Date: 2021-07-26 11:21:45 CEST, cola version: 1.9.4
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First the variable is renamed to res_rh.
res_rh = rh
The partition hierarchy and all available functions which can be applied to res_rh object.
res_rh
#> A 'HierarchicalPartition' object with 'ATC:skmeans' method.
#> On a matrix with 15527 rows and 1600 columns.
#> Performed in total 7650 partitions.
#> There are 30 groups under the following parameters:
#> - min_samples: 16
#> - mean_silhouette_cutoff: 0.9
#> - min_n_signatures: 75 (signatures are selected based on:)
#> - fdr_cutoff: 0.05
#> - group_diff (scaled values): 0.5
#>
#> Hierarchy of the partition:
#> 0, 1600 cols
#> |-- 01, 452 cols, 1273 signatures
#> | |-- 011, 238 cols, 155 signatures
#> | | |-- 0111, 126 cols (a)
#> | | `-- 0112, 112 cols, 72 signatures (c)
#> | |-- 012, 113 cols, 306 signatures
#> | | |-- 0121, 50 cols, 1 signatures (c)
#> | | `-- 0122, 63 cols (a)
#> | `-- 013, 101 cols, 268 signatures
#> | |-- 0131, 47 cols, 4 signatures (c)
#> | `-- 0132, 54 cols (a)
#> |-- 02, 582 cols, 2397 signatures
#> | |-- 021, 289 cols, 397 signatures
#> | | |-- 0211, 147 cols, 174 signatures
#> | | | |-- 02111, 82 cols, 49 signatures (c)
#> | | | `-- 02112, 65 cols, 114 signatures
#> | | | |-- 021121, 32 cols, 0 signatures (c)
#> | | | `-- 021122, 33 cols (a)
#> | | `-- 0212, 142 cols, 108 signatures
#> | | |-- 02121, 80 cols (a)
#> | | `-- 02122, 62 cols, 2 signatures (c)
#> | |-- 022, 218 cols, 689 signatures
#> | | |-- 0221, 118 cols, 287 signatures
#> | | | |-- 02211, 72 cols, 7 signatures (c)
#> | | | `-- 02212, 46 cols, 48 signatures (c)
#> | | `-- 0222, 100 cols, 139 signatures
#> | | |-- 02221, 48 cols, 3 signatures (c)
#> | | `-- 02222, 52 cols, 73 signatures (c)
#> | `-- 023, 75 cols, 1289 signatures
#> | |-- 0231, 34 cols (a)
#> | |-- 0232, 23 cols (b)
#> | `-- 0233, 18 cols (b)
#> `-- 03, 566 cols, 2253 signatures
#> |-- 031, 268 cols, 996 signatures
#> | |-- 0311, 79 cols (a)
#> | |-- 0312, 62 cols (a)
#> | |-- 0313, 58 cols (a)
#> | `-- 0314, 69 cols, 39 signatures (c)
#> |-- 032, 245 cols, 439 signatures
#> | |-- 0321, 150 cols, 248 signatures
#> | | |-- 03211, 69 cols, 87 signatures
#> | | | |-- 032111, 41 cols, 7 signatures (c)
#> | | | `-- 032112, 28 cols (b)
#> | | `-- 03212, 81 cols (a)
#> | `-- 0322, 95 cols, 277 signatures
#> | |-- 03221, 44 cols, 18 signatures (c)
#> | `-- 03222, 51 cols, 22 signatures (c)
#> `-- 033, 53 cols, 446 signatures
#> |-- 0331, 17 cols (b)
#> |-- 0332, 19 cols (b)
#> `-- 0333, 17 cols (b)
#> Stop reason:
#> a) Mean silhouette score was too small
#> b) Subgroup had too few columns.
#> c) There were too few signatures.
#>
#> Following methods can be applied to this 'HierarchicalPartition' object:
#> [1] "all_leaves" "all_nodes" "cola_report" "collect_classes"
#> [5] "colnames" "compare_signatures" "dimension_reduction" "functional_enrichment"
#> [9] "get_anno_col" "get_anno" "get_children_nodes" "get_classes"
#> [13] "get_matrix" "get_signatures" "is_leaf_node" "max_depth"
#> [17] "merge_node" "ncol" "node_info" "node_level"
#> [21] "nrow" "rownames" "show" "split_node"
#> [25] "suggest_best_k" "test_to_known_factors" "top_rows_heatmap" "top_rows_overlap"
#>
#> You can get result for a single node by e.g. object["01"]
The call of hierarchical_partition() was:
#> hierarchical_partition(data = mat, anno = anno, subset = 500, cores = 4)
Dimension of the input matrix:
mat = get_matrix(res_rh)
dim(mat)
#> [1] 15527 1600
All the methods that were tried:
res_rh@param$combination_method
#> [[1]]
#> [1] "ATC" "skmeans"
The density distribution for each sample is visualized as one column in the following heatmap. The clustering is based on the distance which is the Kolmogorov-Smirnov statistic between two distributions.
library(ComplexHeatmap)
densityHeatmap(mat, top_annotation = HeatmapAnnotation(df = get_anno(res_rh),
col = get_anno_col(res_rh)), ylab = "value", cluster_columns = TRUE, show_column_names = FALSE,
mc.cores = 1)
Some values about the hierarchy:
all_nodes(res_rh)
#> [1] "0" "01" "011" "0111" "0112" "012" "0121" "0122" "013" "0131"
#> [11] "0132" "02" "021" "0211" "02111" "02112" "021121" "021122" "0212" "02121"
#> [21] "02122" "022" "0221" "02211" "02212" "0222" "02221" "02222" "023" "0231"
#> [31] "0232" "0233" "03" "031" "0311" "0312" "0313" "0314" "032" "0321"
#> [41] "03211" "032111" "032112" "03212" "0322" "03221" "03222" "033" "0331" "0332"
#> [51] "0333"
all_leaves(res_rh)
#> [1] "0111" "0112" "0121" "0122" "0131" "0132" "02111" "021121" "021122" "02121"
#> [11] "02122" "02211" "02212" "02221" "02222" "0231" "0232" "0233" "0311" "0312"
#> [21] "0313" "0314" "032111" "032112" "03212" "03221" "03222" "0331" "0332" "0333"
node_info(res_rh)
#> id best_method depth best_k n_columns n_signatures p_signatures is_leaf
#> 1 0 ATC:skmeans 1 3 1600 1503 9.68e-02 FALSE
#> 2 01 ATC:skmeans 2 3 452 1273 8.20e-02 FALSE
#> 3 011 ATC:skmeans 3 2 238 155 9.98e-03 FALSE
#> 4 0111 ATC:skmeans 4 3 126 NA NA TRUE
#> 5 0112 ATC:skmeans 4 2 112 72 4.64e-03 TRUE
#> 6 012 ATC:skmeans 3 2 113 306 1.97e-02 FALSE
#> 7 0121 ATC:skmeans 4 2 50 1 6.44e-05 TRUE
#> 8 0122 ATC:skmeans 4 2 63 NA NA TRUE
#> 9 013 ATC:skmeans 3 2 101 268 1.73e-02 FALSE
#> 10 0131 ATC:skmeans 4 2 47 4 2.58e-04 TRUE
#> 11 0132 ATC:skmeans 4 2 54 NA NA TRUE
#> 12 02 ATC:skmeans 2 3 582 2397 1.54e-01 FALSE
#> 13 021 ATC:skmeans 3 2 289 397 2.56e-02 FALSE
#> 14 0211 ATC:skmeans 4 2 147 174 1.12e-02 FALSE
#> 15 02111 ATC:skmeans 5 2 82 49 3.16e-03 TRUE
#> 16 02112 ATC:skmeans 5 2 65 114 7.34e-03 FALSE
#> 17 021121 ATC:skmeans 6 2 32 0 0.00e+00 TRUE
#> 18 021122 ATC:skmeans 6 2 33 NA NA TRUE
#> 19 0212 ATC:skmeans 4 2 142 108 6.96e-03 FALSE
#> 20 02121 ATC:skmeans 5 2 80 NA NA TRUE
#> 21 02122 ATC:skmeans 5 2 62 2 1.29e-04 TRUE
#> 22 022 ATC:skmeans 3 2 218 689 4.44e-02 FALSE
#> 23 0221 ATC:skmeans 4 2 118 287 1.85e-02 FALSE
#> 24 02211 ATC:skmeans 5 2 72 7 4.51e-04 TRUE
#> 25 02212 ATC:skmeans 5 2 46 48 3.09e-03 TRUE
#> 26 0222 ATC:skmeans 4 2 100 139 8.95e-03 FALSE
#> 27 02221 ATC:skmeans 5 2 48 3 1.93e-04 TRUE
#> 28 02222 ATC:skmeans 5 2 52 73 4.70e-03 TRUE
#> 29 023 ATC:skmeans 3 3 75 1289 8.30e-02 FALSE
#> 30 0231 ATC:skmeans 4 3 34 NA NA TRUE
#> 31 0232 not applied 4 NA 23 NA NA TRUE
#> 32 0233 not applied 4 NA 18 NA NA TRUE
#> 33 03 ATC:skmeans 2 3 566 2253 1.45e-01 FALSE
#> 34 031 ATC:skmeans 3 4 268 996 6.41e-02 FALSE
#> 35 0311 ATC:skmeans 4 2 79 NA NA TRUE
#> 36 0312 ATC:skmeans 4 2 62 NA NA TRUE
#> 37 0313 ATC:skmeans 4 2 58 NA NA TRUE
#> 38 0314 ATC:skmeans 4 2 69 39 2.51e-03 TRUE
#> 39 032 ATC:skmeans 3 2 245 439 2.83e-02 FALSE
#> 40 0321 ATC:skmeans 4 2 150 248 1.60e-02 FALSE
#> 41 03211 ATC:skmeans 5 2 69 87 5.60e-03 FALSE
#> 42 032111 ATC:skmeans 6 2 41 7 4.51e-04 TRUE
#> 43 032112 not applied 6 NA 28 NA NA TRUE
#> 44 03212 ATC:skmeans 5 2 81 NA NA TRUE
#> 45 0322 ATC:skmeans 4 2 95 277 1.78e-02 FALSE
#> 46 03221 ATC:skmeans 5 2 44 18 1.16e-03 TRUE
#> 47 03222 ATC:skmeans 5 2 51 22 1.42e-03 TRUE
#> 48 033 ATC:skmeans 3 3 53 446 2.87e-02 FALSE
#> 49 0331 not applied 4 NA 17 NA NA TRUE
#> 50 0332 not applied 4 NA 19 NA NA TRUE
#> 51 0333 not applied 4 NA 17 NA NA TRUE
In the output from node_info(), there are the following columns:
id: The node id.best_method: The best method selected.depth: Depth of the node in the hierarchy.best_k: Best number of groups of the partition on that node.n_columns: Number of columns in the submatrix.n_signatures: Number of signatures with the best_k.p_signatures: Proportion of hte signatures in total number of rows in the matrix.is_leaf: Whether the node is a leaf.Labels of nodes are encoded in a special way. The number of digits correspond to the depth of the node in the hierarchy and the value of the digits correspond to the index of the subgroup in the current node, E.g. a label of “012” means the node is the second subgroup of the partition which is the first subgroup of the root node.
Following table shows the best k (number of partitions) for each node in the
partition hierarchy. Clicking on the node name in the table goes to the
corresponding section for the partitioning on that node.
The cola vignette explains the definition of the metrics used for determining the best number of partitions.
suggest_best_k(res_rh)
| Node | Best method | Is leaf | Best k | 1-PAC | Mean silhouette | Concordance | #samples | |
|---|---|---|---|---|---|---|---|---|
| Node0 | ATC:skmeans | 4 | 0.93 | 0.93 | 0.96 | 1600 | * | |
| Node01 | ATC:skmeans | 3 | 1.00 | 0.97 | 0.99 | 452 | ** | |
| Node011 | ATC:skmeans | 2 | 0.84 | 0.92 | 0.96 | 238 | ||
| Node0111-leaf | ATC:skmeans | ✓ (a) | 3 | 0.85 | 0.86 | 0.94 | 126 | |
| Node0112-leaf | ATC:skmeans | ✓ (c) | 2 | 0.74 | 0.92 | 0.95 | 112 | |
| Node012 | ATC:skmeans | 2 | 1.00 | 0.97 | 0.99 | 113 | ** | |
| Node0121-leaf | ATC:skmeans | ✓ (c) | 2 | 1.00 | 0.95 | 0.98 | 50 | ** |
| Node0122-leaf | ATC:skmeans | ✓ (a) | 2 | 0.73 | 0.90 | 0.95 | 63 | |
| Node013 | ATC:skmeans | 2 | 1.00 | 1.00 | 1.00 | 101 | ** | |
| Node0131-leaf | ATC:skmeans | ✓ (c) | 2 | 0.95 | 0.95 | 0.98 | 47 | * |
| Node0132-leaf | ATC:skmeans | ✓ (a) | 2 | 0.44 | 0.85 | 0.91 | 54 | |
| Node02 | ATC:skmeans | 3 | 1.00 | 0.98 | 0.99 | 582 | ** | |
| Node021 | ATC:skmeans | 2 | 0.86 | 0.90 | 0.96 | 289 | ||
| Node0211 | ATC:skmeans | 4 | 0.94 | 0.93 | 0.96 | 147 | * | |
| Node02111-leaf | ATC:skmeans | ✓ (c) | 2 | 1.00 | 0.96 | 0.98 | 82 | ** |
| Node02112 | ATC:skmeans | 2 | 1.00 | 0.98 | 0.99 | 65 | ** | |
| Node021121-leaf | ATC:skmeans | ✓ (c) | 2 | 0.87 | 0.94 | 0.97 | 32 | |
| Node021122-leaf | ATC:skmeans | ✓ (a) | 2 | 0.36 | 0.66 | 0.86 | 33 | |
| Node0212 | ATC:skmeans | 2 | 0.98 | 0.96 | 0.98 | 142 | ** | |
| Node02121-leaf | ATC:skmeans | ✓ (a) | 2 | 0.78 | 0.89 | 0.95 | 80 | |
| Node02122-leaf | ATC:skmeans | ✓ (c) | 2 | 0.93 | 0.92 | 0.97 | 62 | * |
| Node022 | ATC:skmeans | 2 | 0.97 | 0.96 | 0.98 | 218 | ** | |
| Node0221 | ATC:skmeans | 2 | 0.90 | 0.94 | 0.98 | 118 | ||
| Node02211-leaf | ATC:skmeans | ✓ (c) | 2 | 1.00 | 0.98 | 0.99 | 72 | ** |
| Node02212-leaf | ATC:skmeans | ✓ (c) | 2 | 1.00 | 0.98 | 0.99 | 46 | ** |
| Node0222 | ATC:skmeans | 3 | 0.91 | 0.91 | 0.96 | 100 | * | |
| Node02221-leaf | ATC:skmeans | ✓ (c) | 3 | 0.92 | 0.88 | 0.95 | 48 | * |
| Node02222-leaf | ATC:skmeans | ✓ (c) | 2 | 1.00 | 0.98 | 0.99 | 52 | ** |
| Node023 | ATC:skmeans | 3 | 1.00 | 1.00 | 1.00 | 75 | ** | |
| Node0231-leaf | ATC:skmeans | ✓ (a) | 3 | 0.69 | 0.79 | 0.90 | 34 | |
| Node0232-leaf | not applied | ✓ (b) | 23 | |||||
| Node0233-leaf | not applied | ✓ (b) | 18 | |||||
| Node03 | ATC:skmeans | 4 | 0.95 | 0.94 | 0.97 | 566 | ** | |
| Node031 | ATC:skmeans | 4 | 0.97 | 0.94 | 0.97 | 268 | ** | |
| Node0311-leaf | ATC:skmeans | ✓ (a) | 2 | 0.84 | 0.88 | 0.95 | 79 | |
| Node0312-leaf | ATC:skmeans | ✓ (a) | 2 | 0.60 | 0.81 | 0.92 | 62 | |
| Node0313-leaf | ATC:skmeans | ✓ (a) | 2 | 0.67 | 0.84 | 0.93 | 58 | |
| Node0314-leaf | ATC:skmeans | ✓ (c) | 3 | 0.98 | 0.94 | 0.97 | 69 | ** |
| Node032 | ATC:skmeans | 2 | 0.94 | 0.94 | 0.97 | 245 | * | |
| Node0321 | ATC:skmeans | 2 | 0.92 | 0.92 | 0.97 | 150 | * | |
| Node03211 | ATC:skmeans | 3 | 0.93 | 0.89 | 0.96 | 69 | * | |
| Node032111-leaf | ATC:skmeans | ✓ (c) | 2 | 1.00 | 0.97 | 0.99 | 41 | ** |
| Node032112-leaf | not applied | ✓ (b) | 28 | |||||
| Node03212-leaf | ATC:skmeans | ✓ (a) | 2 | 0.76 | 0.89 | 0.95 | 81 | |
| Node0322 | ATC:skmeans | 3 | 0.94 | 0.91 | 0.97 | 95 | * | |
| Node03221-leaf | ATC:skmeans | ✓ (c) | 2 | 1.00 | 0.96 | 0.98 | 44 | ** |
| Node03222-leaf | ATC:skmeans | ✓ (c) | 2 | 1.00 | 0.96 | 0.98 | 51 | ** |
| Node033 | ATC:skmeans | 3 | 1.00 | 0.98 | 0.99 | 53 | ** | |
| Node0331-leaf | not applied | ✓ (b) | 17 | |||||
| Node0332-leaf | not applied | ✓ (b) | 19 | |||||
| Node0333-leaf | not applied | ✓ (b) | 17 |
Stop reason: a) Mean silhouette score was too small b) Subgroup had too few columns. c) There were too few signatures.
**: 1-PAC > 0.95, *: 1-PAC > 0.9
The nodes of the hierarchy can be merged by setting the merge_node parameters. Here we
control the hierarchy with the min_n_signatures parameter. The value of min_n_signatures is
from node_info().
collect_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 87))
#> Error in dimnames(x) <- dn: length of 'dimnames' [2] not equal to array extent
collect_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 108))
#> Error in dimnames(x) <- dn: length of 'dimnames' [2] not equal to array extent
collect_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 114))
#> Error in dimnames(x) <- dn: length of 'dimnames' [2] not equal to array extent
collect_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 139))
#> Error in dimnames(x) <- dn: length of 'dimnames' [2] not equal to array extent
collect_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 155))
#> Error in dimnames(x) <- dn: length of 'dimnames' [2] not equal to array extent
collect_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 174))
#> Error in dimnames(x) <- dn: length of 'dimnames' [2] not equal to array extent
collect_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 248))
#> Error in dimnames(x) <- dn: length of 'dimnames' [2] not equal to array extent
collect_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 268))
#> Error in dimnames(x) <- dn: length of 'dimnames' [2] not equal to array extent
collect_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 277))
#> Error in dimnames(x) <- dn: length of 'dimnames' [2] not equal to array extent
collect_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 287))
#> Error in dimnames(x) <- dn: length of 'dimnames' [2] not equal to array extent
collect_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 306))
#> Error in dimnames(x) <- dn: length of 'dimnames' [2] not equal to array extent
collect_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 397))
#> Error in dimnames(x) <- dn: length of 'dimnames' [2] not equal to array extent
collect_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 439))
#> Error in dimnames(x) <- dn: length of 'dimnames' [2] not equal to array extent
collect_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 446))
#> Error in dimnames(x) <- dn: length of 'dimnames' [2] not equal to array extent
collect_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 689))
#> Error in dimnames(x) <- dn: length of 'dimnames' [2] not equal to array extent
collect_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 996))
#> Error in dimnames(x) <- dn: length of 'dimnames' [2] not equal to array extent
collect_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 1273))
#> Error in dimnames(x) <- dn: length of 'dimnames' [2] not equal to array extent
collect_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 1289))
#> Error in dimnames(x) <- dn: length of 'dimnames' [2] not equal to array extent
collect_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 1503))
#> Error in dimnames(x) <- dn: length of 'dimnames' [2] not equal to array extent
collect_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 2253))
#> Error in dimnames(x) <- dn: length of 'dimnames' [2] not equal to array extent
collect_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 2397))
#> Error in dimnames(x) <- dn: length of 'dimnames' [2] not equal to array extent
Following shows the table of the partitions (You need to click the show/hide code output link to see it).
get_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 87))
#> Sample_1 Sample_2 Sample_3 Sample_4 Sample_5 Sample_6 Sample_7 Sample_8
#> "0132" "02211" "02212" "02222" "02211" "02211" "02211" "02222"
#> Sample_9 Sample_10 Sample_11 Sample_12 Sample_13 Sample_14 Sample_15 Sample_16
#> "02212" "02211" "02212" "02211" "02211" "02211" "02211" "02212"
#> Sample_17 Sample_18 Sample_19 Sample_20 Sample_21 Sample_22 Sample_23 Sample_24
#> "02222" "02222" "02222" "02221" "02221" "02222" "02221" "02211"
#> Sample_25 Sample_26 Sample_27 Sample_28 Sample_29 Sample_30 Sample_31 Sample_32
#> "02221" "02211" "02222" "02222" "02212" "02222" "0233" "02212"
#> Sample_33 Sample_34 Sample_35 Sample_36 Sample_37 Sample_38 Sample_39 Sample_40
#> "02212" "02212" "02212" "02212" "02212" "02212" "02212" "02212"
#> Sample_41 Sample_42 Sample_43 Sample_44 Sample_45 Sample_46 Sample_47 Sample_48
#> "02212" "02212" "0233" "02212" "02212" "02212" "02212" "02212"
#> Sample_49 Sample_50 Sample_51 Sample_52 Sample_53 Sample_54 Sample_55 Sample_56
#> "02212" "02212" "02212" "02212" "02222" "02221" "02212" "02211"
#> Sample_57 Sample_58 Sample_59 Sample_60 Sample_61 Sample_62 Sample_63 Sample_64
#> "02211" "02211" "02212" "02211" "02221" "02211" "02211" "02211"
#> Sample_65 Sample_66 Sample_67 Sample_68 Sample_69 Sample_70 Sample_71 Sample_72
#> "02211" "02211" "02222" "02211" "02211" "02222" "02222" "021121"
#> Sample_73 Sample_74 Sample_75 Sample_76 Sample_77 Sample_78 Sample_79 Sample_80
#> "0233" "02211" "02221" "02211" "02221" "02211" "02221" "02221"
#> Sample_81 Sample_82 Sample_83 Sample_84 Sample_85 Sample_86 Sample_87 Sample_88
#> "02211" "02221" "02211" "02211" "02222" "02212" "02222" "02222"
#> Sample_89 Sample_90 Sample_91 Sample_92 Sample_93 Sample_94 Sample_95 Sample_96
#> "02222" "02222" "02222" "02222" "02222" "0232" "02221" "02211"
#> Sample_97 Sample_98 Sample_99 Sample_100 Sample_101 Sample_102 Sample_103 Sample_104
#> "02211" "02211" "02211" "02222" "02221" "02212" "02222" "02221"
#> Sample_105 Sample_106 Sample_107 Sample_108 Sample_109 Sample_110 Sample_111 Sample_112
#> "02211" "02211" "02221" "02211" "02211" "02221" "02122" "02221"
#> Sample_113 Sample_114 Sample_115 Sample_116 Sample_117 Sample_118 Sample_119 Sample_120
#> "02221" "0232" "02221" "02222" "02221" "0232" "02221" "02211"
#> Sample_121 Sample_122 Sample_123 Sample_124 Sample_125 Sample_126 Sample_127 Sample_128
#> "0121" "02221" "0232" "02211" "02211" "02222" "02211" "02222"
#> Sample_129 Sample_130 Sample_131 Sample_132 Sample_133 Sample_134 Sample_135 Sample_136
#> "02211" "02222" "02222" "02211" "02222" "02211" "02221" "02211"
#> Sample_137 Sample_138 Sample_139 Sample_140 Sample_141 Sample_142 Sample_143 Sample_144
#> "02211" "02211" "02221" "02211" "02211" "02221" "02221" "0232"
#> Sample_145 Sample_146 Sample_147 Sample_148 Sample_149 Sample_150 Sample_151 Sample_152
#> "02221" "02222" "021121" "021121" "021121" "021121" "02121" "021121"
#> Sample_153 Sample_154 Sample_155 Sample_156 Sample_157 Sample_158 Sample_159 Sample_160
#> "021121" "021121" "021121" "021121" "021121" "021121" "02121" "021121"
#> Sample_161 Sample_162 Sample_163 Sample_164 Sample_165 Sample_166 Sample_167 Sample_168
#> "02121" "02121" "02122" "02121" "02121" "02121" "021121" "021121"
#> Sample_169 Sample_170 Sample_171 Sample_172 Sample_173 Sample_174 Sample_175 Sample_176
#> "02121" "021121" "0232" "02121" "0233" "02121" "021121" "021121"
#> Sample_177 Sample_178 Sample_179 Sample_180 Sample_181 Sample_182 Sample_183 Sample_184
#> "02122" "021121" "02122" "02121" "021121" "021121" "021121" "021121"
#> Sample_185 Sample_186 Sample_187 Sample_188 Sample_189 Sample_190 Sample_191 Sample_192
#> "021121" "021121" "021121" "02121" "02121" "021121" "02121" "02121"
#> Sample_193 Sample_194 Sample_195 Sample_196 Sample_197 Sample_198 Sample_199 Sample_200
#> "021121" "021121" "021121" "021121" "02211" "02211" "02212" "02212"
#> Sample_201 Sample_202 Sample_203 Sample_204 Sample_205 Sample_206 Sample_207 Sample_208
#> "02212" "02212" "02212" "0233" "02212" "02212" "02212" "03222"
#> Sample_209 Sample_210 Sample_211 Sample_212 Sample_213 Sample_214 Sample_215 Sample_216
#> "03222" "03222" "03222" "03222" "03222" "0132" "0132" "03222"
#> Sample_217 Sample_218 Sample_219 Sample_220 Sample_221 Sample_222 Sample_223 Sample_224
#> "0132" "03222" "03222" "03222" "03222" "03222" "03222" "0132"
#> Sample_225 Sample_226 Sample_227 Sample_228 Sample_229 Sample_230 Sample_231 Sample_232
#> "03222" "0132" "03222" "03222" "03222" "03222" "03222" "03222"
#> Sample_233 Sample_234 Sample_235 Sample_236 Sample_237 Sample_238 Sample_239 Sample_240
#> "0333" "03222" "03222" "0132" "03222" "03222" "03222" "03222"
#> Sample_241 Sample_242 Sample_243 Sample_244 Sample_245 Sample_246 Sample_247 Sample_248
#> "0132" "03222" "03212" "03222" "03222" "03222" "03222" "03222"
#> Sample_249 Sample_250 Sample_251 Sample_252 Sample_253 Sample_254 Sample_255 Sample_256
#> "03222" "03222" "03222" "03222" "03222" "0333" "03221" "03222"
#> Sample_257 Sample_258 Sample_259 Sample_260 Sample_261 Sample_262 Sample_263 Sample_264
#> "03222" "0132" "03222" "03222" "03222" "03222" "03222" "0132"
#> Sample_265 Sample_266 Sample_267 Sample_268 Sample_269 Sample_270 Sample_271 Sample_272
#> "0122" "032112" "032112" "032111" "03221" "032111" "0333" "0122"
#> Sample_273 Sample_274 Sample_275 Sample_276 Sample_277 Sample_278 Sample_279 Sample_280
#> "03212" "032111" "0122" "0132" "032112" "0333" "032112" "03222"
#> Sample_281 Sample_282 Sample_283 Sample_284 Sample_285 Sample_286 Sample_287 Sample_288
#> "032112" "032112" "03221" "032112" "032112" "032112" "0132" "0333"
#> Sample_289 Sample_290 Sample_291 Sample_292 Sample_293 Sample_294 Sample_295 Sample_296
#> "0333" "032112" "032112" "032112" "03222" "032112" "032112" "032112"
#> Sample_297 Sample_298 Sample_299 Sample_300 Sample_301 Sample_302 Sample_303 Sample_304
#> "032112" "032112" "032112" "03221" "032112" "0122" "0122" "0122"
#> Sample_305 Sample_306 Sample_307 Sample_308 Sample_309 Sample_310 Sample_311 Sample_312
#> "0122" "03212" "03212" "0122" "0122" "0122" "0122" "0122"
#> Sample_313 Sample_314 Sample_315 Sample_316 Sample_317 Sample_318 Sample_319 Sample_320
#> "0122" "0122" "03212" "0122" "03212" "0122" "0122" "0122"
#> Sample_321 Sample_322 Sample_323 Sample_324 Sample_325 Sample_326 Sample_327 Sample_328
#> "0121" "032111" "0122" "0122" "0122" "0121" "0122" "0122"
#> Sample_329 Sample_330 Sample_331 Sample_332 Sample_333 Sample_334 Sample_335 Sample_336
#> "0121" "0122" "0333" "032111" "032111" "032111" "032111" "032111"
#> Sample_337 Sample_338 Sample_339 Sample_340 Sample_341 Sample_342 Sample_343 Sample_344
#> "032111" "032111" "0333" "03212" "03212" "032111" "032111" "03212"
#> Sample_345 Sample_346 Sample_347 Sample_348 Sample_349 Sample_350 Sample_351 Sample_352
#> "03212" "03212" "032111" "03212" "03212" "0132" "0122" "0132"
#> Sample_353 Sample_354 Sample_355 Sample_356 Sample_357 Sample_358 Sample_359 Sample_360
#> "032112" "032111" "0122" "032111" "032111" "0122" "032111" "0122"
#> Sample_361 Sample_362 Sample_363 Sample_364 Sample_365 Sample_366 Sample_367 Sample_368
#> "032111" "0122" "032111" "032111" "0132" "032111" "032111" "0122"
#> Sample_369 Sample_370 Sample_371 Sample_372 Sample_373 Sample_374 Sample_375 Sample_376
#> "0122" "03212" "0333" "0132" "0132" "032111" "03212" "03212"
#> Sample_377 Sample_378 Sample_379 Sample_380 Sample_381 Sample_382 Sample_383 Sample_384
#> "0122" "03212" "03212" "03212" "0122" "03212" "03212" "03212"
#> Sample_385 Sample_386 Sample_387 Sample_388 Sample_389 Sample_390 Sample_391 Sample_392
#> "0121" "0121" "03212" "032111" "0132" "0132" "03212" "03212"
#> Sample_393 Sample_394 Sample_395 Sample_396 Sample_397 Sample_398 Sample_399 Sample_400
#> "0122" "0132" "0122" "0132" "0122" "0122" "03212" "03212"
#> Sample_401 Sample_402 Sample_403 Sample_404 Sample_405 Sample_406 Sample_407 Sample_408
#> "03212" "03221" "0132" "0132" "0132" "03212" "03212" "0132"
#> Sample_409 Sample_410 Sample_411 Sample_412 Sample_413 Sample_414 Sample_415 Sample_416
#> "0122" "03212" "03212" "0122" "03212" "03212" "0333" "0122"
#> Sample_417 Sample_418 Sample_419 Sample_420 Sample_421 Sample_422 Sample_423 Sample_424
#> "03212" "03212" "03212" "0333" "0122" "0333" "03221" "0122"
#> Sample_425 Sample_426 Sample_427 Sample_428 Sample_429 Sample_430 Sample_431 Sample_432
#> "0122" "03221" "0122" "0122" "03221" "0132" "0122" "0121"
#> Sample_433 Sample_434 Sample_435 Sample_436 Sample_437 Sample_438 Sample_439 Sample_440
#> "0132" "0121" "03221" "0333" "0333" "0132" "0121" "0122"
#> Sample_441 Sample_442 Sample_443 Sample_444 Sample_445 Sample_446 Sample_447 Sample_448
#> "03221" "0122" "0132" "03212" "0121" "0122" "03221" "0132"
#> Sample_449 Sample_450 Sample_451 Sample_452 Sample_453 Sample_454 Sample_455 Sample_456
#> "03221" "03212" "0122" "0121" "03221" "03212" "0132" "0121"
#> Sample_457 Sample_458 Sample_459 Sample_460 Sample_461 Sample_462 Sample_463 Sample_464
#> "0132" "03221" "0122" "03212" "0121" "03212" "0132" "0121"
#> Sample_465 Sample_466 Sample_467 Sample_468 Sample_469 Sample_470 Sample_471 Sample_472
#> "0121" "0121" "03221" "0121" "0122" "03212" "03221" "0121"
#> Sample_473 Sample_474 Sample_475 Sample_476 Sample_477 Sample_478 Sample_479 Sample_480
#> "03212" "0121" "0121" "0132" "0132" "0314" "0111" "0314"
#> Sample_481 Sample_482 Sample_483 Sample_484 Sample_485 Sample_486 Sample_487 Sample_488
#> "0313" "0314" "0314" "0314" "0314" "0112" "0112" "0331"
#> Sample_489 Sample_490 Sample_491 Sample_492 Sample_493 Sample_494 Sample_495 Sample_496
#> "0314" "0112" "0314" "0314" "0314" "0314" "0112" "0314"
#> Sample_497 Sample_498 Sample_499 Sample_500 Sample_501 Sample_502 Sample_503 Sample_504
#> "0314" "0314" "0111" "0111" "0314" "0314" "0314" "0314"
#> Sample_505 Sample_506 Sample_507 Sample_508 Sample_509 Sample_510 Sample_511 Sample_512
#> "0111" "0112" "0111" "0314" "0314" "0314" "0111" "0332"
#> Sample_513 Sample_514 Sample_515 Sample_516 Sample_517 Sample_518 Sample_519 Sample_520
#> "0112" "0314" "0112" "021121" "0112" "0314" "0314" "0112"
#> Sample_521 Sample_522 Sample_523 Sample_524 Sample_525 Sample_526 Sample_527 Sample_528
#> "0112" "0314" "0314" "0314" "0314" "0314" "0112" "0312"
#> Sample_529 Sample_530 Sample_531 Sample_532 Sample_533 Sample_534 Sample_535 Sample_536
#> "0331" "0112" "0332" "0111" "0112" "0314" "0112" "0111"
#> Sample_537 Sample_538 Sample_539 Sample_540 Sample_541 Sample_542 Sample_543 Sample_544
#> "0112" "0314" "0112" "0314" "0314" "0112" "0112" "0112"
#> Sample_545 Sample_546 Sample_547 Sample_548 Sample_549 Sample_550 Sample_551 Sample_552
#> "0331" "0112" "0112" "0314" "0314" "0314" "0314" "0314"
#> Sample_553 Sample_554 Sample_555 Sample_556 Sample_557 Sample_558 Sample_559 Sample_560
#> "0332" "0314" "0314" "0314" "0112" "0112" "0112" "0314"
#> Sample_561 Sample_562 Sample_563 Sample_564 Sample_565 Sample_566 Sample_567 Sample_568
#> "0314" "0314" "0314" "0314" "0112" "0112" "0112" "0314"
#> Sample_569 Sample_570 Sample_571 Sample_572 Sample_573 Sample_574 Sample_575 Sample_576
#> "0111" "03221" "03221" "0132" "0121" "0333" "03212" "03221"
#> Sample_577 Sample_578 Sample_579 Sample_580 Sample_581 Sample_582 Sample_583 Sample_584
#> "0122" "032111" "032111" "032112" "032111" "032111" "032111" "032111"
#> Sample_585 Sample_586 Sample_587 Sample_588 Sample_589 Sample_590 Sample_591 Sample_592
#> "03222" "02212" "02211" "0231" "03222" "0131" "02211" "0231"
#> Sample_593 Sample_594 Sample_595 Sample_596 Sample_597 Sample_598 Sample_599 Sample_600
#> "02212" "0231" "0231" "02222" "0231" "02222" "0231" "0231"
#> Sample_601 Sample_602 Sample_603 Sample_604 Sample_605 Sample_606 Sample_607 Sample_608
#> "02222" "0231" "02222" "02222" "02222" "02222" "02211" "02222"
#> Sample_609 Sample_610 Sample_611 Sample_612 Sample_613 Sample_614 Sample_615 Sample_616
#> "02222" "02222" "02121" "02121" "03222" "0131" "03222" "0131"
#> Sample_617 Sample_618 Sample_619 Sample_620 Sample_621 Sample_622 Sample_623 Sample_624
#> "0131" "0121" "0131" "0131" "0231" "0131" "03221" "03221"
#> Sample_625 Sample_626 Sample_627 Sample_628 Sample_629 Sample_630 Sample_631 Sample_632
#> "0231" "0131" "0231" "0231" "0231" "0131" "0231" "0131"
#> Sample_633 Sample_634 Sample_635 Sample_636 Sample_637 Sample_638 Sample_639 Sample_640
#> "0131" "0131" "0131" "0131" "0131" "0231" "0131" "0232"
#> Sample_641 Sample_642 Sample_643 Sample_644 Sample_645 Sample_646 Sample_647 Sample_648
#> "0131" "0233" "0131" "03212" "0232" "0131" "0131" "0232"
#> Sample_649 Sample_650 Sample_651 Sample_652 Sample_653 Sample_654 Sample_655 Sample_656
#> "0232" "0231" "02212" "02211" "02211" "02212" "02211" "02211"
#> Sample_657 Sample_658 Sample_659 Sample_660 Sample_661 Sample_662 Sample_663 Sample_664
#> "02222" "02212" "02211" "02221" "02211" "02221" "02221" "02211"
#> Sample_665 Sample_666 Sample_667 Sample_668 Sample_669 Sample_670 Sample_671 Sample_672
#> "0232" "02221" "0232" "02221" "02221" "02221" "02211" "02221"
#> Sample_673 Sample_674 Sample_675 Sample_676 Sample_677 Sample_678 Sample_679 Sample_680
#> "02221" "02221" "02221" "02222" "02212" "02212" "02211" "02221"
#> Sample_681 Sample_682 Sample_683 Sample_684 Sample_685 Sample_686 Sample_687 Sample_688
#> "0232" "02221" "02221" "02221" "0121" "02121" "0121" "02122"
#> Sample_689 Sample_690 Sample_691 Sample_692 Sample_693 Sample_694 Sample_695 Sample_696
#> "02221" "02122" "02221" "0121" "02221" "02221" "02121" "02111"
#> Sample_697 Sample_698 Sample_699 Sample_700 Sample_701 Sample_702 Sample_703 Sample_704
#> "02111" "02121" "02121" "02111" "02121" "02111" "02121" "02211"
#> Sample_705 Sample_706 Sample_707 Sample_708 Sample_709 Sample_710 Sample_711 Sample_712
#> "02222" "02111" "02111" "02111" "02111" "0232" "02121" "02111"
#> Sample_713 Sample_714 Sample_715 Sample_716 Sample_717 Sample_718 Sample_719 Sample_720
#> "02111" "02121" "02121" "02121" "02111" "02111" "02111" "0232"
#> Sample_721 Sample_722 Sample_723 Sample_724 Sample_725 Sample_726 Sample_727 Sample_728
#> "02111" "02111" "02121" "02111" "02121" "02111" "02111" "02121"
#> Sample_729 Sample_730 Sample_731 Sample_732 Sample_733 Sample_734 Sample_735 Sample_736
#> "02111" "02111" "0233" "02111" "02111" "02111" "0233" "02111"
#> Sample_737 Sample_738 Sample_739 Sample_740 Sample_741 Sample_742 Sample_743 Sample_744
#> "0121" "0232" "02111" "02121" "02111" "02121" "02111" "02111"
#> Sample_745 Sample_746 Sample_747 Sample_748 Sample_749 Sample_750 Sample_751 Sample_752
#> "02121" "02111" "02111" "02121" "02111" "02111" "02111" "02111"
#> Sample_753 Sample_754 Sample_755 Sample_756 Sample_757 Sample_758 Sample_759 Sample_760
#> "02121" "02121" "02111" "02111" "02111" "02111" "02121" "02111"
#> Sample_761 Sample_762 Sample_763 Sample_764 Sample_765 Sample_766 Sample_767 Sample_768
#> "02111" "02111" "02111" "02111" "02111" "02121" "02111" "0232"
#> Sample_769 Sample_770 Sample_771 Sample_772 Sample_773 Sample_774 Sample_775 Sample_776
#> "02121" "02111" "02111" "02121" "02111" "02111" "02121" "02111"
#> Sample_777 Sample_778 Sample_779 Sample_780 Sample_781 Sample_782 Sample_783 Sample_784
#> "02121" "02121" "02121" "02121" "02111" "02121" "02121" "02121"
#> Sample_785 Sample_786 Sample_787 Sample_788 Sample_789 Sample_790 Sample_791 Sample_792
#> "02121" "02121" "02111" "02121" "02121" "02111" "02111" "02111"
#> Sample_793 Sample_794 Sample_795 Sample_796 Sample_797 Sample_798 Sample_799 Sample_800
#> "02121" "02111" "02111" "02111" "02121" "02121" "02121" "02111"
#> Sample_801 Sample_802 Sample_803 Sample_804 Sample_805 Sample_806 Sample_807 Sample_808
#> "02111" "02122" "02111" "02121" "02111" "02111" "02111" "02111"
#> Sample_809 Sample_810 Sample_811 Sample_812 Sample_813 Sample_814 Sample_815 Sample_816
#> "02111" "02121" "02111" "02111" "02111" "02111" "02111" "02111"
#> Sample_817 Sample_818 Sample_819 Sample_820 Sample_821 Sample_822 Sample_823 Sample_824
#> "02121" "02111" "02121" "0233" "02121" "02111" "02121" "02121"
#> Sample_825 Sample_826 Sample_827 Sample_828 Sample_829 Sample_830 Sample_831 Sample_832
#> "02121" "02121" "02121" "02111" "02121" "02121" "02111" "02111"
#> Sample_833 Sample_834 Sample_835 Sample_836 Sample_837 Sample_838 Sample_839 Sample_840
#> "02111" "02121" "02121" "0233" "02111" "02111" "02121" "02111"
#> Sample_841 Sample_842 Sample_843 Sample_844 Sample_845 Sample_846 Sample_847 Sample_848
#> "02222" "02212" "02212" "02211" "02211" "02211" "02221" "02222"
#> Sample_849 Sample_850 Sample_851 Sample_852 Sample_853 Sample_854 Sample_855 Sample_856
#> "02221" "02211" "02211" "02222" "02211" "02211" "02122" "02122"
#> Sample_857 Sample_858 Sample_859 Sample_860 Sample_861 Sample_862 Sample_863 Sample_864
#> "02122" "021122" "0232" "02122" "02122" "02122" "02122" "021122"
#> Sample_865 Sample_866 Sample_867 Sample_868 Sample_869 Sample_870 Sample_871 Sample_872
#> "0233" "02122" "02122" "021122" "02122" "02122" "02122" "02122"
#> Sample_873 Sample_874 Sample_875 Sample_876 Sample_877 Sample_878 Sample_879 Sample_880
#> "02122" "021122" "021122" "02121" "021122" "021122" "021122" "021122"
#> Sample_881 Sample_882 Sample_883 Sample_884 Sample_885 Sample_886 Sample_887 Sample_888
#> "02122" "0233" "021122" "021122" "02122" "021122" "021122" "021122"
#> Sample_889 Sample_890 Sample_891 Sample_892 Sample_893 Sample_894 Sample_895 Sample_896
#> "02122" "02122" "02122" "02122" "02122" "0232" "02122" "021122"
#> Sample_897 Sample_898 Sample_899 Sample_900 Sample_901 Sample_902 Sample_903 Sample_904
#> "02122" "02122" "021122" "0233" "021122" "02122" "02122" "021122"
#> Sample_905 Sample_906 Sample_907 Sample_908 Sample_909 Sample_910 Sample_911 Sample_912
#> "021122" "02122" "02122" "02122" "02122" "021122" "02122" "021122"
#> Sample_913 Sample_914 Sample_915 Sample_916 Sample_917 Sample_918 Sample_919 Sample_920
#> "02122" "02122" "02122" "021122" "02122" "02122" "02122" "02122"
#> Sample_921 Sample_922 Sample_923 Sample_924 Sample_925 Sample_926 Sample_927 Sample_928
#> "021122" "02122" "021122" "02122" "02122" "021122" "021122" "021122"
#> Sample_929 Sample_930 Sample_931 Sample_932 Sample_933 Sample_934 Sample_935 Sample_936
#> "02122" "021122" "021122" "02122" "02122" "02122" "02122" "02122"
#> Sample_937 Sample_938 Sample_939 Sample_940 Sample_941 Sample_942 Sample_943 Sample_944
#> "02122" "02122" "02122" "02122" "02122" "02122" "02122" "02122"
#> Sample_945 Sample_946 Sample_947 Sample_948 Sample_949 Sample_950 Sample_951 Sample_952
#> "021122" "021122" "021122" "03212" "03212" "0132" "032112" "03212"
#> Sample_953 Sample_954 Sample_955 Sample_956 Sample_957 Sample_958 Sample_959 Sample_960
#> "032112" "03221" "032111" "03212" "032111" "03212" "03212" "03212"
#> Sample_961 Sample_962 Sample_963 Sample_964 Sample_965 Sample_966 Sample_967 Sample_968
#> "032112" "0122" "03212" "032111" "03212" "03212" "032111" "0122"
#> Sample_969 Sample_970 Sample_971 Sample_972 Sample_973 Sample_974 Sample_975 Sample_976
#> "032111" "03212" "032111" "032111" "03212" "032111" "0333" "0132"
#> Sample_977 Sample_978 Sample_979 Sample_980 Sample_981 Sample_982 Sample_983 Sample_984
#> "0132" "0122" "03212" "0132" "0132" "0121" "0122" "03212"
#> Sample_985 Sample_986 Sample_987 Sample_988 Sample_989 Sample_990 Sample_991 Sample_992
#> "0122" "0132" "0122" "0121" "0121" "0121" "0121" "0121"
#> Sample_993 Sample_994 Sample_995 Sample_996 Sample_997 Sample_998 Sample_999 Sample_1000
#> "0121" "0122" "032112" "0132" "03212" "03212" "0132" "03212"
#> Sample_1001 Sample_1002 Sample_1003 Sample_1004 Sample_1005 Sample_1006 Sample_1007 Sample_1008
#> "03212" "03212" "032112" "0333" "03212" "032111" "0122" "0132"
#> Sample_1009 Sample_1010 Sample_1011 Sample_1012 Sample_1013 Sample_1014 Sample_1015 Sample_1016
#> "0132" "0132" "03212" "03221" "03212" "0121" "0121" "0121"
#> Sample_1017 Sample_1018 Sample_1019 Sample_1020 Sample_1021 Sample_1022 Sample_1023 Sample_1024
#> "0121" "0121" "0121" "0121" "0121" "0121" "0121" "0121"
#> Sample_1025 Sample_1026 Sample_1027 Sample_1028 Sample_1029 Sample_1030 Sample_1031 Sample_1032
#> "0121" "0121" "0111" "0111" "0312" "0111" "0332" "0312"
#> Sample_1033 Sample_1034 Sample_1035 Sample_1036 Sample_1037 Sample_1038 Sample_1039 Sample_1040
#> "0312" "0111" "0312" "0111" "0312" "0312" "0111" "0312"
#> Sample_1041 Sample_1042 Sample_1043 Sample_1044 Sample_1045 Sample_1046 Sample_1047 Sample_1048
#> "0112" "0111" "0312" "0312" "0332" "0111" "0312" "0331"
#> Sample_1049 Sample_1050 Sample_1051 Sample_1052 Sample_1053 Sample_1054 Sample_1055 Sample_1056
#> "0111" "0111" "0312" "0111" "03221" "03212" "032112" "0132"
#> Sample_1057 Sample_1058 Sample_1059 Sample_1060 Sample_1061 Sample_1062 Sample_1063 Sample_1064
#> "03221" "0121" "0132" "03212" "03221" "0132" "0312" "0312"
#> Sample_1065 Sample_1066 Sample_1067 Sample_1068 Sample_1069 Sample_1070 Sample_1071 Sample_1072
#> "0312" "0111" "0312" "0312" "0312" "0111" "0312" "0312"
#> Sample_1073 Sample_1074 Sample_1075 Sample_1076 Sample_1077 Sample_1078 Sample_1079 Sample_1080
#> "0111" "0312" "0111" "0111" "0312" "0312" "0312" "0112"
#> Sample_1081 Sample_1082 Sample_1083 Sample_1084 Sample_1085 Sample_1086 Sample_1087 Sample_1088
#> "0312" "0312" "0111" "0312" "0312" "0331" "0111" "0331"
#> Sample_1089 Sample_1090 Sample_1091 Sample_1092 Sample_1093 Sample_1094 Sample_1095 Sample_1096
#> "0332" "0111" "0111" "0312" "0111" "0312" "0312" "0312"
#> Sample_1097 Sample_1098 Sample_1099 Sample_1100 Sample_1101 Sample_1102 Sample_1103 Sample_1104
#> "0111" "0312" "0312" "0112" "0312" "0111" "0112" "0111"
#> Sample_1105 Sample_1106 Sample_1107 Sample_1108 Sample_1109 Sample_1110 Sample_1111 Sample_1112
#> "0111" "0312" "0312" "0112" "0312" "0111" "0312" "0111"
#> Sample_1113 Sample_1114 Sample_1115 Sample_1116 Sample_1117 Sample_1118 Sample_1119 Sample_1120
#> "0111" "0111" "0312" "0111" "0312" "0111" "0112" "0332"
#> Sample_1121 Sample_1122 Sample_1123 Sample_1124 Sample_1125 Sample_1126 Sample_1127 Sample_1128
#> "0111" "0331" "0312" "0312" "0312" "0312" "0312" "0312"
#> Sample_1129 Sample_1130 Sample_1131 Sample_1132 Sample_1133 Sample_1134 Sample_1135 Sample_1136
#> "0111" "0111" "0111" "0332" "0111" "0331" "0112" "0111"
#> Sample_1137 Sample_1138 Sample_1139 Sample_1140 Sample_1141 Sample_1142 Sample_1143 Sample_1144
#> "0111" "0111" "0312" "0331" "0312" "0112" "0111" "0111"
#> Sample_1145 Sample_1146 Sample_1147 Sample_1148 Sample_1149 Sample_1150 Sample_1151 Sample_1152
#> "0312" "0112" "0112" "0111" "0111" "0312" "0111" "0312"
#> Sample_1153 Sample_1154 Sample_1155 Sample_1156 Sample_1157 Sample_1158 Sample_1159 Sample_1160
#> "0111" "0111" "0111" "0312" "0312" "0111" "0112" "0112"
#> Sample_1161 Sample_1162 Sample_1163 Sample_1164 Sample_1165 Sample_1166 Sample_1167 Sample_1168
#> "0312" "0111" "0312" "0312" "0112" "0111" "0112" "0111"
#> Sample_1169 Sample_1170 Sample_1171 Sample_1172 Sample_1173 Sample_1174 Sample_1175 Sample_1176
#> "0312" "0312" "0111" "0111" "0111" "0111" "0312" "0111"
#> Sample_1177 Sample_1178 Sample_1179 Sample_1180 Sample_1181 Sample_1182 Sample_1183 Sample_1184
#> "0111" "0111" "0112" "0312" "0111" "0111" "0331" "0331"
#> Sample_1185 Sample_1186 Sample_1187 Sample_1188 Sample_1189 Sample_1190 Sample_1191 Sample_1192
#> "0111" "0111" "0111" "0111" "032112" "03221" "03222" "0122"
#> Sample_1193 Sample_1194 Sample_1195 Sample_1196 Sample_1197 Sample_1198 Sample_1199 Sample_1200
#> "03221" "03221" "03212" "03212" "03221" "03221" "03221" "03212"
#> Sample_1201 Sample_1202 Sample_1203 Sample_1204 Sample_1205 Sample_1206 Sample_1207 Sample_1208
#> "03212" "03212" "03221" "03221" "03221" "03212" "03221" "0132"
#> Sample_1209 Sample_1210 Sample_1211 Sample_1212 Sample_1213 Sample_1214 Sample_1215 Sample_1216
#> "032111" "03221" "03212" "03212" "03212" "03221" "03221" "0122"
#> Sample_1217 Sample_1218 Sample_1219 Sample_1220 Sample_1221 Sample_1222 Sample_1223 Sample_1224
#> "0132" "0132" "03221" "03212" "03221" "0111" "0311" "0311"
#> Sample_1225 Sample_1226 Sample_1227 Sample_1228 Sample_1229 Sample_1230 Sample_1231 Sample_1232
#> "0112" "0311" "0112" "0112" "0111" "0311" "0311" "0311"
#> Sample_1233 Sample_1234 Sample_1235 Sample_1236 Sample_1237 Sample_1238 Sample_1239 Sample_1240
#> "0311" "0112" "0311" "0112" "0311" "0311" "0112" "0111"
#> Sample_1241 Sample_1242 Sample_1243 Sample_1244 Sample_1245 Sample_1246 Sample_1247 Sample_1248
#> "0112" "0112" "0311" "0311" "0311" "0311" "0311" "0311"
#> Sample_1249 Sample_1250 Sample_1251 Sample_1252 Sample_1253 Sample_1254 Sample_1255 Sample_1256
#> "0112" "0311" "0311" "0311" "0311" "0111" "0332" "0311"
#> Sample_1257 Sample_1258 Sample_1259 Sample_1260 Sample_1261 Sample_1262 Sample_1263 Sample_1264
#> "0311" "0311" "0311" "0311" "0311" "0311" "0311" "0112"
#> Sample_1265 Sample_1266 Sample_1267 Sample_1268 Sample_1269 Sample_1270 Sample_1271 Sample_1272
#> "0311" "0111" "0111" "0332" "0311" "0311" "0311" "0112"
#> Sample_1273 Sample_1274 Sample_1275 Sample_1276 Sample_1277 Sample_1278 Sample_1279 Sample_1280
#> "0311" "0311" "0311" "0311" "0311" "0311" "0111" "0111"
#> Sample_1281 Sample_1282 Sample_1283 Sample_1284 Sample_1285 Sample_1286 Sample_1287 Sample_1288
#> "0311" "0311" "0112" "0311" "0112" "0311" "0311" "0311"
#> Sample_1289 Sample_1290 Sample_1291 Sample_1292 Sample_1293 Sample_1294 Sample_1295 Sample_1296
#> "0311" "0311" "0311" "0311" "0311" "0311" "0311" "0331"
#> Sample_1297 Sample_1298 Sample_1299 Sample_1300 Sample_1301 Sample_1302 Sample_1303 Sample_1304
#> "0311" "0311" "0111" "0311" "0311" "0311" "0311" "0311"
#> Sample_1305 Sample_1306 Sample_1307 Sample_1308 Sample_1309 Sample_1310 Sample_1311 Sample_1312
#> "0311" "0332" "0111" "0111" "0112" "0332" "0311" "0311"
#> Sample_1313 Sample_1314 Sample_1315 Sample_1316 Sample_1317 Sample_1318 Sample_1319 Sample_1320
#> "0112" "0313" "0313" "0311" "0112" "0313" "0311" "0112"
#> Sample_1321 Sample_1322 Sample_1323 Sample_1324 Sample_1325 Sample_1326 Sample_1327 Sample_1328
#> "0314" "0311" "0311" "0311" "0311" "0314" "0311" "0313"
#> Sample_1329 Sample_1330 Sample_1331 Sample_1332 Sample_1333 Sample_1334 Sample_1335 Sample_1336
#> "0314" "0314" "0313" "0314" "0314" "0313" "0111" "0112"
#> Sample_1337 Sample_1338 Sample_1339 Sample_1340 Sample_1341 Sample_1342 Sample_1343 Sample_1344
#> "0332" "0112" "0332" "0311" "0314" "0112" "0112" "0314"
#> Sample_1345 Sample_1346 Sample_1347 Sample_1348 Sample_1349 Sample_1350 Sample_1351 Sample_1352
#> "0332" "0311" "0311" "0112" "0314" "0112" "0314" "0112"
#> Sample_1353 Sample_1354 Sample_1355 Sample_1356 Sample_1357 Sample_1358 Sample_1359 Sample_1360
#> "0311" "0112" "0314" "0314" "0112" "0112" "0314" "0112"
#> Sample_1361 Sample_1362 Sample_1363 Sample_1364 Sample_1365 Sample_1366 Sample_1367 Sample_1368
#> "0314" "0314" "0314" "0112" "0311" "0313" "0111" "0313"
#> Sample_1369 Sample_1370 Sample_1371 Sample_1372 Sample_1373 Sample_1374 Sample_1375 Sample_1376
#> "0311" "0314" "0112" "0314" "0112" "0112" "0112" "0332"
#> Sample_1377 Sample_1378 Sample_1379 Sample_1380 Sample_1381 Sample_1382 Sample_1383 Sample_1384
#> "0311" "0311" "0311" "0311" "0314" "0112" "0313" "0313"
#> Sample_1385 Sample_1386 Sample_1387 Sample_1388 Sample_1389 Sample_1390 Sample_1391 Sample_1392
#> "0313" "0111" "0313" "0112" "0313" "0313" "0313" "0313"
#> Sample_1393 Sample_1394 Sample_1395 Sample_1396 Sample_1397 Sample_1398 Sample_1399 Sample_1400
#> "0313" "0313" "0313" "0313" "0313" "0111" "0112" "0331"
#> Sample_1401 Sample_1402 Sample_1403 Sample_1404 Sample_1405 Sample_1406 Sample_1407 Sample_1408
#> "0313" "0112" "0313" "0313" "0313" "0111" "0313" "0112"
#> Sample_1409 Sample_1410 Sample_1411 Sample_1412 Sample_1413 Sample_1414 Sample_1415 Sample_1416
#> "0111" "0313" "0313" "0313" "0331" "0313" "0313" "0332"
#> Sample_1417 Sample_1418 Sample_1419 Sample_1420 Sample_1421 Sample_1422 Sample_1423 Sample_1424
#> "0313" "0313" "0112" "0313" "0313" "0313" "0111" "0313"
#> Sample_1425 Sample_1426 Sample_1427 Sample_1428 Sample_1429 Sample_1430 Sample_1431 Sample_1432
#> "0111" "0112" "0112" "0313" "0111" "0111" "0111" "0111"
#> Sample_1433 Sample_1434 Sample_1435 Sample_1436 Sample_1437 Sample_1438 Sample_1439 Sample_1440
#> "0111" "0111" "0111" "0112" "0111" "0112" "0111" "0112"
#> Sample_1441 Sample_1442 Sample_1443 Sample_1444 Sample_1445 Sample_1446 Sample_1447 Sample_1448
#> "0111" "0313" "0313" "0111" "0111" "0313" "0313" "0111"
#> Sample_1449 Sample_1450 Sample_1451 Sample_1452 Sample_1453 Sample_1454 Sample_1455 Sample_1456
#> "0111" "0111" "0111" "0111" "0112" "0111" "0111" "0313"
#> Sample_1457 Sample_1458 Sample_1459 Sample_1460 Sample_1461 Sample_1462 Sample_1463 Sample_1464
#> "0313" "0112" "0112" "0331" "0313" "0111" "0314" "0314"
#> Sample_1465 Sample_1466 Sample_1467 Sample_1468 Sample_1469 Sample_1470 Sample_1471 Sample_1472
#> "0112" "0112" "0111" "0313" "0112" "0112" "0112" "0112"
#> Sample_1473 Sample_1474 Sample_1475 Sample_1476 Sample_1477 Sample_1478 Sample_1479 Sample_1480
#> "0112" "0111" "0112" "0112" "0112" "0112" "0111" "0314"
#> Sample_1481 Sample_1482 Sample_1483 Sample_1484 Sample_1485 Sample_1486 Sample_1487 Sample_1488
#> "0112" "0111" "0112" "0112" "0313" "0331" "0111" "0313"
#> Sample_1489 Sample_1490 Sample_1491 Sample_1492 Sample_1493 Sample_1494 Sample_1495 Sample_1496
#> "0111" "0112" "0112" "0112" "0111" "0332" "0112" "0112"
#> Sample_1497 Sample_1498 Sample_1499 Sample_1500 Sample_1501 Sample_1502 Sample_1503 Sample_1504
#> "0112" "0111" "0111" "0112" "0112" "0313" "0313" "0332"
#> Sample_1505 Sample_1506 Sample_1507 Sample_1508 Sample_1509 Sample_1510 Sample_1511 Sample_1512
#> "0331" "0313" "0313" "0313" "0111" "0111" "0313" "0313"
#> Sample_1513 Sample_1514 Sample_1515 Sample_1516 Sample_1517 Sample_1518 Sample_1519 Sample_1520
#> "0313" "0112" "0313" "0111" "02222" "02222" "02211" "0233"
#> Sample_1521 Sample_1522 Sample_1523 Sample_1524 Sample_1525 Sample_1526 Sample_1527 Sample_1528
#> "0232" "0131" "0132" "0233" "0121" "0121" "02121" "02121"
#> Sample_1529 Sample_1530 Sample_1531 Sample_1532 Sample_1533 Sample_1534 Sample_1535 Sample_1536
#> "02121" "0233" "0232" "0232" "02111" "02121" "02222" "02222"
#> Sample_1537 Sample_1538 Sample_1539 Sample_1540 Sample_1541 Sample_1542 Sample_1543 Sample_1544
#> "02211" "02121" "02121" "0131" "0111" "02121" "0233" "0131"
#> Sample_1545 Sample_1546 Sample_1547 Sample_1548 Sample_1549 Sample_1550 Sample_1551 Sample_1552
#> "0111" "021122" "0131" "0231" "03221" "0231" "0231" "0231"
#> Sample_1553 Sample_1554 Sample_1555 Sample_1556 Sample_1557 Sample_1558 Sample_1559 Sample_1560
#> "03212" "02222" "0231" "0131" "0131" "0131" "0231" "0131"
#> Sample_1561 Sample_1562 Sample_1563 Sample_1564 Sample_1565 Sample_1566 Sample_1567 Sample_1568
#> "0231" "0121" "0131" "0131" "0131" "0131" "0131" "0131"
#> Sample_1569 Sample_1570 Sample_1571 Sample_1572 Sample_1573 Sample_1574 Sample_1575 Sample_1576
#> "0312" "02121" "0131" "0131" "0131" "0231" "0231" "0131"
#> Sample_1577 Sample_1578 Sample_1579 Sample_1580 Sample_1581 Sample_1582 Sample_1583 Sample_1584
#> "0131" "0131" "0131" "0131" "03221" "0231" "0131" "0131"
#> Sample_1585 Sample_1586 Sample_1587 Sample_1588 Sample_1589 Sample_1590 Sample_1591 Sample_1592
#> "0231" "0231" "0231" "02222" "0231" "0131" "0131" "0231"
#> Sample_1593 Sample_1594 Sample_1595 Sample_1596 Sample_1597 Sample_1598 Sample_1599 Sample_1600
#> "0231" "0231" "0231" "0232" "03221" "0311" "0233" "0131"
get_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 108))
#> Sample_1 Sample_2 Sample_3 Sample_4 Sample_5 Sample_6 Sample_7 Sample_8
#> "0132" "02211" "02212" "02222" "02211" "02211" "02211" "02222"
#> Sample_9 Sample_10 Sample_11 Sample_12 Sample_13 Sample_14 Sample_15 Sample_16
#> "02212" "02211" "02212" "02211" "02211" "02211" "02211" "02212"
#> Sample_17 Sample_18 Sample_19 Sample_20 Sample_21 Sample_22 Sample_23 Sample_24
#> "02222" "02222" "02222" "02221" "02221" "02222" "02221" "02211"
#> Sample_25 Sample_26 Sample_27 Sample_28 Sample_29 Sample_30 Sample_31 Sample_32
#> "02221" "02211" "02222" "02222" "02212" "02222" "0233" "02212"
#> Sample_33 Sample_34 Sample_35 Sample_36 Sample_37 Sample_38 Sample_39 Sample_40
#> "02212" "02212" "02212" "02212" "02212" "02212" "02212" "02212"
#> Sample_41 Sample_42 Sample_43 Sample_44 Sample_45 Sample_46 Sample_47 Sample_48
#> "02212" "02212" "0233" "02212" "02212" "02212" "02212" "02212"
#> Sample_49 Sample_50 Sample_51 Sample_52 Sample_53 Sample_54 Sample_55 Sample_56
#> "02212" "02212" "02212" "02212" "02222" "02221" "02212" "02211"
#> Sample_57 Sample_58 Sample_59 Sample_60 Sample_61 Sample_62 Sample_63 Sample_64
#> "02211" "02211" "02212" "02211" "02221" "02211" "02211" "02211"
#> Sample_65 Sample_66 Sample_67 Sample_68 Sample_69 Sample_70 Sample_71 Sample_72
#> "02211" "02211" "02222" "02211" "02211" "02222" "02222" "021121"
#> Sample_73 Sample_74 Sample_75 Sample_76 Sample_77 Sample_78 Sample_79 Sample_80
#> "0233" "02211" "02221" "02211" "02221" "02211" "02221" "02221"
#> Sample_81 Sample_82 Sample_83 Sample_84 Sample_85 Sample_86 Sample_87 Sample_88
#> "02211" "02221" "02211" "02211" "02222" "02212" "02222" "02222"
#> Sample_89 Sample_90 Sample_91 Sample_92 Sample_93 Sample_94 Sample_95 Sample_96
#> "02222" "02222" "02222" "02222" "02222" "0232" "02221" "02211"
#> Sample_97 Sample_98 Sample_99 Sample_100 Sample_101 Sample_102 Sample_103 Sample_104
#> "02211" "02211" "02211" "02222" "02221" "02212" "02222" "02221"
#> Sample_105 Sample_106 Sample_107 Sample_108 Sample_109 Sample_110 Sample_111 Sample_112
#> "02211" "02211" "02221" "02211" "02211" "02221" "02122" "02221"
#> Sample_113 Sample_114 Sample_115 Sample_116 Sample_117 Sample_118 Sample_119 Sample_120
#> "02221" "0232" "02221" "02222" "02221" "0232" "02221" "02211"
#> Sample_121 Sample_122 Sample_123 Sample_124 Sample_125 Sample_126 Sample_127 Sample_128
#> "0121" "02221" "0232" "02211" "02211" "02222" "02211" "02222"
#> Sample_129 Sample_130 Sample_131 Sample_132 Sample_133 Sample_134 Sample_135 Sample_136
#> "02211" "02222" "02222" "02211" "02222" "02211" "02221" "02211"
#> Sample_137 Sample_138 Sample_139 Sample_140 Sample_141 Sample_142 Sample_143 Sample_144
#> "02211" "02211" "02221" "02211" "02211" "02221" "02221" "0232"
#> Sample_145 Sample_146 Sample_147 Sample_148 Sample_149 Sample_150 Sample_151 Sample_152
#> "02221" "02222" "021121" "021121" "021121" "021121" "02121" "021121"
#> Sample_153 Sample_154 Sample_155 Sample_156 Sample_157 Sample_158 Sample_159 Sample_160
#> "021121" "021121" "021121" "021121" "021121" "021121" "02121" "021121"
#> Sample_161 Sample_162 Sample_163 Sample_164 Sample_165 Sample_166 Sample_167 Sample_168
#> "02121" "02121" "02122" "02121" "02121" "02121" "021121" "021121"
#> Sample_169 Sample_170 Sample_171 Sample_172 Sample_173 Sample_174 Sample_175 Sample_176
#> "02121" "021121" "0232" "02121" "0233" "02121" "021121" "021121"
#> Sample_177 Sample_178 Sample_179 Sample_180 Sample_181 Sample_182 Sample_183 Sample_184
#> "02122" "021121" "02122" "02121" "021121" "021121" "021121" "021121"
#> Sample_185 Sample_186 Sample_187 Sample_188 Sample_189 Sample_190 Sample_191 Sample_192
#> "021121" "021121" "021121" "02121" "02121" "021121" "02121" "02121"
#> Sample_193 Sample_194 Sample_195 Sample_196 Sample_197 Sample_198 Sample_199 Sample_200
#> "021121" "021121" "021121" "021121" "02211" "02211" "02212" "02212"
#> Sample_201 Sample_202 Sample_203 Sample_204 Sample_205 Sample_206 Sample_207 Sample_208
#> "02212" "02212" "02212" "0233" "02212" "02212" "02212" "03222"
#> Sample_209 Sample_210 Sample_211 Sample_212 Sample_213 Sample_214 Sample_215 Sample_216
#> "03222" "03222" "03222" "03222" "03222" "0132" "0132" "03222"
#> Sample_217 Sample_218 Sample_219 Sample_220 Sample_221 Sample_222 Sample_223 Sample_224
#> "0132" "03222" "03222" "03222" "03222" "03222" "03222" "0132"
#> Sample_225 Sample_226 Sample_227 Sample_228 Sample_229 Sample_230 Sample_231 Sample_232
#> "03222" "0132" "03222" "03222" "03222" "03222" "03222" "03222"
#> Sample_233 Sample_234 Sample_235 Sample_236 Sample_237 Sample_238 Sample_239 Sample_240
#> "0333" "03222" "03222" "0132" "03222" "03222" "03222" "03222"
#> Sample_241 Sample_242 Sample_243 Sample_244 Sample_245 Sample_246 Sample_247 Sample_248
#> "0132" "03222" "03212" "03222" "03222" "03222" "03222" "03222"
#> Sample_249 Sample_250 Sample_251 Sample_252 Sample_253 Sample_254 Sample_255 Sample_256
#> "03222" "03222" "03222" "03222" "03222" "0333" "03221" "03222"
#> Sample_257 Sample_258 Sample_259 Sample_260 Sample_261 Sample_262 Sample_263 Sample_264
#> "03222" "0132" "03222" "03222" "03222" "03222" "03222" "0132"
#> Sample_265 Sample_266 Sample_267 Sample_268 Sample_269 Sample_270 Sample_271 Sample_272
#> "0122" "03211" "03211" "03211" "03221" "03211" "0333" "0122"
#> Sample_273 Sample_274 Sample_275 Sample_276 Sample_277 Sample_278 Sample_279 Sample_280
#> "03212" "03211" "0122" "0132" "03211" "0333" "03211" "03222"
#> Sample_281 Sample_282 Sample_283 Sample_284 Sample_285 Sample_286 Sample_287 Sample_288
#> "03211" "03211" "03221" "03211" "03211" "03211" "0132" "0333"
#> Sample_289 Sample_290 Sample_291 Sample_292 Sample_293 Sample_294 Sample_295 Sample_296
#> "0333" "03211" "03211" "03211" "03222" "03211" "03211" "03211"
#> Sample_297 Sample_298 Sample_299 Sample_300 Sample_301 Sample_302 Sample_303 Sample_304
#> "03211" "03211" "03211" "03221" "03211" "0122" "0122" "0122"
#> Sample_305 Sample_306 Sample_307 Sample_308 Sample_309 Sample_310 Sample_311 Sample_312
#> "0122" "03212" "03212" "0122" "0122" "0122" "0122" "0122"
#> Sample_313 Sample_314 Sample_315 Sample_316 Sample_317 Sample_318 Sample_319 Sample_320
#> "0122" "0122" "03212" "0122" "03212" "0122" "0122" "0122"
#> Sample_321 Sample_322 Sample_323 Sample_324 Sample_325 Sample_326 Sample_327 Sample_328
#> "0121" "03211" "0122" "0122" "0122" "0121" "0122" "0122"
#> Sample_329 Sample_330 Sample_331 Sample_332 Sample_333 Sample_334 Sample_335 Sample_336
#> "0121" "0122" "0333" "03211" "03211" "03211" "03211" "03211"
#> Sample_337 Sample_338 Sample_339 Sample_340 Sample_341 Sample_342 Sample_343 Sample_344
#> "03211" "03211" "0333" "03212" "03212" "03211" "03211" "03212"
#> Sample_345 Sample_346 Sample_347 Sample_348 Sample_349 Sample_350 Sample_351 Sample_352
#> "03212" "03212" "03211" "03212" "03212" "0132" "0122" "0132"
#> Sample_353 Sample_354 Sample_355 Sample_356 Sample_357 Sample_358 Sample_359 Sample_360
#> "03211" "03211" "0122" "03211" "03211" "0122" "03211" "0122"
#> Sample_361 Sample_362 Sample_363 Sample_364 Sample_365 Sample_366 Sample_367 Sample_368
#> "03211" "0122" "03211" "03211" "0132" "03211" "03211" "0122"
#> Sample_369 Sample_370 Sample_371 Sample_372 Sample_373 Sample_374 Sample_375 Sample_376
#> "0122" "03212" "0333" "0132" "0132" "03211" "03212" "03212"
#> Sample_377 Sample_378 Sample_379 Sample_380 Sample_381 Sample_382 Sample_383 Sample_384
#> "0122" "03212" "03212" "03212" "0122" "03212" "03212" "03212"
#> Sample_385 Sample_386 Sample_387 Sample_388 Sample_389 Sample_390 Sample_391 Sample_392
#> "0121" "0121" "03212" "03211" "0132" "0132" "03212" "03212"
#> Sample_393 Sample_394 Sample_395 Sample_396 Sample_397 Sample_398 Sample_399 Sample_400
#> "0122" "0132" "0122" "0132" "0122" "0122" "03212" "03212"
#> Sample_401 Sample_402 Sample_403 Sample_404 Sample_405 Sample_406 Sample_407 Sample_408
#> "03212" "03221" "0132" "0132" "0132" "03212" "03212" "0132"
#> Sample_409 Sample_410 Sample_411 Sample_412 Sample_413 Sample_414 Sample_415 Sample_416
#> "0122" "03212" "03212" "0122" "03212" "03212" "0333" "0122"
#> Sample_417 Sample_418 Sample_419 Sample_420 Sample_421 Sample_422 Sample_423 Sample_424
#> "03212" "03212" "03212" "0333" "0122" "0333" "03221" "0122"
#> Sample_425 Sample_426 Sample_427 Sample_428 Sample_429 Sample_430 Sample_431 Sample_432
#> "0122" "03221" "0122" "0122" "03221" "0132" "0122" "0121"
#> Sample_433 Sample_434 Sample_435 Sample_436 Sample_437 Sample_438 Sample_439 Sample_440
#> "0132" "0121" "03221" "0333" "0333" "0132" "0121" "0122"
#> Sample_441 Sample_442 Sample_443 Sample_444 Sample_445 Sample_446 Sample_447 Sample_448
#> "03221" "0122" "0132" "03212" "0121" "0122" "03221" "0132"
#> Sample_449 Sample_450 Sample_451 Sample_452 Sample_453 Sample_454 Sample_455 Sample_456
#> "03221" "03212" "0122" "0121" "03221" "03212" "0132" "0121"
#> Sample_457 Sample_458 Sample_459 Sample_460 Sample_461 Sample_462 Sample_463 Sample_464
#> "0132" "03221" "0122" "03212" "0121" "03212" "0132" "0121"
#> Sample_465 Sample_466 Sample_467 Sample_468 Sample_469 Sample_470 Sample_471 Sample_472
#> "0121" "0121" "03221" "0121" "0122" "03212" "03221" "0121"
#> Sample_473 Sample_474 Sample_475 Sample_476 Sample_477 Sample_478 Sample_479 Sample_480
#> "03212" "0121" "0121" "0132" "0132" "0314" "0111" "0314"
#> Sample_481 Sample_482 Sample_483 Sample_484 Sample_485 Sample_486 Sample_487 Sample_488
#> "0313" "0314" "0314" "0314" "0314" "0112" "0112" "0331"
#> Sample_489 Sample_490 Sample_491 Sample_492 Sample_493 Sample_494 Sample_495 Sample_496
#> "0314" "0112" "0314" "0314" "0314" "0314" "0112" "0314"
#> Sample_497 Sample_498 Sample_499 Sample_500 Sample_501 Sample_502 Sample_503 Sample_504
#> "0314" "0314" "0111" "0111" "0314" "0314" "0314" "0314"
#> Sample_505 Sample_506 Sample_507 Sample_508 Sample_509 Sample_510 Sample_511 Sample_512
#> "0111" "0112" "0111" "0314" "0314" "0314" "0111" "0332"
#> Sample_513 Sample_514 Sample_515 Sample_516 Sample_517 Sample_518 Sample_519 Sample_520
#> "0112" "0314" "0112" "021121" "0112" "0314" "0314" "0112"
#> Sample_521 Sample_522 Sample_523 Sample_524 Sample_525 Sample_526 Sample_527 Sample_528
#> "0112" "0314" "0314" "0314" "0314" "0314" "0112" "0312"
#> Sample_529 Sample_530 Sample_531 Sample_532 Sample_533 Sample_534 Sample_535 Sample_536
#> "0331" "0112" "0332" "0111" "0112" "0314" "0112" "0111"
#> Sample_537 Sample_538 Sample_539 Sample_540 Sample_541 Sample_542 Sample_543 Sample_544
#> "0112" "0314" "0112" "0314" "0314" "0112" "0112" "0112"
#> Sample_545 Sample_546 Sample_547 Sample_548 Sample_549 Sample_550 Sample_551 Sample_552
#> "0331" "0112" "0112" "0314" "0314" "0314" "0314" "0314"
#> Sample_553 Sample_554 Sample_555 Sample_556 Sample_557 Sample_558 Sample_559 Sample_560
#> "0332" "0314" "0314" "0314" "0112" "0112" "0112" "0314"
#> Sample_561 Sample_562 Sample_563 Sample_564 Sample_565 Sample_566 Sample_567 Sample_568
#> "0314" "0314" "0314" "0314" "0112" "0112" "0112" "0314"
#> Sample_569 Sample_570 Sample_571 Sample_572 Sample_573 Sample_574 Sample_575 Sample_576
#> "0111" "03221" "03221" "0132" "0121" "0333" "03212" "03221"
#> Sample_577 Sample_578 Sample_579 Sample_580 Sample_581 Sample_582 Sample_583 Sample_584
#> "0122" "03211" "03211" "03211" "03211" "03211" "03211" "03211"
#> Sample_585 Sample_586 Sample_587 Sample_588 Sample_589 Sample_590 Sample_591 Sample_592
#> "03222" "02212" "02211" "0231" "03222" "0131" "02211" "0231"
#> Sample_593 Sample_594 Sample_595 Sample_596 Sample_597 Sample_598 Sample_599 Sample_600
#> "02212" "0231" "0231" "02222" "0231" "02222" "0231" "0231"
#> Sample_601 Sample_602 Sample_603 Sample_604 Sample_605 Sample_606 Sample_607 Sample_608
#> "02222" "0231" "02222" "02222" "02222" "02222" "02211" "02222"
#> Sample_609 Sample_610 Sample_611 Sample_612 Sample_613 Sample_614 Sample_615 Sample_616
#> "02222" "02222" "02121" "02121" "03222" "0131" "03222" "0131"
#> Sample_617 Sample_618 Sample_619 Sample_620 Sample_621 Sample_622 Sample_623 Sample_624
#> "0131" "0121" "0131" "0131" "0231" "0131" "03221" "03221"
#> Sample_625 Sample_626 Sample_627 Sample_628 Sample_629 Sample_630 Sample_631 Sample_632
#> "0231" "0131" "0231" "0231" "0231" "0131" "0231" "0131"
#> Sample_633 Sample_634 Sample_635 Sample_636 Sample_637 Sample_638 Sample_639 Sample_640
#> "0131" "0131" "0131" "0131" "0131" "0231" "0131" "0232"
#> Sample_641 Sample_642 Sample_643 Sample_644 Sample_645 Sample_646 Sample_647 Sample_648
#> "0131" "0233" "0131" "03212" "0232" "0131" "0131" "0232"
#> Sample_649 Sample_650 Sample_651 Sample_652 Sample_653 Sample_654 Sample_655 Sample_656
#> "0232" "0231" "02212" "02211" "02211" "02212" "02211" "02211"
#> Sample_657 Sample_658 Sample_659 Sample_660 Sample_661 Sample_662 Sample_663 Sample_664
#> "02222" "02212" "02211" "02221" "02211" "02221" "02221" "02211"
#> Sample_665 Sample_666 Sample_667 Sample_668 Sample_669 Sample_670 Sample_671 Sample_672
#> "0232" "02221" "0232" "02221" "02221" "02221" "02211" "02221"
#> Sample_673 Sample_674 Sample_675 Sample_676 Sample_677 Sample_678 Sample_679 Sample_680
#> "02221" "02221" "02221" "02222" "02212" "02212" "02211" "02221"
#> Sample_681 Sample_682 Sample_683 Sample_684 Sample_685 Sample_686 Sample_687 Sample_688
#> "0232" "02221" "02221" "02221" "0121" "02121" "0121" "02122"
#> Sample_689 Sample_690 Sample_691 Sample_692 Sample_693 Sample_694 Sample_695 Sample_696
#> "02221" "02122" "02221" "0121" "02221" "02221" "02121" "02111"
#> Sample_697 Sample_698 Sample_699 Sample_700 Sample_701 Sample_702 Sample_703 Sample_704
#> "02111" "02121" "02121" "02111" "02121" "02111" "02121" "02211"
#> Sample_705 Sample_706 Sample_707 Sample_708 Sample_709 Sample_710 Sample_711 Sample_712
#> "02222" "02111" "02111" "02111" "02111" "0232" "02121" "02111"
#> Sample_713 Sample_714 Sample_715 Sample_716 Sample_717 Sample_718 Sample_719 Sample_720
#> "02111" "02121" "02121" "02121" "02111" "02111" "02111" "0232"
#> Sample_721 Sample_722 Sample_723 Sample_724 Sample_725 Sample_726 Sample_727 Sample_728
#> "02111" "02111" "02121" "02111" "02121" "02111" "02111" "02121"
#> Sample_729 Sample_730 Sample_731 Sample_732 Sample_733 Sample_734 Sample_735 Sample_736
#> "02111" "02111" "0233" "02111" "02111" "02111" "0233" "02111"
#> Sample_737 Sample_738 Sample_739 Sample_740 Sample_741 Sample_742 Sample_743 Sample_744
#> "0121" "0232" "02111" "02121" "02111" "02121" "02111" "02111"
#> Sample_745 Sample_746 Sample_747 Sample_748 Sample_749 Sample_750 Sample_751 Sample_752
#> "02121" "02111" "02111" "02121" "02111" "02111" "02111" "02111"
#> Sample_753 Sample_754 Sample_755 Sample_756 Sample_757 Sample_758 Sample_759 Sample_760
#> "02121" "02121" "02111" "02111" "02111" "02111" "02121" "02111"
#> Sample_761 Sample_762 Sample_763 Sample_764 Sample_765 Sample_766 Sample_767 Sample_768
#> "02111" "02111" "02111" "02111" "02111" "02121" "02111" "0232"
#> Sample_769 Sample_770 Sample_771 Sample_772 Sample_773 Sample_774 Sample_775 Sample_776
#> "02121" "02111" "02111" "02121" "02111" "02111" "02121" "02111"
#> Sample_777 Sample_778 Sample_779 Sample_780 Sample_781 Sample_782 Sample_783 Sample_784
#> "02121" "02121" "02121" "02121" "02111" "02121" "02121" "02121"
#> Sample_785 Sample_786 Sample_787 Sample_788 Sample_789 Sample_790 Sample_791 Sample_792
#> "02121" "02121" "02111" "02121" "02121" "02111" "02111" "02111"
#> Sample_793 Sample_794 Sample_795 Sample_796 Sample_797 Sample_798 Sample_799 Sample_800
#> "02121" "02111" "02111" "02111" "02121" "02121" "02121" "02111"
#> Sample_801 Sample_802 Sample_803 Sample_804 Sample_805 Sample_806 Sample_807 Sample_808
#> "02111" "02122" "02111" "02121" "02111" "02111" "02111" "02111"
#> Sample_809 Sample_810 Sample_811 Sample_812 Sample_813 Sample_814 Sample_815 Sample_816
#> "02111" "02121" "02111" "02111" "02111" "02111" "02111" "02111"
#> Sample_817 Sample_818 Sample_819 Sample_820 Sample_821 Sample_822 Sample_823 Sample_824
#> "02121" "02111" "02121" "0233" "02121" "02111" "02121" "02121"
#> Sample_825 Sample_826 Sample_827 Sample_828 Sample_829 Sample_830 Sample_831 Sample_832
#> "02121" "02121" "02121" "02111" "02121" "02121" "02111" "02111"
#> Sample_833 Sample_834 Sample_835 Sample_836 Sample_837 Sample_838 Sample_839 Sample_840
#> "02111" "02121" "02121" "0233" "02111" "02111" "02121" "02111"
#> Sample_841 Sample_842 Sample_843 Sample_844 Sample_845 Sample_846 Sample_847 Sample_848
#> "02222" "02212" "02212" "02211" "02211" "02211" "02221" "02222"
#> Sample_849 Sample_850 Sample_851 Sample_852 Sample_853 Sample_854 Sample_855 Sample_856
#> "02221" "02211" "02211" "02222" "02211" "02211" "02122" "02122"
#> Sample_857 Sample_858 Sample_859 Sample_860 Sample_861 Sample_862 Sample_863 Sample_864
#> "02122" "021122" "0232" "02122" "02122" "02122" "02122" "021122"
#> Sample_865 Sample_866 Sample_867 Sample_868 Sample_869 Sample_870 Sample_871 Sample_872
#> "0233" "02122" "02122" "021122" "02122" "02122" "02122" "02122"
#> Sample_873 Sample_874 Sample_875 Sample_876 Sample_877 Sample_878 Sample_879 Sample_880
#> "02122" "021122" "021122" "02121" "021122" "021122" "021122" "021122"
#> Sample_881 Sample_882 Sample_883 Sample_884 Sample_885 Sample_886 Sample_887 Sample_888
#> "02122" "0233" "021122" "021122" "02122" "021122" "021122" "021122"
#> Sample_889 Sample_890 Sample_891 Sample_892 Sample_893 Sample_894 Sample_895 Sample_896
#> "02122" "02122" "02122" "02122" "02122" "0232" "02122" "021122"
#> Sample_897 Sample_898 Sample_899 Sample_900 Sample_901 Sample_902 Sample_903 Sample_904
#> "02122" "02122" "021122" "0233" "021122" "02122" "02122" "021122"
#> Sample_905 Sample_906 Sample_907 Sample_908 Sample_909 Sample_910 Sample_911 Sample_912
#> "021122" "02122" "02122" "02122" "02122" "021122" "02122" "021122"
#> Sample_913 Sample_914 Sample_915 Sample_916 Sample_917 Sample_918 Sample_919 Sample_920
#> "02122" "02122" "02122" "021122" "02122" "02122" "02122" "02122"
#> Sample_921 Sample_922 Sample_923 Sample_924 Sample_925 Sample_926 Sample_927 Sample_928
#> "021122" "02122" "021122" "02122" "02122" "021122" "021122" "021122"
#> Sample_929 Sample_930 Sample_931 Sample_932 Sample_933 Sample_934 Sample_935 Sample_936
#> "02122" "021122" "021122" "02122" "02122" "02122" "02122" "02122"
#> Sample_937 Sample_938 Sample_939 Sample_940 Sample_941 Sample_942 Sample_943 Sample_944
#> "02122" "02122" "02122" "02122" "02122" "02122" "02122" "02122"
#> Sample_945 Sample_946 Sample_947 Sample_948 Sample_949 Sample_950 Sample_951 Sample_952
#> "021122" "021122" "021122" "03212" "03212" "0132" "03211" "03212"
#> Sample_953 Sample_954 Sample_955 Sample_956 Sample_957 Sample_958 Sample_959 Sample_960
#> "03211" "03221" "03211" "03212" "03211" "03212" "03212" "03212"
#> Sample_961 Sample_962 Sample_963 Sample_964 Sample_965 Sample_966 Sample_967 Sample_968
#> "03211" "0122" "03212" "03211" "03212" "03212" "03211" "0122"
#> Sample_969 Sample_970 Sample_971 Sample_972 Sample_973 Sample_974 Sample_975 Sample_976
#> "03211" "03212" "03211" "03211" "03212" "03211" "0333" "0132"
#> Sample_977 Sample_978 Sample_979 Sample_980 Sample_981 Sample_982 Sample_983 Sample_984
#> "0132" "0122" "03212" "0132" "0132" "0121" "0122" "03212"
#> Sample_985 Sample_986 Sample_987 Sample_988 Sample_989 Sample_990 Sample_991 Sample_992
#> "0122" "0132" "0122" "0121" "0121" "0121" "0121" "0121"
#> Sample_993 Sample_994 Sample_995 Sample_996 Sample_997 Sample_998 Sample_999 Sample_1000
#> "0121" "0122" "03211" "0132" "03212" "03212" "0132" "03212"
#> Sample_1001 Sample_1002 Sample_1003 Sample_1004 Sample_1005 Sample_1006 Sample_1007 Sample_1008
#> "03212" "03212" "03211" "0333" "03212" "03211" "0122" "0132"
#> Sample_1009 Sample_1010 Sample_1011 Sample_1012 Sample_1013 Sample_1014 Sample_1015 Sample_1016
#> "0132" "0132" "03212" "03221" "03212" "0121" "0121" "0121"
#> Sample_1017 Sample_1018 Sample_1019 Sample_1020 Sample_1021 Sample_1022 Sample_1023 Sample_1024
#> "0121" "0121" "0121" "0121" "0121" "0121" "0121" "0121"
#> Sample_1025 Sample_1026 Sample_1027 Sample_1028 Sample_1029 Sample_1030 Sample_1031 Sample_1032
#> "0121" "0121" "0111" "0111" "0312" "0111" "0332" "0312"
#> Sample_1033 Sample_1034 Sample_1035 Sample_1036 Sample_1037 Sample_1038 Sample_1039 Sample_1040
#> "0312" "0111" "0312" "0111" "0312" "0312" "0111" "0312"
#> Sample_1041 Sample_1042 Sample_1043 Sample_1044 Sample_1045 Sample_1046 Sample_1047 Sample_1048
#> "0112" "0111" "0312" "0312" "0332" "0111" "0312" "0331"
#> Sample_1049 Sample_1050 Sample_1051 Sample_1052 Sample_1053 Sample_1054 Sample_1055 Sample_1056
#> "0111" "0111" "0312" "0111" "03221" "03212" "03211" "0132"
#> Sample_1057 Sample_1058 Sample_1059 Sample_1060 Sample_1061 Sample_1062 Sample_1063 Sample_1064
#> "03221" "0121" "0132" "03212" "03221" "0132" "0312" "0312"
#> Sample_1065 Sample_1066 Sample_1067 Sample_1068 Sample_1069 Sample_1070 Sample_1071 Sample_1072
#> "0312" "0111" "0312" "0312" "0312" "0111" "0312" "0312"
#> Sample_1073 Sample_1074 Sample_1075 Sample_1076 Sample_1077 Sample_1078 Sample_1079 Sample_1080
#> "0111" "0312" "0111" "0111" "0312" "0312" "0312" "0112"
#> Sample_1081 Sample_1082 Sample_1083 Sample_1084 Sample_1085 Sample_1086 Sample_1087 Sample_1088
#> "0312" "0312" "0111" "0312" "0312" "0331" "0111" "0331"
#> Sample_1089 Sample_1090 Sample_1091 Sample_1092 Sample_1093 Sample_1094 Sample_1095 Sample_1096
#> "0332" "0111" "0111" "0312" "0111" "0312" "0312" "0312"
#> Sample_1097 Sample_1098 Sample_1099 Sample_1100 Sample_1101 Sample_1102 Sample_1103 Sample_1104
#> "0111" "0312" "0312" "0112" "0312" "0111" "0112" "0111"
#> Sample_1105 Sample_1106 Sample_1107 Sample_1108 Sample_1109 Sample_1110 Sample_1111 Sample_1112
#> "0111" "0312" "0312" "0112" "0312" "0111" "0312" "0111"
#> Sample_1113 Sample_1114 Sample_1115 Sample_1116 Sample_1117 Sample_1118 Sample_1119 Sample_1120
#> "0111" "0111" "0312" "0111" "0312" "0111" "0112" "0332"
#> Sample_1121 Sample_1122 Sample_1123 Sample_1124 Sample_1125 Sample_1126 Sample_1127 Sample_1128
#> "0111" "0331" "0312" "0312" "0312" "0312" "0312" "0312"
#> Sample_1129 Sample_1130 Sample_1131 Sample_1132 Sample_1133 Sample_1134 Sample_1135 Sample_1136
#> "0111" "0111" "0111" "0332" "0111" "0331" "0112" "0111"
#> Sample_1137 Sample_1138 Sample_1139 Sample_1140 Sample_1141 Sample_1142 Sample_1143 Sample_1144
#> "0111" "0111" "0312" "0331" "0312" "0112" "0111" "0111"
#> Sample_1145 Sample_1146 Sample_1147 Sample_1148 Sample_1149 Sample_1150 Sample_1151 Sample_1152
#> "0312" "0112" "0112" "0111" "0111" "0312" "0111" "0312"
#> Sample_1153 Sample_1154 Sample_1155 Sample_1156 Sample_1157 Sample_1158 Sample_1159 Sample_1160
#> "0111" "0111" "0111" "0312" "0312" "0111" "0112" "0112"
#> Sample_1161 Sample_1162 Sample_1163 Sample_1164 Sample_1165 Sample_1166 Sample_1167 Sample_1168
#> "0312" "0111" "0312" "0312" "0112" "0111" "0112" "0111"
#> Sample_1169 Sample_1170 Sample_1171 Sample_1172 Sample_1173 Sample_1174 Sample_1175 Sample_1176
#> "0312" "0312" "0111" "0111" "0111" "0111" "0312" "0111"
#> Sample_1177 Sample_1178 Sample_1179 Sample_1180 Sample_1181 Sample_1182 Sample_1183 Sample_1184
#> "0111" "0111" "0112" "0312" "0111" "0111" "0331" "0331"
#> Sample_1185 Sample_1186 Sample_1187 Sample_1188 Sample_1189 Sample_1190 Sample_1191 Sample_1192
#> "0111" "0111" "0111" "0111" "03211" "03221" "03222" "0122"
#> Sample_1193 Sample_1194 Sample_1195 Sample_1196 Sample_1197 Sample_1198 Sample_1199 Sample_1200
#> "03221" "03221" "03212" "03212" "03221" "03221" "03221" "03212"
#> Sample_1201 Sample_1202 Sample_1203 Sample_1204 Sample_1205 Sample_1206 Sample_1207 Sample_1208
#> "03212" "03212" "03221" "03221" "03221" "03212" "03221" "0132"
#> Sample_1209 Sample_1210 Sample_1211 Sample_1212 Sample_1213 Sample_1214 Sample_1215 Sample_1216
#> "03211" "03221" "03212" "03212" "03212" "03221" "03221" "0122"
#> Sample_1217 Sample_1218 Sample_1219 Sample_1220 Sample_1221 Sample_1222 Sample_1223 Sample_1224
#> "0132" "0132" "03221" "03212" "03221" "0111" "0311" "0311"
#> Sample_1225 Sample_1226 Sample_1227 Sample_1228 Sample_1229 Sample_1230 Sample_1231 Sample_1232
#> "0112" "0311" "0112" "0112" "0111" "0311" "0311" "0311"
#> Sample_1233 Sample_1234 Sample_1235 Sample_1236 Sample_1237 Sample_1238 Sample_1239 Sample_1240
#> "0311" "0112" "0311" "0112" "0311" "0311" "0112" "0111"
#> Sample_1241 Sample_1242 Sample_1243 Sample_1244 Sample_1245 Sample_1246 Sample_1247 Sample_1248
#> "0112" "0112" "0311" "0311" "0311" "0311" "0311" "0311"
#> Sample_1249 Sample_1250 Sample_1251 Sample_1252 Sample_1253 Sample_1254 Sample_1255 Sample_1256
#> "0112" "0311" "0311" "0311" "0311" "0111" "0332" "0311"
#> Sample_1257 Sample_1258 Sample_1259 Sample_1260 Sample_1261 Sample_1262 Sample_1263 Sample_1264
#> "0311" "0311" "0311" "0311" "0311" "0311" "0311" "0112"
#> Sample_1265 Sample_1266 Sample_1267 Sample_1268 Sample_1269 Sample_1270 Sample_1271 Sample_1272
#> "0311" "0111" "0111" "0332" "0311" "0311" "0311" "0112"
#> Sample_1273 Sample_1274 Sample_1275 Sample_1276 Sample_1277 Sample_1278 Sample_1279 Sample_1280
#> "0311" "0311" "0311" "0311" "0311" "0311" "0111" "0111"
#> Sample_1281 Sample_1282 Sample_1283 Sample_1284 Sample_1285 Sample_1286 Sample_1287 Sample_1288
#> "0311" "0311" "0112" "0311" "0112" "0311" "0311" "0311"
#> Sample_1289 Sample_1290 Sample_1291 Sample_1292 Sample_1293 Sample_1294 Sample_1295 Sample_1296
#> "0311" "0311" "0311" "0311" "0311" "0311" "0311" "0331"
#> Sample_1297 Sample_1298 Sample_1299 Sample_1300 Sample_1301 Sample_1302 Sample_1303 Sample_1304
#> "0311" "0311" "0111" "0311" "0311" "0311" "0311" "0311"
#> Sample_1305 Sample_1306 Sample_1307 Sample_1308 Sample_1309 Sample_1310 Sample_1311 Sample_1312
#> "0311" "0332" "0111" "0111" "0112" "0332" "0311" "0311"
#> Sample_1313 Sample_1314 Sample_1315 Sample_1316 Sample_1317 Sample_1318 Sample_1319 Sample_1320
#> "0112" "0313" "0313" "0311" "0112" "0313" "0311" "0112"
#> Sample_1321 Sample_1322 Sample_1323 Sample_1324 Sample_1325 Sample_1326 Sample_1327 Sample_1328
#> "0314" "0311" "0311" "0311" "0311" "0314" "0311" "0313"
#> Sample_1329 Sample_1330 Sample_1331 Sample_1332 Sample_1333 Sample_1334 Sample_1335 Sample_1336
#> "0314" "0314" "0313" "0314" "0314" "0313" "0111" "0112"
#> Sample_1337 Sample_1338 Sample_1339 Sample_1340 Sample_1341 Sample_1342 Sample_1343 Sample_1344
#> "0332" "0112" "0332" "0311" "0314" "0112" "0112" "0314"
#> Sample_1345 Sample_1346 Sample_1347 Sample_1348 Sample_1349 Sample_1350 Sample_1351 Sample_1352
#> "0332" "0311" "0311" "0112" "0314" "0112" "0314" "0112"
#> Sample_1353 Sample_1354 Sample_1355 Sample_1356 Sample_1357 Sample_1358 Sample_1359 Sample_1360
#> "0311" "0112" "0314" "0314" "0112" "0112" "0314" "0112"
#> Sample_1361 Sample_1362 Sample_1363 Sample_1364 Sample_1365 Sample_1366 Sample_1367 Sample_1368
#> "0314" "0314" "0314" "0112" "0311" "0313" "0111" "0313"
#> Sample_1369 Sample_1370 Sample_1371 Sample_1372 Sample_1373 Sample_1374 Sample_1375 Sample_1376
#> "0311" "0314" "0112" "0314" "0112" "0112" "0112" "0332"
#> Sample_1377 Sample_1378 Sample_1379 Sample_1380 Sample_1381 Sample_1382 Sample_1383 Sample_1384
#> "0311" "0311" "0311" "0311" "0314" "0112" "0313" "0313"
#> Sample_1385 Sample_1386 Sample_1387 Sample_1388 Sample_1389 Sample_1390 Sample_1391 Sample_1392
#> "0313" "0111" "0313" "0112" "0313" "0313" "0313" "0313"
#> Sample_1393 Sample_1394 Sample_1395 Sample_1396 Sample_1397 Sample_1398 Sample_1399 Sample_1400
#> "0313" "0313" "0313" "0313" "0313" "0111" "0112" "0331"
#> Sample_1401 Sample_1402 Sample_1403 Sample_1404 Sample_1405 Sample_1406 Sample_1407 Sample_1408
#> "0313" "0112" "0313" "0313" "0313" "0111" "0313" "0112"
#> Sample_1409 Sample_1410 Sample_1411 Sample_1412 Sample_1413 Sample_1414 Sample_1415 Sample_1416
#> "0111" "0313" "0313" "0313" "0331" "0313" "0313" "0332"
#> Sample_1417 Sample_1418 Sample_1419 Sample_1420 Sample_1421 Sample_1422 Sample_1423 Sample_1424
#> "0313" "0313" "0112" "0313" "0313" "0313" "0111" "0313"
#> Sample_1425 Sample_1426 Sample_1427 Sample_1428 Sample_1429 Sample_1430 Sample_1431 Sample_1432
#> "0111" "0112" "0112" "0313" "0111" "0111" "0111" "0111"
#> Sample_1433 Sample_1434 Sample_1435 Sample_1436 Sample_1437 Sample_1438 Sample_1439 Sample_1440
#> "0111" "0111" "0111" "0112" "0111" "0112" "0111" "0112"
#> Sample_1441 Sample_1442 Sample_1443 Sample_1444 Sample_1445 Sample_1446 Sample_1447 Sample_1448
#> "0111" "0313" "0313" "0111" "0111" "0313" "0313" "0111"
#> Sample_1449 Sample_1450 Sample_1451 Sample_1452 Sample_1453 Sample_1454 Sample_1455 Sample_1456
#> "0111" "0111" "0111" "0111" "0112" "0111" "0111" "0313"
#> Sample_1457 Sample_1458 Sample_1459 Sample_1460 Sample_1461 Sample_1462 Sample_1463 Sample_1464
#> "0313" "0112" "0112" "0331" "0313" "0111" "0314" "0314"
#> Sample_1465 Sample_1466 Sample_1467 Sample_1468 Sample_1469 Sample_1470 Sample_1471 Sample_1472
#> "0112" "0112" "0111" "0313" "0112" "0112" "0112" "0112"
#> Sample_1473 Sample_1474 Sample_1475 Sample_1476 Sample_1477 Sample_1478 Sample_1479 Sample_1480
#> "0112" "0111" "0112" "0112" "0112" "0112" "0111" "0314"
#> Sample_1481 Sample_1482 Sample_1483 Sample_1484 Sample_1485 Sample_1486 Sample_1487 Sample_1488
#> "0112" "0111" "0112" "0112" "0313" "0331" "0111" "0313"
#> Sample_1489 Sample_1490 Sample_1491 Sample_1492 Sample_1493 Sample_1494 Sample_1495 Sample_1496
#> "0111" "0112" "0112" "0112" "0111" "0332" "0112" "0112"
#> Sample_1497 Sample_1498 Sample_1499 Sample_1500 Sample_1501 Sample_1502 Sample_1503 Sample_1504
#> "0112" "0111" "0111" "0112" "0112" "0313" "0313" "0332"
#> Sample_1505 Sample_1506 Sample_1507 Sample_1508 Sample_1509 Sample_1510 Sample_1511 Sample_1512
#> "0331" "0313" "0313" "0313" "0111" "0111" "0313" "0313"
#> Sample_1513 Sample_1514 Sample_1515 Sample_1516 Sample_1517 Sample_1518 Sample_1519 Sample_1520
#> "0313" "0112" "0313" "0111" "02222" "02222" "02211" "0233"
#> Sample_1521 Sample_1522 Sample_1523 Sample_1524 Sample_1525 Sample_1526 Sample_1527 Sample_1528
#> "0232" "0131" "0132" "0233" "0121" "0121" "02121" "02121"
#> Sample_1529 Sample_1530 Sample_1531 Sample_1532 Sample_1533 Sample_1534 Sample_1535 Sample_1536
#> "02121" "0233" "0232" "0232" "02111" "02121" "02222" "02222"
#> Sample_1537 Sample_1538 Sample_1539 Sample_1540 Sample_1541 Sample_1542 Sample_1543 Sample_1544
#> "02211" "02121" "02121" "0131" "0111" "02121" "0233" "0131"
#> Sample_1545 Sample_1546 Sample_1547 Sample_1548 Sample_1549 Sample_1550 Sample_1551 Sample_1552
#> "0111" "021122" "0131" "0231" "03221" "0231" "0231" "0231"
#> Sample_1553 Sample_1554 Sample_1555 Sample_1556 Sample_1557 Sample_1558 Sample_1559 Sample_1560
#> "03212" "02222" "0231" "0131" "0131" "0131" "0231" "0131"
#> Sample_1561 Sample_1562 Sample_1563 Sample_1564 Sample_1565 Sample_1566 Sample_1567 Sample_1568
#> "0231" "0121" "0131" "0131" "0131" "0131" "0131" "0131"
#> Sample_1569 Sample_1570 Sample_1571 Sample_1572 Sample_1573 Sample_1574 Sample_1575 Sample_1576
#> "0312" "02121" "0131" "0131" "0131" "0231" "0231" "0131"
#> Sample_1577 Sample_1578 Sample_1579 Sample_1580 Sample_1581 Sample_1582 Sample_1583 Sample_1584
#> "0131" "0131" "0131" "0131" "03221" "0231" "0131" "0131"
#> Sample_1585 Sample_1586 Sample_1587 Sample_1588 Sample_1589 Sample_1590 Sample_1591 Sample_1592
#> "0231" "0231" "0231" "02222" "0231" "0131" "0131" "0231"
#> Sample_1593 Sample_1594 Sample_1595 Sample_1596 Sample_1597 Sample_1598 Sample_1599 Sample_1600
#> "0231" "0231" "0231" "0232" "03221" "0311" "0233" "0131"
get_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 114))
#> Sample_1 Sample_2 Sample_3 Sample_4 Sample_5 Sample_6 Sample_7 Sample_8
#> "0132" "02211" "02212" "02222" "02211" "02211" "02211" "02222"
#> Sample_9 Sample_10 Sample_11 Sample_12 Sample_13 Sample_14 Sample_15 Sample_16
#> "02212" "02211" "02212" "02211" "02211" "02211" "02211" "02212"
#> Sample_17 Sample_18 Sample_19 Sample_20 Sample_21 Sample_22 Sample_23 Sample_24
#> "02222" "02222" "02222" "02221" "02221" "02222" "02221" "02211"
#> Sample_25 Sample_26 Sample_27 Sample_28 Sample_29 Sample_30 Sample_31 Sample_32
#> "02221" "02211" "02222" "02222" "02212" "02222" "0233" "02212"
#> Sample_33 Sample_34 Sample_35 Sample_36 Sample_37 Sample_38 Sample_39 Sample_40
#> "02212" "02212" "02212" "02212" "02212" "02212" "02212" "02212"
#> Sample_41 Sample_42 Sample_43 Sample_44 Sample_45 Sample_46 Sample_47 Sample_48
#> "02212" "02212" "0233" "02212" "02212" "02212" "02212" "02212"
#> Sample_49 Sample_50 Sample_51 Sample_52 Sample_53 Sample_54 Sample_55 Sample_56
#> "02212" "02212" "02212" "02212" "02222" "02221" "02212" "02211"
#> Sample_57 Sample_58 Sample_59 Sample_60 Sample_61 Sample_62 Sample_63 Sample_64
#> "02211" "02211" "02212" "02211" "02221" "02211" "02211" "02211"
#> Sample_65 Sample_66 Sample_67 Sample_68 Sample_69 Sample_70 Sample_71 Sample_72
#> "02211" "02211" "02222" "02211" "02211" "02222" "02222" "021121"
#> Sample_73 Sample_74 Sample_75 Sample_76 Sample_77 Sample_78 Sample_79 Sample_80
#> "0233" "02211" "02221" "02211" "02221" "02211" "02221" "02221"
#> Sample_81 Sample_82 Sample_83 Sample_84 Sample_85 Sample_86 Sample_87 Sample_88
#> "02211" "02221" "02211" "02211" "02222" "02212" "02222" "02222"
#> Sample_89 Sample_90 Sample_91 Sample_92 Sample_93 Sample_94 Sample_95 Sample_96
#> "02222" "02222" "02222" "02222" "02222" "0232" "02221" "02211"
#> Sample_97 Sample_98 Sample_99 Sample_100 Sample_101 Sample_102 Sample_103 Sample_104
#> "02211" "02211" "02211" "02222" "02221" "02212" "02222" "02221"
#> Sample_105 Sample_106 Sample_107 Sample_108 Sample_109 Sample_110 Sample_111 Sample_112
#> "02211" "02211" "02221" "02211" "02211" "02221" "0212" "02221"
#> Sample_113 Sample_114 Sample_115 Sample_116 Sample_117 Sample_118 Sample_119 Sample_120
#> "02221" "0232" "02221" "02222" "02221" "0232" "02221" "02211"
#> Sample_121 Sample_122 Sample_123 Sample_124 Sample_125 Sample_126 Sample_127 Sample_128
#> "0121" "02221" "0232" "02211" "02211" "02222" "02211" "02222"
#> Sample_129 Sample_130 Sample_131 Sample_132 Sample_133 Sample_134 Sample_135 Sample_136
#> "02211" "02222" "02222" "02211" "02222" "02211" "02221" "02211"
#> Sample_137 Sample_138 Sample_139 Sample_140 Sample_141 Sample_142 Sample_143 Sample_144
#> "02211" "02211" "02221" "02211" "02211" "02221" "02221" "0232"
#> Sample_145 Sample_146 Sample_147 Sample_148 Sample_149 Sample_150 Sample_151 Sample_152
#> "02221" "02222" "021121" "021121" "021121" "021121" "0212" "021121"
#> Sample_153 Sample_154 Sample_155 Sample_156 Sample_157 Sample_158 Sample_159 Sample_160
#> "021121" "021121" "021121" "021121" "021121" "021121" "0212" "021121"
#> Sample_161 Sample_162 Sample_163 Sample_164 Sample_165 Sample_166 Sample_167 Sample_168
#> "0212" "0212" "0212" "0212" "0212" "0212" "021121" "021121"
#> Sample_169 Sample_170 Sample_171 Sample_172 Sample_173 Sample_174 Sample_175 Sample_176
#> "0212" "021121" "0232" "0212" "0233" "0212" "021121" "021121"
#> Sample_177 Sample_178 Sample_179 Sample_180 Sample_181 Sample_182 Sample_183 Sample_184
#> "0212" "021121" "0212" "0212" "021121" "021121" "021121" "021121"
#> Sample_185 Sample_186 Sample_187 Sample_188 Sample_189 Sample_190 Sample_191 Sample_192
#> "021121" "021121" "021121" "0212" "0212" "021121" "0212" "0212"
#> Sample_193 Sample_194 Sample_195 Sample_196 Sample_197 Sample_198 Sample_199 Sample_200
#> "021121" "021121" "021121" "021121" "02211" "02211" "02212" "02212"
#> Sample_201 Sample_202 Sample_203 Sample_204 Sample_205 Sample_206 Sample_207 Sample_208
#> "02212" "02212" "02212" "0233" "02212" "02212" "02212" "03222"
#> Sample_209 Sample_210 Sample_211 Sample_212 Sample_213 Sample_214 Sample_215 Sample_216
#> "03222" "03222" "03222" "03222" "03222" "0132" "0132" "03222"
#> Sample_217 Sample_218 Sample_219 Sample_220 Sample_221 Sample_222 Sample_223 Sample_224
#> "0132" "03222" "03222" "03222" "03222" "03222" "03222" "0132"
#> Sample_225 Sample_226 Sample_227 Sample_228 Sample_229 Sample_230 Sample_231 Sample_232
#> "03222" "0132" "03222" "03222" "03222" "03222" "03222" "03222"
#> Sample_233 Sample_234 Sample_235 Sample_236 Sample_237 Sample_238 Sample_239 Sample_240
#> "0333" "03222" "03222" "0132" "03222" "03222" "03222" "03222"
#> Sample_241 Sample_242 Sample_243 Sample_244 Sample_245 Sample_246 Sample_247 Sample_248
#> "0132" "03222" "03212" "03222" "03222" "03222" "03222" "03222"
#> Sample_249 Sample_250 Sample_251 Sample_252 Sample_253 Sample_254 Sample_255 Sample_256
#> "03222" "03222" "03222" "03222" "03222" "0333" "03221" "03222"
#> Sample_257 Sample_258 Sample_259 Sample_260 Sample_261 Sample_262 Sample_263 Sample_264
#> "03222" "0132" "03222" "03222" "03222" "03222" "03222" "0132"
#> Sample_265 Sample_266 Sample_267 Sample_268 Sample_269 Sample_270 Sample_271 Sample_272
#> "0122" "03211" "03211" "03211" "03221" "03211" "0333" "0122"
#> Sample_273 Sample_274 Sample_275 Sample_276 Sample_277 Sample_278 Sample_279 Sample_280
#> "03212" "03211" "0122" "0132" "03211" "0333" "03211" "03222"
#> Sample_281 Sample_282 Sample_283 Sample_284 Sample_285 Sample_286 Sample_287 Sample_288
#> "03211" "03211" "03221" "03211" "03211" "03211" "0132" "0333"
#> Sample_289 Sample_290 Sample_291 Sample_292 Sample_293 Sample_294 Sample_295 Sample_296
#> "0333" "03211" "03211" "03211" "03222" "03211" "03211" "03211"
#> Sample_297 Sample_298 Sample_299 Sample_300 Sample_301 Sample_302 Sample_303 Sample_304
#> "03211" "03211" "03211" "03221" "03211" "0122" "0122" "0122"
#> Sample_305 Sample_306 Sample_307 Sample_308 Sample_309 Sample_310 Sample_311 Sample_312
#> "0122" "03212" "03212" "0122" "0122" "0122" "0122" "0122"
#> Sample_313 Sample_314 Sample_315 Sample_316 Sample_317 Sample_318 Sample_319 Sample_320
#> "0122" "0122" "03212" "0122" "03212" "0122" "0122" "0122"
#> Sample_321 Sample_322 Sample_323 Sample_324 Sample_325 Sample_326 Sample_327 Sample_328
#> "0121" "03211" "0122" "0122" "0122" "0121" "0122" "0122"
#> Sample_329 Sample_330 Sample_331 Sample_332 Sample_333 Sample_334 Sample_335 Sample_336
#> "0121" "0122" "0333" "03211" "03211" "03211" "03211" "03211"
#> Sample_337 Sample_338 Sample_339 Sample_340 Sample_341 Sample_342 Sample_343 Sample_344
#> "03211" "03211" "0333" "03212" "03212" "03211" "03211" "03212"
#> Sample_345 Sample_346 Sample_347 Sample_348 Sample_349 Sample_350 Sample_351 Sample_352
#> "03212" "03212" "03211" "03212" "03212" "0132" "0122" "0132"
#> Sample_353 Sample_354 Sample_355 Sample_356 Sample_357 Sample_358 Sample_359 Sample_360
#> "03211" "03211" "0122" "03211" "03211" "0122" "03211" "0122"
#> Sample_361 Sample_362 Sample_363 Sample_364 Sample_365 Sample_366 Sample_367 Sample_368
#> "03211" "0122" "03211" "03211" "0132" "03211" "03211" "0122"
#> Sample_369 Sample_370 Sample_371 Sample_372 Sample_373 Sample_374 Sample_375 Sample_376
#> "0122" "03212" "0333" "0132" "0132" "03211" "03212" "03212"
#> Sample_377 Sample_378 Sample_379 Sample_380 Sample_381 Sample_382 Sample_383 Sample_384
#> "0122" "03212" "03212" "03212" "0122" "03212" "03212" "03212"
#> Sample_385 Sample_386 Sample_387 Sample_388 Sample_389 Sample_390 Sample_391 Sample_392
#> "0121" "0121" "03212" "03211" "0132" "0132" "03212" "03212"
#> Sample_393 Sample_394 Sample_395 Sample_396 Sample_397 Sample_398 Sample_399 Sample_400
#> "0122" "0132" "0122" "0132" "0122" "0122" "03212" "03212"
#> Sample_401 Sample_402 Sample_403 Sample_404 Sample_405 Sample_406 Sample_407 Sample_408
#> "03212" "03221" "0132" "0132" "0132" "03212" "03212" "0132"
#> Sample_409 Sample_410 Sample_411 Sample_412 Sample_413 Sample_414 Sample_415 Sample_416
#> "0122" "03212" "03212" "0122" "03212" "03212" "0333" "0122"
#> Sample_417 Sample_418 Sample_419 Sample_420 Sample_421 Sample_422 Sample_423 Sample_424
#> "03212" "03212" "03212" "0333" "0122" "0333" "03221" "0122"
#> Sample_425 Sample_426 Sample_427 Sample_428 Sample_429 Sample_430 Sample_431 Sample_432
#> "0122" "03221" "0122" "0122" "03221" "0132" "0122" "0121"
#> Sample_433 Sample_434 Sample_435 Sample_436 Sample_437 Sample_438 Sample_439 Sample_440
#> "0132" "0121" "03221" "0333" "0333" "0132" "0121" "0122"
#> Sample_441 Sample_442 Sample_443 Sample_444 Sample_445 Sample_446 Sample_447 Sample_448
#> "03221" "0122" "0132" "03212" "0121" "0122" "03221" "0132"
#> Sample_449 Sample_450 Sample_451 Sample_452 Sample_453 Sample_454 Sample_455 Sample_456
#> "03221" "03212" "0122" "0121" "03221" "03212" "0132" "0121"
#> Sample_457 Sample_458 Sample_459 Sample_460 Sample_461 Sample_462 Sample_463 Sample_464
#> "0132" "03221" "0122" "03212" "0121" "03212" "0132" "0121"
#> Sample_465 Sample_466 Sample_467 Sample_468 Sample_469 Sample_470 Sample_471 Sample_472
#> "0121" "0121" "03221" "0121" "0122" "03212" "03221" "0121"
#> Sample_473 Sample_474 Sample_475 Sample_476 Sample_477 Sample_478 Sample_479 Sample_480
#> "03212" "0121" "0121" "0132" "0132" "0314" "0111" "0314"
#> Sample_481 Sample_482 Sample_483 Sample_484 Sample_485 Sample_486 Sample_487 Sample_488
#> "0313" "0314" "0314" "0314" "0314" "0112" "0112" "0331"
#> Sample_489 Sample_490 Sample_491 Sample_492 Sample_493 Sample_494 Sample_495 Sample_496
#> "0314" "0112" "0314" "0314" "0314" "0314" "0112" "0314"
#> Sample_497 Sample_498 Sample_499 Sample_500 Sample_501 Sample_502 Sample_503 Sample_504
#> "0314" "0314" "0111" "0111" "0314" "0314" "0314" "0314"
#> Sample_505 Sample_506 Sample_507 Sample_508 Sample_509 Sample_510 Sample_511 Sample_512
#> "0111" "0112" "0111" "0314" "0314" "0314" "0111" "0332"
#> Sample_513 Sample_514 Sample_515 Sample_516 Sample_517 Sample_518 Sample_519 Sample_520
#> "0112" "0314" "0112" "021121" "0112" "0314" "0314" "0112"
#> Sample_521 Sample_522 Sample_523 Sample_524 Sample_525 Sample_526 Sample_527 Sample_528
#> "0112" "0314" "0314" "0314" "0314" "0314" "0112" "0312"
#> Sample_529 Sample_530 Sample_531 Sample_532 Sample_533 Sample_534 Sample_535 Sample_536
#> "0331" "0112" "0332" "0111" "0112" "0314" "0112" "0111"
#> Sample_537 Sample_538 Sample_539 Sample_540 Sample_541 Sample_542 Sample_543 Sample_544
#> "0112" "0314" "0112" "0314" "0314" "0112" "0112" "0112"
#> Sample_545 Sample_546 Sample_547 Sample_548 Sample_549 Sample_550 Sample_551 Sample_552
#> "0331" "0112" "0112" "0314" "0314" "0314" "0314" "0314"
#> Sample_553 Sample_554 Sample_555 Sample_556 Sample_557 Sample_558 Sample_559 Sample_560
#> "0332" "0314" "0314" "0314" "0112" "0112" "0112" "0314"
#> Sample_561 Sample_562 Sample_563 Sample_564 Sample_565 Sample_566 Sample_567 Sample_568
#> "0314" "0314" "0314" "0314" "0112" "0112" "0112" "0314"
#> Sample_569 Sample_570 Sample_571 Sample_572 Sample_573 Sample_574 Sample_575 Sample_576
#> "0111" "03221" "03221" "0132" "0121" "0333" "03212" "03221"
#> Sample_577 Sample_578 Sample_579 Sample_580 Sample_581 Sample_582 Sample_583 Sample_584
#> "0122" "03211" "03211" "03211" "03211" "03211" "03211" "03211"
#> Sample_585 Sample_586 Sample_587 Sample_588 Sample_589 Sample_590 Sample_591 Sample_592
#> "03222" "02212" "02211" "0231" "03222" "0131" "02211" "0231"
#> Sample_593 Sample_594 Sample_595 Sample_596 Sample_597 Sample_598 Sample_599 Sample_600
#> "02212" "0231" "0231" "02222" "0231" "02222" "0231" "0231"
#> Sample_601 Sample_602 Sample_603 Sample_604 Sample_605 Sample_606 Sample_607 Sample_608
#> "02222" "0231" "02222" "02222" "02222" "02222" "02211" "02222"
#> Sample_609 Sample_610 Sample_611 Sample_612 Sample_613 Sample_614 Sample_615 Sample_616
#> "02222" "02222" "0212" "0212" "03222" "0131" "03222" "0131"
#> Sample_617 Sample_618 Sample_619 Sample_620 Sample_621 Sample_622 Sample_623 Sample_624
#> "0131" "0121" "0131" "0131" "0231" "0131" "03221" "03221"
#> Sample_625 Sample_626 Sample_627 Sample_628 Sample_629 Sample_630 Sample_631 Sample_632
#> "0231" "0131" "0231" "0231" "0231" "0131" "0231" "0131"
#> Sample_633 Sample_634 Sample_635 Sample_636 Sample_637 Sample_638 Sample_639 Sample_640
#> "0131" "0131" "0131" "0131" "0131" "0231" "0131" "0232"
#> Sample_641 Sample_642 Sample_643 Sample_644 Sample_645 Sample_646 Sample_647 Sample_648
#> "0131" "0233" "0131" "03212" "0232" "0131" "0131" "0232"
#> Sample_649 Sample_650 Sample_651 Sample_652 Sample_653 Sample_654 Sample_655 Sample_656
#> "0232" "0231" "02212" "02211" "02211" "02212" "02211" "02211"
#> Sample_657 Sample_658 Sample_659 Sample_660 Sample_661 Sample_662 Sample_663 Sample_664
#> "02222" "02212" "02211" "02221" "02211" "02221" "02221" "02211"
#> Sample_665 Sample_666 Sample_667 Sample_668 Sample_669 Sample_670 Sample_671 Sample_672
#> "0232" "02221" "0232" "02221" "02221" "02221" "02211" "02221"
#> Sample_673 Sample_674 Sample_675 Sample_676 Sample_677 Sample_678 Sample_679 Sample_680
#> "02221" "02221" "02221" "02222" "02212" "02212" "02211" "02221"
#> Sample_681 Sample_682 Sample_683 Sample_684 Sample_685 Sample_686 Sample_687 Sample_688
#> "0232" "02221" "02221" "02221" "0121" "0212" "0121" "0212"
#> Sample_689 Sample_690 Sample_691 Sample_692 Sample_693 Sample_694 Sample_695 Sample_696
#> "02221" "0212" "02221" "0121" "02221" "02221" "0212" "02111"
#> Sample_697 Sample_698 Sample_699 Sample_700 Sample_701 Sample_702 Sample_703 Sample_704
#> "02111" "0212" "0212" "02111" "0212" "02111" "0212" "02211"
#> Sample_705 Sample_706 Sample_707 Sample_708 Sample_709 Sample_710 Sample_711 Sample_712
#> "02222" "02111" "02111" "02111" "02111" "0232" "0212" "02111"
#> Sample_713 Sample_714 Sample_715 Sample_716 Sample_717 Sample_718 Sample_719 Sample_720
#> "02111" "0212" "0212" "0212" "02111" "02111" "02111" "0232"
#> Sample_721 Sample_722 Sample_723 Sample_724 Sample_725 Sample_726 Sample_727 Sample_728
#> "02111" "02111" "0212" "02111" "0212" "02111" "02111" "0212"
#> Sample_729 Sample_730 Sample_731 Sample_732 Sample_733 Sample_734 Sample_735 Sample_736
#> "02111" "02111" "0233" "02111" "02111" "02111" "0233" "02111"
#> Sample_737 Sample_738 Sample_739 Sample_740 Sample_741 Sample_742 Sample_743 Sample_744
#> "0121" "0232" "02111" "0212" "02111" "0212" "02111" "02111"
#> Sample_745 Sample_746 Sample_747 Sample_748 Sample_749 Sample_750 Sample_751 Sample_752
#> "0212" "02111" "02111" "0212" "02111" "02111" "02111" "02111"
#> Sample_753 Sample_754 Sample_755 Sample_756 Sample_757 Sample_758 Sample_759 Sample_760
#> "0212" "0212" "02111" "02111" "02111" "02111" "0212" "02111"
#> Sample_761 Sample_762 Sample_763 Sample_764 Sample_765 Sample_766 Sample_767 Sample_768
#> "02111" "02111" "02111" "02111" "02111" "0212" "02111" "0232"
#> Sample_769 Sample_770 Sample_771 Sample_772 Sample_773 Sample_774 Sample_775 Sample_776
#> "0212" "02111" "02111" "0212" "02111" "02111" "0212" "02111"
#> Sample_777 Sample_778 Sample_779 Sample_780 Sample_781 Sample_782 Sample_783 Sample_784
#> "0212" "0212" "0212" "0212" "02111" "0212" "0212" "0212"
#> Sample_785 Sample_786 Sample_787 Sample_788 Sample_789 Sample_790 Sample_791 Sample_792
#> "0212" "0212" "02111" "0212" "0212" "02111" "02111" "02111"
#> Sample_793 Sample_794 Sample_795 Sample_796 Sample_797 Sample_798 Sample_799 Sample_800
#> "0212" "02111" "02111" "02111" "0212" "0212" "0212" "02111"
#> Sample_801 Sample_802 Sample_803 Sample_804 Sample_805 Sample_806 Sample_807 Sample_808
#> "02111" "0212" "02111" "0212" "02111" "02111" "02111" "02111"
#> Sample_809 Sample_810 Sample_811 Sample_812 Sample_813 Sample_814 Sample_815 Sample_816
#> "02111" "0212" "02111" "02111" "02111" "02111" "02111" "02111"
#> Sample_817 Sample_818 Sample_819 Sample_820 Sample_821 Sample_822 Sample_823 Sample_824
#> "0212" "02111" "0212" "0233" "0212" "02111" "0212" "0212"
#> Sample_825 Sample_826 Sample_827 Sample_828 Sample_829 Sample_830 Sample_831 Sample_832
#> "0212" "0212" "0212" "02111" "0212" "0212" "02111" "02111"
#> Sample_833 Sample_834 Sample_835 Sample_836 Sample_837 Sample_838 Sample_839 Sample_840
#> "02111" "0212" "0212" "0233" "02111" "02111" "0212" "02111"
#> Sample_841 Sample_842 Sample_843 Sample_844 Sample_845 Sample_846 Sample_847 Sample_848
#> "02222" "02212" "02212" "02211" "02211" "02211" "02221" "02222"
#> Sample_849 Sample_850 Sample_851 Sample_852 Sample_853 Sample_854 Sample_855 Sample_856
#> "02221" "02211" "02211" "02222" "02211" "02211" "0212" "0212"
#> Sample_857 Sample_858 Sample_859 Sample_860 Sample_861 Sample_862 Sample_863 Sample_864
#> "0212" "021122" "0232" "0212" "0212" "0212" "0212" "021122"
#> Sample_865 Sample_866 Sample_867 Sample_868 Sample_869 Sample_870 Sample_871 Sample_872
#> "0233" "0212" "0212" "021122" "0212" "0212" "0212" "0212"
#> Sample_873 Sample_874 Sample_875 Sample_876 Sample_877 Sample_878 Sample_879 Sample_880
#> "0212" "021122" "021122" "0212" "021122" "021122" "021122" "021122"
#> Sample_881 Sample_882 Sample_883 Sample_884 Sample_885 Sample_886 Sample_887 Sample_888
#> "0212" "0233" "021122" "021122" "0212" "021122" "021122" "021122"
#> Sample_889 Sample_890 Sample_891 Sample_892 Sample_893 Sample_894 Sample_895 Sample_896
#> "0212" "0212" "0212" "0212" "0212" "0232" "0212" "021122"
#> Sample_897 Sample_898 Sample_899 Sample_900 Sample_901 Sample_902 Sample_903 Sample_904
#> "0212" "0212" "021122" "0233" "021122" "0212" "0212" "021122"
#> Sample_905 Sample_906 Sample_907 Sample_908 Sample_909 Sample_910 Sample_911 Sample_912
#> "021122" "0212" "0212" "0212" "0212" "021122" "0212" "021122"
#> Sample_913 Sample_914 Sample_915 Sample_916 Sample_917 Sample_918 Sample_919 Sample_920
#> "0212" "0212" "0212" "021122" "0212" "0212" "0212" "0212"
#> Sample_921 Sample_922 Sample_923 Sample_924 Sample_925 Sample_926 Sample_927 Sample_928
#> "021122" "0212" "021122" "0212" "0212" "021122" "021122" "021122"
#> Sample_929 Sample_930 Sample_931 Sample_932 Sample_933 Sample_934 Sample_935 Sample_936
#> "0212" "021122" "021122" "0212" "0212" "0212" "0212" "0212"
#> Sample_937 Sample_938 Sample_939 Sample_940 Sample_941 Sample_942 Sample_943 Sample_944
#> "0212" "0212" "0212" "0212" "0212" "0212" "0212" "0212"
#> Sample_945 Sample_946 Sample_947 Sample_948 Sample_949 Sample_950 Sample_951 Sample_952
#> "021122" "021122" "021122" "03212" "03212" "0132" "03211" "03212"
#> Sample_953 Sample_954 Sample_955 Sample_956 Sample_957 Sample_958 Sample_959 Sample_960
#> "03211" "03221" "03211" "03212" "03211" "03212" "03212" "03212"
#> Sample_961 Sample_962 Sample_963 Sample_964 Sample_965 Sample_966 Sample_967 Sample_968
#> "03211" "0122" "03212" "03211" "03212" "03212" "03211" "0122"
#> Sample_969 Sample_970 Sample_971 Sample_972 Sample_973 Sample_974 Sample_975 Sample_976
#> "03211" "03212" "03211" "03211" "03212" "03211" "0333" "0132"
#> Sample_977 Sample_978 Sample_979 Sample_980 Sample_981 Sample_982 Sample_983 Sample_984
#> "0132" "0122" "03212" "0132" "0132" "0121" "0122" "03212"
#> Sample_985 Sample_986 Sample_987 Sample_988 Sample_989 Sample_990 Sample_991 Sample_992
#> "0122" "0132" "0122" "0121" "0121" "0121" "0121" "0121"
#> Sample_993 Sample_994 Sample_995 Sample_996 Sample_997 Sample_998 Sample_999 Sample_1000
#> "0121" "0122" "03211" "0132" "03212" "03212" "0132" "03212"
#> Sample_1001 Sample_1002 Sample_1003 Sample_1004 Sample_1005 Sample_1006 Sample_1007 Sample_1008
#> "03212" "03212" "03211" "0333" "03212" "03211" "0122" "0132"
#> Sample_1009 Sample_1010 Sample_1011 Sample_1012 Sample_1013 Sample_1014 Sample_1015 Sample_1016
#> "0132" "0132" "03212" "03221" "03212" "0121" "0121" "0121"
#> Sample_1017 Sample_1018 Sample_1019 Sample_1020 Sample_1021 Sample_1022 Sample_1023 Sample_1024
#> "0121" "0121" "0121" "0121" "0121" "0121" "0121" "0121"
#> Sample_1025 Sample_1026 Sample_1027 Sample_1028 Sample_1029 Sample_1030 Sample_1031 Sample_1032
#> "0121" "0121" "0111" "0111" "0312" "0111" "0332" "0312"
#> Sample_1033 Sample_1034 Sample_1035 Sample_1036 Sample_1037 Sample_1038 Sample_1039 Sample_1040
#> "0312" "0111" "0312" "0111" "0312" "0312" "0111" "0312"
#> Sample_1041 Sample_1042 Sample_1043 Sample_1044 Sample_1045 Sample_1046 Sample_1047 Sample_1048
#> "0112" "0111" "0312" "0312" "0332" "0111" "0312" "0331"
#> Sample_1049 Sample_1050 Sample_1051 Sample_1052 Sample_1053 Sample_1054 Sample_1055 Sample_1056
#> "0111" "0111" "0312" "0111" "03221" "03212" "03211" "0132"
#> Sample_1057 Sample_1058 Sample_1059 Sample_1060 Sample_1061 Sample_1062 Sample_1063 Sample_1064
#> "03221" "0121" "0132" "03212" "03221" "0132" "0312" "0312"
#> Sample_1065 Sample_1066 Sample_1067 Sample_1068 Sample_1069 Sample_1070 Sample_1071 Sample_1072
#> "0312" "0111" "0312" "0312" "0312" "0111" "0312" "0312"
#> Sample_1073 Sample_1074 Sample_1075 Sample_1076 Sample_1077 Sample_1078 Sample_1079 Sample_1080
#> "0111" "0312" "0111" "0111" "0312" "0312" "0312" "0112"
#> Sample_1081 Sample_1082 Sample_1083 Sample_1084 Sample_1085 Sample_1086 Sample_1087 Sample_1088
#> "0312" "0312" "0111" "0312" "0312" "0331" "0111" "0331"
#> Sample_1089 Sample_1090 Sample_1091 Sample_1092 Sample_1093 Sample_1094 Sample_1095 Sample_1096
#> "0332" "0111" "0111" "0312" "0111" "0312" "0312" "0312"
#> Sample_1097 Sample_1098 Sample_1099 Sample_1100 Sample_1101 Sample_1102 Sample_1103 Sample_1104
#> "0111" "0312" "0312" "0112" "0312" "0111" "0112" "0111"
#> Sample_1105 Sample_1106 Sample_1107 Sample_1108 Sample_1109 Sample_1110 Sample_1111 Sample_1112
#> "0111" "0312" "0312" "0112" "0312" "0111" "0312" "0111"
#> Sample_1113 Sample_1114 Sample_1115 Sample_1116 Sample_1117 Sample_1118 Sample_1119 Sample_1120
#> "0111" "0111" "0312" "0111" "0312" "0111" "0112" "0332"
#> Sample_1121 Sample_1122 Sample_1123 Sample_1124 Sample_1125 Sample_1126 Sample_1127 Sample_1128
#> "0111" "0331" "0312" "0312" "0312" "0312" "0312" "0312"
#> Sample_1129 Sample_1130 Sample_1131 Sample_1132 Sample_1133 Sample_1134 Sample_1135 Sample_1136
#> "0111" "0111" "0111" "0332" "0111" "0331" "0112" "0111"
#> Sample_1137 Sample_1138 Sample_1139 Sample_1140 Sample_1141 Sample_1142 Sample_1143 Sample_1144
#> "0111" "0111" "0312" "0331" "0312" "0112" "0111" "0111"
#> Sample_1145 Sample_1146 Sample_1147 Sample_1148 Sample_1149 Sample_1150 Sample_1151 Sample_1152
#> "0312" "0112" "0112" "0111" "0111" "0312" "0111" "0312"
#> Sample_1153 Sample_1154 Sample_1155 Sample_1156 Sample_1157 Sample_1158 Sample_1159 Sample_1160
#> "0111" "0111" "0111" "0312" "0312" "0111" "0112" "0112"
#> Sample_1161 Sample_1162 Sample_1163 Sample_1164 Sample_1165 Sample_1166 Sample_1167 Sample_1168
#> "0312" "0111" "0312" "0312" "0112" "0111" "0112" "0111"
#> Sample_1169 Sample_1170 Sample_1171 Sample_1172 Sample_1173 Sample_1174 Sample_1175 Sample_1176
#> "0312" "0312" "0111" "0111" "0111" "0111" "0312" "0111"
#> Sample_1177 Sample_1178 Sample_1179 Sample_1180 Sample_1181 Sample_1182 Sample_1183 Sample_1184
#> "0111" "0111" "0112" "0312" "0111" "0111" "0331" "0331"
#> Sample_1185 Sample_1186 Sample_1187 Sample_1188 Sample_1189 Sample_1190 Sample_1191 Sample_1192
#> "0111" "0111" "0111" "0111" "03211" "03221" "03222" "0122"
#> Sample_1193 Sample_1194 Sample_1195 Sample_1196 Sample_1197 Sample_1198 Sample_1199 Sample_1200
#> "03221" "03221" "03212" "03212" "03221" "03221" "03221" "03212"
#> Sample_1201 Sample_1202 Sample_1203 Sample_1204 Sample_1205 Sample_1206 Sample_1207 Sample_1208
#> "03212" "03212" "03221" "03221" "03221" "03212" "03221" "0132"
#> Sample_1209 Sample_1210 Sample_1211 Sample_1212 Sample_1213 Sample_1214 Sample_1215 Sample_1216
#> "03211" "03221" "03212" "03212" "03212" "03221" "03221" "0122"
#> Sample_1217 Sample_1218 Sample_1219 Sample_1220 Sample_1221 Sample_1222 Sample_1223 Sample_1224
#> "0132" "0132" "03221" "03212" "03221" "0111" "0311" "0311"
#> Sample_1225 Sample_1226 Sample_1227 Sample_1228 Sample_1229 Sample_1230 Sample_1231 Sample_1232
#> "0112" "0311" "0112" "0112" "0111" "0311" "0311" "0311"
#> Sample_1233 Sample_1234 Sample_1235 Sample_1236 Sample_1237 Sample_1238 Sample_1239 Sample_1240
#> "0311" "0112" "0311" "0112" "0311" "0311" "0112" "0111"
#> Sample_1241 Sample_1242 Sample_1243 Sample_1244 Sample_1245 Sample_1246 Sample_1247 Sample_1248
#> "0112" "0112" "0311" "0311" "0311" "0311" "0311" "0311"
#> Sample_1249 Sample_1250 Sample_1251 Sample_1252 Sample_1253 Sample_1254 Sample_1255 Sample_1256
#> "0112" "0311" "0311" "0311" "0311" "0111" "0332" "0311"
#> Sample_1257 Sample_1258 Sample_1259 Sample_1260 Sample_1261 Sample_1262 Sample_1263 Sample_1264
#> "0311" "0311" "0311" "0311" "0311" "0311" "0311" "0112"
#> Sample_1265 Sample_1266 Sample_1267 Sample_1268 Sample_1269 Sample_1270 Sample_1271 Sample_1272
#> "0311" "0111" "0111" "0332" "0311" "0311" "0311" "0112"
#> Sample_1273 Sample_1274 Sample_1275 Sample_1276 Sample_1277 Sample_1278 Sample_1279 Sample_1280
#> "0311" "0311" "0311" "0311" "0311" "0311" "0111" "0111"
#> Sample_1281 Sample_1282 Sample_1283 Sample_1284 Sample_1285 Sample_1286 Sample_1287 Sample_1288
#> "0311" "0311" "0112" "0311" "0112" "0311" "0311" "0311"
#> Sample_1289 Sample_1290 Sample_1291 Sample_1292 Sample_1293 Sample_1294 Sample_1295 Sample_1296
#> "0311" "0311" "0311" "0311" "0311" "0311" "0311" "0331"
#> Sample_1297 Sample_1298 Sample_1299 Sample_1300 Sample_1301 Sample_1302 Sample_1303 Sample_1304
#> "0311" "0311" "0111" "0311" "0311" "0311" "0311" "0311"
#> Sample_1305 Sample_1306 Sample_1307 Sample_1308 Sample_1309 Sample_1310 Sample_1311 Sample_1312
#> "0311" "0332" "0111" "0111" "0112" "0332" "0311" "0311"
#> Sample_1313 Sample_1314 Sample_1315 Sample_1316 Sample_1317 Sample_1318 Sample_1319 Sample_1320
#> "0112" "0313" "0313" "0311" "0112" "0313" "0311" "0112"
#> Sample_1321 Sample_1322 Sample_1323 Sample_1324 Sample_1325 Sample_1326 Sample_1327 Sample_1328
#> "0314" "0311" "0311" "0311" "0311" "0314" "0311" "0313"
#> Sample_1329 Sample_1330 Sample_1331 Sample_1332 Sample_1333 Sample_1334 Sample_1335 Sample_1336
#> "0314" "0314" "0313" "0314" "0314" "0313" "0111" "0112"
#> Sample_1337 Sample_1338 Sample_1339 Sample_1340 Sample_1341 Sample_1342 Sample_1343 Sample_1344
#> "0332" "0112" "0332" "0311" "0314" "0112" "0112" "0314"
#> Sample_1345 Sample_1346 Sample_1347 Sample_1348 Sample_1349 Sample_1350 Sample_1351 Sample_1352
#> "0332" "0311" "0311" "0112" "0314" "0112" "0314" "0112"
#> Sample_1353 Sample_1354 Sample_1355 Sample_1356 Sample_1357 Sample_1358 Sample_1359 Sample_1360
#> "0311" "0112" "0314" "0314" "0112" "0112" "0314" "0112"
#> Sample_1361 Sample_1362 Sample_1363 Sample_1364 Sample_1365 Sample_1366 Sample_1367 Sample_1368
#> "0314" "0314" "0314" "0112" "0311" "0313" "0111" "0313"
#> Sample_1369 Sample_1370 Sample_1371 Sample_1372 Sample_1373 Sample_1374 Sample_1375 Sample_1376
#> "0311" "0314" "0112" "0314" "0112" "0112" "0112" "0332"
#> Sample_1377 Sample_1378 Sample_1379 Sample_1380 Sample_1381 Sample_1382 Sample_1383 Sample_1384
#> "0311" "0311" "0311" "0311" "0314" "0112" "0313" "0313"
#> Sample_1385 Sample_1386 Sample_1387 Sample_1388 Sample_1389 Sample_1390 Sample_1391 Sample_1392
#> "0313" "0111" "0313" "0112" "0313" "0313" "0313" "0313"
#> Sample_1393 Sample_1394 Sample_1395 Sample_1396 Sample_1397 Sample_1398 Sample_1399 Sample_1400
#> "0313" "0313" "0313" "0313" "0313" "0111" "0112" "0331"
#> Sample_1401 Sample_1402 Sample_1403 Sample_1404 Sample_1405 Sample_1406 Sample_1407 Sample_1408
#> "0313" "0112" "0313" "0313" "0313" "0111" "0313" "0112"
#> Sample_1409 Sample_1410 Sample_1411 Sample_1412 Sample_1413 Sample_1414 Sample_1415 Sample_1416
#> "0111" "0313" "0313" "0313" "0331" "0313" "0313" "0332"
#> Sample_1417 Sample_1418 Sample_1419 Sample_1420 Sample_1421 Sample_1422 Sample_1423 Sample_1424
#> "0313" "0313" "0112" "0313" "0313" "0313" "0111" "0313"
#> Sample_1425 Sample_1426 Sample_1427 Sample_1428 Sample_1429 Sample_1430 Sample_1431 Sample_1432
#> "0111" "0112" "0112" "0313" "0111" "0111" "0111" "0111"
#> Sample_1433 Sample_1434 Sample_1435 Sample_1436 Sample_1437 Sample_1438 Sample_1439 Sample_1440
#> "0111" "0111" "0111" "0112" "0111" "0112" "0111" "0112"
#> Sample_1441 Sample_1442 Sample_1443 Sample_1444 Sample_1445 Sample_1446 Sample_1447 Sample_1448
#> "0111" "0313" "0313" "0111" "0111" "0313" "0313" "0111"
#> Sample_1449 Sample_1450 Sample_1451 Sample_1452 Sample_1453 Sample_1454 Sample_1455 Sample_1456
#> "0111" "0111" "0111" "0111" "0112" "0111" "0111" "0313"
#> Sample_1457 Sample_1458 Sample_1459 Sample_1460 Sample_1461 Sample_1462 Sample_1463 Sample_1464
#> "0313" "0112" "0112" "0331" "0313" "0111" "0314" "0314"
#> Sample_1465 Sample_1466 Sample_1467 Sample_1468 Sample_1469 Sample_1470 Sample_1471 Sample_1472
#> "0112" "0112" "0111" "0313" "0112" "0112" "0112" "0112"
#> Sample_1473 Sample_1474 Sample_1475 Sample_1476 Sample_1477 Sample_1478 Sample_1479 Sample_1480
#> "0112" "0111" "0112" "0112" "0112" "0112" "0111" "0314"
#> Sample_1481 Sample_1482 Sample_1483 Sample_1484 Sample_1485 Sample_1486 Sample_1487 Sample_1488
#> "0112" "0111" "0112" "0112" "0313" "0331" "0111" "0313"
#> Sample_1489 Sample_1490 Sample_1491 Sample_1492 Sample_1493 Sample_1494 Sample_1495 Sample_1496
#> "0111" "0112" "0112" "0112" "0111" "0332" "0112" "0112"
#> Sample_1497 Sample_1498 Sample_1499 Sample_1500 Sample_1501 Sample_1502 Sample_1503 Sample_1504
#> "0112" "0111" "0111" "0112" "0112" "0313" "0313" "0332"
#> Sample_1505 Sample_1506 Sample_1507 Sample_1508 Sample_1509 Sample_1510 Sample_1511 Sample_1512
#> "0331" "0313" "0313" "0313" "0111" "0111" "0313" "0313"
#> Sample_1513 Sample_1514 Sample_1515 Sample_1516 Sample_1517 Sample_1518 Sample_1519 Sample_1520
#> "0313" "0112" "0313" "0111" "02222" "02222" "02211" "0233"
#> Sample_1521 Sample_1522 Sample_1523 Sample_1524 Sample_1525 Sample_1526 Sample_1527 Sample_1528
#> "0232" "0131" "0132" "0233" "0121" "0121" "0212" "0212"
#> Sample_1529 Sample_1530 Sample_1531 Sample_1532 Sample_1533 Sample_1534 Sample_1535 Sample_1536
#> "0212" "0233" "0232" "0232" "02111" "0212" "02222" "02222"
#> Sample_1537 Sample_1538 Sample_1539 Sample_1540 Sample_1541 Sample_1542 Sample_1543 Sample_1544
#> "02211" "0212" "0212" "0131" "0111" "0212" "0233" "0131"
#> Sample_1545 Sample_1546 Sample_1547 Sample_1548 Sample_1549 Sample_1550 Sample_1551 Sample_1552
#> "0111" "021122" "0131" "0231" "03221" "0231" "0231" "0231"
#> Sample_1553 Sample_1554 Sample_1555 Sample_1556 Sample_1557 Sample_1558 Sample_1559 Sample_1560
#> "03212" "02222" "0231" "0131" "0131" "0131" "0231" "0131"
#> Sample_1561 Sample_1562 Sample_1563 Sample_1564 Sample_1565 Sample_1566 Sample_1567 Sample_1568
#> "0231" "0121" "0131" "0131" "0131" "0131" "0131" "0131"
#> Sample_1569 Sample_1570 Sample_1571 Sample_1572 Sample_1573 Sample_1574 Sample_1575 Sample_1576
#> "0312" "0212" "0131" "0131" "0131" "0231" "0231" "0131"
#> Sample_1577 Sample_1578 Sample_1579 Sample_1580 Sample_1581 Sample_1582 Sample_1583 Sample_1584
#> "0131" "0131" "0131" "0131" "03221" "0231" "0131" "0131"
#> Sample_1585 Sample_1586 Sample_1587 Sample_1588 Sample_1589 Sample_1590 Sample_1591 Sample_1592
#> "0231" "0231" "0231" "02222" "0231" "0131" "0131" "0231"
#> Sample_1593 Sample_1594 Sample_1595 Sample_1596 Sample_1597 Sample_1598 Sample_1599 Sample_1600
#> "0231" "0231" "0231" "0232" "03221" "0311" "0233" "0131"
get_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 139))
#> Sample_1 Sample_2 Sample_3 Sample_4 Sample_5 Sample_6 Sample_7 Sample_8
#> "0132" "02211" "02212" "02222" "02211" "02211" "02211" "02222"
#> Sample_9 Sample_10 Sample_11 Sample_12 Sample_13 Sample_14 Sample_15 Sample_16
#> "02212" "02211" "02212" "02211" "02211" "02211" "02211" "02212"
#> Sample_17 Sample_18 Sample_19 Sample_20 Sample_21 Sample_22 Sample_23 Sample_24
#> "02222" "02222" "02222" "02221" "02221" "02222" "02221" "02211"
#> Sample_25 Sample_26 Sample_27 Sample_28 Sample_29 Sample_30 Sample_31 Sample_32
#> "02221" "02211" "02222" "02222" "02212" "02222" "0233" "02212"
#> Sample_33 Sample_34 Sample_35 Sample_36 Sample_37 Sample_38 Sample_39 Sample_40
#> "02212" "02212" "02212" "02212" "02212" "02212" "02212" "02212"
#> Sample_41 Sample_42 Sample_43 Sample_44 Sample_45 Sample_46 Sample_47 Sample_48
#> "02212" "02212" "0233" "02212" "02212" "02212" "02212" "02212"
#> Sample_49 Sample_50 Sample_51 Sample_52 Sample_53 Sample_54 Sample_55 Sample_56
#> "02212" "02212" "02212" "02212" "02222" "02221" "02212" "02211"
#> Sample_57 Sample_58 Sample_59 Sample_60 Sample_61 Sample_62 Sample_63 Sample_64
#> "02211" "02211" "02212" "02211" "02221" "02211" "02211" "02211"
#> Sample_65 Sample_66 Sample_67 Sample_68 Sample_69 Sample_70 Sample_71 Sample_72
#> "02211" "02211" "02222" "02211" "02211" "02222" "02222" "02112"
#> Sample_73 Sample_74 Sample_75 Sample_76 Sample_77 Sample_78 Sample_79 Sample_80
#> "0233" "02211" "02221" "02211" "02221" "02211" "02221" "02221"
#> Sample_81 Sample_82 Sample_83 Sample_84 Sample_85 Sample_86 Sample_87 Sample_88
#> "02211" "02221" "02211" "02211" "02222" "02212" "02222" "02222"
#> Sample_89 Sample_90 Sample_91 Sample_92 Sample_93 Sample_94 Sample_95 Sample_96
#> "02222" "02222" "02222" "02222" "02222" "0232" "02221" "02211"
#> Sample_97 Sample_98 Sample_99 Sample_100 Sample_101 Sample_102 Sample_103 Sample_104
#> "02211" "02211" "02211" "02222" "02221" "02212" "02222" "02221"
#> Sample_105 Sample_106 Sample_107 Sample_108 Sample_109 Sample_110 Sample_111 Sample_112
#> "02211" "02211" "02221" "02211" "02211" "02221" "0212" "02221"
#> Sample_113 Sample_114 Sample_115 Sample_116 Sample_117 Sample_118 Sample_119 Sample_120
#> "02221" "0232" "02221" "02222" "02221" "0232" "02221" "02211"
#> Sample_121 Sample_122 Sample_123 Sample_124 Sample_125 Sample_126 Sample_127 Sample_128
#> "0121" "02221" "0232" "02211" "02211" "02222" "02211" "02222"
#> Sample_129 Sample_130 Sample_131 Sample_132 Sample_133 Sample_134 Sample_135 Sample_136
#> "02211" "02222" "02222" "02211" "02222" "02211" "02221" "02211"
#> Sample_137 Sample_138 Sample_139 Sample_140 Sample_141 Sample_142 Sample_143 Sample_144
#> "02211" "02211" "02221" "02211" "02211" "02221" "02221" "0232"
#> Sample_145 Sample_146 Sample_147 Sample_148 Sample_149 Sample_150 Sample_151 Sample_152
#> "02221" "02222" "02112" "02112" "02112" "02112" "0212" "02112"
#> Sample_153 Sample_154 Sample_155 Sample_156 Sample_157 Sample_158 Sample_159 Sample_160
#> "02112" "02112" "02112" "02112" "02112" "02112" "0212" "02112"
#> Sample_161 Sample_162 Sample_163 Sample_164 Sample_165 Sample_166 Sample_167 Sample_168
#> "0212" "0212" "0212" "0212" "0212" "0212" "02112" "02112"
#> Sample_169 Sample_170 Sample_171 Sample_172 Sample_173 Sample_174 Sample_175 Sample_176
#> "0212" "02112" "0232" "0212" "0233" "0212" "02112" "02112"
#> Sample_177 Sample_178 Sample_179 Sample_180 Sample_181 Sample_182 Sample_183 Sample_184
#> "0212" "02112" "0212" "0212" "02112" "02112" "02112" "02112"
#> Sample_185 Sample_186 Sample_187 Sample_188 Sample_189 Sample_190 Sample_191 Sample_192
#> "02112" "02112" "02112" "0212" "0212" "02112" "0212" "0212"
#> Sample_193 Sample_194 Sample_195 Sample_196 Sample_197 Sample_198 Sample_199 Sample_200
#> "02112" "02112" "02112" "02112" "02211" "02211" "02212" "02212"
#> Sample_201 Sample_202 Sample_203 Sample_204 Sample_205 Sample_206 Sample_207 Sample_208
#> "02212" "02212" "02212" "0233" "02212" "02212" "02212" "03222"
#> Sample_209 Sample_210 Sample_211 Sample_212 Sample_213 Sample_214 Sample_215 Sample_216
#> "03222" "03222" "03222" "03222" "03222" "0132" "0132" "03222"
#> Sample_217 Sample_218 Sample_219 Sample_220 Sample_221 Sample_222 Sample_223 Sample_224
#> "0132" "03222" "03222" "03222" "03222" "03222" "03222" "0132"
#> Sample_225 Sample_226 Sample_227 Sample_228 Sample_229 Sample_230 Sample_231 Sample_232
#> "03222" "0132" "03222" "03222" "03222" "03222" "03222" "03222"
#> Sample_233 Sample_234 Sample_235 Sample_236 Sample_237 Sample_238 Sample_239 Sample_240
#> "0333" "03222" "03222" "0132" "03222" "03222" "03222" "03222"
#> Sample_241 Sample_242 Sample_243 Sample_244 Sample_245 Sample_246 Sample_247 Sample_248
#> "0132" "03222" "03212" "03222" "03222" "03222" "03222" "03222"
#> Sample_249 Sample_250 Sample_251 Sample_252 Sample_253 Sample_254 Sample_255 Sample_256
#> "03222" "03222" "03222" "03222" "03222" "0333" "03221" "03222"
#> Sample_257 Sample_258 Sample_259 Sample_260 Sample_261 Sample_262 Sample_263 Sample_264
#> "03222" "0132" "03222" "03222" "03222" "03222" "03222" "0132"
#> Sample_265 Sample_266 Sample_267 Sample_268 Sample_269 Sample_270 Sample_271 Sample_272
#> "0122" "03211" "03211" "03211" "03221" "03211" "0333" "0122"
#> Sample_273 Sample_274 Sample_275 Sample_276 Sample_277 Sample_278 Sample_279 Sample_280
#> "03212" "03211" "0122" "0132" "03211" "0333" "03211" "03222"
#> Sample_281 Sample_282 Sample_283 Sample_284 Sample_285 Sample_286 Sample_287 Sample_288
#> "03211" "03211" "03221" "03211" "03211" "03211" "0132" "0333"
#> Sample_289 Sample_290 Sample_291 Sample_292 Sample_293 Sample_294 Sample_295 Sample_296
#> "0333" "03211" "03211" "03211" "03222" "03211" "03211" "03211"
#> Sample_297 Sample_298 Sample_299 Sample_300 Sample_301 Sample_302 Sample_303 Sample_304
#> "03211" "03211" "03211" "03221" "03211" "0122" "0122" "0122"
#> Sample_305 Sample_306 Sample_307 Sample_308 Sample_309 Sample_310 Sample_311 Sample_312
#> "0122" "03212" "03212" "0122" "0122" "0122" "0122" "0122"
#> Sample_313 Sample_314 Sample_315 Sample_316 Sample_317 Sample_318 Sample_319 Sample_320
#> "0122" "0122" "03212" "0122" "03212" "0122" "0122" "0122"
#> Sample_321 Sample_322 Sample_323 Sample_324 Sample_325 Sample_326 Sample_327 Sample_328
#> "0121" "03211" "0122" "0122" "0122" "0121" "0122" "0122"
#> Sample_329 Sample_330 Sample_331 Sample_332 Sample_333 Sample_334 Sample_335 Sample_336
#> "0121" "0122" "0333" "03211" "03211" "03211" "03211" "03211"
#> Sample_337 Sample_338 Sample_339 Sample_340 Sample_341 Sample_342 Sample_343 Sample_344
#> "03211" "03211" "0333" "03212" "03212" "03211" "03211" "03212"
#> Sample_345 Sample_346 Sample_347 Sample_348 Sample_349 Sample_350 Sample_351 Sample_352
#> "03212" "03212" "03211" "03212" "03212" "0132" "0122" "0132"
#> Sample_353 Sample_354 Sample_355 Sample_356 Sample_357 Sample_358 Sample_359 Sample_360
#> "03211" "03211" "0122" "03211" "03211" "0122" "03211" "0122"
#> Sample_361 Sample_362 Sample_363 Sample_364 Sample_365 Sample_366 Sample_367 Sample_368
#> "03211" "0122" "03211" "03211" "0132" "03211" "03211" "0122"
#> Sample_369 Sample_370 Sample_371 Sample_372 Sample_373 Sample_374 Sample_375 Sample_376
#> "0122" "03212" "0333" "0132" "0132" "03211" "03212" "03212"
#> Sample_377 Sample_378 Sample_379 Sample_380 Sample_381 Sample_382 Sample_383 Sample_384
#> "0122" "03212" "03212" "03212" "0122" "03212" "03212" "03212"
#> Sample_385 Sample_386 Sample_387 Sample_388 Sample_389 Sample_390 Sample_391 Sample_392
#> "0121" "0121" "03212" "03211" "0132" "0132" "03212" "03212"
#> Sample_393 Sample_394 Sample_395 Sample_396 Sample_397 Sample_398 Sample_399 Sample_400
#> "0122" "0132" "0122" "0132" "0122" "0122" "03212" "03212"
#> Sample_401 Sample_402 Sample_403 Sample_404 Sample_405 Sample_406 Sample_407 Sample_408
#> "03212" "03221" "0132" "0132" "0132" "03212" "03212" "0132"
#> Sample_409 Sample_410 Sample_411 Sample_412 Sample_413 Sample_414 Sample_415 Sample_416
#> "0122" "03212" "03212" "0122" "03212" "03212" "0333" "0122"
#> Sample_417 Sample_418 Sample_419 Sample_420 Sample_421 Sample_422 Sample_423 Sample_424
#> "03212" "03212" "03212" "0333" "0122" "0333" "03221" "0122"
#> Sample_425 Sample_426 Sample_427 Sample_428 Sample_429 Sample_430 Sample_431 Sample_432
#> "0122" "03221" "0122" "0122" "03221" "0132" "0122" "0121"
#> Sample_433 Sample_434 Sample_435 Sample_436 Sample_437 Sample_438 Sample_439 Sample_440
#> "0132" "0121" "03221" "0333" "0333" "0132" "0121" "0122"
#> Sample_441 Sample_442 Sample_443 Sample_444 Sample_445 Sample_446 Sample_447 Sample_448
#> "03221" "0122" "0132" "03212" "0121" "0122" "03221" "0132"
#> Sample_449 Sample_450 Sample_451 Sample_452 Sample_453 Sample_454 Sample_455 Sample_456
#> "03221" "03212" "0122" "0121" "03221" "03212" "0132" "0121"
#> Sample_457 Sample_458 Sample_459 Sample_460 Sample_461 Sample_462 Sample_463 Sample_464
#> "0132" "03221" "0122" "03212" "0121" "03212" "0132" "0121"
#> Sample_465 Sample_466 Sample_467 Sample_468 Sample_469 Sample_470 Sample_471 Sample_472
#> "0121" "0121" "03221" "0121" "0122" "03212" "03221" "0121"
#> Sample_473 Sample_474 Sample_475 Sample_476 Sample_477 Sample_478 Sample_479 Sample_480
#> "03212" "0121" "0121" "0132" "0132" "0314" "0111" "0314"
#> Sample_481 Sample_482 Sample_483 Sample_484 Sample_485 Sample_486 Sample_487 Sample_488
#> "0313" "0314" "0314" "0314" "0314" "0112" "0112" "0331"
#> Sample_489 Sample_490 Sample_491 Sample_492 Sample_493 Sample_494 Sample_495 Sample_496
#> "0314" "0112" "0314" "0314" "0314" "0314" "0112" "0314"
#> Sample_497 Sample_498 Sample_499 Sample_500 Sample_501 Sample_502 Sample_503 Sample_504
#> "0314" "0314" "0111" "0111" "0314" "0314" "0314" "0314"
#> Sample_505 Sample_506 Sample_507 Sample_508 Sample_509 Sample_510 Sample_511 Sample_512
#> "0111" "0112" "0111" "0314" "0314" "0314" "0111" "0332"
#> Sample_513 Sample_514 Sample_515 Sample_516 Sample_517 Sample_518 Sample_519 Sample_520
#> "0112" "0314" "0112" "02112" "0112" "0314" "0314" "0112"
#> Sample_521 Sample_522 Sample_523 Sample_524 Sample_525 Sample_526 Sample_527 Sample_528
#> "0112" "0314" "0314" "0314" "0314" "0314" "0112" "0312"
#> Sample_529 Sample_530 Sample_531 Sample_532 Sample_533 Sample_534 Sample_535 Sample_536
#> "0331" "0112" "0332" "0111" "0112" "0314" "0112" "0111"
#> Sample_537 Sample_538 Sample_539 Sample_540 Sample_541 Sample_542 Sample_543 Sample_544
#> "0112" "0314" "0112" "0314" "0314" "0112" "0112" "0112"
#> Sample_545 Sample_546 Sample_547 Sample_548 Sample_549 Sample_550 Sample_551 Sample_552
#> "0331" "0112" "0112" "0314" "0314" "0314" "0314" "0314"
#> Sample_553 Sample_554 Sample_555 Sample_556 Sample_557 Sample_558 Sample_559 Sample_560
#> "0332" "0314" "0314" "0314" "0112" "0112" "0112" "0314"
#> Sample_561 Sample_562 Sample_563 Sample_564 Sample_565 Sample_566 Sample_567 Sample_568
#> "0314" "0314" "0314" "0314" "0112" "0112" "0112" "0314"
#> Sample_569 Sample_570 Sample_571 Sample_572 Sample_573 Sample_574 Sample_575 Sample_576
#> "0111" "03221" "03221" "0132" "0121" "0333" "03212" "03221"
#> Sample_577 Sample_578 Sample_579 Sample_580 Sample_581 Sample_582 Sample_583 Sample_584
#> "0122" "03211" "03211" "03211" "03211" "03211" "03211" "03211"
#> Sample_585 Sample_586 Sample_587 Sample_588 Sample_589 Sample_590 Sample_591 Sample_592
#> "03222" "02212" "02211" "0231" "03222" "0131" "02211" "0231"
#> Sample_593 Sample_594 Sample_595 Sample_596 Sample_597 Sample_598 Sample_599 Sample_600
#> "02212" "0231" "0231" "02222" "0231" "02222" "0231" "0231"
#> Sample_601 Sample_602 Sample_603 Sample_604 Sample_605 Sample_606 Sample_607 Sample_608
#> "02222" "0231" "02222" "02222" "02222" "02222" "02211" "02222"
#> Sample_609 Sample_610 Sample_611 Sample_612 Sample_613 Sample_614 Sample_615 Sample_616
#> "02222" "02222" "0212" "0212" "03222" "0131" "03222" "0131"
#> Sample_617 Sample_618 Sample_619 Sample_620 Sample_621 Sample_622 Sample_623 Sample_624
#> "0131" "0121" "0131" "0131" "0231" "0131" "03221" "03221"
#> Sample_625 Sample_626 Sample_627 Sample_628 Sample_629 Sample_630 Sample_631 Sample_632
#> "0231" "0131" "0231" "0231" "0231" "0131" "0231" "0131"
#> Sample_633 Sample_634 Sample_635 Sample_636 Sample_637 Sample_638 Sample_639 Sample_640
#> "0131" "0131" "0131" "0131" "0131" "0231" "0131" "0232"
#> Sample_641 Sample_642 Sample_643 Sample_644 Sample_645 Sample_646 Sample_647 Sample_648
#> "0131" "0233" "0131" "03212" "0232" "0131" "0131" "0232"
#> Sample_649 Sample_650 Sample_651 Sample_652 Sample_653 Sample_654 Sample_655 Sample_656
#> "0232" "0231" "02212" "02211" "02211" "02212" "02211" "02211"
#> Sample_657 Sample_658 Sample_659 Sample_660 Sample_661 Sample_662 Sample_663 Sample_664
#> "02222" "02212" "02211" "02221" "02211" "02221" "02221" "02211"
#> Sample_665 Sample_666 Sample_667 Sample_668 Sample_669 Sample_670 Sample_671 Sample_672
#> "0232" "02221" "0232" "02221" "02221" "02221" "02211" "02221"
#> Sample_673 Sample_674 Sample_675 Sample_676 Sample_677 Sample_678 Sample_679 Sample_680
#> "02221" "02221" "02221" "02222" "02212" "02212" "02211" "02221"
#> Sample_681 Sample_682 Sample_683 Sample_684 Sample_685 Sample_686 Sample_687 Sample_688
#> "0232" "02221" "02221" "02221" "0121" "0212" "0121" "0212"
#> Sample_689 Sample_690 Sample_691 Sample_692 Sample_693 Sample_694 Sample_695 Sample_696
#> "02221" "0212" "02221" "0121" "02221" "02221" "0212" "02111"
#> Sample_697 Sample_698 Sample_699 Sample_700 Sample_701 Sample_702 Sample_703 Sample_704
#> "02111" "0212" "0212" "02111" "0212" "02111" "0212" "02211"
#> Sample_705 Sample_706 Sample_707 Sample_708 Sample_709 Sample_710 Sample_711 Sample_712
#> "02222" "02111" "02111" "02111" "02111" "0232" "0212" "02111"
#> Sample_713 Sample_714 Sample_715 Sample_716 Sample_717 Sample_718 Sample_719 Sample_720
#> "02111" "0212" "0212" "0212" "02111" "02111" "02111" "0232"
#> Sample_721 Sample_722 Sample_723 Sample_724 Sample_725 Sample_726 Sample_727 Sample_728
#> "02111" "02111" "0212" "02111" "0212" "02111" "02111" "0212"
#> Sample_729 Sample_730 Sample_731 Sample_732 Sample_733 Sample_734 Sample_735 Sample_736
#> "02111" "02111" "0233" "02111" "02111" "02111" "0233" "02111"
#> Sample_737 Sample_738 Sample_739 Sample_740 Sample_741 Sample_742 Sample_743 Sample_744
#> "0121" "0232" "02111" "0212" "02111" "0212" "02111" "02111"
#> Sample_745 Sample_746 Sample_747 Sample_748 Sample_749 Sample_750 Sample_751 Sample_752
#> "0212" "02111" "02111" "0212" "02111" "02111" "02111" "02111"
#> Sample_753 Sample_754 Sample_755 Sample_756 Sample_757 Sample_758 Sample_759 Sample_760
#> "0212" "0212" "02111" "02111" "02111" "02111" "0212" "02111"
#> Sample_761 Sample_762 Sample_763 Sample_764 Sample_765 Sample_766 Sample_767 Sample_768
#> "02111" "02111" "02111" "02111" "02111" "0212" "02111" "0232"
#> Sample_769 Sample_770 Sample_771 Sample_772 Sample_773 Sample_774 Sample_775 Sample_776
#> "0212" "02111" "02111" "0212" "02111" "02111" "0212" "02111"
#> Sample_777 Sample_778 Sample_779 Sample_780 Sample_781 Sample_782 Sample_783 Sample_784
#> "0212" "0212" "0212" "0212" "02111" "0212" "0212" "0212"
#> Sample_785 Sample_786 Sample_787 Sample_788 Sample_789 Sample_790 Sample_791 Sample_792
#> "0212" "0212" "02111" "0212" "0212" "02111" "02111" "02111"
#> Sample_793 Sample_794 Sample_795 Sample_796 Sample_797 Sample_798 Sample_799 Sample_800
#> "0212" "02111" "02111" "02111" "0212" "0212" "0212" "02111"
#> Sample_801 Sample_802 Sample_803 Sample_804 Sample_805 Sample_806 Sample_807 Sample_808
#> "02111" "0212" "02111" "0212" "02111" "02111" "02111" "02111"
#> Sample_809 Sample_810 Sample_811 Sample_812 Sample_813 Sample_814 Sample_815 Sample_816
#> "02111" "0212" "02111" "02111" "02111" "02111" "02111" "02111"
#> Sample_817 Sample_818 Sample_819 Sample_820 Sample_821 Sample_822 Sample_823 Sample_824
#> "0212" "02111" "0212" "0233" "0212" "02111" "0212" "0212"
#> Sample_825 Sample_826 Sample_827 Sample_828 Sample_829 Sample_830 Sample_831 Sample_832
#> "0212" "0212" "0212" "02111" "0212" "0212" "02111" "02111"
#> Sample_833 Sample_834 Sample_835 Sample_836 Sample_837 Sample_838 Sample_839 Sample_840
#> "02111" "0212" "0212" "0233" "02111" "02111" "0212" "02111"
#> Sample_841 Sample_842 Sample_843 Sample_844 Sample_845 Sample_846 Sample_847 Sample_848
#> "02222" "02212" "02212" "02211" "02211" "02211" "02221" "02222"
#> Sample_849 Sample_850 Sample_851 Sample_852 Sample_853 Sample_854 Sample_855 Sample_856
#> "02221" "02211" "02211" "02222" "02211" "02211" "0212" "0212"
#> Sample_857 Sample_858 Sample_859 Sample_860 Sample_861 Sample_862 Sample_863 Sample_864
#> "0212" "02112" "0232" "0212" "0212" "0212" "0212" "02112"
#> Sample_865 Sample_866 Sample_867 Sample_868 Sample_869 Sample_870 Sample_871 Sample_872
#> "0233" "0212" "0212" "02112" "0212" "0212" "0212" "0212"
#> Sample_873 Sample_874 Sample_875 Sample_876 Sample_877 Sample_878 Sample_879 Sample_880
#> "0212" "02112" "02112" "0212" "02112" "02112" "02112" "02112"
#> Sample_881 Sample_882 Sample_883 Sample_884 Sample_885 Sample_886 Sample_887 Sample_888
#> "0212" "0233" "02112" "02112" "0212" "02112" "02112" "02112"
#> Sample_889 Sample_890 Sample_891 Sample_892 Sample_893 Sample_894 Sample_895 Sample_896
#> "0212" "0212" "0212" "0212" "0212" "0232" "0212" "02112"
#> Sample_897 Sample_898 Sample_899 Sample_900 Sample_901 Sample_902 Sample_903 Sample_904
#> "0212" "0212" "02112" "0233" "02112" "0212" "0212" "02112"
#> Sample_905 Sample_906 Sample_907 Sample_908 Sample_909 Sample_910 Sample_911 Sample_912
#> "02112" "0212" "0212" "0212" "0212" "02112" "0212" "02112"
#> Sample_913 Sample_914 Sample_915 Sample_916 Sample_917 Sample_918 Sample_919 Sample_920
#> "0212" "0212" "0212" "02112" "0212" "0212" "0212" "0212"
#> Sample_921 Sample_922 Sample_923 Sample_924 Sample_925 Sample_926 Sample_927 Sample_928
#> "02112" "0212" "02112" "0212" "0212" "02112" "02112" "02112"
#> Sample_929 Sample_930 Sample_931 Sample_932 Sample_933 Sample_934 Sample_935 Sample_936
#> "0212" "02112" "02112" "0212" "0212" "0212" "0212" "0212"
#> Sample_937 Sample_938 Sample_939 Sample_940 Sample_941 Sample_942 Sample_943 Sample_944
#> "0212" "0212" "0212" "0212" "0212" "0212" "0212" "0212"
#> Sample_945 Sample_946 Sample_947 Sample_948 Sample_949 Sample_950 Sample_951 Sample_952
#> "02112" "02112" "02112" "03212" "03212" "0132" "03211" "03212"
#> Sample_953 Sample_954 Sample_955 Sample_956 Sample_957 Sample_958 Sample_959 Sample_960
#> "03211" "03221" "03211" "03212" "03211" "03212" "03212" "03212"
#> Sample_961 Sample_962 Sample_963 Sample_964 Sample_965 Sample_966 Sample_967 Sample_968
#> "03211" "0122" "03212" "03211" "03212" "03212" "03211" "0122"
#> Sample_969 Sample_970 Sample_971 Sample_972 Sample_973 Sample_974 Sample_975 Sample_976
#> "03211" "03212" "03211" "03211" "03212" "03211" "0333" "0132"
#> Sample_977 Sample_978 Sample_979 Sample_980 Sample_981 Sample_982 Sample_983 Sample_984
#> "0132" "0122" "03212" "0132" "0132" "0121" "0122" "03212"
#> Sample_985 Sample_986 Sample_987 Sample_988 Sample_989 Sample_990 Sample_991 Sample_992
#> "0122" "0132" "0122" "0121" "0121" "0121" "0121" "0121"
#> Sample_993 Sample_994 Sample_995 Sample_996 Sample_997 Sample_998 Sample_999 Sample_1000
#> "0121" "0122" "03211" "0132" "03212" "03212" "0132" "03212"
#> Sample_1001 Sample_1002 Sample_1003 Sample_1004 Sample_1005 Sample_1006 Sample_1007 Sample_1008
#> "03212" "03212" "03211" "0333" "03212" "03211" "0122" "0132"
#> Sample_1009 Sample_1010 Sample_1011 Sample_1012 Sample_1013 Sample_1014 Sample_1015 Sample_1016
#> "0132" "0132" "03212" "03221" "03212" "0121" "0121" "0121"
#> Sample_1017 Sample_1018 Sample_1019 Sample_1020 Sample_1021 Sample_1022 Sample_1023 Sample_1024
#> "0121" "0121" "0121" "0121" "0121" "0121" "0121" "0121"
#> Sample_1025 Sample_1026 Sample_1027 Sample_1028 Sample_1029 Sample_1030 Sample_1031 Sample_1032
#> "0121" "0121" "0111" "0111" "0312" "0111" "0332" "0312"
#> Sample_1033 Sample_1034 Sample_1035 Sample_1036 Sample_1037 Sample_1038 Sample_1039 Sample_1040
#> "0312" "0111" "0312" "0111" "0312" "0312" "0111" "0312"
#> Sample_1041 Sample_1042 Sample_1043 Sample_1044 Sample_1045 Sample_1046 Sample_1047 Sample_1048
#> "0112" "0111" "0312" "0312" "0332" "0111" "0312" "0331"
#> Sample_1049 Sample_1050 Sample_1051 Sample_1052 Sample_1053 Sample_1054 Sample_1055 Sample_1056
#> "0111" "0111" "0312" "0111" "03221" "03212" "03211" "0132"
#> Sample_1057 Sample_1058 Sample_1059 Sample_1060 Sample_1061 Sample_1062 Sample_1063 Sample_1064
#> "03221" "0121" "0132" "03212" "03221" "0132" "0312" "0312"
#> Sample_1065 Sample_1066 Sample_1067 Sample_1068 Sample_1069 Sample_1070 Sample_1071 Sample_1072
#> "0312" "0111" "0312" "0312" "0312" "0111" "0312" "0312"
#> Sample_1073 Sample_1074 Sample_1075 Sample_1076 Sample_1077 Sample_1078 Sample_1079 Sample_1080
#> "0111" "0312" "0111" "0111" "0312" "0312" "0312" "0112"
#> Sample_1081 Sample_1082 Sample_1083 Sample_1084 Sample_1085 Sample_1086 Sample_1087 Sample_1088
#> "0312" "0312" "0111" "0312" "0312" "0331" "0111" "0331"
#> Sample_1089 Sample_1090 Sample_1091 Sample_1092 Sample_1093 Sample_1094 Sample_1095 Sample_1096
#> "0332" "0111" "0111" "0312" "0111" "0312" "0312" "0312"
#> Sample_1097 Sample_1098 Sample_1099 Sample_1100 Sample_1101 Sample_1102 Sample_1103 Sample_1104
#> "0111" "0312" "0312" "0112" "0312" "0111" "0112" "0111"
#> Sample_1105 Sample_1106 Sample_1107 Sample_1108 Sample_1109 Sample_1110 Sample_1111 Sample_1112
#> "0111" "0312" "0312" "0112" "0312" "0111" "0312" "0111"
#> Sample_1113 Sample_1114 Sample_1115 Sample_1116 Sample_1117 Sample_1118 Sample_1119 Sample_1120
#> "0111" "0111" "0312" "0111" "0312" "0111" "0112" "0332"
#> Sample_1121 Sample_1122 Sample_1123 Sample_1124 Sample_1125 Sample_1126 Sample_1127 Sample_1128
#> "0111" "0331" "0312" "0312" "0312" "0312" "0312" "0312"
#> Sample_1129 Sample_1130 Sample_1131 Sample_1132 Sample_1133 Sample_1134 Sample_1135 Sample_1136
#> "0111" "0111" "0111" "0332" "0111" "0331" "0112" "0111"
#> Sample_1137 Sample_1138 Sample_1139 Sample_1140 Sample_1141 Sample_1142 Sample_1143 Sample_1144
#> "0111" "0111" "0312" "0331" "0312" "0112" "0111" "0111"
#> Sample_1145 Sample_1146 Sample_1147 Sample_1148 Sample_1149 Sample_1150 Sample_1151 Sample_1152
#> "0312" "0112" "0112" "0111" "0111" "0312" "0111" "0312"
#> Sample_1153 Sample_1154 Sample_1155 Sample_1156 Sample_1157 Sample_1158 Sample_1159 Sample_1160
#> "0111" "0111" "0111" "0312" "0312" "0111" "0112" "0112"
#> Sample_1161 Sample_1162 Sample_1163 Sample_1164 Sample_1165 Sample_1166 Sample_1167 Sample_1168
#> "0312" "0111" "0312" "0312" "0112" "0111" "0112" "0111"
#> Sample_1169 Sample_1170 Sample_1171 Sample_1172 Sample_1173 Sample_1174 Sample_1175 Sample_1176
#> "0312" "0312" "0111" "0111" "0111" "0111" "0312" "0111"
#> Sample_1177 Sample_1178 Sample_1179 Sample_1180 Sample_1181 Sample_1182 Sample_1183 Sample_1184
#> "0111" "0111" "0112" "0312" "0111" "0111" "0331" "0331"
#> Sample_1185 Sample_1186 Sample_1187 Sample_1188 Sample_1189 Sample_1190 Sample_1191 Sample_1192
#> "0111" "0111" "0111" "0111" "03211" "03221" "03222" "0122"
#> Sample_1193 Sample_1194 Sample_1195 Sample_1196 Sample_1197 Sample_1198 Sample_1199 Sample_1200
#> "03221" "03221" "03212" "03212" "03221" "03221" "03221" "03212"
#> Sample_1201 Sample_1202 Sample_1203 Sample_1204 Sample_1205 Sample_1206 Sample_1207 Sample_1208
#> "03212" "03212" "03221" "03221" "03221" "03212" "03221" "0132"
#> Sample_1209 Sample_1210 Sample_1211 Sample_1212 Sample_1213 Sample_1214 Sample_1215 Sample_1216
#> "03211" "03221" "03212" "03212" "03212" "03221" "03221" "0122"
#> Sample_1217 Sample_1218 Sample_1219 Sample_1220 Sample_1221 Sample_1222 Sample_1223 Sample_1224
#> "0132" "0132" "03221" "03212" "03221" "0111" "0311" "0311"
#> Sample_1225 Sample_1226 Sample_1227 Sample_1228 Sample_1229 Sample_1230 Sample_1231 Sample_1232
#> "0112" "0311" "0112" "0112" "0111" "0311" "0311" "0311"
#> Sample_1233 Sample_1234 Sample_1235 Sample_1236 Sample_1237 Sample_1238 Sample_1239 Sample_1240
#> "0311" "0112" "0311" "0112" "0311" "0311" "0112" "0111"
#> Sample_1241 Sample_1242 Sample_1243 Sample_1244 Sample_1245 Sample_1246 Sample_1247 Sample_1248
#> "0112" "0112" "0311" "0311" "0311" "0311" "0311" "0311"
#> Sample_1249 Sample_1250 Sample_1251 Sample_1252 Sample_1253 Sample_1254 Sample_1255 Sample_1256
#> "0112" "0311" "0311" "0311" "0311" "0111" "0332" "0311"
#> Sample_1257 Sample_1258 Sample_1259 Sample_1260 Sample_1261 Sample_1262 Sample_1263 Sample_1264
#> "0311" "0311" "0311" "0311" "0311" "0311" "0311" "0112"
#> Sample_1265 Sample_1266 Sample_1267 Sample_1268 Sample_1269 Sample_1270 Sample_1271 Sample_1272
#> "0311" "0111" "0111" "0332" "0311" "0311" "0311" "0112"
#> Sample_1273 Sample_1274 Sample_1275 Sample_1276 Sample_1277 Sample_1278 Sample_1279 Sample_1280
#> "0311" "0311" "0311" "0311" "0311" "0311" "0111" "0111"
#> Sample_1281 Sample_1282 Sample_1283 Sample_1284 Sample_1285 Sample_1286 Sample_1287 Sample_1288
#> "0311" "0311" "0112" "0311" "0112" "0311" "0311" "0311"
#> Sample_1289 Sample_1290 Sample_1291 Sample_1292 Sample_1293 Sample_1294 Sample_1295 Sample_1296
#> "0311" "0311" "0311" "0311" "0311" "0311" "0311" "0331"
#> Sample_1297 Sample_1298 Sample_1299 Sample_1300 Sample_1301 Sample_1302 Sample_1303 Sample_1304
#> "0311" "0311" "0111" "0311" "0311" "0311" "0311" "0311"
#> Sample_1305 Sample_1306 Sample_1307 Sample_1308 Sample_1309 Sample_1310 Sample_1311 Sample_1312
#> "0311" "0332" "0111" "0111" "0112" "0332" "0311" "0311"
#> Sample_1313 Sample_1314 Sample_1315 Sample_1316 Sample_1317 Sample_1318 Sample_1319 Sample_1320
#> "0112" "0313" "0313" "0311" "0112" "0313" "0311" "0112"
#> Sample_1321 Sample_1322 Sample_1323 Sample_1324 Sample_1325 Sample_1326 Sample_1327 Sample_1328
#> "0314" "0311" "0311" "0311" "0311" "0314" "0311" "0313"
#> Sample_1329 Sample_1330 Sample_1331 Sample_1332 Sample_1333 Sample_1334 Sample_1335 Sample_1336
#> "0314" "0314" "0313" "0314" "0314" "0313" "0111" "0112"
#> Sample_1337 Sample_1338 Sample_1339 Sample_1340 Sample_1341 Sample_1342 Sample_1343 Sample_1344
#> "0332" "0112" "0332" "0311" "0314" "0112" "0112" "0314"
#> Sample_1345 Sample_1346 Sample_1347 Sample_1348 Sample_1349 Sample_1350 Sample_1351 Sample_1352
#> "0332" "0311" "0311" "0112" "0314" "0112" "0314" "0112"
#> Sample_1353 Sample_1354 Sample_1355 Sample_1356 Sample_1357 Sample_1358 Sample_1359 Sample_1360
#> "0311" "0112" "0314" "0314" "0112" "0112" "0314" "0112"
#> Sample_1361 Sample_1362 Sample_1363 Sample_1364 Sample_1365 Sample_1366 Sample_1367 Sample_1368
#> "0314" "0314" "0314" "0112" "0311" "0313" "0111" "0313"
#> Sample_1369 Sample_1370 Sample_1371 Sample_1372 Sample_1373 Sample_1374 Sample_1375 Sample_1376
#> "0311" "0314" "0112" "0314" "0112" "0112" "0112" "0332"
#> Sample_1377 Sample_1378 Sample_1379 Sample_1380 Sample_1381 Sample_1382 Sample_1383 Sample_1384
#> "0311" "0311" "0311" "0311" "0314" "0112" "0313" "0313"
#> Sample_1385 Sample_1386 Sample_1387 Sample_1388 Sample_1389 Sample_1390 Sample_1391 Sample_1392
#> "0313" "0111" "0313" "0112" "0313" "0313" "0313" "0313"
#> Sample_1393 Sample_1394 Sample_1395 Sample_1396 Sample_1397 Sample_1398 Sample_1399 Sample_1400
#> "0313" "0313" "0313" "0313" "0313" "0111" "0112" "0331"
#> Sample_1401 Sample_1402 Sample_1403 Sample_1404 Sample_1405 Sample_1406 Sample_1407 Sample_1408
#> "0313" "0112" "0313" "0313" "0313" "0111" "0313" "0112"
#> Sample_1409 Sample_1410 Sample_1411 Sample_1412 Sample_1413 Sample_1414 Sample_1415 Sample_1416
#> "0111" "0313" "0313" "0313" "0331" "0313" "0313" "0332"
#> Sample_1417 Sample_1418 Sample_1419 Sample_1420 Sample_1421 Sample_1422 Sample_1423 Sample_1424
#> "0313" "0313" "0112" "0313" "0313" "0313" "0111" "0313"
#> Sample_1425 Sample_1426 Sample_1427 Sample_1428 Sample_1429 Sample_1430 Sample_1431 Sample_1432
#> "0111" "0112" "0112" "0313" "0111" "0111" "0111" "0111"
#> Sample_1433 Sample_1434 Sample_1435 Sample_1436 Sample_1437 Sample_1438 Sample_1439 Sample_1440
#> "0111" "0111" "0111" "0112" "0111" "0112" "0111" "0112"
#> Sample_1441 Sample_1442 Sample_1443 Sample_1444 Sample_1445 Sample_1446 Sample_1447 Sample_1448
#> "0111" "0313" "0313" "0111" "0111" "0313" "0313" "0111"
#> Sample_1449 Sample_1450 Sample_1451 Sample_1452 Sample_1453 Sample_1454 Sample_1455 Sample_1456
#> "0111" "0111" "0111" "0111" "0112" "0111" "0111" "0313"
#> Sample_1457 Sample_1458 Sample_1459 Sample_1460 Sample_1461 Sample_1462 Sample_1463 Sample_1464
#> "0313" "0112" "0112" "0331" "0313" "0111" "0314" "0314"
#> Sample_1465 Sample_1466 Sample_1467 Sample_1468 Sample_1469 Sample_1470 Sample_1471 Sample_1472
#> "0112" "0112" "0111" "0313" "0112" "0112" "0112" "0112"
#> Sample_1473 Sample_1474 Sample_1475 Sample_1476 Sample_1477 Sample_1478 Sample_1479 Sample_1480
#> "0112" "0111" "0112" "0112" "0112" "0112" "0111" "0314"
#> Sample_1481 Sample_1482 Sample_1483 Sample_1484 Sample_1485 Sample_1486 Sample_1487 Sample_1488
#> "0112" "0111" "0112" "0112" "0313" "0331" "0111" "0313"
#> Sample_1489 Sample_1490 Sample_1491 Sample_1492 Sample_1493 Sample_1494 Sample_1495 Sample_1496
#> "0111" "0112" "0112" "0112" "0111" "0332" "0112" "0112"
#> Sample_1497 Sample_1498 Sample_1499 Sample_1500 Sample_1501 Sample_1502 Sample_1503 Sample_1504
#> "0112" "0111" "0111" "0112" "0112" "0313" "0313" "0332"
#> Sample_1505 Sample_1506 Sample_1507 Sample_1508 Sample_1509 Sample_1510 Sample_1511 Sample_1512
#> "0331" "0313" "0313" "0313" "0111" "0111" "0313" "0313"
#> Sample_1513 Sample_1514 Sample_1515 Sample_1516 Sample_1517 Sample_1518 Sample_1519 Sample_1520
#> "0313" "0112" "0313" "0111" "02222" "02222" "02211" "0233"
#> Sample_1521 Sample_1522 Sample_1523 Sample_1524 Sample_1525 Sample_1526 Sample_1527 Sample_1528
#> "0232" "0131" "0132" "0233" "0121" "0121" "0212" "0212"
#> Sample_1529 Sample_1530 Sample_1531 Sample_1532 Sample_1533 Sample_1534 Sample_1535 Sample_1536
#> "0212" "0233" "0232" "0232" "02111" "0212" "02222" "02222"
#> Sample_1537 Sample_1538 Sample_1539 Sample_1540 Sample_1541 Sample_1542 Sample_1543 Sample_1544
#> "02211" "0212" "0212" "0131" "0111" "0212" "0233" "0131"
#> Sample_1545 Sample_1546 Sample_1547 Sample_1548 Sample_1549 Sample_1550 Sample_1551 Sample_1552
#> "0111" "02112" "0131" "0231" "03221" "0231" "0231" "0231"
#> Sample_1553 Sample_1554 Sample_1555 Sample_1556 Sample_1557 Sample_1558 Sample_1559 Sample_1560
#> "03212" "02222" "0231" "0131" "0131" "0131" "0231" "0131"
#> Sample_1561 Sample_1562 Sample_1563 Sample_1564 Sample_1565 Sample_1566 Sample_1567 Sample_1568
#> "0231" "0121" "0131" "0131" "0131" "0131" "0131" "0131"
#> Sample_1569 Sample_1570 Sample_1571 Sample_1572 Sample_1573 Sample_1574 Sample_1575 Sample_1576
#> "0312" "0212" "0131" "0131" "0131" "0231" "0231" "0131"
#> Sample_1577 Sample_1578 Sample_1579 Sample_1580 Sample_1581 Sample_1582 Sample_1583 Sample_1584
#> "0131" "0131" "0131" "0131" "03221" "0231" "0131" "0131"
#> Sample_1585 Sample_1586 Sample_1587 Sample_1588 Sample_1589 Sample_1590 Sample_1591 Sample_1592
#> "0231" "0231" "0231" "02222" "0231" "0131" "0131" "0231"
#> Sample_1593 Sample_1594 Sample_1595 Sample_1596 Sample_1597 Sample_1598 Sample_1599 Sample_1600
#> "0231" "0231" "0231" "0232" "03221" "0311" "0233" "0131"
get_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 155))
#> Sample_1 Sample_2 Sample_3 Sample_4 Sample_5 Sample_6 Sample_7 Sample_8
#> "0132" "02211" "02212" "0222" "02211" "02211" "02211" "0222"
#> Sample_9 Sample_10 Sample_11 Sample_12 Sample_13 Sample_14 Sample_15 Sample_16
#> "02212" "02211" "02212" "02211" "02211" "02211" "02211" "02212"
#> Sample_17 Sample_18 Sample_19 Sample_20 Sample_21 Sample_22 Sample_23 Sample_24
#> "0222" "0222" "0222" "0222" "0222" "0222" "0222" "02211"
#> Sample_25 Sample_26 Sample_27 Sample_28 Sample_29 Sample_30 Sample_31 Sample_32
#> "0222" "02211" "0222" "0222" "02212" "0222" "0233" "02212"
#> Sample_33 Sample_34 Sample_35 Sample_36 Sample_37 Sample_38 Sample_39 Sample_40
#> "02212" "02212" "02212" "02212" "02212" "02212" "02212" "02212"
#> Sample_41 Sample_42 Sample_43 Sample_44 Sample_45 Sample_46 Sample_47 Sample_48
#> "02212" "02212" "0233" "02212" "02212" "02212" "02212" "02212"
#> Sample_49 Sample_50 Sample_51 Sample_52 Sample_53 Sample_54 Sample_55 Sample_56
#> "02212" "02212" "02212" "02212" "0222" "0222" "02212" "02211"
#> Sample_57 Sample_58 Sample_59 Sample_60 Sample_61 Sample_62 Sample_63 Sample_64
#> "02211" "02211" "02212" "02211" "0222" "02211" "02211" "02211"
#> Sample_65 Sample_66 Sample_67 Sample_68 Sample_69 Sample_70 Sample_71 Sample_72
#> "02211" "02211" "0222" "02211" "02211" "0222" "0222" "02112"
#> Sample_73 Sample_74 Sample_75 Sample_76 Sample_77 Sample_78 Sample_79 Sample_80
#> "0233" "02211" "0222" "02211" "0222" "02211" "0222" "0222"
#> Sample_81 Sample_82 Sample_83 Sample_84 Sample_85 Sample_86 Sample_87 Sample_88
#> "02211" "0222" "02211" "02211" "0222" "02212" "0222" "0222"
#> Sample_89 Sample_90 Sample_91 Sample_92 Sample_93 Sample_94 Sample_95 Sample_96
#> "0222" "0222" "0222" "0222" "0222" "0232" "0222" "02211"
#> Sample_97 Sample_98 Sample_99 Sample_100 Sample_101 Sample_102 Sample_103 Sample_104
#> "02211" "02211" "02211" "0222" "0222" "02212" "0222" "0222"
#> Sample_105 Sample_106 Sample_107 Sample_108 Sample_109 Sample_110 Sample_111 Sample_112
#> "02211" "02211" "0222" "02211" "02211" "0222" "0212" "0222"
#> Sample_113 Sample_114 Sample_115 Sample_116 Sample_117 Sample_118 Sample_119 Sample_120
#> "0222" "0232" "0222" "0222" "0222" "0232" "0222" "02211"
#> Sample_121 Sample_122 Sample_123 Sample_124 Sample_125 Sample_126 Sample_127 Sample_128
#> "0121" "0222" "0232" "02211" "02211" "0222" "02211" "0222"
#> Sample_129 Sample_130 Sample_131 Sample_132 Sample_133 Sample_134 Sample_135 Sample_136
#> "02211" "0222" "0222" "02211" "0222" "02211" "0222" "02211"
#> Sample_137 Sample_138 Sample_139 Sample_140 Sample_141 Sample_142 Sample_143 Sample_144
#> "02211" "02211" "0222" "02211" "02211" "0222" "0222" "0232"
#> Sample_145 Sample_146 Sample_147 Sample_148 Sample_149 Sample_150 Sample_151 Sample_152
#> "0222" "0222" "02112" "02112" "02112" "02112" "0212" "02112"
#> Sample_153 Sample_154 Sample_155 Sample_156 Sample_157 Sample_158 Sample_159 Sample_160
#> "02112" "02112" "02112" "02112" "02112" "02112" "0212" "02112"
#> Sample_161 Sample_162 Sample_163 Sample_164 Sample_165 Sample_166 Sample_167 Sample_168
#> "0212" "0212" "0212" "0212" "0212" "0212" "02112" "02112"
#> Sample_169 Sample_170 Sample_171 Sample_172 Sample_173 Sample_174 Sample_175 Sample_176
#> "0212" "02112" "0232" "0212" "0233" "0212" "02112" "02112"
#> Sample_177 Sample_178 Sample_179 Sample_180 Sample_181 Sample_182 Sample_183 Sample_184
#> "0212" "02112" "0212" "0212" "02112" "02112" "02112" "02112"
#> Sample_185 Sample_186 Sample_187 Sample_188 Sample_189 Sample_190 Sample_191 Sample_192
#> "02112" "02112" "02112" "0212" "0212" "02112" "0212" "0212"
#> Sample_193 Sample_194 Sample_195 Sample_196 Sample_197 Sample_198 Sample_199 Sample_200
#> "02112" "02112" "02112" "02112" "02211" "02211" "02212" "02212"
#> Sample_201 Sample_202 Sample_203 Sample_204 Sample_205 Sample_206 Sample_207 Sample_208
#> "02212" "02212" "02212" "0233" "02212" "02212" "02212" "03222"
#> Sample_209 Sample_210 Sample_211 Sample_212 Sample_213 Sample_214 Sample_215 Sample_216
#> "03222" "03222" "03222" "03222" "03222" "0132" "0132" "03222"
#> Sample_217 Sample_218 Sample_219 Sample_220 Sample_221 Sample_222 Sample_223 Sample_224
#> "0132" "03222" "03222" "03222" "03222" "03222" "03222" "0132"
#> Sample_225 Sample_226 Sample_227 Sample_228 Sample_229 Sample_230 Sample_231 Sample_232
#> "03222" "0132" "03222" "03222" "03222" "03222" "03222" "03222"
#> Sample_233 Sample_234 Sample_235 Sample_236 Sample_237 Sample_238 Sample_239 Sample_240
#> "0333" "03222" "03222" "0132" "03222" "03222" "03222" "03222"
#> Sample_241 Sample_242 Sample_243 Sample_244 Sample_245 Sample_246 Sample_247 Sample_248
#> "0132" "03222" "03212" "03222" "03222" "03222" "03222" "03222"
#> Sample_249 Sample_250 Sample_251 Sample_252 Sample_253 Sample_254 Sample_255 Sample_256
#> "03222" "03222" "03222" "03222" "03222" "0333" "03221" "03222"
#> Sample_257 Sample_258 Sample_259 Sample_260 Sample_261 Sample_262 Sample_263 Sample_264
#> "03222" "0132" "03222" "03222" "03222" "03222" "03222" "0132"
#> Sample_265 Sample_266 Sample_267 Sample_268 Sample_269 Sample_270 Sample_271 Sample_272
#> "0122" "03211" "03211" "03211" "03221" "03211" "0333" "0122"
#> Sample_273 Sample_274 Sample_275 Sample_276 Sample_277 Sample_278 Sample_279 Sample_280
#> "03212" "03211" "0122" "0132" "03211" "0333" "03211" "03222"
#> Sample_281 Sample_282 Sample_283 Sample_284 Sample_285 Sample_286 Sample_287 Sample_288
#> "03211" "03211" "03221" "03211" "03211" "03211" "0132" "0333"
#> Sample_289 Sample_290 Sample_291 Sample_292 Sample_293 Sample_294 Sample_295 Sample_296
#> "0333" "03211" "03211" "03211" "03222" "03211" "03211" "03211"
#> Sample_297 Sample_298 Sample_299 Sample_300 Sample_301 Sample_302 Sample_303 Sample_304
#> "03211" "03211" "03211" "03221" "03211" "0122" "0122" "0122"
#> Sample_305 Sample_306 Sample_307 Sample_308 Sample_309 Sample_310 Sample_311 Sample_312
#> "0122" "03212" "03212" "0122" "0122" "0122" "0122" "0122"
#> Sample_313 Sample_314 Sample_315 Sample_316 Sample_317 Sample_318 Sample_319 Sample_320
#> "0122" "0122" "03212" "0122" "03212" "0122" "0122" "0122"
#> Sample_321 Sample_322 Sample_323 Sample_324 Sample_325 Sample_326 Sample_327 Sample_328
#> "0121" "03211" "0122" "0122" "0122" "0121" "0122" "0122"
#> Sample_329 Sample_330 Sample_331 Sample_332 Sample_333 Sample_334 Sample_335 Sample_336
#> "0121" "0122" "0333" "03211" "03211" "03211" "03211" "03211"
#> Sample_337 Sample_338 Sample_339 Sample_340 Sample_341 Sample_342 Sample_343 Sample_344
#> "03211" "03211" "0333" "03212" "03212" "03211" "03211" "03212"
#> Sample_345 Sample_346 Sample_347 Sample_348 Sample_349 Sample_350 Sample_351 Sample_352
#> "03212" "03212" "03211" "03212" "03212" "0132" "0122" "0132"
#> Sample_353 Sample_354 Sample_355 Sample_356 Sample_357 Sample_358 Sample_359 Sample_360
#> "03211" "03211" "0122" "03211" "03211" "0122" "03211" "0122"
#> Sample_361 Sample_362 Sample_363 Sample_364 Sample_365 Sample_366 Sample_367 Sample_368
#> "03211" "0122" "03211" "03211" "0132" "03211" "03211" "0122"
#> Sample_369 Sample_370 Sample_371 Sample_372 Sample_373 Sample_374 Sample_375 Sample_376
#> "0122" "03212" "0333" "0132" "0132" "03211" "03212" "03212"
#> Sample_377 Sample_378 Sample_379 Sample_380 Sample_381 Sample_382 Sample_383 Sample_384
#> "0122" "03212" "03212" "03212" "0122" "03212" "03212" "03212"
#> Sample_385 Sample_386 Sample_387 Sample_388 Sample_389 Sample_390 Sample_391 Sample_392
#> "0121" "0121" "03212" "03211" "0132" "0132" "03212" "03212"
#> Sample_393 Sample_394 Sample_395 Sample_396 Sample_397 Sample_398 Sample_399 Sample_400
#> "0122" "0132" "0122" "0132" "0122" "0122" "03212" "03212"
#> Sample_401 Sample_402 Sample_403 Sample_404 Sample_405 Sample_406 Sample_407 Sample_408
#> "03212" "03221" "0132" "0132" "0132" "03212" "03212" "0132"
#> Sample_409 Sample_410 Sample_411 Sample_412 Sample_413 Sample_414 Sample_415 Sample_416
#> "0122" "03212" "03212" "0122" "03212" "03212" "0333" "0122"
#> Sample_417 Sample_418 Sample_419 Sample_420 Sample_421 Sample_422 Sample_423 Sample_424
#> "03212" "03212" "03212" "0333" "0122" "0333" "03221" "0122"
#> Sample_425 Sample_426 Sample_427 Sample_428 Sample_429 Sample_430 Sample_431 Sample_432
#> "0122" "03221" "0122" "0122" "03221" "0132" "0122" "0121"
#> Sample_433 Sample_434 Sample_435 Sample_436 Sample_437 Sample_438 Sample_439 Sample_440
#> "0132" "0121" "03221" "0333" "0333" "0132" "0121" "0122"
#> Sample_441 Sample_442 Sample_443 Sample_444 Sample_445 Sample_446 Sample_447 Sample_448
#> "03221" "0122" "0132" "03212" "0121" "0122" "03221" "0132"
#> Sample_449 Sample_450 Sample_451 Sample_452 Sample_453 Sample_454 Sample_455 Sample_456
#> "03221" "03212" "0122" "0121" "03221" "03212" "0132" "0121"
#> Sample_457 Sample_458 Sample_459 Sample_460 Sample_461 Sample_462 Sample_463 Sample_464
#> "0132" "03221" "0122" "03212" "0121" "03212" "0132" "0121"
#> Sample_465 Sample_466 Sample_467 Sample_468 Sample_469 Sample_470 Sample_471 Sample_472
#> "0121" "0121" "03221" "0121" "0122" "03212" "03221" "0121"
#> Sample_473 Sample_474 Sample_475 Sample_476 Sample_477 Sample_478 Sample_479 Sample_480
#> "03212" "0121" "0121" "0132" "0132" "0314" "0111" "0314"
#> Sample_481 Sample_482 Sample_483 Sample_484 Sample_485 Sample_486 Sample_487 Sample_488
#> "0313" "0314" "0314" "0314" "0314" "0112" "0112" "0331"
#> Sample_489 Sample_490 Sample_491 Sample_492 Sample_493 Sample_494 Sample_495 Sample_496
#> "0314" "0112" "0314" "0314" "0314" "0314" "0112" "0314"
#> Sample_497 Sample_498 Sample_499 Sample_500 Sample_501 Sample_502 Sample_503 Sample_504
#> "0314" "0314" "0111" "0111" "0314" "0314" "0314" "0314"
#> Sample_505 Sample_506 Sample_507 Sample_508 Sample_509 Sample_510 Sample_511 Sample_512
#> "0111" "0112" "0111" "0314" "0314" "0314" "0111" "0332"
#> Sample_513 Sample_514 Sample_515 Sample_516 Sample_517 Sample_518 Sample_519 Sample_520
#> "0112" "0314" "0112" "02112" "0112" "0314" "0314" "0112"
#> Sample_521 Sample_522 Sample_523 Sample_524 Sample_525 Sample_526 Sample_527 Sample_528
#> "0112" "0314" "0314" "0314" "0314" "0314" "0112" "0312"
#> Sample_529 Sample_530 Sample_531 Sample_532 Sample_533 Sample_534 Sample_535 Sample_536
#> "0331" "0112" "0332" "0111" "0112" "0314" "0112" "0111"
#> Sample_537 Sample_538 Sample_539 Sample_540 Sample_541 Sample_542 Sample_543 Sample_544
#> "0112" "0314" "0112" "0314" "0314" "0112" "0112" "0112"
#> Sample_545 Sample_546 Sample_547 Sample_548 Sample_549 Sample_550 Sample_551 Sample_552
#> "0331" "0112" "0112" "0314" "0314" "0314" "0314" "0314"
#> Sample_553 Sample_554 Sample_555 Sample_556 Sample_557 Sample_558 Sample_559 Sample_560
#> "0332" "0314" "0314" "0314" "0112" "0112" "0112" "0314"
#> Sample_561 Sample_562 Sample_563 Sample_564 Sample_565 Sample_566 Sample_567 Sample_568
#> "0314" "0314" "0314" "0314" "0112" "0112" "0112" "0314"
#> Sample_569 Sample_570 Sample_571 Sample_572 Sample_573 Sample_574 Sample_575 Sample_576
#> "0111" "03221" "03221" "0132" "0121" "0333" "03212" "03221"
#> Sample_577 Sample_578 Sample_579 Sample_580 Sample_581 Sample_582 Sample_583 Sample_584
#> "0122" "03211" "03211" "03211" "03211" "03211" "03211" "03211"
#> Sample_585 Sample_586 Sample_587 Sample_588 Sample_589 Sample_590 Sample_591 Sample_592
#> "03222" "02212" "02211" "0231" "03222" "0131" "02211" "0231"
#> Sample_593 Sample_594 Sample_595 Sample_596 Sample_597 Sample_598 Sample_599 Sample_600
#> "02212" "0231" "0231" "0222" "0231" "0222" "0231" "0231"
#> Sample_601 Sample_602 Sample_603 Sample_604 Sample_605 Sample_606 Sample_607 Sample_608
#> "0222" "0231" "0222" "0222" "0222" "0222" "02211" "0222"
#> Sample_609 Sample_610 Sample_611 Sample_612 Sample_613 Sample_614 Sample_615 Sample_616
#> "0222" "0222" "0212" "0212" "03222" "0131" "03222" "0131"
#> Sample_617 Sample_618 Sample_619 Sample_620 Sample_621 Sample_622 Sample_623 Sample_624
#> "0131" "0121" "0131" "0131" "0231" "0131" "03221" "03221"
#> Sample_625 Sample_626 Sample_627 Sample_628 Sample_629 Sample_630 Sample_631 Sample_632
#> "0231" "0131" "0231" "0231" "0231" "0131" "0231" "0131"
#> Sample_633 Sample_634 Sample_635 Sample_636 Sample_637 Sample_638 Sample_639 Sample_640
#> "0131" "0131" "0131" "0131" "0131" "0231" "0131" "0232"
#> Sample_641 Sample_642 Sample_643 Sample_644 Sample_645 Sample_646 Sample_647 Sample_648
#> "0131" "0233" "0131" "03212" "0232" "0131" "0131" "0232"
#> Sample_649 Sample_650 Sample_651 Sample_652 Sample_653 Sample_654 Sample_655 Sample_656
#> "0232" "0231" "02212" "02211" "02211" "02212" "02211" "02211"
#> Sample_657 Sample_658 Sample_659 Sample_660 Sample_661 Sample_662 Sample_663 Sample_664
#> "0222" "02212" "02211" "0222" "02211" "0222" "0222" "02211"
#> Sample_665 Sample_666 Sample_667 Sample_668 Sample_669 Sample_670 Sample_671 Sample_672
#> "0232" "0222" "0232" "0222" "0222" "0222" "02211" "0222"
#> Sample_673 Sample_674 Sample_675 Sample_676 Sample_677 Sample_678 Sample_679 Sample_680
#> "0222" "0222" "0222" "0222" "02212" "02212" "02211" "0222"
#> Sample_681 Sample_682 Sample_683 Sample_684 Sample_685 Sample_686 Sample_687 Sample_688
#> "0232" "0222" "0222" "0222" "0121" "0212" "0121" "0212"
#> Sample_689 Sample_690 Sample_691 Sample_692 Sample_693 Sample_694 Sample_695 Sample_696
#> "0222" "0212" "0222" "0121" "0222" "0222" "0212" "02111"
#> Sample_697 Sample_698 Sample_699 Sample_700 Sample_701 Sample_702 Sample_703 Sample_704
#> "02111" "0212" "0212" "02111" "0212" "02111" "0212" "02211"
#> Sample_705 Sample_706 Sample_707 Sample_708 Sample_709 Sample_710 Sample_711 Sample_712
#> "0222" "02111" "02111" "02111" "02111" "0232" "0212" "02111"
#> Sample_713 Sample_714 Sample_715 Sample_716 Sample_717 Sample_718 Sample_719 Sample_720
#> "02111" "0212" "0212" "0212" "02111" "02111" "02111" "0232"
#> Sample_721 Sample_722 Sample_723 Sample_724 Sample_725 Sample_726 Sample_727 Sample_728
#> "02111" "02111" "0212" "02111" "0212" "02111" "02111" "0212"
#> Sample_729 Sample_730 Sample_731 Sample_732 Sample_733 Sample_734 Sample_735 Sample_736
#> "02111" "02111" "0233" "02111" "02111" "02111" "0233" "02111"
#> Sample_737 Sample_738 Sample_739 Sample_740 Sample_741 Sample_742 Sample_743 Sample_744
#> "0121" "0232" "02111" "0212" "02111" "0212" "02111" "02111"
#> Sample_745 Sample_746 Sample_747 Sample_748 Sample_749 Sample_750 Sample_751 Sample_752
#> "0212" "02111" "02111" "0212" "02111" "02111" "02111" "02111"
#> Sample_753 Sample_754 Sample_755 Sample_756 Sample_757 Sample_758 Sample_759 Sample_760
#> "0212" "0212" "02111" "02111" "02111" "02111" "0212" "02111"
#> Sample_761 Sample_762 Sample_763 Sample_764 Sample_765 Sample_766 Sample_767 Sample_768
#> "02111" "02111" "02111" "02111" "02111" "0212" "02111" "0232"
#> Sample_769 Sample_770 Sample_771 Sample_772 Sample_773 Sample_774 Sample_775 Sample_776
#> "0212" "02111" "02111" "0212" "02111" "02111" "0212" "02111"
#> Sample_777 Sample_778 Sample_779 Sample_780 Sample_781 Sample_782 Sample_783 Sample_784
#> "0212" "0212" "0212" "0212" "02111" "0212" "0212" "0212"
#> Sample_785 Sample_786 Sample_787 Sample_788 Sample_789 Sample_790 Sample_791 Sample_792
#> "0212" "0212" "02111" "0212" "0212" "02111" "02111" "02111"
#> Sample_793 Sample_794 Sample_795 Sample_796 Sample_797 Sample_798 Sample_799 Sample_800
#> "0212" "02111" "02111" "02111" "0212" "0212" "0212" "02111"
#> Sample_801 Sample_802 Sample_803 Sample_804 Sample_805 Sample_806 Sample_807 Sample_808
#> "02111" "0212" "02111" "0212" "02111" "02111" "02111" "02111"
#> Sample_809 Sample_810 Sample_811 Sample_812 Sample_813 Sample_814 Sample_815 Sample_816
#> "02111" "0212" "02111" "02111" "02111" "02111" "02111" "02111"
#> Sample_817 Sample_818 Sample_819 Sample_820 Sample_821 Sample_822 Sample_823 Sample_824
#> "0212" "02111" "0212" "0233" "0212" "02111" "0212" "0212"
#> Sample_825 Sample_826 Sample_827 Sample_828 Sample_829 Sample_830 Sample_831 Sample_832
#> "0212" "0212" "0212" "02111" "0212" "0212" "02111" "02111"
#> Sample_833 Sample_834 Sample_835 Sample_836 Sample_837 Sample_838 Sample_839 Sample_840
#> "02111" "0212" "0212" "0233" "02111" "02111" "0212" "02111"
#> Sample_841 Sample_842 Sample_843 Sample_844 Sample_845 Sample_846 Sample_847 Sample_848
#> "0222" "02212" "02212" "02211" "02211" "02211" "0222" "0222"
#> Sample_849 Sample_850 Sample_851 Sample_852 Sample_853 Sample_854 Sample_855 Sample_856
#> "0222" "02211" "02211" "0222" "02211" "02211" "0212" "0212"
#> Sample_857 Sample_858 Sample_859 Sample_860 Sample_861 Sample_862 Sample_863 Sample_864
#> "0212" "02112" "0232" "0212" "0212" "0212" "0212" "02112"
#> Sample_865 Sample_866 Sample_867 Sample_868 Sample_869 Sample_870 Sample_871 Sample_872
#> "0233" "0212" "0212" "02112" "0212" "0212" "0212" "0212"
#> Sample_873 Sample_874 Sample_875 Sample_876 Sample_877 Sample_878 Sample_879 Sample_880
#> "0212" "02112" "02112" "0212" "02112" "02112" "02112" "02112"
#> Sample_881 Sample_882 Sample_883 Sample_884 Sample_885 Sample_886 Sample_887 Sample_888
#> "0212" "0233" "02112" "02112" "0212" "02112" "02112" "02112"
#> Sample_889 Sample_890 Sample_891 Sample_892 Sample_893 Sample_894 Sample_895 Sample_896
#> "0212" "0212" "0212" "0212" "0212" "0232" "0212" "02112"
#> Sample_897 Sample_898 Sample_899 Sample_900 Sample_901 Sample_902 Sample_903 Sample_904
#> "0212" "0212" "02112" "0233" "02112" "0212" "0212" "02112"
#> Sample_905 Sample_906 Sample_907 Sample_908 Sample_909 Sample_910 Sample_911 Sample_912
#> "02112" "0212" "0212" "0212" "0212" "02112" "0212" "02112"
#> Sample_913 Sample_914 Sample_915 Sample_916 Sample_917 Sample_918 Sample_919 Sample_920
#> "0212" "0212" "0212" "02112" "0212" "0212" "0212" "0212"
#> Sample_921 Sample_922 Sample_923 Sample_924 Sample_925 Sample_926 Sample_927 Sample_928
#> "02112" "0212" "02112" "0212" "0212" "02112" "02112" "02112"
#> Sample_929 Sample_930 Sample_931 Sample_932 Sample_933 Sample_934 Sample_935 Sample_936
#> "0212" "02112" "02112" "0212" "0212" "0212" "0212" "0212"
#> Sample_937 Sample_938 Sample_939 Sample_940 Sample_941 Sample_942 Sample_943 Sample_944
#> "0212" "0212" "0212" "0212" "0212" "0212" "0212" "0212"
#> Sample_945 Sample_946 Sample_947 Sample_948 Sample_949 Sample_950 Sample_951 Sample_952
#> "02112" "02112" "02112" "03212" "03212" "0132" "03211" "03212"
#> Sample_953 Sample_954 Sample_955 Sample_956 Sample_957 Sample_958 Sample_959 Sample_960
#> "03211" "03221" "03211" "03212" "03211" "03212" "03212" "03212"
#> Sample_961 Sample_962 Sample_963 Sample_964 Sample_965 Sample_966 Sample_967 Sample_968
#> "03211" "0122" "03212" "03211" "03212" "03212" "03211" "0122"
#> Sample_969 Sample_970 Sample_971 Sample_972 Sample_973 Sample_974 Sample_975 Sample_976
#> "03211" "03212" "03211" "03211" "03212" "03211" "0333" "0132"
#> Sample_977 Sample_978 Sample_979 Sample_980 Sample_981 Sample_982 Sample_983 Sample_984
#> "0132" "0122" "03212" "0132" "0132" "0121" "0122" "03212"
#> Sample_985 Sample_986 Sample_987 Sample_988 Sample_989 Sample_990 Sample_991 Sample_992
#> "0122" "0132" "0122" "0121" "0121" "0121" "0121" "0121"
#> Sample_993 Sample_994 Sample_995 Sample_996 Sample_997 Sample_998 Sample_999 Sample_1000
#> "0121" "0122" "03211" "0132" "03212" "03212" "0132" "03212"
#> Sample_1001 Sample_1002 Sample_1003 Sample_1004 Sample_1005 Sample_1006 Sample_1007 Sample_1008
#> "03212" "03212" "03211" "0333" "03212" "03211" "0122" "0132"
#> Sample_1009 Sample_1010 Sample_1011 Sample_1012 Sample_1013 Sample_1014 Sample_1015 Sample_1016
#> "0132" "0132" "03212" "03221" "03212" "0121" "0121" "0121"
#> Sample_1017 Sample_1018 Sample_1019 Sample_1020 Sample_1021 Sample_1022 Sample_1023 Sample_1024
#> "0121" "0121" "0121" "0121" "0121" "0121" "0121" "0121"
#> Sample_1025 Sample_1026 Sample_1027 Sample_1028 Sample_1029 Sample_1030 Sample_1031 Sample_1032
#> "0121" "0121" "0111" "0111" "0312" "0111" "0332" "0312"
#> Sample_1033 Sample_1034 Sample_1035 Sample_1036 Sample_1037 Sample_1038 Sample_1039 Sample_1040
#> "0312" "0111" "0312" "0111" "0312" "0312" "0111" "0312"
#> Sample_1041 Sample_1042 Sample_1043 Sample_1044 Sample_1045 Sample_1046 Sample_1047 Sample_1048
#> "0112" "0111" "0312" "0312" "0332" "0111" "0312" "0331"
#> Sample_1049 Sample_1050 Sample_1051 Sample_1052 Sample_1053 Sample_1054 Sample_1055 Sample_1056
#> "0111" "0111" "0312" "0111" "03221" "03212" "03211" "0132"
#> Sample_1057 Sample_1058 Sample_1059 Sample_1060 Sample_1061 Sample_1062 Sample_1063 Sample_1064
#> "03221" "0121" "0132" "03212" "03221" "0132" "0312" "0312"
#> Sample_1065 Sample_1066 Sample_1067 Sample_1068 Sample_1069 Sample_1070 Sample_1071 Sample_1072
#> "0312" "0111" "0312" "0312" "0312" "0111" "0312" "0312"
#> Sample_1073 Sample_1074 Sample_1075 Sample_1076 Sample_1077 Sample_1078 Sample_1079 Sample_1080
#> "0111" "0312" "0111" "0111" "0312" "0312" "0312" "0112"
#> Sample_1081 Sample_1082 Sample_1083 Sample_1084 Sample_1085 Sample_1086 Sample_1087 Sample_1088
#> "0312" "0312" "0111" "0312" "0312" "0331" "0111" "0331"
#> Sample_1089 Sample_1090 Sample_1091 Sample_1092 Sample_1093 Sample_1094 Sample_1095 Sample_1096
#> "0332" "0111" "0111" "0312" "0111" "0312" "0312" "0312"
#> Sample_1097 Sample_1098 Sample_1099 Sample_1100 Sample_1101 Sample_1102 Sample_1103 Sample_1104
#> "0111" "0312" "0312" "0112" "0312" "0111" "0112" "0111"
#> Sample_1105 Sample_1106 Sample_1107 Sample_1108 Sample_1109 Sample_1110 Sample_1111 Sample_1112
#> "0111" "0312" "0312" "0112" "0312" "0111" "0312" "0111"
#> Sample_1113 Sample_1114 Sample_1115 Sample_1116 Sample_1117 Sample_1118 Sample_1119 Sample_1120
#> "0111" "0111" "0312" "0111" "0312" "0111" "0112" "0332"
#> Sample_1121 Sample_1122 Sample_1123 Sample_1124 Sample_1125 Sample_1126 Sample_1127 Sample_1128
#> "0111" "0331" "0312" "0312" "0312" "0312" "0312" "0312"
#> Sample_1129 Sample_1130 Sample_1131 Sample_1132 Sample_1133 Sample_1134 Sample_1135 Sample_1136
#> "0111" "0111" "0111" "0332" "0111" "0331" "0112" "0111"
#> Sample_1137 Sample_1138 Sample_1139 Sample_1140 Sample_1141 Sample_1142 Sample_1143 Sample_1144
#> "0111" "0111" "0312" "0331" "0312" "0112" "0111" "0111"
#> Sample_1145 Sample_1146 Sample_1147 Sample_1148 Sample_1149 Sample_1150 Sample_1151 Sample_1152
#> "0312" "0112" "0112" "0111" "0111" "0312" "0111" "0312"
#> Sample_1153 Sample_1154 Sample_1155 Sample_1156 Sample_1157 Sample_1158 Sample_1159 Sample_1160
#> "0111" "0111" "0111" "0312" "0312" "0111" "0112" "0112"
#> Sample_1161 Sample_1162 Sample_1163 Sample_1164 Sample_1165 Sample_1166 Sample_1167 Sample_1168
#> "0312" "0111" "0312" "0312" "0112" "0111" "0112" "0111"
#> Sample_1169 Sample_1170 Sample_1171 Sample_1172 Sample_1173 Sample_1174 Sample_1175 Sample_1176
#> "0312" "0312" "0111" "0111" "0111" "0111" "0312" "0111"
#> Sample_1177 Sample_1178 Sample_1179 Sample_1180 Sample_1181 Sample_1182 Sample_1183 Sample_1184
#> "0111" "0111" "0112" "0312" "0111" "0111" "0331" "0331"
#> Sample_1185 Sample_1186 Sample_1187 Sample_1188 Sample_1189 Sample_1190 Sample_1191 Sample_1192
#> "0111" "0111" "0111" "0111" "03211" "03221" "03222" "0122"
#> Sample_1193 Sample_1194 Sample_1195 Sample_1196 Sample_1197 Sample_1198 Sample_1199 Sample_1200
#> "03221" "03221" "03212" "03212" "03221" "03221" "03221" "03212"
#> Sample_1201 Sample_1202 Sample_1203 Sample_1204 Sample_1205 Sample_1206 Sample_1207 Sample_1208
#> "03212" "03212" "03221" "03221" "03221" "03212" "03221" "0132"
#> Sample_1209 Sample_1210 Sample_1211 Sample_1212 Sample_1213 Sample_1214 Sample_1215 Sample_1216
#> "03211" "03221" "03212" "03212" "03212" "03221" "03221" "0122"
#> Sample_1217 Sample_1218 Sample_1219 Sample_1220 Sample_1221 Sample_1222 Sample_1223 Sample_1224
#> "0132" "0132" "03221" "03212" "03221" "0111" "0311" "0311"
#> Sample_1225 Sample_1226 Sample_1227 Sample_1228 Sample_1229 Sample_1230 Sample_1231 Sample_1232
#> "0112" "0311" "0112" "0112" "0111" "0311" "0311" "0311"
#> Sample_1233 Sample_1234 Sample_1235 Sample_1236 Sample_1237 Sample_1238 Sample_1239 Sample_1240
#> "0311" "0112" "0311" "0112" "0311" "0311" "0112" "0111"
#> Sample_1241 Sample_1242 Sample_1243 Sample_1244 Sample_1245 Sample_1246 Sample_1247 Sample_1248
#> "0112" "0112" "0311" "0311" "0311" "0311" "0311" "0311"
#> Sample_1249 Sample_1250 Sample_1251 Sample_1252 Sample_1253 Sample_1254 Sample_1255 Sample_1256
#> "0112" "0311" "0311" "0311" "0311" "0111" "0332" "0311"
#> Sample_1257 Sample_1258 Sample_1259 Sample_1260 Sample_1261 Sample_1262 Sample_1263 Sample_1264
#> "0311" "0311" "0311" "0311" "0311" "0311" "0311" "0112"
#> Sample_1265 Sample_1266 Sample_1267 Sample_1268 Sample_1269 Sample_1270 Sample_1271 Sample_1272
#> "0311" "0111" "0111" "0332" "0311" "0311" "0311" "0112"
#> Sample_1273 Sample_1274 Sample_1275 Sample_1276 Sample_1277 Sample_1278 Sample_1279 Sample_1280
#> "0311" "0311" "0311" "0311" "0311" "0311" "0111" "0111"
#> Sample_1281 Sample_1282 Sample_1283 Sample_1284 Sample_1285 Sample_1286 Sample_1287 Sample_1288
#> "0311" "0311" "0112" "0311" "0112" "0311" "0311" "0311"
#> Sample_1289 Sample_1290 Sample_1291 Sample_1292 Sample_1293 Sample_1294 Sample_1295 Sample_1296
#> "0311" "0311" "0311" "0311" "0311" "0311" "0311" "0331"
#> Sample_1297 Sample_1298 Sample_1299 Sample_1300 Sample_1301 Sample_1302 Sample_1303 Sample_1304
#> "0311" "0311" "0111" "0311" "0311" "0311" "0311" "0311"
#> Sample_1305 Sample_1306 Sample_1307 Sample_1308 Sample_1309 Sample_1310 Sample_1311 Sample_1312
#> "0311" "0332" "0111" "0111" "0112" "0332" "0311" "0311"
#> Sample_1313 Sample_1314 Sample_1315 Sample_1316 Sample_1317 Sample_1318 Sample_1319 Sample_1320
#> "0112" "0313" "0313" "0311" "0112" "0313" "0311" "0112"
#> Sample_1321 Sample_1322 Sample_1323 Sample_1324 Sample_1325 Sample_1326 Sample_1327 Sample_1328
#> "0314" "0311" "0311" "0311" "0311" "0314" "0311" "0313"
#> Sample_1329 Sample_1330 Sample_1331 Sample_1332 Sample_1333 Sample_1334 Sample_1335 Sample_1336
#> "0314" "0314" "0313" "0314" "0314" "0313" "0111" "0112"
#> Sample_1337 Sample_1338 Sample_1339 Sample_1340 Sample_1341 Sample_1342 Sample_1343 Sample_1344
#> "0332" "0112" "0332" "0311" "0314" "0112" "0112" "0314"
#> Sample_1345 Sample_1346 Sample_1347 Sample_1348 Sample_1349 Sample_1350 Sample_1351 Sample_1352
#> "0332" "0311" "0311" "0112" "0314" "0112" "0314" "0112"
#> Sample_1353 Sample_1354 Sample_1355 Sample_1356 Sample_1357 Sample_1358 Sample_1359 Sample_1360
#> "0311" "0112" "0314" "0314" "0112" "0112" "0314" "0112"
#> Sample_1361 Sample_1362 Sample_1363 Sample_1364 Sample_1365 Sample_1366 Sample_1367 Sample_1368
#> "0314" "0314" "0314" "0112" "0311" "0313" "0111" "0313"
#> Sample_1369 Sample_1370 Sample_1371 Sample_1372 Sample_1373 Sample_1374 Sample_1375 Sample_1376
#> "0311" "0314" "0112" "0314" "0112" "0112" "0112" "0332"
#> Sample_1377 Sample_1378 Sample_1379 Sample_1380 Sample_1381 Sample_1382 Sample_1383 Sample_1384
#> "0311" "0311" "0311" "0311" "0314" "0112" "0313" "0313"
#> Sample_1385 Sample_1386 Sample_1387 Sample_1388 Sample_1389 Sample_1390 Sample_1391 Sample_1392
#> "0313" "0111" "0313" "0112" "0313" "0313" "0313" "0313"
#> Sample_1393 Sample_1394 Sample_1395 Sample_1396 Sample_1397 Sample_1398 Sample_1399 Sample_1400
#> "0313" "0313" "0313" "0313" "0313" "0111" "0112" "0331"
#> Sample_1401 Sample_1402 Sample_1403 Sample_1404 Sample_1405 Sample_1406 Sample_1407 Sample_1408
#> "0313" "0112" "0313" "0313" "0313" "0111" "0313" "0112"
#> Sample_1409 Sample_1410 Sample_1411 Sample_1412 Sample_1413 Sample_1414 Sample_1415 Sample_1416
#> "0111" "0313" "0313" "0313" "0331" "0313" "0313" "0332"
#> Sample_1417 Sample_1418 Sample_1419 Sample_1420 Sample_1421 Sample_1422 Sample_1423 Sample_1424
#> "0313" "0313" "0112" "0313" "0313" "0313" "0111" "0313"
#> Sample_1425 Sample_1426 Sample_1427 Sample_1428 Sample_1429 Sample_1430 Sample_1431 Sample_1432
#> "0111" "0112" "0112" "0313" "0111" "0111" "0111" "0111"
#> Sample_1433 Sample_1434 Sample_1435 Sample_1436 Sample_1437 Sample_1438 Sample_1439 Sample_1440
#> "0111" "0111" "0111" "0112" "0111" "0112" "0111" "0112"
#> Sample_1441 Sample_1442 Sample_1443 Sample_1444 Sample_1445 Sample_1446 Sample_1447 Sample_1448
#> "0111" "0313" "0313" "0111" "0111" "0313" "0313" "0111"
#> Sample_1449 Sample_1450 Sample_1451 Sample_1452 Sample_1453 Sample_1454 Sample_1455 Sample_1456
#> "0111" "0111" "0111" "0111" "0112" "0111" "0111" "0313"
#> Sample_1457 Sample_1458 Sample_1459 Sample_1460 Sample_1461 Sample_1462 Sample_1463 Sample_1464
#> "0313" "0112" "0112" "0331" "0313" "0111" "0314" "0314"
#> Sample_1465 Sample_1466 Sample_1467 Sample_1468 Sample_1469 Sample_1470 Sample_1471 Sample_1472
#> "0112" "0112" "0111" "0313" "0112" "0112" "0112" "0112"
#> Sample_1473 Sample_1474 Sample_1475 Sample_1476 Sample_1477 Sample_1478 Sample_1479 Sample_1480
#> "0112" "0111" "0112" "0112" "0112" "0112" "0111" "0314"
#> Sample_1481 Sample_1482 Sample_1483 Sample_1484 Sample_1485 Sample_1486 Sample_1487 Sample_1488
#> "0112" "0111" "0112" "0112" "0313" "0331" "0111" "0313"
#> Sample_1489 Sample_1490 Sample_1491 Sample_1492 Sample_1493 Sample_1494 Sample_1495 Sample_1496
#> "0111" "0112" "0112" "0112" "0111" "0332" "0112" "0112"
#> Sample_1497 Sample_1498 Sample_1499 Sample_1500 Sample_1501 Sample_1502 Sample_1503 Sample_1504
#> "0112" "0111" "0111" "0112" "0112" "0313" "0313" "0332"
#> Sample_1505 Sample_1506 Sample_1507 Sample_1508 Sample_1509 Sample_1510 Sample_1511 Sample_1512
#> "0331" "0313" "0313" "0313" "0111" "0111" "0313" "0313"
#> Sample_1513 Sample_1514 Sample_1515 Sample_1516 Sample_1517 Sample_1518 Sample_1519 Sample_1520
#> "0313" "0112" "0313" "0111" "0222" "0222" "02211" "0233"
#> Sample_1521 Sample_1522 Sample_1523 Sample_1524 Sample_1525 Sample_1526 Sample_1527 Sample_1528
#> "0232" "0131" "0132" "0233" "0121" "0121" "0212" "0212"
#> Sample_1529 Sample_1530 Sample_1531 Sample_1532 Sample_1533 Sample_1534 Sample_1535 Sample_1536
#> "0212" "0233" "0232" "0232" "02111" "0212" "0222" "0222"
#> Sample_1537 Sample_1538 Sample_1539 Sample_1540 Sample_1541 Sample_1542 Sample_1543 Sample_1544
#> "02211" "0212" "0212" "0131" "0111" "0212" "0233" "0131"
#> Sample_1545 Sample_1546 Sample_1547 Sample_1548 Sample_1549 Sample_1550 Sample_1551 Sample_1552
#> "0111" "02112" "0131" "0231" "03221" "0231" "0231" "0231"
#> Sample_1553 Sample_1554 Sample_1555 Sample_1556 Sample_1557 Sample_1558 Sample_1559 Sample_1560
#> "03212" "0222" "0231" "0131" "0131" "0131" "0231" "0131"
#> Sample_1561 Sample_1562 Sample_1563 Sample_1564 Sample_1565 Sample_1566 Sample_1567 Sample_1568
#> "0231" "0121" "0131" "0131" "0131" "0131" "0131" "0131"
#> Sample_1569 Sample_1570 Sample_1571 Sample_1572 Sample_1573 Sample_1574 Sample_1575 Sample_1576
#> "0312" "0212" "0131" "0131" "0131" "0231" "0231" "0131"
#> Sample_1577 Sample_1578 Sample_1579 Sample_1580 Sample_1581 Sample_1582 Sample_1583 Sample_1584
#> "0131" "0131" "0131" "0131" "03221" "0231" "0131" "0131"
#> Sample_1585 Sample_1586 Sample_1587 Sample_1588 Sample_1589 Sample_1590 Sample_1591 Sample_1592
#> "0231" "0231" "0231" "0222" "0231" "0131" "0131" "0231"
#> Sample_1593 Sample_1594 Sample_1595 Sample_1596 Sample_1597 Sample_1598 Sample_1599 Sample_1600
#> "0231" "0231" "0231" "0232" "03221" "0311" "0233" "0131"
get_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 174))
#> Sample_1 Sample_2 Sample_3 Sample_4 Sample_5 Sample_6 Sample_7 Sample_8
#> "0132" "02211" "02212" "0222" "02211" "02211" "02211" "0222"
#> Sample_9 Sample_10 Sample_11 Sample_12 Sample_13 Sample_14 Sample_15 Sample_16
#> "02212" "02211" "02212" "02211" "02211" "02211" "02211" "02212"
#> Sample_17 Sample_18 Sample_19 Sample_20 Sample_21 Sample_22 Sample_23 Sample_24
#> "0222" "0222" "0222" "0222" "0222" "0222" "0222" "02211"
#> Sample_25 Sample_26 Sample_27 Sample_28 Sample_29 Sample_30 Sample_31 Sample_32
#> "0222" "02211" "0222" "0222" "02212" "0222" "0233" "02212"
#> Sample_33 Sample_34 Sample_35 Sample_36 Sample_37 Sample_38 Sample_39 Sample_40
#> "02212" "02212" "02212" "02212" "02212" "02212" "02212" "02212"
#> Sample_41 Sample_42 Sample_43 Sample_44 Sample_45 Sample_46 Sample_47 Sample_48
#> "02212" "02212" "0233" "02212" "02212" "02212" "02212" "02212"
#> Sample_49 Sample_50 Sample_51 Sample_52 Sample_53 Sample_54 Sample_55 Sample_56
#> "02212" "02212" "02212" "02212" "0222" "0222" "02212" "02211"
#> Sample_57 Sample_58 Sample_59 Sample_60 Sample_61 Sample_62 Sample_63 Sample_64
#> "02211" "02211" "02212" "02211" "0222" "02211" "02211" "02211"
#> Sample_65 Sample_66 Sample_67 Sample_68 Sample_69 Sample_70 Sample_71 Sample_72
#> "02211" "02211" "0222" "02211" "02211" "0222" "0222" "02112"
#> Sample_73 Sample_74 Sample_75 Sample_76 Sample_77 Sample_78 Sample_79 Sample_80
#> "0233" "02211" "0222" "02211" "0222" "02211" "0222" "0222"
#> Sample_81 Sample_82 Sample_83 Sample_84 Sample_85 Sample_86 Sample_87 Sample_88
#> "02211" "0222" "02211" "02211" "0222" "02212" "0222" "0222"
#> Sample_89 Sample_90 Sample_91 Sample_92 Sample_93 Sample_94 Sample_95 Sample_96
#> "0222" "0222" "0222" "0222" "0222" "0232" "0222" "02211"
#> Sample_97 Sample_98 Sample_99 Sample_100 Sample_101 Sample_102 Sample_103 Sample_104
#> "02211" "02211" "02211" "0222" "0222" "02212" "0222" "0222"
#> Sample_105 Sample_106 Sample_107 Sample_108 Sample_109 Sample_110 Sample_111 Sample_112
#> "02211" "02211" "0222" "02211" "02211" "0222" "0212" "0222"
#> Sample_113 Sample_114 Sample_115 Sample_116 Sample_117 Sample_118 Sample_119 Sample_120
#> "0222" "0232" "0222" "0222" "0222" "0232" "0222" "02211"
#> Sample_121 Sample_122 Sample_123 Sample_124 Sample_125 Sample_126 Sample_127 Sample_128
#> "0121" "0222" "0232" "02211" "02211" "0222" "02211" "0222"
#> Sample_129 Sample_130 Sample_131 Sample_132 Sample_133 Sample_134 Sample_135 Sample_136
#> "02211" "0222" "0222" "02211" "0222" "02211" "0222" "02211"
#> Sample_137 Sample_138 Sample_139 Sample_140 Sample_141 Sample_142 Sample_143 Sample_144
#> "02211" "02211" "0222" "02211" "02211" "0222" "0222" "0232"
#> Sample_145 Sample_146 Sample_147 Sample_148 Sample_149 Sample_150 Sample_151 Sample_152
#> "0222" "0222" "02112" "02112" "02112" "02112" "0212" "02112"
#> Sample_153 Sample_154 Sample_155 Sample_156 Sample_157 Sample_158 Sample_159 Sample_160
#> "02112" "02112" "02112" "02112" "02112" "02112" "0212" "02112"
#> Sample_161 Sample_162 Sample_163 Sample_164 Sample_165 Sample_166 Sample_167 Sample_168
#> "0212" "0212" "0212" "0212" "0212" "0212" "02112" "02112"
#> Sample_169 Sample_170 Sample_171 Sample_172 Sample_173 Sample_174 Sample_175 Sample_176
#> "0212" "02112" "0232" "0212" "0233" "0212" "02112" "02112"
#> Sample_177 Sample_178 Sample_179 Sample_180 Sample_181 Sample_182 Sample_183 Sample_184
#> "0212" "02112" "0212" "0212" "02112" "02112" "02112" "02112"
#> Sample_185 Sample_186 Sample_187 Sample_188 Sample_189 Sample_190 Sample_191 Sample_192
#> "02112" "02112" "02112" "0212" "0212" "02112" "0212" "0212"
#> Sample_193 Sample_194 Sample_195 Sample_196 Sample_197 Sample_198 Sample_199 Sample_200
#> "02112" "02112" "02112" "02112" "02211" "02211" "02212" "02212"
#> Sample_201 Sample_202 Sample_203 Sample_204 Sample_205 Sample_206 Sample_207 Sample_208
#> "02212" "02212" "02212" "0233" "02212" "02212" "02212" "03222"
#> Sample_209 Sample_210 Sample_211 Sample_212 Sample_213 Sample_214 Sample_215 Sample_216
#> "03222" "03222" "03222" "03222" "03222" "0132" "0132" "03222"
#> Sample_217 Sample_218 Sample_219 Sample_220 Sample_221 Sample_222 Sample_223 Sample_224
#> "0132" "03222" "03222" "03222" "03222" "03222" "03222" "0132"
#> Sample_225 Sample_226 Sample_227 Sample_228 Sample_229 Sample_230 Sample_231 Sample_232
#> "03222" "0132" "03222" "03222" "03222" "03222" "03222" "03222"
#> Sample_233 Sample_234 Sample_235 Sample_236 Sample_237 Sample_238 Sample_239 Sample_240
#> "0333" "03222" "03222" "0132" "03222" "03222" "03222" "03222"
#> Sample_241 Sample_242 Sample_243 Sample_244 Sample_245 Sample_246 Sample_247 Sample_248
#> "0132" "03222" "03212" "03222" "03222" "03222" "03222" "03222"
#> Sample_249 Sample_250 Sample_251 Sample_252 Sample_253 Sample_254 Sample_255 Sample_256
#> "03222" "03222" "03222" "03222" "03222" "0333" "03221" "03222"
#> Sample_257 Sample_258 Sample_259 Sample_260 Sample_261 Sample_262 Sample_263 Sample_264
#> "03222" "0132" "03222" "03222" "03222" "03222" "03222" "0132"
#> Sample_265 Sample_266 Sample_267 Sample_268 Sample_269 Sample_270 Sample_271 Sample_272
#> "0122" "03211" "03211" "03211" "03221" "03211" "0333" "0122"
#> Sample_273 Sample_274 Sample_275 Sample_276 Sample_277 Sample_278 Sample_279 Sample_280
#> "03212" "03211" "0122" "0132" "03211" "0333" "03211" "03222"
#> Sample_281 Sample_282 Sample_283 Sample_284 Sample_285 Sample_286 Sample_287 Sample_288
#> "03211" "03211" "03221" "03211" "03211" "03211" "0132" "0333"
#> Sample_289 Sample_290 Sample_291 Sample_292 Sample_293 Sample_294 Sample_295 Sample_296
#> "0333" "03211" "03211" "03211" "03222" "03211" "03211" "03211"
#> Sample_297 Sample_298 Sample_299 Sample_300 Sample_301 Sample_302 Sample_303 Sample_304
#> "03211" "03211" "03211" "03221" "03211" "0122" "0122" "0122"
#> Sample_305 Sample_306 Sample_307 Sample_308 Sample_309 Sample_310 Sample_311 Sample_312
#> "0122" "03212" "03212" "0122" "0122" "0122" "0122" "0122"
#> Sample_313 Sample_314 Sample_315 Sample_316 Sample_317 Sample_318 Sample_319 Sample_320
#> "0122" "0122" "03212" "0122" "03212" "0122" "0122" "0122"
#> Sample_321 Sample_322 Sample_323 Sample_324 Sample_325 Sample_326 Sample_327 Sample_328
#> "0121" "03211" "0122" "0122" "0122" "0121" "0122" "0122"
#> Sample_329 Sample_330 Sample_331 Sample_332 Sample_333 Sample_334 Sample_335 Sample_336
#> "0121" "0122" "0333" "03211" "03211" "03211" "03211" "03211"
#> Sample_337 Sample_338 Sample_339 Sample_340 Sample_341 Sample_342 Sample_343 Sample_344
#> "03211" "03211" "0333" "03212" "03212" "03211" "03211" "03212"
#> Sample_345 Sample_346 Sample_347 Sample_348 Sample_349 Sample_350 Sample_351 Sample_352
#> "03212" "03212" "03211" "03212" "03212" "0132" "0122" "0132"
#> Sample_353 Sample_354 Sample_355 Sample_356 Sample_357 Sample_358 Sample_359 Sample_360
#> "03211" "03211" "0122" "03211" "03211" "0122" "03211" "0122"
#> Sample_361 Sample_362 Sample_363 Sample_364 Sample_365 Sample_366 Sample_367 Sample_368
#> "03211" "0122" "03211" "03211" "0132" "03211" "03211" "0122"
#> Sample_369 Sample_370 Sample_371 Sample_372 Sample_373 Sample_374 Sample_375 Sample_376
#> "0122" "03212" "0333" "0132" "0132" "03211" "03212" "03212"
#> Sample_377 Sample_378 Sample_379 Sample_380 Sample_381 Sample_382 Sample_383 Sample_384
#> "0122" "03212" "03212" "03212" "0122" "03212" "03212" "03212"
#> Sample_385 Sample_386 Sample_387 Sample_388 Sample_389 Sample_390 Sample_391 Sample_392
#> "0121" "0121" "03212" "03211" "0132" "0132" "03212" "03212"
#> Sample_393 Sample_394 Sample_395 Sample_396 Sample_397 Sample_398 Sample_399 Sample_400
#> "0122" "0132" "0122" "0132" "0122" "0122" "03212" "03212"
#> Sample_401 Sample_402 Sample_403 Sample_404 Sample_405 Sample_406 Sample_407 Sample_408
#> "03212" "03221" "0132" "0132" "0132" "03212" "03212" "0132"
#> Sample_409 Sample_410 Sample_411 Sample_412 Sample_413 Sample_414 Sample_415 Sample_416
#> "0122" "03212" "03212" "0122" "03212" "03212" "0333" "0122"
#> Sample_417 Sample_418 Sample_419 Sample_420 Sample_421 Sample_422 Sample_423 Sample_424
#> "03212" "03212" "03212" "0333" "0122" "0333" "03221" "0122"
#> Sample_425 Sample_426 Sample_427 Sample_428 Sample_429 Sample_430 Sample_431 Sample_432
#> "0122" "03221" "0122" "0122" "03221" "0132" "0122" "0121"
#> Sample_433 Sample_434 Sample_435 Sample_436 Sample_437 Sample_438 Sample_439 Sample_440
#> "0132" "0121" "03221" "0333" "0333" "0132" "0121" "0122"
#> Sample_441 Sample_442 Sample_443 Sample_444 Sample_445 Sample_446 Sample_447 Sample_448
#> "03221" "0122" "0132" "03212" "0121" "0122" "03221" "0132"
#> Sample_449 Sample_450 Sample_451 Sample_452 Sample_453 Sample_454 Sample_455 Sample_456
#> "03221" "03212" "0122" "0121" "03221" "03212" "0132" "0121"
#> Sample_457 Sample_458 Sample_459 Sample_460 Sample_461 Sample_462 Sample_463 Sample_464
#> "0132" "03221" "0122" "03212" "0121" "03212" "0132" "0121"
#> Sample_465 Sample_466 Sample_467 Sample_468 Sample_469 Sample_470 Sample_471 Sample_472
#> "0121" "0121" "03221" "0121" "0122" "03212" "03221" "0121"
#> Sample_473 Sample_474 Sample_475 Sample_476 Sample_477 Sample_478 Sample_479 Sample_480
#> "03212" "0121" "0121" "0132" "0132" "0314" "011" "0314"
#> Sample_481 Sample_482 Sample_483 Sample_484 Sample_485 Sample_486 Sample_487 Sample_488
#> "0313" "0314" "0314" "0314" "0314" "011" "011" "0331"
#> Sample_489 Sample_490 Sample_491 Sample_492 Sample_493 Sample_494 Sample_495 Sample_496
#> "0314" "011" "0314" "0314" "0314" "0314" "011" "0314"
#> Sample_497 Sample_498 Sample_499 Sample_500 Sample_501 Sample_502 Sample_503 Sample_504
#> "0314" "0314" "011" "011" "0314" "0314" "0314" "0314"
#> Sample_505 Sample_506 Sample_507 Sample_508 Sample_509 Sample_510 Sample_511 Sample_512
#> "011" "011" "011" "0314" "0314" "0314" "011" "0332"
#> Sample_513 Sample_514 Sample_515 Sample_516 Sample_517 Sample_518 Sample_519 Sample_520
#> "011" "0314" "011" "02112" "011" "0314" "0314" "011"
#> Sample_521 Sample_522 Sample_523 Sample_524 Sample_525 Sample_526 Sample_527 Sample_528
#> "011" "0314" "0314" "0314" "0314" "0314" "011" "0312"
#> Sample_529 Sample_530 Sample_531 Sample_532 Sample_533 Sample_534 Sample_535 Sample_536
#> "0331" "011" "0332" "011" "011" "0314" "011" "011"
#> Sample_537 Sample_538 Sample_539 Sample_540 Sample_541 Sample_542 Sample_543 Sample_544
#> "011" "0314" "011" "0314" "0314" "011" "011" "011"
#> Sample_545 Sample_546 Sample_547 Sample_548 Sample_549 Sample_550 Sample_551 Sample_552
#> "0331" "011" "011" "0314" "0314" "0314" "0314" "0314"
#> Sample_553 Sample_554 Sample_555 Sample_556 Sample_557 Sample_558 Sample_559 Sample_560
#> "0332" "0314" "0314" "0314" "011" "011" "011" "0314"
#> Sample_561 Sample_562 Sample_563 Sample_564 Sample_565 Sample_566 Sample_567 Sample_568
#> "0314" "0314" "0314" "0314" "011" "011" "011" "0314"
#> Sample_569 Sample_570 Sample_571 Sample_572 Sample_573 Sample_574 Sample_575 Sample_576
#> "011" "03221" "03221" "0132" "0121" "0333" "03212" "03221"
#> Sample_577 Sample_578 Sample_579 Sample_580 Sample_581 Sample_582 Sample_583 Sample_584
#> "0122" "03211" "03211" "03211" "03211" "03211" "03211" "03211"
#> Sample_585 Sample_586 Sample_587 Sample_588 Sample_589 Sample_590 Sample_591 Sample_592
#> "03222" "02212" "02211" "0231" "03222" "0131" "02211" "0231"
#> Sample_593 Sample_594 Sample_595 Sample_596 Sample_597 Sample_598 Sample_599 Sample_600
#> "02212" "0231" "0231" "0222" "0231" "0222" "0231" "0231"
#> Sample_601 Sample_602 Sample_603 Sample_604 Sample_605 Sample_606 Sample_607 Sample_608
#> "0222" "0231" "0222" "0222" "0222" "0222" "02211" "0222"
#> Sample_609 Sample_610 Sample_611 Sample_612 Sample_613 Sample_614 Sample_615 Sample_616
#> "0222" "0222" "0212" "0212" "03222" "0131" "03222" "0131"
#> Sample_617 Sample_618 Sample_619 Sample_620 Sample_621 Sample_622 Sample_623 Sample_624
#> "0131" "0121" "0131" "0131" "0231" "0131" "03221" "03221"
#> Sample_625 Sample_626 Sample_627 Sample_628 Sample_629 Sample_630 Sample_631 Sample_632
#> "0231" "0131" "0231" "0231" "0231" "0131" "0231" "0131"
#> Sample_633 Sample_634 Sample_635 Sample_636 Sample_637 Sample_638 Sample_639 Sample_640
#> "0131" "0131" "0131" "0131" "0131" "0231" "0131" "0232"
#> Sample_641 Sample_642 Sample_643 Sample_644 Sample_645 Sample_646 Sample_647 Sample_648
#> "0131" "0233" "0131" "03212" "0232" "0131" "0131" "0232"
#> Sample_649 Sample_650 Sample_651 Sample_652 Sample_653 Sample_654 Sample_655 Sample_656
#> "0232" "0231" "02212" "02211" "02211" "02212" "02211" "02211"
#> Sample_657 Sample_658 Sample_659 Sample_660 Sample_661 Sample_662 Sample_663 Sample_664
#> "0222" "02212" "02211" "0222" "02211" "0222" "0222" "02211"
#> Sample_665 Sample_666 Sample_667 Sample_668 Sample_669 Sample_670 Sample_671 Sample_672
#> "0232" "0222" "0232" "0222" "0222" "0222" "02211" "0222"
#> Sample_673 Sample_674 Sample_675 Sample_676 Sample_677 Sample_678 Sample_679 Sample_680
#> "0222" "0222" "0222" "0222" "02212" "02212" "02211" "0222"
#> Sample_681 Sample_682 Sample_683 Sample_684 Sample_685 Sample_686 Sample_687 Sample_688
#> "0232" "0222" "0222" "0222" "0121" "0212" "0121" "0212"
#> Sample_689 Sample_690 Sample_691 Sample_692 Sample_693 Sample_694 Sample_695 Sample_696
#> "0222" "0212" "0222" "0121" "0222" "0222" "0212" "02111"
#> Sample_697 Sample_698 Sample_699 Sample_700 Sample_701 Sample_702 Sample_703 Sample_704
#> "02111" "0212" "0212" "02111" "0212" "02111" "0212" "02211"
#> Sample_705 Sample_706 Sample_707 Sample_708 Sample_709 Sample_710 Sample_711 Sample_712
#> "0222" "02111" "02111" "02111" "02111" "0232" "0212" "02111"
#> Sample_713 Sample_714 Sample_715 Sample_716 Sample_717 Sample_718 Sample_719 Sample_720
#> "02111" "0212" "0212" "0212" "02111" "02111" "02111" "0232"
#> Sample_721 Sample_722 Sample_723 Sample_724 Sample_725 Sample_726 Sample_727 Sample_728
#> "02111" "02111" "0212" "02111" "0212" "02111" "02111" "0212"
#> Sample_729 Sample_730 Sample_731 Sample_732 Sample_733 Sample_734 Sample_735 Sample_736
#> "02111" "02111" "0233" "02111" "02111" "02111" "0233" "02111"
#> Sample_737 Sample_738 Sample_739 Sample_740 Sample_741 Sample_742 Sample_743 Sample_744
#> "0121" "0232" "02111" "0212" "02111" "0212" "02111" "02111"
#> Sample_745 Sample_746 Sample_747 Sample_748 Sample_749 Sample_750 Sample_751 Sample_752
#> "0212" "02111" "02111" "0212" "02111" "02111" "02111" "02111"
#> Sample_753 Sample_754 Sample_755 Sample_756 Sample_757 Sample_758 Sample_759 Sample_760
#> "0212" "0212" "02111" "02111" "02111" "02111" "0212" "02111"
#> Sample_761 Sample_762 Sample_763 Sample_764 Sample_765 Sample_766 Sample_767 Sample_768
#> "02111" "02111" "02111" "02111" "02111" "0212" "02111" "0232"
#> Sample_769 Sample_770 Sample_771 Sample_772 Sample_773 Sample_774 Sample_775 Sample_776
#> "0212" "02111" "02111" "0212" "02111" "02111" "0212" "02111"
#> Sample_777 Sample_778 Sample_779 Sample_780 Sample_781 Sample_782 Sample_783 Sample_784
#> "0212" "0212" "0212" "0212" "02111" "0212" "0212" "0212"
#> Sample_785 Sample_786 Sample_787 Sample_788 Sample_789 Sample_790 Sample_791 Sample_792
#> "0212" "0212" "02111" "0212" "0212" "02111" "02111" "02111"
#> Sample_793 Sample_794 Sample_795 Sample_796 Sample_797 Sample_798 Sample_799 Sample_800
#> "0212" "02111" "02111" "02111" "0212" "0212" "0212" "02111"
#> Sample_801 Sample_802 Sample_803 Sample_804 Sample_805 Sample_806 Sample_807 Sample_808
#> "02111" "0212" "02111" "0212" "02111" "02111" "02111" "02111"
#> Sample_809 Sample_810 Sample_811 Sample_812 Sample_813 Sample_814 Sample_815 Sample_816
#> "02111" "0212" "02111" "02111" "02111" "02111" "02111" "02111"
#> Sample_817 Sample_818 Sample_819 Sample_820 Sample_821 Sample_822 Sample_823 Sample_824
#> "0212" "02111" "0212" "0233" "0212" "02111" "0212" "0212"
#> Sample_825 Sample_826 Sample_827 Sample_828 Sample_829 Sample_830 Sample_831 Sample_832
#> "0212" "0212" "0212" "02111" "0212" "0212" "02111" "02111"
#> Sample_833 Sample_834 Sample_835 Sample_836 Sample_837 Sample_838 Sample_839 Sample_840
#> "02111" "0212" "0212" "0233" "02111" "02111" "0212" "02111"
#> Sample_841 Sample_842 Sample_843 Sample_844 Sample_845 Sample_846 Sample_847 Sample_848
#> "0222" "02212" "02212" "02211" "02211" "02211" "0222" "0222"
#> Sample_849 Sample_850 Sample_851 Sample_852 Sample_853 Sample_854 Sample_855 Sample_856
#> "0222" "02211" "02211" "0222" "02211" "02211" "0212" "0212"
#> Sample_857 Sample_858 Sample_859 Sample_860 Sample_861 Sample_862 Sample_863 Sample_864
#> "0212" "02112" "0232" "0212" "0212" "0212" "0212" "02112"
#> Sample_865 Sample_866 Sample_867 Sample_868 Sample_869 Sample_870 Sample_871 Sample_872
#> "0233" "0212" "0212" "02112" "0212" "0212" "0212" "0212"
#> Sample_873 Sample_874 Sample_875 Sample_876 Sample_877 Sample_878 Sample_879 Sample_880
#> "0212" "02112" "02112" "0212" "02112" "02112" "02112" "02112"
#> Sample_881 Sample_882 Sample_883 Sample_884 Sample_885 Sample_886 Sample_887 Sample_888
#> "0212" "0233" "02112" "02112" "0212" "02112" "02112" "02112"
#> Sample_889 Sample_890 Sample_891 Sample_892 Sample_893 Sample_894 Sample_895 Sample_896
#> "0212" "0212" "0212" "0212" "0212" "0232" "0212" "02112"
#> Sample_897 Sample_898 Sample_899 Sample_900 Sample_901 Sample_902 Sample_903 Sample_904
#> "0212" "0212" "02112" "0233" "02112" "0212" "0212" "02112"
#> Sample_905 Sample_906 Sample_907 Sample_908 Sample_909 Sample_910 Sample_911 Sample_912
#> "02112" "0212" "0212" "0212" "0212" "02112" "0212" "02112"
#> Sample_913 Sample_914 Sample_915 Sample_916 Sample_917 Sample_918 Sample_919 Sample_920
#> "0212" "0212" "0212" "02112" "0212" "0212" "0212" "0212"
#> Sample_921 Sample_922 Sample_923 Sample_924 Sample_925 Sample_926 Sample_927 Sample_928
#> "02112" "0212" "02112" "0212" "0212" "02112" "02112" "02112"
#> Sample_929 Sample_930 Sample_931 Sample_932 Sample_933 Sample_934 Sample_935 Sample_936
#> "0212" "02112" "02112" "0212" "0212" "0212" "0212" "0212"
#> Sample_937 Sample_938 Sample_939 Sample_940 Sample_941 Sample_942 Sample_943 Sample_944
#> "0212" "0212" "0212" "0212" "0212" "0212" "0212" "0212"
#> Sample_945 Sample_946 Sample_947 Sample_948 Sample_949 Sample_950 Sample_951 Sample_952
#> "02112" "02112" "02112" "03212" "03212" "0132" "03211" "03212"
#> Sample_953 Sample_954 Sample_955 Sample_956 Sample_957 Sample_958 Sample_959 Sample_960
#> "03211" "03221" "03211" "03212" "03211" "03212" "03212" "03212"
#> Sample_961 Sample_962 Sample_963 Sample_964 Sample_965 Sample_966 Sample_967 Sample_968
#> "03211" "0122" "03212" "03211" "03212" "03212" "03211" "0122"
#> Sample_969 Sample_970 Sample_971 Sample_972 Sample_973 Sample_974 Sample_975 Sample_976
#> "03211" "03212" "03211" "03211" "03212" "03211" "0333" "0132"
#> Sample_977 Sample_978 Sample_979 Sample_980 Sample_981 Sample_982 Sample_983 Sample_984
#> "0132" "0122" "03212" "0132" "0132" "0121" "0122" "03212"
#> Sample_985 Sample_986 Sample_987 Sample_988 Sample_989 Sample_990 Sample_991 Sample_992
#> "0122" "0132" "0122" "0121" "0121" "0121" "0121" "0121"
#> Sample_993 Sample_994 Sample_995 Sample_996 Sample_997 Sample_998 Sample_999 Sample_1000
#> "0121" "0122" "03211" "0132" "03212" "03212" "0132" "03212"
#> Sample_1001 Sample_1002 Sample_1003 Sample_1004 Sample_1005 Sample_1006 Sample_1007 Sample_1008
#> "03212" "03212" "03211" "0333" "03212" "03211" "0122" "0132"
#> Sample_1009 Sample_1010 Sample_1011 Sample_1012 Sample_1013 Sample_1014 Sample_1015 Sample_1016
#> "0132" "0132" "03212" "03221" "03212" "0121" "0121" "0121"
#> Sample_1017 Sample_1018 Sample_1019 Sample_1020 Sample_1021 Sample_1022 Sample_1023 Sample_1024
#> "0121" "0121" "0121" "0121" "0121" "0121" "0121" "0121"
#> Sample_1025 Sample_1026 Sample_1027 Sample_1028 Sample_1029 Sample_1030 Sample_1031 Sample_1032
#> "0121" "0121" "011" "011" "0312" "011" "0332" "0312"
#> Sample_1033 Sample_1034 Sample_1035 Sample_1036 Sample_1037 Sample_1038 Sample_1039 Sample_1040
#> "0312" "011" "0312" "011" "0312" "0312" "011" "0312"
#> Sample_1041 Sample_1042 Sample_1043 Sample_1044 Sample_1045 Sample_1046 Sample_1047 Sample_1048
#> "011" "011" "0312" "0312" "0332" "011" "0312" "0331"
#> Sample_1049 Sample_1050 Sample_1051 Sample_1052 Sample_1053 Sample_1054 Sample_1055 Sample_1056
#> "011" "011" "0312" "011" "03221" "03212" "03211" "0132"
#> Sample_1057 Sample_1058 Sample_1059 Sample_1060 Sample_1061 Sample_1062 Sample_1063 Sample_1064
#> "03221" "0121" "0132" "03212" "03221" "0132" "0312" "0312"
#> Sample_1065 Sample_1066 Sample_1067 Sample_1068 Sample_1069 Sample_1070 Sample_1071 Sample_1072
#> "0312" "011" "0312" "0312" "0312" "011" "0312" "0312"
#> Sample_1073 Sample_1074 Sample_1075 Sample_1076 Sample_1077 Sample_1078 Sample_1079 Sample_1080
#> "011" "0312" "011" "011" "0312" "0312" "0312" "011"
#> Sample_1081 Sample_1082 Sample_1083 Sample_1084 Sample_1085 Sample_1086 Sample_1087 Sample_1088
#> "0312" "0312" "011" "0312" "0312" "0331" "011" "0331"
#> Sample_1089 Sample_1090 Sample_1091 Sample_1092 Sample_1093 Sample_1094 Sample_1095 Sample_1096
#> "0332" "011" "011" "0312" "011" "0312" "0312" "0312"
#> Sample_1097 Sample_1098 Sample_1099 Sample_1100 Sample_1101 Sample_1102 Sample_1103 Sample_1104
#> "011" "0312" "0312" "011" "0312" "011" "011" "011"
#> Sample_1105 Sample_1106 Sample_1107 Sample_1108 Sample_1109 Sample_1110 Sample_1111 Sample_1112
#> "011" "0312" "0312" "011" "0312" "011" "0312" "011"
#> Sample_1113 Sample_1114 Sample_1115 Sample_1116 Sample_1117 Sample_1118 Sample_1119 Sample_1120
#> "011" "011" "0312" "011" "0312" "011" "011" "0332"
#> Sample_1121 Sample_1122 Sample_1123 Sample_1124 Sample_1125 Sample_1126 Sample_1127 Sample_1128
#> "011" "0331" "0312" "0312" "0312" "0312" "0312" "0312"
#> Sample_1129 Sample_1130 Sample_1131 Sample_1132 Sample_1133 Sample_1134 Sample_1135 Sample_1136
#> "011" "011" "011" "0332" "011" "0331" "011" "011"
#> Sample_1137 Sample_1138 Sample_1139 Sample_1140 Sample_1141 Sample_1142 Sample_1143 Sample_1144
#> "011" "011" "0312" "0331" "0312" "011" "011" "011"
#> Sample_1145 Sample_1146 Sample_1147 Sample_1148 Sample_1149 Sample_1150 Sample_1151 Sample_1152
#> "0312" "011" "011" "011" "011" "0312" "011" "0312"
#> Sample_1153 Sample_1154 Sample_1155 Sample_1156 Sample_1157 Sample_1158 Sample_1159 Sample_1160
#> "011" "011" "011" "0312" "0312" "011" "011" "011"
#> Sample_1161 Sample_1162 Sample_1163 Sample_1164 Sample_1165 Sample_1166 Sample_1167 Sample_1168
#> "0312" "011" "0312" "0312" "011" "011" "011" "011"
#> Sample_1169 Sample_1170 Sample_1171 Sample_1172 Sample_1173 Sample_1174 Sample_1175 Sample_1176
#> "0312" "0312" "011" "011" "011" "011" "0312" "011"
#> Sample_1177 Sample_1178 Sample_1179 Sample_1180 Sample_1181 Sample_1182 Sample_1183 Sample_1184
#> "011" "011" "011" "0312" "011" "011" "0331" "0331"
#> Sample_1185 Sample_1186 Sample_1187 Sample_1188 Sample_1189 Sample_1190 Sample_1191 Sample_1192
#> "011" "011" "011" "011" "03211" "03221" "03222" "0122"
#> Sample_1193 Sample_1194 Sample_1195 Sample_1196 Sample_1197 Sample_1198 Sample_1199 Sample_1200
#> "03221" "03221" "03212" "03212" "03221" "03221" "03221" "03212"
#> Sample_1201 Sample_1202 Sample_1203 Sample_1204 Sample_1205 Sample_1206 Sample_1207 Sample_1208
#> "03212" "03212" "03221" "03221" "03221" "03212" "03221" "0132"
#> Sample_1209 Sample_1210 Sample_1211 Sample_1212 Sample_1213 Sample_1214 Sample_1215 Sample_1216
#> "03211" "03221" "03212" "03212" "03212" "03221" "03221" "0122"
#> Sample_1217 Sample_1218 Sample_1219 Sample_1220 Sample_1221 Sample_1222 Sample_1223 Sample_1224
#> "0132" "0132" "03221" "03212" "03221" "011" "0311" "0311"
#> Sample_1225 Sample_1226 Sample_1227 Sample_1228 Sample_1229 Sample_1230 Sample_1231 Sample_1232
#> "011" "0311" "011" "011" "011" "0311" "0311" "0311"
#> Sample_1233 Sample_1234 Sample_1235 Sample_1236 Sample_1237 Sample_1238 Sample_1239 Sample_1240
#> "0311" "011" "0311" "011" "0311" "0311" "011" "011"
#> Sample_1241 Sample_1242 Sample_1243 Sample_1244 Sample_1245 Sample_1246 Sample_1247 Sample_1248
#> "011" "011" "0311" "0311" "0311" "0311" "0311" "0311"
#> Sample_1249 Sample_1250 Sample_1251 Sample_1252 Sample_1253 Sample_1254 Sample_1255 Sample_1256
#> "011" "0311" "0311" "0311" "0311" "011" "0332" "0311"
#> Sample_1257 Sample_1258 Sample_1259 Sample_1260 Sample_1261 Sample_1262 Sample_1263 Sample_1264
#> "0311" "0311" "0311" "0311" "0311" "0311" "0311" "011"
#> Sample_1265 Sample_1266 Sample_1267 Sample_1268 Sample_1269 Sample_1270 Sample_1271 Sample_1272
#> "0311" "011" "011" "0332" "0311" "0311" "0311" "011"
#> Sample_1273 Sample_1274 Sample_1275 Sample_1276 Sample_1277 Sample_1278 Sample_1279 Sample_1280
#> "0311" "0311" "0311" "0311" "0311" "0311" "011" "011"
#> Sample_1281 Sample_1282 Sample_1283 Sample_1284 Sample_1285 Sample_1286 Sample_1287 Sample_1288
#> "0311" "0311" "011" "0311" "011" "0311" "0311" "0311"
#> Sample_1289 Sample_1290 Sample_1291 Sample_1292 Sample_1293 Sample_1294 Sample_1295 Sample_1296
#> "0311" "0311" "0311" "0311" "0311" "0311" "0311" "0331"
#> Sample_1297 Sample_1298 Sample_1299 Sample_1300 Sample_1301 Sample_1302 Sample_1303 Sample_1304
#> "0311" "0311" "011" "0311" "0311" "0311" "0311" "0311"
#> Sample_1305 Sample_1306 Sample_1307 Sample_1308 Sample_1309 Sample_1310 Sample_1311 Sample_1312
#> "0311" "0332" "011" "011" "011" "0332" "0311" "0311"
#> Sample_1313 Sample_1314 Sample_1315 Sample_1316 Sample_1317 Sample_1318 Sample_1319 Sample_1320
#> "011" "0313" "0313" "0311" "011" "0313" "0311" "011"
#> Sample_1321 Sample_1322 Sample_1323 Sample_1324 Sample_1325 Sample_1326 Sample_1327 Sample_1328
#> "0314" "0311" "0311" "0311" "0311" "0314" "0311" "0313"
#> Sample_1329 Sample_1330 Sample_1331 Sample_1332 Sample_1333 Sample_1334 Sample_1335 Sample_1336
#> "0314" "0314" "0313" "0314" "0314" "0313" "011" "011"
#> Sample_1337 Sample_1338 Sample_1339 Sample_1340 Sample_1341 Sample_1342 Sample_1343 Sample_1344
#> "0332" "011" "0332" "0311" "0314" "011" "011" "0314"
#> Sample_1345 Sample_1346 Sample_1347 Sample_1348 Sample_1349 Sample_1350 Sample_1351 Sample_1352
#> "0332" "0311" "0311" "011" "0314" "011" "0314" "011"
#> Sample_1353 Sample_1354 Sample_1355 Sample_1356 Sample_1357 Sample_1358 Sample_1359 Sample_1360
#> "0311" "011" "0314" "0314" "011" "011" "0314" "011"
#> Sample_1361 Sample_1362 Sample_1363 Sample_1364 Sample_1365 Sample_1366 Sample_1367 Sample_1368
#> "0314" "0314" "0314" "011" "0311" "0313" "011" "0313"
#> Sample_1369 Sample_1370 Sample_1371 Sample_1372 Sample_1373 Sample_1374 Sample_1375 Sample_1376
#> "0311" "0314" "011" "0314" "011" "011" "011" "0332"
#> Sample_1377 Sample_1378 Sample_1379 Sample_1380 Sample_1381 Sample_1382 Sample_1383 Sample_1384
#> "0311" "0311" "0311" "0311" "0314" "011" "0313" "0313"
#> Sample_1385 Sample_1386 Sample_1387 Sample_1388 Sample_1389 Sample_1390 Sample_1391 Sample_1392
#> "0313" "011" "0313" "011" "0313" "0313" "0313" "0313"
#> Sample_1393 Sample_1394 Sample_1395 Sample_1396 Sample_1397 Sample_1398 Sample_1399 Sample_1400
#> "0313" "0313" "0313" "0313" "0313" "011" "011" "0331"
#> Sample_1401 Sample_1402 Sample_1403 Sample_1404 Sample_1405 Sample_1406 Sample_1407 Sample_1408
#> "0313" "011" "0313" "0313" "0313" "011" "0313" "011"
#> Sample_1409 Sample_1410 Sample_1411 Sample_1412 Sample_1413 Sample_1414 Sample_1415 Sample_1416
#> "011" "0313" "0313" "0313" "0331" "0313" "0313" "0332"
#> Sample_1417 Sample_1418 Sample_1419 Sample_1420 Sample_1421 Sample_1422 Sample_1423 Sample_1424
#> "0313" "0313" "011" "0313" "0313" "0313" "011" "0313"
#> Sample_1425 Sample_1426 Sample_1427 Sample_1428 Sample_1429 Sample_1430 Sample_1431 Sample_1432
#> "011" "011" "011" "0313" "011" "011" "011" "011"
#> Sample_1433 Sample_1434 Sample_1435 Sample_1436 Sample_1437 Sample_1438 Sample_1439 Sample_1440
#> "011" "011" "011" "011" "011" "011" "011" "011"
#> Sample_1441 Sample_1442 Sample_1443 Sample_1444 Sample_1445 Sample_1446 Sample_1447 Sample_1448
#> "011" "0313" "0313" "011" "011" "0313" "0313" "011"
#> Sample_1449 Sample_1450 Sample_1451 Sample_1452 Sample_1453 Sample_1454 Sample_1455 Sample_1456
#> "011" "011" "011" "011" "011" "011" "011" "0313"
#> Sample_1457 Sample_1458 Sample_1459 Sample_1460 Sample_1461 Sample_1462 Sample_1463 Sample_1464
#> "0313" "011" "011" "0331" "0313" "011" "0314" "0314"
#> Sample_1465 Sample_1466 Sample_1467 Sample_1468 Sample_1469 Sample_1470 Sample_1471 Sample_1472
#> "011" "011" "011" "0313" "011" "011" "011" "011"
#> Sample_1473 Sample_1474 Sample_1475 Sample_1476 Sample_1477 Sample_1478 Sample_1479 Sample_1480
#> "011" "011" "011" "011" "011" "011" "011" "0314"
#> Sample_1481 Sample_1482 Sample_1483 Sample_1484 Sample_1485 Sample_1486 Sample_1487 Sample_1488
#> "011" "011" "011" "011" "0313" "0331" "011" "0313"
#> Sample_1489 Sample_1490 Sample_1491 Sample_1492 Sample_1493 Sample_1494 Sample_1495 Sample_1496
#> "011" "011" "011" "011" "011" "0332" "011" "011"
#> Sample_1497 Sample_1498 Sample_1499 Sample_1500 Sample_1501 Sample_1502 Sample_1503 Sample_1504
#> "011" "011" "011" "011" "011" "0313" "0313" "0332"
#> Sample_1505 Sample_1506 Sample_1507 Sample_1508 Sample_1509 Sample_1510 Sample_1511 Sample_1512
#> "0331" "0313" "0313" "0313" "011" "011" "0313" "0313"
#> Sample_1513 Sample_1514 Sample_1515 Sample_1516 Sample_1517 Sample_1518 Sample_1519 Sample_1520
#> "0313" "011" "0313" "011" "0222" "0222" "02211" "0233"
#> Sample_1521 Sample_1522 Sample_1523 Sample_1524 Sample_1525 Sample_1526 Sample_1527 Sample_1528
#> "0232" "0131" "0132" "0233" "0121" "0121" "0212" "0212"
#> Sample_1529 Sample_1530 Sample_1531 Sample_1532 Sample_1533 Sample_1534 Sample_1535 Sample_1536
#> "0212" "0233" "0232" "0232" "02111" "0212" "0222" "0222"
#> Sample_1537 Sample_1538 Sample_1539 Sample_1540 Sample_1541 Sample_1542 Sample_1543 Sample_1544
#> "02211" "0212" "0212" "0131" "011" "0212" "0233" "0131"
#> Sample_1545 Sample_1546 Sample_1547 Sample_1548 Sample_1549 Sample_1550 Sample_1551 Sample_1552
#> "011" "02112" "0131" "0231" "03221" "0231" "0231" "0231"
#> Sample_1553 Sample_1554 Sample_1555 Sample_1556 Sample_1557 Sample_1558 Sample_1559 Sample_1560
#> "03212" "0222" "0231" "0131" "0131" "0131" "0231" "0131"
#> Sample_1561 Sample_1562 Sample_1563 Sample_1564 Sample_1565 Sample_1566 Sample_1567 Sample_1568
#> "0231" "0121" "0131" "0131" "0131" "0131" "0131" "0131"
#> Sample_1569 Sample_1570 Sample_1571 Sample_1572 Sample_1573 Sample_1574 Sample_1575 Sample_1576
#> "0312" "0212" "0131" "0131" "0131" "0231" "0231" "0131"
#> Sample_1577 Sample_1578 Sample_1579 Sample_1580 Sample_1581 Sample_1582 Sample_1583 Sample_1584
#> "0131" "0131" "0131" "0131" "03221" "0231" "0131" "0131"
#> Sample_1585 Sample_1586 Sample_1587 Sample_1588 Sample_1589 Sample_1590 Sample_1591 Sample_1592
#> "0231" "0231" "0231" "0222" "0231" "0131" "0131" "0231"
#> Sample_1593 Sample_1594 Sample_1595 Sample_1596 Sample_1597 Sample_1598 Sample_1599 Sample_1600
#> "0231" "0231" "0231" "0232" "03221" "0311" "0233" "0131"
get_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 248))
#> Sample_1 Sample_2 Sample_3 Sample_4 Sample_5 Sample_6 Sample_7 Sample_8
#> "0132" "02211" "02212" "0222" "02211" "02211" "02211" "0222"
#> Sample_9 Sample_10 Sample_11 Sample_12 Sample_13 Sample_14 Sample_15 Sample_16
#> "02212" "02211" "02212" "02211" "02211" "02211" "02211" "02212"
#> Sample_17 Sample_18 Sample_19 Sample_20 Sample_21 Sample_22 Sample_23 Sample_24
#> "0222" "0222" "0222" "0222" "0222" "0222" "0222" "02211"
#> Sample_25 Sample_26 Sample_27 Sample_28 Sample_29 Sample_30 Sample_31 Sample_32
#> "0222" "02211" "0222" "0222" "02212" "0222" "0233" "02212"
#> Sample_33 Sample_34 Sample_35 Sample_36 Sample_37 Sample_38 Sample_39 Sample_40
#> "02212" "02212" "02212" "02212" "02212" "02212" "02212" "02212"
#> Sample_41 Sample_42 Sample_43 Sample_44 Sample_45 Sample_46 Sample_47 Sample_48
#> "02212" "02212" "0233" "02212" "02212" "02212" "02212" "02212"
#> Sample_49 Sample_50 Sample_51 Sample_52 Sample_53 Sample_54 Sample_55 Sample_56
#> "02212" "02212" "02212" "02212" "0222" "0222" "02212" "02211"
#> Sample_57 Sample_58 Sample_59 Sample_60 Sample_61 Sample_62 Sample_63 Sample_64
#> "02211" "02211" "02212" "02211" "0222" "02211" "02211" "02211"
#> Sample_65 Sample_66 Sample_67 Sample_68 Sample_69 Sample_70 Sample_71 Sample_72
#> "02211" "02211" "0222" "02211" "02211" "0222" "0222" "0211"
#> Sample_73 Sample_74 Sample_75 Sample_76 Sample_77 Sample_78 Sample_79 Sample_80
#> "0233" "02211" "0222" "02211" "0222" "02211" "0222" "0222"
#> Sample_81 Sample_82 Sample_83 Sample_84 Sample_85 Sample_86 Sample_87 Sample_88
#> "02211" "0222" "02211" "02211" "0222" "02212" "0222" "0222"
#> Sample_89 Sample_90 Sample_91 Sample_92 Sample_93 Sample_94 Sample_95 Sample_96
#> "0222" "0222" "0222" "0222" "0222" "0232" "0222" "02211"
#> Sample_97 Sample_98 Sample_99 Sample_100 Sample_101 Sample_102 Sample_103 Sample_104
#> "02211" "02211" "02211" "0222" "0222" "02212" "0222" "0222"
#> Sample_105 Sample_106 Sample_107 Sample_108 Sample_109 Sample_110 Sample_111 Sample_112
#> "02211" "02211" "0222" "02211" "02211" "0222" "0212" "0222"
#> Sample_113 Sample_114 Sample_115 Sample_116 Sample_117 Sample_118 Sample_119 Sample_120
#> "0222" "0232" "0222" "0222" "0222" "0232" "0222" "02211"
#> Sample_121 Sample_122 Sample_123 Sample_124 Sample_125 Sample_126 Sample_127 Sample_128
#> "0121" "0222" "0232" "02211" "02211" "0222" "02211" "0222"
#> Sample_129 Sample_130 Sample_131 Sample_132 Sample_133 Sample_134 Sample_135 Sample_136
#> "02211" "0222" "0222" "02211" "0222" "02211" "0222" "02211"
#> Sample_137 Sample_138 Sample_139 Sample_140 Sample_141 Sample_142 Sample_143 Sample_144
#> "02211" "02211" "0222" "02211" "02211" "0222" "0222" "0232"
#> Sample_145 Sample_146 Sample_147 Sample_148 Sample_149 Sample_150 Sample_151 Sample_152
#> "0222" "0222" "0211" "0211" "0211" "0211" "0212" "0211"
#> Sample_153 Sample_154 Sample_155 Sample_156 Sample_157 Sample_158 Sample_159 Sample_160
#> "0211" "0211" "0211" "0211" "0211" "0211" "0212" "0211"
#> Sample_161 Sample_162 Sample_163 Sample_164 Sample_165 Sample_166 Sample_167 Sample_168
#> "0212" "0212" "0212" "0212" "0212" "0212" "0211" "0211"
#> Sample_169 Sample_170 Sample_171 Sample_172 Sample_173 Sample_174 Sample_175 Sample_176
#> "0212" "0211" "0232" "0212" "0233" "0212" "0211" "0211"
#> Sample_177 Sample_178 Sample_179 Sample_180 Sample_181 Sample_182 Sample_183 Sample_184
#> "0212" "0211" "0212" "0212" "0211" "0211" "0211" "0211"
#> Sample_185 Sample_186 Sample_187 Sample_188 Sample_189 Sample_190 Sample_191 Sample_192
#> "0211" "0211" "0211" "0212" "0212" "0211" "0212" "0212"
#> Sample_193 Sample_194 Sample_195 Sample_196 Sample_197 Sample_198 Sample_199 Sample_200
#> "0211" "0211" "0211" "0211" "02211" "02211" "02212" "02212"
#> Sample_201 Sample_202 Sample_203 Sample_204 Sample_205 Sample_206 Sample_207 Sample_208
#> "02212" "02212" "02212" "0233" "02212" "02212" "02212" "03222"
#> Sample_209 Sample_210 Sample_211 Sample_212 Sample_213 Sample_214 Sample_215 Sample_216
#> "03222" "03222" "03222" "03222" "03222" "0132" "0132" "03222"
#> Sample_217 Sample_218 Sample_219 Sample_220 Sample_221 Sample_222 Sample_223 Sample_224
#> "0132" "03222" "03222" "03222" "03222" "03222" "03222" "0132"
#> Sample_225 Sample_226 Sample_227 Sample_228 Sample_229 Sample_230 Sample_231 Sample_232
#> "03222" "0132" "03222" "03222" "03222" "03222" "03222" "03222"
#> Sample_233 Sample_234 Sample_235 Sample_236 Sample_237 Sample_238 Sample_239 Sample_240
#> "0333" "03222" "03222" "0132" "03222" "03222" "03222" "03222"
#> Sample_241 Sample_242 Sample_243 Sample_244 Sample_245 Sample_246 Sample_247 Sample_248
#> "0132" "03222" "03212" "03222" "03222" "03222" "03222" "03222"
#> Sample_249 Sample_250 Sample_251 Sample_252 Sample_253 Sample_254 Sample_255 Sample_256
#> "03222" "03222" "03222" "03222" "03222" "0333" "03221" "03222"
#> Sample_257 Sample_258 Sample_259 Sample_260 Sample_261 Sample_262 Sample_263 Sample_264
#> "03222" "0132" "03222" "03222" "03222" "03222" "03222" "0132"
#> Sample_265 Sample_266 Sample_267 Sample_268 Sample_269 Sample_270 Sample_271 Sample_272
#> "0122" "03211" "03211" "03211" "03221" "03211" "0333" "0122"
#> Sample_273 Sample_274 Sample_275 Sample_276 Sample_277 Sample_278 Sample_279 Sample_280
#> "03212" "03211" "0122" "0132" "03211" "0333" "03211" "03222"
#> Sample_281 Sample_282 Sample_283 Sample_284 Sample_285 Sample_286 Sample_287 Sample_288
#> "03211" "03211" "03221" "03211" "03211" "03211" "0132" "0333"
#> Sample_289 Sample_290 Sample_291 Sample_292 Sample_293 Sample_294 Sample_295 Sample_296
#> "0333" "03211" "03211" "03211" "03222" "03211" "03211" "03211"
#> Sample_297 Sample_298 Sample_299 Sample_300 Sample_301 Sample_302 Sample_303 Sample_304
#> "03211" "03211" "03211" "03221" "03211" "0122" "0122" "0122"
#> Sample_305 Sample_306 Sample_307 Sample_308 Sample_309 Sample_310 Sample_311 Sample_312
#> "0122" "03212" "03212" "0122" "0122" "0122" "0122" "0122"
#> Sample_313 Sample_314 Sample_315 Sample_316 Sample_317 Sample_318 Sample_319 Sample_320
#> "0122" "0122" "03212" "0122" "03212" "0122" "0122" "0122"
#> Sample_321 Sample_322 Sample_323 Sample_324 Sample_325 Sample_326 Sample_327 Sample_328
#> "0121" "03211" "0122" "0122" "0122" "0121" "0122" "0122"
#> Sample_329 Sample_330 Sample_331 Sample_332 Sample_333 Sample_334 Sample_335 Sample_336
#> "0121" "0122" "0333" "03211" "03211" "03211" "03211" "03211"
#> Sample_337 Sample_338 Sample_339 Sample_340 Sample_341 Sample_342 Sample_343 Sample_344
#> "03211" "03211" "0333" "03212" "03212" "03211" "03211" "03212"
#> Sample_345 Sample_346 Sample_347 Sample_348 Sample_349 Sample_350 Sample_351 Sample_352
#> "03212" "03212" "03211" "03212" "03212" "0132" "0122" "0132"
#> Sample_353 Sample_354 Sample_355 Sample_356 Sample_357 Sample_358 Sample_359 Sample_360
#> "03211" "03211" "0122" "03211" "03211" "0122" "03211" "0122"
#> Sample_361 Sample_362 Sample_363 Sample_364 Sample_365 Sample_366 Sample_367 Sample_368
#> "03211" "0122" "03211" "03211" "0132" "03211" "03211" "0122"
#> Sample_369 Sample_370 Sample_371 Sample_372 Sample_373 Sample_374 Sample_375 Sample_376
#> "0122" "03212" "0333" "0132" "0132" "03211" "03212" "03212"
#> Sample_377 Sample_378 Sample_379 Sample_380 Sample_381 Sample_382 Sample_383 Sample_384
#> "0122" "03212" "03212" "03212" "0122" "03212" "03212" "03212"
#> Sample_385 Sample_386 Sample_387 Sample_388 Sample_389 Sample_390 Sample_391 Sample_392
#> "0121" "0121" "03212" "03211" "0132" "0132" "03212" "03212"
#> Sample_393 Sample_394 Sample_395 Sample_396 Sample_397 Sample_398 Sample_399 Sample_400
#> "0122" "0132" "0122" "0132" "0122" "0122" "03212" "03212"
#> Sample_401 Sample_402 Sample_403 Sample_404 Sample_405 Sample_406 Sample_407 Sample_408
#> "03212" "03221" "0132" "0132" "0132" "03212" "03212" "0132"
#> Sample_409 Sample_410 Sample_411 Sample_412 Sample_413 Sample_414 Sample_415 Sample_416
#> "0122" "03212" "03212" "0122" "03212" "03212" "0333" "0122"
#> Sample_417 Sample_418 Sample_419 Sample_420 Sample_421 Sample_422 Sample_423 Sample_424
#> "03212" "03212" "03212" "0333" "0122" "0333" "03221" "0122"
#> Sample_425 Sample_426 Sample_427 Sample_428 Sample_429 Sample_430 Sample_431 Sample_432
#> "0122" "03221" "0122" "0122" "03221" "0132" "0122" "0121"
#> Sample_433 Sample_434 Sample_435 Sample_436 Sample_437 Sample_438 Sample_439 Sample_440
#> "0132" "0121" "03221" "0333" "0333" "0132" "0121" "0122"
#> Sample_441 Sample_442 Sample_443 Sample_444 Sample_445 Sample_446 Sample_447 Sample_448
#> "03221" "0122" "0132" "03212" "0121" "0122" "03221" "0132"
#> Sample_449 Sample_450 Sample_451 Sample_452 Sample_453 Sample_454 Sample_455 Sample_456
#> "03221" "03212" "0122" "0121" "03221" "03212" "0132" "0121"
#> Sample_457 Sample_458 Sample_459 Sample_460 Sample_461 Sample_462 Sample_463 Sample_464
#> "0132" "03221" "0122" "03212" "0121" "03212" "0132" "0121"
#> Sample_465 Sample_466 Sample_467 Sample_468 Sample_469 Sample_470 Sample_471 Sample_472
#> "0121" "0121" "03221" "0121" "0122" "03212" "03221" "0121"
#> Sample_473 Sample_474 Sample_475 Sample_476 Sample_477 Sample_478 Sample_479 Sample_480
#> "03212" "0121" "0121" "0132" "0132" "0314" "011" "0314"
#> Sample_481 Sample_482 Sample_483 Sample_484 Sample_485 Sample_486 Sample_487 Sample_488
#> "0313" "0314" "0314" "0314" "0314" "011" "011" "0331"
#> Sample_489 Sample_490 Sample_491 Sample_492 Sample_493 Sample_494 Sample_495 Sample_496
#> "0314" "011" "0314" "0314" "0314" "0314" "011" "0314"
#> Sample_497 Sample_498 Sample_499 Sample_500 Sample_501 Sample_502 Sample_503 Sample_504
#> "0314" "0314" "011" "011" "0314" "0314" "0314" "0314"
#> Sample_505 Sample_506 Sample_507 Sample_508 Sample_509 Sample_510 Sample_511 Sample_512
#> "011" "011" "011" "0314" "0314" "0314" "011" "0332"
#> Sample_513 Sample_514 Sample_515 Sample_516 Sample_517 Sample_518 Sample_519 Sample_520
#> "011" "0314" "011" "0211" "011" "0314" "0314" "011"
#> Sample_521 Sample_522 Sample_523 Sample_524 Sample_525 Sample_526 Sample_527 Sample_528
#> "011" "0314" "0314" "0314" "0314" "0314" "011" "0312"
#> Sample_529 Sample_530 Sample_531 Sample_532 Sample_533 Sample_534 Sample_535 Sample_536
#> "0331" "011" "0332" "011" "011" "0314" "011" "011"
#> Sample_537 Sample_538 Sample_539 Sample_540 Sample_541 Sample_542 Sample_543 Sample_544
#> "011" "0314" "011" "0314" "0314" "011" "011" "011"
#> Sample_545 Sample_546 Sample_547 Sample_548 Sample_549 Sample_550 Sample_551 Sample_552
#> "0331" "011" "011" "0314" "0314" "0314" "0314" "0314"
#> Sample_553 Sample_554 Sample_555 Sample_556 Sample_557 Sample_558 Sample_559 Sample_560
#> "0332" "0314" "0314" "0314" "011" "011" "011" "0314"
#> Sample_561 Sample_562 Sample_563 Sample_564 Sample_565 Sample_566 Sample_567 Sample_568
#> "0314" "0314" "0314" "0314" "011" "011" "011" "0314"
#> Sample_569 Sample_570 Sample_571 Sample_572 Sample_573 Sample_574 Sample_575 Sample_576
#> "011" "03221" "03221" "0132" "0121" "0333" "03212" "03221"
#> Sample_577 Sample_578 Sample_579 Sample_580 Sample_581 Sample_582 Sample_583 Sample_584
#> "0122" "03211" "03211" "03211" "03211" "03211" "03211" "03211"
#> Sample_585 Sample_586 Sample_587 Sample_588 Sample_589 Sample_590 Sample_591 Sample_592
#> "03222" "02212" "02211" "0231" "03222" "0131" "02211" "0231"
#> Sample_593 Sample_594 Sample_595 Sample_596 Sample_597 Sample_598 Sample_599 Sample_600
#> "02212" "0231" "0231" "0222" "0231" "0222" "0231" "0231"
#> Sample_601 Sample_602 Sample_603 Sample_604 Sample_605 Sample_606 Sample_607 Sample_608
#> "0222" "0231" "0222" "0222" "0222" "0222" "02211" "0222"
#> Sample_609 Sample_610 Sample_611 Sample_612 Sample_613 Sample_614 Sample_615 Sample_616
#> "0222" "0222" "0212" "0212" "03222" "0131" "03222" "0131"
#> Sample_617 Sample_618 Sample_619 Sample_620 Sample_621 Sample_622 Sample_623 Sample_624
#> "0131" "0121" "0131" "0131" "0231" "0131" "03221" "03221"
#> Sample_625 Sample_626 Sample_627 Sample_628 Sample_629 Sample_630 Sample_631 Sample_632
#> "0231" "0131" "0231" "0231" "0231" "0131" "0231" "0131"
#> Sample_633 Sample_634 Sample_635 Sample_636 Sample_637 Sample_638 Sample_639 Sample_640
#> "0131" "0131" "0131" "0131" "0131" "0231" "0131" "0232"
#> Sample_641 Sample_642 Sample_643 Sample_644 Sample_645 Sample_646 Sample_647 Sample_648
#> "0131" "0233" "0131" "03212" "0232" "0131" "0131" "0232"
#> Sample_649 Sample_650 Sample_651 Sample_652 Sample_653 Sample_654 Sample_655 Sample_656
#> "0232" "0231" "02212" "02211" "02211" "02212" "02211" "02211"
#> Sample_657 Sample_658 Sample_659 Sample_660 Sample_661 Sample_662 Sample_663 Sample_664
#> "0222" "02212" "02211" "0222" "02211" "0222" "0222" "02211"
#> Sample_665 Sample_666 Sample_667 Sample_668 Sample_669 Sample_670 Sample_671 Sample_672
#> "0232" "0222" "0232" "0222" "0222" "0222" "02211" "0222"
#> Sample_673 Sample_674 Sample_675 Sample_676 Sample_677 Sample_678 Sample_679 Sample_680
#> "0222" "0222" "0222" "0222" "02212" "02212" "02211" "0222"
#> Sample_681 Sample_682 Sample_683 Sample_684 Sample_685 Sample_686 Sample_687 Sample_688
#> "0232" "0222" "0222" "0222" "0121" "0212" "0121" "0212"
#> Sample_689 Sample_690 Sample_691 Sample_692 Sample_693 Sample_694 Sample_695 Sample_696
#> "0222" "0212" "0222" "0121" "0222" "0222" "0212" "0211"
#> Sample_697 Sample_698 Sample_699 Sample_700 Sample_701 Sample_702 Sample_703 Sample_704
#> "0211" "0212" "0212" "0211" "0212" "0211" "0212" "02211"
#> Sample_705 Sample_706 Sample_707 Sample_708 Sample_709 Sample_710 Sample_711 Sample_712
#> "0222" "0211" "0211" "0211" "0211" "0232" "0212" "0211"
#> Sample_713 Sample_714 Sample_715 Sample_716 Sample_717 Sample_718 Sample_719 Sample_720
#> "0211" "0212" "0212" "0212" "0211" "0211" "0211" "0232"
#> Sample_721 Sample_722 Sample_723 Sample_724 Sample_725 Sample_726 Sample_727 Sample_728
#> "0211" "0211" "0212" "0211" "0212" "0211" "0211" "0212"
#> Sample_729 Sample_730 Sample_731 Sample_732 Sample_733 Sample_734 Sample_735 Sample_736
#> "0211" "0211" "0233" "0211" "0211" "0211" "0233" "0211"
#> Sample_737 Sample_738 Sample_739 Sample_740 Sample_741 Sample_742 Sample_743 Sample_744
#> "0121" "0232" "0211" "0212" "0211" "0212" "0211" "0211"
#> Sample_745 Sample_746 Sample_747 Sample_748 Sample_749 Sample_750 Sample_751 Sample_752
#> "0212" "0211" "0211" "0212" "0211" "0211" "0211" "0211"
#> Sample_753 Sample_754 Sample_755 Sample_756 Sample_757 Sample_758 Sample_759 Sample_760
#> "0212" "0212" "0211" "0211" "0211" "0211" "0212" "0211"
#> Sample_761 Sample_762 Sample_763 Sample_764 Sample_765 Sample_766 Sample_767 Sample_768
#> "0211" "0211" "0211" "0211" "0211" "0212" "0211" "0232"
#> Sample_769 Sample_770 Sample_771 Sample_772 Sample_773 Sample_774 Sample_775 Sample_776
#> "0212" "0211" "0211" "0212" "0211" "0211" "0212" "0211"
#> Sample_777 Sample_778 Sample_779 Sample_780 Sample_781 Sample_782 Sample_783 Sample_784
#> "0212" "0212" "0212" "0212" "0211" "0212" "0212" "0212"
#> Sample_785 Sample_786 Sample_787 Sample_788 Sample_789 Sample_790 Sample_791 Sample_792
#> "0212" "0212" "0211" "0212" "0212" "0211" "0211" "0211"
#> Sample_793 Sample_794 Sample_795 Sample_796 Sample_797 Sample_798 Sample_799 Sample_800
#> "0212" "0211" "0211" "0211" "0212" "0212" "0212" "0211"
#> Sample_801 Sample_802 Sample_803 Sample_804 Sample_805 Sample_806 Sample_807 Sample_808
#> "0211" "0212" "0211" "0212" "0211" "0211" "0211" "0211"
#> Sample_809 Sample_810 Sample_811 Sample_812 Sample_813 Sample_814 Sample_815 Sample_816
#> "0211" "0212" "0211" "0211" "0211" "0211" "0211" "0211"
#> Sample_817 Sample_818 Sample_819 Sample_820 Sample_821 Sample_822 Sample_823 Sample_824
#> "0212" "0211" "0212" "0233" "0212" "0211" "0212" "0212"
#> Sample_825 Sample_826 Sample_827 Sample_828 Sample_829 Sample_830 Sample_831 Sample_832
#> "0212" "0212" "0212" "0211" "0212" "0212" "0211" "0211"
#> Sample_833 Sample_834 Sample_835 Sample_836 Sample_837 Sample_838 Sample_839 Sample_840
#> "0211" "0212" "0212" "0233" "0211" "0211" "0212" "0211"
#> Sample_841 Sample_842 Sample_843 Sample_844 Sample_845 Sample_846 Sample_847 Sample_848
#> "0222" "02212" "02212" "02211" "02211" "02211" "0222" "0222"
#> Sample_849 Sample_850 Sample_851 Sample_852 Sample_853 Sample_854 Sample_855 Sample_856
#> "0222" "02211" "02211" "0222" "02211" "02211" "0212" "0212"
#> Sample_857 Sample_858 Sample_859 Sample_860 Sample_861 Sample_862 Sample_863 Sample_864
#> "0212" "0211" "0232" "0212" "0212" "0212" "0212" "0211"
#> Sample_865 Sample_866 Sample_867 Sample_868 Sample_869 Sample_870 Sample_871 Sample_872
#> "0233" "0212" "0212" "0211" "0212" "0212" "0212" "0212"
#> Sample_873 Sample_874 Sample_875 Sample_876 Sample_877 Sample_878 Sample_879 Sample_880
#> "0212" "0211" "0211" "0212" "0211" "0211" "0211" "0211"
#> Sample_881 Sample_882 Sample_883 Sample_884 Sample_885 Sample_886 Sample_887 Sample_888
#> "0212" "0233" "0211" "0211" "0212" "0211" "0211" "0211"
#> Sample_889 Sample_890 Sample_891 Sample_892 Sample_893 Sample_894 Sample_895 Sample_896
#> "0212" "0212" "0212" "0212" "0212" "0232" "0212" "0211"
#> Sample_897 Sample_898 Sample_899 Sample_900 Sample_901 Sample_902 Sample_903 Sample_904
#> "0212" "0212" "0211" "0233" "0211" "0212" "0212" "0211"
#> Sample_905 Sample_906 Sample_907 Sample_908 Sample_909 Sample_910 Sample_911 Sample_912
#> "0211" "0212" "0212" "0212" "0212" "0211" "0212" "0211"
#> Sample_913 Sample_914 Sample_915 Sample_916 Sample_917 Sample_918 Sample_919 Sample_920
#> "0212" "0212" "0212" "0211" "0212" "0212" "0212" "0212"
#> Sample_921 Sample_922 Sample_923 Sample_924 Sample_925 Sample_926 Sample_927 Sample_928
#> "0211" "0212" "0211" "0212" "0212" "0211" "0211" "0211"
#> Sample_929 Sample_930 Sample_931 Sample_932 Sample_933 Sample_934 Sample_935 Sample_936
#> "0212" "0211" "0211" "0212" "0212" "0212" "0212" "0212"
#> Sample_937 Sample_938 Sample_939 Sample_940 Sample_941 Sample_942 Sample_943 Sample_944
#> "0212" "0212" "0212" "0212" "0212" "0212" "0212" "0212"
#> Sample_945 Sample_946 Sample_947 Sample_948 Sample_949 Sample_950 Sample_951 Sample_952
#> "0211" "0211" "0211" "03212" "03212" "0132" "03211" "03212"
#> Sample_953 Sample_954 Sample_955 Sample_956 Sample_957 Sample_958 Sample_959 Sample_960
#> "03211" "03221" "03211" "03212" "03211" "03212" "03212" "03212"
#> Sample_961 Sample_962 Sample_963 Sample_964 Sample_965 Sample_966 Sample_967 Sample_968
#> "03211" "0122" "03212" "03211" "03212" "03212" "03211" "0122"
#> Sample_969 Sample_970 Sample_971 Sample_972 Sample_973 Sample_974 Sample_975 Sample_976
#> "03211" "03212" "03211" "03211" "03212" "03211" "0333" "0132"
#> Sample_977 Sample_978 Sample_979 Sample_980 Sample_981 Sample_982 Sample_983 Sample_984
#> "0132" "0122" "03212" "0132" "0132" "0121" "0122" "03212"
#> Sample_985 Sample_986 Sample_987 Sample_988 Sample_989 Sample_990 Sample_991 Sample_992
#> "0122" "0132" "0122" "0121" "0121" "0121" "0121" "0121"
#> Sample_993 Sample_994 Sample_995 Sample_996 Sample_997 Sample_998 Sample_999 Sample_1000
#> "0121" "0122" "03211" "0132" "03212" "03212" "0132" "03212"
#> Sample_1001 Sample_1002 Sample_1003 Sample_1004 Sample_1005 Sample_1006 Sample_1007 Sample_1008
#> "03212" "03212" "03211" "0333" "03212" "03211" "0122" "0132"
#> Sample_1009 Sample_1010 Sample_1011 Sample_1012 Sample_1013 Sample_1014 Sample_1015 Sample_1016
#> "0132" "0132" "03212" "03221" "03212" "0121" "0121" "0121"
#> Sample_1017 Sample_1018 Sample_1019 Sample_1020 Sample_1021 Sample_1022 Sample_1023 Sample_1024
#> "0121" "0121" "0121" "0121" "0121" "0121" "0121" "0121"
#> Sample_1025 Sample_1026 Sample_1027 Sample_1028 Sample_1029 Sample_1030 Sample_1031 Sample_1032
#> "0121" "0121" "011" "011" "0312" "011" "0332" "0312"
#> Sample_1033 Sample_1034 Sample_1035 Sample_1036 Sample_1037 Sample_1038 Sample_1039 Sample_1040
#> "0312" "011" "0312" "011" "0312" "0312" "011" "0312"
#> Sample_1041 Sample_1042 Sample_1043 Sample_1044 Sample_1045 Sample_1046 Sample_1047 Sample_1048
#> "011" "011" "0312" "0312" "0332" "011" "0312" "0331"
#> Sample_1049 Sample_1050 Sample_1051 Sample_1052 Sample_1053 Sample_1054 Sample_1055 Sample_1056
#> "011" "011" "0312" "011" "03221" "03212" "03211" "0132"
#> Sample_1057 Sample_1058 Sample_1059 Sample_1060 Sample_1061 Sample_1062 Sample_1063 Sample_1064
#> "03221" "0121" "0132" "03212" "03221" "0132" "0312" "0312"
#> Sample_1065 Sample_1066 Sample_1067 Sample_1068 Sample_1069 Sample_1070 Sample_1071 Sample_1072
#> "0312" "011" "0312" "0312" "0312" "011" "0312" "0312"
#> Sample_1073 Sample_1074 Sample_1075 Sample_1076 Sample_1077 Sample_1078 Sample_1079 Sample_1080
#> "011" "0312" "011" "011" "0312" "0312" "0312" "011"
#> Sample_1081 Sample_1082 Sample_1083 Sample_1084 Sample_1085 Sample_1086 Sample_1087 Sample_1088
#> "0312" "0312" "011" "0312" "0312" "0331" "011" "0331"
#> Sample_1089 Sample_1090 Sample_1091 Sample_1092 Sample_1093 Sample_1094 Sample_1095 Sample_1096
#> "0332" "011" "011" "0312" "011" "0312" "0312" "0312"
#> Sample_1097 Sample_1098 Sample_1099 Sample_1100 Sample_1101 Sample_1102 Sample_1103 Sample_1104
#> "011" "0312" "0312" "011" "0312" "011" "011" "011"
#> Sample_1105 Sample_1106 Sample_1107 Sample_1108 Sample_1109 Sample_1110 Sample_1111 Sample_1112
#> "011" "0312" "0312" "011" "0312" "011" "0312" "011"
#> Sample_1113 Sample_1114 Sample_1115 Sample_1116 Sample_1117 Sample_1118 Sample_1119 Sample_1120
#> "011" "011" "0312" "011" "0312" "011" "011" "0332"
#> Sample_1121 Sample_1122 Sample_1123 Sample_1124 Sample_1125 Sample_1126 Sample_1127 Sample_1128
#> "011" "0331" "0312" "0312" "0312" "0312" "0312" "0312"
#> Sample_1129 Sample_1130 Sample_1131 Sample_1132 Sample_1133 Sample_1134 Sample_1135 Sample_1136
#> "011" "011" "011" "0332" "011" "0331" "011" "011"
#> Sample_1137 Sample_1138 Sample_1139 Sample_1140 Sample_1141 Sample_1142 Sample_1143 Sample_1144
#> "011" "011" "0312" "0331" "0312" "011" "011" "011"
#> Sample_1145 Sample_1146 Sample_1147 Sample_1148 Sample_1149 Sample_1150 Sample_1151 Sample_1152
#> "0312" "011" "011" "011" "011" "0312" "011" "0312"
#> Sample_1153 Sample_1154 Sample_1155 Sample_1156 Sample_1157 Sample_1158 Sample_1159 Sample_1160
#> "011" "011" "011" "0312" "0312" "011" "011" "011"
#> Sample_1161 Sample_1162 Sample_1163 Sample_1164 Sample_1165 Sample_1166 Sample_1167 Sample_1168
#> "0312" "011" "0312" "0312" "011" "011" "011" "011"
#> Sample_1169 Sample_1170 Sample_1171 Sample_1172 Sample_1173 Sample_1174 Sample_1175 Sample_1176
#> "0312" "0312" "011" "011" "011" "011" "0312" "011"
#> Sample_1177 Sample_1178 Sample_1179 Sample_1180 Sample_1181 Sample_1182 Sample_1183 Sample_1184
#> "011" "011" "011" "0312" "011" "011" "0331" "0331"
#> Sample_1185 Sample_1186 Sample_1187 Sample_1188 Sample_1189 Sample_1190 Sample_1191 Sample_1192
#> "011" "011" "011" "011" "03211" "03221" "03222" "0122"
#> Sample_1193 Sample_1194 Sample_1195 Sample_1196 Sample_1197 Sample_1198 Sample_1199 Sample_1200
#> "03221" "03221" "03212" "03212" "03221" "03221" "03221" "03212"
#> Sample_1201 Sample_1202 Sample_1203 Sample_1204 Sample_1205 Sample_1206 Sample_1207 Sample_1208
#> "03212" "03212" "03221" "03221" "03221" "03212" "03221" "0132"
#> Sample_1209 Sample_1210 Sample_1211 Sample_1212 Sample_1213 Sample_1214 Sample_1215 Sample_1216
#> "03211" "03221" "03212" "03212" "03212" "03221" "03221" "0122"
#> Sample_1217 Sample_1218 Sample_1219 Sample_1220 Sample_1221 Sample_1222 Sample_1223 Sample_1224
#> "0132" "0132" "03221" "03212" "03221" "011" "0311" "0311"
#> Sample_1225 Sample_1226 Sample_1227 Sample_1228 Sample_1229 Sample_1230 Sample_1231 Sample_1232
#> "011" "0311" "011" "011" "011" "0311" "0311" "0311"
#> Sample_1233 Sample_1234 Sample_1235 Sample_1236 Sample_1237 Sample_1238 Sample_1239 Sample_1240
#> "0311" "011" "0311" "011" "0311" "0311" "011" "011"
#> Sample_1241 Sample_1242 Sample_1243 Sample_1244 Sample_1245 Sample_1246 Sample_1247 Sample_1248
#> "011" "011" "0311" "0311" "0311" "0311" "0311" "0311"
#> Sample_1249 Sample_1250 Sample_1251 Sample_1252 Sample_1253 Sample_1254 Sample_1255 Sample_1256
#> "011" "0311" "0311" "0311" "0311" "011" "0332" "0311"
#> Sample_1257 Sample_1258 Sample_1259 Sample_1260 Sample_1261 Sample_1262 Sample_1263 Sample_1264
#> "0311" "0311" "0311" "0311" "0311" "0311" "0311" "011"
#> Sample_1265 Sample_1266 Sample_1267 Sample_1268 Sample_1269 Sample_1270 Sample_1271 Sample_1272
#> "0311" "011" "011" "0332" "0311" "0311" "0311" "011"
#> Sample_1273 Sample_1274 Sample_1275 Sample_1276 Sample_1277 Sample_1278 Sample_1279 Sample_1280
#> "0311" "0311" "0311" "0311" "0311" "0311" "011" "011"
#> Sample_1281 Sample_1282 Sample_1283 Sample_1284 Sample_1285 Sample_1286 Sample_1287 Sample_1288
#> "0311" "0311" "011" "0311" "011" "0311" "0311" "0311"
#> Sample_1289 Sample_1290 Sample_1291 Sample_1292 Sample_1293 Sample_1294 Sample_1295 Sample_1296
#> "0311" "0311" "0311" "0311" "0311" "0311" "0311" "0331"
#> Sample_1297 Sample_1298 Sample_1299 Sample_1300 Sample_1301 Sample_1302 Sample_1303 Sample_1304
#> "0311" "0311" "011" "0311" "0311" "0311" "0311" "0311"
#> Sample_1305 Sample_1306 Sample_1307 Sample_1308 Sample_1309 Sample_1310 Sample_1311 Sample_1312
#> "0311" "0332" "011" "011" "011" "0332" "0311" "0311"
#> Sample_1313 Sample_1314 Sample_1315 Sample_1316 Sample_1317 Sample_1318 Sample_1319 Sample_1320
#> "011" "0313" "0313" "0311" "011" "0313" "0311" "011"
#> Sample_1321 Sample_1322 Sample_1323 Sample_1324 Sample_1325 Sample_1326 Sample_1327 Sample_1328
#> "0314" "0311" "0311" "0311" "0311" "0314" "0311" "0313"
#> Sample_1329 Sample_1330 Sample_1331 Sample_1332 Sample_1333 Sample_1334 Sample_1335 Sample_1336
#> "0314" "0314" "0313" "0314" "0314" "0313" "011" "011"
#> Sample_1337 Sample_1338 Sample_1339 Sample_1340 Sample_1341 Sample_1342 Sample_1343 Sample_1344
#> "0332" "011" "0332" "0311" "0314" "011" "011" "0314"
#> Sample_1345 Sample_1346 Sample_1347 Sample_1348 Sample_1349 Sample_1350 Sample_1351 Sample_1352
#> "0332" "0311" "0311" "011" "0314" "011" "0314" "011"
#> Sample_1353 Sample_1354 Sample_1355 Sample_1356 Sample_1357 Sample_1358 Sample_1359 Sample_1360
#> "0311" "011" "0314" "0314" "011" "011" "0314" "011"
#> Sample_1361 Sample_1362 Sample_1363 Sample_1364 Sample_1365 Sample_1366 Sample_1367 Sample_1368
#> "0314" "0314" "0314" "011" "0311" "0313" "011" "0313"
#> Sample_1369 Sample_1370 Sample_1371 Sample_1372 Sample_1373 Sample_1374 Sample_1375 Sample_1376
#> "0311" "0314" "011" "0314" "011" "011" "011" "0332"
#> Sample_1377 Sample_1378 Sample_1379 Sample_1380 Sample_1381 Sample_1382 Sample_1383 Sample_1384
#> "0311" "0311" "0311" "0311" "0314" "011" "0313" "0313"
#> Sample_1385 Sample_1386 Sample_1387 Sample_1388 Sample_1389 Sample_1390 Sample_1391 Sample_1392
#> "0313" "011" "0313" "011" "0313" "0313" "0313" "0313"
#> Sample_1393 Sample_1394 Sample_1395 Sample_1396 Sample_1397 Sample_1398 Sample_1399 Sample_1400
#> "0313" "0313" "0313" "0313" "0313" "011" "011" "0331"
#> Sample_1401 Sample_1402 Sample_1403 Sample_1404 Sample_1405 Sample_1406 Sample_1407 Sample_1408
#> "0313" "011" "0313" "0313" "0313" "011" "0313" "011"
#> Sample_1409 Sample_1410 Sample_1411 Sample_1412 Sample_1413 Sample_1414 Sample_1415 Sample_1416
#> "011" "0313" "0313" "0313" "0331" "0313" "0313" "0332"
#> Sample_1417 Sample_1418 Sample_1419 Sample_1420 Sample_1421 Sample_1422 Sample_1423 Sample_1424
#> "0313" "0313" "011" "0313" "0313" "0313" "011" "0313"
#> Sample_1425 Sample_1426 Sample_1427 Sample_1428 Sample_1429 Sample_1430 Sample_1431 Sample_1432
#> "011" "011" "011" "0313" "011" "011" "011" "011"
#> Sample_1433 Sample_1434 Sample_1435 Sample_1436 Sample_1437 Sample_1438 Sample_1439 Sample_1440
#> "011" "011" "011" "011" "011" "011" "011" "011"
#> Sample_1441 Sample_1442 Sample_1443 Sample_1444 Sample_1445 Sample_1446 Sample_1447 Sample_1448
#> "011" "0313" "0313" "011" "011" "0313" "0313" "011"
#> Sample_1449 Sample_1450 Sample_1451 Sample_1452 Sample_1453 Sample_1454 Sample_1455 Sample_1456
#> "011" "011" "011" "011" "011" "011" "011" "0313"
#> Sample_1457 Sample_1458 Sample_1459 Sample_1460 Sample_1461 Sample_1462 Sample_1463 Sample_1464
#> "0313" "011" "011" "0331" "0313" "011" "0314" "0314"
#> Sample_1465 Sample_1466 Sample_1467 Sample_1468 Sample_1469 Sample_1470 Sample_1471 Sample_1472
#> "011" "011" "011" "0313" "011" "011" "011" "011"
#> Sample_1473 Sample_1474 Sample_1475 Sample_1476 Sample_1477 Sample_1478 Sample_1479 Sample_1480
#> "011" "011" "011" "011" "011" "011" "011" "0314"
#> Sample_1481 Sample_1482 Sample_1483 Sample_1484 Sample_1485 Sample_1486 Sample_1487 Sample_1488
#> "011" "011" "011" "011" "0313" "0331" "011" "0313"
#> Sample_1489 Sample_1490 Sample_1491 Sample_1492 Sample_1493 Sample_1494 Sample_1495 Sample_1496
#> "011" "011" "011" "011" "011" "0332" "011" "011"
#> Sample_1497 Sample_1498 Sample_1499 Sample_1500 Sample_1501 Sample_1502 Sample_1503 Sample_1504
#> "011" "011" "011" "011" "011" "0313" "0313" "0332"
#> Sample_1505 Sample_1506 Sample_1507 Sample_1508 Sample_1509 Sample_1510 Sample_1511 Sample_1512
#> "0331" "0313" "0313" "0313" "011" "011" "0313" "0313"
#> Sample_1513 Sample_1514 Sample_1515 Sample_1516 Sample_1517 Sample_1518 Sample_1519 Sample_1520
#> "0313" "011" "0313" "011" "0222" "0222" "02211" "0233"
#> Sample_1521 Sample_1522 Sample_1523 Sample_1524 Sample_1525 Sample_1526 Sample_1527 Sample_1528
#> "0232" "0131" "0132" "0233" "0121" "0121" "0212" "0212"
#> Sample_1529 Sample_1530 Sample_1531 Sample_1532 Sample_1533 Sample_1534 Sample_1535 Sample_1536
#> "0212" "0233" "0232" "0232" "0211" "0212" "0222" "0222"
#> Sample_1537 Sample_1538 Sample_1539 Sample_1540 Sample_1541 Sample_1542 Sample_1543 Sample_1544
#> "02211" "0212" "0212" "0131" "011" "0212" "0233" "0131"
#> Sample_1545 Sample_1546 Sample_1547 Sample_1548 Sample_1549 Sample_1550 Sample_1551 Sample_1552
#> "011" "0211" "0131" "0231" "03221" "0231" "0231" "0231"
#> Sample_1553 Sample_1554 Sample_1555 Sample_1556 Sample_1557 Sample_1558 Sample_1559 Sample_1560
#> "03212" "0222" "0231" "0131" "0131" "0131" "0231" "0131"
#> Sample_1561 Sample_1562 Sample_1563 Sample_1564 Sample_1565 Sample_1566 Sample_1567 Sample_1568
#> "0231" "0121" "0131" "0131" "0131" "0131" "0131" "0131"
#> Sample_1569 Sample_1570 Sample_1571 Sample_1572 Sample_1573 Sample_1574 Sample_1575 Sample_1576
#> "0312" "0212" "0131" "0131" "0131" "0231" "0231" "0131"
#> Sample_1577 Sample_1578 Sample_1579 Sample_1580 Sample_1581 Sample_1582 Sample_1583 Sample_1584
#> "0131" "0131" "0131" "0131" "03221" "0231" "0131" "0131"
#> Sample_1585 Sample_1586 Sample_1587 Sample_1588 Sample_1589 Sample_1590 Sample_1591 Sample_1592
#> "0231" "0231" "0231" "0222" "0231" "0131" "0131" "0231"
#> Sample_1593 Sample_1594 Sample_1595 Sample_1596 Sample_1597 Sample_1598 Sample_1599 Sample_1600
#> "0231" "0231" "0231" "0232" "03221" "0311" "0233" "0131"
get_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 268))
#> Sample_1 Sample_2 Sample_3 Sample_4 Sample_5 Sample_6 Sample_7 Sample_8
#> "0132" "02211" "02212" "0222" "02211" "02211" "02211" "0222"
#> Sample_9 Sample_10 Sample_11 Sample_12 Sample_13 Sample_14 Sample_15 Sample_16
#> "02212" "02211" "02212" "02211" "02211" "02211" "02211" "02212"
#> Sample_17 Sample_18 Sample_19 Sample_20 Sample_21 Sample_22 Sample_23 Sample_24
#> "0222" "0222" "0222" "0222" "0222" "0222" "0222" "02211"
#> Sample_25 Sample_26 Sample_27 Sample_28 Sample_29 Sample_30 Sample_31 Sample_32
#> "0222" "02211" "0222" "0222" "02212" "0222" "0233" "02212"
#> Sample_33 Sample_34 Sample_35 Sample_36 Sample_37 Sample_38 Sample_39 Sample_40
#> "02212" "02212" "02212" "02212" "02212" "02212" "02212" "02212"
#> Sample_41 Sample_42 Sample_43 Sample_44 Sample_45 Sample_46 Sample_47 Sample_48
#> "02212" "02212" "0233" "02212" "02212" "02212" "02212" "02212"
#> Sample_49 Sample_50 Sample_51 Sample_52 Sample_53 Sample_54 Sample_55 Sample_56
#> "02212" "02212" "02212" "02212" "0222" "0222" "02212" "02211"
#> Sample_57 Sample_58 Sample_59 Sample_60 Sample_61 Sample_62 Sample_63 Sample_64
#> "02211" "02211" "02212" "02211" "0222" "02211" "02211" "02211"
#> Sample_65 Sample_66 Sample_67 Sample_68 Sample_69 Sample_70 Sample_71 Sample_72
#> "02211" "02211" "0222" "02211" "02211" "0222" "0222" "0211"
#> Sample_73 Sample_74 Sample_75 Sample_76 Sample_77 Sample_78 Sample_79 Sample_80
#> "0233" "02211" "0222" "02211" "0222" "02211" "0222" "0222"
#> Sample_81 Sample_82 Sample_83 Sample_84 Sample_85 Sample_86 Sample_87 Sample_88
#> "02211" "0222" "02211" "02211" "0222" "02212" "0222" "0222"
#> Sample_89 Sample_90 Sample_91 Sample_92 Sample_93 Sample_94 Sample_95 Sample_96
#> "0222" "0222" "0222" "0222" "0222" "0232" "0222" "02211"
#> Sample_97 Sample_98 Sample_99 Sample_100 Sample_101 Sample_102 Sample_103 Sample_104
#> "02211" "02211" "02211" "0222" "0222" "02212" "0222" "0222"
#> Sample_105 Sample_106 Sample_107 Sample_108 Sample_109 Sample_110 Sample_111 Sample_112
#> "02211" "02211" "0222" "02211" "02211" "0222" "0212" "0222"
#> Sample_113 Sample_114 Sample_115 Sample_116 Sample_117 Sample_118 Sample_119 Sample_120
#> "0222" "0232" "0222" "0222" "0222" "0232" "0222" "02211"
#> Sample_121 Sample_122 Sample_123 Sample_124 Sample_125 Sample_126 Sample_127 Sample_128
#> "0121" "0222" "0232" "02211" "02211" "0222" "02211" "0222"
#> Sample_129 Sample_130 Sample_131 Sample_132 Sample_133 Sample_134 Sample_135 Sample_136
#> "02211" "0222" "0222" "02211" "0222" "02211" "0222" "02211"
#> Sample_137 Sample_138 Sample_139 Sample_140 Sample_141 Sample_142 Sample_143 Sample_144
#> "02211" "02211" "0222" "02211" "02211" "0222" "0222" "0232"
#> Sample_145 Sample_146 Sample_147 Sample_148 Sample_149 Sample_150 Sample_151 Sample_152
#> "0222" "0222" "0211" "0211" "0211" "0211" "0212" "0211"
#> Sample_153 Sample_154 Sample_155 Sample_156 Sample_157 Sample_158 Sample_159 Sample_160
#> "0211" "0211" "0211" "0211" "0211" "0211" "0212" "0211"
#> Sample_161 Sample_162 Sample_163 Sample_164 Sample_165 Sample_166 Sample_167 Sample_168
#> "0212" "0212" "0212" "0212" "0212" "0212" "0211" "0211"
#> Sample_169 Sample_170 Sample_171 Sample_172 Sample_173 Sample_174 Sample_175 Sample_176
#> "0212" "0211" "0232" "0212" "0233" "0212" "0211" "0211"
#> Sample_177 Sample_178 Sample_179 Sample_180 Sample_181 Sample_182 Sample_183 Sample_184
#> "0212" "0211" "0212" "0212" "0211" "0211" "0211" "0211"
#> Sample_185 Sample_186 Sample_187 Sample_188 Sample_189 Sample_190 Sample_191 Sample_192
#> "0211" "0211" "0211" "0212" "0212" "0211" "0212" "0212"
#> Sample_193 Sample_194 Sample_195 Sample_196 Sample_197 Sample_198 Sample_199 Sample_200
#> "0211" "0211" "0211" "0211" "02211" "02211" "02212" "02212"
#> Sample_201 Sample_202 Sample_203 Sample_204 Sample_205 Sample_206 Sample_207 Sample_208
#> "02212" "02212" "02212" "0233" "02212" "02212" "02212" "03222"
#> Sample_209 Sample_210 Sample_211 Sample_212 Sample_213 Sample_214 Sample_215 Sample_216
#> "03222" "03222" "03222" "03222" "03222" "0132" "0132" "03222"
#> Sample_217 Sample_218 Sample_219 Sample_220 Sample_221 Sample_222 Sample_223 Sample_224
#> "0132" "03222" "03222" "03222" "03222" "03222" "03222" "0132"
#> Sample_225 Sample_226 Sample_227 Sample_228 Sample_229 Sample_230 Sample_231 Sample_232
#> "03222" "0132" "03222" "03222" "03222" "03222" "03222" "03222"
#> Sample_233 Sample_234 Sample_235 Sample_236 Sample_237 Sample_238 Sample_239 Sample_240
#> "0333" "03222" "03222" "0132" "03222" "03222" "03222" "03222"
#> Sample_241 Sample_242 Sample_243 Sample_244 Sample_245 Sample_246 Sample_247 Sample_248
#> "0132" "03222" "0321" "03222" "03222" "03222" "03222" "03222"
#> Sample_249 Sample_250 Sample_251 Sample_252 Sample_253 Sample_254 Sample_255 Sample_256
#> "03222" "03222" "03222" "03222" "03222" "0333" "03221" "03222"
#> Sample_257 Sample_258 Sample_259 Sample_260 Sample_261 Sample_262 Sample_263 Sample_264
#> "03222" "0132" "03222" "03222" "03222" "03222" "03222" "0132"
#> Sample_265 Sample_266 Sample_267 Sample_268 Sample_269 Sample_270 Sample_271 Sample_272
#> "0122" "0321" "0321" "0321" "03221" "0321" "0333" "0122"
#> Sample_273 Sample_274 Sample_275 Sample_276 Sample_277 Sample_278 Sample_279 Sample_280
#> "0321" "0321" "0122" "0132" "0321" "0333" "0321" "03222"
#> Sample_281 Sample_282 Sample_283 Sample_284 Sample_285 Sample_286 Sample_287 Sample_288
#> "0321" "0321" "03221" "0321" "0321" "0321" "0132" "0333"
#> Sample_289 Sample_290 Sample_291 Sample_292 Sample_293 Sample_294 Sample_295 Sample_296
#> "0333" "0321" "0321" "0321" "03222" "0321" "0321" "0321"
#> Sample_297 Sample_298 Sample_299 Sample_300 Sample_301 Sample_302 Sample_303 Sample_304
#> "0321" "0321" "0321" "03221" "0321" "0122" "0122" "0122"
#> Sample_305 Sample_306 Sample_307 Sample_308 Sample_309 Sample_310 Sample_311 Sample_312
#> "0122" "0321" "0321" "0122" "0122" "0122" "0122" "0122"
#> Sample_313 Sample_314 Sample_315 Sample_316 Sample_317 Sample_318 Sample_319 Sample_320
#> "0122" "0122" "0321" "0122" "0321" "0122" "0122" "0122"
#> Sample_321 Sample_322 Sample_323 Sample_324 Sample_325 Sample_326 Sample_327 Sample_328
#> "0121" "0321" "0122" "0122" "0122" "0121" "0122" "0122"
#> Sample_329 Sample_330 Sample_331 Sample_332 Sample_333 Sample_334 Sample_335 Sample_336
#> "0121" "0122" "0333" "0321" "0321" "0321" "0321" "0321"
#> Sample_337 Sample_338 Sample_339 Sample_340 Sample_341 Sample_342 Sample_343 Sample_344
#> "0321" "0321" "0333" "0321" "0321" "0321" "0321" "0321"
#> Sample_345 Sample_346 Sample_347 Sample_348 Sample_349 Sample_350 Sample_351 Sample_352
#> "0321" "0321" "0321" "0321" "0321" "0132" "0122" "0132"
#> Sample_353 Sample_354 Sample_355 Sample_356 Sample_357 Sample_358 Sample_359 Sample_360
#> "0321" "0321" "0122" "0321" "0321" "0122" "0321" "0122"
#> Sample_361 Sample_362 Sample_363 Sample_364 Sample_365 Sample_366 Sample_367 Sample_368
#> "0321" "0122" "0321" "0321" "0132" "0321" "0321" "0122"
#> Sample_369 Sample_370 Sample_371 Sample_372 Sample_373 Sample_374 Sample_375 Sample_376
#> "0122" "0321" "0333" "0132" "0132" "0321" "0321" "0321"
#> Sample_377 Sample_378 Sample_379 Sample_380 Sample_381 Sample_382 Sample_383 Sample_384
#> "0122" "0321" "0321" "0321" "0122" "0321" "0321" "0321"
#> Sample_385 Sample_386 Sample_387 Sample_388 Sample_389 Sample_390 Sample_391 Sample_392
#> "0121" "0121" "0321" "0321" "0132" "0132" "0321" "0321"
#> Sample_393 Sample_394 Sample_395 Sample_396 Sample_397 Sample_398 Sample_399 Sample_400
#> "0122" "0132" "0122" "0132" "0122" "0122" "0321" "0321"
#> Sample_401 Sample_402 Sample_403 Sample_404 Sample_405 Sample_406 Sample_407 Sample_408
#> "0321" "03221" "0132" "0132" "0132" "0321" "0321" "0132"
#> Sample_409 Sample_410 Sample_411 Sample_412 Sample_413 Sample_414 Sample_415 Sample_416
#> "0122" "0321" "0321" "0122" "0321" "0321" "0333" "0122"
#> Sample_417 Sample_418 Sample_419 Sample_420 Sample_421 Sample_422 Sample_423 Sample_424
#> "0321" "0321" "0321" "0333" "0122" "0333" "03221" "0122"
#> Sample_425 Sample_426 Sample_427 Sample_428 Sample_429 Sample_430 Sample_431 Sample_432
#> "0122" "03221" "0122" "0122" "03221" "0132" "0122" "0121"
#> Sample_433 Sample_434 Sample_435 Sample_436 Sample_437 Sample_438 Sample_439 Sample_440
#> "0132" "0121" "03221" "0333" "0333" "0132" "0121" "0122"
#> Sample_441 Sample_442 Sample_443 Sample_444 Sample_445 Sample_446 Sample_447 Sample_448
#> "03221" "0122" "0132" "0321" "0121" "0122" "03221" "0132"
#> Sample_449 Sample_450 Sample_451 Sample_452 Sample_453 Sample_454 Sample_455 Sample_456
#> "03221" "0321" "0122" "0121" "03221" "0321" "0132" "0121"
#> Sample_457 Sample_458 Sample_459 Sample_460 Sample_461 Sample_462 Sample_463 Sample_464
#> "0132" "03221" "0122" "0321" "0121" "0321" "0132" "0121"
#> Sample_465 Sample_466 Sample_467 Sample_468 Sample_469 Sample_470 Sample_471 Sample_472
#> "0121" "0121" "03221" "0121" "0122" "0321" "03221" "0121"
#> Sample_473 Sample_474 Sample_475 Sample_476 Sample_477 Sample_478 Sample_479 Sample_480
#> "0321" "0121" "0121" "0132" "0132" "0314" "011" "0314"
#> Sample_481 Sample_482 Sample_483 Sample_484 Sample_485 Sample_486 Sample_487 Sample_488
#> "0313" "0314" "0314" "0314" "0314" "011" "011" "0331"
#> Sample_489 Sample_490 Sample_491 Sample_492 Sample_493 Sample_494 Sample_495 Sample_496
#> "0314" "011" "0314" "0314" "0314" "0314" "011" "0314"
#> Sample_497 Sample_498 Sample_499 Sample_500 Sample_501 Sample_502 Sample_503 Sample_504
#> "0314" "0314" "011" "011" "0314" "0314" "0314" "0314"
#> Sample_505 Sample_506 Sample_507 Sample_508 Sample_509 Sample_510 Sample_511 Sample_512
#> "011" "011" "011" "0314" "0314" "0314" "011" "0332"
#> Sample_513 Sample_514 Sample_515 Sample_516 Sample_517 Sample_518 Sample_519 Sample_520
#> "011" "0314" "011" "0211" "011" "0314" "0314" "011"
#> Sample_521 Sample_522 Sample_523 Sample_524 Sample_525 Sample_526 Sample_527 Sample_528
#> "011" "0314" "0314" "0314" "0314" "0314" "011" "0312"
#> Sample_529 Sample_530 Sample_531 Sample_532 Sample_533 Sample_534 Sample_535 Sample_536
#> "0331" "011" "0332" "011" "011" "0314" "011" "011"
#> Sample_537 Sample_538 Sample_539 Sample_540 Sample_541 Sample_542 Sample_543 Sample_544
#> "011" "0314" "011" "0314" "0314" "011" "011" "011"
#> Sample_545 Sample_546 Sample_547 Sample_548 Sample_549 Sample_550 Sample_551 Sample_552
#> "0331" "011" "011" "0314" "0314" "0314" "0314" "0314"
#> Sample_553 Sample_554 Sample_555 Sample_556 Sample_557 Sample_558 Sample_559 Sample_560
#> "0332" "0314" "0314" "0314" "011" "011" "011" "0314"
#> Sample_561 Sample_562 Sample_563 Sample_564 Sample_565 Sample_566 Sample_567 Sample_568
#> "0314" "0314" "0314" "0314" "011" "011" "011" "0314"
#> Sample_569 Sample_570 Sample_571 Sample_572 Sample_573 Sample_574 Sample_575 Sample_576
#> "011" "03221" "03221" "0132" "0121" "0333" "0321" "03221"
#> Sample_577 Sample_578 Sample_579 Sample_580 Sample_581 Sample_582 Sample_583 Sample_584
#> "0122" "0321" "0321" "0321" "0321" "0321" "0321" "0321"
#> Sample_585 Sample_586 Sample_587 Sample_588 Sample_589 Sample_590 Sample_591 Sample_592
#> "03222" "02212" "02211" "0231" "03222" "0131" "02211" "0231"
#> Sample_593 Sample_594 Sample_595 Sample_596 Sample_597 Sample_598 Sample_599 Sample_600
#> "02212" "0231" "0231" "0222" "0231" "0222" "0231" "0231"
#> Sample_601 Sample_602 Sample_603 Sample_604 Sample_605 Sample_606 Sample_607 Sample_608
#> "0222" "0231" "0222" "0222" "0222" "0222" "02211" "0222"
#> Sample_609 Sample_610 Sample_611 Sample_612 Sample_613 Sample_614 Sample_615 Sample_616
#> "0222" "0222" "0212" "0212" "03222" "0131" "03222" "0131"
#> Sample_617 Sample_618 Sample_619 Sample_620 Sample_621 Sample_622 Sample_623 Sample_624
#> "0131" "0121" "0131" "0131" "0231" "0131" "03221" "03221"
#> Sample_625 Sample_626 Sample_627 Sample_628 Sample_629 Sample_630 Sample_631 Sample_632
#> "0231" "0131" "0231" "0231" "0231" "0131" "0231" "0131"
#> Sample_633 Sample_634 Sample_635 Sample_636 Sample_637 Sample_638 Sample_639 Sample_640
#> "0131" "0131" "0131" "0131" "0131" "0231" "0131" "0232"
#> Sample_641 Sample_642 Sample_643 Sample_644 Sample_645 Sample_646 Sample_647 Sample_648
#> "0131" "0233" "0131" "0321" "0232" "0131" "0131" "0232"
#> Sample_649 Sample_650 Sample_651 Sample_652 Sample_653 Sample_654 Sample_655 Sample_656
#> "0232" "0231" "02212" "02211" "02211" "02212" "02211" "02211"
#> Sample_657 Sample_658 Sample_659 Sample_660 Sample_661 Sample_662 Sample_663 Sample_664
#> "0222" "02212" "02211" "0222" "02211" "0222" "0222" "02211"
#> Sample_665 Sample_666 Sample_667 Sample_668 Sample_669 Sample_670 Sample_671 Sample_672
#> "0232" "0222" "0232" "0222" "0222" "0222" "02211" "0222"
#> Sample_673 Sample_674 Sample_675 Sample_676 Sample_677 Sample_678 Sample_679 Sample_680
#> "0222" "0222" "0222" "0222" "02212" "02212" "02211" "0222"
#> Sample_681 Sample_682 Sample_683 Sample_684 Sample_685 Sample_686 Sample_687 Sample_688
#> "0232" "0222" "0222" "0222" "0121" "0212" "0121" "0212"
#> Sample_689 Sample_690 Sample_691 Sample_692 Sample_693 Sample_694 Sample_695 Sample_696
#> "0222" "0212" "0222" "0121" "0222" "0222" "0212" "0211"
#> Sample_697 Sample_698 Sample_699 Sample_700 Sample_701 Sample_702 Sample_703 Sample_704
#> "0211" "0212" "0212" "0211" "0212" "0211" "0212" "02211"
#> Sample_705 Sample_706 Sample_707 Sample_708 Sample_709 Sample_710 Sample_711 Sample_712
#> "0222" "0211" "0211" "0211" "0211" "0232" "0212" "0211"
#> Sample_713 Sample_714 Sample_715 Sample_716 Sample_717 Sample_718 Sample_719 Sample_720
#> "0211" "0212" "0212" "0212" "0211" "0211" "0211" "0232"
#> Sample_721 Sample_722 Sample_723 Sample_724 Sample_725 Sample_726 Sample_727 Sample_728
#> "0211" "0211" "0212" "0211" "0212" "0211" "0211" "0212"
#> Sample_729 Sample_730 Sample_731 Sample_732 Sample_733 Sample_734 Sample_735 Sample_736
#> "0211" "0211" "0233" "0211" "0211" "0211" "0233" "0211"
#> Sample_737 Sample_738 Sample_739 Sample_740 Sample_741 Sample_742 Sample_743 Sample_744
#> "0121" "0232" "0211" "0212" "0211" "0212" "0211" "0211"
#> Sample_745 Sample_746 Sample_747 Sample_748 Sample_749 Sample_750 Sample_751 Sample_752
#> "0212" "0211" "0211" "0212" "0211" "0211" "0211" "0211"
#> Sample_753 Sample_754 Sample_755 Sample_756 Sample_757 Sample_758 Sample_759 Sample_760
#> "0212" "0212" "0211" "0211" "0211" "0211" "0212" "0211"
#> Sample_761 Sample_762 Sample_763 Sample_764 Sample_765 Sample_766 Sample_767 Sample_768
#> "0211" "0211" "0211" "0211" "0211" "0212" "0211" "0232"
#> Sample_769 Sample_770 Sample_771 Sample_772 Sample_773 Sample_774 Sample_775 Sample_776
#> "0212" "0211" "0211" "0212" "0211" "0211" "0212" "0211"
#> Sample_777 Sample_778 Sample_779 Sample_780 Sample_781 Sample_782 Sample_783 Sample_784
#> "0212" "0212" "0212" "0212" "0211" "0212" "0212" "0212"
#> Sample_785 Sample_786 Sample_787 Sample_788 Sample_789 Sample_790 Sample_791 Sample_792
#> "0212" "0212" "0211" "0212" "0212" "0211" "0211" "0211"
#> Sample_793 Sample_794 Sample_795 Sample_796 Sample_797 Sample_798 Sample_799 Sample_800
#> "0212" "0211" "0211" "0211" "0212" "0212" "0212" "0211"
#> Sample_801 Sample_802 Sample_803 Sample_804 Sample_805 Sample_806 Sample_807 Sample_808
#> "0211" "0212" "0211" "0212" "0211" "0211" "0211" "0211"
#> Sample_809 Sample_810 Sample_811 Sample_812 Sample_813 Sample_814 Sample_815 Sample_816
#> "0211" "0212" "0211" "0211" "0211" "0211" "0211" "0211"
#> Sample_817 Sample_818 Sample_819 Sample_820 Sample_821 Sample_822 Sample_823 Sample_824
#> "0212" "0211" "0212" "0233" "0212" "0211" "0212" "0212"
#> Sample_825 Sample_826 Sample_827 Sample_828 Sample_829 Sample_830 Sample_831 Sample_832
#> "0212" "0212" "0212" "0211" "0212" "0212" "0211" "0211"
#> Sample_833 Sample_834 Sample_835 Sample_836 Sample_837 Sample_838 Sample_839 Sample_840
#> "0211" "0212" "0212" "0233" "0211" "0211" "0212" "0211"
#> Sample_841 Sample_842 Sample_843 Sample_844 Sample_845 Sample_846 Sample_847 Sample_848
#> "0222" "02212" "02212" "02211" "02211" "02211" "0222" "0222"
#> Sample_849 Sample_850 Sample_851 Sample_852 Sample_853 Sample_854 Sample_855 Sample_856
#> "0222" "02211" "02211" "0222" "02211" "02211" "0212" "0212"
#> Sample_857 Sample_858 Sample_859 Sample_860 Sample_861 Sample_862 Sample_863 Sample_864
#> "0212" "0211" "0232" "0212" "0212" "0212" "0212" "0211"
#> Sample_865 Sample_866 Sample_867 Sample_868 Sample_869 Sample_870 Sample_871 Sample_872
#> "0233" "0212" "0212" "0211" "0212" "0212" "0212" "0212"
#> Sample_873 Sample_874 Sample_875 Sample_876 Sample_877 Sample_878 Sample_879 Sample_880
#> "0212" "0211" "0211" "0212" "0211" "0211" "0211" "0211"
#> Sample_881 Sample_882 Sample_883 Sample_884 Sample_885 Sample_886 Sample_887 Sample_888
#> "0212" "0233" "0211" "0211" "0212" "0211" "0211" "0211"
#> Sample_889 Sample_890 Sample_891 Sample_892 Sample_893 Sample_894 Sample_895 Sample_896
#> "0212" "0212" "0212" "0212" "0212" "0232" "0212" "0211"
#> Sample_897 Sample_898 Sample_899 Sample_900 Sample_901 Sample_902 Sample_903 Sample_904
#> "0212" "0212" "0211" "0233" "0211" "0212" "0212" "0211"
#> Sample_905 Sample_906 Sample_907 Sample_908 Sample_909 Sample_910 Sample_911 Sample_912
#> "0211" "0212" "0212" "0212" "0212" "0211" "0212" "0211"
#> Sample_913 Sample_914 Sample_915 Sample_916 Sample_917 Sample_918 Sample_919 Sample_920
#> "0212" "0212" "0212" "0211" "0212" "0212" "0212" "0212"
#> Sample_921 Sample_922 Sample_923 Sample_924 Sample_925 Sample_926 Sample_927 Sample_928
#> "0211" "0212" "0211" "0212" "0212" "0211" "0211" "0211"
#> Sample_929 Sample_930 Sample_931 Sample_932 Sample_933 Sample_934 Sample_935 Sample_936
#> "0212" "0211" "0211" "0212" "0212" "0212" "0212" "0212"
#> Sample_937 Sample_938 Sample_939 Sample_940 Sample_941 Sample_942 Sample_943 Sample_944
#> "0212" "0212" "0212" "0212" "0212" "0212" "0212" "0212"
#> Sample_945 Sample_946 Sample_947 Sample_948 Sample_949 Sample_950 Sample_951 Sample_952
#> "0211" "0211" "0211" "0321" "0321" "0132" "0321" "0321"
#> Sample_953 Sample_954 Sample_955 Sample_956 Sample_957 Sample_958 Sample_959 Sample_960
#> "0321" "03221" "0321" "0321" "0321" "0321" "0321" "0321"
#> Sample_961 Sample_962 Sample_963 Sample_964 Sample_965 Sample_966 Sample_967 Sample_968
#> "0321" "0122" "0321" "0321" "0321" "0321" "0321" "0122"
#> Sample_969 Sample_970 Sample_971 Sample_972 Sample_973 Sample_974 Sample_975 Sample_976
#> "0321" "0321" "0321" "0321" "0321" "0321" "0333" "0132"
#> Sample_977 Sample_978 Sample_979 Sample_980 Sample_981 Sample_982 Sample_983 Sample_984
#> "0132" "0122" "0321" "0132" "0132" "0121" "0122" "0321"
#> Sample_985 Sample_986 Sample_987 Sample_988 Sample_989 Sample_990 Sample_991 Sample_992
#> "0122" "0132" "0122" "0121" "0121" "0121" "0121" "0121"
#> Sample_993 Sample_994 Sample_995 Sample_996 Sample_997 Sample_998 Sample_999 Sample_1000
#> "0121" "0122" "0321" "0132" "0321" "0321" "0132" "0321"
#> Sample_1001 Sample_1002 Sample_1003 Sample_1004 Sample_1005 Sample_1006 Sample_1007 Sample_1008
#> "0321" "0321" "0321" "0333" "0321" "0321" "0122" "0132"
#> Sample_1009 Sample_1010 Sample_1011 Sample_1012 Sample_1013 Sample_1014 Sample_1015 Sample_1016
#> "0132" "0132" "0321" "03221" "0321" "0121" "0121" "0121"
#> Sample_1017 Sample_1018 Sample_1019 Sample_1020 Sample_1021 Sample_1022 Sample_1023 Sample_1024
#> "0121" "0121" "0121" "0121" "0121" "0121" "0121" "0121"
#> Sample_1025 Sample_1026 Sample_1027 Sample_1028 Sample_1029 Sample_1030 Sample_1031 Sample_1032
#> "0121" "0121" "011" "011" "0312" "011" "0332" "0312"
#> Sample_1033 Sample_1034 Sample_1035 Sample_1036 Sample_1037 Sample_1038 Sample_1039 Sample_1040
#> "0312" "011" "0312" "011" "0312" "0312" "011" "0312"
#> Sample_1041 Sample_1042 Sample_1043 Sample_1044 Sample_1045 Sample_1046 Sample_1047 Sample_1048
#> "011" "011" "0312" "0312" "0332" "011" "0312" "0331"
#> Sample_1049 Sample_1050 Sample_1051 Sample_1052 Sample_1053 Sample_1054 Sample_1055 Sample_1056
#> "011" "011" "0312" "011" "03221" "0321" "0321" "0132"
#> Sample_1057 Sample_1058 Sample_1059 Sample_1060 Sample_1061 Sample_1062 Sample_1063 Sample_1064
#> "03221" "0121" "0132" "0321" "03221" "0132" "0312" "0312"
#> Sample_1065 Sample_1066 Sample_1067 Sample_1068 Sample_1069 Sample_1070 Sample_1071 Sample_1072
#> "0312" "011" "0312" "0312" "0312" "011" "0312" "0312"
#> Sample_1073 Sample_1074 Sample_1075 Sample_1076 Sample_1077 Sample_1078 Sample_1079 Sample_1080
#> "011" "0312" "011" "011" "0312" "0312" "0312" "011"
#> Sample_1081 Sample_1082 Sample_1083 Sample_1084 Sample_1085 Sample_1086 Sample_1087 Sample_1088
#> "0312" "0312" "011" "0312" "0312" "0331" "011" "0331"
#> Sample_1089 Sample_1090 Sample_1091 Sample_1092 Sample_1093 Sample_1094 Sample_1095 Sample_1096
#> "0332" "011" "011" "0312" "011" "0312" "0312" "0312"
#> Sample_1097 Sample_1098 Sample_1099 Sample_1100 Sample_1101 Sample_1102 Sample_1103 Sample_1104
#> "011" "0312" "0312" "011" "0312" "011" "011" "011"
#> Sample_1105 Sample_1106 Sample_1107 Sample_1108 Sample_1109 Sample_1110 Sample_1111 Sample_1112
#> "011" "0312" "0312" "011" "0312" "011" "0312" "011"
#> Sample_1113 Sample_1114 Sample_1115 Sample_1116 Sample_1117 Sample_1118 Sample_1119 Sample_1120
#> "011" "011" "0312" "011" "0312" "011" "011" "0332"
#> Sample_1121 Sample_1122 Sample_1123 Sample_1124 Sample_1125 Sample_1126 Sample_1127 Sample_1128
#> "011" "0331" "0312" "0312" "0312" "0312" "0312" "0312"
#> Sample_1129 Sample_1130 Sample_1131 Sample_1132 Sample_1133 Sample_1134 Sample_1135 Sample_1136
#> "011" "011" "011" "0332" "011" "0331" "011" "011"
#> Sample_1137 Sample_1138 Sample_1139 Sample_1140 Sample_1141 Sample_1142 Sample_1143 Sample_1144
#> "011" "011" "0312" "0331" "0312" "011" "011" "011"
#> Sample_1145 Sample_1146 Sample_1147 Sample_1148 Sample_1149 Sample_1150 Sample_1151 Sample_1152
#> "0312" "011" "011" "011" "011" "0312" "011" "0312"
#> Sample_1153 Sample_1154 Sample_1155 Sample_1156 Sample_1157 Sample_1158 Sample_1159 Sample_1160
#> "011" "011" "011" "0312" "0312" "011" "011" "011"
#> Sample_1161 Sample_1162 Sample_1163 Sample_1164 Sample_1165 Sample_1166 Sample_1167 Sample_1168
#> "0312" "011" "0312" "0312" "011" "011" "011" "011"
#> Sample_1169 Sample_1170 Sample_1171 Sample_1172 Sample_1173 Sample_1174 Sample_1175 Sample_1176
#> "0312" "0312" "011" "011" "011" "011" "0312" "011"
#> Sample_1177 Sample_1178 Sample_1179 Sample_1180 Sample_1181 Sample_1182 Sample_1183 Sample_1184
#> "011" "011" "011" "0312" "011" "011" "0331" "0331"
#> Sample_1185 Sample_1186 Sample_1187 Sample_1188 Sample_1189 Sample_1190 Sample_1191 Sample_1192
#> "011" "011" "011" "011" "0321" "03221" "03222" "0122"
#> Sample_1193 Sample_1194 Sample_1195 Sample_1196 Sample_1197 Sample_1198 Sample_1199 Sample_1200
#> "03221" "03221" "0321" "0321" "03221" "03221" "03221" "0321"
#> Sample_1201 Sample_1202 Sample_1203 Sample_1204 Sample_1205 Sample_1206 Sample_1207 Sample_1208
#> "0321" "0321" "03221" "03221" "03221" "0321" "03221" "0132"
#> Sample_1209 Sample_1210 Sample_1211 Sample_1212 Sample_1213 Sample_1214 Sample_1215 Sample_1216
#> "0321" "03221" "0321" "0321" "0321" "03221" "03221" "0122"
#> Sample_1217 Sample_1218 Sample_1219 Sample_1220 Sample_1221 Sample_1222 Sample_1223 Sample_1224
#> "0132" "0132" "03221" "0321" "03221" "011" "0311" "0311"
#> Sample_1225 Sample_1226 Sample_1227 Sample_1228 Sample_1229 Sample_1230 Sample_1231 Sample_1232
#> "011" "0311" "011" "011" "011" "0311" "0311" "0311"
#> Sample_1233 Sample_1234 Sample_1235 Sample_1236 Sample_1237 Sample_1238 Sample_1239 Sample_1240
#> "0311" "011" "0311" "011" "0311" "0311" "011" "011"
#> Sample_1241 Sample_1242 Sample_1243 Sample_1244 Sample_1245 Sample_1246 Sample_1247 Sample_1248
#> "011" "011" "0311" "0311" "0311" "0311" "0311" "0311"
#> Sample_1249 Sample_1250 Sample_1251 Sample_1252 Sample_1253 Sample_1254 Sample_1255 Sample_1256
#> "011" "0311" "0311" "0311" "0311" "011" "0332" "0311"
#> Sample_1257 Sample_1258 Sample_1259 Sample_1260 Sample_1261 Sample_1262 Sample_1263 Sample_1264
#> "0311" "0311" "0311" "0311" "0311" "0311" "0311" "011"
#> Sample_1265 Sample_1266 Sample_1267 Sample_1268 Sample_1269 Sample_1270 Sample_1271 Sample_1272
#> "0311" "011" "011" "0332" "0311" "0311" "0311" "011"
#> Sample_1273 Sample_1274 Sample_1275 Sample_1276 Sample_1277 Sample_1278 Sample_1279 Sample_1280
#> "0311" "0311" "0311" "0311" "0311" "0311" "011" "011"
#> Sample_1281 Sample_1282 Sample_1283 Sample_1284 Sample_1285 Sample_1286 Sample_1287 Sample_1288
#> "0311" "0311" "011" "0311" "011" "0311" "0311" "0311"
#> Sample_1289 Sample_1290 Sample_1291 Sample_1292 Sample_1293 Sample_1294 Sample_1295 Sample_1296
#> "0311" "0311" "0311" "0311" "0311" "0311" "0311" "0331"
#> Sample_1297 Sample_1298 Sample_1299 Sample_1300 Sample_1301 Sample_1302 Sample_1303 Sample_1304
#> "0311" "0311" "011" "0311" "0311" "0311" "0311" "0311"
#> Sample_1305 Sample_1306 Sample_1307 Sample_1308 Sample_1309 Sample_1310 Sample_1311 Sample_1312
#> "0311" "0332" "011" "011" "011" "0332" "0311" "0311"
#> Sample_1313 Sample_1314 Sample_1315 Sample_1316 Sample_1317 Sample_1318 Sample_1319 Sample_1320
#> "011" "0313" "0313" "0311" "011" "0313" "0311" "011"
#> Sample_1321 Sample_1322 Sample_1323 Sample_1324 Sample_1325 Sample_1326 Sample_1327 Sample_1328
#> "0314" "0311" "0311" "0311" "0311" "0314" "0311" "0313"
#> Sample_1329 Sample_1330 Sample_1331 Sample_1332 Sample_1333 Sample_1334 Sample_1335 Sample_1336
#> "0314" "0314" "0313" "0314" "0314" "0313" "011" "011"
#> Sample_1337 Sample_1338 Sample_1339 Sample_1340 Sample_1341 Sample_1342 Sample_1343 Sample_1344
#> "0332" "011" "0332" "0311" "0314" "011" "011" "0314"
#> Sample_1345 Sample_1346 Sample_1347 Sample_1348 Sample_1349 Sample_1350 Sample_1351 Sample_1352
#> "0332" "0311" "0311" "011" "0314" "011" "0314" "011"
#> Sample_1353 Sample_1354 Sample_1355 Sample_1356 Sample_1357 Sample_1358 Sample_1359 Sample_1360
#> "0311" "011" "0314" "0314" "011" "011" "0314" "011"
#> Sample_1361 Sample_1362 Sample_1363 Sample_1364 Sample_1365 Sample_1366 Sample_1367 Sample_1368
#> "0314" "0314" "0314" "011" "0311" "0313" "011" "0313"
#> Sample_1369 Sample_1370 Sample_1371 Sample_1372 Sample_1373 Sample_1374 Sample_1375 Sample_1376
#> "0311" "0314" "011" "0314" "011" "011" "011" "0332"
#> Sample_1377 Sample_1378 Sample_1379 Sample_1380 Sample_1381 Sample_1382 Sample_1383 Sample_1384
#> "0311" "0311" "0311" "0311" "0314" "011" "0313" "0313"
#> Sample_1385 Sample_1386 Sample_1387 Sample_1388 Sample_1389 Sample_1390 Sample_1391 Sample_1392
#> "0313" "011" "0313" "011" "0313" "0313" "0313" "0313"
#> Sample_1393 Sample_1394 Sample_1395 Sample_1396 Sample_1397 Sample_1398 Sample_1399 Sample_1400
#> "0313" "0313" "0313" "0313" "0313" "011" "011" "0331"
#> Sample_1401 Sample_1402 Sample_1403 Sample_1404 Sample_1405 Sample_1406 Sample_1407 Sample_1408
#> "0313" "011" "0313" "0313" "0313" "011" "0313" "011"
#> Sample_1409 Sample_1410 Sample_1411 Sample_1412 Sample_1413 Sample_1414 Sample_1415 Sample_1416
#> "011" "0313" "0313" "0313" "0331" "0313" "0313" "0332"
#> Sample_1417 Sample_1418 Sample_1419 Sample_1420 Sample_1421 Sample_1422 Sample_1423 Sample_1424
#> "0313" "0313" "011" "0313" "0313" "0313" "011" "0313"
#> Sample_1425 Sample_1426 Sample_1427 Sample_1428 Sample_1429 Sample_1430 Sample_1431 Sample_1432
#> "011" "011" "011" "0313" "011" "011" "011" "011"
#> Sample_1433 Sample_1434 Sample_1435 Sample_1436 Sample_1437 Sample_1438 Sample_1439 Sample_1440
#> "011" "011" "011" "011" "011" "011" "011" "011"
#> Sample_1441 Sample_1442 Sample_1443 Sample_1444 Sample_1445 Sample_1446 Sample_1447 Sample_1448
#> "011" "0313" "0313" "011" "011" "0313" "0313" "011"
#> Sample_1449 Sample_1450 Sample_1451 Sample_1452 Sample_1453 Sample_1454 Sample_1455 Sample_1456
#> "011" "011" "011" "011" "011" "011" "011" "0313"
#> Sample_1457 Sample_1458 Sample_1459 Sample_1460 Sample_1461 Sample_1462 Sample_1463 Sample_1464
#> "0313" "011" "011" "0331" "0313" "011" "0314" "0314"
#> Sample_1465 Sample_1466 Sample_1467 Sample_1468 Sample_1469 Sample_1470 Sample_1471 Sample_1472
#> "011" "011" "011" "0313" "011" "011" "011" "011"
#> Sample_1473 Sample_1474 Sample_1475 Sample_1476 Sample_1477 Sample_1478 Sample_1479 Sample_1480
#> "011" "011" "011" "011" "011" "011" "011" "0314"
#> Sample_1481 Sample_1482 Sample_1483 Sample_1484 Sample_1485 Sample_1486 Sample_1487 Sample_1488
#> "011" "011" "011" "011" "0313" "0331" "011" "0313"
#> Sample_1489 Sample_1490 Sample_1491 Sample_1492 Sample_1493 Sample_1494 Sample_1495 Sample_1496
#> "011" "011" "011" "011" "011" "0332" "011" "011"
#> Sample_1497 Sample_1498 Sample_1499 Sample_1500 Sample_1501 Sample_1502 Sample_1503 Sample_1504
#> "011" "011" "011" "011" "011" "0313" "0313" "0332"
#> Sample_1505 Sample_1506 Sample_1507 Sample_1508 Sample_1509 Sample_1510 Sample_1511 Sample_1512
#> "0331" "0313" "0313" "0313" "011" "011" "0313" "0313"
#> Sample_1513 Sample_1514 Sample_1515 Sample_1516 Sample_1517 Sample_1518 Sample_1519 Sample_1520
#> "0313" "011" "0313" "011" "0222" "0222" "02211" "0233"
#> Sample_1521 Sample_1522 Sample_1523 Sample_1524 Sample_1525 Sample_1526 Sample_1527 Sample_1528
#> "0232" "0131" "0132" "0233" "0121" "0121" "0212" "0212"
#> Sample_1529 Sample_1530 Sample_1531 Sample_1532 Sample_1533 Sample_1534 Sample_1535 Sample_1536
#> "0212" "0233" "0232" "0232" "0211" "0212" "0222" "0222"
#> Sample_1537 Sample_1538 Sample_1539 Sample_1540 Sample_1541 Sample_1542 Sample_1543 Sample_1544
#> "02211" "0212" "0212" "0131" "011" "0212" "0233" "0131"
#> Sample_1545 Sample_1546 Sample_1547 Sample_1548 Sample_1549 Sample_1550 Sample_1551 Sample_1552
#> "011" "0211" "0131" "0231" "03221" "0231" "0231" "0231"
#> Sample_1553 Sample_1554 Sample_1555 Sample_1556 Sample_1557 Sample_1558 Sample_1559 Sample_1560
#> "0321" "0222" "0231" "0131" "0131" "0131" "0231" "0131"
#> Sample_1561 Sample_1562 Sample_1563 Sample_1564 Sample_1565 Sample_1566 Sample_1567 Sample_1568
#> "0231" "0121" "0131" "0131" "0131" "0131" "0131" "0131"
#> Sample_1569 Sample_1570 Sample_1571 Sample_1572 Sample_1573 Sample_1574 Sample_1575 Sample_1576
#> "0312" "0212" "0131" "0131" "0131" "0231" "0231" "0131"
#> Sample_1577 Sample_1578 Sample_1579 Sample_1580 Sample_1581 Sample_1582 Sample_1583 Sample_1584
#> "0131" "0131" "0131" "0131" "03221" "0231" "0131" "0131"
#> Sample_1585 Sample_1586 Sample_1587 Sample_1588 Sample_1589 Sample_1590 Sample_1591 Sample_1592
#> "0231" "0231" "0231" "0222" "0231" "0131" "0131" "0231"
#> Sample_1593 Sample_1594 Sample_1595 Sample_1596 Sample_1597 Sample_1598 Sample_1599 Sample_1600
#> "0231" "0231" "0231" "0232" "03221" "0311" "0233" "0131"
get_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 277))
#> Sample_1 Sample_2 Sample_3 Sample_4 Sample_5 Sample_6 Sample_7 Sample_8
#> "013" "02211" "02212" "0222" "02211" "02211" "02211" "0222"
#> Sample_9 Sample_10 Sample_11 Sample_12 Sample_13 Sample_14 Sample_15 Sample_16
#> "02212" "02211" "02212" "02211" "02211" "02211" "02211" "02212"
#> Sample_17 Sample_18 Sample_19 Sample_20 Sample_21 Sample_22 Sample_23 Sample_24
#> "0222" "0222" "0222" "0222" "0222" "0222" "0222" "02211"
#> Sample_25 Sample_26 Sample_27 Sample_28 Sample_29 Sample_30 Sample_31 Sample_32
#> "0222" "02211" "0222" "0222" "02212" "0222" "0233" "02212"
#> Sample_33 Sample_34 Sample_35 Sample_36 Sample_37 Sample_38 Sample_39 Sample_40
#> "02212" "02212" "02212" "02212" "02212" "02212" "02212" "02212"
#> Sample_41 Sample_42 Sample_43 Sample_44 Sample_45 Sample_46 Sample_47 Sample_48
#> "02212" "02212" "0233" "02212" "02212" "02212" "02212" "02212"
#> Sample_49 Sample_50 Sample_51 Sample_52 Sample_53 Sample_54 Sample_55 Sample_56
#> "02212" "02212" "02212" "02212" "0222" "0222" "02212" "02211"
#> Sample_57 Sample_58 Sample_59 Sample_60 Sample_61 Sample_62 Sample_63 Sample_64
#> "02211" "02211" "02212" "02211" "0222" "02211" "02211" "02211"
#> Sample_65 Sample_66 Sample_67 Sample_68 Sample_69 Sample_70 Sample_71 Sample_72
#> "02211" "02211" "0222" "02211" "02211" "0222" "0222" "0211"
#> Sample_73 Sample_74 Sample_75 Sample_76 Sample_77 Sample_78 Sample_79 Sample_80
#> "0233" "02211" "0222" "02211" "0222" "02211" "0222" "0222"
#> Sample_81 Sample_82 Sample_83 Sample_84 Sample_85 Sample_86 Sample_87 Sample_88
#> "02211" "0222" "02211" "02211" "0222" "02212" "0222" "0222"
#> Sample_89 Sample_90 Sample_91 Sample_92 Sample_93 Sample_94 Sample_95 Sample_96
#> "0222" "0222" "0222" "0222" "0222" "0232" "0222" "02211"
#> Sample_97 Sample_98 Sample_99 Sample_100 Sample_101 Sample_102 Sample_103 Sample_104
#> "02211" "02211" "02211" "0222" "0222" "02212" "0222" "0222"
#> Sample_105 Sample_106 Sample_107 Sample_108 Sample_109 Sample_110 Sample_111 Sample_112
#> "02211" "02211" "0222" "02211" "02211" "0222" "0212" "0222"
#> Sample_113 Sample_114 Sample_115 Sample_116 Sample_117 Sample_118 Sample_119 Sample_120
#> "0222" "0232" "0222" "0222" "0222" "0232" "0222" "02211"
#> Sample_121 Sample_122 Sample_123 Sample_124 Sample_125 Sample_126 Sample_127 Sample_128
#> "0121" "0222" "0232" "02211" "02211" "0222" "02211" "0222"
#> Sample_129 Sample_130 Sample_131 Sample_132 Sample_133 Sample_134 Sample_135 Sample_136
#> "02211" "0222" "0222" "02211" "0222" "02211" "0222" "02211"
#> Sample_137 Sample_138 Sample_139 Sample_140 Sample_141 Sample_142 Sample_143 Sample_144
#> "02211" "02211" "0222" "02211" "02211" "0222" "0222" "0232"
#> Sample_145 Sample_146 Sample_147 Sample_148 Sample_149 Sample_150 Sample_151 Sample_152
#> "0222" "0222" "0211" "0211" "0211" "0211" "0212" "0211"
#> Sample_153 Sample_154 Sample_155 Sample_156 Sample_157 Sample_158 Sample_159 Sample_160
#> "0211" "0211" "0211" "0211" "0211" "0211" "0212" "0211"
#> Sample_161 Sample_162 Sample_163 Sample_164 Sample_165 Sample_166 Sample_167 Sample_168
#> "0212" "0212" "0212" "0212" "0212" "0212" "0211" "0211"
#> Sample_169 Sample_170 Sample_171 Sample_172 Sample_173 Sample_174 Sample_175 Sample_176
#> "0212" "0211" "0232" "0212" "0233" "0212" "0211" "0211"
#> Sample_177 Sample_178 Sample_179 Sample_180 Sample_181 Sample_182 Sample_183 Sample_184
#> "0212" "0211" "0212" "0212" "0211" "0211" "0211" "0211"
#> Sample_185 Sample_186 Sample_187 Sample_188 Sample_189 Sample_190 Sample_191 Sample_192
#> "0211" "0211" "0211" "0212" "0212" "0211" "0212" "0212"
#> Sample_193 Sample_194 Sample_195 Sample_196 Sample_197 Sample_198 Sample_199 Sample_200
#> "0211" "0211" "0211" "0211" "02211" "02211" "02212" "02212"
#> Sample_201 Sample_202 Sample_203 Sample_204 Sample_205 Sample_206 Sample_207 Sample_208
#> "02212" "02212" "02212" "0233" "02212" "02212" "02212" "03222"
#> Sample_209 Sample_210 Sample_211 Sample_212 Sample_213 Sample_214 Sample_215 Sample_216
#> "03222" "03222" "03222" "03222" "03222" "013" "013" "03222"
#> Sample_217 Sample_218 Sample_219 Sample_220 Sample_221 Sample_222 Sample_223 Sample_224
#> "013" "03222" "03222" "03222" "03222" "03222" "03222" "013"
#> Sample_225 Sample_226 Sample_227 Sample_228 Sample_229 Sample_230 Sample_231 Sample_232
#> "03222" "013" "03222" "03222" "03222" "03222" "03222" "03222"
#> Sample_233 Sample_234 Sample_235 Sample_236 Sample_237 Sample_238 Sample_239 Sample_240
#> "0333" "03222" "03222" "013" "03222" "03222" "03222" "03222"
#> Sample_241 Sample_242 Sample_243 Sample_244 Sample_245 Sample_246 Sample_247 Sample_248
#> "013" "03222" "0321" "03222" "03222" "03222" "03222" "03222"
#> Sample_249 Sample_250 Sample_251 Sample_252 Sample_253 Sample_254 Sample_255 Sample_256
#> "03222" "03222" "03222" "03222" "03222" "0333" "03221" "03222"
#> Sample_257 Sample_258 Sample_259 Sample_260 Sample_261 Sample_262 Sample_263 Sample_264
#> "03222" "013" "03222" "03222" "03222" "03222" "03222" "013"
#> Sample_265 Sample_266 Sample_267 Sample_268 Sample_269 Sample_270 Sample_271 Sample_272
#> "0122" "0321" "0321" "0321" "03221" "0321" "0333" "0122"
#> Sample_273 Sample_274 Sample_275 Sample_276 Sample_277 Sample_278 Sample_279 Sample_280
#> "0321" "0321" "0122" "013" "0321" "0333" "0321" "03222"
#> Sample_281 Sample_282 Sample_283 Sample_284 Sample_285 Sample_286 Sample_287 Sample_288
#> "0321" "0321" "03221" "0321" "0321" "0321" "013" "0333"
#> Sample_289 Sample_290 Sample_291 Sample_292 Sample_293 Sample_294 Sample_295 Sample_296
#> "0333" "0321" "0321" "0321" "03222" "0321" "0321" "0321"
#> Sample_297 Sample_298 Sample_299 Sample_300 Sample_301 Sample_302 Sample_303 Sample_304
#> "0321" "0321" "0321" "03221" "0321" "0122" "0122" "0122"
#> Sample_305 Sample_306 Sample_307 Sample_308 Sample_309 Sample_310 Sample_311 Sample_312
#> "0122" "0321" "0321" "0122" "0122" "0122" "0122" "0122"
#> Sample_313 Sample_314 Sample_315 Sample_316 Sample_317 Sample_318 Sample_319 Sample_320
#> "0122" "0122" "0321" "0122" "0321" "0122" "0122" "0122"
#> Sample_321 Sample_322 Sample_323 Sample_324 Sample_325 Sample_326 Sample_327 Sample_328
#> "0121" "0321" "0122" "0122" "0122" "0121" "0122" "0122"
#> Sample_329 Sample_330 Sample_331 Sample_332 Sample_333 Sample_334 Sample_335 Sample_336
#> "0121" "0122" "0333" "0321" "0321" "0321" "0321" "0321"
#> Sample_337 Sample_338 Sample_339 Sample_340 Sample_341 Sample_342 Sample_343 Sample_344
#> "0321" "0321" "0333" "0321" "0321" "0321" "0321" "0321"
#> Sample_345 Sample_346 Sample_347 Sample_348 Sample_349 Sample_350 Sample_351 Sample_352
#> "0321" "0321" "0321" "0321" "0321" "013" "0122" "013"
#> Sample_353 Sample_354 Sample_355 Sample_356 Sample_357 Sample_358 Sample_359 Sample_360
#> "0321" "0321" "0122" "0321" "0321" "0122" "0321" "0122"
#> Sample_361 Sample_362 Sample_363 Sample_364 Sample_365 Sample_366 Sample_367 Sample_368
#> "0321" "0122" "0321" "0321" "013" "0321" "0321" "0122"
#> Sample_369 Sample_370 Sample_371 Sample_372 Sample_373 Sample_374 Sample_375 Sample_376
#> "0122" "0321" "0333" "013" "013" "0321" "0321" "0321"
#> Sample_377 Sample_378 Sample_379 Sample_380 Sample_381 Sample_382 Sample_383 Sample_384
#> "0122" "0321" "0321" "0321" "0122" "0321" "0321" "0321"
#> Sample_385 Sample_386 Sample_387 Sample_388 Sample_389 Sample_390 Sample_391 Sample_392
#> "0121" "0121" "0321" "0321" "013" "013" "0321" "0321"
#> Sample_393 Sample_394 Sample_395 Sample_396 Sample_397 Sample_398 Sample_399 Sample_400
#> "0122" "013" "0122" "013" "0122" "0122" "0321" "0321"
#> Sample_401 Sample_402 Sample_403 Sample_404 Sample_405 Sample_406 Sample_407 Sample_408
#> "0321" "03221" "013" "013" "013" "0321" "0321" "013"
#> Sample_409 Sample_410 Sample_411 Sample_412 Sample_413 Sample_414 Sample_415 Sample_416
#> "0122" "0321" "0321" "0122" "0321" "0321" "0333" "0122"
#> Sample_417 Sample_418 Sample_419 Sample_420 Sample_421 Sample_422 Sample_423 Sample_424
#> "0321" "0321" "0321" "0333" "0122" "0333" "03221" "0122"
#> Sample_425 Sample_426 Sample_427 Sample_428 Sample_429 Sample_430 Sample_431 Sample_432
#> "0122" "03221" "0122" "0122" "03221" "013" "0122" "0121"
#> Sample_433 Sample_434 Sample_435 Sample_436 Sample_437 Sample_438 Sample_439 Sample_440
#> "013" "0121" "03221" "0333" "0333" "013" "0121" "0122"
#> Sample_441 Sample_442 Sample_443 Sample_444 Sample_445 Sample_446 Sample_447 Sample_448
#> "03221" "0122" "013" "0321" "0121" "0122" "03221" "013"
#> Sample_449 Sample_450 Sample_451 Sample_452 Sample_453 Sample_454 Sample_455 Sample_456
#> "03221" "0321" "0122" "0121" "03221" "0321" "013" "0121"
#> Sample_457 Sample_458 Sample_459 Sample_460 Sample_461 Sample_462 Sample_463 Sample_464
#> "013" "03221" "0122" "0321" "0121" "0321" "013" "0121"
#> Sample_465 Sample_466 Sample_467 Sample_468 Sample_469 Sample_470 Sample_471 Sample_472
#> "0121" "0121" "03221" "0121" "0122" "0321" "03221" "0121"
#> Sample_473 Sample_474 Sample_475 Sample_476 Sample_477 Sample_478 Sample_479 Sample_480
#> "0321" "0121" "0121" "013" "013" "0314" "011" "0314"
#> Sample_481 Sample_482 Sample_483 Sample_484 Sample_485 Sample_486 Sample_487 Sample_488
#> "0313" "0314" "0314" "0314" "0314" "011" "011" "0331"
#> Sample_489 Sample_490 Sample_491 Sample_492 Sample_493 Sample_494 Sample_495 Sample_496
#> "0314" "011" "0314" "0314" "0314" "0314" "011" "0314"
#> Sample_497 Sample_498 Sample_499 Sample_500 Sample_501 Sample_502 Sample_503 Sample_504
#> "0314" "0314" "011" "011" "0314" "0314" "0314" "0314"
#> Sample_505 Sample_506 Sample_507 Sample_508 Sample_509 Sample_510 Sample_511 Sample_512
#> "011" "011" "011" "0314" "0314" "0314" "011" "0332"
#> Sample_513 Sample_514 Sample_515 Sample_516 Sample_517 Sample_518 Sample_519 Sample_520
#> "011" "0314" "011" "0211" "011" "0314" "0314" "011"
#> Sample_521 Sample_522 Sample_523 Sample_524 Sample_525 Sample_526 Sample_527 Sample_528
#> "011" "0314" "0314" "0314" "0314" "0314" "011" "0312"
#> Sample_529 Sample_530 Sample_531 Sample_532 Sample_533 Sample_534 Sample_535 Sample_536
#> "0331" "011" "0332" "011" "011" "0314" "011" "011"
#> Sample_537 Sample_538 Sample_539 Sample_540 Sample_541 Sample_542 Sample_543 Sample_544
#> "011" "0314" "011" "0314" "0314" "011" "011" "011"
#> Sample_545 Sample_546 Sample_547 Sample_548 Sample_549 Sample_550 Sample_551 Sample_552
#> "0331" "011" "011" "0314" "0314" "0314" "0314" "0314"
#> Sample_553 Sample_554 Sample_555 Sample_556 Sample_557 Sample_558 Sample_559 Sample_560
#> "0332" "0314" "0314" "0314" "011" "011" "011" "0314"
#> Sample_561 Sample_562 Sample_563 Sample_564 Sample_565 Sample_566 Sample_567 Sample_568
#> "0314" "0314" "0314" "0314" "011" "011" "011" "0314"
#> Sample_569 Sample_570 Sample_571 Sample_572 Sample_573 Sample_574 Sample_575 Sample_576
#> "011" "03221" "03221" "013" "0121" "0333" "0321" "03221"
#> Sample_577 Sample_578 Sample_579 Sample_580 Sample_581 Sample_582 Sample_583 Sample_584
#> "0122" "0321" "0321" "0321" "0321" "0321" "0321" "0321"
#> Sample_585 Sample_586 Sample_587 Sample_588 Sample_589 Sample_590 Sample_591 Sample_592
#> "03222" "02212" "02211" "0231" "03222" "013" "02211" "0231"
#> Sample_593 Sample_594 Sample_595 Sample_596 Sample_597 Sample_598 Sample_599 Sample_600
#> "02212" "0231" "0231" "0222" "0231" "0222" "0231" "0231"
#> Sample_601 Sample_602 Sample_603 Sample_604 Sample_605 Sample_606 Sample_607 Sample_608
#> "0222" "0231" "0222" "0222" "0222" "0222" "02211" "0222"
#> Sample_609 Sample_610 Sample_611 Sample_612 Sample_613 Sample_614 Sample_615 Sample_616
#> "0222" "0222" "0212" "0212" "03222" "013" "03222" "013"
#> Sample_617 Sample_618 Sample_619 Sample_620 Sample_621 Sample_622 Sample_623 Sample_624
#> "013" "0121" "013" "013" "0231" "013" "03221" "03221"
#> Sample_625 Sample_626 Sample_627 Sample_628 Sample_629 Sample_630 Sample_631 Sample_632
#> "0231" "013" "0231" "0231" "0231" "013" "0231" "013"
#> Sample_633 Sample_634 Sample_635 Sample_636 Sample_637 Sample_638 Sample_639 Sample_640
#> "013" "013" "013" "013" "013" "0231" "013" "0232"
#> Sample_641 Sample_642 Sample_643 Sample_644 Sample_645 Sample_646 Sample_647 Sample_648
#> "013" "0233" "013" "0321" "0232" "013" "013" "0232"
#> Sample_649 Sample_650 Sample_651 Sample_652 Sample_653 Sample_654 Sample_655 Sample_656
#> "0232" "0231" "02212" "02211" "02211" "02212" "02211" "02211"
#> Sample_657 Sample_658 Sample_659 Sample_660 Sample_661 Sample_662 Sample_663 Sample_664
#> "0222" "02212" "02211" "0222" "02211" "0222" "0222" "02211"
#> Sample_665 Sample_666 Sample_667 Sample_668 Sample_669 Sample_670 Sample_671 Sample_672
#> "0232" "0222" "0232" "0222" "0222" "0222" "02211" "0222"
#> Sample_673 Sample_674 Sample_675 Sample_676 Sample_677 Sample_678 Sample_679 Sample_680
#> "0222" "0222" "0222" "0222" "02212" "02212" "02211" "0222"
#> Sample_681 Sample_682 Sample_683 Sample_684 Sample_685 Sample_686 Sample_687 Sample_688
#> "0232" "0222" "0222" "0222" "0121" "0212" "0121" "0212"
#> Sample_689 Sample_690 Sample_691 Sample_692 Sample_693 Sample_694 Sample_695 Sample_696
#> "0222" "0212" "0222" "0121" "0222" "0222" "0212" "0211"
#> Sample_697 Sample_698 Sample_699 Sample_700 Sample_701 Sample_702 Sample_703 Sample_704
#> "0211" "0212" "0212" "0211" "0212" "0211" "0212" "02211"
#> Sample_705 Sample_706 Sample_707 Sample_708 Sample_709 Sample_710 Sample_711 Sample_712
#> "0222" "0211" "0211" "0211" "0211" "0232" "0212" "0211"
#> Sample_713 Sample_714 Sample_715 Sample_716 Sample_717 Sample_718 Sample_719 Sample_720
#> "0211" "0212" "0212" "0212" "0211" "0211" "0211" "0232"
#> Sample_721 Sample_722 Sample_723 Sample_724 Sample_725 Sample_726 Sample_727 Sample_728
#> "0211" "0211" "0212" "0211" "0212" "0211" "0211" "0212"
#> Sample_729 Sample_730 Sample_731 Sample_732 Sample_733 Sample_734 Sample_735 Sample_736
#> "0211" "0211" "0233" "0211" "0211" "0211" "0233" "0211"
#> Sample_737 Sample_738 Sample_739 Sample_740 Sample_741 Sample_742 Sample_743 Sample_744
#> "0121" "0232" "0211" "0212" "0211" "0212" "0211" "0211"
#> Sample_745 Sample_746 Sample_747 Sample_748 Sample_749 Sample_750 Sample_751 Sample_752
#> "0212" "0211" "0211" "0212" "0211" "0211" "0211" "0211"
#> Sample_753 Sample_754 Sample_755 Sample_756 Sample_757 Sample_758 Sample_759 Sample_760
#> "0212" "0212" "0211" "0211" "0211" "0211" "0212" "0211"
#> Sample_761 Sample_762 Sample_763 Sample_764 Sample_765 Sample_766 Sample_767 Sample_768
#> "0211" "0211" "0211" "0211" "0211" "0212" "0211" "0232"
#> Sample_769 Sample_770 Sample_771 Sample_772 Sample_773 Sample_774 Sample_775 Sample_776
#> "0212" "0211" "0211" "0212" "0211" "0211" "0212" "0211"
#> Sample_777 Sample_778 Sample_779 Sample_780 Sample_781 Sample_782 Sample_783 Sample_784
#> "0212" "0212" "0212" "0212" "0211" "0212" "0212" "0212"
#> Sample_785 Sample_786 Sample_787 Sample_788 Sample_789 Sample_790 Sample_791 Sample_792
#> "0212" "0212" "0211" "0212" "0212" "0211" "0211" "0211"
#> Sample_793 Sample_794 Sample_795 Sample_796 Sample_797 Sample_798 Sample_799 Sample_800
#> "0212" "0211" "0211" "0211" "0212" "0212" "0212" "0211"
#> Sample_801 Sample_802 Sample_803 Sample_804 Sample_805 Sample_806 Sample_807 Sample_808
#> "0211" "0212" "0211" "0212" "0211" "0211" "0211" "0211"
#> Sample_809 Sample_810 Sample_811 Sample_812 Sample_813 Sample_814 Sample_815 Sample_816
#> "0211" "0212" "0211" "0211" "0211" "0211" "0211" "0211"
#> Sample_817 Sample_818 Sample_819 Sample_820 Sample_821 Sample_822 Sample_823 Sample_824
#> "0212" "0211" "0212" "0233" "0212" "0211" "0212" "0212"
#> Sample_825 Sample_826 Sample_827 Sample_828 Sample_829 Sample_830 Sample_831 Sample_832
#> "0212" "0212" "0212" "0211" "0212" "0212" "0211" "0211"
#> Sample_833 Sample_834 Sample_835 Sample_836 Sample_837 Sample_838 Sample_839 Sample_840
#> "0211" "0212" "0212" "0233" "0211" "0211" "0212" "0211"
#> Sample_841 Sample_842 Sample_843 Sample_844 Sample_845 Sample_846 Sample_847 Sample_848
#> "0222" "02212" "02212" "02211" "02211" "02211" "0222" "0222"
#> Sample_849 Sample_850 Sample_851 Sample_852 Sample_853 Sample_854 Sample_855 Sample_856
#> "0222" "02211" "02211" "0222" "02211" "02211" "0212" "0212"
#> Sample_857 Sample_858 Sample_859 Sample_860 Sample_861 Sample_862 Sample_863 Sample_864
#> "0212" "0211" "0232" "0212" "0212" "0212" "0212" "0211"
#> Sample_865 Sample_866 Sample_867 Sample_868 Sample_869 Sample_870 Sample_871 Sample_872
#> "0233" "0212" "0212" "0211" "0212" "0212" "0212" "0212"
#> Sample_873 Sample_874 Sample_875 Sample_876 Sample_877 Sample_878 Sample_879 Sample_880
#> "0212" "0211" "0211" "0212" "0211" "0211" "0211" "0211"
#> Sample_881 Sample_882 Sample_883 Sample_884 Sample_885 Sample_886 Sample_887 Sample_888
#> "0212" "0233" "0211" "0211" "0212" "0211" "0211" "0211"
#> Sample_889 Sample_890 Sample_891 Sample_892 Sample_893 Sample_894 Sample_895 Sample_896
#> "0212" "0212" "0212" "0212" "0212" "0232" "0212" "0211"
#> Sample_897 Sample_898 Sample_899 Sample_900 Sample_901 Sample_902 Sample_903 Sample_904
#> "0212" "0212" "0211" "0233" "0211" "0212" "0212" "0211"
#> Sample_905 Sample_906 Sample_907 Sample_908 Sample_909 Sample_910 Sample_911 Sample_912
#> "0211" "0212" "0212" "0212" "0212" "0211" "0212" "0211"
#> Sample_913 Sample_914 Sample_915 Sample_916 Sample_917 Sample_918 Sample_919 Sample_920
#> "0212" "0212" "0212" "0211" "0212" "0212" "0212" "0212"
#> Sample_921 Sample_922 Sample_923 Sample_924 Sample_925 Sample_926 Sample_927 Sample_928
#> "0211" "0212" "0211" "0212" "0212" "0211" "0211" "0211"
#> Sample_929 Sample_930 Sample_931 Sample_932 Sample_933 Sample_934 Sample_935 Sample_936
#> "0212" "0211" "0211" "0212" "0212" "0212" "0212" "0212"
#> Sample_937 Sample_938 Sample_939 Sample_940 Sample_941 Sample_942 Sample_943 Sample_944
#> "0212" "0212" "0212" "0212" "0212" "0212" "0212" "0212"
#> Sample_945 Sample_946 Sample_947 Sample_948 Sample_949 Sample_950 Sample_951 Sample_952
#> "0211" "0211" "0211" "0321" "0321" "013" "0321" "0321"
#> Sample_953 Sample_954 Sample_955 Sample_956 Sample_957 Sample_958 Sample_959 Sample_960
#> "0321" "03221" "0321" "0321" "0321" "0321" "0321" "0321"
#> Sample_961 Sample_962 Sample_963 Sample_964 Sample_965 Sample_966 Sample_967 Sample_968
#> "0321" "0122" "0321" "0321" "0321" "0321" "0321" "0122"
#> Sample_969 Sample_970 Sample_971 Sample_972 Sample_973 Sample_974 Sample_975 Sample_976
#> "0321" "0321" "0321" "0321" "0321" "0321" "0333" "013"
#> Sample_977 Sample_978 Sample_979 Sample_980 Sample_981 Sample_982 Sample_983 Sample_984
#> "013" "0122" "0321" "013" "013" "0121" "0122" "0321"
#> Sample_985 Sample_986 Sample_987 Sample_988 Sample_989 Sample_990 Sample_991 Sample_992
#> "0122" "013" "0122" "0121" "0121" "0121" "0121" "0121"
#> Sample_993 Sample_994 Sample_995 Sample_996 Sample_997 Sample_998 Sample_999 Sample_1000
#> "0121" "0122" "0321" "013" "0321" "0321" "013" "0321"
#> Sample_1001 Sample_1002 Sample_1003 Sample_1004 Sample_1005 Sample_1006 Sample_1007 Sample_1008
#> "0321" "0321" "0321" "0333" "0321" "0321" "0122" "013"
#> Sample_1009 Sample_1010 Sample_1011 Sample_1012 Sample_1013 Sample_1014 Sample_1015 Sample_1016
#> "013" "013" "0321" "03221" "0321" "0121" "0121" "0121"
#> Sample_1017 Sample_1018 Sample_1019 Sample_1020 Sample_1021 Sample_1022 Sample_1023 Sample_1024
#> "0121" "0121" "0121" "0121" "0121" "0121" "0121" "0121"
#> Sample_1025 Sample_1026 Sample_1027 Sample_1028 Sample_1029 Sample_1030 Sample_1031 Sample_1032
#> "0121" "0121" "011" "011" "0312" "011" "0332" "0312"
#> Sample_1033 Sample_1034 Sample_1035 Sample_1036 Sample_1037 Sample_1038 Sample_1039 Sample_1040
#> "0312" "011" "0312" "011" "0312" "0312" "011" "0312"
#> Sample_1041 Sample_1042 Sample_1043 Sample_1044 Sample_1045 Sample_1046 Sample_1047 Sample_1048
#> "011" "011" "0312" "0312" "0332" "011" "0312" "0331"
#> Sample_1049 Sample_1050 Sample_1051 Sample_1052 Sample_1053 Sample_1054 Sample_1055 Sample_1056
#> "011" "011" "0312" "011" "03221" "0321" "0321" "013"
#> Sample_1057 Sample_1058 Sample_1059 Sample_1060 Sample_1061 Sample_1062 Sample_1063 Sample_1064
#> "03221" "0121" "013" "0321" "03221" "013" "0312" "0312"
#> Sample_1065 Sample_1066 Sample_1067 Sample_1068 Sample_1069 Sample_1070 Sample_1071 Sample_1072
#> "0312" "011" "0312" "0312" "0312" "011" "0312" "0312"
#> Sample_1073 Sample_1074 Sample_1075 Sample_1076 Sample_1077 Sample_1078 Sample_1079 Sample_1080
#> "011" "0312" "011" "011" "0312" "0312" "0312" "011"
#> Sample_1081 Sample_1082 Sample_1083 Sample_1084 Sample_1085 Sample_1086 Sample_1087 Sample_1088
#> "0312" "0312" "011" "0312" "0312" "0331" "011" "0331"
#> Sample_1089 Sample_1090 Sample_1091 Sample_1092 Sample_1093 Sample_1094 Sample_1095 Sample_1096
#> "0332" "011" "011" "0312" "011" "0312" "0312" "0312"
#> Sample_1097 Sample_1098 Sample_1099 Sample_1100 Sample_1101 Sample_1102 Sample_1103 Sample_1104
#> "011" "0312" "0312" "011" "0312" "011" "011" "011"
#> Sample_1105 Sample_1106 Sample_1107 Sample_1108 Sample_1109 Sample_1110 Sample_1111 Sample_1112
#> "011" "0312" "0312" "011" "0312" "011" "0312" "011"
#> Sample_1113 Sample_1114 Sample_1115 Sample_1116 Sample_1117 Sample_1118 Sample_1119 Sample_1120
#> "011" "011" "0312" "011" "0312" "011" "011" "0332"
#> Sample_1121 Sample_1122 Sample_1123 Sample_1124 Sample_1125 Sample_1126 Sample_1127 Sample_1128
#> "011" "0331" "0312" "0312" "0312" "0312" "0312" "0312"
#> Sample_1129 Sample_1130 Sample_1131 Sample_1132 Sample_1133 Sample_1134 Sample_1135 Sample_1136
#> "011" "011" "011" "0332" "011" "0331" "011" "011"
#> Sample_1137 Sample_1138 Sample_1139 Sample_1140 Sample_1141 Sample_1142 Sample_1143 Sample_1144
#> "011" "011" "0312" "0331" "0312" "011" "011" "011"
#> Sample_1145 Sample_1146 Sample_1147 Sample_1148 Sample_1149 Sample_1150 Sample_1151 Sample_1152
#> "0312" "011" "011" "011" "011" "0312" "011" "0312"
#> Sample_1153 Sample_1154 Sample_1155 Sample_1156 Sample_1157 Sample_1158 Sample_1159 Sample_1160
#> "011" "011" "011" "0312" "0312" "011" "011" "011"
#> Sample_1161 Sample_1162 Sample_1163 Sample_1164 Sample_1165 Sample_1166 Sample_1167 Sample_1168
#> "0312" "011" "0312" "0312" "011" "011" "011" "011"
#> Sample_1169 Sample_1170 Sample_1171 Sample_1172 Sample_1173 Sample_1174 Sample_1175 Sample_1176
#> "0312" "0312" "011" "011" "011" "011" "0312" "011"
#> Sample_1177 Sample_1178 Sample_1179 Sample_1180 Sample_1181 Sample_1182 Sample_1183 Sample_1184
#> "011" "011" "011" "0312" "011" "011" "0331" "0331"
#> Sample_1185 Sample_1186 Sample_1187 Sample_1188 Sample_1189 Sample_1190 Sample_1191 Sample_1192
#> "011" "011" "011" "011" "0321" "03221" "03222" "0122"
#> Sample_1193 Sample_1194 Sample_1195 Sample_1196 Sample_1197 Sample_1198 Sample_1199 Sample_1200
#> "03221" "03221" "0321" "0321" "03221" "03221" "03221" "0321"
#> Sample_1201 Sample_1202 Sample_1203 Sample_1204 Sample_1205 Sample_1206 Sample_1207 Sample_1208
#> "0321" "0321" "03221" "03221" "03221" "0321" "03221" "013"
#> Sample_1209 Sample_1210 Sample_1211 Sample_1212 Sample_1213 Sample_1214 Sample_1215 Sample_1216
#> "0321" "03221" "0321" "0321" "0321" "03221" "03221" "0122"
#> Sample_1217 Sample_1218 Sample_1219 Sample_1220 Sample_1221 Sample_1222 Sample_1223 Sample_1224
#> "013" "013" "03221" "0321" "03221" "011" "0311" "0311"
#> Sample_1225 Sample_1226 Sample_1227 Sample_1228 Sample_1229 Sample_1230 Sample_1231 Sample_1232
#> "011" "0311" "011" "011" "011" "0311" "0311" "0311"
#> Sample_1233 Sample_1234 Sample_1235 Sample_1236 Sample_1237 Sample_1238 Sample_1239 Sample_1240
#> "0311" "011" "0311" "011" "0311" "0311" "011" "011"
#> Sample_1241 Sample_1242 Sample_1243 Sample_1244 Sample_1245 Sample_1246 Sample_1247 Sample_1248
#> "011" "011" "0311" "0311" "0311" "0311" "0311" "0311"
#> Sample_1249 Sample_1250 Sample_1251 Sample_1252 Sample_1253 Sample_1254 Sample_1255 Sample_1256
#> "011" "0311" "0311" "0311" "0311" "011" "0332" "0311"
#> Sample_1257 Sample_1258 Sample_1259 Sample_1260 Sample_1261 Sample_1262 Sample_1263 Sample_1264
#> "0311" "0311" "0311" "0311" "0311" "0311" "0311" "011"
#> Sample_1265 Sample_1266 Sample_1267 Sample_1268 Sample_1269 Sample_1270 Sample_1271 Sample_1272
#> "0311" "011" "011" "0332" "0311" "0311" "0311" "011"
#> Sample_1273 Sample_1274 Sample_1275 Sample_1276 Sample_1277 Sample_1278 Sample_1279 Sample_1280
#> "0311" "0311" "0311" "0311" "0311" "0311" "011" "011"
#> Sample_1281 Sample_1282 Sample_1283 Sample_1284 Sample_1285 Sample_1286 Sample_1287 Sample_1288
#> "0311" "0311" "011" "0311" "011" "0311" "0311" "0311"
#> Sample_1289 Sample_1290 Sample_1291 Sample_1292 Sample_1293 Sample_1294 Sample_1295 Sample_1296
#> "0311" "0311" "0311" "0311" "0311" "0311" "0311" "0331"
#> Sample_1297 Sample_1298 Sample_1299 Sample_1300 Sample_1301 Sample_1302 Sample_1303 Sample_1304
#> "0311" "0311" "011" "0311" "0311" "0311" "0311" "0311"
#> Sample_1305 Sample_1306 Sample_1307 Sample_1308 Sample_1309 Sample_1310 Sample_1311 Sample_1312
#> "0311" "0332" "011" "011" "011" "0332" "0311" "0311"
#> Sample_1313 Sample_1314 Sample_1315 Sample_1316 Sample_1317 Sample_1318 Sample_1319 Sample_1320
#> "011" "0313" "0313" "0311" "011" "0313" "0311" "011"
#> Sample_1321 Sample_1322 Sample_1323 Sample_1324 Sample_1325 Sample_1326 Sample_1327 Sample_1328
#> "0314" "0311" "0311" "0311" "0311" "0314" "0311" "0313"
#> Sample_1329 Sample_1330 Sample_1331 Sample_1332 Sample_1333 Sample_1334 Sample_1335 Sample_1336
#> "0314" "0314" "0313" "0314" "0314" "0313" "011" "011"
#> Sample_1337 Sample_1338 Sample_1339 Sample_1340 Sample_1341 Sample_1342 Sample_1343 Sample_1344
#> "0332" "011" "0332" "0311" "0314" "011" "011" "0314"
#> Sample_1345 Sample_1346 Sample_1347 Sample_1348 Sample_1349 Sample_1350 Sample_1351 Sample_1352
#> "0332" "0311" "0311" "011" "0314" "011" "0314" "011"
#> Sample_1353 Sample_1354 Sample_1355 Sample_1356 Sample_1357 Sample_1358 Sample_1359 Sample_1360
#> "0311" "011" "0314" "0314" "011" "011" "0314" "011"
#> Sample_1361 Sample_1362 Sample_1363 Sample_1364 Sample_1365 Sample_1366 Sample_1367 Sample_1368
#> "0314" "0314" "0314" "011" "0311" "0313" "011" "0313"
#> Sample_1369 Sample_1370 Sample_1371 Sample_1372 Sample_1373 Sample_1374 Sample_1375 Sample_1376
#> "0311" "0314" "011" "0314" "011" "011" "011" "0332"
#> Sample_1377 Sample_1378 Sample_1379 Sample_1380 Sample_1381 Sample_1382 Sample_1383 Sample_1384
#> "0311" "0311" "0311" "0311" "0314" "011" "0313" "0313"
#> Sample_1385 Sample_1386 Sample_1387 Sample_1388 Sample_1389 Sample_1390 Sample_1391 Sample_1392
#> "0313" "011" "0313" "011" "0313" "0313" "0313" "0313"
#> Sample_1393 Sample_1394 Sample_1395 Sample_1396 Sample_1397 Sample_1398 Sample_1399 Sample_1400
#> "0313" "0313" "0313" "0313" "0313" "011" "011" "0331"
#> Sample_1401 Sample_1402 Sample_1403 Sample_1404 Sample_1405 Sample_1406 Sample_1407 Sample_1408
#> "0313" "011" "0313" "0313" "0313" "011" "0313" "011"
#> Sample_1409 Sample_1410 Sample_1411 Sample_1412 Sample_1413 Sample_1414 Sample_1415 Sample_1416
#> "011" "0313" "0313" "0313" "0331" "0313" "0313" "0332"
#> Sample_1417 Sample_1418 Sample_1419 Sample_1420 Sample_1421 Sample_1422 Sample_1423 Sample_1424
#> "0313" "0313" "011" "0313" "0313" "0313" "011" "0313"
#> Sample_1425 Sample_1426 Sample_1427 Sample_1428 Sample_1429 Sample_1430 Sample_1431 Sample_1432
#> "011" "011" "011" "0313" "011" "011" "011" "011"
#> Sample_1433 Sample_1434 Sample_1435 Sample_1436 Sample_1437 Sample_1438 Sample_1439 Sample_1440
#> "011" "011" "011" "011" "011" "011" "011" "011"
#> Sample_1441 Sample_1442 Sample_1443 Sample_1444 Sample_1445 Sample_1446 Sample_1447 Sample_1448
#> "011" "0313" "0313" "011" "011" "0313" "0313" "011"
#> Sample_1449 Sample_1450 Sample_1451 Sample_1452 Sample_1453 Sample_1454 Sample_1455 Sample_1456
#> "011" "011" "011" "011" "011" "011" "011" "0313"
#> Sample_1457 Sample_1458 Sample_1459 Sample_1460 Sample_1461 Sample_1462 Sample_1463 Sample_1464
#> "0313" "011" "011" "0331" "0313" "011" "0314" "0314"
#> Sample_1465 Sample_1466 Sample_1467 Sample_1468 Sample_1469 Sample_1470 Sample_1471 Sample_1472
#> "011" "011" "011" "0313" "011" "011" "011" "011"
#> Sample_1473 Sample_1474 Sample_1475 Sample_1476 Sample_1477 Sample_1478 Sample_1479 Sample_1480
#> "011" "011" "011" "011" "011" "011" "011" "0314"
#> Sample_1481 Sample_1482 Sample_1483 Sample_1484 Sample_1485 Sample_1486 Sample_1487 Sample_1488
#> "011" "011" "011" "011" "0313" "0331" "011" "0313"
#> Sample_1489 Sample_1490 Sample_1491 Sample_1492 Sample_1493 Sample_1494 Sample_1495 Sample_1496
#> "011" "011" "011" "011" "011" "0332" "011" "011"
#> Sample_1497 Sample_1498 Sample_1499 Sample_1500 Sample_1501 Sample_1502 Sample_1503 Sample_1504
#> "011" "011" "011" "011" "011" "0313" "0313" "0332"
#> Sample_1505 Sample_1506 Sample_1507 Sample_1508 Sample_1509 Sample_1510 Sample_1511 Sample_1512
#> "0331" "0313" "0313" "0313" "011" "011" "0313" "0313"
#> Sample_1513 Sample_1514 Sample_1515 Sample_1516 Sample_1517 Sample_1518 Sample_1519 Sample_1520
#> "0313" "011" "0313" "011" "0222" "0222" "02211" "0233"
#> Sample_1521 Sample_1522 Sample_1523 Sample_1524 Sample_1525 Sample_1526 Sample_1527 Sample_1528
#> "0232" "013" "013" "0233" "0121" "0121" "0212" "0212"
#> Sample_1529 Sample_1530 Sample_1531 Sample_1532 Sample_1533 Sample_1534 Sample_1535 Sample_1536
#> "0212" "0233" "0232" "0232" "0211" "0212" "0222" "0222"
#> Sample_1537 Sample_1538 Sample_1539 Sample_1540 Sample_1541 Sample_1542 Sample_1543 Sample_1544
#> "02211" "0212" "0212" "013" "011" "0212" "0233" "013"
#> Sample_1545 Sample_1546 Sample_1547 Sample_1548 Sample_1549 Sample_1550 Sample_1551 Sample_1552
#> "011" "0211" "013" "0231" "03221" "0231" "0231" "0231"
#> Sample_1553 Sample_1554 Sample_1555 Sample_1556 Sample_1557 Sample_1558 Sample_1559 Sample_1560
#> "0321" "0222" "0231" "013" "013" "013" "0231" "013"
#> Sample_1561 Sample_1562 Sample_1563 Sample_1564 Sample_1565 Sample_1566 Sample_1567 Sample_1568
#> "0231" "0121" "013" "013" "013" "013" "013" "013"
#> Sample_1569 Sample_1570 Sample_1571 Sample_1572 Sample_1573 Sample_1574 Sample_1575 Sample_1576
#> "0312" "0212" "013" "013" "013" "0231" "0231" "013"
#> Sample_1577 Sample_1578 Sample_1579 Sample_1580 Sample_1581 Sample_1582 Sample_1583 Sample_1584
#> "013" "013" "013" "013" "03221" "0231" "013" "013"
#> Sample_1585 Sample_1586 Sample_1587 Sample_1588 Sample_1589 Sample_1590 Sample_1591 Sample_1592
#> "0231" "0231" "0231" "0222" "0231" "013" "013" "0231"
#> Sample_1593 Sample_1594 Sample_1595 Sample_1596 Sample_1597 Sample_1598 Sample_1599 Sample_1600
#> "0231" "0231" "0231" "0232" "03221" "0311" "0233" "013"
get_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 287))
#> Sample_1 Sample_2 Sample_3 Sample_4 Sample_5 Sample_6 Sample_7 Sample_8
#> "013" "02211" "02212" "0222" "02211" "02211" "02211" "0222"
#> Sample_9 Sample_10 Sample_11 Sample_12 Sample_13 Sample_14 Sample_15 Sample_16
#> "02212" "02211" "02212" "02211" "02211" "02211" "02211" "02212"
#> Sample_17 Sample_18 Sample_19 Sample_20 Sample_21 Sample_22 Sample_23 Sample_24
#> "0222" "0222" "0222" "0222" "0222" "0222" "0222" "02211"
#> Sample_25 Sample_26 Sample_27 Sample_28 Sample_29 Sample_30 Sample_31 Sample_32
#> "0222" "02211" "0222" "0222" "02212" "0222" "0233" "02212"
#> Sample_33 Sample_34 Sample_35 Sample_36 Sample_37 Sample_38 Sample_39 Sample_40
#> "02212" "02212" "02212" "02212" "02212" "02212" "02212" "02212"
#> Sample_41 Sample_42 Sample_43 Sample_44 Sample_45 Sample_46 Sample_47 Sample_48
#> "02212" "02212" "0233" "02212" "02212" "02212" "02212" "02212"
#> Sample_49 Sample_50 Sample_51 Sample_52 Sample_53 Sample_54 Sample_55 Sample_56
#> "02212" "02212" "02212" "02212" "0222" "0222" "02212" "02211"
#> Sample_57 Sample_58 Sample_59 Sample_60 Sample_61 Sample_62 Sample_63 Sample_64
#> "02211" "02211" "02212" "02211" "0222" "02211" "02211" "02211"
#> Sample_65 Sample_66 Sample_67 Sample_68 Sample_69 Sample_70 Sample_71 Sample_72
#> "02211" "02211" "0222" "02211" "02211" "0222" "0222" "0211"
#> Sample_73 Sample_74 Sample_75 Sample_76 Sample_77 Sample_78 Sample_79 Sample_80
#> "0233" "02211" "0222" "02211" "0222" "02211" "0222" "0222"
#> Sample_81 Sample_82 Sample_83 Sample_84 Sample_85 Sample_86 Sample_87 Sample_88
#> "02211" "0222" "02211" "02211" "0222" "02212" "0222" "0222"
#> Sample_89 Sample_90 Sample_91 Sample_92 Sample_93 Sample_94 Sample_95 Sample_96
#> "0222" "0222" "0222" "0222" "0222" "0232" "0222" "02211"
#> Sample_97 Sample_98 Sample_99 Sample_100 Sample_101 Sample_102 Sample_103 Sample_104
#> "02211" "02211" "02211" "0222" "0222" "02212" "0222" "0222"
#> Sample_105 Sample_106 Sample_107 Sample_108 Sample_109 Sample_110 Sample_111 Sample_112
#> "02211" "02211" "0222" "02211" "02211" "0222" "0212" "0222"
#> Sample_113 Sample_114 Sample_115 Sample_116 Sample_117 Sample_118 Sample_119 Sample_120
#> "0222" "0232" "0222" "0222" "0222" "0232" "0222" "02211"
#> Sample_121 Sample_122 Sample_123 Sample_124 Sample_125 Sample_126 Sample_127 Sample_128
#> "0121" "0222" "0232" "02211" "02211" "0222" "02211" "0222"
#> Sample_129 Sample_130 Sample_131 Sample_132 Sample_133 Sample_134 Sample_135 Sample_136
#> "02211" "0222" "0222" "02211" "0222" "02211" "0222" "02211"
#> Sample_137 Sample_138 Sample_139 Sample_140 Sample_141 Sample_142 Sample_143 Sample_144
#> "02211" "02211" "0222" "02211" "02211" "0222" "0222" "0232"
#> Sample_145 Sample_146 Sample_147 Sample_148 Sample_149 Sample_150 Sample_151 Sample_152
#> "0222" "0222" "0211" "0211" "0211" "0211" "0212" "0211"
#> Sample_153 Sample_154 Sample_155 Sample_156 Sample_157 Sample_158 Sample_159 Sample_160
#> "0211" "0211" "0211" "0211" "0211" "0211" "0212" "0211"
#> Sample_161 Sample_162 Sample_163 Sample_164 Sample_165 Sample_166 Sample_167 Sample_168
#> "0212" "0212" "0212" "0212" "0212" "0212" "0211" "0211"
#> Sample_169 Sample_170 Sample_171 Sample_172 Sample_173 Sample_174 Sample_175 Sample_176
#> "0212" "0211" "0232" "0212" "0233" "0212" "0211" "0211"
#> Sample_177 Sample_178 Sample_179 Sample_180 Sample_181 Sample_182 Sample_183 Sample_184
#> "0212" "0211" "0212" "0212" "0211" "0211" "0211" "0211"
#> Sample_185 Sample_186 Sample_187 Sample_188 Sample_189 Sample_190 Sample_191 Sample_192
#> "0211" "0211" "0211" "0212" "0212" "0211" "0212" "0212"
#> Sample_193 Sample_194 Sample_195 Sample_196 Sample_197 Sample_198 Sample_199 Sample_200
#> "0211" "0211" "0211" "0211" "02211" "02211" "02212" "02212"
#> Sample_201 Sample_202 Sample_203 Sample_204 Sample_205 Sample_206 Sample_207 Sample_208
#> "02212" "02212" "02212" "0233" "02212" "02212" "02212" "0322"
#> Sample_209 Sample_210 Sample_211 Sample_212 Sample_213 Sample_214 Sample_215 Sample_216
#> "0322" "0322" "0322" "0322" "0322" "013" "013" "0322"
#> Sample_217 Sample_218 Sample_219 Sample_220 Sample_221 Sample_222 Sample_223 Sample_224
#> "013" "0322" "0322" "0322" "0322" "0322" "0322" "013"
#> Sample_225 Sample_226 Sample_227 Sample_228 Sample_229 Sample_230 Sample_231 Sample_232
#> "0322" "013" "0322" "0322" "0322" "0322" "0322" "0322"
#> Sample_233 Sample_234 Sample_235 Sample_236 Sample_237 Sample_238 Sample_239 Sample_240
#> "0333" "0322" "0322" "013" "0322" "0322" "0322" "0322"
#> Sample_241 Sample_242 Sample_243 Sample_244 Sample_245 Sample_246 Sample_247 Sample_248
#> "013" "0322" "0321" "0322" "0322" "0322" "0322" "0322"
#> Sample_249 Sample_250 Sample_251 Sample_252 Sample_253 Sample_254 Sample_255 Sample_256
#> "0322" "0322" "0322" "0322" "0322" "0333" "0322" "0322"
#> Sample_257 Sample_258 Sample_259 Sample_260 Sample_261 Sample_262 Sample_263 Sample_264
#> "0322" "013" "0322" "0322" "0322" "0322" "0322" "013"
#> Sample_265 Sample_266 Sample_267 Sample_268 Sample_269 Sample_270 Sample_271 Sample_272
#> "0122" "0321" "0321" "0321" "0322" "0321" "0333" "0122"
#> Sample_273 Sample_274 Sample_275 Sample_276 Sample_277 Sample_278 Sample_279 Sample_280
#> "0321" "0321" "0122" "013" "0321" "0333" "0321" "0322"
#> Sample_281 Sample_282 Sample_283 Sample_284 Sample_285 Sample_286 Sample_287 Sample_288
#> "0321" "0321" "0322" "0321" "0321" "0321" "013" "0333"
#> Sample_289 Sample_290 Sample_291 Sample_292 Sample_293 Sample_294 Sample_295 Sample_296
#> "0333" "0321" "0321" "0321" "0322" "0321" "0321" "0321"
#> Sample_297 Sample_298 Sample_299 Sample_300 Sample_301 Sample_302 Sample_303 Sample_304
#> "0321" "0321" "0321" "0322" "0321" "0122" "0122" "0122"
#> Sample_305 Sample_306 Sample_307 Sample_308 Sample_309 Sample_310 Sample_311 Sample_312
#> "0122" "0321" "0321" "0122" "0122" "0122" "0122" "0122"
#> Sample_313 Sample_314 Sample_315 Sample_316 Sample_317 Sample_318 Sample_319 Sample_320
#> "0122" "0122" "0321" "0122" "0321" "0122" "0122" "0122"
#> Sample_321 Sample_322 Sample_323 Sample_324 Sample_325 Sample_326 Sample_327 Sample_328
#> "0121" "0321" "0122" "0122" "0122" "0121" "0122" "0122"
#> Sample_329 Sample_330 Sample_331 Sample_332 Sample_333 Sample_334 Sample_335 Sample_336
#> "0121" "0122" "0333" "0321" "0321" "0321" "0321" "0321"
#> Sample_337 Sample_338 Sample_339 Sample_340 Sample_341 Sample_342 Sample_343 Sample_344
#> "0321" "0321" "0333" "0321" "0321" "0321" "0321" "0321"
#> Sample_345 Sample_346 Sample_347 Sample_348 Sample_349 Sample_350 Sample_351 Sample_352
#> "0321" "0321" "0321" "0321" "0321" "013" "0122" "013"
#> Sample_353 Sample_354 Sample_355 Sample_356 Sample_357 Sample_358 Sample_359 Sample_360
#> "0321" "0321" "0122" "0321" "0321" "0122" "0321" "0122"
#> Sample_361 Sample_362 Sample_363 Sample_364 Sample_365 Sample_366 Sample_367 Sample_368
#> "0321" "0122" "0321" "0321" "013" "0321" "0321" "0122"
#> Sample_369 Sample_370 Sample_371 Sample_372 Sample_373 Sample_374 Sample_375 Sample_376
#> "0122" "0321" "0333" "013" "013" "0321" "0321" "0321"
#> Sample_377 Sample_378 Sample_379 Sample_380 Sample_381 Sample_382 Sample_383 Sample_384
#> "0122" "0321" "0321" "0321" "0122" "0321" "0321" "0321"
#> Sample_385 Sample_386 Sample_387 Sample_388 Sample_389 Sample_390 Sample_391 Sample_392
#> "0121" "0121" "0321" "0321" "013" "013" "0321" "0321"
#> Sample_393 Sample_394 Sample_395 Sample_396 Sample_397 Sample_398 Sample_399 Sample_400
#> "0122" "013" "0122" "013" "0122" "0122" "0321" "0321"
#> Sample_401 Sample_402 Sample_403 Sample_404 Sample_405 Sample_406 Sample_407 Sample_408
#> "0321" "0322" "013" "013" "013" "0321" "0321" "013"
#> Sample_409 Sample_410 Sample_411 Sample_412 Sample_413 Sample_414 Sample_415 Sample_416
#> "0122" "0321" "0321" "0122" "0321" "0321" "0333" "0122"
#> Sample_417 Sample_418 Sample_419 Sample_420 Sample_421 Sample_422 Sample_423 Sample_424
#> "0321" "0321" "0321" "0333" "0122" "0333" "0322" "0122"
#> Sample_425 Sample_426 Sample_427 Sample_428 Sample_429 Sample_430 Sample_431 Sample_432
#> "0122" "0322" "0122" "0122" "0322" "013" "0122" "0121"
#> Sample_433 Sample_434 Sample_435 Sample_436 Sample_437 Sample_438 Sample_439 Sample_440
#> "013" "0121" "0322" "0333" "0333" "013" "0121" "0122"
#> Sample_441 Sample_442 Sample_443 Sample_444 Sample_445 Sample_446 Sample_447 Sample_448
#> "0322" "0122" "013" "0321" "0121" "0122" "0322" "013"
#> Sample_449 Sample_450 Sample_451 Sample_452 Sample_453 Sample_454 Sample_455 Sample_456
#> "0322" "0321" "0122" "0121" "0322" "0321" "013" "0121"
#> Sample_457 Sample_458 Sample_459 Sample_460 Sample_461 Sample_462 Sample_463 Sample_464
#> "013" "0322" "0122" "0321" "0121" "0321" "013" "0121"
#> Sample_465 Sample_466 Sample_467 Sample_468 Sample_469 Sample_470 Sample_471 Sample_472
#> "0121" "0121" "0322" "0121" "0122" "0321" "0322" "0121"
#> Sample_473 Sample_474 Sample_475 Sample_476 Sample_477 Sample_478 Sample_479 Sample_480
#> "0321" "0121" "0121" "013" "013" "0314" "011" "0314"
#> Sample_481 Sample_482 Sample_483 Sample_484 Sample_485 Sample_486 Sample_487 Sample_488
#> "0313" "0314" "0314" "0314" "0314" "011" "011" "0331"
#> Sample_489 Sample_490 Sample_491 Sample_492 Sample_493 Sample_494 Sample_495 Sample_496
#> "0314" "011" "0314" "0314" "0314" "0314" "011" "0314"
#> Sample_497 Sample_498 Sample_499 Sample_500 Sample_501 Sample_502 Sample_503 Sample_504
#> "0314" "0314" "011" "011" "0314" "0314" "0314" "0314"
#> Sample_505 Sample_506 Sample_507 Sample_508 Sample_509 Sample_510 Sample_511 Sample_512
#> "011" "011" "011" "0314" "0314" "0314" "011" "0332"
#> Sample_513 Sample_514 Sample_515 Sample_516 Sample_517 Sample_518 Sample_519 Sample_520
#> "011" "0314" "011" "0211" "011" "0314" "0314" "011"
#> Sample_521 Sample_522 Sample_523 Sample_524 Sample_525 Sample_526 Sample_527 Sample_528
#> "011" "0314" "0314" "0314" "0314" "0314" "011" "0312"
#> Sample_529 Sample_530 Sample_531 Sample_532 Sample_533 Sample_534 Sample_535 Sample_536
#> "0331" "011" "0332" "011" "011" "0314" "011" "011"
#> Sample_537 Sample_538 Sample_539 Sample_540 Sample_541 Sample_542 Sample_543 Sample_544
#> "011" "0314" "011" "0314" "0314" "011" "011" "011"
#> Sample_545 Sample_546 Sample_547 Sample_548 Sample_549 Sample_550 Sample_551 Sample_552
#> "0331" "011" "011" "0314" "0314" "0314" "0314" "0314"
#> Sample_553 Sample_554 Sample_555 Sample_556 Sample_557 Sample_558 Sample_559 Sample_560
#> "0332" "0314" "0314" "0314" "011" "011" "011" "0314"
#> Sample_561 Sample_562 Sample_563 Sample_564 Sample_565 Sample_566 Sample_567 Sample_568
#> "0314" "0314" "0314" "0314" "011" "011" "011" "0314"
#> Sample_569 Sample_570 Sample_571 Sample_572 Sample_573 Sample_574 Sample_575 Sample_576
#> "011" "0322" "0322" "013" "0121" "0333" "0321" "0322"
#> Sample_577 Sample_578 Sample_579 Sample_580 Sample_581 Sample_582 Sample_583 Sample_584
#> "0122" "0321" "0321" "0321" "0321" "0321" "0321" "0321"
#> Sample_585 Sample_586 Sample_587 Sample_588 Sample_589 Sample_590 Sample_591 Sample_592
#> "0322" "02212" "02211" "0231" "0322" "013" "02211" "0231"
#> Sample_593 Sample_594 Sample_595 Sample_596 Sample_597 Sample_598 Sample_599 Sample_600
#> "02212" "0231" "0231" "0222" "0231" "0222" "0231" "0231"
#> Sample_601 Sample_602 Sample_603 Sample_604 Sample_605 Sample_606 Sample_607 Sample_608
#> "0222" "0231" "0222" "0222" "0222" "0222" "02211" "0222"
#> Sample_609 Sample_610 Sample_611 Sample_612 Sample_613 Sample_614 Sample_615 Sample_616
#> "0222" "0222" "0212" "0212" "0322" "013" "0322" "013"
#> Sample_617 Sample_618 Sample_619 Sample_620 Sample_621 Sample_622 Sample_623 Sample_624
#> "013" "0121" "013" "013" "0231" "013" "0322" "0322"
#> Sample_625 Sample_626 Sample_627 Sample_628 Sample_629 Sample_630 Sample_631 Sample_632
#> "0231" "013" "0231" "0231" "0231" "013" "0231" "013"
#> Sample_633 Sample_634 Sample_635 Sample_636 Sample_637 Sample_638 Sample_639 Sample_640
#> "013" "013" "013" "013" "013" "0231" "013" "0232"
#> Sample_641 Sample_642 Sample_643 Sample_644 Sample_645 Sample_646 Sample_647 Sample_648
#> "013" "0233" "013" "0321" "0232" "013" "013" "0232"
#> Sample_649 Sample_650 Sample_651 Sample_652 Sample_653 Sample_654 Sample_655 Sample_656
#> "0232" "0231" "02212" "02211" "02211" "02212" "02211" "02211"
#> Sample_657 Sample_658 Sample_659 Sample_660 Sample_661 Sample_662 Sample_663 Sample_664
#> "0222" "02212" "02211" "0222" "02211" "0222" "0222" "02211"
#> Sample_665 Sample_666 Sample_667 Sample_668 Sample_669 Sample_670 Sample_671 Sample_672
#> "0232" "0222" "0232" "0222" "0222" "0222" "02211" "0222"
#> Sample_673 Sample_674 Sample_675 Sample_676 Sample_677 Sample_678 Sample_679 Sample_680
#> "0222" "0222" "0222" "0222" "02212" "02212" "02211" "0222"
#> Sample_681 Sample_682 Sample_683 Sample_684 Sample_685 Sample_686 Sample_687 Sample_688
#> "0232" "0222" "0222" "0222" "0121" "0212" "0121" "0212"
#> Sample_689 Sample_690 Sample_691 Sample_692 Sample_693 Sample_694 Sample_695 Sample_696
#> "0222" "0212" "0222" "0121" "0222" "0222" "0212" "0211"
#> Sample_697 Sample_698 Sample_699 Sample_700 Sample_701 Sample_702 Sample_703 Sample_704
#> "0211" "0212" "0212" "0211" "0212" "0211" "0212" "02211"
#> Sample_705 Sample_706 Sample_707 Sample_708 Sample_709 Sample_710 Sample_711 Sample_712
#> "0222" "0211" "0211" "0211" "0211" "0232" "0212" "0211"
#> Sample_713 Sample_714 Sample_715 Sample_716 Sample_717 Sample_718 Sample_719 Sample_720
#> "0211" "0212" "0212" "0212" "0211" "0211" "0211" "0232"
#> Sample_721 Sample_722 Sample_723 Sample_724 Sample_725 Sample_726 Sample_727 Sample_728
#> "0211" "0211" "0212" "0211" "0212" "0211" "0211" "0212"
#> Sample_729 Sample_730 Sample_731 Sample_732 Sample_733 Sample_734 Sample_735 Sample_736
#> "0211" "0211" "0233" "0211" "0211" "0211" "0233" "0211"
#> Sample_737 Sample_738 Sample_739 Sample_740 Sample_741 Sample_742 Sample_743 Sample_744
#> "0121" "0232" "0211" "0212" "0211" "0212" "0211" "0211"
#> Sample_745 Sample_746 Sample_747 Sample_748 Sample_749 Sample_750 Sample_751 Sample_752
#> "0212" "0211" "0211" "0212" "0211" "0211" "0211" "0211"
#> Sample_753 Sample_754 Sample_755 Sample_756 Sample_757 Sample_758 Sample_759 Sample_760
#> "0212" "0212" "0211" "0211" "0211" "0211" "0212" "0211"
#> Sample_761 Sample_762 Sample_763 Sample_764 Sample_765 Sample_766 Sample_767 Sample_768
#> "0211" "0211" "0211" "0211" "0211" "0212" "0211" "0232"
#> Sample_769 Sample_770 Sample_771 Sample_772 Sample_773 Sample_774 Sample_775 Sample_776
#> "0212" "0211" "0211" "0212" "0211" "0211" "0212" "0211"
#> Sample_777 Sample_778 Sample_779 Sample_780 Sample_781 Sample_782 Sample_783 Sample_784
#> "0212" "0212" "0212" "0212" "0211" "0212" "0212" "0212"
#> Sample_785 Sample_786 Sample_787 Sample_788 Sample_789 Sample_790 Sample_791 Sample_792
#> "0212" "0212" "0211" "0212" "0212" "0211" "0211" "0211"
#> Sample_793 Sample_794 Sample_795 Sample_796 Sample_797 Sample_798 Sample_799 Sample_800
#> "0212" "0211" "0211" "0211" "0212" "0212" "0212" "0211"
#> Sample_801 Sample_802 Sample_803 Sample_804 Sample_805 Sample_806 Sample_807 Sample_808
#> "0211" "0212" "0211" "0212" "0211" "0211" "0211" "0211"
#> Sample_809 Sample_810 Sample_811 Sample_812 Sample_813 Sample_814 Sample_815 Sample_816
#> "0211" "0212" "0211" "0211" "0211" "0211" "0211" "0211"
#> Sample_817 Sample_818 Sample_819 Sample_820 Sample_821 Sample_822 Sample_823 Sample_824
#> "0212" "0211" "0212" "0233" "0212" "0211" "0212" "0212"
#> Sample_825 Sample_826 Sample_827 Sample_828 Sample_829 Sample_830 Sample_831 Sample_832
#> "0212" "0212" "0212" "0211" "0212" "0212" "0211" "0211"
#> Sample_833 Sample_834 Sample_835 Sample_836 Sample_837 Sample_838 Sample_839 Sample_840
#> "0211" "0212" "0212" "0233" "0211" "0211" "0212" "0211"
#> Sample_841 Sample_842 Sample_843 Sample_844 Sample_845 Sample_846 Sample_847 Sample_848
#> "0222" "02212" "02212" "02211" "02211" "02211" "0222" "0222"
#> Sample_849 Sample_850 Sample_851 Sample_852 Sample_853 Sample_854 Sample_855 Sample_856
#> "0222" "02211" "02211" "0222" "02211" "02211" "0212" "0212"
#> Sample_857 Sample_858 Sample_859 Sample_860 Sample_861 Sample_862 Sample_863 Sample_864
#> "0212" "0211" "0232" "0212" "0212" "0212" "0212" "0211"
#> Sample_865 Sample_866 Sample_867 Sample_868 Sample_869 Sample_870 Sample_871 Sample_872
#> "0233" "0212" "0212" "0211" "0212" "0212" "0212" "0212"
#> Sample_873 Sample_874 Sample_875 Sample_876 Sample_877 Sample_878 Sample_879 Sample_880
#> "0212" "0211" "0211" "0212" "0211" "0211" "0211" "0211"
#> Sample_881 Sample_882 Sample_883 Sample_884 Sample_885 Sample_886 Sample_887 Sample_888
#> "0212" "0233" "0211" "0211" "0212" "0211" "0211" "0211"
#> Sample_889 Sample_890 Sample_891 Sample_892 Sample_893 Sample_894 Sample_895 Sample_896
#> "0212" "0212" "0212" "0212" "0212" "0232" "0212" "0211"
#> Sample_897 Sample_898 Sample_899 Sample_900 Sample_901 Sample_902 Sample_903 Sample_904
#> "0212" "0212" "0211" "0233" "0211" "0212" "0212" "0211"
#> Sample_905 Sample_906 Sample_907 Sample_908 Sample_909 Sample_910 Sample_911 Sample_912
#> "0211" "0212" "0212" "0212" "0212" "0211" "0212" "0211"
#> Sample_913 Sample_914 Sample_915 Sample_916 Sample_917 Sample_918 Sample_919 Sample_920
#> "0212" "0212" "0212" "0211" "0212" "0212" "0212" "0212"
#> Sample_921 Sample_922 Sample_923 Sample_924 Sample_925 Sample_926 Sample_927 Sample_928
#> "0211" "0212" "0211" "0212" "0212" "0211" "0211" "0211"
#> Sample_929 Sample_930 Sample_931 Sample_932 Sample_933 Sample_934 Sample_935 Sample_936
#> "0212" "0211" "0211" "0212" "0212" "0212" "0212" "0212"
#> Sample_937 Sample_938 Sample_939 Sample_940 Sample_941 Sample_942 Sample_943 Sample_944
#> "0212" "0212" "0212" "0212" "0212" "0212" "0212" "0212"
#> Sample_945 Sample_946 Sample_947 Sample_948 Sample_949 Sample_950 Sample_951 Sample_952
#> "0211" "0211" "0211" "0321" "0321" "013" "0321" "0321"
#> Sample_953 Sample_954 Sample_955 Sample_956 Sample_957 Sample_958 Sample_959 Sample_960
#> "0321" "0322" "0321" "0321" "0321" "0321" "0321" "0321"
#> Sample_961 Sample_962 Sample_963 Sample_964 Sample_965 Sample_966 Sample_967 Sample_968
#> "0321" "0122" "0321" "0321" "0321" "0321" "0321" "0122"
#> Sample_969 Sample_970 Sample_971 Sample_972 Sample_973 Sample_974 Sample_975 Sample_976
#> "0321" "0321" "0321" "0321" "0321" "0321" "0333" "013"
#> Sample_977 Sample_978 Sample_979 Sample_980 Sample_981 Sample_982 Sample_983 Sample_984
#> "013" "0122" "0321" "013" "013" "0121" "0122" "0321"
#> Sample_985 Sample_986 Sample_987 Sample_988 Sample_989 Sample_990 Sample_991 Sample_992
#> "0122" "013" "0122" "0121" "0121" "0121" "0121" "0121"
#> Sample_993 Sample_994 Sample_995 Sample_996 Sample_997 Sample_998 Sample_999 Sample_1000
#> "0121" "0122" "0321" "013" "0321" "0321" "013" "0321"
#> Sample_1001 Sample_1002 Sample_1003 Sample_1004 Sample_1005 Sample_1006 Sample_1007 Sample_1008
#> "0321" "0321" "0321" "0333" "0321" "0321" "0122" "013"
#> Sample_1009 Sample_1010 Sample_1011 Sample_1012 Sample_1013 Sample_1014 Sample_1015 Sample_1016
#> "013" "013" "0321" "0322" "0321" "0121" "0121" "0121"
#> Sample_1017 Sample_1018 Sample_1019 Sample_1020 Sample_1021 Sample_1022 Sample_1023 Sample_1024
#> "0121" "0121" "0121" "0121" "0121" "0121" "0121" "0121"
#> Sample_1025 Sample_1026 Sample_1027 Sample_1028 Sample_1029 Sample_1030 Sample_1031 Sample_1032
#> "0121" "0121" "011" "011" "0312" "011" "0332" "0312"
#> Sample_1033 Sample_1034 Sample_1035 Sample_1036 Sample_1037 Sample_1038 Sample_1039 Sample_1040
#> "0312" "011" "0312" "011" "0312" "0312" "011" "0312"
#> Sample_1041 Sample_1042 Sample_1043 Sample_1044 Sample_1045 Sample_1046 Sample_1047 Sample_1048
#> "011" "011" "0312" "0312" "0332" "011" "0312" "0331"
#> Sample_1049 Sample_1050 Sample_1051 Sample_1052 Sample_1053 Sample_1054 Sample_1055 Sample_1056
#> "011" "011" "0312" "011" "0322" "0321" "0321" "013"
#> Sample_1057 Sample_1058 Sample_1059 Sample_1060 Sample_1061 Sample_1062 Sample_1063 Sample_1064
#> "0322" "0121" "013" "0321" "0322" "013" "0312" "0312"
#> Sample_1065 Sample_1066 Sample_1067 Sample_1068 Sample_1069 Sample_1070 Sample_1071 Sample_1072
#> "0312" "011" "0312" "0312" "0312" "011" "0312" "0312"
#> Sample_1073 Sample_1074 Sample_1075 Sample_1076 Sample_1077 Sample_1078 Sample_1079 Sample_1080
#> "011" "0312" "011" "011" "0312" "0312" "0312" "011"
#> Sample_1081 Sample_1082 Sample_1083 Sample_1084 Sample_1085 Sample_1086 Sample_1087 Sample_1088
#> "0312" "0312" "011" "0312" "0312" "0331" "011" "0331"
#> Sample_1089 Sample_1090 Sample_1091 Sample_1092 Sample_1093 Sample_1094 Sample_1095 Sample_1096
#> "0332" "011" "011" "0312" "011" "0312" "0312" "0312"
#> Sample_1097 Sample_1098 Sample_1099 Sample_1100 Sample_1101 Sample_1102 Sample_1103 Sample_1104
#> "011" "0312" "0312" "011" "0312" "011" "011" "011"
#> Sample_1105 Sample_1106 Sample_1107 Sample_1108 Sample_1109 Sample_1110 Sample_1111 Sample_1112
#> "011" "0312" "0312" "011" "0312" "011" "0312" "011"
#> Sample_1113 Sample_1114 Sample_1115 Sample_1116 Sample_1117 Sample_1118 Sample_1119 Sample_1120
#> "011" "011" "0312" "011" "0312" "011" "011" "0332"
#> Sample_1121 Sample_1122 Sample_1123 Sample_1124 Sample_1125 Sample_1126 Sample_1127 Sample_1128
#> "011" "0331" "0312" "0312" "0312" "0312" "0312" "0312"
#> Sample_1129 Sample_1130 Sample_1131 Sample_1132 Sample_1133 Sample_1134 Sample_1135 Sample_1136
#> "011" "011" "011" "0332" "011" "0331" "011" "011"
#> Sample_1137 Sample_1138 Sample_1139 Sample_1140 Sample_1141 Sample_1142 Sample_1143 Sample_1144
#> "011" "011" "0312" "0331" "0312" "011" "011" "011"
#> Sample_1145 Sample_1146 Sample_1147 Sample_1148 Sample_1149 Sample_1150 Sample_1151 Sample_1152
#> "0312" "011" "011" "011" "011" "0312" "011" "0312"
#> Sample_1153 Sample_1154 Sample_1155 Sample_1156 Sample_1157 Sample_1158 Sample_1159 Sample_1160
#> "011" "011" "011" "0312" "0312" "011" "011" "011"
#> Sample_1161 Sample_1162 Sample_1163 Sample_1164 Sample_1165 Sample_1166 Sample_1167 Sample_1168
#> "0312" "011" "0312" "0312" "011" "011" "011" "011"
#> Sample_1169 Sample_1170 Sample_1171 Sample_1172 Sample_1173 Sample_1174 Sample_1175 Sample_1176
#> "0312" "0312" "011" "011" "011" "011" "0312" "011"
#> Sample_1177 Sample_1178 Sample_1179 Sample_1180 Sample_1181 Sample_1182 Sample_1183 Sample_1184
#> "011" "011" "011" "0312" "011" "011" "0331" "0331"
#> Sample_1185 Sample_1186 Sample_1187 Sample_1188 Sample_1189 Sample_1190 Sample_1191 Sample_1192
#> "011" "011" "011" "011" "0321" "0322" "0322" "0122"
#> Sample_1193 Sample_1194 Sample_1195 Sample_1196 Sample_1197 Sample_1198 Sample_1199 Sample_1200
#> "0322" "0322" "0321" "0321" "0322" "0322" "0322" "0321"
#> Sample_1201 Sample_1202 Sample_1203 Sample_1204 Sample_1205 Sample_1206 Sample_1207 Sample_1208
#> "0321" "0321" "0322" "0322" "0322" "0321" "0322" "013"
#> Sample_1209 Sample_1210 Sample_1211 Sample_1212 Sample_1213 Sample_1214 Sample_1215 Sample_1216
#> "0321" "0322" "0321" "0321" "0321" "0322" "0322" "0122"
#> Sample_1217 Sample_1218 Sample_1219 Sample_1220 Sample_1221 Sample_1222 Sample_1223 Sample_1224
#> "013" "013" "0322" "0321" "0322" "011" "0311" "0311"
#> Sample_1225 Sample_1226 Sample_1227 Sample_1228 Sample_1229 Sample_1230 Sample_1231 Sample_1232
#> "011" "0311" "011" "011" "011" "0311" "0311" "0311"
#> Sample_1233 Sample_1234 Sample_1235 Sample_1236 Sample_1237 Sample_1238 Sample_1239 Sample_1240
#> "0311" "011" "0311" "011" "0311" "0311" "011" "011"
#> Sample_1241 Sample_1242 Sample_1243 Sample_1244 Sample_1245 Sample_1246 Sample_1247 Sample_1248
#> "011" "011" "0311" "0311" "0311" "0311" "0311" "0311"
#> Sample_1249 Sample_1250 Sample_1251 Sample_1252 Sample_1253 Sample_1254 Sample_1255 Sample_1256
#> "011" "0311" "0311" "0311" "0311" "011" "0332" "0311"
#> Sample_1257 Sample_1258 Sample_1259 Sample_1260 Sample_1261 Sample_1262 Sample_1263 Sample_1264
#> "0311" "0311" "0311" "0311" "0311" "0311" "0311" "011"
#> Sample_1265 Sample_1266 Sample_1267 Sample_1268 Sample_1269 Sample_1270 Sample_1271 Sample_1272
#> "0311" "011" "011" "0332" "0311" "0311" "0311" "011"
#> Sample_1273 Sample_1274 Sample_1275 Sample_1276 Sample_1277 Sample_1278 Sample_1279 Sample_1280
#> "0311" "0311" "0311" "0311" "0311" "0311" "011" "011"
#> Sample_1281 Sample_1282 Sample_1283 Sample_1284 Sample_1285 Sample_1286 Sample_1287 Sample_1288
#> "0311" "0311" "011" "0311" "011" "0311" "0311" "0311"
#> Sample_1289 Sample_1290 Sample_1291 Sample_1292 Sample_1293 Sample_1294 Sample_1295 Sample_1296
#> "0311" "0311" "0311" "0311" "0311" "0311" "0311" "0331"
#> Sample_1297 Sample_1298 Sample_1299 Sample_1300 Sample_1301 Sample_1302 Sample_1303 Sample_1304
#> "0311" "0311" "011" "0311" "0311" "0311" "0311" "0311"
#> Sample_1305 Sample_1306 Sample_1307 Sample_1308 Sample_1309 Sample_1310 Sample_1311 Sample_1312
#> "0311" "0332" "011" "011" "011" "0332" "0311" "0311"
#> Sample_1313 Sample_1314 Sample_1315 Sample_1316 Sample_1317 Sample_1318 Sample_1319 Sample_1320
#> "011" "0313" "0313" "0311" "011" "0313" "0311" "011"
#> Sample_1321 Sample_1322 Sample_1323 Sample_1324 Sample_1325 Sample_1326 Sample_1327 Sample_1328
#> "0314" "0311" "0311" "0311" "0311" "0314" "0311" "0313"
#> Sample_1329 Sample_1330 Sample_1331 Sample_1332 Sample_1333 Sample_1334 Sample_1335 Sample_1336
#> "0314" "0314" "0313" "0314" "0314" "0313" "011" "011"
#> Sample_1337 Sample_1338 Sample_1339 Sample_1340 Sample_1341 Sample_1342 Sample_1343 Sample_1344
#> "0332" "011" "0332" "0311" "0314" "011" "011" "0314"
#> Sample_1345 Sample_1346 Sample_1347 Sample_1348 Sample_1349 Sample_1350 Sample_1351 Sample_1352
#> "0332" "0311" "0311" "011" "0314" "011" "0314" "011"
#> Sample_1353 Sample_1354 Sample_1355 Sample_1356 Sample_1357 Sample_1358 Sample_1359 Sample_1360
#> "0311" "011" "0314" "0314" "011" "011" "0314" "011"
#> Sample_1361 Sample_1362 Sample_1363 Sample_1364 Sample_1365 Sample_1366 Sample_1367 Sample_1368
#> "0314" "0314" "0314" "011" "0311" "0313" "011" "0313"
#> Sample_1369 Sample_1370 Sample_1371 Sample_1372 Sample_1373 Sample_1374 Sample_1375 Sample_1376
#> "0311" "0314" "011" "0314" "011" "011" "011" "0332"
#> Sample_1377 Sample_1378 Sample_1379 Sample_1380 Sample_1381 Sample_1382 Sample_1383 Sample_1384
#> "0311" "0311" "0311" "0311" "0314" "011" "0313" "0313"
#> Sample_1385 Sample_1386 Sample_1387 Sample_1388 Sample_1389 Sample_1390 Sample_1391 Sample_1392
#> "0313" "011" "0313" "011" "0313" "0313" "0313" "0313"
#> Sample_1393 Sample_1394 Sample_1395 Sample_1396 Sample_1397 Sample_1398 Sample_1399 Sample_1400
#> "0313" "0313" "0313" "0313" "0313" "011" "011" "0331"
#> Sample_1401 Sample_1402 Sample_1403 Sample_1404 Sample_1405 Sample_1406 Sample_1407 Sample_1408
#> "0313" "011" "0313" "0313" "0313" "011" "0313" "011"
#> Sample_1409 Sample_1410 Sample_1411 Sample_1412 Sample_1413 Sample_1414 Sample_1415 Sample_1416
#> "011" "0313" "0313" "0313" "0331" "0313" "0313" "0332"
#> Sample_1417 Sample_1418 Sample_1419 Sample_1420 Sample_1421 Sample_1422 Sample_1423 Sample_1424
#> "0313" "0313" "011" "0313" "0313" "0313" "011" "0313"
#> Sample_1425 Sample_1426 Sample_1427 Sample_1428 Sample_1429 Sample_1430 Sample_1431 Sample_1432
#> "011" "011" "011" "0313" "011" "011" "011" "011"
#> Sample_1433 Sample_1434 Sample_1435 Sample_1436 Sample_1437 Sample_1438 Sample_1439 Sample_1440
#> "011" "011" "011" "011" "011" "011" "011" "011"
#> Sample_1441 Sample_1442 Sample_1443 Sample_1444 Sample_1445 Sample_1446 Sample_1447 Sample_1448
#> "011" "0313" "0313" "011" "011" "0313" "0313" "011"
#> Sample_1449 Sample_1450 Sample_1451 Sample_1452 Sample_1453 Sample_1454 Sample_1455 Sample_1456
#> "011" "011" "011" "011" "011" "011" "011" "0313"
#> Sample_1457 Sample_1458 Sample_1459 Sample_1460 Sample_1461 Sample_1462 Sample_1463 Sample_1464
#> "0313" "011" "011" "0331" "0313" "011" "0314" "0314"
#> Sample_1465 Sample_1466 Sample_1467 Sample_1468 Sample_1469 Sample_1470 Sample_1471 Sample_1472
#> "011" "011" "011" "0313" "011" "011" "011" "011"
#> Sample_1473 Sample_1474 Sample_1475 Sample_1476 Sample_1477 Sample_1478 Sample_1479 Sample_1480
#> "011" "011" "011" "011" "011" "011" "011" "0314"
#> Sample_1481 Sample_1482 Sample_1483 Sample_1484 Sample_1485 Sample_1486 Sample_1487 Sample_1488
#> "011" "011" "011" "011" "0313" "0331" "011" "0313"
#> Sample_1489 Sample_1490 Sample_1491 Sample_1492 Sample_1493 Sample_1494 Sample_1495 Sample_1496
#> "011" "011" "011" "011" "011" "0332" "011" "011"
#> Sample_1497 Sample_1498 Sample_1499 Sample_1500 Sample_1501 Sample_1502 Sample_1503 Sample_1504
#> "011" "011" "011" "011" "011" "0313" "0313" "0332"
#> Sample_1505 Sample_1506 Sample_1507 Sample_1508 Sample_1509 Sample_1510 Sample_1511 Sample_1512
#> "0331" "0313" "0313" "0313" "011" "011" "0313" "0313"
#> Sample_1513 Sample_1514 Sample_1515 Sample_1516 Sample_1517 Sample_1518 Sample_1519 Sample_1520
#> "0313" "011" "0313" "011" "0222" "0222" "02211" "0233"
#> Sample_1521 Sample_1522 Sample_1523 Sample_1524 Sample_1525 Sample_1526 Sample_1527 Sample_1528
#> "0232" "013" "013" "0233" "0121" "0121" "0212" "0212"
#> Sample_1529 Sample_1530 Sample_1531 Sample_1532 Sample_1533 Sample_1534 Sample_1535 Sample_1536
#> "0212" "0233" "0232" "0232" "0211" "0212" "0222" "0222"
#> Sample_1537 Sample_1538 Sample_1539 Sample_1540 Sample_1541 Sample_1542 Sample_1543 Sample_1544
#> "02211" "0212" "0212" "013" "011" "0212" "0233" "013"
#> Sample_1545 Sample_1546 Sample_1547 Sample_1548 Sample_1549 Sample_1550 Sample_1551 Sample_1552
#> "011" "0211" "013" "0231" "0322" "0231" "0231" "0231"
#> Sample_1553 Sample_1554 Sample_1555 Sample_1556 Sample_1557 Sample_1558 Sample_1559 Sample_1560
#> "0321" "0222" "0231" "013" "013" "013" "0231" "013"
#> Sample_1561 Sample_1562 Sample_1563 Sample_1564 Sample_1565 Sample_1566 Sample_1567 Sample_1568
#> "0231" "0121" "013" "013" "013" "013" "013" "013"
#> Sample_1569 Sample_1570 Sample_1571 Sample_1572 Sample_1573 Sample_1574 Sample_1575 Sample_1576
#> "0312" "0212" "013" "013" "013" "0231" "0231" "013"
#> Sample_1577 Sample_1578 Sample_1579 Sample_1580 Sample_1581 Sample_1582 Sample_1583 Sample_1584
#> "013" "013" "013" "013" "0322" "0231" "013" "013"
#> Sample_1585 Sample_1586 Sample_1587 Sample_1588 Sample_1589 Sample_1590 Sample_1591 Sample_1592
#> "0231" "0231" "0231" "0222" "0231" "013" "013" "0231"
#> Sample_1593 Sample_1594 Sample_1595 Sample_1596 Sample_1597 Sample_1598 Sample_1599 Sample_1600
#> "0231" "0231" "0231" "0232" "0322" "0311" "0233" "013"
get_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 306))
#> Sample_1 Sample_2 Sample_3 Sample_4 Sample_5 Sample_6 Sample_7 Sample_8
#> "013" "0221" "0221" "0222" "0221" "0221" "0221" "0222"
#> Sample_9 Sample_10 Sample_11 Sample_12 Sample_13 Sample_14 Sample_15 Sample_16
#> "0221" "0221" "0221" "0221" "0221" "0221" "0221" "0221"
#> Sample_17 Sample_18 Sample_19 Sample_20 Sample_21 Sample_22 Sample_23 Sample_24
#> "0222" "0222" "0222" "0222" "0222" "0222" "0222" "0221"
#> Sample_25 Sample_26 Sample_27 Sample_28 Sample_29 Sample_30 Sample_31 Sample_32
#> "0222" "0221" "0222" "0222" "0221" "0222" "0233" "0221"
#> Sample_33 Sample_34 Sample_35 Sample_36 Sample_37 Sample_38 Sample_39 Sample_40
#> "0221" "0221" "0221" "0221" "0221" "0221" "0221" "0221"
#> Sample_41 Sample_42 Sample_43 Sample_44 Sample_45 Sample_46 Sample_47 Sample_48
#> "0221" "0221" "0233" "0221" "0221" "0221" "0221" "0221"
#> Sample_49 Sample_50 Sample_51 Sample_52 Sample_53 Sample_54 Sample_55 Sample_56
#> "0221" "0221" "0221" "0221" "0222" "0222" "0221" "0221"
#> Sample_57 Sample_58 Sample_59 Sample_60 Sample_61 Sample_62 Sample_63 Sample_64
#> "0221" "0221" "0221" "0221" "0222" "0221" "0221" "0221"
#> Sample_65 Sample_66 Sample_67 Sample_68 Sample_69 Sample_70 Sample_71 Sample_72
#> "0221" "0221" "0222" "0221" "0221" "0222" "0222" "0211"
#> Sample_73 Sample_74 Sample_75 Sample_76 Sample_77 Sample_78 Sample_79 Sample_80
#> "0233" "0221" "0222" "0221" "0222" "0221" "0222" "0222"
#> Sample_81 Sample_82 Sample_83 Sample_84 Sample_85 Sample_86 Sample_87 Sample_88
#> "0221" "0222" "0221" "0221" "0222" "0221" "0222" "0222"
#> Sample_89 Sample_90 Sample_91 Sample_92 Sample_93 Sample_94 Sample_95 Sample_96
#> "0222" "0222" "0222" "0222" "0222" "0232" "0222" "0221"
#> Sample_97 Sample_98 Sample_99 Sample_100 Sample_101 Sample_102 Sample_103 Sample_104
#> "0221" "0221" "0221" "0222" "0222" "0221" "0222" "0222"
#> Sample_105 Sample_106 Sample_107 Sample_108 Sample_109 Sample_110 Sample_111 Sample_112
#> "0221" "0221" "0222" "0221" "0221" "0222" "0212" "0222"
#> Sample_113 Sample_114 Sample_115 Sample_116 Sample_117 Sample_118 Sample_119 Sample_120
#> "0222" "0232" "0222" "0222" "0222" "0232" "0222" "0221"
#> Sample_121 Sample_122 Sample_123 Sample_124 Sample_125 Sample_126 Sample_127 Sample_128
#> "0121" "0222" "0232" "0221" "0221" "0222" "0221" "0222"
#> Sample_129 Sample_130 Sample_131 Sample_132 Sample_133 Sample_134 Sample_135 Sample_136
#> "0221" "0222" "0222" "0221" "0222" "0221" "0222" "0221"
#> Sample_137 Sample_138 Sample_139 Sample_140 Sample_141 Sample_142 Sample_143 Sample_144
#> "0221" "0221" "0222" "0221" "0221" "0222" "0222" "0232"
#> Sample_145 Sample_146 Sample_147 Sample_148 Sample_149 Sample_150 Sample_151 Sample_152
#> "0222" "0222" "0211" "0211" "0211" "0211" "0212" "0211"
#> Sample_153 Sample_154 Sample_155 Sample_156 Sample_157 Sample_158 Sample_159 Sample_160
#> "0211" "0211" "0211" "0211" "0211" "0211" "0212" "0211"
#> Sample_161 Sample_162 Sample_163 Sample_164 Sample_165 Sample_166 Sample_167 Sample_168
#> "0212" "0212" "0212" "0212" "0212" "0212" "0211" "0211"
#> Sample_169 Sample_170 Sample_171 Sample_172 Sample_173 Sample_174 Sample_175 Sample_176
#> "0212" "0211" "0232" "0212" "0233" "0212" "0211" "0211"
#> Sample_177 Sample_178 Sample_179 Sample_180 Sample_181 Sample_182 Sample_183 Sample_184
#> "0212" "0211" "0212" "0212" "0211" "0211" "0211" "0211"
#> Sample_185 Sample_186 Sample_187 Sample_188 Sample_189 Sample_190 Sample_191 Sample_192
#> "0211" "0211" "0211" "0212" "0212" "0211" "0212" "0212"
#> Sample_193 Sample_194 Sample_195 Sample_196 Sample_197 Sample_198 Sample_199 Sample_200
#> "0211" "0211" "0211" "0211" "0221" "0221" "0221" "0221"
#> Sample_201 Sample_202 Sample_203 Sample_204 Sample_205 Sample_206 Sample_207 Sample_208
#> "0221" "0221" "0221" "0233" "0221" "0221" "0221" "0322"
#> Sample_209 Sample_210 Sample_211 Sample_212 Sample_213 Sample_214 Sample_215 Sample_216
#> "0322" "0322" "0322" "0322" "0322" "013" "013" "0322"
#> Sample_217 Sample_218 Sample_219 Sample_220 Sample_221 Sample_222 Sample_223 Sample_224
#> "013" "0322" "0322" "0322" "0322" "0322" "0322" "013"
#> Sample_225 Sample_226 Sample_227 Sample_228 Sample_229 Sample_230 Sample_231 Sample_232
#> "0322" "013" "0322" "0322" "0322" "0322" "0322" "0322"
#> Sample_233 Sample_234 Sample_235 Sample_236 Sample_237 Sample_238 Sample_239 Sample_240
#> "0333" "0322" "0322" "013" "0322" "0322" "0322" "0322"
#> Sample_241 Sample_242 Sample_243 Sample_244 Sample_245 Sample_246 Sample_247 Sample_248
#> "013" "0322" "0321" "0322" "0322" "0322" "0322" "0322"
#> Sample_249 Sample_250 Sample_251 Sample_252 Sample_253 Sample_254 Sample_255 Sample_256
#> "0322" "0322" "0322" "0322" "0322" "0333" "0322" "0322"
#> Sample_257 Sample_258 Sample_259 Sample_260 Sample_261 Sample_262 Sample_263 Sample_264
#> "0322" "013" "0322" "0322" "0322" "0322" "0322" "013"
#> Sample_265 Sample_266 Sample_267 Sample_268 Sample_269 Sample_270 Sample_271 Sample_272
#> "0122" "0321" "0321" "0321" "0322" "0321" "0333" "0122"
#> Sample_273 Sample_274 Sample_275 Sample_276 Sample_277 Sample_278 Sample_279 Sample_280
#> "0321" "0321" "0122" "013" "0321" "0333" "0321" "0322"
#> Sample_281 Sample_282 Sample_283 Sample_284 Sample_285 Sample_286 Sample_287 Sample_288
#> "0321" "0321" "0322" "0321" "0321" "0321" "013" "0333"
#> Sample_289 Sample_290 Sample_291 Sample_292 Sample_293 Sample_294 Sample_295 Sample_296
#> "0333" "0321" "0321" "0321" "0322" "0321" "0321" "0321"
#> Sample_297 Sample_298 Sample_299 Sample_300 Sample_301 Sample_302 Sample_303 Sample_304
#> "0321" "0321" "0321" "0322" "0321" "0122" "0122" "0122"
#> Sample_305 Sample_306 Sample_307 Sample_308 Sample_309 Sample_310 Sample_311 Sample_312
#> "0122" "0321" "0321" "0122" "0122" "0122" "0122" "0122"
#> Sample_313 Sample_314 Sample_315 Sample_316 Sample_317 Sample_318 Sample_319 Sample_320
#> "0122" "0122" "0321" "0122" "0321" "0122" "0122" "0122"
#> Sample_321 Sample_322 Sample_323 Sample_324 Sample_325 Sample_326 Sample_327 Sample_328
#> "0121" "0321" "0122" "0122" "0122" "0121" "0122" "0122"
#> Sample_329 Sample_330 Sample_331 Sample_332 Sample_333 Sample_334 Sample_335 Sample_336
#> "0121" "0122" "0333" "0321" "0321" "0321" "0321" "0321"
#> Sample_337 Sample_338 Sample_339 Sample_340 Sample_341 Sample_342 Sample_343 Sample_344
#> "0321" "0321" "0333" "0321" "0321" "0321" "0321" "0321"
#> Sample_345 Sample_346 Sample_347 Sample_348 Sample_349 Sample_350 Sample_351 Sample_352
#> "0321" "0321" "0321" "0321" "0321" "013" "0122" "013"
#> Sample_353 Sample_354 Sample_355 Sample_356 Sample_357 Sample_358 Sample_359 Sample_360
#> "0321" "0321" "0122" "0321" "0321" "0122" "0321" "0122"
#> Sample_361 Sample_362 Sample_363 Sample_364 Sample_365 Sample_366 Sample_367 Sample_368
#> "0321" "0122" "0321" "0321" "013" "0321" "0321" "0122"
#> Sample_369 Sample_370 Sample_371 Sample_372 Sample_373 Sample_374 Sample_375 Sample_376
#> "0122" "0321" "0333" "013" "013" "0321" "0321" "0321"
#> Sample_377 Sample_378 Sample_379 Sample_380 Sample_381 Sample_382 Sample_383 Sample_384
#> "0122" "0321" "0321" "0321" "0122" "0321" "0321" "0321"
#> Sample_385 Sample_386 Sample_387 Sample_388 Sample_389 Sample_390 Sample_391 Sample_392
#> "0121" "0121" "0321" "0321" "013" "013" "0321" "0321"
#> Sample_393 Sample_394 Sample_395 Sample_396 Sample_397 Sample_398 Sample_399 Sample_400
#> "0122" "013" "0122" "013" "0122" "0122" "0321" "0321"
#> Sample_401 Sample_402 Sample_403 Sample_404 Sample_405 Sample_406 Sample_407 Sample_408
#> "0321" "0322" "013" "013" "013" "0321" "0321" "013"
#> Sample_409 Sample_410 Sample_411 Sample_412 Sample_413 Sample_414 Sample_415 Sample_416
#> "0122" "0321" "0321" "0122" "0321" "0321" "0333" "0122"
#> Sample_417 Sample_418 Sample_419 Sample_420 Sample_421 Sample_422 Sample_423 Sample_424
#> "0321" "0321" "0321" "0333" "0122" "0333" "0322" "0122"
#> Sample_425 Sample_426 Sample_427 Sample_428 Sample_429 Sample_430 Sample_431 Sample_432
#> "0122" "0322" "0122" "0122" "0322" "013" "0122" "0121"
#> Sample_433 Sample_434 Sample_435 Sample_436 Sample_437 Sample_438 Sample_439 Sample_440
#> "013" "0121" "0322" "0333" "0333" "013" "0121" "0122"
#> Sample_441 Sample_442 Sample_443 Sample_444 Sample_445 Sample_446 Sample_447 Sample_448
#> "0322" "0122" "013" "0321" "0121" "0122" "0322" "013"
#> Sample_449 Sample_450 Sample_451 Sample_452 Sample_453 Sample_454 Sample_455 Sample_456
#> "0322" "0321" "0122" "0121" "0322" "0321" "013" "0121"
#> Sample_457 Sample_458 Sample_459 Sample_460 Sample_461 Sample_462 Sample_463 Sample_464
#> "013" "0322" "0122" "0321" "0121" "0321" "013" "0121"
#> Sample_465 Sample_466 Sample_467 Sample_468 Sample_469 Sample_470 Sample_471 Sample_472
#> "0121" "0121" "0322" "0121" "0122" "0321" "0322" "0121"
#> Sample_473 Sample_474 Sample_475 Sample_476 Sample_477 Sample_478 Sample_479 Sample_480
#> "0321" "0121" "0121" "013" "013" "0314" "011" "0314"
#> Sample_481 Sample_482 Sample_483 Sample_484 Sample_485 Sample_486 Sample_487 Sample_488
#> "0313" "0314" "0314" "0314" "0314" "011" "011" "0331"
#> Sample_489 Sample_490 Sample_491 Sample_492 Sample_493 Sample_494 Sample_495 Sample_496
#> "0314" "011" "0314" "0314" "0314" "0314" "011" "0314"
#> Sample_497 Sample_498 Sample_499 Sample_500 Sample_501 Sample_502 Sample_503 Sample_504
#> "0314" "0314" "011" "011" "0314" "0314" "0314" "0314"
#> Sample_505 Sample_506 Sample_507 Sample_508 Sample_509 Sample_510 Sample_511 Sample_512
#> "011" "011" "011" "0314" "0314" "0314" "011" "0332"
#> Sample_513 Sample_514 Sample_515 Sample_516 Sample_517 Sample_518 Sample_519 Sample_520
#> "011" "0314" "011" "0211" "011" "0314" "0314" "011"
#> Sample_521 Sample_522 Sample_523 Sample_524 Sample_525 Sample_526 Sample_527 Sample_528
#> "011" "0314" "0314" "0314" "0314" "0314" "011" "0312"
#> Sample_529 Sample_530 Sample_531 Sample_532 Sample_533 Sample_534 Sample_535 Sample_536
#> "0331" "011" "0332" "011" "011" "0314" "011" "011"
#> Sample_537 Sample_538 Sample_539 Sample_540 Sample_541 Sample_542 Sample_543 Sample_544
#> "011" "0314" "011" "0314" "0314" "011" "011" "011"
#> Sample_545 Sample_546 Sample_547 Sample_548 Sample_549 Sample_550 Sample_551 Sample_552
#> "0331" "011" "011" "0314" "0314" "0314" "0314" "0314"
#> Sample_553 Sample_554 Sample_555 Sample_556 Sample_557 Sample_558 Sample_559 Sample_560
#> "0332" "0314" "0314" "0314" "011" "011" "011" "0314"
#> Sample_561 Sample_562 Sample_563 Sample_564 Sample_565 Sample_566 Sample_567 Sample_568
#> "0314" "0314" "0314" "0314" "011" "011" "011" "0314"
#> Sample_569 Sample_570 Sample_571 Sample_572 Sample_573 Sample_574 Sample_575 Sample_576
#> "011" "0322" "0322" "013" "0121" "0333" "0321" "0322"
#> Sample_577 Sample_578 Sample_579 Sample_580 Sample_581 Sample_582 Sample_583 Sample_584
#> "0122" "0321" "0321" "0321" "0321" "0321" "0321" "0321"
#> Sample_585 Sample_586 Sample_587 Sample_588 Sample_589 Sample_590 Sample_591 Sample_592
#> "0322" "0221" "0221" "0231" "0322" "013" "0221" "0231"
#> Sample_593 Sample_594 Sample_595 Sample_596 Sample_597 Sample_598 Sample_599 Sample_600
#> "0221" "0231" "0231" "0222" "0231" "0222" "0231" "0231"
#> Sample_601 Sample_602 Sample_603 Sample_604 Sample_605 Sample_606 Sample_607 Sample_608
#> "0222" "0231" "0222" "0222" "0222" "0222" "0221" "0222"
#> Sample_609 Sample_610 Sample_611 Sample_612 Sample_613 Sample_614 Sample_615 Sample_616
#> "0222" "0222" "0212" "0212" "0322" "013" "0322" "013"
#> Sample_617 Sample_618 Sample_619 Sample_620 Sample_621 Sample_622 Sample_623 Sample_624
#> "013" "0121" "013" "013" "0231" "013" "0322" "0322"
#> Sample_625 Sample_626 Sample_627 Sample_628 Sample_629 Sample_630 Sample_631 Sample_632
#> "0231" "013" "0231" "0231" "0231" "013" "0231" "013"
#> Sample_633 Sample_634 Sample_635 Sample_636 Sample_637 Sample_638 Sample_639 Sample_640
#> "013" "013" "013" "013" "013" "0231" "013" "0232"
#> Sample_641 Sample_642 Sample_643 Sample_644 Sample_645 Sample_646 Sample_647 Sample_648
#> "013" "0233" "013" "0321" "0232" "013" "013" "0232"
#> Sample_649 Sample_650 Sample_651 Sample_652 Sample_653 Sample_654 Sample_655 Sample_656
#> "0232" "0231" "0221" "0221" "0221" "0221" "0221" "0221"
#> Sample_657 Sample_658 Sample_659 Sample_660 Sample_661 Sample_662 Sample_663 Sample_664
#> "0222" "0221" "0221" "0222" "0221" "0222" "0222" "0221"
#> Sample_665 Sample_666 Sample_667 Sample_668 Sample_669 Sample_670 Sample_671 Sample_672
#> "0232" "0222" "0232" "0222" "0222" "0222" "0221" "0222"
#> Sample_673 Sample_674 Sample_675 Sample_676 Sample_677 Sample_678 Sample_679 Sample_680
#> "0222" "0222" "0222" "0222" "0221" "0221" "0221" "0222"
#> Sample_681 Sample_682 Sample_683 Sample_684 Sample_685 Sample_686 Sample_687 Sample_688
#> "0232" "0222" "0222" "0222" "0121" "0212" "0121" "0212"
#> Sample_689 Sample_690 Sample_691 Sample_692 Sample_693 Sample_694 Sample_695 Sample_696
#> "0222" "0212" "0222" "0121" "0222" "0222" "0212" "0211"
#> Sample_697 Sample_698 Sample_699 Sample_700 Sample_701 Sample_702 Sample_703 Sample_704
#> "0211" "0212" "0212" "0211" "0212" "0211" "0212" "0221"
#> Sample_705 Sample_706 Sample_707 Sample_708 Sample_709 Sample_710 Sample_711 Sample_712
#> "0222" "0211" "0211" "0211" "0211" "0232" "0212" "0211"
#> Sample_713 Sample_714 Sample_715 Sample_716 Sample_717 Sample_718 Sample_719 Sample_720
#> "0211" "0212" "0212" "0212" "0211" "0211" "0211" "0232"
#> Sample_721 Sample_722 Sample_723 Sample_724 Sample_725 Sample_726 Sample_727 Sample_728
#> "0211" "0211" "0212" "0211" "0212" "0211" "0211" "0212"
#> Sample_729 Sample_730 Sample_731 Sample_732 Sample_733 Sample_734 Sample_735 Sample_736
#> "0211" "0211" "0233" "0211" "0211" "0211" "0233" "0211"
#> Sample_737 Sample_738 Sample_739 Sample_740 Sample_741 Sample_742 Sample_743 Sample_744
#> "0121" "0232" "0211" "0212" "0211" "0212" "0211" "0211"
#> Sample_745 Sample_746 Sample_747 Sample_748 Sample_749 Sample_750 Sample_751 Sample_752
#> "0212" "0211" "0211" "0212" "0211" "0211" "0211" "0211"
#> Sample_753 Sample_754 Sample_755 Sample_756 Sample_757 Sample_758 Sample_759 Sample_760
#> "0212" "0212" "0211" "0211" "0211" "0211" "0212" "0211"
#> Sample_761 Sample_762 Sample_763 Sample_764 Sample_765 Sample_766 Sample_767 Sample_768
#> "0211" "0211" "0211" "0211" "0211" "0212" "0211" "0232"
#> Sample_769 Sample_770 Sample_771 Sample_772 Sample_773 Sample_774 Sample_775 Sample_776
#> "0212" "0211" "0211" "0212" "0211" "0211" "0212" "0211"
#> Sample_777 Sample_778 Sample_779 Sample_780 Sample_781 Sample_782 Sample_783 Sample_784
#> "0212" "0212" "0212" "0212" "0211" "0212" "0212" "0212"
#> Sample_785 Sample_786 Sample_787 Sample_788 Sample_789 Sample_790 Sample_791 Sample_792
#> "0212" "0212" "0211" "0212" "0212" "0211" "0211" "0211"
#> Sample_793 Sample_794 Sample_795 Sample_796 Sample_797 Sample_798 Sample_799 Sample_800
#> "0212" "0211" "0211" "0211" "0212" "0212" "0212" "0211"
#> Sample_801 Sample_802 Sample_803 Sample_804 Sample_805 Sample_806 Sample_807 Sample_808
#> "0211" "0212" "0211" "0212" "0211" "0211" "0211" "0211"
#> Sample_809 Sample_810 Sample_811 Sample_812 Sample_813 Sample_814 Sample_815 Sample_816
#> "0211" "0212" "0211" "0211" "0211" "0211" "0211" "0211"
#> Sample_817 Sample_818 Sample_819 Sample_820 Sample_821 Sample_822 Sample_823 Sample_824
#> "0212" "0211" "0212" "0233" "0212" "0211" "0212" "0212"
#> Sample_825 Sample_826 Sample_827 Sample_828 Sample_829 Sample_830 Sample_831 Sample_832
#> "0212" "0212" "0212" "0211" "0212" "0212" "0211" "0211"
#> Sample_833 Sample_834 Sample_835 Sample_836 Sample_837 Sample_838 Sample_839 Sample_840
#> "0211" "0212" "0212" "0233" "0211" "0211" "0212" "0211"
#> Sample_841 Sample_842 Sample_843 Sample_844 Sample_845 Sample_846 Sample_847 Sample_848
#> "0222" "0221" "0221" "0221" "0221" "0221" "0222" "0222"
#> Sample_849 Sample_850 Sample_851 Sample_852 Sample_853 Sample_854 Sample_855 Sample_856
#> "0222" "0221" "0221" "0222" "0221" "0221" "0212" "0212"
#> Sample_857 Sample_858 Sample_859 Sample_860 Sample_861 Sample_862 Sample_863 Sample_864
#> "0212" "0211" "0232" "0212" "0212" "0212" "0212" "0211"
#> Sample_865 Sample_866 Sample_867 Sample_868 Sample_869 Sample_870 Sample_871 Sample_872
#> "0233" "0212" "0212" "0211" "0212" "0212" "0212" "0212"
#> Sample_873 Sample_874 Sample_875 Sample_876 Sample_877 Sample_878 Sample_879 Sample_880
#> "0212" "0211" "0211" "0212" "0211" "0211" "0211" "0211"
#> Sample_881 Sample_882 Sample_883 Sample_884 Sample_885 Sample_886 Sample_887 Sample_888
#> "0212" "0233" "0211" "0211" "0212" "0211" "0211" "0211"
#> Sample_889 Sample_890 Sample_891 Sample_892 Sample_893 Sample_894 Sample_895 Sample_896
#> "0212" "0212" "0212" "0212" "0212" "0232" "0212" "0211"
#> Sample_897 Sample_898 Sample_899 Sample_900 Sample_901 Sample_902 Sample_903 Sample_904
#> "0212" "0212" "0211" "0233" "0211" "0212" "0212" "0211"
#> Sample_905 Sample_906 Sample_907 Sample_908 Sample_909 Sample_910 Sample_911 Sample_912
#> "0211" "0212" "0212" "0212" "0212" "0211" "0212" "0211"
#> Sample_913 Sample_914 Sample_915 Sample_916 Sample_917 Sample_918 Sample_919 Sample_920
#> "0212" "0212" "0212" "0211" "0212" "0212" "0212" "0212"
#> Sample_921 Sample_922 Sample_923 Sample_924 Sample_925 Sample_926 Sample_927 Sample_928
#> "0211" "0212" "0211" "0212" "0212" "0211" "0211" "0211"
#> Sample_929 Sample_930 Sample_931 Sample_932 Sample_933 Sample_934 Sample_935 Sample_936
#> "0212" "0211" "0211" "0212" "0212" "0212" "0212" "0212"
#> Sample_937 Sample_938 Sample_939 Sample_940 Sample_941 Sample_942 Sample_943 Sample_944
#> "0212" "0212" "0212" "0212" "0212" "0212" "0212" "0212"
#> Sample_945 Sample_946 Sample_947 Sample_948 Sample_949 Sample_950 Sample_951 Sample_952
#> "0211" "0211" "0211" "0321" "0321" "013" "0321" "0321"
#> Sample_953 Sample_954 Sample_955 Sample_956 Sample_957 Sample_958 Sample_959 Sample_960
#> "0321" "0322" "0321" "0321" "0321" "0321" "0321" "0321"
#> Sample_961 Sample_962 Sample_963 Sample_964 Sample_965 Sample_966 Sample_967 Sample_968
#> "0321" "0122" "0321" "0321" "0321" "0321" "0321" "0122"
#> Sample_969 Sample_970 Sample_971 Sample_972 Sample_973 Sample_974 Sample_975 Sample_976
#> "0321" "0321" "0321" "0321" "0321" "0321" "0333" "013"
#> Sample_977 Sample_978 Sample_979 Sample_980 Sample_981 Sample_982 Sample_983 Sample_984
#> "013" "0122" "0321" "013" "013" "0121" "0122" "0321"
#> Sample_985 Sample_986 Sample_987 Sample_988 Sample_989 Sample_990 Sample_991 Sample_992
#> "0122" "013" "0122" "0121" "0121" "0121" "0121" "0121"
#> Sample_993 Sample_994 Sample_995 Sample_996 Sample_997 Sample_998 Sample_999 Sample_1000
#> "0121" "0122" "0321" "013" "0321" "0321" "013" "0321"
#> Sample_1001 Sample_1002 Sample_1003 Sample_1004 Sample_1005 Sample_1006 Sample_1007 Sample_1008
#> "0321" "0321" "0321" "0333" "0321" "0321" "0122" "013"
#> Sample_1009 Sample_1010 Sample_1011 Sample_1012 Sample_1013 Sample_1014 Sample_1015 Sample_1016
#> "013" "013" "0321" "0322" "0321" "0121" "0121" "0121"
#> Sample_1017 Sample_1018 Sample_1019 Sample_1020 Sample_1021 Sample_1022 Sample_1023 Sample_1024
#> "0121" "0121" "0121" "0121" "0121" "0121" "0121" "0121"
#> Sample_1025 Sample_1026 Sample_1027 Sample_1028 Sample_1029 Sample_1030 Sample_1031 Sample_1032
#> "0121" "0121" "011" "011" "0312" "011" "0332" "0312"
#> Sample_1033 Sample_1034 Sample_1035 Sample_1036 Sample_1037 Sample_1038 Sample_1039 Sample_1040
#> "0312" "011" "0312" "011" "0312" "0312" "011" "0312"
#> Sample_1041 Sample_1042 Sample_1043 Sample_1044 Sample_1045 Sample_1046 Sample_1047 Sample_1048
#> "011" "011" "0312" "0312" "0332" "011" "0312" "0331"
#> Sample_1049 Sample_1050 Sample_1051 Sample_1052 Sample_1053 Sample_1054 Sample_1055 Sample_1056
#> "011" "011" "0312" "011" "0322" "0321" "0321" "013"
#> Sample_1057 Sample_1058 Sample_1059 Sample_1060 Sample_1061 Sample_1062 Sample_1063 Sample_1064
#> "0322" "0121" "013" "0321" "0322" "013" "0312" "0312"
#> Sample_1065 Sample_1066 Sample_1067 Sample_1068 Sample_1069 Sample_1070 Sample_1071 Sample_1072
#> "0312" "011" "0312" "0312" "0312" "011" "0312" "0312"
#> Sample_1073 Sample_1074 Sample_1075 Sample_1076 Sample_1077 Sample_1078 Sample_1079 Sample_1080
#> "011" "0312" "011" "011" "0312" "0312" "0312" "011"
#> Sample_1081 Sample_1082 Sample_1083 Sample_1084 Sample_1085 Sample_1086 Sample_1087 Sample_1088
#> "0312" "0312" "011" "0312" "0312" "0331" "011" "0331"
#> Sample_1089 Sample_1090 Sample_1091 Sample_1092 Sample_1093 Sample_1094 Sample_1095 Sample_1096
#> "0332" "011" "011" "0312" "011" "0312" "0312" "0312"
#> Sample_1097 Sample_1098 Sample_1099 Sample_1100 Sample_1101 Sample_1102 Sample_1103 Sample_1104
#> "011" "0312" "0312" "011" "0312" "011" "011" "011"
#> Sample_1105 Sample_1106 Sample_1107 Sample_1108 Sample_1109 Sample_1110 Sample_1111 Sample_1112
#> "011" "0312" "0312" "011" "0312" "011" "0312" "011"
#> Sample_1113 Sample_1114 Sample_1115 Sample_1116 Sample_1117 Sample_1118 Sample_1119 Sample_1120
#> "011" "011" "0312" "011" "0312" "011" "011" "0332"
#> Sample_1121 Sample_1122 Sample_1123 Sample_1124 Sample_1125 Sample_1126 Sample_1127 Sample_1128
#> "011" "0331" "0312" "0312" "0312" "0312" "0312" "0312"
#> Sample_1129 Sample_1130 Sample_1131 Sample_1132 Sample_1133 Sample_1134 Sample_1135 Sample_1136
#> "011" "011" "011" "0332" "011" "0331" "011" "011"
#> Sample_1137 Sample_1138 Sample_1139 Sample_1140 Sample_1141 Sample_1142 Sample_1143 Sample_1144
#> "011" "011" "0312" "0331" "0312" "011" "011" "011"
#> Sample_1145 Sample_1146 Sample_1147 Sample_1148 Sample_1149 Sample_1150 Sample_1151 Sample_1152
#> "0312" "011" "011" "011" "011" "0312" "011" "0312"
#> Sample_1153 Sample_1154 Sample_1155 Sample_1156 Sample_1157 Sample_1158 Sample_1159 Sample_1160
#> "011" "011" "011" "0312" "0312" "011" "011" "011"
#> Sample_1161 Sample_1162 Sample_1163 Sample_1164 Sample_1165 Sample_1166 Sample_1167 Sample_1168
#> "0312" "011" "0312" "0312" "011" "011" "011" "011"
#> Sample_1169 Sample_1170 Sample_1171 Sample_1172 Sample_1173 Sample_1174 Sample_1175 Sample_1176
#> "0312" "0312" "011" "011" "011" "011" "0312" "011"
#> Sample_1177 Sample_1178 Sample_1179 Sample_1180 Sample_1181 Sample_1182 Sample_1183 Sample_1184
#> "011" "011" "011" "0312" "011" "011" "0331" "0331"
#> Sample_1185 Sample_1186 Sample_1187 Sample_1188 Sample_1189 Sample_1190 Sample_1191 Sample_1192
#> "011" "011" "011" "011" "0321" "0322" "0322" "0122"
#> Sample_1193 Sample_1194 Sample_1195 Sample_1196 Sample_1197 Sample_1198 Sample_1199 Sample_1200
#> "0322" "0322" "0321" "0321" "0322" "0322" "0322" "0321"
#> Sample_1201 Sample_1202 Sample_1203 Sample_1204 Sample_1205 Sample_1206 Sample_1207 Sample_1208
#> "0321" "0321" "0322" "0322" "0322" "0321" "0322" "013"
#> Sample_1209 Sample_1210 Sample_1211 Sample_1212 Sample_1213 Sample_1214 Sample_1215 Sample_1216
#> "0321" "0322" "0321" "0321" "0321" "0322" "0322" "0122"
#> Sample_1217 Sample_1218 Sample_1219 Sample_1220 Sample_1221 Sample_1222 Sample_1223 Sample_1224
#> "013" "013" "0322" "0321" "0322" "011" "0311" "0311"
#> Sample_1225 Sample_1226 Sample_1227 Sample_1228 Sample_1229 Sample_1230 Sample_1231 Sample_1232
#> "011" "0311" "011" "011" "011" "0311" "0311" "0311"
#> Sample_1233 Sample_1234 Sample_1235 Sample_1236 Sample_1237 Sample_1238 Sample_1239 Sample_1240
#> "0311" "011" "0311" "011" "0311" "0311" "011" "011"
#> Sample_1241 Sample_1242 Sample_1243 Sample_1244 Sample_1245 Sample_1246 Sample_1247 Sample_1248
#> "011" "011" "0311" "0311" "0311" "0311" "0311" "0311"
#> Sample_1249 Sample_1250 Sample_1251 Sample_1252 Sample_1253 Sample_1254 Sample_1255 Sample_1256
#> "011" "0311" "0311" "0311" "0311" "011" "0332" "0311"
#> Sample_1257 Sample_1258 Sample_1259 Sample_1260 Sample_1261 Sample_1262 Sample_1263 Sample_1264
#> "0311" "0311" "0311" "0311" "0311" "0311" "0311" "011"
#> Sample_1265 Sample_1266 Sample_1267 Sample_1268 Sample_1269 Sample_1270 Sample_1271 Sample_1272
#> "0311" "011" "011" "0332" "0311" "0311" "0311" "011"
#> Sample_1273 Sample_1274 Sample_1275 Sample_1276 Sample_1277 Sample_1278 Sample_1279 Sample_1280
#> "0311" "0311" "0311" "0311" "0311" "0311" "011" "011"
#> Sample_1281 Sample_1282 Sample_1283 Sample_1284 Sample_1285 Sample_1286 Sample_1287 Sample_1288
#> "0311" "0311" "011" "0311" "011" "0311" "0311" "0311"
#> Sample_1289 Sample_1290 Sample_1291 Sample_1292 Sample_1293 Sample_1294 Sample_1295 Sample_1296
#> "0311" "0311" "0311" "0311" "0311" "0311" "0311" "0331"
#> Sample_1297 Sample_1298 Sample_1299 Sample_1300 Sample_1301 Sample_1302 Sample_1303 Sample_1304
#> "0311" "0311" "011" "0311" "0311" "0311" "0311" "0311"
#> Sample_1305 Sample_1306 Sample_1307 Sample_1308 Sample_1309 Sample_1310 Sample_1311 Sample_1312
#> "0311" "0332" "011" "011" "011" "0332" "0311" "0311"
#> Sample_1313 Sample_1314 Sample_1315 Sample_1316 Sample_1317 Sample_1318 Sample_1319 Sample_1320
#> "011" "0313" "0313" "0311" "011" "0313" "0311" "011"
#> Sample_1321 Sample_1322 Sample_1323 Sample_1324 Sample_1325 Sample_1326 Sample_1327 Sample_1328
#> "0314" "0311" "0311" "0311" "0311" "0314" "0311" "0313"
#> Sample_1329 Sample_1330 Sample_1331 Sample_1332 Sample_1333 Sample_1334 Sample_1335 Sample_1336
#> "0314" "0314" "0313" "0314" "0314" "0313" "011" "011"
#> Sample_1337 Sample_1338 Sample_1339 Sample_1340 Sample_1341 Sample_1342 Sample_1343 Sample_1344
#> "0332" "011" "0332" "0311" "0314" "011" "011" "0314"
#> Sample_1345 Sample_1346 Sample_1347 Sample_1348 Sample_1349 Sample_1350 Sample_1351 Sample_1352
#> "0332" "0311" "0311" "011" "0314" "011" "0314" "011"
#> Sample_1353 Sample_1354 Sample_1355 Sample_1356 Sample_1357 Sample_1358 Sample_1359 Sample_1360
#> "0311" "011" "0314" "0314" "011" "011" "0314" "011"
#> Sample_1361 Sample_1362 Sample_1363 Sample_1364 Sample_1365 Sample_1366 Sample_1367 Sample_1368
#> "0314" "0314" "0314" "011" "0311" "0313" "011" "0313"
#> Sample_1369 Sample_1370 Sample_1371 Sample_1372 Sample_1373 Sample_1374 Sample_1375 Sample_1376
#> "0311" "0314" "011" "0314" "011" "011" "011" "0332"
#> Sample_1377 Sample_1378 Sample_1379 Sample_1380 Sample_1381 Sample_1382 Sample_1383 Sample_1384
#> "0311" "0311" "0311" "0311" "0314" "011" "0313" "0313"
#> Sample_1385 Sample_1386 Sample_1387 Sample_1388 Sample_1389 Sample_1390 Sample_1391 Sample_1392
#> "0313" "011" "0313" "011" "0313" "0313" "0313" "0313"
#> Sample_1393 Sample_1394 Sample_1395 Sample_1396 Sample_1397 Sample_1398 Sample_1399 Sample_1400
#> "0313" "0313" "0313" "0313" "0313" "011" "011" "0331"
#> Sample_1401 Sample_1402 Sample_1403 Sample_1404 Sample_1405 Sample_1406 Sample_1407 Sample_1408
#> "0313" "011" "0313" "0313" "0313" "011" "0313" "011"
#> Sample_1409 Sample_1410 Sample_1411 Sample_1412 Sample_1413 Sample_1414 Sample_1415 Sample_1416
#> "011" "0313" "0313" "0313" "0331" "0313" "0313" "0332"
#> Sample_1417 Sample_1418 Sample_1419 Sample_1420 Sample_1421 Sample_1422 Sample_1423 Sample_1424
#> "0313" "0313" "011" "0313" "0313" "0313" "011" "0313"
#> Sample_1425 Sample_1426 Sample_1427 Sample_1428 Sample_1429 Sample_1430 Sample_1431 Sample_1432
#> "011" "011" "011" "0313" "011" "011" "011" "011"
#> Sample_1433 Sample_1434 Sample_1435 Sample_1436 Sample_1437 Sample_1438 Sample_1439 Sample_1440
#> "011" "011" "011" "011" "011" "011" "011" "011"
#> Sample_1441 Sample_1442 Sample_1443 Sample_1444 Sample_1445 Sample_1446 Sample_1447 Sample_1448
#> "011" "0313" "0313" "011" "011" "0313" "0313" "011"
#> Sample_1449 Sample_1450 Sample_1451 Sample_1452 Sample_1453 Sample_1454 Sample_1455 Sample_1456
#> "011" "011" "011" "011" "011" "011" "011" "0313"
#> Sample_1457 Sample_1458 Sample_1459 Sample_1460 Sample_1461 Sample_1462 Sample_1463 Sample_1464
#> "0313" "011" "011" "0331" "0313" "011" "0314" "0314"
#> Sample_1465 Sample_1466 Sample_1467 Sample_1468 Sample_1469 Sample_1470 Sample_1471 Sample_1472
#> "011" "011" "011" "0313" "011" "011" "011" "011"
#> Sample_1473 Sample_1474 Sample_1475 Sample_1476 Sample_1477 Sample_1478 Sample_1479 Sample_1480
#> "011" "011" "011" "011" "011" "011" "011" "0314"
#> Sample_1481 Sample_1482 Sample_1483 Sample_1484 Sample_1485 Sample_1486 Sample_1487 Sample_1488
#> "011" "011" "011" "011" "0313" "0331" "011" "0313"
#> Sample_1489 Sample_1490 Sample_1491 Sample_1492 Sample_1493 Sample_1494 Sample_1495 Sample_1496
#> "011" "011" "011" "011" "011" "0332" "011" "011"
#> Sample_1497 Sample_1498 Sample_1499 Sample_1500 Sample_1501 Sample_1502 Sample_1503 Sample_1504
#> "011" "011" "011" "011" "011" "0313" "0313" "0332"
#> Sample_1505 Sample_1506 Sample_1507 Sample_1508 Sample_1509 Sample_1510 Sample_1511 Sample_1512
#> "0331" "0313" "0313" "0313" "011" "011" "0313" "0313"
#> Sample_1513 Sample_1514 Sample_1515 Sample_1516 Sample_1517 Sample_1518 Sample_1519 Sample_1520
#> "0313" "011" "0313" "011" "0222" "0222" "0221" "0233"
#> Sample_1521 Sample_1522 Sample_1523 Sample_1524 Sample_1525 Sample_1526 Sample_1527 Sample_1528
#> "0232" "013" "013" "0233" "0121" "0121" "0212" "0212"
#> Sample_1529 Sample_1530 Sample_1531 Sample_1532 Sample_1533 Sample_1534 Sample_1535 Sample_1536
#> "0212" "0233" "0232" "0232" "0211" "0212" "0222" "0222"
#> Sample_1537 Sample_1538 Sample_1539 Sample_1540 Sample_1541 Sample_1542 Sample_1543 Sample_1544
#> "0221" "0212" "0212" "013" "011" "0212" "0233" "013"
#> Sample_1545 Sample_1546 Sample_1547 Sample_1548 Sample_1549 Sample_1550 Sample_1551 Sample_1552
#> "011" "0211" "013" "0231" "0322" "0231" "0231" "0231"
#> Sample_1553 Sample_1554 Sample_1555 Sample_1556 Sample_1557 Sample_1558 Sample_1559 Sample_1560
#> "0321" "0222" "0231" "013" "013" "013" "0231" "013"
#> Sample_1561 Sample_1562 Sample_1563 Sample_1564 Sample_1565 Sample_1566 Sample_1567 Sample_1568
#> "0231" "0121" "013" "013" "013" "013" "013" "013"
#> Sample_1569 Sample_1570 Sample_1571 Sample_1572 Sample_1573 Sample_1574 Sample_1575 Sample_1576
#> "0312" "0212" "013" "013" "013" "0231" "0231" "013"
#> Sample_1577 Sample_1578 Sample_1579 Sample_1580 Sample_1581 Sample_1582 Sample_1583 Sample_1584
#> "013" "013" "013" "013" "0322" "0231" "013" "013"
#> Sample_1585 Sample_1586 Sample_1587 Sample_1588 Sample_1589 Sample_1590 Sample_1591 Sample_1592
#> "0231" "0231" "0231" "0222" "0231" "013" "013" "0231"
#> Sample_1593 Sample_1594 Sample_1595 Sample_1596 Sample_1597 Sample_1598 Sample_1599 Sample_1600
#> "0231" "0231" "0231" "0232" "0322" "0311" "0233" "013"
get_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 397))
#> Sample_1 Sample_2 Sample_3 Sample_4 Sample_5 Sample_6 Sample_7 Sample_8
#> "013" "0221" "0221" "0222" "0221" "0221" "0221" "0222"
#> Sample_9 Sample_10 Sample_11 Sample_12 Sample_13 Sample_14 Sample_15 Sample_16
#> "0221" "0221" "0221" "0221" "0221" "0221" "0221" "0221"
#> Sample_17 Sample_18 Sample_19 Sample_20 Sample_21 Sample_22 Sample_23 Sample_24
#> "0222" "0222" "0222" "0222" "0222" "0222" "0222" "0221"
#> Sample_25 Sample_26 Sample_27 Sample_28 Sample_29 Sample_30 Sample_31 Sample_32
#> "0222" "0221" "0222" "0222" "0221" "0222" "0233" "0221"
#> Sample_33 Sample_34 Sample_35 Sample_36 Sample_37 Sample_38 Sample_39 Sample_40
#> "0221" "0221" "0221" "0221" "0221" "0221" "0221" "0221"
#> Sample_41 Sample_42 Sample_43 Sample_44 Sample_45 Sample_46 Sample_47 Sample_48
#> "0221" "0221" "0233" "0221" "0221" "0221" "0221" "0221"
#> Sample_49 Sample_50 Sample_51 Sample_52 Sample_53 Sample_54 Sample_55 Sample_56
#> "0221" "0221" "0221" "0221" "0222" "0222" "0221" "0221"
#> Sample_57 Sample_58 Sample_59 Sample_60 Sample_61 Sample_62 Sample_63 Sample_64
#> "0221" "0221" "0221" "0221" "0222" "0221" "0221" "0221"
#> Sample_65 Sample_66 Sample_67 Sample_68 Sample_69 Sample_70 Sample_71 Sample_72
#> "0221" "0221" "0222" "0221" "0221" "0222" "0222" "0211"
#> Sample_73 Sample_74 Sample_75 Sample_76 Sample_77 Sample_78 Sample_79 Sample_80
#> "0233" "0221" "0222" "0221" "0222" "0221" "0222" "0222"
#> Sample_81 Sample_82 Sample_83 Sample_84 Sample_85 Sample_86 Sample_87 Sample_88
#> "0221" "0222" "0221" "0221" "0222" "0221" "0222" "0222"
#> Sample_89 Sample_90 Sample_91 Sample_92 Sample_93 Sample_94 Sample_95 Sample_96
#> "0222" "0222" "0222" "0222" "0222" "0232" "0222" "0221"
#> Sample_97 Sample_98 Sample_99 Sample_100 Sample_101 Sample_102 Sample_103 Sample_104
#> "0221" "0221" "0221" "0222" "0222" "0221" "0222" "0222"
#> Sample_105 Sample_106 Sample_107 Sample_108 Sample_109 Sample_110 Sample_111 Sample_112
#> "0221" "0221" "0222" "0221" "0221" "0222" "0212" "0222"
#> Sample_113 Sample_114 Sample_115 Sample_116 Sample_117 Sample_118 Sample_119 Sample_120
#> "0222" "0232" "0222" "0222" "0222" "0232" "0222" "0221"
#> Sample_121 Sample_122 Sample_123 Sample_124 Sample_125 Sample_126 Sample_127 Sample_128
#> "012" "0222" "0232" "0221" "0221" "0222" "0221" "0222"
#> Sample_129 Sample_130 Sample_131 Sample_132 Sample_133 Sample_134 Sample_135 Sample_136
#> "0221" "0222" "0222" "0221" "0222" "0221" "0222" "0221"
#> Sample_137 Sample_138 Sample_139 Sample_140 Sample_141 Sample_142 Sample_143 Sample_144
#> "0221" "0221" "0222" "0221" "0221" "0222" "0222" "0232"
#> Sample_145 Sample_146 Sample_147 Sample_148 Sample_149 Sample_150 Sample_151 Sample_152
#> "0222" "0222" "0211" "0211" "0211" "0211" "0212" "0211"
#> Sample_153 Sample_154 Sample_155 Sample_156 Sample_157 Sample_158 Sample_159 Sample_160
#> "0211" "0211" "0211" "0211" "0211" "0211" "0212" "0211"
#> Sample_161 Sample_162 Sample_163 Sample_164 Sample_165 Sample_166 Sample_167 Sample_168
#> "0212" "0212" "0212" "0212" "0212" "0212" "0211" "0211"
#> Sample_169 Sample_170 Sample_171 Sample_172 Sample_173 Sample_174 Sample_175 Sample_176
#> "0212" "0211" "0232" "0212" "0233" "0212" "0211" "0211"
#> Sample_177 Sample_178 Sample_179 Sample_180 Sample_181 Sample_182 Sample_183 Sample_184
#> "0212" "0211" "0212" "0212" "0211" "0211" "0211" "0211"
#> Sample_185 Sample_186 Sample_187 Sample_188 Sample_189 Sample_190 Sample_191 Sample_192
#> "0211" "0211" "0211" "0212" "0212" "0211" "0212" "0212"
#> Sample_193 Sample_194 Sample_195 Sample_196 Sample_197 Sample_198 Sample_199 Sample_200
#> "0211" "0211" "0211" "0211" "0221" "0221" "0221" "0221"
#> Sample_201 Sample_202 Sample_203 Sample_204 Sample_205 Sample_206 Sample_207 Sample_208
#> "0221" "0221" "0221" "0233" "0221" "0221" "0221" "0322"
#> Sample_209 Sample_210 Sample_211 Sample_212 Sample_213 Sample_214 Sample_215 Sample_216
#> "0322" "0322" "0322" "0322" "0322" "013" "013" "0322"
#> Sample_217 Sample_218 Sample_219 Sample_220 Sample_221 Sample_222 Sample_223 Sample_224
#> "013" "0322" "0322" "0322" "0322" "0322" "0322" "013"
#> Sample_225 Sample_226 Sample_227 Sample_228 Sample_229 Sample_230 Sample_231 Sample_232
#> "0322" "013" "0322" "0322" "0322" "0322" "0322" "0322"
#> Sample_233 Sample_234 Sample_235 Sample_236 Sample_237 Sample_238 Sample_239 Sample_240
#> "0333" "0322" "0322" "013" "0322" "0322" "0322" "0322"
#> Sample_241 Sample_242 Sample_243 Sample_244 Sample_245 Sample_246 Sample_247 Sample_248
#> "013" "0322" "0321" "0322" "0322" "0322" "0322" "0322"
#> Sample_249 Sample_250 Sample_251 Sample_252 Sample_253 Sample_254 Sample_255 Sample_256
#> "0322" "0322" "0322" "0322" "0322" "0333" "0322" "0322"
#> Sample_257 Sample_258 Sample_259 Sample_260 Sample_261 Sample_262 Sample_263 Sample_264
#> "0322" "013" "0322" "0322" "0322" "0322" "0322" "013"
#> Sample_265 Sample_266 Sample_267 Sample_268 Sample_269 Sample_270 Sample_271 Sample_272
#> "012" "0321" "0321" "0321" "0322" "0321" "0333" "012"
#> Sample_273 Sample_274 Sample_275 Sample_276 Sample_277 Sample_278 Sample_279 Sample_280
#> "0321" "0321" "012" "013" "0321" "0333" "0321" "0322"
#> Sample_281 Sample_282 Sample_283 Sample_284 Sample_285 Sample_286 Sample_287 Sample_288
#> "0321" "0321" "0322" "0321" "0321" "0321" "013" "0333"
#> Sample_289 Sample_290 Sample_291 Sample_292 Sample_293 Sample_294 Sample_295 Sample_296
#> "0333" "0321" "0321" "0321" "0322" "0321" "0321" "0321"
#> Sample_297 Sample_298 Sample_299 Sample_300 Sample_301 Sample_302 Sample_303 Sample_304
#> "0321" "0321" "0321" "0322" "0321" "012" "012" "012"
#> Sample_305 Sample_306 Sample_307 Sample_308 Sample_309 Sample_310 Sample_311 Sample_312
#> "012" "0321" "0321" "012" "012" "012" "012" "012"
#> Sample_313 Sample_314 Sample_315 Sample_316 Sample_317 Sample_318 Sample_319 Sample_320
#> "012" "012" "0321" "012" "0321" "012" "012" "012"
#> Sample_321 Sample_322 Sample_323 Sample_324 Sample_325 Sample_326 Sample_327 Sample_328
#> "012" "0321" "012" "012" "012" "012" "012" "012"
#> Sample_329 Sample_330 Sample_331 Sample_332 Sample_333 Sample_334 Sample_335 Sample_336
#> "012" "012" "0333" "0321" "0321" "0321" "0321" "0321"
#> Sample_337 Sample_338 Sample_339 Sample_340 Sample_341 Sample_342 Sample_343 Sample_344
#> "0321" "0321" "0333" "0321" "0321" "0321" "0321" "0321"
#> Sample_345 Sample_346 Sample_347 Sample_348 Sample_349 Sample_350 Sample_351 Sample_352
#> "0321" "0321" "0321" "0321" "0321" "013" "012" "013"
#> Sample_353 Sample_354 Sample_355 Sample_356 Sample_357 Sample_358 Sample_359 Sample_360
#> "0321" "0321" "012" "0321" "0321" "012" "0321" "012"
#> Sample_361 Sample_362 Sample_363 Sample_364 Sample_365 Sample_366 Sample_367 Sample_368
#> "0321" "012" "0321" "0321" "013" "0321" "0321" "012"
#> Sample_369 Sample_370 Sample_371 Sample_372 Sample_373 Sample_374 Sample_375 Sample_376
#> "012" "0321" "0333" "013" "013" "0321" "0321" "0321"
#> Sample_377 Sample_378 Sample_379 Sample_380 Sample_381 Sample_382 Sample_383 Sample_384
#> "012" "0321" "0321" "0321" "012" "0321" "0321" "0321"
#> Sample_385 Sample_386 Sample_387 Sample_388 Sample_389 Sample_390 Sample_391 Sample_392
#> "012" "012" "0321" "0321" "013" "013" "0321" "0321"
#> Sample_393 Sample_394 Sample_395 Sample_396 Sample_397 Sample_398 Sample_399 Sample_400
#> "012" "013" "012" "013" "012" "012" "0321" "0321"
#> Sample_401 Sample_402 Sample_403 Sample_404 Sample_405 Sample_406 Sample_407 Sample_408
#> "0321" "0322" "013" "013" "013" "0321" "0321" "013"
#> Sample_409 Sample_410 Sample_411 Sample_412 Sample_413 Sample_414 Sample_415 Sample_416
#> "012" "0321" "0321" "012" "0321" "0321" "0333" "012"
#> Sample_417 Sample_418 Sample_419 Sample_420 Sample_421 Sample_422 Sample_423 Sample_424
#> "0321" "0321" "0321" "0333" "012" "0333" "0322" "012"
#> Sample_425 Sample_426 Sample_427 Sample_428 Sample_429 Sample_430 Sample_431 Sample_432
#> "012" "0322" "012" "012" "0322" "013" "012" "012"
#> Sample_433 Sample_434 Sample_435 Sample_436 Sample_437 Sample_438 Sample_439 Sample_440
#> "013" "012" "0322" "0333" "0333" "013" "012" "012"
#> Sample_441 Sample_442 Sample_443 Sample_444 Sample_445 Sample_446 Sample_447 Sample_448
#> "0322" "012" "013" "0321" "012" "012" "0322" "013"
#> Sample_449 Sample_450 Sample_451 Sample_452 Sample_453 Sample_454 Sample_455 Sample_456
#> "0322" "0321" "012" "012" "0322" "0321" "013" "012"
#> Sample_457 Sample_458 Sample_459 Sample_460 Sample_461 Sample_462 Sample_463 Sample_464
#> "013" "0322" "012" "0321" "012" "0321" "013" "012"
#> Sample_465 Sample_466 Sample_467 Sample_468 Sample_469 Sample_470 Sample_471 Sample_472
#> "012" "012" "0322" "012" "012" "0321" "0322" "012"
#> Sample_473 Sample_474 Sample_475 Sample_476 Sample_477 Sample_478 Sample_479 Sample_480
#> "0321" "012" "012" "013" "013" "0314" "011" "0314"
#> Sample_481 Sample_482 Sample_483 Sample_484 Sample_485 Sample_486 Sample_487 Sample_488
#> "0313" "0314" "0314" "0314" "0314" "011" "011" "0331"
#> Sample_489 Sample_490 Sample_491 Sample_492 Sample_493 Sample_494 Sample_495 Sample_496
#> "0314" "011" "0314" "0314" "0314" "0314" "011" "0314"
#> Sample_497 Sample_498 Sample_499 Sample_500 Sample_501 Sample_502 Sample_503 Sample_504
#> "0314" "0314" "011" "011" "0314" "0314" "0314" "0314"
#> Sample_505 Sample_506 Sample_507 Sample_508 Sample_509 Sample_510 Sample_511 Sample_512
#> "011" "011" "011" "0314" "0314" "0314" "011" "0332"
#> Sample_513 Sample_514 Sample_515 Sample_516 Sample_517 Sample_518 Sample_519 Sample_520
#> "011" "0314" "011" "0211" "011" "0314" "0314" "011"
#> Sample_521 Sample_522 Sample_523 Sample_524 Sample_525 Sample_526 Sample_527 Sample_528
#> "011" "0314" "0314" "0314" "0314" "0314" "011" "0312"
#> Sample_529 Sample_530 Sample_531 Sample_532 Sample_533 Sample_534 Sample_535 Sample_536
#> "0331" "011" "0332" "011" "011" "0314" "011" "011"
#> Sample_537 Sample_538 Sample_539 Sample_540 Sample_541 Sample_542 Sample_543 Sample_544
#> "011" "0314" "011" "0314" "0314" "011" "011" "011"
#> Sample_545 Sample_546 Sample_547 Sample_548 Sample_549 Sample_550 Sample_551 Sample_552
#> "0331" "011" "011" "0314" "0314" "0314" "0314" "0314"
#> Sample_553 Sample_554 Sample_555 Sample_556 Sample_557 Sample_558 Sample_559 Sample_560
#> "0332" "0314" "0314" "0314" "011" "011" "011" "0314"
#> Sample_561 Sample_562 Sample_563 Sample_564 Sample_565 Sample_566 Sample_567 Sample_568
#> "0314" "0314" "0314" "0314" "011" "011" "011" "0314"
#> Sample_569 Sample_570 Sample_571 Sample_572 Sample_573 Sample_574 Sample_575 Sample_576
#> "011" "0322" "0322" "013" "012" "0333" "0321" "0322"
#> Sample_577 Sample_578 Sample_579 Sample_580 Sample_581 Sample_582 Sample_583 Sample_584
#> "012" "0321" "0321" "0321" "0321" "0321" "0321" "0321"
#> Sample_585 Sample_586 Sample_587 Sample_588 Sample_589 Sample_590 Sample_591 Sample_592
#> "0322" "0221" "0221" "0231" "0322" "013" "0221" "0231"
#> Sample_593 Sample_594 Sample_595 Sample_596 Sample_597 Sample_598 Sample_599 Sample_600
#> "0221" "0231" "0231" "0222" "0231" "0222" "0231" "0231"
#> Sample_601 Sample_602 Sample_603 Sample_604 Sample_605 Sample_606 Sample_607 Sample_608
#> "0222" "0231" "0222" "0222" "0222" "0222" "0221" "0222"
#> Sample_609 Sample_610 Sample_611 Sample_612 Sample_613 Sample_614 Sample_615 Sample_616
#> "0222" "0222" "0212" "0212" "0322" "013" "0322" "013"
#> Sample_617 Sample_618 Sample_619 Sample_620 Sample_621 Sample_622 Sample_623 Sample_624
#> "013" "012" "013" "013" "0231" "013" "0322" "0322"
#> Sample_625 Sample_626 Sample_627 Sample_628 Sample_629 Sample_630 Sample_631 Sample_632
#> "0231" "013" "0231" "0231" "0231" "013" "0231" "013"
#> Sample_633 Sample_634 Sample_635 Sample_636 Sample_637 Sample_638 Sample_639 Sample_640
#> "013" "013" "013" "013" "013" "0231" "013" "0232"
#> Sample_641 Sample_642 Sample_643 Sample_644 Sample_645 Sample_646 Sample_647 Sample_648
#> "013" "0233" "013" "0321" "0232" "013" "013" "0232"
#> Sample_649 Sample_650 Sample_651 Sample_652 Sample_653 Sample_654 Sample_655 Sample_656
#> "0232" "0231" "0221" "0221" "0221" "0221" "0221" "0221"
#> Sample_657 Sample_658 Sample_659 Sample_660 Sample_661 Sample_662 Sample_663 Sample_664
#> "0222" "0221" "0221" "0222" "0221" "0222" "0222" "0221"
#> Sample_665 Sample_666 Sample_667 Sample_668 Sample_669 Sample_670 Sample_671 Sample_672
#> "0232" "0222" "0232" "0222" "0222" "0222" "0221" "0222"
#> Sample_673 Sample_674 Sample_675 Sample_676 Sample_677 Sample_678 Sample_679 Sample_680
#> "0222" "0222" "0222" "0222" "0221" "0221" "0221" "0222"
#> Sample_681 Sample_682 Sample_683 Sample_684 Sample_685 Sample_686 Sample_687 Sample_688
#> "0232" "0222" "0222" "0222" "012" "0212" "012" "0212"
#> Sample_689 Sample_690 Sample_691 Sample_692 Sample_693 Sample_694 Sample_695 Sample_696
#> "0222" "0212" "0222" "012" "0222" "0222" "0212" "0211"
#> Sample_697 Sample_698 Sample_699 Sample_700 Sample_701 Sample_702 Sample_703 Sample_704
#> "0211" "0212" "0212" "0211" "0212" "0211" "0212" "0221"
#> Sample_705 Sample_706 Sample_707 Sample_708 Sample_709 Sample_710 Sample_711 Sample_712
#> "0222" "0211" "0211" "0211" "0211" "0232" "0212" "0211"
#> Sample_713 Sample_714 Sample_715 Sample_716 Sample_717 Sample_718 Sample_719 Sample_720
#> "0211" "0212" "0212" "0212" "0211" "0211" "0211" "0232"
#> Sample_721 Sample_722 Sample_723 Sample_724 Sample_725 Sample_726 Sample_727 Sample_728
#> "0211" "0211" "0212" "0211" "0212" "0211" "0211" "0212"
#> Sample_729 Sample_730 Sample_731 Sample_732 Sample_733 Sample_734 Sample_735 Sample_736
#> "0211" "0211" "0233" "0211" "0211" "0211" "0233" "0211"
#> Sample_737 Sample_738 Sample_739 Sample_740 Sample_741 Sample_742 Sample_743 Sample_744
#> "012" "0232" "0211" "0212" "0211" "0212" "0211" "0211"
#> Sample_745 Sample_746 Sample_747 Sample_748 Sample_749 Sample_750 Sample_751 Sample_752
#> "0212" "0211" "0211" "0212" "0211" "0211" "0211" "0211"
#> Sample_753 Sample_754 Sample_755 Sample_756 Sample_757 Sample_758 Sample_759 Sample_760
#> "0212" "0212" "0211" "0211" "0211" "0211" "0212" "0211"
#> Sample_761 Sample_762 Sample_763 Sample_764 Sample_765 Sample_766 Sample_767 Sample_768
#> "0211" "0211" "0211" "0211" "0211" "0212" "0211" "0232"
#> Sample_769 Sample_770 Sample_771 Sample_772 Sample_773 Sample_774 Sample_775 Sample_776
#> "0212" "0211" "0211" "0212" "0211" "0211" "0212" "0211"
#> Sample_777 Sample_778 Sample_779 Sample_780 Sample_781 Sample_782 Sample_783 Sample_784
#> "0212" "0212" "0212" "0212" "0211" "0212" "0212" "0212"
#> Sample_785 Sample_786 Sample_787 Sample_788 Sample_789 Sample_790 Sample_791 Sample_792
#> "0212" "0212" "0211" "0212" "0212" "0211" "0211" "0211"
#> Sample_793 Sample_794 Sample_795 Sample_796 Sample_797 Sample_798 Sample_799 Sample_800
#> "0212" "0211" "0211" "0211" "0212" "0212" "0212" "0211"
#> Sample_801 Sample_802 Sample_803 Sample_804 Sample_805 Sample_806 Sample_807 Sample_808
#> "0211" "0212" "0211" "0212" "0211" "0211" "0211" "0211"
#> Sample_809 Sample_810 Sample_811 Sample_812 Sample_813 Sample_814 Sample_815 Sample_816
#> "0211" "0212" "0211" "0211" "0211" "0211" "0211" "0211"
#> Sample_817 Sample_818 Sample_819 Sample_820 Sample_821 Sample_822 Sample_823 Sample_824
#> "0212" "0211" "0212" "0233" "0212" "0211" "0212" "0212"
#> Sample_825 Sample_826 Sample_827 Sample_828 Sample_829 Sample_830 Sample_831 Sample_832
#> "0212" "0212" "0212" "0211" "0212" "0212" "0211" "0211"
#> Sample_833 Sample_834 Sample_835 Sample_836 Sample_837 Sample_838 Sample_839 Sample_840
#> "0211" "0212" "0212" "0233" "0211" "0211" "0212" "0211"
#> Sample_841 Sample_842 Sample_843 Sample_844 Sample_845 Sample_846 Sample_847 Sample_848
#> "0222" "0221" "0221" "0221" "0221" "0221" "0222" "0222"
#> Sample_849 Sample_850 Sample_851 Sample_852 Sample_853 Sample_854 Sample_855 Sample_856
#> "0222" "0221" "0221" "0222" "0221" "0221" "0212" "0212"
#> Sample_857 Sample_858 Sample_859 Sample_860 Sample_861 Sample_862 Sample_863 Sample_864
#> "0212" "0211" "0232" "0212" "0212" "0212" "0212" "0211"
#> Sample_865 Sample_866 Sample_867 Sample_868 Sample_869 Sample_870 Sample_871 Sample_872
#> "0233" "0212" "0212" "0211" "0212" "0212" "0212" "0212"
#> Sample_873 Sample_874 Sample_875 Sample_876 Sample_877 Sample_878 Sample_879 Sample_880
#> "0212" "0211" "0211" "0212" "0211" "0211" "0211" "0211"
#> Sample_881 Sample_882 Sample_883 Sample_884 Sample_885 Sample_886 Sample_887 Sample_888
#> "0212" "0233" "0211" "0211" "0212" "0211" "0211" "0211"
#> Sample_889 Sample_890 Sample_891 Sample_892 Sample_893 Sample_894 Sample_895 Sample_896
#> "0212" "0212" "0212" "0212" "0212" "0232" "0212" "0211"
#> Sample_897 Sample_898 Sample_899 Sample_900 Sample_901 Sample_902 Sample_903 Sample_904
#> "0212" "0212" "0211" "0233" "0211" "0212" "0212" "0211"
#> Sample_905 Sample_906 Sample_907 Sample_908 Sample_909 Sample_910 Sample_911 Sample_912
#> "0211" "0212" "0212" "0212" "0212" "0211" "0212" "0211"
#> Sample_913 Sample_914 Sample_915 Sample_916 Sample_917 Sample_918 Sample_919 Sample_920
#> "0212" "0212" "0212" "0211" "0212" "0212" "0212" "0212"
#> Sample_921 Sample_922 Sample_923 Sample_924 Sample_925 Sample_926 Sample_927 Sample_928
#> "0211" "0212" "0211" "0212" "0212" "0211" "0211" "0211"
#> Sample_929 Sample_930 Sample_931 Sample_932 Sample_933 Sample_934 Sample_935 Sample_936
#> "0212" "0211" "0211" "0212" "0212" "0212" "0212" "0212"
#> Sample_937 Sample_938 Sample_939 Sample_940 Sample_941 Sample_942 Sample_943 Sample_944
#> "0212" "0212" "0212" "0212" "0212" "0212" "0212" "0212"
#> Sample_945 Sample_946 Sample_947 Sample_948 Sample_949 Sample_950 Sample_951 Sample_952
#> "0211" "0211" "0211" "0321" "0321" "013" "0321" "0321"
#> Sample_953 Sample_954 Sample_955 Sample_956 Sample_957 Sample_958 Sample_959 Sample_960
#> "0321" "0322" "0321" "0321" "0321" "0321" "0321" "0321"
#> Sample_961 Sample_962 Sample_963 Sample_964 Sample_965 Sample_966 Sample_967 Sample_968
#> "0321" "012" "0321" "0321" "0321" "0321" "0321" "012"
#> Sample_969 Sample_970 Sample_971 Sample_972 Sample_973 Sample_974 Sample_975 Sample_976
#> "0321" "0321" "0321" "0321" "0321" "0321" "0333" "013"
#> Sample_977 Sample_978 Sample_979 Sample_980 Sample_981 Sample_982 Sample_983 Sample_984
#> "013" "012" "0321" "013" "013" "012" "012" "0321"
#> Sample_985 Sample_986 Sample_987 Sample_988 Sample_989 Sample_990 Sample_991 Sample_992
#> "012" "013" "012" "012" "012" "012" "012" "012"
#> Sample_993 Sample_994 Sample_995 Sample_996 Sample_997 Sample_998 Sample_999 Sample_1000
#> "012" "012" "0321" "013" "0321" "0321" "013" "0321"
#> Sample_1001 Sample_1002 Sample_1003 Sample_1004 Sample_1005 Sample_1006 Sample_1007 Sample_1008
#> "0321" "0321" "0321" "0333" "0321" "0321" "012" "013"
#> Sample_1009 Sample_1010 Sample_1011 Sample_1012 Sample_1013 Sample_1014 Sample_1015 Sample_1016
#> "013" "013" "0321" "0322" "0321" "012" "012" "012"
#> Sample_1017 Sample_1018 Sample_1019 Sample_1020 Sample_1021 Sample_1022 Sample_1023 Sample_1024
#> "012" "012" "012" "012" "012" "012" "012" "012"
#> Sample_1025 Sample_1026 Sample_1027 Sample_1028 Sample_1029 Sample_1030 Sample_1031 Sample_1032
#> "012" "012" "011" "011" "0312" "011" "0332" "0312"
#> Sample_1033 Sample_1034 Sample_1035 Sample_1036 Sample_1037 Sample_1038 Sample_1039 Sample_1040
#> "0312" "011" "0312" "011" "0312" "0312" "011" "0312"
#> Sample_1041 Sample_1042 Sample_1043 Sample_1044 Sample_1045 Sample_1046 Sample_1047 Sample_1048
#> "011" "011" "0312" "0312" "0332" "011" "0312" "0331"
#> Sample_1049 Sample_1050 Sample_1051 Sample_1052 Sample_1053 Sample_1054 Sample_1055 Sample_1056
#> "011" "011" "0312" "011" "0322" "0321" "0321" "013"
#> Sample_1057 Sample_1058 Sample_1059 Sample_1060 Sample_1061 Sample_1062 Sample_1063 Sample_1064
#> "0322" "012" "013" "0321" "0322" "013" "0312" "0312"
#> Sample_1065 Sample_1066 Sample_1067 Sample_1068 Sample_1069 Sample_1070 Sample_1071 Sample_1072
#> "0312" "011" "0312" "0312" "0312" "011" "0312" "0312"
#> Sample_1073 Sample_1074 Sample_1075 Sample_1076 Sample_1077 Sample_1078 Sample_1079 Sample_1080
#> "011" "0312" "011" "011" "0312" "0312" "0312" "011"
#> Sample_1081 Sample_1082 Sample_1083 Sample_1084 Sample_1085 Sample_1086 Sample_1087 Sample_1088
#> "0312" "0312" "011" "0312" "0312" "0331" "011" "0331"
#> Sample_1089 Sample_1090 Sample_1091 Sample_1092 Sample_1093 Sample_1094 Sample_1095 Sample_1096
#> "0332" "011" "011" "0312" "011" "0312" "0312" "0312"
#> Sample_1097 Sample_1098 Sample_1099 Sample_1100 Sample_1101 Sample_1102 Sample_1103 Sample_1104
#> "011" "0312" "0312" "011" "0312" "011" "011" "011"
#> Sample_1105 Sample_1106 Sample_1107 Sample_1108 Sample_1109 Sample_1110 Sample_1111 Sample_1112
#> "011" "0312" "0312" "011" "0312" "011" "0312" "011"
#> Sample_1113 Sample_1114 Sample_1115 Sample_1116 Sample_1117 Sample_1118 Sample_1119 Sample_1120
#> "011" "011" "0312" "011" "0312" "011" "011" "0332"
#> Sample_1121 Sample_1122 Sample_1123 Sample_1124 Sample_1125 Sample_1126 Sample_1127 Sample_1128
#> "011" "0331" "0312" "0312" "0312" "0312" "0312" "0312"
#> Sample_1129 Sample_1130 Sample_1131 Sample_1132 Sample_1133 Sample_1134 Sample_1135 Sample_1136
#> "011" "011" "011" "0332" "011" "0331" "011" "011"
#> Sample_1137 Sample_1138 Sample_1139 Sample_1140 Sample_1141 Sample_1142 Sample_1143 Sample_1144
#> "011" "011" "0312" "0331" "0312" "011" "011" "011"
#> Sample_1145 Sample_1146 Sample_1147 Sample_1148 Sample_1149 Sample_1150 Sample_1151 Sample_1152
#> "0312" "011" "011" "011" "011" "0312" "011" "0312"
#> Sample_1153 Sample_1154 Sample_1155 Sample_1156 Sample_1157 Sample_1158 Sample_1159 Sample_1160
#> "011" "011" "011" "0312" "0312" "011" "011" "011"
#> Sample_1161 Sample_1162 Sample_1163 Sample_1164 Sample_1165 Sample_1166 Sample_1167 Sample_1168
#> "0312" "011" "0312" "0312" "011" "011" "011" "011"
#> Sample_1169 Sample_1170 Sample_1171 Sample_1172 Sample_1173 Sample_1174 Sample_1175 Sample_1176
#> "0312" "0312" "011" "011" "011" "011" "0312" "011"
#> Sample_1177 Sample_1178 Sample_1179 Sample_1180 Sample_1181 Sample_1182 Sample_1183 Sample_1184
#> "011" "011" "011" "0312" "011" "011" "0331" "0331"
#> Sample_1185 Sample_1186 Sample_1187 Sample_1188 Sample_1189 Sample_1190 Sample_1191 Sample_1192
#> "011" "011" "011" "011" "0321" "0322" "0322" "012"
#> Sample_1193 Sample_1194 Sample_1195 Sample_1196 Sample_1197 Sample_1198 Sample_1199 Sample_1200
#> "0322" "0322" "0321" "0321" "0322" "0322" "0322" "0321"
#> Sample_1201 Sample_1202 Sample_1203 Sample_1204 Sample_1205 Sample_1206 Sample_1207 Sample_1208
#> "0321" "0321" "0322" "0322" "0322" "0321" "0322" "013"
#> Sample_1209 Sample_1210 Sample_1211 Sample_1212 Sample_1213 Sample_1214 Sample_1215 Sample_1216
#> "0321" "0322" "0321" "0321" "0321" "0322" "0322" "012"
#> Sample_1217 Sample_1218 Sample_1219 Sample_1220 Sample_1221 Sample_1222 Sample_1223 Sample_1224
#> "013" "013" "0322" "0321" "0322" "011" "0311" "0311"
#> Sample_1225 Sample_1226 Sample_1227 Sample_1228 Sample_1229 Sample_1230 Sample_1231 Sample_1232
#> "011" "0311" "011" "011" "011" "0311" "0311" "0311"
#> Sample_1233 Sample_1234 Sample_1235 Sample_1236 Sample_1237 Sample_1238 Sample_1239 Sample_1240
#> "0311" "011" "0311" "011" "0311" "0311" "011" "011"
#> Sample_1241 Sample_1242 Sample_1243 Sample_1244 Sample_1245 Sample_1246 Sample_1247 Sample_1248
#> "011" "011" "0311" "0311" "0311" "0311" "0311" "0311"
#> Sample_1249 Sample_1250 Sample_1251 Sample_1252 Sample_1253 Sample_1254 Sample_1255 Sample_1256
#> "011" "0311" "0311" "0311" "0311" "011" "0332" "0311"
#> Sample_1257 Sample_1258 Sample_1259 Sample_1260 Sample_1261 Sample_1262 Sample_1263 Sample_1264
#> "0311" "0311" "0311" "0311" "0311" "0311" "0311" "011"
#> Sample_1265 Sample_1266 Sample_1267 Sample_1268 Sample_1269 Sample_1270 Sample_1271 Sample_1272
#> "0311" "011" "011" "0332" "0311" "0311" "0311" "011"
#> Sample_1273 Sample_1274 Sample_1275 Sample_1276 Sample_1277 Sample_1278 Sample_1279 Sample_1280
#> "0311" "0311" "0311" "0311" "0311" "0311" "011" "011"
#> Sample_1281 Sample_1282 Sample_1283 Sample_1284 Sample_1285 Sample_1286 Sample_1287 Sample_1288
#> "0311" "0311" "011" "0311" "011" "0311" "0311" "0311"
#> Sample_1289 Sample_1290 Sample_1291 Sample_1292 Sample_1293 Sample_1294 Sample_1295 Sample_1296
#> "0311" "0311" "0311" "0311" "0311" "0311" "0311" "0331"
#> Sample_1297 Sample_1298 Sample_1299 Sample_1300 Sample_1301 Sample_1302 Sample_1303 Sample_1304
#> "0311" "0311" "011" "0311" "0311" "0311" "0311" "0311"
#> Sample_1305 Sample_1306 Sample_1307 Sample_1308 Sample_1309 Sample_1310 Sample_1311 Sample_1312
#> "0311" "0332" "011" "011" "011" "0332" "0311" "0311"
#> Sample_1313 Sample_1314 Sample_1315 Sample_1316 Sample_1317 Sample_1318 Sample_1319 Sample_1320
#> "011" "0313" "0313" "0311" "011" "0313" "0311" "011"
#> Sample_1321 Sample_1322 Sample_1323 Sample_1324 Sample_1325 Sample_1326 Sample_1327 Sample_1328
#> "0314" "0311" "0311" "0311" "0311" "0314" "0311" "0313"
#> Sample_1329 Sample_1330 Sample_1331 Sample_1332 Sample_1333 Sample_1334 Sample_1335 Sample_1336
#> "0314" "0314" "0313" "0314" "0314" "0313" "011" "011"
#> Sample_1337 Sample_1338 Sample_1339 Sample_1340 Sample_1341 Sample_1342 Sample_1343 Sample_1344
#> "0332" "011" "0332" "0311" "0314" "011" "011" "0314"
#> Sample_1345 Sample_1346 Sample_1347 Sample_1348 Sample_1349 Sample_1350 Sample_1351 Sample_1352
#> "0332" "0311" "0311" "011" "0314" "011" "0314" "011"
#> Sample_1353 Sample_1354 Sample_1355 Sample_1356 Sample_1357 Sample_1358 Sample_1359 Sample_1360
#> "0311" "011" "0314" "0314" "011" "011" "0314" "011"
#> Sample_1361 Sample_1362 Sample_1363 Sample_1364 Sample_1365 Sample_1366 Sample_1367 Sample_1368
#> "0314" "0314" "0314" "011" "0311" "0313" "011" "0313"
#> Sample_1369 Sample_1370 Sample_1371 Sample_1372 Sample_1373 Sample_1374 Sample_1375 Sample_1376
#> "0311" "0314" "011" "0314" "011" "011" "011" "0332"
#> Sample_1377 Sample_1378 Sample_1379 Sample_1380 Sample_1381 Sample_1382 Sample_1383 Sample_1384
#> "0311" "0311" "0311" "0311" "0314" "011" "0313" "0313"
#> Sample_1385 Sample_1386 Sample_1387 Sample_1388 Sample_1389 Sample_1390 Sample_1391 Sample_1392
#> "0313" "011" "0313" "011" "0313" "0313" "0313" "0313"
#> Sample_1393 Sample_1394 Sample_1395 Sample_1396 Sample_1397 Sample_1398 Sample_1399 Sample_1400
#> "0313" "0313" "0313" "0313" "0313" "011" "011" "0331"
#> Sample_1401 Sample_1402 Sample_1403 Sample_1404 Sample_1405 Sample_1406 Sample_1407 Sample_1408
#> "0313" "011" "0313" "0313" "0313" "011" "0313" "011"
#> Sample_1409 Sample_1410 Sample_1411 Sample_1412 Sample_1413 Sample_1414 Sample_1415 Sample_1416
#> "011" "0313" "0313" "0313" "0331" "0313" "0313" "0332"
#> Sample_1417 Sample_1418 Sample_1419 Sample_1420 Sample_1421 Sample_1422 Sample_1423 Sample_1424
#> "0313" "0313" "011" "0313" "0313" "0313" "011" "0313"
#> Sample_1425 Sample_1426 Sample_1427 Sample_1428 Sample_1429 Sample_1430 Sample_1431 Sample_1432
#> "011" "011" "011" "0313" "011" "011" "011" "011"
#> Sample_1433 Sample_1434 Sample_1435 Sample_1436 Sample_1437 Sample_1438 Sample_1439 Sample_1440
#> "011" "011" "011" "011" "011" "011" "011" "011"
#> Sample_1441 Sample_1442 Sample_1443 Sample_1444 Sample_1445 Sample_1446 Sample_1447 Sample_1448
#> "011" "0313" "0313" "011" "011" "0313" "0313" "011"
#> Sample_1449 Sample_1450 Sample_1451 Sample_1452 Sample_1453 Sample_1454 Sample_1455 Sample_1456
#> "011" "011" "011" "011" "011" "011" "011" "0313"
#> Sample_1457 Sample_1458 Sample_1459 Sample_1460 Sample_1461 Sample_1462 Sample_1463 Sample_1464
#> "0313" "011" "011" "0331" "0313" "011" "0314" "0314"
#> Sample_1465 Sample_1466 Sample_1467 Sample_1468 Sample_1469 Sample_1470 Sample_1471 Sample_1472
#> "011" "011" "011" "0313" "011" "011" "011" "011"
#> Sample_1473 Sample_1474 Sample_1475 Sample_1476 Sample_1477 Sample_1478 Sample_1479 Sample_1480
#> "011" "011" "011" "011" "011" "011" "011" "0314"
#> Sample_1481 Sample_1482 Sample_1483 Sample_1484 Sample_1485 Sample_1486 Sample_1487 Sample_1488
#> "011" "011" "011" "011" "0313" "0331" "011" "0313"
#> Sample_1489 Sample_1490 Sample_1491 Sample_1492 Sample_1493 Sample_1494 Sample_1495 Sample_1496
#> "011" "011" "011" "011" "011" "0332" "011" "011"
#> Sample_1497 Sample_1498 Sample_1499 Sample_1500 Sample_1501 Sample_1502 Sample_1503 Sample_1504
#> "011" "011" "011" "011" "011" "0313" "0313" "0332"
#> Sample_1505 Sample_1506 Sample_1507 Sample_1508 Sample_1509 Sample_1510 Sample_1511 Sample_1512
#> "0331" "0313" "0313" "0313" "011" "011" "0313" "0313"
#> Sample_1513 Sample_1514 Sample_1515 Sample_1516 Sample_1517 Sample_1518 Sample_1519 Sample_1520
#> "0313" "011" "0313" "011" "0222" "0222" "0221" "0233"
#> Sample_1521 Sample_1522 Sample_1523 Sample_1524 Sample_1525 Sample_1526 Sample_1527 Sample_1528
#> "0232" "013" "013" "0233" "012" "012" "0212" "0212"
#> Sample_1529 Sample_1530 Sample_1531 Sample_1532 Sample_1533 Sample_1534 Sample_1535 Sample_1536
#> "0212" "0233" "0232" "0232" "0211" "0212" "0222" "0222"
#> Sample_1537 Sample_1538 Sample_1539 Sample_1540 Sample_1541 Sample_1542 Sample_1543 Sample_1544
#> "0221" "0212" "0212" "013" "011" "0212" "0233" "013"
#> Sample_1545 Sample_1546 Sample_1547 Sample_1548 Sample_1549 Sample_1550 Sample_1551 Sample_1552
#> "011" "0211" "013" "0231" "0322" "0231" "0231" "0231"
#> Sample_1553 Sample_1554 Sample_1555 Sample_1556 Sample_1557 Sample_1558 Sample_1559 Sample_1560
#> "0321" "0222" "0231" "013" "013" "013" "0231" "013"
#> Sample_1561 Sample_1562 Sample_1563 Sample_1564 Sample_1565 Sample_1566 Sample_1567 Sample_1568
#> "0231" "012" "013" "013" "013" "013" "013" "013"
#> Sample_1569 Sample_1570 Sample_1571 Sample_1572 Sample_1573 Sample_1574 Sample_1575 Sample_1576
#> "0312" "0212" "013" "013" "013" "0231" "0231" "013"
#> Sample_1577 Sample_1578 Sample_1579 Sample_1580 Sample_1581 Sample_1582 Sample_1583 Sample_1584
#> "013" "013" "013" "013" "0322" "0231" "013" "013"
#> Sample_1585 Sample_1586 Sample_1587 Sample_1588 Sample_1589 Sample_1590 Sample_1591 Sample_1592
#> "0231" "0231" "0231" "0222" "0231" "013" "013" "0231"
#> Sample_1593 Sample_1594 Sample_1595 Sample_1596 Sample_1597 Sample_1598 Sample_1599 Sample_1600
#> "0231" "0231" "0231" "0232" "0322" "0311" "0233" "013"
get_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 439))
#> Sample_1 Sample_2 Sample_3 Sample_4 Sample_5 Sample_6 Sample_7 Sample_8
#> "013" "0221" "0221" "0222" "0221" "0221" "0221" "0222"
#> Sample_9 Sample_10 Sample_11 Sample_12 Sample_13 Sample_14 Sample_15 Sample_16
#> "0221" "0221" "0221" "0221" "0221" "0221" "0221" "0221"
#> Sample_17 Sample_18 Sample_19 Sample_20 Sample_21 Sample_22 Sample_23 Sample_24
#> "0222" "0222" "0222" "0222" "0222" "0222" "0222" "0221"
#> Sample_25 Sample_26 Sample_27 Sample_28 Sample_29 Sample_30 Sample_31 Sample_32
#> "0222" "0221" "0222" "0222" "0221" "0222" "0233" "0221"
#> Sample_33 Sample_34 Sample_35 Sample_36 Sample_37 Sample_38 Sample_39 Sample_40
#> "0221" "0221" "0221" "0221" "0221" "0221" "0221" "0221"
#> Sample_41 Sample_42 Sample_43 Sample_44 Sample_45 Sample_46 Sample_47 Sample_48
#> "0221" "0221" "0233" "0221" "0221" "0221" "0221" "0221"
#> Sample_49 Sample_50 Sample_51 Sample_52 Sample_53 Sample_54 Sample_55 Sample_56
#> "0221" "0221" "0221" "0221" "0222" "0222" "0221" "0221"
#> Sample_57 Sample_58 Sample_59 Sample_60 Sample_61 Sample_62 Sample_63 Sample_64
#> "0221" "0221" "0221" "0221" "0222" "0221" "0221" "0221"
#> Sample_65 Sample_66 Sample_67 Sample_68 Sample_69 Sample_70 Sample_71 Sample_72
#> "0221" "0221" "0222" "0221" "0221" "0222" "0222" "021"
#> Sample_73 Sample_74 Sample_75 Sample_76 Sample_77 Sample_78 Sample_79 Sample_80
#> "0233" "0221" "0222" "0221" "0222" "0221" "0222" "0222"
#> Sample_81 Sample_82 Sample_83 Sample_84 Sample_85 Sample_86 Sample_87 Sample_88
#> "0221" "0222" "0221" "0221" "0222" "0221" "0222" "0222"
#> Sample_89 Sample_90 Sample_91 Sample_92 Sample_93 Sample_94 Sample_95 Sample_96
#> "0222" "0222" "0222" "0222" "0222" "0232" "0222" "0221"
#> Sample_97 Sample_98 Sample_99 Sample_100 Sample_101 Sample_102 Sample_103 Sample_104
#> "0221" "0221" "0221" "0222" "0222" "0221" "0222" "0222"
#> Sample_105 Sample_106 Sample_107 Sample_108 Sample_109 Sample_110 Sample_111 Sample_112
#> "0221" "0221" "0222" "0221" "0221" "0222" "021" "0222"
#> Sample_113 Sample_114 Sample_115 Sample_116 Sample_117 Sample_118 Sample_119 Sample_120
#> "0222" "0232" "0222" "0222" "0222" "0232" "0222" "0221"
#> Sample_121 Sample_122 Sample_123 Sample_124 Sample_125 Sample_126 Sample_127 Sample_128
#> "012" "0222" "0232" "0221" "0221" "0222" "0221" "0222"
#> Sample_129 Sample_130 Sample_131 Sample_132 Sample_133 Sample_134 Sample_135 Sample_136
#> "0221" "0222" "0222" "0221" "0222" "0221" "0222" "0221"
#> Sample_137 Sample_138 Sample_139 Sample_140 Sample_141 Sample_142 Sample_143 Sample_144
#> "0221" "0221" "0222" "0221" "0221" "0222" "0222" "0232"
#> Sample_145 Sample_146 Sample_147 Sample_148 Sample_149 Sample_150 Sample_151 Sample_152
#> "0222" "0222" "021" "021" "021" "021" "021" "021"
#> Sample_153 Sample_154 Sample_155 Sample_156 Sample_157 Sample_158 Sample_159 Sample_160
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_161 Sample_162 Sample_163 Sample_164 Sample_165 Sample_166 Sample_167 Sample_168
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_169 Sample_170 Sample_171 Sample_172 Sample_173 Sample_174 Sample_175 Sample_176
#> "021" "021" "0232" "021" "0233" "021" "021" "021"
#> Sample_177 Sample_178 Sample_179 Sample_180 Sample_181 Sample_182 Sample_183 Sample_184
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_185 Sample_186 Sample_187 Sample_188 Sample_189 Sample_190 Sample_191 Sample_192
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_193 Sample_194 Sample_195 Sample_196 Sample_197 Sample_198 Sample_199 Sample_200
#> "021" "021" "021" "021" "0221" "0221" "0221" "0221"
#> Sample_201 Sample_202 Sample_203 Sample_204 Sample_205 Sample_206 Sample_207 Sample_208
#> "0221" "0221" "0221" "0233" "0221" "0221" "0221" "0322"
#> Sample_209 Sample_210 Sample_211 Sample_212 Sample_213 Sample_214 Sample_215 Sample_216
#> "0322" "0322" "0322" "0322" "0322" "013" "013" "0322"
#> Sample_217 Sample_218 Sample_219 Sample_220 Sample_221 Sample_222 Sample_223 Sample_224
#> "013" "0322" "0322" "0322" "0322" "0322" "0322" "013"
#> Sample_225 Sample_226 Sample_227 Sample_228 Sample_229 Sample_230 Sample_231 Sample_232
#> "0322" "013" "0322" "0322" "0322" "0322" "0322" "0322"
#> Sample_233 Sample_234 Sample_235 Sample_236 Sample_237 Sample_238 Sample_239 Sample_240
#> "0333" "0322" "0322" "013" "0322" "0322" "0322" "0322"
#> Sample_241 Sample_242 Sample_243 Sample_244 Sample_245 Sample_246 Sample_247 Sample_248
#> "013" "0322" "0321" "0322" "0322" "0322" "0322" "0322"
#> Sample_249 Sample_250 Sample_251 Sample_252 Sample_253 Sample_254 Sample_255 Sample_256
#> "0322" "0322" "0322" "0322" "0322" "0333" "0322" "0322"
#> Sample_257 Sample_258 Sample_259 Sample_260 Sample_261 Sample_262 Sample_263 Sample_264
#> "0322" "013" "0322" "0322" "0322" "0322" "0322" "013"
#> Sample_265 Sample_266 Sample_267 Sample_268 Sample_269 Sample_270 Sample_271 Sample_272
#> "012" "0321" "0321" "0321" "0322" "0321" "0333" "012"
#> Sample_273 Sample_274 Sample_275 Sample_276 Sample_277 Sample_278 Sample_279 Sample_280
#> "0321" "0321" "012" "013" "0321" "0333" "0321" "0322"
#> Sample_281 Sample_282 Sample_283 Sample_284 Sample_285 Sample_286 Sample_287 Sample_288
#> "0321" "0321" "0322" "0321" "0321" "0321" "013" "0333"
#> Sample_289 Sample_290 Sample_291 Sample_292 Sample_293 Sample_294 Sample_295 Sample_296
#> "0333" "0321" "0321" "0321" "0322" "0321" "0321" "0321"
#> Sample_297 Sample_298 Sample_299 Sample_300 Sample_301 Sample_302 Sample_303 Sample_304
#> "0321" "0321" "0321" "0322" "0321" "012" "012" "012"
#> Sample_305 Sample_306 Sample_307 Sample_308 Sample_309 Sample_310 Sample_311 Sample_312
#> "012" "0321" "0321" "012" "012" "012" "012" "012"
#> Sample_313 Sample_314 Sample_315 Sample_316 Sample_317 Sample_318 Sample_319 Sample_320
#> "012" "012" "0321" "012" "0321" "012" "012" "012"
#> Sample_321 Sample_322 Sample_323 Sample_324 Sample_325 Sample_326 Sample_327 Sample_328
#> "012" "0321" "012" "012" "012" "012" "012" "012"
#> Sample_329 Sample_330 Sample_331 Sample_332 Sample_333 Sample_334 Sample_335 Sample_336
#> "012" "012" "0333" "0321" "0321" "0321" "0321" "0321"
#> Sample_337 Sample_338 Sample_339 Sample_340 Sample_341 Sample_342 Sample_343 Sample_344
#> "0321" "0321" "0333" "0321" "0321" "0321" "0321" "0321"
#> Sample_345 Sample_346 Sample_347 Sample_348 Sample_349 Sample_350 Sample_351 Sample_352
#> "0321" "0321" "0321" "0321" "0321" "013" "012" "013"
#> Sample_353 Sample_354 Sample_355 Sample_356 Sample_357 Sample_358 Sample_359 Sample_360
#> "0321" "0321" "012" "0321" "0321" "012" "0321" "012"
#> Sample_361 Sample_362 Sample_363 Sample_364 Sample_365 Sample_366 Sample_367 Sample_368
#> "0321" "012" "0321" "0321" "013" "0321" "0321" "012"
#> Sample_369 Sample_370 Sample_371 Sample_372 Sample_373 Sample_374 Sample_375 Sample_376
#> "012" "0321" "0333" "013" "013" "0321" "0321" "0321"
#> Sample_377 Sample_378 Sample_379 Sample_380 Sample_381 Sample_382 Sample_383 Sample_384
#> "012" "0321" "0321" "0321" "012" "0321" "0321" "0321"
#> Sample_385 Sample_386 Sample_387 Sample_388 Sample_389 Sample_390 Sample_391 Sample_392
#> "012" "012" "0321" "0321" "013" "013" "0321" "0321"
#> Sample_393 Sample_394 Sample_395 Sample_396 Sample_397 Sample_398 Sample_399 Sample_400
#> "012" "013" "012" "013" "012" "012" "0321" "0321"
#> Sample_401 Sample_402 Sample_403 Sample_404 Sample_405 Sample_406 Sample_407 Sample_408
#> "0321" "0322" "013" "013" "013" "0321" "0321" "013"
#> Sample_409 Sample_410 Sample_411 Sample_412 Sample_413 Sample_414 Sample_415 Sample_416
#> "012" "0321" "0321" "012" "0321" "0321" "0333" "012"
#> Sample_417 Sample_418 Sample_419 Sample_420 Sample_421 Sample_422 Sample_423 Sample_424
#> "0321" "0321" "0321" "0333" "012" "0333" "0322" "012"
#> Sample_425 Sample_426 Sample_427 Sample_428 Sample_429 Sample_430 Sample_431 Sample_432
#> "012" "0322" "012" "012" "0322" "013" "012" "012"
#> Sample_433 Sample_434 Sample_435 Sample_436 Sample_437 Sample_438 Sample_439 Sample_440
#> "013" "012" "0322" "0333" "0333" "013" "012" "012"
#> Sample_441 Sample_442 Sample_443 Sample_444 Sample_445 Sample_446 Sample_447 Sample_448
#> "0322" "012" "013" "0321" "012" "012" "0322" "013"
#> Sample_449 Sample_450 Sample_451 Sample_452 Sample_453 Sample_454 Sample_455 Sample_456
#> "0322" "0321" "012" "012" "0322" "0321" "013" "012"
#> Sample_457 Sample_458 Sample_459 Sample_460 Sample_461 Sample_462 Sample_463 Sample_464
#> "013" "0322" "012" "0321" "012" "0321" "013" "012"
#> Sample_465 Sample_466 Sample_467 Sample_468 Sample_469 Sample_470 Sample_471 Sample_472
#> "012" "012" "0322" "012" "012" "0321" "0322" "012"
#> Sample_473 Sample_474 Sample_475 Sample_476 Sample_477 Sample_478 Sample_479 Sample_480
#> "0321" "012" "012" "013" "013" "0314" "011" "0314"
#> Sample_481 Sample_482 Sample_483 Sample_484 Sample_485 Sample_486 Sample_487 Sample_488
#> "0313" "0314" "0314" "0314" "0314" "011" "011" "0331"
#> Sample_489 Sample_490 Sample_491 Sample_492 Sample_493 Sample_494 Sample_495 Sample_496
#> "0314" "011" "0314" "0314" "0314" "0314" "011" "0314"
#> Sample_497 Sample_498 Sample_499 Sample_500 Sample_501 Sample_502 Sample_503 Sample_504
#> "0314" "0314" "011" "011" "0314" "0314" "0314" "0314"
#> Sample_505 Sample_506 Sample_507 Sample_508 Sample_509 Sample_510 Sample_511 Sample_512
#> "011" "011" "011" "0314" "0314" "0314" "011" "0332"
#> Sample_513 Sample_514 Sample_515 Sample_516 Sample_517 Sample_518 Sample_519 Sample_520
#> "011" "0314" "011" "021" "011" "0314" "0314" "011"
#> Sample_521 Sample_522 Sample_523 Sample_524 Sample_525 Sample_526 Sample_527 Sample_528
#> "011" "0314" "0314" "0314" "0314" "0314" "011" "0312"
#> Sample_529 Sample_530 Sample_531 Sample_532 Sample_533 Sample_534 Sample_535 Sample_536
#> "0331" "011" "0332" "011" "011" "0314" "011" "011"
#> Sample_537 Sample_538 Sample_539 Sample_540 Sample_541 Sample_542 Sample_543 Sample_544
#> "011" "0314" "011" "0314" "0314" "011" "011" "011"
#> Sample_545 Sample_546 Sample_547 Sample_548 Sample_549 Sample_550 Sample_551 Sample_552
#> "0331" "011" "011" "0314" "0314" "0314" "0314" "0314"
#> Sample_553 Sample_554 Sample_555 Sample_556 Sample_557 Sample_558 Sample_559 Sample_560
#> "0332" "0314" "0314" "0314" "011" "011" "011" "0314"
#> Sample_561 Sample_562 Sample_563 Sample_564 Sample_565 Sample_566 Sample_567 Sample_568
#> "0314" "0314" "0314" "0314" "011" "011" "011" "0314"
#> Sample_569 Sample_570 Sample_571 Sample_572 Sample_573 Sample_574 Sample_575 Sample_576
#> "011" "0322" "0322" "013" "012" "0333" "0321" "0322"
#> Sample_577 Sample_578 Sample_579 Sample_580 Sample_581 Sample_582 Sample_583 Sample_584
#> "012" "0321" "0321" "0321" "0321" "0321" "0321" "0321"
#> Sample_585 Sample_586 Sample_587 Sample_588 Sample_589 Sample_590 Sample_591 Sample_592
#> "0322" "0221" "0221" "0231" "0322" "013" "0221" "0231"
#> Sample_593 Sample_594 Sample_595 Sample_596 Sample_597 Sample_598 Sample_599 Sample_600
#> "0221" "0231" "0231" "0222" "0231" "0222" "0231" "0231"
#> Sample_601 Sample_602 Sample_603 Sample_604 Sample_605 Sample_606 Sample_607 Sample_608
#> "0222" "0231" "0222" "0222" "0222" "0222" "0221" "0222"
#> Sample_609 Sample_610 Sample_611 Sample_612 Sample_613 Sample_614 Sample_615 Sample_616
#> "0222" "0222" "021" "021" "0322" "013" "0322" "013"
#> Sample_617 Sample_618 Sample_619 Sample_620 Sample_621 Sample_622 Sample_623 Sample_624
#> "013" "012" "013" "013" "0231" "013" "0322" "0322"
#> Sample_625 Sample_626 Sample_627 Sample_628 Sample_629 Sample_630 Sample_631 Sample_632
#> "0231" "013" "0231" "0231" "0231" "013" "0231" "013"
#> Sample_633 Sample_634 Sample_635 Sample_636 Sample_637 Sample_638 Sample_639 Sample_640
#> "013" "013" "013" "013" "013" "0231" "013" "0232"
#> Sample_641 Sample_642 Sample_643 Sample_644 Sample_645 Sample_646 Sample_647 Sample_648
#> "013" "0233" "013" "0321" "0232" "013" "013" "0232"
#> Sample_649 Sample_650 Sample_651 Sample_652 Sample_653 Sample_654 Sample_655 Sample_656
#> "0232" "0231" "0221" "0221" "0221" "0221" "0221" "0221"
#> Sample_657 Sample_658 Sample_659 Sample_660 Sample_661 Sample_662 Sample_663 Sample_664
#> "0222" "0221" "0221" "0222" "0221" "0222" "0222" "0221"
#> Sample_665 Sample_666 Sample_667 Sample_668 Sample_669 Sample_670 Sample_671 Sample_672
#> "0232" "0222" "0232" "0222" "0222" "0222" "0221" "0222"
#> Sample_673 Sample_674 Sample_675 Sample_676 Sample_677 Sample_678 Sample_679 Sample_680
#> "0222" "0222" "0222" "0222" "0221" "0221" "0221" "0222"
#> Sample_681 Sample_682 Sample_683 Sample_684 Sample_685 Sample_686 Sample_687 Sample_688
#> "0232" "0222" "0222" "0222" "012" "021" "012" "021"
#> Sample_689 Sample_690 Sample_691 Sample_692 Sample_693 Sample_694 Sample_695 Sample_696
#> "0222" "021" "0222" "012" "0222" "0222" "021" "021"
#> Sample_697 Sample_698 Sample_699 Sample_700 Sample_701 Sample_702 Sample_703 Sample_704
#> "021" "021" "021" "021" "021" "021" "021" "0221"
#> Sample_705 Sample_706 Sample_707 Sample_708 Sample_709 Sample_710 Sample_711 Sample_712
#> "0222" "021" "021" "021" "021" "0232" "021" "021"
#> Sample_713 Sample_714 Sample_715 Sample_716 Sample_717 Sample_718 Sample_719 Sample_720
#> "021" "021" "021" "021" "021" "021" "021" "0232"
#> Sample_721 Sample_722 Sample_723 Sample_724 Sample_725 Sample_726 Sample_727 Sample_728
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_729 Sample_730 Sample_731 Sample_732 Sample_733 Sample_734 Sample_735 Sample_736
#> "021" "021" "0233" "021" "021" "021" "0233" "021"
#> Sample_737 Sample_738 Sample_739 Sample_740 Sample_741 Sample_742 Sample_743 Sample_744
#> "012" "0232" "021" "021" "021" "021" "021" "021"
#> Sample_745 Sample_746 Sample_747 Sample_748 Sample_749 Sample_750 Sample_751 Sample_752
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_753 Sample_754 Sample_755 Sample_756 Sample_757 Sample_758 Sample_759 Sample_760
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_761 Sample_762 Sample_763 Sample_764 Sample_765 Sample_766 Sample_767 Sample_768
#> "021" "021" "021" "021" "021" "021" "021" "0232"
#> Sample_769 Sample_770 Sample_771 Sample_772 Sample_773 Sample_774 Sample_775 Sample_776
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_777 Sample_778 Sample_779 Sample_780 Sample_781 Sample_782 Sample_783 Sample_784
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_785 Sample_786 Sample_787 Sample_788 Sample_789 Sample_790 Sample_791 Sample_792
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_793 Sample_794 Sample_795 Sample_796 Sample_797 Sample_798 Sample_799 Sample_800
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_801 Sample_802 Sample_803 Sample_804 Sample_805 Sample_806 Sample_807 Sample_808
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_809 Sample_810 Sample_811 Sample_812 Sample_813 Sample_814 Sample_815 Sample_816
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_817 Sample_818 Sample_819 Sample_820 Sample_821 Sample_822 Sample_823 Sample_824
#> "021" "021" "021" "0233" "021" "021" "021" "021"
#> Sample_825 Sample_826 Sample_827 Sample_828 Sample_829 Sample_830 Sample_831 Sample_832
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_833 Sample_834 Sample_835 Sample_836 Sample_837 Sample_838 Sample_839 Sample_840
#> "021" "021" "021" "0233" "021" "021" "021" "021"
#> Sample_841 Sample_842 Sample_843 Sample_844 Sample_845 Sample_846 Sample_847 Sample_848
#> "0222" "0221" "0221" "0221" "0221" "0221" "0222" "0222"
#> Sample_849 Sample_850 Sample_851 Sample_852 Sample_853 Sample_854 Sample_855 Sample_856
#> "0222" "0221" "0221" "0222" "0221" "0221" "021" "021"
#> Sample_857 Sample_858 Sample_859 Sample_860 Sample_861 Sample_862 Sample_863 Sample_864
#> "021" "021" "0232" "021" "021" "021" "021" "021"
#> Sample_865 Sample_866 Sample_867 Sample_868 Sample_869 Sample_870 Sample_871 Sample_872
#> "0233" "021" "021" "021" "021" "021" "021" "021"
#> Sample_873 Sample_874 Sample_875 Sample_876 Sample_877 Sample_878 Sample_879 Sample_880
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_881 Sample_882 Sample_883 Sample_884 Sample_885 Sample_886 Sample_887 Sample_888
#> "021" "0233" "021" "021" "021" "021" "021" "021"
#> Sample_889 Sample_890 Sample_891 Sample_892 Sample_893 Sample_894 Sample_895 Sample_896
#> "021" "021" "021" "021" "021" "0232" "021" "021"
#> Sample_897 Sample_898 Sample_899 Sample_900 Sample_901 Sample_902 Sample_903 Sample_904
#> "021" "021" "021" "0233" "021" "021" "021" "021"
#> Sample_905 Sample_906 Sample_907 Sample_908 Sample_909 Sample_910 Sample_911 Sample_912
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_913 Sample_914 Sample_915 Sample_916 Sample_917 Sample_918 Sample_919 Sample_920
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_921 Sample_922 Sample_923 Sample_924 Sample_925 Sample_926 Sample_927 Sample_928
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_929 Sample_930 Sample_931 Sample_932 Sample_933 Sample_934 Sample_935 Sample_936
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_937 Sample_938 Sample_939 Sample_940 Sample_941 Sample_942 Sample_943 Sample_944
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_945 Sample_946 Sample_947 Sample_948 Sample_949 Sample_950 Sample_951 Sample_952
#> "021" "021" "021" "0321" "0321" "013" "0321" "0321"
#> Sample_953 Sample_954 Sample_955 Sample_956 Sample_957 Sample_958 Sample_959 Sample_960
#> "0321" "0322" "0321" "0321" "0321" "0321" "0321" "0321"
#> Sample_961 Sample_962 Sample_963 Sample_964 Sample_965 Sample_966 Sample_967 Sample_968
#> "0321" "012" "0321" "0321" "0321" "0321" "0321" "012"
#> Sample_969 Sample_970 Sample_971 Sample_972 Sample_973 Sample_974 Sample_975 Sample_976
#> "0321" "0321" "0321" "0321" "0321" "0321" "0333" "013"
#> Sample_977 Sample_978 Sample_979 Sample_980 Sample_981 Sample_982 Sample_983 Sample_984
#> "013" "012" "0321" "013" "013" "012" "012" "0321"
#> Sample_985 Sample_986 Sample_987 Sample_988 Sample_989 Sample_990 Sample_991 Sample_992
#> "012" "013" "012" "012" "012" "012" "012" "012"
#> Sample_993 Sample_994 Sample_995 Sample_996 Sample_997 Sample_998 Sample_999 Sample_1000
#> "012" "012" "0321" "013" "0321" "0321" "013" "0321"
#> Sample_1001 Sample_1002 Sample_1003 Sample_1004 Sample_1005 Sample_1006 Sample_1007 Sample_1008
#> "0321" "0321" "0321" "0333" "0321" "0321" "012" "013"
#> Sample_1009 Sample_1010 Sample_1011 Sample_1012 Sample_1013 Sample_1014 Sample_1015 Sample_1016
#> "013" "013" "0321" "0322" "0321" "012" "012" "012"
#> Sample_1017 Sample_1018 Sample_1019 Sample_1020 Sample_1021 Sample_1022 Sample_1023 Sample_1024
#> "012" "012" "012" "012" "012" "012" "012" "012"
#> Sample_1025 Sample_1026 Sample_1027 Sample_1028 Sample_1029 Sample_1030 Sample_1031 Sample_1032
#> "012" "012" "011" "011" "0312" "011" "0332" "0312"
#> Sample_1033 Sample_1034 Sample_1035 Sample_1036 Sample_1037 Sample_1038 Sample_1039 Sample_1040
#> "0312" "011" "0312" "011" "0312" "0312" "011" "0312"
#> Sample_1041 Sample_1042 Sample_1043 Sample_1044 Sample_1045 Sample_1046 Sample_1047 Sample_1048
#> "011" "011" "0312" "0312" "0332" "011" "0312" "0331"
#> Sample_1049 Sample_1050 Sample_1051 Sample_1052 Sample_1053 Sample_1054 Sample_1055 Sample_1056
#> "011" "011" "0312" "011" "0322" "0321" "0321" "013"
#> Sample_1057 Sample_1058 Sample_1059 Sample_1060 Sample_1061 Sample_1062 Sample_1063 Sample_1064
#> "0322" "012" "013" "0321" "0322" "013" "0312" "0312"
#> Sample_1065 Sample_1066 Sample_1067 Sample_1068 Sample_1069 Sample_1070 Sample_1071 Sample_1072
#> "0312" "011" "0312" "0312" "0312" "011" "0312" "0312"
#> Sample_1073 Sample_1074 Sample_1075 Sample_1076 Sample_1077 Sample_1078 Sample_1079 Sample_1080
#> "011" "0312" "011" "011" "0312" "0312" "0312" "011"
#> Sample_1081 Sample_1082 Sample_1083 Sample_1084 Sample_1085 Sample_1086 Sample_1087 Sample_1088
#> "0312" "0312" "011" "0312" "0312" "0331" "011" "0331"
#> Sample_1089 Sample_1090 Sample_1091 Sample_1092 Sample_1093 Sample_1094 Sample_1095 Sample_1096
#> "0332" "011" "011" "0312" "011" "0312" "0312" "0312"
#> Sample_1097 Sample_1098 Sample_1099 Sample_1100 Sample_1101 Sample_1102 Sample_1103 Sample_1104
#> "011" "0312" "0312" "011" "0312" "011" "011" "011"
#> Sample_1105 Sample_1106 Sample_1107 Sample_1108 Sample_1109 Sample_1110 Sample_1111 Sample_1112
#> "011" "0312" "0312" "011" "0312" "011" "0312" "011"
#> Sample_1113 Sample_1114 Sample_1115 Sample_1116 Sample_1117 Sample_1118 Sample_1119 Sample_1120
#> "011" "011" "0312" "011" "0312" "011" "011" "0332"
#> Sample_1121 Sample_1122 Sample_1123 Sample_1124 Sample_1125 Sample_1126 Sample_1127 Sample_1128
#> "011" "0331" "0312" "0312" "0312" "0312" "0312" "0312"
#> Sample_1129 Sample_1130 Sample_1131 Sample_1132 Sample_1133 Sample_1134 Sample_1135 Sample_1136
#> "011" "011" "011" "0332" "011" "0331" "011" "011"
#> Sample_1137 Sample_1138 Sample_1139 Sample_1140 Sample_1141 Sample_1142 Sample_1143 Sample_1144
#> "011" "011" "0312" "0331" "0312" "011" "011" "011"
#> Sample_1145 Sample_1146 Sample_1147 Sample_1148 Sample_1149 Sample_1150 Sample_1151 Sample_1152
#> "0312" "011" "011" "011" "011" "0312" "011" "0312"
#> Sample_1153 Sample_1154 Sample_1155 Sample_1156 Sample_1157 Sample_1158 Sample_1159 Sample_1160
#> "011" "011" "011" "0312" "0312" "011" "011" "011"
#> Sample_1161 Sample_1162 Sample_1163 Sample_1164 Sample_1165 Sample_1166 Sample_1167 Sample_1168
#> "0312" "011" "0312" "0312" "011" "011" "011" "011"
#> Sample_1169 Sample_1170 Sample_1171 Sample_1172 Sample_1173 Sample_1174 Sample_1175 Sample_1176
#> "0312" "0312" "011" "011" "011" "011" "0312" "011"
#> Sample_1177 Sample_1178 Sample_1179 Sample_1180 Sample_1181 Sample_1182 Sample_1183 Sample_1184
#> "011" "011" "011" "0312" "011" "011" "0331" "0331"
#> Sample_1185 Sample_1186 Sample_1187 Sample_1188 Sample_1189 Sample_1190 Sample_1191 Sample_1192
#> "011" "011" "011" "011" "0321" "0322" "0322" "012"
#> Sample_1193 Sample_1194 Sample_1195 Sample_1196 Sample_1197 Sample_1198 Sample_1199 Sample_1200
#> "0322" "0322" "0321" "0321" "0322" "0322" "0322" "0321"
#> Sample_1201 Sample_1202 Sample_1203 Sample_1204 Sample_1205 Sample_1206 Sample_1207 Sample_1208
#> "0321" "0321" "0322" "0322" "0322" "0321" "0322" "013"
#> Sample_1209 Sample_1210 Sample_1211 Sample_1212 Sample_1213 Sample_1214 Sample_1215 Sample_1216
#> "0321" "0322" "0321" "0321" "0321" "0322" "0322" "012"
#> Sample_1217 Sample_1218 Sample_1219 Sample_1220 Sample_1221 Sample_1222 Sample_1223 Sample_1224
#> "013" "013" "0322" "0321" "0322" "011" "0311" "0311"
#> Sample_1225 Sample_1226 Sample_1227 Sample_1228 Sample_1229 Sample_1230 Sample_1231 Sample_1232
#> "011" "0311" "011" "011" "011" "0311" "0311" "0311"
#> Sample_1233 Sample_1234 Sample_1235 Sample_1236 Sample_1237 Sample_1238 Sample_1239 Sample_1240
#> "0311" "011" "0311" "011" "0311" "0311" "011" "011"
#> Sample_1241 Sample_1242 Sample_1243 Sample_1244 Sample_1245 Sample_1246 Sample_1247 Sample_1248
#> "011" "011" "0311" "0311" "0311" "0311" "0311" "0311"
#> Sample_1249 Sample_1250 Sample_1251 Sample_1252 Sample_1253 Sample_1254 Sample_1255 Sample_1256
#> "011" "0311" "0311" "0311" "0311" "011" "0332" "0311"
#> Sample_1257 Sample_1258 Sample_1259 Sample_1260 Sample_1261 Sample_1262 Sample_1263 Sample_1264
#> "0311" "0311" "0311" "0311" "0311" "0311" "0311" "011"
#> Sample_1265 Sample_1266 Sample_1267 Sample_1268 Sample_1269 Sample_1270 Sample_1271 Sample_1272
#> "0311" "011" "011" "0332" "0311" "0311" "0311" "011"
#> Sample_1273 Sample_1274 Sample_1275 Sample_1276 Sample_1277 Sample_1278 Sample_1279 Sample_1280
#> "0311" "0311" "0311" "0311" "0311" "0311" "011" "011"
#> Sample_1281 Sample_1282 Sample_1283 Sample_1284 Sample_1285 Sample_1286 Sample_1287 Sample_1288
#> "0311" "0311" "011" "0311" "011" "0311" "0311" "0311"
#> Sample_1289 Sample_1290 Sample_1291 Sample_1292 Sample_1293 Sample_1294 Sample_1295 Sample_1296
#> "0311" "0311" "0311" "0311" "0311" "0311" "0311" "0331"
#> Sample_1297 Sample_1298 Sample_1299 Sample_1300 Sample_1301 Sample_1302 Sample_1303 Sample_1304
#> "0311" "0311" "011" "0311" "0311" "0311" "0311" "0311"
#> Sample_1305 Sample_1306 Sample_1307 Sample_1308 Sample_1309 Sample_1310 Sample_1311 Sample_1312
#> "0311" "0332" "011" "011" "011" "0332" "0311" "0311"
#> Sample_1313 Sample_1314 Sample_1315 Sample_1316 Sample_1317 Sample_1318 Sample_1319 Sample_1320
#> "011" "0313" "0313" "0311" "011" "0313" "0311" "011"
#> Sample_1321 Sample_1322 Sample_1323 Sample_1324 Sample_1325 Sample_1326 Sample_1327 Sample_1328
#> "0314" "0311" "0311" "0311" "0311" "0314" "0311" "0313"
#> Sample_1329 Sample_1330 Sample_1331 Sample_1332 Sample_1333 Sample_1334 Sample_1335 Sample_1336
#> "0314" "0314" "0313" "0314" "0314" "0313" "011" "011"
#> Sample_1337 Sample_1338 Sample_1339 Sample_1340 Sample_1341 Sample_1342 Sample_1343 Sample_1344
#> "0332" "011" "0332" "0311" "0314" "011" "011" "0314"
#> Sample_1345 Sample_1346 Sample_1347 Sample_1348 Sample_1349 Sample_1350 Sample_1351 Sample_1352
#> "0332" "0311" "0311" "011" "0314" "011" "0314" "011"
#> Sample_1353 Sample_1354 Sample_1355 Sample_1356 Sample_1357 Sample_1358 Sample_1359 Sample_1360
#> "0311" "011" "0314" "0314" "011" "011" "0314" "011"
#> Sample_1361 Sample_1362 Sample_1363 Sample_1364 Sample_1365 Sample_1366 Sample_1367 Sample_1368
#> "0314" "0314" "0314" "011" "0311" "0313" "011" "0313"
#> Sample_1369 Sample_1370 Sample_1371 Sample_1372 Sample_1373 Sample_1374 Sample_1375 Sample_1376
#> "0311" "0314" "011" "0314" "011" "011" "011" "0332"
#> Sample_1377 Sample_1378 Sample_1379 Sample_1380 Sample_1381 Sample_1382 Sample_1383 Sample_1384
#> "0311" "0311" "0311" "0311" "0314" "011" "0313" "0313"
#> Sample_1385 Sample_1386 Sample_1387 Sample_1388 Sample_1389 Sample_1390 Sample_1391 Sample_1392
#> "0313" "011" "0313" "011" "0313" "0313" "0313" "0313"
#> Sample_1393 Sample_1394 Sample_1395 Sample_1396 Sample_1397 Sample_1398 Sample_1399 Sample_1400
#> "0313" "0313" "0313" "0313" "0313" "011" "011" "0331"
#> Sample_1401 Sample_1402 Sample_1403 Sample_1404 Sample_1405 Sample_1406 Sample_1407 Sample_1408
#> "0313" "011" "0313" "0313" "0313" "011" "0313" "011"
#> Sample_1409 Sample_1410 Sample_1411 Sample_1412 Sample_1413 Sample_1414 Sample_1415 Sample_1416
#> "011" "0313" "0313" "0313" "0331" "0313" "0313" "0332"
#> Sample_1417 Sample_1418 Sample_1419 Sample_1420 Sample_1421 Sample_1422 Sample_1423 Sample_1424
#> "0313" "0313" "011" "0313" "0313" "0313" "011" "0313"
#> Sample_1425 Sample_1426 Sample_1427 Sample_1428 Sample_1429 Sample_1430 Sample_1431 Sample_1432
#> "011" "011" "011" "0313" "011" "011" "011" "011"
#> Sample_1433 Sample_1434 Sample_1435 Sample_1436 Sample_1437 Sample_1438 Sample_1439 Sample_1440
#> "011" "011" "011" "011" "011" "011" "011" "011"
#> Sample_1441 Sample_1442 Sample_1443 Sample_1444 Sample_1445 Sample_1446 Sample_1447 Sample_1448
#> "011" "0313" "0313" "011" "011" "0313" "0313" "011"
#> Sample_1449 Sample_1450 Sample_1451 Sample_1452 Sample_1453 Sample_1454 Sample_1455 Sample_1456
#> "011" "011" "011" "011" "011" "011" "011" "0313"
#> Sample_1457 Sample_1458 Sample_1459 Sample_1460 Sample_1461 Sample_1462 Sample_1463 Sample_1464
#> "0313" "011" "011" "0331" "0313" "011" "0314" "0314"
#> Sample_1465 Sample_1466 Sample_1467 Sample_1468 Sample_1469 Sample_1470 Sample_1471 Sample_1472
#> "011" "011" "011" "0313" "011" "011" "011" "011"
#> Sample_1473 Sample_1474 Sample_1475 Sample_1476 Sample_1477 Sample_1478 Sample_1479 Sample_1480
#> "011" "011" "011" "011" "011" "011" "011" "0314"
#> Sample_1481 Sample_1482 Sample_1483 Sample_1484 Sample_1485 Sample_1486 Sample_1487 Sample_1488
#> "011" "011" "011" "011" "0313" "0331" "011" "0313"
#> Sample_1489 Sample_1490 Sample_1491 Sample_1492 Sample_1493 Sample_1494 Sample_1495 Sample_1496
#> "011" "011" "011" "011" "011" "0332" "011" "011"
#> Sample_1497 Sample_1498 Sample_1499 Sample_1500 Sample_1501 Sample_1502 Sample_1503 Sample_1504
#> "011" "011" "011" "011" "011" "0313" "0313" "0332"
#> Sample_1505 Sample_1506 Sample_1507 Sample_1508 Sample_1509 Sample_1510 Sample_1511 Sample_1512
#> "0331" "0313" "0313" "0313" "011" "011" "0313" "0313"
#> Sample_1513 Sample_1514 Sample_1515 Sample_1516 Sample_1517 Sample_1518 Sample_1519 Sample_1520
#> "0313" "011" "0313" "011" "0222" "0222" "0221" "0233"
#> Sample_1521 Sample_1522 Sample_1523 Sample_1524 Sample_1525 Sample_1526 Sample_1527 Sample_1528
#> "0232" "013" "013" "0233" "012" "012" "021" "021"
#> Sample_1529 Sample_1530 Sample_1531 Sample_1532 Sample_1533 Sample_1534 Sample_1535 Sample_1536
#> "021" "0233" "0232" "0232" "021" "021" "0222" "0222"
#> Sample_1537 Sample_1538 Sample_1539 Sample_1540 Sample_1541 Sample_1542 Sample_1543 Sample_1544
#> "0221" "021" "021" "013" "011" "021" "0233" "013"
#> Sample_1545 Sample_1546 Sample_1547 Sample_1548 Sample_1549 Sample_1550 Sample_1551 Sample_1552
#> "011" "021" "013" "0231" "0322" "0231" "0231" "0231"
#> Sample_1553 Sample_1554 Sample_1555 Sample_1556 Sample_1557 Sample_1558 Sample_1559 Sample_1560
#> "0321" "0222" "0231" "013" "013" "013" "0231" "013"
#> Sample_1561 Sample_1562 Sample_1563 Sample_1564 Sample_1565 Sample_1566 Sample_1567 Sample_1568
#> "0231" "012" "013" "013" "013" "013" "013" "013"
#> Sample_1569 Sample_1570 Sample_1571 Sample_1572 Sample_1573 Sample_1574 Sample_1575 Sample_1576
#> "0312" "021" "013" "013" "013" "0231" "0231" "013"
#> Sample_1577 Sample_1578 Sample_1579 Sample_1580 Sample_1581 Sample_1582 Sample_1583 Sample_1584
#> "013" "013" "013" "013" "0322" "0231" "013" "013"
#> Sample_1585 Sample_1586 Sample_1587 Sample_1588 Sample_1589 Sample_1590 Sample_1591 Sample_1592
#> "0231" "0231" "0231" "0222" "0231" "013" "013" "0231"
#> Sample_1593 Sample_1594 Sample_1595 Sample_1596 Sample_1597 Sample_1598 Sample_1599 Sample_1600
#> "0231" "0231" "0231" "0232" "0322" "0311" "0233" "013"
get_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 446))
#> Sample_1 Sample_2 Sample_3 Sample_4 Sample_5 Sample_6 Sample_7 Sample_8
#> "013" "0221" "0221" "0222" "0221" "0221" "0221" "0222"
#> Sample_9 Sample_10 Sample_11 Sample_12 Sample_13 Sample_14 Sample_15 Sample_16
#> "0221" "0221" "0221" "0221" "0221" "0221" "0221" "0221"
#> Sample_17 Sample_18 Sample_19 Sample_20 Sample_21 Sample_22 Sample_23 Sample_24
#> "0222" "0222" "0222" "0222" "0222" "0222" "0222" "0221"
#> Sample_25 Sample_26 Sample_27 Sample_28 Sample_29 Sample_30 Sample_31 Sample_32
#> "0222" "0221" "0222" "0222" "0221" "0222" "0233" "0221"
#> Sample_33 Sample_34 Sample_35 Sample_36 Sample_37 Sample_38 Sample_39 Sample_40
#> "0221" "0221" "0221" "0221" "0221" "0221" "0221" "0221"
#> Sample_41 Sample_42 Sample_43 Sample_44 Sample_45 Sample_46 Sample_47 Sample_48
#> "0221" "0221" "0233" "0221" "0221" "0221" "0221" "0221"
#> Sample_49 Sample_50 Sample_51 Sample_52 Sample_53 Sample_54 Sample_55 Sample_56
#> "0221" "0221" "0221" "0221" "0222" "0222" "0221" "0221"
#> Sample_57 Sample_58 Sample_59 Sample_60 Sample_61 Sample_62 Sample_63 Sample_64
#> "0221" "0221" "0221" "0221" "0222" "0221" "0221" "0221"
#> Sample_65 Sample_66 Sample_67 Sample_68 Sample_69 Sample_70 Sample_71 Sample_72
#> "0221" "0221" "0222" "0221" "0221" "0222" "0222" "021"
#> Sample_73 Sample_74 Sample_75 Sample_76 Sample_77 Sample_78 Sample_79 Sample_80
#> "0233" "0221" "0222" "0221" "0222" "0221" "0222" "0222"
#> Sample_81 Sample_82 Sample_83 Sample_84 Sample_85 Sample_86 Sample_87 Sample_88
#> "0221" "0222" "0221" "0221" "0222" "0221" "0222" "0222"
#> Sample_89 Sample_90 Sample_91 Sample_92 Sample_93 Sample_94 Sample_95 Sample_96
#> "0222" "0222" "0222" "0222" "0222" "0232" "0222" "0221"
#> Sample_97 Sample_98 Sample_99 Sample_100 Sample_101 Sample_102 Sample_103 Sample_104
#> "0221" "0221" "0221" "0222" "0222" "0221" "0222" "0222"
#> Sample_105 Sample_106 Sample_107 Sample_108 Sample_109 Sample_110 Sample_111 Sample_112
#> "0221" "0221" "0222" "0221" "0221" "0222" "021" "0222"
#> Sample_113 Sample_114 Sample_115 Sample_116 Sample_117 Sample_118 Sample_119 Sample_120
#> "0222" "0232" "0222" "0222" "0222" "0232" "0222" "0221"
#> Sample_121 Sample_122 Sample_123 Sample_124 Sample_125 Sample_126 Sample_127 Sample_128
#> "012" "0222" "0232" "0221" "0221" "0222" "0221" "0222"
#> Sample_129 Sample_130 Sample_131 Sample_132 Sample_133 Sample_134 Sample_135 Sample_136
#> "0221" "0222" "0222" "0221" "0222" "0221" "0222" "0221"
#> Sample_137 Sample_138 Sample_139 Sample_140 Sample_141 Sample_142 Sample_143 Sample_144
#> "0221" "0221" "0222" "0221" "0221" "0222" "0222" "0232"
#> Sample_145 Sample_146 Sample_147 Sample_148 Sample_149 Sample_150 Sample_151 Sample_152
#> "0222" "0222" "021" "021" "021" "021" "021" "021"
#> Sample_153 Sample_154 Sample_155 Sample_156 Sample_157 Sample_158 Sample_159 Sample_160
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_161 Sample_162 Sample_163 Sample_164 Sample_165 Sample_166 Sample_167 Sample_168
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_169 Sample_170 Sample_171 Sample_172 Sample_173 Sample_174 Sample_175 Sample_176
#> "021" "021" "0232" "021" "0233" "021" "021" "021"
#> Sample_177 Sample_178 Sample_179 Sample_180 Sample_181 Sample_182 Sample_183 Sample_184
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_185 Sample_186 Sample_187 Sample_188 Sample_189 Sample_190 Sample_191 Sample_192
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_193 Sample_194 Sample_195 Sample_196 Sample_197 Sample_198 Sample_199 Sample_200
#> "021" "021" "021" "021" "0221" "0221" "0221" "0221"
#> Sample_201 Sample_202 Sample_203 Sample_204 Sample_205 Sample_206 Sample_207 Sample_208
#> "0221" "0221" "0221" "0233" "0221" "0221" "0221" "032"
#> Sample_209 Sample_210 Sample_211 Sample_212 Sample_213 Sample_214 Sample_215 Sample_216
#> "032" "032" "032" "032" "032" "013" "013" "032"
#> Sample_217 Sample_218 Sample_219 Sample_220 Sample_221 Sample_222 Sample_223 Sample_224
#> "013" "032" "032" "032" "032" "032" "032" "013"
#> Sample_225 Sample_226 Sample_227 Sample_228 Sample_229 Sample_230 Sample_231 Sample_232
#> "032" "013" "032" "032" "032" "032" "032" "032"
#> Sample_233 Sample_234 Sample_235 Sample_236 Sample_237 Sample_238 Sample_239 Sample_240
#> "0333" "032" "032" "013" "032" "032" "032" "032"
#> Sample_241 Sample_242 Sample_243 Sample_244 Sample_245 Sample_246 Sample_247 Sample_248
#> "013" "032" "032" "032" "032" "032" "032" "032"
#> Sample_249 Sample_250 Sample_251 Sample_252 Sample_253 Sample_254 Sample_255 Sample_256
#> "032" "032" "032" "032" "032" "0333" "032" "032"
#> Sample_257 Sample_258 Sample_259 Sample_260 Sample_261 Sample_262 Sample_263 Sample_264
#> "032" "013" "032" "032" "032" "032" "032" "013"
#> Sample_265 Sample_266 Sample_267 Sample_268 Sample_269 Sample_270 Sample_271 Sample_272
#> "012" "032" "032" "032" "032" "032" "0333" "012"
#> Sample_273 Sample_274 Sample_275 Sample_276 Sample_277 Sample_278 Sample_279 Sample_280
#> "032" "032" "012" "013" "032" "0333" "032" "032"
#> Sample_281 Sample_282 Sample_283 Sample_284 Sample_285 Sample_286 Sample_287 Sample_288
#> "032" "032" "032" "032" "032" "032" "013" "0333"
#> Sample_289 Sample_290 Sample_291 Sample_292 Sample_293 Sample_294 Sample_295 Sample_296
#> "0333" "032" "032" "032" "032" "032" "032" "032"
#> Sample_297 Sample_298 Sample_299 Sample_300 Sample_301 Sample_302 Sample_303 Sample_304
#> "032" "032" "032" "032" "032" "012" "012" "012"
#> Sample_305 Sample_306 Sample_307 Sample_308 Sample_309 Sample_310 Sample_311 Sample_312
#> "012" "032" "032" "012" "012" "012" "012" "012"
#> Sample_313 Sample_314 Sample_315 Sample_316 Sample_317 Sample_318 Sample_319 Sample_320
#> "012" "012" "032" "012" "032" "012" "012" "012"
#> Sample_321 Sample_322 Sample_323 Sample_324 Sample_325 Sample_326 Sample_327 Sample_328
#> "012" "032" "012" "012" "012" "012" "012" "012"
#> Sample_329 Sample_330 Sample_331 Sample_332 Sample_333 Sample_334 Sample_335 Sample_336
#> "012" "012" "0333" "032" "032" "032" "032" "032"
#> Sample_337 Sample_338 Sample_339 Sample_340 Sample_341 Sample_342 Sample_343 Sample_344
#> "032" "032" "0333" "032" "032" "032" "032" "032"
#> Sample_345 Sample_346 Sample_347 Sample_348 Sample_349 Sample_350 Sample_351 Sample_352
#> "032" "032" "032" "032" "032" "013" "012" "013"
#> Sample_353 Sample_354 Sample_355 Sample_356 Sample_357 Sample_358 Sample_359 Sample_360
#> "032" "032" "012" "032" "032" "012" "032" "012"
#> Sample_361 Sample_362 Sample_363 Sample_364 Sample_365 Sample_366 Sample_367 Sample_368
#> "032" "012" "032" "032" "013" "032" "032" "012"
#> Sample_369 Sample_370 Sample_371 Sample_372 Sample_373 Sample_374 Sample_375 Sample_376
#> "012" "032" "0333" "013" "013" "032" "032" "032"
#> Sample_377 Sample_378 Sample_379 Sample_380 Sample_381 Sample_382 Sample_383 Sample_384
#> "012" "032" "032" "032" "012" "032" "032" "032"
#> Sample_385 Sample_386 Sample_387 Sample_388 Sample_389 Sample_390 Sample_391 Sample_392
#> "012" "012" "032" "032" "013" "013" "032" "032"
#> Sample_393 Sample_394 Sample_395 Sample_396 Sample_397 Sample_398 Sample_399 Sample_400
#> "012" "013" "012" "013" "012" "012" "032" "032"
#> Sample_401 Sample_402 Sample_403 Sample_404 Sample_405 Sample_406 Sample_407 Sample_408
#> "032" "032" "013" "013" "013" "032" "032" "013"
#> Sample_409 Sample_410 Sample_411 Sample_412 Sample_413 Sample_414 Sample_415 Sample_416
#> "012" "032" "032" "012" "032" "032" "0333" "012"
#> Sample_417 Sample_418 Sample_419 Sample_420 Sample_421 Sample_422 Sample_423 Sample_424
#> "032" "032" "032" "0333" "012" "0333" "032" "012"
#> Sample_425 Sample_426 Sample_427 Sample_428 Sample_429 Sample_430 Sample_431 Sample_432
#> "012" "032" "012" "012" "032" "013" "012" "012"
#> Sample_433 Sample_434 Sample_435 Sample_436 Sample_437 Sample_438 Sample_439 Sample_440
#> "013" "012" "032" "0333" "0333" "013" "012" "012"
#> Sample_441 Sample_442 Sample_443 Sample_444 Sample_445 Sample_446 Sample_447 Sample_448
#> "032" "012" "013" "032" "012" "012" "032" "013"
#> Sample_449 Sample_450 Sample_451 Sample_452 Sample_453 Sample_454 Sample_455 Sample_456
#> "032" "032" "012" "012" "032" "032" "013" "012"
#> Sample_457 Sample_458 Sample_459 Sample_460 Sample_461 Sample_462 Sample_463 Sample_464
#> "013" "032" "012" "032" "012" "032" "013" "012"
#> Sample_465 Sample_466 Sample_467 Sample_468 Sample_469 Sample_470 Sample_471 Sample_472
#> "012" "012" "032" "012" "012" "032" "032" "012"
#> Sample_473 Sample_474 Sample_475 Sample_476 Sample_477 Sample_478 Sample_479 Sample_480
#> "032" "012" "012" "013" "013" "0314" "011" "0314"
#> Sample_481 Sample_482 Sample_483 Sample_484 Sample_485 Sample_486 Sample_487 Sample_488
#> "0313" "0314" "0314" "0314" "0314" "011" "011" "0331"
#> Sample_489 Sample_490 Sample_491 Sample_492 Sample_493 Sample_494 Sample_495 Sample_496
#> "0314" "011" "0314" "0314" "0314" "0314" "011" "0314"
#> Sample_497 Sample_498 Sample_499 Sample_500 Sample_501 Sample_502 Sample_503 Sample_504
#> "0314" "0314" "011" "011" "0314" "0314" "0314" "0314"
#> Sample_505 Sample_506 Sample_507 Sample_508 Sample_509 Sample_510 Sample_511 Sample_512
#> "011" "011" "011" "0314" "0314" "0314" "011" "0332"
#> Sample_513 Sample_514 Sample_515 Sample_516 Sample_517 Sample_518 Sample_519 Sample_520
#> "011" "0314" "011" "021" "011" "0314" "0314" "011"
#> Sample_521 Sample_522 Sample_523 Sample_524 Sample_525 Sample_526 Sample_527 Sample_528
#> "011" "0314" "0314" "0314" "0314" "0314" "011" "0312"
#> Sample_529 Sample_530 Sample_531 Sample_532 Sample_533 Sample_534 Sample_535 Sample_536
#> "0331" "011" "0332" "011" "011" "0314" "011" "011"
#> Sample_537 Sample_538 Sample_539 Sample_540 Sample_541 Sample_542 Sample_543 Sample_544
#> "011" "0314" "011" "0314" "0314" "011" "011" "011"
#> Sample_545 Sample_546 Sample_547 Sample_548 Sample_549 Sample_550 Sample_551 Sample_552
#> "0331" "011" "011" "0314" "0314" "0314" "0314" "0314"
#> Sample_553 Sample_554 Sample_555 Sample_556 Sample_557 Sample_558 Sample_559 Sample_560
#> "0332" "0314" "0314" "0314" "011" "011" "011" "0314"
#> Sample_561 Sample_562 Sample_563 Sample_564 Sample_565 Sample_566 Sample_567 Sample_568
#> "0314" "0314" "0314" "0314" "011" "011" "011" "0314"
#> Sample_569 Sample_570 Sample_571 Sample_572 Sample_573 Sample_574 Sample_575 Sample_576
#> "011" "032" "032" "013" "012" "0333" "032" "032"
#> Sample_577 Sample_578 Sample_579 Sample_580 Sample_581 Sample_582 Sample_583 Sample_584
#> "012" "032" "032" "032" "032" "032" "032" "032"
#> Sample_585 Sample_586 Sample_587 Sample_588 Sample_589 Sample_590 Sample_591 Sample_592
#> "032" "0221" "0221" "0231" "032" "013" "0221" "0231"
#> Sample_593 Sample_594 Sample_595 Sample_596 Sample_597 Sample_598 Sample_599 Sample_600
#> "0221" "0231" "0231" "0222" "0231" "0222" "0231" "0231"
#> Sample_601 Sample_602 Sample_603 Sample_604 Sample_605 Sample_606 Sample_607 Sample_608
#> "0222" "0231" "0222" "0222" "0222" "0222" "0221" "0222"
#> Sample_609 Sample_610 Sample_611 Sample_612 Sample_613 Sample_614 Sample_615 Sample_616
#> "0222" "0222" "021" "021" "032" "013" "032" "013"
#> Sample_617 Sample_618 Sample_619 Sample_620 Sample_621 Sample_622 Sample_623 Sample_624
#> "013" "012" "013" "013" "0231" "013" "032" "032"
#> Sample_625 Sample_626 Sample_627 Sample_628 Sample_629 Sample_630 Sample_631 Sample_632
#> "0231" "013" "0231" "0231" "0231" "013" "0231" "013"
#> Sample_633 Sample_634 Sample_635 Sample_636 Sample_637 Sample_638 Sample_639 Sample_640
#> "013" "013" "013" "013" "013" "0231" "013" "0232"
#> Sample_641 Sample_642 Sample_643 Sample_644 Sample_645 Sample_646 Sample_647 Sample_648
#> "013" "0233" "013" "032" "0232" "013" "013" "0232"
#> Sample_649 Sample_650 Sample_651 Sample_652 Sample_653 Sample_654 Sample_655 Sample_656
#> "0232" "0231" "0221" "0221" "0221" "0221" "0221" "0221"
#> Sample_657 Sample_658 Sample_659 Sample_660 Sample_661 Sample_662 Sample_663 Sample_664
#> "0222" "0221" "0221" "0222" "0221" "0222" "0222" "0221"
#> Sample_665 Sample_666 Sample_667 Sample_668 Sample_669 Sample_670 Sample_671 Sample_672
#> "0232" "0222" "0232" "0222" "0222" "0222" "0221" "0222"
#> Sample_673 Sample_674 Sample_675 Sample_676 Sample_677 Sample_678 Sample_679 Sample_680
#> "0222" "0222" "0222" "0222" "0221" "0221" "0221" "0222"
#> Sample_681 Sample_682 Sample_683 Sample_684 Sample_685 Sample_686 Sample_687 Sample_688
#> "0232" "0222" "0222" "0222" "012" "021" "012" "021"
#> Sample_689 Sample_690 Sample_691 Sample_692 Sample_693 Sample_694 Sample_695 Sample_696
#> "0222" "021" "0222" "012" "0222" "0222" "021" "021"
#> Sample_697 Sample_698 Sample_699 Sample_700 Sample_701 Sample_702 Sample_703 Sample_704
#> "021" "021" "021" "021" "021" "021" "021" "0221"
#> Sample_705 Sample_706 Sample_707 Sample_708 Sample_709 Sample_710 Sample_711 Sample_712
#> "0222" "021" "021" "021" "021" "0232" "021" "021"
#> Sample_713 Sample_714 Sample_715 Sample_716 Sample_717 Sample_718 Sample_719 Sample_720
#> "021" "021" "021" "021" "021" "021" "021" "0232"
#> Sample_721 Sample_722 Sample_723 Sample_724 Sample_725 Sample_726 Sample_727 Sample_728
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_729 Sample_730 Sample_731 Sample_732 Sample_733 Sample_734 Sample_735 Sample_736
#> "021" "021" "0233" "021" "021" "021" "0233" "021"
#> Sample_737 Sample_738 Sample_739 Sample_740 Sample_741 Sample_742 Sample_743 Sample_744
#> "012" "0232" "021" "021" "021" "021" "021" "021"
#> Sample_745 Sample_746 Sample_747 Sample_748 Sample_749 Sample_750 Sample_751 Sample_752
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_753 Sample_754 Sample_755 Sample_756 Sample_757 Sample_758 Sample_759 Sample_760
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_761 Sample_762 Sample_763 Sample_764 Sample_765 Sample_766 Sample_767 Sample_768
#> "021" "021" "021" "021" "021" "021" "021" "0232"
#> Sample_769 Sample_770 Sample_771 Sample_772 Sample_773 Sample_774 Sample_775 Sample_776
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_777 Sample_778 Sample_779 Sample_780 Sample_781 Sample_782 Sample_783 Sample_784
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_785 Sample_786 Sample_787 Sample_788 Sample_789 Sample_790 Sample_791 Sample_792
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_793 Sample_794 Sample_795 Sample_796 Sample_797 Sample_798 Sample_799 Sample_800
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_801 Sample_802 Sample_803 Sample_804 Sample_805 Sample_806 Sample_807 Sample_808
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_809 Sample_810 Sample_811 Sample_812 Sample_813 Sample_814 Sample_815 Sample_816
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_817 Sample_818 Sample_819 Sample_820 Sample_821 Sample_822 Sample_823 Sample_824
#> "021" "021" "021" "0233" "021" "021" "021" "021"
#> Sample_825 Sample_826 Sample_827 Sample_828 Sample_829 Sample_830 Sample_831 Sample_832
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_833 Sample_834 Sample_835 Sample_836 Sample_837 Sample_838 Sample_839 Sample_840
#> "021" "021" "021" "0233" "021" "021" "021" "021"
#> Sample_841 Sample_842 Sample_843 Sample_844 Sample_845 Sample_846 Sample_847 Sample_848
#> "0222" "0221" "0221" "0221" "0221" "0221" "0222" "0222"
#> Sample_849 Sample_850 Sample_851 Sample_852 Sample_853 Sample_854 Sample_855 Sample_856
#> "0222" "0221" "0221" "0222" "0221" "0221" "021" "021"
#> Sample_857 Sample_858 Sample_859 Sample_860 Sample_861 Sample_862 Sample_863 Sample_864
#> "021" "021" "0232" "021" "021" "021" "021" "021"
#> Sample_865 Sample_866 Sample_867 Sample_868 Sample_869 Sample_870 Sample_871 Sample_872
#> "0233" "021" "021" "021" "021" "021" "021" "021"
#> Sample_873 Sample_874 Sample_875 Sample_876 Sample_877 Sample_878 Sample_879 Sample_880
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_881 Sample_882 Sample_883 Sample_884 Sample_885 Sample_886 Sample_887 Sample_888
#> "021" "0233" "021" "021" "021" "021" "021" "021"
#> Sample_889 Sample_890 Sample_891 Sample_892 Sample_893 Sample_894 Sample_895 Sample_896
#> "021" "021" "021" "021" "021" "0232" "021" "021"
#> Sample_897 Sample_898 Sample_899 Sample_900 Sample_901 Sample_902 Sample_903 Sample_904
#> "021" "021" "021" "0233" "021" "021" "021" "021"
#> Sample_905 Sample_906 Sample_907 Sample_908 Sample_909 Sample_910 Sample_911 Sample_912
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_913 Sample_914 Sample_915 Sample_916 Sample_917 Sample_918 Sample_919 Sample_920
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_921 Sample_922 Sample_923 Sample_924 Sample_925 Sample_926 Sample_927 Sample_928
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_929 Sample_930 Sample_931 Sample_932 Sample_933 Sample_934 Sample_935 Sample_936
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_937 Sample_938 Sample_939 Sample_940 Sample_941 Sample_942 Sample_943 Sample_944
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_945 Sample_946 Sample_947 Sample_948 Sample_949 Sample_950 Sample_951 Sample_952
#> "021" "021" "021" "032" "032" "013" "032" "032"
#> Sample_953 Sample_954 Sample_955 Sample_956 Sample_957 Sample_958 Sample_959 Sample_960
#> "032" "032" "032" "032" "032" "032" "032" "032"
#> Sample_961 Sample_962 Sample_963 Sample_964 Sample_965 Sample_966 Sample_967 Sample_968
#> "032" "012" "032" "032" "032" "032" "032" "012"
#> Sample_969 Sample_970 Sample_971 Sample_972 Sample_973 Sample_974 Sample_975 Sample_976
#> "032" "032" "032" "032" "032" "032" "0333" "013"
#> Sample_977 Sample_978 Sample_979 Sample_980 Sample_981 Sample_982 Sample_983 Sample_984
#> "013" "012" "032" "013" "013" "012" "012" "032"
#> Sample_985 Sample_986 Sample_987 Sample_988 Sample_989 Sample_990 Sample_991 Sample_992
#> "012" "013" "012" "012" "012" "012" "012" "012"
#> Sample_993 Sample_994 Sample_995 Sample_996 Sample_997 Sample_998 Sample_999 Sample_1000
#> "012" "012" "032" "013" "032" "032" "013" "032"
#> Sample_1001 Sample_1002 Sample_1003 Sample_1004 Sample_1005 Sample_1006 Sample_1007 Sample_1008
#> "032" "032" "032" "0333" "032" "032" "012" "013"
#> Sample_1009 Sample_1010 Sample_1011 Sample_1012 Sample_1013 Sample_1014 Sample_1015 Sample_1016
#> "013" "013" "032" "032" "032" "012" "012" "012"
#> Sample_1017 Sample_1018 Sample_1019 Sample_1020 Sample_1021 Sample_1022 Sample_1023 Sample_1024
#> "012" "012" "012" "012" "012" "012" "012" "012"
#> Sample_1025 Sample_1026 Sample_1027 Sample_1028 Sample_1029 Sample_1030 Sample_1031 Sample_1032
#> "012" "012" "011" "011" "0312" "011" "0332" "0312"
#> Sample_1033 Sample_1034 Sample_1035 Sample_1036 Sample_1037 Sample_1038 Sample_1039 Sample_1040
#> "0312" "011" "0312" "011" "0312" "0312" "011" "0312"
#> Sample_1041 Sample_1042 Sample_1043 Sample_1044 Sample_1045 Sample_1046 Sample_1047 Sample_1048
#> "011" "011" "0312" "0312" "0332" "011" "0312" "0331"
#> Sample_1049 Sample_1050 Sample_1051 Sample_1052 Sample_1053 Sample_1054 Sample_1055 Sample_1056
#> "011" "011" "0312" "011" "032" "032" "032" "013"
#> Sample_1057 Sample_1058 Sample_1059 Sample_1060 Sample_1061 Sample_1062 Sample_1063 Sample_1064
#> "032" "012" "013" "032" "032" "013" "0312" "0312"
#> Sample_1065 Sample_1066 Sample_1067 Sample_1068 Sample_1069 Sample_1070 Sample_1071 Sample_1072
#> "0312" "011" "0312" "0312" "0312" "011" "0312" "0312"
#> Sample_1073 Sample_1074 Sample_1075 Sample_1076 Sample_1077 Sample_1078 Sample_1079 Sample_1080
#> "011" "0312" "011" "011" "0312" "0312" "0312" "011"
#> Sample_1081 Sample_1082 Sample_1083 Sample_1084 Sample_1085 Sample_1086 Sample_1087 Sample_1088
#> "0312" "0312" "011" "0312" "0312" "0331" "011" "0331"
#> Sample_1089 Sample_1090 Sample_1091 Sample_1092 Sample_1093 Sample_1094 Sample_1095 Sample_1096
#> "0332" "011" "011" "0312" "011" "0312" "0312" "0312"
#> Sample_1097 Sample_1098 Sample_1099 Sample_1100 Sample_1101 Sample_1102 Sample_1103 Sample_1104
#> "011" "0312" "0312" "011" "0312" "011" "011" "011"
#> Sample_1105 Sample_1106 Sample_1107 Sample_1108 Sample_1109 Sample_1110 Sample_1111 Sample_1112
#> "011" "0312" "0312" "011" "0312" "011" "0312" "011"
#> Sample_1113 Sample_1114 Sample_1115 Sample_1116 Sample_1117 Sample_1118 Sample_1119 Sample_1120
#> "011" "011" "0312" "011" "0312" "011" "011" "0332"
#> Sample_1121 Sample_1122 Sample_1123 Sample_1124 Sample_1125 Sample_1126 Sample_1127 Sample_1128
#> "011" "0331" "0312" "0312" "0312" "0312" "0312" "0312"
#> Sample_1129 Sample_1130 Sample_1131 Sample_1132 Sample_1133 Sample_1134 Sample_1135 Sample_1136
#> "011" "011" "011" "0332" "011" "0331" "011" "011"
#> Sample_1137 Sample_1138 Sample_1139 Sample_1140 Sample_1141 Sample_1142 Sample_1143 Sample_1144
#> "011" "011" "0312" "0331" "0312" "011" "011" "011"
#> Sample_1145 Sample_1146 Sample_1147 Sample_1148 Sample_1149 Sample_1150 Sample_1151 Sample_1152
#> "0312" "011" "011" "011" "011" "0312" "011" "0312"
#> Sample_1153 Sample_1154 Sample_1155 Sample_1156 Sample_1157 Sample_1158 Sample_1159 Sample_1160
#> "011" "011" "011" "0312" "0312" "011" "011" "011"
#> Sample_1161 Sample_1162 Sample_1163 Sample_1164 Sample_1165 Sample_1166 Sample_1167 Sample_1168
#> "0312" "011" "0312" "0312" "011" "011" "011" "011"
#> Sample_1169 Sample_1170 Sample_1171 Sample_1172 Sample_1173 Sample_1174 Sample_1175 Sample_1176
#> "0312" "0312" "011" "011" "011" "011" "0312" "011"
#> Sample_1177 Sample_1178 Sample_1179 Sample_1180 Sample_1181 Sample_1182 Sample_1183 Sample_1184
#> "011" "011" "011" "0312" "011" "011" "0331" "0331"
#> Sample_1185 Sample_1186 Sample_1187 Sample_1188 Sample_1189 Sample_1190 Sample_1191 Sample_1192
#> "011" "011" "011" "011" "032" "032" "032" "012"
#> Sample_1193 Sample_1194 Sample_1195 Sample_1196 Sample_1197 Sample_1198 Sample_1199 Sample_1200
#> "032" "032" "032" "032" "032" "032" "032" "032"
#> Sample_1201 Sample_1202 Sample_1203 Sample_1204 Sample_1205 Sample_1206 Sample_1207 Sample_1208
#> "032" "032" "032" "032" "032" "032" "032" "013"
#> Sample_1209 Sample_1210 Sample_1211 Sample_1212 Sample_1213 Sample_1214 Sample_1215 Sample_1216
#> "032" "032" "032" "032" "032" "032" "032" "012"
#> Sample_1217 Sample_1218 Sample_1219 Sample_1220 Sample_1221 Sample_1222 Sample_1223 Sample_1224
#> "013" "013" "032" "032" "032" "011" "0311" "0311"
#> Sample_1225 Sample_1226 Sample_1227 Sample_1228 Sample_1229 Sample_1230 Sample_1231 Sample_1232
#> "011" "0311" "011" "011" "011" "0311" "0311" "0311"
#> Sample_1233 Sample_1234 Sample_1235 Sample_1236 Sample_1237 Sample_1238 Sample_1239 Sample_1240
#> "0311" "011" "0311" "011" "0311" "0311" "011" "011"
#> Sample_1241 Sample_1242 Sample_1243 Sample_1244 Sample_1245 Sample_1246 Sample_1247 Sample_1248
#> "011" "011" "0311" "0311" "0311" "0311" "0311" "0311"
#> Sample_1249 Sample_1250 Sample_1251 Sample_1252 Sample_1253 Sample_1254 Sample_1255 Sample_1256
#> "011" "0311" "0311" "0311" "0311" "011" "0332" "0311"
#> Sample_1257 Sample_1258 Sample_1259 Sample_1260 Sample_1261 Sample_1262 Sample_1263 Sample_1264
#> "0311" "0311" "0311" "0311" "0311" "0311" "0311" "011"
#> Sample_1265 Sample_1266 Sample_1267 Sample_1268 Sample_1269 Sample_1270 Sample_1271 Sample_1272
#> "0311" "011" "011" "0332" "0311" "0311" "0311" "011"
#> Sample_1273 Sample_1274 Sample_1275 Sample_1276 Sample_1277 Sample_1278 Sample_1279 Sample_1280
#> "0311" "0311" "0311" "0311" "0311" "0311" "011" "011"
#> Sample_1281 Sample_1282 Sample_1283 Sample_1284 Sample_1285 Sample_1286 Sample_1287 Sample_1288
#> "0311" "0311" "011" "0311" "011" "0311" "0311" "0311"
#> Sample_1289 Sample_1290 Sample_1291 Sample_1292 Sample_1293 Sample_1294 Sample_1295 Sample_1296
#> "0311" "0311" "0311" "0311" "0311" "0311" "0311" "0331"
#> Sample_1297 Sample_1298 Sample_1299 Sample_1300 Sample_1301 Sample_1302 Sample_1303 Sample_1304
#> "0311" "0311" "011" "0311" "0311" "0311" "0311" "0311"
#> Sample_1305 Sample_1306 Sample_1307 Sample_1308 Sample_1309 Sample_1310 Sample_1311 Sample_1312
#> "0311" "0332" "011" "011" "011" "0332" "0311" "0311"
#> Sample_1313 Sample_1314 Sample_1315 Sample_1316 Sample_1317 Sample_1318 Sample_1319 Sample_1320
#> "011" "0313" "0313" "0311" "011" "0313" "0311" "011"
#> Sample_1321 Sample_1322 Sample_1323 Sample_1324 Sample_1325 Sample_1326 Sample_1327 Sample_1328
#> "0314" "0311" "0311" "0311" "0311" "0314" "0311" "0313"
#> Sample_1329 Sample_1330 Sample_1331 Sample_1332 Sample_1333 Sample_1334 Sample_1335 Sample_1336
#> "0314" "0314" "0313" "0314" "0314" "0313" "011" "011"
#> Sample_1337 Sample_1338 Sample_1339 Sample_1340 Sample_1341 Sample_1342 Sample_1343 Sample_1344
#> "0332" "011" "0332" "0311" "0314" "011" "011" "0314"
#> Sample_1345 Sample_1346 Sample_1347 Sample_1348 Sample_1349 Sample_1350 Sample_1351 Sample_1352
#> "0332" "0311" "0311" "011" "0314" "011" "0314" "011"
#> Sample_1353 Sample_1354 Sample_1355 Sample_1356 Sample_1357 Sample_1358 Sample_1359 Sample_1360
#> "0311" "011" "0314" "0314" "011" "011" "0314" "011"
#> Sample_1361 Sample_1362 Sample_1363 Sample_1364 Sample_1365 Sample_1366 Sample_1367 Sample_1368
#> "0314" "0314" "0314" "011" "0311" "0313" "011" "0313"
#> Sample_1369 Sample_1370 Sample_1371 Sample_1372 Sample_1373 Sample_1374 Sample_1375 Sample_1376
#> "0311" "0314" "011" "0314" "011" "011" "011" "0332"
#> Sample_1377 Sample_1378 Sample_1379 Sample_1380 Sample_1381 Sample_1382 Sample_1383 Sample_1384
#> "0311" "0311" "0311" "0311" "0314" "011" "0313" "0313"
#> Sample_1385 Sample_1386 Sample_1387 Sample_1388 Sample_1389 Sample_1390 Sample_1391 Sample_1392
#> "0313" "011" "0313" "011" "0313" "0313" "0313" "0313"
#> Sample_1393 Sample_1394 Sample_1395 Sample_1396 Sample_1397 Sample_1398 Sample_1399 Sample_1400
#> "0313" "0313" "0313" "0313" "0313" "011" "011" "0331"
#> Sample_1401 Sample_1402 Sample_1403 Sample_1404 Sample_1405 Sample_1406 Sample_1407 Sample_1408
#> "0313" "011" "0313" "0313" "0313" "011" "0313" "011"
#> Sample_1409 Sample_1410 Sample_1411 Sample_1412 Sample_1413 Sample_1414 Sample_1415 Sample_1416
#> "011" "0313" "0313" "0313" "0331" "0313" "0313" "0332"
#> Sample_1417 Sample_1418 Sample_1419 Sample_1420 Sample_1421 Sample_1422 Sample_1423 Sample_1424
#> "0313" "0313" "011" "0313" "0313" "0313" "011" "0313"
#> Sample_1425 Sample_1426 Sample_1427 Sample_1428 Sample_1429 Sample_1430 Sample_1431 Sample_1432
#> "011" "011" "011" "0313" "011" "011" "011" "011"
#> Sample_1433 Sample_1434 Sample_1435 Sample_1436 Sample_1437 Sample_1438 Sample_1439 Sample_1440
#> "011" "011" "011" "011" "011" "011" "011" "011"
#> Sample_1441 Sample_1442 Sample_1443 Sample_1444 Sample_1445 Sample_1446 Sample_1447 Sample_1448
#> "011" "0313" "0313" "011" "011" "0313" "0313" "011"
#> Sample_1449 Sample_1450 Sample_1451 Sample_1452 Sample_1453 Sample_1454 Sample_1455 Sample_1456
#> "011" "011" "011" "011" "011" "011" "011" "0313"
#> Sample_1457 Sample_1458 Sample_1459 Sample_1460 Sample_1461 Sample_1462 Sample_1463 Sample_1464
#> "0313" "011" "011" "0331" "0313" "011" "0314" "0314"
#> Sample_1465 Sample_1466 Sample_1467 Sample_1468 Sample_1469 Sample_1470 Sample_1471 Sample_1472
#> "011" "011" "011" "0313" "011" "011" "011" "011"
#> Sample_1473 Sample_1474 Sample_1475 Sample_1476 Sample_1477 Sample_1478 Sample_1479 Sample_1480
#> "011" "011" "011" "011" "011" "011" "011" "0314"
#> Sample_1481 Sample_1482 Sample_1483 Sample_1484 Sample_1485 Sample_1486 Sample_1487 Sample_1488
#> "011" "011" "011" "011" "0313" "0331" "011" "0313"
#> Sample_1489 Sample_1490 Sample_1491 Sample_1492 Sample_1493 Sample_1494 Sample_1495 Sample_1496
#> "011" "011" "011" "011" "011" "0332" "011" "011"
#> Sample_1497 Sample_1498 Sample_1499 Sample_1500 Sample_1501 Sample_1502 Sample_1503 Sample_1504
#> "011" "011" "011" "011" "011" "0313" "0313" "0332"
#> Sample_1505 Sample_1506 Sample_1507 Sample_1508 Sample_1509 Sample_1510 Sample_1511 Sample_1512
#> "0331" "0313" "0313" "0313" "011" "011" "0313" "0313"
#> Sample_1513 Sample_1514 Sample_1515 Sample_1516 Sample_1517 Sample_1518 Sample_1519 Sample_1520
#> "0313" "011" "0313" "011" "0222" "0222" "0221" "0233"
#> Sample_1521 Sample_1522 Sample_1523 Sample_1524 Sample_1525 Sample_1526 Sample_1527 Sample_1528
#> "0232" "013" "013" "0233" "012" "012" "021" "021"
#> Sample_1529 Sample_1530 Sample_1531 Sample_1532 Sample_1533 Sample_1534 Sample_1535 Sample_1536
#> "021" "0233" "0232" "0232" "021" "021" "0222" "0222"
#> Sample_1537 Sample_1538 Sample_1539 Sample_1540 Sample_1541 Sample_1542 Sample_1543 Sample_1544
#> "0221" "021" "021" "013" "011" "021" "0233" "013"
#> Sample_1545 Sample_1546 Sample_1547 Sample_1548 Sample_1549 Sample_1550 Sample_1551 Sample_1552
#> "011" "021" "013" "0231" "032" "0231" "0231" "0231"
#> Sample_1553 Sample_1554 Sample_1555 Sample_1556 Sample_1557 Sample_1558 Sample_1559 Sample_1560
#> "032" "0222" "0231" "013" "013" "013" "0231" "013"
#> Sample_1561 Sample_1562 Sample_1563 Sample_1564 Sample_1565 Sample_1566 Sample_1567 Sample_1568
#> "0231" "012" "013" "013" "013" "013" "013" "013"
#> Sample_1569 Sample_1570 Sample_1571 Sample_1572 Sample_1573 Sample_1574 Sample_1575 Sample_1576
#> "0312" "021" "013" "013" "013" "0231" "0231" "013"
#> Sample_1577 Sample_1578 Sample_1579 Sample_1580 Sample_1581 Sample_1582 Sample_1583 Sample_1584
#> "013" "013" "013" "013" "032" "0231" "013" "013"
#> Sample_1585 Sample_1586 Sample_1587 Sample_1588 Sample_1589 Sample_1590 Sample_1591 Sample_1592
#> "0231" "0231" "0231" "0222" "0231" "013" "013" "0231"
#> Sample_1593 Sample_1594 Sample_1595 Sample_1596 Sample_1597 Sample_1598 Sample_1599 Sample_1600
#> "0231" "0231" "0231" "0232" "032" "0311" "0233" "013"
get_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 689))
#> Sample_1 Sample_2 Sample_3 Sample_4 Sample_5 Sample_6 Sample_7 Sample_8
#> "013" "0221" "0221" "0222" "0221" "0221" "0221" "0222"
#> Sample_9 Sample_10 Sample_11 Sample_12 Sample_13 Sample_14 Sample_15 Sample_16
#> "0221" "0221" "0221" "0221" "0221" "0221" "0221" "0221"
#> Sample_17 Sample_18 Sample_19 Sample_20 Sample_21 Sample_22 Sample_23 Sample_24
#> "0222" "0222" "0222" "0222" "0222" "0222" "0222" "0221"
#> Sample_25 Sample_26 Sample_27 Sample_28 Sample_29 Sample_30 Sample_31 Sample_32
#> "0222" "0221" "0222" "0222" "0221" "0222" "0233" "0221"
#> Sample_33 Sample_34 Sample_35 Sample_36 Sample_37 Sample_38 Sample_39 Sample_40
#> "0221" "0221" "0221" "0221" "0221" "0221" "0221" "0221"
#> Sample_41 Sample_42 Sample_43 Sample_44 Sample_45 Sample_46 Sample_47 Sample_48
#> "0221" "0221" "0233" "0221" "0221" "0221" "0221" "0221"
#> Sample_49 Sample_50 Sample_51 Sample_52 Sample_53 Sample_54 Sample_55 Sample_56
#> "0221" "0221" "0221" "0221" "0222" "0222" "0221" "0221"
#> Sample_57 Sample_58 Sample_59 Sample_60 Sample_61 Sample_62 Sample_63 Sample_64
#> "0221" "0221" "0221" "0221" "0222" "0221" "0221" "0221"
#> Sample_65 Sample_66 Sample_67 Sample_68 Sample_69 Sample_70 Sample_71 Sample_72
#> "0221" "0221" "0222" "0221" "0221" "0222" "0222" "021"
#> Sample_73 Sample_74 Sample_75 Sample_76 Sample_77 Sample_78 Sample_79 Sample_80
#> "0233" "0221" "0222" "0221" "0222" "0221" "0222" "0222"
#> Sample_81 Sample_82 Sample_83 Sample_84 Sample_85 Sample_86 Sample_87 Sample_88
#> "0221" "0222" "0221" "0221" "0222" "0221" "0222" "0222"
#> Sample_89 Sample_90 Sample_91 Sample_92 Sample_93 Sample_94 Sample_95 Sample_96
#> "0222" "0222" "0222" "0222" "0222" "0232" "0222" "0221"
#> Sample_97 Sample_98 Sample_99 Sample_100 Sample_101 Sample_102 Sample_103 Sample_104
#> "0221" "0221" "0221" "0222" "0222" "0221" "0222" "0222"
#> Sample_105 Sample_106 Sample_107 Sample_108 Sample_109 Sample_110 Sample_111 Sample_112
#> "0221" "0221" "0222" "0221" "0221" "0222" "021" "0222"
#> Sample_113 Sample_114 Sample_115 Sample_116 Sample_117 Sample_118 Sample_119 Sample_120
#> "0222" "0232" "0222" "0222" "0222" "0232" "0222" "0221"
#> Sample_121 Sample_122 Sample_123 Sample_124 Sample_125 Sample_126 Sample_127 Sample_128
#> "012" "0222" "0232" "0221" "0221" "0222" "0221" "0222"
#> Sample_129 Sample_130 Sample_131 Sample_132 Sample_133 Sample_134 Sample_135 Sample_136
#> "0221" "0222" "0222" "0221" "0222" "0221" "0222" "0221"
#> Sample_137 Sample_138 Sample_139 Sample_140 Sample_141 Sample_142 Sample_143 Sample_144
#> "0221" "0221" "0222" "0221" "0221" "0222" "0222" "0232"
#> Sample_145 Sample_146 Sample_147 Sample_148 Sample_149 Sample_150 Sample_151 Sample_152
#> "0222" "0222" "021" "021" "021" "021" "021" "021"
#> Sample_153 Sample_154 Sample_155 Sample_156 Sample_157 Sample_158 Sample_159 Sample_160
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_161 Sample_162 Sample_163 Sample_164 Sample_165 Sample_166 Sample_167 Sample_168
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_169 Sample_170 Sample_171 Sample_172 Sample_173 Sample_174 Sample_175 Sample_176
#> "021" "021" "0232" "021" "0233" "021" "021" "021"
#> Sample_177 Sample_178 Sample_179 Sample_180 Sample_181 Sample_182 Sample_183 Sample_184
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_185 Sample_186 Sample_187 Sample_188 Sample_189 Sample_190 Sample_191 Sample_192
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_193 Sample_194 Sample_195 Sample_196 Sample_197 Sample_198 Sample_199 Sample_200
#> "021" "021" "021" "021" "0221" "0221" "0221" "0221"
#> Sample_201 Sample_202 Sample_203 Sample_204 Sample_205 Sample_206 Sample_207 Sample_208
#> "0221" "0221" "0221" "0233" "0221" "0221" "0221" "032"
#> Sample_209 Sample_210 Sample_211 Sample_212 Sample_213 Sample_214 Sample_215 Sample_216
#> "032" "032" "032" "032" "032" "013" "013" "032"
#> Sample_217 Sample_218 Sample_219 Sample_220 Sample_221 Sample_222 Sample_223 Sample_224
#> "013" "032" "032" "032" "032" "032" "032" "013"
#> Sample_225 Sample_226 Sample_227 Sample_228 Sample_229 Sample_230 Sample_231 Sample_232
#> "032" "013" "032" "032" "032" "032" "032" "032"
#> Sample_233 Sample_234 Sample_235 Sample_236 Sample_237 Sample_238 Sample_239 Sample_240
#> "033" "032" "032" "013" "032" "032" "032" "032"
#> Sample_241 Sample_242 Sample_243 Sample_244 Sample_245 Sample_246 Sample_247 Sample_248
#> "013" "032" "032" "032" "032" "032" "032" "032"
#> Sample_249 Sample_250 Sample_251 Sample_252 Sample_253 Sample_254 Sample_255 Sample_256
#> "032" "032" "032" "032" "032" "033" "032" "032"
#> Sample_257 Sample_258 Sample_259 Sample_260 Sample_261 Sample_262 Sample_263 Sample_264
#> "032" "013" "032" "032" "032" "032" "032" "013"
#> Sample_265 Sample_266 Sample_267 Sample_268 Sample_269 Sample_270 Sample_271 Sample_272
#> "012" "032" "032" "032" "032" "032" "033" "012"
#> Sample_273 Sample_274 Sample_275 Sample_276 Sample_277 Sample_278 Sample_279 Sample_280
#> "032" "032" "012" "013" "032" "033" "032" "032"
#> Sample_281 Sample_282 Sample_283 Sample_284 Sample_285 Sample_286 Sample_287 Sample_288
#> "032" "032" "032" "032" "032" "032" "013" "033"
#> Sample_289 Sample_290 Sample_291 Sample_292 Sample_293 Sample_294 Sample_295 Sample_296
#> "033" "032" "032" "032" "032" "032" "032" "032"
#> Sample_297 Sample_298 Sample_299 Sample_300 Sample_301 Sample_302 Sample_303 Sample_304
#> "032" "032" "032" "032" "032" "012" "012" "012"
#> Sample_305 Sample_306 Sample_307 Sample_308 Sample_309 Sample_310 Sample_311 Sample_312
#> "012" "032" "032" "012" "012" "012" "012" "012"
#> Sample_313 Sample_314 Sample_315 Sample_316 Sample_317 Sample_318 Sample_319 Sample_320
#> "012" "012" "032" "012" "032" "012" "012" "012"
#> Sample_321 Sample_322 Sample_323 Sample_324 Sample_325 Sample_326 Sample_327 Sample_328
#> "012" "032" "012" "012" "012" "012" "012" "012"
#> Sample_329 Sample_330 Sample_331 Sample_332 Sample_333 Sample_334 Sample_335 Sample_336
#> "012" "012" "033" "032" "032" "032" "032" "032"
#> Sample_337 Sample_338 Sample_339 Sample_340 Sample_341 Sample_342 Sample_343 Sample_344
#> "032" "032" "033" "032" "032" "032" "032" "032"
#> Sample_345 Sample_346 Sample_347 Sample_348 Sample_349 Sample_350 Sample_351 Sample_352
#> "032" "032" "032" "032" "032" "013" "012" "013"
#> Sample_353 Sample_354 Sample_355 Sample_356 Sample_357 Sample_358 Sample_359 Sample_360
#> "032" "032" "012" "032" "032" "012" "032" "012"
#> Sample_361 Sample_362 Sample_363 Sample_364 Sample_365 Sample_366 Sample_367 Sample_368
#> "032" "012" "032" "032" "013" "032" "032" "012"
#> Sample_369 Sample_370 Sample_371 Sample_372 Sample_373 Sample_374 Sample_375 Sample_376
#> "012" "032" "033" "013" "013" "032" "032" "032"
#> Sample_377 Sample_378 Sample_379 Sample_380 Sample_381 Sample_382 Sample_383 Sample_384
#> "012" "032" "032" "032" "012" "032" "032" "032"
#> Sample_385 Sample_386 Sample_387 Sample_388 Sample_389 Sample_390 Sample_391 Sample_392
#> "012" "012" "032" "032" "013" "013" "032" "032"
#> Sample_393 Sample_394 Sample_395 Sample_396 Sample_397 Sample_398 Sample_399 Sample_400
#> "012" "013" "012" "013" "012" "012" "032" "032"
#> Sample_401 Sample_402 Sample_403 Sample_404 Sample_405 Sample_406 Sample_407 Sample_408
#> "032" "032" "013" "013" "013" "032" "032" "013"
#> Sample_409 Sample_410 Sample_411 Sample_412 Sample_413 Sample_414 Sample_415 Sample_416
#> "012" "032" "032" "012" "032" "032" "033" "012"
#> Sample_417 Sample_418 Sample_419 Sample_420 Sample_421 Sample_422 Sample_423 Sample_424
#> "032" "032" "032" "033" "012" "033" "032" "012"
#> Sample_425 Sample_426 Sample_427 Sample_428 Sample_429 Sample_430 Sample_431 Sample_432
#> "012" "032" "012" "012" "032" "013" "012" "012"
#> Sample_433 Sample_434 Sample_435 Sample_436 Sample_437 Sample_438 Sample_439 Sample_440
#> "013" "012" "032" "033" "033" "013" "012" "012"
#> Sample_441 Sample_442 Sample_443 Sample_444 Sample_445 Sample_446 Sample_447 Sample_448
#> "032" "012" "013" "032" "012" "012" "032" "013"
#> Sample_449 Sample_450 Sample_451 Sample_452 Sample_453 Sample_454 Sample_455 Sample_456
#> "032" "032" "012" "012" "032" "032" "013" "012"
#> Sample_457 Sample_458 Sample_459 Sample_460 Sample_461 Sample_462 Sample_463 Sample_464
#> "013" "032" "012" "032" "012" "032" "013" "012"
#> Sample_465 Sample_466 Sample_467 Sample_468 Sample_469 Sample_470 Sample_471 Sample_472
#> "012" "012" "032" "012" "012" "032" "032" "012"
#> Sample_473 Sample_474 Sample_475 Sample_476 Sample_477 Sample_478 Sample_479 Sample_480
#> "032" "012" "012" "013" "013" "0314" "011" "0314"
#> Sample_481 Sample_482 Sample_483 Sample_484 Sample_485 Sample_486 Sample_487 Sample_488
#> "0313" "0314" "0314" "0314" "0314" "011" "011" "033"
#> Sample_489 Sample_490 Sample_491 Sample_492 Sample_493 Sample_494 Sample_495 Sample_496
#> "0314" "011" "0314" "0314" "0314" "0314" "011" "0314"
#> Sample_497 Sample_498 Sample_499 Sample_500 Sample_501 Sample_502 Sample_503 Sample_504
#> "0314" "0314" "011" "011" "0314" "0314" "0314" "0314"
#> Sample_505 Sample_506 Sample_507 Sample_508 Sample_509 Sample_510 Sample_511 Sample_512
#> "011" "011" "011" "0314" "0314" "0314" "011" "033"
#> Sample_513 Sample_514 Sample_515 Sample_516 Sample_517 Sample_518 Sample_519 Sample_520
#> "011" "0314" "011" "021" "011" "0314" "0314" "011"
#> Sample_521 Sample_522 Sample_523 Sample_524 Sample_525 Sample_526 Sample_527 Sample_528
#> "011" "0314" "0314" "0314" "0314" "0314" "011" "0312"
#> Sample_529 Sample_530 Sample_531 Sample_532 Sample_533 Sample_534 Sample_535 Sample_536
#> "033" "011" "033" "011" "011" "0314" "011" "011"
#> Sample_537 Sample_538 Sample_539 Sample_540 Sample_541 Sample_542 Sample_543 Sample_544
#> "011" "0314" "011" "0314" "0314" "011" "011" "011"
#> Sample_545 Sample_546 Sample_547 Sample_548 Sample_549 Sample_550 Sample_551 Sample_552
#> "033" "011" "011" "0314" "0314" "0314" "0314" "0314"
#> Sample_553 Sample_554 Sample_555 Sample_556 Sample_557 Sample_558 Sample_559 Sample_560
#> "033" "0314" "0314" "0314" "011" "011" "011" "0314"
#> Sample_561 Sample_562 Sample_563 Sample_564 Sample_565 Sample_566 Sample_567 Sample_568
#> "0314" "0314" "0314" "0314" "011" "011" "011" "0314"
#> Sample_569 Sample_570 Sample_571 Sample_572 Sample_573 Sample_574 Sample_575 Sample_576
#> "011" "032" "032" "013" "012" "033" "032" "032"
#> Sample_577 Sample_578 Sample_579 Sample_580 Sample_581 Sample_582 Sample_583 Sample_584
#> "012" "032" "032" "032" "032" "032" "032" "032"
#> Sample_585 Sample_586 Sample_587 Sample_588 Sample_589 Sample_590 Sample_591 Sample_592
#> "032" "0221" "0221" "0231" "032" "013" "0221" "0231"
#> Sample_593 Sample_594 Sample_595 Sample_596 Sample_597 Sample_598 Sample_599 Sample_600
#> "0221" "0231" "0231" "0222" "0231" "0222" "0231" "0231"
#> Sample_601 Sample_602 Sample_603 Sample_604 Sample_605 Sample_606 Sample_607 Sample_608
#> "0222" "0231" "0222" "0222" "0222" "0222" "0221" "0222"
#> Sample_609 Sample_610 Sample_611 Sample_612 Sample_613 Sample_614 Sample_615 Sample_616
#> "0222" "0222" "021" "021" "032" "013" "032" "013"
#> Sample_617 Sample_618 Sample_619 Sample_620 Sample_621 Sample_622 Sample_623 Sample_624
#> "013" "012" "013" "013" "0231" "013" "032" "032"
#> Sample_625 Sample_626 Sample_627 Sample_628 Sample_629 Sample_630 Sample_631 Sample_632
#> "0231" "013" "0231" "0231" "0231" "013" "0231" "013"
#> Sample_633 Sample_634 Sample_635 Sample_636 Sample_637 Sample_638 Sample_639 Sample_640
#> "013" "013" "013" "013" "013" "0231" "013" "0232"
#> Sample_641 Sample_642 Sample_643 Sample_644 Sample_645 Sample_646 Sample_647 Sample_648
#> "013" "0233" "013" "032" "0232" "013" "013" "0232"
#> Sample_649 Sample_650 Sample_651 Sample_652 Sample_653 Sample_654 Sample_655 Sample_656
#> "0232" "0231" "0221" "0221" "0221" "0221" "0221" "0221"
#> Sample_657 Sample_658 Sample_659 Sample_660 Sample_661 Sample_662 Sample_663 Sample_664
#> "0222" "0221" "0221" "0222" "0221" "0222" "0222" "0221"
#> Sample_665 Sample_666 Sample_667 Sample_668 Sample_669 Sample_670 Sample_671 Sample_672
#> "0232" "0222" "0232" "0222" "0222" "0222" "0221" "0222"
#> Sample_673 Sample_674 Sample_675 Sample_676 Sample_677 Sample_678 Sample_679 Sample_680
#> "0222" "0222" "0222" "0222" "0221" "0221" "0221" "0222"
#> Sample_681 Sample_682 Sample_683 Sample_684 Sample_685 Sample_686 Sample_687 Sample_688
#> "0232" "0222" "0222" "0222" "012" "021" "012" "021"
#> Sample_689 Sample_690 Sample_691 Sample_692 Sample_693 Sample_694 Sample_695 Sample_696
#> "0222" "021" "0222" "012" "0222" "0222" "021" "021"
#> Sample_697 Sample_698 Sample_699 Sample_700 Sample_701 Sample_702 Sample_703 Sample_704
#> "021" "021" "021" "021" "021" "021" "021" "0221"
#> Sample_705 Sample_706 Sample_707 Sample_708 Sample_709 Sample_710 Sample_711 Sample_712
#> "0222" "021" "021" "021" "021" "0232" "021" "021"
#> Sample_713 Sample_714 Sample_715 Sample_716 Sample_717 Sample_718 Sample_719 Sample_720
#> "021" "021" "021" "021" "021" "021" "021" "0232"
#> Sample_721 Sample_722 Sample_723 Sample_724 Sample_725 Sample_726 Sample_727 Sample_728
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_729 Sample_730 Sample_731 Sample_732 Sample_733 Sample_734 Sample_735 Sample_736
#> "021" "021" "0233" "021" "021" "021" "0233" "021"
#> Sample_737 Sample_738 Sample_739 Sample_740 Sample_741 Sample_742 Sample_743 Sample_744
#> "012" "0232" "021" "021" "021" "021" "021" "021"
#> Sample_745 Sample_746 Sample_747 Sample_748 Sample_749 Sample_750 Sample_751 Sample_752
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_753 Sample_754 Sample_755 Sample_756 Sample_757 Sample_758 Sample_759 Sample_760
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_761 Sample_762 Sample_763 Sample_764 Sample_765 Sample_766 Sample_767 Sample_768
#> "021" "021" "021" "021" "021" "021" "021" "0232"
#> Sample_769 Sample_770 Sample_771 Sample_772 Sample_773 Sample_774 Sample_775 Sample_776
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_777 Sample_778 Sample_779 Sample_780 Sample_781 Sample_782 Sample_783 Sample_784
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_785 Sample_786 Sample_787 Sample_788 Sample_789 Sample_790 Sample_791 Sample_792
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_793 Sample_794 Sample_795 Sample_796 Sample_797 Sample_798 Sample_799 Sample_800
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_801 Sample_802 Sample_803 Sample_804 Sample_805 Sample_806 Sample_807 Sample_808
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_809 Sample_810 Sample_811 Sample_812 Sample_813 Sample_814 Sample_815 Sample_816
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_817 Sample_818 Sample_819 Sample_820 Sample_821 Sample_822 Sample_823 Sample_824
#> "021" "021" "021" "0233" "021" "021" "021" "021"
#> Sample_825 Sample_826 Sample_827 Sample_828 Sample_829 Sample_830 Sample_831 Sample_832
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_833 Sample_834 Sample_835 Sample_836 Sample_837 Sample_838 Sample_839 Sample_840
#> "021" "021" "021" "0233" "021" "021" "021" "021"
#> Sample_841 Sample_842 Sample_843 Sample_844 Sample_845 Sample_846 Sample_847 Sample_848
#> "0222" "0221" "0221" "0221" "0221" "0221" "0222" "0222"
#> Sample_849 Sample_850 Sample_851 Sample_852 Sample_853 Sample_854 Sample_855 Sample_856
#> "0222" "0221" "0221" "0222" "0221" "0221" "021" "021"
#> Sample_857 Sample_858 Sample_859 Sample_860 Sample_861 Sample_862 Sample_863 Sample_864
#> "021" "021" "0232" "021" "021" "021" "021" "021"
#> Sample_865 Sample_866 Sample_867 Sample_868 Sample_869 Sample_870 Sample_871 Sample_872
#> "0233" "021" "021" "021" "021" "021" "021" "021"
#> Sample_873 Sample_874 Sample_875 Sample_876 Sample_877 Sample_878 Sample_879 Sample_880
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_881 Sample_882 Sample_883 Sample_884 Sample_885 Sample_886 Sample_887 Sample_888
#> "021" "0233" "021" "021" "021" "021" "021" "021"
#> Sample_889 Sample_890 Sample_891 Sample_892 Sample_893 Sample_894 Sample_895 Sample_896
#> "021" "021" "021" "021" "021" "0232" "021" "021"
#> Sample_897 Sample_898 Sample_899 Sample_900 Sample_901 Sample_902 Sample_903 Sample_904
#> "021" "021" "021" "0233" "021" "021" "021" "021"
#> Sample_905 Sample_906 Sample_907 Sample_908 Sample_909 Sample_910 Sample_911 Sample_912
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_913 Sample_914 Sample_915 Sample_916 Sample_917 Sample_918 Sample_919 Sample_920
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_921 Sample_922 Sample_923 Sample_924 Sample_925 Sample_926 Sample_927 Sample_928
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_929 Sample_930 Sample_931 Sample_932 Sample_933 Sample_934 Sample_935 Sample_936
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_937 Sample_938 Sample_939 Sample_940 Sample_941 Sample_942 Sample_943 Sample_944
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_945 Sample_946 Sample_947 Sample_948 Sample_949 Sample_950 Sample_951 Sample_952
#> "021" "021" "021" "032" "032" "013" "032" "032"
#> Sample_953 Sample_954 Sample_955 Sample_956 Sample_957 Sample_958 Sample_959 Sample_960
#> "032" "032" "032" "032" "032" "032" "032" "032"
#> Sample_961 Sample_962 Sample_963 Sample_964 Sample_965 Sample_966 Sample_967 Sample_968
#> "032" "012" "032" "032" "032" "032" "032" "012"
#> Sample_969 Sample_970 Sample_971 Sample_972 Sample_973 Sample_974 Sample_975 Sample_976
#> "032" "032" "032" "032" "032" "032" "033" "013"
#> Sample_977 Sample_978 Sample_979 Sample_980 Sample_981 Sample_982 Sample_983 Sample_984
#> "013" "012" "032" "013" "013" "012" "012" "032"
#> Sample_985 Sample_986 Sample_987 Sample_988 Sample_989 Sample_990 Sample_991 Sample_992
#> "012" "013" "012" "012" "012" "012" "012" "012"
#> Sample_993 Sample_994 Sample_995 Sample_996 Sample_997 Sample_998 Sample_999 Sample_1000
#> "012" "012" "032" "013" "032" "032" "013" "032"
#> Sample_1001 Sample_1002 Sample_1003 Sample_1004 Sample_1005 Sample_1006 Sample_1007 Sample_1008
#> "032" "032" "032" "033" "032" "032" "012" "013"
#> Sample_1009 Sample_1010 Sample_1011 Sample_1012 Sample_1013 Sample_1014 Sample_1015 Sample_1016
#> "013" "013" "032" "032" "032" "012" "012" "012"
#> Sample_1017 Sample_1018 Sample_1019 Sample_1020 Sample_1021 Sample_1022 Sample_1023 Sample_1024
#> "012" "012" "012" "012" "012" "012" "012" "012"
#> Sample_1025 Sample_1026 Sample_1027 Sample_1028 Sample_1029 Sample_1030 Sample_1031 Sample_1032
#> "012" "012" "011" "011" "0312" "011" "033" "0312"
#> Sample_1033 Sample_1034 Sample_1035 Sample_1036 Sample_1037 Sample_1038 Sample_1039 Sample_1040
#> "0312" "011" "0312" "011" "0312" "0312" "011" "0312"
#> Sample_1041 Sample_1042 Sample_1043 Sample_1044 Sample_1045 Sample_1046 Sample_1047 Sample_1048
#> "011" "011" "0312" "0312" "033" "011" "0312" "033"
#> Sample_1049 Sample_1050 Sample_1051 Sample_1052 Sample_1053 Sample_1054 Sample_1055 Sample_1056
#> "011" "011" "0312" "011" "032" "032" "032" "013"
#> Sample_1057 Sample_1058 Sample_1059 Sample_1060 Sample_1061 Sample_1062 Sample_1063 Sample_1064
#> "032" "012" "013" "032" "032" "013" "0312" "0312"
#> Sample_1065 Sample_1066 Sample_1067 Sample_1068 Sample_1069 Sample_1070 Sample_1071 Sample_1072
#> "0312" "011" "0312" "0312" "0312" "011" "0312" "0312"
#> Sample_1073 Sample_1074 Sample_1075 Sample_1076 Sample_1077 Sample_1078 Sample_1079 Sample_1080
#> "011" "0312" "011" "011" "0312" "0312" "0312" "011"
#> Sample_1081 Sample_1082 Sample_1083 Sample_1084 Sample_1085 Sample_1086 Sample_1087 Sample_1088
#> "0312" "0312" "011" "0312" "0312" "033" "011" "033"
#> Sample_1089 Sample_1090 Sample_1091 Sample_1092 Sample_1093 Sample_1094 Sample_1095 Sample_1096
#> "033" "011" "011" "0312" "011" "0312" "0312" "0312"
#> Sample_1097 Sample_1098 Sample_1099 Sample_1100 Sample_1101 Sample_1102 Sample_1103 Sample_1104
#> "011" "0312" "0312" "011" "0312" "011" "011" "011"
#> Sample_1105 Sample_1106 Sample_1107 Sample_1108 Sample_1109 Sample_1110 Sample_1111 Sample_1112
#> "011" "0312" "0312" "011" "0312" "011" "0312" "011"
#> Sample_1113 Sample_1114 Sample_1115 Sample_1116 Sample_1117 Sample_1118 Sample_1119 Sample_1120
#> "011" "011" "0312" "011" "0312" "011" "011" "033"
#> Sample_1121 Sample_1122 Sample_1123 Sample_1124 Sample_1125 Sample_1126 Sample_1127 Sample_1128
#> "011" "033" "0312" "0312" "0312" "0312" "0312" "0312"
#> Sample_1129 Sample_1130 Sample_1131 Sample_1132 Sample_1133 Sample_1134 Sample_1135 Sample_1136
#> "011" "011" "011" "033" "011" "033" "011" "011"
#> Sample_1137 Sample_1138 Sample_1139 Sample_1140 Sample_1141 Sample_1142 Sample_1143 Sample_1144
#> "011" "011" "0312" "033" "0312" "011" "011" "011"
#> Sample_1145 Sample_1146 Sample_1147 Sample_1148 Sample_1149 Sample_1150 Sample_1151 Sample_1152
#> "0312" "011" "011" "011" "011" "0312" "011" "0312"
#> Sample_1153 Sample_1154 Sample_1155 Sample_1156 Sample_1157 Sample_1158 Sample_1159 Sample_1160
#> "011" "011" "011" "0312" "0312" "011" "011" "011"
#> Sample_1161 Sample_1162 Sample_1163 Sample_1164 Sample_1165 Sample_1166 Sample_1167 Sample_1168
#> "0312" "011" "0312" "0312" "011" "011" "011" "011"
#> Sample_1169 Sample_1170 Sample_1171 Sample_1172 Sample_1173 Sample_1174 Sample_1175 Sample_1176
#> "0312" "0312" "011" "011" "011" "011" "0312" "011"
#> Sample_1177 Sample_1178 Sample_1179 Sample_1180 Sample_1181 Sample_1182 Sample_1183 Sample_1184
#> "011" "011" "011" "0312" "011" "011" "033" "033"
#> Sample_1185 Sample_1186 Sample_1187 Sample_1188 Sample_1189 Sample_1190 Sample_1191 Sample_1192
#> "011" "011" "011" "011" "032" "032" "032" "012"
#> Sample_1193 Sample_1194 Sample_1195 Sample_1196 Sample_1197 Sample_1198 Sample_1199 Sample_1200
#> "032" "032" "032" "032" "032" "032" "032" "032"
#> Sample_1201 Sample_1202 Sample_1203 Sample_1204 Sample_1205 Sample_1206 Sample_1207 Sample_1208
#> "032" "032" "032" "032" "032" "032" "032" "013"
#> Sample_1209 Sample_1210 Sample_1211 Sample_1212 Sample_1213 Sample_1214 Sample_1215 Sample_1216
#> "032" "032" "032" "032" "032" "032" "032" "012"
#> Sample_1217 Sample_1218 Sample_1219 Sample_1220 Sample_1221 Sample_1222 Sample_1223 Sample_1224
#> "013" "013" "032" "032" "032" "011" "0311" "0311"
#> Sample_1225 Sample_1226 Sample_1227 Sample_1228 Sample_1229 Sample_1230 Sample_1231 Sample_1232
#> "011" "0311" "011" "011" "011" "0311" "0311" "0311"
#> Sample_1233 Sample_1234 Sample_1235 Sample_1236 Sample_1237 Sample_1238 Sample_1239 Sample_1240
#> "0311" "011" "0311" "011" "0311" "0311" "011" "011"
#> Sample_1241 Sample_1242 Sample_1243 Sample_1244 Sample_1245 Sample_1246 Sample_1247 Sample_1248
#> "011" "011" "0311" "0311" "0311" "0311" "0311" "0311"
#> Sample_1249 Sample_1250 Sample_1251 Sample_1252 Sample_1253 Sample_1254 Sample_1255 Sample_1256
#> "011" "0311" "0311" "0311" "0311" "011" "033" "0311"
#> Sample_1257 Sample_1258 Sample_1259 Sample_1260 Sample_1261 Sample_1262 Sample_1263 Sample_1264
#> "0311" "0311" "0311" "0311" "0311" "0311" "0311" "011"
#> Sample_1265 Sample_1266 Sample_1267 Sample_1268 Sample_1269 Sample_1270 Sample_1271 Sample_1272
#> "0311" "011" "011" "033" "0311" "0311" "0311" "011"
#> Sample_1273 Sample_1274 Sample_1275 Sample_1276 Sample_1277 Sample_1278 Sample_1279 Sample_1280
#> "0311" "0311" "0311" "0311" "0311" "0311" "011" "011"
#> Sample_1281 Sample_1282 Sample_1283 Sample_1284 Sample_1285 Sample_1286 Sample_1287 Sample_1288
#> "0311" "0311" "011" "0311" "011" "0311" "0311" "0311"
#> Sample_1289 Sample_1290 Sample_1291 Sample_1292 Sample_1293 Sample_1294 Sample_1295 Sample_1296
#> "0311" "0311" "0311" "0311" "0311" "0311" "0311" "033"
#> Sample_1297 Sample_1298 Sample_1299 Sample_1300 Sample_1301 Sample_1302 Sample_1303 Sample_1304
#> "0311" "0311" "011" "0311" "0311" "0311" "0311" "0311"
#> Sample_1305 Sample_1306 Sample_1307 Sample_1308 Sample_1309 Sample_1310 Sample_1311 Sample_1312
#> "0311" "033" "011" "011" "011" "033" "0311" "0311"
#> Sample_1313 Sample_1314 Sample_1315 Sample_1316 Sample_1317 Sample_1318 Sample_1319 Sample_1320
#> "011" "0313" "0313" "0311" "011" "0313" "0311" "011"
#> Sample_1321 Sample_1322 Sample_1323 Sample_1324 Sample_1325 Sample_1326 Sample_1327 Sample_1328
#> "0314" "0311" "0311" "0311" "0311" "0314" "0311" "0313"
#> Sample_1329 Sample_1330 Sample_1331 Sample_1332 Sample_1333 Sample_1334 Sample_1335 Sample_1336
#> "0314" "0314" "0313" "0314" "0314" "0313" "011" "011"
#> Sample_1337 Sample_1338 Sample_1339 Sample_1340 Sample_1341 Sample_1342 Sample_1343 Sample_1344
#> "033" "011" "033" "0311" "0314" "011" "011" "0314"
#> Sample_1345 Sample_1346 Sample_1347 Sample_1348 Sample_1349 Sample_1350 Sample_1351 Sample_1352
#> "033" "0311" "0311" "011" "0314" "011" "0314" "011"
#> Sample_1353 Sample_1354 Sample_1355 Sample_1356 Sample_1357 Sample_1358 Sample_1359 Sample_1360
#> "0311" "011" "0314" "0314" "011" "011" "0314" "011"
#> Sample_1361 Sample_1362 Sample_1363 Sample_1364 Sample_1365 Sample_1366 Sample_1367 Sample_1368
#> "0314" "0314" "0314" "011" "0311" "0313" "011" "0313"
#> Sample_1369 Sample_1370 Sample_1371 Sample_1372 Sample_1373 Sample_1374 Sample_1375 Sample_1376
#> "0311" "0314" "011" "0314" "011" "011" "011" "033"
#> Sample_1377 Sample_1378 Sample_1379 Sample_1380 Sample_1381 Sample_1382 Sample_1383 Sample_1384
#> "0311" "0311" "0311" "0311" "0314" "011" "0313" "0313"
#> Sample_1385 Sample_1386 Sample_1387 Sample_1388 Sample_1389 Sample_1390 Sample_1391 Sample_1392
#> "0313" "011" "0313" "011" "0313" "0313" "0313" "0313"
#> Sample_1393 Sample_1394 Sample_1395 Sample_1396 Sample_1397 Sample_1398 Sample_1399 Sample_1400
#> "0313" "0313" "0313" "0313" "0313" "011" "011" "033"
#> Sample_1401 Sample_1402 Sample_1403 Sample_1404 Sample_1405 Sample_1406 Sample_1407 Sample_1408
#> "0313" "011" "0313" "0313" "0313" "011" "0313" "011"
#> Sample_1409 Sample_1410 Sample_1411 Sample_1412 Sample_1413 Sample_1414 Sample_1415 Sample_1416
#> "011" "0313" "0313" "0313" "033" "0313" "0313" "033"
#> Sample_1417 Sample_1418 Sample_1419 Sample_1420 Sample_1421 Sample_1422 Sample_1423 Sample_1424
#> "0313" "0313" "011" "0313" "0313" "0313" "011" "0313"
#> Sample_1425 Sample_1426 Sample_1427 Sample_1428 Sample_1429 Sample_1430 Sample_1431 Sample_1432
#> "011" "011" "011" "0313" "011" "011" "011" "011"
#> Sample_1433 Sample_1434 Sample_1435 Sample_1436 Sample_1437 Sample_1438 Sample_1439 Sample_1440
#> "011" "011" "011" "011" "011" "011" "011" "011"
#> Sample_1441 Sample_1442 Sample_1443 Sample_1444 Sample_1445 Sample_1446 Sample_1447 Sample_1448
#> "011" "0313" "0313" "011" "011" "0313" "0313" "011"
#> Sample_1449 Sample_1450 Sample_1451 Sample_1452 Sample_1453 Sample_1454 Sample_1455 Sample_1456
#> "011" "011" "011" "011" "011" "011" "011" "0313"
#> Sample_1457 Sample_1458 Sample_1459 Sample_1460 Sample_1461 Sample_1462 Sample_1463 Sample_1464
#> "0313" "011" "011" "033" "0313" "011" "0314" "0314"
#> Sample_1465 Sample_1466 Sample_1467 Sample_1468 Sample_1469 Sample_1470 Sample_1471 Sample_1472
#> "011" "011" "011" "0313" "011" "011" "011" "011"
#> Sample_1473 Sample_1474 Sample_1475 Sample_1476 Sample_1477 Sample_1478 Sample_1479 Sample_1480
#> "011" "011" "011" "011" "011" "011" "011" "0314"
#> Sample_1481 Sample_1482 Sample_1483 Sample_1484 Sample_1485 Sample_1486 Sample_1487 Sample_1488
#> "011" "011" "011" "011" "0313" "033" "011" "0313"
#> Sample_1489 Sample_1490 Sample_1491 Sample_1492 Sample_1493 Sample_1494 Sample_1495 Sample_1496
#> "011" "011" "011" "011" "011" "033" "011" "011"
#> Sample_1497 Sample_1498 Sample_1499 Sample_1500 Sample_1501 Sample_1502 Sample_1503 Sample_1504
#> "011" "011" "011" "011" "011" "0313" "0313" "033"
#> Sample_1505 Sample_1506 Sample_1507 Sample_1508 Sample_1509 Sample_1510 Sample_1511 Sample_1512
#> "033" "0313" "0313" "0313" "011" "011" "0313" "0313"
#> Sample_1513 Sample_1514 Sample_1515 Sample_1516 Sample_1517 Sample_1518 Sample_1519 Sample_1520
#> "0313" "011" "0313" "011" "0222" "0222" "0221" "0233"
#> Sample_1521 Sample_1522 Sample_1523 Sample_1524 Sample_1525 Sample_1526 Sample_1527 Sample_1528
#> "0232" "013" "013" "0233" "012" "012" "021" "021"
#> Sample_1529 Sample_1530 Sample_1531 Sample_1532 Sample_1533 Sample_1534 Sample_1535 Sample_1536
#> "021" "0233" "0232" "0232" "021" "021" "0222" "0222"
#> Sample_1537 Sample_1538 Sample_1539 Sample_1540 Sample_1541 Sample_1542 Sample_1543 Sample_1544
#> "0221" "021" "021" "013" "011" "021" "0233" "013"
#> Sample_1545 Sample_1546 Sample_1547 Sample_1548 Sample_1549 Sample_1550 Sample_1551 Sample_1552
#> "011" "021" "013" "0231" "032" "0231" "0231" "0231"
#> Sample_1553 Sample_1554 Sample_1555 Sample_1556 Sample_1557 Sample_1558 Sample_1559 Sample_1560
#> "032" "0222" "0231" "013" "013" "013" "0231" "013"
#> Sample_1561 Sample_1562 Sample_1563 Sample_1564 Sample_1565 Sample_1566 Sample_1567 Sample_1568
#> "0231" "012" "013" "013" "013" "013" "013" "013"
#> Sample_1569 Sample_1570 Sample_1571 Sample_1572 Sample_1573 Sample_1574 Sample_1575 Sample_1576
#> "0312" "021" "013" "013" "013" "0231" "0231" "013"
#> Sample_1577 Sample_1578 Sample_1579 Sample_1580 Sample_1581 Sample_1582 Sample_1583 Sample_1584
#> "013" "013" "013" "013" "032" "0231" "013" "013"
#> Sample_1585 Sample_1586 Sample_1587 Sample_1588 Sample_1589 Sample_1590 Sample_1591 Sample_1592
#> "0231" "0231" "0231" "0222" "0231" "013" "013" "0231"
#> Sample_1593 Sample_1594 Sample_1595 Sample_1596 Sample_1597 Sample_1598 Sample_1599 Sample_1600
#> "0231" "0231" "0231" "0232" "032" "0311" "0233" "013"
get_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 996))
#> Sample_1 Sample_2 Sample_3 Sample_4 Sample_5 Sample_6 Sample_7 Sample_8
#> "013" "022" "022" "022" "022" "022" "022" "022"
#> Sample_9 Sample_10 Sample_11 Sample_12 Sample_13 Sample_14 Sample_15 Sample_16
#> "022" "022" "022" "022" "022" "022" "022" "022"
#> Sample_17 Sample_18 Sample_19 Sample_20 Sample_21 Sample_22 Sample_23 Sample_24
#> "022" "022" "022" "022" "022" "022" "022" "022"
#> Sample_25 Sample_26 Sample_27 Sample_28 Sample_29 Sample_30 Sample_31 Sample_32
#> "022" "022" "022" "022" "022" "022" "0233" "022"
#> Sample_33 Sample_34 Sample_35 Sample_36 Sample_37 Sample_38 Sample_39 Sample_40
#> "022" "022" "022" "022" "022" "022" "022" "022"
#> Sample_41 Sample_42 Sample_43 Sample_44 Sample_45 Sample_46 Sample_47 Sample_48
#> "022" "022" "0233" "022" "022" "022" "022" "022"
#> Sample_49 Sample_50 Sample_51 Sample_52 Sample_53 Sample_54 Sample_55 Sample_56
#> "022" "022" "022" "022" "022" "022" "022" "022"
#> Sample_57 Sample_58 Sample_59 Sample_60 Sample_61 Sample_62 Sample_63 Sample_64
#> "022" "022" "022" "022" "022" "022" "022" "022"
#> Sample_65 Sample_66 Sample_67 Sample_68 Sample_69 Sample_70 Sample_71 Sample_72
#> "022" "022" "022" "022" "022" "022" "022" "021"
#> Sample_73 Sample_74 Sample_75 Sample_76 Sample_77 Sample_78 Sample_79 Sample_80
#> "0233" "022" "022" "022" "022" "022" "022" "022"
#> Sample_81 Sample_82 Sample_83 Sample_84 Sample_85 Sample_86 Sample_87 Sample_88
#> "022" "022" "022" "022" "022" "022" "022" "022"
#> Sample_89 Sample_90 Sample_91 Sample_92 Sample_93 Sample_94 Sample_95 Sample_96
#> "022" "022" "022" "022" "022" "0232" "022" "022"
#> Sample_97 Sample_98 Sample_99 Sample_100 Sample_101 Sample_102 Sample_103 Sample_104
#> "022" "022" "022" "022" "022" "022" "022" "022"
#> Sample_105 Sample_106 Sample_107 Sample_108 Sample_109 Sample_110 Sample_111 Sample_112
#> "022" "022" "022" "022" "022" "022" "021" "022"
#> Sample_113 Sample_114 Sample_115 Sample_116 Sample_117 Sample_118 Sample_119 Sample_120
#> "022" "0232" "022" "022" "022" "0232" "022" "022"
#> Sample_121 Sample_122 Sample_123 Sample_124 Sample_125 Sample_126 Sample_127 Sample_128
#> "012" "022" "0232" "022" "022" "022" "022" "022"
#> Sample_129 Sample_130 Sample_131 Sample_132 Sample_133 Sample_134 Sample_135 Sample_136
#> "022" "022" "022" "022" "022" "022" "022" "022"
#> Sample_137 Sample_138 Sample_139 Sample_140 Sample_141 Sample_142 Sample_143 Sample_144
#> "022" "022" "022" "022" "022" "022" "022" "0232"
#> Sample_145 Sample_146 Sample_147 Sample_148 Sample_149 Sample_150 Sample_151 Sample_152
#> "022" "022" "021" "021" "021" "021" "021" "021"
#> Sample_153 Sample_154 Sample_155 Sample_156 Sample_157 Sample_158 Sample_159 Sample_160
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_161 Sample_162 Sample_163 Sample_164 Sample_165 Sample_166 Sample_167 Sample_168
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_169 Sample_170 Sample_171 Sample_172 Sample_173 Sample_174 Sample_175 Sample_176
#> "021" "021" "0232" "021" "0233" "021" "021" "021"
#> Sample_177 Sample_178 Sample_179 Sample_180 Sample_181 Sample_182 Sample_183 Sample_184
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_185 Sample_186 Sample_187 Sample_188 Sample_189 Sample_190 Sample_191 Sample_192
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_193 Sample_194 Sample_195 Sample_196 Sample_197 Sample_198 Sample_199 Sample_200
#> "021" "021" "021" "021" "022" "022" "022" "022"
#> Sample_201 Sample_202 Sample_203 Sample_204 Sample_205 Sample_206 Sample_207 Sample_208
#> "022" "022" "022" "0233" "022" "022" "022" "032"
#> Sample_209 Sample_210 Sample_211 Sample_212 Sample_213 Sample_214 Sample_215 Sample_216
#> "032" "032" "032" "032" "032" "013" "013" "032"
#> Sample_217 Sample_218 Sample_219 Sample_220 Sample_221 Sample_222 Sample_223 Sample_224
#> "013" "032" "032" "032" "032" "032" "032" "013"
#> Sample_225 Sample_226 Sample_227 Sample_228 Sample_229 Sample_230 Sample_231 Sample_232
#> "032" "013" "032" "032" "032" "032" "032" "032"
#> Sample_233 Sample_234 Sample_235 Sample_236 Sample_237 Sample_238 Sample_239 Sample_240
#> "033" "032" "032" "013" "032" "032" "032" "032"
#> Sample_241 Sample_242 Sample_243 Sample_244 Sample_245 Sample_246 Sample_247 Sample_248
#> "013" "032" "032" "032" "032" "032" "032" "032"
#> Sample_249 Sample_250 Sample_251 Sample_252 Sample_253 Sample_254 Sample_255 Sample_256
#> "032" "032" "032" "032" "032" "033" "032" "032"
#> Sample_257 Sample_258 Sample_259 Sample_260 Sample_261 Sample_262 Sample_263 Sample_264
#> "032" "013" "032" "032" "032" "032" "032" "013"
#> Sample_265 Sample_266 Sample_267 Sample_268 Sample_269 Sample_270 Sample_271 Sample_272
#> "012" "032" "032" "032" "032" "032" "033" "012"
#> Sample_273 Sample_274 Sample_275 Sample_276 Sample_277 Sample_278 Sample_279 Sample_280
#> "032" "032" "012" "013" "032" "033" "032" "032"
#> Sample_281 Sample_282 Sample_283 Sample_284 Sample_285 Sample_286 Sample_287 Sample_288
#> "032" "032" "032" "032" "032" "032" "013" "033"
#> Sample_289 Sample_290 Sample_291 Sample_292 Sample_293 Sample_294 Sample_295 Sample_296
#> "033" "032" "032" "032" "032" "032" "032" "032"
#> Sample_297 Sample_298 Sample_299 Sample_300 Sample_301 Sample_302 Sample_303 Sample_304
#> "032" "032" "032" "032" "032" "012" "012" "012"
#> Sample_305 Sample_306 Sample_307 Sample_308 Sample_309 Sample_310 Sample_311 Sample_312
#> "012" "032" "032" "012" "012" "012" "012" "012"
#> Sample_313 Sample_314 Sample_315 Sample_316 Sample_317 Sample_318 Sample_319 Sample_320
#> "012" "012" "032" "012" "032" "012" "012" "012"
#> Sample_321 Sample_322 Sample_323 Sample_324 Sample_325 Sample_326 Sample_327 Sample_328
#> "012" "032" "012" "012" "012" "012" "012" "012"
#> Sample_329 Sample_330 Sample_331 Sample_332 Sample_333 Sample_334 Sample_335 Sample_336
#> "012" "012" "033" "032" "032" "032" "032" "032"
#> Sample_337 Sample_338 Sample_339 Sample_340 Sample_341 Sample_342 Sample_343 Sample_344
#> "032" "032" "033" "032" "032" "032" "032" "032"
#> Sample_345 Sample_346 Sample_347 Sample_348 Sample_349 Sample_350 Sample_351 Sample_352
#> "032" "032" "032" "032" "032" "013" "012" "013"
#> Sample_353 Sample_354 Sample_355 Sample_356 Sample_357 Sample_358 Sample_359 Sample_360
#> "032" "032" "012" "032" "032" "012" "032" "012"
#> Sample_361 Sample_362 Sample_363 Sample_364 Sample_365 Sample_366 Sample_367 Sample_368
#> "032" "012" "032" "032" "013" "032" "032" "012"
#> Sample_369 Sample_370 Sample_371 Sample_372 Sample_373 Sample_374 Sample_375 Sample_376
#> "012" "032" "033" "013" "013" "032" "032" "032"
#> Sample_377 Sample_378 Sample_379 Sample_380 Sample_381 Sample_382 Sample_383 Sample_384
#> "012" "032" "032" "032" "012" "032" "032" "032"
#> Sample_385 Sample_386 Sample_387 Sample_388 Sample_389 Sample_390 Sample_391 Sample_392
#> "012" "012" "032" "032" "013" "013" "032" "032"
#> Sample_393 Sample_394 Sample_395 Sample_396 Sample_397 Sample_398 Sample_399 Sample_400
#> "012" "013" "012" "013" "012" "012" "032" "032"
#> Sample_401 Sample_402 Sample_403 Sample_404 Sample_405 Sample_406 Sample_407 Sample_408
#> "032" "032" "013" "013" "013" "032" "032" "013"
#> Sample_409 Sample_410 Sample_411 Sample_412 Sample_413 Sample_414 Sample_415 Sample_416
#> "012" "032" "032" "012" "032" "032" "033" "012"
#> Sample_417 Sample_418 Sample_419 Sample_420 Sample_421 Sample_422 Sample_423 Sample_424
#> "032" "032" "032" "033" "012" "033" "032" "012"
#> Sample_425 Sample_426 Sample_427 Sample_428 Sample_429 Sample_430 Sample_431 Sample_432
#> "012" "032" "012" "012" "032" "013" "012" "012"
#> Sample_433 Sample_434 Sample_435 Sample_436 Sample_437 Sample_438 Sample_439 Sample_440
#> "013" "012" "032" "033" "033" "013" "012" "012"
#> Sample_441 Sample_442 Sample_443 Sample_444 Sample_445 Sample_446 Sample_447 Sample_448
#> "032" "012" "013" "032" "012" "012" "032" "013"
#> Sample_449 Sample_450 Sample_451 Sample_452 Sample_453 Sample_454 Sample_455 Sample_456
#> "032" "032" "012" "012" "032" "032" "013" "012"
#> Sample_457 Sample_458 Sample_459 Sample_460 Sample_461 Sample_462 Sample_463 Sample_464
#> "013" "032" "012" "032" "012" "032" "013" "012"
#> Sample_465 Sample_466 Sample_467 Sample_468 Sample_469 Sample_470 Sample_471 Sample_472
#> "012" "012" "032" "012" "012" "032" "032" "012"
#> Sample_473 Sample_474 Sample_475 Sample_476 Sample_477 Sample_478 Sample_479 Sample_480
#> "032" "012" "012" "013" "013" "0314" "011" "0314"
#> Sample_481 Sample_482 Sample_483 Sample_484 Sample_485 Sample_486 Sample_487 Sample_488
#> "0313" "0314" "0314" "0314" "0314" "011" "011" "033"
#> Sample_489 Sample_490 Sample_491 Sample_492 Sample_493 Sample_494 Sample_495 Sample_496
#> "0314" "011" "0314" "0314" "0314" "0314" "011" "0314"
#> Sample_497 Sample_498 Sample_499 Sample_500 Sample_501 Sample_502 Sample_503 Sample_504
#> "0314" "0314" "011" "011" "0314" "0314" "0314" "0314"
#> Sample_505 Sample_506 Sample_507 Sample_508 Sample_509 Sample_510 Sample_511 Sample_512
#> "011" "011" "011" "0314" "0314" "0314" "011" "033"
#> Sample_513 Sample_514 Sample_515 Sample_516 Sample_517 Sample_518 Sample_519 Sample_520
#> "011" "0314" "011" "021" "011" "0314" "0314" "011"
#> Sample_521 Sample_522 Sample_523 Sample_524 Sample_525 Sample_526 Sample_527 Sample_528
#> "011" "0314" "0314" "0314" "0314" "0314" "011" "0312"
#> Sample_529 Sample_530 Sample_531 Sample_532 Sample_533 Sample_534 Sample_535 Sample_536
#> "033" "011" "033" "011" "011" "0314" "011" "011"
#> Sample_537 Sample_538 Sample_539 Sample_540 Sample_541 Sample_542 Sample_543 Sample_544
#> "011" "0314" "011" "0314" "0314" "011" "011" "011"
#> Sample_545 Sample_546 Sample_547 Sample_548 Sample_549 Sample_550 Sample_551 Sample_552
#> "033" "011" "011" "0314" "0314" "0314" "0314" "0314"
#> Sample_553 Sample_554 Sample_555 Sample_556 Sample_557 Sample_558 Sample_559 Sample_560
#> "033" "0314" "0314" "0314" "011" "011" "011" "0314"
#> Sample_561 Sample_562 Sample_563 Sample_564 Sample_565 Sample_566 Sample_567 Sample_568
#> "0314" "0314" "0314" "0314" "011" "011" "011" "0314"
#> Sample_569 Sample_570 Sample_571 Sample_572 Sample_573 Sample_574 Sample_575 Sample_576
#> "011" "032" "032" "013" "012" "033" "032" "032"
#> Sample_577 Sample_578 Sample_579 Sample_580 Sample_581 Sample_582 Sample_583 Sample_584
#> "012" "032" "032" "032" "032" "032" "032" "032"
#> Sample_585 Sample_586 Sample_587 Sample_588 Sample_589 Sample_590 Sample_591 Sample_592
#> "032" "022" "022" "0231" "032" "013" "022" "0231"
#> Sample_593 Sample_594 Sample_595 Sample_596 Sample_597 Sample_598 Sample_599 Sample_600
#> "022" "0231" "0231" "022" "0231" "022" "0231" "0231"
#> Sample_601 Sample_602 Sample_603 Sample_604 Sample_605 Sample_606 Sample_607 Sample_608
#> "022" "0231" "022" "022" "022" "022" "022" "022"
#> Sample_609 Sample_610 Sample_611 Sample_612 Sample_613 Sample_614 Sample_615 Sample_616
#> "022" "022" "021" "021" "032" "013" "032" "013"
#> Sample_617 Sample_618 Sample_619 Sample_620 Sample_621 Sample_622 Sample_623 Sample_624
#> "013" "012" "013" "013" "0231" "013" "032" "032"
#> Sample_625 Sample_626 Sample_627 Sample_628 Sample_629 Sample_630 Sample_631 Sample_632
#> "0231" "013" "0231" "0231" "0231" "013" "0231" "013"
#> Sample_633 Sample_634 Sample_635 Sample_636 Sample_637 Sample_638 Sample_639 Sample_640
#> "013" "013" "013" "013" "013" "0231" "013" "0232"
#> Sample_641 Sample_642 Sample_643 Sample_644 Sample_645 Sample_646 Sample_647 Sample_648
#> "013" "0233" "013" "032" "0232" "013" "013" "0232"
#> Sample_649 Sample_650 Sample_651 Sample_652 Sample_653 Sample_654 Sample_655 Sample_656
#> "0232" "0231" "022" "022" "022" "022" "022" "022"
#> Sample_657 Sample_658 Sample_659 Sample_660 Sample_661 Sample_662 Sample_663 Sample_664
#> "022" "022" "022" "022" "022" "022" "022" "022"
#> Sample_665 Sample_666 Sample_667 Sample_668 Sample_669 Sample_670 Sample_671 Sample_672
#> "0232" "022" "0232" "022" "022" "022" "022" "022"
#> Sample_673 Sample_674 Sample_675 Sample_676 Sample_677 Sample_678 Sample_679 Sample_680
#> "022" "022" "022" "022" "022" "022" "022" "022"
#> Sample_681 Sample_682 Sample_683 Sample_684 Sample_685 Sample_686 Sample_687 Sample_688
#> "0232" "022" "022" "022" "012" "021" "012" "021"
#> Sample_689 Sample_690 Sample_691 Sample_692 Sample_693 Sample_694 Sample_695 Sample_696
#> "022" "021" "022" "012" "022" "022" "021" "021"
#> Sample_697 Sample_698 Sample_699 Sample_700 Sample_701 Sample_702 Sample_703 Sample_704
#> "021" "021" "021" "021" "021" "021" "021" "022"
#> Sample_705 Sample_706 Sample_707 Sample_708 Sample_709 Sample_710 Sample_711 Sample_712
#> "022" "021" "021" "021" "021" "0232" "021" "021"
#> Sample_713 Sample_714 Sample_715 Sample_716 Sample_717 Sample_718 Sample_719 Sample_720
#> "021" "021" "021" "021" "021" "021" "021" "0232"
#> Sample_721 Sample_722 Sample_723 Sample_724 Sample_725 Sample_726 Sample_727 Sample_728
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_729 Sample_730 Sample_731 Sample_732 Sample_733 Sample_734 Sample_735 Sample_736
#> "021" "021" "0233" "021" "021" "021" "0233" "021"
#> Sample_737 Sample_738 Sample_739 Sample_740 Sample_741 Sample_742 Sample_743 Sample_744
#> "012" "0232" "021" "021" "021" "021" "021" "021"
#> Sample_745 Sample_746 Sample_747 Sample_748 Sample_749 Sample_750 Sample_751 Sample_752
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_753 Sample_754 Sample_755 Sample_756 Sample_757 Sample_758 Sample_759 Sample_760
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_761 Sample_762 Sample_763 Sample_764 Sample_765 Sample_766 Sample_767 Sample_768
#> "021" "021" "021" "021" "021" "021" "021" "0232"
#> Sample_769 Sample_770 Sample_771 Sample_772 Sample_773 Sample_774 Sample_775 Sample_776
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_777 Sample_778 Sample_779 Sample_780 Sample_781 Sample_782 Sample_783 Sample_784
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_785 Sample_786 Sample_787 Sample_788 Sample_789 Sample_790 Sample_791 Sample_792
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_793 Sample_794 Sample_795 Sample_796 Sample_797 Sample_798 Sample_799 Sample_800
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_801 Sample_802 Sample_803 Sample_804 Sample_805 Sample_806 Sample_807 Sample_808
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_809 Sample_810 Sample_811 Sample_812 Sample_813 Sample_814 Sample_815 Sample_816
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_817 Sample_818 Sample_819 Sample_820 Sample_821 Sample_822 Sample_823 Sample_824
#> "021" "021" "021" "0233" "021" "021" "021" "021"
#> Sample_825 Sample_826 Sample_827 Sample_828 Sample_829 Sample_830 Sample_831 Sample_832
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_833 Sample_834 Sample_835 Sample_836 Sample_837 Sample_838 Sample_839 Sample_840
#> "021" "021" "021" "0233" "021" "021" "021" "021"
#> Sample_841 Sample_842 Sample_843 Sample_844 Sample_845 Sample_846 Sample_847 Sample_848
#> "022" "022" "022" "022" "022" "022" "022" "022"
#> Sample_849 Sample_850 Sample_851 Sample_852 Sample_853 Sample_854 Sample_855 Sample_856
#> "022" "022" "022" "022" "022" "022" "021" "021"
#> Sample_857 Sample_858 Sample_859 Sample_860 Sample_861 Sample_862 Sample_863 Sample_864
#> "021" "021" "0232" "021" "021" "021" "021" "021"
#> Sample_865 Sample_866 Sample_867 Sample_868 Sample_869 Sample_870 Sample_871 Sample_872
#> "0233" "021" "021" "021" "021" "021" "021" "021"
#> Sample_873 Sample_874 Sample_875 Sample_876 Sample_877 Sample_878 Sample_879 Sample_880
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_881 Sample_882 Sample_883 Sample_884 Sample_885 Sample_886 Sample_887 Sample_888
#> "021" "0233" "021" "021" "021" "021" "021" "021"
#> Sample_889 Sample_890 Sample_891 Sample_892 Sample_893 Sample_894 Sample_895 Sample_896
#> "021" "021" "021" "021" "021" "0232" "021" "021"
#> Sample_897 Sample_898 Sample_899 Sample_900 Sample_901 Sample_902 Sample_903 Sample_904
#> "021" "021" "021" "0233" "021" "021" "021" "021"
#> Sample_905 Sample_906 Sample_907 Sample_908 Sample_909 Sample_910 Sample_911 Sample_912
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_913 Sample_914 Sample_915 Sample_916 Sample_917 Sample_918 Sample_919 Sample_920
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_921 Sample_922 Sample_923 Sample_924 Sample_925 Sample_926 Sample_927 Sample_928
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_929 Sample_930 Sample_931 Sample_932 Sample_933 Sample_934 Sample_935 Sample_936
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_937 Sample_938 Sample_939 Sample_940 Sample_941 Sample_942 Sample_943 Sample_944
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_945 Sample_946 Sample_947 Sample_948 Sample_949 Sample_950 Sample_951 Sample_952
#> "021" "021" "021" "032" "032" "013" "032" "032"
#> Sample_953 Sample_954 Sample_955 Sample_956 Sample_957 Sample_958 Sample_959 Sample_960
#> "032" "032" "032" "032" "032" "032" "032" "032"
#> Sample_961 Sample_962 Sample_963 Sample_964 Sample_965 Sample_966 Sample_967 Sample_968
#> "032" "012" "032" "032" "032" "032" "032" "012"
#> Sample_969 Sample_970 Sample_971 Sample_972 Sample_973 Sample_974 Sample_975 Sample_976
#> "032" "032" "032" "032" "032" "032" "033" "013"
#> Sample_977 Sample_978 Sample_979 Sample_980 Sample_981 Sample_982 Sample_983 Sample_984
#> "013" "012" "032" "013" "013" "012" "012" "032"
#> Sample_985 Sample_986 Sample_987 Sample_988 Sample_989 Sample_990 Sample_991 Sample_992
#> "012" "013" "012" "012" "012" "012" "012" "012"
#> Sample_993 Sample_994 Sample_995 Sample_996 Sample_997 Sample_998 Sample_999 Sample_1000
#> "012" "012" "032" "013" "032" "032" "013" "032"
#> Sample_1001 Sample_1002 Sample_1003 Sample_1004 Sample_1005 Sample_1006 Sample_1007 Sample_1008
#> "032" "032" "032" "033" "032" "032" "012" "013"
#> Sample_1009 Sample_1010 Sample_1011 Sample_1012 Sample_1013 Sample_1014 Sample_1015 Sample_1016
#> "013" "013" "032" "032" "032" "012" "012" "012"
#> Sample_1017 Sample_1018 Sample_1019 Sample_1020 Sample_1021 Sample_1022 Sample_1023 Sample_1024
#> "012" "012" "012" "012" "012" "012" "012" "012"
#> Sample_1025 Sample_1026 Sample_1027 Sample_1028 Sample_1029 Sample_1030 Sample_1031 Sample_1032
#> "012" "012" "011" "011" "0312" "011" "033" "0312"
#> Sample_1033 Sample_1034 Sample_1035 Sample_1036 Sample_1037 Sample_1038 Sample_1039 Sample_1040
#> "0312" "011" "0312" "011" "0312" "0312" "011" "0312"
#> Sample_1041 Sample_1042 Sample_1043 Sample_1044 Sample_1045 Sample_1046 Sample_1047 Sample_1048
#> "011" "011" "0312" "0312" "033" "011" "0312" "033"
#> Sample_1049 Sample_1050 Sample_1051 Sample_1052 Sample_1053 Sample_1054 Sample_1055 Sample_1056
#> "011" "011" "0312" "011" "032" "032" "032" "013"
#> Sample_1057 Sample_1058 Sample_1059 Sample_1060 Sample_1061 Sample_1062 Sample_1063 Sample_1064
#> "032" "012" "013" "032" "032" "013" "0312" "0312"
#> Sample_1065 Sample_1066 Sample_1067 Sample_1068 Sample_1069 Sample_1070 Sample_1071 Sample_1072
#> "0312" "011" "0312" "0312" "0312" "011" "0312" "0312"
#> Sample_1073 Sample_1074 Sample_1075 Sample_1076 Sample_1077 Sample_1078 Sample_1079 Sample_1080
#> "011" "0312" "011" "011" "0312" "0312" "0312" "011"
#> Sample_1081 Sample_1082 Sample_1083 Sample_1084 Sample_1085 Sample_1086 Sample_1087 Sample_1088
#> "0312" "0312" "011" "0312" "0312" "033" "011" "033"
#> Sample_1089 Sample_1090 Sample_1091 Sample_1092 Sample_1093 Sample_1094 Sample_1095 Sample_1096
#> "033" "011" "011" "0312" "011" "0312" "0312" "0312"
#> Sample_1097 Sample_1098 Sample_1099 Sample_1100 Sample_1101 Sample_1102 Sample_1103 Sample_1104
#> "011" "0312" "0312" "011" "0312" "011" "011" "011"
#> Sample_1105 Sample_1106 Sample_1107 Sample_1108 Sample_1109 Sample_1110 Sample_1111 Sample_1112
#> "011" "0312" "0312" "011" "0312" "011" "0312" "011"
#> Sample_1113 Sample_1114 Sample_1115 Sample_1116 Sample_1117 Sample_1118 Sample_1119 Sample_1120
#> "011" "011" "0312" "011" "0312" "011" "011" "033"
#> Sample_1121 Sample_1122 Sample_1123 Sample_1124 Sample_1125 Sample_1126 Sample_1127 Sample_1128
#> "011" "033" "0312" "0312" "0312" "0312" "0312" "0312"
#> Sample_1129 Sample_1130 Sample_1131 Sample_1132 Sample_1133 Sample_1134 Sample_1135 Sample_1136
#> "011" "011" "011" "033" "011" "033" "011" "011"
#> Sample_1137 Sample_1138 Sample_1139 Sample_1140 Sample_1141 Sample_1142 Sample_1143 Sample_1144
#> "011" "011" "0312" "033" "0312" "011" "011" "011"
#> Sample_1145 Sample_1146 Sample_1147 Sample_1148 Sample_1149 Sample_1150 Sample_1151 Sample_1152
#> "0312" "011" "011" "011" "011" "0312" "011" "0312"
#> Sample_1153 Sample_1154 Sample_1155 Sample_1156 Sample_1157 Sample_1158 Sample_1159 Sample_1160
#> "011" "011" "011" "0312" "0312" "011" "011" "011"
#> Sample_1161 Sample_1162 Sample_1163 Sample_1164 Sample_1165 Sample_1166 Sample_1167 Sample_1168
#> "0312" "011" "0312" "0312" "011" "011" "011" "011"
#> Sample_1169 Sample_1170 Sample_1171 Sample_1172 Sample_1173 Sample_1174 Sample_1175 Sample_1176
#> "0312" "0312" "011" "011" "011" "011" "0312" "011"
#> Sample_1177 Sample_1178 Sample_1179 Sample_1180 Sample_1181 Sample_1182 Sample_1183 Sample_1184
#> "011" "011" "011" "0312" "011" "011" "033" "033"
#> Sample_1185 Sample_1186 Sample_1187 Sample_1188 Sample_1189 Sample_1190 Sample_1191 Sample_1192
#> "011" "011" "011" "011" "032" "032" "032" "012"
#> Sample_1193 Sample_1194 Sample_1195 Sample_1196 Sample_1197 Sample_1198 Sample_1199 Sample_1200
#> "032" "032" "032" "032" "032" "032" "032" "032"
#> Sample_1201 Sample_1202 Sample_1203 Sample_1204 Sample_1205 Sample_1206 Sample_1207 Sample_1208
#> "032" "032" "032" "032" "032" "032" "032" "013"
#> Sample_1209 Sample_1210 Sample_1211 Sample_1212 Sample_1213 Sample_1214 Sample_1215 Sample_1216
#> "032" "032" "032" "032" "032" "032" "032" "012"
#> Sample_1217 Sample_1218 Sample_1219 Sample_1220 Sample_1221 Sample_1222 Sample_1223 Sample_1224
#> "013" "013" "032" "032" "032" "011" "0311" "0311"
#> Sample_1225 Sample_1226 Sample_1227 Sample_1228 Sample_1229 Sample_1230 Sample_1231 Sample_1232
#> "011" "0311" "011" "011" "011" "0311" "0311" "0311"
#> Sample_1233 Sample_1234 Sample_1235 Sample_1236 Sample_1237 Sample_1238 Sample_1239 Sample_1240
#> "0311" "011" "0311" "011" "0311" "0311" "011" "011"
#> Sample_1241 Sample_1242 Sample_1243 Sample_1244 Sample_1245 Sample_1246 Sample_1247 Sample_1248
#> "011" "011" "0311" "0311" "0311" "0311" "0311" "0311"
#> Sample_1249 Sample_1250 Sample_1251 Sample_1252 Sample_1253 Sample_1254 Sample_1255 Sample_1256
#> "011" "0311" "0311" "0311" "0311" "011" "033" "0311"
#> Sample_1257 Sample_1258 Sample_1259 Sample_1260 Sample_1261 Sample_1262 Sample_1263 Sample_1264
#> "0311" "0311" "0311" "0311" "0311" "0311" "0311" "011"
#> Sample_1265 Sample_1266 Sample_1267 Sample_1268 Sample_1269 Sample_1270 Sample_1271 Sample_1272
#> "0311" "011" "011" "033" "0311" "0311" "0311" "011"
#> Sample_1273 Sample_1274 Sample_1275 Sample_1276 Sample_1277 Sample_1278 Sample_1279 Sample_1280
#> "0311" "0311" "0311" "0311" "0311" "0311" "011" "011"
#> Sample_1281 Sample_1282 Sample_1283 Sample_1284 Sample_1285 Sample_1286 Sample_1287 Sample_1288
#> "0311" "0311" "011" "0311" "011" "0311" "0311" "0311"
#> Sample_1289 Sample_1290 Sample_1291 Sample_1292 Sample_1293 Sample_1294 Sample_1295 Sample_1296
#> "0311" "0311" "0311" "0311" "0311" "0311" "0311" "033"
#> Sample_1297 Sample_1298 Sample_1299 Sample_1300 Sample_1301 Sample_1302 Sample_1303 Sample_1304
#> "0311" "0311" "011" "0311" "0311" "0311" "0311" "0311"
#> Sample_1305 Sample_1306 Sample_1307 Sample_1308 Sample_1309 Sample_1310 Sample_1311 Sample_1312
#> "0311" "033" "011" "011" "011" "033" "0311" "0311"
#> Sample_1313 Sample_1314 Sample_1315 Sample_1316 Sample_1317 Sample_1318 Sample_1319 Sample_1320
#> "011" "0313" "0313" "0311" "011" "0313" "0311" "011"
#> Sample_1321 Sample_1322 Sample_1323 Sample_1324 Sample_1325 Sample_1326 Sample_1327 Sample_1328
#> "0314" "0311" "0311" "0311" "0311" "0314" "0311" "0313"
#> Sample_1329 Sample_1330 Sample_1331 Sample_1332 Sample_1333 Sample_1334 Sample_1335 Sample_1336
#> "0314" "0314" "0313" "0314" "0314" "0313" "011" "011"
#> Sample_1337 Sample_1338 Sample_1339 Sample_1340 Sample_1341 Sample_1342 Sample_1343 Sample_1344
#> "033" "011" "033" "0311" "0314" "011" "011" "0314"
#> Sample_1345 Sample_1346 Sample_1347 Sample_1348 Sample_1349 Sample_1350 Sample_1351 Sample_1352
#> "033" "0311" "0311" "011" "0314" "011" "0314" "011"
#> Sample_1353 Sample_1354 Sample_1355 Sample_1356 Sample_1357 Sample_1358 Sample_1359 Sample_1360
#> "0311" "011" "0314" "0314" "011" "011" "0314" "011"
#> Sample_1361 Sample_1362 Sample_1363 Sample_1364 Sample_1365 Sample_1366 Sample_1367 Sample_1368
#> "0314" "0314" "0314" "011" "0311" "0313" "011" "0313"
#> Sample_1369 Sample_1370 Sample_1371 Sample_1372 Sample_1373 Sample_1374 Sample_1375 Sample_1376
#> "0311" "0314" "011" "0314" "011" "011" "011" "033"
#> Sample_1377 Sample_1378 Sample_1379 Sample_1380 Sample_1381 Sample_1382 Sample_1383 Sample_1384
#> "0311" "0311" "0311" "0311" "0314" "011" "0313" "0313"
#> Sample_1385 Sample_1386 Sample_1387 Sample_1388 Sample_1389 Sample_1390 Sample_1391 Sample_1392
#> "0313" "011" "0313" "011" "0313" "0313" "0313" "0313"
#> Sample_1393 Sample_1394 Sample_1395 Sample_1396 Sample_1397 Sample_1398 Sample_1399 Sample_1400
#> "0313" "0313" "0313" "0313" "0313" "011" "011" "033"
#> Sample_1401 Sample_1402 Sample_1403 Sample_1404 Sample_1405 Sample_1406 Sample_1407 Sample_1408
#> "0313" "011" "0313" "0313" "0313" "011" "0313" "011"
#> Sample_1409 Sample_1410 Sample_1411 Sample_1412 Sample_1413 Sample_1414 Sample_1415 Sample_1416
#> "011" "0313" "0313" "0313" "033" "0313" "0313" "033"
#> Sample_1417 Sample_1418 Sample_1419 Sample_1420 Sample_1421 Sample_1422 Sample_1423 Sample_1424
#> "0313" "0313" "011" "0313" "0313" "0313" "011" "0313"
#> Sample_1425 Sample_1426 Sample_1427 Sample_1428 Sample_1429 Sample_1430 Sample_1431 Sample_1432
#> "011" "011" "011" "0313" "011" "011" "011" "011"
#> Sample_1433 Sample_1434 Sample_1435 Sample_1436 Sample_1437 Sample_1438 Sample_1439 Sample_1440
#> "011" "011" "011" "011" "011" "011" "011" "011"
#> Sample_1441 Sample_1442 Sample_1443 Sample_1444 Sample_1445 Sample_1446 Sample_1447 Sample_1448
#> "011" "0313" "0313" "011" "011" "0313" "0313" "011"
#> Sample_1449 Sample_1450 Sample_1451 Sample_1452 Sample_1453 Sample_1454 Sample_1455 Sample_1456
#> "011" "011" "011" "011" "011" "011" "011" "0313"
#> Sample_1457 Sample_1458 Sample_1459 Sample_1460 Sample_1461 Sample_1462 Sample_1463 Sample_1464
#> "0313" "011" "011" "033" "0313" "011" "0314" "0314"
#> Sample_1465 Sample_1466 Sample_1467 Sample_1468 Sample_1469 Sample_1470 Sample_1471 Sample_1472
#> "011" "011" "011" "0313" "011" "011" "011" "011"
#> Sample_1473 Sample_1474 Sample_1475 Sample_1476 Sample_1477 Sample_1478 Sample_1479 Sample_1480
#> "011" "011" "011" "011" "011" "011" "011" "0314"
#> Sample_1481 Sample_1482 Sample_1483 Sample_1484 Sample_1485 Sample_1486 Sample_1487 Sample_1488
#> "011" "011" "011" "011" "0313" "033" "011" "0313"
#> Sample_1489 Sample_1490 Sample_1491 Sample_1492 Sample_1493 Sample_1494 Sample_1495 Sample_1496
#> "011" "011" "011" "011" "011" "033" "011" "011"
#> Sample_1497 Sample_1498 Sample_1499 Sample_1500 Sample_1501 Sample_1502 Sample_1503 Sample_1504
#> "011" "011" "011" "011" "011" "0313" "0313" "033"
#> Sample_1505 Sample_1506 Sample_1507 Sample_1508 Sample_1509 Sample_1510 Sample_1511 Sample_1512
#> "033" "0313" "0313" "0313" "011" "011" "0313" "0313"
#> Sample_1513 Sample_1514 Sample_1515 Sample_1516 Sample_1517 Sample_1518 Sample_1519 Sample_1520
#> "0313" "011" "0313" "011" "022" "022" "022" "0233"
#> Sample_1521 Sample_1522 Sample_1523 Sample_1524 Sample_1525 Sample_1526 Sample_1527 Sample_1528
#> "0232" "013" "013" "0233" "012" "012" "021" "021"
#> Sample_1529 Sample_1530 Sample_1531 Sample_1532 Sample_1533 Sample_1534 Sample_1535 Sample_1536
#> "021" "0233" "0232" "0232" "021" "021" "022" "022"
#> Sample_1537 Sample_1538 Sample_1539 Sample_1540 Sample_1541 Sample_1542 Sample_1543 Sample_1544
#> "022" "021" "021" "013" "011" "021" "0233" "013"
#> Sample_1545 Sample_1546 Sample_1547 Sample_1548 Sample_1549 Sample_1550 Sample_1551 Sample_1552
#> "011" "021" "013" "0231" "032" "0231" "0231" "0231"
#> Sample_1553 Sample_1554 Sample_1555 Sample_1556 Sample_1557 Sample_1558 Sample_1559 Sample_1560
#> "032" "022" "0231" "013" "013" "013" "0231" "013"
#> Sample_1561 Sample_1562 Sample_1563 Sample_1564 Sample_1565 Sample_1566 Sample_1567 Sample_1568
#> "0231" "012" "013" "013" "013" "013" "013" "013"
#> Sample_1569 Sample_1570 Sample_1571 Sample_1572 Sample_1573 Sample_1574 Sample_1575 Sample_1576
#> "0312" "021" "013" "013" "013" "0231" "0231" "013"
#> Sample_1577 Sample_1578 Sample_1579 Sample_1580 Sample_1581 Sample_1582 Sample_1583 Sample_1584
#> "013" "013" "013" "013" "032" "0231" "013" "013"
#> Sample_1585 Sample_1586 Sample_1587 Sample_1588 Sample_1589 Sample_1590 Sample_1591 Sample_1592
#> "0231" "0231" "0231" "022" "0231" "013" "013" "0231"
#> Sample_1593 Sample_1594 Sample_1595 Sample_1596 Sample_1597 Sample_1598 Sample_1599 Sample_1600
#> "0231" "0231" "0231" "0232" "032" "0311" "0233" "013"
get_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 1273))
#> Sample_1 Sample_2 Sample_3 Sample_4 Sample_5 Sample_6 Sample_7 Sample_8
#> "013" "022" "022" "022" "022" "022" "022" "022"
#> Sample_9 Sample_10 Sample_11 Sample_12 Sample_13 Sample_14 Sample_15 Sample_16
#> "022" "022" "022" "022" "022" "022" "022" "022"
#> Sample_17 Sample_18 Sample_19 Sample_20 Sample_21 Sample_22 Sample_23 Sample_24
#> "022" "022" "022" "022" "022" "022" "022" "022"
#> Sample_25 Sample_26 Sample_27 Sample_28 Sample_29 Sample_30 Sample_31 Sample_32
#> "022" "022" "022" "022" "022" "022" "0233" "022"
#> Sample_33 Sample_34 Sample_35 Sample_36 Sample_37 Sample_38 Sample_39 Sample_40
#> "022" "022" "022" "022" "022" "022" "022" "022"
#> Sample_41 Sample_42 Sample_43 Sample_44 Sample_45 Sample_46 Sample_47 Sample_48
#> "022" "022" "0233" "022" "022" "022" "022" "022"
#> Sample_49 Sample_50 Sample_51 Sample_52 Sample_53 Sample_54 Sample_55 Sample_56
#> "022" "022" "022" "022" "022" "022" "022" "022"
#> Sample_57 Sample_58 Sample_59 Sample_60 Sample_61 Sample_62 Sample_63 Sample_64
#> "022" "022" "022" "022" "022" "022" "022" "022"
#> Sample_65 Sample_66 Sample_67 Sample_68 Sample_69 Sample_70 Sample_71 Sample_72
#> "022" "022" "022" "022" "022" "022" "022" "021"
#> Sample_73 Sample_74 Sample_75 Sample_76 Sample_77 Sample_78 Sample_79 Sample_80
#> "0233" "022" "022" "022" "022" "022" "022" "022"
#> Sample_81 Sample_82 Sample_83 Sample_84 Sample_85 Sample_86 Sample_87 Sample_88
#> "022" "022" "022" "022" "022" "022" "022" "022"
#> Sample_89 Sample_90 Sample_91 Sample_92 Sample_93 Sample_94 Sample_95 Sample_96
#> "022" "022" "022" "022" "022" "0232" "022" "022"
#> Sample_97 Sample_98 Sample_99 Sample_100 Sample_101 Sample_102 Sample_103 Sample_104
#> "022" "022" "022" "022" "022" "022" "022" "022"
#> Sample_105 Sample_106 Sample_107 Sample_108 Sample_109 Sample_110 Sample_111 Sample_112
#> "022" "022" "022" "022" "022" "022" "021" "022"
#> Sample_113 Sample_114 Sample_115 Sample_116 Sample_117 Sample_118 Sample_119 Sample_120
#> "022" "0232" "022" "022" "022" "0232" "022" "022"
#> Sample_121 Sample_122 Sample_123 Sample_124 Sample_125 Sample_126 Sample_127 Sample_128
#> "012" "022" "0232" "022" "022" "022" "022" "022"
#> Sample_129 Sample_130 Sample_131 Sample_132 Sample_133 Sample_134 Sample_135 Sample_136
#> "022" "022" "022" "022" "022" "022" "022" "022"
#> Sample_137 Sample_138 Sample_139 Sample_140 Sample_141 Sample_142 Sample_143 Sample_144
#> "022" "022" "022" "022" "022" "022" "022" "0232"
#> Sample_145 Sample_146 Sample_147 Sample_148 Sample_149 Sample_150 Sample_151 Sample_152
#> "022" "022" "021" "021" "021" "021" "021" "021"
#> Sample_153 Sample_154 Sample_155 Sample_156 Sample_157 Sample_158 Sample_159 Sample_160
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_161 Sample_162 Sample_163 Sample_164 Sample_165 Sample_166 Sample_167 Sample_168
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_169 Sample_170 Sample_171 Sample_172 Sample_173 Sample_174 Sample_175 Sample_176
#> "021" "021" "0232" "021" "0233" "021" "021" "021"
#> Sample_177 Sample_178 Sample_179 Sample_180 Sample_181 Sample_182 Sample_183 Sample_184
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_185 Sample_186 Sample_187 Sample_188 Sample_189 Sample_190 Sample_191 Sample_192
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_193 Sample_194 Sample_195 Sample_196 Sample_197 Sample_198 Sample_199 Sample_200
#> "021" "021" "021" "021" "022" "022" "022" "022"
#> Sample_201 Sample_202 Sample_203 Sample_204 Sample_205 Sample_206 Sample_207 Sample_208
#> "022" "022" "022" "0233" "022" "022" "022" "032"
#> Sample_209 Sample_210 Sample_211 Sample_212 Sample_213 Sample_214 Sample_215 Sample_216
#> "032" "032" "032" "032" "032" "013" "013" "032"
#> Sample_217 Sample_218 Sample_219 Sample_220 Sample_221 Sample_222 Sample_223 Sample_224
#> "013" "032" "032" "032" "032" "032" "032" "013"
#> Sample_225 Sample_226 Sample_227 Sample_228 Sample_229 Sample_230 Sample_231 Sample_232
#> "032" "013" "032" "032" "032" "032" "032" "032"
#> Sample_233 Sample_234 Sample_235 Sample_236 Sample_237 Sample_238 Sample_239 Sample_240
#> "033" "032" "032" "013" "032" "032" "032" "032"
#> Sample_241 Sample_242 Sample_243 Sample_244 Sample_245 Sample_246 Sample_247 Sample_248
#> "013" "032" "032" "032" "032" "032" "032" "032"
#> Sample_249 Sample_250 Sample_251 Sample_252 Sample_253 Sample_254 Sample_255 Sample_256
#> "032" "032" "032" "032" "032" "033" "032" "032"
#> Sample_257 Sample_258 Sample_259 Sample_260 Sample_261 Sample_262 Sample_263 Sample_264
#> "032" "013" "032" "032" "032" "032" "032" "013"
#> Sample_265 Sample_266 Sample_267 Sample_268 Sample_269 Sample_270 Sample_271 Sample_272
#> "012" "032" "032" "032" "032" "032" "033" "012"
#> Sample_273 Sample_274 Sample_275 Sample_276 Sample_277 Sample_278 Sample_279 Sample_280
#> "032" "032" "012" "013" "032" "033" "032" "032"
#> Sample_281 Sample_282 Sample_283 Sample_284 Sample_285 Sample_286 Sample_287 Sample_288
#> "032" "032" "032" "032" "032" "032" "013" "033"
#> Sample_289 Sample_290 Sample_291 Sample_292 Sample_293 Sample_294 Sample_295 Sample_296
#> "033" "032" "032" "032" "032" "032" "032" "032"
#> Sample_297 Sample_298 Sample_299 Sample_300 Sample_301 Sample_302 Sample_303 Sample_304
#> "032" "032" "032" "032" "032" "012" "012" "012"
#> Sample_305 Sample_306 Sample_307 Sample_308 Sample_309 Sample_310 Sample_311 Sample_312
#> "012" "032" "032" "012" "012" "012" "012" "012"
#> Sample_313 Sample_314 Sample_315 Sample_316 Sample_317 Sample_318 Sample_319 Sample_320
#> "012" "012" "032" "012" "032" "012" "012" "012"
#> Sample_321 Sample_322 Sample_323 Sample_324 Sample_325 Sample_326 Sample_327 Sample_328
#> "012" "032" "012" "012" "012" "012" "012" "012"
#> Sample_329 Sample_330 Sample_331 Sample_332 Sample_333 Sample_334 Sample_335 Sample_336
#> "012" "012" "033" "032" "032" "032" "032" "032"
#> Sample_337 Sample_338 Sample_339 Sample_340 Sample_341 Sample_342 Sample_343 Sample_344
#> "032" "032" "033" "032" "032" "032" "032" "032"
#> Sample_345 Sample_346 Sample_347 Sample_348 Sample_349 Sample_350 Sample_351 Sample_352
#> "032" "032" "032" "032" "032" "013" "012" "013"
#> Sample_353 Sample_354 Sample_355 Sample_356 Sample_357 Sample_358 Sample_359 Sample_360
#> "032" "032" "012" "032" "032" "012" "032" "012"
#> Sample_361 Sample_362 Sample_363 Sample_364 Sample_365 Sample_366 Sample_367 Sample_368
#> "032" "012" "032" "032" "013" "032" "032" "012"
#> Sample_369 Sample_370 Sample_371 Sample_372 Sample_373 Sample_374 Sample_375 Sample_376
#> "012" "032" "033" "013" "013" "032" "032" "032"
#> Sample_377 Sample_378 Sample_379 Sample_380 Sample_381 Sample_382 Sample_383 Sample_384
#> "012" "032" "032" "032" "012" "032" "032" "032"
#> Sample_385 Sample_386 Sample_387 Sample_388 Sample_389 Sample_390 Sample_391 Sample_392
#> "012" "012" "032" "032" "013" "013" "032" "032"
#> Sample_393 Sample_394 Sample_395 Sample_396 Sample_397 Sample_398 Sample_399 Sample_400
#> "012" "013" "012" "013" "012" "012" "032" "032"
#> Sample_401 Sample_402 Sample_403 Sample_404 Sample_405 Sample_406 Sample_407 Sample_408
#> "032" "032" "013" "013" "013" "032" "032" "013"
#> Sample_409 Sample_410 Sample_411 Sample_412 Sample_413 Sample_414 Sample_415 Sample_416
#> "012" "032" "032" "012" "032" "032" "033" "012"
#> Sample_417 Sample_418 Sample_419 Sample_420 Sample_421 Sample_422 Sample_423 Sample_424
#> "032" "032" "032" "033" "012" "033" "032" "012"
#> Sample_425 Sample_426 Sample_427 Sample_428 Sample_429 Sample_430 Sample_431 Sample_432
#> "012" "032" "012" "012" "032" "013" "012" "012"
#> Sample_433 Sample_434 Sample_435 Sample_436 Sample_437 Sample_438 Sample_439 Sample_440
#> "013" "012" "032" "033" "033" "013" "012" "012"
#> Sample_441 Sample_442 Sample_443 Sample_444 Sample_445 Sample_446 Sample_447 Sample_448
#> "032" "012" "013" "032" "012" "012" "032" "013"
#> Sample_449 Sample_450 Sample_451 Sample_452 Sample_453 Sample_454 Sample_455 Sample_456
#> "032" "032" "012" "012" "032" "032" "013" "012"
#> Sample_457 Sample_458 Sample_459 Sample_460 Sample_461 Sample_462 Sample_463 Sample_464
#> "013" "032" "012" "032" "012" "032" "013" "012"
#> Sample_465 Sample_466 Sample_467 Sample_468 Sample_469 Sample_470 Sample_471 Sample_472
#> "012" "012" "032" "012" "012" "032" "032" "012"
#> Sample_473 Sample_474 Sample_475 Sample_476 Sample_477 Sample_478 Sample_479 Sample_480
#> "032" "012" "012" "013" "013" "031" "011" "031"
#> Sample_481 Sample_482 Sample_483 Sample_484 Sample_485 Sample_486 Sample_487 Sample_488
#> "031" "031" "031" "031" "031" "011" "011" "033"
#> Sample_489 Sample_490 Sample_491 Sample_492 Sample_493 Sample_494 Sample_495 Sample_496
#> "031" "011" "031" "031" "031" "031" "011" "031"
#> Sample_497 Sample_498 Sample_499 Sample_500 Sample_501 Sample_502 Sample_503 Sample_504
#> "031" "031" "011" "011" "031" "031" "031" "031"
#> Sample_505 Sample_506 Sample_507 Sample_508 Sample_509 Sample_510 Sample_511 Sample_512
#> "011" "011" "011" "031" "031" "031" "011" "033"
#> Sample_513 Sample_514 Sample_515 Sample_516 Sample_517 Sample_518 Sample_519 Sample_520
#> "011" "031" "011" "021" "011" "031" "031" "011"
#> Sample_521 Sample_522 Sample_523 Sample_524 Sample_525 Sample_526 Sample_527 Sample_528
#> "011" "031" "031" "031" "031" "031" "011" "031"
#> Sample_529 Sample_530 Sample_531 Sample_532 Sample_533 Sample_534 Sample_535 Sample_536
#> "033" "011" "033" "011" "011" "031" "011" "011"
#> Sample_537 Sample_538 Sample_539 Sample_540 Sample_541 Sample_542 Sample_543 Sample_544
#> "011" "031" "011" "031" "031" "011" "011" "011"
#> Sample_545 Sample_546 Sample_547 Sample_548 Sample_549 Sample_550 Sample_551 Sample_552
#> "033" "011" "011" "031" "031" "031" "031" "031"
#> Sample_553 Sample_554 Sample_555 Sample_556 Sample_557 Sample_558 Sample_559 Sample_560
#> "033" "031" "031" "031" "011" "011" "011" "031"
#> Sample_561 Sample_562 Sample_563 Sample_564 Sample_565 Sample_566 Sample_567 Sample_568
#> "031" "031" "031" "031" "011" "011" "011" "031"
#> Sample_569 Sample_570 Sample_571 Sample_572 Sample_573 Sample_574 Sample_575 Sample_576
#> "011" "032" "032" "013" "012" "033" "032" "032"
#> Sample_577 Sample_578 Sample_579 Sample_580 Sample_581 Sample_582 Sample_583 Sample_584
#> "012" "032" "032" "032" "032" "032" "032" "032"
#> Sample_585 Sample_586 Sample_587 Sample_588 Sample_589 Sample_590 Sample_591 Sample_592
#> "032" "022" "022" "0231" "032" "013" "022" "0231"
#> Sample_593 Sample_594 Sample_595 Sample_596 Sample_597 Sample_598 Sample_599 Sample_600
#> "022" "0231" "0231" "022" "0231" "022" "0231" "0231"
#> Sample_601 Sample_602 Sample_603 Sample_604 Sample_605 Sample_606 Sample_607 Sample_608
#> "022" "0231" "022" "022" "022" "022" "022" "022"
#> Sample_609 Sample_610 Sample_611 Sample_612 Sample_613 Sample_614 Sample_615 Sample_616
#> "022" "022" "021" "021" "032" "013" "032" "013"
#> Sample_617 Sample_618 Sample_619 Sample_620 Sample_621 Sample_622 Sample_623 Sample_624
#> "013" "012" "013" "013" "0231" "013" "032" "032"
#> Sample_625 Sample_626 Sample_627 Sample_628 Sample_629 Sample_630 Sample_631 Sample_632
#> "0231" "013" "0231" "0231" "0231" "013" "0231" "013"
#> Sample_633 Sample_634 Sample_635 Sample_636 Sample_637 Sample_638 Sample_639 Sample_640
#> "013" "013" "013" "013" "013" "0231" "013" "0232"
#> Sample_641 Sample_642 Sample_643 Sample_644 Sample_645 Sample_646 Sample_647 Sample_648
#> "013" "0233" "013" "032" "0232" "013" "013" "0232"
#> Sample_649 Sample_650 Sample_651 Sample_652 Sample_653 Sample_654 Sample_655 Sample_656
#> "0232" "0231" "022" "022" "022" "022" "022" "022"
#> Sample_657 Sample_658 Sample_659 Sample_660 Sample_661 Sample_662 Sample_663 Sample_664
#> "022" "022" "022" "022" "022" "022" "022" "022"
#> Sample_665 Sample_666 Sample_667 Sample_668 Sample_669 Sample_670 Sample_671 Sample_672
#> "0232" "022" "0232" "022" "022" "022" "022" "022"
#> Sample_673 Sample_674 Sample_675 Sample_676 Sample_677 Sample_678 Sample_679 Sample_680
#> "022" "022" "022" "022" "022" "022" "022" "022"
#> Sample_681 Sample_682 Sample_683 Sample_684 Sample_685 Sample_686 Sample_687 Sample_688
#> "0232" "022" "022" "022" "012" "021" "012" "021"
#> Sample_689 Sample_690 Sample_691 Sample_692 Sample_693 Sample_694 Sample_695 Sample_696
#> "022" "021" "022" "012" "022" "022" "021" "021"
#> Sample_697 Sample_698 Sample_699 Sample_700 Sample_701 Sample_702 Sample_703 Sample_704
#> "021" "021" "021" "021" "021" "021" "021" "022"
#> Sample_705 Sample_706 Sample_707 Sample_708 Sample_709 Sample_710 Sample_711 Sample_712
#> "022" "021" "021" "021" "021" "0232" "021" "021"
#> Sample_713 Sample_714 Sample_715 Sample_716 Sample_717 Sample_718 Sample_719 Sample_720
#> "021" "021" "021" "021" "021" "021" "021" "0232"
#> Sample_721 Sample_722 Sample_723 Sample_724 Sample_725 Sample_726 Sample_727 Sample_728
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_729 Sample_730 Sample_731 Sample_732 Sample_733 Sample_734 Sample_735 Sample_736
#> "021" "021" "0233" "021" "021" "021" "0233" "021"
#> Sample_737 Sample_738 Sample_739 Sample_740 Sample_741 Sample_742 Sample_743 Sample_744
#> "012" "0232" "021" "021" "021" "021" "021" "021"
#> Sample_745 Sample_746 Sample_747 Sample_748 Sample_749 Sample_750 Sample_751 Sample_752
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_753 Sample_754 Sample_755 Sample_756 Sample_757 Sample_758 Sample_759 Sample_760
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_761 Sample_762 Sample_763 Sample_764 Sample_765 Sample_766 Sample_767 Sample_768
#> "021" "021" "021" "021" "021" "021" "021" "0232"
#> Sample_769 Sample_770 Sample_771 Sample_772 Sample_773 Sample_774 Sample_775 Sample_776
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_777 Sample_778 Sample_779 Sample_780 Sample_781 Sample_782 Sample_783 Sample_784
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_785 Sample_786 Sample_787 Sample_788 Sample_789 Sample_790 Sample_791 Sample_792
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_793 Sample_794 Sample_795 Sample_796 Sample_797 Sample_798 Sample_799 Sample_800
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_801 Sample_802 Sample_803 Sample_804 Sample_805 Sample_806 Sample_807 Sample_808
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_809 Sample_810 Sample_811 Sample_812 Sample_813 Sample_814 Sample_815 Sample_816
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_817 Sample_818 Sample_819 Sample_820 Sample_821 Sample_822 Sample_823 Sample_824
#> "021" "021" "021" "0233" "021" "021" "021" "021"
#> Sample_825 Sample_826 Sample_827 Sample_828 Sample_829 Sample_830 Sample_831 Sample_832
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_833 Sample_834 Sample_835 Sample_836 Sample_837 Sample_838 Sample_839 Sample_840
#> "021" "021" "021" "0233" "021" "021" "021" "021"
#> Sample_841 Sample_842 Sample_843 Sample_844 Sample_845 Sample_846 Sample_847 Sample_848
#> "022" "022" "022" "022" "022" "022" "022" "022"
#> Sample_849 Sample_850 Sample_851 Sample_852 Sample_853 Sample_854 Sample_855 Sample_856
#> "022" "022" "022" "022" "022" "022" "021" "021"
#> Sample_857 Sample_858 Sample_859 Sample_860 Sample_861 Sample_862 Sample_863 Sample_864
#> "021" "021" "0232" "021" "021" "021" "021" "021"
#> Sample_865 Sample_866 Sample_867 Sample_868 Sample_869 Sample_870 Sample_871 Sample_872
#> "0233" "021" "021" "021" "021" "021" "021" "021"
#> Sample_873 Sample_874 Sample_875 Sample_876 Sample_877 Sample_878 Sample_879 Sample_880
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_881 Sample_882 Sample_883 Sample_884 Sample_885 Sample_886 Sample_887 Sample_888
#> "021" "0233" "021" "021" "021" "021" "021" "021"
#> Sample_889 Sample_890 Sample_891 Sample_892 Sample_893 Sample_894 Sample_895 Sample_896
#> "021" "021" "021" "021" "021" "0232" "021" "021"
#> Sample_897 Sample_898 Sample_899 Sample_900 Sample_901 Sample_902 Sample_903 Sample_904
#> "021" "021" "021" "0233" "021" "021" "021" "021"
#> Sample_905 Sample_906 Sample_907 Sample_908 Sample_909 Sample_910 Sample_911 Sample_912
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_913 Sample_914 Sample_915 Sample_916 Sample_917 Sample_918 Sample_919 Sample_920
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_921 Sample_922 Sample_923 Sample_924 Sample_925 Sample_926 Sample_927 Sample_928
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_929 Sample_930 Sample_931 Sample_932 Sample_933 Sample_934 Sample_935 Sample_936
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_937 Sample_938 Sample_939 Sample_940 Sample_941 Sample_942 Sample_943 Sample_944
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_945 Sample_946 Sample_947 Sample_948 Sample_949 Sample_950 Sample_951 Sample_952
#> "021" "021" "021" "032" "032" "013" "032" "032"
#> Sample_953 Sample_954 Sample_955 Sample_956 Sample_957 Sample_958 Sample_959 Sample_960
#> "032" "032" "032" "032" "032" "032" "032" "032"
#> Sample_961 Sample_962 Sample_963 Sample_964 Sample_965 Sample_966 Sample_967 Sample_968
#> "032" "012" "032" "032" "032" "032" "032" "012"
#> Sample_969 Sample_970 Sample_971 Sample_972 Sample_973 Sample_974 Sample_975 Sample_976
#> "032" "032" "032" "032" "032" "032" "033" "013"
#> Sample_977 Sample_978 Sample_979 Sample_980 Sample_981 Sample_982 Sample_983 Sample_984
#> "013" "012" "032" "013" "013" "012" "012" "032"
#> Sample_985 Sample_986 Sample_987 Sample_988 Sample_989 Sample_990 Sample_991 Sample_992
#> "012" "013" "012" "012" "012" "012" "012" "012"
#> Sample_993 Sample_994 Sample_995 Sample_996 Sample_997 Sample_998 Sample_999 Sample_1000
#> "012" "012" "032" "013" "032" "032" "013" "032"
#> Sample_1001 Sample_1002 Sample_1003 Sample_1004 Sample_1005 Sample_1006 Sample_1007 Sample_1008
#> "032" "032" "032" "033" "032" "032" "012" "013"
#> Sample_1009 Sample_1010 Sample_1011 Sample_1012 Sample_1013 Sample_1014 Sample_1015 Sample_1016
#> "013" "013" "032" "032" "032" "012" "012" "012"
#> Sample_1017 Sample_1018 Sample_1019 Sample_1020 Sample_1021 Sample_1022 Sample_1023 Sample_1024
#> "012" "012" "012" "012" "012" "012" "012" "012"
#> Sample_1025 Sample_1026 Sample_1027 Sample_1028 Sample_1029 Sample_1030 Sample_1031 Sample_1032
#> "012" "012" "011" "011" "031" "011" "033" "031"
#> Sample_1033 Sample_1034 Sample_1035 Sample_1036 Sample_1037 Sample_1038 Sample_1039 Sample_1040
#> "031" "011" "031" "011" "031" "031" "011" "031"
#> Sample_1041 Sample_1042 Sample_1043 Sample_1044 Sample_1045 Sample_1046 Sample_1047 Sample_1048
#> "011" "011" "031" "031" "033" "011" "031" "033"
#> Sample_1049 Sample_1050 Sample_1051 Sample_1052 Sample_1053 Sample_1054 Sample_1055 Sample_1056
#> "011" "011" "031" "011" "032" "032" "032" "013"
#> Sample_1057 Sample_1058 Sample_1059 Sample_1060 Sample_1061 Sample_1062 Sample_1063 Sample_1064
#> "032" "012" "013" "032" "032" "013" "031" "031"
#> Sample_1065 Sample_1066 Sample_1067 Sample_1068 Sample_1069 Sample_1070 Sample_1071 Sample_1072
#> "031" "011" "031" "031" "031" "011" "031" "031"
#> Sample_1073 Sample_1074 Sample_1075 Sample_1076 Sample_1077 Sample_1078 Sample_1079 Sample_1080
#> "011" "031" "011" "011" "031" "031" "031" "011"
#> Sample_1081 Sample_1082 Sample_1083 Sample_1084 Sample_1085 Sample_1086 Sample_1087 Sample_1088
#> "031" "031" "011" "031" "031" "033" "011" "033"
#> Sample_1089 Sample_1090 Sample_1091 Sample_1092 Sample_1093 Sample_1094 Sample_1095 Sample_1096
#> "033" "011" "011" "031" "011" "031" "031" "031"
#> Sample_1097 Sample_1098 Sample_1099 Sample_1100 Sample_1101 Sample_1102 Sample_1103 Sample_1104
#> "011" "031" "031" "011" "031" "011" "011" "011"
#> Sample_1105 Sample_1106 Sample_1107 Sample_1108 Sample_1109 Sample_1110 Sample_1111 Sample_1112
#> "011" "031" "031" "011" "031" "011" "031" "011"
#> Sample_1113 Sample_1114 Sample_1115 Sample_1116 Sample_1117 Sample_1118 Sample_1119 Sample_1120
#> "011" "011" "031" "011" "031" "011" "011" "033"
#> Sample_1121 Sample_1122 Sample_1123 Sample_1124 Sample_1125 Sample_1126 Sample_1127 Sample_1128
#> "011" "033" "031" "031" "031" "031" "031" "031"
#> Sample_1129 Sample_1130 Sample_1131 Sample_1132 Sample_1133 Sample_1134 Sample_1135 Sample_1136
#> "011" "011" "011" "033" "011" "033" "011" "011"
#> Sample_1137 Sample_1138 Sample_1139 Sample_1140 Sample_1141 Sample_1142 Sample_1143 Sample_1144
#> "011" "011" "031" "033" "031" "011" "011" "011"
#> Sample_1145 Sample_1146 Sample_1147 Sample_1148 Sample_1149 Sample_1150 Sample_1151 Sample_1152
#> "031" "011" "011" "011" "011" "031" "011" "031"
#> Sample_1153 Sample_1154 Sample_1155 Sample_1156 Sample_1157 Sample_1158 Sample_1159 Sample_1160
#> "011" "011" "011" "031" "031" "011" "011" "011"
#> Sample_1161 Sample_1162 Sample_1163 Sample_1164 Sample_1165 Sample_1166 Sample_1167 Sample_1168
#> "031" "011" "031" "031" "011" "011" "011" "011"
#> Sample_1169 Sample_1170 Sample_1171 Sample_1172 Sample_1173 Sample_1174 Sample_1175 Sample_1176
#> "031" "031" "011" "011" "011" "011" "031" "011"
#> Sample_1177 Sample_1178 Sample_1179 Sample_1180 Sample_1181 Sample_1182 Sample_1183 Sample_1184
#> "011" "011" "011" "031" "011" "011" "033" "033"
#> Sample_1185 Sample_1186 Sample_1187 Sample_1188 Sample_1189 Sample_1190 Sample_1191 Sample_1192
#> "011" "011" "011" "011" "032" "032" "032" "012"
#> Sample_1193 Sample_1194 Sample_1195 Sample_1196 Sample_1197 Sample_1198 Sample_1199 Sample_1200
#> "032" "032" "032" "032" "032" "032" "032" "032"
#> Sample_1201 Sample_1202 Sample_1203 Sample_1204 Sample_1205 Sample_1206 Sample_1207 Sample_1208
#> "032" "032" "032" "032" "032" "032" "032" "013"
#> Sample_1209 Sample_1210 Sample_1211 Sample_1212 Sample_1213 Sample_1214 Sample_1215 Sample_1216
#> "032" "032" "032" "032" "032" "032" "032" "012"
#> Sample_1217 Sample_1218 Sample_1219 Sample_1220 Sample_1221 Sample_1222 Sample_1223 Sample_1224
#> "013" "013" "032" "032" "032" "011" "031" "031"
#> Sample_1225 Sample_1226 Sample_1227 Sample_1228 Sample_1229 Sample_1230 Sample_1231 Sample_1232
#> "011" "031" "011" "011" "011" "031" "031" "031"
#> Sample_1233 Sample_1234 Sample_1235 Sample_1236 Sample_1237 Sample_1238 Sample_1239 Sample_1240
#> "031" "011" "031" "011" "031" "031" "011" "011"
#> Sample_1241 Sample_1242 Sample_1243 Sample_1244 Sample_1245 Sample_1246 Sample_1247 Sample_1248
#> "011" "011" "031" "031" "031" "031" "031" "031"
#> Sample_1249 Sample_1250 Sample_1251 Sample_1252 Sample_1253 Sample_1254 Sample_1255 Sample_1256
#> "011" "031" "031" "031" "031" "011" "033" "031"
#> Sample_1257 Sample_1258 Sample_1259 Sample_1260 Sample_1261 Sample_1262 Sample_1263 Sample_1264
#> "031" "031" "031" "031" "031" "031" "031" "011"
#> Sample_1265 Sample_1266 Sample_1267 Sample_1268 Sample_1269 Sample_1270 Sample_1271 Sample_1272
#> "031" "011" "011" "033" "031" "031" "031" "011"
#> Sample_1273 Sample_1274 Sample_1275 Sample_1276 Sample_1277 Sample_1278 Sample_1279 Sample_1280
#> "031" "031" "031" "031" "031" "031" "011" "011"
#> Sample_1281 Sample_1282 Sample_1283 Sample_1284 Sample_1285 Sample_1286 Sample_1287 Sample_1288
#> "031" "031" "011" "031" "011" "031" "031" "031"
#> Sample_1289 Sample_1290 Sample_1291 Sample_1292 Sample_1293 Sample_1294 Sample_1295 Sample_1296
#> "031" "031" "031" "031" "031" "031" "031" "033"
#> Sample_1297 Sample_1298 Sample_1299 Sample_1300 Sample_1301 Sample_1302 Sample_1303 Sample_1304
#> "031" "031" "011" "031" "031" "031" "031" "031"
#> Sample_1305 Sample_1306 Sample_1307 Sample_1308 Sample_1309 Sample_1310 Sample_1311 Sample_1312
#> "031" "033" "011" "011" "011" "033" "031" "031"
#> Sample_1313 Sample_1314 Sample_1315 Sample_1316 Sample_1317 Sample_1318 Sample_1319 Sample_1320
#> "011" "031" "031" "031" "011" "031" "031" "011"
#> Sample_1321 Sample_1322 Sample_1323 Sample_1324 Sample_1325 Sample_1326 Sample_1327 Sample_1328
#> "031" "031" "031" "031" "031" "031" "031" "031"
#> Sample_1329 Sample_1330 Sample_1331 Sample_1332 Sample_1333 Sample_1334 Sample_1335 Sample_1336
#> "031" "031" "031" "031" "031" "031" "011" "011"
#> Sample_1337 Sample_1338 Sample_1339 Sample_1340 Sample_1341 Sample_1342 Sample_1343 Sample_1344
#> "033" "011" "033" "031" "031" "011" "011" "031"
#> Sample_1345 Sample_1346 Sample_1347 Sample_1348 Sample_1349 Sample_1350 Sample_1351 Sample_1352
#> "033" "031" "031" "011" "031" "011" "031" "011"
#> Sample_1353 Sample_1354 Sample_1355 Sample_1356 Sample_1357 Sample_1358 Sample_1359 Sample_1360
#> "031" "011" "031" "031" "011" "011" "031" "011"
#> Sample_1361 Sample_1362 Sample_1363 Sample_1364 Sample_1365 Sample_1366 Sample_1367 Sample_1368
#> "031" "031" "031" "011" "031" "031" "011" "031"
#> Sample_1369 Sample_1370 Sample_1371 Sample_1372 Sample_1373 Sample_1374 Sample_1375 Sample_1376
#> "031" "031" "011" "031" "011" "011" "011" "033"
#> Sample_1377 Sample_1378 Sample_1379 Sample_1380 Sample_1381 Sample_1382 Sample_1383 Sample_1384
#> "031" "031" "031" "031" "031" "011" "031" "031"
#> Sample_1385 Sample_1386 Sample_1387 Sample_1388 Sample_1389 Sample_1390 Sample_1391 Sample_1392
#> "031" "011" "031" "011" "031" "031" "031" "031"
#> Sample_1393 Sample_1394 Sample_1395 Sample_1396 Sample_1397 Sample_1398 Sample_1399 Sample_1400
#> "031" "031" "031" "031" "031" "011" "011" "033"
#> Sample_1401 Sample_1402 Sample_1403 Sample_1404 Sample_1405 Sample_1406 Sample_1407 Sample_1408
#> "031" "011" "031" "031" "031" "011" "031" "011"
#> Sample_1409 Sample_1410 Sample_1411 Sample_1412 Sample_1413 Sample_1414 Sample_1415 Sample_1416
#> "011" "031" "031" "031" "033" "031" "031" "033"
#> Sample_1417 Sample_1418 Sample_1419 Sample_1420 Sample_1421 Sample_1422 Sample_1423 Sample_1424
#> "031" "031" "011" "031" "031" "031" "011" "031"
#> Sample_1425 Sample_1426 Sample_1427 Sample_1428 Sample_1429 Sample_1430 Sample_1431 Sample_1432
#> "011" "011" "011" "031" "011" "011" "011" "011"
#> Sample_1433 Sample_1434 Sample_1435 Sample_1436 Sample_1437 Sample_1438 Sample_1439 Sample_1440
#> "011" "011" "011" "011" "011" "011" "011" "011"
#> Sample_1441 Sample_1442 Sample_1443 Sample_1444 Sample_1445 Sample_1446 Sample_1447 Sample_1448
#> "011" "031" "031" "011" "011" "031" "031" "011"
#> Sample_1449 Sample_1450 Sample_1451 Sample_1452 Sample_1453 Sample_1454 Sample_1455 Sample_1456
#> "011" "011" "011" "011" "011" "011" "011" "031"
#> Sample_1457 Sample_1458 Sample_1459 Sample_1460 Sample_1461 Sample_1462 Sample_1463 Sample_1464
#> "031" "011" "011" "033" "031" "011" "031" "031"
#> Sample_1465 Sample_1466 Sample_1467 Sample_1468 Sample_1469 Sample_1470 Sample_1471 Sample_1472
#> "011" "011" "011" "031" "011" "011" "011" "011"
#> Sample_1473 Sample_1474 Sample_1475 Sample_1476 Sample_1477 Sample_1478 Sample_1479 Sample_1480
#> "011" "011" "011" "011" "011" "011" "011" "031"
#> Sample_1481 Sample_1482 Sample_1483 Sample_1484 Sample_1485 Sample_1486 Sample_1487 Sample_1488
#> "011" "011" "011" "011" "031" "033" "011" "031"
#> Sample_1489 Sample_1490 Sample_1491 Sample_1492 Sample_1493 Sample_1494 Sample_1495 Sample_1496
#> "011" "011" "011" "011" "011" "033" "011" "011"
#> Sample_1497 Sample_1498 Sample_1499 Sample_1500 Sample_1501 Sample_1502 Sample_1503 Sample_1504
#> "011" "011" "011" "011" "011" "031" "031" "033"
#> Sample_1505 Sample_1506 Sample_1507 Sample_1508 Sample_1509 Sample_1510 Sample_1511 Sample_1512
#> "033" "031" "031" "031" "011" "011" "031" "031"
#> Sample_1513 Sample_1514 Sample_1515 Sample_1516 Sample_1517 Sample_1518 Sample_1519 Sample_1520
#> "031" "011" "031" "011" "022" "022" "022" "0233"
#> Sample_1521 Sample_1522 Sample_1523 Sample_1524 Sample_1525 Sample_1526 Sample_1527 Sample_1528
#> "0232" "013" "013" "0233" "012" "012" "021" "021"
#> Sample_1529 Sample_1530 Sample_1531 Sample_1532 Sample_1533 Sample_1534 Sample_1535 Sample_1536
#> "021" "0233" "0232" "0232" "021" "021" "022" "022"
#> Sample_1537 Sample_1538 Sample_1539 Sample_1540 Sample_1541 Sample_1542 Sample_1543 Sample_1544
#> "022" "021" "021" "013" "011" "021" "0233" "013"
#> Sample_1545 Sample_1546 Sample_1547 Sample_1548 Sample_1549 Sample_1550 Sample_1551 Sample_1552
#> "011" "021" "013" "0231" "032" "0231" "0231" "0231"
#> Sample_1553 Sample_1554 Sample_1555 Sample_1556 Sample_1557 Sample_1558 Sample_1559 Sample_1560
#> "032" "022" "0231" "013" "013" "013" "0231" "013"
#> Sample_1561 Sample_1562 Sample_1563 Sample_1564 Sample_1565 Sample_1566 Sample_1567 Sample_1568
#> "0231" "012" "013" "013" "013" "013" "013" "013"
#> Sample_1569 Sample_1570 Sample_1571 Sample_1572 Sample_1573 Sample_1574 Sample_1575 Sample_1576
#> "031" "021" "013" "013" "013" "0231" "0231" "013"
#> Sample_1577 Sample_1578 Sample_1579 Sample_1580 Sample_1581 Sample_1582 Sample_1583 Sample_1584
#> "013" "013" "013" "013" "032" "0231" "013" "013"
#> Sample_1585 Sample_1586 Sample_1587 Sample_1588 Sample_1589 Sample_1590 Sample_1591 Sample_1592
#> "0231" "0231" "0231" "022" "0231" "013" "013" "0231"
#> Sample_1593 Sample_1594 Sample_1595 Sample_1596 Sample_1597 Sample_1598 Sample_1599 Sample_1600
#> "0231" "0231" "0231" "0232" "032" "031" "0233" "013"
get_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 1289))
#> Sample_1 Sample_2 Sample_3 Sample_4 Sample_5 Sample_6 Sample_7 Sample_8
#> "01" "022" "022" "022" "022" "022" "022" "022"
#> Sample_9 Sample_10 Sample_11 Sample_12 Sample_13 Sample_14 Sample_15 Sample_16
#> "022" "022" "022" "022" "022" "022" "022" "022"
#> Sample_17 Sample_18 Sample_19 Sample_20 Sample_21 Sample_22 Sample_23 Sample_24
#> "022" "022" "022" "022" "022" "022" "022" "022"
#> Sample_25 Sample_26 Sample_27 Sample_28 Sample_29 Sample_30 Sample_31 Sample_32
#> "022" "022" "022" "022" "022" "022" "0233" "022"
#> Sample_33 Sample_34 Sample_35 Sample_36 Sample_37 Sample_38 Sample_39 Sample_40
#> "022" "022" "022" "022" "022" "022" "022" "022"
#> Sample_41 Sample_42 Sample_43 Sample_44 Sample_45 Sample_46 Sample_47 Sample_48
#> "022" "022" "0233" "022" "022" "022" "022" "022"
#> Sample_49 Sample_50 Sample_51 Sample_52 Sample_53 Sample_54 Sample_55 Sample_56
#> "022" "022" "022" "022" "022" "022" "022" "022"
#> Sample_57 Sample_58 Sample_59 Sample_60 Sample_61 Sample_62 Sample_63 Sample_64
#> "022" "022" "022" "022" "022" "022" "022" "022"
#> Sample_65 Sample_66 Sample_67 Sample_68 Sample_69 Sample_70 Sample_71 Sample_72
#> "022" "022" "022" "022" "022" "022" "022" "021"
#> Sample_73 Sample_74 Sample_75 Sample_76 Sample_77 Sample_78 Sample_79 Sample_80
#> "0233" "022" "022" "022" "022" "022" "022" "022"
#> Sample_81 Sample_82 Sample_83 Sample_84 Sample_85 Sample_86 Sample_87 Sample_88
#> "022" "022" "022" "022" "022" "022" "022" "022"
#> Sample_89 Sample_90 Sample_91 Sample_92 Sample_93 Sample_94 Sample_95 Sample_96
#> "022" "022" "022" "022" "022" "0232" "022" "022"
#> Sample_97 Sample_98 Sample_99 Sample_100 Sample_101 Sample_102 Sample_103 Sample_104
#> "022" "022" "022" "022" "022" "022" "022" "022"
#> Sample_105 Sample_106 Sample_107 Sample_108 Sample_109 Sample_110 Sample_111 Sample_112
#> "022" "022" "022" "022" "022" "022" "021" "022"
#> Sample_113 Sample_114 Sample_115 Sample_116 Sample_117 Sample_118 Sample_119 Sample_120
#> "022" "0232" "022" "022" "022" "0232" "022" "022"
#> Sample_121 Sample_122 Sample_123 Sample_124 Sample_125 Sample_126 Sample_127 Sample_128
#> "01" "022" "0232" "022" "022" "022" "022" "022"
#> Sample_129 Sample_130 Sample_131 Sample_132 Sample_133 Sample_134 Sample_135 Sample_136
#> "022" "022" "022" "022" "022" "022" "022" "022"
#> Sample_137 Sample_138 Sample_139 Sample_140 Sample_141 Sample_142 Sample_143 Sample_144
#> "022" "022" "022" "022" "022" "022" "022" "0232"
#> Sample_145 Sample_146 Sample_147 Sample_148 Sample_149 Sample_150 Sample_151 Sample_152
#> "022" "022" "021" "021" "021" "021" "021" "021"
#> Sample_153 Sample_154 Sample_155 Sample_156 Sample_157 Sample_158 Sample_159 Sample_160
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_161 Sample_162 Sample_163 Sample_164 Sample_165 Sample_166 Sample_167 Sample_168
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_169 Sample_170 Sample_171 Sample_172 Sample_173 Sample_174 Sample_175 Sample_176
#> "021" "021" "0232" "021" "0233" "021" "021" "021"
#> Sample_177 Sample_178 Sample_179 Sample_180 Sample_181 Sample_182 Sample_183 Sample_184
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_185 Sample_186 Sample_187 Sample_188 Sample_189 Sample_190 Sample_191 Sample_192
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_193 Sample_194 Sample_195 Sample_196 Sample_197 Sample_198 Sample_199 Sample_200
#> "021" "021" "021" "021" "022" "022" "022" "022"
#> Sample_201 Sample_202 Sample_203 Sample_204 Sample_205 Sample_206 Sample_207 Sample_208
#> "022" "022" "022" "0233" "022" "022" "022" "032"
#> Sample_209 Sample_210 Sample_211 Sample_212 Sample_213 Sample_214 Sample_215 Sample_216
#> "032" "032" "032" "032" "032" "01" "01" "032"
#> Sample_217 Sample_218 Sample_219 Sample_220 Sample_221 Sample_222 Sample_223 Sample_224
#> "01" "032" "032" "032" "032" "032" "032" "01"
#> Sample_225 Sample_226 Sample_227 Sample_228 Sample_229 Sample_230 Sample_231 Sample_232
#> "032" "01" "032" "032" "032" "032" "032" "032"
#> Sample_233 Sample_234 Sample_235 Sample_236 Sample_237 Sample_238 Sample_239 Sample_240
#> "033" "032" "032" "01" "032" "032" "032" "032"
#> Sample_241 Sample_242 Sample_243 Sample_244 Sample_245 Sample_246 Sample_247 Sample_248
#> "01" "032" "032" "032" "032" "032" "032" "032"
#> Sample_249 Sample_250 Sample_251 Sample_252 Sample_253 Sample_254 Sample_255 Sample_256
#> "032" "032" "032" "032" "032" "033" "032" "032"
#> Sample_257 Sample_258 Sample_259 Sample_260 Sample_261 Sample_262 Sample_263 Sample_264
#> "032" "01" "032" "032" "032" "032" "032" "01"
#> Sample_265 Sample_266 Sample_267 Sample_268 Sample_269 Sample_270 Sample_271 Sample_272
#> "01" "032" "032" "032" "032" "032" "033" "01"
#> Sample_273 Sample_274 Sample_275 Sample_276 Sample_277 Sample_278 Sample_279 Sample_280
#> "032" "032" "01" "01" "032" "033" "032" "032"
#> Sample_281 Sample_282 Sample_283 Sample_284 Sample_285 Sample_286 Sample_287 Sample_288
#> "032" "032" "032" "032" "032" "032" "01" "033"
#> Sample_289 Sample_290 Sample_291 Sample_292 Sample_293 Sample_294 Sample_295 Sample_296
#> "033" "032" "032" "032" "032" "032" "032" "032"
#> Sample_297 Sample_298 Sample_299 Sample_300 Sample_301 Sample_302 Sample_303 Sample_304
#> "032" "032" "032" "032" "032" "01" "01" "01"
#> Sample_305 Sample_306 Sample_307 Sample_308 Sample_309 Sample_310 Sample_311 Sample_312
#> "01" "032" "032" "01" "01" "01" "01" "01"
#> Sample_313 Sample_314 Sample_315 Sample_316 Sample_317 Sample_318 Sample_319 Sample_320
#> "01" "01" "032" "01" "032" "01" "01" "01"
#> Sample_321 Sample_322 Sample_323 Sample_324 Sample_325 Sample_326 Sample_327 Sample_328
#> "01" "032" "01" "01" "01" "01" "01" "01"
#> Sample_329 Sample_330 Sample_331 Sample_332 Sample_333 Sample_334 Sample_335 Sample_336
#> "01" "01" "033" "032" "032" "032" "032" "032"
#> Sample_337 Sample_338 Sample_339 Sample_340 Sample_341 Sample_342 Sample_343 Sample_344
#> "032" "032" "033" "032" "032" "032" "032" "032"
#> Sample_345 Sample_346 Sample_347 Sample_348 Sample_349 Sample_350 Sample_351 Sample_352
#> "032" "032" "032" "032" "032" "01" "01" "01"
#> Sample_353 Sample_354 Sample_355 Sample_356 Sample_357 Sample_358 Sample_359 Sample_360
#> "032" "032" "01" "032" "032" "01" "032" "01"
#> Sample_361 Sample_362 Sample_363 Sample_364 Sample_365 Sample_366 Sample_367 Sample_368
#> "032" "01" "032" "032" "01" "032" "032" "01"
#> Sample_369 Sample_370 Sample_371 Sample_372 Sample_373 Sample_374 Sample_375 Sample_376
#> "01" "032" "033" "01" "01" "032" "032" "032"
#> Sample_377 Sample_378 Sample_379 Sample_380 Sample_381 Sample_382 Sample_383 Sample_384
#> "01" "032" "032" "032" "01" "032" "032" "032"
#> Sample_385 Sample_386 Sample_387 Sample_388 Sample_389 Sample_390 Sample_391 Sample_392
#> "01" "01" "032" "032" "01" "01" "032" "032"
#> Sample_393 Sample_394 Sample_395 Sample_396 Sample_397 Sample_398 Sample_399 Sample_400
#> "01" "01" "01" "01" "01" "01" "032" "032"
#> Sample_401 Sample_402 Sample_403 Sample_404 Sample_405 Sample_406 Sample_407 Sample_408
#> "032" "032" "01" "01" "01" "032" "032" "01"
#> Sample_409 Sample_410 Sample_411 Sample_412 Sample_413 Sample_414 Sample_415 Sample_416
#> "01" "032" "032" "01" "032" "032" "033" "01"
#> Sample_417 Sample_418 Sample_419 Sample_420 Sample_421 Sample_422 Sample_423 Sample_424
#> "032" "032" "032" "033" "01" "033" "032" "01"
#> Sample_425 Sample_426 Sample_427 Sample_428 Sample_429 Sample_430 Sample_431 Sample_432
#> "01" "032" "01" "01" "032" "01" "01" "01"
#> Sample_433 Sample_434 Sample_435 Sample_436 Sample_437 Sample_438 Sample_439 Sample_440
#> "01" "01" "032" "033" "033" "01" "01" "01"
#> Sample_441 Sample_442 Sample_443 Sample_444 Sample_445 Sample_446 Sample_447 Sample_448
#> "032" "01" "01" "032" "01" "01" "032" "01"
#> Sample_449 Sample_450 Sample_451 Sample_452 Sample_453 Sample_454 Sample_455 Sample_456
#> "032" "032" "01" "01" "032" "032" "01" "01"
#> Sample_457 Sample_458 Sample_459 Sample_460 Sample_461 Sample_462 Sample_463 Sample_464
#> "01" "032" "01" "032" "01" "032" "01" "01"
#> Sample_465 Sample_466 Sample_467 Sample_468 Sample_469 Sample_470 Sample_471 Sample_472
#> "01" "01" "032" "01" "01" "032" "032" "01"
#> Sample_473 Sample_474 Sample_475 Sample_476 Sample_477 Sample_478 Sample_479 Sample_480
#> "032" "01" "01" "01" "01" "031" "01" "031"
#> Sample_481 Sample_482 Sample_483 Sample_484 Sample_485 Sample_486 Sample_487 Sample_488
#> "031" "031" "031" "031" "031" "01" "01" "033"
#> Sample_489 Sample_490 Sample_491 Sample_492 Sample_493 Sample_494 Sample_495 Sample_496
#> "031" "01" "031" "031" "031" "031" "01" "031"
#> Sample_497 Sample_498 Sample_499 Sample_500 Sample_501 Sample_502 Sample_503 Sample_504
#> "031" "031" "01" "01" "031" "031" "031" "031"
#> Sample_505 Sample_506 Sample_507 Sample_508 Sample_509 Sample_510 Sample_511 Sample_512
#> "01" "01" "01" "031" "031" "031" "01" "033"
#> Sample_513 Sample_514 Sample_515 Sample_516 Sample_517 Sample_518 Sample_519 Sample_520
#> "01" "031" "01" "021" "01" "031" "031" "01"
#> Sample_521 Sample_522 Sample_523 Sample_524 Sample_525 Sample_526 Sample_527 Sample_528
#> "01" "031" "031" "031" "031" "031" "01" "031"
#> Sample_529 Sample_530 Sample_531 Sample_532 Sample_533 Sample_534 Sample_535 Sample_536
#> "033" "01" "033" "01" "01" "031" "01" "01"
#> Sample_537 Sample_538 Sample_539 Sample_540 Sample_541 Sample_542 Sample_543 Sample_544
#> "01" "031" "01" "031" "031" "01" "01" "01"
#> Sample_545 Sample_546 Sample_547 Sample_548 Sample_549 Sample_550 Sample_551 Sample_552
#> "033" "01" "01" "031" "031" "031" "031" "031"
#> Sample_553 Sample_554 Sample_555 Sample_556 Sample_557 Sample_558 Sample_559 Sample_560
#> "033" "031" "031" "031" "01" "01" "01" "031"
#> Sample_561 Sample_562 Sample_563 Sample_564 Sample_565 Sample_566 Sample_567 Sample_568
#> "031" "031" "031" "031" "01" "01" "01" "031"
#> Sample_569 Sample_570 Sample_571 Sample_572 Sample_573 Sample_574 Sample_575 Sample_576
#> "01" "032" "032" "01" "01" "033" "032" "032"
#> Sample_577 Sample_578 Sample_579 Sample_580 Sample_581 Sample_582 Sample_583 Sample_584
#> "01" "032" "032" "032" "032" "032" "032" "032"
#> Sample_585 Sample_586 Sample_587 Sample_588 Sample_589 Sample_590 Sample_591 Sample_592
#> "032" "022" "022" "0231" "032" "01" "022" "0231"
#> Sample_593 Sample_594 Sample_595 Sample_596 Sample_597 Sample_598 Sample_599 Sample_600
#> "022" "0231" "0231" "022" "0231" "022" "0231" "0231"
#> Sample_601 Sample_602 Sample_603 Sample_604 Sample_605 Sample_606 Sample_607 Sample_608
#> "022" "0231" "022" "022" "022" "022" "022" "022"
#> Sample_609 Sample_610 Sample_611 Sample_612 Sample_613 Sample_614 Sample_615 Sample_616
#> "022" "022" "021" "021" "032" "01" "032" "01"
#> Sample_617 Sample_618 Sample_619 Sample_620 Sample_621 Sample_622 Sample_623 Sample_624
#> "01" "01" "01" "01" "0231" "01" "032" "032"
#> Sample_625 Sample_626 Sample_627 Sample_628 Sample_629 Sample_630 Sample_631 Sample_632
#> "0231" "01" "0231" "0231" "0231" "01" "0231" "01"
#> Sample_633 Sample_634 Sample_635 Sample_636 Sample_637 Sample_638 Sample_639 Sample_640
#> "01" "01" "01" "01" "01" "0231" "01" "0232"
#> Sample_641 Sample_642 Sample_643 Sample_644 Sample_645 Sample_646 Sample_647 Sample_648
#> "01" "0233" "01" "032" "0232" "01" "01" "0232"
#> Sample_649 Sample_650 Sample_651 Sample_652 Sample_653 Sample_654 Sample_655 Sample_656
#> "0232" "0231" "022" "022" "022" "022" "022" "022"
#> Sample_657 Sample_658 Sample_659 Sample_660 Sample_661 Sample_662 Sample_663 Sample_664
#> "022" "022" "022" "022" "022" "022" "022" "022"
#> Sample_665 Sample_666 Sample_667 Sample_668 Sample_669 Sample_670 Sample_671 Sample_672
#> "0232" "022" "0232" "022" "022" "022" "022" "022"
#> Sample_673 Sample_674 Sample_675 Sample_676 Sample_677 Sample_678 Sample_679 Sample_680
#> "022" "022" "022" "022" "022" "022" "022" "022"
#> Sample_681 Sample_682 Sample_683 Sample_684 Sample_685 Sample_686 Sample_687 Sample_688
#> "0232" "022" "022" "022" "01" "021" "01" "021"
#> Sample_689 Sample_690 Sample_691 Sample_692 Sample_693 Sample_694 Sample_695 Sample_696
#> "022" "021" "022" "01" "022" "022" "021" "021"
#> Sample_697 Sample_698 Sample_699 Sample_700 Sample_701 Sample_702 Sample_703 Sample_704
#> "021" "021" "021" "021" "021" "021" "021" "022"
#> Sample_705 Sample_706 Sample_707 Sample_708 Sample_709 Sample_710 Sample_711 Sample_712
#> "022" "021" "021" "021" "021" "0232" "021" "021"
#> Sample_713 Sample_714 Sample_715 Sample_716 Sample_717 Sample_718 Sample_719 Sample_720
#> "021" "021" "021" "021" "021" "021" "021" "0232"
#> Sample_721 Sample_722 Sample_723 Sample_724 Sample_725 Sample_726 Sample_727 Sample_728
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_729 Sample_730 Sample_731 Sample_732 Sample_733 Sample_734 Sample_735 Sample_736
#> "021" "021" "0233" "021" "021" "021" "0233" "021"
#> Sample_737 Sample_738 Sample_739 Sample_740 Sample_741 Sample_742 Sample_743 Sample_744
#> "01" "0232" "021" "021" "021" "021" "021" "021"
#> Sample_745 Sample_746 Sample_747 Sample_748 Sample_749 Sample_750 Sample_751 Sample_752
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_753 Sample_754 Sample_755 Sample_756 Sample_757 Sample_758 Sample_759 Sample_760
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_761 Sample_762 Sample_763 Sample_764 Sample_765 Sample_766 Sample_767 Sample_768
#> "021" "021" "021" "021" "021" "021" "021" "0232"
#> Sample_769 Sample_770 Sample_771 Sample_772 Sample_773 Sample_774 Sample_775 Sample_776
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_777 Sample_778 Sample_779 Sample_780 Sample_781 Sample_782 Sample_783 Sample_784
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_785 Sample_786 Sample_787 Sample_788 Sample_789 Sample_790 Sample_791 Sample_792
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_793 Sample_794 Sample_795 Sample_796 Sample_797 Sample_798 Sample_799 Sample_800
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_801 Sample_802 Sample_803 Sample_804 Sample_805 Sample_806 Sample_807 Sample_808
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_809 Sample_810 Sample_811 Sample_812 Sample_813 Sample_814 Sample_815 Sample_816
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_817 Sample_818 Sample_819 Sample_820 Sample_821 Sample_822 Sample_823 Sample_824
#> "021" "021" "021" "0233" "021" "021" "021" "021"
#> Sample_825 Sample_826 Sample_827 Sample_828 Sample_829 Sample_830 Sample_831 Sample_832
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_833 Sample_834 Sample_835 Sample_836 Sample_837 Sample_838 Sample_839 Sample_840
#> "021" "021" "021" "0233" "021" "021" "021" "021"
#> Sample_841 Sample_842 Sample_843 Sample_844 Sample_845 Sample_846 Sample_847 Sample_848
#> "022" "022" "022" "022" "022" "022" "022" "022"
#> Sample_849 Sample_850 Sample_851 Sample_852 Sample_853 Sample_854 Sample_855 Sample_856
#> "022" "022" "022" "022" "022" "022" "021" "021"
#> Sample_857 Sample_858 Sample_859 Sample_860 Sample_861 Sample_862 Sample_863 Sample_864
#> "021" "021" "0232" "021" "021" "021" "021" "021"
#> Sample_865 Sample_866 Sample_867 Sample_868 Sample_869 Sample_870 Sample_871 Sample_872
#> "0233" "021" "021" "021" "021" "021" "021" "021"
#> Sample_873 Sample_874 Sample_875 Sample_876 Sample_877 Sample_878 Sample_879 Sample_880
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_881 Sample_882 Sample_883 Sample_884 Sample_885 Sample_886 Sample_887 Sample_888
#> "021" "0233" "021" "021" "021" "021" "021" "021"
#> Sample_889 Sample_890 Sample_891 Sample_892 Sample_893 Sample_894 Sample_895 Sample_896
#> "021" "021" "021" "021" "021" "0232" "021" "021"
#> Sample_897 Sample_898 Sample_899 Sample_900 Sample_901 Sample_902 Sample_903 Sample_904
#> "021" "021" "021" "0233" "021" "021" "021" "021"
#> Sample_905 Sample_906 Sample_907 Sample_908 Sample_909 Sample_910 Sample_911 Sample_912
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_913 Sample_914 Sample_915 Sample_916 Sample_917 Sample_918 Sample_919 Sample_920
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_921 Sample_922 Sample_923 Sample_924 Sample_925 Sample_926 Sample_927 Sample_928
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_929 Sample_930 Sample_931 Sample_932 Sample_933 Sample_934 Sample_935 Sample_936
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_937 Sample_938 Sample_939 Sample_940 Sample_941 Sample_942 Sample_943 Sample_944
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_945 Sample_946 Sample_947 Sample_948 Sample_949 Sample_950 Sample_951 Sample_952
#> "021" "021" "021" "032" "032" "01" "032" "032"
#> Sample_953 Sample_954 Sample_955 Sample_956 Sample_957 Sample_958 Sample_959 Sample_960
#> "032" "032" "032" "032" "032" "032" "032" "032"
#> Sample_961 Sample_962 Sample_963 Sample_964 Sample_965 Sample_966 Sample_967 Sample_968
#> "032" "01" "032" "032" "032" "032" "032" "01"
#> Sample_969 Sample_970 Sample_971 Sample_972 Sample_973 Sample_974 Sample_975 Sample_976
#> "032" "032" "032" "032" "032" "032" "033" "01"
#> Sample_977 Sample_978 Sample_979 Sample_980 Sample_981 Sample_982 Sample_983 Sample_984
#> "01" "01" "032" "01" "01" "01" "01" "032"
#> Sample_985 Sample_986 Sample_987 Sample_988 Sample_989 Sample_990 Sample_991 Sample_992
#> "01" "01" "01" "01" "01" "01" "01" "01"
#> Sample_993 Sample_994 Sample_995 Sample_996 Sample_997 Sample_998 Sample_999 Sample_1000
#> "01" "01" "032" "01" "032" "032" "01" "032"
#> Sample_1001 Sample_1002 Sample_1003 Sample_1004 Sample_1005 Sample_1006 Sample_1007 Sample_1008
#> "032" "032" "032" "033" "032" "032" "01" "01"
#> Sample_1009 Sample_1010 Sample_1011 Sample_1012 Sample_1013 Sample_1014 Sample_1015 Sample_1016
#> "01" "01" "032" "032" "032" "01" "01" "01"
#> Sample_1017 Sample_1018 Sample_1019 Sample_1020 Sample_1021 Sample_1022 Sample_1023 Sample_1024
#> "01" "01" "01" "01" "01" "01" "01" "01"
#> Sample_1025 Sample_1026 Sample_1027 Sample_1028 Sample_1029 Sample_1030 Sample_1031 Sample_1032
#> "01" "01" "01" "01" "031" "01" "033" "031"
#> Sample_1033 Sample_1034 Sample_1035 Sample_1036 Sample_1037 Sample_1038 Sample_1039 Sample_1040
#> "031" "01" "031" "01" "031" "031" "01" "031"
#> Sample_1041 Sample_1042 Sample_1043 Sample_1044 Sample_1045 Sample_1046 Sample_1047 Sample_1048
#> "01" "01" "031" "031" "033" "01" "031" "033"
#> Sample_1049 Sample_1050 Sample_1051 Sample_1052 Sample_1053 Sample_1054 Sample_1055 Sample_1056
#> "01" "01" "031" "01" "032" "032" "032" "01"
#> Sample_1057 Sample_1058 Sample_1059 Sample_1060 Sample_1061 Sample_1062 Sample_1063 Sample_1064
#> "032" "01" "01" "032" "032" "01" "031" "031"
#> Sample_1065 Sample_1066 Sample_1067 Sample_1068 Sample_1069 Sample_1070 Sample_1071 Sample_1072
#> "031" "01" "031" "031" "031" "01" "031" "031"
#> Sample_1073 Sample_1074 Sample_1075 Sample_1076 Sample_1077 Sample_1078 Sample_1079 Sample_1080
#> "01" "031" "01" "01" "031" "031" "031" "01"
#> Sample_1081 Sample_1082 Sample_1083 Sample_1084 Sample_1085 Sample_1086 Sample_1087 Sample_1088
#> "031" "031" "01" "031" "031" "033" "01" "033"
#> Sample_1089 Sample_1090 Sample_1091 Sample_1092 Sample_1093 Sample_1094 Sample_1095 Sample_1096
#> "033" "01" "01" "031" "01" "031" "031" "031"
#> Sample_1097 Sample_1098 Sample_1099 Sample_1100 Sample_1101 Sample_1102 Sample_1103 Sample_1104
#> "01" "031" "031" "01" "031" "01" "01" "01"
#> Sample_1105 Sample_1106 Sample_1107 Sample_1108 Sample_1109 Sample_1110 Sample_1111 Sample_1112
#> "01" "031" "031" "01" "031" "01" "031" "01"
#> Sample_1113 Sample_1114 Sample_1115 Sample_1116 Sample_1117 Sample_1118 Sample_1119 Sample_1120
#> "01" "01" "031" "01" "031" "01" "01" "033"
#> Sample_1121 Sample_1122 Sample_1123 Sample_1124 Sample_1125 Sample_1126 Sample_1127 Sample_1128
#> "01" "033" "031" "031" "031" "031" "031" "031"
#> Sample_1129 Sample_1130 Sample_1131 Sample_1132 Sample_1133 Sample_1134 Sample_1135 Sample_1136
#> "01" "01" "01" "033" "01" "033" "01" "01"
#> Sample_1137 Sample_1138 Sample_1139 Sample_1140 Sample_1141 Sample_1142 Sample_1143 Sample_1144
#> "01" "01" "031" "033" "031" "01" "01" "01"
#> Sample_1145 Sample_1146 Sample_1147 Sample_1148 Sample_1149 Sample_1150 Sample_1151 Sample_1152
#> "031" "01" "01" "01" "01" "031" "01" "031"
#> Sample_1153 Sample_1154 Sample_1155 Sample_1156 Sample_1157 Sample_1158 Sample_1159 Sample_1160
#> "01" "01" "01" "031" "031" "01" "01" "01"
#> Sample_1161 Sample_1162 Sample_1163 Sample_1164 Sample_1165 Sample_1166 Sample_1167 Sample_1168
#> "031" "01" "031" "031" "01" "01" "01" "01"
#> Sample_1169 Sample_1170 Sample_1171 Sample_1172 Sample_1173 Sample_1174 Sample_1175 Sample_1176
#> "031" "031" "01" "01" "01" "01" "031" "01"
#> Sample_1177 Sample_1178 Sample_1179 Sample_1180 Sample_1181 Sample_1182 Sample_1183 Sample_1184
#> "01" "01" "01" "031" "01" "01" "033" "033"
#> Sample_1185 Sample_1186 Sample_1187 Sample_1188 Sample_1189 Sample_1190 Sample_1191 Sample_1192
#> "01" "01" "01" "01" "032" "032" "032" "01"
#> Sample_1193 Sample_1194 Sample_1195 Sample_1196 Sample_1197 Sample_1198 Sample_1199 Sample_1200
#> "032" "032" "032" "032" "032" "032" "032" "032"
#> Sample_1201 Sample_1202 Sample_1203 Sample_1204 Sample_1205 Sample_1206 Sample_1207 Sample_1208
#> "032" "032" "032" "032" "032" "032" "032" "01"
#> Sample_1209 Sample_1210 Sample_1211 Sample_1212 Sample_1213 Sample_1214 Sample_1215 Sample_1216
#> "032" "032" "032" "032" "032" "032" "032" "01"
#> Sample_1217 Sample_1218 Sample_1219 Sample_1220 Sample_1221 Sample_1222 Sample_1223 Sample_1224
#> "01" "01" "032" "032" "032" "01" "031" "031"
#> Sample_1225 Sample_1226 Sample_1227 Sample_1228 Sample_1229 Sample_1230 Sample_1231 Sample_1232
#> "01" "031" "01" "01" "01" "031" "031" "031"
#> Sample_1233 Sample_1234 Sample_1235 Sample_1236 Sample_1237 Sample_1238 Sample_1239 Sample_1240
#> "031" "01" "031" "01" "031" "031" "01" "01"
#> Sample_1241 Sample_1242 Sample_1243 Sample_1244 Sample_1245 Sample_1246 Sample_1247 Sample_1248
#> "01" "01" "031" "031" "031" "031" "031" "031"
#> Sample_1249 Sample_1250 Sample_1251 Sample_1252 Sample_1253 Sample_1254 Sample_1255 Sample_1256
#> "01" "031" "031" "031" "031" "01" "033" "031"
#> Sample_1257 Sample_1258 Sample_1259 Sample_1260 Sample_1261 Sample_1262 Sample_1263 Sample_1264
#> "031" "031" "031" "031" "031" "031" "031" "01"
#> Sample_1265 Sample_1266 Sample_1267 Sample_1268 Sample_1269 Sample_1270 Sample_1271 Sample_1272
#> "031" "01" "01" "033" "031" "031" "031" "01"
#> Sample_1273 Sample_1274 Sample_1275 Sample_1276 Sample_1277 Sample_1278 Sample_1279 Sample_1280
#> "031" "031" "031" "031" "031" "031" "01" "01"
#> Sample_1281 Sample_1282 Sample_1283 Sample_1284 Sample_1285 Sample_1286 Sample_1287 Sample_1288
#> "031" "031" "01" "031" "01" "031" "031" "031"
#> Sample_1289 Sample_1290 Sample_1291 Sample_1292 Sample_1293 Sample_1294 Sample_1295 Sample_1296
#> "031" "031" "031" "031" "031" "031" "031" "033"
#> Sample_1297 Sample_1298 Sample_1299 Sample_1300 Sample_1301 Sample_1302 Sample_1303 Sample_1304
#> "031" "031" "01" "031" "031" "031" "031" "031"
#> Sample_1305 Sample_1306 Sample_1307 Sample_1308 Sample_1309 Sample_1310 Sample_1311 Sample_1312
#> "031" "033" "01" "01" "01" "033" "031" "031"
#> Sample_1313 Sample_1314 Sample_1315 Sample_1316 Sample_1317 Sample_1318 Sample_1319 Sample_1320
#> "01" "031" "031" "031" "01" "031" "031" "01"
#> Sample_1321 Sample_1322 Sample_1323 Sample_1324 Sample_1325 Sample_1326 Sample_1327 Sample_1328
#> "031" "031" "031" "031" "031" "031" "031" "031"
#> Sample_1329 Sample_1330 Sample_1331 Sample_1332 Sample_1333 Sample_1334 Sample_1335 Sample_1336
#> "031" "031" "031" "031" "031" "031" "01" "01"
#> Sample_1337 Sample_1338 Sample_1339 Sample_1340 Sample_1341 Sample_1342 Sample_1343 Sample_1344
#> "033" "01" "033" "031" "031" "01" "01" "031"
#> Sample_1345 Sample_1346 Sample_1347 Sample_1348 Sample_1349 Sample_1350 Sample_1351 Sample_1352
#> "033" "031" "031" "01" "031" "01" "031" "01"
#> Sample_1353 Sample_1354 Sample_1355 Sample_1356 Sample_1357 Sample_1358 Sample_1359 Sample_1360
#> "031" "01" "031" "031" "01" "01" "031" "01"
#> Sample_1361 Sample_1362 Sample_1363 Sample_1364 Sample_1365 Sample_1366 Sample_1367 Sample_1368
#> "031" "031" "031" "01" "031" "031" "01" "031"
#> Sample_1369 Sample_1370 Sample_1371 Sample_1372 Sample_1373 Sample_1374 Sample_1375 Sample_1376
#> "031" "031" "01" "031" "01" "01" "01" "033"
#> Sample_1377 Sample_1378 Sample_1379 Sample_1380 Sample_1381 Sample_1382 Sample_1383 Sample_1384
#> "031" "031" "031" "031" "031" "01" "031" "031"
#> Sample_1385 Sample_1386 Sample_1387 Sample_1388 Sample_1389 Sample_1390 Sample_1391 Sample_1392
#> "031" "01" "031" "01" "031" "031" "031" "031"
#> Sample_1393 Sample_1394 Sample_1395 Sample_1396 Sample_1397 Sample_1398 Sample_1399 Sample_1400
#> "031" "031" "031" "031" "031" "01" "01" "033"
#> Sample_1401 Sample_1402 Sample_1403 Sample_1404 Sample_1405 Sample_1406 Sample_1407 Sample_1408
#> "031" "01" "031" "031" "031" "01" "031" "01"
#> Sample_1409 Sample_1410 Sample_1411 Sample_1412 Sample_1413 Sample_1414 Sample_1415 Sample_1416
#> "01" "031" "031" "031" "033" "031" "031" "033"
#> Sample_1417 Sample_1418 Sample_1419 Sample_1420 Sample_1421 Sample_1422 Sample_1423 Sample_1424
#> "031" "031" "01" "031" "031" "031" "01" "031"
#> Sample_1425 Sample_1426 Sample_1427 Sample_1428 Sample_1429 Sample_1430 Sample_1431 Sample_1432
#> "01" "01" "01" "031" "01" "01" "01" "01"
#> Sample_1433 Sample_1434 Sample_1435 Sample_1436 Sample_1437 Sample_1438 Sample_1439 Sample_1440
#> "01" "01" "01" "01" "01" "01" "01" "01"
#> Sample_1441 Sample_1442 Sample_1443 Sample_1444 Sample_1445 Sample_1446 Sample_1447 Sample_1448
#> "01" "031" "031" "01" "01" "031" "031" "01"
#> Sample_1449 Sample_1450 Sample_1451 Sample_1452 Sample_1453 Sample_1454 Sample_1455 Sample_1456
#> "01" "01" "01" "01" "01" "01" "01" "031"
#> Sample_1457 Sample_1458 Sample_1459 Sample_1460 Sample_1461 Sample_1462 Sample_1463 Sample_1464
#> "031" "01" "01" "033" "031" "01" "031" "031"
#> Sample_1465 Sample_1466 Sample_1467 Sample_1468 Sample_1469 Sample_1470 Sample_1471 Sample_1472
#> "01" "01" "01" "031" "01" "01" "01" "01"
#> Sample_1473 Sample_1474 Sample_1475 Sample_1476 Sample_1477 Sample_1478 Sample_1479 Sample_1480
#> "01" "01" "01" "01" "01" "01" "01" "031"
#> Sample_1481 Sample_1482 Sample_1483 Sample_1484 Sample_1485 Sample_1486 Sample_1487 Sample_1488
#> "01" "01" "01" "01" "031" "033" "01" "031"
#> Sample_1489 Sample_1490 Sample_1491 Sample_1492 Sample_1493 Sample_1494 Sample_1495 Sample_1496
#> "01" "01" "01" "01" "01" "033" "01" "01"
#> Sample_1497 Sample_1498 Sample_1499 Sample_1500 Sample_1501 Sample_1502 Sample_1503 Sample_1504
#> "01" "01" "01" "01" "01" "031" "031" "033"
#> Sample_1505 Sample_1506 Sample_1507 Sample_1508 Sample_1509 Sample_1510 Sample_1511 Sample_1512
#> "033" "031" "031" "031" "01" "01" "031" "031"
#> Sample_1513 Sample_1514 Sample_1515 Sample_1516 Sample_1517 Sample_1518 Sample_1519 Sample_1520
#> "031" "01" "031" "01" "022" "022" "022" "0233"
#> Sample_1521 Sample_1522 Sample_1523 Sample_1524 Sample_1525 Sample_1526 Sample_1527 Sample_1528
#> "0232" "01" "01" "0233" "01" "01" "021" "021"
#> Sample_1529 Sample_1530 Sample_1531 Sample_1532 Sample_1533 Sample_1534 Sample_1535 Sample_1536
#> "021" "0233" "0232" "0232" "021" "021" "022" "022"
#> Sample_1537 Sample_1538 Sample_1539 Sample_1540 Sample_1541 Sample_1542 Sample_1543 Sample_1544
#> "022" "021" "021" "01" "01" "021" "0233" "01"
#> Sample_1545 Sample_1546 Sample_1547 Sample_1548 Sample_1549 Sample_1550 Sample_1551 Sample_1552
#> "01" "021" "01" "0231" "032" "0231" "0231" "0231"
#> Sample_1553 Sample_1554 Sample_1555 Sample_1556 Sample_1557 Sample_1558 Sample_1559 Sample_1560
#> "032" "022" "0231" "01" "01" "01" "0231" "01"
#> Sample_1561 Sample_1562 Sample_1563 Sample_1564 Sample_1565 Sample_1566 Sample_1567 Sample_1568
#> "0231" "01" "01" "01" "01" "01" "01" "01"
#> Sample_1569 Sample_1570 Sample_1571 Sample_1572 Sample_1573 Sample_1574 Sample_1575 Sample_1576
#> "031" "021" "01" "01" "01" "0231" "0231" "01"
#> Sample_1577 Sample_1578 Sample_1579 Sample_1580 Sample_1581 Sample_1582 Sample_1583 Sample_1584
#> "01" "01" "01" "01" "032" "0231" "01" "01"
#> Sample_1585 Sample_1586 Sample_1587 Sample_1588 Sample_1589 Sample_1590 Sample_1591 Sample_1592
#> "0231" "0231" "0231" "022" "0231" "01" "01" "0231"
#> Sample_1593 Sample_1594 Sample_1595 Sample_1596 Sample_1597 Sample_1598 Sample_1599 Sample_1600
#> "0231" "0231" "0231" "0232" "032" "031" "0233" "01"
get_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 1503))
#> Sample_1 Sample_2 Sample_3 Sample_4 Sample_5 Sample_6 Sample_7 Sample_8
#> "01" "022" "022" "022" "022" "022" "022" "022"
#> Sample_9 Sample_10 Sample_11 Sample_12 Sample_13 Sample_14 Sample_15 Sample_16
#> "022" "022" "022" "022" "022" "022" "022" "022"
#> Sample_17 Sample_18 Sample_19 Sample_20 Sample_21 Sample_22 Sample_23 Sample_24
#> "022" "022" "022" "022" "022" "022" "022" "022"
#> Sample_25 Sample_26 Sample_27 Sample_28 Sample_29 Sample_30 Sample_31 Sample_32
#> "022" "022" "022" "022" "022" "022" "023" "022"
#> Sample_33 Sample_34 Sample_35 Sample_36 Sample_37 Sample_38 Sample_39 Sample_40
#> "022" "022" "022" "022" "022" "022" "022" "022"
#> Sample_41 Sample_42 Sample_43 Sample_44 Sample_45 Sample_46 Sample_47 Sample_48
#> "022" "022" "023" "022" "022" "022" "022" "022"
#> Sample_49 Sample_50 Sample_51 Sample_52 Sample_53 Sample_54 Sample_55 Sample_56
#> "022" "022" "022" "022" "022" "022" "022" "022"
#> Sample_57 Sample_58 Sample_59 Sample_60 Sample_61 Sample_62 Sample_63 Sample_64
#> "022" "022" "022" "022" "022" "022" "022" "022"
#> Sample_65 Sample_66 Sample_67 Sample_68 Sample_69 Sample_70 Sample_71 Sample_72
#> "022" "022" "022" "022" "022" "022" "022" "021"
#> Sample_73 Sample_74 Sample_75 Sample_76 Sample_77 Sample_78 Sample_79 Sample_80
#> "023" "022" "022" "022" "022" "022" "022" "022"
#> Sample_81 Sample_82 Sample_83 Sample_84 Sample_85 Sample_86 Sample_87 Sample_88
#> "022" "022" "022" "022" "022" "022" "022" "022"
#> Sample_89 Sample_90 Sample_91 Sample_92 Sample_93 Sample_94 Sample_95 Sample_96
#> "022" "022" "022" "022" "022" "023" "022" "022"
#> Sample_97 Sample_98 Sample_99 Sample_100 Sample_101 Sample_102 Sample_103 Sample_104
#> "022" "022" "022" "022" "022" "022" "022" "022"
#> Sample_105 Sample_106 Sample_107 Sample_108 Sample_109 Sample_110 Sample_111 Sample_112
#> "022" "022" "022" "022" "022" "022" "021" "022"
#> Sample_113 Sample_114 Sample_115 Sample_116 Sample_117 Sample_118 Sample_119 Sample_120
#> "022" "023" "022" "022" "022" "023" "022" "022"
#> Sample_121 Sample_122 Sample_123 Sample_124 Sample_125 Sample_126 Sample_127 Sample_128
#> "01" "022" "023" "022" "022" "022" "022" "022"
#> Sample_129 Sample_130 Sample_131 Sample_132 Sample_133 Sample_134 Sample_135 Sample_136
#> "022" "022" "022" "022" "022" "022" "022" "022"
#> Sample_137 Sample_138 Sample_139 Sample_140 Sample_141 Sample_142 Sample_143 Sample_144
#> "022" "022" "022" "022" "022" "022" "022" "023"
#> Sample_145 Sample_146 Sample_147 Sample_148 Sample_149 Sample_150 Sample_151 Sample_152
#> "022" "022" "021" "021" "021" "021" "021" "021"
#> Sample_153 Sample_154 Sample_155 Sample_156 Sample_157 Sample_158 Sample_159 Sample_160
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_161 Sample_162 Sample_163 Sample_164 Sample_165 Sample_166 Sample_167 Sample_168
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_169 Sample_170 Sample_171 Sample_172 Sample_173 Sample_174 Sample_175 Sample_176
#> "021" "021" "023" "021" "023" "021" "021" "021"
#> Sample_177 Sample_178 Sample_179 Sample_180 Sample_181 Sample_182 Sample_183 Sample_184
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_185 Sample_186 Sample_187 Sample_188 Sample_189 Sample_190 Sample_191 Sample_192
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_193 Sample_194 Sample_195 Sample_196 Sample_197 Sample_198 Sample_199 Sample_200
#> "021" "021" "021" "021" "022" "022" "022" "022"
#> Sample_201 Sample_202 Sample_203 Sample_204 Sample_205 Sample_206 Sample_207 Sample_208
#> "022" "022" "022" "023" "022" "022" "022" "032"
#> Sample_209 Sample_210 Sample_211 Sample_212 Sample_213 Sample_214 Sample_215 Sample_216
#> "032" "032" "032" "032" "032" "01" "01" "032"
#> Sample_217 Sample_218 Sample_219 Sample_220 Sample_221 Sample_222 Sample_223 Sample_224
#> "01" "032" "032" "032" "032" "032" "032" "01"
#> Sample_225 Sample_226 Sample_227 Sample_228 Sample_229 Sample_230 Sample_231 Sample_232
#> "032" "01" "032" "032" "032" "032" "032" "032"
#> Sample_233 Sample_234 Sample_235 Sample_236 Sample_237 Sample_238 Sample_239 Sample_240
#> "033" "032" "032" "01" "032" "032" "032" "032"
#> Sample_241 Sample_242 Sample_243 Sample_244 Sample_245 Sample_246 Sample_247 Sample_248
#> "01" "032" "032" "032" "032" "032" "032" "032"
#> Sample_249 Sample_250 Sample_251 Sample_252 Sample_253 Sample_254 Sample_255 Sample_256
#> "032" "032" "032" "032" "032" "033" "032" "032"
#> Sample_257 Sample_258 Sample_259 Sample_260 Sample_261 Sample_262 Sample_263 Sample_264
#> "032" "01" "032" "032" "032" "032" "032" "01"
#> Sample_265 Sample_266 Sample_267 Sample_268 Sample_269 Sample_270 Sample_271 Sample_272
#> "01" "032" "032" "032" "032" "032" "033" "01"
#> Sample_273 Sample_274 Sample_275 Sample_276 Sample_277 Sample_278 Sample_279 Sample_280
#> "032" "032" "01" "01" "032" "033" "032" "032"
#> Sample_281 Sample_282 Sample_283 Sample_284 Sample_285 Sample_286 Sample_287 Sample_288
#> "032" "032" "032" "032" "032" "032" "01" "033"
#> Sample_289 Sample_290 Sample_291 Sample_292 Sample_293 Sample_294 Sample_295 Sample_296
#> "033" "032" "032" "032" "032" "032" "032" "032"
#> Sample_297 Sample_298 Sample_299 Sample_300 Sample_301 Sample_302 Sample_303 Sample_304
#> "032" "032" "032" "032" "032" "01" "01" "01"
#> Sample_305 Sample_306 Sample_307 Sample_308 Sample_309 Sample_310 Sample_311 Sample_312
#> "01" "032" "032" "01" "01" "01" "01" "01"
#> Sample_313 Sample_314 Sample_315 Sample_316 Sample_317 Sample_318 Sample_319 Sample_320
#> "01" "01" "032" "01" "032" "01" "01" "01"
#> Sample_321 Sample_322 Sample_323 Sample_324 Sample_325 Sample_326 Sample_327 Sample_328
#> "01" "032" "01" "01" "01" "01" "01" "01"
#> Sample_329 Sample_330 Sample_331 Sample_332 Sample_333 Sample_334 Sample_335 Sample_336
#> "01" "01" "033" "032" "032" "032" "032" "032"
#> Sample_337 Sample_338 Sample_339 Sample_340 Sample_341 Sample_342 Sample_343 Sample_344
#> "032" "032" "033" "032" "032" "032" "032" "032"
#> Sample_345 Sample_346 Sample_347 Sample_348 Sample_349 Sample_350 Sample_351 Sample_352
#> "032" "032" "032" "032" "032" "01" "01" "01"
#> Sample_353 Sample_354 Sample_355 Sample_356 Sample_357 Sample_358 Sample_359 Sample_360
#> "032" "032" "01" "032" "032" "01" "032" "01"
#> Sample_361 Sample_362 Sample_363 Sample_364 Sample_365 Sample_366 Sample_367 Sample_368
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#> Sample_377 Sample_378 Sample_379 Sample_380 Sample_381 Sample_382 Sample_383 Sample_384
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#> Sample_385 Sample_386 Sample_387 Sample_388 Sample_389 Sample_390 Sample_391 Sample_392
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#> Sample_401 Sample_402 Sample_403 Sample_404 Sample_405 Sample_406 Sample_407 Sample_408
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#> Sample_409 Sample_410 Sample_411 Sample_412 Sample_413 Sample_414 Sample_415 Sample_416
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#> Sample_417 Sample_418 Sample_419 Sample_420 Sample_421 Sample_422 Sample_423 Sample_424
#> "032" "032" "032" "033" "01" "033" "032" "01"
#> Sample_425 Sample_426 Sample_427 Sample_428 Sample_429 Sample_430 Sample_431 Sample_432
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#> Sample_433 Sample_434 Sample_435 Sample_436 Sample_437 Sample_438 Sample_439 Sample_440
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#> Sample_441 Sample_442 Sample_443 Sample_444 Sample_445 Sample_446 Sample_447 Sample_448
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#> Sample_449 Sample_450 Sample_451 Sample_452 Sample_453 Sample_454 Sample_455 Sample_456
#> "032" "032" "01" "01" "032" "032" "01" "01"
#> Sample_457 Sample_458 Sample_459 Sample_460 Sample_461 Sample_462 Sample_463 Sample_464
#> "01" "032" "01" "032" "01" "032" "01" "01"
#> Sample_465 Sample_466 Sample_467 Sample_468 Sample_469 Sample_470 Sample_471 Sample_472
#> "01" "01" "032" "01" "01" "032" "032" "01"
#> Sample_473 Sample_474 Sample_475 Sample_476 Sample_477 Sample_478 Sample_479 Sample_480
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#> Sample_481 Sample_482 Sample_483 Sample_484 Sample_485 Sample_486 Sample_487 Sample_488
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#> Sample_489 Sample_490 Sample_491 Sample_492 Sample_493 Sample_494 Sample_495 Sample_496
#> "031" "01" "031" "031" "031" "031" "01" "031"
#> Sample_497 Sample_498 Sample_499 Sample_500 Sample_501 Sample_502 Sample_503 Sample_504
#> "031" "031" "01" "01" "031" "031" "031" "031"
#> Sample_505 Sample_506 Sample_507 Sample_508 Sample_509 Sample_510 Sample_511 Sample_512
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#> Sample_513 Sample_514 Sample_515 Sample_516 Sample_517 Sample_518 Sample_519 Sample_520
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#> Sample_521 Sample_522 Sample_523 Sample_524 Sample_525 Sample_526 Sample_527 Sample_528
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#> Sample_529 Sample_530 Sample_531 Sample_532 Sample_533 Sample_534 Sample_535 Sample_536
#> "033" "01" "033" "01" "01" "031" "01" "01"
#> Sample_537 Sample_538 Sample_539 Sample_540 Sample_541 Sample_542 Sample_543 Sample_544
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#> Sample_545 Sample_546 Sample_547 Sample_548 Sample_549 Sample_550 Sample_551 Sample_552
#> "033" "01" "01" "031" "031" "031" "031" "031"
#> Sample_553 Sample_554 Sample_555 Sample_556 Sample_557 Sample_558 Sample_559 Sample_560
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#> Sample_561 Sample_562 Sample_563 Sample_564 Sample_565 Sample_566 Sample_567 Sample_568
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#> Sample_569 Sample_570 Sample_571 Sample_572 Sample_573 Sample_574 Sample_575 Sample_576
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#> "01" "032" "032" "032" "032" "032" "032" "032"
#> Sample_585 Sample_586 Sample_587 Sample_588 Sample_589 Sample_590 Sample_591 Sample_592
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#> "022" "023" "023" "022" "023" "022" "023" "023"
#> Sample_601 Sample_602 Sample_603 Sample_604 Sample_605 Sample_606 Sample_607 Sample_608
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#> Sample_609 Sample_610 Sample_611 Sample_612 Sample_613 Sample_614 Sample_615 Sample_616
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#> Sample_625 Sample_626 Sample_627 Sample_628 Sample_629 Sample_630 Sample_631 Sample_632
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#> Sample_641 Sample_642 Sample_643 Sample_644 Sample_645 Sample_646 Sample_647 Sample_648
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#> Sample_649 Sample_650 Sample_651 Sample_652 Sample_653 Sample_654 Sample_655 Sample_656
#> "023" "023" "022" "022" "022" "022" "022" "022"
#> Sample_657 Sample_658 Sample_659 Sample_660 Sample_661 Sample_662 Sample_663 Sample_664
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#> Sample_689 Sample_690 Sample_691 Sample_692 Sample_693 Sample_694 Sample_695 Sample_696
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#> Sample_697 Sample_698 Sample_699 Sample_700 Sample_701 Sample_702 Sample_703 Sample_704
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#> Sample_705 Sample_706 Sample_707 Sample_708 Sample_709 Sample_710 Sample_711 Sample_712
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#> Sample_713 Sample_714 Sample_715 Sample_716 Sample_717 Sample_718 Sample_719 Sample_720
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#> Sample_745 Sample_746 Sample_747 Sample_748 Sample_749 Sample_750 Sample_751 Sample_752
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#> Sample_753 Sample_754 Sample_755 Sample_756 Sample_757 Sample_758 Sample_759 Sample_760
#> "021" "021" "021" "021" "021" "021" "021" "021"
#> Sample_761 Sample_762 Sample_763 Sample_764 Sample_765 Sample_766 Sample_767 Sample_768
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#> Sample_769 Sample_770 Sample_771 Sample_772 Sample_773 Sample_774 Sample_775 Sample_776
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#> Sample_777 Sample_778 Sample_779 Sample_780 Sample_781 Sample_782 Sample_783 Sample_784
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#> Sample_785 Sample_786 Sample_787 Sample_788 Sample_789 Sample_790 Sample_791 Sample_792
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#> Sample_793 Sample_794 Sample_795 Sample_796 Sample_797 Sample_798 Sample_799 Sample_800
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#> Sample_801 Sample_802 Sample_803 Sample_804 Sample_805 Sample_806 Sample_807 Sample_808
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#> Sample_809 Sample_810 Sample_811 Sample_812 Sample_813 Sample_814 Sample_815 Sample_816
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#> Sample_817 Sample_818 Sample_819 Sample_820 Sample_821 Sample_822 Sample_823 Sample_824
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#> Sample_825 Sample_826 Sample_827 Sample_828 Sample_829 Sample_830 Sample_831 Sample_832
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#> Sample_833 Sample_834 Sample_835 Sample_836 Sample_837 Sample_838 Sample_839 Sample_840
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#> Sample_841 Sample_842 Sample_843 Sample_844 Sample_845 Sample_846 Sample_847 Sample_848
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#> Sample_849 Sample_850 Sample_851 Sample_852 Sample_853 Sample_854 Sample_855 Sample_856
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#> Sample_857 Sample_858 Sample_859 Sample_860 Sample_861 Sample_862 Sample_863 Sample_864
#> "021" "021" "023" "021" "021" "021" "021" "021"
#> Sample_865 Sample_866 Sample_867 Sample_868 Sample_869 Sample_870 Sample_871 Sample_872
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#> Sample_873 Sample_874 Sample_875 Sample_876 Sample_877 Sample_878 Sample_879 Sample_880
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#> Sample_881 Sample_882 Sample_883 Sample_884 Sample_885 Sample_886 Sample_887 Sample_888
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#> Sample_889 Sample_890 Sample_891 Sample_892 Sample_893 Sample_894 Sample_895 Sample_896
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#> Sample_897 Sample_898 Sample_899 Sample_900 Sample_901 Sample_902 Sample_903 Sample_904
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#> Sample_905 Sample_906 Sample_907 Sample_908 Sample_909 Sample_910 Sample_911 Sample_912
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#> Sample_913 Sample_914 Sample_915 Sample_916 Sample_917 Sample_918 Sample_919 Sample_920
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#> Sample_921 Sample_922 Sample_923 Sample_924 Sample_925 Sample_926 Sample_927 Sample_928
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#> Sample_929 Sample_930 Sample_931 Sample_932 Sample_933 Sample_934 Sample_935 Sample_936
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#> Sample_953 Sample_954 Sample_955 Sample_956 Sample_957 Sample_958 Sample_959 Sample_960
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#> Sample_961 Sample_962 Sample_963 Sample_964 Sample_965 Sample_966 Sample_967 Sample_968
#> "032" "01" "032" "032" "032" "032" "032" "01"
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#> Sample_985 Sample_986 Sample_987 Sample_988 Sample_989 Sample_990 Sample_991 Sample_992
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#> Sample_993 Sample_994 Sample_995 Sample_996 Sample_997 Sample_998 Sample_999 Sample_1000
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#> Sample_1001 Sample_1002 Sample_1003 Sample_1004 Sample_1005 Sample_1006 Sample_1007 Sample_1008
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#> Sample_1017 Sample_1018 Sample_1019 Sample_1020 Sample_1021 Sample_1022 Sample_1023 Sample_1024
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#> Sample_1057 Sample_1058 Sample_1059 Sample_1060 Sample_1061 Sample_1062 Sample_1063 Sample_1064
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#> Sample_1065 Sample_1066 Sample_1067 Sample_1068 Sample_1069 Sample_1070 Sample_1071 Sample_1072
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#> Sample_1081 Sample_1082 Sample_1083 Sample_1084 Sample_1085 Sample_1086 Sample_1087 Sample_1088
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#> Sample_1105 Sample_1106 Sample_1107 Sample_1108 Sample_1109 Sample_1110 Sample_1111 Sample_1112
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#> Sample_1153 Sample_1154 Sample_1155 Sample_1156 Sample_1157 Sample_1158 Sample_1159 Sample_1160
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#> Sample_1161 Sample_1162 Sample_1163 Sample_1164 Sample_1165 Sample_1166 Sample_1167 Sample_1168
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#> Sample_1169 Sample_1170 Sample_1171 Sample_1172 Sample_1173 Sample_1174 Sample_1175 Sample_1176
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#> Sample_1185 Sample_1186 Sample_1187 Sample_1188 Sample_1189 Sample_1190 Sample_1191 Sample_1192
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#> Sample_1193 Sample_1194 Sample_1195 Sample_1196 Sample_1197 Sample_1198 Sample_1199 Sample_1200
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#> Sample_1201 Sample_1202 Sample_1203 Sample_1204 Sample_1205 Sample_1206 Sample_1207 Sample_1208
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#> Sample_1209 Sample_1210 Sample_1211 Sample_1212 Sample_1213 Sample_1214 Sample_1215 Sample_1216
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#> Sample_1225 Sample_1226 Sample_1227 Sample_1228 Sample_1229 Sample_1230 Sample_1231 Sample_1232
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#> Sample_1233 Sample_1234 Sample_1235 Sample_1236 Sample_1237 Sample_1238 Sample_1239 Sample_1240
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#> Sample_1241 Sample_1242 Sample_1243 Sample_1244 Sample_1245 Sample_1246 Sample_1247 Sample_1248
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#> Sample_1249 Sample_1250 Sample_1251 Sample_1252 Sample_1253 Sample_1254 Sample_1255 Sample_1256
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#> Sample_1257 Sample_1258 Sample_1259 Sample_1260 Sample_1261 Sample_1262 Sample_1263 Sample_1264
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#> Sample_1265 Sample_1266 Sample_1267 Sample_1268 Sample_1269 Sample_1270 Sample_1271 Sample_1272
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#> Sample_1273 Sample_1274 Sample_1275 Sample_1276 Sample_1277 Sample_1278 Sample_1279 Sample_1280
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#> Sample_1281 Sample_1282 Sample_1283 Sample_1284 Sample_1285 Sample_1286 Sample_1287 Sample_1288
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#> Sample_1289 Sample_1290 Sample_1291 Sample_1292 Sample_1293 Sample_1294 Sample_1295 Sample_1296
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#> Sample_1297 Sample_1298 Sample_1299 Sample_1300 Sample_1301 Sample_1302 Sample_1303 Sample_1304
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#> Sample_1305 Sample_1306 Sample_1307 Sample_1308 Sample_1309 Sample_1310 Sample_1311 Sample_1312
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#> Sample_1313 Sample_1314 Sample_1315 Sample_1316 Sample_1317 Sample_1318 Sample_1319 Sample_1320
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#> Sample_1321 Sample_1322 Sample_1323 Sample_1324 Sample_1325 Sample_1326 Sample_1327 Sample_1328
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#> Sample_1329 Sample_1330 Sample_1331 Sample_1332 Sample_1333 Sample_1334 Sample_1335 Sample_1336
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#> Sample_1337 Sample_1338 Sample_1339 Sample_1340 Sample_1341 Sample_1342 Sample_1343 Sample_1344
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#> Sample_1345 Sample_1346 Sample_1347 Sample_1348 Sample_1349 Sample_1350 Sample_1351 Sample_1352
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#> Sample_1353 Sample_1354 Sample_1355 Sample_1356 Sample_1357 Sample_1358 Sample_1359 Sample_1360
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#> Sample_1361 Sample_1362 Sample_1363 Sample_1364 Sample_1365 Sample_1366 Sample_1367 Sample_1368
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#> Sample_1369 Sample_1370 Sample_1371 Sample_1372 Sample_1373 Sample_1374 Sample_1375 Sample_1376
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#> Sample_1377 Sample_1378 Sample_1379 Sample_1380 Sample_1381 Sample_1382 Sample_1383 Sample_1384
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#> Sample_1385 Sample_1386 Sample_1387 Sample_1388 Sample_1389 Sample_1390 Sample_1391 Sample_1392
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#> Sample_1393 Sample_1394 Sample_1395 Sample_1396 Sample_1397 Sample_1398 Sample_1399 Sample_1400
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#> Sample_1401 Sample_1402 Sample_1403 Sample_1404 Sample_1405 Sample_1406 Sample_1407 Sample_1408
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#> Sample_1409 Sample_1410 Sample_1411 Sample_1412 Sample_1413 Sample_1414 Sample_1415 Sample_1416
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#> Sample_1417 Sample_1418 Sample_1419 Sample_1420 Sample_1421 Sample_1422 Sample_1423 Sample_1424
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#> Sample_1425 Sample_1426 Sample_1427 Sample_1428 Sample_1429 Sample_1430 Sample_1431 Sample_1432
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#> "01" "01" "01" "01" "01" "01" "01" "01"
#> Sample_1441 Sample_1442 Sample_1443 Sample_1444 Sample_1445 Sample_1446 Sample_1447 Sample_1448
#> "01" "031" "031" "01" "01" "031" "031" "01"
#> Sample_1449 Sample_1450 Sample_1451 Sample_1452 Sample_1453 Sample_1454 Sample_1455 Sample_1456
#> "01" "01" "01" "01" "01" "01" "01" "031"
#> Sample_1457 Sample_1458 Sample_1459 Sample_1460 Sample_1461 Sample_1462 Sample_1463 Sample_1464
#> "031" "01" "01" "033" "031" "01" "031" "031"
#> Sample_1465 Sample_1466 Sample_1467 Sample_1468 Sample_1469 Sample_1470 Sample_1471 Sample_1472
#> "01" "01" "01" "031" "01" "01" "01" "01"
#> Sample_1473 Sample_1474 Sample_1475 Sample_1476 Sample_1477 Sample_1478 Sample_1479 Sample_1480
#> "01" "01" "01" "01" "01" "01" "01" "031"
#> Sample_1481 Sample_1482 Sample_1483 Sample_1484 Sample_1485 Sample_1486 Sample_1487 Sample_1488
#> "01" "01" "01" "01" "031" "033" "01" "031"
#> Sample_1489 Sample_1490 Sample_1491 Sample_1492 Sample_1493 Sample_1494 Sample_1495 Sample_1496
#> "01" "01" "01" "01" "01" "033" "01" "01"
#> Sample_1497 Sample_1498 Sample_1499 Sample_1500 Sample_1501 Sample_1502 Sample_1503 Sample_1504
#> "01" "01" "01" "01" "01" "031" "031" "033"
#> Sample_1505 Sample_1506 Sample_1507 Sample_1508 Sample_1509 Sample_1510 Sample_1511 Sample_1512
#> "033" "031" "031" "031" "01" "01" "031" "031"
#> Sample_1513 Sample_1514 Sample_1515 Sample_1516 Sample_1517 Sample_1518 Sample_1519 Sample_1520
#> "031" "01" "031" "01" "022" "022" "022" "023"
#> Sample_1521 Sample_1522 Sample_1523 Sample_1524 Sample_1525 Sample_1526 Sample_1527 Sample_1528
#> "023" "01" "01" "023" "01" "01" "021" "021"
#> Sample_1529 Sample_1530 Sample_1531 Sample_1532 Sample_1533 Sample_1534 Sample_1535 Sample_1536
#> "021" "023" "023" "023" "021" "021" "022" "022"
#> Sample_1537 Sample_1538 Sample_1539 Sample_1540 Sample_1541 Sample_1542 Sample_1543 Sample_1544
#> "022" "021" "021" "01" "01" "021" "023" "01"
#> Sample_1545 Sample_1546 Sample_1547 Sample_1548 Sample_1549 Sample_1550 Sample_1551 Sample_1552
#> "01" "021" "01" "023" "032" "023" "023" "023"
#> Sample_1553 Sample_1554 Sample_1555 Sample_1556 Sample_1557 Sample_1558 Sample_1559 Sample_1560
#> "032" "022" "023" "01" "01" "01" "023" "01"
#> Sample_1561 Sample_1562 Sample_1563 Sample_1564 Sample_1565 Sample_1566 Sample_1567 Sample_1568
#> "023" "01" "01" "01" "01" "01" "01" "01"
#> Sample_1569 Sample_1570 Sample_1571 Sample_1572 Sample_1573 Sample_1574 Sample_1575 Sample_1576
#> "031" "021" "01" "01" "01" "023" "023" "01"
#> Sample_1577 Sample_1578 Sample_1579 Sample_1580 Sample_1581 Sample_1582 Sample_1583 Sample_1584
#> "01" "01" "01" "01" "032" "023" "01" "01"
#> Sample_1585 Sample_1586 Sample_1587 Sample_1588 Sample_1589 Sample_1590 Sample_1591 Sample_1592
#> "023" "023" "023" "022" "023" "01" "01" "023"
#> Sample_1593 Sample_1594 Sample_1595 Sample_1596 Sample_1597 Sample_1598 Sample_1599 Sample_1600
#> "023" "023" "023" "023" "032" "031" "023" "01"
get_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 2253))
#> Sample_1 Sample_2 Sample_3 Sample_4 Sample_5 Sample_6 Sample_7 Sample_8
#> NA NA NA NA NA NA NA NA
#> Sample_9 Sample_10 Sample_11 Sample_12 Sample_13 Sample_14 Sample_15 Sample_16
#> NA NA NA NA NA NA NA NA
#> Sample_17 Sample_18 Sample_19 Sample_20 Sample_21 Sample_22 Sample_23 Sample_24
#> NA NA NA NA NA NA NA NA
#> Sample_25 Sample_26 Sample_27 Sample_28 Sample_29 Sample_30 Sample_31 Sample_32
#> NA NA NA NA NA NA NA NA
#> Sample_33 Sample_34 Sample_35 Sample_36 Sample_37 Sample_38 Sample_39 Sample_40
#> NA NA NA NA NA NA NA NA
#> Sample_41 Sample_42 Sample_43 Sample_44 Sample_45 Sample_46 Sample_47 Sample_48
#> NA NA NA NA NA NA NA NA
#> Sample_49 Sample_50 Sample_51 Sample_52 Sample_53 Sample_54 Sample_55 Sample_56
#> NA NA NA NA NA NA NA NA
#> Sample_57 Sample_58 Sample_59 Sample_60 Sample_61 Sample_62 Sample_63 Sample_64
#> NA NA NA NA NA NA NA NA
#> Sample_65 Sample_66 Sample_67 Sample_68 Sample_69 Sample_70 Sample_71 Sample_72
#> NA NA NA NA NA NA NA NA
#> Sample_73 Sample_74 Sample_75 Sample_76 Sample_77 Sample_78 Sample_79 Sample_80
#> NA NA NA NA NA NA NA NA
#> Sample_81 Sample_82 Sample_83 Sample_84 Sample_85 Sample_86 Sample_87 Sample_88
#> NA NA NA NA NA NA NA NA
#> Sample_89 Sample_90 Sample_91 Sample_92 Sample_93 Sample_94 Sample_95 Sample_96
#> NA NA NA NA NA NA NA NA
#> Sample_97 Sample_98 Sample_99 Sample_100 Sample_101 Sample_102 Sample_103 Sample_104
#> NA NA NA NA NA NA NA NA
#> Sample_105 Sample_106 Sample_107 Sample_108 Sample_109 Sample_110 Sample_111 Sample_112
#> NA NA NA NA NA NA NA NA
#> Sample_113 Sample_114 Sample_115 Sample_116 Sample_117 Sample_118 Sample_119 Sample_120
#> NA NA NA NA NA NA NA NA
#> Sample_121 Sample_122 Sample_123 Sample_124 Sample_125 Sample_126 Sample_127 Sample_128
#> NA NA NA NA NA NA NA NA
#> Sample_129 Sample_130 Sample_131 Sample_132 Sample_133 Sample_134 Sample_135 Sample_136
#> NA NA NA NA NA NA NA NA
#> Sample_137 Sample_138 Sample_139 Sample_140 Sample_141 Sample_142 Sample_143 Sample_144
#> NA NA NA NA NA NA NA NA
#> Sample_145 Sample_146 Sample_147 Sample_148 Sample_149 Sample_150 Sample_151 Sample_152
#> NA NA NA NA NA NA NA NA
#> Sample_153 Sample_154 Sample_155 Sample_156 Sample_157 Sample_158 Sample_159 Sample_160
#> NA NA NA NA NA NA NA NA
#> Sample_161 Sample_162 Sample_163 Sample_164 Sample_165 Sample_166 Sample_167 Sample_168
#> NA NA NA NA NA NA NA NA
#> Sample_169 Sample_170 Sample_171 Sample_172 Sample_173 Sample_174 Sample_175 Sample_176
#> NA NA NA NA NA NA NA NA
#> Sample_177 Sample_178 Sample_179 Sample_180 Sample_181 Sample_182 Sample_183 Sample_184
#> NA NA NA NA NA NA NA NA
#> Sample_185 Sample_186 Sample_187 Sample_188 Sample_189 Sample_190 Sample_191 Sample_192
#> NA NA NA NA NA NA NA NA
#> Sample_193 Sample_194 Sample_195 Sample_196 Sample_197 Sample_198 Sample_199 Sample_200
#> NA NA NA NA NA NA NA NA
#> Sample_201 Sample_202 Sample_203 Sample_204 Sample_205 Sample_206 Sample_207 Sample_208
#> NA NA NA NA NA NA NA NA
#> Sample_209 Sample_210 Sample_211 Sample_212 Sample_213 Sample_214 Sample_215 Sample_216
#> NA NA NA NA NA NA NA NA
#> Sample_217 Sample_218 Sample_219 Sample_220 Sample_221 Sample_222 Sample_223 Sample_224
#> NA NA NA NA NA NA NA NA
#> Sample_225 Sample_226 Sample_227 Sample_228 Sample_229 Sample_230 Sample_231 Sample_232
#> NA NA NA NA NA NA NA NA
#> Sample_233 Sample_234 Sample_235 Sample_236 Sample_237 Sample_238 Sample_239 Sample_240
#> NA NA NA NA NA NA NA NA
#> Sample_241 Sample_242 Sample_243 Sample_244 Sample_245 Sample_246 Sample_247 Sample_248
#> NA NA NA NA NA NA NA NA
#> Sample_249 Sample_250 Sample_251 Sample_252 Sample_253 Sample_254 Sample_255 Sample_256
#> NA NA NA NA NA NA NA NA
#> Sample_257 Sample_258 Sample_259 Sample_260 Sample_261 Sample_262 Sample_263 Sample_264
#> NA NA NA NA NA NA NA NA
#> Sample_265 Sample_266 Sample_267 Sample_268 Sample_269 Sample_270 Sample_271 Sample_272
#> NA NA NA NA NA NA NA NA
#> Sample_273 Sample_274 Sample_275 Sample_276 Sample_277 Sample_278 Sample_279 Sample_280
#> NA NA NA NA NA NA NA NA
#> Sample_281 Sample_282 Sample_283 Sample_284 Sample_285 Sample_286 Sample_287 Sample_288
#> NA NA NA NA NA NA NA NA
#> Sample_289 Sample_290 Sample_291 Sample_292 Sample_293 Sample_294 Sample_295 Sample_296
#> NA NA NA NA NA NA NA NA
#> Sample_297 Sample_298 Sample_299 Sample_300 Sample_301 Sample_302 Sample_303 Sample_304
#> NA NA NA NA NA NA NA NA
#> Sample_305 Sample_306 Sample_307 Sample_308 Sample_309 Sample_310 Sample_311 Sample_312
#> NA NA NA NA NA NA NA NA
#> Sample_313 Sample_314 Sample_315 Sample_316 Sample_317 Sample_318 Sample_319 Sample_320
#> NA NA NA NA NA NA NA NA
#> Sample_321 Sample_322 Sample_323 Sample_324 Sample_325 Sample_326 Sample_327 Sample_328
#> NA NA NA NA NA NA NA NA
#> Sample_329 Sample_330 Sample_331 Sample_332 Sample_333 Sample_334 Sample_335 Sample_336
#> NA NA NA NA NA NA NA NA
#> Sample_337 Sample_338 Sample_339 Sample_340 Sample_341 Sample_342 Sample_343 Sample_344
#> NA NA NA NA NA NA NA NA
#> Sample_345 Sample_346 Sample_347 Sample_348 Sample_349 Sample_350 Sample_351 Sample_352
#> NA NA NA NA NA NA NA NA
#> Sample_353 Sample_354 Sample_355 Sample_356 Sample_357 Sample_358 Sample_359 Sample_360
#> NA NA NA NA NA NA NA NA
#> Sample_361 Sample_362 Sample_363 Sample_364 Sample_365 Sample_366 Sample_367 Sample_368
#> NA NA NA NA NA NA NA NA
#> Sample_369 Sample_370 Sample_371 Sample_372 Sample_373 Sample_374 Sample_375 Sample_376
#> NA NA NA NA NA NA NA NA
#> Sample_377 Sample_378 Sample_379 Sample_380 Sample_381 Sample_382 Sample_383 Sample_384
#> NA NA NA NA NA NA NA NA
#> Sample_385 Sample_386 Sample_387 Sample_388 Sample_389 Sample_390 Sample_391 Sample_392
#> NA NA NA NA NA NA NA NA
#> Sample_393 Sample_394 Sample_395 Sample_396 Sample_397 Sample_398 Sample_399 Sample_400
#> NA NA NA NA NA NA NA NA
#> Sample_401 Sample_402 Sample_403 Sample_404 Sample_405 Sample_406 Sample_407 Sample_408
#> NA NA NA NA NA NA NA NA
#> Sample_409 Sample_410 Sample_411 Sample_412 Sample_413 Sample_414 Sample_415 Sample_416
#> NA NA NA NA NA NA NA NA
#> Sample_417 Sample_418 Sample_419 Sample_420 Sample_421 Sample_422 Sample_423 Sample_424
#> NA NA NA NA NA NA NA NA
#> Sample_425 Sample_426 Sample_427 Sample_428 Sample_429 Sample_430 Sample_431 Sample_432
#> NA NA NA NA NA NA NA NA
#> Sample_433 Sample_434 Sample_435 Sample_436 Sample_437 Sample_438 Sample_439 Sample_440
#> NA NA NA NA NA NA NA NA
#> Sample_441 Sample_442 Sample_443 Sample_444 Sample_445 Sample_446 Sample_447 Sample_448
#> NA NA NA NA NA NA NA NA
#> Sample_449 Sample_450 Sample_451 Sample_452 Sample_453 Sample_454 Sample_455 Sample_456
#> NA NA NA NA NA NA NA NA
#> Sample_457 Sample_458 Sample_459 Sample_460 Sample_461 Sample_462 Sample_463 Sample_464
#> NA NA NA NA NA NA NA NA
#> Sample_465 Sample_466 Sample_467 Sample_468 Sample_469 Sample_470 Sample_471 Sample_472
#> NA NA NA NA NA NA NA NA
#> Sample_473 Sample_474 Sample_475 Sample_476 Sample_477 Sample_478 Sample_479 Sample_480
#> NA NA NA NA NA NA NA NA
#> Sample_481 Sample_482 Sample_483 Sample_484 Sample_485 Sample_486 Sample_487 Sample_488
#> NA NA NA NA NA NA NA NA
#> Sample_489 Sample_490 Sample_491 Sample_492 Sample_493 Sample_494 Sample_495 Sample_496
#> NA NA NA NA NA NA NA NA
#> Sample_497 Sample_498 Sample_499 Sample_500 Sample_501 Sample_502 Sample_503 Sample_504
#> NA NA NA NA NA NA NA NA
#> Sample_505 Sample_506 Sample_507 Sample_508 Sample_509 Sample_510 Sample_511 Sample_512
#> NA NA NA NA NA NA NA NA
#> Sample_513 Sample_514 Sample_515 Sample_516 Sample_517 Sample_518 Sample_519 Sample_520
#> NA NA NA NA NA NA NA NA
#> Sample_521 Sample_522 Sample_523 Sample_524 Sample_525 Sample_526 Sample_527 Sample_528
#> NA NA NA NA NA NA NA NA
#> Sample_529 Sample_530 Sample_531 Sample_532 Sample_533 Sample_534 Sample_535 Sample_536
#> NA NA NA NA NA NA NA NA
#> Sample_537 Sample_538 Sample_539 Sample_540 Sample_541 Sample_542 Sample_543 Sample_544
#> NA NA NA NA NA NA NA NA
#> Sample_545 Sample_546 Sample_547 Sample_548 Sample_549 Sample_550 Sample_551 Sample_552
#> NA NA NA NA NA NA NA NA
#> Sample_553 Sample_554 Sample_555 Sample_556 Sample_557 Sample_558 Sample_559 Sample_560
#> NA NA NA NA NA NA NA NA
#> Sample_561 Sample_562 Sample_563 Sample_564 Sample_565 Sample_566 Sample_567 Sample_568
#> NA NA NA NA NA NA NA NA
#> Sample_569 Sample_570 Sample_571 Sample_572 Sample_573 Sample_574 Sample_575 Sample_576
#> NA NA NA NA NA NA NA NA
#> Sample_577 Sample_578 Sample_579 Sample_580 Sample_581 Sample_582 Sample_583 Sample_584
#> NA NA NA NA NA NA NA NA
#> Sample_585 Sample_586 Sample_587 Sample_588 Sample_589 Sample_590 Sample_591 Sample_592
#> NA NA NA NA NA NA NA NA
#> Sample_593 Sample_594 Sample_595 Sample_596 Sample_597 Sample_598 Sample_599 Sample_600
#> NA NA NA NA NA NA NA NA
#> Sample_601 Sample_602 Sample_603 Sample_604 Sample_605 Sample_606 Sample_607 Sample_608
#> NA NA NA NA NA NA NA NA
#> Sample_609 Sample_610 Sample_611 Sample_612 Sample_613 Sample_614 Sample_615 Sample_616
#> NA NA NA NA NA NA NA NA
#> Sample_617 Sample_618 Sample_619 Sample_620 Sample_621 Sample_622 Sample_623 Sample_624
#> NA NA NA NA NA NA NA NA
#> Sample_625 Sample_626 Sample_627 Sample_628 Sample_629 Sample_630 Sample_631 Sample_632
#> NA NA NA NA NA NA NA NA
#> Sample_633 Sample_634 Sample_635 Sample_636 Sample_637 Sample_638 Sample_639 Sample_640
#> NA NA NA NA NA NA NA NA
#> Sample_641 Sample_642 Sample_643 Sample_644 Sample_645 Sample_646 Sample_647 Sample_648
#> NA NA NA NA NA NA NA NA
#> Sample_649 Sample_650 Sample_651 Sample_652 Sample_653 Sample_654 Sample_655 Sample_656
#> NA NA NA NA NA NA NA NA
#> Sample_657 Sample_658 Sample_659 Sample_660 Sample_661 Sample_662 Sample_663 Sample_664
#> NA NA NA NA NA NA NA NA
#> Sample_665 Sample_666 Sample_667 Sample_668 Sample_669 Sample_670 Sample_671 Sample_672
#> NA NA NA NA NA NA NA NA
#> Sample_673 Sample_674 Sample_675 Sample_676 Sample_677 Sample_678 Sample_679 Sample_680
#> NA NA NA NA NA NA NA NA
#> Sample_681 Sample_682 Sample_683 Sample_684 Sample_685 Sample_686 Sample_687 Sample_688
#> NA NA NA NA NA NA NA NA
#> Sample_689 Sample_690 Sample_691 Sample_692 Sample_693 Sample_694 Sample_695 Sample_696
#> NA NA NA NA NA NA NA NA
#> Sample_697 Sample_698 Sample_699 Sample_700 Sample_701 Sample_702 Sample_703 Sample_704
#> NA NA NA NA NA NA NA NA
#> Sample_705 Sample_706 Sample_707 Sample_708 Sample_709 Sample_710 Sample_711 Sample_712
#> NA NA NA NA NA NA NA NA
#> Sample_713 Sample_714 Sample_715 Sample_716 Sample_717 Sample_718 Sample_719 Sample_720
#> NA NA NA NA NA NA NA NA
#> Sample_721 Sample_722 Sample_723 Sample_724 Sample_725 Sample_726 Sample_727 Sample_728
#> NA NA NA NA NA NA NA NA
#> Sample_729 Sample_730 Sample_731 Sample_732 Sample_733 Sample_734 Sample_735 Sample_736
#> NA NA NA NA NA NA NA NA
#> Sample_737 Sample_738 Sample_739 Sample_740 Sample_741 Sample_742 Sample_743 Sample_744
#> NA NA NA NA NA NA NA NA
#> Sample_745 Sample_746 Sample_747 Sample_748 Sample_749 Sample_750 Sample_751 Sample_752
#> NA NA NA NA NA NA NA NA
#> Sample_753 Sample_754 Sample_755 Sample_756 Sample_757 Sample_758 Sample_759 Sample_760
#> NA NA NA NA NA NA NA NA
#> Sample_761 Sample_762 Sample_763 Sample_764 Sample_765 Sample_766 Sample_767 Sample_768
#> NA NA NA NA NA NA NA NA
#> Sample_769 Sample_770 Sample_771 Sample_772 Sample_773 Sample_774 Sample_775 Sample_776
#> NA NA NA NA NA NA NA NA
#> Sample_777 Sample_778 Sample_779 Sample_780 Sample_781 Sample_782 Sample_783 Sample_784
#> NA NA NA NA NA NA NA NA
#> Sample_785 Sample_786 Sample_787 Sample_788 Sample_789 Sample_790 Sample_791 Sample_792
#> NA NA NA NA NA NA NA NA
#> Sample_793 Sample_794 Sample_795 Sample_796 Sample_797 Sample_798 Sample_799 Sample_800
#> NA NA NA NA NA NA NA NA
#> Sample_801 Sample_802 Sample_803 Sample_804 Sample_805 Sample_806 Sample_807 Sample_808
#> NA NA NA NA NA NA NA NA
#> Sample_809 Sample_810 Sample_811 Sample_812 Sample_813 Sample_814 Sample_815 Sample_816
#> NA NA NA NA NA NA NA NA
#> Sample_817 Sample_818 Sample_819 Sample_820 Sample_821 Sample_822 Sample_823 Sample_824
#> NA NA NA NA NA NA NA NA
#> Sample_825 Sample_826 Sample_827 Sample_828 Sample_829 Sample_830 Sample_831 Sample_832
#> NA NA NA NA NA NA NA NA
#> Sample_833 Sample_834 Sample_835 Sample_836 Sample_837 Sample_838 Sample_839 Sample_840
#> NA NA NA NA NA NA NA NA
#> Sample_841 Sample_842 Sample_843 Sample_844 Sample_845 Sample_846 Sample_847 Sample_848
#> NA NA NA NA NA NA NA NA
#> Sample_849 Sample_850 Sample_851 Sample_852 Sample_853 Sample_854 Sample_855 Sample_856
#> NA NA NA NA NA NA NA NA
#> Sample_857 Sample_858 Sample_859 Sample_860 Sample_861 Sample_862 Sample_863 Sample_864
#> NA NA NA NA NA NA NA NA
#> Sample_865 Sample_866 Sample_867 Sample_868 Sample_869 Sample_870 Sample_871 Sample_872
#> NA NA NA NA NA NA NA NA
#> Sample_873 Sample_874 Sample_875 Sample_876 Sample_877 Sample_878 Sample_879 Sample_880
#> NA NA NA NA NA NA NA NA
#> Sample_881 Sample_882 Sample_883 Sample_884 Sample_885 Sample_886 Sample_887 Sample_888
#> NA NA NA NA NA NA NA NA
#> Sample_889 Sample_890 Sample_891 Sample_892 Sample_893 Sample_894 Sample_895 Sample_896
#> NA NA NA NA NA NA NA NA
#> Sample_897 Sample_898 Sample_899 Sample_900 Sample_901 Sample_902 Sample_903 Sample_904
#> NA NA NA NA NA NA NA NA
#> Sample_905 Sample_906 Sample_907 Sample_908 Sample_909 Sample_910 Sample_911 Sample_912
#> NA NA NA NA NA NA NA NA
#> Sample_913 Sample_914 Sample_915 Sample_916 Sample_917 Sample_918 Sample_919 Sample_920
#> NA NA NA NA NA NA NA NA
#> Sample_921 Sample_922 Sample_923 Sample_924 Sample_925 Sample_926 Sample_927 Sample_928
#> NA NA NA NA NA NA NA NA
#> Sample_929 Sample_930 Sample_931 Sample_932 Sample_933 Sample_934 Sample_935 Sample_936
#> NA NA NA NA NA NA NA NA
#> Sample_937 Sample_938 Sample_939 Sample_940 Sample_941 Sample_942 Sample_943 Sample_944
#> NA NA NA NA NA NA NA NA
#> Sample_945 Sample_946 Sample_947 Sample_948 Sample_949 Sample_950 Sample_951 Sample_952
#> NA NA NA NA NA NA NA NA
#> Sample_953 Sample_954 Sample_955 Sample_956 Sample_957 Sample_958 Sample_959 Sample_960
#> NA NA NA NA NA NA NA NA
#> Sample_961 Sample_962 Sample_963 Sample_964 Sample_965 Sample_966 Sample_967 Sample_968
#> NA NA NA NA NA NA NA NA
#> Sample_969 Sample_970 Sample_971 Sample_972 Sample_973 Sample_974 Sample_975 Sample_976
#> NA NA NA NA NA NA NA NA
#> Sample_977 Sample_978 Sample_979 Sample_980 Sample_981 Sample_982 Sample_983 Sample_984
#> NA NA NA NA NA NA NA NA
#> Sample_985 Sample_986 Sample_987 Sample_988 Sample_989 Sample_990 Sample_991 Sample_992
#> NA NA NA NA NA NA NA NA
#> Sample_993 Sample_994 Sample_995 Sample_996 Sample_997 Sample_998 Sample_999 Sample_1000
#> NA NA NA NA NA NA NA NA
#> Sample_1001 Sample_1002 Sample_1003 Sample_1004 Sample_1005 Sample_1006 Sample_1007 Sample_1008
#> NA NA NA NA NA NA NA NA
#> Sample_1009 Sample_1010 Sample_1011 Sample_1012 Sample_1013 Sample_1014 Sample_1015 Sample_1016
#> NA NA NA NA NA NA NA NA
#> Sample_1017 Sample_1018 Sample_1019 Sample_1020 Sample_1021 Sample_1022 Sample_1023 Sample_1024
#> NA NA NA NA NA NA NA NA
#> Sample_1025 Sample_1026 Sample_1027 Sample_1028 Sample_1029 Sample_1030 Sample_1031 Sample_1032
#> NA NA NA NA NA NA NA NA
#> Sample_1033 Sample_1034 Sample_1035 Sample_1036 Sample_1037 Sample_1038 Sample_1039 Sample_1040
#> NA NA NA NA NA NA NA NA
#> Sample_1041 Sample_1042 Sample_1043 Sample_1044 Sample_1045 Sample_1046 Sample_1047 Sample_1048
#> NA NA NA NA NA NA NA NA
#> Sample_1049 Sample_1050 Sample_1051 Sample_1052 Sample_1053 Sample_1054 Sample_1055 Sample_1056
#> NA NA NA NA NA NA NA NA
#> Sample_1057 Sample_1058 Sample_1059 Sample_1060 Sample_1061 Sample_1062 Sample_1063 Sample_1064
#> NA NA NA NA NA NA NA NA
#> Sample_1065 Sample_1066 Sample_1067 Sample_1068 Sample_1069 Sample_1070 Sample_1071 Sample_1072
#> NA NA NA NA NA NA NA NA
#> Sample_1073 Sample_1074 Sample_1075 Sample_1076 Sample_1077 Sample_1078 Sample_1079 Sample_1080
#> NA NA NA NA NA NA NA NA
#> Sample_1081 Sample_1082 Sample_1083 Sample_1084 Sample_1085 Sample_1086 Sample_1087 Sample_1088
#> NA NA NA NA NA NA NA NA
#> Sample_1089 Sample_1090 Sample_1091 Sample_1092 Sample_1093 Sample_1094 Sample_1095 Sample_1096
#> NA NA NA NA NA NA NA NA
#> Sample_1097 Sample_1098 Sample_1099 Sample_1100 Sample_1101 Sample_1102 Sample_1103 Sample_1104
#> NA NA NA NA NA NA NA NA
#> Sample_1105 Sample_1106 Sample_1107 Sample_1108 Sample_1109 Sample_1110 Sample_1111 Sample_1112
#> NA NA NA NA NA NA NA NA
#> Sample_1113 Sample_1114 Sample_1115 Sample_1116 Sample_1117 Sample_1118 Sample_1119 Sample_1120
#> NA NA NA NA NA NA NA NA
#> Sample_1121 Sample_1122 Sample_1123 Sample_1124 Sample_1125 Sample_1126 Sample_1127 Sample_1128
#> NA NA NA NA NA NA NA NA
#> Sample_1129 Sample_1130 Sample_1131 Sample_1132 Sample_1133 Sample_1134 Sample_1135 Sample_1136
#> NA NA NA NA NA NA NA NA
#> Sample_1137 Sample_1138 Sample_1139 Sample_1140 Sample_1141 Sample_1142 Sample_1143 Sample_1144
#> NA NA NA NA NA NA NA NA
#> Sample_1145 Sample_1146 Sample_1147 Sample_1148 Sample_1149 Sample_1150 Sample_1151 Sample_1152
#> NA NA NA NA NA NA NA NA
#> Sample_1153 Sample_1154 Sample_1155 Sample_1156 Sample_1157 Sample_1158 Sample_1159 Sample_1160
#> NA NA NA NA NA NA NA NA
#> Sample_1161 Sample_1162 Sample_1163 Sample_1164 Sample_1165 Sample_1166 Sample_1167 Sample_1168
#> NA NA NA NA NA NA NA NA
#> Sample_1169 Sample_1170 Sample_1171 Sample_1172 Sample_1173 Sample_1174 Sample_1175 Sample_1176
#> NA NA NA NA NA NA NA NA
#> Sample_1177 Sample_1178 Sample_1179 Sample_1180 Sample_1181 Sample_1182 Sample_1183 Sample_1184
#> NA NA NA NA NA NA NA NA
#> Sample_1185 Sample_1186 Sample_1187 Sample_1188 Sample_1189 Sample_1190 Sample_1191 Sample_1192
#> NA NA NA NA NA NA NA NA
#> Sample_1193 Sample_1194 Sample_1195 Sample_1196 Sample_1197 Sample_1198 Sample_1199 Sample_1200
#> NA NA NA NA NA NA NA NA
#> Sample_1201 Sample_1202 Sample_1203 Sample_1204 Sample_1205 Sample_1206 Sample_1207 Sample_1208
#> NA NA NA NA NA NA NA NA
#> Sample_1209 Sample_1210 Sample_1211 Sample_1212 Sample_1213 Sample_1214 Sample_1215 Sample_1216
#> NA NA NA NA NA NA NA NA
#> Sample_1217 Sample_1218 Sample_1219 Sample_1220 Sample_1221 Sample_1222 Sample_1223 Sample_1224
#> NA NA NA NA NA NA NA NA
#> Sample_1225 Sample_1226 Sample_1227 Sample_1228 Sample_1229 Sample_1230 Sample_1231 Sample_1232
#> NA NA NA NA NA NA NA NA
#> Sample_1233 Sample_1234 Sample_1235 Sample_1236 Sample_1237 Sample_1238 Sample_1239 Sample_1240
#> NA NA NA NA NA NA NA NA
#> Sample_1241 Sample_1242 Sample_1243 Sample_1244 Sample_1245 Sample_1246 Sample_1247 Sample_1248
#> NA NA NA NA NA NA NA NA
#> Sample_1249 Sample_1250 Sample_1251 Sample_1252 Sample_1253 Sample_1254 Sample_1255 Sample_1256
#> NA NA NA NA NA NA NA NA
#> Sample_1257 Sample_1258 Sample_1259 Sample_1260 Sample_1261 Sample_1262 Sample_1263 Sample_1264
#> NA NA NA NA NA NA NA NA
#> Sample_1265 Sample_1266 Sample_1267 Sample_1268 Sample_1269 Sample_1270 Sample_1271 Sample_1272
#> NA NA NA NA NA NA NA NA
#> Sample_1273 Sample_1274 Sample_1275 Sample_1276 Sample_1277 Sample_1278 Sample_1279 Sample_1280
#> NA NA NA NA NA NA NA NA
#> Sample_1281 Sample_1282 Sample_1283 Sample_1284 Sample_1285 Sample_1286 Sample_1287 Sample_1288
#> NA NA NA NA NA NA NA NA
#> Sample_1289 Sample_1290 Sample_1291 Sample_1292 Sample_1293 Sample_1294 Sample_1295 Sample_1296
#> NA NA NA NA NA NA NA NA
#> Sample_1297 Sample_1298 Sample_1299 Sample_1300 Sample_1301 Sample_1302 Sample_1303 Sample_1304
#> NA NA NA NA NA NA NA NA
#> Sample_1305 Sample_1306 Sample_1307 Sample_1308 Sample_1309 Sample_1310 Sample_1311 Sample_1312
#> NA NA NA NA NA NA NA NA
#> Sample_1313 Sample_1314 Sample_1315 Sample_1316 Sample_1317 Sample_1318 Sample_1319 Sample_1320
#> NA NA NA NA NA NA NA NA
#> Sample_1321 Sample_1322 Sample_1323 Sample_1324 Sample_1325 Sample_1326 Sample_1327 Sample_1328
#> NA NA NA NA NA NA NA NA
#> Sample_1329 Sample_1330 Sample_1331 Sample_1332 Sample_1333 Sample_1334 Sample_1335 Sample_1336
#> NA NA NA NA NA NA NA NA
#> Sample_1337 Sample_1338 Sample_1339 Sample_1340 Sample_1341 Sample_1342 Sample_1343 Sample_1344
#> NA NA NA NA NA NA NA NA
#> Sample_1345 Sample_1346 Sample_1347 Sample_1348 Sample_1349 Sample_1350 Sample_1351 Sample_1352
#> NA NA NA NA NA NA NA NA
#> Sample_1353 Sample_1354 Sample_1355 Sample_1356 Sample_1357 Sample_1358 Sample_1359 Sample_1360
#> NA NA NA NA NA NA NA NA
#> Sample_1361 Sample_1362 Sample_1363 Sample_1364 Sample_1365 Sample_1366 Sample_1367 Sample_1368
#> NA NA NA NA NA NA NA NA
#> Sample_1369 Sample_1370 Sample_1371 Sample_1372 Sample_1373 Sample_1374 Sample_1375 Sample_1376
#> NA NA NA NA NA NA NA NA
#> Sample_1377 Sample_1378 Sample_1379 Sample_1380 Sample_1381 Sample_1382 Sample_1383 Sample_1384
#> NA NA NA NA NA NA NA NA
#> Sample_1385 Sample_1386 Sample_1387 Sample_1388 Sample_1389 Sample_1390 Sample_1391 Sample_1392
#> NA NA NA NA NA NA NA NA
#> Sample_1393 Sample_1394 Sample_1395 Sample_1396 Sample_1397 Sample_1398 Sample_1399 Sample_1400
#> NA NA NA NA NA NA NA NA
#> Sample_1401 Sample_1402 Sample_1403 Sample_1404 Sample_1405 Sample_1406 Sample_1407 Sample_1408
#> NA NA NA NA NA NA NA NA
#> Sample_1409 Sample_1410 Sample_1411 Sample_1412 Sample_1413 Sample_1414 Sample_1415 Sample_1416
#> NA NA NA NA NA NA NA NA
#> Sample_1417 Sample_1418 Sample_1419 Sample_1420 Sample_1421 Sample_1422 Sample_1423 Sample_1424
#> NA NA NA NA NA NA NA NA
#> Sample_1425 Sample_1426 Sample_1427 Sample_1428 Sample_1429 Sample_1430 Sample_1431 Sample_1432
#> NA NA NA NA NA NA NA NA
#> Sample_1433 Sample_1434 Sample_1435 Sample_1436 Sample_1437 Sample_1438 Sample_1439 Sample_1440
#> NA NA NA NA NA NA NA NA
#> Sample_1441 Sample_1442 Sample_1443 Sample_1444 Sample_1445 Sample_1446 Sample_1447 Sample_1448
#> NA NA NA NA NA NA NA NA
#> Sample_1449 Sample_1450 Sample_1451 Sample_1452 Sample_1453 Sample_1454 Sample_1455 Sample_1456
#> NA NA NA NA NA NA NA NA
#> Sample_1457 Sample_1458 Sample_1459 Sample_1460 Sample_1461 Sample_1462 Sample_1463 Sample_1464
#> NA NA NA NA NA NA NA NA
#> Sample_1465 Sample_1466 Sample_1467 Sample_1468 Sample_1469 Sample_1470 Sample_1471 Sample_1472
#> NA NA NA NA NA NA NA NA
#> Sample_1473 Sample_1474 Sample_1475 Sample_1476 Sample_1477 Sample_1478 Sample_1479 Sample_1480
#> NA NA NA NA NA NA NA NA
#> Sample_1481 Sample_1482 Sample_1483 Sample_1484 Sample_1485 Sample_1486 Sample_1487 Sample_1488
#> NA NA NA NA NA NA NA NA
#> Sample_1489 Sample_1490 Sample_1491 Sample_1492 Sample_1493 Sample_1494 Sample_1495 Sample_1496
#> NA NA NA NA NA NA NA NA
#> Sample_1497 Sample_1498 Sample_1499 Sample_1500 Sample_1501 Sample_1502 Sample_1503 Sample_1504
#> NA NA NA NA NA NA NA NA
#> Sample_1505 Sample_1506 Sample_1507 Sample_1508 Sample_1509 Sample_1510 Sample_1511 Sample_1512
#> NA NA NA NA NA NA NA NA
#> Sample_1513 Sample_1514 Sample_1515 Sample_1516 Sample_1517 Sample_1518 Sample_1519 Sample_1520
#> NA NA NA NA NA NA NA NA
#> Sample_1521 Sample_1522 Sample_1523 Sample_1524 Sample_1525 Sample_1526 Sample_1527 Sample_1528
#> NA NA NA NA NA NA NA NA
#> Sample_1529 Sample_1530 Sample_1531 Sample_1532 Sample_1533 Sample_1534 Sample_1535 Sample_1536
#> NA NA NA NA NA NA NA NA
#> Sample_1537 Sample_1538 Sample_1539 Sample_1540 Sample_1541 Sample_1542 Sample_1543 Sample_1544
#> NA NA NA NA NA NA NA NA
#> Sample_1545 Sample_1546 Sample_1547 Sample_1548 Sample_1549 Sample_1550 Sample_1551 Sample_1552
#> NA NA NA NA NA NA NA NA
#> Sample_1553 Sample_1554 Sample_1555 Sample_1556 Sample_1557 Sample_1558 Sample_1559 Sample_1560
#> NA NA NA NA NA NA NA NA
#> Sample_1561 Sample_1562 Sample_1563 Sample_1564 Sample_1565 Sample_1566 Sample_1567 Sample_1568
#> NA NA NA NA NA NA NA NA
#> Sample_1569 Sample_1570 Sample_1571 Sample_1572 Sample_1573 Sample_1574 Sample_1575 Sample_1576
#> NA NA NA NA NA NA NA NA
#> Sample_1577 Sample_1578 Sample_1579 Sample_1580 Sample_1581 Sample_1582 Sample_1583 Sample_1584
#> NA NA NA NA NA NA NA NA
#> Sample_1585 Sample_1586 Sample_1587 Sample_1588 Sample_1589 Sample_1590 Sample_1591 Sample_1592
#> NA NA NA NA NA NA NA NA
#> Sample_1593 Sample_1594 Sample_1595 Sample_1596 Sample_1597 Sample_1598 Sample_1599 Sample_1600
#> NA NA NA NA NA NA NA NA
get_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 2397))
#> Sample_1 Sample_2 Sample_3 Sample_4 Sample_5 Sample_6 Sample_7 Sample_8
#> NA NA NA NA NA NA NA NA
#> Sample_9 Sample_10 Sample_11 Sample_12 Sample_13 Sample_14 Sample_15 Sample_16
#> NA NA NA NA NA NA NA NA
#> Sample_17 Sample_18 Sample_19 Sample_20 Sample_21 Sample_22 Sample_23 Sample_24
#> NA NA NA NA NA NA NA NA
#> Sample_25 Sample_26 Sample_27 Sample_28 Sample_29 Sample_30 Sample_31 Sample_32
#> NA NA NA NA NA NA NA NA
#> Sample_33 Sample_34 Sample_35 Sample_36 Sample_37 Sample_38 Sample_39 Sample_40
#> NA NA NA NA NA NA NA NA
#> Sample_41 Sample_42 Sample_43 Sample_44 Sample_45 Sample_46 Sample_47 Sample_48
#> NA NA NA NA NA NA NA NA
#> Sample_49 Sample_50 Sample_51 Sample_52 Sample_53 Sample_54 Sample_55 Sample_56
#> NA NA NA NA NA NA NA NA
#> Sample_57 Sample_58 Sample_59 Sample_60 Sample_61 Sample_62 Sample_63 Sample_64
#> NA NA NA NA NA NA NA NA
#> Sample_65 Sample_66 Sample_67 Sample_68 Sample_69 Sample_70 Sample_71 Sample_72
#> NA NA NA NA NA NA NA NA
#> Sample_73 Sample_74 Sample_75 Sample_76 Sample_77 Sample_78 Sample_79 Sample_80
#> NA NA NA NA NA NA NA NA
#> Sample_81 Sample_82 Sample_83 Sample_84 Sample_85 Sample_86 Sample_87 Sample_88
#> NA NA NA NA NA NA NA NA
#> Sample_89 Sample_90 Sample_91 Sample_92 Sample_93 Sample_94 Sample_95 Sample_96
#> NA NA NA NA NA NA NA NA
#> Sample_97 Sample_98 Sample_99 Sample_100 Sample_101 Sample_102 Sample_103 Sample_104
#> NA NA NA NA NA NA NA NA
#> Sample_105 Sample_106 Sample_107 Sample_108 Sample_109 Sample_110 Sample_111 Sample_112
#> NA NA NA NA NA NA NA NA
#> Sample_113 Sample_114 Sample_115 Sample_116 Sample_117 Sample_118 Sample_119 Sample_120
#> NA NA NA NA NA NA NA NA
#> Sample_121 Sample_122 Sample_123 Sample_124 Sample_125 Sample_126 Sample_127 Sample_128
#> NA NA NA NA NA NA NA NA
#> Sample_129 Sample_130 Sample_131 Sample_132 Sample_133 Sample_134 Sample_135 Sample_136
#> NA NA NA NA NA NA NA NA
#> Sample_137 Sample_138 Sample_139 Sample_140 Sample_141 Sample_142 Sample_143 Sample_144
#> NA NA NA NA NA NA NA NA
#> Sample_145 Sample_146 Sample_147 Sample_148 Sample_149 Sample_150 Sample_151 Sample_152
#> NA NA NA NA NA NA NA NA
#> Sample_153 Sample_154 Sample_155 Sample_156 Sample_157 Sample_158 Sample_159 Sample_160
#> NA NA NA NA NA NA NA NA
#> Sample_161 Sample_162 Sample_163 Sample_164 Sample_165 Sample_166 Sample_167 Sample_168
#> NA NA NA NA NA NA NA NA
#> Sample_169 Sample_170 Sample_171 Sample_172 Sample_173 Sample_174 Sample_175 Sample_176
#> NA NA NA NA NA NA NA NA
#> Sample_177 Sample_178 Sample_179 Sample_180 Sample_181 Sample_182 Sample_183 Sample_184
#> NA NA NA NA NA NA NA NA
#> Sample_185 Sample_186 Sample_187 Sample_188 Sample_189 Sample_190 Sample_191 Sample_192
#> NA NA NA NA NA NA NA NA
#> Sample_193 Sample_194 Sample_195 Sample_196 Sample_197 Sample_198 Sample_199 Sample_200
#> NA NA NA NA NA NA NA NA
#> Sample_201 Sample_202 Sample_203 Sample_204 Sample_205 Sample_206 Sample_207 Sample_208
#> NA NA NA NA NA NA NA NA
#> Sample_209 Sample_210 Sample_211 Sample_212 Sample_213 Sample_214 Sample_215 Sample_216
#> NA NA NA NA NA NA NA NA
#> Sample_217 Sample_218 Sample_219 Sample_220 Sample_221 Sample_222 Sample_223 Sample_224
#> NA NA NA NA NA NA NA NA
#> Sample_225 Sample_226 Sample_227 Sample_228 Sample_229 Sample_230 Sample_231 Sample_232
#> NA NA NA NA NA NA NA NA
#> Sample_233 Sample_234 Sample_235 Sample_236 Sample_237 Sample_238 Sample_239 Sample_240
#> NA NA NA NA NA NA NA NA
#> Sample_241 Sample_242 Sample_243 Sample_244 Sample_245 Sample_246 Sample_247 Sample_248
#> NA NA NA NA NA NA NA NA
#> Sample_249 Sample_250 Sample_251 Sample_252 Sample_253 Sample_254 Sample_255 Sample_256
#> NA NA NA NA NA NA NA NA
#> Sample_257 Sample_258 Sample_259 Sample_260 Sample_261 Sample_262 Sample_263 Sample_264
#> NA NA NA NA NA NA NA NA
#> Sample_265 Sample_266 Sample_267 Sample_268 Sample_269 Sample_270 Sample_271 Sample_272
#> NA NA NA NA NA NA NA NA
#> Sample_273 Sample_274 Sample_275 Sample_276 Sample_277 Sample_278 Sample_279 Sample_280
#> NA NA NA NA NA NA NA NA
#> Sample_281 Sample_282 Sample_283 Sample_284 Sample_285 Sample_286 Sample_287 Sample_288
#> NA NA NA NA NA NA NA NA
#> Sample_289 Sample_290 Sample_291 Sample_292 Sample_293 Sample_294 Sample_295 Sample_296
#> NA NA NA NA NA NA NA NA
#> Sample_297 Sample_298 Sample_299 Sample_300 Sample_301 Sample_302 Sample_303 Sample_304
#> NA NA NA NA NA NA NA NA
#> Sample_305 Sample_306 Sample_307 Sample_308 Sample_309 Sample_310 Sample_311 Sample_312
#> NA NA NA NA NA NA NA NA
#> Sample_313 Sample_314 Sample_315 Sample_316 Sample_317 Sample_318 Sample_319 Sample_320
#> NA NA NA NA NA NA NA NA
#> Sample_321 Sample_322 Sample_323 Sample_324 Sample_325 Sample_326 Sample_327 Sample_328
#> NA NA NA NA NA NA NA NA
#> Sample_329 Sample_330 Sample_331 Sample_332 Sample_333 Sample_334 Sample_335 Sample_336
#> NA NA NA NA NA NA NA NA
#> Sample_337 Sample_338 Sample_339 Sample_340 Sample_341 Sample_342 Sample_343 Sample_344
#> NA NA NA NA NA NA NA NA
#> Sample_345 Sample_346 Sample_347 Sample_348 Sample_349 Sample_350 Sample_351 Sample_352
#> NA NA NA NA NA NA NA NA
#> Sample_353 Sample_354 Sample_355 Sample_356 Sample_357 Sample_358 Sample_359 Sample_360
#> NA NA NA NA NA NA NA NA
#> Sample_361 Sample_362 Sample_363 Sample_364 Sample_365 Sample_366 Sample_367 Sample_368
#> NA NA NA NA NA NA NA NA
#> Sample_369 Sample_370 Sample_371 Sample_372 Sample_373 Sample_374 Sample_375 Sample_376
#> NA NA NA NA NA NA NA NA
#> Sample_377 Sample_378 Sample_379 Sample_380 Sample_381 Sample_382 Sample_383 Sample_384
#> NA NA NA NA NA NA NA NA
#> Sample_385 Sample_386 Sample_387 Sample_388 Sample_389 Sample_390 Sample_391 Sample_392
#> NA NA NA NA NA NA NA NA
#> Sample_393 Sample_394 Sample_395 Sample_396 Sample_397 Sample_398 Sample_399 Sample_400
#> NA NA NA NA NA NA NA NA
#> Sample_401 Sample_402 Sample_403 Sample_404 Sample_405 Sample_406 Sample_407 Sample_408
#> NA NA NA NA NA NA NA NA
#> Sample_409 Sample_410 Sample_411 Sample_412 Sample_413 Sample_414 Sample_415 Sample_416
#> NA NA NA NA NA NA NA NA
#> Sample_417 Sample_418 Sample_419 Sample_420 Sample_421 Sample_422 Sample_423 Sample_424
#> NA NA NA NA NA NA NA NA
#> Sample_425 Sample_426 Sample_427 Sample_428 Sample_429 Sample_430 Sample_431 Sample_432
#> NA NA NA NA NA NA NA NA
#> Sample_433 Sample_434 Sample_435 Sample_436 Sample_437 Sample_438 Sample_439 Sample_440
#> NA NA NA NA NA NA NA NA
#> Sample_441 Sample_442 Sample_443 Sample_444 Sample_445 Sample_446 Sample_447 Sample_448
#> NA NA NA NA NA NA NA NA
#> Sample_449 Sample_450 Sample_451 Sample_452 Sample_453 Sample_454 Sample_455 Sample_456
#> NA NA NA NA NA NA NA NA
#> Sample_457 Sample_458 Sample_459 Sample_460 Sample_461 Sample_462 Sample_463 Sample_464
#> NA NA NA NA NA NA NA NA
#> Sample_465 Sample_466 Sample_467 Sample_468 Sample_469 Sample_470 Sample_471 Sample_472
#> NA NA NA NA NA NA NA NA
#> Sample_473 Sample_474 Sample_475 Sample_476 Sample_477 Sample_478 Sample_479 Sample_480
#> NA NA NA NA NA NA NA NA
#> Sample_481 Sample_482 Sample_483 Sample_484 Sample_485 Sample_486 Sample_487 Sample_488
#> NA NA NA NA NA NA NA NA
#> Sample_489 Sample_490 Sample_491 Sample_492 Sample_493 Sample_494 Sample_495 Sample_496
#> NA NA NA NA NA NA NA NA
#> Sample_497 Sample_498 Sample_499 Sample_500 Sample_501 Sample_502 Sample_503 Sample_504
#> NA NA NA NA NA NA NA NA
#> Sample_505 Sample_506 Sample_507 Sample_508 Sample_509 Sample_510 Sample_511 Sample_512
#> NA NA NA NA NA NA NA NA
#> Sample_513 Sample_514 Sample_515 Sample_516 Sample_517 Sample_518 Sample_519 Sample_520
#> NA NA NA NA NA NA NA NA
#> Sample_521 Sample_522 Sample_523 Sample_524 Sample_525 Sample_526 Sample_527 Sample_528
#> NA NA NA NA NA NA NA NA
#> Sample_529 Sample_530 Sample_531 Sample_532 Sample_533 Sample_534 Sample_535 Sample_536
#> NA NA NA NA NA NA NA NA
#> Sample_537 Sample_538 Sample_539 Sample_540 Sample_541 Sample_542 Sample_543 Sample_544
#> NA NA NA NA NA NA NA NA
#> Sample_545 Sample_546 Sample_547 Sample_548 Sample_549 Sample_550 Sample_551 Sample_552
#> NA NA NA NA NA NA NA NA
#> Sample_553 Sample_554 Sample_555 Sample_556 Sample_557 Sample_558 Sample_559 Sample_560
#> NA NA NA NA NA NA NA NA
#> Sample_561 Sample_562 Sample_563 Sample_564 Sample_565 Sample_566 Sample_567 Sample_568
#> NA NA NA NA NA NA NA NA
#> Sample_569 Sample_570 Sample_571 Sample_572 Sample_573 Sample_574 Sample_575 Sample_576
#> NA NA NA NA NA NA NA NA
#> Sample_577 Sample_578 Sample_579 Sample_580 Sample_581 Sample_582 Sample_583 Sample_584
#> NA NA NA NA NA NA NA NA
#> Sample_585 Sample_586 Sample_587 Sample_588 Sample_589 Sample_590 Sample_591 Sample_592
#> NA NA NA NA NA NA NA NA
#> Sample_593 Sample_594 Sample_595 Sample_596 Sample_597 Sample_598 Sample_599 Sample_600
#> NA NA NA NA NA NA NA NA
#> Sample_601 Sample_602 Sample_603 Sample_604 Sample_605 Sample_606 Sample_607 Sample_608
#> NA NA NA NA NA NA NA NA
#> Sample_609 Sample_610 Sample_611 Sample_612 Sample_613 Sample_614 Sample_615 Sample_616
#> NA NA NA NA NA NA NA NA
#> Sample_617 Sample_618 Sample_619 Sample_620 Sample_621 Sample_622 Sample_623 Sample_624
#> NA NA NA NA NA NA NA NA
#> Sample_625 Sample_626 Sample_627 Sample_628 Sample_629 Sample_630 Sample_631 Sample_632
#> NA NA NA NA NA NA NA NA
#> Sample_633 Sample_634 Sample_635 Sample_636 Sample_637 Sample_638 Sample_639 Sample_640
#> NA NA NA NA NA NA NA NA
#> Sample_641 Sample_642 Sample_643 Sample_644 Sample_645 Sample_646 Sample_647 Sample_648
#> NA NA NA NA NA NA NA NA
#> Sample_649 Sample_650 Sample_651 Sample_652 Sample_653 Sample_654 Sample_655 Sample_656
#> NA NA NA NA NA NA NA NA
#> Sample_657 Sample_658 Sample_659 Sample_660 Sample_661 Sample_662 Sample_663 Sample_664
#> NA NA NA NA NA NA NA NA
#> Sample_665 Sample_666 Sample_667 Sample_668 Sample_669 Sample_670 Sample_671 Sample_672
#> NA NA NA NA NA NA NA NA
#> Sample_673 Sample_674 Sample_675 Sample_676 Sample_677 Sample_678 Sample_679 Sample_680
#> NA NA NA NA NA NA NA NA
#> Sample_681 Sample_682 Sample_683 Sample_684 Sample_685 Sample_686 Sample_687 Sample_688
#> NA NA NA NA NA NA NA NA
#> Sample_689 Sample_690 Sample_691 Sample_692 Sample_693 Sample_694 Sample_695 Sample_696
#> NA NA NA NA NA NA NA NA
#> Sample_697 Sample_698 Sample_699 Sample_700 Sample_701 Sample_702 Sample_703 Sample_704
#> NA NA NA NA NA NA NA NA
#> Sample_705 Sample_706 Sample_707 Sample_708 Sample_709 Sample_710 Sample_711 Sample_712
#> NA NA NA NA NA NA NA NA
#> Sample_713 Sample_714 Sample_715 Sample_716 Sample_717 Sample_718 Sample_719 Sample_720
#> NA NA NA NA NA NA NA NA
#> Sample_721 Sample_722 Sample_723 Sample_724 Sample_725 Sample_726 Sample_727 Sample_728
#> NA NA NA NA NA NA NA NA
#> Sample_729 Sample_730 Sample_731 Sample_732 Sample_733 Sample_734 Sample_735 Sample_736
#> NA NA NA NA NA NA NA NA
#> Sample_737 Sample_738 Sample_739 Sample_740 Sample_741 Sample_742 Sample_743 Sample_744
#> NA NA NA NA NA NA NA NA
#> Sample_745 Sample_746 Sample_747 Sample_748 Sample_749 Sample_750 Sample_751 Sample_752
#> NA NA NA NA NA NA NA NA
#> Sample_753 Sample_754 Sample_755 Sample_756 Sample_757 Sample_758 Sample_759 Sample_760
#> NA NA NA NA NA NA NA NA
#> Sample_761 Sample_762 Sample_763 Sample_764 Sample_765 Sample_766 Sample_767 Sample_768
#> NA NA NA NA NA NA NA NA
#> Sample_769 Sample_770 Sample_771 Sample_772 Sample_773 Sample_774 Sample_775 Sample_776
#> NA NA NA NA NA NA NA NA
#> Sample_777 Sample_778 Sample_779 Sample_780 Sample_781 Sample_782 Sample_783 Sample_784
#> NA NA NA NA NA NA NA NA
#> Sample_785 Sample_786 Sample_787 Sample_788 Sample_789 Sample_790 Sample_791 Sample_792
#> NA NA NA NA NA NA NA NA
#> Sample_793 Sample_794 Sample_795 Sample_796 Sample_797 Sample_798 Sample_799 Sample_800
#> NA NA NA NA NA NA NA NA
#> Sample_801 Sample_802 Sample_803 Sample_804 Sample_805 Sample_806 Sample_807 Sample_808
#> NA NA NA NA NA NA NA NA
#> Sample_809 Sample_810 Sample_811 Sample_812 Sample_813 Sample_814 Sample_815 Sample_816
#> NA NA NA NA NA NA NA NA
#> Sample_817 Sample_818 Sample_819 Sample_820 Sample_821 Sample_822 Sample_823 Sample_824
#> NA NA NA NA NA NA NA NA
#> Sample_825 Sample_826 Sample_827 Sample_828 Sample_829 Sample_830 Sample_831 Sample_832
#> NA NA NA NA NA NA NA NA
#> Sample_833 Sample_834 Sample_835 Sample_836 Sample_837 Sample_838 Sample_839 Sample_840
#> NA NA NA NA NA NA NA NA
#> Sample_841 Sample_842 Sample_843 Sample_844 Sample_845 Sample_846 Sample_847 Sample_848
#> NA NA NA NA NA NA NA NA
#> Sample_849 Sample_850 Sample_851 Sample_852 Sample_853 Sample_854 Sample_855 Sample_856
#> NA NA NA NA NA NA NA NA
#> Sample_857 Sample_858 Sample_859 Sample_860 Sample_861 Sample_862 Sample_863 Sample_864
#> NA NA NA NA NA NA NA NA
#> Sample_865 Sample_866 Sample_867 Sample_868 Sample_869 Sample_870 Sample_871 Sample_872
#> NA NA NA NA NA NA NA NA
#> Sample_873 Sample_874 Sample_875 Sample_876 Sample_877 Sample_878 Sample_879 Sample_880
#> NA NA NA NA NA NA NA NA
#> Sample_881 Sample_882 Sample_883 Sample_884 Sample_885 Sample_886 Sample_887 Sample_888
#> NA NA NA NA NA NA NA NA
#> Sample_889 Sample_890 Sample_891 Sample_892 Sample_893 Sample_894 Sample_895 Sample_896
#> NA NA NA NA NA NA NA NA
#> Sample_897 Sample_898 Sample_899 Sample_900 Sample_901 Sample_902 Sample_903 Sample_904
#> NA NA NA NA NA NA NA NA
#> Sample_905 Sample_906 Sample_907 Sample_908 Sample_909 Sample_910 Sample_911 Sample_912
#> NA NA NA NA NA NA NA NA
#> Sample_913 Sample_914 Sample_915 Sample_916 Sample_917 Sample_918 Sample_919 Sample_920
#> NA NA NA NA NA NA NA NA
#> Sample_921 Sample_922 Sample_923 Sample_924 Sample_925 Sample_926 Sample_927 Sample_928
#> NA NA NA NA NA NA NA NA
#> Sample_929 Sample_930 Sample_931 Sample_932 Sample_933 Sample_934 Sample_935 Sample_936
#> NA NA NA NA NA NA NA NA
#> Sample_937 Sample_938 Sample_939 Sample_940 Sample_941 Sample_942 Sample_943 Sample_944
#> NA NA NA NA NA NA NA NA
#> Sample_945 Sample_946 Sample_947 Sample_948 Sample_949 Sample_950 Sample_951 Sample_952
#> NA NA NA NA NA NA NA NA
#> Sample_953 Sample_954 Sample_955 Sample_956 Sample_957 Sample_958 Sample_959 Sample_960
#> NA NA NA NA NA NA NA NA
#> Sample_961 Sample_962 Sample_963 Sample_964 Sample_965 Sample_966 Sample_967 Sample_968
#> NA NA NA NA NA NA NA NA
#> Sample_969 Sample_970 Sample_971 Sample_972 Sample_973 Sample_974 Sample_975 Sample_976
#> NA NA NA NA NA NA NA NA
#> Sample_977 Sample_978 Sample_979 Sample_980 Sample_981 Sample_982 Sample_983 Sample_984
#> NA NA NA NA NA NA NA NA
#> Sample_985 Sample_986 Sample_987 Sample_988 Sample_989 Sample_990 Sample_991 Sample_992
#> NA NA NA NA NA NA NA NA
#> Sample_993 Sample_994 Sample_995 Sample_996 Sample_997 Sample_998 Sample_999 Sample_1000
#> NA NA NA NA NA NA NA NA
#> Sample_1001 Sample_1002 Sample_1003 Sample_1004 Sample_1005 Sample_1006 Sample_1007 Sample_1008
#> NA NA NA NA NA NA NA NA
#> Sample_1009 Sample_1010 Sample_1011 Sample_1012 Sample_1013 Sample_1014 Sample_1015 Sample_1016
#> NA NA NA NA NA NA NA NA
#> Sample_1017 Sample_1018 Sample_1019 Sample_1020 Sample_1021 Sample_1022 Sample_1023 Sample_1024
#> NA NA NA NA NA NA NA NA
#> Sample_1025 Sample_1026 Sample_1027 Sample_1028 Sample_1029 Sample_1030 Sample_1031 Sample_1032
#> NA NA NA NA NA NA NA NA
#> Sample_1033 Sample_1034 Sample_1035 Sample_1036 Sample_1037 Sample_1038 Sample_1039 Sample_1040
#> NA NA NA NA NA NA NA NA
#> Sample_1041 Sample_1042 Sample_1043 Sample_1044 Sample_1045 Sample_1046 Sample_1047 Sample_1048
#> NA NA NA NA NA NA NA NA
#> Sample_1049 Sample_1050 Sample_1051 Sample_1052 Sample_1053 Sample_1054 Sample_1055 Sample_1056
#> NA NA NA NA NA NA NA NA
#> Sample_1057 Sample_1058 Sample_1059 Sample_1060 Sample_1061 Sample_1062 Sample_1063 Sample_1064
#> NA NA NA NA NA NA NA NA
#> Sample_1065 Sample_1066 Sample_1067 Sample_1068 Sample_1069 Sample_1070 Sample_1071 Sample_1072
#> NA NA NA NA NA NA NA NA
#> Sample_1073 Sample_1074 Sample_1075 Sample_1076 Sample_1077 Sample_1078 Sample_1079 Sample_1080
#> NA NA NA NA NA NA NA NA
#> Sample_1081 Sample_1082 Sample_1083 Sample_1084 Sample_1085 Sample_1086 Sample_1087 Sample_1088
#> NA NA NA NA NA NA NA NA
#> Sample_1089 Sample_1090 Sample_1091 Sample_1092 Sample_1093 Sample_1094 Sample_1095 Sample_1096
#> NA NA NA NA NA NA NA NA
#> Sample_1097 Sample_1098 Sample_1099 Sample_1100 Sample_1101 Sample_1102 Sample_1103 Sample_1104
#> NA NA NA NA NA NA NA NA
#> Sample_1105 Sample_1106 Sample_1107 Sample_1108 Sample_1109 Sample_1110 Sample_1111 Sample_1112
#> NA NA NA NA NA NA NA NA
#> Sample_1113 Sample_1114 Sample_1115 Sample_1116 Sample_1117 Sample_1118 Sample_1119 Sample_1120
#> NA NA NA NA NA NA NA NA
#> Sample_1121 Sample_1122 Sample_1123 Sample_1124 Sample_1125 Sample_1126 Sample_1127 Sample_1128
#> NA NA NA NA NA NA NA NA
#> Sample_1129 Sample_1130 Sample_1131 Sample_1132 Sample_1133 Sample_1134 Sample_1135 Sample_1136
#> NA NA NA NA NA NA NA NA
#> Sample_1137 Sample_1138 Sample_1139 Sample_1140 Sample_1141 Sample_1142 Sample_1143 Sample_1144
#> NA NA NA NA NA NA NA NA
#> Sample_1145 Sample_1146 Sample_1147 Sample_1148 Sample_1149 Sample_1150 Sample_1151 Sample_1152
#> NA NA NA NA NA NA NA NA
#> Sample_1153 Sample_1154 Sample_1155 Sample_1156 Sample_1157 Sample_1158 Sample_1159 Sample_1160
#> NA NA NA NA NA NA NA NA
#> Sample_1161 Sample_1162 Sample_1163 Sample_1164 Sample_1165 Sample_1166 Sample_1167 Sample_1168
#> NA NA NA NA NA NA NA NA
#> Sample_1169 Sample_1170 Sample_1171 Sample_1172 Sample_1173 Sample_1174 Sample_1175 Sample_1176
#> NA NA NA NA NA NA NA NA
#> Sample_1177 Sample_1178 Sample_1179 Sample_1180 Sample_1181 Sample_1182 Sample_1183 Sample_1184
#> NA NA NA NA NA NA NA NA
#> Sample_1185 Sample_1186 Sample_1187 Sample_1188 Sample_1189 Sample_1190 Sample_1191 Sample_1192
#> NA NA NA NA NA NA NA NA
#> Sample_1193 Sample_1194 Sample_1195 Sample_1196 Sample_1197 Sample_1198 Sample_1199 Sample_1200
#> NA NA NA NA NA NA NA NA
#> Sample_1201 Sample_1202 Sample_1203 Sample_1204 Sample_1205 Sample_1206 Sample_1207 Sample_1208
#> NA NA NA NA NA NA NA NA
#> Sample_1209 Sample_1210 Sample_1211 Sample_1212 Sample_1213 Sample_1214 Sample_1215 Sample_1216
#> NA NA NA NA NA NA NA NA
#> Sample_1217 Sample_1218 Sample_1219 Sample_1220 Sample_1221 Sample_1222 Sample_1223 Sample_1224
#> NA NA NA NA NA NA NA NA
#> Sample_1225 Sample_1226 Sample_1227 Sample_1228 Sample_1229 Sample_1230 Sample_1231 Sample_1232
#> NA NA NA NA NA NA NA NA
#> Sample_1233 Sample_1234 Sample_1235 Sample_1236 Sample_1237 Sample_1238 Sample_1239 Sample_1240
#> NA NA NA NA NA NA NA NA
#> Sample_1241 Sample_1242 Sample_1243 Sample_1244 Sample_1245 Sample_1246 Sample_1247 Sample_1248
#> NA NA NA NA NA NA NA NA
#> Sample_1249 Sample_1250 Sample_1251 Sample_1252 Sample_1253 Sample_1254 Sample_1255 Sample_1256
#> NA NA NA NA NA NA NA NA
#> Sample_1257 Sample_1258 Sample_1259 Sample_1260 Sample_1261 Sample_1262 Sample_1263 Sample_1264
#> NA NA NA NA NA NA NA NA
#> Sample_1265 Sample_1266 Sample_1267 Sample_1268 Sample_1269 Sample_1270 Sample_1271 Sample_1272
#> NA NA NA NA NA NA NA NA
#> Sample_1273 Sample_1274 Sample_1275 Sample_1276 Sample_1277 Sample_1278 Sample_1279 Sample_1280
#> NA NA NA NA NA NA NA NA
#> Sample_1281 Sample_1282 Sample_1283 Sample_1284 Sample_1285 Sample_1286 Sample_1287 Sample_1288
#> NA NA NA NA NA NA NA NA
#> Sample_1289 Sample_1290 Sample_1291 Sample_1292 Sample_1293 Sample_1294 Sample_1295 Sample_1296
#> NA NA NA NA NA NA NA NA
#> Sample_1297 Sample_1298 Sample_1299 Sample_1300 Sample_1301 Sample_1302 Sample_1303 Sample_1304
#> NA NA NA NA NA NA NA NA
#> Sample_1305 Sample_1306 Sample_1307 Sample_1308 Sample_1309 Sample_1310 Sample_1311 Sample_1312
#> NA NA NA NA NA NA NA NA
#> Sample_1313 Sample_1314 Sample_1315 Sample_1316 Sample_1317 Sample_1318 Sample_1319 Sample_1320
#> NA NA NA NA NA NA NA NA
#> Sample_1321 Sample_1322 Sample_1323 Sample_1324 Sample_1325 Sample_1326 Sample_1327 Sample_1328
#> NA NA NA NA NA NA NA NA
#> Sample_1329 Sample_1330 Sample_1331 Sample_1332 Sample_1333 Sample_1334 Sample_1335 Sample_1336
#> NA NA NA NA NA NA NA NA
#> Sample_1337 Sample_1338 Sample_1339 Sample_1340 Sample_1341 Sample_1342 Sample_1343 Sample_1344
#> NA NA NA NA NA NA NA NA
#> Sample_1345 Sample_1346 Sample_1347 Sample_1348 Sample_1349 Sample_1350 Sample_1351 Sample_1352
#> NA NA NA NA NA NA NA NA
#> Sample_1353 Sample_1354 Sample_1355 Sample_1356 Sample_1357 Sample_1358 Sample_1359 Sample_1360
#> NA NA NA NA NA NA NA NA
#> Sample_1361 Sample_1362 Sample_1363 Sample_1364 Sample_1365 Sample_1366 Sample_1367 Sample_1368
#> NA NA NA NA NA NA NA NA
#> Sample_1369 Sample_1370 Sample_1371 Sample_1372 Sample_1373 Sample_1374 Sample_1375 Sample_1376
#> NA NA NA NA NA NA NA NA
#> Sample_1377 Sample_1378 Sample_1379 Sample_1380 Sample_1381 Sample_1382 Sample_1383 Sample_1384
#> NA NA NA NA NA NA NA NA
#> Sample_1385 Sample_1386 Sample_1387 Sample_1388 Sample_1389 Sample_1390 Sample_1391 Sample_1392
#> NA NA NA NA NA NA NA NA
#> Sample_1393 Sample_1394 Sample_1395 Sample_1396 Sample_1397 Sample_1398 Sample_1399 Sample_1400
#> NA NA NA NA NA NA NA NA
#> Sample_1401 Sample_1402 Sample_1403 Sample_1404 Sample_1405 Sample_1406 Sample_1407 Sample_1408
#> NA NA NA NA NA NA NA NA
#> Sample_1409 Sample_1410 Sample_1411 Sample_1412 Sample_1413 Sample_1414 Sample_1415 Sample_1416
#> NA NA NA NA NA NA NA NA
#> Sample_1417 Sample_1418 Sample_1419 Sample_1420 Sample_1421 Sample_1422 Sample_1423 Sample_1424
#> NA NA NA NA NA NA NA NA
#> Sample_1425 Sample_1426 Sample_1427 Sample_1428 Sample_1429 Sample_1430 Sample_1431 Sample_1432
#> NA NA NA NA NA NA NA NA
#> Sample_1433 Sample_1434 Sample_1435 Sample_1436 Sample_1437 Sample_1438 Sample_1439 Sample_1440
#> NA NA NA NA NA NA NA NA
#> Sample_1441 Sample_1442 Sample_1443 Sample_1444 Sample_1445 Sample_1446 Sample_1447 Sample_1448
#> NA NA NA NA NA NA NA NA
#> Sample_1449 Sample_1450 Sample_1451 Sample_1452 Sample_1453 Sample_1454 Sample_1455 Sample_1456
#> NA NA NA NA NA NA NA NA
#> Sample_1457 Sample_1458 Sample_1459 Sample_1460 Sample_1461 Sample_1462 Sample_1463 Sample_1464
#> NA NA NA NA NA NA NA NA
#> Sample_1465 Sample_1466 Sample_1467 Sample_1468 Sample_1469 Sample_1470 Sample_1471 Sample_1472
#> NA NA NA NA NA NA NA NA
#> Sample_1473 Sample_1474 Sample_1475 Sample_1476 Sample_1477 Sample_1478 Sample_1479 Sample_1480
#> NA NA NA NA NA NA NA NA
#> Sample_1481 Sample_1482 Sample_1483 Sample_1484 Sample_1485 Sample_1486 Sample_1487 Sample_1488
#> NA NA NA NA NA NA NA NA
#> Sample_1489 Sample_1490 Sample_1491 Sample_1492 Sample_1493 Sample_1494 Sample_1495 Sample_1496
#> NA NA NA NA NA NA NA NA
#> Sample_1497 Sample_1498 Sample_1499 Sample_1500 Sample_1501 Sample_1502 Sample_1503 Sample_1504
#> NA NA NA NA NA NA NA NA
#> Sample_1505 Sample_1506 Sample_1507 Sample_1508 Sample_1509 Sample_1510 Sample_1511 Sample_1512
#> NA NA NA NA NA NA NA NA
#> Sample_1513 Sample_1514 Sample_1515 Sample_1516 Sample_1517 Sample_1518 Sample_1519 Sample_1520
#> NA NA NA NA NA NA NA NA
#> Sample_1521 Sample_1522 Sample_1523 Sample_1524 Sample_1525 Sample_1526 Sample_1527 Sample_1528
#> NA NA NA NA NA NA NA NA
#> Sample_1529 Sample_1530 Sample_1531 Sample_1532 Sample_1533 Sample_1534 Sample_1535 Sample_1536
#> NA NA NA NA NA NA NA NA
#> Sample_1537 Sample_1538 Sample_1539 Sample_1540 Sample_1541 Sample_1542 Sample_1543 Sample_1544
#> NA NA NA NA NA NA NA NA
#> Sample_1545 Sample_1546 Sample_1547 Sample_1548 Sample_1549 Sample_1550 Sample_1551 Sample_1552
#> NA NA NA NA NA NA NA NA
#> Sample_1553 Sample_1554 Sample_1555 Sample_1556 Sample_1557 Sample_1558 Sample_1559 Sample_1560
#> NA NA NA NA NA NA NA NA
#> Sample_1561 Sample_1562 Sample_1563 Sample_1564 Sample_1565 Sample_1566 Sample_1567 Sample_1568
#> NA NA NA NA NA NA NA NA
#> Sample_1569 Sample_1570 Sample_1571 Sample_1572 Sample_1573 Sample_1574 Sample_1575 Sample_1576
#> NA NA NA NA NA NA NA NA
#> Sample_1577 Sample_1578 Sample_1579 Sample_1580 Sample_1581 Sample_1582 Sample_1583 Sample_1584
#> NA NA NA NA NA NA NA NA
#> Sample_1585 Sample_1586 Sample_1587 Sample_1588 Sample_1589 Sample_1590 Sample_1591 Sample_1592
#> NA NA NA NA NA NA NA NA
#> Sample_1593 Sample_1594 Sample_1595 Sample_1596 Sample_1597 Sample_1598 Sample_1599 Sample_1600
#> NA NA NA NA NA NA NA NA
Heatmaps of the top rows:
top_rows_heatmap(res_rh)
Top rows on each node:
top_rows_overlap(res_rh, method = "upset")
UMAP plot which shows how samples are separated.
par(mfrow = c(1, 2))
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 87),
method = "UMAP", top_value_method = "SD", top_n = 2000, scale_rows = FALSE)
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 87),
method = "UMAP", top_value_method = "ATC", top_n = 2000, scale_rows = TRUE)
par(mfrow = c(1, 2))
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 108),
method = "UMAP", top_value_method = "SD", top_n = 2000, scale_rows = FALSE)
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 108),
method = "UMAP", top_value_method = "ATC", top_n = 2000, scale_rows = TRUE)
par(mfrow = c(1, 2))
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 114),
method = "UMAP", top_value_method = "SD", top_n = 2000, scale_rows = FALSE)
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 114),
method = "UMAP", top_value_method = "ATC", top_n = 2000, scale_rows = TRUE)
par(mfrow = c(1, 2))
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 139),
method = "UMAP", top_value_method = "SD", top_n = 2000, scale_rows = FALSE)
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 139),
method = "UMAP", top_value_method = "ATC", top_n = 2000, scale_rows = TRUE)
par(mfrow = c(1, 2))
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 155),
method = "UMAP", top_value_method = "SD", top_n = 2000, scale_rows = FALSE)
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 155),
method = "UMAP", top_value_method = "ATC", top_n = 2000, scale_rows = TRUE)
par(mfrow = c(1, 2))
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 174),
method = "UMAP", top_value_method = "SD", top_n = 2000, scale_rows = FALSE)
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 174),
method = "UMAP", top_value_method = "ATC", top_n = 2000, scale_rows = TRUE)
par(mfrow = c(1, 2))
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 248),
method = "UMAP", top_value_method = "SD", top_n = 2000, scale_rows = FALSE)
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 248),
method = "UMAP", top_value_method = "ATC", top_n = 2000, scale_rows = TRUE)
par(mfrow = c(1, 2))
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 268),
method = "UMAP", top_value_method = "SD", top_n = 2000, scale_rows = FALSE)
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 268),
method = "UMAP", top_value_method = "ATC", top_n = 2000, scale_rows = TRUE)
par(mfrow = c(1, 2))
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 277),
method = "UMAP", top_value_method = "SD", top_n = 2000, scale_rows = FALSE)
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 277),
method = "UMAP", top_value_method = "ATC", top_n = 2000, scale_rows = TRUE)
par(mfrow = c(1, 2))
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 287),
method = "UMAP", top_value_method = "SD", top_n = 2000, scale_rows = FALSE)
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 287),
method = "UMAP", top_value_method = "ATC", top_n = 2000, scale_rows = TRUE)
par(mfrow = c(1, 2))
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 306),
method = "UMAP", top_value_method = "SD", top_n = 2000, scale_rows = FALSE)
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 306),
method = "UMAP", top_value_method = "ATC", top_n = 2000, scale_rows = TRUE)
par(mfrow = c(1, 2))
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 397),
method = "UMAP", top_value_method = "SD", top_n = 2000, scale_rows = FALSE)
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 397),
method = "UMAP", top_value_method = "ATC", top_n = 2000, scale_rows = TRUE)
par(mfrow = c(1, 2))
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 439),
method = "UMAP", top_value_method = "SD", top_n = 2000, scale_rows = FALSE)
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 439),
method = "UMAP", top_value_method = "ATC", top_n = 2000, scale_rows = TRUE)
par(mfrow = c(1, 2))
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 446),
method = "UMAP", top_value_method = "SD", top_n = 2000, scale_rows = FALSE)
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 446),
method = "UMAP", top_value_method = "ATC", top_n = 2000, scale_rows = TRUE)
par(mfrow = c(1, 2))
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 689),
method = "UMAP", top_value_method = "SD", top_n = 2000, scale_rows = FALSE)
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 689),
method = "UMAP", top_value_method = "ATC", top_n = 2000, scale_rows = TRUE)
par(mfrow = c(1, 2))
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 996),
method = "UMAP", top_value_method = "SD", top_n = 2000, scale_rows = FALSE)
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 996),
method = "UMAP", top_value_method = "ATC", top_n = 2000, scale_rows = TRUE)
par(mfrow = c(1, 2))
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 1273),
method = "UMAP", top_value_method = "SD", top_n = 2000, scale_rows = FALSE)
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 1273),
method = "UMAP", top_value_method = "ATC", top_n = 2000, scale_rows = TRUE)
par(mfrow = c(1, 2))
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 1289),
method = "UMAP", top_value_method = "SD", top_n = 2000, scale_rows = FALSE)
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 1289),
method = "UMAP", top_value_method = "ATC", top_n = 2000, scale_rows = TRUE)
par(mfrow = c(1, 2))
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 1503),
method = "UMAP", top_value_method = "SD", top_n = 2000, scale_rows = FALSE)
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 1503),
method = "UMAP", top_value_method = "ATC", top_n = 2000, scale_rows = TRUE)
par(mfrow = c(1, 2))
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 2253),
method = "UMAP", top_value_method = "SD", top_n = 2000, scale_rows = FALSE)
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 2253),
method = "UMAP", top_value_method = "ATC", top_n = 2000, scale_rows = TRUE)
par(mfrow = c(1, 2))
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 2397),
method = "UMAP", top_value_method = "SD", top_n = 2000, scale_rows = FALSE)
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 2397),
method = "UMAP", top_value_method = "ATC", top_n = 2000, scale_rows = TRUE)
Signatures on the heatmap are the union of all signatures found on every node on the hierarchy. The number of k-means on rows are automatically selected by the function.
get_signatures(res_rh, merge_node = merge_node_param(min_n_signatures = 87))
get_signatures(res_rh, merge_node = merge_node_param(min_n_signatures = 108))
get_signatures(res_rh, merge_node = merge_node_param(min_n_signatures = 114))
get_signatures(res_rh, merge_node = merge_node_param(min_n_signatures = 139))
get_signatures(res_rh, merge_node = merge_node_param(min_n_signatures = 155))
get_signatures(res_rh, merge_node = merge_node_param(min_n_signatures = 174))
get_signatures(res_rh, merge_node = merge_node_param(min_n_signatures = 248))
get_signatures(res_rh, merge_node = merge_node_param(min_n_signatures = 268))
get_signatures(res_rh, merge_node = merge_node_param(min_n_signatures = 277))
get_signatures(res_rh, merge_node = merge_node_param(min_n_signatures = 287))
get_signatures(res_rh, merge_node = merge_node_param(min_n_signatures = 306))
get_signatures(res_rh, merge_node = merge_node_param(min_n_signatures = 397))
get_signatures(res_rh, merge_node = merge_node_param(min_n_signatures = 439))
get_signatures(res_rh, merge_node = merge_node_param(min_n_signatures = 446))
get_signatures(res_rh, merge_node = merge_node_param(min_n_signatures = 689))
get_signatures(res_rh, merge_node = merge_node_param(min_n_signatures = 996))
get_signatures(res_rh, merge_node = merge_node_param(min_n_signatures = 1273))
get_signatures(res_rh, merge_node = merge_node_param(min_n_signatures = 1289))
get_signatures(res_rh, merge_node = merge_node_param(min_n_signatures = 1503))
get_signatures(res_rh, merge_node = merge_node_param(min_n_signatures = 2253))
#> Error in names(x) <- value: 'names' attribute [1] must be the same length as the vector [0]
get_signatures(res_rh, merge_node = merge_node_param(min_n_signatures = 2397))
#> Error in names(x) <- value: 'names' attribute [1] must be the same length as the vector [0]
Compare signatures from different nodes:
compare_signatures(res_rh, verbose = FALSE)
If there are too many signatures, top_signatures = ... can be set to only show the
signatures with the highest FDRs. Note it only works on every node and the final signatures
are the union of all signatures of all nodes.
# code only for demonstration
# e.g. to show the top 500 most significant rows on each node.
tb = get_signature(res_rh, top_signatures = 500)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res_rh, merge_node = merge_node_param(min_n_signatures = 87))
#> age cell.type
#> class NaN 0
test_to_known_factors(res_rh, merge_node = merge_node_param(min_n_signatures = 108))
#> age cell.type
#> class NaN 0
test_to_known_factors(res_rh, merge_node = merge_node_param(min_n_signatures = 114))
#> age cell.type
#> class NaN 0
test_to_known_factors(res_rh, merge_node = merge_node_param(min_n_signatures = 139))
#> age cell.type
#> class NaN 0
test_to_known_factors(res_rh, merge_node = merge_node_param(min_n_signatures = 155))
#> age cell.type
#> class NaN 0
test_to_known_factors(res_rh, merge_node = merge_node_param(min_n_signatures = 174))
#> age cell.type
#> class NaN 0
test_to_known_factors(res_rh, merge_node = merge_node_param(min_n_signatures = 248))
#> age cell.type
#> class NaN 0
test_to_known_factors(res_rh, merge_node = merge_node_param(min_n_signatures = 268))
#> age cell.type
#> class NaN 0
test_to_known_factors(res_rh, merge_node = merge_node_param(min_n_signatures = 277))
#> age cell.type
#> class NaN 0
test_to_known_factors(res_rh, merge_node = merge_node_param(min_n_signatures = 287))
#> age cell.type
#> class NaN 0
test_to_known_factors(res_rh, merge_node = merge_node_param(min_n_signatures = 306))
#> age cell.type
#> class NaN 0
test_to_known_factors(res_rh, merge_node = merge_node_param(min_n_signatures = 397))
#> age cell.type
#> class NaN 0
test_to_known_factors(res_rh, merge_node = merge_node_param(min_n_signatures = 439))
#> age cell.type
#> class NaN 0
test_to_known_factors(res_rh, merge_node = merge_node_param(min_n_signatures = 446))
#> age cell.type
#> class NaN 0
test_to_known_factors(res_rh, merge_node = merge_node_param(min_n_signatures = 689))
#> age cell.type
#> class NaN 0
test_to_known_factors(res_rh, merge_node = merge_node_param(min_n_signatures = 996))
#> age cell.type
#> class NaN 0
test_to_known_factors(res_rh, merge_node = merge_node_param(min_n_signatures = 1273))
#> age cell.type
#> class 3.21e-19 0
test_to_known_factors(res_rh, merge_node = merge_node_param(min_n_signatures = 1289))
#> age cell.type
#> class 2.64e-17 0
test_to_known_factors(res_rh, merge_node = merge_node_param(min_n_signatures = 1503))
#> age cell.type
#> class 2.56e-21 0
test_to_known_factors(res_rh, merge_node = merge_node_param(min_n_signatures = 2253))
#> age cell.type
#> class NA NA
test_to_known_factors(res_rh, merge_node = merge_node_param(min_n_signatures = 2397))
#> age cell.type
#> class NA NA
Child nodes: Node01 , Node02 , Node03 .
The object with results only for a single top-value method and a single partitioning method can be extracted as:
res = res_rh["0"]
A summary of res and all the functions that can be applied to it:
res
#> A 'DownSamplingConsensusPartition' object with k = 2, 3, 4.
#> On a matrix with 15527 rows and 500 columns, randomly sampled from 1600 columns.
#> Top rows (1475) are extracted by 'ATC' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 150 partitions by row resampling.
#> Best k for subgroups seems to be 4.
#>
#> Following methods can be applied to this 'DownSamplingConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_partitions"
#> [7] "compare_signatures" "consensus_heatmap" "dimension_reduction"
#> [10] "functional_enrichment" "get_anno_col" "get_anno"
#> [13] "get_classes" "get_consensus" "get_matrix"
#> [16] "get_membership" "get_param" "get_signatures"
#> [19] "get_stats" "is_best_k" "is_stable_k"
#> [22] "membership_heatmap" "ncol" "nrow"
#> [25] "plot_ecdf" "predict_classes" "rownames"
#> [28] "select_partition_number" "show" "suggest_best_k"
#> [31] "test_to_known_factors" "top_rows_heatmap"
collect_plots() function collects all the plots made from res for all k (number of subgroups)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, higher 1-PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.989 0.995 0.483 0.519 0.519
#> 3 3 1.000 0.978 0.991 0.381 0.774 0.581
#> 4 4 0.931 0.927 0.960 0.109 0.860 0.620
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 4
#> attr(,"optional")
#> [1] 2 3
There is also optional best \(k\) = 2 3 that is worth to check.
Following is the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall subgroup
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
get_classes(res, k = 2)
#> class p
#> Sample_1 2 1.000
#> Sample_2 2 0.000
#> Sample_3 2 0.000
#> Sample_4 2 0.000
#> Sample_5 2 0.000
#> Sample_6 2 0.000
#> Sample_7 2 0.000
#> Sample_8 2 0.000
#> Sample_9 2 0.000
#> Sample_10 2 0.000
#> Sample_11 2 0.000
#> Sample_12 2 0.000
#> Sample_13 2 0.000
#> Sample_14 2 0.000
#> Sample_15 2 0.000
#> Sample_16 2 0.000
#> Sample_17 2 0.000
#> Sample_18 2 0.000
#> Sample_19 2 0.747
#> Sample_20 2 0.000
#> Sample_21 2 0.000
#> Sample_22 2 0.000
#> Sample_23 2 0.000
#> Sample_24 2 0.000
#> Sample_25 2 0.000
#> Sample_26 2 0.000
#> Sample_27 2 0.000
#> Sample_28 2 0.000
#> Sample_29 2 0.000
#> Sample_30 2 0.000
#> Sample_31 2 0.000
#> Sample_32 2 0.000
#> Sample_33 2 0.000
#> Sample_34 2 0.000
#> Sample_35 2 0.000
#> Sample_36 2 0.000
#> Sample_37 2 0.000
#> Sample_38 2 0.000
#> Sample_39 2 0.000
#> Sample_40 2 0.000
#> Sample_41 2 0.000
#> Sample_42 2 0.000
#> Sample_43 2 0.000
#> Sample_44 2 0.000
#> Sample_45 2 0.000
#> Sample_46 2 0.000
#> Sample_47 2 0.000
#> Sample_48 2 0.000
#> Sample_49 2 0.000
#> Sample_50 2 0.000
#> Sample_51 2 0.000
#> Sample_52 2 0.000
#> Sample_53 2 0.000
#> Sample_54 2 0.000
#> Sample_55 2 0.000
#> Sample_56 2 0.000
#> Sample_57 2 0.000
#> Sample_58 2 0.000
#> Sample_59 2 0.000
#> Sample_60 2 0.000
#> Sample_61 2 0.000
#> Sample_62 2 0.000
#> Sample_63 2 0.000
#> Sample_64 2 0.000
#> Sample_65 2 0.000
#> Sample_66 2 0.000
#> Sample_67 2 0.000
#> Sample_68 2 0.000
#> Sample_69 2 0.000
#> Sample_70 2 0.000
#> Sample_71 2 0.000
#> Sample_72 2 0.000
#> Sample_73 2 0.000
#> Sample_74 2 0.000
#> Sample_75 2 0.000
#> Sample_76 2 0.000
#> Sample_77 2 0.000
#> Sample_78 2 0.000
#> Sample_79 2 0.000
#> Sample_80 2 0.000
#> Sample_81 2 0.000
#> Sample_82 2 0.000
#> Sample_83 2 0.000
#> Sample_84 2 0.000
#> Sample_85 2 0.000
#> Sample_86 2 0.000
#> Sample_87 2 0.000
#> Sample_88 2 0.000
#> Sample_89 2 0.000
#> Sample_90 2 0.000
#> Sample_91 2 0.000
#> Sample_92 2 0.000
#> Sample_93 2 0.000
#> Sample_94 2 0.000
#> Sample_95 2 0.000
#> Sample_96 2 0.000
#> Sample_97 2 0.000
#> Sample_98 2 0.000
#> Sample_99 2 0.000
#> Sample_100 2 0.000
#> Sample_101 2 0.000
#> Sample_102 2 0.000
#> Sample_103 2 0.000
#> Sample_104 2 0.000
#> Sample_105 2 0.000
#> Sample_106 2 0.000
#> Sample_107 2 0.000
#> Sample_108 2 0.000
#> Sample_109 2 0.000
#> Sample_110 2 0.000
#> Sample_111 2 0.498
#> Sample_112 2 0.000
#> Sample_113 2 0.000
#> Sample_114 2 0.000
#> Sample_115 2 0.000
#> Sample_116 2 1.000
#> Sample_117 2 0.000
#> Sample_118 2 0.000
#> Sample_119 2 0.000
#> Sample_120 2 0.000
#> Sample_121 2 1.000
#> Sample_122 2 0.000
#> Sample_123 2 0.000
#> Sample_124 2 0.000
#> Sample_125 2 0.000
#> Sample_126 2 0.000
#> Sample_127 2 0.000
#> Sample_128 2 0.000
#> Sample_129 2 0.000
#> Sample_130 2 0.000
#> Sample_131 2 0.000
#> Sample_132 2 0.000
#> Sample_133 2 0.000
#> Sample_134 2 0.000
#> Sample_135 2 0.000
#> Sample_136 2 0.000
#> Sample_137 2 0.000
#> Sample_138 2 0.000
#> Sample_139 2 0.000
#> Sample_140 2 0.000
#> Sample_141 2 0.000
#> Sample_142 2 0.000
#> Sample_143 2 0.000
#> Sample_144 2 0.000
#> Sample_145 2 0.000
#> Sample_146 2 0.498
#> Sample_147 2 0.000
#> Sample_148 2 0.000
#> Sample_149 2 0.000
#> Sample_150 2 0.000
#> Sample_151 2 0.000
#> Sample_152 2 0.000
#> Sample_153 2 0.000
#> Sample_154 2 0.000
#> Sample_155 2 0.000
#> Sample_156 2 0.000
#> Sample_157 2 0.000
#> Sample_158 2 0.000
#> Sample_159 2 0.000
#> Sample_160 2 0.000
#> Sample_161 2 0.000
#> Sample_162 2 0.000
#> Sample_163 2 0.000
#> Sample_164 2 0.000
#> Sample_165 2 0.000
#> Sample_166 2 0.000
#> Sample_167 2 0.000
#> Sample_168 2 0.000
#> Sample_169 2 0.000
#> Sample_170 2 0.000
#> Sample_171 2 0.000
#> Sample_172 2 0.000
#> Sample_173 2 0.000
#> Sample_174 2 0.000
#> Sample_175 2 0.000
#> Sample_176 2 0.000
#> Sample_177 2 0.000
#> Sample_178 2 0.000
#> Sample_179 2 0.000
#> Sample_180 2 0.000
#> Sample_181 2 0.000
#> Sample_182 2 0.000
#> Sample_183 2 0.000
#> Sample_184 2 0.000
#> Sample_185 2 0.000
#> Sample_186 2 0.000
#> Sample_187 2 0.000
#> Sample_188 2 0.000
#> Sample_189 2 0.000
#> Sample_190 2 0.000
#> Sample_191 2 0.000
#> Sample_192 2 0.000
#> Sample_193 2 0.000
#> Sample_194 2 0.000
#> Sample_195 2 0.000
#> Sample_196 2 0.000
#> Sample_197 2 0.000
#> Sample_198 2 0.000
#> Sample_199 2 0.000
#> Sample_200 2 0.000
#> Sample_201 2 0.000
#> Sample_202 2 0.000
#> Sample_203 2 0.000
#> Sample_204 2 0.000
#> Sample_205 2 0.000
#> Sample_206 2 0.000
#> Sample_207 2 0.000
#> Sample_208 1 0.000
#> Sample_209 1 0.000
#> Sample_210 1 0.000
#> Sample_211 1 0.000
#> Sample_212 1 0.000
#> Sample_213 1 0.000
#> Sample_214 1 0.000
#> Sample_215 1 0.000
#> Sample_216 1 0.000
#> Sample_217 1 0.000
#> Sample_218 1 0.000
#> Sample_219 1 0.000
#> Sample_220 1 0.000
#> Sample_221 1 0.000
#> Sample_222 1 0.000
#> Sample_223 1 0.000
#> Sample_224 1 0.000
#> Sample_225 1 0.000
#> Sample_226 1 0.000
#> Sample_227 1 0.000
#> Sample_228 1 0.000
#> Sample_229 1 0.000
#> Sample_230 1 0.000
#> Sample_231 1 0.000
#> Sample_232 1 0.000
#> Sample_233 1 0.000
#> Sample_234 1 0.000
#> Sample_235 1 0.000
#> Sample_236 1 0.000
#> Sample_237 1 0.000
#> Sample_238 1 0.000
#> Sample_239 1 0.000
#> Sample_240 1 0.000
#> Sample_241 1 0.000
#> Sample_242 1 0.000
#> Sample_243 1 0.000
#> Sample_244 1 0.000
#> Sample_245 1 0.000
#> Sample_246 1 0.000
#> Sample_247 1 0.000
#> Sample_248 1 0.000
#> Sample_249 1 0.000
#> Sample_250 1 0.000
#> Sample_251 1 0.000
#> Sample_252 1 0.000
#> Sample_253 1 0.000
#> Sample_254 1 0.000
#> Sample_255 1 0.000
#> Sample_256 1 0.000
#> Sample_257 1 0.000
#> Sample_258 1 0.000
#> Sample_259 1 0.000
#> Sample_260 1 0.000
#> Sample_261 1 0.000
#> Sample_262 1 0.000
#> Sample_263 1 0.000
#> Sample_264 1 0.000
#> Sample_265 1 0.000
#> Sample_266 1 0.000
#> Sample_267 1 0.000
#> Sample_268 1 0.000
#> Sample_269 1 0.000
#> Sample_270 1 0.000
#> Sample_271 1 0.249
#> Sample_272 1 0.000
#> Sample_273 1 0.000
#> Sample_274 1 0.000
#> Sample_275 1 0.000
#> Sample_276 1 0.000
#> Sample_277 1 0.000
#> Sample_278 1 0.000
#> Sample_279 1 0.000
#> Sample_280 1 0.000
#> Sample_281 1 0.000
#> Sample_282 1 0.000
#> Sample_283 1 0.000
#> Sample_284 1 0.000
#> Sample_285 1 0.000
#> Sample_286 1 0.000
#> Sample_287 1 0.000
#> Sample_288 1 0.000
#> Sample_289 1 0.000
#> Sample_290 1 0.000
#> Sample_291 1 0.000
#> Sample_292 1 0.000
#> Sample_293 1 0.000
#> Sample_294 1 0.000
#> Sample_295 1 0.000
#> Sample_296 1 0.000
#> Sample_297 1 0.000
#> Sample_298 1 0.000
#> Sample_299 1 0.000
#> Sample_300 1 0.000
#> Sample_301 1 0.000
#> Sample_302 1 0.000
#> Sample_303 1 0.000
#> Sample_304 1 0.000
#> Sample_305 1 0.000
#> Sample_306 1 0.000
#> Sample_307 1 0.000
#> Sample_308 1 0.000
#> Sample_309 1 0.000
#> Sample_310 1 0.000
#> Sample_311 1 0.000
#> Sample_312 1 0.000
#> Sample_313 1 0.000
#> Sample_314 1 0.000
#> Sample_315 1 0.000
#> Sample_316 1 0.000
#> Sample_317 1 0.000
#> Sample_318 1 0.000
#> Sample_319 1 0.000
#> Sample_320 1 0.000
#> Sample_321 1 0.000
#> Sample_322 1 0.000
#> Sample_323 1 0.000
#> Sample_324 1 0.000
#> Sample_325 1 0.000
#> Sample_326 1 0.000
#> Sample_327 1 0.000
#> Sample_328 1 0.000
#> Sample_329 1 0.000
#> Sample_330 1 0.000
#> Sample_331 2 0.751
#> Sample_332 1 0.000
#> Sample_333 1 0.000
#> Sample_334 1 0.000
#> Sample_335 1 0.000
#> Sample_336 1 0.000
#> Sample_337 1 0.000
#> Sample_338 1 0.000
#> Sample_339 1 0.000
#> Sample_340 1 0.000
#> Sample_341 1 0.000
#> Sample_342 1 0.000
#> Sample_343 1 0.000
#> Sample_344 1 0.000
#> Sample_345 1 0.000
#> Sample_346 1 0.000
#> Sample_347 1 0.000
#> Sample_348 1 0.000
#> Sample_349 1 0.000
#> Sample_350 1 0.000
#> Sample_351 1 0.000
#> Sample_352 1 0.000
#> Sample_353 1 0.000
#> Sample_354 1 0.000
#> Sample_355 1 0.000
#> Sample_356 1 0.000
#> Sample_357 1 0.000
#> Sample_358 1 0.000
#> Sample_359 1 0.000
#> Sample_360 1 0.000
#> Sample_361 1 0.000
#> Sample_362 1 0.000
#> Sample_363 1 0.000
#> Sample_364 1 0.000
#> Sample_365 1 0.000
#> Sample_366 1 0.000
#> Sample_367 1 0.000
#> Sample_368 1 0.000
#> Sample_369 1 0.000
#> Sample_370 1 0.000
#> Sample_371 1 0.000
#> Sample_372 1 0.000
#> Sample_373 1 0.000
#> Sample_374 1 0.000
#> Sample_375 1 0.000
#> Sample_376 1 0.000
#> Sample_377 1 0.000
#> Sample_378 1 0.000
#> Sample_379 1 0.000
#> Sample_380 1 0.000
#> Sample_381 1 0.000
#> Sample_382 1 0.000
#> Sample_383 1 0.000
#> Sample_384 1 0.000
#> Sample_385 1 0.000
#> Sample_386 1 0.000
#> Sample_387 1 0.000
#> Sample_388 1 0.000
#> Sample_389 1 0.000
#> Sample_390 1 0.000
#> Sample_391 1 0.000
#> Sample_392 1 0.000
#> Sample_393 1 0.000
#> Sample_394 1 0.000
#> Sample_395 1 0.000
#> Sample_396 1 0.000
#> Sample_397 1 0.000
#> Sample_398 1 0.000
#> Sample_399 1 0.000
#> Sample_400 1 0.000
#> Sample_401 1 0.000
#> Sample_402 1 0.000
#> Sample_403 1 0.000
#> Sample_404 1 0.000
#> Sample_405 1 0.000
#> Sample_406 1 0.000
#> Sample_407 1 0.000
#> Sample_408 1 0.000
#> Sample_409 1 0.000
#> Sample_410 1 0.000
#> Sample_411 1 0.000
#> Sample_412 1 0.000
#> Sample_413 1 0.000
#> Sample_414 1 0.000
#> Sample_415 1 0.000
#> Sample_416 1 0.000
#> Sample_417 1 0.000
#> Sample_418 1 0.000
#> Sample_419 1 0.000
#> Sample_420 1 0.000
#> Sample_421 1 0.000
#> Sample_422 1 0.000
#> Sample_423 1 0.000
#> Sample_424 1 0.000
#> Sample_425 1 0.000
#> Sample_426 1 0.000
#> Sample_427 1 0.000
#> Sample_428 1 0.000
#> Sample_429 1 0.000
#> Sample_430 1 0.000
#> Sample_431 1 0.000
#> Sample_432 1 0.000
#> Sample_433 1 0.000
#> Sample_434 1 0.000
#> Sample_435 1 0.000
#> Sample_436 2 0.747
#> Sample_437 1 0.000
#> Sample_438 1 0.000
#> Sample_439 1 0.000
#> Sample_440 1 0.000
#> Sample_441 1 0.000
#> Sample_442 1 0.000
#> Sample_443 1 0.000
#> Sample_444 1 0.000
#> Sample_445 1 0.000
#> Sample_446 1 0.000
#> Sample_447 1 0.000
#> Sample_448 1 0.000
#> Sample_449 1 0.000
#> Sample_450 1 0.000
#> Sample_451 1 0.000
#> Sample_452 1 0.000
#> Sample_453 1 0.000
#> Sample_454 1 0.000
#> Sample_455 1 0.000
#> Sample_456 1 0.000
#> Sample_457 1 0.000
#> Sample_458 1 0.000
#> Sample_459 1 0.000
#> Sample_460 1 0.000
#> Sample_461 1 0.000
#> Sample_462 1 0.000
#> Sample_463 1 0.000
#> Sample_464 1 0.000
#> Sample_465 1 0.000
#> Sample_466 1 0.000
#> Sample_467 1 0.000
#> Sample_468 1 0.000
#> Sample_469 1 0.000
#> Sample_470 1 0.000
#> Sample_471 1 0.000
#> Sample_472 1 0.000
#> Sample_473 1 0.000
#> Sample_474 1 0.000
#> Sample_475 1 0.000
#> Sample_476 1 0.000
#> Sample_477 1 0.000
#> Sample_478 1 0.000
#> Sample_479 1 0.000
#> Sample_480 1 0.000
#> Sample_481 1 0.000
#> Sample_482 1 0.000
#> Sample_483 1 0.000
#> Sample_484 1 0.000
#> Sample_485 1 0.000
#> Sample_486 1 0.000
#> Sample_487 1 0.000
#> Sample_488 1 0.000
#> Sample_489 1 0.000
#> Sample_490 1 0.000
#> Sample_491 1 0.000
#> Sample_492 1 0.000
#> Sample_493 1 0.000
#> Sample_494 1 0.000
#> Sample_495 1 0.000
#> Sample_496 1 0.000
#> Sample_497 1 0.000
#> Sample_498 1 0.000
#> Sample_499 1 0.000
#> Sample_500 1 0.000
#> Sample_501 1 0.000
#> Sample_502 1 0.000
#> Sample_503 1 0.000
#> Sample_504 1 0.000
#> Sample_505 1 0.000
#> Sample_506 1 0.000
#> Sample_507 1 0.000
#> Sample_508 1 0.000
#> Sample_509 1 0.000
#> Sample_510 1 0.000
#> Sample_511 1 0.000
#> Sample_512 1 0.000
#> Sample_513 1 0.000
#> Sample_514 1 0.000
#> Sample_515 1 0.000
#> Sample_516 2 0.502
#> Sample_517 1 0.000
#> Sample_518 1 0.000
#> Sample_519 1 0.000
#> Sample_520 1 0.000
#> Sample_521 1 0.000
#> Sample_522 1 0.000
#> Sample_523 1 0.000
#> Sample_524 1 0.000
#> Sample_525 1 0.000
#> Sample_526 1 0.000
#> Sample_527 1 0.000
#> Sample_528 1 0.000
#> Sample_529 1 1.000
#> Sample_530 1 0.000
#> Sample_531 1 0.000
#> Sample_532 1 0.000
#> Sample_533 1 0.000
#> Sample_534 1 0.000
#> Sample_535 1 0.000
#> Sample_536 1 0.000
#> Sample_537 1 0.000
#> Sample_538 1 0.000
#> Sample_539 1 0.000
#> Sample_540 1 0.000
#> Sample_541 1 0.000
#> Sample_542 1 0.000
#> Sample_543 1 0.000
#> Sample_544 1 0.000
#> Sample_545 1 0.000
#> Sample_546 1 0.000
#> Sample_547 1 0.000
#> Sample_548 1 0.000
#> Sample_549 1 0.000
#> Sample_550 1 0.000
#> Sample_551 1 0.000
#> Sample_552 1 0.000
#> Sample_553 1 0.000
#> Sample_554 1 0.000
#> Sample_555 1 0.000
#> Sample_556 1 0.000
#> Sample_557 1 0.000
#> Sample_558 1 0.000
#> Sample_559 1 0.000
#> Sample_560 1 0.000
#> Sample_561 1 0.000
#> Sample_562 1 0.000
#> Sample_563 1 0.000
#> Sample_564 1 0.000
#> Sample_565 1 0.000
#> Sample_566 1 0.000
#> Sample_567 1 0.000
#> Sample_568 1 0.000
#> Sample_569 1 0.000
#> Sample_570 1 0.000
#> Sample_571 1 0.000
#> Sample_572 1 0.000
#> Sample_573 1 0.000
#> Sample_574 1 0.000
#> Sample_575 1 0.000
#> Sample_576 1 0.000
#> Sample_577 1 0.000
#> Sample_578 1 0.000
#> Sample_579 1 0.000
#> Sample_580 1 0.000
#> Sample_581 1 0.000
#> Sample_582 1 0.000
#> Sample_583 1 0.000
#> Sample_584 1 0.000
#> Sample_585 1 0.498
#> Sample_586 2 0.000
#> Sample_587 2 0.000
#> Sample_588 2 0.000
#> Sample_589 1 0.000
#> Sample_590 1 1.000
#> Sample_591 2 0.000
#> Sample_592 2 0.000
#> Sample_593 2 0.000
#> Sample_594 2 0.000
#> Sample_595 2 1.000
#> Sample_596 2 1.000
#> Sample_597 2 1.000
#> Sample_598 2 0.249
#> Sample_599 2 0.000
#> Sample_600 2 0.000
#> Sample_601 2 0.000
#> Sample_602 2 0.000
#> Sample_603 2 0.000
#> Sample_604 2 0.000
#> Sample_605 2 0.000
#> Sample_606 2 0.000
#> Sample_607 2 0.000
#> Sample_608 2 0.498
#> Sample_609 2 0.000
#> Sample_610 2 0.249
#> Sample_611 2 0.000
#> Sample_612 2 0.249
#> Sample_613 1 1.000
#> Sample_614 2 0.751
#> Sample_615 1 0.249
#> Sample_616 1 1.000
#> Sample_617 2 1.000
#> Sample_618 1 0.751
#> Sample_619 1 0.751
#> Sample_620 1 0.498
#> Sample_621 2 0.249
#> Sample_622 1 1.000
#> Sample_623 1 1.000
#> Sample_624 1 0.502
#> Sample_625 2 0.249
#> Sample_626 2 1.000
#> Sample_627 2 0.000
#> Sample_628 2 1.000
#> Sample_629 2 0.000
#> Sample_630 1 0.498
#> Sample_631 2 0.000
#> Sample_632 1 0.747
#> Sample_633 1 0.249
#> Sample_634 2 0.751
#> Sample_635 1 1.000
#> Sample_636 1 1.000
#> Sample_637 1 0.000
#> Sample_638 2 1.000
#> Sample_639 1 0.000
#> Sample_640 2 0.000
#> Sample_641 1 1.000
#> Sample_642 2 0.249
#> Sample_643 1 1.000
#> Sample_644 1 0.249
#> Sample_645 2 0.000
#> Sample_646 2 0.751
#> Sample_647 2 1.000
#> Sample_648 2 0.000
#> Sample_649 2 0.000
#> Sample_650 2 0.000
#> Sample_651 2 0.000
#> Sample_652 2 0.000
#> Sample_653 2 0.000
#> Sample_654 2 0.000
#> Sample_655 2 0.000
#> Sample_656 2 0.000
#> Sample_657 2 0.000
#> Sample_658 2 0.000
#> Sample_659 2 0.000
#> Sample_660 2 0.000
#> Sample_661 2 0.000
#> Sample_662 2 0.000
#> Sample_663 2 0.000
#> Sample_664 2 0.000
#> Sample_665 2 0.000
#> Sample_666 2 0.000
#> Sample_667 2 0.000
#> Sample_668 2 0.000
#> Sample_669 2 0.000
#> Sample_670 2 0.000
#> Sample_671 2 0.000
#> Sample_672 2 0.000
#> Sample_673 2 0.000
#> Sample_674 2 0.000
#> Sample_675 2 0.000
#> Sample_676 2 0.000
#> Sample_677 2 0.000
#> Sample_678 2 0.000
#> Sample_679 2 0.000
#> Sample_680 2 0.000
#> Sample_681 2 0.000
#> Sample_682 2 0.000
#> Sample_683 2 0.000
#> Sample_684 2 0.000
#> Sample_685 2 0.502
#> Sample_686 2 0.249
#> Sample_687 2 1.000
#> Sample_688 2 0.000
#> Sample_689 2 0.747
#> Sample_690 2 0.000
#> Sample_691 2 0.000
#> Sample_692 2 0.249
#> Sample_693 2 0.000
#> Sample_694 2 0.000
#> Sample_695 2 0.000
#> Sample_696 2 0.000
#> Sample_697 2 0.000
#> Sample_698 2 0.000
#> Sample_699 2 0.000
#> Sample_700 2 0.000
#> Sample_701 2 0.000
#> Sample_702 2 0.000
#> Sample_703 2 0.000
#> Sample_704 2 0.000
#> Sample_705 2 0.000
#> Sample_706 2 0.000
#> Sample_707 2 0.000
#> Sample_708 2 0.000
#> Sample_709 2 0.000
#> Sample_710 2 0.000
#> Sample_711 2 0.000
#> Sample_712 2 0.000
#> Sample_713 2 0.000
#> Sample_714 2 0.000
#> Sample_715 2 0.000
#> Sample_716 2 0.000
#> Sample_717 2 0.000
#> Sample_718 2 0.000
#> Sample_719 2 0.000
#> Sample_720 2 0.000
#> Sample_721 2 0.000
#> Sample_722 2 0.000
#> Sample_723 2 0.000
#> Sample_724 2 0.000
#> Sample_725 2 0.000
#> Sample_726 2 0.000
#> Sample_727 2 0.000
#> Sample_728 2 0.000
#> Sample_729 2 0.000
#> Sample_730 2 0.000
#> Sample_731 2 0.000
#> Sample_732 2 0.000
#> Sample_733 2 0.000
#> Sample_734 2 0.000
#> Sample_735 2 0.000
#> Sample_736 2 0.000
#> Sample_737 2 1.000
#> Sample_738 2 0.000
#> Sample_739 2 0.000
#> Sample_740 2 0.000
#> Sample_741 2 0.000
#> Sample_742 2 0.000
#> Sample_743 2 0.000
#> Sample_744 2 0.000
#> Sample_745 2 0.000
#> Sample_746 2 0.000
#> Sample_747 2 0.000
#> Sample_748 2 0.000
#> Sample_749 2 0.000
#> Sample_750 2 0.000
#> Sample_751 2 0.000
#> Sample_752 2 0.000
#> Sample_753 2 0.000
#> Sample_754 2 0.000
#> Sample_755 2 0.000
#> Sample_756 2 0.000
#> Sample_757 2 0.000
#> Sample_758 2 0.000
#> Sample_759 2 0.000
#> Sample_760 2 0.000
#> Sample_761 2 0.000
#> Sample_762 2 0.000
#> Sample_763 2 0.000
#> Sample_764 2 0.000
#> Sample_765 2 0.000
#> Sample_766 2 0.000
#> Sample_767 2 0.000
#> Sample_768 2 0.000
#> Sample_769 2 0.000
#> Sample_770 2 0.000
#> Sample_771 2 0.000
#> Sample_772 2 0.000
#> Sample_773 2 0.000
#> Sample_774 2 0.000
#> Sample_775 2 0.000
#> Sample_776 2 0.000
#> Sample_777 2 0.000
#> Sample_778 2 0.000
#> Sample_779 2 0.000
#> Sample_780 2 0.000
#> Sample_781 2 0.000
#> Sample_782 2 0.000
#> Sample_783 2 0.000
#> Sample_784 2 0.000
#> Sample_785 2 0.000
#> Sample_786 2 0.000
#> Sample_787 2 0.000
#> Sample_788 2 0.000
#> Sample_789 2 0.000
#> Sample_790 2 0.000
#> Sample_791 2 0.000
#> Sample_792 2 0.000
#> Sample_793 2 0.000
#> Sample_794 2 0.000
#> Sample_795 2 0.000
#> Sample_796 2 0.000
#> Sample_797 2 0.000
#> Sample_798 2 0.000
#> Sample_799 2 0.000
#> Sample_800 2 0.000
#> Sample_801 2 0.000
#> Sample_802 2 0.000
#> Sample_803 2 0.000
#> Sample_804 2 0.000
#> Sample_805 2 0.000
#> Sample_806 2 0.000
#> Sample_807 2 0.000
#> Sample_808 2 0.000
#> Sample_809 2 0.000
#> Sample_810 2 0.000
#> Sample_811 2 0.000
#> Sample_812 2 0.000
#> Sample_813 2 0.000
#> Sample_814 2 0.000
#> Sample_815 2 0.000
#> Sample_816 2 0.000
#> Sample_817 2 0.000
#> Sample_818 2 0.000
#> Sample_819 2 0.000
#> Sample_820 2 0.000
#> Sample_821 2 0.000
#> Sample_822 2 0.000
#> Sample_823 2 0.000
#> Sample_824 2 0.000
#> Sample_825 2 0.000
#> Sample_826 2 0.000
#> Sample_827 2 0.000
#> Sample_828 2 0.000
#> Sample_829 2 0.000
#> Sample_830 2 0.000
#> Sample_831 2 0.000
#> Sample_832 2 0.000
#> Sample_833 2 0.000
#> Sample_834 2 0.000
#> Sample_835 2 0.000
#> Sample_836 2 0.000
#> Sample_837 2 0.000
#> Sample_838 2 0.000
#> Sample_839 2 0.000
#> Sample_840 2 0.000
#> Sample_841 2 0.000
#> Sample_842 2 0.000
#> Sample_843 2 0.000
#> Sample_844 2 0.000
#> Sample_845 2 0.000
#> Sample_846 2 0.000
#> Sample_847 2 0.000
#> Sample_848 2 1.000
#> Sample_849 2 0.000
#> Sample_850 2 0.000
#> Sample_851 2 0.000
#> Sample_852 2 0.000
#> Sample_853 2 0.000
#> Sample_854 2 0.000
#> Sample_855 2 0.000
#> Sample_856 2 0.000
#> Sample_857 2 0.000
#> Sample_858 2 0.000
#> Sample_859 2 0.000
#> Sample_860 2 0.000
#> Sample_861 2 0.000
#> Sample_862 2 0.000
#> Sample_863 2 0.000
#> Sample_864 2 0.000
#> Sample_865 2 0.000
#> Sample_866 2 0.000
#> Sample_867 2 0.000
#> Sample_868 2 0.000
#> Sample_869 2 0.000
#> Sample_870 2 0.000
#> Sample_871 2 0.000
#> Sample_872 2 0.000
#> Sample_873 2 0.000
#> Sample_874 2 0.000
#> Sample_875 2 0.000
#> Sample_876 2 0.000
#> Sample_877 2 0.000
#> Sample_878 2 0.000
#> Sample_879 2 0.000
#> Sample_880 2 0.000
#> Sample_881 2 0.000
#> Sample_882 2 0.000
#> Sample_883 2 0.000
#> Sample_884 2 0.000
#> Sample_885 2 0.000
#> Sample_886 2 0.000
#> Sample_887 2 0.000
#> Sample_888 2 0.000
#> Sample_889 2 0.000
#> Sample_890 2 0.000
#> Sample_891 2 0.000
#> Sample_892 2 0.000
#> Sample_893 2 0.000
#> Sample_894 2 0.000
#> Sample_895 2 0.000
#> Sample_896 2 0.000
#> Sample_897 2 0.000
#> Sample_898 2 0.000
#> Sample_899 2 0.000
#> Sample_900 2 0.000
#> Sample_901 2 0.000
#> Sample_902 2 0.000
#> Sample_903 2 0.000
#> Sample_904 2 0.000
#> Sample_905 2 0.000
#> Sample_906 2 0.000
#> Sample_907 2 0.000
#> Sample_908 2 0.000
#> Sample_909 2 0.000
#> Sample_910 2 0.000
#> Sample_911 2 0.000
#> Sample_912 2 0.000
#> Sample_913 2 0.000
#> Sample_914 2 0.000
#> Sample_915 2 0.000
#> Sample_916 2 0.000
#> Sample_917 2 0.000
#> Sample_918 2 0.000
#> Sample_919 2 0.000
#> Sample_920 2 0.000
#> Sample_921 2 0.000
#> Sample_922 2 0.000
#> Sample_923 2 0.000
#> Sample_924 2 0.000
#> Sample_925 2 0.000
#> Sample_926 2 0.000
#> Sample_927 2 0.000
#> Sample_928 2 0.000
#> Sample_929 2 0.000
#> Sample_930 2 0.000
#> Sample_931 2 0.000
#> Sample_932 2 0.000
#> Sample_933 2 0.000
#> Sample_934 2 0.000
#> Sample_935 2 0.000
#> Sample_936 2 0.000
#> Sample_937 2 0.000
#> Sample_938 2 0.000
#> Sample_939 2 0.000
#> Sample_940 2 0.000
#> Sample_941 2 0.000
#> Sample_942 2 0.000
#> Sample_943 2 0.000
#> Sample_944 2 0.000
#> Sample_945 2 0.000
#> Sample_946 2 0.000
#> Sample_947 2 0.000
#> Sample_948 1 0.000
#> Sample_949 1 0.000
#> Sample_950 1 0.000
#> Sample_951 1 0.000
#> Sample_952 1 0.000
#> Sample_953 1 0.000
#> Sample_954 1 0.000
#> Sample_955 1 0.000
#> Sample_956 1 0.000
#> Sample_957 1 0.000
#> Sample_958 1 0.000
#> Sample_959 1 0.000
#> Sample_960 1 0.000
#> Sample_961 1 0.000
#> Sample_962 1 0.000
#> Sample_963 1 0.000
#> Sample_964 1 0.000
#> Sample_965 1 0.000
#> Sample_966 1 0.000
#> Sample_967 1 0.000
#> Sample_968 1 0.000
#> Sample_969 1 0.000
#> Sample_970 1 0.000
#> Sample_971 1 0.000
#> Sample_972 1 0.000
#> Sample_973 1 0.000
#> Sample_974 1 0.000
#> Sample_975 1 0.000
#> Sample_976 1 0.000
#> Sample_977 1 0.000
#> Sample_978 1 0.000
#> Sample_979 1 0.000
#> Sample_980 1 0.000
#> Sample_981 1 0.000
#> Sample_982 1 0.000
#> Sample_983 1 0.000
#> Sample_984 1 0.000
#> Sample_985 1 0.000
#> Sample_986 1 0.000
#> Sample_987 1 0.000
#> Sample_988 1 0.000
#> Sample_989 1 0.000
#> Sample_990 1 0.000
#> Sample_991 1 0.000
#> Sample_992 1 0.000
#> Sample_993 1 0.000
#> Sample_994 1 0.000
#> Sample_995 1 0.000
#> Sample_996 1 0.000
#> Sample_997 1 0.000
#> Sample_998 1 0.000
#> Sample_999 1 0.253
#> Sample_1000 1 0.000
#> Sample_1001 1 0.000
#> Sample_1002 1 0.000
#> Sample_1003 1 0.000
#> Sample_1004 2 1.000
#> Sample_1005 1 0.000
#> Sample_1006 1 0.000
#> Sample_1007 1 0.000
#> Sample_1008 1 0.000
#> Sample_1009 1 0.000
#> Sample_1010 1 0.249
#> Sample_1011 1 0.000
#> Sample_1012 1 0.000
#> Sample_1013 1 0.000
#> Sample_1014 1 0.000
#> Sample_1015 1 0.000
#> Sample_1016 1 0.000
#> Sample_1017 1 0.000
#> Sample_1018 1 0.000
#> Sample_1019 1 0.000
#> Sample_1020 1 0.000
#> Sample_1021 1 0.000
#> Sample_1022 1 0.000
#> Sample_1023 1 0.000
#> Sample_1024 1 0.000
#> Sample_1025 1 0.000
#> Sample_1026 1 0.000
#> Sample_1027 1 0.000
#> Sample_1028 1 0.000
#> Sample_1029 1 0.000
#> Sample_1030 1 0.000
#> Sample_1031 1 1.000
#> Sample_1032 1 0.000
#> Sample_1033 1 0.000
#> Sample_1034 1 0.000
#> Sample_1035 1 0.000
#> Sample_1036 1 0.000
#> Sample_1037 1 0.000
#> Sample_1038 1 0.000
#> Sample_1039 1 0.000
#> Sample_1040 1 0.000
#> Sample_1041 1 0.000
#> Sample_1042 1 0.000
#> Sample_1043 1 0.000
#> Sample_1044 1 0.000
#> Sample_1045 1 0.000
#> Sample_1046 1 0.000
#> Sample_1047 1 0.000
#> Sample_1048 2 1.000
#> Sample_1049 1 0.000
#> Sample_1050 1 0.000
#> Sample_1051 1 0.000
#> Sample_1052 1 0.000
#> Sample_1053 1 0.000
#> Sample_1054 1 0.000
#> Sample_1055 1 0.000
#> Sample_1056 1 0.000
#> Sample_1057 1 0.000
#> Sample_1058 1 0.000
#> Sample_1059 1 0.000
#> Sample_1060 1 0.000
#> Sample_1061 1 0.000
#> Sample_1062 1 0.000
#> Sample_1063 1 0.000
#> Sample_1064 1 0.000
#> Sample_1065 1 0.000
#> Sample_1066 1 0.000
#> Sample_1067 1 0.000
#> Sample_1068 1 0.000
#> Sample_1069 1 0.000
#> Sample_1070 1 0.000
#> Sample_1071 1 0.000
#> Sample_1072 1 0.000
#> Sample_1073 1 0.000
#> Sample_1074 1 0.000
#> Sample_1075 1 0.000
#> Sample_1076 1 0.000
#> Sample_1077 1 0.000
#> Sample_1078 1 0.000
#> Sample_1079 1 0.000
#> Sample_1080 1 0.000
#> Sample_1081 1 0.000
#> Sample_1082 1 0.000
#> Sample_1083 1 0.000
#> Sample_1084 1 0.000
#> Sample_1085 1 0.000
#> Sample_1086 2 0.751
#> Sample_1087 1 0.000
#> Sample_1088 1 0.000
#> Sample_1089 1 0.000
#> Sample_1090 1 0.000
#> Sample_1091 1 0.000
#> Sample_1092 1 0.000
#> Sample_1093 1 0.000
#> Sample_1094 1 0.000
#> Sample_1095 1 0.000
#> Sample_1096 1 0.000
#> Sample_1097 1 0.000
#> Sample_1098 1 0.000
#> Sample_1099 1 0.000
#> Sample_1100 1 0.000
#> Sample_1101 1 0.000
#> Sample_1102 1 0.000
#> Sample_1103 1 0.000
#> Sample_1104 1 0.000
#> Sample_1105 1 0.000
#> Sample_1106 1 0.000
#> Sample_1107 1 0.000
#> Sample_1108 1 0.000
#> Sample_1109 1 0.000
#> Sample_1110 1 0.000
#> Sample_1111 1 0.000
#> Sample_1112 1 0.000
#> Sample_1113 1 0.000
#> Sample_1114 1 0.000
#> Sample_1115 1 0.000
#> Sample_1116 1 0.000
#> Sample_1117 1 0.000
#> Sample_1118 1 0.000
#> Sample_1119 1 0.000
#> Sample_1120 1 1.000
#> Sample_1121 1 0.000
#> Sample_1122 1 0.000
#> Sample_1123 1 0.000
#> Sample_1124 1 0.000
#> Sample_1125 1 0.000
#> Sample_1126 1 0.000
#> Sample_1127 1 0.000
#> Sample_1128 1 0.000
#> Sample_1129 1 0.000
#> Sample_1130 1 0.000
#> Sample_1131 1 0.000
#> Sample_1132 1 0.000
#> Sample_1133 1 0.000
#> Sample_1134 1 0.498
#> Sample_1135 1 0.000
#> Sample_1136 1 0.000
#> Sample_1137 1 1.000
#> Sample_1138 1 0.000
#> Sample_1139 1 0.000
#> Sample_1140 1 1.000
#> Sample_1141 1 0.000
#> Sample_1142 1 0.000
#> Sample_1143 1 0.000
#> Sample_1144 1 0.000
#> Sample_1145 1 0.000
#> Sample_1146 1 0.000
#> Sample_1147 1 0.000
#> Sample_1148 1 0.000
#> Sample_1149 1 0.000
#> Sample_1150 1 0.000
#> Sample_1151 1 0.000
#> Sample_1152 1 0.000
#> Sample_1153 1 0.000
#> Sample_1154 1 0.000
#> Sample_1155 1 0.000
#> Sample_1156 1 0.000
#> Sample_1157 1 0.000
#> Sample_1158 1 0.000
#> Sample_1159 1 0.000
#> Sample_1160 1 0.000
#> Sample_1161 1 0.000
#> Sample_1162 1 0.000
#> Sample_1163 1 0.000
#> Sample_1164 1 0.000
#> Sample_1165 1 0.000
#> Sample_1166 1 0.000
#> Sample_1167 1 0.000
#> Sample_1168 1 0.000
#> Sample_1169 1 0.000
#> Sample_1170 1 0.000
#> Sample_1171 1 0.000
#> Sample_1172 1 0.000
#> Sample_1173 1 0.000
#> Sample_1174 1 0.000
#> Sample_1175 1 0.000
#> Sample_1176 1 0.000
#> Sample_1177 1 0.000
#> Sample_1178 1 0.000
#> Sample_1179 1 0.000
#> Sample_1180 1 0.000
#> Sample_1181 1 0.000
#> Sample_1182 1 0.000
#> Sample_1183 1 0.249
#> Sample_1184 1 0.000
#> Sample_1185 1 0.000
#> Sample_1186 1 0.000
#> Sample_1187 1 0.000
#> Sample_1188 1 0.000
#> Sample_1189 1 0.000
#> Sample_1190 1 0.000
#> Sample_1191 1 0.000
#> Sample_1192 1 0.000
#> Sample_1193 1 0.000
#> Sample_1194 1 0.000
#> Sample_1195 1 0.000
#> Sample_1196 1 0.000
#> Sample_1197 1 0.000
#> Sample_1198 1 0.000
#> Sample_1199 1 0.000
#> Sample_1200 1 0.000
#> Sample_1201 1 0.000
#> Sample_1202 1 0.000
#> Sample_1203 1 0.000
#> Sample_1204 1 0.000
#> Sample_1205 1 0.000
#> Sample_1206 1 0.000
#> Sample_1207 1 0.000
#> Sample_1208 1 0.000
#> Sample_1209 1 0.000
#> Sample_1210 1 0.000
#> Sample_1211 1 0.000
#> Sample_1212 1 0.000
#> Sample_1213 1 0.000
#> Sample_1214 1 0.000
#> Sample_1215 1 0.000
#> Sample_1216 1 0.000
#> Sample_1217 1 0.000
#> Sample_1218 1 0.000
#> Sample_1219 1 0.000
#> Sample_1220 1 0.000
#> Sample_1221 1 0.000
#> Sample_1222 1 0.000
#> Sample_1223 1 0.000
#> Sample_1224 1 0.000
#> Sample_1225 1 0.000
#> Sample_1226 1 0.000
#> Sample_1227 1 0.000
#> Sample_1228 1 0.000
#> Sample_1229 1 0.000
#> Sample_1230 1 0.000
#> Sample_1231 1 0.000
#> Sample_1232 1 0.000
#> Sample_1233 1 0.000
#> Sample_1234 1 0.000
#> Sample_1235 1 0.000
#> Sample_1236 1 0.000
#> Sample_1237 1 0.000
#> Sample_1238 1 0.000
#> Sample_1239 1 0.000
#> Sample_1240 1 0.000
#> Sample_1241 1 0.000
#> Sample_1242 1 0.000
#> Sample_1243 1 0.000
#> Sample_1244 1 0.000
#> Sample_1245 1 0.000
#> Sample_1246 1 0.000
#> Sample_1247 1 0.000
#> Sample_1248 1 0.000
#> Sample_1249 1 0.000
#> Sample_1250 1 0.000
#> Sample_1251 1 0.000
#> Sample_1252 1 0.000
#> Sample_1253 1 0.000
#> Sample_1254 1 0.000
#> Sample_1255 1 1.000
#> Sample_1256 1 0.000
#> Sample_1257 1 0.000
#> Sample_1258 1 0.000
#> Sample_1259 1 0.000
#> Sample_1260 1 0.000
#> Sample_1261 1 0.000
#> Sample_1262 1 0.000
#> Sample_1263 1 0.000
#> Sample_1264 1 0.000
#> Sample_1265 1 0.000
#> Sample_1266 1 0.000
#> Sample_1267 1 0.000
#> Sample_1268 1 0.000
#> Sample_1269 1 0.000
#> Sample_1270 1 0.000
#> Sample_1271 1 0.000
#> Sample_1272 1 0.000
#> Sample_1273 1 0.000
#> Sample_1274 1 0.000
#> Sample_1275 1 0.000
#> Sample_1276 1 0.000
#> Sample_1277 1 0.000
#> Sample_1278 1 0.000
#> Sample_1279 1 0.000
#> Sample_1280 1 0.000
#> Sample_1281 1 0.000
#> Sample_1282 1 0.000
#> Sample_1283 1 0.000
#> Sample_1284 1 0.000
#> Sample_1285 1 0.000
#> Sample_1286 1 0.000
#> Sample_1287 1 0.000
#> Sample_1288 1 0.000
#> Sample_1289 1 0.000
#> Sample_1290 1 0.000
#> Sample_1291 1 0.000
#> Sample_1292 1 0.000
#> Sample_1293 1 0.000
#> Sample_1294 1 0.000
#> Sample_1295 1 0.000
#> Sample_1296 1 0.000
#> Sample_1297 1 0.000
#> Sample_1298 1 0.000
#> Sample_1299 1 0.000
#> Sample_1300 1 0.000
#> Sample_1301 1 0.000
#> Sample_1302 1 0.000
#> Sample_1303 1 0.000
#> Sample_1304 1 0.000
#> Sample_1305 1 0.000
#> Sample_1306 1 0.751
#> Sample_1307 1 0.000
#> Sample_1308 1 0.000
#> Sample_1309 1 0.000
#> Sample_1310 1 0.249
#> Sample_1311 1 0.000
#> Sample_1312 1 0.000
#> Sample_1313 1 0.000
#> Sample_1314 1 0.000
#> Sample_1315 1 0.000
#> Sample_1316 1 0.000
#> Sample_1317 1 0.000
#> Sample_1318 1 0.000
#> Sample_1319 1 0.000
#> Sample_1320 1 0.000
#> Sample_1321 1 0.000
#> Sample_1322 1 0.000
#> Sample_1323 1 0.000
#> Sample_1324 1 0.000
#> Sample_1325 1 0.000
#> Sample_1326 1 0.000
#> Sample_1327 1 0.000
#> Sample_1328 1 0.000
#> Sample_1329 1 0.000
#> Sample_1330 1 0.000
#> Sample_1331 1 0.000
#> Sample_1332 1 0.000
#> Sample_1333 1 0.000
#> Sample_1334 1 0.000
#> Sample_1335 1 0.000
#> Sample_1336 1 0.000
#> Sample_1337 1 0.000
#> Sample_1338 1 0.000
#> Sample_1339 1 0.000
#> Sample_1340 1 0.000
#> Sample_1341 1 0.000
#> Sample_1342 1 0.000
#> Sample_1343 1 0.000
#> Sample_1344 1 0.000
#> Sample_1345 1 0.000
#> Sample_1346 1 0.000
#> Sample_1347 1 0.000
#> Sample_1348 1 0.000
#> Sample_1349 1 0.000
#> Sample_1350 1 0.000
#> Sample_1351 1 0.000
#> Sample_1352 1 0.000
#> Sample_1353 1 0.000
#> Sample_1354 1 0.000
#> Sample_1355 1 0.000
#> Sample_1356 1 0.000
#> Sample_1357 1 0.000
#> Sample_1358 1 0.000
#> Sample_1359 1 0.000
#> Sample_1360 1 0.000
#> Sample_1361 1 0.000
#> Sample_1362 1 0.000
#> Sample_1363 1 0.000
#> Sample_1364 1 0.000
#> Sample_1365 1 0.000
#> Sample_1366 1 0.000
#> Sample_1367 1 0.000
#> Sample_1368 1 0.000
#> Sample_1369 1 0.000
#> Sample_1370 1 0.000
#> Sample_1371 1 0.000
#> Sample_1372 1 0.000
#> Sample_1373 1 0.000
#> Sample_1374 1 0.000
#> Sample_1375 1 0.000
#> Sample_1376 2 1.000
#> Sample_1377 1 0.000
#> Sample_1378 1 0.000
#> Sample_1379 1 0.000
#> Sample_1380 1 0.000
#> Sample_1381 1 0.000
#> Sample_1382 1 0.000
#> Sample_1383 1 0.000
#> Sample_1384 1 0.000
#> Sample_1385 1 0.000
#> Sample_1386 1 0.000
#> Sample_1387 1 0.000
#> Sample_1388 1 0.000
#> Sample_1389 1 0.000
#> Sample_1390 1 0.000
#> Sample_1391 1 0.000
#> Sample_1392 1 0.000
#> Sample_1393 1 0.000
#> Sample_1394 1 0.000
#> Sample_1395 1 0.000
#> Sample_1396 1 0.000
#> Sample_1397 1 0.000
#> Sample_1398 1 0.000
#> Sample_1399 1 0.000
#> Sample_1400 1 0.000
#> Sample_1401 1 0.000
#> Sample_1402 1 0.000
#> Sample_1403 1 0.000
#> Sample_1404 1 0.000
#> Sample_1405 1 0.000
#> Sample_1406 1 0.000
#> Sample_1407 1 0.000
#> Sample_1408 1 0.000
#> Sample_1409 1 0.000
#> Sample_1410 1 0.000
#> Sample_1411 1 0.000
#> Sample_1412 1 0.000
#> Sample_1413 1 0.000
#> Sample_1414 1 0.000
#> Sample_1415 1 0.000
#> Sample_1416 1 0.000
#> Sample_1417 1 0.000
#> Sample_1418 1 0.000
#> Sample_1419 1 0.000
#> Sample_1420 1 0.000
#> Sample_1421 1 0.000
#> Sample_1422 1 0.000
#> Sample_1423 1 0.000
#> Sample_1424 1 0.000
#> Sample_1425 1 0.000
#> Sample_1426 1 0.000
#> Sample_1427 1 0.000
#> Sample_1428 1 0.000
#> Sample_1429 1 0.000
#> Sample_1430 1 0.000
#> Sample_1431 1 0.000
#> Sample_1432 1 0.000
#> Sample_1433 1 0.000
#> Sample_1434 1 0.000
#> Sample_1435 1 0.000
#> Sample_1436 1 0.000
#> Sample_1437 1 0.000
#> Sample_1438 1 0.000
#> Sample_1439 1 0.000
#> Sample_1440 1 0.000
#> Sample_1441 1 0.000
#> Sample_1442 1 0.000
#> Sample_1443 1 0.000
#> Sample_1444 1 0.000
#> Sample_1445 1 0.000
#> Sample_1446 1 0.000
#> Sample_1447 1 0.000
#> Sample_1448 1 0.000
#> Sample_1449 1 0.000
#> Sample_1450 1 0.000
#> Sample_1451 1 0.000
#> Sample_1452 1 0.000
#> Sample_1453 1 0.000
#> Sample_1454 1 0.000
#> Sample_1455 1 0.000
#> Sample_1456 1 0.000
#> Sample_1457 1 0.000
#> Sample_1458 1 0.000
#> Sample_1459 1 0.000
#> Sample_1460 1 0.498
#> Sample_1461 1 0.000
#> Sample_1462 1 0.000
#> Sample_1463 1 0.000
#> Sample_1464 1 0.000
#> Sample_1465 1 0.000
#> Sample_1466 1 0.000
#> Sample_1467 1 0.000
#> Sample_1468 1 0.000
#> Sample_1469 1 0.000
#> Sample_1470 1 0.000
#> Sample_1471 1 0.000
#> Sample_1472 1 0.000
#> Sample_1473 1 0.000
#> Sample_1474 1 0.000
#> Sample_1475 1 0.000
#> Sample_1476 1 0.000
#> Sample_1477 1 0.000
#> Sample_1478 1 0.000
#> Sample_1479 1 0.000
#> Sample_1480 1 0.000
#> Sample_1481 1 0.000
#> Sample_1482 1 0.000
#> Sample_1483 1 0.000
#> Sample_1484 1 0.000
#> Sample_1485 1 0.000
#> Sample_1486 1 0.000
#> Sample_1487 1 0.000
#> Sample_1488 1 0.000
#> Sample_1489 1 0.000
#> Sample_1490 1 0.000
#> Sample_1491 1 0.000
#> Sample_1492 1 0.000
#> Sample_1493 1 0.000
#> Sample_1494 1 0.000
#> Sample_1495 1 0.000
#> Sample_1496 1 0.000
#> Sample_1497 1 0.000
#> Sample_1498 1 0.000
#> Sample_1499 1 0.000
#> Sample_1500 1 0.000
#> Sample_1501 1 0.000
#> Sample_1502 1 0.000
#> Sample_1503 1 0.000
#> Sample_1504 1 0.000
#> Sample_1505 1 0.000
#> Sample_1506 1 0.000
#> Sample_1507 1 0.000
#> Sample_1508 1 0.000
#> Sample_1509 1 0.000
#> Sample_1510 1 0.000
#> Sample_1511 1 0.000
#> Sample_1512 1 0.000
#> Sample_1513 1 0.000
#> Sample_1514 1 0.000
#> Sample_1515 1 0.000
#> Sample_1516 1 0.000
#> Sample_1517 2 0.000
#> Sample_1518 2 0.000
#> Sample_1519 2 0.000
#> Sample_1520 2 0.000
#> Sample_1521 2 0.000
#> Sample_1522 2 0.747
#> Sample_1523 1 1.000
#> Sample_1524 2 0.000
#> Sample_1525 1 1.000
#> Sample_1526 1 1.000
#> Sample_1527 2 0.751
#> Sample_1528 2 0.000
#> Sample_1529 2 0.000
#> Sample_1530 2 0.000
#> Sample_1531 2 0.000
#> Sample_1532 2 0.000
#> Sample_1533 2 0.000
#> Sample_1534 2 0.000
#> Sample_1535 2 0.747
#> Sample_1536 2 0.249
#> Sample_1537 2 0.000
#> Sample_1538 2 0.000
#> Sample_1539 2 0.751
#> Sample_1540 2 1.000
#> Sample_1541 1 0.747
#> Sample_1542 2 1.000
#> Sample_1543 2 0.000
#> Sample_1544 2 1.000
#> Sample_1545 1 1.000
#> Sample_1546 2 0.000
#> Sample_1547 1 0.000
#> Sample_1548 2 0.000
#> Sample_1549 1 0.751
#> Sample_1550 2 0.000
#> Sample_1551 2 0.751
#> Sample_1552 2 0.249
#> Sample_1553 1 0.000
#> Sample_1554 2 1.000
#> Sample_1555 2 0.000
#> Sample_1556 1 0.000
#> Sample_1557 1 0.000
#> Sample_1558 1 0.249
#> Sample_1559 2 0.747
#> Sample_1560 1 0.000
#> Sample_1561 2 0.747
#> Sample_1562 1 1.000
#> Sample_1563 1 1.000
#> Sample_1564 1 0.747
#> Sample_1565 1 0.000
#> Sample_1566 1 0.253
#> Sample_1567 1 0.000
#> Sample_1568 1 0.000
#> Sample_1569 1 0.751
#> Sample_1570 2 0.000
#> Sample_1571 1 1.000
#> Sample_1572 1 1.000
#> Sample_1573 1 0.000
#> Sample_1574 2 0.249
#> Sample_1575 2 0.000
#> Sample_1576 2 1.000
#> Sample_1577 1 0.253
#> Sample_1578 1 0.502
#> Sample_1579 1 0.000
#> Sample_1580 1 0.751
#> Sample_1581 1 0.498
#> Sample_1582 2 0.249
#> Sample_1583 1 0.000
#> Sample_1584 1 0.498
#> Sample_1585 2 0.747
#> Sample_1586 2 0.000
#> Sample_1587 2 0.502
#> Sample_1588 2 1.000
#> Sample_1589 2 0.000
#> Sample_1590 1 1.000
#> Sample_1591 1 1.000
#> Sample_1592 2 0.000
#> Sample_1593 2 0.000
#> Sample_1594 2 0.249
#> Sample_1595 2 0.000
#> Sample_1596 2 0.000
#> Sample_1597 1 0.000
#> Sample_1598 1 0.000
#> Sample_1599 2 0.000
#> Sample_1600 1 0.502
get_classes(res, k = 3)
#> class p
#> Sample_1 1 0.000
#> Sample_2 2 0.000
#> Sample_3 2 0.000
#> Sample_4 2 0.000
#> Sample_5 2 0.000
#> Sample_6 2 0.000
#> Sample_7 2 0.000
#> Sample_8 2 0.000
#> Sample_9 2 0.000
#> Sample_10 2 0.000
#> Sample_11 2 0.000
#> Sample_12 2 0.000
#> Sample_13 2 0.000
#> Sample_14 2 0.000
#> Sample_15 2 0.000
#> Sample_16 2 0.000
#> Sample_17 2 0.000
#> Sample_18 2 0.000
#> Sample_19 2 0.000
#> Sample_20 2 0.000
#> Sample_21 2 0.000
#> Sample_22 2 0.000
#> Sample_23 2 0.000
#> Sample_24 2 0.000
#> Sample_25 2 0.000
#> Sample_26 2 0.000
#> Sample_27 2 0.000
#> Sample_28 2 0.000
#> Sample_29 2 0.000
#> Sample_30 2 0.000
#> Sample_31 2 0.000
#> Sample_32 2 0.000
#> Sample_33 2 0.000
#> Sample_34 2 0.000
#> Sample_35 2 0.000
#> Sample_36 2 0.000
#> Sample_37 2 0.000
#> Sample_38 2 0.000
#> Sample_39 2 0.000
#> Sample_40 2 0.000
#> Sample_41 2 0.000
#> Sample_42 2 0.000
#> Sample_43 2 0.000
#> Sample_44 2 0.000
#> Sample_45 2 0.000
#> Sample_46 2 0.000
#> Sample_47 2 0.000
#> Sample_48 2 0.000
#> Sample_49 2 0.000
#> Sample_50 2 0.000
#> Sample_51 2 0.000
#> Sample_52 2 0.000
#> Sample_53 2 0.000
#> Sample_54 2 0.000
#> Sample_55 2 0.000
#> Sample_56 2 0.000
#> Sample_57 2 0.000
#> Sample_58 2 0.000
#> Sample_59 2 0.000
#> Sample_60 2 0.000
#> Sample_61 2 0.000
#> Sample_62 2 0.000
#> Sample_63 2 0.000
#> Sample_64 2 0.000
#> Sample_65 2 0.000
#> Sample_66 2 0.000
#> Sample_67 2 0.000
#> Sample_68 2 0.000
#> Sample_69 2 0.000
#> Sample_70 2 0.000
#> Sample_71 2 0.000
#> Sample_72 2 0.000
#> Sample_73 2 0.000
#> Sample_74 2 0.000
#> Sample_75 2 0.000
#> Sample_76 2 0.000
#> Sample_77 2 0.000
#> Sample_78 2 0.000
#> Sample_79 2 0.000
#> Sample_80 2 0.000
#> Sample_81 2 0.000
#> Sample_82 2 0.000
#> Sample_83 2 0.000
#> Sample_84 2 0.000
#> Sample_85 2 0.000
#> Sample_86 2 0.000
#> Sample_87 2 0.000
#> Sample_88 2 0.000
#> Sample_89 2 0.000
#> Sample_90 2 0.000
#> Sample_91 2 0.000
#> Sample_92 2 0.000
#> Sample_93 2 0.000
#> Sample_94 2 0.000
#> Sample_95 2 0.000
#> Sample_96 2 0.000
#> Sample_97 2 0.000
#> Sample_98 2 0.000
#> Sample_99 2 0.000
#> Sample_100 2 0.000
#> Sample_101 2 0.000
#> Sample_102 2 0.000
#> Sample_103 2 0.000
#> Sample_104 2 0.000
#> Sample_105 2 0.000
#> Sample_106 2 0.000
#> Sample_107 2 0.000
#> Sample_108 2 0.000
#> Sample_109 2 0.000
#> Sample_110 2 0.000
#> Sample_111 2 1.000
#> Sample_112 2 0.000
#> Sample_113 2 0.000
#> Sample_114 2 0.000
#> Sample_115 2 0.000
#> Sample_116 2 0.000
#> Sample_117 2 0.000
#> Sample_118 2 0.000
#> Sample_119 2 0.000
#> Sample_120 2 0.000
#> Sample_121 1 1.000
#> Sample_122 2 0.000
#> Sample_123 2 0.000
#> Sample_124 2 0.000
#> Sample_125 2 0.000
#> Sample_126 2 0.000
#> Sample_127 2 0.000
#> Sample_128 2 0.000
#> Sample_129 2 0.000
#> Sample_130 2 0.000
#> Sample_131 2 0.000
#> Sample_132 2 0.000
#> Sample_133 2 0.000
#> Sample_134 2 0.000
#> Sample_135 2 0.000
#> Sample_136 2 0.000
#> Sample_137 2 0.000
#> Sample_138 2 0.000
#> Sample_139 2 0.000
#> Sample_140 2 0.000
#> Sample_141 2 0.000
#> Sample_142 2 0.000
#> Sample_143 2 0.000
#> Sample_144 2 0.000
#> Sample_145 2 0.000
#> Sample_146 2 0.000
#> Sample_147 2 0.000
#> Sample_148 2 0.000
#> Sample_149 2 0.000
#> Sample_150 2 0.000
#> Sample_151 2 0.000
#> Sample_152 2 0.000
#> Sample_153 2 0.000
#> Sample_154 2 0.000
#> Sample_155 2 0.000
#> Sample_156 2 0.000
#> Sample_157 2 0.000
#> Sample_158 2 0.000
#> Sample_159 2 0.000
#> Sample_160 2 0.000
#> Sample_161 2 0.000
#> Sample_162 2 0.000
#> Sample_163 2 0.000
#> Sample_164 2 0.000
#> Sample_165 2 0.000
#> Sample_166 2 0.000
#> Sample_167 2 0.000
#> Sample_168 2 0.000
#> Sample_169 2 0.000
#> Sample_170 2 0.000
#> Sample_171 2 0.000
#> Sample_172 2 0.000
#> Sample_173 2 0.000
#> Sample_174 2 0.000
#> Sample_175 2 0.000
#> Sample_176 2 0.000
#> Sample_177 2 0.000
#> Sample_178 2 0.000
#> Sample_179 2 0.000
#> Sample_180 2 0.000
#> Sample_181 2 0.000
#> Sample_182 2 0.000
#> Sample_183 2 0.000
#> Sample_184 2 0.000
#> Sample_185 2 0.000
#> Sample_186 2 0.000
#> Sample_187 2 0.000
#> Sample_188 2 0.000
#> Sample_189 2 0.000
#> Sample_190 2 0.000
#> Sample_191 2 0.000
#> Sample_192 2 0.000
#> Sample_193 2 0.000
#> Sample_194 2 0.000
#> Sample_195 2 0.000
#> Sample_196 2 0.000
#> Sample_197 2 0.000
#> Sample_198 2 0.000
#> Sample_199 2 0.000
#> Sample_200 2 0.000
#> Sample_201 2 0.000
#> Sample_202 2 0.000
#> Sample_203 2 0.000
#> Sample_204 2 0.000
#> Sample_205 2 0.000
#> Sample_206 2 0.000
#> Sample_207 2 0.000
#> Sample_208 3 0.000
#> Sample_209 3 0.000
#> Sample_210 3 0.000
#> Sample_211 3 0.000
#> Sample_212 3 0.000
#> Sample_213 3 0.000
#> Sample_214 1 0.249
#> Sample_215 1 1.000
#> Sample_216 3 1.000
#> Sample_217 1 0.000
#> Sample_218 3 0.000
#> Sample_219 3 0.502
#> Sample_220 3 0.000
#> Sample_221 3 0.000
#> Sample_222 3 0.000
#> Sample_223 3 0.000
#> Sample_224 1 1.000
#> Sample_225 3 0.000
#> Sample_226 1 0.502
#> Sample_227 3 0.000
#> Sample_228 3 0.000
#> Sample_229 3 0.000
#> Sample_230 3 0.000
#> Sample_231 3 0.000
#> Sample_232 3 0.000
#> Sample_233 3 0.000
#> Sample_234 3 0.000
#> Sample_235 3 0.000
#> Sample_236 1 1.000
#> Sample_237 3 1.000
#> Sample_238 3 0.000
#> Sample_239 3 0.000
#> Sample_240 3 0.000
#> Sample_241 1 1.000
#> Sample_242 3 0.000
#> Sample_243 3 0.000
#> Sample_244 3 0.000
#> Sample_245 3 0.000
#> Sample_246 3 0.000
#> Sample_247 3 0.000
#> Sample_248 3 0.000
#> Sample_249 3 0.751
#> Sample_250 3 0.000
#> Sample_251 3 0.000
#> Sample_252 3 0.000
#> Sample_253 3 0.000
#> Sample_254 3 0.000
#> Sample_255 3 0.000
#> Sample_256 3 0.000
#> Sample_257 3 0.000
#> Sample_258 1 1.000
#> Sample_259 3 0.000
#> Sample_260 3 0.000
#> Sample_261 3 0.000
#> Sample_262 3 0.000
#> Sample_263 3 0.000
#> Sample_264 1 1.000
#> Sample_265 1 1.000
#> Sample_266 3 0.000
#> Sample_267 3 0.000
#> Sample_268 3 0.000
#> Sample_269 3 0.000
#> Sample_270 3 0.000
#> Sample_271 3 0.000
#> Sample_272 1 1.000
#> Sample_273 3 0.249
#> Sample_274 3 1.000
#> Sample_275 1 1.000
#> Sample_276 1 1.000
#> Sample_277 3 1.000
#> Sample_278 3 0.000
#> Sample_279 3 0.000
#> Sample_280 3 0.000
#> Sample_281 3 0.000
#> Sample_282 3 0.000
#> Sample_283 3 0.000
#> Sample_284 3 0.000
#> Sample_285 3 0.000
#> Sample_286 3 0.000
#> Sample_287 1 1.000
#> Sample_288 3 0.000
#> Sample_289 3 0.000
#> Sample_290 3 0.751
#> Sample_291 3 0.000
#> Sample_292 3 0.000
#> Sample_293 3 0.000
#> Sample_294 3 0.000
#> Sample_295 3 0.000
#> Sample_296 3 0.000
#> Sample_297 3 0.000
#> Sample_298 3 0.000
#> Sample_299 3 0.000
#> Sample_300 3 0.000
#> Sample_301 3 0.000
#> Sample_302 1 1.000
#> Sample_303 1 0.253
#> Sample_304 1 0.000
#> Sample_305 1 0.502
#> Sample_306 3 0.000
#> Sample_307 3 0.502
#> Sample_308 1 0.249
#> Sample_309 1 0.498
#> Sample_310 1 0.000
#> Sample_311 1 0.747
#> Sample_312 1 0.000
#> Sample_313 1 1.000
#> Sample_314 1 1.000
#> Sample_315 3 0.000
#> Sample_316 1 0.000
#> Sample_317 3 0.000
#> Sample_318 1 0.000
#> Sample_319 1 0.000
#> Sample_320 1 0.000
#> Sample_321 1 0.000
#> Sample_322 3 1.000
#> Sample_323 1 0.000
#> Sample_324 1 1.000
#> Sample_325 1 1.000
#> Sample_326 1 0.249
#> Sample_327 1 0.249
#> Sample_328 1 0.253
#> Sample_329 1 0.000
#> Sample_330 1 0.000
#> Sample_331 3 0.000
#> Sample_332 3 0.000
#> Sample_333 3 0.000
#> Sample_334 3 0.000
#> Sample_335 3 0.000
#> Sample_336 3 0.000
#> Sample_337 3 0.000
#> Sample_338 3 0.747
#> Sample_339 3 0.000
#> Sample_340 3 0.000
#> Sample_341 3 0.000
#> Sample_342 3 0.000
#> Sample_343 3 0.000
#> Sample_344 3 0.000
#> Sample_345 3 0.000
#> Sample_346 3 0.253
#> Sample_347 3 0.000
#> Sample_348 3 0.000
#> Sample_349 3 0.502
#> Sample_350 1 1.000
#> Sample_351 1 0.751
#> Sample_352 1 0.751
#> Sample_353 3 0.000
#> Sample_354 3 0.000
#> Sample_355 1 0.751
#> Sample_356 3 0.000
#> Sample_357 3 0.000
#> Sample_358 1 0.000
#> Sample_359 3 0.000
#> Sample_360 1 1.000
#> Sample_361 3 0.000
#> Sample_362 1 1.000
#> Sample_363 3 0.000
#> Sample_364 3 1.000
#> Sample_365 1 0.498
#> Sample_366 3 0.000
#> Sample_367 3 0.000
#> Sample_368 1 1.000
#> Sample_369 1 1.000
#> Sample_370 3 0.751
#> Sample_371 3 0.000
#> Sample_372 1 0.000
#> Sample_373 1 0.751
#> Sample_374 3 0.253
#> Sample_375 3 0.000
#> Sample_376 3 0.000
#> Sample_377 1 1.000
#> Sample_378 3 0.000
#> Sample_379 3 1.000
#> Sample_380 3 0.000
#> Sample_381 1 0.000
#> Sample_382 3 0.000
#> Sample_383 3 0.000
#> Sample_384 3 0.000
#> Sample_385 1 0.751
#> Sample_386 1 0.000
#> Sample_387 3 1.000
#> Sample_388 3 0.000
#> Sample_389 1 1.000
#> Sample_390 1 0.000
#> Sample_391 3 0.000
#> Sample_392 3 0.000
#> Sample_393 1 0.000
#> Sample_394 1 0.751
#> Sample_395 1 1.000
#> Sample_396 1 1.000
#> Sample_397 1 1.000
#> Sample_398 1 1.000
#> Sample_399 3 0.000
#> Sample_400 3 0.249
#> Sample_401 3 0.751
#> Sample_402 3 0.000
#> Sample_403 1 1.000
#> Sample_404 1 0.000
#> Sample_405 1 0.249
#> Sample_406 3 0.751
#> Sample_407 3 0.000
#> Sample_408 1 0.502
#> Sample_409 1 0.000
#> Sample_410 3 1.000
#> Sample_411 3 1.000
#> Sample_412 1 0.498
#> Sample_413 3 0.000
#> Sample_414 3 1.000
#> Sample_415 3 0.000
#> Sample_416 1 0.000
#> Sample_417 3 0.000
#> Sample_418 3 0.000
#> Sample_419 3 1.000
#> Sample_420 3 0.000
#> Sample_421 1 1.000
#> Sample_422 3 0.000
#> Sample_423 3 0.000
#> Sample_424 1 0.751
#> Sample_425 1 1.000
#> Sample_426 3 0.000
#> Sample_427 1 1.000
#> Sample_428 1 1.000
#> Sample_429 3 0.000
#> Sample_430 1 1.000
#> Sample_431 1 1.000
#> Sample_432 1 0.000
#> Sample_433 1 1.000
#> Sample_434 1 0.000
#> Sample_435 3 0.000
#> Sample_436 3 0.000
#> Sample_437 3 0.000
#> Sample_438 1 1.000
#> Sample_439 1 0.000
#> Sample_440 1 0.751
#> Sample_441 3 0.000
#> Sample_442 1 0.249
#> Sample_443 1 0.249
#> Sample_444 3 1.000
#> Sample_445 1 0.000
#> Sample_446 1 0.747
#> Sample_447 3 0.000
#> Sample_448 1 0.000
#> Sample_449 3 0.000
#> Sample_450 3 1.000
#> Sample_451 1 0.000
#> Sample_452 1 0.000
#> Sample_453 3 1.000
#> Sample_454 3 0.000
#> Sample_455 1 0.751
#> Sample_456 1 0.253
#> Sample_457 1 0.751
#> Sample_458 3 0.000
#> Sample_459 1 0.000
#> Sample_460 3 0.000
#> Sample_461 1 0.000
#> Sample_462 3 0.000
#> Sample_463 1 1.000
#> Sample_464 1 0.000
#> Sample_465 1 0.000
#> Sample_466 1 0.000
#> Sample_467 3 0.751
#> Sample_468 1 0.000
#> Sample_469 1 0.000
#> Sample_470 3 0.502
#> Sample_471 3 0.000
#> Sample_472 1 0.000
#> Sample_473 3 0.000
#> Sample_474 1 0.000
#> Sample_475 1 0.000
#> Sample_476 1 1.000
#> Sample_477 1 0.000
#> Sample_478 3 0.000
#> Sample_479 1 1.000
#> Sample_480 3 0.751
#> Sample_481 3 1.000
#> Sample_482 3 0.000
#> Sample_483 3 0.751
#> Sample_484 3 0.000
#> Sample_485 3 0.000
#> Sample_486 1 0.249
#> Sample_487 1 1.000
#> Sample_488 3 0.000
#> Sample_489 3 0.000
#> Sample_490 1 1.000
#> Sample_491 3 0.000
#> Sample_492 3 0.000
#> Sample_493 3 1.000
#> Sample_494 3 0.000
#> Sample_495 1 1.000
#> Sample_496 3 0.000
#> Sample_497 3 0.000
#> Sample_498 3 0.000
#> Sample_499 1 1.000
#> Sample_500 1 0.000
#> Sample_501 3 0.000
#> Sample_502 3 0.000
#> Sample_503 3 0.000
#> Sample_504 3 0.000
#> Sample_505 1 0.751
#> Sample_506 1 0.498
#> Sample_507 1 0.000
#> Sample_508 3 0.751
#> Sample_509 3 0.000
#> Sample_510 3 0.253
#> Sample_511 1 0.000
#> Sample_512 3 0.000
#> Sample_513 1 0.000
#> Sample_514 3 0.000
#> Sample_515 1 0.249
#> Sample_516 2 1.000
#> Sample_517 1 0.751
#> Sample_518 3 1.000
#> Sample_519 3 0.000
#> Sample_520 1 0.498
#> Sample_521 1 1.000
#> Sample_522 3 0.000
#> Sample_523 3 0.751
#> Sample_524 3 0.000
#> Sample_525 3 0.502
#> Sample_526 3 1.000
#> Sample_527 1 0.000
#> Sample_528 3 1.000
#> Sample_529 3 0.000
#> Sample_530 1 0.000
#> Sample_531 3 0.000
#> Sample_532 1 0.498
#> Sample_533 1 1.000
#> Sample_534 3 0.000
#> Sample_535 1 1.000
#> Sample_536 1 0.751
#> Sample_537 1 0.751
#> Sample_538 3 0.000
#> Sample_539 1 0.747
#> Sample_540 3 0.000
#> Sample_541 3 0.000
#> Sample_542 1 0.249
#> Sample_543 1 0.747
#> Sample_544 1 0.249
#> Sample_545 3 0.000
#> Sample_546 1 1.000
#> Sample_547 1 1.000
#> Sample_548 3 0.253
#> Sample_549 3 0.000
#> Sample_550 3 0.498
#> Sample_551 3 0.000
#> Sample_552 3 0.000
#> Sample_553 3 0.000
#> Sample_554 3 0.000
#> Sample_555 3 0.000
#> Sample_556 3 1.000
#> Sample_557 1 0.249
#> Sample_558 1 0.502
#> Sample_559 1 0.249
#> Sample_560 3 0.000
#> Sample_561 3 0.253
#> Sample_562 3 0.498
#> Sample_563 3 0.000
#> Sample_564 3 0.000
#> Sample_565 1 0.751
#> Sample_566 1 0.000
#> Sample_567 1 0.249
#> Sample_568 3 0.000
#> Sample_569 1 0.000
#> Sample_570 3 0.000
#> Sample_571 3 0.000
#> Sample_572 1 0.000
#> Sample_573 1 0.000
#> Sample_574 3 0.000
#> Sample_575 3 0.751
#> Sample_576 3 0.000
#> Sample_577 1 0.249
#> Sample_578 3 0.000
#> Sample_579 3 0.000
#> Sample_580 3 0.000
#> Sample_581 3 0.000
#> Sample_582 3 0.000
#> Sample_583 3 0.249
#> Sample_584 3 0.000
#> Sample_585 3 0.000
#> Sample_586 2 0.000
#> Sample_587 2 0.000
#> Sample_588 2 0.000
#> Sample_589 3 0.000
#> Sample_590 1 0.000
#> Sample_591 2 0.000
#> Sample_592 2 0.000
#> Sample_593 2 0.000
#> Sample_594 2 0.000
#> Sample_595 2 0.000
#> Sample_596 2 0.253
#> Sample_597 2 0.249
#> Sample_598 2 0.000
#> Sample_599 2 0.000
#> Sample_600 2 0.000
#> Sample_601 2 0.000
#> Sample_602 2 0.000
#> Sample_603 2 0.000
#> Sample_604 2 0.000
#> Sample_605 2 0.000
#> Sample_606 2 0.000
#> Sample_607 2 0.000
#> Sample_608 2 0.000
#> Sample_609 2 0.000
#> Sample_610 2 0.000
#> Sample_611 2 0.000
#> Sample_612 2 1.000
#> Sample_613 3 0.000
#> Sample_614 1 1.000
#> Sample_615 3 0.000
#> Sample_616 1 0.000
#> Sample_617 1 0.498
#> Sample_618 1 0.000
#> Sample_619 1 0.000
#> Sample_620 1 1.000
#> Sample_621 2 0.000
#> Sample_622 1 0.253
#> Sample_623 3 0.498
#> Sample_624 3 0.000
#> Sample_625 2 0.000
#> Sample_626 1 1.000
#> Sample_627 2 0.000
#> Sample_628 2 0.502
#> Sample_629 2 0.000
#> Sample_630 1 0.000
#> Sample_631 2 0.000
#> Sample_632 1 0.498
#> Sample_633 1 0.000
#> Sample_634 1 0.000
#> Sample_635 1 0.249
#> Sample_636 1 0.000
#> Sample_637 1 0.000
#> Sample_638 2 0.249
#> Sample_639 1 0.000
#> Sample_640 2 0.000
#> Sample_641 1 0.000
#> Sample_642 2 0.000
#> Sample_643 1 0.000
#> Sample_644 3 0.000
#> Sample_645 2 0.249
#> Sample_646 1 0.751
#> Sample_647 1 0.502
#> Sample_648 2 0.751
#> Sample_649 2 0.751
#> Sample_650 2 0.000
#> Sample_651 2 0.000
#> Sample_652 2 0.000
#> Sample_653 2 0.000
#> Sample_654 2 0.000
#> Sample_655 2 0.000
#> Sample_656 2 0.000
#> Sample_657 2 0.000
#> Sample_658 2 0.000
#> Sample_659 2 0.000
#> Sample_660 2 0.000
#> Sample_661 2 0.000
#> Sample_662 2 0.000
#> Sample_663 2 0.000
#> Sample_664 2 0.000
#> Sample_665 2 0.000
#> Sample_666 2 0.000
#> Sample_667 2 0.000
#> Sample_668 2 0.000
#> Sample_669 2 0.000
#> Sample_670 2 0.000
#> Sample_671 2 0.000
#> Sample_672 2 0.000
#> Sample_673 2 0.000
#> Sample_674 2 0.000
#> Sample_675 2 0.000
#> Sample_676 2 0.000
#> Sample_677 2 0.000
#> Sample_678 2 0.000
#> Sample_679 2 0.000
#> Sample_680 2 0.000
#> Sample_681 2 0.000
#> Sample_682 2 0.000
#> Sample_683 2 0.502
#> Sample_684 2 0.000
#> Sample_685 1 1.000
#> Sample_686 2 1.000
#> Sample_687 1 1.000
#> Sample_688 2 0.000
#> Sample_689 2 1.000
#> Sample_690 2 0.000
#> Sample_691 2 0.000
#> Sample_692 1 1.000
#> Sample_693 2 0.000
#> Sample_694 2 0.498
#> Sample_695 2 0.000
#> Sample_696 2 0.000
#> Sample_697 2 0.000
#> Sample_698 2 0.000
#> Sample_699 2 0.000
#> Sample_700 2 0.000
#> Sample_701 2 0.000
#> Sample_702 2 0.000
#> Sample_703 2 0.000
#> Sample_704 2 0.000
#> Sample_705 2 0.000
#> Sample_706 2 0.000
#> Sample_707 2 0.000
#> Sample_708 2 0.000
#> Sample_709 2 0.000
#> Sample_710 2 0.000
#> Sample_711 2 0.000
#> Sample_712 2 0.000
#> Sample_713 2 0.000
#> Sample_714 2 0.000
#> Sample_715 2 0.000
#> Sample_716 2 0.000
#> Sample_717 2 0.000
#> Sample_718 2 0.000
#> Sample_719 2 0.000
#> Sample_720 2 0.498
#> Sample_721 2 0.000
#> Sample_722 2 0.000
#> Sample_723 2 0.000
#> Sample_724 2 0.000
#> Sample_725 2 0.000
#> Sample_726 2 0.000
#> Sample_727 2 0.000
#> Sample_728 2 0.000
#> Sample_729 2 0.000
#> Sample_730 2 0.000
#> Sample_731 2 0.000
#> Sample_732 2 0.000
#> Sample_733 2 0.000
#> Sample_734 2 0.000
#> Sample_735 2 0.000
#> Sample_736 2 0.000
#> Sample_737 1 1.000
#> Sample_738 2 0.000
#> Sample_739 2 0.000
#> Sample_740 2 0.000
#> Sample_741 2 0.000
#> Sample_742 2 0.000
#> Sample_743 2 0.000
#> Sample_744 2 0.000
#> Sample_745 2 0.000
#> Sample_746 2 0.000
#> Sample_747 2 0.000
#> Sample_748 2 0.000
#> Sample_749 2 0.000
#> Sample_750 2 0.000
#> Sample_751 2 0.000
#> Sample_752 2 0.000
#> Sample_753 2 0.000
#> Sample_754 2 0.000
#> Sample_755 2 0.000
#> Sample_756 2 0.000
#> Sample_757 2 0.000
#> Sample_758 2 0.000
#> Sample_759 2 0.000
#> Sample_760 2 0.000
#> Sample_761 2 0.000
#> Sample_762 2 0.000
#> Sample_763 2 0.000
#> Sample_764 2 0.000
#> Sample_765 2 0.000
#> Sample_766 2 0.000
#> Sample_767 2 0.000
#> Sample_768 2 0.000
#> Sample_769 2 0.000
#> Sample_770 2 0.000
#> Sample_771 2 0.000
#> Sample_772 2 0.000
#> Sample_773 2 0.000
#> Sample_774 2 0.000
#> Sample_775 2 0.000
#> Sample_776 2 0.000
#> Sample_777 2 0.000
#> Sample_778 2 0.000
#> Sample_779 2 0.000
#> Sample_780 2 0.000
#> Sample_781 2 0.000
#> Sample_782 2 0.000
#> Sample_783 2 0.000
#> Sample_784 2 0.000
#> Sample_785 2 0.000
#> Sample_786 2 0.000
#> Sample_787 2 0.000
#> Sample_788 2 0.000
#> Sample_789 2 0.000
#> Sample_790 2 0.000
#> Sample_791 2 0.000
#> Sample_792 2 0.000
#> Sample_793 2 0.000
#> Sample_794 2 0.000
#> Sample_795 2 0.000
#> Sample_796 2 0.000
#> Sample_797 2 0.000
#> Sample_798 2 0.000
#> Sample_799 2 0.000
#> Sample_800 2 0.000
#> Sample_801 2 0.000
#> Sample_802 2 0.000
#> Sample_803 2 0.000
#> Sample_804 2 0.000
#> Sample_805 2 0.000
#> Sample_806 2 0.000
#> Sample_807 2 0.000
#> Sample_808 2 0.000
#> Sample_809 2 0.000
#> Sample_810 2 0.000
#> Sample_811 2 0.000
#> Sample_812 2 0.000
#> Sample_813 2 0.000
#> Sample_814 2 0.000
#> Sample_815 2 0.000
#> Sample_816 2 0.000
#> Sample_817 2 0.000
#> Sample_818 2 0.000
#> Sample_819 2 0.000
#> Sample_820 2 0.000
#> Sample_821 2 0.000
#> Sample_822 2 0.000
#> Sample_823 2 0.000
#> Sample_824 2 0.000
#> Sample_825 2 0.000
#> Sample_826 2 0.000
#> Sample_827 2 0.000
#> Sample_828 2 0.000
#> Sample_829 2 0.000
#> Sample_830 2 0.000
#> Sample_831 2 0.000
#> Sample_832 2 0.000
#> Sample_833 2 0.000
#> Sample_834 2 0.000
#> Sample_835 2 0.000
#> Sample_836 2 0.000
#> Sample_837 2 0.000
#> Sample_838 2 0.000
#> Sample_839 2 0.000
#> Sample_840 2 0.000
#> Sample_841 2 0.000
#> Sample_842 2 0.000
#> Sample_843 2 0.000
#> Sample_844 2 0.000
#> Sample_845 2 0.000
#> Sample_846 2 0.000
#> Sample_847 2 0.000
#> Sample_848 2 0.000
#> Sample_849 2 0.000
#> Sample_850 2 0.000
#> Sample_851 2 0.000
#> Sample_852 2 0.000
#> Sample_853 2 0.000
#> Sample_854 2 0.000
#> Sample_855 2 0.000
#> Sample_856 2 0.000
#> Sample_857 2 0.000
#> Sample_858 2 0.000
#> Sample_859 2 0.000
#> Sample_860 2 0.000
#> Sample_861 2 0.000
#> Sample_862 2 0.000
#> Sample_863 2 0.000
#> Sample_864 2 0.000
#> Sample_865 2 0.000
#> Sample_866 2 0.000
#> Sample_867 2 0.000
#> Sample_868 2 0.000
#> Sample_869 2 0.000
#> Sample_870 2 0.000
#> Sample_871 2 0.000
#> Sample_872 2 0.000
#> Sample_873 2 0.000
#> Sample_874 2 0.000
#> Sample_875 2 0.000
#> Sample_876 2 0.000
#> Sample_877 2 0.000
#> Sample_878 2 0.000
#> Sample_879 2 0.000
#> Sample_880 2 0.000
#> Sample_881 2 0.000
#> Sample_882 2 0.000
#> Sample_883 2 0.000
#> Sample_884 2 0.000
#> Sample_885 2 0.000
#> Sample_886 2 0.000
#> Sample_887 2 0.000
#> Sample_888 2 0.000
#> Sample_889 2 0.000
#> Sample_890 2 0.000
#> Sample_891 2 0.000
#> Sample_892 2 0.000
#> Sample_893 2 0.000
#> Sample_894 2 0.000
#> Sample_895 2 0.000
#> Sample_896 2 0.000
#> Sample_897 2 0.000
#> Sample_898 2 0.000
#> Sample_899 2 0.000
#> Sample_900 2 0.000
#> Sample_901 2 0.000
#> Sample_902 2 0.000
#> Sample_903 2 0.000
#> Sample_904 2 0.000
#> Sample_905 2 0.000
#> Sample_906 2 0.000
#> Sample_907 2 0.000
#> Sample_908 2 0.000
#> Sample_909 2 0.000
#> Sample_910 2 0.000
#> Sample_911 2 0.000
#> Sample_912 2 0.000
#> Sample_913 2 0.000
#> Sample_914 2 0.000
#> Sample_915 2 0.000
#> Sample_916 2 0.000
#> Sample_917 2 0.000
#> Sample_918 2 0.000
#> Sample_919 2 0.000
#> Sample_920 2 0.000
#> Sample_921 2 0.000
#> Sample_922 2 0.000
#> Sample_923 2 0.000
#> Sample_924 2 0.000
#> Sample_925 2 0.000
#> Sample_926 2 0.000
#> Sample_927 2 0.000
#> Sample_928 2 0.000
#> Sample_929 2 0.000
#> Sample_930 2 0.000
#> Sample_931 2 0.000
#> Sample_932 2 0.000
#> Sample_933 2 0.000
#> Sample_934 2 0.000
#> Sample_935 2 0.000
#> Sample_936 2 0.000
#> Sample_937 2 0.000
#> Sample_938 2 0.000
#> Sample_939 2 0.000
#> Sample_940 2 0.000
#> Sample_941 2 0.000
#> Sample_942 2 0.000
#> Sample_943 2 0.000
#> Sample_944 2 0.000
#> Sample_945 2 0.000
#> Sample_946 2 0.000
#> Sample_947 2 0.000
#> Sample_948 3 0.000
#> Sample_949 3 0.000
#> Sample_950 1 1.000
#> Sample_951 3 0.000
#> Sample_952 3 0.000
#> Sample_953 3 0.000
#> Sample_954 3 0.000
#> Sample_955 3 0.000
#> Sample_956 3 0.000
#> Sample_957 3 0.000
#> Sample_958 3 0.000
#> Sample_959 3 0.000
#> Sample_960 3 0.000
#> Sample_961 3 0.000
#> Sample_962 1 1.000
#> Sample_963 3 0.000
#> Sample_964 3 0.000
#> Sample_965 3 0.000
#> Sample_966 3 1.000
#> Sample_967 3 0.000
#> Sample_968 1 1.000
#> Sample_969 3 0.000
#> Sample_970 3 1.000
#> Sample_971 3 0.000
#> Sample_972 3 0.000
#> Sample_973 3 0.000
#> Sample_974 3 0.000
#> Sample_975 3 0.751
#> Sample_976 1 0.253
#> Sample_977 1 0.498
#> Sample_978 1 0.000
#> Sample_979 3 0.000
#> Sample_980 1 0.000
#> Sample_981 1 0.000
#> Sample_982 1 0.000
#> Sample_983 1 0.000
#> Sample_984 3 0.000
#> Sample_985 1 0.000
#> Sample_986 1 0.000
#> Sample_987 1 0.000
#> Sample_988 1 0.000
#> Sample_989 1 0.000
#> Sample_990 1 0.000
#> Sample_991 1 0.000
#> Sample_992 1 0.000
#> Sample_993 1 0.000
#> Sample_994 1 0.000
#> Sample_995 3 0.000
#> Sample_996 1 1.000
#> Sample_997 3 0.000
#> Sample_998 3 1.000
#> Sample_999 1 1.000
#> Sample_1000 3 0.000
#> Sample_1001 3 0.000
#> Sample_1002 3 0.751
#> Sample_1003 3 0.000
#> Sample_1004 3 0.000
#> Sample_1005 3 0.000
#> Sample_1006 3 1.000
#> Sample_1007 1 1.000
#> Sample_1008 1 1.000
#> Sample_1009 1 1.000
#> Sample_1010 1 1.000
#> Sample_1011 3 0.000
#> Sample_1012 3 0.751
#> Sample_1013 3 1.000
#> Sample_1014 1 0.000
#> Sample_1015 1 0.000
#> Sample_1016 1 0.000
#> Sample_1017 1 0.000
#> Sample_1018 1 0.000
#> Sample_1019 1 0.000
#> Sample_1020 1 0.000
#> Sample_1021 1 0.000
#> Sample_1022 1 0.000
#> Sample_1023 1 0.000
#> Sample_1024 1 0.000
#> Sample_1025 1 0.000
#> Sample_1026 1 0.000
#> Sample_1027 1 0.249
#> Sample_1028 1 0.000
#> Sample_1029 3 0.000
#> Sample_1030 1 0.747
#> Sample_1031 3 0.000
#> Sample_1032 3 0.000
#> Sample_1033 3 0.000
#> Sample_1034 1 1.000
#> Sample_1035 3 0.000
#> Sample_1036 1 1.000
#> Sample_1037 3 0.000
#> Sample_1038 3 1.000
#> Sample_1039 1 1.000
#> Sample_1040 3 0.751
#> Sample_1041 1 1.000
#> Sample_1042 1 0.249
#> Sample_1043 3 0.000
#> Sample_1044 3 0.000
#> Sample_1045 3 0.000
#> Sample_1046 1 0.249
#> Sample_1047 3 0.000
#> Sample_1048 3 1.000
#> Sample_1049 1 1.000
#> Sample_1050 1 0.000
#> Sample_1051 3 1.000
#> Sample_1052 1 0.000
#> Sample_1053 3 0.000
#> Sample_1054 3 0.000
#> Sample_1055 3 0.000
#> Sample_1056 1 1.000
#> Sample_1057 3 0.000
#> Sample_1058 1 0.000
#> Sample_1059 1 1.000
#> Sample_1060 3 0.000
#> Sample_1061 3 0.000
#> Sample_1062 1 1.000
#> Sample_1063 3 0.000
#> Sample_1064 3 0.000
#> Sample_1065 3 0.249
#> Sample_1066 1 1.000
#> Sample_1067 3 0.000
#> Sample_1068 3 0.000
#> Sample_1069 3 0.000
#> Sample_1070 1 0.000
#> Sample_1071 3 0.000
#> Sample_1072 3 0.000
#> Sample_1073 1 0.000
#> Sample_1074 3 0.000
#> Sample_1075 1 1.000
#> Sample_1076 1 0.000
#> Sample_1077 3 0.751
#> Sample_1078 3 1.000
#> Sample_1079 3 0.249
#> Sample_1080 1 1.000
#> Sample_1081 3 0.000
#> Sample_1082 3 0.000
#> Sample_1083 1 1.000
#> Sample_1084 3 0.000
#> Sample_1085 3 0.000
#> Sample_1086 3 0.000
#> Sample_1087 1 0.000
#> Sample_1088 3 0.000
#> Sample_1089 3 0.000
#> Sample_1090 1 0.747
#> Sample_1091 1 1.000
#> Sample_1092 3 0.000
#> Sample_1093 1 1.000
#> Sample_1094 3 0.000
#> Sample_1095 3 1.000
#> Sample_1096 3 0.000
#> Sample_1097 1 1.000
#> Sample_1098 3 0.000
#> Sample_1099 3 0.000
#> Sample_1100 1 1.000
#> Sample_1101 3 1.000
#> Sample_1102 1 0.000
#> Sample_1103 1 0.249
#> Sample_1104 1 0.000
#> Sample_1105 1 0.000
#> Sample_1106 3 0.747
#> Sample_1107 3 0.000
#> Sample_1108 1 0.249
#> Sample_1109 3 0.000
#> Sample_1110 1 1.000
#> Sample_1111 3 0.000
#> Sample_1112 1 0.502
#> Sample_1113 1 0.747
#> Sample_1114 1 1.000
#> Sample_1115 3 0.000
#> Sample_1116 1 1.000
#> Sample_1117 3 1.000
#> Sample_1118 1 0.000
#> Sample_1119 1 1.000
#> Sample_1120 3 0.000
#> Sample_1121 1 0.000
#> Sample_1122 3 0.000
#> Sample_1123 3 0.747
#> Sample_1124 3 0.000
#> Sample_1125 3 0.000
#> Sample_1126 3 0.000
#> Sample_1127 3 0.000
#> Sample_1128 3 0.000
#> Sample_1129 1 1.000
#> Sample_1130 1 0.000
#> Sample_1131 1 0.000
#> Sample_1132 3 0.249
#> Sample_1133 1 0.000
#> Sample_1134 3 0.000
#> Sample_1135 1 0.249
#> Sample_1136 1 0.000
#> Sample_1137 1 1.000
#> Sample_1138 1 0.751
#> Sample_1139 3 0.000
#> Sample_1140 3 0.000
#> Sample_1141 3 0.000
#> Sample_1142 1 1.000
#> Sample_1143 1 1.000
#> Sample_1144 1 0.502
#> Sample_1145 3 0.000
#> Sample_1146 1 0.751
#> Sample_1147 1 0.498
#> Sample_1148 1 1.000
#> Sample_1149 1 1.000
#> Sample_1150 3 0.000
#> Sample_1151 1 0.000
#> Sample_1152 3 0.751
#> Sample_1153 1 1.000
#> Sample_1154 1 0.249
#> Sample_1155 1 1.000
#> Sample_1156 3 0.000
#> Sample_1157 3 0.751
#> Sample_1158 1 0.000
#> Sample_1159 1 0.751
#> Sample_1160 1 0.249
#> Sample_1161 3 0.000
#> Sample_1162 1 1.000
#> Sample_1163 3 1.000
#> Sample_1164 3 0.000
#> Sample_1165 1 1.000
#> Sample_1166 1 0.000
#> Sample_1167 1 0.747
#> Sample_1168 1 0.000
#> Sample_1169 3 0.000
#> Sample_1170 3 0.000
#> Sample_1171 1 0.502
#> Sample_1172 1 0.000
#> Sample_1173 1 0.000
#> Sample_1174 1 0.000
#> Sample_1175 3 0.000
#> Sample_1176 1 0.253
#> Sample_1177 1 1.000
#> Sample_1178 1 0.000
#> Sample_1179 1 0.000
#> Sample_1180 3 0.000
#> Sample_1181 1 0.249
#> Sample_1182 1 1.000
#> Sample_1183 3 0.000
#> Sample_1184 3 0.000
#> Sample_1185 1 1.000
#> Sample_1186 1 1.000
#> Sample_1187 1 1.000
#> Sample_1188 1 0.000
#> Sample_1189 3 0.000
#> Sample_1190 3 0.000
#> Sample_1191 3 0.000
#> Sample_1192 1 0.751
#> Sample_1193 3 0.000
#> Sample_1194 3 0.000
#> Sample_1195 3 0.000
#> Sample_1196 3 0.000
#> Sample_1197 3 0.000
#> Sample_1198 3 0.000
#> Sample_1199 3 0.000
#> Sample_1200 3 0.000
#> Sample_1201 3 0.000
#> Sample_1202 3 0.249
#> Sample_1203 3 0.000
#> Sample_1204 3 0.000
#> Sample_1205 3 0.000
#> Sample_1206 3 0.751
#> Sample_1207 3 0.000
#> Sample_1208 1 1.000
#> Sample_1209 3 1.000
#> Sample_1210 3 0.000
#> Sample_1211 3 0.000
#> Sample_1212 3 0.000
#> Sample_1213 3 0.000
#> Sample_1214 3 0.000
#> Sample_1215 3 0.000
#> Sample_1216 1 0.747
#> Sample_1217 1 1.000
#> Sample_1218 1 0.000
#> Sample_1219 3 0.000
#> Sample_1220 3 0.000
#> Sample_1221 3 0.000
#> Sample_1222 1 1.000
#> Sample_1223 3 0.000
#> Sample_1224 3 0.000
#> Sample_1225 1 1.000
#> Sample_1226 3 0.000
#> Sample_1227 1 0.249
#> Sample_1228 1 1.000
#> Sample_1229 1 0.000
#> Sample_1230 3 0.000
#> Sample_1231 3 0.000
#> Sample_1232 3 1.000
#> Sample_1233 3 0.000
#> Sample_1234 1 1.000
#> Sample_1235 3 0.000
#> Sample_1236 1 0.249
#> Sample_1237 3 0.000
#> Sample_1238 3 0.000
#> Sample_1239 1 1.000
#> Sample_1240 1 0.747
#> Sample_1241 1 1.000
#> Sample_1242 1 0.747
#> Sample_1243 3 0.000
#> Sample_1244 3 0.000
#> Sample_1245 3 1.000
#> Sample_1246 3 0.000
#> Sample_1247 3 0.000
#> Sample_1248 3 0.000
#> Sample_1249 1 0.751
#> Sample_1250 3 1.000
#> Sample_1251 3 0.751
#> Sample_1252 3 0.000
#> Sample_1253 3 0.000
#> Sample_1254 1 0.502
#> Sample_1255 3 0.000
#> Sample_1256 3 0.000
#> Sample_1257 3 0.000
#> Sample_1258 3 0.000
#> Sample_1259 3 0.000
#> Sample_1260 3 0.000
#> Sample_1261 3 0.000
#> Sample_1262 3 0.000
#> Sample_1263 3 0.000
#> Sample_1264 1 1.000
#> Sample_1265 3 0.000
#> Sample_1266 1 0.000
#> Sample_1267 1 0.000
#> Sample_1268 3 1.000
#> Sample_1269 3 1.000
#> Sample_1270 3 0.000
#> Sample_1271 3 0.000
#> Sample_1272 1 1.000
#> Sample_1273 3 0.000
#> Sample_1274 3 0.000
#> Sample_1275 3 1.000
#> Sample_1276 3 0.000
#> Sample_1277 3 0.000
#> Sample_1278 3 0.000
#> Sample_1279 1 0.751
#> Sample_1280 1 1.000
#> Sample_1281 3 0.000
#> Sample_1282 3 0.000
#> Sample_1283 1 1.000
#> Sample_1284 3 0.000
#> Sample_1285 1 1.000
#> Sample_1286 3 1.000
#> Sample_1287 3 0.000
#> Sample_1288 3 0.000
#> Sample_1289 3 0.000
#> Sample_1290 3 1.000
#> Sample_1291 3 0.000
#> Sample_1292 3 0.000
#> Sample_1293 3 0.751
#> Sample_1294 3 0.000
#> Sample_1295 3 0.000
#> Sample_1296 3 0.000
#> Sample_1297 3 0.000
#> Sample_1298 3 0.000
#> Sample_1299 1 1.000
#> Sample_1300 3 0.000
#> Sample_1301 3 0.000
#> Sample_1302 3 0.000
#> Sample_1303 3 0.000
#> Sample_1304 3 0.751
#> Sample_1305 3 0.000
#> Sample_1306 3 0.000
#> Sample_1307 1 1.000
#> Sample_1308 1 0.000
#> Sample_1309 1 1.000
#> Sample_1310 3 0.000
#> Sample_1311 3 0.000
#> Sample_1312 3 0.000
#> Sample_1313 1 1.000
#> Sample_1314 3 0.000
#> Sample_1315 3 0.000
#> Sample_1316 3 0.000
#> Sample_1317 1 0.498
#> Sample_1318 3 0.000
#> Sample_1319 3 0.000
#> Sample_1320 1 0.000
#> Sample_1321 3 0.000
#> Sample_1322 3 1.000
#> Sample_1323 3 0.000
#> Sample_1324 3 0.000
#> Sample_1325 3 0.000
#> Sample_1326 3 0.000
#> Sample_1327 3 1.000
#> Sample_1328 3 1.000
#> Sample_1329 3 0.249
#> Sample_1330 3 0.000
#> Sample_1331 3 0.000
#> Sample_1332 3 0.000
#> Sample_1333 3 0.000
#> Sample_1334 3 0.000
#> Sample_1335 1 1.000
#> Sample_1336 1 0.249
#> Sample_1337 3 0.000
#> Sample_1338 1 0.000
#> Sample_1339 3 0.000
#> Sample_1340 3 0.000
#> Sample_1341 3 0.000
#> Sample_1342 1 0.751
#> Sample_1343 1 0.751
#> Sample_1344 3 0.000
#> Sample_1345 3 0.000
#> Sample_1346 3 0.000
#> Sample_1347 3 1.000
#> Sample_1348 1 0.249
#> Sample_1349 3 0.000
#> Sample_1350 1 0.751
#> Sample_1351 3 0.000
#> Sample_1352 1 0.000
#> Sample_1353 3 0.502
#> Sample_1354 1 1.000
#> Sample_1355 3 0.000
#> Sample_1356 3 0.253
#> Sample_1357 1 1.000
#> Sample_1358 1 0.000
#> Sample_1359 3 0.000
#> Sample_1360 1 1.000
#> Sample_1361 3 0.000
#> Sample_1362 3 0.000
#> Sample_1363 3 0.000
#> Sample_1364 1 0.000
#> Sample_1365 3 0.000
#> Sample_1366 3 0.000
#> Sample_1367 1 1.000
#> Sample_1368 3 0.000
#> Sample_1369 3 0.000
#> Sample_1370 3 0.249
#> Sample_1371 1 1.000
#> Sample_1372 3 0.000
#> Sample_1373 1 1.000
#> Sample_1374 1 1.000
#> Sample_1375 1 1.000
#> Sample_1376 3 0.000
#> Sample_1377 3 0.000
#> Sample_1378 3 0.000
#> Sample_1379 3 0.000
#> Sample_1380 3 0.000
#> Sample_1381 3 0.000
#> Sample_1382 1 1.000
#> Sample_1383 3 0.000
#> Sample_1384 3 0.000
#> Sample_1385 3 0.249
#> Sample_1386 1 0.249
#> Sample_1387 3 0.000
#> Sample_1388 1 1.000
#> Sample_1389 3 0.000
#> Sample_1390 3 0.000
#> Sample_1391 3 0.000
#> Sample_1392 3 0.000
#> Sample_1393 3 0.000
#> Sample_1394 3 0.000
#> Sample_1395 3 0.000
#> Sample_1396 3 0.502
#> Sample_1397 3 0.000
#> Sample_1398 1 1.000
#> Sample_1399 1 1.000
#> Sample_1400 3 0.000
#> Sample_1401 3 0.000
#> Sample_1402 1 0.000
#> Sample_1403 3 0.000
#> Sample_1404 3 0.249
#> Sample_1405 3 0.000
#> Sample_1406 1 1.000
#> Sample_1407 3 1.000
#> Sample_1408 1 1.000
#> Sample_1409 1 0.000
#> Sample_1410 3 0.000
#> Sample_1411 3 0.000
#> Sample_1412 3 0.000
#> Sample_1413 3 0.000
#> Sample_1414 3 0.000
#> Sample_1415 3 0.000
#> Sample_1416 3 0.000
#> Sample_1417 3 0.000
#> Sample_1418 3 0.000
#> Sample_1419 1 1.000
#> Sample_1420 3 0.000
#> Sample_1421 3 0.000
#> Sample_1422 3 0.000
#> Sample_1423 1 1.000
#> Sample_1424 3 0.000
#> Sample_1425 1 0.747
#> Sample_1426 1 0.249
#> Sample_1427 1 1.000
#> Sample_1428 3 0.000
#> Sample_1429 1 0.000
#> Sample_1430 1 1.000
#> Sample_1431 1 0.000
#> Sample_1432 1 0.000
#> Sample_1433 1 0.249
#> Sample_1434 1 0.000
#> Sample_1435 1 1.000
#> Sample_1436 1 0.000
#> Sample_1437 1 0.751
#> Sample_1438 1 0.747
#> Sample_1439 1 0.000
#> Sample_1440 1 0.498
#> Sample_1441 1 0.751
#> Sample_1442 3 1.000
#> Sample_1443 3 1.000
#> Sample_1444 1 1.000
#> Sample_1445 1 1.000
#> Sample_1446 3 0.751
#> Sample_1447 3 1.000
#> Sample_1448 1 0.000
#> Sample_1449 1 0.000
#> Sample_1450 1 0.000
#> Sample_1451 1 1.000
#> Sample_1452 1 0.000
#> Sample_1453 1 1.000
#> Sample_1454 1 0.000
#> Sample_1455 1 0.000
#> Sample_1456 3 1.000
#> Sample_1457 3 1.000
#> Sample_1458 1 1.000
#> Sample_1459 1 1.000
#> Sample_1460 3 0.249
#> Sample_1461 3 0.000
#> Sample_1462 1 0.751
#> Sample_1463 3 0.000
#> Sample_1464 3 0.000
#> Sample_1465 1 0.000
#> Sample_1466 1 0.000
#> Sample_1467 1 0.000
#> Sample_1468 3 1.000
#> Sample_1469 1 1.000
#> Sample_1470 1 0.498
#> Sample_1471 1 1.000
#> Sample_1472 1 0.502
#> Sample_1473 1 1.000
#> Sample_1474 1 0.000
#> Sample_1475 1 0.751
#> Sample_1476 1 0.000
#> Sample_1477 1 1.000
#> Sample_1478 1 0.000
#> Sample_1479 1 1.000
#> Sample_1480 3 0.000
#> Sample_1481 1 1.000
#> Sample_1482 1 0.000
#> Sample_1483 1 1.000
#> Sample_1484 1 0.249
#> Sample_1485 3 0.253
#> Sample_1486 3 0.000
#> Sample_1487 1 1.000
#> Sample_1488 3 0.000
#> Sample_1489 1 0.000
#> Sample_1490 1 1.000
#> Sample_1491 1 1.000
#> Sample_1492 1 1.000
#> Sample_1493 1 0.249
#> Sample_1494 3 0.000
#> Sample_1495 1 1.000
#> Sample_1496 1 0.751
#> Sample_1497 1 0.498
#> Sample_1498 1 0.000
#> Sample_1499 1 0.249
#> Sample_1500 1 0.000
#> Sample_1501 1 0.249
#> Sample_1502 3 0.000
#> Sample_1503 3 0.000
#> Sample_1504 3 0.000
#> Sample_1505 3 0.000
#> Sample_1506 3 0.000
#> Sample_1507 3 1.000
#> Sample_1508 3 0.000
#> Sample_1509 1 1.000
#> Sample_1510 1 0.747
#> Sample_1511 3 0.000
#> Sample_1512 3 0.000
#> Sample_1513 3 0.000
#> Sample_1514 1 0.249
#> Sample_1515 3 0.000
#> Sample_1516 1 0.000
#> Sample_1517 2 0.000
#> Sample_1518 2 0.000
#> Sample_1519 2 0.000
#> Sample_1520 2 0.000
#> Sample_1521 2 0.000
#> Sample_1522 1 1.000
#> Sample_1523 1 1.000
#> Sample_1524 2 0.000
#> Sample_1525 1 0.000
#> Sample_1526 1 0.000
#> Sample_1527 2 0.000
#> Sample_1528 2 0.000
#> Sample_1529 2 0.000
#> Sample_1530 2 0.000
#> Sample_1531 2 0.000
#> Sample_1532 2 0.000
#> Sample_1533 2 0.000
#> Sample_1534 2 0.000
#> Sample_1535 2 1.000
#> Sample_1536 2 0.000
#> Sample_1537 2 0.000
#> Sample_1538 2 0.000
#> Sample_1539 2 0.751
#> Sample_1540 1 1.000
#> Sample_1541 1 0.000
#> Sample_1542 2 1.000
#> Sample_1543 2 0.000
#> Sample_1544 1 0.000
#> Sample_1545 1 1.000
#> Sample_1546 2 0.000
#> Sample_1547 1 0.498
#> Sample_1548 2 0.000
#> Sample_1549 3 0.000
#> Sample_1550 2 0.000
#> Sample_1551 2 1.000
#> Sample_1552 2 0.751
#> Sample_1553 3 0.747
#> Sample_1554 2 0.000
#> Sample_1555 2 0.000
#> Sample_1556 1 0.000
#> Sample_1557 1 0.249
#> Sample_1558 1 1.000
#> Sample_1559 2 1.000
#> Sample_1560 1 0.498
#> Sample_1561 2 0.502
#> Sample_1562 1 0.000
#> Sample_1563 1 0.000
#> Sample_1564 1 0.249
#> Sample_1565 1 0.000
#> Sample_1566 1 0.000
#> Sample_1567 1 0.000
#> Sample_1568 1 0.000
#> Sample_1569 3 1.000
#> Sample_1570 2 0.000
#> Sample_1571 1 0.249
#> Sample_1572 1 0.000
#> Sample_1573 1 0.000
#> Sample_1574 2 0.000
#> Sample_1575 2 0.000
#> Sample_1576 1 0.751
#> Sample_1577 1 0.000
#> Sample_1578 1 0.000
#> Sample_1579 1 0.000
#> Sample_1580 1 0.000
#> Sample_1581 3 0.249
#> Sample_1582 2 0.502
#> Sample_1583 1 0.253
#> Sample_1584 1 0.498
#> Sample_1585 2 0.000
#> Sample_1586 2 0.000
#> Sample_1587 2 0.000
#> Sample_1588 2 1.000
#> Sample_1589 2 0.000
#> Sample_1590 1 0.000
#> Sample_1591 1 0.502
#> Sample_1592 2 0.000
#> Sample_1593 2 0.000
#> Sample_1594 2 0.253
#> Sample_1595 2 1.000
#> Sample_1596 2 0.000
#> Sample_1597 3 0.000
#> Sample_1598 3 0.249
#> Sample_1599 2 0.000
#> Sample_1600 1 0.000
get_classes(res, k = 4)
#> class p
#> Sample_1 1 0.000
#> Sample_2 2 0.000
#> Sample_3 2 0.000
#> Sample_4 2 0.000
#> Sample_5 2 0.000
#> Sample_6 2 0.000
#> Sample_7 2 0.000
#> Sample_8 2 0.000
#> Sample_9 2 0.000
#> Sample_10 2 0.000
#> Sample_11 2 0.000
#> Sample_12 2 0.000
#> Sample_13 2 0.000
#> Sample_14 2 0.000
#> Sample_15 2 0.000
#> Sample_16 2 0.000
#> Sample_17 2 0.000
#> Sample_18 2 0.000
#> Sample_19 2 0.000
#> Sample_20 2 0.000
#> Sample_21 2 0.000
#> Sample_22 2 0.000
#> Sample_23 2 0.000
#> Sample_24 2 0.000
#> Sample_25 2 0.000
#> Sample_26 2 0.000
#> Sample_27 2 0.000
#> Sample_28 2 0.000
#> Sample_29 2 0.000
#> Sample_30 2 0.000
#> Sample_31 2 0.000
#> Sample_32 2 0.000
#> Sample_33 2 0.000
#> Sample_34 2 0.000
#> Sample_35 2 0.000
#> Sample_36 2 0.000
#> Sample_37 2 0.000
#> Sample_38 2 0.000
#> Sample_39 2 0.000
#> Sample_40 2 0.000
#> Sample_41 2 0.000
#> Sample_42 2 0.000
#> Sample_43 2 0.000
#> Sample_44 2 0.000
#> Sample_45 2 0.000
#> Sample_46 2 0.000
#> Sample_47 2 0.000
#> Sample_48 2 0.000
#> Sample_49 2 0.000
#> Sample_50 2 0.000
#> Sample_51 2 0.000
#> Sample_52 2 0.000
#> Sample_53 2 0.000
#> Sample_54 2 0.000
#> Sample_55 2 0.000
#> Sample_56 2 0.000
#> Sample_57 2 0.000
#> Sample_58 2 0.000
#> Sample_59 2 0.000
#> Sample_60 2 0.000
#> Sample_61 2 0.000
#> Sample_62 2 0.000
#> Sample_63 2 0.000
#> Sample_64 2 0.000
#> Sample_65 2 0.000
#> Sample_66 2 0.000
#> Sample_67 2 0.000
#> Sample_68 2 0.000
#> Sample_69 2 0.000
#> Sample_70 2 0.000
#> Sample_71 2 0.000
#> Sample_72 2 0.000
#> Sample_73 2 0.000
#> Sample_74 2 0.000
#> Sample_75 2 0.000
#> Sample_76 2 0.000
#> Sample_77 2 0.000
#> Sample_78 2 0.000
#> Sample_79 2 0.000
#> Sample_80 2 0.000
#> Sample_81 2 0.000
#> Sample_82 2 0.000
#> Sample_83 2 0.000
#> Sample_84 2 0.000
#> Sample_85 2 0.000
#> Sample_86 2 0.000
#> Sample_87 2 0.000
#> Sample_88 2 0.000
#> Sample_89 2 0.000
#> Sample_90 2 0.000
#> Sample_91 2 0.000
#> Sample_92 2 0.000
#> Sample_93 2 0.000
#> Sample_94 2 0.000
#> Sample_95 2 0.000
#> Sample_96 2 0.000
#> Sample_97 2 0.000
#> Sample_98 2 0.000
#> Sample_99 2 0.000
#> Sample_100 2 0.000
#> Sample_101 2 0.000
#> Sample_102 2 0.000
#> Sample_103 2 0.000
#> Sample_104 2 0.000
#> Sample_105 2 0.000
#> Sample_106 2 0.000
#> Sample_107 2 0.000
#> Sample_108 2 0.000
#> Sample_109 2 0.000
#> Sample_110 2 0.000
#> Sample_111 1 1.000
#> Sample_112 2 0.000
#> Sample_113 2 0.000
#> Sample_114 2 0.000
#> Sample_115 2 0.000
#> Sample_116 2 0.249
#> Sample_117 2 0.000
#> Sample_118 2 0.000
#> Sample_119 2 0.000
#> Sample_120 2 0.000
#> Sample_121 1 1.000
#> Sample_122 2 0.000
#> Sample_123 2 0.000
#> Sample_124 2 0.000
#> Sample_125 2 0.000
#> Sample_126 2 0.000
#> Sample_127 2 0.000
#> Sample_128 2 0.000
#> Sample_129 2 0.000
#> Sample_130 2 0.000
#> Sample_131 2 0.000
#> Sample_132 2 0.000
#> Sample_133 2 0.000
#> Sample_134 2 0.000
#> Sample_135 2 0.000
#> Sample_136 2 0.000
#> Sample_137 2 0.000
#> Sample_138 2 0.000
#> Sample_139 2 0.000
#> Sample_140 2 0.000
#> Sample_141 2 0.000
#> Sample_142 2 0.000
#> Sample_143 2 0.000
#> Sample_144 2 0.000
#> Sample_145 2 0.000
#> Sample_146 2 0.000
#> Sample_147 2 0.000
#> Sample_148 2 0.000
#> Sample_149 2 0.000
#> Sample_150 2 0.000
#> Sample_151 2 0.000
#> Sample_152 2 0.000
#> Sample_153 2 0.000
#> Sample_154 2 0.000
#> Sample_155 2 0.000
#> Sample_156 2 0.000
#> Sample_157 2 0.000
#> Sample_158 2 0.000
#> Sample_159 2 0.000
#> Sample_160 2 0.000
#> Sample_161 2 0.000
#> Sample_162 2 0.000
#> Sample_163 2 0.000
#> Sample_164 2 0.000
#> Sample_165 2 0.000
#> Sample_166 2 0.000
#> Sample_167 2 0.000
#> Sample_168 2 0.000
#> Sample_169 2 0.000
#> Sample_170 2 0.000
#> Sample_171 2 0.000
#> Sample_172 2 0.000
#> Sample_173 2 0.000
#> Sample_174 2 0.000
#> Sample_175 2 0.000
#> Sample_176 2 0.000
#> Sample_177 2 0.000
#> Sample_178 2 0.000
#> Sample_179 2 0.000
#> Sample_180 2 0.000
#> Sample_181 2 0.000
#> Sample_182 2 0.000
#> Sample_183 2 0.000
#> Sample_184 2 0.000
#> Sample_185 2 0.000
#> Sample_186 2 0.000
#> Sample_187 2 0.000
#> Sample_188 2 0.000
#> Sample_189 2 0.000
#> Sample_190 2 0.000
#> Sample_191 2 0.000
#> Sample_192 2 0.000
#> Sample_193 2 0.000
#> Sample_194 2 0.000
#> Sample_195 2 0.000
#> Sample_196 2 0.000
#> Sample_197 2 0.000
#> Sample_198 2 0.000
#> Sample_199 2 0.000
#> Sample_200 2 0.000
#> Sample_201 2 0.000
#> Sample_202 2 0.000
#> Sample_203 2 0.000
#> Sample_204 2 0.000
#> Sample_205 2 0.000
#> Sample_206 2 0.000
#> Sample_207 2 0.000
#> Sample_208 4 0.000
#> Sample_209 4 0.000
#> Sample_210 4 0.000
#> Sample_211 4 0.000
#> Sample_212 4 0.000
#> Sample_213 4 0.000
#> Sample_214 4 0.000
#> Sample_215 4 0.000
#> Sample_216 4 0.000
#> Sample_217 4 0.000
#> Sample_218 4 0.000
#> Sample_219 4 0.249
#> Sample_220 4 0.000
#> Sample_221 4 0.000
#> Sample_222 4 0.000
#> Sample_223 4 0.000
#> Sample_224 1 1.000
#> Sample_225 4 0.000
#> Sample_226 4 0.498
#> Sample_227 4 0.000
#> Sample_228 4 0.000
#> Sample_229 4 0.000
#> Sample_230 4 0.000
#> Sample_231 4 0.000
#> Sample_232 4 0.000
#> Sample_233 4 0.000
#> Sample_234 4 0.000
#> Sample_235 4 0.000
#> Sample_236 4 0.000
#> Sample_237 4 0.000
#> Sample_238 4 0.000
#> Sample_239 4 0.000
#> Sample_240 4 0.000
#> Sample_241 4 1.000
#> Sample_242 4 0.000
#> Sample_243 4 0.000
#> Sample_244 4 0.000
#> Sample_245 4 0.000
#> Sample_246 4 0.000
#> Sample_247 4 0.000
#> Sample_248 4 0.000
#> Sample_249 4 0.000
#> Sample_250 4 0.000
#> Sample_251 4 0.000
#> Sample_252 4 0.000
#> Sample_253 4 0.000
#> Sample_254 4 0.000
#> Sample_255 4 0.000
#> Sample_256 4 0.000
#> Sample_257 4 0.000
#> Sample_258 1 1.000
#> Sample_259 4 0.000
#> Sample_260 4 0.000
#> Sample_261 4 0.000
#> Sample_262 4 0.000
#> Sample_263 4 0.000
#> Sample_264 4 0.000
#> Sample_265 1 1.000
#> Sample_266 4 0.000
#> Sample_267 4 0.000
#> Sample_268 4 0.000
#> Sample_269 4 0.000
#> Sample_270 4 0.000
#> Sample_271 4 0.000
#> Sample_272 4 0.502
#> Sample_273 4 0.000
#> Sample_274 4 0.000
#> Sample_275 4 1.000
#> Sample_276 1 1.000
#> Sample_277 4 0.000
#> Sample_278 4 0.000
#> Sample_279 4 0.000
#> Sample_280 4 0.000
#> Sample_281 4 0.000
#> Sample_282 4 0.000
#> Sample_283 4 0.000
#> Sample_284 4 0.000
#> Sample_285 4 0.000
#> Sample_286 4 0.000
#> Sample_287 4 1.000
#> Sample_288 4 0.000
#> Sample_289 4 0.000
#> Sample_290 4 0.000
#> Sample_291 4 0.000
#> Sample_292 4 0.000
#> Sample_293 4 0.000
#> Sample_294 4 0.000
#> Sample_295 4 0.000
#> Sample_296 4 0.000
#> Sample_297 4 0.000
#> Sample_298 4 0.000
#> Sample_299 4 0.000
#> Sample_300 4 0.000
#> Sample_301 4 0.000
#> Sample_302 1 0.249
#> Sample_303 1 1.000
#> Sample_304 1 1.000
#> Sample_305 4 1.000
#> Sample_306 4 0.000
#> Sample_307 4 0.000
#> Sample_308 1 0.000
#> Sample_309 1 1.000
#> Sample_310 1 0.502
#> Sample_311 4 0.000
#> Sample_312 1 0.249
#> Sample_313 1 1.000
#> Sample_314 1 1.000
#> Sample_315 4 0.000
#> Sample_316 1 0.502
#> Sample_317 4 0.000
#> Sample_318 1 0.751
#> Sample_319 1 0.000
#> Sample_320 1 0.000
#> Sample_321 1 0.000
#> Sample_322 4 0.000
#> Sample_323 1 1.000
#> Sample_324 4 1.000
#> Sample_325 1 1.000
#> Sample_326 1 0.000
#> Sample_327 1 0.751
#> Sample_328 1 0.502
#> Sample_329 1 0.000
#> Sample_330 1 0.000
#> Sample_331 4 0.000
#> Sample_332 4 0.000
#> Sample_333 4 0.000
#> Sample_334 4 0.000
#> Sample_335 4 0.000
#> Sample_336 4 0.000
#> Sample_337 4 0.000
#> Sample_338 4 0.253
#> Sample_339 4 0.000
#> Sample_340 4 0.000
#> Sample_341 4 0.000
#> Sample_342 4 0.000
#> Sample_343 4 0.000
#> Sample_344 4 0.498
#> Sample_345 4 0.000
#> Sample_346 4 0.000
#> Sample_347 4 0.000
#> Sample_348 4 0.000
#> Sample_349 4 0.000
#> Sample_350 4 0.751
#> Sample_351 1 1.000
#> Sample_352 4 1.000
#> Sample_353 4 0.000
#> Sample_354 4 0.000
#> Sample_355 4 0.000
#> Sample_356 4 0.000
#> Sample_357 4 0.000
#> Sample_358 1 0.751
#> Sample_359 4 0.000
#> Sample_360 4 0.000
#> Sample_361 4 0.000
#> Sample_362 4 0.000
#> Sample_363 4 0.000
#> Sample_364 4 0.000
#> Sample_365 4 0.000
#> Sample_366 4 0.000
#> Sample_367 4 0.000
#> Sample_368 4 0.000
#> Sample_369 4 0.751
#> Sample_370 4 0.000
#> Sample_371 4 0.000
#> Sample_372 4 0.000
#> Sample_373 4 0.000
#> Sample_374 4 0.000
#> Sample_375 4 0.000
#> Sample_376 4 0.000
#> Sample_377 1 1.000
#> Sample_378 4 0.000
#> Sample_379 4 0.000
#> Sample_380 4 0.000
#> Sample_381 4 0.253
#> Sample_382 4 0.000
#> Sample_383 4 0.000
#> Sample_384 4 0.000
#> Sample_385 4 1.000
#> Sample_386 1 0.249
#> Sample_387 4 0.000
#> Sample_388 4 0.000
#> Sample_389 4 0.751
#> Sample_390 1 0.502
#> Sample_391 4 0.000
#> Sample_392 4 0.000
#> Sample_393 1 0.000
#> Sample_394 4 0.000
#> Sample_395 4 0.498
#> Sample_396 4 1.000
#> Sample_397 4 1.000
#> Sample_398 4 0.000
#> Sample_399 4 0.000
#> Sample_400 4 0.000
#> Sample_401 4 0.000
#> Sample_402 4 0.000
#> Sample_403 4 0.000
#> Sample_404 4 0.000
#> Sample_405 4 1.000
#> Sample_406 4 0.000
#> Sample_407 4 0.000
#> Sample_408 4 0.751
#> Sample_409 1 0.000
#> Sample_410 4 0.000
#> Sample_411 4 0.751
#> Sample_412 1 0.000
#> Sample_413 4 0.000
#> Sample_414 4 0.000
#> Sample_415 4 0.000
#> Sample_416 1 0.249
#> Sample_417 4 0.000
#> Sample_418 4 0.000
#> Sample_419 4 0.000
#> Sample_420 4 0.000
#> Sample_421 1 1.000
#> Sample_422 4 0.000
#> Sample_423 4 0.000
#> Sample_424 4 1.000
#> Sample_425 1 0.498
#> Sample_426 4 0.000
#> Sample_427 1 1.000
#> Sample_428 1 1.000
#> Sample_429 4 0.000
#> Sample_430 4 0.747
#> Sample_431 4 1.000
#> Sample_432 1 1.000
#> Sample_433 4 0.249
#> Sample_434 1 0.249
#> Sample_435 4 0.000
#> Sample_436 4 0.000
#> Sample_437 4 0.000
#> Sample_438 4 0.000
#> Sample_439 1 0.249
#> Sample_440 1 0.751
#> Sample_441 4 0.000
#> Sample_442 1 0.751
#> Sample_443 1 1.000
#> Sample_444 4 0.751
#> Sample_445 1 0.249
#> Sample_446 4 0.000
#> Sample_447 4 0.000
#> Sample_448 1 0.000
#> Sample_449 4 0.000
#> Sample_450 4 0.498
#> Sample_451 1 0.498
#> Sample_452 1 0.000
#> Sample_453 4 0.000
#> Sample_454 4 0.000
#> Sample_455 4 0.000
#> Sample_456 1 0.000
#> Sample_457 4 0.000
#> Sample_458 4 0.000
#> Sample_459 1 1.000
#> Sample_460 4 0.000
#> Sample_461 1 1.000
#> Sample_462 4 0.000
#> Sample_463 4 1.000
#> Sample_464 1 0.000
#> Sample_465 1 0.000
#> Sample_466 1 0.000
#> Sample_467 4 0.000
#> Sample_468 1 1.000
#> Sample_469 1 1.000
#> Sample_470 4 0.000
#> Sample_471 4 0.000
#> Sample_472 1 0.498
#> Sample_473 4 0.000
#> Sample_474 1 1.000
#> Sample_475 1 0.000
#> Sample_476 4 0.249
#> Sample_477 1 0.751
#> Sample_478 3 0.000
#> Sample_479 3 1.000
#> Sample_480 3 0.498
#> Sample_481 3 0.249
#> Sample_482 3 0.000
#> Sample_483 3 0.000
#> Sample_484 3 0.000
#> Sample_485 3 0.000
#> Sample_486 1 1.000
#> Sample_487 3 1.000
#> Sample_488 3 0.502
#> Sample_489 3 0.000
#> Sample_490 3 1.000
#> Sample_491 3 0.000
#> Sample_492 3 0.000
#> Sample_493 3 1.000
#> Sample_494 3 0.000
#> Sample_495 3 1.000
#> Sample_496 3 0.000
#> Sample_497 3 0.751
#> Sample_498 3 0.000
#> Sample_499 3 1.000
#> Sample_500 3 1.000
#> Sample_501 3 0.000
#> Sample_502 3 0.000
#> Sample_503 3 0.000
#> Sample_504 3 1.000
#> Sample_505 1 1.000
#> Sample_506 3 0.502
#> Sample_507 1 1.000
#> Sample_508 3 1.000
#> Sample_509 3 0.000
#> Sample_510 3 0.253
#> Sample_511 1 1.000
#> Sample_512 3 0.000
#> Sample_513 1 1.000
#> Sample_514 3 0.000
#> Sample_515 3 1.000
#> Sample_516 3 0.000
#> Sample_517 1 1.000
#> Sample_518 3 1.000
#> Sample_519 3 0.000
#> Sample_520 3 1.000
#> Sample_521 3 1.000
#> Sample_522 3 0.000
#> Sample_523 3 0.000
#> Sample_524 3 0.000
#> Sample_525 3 0.000
#> Sample_526 3 0.000
#> Sample_527 1 1.000
#> Sample_528 3 0.751
#> Sample_529 3 0.000
#> Sample_530 1 0.249
#> Sample_531 3 0.000
#> Sample_532 1 1.000
#> Sample_533 3 1.000
#> Sample_534 3 0.000
#> Sample_535 3 1.000
#> Sample_536 1 1.000
#> Sample_537 3 1.000
#> Sample_538 3 0.502
#> Sample_539 3 1.000
#> Sample_540 3 0.000
#> Sample_541 3 0.000
#> Sample_542 1 1.000
#> Sample_543 1 1.000
#> Sample_544 3 1.000
#> Sample_545 3 0.000
#> Sample_546 1 1.000
#> Sample_547 3 1.000
#> Sample_548 3 0.000
#> Sample_549 3 0.000
#> Sample_550 3 0.000
#> Sample_551 3 0.249
#> Sample_552 3 0.000
#> Sample_553 3 0.000
#> Sample_554 3 0.000
#> Sample_555 3 0.751
#> Sample_556 3 0.253
#> Sample_557 1 1.000
#> Sample_558 1 1.000
#> Sample_559 1 1.000
#> Sample_560 3 0.000
#> Sample_561 3 0.000
#> Sample_562 3 0.000
#> Sample_563 3 0.000
#> Sample_564 3 0.253
#> Sample_565 3 0.747
#> Sample_566 1 1.000
#> Sample_567 3 1.000
#> Sample_568 3 0.000
#> Sample_569 1 0.751
#> Sample_570 4 0.000
#> Sample_571 4 0.000
#> Sample_572 4 0.000
#> Sample_573 1 0.000
#> Sample_574 4 0.000
#> Sample_575 4 0.000
#> Sample_576 4 0.000
#> Sample_577 4 1.000
#> Sample_578 4 0.000
#> Sample_579 4 0.000
#> Sample_580 4 0.000
#> Sample_581 4 0.000
#> Sample_582 4 0.000
#> Sample_583 4 0.000
#> Sample_584 4 0.000
#> Sample_585 4 0.000
#> Sample_586 2 0.000
#> Sample_587 2 0.000
#> Sample_588 2 0.000
#> Sample_589 4 0.000
#> Sample_590 1 0.498
#> Sample_591 2 0.000
#> Sample_592 2 0.000
#> Sample_593 2 0.000
#> Sample_594 4 0.000
#> Sample_595 2 1.000
#> Sample_596 2 1.000
#> Sample_597 4 0.000
#> Sample_598 2 0.000
#> Sample_599 2 0.000
#> Sample_600 2 0.000
#> Sample_601 2 0.000
#> Sample_602 2 0.000
#> Sample_603 2 0.000
#> Sample_604 2 0.498
#> Sample_605 2 0.000
#> Sample_606 2 0.000
#> Sample_607 2 0.000
#> Sample_608 2 0.498
#> Sample_609 2 0.000
#> Sample_610 4 1.000
#> Sample_611 2 0.000
#> Sample_612 1 1.000
#> Sample_613 4 0.000
#> Sample_614 4 0.000
#> Sample_615 4 0.000
#> Sample_616 1 1.000
#> Sample_617 4 0.000
#> Sample_618 1 0.747
#> Sample_619 4 1.000
#> Sample_620 4 0.000
#> Sample_621 4 0.000
#> Sample_622 4 0.000
#> Sample_623 4 0.000
#> Sample_624 4 0.000
#> Sample_625 4 0.253
#> Sample_626 4 0.000
#> Sample_627 4 0.000
#> Sample_628 4 0.000
#> Sample_629 4 0.000
#> Sample_630 1 0.751
#> Sample_631 4 0.000
#> Sample_632 4 0.000
#> Sample_633 4 0.000
#> Sample_634 4 0.000
#> Sample_635 4 0.000
#> Sample_636 4 0.000
#> Sample_637 4 0.000
#> Sample_638 4 0.000
#> Sample_639 1 1.000
#> Sample_640 4 0.000
#> Sample_641 4 0.000
#> Sample_642 4 0.000
#> Sample_643 1 1.000
#> Sample_644 4 0.000
#> Sample_645 4 0.249
#> Sample_646 4 0.000
#> Sample_647 4 0.000
#> Sample_648 4 0.000
#> Sample_649 4 0.000
#> Sample_650 4 0.253
#> Sample_651 2 0.751
#> Sample_652 2 0.000
#> Sample_653 2 0.000
#> Sample_654 2 0.000
#> Sample_655 2 0.000
#> Sample_656 2 0.000
#> Sample_657 2 0.000
#> Sample_658 2 0.000
#> Sample_659 2 0.000
#> Sample_660 2 0.000
#> Sample_661 2 0.000
#> Sample_662 2 0.000
#> Sample_663 2 0.000
#> Sample_664 2 0.000
#> Sample_665 2 0.000
#> Sample_666 2 0.000
#> Sample_667 2 0.000
#> Sample_668 2 0.000
#> Sample_669 2 0.000
#> Sample_670 2 0.000
#> Sample_671 2 0.000
#> Sample_672 2 0.000
#> Sample_673 2 0.000
#> Sample_674 2 0.000
#> Sample_675 2 0.000
#> Sample_676 2 0.000
#> Sample_677 2 0.000
#> Sample_678 2 0.000
#> Sample_679 2 0.000
#> Sample_680 2 0.000
#> Sample_681 2 0.000
#> Sample_682 2 0.000
#> Sample_683 2 0.249
#> Sample_684 2 0.000
#> Sample_685 2 1.000
#> Sample_686 2 1.000
#> Sample_687 1 0.498
#> Sample_688 2 0.000
#> Sample_689 2 0.000
#> Sample_690 2 0.000
#> Sample_691 2 0.000
#> Sample_692 2 1.000
#> Sample_693 2 0.000
#> Sample_694 2 0.000
#> Sample_695 2 0.000
#> Sample_696 2 0.000
#> Sample_697 2 0.000
#> Sample_698 2 0.000
#> Sample_699 2 0.000
#> Sample_700 2 0.000
#> Sample_701 2 0.000
#> Sample_702 2 0.000
#> Sample_703 2 1.000
#> Sample_704 2 0.000
#> Sample_705 2 0.000
#> Sample_706 2 0.000
#> Sample_707 2 0.000
#> Sample_708 2 0.000
#> Sample_709 2 0.000
#> Sample_710 2 0.000
#> Sample_711 2 0.000
#> Sample_712 2 0.000
#> Sample_713 2 0.000
#> Sample_714 2 0.000
#> Sample_715 2 0.000
#> Sample_716 2 0.000
#> Sample_717 2 0.000
#> Sample_718 2 0.000
#> Sample_719 2 0.000
#> Sample_720 3 1.000
#> Sample_721 2 0.000
#> Sample_722 2 0.000
#> Sample_723 2 0.000
#> Sample_724 2 0.000
#> Sample_725 2 0.000
#> Sample_726 2 0.000
#> Sample_727 2 0.000
#> Sample_728 2 0.000
#> Sample_729 2 0.000
#> Sample_730 2 0.000
#> Sample_731 2 0.000
#> Sample_732 2 0.000
#> Sample_733 2 0.000
#> Sample_734 2 0.000
#> Sample_735 2 0.000
#> Sample_736 2 0.000
#> Sample_737 1 1.000
#> Sample_738 2 0.000
#> Sample_739 2 0.000
#> Sample_740 2 0.000
#> Sample_741 2 0.000
#> Sample_742 2 0.000
#> Sample_743 2 0.000
#> Sample_744 2 0.000
#> Sample_745 2 0.000
#> Sample_746 2 0.000
#> Sample_747 2 0.000
#> Sample_748 2 0.000
#> Sample_749 2 0.000
#> Sample_750 2 0.000
#> Sample_751 2 0.000
#> Sample_752 2 0.000
#> Sample_753 2 0.000
#> Sample_754 2 0.000
#> Sample_755 2 0.000
#> Sample_756 2 0.000
#> Sample_757 2 0.000
#> Sample_758 2 0.000
#> Sample_759 2 0.000
#> Sample_760 2 0.000
#> Sample_761 2 0.000
#> Sample_762 2 0.000
#> Sample_763 2 0.000
#> Sample_764 2 0.000
#> Sample_765 2 0.000
#> Sample_766 2 0.000
#> Sample_767 2 0.000
#> Sample_768 2 0.000
#> Sample_769 2 0.000
#> Sample_770 2 0.000
#> Sample_771 2 0.000
#> Sample_772 2 0.000
#> Sample_773 2 0.000
#> Sample_774 2 0.000
#> Sample_775 2 0.000
#> Sample_776 2 0.000
#> Sample_777 2 0.000
#> Sample_778 2 0.000
#> Sample_779 2 0.000
#> Sample_780 2 0.000
#> Sample_781 2 0.000
#> Sample_782 2 0.000
#> Sample_783 2 0.000
#> Sample_784 2 0.000
#> Sample_785 2 0.000
#> Sample_786 2 0.000
#> Sample_787 2 0.000
#> Sample_788 2 0.000
#> Sample_789 2 0.000
#> Sample_790 2 0.000
#> Sample_791 2 0.000
#> Sample_792 2 0.000
#> Sample_793 2 0.000
#> Sample_794 2 0.000
#> Sample_795 2 0.000
#> Sample_796 2 0.000
#> Sample_797 2 0.000
#> Sample_798 2 0.000
#> Sample_799 2 0.000
#> Sample_800 2 0.000
#> Sample_801 2 0.000
#> Sample_802 2 0.000
#> Sample_803 2 0.000
#> Sample_804 2 0.000
#> Sample_805 2 0.000
#> Sample_806 2 0.000
#> Sample_807 2 0.000
#> Sample_808 2 0.000
#> Sample_809 2 0.000
#> Sample_810 2 0.000
#> Sample_811 2 0.000
#> Sample_812 2 0.000
#> Sample_813 2 0.000
#> Sample_814 2 0.000
#> Sample_815 2 0.000
#> Sample_816 2 0.000
#> Sample_817 2 0.000
#> Sample_818 2 0.000
#> Sample_819 2 0.000
#> Sample_820 2 0.000
#> Sample_821 2 0.000
#> Sample_822 2 0.000
#> Sample_823 2 0.000
#> Sample_824 2 0.000
#> Sample_825 2 0.000
#> Sample_826 2 0.000
#> Sample_827 2 0.000
#> Sample_828 2 0.000
#> Sample_829 2 0.000
#> Sample_830 2 0.000
#> Sample_831 2 0.000
#> Sample_832 2 0.000
#> Sample_833 2 0.000
#> Sample_834 2 0.000
#> Sample_835 2 0.000
#> Sample_836 2 0.000
#> Sample_837 2 0.000
#> Sample_838 2 0.000
#> Sample_839 2 0.000
#> Sample_840 2 0.000
#> Sample_841 2 0.000
#> Sample_842 2 0.000
#> Sample_843 2 0.000
#> Sample_844 2 0.000
#> Sample_845 2 0.000
#> Sample_846 2 0.000
#> Sample_847 2 0.000
#> Sample_848 2 0.000
#> Sample_849 2 0.000
#> Sample_850 2 0.000
#> Sample_851 2 0.000
#> Sample_852 2 0.000
#> Sample_853 2 0.000
#> Sample_854 2 0.000
#> Sample_855 2 0.000
#> Sample_856 2 0.000
#> Sample_857 2 0.000
#> Sample_858 2 0.000
#> Sample_859 2 0.000
#> Sample_860 2 0.000
#> Sample_861 2 0.000
#> Sample_862 2 0.000
#> Sample_863 2 0.000
#> Sample_864 2 0.000
#> Sample_865 2 0.000
#> Sample_866 2 0.000
#> Sample_867 2 0.000
#> Sample_868 2 0.000
#> Sample_869 2 0.000
#> Sample_870 2 0.000
#> Sample_871 2 0.000
#> Sample_872 2 0.000
#> Sample_873 2 0.000
#> Sample_874 2 0.000
#> Sample_875 2 0.000
#> Sample_876 2 0.000
#> Sample_877 2 0.000
#> Sample_878 2 0.000
#> Sample_879 2 0.000
#> Sample_880 2 0.000
#> Sample_881 2 0.000
#> Sample_882 2 0.000
#> Sample_883 2 0.000
#> Sample_884 2 0.000
#> Sample_885 2 0.000
#> Sample_886 2 0.000
#> Sample_887 2 0.000
#> Sample_888 2 0.000
#> Sample_889 2 0.000
#> Sample_890 2 0.000
#> Sample_891 2 0.000
#> Sample_892 2 0.000
#> Sample_893 2 0.000
#> Sample_894 2 0.000
#> Sample_895 2 0.000
#> Sample_896 2 0.000
#> Sample_897 2 0.000
#> Sample_898 2 0.000
#> Sample_899 2 0.000
#> Sample_900 2 0.000
#> Sample_901 2 0.000
#> Sample_902 2 0.000
#> Sample_903 2 0.000
#> Sample_904 2 0.000
#> Sample_905 2 0.000
#> Sample_906 2 0.000
#> Sample_907 2 0.000
#> Sample_908 2 0.000
#> Sample_909 2 0.000
#> Sample_910 2 0.000
#> Sample_911 2 0.000
#> Sample_912 2 0.000
#> Sample_913 2 0.000
#> Sample_914 2 0.000
#> Sample_915 2 0.000
#> Sample_916 2 0.000
#> Sample_917 2 0.000
#> Sample_918 2 0.000
#> Sample_919 2 0.000
#> Sample_920 2 0.000
#> Sample_921 2 0.000
#> Sample_922 2 0.000
#> Sample_923 2 0.000
#> Sample_924 2 0.000
#> Sample_925 2 0.000
#> Sample_926 2 0.000
#> Sample_927 2 0.000
#> Sample_928 2 0.000
#> Sample_929 2 0.000
#> Sample_930 2 0.000
#> Sample_931 2 0.000
#> Sample_932 2 0.000
#> Sample_933 2 0.000
#> Sample_934 2 0.000
#> Sample_935 2 0.000
#> Sample_936 2 0.000
#> Sample_937 2 0.000
#> Sample_938 2 0.000
#> Sample_939 2 0.000
#> Sample_940 2 0.000
#> Sample_941 2 0.000
#> Sample_942 2 0.000
#> Sample_943 2 0.000
#> Sample_944 2 0.000
#> Sample_945 2 0.000
#> Sample_946 2 0.000
#> Sample_947 2 0.000
#> Sample_948 4 0.000
#> Sample_949 4 0.000
#> Sample_950 4 0.000
#> Sample_951 4 0.000
#> Sample_952 4 0.000
#> Sample_953 4 0.000
#> Sample_954 4 0.000
#> Sample_955 4 0.000
#> Sample_956 4 0.000
#> Sample_957 4 0.000
#> Sample_958 4 0.000
#> Sample_959 4 0.000
#> Sample_960 4 0.000
#> Sample_961 4 0.000
#> Sample_962 4 0.498
#> Sample_963 4 0.000
#> Sample_964 4 0.000
#> Sample_965 4 0.000
#> Sample_966 4 0.000
#> Sample_967 4 0.000
#> Sample_968 4 1.000
#> Sample_969 4 0.000
#> Sample_970 4 1.000
#> Sample_971 4 0.000
#> Sample_972 4 0.000
#> Sample_973 4 0.000
#> Sample_974 4 0.000
#> Sample_975 4 0.000
#> Sample_976 4 0.751
#> Sample_977 4 0.000
#> Sample_978 1 0.751
#> Sample_979 4 0.000
#> Sample_980 4 0.000
#> Sample_981 4 0.000
#> Sample_982 1 0.000
#> Sample_983 4 0.000
#> Sample_984 4 0.000
#> Sample_985 1 0.249
#> Sample_986 4 0.000
#> Sample_987 4 0.747
#> Sample_988 1 0.000
#> Sample_989 1 0.000
#> Sample_990 1 0.000
#> Sample_991 1 0.000
#> Sample_992 1 0.498
#> Sample_993 1 0.000
#> Sample_994 1 0.000
#> Sample_995 4 0.000
#> Sample_996 1 1.000
#> Sample_997 4 0.000
#> Sample_998 4 0.751
#> Sample_999 4 0.000
#> Sample_1000 4 0.000
#> Sample_1001 4 0.000
#> Sample_1002 4 0.000
#> Sample_1003 4 0.000
#> Sample_1004 4 0.000
#> Sample_1005 4 0.000
#> Sample_1006 4 0.000
#> Sample_1007 4 0.000
#> Sample_1008 4 1.000
#> Sample_1009 4 1.000
#> Sample_1010 4 0.000
#> Sample_1011 4 0.000
#> Sample_1012 4 0.000
#> Sample_1013 4 0.000
#> Sample_1014 1 0.000
#> Sample_1015 1 0.000
#> Sample_1016 1 0.000
#> Sample_1017 1 0.000
#> Sample_1018 1 0.000
#> Sample_1019 1 0.000
#> Sample_1020 1 0.000
#> Sample_1021 1 0.000
#> Sample_1022 1 0.000
#> Sample_1023 1 0.000
#> Sample_1024 1 0.000
#> Sample_1025 1 0.000
#> Sample_1026 1 0.000
#> Sample_1027 1 1.000
#> Sample_1028 1 1.000
#> Sample_1029 3 0.000
#> Sample_1030 1 1.000
#> Sample_1031 3 0.000
#> Sample_1032 3 0.000
#> Sample_1033 3 0.000
#> Sample_1034 1 1.000
#> Sample_1035 3 0.000
#> Sample_1036 3 0.000
#> Sample_1037 3 0.000
#> Sample_1038 3 0.502
#> Sample_1039 1 1.000
#> Sample_1040 3 0.751
#> Sample_1041 3 1.000
#> Sample_1042 1 1.000
#> Sample_1043 3 0.000
#> Sample_1044 3 0.000
#> Sample_1045 3 0.000
#> Sample_1046 1 1.000
#> Sample_1047 3 0.000
#> Sample_1048 3 1.000
#> Sample_1049 3 1.000
#> Sample_1050 1 0.253
#> Sample_1051 3 1.000
#> Sample_1052 1 1.000
#> Sample_1053 4 0.000
#> Sample_1054 4 0.000
#> Sample_1055 4 0.000
#> Sample_1056 1 1.000
#> Sample_1057 4 0.000
#> Sample_1058 1 0.000
#> Sample_1059 4 1.000
#> Sample_1060 4 0.000
#> Sample_1061 4 0.000
#> Sample_1062 4 0.000
#> Sample_1063 3 0.000
#> Sample_1064 3 0.000
#> Sample_1065 3 0.000
#> Sample_1066 1 1.000
#> Sample_1067 3 0.000
#> Sample_1068 3 0.000
#> Sample_1069 3 0.000
#> Sample_1070 1 0.502
#> Sample_1071 3 0.000
#> Sample_1072 3 0.000
#> Sample_1073 1 1.000
#> Sample_1074 3 0.000
#> Sample_1075 3 1.000
#> Sample_1076 1 1.000
#> Sample_1077 3 0.000
#> Sample_1078 3 1.000
#> Sample_1079 3 0.253
#> Sample_1080 3 0.751
#> Sample_1081 3 0.000
#> Sample_1082 3 0.000
#> Sample_1083 3 1.000
#> Sample_1084 3 0.000
#> Sample_1085 3 0.000
#> Sample_1086 3 0.000
#> Sample_1087 1 0.751
#> Sample_1088 3 0.000
#> Sample_1089 3 0.000
#> Sample_1090 1 1.000
#> Sample_1091 1 1.000
#> Sample_1092 3 0.000
#> Sample_1093 3 0.502
#> Sample_1094 3 0.000
#> Sample_1095 3 0.249
#> Sample_1096 3 0.000
#> Sample_1097 3 1.000
#> Sample_1098 3 0.000
#> Sample_1099 3 0.000
#> Sample_1100 3 1.000
#> Sample_1101 3 1.000
#> Sample_1102 1 0.253
#> Sample_1103 1 1.000
#> Sample_1104 1 0.751
#> Sample_1105 1 1.000
#> Sample_1106 3 0.253
#> Sample_1107 3 0.000
#> Sample_1108 1 1.000
#> Sample_1109 3 0.000
#> Sample_1110 1 1.000
#> Sample_1111 3 0.000
#> Sample_1112 1 1.000
#> Sample_1113 1 1.000
#> Sample_1114 3 1.000
#> Sample_1115 3 0.000
#> Sample_1116 3 1.000
#> Sample_1117 3 1.000
#> Sample_1118 1 0.751
#> Sample_1119 3 1.000
#> Sample_1120 3 0.000
#> Sample_1121 1 1.000
#> Sample_1122 3 0.000
#> Sample_1123 3 1.000
#> Sample_1124 3 0.000
#> Sample_1125 3 0.000
#> Sample_1126 3 0.000
#> Sample_1127 3 0.502
#> Sample_1128 3 0.000
#> Sample_1129 1 1.000
#> Sample_1130 1 0.498
#> Sample_1131 1 1.000
#> Sample_1132 3 0.249
#> Sample_1133 1 0.751
#> Sample_1134 3 0.000
#> Sample_1135 1 1.000
#> Sample_1136 1 0.000
#> Sample_1137 1 1.000
#> Sample_1138 1 1.000
#> Sample_1139 3 0.000
#> Sample_1140 3 0.000
#> Sample_1141 3 0.249
#> Sample_1142 3 1.000
#> Sample_1143 3 1.000
#> Sample_1144 1 1.000
#> Sample_1145 3 0.000
#> Sample_1146 1 1.000
#> Sample_1147 1 1.000
#> Sample_1148 3 1.000
#> Sample_1149 1 1.000
#> Sample_1150 3 0.000
#> Sample_1151 1 1.000
#> Sample_1152 3 1.000
#> Sample_1153 3 1.000
#> Sample_1154 1 0.751
#> Sample_1155 3 1.000
#> Sample_1156 3 0.000
#> Sample_1157 3 0.751
#> Sample_1158 1 1.000
#> Sample_1159 1 1.000
#> Sample_1160 3 0.000
#> Sample_1161 3 0.000
#> Sample_1162 1 1.000
#> Sample_1163 3 0.751
#> Sample_1164 3 0.000
#> Sample_1165 1 1.000
#> Sample_1166 1 1.000
#> Sample_1167 3 1.000
#> Sample_1168 1 1.000
#> Sample_1169 3 0.000
#> Sample_1170 3 0.000
#> Sample_1171 1 1.000
#> Sample_1172 1 0.000
#> Sample_1173 1 0.502
#> Sample_1174 1 0.502
#> Sample_1175 3 0.000
#> Sample_1176 1 1.000
#> Sample_1177 1 1.000
#> Sample_1178 1 1.000
#> Sample_1179 1 1.000
#> Sample_1180 3 0.751
#> Sample_1181 1 1.000
#> Sample_1182 3 1.000
#> Sample_1183 3 0.000
#> Sample_1184 3 0.000
#> Sample_1185 1 1.000
#> Sample_1186 1 1.000
#> Sample_1187 3 1.000
#> Sample_1188 1 1.000
#> Sample_1189 4 0.000
#> Sample_1190 4 0.000
#> Sample_1191 4 0.000
#> Sample_1192 4 1.000
#> Sample_1193 4 0.000
#> Sample_1194 4 0.000
#> Sample_1195 4 0.000
#> Sample_1196 4 0.000
#> Sample_1197 4 0.000
#> Sample_1198 4 0.000
#> Sample_1199 4 0.000
#> Sample_1200 4 0.000
#> Sample_1201 4 0.000
#> Sample_1202 4 0.000
#> Sample_1203 4 0.000
#> Sample_1204 4 0.000
#> Sample_1205 4 0.000
#> Sample_1206 4 0.751
#> Sample_1207 4 0.000
#> Sample_1208 4 0.000
#> Sample_1209 4 0.000
#> Sample_1210 4 0.000
#> Sample_1211 4 0.000
#> Sample_1212 4 0.000
#> Sample_1213 4 0.000
#> Sample_1214 4 0.000
#> Sample_1215 4 0.000
#> Sample_1216 4 0.253
#> Sample_1217 4 0.000
#> Sample_1218 4 1.000
#> Sample_1219 4 0.000
#> Sample_1220 4 0.000
#> Sample_1221 4 0.000
#> Sample_1222 3 1.000
#> Sample_1223 3 0.000
#> Sample_1224 3 0.000
#> Sample_1225 3 0.751
#> Sample_1226 3 0.000
#> Sample_1227 3 1.000
#> Sample_1228 3 0.000
#> Sample_1229 1 1.000
#> Sample_1230 3 0.000
#> Sample_1231 3 0.000
#> Sample_1232 3 0.000
#> Sample_1233 3 0.000
#> Sample_1234 3 0.000
#> Sample_1235 3 0.000
#> Sample_1236 3 1.000
#> Sample_1237 3 0.000
#> Sample_1238 3 0.000
#> Sample_1239 3 1.000
#> Sample_1240 1 1.000
#> Sample_1241 3 1.000
#> Sample_1242 1 1.000
#> Sample_1243 3 0.000
#> Sample_1244 3 0.000
#> Sample_1245 3 0.253
#> Sample_1246 3 0.000
#> Sample_1247 3 0.000
#> Sample_1248 3 0.000
#> Sample_1249 3 1.000
#> Sample_1250 3 0.000
#> Sample_1251 3 0.000
#> Sample_1252 3 0.000
#> Sample_1253 3 0.000
#> Sample_1254 3 1.000
#> Sample_1255 3 0.000
#> Sample_1256 3 0.000
#> Sample_1257 3 0.000
#> Sample_1258 3 0.000
#> Sample_1259 3 0.000
#> Sample_1260 3 0.502
#> Sample_1261 3 0.000
#> Sample_1262 3 0.000
#> Sample_1263 3 0.000
#> Sample_1264 3 0.249
#> Sample_1265 3 0.000
#> Sample_1266 1 1.000
#> Sample_1267 1 1.000
#> Sample_1268 3 0.000
#> Sample_1269 3 0.000
#> Sample_1270 3 0.249
#> Sample_1271 3 0.000
#> Sample_1272 3 0.502
#> Sample_1273 3 0.000
#> Sample_1274 3 0.000
#> Sample_1275 3 0.000
#> Sample_1276 3 0.000
#> Sample_1277 3 0.000
#> Sample_1278 3 0.000
#> Sample_1279 3 1.000
#> Sample_1280 1 1.000
#> Sample_1281 3 0.000
#> Sample_1282 3 0.000
#> Sample_1283 3 0.498
#> Sample_1284 3 0.000
#> Sample_1285 3 0.502
#> Sample_1286 3 0.000
#> Sample_1287 3 0.000
#> Sample_1288 3 0.000
#> Sample_1289 3 0.000
#> Sample_1290 3 0.751
#> Sample_1291 3 0.000
#> Sample_1292 3 0.000
#> Sample_1293 3 0.000
#> Sample_1294 3 0.000
#> Sample_1295 3 0.000
#> Sample_1296 3 0.000
#> Sample_1297 3 0.000
#> Sample_1298 3 0.000
#> Sample_1299 3 1.000
#> Sample_1300 3 0.000
#> Sample_1301 3 0.000
#> Sample_1302 3 0.000
#> Sample_1303 3 0.000
#> Sample_1304 3 0.000
#> Sample_1305 3 0.000
#> Sample_1306 3 0.000
#> Sample_1307 3 1.000
#> Sample_1308 1 1.000
#> Sample_1309 3 1.000
#> Sample_1310 3 0.000
#> Sample_1311 3 0.000
#> Sample_1312 3 0.000
#> Sample_1313 1 1.000
#> Sample_1314 3 0.000
#> Sample_1315 3 0.000
#> Sample_1316 3 0.000
#> Sample_1317 3 1.000
#> Sample_1318 3 0.000
#> Sample_1319 3 0.000
#> Sample_1320 1 1.000
#> Sample_1321 3 0.000
#> Sample_1322 3 0.000
#> Sample_1323 3 0.000
#> Sample_1324 3 0.000
#> Sample_1325 3 0.000
#> Sample_1326 3 0.000
#> Sample_1327 3 0.000
#> Sample_1328 3 1.000
#> Sample_1329 3 0.000
#> Sample_1330 3 0.000
#> Sample_1331 3 0.000
#> Sample_1332 3 0.000
#> Sample_1333 3 0.000
#> Sample_1334 3 0.000
#> Sample_1335 3 1.000
#> Sample_1336 3 1.000
#> Sample_1337 3 0.000
#> Sample_1338 1 1.000
#> Sample_1339 3 0.000
#> Sample_1340 3 0.000
#> Sample_1341 3 0.249
#> Sample_1342 1 1.000
#> Sample_1343 3 0.502
#> Sample_1344 3 0.000
#> Sample_1345 3 0.000
#> Sample_1346 3 0.000
#> Sample_1347 3 1.000
#> Sample_1348 1 1.000
#> Sample_1349 3 0.000
#> Sample_1350 3 1.000
#> Sample_1351 3 0.502
#> Sample_1352 3 1.000
#> Sample_1353 3 1.000
#> Sample_1354 3 1.000
#> Sample_1355 3 0.000
#> Sample_1356 3 0.000
#> Sample_1357 3 0.502
#> Sample_1358 1 0.502
#> Sample_1359 3 0.000
#> Sample_1360 3 0.253
#> Sample_1361 3 0.000
#> Sample_1362 3 0.000
#> Sample_1363 3 0.000
#> Sample_1364 1 0.000
#> Sample_1365 3 0.000
#> Sample_1366 3 0.000
#> Sample_1367 3 1.000
#> Sample_1368 3 0.000
#> Sample_1369 3 0.000
#> Sample_1370 3 0.000
#> Sample_1371 3 1.000
#> Sample_1372 3 0.000
#> Sample_1373 1 1.000
#> Sample_1374 3 1.000
#> Sample_1375 3 1.000
#> Sample_1376 3 0.249
#> Sample_1377 3 0.000
#> Sample_1378 3 0.000
#> Sample_1379 3 0.000
#> Sample_1380 3 0.249
#> Sample_1381 3 0.000
#> Sample_1382 3 1.000
#> Sample_1383 3 0.000
#> Sample_1384 3 0.000
#> Sample_1385 3 0.502
#> Sample_1386 1 1.000
#> Sample_1387 3 0.000
#> Sample_1388 3 1.000
#> Sample_1389 3 0.000
#> Sample_1390 3 0.000
#> Sample_1391 3 0.000
#> Sample_1392 3 0.000
#> Sample_1393 3 0.000
#> Sample_1394 3 0.000
#> Sample_1395 3 0.000
#> Sample_1396 3 0.502
#> Sample_1397 3 0.000
#> Sample_1398 3 1.000
#> Sample_1399 1 1.000
#> Sample_1400 3 0.000
#> Sample_1401 3 0.000
#> Sample_1402 1 1.000
#> Sample_1403 3 0.000
#> Sample_1404 3 0.000
#> Sample_1405 3 0.000
#> Sample_1406 3 1.000
#> Sample_1407 3 0.249
#> Sample_1408 3 1.000
#> Sample_1409 1 0.502
#> Sample_1410 3 0.751
#> Sample_1411 3 0.000
#> Sample_1412 3 0.000
#> Sample_1413 3 0.000
#> Sample_1414 3 0.000
#> Sample_1415 3 0.000
#> Sample_1416 3 0.000
#> Sample_1417 3 0.000
#> Sample_1418 3 0.253
#> Sample_1419 3 1.000
#> Sample_1420 3 0.000
#> Sample_1421 3 0.000
#> Sample_1422 3 0.000
#> Sample_1423 3 1.000
#> Sample_1424 3 0.000
#> Sample_1425 1 1.000
#> Sample_1426 1 1.000
#> Sample_1427 3 1.000
#> Sample_1428 3 0.000
#> Sample_1429 1 0.000
#> Sample_1430 3 0.253
#> Sample_1431 1 1.000
#> Sample_1432 1 0.502
#> Sample_1433 1 1.000
#> Sample_1434 1 0.000
#> Sample_1435 1 1.000
#> Sample_1436 1 1.000
#> Sample_1437 1 1.000
#> Sample_1438 3 1.000
#> Sample_1439 1 0.000
#> Sample_1440 1 1.000
#> Sample_1441 3 1.000
#> Sample_1442 3 0.751
#> Sample_1443 3 0.253
#> Sample_1444 1 1.000
#> Sample_1445 3 1.000
#> Sample_1446 3 1.000
#> Sample_1447 3 0.498
#> Sample_1448 1 0.751
#> Sample_1449 1 0.751
#> Sample_1450 1 1.000
#> Sample_1451 1 1.000
#> Sample_1452 1 0.000
#> Sample_1453 3 1.000
#> Sample_1454 1 0.000
#> Sample_1455 1 1.000
#> Sample_1456 3 1.000
#> Sample_1457 3 1.000
#> Sample_1458 3 0.249
#> Sample_1459 3 1.000
#> Sample_1460 3 1.000
#> Sample_1461 3 0.000
#> Sample_1462 1 1.000
#> Sample_1463 3 0.249
#> Sample_1464 3 0.000
#> Sample_1465 1 1.000
#> Sample_1466 1 0.498
#> Sample_1467 1 0.751
#> Sample_1468 3 0.253
#> Sample_1469 3 1.000
#> Sample_1470 3 0.000
#> Sample_1471 3 1.000
#> Sample_1472 3 1.000
#> Sample_1473 1 1.000
#> Sample_1474 1 1.000
#> Sample_1475 3 1.000
#> Sample_1476 1 1.000
#> Sample_1477 3 1.000
#> Sample_1478 1 1.000
#> Sample_1479 1 1.000
#> Sample_1480 3 0.000
#> Sample_1481 3 1.000
#> Sample_1482 1 0.751
#> Sample_1483 1 1.000
#> Sample_1484 1 1.000
#> Sample_1485 3 0.253
#> Sample_1486 3 0.000
#> Sample_1487 1 1.000
#> Sample_1488 3 0.000
#> Sample_1489 1 0.000
#> Sample_1490 1 1.000
#> Sample_1491 1 1.000
#> Sample_1492 3 1.000
#> Sample_1493 1 1.000
#> Sample_1494 3 0.498
#> Sample_1495 3 1.000
#> Sample_1496 3 1.000
#> Sample_1497 1 1.000
#> Sample_1498 1 0.502
#> Sample_1499 1 1.000
#> Sample_1500 1 1.000
#> Sample_1501 1 1.000
#> Sample_1502 3 0.000
#> Sample_1503 3 0.000
#> Sample_1504 3 0.000
#> Sample_1505 3 0.000
#> Sample_1506 3 0.000
#> Sample_1507 3 1.000
#> Sample_1508 3 1.000
#> Sample_1509 1 1.000
#> Sample_1510 1 1.000
#> Sample_1511 3 0.000
#> Sample_1512 3 0.000
#> Sample_1513 3 0.000
#> Sample_1514 3 1.000
#> Sample_1515 3 0.000
#> Sample_1516 1 1.000
#> Sample_1517 2 0.000
#> Sample_1518 2 0.000
#> Sample_1519 2 0.000
#> Sample_1520 2 0.000
#> Sample_1521 2 1.000
#> Sample_1522 4 0.000
#> Sample_1523 4 0.000
#> Sample_1524 4 0.000
#> Sample_1525 1 0.000
#> Sample_1526 1 0.000
#> Sample_1527 2 1.000
#> Sample_1528 2 0.000
#> Sample_1529 2 0.751
#> Sample_1530 2 0.000
#> Sample_1531 2 0.000
#> Sample_1532 2 0.000
#> Sample_1533 2 0.000
#> Sample_1534 2 0.253
#> Sample_1535 4 0.000
#> Sample_1536 2 0.000
#> Sample_1537 2 0.000
#> Sample_1538 2 0.000
#> Sample_1539 2 1.000
#> Sample_1540 1 0.502
#> Sample_1541 1 0.498
#> Sample_1542 2 0.751
#> Sample_1543 2 0.000
#> Sample_1544 1 0.000
#> Sample_1545 1 1.000
#> Sample_1546 2 0.000
#> Sample_1547 4 0.000
#> Sample_1548 4 0.000
#> Sample_1549 4 0.000
#> Sample_1550 4 0.000
#> Sample_1551 4 0.000
#> Sample_1552 4 0.000
#> Sample_1553 4 0.000
#> Sample_1554 4 0.000
#> Sample_1555 4 0.000
#> Sample_1556 4 0.000
#> Sample_1557 4 0.000
#> Sample_1558 4 0.000
#> Sample_1559 4 0.000
#> Sample_1560 4 0.000
#> Sample_1561 4 0.000
#> Sample_1562 1 0.000
#> Sample_1563 1 0.000
#> Sample_1564 4 0.000
#> Sample_1565 1 1.000
#> Sample_1566 1 0.747
#> Sample_1567 1 0.000
#> Sample_1568 4 1.000
#> Sample_1569 3 0.751
#> Sample_1570 2 0.502
#> Sample_1571 4 0.000
#> Sample_1572 4 1.000
#> Sample_1573 4 0.751
#> Sample_1574 4 0.000
#> Sample_1575 4 0.000
#> Sample_1576 4 0.000
#> Sample_1577 1 0.253
#> Sample_1578 4 0.000
#> Sample_1579 4 0.000
#> Sample_1580 4 0.000
#> Sample_1581 4 0.000
#> Sample_1582 4 0.000
#> Sample_1583 4 0.000
#> Sample_1584 4 0.000
#> Sample_1585 4 0.000
#> Sample_1586 4 0.000
#> Sample_1587 4 0.000
#> Sample_1588 4 0.000
#> Sample_1589 4 0.000
#> Sample_1590 4 1.000
#> Sample_1591 4 0.000
#> Sample_1592 4 0.751
#> Sample_1593 4 0.000
#> Sample_1594 4 0.000
#> Sample_1595 4 0.000
#> Sample_1596 2 0.000
#> Sample_1597 4 0.000
#> Sample_1598 3 0.000
#> Sample_1599 2 0.000
#> Sample_1600 1 0.249
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
As soon as the classes for columns are determined, the signatures that are significantly different between subgroups can be looked for. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. To get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows (which is done by automatically selecting number of clusters).If there are too many signatures, top_signatures = ... can be set to only show the
signatures with the highest FDRs:
# code only for demonstration
# e.g. to show the top 500 most significant rows
tb = get_signature(res, k = ..., top_signatures = 500)
If the signatures are defined as these which are uniquely high in current group, diff_method argument
can be set to "uniquely_high_in_one_group":
# code only for demonstration
tb = get_signature(res, k = ..., diff_method = "uniquely_high_in_one_group")
UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n_sample age(p-value) cell.type(p-value) k
#> ATC:skmeans 1487 3.25e-01 4.41e-309 2
#> ATC:skmeans 1179 1.00e-07 5.38e-243 3
#> ATC:skmeans 1176 9.06e-14 3.32e-251 4
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
Parent node: Node0. Child nodes: Node011 , Node012 , Node013 , Node021 , Node022 , Node023 , Node031 , Node032 , Node033 .
The object with results only for a single top-value method and a single partitioning method can be extracted as:
res = res_rh["01"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4.
#> On a matrix with 14749 rows and 452 columns.
#> Top rows (885) are extracted by 'ATC' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 150 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_partitions"
#> [7] "compare_signatures" "consensus_heatmap" "dimension_reduction"
#> [10] "functional_enrichment" "get_anno_col" "get_anno"
#> [13] "get_classes" "get_consensus" "get_matrix"
#> [16] "get_membership" "get_param" "get_signatures"
#> [19] "get_stats" "is_best_k" "is_stable_k"
#> [22] "membership_heatmap" "ncol" "nrow"
#> [25] "plot_ecdf" "predict_classes" "rownames"
#> [28] "select_partition_number" "show" "suggest_best_k"
#> [31] "test_to_known_factors" "top_rows_heatmap"
collect_plots() function collects all the plots made from res for all k (number of subgroups)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, higher 1-PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.996 0.998 0.499 0.501 0.501
#> 3 3 1.000 0.971 0.986 0.225 0.878 0.760
#> 4 4 0.702 0.832 0.882 0.197 0.855 0.632
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 3
#> attr(,"optional")
#> [1] 2
There is also optional best \(k\) = 2 that is worth to check.
Following is the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall subgroup
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> Sample_1 2 0.000 0.998 0.00 1.00
#> Sample_121 2 0.000 0.998 0.00 1.00
#> Sample_214 2 0.000 0.998 0.00 1.00
#> Sample_215 2 0.000 0.998 0.00 1.00
#> Sample_217 2 0.000 0.998 0.00 1.00
#> Sample_224 2 0.000 0.998 0.00 1.00
#> Sample_226 2 0.000 0.998 0.00 1.00
#> Sample_236 2 0.000 0.998 0.00 1.00
#> Sample_241 2 0.000 0.998 0.00 1.00
#> Sample_258 2 0.000 0.998 0.00 1.00
#> Sample_264 2 0.000 0.998 0.00 1.00
#> Sample_265 2 0.000 0.998 0.00 1.00
#> Sample_272 2 0.000 0.998 0.00 1.00
#> Sample_275 2 0.000 0.998 0.00 1.00
#> Sample_276 2 0.000 0.998 0.00 1.00
#> Sample_287 2 0.000 0.998 0.00 1.00
#> Sample_302 1 0.680 0.780 0.82 0.18
#> Sample_303 2 0.000 0.998 0.00 1.00
#> Sample_304 2 0.000 0.998 0.00 1.00
#> Sample_305 2 0.000 0.998 0.00 1.00
#> Sample_308 2 0.000 0.998 0.00 1.00
#> Sample_309 2 0.000 0.998 0.00 1.00
#> Sample_310 2 0.000 0.998 0.00 1.00
#> Sample_311 2 0.000 0.998 0.00 1.00
#> Sample_312 2 0.000 0.998 0.00 1.00
#> Sample_313 2 0.000 0.998 0.00 1.00
#> Sample_314 2 0.000 0.998 0.00 1.00
#> Sample_316 2 0.000 0.998 0.00 1.00
#> Sample_318 2 0.000 0.998 0.00 1.00
#> Sample_319 2 0.000 0.998 0.00 1.00
#> Sample_320 2 0.000 0.998 0.00 1.00
#> Sample_321 2 0.000 0.998 0.00 1.00
#> Sample_323 2 0.000 0.998 0.00 1.00
#> Sample_324 2 0.000 0.998 0.00 1.00
#> Sample_325 2 0.000 0.998 0.00 1.00
#> Sample_326 2 0.000 0.998 0.00 1.00
#> Sample_327 2 0.000 0.998 0.00 1.00
#> Sample_328 2 0.000 0.998 0.00 1.00
#> Sample_329 2 0.000 0.998 0.00 1.00
#> Sample_330 2 0.000 0.998 0.00 1.00
#> Sample_350 2 0.000 0.998 0.00 1.00
#> Sample_351 2 0.000 0.998 0.00 1.00
#> Sample_352 2 0.000 0.998 0.00 1.00
#> Sample_355 2 0.000 0.998 0.00 1.00
#> Sample_358 2 0.000 0.998 0.00 1.00
#> Sample_360 2 0.000 0.998 0.00 1.00
#> Sample_362 2 0.000 0.998 0.00 1.00
#> Sample_365 2 0.000 0.998 0.00 1.00
#> Sample_368 2 0.000 0.998 0.00 1.00
#> Sample_369 2 0.000 0.998 0.00 1.00
#> Sample_372 2 0.000 0.998 0.00 1.00
#> Sample_373 2 0.000 0.998 0.00 1.00
#> Sample_377 2 0.000 0.998 0.00 1.00
#> Sample_381 2 0.000 0.998 0.00 1.00
#> Sample_385 2 0.000 0.998 0.00 1.00
#> Sample_386 2 0.000 0.998 0.00 1.00
#> Sample_389 2 0.000 0.998 0.00 1.00
#> Sample_390 2 0.000 0.998 0.00 1.00
#> Sample_393 2 0.000 0.998 0.00 1.00
#> Sample_394 2 0.000 0.998 0.00 1.00
#> Sample_395 2 0.000 0.998 0.00 1.00
#> Sample_396 2 0.000 0.998 0.00 1.00
#> Sample_397 2 0.000 0.998 0.00 1.00
#> Sample_398 2 0.000 0.998 0.00 1.00
#> Sample_403 2 0.000 0.998 0.00 1.00
#> Sample_404 2 0.000 0.998 0.00 1.00
#> Sample_405 2 0.000 0.998 0.00 1.00
#> Sample_408 2 0.000 0.998 0.00 1.00
#> Sample_409 2 0.000 0.998 0.00 1.00
#> Sample_412 2 0.000 0.998 0.00 1.00
#> Sample_416 2 0.000 0.998 0.00 1.00
#> Sample_421 2 0.000 0.998 0.00 1.00
#> Sample_424 2 0.000 0.998 0.00 1.00
#> Sample_425 2 0.000 0.998 0.00 1.00
#> Sample_427 2 0.000 0.998 0.00 1.00
#> Sample_428 2 0.000 0.998 0.00 1.00
#> Sample_430 2 0.000 0.998 0.00 1.00
#> Sample_431 2 0.000 0.998 0.00 1.00
#> Sample_432 2 0.000 0.998 0.00 1.00
#> Sample_433 2 0.000 0.998 0.00 1.00
#> Sample_434 2 0.000 0.998 0.00 1.00
#> Sample_438 2 0.000 0.998 0.00 1.00
#> Sample_439 2 0.000 0.998 0.00 1.00
#> Sample_440 2 0.000 0.998 0.00 1.00
#> Sample_442 2 0.000 0.998 0.00 1.00
#> Sample_443 2 0.000 0.998 0.00 1.00
#> Sample_445 2 0.000 0.998 0.00 1.00
#> Sample_446 2 0.000 0.998 0.00 1.00
#> Sample_448 2 0.000 0.998 0.00 1.00
#> Sample_451 2 0.000 0.998 0.00 1.00
#> Sample_452 2 0.000 0.998 0.00 1.00
#> Sample_455 2 0.000 0.998 0.00 1.00
#> Sample_456 2 0.000 0.998 0.00 1.00
#> Sample_457 2 0.000 0.998 0.00 1.00
#> Sample_459 2 0.000 0.998 0.00 1.00
#> Sample_461 2 0.000 0.998 0.00 1.00
#> Sample_463 2 0.000 0.998 0.00 1.00
#> Sample_464 2 0.000 0.998 0.00 1.00
#> Sample_465 2 0.000 0.998 0.00 1.00
#> Sample_466 2 0.000 0.998 0.00 1.00
#> Sample_468 2 0.000 0.998 0.00 1.00
#> Sample_469 2 0.000 0.998 0.00 1.00
#> Sample_472 2 0.000 0.998 0.00 1.00
#> Sample_474 2 0.000 0.998 0.00 1.00
#> Sample_475 2 0.000 0.998 0.00 1.00
#> Sample_476 2 0.000 0.998 0.00 1.00
#> Sample_477 2 0.000 0.998 0.00 1.00
#> Sample_479 1 0.000 0.998 1.00 0.00
#> Sample_486 1 0.000 0.998 1.00 0.00
#> Sample_487 1 0.000 0.998 1.00 0.00
#> Sample_490 1 0.000 0.998 1.00 0.00
#> Sample_495 1 0.000 0.998 1.00 0.00
#> Sample_499 1 0.000 0.998 1.00 0.00
#> Sample_500 1 0.000 0.998 1.00 0.00
#> Sample_505 1 0.000 0.998 1.00 0.00
#> Sample_506 1 0.000 0.998 1.00 0.00
#> Sample_507 1 0.000 0.998 1.00 0.00
#> Sample_511 1 0.000 0.998 1.00 0.00
#> Sample_513 1 0.000 0.998 1.00 0.00
#> Sample_515 1 0.000 0.998 1.00 0.00
#> Sample_517 1 0.000 0.998 1.00 0.00
#> Sample_520 1 0.000 0.998 1.00 0.00
#> Sample_521 1 0.000 0.998 1.00 0.00
#> Sample_527 1 0.000 0.998 1.00 0.00
#> Sample_530 1 0.000 0.998 1.00 0.00
#> Sample_532 1 0.000 0.998 1.00 0.00
#> Sample_533 1 0.000 0.998 1.00 0.00
#> Sample_535 1 0.000 0.998 1.00 0.00
#> Sample_536 1 0.000 0.998 1.00 0.00
#> Sample_537 1 0.000 0.998 1.00 0.00
#> Sample_539 1 0.000 0.998 1.00 0.00
#> Sample_542 1 0.000 0.998 1.00 0.00
#> Sample_543 1 0.000 0.998 1.00 0.00
#> Sample_544 1 0.000 0.998 1.00 0.00
#> Sample_546 1 0.000 0.998 1.00 0.00
#> Sample_547 1 0.000 0.998 1.00 0.00
#> Sample_557 1 0.000 0.998 1.00 0.00
#> Sample_558 1 0.000 0.998 1.00 0.00
#> Sample_559 1 0.000 0.998 1.00 0.00
#> Sample_565 1 0.000 0.998 1.00 0.00
#> Sample_566 1 0.000 0.998 1.00 0.00
#> Sample_567 1 0.000 0.998 1.00 0.00
#> Sample_569 1 0.000 0.998 1.00 0.00
#> Sample_572 2 0.000 0.998 0.00 1.00
#> Sample_573 2 0.141 0.978 0.02 0.98
#> Sample_577 2 0.000 0.998 0.00 1.00
#> Sample_590 2 0.000 0.998 0.00 1.00
#> Sample_614 2 0.000 0.998 0.00 1.00
#> Sample_616 2 0.000 0.998 0.00 1.00
#> Sample_617 2 0.000 0.998 0.00 1.00
#> Sample_618 2 0.000 0.998 0.00 1.00
#> Sample_619 2 0.000 0.998 0.00 1.00
#> Sample_620 2 0.000 0.998 0.00 1.00
#> Sample_622 2 0.000 0.998 0.00 1.00
#> Sample_626 2 0.000 0.998 0.00 1.00
#> Sample_630 2 0.000 0.998 0.00 1.00
#> Sample_632 2 0.000 0.998 0.00 1.00
#> Sample_633 2 0.000 0.998 0.00 1.00
#> Sample_634 2 0.000 0.998 0.00 1.00
#> Sample_635 2 0.000 0.998 0.00 1.00
#> Sample_636 2 0.000 0.998 0.00 1.00
#> Sample_637 2 0.000 0.998 0.00 1.00
#> Sample_639 2 0.000 0.998 0.00 1.00
#> Sample_641 2 0.000 0.998 0.00 1.00
#> Sample_643 2 0.000 0.998 0.00 1.00
#> Sample_646 2 0.000 0.998 0.00 1.00
#> Sample_647 2 0.000 0.998 0.00 1.00
#> Sample_685 2 0.000 0.998 0.00 1.00
#> Sample_687 2 0.000 0.998 0.00 1.00
#> Sample_692 2 0.000 0.998 0.00 1.00
#> Sample_737 1 0.795 0.684 0.76 0.24
#> Sample_950 2 0.000 0.998 0.00 1.00
#> Sample_962 2 0.000 0.998 0.00 1.00
#> Sample_968 2 0.000 0.998 0.00 1.00
#> Sample_976 2 0.000 0.998 0.00 1.00
#> Sample_977 2 0.000 0.998 0.00 1.00
#> Sample_978 2 0.000 0.998 0.00 1.00
#> Sample_980 2 0.000 0.998 0.00 1.00
#> Sample_981 2 0.000 0.998 0.00 1.00
#> Sample_982 2 0.000 0.998 0.00 1.00
#> Sample_983 2 0.000 0.998 0.00 1.00
#> Sample_985 2 0.000 0.998 0.00 1.00
#> Sample_986 2 0.000 0.998 0.00 1.00
#> Sample_987 2 0.000 0.998 0.00 1.00
#> Sample_988 2 0.000 0.998 0.00 1.00
#> Sample_989 2 0.000 0.998 0.00 1.00
#> Sample_990 2 0.000 0.998 0.00 1.00
#> Sample_991 2 0.000 0.998 0.00 1.00
#> Sample_992 2 0.000 0.998 0.00 1.00
#> Sample_993 2 0.000 0.998 0.00 1.00
#> Sample_994 2 0.000 0.998 0.00 1.00
#> Sample_996 2 0.000 0.998 0.00 1.00
#> Sample_999 2 0.000 0.998 0.00 1.00
#> Sample_1007 2 0.000 0.998 0.00 1.00
#> Sample_1008 2 0.000 0.998 0.00 1.00
#> Sample_1009 2 0.000 0.998 0.00 1.00
#> Sample_1010 2 0.000 0.998 0.00 1.00
#> Sample_1014 2 0.000 0.998 0.00 1.00
#> Sample_1015 2 0.000 0.998 0.00 1.00
#> Sample_1016 2 0.000 0.998 0.00 1.00
#> Sample_1017 2 0.000 0.998 0.00 1.00
#> Sample_1018 2 0.000 0.998 0.00 1.00
#> Sample_1019 2 0.000 0.998 0.00 1.00
#> Sample_1020 2 0.000 0.998 0.00 1.00
#> Sample_1021 2 0.000 0.998 0.00 1.00
#> Sample_1022 1 0.000 0.998 1.00 0.00
#> Sample_1023 2 0.529 0.863 0.12 0.88
#> Sample_1024 2 0.000 0.998 0.00 1.00
#> Sample_1025 2 0.000 0.998 0.00 1.00
#> Sample_1026 2 0.000 0.998 0.00 1.00
#> Sample_1027 1 0.000 0.998 1.00 0.00
#> Sample_1028 1 0.000 0.998 1.00 0.00
#> Sample_1030 1 0.000 0.998 1.00 0.00
#> Sample_1034 1 0.000 0.998 1.00 0.00
#> Sample_1036 1 0.000 0.998 1.00 0.00
#> Sample_1039 1 0.000 0.998 1.00 0.00
#> Sample_1041 1 0.000 0.998 1.00 0.00
#> Sample_1042 1 0.000 0.998 1.00 0.00
#> Sample_1046 1 0.000 0.998 1.00 0.00
#> Sample_1049 1 0.000 0.998 1.00 0.00
#> Sample_1050 1 0.000 0.998 1.00 0.00
#> Sample_1052 1 0.000 0.998 1.00 0.00
#> Sample_1056 2 0.000 0.998 0.00 1.00
#> Sample_1058 2 0.000 0.998 0.00 1.00
#> Sample_1059 2 0.000 0.998 0.00 1.00
#> Sample_1062 2 0.000 0.998 0.00 1.00
#> Sample_1066 1 0.000 0.998 1.00 0.00
#> Sample_1070 1 0.000 0.998 1.00 0.00
#> Sample_1073 1 0.000 0.998 1.00 0.00
#> Sample_1075 1 0.000 0.998 1.00 0.00
#> Sample_1076 1 0.000 0.998 1.00 0.00
#> Sample_1080 1 0.000 0.998 1.00 0.00
#> Sample_1083 1 0.000 0.998 1.00 0.00
#> Sample_1087 1 0.000 0.998 1.00 0.00
#> Sample_1090 1 0.000 0.998 1.00 0.00
#> Sample_1091 1 0.000 0.998 1.00 0.00
#> Sample_1093 1 0.000 0.998 1.00 0.00
#> Sample_1097 1 0.000 0.998 1.00 0.00
#> Sample_1100 1 0.000 0.998 1.00 0.00
#> Sample_1102 1 0.000 0.998 1.00 0.00
#> Sample_1103 1 0.000 0.998 1.00 0.00
#> Sample_1104 1 0.000 0.998 1.00 0.00
#> Sample_1105 1 0.000 0.998 1.00 0.00
#> Sample_1108 1 0.000 0.998 1.00 0.00
#> Sample_1110 1 0.000 0.998 1.00 0.00
#> Sample_1112 1 0.000 0.998 1.00 0.00
#> Sample_1113 1 0.000 0.998 1.00 0.00
#> Sample_1114 1 0.000 0.998 1.00 0.00
#> Sample_1116 1 0.000 0.998 1.00 0.00
#> Sample_1118 1 0.000 0.998 1.00 0.00
#> Sample_1119 1 0.000 0.998 1.00 0.00
#> Sample_1121 1 0.000 0.998 1.00 0.00
#> Sample_1129 1 0.000 0.998 1.00 0.00
#> Sample_1130 1 0.000 0.998 1.00 0.00
#> Sample_1131 1 0.000 0.998 1.00 0.00
#> Sample_1133 1 0.000 0.998 1.00 0.00
#> Sample_1135 1 0.000 0.998 1.00 0.00
#> Sample_1136 1 0.000 0.998 1.00 0.00
#> Sample_1137 1 0.000 0.998 1.00 0.00
#> Sample_1138 1 0.000 0.998 1.00 0.00
#> Sample_1142 1 0.000 0.998 1.00 0.00
#> Sample_1143 1 0.000 0.998 1.00 0.00
#> Sample_1144 1 0.000 0.998 1.00 0.00
#> Sample_1146 1 0.000 0.998 1.00 0.00
#> Sample_1147 1 0.000 0.998 1.00 0.00
#> Sample_1148 1 0.000 0.998 1.00 0.00
#> Sample_1149 1 0.000 0.998 1.00 0.00
#> Sample_1151 1 0.000 0.998 1.00 0.00
#> Sample_1153 1 0.000 0.998 1.00 0.00
#> Sample_1154 1 0.000 0.998 1.00 0.00
#> Sample_1155 1 0.000 0.998 1.00 0.00
#> Sample_1158 1 0.000 0.998 1.00 0.00
#> Sample_1159 1 0.000 0.998 1.00 0.00
#> Sample_1160 1 0.000 0.998 1.00 0.00
#> Sample_1162 1 0.000 0.998 1.00 0.00
#> Sample_1165 1 0.000 0.998 1.00 0.00
#> Sample_1166 1 0.000 0.998 1.00 0.00
#> Sample_1167 1 0.000 0.998 1.00 0.00
#> Sample_1168 1 0.000 0.998 1.00 0.00
#> Sample_1171 1 0.000 0.998 1.00 0.00
#> Sample_1172 1 0.000 0.998 1.00 0.00
#> Sample_1173 1 0.000 0.998 1.00 0.00
#> Sample_1174 1 0.000 0.998 1.00 0.00
#> Sample_1176 1 0.000 0.998 1.00 0.00
#> Sample_1177 1 0.000 0.998 1.00 0.00
#> Sample_1178 1 0.000 0.998 1.00 0.00
#> Sample_1179 1 0.000 0.998 1.00 0.00
#> Sample_1181 1 0.000 0.998 1.00 0.00
#> Sample_1182 1 0.000 0.998 1.00 0.00
#> Sample_1185 1 0.000 0.998 1.00 0.00
#> Sample_1186 1 0.000 0.998 1.00 0.00
#> Sample_1187 1 0.000 0.998 1.00 0.00
#> Sample_1188 1 0.000 0.998 1.00 0.00
#> Sample_1192 2 0.000 0.998 0.00 1.00
#> Sample_1208 2 0.000 0.998 0.00 1.00
#> Sample_1216 2 0.000 0.998 0.00 1.00
#> Sample_1217 2 0.000 0.998 0.00 1.00
#> Sample_1218 2 0.000 0.998 0.00 1.00
#> Sample_1222 1 0.000 0.998 1.00 0.00
#> Sample_1225 1 0.000 0.998 1.00 0.00
#> Sample_1227 1 0.000 0.998 1.00 0.00
#> Sample_1228 1 0.000 0.998 1.00 0.00
#> Sample_1229 1 0.000 0.998 1.00 0.00
#> Sample_1234 1 0.000 0.998 1.00 0.00
#> Sample_1236 1 0.000 0.998 1.00 0.00
#> Sample_1239 1 0.000 0.998 1.00 0.00
#> Sample_1240 1 0.000 0.998 1.00 0.00
#> Sample_1241 1 0.000 0.998 1.00 0.00
#> Sample_1242 1 0.000 0.998 1.00 0.00
#> Sample_1249 1 0.000 0.998 1.00 0.00
#> Sample_1254 1 0.000 0.998 1.00 0.00
#> Sample_1264 1 0.000 0.998 1.00 0.00
#> Sample_1266 1 0.000 0.998 1.00 0.00
#> Sample_1267 1 0.000 0.998 1.00 0.00
#> Sample_1272 1 0.000 0.998 1.00 0.00
#> Sample_1279 1 0.000 0.998 1.00 0.00
#> Sample_1280 1 0.000 0.998 1.00 0.00
#> Sample_1283 1 0.000 0.998 1.00 0.00
#> Sample_1285 1 0.000 0.998 1.00 0.00
#> Sample_1299 1 0.000 0.998 1.00 0.00
#> Sample_1307 1 0.000 0.998 1.00 0.00
#> Sample_1308 1 0.000 0.998 1.00 0.00
#> Sample_1309 1 0.000 0.998 1.00 0.00
#> Sample_1313 1 0.000 0.998 1.00 0.00
#> Sample_1317 1 0.000 0.998 1.00 0.00
#> Sample_1320 1 0.000 0.998 1.00 0.00
#> Sample_1335 1 0.000 0.998 1.00 0.00
#> Sample_1336 1 0.000 0.998 1.00 0.00
#> Sample_1338 1 0.000 0.998 1.00 0.00
#> Sample_1342 1 0.000 0.998 1.00 0.00
#> Sample_1343 1 0.000 0.998 1.00 0.00
#> Sample_1348 1 0.000 0.998 1.00 0.00
#> Sample_1350 1 0.000 0.998 1.00 0.00
#> Sample_1352 1 0.000 0.998 1.00 0.00
#> Sample_1354 1 0.000 0.998 1.00 0.00
#> Sample_1357 1 0.000 0.998 1.00 0.00
#> Sample_1358 1 0.000 0.998 1.00 0.00
#> Sample_1360 1 0.000 0.998 1.00 0.00
#> Sample_1364 1 0.000 0.998 1.00 0.00
#> Sample_1367 1 0.000 0.998 1.00 0.00
#> Sample_1371 1 0.000 0.998 1.00 0.00
#> Sample_1373 1 0.000 0.998 1.00 0.00
#> Sample_1374 1 0.000 0.998 1.00 0.00
#> Sample_1375 1 0.000 0.998 1.00 0.00
#> Sample_1382 1 0.000 0.998 1.00 0.00
#> Sample_1386 1 0.000 0.998 1.00 0.00
#> Sample_1388 1 0.000 0.998 1.00 0.00
#> Sample_1398 1 0.000 0.998 1.00 0.00
#> Sample_1399 1 0.000 0.998 1.00 0.00
#> Sample_1402 1 0.000 0.998 1.00 0.00
#> Sample_1406 1 0.000 0.998 1.00 0.00
#> Sample_1408 1 0.000 0.998 1.00 0.00
#> Sample_1409 1 0.000 0.998 1.00 0.00
#> Sample_1419 1 0.000 0.998 1.00 0.00
#> Sample_1423 1 0.000 0.998 1.00 0.00
#> Sample_1425 1 0.000 0.998 1.00 0.00
#> Sample_1426 1 0.000 0.998 1.00 0.00
#> Sample_1427 1 0.000 0.998 1.00 0.00
#> Sample_1429 1 0.000 0.998 1.00 0.00
#> Sample_1430 1 0.000 0.998 1.00 0.00
#> Sample_1431 1 0.000 0.998 1.00 0.00
#> Sample_1432 1 0.000 0.998 1.00 0.00
#> Sample_1433 1 0.000 0.998 1.00 0.00
#> Sample_1434 1 0.000 0.998 1.00 0.00
#> Sample_1435 1 0.000 0.998 1.00 0.00
#> Sample_1436 1 0.000 0.998 1.00 0.00
#> Sample_1437 1 0.000 0.998 1.00 0.00
#> Sample_1438 1 0.000 0.998 1.00 0.00
#> Sample_1439 1 0.000 0.998 1.00 0.00
#> Sample_1440 1 0.000 0.998 1.00 0.00
#> Sample_1441 1 0.000 0.998 1.00 0.00
#> Sample_1444 1 0.000 0.998 1.00 0.00
#> Sample_1445 1 0.000 0.998 1.00 0.00
#> Sample_1448 1 0.000 0.998 1.00 0.00
#> Sample_1449 1 0.000 0.998 1.00 0.00
#> Sample_1450 1 0.000 0.998 1.00 0.00
#> Sample_1451 1 0.000 0.998 1.00 0.00
#> Sample_1452 1 0.000 0.998 1.00 0.00
#> Sample_1453 1 0.000 0.998 1.00 0.00
#> Sample_1454 1 0.000 0.998 1.00 0.00
#> Sample_1455 1 0.000 0.998 1.00 0.00
#> Sample_1458 1 0.000 0.998 1.00 0.00
#> Sample_1459 1 0.000 0.998 1.00 0.00
#> Sample_1462 1 0.000 0.998 1.00 0.00
#> Sample_1465 1 0.000 0.998 1.00 0.00
#> Sample_1466 1 0.000 0.998 1.00 0.00
#> Sample_1467 1 0.000 0.998 1.00 0.00
#> Sample_1469 1 0.000 0.998 1.00 0.00
#> Sample_1470 1 0.000 0.998 1.00 0.00
#> Sample_1471 1 0.000 0.998 1.00 0.00
#> Sample_1472 1 0.000 0.998 1.00 0.00
#> Sample_1473 1 0.000 0.998 1.00 0.00
#> Sample_1474 1 0.000 0.998 1.00 0.00
#> Sample_1475 1 0.000 0.998 1.00 0.00
#> Sample_1476 1 0.000 0.998 1.00 0.00
#> Sample_1477 1 0.000 0.998 1.00 0.00
#> Sample_1478 1 0.000 0.998 1.00 0.00
#> Sample_1479 1 0.000 0.998 1.00 0.00
#> Sample_1481 1 0.000 0.998 1.00 0.00
#> Sample_1482 1 0.000 0.998 1.00 0.00
#> Sample_1483 1 0.000 0.998 1.00 0.00
#> Sample_1484 1 0.000 0.998 1.00 0.00
#> Sample_1487 1 0.000 0.998 1.00 0.00
#> Sample_1489 1 0.000 0.998 1.00 0.00
#> Sample_1490 1 0.000 0.998 1.00 0.00
#> Sample_1491 1 0.000 0.998 1.00 0.00
#> Sample_1492 1 0.000 0.998 1.00 0.00
#> Sample_1493 1 0.000 0.998 1.00 0.00
#> Sample_1495 1 0.000 0.998 1.00 0.00
#> Sample_1496 1 0.000 0.998 1.00 0.00
#> Sample_1497 1 0.000 0.998 1.00 0.00
#> Sample_1498 1 0.000 0.998 1.00 0.00
#> Sample_1499 1 0.000 0.998 1.00 0.00
#> Sample_1500 1 0.000 0.998 1.00 0.00
#> Sample_1501 1 0.000 0.998 1.00 0.00
#> Sample_1509 1 0.000 0.998 1.00 0.00
#> Sample_1510 1 0.000 0.998 1.00 0.00
#> Sample_1514 1 0.000 0.998 1.00 0.00
#> Sample_1516 1 0.000 0.998 1.00 0.00
#> Sample_1522 2 0.000 0.998 0.00 1.00
#> Sample_1523 2 0.000 0.998 0.00 1.00
#> Sample_1525 2 0.000 0.998 0.00 1.00
#> Sample_1526 2 0.000 0.998 0.00 1.00
#> Sample_1540 2 0.827 0.648 0.26 0.74
#> Sample_1541 1 0.000 0.998 1.00 0.00
#> Sample_1544 2 0.000 0.998 0.00 1.00
#> Sample_1545 1 0.000 0.998 1.00 0.00
#> Sample_1547 2 0.000 0.998 0.00 1.00
#> Sample_1556 2 0.000 0.998 0.00 1.00
#> Sample_1557 2 0.000 0.998 0.00 1.00
#> Sample_1558 2 0.000 0.998 0.00 1.00
#> Sample_1560 2 0.000 0.998 0.00 1.00
#> Sample_1562 2 0.000 0.998 0.00 1.00
#> Sample_1563 2 0.000 0.998 0.00 1.00
#> Sample_1564 2 0.000 0.998 0.00 1.00
#> Sample_1565 2 0.000 0.998 0.00 1.00
#> Sample_1566 2 0.000 0.998 0.00 1.00
#> Sample_1567 2 0.000 0.998 0.00 1.00
#> Sample_1568 2 0.000 0.998 0.00 1.00
#> Sample_1571 2 0.000 0.998 0.00 1.00
#> Sample_1572 2 0.000 0.998 0.00 1.00
#> Sample_1573 2 0.000 0.998 0.00 1.00
#> Sample_1576 2 0.000 0.998 0.00 1.00
#> Sample_1577 2 0.000 0.998 0.00 1.00
#> Sample_1578 2 0.000 0.998 0.00 1.00
#> Sample_1579 2 0.000 0.998 0.00 1.00
#> Sample_1580 2 0.000 0.998 0.00 1.00
#> Sample_1583 2 0.000 0.998 0.00 1.00
#> Sample_1584 2 0.000 0.998 0.00 1.00
#> Sample_1590 2 0.000 0.998 0.00 1.00
#> Sample_1591 2 0.000 0.998 0.00 1.00
#> Sample_1600 1 0.000 0.998 1.00 0.00
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> Sample_1 3 0.0000 0.9655 0.00 0.00 1.00
#> Sample_121 2 0.0892 0.9604 0.00 0.98 0.02
#> Sample_214 3 0.0892 0.9628 0.00 0.02 0.98
#> Sample_215 3 0.0892 0.9628 0.00 0.02 0.98
#> Sample_217 3 0.0892 0.9628 0.00 0.02 0.98
#> Sample_224 3 0.0000 0.9655 0.00 0.00 1.00
#> Sample_226 3 0.0892 0.9628 0.00 0.02 0.98
#> Sample_236 3 0.1529 0.9514 0.00 0.04 0.96
#> Sample_241 3 0.0892 0.9628 0.00 0.02 0.98
#> Sample_258 3 0.6244 0.2522 0.00 0.44 0.56
#> Sample_264 3 0.1529 0.9514 0.00 0.04 0.96
#> Sample_265 2 0.0000 0.9736 0.00 1.00 0.00
#> Sample_272 2 0.0892 0.9614 0.00 0.98 0.02
#> Sample_275 2 0.0892 0.9614 0.00 0.98 0.02
#> Sample_276 3 0.1529 0.9514 0.00 0.04 0.96
#> Sample_287 3 0.1529 0.9514 0.00 0.04 0.96
#> Sample_302 2 0.3686 0.7938 0.14 0.86 0.00
#> Sample_303 2 0.0000 0.9736 0.00 1.00 0.00
#> Sample_304 2 0.0000 0.9736 0.00 1.00 0.00
#> Sample_305 2 0.0000 0.9736 0.00 1.00 0.00
#> Sample_308 2 0.0000 0.9736 0.00 1.00 0.00
#> Sample_309 2 0.0000 0.9736 0.00 1.00 0.00
#> Sample_310 2 0.0000 0.9736 0.00 1.00 0.00
#> Sample_311 2 0.0000 0.9736 0.00 1.00 0.00
#> Sample_312 2 0.0000 0.9736 0.00 1.00 0.00
#> Sample_313 2 0.0000 0.9736 0.00 1.00 0.00
#> Sample_314 2 0.0000 0.9736 0.00 1.00 0.00
#> Sample_316 2 0.0000 0.9736 0.00 1.00 0.00
#> Sample_318 2 0.0000 0.9736 0.00 1.00 0.00
#> Sample_319 2 0.0000 0.9736 0.00 1.00 0.00
#> Sample_320 2 0.0000 0.9736 0.00 1.00 0.00
#> Sample_321 2 0.0000 0.9736 0.00 1.00 0.00
#> Sample_323 2 0.0000 0.9736 0.00 1.00 0.00
#> Sample_324 2 0.0000 0.9736 0.00 1.00 0.00
#> Sample_325 2 0.4796 0.7143 0.00 0.78 0.22
#> Sample_326 2 0.0000 0.9736 0.00 1.00 0.00
#> Sample_327 2 0.0000 0.9736 0.00 1.00 0.00
#> Sample_328 2 0.0000 0.9736 0.00 1.00 0.00
#> Sample_329 2 0.0000 0.9736 0.00 1.00 0.00
#> Sample_330 2 0.0000 0.9736 0.00 1.00 0.00
#> Sample_350 3 0.0892 0.9628 0.00 0.02 0.98
#> Sample_351 2 0.0000 0.9736 0.00 1.00 0.00
#> Sample_352 3 0.0892 0.9628 0.00 0.02 0.98
#> Sample_355 2 0.4555 0.7530 0.00 0.80 0.20
#> Sample_358 2 0.0892 0.9614 0.00 0.98 0.02
#> Sample_360 2 0.0892 0.9614 0.00 0.98 0.02
#> Sample_362 2 0.4002 0.8111 0.00 0.84 0.16
#> Sample_365 3 0.0892 0.9628 0.00 0.02 0.98
#> Sample_368 2 0.0892 0.9614 0.00 0.98 0.02
#> Sample_369 2 0.2959 0.8846 0.00 0.90 0.10
#> Sample_372 3 0.6302 0.0941 0.00 0.48 0.52
#> Sample_373 3 0.1529 0.9514 0.00 0.04 0.96
#> Sample_377 2 0.0000 0.9736 0.00 1.00 0.00
#> Sample_381 2 0.0000 0.9736 0.00 1.00 0.00
#> Sample_385 2 0.0000 0.9736 0.00 1.00 0.00
#> Sample_386 2 0.0000 0.9736 0.00 1.00 0.00
#> Sample_389 3 0.0892 0.9628 0.00 0.02 0.98
#> Sample_390 3 0.1529 0.9514 0.00 0.04 0.96
#> Sample_393 2 0.0000 0.9736 0.00 1.00 0.00
#> Sample_394 3 0.0892 0.9628 0.00 0.02 0.98
#> Sample_395 2 0.5948 0.4331 0.00 0.64 0.36
#> Sample_396 3 0.1529 0.9514 0.00 0.04 0.96
#> Sample_397 2 0.2537 0.9054 0.00 0.92 0.08
#> Sample_398 2 0.0000 0.9736 0.00 1.00 0.00
#> Sample_403 3 0.4796 0.7406 0.00 0.22 0.78
#> Sample_404 3 0.0892 0.9628 0.00 0.02 0.98
#> Sample_405 3 0.0892 0.9628 0.00 0.02 0.98
#> Sample_408 3 0.1529 0.9514 0.00 0.04 0.96
#> Sample_409 2 0.0000 0.9736 0.00 1.00 0.00
#> Sample_412 2 0.0000 0.9736 0.00 1.00 0.00
#> Sample_416 2 0.0000 0.9736 0.00 1.00 0.00
#> Sample_421 2 0.0000 0.9736 0.00 1.00 0.00
#> Sample_424 2 0.0000 0.9736 0.00 1.00 0.00
#> Sample_425 2 0.0000 0.9736 0.00 1.00 0.00
#> Sample_427 2 0.0000 0.9736 0.00 1.00 0.00
#> Sample_428 2 0.0000 0.9736 0.00 1.00 0.00
#> Sample_430 3 0.0892 0.9628 0.00 0.02 0.98
#> Sample_431 2 0.0892 0.9612 0.00 0.98 0.02
#> Sample_432 2 0.0000 0.9736 0.00 1.00 0.00
#> Sample_433 3 0.0892 0.9628 0.00 0.02 0.98
#> Sample_434 2 0.0000 0.9736 0.00 1.00 0.00
#> Sample_438 3 0.0892 0.9628 0.00 0.02 0.98
#> Sample_439 2 0.0000 0.9736 0.00 1.00 0.00
#> Sample_440 2 0.0000 0.9736 0.00 1.00 0.00
#> Sample_442 2 0.1529 0.9455 0.00 0.96 0.04
#> Sample_443 3 0.2537 0.9158 0.00 0.08 0.92
#> Sample_445 2 0.0000 0.9736 0.00 1.00 0.00
#> Sample_446 2 0.1529 0.9452 0.00 0.96 0.04
#> Sample_448 3 0.5016 0.7128 0.00 0.24 0.76
#> Sample_451 2 0.0000 0.9736 0.00 1.00 0.00
#> Sample_452 2 0.0000 0.9736 0.00 1.00 0.00
#> Sample_455 3 0.4291 0.8007 0.00 0.18 0.82
#> Sample_456 2 0.0000 0.9736 0.00 1.00 0.00
#> Sample_457 3 0.0892 0.9628 0.00 0.02 0.98
#> Sample_459 2 0.0000 0.9736 0.00 1.00 0.00
#> Sample_461 2 0.0000 0.9736 0.00 1.00 0.00
#> Sample_463 3 0.5016 0.7136 0.00 0.24 0.76
#> Sample_464 2 0.0000 0.9736 0.00 1.00 0.00
#> Sample_465 2 0.0000 0.9736 0.00 1.00 0.00
#> Sample_466 2 0.0000 0.9736 0.00 1.00 0.00
#> Sample_468 2 0.0892 0.9612 0.00 0.98 0.02
#> Sample_469 2 0.0000 0.9736 0.00 1.00 0.00
#> Sample_472 2 0.0000 0.9736 0.00 1.00 0.00
#> Sample_474 2 0.0000 0.9736 0.00 1.00 0.00
#> Sample_475 2 0.0000 0.9736 0.00 1.00 0.00
#> Sample_476 3 0.1529 0.9514 0.00 0.04 0.96
#> Sample_477 3 0.1529 0.9514 0.00 0.04 0.96
#> Sample_479 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_486 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_487 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_490 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_495 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_499 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_500 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_505 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_506 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_507 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_511 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_513 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_515 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_517 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_520 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_521 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_527 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_530 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_532 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_533 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_535 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_536 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_537 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_539 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_542 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_543 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_544 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_546 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_547 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_557 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_558 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_559 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_565 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_566 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_567 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_569 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_572 3 0.0892 0.9628 0.00 0.02 0.98
#> Sample_573 2 0.0000 0.9736 0.00 1.00 0.00
#> Sample_577 2 0.0000 0.9736 0.00 1.00 0.00
#> Sample_590 3 0.0000 0.9655 0.00 0.00 1.00
#> Sample_614 3 0.0000 0.9655 0.00 0.00 1.00
#> Sample_616 3 0.0000 0.9655 0.00 0.00 1.00
#> Sample_617 3 0.0000 0.9655 0.00 0.00 1.00
#> Sample_618 2 0.0892 0.9604 0.00 0.98 0.02
#> Sample_619 3 0.0000 0.9655 0.00 0.00 1.00
#> Sample_620 3 0.0000 0.9655 0.00 0.00 1.00
#> Sample_622 3 0.0000 0.9655 0.00 0.00 1.00
#> Sample_626 3 0.0000 0.9655 0.00 0.00 1.00
#> Sample_630 3 0.0000 0.9655 0.00 0.00 1.00
#> Sample_632 3 0.0000 0.9655 0.00 0.00 1.00
#> Sample_633 3 0.0000 0.9655 0.00 0.00 1.00
#> Sample_634 3 0.0000 0.9655 0.00 0.00 1.00
#> Sample_635 3 0.0000 0.9655 0.00 0.00 1.00
#> Sample_636 3 0.0000 0.9655 0.00 0.00 1.00
#> Sample_637 3 0.0000 0.9655 0.00 0.00 1.00
#> Sample_639 3 0.0000 0.9655 0.00 0.00 1.00
#> Sample_641 3 0.0000 0.9655 0.00 0.00 1.00
#> Sample_643 3 0.0000 0.9655 0.00 0.00 1.00
#> Sample_646 3 0.0000 0.9655 0.00 0.00 1.00
#> Sample_647 3 0.0000 0.9655 0.00 0.00 1.00
#> Sample_685 2 0.0892 0.9604 0.00 0.98 0.02
#> Sample_687 2 0.0892 0.9604 0.00 0.98 0.02
#> Sample_692 2 0.0892 0.9604 0.00 0.98 0.02
#> Sample_737 2 0.3832 0.8404 0.10 0.88 0.02
#> Sample_950 3 0.0892 0.9628 0.00 0.02 0.98
#> Sample_962 2 0.0000 0.9736 0.00 1.00 0.00
#> Sample_968 2 0.0000 0.9736 0.00 1.00 0.00
#> Sample_976 3 0.0892 0.9628 0.00 0.02 0.98
#> Sample_977 3 0.0892 0.9628 0.00 0.02 0.98
#> Sample_978 2 0.1529 0.9453 0.00 0.96 0.04
#> Sample_980 3 0.0892 0.9628 0.00 0.02 0.98
#> Sample_981 3 0.4796 0.7411 0.00 0.22 0.78
#> Sample_982 2 0.0000 0.9736 0.00 1.00 0.00
#> Sample_983 2 0.0892 0.9611 0.00 0.98 0.02
#> Sample_985 2 0.1529 0.9453 0.00 0.96 0.04
#> Sample_986 3 0.0000 0.9655 0.00 0.00 1.00
#> Sample_987 2 0.0000 0.9736 0.00 1.00 0.00
#> Sample_988 2 0.0000 0.9736 0.00 1.00 0.00
#> Sample_989 2 0.0000 0.9736 0.00 1.00 0.00
#> Sample_990 2 0.6302 0.0223 0.00 0.52 0.48
#> Sample_991 2 0.0000 0.9736 0.00 1.00 0.00
#> Sample_992 2 0.0000 0.9736 0.00 1.00 0.00
#> Sample_993 2 0.0000 0.9736 0.00 1.00 0.00
#> Sample_994 2 0.0000 0.9736 0.00 1.00 0.00
#> Sample_996 3 0.0892 0.9628 0.00 0.02 0.98
#> Sample_999 3 0.0000 0.9655 0.00 0.00 1.00
#> Sample_1007 2 0.2959 0.8854 0.00 0.90 0.10
#> Sample_1008 3 0.0892 0.9628 0.00 0.02 0.98
#> Sample_1009 3 0.2066 0.9350 0.00 0.06 0.94
#> Sample_1010 3 0.0000 0.9655 0.00 0.00 1.00
#> Sample_1014 2 0.0000 0.9736 0.00 1.00 0.00
#> Sample_1015 2 0.0000 0.9736 0.00 1.00 0.00
#> Sample_1016 2 0.0000 0.9736 0.00 1.00 0.00
#> Sample_1017 2 0.0000 0.9736 0.00 1.00 0.00
#> Sample_1018 2 0.0000 0.9736 0.00 1.00 0.00
#> Sample_1019 2 0.0000 0.9736 0.00 1.00 0.00
#> Sample_1020 2 0.0000 0.9736 0.00 1.00 0.00
#> Sample_1021 2 0.0000 0.9736 0.00 1.00 0.00
#> Sample_1022 2 0.0892 0.9524 0.02 0.98 0.00
#> Sample_1023 2 0.0000 0.9736 0.00 1.00 0.00
#> Sample_1024 2 0.0000 0.9736 0.00 1.00 0.00
#> Sample_1025 2 0.0000 0.9736 0.00 1.00 0.00
#> Sample_1026 2 0.0000 0.9736 0.00 1.00 0.00
#> Sample_1027 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1028 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1030 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1034 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1036 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1039 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1041 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1042 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1046 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1049 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1050 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1052 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1056 3 0.0892 0.9628 0.00 0.02 0.98
#> Sample_1058 2 0.4291 0.7780 0.00 0.82 0.18
#> Sample_1059 3 0.1529 0.9514 0.00 0.04 0.96
#> Sample_1062 3 0.0892 0.9628 0.00 0.02 0.98
#> Sample_1066 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1070 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1073 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1075 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1076 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1080 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1083 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1087 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1090 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1091 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1093 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1097 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1100 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1102 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1103 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1104 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1105 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1108 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1110 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1112 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1113 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1114 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1116 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1118 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1119 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1121 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1129 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1130 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1131 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1133 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1135 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1136 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1137 1 0.0892 0.9805 0.98 0.00 0.02
#> Sample_1138 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1142 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1143 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1144 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1146 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1147 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1148 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1149 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1151 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1153 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1154 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1155 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1158 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1159 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1160 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1162 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1165 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1166 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1167 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1168 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1171 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1172 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1173 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1174 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1176 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1177 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1178 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1179 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1181 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1182 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1185 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1186 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1187 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1188 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1192 2 0.1529 0.9457 0.00 0.96 0.04
#> Sample_1208 3 0.1529 0.9514 0.00 0.04 0.96
#> Sample_1216 2 0.1529 0.9452 0.00 0.96 0.04
#> Sample_1217 3 0.0892 0.9628 0.00 0.02 0.98
#> Sample_1218 3 0.5016 0.7120 0.00 0.24 0.76
#> Sample_1222 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1225 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1227 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1228 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1229 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1234 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1236 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1239 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1240 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1241 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1242 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1249 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1254 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1264 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1266 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1267 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1272 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1279 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1280 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1283 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1285 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1299 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1307 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1308 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1309 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1313 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1317 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1320 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1335 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1336 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1338 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1342 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1343 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1348 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1350 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1352 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1354 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1357 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1358 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1360 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1364 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1367 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1371 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1373 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1374 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1375 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1382 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1386 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1388 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1398 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1399 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1402 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1406 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1408 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1409 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1419 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1423 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1425 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1426 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1427 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1429 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1430 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1431 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1432 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1433 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1434 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1435 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1436 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1437 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1438 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1439 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1440 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1441 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1444 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1445 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1448 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1449 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1450 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1451 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1452 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1453 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1454 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1455 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1458 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1459 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1462 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1465 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1466 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1467 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1469 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1470 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1471 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1472 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1473 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1474 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1475 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1476 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1477 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1478 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1479 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1481 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1482 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1483 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1484 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1487 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1489 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1490 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1491 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1492 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1493 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1495 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1496 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1497 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1498 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1499 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1500 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1501 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1509 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1510 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1514 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1516 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1522 3 0.0000 0.9655 0.00 0.00 1.00
#> Sample_1523 3 0.0000 0.9655 0.00 0.00 1.00
#> Sample_1525 2 0.0892 0.9604 0.00 0.98 0.02
#> Sample_1526 2 0.0892 0.9604 0.00 0.98 0.02
#> Sample_1540 3 0.0892 0.9472 0.02 0.00 0.98
#> Sample_1541 1 0.0000 0.9997 1.00 0.00 0.00
#> Sample_1544 3 0.0000 0.9655 0.00 0.00 1.00
#> Sample_1545 1 0.2066 0.9388 0.94 0.00 0.06
#> Sample_1547 3 0.0000 0.9655 0.00 0.00 1.00
#> Sample_1556 3 0.0000 0.9655 0.00 0.00 1.00
#> Sample_1557 3 0.0000 0.9655 0.00 0.00 1.00
#> Sample_1558 3 0.0000 0.9655 0.00 0.00 1.00
#> Sample_1560 3 0.0000 0.9655 0.00 0.00 1.00
#> Sample_1562 2 0.1529 0.9465 0.00 0.96 0.04
#> Sample_1563 3 0.0000 0.9655 0.00 0.00 1.00
#> Sample_1564 3 0.0000 0.9655 0.00 0.00 1.00
#> Sample_1565 3 0.0000 0.9655 0.00 0.00 1.00
#> Sample_1566 3 0.0000 0.9655 0.00 0.00 1.00
#> Sample_1567 3 0.0000 0.9655 0.00 0.00 1.00
#> Sample_1568 3 0.0000 0.9655 0.00 0.00 1.00
#> Sample_1571 3 0.0000 0.9655 0.00 0.00 1.00
#> Sample_1572 3 0.0000 0.9655 0.00 0.00 1.00
#> Sample_1573 3 0.0000 0.9655 0.00 0.00 1.00
#> Sample_1576 3 0.0000 0.9655 0.00 0.00 1.00
#> Sample_1577 3 0.0000 0.9655 0.00 0.00 1.00
#> Sample_1578 3 0.0000 0.9655 0.00 0.00 1.00
#> Sample_1579 3 0.0000 0.9655 0.00 0.00 1.00
#> Sample_1580 3 0.0000 0.9655 0.00 0.00 1.00
#> Sample_1583 3 0.0000 0.9655 0.00 0.00 1.00
#> Sample_1584 3 0.0000 0.9655 0.00 0.00 1.00
#> Sample_1590 3 0.0000 0.9655 0.00 0.00 1.00
#> Sample_1591 3 0.0000 0.9655 0.00 0.00 1.00
#> Sample_1600 3 0.0000 0.9655 0.00 0.00 1.00
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> Sample_1 3 0.0000 0.8612 0.00 0.00 1.00 0.00
#> Sample_121 2 0.5151 0.8090 0.00 0.76 0.10 0.14
#> Sample_214 3 0.3975 0.8182 0.00 0.24 0.76 0.00
#> Sample_215 3 0.3975 0.8182 0.00 0.24 0.76 0.00
#> Sample_217 3 0.3975 0.8182 0.00 0.24 0.76 0.00
#> Sample_224 3 0.2011 0.8509 0.00 0.08 0.92 0.00
#> Sample_226 3 0.3975 0.8182 0.00 0.24 0.76 0.00
#> Sample_236 3 0.3975 0.8182 0.00 0.24 0.76 0.00
#> Sample_241 3 0.3975 0.8182 0.00 0.24 0.76 0.00
#> Sample_258 2 0.4624 0.2830 0.00 0.66 0.34 0.00
#> Sample_264 3 0.3975 0.8182 0.00 0.24 0.76 0.00
#> Sample_265 2 0.2011 0.8929 0.00 0.92 0.00 0.08
#> Sample_272 2 0.2011 0.8157 0.00 0.92 0.08 0.00
#> Sample_275 2 0.0707 0.8669 0.00 0.98 0.02 0.00
#> Sample_276 3 0.4522 0.7306 0.00 0.32 0.68 0.00
#> Sample_287 3 0.4277 0.7811 0.00 0.28 0.72 0.00
#> Sample_302 2 0.4790 0.3795 0.00 0.62 0.00 0.38
#> Sample_303 2 0.1637 0.8914 0.00 0.94 0.00 0.06
#> Sample_304 2 0.0707 0.8844 0.00 0.98 0.00 0.02
#> Sample_305 2 0.0000 0.8776 0.00 1.00 0.00 0.00
#> Sample_308 2 0.2921 0.8857 0.00 0.86 0.00 0.14
#> Sample_309 2 0.2345 0.8925 0.00 0.90 0.00 0.10
#> Sample_310 2 0.0000 0.8776 0.00 1.00 0.00 0.00
#> Sample_311 2 0.1211 0.8889 0.00 0.96 0.00 0.04
#> Sample_312 2 0.0000 0.8776 0.00 1.00 0.00 0.00
#> Sample_313 2 0.1211 0.8889 0.00 0.96 0.00 0.04
#> Sample_314 2 0.0000 0.8776 0.00 1.00 0.00 0.00
#> Sample_316 2 0.2921 0.8857 0.00 0.86 0.00 0.14
#> Sample_318 2 0.2647 0.8902 0.00 0.88 0.00 0.12
#> Sample_319 2 0.1637 0.8914 0.00 0.94 0.00 0.06
#> Sample_320 2 0.2345 0.8927 0.00 0.90 0.00 0.10
#> Sample_321 2 0.2647 0.8902 0.00 0.88 0.00 0.12
#> Sample_323 2 0.0707 0.8844 0.00 0.98 0.00 0.02
#> Sample_324 2 0.0000 0.8776 0.00 1.00 0.00 0.00
#> Sample_325 2 0.2345 0.7967 0.00 0.90 0.10 0.00
#> Sample_326 2 0.2921 0.8857 0.00 0.86 0.00 0.14
#> Sample_327 2 0.2011 0.8929 0.00 0.92 0.00 0.08
#> Sample_328 2 0.0707 0.8844 0.00 0.98 0.00 0.02
#> Sample_329 2 0.2345 0.8925 0.00 0.90 0.00 0.10
#> Sample_330 2 0.0707 0.8844 0.00 0.98 0.00 0.02
#> Sample_350 3 0.3975 0.8182 0.00 0.24 0.76 0.00
#> Sample_351 2 0.0707 0.8844 0.00 0.98 0.00 0.02
#> Sample_352 3 0.3975 0.8182 0.00 0.24 0.76 0.00
#> Sample_355 2 0.3801 0.6029 0.00 0.78 0.22 0.00
#> Sample_358 2 0.2345 0.7922 0.00 0.90 0.10 0.00
#> Sample_360 2 0.1637 0.8367 0.00 0.94 0.06 0.00
#> Sample_362 2 0.3610 0.6394 0.00 0.80 0.20 0.00
#> Sample_365 3 0.3801 0.8249 0.00 0.22 0.78 0.00
#> Sample_368 2 0.1637 0.8362 0.00 0.94 0.06 0.00
#> Sample_369 2 0.3172 0.7093 0.00 0.84 0.16 0.00
#> Sample_372 2 0.4907 -0.0401 0.00 0.58 0.42 0.00
#> Sample_373 3 0.4277 0.7811 0.00 0.28 0.72 0.00
#> Sample_377 2 0.0000 0.8776 0.00 1.00 0.00 0.00
#> Sample_381 2 0.0000 0.8776 0.00 1.00 0.00 0.00
#> Sample_385 2 0.2921 0.8857 0.00 0.86 0.00 0.14
#> Sample_386 2 0.2647 0.8902 0.00 0.88 0.00 0.12
#> Sample_389 3 0.3975 0.8182 0.00 0.24 0.76 0.00
#> Sample_390 3 0.4406 0.7577 0.00 0.30 0.70 0.00
#> Sample_393 2 0.2647 0.8902 0.00 0.88 0.00 0.12
#> Sample_394 3 0.3975 0.8182 0.00 0.24 0.76 0.00
#> Sample_395 2 0.4277 0.4612 0.00 0.72 0.28 0.00
#> Sample_396 3 0.4406 0.7572 0.00 0.30 0.70 0.00
#> Sample_397 2 0.2647 0.7704 0.00 0.88 0.12 0.00
#> Sample_398 2 0.0000 0.8776 0.00 1.00 0.00 0.00
#> Sample_403 3 0.4948 0.4949 0.00 0.44 0.56 0.00
#> Sample_404 3 0.3610 0.8299 0.00 0.20 0.80 0.00
#> Sample_405 3 0.3975 0.8182 0.00 0.24 0.76 0.00
#> Sample_408 3 0.4134 0.8018 0.00 0.26 0.74 0.00
#> Sample_409 2 0.2011 0.8931 0.00 0.92 0.00 0.08
#> Sample_412 2 0.0000 0.8776 0.00 1.00 0.00 0.00
#> Sample_416 2 0.2011 0.8935 0.00 0.92 0.00 0.08
#> Sample_421 2 0.0000 0.8776 0.00 1.00 0.00 0.00
#> Sample_424 2 0.0000 0.8776 0.00 1.00 0.00 0.00
#> Sample_425 2 0.0000 0.8776 0.00 1.00 0.00 0.00
#> Sample_427 2 0.0000 0.8776 0.00 1.00 0.00 0.00
#> Sample_428 2 0.0000 0.8776 0.00 1.00 0.00 0.00
#> Sample_430 3 0.3975 0.8182 0.00 0.24 0.76 0.00
#> Sample_431 2 0.0000 0.8776 0.00 1.00 0.00 0.00
#> Sample_432 2 0.2647 0.8902 0.00 0.88 0.00 0.12
#> Sample_433 3 0.3975 0.8182 0.00 0.24 0.76 0.00
#> Sample_434 2 0.2921 0.8857 0.00 0.86 0.00 0.14
#> Sample_438 3 0.3975 0.8182 0.00 0.24 0.76 0.00
#> Sample_439 2 0.2921 0.8857 0.00 0.86 0.00 0.14
#> Sample_440 2 0.2921 0.8857 0.00 0.86 0.00 0.14
#> Sample_442 2 0.1211 0.8539 0.00 0.96 0.04 0.00
#> Sample_443 3 0.4406 0.7485 0.00 0.30 0.70 0.00
#> Sample_445 2 0.2647 0.8901 0.00 0.88 0.00 0.12
#> Sample_446 2 0.1211 0.8539 0.00 0.96 0.04 0.00
#> Sample_448 3 0.6617 0.4785 0.00 0.28 0.60 0.12
#> Sample_451 2 0.0000 0.8776 0.00 1.00 0.00 0.00
#> Sample_452 2 0.2921 0.8857 0.00 0.86 0.00 0.14
#> Sample_455 3 0.4855 0.5963 0.00 0.40 0.60 0.00
#> Sample_456 2 0.2921 0.8857 0.00 0.86 0.00 0.14
#> Sample_457 3 0.3975 0.8182 0.00 0.24 0.76 0.00
#> Sample_459 2 0.0707 0.8673 0.00 0.98 0.02 0.00
#> Sample_461 2 0.2345 0.8926 0.00 0.90 0.00 0.10
#> Sample_463 3 0.4977 0.4577 0.00 0.46 0.54 0.00
#> Sample_464 2 0.2647 0.8902 0.00 0.88 0.00 0.12
#> Sample_465 2 0.2921 0.8857 0.00 0.86 0.00 0.14
#> Sample_466 2 0.2921 0.8857 0.00 0.86 0.00 0.14
#> Sample_468 2 0.4491 0.8609 0.00 0.80 0.06 0.14
#> Sample_469 2 0.0000 0.8776 0.00 1.00 0.00 0.00
#> Sample_472 2 0.2011 0.8929 0.00 0.92 0.00 0.08
#> Sample_474 2 0.0707 0.8848 0.00 0.98 0.00 0.02
#> Sample_475 2 0.2921 0.8857 0.00 0.86 0.00 0.14
#> Sample_476 3 0.4134 0.8007 0.00 0.26 0.74 0.00
#> Sample_477 3 0.4277 0.7807 0.00 0.28 0.72 0.00
#> Sample_479 1 0.1637 0.8726 0.94 0.00 0.00 0.06
#> Sample_486 1 0.4522 0.4168 0.68 0.00 0.00 0.32
#> Sample_487 4 0.2921 0.9404 0.14 0.00 0.00 0.86
#> Sample_490 4 0.2921 0.9404 0.14 0.00 0.00 0.86
#> Sample_495 4 0.2921 0.9404 0.14 0.00 0.00 0.86
#> Sample_499 1 0.0000 0.9137 1.00 0.00 0.00 0.00
#> Sample_500 4 0.3801 0.8773 0.22 0.00 0.00 0.78
#> Sample_505 1 0.0000 0.9137 1.00 0.00 0.00 0.00
#> Sample_506 4 0.2921 0.9404 0.14 0.00 0.00 0.86
#> Sample_507 1 0.0707 0.9022 0.98 0.00 0.00 0.02
#> Sample_511 1 0.0000 0.9137 1.00 0.00 0.00 0.00
#> Sample_513 4 0.2921 0.9404 0.14 0.00 0.00 0.86
#> Sample_515 4 0.2921 0.9404 0.14 0.00 0.00 0.86
#> Sample_517 4 0.2921 0.9404 0.14 0.00 0.00 0.86
#> Sample_520 4 0.2921 0.9404 0.14 0.00 0.00 0.86
#> Sample_521 4 0.2921 0.9404 0.14 0.00 0.00 0.86
#> Sample_527 4 0.2921 0.9404 0.14 0.00 0.00 0.86
#> Sample_530 4 0.2921 0.9404 0.14 0.00 0.00 0.86
#> Sample_532 1 0.0000 0.9137 1.00 0.00 0.00 0.00
#> Sample_533 4 0.2921 0.9404 0.14 0.00 0.00 0.86
#> Sample_535 4 0.2921 0.9404 0.14 0.00 0.00 0.86
#> Sample_536 4 0.4994 0.3517 0.48 0.00 0.00 0.52
#> Sample_537 4 0.2921 0.9404 0.14 0.00 0.00 0.86
#> Sample_539 4 0.2921 0.9404 0.14 0.00 0.00 0.86
#> Sample_542 4 0.2921 0.9404 0.14 0.00 0.00 0.86
#> Sample_543 4 0.4406 0.7802 0.30 0.00 0.00 0.70
#> Sample_544 4 0.2921 0.9404 0.14 0.00 0.00 0.86
#> Sample_546 4 0.3400 0.9133 0.18 0.00 0.00 0.82
#> Sample_547 4 0.2921 0.9404 0.14 0.00 0.00 0.86
#> Sample_557 4 0.2921 0.9404 0.14 0.00 0.00 0.86
#> Sample_558 4 0.4907 0.5344 0.42 0.00 0.00 0.58
#> Sample_559 4 0.4713 0.6721 0.36 0.00 0.00 0.64
#> Sample_565 4 0.2921 0.9404 0.14 0.00 0.00 0.86
#> Sample_566 4 0.2921 0.9404 0.14 0.00 0.00 0.86
#> Sample_567 4 0.2921 0.9404 0.14 0.00 0.00 0.86
#> Sample_569 1 0.0000 0.9137 1.00 0.00 0.00 0.00
#> Sample_572 3 0.3975 0.8182 0.00 0.24 0.76 0.00
#> Sample_573 2 0.4841 0.8169 0.08 0.78 0.00 0.14
#> Sample_577 2 0.1211 0.8892 0.00 0.96 0.00 0.04
#> Sample_590 3 0.0000 0.8612 0.00 0.00 1.00 0.00
#> Sample_614 3 0.0000 0.8612 0.00 0.00 1.00 0.00
#> Sample_616 3 0.0000 0.8612 0.00 0.00 1.00 0.00
#> Sample_617 3 0.0000 0.8612 0.00 0.00 1.00 0.00
#> Sample_618 2 0.5151 0.8090 0.00 0.76 0.10 0.14
#> Sample_619 3 0.0000 0.8612 0.00 0.00 1.00 0.00
#> Sample_620 3 0.0000 0.8612 0.00 0.00 1.00 0.00
#> Sample_622 3 0.0000 0.8612 0.00 0.00 1.00 0.00
#> Sample_626 3 0.0000 0.8612 0.00 0.00 1.00 0.00
#> Sample_630 3 0.0000 0.8612 0.00 0.00 1.00 0.00
#> Sample_632 3 0.0000 0.8612 0.00 0.00 1.00 0.00
#> Sample_633 3 0.0000 0.8612 0.00 0.00 1.00 0.00
#> Sample_634 3 0.0000 0.8612 0.00 0.00 1.00 0.00
#> Sample_635 3 0.0000 0.8612 0.00 0.00 1.00 0.00
#> Sample_636 3 0.0000 0.8612 0.00 0.00 1.00 0.00
#> Sample_637 3 0.0000 0.8612 0.00 0.00 1.00 0.00
#> Sample_639 3 0.0000 0.8612 0.00 0.00 1.00 0.00
#> Sample_641 3 0.0000 0.8612 0.00 0.00 1.00 0.00
#> Sample_643 3 0.0000 0.8612 0.00 0.00 1.00 0.00
#> Sample_646 3 0.0000 0.8612 0.00 0.00 1.00 0.00
#> Sample_647 3 0.0000 0.8612 0.00 0.00 1.00 0.00
#> Sample_685 2 0.5151 0.8090 0.00 0.76 0.10 0.14
#> Sample_687 2 0.5151 0.8090 0.00 0.76 0.10 0.14
#> Sample_692 2 0.5151 0.8090 0.00 0.76 0.10 0.14
#> Sample_737 1 0.5637 0.6656 0.76 0.06 0.04 0.14
#> Sample_950 3 0.4277 0.7814 0.00 0.28 0.72 0.00
#> Sample_962 2 0.0000 0.8776 0.00 1.00 0.00 0.00
#> Sample_968 2 0.1211 0.8893 0.00 0.96 0.00 0.04
#> Sample_976 3 0.3975 0.8182 0.00 0.24 0.76 0.00
#> Sample_977 3 0.3975 0.8182 0.00 0.24 0.76 0.00
#> Sample_978 2 0.2011 0.8178 0.00 0.92 0.08 0.00
#> Sample_980 3 0.3975 0.8182 0.00 0.24 0.76 0.00
#> Sample_981 3 0.4790 0.6322 0.00 0.38 0.62 0.00
#> Sample_982 2 0.2921 0.8857 0.00 0.86 0.00 0.14
#> Sample_983 2 0.1637 0.8377 0.00 0.94 0.06 0.00
#> Sample_985 2 0.1637 0.8383 0.00 0.94 0.06 0.00
#> Sample_986 3 0.0000 0.8612 0.00 0.00 1.00 0.00
#> Sample_987 2 0.2011 0.8934 0.00 0.92 0.00 0.08
#> Sample_988 2 0.2921 0.8857 0.00 0.86 0.00 0.14
#> Sample_989 2 0.1211 0.8889 0.00 0.96 0.00 0.04
#> Sample_990 2 0.5860 0.1516 0.00 0.58 0.38 0.04
#> Sample_991 2 0.2921 0.8857 0.00 0.86 0.00 0.14
#> Sample_992 2 0.2345 0.8927 0.00 0.90 0.00 0.10
#> Sample_993 2 0.2921 0.8857 0.00 0.86 0.00 0.14
#> Sample_994 2 0.2921 0.8857 0.00 0.86 0.00 0.14
#> Sample_996 3 0.3801 0.8249 0.00 0.22 0.78 0.00
#> Sample_999 3 0.0000 0.8612 0.00 0.00 1.00 0.00
#> Sample_1007 2 0.3400 0.6775 0.00 0.82 0.18 0.00
#> Sample_1008 3 0.3975 0.8182 0.00 0.24 0.76 0.00
#> Sample_1009 3 0.4134 0.8007 0.00 0.26 0.74 0.00
#> Sample_1010 3 0.2921 0.8421 0.00 0.14 0.86 0.00
#> Sample_1014 2 0.2921 0.8857 0.00 0.86 0.00 0.14
#> Sample_1015 2 0.2921 0.8857 0.00 0.86 0.00 0.14
#> Sample_1016 2 0.2921 0.8857 0.00 0.86 0.00 0.14
#> Sample_1017 2 0.2921 0.8857 0.00 0.86 0.00 0.14
#> Sample_1018 2 0.2921 0.8857 0.00 0.86 0.00 0.14
#> Sample_1019 2 0.2921 0.8857 0.00 0.86 0.00 0.14
#> Sample_1020 2 0.2921 0.8857 0.00 0.86 0.00 0.14
#> Sample_1021 2 0.4088 0.8614 0.00 0.82 0.04 0.14
#> Sample_1022 1 0.7206 -0.1064 0.46 0.40 0.00 0.14
#> Sample_1023 2 0.5151 0.7943 0.10 0.76 0.00 0.14
#> Sample_1024 2 0.2921 0.8857 0.00 0.86 0.00 0.14
#> Sample_1025 2 0.2921 0.8857 0.00 0.86 0.00 0.14
#> Sample_1026 2 0.2921 0.8857 0.00 0.86 0.00 0.14
#> Sample_1027 1 0.0000 0.9137 1.00 0.00 0.00 0.00
#> Sample_1028 1 0.0000 0.9137 1.00 0.00 0.00 0.00
#> Sample_1030 1 0.0000 0.9137 1.00 0.00 0.00 0.00
#> Sample_1034 1 0.0000 0.9137 1.00 0.00 0.00 0.00
#> Sample_1036 1 0.4977 -0.1327 0.54 0.00 0.00 0.46
#> Sample_1039 1 0.0000 0.9137 1.00 0.00 0.00 0.00
#> Sample_1041 4 0.3975 0.8581 0.24 0.00 0.00 0.76
#> Sample_1042 1 0.0000 0.9137 1.00 0.00 0.00 0.00
#> Sample_1046 1 0.0000 0.9137 1.00 0.00 0.00 0.00
#> Sample_1049 1 0.0000 0.9137 1.00 0.00 0.00 0.00
#> Sample_1050 1 0.0000 0.9137 1.00 0.00 0.00 0.00
#> Sample_1052 1 0.0000 0.9137 1.00 0.00 0.00 0.00
#> Sample_1056 3 0.3801 0.8249 0.00 0.22 0.78 0.00
#> Sample_1058 2 0.5175 0.8196 0.00 0.76 0.12 0.12
#> Sample_1059 3 0.3975 0.8182 0.00 0.24 0.76 0.00
#> Sample_1062 3 0.3975 0.8182 0.00 0.24 0.76 0.00
#> Sample_1066 1 0.0707 0.9029 0.98 0.00 0.00 0.02
#> Sample_1070 1 0.0000 0.9137 1.00 0.00 0.00 0.00
#> Sample_1073 1 0.0000 0.9137 1.00 0.00 0.00 0.00
#> Sample_1075 1 0.0000 0.9137 1.00 0.00 0.00 0.00
#> Sample_1076 1 0.0000 0.9137 1.00 0.00 0.00 0.00
#> Sample_1080 1 0.2921 0.7828 0.86 0.00 0.00 0.14
#> Sample_1083 1 0.0000 0.9137 1.00 0.00 0.00 0.00
#> Sample_1087 1 0.0000 0.9137 1.00 0.00 0.00 0.00
#> Sample_1090 1 0.4713 0.3005 0.64 0.00 0.00 0.36
#> Sample_1091 4 0.2921 0.9404 0.14 0.00 0.00 0.86
#> Sample_1093 1 0.0000 0.9137 1.00 0.00 0.00 0.00
#> Sample_1097 1 0.0000 0.9137 1.00 0.00 0.00 0.00
#> Sample_1100 4 0.2921 0.9404 0.14 0.00 0.00 0.86
#> Sample_1102 1 0.0000 0.9137 1.00 0.00 0.00 0.00
#> Sample_1103 4 0.2921 0.9404 0.14 0.00 0.00 0.86
#> Sample_1104 1 0.0707 0.9023 0.98 0.00 0.00 0.02
#> Sample_1105 1 0.0000 0.9137 1.00 0.00 0.00 0.00
#> Sample_1108 4 0.3172 0.9263 0.16 0.00 0.00 0.84
#> Sample_1110 4 0.3801 0.8790 0.22 0.00 0.00 0.78
#> Sample_1112 1 0.1211 0.8884 0.96 0.00 0.00 0.04
#> Sample_1113 1 0.1211 0.8888 0.96 0.00 0.00 0.04
#> Sample_1114 1 0.0000 0.9137 1.00 0.00 0.00 0.00
#> Sample_1116 1 0.0000 0.9137 1.00 0.00 0.00 0.00
#> Sample_1118 1 0.4277 0.5351 0.72 0.00 0.00 0.28
#> Sample_1119 4 0.3610 0.8978 0.20 0.00 0.00 0.80
#> Sample_1121 1 0.0707 0.9028 0.98 0.00 0.00 0.02
#> Sample_1129 1 0.0000 0.9137 1.00 0.00 0.00 0.00
#> Sample_1130 1 0.0000 0.9137 1.00 0.00 0.00 0.00
#> Sample_1131 1 0.3172 0.7540 0.84 0.00 0.00 0.16
#> Sample_1133 1 0.4522 0.4197 0.68 0.00 0.00 0.32
#> Sample_1135 4 0.3610 0.8959 0.20 0.00 0.00 0.80
#> Sample_1136 1 0.0000 0.9137 1.00 0.00 0.00 0.00
#> Sample_1137 1 0.2921 0.7756 0.86 0.00 0.14 0.00
#> Sample_1138 1 0.0000 0.9137 1.00 0.00 0.00 0.00
#> Sample_1142 4 0.2921 0.9404 0.14 0.00 0.00 0.86
#> Sample_1143 1 0.0000 0.9137 1.00 0.00 0.00 0.00
#> Sample_1144 1 0.3801 0.6556 0.78 0.00 0.00 0.22
#> Sample_1146 4 0.3975 0.8544 0.24 0.00 0.00 0.76
#> Sample_1147 4 0.2921 0.9404 0.14 0.00 0.00 0.86
#> Sample_1148 1 0.2011 0.8520 0.92 0.00 0.00 0.08
#> Sample_1149 1 0.0000 0.9137 1.00 0.00 0.00 0.00
#> Sample_1151 1 0.0000 0.9137 1.00 0.00 0.00 0.00
#> Sample_1153 1 0.2647 0.8101 0.88 0.00 0.00 0.12
#> Sample_1154 1 0.0000 0.9137 1.00 0.00 0.00 0.00
#> Sample_1155 1 0.3610 0.6902 0.80 0.00 0.00 0.20
#> Sample_1158 1 0.0000 0.9137 1.00 0.00 0.00 0.00
#> Sample_1159 1 0.4948 -0.0408 0.56 0.00 0.00 0.44
#> Sample_1160 4 0.2921 0.9404 0.14 0.00 0.00 0.86
#> Sample_1162 1 0.0707 0.9025 0.98 0.00 0.00 0.02
#> Sample_1165 1 0.2345 0.8328 0.90 0.00 0.00 0.10
#> Sample_1166 1 0.0000 0.9137 1.00 0.00 0.00 0.00
#> Sample_1167 4 0.3400 0.9144 0.18 0.00 0.00 0.82
#> Sample_1168 1 0.0000 0.9137 1.00 0.00 0.00 0.00
#> Sample_1171 1 0.0000 0.9137 1.00 0.00 0.00 0.00
#> Sample_1172 1 0.1211 0.8743 0.96 0.00 0.00 0.04
#> Sample_1173 1 0.4277 0.5291 0.72 0.00 0.00 0.28
#> Sample_1174 1 0.4134 0.5744 0.74 0.00 0.00 0.26
#> Sample_1176 4 0.4134 0.8352 0.26 0.00 0.00 0.74
#> Sample_1177 1 0.0000 0.9137 1.00 0.00 0.00 0.00
#> Sample_1178 1 0.0000 0.9137 1.00 0.00 0.00 0.00
#> Sample_1179 4 0.2921 0.9404 0.14 0.00 0.00 0.86
#> Sample_1181 1 0.0000 0.9137 1.00 0.00 0.00 0.00
#> Sample_1182 1 0.0000 0.9137 1.00 0.00 0.00 0.00
#> Sample_1185 1 0.0000 0.9137 1.00 0.00 0.00 0.00
#> Sample_1186 1 0.0707 0.9026 0.98 0.00 0.00 0.02
#> Sample_1187 1 0.0000 0.9137 1.00 0.00 0.00 0.00
#> Sample_1188 1 0.0000 0.9137 1.00 0.00 0.00 0.00
#> Sample_1192 2 0.2921 0.7412 0.00 0.86 0.14 0.00
#> Sample_1208 3 0.3975 0.8182 0.00 0.24 0.76 0.00
#> Sample_1216 2 0.1211 0.8539 0.00 0.96 0.04 0.00
#> Sample_1217 3 0.3975 0.8182 0.00 0.24 0.76 0.00
#> Sample_1218 3 0.4790 0.6303 0.00 0.38 0.62 0.00
#> Sample_1222 1 0.4907 0.0641 0.58 0.00 0.00 0.42
#> Sample_1225 4 0.2921 0.9404 0.14 0.00 0.00 0.86
#> Sample_1227 4 0.2921 0.9404 0.14 0.00 0.00 0.86
#> Sample_1228 4 0.2921 0.9404 0.14 0.00 0.00 0.86
#> Sample_1229 1 0.0707 0.8955 0.98 0.00 0.00 0.02
#> Sample_1234 4 0.2921 0.9404 0.14 0.00 0.00 0.86
#> Sample_1236 1 0.4855 0.1428 0.60 0.00 0.00 0.40
#> Sample_1239 1 0.4406 0.4813 0.70 0.00 0.00 0.30
#> Sample_1240 1 0.0000 0.9137 1.00 0.00 0.00 0.00
#> Sample_1241 4 0.2921 0.9404 0.14 0.00 0.00 0.86
#> Sample_1242 1 0.4994 -0.2104 0.52 0.00 0.00 0.48
#> Sample_1249 4 0.2921 0.9404 0.14 0.00 0.00 0.86
#> Sample_1254 4 0.4907 0.5344 0.42 0.00 0.00 0.58
#> Sample_1264 4 0.2921 0.9404 0.14 0.00 0.00 0.86
#> Sample_1266 1 0.0000 0.9137 1.00 0.00 0.00 0.00
#> Sample_1267 1 0.0000 0.9137 1.00 0.00 0.00 0.00
#> Sample_1272 4 0.2921 0.9404 0.14 0.00 0.00 0.86
#> Sample_1279 1 0.0000 0.9137 1.00 0.00 0.00 0.00
#> Sample_1280 1 0.2011 0.8543 0.92 0.00 0.00 0.08
#> Sample_1283 4 0.2921 0.9404 0.14 0.00 0.00 0.86
#> Sample_1285 4 0.2921 0.9404 0.14 0.00 0.00 0.86
#> Sample_1299 1 0.0000 0.9137 1.00 0.00 0.00 0.00
#> Sample_1307 4 0.3610 0.8977 0.20 0.00 0.00 0.80
#> Sample_1308 1 0.0707 0.9026 0.98 0.00 0.00 0.02
#> Sample_1309 4 0.2921 0.9404 0.14 0.00 0.00 0.86
#> Sample_1313 4 0.4134 0.8321 0.26 0.00 0.00 0.74
#> Sample_1317 4 0.2921 0.9404 0.14 0.00 0.00 0.86
#> Sample_1320 4 0.2921 0.9404 0.14 0.00 0.00 0.86
#> Sample_1335 1 0.2921 0.7835 0.86 0.00 0.00 0.14
#> Sample_1336 4 0.2921 0.9404 0.14 0.00 0.00 0.86
#> Sample_1338 4 0.2921 0.9404 0.14 0.00 0.00 0.86
#> Sample_1342 4 0.2921 0.9404 0.14 0.00 0.00 0.86
#> Sample_1343 4 0.2921 0.9404 0.14 0.00 0.00 0.86
#> Sample_1348 4 0.2921 0.9404 0.14 0.00 0.00 0.86
#> Sample_1350 4 0.3172 0.9277 0.16 0.00 0.00 0.84
#> Sample_1352 4 0.2921 0.9404 0.14 0.00 0.00 0.86
#> Sample_1354 4 0.2921 0.9404 0.14 0.00 0.00 0.86
#> Sample_1357 4 0.2921 0.9404 0.14 0.00 0.00 0.86
#> Sample_1358 4 0.2921 0.9404 0.14 0.00 0.00 0.86
#> Sample_1360 4 0.2921 0.9404 0.14 0.00 0.00 0.86
#> Sample_1364 4 0.2921 0.9404 0.14 0.00 0.00 0.86
#> Sample_1367 1 0.4994 -0.2059 0.52 0.00 0.00 0.48
#> Sample_1371 4 0.2921 0.9404 0.14 0.00 0.00 0.86
#> Sample_1373 4 0.2921 0.9404 0.14 0.00 0.00 0.86
#> Sample_1374 4 0.2921 0.9404 0.14 0.00 0.00 0.86
#> Sample_1375 4 0.2921 0.9404 0.14 0.00 0.00 0.86
#> Sample_1382 4 0.2921 0.9404 0.14 0.00 0.00 0.86
#> Sample_1386 1 0.0000 0.9137 1.00 0.00 0.00 0.00
#> Sample_1388 4 0.4277 0.8081 0.28 0.00 0.00 0.72
#> Sample_1398 1 0.0000 0.9137 1.00 0.00 0.00 0.00
#> Sample_1399 4 0.4994 0.3622 0.48 0.00 0.00 0.52
#> Sample_1402 4 0.2921 0.9404 0.14 0.00 0.00 0.86
#> Sample_1406 1 0.1211 0.8889 0.96 0.00 0.00 0.04
#> Sample_1408 4 0.2921 0.9404 0.14 0.00 0.00 0.86
#> Sample_1409 1 0.0000 0.9137 1.00 0.00 0.00 0.00
#> Sample_1419 1 0.4977 -0.1230 0.54 0.00 0.00 0.46
#> Sample_1423 1 0.0000 0.9137 1.00 0.00 0.00 0.00
#> Sample_1425 1 0.0000 0.9137 1.00 0.00 0.00 0.00
#> Sample_1426 4 0.2921 0.9404 0.14 0.00 0.00 0.86
#> Sample_1427 1 0.2647 0.8088 0.88 0.00 0.00 0.12
#> Sample_1429 1 0.0000 0.9137 1.00 0.00 0.00 0.00
#> Sample_1430 1 0.0707 0.9027 0.98 0.00 0.00 0.02
#> Sample_1431 1 0.2647 0.8120 0.88 0.00 0.00 0.12
#> Sample_1432 1 0.0000 0.9137 1.00 0.00 0.00 0.00
#> Sample_1433 1 0.0000 0.9137 1.00 0.00 0.00 0.00
#> Sample_1434 1 0.0000 0.9137 1.00 0.00 0.00 0.00
#> Sample_1435 1 0.0000 0.9137 1.00 0.00 0.00 0.00
#> Sample_1436 1 0.0707 0.9025 0.98 0.00 0.00 0.02
#> Sample_1437 1 0.0000 0.9137 1.00 0.00 0.00 0.00
#> Sample_1438 4 0.2921 0.9404 0.14 0.00 0.00 0.86
#> Sample_1439 1 0.0000 0.9137 1.00 0.00 0.00 0.00
#> Sample_1440 4 0.4277 0.8063 0.28 0.00 0.00 0.72
#> Sample_1441 1 0.0707 0.9027 0.98 0.00 0.00 0.02
#> Sample_1444 1 0.0000 0.9137 1.00 0.00 0.00 0.00
#> Sample_1445 1 0.0707 0.9027 0.98 0.00 0.00 0.02
#> Sample_1448 1 0.0000 0.9137 1.00 0.00 0.00 0.00
#> Sample_1449 1 0.0000 0.9137 1.00 0.00 0.00 0.00
#> Sample_1450 1 0.0000 0.9137 1.00 0.00 0.00 0.00
#> Sample_1451 1 0.0000 0.9137 1.00 0.00 0.00 0.00
#> Sample_1452 1 0.0000 0.9137 1.00 0.00 0.00 0.00
#> Sample_1453 4 0.4855 0.5845 0.40 0.00 0.00 0.60
#> Sample_1454 1 0.0000 0.9137 1.00 0.00 0.00 0.00
#> Sample_1455 1 0.0000 0.9137 1.00 0.00 0.00 0.00
#> Sample_1458 1 0.0707 0.9027 0.98 0.00 0.00 0.02
#> Sample_1459 1 0.4977 -0.1502 0.54 0.00 0.00 0.46
#> Sample_1462 1 0.2647 0.8079 0.88 0.00 0.00 0.12
#> Sample_1465 4 0.2921 0.9404 0.14 0.00 0.00 0.86
#> Sample_1466 4 0.2921 0.9404 0.14 0.00 0.00 0.86
#> Sample_1467 1 0.0000 0.9137 1.00 0.00 0.00 0.00
#> Sample_1469 4 0.4977 0.4258 0.46 0.00 0.00 0.54
#> Sample_1470 4 0.3172 0.9277 0.16 0.00 0.00 0.84
#> Sample_1471 4 0.3400 0.9145 0.18 0.00 0.00 0.82
#> Sample_1472 4 0.2921 0.9404 0.14 0.00 0.00 0.86
#> Sample_1473 4 0.3801 0.8792 0.22 0.00 0.00 0.78
#> Sample_1474 1 0.0000 0.9137 1.00 0.00 0.00 0.00
#> Sample_1475 4 0.4277 0.8068 0.28 0.00 0.00 0.72
#> Sample_1476 1 0.1211 0.8896 0.96 0.00 0.00 0.04
#> Sample_1477 4 0.2921 0.9404 0.14 0.00 0.00 0.86
#> Sample_1478 4 0.4855 0.5834 0.40 0.00 0.00 0.60
#> Sample_1479 1 0.0000 0.9137 1.00 0.00 0.00 0.00
#> Sample_1481 4 0.3172 0.9276 0.16 0.00 0.00 0.84
#> Sample_1482 1 0.0000 0.9137 1.00 0.00 0.00 0.00
#> Sample_1483 1 0.0707 0.9028 0.98 0.00 0.00 0.02
#> Sample_1484 4 0.3610 0.8973 0.20 0.00 0.00 0.80
#> Sample_1487 1 0.0000 0.9137 1.00 0.00 0.00 0.00
#> Sample_1489 1 0.1211 0.8882 0.96 0.00 0.00 0.04
#> Sample_1490 4 0.4977 0.4305 0.46 0.00 0.00 0.54
#> Sample_1491 1 0.1211 0.8889 0.96 0.00 0.00 0.04
#> Sample_1492 1 0.3400 0.7275 0.82 0.00 0.00 0.18
#> Sample_1493 1 0.0000 0.9137 1.00 0.00 0.00 0.00
#> Sample_1495 4 0.2921 0.9404 0.14 0.00 0.00 0.86
#> Sample_1496 1 0.1637 0.8716 0.94 0.00 0.00 0.06
#> Sample_1497 4 0.4624 0.7135 0.34 0.00 0.00 0.66
#> Sample_1498 1 0.0000 0.9137 1.00 0.00 0.00 0.00
#> Sample_1499 1 0.0000 0.9137 1.00 0.00 0.00 0.00
#> Sample_1500 4 0.4406 0.7771 0.30 0.00 0.00 0.70
#> Sample_1501 1 0.1637 0.8734 0.94 0.00 0.00 0.06
#> Sample_1509 1 0.0707 0.9027 0.98 0.00 0.00 0.02
#> Sample_1510 1 0.0000 0.9137 1.00 0.00 0.00 0.00
#> Sample_1514 4 0.3172 0.9273 0.16 0.00 0.00 0.84
#> Sample_1516 1 0.0000 0.9137 1.00 0.00 0.00 0.00
#> Sample_1522 3 0.0000 0.8612 0.00 0.00 1.00 0.00
#> Sample_1523 3 0.0000 0.8612 0.00 0.00 1.00 0.00
#> Sample_1525 2 0.5151 0.8090 0.00 0.76 0.10 0.14
#> Sample_1526 2 0.5151 0.8090 0.00 0.76 0.10 0.14
#> Sample_1540 3 0.2011 0.7965 0.08 0.00 0.92 0.00
#> Sample_1541 1 0.2345 0.8043 0.90 0.00 0.00 0.10
#> Sample_1544 3 0.0000 0.8612 0.00 0.00 1.00 0.00
#> Sample_1545 1 0.4553 0.6892 0.78 0.00 0.18 0.04
#> Sample_1547 3 0.0000 0.8612 0.00 0.00 1.00 0.00
#> Sample_1556 3 0.0000 0.8612 0.00 0.00 1.00 0.00
#> Sample_1557 3 0.0000 0.8612 0.00 0.00 1.00 0.00
#> Sample_1558 3 0.0000 0.8612 0.00 0.00 1.00 0.00
#> Sample_1560 3 0.0000 0.8612 0.00 0.00 1.00 0.00
#> Sample_1562 2 0.5428 0.7959 0.00 0.74 0.12 0.14
#> Sample_1563 3 0.0000 0.8612 0.00 0.00 1.00 0.00
#> Sample_1564 3 0.0000 0.8612 0.00 0.00 1.00 0.00
#> Sample_1565 3 0.0000 0.8612 0.00 0.00 1.00 0.00
#> Sample_1566 3 0.0000 0.8612 0.00 0.00 1.00 0.00
#> Sample_1567 3 0.0000 0.8612 0.00 0.00 1.00 0.00
#> Sample_1568 3 0.0000 0.8612 0.00 0.00 1.00 0.00
#> Sample_1571 3 0.0000 0.8612 0.00 0.00 1.00 0.00
#> Sample_1572 3 0.0000 0.8612 0.00 0.00 1.00 0.00
#> Sample_1573 3 0.0000 0.8612 0.00 0.00 1.00 0.00
#> Sample_1576 3 0.0000 0.8612 0.00 0.00 1.00 0.00
#> Sample_1577 3 0.0000 0.8612 0.00 0.00 1.00 0.00
#> Sample_1578 3 0.0000 0.8612 0.00 0.00 1.00 0.00
#> Sample_1579 3 0.0000 0.8612 0.00 0.00 1.00 0.00
#> Sample_1580 3 0.0000 0.8612 0.00 0.00 1.00 0.00
#> Sample_1583 3 0.0000 0.8612 0.00 0.00 1.00 0.00
#> Sample_1584 3 0.0000 0.8612 0.00 0.00 1.00 0.00
#> Sample_1590 3 0.0000 0.8612 0.00 0.00 1.00 0.00
#> Sample_1591 3 0.0000 0.8612 0.00 0.00 1.00 0.00
#> Sample_1600 3 0.4790 0.3166 0.00 0.00 0.62 0.38
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
As soon as the classes for columns are determined, the signatures that are significantly different between subgroups can be looked for. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. To get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows (which is done by automatically selecting number of clusters).If there are too many signatures, top_signatures = ... can be set to only show the
signatures with the highest FDRs:
# code only for demonstration
# e.g. to show the top 500 most significant rows
tb = get_signature(res, k = ..., top_signatures = 500)
If the signatures are defined as these which are uniquely high in current group, diff_method argument
can be set to "uniquely_high_in_one_group":
# code only for demonstration
tb = get_signature(res, k = ..., diff_method = "uniquely_high_in_one_group")
UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n_sample age(p-value) cell.type(p-value) k
#> ATC:skmeans 452 0.1290 9.07e-12 2
#> ATC:skmeans 448 0.0371 5.07e-32 3
#> ATC:skmeans 426 0.0131 1.18e-26 4
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
Parent node: Node01. Child nodes: Node0111-leaf , Node0112-leaf , Node0121-leaf , Node0122-leaf , Node0131-leaf , Node0132-leaf , Node0211 , Node0212 , Node0221 , Node0222 , Node0231-leaf , Node0232-leaf , Node0233-leaf , Node0311-leaf , Node0312-leaf , Node0313-leaf , Node0314-leaf , Node0321 , Node0322 , Node0331-leaf , Node0332-leaf , Node0333-leaf .
The object with results only for a single top-value method and a single partitioning method can be extracted as:
res = res_rh["011"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4.
#> On a matrix with 14725 rows and 238 columns.
#> Top rows (663) are extracted by 'ATC' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 150 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_partitions"
#> [7] "compare_signatures" "consensus_heatmap" "dimension_reduction"
#> [10] "functional_enrichment" "get_anno_col" "get_anno"
#> [13] "get_classes" "get_consensus" "get_matrix"
#> [16] "get_membership" "get_param" "get_signatures"
#> [19] "get_stats" "is_best_k" "is_stable_k"
#> [22] "membership_heatmap" "ncol" "nrow"
#> [25] "plot_ecdf" "predict_classes" "rownames"
#> [28] "select_partition_number" "show" "suggest_best_k"
#> [31] "test_to_known_factors" "top_rows_heatmap"
collect_plots() function collects all the plots made from res for all k (number of subgroups)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, higher 1-PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.838 0.916 0.964 0.500 0.500 0.500
#> 3 3 0.747 0.833 0.916 0.331 0.741 0.526
#> 4 4 0.575 0.644 0.811 0.122 0.837 0.565
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 2
Following is the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall subgroup
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> Sample_479 1 0.999 0.054 0.52 0.48
#> Sample_486 2 0.529 0.854 0.12 0.88
#> Sample_487 2 0.000 0.956 0.00 1.00
#> Sample_490 2 0.000 0.956 0.00 1.00
#> Sample_495 2 0.000 0.956 0.00 1.00
#> Sample_499 1 0.141 0.951 0.98 0.02
#> Sample_500 1 0.327 0.915 0.94 0.06
#> Sample_505 1 0.000 0.968 1.00 0.00
#> Sample_506 2 0.000 0.956 0.00 1.00
#> Sample_507 1 0.000 0.968 1.00 0.00
#> Sample_511 1 0.000 0.968 1.00 0.00
#> Sample_513 2 0.000 0.956 0.00 1.00
#> Sample_515 2 0.000 0.956 0.00 1.00
#> Sample_517 2 0.000 0.956 0.00 1.00
#> Sample_520 2 0.000 0.956 0.00 1.00
#> Sample_521 2 0.000 0.956 0.00 1.00
#> Sample_527 2 0.000 0.956 0.00 1.00
#> Sample_530 2 0.242 0.928 0.04 0.96
#> Sample_532 1 0.469 0.872 0.90 0.10
#> Sample_533 2 0.000 0.956 0.00 1.00
#> Sample_535 2 0.000 0.956 0.00 1.00
#> Sample_536 1 0.529 0.850 0.88 0.12
#> Sample_537 2 0.000 0.956 0.00 1.00
#> Sample_539 2 0.000 0.956 0.00 1.00
#> Sample_542 2 0.000 0.956 0.00 1.00
#> Sample_543 2 0.000 0.956 0.00 1.00
#> Sample_544 2 0.000 0.956 0.00 1.00
#> Sample_546 2 0.000 0.956 0.00 1.00
#> Sample_547 2 0.000 0.956 0.00 1.00
#> Sample_557 2 0.000 0.956 0.00 1.00
#> Sample_558 2 0.760 0.729 0.22 0.78
#> Sample_559 2 0.634 0.810 0.16 0.84
#> Sample_565 2 0.000 0.956 0.00 1.00
#> Sample_566 2 0.000 0.956 0.00 1.00
#> Sample_567 2 0.000 0.956 0.00 1.00
#> Sample_569 1 0.000 0.968 1.00 0.00
#> Sample_1027 1 0.000 0.968 1.00 0.00
#> Sample_1028 1 0.000 0.968 1.00 0.00
#> Sample_1030 1 0.971 0.326 0.60 0.40
#> Sample_1034 1 0.000 0.968 1.00 0.00
#> Sample_1036 1 0.634 0.801 0.84 0.16
#> Sample_1039 1 0.000 0.968 1.00 0.00
#> Sample_1041 2 0.242 0.928 0.04 0.96
#> Sample_1042 1 0.000 0.968 1.00 0.00
#> Sample_1046 1 0.000 0.968 1.00 0.00
#> Sample_1049 1 0.000 0.968 1.00 0.00
#> Sample_1050 1 0.000 0.968 1.00 0.00
#> Sample_1052 1 0.000 0.968 1.00 0.00
#> Sample_1066 1 0.000 0.968 1.00 0.00
#> Sample_1070 1 0.000 0.968 1.00 0.00
#> Sample_1073 1 0.000 0.968 1.00 0.00
#> Sample_1075 1 0.000 0.968 1.00 0.00
#> Sample_1076 1 0.000 0.968 1.00 0.00
#> Sample_1080 2 0.000 0.956 0.00 1.00
#> Sample_1083 1 0.000 0.968 1.00 0.00
#> Sample_1087 1 0.000 0.968 1.00 0.00
#> Sample_1090 1 0.000 0.968 1.00 0.00
#> Sample_1091 1 0.827 0.644 0.74 0.26
#> Sample_1093 1 0.000 0.968 1.00 0.00
#> Sample_1097 1 0.855 0.609 0.72 0.28
#> Sample_1100 2 0.000 0.956 0.00 1.00
#> Sample_1102 1 0.000 0.968 1.00 0.00
#> Sample_1103 2 0.000 0.956 0.00 1.00
#> Sample_1104 1 0.141 0.951 0.98 0.02
#> Sample_1105 1 0.000 0.968 1.00 0.00
#> Sample_1108 2 0.584 0.833 0.14 0.86
#> Sample_1110 1 0.000 0.968 1.00 0.00
#> Sample_1112 1 0.000 0.968 1.00 0.00
#> Sample_1113 1 0.000 0.968 1.00 0.00
#> Sample_1114 1 0.000 0.968 1.00 0.00
#> Sample_1116 1 0.000 0.968 1.00 0.00
#> Sample_1118 1 0.000 0.968 1.00 0.00
#> Sample_1119 2 0.995 0.176 0.46 0.54
#> Sample_1121 1 0.000 0.968 1.00 0.00
#> Sample_1129 1 0.000 0.968 1.00 0.00
#> Sample_1130 1 0.000 0.968 1.00 0.00
#> Sample_1131 1 0.000 0.968 1.00 0.00
#> Sample_1133 1 0.141 0.951 0.98 0.02
#> Sample_1135 2 0.000 0.956 0.00 1.00
#> Sample_1136 1 0.000 0.968 1.00 0.00
#> Sample_1137 1 0.000 0.968 1.00 0.00
#> Sample_1138 1 0.000 0.968 1.00 0.00
#> Sample_1142 2 0.925 0.509 0.34 0.66
#> Sample_1143 1 0.000 0.968 1.00 0.00
#> Sample_1144 1 0.000 0.968 1.00 0.00
#> Sample_1146 2 0.971 0.360 0.40 0.60
#> Sample_1147 2 0.000 0.956 0.00 1.00
#> Sample_1148 1 0.000 0.968 1.00 0.00
#> Sample_1149 1 0.000 0.968 1.00 0.00
#> Sample_1151 1 0.000 0.968 1.00 0.00
#> Sample_1153 1 0.141 0.951 0.98 0.02
#> Sample_1154 1 0.000 0.968 1.00 0.00
#> Sample_1155 1 0.000 0.968 1.00 0.00
#> Sample_1158 1 0.000 0.968 1.00 0.00
#> Sample_1159 2 0.990 0.243 0.44 0.56
#> Sample_1160 2 0.000 0.956 0.00 1.00
#> Sample_1162 1 0.000 0.968 1.00 0.00
#> Sample_1165 2 0.000 0.956 0.00 1.00
#> Sample_1166 1 0.000 0.968 1.00 0.00
#> Sample_1167 2 0.584 0.833 0.14 0.86
#> Sample_1168 1 0.000 0.968 1.00 0.00
#> Sample_1171 1 0.000 0.968 1.00 0.00
#> Sample_1172 1 0.000 0.968 1.00 0.00
#> Sample_1173 1 0.000 0.968 1.00 0.00
#> Sample_1174 1 0.000 0.968 1.00 0.00
#> Sample_1176 1 0.943 0.434 0.64 0.36
#> Sample_1177 1 0.000 0.968 1.00 0.00
#> Sample_1178 1 0.000 0.968 1.00 0.00
#> Sample_1179 2 0.000 0.956 0.00 1.00
#> Sample_1181 1 0.000 0.968 1.00 0.00
#> Sample_1182 1 0.000 0.968 1.00 0.00
#> Sample_1185 1 0.000 0.968 1.00 0.00
#> Sample_1186 1 0.000 0.968 1.00 0.00
#> Sample_1187 1 0.000 0.968 1.00 0.00
#> Sample_1188 1 0.000 0.968 1.00 0.00
#> Sample_1222 1 0.795 0.684 0.76 0.24
#> Sample_1225 2 0.000 0.956 0.00 1.00
#> Sample_1227 2 0.000 0.956 0.00 1.00
#> Sample_1228 2 0.000 0.956 0.00 1.00
#> Sample_1229 1 0.000 0.968 1.00 0.00
#> Sample_1234 2 0.000 0.956 0.00 1.00
#> Sample_1236 2 0.000 0.956 0.00 1.00
#> Sample_1239 2 0.855 0.626 0.28 0.72
#> Sample_1240 1 0.000 0.968 1.00 0.00
#> Sample_1241 2 0.000 0.956 0.00 1.00
#> Sample_1242 2 0.000 0.956 0.00 1.00
#> Sample_1249 2 0.000 0.956 0.00 1.00
#> Sample_1254 1 0.000 0.968 1.00 0.00
#> Sample_1264 2 0.000 0.956 0.00 1.00
#> Sample_1266 1 0.000 0.968 1.00 0.00
#> Sample_1267 1 0.000 0.968 1.00 0.00
#> Sample_1272 2 0.000 0.956 0.00 1.00
#> Sample_1279 1 0.000 0.968 1.00 0.00
#> Sample_1280 1 0.141 0.951 0.98 0.02
#> Sample_1283 2 0.000 0.956 0.00 1.00
#> Sample_1285 2 0.000 0.956 0.00 1.00
#> Sample_1299 1 0.000 0.968 1.00 0.00
#> Sample_1307 1 0.000 0.968 1.00 0.00
#> Sample_1308 1 0.680 0.775 0.82 0.18
#> Sample_1309 2 0.000 0.956 0.00 1.00
#> Sample_1313 2 0.242 0.927 0.04 0.96
#> Sample_1317 2 0.000 0.956 0.00 1.00
#> Sample_1320 2 0.000 0.956 0.00 1.00
#> Sample_1335 1 0.943 0.438 0.64 0.36
#> Sample_1336 2 0.000 0.956 0.00 1.00
#> Sample_1338 2 0.000 0.956 0.00 1.00
#> Sample_1342 2 0.000 0.956 0.00 1.00
#> Sample_1343 2 0.000 0.956 0.00 1.00
#> Sample_1348 2 0.000 0.956 0.00 1.00
#> Sample_1350 2 0.000 0.956 0.00 1.00
#> Sample_1352 2 0.000 0.956 0.00 1.00
#> Sample_1354 2 0.000 0.956 0.00 1.00
#> Sample_1357 2 0.000 0.956 0.00 1.00
#> Sample_1358 2 0.000 0.956 0.00 1.00
#> Sample_1360 2 0.000 0.956 0.00 1.00
#> Sample_1364 2 0.000 0.956 0.00 1.00
#> Sample_1367 1 0.000 0.968 1.00 0.00
#> Sample_1371 2 0.000 0.956 0.00 1.00
#> Sample_1373 2 0.000 0.956 0.00 1.00
#> Sample_1374 2 0.000 0.956 0.00 1.00
#> Sample_1375 2 0.000 0.956 0.00 1.00
#> Sample_1382 2 0.000 0.956 0.00 1.00
#> Sample_1386 1 0.000 0.968 1.00 0.00
#> Sample_1388 2 0.000 0.956 0.00 1.00
#> Sample_1398 1 0.000 0.968 1.00 0.00
#> Sample_1399 2 0.000 0.956 0.00 1.00
#> Sample_1402 2 0.000 0.956 0.00 1.00
#> Sample_1406 1 0.000 0.968 1.00 0.00
#> Sample_1408 2 0.000 0.956 0.00 1.00
#> Sample_1409 1 0.000 0.968 1.00 0.00
#> Sample_1419 2 0.000 0.956 0.00 1.00
#> Sample_1423 1 0.000 0.968 1.00 0.00
#> Sample_1425 1 0.000 0.968 1.00 0.00
#> Sample_1426 2 0.000 0.956 0.00 1.00
#> Sample_1427 2 0.529 0.857 0.12 0.88
#> Sample_1429 1 0.000 0.968 1.00 0.00
#> Sample_1430 1 0.000 0.968 1.00 0.00
#> Sample_1431 1 0.795 0.683 0.76 0.24
#> Sample_1432 1 0.000 0.968 1.00 0.00
#> Sample_1433 1 0.000 0.968 1.00 0.00
#> Sample_1434 1 0.000 0.968 1.00 0.00
#> Sample_1435 1 0.000 0.968 1.00 0.00
#> Sample_1436 2 0.584 0.833 0.14 0.86
#> Sample_1437 1 0.000 0.968 1.00 0.00
#> Sample_1438 2 0.000 0.956 0.00 1.00
#> Sample_1439 1 0.000 0.968 1.00 0.00
#> Sample_1440 2 0.000 0.956 0.00 1.00
#> Sample_1441 1 0.242 0.934 0.96 0.04
#> Sample_1444 1 0.000 0.968 1.00 0.00
#> Sample_1445 1 0.000 0.968 1.00 0.00
#> Sample_1448 1 0.000 0.968 1.00 0.00
#> Sample_1449 1 0.000 0.968 1.00 0.00
#> Sample_1450 1 0.000 0.968 1.00 0.00
#> Sample_1451 1 0.000 0.968 1.00 0.00
#> Sample_1452 1 0.000 0.968 1.00 0.00
#> Sample_1453 2 0.000 0.956 0.00 1.00
#> Sample_1454 1 0.000 0.968 1.00 0.00
#> Sample_1455 1 0.000 0.968 1.00 0.00
#> Sample_1458 2 0.000 0.956 0.00 1.00
#> Sample_1459 2 0.000 0.956 0.00 1.00
#> Sample_1462 1 0.000 0.968 1.00 0.00
#> Sample_1465 2 0.000 0.956 0.00 1.00
#> Sample_1466 2 0.000 0.956 0.00 1.00
#> Sample_1467 1 0.000 0.968 1.00 0.00
#> Sample_1469 2 0.469 0.876 0.10 0.90
#> Sample_1470 2 0.000 0.956 0.00 1.00
#> Sample_1471 2 0.000 0.956 0.00 1.00
#> Sample_1472 2 0.000 0.956 0.00 1.00
#> Sample_1473 2 0.402 0.894 0.08 0.92
#> Sample_1474 1 0.000 0.968 1.00 0.00
#> Sample_1475 2 0.000 0.956 0.00 1.00
#> Sample_1476 2 0.943 0.465 0.36 0.64
#> Sample_1477 2 0.000 0.956 0.00 1.00
#> Sample_1478 2 0.722 0.759 0.20 0.80
#> Sample_1479 1 0.000 0.968 1.00 0.00
#> Sample_1481 2 0.000 0.956 0.00 1.00
#> Sample_1482 1 0.000 0.968 1.00 0.00
#> Sample_1483 2 0.971 0.361 0.40 0.60
#> Sample_1484 2 0.000 0.956 0.00 1.00
#> Sample_1487 1 0.000 0.968 1.00 0.00
#> Sample_1489 1 0.981 0.262 0.58 0.42
#> Sample_1490 2 0.000 0.956 0.00 1.00
#> Sample_1491 2 0.000 0.956 0.00 1.00
#> Sample_1492 2 0.141 0.942 0.02 0.98
#> Sample_1493 1 0.000 0.968 1.00 0.00
#> Sample_1495 2 0.000 0.956 0.00 1.00
#> Sample_1496 2 0.634 0.814 0.16 0.84
#> Sample_1497 2 0.000 0.956 0.00 1.00
#> Sample_1498 1 0.000 0.968 1.00 0.00
#> Sample_1499 1 0.000 0.968 1.00 0.00
#> Sample_1500 2 0.242 0.928 0.04 0.96
#> Sample_1501 2 0.795 0.698 0.24 0.76
#> Sample_1509 1 0.000 0.968 1.00 0.00
#> Sample_1510 1 0.000 0.968 1.00 0.00
#> Sample_1514 2 0.000 0.956 0.00 1.00
#> Sample_1516 1 0.000 0.968 1.00 0.00
#> Sample_1541 1 0.000 0.968 1.00 0.00
#> Sample_1545 1 0.000 0.968 1.00 0.00
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> Sample_479 3 0.2066 0.8307 0.00 0.06 0.94
#> Sample_486 2 0.7091 0.4890 0.04 0.64 0.32
#> Sample_487 2 0.0000 0.9217 0.00 1.00 0.00
#> Sample_490 2 0.0000 0.9217 0.00 1.00 0.00
#> Sample_495 2 0.2537 0.8672 0.08 0.92 0.00
#> Sample_499 3 0.5643 0.7295 0.22 0.02 0.76
#> Sample_500 1 0.2414 0.9203 0.94 0.02 0.04
#> Sample_505 1 0.1529 0.9327 0.96 0.00 0.04
#> Sample_506 2 0.0000 0.9217 0.00 1.00 0.00
#> Sample_507 1 0.5835 0.3859 0.66 0.00 0.34
#> Sample_511 3 0.5706 0.5997 0.32 0.00 0.68
#> Sample_513 2 0.0000 0.9217 0.00 1.00 0.00
#> Sample_515 2 0.0000 0.9217 0.00 1.00 0.00
#> Sample_517 2 0.0000 0.9217 0.00 1.00 0.00
#> Sample_520 2 0.0000 0.9217 0.00 1.00 0.00
#> Sample_521 2 0.0000 0.9217 0.00 1.00 0.00
#> Sample_527 2 0.0000 0.9217 0.00 1.00 0.00
#> Sample_530 2 0.6126 0.3303 0.40 0.60 0.00
#> Sample_532 3 0.0892 0.8603 0.02 0.00 0.98
#> Sample_533 2 0.0000 0.9217 0.00 1.00 0.00
#> Sample_535 2 0.0000 0.9217 0.00 1.00 0.00
#> Sample_536 3 0.6126 0.4693 0.40 0.00 0.60
#> Sample_537 2 0.0000 0.9217 0.00 1.00 0.00
#> Sample_539 2 0.0000 0.9217 0.00 1.00 0.00
#> Sample_542 2 0.0000 0.9217 0.00 1.00 0.00
#> Sample_543 2 0.0892 0.9122 0.02 0.98 0.00
#> Sample_544 2 0.0000 0.9217 0.00 1.00 0.00
#> Sample_546 2 0.1529 0.8994 0.04 0.96 0.00
#> Sample_547 2 0.0000 0.9217 0.00 1.00 0.00
#> Sample_557 2 0.0000 0.9217 0.00 1.00 0.00
#> Sample_558 1 0.1529 0.9270 0.96 0.04 0.00
#> Sample_559 1 0.5016 0.6909 0.76 0.24 0.00
#> Sample_565 2 0.0000 0.9217 0.00 1.00 0.00
#> Sample_566 2 0.0000 0.9217 0.00 1.00 0.00
#> Sample_567 2 0.0000 0.9217 0.00 1.00 0.00
#> Sample_569 3 0.4555 0.7493 0.20 0.00 0.80
#> Sample_1027 1 0.0892 0.9415 0.98 0.00 0.02
#> Sample_1028 1 0.3686 0.8191 0.86 0.00 0.14
#> Sample_1030 1 0.6443 0.6239 0.72 0.04 0.24
#> Sample_1034 1 0.2066 0.9162 0.94 0.00 0.06
#> Sample_1036 1 0.0000 0.9456 1.00 0.00 0.00
#> Sample_1039 1 0.0892 0.9418 0.98 0.00 0.02
#> Sample_1041 1 0.2066 0.9102 0.94 0.06 0.00
#> Sample_1042 1 0.2537 0.8961 0.92 0.00 0.08
#> Sample_1046 1 0.1529 0.9326 0.96 0.00 0.04
#> Sample_1049 1 0.1529 0.9293 0.96 0.00 0.04
#> Sample_1050 3 0.6192 0.3979 0.42 0.00 0.58
#> Sample_1052 1 0.1529 0.9326 0.96 0.00 0.04
#> Sample_1066 1 0.2066 0.9214 0.94 0.00 0.06
#> Sample_1070 3 0.6309 0.1623 0.50 0.00 0.50
#> Sample_1073 1 0.0892 0.9412 0.98 0.00 0.02
#> Sample_1075 1 0.0892 0.9418 0.98 0.00 0.02
#> Sample_1076 1 0.0000 0.9456 1.00 0.00 0.00
#> Sample_1080 2 0.0892 0.9119 0.02 0.98 0.00
#> Sample_1083 1 0.1529 0.9326 0.96 0.00 0.04
#> Sample_1087 3 0.6280 0.2944 0.46 0.00 0.54
#> Sample_1090 1 0.0000 0.9456 1.00 0.00 0.00
#> Sample_1091 1 0.0892 0.9390 0.98 0.02 0.00
#> Sample_1093 1 0.0000 0.9456 1.00 0.00 0.00
#> Sample_1097 1 0.2537 0.8923 0.92 0.08 0.00
#> Sample_1100 2 0.2066 0.8840 0.06 0.94 0.00
#> Sample_1102 1 0.0892 0.9418 0.98 0.00 0.02
#> Sample_1103 2 0.5560 0.5705 0.30 0.70 0.00
#> Sample_1104 1 0.0892 0.9390 0.98 0.02 0.00
#> Sample_1105 1 0.0892 0.9412 0.98 0.00 0.02
#> Sample_1108 1 0.3686 0.8269 0.86 0.14 0.00
#> Sample_1110 1 0.0892 0.9390 0.98 0.02 0.00
#> Sample_1112 1 0.1529 0.9318 0.96 0.00 0.04
#> Sample_1113 1 0.0000 0.9456 1.00 0.00 0.00
#> Sample_1114 3 0.6309 0.1640 0.50 0.00 0.50
#> Sample_1116 1 0.1529 0.9326 0.96 0.00 0.04
#> Sample_1118 1 0.0892 0.9398 0.98 0.00 0.02
#> Sample_1119 1 0.3042 0.8968 0.92 0.04 0.04
#> Sample_1121 1 0.0000 0.9456 1.00 0.00 0.00
#> Sample_1129 1 0.0000 0.9456 1.00 0.00 0.00
#> Sample_1130 1 0.1529 0.9326 0.96 0.00 0.04
#> Sample_1131 1 0.0000 0.9456 1.00 0.00 0.00
#> Sample_1133 1 0.0892 0.9390 0.98 0.02 0.00
#> Sample_1135 2 0.5016 0.6715 0.24 0.76 0.00
#> Sample_1136 1 0.1529 0.9326 0.96 0.00 0.04
#> Sample_1137 3 0.6280 0.3001 0.46 0.00 0.54
#> Sample_1138 1 0.0892 0.9418 0.98 0.00 0.02
#> Sample_1142 1 0.1529 0.9260 0.96 0.04 0.00
#> Sample_1143 1 0.0000 0.9456 1.00 0.00 0.00
#> Sample_1144 1 0.0892 0.9390 0.98 0.02 0.00
#> Sample_1146 1 0.2066 0.9102 0.94 0.06 0.00
#> Sample_1147 1 0.4002 0.7989 0.84 0.16 0.00
#> Sample_1148 1 0.0000 0.9456 1.00 0.00 0.00
#> Sample_1149 1 0.0892 0.9418 0.98 0.00 0.02
#> Sample_1151 1 0.0000 0.9456 1.00 0.00 0.00
#> Sample_1153 1 0.1529 0.9292 0.96 0.00 0.04
#> Sample_1154 1 0.0000 0.9456 1.00 0.00 0.00
#> Sample_1155 1 0.0000 0.9456 1.00 0.00 0.00
#> Sample_1158 1 0.0892 0.9390 0.98 0.02 0.00
#> Sample_1159 1 0.0892 0.9390 0.98 0.02 0.00
#> Sample_1160 2 0.5948 0.4378 0.36 0.64 0.00
#> Sample_1162 1 0.0000 0.9456 1.00 0.00 0.00
#> Sample_1165 2 0.6302 0.0806 0.48 0.52 0.00
#> Sample_1166 1 0.0892 0.9418 0.98 0.00 0.02
#> Sample_1167 1 0.2066 0.9102 0.94 0.06 0.00
#> Sample_1168 1 0.0000 0.9456 1.00 0.00 0.00
#> Sample_1171 1 0.0000 0.9456 1.00 0.00 0.00
#> Sample_1172 3 0.6302 0.2339 0.48 0.00 0.52
#> Sample_1173 1 0.0000 0.9456 1.00 0.00 0.00
#> Sample_1174 1 0.0000 0.9456 1.00 0.00 0.00
#> Sample_1176 1 0.1529 0.9260 0.96 0.04 0.00
#> Sample_1177 1 0.0000 0.9456 1.00 0.00 0.00
#> Sample_1178 1 0.1529 0.9326 0.96 0.00 0.04
#> Sample_1179 2 0.0000 0.9217 0.00 1.00 0.00
#> Sample_1181 1 0.0892 0.9418 0.98 0.00 0.02
#> Sample_1182 1 0.1529 0.9326 0.96 0.00 0.04
#> Sample_1185 1 0.0000 0.9456 1.00 0.00 0.00
#> Sample_1186 1 0.0000 0.9456 1.00 0.00 0.00
#> Sample_1187 1 0.2066 0.9142 0.94 0.00 0.06
#> Sample_1188 1 0.0892 0.9390 0.98 0.02 0.00
#> Sample_1222 3 0.5334 0.7962 0.06 0.12 0.82
#> Sample_1225 2 0.2066 0.9001 0.00 0.94 0.06
#> Sample_1227 2 0.0000 0.9217 0.00 1.00 0.00
#> Sample_1228 2 0.2959 0.8725 0.00 0.90 0.10
#> Sample_1229 3 0.4291 0.7682 0.18 0.00 0.82
#> Sample_1234 2 0.0892 0.9168 0.00 0.98 0.02
#> Sample_1236 2 0.0000 0.9217 0.00 1.00 0.00
#> Sample_1239 3 0.5016 0.6216 0.00 0.24 0.76
#> Sample_1240 3 0.2959 0.8320 0.10 0.00 0.90
#> Sample_1241 2 0.0892 0.9168 0.00 0.98 0.02
#> Sample_1242 2 0.0000 0.9217 0.00 1.00 0.00
#> Sample_1249 2 0.0892 0.9168 0.00 0.98 0.02
#> Sample_1254 3 0.6244 0.3507 0.44 0.00 0.56
#> Sample_1264 2 0.0892 0.9168 0.00 0.98 0.02
#> Sample_1266 3 0.1529 0.8586 0.04 0.00 0.96
#> Sample_1267 3 0.0892 0.8603 0.02 0.00 0.98
#> Sample_1272 2 0.0892 0.9168 0.00 0.98 0.02
#> Sample_1279 3 0.1529 0.8586 0.04 0.00 0.96
#> Sample_1280 3 0.0892 0.8545 0.00 0.02 0.98
#> Sample_1283 2 0.0000 0.9217 0.00 1.00 0.00
#> Sample_1285 2 0.0892 0.9168 0.00 0.98 0.02
#> Sample_1299 3 0.1529 0.8586 0.04 0.00 0.96
#> Sample_1307 3 0.0000 0.8568 0.00 0.00 1.00
#> Sample_1308 3 0.0000 0.8568 0.00 0.00 1.00
#> Sample_1309 2 0.0892 0.9168 0.00 0.98 0.02
#> Sample_1313 2 0.5216 0.6880 0.00 0.74 0.26
#> Sample_1317 2 0.0000 0.9217 0.00 1.00 0.00
#> Sample_1320 2 0.0000 0.9217 0.00 1.00 0.00
#> Sample_1335 3 0.2537 0.8173 0.00 0.08 0.92
#> Sample_1336 2 0.0000 0.9217 0.00 1.00 0.00
#> Sample_1338 2 0.0000 0.9217 0.00 1.00 0.00
#> Sample_1342 2 0.2414 0.9049 0.02 0.94 0.04
#> Sample_1343 2 0.1529 0.9088 0.00 0.96 0.04
#> Sample_1348 2 0.1529 0.9088 0.00 0.96 0.04
#> Sample_1350 2 0.1529 0.9088 0.00 0.96 0.04
#> Sample_1352 2 0.0000 0.9217 0.00 1.00 0.00
#> Sample_1354 2 0.2066 0.8858 0.06 0.94 0.00
#> Sample_1357 2 0.0000 0.9217 0.00 1.00 0.00
#> Sample_1358 2 0.0000 0.9217 0.00 1.00 0.00
#> Sample_1360 2 0.0000 0.9217 0.00 1.00 0.00
#> Sample_1364 2 0.0000 0.9217 0.00 1.00 0.00
#> Sample_1367 3 0.2537 0.8432 0.08 0.00 0.92
#> Sample_1371 2 0.0000 0.9217 0.00 1.00 0.00
#> Sample_1373 2 0.0000 0.9217 0.00 1.00 0.00
#> Sample_1374 2 0.0000 0.9217 0.00 1.00 0.00
#> Sample_1375 2 0.0000 0.9217 0.00 1.00 0.00
#> Sample_1382 2 0.0892 0.9168 0.00 0.98 0.02
#> Sample_1386 3 0.0000 0.8568 0.00 0.00 1.00
#> Sample_1388 2 0.4796 0.7507 0.00 0.78 0.22
#> Sample_1398 3 0.1529 0.8586 0.04 0.00 0.96
#> Sample_1399 3 0.5016 0.6142 0.00 0.24 0.76
#> Sample_1402 2 0.2537 0.8840 0.00 0.92 0.08
#> Sample_1406 3 0.0892 0.8603 0.02 0.00 0.98
#> Sample_1408 2 0.1529 0.9088 0.00 0.96 0.04
#> Sample_1409 3 0.0892 0.8603 0.02 0.00 0.98
#> Sample_1419 2 0.5016 0.7257 0.00 0.76 0.24
#> Sample_1423 3 0.0892 0.8603 0.02 0.00 0.98
#> Sample_1425 3 0.1529 0.8586 0.04 0.00 0.96
#> Sample_1426 2 0.0000 0.9217 0.00 1.00 0.00
#> Sample_1427 3 0.2066 0.8274 0.00 0.06 0.94
#> Sample_1429 3 0.0892 0.8603 0.02 0.00 0.98
#> Sample_1430 3 0.0000 0.8568 0.00 0.00 1.00
#> Sample_1431 3 0.0892 0.8520 0.02 0.00 0.98
#> Sample_1432 3 0.2066 0.8527 0.06 0.00 0.94
#> Sample_1433 3 0.0000 0.8568 0.00 0.00 1.00
#> Sample_1434 3 0.2066 0.8527 0.06 0.00 0.94
#> Sample_1435 3 0.0892 0.8603 0.02 0.00 0.98
#> Sample_1436 3 0.1529 0.8415 0.00 0.04 0.96
#> Sample_1437 3 0.0000 0.8568 0.00 0.00 1.00
#> Sample_1438 2 0.0000 0.9217 0.00 1.00 0.00
#> Sample_1439 3 0.2066 0.8527 0.06 0.00 0.94
#> Sample_1440 2 0.5706 0.5980 0.00 0.68 0.32
#> Sample_1441 3 0.0000 0.8568 0.00 0.00 1.00
#> Sample_1444 3 0.0000 0.8568 0.00 0.00 1.00
#> Sample_1445 3 0.0000 0.8568 0.00 0.00 1.00
#> Sample_1448 3 0.3686 0.8034 0.14 0.00 0.86
#> Sample_1449 3 0.5948 0.5277 0.36 0.00 0.64
#> Sample_1450 3 0.4555 0.7493 0.20 0.00 0.80
#> Sample_1451 3 0.1529 0.8586 0.04 0.00 0.96
#> Sample_1452 3 0.2066 0.8527 0.06 0.00 0.94
#> Sample_1453 2 0.4291 0.7961 0.00 0.82 0.18
#> Sample_1454 3 0.1529 0.8586 0.04 0.00 0.96
#> Sample_1455 3 0.1529 0.8586 0.04 0.00 0.96
#> Sample_1458 2 0.5835 0.5578 0.00 0.66 0.34
#> Sample_1459 2 0.0892 0.9168 0.00 0.98 0.02
#> Sample_1462 1 0.4002 0.7953 0.84 0.00 0.16
#> Sample_1465 2 0.0000 0.9217 0.00 1.00 0.00
#> Sample_1466 2 0.0000 0.9217 0.00 1.00 0.00
#> Sample_1467 1 0.4555 0.7192 0.80 0.00 0.20
#> Sample_1469 3 0.3340 0.7729 0.00 0.12 0.88
#> Sample_1470 2 0.1529 0.9088 0.00 0.96 0.04
#> Sample_1471 2 0.2066 0.8997 0.00 0.94 0.06
#> Sample_1472 2 0.2537 0.8858 0.00 0.92 0.08
#> Sample_1473 2 0.8576 0.5080 0.16 0.60 0.24
#> Sample_1474 3 0.2537 0.8438 0.08 0.00 0.92
#> Sample_1475 2 0.7948 0.4982 0.08 0.60 0.32
#> Sample_1476 3 0.6922 0.6337 0.08 0.20 0.72
#> Sample_1477 2 0.0892 0.9168 0.00 0.98 0.02
#> Sample_1478 3 0.8953 0.4665 0.18 0.26 0.56
#> Sample_1479 3 0.0892 0.8603 0.02 0.00 0.98
#> Sample_1481 2 0.3686 0.8364 0.00 0.86 0.14
#> Sample_1482 3 0.1529 0.8586 0.04 0.00 0.96
#> Sample_1483 3 0.4796 0.6454 0.00 0.22 0.78
#> Sample_1484 2 0.0000 0.9217 0.00 1.00 0.00
#> Sample_1487 3 0.0892 0.8603 0.02 0.00 0.98
#> Sample_1489 3 0.7901 0.2980 0.40 0.06 0.54
#> Sample_1490 2 0.4291 0.7990 0.00 0.82 0.18
#> Sample_1491 2 0.4796 0.7516 0.00 0.78 0.22
#> Sample_1492 2 0.6302 0.2019 0.00 0.52 0.48
#> Sample_1493 3 0.0000 0.8568 0.00 0.00 1.00
#> Sample_1495 2 0.1529 0.9088 0.00 0.96 0.04
#> Sample_1496 3 0.4555 0.6770 0.00 0.20 0.80
#> Sample_1497 2 0.2537 0.8881 0.00 0.92 0.08
#> Sample_1498 3 0.0000 0.8568 0.00 0.00 1.00
#> Sample_1499 3 0.0000 0.8568 0.00 0.00 1.00
#> Sample_1500 3 0.4796 0.6442 0.00 0.22 0.78
#> Sample_1501 3 0.0892 0.8500 0.00 0.02 0.98
#> Sample_1509 3 0.0000 0.8568 0.00 0.00 1.00
#> Sample_1510 3 0.0892 0.8603 0.02 0.00 0.98
#> Sample_1514 2 0.3340 0.8542 0.00 0.88 0.12
#> Sample_1516 3 0.2066 0.8527 0.06 0.00 0.94
#> Sample_1541 3 0.3686 0.8031 0.14 0.00 0.86
#> Sample_1545 3 0.6192 0.4059 0.42 0.00 0.58
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> Sample_479 2 0.4406 0.48028 0.00 0.70 0.30 0.00
#> Sample_486 2 0.5327 0.58650 0.00 0.72 0.06 0.22
#> Sample_487 2 0.4790 0.47674 0.00 0.62 0.00 0.38
#> Sample_490 4 0.4948 -0.11072 0.00 0.44 0.00 0.56
#> Sample_495 2 0.5106 0.58502 0.04 0.72 0.00 0.24
#> Sample_499 2 0.6720 0.28440 0.12 0.58 0.30 0.00
#> Sample_500 2 0.8102 0.21429 0.34 0.48 0.14 0.04
#> Sample_505 1 0.6299 0.44508 0.60 0.32 0.08 0.00
#> Sample_506 2 0.4907 0.42203 0.00 0.58 0.00 0.42
#> Sample_507 2 0.7805 0.08029 0.30 0.42 0.28 0.00
#> Sample_511 3 0.5962 0.63896 0.26 0.08 0.66 0.00
#> Sample_513 2 0.4406 0.55648 0.00 0.70 0.00 0.30
#> Sample_515 2 0.3975 0.59469 0.00 0.76 0.00 0.24
#> Sample_517 2 0.4134 0.58304 0.00 0.74 0.00 0.26
#> Sample_520 2 0.4134 0.58440 0.00 0.74 0.00 0.26
#> Sample_521 2 0.3610 0.61021 0.00 0.80 0.00 0.20
#> Sample_527 2 0.3801 0.60327 0.00 0.78 0.00 0.22
#> Sample_530 2 0.5383 0.59562 0.10 0.74 0.00 0.16
#> Sample_532 2 0.5606 0.06171 0.02 0.50 0.48 0.00
#> Sample_533 2 0.4277 0.57107 0.00 0.72 0.00 0.28
#> Sample_535 2 0.4406 0.55448 0.00 0.70 0.00 0.30
#> Sample_536 2 0.5744 0.58604 0.08 0.74 0.16 0.02
#> Sample_537 2 0.4907 0.42203 0.00 0.58 0.00 0.42
#> Sample_539 2 0.4907 0.42203 0.00 0.58 0.00 0.42
#> Sample_542 2 0.4624 0.52147 0.00 0.66 0.00 0.34
#> Sample_543 2 0.2011 0.62920 0.00 0.92 0.00 0.08
#> Sample_544 2 0.4907 0.42136 0.00 0.58 0.00 0.42
#> Sample_546 2 0.3606 0.62531 0.02 0.84 0.00 0.14
#> Sample_547 2 0.4713 0.49991 0.00 0.64 0.00 0.36
#> Sample_557 2 0.4713 0.50195 0.00 0.64 0.00 0.36
#> Sample_558 2 0.4948 0.15336 0.44 0.56 0.00 0.00
#> Sample_559 2 0.5006 0.58396 0.16 0.78 0.02 0.04
#> Sample_565 2 0.4907 0.42203 0.00 0.58 0.00 0.42
#> Sample_566 2 0.4713 0.49991 0.00 0.64 0.00 0.36
#> Sample_567 2 0.4977 0.34605 0.00 0.54 0.00 0.46
#> Sample_569 3 0.3400 0.77072 0.18 0.00 0.82 0.00
#> Sample_1027 1 0.1411 0.86286 0.96 0.02 0.02 0.00
#> Sample_1028 1 0.4406 0.57477 0.70 0.00 0.30 0.00
#> Sample_1030 1 0.7049 0.59084 0.68 0.10 0.12 0.10
#> Sample_1034 1 0.3801 0.71373 0.78 0.00 0.22 0.00
#> Sample_1036 1 0.1211 0.86039 0.96 0.00 0.04 0.00
#> Sample_1039 1 0.0707 0.86445 0.98 0.00 0.02 0.00
#> Sample_1041 1 0.4332 0.71715 0.80 0.16 0.00 0.04
#> Sample_1042 1 0.4624 0.48710 0.66 0.00 0.34 0.00
#> Sample_1046 1 0.3400 0.76207 0.82 0.00 0.18 0.00
#> Sample_1049 1 0.2011 0.84355 0.92 0.00 0.08 0.00
#> Sample_1050 3 0.3975 0.70395 0.24 0.00 0.76 0.00
#> Sample_1052 1 0.3400 0.76409 0.82 0.00 0.18 0.00
#> Sample_1066 1 0.1637 0.86035 0.94 0.00 0.06 0.00
#> Sample_1070 3 0.4994 0.12489 0.48 0.00 0.52 0.00
#> Sample_1073 1 0.1211 0.86039 0.96 0.00 0.04 0.00
#> Sample_1075 1 0.1637 0.85387 0.94 0.00 0.06 0.00
#> Sample_1076 1 0.0000 0.86430 1.00 0.00 0.00 0.00
#> Sample_1080 4 0.6212 -0.04927 0.06 0.38 0.00 0.56
#> Sample_1083 1 0.3801 0.71673 0.78 0.00 0.22 0.00
#> Sample_1087 3 0.4522 0.57761 0.32 0.00 0.68 0.00
#> Sample_1090 1 0.0707 0.86151 0.98 0.02 0.00 0.00
#> Sample_1091 1 0.2345 0.81817 0.90 0.10 0.00 0.00
#> Sample_1093 1 0.0000 0.86430 1.00 0.00 0.00 0.00
#> Sample_1097 1 0.3611 0.79768 0.86 0.06 0.00 0.08
#> Sample_1100 2 0.6554 0.38701 0.08 0.52 0.00 0.40
#> Sample_1102 1 0.2921 0.80364 0.86 0.00 0.14 0.00
#> Sample_1103 2 0.7748 0.34424 0.28 0.44 0.00 0.28
#> Sample_1104 1 0.1211 0.85547 0.96 0.04 0.00 0.00
#> Sample_1105 1 0.2345 0.83373 0.90 0.00 0.10 0.00
#> Sample_1108 1 0.4797 0.60904 0.72 0.02 0.00 0.26
#> Sample_1110 1 0.1211 0.85704 0.96 0.04 0.00 0.00
#> Sample_1112 1 0.2647 0.81368 0.88 0.00 0.12 0.00
#> Sample_1113 1 0.0707 0.86419 0.98 0.00 0.02 0.00
#> Sample_1114 3 0.4522 0.57836 0.32 0.00 0.68 0.00
#> Sample_1116 1 0.2011 0.84459 0.92 0.00 0.08 0.00
#> Sample_1118 1 0.2647 0.82378 0.88 0.00 0.12 0.00
#> Sample_1119 1 0.3037 0.81973 0.88 0.10 0.02 0.00
#> Sample_1121 1 0.2011 0.84195 0.92 0.00 0.08 0.00
#> Sample_1129 1 0.0000 0.86430 1.00 0.00 0.00 0.00
#> Sample_1130 1 0.3975 0.68581 0.76 0.00 0.24 0.00
#> Sample_1131 1 0.0000 0.86430 1.00 0.00 0.00 0.00
#> Sample_1133 1 0.1637 0.84513 0.94 0.06 0.00 0.00
#> Sample_1135 4 0.7121 0.05272 0.16 0.30 0.00 0.54
#> Sample_1136 1 0.4406 0.57881 0.70 0.00 0.30 0.00
#> Sample_1137 3 0.4797 0.67763 0.26 0.02 0.72 0.00
#> Sample_1138 1 0.0707 0.86419 0.98 0.00 0.02 0.00
#> Sample_1142 1 0.2921 0.77974 0.86 0.14 0.00 0.00
#> Sample_1143 1 0.0000 0.86430 1.00 0.00 0.00 0.00
#> Sample_1144 1 0.1211 0.85547 0.96 0.04 0.00 0.00
#> Sample_1146 1 0.3606 0.76024 0.84 0.14 0.00 0.02
#> Sample_1147 1 0.5147 0.63198 0.74 0.20 0.00 0.06
#> Sample_1148 1 0.0000 0.86430 1.00 0.00 0.00 0.00
#> Sample_1149 1 0.3801 0.71582 0.78 0.00 0.22 0.00
#> Sample_1151 1 0.1211 0.86180 0.96 0.00 0.04 0.00
#> Sample_1153 1 0.3525 0.79592 0.86 0.10 0.04 0.00
#> Sample_1154 1 0.1211 0.86039 0.96 0.00 0.04 0.00
#> Sample_1155 1 0.0000 0.86430 1.00 0.00 0.00 0.00
#> Sample_1158 1 0.0707 0.86326 0.98 0.02 0.00 0.00
#> Sample_1159 1 0.2647 0.79958 0.88 0.12 0.00 0.00
#> Sample_1160 2 0.7427 0.40411 0.30 0.50 0.00 0.20
#> Sample_1162 1 0.1211 0.85547 0.96 0.04 0.00 0.00
#> Sample_1165 1 0.6586 0.08813 0.50 0.42 0.00 0.08
#> Sample_1166 1 0.0707 0.86445 0.98 0.00 0.02 0.00
#> Sample_1167 1 0.3335 0.78308 0.86 0.12 0.00 0.02
#> Sample_1168 1 0.1211 0.86013 0.96 0.00 0.04 0.00
#> Sample_1171 1 0.0000 0.86430 1.00 0.00 0.00 0.00
#> Sample_1172 3 0.3801 0.72956 0.22 0.00 0.78 0.00
#> Sample_1173 1 0.0000 0.86430 1.00 0.00 0.00 0.00
#> Sample_1174 1 0.0000 0.86430 1.00 0.00 0.00 0.00
#> Sample_1176 1 0.2921 0.77974 0.86 0.14 0.00 0.00
#> Sample_1177 1 0.1211 0.85547 0.96 0.04 0.00 0.00
#> Sample_1178 1 0.3172 0.78445 0.84 0.00 0.16 0.00
#> Sample_1179 2 0.5860 0.45460 0.04 0.58 0.00 0.38
#> Sample_1181 1 0.2011 0.84455 0.92 0.00 0.08 0.00
#> Sample_1182 1 0.3172 0.78307 0.84 0.00 0.16 0.00
#> Sample_1185 1 0.1637 0.85387 0.94 0.00 0.06 0.00
#> Sample_1186 1 0.0707 0.86151 0.98 0.02 0.00 0.00
#> Sample_1187 1 0.2335 0.84160 0.92 0.06 0.02 0.00
#> Sample_1188 1 0.1211 0.85547 0.96 0.04 0.00 0.00
#> Sample_1222 4 0.3610 0.62573 0.00 0.00 0.20 0.80
#> Sample_1225 4 0.0707 0.79034 0.00 0.02 0.00 0.98
#> Sample_1227 4 0.0000 0.79028 0.00 0.00 0.00 1.00
#> Sample_1228 4 0.1411 0.77956 0.00 0.02 0.02 0.96
#> Sample_1229 3 0.3172 0.78407 0.16 0.00 0.84 0.00
#> Sample_1234 4 0.0000 0.79028 0.00 0.00 0.00 1.00
#> Sample_1236 4 0.0000 0.79028 0.00 0.00 0.00 1.00
#> Sample_1239 4 0.5147 0.58196 0.00 0.06 0.20 0.74
#> Sample_1240 3 0.2921 0.79778 0.14 0.00 0.86 0.00
#> Sample_1241 4 0.0707 0.79034 0.00 0.02 0.00 0.98
#> Sample_1242 4 0.0000 0.79028 0.00 0.00 0.00 1.00
#> Sample_1249 4 0.0707 0.79034 0.00 0.02 0.00 0.98
#> Sample_1254 3 0.7198 0.46192 0.18 0.00 0.54 0.28
#> Sample_1264 4 0.0707 0.79034 0.00 0.02 0.00 0.98
#> Sample_1266 3 0.2647 0.80602 0.12 0.00 0.88 0.00
#> Sample_1267 3 0.4079 0.70408 0.02 0.00 0.80 0.18
#> Sample_1272 4 0.0000 0.79028 0.00 0.00 0.00 1.00
#> Sample_1279 3 0.2830 0.80404 0.06 0.00 0.90 0.04
#> Sample_1280 3 0.4755 0.68278 0.04 0.00 0.76 0.20
#> Sample_1283 4 0.0000 0.79028 0.00 0.00 0.00 1.00
#> Sample_1285 4 0.0707 0.79034 0.00 0.02 0.00 0.98
#> Sample_1299 3 0.2011 0.81212 0.08 0.00 0.92 0.00
#> Sample_1307 4 0.5987 0.07785 0.00 0.04 0.44 0.52
#> Sample_1308 4 0.6336 0.00309 0.00 0.06 0.46 0.48
#> Sample_1309 4 0.0707 0.79034 0.00 0.02 0.00 0.98
#> Sample_1313 4 0.1211 0.78247 0.00 0.04 0.00 0.96
#> Sample_1317 4 0.1637 0.74770 0.00 0.06 0.00 0.94
#> Sample_1320 4 0.1211 0.76733 0.00 0.04 0.00 0.96
#> Sample_1335 4 0.6586 0.13757 0.00 0.08 0.42 0.50
#> Sample_1336 4 0.0000 0.79028 0.00 0.00 0.00 1.00
#> Sample_1338 4 0.2011 0.72670 0.00 0.08 0.00 0.92
#> Sample_1342 4 0.1913 0.76017 0.04 0.02 0.00 0.94
#> Sample_1343 4 0.1211 0.78223 0.00 0.04 0.00 0.96
#> Sample_1348 4 0.0707 0.79034 0.00 0.02 0.00 0.98
#> Sample_1350 4 0.1211 0.78189 0.00 0.04 0.00 0.96
#> Sample_1352 4 0.0000 0.79028 0.00 0.00 0.00 1.00
#> Sample_1354 4 0.1913 0.77186 0.04 0.02 0.00 0.94
#> Sample_1357 4 0.0707 0.78185 0.02 0.00 0.00 0.98
#> Sample_1358 4 0.0000 0.79028 0.00 0.00 0.00 1.00
#> Sample_1360 4 0.0000 0.79028 0.00 0.00 0.00 1.00
#> Sample_1364 4 0.1211 0.76733 0.00 0.04 0.00 0.96
#> Sample_1367 3 0.4977 0.14840 0.00 0.00 0.54 0.46
#> Sample_1371 4 0.0707 0.78008 0.00 0.02 0.00 0.98
#> Sample_1373 4 0.0000 0.79028 0.00 0.00 0.00 1.00
#> Sample_1374 4 0.1211 0.76703 0.00 0.04 0.00 0.96
#> Sample_1375 4 0.0000 0.79028 0.00 0.00 0.00 1.00
#> Sample_1382 4 0.1211 0.78247 0.00 0.04 0.00 0.96
#> Sample_1386 3 0.2345 0.72305 0.00 0.10 0.90 0.00
#> Sample_1388 2 0.7121 0.20131 0.00 0.54 0.16 0.30
#> Sample_1398 3 0.1637 0.81091 0.06 0.00 0.94 0.00
#> Sample_1399 4 0.7738 0.19618 0.00 0.30 0.26 0.44
#> Sample_1402 4 0.4731 0.63229 0.00 0.16 0.06 0.78
#> Sample_1406 3 0.1637 0.81091 0.06 0.00 0.94 0.00
#> Sample_1408 4 0.3335 0.71108 0.00 0.12 0.02 0.86
#> Sample_1409 3 0.1211 0.80564 0.04 0.00 0.96 0.00
#> Sample_1419 4 0.6808 0.32619 0.00 0.32 0.12 0.56
#> Sample_1423 3 0.2830 0.80146 0.06 0.04 0.90 0.00
#> Sample_1425 3 0.1211 0.80564 0.04 0.00 0.96 0.00
#> Sample_1426 4 0.3335 0.71803 0.00 0.12 0.02 0.86
#> Sample_1427 3 0.6477 0.36331 0.00 0.30 0.60 0.10
#> Sample_1429 3 0.1211 0.80564 0.04 0.00 0.96 0.00
#> Sample_1430 3 0.3037 0.71174 0.00 0.10 0.88 0.02
#> Sample_1431 3 0.6966 0.45275 0.02 0.18 0.64 0.16
#> Sample_1432 3 0.2011 0.81204 0.08 0.00 0.92 0.00
#> Sample_1433 3 0.1211 0.76513 0.00 0.04 0.96 0.00
#> Sample_1434 3 0.2647 0.80602 0.12 0.00 0.88 0.00
#> Sample_1435 3 0.1913 0.79980 0.04 0.02 0.94 0.00
#> Sample_1436 3 0.6976 0.33786 0.00 0.24 0.58 0.18
#> Sample_1437 3 0.1211 0.76513 0.00 0.04 0.96 0.00
#> Sample_1438 2 0.6323 0.16755 0.00 0.50 0.06 0.44
#> Sample_1439 3 0.2647 0.80602 0.12 0.00 0.88 0.00
#> Sample_1440 4 0.7365 0.12770 0.00 0.40 0.16 0.44
#> Sample_1441 3 0.3975 0.57862 0.00 0.24 0.76 0.00
#> Sample_1444 3 0.2011 0.73772 0.00 0.08 0.92 0.00
#> Sample_1445 3 0.1637 0.75235 0.00 0.06 0.94 0.00
#> Sample_1448 3 0.3172 0.78407 0.16 0.00 0.84 0.00
#> Sample_1449 3 0.3400 0.77072 0.18 0.00 0.82 0.00
#> Sample_1450 3 0.3400 0.77072 0.18 0.00 0.82 0.00
#> Sample_1451 3 0.2830 0.78281 0.04 0.06 0.90 0.00
#> Sample_1452 3 0.2647 0.80602 0.12 0.00 0.88 0.00
#> Sample_1453 4 0.6805 0.22736 0.00 0.40 0.10 0.50
#> Sample_1454 3 0.2345 0.80979 0.10 0.00 0.90 0.00
#> Sample_1455 3 0.1637 0.81113 0.06 0.00 0.94 0.00
#> Sample_1458 4 0.7344 0.16523 0.00 0.38 0.16 0.46
#> Sample_1459 2 0.5661 0.49677 0.00 0.70 0.08 0.22
#> Sample_1462 1 0.6933 0.32927 0.56 0.14 0.30 0.00
#> Sample_1465 2 0.3400 0.61056 0.00 0.82 0.00 0.18
#> Sample_1466 2 0.3821 0.62961 0.00 0.84 0.04 0.12
#> Sample_1467 1 0.4406 0.57261 0.70 0.00 0.30 0.00
#> Sample_1469 2 0.3975 0.53503 0.00 0.76 0.24 0.00
#> Sample_1470 2 0.2706 0.61760 0.00 0.90 0.08 0.02
#> Sample_1471 2 0.3335 0.59386 0.00 0.86 0.12 0.02
#> Sample_1472 2 0.3335 0.60685 0.00 0.86 0.12 0.02
#> Sample_1473 2 0.5637 0.57659 0.04 0.76 0.14 0.06
#> Sample_1474 3 0.2921 0.79778 0.14 0.00 0.86 0.00
#> Sample_1475 2 0.4332 0.58110 0.04 0.80 0.16 0.00
#> Sample_1476 2 0.5062 0.44509 0.00 0.68 0.30 0.02
#> Sample_1477 2 0.5915 0.37975 0.00 0.56 0.04 0.40
#> Sample_1478 2 0.9470 0.22434 0.18 0.38 0.30 0.14
#> Sample_1479 3 0.3400 0.65249 0.00 0.18 0.82 0.00
#> Sample_1481 2 0.2647 0.60070 0.00 0.88 0.12 0.00
#> Sample_1482 3 0.1637 0.81091 0.06 0.00 0.94 0.00
#> Sample_1483 2 0.3975 0.53941 0.00 0.76 0.24 0.00
#> Sample_1484 2 0.1913 0.62150 0.00 0.94 0.04 0.02
#> Sample_1487 3 0.2830 0.78307 0.04 0.06 0.90 0.00
#> Sample_1489 2 0.5327 0.52889 0.06 0.72 0.22 0.00
#> Sample_1490 2 0.2345 0.60735 0.00 0.90 0.10 0.00
#> Sample_1491 2 0.2647 0.60070 0.00 0.88 0.12 0.00
#> Sample_1492 2 0.4755 0.55053 0.00 0.76 0.20 0.04
#> Sample_1493 3 0.5062 0.47968 0.02 0.30 0.68 0.00
#> Sample_1495 2 0.4894 0.56874 0.00 0.78 0.10 0.12
#> Sample_1496 2 0.6611 0.01608 0.02 0.48 0.46 0.04
#> Sample_1497 2 0.3335 0.60893 0.00 0.86 0.12 0.02
#> Sample_1498 3 0.4713 0.38668 0.00 0.36 0.64 0.00
#> Sample_1499 3 0.4713 0.38462 0.00 0.36 0.64 0.00
#> Sample_1500 2 0.4797 0.50376 0.00 0.72 0.26 0.02
#> Sample_1501 2 0.4277 0.48516 0.00 0.72 0.28 0.00
#> Sample_1509 3 0.1211 0.76513 0.00 0.04 0.96 0.00
#> Sample_1510 3 0.1211 0.80564 0.04 0.00 0.96 0.00
#> Sample_1514 4 0.7220 0.09954 0.00 0.42 0.14 0.44
#> Sample_1516 3 0.2011 0.81200 0.08 0.00 0.92 0.00
#> Sample_1541 3 0.2921 0.79778 0.14 0.00 0.86 0.00
#> Sample_1545 3 0.4797 0.67763 0.26 0.02 0.72 0.00
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
As soon as the classes for columns are determined, the signatures that are significantly different between subgroups can be looked for. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. To get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows (which is done by automatically selecting number of clusters).If there are too many signatures, top_signatures = ... can be set to only show the
signatures with the highest FDRs:
# code only for demonstration
# e.g. to show the top 500 most significant rows
tb = get_signature(res, k = ..., top_signatures = 500)
If the signatures are defined as these which are uniquely high in current group, diff_method argument
can be set to "uniquely_high_in_one_group":
# code only for demonstration
tb = get_signature(res, k = ..., diff_method = "uniquely_high_in_one_group")
UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n_sample age(p-value) cell.type(p-value) k
#> ATC:skmeans 228 1.26e-04 0.616 2
#> ATC:skmeans 220 2.83e-21 0.387 3
#> ATC:skmeans 186 4.10e-40 0.208 4
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
Parent node: Node01. Child nodes: Node0111-leaf , Node0112-leaf , Node0121-leaf , Node0122-leaf , Node0131-leaf , Node0132-leaf , Node0211 , Node0212 , Node0221 , Node0222 , Node0231-leaf , Node0232-leaf , Node0233-leaf , Node0311-leaf , Node0312-leaf , Node0313-leaf , Node0314-leaf , Node0321 , Node0322 , Node0331-leaf , Node0332-leaf , Node0333-leaf .
The object with results only for a single top-value method and a single partitioning method can be extracted as:
res = res_rh["012"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4.
#> On a matrix with 14725 rows and 113 columns.
#> Top rows (655) are extracted by 'ATC' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 150 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_partitions"
#> [7] "compare_signatures" "consensus_heatmap" "dimension_reduction"
#> [10] "functional_enrichment" "get_anno_col" "get_anno"
#> [13] "get_classes" "get_consensus" "get_matrix"
#> [16] "get_membership" "get_param" "get_signatures"
#> [19] "get_stats" "is_best_k" "is_stable_k"
#> [22] "membership_heatmap" "ncol" "nrow"
#> [25] "plot_ecdf" "predict_classes" "rownames"
#> [28] "select_partition_number" "show" "suggest_best_k"
#> [31] "test_to_known_factors" "top_rows_heatmap"
collect_plots() function collects all the plots made from res for all k (number of subgroups)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, higher 1-PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.968 0.987 0.500 0.502 0.502
#> 3 3 0.638 0.762 0.875 0.278 0.835 0.681
#> 4 4 0.558 0.664 0.792 0.122 0.883 0.710
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 2
Following is the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall subgroup
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> Sample_121 1 0.000 0.992 1.00 0.00
#> Sample_265 2 0.904 0.533 0.32 0.68
#> Sample_272 2 0.000 0.982 0.00 1.00
#> Sample_275 2 0.000 0.982 0.00 1.00
#> Sample_302 2 0.000 0.982 0.00 1.00
#> Sample_303 2 0.000 0.982 0.00 1.00
#> Sample_304 2 0.000 0.982 0.00 1.00
#> Sample_305 2 0.000 0.982 0.00 1.00
#> Sample_308 2 0.000 0.982 0.00 1.00
#> Sample_309 2 0.000 0.982 0.00 1.00
#> Sample_310 2 0.000 0.982 0.00 1.00
#> Sample_311 2 0.000 0.982 0.00 1.00
#> Sample_312 2 0.000 0.982 0.00 1.00
#> Sample_313 2 0.000 0.982 0.00 1.00
#> Sample_314 2 0.000 0.982 0.00 1.00
#> Sample_316 2 0.000 0.982 0.00 1.00
#> Sample_318 2 0.000 0.982 0.00 1.00
#> Sample_319 2 0.000 0.982 0.00 1.00
#> Sample_320 2 0.000 0.982 0.00 1.00
#> Sample_321 1 0.000 0.992 1.00 0.00
#> Sample_323 2 0.000 0.982 0.00 1.00
#> Sample_324 2 0.000 0.982 0.00 1.00
#> Sample_325 2 0.000 0.982 0.00 1.00
#> Sample_326 1 0.000 0.992 1.00 0.00
#> Sample_327 2 0.000 0.982 0.00 1.00
#> Sample_328 2 0.000 0.982 0.00 1.00
#> Sample_329 1 0.000 0.992 1.00 0.00
#> Sample_330 2 0.000 0.982 0.00 1.00
#> Sample_351 2 0.000 0.982 0.00 1.00
#> Sample_355 2 0.000 0.982 0.00 1.00
#> Sample_358 2 0.141 0.963 0.02 0.98
#> Sample_360 2 0.000 0.982 0.00 1.00
#> Sample_362 2 0.000 0.982 0.00 1.00
#> Sample_368 2 0.000 0.982 0.00 1.00
#> Sample_369 2 0.000 0.982 0.00 1.00
#> Sample_377 2 0.000 0.982 0.00 1.00
#> Sample_381 2 0.000 0.982 0.00 1.00
#> Sample_385 1 0.000 0.992 1.00 0.00
#> Sample_386 1 0.000 0.992 1.00 0.00
#> Sample_393 2 0.000 0.982 0.00 1.00
#> Sample_395 2 0.000 0.982 0.00 1.00
#> Sample_397 2 0.000 0.982 0.00 1.00
#> Sample_398 2 0.000 0.982 0.00 1.00
#> Sample_409 2 0.000 0.982 0.00 1.00
#> Sample_412 2 0.000 0.982 0.00 1.00
#> Sample_416 2 0.000 0.982 0.00 1.00
#> Sample_421 2 0.000 0.982 0.00 1.00
#> Sample_424 2 0.000 0.982 0.00 1.00
#> Sample_425 2 0.000 0.982 0.00 1.00
#> Sample_427 2 0.000 0.982 0.00 1.00
#> Sample_428 2 0.000 0.982 0.00 1.00
#> Sample_431 2 0.000 0.982 0.00 1.00
#> Sample_432 1 0.000 0.992 1.00 0.00
#> Sample_434 1 0.000 0.992 1.00 0.00
#> Sample_439 1 0.000 0.992 1.00 0.00
#> Sample_440 2 0.000 0.982 0.00 1.00
#> Sample_442 2 0.000 0.982 0.00 1.00
#> Sample_445 1 0.000 0.992 1.00 0.00
#> Sample_446 2 0.000 0.982 0.00 1.00
#> Sample_451 2 0.000 0.982 0.00 1.00
#> Sample_452 1 0.000 0.992 1.00 0.00
#> Sample_456 1 0.402 0.914 0.92 0.08
#> Sample_459 2 0.000 0.982 0.00 1.00
#> Sample_461 1 0.141 0.975 0.98 0.02
#> Sample_464 1 0.000 0.992 1.00 0.00
#> Sample_465 1 0.000 0.992 1.00 0.00
#> Sample_466 1 0.000 0.992 1.00 0.00
#> Sample_468 1 0.000 0.992 1.00 0.00
#> Sample_469 2 0.000 0.982 0.00 1.00
#> Sample_472 1 0.327 0.936 0.94 0.06
#> Sample_474 1 0.584 0.839 0.86 0.14
#> Sample_475 1 0.000 0.992 1.00 0.00
#> Sample_573 1 0.000 0.992 1.00 0.00
#> Sample_577 2 0.000 0.982 0.00 1.00
#> Sample_618 1 0.000 0.992 1.00 0.00
#> Sample_685 1 0.000 0.992 1.00 0.00
#> Sample_687 1 0.000 0.992 1.00 0.00
#> Sample_692 1 0.000 0.992 1.00 0.00
#> Sample_737 1 0.000 0.992 1.00 0.00
#> Sample_962 2 0.000 0.982 0.00 1.00
#> Sample_968 2 0.000 0.982 0.00 1.00
#> Sample_978 2 0.000 0.982 0.00 1.00
#> Sample_982 1 0.000 0.992 1.00 0.00
#> Sample_983 2 0.000 0.982 0.00 1.00
#> Sample_985 2 0.000 0.982 0.00 1.00
#> Sample_987 2 0.904 0.535 0.32 0.68
#> Sample_988 1 0.000 0.992 1.00 0.00
#> Sample_989 1 0.327 0.937 0.94 0.06
#> Sample_990 1 0.000 0.992 1.00 0.00
#> Sample_991 1 0.000 0.992 1.00 0.00
#> Sample_992 1 0.000 0.992 1.00 0.00
#> Sample_993 1 0.000 0.992 1.00 0.00
#> Sample_994 2 0.995 0.161 0.46 0.54
#> Sample_1007 2 0.000 0.982 0.00 1.00
#> Sample_1014 1 0.000 0.992 1.00 0.00
#> Sample_1015 1 0.000 0.992 1.00 0.00
#> Sample_1016 1 0.000 0.992 1.00 0.00
#> Sample_1017 1 0.000 0.992 1.00 0.00
#> Sample_1018 1 0.000 0.992 1.00 0.00
#> Sample_1019 1 0.000 0.992 1.00 0.00
#> Sample_1020 1 0.000 0.992 1.00 0.00
#> Sample_1021 1 0.000 0.992 1.00 0.00
#> Sample_1022 1 0.000 0.992 1.00 0.00
#> Sample_1023 1 0.000 0.992 1.00 0.00
#> Sample_1024 1 0.000 0.992 1.00 0.00
#> Sample_1025 1 0.000 0.992 1.00 0.00
#> Sample_1026 1 0.000 0.992 1.00 0.00
#> Sample_1058 1 0.000 0.992 1.00 0.00
#> Sample_1192 2 0.000 0.982 0.00 1.00
#> Sample_1216 2 0.000 0.982 0.00 1.00
#> Sample_1525 1 0.000 0.992 1.00 0.00
#> Sample_1526 1 0.000 0.992 1.00 0.00
#> Sample_1562 1 0.000 0.992 1.00 0.00
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> Sample_121 1 0.0000 0.8870 1.00 0.00 0.00
#> Sample_265 3 0.9602 0.2123 0.20 0.40 0.40
#> Sample_272 2 0.1529 0.8983 0.00 0.96 0.04
#> Sample_275 2 0.1529 0.9003 0.00 0.96 0.04
#> Sample_302 2 0.2066 0.8959 0.00 0.94 0.06
#> Sample_303 2 0.0000 0.8988 0.00 1.00 0.00
#> Sample_304 2 0.2066 0.8959 0.00 0.94 0.06
#> Sample_305 2 0.2066 0.8959 0.00 0.94 0.06
#> Sample_308 2 0.1529 0.9003 0.00 0.96 0.04
#> Sample_309 2 0.0892 0.8960 0.00 0.98 0.02
#> Sample_310 2 0.2066 0.8959 0.00 0.94 0.06
#> Sample_311 2 0.0000 0.8988 0.00 1.00 0.00
#> Sample_312 2 0.3686 0.8332 0.00 0.86 0.14
#> Sample_313 2 0.2537 0.8917 0.00 0.92 0.08
#> Sample_314 2 0.2537 0.8919 0.00 0.92 0.08
#> Sample_316 2 0.2959 0.8729 0.00 0.90 0.10
#> Sample_318 2 0.1529 0.8866 0.00 0.96 0.04
#> Sample_319 2 0.0892 0.8960 0.00 0.98 0.02
#> Sample_320 2 0.0000 0.8988 0.00 1.00 0.00
#> Sample_321 1 0.4551 0.7819 0.84 0.02 0.14
#> Sample_323 2 0.0892 0.8960 0.00 0.98 0.02
#> Sample_324 2 0.2537 0.8919 0.00 0.92 0.08
#> Sample_325 2 0.2959 0.8828 0.00 0.90 0.10
#> Sample_326 1 0.3340 0.8155 0.88 0.00 0.12
#> Sample_327 2 0.4002 0.7688 0.00 0.84 0.16
#> Sample_328 2 0.1529 0.8860 0.00 0.96 0.04
#> Sample_329 1 0.0892 0.8782 0.98 0.00 0.02
#> Sample_330 2 0.2537 0.8919 0.00 0.92 0.08
#> Sample_351 2 0.3340 0.8707 0.00 0.88 0.12
#> Sample_355 2 0.0892 0.8960 0.00 0.98 0.02
#> Sample_358 2 0.5334 0.7437 0.06 0.82 0.12
#> Sample_360 2 0.2066 0.8731 0.00 0.94 0.06
#> Sample_362 2 0.0000 0.8988 0.00 1.00 0.00
#> Sample_368 2 0.1529 0.8864 0.00 0.96 0.04
#> Sample_369 2 0.0892 0.8960 0.00 0.98 0.02
#> Sample_377 2 0.3686 0.8561 0.00 0.86 0.14
#> Sample_381 2 0.3686 0.8561 0.00 0.86 0.14
#> Sample_385 1 0.6407 0.6923 0.76 0.08 0.16
#> Sample_386 1 0.6495 0.6730 0.74 0.06 0.20
#> Sample_393 2 0.0892 0.8960 0.00 0.98 0.02
#> Sample_395 2 0.5216 0.7128 0.00 0.74 0.26
#> Sample_397 2 0.6302 0.0536 0.00 0.52 0.48
#> Sample_398 2 0.0892 0.8960 0.00 0.98 0.02
#> Sample_409 2 0.3340 0.8909 0.00 0.88 0.12
#> Sample_412 2 0.2537 0.8919 0.00 0.92 0.08
#> Sample_416 2 0.3686 0.8575 0.00 0.86 0.14
#> Sample_421 2 0.2066 0.8959 0.00 0.94 0.06
#> Sample_424 2 0.3686 0.8561 0.00 0.86 0.14
#> Sample_425 2 0.4002 0.8370 0.00 0.84 0.16
#> Sample_427 2 0.2959 0.8828 0.00 0.90 0.10
#> Sample_428 2 0.3340 0.8707 0.00 0.88 0.12
#> Sample_431 3 0.5948 0.4130 0.00 0.36 0.64
#> Sample_432 1 0.4002 0.7889 0.84 0.00 0.16
#> Sample_434 1 0.0000 0.8870 1.00 0.00 0.00
#> Sample_439 1 0.0892 0.8783 0.98 0.00 0.02
#> Sample_440 3 0.6309 -0.0291 0.00 0.50 0.50
#> Sample_442 3 0.5948 0.4081 0.00 0.36 0.64
#> Sample_445 1 0.5147 0.7423 0.80 0.02 0.18
#> Sample_446 3 0.4555 0.6536 0.00 0.20 0.80
#> Sample_451 3 0.2537 0.6858 0.00 0.08 0.92
#> Sample_452 1 0.0000 0.8870 1.00 0.00 0.00
#> Sample_456 3 0.7555 0.0954 0.44 0.04 0.52
#> Sample_459 3 0.0000 0.6761 0.00 0.00 1.00
#> Sample_461 3 0.3340 0.6208 0.12 0.00 0.88
#> Sample_464 1 0.5016 0.7108 0.76 0.00 0.24
#> Sample_465 1 0.6309 0.1616 0.50 0.00 0.50
#> Sample_466 1 0.0000 0.8870 1.00 0.00 0.00
#> Sample_468 1 0.6045 0.4202 0.62 0.00 0.38
#> Sample_469 2 0.6126 0.4447 0.00 0.60 0.40
#> Sample_472 1 0.7553 0.5054 0.62 0.06 0.32
#> Sample_474 3 0.9110 -0.0171 0.42 0.14 0.44
#> Sample_475 1 0.0000 0.8870 1.00 0.00 0.00
#> Sample_573 1 0.0000 0.8870 1.00 0.00 0.00
#> Sample_577 2 0.4291 0.8168 0.00 0.82 0.18
#> Sample_618 1 0.0000 0.8870 1.00 0.00 0.00
#> Sample_685 1 0.0000 0.8870 1.00 0.00 0.00
#> Sample_687 1 0.0000 0.8870 1.00 0.00 0.00
#> Sample_692 1 0.0000 0.8870 1.00 0.00 0.00
#> Sample_737 1 0.0000 0.8870 1.00 0.00 0.00
#> Sample_962 2 0.0892 0.8960 0.00 0.98 0.02
#> Sample_968 2 0.4002 0.7692 0.00 0.84 0.16
#> Sample_978 3 0.4291 0.6707 0.00 0.18 0.82
#> Sample_982 1 0.0892 0.8771 0.98 0.00 0.02
#> Sample_983 3 0.4291 0.6707 0.00 0.18 0.82
#> Sample_985 3 0.4555 0.6660 0.00 0.20 0.80
#> Sample_987 3 0.0892 0.6804 0.02 0.00 0.98
#> Sample_988 1 0.1529 0.8649 0.96 0.00 0.04
#> Sample_989 3 0.6803 0.5589 0.28 0.04 0.68
#> Sample_990 3 0.5560 0.5326 0.30 0.00 0.70
#> Sample_991 1 0.0000 0.8870 1.00 0.00 0.00
#> Sample_992 1 0.2066 0.8495 0.94 0.00 0.06
#> Sample_993 1 0.1529 0.8646 0.96 0.00 0.04
#> Sample_994 3 0.5416 0.6985 0.08 0.10 0.82
#> Sample_1007 2 0.0892 0.8960 0.00 0.98 0.02
#> Sample_1014 1 0.4002 0.7433 0.84 0.00 0.16
#> Sample_1015 1 0.1529 0.8660 0.96 0.00 0.04
#> Sample_1016 1 0.6302 -0.0428 0.52 0.00 0.48
#> Sample_1017 3 0.5706 0.4998 0.32 0.00 0.68
#> Sample_1018 3 0.5560 0.5326 0.30 0.00 0.70
#> Sample_1019 1 0.5560 0.5060 0.70 0.00 0.30
#> Sample_1020 1 0.0000 0.8870 1.00 0.00 0.00
#> Sample_1021 3 0.5016 0.5956 0.24 0.00 0.76
#> Sample_1022 1 0.0000 0.8870 1.00 0.00 0.00
#> Sample_1023 1 0.0000 0.8870 1.00 0.00 0.00
#> Sample_1024 1 0.0000 0.8870 1.00 0.00 0.00
#> Sample_1025 1 0.0000 0.8870 1.00 0.00 0.00
#> Sample_1026 1 0.0000 0.8870 1.00 0.00 0.00
#> Sample_1058 1 0.4291 0.7720 0.82 0.00 0.18
#> Sample_1192 2 0.0892 0.8960 0.00 0.98 0.02
#> Sample_1216 2 0.2537 0.8919 0.00 0.92 0.08
#> Sample_1525 1 0.0000 0.8870 1.00 0.00 0.00
#> Sample_1526 1 0.0000 0.8870 1.00 0.00 0.00
#> Sample_1562 1 0.0000 0.8870 1.00 0.00 0.00
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> Sample_121 1 0.0000 0.90487 1.00 0.00 0.00 0.00
#> Sample_265 4 0.4011 0.53902 0.04 0.06 0.04 0.86
#> Sample_272 2 0.2345 0.76141 0.00 0.90 0.10 0.00
#> Sample_275 2 0.4088 0.73271 0.00 0.82 0.04 0.14
#> Sample_302 2 0.3037 0.76002 0.00 0.88 0.10 0.02
#> Sample_303 2 0.3198 0.75721 0.00 0.88 0.04 0.08
#> Sample_304 2 0.2921 0.75267 0.00 0.86 0.14 0.00
#> Sample_305 2 0.2647 0.75777 0.00 0.88 0.12 0.00
#> Sample_308 2 0.4292 0.75035 0.00 0.82 0.08 0.10
#> Sample_309 2 0.3335 0.72795 0.00 0.86 0.02 0.12
#> Sample_310 2 0.3606 0.75535 0.00 0.84 0.14 0.02
#> Sample_311 2 0.3611 0.75492 0.00 0.86 0.06 0.08
#> Sample_312 2 0.5489 0.60073 0.00 0.70 0.06 0.24
#> Sample_313 2 0.5147 0.71385 0.00 0.74 0.20 0.06
#> Sample_314 2 0.3975 0.71720 0.00 0.76 0.24 0.00
#> Sample_316 2 0.2830 0.75961 0.00 0.90 0.06 0.04
#> Sample_318 2 0.4284 0.71694 0.00 0.78 0.02 0.20
#> Sample_319 2 0.3400 0.71111 0.00 0.82 0.00 0.18
#> Sample_320 2 0.4332 0.70228 0.00 0.80 0.04 0.16
#> Sample_321 1 0.7774 0.00104 0.52 0.06 0.08 0.34
#> Sample_323 2 0.2647 0.73519 0.00 0.88 0.00 0.12
#> Sample_324 2 0.4797 0.69806 0.00 0.72 0.26 0.02
#> Sample_325 2 0.4134 0.70858 0.00 0.74 0.26 0.00
#> Sample_326 1 0.3801 0.64407 0.78 0.00 0.00 0.22
#> Sample_327 2 0.5106 0.64901 0.00 0.72 0.04 0.24
#> Sample_328 2 0.4491 0.72985 0.00 0.80 0.06 0.14
#> Sample_329 1 0.2647 0.79824 0.88 0.00 0.00 0.12
#> Sample_330 2 0.3606 0.75589 0.00 0.84 0.14 0.02
#> Sample_351 2 0.5256 0.69874 0.00 0.70 0.26 0.04
#> Sample_355 2 0.4755 0.68004 0.00 0.76 0.04 0.20
#> Sample_358 2 0.6425 0.46509 0.02 0.58 0.04 0.36
#> Sample_360 2 0.4939 0.66661 0.00 0.74 0.04 0.22
#> Sample_362 2 0.4731 0.71326 0.00 0.78 0.06 0.16
#> Sample_368 2 0.4939 0.67769 0.00 0.74 0.04 0.22
#> Sample_369 2 0.4949 0.70509 0.00 0.76 0.06 0.18
#> Sample_377 2 0.5392 0.67362 0.00 0.68 0.28 0.04
#> Sample_381 2 0.5256 0.71948 0.00 0.70 0.26 0.04
#> Sample_385 4 0.5913 0.56138 0.34 0.02 0.02 0.62
#> Sample_386 4 0.2647 0.62384 0.12 0.00 0.00 0.88
#> Sample_393 2 0.4553 0.69329 0.00 0.78 0.04 0.18
#> Sample_395 4 0.5594 0.38984 0.00 0.18 0.10 0.72
#> Sample_397 2 0.6005 0.42258 0.00 0.50 0.46 0.04
#> Sample_398 2 0.3821 0.75437 0.00 0.84 0.04 0.12
#> Sample_409 2 0.6286 0.69252 0.00 0.66 0.14 0.20
#> Sample_412 2 0.4284 0.74213 0.00 0.78 0.20 0.02
#> Sample_416 2 0.6201 0.65129 0.00 0.62 0.30 0.08
#> Sample_421 2 0.5106 0.71020 0.00 0.72 0.24 0.04
#> Sample_424 2 0.4936 0.68641 0.00 0.70 0.28 0.02
#> Sample_425 2 0.4797 0.69806 0.00 0.72 0.26 0.02
#> Sample_427 2 0.4797 0.69806 0.00 0.72 0.26 0.02
#> Sample_428 2 0.5820 0.70882 0.00 0.68 0.24 0.08
#> Sample_431 2 0.7021 0.41847 0.00 0.48 0.40 0.12
#> Sample_432 4 0.4790 0.51554 0.38 0.00 0.00 0.62
#> Sample_434 1 0.1211 0.87730 0.96 0.00 0.00 0.04
#> Sample_439 1 0.4277 0.48594 0.72 0.00 0.00 0.28
#> Sample_440 2 0.6382 0.56187 0.00 0.58 0.34 0.08
#> Sample_442 2 0.7344 0.40196 0.00 0.46 0.38 0.16
#> Sample_445 4 0.4406 0.60484 0.30 0.00 0.00 0.70
#> Sample_446 3 0.5784 0.37242 0.00 0.20 0.70 0.10
#> Sample_451 3 0.5860 0.26078 0.00 0.04 0.58 0.38
#> Sample_452 1 0.0707 0.89055 0.98 0.00 0.00 0.02
#> Sample_456 4 0.8758 0.21159 0.28 0.04 0.30 0.38
#> Sample_459 4 0.5915 0.14766 0.00 0.04 0.40 0.56
#> Sample_461 4 0.6350 0.33070 0.04 0.02 0.34 0.60
#> Sample_464 4 0.4977 0.34696 0.46 0.00 0.00 0.54
#> Sample_465 4 0.6104 0.57700 0.18 0.00 0.14 0.68
#> Sample_466 1 0.0000 0.90487 1.00 0.00 0.00 0.00
#> Sample_468 4 0.6510 0.47808 0.38 0.00 0.08 0.54
#> Sample_469 4 0.6686 0.31774 0.00 0.18 0.20 0.62
#> Sample_472 4 0.3335 0.62364 0.12 0.00 0.02 0.86
#> Sample_474 4 0.4011 0.58713 0.06 0.04 0.04 0.86
#> Sample_475 1 0.0000 0.90487 1.00 0.00 0.00 0.00
#> Sample_573 1 0.0000 0.90487 1.00 0.00 0.00 0.00
#> Sample_577 2 0.5793 0.62211 0.00 0.60 0.36 0.04
#> Sample_618 1 0.0000 0.90487 1.00 0.00 0.00 0.00
#> Sample_685 1 0.0000 0.90487 1.00 0.00 0.00 0.00
#> Sample_687 1 0.0000 0.90487 1.00 0.00 0.00 0.00
#> Sample_692 1 0.0000 0.90487 1.00 0.00 0.00 0.00
#> Sample_737 1 0.0000 0.90487 1.00 0.00 0.00 0.00
#> Sample_962 2 0.2706 0.75788 0.00 0.90 0.02 0.08
#> Sample_968 2 0.6005 0.33441 0.00 0.50 0.04 0.46
#> Sample_978 3 0.2335 0.57680 0.00 0.06 0.92 0.02
#> Sample_982 1 0.0000 0.90487 1.00 0.00 0.00 0.00
#> Sample_983 3 0.1211 0.59755 0.00 0.04 0.96 0.00
#> Sample_985 3 0.3935 0.57579 0.00 0.10 0.84 0.06
#> Sample_987 3 0.5355 0.25855 0.00 0.02 0.62 0.36
#> Sample_988 1 0.2345 0.83015 0.90 0.00 0.10 0.00
#> Sample_989 3 0.6463 0.56670 0.18 0.06 0.70 0.06
#> Sample_990 3 0.4472 0.58608 0.22 0.00 0.76 0.02
#> Sample_991 1 0.0000 0.90487 1.00 0.00 0.00 0.00
#> Sample_992 1 0.2345 0.82726 0.90 0.00 0.10 0.00
#> Sample_993 1 0.2647 0.80914 0.88 0.00 0.12 0.00
#> Sample_994 3 0.1211 0.59755 0.00 0.04 0.96 0.00
#> Sample_1007 2 0.3821 0.72304 0.00 0.84 0.04 0.12
#> Sample_1014 1 0.2345 0.82703 0.90 0.00 0.10 0.00
#> Sample_1015 1 0.2706 0.83577 0.90 0.00 0.02 0.08
#> Sample_1016 3 0.5606 0.11821 0.48 0.00 0.50 0.02
#> Sample_1017 3 0.4406 0.49737 0.30 0.00 0.70 0.00
#> Sample_1018 3 0.3975 0.57965 0.24 0.00 0.76 0.00
#> Sample_1019 1 0.4790 0.31140 0.62 0.00 0.38 0.00
#> Sample_1020 1 0.0000 0.90487 1.00 0.00 0.00 0.00
#> Sample_1021 3 0.3801 0.59621 0.22 0.00 0.78 0.00
#> Sample_1022 1 0.0000 0.90487 1.00 0.00 0.00 0.00
#> Sample_1023 1 0.0000 0.90487 1.00 0.00 0.00 0.00
#> Sample_1024 1 0.0000 0.90487 1.00 0.00 0.00 0.00
#> Sample_1025 1 0.0000 0.90487 1.00 0.00 0.00 0.00
#> Sample_1026 1 0.0000 0.90487 1.00 0.00 0.00 0.00
#> Sample_1058 1 0.6089 0.33971 0.64 0.00 0.08 0.28
#> Sample_1192 2 0.4553 0.68913 0.00 0.78 0.04 0.18
#> Sample_1216 2 0.3801 0.72912 0.00 0.78 0.22 0.00
#> Sample_1525 1 0.0000 0.90487 1.00 0.00 0.00 0.00
#> Sample_1526 1 0.0000 0.90487 1.00 0.00 0.00 0.00
#> Sample_1562 1 0.0000 0.90487 1.00 0.00 0.00 0.00
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
As soon as the classes for columns are determined, the signatures that are significantly different between subgroups can be looked for. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. To get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows (which is done by automatically selecting number of clusters).If there are too many signatures, top_signatures = ... can be set to only show the
signatures with the highest FDRs:
# code only for demonstration
# e.g. to show the top 500 most significant rows
tb = get_signature(res, k = ..., top_signatures = 500)
If the signatures are defined as these which are uniquely high in current group, diff_method argument
can be set to "uniquely_high_in_one_group":
# code only for demonstration
tb = get_signature(res, k = ..., diff_method = "uniquely_high_in_one_group")
UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n_sample age(p-value) cell.type(p-value) k
#> ATC:skmeans 112 0.681 0.00445 2
#> ATC:skmeans 101 0.617 0.01430 3
#> ATC:skmeans 92 NaN 0.00133 4
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
Parent node: Node01. Child nodes: Node0111-leaf , Node0112-leaf , Node0121-leaf , Node0122-leaf , Node0131-leaf , Node0132-leaf , Node0211 , Node0212 , Node0221 , Node0222 , Node0231-leaf , Node0232-leaf , Node0233-leaf , Node0311-leaf , Node0312-leaf , Node0313-leaf , Node0314-leaf , Node0321 , Node0322 , Node0331-leaf , Node0332-leaf , Node0333-leaf .
The object with results only for a single top-value method and a single partitioning method can be extracted as:
res = res_rh["013"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4.
#> On a matrix with 14739 rows and 101 columns.
#> Top rows (697) are extracted by 'ATC' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 150 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_partitions"
#> [7] "compare_signatures" "consensus_heatmap" "dimension_reduction"
#> [10] "functional_enrichment" "get_anno_col" "get_anno"
#> [13] "get_classes" "get_consensus" "get_matrix"
#> [16] "get_membership" "get_param" "get_signatures"
#> [19] "get_stats" "is_best_k" "is_stable_k"
#> [22] "membership_heatmap" "ncol" "nrow"
#> [25] "plot_ecdf" "predict_classes" "rownames"
#> [28] "select_partition_number" "show" "suggest_best_k"
#> [31] "test_to_known_factors" "top_rows_heatmap"
collect_plots() function collects all the plots made from res for all k (number of subgroups)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, higher 1-PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 1.000 1.000 0.503 0.497 0.497
#> 3 3 0.801 0.886 0.922 0.291 0.842 0.685
#> 4 4 0.786 0.801 0.910 0.142 0.887 0.686
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 2
Following is the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall subgroup
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> Sample_1 2 0 1 0 1
#> Sample_214 2 0 1 0 1
#> Sample_215 2 0 1 0 1
#> Sample_217 2 0 1 0 1
#> Sample_224 2 0 1 0 1
#> Sample_226 2 0 1 0 1
#> Sample_236 2 0 1 0 1
#> Sample_241 2 0 1 0 1
#> Sample_258 2 0 1 0 1
#> Sample_264 2 0 1 0 1
#> Sample_276 2 0 1 0 1
#> Sample_287 2 0 1 0 1
#> Sample_350 2 0 1 0 1
#> Sample_352 2 0 1 0 1
#> Sample_365 2 0 1 0 1
#> Sample_372 2 0 1 0 1
#> Sample_373 2 0 1 0 1
#> Sample_389 2 0 1 0 1
#> Sample_390 2 0 1 0 1
#> Sample_394 2 0 1 0 1
#> Sample_396 2 0 1 0 1
#> Sample_403 2 0 1 0 1
#> Sample_404 2 0 1 0 1
#> Sample_405 2 0 1 0 1
#> Sample_408 2 0 1 0 1
#> Sample_430 2 0 1 0 1
#> Sample_433 2 0 1 0 1
#> Sample_438 2 0 1 0 1
#> Sample_443 2 0 1 0 1
#> Sample_448 2 0 1 0 1
#> Sample_455 2 0 1 0 1
#> Sample_457 2 0 1 0 1
#> Sample_463 2 0 1 0 1
#> Sample_476 2 0 1 0 1
#> Sample_477 2 0 1 0 1
#> Sample_572 2 0 1 0 1
#> Sample_590 1 0 1 1 0
#> Sample_614 1 0 1 1 0
#> Sample_616 1 0 1 1 0
#> Sample_617 1 0 1 1 0
#> Sample_619 1 0 1 1 0
#> Sample_620 1 0 1 1 0
#> Sample_622 1 0 1 1 0
#> Sample_626 1 0 1 1 0
#> Sample_630 1 0 1 1 0
#> Sample_632 1 0 1 1 0
#> Sample_633 1 0 1 1 0
#> Sample_634 1 0 1 1 0
#> Sample_635 1 0 1 1 0
#> Sample_636 1 0 1 1 0
#> Sample_637 1 0 1 1 0
#> Sample_639 1 0 1 1 0
#> Sample_641 1 0 1 1 0
#> Sample_643 1 0 1 1 0
#> Sample_646 1 0 1 1 0
#> Sample_647 1 0 1 1 0
#> Sample_950 2 0 1 0 1
#> Sample_976 2 0 1 0 1
#> Sample_977 2 0 1 0 1
#> Sample_980 2 0 1 0 1
#> Sample_981 2 0 1 0 1
#> Sample_986 2 0 1 0 1
#> Sample_996 2 0 1 0 1
#> Sample_999 2 0 1 0 1
#> Sample_1008 2 0 1 0 1
#> Sample_1009 2 0 1 0 1
#> Sample_1010 2 0 1 0 1
#> Sample_1056 2 0 1 0 1
#> Sample_1059 2 0 1 0 1
#> Sample_1062 2 0 1 0 1
#> Sample_1208 2 0 1 0 1
#> Sample_1217 2 0 1 0 1
#> Sample_1218 2 0 1 0 1
#> Sample_1522 1 0 1 1 0
#> Sample_1523 2 0 1 0 1
#> Sample_1540 1 0 1 1 0
#> Sample_1544 1 0 1 1 0
#> Sample_1547 1 0 1 1 0
#> Sample_1556 1 0 1 1 0
#> Sample_1557 1 0 1 1 0
#> Sample_1558 1 0 1 1 0
#> Sample_1560 1 0 1 1 0
#> Sample_1563 1 0 1 1 0
#> Sample_1564 1 0 1 1 0
#> Sample_1565 1 0 1 1 0
#> Sample_1566 1 0 1 1 0
#> Sample_1567 1 0 1 1 0
#> Sample_1568 1 0 1 1 0
#> Sample_1571 1 0 1 1 0
#> Sample_1572 1 0 1 1 0
#> Sample_1573 1 0 1 1 0
#> Sample_1576 1 0 1 1 0
#> Sample_1577 1 0 1 1 0
#> Sample_1578 1 0 1 1 0
#> Sample_1579 1 0 1 1 0
#> Sample_1580 1 0 1 1 0
#> Sample_1583 1 0 1 1 0
#> Sample_1584 1 0 1 1 0
#> Sample_1590 1 0 1 1 0
#> Sample_1591 1 0 1 1 0
#> Sample_1600 1 0 1 1 0
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> Sample_1 3 0.3340 0.8887 0.00 0.12 0.88
#> Sample_214 3 0.2537 0.8871 0.00 0.08 0.92
#> Sample_215 3 0.6126 0.5585 0.00 0.40 0.60
#> Sample_217 3 0.2066 0.8756 0.00 0.06 0.94
#> Sample_224 3 0.3340 0.8887 0.00 0.12 0.88
#> Sample_226 3 0.2959 0.8958 0.00 0.10 0.90
#> Sample_236 3 0.2959 0.8958 0.00 0.10 0.90
#> Sample_241 3 0.3340 0.8877 0.00 0.12 0.88
#> Sample_258 3 0.3686 0.8781 0.00 0.14 0.86
#> Sample_264 3 0.2959 0.8958 0.00 0.10 0.90
#> Sample_276 3 0.5948 0.6363 0.00 0.36 0.64
#> Sample_287 2 0.6126 0.0686 0.00 0.60 0.40
#> Sample_350 2 0.0000 0.9658 0.00 1.00 0.00
#> Sample_352 2 0.0000 0.9658 0.00 1.00 0.00
#> Sample_365 3 0.2066 0.8177 0.06 0.00 0.94
#> Sample_372 3 0.2414 0.8585 0.02 0.04 0.94
#> Sample_373 3 0.5397 0.7498 0.00 0.28 0.72
#> Sample_389 2 0.2537 0.8890 0.00 0.92 0.08
#> Sample_390 2 0.1529 0.9319 0.00 0.96 0.04
#> Sample_394 3 0.6192 0.4993 0.00 0.42 0.58
#> Sample_396 2 0.0000 0.9658 0.00 1.00 0.00
#> Sample_403 3 0.6045 0.5865 0.00 0.38 0.62
#> Sample_404 2 0.0000 0.9658 0.00 1.00 0.00
#> Sample_405 2 0.0000 0.9658 0.00 1.00 0.00
#> Sample_408 2 0.0000 0.9658 0.00 1.00 0.00
#> Sample_430 2 0.0000 0.9658 0.00 1.00 0.00
#> Sample_433 2 0.0000 0.9658 0.00 1.00 0.00
#> Sample_438 2 0.0000 0.9658 0.00 1.00 0.00
#> Sample_443 2 0.0892 0.9503 0.00 0.98 0.02
#> Sample_448 2 0.0000 0.9658 0.00 1.00 0.00
#> Sample_455 2 0.0000 0.9658 0.00 1.00 0.00
#> Sample_457 2 0.0000 0.9658 0.00 1.00 0.00
#> Sample_463 2 0.0000 0.9658 0.00 1.00 0.00
#> Sample_476 2 0.0000 0.9658 0.00 1.00 0.00
#> Sample_477 2 0.0000 0.9658 0.00 1.00 0.00
#> Sample_572 2 0.1529 0.9371 0.00 0.96 0.04
#> Sample_590 1 0.2066 0.9359 0.94 0.00 0.06
#> Sample_614 1 0.0000 0.9449 1.00 0.00 0.00
#> Sample_616 1 0.2066 0.9360 0.94 0.00 0.06
#> Sample_617 1 0.2066 0.9287 0.94 0.00 0.06
#> Sample_619 1 0.1529 0.9463 0.96 0.00 0.04
#> Sample_620 1 0.0892 0.9470 0.98 0.00 0.02
#> Sample_622 1 0.1529 0.9349 0.96 0.00 0.04
#> Sample_626 1 0.0892 0.9469 0.98 0.00 0.02
#> Sample_630 1 0.2066 0.9287 0.94 0.00 0.06
#> Sample_632 1 0.7948 0.2329 0.52 0.42 0.06
#> Sample_633 1 0.2066 0.9287 0.94 0.00 0.06
#> Sample_634 1 0.2066 0.9287 0.94 0.00 0.06
#> Sample_635 1 0.2066 0.9287 0.94 0.00 0.06
#> Sample_636 1 0.1529 0.9463 0.96 0.00 0.04
#> Sample_637 1 0.7979 0.1674 0.50 0.44 0.06
#> Sample_639 1 0.2066 0.9287 0.94 0.00 0.06
#> Sample_641 1 0.2066 0.9287 0.94 0.00 0.06
#> Sample_643 1 0.2066 0.9457 0.94 0.00 0.06
#> Sample_646 1 0.3572 0.9018 0.90 0.04 0.06
#> Sample_647 1 0.0892 0.9469 0.98 0.00 0.02
#> Sample_950 2 0.1529 0.9320 0.00 0.96 0.04
#> Sample_976 2 0.0000 0.9658 0.00 1.00 0.00
#> Sample_977 2 0.0000 0.9658 0.00 1.00 0.00
#> Sample_980 2 0.0000 0.9658 0.00 1.00 0.00
#> Sample_981 2 0.0000 0.9658 0.00 1.00 0.00
#> Sample_986 2 0.2947 0.8702 0.02 0.92 0.06
#> Sample_996 3 0.2959 0.8958 0.00 0.10 0.90
#> Sample_999 3 0.2959 0.8958 0.00 0.10 0.90
#> Sample_1008 3 0.2959 0.8958 0.00 0.10 0.90
#> Sample_1009 3 0.2959 0.8958 0.00 0.10 0.90
#> Sample_1010 3 0.2959 0.8958 0.00 0.10 0.90
#> Sample_1056 3 0.2959 0.8958 0.00 0.10 0.90
#> Sample_1059 3 0.2959 0.8958 0.00 0.10 0.90
#> Sample_1062 3 0.2066 0.8756 0.00 0.06 0.94
#> Sample_1208 2 0.1529 0.9371 0.00 0.96 0.04
#> Sample_1217 2 0.0000 0.9658 0.00 1.00 0.00
#> Sample_1218 3 0.6244 0.4686 0.00 0.44 0.56
#> Sample_1522 3 0.3686 0.7436 0.14 0.00 0.86
#> Sample_1523 3 0.2947 0.8751 0.02 0.06 0.92
#> Sample_1540 1 0.1529 0.9463 0.96 0.00 0.04
#> Sample_1544 1 0.1529 0.9463 0.96 0.00 0.04
#> Sample_1547 1 0.2066 0.9287 0.94 0.00 0.06
#> Sample_1556 1 0.2066 0.9287 0.94 0.00 0.06
#> Sample_1557 1 0.0892 0.9409 0.98 0.00 0.02
#> Sample_1558 1 0.2066 0.9287 0.94 0.00 0.06
#> Sample_1560 1 0.1529 0.9463 0.96 0.00 0.04
#> Sample_1563 1 0.1529 0.9463 0.96 0.00 0.04
#> Sample_1564 1 0.0892 0.9470 0.98 0.00 0.02
#> Sample_1565 1 0.1529 0.9463 0.96 0.00 0.04
#> Sample_1566 1 0.0892 0.9470 0.98 0.00 0.02
#> Sample_1567 1 0.0000 0.9449 1.00 0.00 0.00
#> Sample_1568 1 0.0892 0.9470 0.98 0.00 0.02
#> Sample_1571 1 0.1529 0.9463 0.96 0.00 0.04
#> Sample_1572 1 0.1529 0.9463 0.96 0.00 0.04
#> Sample_1573 1 0.1529 0.9463 0.96 0.00 0.04
#> Sample_1576 1 0.1529 0.9463 0.96 0.00 0.04
#> Sample_1577 1 0.1529 0.9463 0.96 0.00 0.04
#> Sample_1578 1 0.1529 0.9463 0.96 0.00 0.04
#> Sample_1579 1 0.1529 0.9463 0.96 0.00 0.04
#> Sample_1580 1 0.1529 0.9463 0.96 0.00 0.04
#> Sample_1583 1 0.0000 0.9449 1.00 0.00 0.00
#> Sample_1584 1 0.2066 0.9287 0.94 0.00 0.06
#> Sample_1590 1 0.1529 0.9463 0.96 0.00 0.04
#> Sample_1591 1 0.0892 0.9470 0.98 0.00 0.02
#> Sample_1600 1 0.2066 0.9391 0.94 0.00 0.06
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> Sample_1 3 0.2011 0.8501 0.00 0.08 0.92 0.00
#> Sample_214 3 0.1411 0.8654 0.00 0.02 0.96 0.02
#> Sample_215 3 0.4977 0.1882 0.00 0.46 0.54 0.00
#> Sample_217 3 0.3106 0.8426 0.04 0.04 0.90 0.02
#> Sample_224 3 0.2011 0.8501 0.00 0.08 0.92 0.00
#> Sample_226 3 0.0707 0.8737 0.00 0.02 0.98 0.00
#> Sample_236 3 0.1211 0.8684 0.00 0.04 0.96 0.00
#> Sample_241 3 0.1637 0.8637 0.00 0.06 0.94 0.00
#> Sample_258 3 0.1637 0.8608 0.00 0.06 0.94 0.00
#> Sample_264 3 0.0707 0.8737 0.00 0.02 0.98 0.00
#> Sample_276 2 0.4994 -0.0269 0.00 0.52 0.48 0.00
#> Sample_287 2 0.3610 0.7315 0.00 0.80 0.20 0.00
#> Sample_350 2 0.0707 0.9174 0.00 0.98 0.02 0.00
#> Sample_352 2 0.0707 0.9174 0.00 0.98 0.02 0.00
#> Sample_365 3 0.3030 0.8328 0.06 0.02 0.90 0.02
#> Sample_372 3 0.1411 0.8654 0.00 0.02 0.96 0.02
#> Sample_373 3 0.5271 0.5059 0.00 0.34 0.64 0.02
#> Sample_389 2 0.3335 0.8276 0.00 0.86 0.12 0.02
#> Sample_390 2 0.3037 0.8581 0.00 0.88 0.10 0.02
#> Sample_394 3 0.5428 0.3668 0.00 0.38 0.60 0.02
#> Sample_396 2 0.1211 0.9099 0.00 0.96 0.04 0.00
#> Sample_403 3 0.4948 0.2626 0.00 0.44 0.56 0.00
#> Sample_404 2 0.1411 0.9150 0.00 0.96 0.02 0.02
#> Sample_405 2 0.0707 0.9112 0.00 0.98 0.00 0.02
#> Sample_408 2 0.0707 0.9174 0.00 0.98 0.02 0.00
#> Sample_430 2 0.0707 0.9174 0.00 0.98 0.02 0.00
#> Sample_433 2 0.0707 0.9174 0.00 0.98 0.02 0.00
#> Sample_438 2 0.0707 0.9174 0.00 0.98 0.02 0.00
#> Sample_443 2 0.1211 0.9016 0.00 0.96 0.04 0.00
#> Sample_448 2 0.0707 0.9110 0.00 0.98 0.02 0.00
#> Sample_455 2 0.0707 0.9174 0.00 0.98 0.02 0.00
#> Sample_457 2 0.0707 0.9110 0.00 0.98 0.02 0.00
#> Sample_463 2 0.0707 0.9174 0.00 0.98 0.02 0.00
#> Sample_476 2 0.0707 0.9174 0.00 0.98 0.02 0.00
#> Sample_477 2 0.0707 0.9110 0.00 0.98 0.02 0.00
#> Sample_572 2 0.2335 0.8825 0.00 0.92 0.06 0.02
#> Sample_590 1 0.0000 0.9103 1.00 0.00 0.00 0.00
#> Sample_614 1 0.2647 0.8340 0.88 0.00 0.00 0.12
#> Sample_616 1 0.0000 0.9103 1.00 0.00 0.00 0.00
#> Sample_617 4 0.0707 0.8783 0.02 0.00 0.00 0.98
#> Sample_619 1 0.0000 0.9103 1.00 0.00 0.00 0.00
#> Sample_620 1 0.3801 0.7132 0.78 0.00 0.00 0.22
#> Sample_622 1 0.4855 0.2940 0.60 0.00 0.00 0.40
#> Sample_626 1 0.3400 0.7669 0.82 0.00 0.00 0.18
#> Sample_630 4 0.4948 0.2417 0.44 0.00 0.00 0.56
#> Sample_632 4 0.0707 0.8659 0.00 0.02 0.00 0.98
#> Sample_633 4 0.0707 0.8783 0.02 0.00 0.00 0.98
#> Sample_634 4 0.0707 0.8783 0.02 0.00 0.00 0.98
#> Sample_635 4 0.0707 0.8783 0.02 0.00 0.00 0.98
#> Sample_636 1 0.0000 0.9103 1.00 0.00 0.00 0.00
#> Sample_637 4 0.0707 0.8659 0.00 0.02 0.00 0.98
#> Sample_639 4 0.2011 0.8477 0.08 0.00 0.00 0.92
#> Sample_641 4 0.1211 0.8701 0.04 0.00 0.00 0.96
#> Sample_643 1 0.3610 0.7426 0.80 0.00 0.00 0.20
#> Sample_646 4 0.0707 0.8659 0.00 0.02 0.00 0.98
#> Sample_647 1 0.3975 0.6795 0.76 0.00 0.00 0.24
#> Sample_950 2 0.3037 0.8544 0.00 0.88 0.10 0.02
#> Sample_976 2 0.0707 0.9174 0.00 0.98 0.02 0.00
#> Sample_977 2 0.0707 0.9112 0.00 0.98 0.00 0.02
#> Sample_980 2 0.0707 0.9174 0.00 0.98 0.02 0.00
#> Sample_981 2 0.0000 0.9141 0.00 1.00 0.00 0.00
#> Sample_986 2 0.2921 0.8149 0.00 0.86 0.00 0.14
#> Sample_996 3 0.0000 0.8756 0.00 0.00 1.00 0.00
#> Sample_999 3 0.0000 0.8756 0.00 0.00 1.00 0.00
#> Sample_1008 3 0.0000 0.8756 0.00 0.00 1.00 0.00
#> Sample_1009 3 0.0000 0.8756 0.00 0.00 1.00 0.00
#> Sample_1010 3 0.0000 0.8756 0.00 0.00 1.00 0.00
#> Sample_1056 3 0.0000 0.8756 0.00 0.00 1.00 0.00
#> Sample_1059 3 0.1211 0.8696 0.00 0.04 0.96 0.00
#> Sample_1062 3 0.0707 0.8687 0.00 0.02 0.98 0.00
#> Sample_1208 2 0.3037 0.8552 0.00 0.88 0.10 0.02
#> Sample_1217 2 0.0707 0.9112 0.00 0.98 0.00 0.02
#> Sample_1218 2 0.5535 0.1664 0.00 0.56 0.42 0.02
#> Sample_1522 3 0.5271 0.4358 0.34 0.00 0.64 0.02
#> Sample_1523 3 0.0000 0.8756 0.00 0.00 1.00 0.00
#> Sample_1540 1 0.0000 0.9103 1.00 0.00 0.00 0.00
#> Sample_1544 1 0.3801 0.7089 0.78 0.00 0.00 0.22
#> Sample_1547 4 0.0707 0.8783 0.02 0.00 0.00 0.98
#> Sample_1556 4 0.0707 0.8783 0.02 0.00 0.00 0.98
#> Sample_1557 1 0.4977 0.0894 0.54 0.00 0.00 0.46
#> Sample_1558 4 0.2921 0.7985 0.14 0.00 0.00 0.86
#> Sample_1560 1 0.0000 0.9103 1.00 0.00 0.00 0.00
#> Sample_1563 1 0.0000 0.9103 1.00 0.00 0.00 0.00
#> Sample_1564 1 0.0707 0.8997 0.98 0.00 0.00 0.02
#> Sample_1565 1 0.0000 0.9103 1.00 0.00 0.00 0.00
#> Sample_1566 1 0.0000 0.9103 1.00 0.00 0.00 0.00
#> Sample_1567 1 0.3172 0.7805 0.84 0.00 0.00 0.16
#> Sample_1568 1 0.0000 0.9103 1.00 0.00 0.00 0.00
#> Sample_1571 1 0.0000 0.9103 1.00 0.00 0.00 0.00
#> Sample_1572 1 0.0000 0.9103 1.00 0.00 0.00 0.00
#> Sample_1573 1 0.0000 0.9103 1.00 0.00 0.00 0.00
#> Sample_1576 1 0.0000 0.9103 1.00 0.00 0.00 0.00
#> Sample_1577 1 0.0000 0.9103 1.00 0.00 0.00 0.00
#> Sample_1578 1 0.0000 0.9103 1.00 0.00 0.00 0.00
#> Sample_1579 1 0.0000 0.9103 1.00 0.00 0.00 0.00
#> Sample_1580 1 0.0000 0.9103 1.00 0.00 0.00 0.00
#> Sample_1583 1 0.2345 0.8471 0.90 0.00 0.00 0.10
#> Sample_1584 4 0.4624 0.4964 0.34 0.00 0.00 0.66
#> Sample_1590 1 0.0000 0.9103 1.00 0.00 0.00 0.00
#> Sample_1591 1 0.0707 0.8996 0.98 0.00 0.00 0.02
#> Sample_1600 4 0.5000 0.0234 0.50 0.00 0.00 0.50
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
As soon as the classes for columns are determined, the signatures that are significantly different between subgroups can be looked for. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. To get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows (which is done by automatically selecting number of clusters).If there are too many signatures, top_signatures = ... can be set to only show the
signatures with the highest FDRs:
# code only for demonstration
# e.g. to show the top 500 most significant rows
tb = get_signature(res, k = ..., top_signatures = 500)
If the signatures are defined as these which are uniquely high in current group, diff_method argument
can be set to "uniquely_high_in_one_group":
# code only for demonstration
tb = get_signature(res, k = ..., diff_method = "uniquely_high_in_one_group")
UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n_sample age(p-value) cell.type(p-value) k
#> ATC:skmeans 101 1.02e-05 6.60e-19 2
#> ATC:skmeans 96 1.19e-03 1.47e-16 3
#> ATC:skmeans 90 5.42e-03 2.77e-13 4
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
Parent node: Node0. Child nodes: Node011 , Node012 , Node013 , Node021 , Node022 , Node023 , Node031 , Node032 , Node033 .
The object with results only for a single top-value method and a single partitioning method can be extracted as:
res = res_rh["02"]
A summary of res and all the functions that can be applied to it:
res
#> A 'DownSamplingConsensusPartition' object with k = 2, 3, 4.
#> On a matrix with 15527 rows and 500 columns, randomly sampled from 582 columns.
#> Top rows (1475) are extracted by 'ATC' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 150 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'DownSamplingConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_partitions"
#> [7] "compare_signatures" "consensus_heatmap" "dimension_reduction"
#> [10] "functional_enrichment" "get_anno_col" "get_anno"
#> [13] "get_classes" "get_consensus" "get_matrix"
#> [16] "get_membership" "get_param" "get_signatures"
#> [19] "get_stats" "is_best_k" "is_stable_k"
#> [22] "membership_heatmap" "ncol" "nrow"
#> [25] "plot_ecdf" "predict_classes" "rownames"
#> [28] "select_partition_number" "show" "suggest_best_k"
#> [31] "test_to_known_factors" "top_rows_heatmap"
collect_plots() function collects all the plots made from res for all k (number of subgroups)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, higher 1-PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.993 0.997 0.500 0.500 0.500
#> 3 3 1.000 0.985 0.994 0.224 0.874 0.751
#> 4 4 0.819 0.883 0.896 0.167 0.868 0.663
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 3
#> attr(,"optional")
#> [1] 2
There is also optional best \(k\) = 2 that is worth to check.
Following is the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall subgroup
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
get_classes(res, k = 2)
#> class p
#> Sample_2 2 0.000
#> Sample_3 2 0.000
#> Sample_4 2 0.000
#> Sample_5 2 0.000
#> Sample_6 2 0.000
#> Sample_7 2 0.000
#> Sample_8 2 0.000
#> Sample_9 2 0.000
#> Sample_10 2 0.000
#> Sample_11 2 0.000
#> Sample_12 2 0.000
#> Sample_13 2 0.000
#> Sample_14 2 0.000
#> Sample_15 2 0.000
#> Sample_16 2 0.000
#> Sample_17 2 0.000
#> Sample_18 2 0.000
#> Sample_19 2 0.000
#> Sample_20 2 0.000
#> Sample_21 2 0.000
#> Sample_22 2 0.000
#> Sample_23 2 0.000
#> Sample_24 2 0.000
#> Sample_25 2 0.000
#> Sample_26 2 0.000
#> Sample_27 2 0.000
#> Sample_28 2 0.000
#> Sample_29 2 0.000
#> Sample_30 2 0.000
#> Sample_31 2 0.000
#> Sample_32 2 0.000
#> Sample_33 2 0.000
#> Sample_34 2 0.000
#> Sample_35 2 0.000
#> Sample_36 2 0.000
#> Sample_37 2 0.000
#> Sample_38 2 0.000
#> Sample_39 2 0.000
#> Sample_40 2 0.000
#> Sample_41 2 0.000
#> Sample_42 2 0.000
#> Sample_43 2 0.000
#> Sample_44 2 0.000
#> Sample_45 2 0.000
#> Sample_46 2 0.000
#> Sample_47 2 0.000
#> Sample_48 2 0.000
#> Sample_49 2 0.000
#> Sample_50 2 0.000
#> Sample_51 2 0.000
#> Sample_52 2 0.000
#> Sample_53 2 0.000
#> Sample_54 2 0.000
#> Sample_55 2 0.000
#> Sample_56 2 0.000
#> Sample_57 2 0.000
#> Sample_58 2 0.000
#> Sample_59 2 0.000
#> Sample_60 2 0.000
#> Sample_61 2 0.000
#> Sample_62 2 0.000
#> Sample_63 2 0.000
#> Sample_64 2 0.000
#> Sample_65 2 0.000
#> Sample_66 2 0.000
#> Sample_67 2 0.000
#> Sample_68 2 0.000
#> Sample_69 2 0.000
#> Sample_70 2 0.000
#> Sample_71 2 0.000
#> Sample_72 1 1.000
#> Sample_73 2 0.000
#> Sample_74 2 0.000
#> Sample_75 2 0.000
#> Sample_76 2 0.000
#> Sample_77 2 0.000
#> Sample_78 2 0.000
#> Sample_79 2 0.000
#> Sample_80 2 0.000
#> Sample_81 2 0.000
#> Sample_82 2 0.000
#> Sample_83 2 0.000
#> Sample_84 2 0.000
#> Sample_85 2 0.000
#> Sample_86 2 0.000
#> Sample_87 2 0.000
#> Sample_88 2 0.000
#> Sample_89 2 0.000
#> Sample_90 2 0.000
#> Sample_91 2 0.000
#> Sample_92 2 0.000
#> Sample_93 2 0.000
#> Sample_94 2 0.000
#> Sample_95 2 0.000
#> Sample_96 2 0.000
#> Sample_97 2 0.000
#> Sample_98 2 0.000
#> Sample_99 2 0.000
#> Sample_100 2 0.000
#> Sample_101 2 0.000
#> Sample_102 2 0.000
#> Sample_103 2 0.000
#> Sample_104 2 0.000
#> Sample_105 2 0.000
#> Sample_106 2 0.000
#> Sample_107 2 0.000
#> Sample_108 2 0.000
#> Sample_109 2 0.000
#> Sample_110 2 0.000
#> Sample_111 1 0.751
#> Sample_112 2 0.000
#> Sample_113 2 0.000
#> Sample_114 2 0.000
#> Sample_115 2 0.000
#> Sample_116 2 0.000
#> Sample_117 2 0.000
#> Sample_118 2 0.000
#> Sample_119 2 0.000
#> Sample_120 2 0.000
#> Sample_122 2 0.000
#> Sample_123 2 0.000
#> Sample_124 2 0.000
#> Sample_125 2 0.000
#> Sample_126 2 0.000
#> Sample_127 2 0.000
#> Sample_128 2 0.000
#> Sample_129 2 0.000
#> Sample_130 2 0.000
#> Sample_131 2 0.000
#> Sample_132 2 0.000
#> Sample_133 2 0.000
#> Sample_134 2 0.000
#> Sample_135 2 0.000
#> Sample_136 2 0.000
#> Sample_137 2 0.000
#> Sample_138 2 0.000
#> Sample_139 2 0.000
#> Sample_140 2 0.000
#> Sample_141 2 0.000
#> Sample_142 2 0.000
#> Sample_143 2 0.000
#> Sample_144 2 0.000
#> Sample_145 2 0.000
#> Sample_146 2 0.000
#> Sample_147 1 0.000
#> Sample_148 1 0.000
#> Sample_149 1 0.000
#> Sample_150 1 0.000
#> Sample_151 1 0.000
#> Sample_152 1 0.000
#> Sample_153 1 0.000
#> Sample_154 1 0.000
#> Sample_155 1 0.000
#> Sample_156 1 0.000
#> Sample_157 1 0.000
#> Sample_158 1 0.000
#> Sample_159 1 0.000
#> Sample_160 1 0.000
#> Sample_161 1 0.000
#> Sample_162 1 0.000
#> Sample_163 1 0.000
#> Sample_164 1 0.000
#> Sample_165 1 0.000
#> Sample_166 1 0.000
#> Sample_167 1 0.000
#> Sample_168 1 0.000
#> Sample_169 1 0.000
#> Sample_170 1 0.000
#> Sample_171 1 0.000
#> Sample_172 1 0.000
#> Sample_173 1 0.000
#> Sample_174 1 0.000
#> Sample_175 1 0.000
#> Sample_176 1 0.000
#> Sample_177 1 0.000
#> Sample_178 1 0.000
#> Sample_179 1 0.000
#> Sample_180 1 0.000
#> Sample_181 1 0.000
#> Sample_182 1 0.000
#> Sample_183 1 0.000
#> Sample_184 1 0.000
#> Sample_185 1 0.000
#> Sample_186 1 0.000
#> Sample_187 1 0.000
#> Sample_188 1 0.000
#> Sample_189 1 0.000
#> Sample_190 1 0.000
#> Sample_191 1 0.000
#> Sample_192 1 0.000
#> Sample_193 1 0.000
#> Sample_194 1 0.000
#> Sample_195 1 0.000
#> Sample_196 1 0.000
#> Sample_197 2 0.000
#> Sample_198 2 0.000
#> Sample_199 2 0.000
#> Sample_200 2 0.000
#> Sample_201 2 0.000
#> Sample_202 2 0.000
#> Sample_203 2 0.000
#> Sample_204 2 0.000
#> Sample_205 2 0.000
#> Sample_206 2 0.000
#> Sample_207 2 0.000
#> Sample_516 1 0.000
#> Sample_586 2 0.000
#> Sample_587 2 0.000
#> Sample_588 2 0.000
#> Sample_591 2 0.000
#> Sample_592 2 0.000
#> Sample_593 2 0.000
#> Sample_594 2 0.000
#> Sample_595 1 1.000
#> Sample_596 2 0.000
#> Sample_597 2 0.000
#> Sample_598 2 0.000
#> Sample_599 2 0.000
#> Sample_600 2 0.000
#> Sample_601 2 0.000
#> Sample_602 2 0.000
#> Sample_603 2 0.000
#> Sample_604 2 0.000
#> Sample_605 2 0.000
#> Sample_606 2 0.000
#> Sample_607 2 0.000
#> Sample_608 2 0.000
#> Sample_609 2 0.000
#> Sample_610 2 0.000
#> Sample_611 1 0.000
#> Sample_612 1 0.000
#> Sample_621 2 0.000
#> Sample_625 2 0.000
#> Sample_627 2 0.000
#> Sample_628 2 0.000
#> Sample_629 2 0.000
#> Sample_631 2 0.000
#> Sample_638 2 0.000
#> Sample_640 2 0.000
#> Sample_642 2 0.000
#> Sample_645 2 0.000
#> Sample_648 2 0.000
#> Sample_649 2 0.000
#> Sample_650 2 0.000
#> Sample_651 2 0.000
#> Sample_652 2 0.000
#> Sample_653 2 0.000
#> Sample_654 2 0.000
#> Sample_655 2 0.000
#> Sample_656 2 0.000
#> Sample_657 2 0.000
#> Sample_658 2 0.000
#> Sample_659 2 0.000
#> Sample_660 2 0.000
#> Sample_661 2 0.000
#> Sample_662 2 0.000
#> Sample_663 2 0.000
#> Sample_664 2 0.000
#> Sample_665 2 0.000
#> Sample_666 2 0.000
#> Sample_667 2 0.000
#> Sample_668 2 0.000
#> Sample_669 2 0.000
#> Sample_670 2 0.000
#> Sample_671 2 0.000
#> Sample_672 2 0.000
#> Sample_673 2 0.000
#> Sample_674 2 0.000
#> Sample_675 2 0.000
#> Sample_676 2 0.000
#> Sample_677 2 0.000
#> Sample_678 2 0.000
#> Sample_679 2 0.000
#> Sample_680 2 0.000
#> Sample_681 2 0.000
#> Sample_682 2 0.000
#> Sample_683 2 0.000
#> Sample_684 2 0.000
#> Sample_686 1 1.000
#> Sample_688 1 1.000
#> Sample_689 2 0.000
#> Sample_690 2 0.751
#> Sample_691 2 0.000
#> Sample_693 2 0.000
#> Sample_694 2 0.000
#> Sample_695 1 0.000
#> Sample_696 1 0.000
#> Sample_697 1 0.000
#> Sample_698 1 0.000
#> Sample_699 1 0.000
#> Sample_700 1 0.000
#> Sample_701 1 0.000
#> Sample_702 1 0.000
#> Sample_703 1 0.000
#> Sample_704 2 0.000
#> Sample_705 2 0.000
#> Sample_706 1 0.000
#> Sample_707 1 0.000
#> Sample_708 1 0.000
#> Sample_709 1 0.000
#> Sample_710 1 0.000
#> Sample_711 1 0.000
#> Sample_712 1 0.000
#> Sample_713 1 0.000
#> Sample_714 1 0.000
#> Sample_715 1 0.000
#> Sample_716 1 0.000
#> Sample_717 1 0.000
#> Sample_718 1 0.000
#> Sample_719 1 0.000
#> Sample_720 1 0.000
#> Sample_721 1 0.000
#> Sample_722 1 0.000
#> Sample_723 1 0.000
#> Sample_724 1 0.000
#> Sample_725 1 0.000
#> Sample_726 1 0.000
#> Sample_727 1 0.000
#> Sample_728 1 0.000
#> Sample_729 1 0.000
#> Sample_730 1 0.000
#> Sample_731 1 0.000
#> Sample_732 1 0.000
#> Sample_733 1 0.000
#> Sample_734 1 0.000
#> Sample_735 1 0.000
#> Sample_736 1 0.000
#> Sample_738 1 0.000
#> Sample_739 1 0.000
#> Sample_740 1 0.000
#> Sample_741 1 0.000
#> Sample_742 1 0.000
#> Sample_743 1 0.000
#> Sample_744 1 0.000
#> Sample_745 1 0.000
#> Sample_746 1 0.000
#> Sample_747 1 0.000
#> Sample_748 1 0.000
#> Sample_749 1 0.000
#> Sample_750 1 0.000
#> Sample_751 1 0.000
#> Sample_752 1 0.000
#> Sample_753 1 0.000
#> Sample_754 1 0.000
#> Sample_755 1 0.000
#> Sample_756 1 0.000
#> Sample_757 1 0.000
#> Sample_758 1 0.000
#> Sample_759 1 0.000
#> Sample_760 1 0.000
#> Sample_761 1 0.000
#> Sample_762 1 0.000
#> Sample_763 1 0.000
#> Sample_764 1 0.000
#> Sample_765 1 0.000
#> Sample_766 1 0.000
#> Sample_767 1 0.000
#> Sample_768 1 0.000
#> Sample_769 1 0.000
#> Sample_770 1 0.000
#> Sample_771 1 0.000
#> Sample_772 1 0.000
#> Sample_773 1 0.000
#> Sample_774 1 0.000
#> Sample_775 1 0.000
#> Sample_776 1 0.000
#> Sample_777 1 0.000
#> Sample_778 1 0.000
#> Sample_779 1 0.000
#> Sample_780 1 0.000
#> Sample_781 1 0.000
#> Sample_782 1 0.000
#> Sample_783 1 0.000
#> Sample_784 1 0.000
#> Sample_785 1 0.000
#> Sample_786 1 0.000
#> Sample_787 1 0.000
#> Sample_788 1 0.000
#> Sample_789 1 0.000
#> Sample_790 1 0.000
#> Sample_791 1 0.000
#> Sample_792 1 0.000
#> Sample_793 1 0.000
#> Sample_794 1 0.000
#> Sample_795 1 0.000
#> Sample_796 1 0.000
#> Sample_797 1 0.000
#> Sample_798 1 0.000
#> Sample_799 1 0.000
#> Sample_800 1 0.000
#> Sample_801 1 0.000
#> Sample_802 1 0.000
#> Sample_803 1 0.000
#> Sample_804 1 0.000
#> Sample_805 1 0.000
#> Sample_806 1 0.000
#> Sample_807 1 0.000
#> Sample_808 1 0.000
#> Sample_809 1 0.000
#> Sample_810 1 0.000
#> Sample_811 1 0.000
#> Sample_812 1 0.000
#> Sample_813 1 0.000
#> Sample_814 1 0.000
#> Sample_815 1 0.000
#> Sample_816 1 0.000
#> Sample_817 1 0.000
#> Sample_818 1 0.000
#> Sample_819 1 0.000
#> Sample_820 1 0.000
#> Sample_821 1 0.000
#> Sample_822 1 0.000
#> Sample_823 1 0.000
#> Sample_824 1 0.000
#> Sample_825 1 0.000
#> Sample_826 1 0.000
#> Sample_827 1 0.000
#> Sample_828 1 0.000
#> Sample_829 1 0.000
#> Sample_830 1 0.000
#> Sample_831 1 0.000
#> Sample_832 1 0.000
#> Sample_833 1 0.000
#> Sample_834 1 0.000
#> Sample_835 1 0.000
#> Sample_836 1 0.000
#> Sample_837 1 0.000
#> Sample_838 1 0.000
#> Sample_839 1 0.000
#> Sample_840 1 0.000
#> Sample_841 2 0.000
#> Sample_842 2 0.000
#> Sample_843 2 0.000
#> Sample_844 2 0.000
#> Sample_845 2 0.000
#> Sample_846 2 0.000
#> Sample_847 2 0.000
#> Sample_848 2 0.000
#> Sample_849 2 0.000
#> Sample_850 2 0.000
#> Sample_851 2 0.000
#> Sample_852 2 0.000
#> Sample_853 2 0.000
#> Sample_854 2 0.000
#> Sample_855 1 0.000
#> Sample_856 1 0.000
#> Sample_857 1 0.000
#> Sample_858 1 0.000
#> Sample_859 1 0.000
#> Sample_860 1 0.000
#> Sample_861 1 0.000
#> Sample_862 1 0.000
#> Sample_863 1 0.000
#> Sample_864 1 0.000
#> Sample_865 1 0.000
#> Sample_866 1 0.000
#> Sample_867 1 0.000
#> Sample_868 1 0.000
#> Sample_869 1 0.000
#> Sample_870 1 0.000
#> Sample_871 1 0.000
#> Sample_872 1 0.000
#> Sample_873 1 0.000
#> Sample_874 1 0.000
#> Sample_875 1 0.000
#> Sample_876 1 0.000
#> Sample_877 1 0.000
#> Sample_878 1 0.000
#> Sample_879 1 0.000
#> Sample_880 1 0.000
#> Sample_881 1 0.000
#> Sample_882 1 0.000
#> Sample_883 1 0.000
#> Sample_884 1 0.000
#> Sample_885 1 0.000
#> Sample_886 1 0.000
#> Sample_887 1 0.000
#> Sample_888 1 0.000
#> Sample_889 1 0.000
#> Sample_890 1 0.000
#> Sample_891 1 0.000
#> Sample_892 1 0.000
#> Sample_893 1 0.000
#> Sample_894 1 0.000
#> Sample_895 1 0.000
#> Sample_896 1 0.000
#> Sample_897 1 0.000
#> Sample_898 1 0.000
#> Sample_899 1 0.000
#> Sample_900 1 0.000
#> Sample_901 1 0.000
#> Sample_902 1 0.000
#> Sample_903 1 0.000
#> Sample_904 1 0.000
#> Sample_905 1 0.000
#> Sample_906 1 0.000
#> Sample_907 1 0.000
#> Sample_908 1 0.000
#> Sample_909 1 0.000
#> Sample_910 1 0.000
#> Sample_911 1 0.000
#> Sample_912 1 0.000
#> Sample_913 1 0.000
#> Sample_914 1 0.000
#> Sample_915 1 0.000
#> Sample_916 1 0.000
#> Sample_917 1 0.000
#> Sample_918 1 0.000
#> Sample_919 1 0.000
#> Sample_920 1 0.000
#> Sample_921 1 0.000
#> Sample_922 1 0.000
#> Sample_923 1 0.000
#> Sample_924 1 0.000
#> Sample_925 1 0.000
#> Sample_926 1 0.000
#> Sample_927 1 0.000
#> Sample_928 1 0.000
#> Sample_929 1 0.000
#> Sample_930 1 0.000
#> Sample_931 1 0.000
#> Sample_932 1 0.000
#> Sample_933 1 0.000
#> Sample_934 1 0.000
#> Sample_935 1 0.000
#> Sample_936 1 0.000
#> Sample_937 1 0.000
#> Sample_938 1 0.000
#> Sample_939 1 0.000
#> Sample_940 1 0.000
#> Sample_941 1 0.000
#> Sample_942 1 0.000
#> Sample_943 1 0.000
#> Sample_944 1 0.000
#> Sample_945 1 0.000
#> Sample_946 1 0.000
#> Sample_947 1 0.000
#> Sample_1517 2 0.000
#> Sample_1518 2 0.000
#> Sample_1519 2 0.000
#> Sample_1520 2 0.000
#> Sample_1521 2 0.000
#> Sample_1524 2 0.000
#> Sample_1527 1 0.000
#> Sample_1528 1 0.000
#> Sample_1529 1 0.000
#> Sample_1530 1 0.000
#> Sample_1531 1 0.000
#> Sample_1532 1 0.000
#> Sample_1533 1 0.000
#> Sample_1534 1 0.000
#> Sample_1535 2 0.000
#> Sample_1536 2 0.000
#> Sample_1537 2 0.000
#> Sample_1538 1 0.000
#> Sample_1539 1 0.000
#> Sample_1542 1 0.000
#> Sample_1543 1 0.000
#> Sample_1546 1 0.000
#> Sample_1548 2 0.000
#> Sample_1550 2 0.000
#> Sample_1551 2 0.000
#> Sample_1552 2 0.000
#> Sample_1554 2 0.000
#> Sample_1555 2 0.000
#> Sample_1559 2 0.000
#> Sample_1561 2 0.000
#> Sample_1570 1 0.000
#> Sample_1574 2 0.000
#> Sample_1575 2 0.000
#> Sample_1582 2 0.000
#> Sample_1585 2 0.000
#> Sample_1586 2 0.000
#> Sample_1587 2 0.000
#> Sample_1588 2 0.000
#> Sample_1589 2 0.000
#> Sample_1592 2 0.000
#> Sample_1593 2 0.000
#> Sample_1594 2 0.000
#> Sample_1595 2 0.000
#> Sample_1596 2 0.000
#> Sample_1599 1 0.000
get_classes(res, k = 3)
#> class p
#> Sample_2 2 0.000
#> Sample_3 2 0.000
#> Sample_4 2 0.000
#> Sample_5 2 0.000
#> Sample_6 2 0.000
#> Sample_7 2 0.000
#> Sample_8 2 0.000
#> Sample_9 2 0.000
#> Sample_10 2 0.000
#> Sample_11 2 0.000
#> Sample_12 2 0.000
#> Sample_13 2 0.000
#> Sample_14 2 0.000
#> Sample_15 2 0.000
#> Sample_16 2 0.000
#> Sample_17 2 0.000
#> Sample_18 2 0.000
#> Sample_19 2 0.000
#> Sample_20 2 0.000
#> Sample_21 2 0.000
#> Sample_22 2 0.000
#> Sample_23 2 0.000
#> Sample_24 2 0.000
#> Sample_25 2 0.000
#> Sample_26 2 0.000
#> Sample_27 2 0.000
#> Sample_28 2 0.000
#> Sample_29 2 0.000
#> Sample_30 2 0.000
#> Sample_31 3 0.000
#> Sample_32 2 0.000
#> Sample_33 2 0.000
#> Sample_34 2 0.000
#> Sample_35 2 0.000
#> Sample_36 2 0.000
#> Sample_37 2 0.000
#> Sample_38 2 0.000
#> Sample_39 2 0.000
#> Sample_40 2 0.000
#> Sample_41 2 0.000
#> Sample_42 2 0.000
#> Sample_43 3 0.000
#> Sample_44 2 0.000
#> Sample_45 2 0.000
#> Sample_46 2 0.000
#> Sample_47 2 0.000
#> Sample_48 2 0.000
#> Sample_49 2 0.000
#> Sample_50 2 0.000
#> Sample_51 2 0.000
#> Sample_52 2 0.000
#> Sample_53 2 0.000
#> Sample_54 2 0.000
#> Sample_55 2 0.000
#> Sample_56 2 0.000
#> Sample_57 2 0.000
#> Sample_58 2 0.000
#> Sample_59 2 0.000
#> Sample_60 2 0.000
#> Sample_61 2 0.000
#> Sample_62 2 0.000
#> Sample_63 2 0.747
#> Sample_64 2 0.000
#> Sample_65 2 0.000
#> Sample_66 2 0.000
#> Sample_67 2 0.000
#> Sample_68 2 0.000
#> Sample_69 2 0.000
#> Sample_70 2 0.253
#> Sample_71 2 0.000
#> Sample_72 1 1.000
#> Sample_73 3 0.000
#> Sample_74 2 0.000
#> Sample_75 2 0.000
#> Sample_76 2 0.000
#> Sample_77 2 0.000
#> Sample_78 2 0.000
#> Sample_79 2 0.000
#> Sample_80 2 0.000
#> Sample_81 2 0.000
#> Sample_82 2 0.000
#> Sample_83 2 0.000
#> Sample_84 2 0.000
#> Sample_85 2 0.000
#> Sample_86 2 0.000
#> Sample_87 2 0.000
#> Sample_88 2 0.000
#> Sample_89 2 0.000
#> Sample_90 2 0.000
#> Sample_91 2 0.000
#> Sample_92 2 0.000
#> Sample_93 2 0.000
#> Sample_94 3 0.000
#> Sample_95 2 0.000
#> Sample_96 2 0.000
#> Sample_97 2 0.000
#> Sample_98 2 0.000
#> Sample_99 2 0.000
#> Sample_100 2 0.000
#> Sample_101 2 0.000
#> Sample_102 2 0.000
#> Sample_103 2 0.000
#> Sample_104 2 0.000
#> Sample_105 2 0.000
#> Sample_106 2 0.000
#> Sample_107 2 0.000
#> Sample_108 2 0.000
#> Sample_109 2 0.000
#> Sample_110 2 0.000
#> Sample_111 1 0.751
#> Sample_112 2 0.000
#> Sample_113 2 0.000
#> Sample_114 3 0.000
#> Sample_115 2 0.000
#> Sample_116 2 0.000
#> Sample_117 2 0.000
#> Sample_118 3 0.000
#> Sample_119 2 0.000
#> Sample_120 2 0.000
#> Sample_122 2 0.000
#> Sample_123 3 0.000
#> Sample_124 2 0.000
#> Sample_125 2 0.000
#> Sample_126 2 0.000
#> Sample_127 2 0.000
#> Sample_128 2 0.000
#> Sample_129 2 0.000
#> Sample_130 2 0.000
#> Sample_131 2 0.000
#> Sample_132 2 0.000
#> Sample_133 2 0.000
#> Sample_134 2 0.000
#> Sample_135 2 0.000
#> Sample_136 2 0.000
#> Sample_137 2 0.000
#> Sample_138 2 0.000
#> Sample_139 2 0.000
#> Sample_140 2 0.000
#> Sample_141 2 0.000
#> Sample_142 2 0.000
#> Sample_143 2 0.000
#> Sample_144 3 0.000
#> Sample_145 2 0.000
#> Sample_146 2 0.000
#> Sample_147 1 0.000
#> Sample_148 1 0.000
#> Sample_149 1 0.000
#> Sample_150 1 0.000
#> Sample_151 1 0.000
#> Sample_152 1 0.000
#> Sample_153 1 0.000
#> Sample_154 1 0.000
#> Sample_155 1 0.000
#> Sample_156 1 0.000
#> Sample_157 1 0.000
#> Sample_158 1 0.000
#> Sample_159 1 0.000
#> Sample_160 1 0.000
#> Sample_161 1 0.000
#> Sample_162 1 0.000
#> Sample_163 1 0.000
#> Sample_164 1 0.000
#> Sample_165 1 0.000
#> Sample_166 1 0.000
#> Sample_167 1 0.000
#> Sample_168 1 0.000
#> Sample_169 1 0.000
#> Sample_170 1 0.000
#> Sample_171 3 0.000
#> Sample_172 1 0.000
#> Sample_173 3 0.000
#> Sample_174 1 0.000
#> Sample_175 1 0.000
#> Sample_176 1 0.000
#> Sample_177 1 0.000
#> Sample_178 1 0.000
#> Sample_179 1 0.000
#> Sample_180 1 0.000
#> Sample_181 1 0.000
#> Sample_182 1 0.000
#> Sample_183 1 0.000
#> Sample_184 1 0.000
#> Sample_185 1 0.000
#> Sample_186 1 0.000
#> Sample_187 1 0.000
#> Sample_188 1 0.000
#> Sample_189 1 0.000
#> Sample_190 1 0.000
#> Sample_191 1 0.000
#> Sample_192 1 0.000
#> Sample_193 1 0.000
#> Sample_194 1 0.000
#> Sample_195 1 0.000
#> Sample_196 1 0.000
#> Sample_197 2 0.000
#> Sample_198 2 0.000
#> Sample_199 2 0.000
#> Sample_200 2 0.000
#> Sample_201 2 0.000
#> Sample_202 2 0.000
#> Sample_203 2 0.000
#> Sample_204 3 0.000
#> Sample_205 2 0.000
#> Sample_206 2 0.000
#> Sample_207 2 0.000
#> Sample_516 1 1.000
#> Sample_586 2 0.000
#> Sample_587 2 1.000
#> Sample_588 3 1.000
#> Sample_591 2 1.000
#> Sample_592 3 0.751
#> Sample_593 2 0.000
#> Sample_594 3 0.249
#> Sample_595 3 0.751
#> Sample_596 2 0.000
#> Sample_597 3 0.249
#> Sample_598 2 0.249
#> Sample_599 3 1.000
#> Sample_600 3 0.498
#> Sample_601 2 0.000
#> Sample_602 3 1.000
#> Sample_603 2 0.000
#> Sample_604 2 0.000
#> Sample_605 2 0.000
#> Sample_606 2 0.000
#> Sample_607 2 0.747
#> Sample_608 2 0.000
#> Sample_609 2 0.253
#> Sample_610 2 0.751
#> Sample_611 1 0.000
#> Sample_612 1 0.000
#> Sample_621 3 0.000
#> Sample_625 3 0.000
#> Sample_627 3 0.249
#> Sample_628 3 0.000
#> Sample_629 3 0.000
#> Sample_631 3 0.000
#> Sample_638 3 0.000
#> Sample_640 3 0.000
#> Sample_642 3 0.000
#> Sample_645 3 0.000
#> Sample_648 3 0.000
#> Sample_649 3 0.000
#> Sample_650 3 0.000
#> Sample_651 2 0.000
#> Sample_652 2 0.000
#> Sample_653 2 0.000
#> Sample_654 2 0.000
#> Sample_655 2 0.000
#> Sample_656 2 0.000
#> Sample_657 2 0.000
#> Sample_658 2 0.000
#> Sample_659 2 0.000
#> Sample_660 2 0.000
#> Sample_661 2 0.000
#> Sample_662 2 0.000
#> Sample_663 2 0.000
#> Sample_664 2 0.000
#> Sample_665 3 0.000
#> Sample_666 2 0.000
#> Sample_667 3 0.000
#> Sample_668 2 0.253
#> Sample_669 2 0.000
#> Sample_670 2 0.000
#> Sample_671 2 0.000
#> Sample_672 2 0.000
#> Sample_673 2 0.000
#> Sample_674 2 0.000
#> Sample_675 2 0.000
#> Sample_676 2 0.000
#> Sample_677 2 0.000
#> Sample_678 2 0.000
#> Sample_679 2 0.000
#> Sample_680 2 0.000
#> Sample_681 3 0.000
#> Sample_682 2 0.000
#> Sample_683 2 0.000
#> Sample_684 2 0.498
#> Sample_686 1 0.751
#> Sample_688 1 1.000
#> Sample_689 2 0.000
#> Sample_690 1 1.000
#> Sample_691 2 0.000
#> Sample_693 2 0.000
#> Sample_694 2 0.249
#> Sample_695 1 0.000
#> Sample_696 1 0.000
#> Sample_697 1 0.000
#> Sample_698 1 0.000
#> Sample_699 1 0.000
#> Sample_700 1 0.000
#> Sample_701 1 0.000
#> Sample_702 1 0.000
#> Sample_703 1 0.000
#> Sample_704 2 0.000
#> Sample_705 2 0.000
#> Sample_706 1 0.000
#> Sample_707 1 0.000
#> Sample_708 1 0.000
#> Sample_709 1 0.000
#> Sample_710 3 0.000
#> Sample_711 1 0.000
#> Sample_712 1 0.000
#> Sample_713 1 0.000
#> Sample_714 1 0.000
#> Sample_715 1 0.000
#> Sample_716 1 0.000
#> Sample_717 1 0.000
#> Sample_718 1 0.000
#> Sample_719 1 0.000
#> Sample_720 3 0.000
#> Sample_721 1 0.000
#> Sample_722 1 0.000
#> Sample_723 1 0.000
#> Sample_724 1 0.000
#> Sample_725 1 0.000
#> Sample_726 1 0.000
#> Sample_727 1 0.000
#> Sample_728 1 0.000
#> Sample_729 1 0.000
#> Sample_730 1 0.000
#> Sample_731 3 0.000
#> Sample_732 1 0.000
#> Sample_733 1 0.000
#> Sample_734 1 0.000
#> Sample_735 3 0.000
#> Sample_736 1 0.000
#> Sample_738 3 0.000
#> Sample_739 1 0.000
#> Sample_740 1 0.000
#> Sample_741 1 0.000
#> Sample_742 1 0.000
#> Sample_743 1 0.000
#> Sample_744 1 0.000
#> Sample_745 1 0.000
#> Sample_746 1 0.000
#> Sample_747 1 0.000
#> Sample_748 1 0.000
#> Sample_749 1 0.000
#> Sample_750 1 0.000
#> Sample_751 1 0.000
#> Sample_752 1 0.000
#> Sample_753 1 0.000
#> Sample_754 1 0.000
#> Sample_755 1 0.000
#> Sample_756 1 0.000
#> Sample_757 1 0.000
#> Sample_758 1 0.000
#> Sample_759 1 0.000
#> Sample_760 1 0.000
#> Sample_761 1 0.000
#> Sample_762 1 0.000
#> Sample_763 1 0.000
#> Sample_764 1 0.000
#> Sample_765 1 0.000
#> Sample_766 1 0.000
#> Sample_767 1 0.000
#> Sample_768 3 0.000
#> Sample_769 1 0.000
#> Sample_770 1 0.000
#> Sample_771 1 0.000
#> Sample_772 1 0.000
#> Sample_773 1 0.000
#> Sample_774 1 0.000
#> Sample_775 1 0.000
#> Sample_776 1 0.000
#> Sample_777 1 0.000
#> Sample_778 1 0.000
#> Sample_779 1 0.000
#> Sample_780 1 0.000
#> Sample_781 1 0.000
#> Sample_782 1 0.000
#> Sample_783 1 0.000
#> Sample_784 1 0.000
#> Sample_785 1 0.000
#> Sample_786 1 0.000
#> Sample_787 1 0.000
#> Sample_788 1 0.000
#> Sample_789 1 0.000
#> Sample_790 1 0.000
#> Sample_791 1 0.000
#> Sample_792 1 0.000
#> Sample_793 1 0.000
#> Sample_794 1 0.000
#> Sample_795 1 0.000
#> Sample_796 1 0.000
#> Sample_797 1 0.000
#> Sample_798 1 0.000
#> Sample_799 1 0.000
#> Sample_800 1 0.000
#> Sample_801 1 0.000
#> Sample_802 1 0.000
#> Sample_803 1 0.000
#> Sample_804 1 0.000
#> Sample_805 1 0.000
#> Sample_806 1 0.000
#> Sample_807 1 0.000
#> Sample_808 1 0.000
#> Sample_809 1 0.000
#> Sample_810 1 0.000
#> Sample_811 1 0.000
#> Sample_812 1 0.000
#> Sample_813 1 0.000
#> Sample_814 1 0.000
#> Sample_815 1 0.000
#> Sample_816 1 0.000
#> Sample_817 1 0.000
#> Sample_818 1 0.000
#> Sample_819 1 0.000
#> Sample_820 3 0.000
#> Sample_821 1 0.000
#> Sample_822 1 0.000
#> Sample_823 1 0.000
#> Sample_824 1 0.000
#> Sample_825 1 0.000
#> Sample_826 1 0.000
#> Sample_827 1 0.000
#> Sample_828 1 0.000
#> Sample_829 1 0.000
#> Sample_830 1 0.000
#> Sample_831 1 0.000
#> Sample_832 1 0.000
#> Sample_833 1 0.000
#> Sample_834 1 0.000
#> Sample_835 1 0.000
#> Sample_836 3 0.000
#> Sample_837 1 0.000
#> Sample_838 1 0.000
#> Sample_839 1 0.000
#> Sample_840 1 0.000
#> Sample_841 2 0.000
#> Sample_842 2 0.000
#> Sample_843 2 0.000
#> Sample_844 2 0.000
#> Sample_845 2 0.000
#> Sample_846 2 0.000
#> Sample_847 2 0.000
#> Sample_848 2 0.000
#> Sample_849 2 0.000
#> Sample_850 2 0.000
#> Sample_851 2 0.000
#> Sample_852 2 0.000
#> Sample_853 2 0.000
#> Sample_854 2 0.000
#> Sample_855 1 0.000
#> Sample_856 1 0.000
#> Sample_857 1 0.000
#> Sample_858 1 0.000
#> Sample_859 3 1.000
#> Sample_860 1 0.000
#> Sample_861 1 0.000
#> Sample_862 1 0.000
#> Sample_863 1 0.000
#> Sample_864 1 0.000
#> Sample_865 3 0.000
#> Sample_866 1 0.000
#> Sample_867 1 0.000
#> Sample_868 1 0.000
#> Sample_869 1 0.000
#> Sample_870 1 0.000
#> Sample_871 1 0.000
#> Sample_872 1 0.000
#> Sample_873 1 0.000
#> Sample_874 1 0.000
#> Sample_875 1 0.000
#> Sample_876 1 0.000
#> Sample_877 1 0.000
#> Sample_878 1 0.000
#> Sample_879 1 0.000
#> Sample_880 1 0.000
#> Sample_881 1 0.000
#> Sample_882 3 0.000
#> Sample_883 1 0.000
#> Sample_884 1 0.000
#> Sample_885 1 0.000
#> Sample_886 1 0.000
#> Sample_887 1 0.000
#> Sample_888 1 0.000
#> Sample_889 1 0.000
#> Sample_890 1 0.000
#> Sample_891 1 0.000
#> Sample_892 1 0.000
#> Sample_893 1 0.000
#> Sample_894 3 0.000
#> Sample_895 1 0.000
#> Sample_896 1 0.000
#> Sample_897 1 0.000
#> Sample_898 1 0.000
#> Sample_899 1 0.000
#> Sample_900 3 0.000
#> Sample_901 1 0.000
#> Sample_902 1 0.000
#> Sample_903 1 0.000
#> Sample_904 1 0.000
#> Sample_905 1 0.000
#> Sample_906 1 0.000
#> Sample_907 1 0.000
#> Sample_908 1 0.000
#> Sample_909 1 0.000
#> Sample_910 1 0.000
#> Sample_911 1 0.000
#> Sample_912 1 0.000
#> Sample_913 1 0.000
#> Sample_914 1 0.000
#> Sample_915 1 0.000
#> Sample_916 1 0.000
#> Sample_917 1 0.000
#> Sample_918 1 0.000
#> Sample_919 1 0.000
#> Sample_920 1 0.000
#> Sample_921 1 0.000
#> Sample_922 1 0.000
#> Sample_923 1 0.000
#> Sample_924 1 0.000
#> Sample_925 1 0.000
#> Sample_926 1 0.000
#> Sample_927 1 0.000
#> Sample_928 1 0.000
#> Sample_929 1 0.000
#> Sample_930 1 0.000
#> Sample_931 1 0.000
#> Sample_932 1 0.000
#> Sample_933 1 0.000
#> Sample_934 1 0.000
#> Sample_935 1 0.000
#> Sample_936 1 0.000
#> Sample_937 1 0.000
#> Sample_938 1 0.000
#> Sample_939 1 0.000
#> Sample_940 1 0.000
#> Sample_941 1 0.000
#> Sample_942 1 0.000
#> Sample_943 1 0.000
#> Sample_944 1 0.000
#> Sample_945 1 0.000
#> Sample_946 1 0.000
#> Sample_947 1 0.000
#> Sample_1517 2 0.498
#> Sample_1518 2 0.000
#> Sample_1519 2 0.000
#> Sample_1520 3 0.000
#> Sample_1521 3 0.000
#> Sample_1524 3 0.000
#> Sample_1527 1 0.000
#> Sample_1528 1 0.000
#> Sample_1529 1 0.751
#> Sample_1530 3 0.000
#> Sample_1531 3 0.000
#> Sample_1532 3 0.000
#> Sample_1533 1 0.751
#> Sample_1534 1 0.000
#> Sample_1535 2 0.000
#> Sample_1536 2 0.000
#> Sample_1537 2 0.000
#> Sample_1538 1 0.000
#> Sample_1539 1 0.000
#> Sample_1542 1 0.000
#> Sample_1543 3 0.000
#> Sample_1546 1 0.747
#> Sample_1548 3 0.000
#> Sample_1550 3 0.000
#> Sample_1551 3 0.000
#> Sample_1552 3 0.000
#> Sample_1554 2 0.249
#> Sample_1555 3 0.498
#> Sample_1559 3 0.000
#> Sample_1561 3 0.253
#> Sample_1570 1 1.000
#> Sample_1574 3 0.000
#> Sample_1575 3 0.000
#> Sample_1582 3 0.000
#> Sample_1585 3 0.000
#> Sample_1586 3 0.000
#> Sample_1587 3 0.000
#> Sample_1588 2 0.502
#> Sample_1589 3 0.000
#> Sample_1592 3 0.000
#> Sample_1593 3 0.000
#> Sample_1594 3 0.000
#> Sample_1595 3 0.000
#> Sample_1596 3 0.000
#> Sample_1599 3 0.000
get_classes(res, k = 4)
#> class p
#> Sample_2 2 0.000
#> Sample_3 2 0.000
#> Sample_4 2 0.000
#> Sample_5 2 0.000
#> Sample_6 2 0.000
#> Sample_7 2 0.000
#> Sample_8 2 0.000
#> Sample_9 2 0.000
#> Sample_10 2 0.000
#> Sample_11 2 0.000
#> Sample_12 2 0.000
#> Sample_13 2 0.000
#> Sample_14 2 0.000
#> Sample_15 2 0.000
#> Sample_16 2 0.000
#> Sample_17 2 0.000
#> Sample_18 2 0.000
#> Sample_19 2 0.000
#> Sample_20 2 0.000
#> Sample_21 2 0.000
#> Sample_22 2 0.000
#> Sample_23 2 0.000
#> Sample_24 2 0.000
#> Sample_25 2 0.000
#> Sample_26 2 0.000
#> Sample_27 2 0.000
#> Sample_28 2 0.000
#> Sample_29 2 0.000
#> Sample_30 2 0.000
#> Sample_31 3 0.000
#> Sample_32 2 0.000
#> Sample_33 2 0.000
#> Sample_34 2 0.000
#> Sample_35 2 0.000
#> Sample_36 2 0.000
#> Sample_37 2 0.000
#> Sample_38 2 0.000
#> Sample_39 2 0.000
#> Sample_40 2 0.000
#> Sample_41 2 0.000
#> Sample_42 2 0.000
#> Sample_43 3 0.000
#> Sample_44 2 0.000
#> Sample_45 2 0.000
#> Sample_46 2 0.000
#> Sample_47 2 0.000
#> Sample_48 2 0.000
#> Sample_49 2 0.000
#> Sample_50 2 0.000
#> Sample_51 2 0.000
#> Sample_52 2 0.000
#> Sample_53 2 0.000
#> Sample_54 2 0.000
#> Sample_55 2 0.000
#> Sample_56 2 0.000
#> Sample_57 2 0.000
#> Sample_58 2 0.000
#> Sample_59 2 0.000
#> Sample_60 2 0.000
#> Sample_61 2 0.000
#> Sample_62 2 0.000
#> Sample_63 2 0.000
#> Sample_64 2 0.000
#> Sample_65 2 0.000
#> Sample_66 2 0.000
#> Sample_67 2 0.000
#> Sample_68 2 0.000
#> Sample_69 2 0.000
#> Sample_70 2 0.747
#> Sample_71 2 0.000
#> Sample_72 2 1.000
#> Sample_73 3 0.000
#> Sample_74 2 0.000
#> Sample_75 2 0.000
#> Sample_76 2 0.000
#> Sample_77 2 0.000
#> Sample_78 2 0.000
#> Sample_79 2 0.000
#> Sample_80 2 0.000
#> Sample_81 2 0.000
#> Sample_82 2 0.000
#> Sample_83 2 0.000
#> Sample_84 2 0.000
#> Sample_85 2 0.000
#> Sample_86 2 0.000
#> Sample_87 2 0.000
#> Sample_88 2 0.000
#> Sample_89 2 0.000
#> Sample_90 2 0.000
#> Sample_91 2 0.000
#> Sample_92 2 0.000
#> Sample_93 2 0.000
#> Sample_94 3 0.000
#> Sample_95 2 0.000
#> Sample_96 2 0.000
#> Sample_97 2 0.000
#> Sample_98 2 0.000
#> Sample_99 2 0.000
#> Sample_100 2 0.000
#> Sample_101 2 0.000
#> Sample_102 2 0.000
#> Sample_103 2 0.000
#> Sample_104 2 0.000
#> Sample_105 2 0.000
#> Sample_106 2 0.000
#> Sample_107 2 0.000
#> Sample_108 2 0.000
#> Sample_109 2 0.000
#> Sample_110 2 0.000
#> Sample_111 1 0.000
#> Sample_112 2 0.000
#> Sample_113 2 0.000
#> Sample_114 3 0.000
#> Sample_115 2 0.000
#> Sample_116 2 0.000
#> Sample_117 2 0.000
#> Sample_118 3 0.000
#> Sample_119 2 0.000
#> Sample_120 2 0.000
#> Sample_122 2 0.000
#> Sample_123 3 0.000
#> Sample_124 2 0.000
#> Sample_125 2 0.000
#> Sample_126 2 0.000
#> Sample_127 2 0.000
#> Sample_128 2 0.000
#> Sample_129 2 0.000
#> Sample_130 2 0.000
#> Sample_131 2 0.000
#> Sample_132 2 0.000
#> Sample_133 2 0.000
#> Sample_134 2 0.000
#> Sample_135 2 0.000
#> Sample_136 2 0.000
#> Sample_137 2 0.000
#> Sample_138 2 0.000
#> Sample_139 2 0.000
#> Sample_140 2 0.000
#> Sample_141 2 0.000
#> Sample_142 2 0.000
#> Sample_143 2 0.000
#> Sample_144 3 0.000
#> Sample_145 2 0.000
#> Sample_146 2 0.000
#> Sample_147 1 1.000
#> Sample_148 4 0.000
#> Sample_149 1 1.000
#> Sample_150 1 0.000
#> Sample_151 1 1.000
#> Sample_152 4 0.502
#> Sample_153 1 1.000
#> Sample_154 4 1.000
#> Sample_155 4 1.000
#> Sample_156 4 1.000
#> Sample_157 4 1.000
#> Sample_158 1 1.000
#> Sample_159 1 1.000
#> Sample_160 4 0.751
#> Sample_161 1 1.000
#> Sample_162 1 0.751
#> Sample_163 1 0.751
#> Sample_164 1 1.000
#> Sample_165 1 0.751
#> Sample_166 4 1.000
#> Sample_167 4 0.751
#> Sample_168 4 0.751
#> Sample_169 1 1.000
#> Sample_170 4 0.502
#> Sample_171 3 0.000
#> Sample_172 1 1.000
#> Sample_173 3 0.000
#> Sample_174 1 0.502
#> Sample_175 4 1.000
#> Sample_176 4 0.000
#> Sample_177 1 1.000
#> Sample_178 1 0.751
#> Sample_179 1 1.000
#> Sample_180 1 0.751
#> Sample_181 1 1.000
#> Sample_182 4 0.751
#> Sample_183 4 1.000
#> Sample_184 4 1.000
#> Sample_185 4 0.498
#> Sample_186 4 0.751
#> Sample_187 4 0.000
#> Sample_188 1 1.000
#> Sample_189 1 0.253
#> Sample_190 4 0.000
#> Sample_191 1 1.000
#> Sample_192 4 1.000
#> Sample_193 4 0.000
#> Sample_194 1 1.000
#> Sample_195 1 1.000
#> Sample_196 4 0.498
#> Sample_197 2 0.000
#> Sample_198 2 0.000
#> Sample_199 2 0.000
#> Sample_200 2 0.000
#> Sample_201 2 0.000
#> Sample_202 2 0.000
#> Sample_203 2 0.000
#> Sample_204 3 0.000
#> Sample_205 2 0.000
#> Sample_206 2 0.000
#> Sample_207 2 0.000
#> Sample_516 4 0.747
#> Sample_586 2 0.000
#> Sample_587 2 0.747
#> Sample_588 3 1.000
#> Sample_591 2 1.000
#> Sample_592 3 0.751
#> Sample_593 2 0.000
#> Sample_594 3 0.253
#> Sample_595 3 0.000
#> Sample_596 2 0.000
#> Sample_597 3 0.249
#> Sample_598 2 0.000
#> Sample_599 2 0.751
#> Sample_600 3 0.502
#> Sample_601 2 0.000
#> Sample_602 3 1.000
#> Sample_603 2 0.000
#> Sample_604 2 0.000
#> Sample_605 2 0.498
#> Sample_606 2 0.000
#> Sample_607 2 0.751
#> Sample_608 2 0.000
#> Sample_609 2 0.000
#> Sample_610 2 0.751
#> Sample_611 1 0.751
#> Sample_612 1 0.000
#> Sample_621 3 0.249
#> Sample_625 3 0.000
#> Sample_627 3 0.253
#> Sample_628 3 0.000
#> Sample_629 3 0.000
#> Sample_631 3 0.000
#> Sample_638 3 0.000
#> Sample_640 3 0.000
#> Sample_642 3 0.000
#> Sample_645 3 0.000
#> Sample_648 3 0.000
#> Sample_649 3 0.000
#> Sample_650 3 0.000
#> Sample_651 2 0.249
#> Sample_652 2 0.000
#> Sample_653 2 0.000
#> Sample_654 2 0.000
#> Sample_655 2 0.000
#> Sample_656 2 0.000
#> Sample_657 2 0.000
#> Sample_658 2 0.000
#> Sample_659 2 0.000
#> Sample_660 2 0.000
#> Sample_661 2 0.000
#> Sample_662 2 0.000
#> Sample_663 2 0.000
#> Sample_664 2 0.000
#> Sample_665 3 0.000
#> Sample_666 2 0.000
#> Sample_667 3 0.000
#> Sample_668 2 0.502
#> Sample_669 2 0.000
#> Sample_670 2 0.000
#> Sample_671 2 0.000
#> Sample_672 2 0.000
#> Sample_673 2 0.000
#> Sample_674 2 0.000
#> Sample_675 2 0.000
#> Sample_676 2 0.000
#> Sample_677 2 0.000
#> Sample_678 2 0.000
#> Sample_679 2 0.000
#> Sample_680 2 0.000
#> Sample_681 3 0.000
#> Sample_682 2 0.000
#> Sample_683 2 0.000
#> Sample_684 2 0.000
#> Sample_686 1 0.000
#> Sample_688 1 0.000
#> Sample_689 2 0.000
#> Sample_690 1 0.000
#> Sample_691 2 0.000
#> Sample_693 2 0.000
#> Sample_694 2 0.000
#> Sample_695 1 1.000
#> Sample_696 4 1.000
#> Sample_697 4 0.253
#> Sample_698 4 1.000
#> Sample_699 4 1.000
#> Sample_700 4 0.000
#> Sample_701 1 1.000
#> Sample_702 4 0.498
#> Sample_703 1 0.751
#> Sample_704 2 0.000
#> Sample_705 2 0.000
#> Sample_706 4 0.000
#> Sample_707 4 0.000
#> Sample_708 4 0.751
#> Sample_709 4 0.249
#> Sample_710 3 0.000
#> Sample_711 4 1.000
#> Sample_712 4 1.000
#> Sample_713 4 0.751
#> Sample_714 1 0.751
#> Sample_715 4 1.000
#> Sample_716 1 0.747
#> Sample_717 4 0.000
#> Sample_718 4 1.000
#> Sample_719 4 0.751
#> Sample_720 3 0.000
#> Sample_721 4 1.000
#> Sample_722 4 0.000
#> Sample_723 4 0.751
#> Sample_724 4 0.498
#> Sample_725 4 1.000
#> Sample_726 4 0.000
#> Sample_727 4 0.000
#> Sample_728 4 0.751
#> Sample_729 4 0.000
#> Sample_730 4 0.253
#> Sample_731 3 0.000
#> Sample_732 4 0.249
#> Sample_733 4 0.000
#> Sample_734 4 0.751
#> Sample_735 3 0.000
#> Sample_736 4 0.751
#> Sample_738 3 0.000
#> Sample_739 4 0.000
#> Sample_740 4 1.000
#> Sample_741 4 0.000
#> Sample_742 4 0.751
#> Sample_743 4 0.000
#> Sample_744 4 0.000
#> Sample_745 1 1.000
#> Sample_746 4 0.751
#> Sample_747 4 0.253
#> Sample_748 1 1.000
#> Sample_749 4 0.751
#> Sample_750 4 0.502
#> Sample_751 4 0.000
#> Sample_752 1 1.000
#> Sample_753 1 1.000
#> Sample_754 4 0.751
#> Sample_755 4 0.502
#> Sample_756 4 0.000
#> Sample_757 4 0.747
#> Sample_758 4 0.000
#> Sample_759 1 1.000
#> Sample_760 4 0.000
#> Sample_761 4 0.747
#> Sample_762 4 0.498
#> Sample_763 4 0.000
#> Sample_764 4 0.000
#> Sample_765 4 1.000
#> Sample_766 4 1.000
#> Sample_767 4 0.249
#> Sample_768 3 0.000
#> Sample_769 4 1.000
#> Sample_770 4 0.000
#> Sample_771 4 0.747
#> Sample_772 1 0.253
#> Sample_773 4 0.498
#> Sample_774 4 0.000
#> Sample_775 1 1.000
#> Sample_776 4 0.249
#> Sample_777 1 0.249
#> Sample_778 4 1.000
#> Sample_779 1 0.751
#> Sample_780 1 1.000
#> Sample_781 4 0.751
#> Sample_782 4 1.000
#> Sample_783 1 0.502
#> Sample_784 1 0.000
#> Sample_785 1 1.000
#> Sample_786 1 1.000
#> Sample_787 4 1.000
#> Sample_788 1 1.000
#> Sample_789 1 1.000
#> Sample_790 4 0.498
#> Sample_791 4 0.747
#> Sample_792 4 0.751
#> Sample_793 1 1.000
#> Sample_794 4 0.751
#> Sample_795 4 0.000
#> Sample_796 4 1.000
#> Sample_797 1 1.000
#> Sample_798 1 0.249
#> Sample_799 1 0.502
#> Sample_800 4 0.751
#> Sample_801 4 0.498
#> Sample_802 4 1.000
#> Sample_803 4 0.000
#> Sample_804 1 1.000
#> Sample_805 4 0.498
#> Sample_806 4 0.751
#> Sample_807 4 1.000
#> Sample_808 4 1.000
#> Sample_809 4 0.751
#> Sample_810 1 1.000
#> Sample_811 4 1.000
#> Sample_812 4 0.751
#> Sample_813 1 1.000
#> Sample_814 4 1.000
#> Sample_815 4 0.000
#> Sample_816 4 0.000
#> Sample_817 1 1.000
#> Sample_818 4 1.000
#> Sample_819 1 1.000
#> Sample_820 3 0.000
#> Sample_821 1 1.000
#> Sample_822 4 0.253
#> Sample_823 1 1.000
#> Sample_824 1 0.751
#> Sample_825 1 0.751
#> Sample_826 1 1.000
#> Sample_827 1 1.000
#> Sample_828 4 0.000
#> Sample_829 4 1.000
#> Sample_830 4 1.000
#> Sample_831 4 1.000
#> Sample_832 1 0.751
#> Sample_833 4 1.000
#> Sample_834 1 1.000
#> Sample_835 1 0.502
#> Sample_836 3 0.000
#> Sample_837 4 0.751
#> Sample_838 1 1.000
#> Sample_839 1 1.000
#> Sample_840 4 1.000
#> Sample_841 2 0.000
#> Sample_842 2 0.000
#> Sample_843 2 0.000
#> Sample_844 2 0.000
#> Sample_845 2 0.000
#> Sample_846 2 0.000
#> Sample_847 2 0.253
#> Sample_848 2 0.000
#> Sample_849 2 0.000
#> Sample_850 2 0.000
#> Sample_851 2 0.000
#> Sample_852 2 0.000
#> Sample_853 2 0.000
#> Sample_854 2 0.000
#> Sample_855 1 1.000
#> Sample_856 1 0.498
#> Sample_857 1 0.000
#> Sample_858 4 1.000
#> Sample_859 3 0.751
#> Sample_860 1 0.751
#> Sample_861 1 0.751
#> Sample_862 1 1.000
#> Sample_863 1 0.747
#> Sample_864 1 1.000
#> Sample_865 3 0.000
#> Sample_866 4 0.751
#> Sample_867 1 1.000
#> Sample_868 1 1.000
#> Sample_869 1 1.000
#> Sample_870 1 0.747
#> Sample_871 1 1.000
#> Sample_872 1 0.751
#> Sample_873 1 0.751
#> Sample_874 4 0.498
#> Sample_875 4 1.000
#> Sample_876 1 1.000
#> Sample_877 4 1.000
#> Sample_878 4 0.751
#> Sample_879 4 0.751
#> Sample_880 4 0.000
#> Sample_881 1 1.000
#> Sample_882 3 0.000
#> Sample_883 4 1.000
#> Sample_884 4 0.751
#> Sample_885 1 0.502
#> Sample_886 4 1.000
#> Sample_887 4 0.249
#> Sample_888 4 0.751
#> Sample_889 1 1.000
#> Sample_890 4 1.000
#> Sample_891 1 1.000
#> Sample_892 4 1.000
#> Sample_893 1 1.000
#> Sample_894 3 0.000
#> Sample_895 1 1.000
#> Sample_896 4 0.253
#> Sample_897 1 0.502
#> Sample_898 1 0.000
#> Sample_899 1 0.751
#> Sample_900 3 0.000
#> Sample_901 4 1.000
#> Sample_902 1 1.000
#> Sample_903 1 0.751
#> Sample_904 4 0.249
#> Sample_905 4 0.502
#> Sample_906 1 1.000
#> Sample_907 1 1.000
#> Sample_908 1 1.000
#> Sample_909 1 0.502
#> Sample_910 4 0.253
#> Sample_911 1 0.498
#> Sample_912 1 1.000
#> Sample_913 1 0.000
#> Sample_914 1 0.000
#> Sample_915 1 1.000
#> Sample_916 4 1.000
#> Sample_917 1 0.249
#> Sample_918 1 0.000
#> Sample_919 1 0.498
#> Sample_920 1 0.000
#> Sample_921 1 1.000
#> Sample_922 1 0.000
#> Sample_923 4 1.000
#> Sample_924 1 0.000
#> Sample_925 1 1.000
#> Sample_926 4 1.000
#> Sample_927 4 1.000
#> Sample_928 4 0.249
#> Sample_929 1 0.000
#> Sample_930 4 1.000
#> Sample_931 4 1.000
#> Sample_932 1 0.000
#> Sample_933 1 0.249
#> Sample_934 1 0.751
#> Sample_935 1 0.747
#> Sample_936 1 0.751
#> Sample_937 1 0.502
#> Sample_938 1 0.000
#> Sample_939 1 1.000
#> Sample_940 1 1.000
#> Sample_941 1 0.000
#> Sample_942 1 0.000
#> Sample_943 1 0.253
#> Sample_944 1 1.000
#> Sample_945 1 1.000
#> Sample_946 4 0.751
#> Sample_947 4 1.000
#> Sample_1517 2 0.747
#> Sample_1518 2 0.000
#> Sample_1519 2 1.000
#> Sample_1520 3 0.000
#> Sample_1521 3 0.000
#> Sample_1524 3 0.000
#> Sample_1527 1 0.000
#> Sample_1528 1 0.498
#> Sample_1529 1 1.000
#> Sample_1530 3 0.000
#> Sample_1531 3 0.000
#> Sample_1532 3 0.000
#> Sample_1533 4 1.000
#> Sample_1534 1 0.498
#> Sample_1535 2 0.000
#> Sample_1536 2 0.249
#> Sample_1537 2 0.000
#> Sample_1538 1 0.751
#> Sample_1539 1 0.000
#> Sample_1542 1 0.249
#> Sample_1543 3 0.000
#> Sample_1546 1 0.751
#> Sample_1548 3 0.000
#> Sample_1550 3 0.000
#> Sample_1551 3 0.000
#> Sample_1552 3 0.000
#> Sample_1554 2 0.751
#> Sample_1555 3 0.000
#> Sample_1559 3 0.000
#> Sample_1561 3 0.000
#> Sample_1570 4 1.000
#> Sample_1574 3 0.000
#> Sample_1575 3 0.000
#> Sample_1582 3 0.000
#> Sample_1585 3 0.253
#> Sample_1586 3 0.000
#> Sample_1587 3 0.000
#> Sample_1588 2 0.000
#> Sample_1589 3 0.000
#> Sample_1592 3 0.000
#> Sample_1593 3 0.000
#> Sample_1594 3 0.000
#> Sample_1595 3 0.000
#> Sample_1596 3 0.000
#> Sample_1599 3 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
As soon as the classes for columns are determined, the signatures that are significantly different between subgroups can be looked for. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. To get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows (which is done by automatically selecting number of clusters).If there are too many signatures, top_signatures = ... can be set to only show the
signatures with the highest FDRs:
# code only for demonstration
# e.g. to show the top 500 most significant rows
tb = get_signature(res, k = ..., top_signatures = 500)
If the signatures are defined as these which are uniquely high in current group, diff_method argument
can be set to "uniquely_high_in_one_group":
# code only for demonstration
tb = get_signature(res, k = ..., diff_method = "uniquely_high_in_one_group")
UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n_sample age(p-value) cell.type(p-value) k
#> ATC:skmeans 576 4.84e-07 5.19e-09 2
#> ATC:skmeans 546 5.10e-08 1.01e-102 3
#> ATC:skmeans 323 1.97e-09 4.64e-57 4
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
Parent node: Node02. Child nodes: Node0111-leaf , Node0112-leaf , Node0121-leaf , Node0122-leaf , Node0131-leaf , Node0132-leaf , Node0211 , Node0212 , Node0221 , Node0222 , Node0231-leaf , Node0232-leaf , Node0233-leaf , Node0311-leaf , Node0312-leaf , Node0313-leaf , Node0314-leaf , Node0321 , Node0322 , Node0331-leaf , Node0332-leaf , Node0333-leaf .
The object with results only for a single top-value method and a single partitioning method can be extracted as:
res = res_rh["021"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4.
#> On a matrix with 14740 rows and 289 columns.
#> Top rows (1000) are extracted by 'ATC' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 150 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_partitions"
#> [7] "compare_signatures" "consensus_heatmap" "dimension_reduction"
#> [10] "functional_enrichment" "get_anno_col" "get_anno"
#> [13] "get_classes" "get_consensus" "get_matrix"
#> [16] "get_membership" "get_param" "get_signatures"
#> [19] "get_stats" "is_best_k" "is_stable_k"
#> [22] "membership_heatmap" "ncol" "nrow"
#> [25] "plot_ecdf" "predict_classes" "rownames"
#> [28] "select_partition_number" "show" "suggest_best_k"
#> [31] "test_to_known_factors" "top_rows_heatmap"
collect_plots() function collects all the plots made from res for all k (number of subgroups)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, higher 1-PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.865 0.900 0.960 0.502 0.498 0.498
#> 3 3 0.750 0.816 0.908 0.302 0.764 0.561
#> 4 4 0.647 0.628 0.779 0.116 0.827 0.562
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 2
Following is the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall subgroup
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> Sample_72 1 0.000 0.9582 1.00 0.00
#> Sample_111 2 0.000 0.9578 0.00 1.00
#> Sample_147 1 0.000 0.9582 1.00 0.00
#> Sample_148 1 0.000 0.9582 1.00 0.00
#> Sample_149 1 0.000 0.9582 1.00 0.00
#> Sample_150 1 0.000 0.9582 1.00 0.00
#> Sample_151 2 0.000 0.9578 0.00 1.00
#> Sample_152 1 0.000 0.9582 1.00 0.00
#> Sample_153 1 0.000 0.9582 1.00 0.00
#> Sample_154 1 0.000 0.9582 1.00 0.00
#> Sample_155 1 0.000 0.9582 1.00 0.00
#> Sample_156 1 0.000 0.9582 1.00 0.00
#> Sample_157 1 0.000 0.9582 1.00 0.00
#> Sample_158 1 0.000 0.9582 1.00 0.00
#> Sample_159 2 0.000 0.9578 0.00 1.00
#> Sample_160 1 0.000 0.9582 1.00 0.00
#> Sample_161 2 0.000 0.9578 0.00 1.00
#> Sample_162 2 0.000 0.9578 0.00 1.00
#> Sample_163 2 0.000 0.9578 0.00 1.00
#> Sample_164 2 0.000 0.9578 0.00 1.00
#> Sample_165 2 0.000 0.9578 0.00 1.00
#> Sample_166 2 0.000 0.9578 0.00 1.00
#> Sample_167 1 0.000 0.9582 1.00 0.00
#> Sample_168 1 0.000 0.9582 1.00 0.00
#> Sample_169 2 0.000 0.9578 0.00 1.00
#> Sample_170 1 0.000 0.9582 1.00 0.00
#> Sample_172 2 0.402 0.8897 0.08 0.92
#> Sample_174 2 0.000 0.9578 0.00 1.00
#> Sample_175 1 0.000 0.9582 1.00 0.00
#> Sample_176 1 0.000 0.9582 1.00 0.00
#> Sample_177 2 0.000 0.9578 0.00 1.00
#> Sample_178 1 0.000 0.9582 1.00 0.00
#> Sample_179 2 0.000 0.9578 0.00 1.00
#> Sample_180 2 0.000 0.9578 0.00 1.00
#> Sample_181 1 0.000 0.9582 1.00 0.00
#> Sample_182 1 0.000 0.9582 1.00 0.00
#> Sample_183 1 0.000 0.9582 1.00 0.00
#> Sample_184 1 0.000 0.9582 1.00 0.00
#> Sample_185 1 0.000 0.9582 1.00 0.00
#> Sample_186 1 0.000 0.9582 1.00 0.00
#> Sample_187 1 0.971 0.3368 0.60 0.40
#> Sample_188 2 0.000 0.9578 0.00 1.00
#> Sample_189 2 0.000 0.9578 0.00 1.00
#> Sample_190 1 0.000 0.9582 1.00 0.00
#> Sample_191 2 0.855 0.6144 0.28 0.72
#> Sample_192 2 0.995 0.1556 0.46 0.54
#> Sample_193 1 0.000 0.9582 1.00 0.00
#> Sample_194 1 0.990 0.2116 0.56 0.44
#> Sample_195 1 0.327 0.9050 0.94 0.06
#> Sample_196 1 0.327 0.9046 0.94 0.06
#> Sample_516 1 0.000 0.9582 1.00 0.00
#> Sample_611 2 0.000 0.9578 0.00 1.00
#> Sample_612 2 0.000 0.9578 0.00 1.00
#> Sample_686 2 0.000 0.9578 0.00 1.00
#> Sample_688 2 0.000 0.9578 0.00 1.00
#> Sample_690 2 0.000 0.9578 0.00 1.00
#> Sample_695 2 0.000 0.9578 0.00 1.00
#> Sample_696 1 0.000 0.9582 1.00 0.00
#> Sample_697 1 0.990 0.2112 0.56 0.44
#> Sample_698 2 0.000 0.9578 0.00 1.00
#> Sample_699 2 0.000 0.9578 0.00 1.00
#> Sample_700 1 0.000 0.9582 1.00 0.00
#> Sample_701 2 0.402 0.8894 0.08 0.92
#> Sample_702 1 0.000 0.9582 1.00 0.00
#> Sample_703 2 0.000 0.9578 0.00 1.00
#> Sample_706 1 0.000 0.9582 1.00 0.00
#> Sample_707 1 0.000 0.9582 1.00 0.00
#> Sample_708 1 0.000 0.9582 1.00 0.00
#> Sample_709 1 0.000 0.9582 1.00 0.00
#> Sample_711 2 0.000 0.9578 0.00 1.00
#> Sample_712 1 0.000 0.9582 1.00 0.00
#> Sample_713 1 0.000 0.9582 1.00 0.00
#> Sample_714 2 0.000 0.9578 0.00 1.00
#> Sample_715 2 0.000 0.9578 0.00 1.00
#> Sample_716 2 0.000 0.9578 0.00 1.00
#> Sample_717 1 0.000 0.9582 1.00 0.00
#> Sample_718 1 0.000 0.9582 1.00 0.00
#> Sample_719 1 0.000 0.9582 1.00 0.00
#> Sample_721 1 0.000 0.9582 1.00 0.00
#> Sample_722 1 0.000 0.9582 1.00 0.00
#> Sample_723 2 0.000 0.9578 0.00 1.00
#> Sample_724 1 0.722 0.7381 0.80 0.20
#> Sample_725 2 0.469 0.8705 0.10 0.90
#> Sample_726 1 0.000 0.9582 1.00 0.00
#> Sample_727 1 0.000 0.9582 1.00 0.00
#> Sample_728 2 0.000 0.9578 0.00 1.00
#> Sample_729 1 0.000 0.9582 1.00 0.00
#> Sample_730 1 0.000 0.9582 1.00 0.00
#> Sample_732 1 0.000 0.9582 1.00 0.00
#> Sample_733 1 0.000 0.9582 1.00 0.00
#> Sample_734 1 0.000 0.9582 1.00 0.00
#> Sample_736 1 0.000 0.9582 1.00 0.00
#> Sample_739 1 0.000 0.9582 1.00 0.00
#> Sample_740 2 0.000 0.9578 0.00 1.00
#> Sample_741 1 0.000 0.9582 1.00 0.00
#> Sample_742 2 0.995 0.1540 0.46 0.54
#> Sample_743 1 0.000 0.9582 1.00 0.00
#> Sample_744 1 0.000 0.9582 1.00 0.00
#> Sample_745 2 0.990 0.2232 0.44 0.56
#> Sample_746 1 0.000 0.9582 1.00 0.00
#> Sample_747 1 0.000 0.9582 1.00 0.00
#> Sample_748 2 0.141 0.9423 0.02 0.98
#> Sample_749 1 0.000 0.9582 1.00 0.00
#> Sample_750 1 0.000 0.9582 1.00 0.00
#> Sample_751 1 0.000 0.9582 1.00 0.00
#> Sample_752 1 0.000 0.9582 1.00 0.00
#> Sample_753 2 0.000 0.9578 0.00 1.00
#> Sample_754 2 0.000 0.9578 0.00 1.00
#> Sample_755 1 0.000 0.9582 1.00 0.00
#> Sample_756 1 0.000 0.9582 1.00 0.00
#> Sample_757 1 0.827 0.6420 0.74 0.26
#> Sample_758 1 0.000 0.9582 1.00 0.00
#> Sample_759 2 0.000 0.9578 0.00 1.00
#> Sample_760 1 0.000 0.9582 1.00 0.00
#> Sample_761 1 0.000 0.9582 1.00 0.00
#> Sample_762 1 0.000 0.9582 1.00 0.00
#> Sample_763 1 0.000 0.9582 1.00 0.00
#> Sample_764 1 0.000 0.9582 1.00 0.00
#> Sample_765 1 0.000 0.9582 1.00 0.00
#> Sample_766 2 0.327 0.9083 0.06 0.94
#> Sample_767 1 0.680 0.7683 0.82 0.18
#> Sample_769 2 0.000 0.9578 0.00 1.00
#> Sample_770 1 0.000 0.9582 1.00 0.00
#> Sample_771 1 0.000 0.9582 1.00 0.00
#> Sample_772 2 0.000 0.9578 0.00 1.00
#> Sample_773 1 0.000 0.9582 1.00 0.00
#> Sample_774 1 0.000 0.9582 1.00 0.00
#> Sample_775 2 0.000 0.9578 0.00 1.00
#> Sample_776 1 0.000 0.9582 1.00 0.00
#> Sample_777 2 0.000 0.9578 0.00 1.00
#> Sample_778 2 0.000 0.9578 0.00 1.00
#> Sample_779 2 0.000 0.9578 0.00 1.00
#> Sample_780 2 0.000 0.9578 0.00 1.00
#> Sample_781 1 0.000 0.9582 1.00 0.00
#> Sample_782 2 0.000 0.9578 0.00 1.00
#> Sample_783 2 0.000 0.9578 0.00 1.00
#> Sample_784 2 0.000 0.9578 0.00 1.00
#> Sample_785 2 0.000 0.9578 0.00 1.00
#> Sample_786 2 0.327 0.9074 0.06 0.94
#> Sample_787 1 0.000 0.9582 1.00 0.00
#> Sample_788 2 0.680 0.7746 0.18 0.82
#> Sample_789 2 0.469 0.8688 0.10 0.90
#> Sample_790 1 0.000 0.9582 1.00 0.00
#> Sample_791 1 0.000 0.9582 1.00 0.00
#> Sample_792 1 0.000 0.9582 1.00 0.00
#> Sample_793 2 0.000 0.9578 0.00 1.00
#> Sample_794 1 0.000 0.9582 1.00 0.00
#> Sample_795 1 0.000 0.9582 1.00 0.00
#> Sample_796 1 0.000 0.9582 1.00 0.00
#> Sample_797 2 0.000 0.9578 0.00 1.00
#> Sample_798 2 0.000 0.9578 0.00 1.00
#> Sample_799 2 0.881 0.5760 0.30 0.70
#> Sample_800 1 0.000 0.9582 1.00 0.00
#> Sample_801 1 0.000 0.9582 1.00 0.00
#> Sample_802 2 0.000 0.9578 0.00 1.00
#> Sample_803 1 0.000 0.9582 1.00 0.00
#> Sample_804 2 0.000 0.9578 0.00 1.00
#> Sample_805 1 0.469 0.8636 0.90 0.10
#> Sample_806 1 0.000 0.9582 1.00 0.00
#> Sample_807 1 0.000 0.9582 1.00 0.00
#> Sample_808 1 0.000 0.9582 1.00 0.00
#> Sample_809 1 0.000 0.9582 1.00 0.00
#> Sample_810 2 0.000 0.9578 0.00 1.00
#> Sample_811 1 0.000 0.9582 1.00 0.00
#> Sample_812 1 0.000 0.9582 1.00 0.00
#> Sample_813 1 0.000 0.9582 1.00 0.00
#> Sample_814 1 0.000 0.9582 1.00 0.00
#> Sample_815 1 0.000 0.9582 1.00 0.00
#> Sample_816 1 0.000 0.9582 1.00 0.00
#> Sample_817 2 0.000 0.9578 0.00 1.00
#> Sample_818 1 0.000 0.9582 1.00 0.00
#> Sample_819 2 0.000 0.9578 0.00 1.00
#> Sample_821 2 0.000 0.9578 0.00 1.00
#> Sample_822 1 0.000 0.9582 1.00 0.00
#> Sample_823 2 0.000 0.9578 0.00 1.00
#> Sample_824 2 0.000 0.9578 0.00 1.00
#> Sample_825 2 0.000 0.9578 0.00 1.00
#> Sample_826 2 0.000 0.9578 0.00 1.00
#> Sample_827 2 0.141 0.9423 0.02 0.98
#> Sample_828 1 0.000 0.9582 1.00 0.00
#> Sample_829 2 0.242 0.9262 0.04 0.96
#> Sample_830 2 0.402 0.8897 0.08 0.92
#> Sample_831 1 0.981 0.2753 0.58 0.42
#> Sample_832 1 0.000 0.9582 1.00 0.00
#> Sample_833 1 0.000 0.9582 1.00 0.00
#> Sample_834 2 0.000 0.9578 0.00 1.00
#> Sample_835 2 0.000 0.9578 0.00 1.00
#> Sample_837 1 0.000 0.9582 1.00 0.00
#> Sample_838 1 0.000 0.9582 1.00 0.00
#> Sample_839 2 0.000 0.9578 0.00 1.00
#> Sample_840 1 0.999 0.0675 0.52 0.48
#> Sample_855 2 0.000 0.9578 0.00 1.00
#> Sample_856 2 0.000 0.9578 0.00 1.00
#> Sample_857 2 0.000 0.9578 0.00 1.00
#> Sample_858 1 0.000 0.9582 1.00 0.00
#> Sample_860 2 0.000 0.9578 0.00 1.00
#> Sample_861 2 0.000 0.9578 0.00 1.00
#> Sample_862 2 0.634 0.7975 0.16 0.84
#> Sample_863 2 0.000 0.9578 0.00 1.00
#> Sample_864 1 0.000 0.9582 1.00 0.00
#> Sample_866 2 0.855 0.6136 0.28 0.72
#> Sample_867 2 0.141 0.9423 0.02 0.98
#> Sample_868 1 0.925 0.4874 0.66 0.34
#> Sample_869 2 0.000 0.9578 0.00 1.00
#> Sample_870 2 0.141 0.9423 0.02 0.98
#> Sample_871 2 0.000 0.9578 0.00 1.00
#> Sample_872 2 0.000 0.9578 0.00 1.00
#> Sample_873 2 0.000 0.9578 0.00 1.00
#> Sample_874 1 0.000 0.9582 1.00 0.00
#> Sample_875 1 0.000 0.9582 1.00 0.00
#> Sample_876 2 0.000 0.9578 0.00 1.00
#> Sample_877 1 0.000 0.9582 1.00 0.00
#> Sample_878 1 0.000 0.9582 1.00 0.00
#> Sample_879 1 0.827 0.6443 0.74 0.26
#> Sample_880 1 0.000 0.9582 1.00 0.00
#> Sample_881 2 0.000 0.9578 0.00 1.00
#> Sample_883 1 0.141 0.9412 0.98 0.02
#> Sample_884 1 0.141 0.9412 0.98 0.02
#> Sample_885 2 0.000 0.9578 0.00 1.00
#> Sample_886 1 0.000 0.9582 1.00 0.00
#> Sample_887 1 0.000 0.9582 1.00 0.00
#> Sample_888 1 0.634 0.7919 0.84 0.16
#> Sample_889 2 0.000 0.9578 0.00 1.00
#> Sample_890 2 0.000 0.9578 0.00 1.00
#> Sample_891 2 0.000 0.9578 0.00 1.00
#> Sample_892 2 0.000 0.9578 0.00 1.00
#> Sample_893 2 0.000 0.9578 0.00 1.00
#> Sample_895 2 0.881 0.5750 0.30 0.70
#> Sample_896 1 0.971 0.3367 0.60 0.40
#> Sample_897 2 0.000 0.9578 0.00 1.00
#> Sample_898 2 0.000 0.9578 0.00 1.00
#> Sample_899 1 0.971 0.3392 0.60 0.40
#> Sample_901 1 0.000 0.9582 1.00 0.00
#> Sample_902 2 0.000 0.9578 0.00 1.00
#> Sample_903 2 0.000 0.9578 0.00 1.00
#> Sample_904 1 0.000 0.9582 1.00 0.00
#> Sample_905 1 0.000 0.9582 1.00 0.00
#> Sample_906 2 0.000 0.9578 0.00 1.00
#> Sample_907 2 0.000 0.9578 0.00 1.00
#> Sample_908 2 0.000 0.9578 0.00 1.00
#> Sample_909 2 0.000 0.9578 0.00 1.00
#> Sample_910 1 0.000 0.9582 1.00 0.00
#> Sample_911 2 0.000 0.9578 0.00 1.00
#> Sample_912 1 0.995 0.1522 0.54 0.46
#> Sample_913 2 0.000 0.9578 0.00 1.00
#> Sample_914 2 0.000 0.9578 0.00 1.00
#> Sample_915 2 0.000 0.9578 0.00 1.00
#> Sample_916 1 0.000 0.9582 1.00 0.00
#> Sample_917 2 0.000 0.9578 0.00 1.00
#> Sample_918 2 0.000 0.9578 0.00 1.00
#> Sample_919 2 0.000 0.9578 0.00 1.00
#> Sample_920 2 0.925 0.4905 0.34 0.66
#> Sample_921 1 0.000 0.9582 1.00 0.00
#> Sample_922 2 0.000 0.9578 0.00 1.00
#> Sample_923 1 0.584 0.8187 0.86 0.14
#> Sample_924 2 0.000 0.9578 0.00 1.00
#> Sample_925 2 0.000 0.9578 0.00 1.00
#> Sample_926 1 0.999 0.0748 0.52 0.48
#> Sample_927 1 0.000 0.9582 1.00 0.00
#> Sample_928 1 0.000 0.9582 1.00 0.00
#> Sample_929 2 0.958 0.3943 0.38 0.62
#> Sample_930 1 0.000 0.9582 1.00 0.00
#> Sample_931 1 0.000 0.9582 1.00 0.00
#> Sample_932 2 0.971 0.3397 0.40 0.60
#> Sample_933 2 0.000 0.9578 0.00 1.00
#> Sample_934 2 0.584 0.8224 0.14 0.86
#> Sample_935 2 0.000 0.9578 0.00 1.00
#> Sample_936 2 0.000 0.9578 0.00 1.00
#> Sample_937 2 0.000 0.9578 0.00 1.00
#> Sample_938 2 0.000 0.9578 0.00 1.00
#> Sample_939 2 0.000 0.9578 0.00 1.00
#> Sample_940 2 0.000 0.9578 0.00 1.00
#> Sample_941 2 0.000 0.9578 0.00 1.00
#> Sample_942 2 0.000 0.9578 0.00 1.00
#> Sample_943 2 0.000 0.9578 0.00 1.00
#> Sample_944 2 0.999 0.0739 0.48 0.52
#> Sample_945 1 0.000 0.9582 1.00 0.00
#> Sample_946 1 0.000 0.9582 1.00 0.00
#> Sample_947 1 0.000 0.9582 1.00 0.00
#> Sample_1527 2 0.000 0.9578 0.00 1.00
#> Sample_1528 2 0.000 0.9578 0.00 1.00
#> Sample_1529 2 0.000 0.9578 0.00 1.00
#> Sample_1533 1 0.000 0.9582 1.00 0.00
#> Sample_1534 2 0.000 0.9578 0.00 1.00
#> Sample_1538 2 0.000 0.9578 0.00 1.00
#> Sample_1539 2 0.000 0.9578 0.00 1.00
#> Sample_1542 2 0.000 0.9578 0.00 1.00
#> Sample_1546 1 0.402 0.8854 0.92 0.08
#> Sample_1570 2 0.958 0.4005 0.38 0.62
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> Sample_72 3 0.5948 0.6218 0.36 0.00 0.64
#> Sample_111 2 0.0892 0.9251 0.00 0.98 0.02
#> Sample_147 3 0.6045 0.5970 0.38 0.00 0.62
#> Sample_148 1 0.6045 0.1248 0.62 0.00 0.38
#> Sample_149 3 0.2066 0.8074 0.06 0.00 0.94
#> Sample_150 3 0.1529 0.8090 0.04 0.00 0.96
#> Sample_151 2 0.0000 0.9281 0.00 1.00 0.00
#> Sample_152 3 0.5706 0.6634 0.32 0.00 0.68
#> Sample_153 3 0.6192 0.5192 0.42 0.00 0.58
#> Sample_154 3 0.6045 0.5970 0.38 0.00 0.62
#> Sample_155 3 0.4796 0.7412 0.22 0.00 0.78
#> Sample_156 3 0.6045 0.5970 0.38 0.00 0.62
#> Sample_157 3 0.5835 0.6435 0.34 0.00 0.66
#> Sample_158 3 0.6045 0.5970 0.38 0.00 0.62
#> Sample_159 2 0.1529 0.9061 0.00 0.96 0.04
#> Sample_160 3 0.6045 0.5970 0.38 0.00 0.62
#> Sample_161 2 0.0000 0.9281 0.00 1.00 0.00
#> Sample_162 2 0.0000 0.9281 0.00 1.00 0.00
#> Sample_163 2 0.2066 0.9129 0.00 0.94 0.06
#> Sample_164 2 0.0892 0.9186 0.00 0.98 0.02
#> Sample_165 2 0.0000 0.9281 0.00 1.00 0.00
#> Sample_166 2 0.0000 0.9281 0.00 1.00 0.00
#> Sample_167 3 0.6045 0.5970 0.38 0.00 0.62
#> Sample_168 3 0.6045 0.5970 0.38 0.00 0.62
#> Sample_169 2 0.4796 0.7095 0.00 0.78 0.22
#> Sample_170 3 0.6045 0.5970 0.38 0.00 0.62
#> Sample_172 3 0.7760 0.4586 0.06 0.36 0.58
#> Sample_174 2 0.0892 0.9251 0.00 0.98 0.02
#> Sample_175 3 0.6244 0.4777 0.44 0.00 0.56
#> Sample_176 1 0.6126 0.0454 0.60 0.00 0.40
#> Sample_177 2 0.0892 0.9251 0.00 0.98 0.02
#> Sample_178 3 0.5216 0.7140 0.26 0.00 0.74
#> Sample_179 2 0.2537 0.9011 0.00 0.92 0.08
#> Sample_180 2 0.0000 0.9281 0.00 1.00 0.00
#> Sample_181 3 0.6045 0.5970 0.38 0.00 0.62
#> Sample_182 1 0.6280 -0.2036 0.54 0.00 0.46
#> Sample_183 3 0.5835 0.6444 0.34 0.00 0.66
#> Sample_184 3 0.6045 0.5970 0.38 0.00 0.62
#> Sample_185 3 0.6045 0.5970 0.38 0.00 0.62
#> Sample_186 3 0.6045 0.5970 0.38 0.00 0.62
#> Sample_187 3 0.1529 0.8110 0.04 0.00 0.96
#> Sample_188 2 0.0000 0.9281 0.00 1.00 0.00
#> Sample_189 2 0.0000 0.9281 0.00 1.00 0.00
#> Sample_190 3 0.6192 0.5221 0.42 0.00 0.58
#> Sample_191 3 0.7633 0.6757 0.12 0.20 0.68
#> Sample_192 3 0.8733 0.5784 0.16 0.26 0.58
#> Sample_193 3 0.5948 0.6211 0.36 0.00 0.64
#> Sample_194 3 0.7760 0.5481 0.36 0.06 0.58
#> Sample_195 3 0.2947 0.8073 0.06 0.02 0.92
#> Sample_196 3 0.6849 0.5730 0.38 0.02 0.60
#> Sample_516 3 0.5835 0.6440 0.34 0.00 0.66
#> Sample_611 2 0.0892 0.9251 0.00 0.98 0.02
#> Sample_612 2 0.0000 0.9281 0.00 1.00 0.00
#> Sample_686 2 0.0000 0.9281 0.00 1.00 0.00
#> Sample_688 2 0.0000 0.9281 0.00 1.00 0.00
#> Sample_690 2 0.0000 0.9281 0.00 1.00 0.00
#> Sample_695 2 0.2959 0.8499 0.10 0.90 0.00
#> Sample_696 1 0.0892 0.9107 0.98 0.00 0.02
#> Sample_697 1 0.5016 0.6540 0.76 0.24 0.00
#> Sample_698 2 0.0000 0.9281 0.00 1.00 0.00
#> Sample_699 2 0.0000 0.9281 0.00 1.00 0.00
#> Sample_700 1 0.0000 0.9285 1.00 0.00 0.00
#> Sample_701 2 0.5835 0.4747 0.34 0.66 0.00
#> Sample_702 1 0.0000 0.9285 1.00 0.00 0.00
#> Sample_703 2 0.0000 0.9281 0.00 1.00 0.00
#> Sample_706 1 0.0000 0.9285 1.00 0.00 0.00
#> Sample_707 1 0.0000 0.9285 1.00 0.00 0.00
#> Sample_708 1 0.0000 0.9285 1.00 0.00 0.00
#> Sample_709 1 0.3340 0.8057 0.88 0.00 0.12
#> Sample_711 2 0.0000 0.9281 0.00 1.00 0.00
#> Sample_712 1 0.0000 0.9285 1.00 0.00 0.00
#> Sample_713 1 0.0000 0.9285 1.00 0.00 0.00
#> Sample_714 2 0.0000 0.9281 0.00 1.00 0.00
#> Sample_715 2 0.0000 0.9281 0.00 1.00 0.00
#> Sample_716 2 0.0000 0.9281 0.00 1.00 0.00
#> Sample_717 1 0.0000 0.9285 1.00 0.00 0.00
#> Sample_718 1 0.0000 0.9285 1.00 0.00 0.00
#> Sample_719 1 0.0892 0.9118 0.98 0.02 0.00
#> Sample_721 1 0.0000 0.9285 1.00 0.00 0.00
#> Sample_722 1 0.0000 0.9285 1.00 0.00 0.00
#> Sample_723 2 0.0000 0.9281 0.00 1.00 0.00
#> Sample_724 1 0.2537 0.8540 0.92 0.08 0.00
#> Sample_725 2 0.5216 0.6385 0.26 0.74 0.00
#> Sample_726 1 0.0000 0.9285 1.00 0.00 0.00
#> Sample_727 1 0.0000 0.9285 1.00 0.00 0.00
#> Sample_728 2 0.0000 0.9281 0.00 1.00 0.00
#> Sample_729 1 0.0000 0.9285 1.00 0.00 0.00
#> Sample_730 1 0.0000 0.9285 1.00 0.00 0.00
#> Sample_732 1 0.0000 0.9285 1.00 0.00 0.00
#> Sample_733 1 0.0000 0.9285 1.00 0.00 0.00
#> Sample_734 1 0.0892 0.9120 0.98 0.02 0.00
#> Sample_736 1 0.0000 0.9285 1.00 0.00 0.00
#> Sample_739 1 0.0000 0.9285 1.00 0.00 0.00
#> Sample_740 2 0.2959 0.8497 0.10 0.90 0.00
#> Sample_741 1 0.0000 0.9285 1.00 0.00 0.00
#> Sample_742 1 0.5948 0.4407 0.64 0.36 0.00
#> Sample_743 1 0.0000 0.9285 1.00 0.00 0.00
#> Sample_744 1 0.0000 0.9285 1.00 0.00 0.00
#> Sample_745 1 0.8587 0.2348 0.50 0.40 0.10
#> Sample_746 1 0.0000 0.9285 1.00 0.00 0.00
#> Sample_747 1 0.0000 0.9285 1.00 0.00 0.00
#> Sample_748 2 0.5948 0.4298 0.36 0.64 0.00
#> Sample_749 1 0.0000 0.9285 1.00 0.00 0.00
#> Sample_750 1 0.0000 0.9285 1.00 0.00 0.00
#> Sample_751 1 0.0000 0.9285 1.00 0.00 0.00
#> Sample_752 1 0.0000 0.9285 1.00 0.00 0.00
#> Sample_753 2 0.0000 0.9281 0.00 1.00 0.00
#> Sample_754 2 0.0000 0.9281 0.00 1.00 0.00
#> Sample_755 1 0.0000 0.9285 1.00 0.00 0.00
#> Sample_756 1 0.0000 0.9285 1.00 0.00 0.00
#> Sample_757 1 0.2066 0.8739 0.94 0.06 0.00
#> Sample_758 1 0.0000 0.9285 1.00 0.00 0.00
#> Sample_759 2 0.0000 0.9281 0.00 1.00 0.00
#> Sample_760 1 0.0000 0.9285 1.00 0.00 0.00
#> Sample_761 1 0.0000 0.9285 1.00 0.00 0.00
#> Sample_762 1 0.0000 0.9285 1.00 0.00 0.00
#> Sample_763 1 0.0000 0.9285 1.00 0.00 0.00
#> Sample_764 1 0.0000 0.9285 1.00 0.00 0.00
#> Sample_765 1 0.0000 0.9285 1.00 0.00 0.00
#> Sample_766 2 0.5216 0.6383 0.26 0.74 0.00
#> Sample_767 1 0.3572 0.8461 0.90 0.06 0.04
#> Sample_769 2 0.0000 0.9281 0.00 1.00 0.00
#> Sample_770 1 0.0000 0.9285 1.00 0.00 0.00
#> Sample_771 1 0.2066 0.8739 0.94 0.06 0.00
#> Sample_772 2 0.0000 0.9281 0.00 1.00 0.00
#> Sample_773 1 0.0000 0.9285 1.00 0.00 0.00
#> Sample_774 1 0.0000 0.9285 1.00 0.00 0.00
#> Sample_775 2 0.0000 0.9281 0.00 1.00 0.00
#> Sample_776 1 0.0000 0.9285 1.00 0.00 0.00
#> Sample_777 2 0.0000 0.9281 0.00 1.00 0.00
#> Sample_778 2 0.0000 0.9281 0.00 1.00 0.00
#> Sample_779 2 0.0000 0.9281 0.00 1.00 0.00
#> Sample_780 2 0.0000 0.9281 0.00 1.00 0.00
#> Sample_781 1 0.0000 0.9285 1.00 0.00 0.00
#> Sample_782 2 0.0000 0.9281 0.00 1.00 0.00
#> Sample_783 2 0.0000 0.9281 0.00 1.00 0.00
#> Sample_784 2 0.0000 0.9281 0.00 1.00 0.00
#> Sample_785 2 0.0000 0.9281 0.00 1.00 0.00
#> Sample_786 1 0.6244 0.2227 0.56 0.44 0.00
#> Sample_787 1 0.0000 0.9285 1.00 0.00 0.00
#> Sample_788 2 0.6192 0.2689 0.42 0.58 0.00
#> Sample_789 1 0.6302 0.0858 0.52 0.48 0.00
#> Sample_790 1 0.0000 0.9285 1.00 0.00 0.00
#> Sample_791 1 0.0000 0.9285 1.00 0.00 0.00
#> Sample_792 1 0.2066 0.8637 0.94 0.00 0.06
#> Sample_793 2 0.0000 0.9281 0.00 1.00 0.00
#> Sample_794 1 0.0000 0.9285 1.00 0.00 0.00
#> Sample_795 1 0.0000 0.9285 1.00 0.00 0.00
#> Sample_796 1 0.0000 0.9285 1.00 0.00 0.00
#> Sample_797 2 0.0000 0.9281 0.00 1.00 0.00
#> Sample_798 2 0.3340 0.8283 0.12 0.88 0.00
#> Sample_799 1 0.4002 0.7610 0.84 0.16 0.00
#> Sample_800 1 0.0000 0.9285 1.00 0.00 0.00
#> Sample_801 1 0.0000 0.9285 1.00 0.00 0.00
#> Sample_802 2 0.3415 0.8831 0.02 0.90 0.08
#> Sample_803 1 0.0000 0.9285 1.00 0.00 0.00
#> Sample_804 2 0.0892 0.9173 0.02 0.98 0.00
#> Sample_805 1 0.0892 0.9117 0.98 0.02 0.00
#> Sample_806 1 0.0000 0.9285 1.00 0.00 0.00
#> Sample_807 1 0.0000 0.9285 1.00 0.00 0.00
#> Sample_808 1 0.0000 0.9285 1.00 0.00 0.00
#> Sample_809 1 0.0000 0.9285 1.00 0.00 0.00
#> Sample_810 2 0.0000 0.9281 0.00 1.00 0.00
#> Sample_811 1 0.0000 0.9285 1.00 0.00 0.00
#> Sample_812 1 0.0000 0.9285 1.00 0.00 0.00
#> Sample_813 1 0.0000 0.9285 1.00 0.00 0.00
#> Sample_814 1 0.1529 0.8937 0.96 0.04 0.00
#> Sample_815 1 0.0000 0.9285 1.00 0.00 0.00
#> Sample_816 1 0.0000 0.9285 1.00 0.00 0.00
#> Sample_817 2 0.0000 0.9281 0.00 1.00 0.00
#> Sample_818 1 0.0000 0.9285 1.00 0.00 0.00
#> Sample_819 2 0.6280 0.1342 0.46 0.54 0.00
#> Sample_821 2 0.0000 0.9281 0.00 1.00 0.00
#> Sample_822 1 0.0000 0.9285 1.00 0.00 0.00
#> Sample_823 2 0.0000 0.9281 0.00 1.00 0.00
#> Sample_824 2 0.0000 0.9281 0.00 1.00 0.00
#> Sample_825 2 0.0000 0.9281 0.00 1.00 0.00
#> Sample_826 2 0.0000 0.9281 0.00 1.00 0.00
#> Sample_827 2 0.6244 0.2028 0.44 0.56 0.00
#> Sample_828 1 0.0000 0.9285 1.00 0.00 0.00
#> Sample_829 2 0.7586 -0.0235 0.48 0.48 0.04
#> Sample_830 1 0.5835 0.4880 0.66 0.34 0.00
#> Sample_831 1 0.3686 0.7834 0.86 0.14 0.00
#> Sample_832 1 0.0892 0.9118 0.98 0.02 0.00
#> Sample_833 1 0.0000 0.9285 1.00 0.00 0.00
#> Sample_834 2 0.0000 0.9281 0.00 1.00 0.00
#> Sample_835 2 0.0000 0.9281 0.00 1.00 0.00
#> Sample_837 1 0.0000 0.9285 1.00 0.00 0.00
#> Sample_838 1 0.0000 0.9285 1.00 0.00 0.00
#> Sample_839 2 0.0000 0.9281 0.00 1.00 0.00
#> Sample_840 1 0.2537 0.8545 0.92 0.08 0.00
#> Sample_855 2 0.0000 0.9281 0.00 1.00 0.00
#> Sample_856 2 0.3686 0.8529 0.00 0.86 0.14
#> Sample_857 2 0.5560 0.6491 0.00 0.70 0.30
#> Sample_858 1 0.4002 0.7245 0.84 0.00 0.16
#> Sample_860 2 0.2066 0.9129 0.00 0.94 0.06
#> Sample_861 3 0.6302 -0.1365 0.00 0.48 0.52
#> Sample_862 3 0.0000 0.8081 0.00 0.00 1.00
#> Sample_863 2 0.2066 0.9129 0.00 0.94 0.06
#> Sample_864 3 0.1529 0.8095 0.04 0.00 0.96
#> Sample_866 3 0.4551 0.6969 0.02 0.14 0.84
#> Sample_867 3 0.3686 0.7029 0.00 0.14 0.86
#> Sample_868 3 0.0000 0.8081 0.00 0.00 1.00
#> Sample_869 2 0.6126 0.4647 0.00 0.60 0.40
#> Sample_870 3 0.2959 0.7521 0.00 0.10 0.90
#> Sample_871 2 0.4796 0.7627 0.00 0.78 0.22
#> Sample_872 2 0.2066 0.9129 0.00 0.94 0.06
#> Sample_873 2 0.2066 0.9129 0.00 0.94 0.06
#> Sample_874 3 0.2959 0.7978 0.10 0.00 0.90
#> Sample_875 3 0.0000 0.8081 0.00 0.00 1.00
#> Sample_876 2 0.1529 0.9200 0.00 0.96 0.04
#> Sample_877 3 0.0000 0.8081 0.00 0.00 1.00
#> Sample_878 3 0.4555 0.7541 0.20 0.00 0.80
#> Sample_879 3 0.0000 0.8081 0.00 0.00 1.00
#> Sample_880 3 0.0000 0.8081 0.00 0.00 1.00
#> Sample_881 3 0.5835 0.3268 0.00 0.34 0.66
#> Sample_883 3 0.0000 0.8081 0.00 0.00 1.00
#> Sample_884 3 0.0000 0.8081 0.00 0.00 1.00
#> Sample_885 2 0.0000 0.9281 0.00 1.00 0.00
#> Sample_886 3 0.2959 0.7993 0.10 0.00 0.90
#> Sample_887 3 0.2066 0.8074 0.06 0.00 0.94
#> Sample_888 3 0.0000 0.8081 0.00 0.00 1.00
#> Sample_889 2 0.2959 0.8853 0.00 0.90 0.10
#> Sample_890 2 0.4291 0.8105 0.00 0.82 0.18
#> Sample_891 2 0.2066 0.9129 0.00 0.94 0.06
#> Sample_892 2 0.2066 0.9129 0.00 0.94 0.06
#> Sample_893 2 0.2066 0.9129 0.00 0.94 0.06
#> Sample_895 3 0.0000 0.8081 0.00 0.00 1.00
#> Sample_896 3 0.0000 0.8081 0.00 0.00 1.00
#> Sample_897 2 0.2066 0.9129 0.00 0.94 0.06
#> Sample_898 3 0.4002 0.6855 0.00 0.16 0.84
#> Sample_899 3 0.0000 0.8081 0.00 0.00 1.00
#> Sample_901 3 0.0000 0.8081 0.00 0.00 1.00
#> Sample_902 2 0.2066 0.9129 0.00 0.94 0.06
#> Sample_903 2 0.4002 0.8312 0.00 0.84 0.16
#> Sample_904 3 0.2066 0.8074 0.06 0.00 0.94
#> Sample_905 3 0.2066 0.8093 0.06 0.00 0.94
#> Sample_906 3 0.3686 0.7032 0.00 0.14 0.86
#> Sample_907 2 0.2066 0.9129 0.00 0.94 0.06
#> Sample_908 2 0.2066 0.9129 0.00 0.94 0.06
#> Sample_909 2 0.2066 0.9129 0.00 0.94 0.06
#> Sample_910 3 0.2066 0.8074 0.06 0.00 0.94
#> Sample_911 2 0.2066 0.9129 0.00 0.94 0.06
#> Sample_912 3 0.0000 0.8081 0.00 0.00 1.00
#> Sample_913 2 0.2066 0.9129 0.00 0.94 0.06
#> Sample_914 2 0.2066 0.9129 0.00 0.94 0.06
#> Sample_915 2 0.2066 0.9129 0.00 0.94 0.06
#> Sample_916 3 0.0000 0.8081 0.00 0.00 1.00
#> Sample_917 2 0.2066 0.9129 0.00 0.94 0.06
#> Sample_918 2 0.2066 0.9129 0.00 0.94 0.06
#> Sample_919 2 0.2066 0.9129 0.00 0.94 0.06
#> Sample_920 3 0.0000 0.8081 0.00 0.00 1.00
#> Sample_921 3 0.2537 0.8033 0.08 0.00 0.92
#> Sample_922 2 0.0892 0.9251 0.00 0.98 0.02
#> Sample_923 3 0.0000 0.8081 0.00 0.00 1.00
#> Sample_924 2 0.2066 0.9129 0.00 0.94 0.06
#> Sample_925 2 0.5216 0.7092 0.00 0.74 0.26
#> Sample_926 3 0.0000 0.8081 0.00 0.00 1.00
#> Sample_927 3 0.4555 0.7519 0.20 0.00 0.80
#> Sample_928 3 0.5397 0.6992 0.28 0.00 0.72
#> Sample_929 3 0.0000 0.8081 0.00 0.00 1.00
#> Sample_930 3 0.1529 0.8095 0.04 0.00 0.96
#> Sample_931 3 0.2066 0.8074 0.06 0.00 0.94
#> Sample_932 3 0.0000 0.8081 0.00 0.00 1.00
#> Sample_933 2 0.4555 0.7866 0.00 0.80 0.20
#> Sample_934 3 0.0000 0.8081 0.00 0.00 1.00
#> Sample_935 2 0.2066 0.9129 0.00 0.94 0.06
#> Sample_936 2 0.2537 0.9006 0.00 0.92 0.08
#> Sample_937 2 0.2066 0.9129 0.00 0.94 0.06
#> Sample_938 2 0.2066 0.9129 0.00 0.94 0.06
#> Sample_939 2 0.5397 0.6800 0.00 0.72 0.28
#> Sample_940 2 0.1529 0.9199 0.00 0.96 0.04
#> Sample_941 2 0.0000 0.9281 0.00 1.00 0.00
#> Sample_942 2 0.1529 0.9199 0.00 0.96 0.04
#> Sample_943 2 0.1529 0.9199 0.00 0.96 0.04
#> Sample_944 3 0.0000 0.8081 0.00 0.00 1.00
#> Sample_945 3 0.2066 0.8074 0.06 0.00 0.94
#> Sample_946 3 0.0892 0.8091 0.02 0.00 0.98
#> Sample_947 3 0.2066 0.8074 0.06 0.00 0.94
#> Sample_1527 2 0.0892 0.9251 0.00 0.98 0.02
#> Sample_1528 2 0.0000 0.9281 0.00 1.00 0.00
#> Sample_1529 2 0.0000 0.9281 0.00 1.00 0.00
#> Sample_1533 1 0.0000 0.9285 1.00 0.00 0.00
#> Sample_1534 2 0.0000 0.9281 0.00 1.00 0.00
#> Sample_1538 2 0.0000 0.9281 0.00 1.00 0.00
#> Sample_1539 2 0.0892 0.9251 0.00 0.98 0.02
#> Sample_1542 2 0.0892 0.9251 0.00 0.98 0.02
#> Sample_1546 3 0.0000 0.8081 0.00 0.00 1.00
#> Sample_1570 1 0.4555 0.7039 0.80 0.20 0.00
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> Sample_72 1 0.6766 0.43223 0.52 0.00 0.10 0.38
#> Sample_111 2 0.0000 0.85369 0.00 1.00 0.00 0.00
#> Sample_147 1 0.6554 0.42898 0.52 0.00 0.08 0.40
#> Sample_148 1 0.6011 0.36904 0.48 0.00 0.04 0.48
#> Sample_149 3 0.6089 0.58730 0.08 0.00 0.64 0.28
#> Sample_150 3 0.7867 0.38499 0.16 0.02 0.48 0.34
#> Sample_151 2 0.4624 0.48945 0.00 0.66 0.00 0.34
#> Sample_152 1 0.6766 0.43246 0.52 0.00 0.10 0.38
#> Sample_153 4 0.5987 -0.33981 0.44 0.00 0.04 0.52
#> Sample_154 1 0.6510 0.44878 0.54 0.00 0.08 0.38
#> Sample_155 1 0.7206 0.36844 0.46 0.00 0.14 0.40
#> Sample_156 1 0.6510 0.44878 0.54 0.00 0.08 0.38
#> Sample_157 1 0.6766 0.43163 0.52 0.00 0.10 0.38
#> Sample_158 1 0.6510 0.44878 0.54 0.00 0.08 0.38
#> Sample_159 4 0.4790 -0.00595 0.00 0.38 0.00 0.62
#> Sample_160 1 0.6554 0.42898 0.52 0.00 0.08 0.40
#> Sample_161 2 0.4855 0.39546 0.00 0.60 0.00 0.40
#> Sample_162 2 0.3400 0.70717 0.00 0.82 0.00 0.18
#> Sample_163 2 0.0707 0.85038 0.00 0.98 0.02 0.00
#> Sample_164 2 0.4907 0.39606 0.00 0.58 0.00 0.42
#> Sample_165 2 0.0000 0.85369 0.00 1.00 0.00 0.00
#> Sample_166 2 0.3801 0.71324 0.00 0.78 0.00 0.22
#> Sample_167 1 0.5860 0.47019 0.58 0.00 0.04 0.38
#> Sample_168 1 0.6212 0.45953 0.56 0.00 0.06 0.38
#> Sample_169 2 0.5487 0.34344 0.02 0.58 0.00 0.40
#> Sample_170 1 0.6510 0.44878 0.54 0.00 0.08 0.38
#> Sample_172 4 0.8581 -0.06268 0.08 0.40 0.12 0.40
#> Sample_174 2 0.4624 0.47328 0.00 0.66 0.00 0.34
#> Sample_175 1 0.6262 0.44301 0.54 0.00 0.06 0.40
#> Sample_176 1 0.4642 0.57067 0.74 0.00 0.02 0.24
#> Sample_177 2 0.0707 0.85038 0.00 0.98 0.02 0.00
#> Sample_178 1 0.7021 0.39105 0.48 0.00 0.12 0.40
#> Sample_179 2 0.1913 0.83530 0.00 0.94 0.02 0.04
#> Sample_180 2 0.0000 0.85369 0.00 1.00 0.00 0.00
#> Sample_181 1 0.6510 0.44878 0.54 0.00 0.08 0.38
#> Sample_182 1 0.5957 0.43921 0.54 0.00 0.04 0.42
#> Sample_183 4 0.6831 -0.33359 0.42 0.00 0.10 0.48
#> Sample_184 1 0.6510 0.44878 0.54 0.00 0.08 0.38
#> Sample_185 1 0.6766 0.43163 0.52 0.00 0.10 0.38
#> Sample_186 1 0.6510 0.44878 0.54 0.00 0.08 0.38
#> Sample_187 3 0.8186 0.31273 0.14 0.04 0.42 0.40
#> Sample_188 2 0.2647 0.81057 0.00 0.88 0.00 0.12
#> Sample_189 2 0.0000 0.85369 0.00 1.00 0.00 0.00
#> Sample_190 1 0.5860 0.47019 0.58 0.00 0.04 0.38
#> Sample_191 4 0.6708 0.23523 0.04 0.18 0.10 0.68
#> Sample_192 4 0.6336 0.24585 0.14 0.14 0.02 0.70
#> Sample_193 1 0.6510 0.44878 0.54 0.00 0.08 0.38
#> Sample_194 4 0.0707 0.42596 0.02 0.00 0.00 0.98
#> Sample_195 4 0.8829 -0.22640 0.20 0.06 0.34 0.40
#> Sample_196 4 0.2335 0.37999 0.02 0.00 0.06 0.92
#> Sample_516 1 0.6554 0.43153 0.52 0.00 0.08 0.40
#> Sample_611 2 0.0000 0.85369 0.00 1.00 0.00 0.00
#> Sample_612 2 0.0000 0.85369 0.00 1.00 0.00 0.00
#> Sample_686 2 0.0000 0.85369 0.00 1.00 0.00 0.00
#> Sample_688 2 0.0000 0.85369 0.00 1.00 0.00 0.00
#> Sample_690 2 0.0000 0.85369 0.00 1.00 0.00 0.00
#> Sample_695 4 0.5355 0.31147 0.02 0.36 0.00 0.62
#> Sample_696 1 0.0000 0.73255 1.00 0.00 0.00 0.00
#> Sample_697 4 0.6150 0.54880 0.36 0.06 0.00 0.58
#> Sample_698 2 0.4907 0.30587 0.00 0.58 0.00 0.42
#> Sample_699 2 0.4522 0.54276 0.00 0.68 0.00 0.32
#> Sample_700 1 0.0000 0.73255 1.00 0.00 0.00 0.00
#> Sample_701 4 0.7004 0.58053 0.20 0.22 0.00 0.58
#> Sample_702 1 0.0000 0.73255 1.00 0.00 0.00 0.00
#> Sample_703 2 0.0707 0.84950 0.00 0.98 0.00 0.02
#> Sample_706 1 0.4624 0.11268 0.66 0.00 0.00 0.34
#> Sample_707 1 0.0000 0.73255 1.00 0.00 0.00 0.00
#> Sample_708 4 0.4994 0.39933 0.48 0.00 0.00 0.52
#> Sample_709 1 0.7285 -0.11594 0.52 0.00 0.18 0.30
#> Sample_711 2 0.2345 0.81668 0.00 0.90 0.00 0.10
#> Sample_712 1 0.0000 0.73255 1.00 0.00 0.00 0.00
#> Sample_713 1 0.4855 -0.04261 0.60 0.00 0.00 0.40
#> Sample_714 2 0.1637 0.83665 0.00 0.94 0.00 0.06
#> Sample_715 2 0.0000 0.85369 0.00 1.00 0.00 0.00
#> Sample_716 2 0.2647 0.80345 0.00 0.88 0.00 0.12
#> Sample_717 1 0.1637 0.67334 0.94 0.00 0.00 0.06
#> Sample_718 1 0.0000 0.73255 1.00 0.00 0.00 0.00
#> Sample_719 4 0.4948 0.46797 0.44 0.00 0.00 0.56
#> Sample_721 4 0.4977 0.43097 0.46 0.00 0.00 0.54
#> Sample_722 1 0.0000 0.73255 1.00 0.00 0.00 0.00
#> Sample_723 2 0.4624 0.50350 0.00 0.66 0.00 0.34
#> Sample_724 4 0.5428 0.53647 0.38 0.02 0.00 0.60
#> Sample_725 4 0.6805 0.11235 0.10 0.40 0.00 0.50
#> Sample_726 1 0.0000 0.73255 1.00 0.00 0.00 0.00
#> Sample_727 1 0.0000 0.73255 1.00 0.00 0.00 0.00
#> Sample_728 2 0.3400 0.75036 0.00 0.82 0.00 0.18
#> Sample_729 1 0.0000 0.73255 1.00 0.00 0.00 0.00
#> Sample_730 1 0.4855 -0.10416 0.60 0.00 0.00 0.40
#> Sample_732 1 0.0000 0.73255 1.00 0.00 0.00 0.00
#> Sample_733 1 0.3400 0.50504 0.82 0.00 0.00 0.18
#> Sample_734 4 0.4907 0.49407 0.42 0.00 0.00 0.58
#> Sample_736 1 0.4277 0.28773 0.72 0.00 0.00 0.28
#> Sample_739 1 0.2647 0.60135 0.88 0.00 0.00 0.12
#> Sample_740 4 0.5606 -0.03115 0.02 0.48 0.00 0.50
#> Sample_741 1 0.0000 0.73255 1.00 0.00 0.00 0.00
#> Sample_742 1 0.7610 -0.42720 0.40 0.20 0.00 0.40
#> Sample_743 1 0.0000 0.73255 1.00 0.00 0.00 0.00
#> Sample_744 1 0.4406 0.23614 0.70 0.00 0.00 0.30
#> Sample_745 4 0.4827 0.49136 0.06 0.06 0.06 0.82
#> Sample_746 1 0.0000 0.73255 1.00 0.00 0.00 0.00
#> Sample_747 1 0.0000 0.73255 1.00 0.00 0.00 0.00
#> Sample_748 4 0.6714 0.36238 0.10 0.36 0.00 0.54
#> Sample_749 1 0.0000 0.73255 1.00 0.00 0.00 0.00
#> Sample_750 1 0.0000 0.73255 1.00 0.00 0.00 0.00
#> Sample_751 1 0.0000 0.73255 1.00 0.00 0.00 0.00
#> Sample_752 1 0.2011 0.64910 0.92 0.00 0.00 0.08
#> Sample_753 2 0.3801 0.70040 0.00 0.78 0.00 0.22
#> Sample_754 2 0.3975 0.67581 0.00 0.76 0.00 0.24
#> Sample_755 1 0.0000 0.73255 1.00 0.00 0.00 0.00
#> Sample_756 4 0.4977 0.43456 0.46 0.00 0.00 0.54
#> Sample_757 4 0.4790 0.53082 0.38 0.00 0.00 0.62
#> Sample_758 1 0.0000 0.73255 1.00 0.00 0.00 0.00
#> Sample_759 2 0.0000 0.85369 0.00 1.00 0.00 0.00
#> Sample_760 1 0.0000 0.73255 1.00 0.00 0.00 0.00
#> Sample_761 1 0.0000 0.73255 1.00 0.00 0.00 0.00
#> Sample_762 1 0.5000 -0.36411 0.50 0.00 0.00 0.50
#> Sample_763 1 0.0000 0.73255 1.00 0.00 0.00 0.00
#> Sample_764 1 0.1637 0.67556 0.94 0.00 0.00 0.06
#> Sample_765 1 0.0000 0.73255 1.00 0.00 0.00 0.00
#> Sample_766 4 0.7232 0.54587 0.14 0.24 0.02 0.60
#> Sample_767 4 0.5915 0.49381 0.40 0.00 0.04 0.56
#> Sample_769 2 0.4406 0.57822 0.00 0.70 0.00 0.30
#> Sample_770 1 0.0000 0.73255 1.00 0.00 0.00 0.00
#> Sample_771 4 0.4855 0.51268 0.40 0.00 0.00 0.60
#> Sample_772 2 0.2647 0.80345 0.00 0.88 0.00 0.12
#> Sample_773 4 0.4977 0.43456 0.46 0.00 0.00 0.54
#> Sample_774 1 0.0000 0.73255 1.00 0.00 0.00 0.00
#> Sample_775 2 0.2647 0.80345 0.00 0.88 0.00 0.12
#> Sample_776 1 0.0000 0.73255 1.00 0.00 0.00 0.00
#> Sample_777 2 0.0707 0.84974 0.00 0.98 0.00 0.02
#> Sample_778 2 0.2647 0.80345 0.00 0.88 0.00 0.12
#> Sample_779 2 0.2345 0.81927 0.00 0.90 0.00 0.10
#> Sample_780 2 0.0000 0.85369 0.00 1.00 0.00 0.00
#> Sample_781 1 0.0000 0.73255 1.00 0.00 0.00 0.00
#> Sample_782 2 0.2647 0.80345 0.00 0.88 0.00 0.12
#> Sample_783 2 0.0707 0.84974 0.00 0.98 0.00 0.02
#> Sample_784 2 0.1211 0.84342 0.00 0.96 0.00 0.04
#> Sample_785 2 0.4522 0.55518 0.00 0.68 0.00 0.32
#> Sample_786 4 0.6656 0.57331 0.16 0.22 0.00 0.62
#> Sample_787 1 0.0000 0.73255 1.00 0.00 0.00 0.00
#> Sample_788 2 0.7456 0.01508 0.18 0.46 0.00 0.36
#> Sample_789 4 0.4731 0.55008 0.06 0.16 0.00 0.78
#> Sample_790 1 0.0000 0.73255 1.00 0.00 0.00 0.00
#> Sample_791 1 0.4134 0.32886 0.74 0.00 0.00 0.26
#> Sample_792 1 0.1211 0.70655 0.96 0.00 0.04 0.00
#> Sample_793 2 0.4624 0.50820 0.00 0.66 0.00 0.34
#> Sample_794 1 0.0000 0.73255 1.00 0.00 0.00 0.00
#> Sample_795 1 0.0000 0.73255 1.00 0.00 0.00 0.00
#> Sample_796 4 0.4907 0.49487 0.42 0.00 0.00 0.58
#> Sample_797 2 0.2345 0.81668 0.00 0.90 0.00 0.10
#> Sample_798 4 0.5535 0.17244 0.02 0.42 0.00 0.56
#> Sample_799 4 0.5713 0.55938 0.34 0.04 0.00 0.62
#> Sample_800 1 0.0000 0.73255 1.00 0.00 0.00 0.00
#> Sample_801 1 0.0707 0.71457 0.98 0.00 0.00 0.02
#> Sample_802 2 0.5327 0.65498 0.00 0.72 0.22 0.06
#> Sample_803 1 0.0000 0.73255 1.00 0.00 0.00 0.00
#> Sample_804 4 0.4907 0.17882 0.00 0.42 0.00 0.58
#> Sample_805 4 0.5428 0.53535 0.38 0.02 0.00 0.60
#> Sample_806 1 0.0000 0.73255 1.00 0.00 0.00 0.00
#> Sample_807 1 0.0000 0.73255 1.00 0.00 0.00 0.00
#> Sample_808 1 0.0000 0.73255 1.00 0.00 0.00 0.00
#> Sample_809 1 0.0000 0.73255 1.00 0.00 0.00 0.00
#> Sample_810 2 0.2647 0.80345 0.00 0.88 0.00 0.12
#> Sample_811 1 0.0000 0.73255 1.00 0.00 0.00 0.00
#> Sample_812 1 0.0000 0.73255 1.00 0.00 0.00 0.00
#> Sample_813 1 0.0707 0.72237 0.98 0.00 0.00 0.02
#> Sample_814 4 0.4790 0.53082 0.38 0.00 0.00 0.62
#> Sample_815 1 0.0000 0.73255 1.00 0.00 0.00 0.00
#> Sample_816 1 0.0000 0.73255 1.00 0.00 0.00 0.00
#> Sample_817 2 0.2011 0.82755 0.00 0.92 0.00 0.08
#> Sample_818 4 0.4907 0.49027 0.42 0.00 0.00 0.58
#> Sample_819 4 0.6594 0.54666 0.14 0.24 0.00 0.62
#> Sample_821 4 0.4994 -0.01193 0.00 0.48 0.00 0.52
#> Sample_822 1 0.5000 -0.36041 0.50 0.00 0.00 0.50
#> Sample_823 2 0.2921 0.79046 0.00 0.86 0.00 0.14
#> Sample_824 2 0.0000 0.85369 0.00 1.00 0.00 0.00
#> Sample_825 2 0.4406 0.59727 0.00 0.70 0.00 0.30
#> Sample_826 2 0.4948 0.25479 0.00 0.56 0.00 0.44
#> Sample_827 4 0.6921 0.53078 0.16 0.26 0.00 0.58
#> Sample_828 1 0.0000 0.73255 1.00 0.00 0.00 0.00
#> Sample_829 4 0.7770 0.57690 0.22 0.22 0.02 0.54
#> Sample_830 4 0.6370 0.58033 0.28 0.10 0.00 0.62
#> Sample_831 4 0.5355 0.52852 0.36 0.00 0.02 0.62
#> Sample_832 4 0.4907 0.49407 0.42 0.00 0.00 0.58
#> Sample_833 1 0.0000 0.73255 1.00 0.00 0.00 0.00
#> Sample_834 2 0.2345 0.81640 0.00 0.90 0.00 0.10
#> Sample_835 2 0.2345 0.81690 0.00 0.90 0.00 0.10
#> Sample_837 1 0.0000 0.73255 1.00 0.00 0.00 0.00
#> Sample_838 1 0.3172 0.53149 0.84 0.00 0.00 0.16
#> Sample_839 2 0.4948 0.24935 0.00 0.56 0.00 0.44
#> Sample_840 4 0.5619 0.56359 0.32 0.04 0.00 0.64
#> Sample_855 2 0.4079 0.75956 0.00 0.80 0.02 0.18
#> Sample_856 2 0.3801 0.69514 0.00 0.78 0.22 0.00
#> Sample_857 3 0.4406 0.55335 0.00 0.30 0.70 0.00
#> Sample_858 1 0.3525 0.64558 0.86 0.00 0.10 0.04
#> Sample_860 2 0.1211 0.84586 0.00 0.96 0.04 0.00
#> Sample_861 3 0.4277 0.59407 0.00 0.28 0.72 0.00
#> Sample_862 3 0.1211 0.83917 0.00 0.04 0.96 0.00
#> Sample_863 2 0.1637 0.83857 0.00 0.94 0.06 0.00
#> Sample_864 3 0.1913 0.83547 0.04 0.00 0.94 0.02
#> Sample_866 3 0.1211 0.83994 0.00 0.04 0.96 0.00
#> Sample_867 3 0.1211 0.83917 0.00 0.04 0.96 0.00
#> Sample_868 3 0.0000 0.86104 0.00 0.00 1.00 0.00
#> Sample_869 3 0.3400 0.72087 0.00 0.18 0.82 0.00
#> Sample_870 3 0.4088 0.75714 0.00 0.14 0.82 0.04
#> Sample_871 2 0.4277 0.61030 0.00 0.72 0.28 0.00
#> Sample_872 2 0.1211 0.84586 0.00 0.96 0.04 0.00
#> Sample_873 2 0.1211 0.84586 0.00 0.96 0.04 0.00
#> Sample_874 3 0.6262 0.24448 0.40 0.00 0.54 0.06
#> Sample_875 3 0.0000 0.86104 0.00 0.00 1.00 0.00
#> Sample_876 2 0.0000 0.85369 0.00 1.00 0.00 0.00
#> Sample_877 3 0.0000 0.86104 0.00 0.00 1.00 0.00
#> Sample_878 3 0.5512 0.51954 0.30 0.00 0.66 0.04
#> Sample_879 3 0.0000 0.86104 0.00 0.00 1.00 0.00
#> Sample_880 3 0.0000 0.86104 0.00 0.00 1.00 0.00
#> Sample_881 3 0.3172 0.74894 0.00 0.16 0.84 0.00
#> Sample_883 3 0.0000 0.86104 0.00 0.00 1.00 0.00
#> Sample_884 3 0.0000 0.86104 0.00 0.00 1.00 0.00
#> Sample_885 2 0.0000 0.85369 0.00 1.00 0.00 0.00
#> Sample_886 3 0.4553 0.74322 0.04 0.00 0.78 0.18
#> Sample_887 3 0.2335 0.82430 0.06 0.00 0.92 0.02
#> Sample_888 3 0.0000 0.86104 0.00 0.00 1.00 0.00
#> Sample_889 2 0.2647 0.79433 0.00 0.88 0.12 0.00
#> Sample_890 2 0.6941 0.28988 0.00 0.52 0.36 0.12
#> Sample_891 2 0.1211 0.84586 0.00 0.96 0.04 0.00
#> Sample_892 2 0.1211 0.84586 0.00 0.96 0.04 0.00
#> Sample_893 2 0.1211 0.84586 0.00 0.96 0.04 0.00
#> Sample_895 3 0.0000 0.86104 0.00 0.00 1.00 0.00
#> Sample_896 3 0.0000 0.86104 0.00 0.00 1.00 0.00
#> Sample_897 2 0.2011 0.82348 0.00 0.92 0.08 0.00
#> Sample_898 3 0.4332 0.73489 0.00 0.16 0.80 0.04
#> Sample_899 3 0.0000 0.86104 0.00 0.00 1.00 0.00
#> Sample_901 3 0.0000 0.86104 0.00 0.00 1.00 0.00
#> Sample_902 2 0.1211 0.84586 0.00 0.96 0.04 0.00
#> Sample_903 2 0.3801 0.68879 0.00 0.78 0.22 0.00
#> Sample_904 3 0.2345 0.80595 0.10 0.00 0.90 0.00
#> Sample_905 3 0.4949 0.69436 0.18 0.00 0.76 0.06
#> Sample_906 3 0.1637 0.82411 0.00 0.06 0.94 0.00
#> Sample_907 2 0.1211 0.84586 0.00 0.96 0.04 0.00
#> Sample_908 2 0.1211 0.84586 0.00 0.96 0.04 0.00
#> Sample_909 2 0.2647 0.78935 0.00 0.88 0.12 0.00
#> Sample_910 3 0.2921 0.77181 0.14 0.00 0.86 0.00
#> Sample_911 2 0.1211 0.84586 0.00 0.96 0.04 0.00
#> Sample_912 3 0.0000 0.86104 0.00 0.00 1.00 0.00
#> Sample_913 2 0.1211 0.84586 0.00 0.96 0.04 0.00
#> Sample_914 2 0.0707 0.85038 0.00 0.98 0.02 0.00
#> Sample_915 2 0.0000 0.85369 0.00 1.00 0.00 0.00
#> Sample_916 3 0.0000 0.86104 0.00 0.00 1.00 0.00
#> Sample_917 2 0.1211 0.84586 0.00 0.96 0.04 0.00
#> Sample_918 2 0.0000 0.85369 0.00 1.00 0.00 0.00
#> Sample_919 2 0.1211 0.84586 0.00 0.96 0.04 0.00
#> Sample_920 3 0.0000 0.86104 0.00 0.00 1.00 0.00
#> Sample_921 3 0.5489 0.64380 0.06 0.00 0.70 0.24
#> Sample_922 2 0.0000 0.85369 0.00 1.00 0.00 0.00
#> Sample_923 3 0.0000 0.86104 0.00 0.00 1.00 0.00
#> Sample_924 2 0.1211 0.84586 0.00 0.96 0.04 0.00
#> Sample_925 2 0.4977 0.17520 0.00 0.54 0.46 0.00
#> Sample_926 3 0.0000 0.86104 0.00 0.00 1.00 0.00
#> Sample_927 3 0.5355 0.40852 0.36 0.00 0.62 0.02
#> Sample_928 1 0.7499 0.03095 0.42 0.00 0.40 0.18
#> Sample_929 3 0.1211 0.84695 0.00 0.00 0.96 0.04
#> Sample_930 3 0.0707 0.85280 0.00 0.00 0.98 0.02
#> Sample_931 3 0.3853 0.74220 0.16 0.00 0.82 0.02
#> Sample_932 3 0.1211 0.83917 0.00 0.04 0.96 0.00
#> Sample_933 2 0.4406 0.58396 0.00 0.70 0.30 0.00
#> Sample_934 3 0.0000 0.86104 0.00 0.00 1.00 0.00
#> Sample_935 2 0.1211 0.84586 0.00 0.96 0.04 0.00
#> Sample_936 2 0.1637 0.83575 0.00 0.94 0.06 0.00
#> Sample_937 2 0.1211 0.84586 0.00 0.96 0.04 0.00
#> Sample_938 2 0.0707 0.85038 0.00 0.98 0.02 0.00
#> Sample_939 3 0.4855 0.31533 0.00 0.40 0.60 0.00
#> Sample_940 2 0.0000 0.85369 0.00 1.00 0.00 0.00
#> Sample_941 2 0.0000 0.85369 0.00 1.00 0.00 0.00
#> Sample_942 2 0.0000 0.85369 0.00 1.00 0.00 0.00
#> Sample_943 2 0.0000 0.85369 0.00 1.00 0.00 0.00
#> Sample_944 3 0.0000 0.86104 0.00 0.00 1.00 0.00
#> Sample_945 3 0.1411 0.84455 0.02 0.00 0.96 0.02
#> Sample_946 3 0.0000 0.86104 0.00 0.00 1.00 0.00
#> Sample_947 3 0.2706 0.81186 0.08 0.00 0.90 0.02
#> Sample_1527 2 0.0000 0.85369 0.00 1.00 0.00 0.00
#> Sample_1528 2 0.2011 0.82790 0.00 0.92 0.00 0.08
#> Sample_1529 2 0.0000 0.85369 0.00 1.00 0.00 0.00
#> Sample_1533 1 0.0000 0.73255 1.00 0.00 0.00 0.00
#> Sample_1534 2 0.2647 0.80345 0.00 0.88 0.00 0.12
#> Sample_1538 2 0.2647 0.80519 0.00 0.88 0.00 0.12
#> Sample_1539 2 0.0000 0.85369 0.00 1.00 0.00 0.00
#> Sample_1542 2 0.0000 0.85369 0.00 1.00 0.00 0.00
#> Sample_1546 3 0.0000 0.86104 0.00 0.00 1.00 0.00
#> Sample_1570 4 0.6074 0.55948 0.34 0.06 0.00 0.60
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
As soon as the classes for columns are determined, the signatures that are significantly different between subgroups can be looked for. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. To get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows (which is done by automatically selecting number of clusters).If there are too many signatures, top_signatures = ... can be set to only show the
signatures with the highest FDRs:
# code only for demonstration
# e.g. to show the top 500 most significant rows
tb = get_signature(res, k = ..., top_signatures = 500)
If the signatures are defined as these which are uniquely high in current group, diff_method argument
can be set to "uniquely_high_in_one_group":
# code only for demonstration
tb = get_signature(res, k = ..., diff_method = "uniquely_high_in_one_group")
UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n_sample age(p-value) cell.type(p-value) k
#> ATC:skmeans 271 2.10e-01 0.1113 2
#> ATC:skmeans 270 7.39e-48 0.0520 3
#> ATC:skmeans 212 NaN 0.0028 4
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
Parent node: Node021. Child nodes: Node02111-leaf , Node02112 , Node02121-leaf , Node02122-leaf , Node02211-leaf , Node02212-leaf , Node02221-leaf , Node02222-leaf , Node03211 , Node03212-leaf , Node03221-leaf , Node03222-leaf .
The object with results only for a single top-value method and a single partitioning method can be extracted as:
res = res_rh["0211"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4.
#> On a matrix with 14696 rows and 147 columns.
#> Top rows (582) are extracted by 'ATC' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 150 partitions by row resampling.
#> Best k for subgroups seems to be 4.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_partitions"
#> [7] "compare_signatures" "consensus_heatmap" "dimension_reduction"
#> [10] "functional_enrichment" "get_anno_col" "get_anno"
#> [13] "get_classes" "get_consensus" "get_matrix"
#> [16] "get_membership" "get_param" "get_signatures"
#> [19] "get_stats" "is_best_k" "is_stable_k"
#> [22] "membership_heatmap" "ncol" "nrow"
#> [25] "plot_ecdf" "predict_classes" "rownames"
#> [28] "select_partition_number" "show" "suggest_best_k"
#> [31] "test_to_known_factors" "top_rows_heatmap"
collect_plots() function collects all the plots made from res for all k (number of subgroups)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, higher 1-PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.989 0.996 0.498 0.503 0.503
#> 3 3 0.978 0.948 0.978 0.248 0.850 0.711
#> 4 4 0.938 0.928 0.963 0.208 0.822 0.561
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 4
#> attr(,"optional")
#> [1] 2 3
There is also optional best \(k\) = 2 3 that is worth to check.
Following is the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall subgroup
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> Sample_72 2 0.000 0.998 0.00 1.00
#> Sample_147 2 0.000 0.998 0.00 1.00
#> Sample_148 2 0.000 0.998 0.00 1.00
#> Sample_149 2 0.000 0.998 0.00 1.00
#> Sample_150 2 0.000 0.998 0.00 1.00
#> Sample_152 2 0.000 0.998 0.00 1.00
#> Sample_153 2 0.000 0.998 0.00 1.00
#> Sample_154 2 0.000 0.998 0.00 1.00
#> Sample_155 2 0.000 0.998 0.00 1.00
#> Sample_156 2 0.000 0.998 0.00 1.00
#> Sample_157 2 0.000 0.998 0.00 1.00
#> Sample_158 2 0.000 0.998 0.00 1.00
#> Sample_160 2 0.000 0.998 0.00 1.00
#> Sample_167 2 0.000 0.998 0.00 1.00
#> Sample_168 2 0.000 0.998 0.00 1.00
#> Sample_170 2 0.000 0.998 0.00 1.00
#> Sample_175 2 0.000 0.998 0.00 1.00
#> Sample_176 2 0.000 0.998 0.00 1.00
#> Sample_178 2 0.000 0.998 0.00 1.00
#> Sample_181 2 0.000 0.998 0.00 1.00
#> Sample_182 2 0.000 0.998 0.00 1.00
#> Sample_183 2 0.000 0.998 0.00 1.00
#> Sample_184 2 0.000 0.998 0.00 1.00
#> Sample_185 2 0.000 0.998 0.00 1.00
#> Sample_186 2 0.000 0.998 0.00 1.00
#> Sample_187 2 0.000 0.998 0.00 1.00
#> Sample_190 2 0.000 0.998 0.00 1.00
#> Sample_193 2 0.000 0.998 0.00 1.00
#> Sample_194 2 0.000 0.998 0.00 1.00
#> Sample_195 2 0.000 0.998 0.00 1.00
#> Sample_196 2 0.141 0.979 0.02 0.98
#> Sample_516 2 0.000 0.998 0.00 1.00
#> Sample_696 1 0.000 0.993 1.00 0.00
#> Sample_697 1 0.000 0.993 1.00 0.00
#> Sample_700 1 0.000 0.993 1.00 0.00
#> Sample_702 1 0.000 0.993 1.00 0.00
#> Sample_706 1 0.000 0.993 1.00 0.00
#> Sample_707 1 0.000 0.993 1.00 0.00
#> Sample_708 1 0.000 0.993 1.00 0.00
#> Sample_709 1 0.000 0.993 1.00 0.00
#> Sample_712 1 0.000 0.993 1.00 0.00
#> Sample_713 1 0.000 0.993 1.00 0.00
#> Sample_717 1 0.000 0.993 1.00 0.00
#> Sample_718 1 0.000 0.993 1.00 0.00
#> Sample_719 1 0.000 0.993 1.00 0.00
#> Sample_721 1 0.000 0.993 1.00 0.00
#> Sample_722 1 0.141 0.974 0.98 0.02
#> Sample_724 1 0.000 0.993 1.00 0.00
#> Sample_726 1 0.000 0.993 1.00 0.00
#> Sample_727 1 0.000 0.993 1.00 0.00
#> Sample_729 1 0.990 0.219 0.56 0.44
#> Sample_730 1 0.000 0.993 1.00 0.00
#> Sample_732 1 0.000 0.993 1.00 0.00
#> Sample_733 1 0.000 0.993 1.00 0.00
#> Sample_734 1 0.000 0.993 1.00 0.00
#> Sample_736 1 0.000 0.993 1.00 0.00
#> Sample_739 1 0.000 0.993 1.00 0.00
#> Sample_741 1 0.000 0.993 1.00 0.00
#> Sample_743 1 0.000 0.993 1.00 0.00
#> Sample_744 1 0.000 0.993 1.00 0.00
#> Sample_746 1 0.000 0.993 1.00 0.00
#> Sample_747 1 0.000 0.993 1.00 0.00
#> Sample_749 1 0.000 0.993 1.00 0.00
#> Sample_750 1 0.000 0.993 1.00 0.00
#> Sample_751 1 0.000 0.993 1.00 0.00
#> Sample_752 1 0.000 0.993 1.00 0.00
#> Sample_755 1 0.000 0.993 1.00 0.00
#> Sample_756 1 0.000 0.993 1.00 0.00
#> Sample_757 1 0.000 0.993 1.00 0.00
#> Sample_758 1 0.000 0.993 1.00 0.00
#> Sample_760 1 0.000 0.993 1.00 0.00
#> Sample_761 1 0.000 0.993 1.00 0.00
#> Sample_762 1 0.000 0.993 1.00 0.00
#> Sample_763 1 0.000 0.993 1.00 0.00
#> Sample_764 1 0.000 0.993 1.00 0.00
#> Sample_765 1 0.000 0.993 1.00 0.00
#> Sample_767 1 0.000 0.993 1.00 0.00
#> Sample_770 1 0.000 0.993 1.00 0.00
#> Sample_771 1 0.000 0.993 1.00 0.00
#> Sample_773 1 0.000 0.993 1.00 0.00
#> Sample_774 1 0.000 0.993 1.00 0.00
#> Sample_776 1 0.000 0.993 1.00 0.00
#> Sample_781 1 0.000 0.993 1.00 0.00
#> Sample_787 1 0.000 0.993 1.00 0.00
#> Sample_790 1 0.000 0.993 1.00 0.00
#> Sample_791 1 0.000 0.993 1.00 0.00
#> Sample_792 1 0.242 0.954 0.96 0.04
#> Sample_794 1 0.000 0.993 1.00 0.00
#> Sample_795 1 0.000 0.993 1.00 0.00
#> Sample_796 1 0.000 0.993 1.00 0.00
#> Sample_800 1 0.000 0.993 1.00 0.00
#> Sample_801 1 0.000 0.993 1.00 0.00
#> Sample_803 1 0.000 0.993 1.00 0.00
#> Sample_805 1 0.000 0.993 1.00 0.00
#> Sample_806 1 0.000 0.993 1.00 0.00
#> Sample_807 1 0.000 0.993 1.00 0.00
#> Sample_808 1 0.000 0.993 1.00 0.00
#> Sample_809 1 0.000 0.993 1.00 0.00
#> Sample_811 1 0.242 0.954 0.96 0.04
#> Sample_812 1 0.000 0.993 1.00 0.00
#> Sample_813 1 0.000 0.993 1.00 0.00
#> Sample_814 1 0.000 0.993 1.00 0.00
#> Sample_815 1 0.000 0.993 1.00 0.00
#> Sample_816 1 0.000 0.993 1.00 0.00
#> Sample_818 1 0.000 0.993 1.00 0.00
#> Sample_822 1 0.000 0.993 1.00 0.00
#> Sample_828 1 0.000 0.993 1.00 0.00
#> Sample_831 1 0.000 0.993 1.00 0.00
#> Sample_832 1 0.000 0.993 1.00 0.00
#> Sample_833 1 0.000 0.993 1.00 0.00
#> Sample_837 1 0.000 0.993 1.00 0.00
#> Sample_838 1 0.000 0.993 1.00 0.00
#> Sample_840 1 0.000 0.993 1.00 0.00
#> Sample_858 2 0.000 0.998 0.00 1.00
#> Sample_864 2 0.000 0.998 0.00 1.00
#> Sample_868 2 0.000 0.998 0.00 1.00
#> Sample_874 2 0.000 0.998 0.00 1.00
#> Sample_875 2 0.000 0.998 0.00 1.00
#> Sample_877 2 0.000 0.998 0.00 1.00
#> Sample_878 2 0.000 0.998 0.00 1.00
#> Sample_879 2 0.000 0.998 0.00 1.00
#> Sample_880 2 0.000 0.998 0.00 1.00
#> Sample_883 2 0.000 0.998 0.00 1.00
#> Sample_884 2 0.000 0.998 0.00 1.00
#> Sample_886 2 0.000 0.998 0.00 1.00
#> Sample_887 2 0.000 0.998 0.00 1.00
#> Sample_888 2 0.000 0.998 0.00 1.00
#> Sample_896 2 0.000 0.998 0.00 1.00
#> Sample_899 2 0.000 0.998 0.00 1.00
#> Sample_901 2 0.000 0.998 0.00 1.00
#> Sample_904 2 0.000 0.998 0.00 1.00
#> Sample_905 2 0.000 0.998 0.00 1.00
#> Sample_910 2 0.000 0.998 0.00 1.00
#> Sample_912 2 0.000 0.998 0.00 1.00
#> Sample_916 2 0.000 0.998 0.00 1.00
#> Sample_921 2 0.402 0.913 0.08 0.92
#> Sample_923 2 0.000 0.998 0.00 1.00
#> Sample_926 2 0.000 0.998 0.00 1.00
#> Sample_927 2 0.000 0.998 0.00 1.00
#> Sample_928 2 0.000 0.998 0.00 1.00
#> Sample_930 2 0.000 0.998 0.00 1.00
#> Sample_931 2 0.000 0.998 0.00 1.00
#> Sample_945 2 0.000 0.998 0.00 1.00
#> Sample_946 2 0.000 0.998 0.00 1.00
#> Sample_947 2 0.000 0.998 0.00 1.00
#> Sample_1533 1 0.000 0.993 1.00 0.00
#> Sample_1546 2 0.000 0.998 0.00 1.00
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> Sample_72 2 0.0000 0.973 0.00 1.00 0.00
#> Sample_147 2 0.0000 0.973 0.00 1.00 0.00
#> Sample_148 2 0.0000 0.973 0.00 1.00 0.00
#> Sample_149 2 0.0892 0.954 0.00 0.98 0.02
#> Sample_150 2 0.0000 0.973 0.00 1.00 0.00
#> Sample_152 2 0.0000 0.973 0.00 1.00 0.00
#> Sample_153 2 0.0000 0.973 0.00 1.00 0.00
#> Sample_154 2 0.0000 0.973 0.00 1.00 0.00
#> Sample_155 2 0.0000 0.973 0.00 1.00 0.00
#> Sample_156 2 0.0000 0.973 0.00 1.00 0.00
#> Sample_157 2 0.0000 0.973 0.00 1.00 0.00
#> Sample_158 2 0.0000 0.973 0.00 1.00 0.00
#> Sample_160 2 0.0000 0.973 0.00 1.00 0.00
#> Sample_167 2 0.0000 0.973 0.00 1.00 0.00
#> Sample_168 2 0.0000 0.973 0.00 1.00 0.00
#> Sample_170 2 0.0000 0.973 0.00 1.00 0.00
#> Sample_175 2 0.0000 0.973 0.00 1.00 0.00
#> Sample_176 2 0.0000 0.973 0.00 1.00 0.00
#> Sample_178 2 0.0000 0.973 0.00 1.00 0.00
#> Sample_181 2 0.0000 0.973 0.00 1.00 0.00
#> Sample_182 2 0.0000 0.973 0.00 1.00 0.00
#> Sample_183 2 0.0000 0.973 0.00 1.00 0.00
#> Sample_184 2 0.0000 0.973 0.00 1.00 0.00
#> Sample_185 2 0.0000 0.973 0.00 1.00 0.00
#> Sample_186 2 0.0000 0.973 0.00 1.00 0.00
#> Sample_187 2 0.0000 0.973 0.00 1.00 0.00
#> Sample_190 2 0.0000 0.973 0.00 1.00 0.00
#> Sample_193 2 0.0000 0.973 0.00 1.00 0.00
#> Sample_194 2 0.0000 0.973 0.00 1.00 0.00
#> Sample_195 2 0.0000 0.973 0.00 1.00 0.00
#> Sample_196 2 0.0000 0.973 0.00 1.00 0.00
#> Sample_516 2 0.0000 0.973 0.00 1.00 0.00
#> Sample_696 3 0.0892 0.968 0.02 0.00 0.98
#> Sample_697 1 0.0000 0.973 1.00 0.00 0.00
#> Sample_700 1 0.6280 0.178 0.54 0.00 0.46
#> Sample_702 1 0.0000 0.973 1.00 0.00 0.00
#> Sample_706 1 0.0000 0.973 1.00 0.00 0.00
#> Sample_707 1 0.2959 0.882 0.90 0.00 0.10
#> Sample_708 1 0.0000 0.973 1.00 0.00 0.00
#> Sample_709 1 0.5016 0.691 0.76 0.00 0.24
#> Sample_712 1 0.0000 0.973 1.00 0.00 0.00
#> Sample_713 1 0.0000 0.973 1.00 0.00 0.00
#> Sample_717 1 0.0000 0.973 1.00 0.00 0.00
#> Sample_718 1 0.0000 0.973 1.00 0.00 0.00
#> Sample_719 1 0.0000 0.973 1.00 0.00 0.00
#> Sample_721 1 0.0000 0.973 1.00 0.00 0.00
#> Sample_722 3 0.0000 0.989 0.00 0.00 1.00
#> Sample_724 1 0.0000 0.973 1.00 0.00 0.00
#> Sample_726 1 0.0000 0.973 1.00 0.00 0.00
#> Sample_727 1 0.0000 0.973 1.00 0.00 0.00
#> Sample_729 3 0.2414 0.934 0.04 0.02 0.94
#> Sample_730 1 0.0000 0.973 1.00 0.00 0.00
#> Sample_732 1 0.0000 0.973 1.00 0.00 0.00
#> Sample_733 1 0.0000 0.973 1.00 0.00 0.00
#> Sample_734 1 0.0000 0.973 1.00 0.00 0.00
#> Sample_736 1 0.0000 0.973 1.00 0.00 0.00
#> Sample_739 1 0.0000 0.973 1.00 0.00 0.00
#> Sample_741 1 0.0000 0.973 1.00 0.00 0.00
#> Sample_743 1 0.0000 0.973 1.00 0.00 0.00
#> Sample_744 1 0.0000 0.973 1.00 0.00 0.00
#> Sample_746 1 0.0000 0.973 1.00 0.00 0.00
#> Sample_747 1 0.0000 0.973 1.00 0.00 0.00
#> Sample_749 1 0.0000 0.973 1.00 0.00 0.00
#> Sample_750 1 0.2066 0.922 0.94 0.00 0.06
#> Sample_751 1 0.0000 0.973 1.00 0.00 0.00
#> Sample_752 1 0.0000 0.973 1.00 0.00 0.00
#> Sample_755 1 0.0000 0.973 1.00 0.00 0.00
#> Sample_756 1 0.0000 0.973 1.00 0.00 0.00
#> Sample_757 1 0.0000 0.973 1.00 0.00 0.00
#> Sample_758 1 0.0000 0.973 1.00 0.00 0.00
#> Sample_760 1 0.0000 0.973 1.00 0.00 0.00
#> Sample_761 1 0.0000 0.973 1.00 0.00 0.00
#> Sample_762 1 0.0000 0.973 1.00 0.00 0.00
#> Sample_763 1 0.0000 0.973 1.00 0.00 0.00
#> Sample_764 1 0.0000 0.973 1.00 0.00 0.00
#> Sample_765 1 0.0000 0.973 1.00 0.00 0.00
#> Sample_767 1 0.0000 0.973 1.00 0.00 0.00
#> Sample_770 3 0.2066 0.919 0.06 0.00 0.94
#> Sample_771 1 0.0000 0.973 1.00 0.00 0.00
#> Sample_773 1 0.0000 0.973 1.00 0.00 0.00
#> Sample_774 1 0.0000 0.973 1.00 0.00 0.00
#> Sample_776 1 0.0000 0.973 1.00 0.00 0.00
#> Sample_781 1 0.0000 0.973 1.00 0.00 0.00
#> Sample_787 1 0.1529 0.940 0.96 0.04 0.00
#> Sample_790 1 0.0000 0.973 1.00 0.00 0.00
#> Sample_791 1 0.0000 0.973 1.00 0.00 0.00
#> Sample_792 1 0.9059 0.134 0.48 0.14 0.38
#> Sample_794 1 0.0000 0.973 1.00 0.00 0.00
#> Sample_795 1 0.0000 0.973 1.00 0.00 0.00
#> Sample_796 1 0.0000 0.973 1.00 0.00 0.00
#> Sample_800 1 0.2066 0.922 0.94 0.06 0.00
#> Sample_801 1 0.0000 0.973 1.00 0.00 0.00
#> Sample_803 1 0.0000 0.973 1.00 0.00 0.00
#> Sample_805 1 0.0000 0.973 1.00 0.00 0.00
#> Sample_806 1 0.2959 0.882 0.90 0.10 0.00
#> Sample_807 1 0.0000 0.973 1.00 0.00 0.00
#> Sample_808 1 0.0000 0.973 1.00 0.00 0.00
#> Sample_809 1 0.0000 0.973 1.00 0.00 0.00
#> Sample_811 2 0.6984 0.229 0.42 0.56 0.02
#> Sample_812 1 0.0892 0.957 0.98 0.02 0.00
#> Sample_813 1 0.2537 0.903 0.92 0.08 0.00
#> Sample_814 1 0.0000 0.973 1.00 0.00 0.00
#> Sample_815 1 0.0000 0.973 1.00 0.00 0.00
#> Sample_816 1 0.0000 0.973 1.00 0.00 0.00
#> Sample_818 1 0.0000 0.973 1.00 0.00 0.00
#> Sample_822 1 0.0000 0.973 1.00 0.00 0.00
#> Sample_828 1 0.0000 0.973 1.00 0.00 0.00
#> Sample_831 1 0.0000 0.973 1.00 0.00 0.00
#> Sample_832 1 0.0000 0.973 1.00 0.00 0.00
#> Sample_833 1 0.5406 0.732 0.78 0.20 0.02
#> Sample_837 1 0.0000 0.973 1.00 0.00 0.00
#> Sample_838 1 0.0000 0.973 1.00 0.00 0.00
#> Sample_840 1 0.0000 0.973 1.00 0.00 0.00
#> Sample_858 3 0.0892 0.972 0.00 0.02 0.98
#> Sample_864 3 0.0000 0.989 0.00 0.00 1.00
#> Sample_868 3 0.0000 0.989 0.00 0.00 1.00
#> Sample_874 3 0.0000 0.989 0.00 0.00 1.00
#> Sample_875 3 0.0000 0.989 0.00 0.00 1.00
#> Sample_877 3 0.0000 0.989 0.00 0.00 1.00
#> Sample_878 3 0.0000 0.989 0.00 0.00 1.00
#> Sample_879 3 0.0000 0.989 0.00 0.00 1.00
#> Sample_880 3 0.0000 0.989 0.00 0.00 1.00
#> Sample_883 3 0.0000 0.989 0.00 0.00 1.00
#> Sample_884 3 0.0000 0.989 0.00 0.00 1.00
#> Sample_886 3 0.4291 0.778 0.00 0.18 0.82
#> Sample_887 3 0.0000 0.989 0.00 0.00 1.00
#> Sample_888 3 0.0000 0.989 0.00 0.00 1.00
#> Sample_896 3 0.0000 0.989 0.00 0.00 1.00
#> Sample_899 3 0.0000 0.989 0.00 0.00 1.00
#> Sample_901 3 0.0000 0.989 0.00 0.00 1.00
#> Sample_904 3 0.0000 0.989 0.00 0.00 1.00
#> Sample_905 3 0.0000 0.989 0.00 0.00 1.00
#> Sample_910 3 0.0000 0.989 0.00 0.00 1.00
#> Sample_912 3 0.0000 0.989 0.00 0.00 1.00
#> Sample_916 3 0.0000 0.989 0.00 0.00 1.00
#> Sample_921 2 0.8219 0.559 0.18 0.64 0.18
#> Sample_923 3 0.0000 0.989 0.00 0.00 1.00
#> Sample_926 3 0.0000 0.989 0.00 0.00 1.00
#> Sample_927 3 0.0000 0.989 0.00 0.00 1.00
#> Sample_928 3 0.0000 0.989 0.00 0.00 1.00
#> Sample_930 3 0.0000 0.989 0.00 0.00 1.00
#> Sample_931 3 0.0000 0.989 0.00 0.00 1.00
#> Sample_945 3 0.0000 0.989 0.00 0.00 1.00
#> Sample_946 3 0.0000 0.989 0.00 0.00 1.00
#> Sample_947 3 0.0000 0.989 0.00 0.00 1.00
#> Sample_1533 1 0.3340 0.858 0.88 0.12 0.00
#> Sample_1546 3 0.0000 0.989 0.00 0.00 1.00
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> Sample_72 2 0.0707 0.99680 0.00 0.98 0.02 0.00
#> Sample_147 2 0.0707 0.99680 0.00 0.98 0.02 0.00
#> Sample_148 2 0.0707 0.97668 0.00 0.98 0.00 0.02
#> Sample_149 2 0.0707 0.99680 0.00 0.98 0.02 0.00
#> Sample_150 2 0.0707 0.99680 0.00 0.98 0.02 0.00
#> Sample_152 2 0.0707 0.99680 0.00 0.98 0.02 0.00
#> Sample_153 2 0.0707 0.99680 0.00 0.98 0.02 0.00
#> Sample_154 2 0.0707 0.99680 0.00 0.98 0.02 0.00
#> Sample_155 2 0.0707 0.99680 0.00 0.98 0.02 0.00
#> Sample_156 2 0.0707 0.99680 0.00 0.98 0.02 0.00
#> Sample_157 2 0.0707 0.99680 0.00 0.98 0.02 0.00
#> Sample_158 2 0.0707 0.99680 0.00 0.98 0.02 0.00
#> Sample_160 2 0.0707 0.99680 0.00 0.98 0.02 0.00
#> Sample_167 2 0.0707 0.99680 0.00 0.98 0.02 0.00
#> Sample_168 2 0.0707 0.99680 0.00 0.98 0.02 0.00
#> Sample_170 2 0.0707 0.99680 0.00 0.98 0.02 0.00
#> Sample_175 2 0.0707 0.99680 0.00 0.98 0.02 0.00
#> Sample_176 2 0.0707 0.97642 0.02 0.98 0.00 0.00
#> Sample_178 2 0.0707 0.99680 0.00 0.98 0.02 0.00
#> Sample_181 2 0.0707 0.99680 0.00 0.98 0.02 0.00
#> Sample_182 2 0.0707 0.99680 0.00 0.98 0.02 0.00
#> Sample_183 2 0.0707 0.99680 0.00 0.98 0.02 0.00
#> Sample_184 2 0.0707 0.99680 0.00 0.98 0.02 0.00
#> Sample_185 2 0.0707 0.99680 0.00 0.98 0.02 0.00
#> Sample_186 2 0.0707 0.99680 0.00 0.98 0.02 0.00
#> Sample_187 2 0.0707 0.99680 0.00 0.98 0.02 0.00
#> Sample_190 2 0.0707 0.99680 0.00 0.98 0.02 0.00
#> Sample_193 2 0.0707 0.99680 0.00 0.98 0.02 0.00
#> Sample_194 2 0.0707 0.97668 0.00 0.98 0.00 0.02
#> Sample_195 2 0.0707 0.99680 0.00 0.98 0.02 0.00
#> Sample_196 2 0.0707 0.97668 0.00 0.98 0.00 0.02
#> Sample_516 2 0.0707 0.99680 0.00 0.98 0.02 0.00
#> Sample_696 1 0.3975 0.67865 0.76 0.00 0.24 0.00
#> Sample_697 4 0.0000 0.96654 0.00 0.00 0.00 1.00
#> Sample_700 1 0.0000 0.92156 1.00 0.00 0.00 0.00
#> Sample_702 1 0.0707 0.91982 0.98 0.02 0.00 0.00
#> Sample_706 4 0.4284 0.70629 0.20 0.02 0.00 0.78
#> Sample_707 1 0.0000 0.92156 1.00 0.00 0.00 0.00
#> Sample_708 4 0.0000 0.96654 0.00 0.00 0.00 1.00
#> Sample_709 4 0.0000 0.96654 0.00 0.00 0.00 1.00
#> Sample_712 1 0.5173 0.55512 0.66 0.02 0.00 0.32
#> Sample_713 4 0.0000 0.96654 0.00 0.00 0.00 1.00
#> Sample_717 1 0.2706 0.87582 0.90 0.02 0.00 0.08
#> Sample_718 1 0.5606 0.12126 0.50 0.02 0.00 0.48
#> Sample_719 4 0.0000 0.96654 0.00 0.00 0.00 1.00
#> Sample_721 4 0.0000 0.96654 0.00 0.00 0.00 1.00
#> Sample_722 1 0.3801 0.70620 0.78 0.00 0.22 0.00
#> Sample_724 4 0.0000 0.96654 0.00 0.00 0.00 1.00
#> Sample_726 4 0.0707 0.94982 0.02 0.00 0.00 0.98
#> Sample_727 1 0.0000 0.92156 1.00 0.00 0.00 0.00
#> Sample_729 1 0.0000 0.92156 1.00 0.00 0.00 0.00
#> Sample_730 4 0.0000 0.96654 0.00 0.00 0.00 1.00
#> Sample_732 1 0.0000 0.92156 1.00 0.00 0.00 0.00
#> Sample_733 4 0.0000 0.96654 0.00 0.00 0.00 1.00
#> Sample_734 4 0.0000 0.96654 0.00 0.00 0.00 1.00
#> Sample_736 4 0.0000 0.96654 0.00 0.00 0.00 1.00
#> Sample_739 4 0.0000 0.96654 0.00 0.00 0.00 1.00
#> Sample_741 1 0.3975 0.69766 0.76 0.00 0.00 0.24
#> Sample_743 1 0.0000 0.92156 1.00 0.00 0.00 0.00
#> Sample_744 4 0.0000 0.96654 0.00 0.00 0.00 1.00
#> Sample_746 1 0.4406 0.60357 0.70 0.00 0.00 0.30
#> Sample_747 1 0.4079 0.77521 0.80 0.02 0.00 0.18
#> Sample_749 1 0.0000 0.92156 1.00 0.00 0.00 0.00
#> Sample_750 1 0.3037 0.85848 0.88 0.02 0.00 0.10
#> Sample_751 1 0.0000 0.92156 1.00 0.00 0.00 0.00
#> Sample_752 4 0.0000 0.96654 0.00 0.00 0.00 1.00
#> Sample_755 1 0.1411 0.91307 0.96 0.02 0.00 0.02
#> Sample_756 4 0.0000 0.96654 0.00 0.00 0.00 1.00
#> Sample_757 4 0.0000 0.96654 0.00 0.00 0.00 1.00
#> Sample_758 1 0.0000 0.92156 1.00 0.00 0.00 0.00
#> Sample_760 1 0.0000 0.92156 1.00 0.00 0.00 0.00
#> Sample_761 4 0.4994 0.00715 0.48 0.00 0.00 0.52
#> Sample_762 4 0.0000 0.96654 0.00 0.00 0.00 1.00
#> Sample_763 1 0.2011 0.87617 0.92 0.00 0.00 0.08
#> Sample_764 4 0.0000 0.96654 0.00 0.00 0.00 1.00
#> Sample_765 1 0.0707 0.91982 0.98 0.02 0.00 0.00
#> Sample_767 4 0.0000 0.96654 0.00 0.00 0.00 1.00
#> Sample_770 1 0.0000 0.92156 1.00 0.00 0.00 0.00
#> Sample_771 4 0.0000 0.96654 0.00 0.00 0.00 1.00
#> Sample_773 4 0.0000 0.96654 0.00 0.00 0.00 1.00
#> Sample_774 1 0.5487 0.38013 0.58 0.02 0.00 0.40
#> Sample_776 4 0.4406 0.54663 0.30 0.00 0.00 0.70
#> Sample_781 1 0.0707 0.91982 0.98 0.02 0.00 0.00
#> Sample_787 1 0.0707 0.91982 0.98 0.02 0.00 0.00
#> Sample_790 1 0.0000 0.92156 1.00 0.00 0.00 0.00
#> Sample_791 4 0.0000 0.96654 0.00 0.00 0.00 1.00
#> Sample_792 1 0.0707 0.91982 0.98 0.02 0.00 0.00
#> Sample_794 4 0.0000 0.96654 0.00 0.00 0.00 1.00
#> Sample_795 4 0.0000 0.96654 0.00 0.00 0.00 1.00
#> Sample_796 4 0.0000 0.96654 0.00 0.00 0.00 1.00
#> Sample_800 1 0.0707 0.91982 0.98 0.02 0.00 0.00
#> Sample_801 4 0.0000 0.96654 0.00 0.00 0.00 1.00
#> Sample_803 1 0.0000 0.92156 1.00 0.00 0.00 0.00
#> Sample_805 4 0.0000 0.96654 0.00 0.00 0.00 1.00
#> Sample_806 1 0.0707 0.91982 0.98 0.02 0.00 0.00
#> Sample_807 1 0.0707 0.91982 0.98 0.02 0.00 0.00
#> Sample_808 1 0.0000 0.92156 1.00 0.00 0.00 0.00
#> Sample_809 1 0.0000 0.92156 1.00 0.00 0.00 0.00
#> Sample_811 1 0.0707 0.91982 0.98 0.02 0.00 0.00
#> Sample_812 1 0.0000 0.92156 1.00 0.00 0.00 0.00
#> Sample_813 1 0.0707 0.91982 0.98 0.02 0.00 0.00
#> Sample_814 4 0.0000 0.96654 0.00 0.00 0.00 1.00
#> Sample_815 1 0.0707 0.91982 0.98 0.02 0.00 0.00
#> Sample_816 1 0.2345 0.85959 0.90 0.00 0.00 0.10
#> Sample_818 4 0.0000 0.96654 0.00 0.00 0.00 1.00
#> Sample_822 4 0.3335 0.82435 0.12 0.02 0.00 0.86
#> Sample_828 1 0.0000 0.92156 1.00 0.00 0.00 0.00
#> Sample_831 4 0.0000 0.96654 0.00 0.00 0.00 1.00
#> Sample_832 4 0.0000 0.96654 0.00 0.00 0.00 1.00
#> Sample_833 1 0.0707 0.91982 0.98 0.02 0.00 0.00
#> Sample_837 1 0.4797 0.66351 0.72 0.02 0.00 0.26
#> Sample_838 1 0.1913 0.90143 0.94 0.02 0.00 0.04
#> Sample_840 4 0.0000 0.96654 0.00 0.00 0.00 1.00
#> Sample_858 3 0.3172 0.80881 0.00 0.00 0.84 0.16
#> Sample_864 3 0.0000 0.99322 0.00 0.00 1.00 0.00
#> Sample_868 3 0.0000 0.99322 0.00 0.00 1.00 0.00
#> Sample_874 3 0.0000 0.99322 0.00 0.00 1.00 0.00
#> Sample_875 3 0.0000 0.99322 0.00 0.00 1.00 0.00
#> Sample_877 3 0.0000 0.99322 0.00 0.00 1.00 0.00
#> Sample_878 3 0.0000 0.99322 0.00 0.00 1.00 0.00
#> Sample_879 3 0.0000 0.99322 0.00 0.00 1.00 0.00
#> Sample_880 3 0.0000 0.99322 0.00 0.00 1.00 0.00
#> Sample_883 3 0.0000 0.99322 0.00 0.00 1.00 0.00
#> Sample_884 3 0.0000 0.99322 0.00 0.00 1.00 0.00
#> Sample_886 3 0.1211 0.95345 0.00 0.04 0.96 0.00
#> Sample_887 3 0.0000 0.99322 0.00 0.00 1.00 0.00
#> Sample_888 3 0.0000 0.99322 0.00 0.00 1.00 0.00
#> Sample_896 3 0.0000 0.99322 0.00 0.00 1.00 0.00
#> Sample_899 3 0.0000 0.99322 0.00 0.00 1.00 0.00
#> Sample_901 3 0.0000 0.99322 0.00 0.00 1.00 0.00
#> Sample_904 3 0.0000 0.99322 0.00 0.00 1.00 0.00
#> Sample_905 3 0.0000 0.99322 0.00 0.00 1.00 0.00
#> Sample_910 3 0.0000 0.99322 0.00 0.00 1.00 0.00
#> Sample_912 3 0.0000 0.99322 0.00 0.00 1.00 0.00
#> Sample_916 3 0.0000 0.99322 0.00 0.00 1.00 0.00
#> Sample_921 4 0.0000 0.96654 0.00 0.00 0.00 1.00
#> Sample_923 3 0.0000 0.99322 0.00 0.00 1.00 0.00
#> Sample_926 3 0.0000 0.99322 0.00 0.00 1.00 0.00
#> Sample_927 3 0.0000 0.99322 0.00 0.00 1.00 0.00
#> Sample_928 3 0.0000 0.99322 0.00 0.00 1.00 0.00
#> Sample_930 3 0.0000 0.99322 0.00 0.00 1.00 0.00
#> Sample_931 3 0.0000 0.99322 0.00 0.00 1.00 0.00
#> Sample_945 3 0.0000 0.99322 0.00 0.00 1.00 0.00
#> Sample_946 3 0.0000 0.99322 0.00 0.00 1.00 0.00
#> Sample_947 3 0.0000 0.99322 0.00 0.00 1.00 0.00
#> Sample_1533 1 0.0000 0.92156 1.00 0.00 0.00 0.00
#> Sample_1546 3 0.0000 0.99322 0.00 0.00 1.00 0.00
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
As soon as the classes for columns are determined, the signatures that are significantly different between subgroups can be looked for. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. To get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows (which is done by automatically selecting number of clusters).If there are too many signatures, top_signatures = ... can be set to only show the
signatures with the highest FDRs:
# code only for demonstration
# e.g. to show the top 500 most significant rows
tb = get_signature(res, k = ..., top_signatures = 500)
If the signatures are defined as these which are uniquely high in current group, diff_method argument
can be set to "uniquely_high_in_one_group":
# code only for demonstration
tb = get_signature(res, k = ..., diff_method = "uniquely_high_in_one_group")
UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n_sample age(p-value) cell.type(p-value) k
#> ATC:skmeans 146 NaN 0.726 2
#> ATC:skmeans 144 NaN 0.505 3
#> ATC:skmeans 144 NaN 0.569 4
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
Parent node: Node0211. Child nodes: Node021121-leaf , Node021122-leaf , Node032111-leaf , Node032112-leaf .
The object with results only for a single top-value method and a single partitioning method can be extracted as:
res = res_rh["02112"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4.
#> On a matrix with 14549 rows and 65 columns.
#> Top rows (502) are extracted by 'ATC' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 150 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_partitions"
#> [7] "compare_signatures" "consensus_heatmap" "dimension_reduction"
#> [10] "functional_enrichment" "get_anno_col" "get_anno"
#> [13] "get_classes" "get_consensus" "get_matrix"
#> [16] "get_membership" "get_param" "get_signatures"
#> [19] "get_stats" "is_best_k" "is_stable_k"
#> [22] "membership_heatmap" "ncol" "nrow"
#> [25] "plot_ecdf" "predict_classes" "rownames"
#> [28] "select_partition_number" "show" "suggest_best_k"
#> [31] "test_to_known_factors" "top_rows_heatmap"
collect_plots() function collects all the plots made from res for all k (number of subgroups)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, higher 1-PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.976 0.990 0.508 0.492 0.492
#> 3 3 0.854 0.883 0.935 0.192 0.899 0.797
#> 4 4 0.584 0.654 0.809 0.139 0.984 0.959
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 2
Following is the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall subgroup
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> Sample_72 1 0.000 1.000 1.00 0.00
#> Sample_147 1 0.000 1.000 1.00 0.00
#> Sample_148 1 0.000 1.000 1.00 0.00
#> Sample_149 1 0.000 1.000 1.00 0.00
#> Sample_150 1 0.000 1.000 1.00 0.00
#> Sample_152 1 0.000 1.000 1.00 0.00
#> Sample_153 1 0.000 1.000 1.00 0.00
#> Sample_154 1 0.000 1.000 1.00 0.00
#> Sample_155 1 0.000 1.000 1.00 0.00
#> Sample_156 1 0.000 1.000 1.00 0.00
#> Sample_157 1 0.000 1.000 1.00 0.00
#> Sample_158 1 0.000 1.000 1.00 0.00
#> Sample_160 1 0.000 1.000 1.00 0.00
#> Sample_167 1 0.000 1.000 1.00 0.00
#> Sample_168 1 0.000 1.000 1.00 0.00
#> Sample_170 1 0.000 1.000 1.00 0.00
#> Sample_175 1 0.000 1.000 1.00 0.00
#> Sample_176 1 0.000 1.000 1.00 0.00
#> Sample_178 1 0.000 1.000 1.00 0.00
#> Sample_181 1 0.000 1.000 1.00 0.00
#> Sample_182 1 0.000 1.000 1.00 0.00
#> Sample_183 1 0.000 1.000 1.00 0.00
#> Sample_184 1 0.000 1.000 1.00 0.00
#> Sample_185 1 0.000 1.000 1.00 0.00
#> Sample_186 1 0.000 1.000 1.00 0.00
#> Sample_187 1 0.000 1.000 1.00 0.00
#> Sample_190 1 0.000 1.000 1.00 0.00
#> Sample_193 1 0.000 1.000 1.00 0.00
#> Sample_194 1 0.000 1.000 1.00 0.00
#> Sample_195 1 0.000 1.000 1.00 0.00
#> Sample_196 1 0.000 1.000 1.00 0.00
#> Sample_516 1 0.000 1.000 1.00 0.00
#> Sample_858 2 0.000 0.980 0.00 1.00
#> Sample_864 2 0.000 0.980 0.00 1.00
#> Sample_868 2 0.000 0.980 0.00 1.00
#> Sample_874 2 0.000 0.980 0.00 1.00
#> Sample_875 2 0.000 0.980 0.00 1.00
#> Sample_877 2 0.000 0.980 0.00 1.00
#> Sample_878 2 0.000 0.980 0.00 1.00
#> Sample_879 2 0.000 0.980 0.00 1.00
#> Sample_880 2 0.000 0.980 0.00 1.00
#> Sample_883 2 0.000 0.980 0.00 1.00
#> Sample_884 2 0.000 0.980 0.00 1.00
#> Sample_886 2 0.855 0.619 0.28 0.72
#> Sample_887 2 0.000 0.980 0.00 1.00
#> Sample_888 2 0.000 0.980 0.00 1.00
#> Sample_896 2 0.000 0.980 0.00 1.00
#> Sample_899 2 0.000 0.980 0.00 1.00
#> Sample_901 2 0.000 0.980 0.00 1.00
#> Sample_904 2 0.000 0.980 0.00 1.00
#> Sample_905 2 0.000 0.980 0.00 1.00
#> Sample_910 2 0.000 0.980 0.00 1.00
#> Sample_912 2 0.000 0.980 0.00 1.00
#> Sample_916 2 0.000 0.980 0.00 1.00
#> Sample_921 2 0.943 0.450 0.36 0.64
#> Sample_923 2 0.000 0.980 0.00 1.00
#> Sample_926 2 0.000 0.980 0.00 1.00
#> Sample_927 2 0.000 0.980 0.00 1.00
#> Sample_928 2 0.000 0.980 0.00 1.00
#> Sample_930 2 0.000 0.980 0.00 1.00
#> Sample_931 2 0.000 0.980 0.00 1.00
#> Sample_945 2 0.000 0.980 0.00 1.00
#> Sample_946 2 0.000 0.980 0.00 1.00
#> Sample_947 2 0.000 0.980 0.00 1.00
#> Sample_1546 2 0.000 0.980 0.00 1.00
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> Sample_72 1 0.2959 0.882 0.90 0.00 0.10
#> Sample_147 1 0.0892 0.940 0.98 0.00 0.02
#> Sample_148 1 0.1529 0.930 0.96 0.00 0.04
#> Sample_149 1 0.3686 0.830 0.86 0.00 0.14
#> Sample_150 1 0.6280 0.155 0.54 0.00 0.46
#> Sample_152 1 0.0000 0.942 1.00 0.00 0.00
#> Sample_153 1 0.0892 0.940 0.98 0.00 0.02
#> Sample_154 1 0.0892 0.940 0.98 0.00 0.02
#> Sample_155 1 0.1529 0.940 0.96 0.00 0.04
#> Sample_156 1 0.0892 0.940 0.98 0.00 0.02
#> Sample_157 1 0.1529 0.940 0.96 0.00 0.04
#> Sample_158 1 0.0892 0.940 0.98 0.00 0.02
#> Sample_160 1 0.0000 0.942 1.00 0.00 0.00
#> Sample_167 1 0.0892 0.940 0.98 0.00 0.02
#> Sample_168 1 0.0000 0.942 1.00 0.00 0.00
#> Sample_170 1 0.0892 0.940 0.98 0.00 0.02
#> Sample_175 1 0.0892 0.940 0.98 0.00 0.02
#> Sample_176 3 0.4002 0.707 0.16 0.00 0.84
#> Sample_178 1 0.0892 0.940 0.98 0.00 0.02
#> Sample_181 1 0.0892 0.940 0.98 0.00 0.02
#> Sample_182 1 0.2066 0.916 0.94 0.00 0.06
#> Sample_183 1 0.0892 0.940 0.98 0.00 0.02
#> Sample_184 1 0.0892 0.940 0.98 0.00 0.02
#> Sample_185 1 0.0000 0.942 1.00 0.00 0.00
#> Sample_186 1 0.0892 0.940 0.98 0.00 0.02
#> Sample_187 1 0.6045 0.391 0.62 0.00 0.38
#> Sample_190 1 0.0892 0.940 0.98 0.00 0.02
#> Sample_193 1 0.0892 0.940 0.98 0.00 0.02
#> Sample_194 1 0.1529 0.930 0.96 0.00 0.04
#> Sample_195 1 0.0000 0.942 1.00 0.00 0.00
#> Sample_196 1 0.0892 0.940 0.98 0.00 0.02
#> Sample_516 3 0.3686 0.719 0.14 0.00 0.86
#> Sample_858 3 0.6126 0.343 0.00 0.40 0.60
#> Sample_864 2 0.1529 0.946 0.00 0.96 0.04
#> Sample_868 2 0.0000 0.960 0.00 1.00 0.00
#> Sample_874 3 0.3340 0.754 0.00 0.12 0.88
#> Sample_875 2 0.1529 0.945 0.00 0.96 0.04
#> Sample_877 2 0.0892 0.956 0.00 0.98 0.02
#> Sample_878 2 0.3340 0.872 0.00 0.88 0.12
#> Sample_879 2 0.0892 0.956 0.00 0.98 0.02
#> Sample_880 2 0.4002 0.814 0.00 0.84 0.16
#> Sample_883 2 0.0000 0.960 0.00 1.00 0.00
#> Sample_884 2 0.0892 0.956 0.00 0.98 0.02
#> Sample_886 3 0.4097 0.769 0.06 0.06 0.88
#> Sample_887 2 0.0892 0.956 0.00 0.98 0.02
#> Sample_888 2 0.0000 0.960 0.00 1.00 0.00
#> Sample_896 2 0.0000 0.960 0.00 1.00 0.00
#> Sample_899 2 0.0000 0.960 0.00 1.00 0.00
#> Sample_901 2 0.0000 0.960 0.00 1.00 0.00
#> Sample_904 2 0.1529 0.945 0.00 0.96 0.04
#> Sample_905 2 0.2959 0.892 0.00 0.90 0.10
#> Sample_910 2 0.0000 0.960 0.00 1.00 0.00
#> Sample_912 2 0.1529 0.945 0.00 0.96 0.04
#> Sample_916 2 0.0892 0.956 0.00 0.98 0.02
#> Sample_921 3 0.9930 0.326 0.28 0.34 0.38
#> Sample_923 2 0.2537 0.915 0.00 0.92 0.08
#> Sample_926 2 0.1529 0.945 0.00 0.96 0.04
#> Sample_927 2 0.0000 0.960 0.00 1.00 0.00
#> Sample_928 2 0.1529 0.945 0.00 0.96 0.04
#> Sample_930 2 0.0892 0.954 0.00 0.98 0.02
#> Sample_931 2 0.1529 0.945 0.00 0.96 0.04
#> Sample_945 2 0.2959 0.895 0.00 0.90 0.10
#> Sample_946 2 0.0000 0.960 0.00 1.00 0.00
#> Sample_947 2 0.0000 0.960 0.00 1.00 0.00
#> Sample_1546 3 0.3340 0.752 0.00 0.12 0.88
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> Sample_72 1 0.6453 0.6024 0.56 0.00 0.08 0.36
#> Sample_147 1 0.4491 0.8293 0.80 0.00 0.06 0.14
#> Sample_148 1 0.5486 0.7816 0.72 0.00 0.08 0.20
#> Sample_149 1 0.4949 0.7269 0.76 0.00 0.06 0.18
#> Sample_150 1 0.6212 0.4019 0.56 0.00 0.38 0.06
#> Sample_152 1 0.3853 0.8195 0.82 0.00 0.02 0.16
#> Sample_153 1 0.4332 0.8240 0.80 0.00 0.04 0.16
#> Sample_154 1 0.1913 0.8300 0.94 0.00 0.02 0.04
#> Sample_155 1 0.1913 0.8446 0.94 0.00 0.02 0.04
#> Sample_156 1 0.3037 0.8294 0.88 0.00 0.02 0.10
#> Sample_157 1 0.2706 0.8309 0.90 0.00 0.02 0.08
#> Sample_158 1 0.1913 0.8300 0.94 0.00 0.02 0.04
#> Sample_160 1 0.1913 0.8477 0.94 0.00 0.04 0.02
#> Sample_167 1 0.1411 0.8379 0.96 0.00 0.02 0.02
#> Sample_168 1 0.1913 0.8404 0.94 0.00 0.02 0.04
#> Sample_170 1 0.3247 0.8454 0.88 0.00 0.06 0.06
#> Sample_175 1 0.4227 0.8265 0.82 0.00 0.06 0.12
#> Sample_176 3 0.1211 0.6811 0.04 0.00 0.96 0.00
#> Sample_178 1 0.1211 0.8350 0.96 0.00 0.00 0.04
#> Sample_181 1 0.3525 0.7941 0.86 0.00 0.04 0.10
#> Sample_182 1 0.5291 0.7916 0.74 0.00 0.08 0.18
#> Sample_183 1 0.4491 0.8198 0.80 0.00 0.06 0.14
#> Sample_184 1 0.3611 0.8193 0.86 0.00 0.06 0.08
#> Sample_185 1 0.2335 0.8486 0.92 0.00 0.02 0.06
#> Sample_186 1 0.2706 0.8287 0.90 0.00 0.02 0.08
#> Sample_187 1 0.6881 0.2953 0.54 0.00 0.34 0.12
#> Sample_190 1 0.4949 0.8051 0.76 0.00 0.06 0.18
#> Sample_193 1 0.3172 0.8238 0.84 0.00 0.00 0.16
#> Sample_194 1 0.5291 0.7916 0.74 0.00 0.08 0.18
#> Sample_195 1 0.1913 0.8485 0.94 0.00 0.04 0.02
#> Sample_196 1 0.4731 0.8123 0.78 0.00 0.06 0.16
#> Sample_516 3 0.1211 0.6806 0.04 0.00 0.96 0.00
#> Sample_858 3 0.7583 0.2050 0.00 0.28 0.48 0.24
#> Sample_864 2 0.5636 0.4506 0.00 0.68 0.06 0.26
#> Sample_868 2 0.2647 0.7311 0.00 0.88 0.00 0.12
#> Sample_874 3 0.3037 0.6898 0.00 0.10 0.88 0.02
#> Sample_875 2 0.2345 0.7367 0.00 0.90 0.00 0.10
#> Sample_877 2 0.2011 0.7457 0.00 0.92 0.00 0.08
#> Sample_878 2 0.5767 0.4598 0.00 0.66 0.06 0.28
#> Sample_879 2 0.1211 0.7425 0.00 0.96 0.00 0.04
#> Sample_880 2 0.5428 0.5270 0.00 0.74 0.14 0.12
#> Sample_883 2 0.1637 0.7436 0.00 0.94 0.00 0.06
#> Sample_884 2 0.2345 0.7333 0.00 0.90 0.00 0.10
#> Sample_886 3 0.8629 0.1865 0.06 0.16 0.40 0.38
#> Sample_887 2 0.4841 0.6451 0.00 0.78 0.08 0.14
#> Sample_888 2 0.1211 0.7427 0.00 0.96 0.00 0.04
#> Sample_896 2 0.2011 0.7345 0.00 0.92 0.00 0.08
#> Sample_899 2 0.0707 0.7445 0.00 0.98 0.00 0.02
#> Sample_901 2 0.2011 0.7433 0.00 0.92 0.00 0.08
#> Sample_904 2 0.2345 0.7269 0.00 0.90 0.00 0.10
#> Sample_905 2 0.6941 0.0803 0.00 0.52 0.12 0.36
#> Sample_910 2 0.3172 0.7108 0.00 0.84 0.00 0.16
#> Sample_912 2 0.4134 0.6501 0.00 0.74 0.00 0.26
#> Sample_916 2 0.1211 0.7449 0.00 0.96 0.00 0.04
#> Sample_921 4 0.5132 -0.1275 0.08 0.04 0.08 0.80
#> Sample_923 2 0.4624 0.4428 0.00 0.66 0.00 0.34
#> Sample_926 2 0.4406 0.5242 0.00 0.70 0.00 0.30
#> Sample_927 2 0.3400 0.6887 0.00 0.82 0.00 0.18
#> Sample_928 4 0.5000 -0.4208 0.00 0.50 0.00 0.50
#> Sample_930 2 0.4855 0.2665 0.00 0.60 0.00 0.40
#> Sample_931 2 0.4642 0.5851 0.00 0.74 0.02 0.24
#> Sample_945 2 0.6586 0.1053 0.00 0.50 0.08 0.42
#> Sample_946 2 0.0707 0.7425 0.00 0.98 0.00 0.02
#> Sample_947 2 0.3400 0.6879 0.00 0.82 0.00 0.18
#> Sample_1546 3 0.3525 0.6950 0.00 0.10 0.86 0.04
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
As soon as the classes for columns are determined, the signatures that are significantly different between subgroups can be looked for. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. To get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows (which is done by automatically selecting number of clusters).If there are too many signatures, top_signatures = ... can be set to only show the
signatures with the highest FDRs:
# code only for demonstration
# e.g. to show the top 500 most significant rows
tb = get_signature(res, k = ..., top_signatures = 500)
If the signatures are defined as these which are uniquely high in current group, diff_method argument
can be set to "uniquely_high_in_one_group":
# code only for demonstration
tb = get_signature(res, k = ..., diff_method = "uniquely_high_in_one_group")
UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n_sample age(p-value) cell.type(p-value) k
#> ATC:skmeans 64 7.66e-06 3.89e-01 2
#> ATC:skmeans 61 4.09e-03 4.59e-04 3
#> ATC:skmeans 53 NaN 4.06e-05 4
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
Parent node: Node021. Child nodes: Node02111-leaf , Node02112 , Node02121-leaf , Node02122-leaf , Node02211-leaf , Node02212-leaf , Node02221-leaf , Node02222-leaf , Node03211 , Node03212-leaf , Node03221-leaf , Node03222-leaf .
The object with results only for a single top-value method and a single partitioning method can be extracted as:
res = res_rh["0212"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4.
#> On a matrix with 14692 rows and 142 columns.
#> Top rows (492) are extracted by 'ATC' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 150 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_partitions"
#> [7] "compare_signatures" "consensus_heatmap" "dimension_reduction"
#> [10] "functional_enrichment" "get_anno_col" "get_anno"
#> [13] "get_classes" "get_consensus" "get_matrix"
#> [16] "get_membership" "get_param" "get_signatures"
#> [19] "get_stats" "is_best_k" "is_stable_k"
#> [22] "membership_heatmap" "ncol" "nrow"
#> [25] "plot_ecdf" "predict_classes" "rownames"
#> [28] "select_partition_number" "show" "suggest_best_k"
#> [31] "test_to_known_factors" "top_rows_heatmap"
collect_plots() function collects all the plots made from res for all k (number of subgroups)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, higher 1-PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.984 0.957 0.982 0.498 0.505 0.505
#> 3 3 0.653 0.756 0.891 0.321 0.797 0.618
#> 4 4 0.720 0.747 0.871 0.112 0.865 0.647
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 2
Following is the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall subgroup
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> Sample_111 2 0.990 0.182 0.44 0.56
#> Sample_151 1 0.141 0.961 0.98 0.02
#> Sample_159 1 0.000 0.977 1.00 0.00
#> Sample_161 1 0.000 0.977 1.00 0.00
#> Sample_162 1 0.000 0.977 1.00 0.00
#> Sample_163 2 0.000 0.987 0.00 1.00
#> Sample_164 1 0.000 0.977 1.00 0.00
#> Sample_165 1 0.000 0.977 1.00 0.00
#> Sample_166 1 0.000 0.977 1.00 0.00
#> Sample_169 1 0.242 0.943 0.96 0.04
#> Sample_172 1 0.000 0.977 1.00 0.00
#> Sample_174 1 0.722 0.758 0.80 0.20
#> Sample_177 2 0.000 0.987 0.00 1.00
#> Sample_179 2 0.000 0.987 0.00 1.00
#> Sample_180 1 0.000 0.977 1.00 0.00
#> Sample_188 1 0.000 0.977 1.00 0.00
#> Sample_189 1 0.000 0.977 1.00 0.00
#> Sample_191 1 0.000 0.977 1.00 0.00
#> Sample_192 1 0.000 0.977 1.00 0.00
#> Sample_611 1 0.000 0.977 1.00 0.00
#> Sample_612 1 0.000 0.977 1.00 0.00
#> Sample_686 1 0.722 0.758 0.80 0.20
#> Sample_688 2 0.000 0.987 0.00 1.00
#> Sample_690 2 0.000 0.987 0.00 1.00
#> Sample_695 1 0.000 0.977 1.00 0.00
#> Sample_698 1 0.000 0.977 1.00 0.00
#> Sample_699 1 0.000 0.977 1.00 0.00
#> Sample_701 1 0.000 0.977 1.00 0.00
#> Sample_703 1 0.000 0.977 1.00 0.00
#> Sample_711 1 0.000 0.977 1.00 0.00
#> Sample_714 1 0.000 0.977 1.00 0.00
#> Sample_715 1 0.000 0.977 1.00 0.00
#> Sample_716 1 0.000 0.977 1.00 0.00
#> Sample_723 1 0.000 0.977 1.00 0.00
#> Sample_725 1 0.000 0.977 1.00 0.00
#> Sample_728 1 0.000 0.977 1.00 0.00
#> Sample_740 1 0.000 0.977 1.00 0.00
#> Sample_742 1 0.402 0.904 0.92 0.08
#> Sample_745 1 0.000 0.977 1.00 0.00
#> Sample_748 1 0.000 0.977 1.00 0.00
#> Sample_753 1 0.000 0.977 1.00 0.00
#> Sample_754 1 0.000 0.977 1.00 0.00
#> Sample_759 1 0.000 0.977 1.00 0.00
#> Sample_766 1 0.000 0.977 1.00 0.00
#> Sample_769 1 0.000 0.977 1.00 0.00
#> Sample_772 1 0.000 0.977 1.00 0.00
#> Sample_775 1 0.000 0.977 1.00 0.00
#> Sample_777 1 0.000 0.977 1.00 0.00
#> Sample_778 1 0.000 0.977 1.00 0.00
#> Sample_779 1 0.000 0.977 1.00 0.00
#> Sample_780 1 0.000 0.977 1.00 0.00
#> Sample_782 1 0.000 0.977 1.00 0.00
#> Sample_783 1 0.000 0.977 1.00 0.00
#> Sample_784 1 0.000 0.977 1.00 0.00
#> Sample_785 1 0.000 0.977 1.00 0.00
#> Sample_786 1 0.000 0.977 1.00 0.00
#> Sample_788 1 0.141 0.961 0.98 0.02
#> Sample_789 1 0.000 0.977 1.00 0.00
#> Sample_793 1 0.000 0.977 1.00 0.00
#> Sample_797 1 0.000 0.977 1.00 0.00
#> Sample_798 1 0.000 0.977 1.00 0.00
#> Sample_799 1 0.000 0.977 1.00 0.00
#> Sample_802 2 0.327 0.928 0.06 0.94
#> Sample_804 1 0.000 0.977 1.00 0.00
#> Sample_810 1 0.000 0.977 1.00 0.00
#> Sample_817 1 0.000 0.977 1.00 0.00
#> Sample_819 1 0.000 0.977 1.00 0.00
#> Sample_821 1 0.000 0.977 1.00 0.00
#> Sample_823 1 0.000 0.977 1.00 0.00
#> Sample_824 1 0.943 0.455 0.64 0.36
#> Sample_825 1 0.000 0.977 1.00 0.00
#> Sample_826 1 0.000 0.977 1.00 0.00
#> Sample_827 1 0.000 0.977 1.00 0.00
#> Sample_829 1 0.971 0.342 0.60 0.40
#> Sample_830 1 0.000 0.977 1.00 0.00
#> Sample_834 1 0.000 0.977 1.00 0.00
#> Sample_835 1 0.000 0.977 1.00 0.00
#> Sample_839 1 0.000 0.977 1.00 0.00
#> Sample_855 2 0.584 0.834 0.14 0.86
#> Sample_856 2 0.141 0.968 0.02 0.98
#> Sample_857 2 0.000 0.987 0.00 1.00
#> Sample_860 2 0.000 0.987 0.00 1.00
#> Sample_861 2 0.000 0.987 0.00 1.00
#> Sample_862 2 0.000 0.987 0.00 1.00
#> Sample_863 2 0.000 0.987 0.00 1.00
#> Sample_866 2 0.000 0.987 0.00 1.00
#> Sample_867 2 0.000 0.987 0.00 1.00
#> Sample_869 2 0.000 0.987 0.00 1.00
#> Sample_870 2 0.000 0.987 0.00 1.00
#> Sample_871 2 0.000 0.987 0.00 1.00
#> Sample_872 2 0.000 0.987 0.00 1.00
#> Sample_873 2 0.000 0.987 0.00 1.00
#> Sample_876 1 0.795 0.692 0.76 0.24
#> Sample_881 2 0.000 0.987 0.00 1.00
#> Sample_885 2 0.000 0.987 0.00 1.00
#> Sample_889 2 0.000 0.987 0.00 1.00
#> Sample_890 2 0.000 0.987 0.00 1.00
#> Sample_891 2 0.000 0.987 0.00 1.00
#> Sample_892 2 0.000 0.987 0.00 1.00
#> Sample_893 2 0.469 0.883 0.10 0.90
#> Sample_895 2 0.000 0.987 0.00 1.00
#> Sample_897 2 0.000 0.987 0.00 1.00
#> Sample_898 2 0.000 0.987 0.00 1.00
#> Sample_902 2 0.000 0.987 0.00 1.00
#> Sample_903 2 0.000 0.987 0.00 1.00
#> Sample_906 2 0.000 0.987 0.00 1.00
#> Sample_907 2 0.000 0.987 0.00 1.00
#> Sample_908 2 0.000 0.987 0.00 1.00
#> Sample_909 2 0.000 0.987 0.00 1.00
#> Sample_911 2 0.000 0.987 0.00 1.00
#> Sample_913 2 0.000 0.987 0.00 1.00
#> Sample_914 2 0.000 0.987 0.00 1.00
#> Sample_915 2 0.000 0.987 0.00 1.00
#> Sample_917 2 0.000 0.987 0.00 1.00
#> Sample_918 2 0.000 0.987 0.00 1.00
#> Sample_919 2 0.000 0.987 0.00 1.00
#> Sample_920 2 0.000 0.987 0.00 1.00
#> Sample_922 2 0.000 0.987 0.00 1.00
#> Sample_924 2 0.000 0.987 0.00 1.00
#> Sample_925 2 0.000 0.987 0.00 1.00
#> Sample_929 2 0.000 0.987 0.00 1.00
#> Sample_932 2 0.000 0.987 0.00 1.00
#> Sample_933 2 0.000 0.987 0.00 1.00
#> Sample_934 2 0.000 0.987 0.00 1.00
#> Sample_935 2 0.000 0.987 0.00 1.00
#> Sample_936 2 0.000 0.987 0.00 1.00
#> Sample_937 2 0.000 0.987 0.00 1.00
#> Sample_938 2 0.000 0.987 0.00 1.00
#> Sample_939 2 0.000 0.987 0.00 1.00
#> Sample_940 2 0.000 0.987 0.00 1.00
#> Sample_941 2 0.000 0.987 0.00 1.00
#> Sample_942 2 0.000 0.987 0.00 1.00
#> Sample_943 2 0.000 0.987 0.00 1.00
#> Sample_944 2 0.000 0.987 0.00 1.00
#> Sample_1527 1 0.141 0.961 0.98 0.02
#> Sample_1528 1 0.000 0.977 1.00 0.00
#> Sample_1529 1 0.000 0.977 1.00 0.00
#> Sample_1534 1 0.000 0.977 1.00 0.00
#> Sample_1538 1 0.000 0.977 1.00 0.00
#> Sample_1539 1 0.242 0.944 0.96 0.04
#> Sample_1542 1 0.680 0.786 0.82 0.18
#> Sample_1570 1 0.000 0.977 1.00 0.00
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> Sample_111 3 0.0000 0.795 0.00 0.00 1.00
#> Sample_151 1 0.6758 0.503 0.62 0.02 0.36
#> Sample_159 1 0.5948 0.529 0.64 0.00 0.36
#> Sample_161 1 0.5948 0.529 0.64 0.00 0.36
#> Sample_162 1 0.6045 0.499 0.62 0.00 0.38
#> Sample_163 2 0.6126 0.311 0.00 0.60 0.40
#> Sample_164 1 0.5948 0.529 0.64 0.00 0.36
#> Sample_165 1 0.5216 0.649 0.74 0.00 0.26
#> Sample_166 3 0.2537 0.754 0.08 0.00 0.92
#> Sample_169 1 0.6244 0.370 0.56 0.00 0.44
#> Sample_172 3 0.2959 0.734 0.10 0.00 0.90
#> Sample_174 3 0.4291 0.644 0.18 0.00 0.82
#> Sample_177 2 0.6053 0.581 0.02 0.72 0.26
#> Sample_179 3 0.5835 0.440 0.00 0.34 0.66
#> Sample_180 1 0.5948 0.529 0.64 0.00 0.36
#> Sample_188 1 0.5948 0.529 0.64 0.00 0.36
#> Sample_189 1 0.2959 0.791 0.90 0.00 0.10
#> Sample_191 1 0.5948 0.529 0.64 0.00 0.36
#> Sample_192 1 0.5948 0.529 0.64 0.00 0.36
#> Sample_611 3 0.0000 0.795 0.00 0.00 1.00
#> Sample_612 3 0.0000 0.795 0.00 0.00 1.00
#> Sample_686 1 0.2537 0.788 0.92 0.08 0.00
#> Sample_688 3 0.6126 0.377 0.00 0.40 0.60
#> Sample_690 3 0.5397 0.612 0.00 0.28 0.72
#> Sample_695 1 0.0892 0.839 0.98 0.00 0.02
#> Sample_698 1 0.0000 0.850 1.00 0.00 0.00
#> Sample_699 1 0.0000 0.850 1.00 0.00 0.00
#> Sample_701 1 0.0000 0.850 1.00 0.00 0.00
#> Sample_703 3 0.0000 0.795 0.00 0.00 1.00
#> Sample_711 1 0.0000 0.850 1.00 0.00 0.00
#> Sample_714 1 0.0000 0.850 1.00 0.00 0.00
#> Sample_715 1 0.5706 0.504 0.68 0.00 0.32
#> Sample_716 1 0.0000 0.850 1.00 0.00 0.00
#> Sample_723 1 0.4555 0.663 0.80 0.00 0.20
#> Sample_725 3 0.6045 0.391 0.38 0.00 0.62
#> Sample_728 1 0.0000 0.850 1.00 0.00 0.00
#> Sample_740 3 0.4796 0.670 0.22 0.00 0.78
#> Sample_742 3 0.5016 0.641 0.24 0.00 0.76
#> Sample_745 1 0.0892 0.839 0.98 0.00 0.02
#> Sample_748 1 0.0000 0.850 1.00 0.00 0.00
#> Sample_753 1 0.6309 -0.117 0.50 0.00 0.50
#> Sample_754 1 0.0000 0.850 1.00 0.00 0.00
#> Sample_759 1 0.0000 0.850 1.00 0.00 0.00
#> Sample_766 1 0.0000 0.850 1.00 0.00 0.00
#> Sample_769 1 0.0000 0.850 1.00 0.00 0.00
#> Sample_772 1 0.0000 0.850 1.00 0.00 0.00
#> Sample_775 1 0.0000 0.850 1.00 0.00 0.00
#> Sample_777 3 0.5216 0.620 0.26 0.00 0.74
#> Sample_778 1 0.0000 0.850 1.00 0.00 0.00
#> Sample_779 3 0.5397 0.590 0.28 0.00 0.72
#> Sample_780 1 0.0000 0.850 1.00 0.00 0.00
#> Sample_782 1 0.0000 0.850 1.00 0.00 0.00
#> Sample_783 1 0.5016 0.629 0.76 0.00 0.24
#> Sample_784 1 0.2959 0.792 0.90 0.00 0.10
#> Sample_785 1 0.0892 0.840 0.98 0.00 0.02
#> Sample_786 1 0.0000 0.850 1.00 0.00 0.00
#> Sample_788 3 0.5216 0.580 0.26 0.00 0.74
#> Sample_789 1 0.0000 0.850 1.00 0.00 0.00
#> Sample_793 1 0.0000 0.850 1.00 0.00 0.00
#> Sample_797 1 0.0000 0.850 1.00 0.00 0.00
#> Sample_798 1 0.4291 0.730 0.82 0.00 0.18
#> Sample_799 1 0.0000 0.850 1.00 0.00 0.00
#> Sample_802 2 0.6927 0.552 0.24 0.70 0.06
#> Sample_804 1 0.0000 0.850 1.00 0.00 0.00
#> Sample_810 1 0.0000 0.850 1.00 0.00 0.00
#> Sample_817 1 0.0000 0.850 1.00 0.00 0.00
#> Sample_819 1 0.0000 0.850 1.00 0.00 0.00
#> Sample_821 1 0.0000 0.850 1.00 0.00 0.00
#> Sample_823 1 0.0000 0.850 1.00 0.00 0.00
#> Sample_824 1 0.5216 0.557 0.74 0.26 0.00
#> Sample_825 1 0.0000 0.850 1.00 0.00 0.00
#> Sample_826 1 0.1529 0.826 0.96 0.00 0.04
#> Sample_827 1 0.0000 0.850 1.00 0.00 0.00
#> Sample_829 1 0.4002 0.702 0.84 0.16 0.00
#> Sample_830 1 0.0000 0.850 1.00 0.00 0.00
#> Sample_834 1 0.0000 0.850 1.00 0.00 0.00
#> Sample_835 1 0.0000 0.850 1.00 0.00 0.00
#> Sample_839 1 0.6192 0.176 0.58 0.00 0.42
#> Sample_855 1 0.9940 -0.246 0.36 0.28 0.36
#> Sample_856 2 0.6651 0.432 0.02 0.64 0.34
#> Sample_857 2 0.3686 0.806 0.00 0.86 0.14
#> Sample_860 2 0.3415 0.859 0.02 0.90 0.08
#> Sample_861 2 0.3686 0.808 0.00 0.86 0.14
#> Sample_862 3 0.5216 0.628 0.00 0.26 0.74
#> Sample_863 3 0.6244 0.261 0.00 0.44 0.56
#> Sample_866 3 0.5016 0.653 0.00 0.24 0.76
#> Sample_867 3 0.5948 0.456 0.00 0.36 0.64
#> Sample_869 2 0.0000 0.933 0.00 1.00 0.00
#> Sample_870 2 0.4002 0.789 0.00 0.84 0.16
#> Sample_871 2 0.0000 0.933 0.00 1.00 0.00
#> Sample_872 2 0.0000 0.933 0.00 1.00 0.00
#> Sample_873 2 0.0000 0.933 0.00 1.00 0.00
#> Sample_876 1 0.8399 0.474 0.62 0.22 0.16
#> Sample_881 2 0.2959 0.851 0.00 0.90 0.10
#> Sample_885 2 0.0892 0.915 0.02 0.98 0.00
#> Sample_889 2 0.0000 0.933 0.00 1.00 0.00
#> Sample_890 2 0.6633 0.562 0.04 0.70 0.26
#> Sample_891 2 0.0000 0.933 0.00 1.00 0.00
#> Sample_892 2 0.0000 0.933 0.00 1.00 0.00
#> Sample_893 2 0.8215 0.191 0.08 0.54 0.38
#> Sample_895 2 0.0000 0.933 0.00 1.00 0.00
#> Sample_897 2 0.0000 0.933 0.00 1.00 0.00
#> Sample_898 2 0.0892 0.919 0.00 0.98 0.02
#> Sample_902 2 0.0000 0.933 0.00 1.00 0.00
#> Sample_903 2 0.0000 0.933 0.00 1.00 0.00
#> Sample_906 2 0.0000 0.933 0.00 1.00 0.00
#> Sample_907 2 0.0000 0.933 0.00 1.00 0.00
#> Sample_908 2 0.0000 0.933 0.00 1.00 0.00
#> Sample_909 3 0.1529 0.792 0.00 0.04 0.96
#> Sample_911 2 0.0000 0.933 0.00 1.00 0.00
#> Sample_913 2 0.0000 0.933 0.00 1.00 0.00
#> Sample_914 2 0.0000 0.933 0.00 1.00 0.00
#> Sample_915 2 0.0000 0.933 0.00 1.00 0.00
#> Sample_917 2 0.0000 0.933 0.00 1.00 0.00
#> Sample_918 2 0.0000 0.933 0.00 1.00 0.00
#> Sample_919 2 0.0000 0.933 0.00 1.00 0.00
#> Sample_920 2 0.0000 0.933 0.00 1.00 0.00
#> Sample_922 2 0.0000 0.933 0.00 1.00 0.00
#> Sample_924 2 0.0000 0.933 0.00 1.00 0.00
#> Sample_925 2 0.0000 0.933 0.00 1.00 0.00
#> Sample_929 3 0.5016 0.658 0.00 0.24 0.76
#> Sample_932 2 0.3686 0.801 0.00 0.86 0.14
#> Sample_933 2 0.0000 0.933 0.00 1.00 0.00
#> Sample_934 2 0.0000 0.933 0.00 1.00 0.00
#> Sample_935 2 0.0000 0.933 0.00 1.00 0.00
#> Sample_936 2 0.0892 0.919 0.00 0.98 0.02
#> Sample_937 2 0.0000 0.933 0.00 1.00 0.00
#> Sample_938 2 0.0000 0.933 0.00 1.00 0.00
#> Sample_939 2 0.0000 0.933 0.00 1.00 0.00
#> Sample_940 2 0.0000 0.933 0.00 1.00 0.00
#> Sample_941 2 0.0000 0.933 0.00 1.00 0.00
#> Sample_942 2 0.0000 0.933 0.00 1.00 0.00
#> Sample_943 2 0.0000 0.933 0.00 1.00 0.00
#> Sample_944 2 0.0000 0.933 0.00 1.00 0.00
#> Sample_1527 3 0.0000 0.795 0.00 0.00 1.00
#> Sample_1528 3 0.2066 0.779 0.06 0.00 0.94
#> Sample_1529 3 0.0000 0.795 0.00 0.00 1.00
#> Sample_1534 3 0.5216 0.612 0.26 0.00 0.74
#> Sample_1538 3 0.0000 0.795 0.00 0.00 1.00
#> Sample_1539 3 0.0000 0.795 0.00 0.00 1.00
#> Sample_1542 3 0.0000 0.795 0.00 0.00 1.00
#> Sample_1570 3 0.0000 0.795 0.00 0.00 1.00
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> Sample_111 3 0.1637 0.7302 0.00 0.00 0.94 0.06
#> Sample_151 4 0.2335 0.8359 0.06 0.00 0.02 0.92
#> Sample_159 4 0.2011 0.8326 0.08 0.00 0.00 0.92
#> Sample_161 4 0.2011 0.8326 0.08 0.00 0.00 0.92
#> Sample_162 4 0.2335 0.8359 0.06 0.00 0.02 0.92
#> Sample_163 4 0.3525 0.7580 0.00 0.04 0.10 0.86
#> Sample_164 4 0.2011 0.8326 0.08 0.00 0.00 0.92
#> Sample_165 4 0.2345 0.8160 0.10 0.00 0.00 0.90
#> Sample_166 4 0.4907 0.1574 0.00 0.00 0.42 0.58
#> Sample_169 4 0.2411 0.8265 0.04 0.00 0.04 0.92
#> Sample_172 4 0.0000 0.7813 0.00 0.00 0.00 1.00
#> Sample_174 4 0.2011 0.7923 0.00 0.00 0.08 0.92
#> Sample_177 4 0.3400 0.6900 0.00 0.18 0.00 0.82
#> Sample_179 4 0.2011 0.7923 0.00 0.00 0.08 0.92
#> Sample_180 4 0.1211 0.8213 0.04 0.00 0.00 0.96
#> Sample_188 4 0.2011 0.8326 0.08 0.00 0.00 0.92
#> Sample_189 4 0.4406 0.5772 0.30 0.00 0.00 0.70
#> Sample_191 4 0.2335 0.8359 0.06 0.00 0.02 0.92
#> Sample_192 4 0.1637 0.8315 0.06 0.00 0.00 0.94
#> Sample_611 3 0.4134 0.7008 0.00 0.00 0.74 0.26
#> Sample_612 3 0.3975 0.7113 0.00 0.00 0.76 0.24
#> Sample_686 1 0.4079 0.7320 0.80 0.18 0.00 0.02
#> Sample_688 3 0.2011 0.6776 0.00 0.08 0.92 0.00
#> Sample_690 3 0.2411 0.6932 0.00 0.04 0.92 0.04
#> Sample_695 1 0.0000 0.9055 1.00 0.00 0.00 0.00
#> Sample_698 1 0.0000 0.9055 1.00 0.00 0.00 0.00
#> Sample_699 1 0.0000 0.9055 1.00 0.00 0.00 0.00
#> Sample_701 1 0.0000 0.9055 1.00 0.00 0.00 0.00
#> Sample_703 3 0.4277 0.6842 0.00 0.00 0.72 0.28
#> Sample_711 1 0.0000 0.9055 1.00 0.00 0.00 0.00
#> Sample_714 1 0.1211 0.8787 0.96 0.04 0.00 0.00
#> Sample_715 1 0.6370 0.5071 0.62 0.00 0.10 0.28
#> Sample_716 1 0.0000 0.9055 1.00 0.00 0.00 0.00
#> Sample_723 1 0.1637 0.8676 0.94 0.00 0.00 0.06
#> Sample_725 1 0.7653 0.0766 0.46 0.00 0.24 0.30
#> Sample_728 1 0.0000 0.9055 1.00 0.00 0.00 0.00
#> Sample_740 3 0.6976 0.4703 0.24 0.00 0.58 0.18
#> Sample_742 3 0.4292 0.7046 0.10 0.00 0.82 0.08
#> Sample_745 1 0.3198 0.8343 0.88 0.00 0.04 0.08
#> Sample_748 1 0.0000 0.9055 1.00 0.00 0.00 0.00
#> Sample_753 1 0.4731 0.7093 0.78 0.00 0.16 0.06
#> Sample_754 1 0.0000 0.9055 1.00 0.00 0.00 0.00
#> Sample_759 1 0.1211 0.8801 0.96 0.04 0.00 0.00
#> Sample_766 1 0.0000 0.9055 1.00 0.00 0.00 0.00
#> Sample_769 1 0.0000 0.9055 1.00 0.00 0.00 0.00
#> Sample_772 1 0.0000 0.9055 1.00 0.00 0.00 0.00
#> Sample_775 1 0.0000 0.9055 1.00 0.00 0.00 0.00
#> Sample_777 1 0.7179 0.0308 0.48 0.00 0.38 0.14
#> Sample_778 1 0.0000 0.9055 1.00 0.00 0.00 0.00
#> Sample_779 3 0.7738 0.3415 0.26 0.00 0.44 0.30
#> Sample_780 1 0.0000 0.9055 1.00 0.00 0.00 0.00
#> Sample_782 1 0.0000 0.9055 1.00 0.00 0.00 0.00
#> Sample_783 1 0.5784 0.5928 0.70 0.00 0.20 0.10
#> Sample_784 1 0.2647 0.8348 0.88 0.00 0.00 0.12
#> Sample_785 1 0.2921 0.7927 0.86 0.00 0.00 0.14
#> Sample_786 1 0.0000 0.9055 1.00 0.00 0.00 0.00
#> Sample_788 3 0.6831 0.0534 0.10 0.00 0.48 0.42
#> Sample_789 1 0.0000 0.9055 1.00 0.00 0.00 0.00
#> Sample_793 1 0.0000 0.9055 1.00 0.00 0.00 0.00
#> Sample_797 1 0.0000 0.9055 1.00 0.00 0.00 0.00
#> Sample_798 1 0.4277 0.6028 0.72 0.00 0.00 0.28
#> Sample_799 1 0.0000 0.9055 1.00 0.00 0.00 0.00
#> Sample_802 1 0.8453 -0.1472 0.36 0.30 0.32 0.02
#> Sample_804 1 0.0000 0.9055 1.00 0.00 0.00 0.00
#> Sample_810 1 0.0707 0.8943 0.98 0.00 0.00 0.02
#> Sample_817 1 0.0000 0.9055 1.00 0.00 0.00 0.00
#> Sample_819 1 0.0000 0.9055 1.00 0.00 0.00 0.00
#> Sample_821 1 0.0000 0.9055 1.00 0.00 0.00 0.00
#> Sample_823 1 0.0000 0.9055 1.00 0.00 0.00 0.00
#> Sample_824 1 0.3400 0.7406 0.82 0.18 0.00 0.00
#> Sample_825 1 0.1211 0.8811 0.96 0.00 0.00 0.04
#> Sample_826 1 0.0000 0.9055 1.00 0.00 0.00 0.00
#> Sample_827 1 0.0707 0.8947 0.98 0.00 0.02 0.00
#> Sample_829 1 0.2011 0.8461 0.92 0.08 0.00 0.00
#> Sample_830 1 0.0000 0.9055 1.00 0.00 0.00 0.00
#> Sample_834 1 0.0000 0.9055 1.00 0.00 0.00 0.00
#> Sample_835 1 0.0000 0.9055 1.00 0.00 0.00 0.00
#> Sample_839 1 0.5883 0.4542 0.64 0.00 0.30 0.06
#> Sample_855 3 0.9078 0.1925 0.34 0.20 0.38 0.08
#> Sample_856 2 0.7499 0.1672 0.00 0.42 0.40 0.18
#> Sample_857 2 0.3400 0.7882 0.00 0.82 0.18 0.00
#> Sample_860 2 0.7577 0.5267 0.04 0.60 0.16 0.20
#> Sample_861 2 0.4907 0.5112 0.00 0.58 0.42 0.00
#> Sample_862 3 0.0000 0.7261 0.00 0.00 1.00 0.00
#> Sample_863 3 0.2921 0.6217 0.00 0.14 0.86 0.00
#> Sample_866 3 0.0000 0.7261 0.00 0.00 1.00 0.00
#> Sample_867 3 0.0000 0.7261 0.00 0.00 1.00 0.00
#> Sample_869 2 0.1637 0.8605 0.00 0.94 0.06 0.00
#> Sample_870 2 0.7040 0.2896 0.00 0.46 0.42 0.12
#> Sample_871 2 0.0707 0.8732 0.00 0.98 0.02 0.00
#> Sample_872 2 0.0000 0.8786 0.00 1.00 0.00 0.00
#> Sample_873 2 0.0000 0.8786 0.00 1.00 0.00 0.00
#> Sample_876 4 0.7674 0.3794 0.28 0.26 0.00 0.46
#> Sample_881 2 0.4713 0.6162 0.00 0.64 0.36 0.00
#> Sample_885 2 0.0707 0.8639 0.02 0.98 0.00 0.00
#> Sample_889 2 0.0000 0.8786 0.00 1.00 0.00 0.00
#> Sample_890 3 0.7329 -0.0691 0.04 0.44 0.46 0.06
#> Sample_891 2 0.0000 0.8786 0.00 1.00 0.00 0.00
#> Sample_892 2 0.0000 0.8786 0.00 1.00 0.00 0.00
#> Sample_893 4 0.8293 0.1845 0.02 0.24 0.32 0.42
#> Sample_895 2 0.4134 0.7289 0.00 0.74 0.26 0.00
#> Sample_897 2 0.3400 0.7925 0.00 0.82 0.18 0.00
#> Sample_898 2 0.5271 0.6202 0.00 0.64 0.34 0.02
#> Sample_902 2 0.0000 0.8786 0.00 1.00 0.00 0.00
#> Sample_903 2 0.0000 0.8786 0.00 1.00 0.00 0.00
#> Sample_906 2 0.1211 0.8672 0.00 0.96 0.04 0.00
#> Sample_907 2 0.0000 0.8786 0.00 1.00 0.00 0.00
#> Sample_908 2 0.0000 0.8786 0.00 1.00 0.00 0.00
#> Sample_909 3 0.0000 0.7261 0.00 0.00 1.00 0.00
#> Sample_911 2 0.0000 0.8786 0.00 1.00 0.00 0.00
#> Sample_913 2 0.0000 0.8786 0.00 1.00 0.00 0.00
#> Sample_914 2 0.0000 0.8786 0.00 1.00 0.00 0.00
#> Sample_915 2 0.0000 0.8786 0.00 1.00 0.00 0.00
#> Sample_917 2 0.0000 0.8786 0.00 1.00 0.00 0.00
#> Sample_918 2 0.0000 0.8786 0.00 1.00 0.00 0.00
#> Sample_919 2 0.0000 0.8786 0.00 1.00 0.00 0.00
#> Sample_920 2 0.4406 0.6884 0.00 0.70 0.30 0.00
#> Sample_922 2 0.0000 0.8786 0.00 1.00 0.00 0.00
#> Sample_924 2 0.0000 0.8786 0.00 1.00 0.00 0.00
#> Sample_925 2 0.3172 0.8057 0.00 0.84 0.16 0.00
#> Sample_929 3 0.0000 0.7261 0.00 0.00 1.00 0.00
#> Sample_932 2 0.4948 0.4685 0.00 0.56 0.44 0.00
#> Sample_933 2 0.1211 0.8678 0.00 0.96 0.04 0.00
#> Sample_934 2 0.3975 0.7461 0.00 0.76 0.24 0.00
#> Sample_935 2 0.0707 0.8732 0.00 0.98 0.02 0.00
#> Sample_936 2 0.4790 0.5817 0.00 0.62 0.38 0.00
#> Sample_937 2 0.0000 0.8786 0.00 1.00 0.00 0.00
#> Sample_938 2 0.0000 0.8786 0.00 1.00 0.00 0.00
#> Sample_939 2 0.4277 0.7103 0.00 0.72 0.28 0.00
#> Sample_940 2 0.0000 0.8786 0.00 1.00 0.00 0.00
#> Sample_941 2 0.0000 0.8786 0.00 1.00 0.00 0.00
#> Sample_942 2 0.0000 0.8786 0.00 1.00 0.00 0.00
#> Sample_943 2 0.2921 0.7733 0.00 0.86 0.00 0.14
#> Sample_944 2 0.1637 0.8590 0.00 0.94 0.06 0.00
#> Sample_1527 3 0.3610 0.7273 0.00 0.00 0.80 0.20
#> Sample_1528 3 0.5327 0.6903 0.06 0.00 0.72 0.22
#> Sample_1529 3 0.4134 0.7008 0.00 0.00 0.74 0.26
#> Sample_1534 3 0.6049 0.6460 0.12 0.00 0.68 0.20
#> Sample_1538 3 0.4134 0.7008 0.00 0.00 0.74 0.26
#> Sample_1539 3 0.4134 0.7008 0.00 0.00 0.74 0.26
#> Sample_1542 3 0.4134 0.7008 0.00 0.00 0.74 0.26
#> Sample_1570 3 0.3400 0.7307 0.00 0.00 0.82 0.18
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
As soon as the classes for columns are determined, the signatures that are significantly different between subgroups can be looked for. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. To get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows (which is done by automatically selecting number of clusters).If there are too many signatures, top_signatures = ... can be set to only show the
signatures with the highest FDRs:
# code only for demonstration
# e.g. to show the top 500 most significant rows
tb = get_signature(res, k = ..., top_signatures = 500)
If the signatures are defined as these which are uniquely high in current group, diff_method argument
can be set to "uniquely_high_in_one_group":
# code only for demonstration
tb = get_signature(res, k = ..., diff_method = "uniquely_high_in_one_group")
UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n_sample age(p-value) cell.type(p-value) k
#> ATC:skmeans 139 1.88e-08 2.27e-02 2
#> ATC:skmeans 128 9.59e-09 4.61e-09 3
#> ATC:skmeans 127 NaN 9.44e-10 4
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
Parent node: Node02. Child nodes: Node0111-leaf , Node0112-leaf , Node0121-leaf , Node0122-leaf , Node0131-leaf , Node0132-leaf , Node0211 , Node0212 , Node0221 , Node0222 , Node0231-leaf , Node0232-leaf , Node0233-leaf , Node0311-leaf , Node0312-leaf , Node0313-leaf , Node0314-leaf , Node0321 , Node0322 , Node0331-leaf , Node0332-leaf , Node0333-leaf .
The object with results only for a single top-value method and a single partitioning method can be extracted as:
res = res_rh["022"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4.
#> On a matrix with 14744 rows and 218 columns.
#> Top rows (1407) are extracted by 'ATC' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 150 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_partitions"
#> [7] "compare_signatures" "consensus_heatmap" "dimension_reduction"
#> [10] "functional_enrichment" "get_anno_col" "get_anno"
#> [13] "get_classes" "get_consensus" "get_matrix"
#> [16] "get_membership" "get_param" "get_signatures"
#> [19] "get_stats" "is_best_k" "is_stable_k"
#> [22] "membership_heatmap" "ncol" "nrow"
#> [25] "plot_ecdf" "predict_classes" "rownames"
#> [28] "select_partition_number" "show" "suggest_best_k"
#> [31] "test_to_known_factors" "top_rows_heatmap"
collect_plots() function collects all the plots made from res for all k (number of subgroups)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, higher 1-PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.971 0.957 0.982 0.500 0.501 0.501
#> 3 3 0.701 0.827 0.901 0.313 0.782 0.589
#> 4 4 0.534 0.558 0.779 0.121 0.864 0.632
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 2
Following is the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall subgroup
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> Sample_2 1 0.141 0.9637 0.98 0.02
#> Sample_3 1 0.000 0.9815 1.00 0.00
#> Sample_4 2 0.904 0.5337 0.32 0.68
#> Sample_5 1 0.000 0.9815 1.00 0.00
#> Sample_6 1 0.000 0.9815 1.00 0.00
#> Sample_7 1 0.000 0.9815 1.00 0.00
#> Sample_8 2 0.000 0.9824 0.00 1.00
#> Sample_9 1 0.000 0.9815 1.00 0.00
#> Sample_10 1 0.000 0.9815 1.00 0.00
#> Sample_11 1 0.000 0.9815 1.00 0.00
#> Sample_12 1 0.000 0.9815 1.00 0.00
#> Sample_13 1 0.000 0.9815 1.00 0.00
#> Sample_14 1 0.000 0.9815 1.00 0.00
#> Sample_15 1 0.000 0.9815 1.00 0.00
#> Sample_16 1 0.000 0.9815 1.00 0.00
#> Sample_17 2 0.000 0.9824 0.00 1.00
#> Sample_18 2 0.000 0.9824 0.00 1.00
#> Sample_19 2 0.000 0.9824 0.00 1.00
#> Sample_20 2 0.000 0.9824 0.00 1.00
#> Sample_21 2 0.000 0.9824 0.00 1.00
#> Sample_22 2 0.000 0.9824 0.00 1.00
#> Sample_23 2 0.000 0.9824 0.00 1.00
#> Sample_24 1 0.141 0.9637 0.98 0.02
#> Sample_25 2 0.000 0.9824 0.00 1.00
#> Sample_26 1 0.999 0.0722 0.52 0.48
#> Sample_27 2 0.000 0.9824 0.00 1.00
#> Sample_28 2 0.000 0.9824 0.00 1.00
#> Sample_29 1 0.000 0.9815 1.00 0.00
#> Sample_30 2 0.000 0.9824 0.00 1.00
#> Sample_32 1 0.000 0.9815 1.00 0.00
#> Sample_33 1 0.000 0.9815 1.00 0.00
#> Sample_34 1 0.000 0.9815 1.00 0.00
#> Sample_35 1 0.000 0.9815 1.00 0.00
#> Sample_36 1 0.000 0.9815 1.00 0.00
#> Sample_37 1 0.000 0.9815 1.00 0.00
#> Sample_38 1 0.000 0.9815 1.00 0.00
#> Sample_39 1 0.000 0.9815 1.00 0.00
#> Sample_40 1 0.000 0.9815 1.00 0.00
#> Sample_41 1 0.000 0.9815 1.00 0.00
#> Sample_42 1 0.000 0.9815 1.00 0.00
#> Sample_44 1 0.000 0.9815 1.00 0.00
#> Sample_45 1 0.000 0.9815 1.00 0.00
#> Sample_46 1 0.000 0.9815 1.00 0.00
#> Sample_47 1 0.000 0.9815 1.00 0.00
#> Sample_48 1 0.000 0.9815 1.00 0.00
#> Sample_49 1 0.000 0.9815 1.00 0.00
#> Sample_50 1 0.000 0.9815 1.00 0.00
#> Sample_51 1 0.000 0.9815 1.00 0.00
#> Sample_52 1 0.000 0.9815 1.00 0.00
#> Sample_53 2 0.000 0.9824 0.00 1.00
#> Sample_54 2 0.000 0.9824 0.00 1.00
#> Sample_55 1 0.000 0.9815 1.00 0.00
#> Sample_56 1 0.000 0.9815 1.00 0.00
#> Sample_57 1 0.000 0.9815 1.00 0.00
#> Sample_58 1 0.000 0.9815 1.00 0.00
#> Sample_59 1 0.000 0.9815 1.00 0.00
#> Sample_60 1 0.000 0.9815 1.00 0.00
#> Sample_61 2 0.000 0.9824 0.00 1.00
#> Sample_62 1 0.000 0.9815 1.00 0.00
#> Sample_63 1 0.000 0.9815 1.00 0.00
#> Sample_64 1 0.000 0.9815 1.00 0.00
#> Sample_65 1 0.000 0.9815 1.00 0.00
#> Sample_66 1 0.000 0.9815 1.00 0.00
#> Sample_67 2 0.000 0.9824 0.00 1.00
#> Sample_68 1 0.000 0.9815 1.00 0.00
#> Sample_69 1 0.000 0.9815 1.00 0.00
#> Sample_70 2 0.995 0.1434 0.46 0.54
#> Sample_71 2 0.000 0.9824 0.00 1.00
#> Sample_74 1 0.000 0.9815 1.00 0.00
#> Sample_75 2 0.000 0.9824 0.00 1.00
#> Sample_76 1 0.000 0.9815 1.00 0.00
#> Sample_77 2 0.000 0.9824 0.00 1.00
#> Sample_78 1 0.000 0.9815 1.00 0.00
#> Sample_79 2 0.000 0.9824 0.00 1.00
#> Sample_80 2 0.000 0.9824 0.00 1.00
#> Sample_81 1 0.141 0.9637 0.98 0.02
#> Sample_82 2 0.000 0.9824 0.00 1.00
#> Sample_83 1 0.760 0.7185 0.78 0.22
#> Sample_84 1 0.680 0.7796 0.82 0.18
#> Sample_85 2 0.827 0.6479 0.26 0.74
#> Sample_86 1 0.000 0.9815 1.00 0.00
#> Sample_87 2 0.000 0.9824 0.00 1.00
#> Sample_88 2 0.529 0.8606 0.12 0.88
#> Sample_89 2 0.000 0.9824 0.00 1.00
#> Sample_90 2 0.000 0.9824 0.00 1.00
#> Sample_91 2 0.000 0.9824 0.00 1.00
#> Sample_92 2 0.000 0.9824 0.00 1.00
#> Sample_93 2 0.584 0.8363 0.14 0.86
#> Sample_95 2 0.327 0.9266 0.06 0.94
#> Sample_96 1 0.000 0.9815 1.00 0.00
#> Sample_97 1 0.881 0.5741 0.70 0.30
#> Sample_98 1 0.000 0.9815 1.00 0.00
#> Sample_99 1 0.000 0.9815 1.00 0.00
#> Sample_100 2 0.000 0.9824 0.00 1.00
#> Sample_101 2 0.000 0.9824 0.00 1.00
#> Sample_102 1 0.995 0.1475 0.54 0.46
#> Sample_103 2 0.000 0.9824 0.00 1.00
#> Sample_104 2 0.000 0.9824 0.00 1.00
#> Sample_105 1 0.000 0.9815 1.00 0.00
#> Sample_106 1 0.000 0.9815 1.00 0.00
#> Sample_107 2 0.000 0.9824 0.00 1.00
#> Sample_108 1 0.000 0.9815 1.00 0.00
#> Sample_109 1 0.000 0.9815 1.00 0.00
#> Sample_110 2 0.000 0.9824 0.00 1.00
#> Sample_112 2 0.000 0.9824 0.00 1.00
#> Sample_113 2 0.000 0.9824 0.00 1.00
#> Sample_115 2 0.000 0.9824 0.00 1.00
#> Sample_116 2 0.000 0.9824 0.00 1.00
#> Sample_117 2 0.000 0.9824 0.00 1.00
#> Sample_119 2 0.000 0.9824 0.00 1.00
#> Sample_120 1 0.000 0.9815 1.00 0.00
#> Sample_122 2 0.000 0.9824 0.00 1.00
#> Sample_124 1 0.000 0.9815 1.00 0.00
#> Sample_125 1 0.000 0.9815 1.00 0.00
#> Sample_126 2 0.000 0.9824 0.00 1.00
#> Sample_127 1 0.000 0.9815 1.00 0.00
#> Sample_128 2 0.000 0.9824 0.00 1.00
#> Sample_129 1 0.000 0.9815 1.00 0.00
#> Sample_130 2 0.000 0.9824 0.00 1.00
#> Sample_131 2 0.000 0.9824 0.00 1.00
#> Sample_132 1 0.000 0.9815 1.00 0.00
#> Sample_133 2 0.000 0.9824 0.00 1.00
#> Sample_134 1 0.680 0.7779 0.82 0.18
#> Sample_135 2 0.000 0.9824 0.00 1.00
#> Sample_136 1 0.000 0.9815 1.00 0.00
#> Sample_137 1 0.000 0.9815 1.00 0.00
#> Sample_138 1 0.000 0.9815 1.00 0.00
#> Sample_139 2 0.000 0.9824 0.00 1.00
#> Sample_140 1 0.000 0.9815 1.00 0.00
#> Sample_141 1 0.000 0.9815 1.00 0.00
#> Sample_142 2 0.000 0.9824 0.00 1.00
#> Sample_143 2 0.000 0.9824 0.00 1.00
#> Sample_145 2 0.000 0.9824 0.00 1.00
#> Sample_146 2 0.000 0.9824 0.00 1.00
#> Sample_197 1 0.000 0.9815 1.00 0.00
#> Sample_198 1 0.000 0.9815 1.00 0.00
#> Sample_199 1 0.000 0.9815 1.00 0.00
#> Sample_200 1 0.000 0.9815 1.00 0.00
#> Sample_201 1 0.000 0.9815 1.00 0.00
#> Sample_202 1 0.000 0.9815 1.00 0.00
#> Sample_203 1 0.000 0.9815 1.00 0.00
#> Sample_205 1 0.000 0.9815 1.00 0.00
#> Sample_206 1 0.000 0.9815 1.00 0.00
#> Sample_207 1 0.000 0.9815 1.00 0.00
#> Sample_586 1 0.000 0.9815 1.00 0.00
#> Sample_587 1 0.000 0.9815 1.00 0.00
#> Sample_591 1 0.000 0.9815 1.00 0.00
#> Sample_593 1 0.000 0.9815 1.00 0.00
#> Sample_596 2 0.000 0.9824 0.00 1.00
#> Sample_598 2 0.000 0.9824 0.00 1.00
#> Sample_601 2 0.000 0.9824 0.00 1.00
#> Sample_603 2 0.000 0.9824 0.00 1.00
#> Sample_604 2 0.000 0.9824 0.00 1.00
#> Sample_605 2 0.000 0.9824 0.00 1.00
#> Sample_606 2 0.000 0.9824 0.00 1.00
#> Sample_607 1 0.000 0.9815 1.00 0.00
#> Sample_608 2 0.000 0.9824 0.00 1.00
#> Sample_609 2 0.000 0.9824 0.00 1.00
#> Sample_610 2 0.000 0.9824 0.00 1.00
#> Sample_651 1 0.000 0.9815 1.00 0.00
#> Sample_652 1 0.000 0.9815 1.00 0.00
#> Sample_653 1 0.327 0.9242 0.94 0.06
#> Sample_654 1 0.000 0.9815 1.00 0.00
#> Sample_655 1 0.000 0.9815 1.00 0.00
#> Sample_656 1 0.000 0.9815 1.00 0.00
#> Sample_657 2 0.000 0.9824 0.00 1.00
#> Sample_658 1 0.000 0.9815 1.00 0.00
#> Sample_659 1 0.000 0.9815 1.00 0.00
#> Sample_660 2 0.000 0.9824 0.00 1.00
#> Sample_661 1 0.000 0.9815 1.00 0.00
#> Sample_662 2 0.000 0.9824 0.00 1.00
#> Sample_663 2 0.000 0.9824 0.00 1.00
#> Sample_664 1 0.000 0.9815 1.00 0.00
#> Sample_666 2 0.000 0.9824 0.00 1.00
#> Sample_668 2 0.000 0.9824 0.00 1.00
#> Sample_669 2 0.000 0.9824 0.00 1.00
#> Sample_670 2 0.634 0.8098 0.16 0.84
#> Sample_671 1 0.000 0.9815 1.00 0.00
#> Sample_672 2 0.000 0.9824 0.00 1.00
#> Sample_673 2 0.000 0.9824 0.00 1.00
#> Sample_674 2 0.000 0.9824 0.00 1.00
#> Sample_675 2 0.000 0.9824 0.00 1.00
#> Sample_676 2 0.000 0.9824 0.00 1.00
#> Sample_677 1 0.634 0.8066 0.84 0.16
#> Sample_678 1 0.000 0.9815 1.00 0.00
#> Sample_679 1 0.000 0.9815 1.00 0.00
#> Sample_680 2 0.000 0.9824 0.00 1.00
#> Sample_682 2 0.000 0.9824 0.00 1.00
#> Sample_683 2 0.000 0.9824 0.00 1.00
#> Sample_684 2 0.000 0.9824 0.00 1.00
#> Sample_689 2 0.000 0.9824 0.00 1.00
#> Sample_691 2 0.000 0.9824 0.00 1.00
#> Sample_693 2 0.000 0.9824 0.00 1.00
#> Sample_694 2 0.000 0.9824 0.00 1.00
#> Sample_704 1 0.000 0.9815 1.00 0.00
#> Sample_705 2 0.469 0.8845 0.10 0.90
#> Sample_841 2 0.000 0.9824 0.00 1.00
#> Sample_842 1 0.000 0.9815 1.00 0.00
#> Sample_843 1 0.141 0.9637 0.98 0.02
#> Sample_844 1 0.000 0.9815 1.00 0.00
#> Sample_845 1 0.000 0.9815 1.00 0.00
#> Sample_846 1 0.000 0.9815 1.00 0.00
#> Sample_847 2 0.402 0.9057 0.08 0.92
#> Sample_848 2 0.000 0.9824 0.00 1.00
#> Sample_849 2 0.000 0.9824 0.00 1.00
#> Sample_850 1 0.000 0.9815 1.00 0.00
#> Sample_851 1 0.000 0.9815 1.00 0.00
#> Sample_852 2 0.000 0.9824 0.00 1.00
#> Sample_853 1 0.000 0.9815 1.00 0.00
#> Sample_854 1 0.000 0.9815 1.00 0.00
#> Sample_1517 2 0.000 0.9824 0.00 1.00
#> Sample_1518 2 0.000 0.9824 0.00 1.00
#> Sample_1519 1 0.000 0.9815 1.00 0.00
#> Sample_1535 2 0.000 0.9824 0.00 1.00
#> Sample_1536 2 0.000 0.9824 0.00 1.00
#> Sample_1537 1 0.000 0.9815 1.00 0.00
#> Sample_1554 2 0.000 0.9824 0.00 1.00
#> Sample_1588 2 0.000 0.9824 0.00 1.00
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> Sample_2 1 0.6956 0.5077 0.66 0.04 0.30
#> Sample_3 1 0.0000 0.8835 1.00 0.00 0.00
#> Sample_4 1 0.7633 0.5899 0.68 0.20 0.12
#> Sample_5 1 0.3686 0.8264 0.86 0.00 0.14
#> Sample_6 1 0.2066 0.8545 0.94 0.00 0.06
#> Sample_7 1 0.1529 0.8660 0.96 0.00 0.04
#> Sample_8 2 0.0000 0.9546 0.00 1.00 0.00
#> Sample_9 1 0.1529 0.8660 0.96 0.00 0.04
#> Sample_10 1 0.2537 0.8418 0.92 0.00 0.08
#> Sample_11 1 0.1529 0.8660 0.96 0.00 0.04
#> Sample_12 1 0.2959 0.8287 0.90 0.00 0.10
#> Sample_13 1 0.3340 0.8093 0.88 0.00 0.12
#> Sample_14 3 0.5706 0.6379 0.32 0.00 0.68
#> Sample_15 1 0.2959 0.8290 0.90 0.00 0.10
#> Sample_16 1 0.0000 0.8835 1.00 0.00 0.00
#> Sample_17 2 0.0000 0.9546 0.00 1.00 0.00
#> Sample_18 2 0.0892 0.9503 0.00 0.98 0.02
#> Sample_19 2 0.0892 0.9503 0.00 0.98 0.02
#> Sample_20 2 0.0892 0.9503 0.00 0.98 0.02
#> Sample_21 2 0.1529 0.9521 0.00 0.96 0.04
#> Sample_22 2 0.1781 0.9432 0.02 0.96 0.02
#> Sample_23 2 0.0892 0.9535 0.00 0.98 0.02
#> Sample_24 1 0.2947 0.8579 0.92 0.02 0.06
#> Sample_25 2 0.0000 0.9546 0.00 1.00 0.00
#> Sample_26 1 0.9147 0.3407 0.54 0.20 0.26
#> Sample_27 2 0.1529 0.9427 0.00 0.96 0.04
#> Sample_28 2 0.1529 0.9427 0.00 0.96 0.04
#> Sample_29 1 0.0000 0.8835 1.00 0.00 0.00
#> Sample_30 2 0.1529 0.9427 0.00 0.96 0.04
#> Sample_32 1 0.0000 0.8835 1.00 0.00 0.00
#> Sample_33 1 0.0000 0.8835 1.00 0.00 0.00
#> Sample_34 1 0.0000 0.8835 1.00 0.00 0.00
#> Sample_35 1 0.0000 0.8835 1.00 0.00 0.00
#> Sample_36 1 0.0000 0.8835 1.00 0.00 0.00
#> Sample_37 1 0.0000 0.8835 1.00 0.00 0.00
#> Sample_38 1 0.0000 0.8835 1.00 0.00 0.00
#> Sample_39 1 0.0000 0.8835 1.00 0.00 0.00
#> Sample_40 1 0.0000 0.8835 1.00 0.00 0.00
#> Sample_41 1 0.0000 0.8835 1.00 0.00 0.00
#> Sample_42 1 0.0000 0.8835 1.00 0.00 0.00
#> Sample_44 1 0.0000 0.8835 1.00 0.00 0.00
#> Sample_45 1 0.0000 0.8835 1.00 0.00 0.00
#> Sample_46 1 0.0000 0.8835 1.00 0.00 0.00
#> Sample_47 1 0.0000 0.8835 1.00 0.00 0.00
#> Sample_48 1 0.0000 0.8835 1.00 0.00 0.00
#> Sample_49 1 0.0000 0.8835 1.00 0.00 0.00
#> Sample_50 1 0.0892 0.8743 0.98 0.00 0.02
#> Sample_51 1 0.0000 0.8835 1.00 0.00 0.00
#> Sample_52 1 0.0000 0.8835 1.00 0.00 0.00
#> Sample_53 2 0.0000 0.9546 0.00 1.00 0.00
#> Sample_54 3 0.5706 0.5157 0.00 0.32 0.68
#> Sample_55 1 0.4555 0.6936 0.80 0.00 0.20
#> Sample_56 1 0.3340 0.8099 0.88 0.00 0.12
#> Sample_57 1 0.5397 0.5613 0.72 0.00 0.28
#> Sample_58 1 0.5216 0.6083 0.74 0.00 0.26
#> Sample_59 1 0.0000 0.8835 1.00 0.00 0.00
#> Sample_60 3 0.4555 0.7865 0.20 0.00 0.80
#> Sample_61 3 0.6192 0.2751 0.00 0.42 0.58
#> Sample_62 3 0.4291 0.8056 0.18 0.00 0.82
#> Sample_63 3 0.2537 0.8475 0.08 0.00 0.92
#> Sample_64 3 0.2959 0.8470 0.10 0.00 0.90
#> Sample_65 3 0.4291 0.8037 0.18 0.00 0.82
#> Sample_66 3 0.2537 0.8475 0.08 0.00 0.92
#> Sample_67 2 0.0000 0.9546 0.00 1.00 0.00
#> Sample_68 3 0.6045 0.5315 0.38 0.00 0.62
#> Sample_69 3 0.4555 0.7898 0.20 0.00 0.80
#> Sample_70 3 0.9773 0.2658 0.34 0.24 0.42
#> Sample_71 2 0.3340 0.8717 0.00 0.88 0.12
#> Sample_74 3 0.6280 0.3106 0.46 0.00 0.54
#> Sample_75 2 0.0892 0.9535 0.00 0.98 0.02
#> Sample_76 1 0.1529 0.8664 0.96 0.00 0.04
#> Sample_77 2 0.3686 0.8492 0.00 0.86 0.14
#> Sample_78 3 0.2537 0.8475 0.08 0.00 0.92
#> Sample_79 2 0.0892 0.9535 0.00 0.98 0.02
#> Sample_80 2 0.0892 0.9535 0.00 0.98 0.02
#> Sample_81 3 0.3832 0.8340 0.10 0.02 0.88
#> Sample_82 2 0.2959 0.8955 0.00 0.90 0.10
#> Sample_83 3 0.2947 0.8162 0.02 0.06 0.92
#> Sample_84 3 0.0000 0.8059 0.00 0.00 1.00
#> Sample_85 3 0.8483 0.4777 0.14 0.26 0.60
#> Sample_86 1 0.0000 0.8835 1.00 0.00 0.00
#> Sample_87 2 0.0892 0.9503 0.00 0.98 0.02
#> Sample_88 1 0.7277 0.5263 0.66 0.28 0.06
#> Sample_89 2 0.3572 0.9038 0.04 0.90 0.06
#> Sample_90 2 0.0892 0.9503 0.00 0.98 0.02
#> Sample_91 2 0.0892 0.9503 0.00 0.98 0.02
#> Sample_92 2 0.0892 0.9503 0.00 0.98 0.02
#> Sample_93 1 0.7665 0.4340 0.60 0.34 0.06
#> Sample_95 3 0.2959 0.7744 0.00 0.10 0.90
#> Sample_96 3 0.3340 0.8418 0.12 0.00 0.88
#> Sample_97 3 0.3572 0.8220 0.06 0.04 0.90
#> Sample_98 3 0.2959 0.8470 0.10 0.00 0.90
#> Sample_99 3 0.2959 0.8470 0.10 0.00 0.90
#> Sample_100 2 0.0000 0.9546 0.00 1.00 0.00
#> Sample_101 2 0.1781 0.9431 0.02 0.96 0.02
#> Sample_102 1 0.9008 0.2965 0.50 0.36 0.14
#> Sample_103 2 0.2066 0.9332 0.00 0.94 0.06
#> Sample_104 2 0.6192 0.2862 0.00 0.58 0.42
#> Sample_105 3 0.6244 0.4017 0.44 0.00 0.56
#> Sample_106 3 0.2959 0.8470 0.10 0.00 0.90
#> Sample_107 2 0.3686 0.8462 0.00 0.86 0.14
#> Sample_108 1 0.1529 0.8654 0.96 0.00 0.04
#> Sample_109 3 0.2959 0.8470 0.10 0.00 0.90
#> Sample_110 2 0.0892 0.9535 0.00 0.98 0.02
#> Sample_112 2 0.0000 0.9546 0.00 1.00 0.00
#> Sample_113 2 0.1529 0.9435 0.00 0.96 0.04
#> Sample_115 2 0.0892 0.9535 0.00 0.98 0.02
#> Sample_116 2 0.0000 0.9546 0.00 1.00 0.00
#> Sample_117 2 0.0892 0.9535 0.00 0.98 0.02
#> Sample_119 3 0.3686 0.7623 0.00 0.14 0.86
#> Sample_120 3 0.2959 0.8470 0.10 0.00 0.90
#> Sample_122 2 0.0892 0.9535 0.00 0.98 0.02
#> Sample_124 3 0.3415 0.8474 0.08 0.02 0.90
#> Sample_125 3 0.4555 0.7968 0.20 0.00 0.80
#> Sample_126 2 0.3340 0.8746 0.00 0.88 0.12
#> Sample_127 3 0.3415 0.8474 0.08 0.02 0.90
#> Sample_128 2 0.0000 0.9546 0.00 1.00 0.00
#> Sample_129 3 0.2537 0.8475 0.08 0.00 0.92
#> Sample_130 2 0.0000 0.9546 0.00 1.00 0.00
#> Sample_131 2 0.0000 0.9546 0.00 1.00 0.00
#> Sample_132 3 0.2959 0.8470 0.10 0.00 0.90
#> Sample_133 2 0.0000 0.9546 0.00 1.00 0.00
#> Sample_134 3 0.3042 0.8298 0.04 0.04 0.92
#> Sample_135 2 0.0892 0.9535 0.00 0.98 0.02
#> Sample_136 3 0.6192 0.4591 0.42 0.00 0.58
#> Sample_137 3 0.2959 0.8470 0.10 0.00 0.90
#> Sample_138 3 0.3686 0.8317 0.14 0.00 0.86
#> Sample_139 2 0.0892 0.9535 0.00 0.98 0.02
#> Sample_140 3 0.2959 0.8470 0.10 0.00 0.90
#> Sample_141 3 0.2537 0.8475 0.08 0.00 0.92
#> Sample_142 2 0.1529 0.9436 0.00 0.96 0.04
#> Sample_143 2 0.0892 0.9535 0.00 0.98 0.02
#> Sample_145 2 0.2537 0.9129 0.00 0.92 0.08
#> Sample_146 2 0.0000 0.9546 0.00 1.00 0.00
#> Sample_197 3 0.6280 0.3615 0.46 0.00 0.54
#> Sample_198 3 0.3415 0.8474 0.08 0.02 0.90
#> Sample_199 1 0.2947 0.8361 0.92 0.02 0.06
#> Sample_200 1 0.0000 0.8835 1.00 0.00 0.00
#> Sample_201 1 0.0000 0.8835 1.00 0.00 0.00
#> Sample_202 1 0.0000 0.8835 1.00 0.00 0.00
#> Sample_203 1 0.0000 0.8835 1.00 0.00 0.00
#> Sample_205 1 0.0000 0.8835 1.00 0.00 0.00
#> Sample_206 1 0.0000 0.8835 1.00 0.00 0.00
#> Sample_207 1 0.0000 0.8835 1.00 0.00 0.00
#> Sample_586 1 0.2066 0.8501 0.94 0.00 0.06
#> Sample_587 1 0.3686 0.8266 0.86 0.00 0.14
#> Sample_591 1 0.6309 0.0399 0.50 0.00 0.50
#> Sample_593 1 0.2537 0.8359 0.92 0.00 0.08
#> Sample_596 2 0.2537 0.9224 0.00 0.92 0.08
#> Sample_598 2 0.1529 0.9421 0.00 0.96 0.04
#> Sample_601 2 0.2537 0.9224 0.00 0.92 0.08
#> Sample_603 2 0.2537 0.9224 0.00 0.92 0.08
#> Sample_604 2 0.2537 0.9224 0.00 0.92 0.08
#> Sample_605 2 0.2537 0.9224 0.00 0.92 0.08
#> Sample_606 2 0.2537 0.9224 0.00 0.92 0.08
#> Sample_607 3 0.5147 0.6989 0.18 0.02 0.80
#> Sample_608 2 0.1529 0.9421 0.00 0.96 0.04
#> Sample_609 2 0.0000 0.9546 0.00 1.00 0.00
#> Sample_610 2 0.0000 0.9546 0.00 1.00 0.00
#> Sample_651 1 0.3415 0.8206 0.90 0.02 0.08
#> Sample_652 3 0.2066 0.8415 0.06 0.00 0.94
#> Sample_653 3 0.2947 0.8403 0.06 0.02 0.92
#> Sample_654 1 0.0000 0.8835 1.00 0.00 0.00
#> Sample_655 3 0.4555 0.7881 0.20 0.00 0.80
#> Sample_656 3 0.2959 0.8470 0.10 0.00 0.90
#> Sample_657 2 0.0000 0.9546 0.00 1.00 0.00
#> Sample_658 1 0.4002 0.7516 0.84 0.00 0.16
#> Sample_659 3 0.6045 0.5195 0.38 0.00 0.62
#> Sample_660 2 0.0000 0.9546 0.00 1.00 0.00
#> Sample_661 1 0.6302 -0.1461 0.52 0.00 0.48
#> Sample_662 2 0.0892 0.9535 0.00 0.98 0.02
#> Sample_663 2 0.0892 0.9535 0.00 0.98 0.02
#> Sample_664 3 0.2959 0.8470 0.10 0.00 0.90
#> Sample_666 3 0.4002 0.7447 0.00 0.16 0.84
#> Sample_668 2 0.0892 0.9535 0.00 0.98 0.02
#> Sample_669 2 0.0892 0.9535 0.00 0.98 0.02
#> Sample_670 3 0.3042 0.8273 0.04 0.04 0.92
#> Sample_671 3 0.2537 0.8475 0.08 0.00 0.92
#> Sample_672 2 0.0892 0.9535 0.00 0.98 0.02
#> Sample_673 2 0.0892 0.9535 0.00 0.98 0.02
#> Sample_674 2 0.0892 0.9535 0.00 0.98 0.02
#> Sample_675 2 0.0892 0.9535 0.00 0.98 0.02
#> Sample_676 2 0.5397 0.5986 0.00 0.72 0.28
#> Sample_677 1 0.4035 0.8073 0.88 0.04 0.08
#> Sample_678 1 0.0892 0.8743 0.98 0.00 0.02
#> Sample_679 1 0.2066 0.8524 0.94 0.00 0.06
#> Sample_680 2 0.0892 0.9535 0.00 0.98 0.02
#> Sample_682 2 0.0892 0.9535 0.00 0.98 0.02
#> Sample_683 2 0.0892 0.9535 0.00 0.98 0.02
#> Sample_684 2 0.0892 0.9535 0.00 0.98 0.02
#> Sample_689 2 0.0892 0.9535 0.00 0.98 0.02
#> Sample_691 2 0.0892 0.9535 0.00 0.98 0.02
#> Sample_693 2 0.0892 0.9535 0.00 0.98 0.02
#> Sample_694 2 0.0892 0.9535 0.00 0.98 0.02
#> Sample_704 3 0.5948 0.5608 0.36 0.00 0.64
#> Sample_705 2 0.8733 0.4575 0.16 0.58 0.26
#> Sample_841 2 0.2066 0.9327 0.00 0.94 0.06
#> Sample_842 1 0.0000 0.8835 1.00 0.00 0.00
#> Sample_843 1 0.3415 0.8206 0.90 0.02 0.08
#> Sample_844 1 0.3686 0.7870 0.86 0.00 0.14
#> Sample_845 1 0.4002 0.7647 0.84 0.00 0.16
#> Sample_846 3 0.5706 0.6436 0.32 0.00 0.68
#> Sample_847 3 0.2066 0.8064 0.00 0.06 0.94
#> Sample_848 2 0.0000 0.9546 0.00 1.00 0.00
#> Sample_849 3 0.5216 0.6234 0.00 0.26 0.74
#> Sample_850 1 0.6192 0.1250 0.58 0.00 0.42
#> Sample_851 3 0.2537 0.8475 0.08 0.00 0.92
#> Sample_852 2 0.0892 0.9503 0.00 0.98 0.02
#> Sample_853 1 0.1529 0.8668 0.96 0.00 0.04
#> Sample_854 1 0.5016 0.6500 0.76 0.00 0.24
#> Sample_1517 2 0.2537 0.9224 0.00 0.92 0.08
#> Sample_1518 2 0.2537 0.9224 0.00 0.92 0.08
#> Sample_1519 1 0.5147 0.7793 0.80 0.02 0.18
#> Sample_1535 2 0.2537 0.9224 0.00 0.92 0.08
#> Sample_1536 2 0.2537 0.9224 0.00 0.92 0.08
#> Sample_1537 1 0.3340 0.8317 0.88 0.00 0.12
#> Sample_1554 2 0.2537 0.9224 0.00 0.92 0.08
#> Sample_1588 2 0.0000 0.9546 0.00 1.00 0.00
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> Sample_2 1 0.7896 -0.04505 0.46 0.08 0.40 0.06
#> Sample_3 1 0.0707 0.78038 0.98 0.00 0.02 0.00
#> Sample_4 1 0.9159 -0.08801 0.36 0.22 0.08 0.34
#> Sample_5 1 0.7346 0.43703 0.52 0.00 0.20 0.28
#> Sample_6 1 0.3198 0.73541 0.88 0.00 0.08 0.04
#> Sample_7 1 0.4227 0.69794 0.82 0.00 0.12 0.06
#> Sample_8 2 0.3172 0.66134 0.00 0.84 0.00 0.16
#> Sample_9 1 0.3821 0.70711 0.84 0.00 0.12 0.04
#> Sample_10 1 0.5147 0.61838 0.74 0.00 0.20 0.06
#> Sample_11 1 0.4227 0.69794 0.82 0.00 0.12 0.06
#> Sample_12 1 0.4731 0.66538 0.78 0.00 0.16 0.06
#> Sample_13 1 0.5256 0.54168 0.70 0.00 0.26 0.04
#> Sample_14 3 0.5883 0.57257 0.30 0.00 0.64 0.06
#> Sample_15 1 0.5767 0.48505 0.66 0.00 0.28 0.06
#> Sample_16 1 0.2335 0.77083 0.92 0.00 0.02 0.06
#> Sample_17 2 0.4994 -0.03142 0.00 0.52 0.00 0.48
#> Sample_18 4 0.4994 0.10252 0.00 0.48 0.00 0.52
#> Sample_19 2 0.4948 0.08748 0.00 0.56 0.00 0.44
#> Sample_20 2 0.4907 0.15655 0.00 0.58 0.00 0.42
#> Sample_21 2 0.4406 0.46586 0.00 0.70 0.00 0.30
#> Sample_22 4 0.5594 0.16987 0.00 0.46 0.02 0.52
#> Sample_23 2 0.0000 0.74657 0.00 1.00 0.00 0.00
#> Sample_24 1 0.8033 0.37520 0.48 0.02 0.20 0.30
#> Sample_25 2 0.4790 0.27361 0.00 0.62 0.00 0.38
#> Sample_26 1 0.9874 -0.16291 0.30 0.28 0.18 0.24
#> Sample_27 4 0.5512 0.46252 0.00 0.30 0.04 0.66
#> Sample_28 4 0.5487 0.33643 0.00 0.40 0.02 0.58
#> Sample_29 1 0.0000 0.78619 1.00 0.00 0.00 0.00
#> Sample_30 4 0.5535 0.28984 0.00 0.42 0.02 0.56
#> Sample_32 1 0.0000 0.78619 1.00 0.00 0.00 0.00
#> Sample_33 1 0.0000 0.78619 1.00 0.00 0.00 0.00
#> Sample_34 1 0.0000 0.78619 1.00 0.00 0.00 0.00
#> Sample_35 1 0.0000 0.78619 1.00 0.00 0.00 0.00
#> Sample_36 1 0.0000 0.78619 1.00 0.00 0.00 0.00
#> Sample_37 1 0.0000 0.78619 1.00 0.00 0.00 0.00
#> Sample_38 1 0.0000 0.78619 1.00 0.00 0.00 0.00
#> Sample_39 1 0.0000 0.78619 1.00 0.00 0.00 0.00
#> Sample_40 1 0.0000 0.78619 1.00 0.00 0.00 0.00
#> Sample_41 1 0.0000 0.78619 1.00 0.00 0.00 0.00
#> Sample_42 1 0.0000 0.78619 1.00 0.00 0.00 0.00
#> Sample_44 1 0.0000 0.78619 1.00 0.00 0.00 0.00
#> Sample_45 1 0.0000 0.78619 1.00 0.00 0.00 0.00
#> Sample_46 1 0.0000 0.78619 1.00 0.00 0.00 0.00
#> Sample_47 1 0.0000 0.78619 1.00 0.00 0.00 0.00
#> Sample_48 1 0.0000 0.78619 1.00 0.00 0.00 0.00
#> Sample_49 1 0.1637 0.76777 0.94 0.00 0.00 0.06
#> Sample_50 1 0.2335 0.76263 0.92 0.00 0.02 0.06
#> Sample_51 1 0.0000 0.78619 1.00 0.00 0.00 0.00
#> Sample_52 1 0.0000 0.78619 1.00 0.00 0.00 0.00
#> Sample_53 2 0.2345 0.70472 0.00 0.90 0.00 0.10
#> Sample_54 2 0.4936 0.43613 0.00 0.70 0.28 0.02
#> Sample_55 1 0.3400 0.67351 0.82 0.00 0.18 0.00
#> Sample_56 1 0.5106 0.58026 0.72 0.00 0.24 0.04
#> Sample_57 1 0.5860 0.27413 0.58 0.00 0.38 0.04
#> Sample_58 1 0.5793 0.32928 0.60 0.00 0.36 0.04
#> Sample_59 1 0.2411 0.77400 0.92 0.00 0.04 0.04
#> Sample_60 3 0.4755 0.69608 0.20 0.00 0.76 0.04
#> Sample_61 2 0.5062 0.39597 0.00 0.68 0.30 0.02
#> Sample_62 3 0.4939 0.67618 0.22 0.00 0.74 0.04
#> Sample_63 3 0.4292 0.75690 0.10 0.00 0.82 0.08
#> Sample_64 3 0.3525 0.75770 0.10 0.00 0.86 0.04
#> Sample_65 3 0.5512 0.56478 0.30 0.00 0.66 0.04
#> Sample_66 3 0.3611 0.76464 0.06 0.00 0.86 0.08
#> Sample_67 2 0.4134 0.50157 0.00 0.74 0.00 0.26
#> Sample_68 3 0.5987 0.23912 0.44 0.00 0.52 0.04
#> Sample_69 3 0.5512 0.58582 0.30 0.00 0.66 0.04
#> Sample_70 4 0.9032 0.16926 0.12 0.14 0.30 0.44
#> Sample_71 2 0.4581 0.60466 0.00 0.80 0.12 0.08
#> Sample_74 1 0.5915 0.20897 0.56 0.00 0.40 0.04
#> Sample_75 2 0.1637 0.72670 0.00 0.94 0.00 0.06
#> Sample_76 1 0.5256 0.53725 0.70 0.00 0.26 0.04
#> Sample_77 2 0.2345 0.69480 0.00 0.90 0.10 0.00
#> Sample_78 3 0.1913 0.75952 0.04 0.00 0.94 0.02
#> Sample_79 2 0.1637 0.72597 0.00 0.94 0.00 0.06
#> Sample_80 2 0.2411 0.72329 0.00 0.92 0.04 0.04
#> Sample_81 3 0.3398 0.72769 0.08 0.02 0.88 0.02
#> Sample_82 2 0.2335 0.70118 0.00 0.92 0.06 0.02
#> Sample_83 3 0.3525 0.70121 0.00 0.10 0.86 0.04
#> Sample_84 3 0.7359 0.35002 0.04 0.08 0.56 0.32
#> Sample_85 4 0.9530 0.28735 0.12 0.28 0.24 0.36
#> Sample_86 1 0.2706 0.74959 0.90 0.00 0.02 0.08
#> Sample_87 4 0.5487 0.31933 0.00 0.40 0.02 0.58
#> Sample_88 4 0.7198 0.40211 0.32 0.10 0.02 0.56
#> Sample_89 4 0.6316 0.53897 0.12 0.16 0.02 0.70
#> Sample_90 4 0.5000 0.06902 0.00 0.50 0.00 0.50
#> Sample_91 4 0.5487 0.30556 0.00 0.40 0.02 0.58
#> Sample_92 4 0.4948 0.23136 0.00 0.44 0.00 0.56
#> Sample_93 4 0.6595 0.50115 0.24 0.06 0.04 0.66
#> Sample_95 3 0.5428 0.27085 0.00 0.38 0.60 0.02
#> Sample_96 3 0.2345 0.76261 0.10 0.00 0.90 0.00
#> Sample_97 3 0.6147 0.59744 0.10 0.06 0.74 0.10
#> Sample_98 3 0.2647 0.75937 0.12 0.00 0.88 0.00
#> Sample_99 3 0.2706 0.76593 0.08 0.00 0.90 0.02
#> Sample_100 2 0.4522 0.37960 0.00 0.68 0.00 0.32
#> Sample_101 2 0.5993 0.20106 0.02 0.60 0.02 0.36
#> Sample_102 4 0.7053 0.48935 0.22 0.04 0.10 0.64
#> Sample_103 4 0.5355 0.36022 0.00 0.36 0.02 0.62
#> Sample_104 2 0.6617 0.34156 0.00 0.60 0.28 0.12
#> Sample_105 3 0.5487 0.40102 0.40 0.00 0.58 0.02
#> Sample_106 3 0.3037 0.76172 0.10 0.00 0.88 0.02
#> Sample_107 2 0.4755 0.58392 0.00 0.76 0.04 0.20
#> Sample_108 1 0.4088 0.70266 0.82 0.00 0.14 0.04
#> Sample_109 3 0.2011 0.76386 0.08 0.00 0.92 0.00
#> Sample_110 2 0.0707 0.73856 0.00 0.98 0.00 0.02
#> Sample_112 2 0.1637 0.72456 0.00 0.94 0.00 0.06
#> Sample_113 2 0.2335 0.70498 0.00 0.92 0.06 0.02
#> Sample_115 2 0.1913 0.73268 0.00 0.94 0.02 0.04
#> Sample_116 2 0.4790 0.23282 0.00 0.62 0.00 0.38
#> Sample_117 2 0.0707 0.73856 0.00 0.98 0.00 0.02
#> Sample_119 3 0.5594 0.09752 0.00 0.46 0.52 0.02
#> Sample_120 3 0.2830 0.76438 0.06 0.00 0.90 0.04
#> Sample_122 2 0.0707 0.73856 0.00 0.98 0.00 0.02
#> Sample_124 3 0.5637 0.70374 0.14 0.06 0.76 0.04
#> Sample_125 3 0.3606 0.75218 0.14 0.00 0.84 0.02
#> Sample_126 4 0.7581 0.25037 0.00 0.36 0.20 0.44
#> Sample_127 3 0.1913 0.75952 0.04 0.00 0.94 0.02
#> Sample_128 2 0.5355 0.30114 0.00 0.62 0.02 0.36
#> Sample_129 3 0.1913 0.75952 0.04 0.00 0.94 0.02
#> Sample_130 2 0.5327 0.56792 0.00 0.72 0.06 0.22
#> Sample_131 2 0.5570 0.09169 0.00 0.54 0.02 0.44
#> Sample_132 3 0.2830 0.76583 0.06 0.00 0.90 0.04
#> Sample_133 2 0.3610 0.59363 0.00 0.80 0.00 0.20
#> Sample_134 3 0.3725 0.70642 0.02 0.10 0.86 0.02
#> Sample_135 2 0.0000 0.74657 0.00 1.00 0.00 0.00
#> Sample_136 3 0.5355 0.49084 0.36 0.00 0.62 0.02
#> Sample_137 3 0.3400 0.73294 0.18 0.00 0.82 0.00
#> Sample_138 3 0.4472 0.69861 0.22 0.00 0.76 0.02
#> Sample_139 2 0.0707 0.74072 0.00 0.98 0.00 0.02
#> Sample_140 3 0.1211 0.76073 0.04 0.00 0.96 0.00
#> Sample_141 3 0.3398 0.73043 0.02 0.08 0.88 0.02
#> Sample_142 2 0.0707 0.73955 0.00 0.98 0.02 0.00
#> Sample_143 2 0.0000 0.74657 0.00 1.00 0.00 0.00
#> Sample_145 2 0.1411 0.72868 0.00 0.96 0.02 0.02
#> Sample_146 2 0.4624 0.33748 0.00 0.66 0.00 0.34
#> Sample_197 3 0.5606 0.11304 0.48 0.00 0.50 0.02
#> Sample_198 3 0.2611 0.75345 0.02 0.02 0.92 0.04
#> Sample_199 1 0.5860 0.31261 0.58 0.00 0.04 0.38
#> Sample_200 1 0.3611 0.72532 0.86 0.00 0.06 0.08
#> Sample_201 1 0.1913 0.76657 0.94 0.00 0.02 0.04
#> Sample_202 1 0.0707 0.78179 0.98 0.00 0.00 0.02
#> Sample_203 1 0.0707 0.78179 0.98 0.00 0.00 0.02
#> Sample_205 1 0.0000 0.78619 1.00 0.00 0.00 0.00
#> Sample_206 1 0.1411 0.77424 0.96 0.00 0.02 0.02
#> Sample_207 1 0.0707 0.78038 0.98 0.00 0.02 0.00
#> Sample_586 1 0.5860 0.40349 0.58 0.00 0.04 0.38
#> Sample_587 1 0.6554 0.34337 0.52 0.00 0.08 0.40
#> Sample_591 1 0.7832 0.11206 0.38 0.00 0.26 0.36
#> Sample_593 1 0.4797 0.57708 0.72 0.00 0.02 0.26
#> Sample_596 4 0.4797 0.52997 0.00 0.26 0.02 0.72
#> Sample_598 2 0.5355 0.22447 0.00 0.62 0.02 0.36
#> Sample_601 4 0.4284 0.56232 0.00 0.20 0.02 0.78
#> Sample_603 4 0.4284 0.56645 0.00 0.20 0.02 0.78
#> Sample_604 4 0.3335 0.57496 0.00 0.12 0.02 0.86
#> Sample_605 4 0.4472 0.55197 0.00 0.22 0.02 0.76
#> Sample_606 4 0.4642 0.54316 0.00 0.24 0.02 0.74
#> Sample_607 3 0.7372 0.24575 0.16 0.00 0.42 0.42
#> Sample_608 2 0.4713 0.34774 0.00 0.64 0.00 0.36
#> Sample_609 2 0.2647 0.69423 0.00 0.88 0.00 0.12
#> Sample_610 2 0.2647 0.69283 0.00 0.88 0.00 0.12
#> Sample_651 4 0.5428 0.14469 0.38 0.00 0.02 0.60
#> Sample_652 3 0.3935 0.74595 0.06 0.00 0.84 0.10
#> Sample_653 3 0.1913 0.76066 0.04 0.02 0.94 0.00
#> Sample_654 1 0.1913 0.76845 0.94 0.00 0.02 0.04
#> Sample_655 3 0.5327 0.65024 0.22 0.00 0.72 0.06
#> Sample_656 3 0.2011 0.76490 0.08 0.00 0.92 0.00
#> Sample_657 2 0.4936 0.45638 0.00 0.70 0.02 0.28
#> Sample_658 1 0.4936 0.51793 0.70 0.00 0.28 0.02
#> Sample_659 3 0.5860 0.41275 0.38 0.00 0.58 0.04
#> Sample_660 2 0.3801 0.58291 0.00 0.78 0.00 0.22
#> Sample_661 3 0.5428 0.37620 0.38 0.00 0.60 0.02
#> Sample_662 2 0.0000 0.74657 0.00 1.00 0.00 0.00
#> Sample_663 2 0.0000 0.74657 0.00 1.00 0.00 0.00
#> Sample_664 3 0.3335 0.75623 0.12 0.00 0.86 0.02
#> Sample_666 2 0.4994 0.00179 0.00 0.52 0.48 0.00
#> Sample_668 2 0.0707 0.73906 0.00 0.98 0.00 0.02
#> Sample_669 2 0.0000 0.74657 0.00 1.00 0.00 0.00
#> Sample_670 3 0.5913 0.44002 0.02 0.34 0.62 0.02
#> Sample_671 3 0.3037 0.76339 0.10 0.00 0.88 0.02
#> Sample_672 2 0.0000 0.74657 0.00 1.00 0.00 0.00
#> Sample_673 2 0.0000 0.74657 0.00 1.00 0.00 0.00
#> Sample_674 2 0.0000 0.74657 0.00 1.00 0.00 0.00
#> Sample_675 2 0.0000 0.74657 0.00 1.00 0.00 0.00
#> Sample_676 2 0.7581 -0.04548 0.00 0.44 0.20 0.36
#> Sample_677 4 0.6212 0.21041 0.38 0.00 0.06 0.56
#> Sample_678 1 0.3935 0.71404 0.84 0.00 0.06 0.10
#> Sample_679 1 0.4284 0.64316 0.78 0.00 0.20 0.02
#> Sample_680 2 0.0000 0.74657 0.00 1.00 0.00 0.00
#> Sample_682 2 0.0000 0.74657 0.00 1.00 0.00 0.00
#> Sample_683 2 0.0000 0.74657 0.00 1.00 0.00 0.00
#> Sample_684 2 0.0000 0.74657 0.00 1.00 0.00 0.00
#> Sample_689 2 0.0000 0.74657 0.00 1.00 0.00 0.00
#> Sample_691 2 0.0000 0.74657 0.00 1.00 0.00 0.00
#> Sample_693 2 0.0000 0.74657 0.00 1.00 0.00 0.00
#> Sample_694 2 0.0000 0.74657 0.00 1.00 0.00 0.00
#> Sample_704 3 0.5256 0.59830 0.26 0.00 0.70 0.04
#> Sample_705 4 0.8204 0.44833 0.06 0.24 0.16 0.54
#> Sample_841 4 0.4522 0.44424 0.00 0.32 0.00 0.68
#> Sample_842 1 0.1411 0.77550 0.96 0.00 0.02 0.02
#> Sample_843 1 0.5713 0.38224 0.62 0.00 0.04 0.34
#> Sample_844 1 0.5106 0.58051 0.72 0.00 0.24 0.04
#> Sample_845 1 0.5392 0.50621 0.68 0.00 0.28 0.04
#> Sample_846 3 0.5713 0.50183 0.34 0.00 0.62 0.04
#> Sample_847 3 0.6881 0.27948 0.00 0.34 0.54 0.12
#> Sample_848 2 0.4790 0.23282 0.00 0.62 0.00 0.38
#> Sample_849 2 0.5606 0.01898 0.00 0.50 0.48 0.02
#> Sample_850 3 0.5957 0.27822 0.42 0.00 0.54 0.04
#> Sample_851 3 0.1913 0.76308 0.04 0.00 0.94 0.02
#> Sample_852 4 0.4713 0.39249 0.00 0.36 0.00 0.64
#> Sample_853 1 0.3335 0.72184 0.86 0.00 0.12 0.02
#> Sample_854 1 0.6005 0.04115 0.50 0.00 0.46 0.04
#> Sample_1517 4 0.4284 0.56081 0.00 0.20 0.02 0.78
#> Sample_1518 4 0.4617 0.56881 0.06 0.10 0.02 0.82
#> Sample_1519 4 0.6933 0.12663 0.30 0.00 0.14 0.56
#> Sample_1535 4 0.4472 0.55745 0.00 0.22 0.02 0.76
#> Sample_1536 4 0.4642 0.54316 0.00 0.24 0.02 0.74
#> Sample_1537 4 0.7261 0.07954 0.34 0.00 0.16 0.50
#> Sample_1554 4 0.4472 0.55745 0.00 0.22 0.02 0.76
#> Sample_1588 2 0.3610 0.59568 0.00 0.80 0.00 0.20
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
As soon as the classes for columns are determined, the signatures that are significantly different between subgroups can be looked for. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. To get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows (which is done by automatically selecting number of clusters).If there are too many signatures, top_signatures = ... can be set to only show the
signatures with the highest FDRs:
# code only for demonstration
# e.g. to show the top 500 most significant rows
tb = get_signature(res, k = ..., top_signatures = 500)
If the signatures are defined as these which are uniquely high in current group, diff_method argument
can be set to "uniquely_high_in_one_group":
# code only for demonstration
tb = get_signature(res, k = ..., diff_method = "uniquely_high_in_one_group")
UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n_sample age(p-value) cell.type(p-value) k
#> ATC:skmeans 215 0.0916 1.16e-01 2
#> ATC:skmeans 203 0.0389 8.55e-02 3
#> ATC:skmeans 147 0.0105 3.92e-17 4
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
Parent node: Node022. Child nodes: Node02111-leaf , Node02112 , Node02121-leaf , Node02122-leaf , Node02211-leaf , Node02212-leaf , Node02221-leaf , Node02222-leaf , Node03211 , Node03212-leaf , Node03221-leaf , Node03222-leaf .
The object with results only for a single top-value method and a single partitioning method can be extracted as:
res = res_rh["0221"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4.
#> On a matrix with 14727 rows and 118 columns.
#> Top rows (1005) are extracted by 'ATC' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 150 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_partitions"
#> [7] "compare_signatures" "consensus_heatmap" "dimension_reduction"
#> [10] "functional_enrichment" "get_anno_col" "get_anno"
#> [13] "get_classes" "get_consensus" "get_matrix"
#> [16] "get_membership" "get_param" "get_signatures"
#> [19] "get_stats" "is_best_k" "is_stable_k"
#> [22] "membership_heatmap" "ncol" "nrow"
#> [25] "plot_ecdf" "predict_classes" "rownames"
#> [28] "select_partition_number" "show" "suggest_best_k"
#> [31] "test_to_known_factors" "top_rows_heatmap"
collect_plots() function collects all the plots made from res for all k (number of subgroups)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, higher 1-PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.895 0.942 0.975 0.488 0.520 0.520
#> 3 3 0.472 0.635 0.809 0.363 0.759 0.557
#> 4 4 0.518 0.426 0.618 0.111 0.782 0.459
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 2
Following is the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall subgroup
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> Sample_2 1 0.000 0.962 1.00 0.00
#> Sample_3 2 0.000 0.993 0.00 1.00
#> Sample_5 1 0.529 0.857 0.88 0.12
#> Sample_6 1 0.000 0.962 1.00 0.00
#> Sample_7 1 0.584 0.834 0.86 0.14
#> Sample_9 2 0.000 0.993 0.00 1.00
#> Sample_10 1 0.000 0.962 1.00 0.00
#> Sample_11 2 0.000 0.993 0.00 1.00
#> Sample_12 1 0.000 0.962 1.00 0.00
#> Sample_13 1 0.000 0.962 1.00 0.00
#> Sample_14 1 0.000 0.962 1.00 0.00
#> Sample_15 1 0.000 0.962 1.00 0.00
#> Sample_16 2 0.000 0.993 0.00 1.00
#> Sample_24 1 0.000 0.962 1.00 0.00
#> Sample_26 1 0.584 0.833 0.86 0.14
#> Sample_29 2 0.000 0.993 0.00 1.00
#> Sample_32 2 0.000 0.993 0.00 1.00
#> Sample_33 2 0.000 0.993 0.00 1.00
#> Sample_34 2 0.000 0.993 0.00 1.00
#> Sample_35 2 0.000 0.993 0.00 1.00
#> Sample_36 2 0.000 0.993 0.00 1.00
#> Sample_37 2 0.000 0.993 0.00 1.00
#> Sample_38 2 0.000 0.993 0.00 1.00
#> Sample_39 2 0.000 0.993 0.00 1.00
#> Sample_40 2 0.000 0.993 0.00 1.00
#> Sample_41 2 0.000 0.993 0.00 1.00
#> Sample_42 2 0.000 0.993 0.00 1.00
#> Sample_44 2 0.000 0.993 0.00 1.00
#> Sample_45 2 0.000 0.993 0.00 1.00
#> Sample_46 2 0.000 0.993 0.00 1.00
#> Sample_47 2 0.000 0.993 0.00 1.00
#> Sample_48 2 0.000 0.993 0.00 1.00
#> Sample_49 2 0.000 0.993 0.00 1.00
#> Sample_50 2 0.000 0.993 0.00 1.00
#> Sample_51 2 0.000 0.993 0.00 1.00
#> Sample_52 2 0.000 0.993 0.00 1.00
#> Sample_55 2 0.000 0.993 0.00 1.00
#> Sample_56 1 0.242 0.931 0.96 0.04
#> Sample_57 1 0.000 0.962 1.00 0.00
#> Sample_58 1 0.000 0.962 1.00 0.00
#> Sample_59 2 0.000 0.993 0.00 1.00
#> Sample_60 1 0.000 0.962 1.00 0.00
#> Sample_62 1 0.000 0.962 1.00 0.00
#> Sample_63 1 0.000 0.962 1.00 0.00
#> Sample_64 1 0.000 0.962 1.00 0.00
#> Sample_65 1 0.000 0.962 1.00 0.00
#> Sample_66 1 0.000 0.962 1.00 0.00
#> Sample_68 1 0.000 0.962 1.00 0.00
#> Sample_69 1 0.000 0.962 1.00 0.00
#> Sample_74 1 0.000 0.962 1.00 0.00
#> Sample_76 1 0.999 0.115 0.52 0.48
#> Sample_78 1 0.000 0.962 1.00 0.00
#> Sample_81 1 0.000 0.962 1.00 0.00
#> Sample_83 1 0.000 0.962 1.00 0.00
#> Sample_84 1 0.000 0.962 1.00 0.00
#> Sample_86 2 0.000 0.993 0.00 1.00
#> Sample_96 1 0.000 0.962 1.00 0.00
#> Sample_97 1 0.000 0.962 1.00 0.00
#> Sample_98 1 0.000 0.962 1.00 0.00
#> Sample_99 1 0.000 0.962 1.00 0.00
#> Sample_102 2 0.402 0.910 0.08 0.92
#> Sample_105 1 0.141 0.947 0.98 0.02
#> Sample_106 1 0.000 0.962 1.00 0.00
#> Sample_108 1 0.981 0.311 0.58 0.42
#> Sample_109 1 0.000 0.962 1.00 0.00
#> Sample_120 1 0.000 0.962 1.00 0.00
#> Sample_124 1 0.000 0.962 1.00 0.00
#> Sample_125 1 0.000 0.962 1.00 0.00
#> Sample_127 1 0.000 0.962 1.00 0.00
#> Sample_129 1 0.000 0.962 1.00 0.00
#> Sample_132 1 0.000 0.962 1.00 0.00
#> Sample_134 1 0.000 0.962 1.00 0.00
#> Sample_136 1 0.000 0.962 1.00 0.00
#> Sample_137 1 0.000 0.962 1.00 0.00
#> Sample_138 1 0.000 0.962 1.00 0.00
#> Sample_140 1 0.000 0.962 1.00 0.00
#> Sample_141 1 0.000 0.962 1.00 0.00
#> Sample_197 1 0.881 0.592 0.70 0.30
#> Sample_198 1 0.000 0.962 1.00 0.00
#> Sample_199 2 0.000 0.993 0.00 1.00
#> Sample_200 2 0.000 0.993 0.00 1.00
#> Sample_201 2 0.000 0.993 0.00 1.00
#> Sample_202 2 0.000 0.993 0.00 1.00
#> Sample_203 2 0.000 0.993 0.00 1.00
#> Sample_205 2 0.000 0.993 0.00 1.00
#> Sample_206 2 0.000 0.993 0.00 1.00
#> Sample_207 2 0.000 0.993 0.00 1.00
#> Sample_586 2 0.000 0.993 0.00 1.00
#> Sample_587 1 0.000 0.962 1.00 0.00
#> Sample_591 1 0.000 0.962 1.00 0.00
#> Sample_593 2 0.000 0.993 0.00 1.00
#> Sample_607 1 0.000 0.962 1.00 0.00
#> Sample_651 2 0.000 0.993 0.00 1.00
#> Sample_652 1 0.000 0.962 1.00 0.00
#> Sample_653 1 0.000 0.962 1.00 0.00
#> Sample_654 2 0.469 0.886 0.10 0.90
#> Sample_655 1 0.000 0.962 1.00 0.00
#> Sample_656 1 0.000 0.962 1.00 0.00
#> Sample_658 2 0.469 0.887 0.10 0.90
#> Sample_659 1 0.000 0.962 1.00 0.00
#> Sample_661 1 0.000 0.962 1.00 0.00
#> Sample_664 1 0.000 0.962 1.00 0.00
#> Sample_671 1 0.000 0.962 1.00 0.00
#> Sample_677 2 0.000 0.993 0.00 1.00
#> Sample_678 2 0.000 0.993 0.00 1.00
#> Sample_679 1 0.795 0.696 0.76 0.24
#> Sample_704 1 0.000 0.962 1.00 0.00
#> Sample_842 2 0.000 0.993 0.00 1.00
#> Sample_843 2 0.000 0.993 0.00 1.00
#> Sample_844 1 0.000 0.962 1.00 0.00
#> Sample_845 1 0.000 0.962 1.00 0.00
#> Sample_846 1 0.000 0.962 1.00 0.00
#> Sample_850 1 0.634 0.810 0.84 0.16
#> Sample_851 1 0.000 0.962 1.00 0.00
#> Sample_853 1 0.242 0.932 0.96 0.04
#> Sample_854 1 0.990 0.255 0.56 0.44
#> Sample_1519 1 0.000 0.962 1.00 0.00
#> Sample_1537 1 0.469 0.877 0.90 0.10
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> Sample_2 3 0.5706 0.527 0.32 0.00 0.68
#> Sample_3 2 0.3042 0.874 0.04 0.92 0.04
#> Sample_5 1 0.3340 0.664 0.88 0.12 0.00
#> Sample_6 1 0.5835 0.265 0.66 0.00 0.34
#> Sample_7 3 0.5948 0.416 0.36 0.00 0.64
#> Sample_9 3 0.8472 0.118 0.10 0.36 0.54
#> Sample_10 3 0.5560 0.522 0.30 0.00 0.70
#> Sample_11 3 0.8853 0.363 0.32 0.14 0.54
#> Sample_12 3 0.5706 0.499 0.32 0.00 0.68
#> Sample_13 3 0.5835 0.446 0.34 0.00 0.66
#> Sample_14 3 0.5216 0.636 0.26 0.00 0.74
#> Sample_15 3 0.4796 0.617 0.22 0.00 0.78
#> Sample_16 2 0.5667 0.791 0.14 0.80 0.06
#> Sample_24 1 0.2066 0.710 0.94 0.00 0.06
#> Sample_26 1 0.2537 0.699 0.92 0.08 0.00
#> Sample_29 2 0.2959 0.868 0.00 0.90 0.10
#> Sample_32 2 0.2959 0.868 0.00 0.90 0.10
#> Sample_33 2 0.2959 0.868 0.00 0.90 0.10
#> Sample_34 2 0.6309 0.219 0.00 0.50 0.50
#> Sample_35 2 0.2959 0.868 0.00 0.90 0.10
#> Sample_36 2 0.2959 0.868 0.00 0.90 0.10
#> Sample_37 2 0.2959 0.868 0.00 0.90 0.10
#> Sample_38 2 0.2959 0.868 0.00 0.90 0.10
#> Sample_39 2 0.2959 0.868 0.00 0.90 0.10
#> Sample_40 2 0.2959 0.868 0.00 0.90 0.10
#> Sample_41 2 0.2959 0.868 0.00 0.90 0.10
#> Sample_42 2 0.2959 0.868 0.00 0.90 0.10
#> Sample_44 2 0.2959 0.868 0.00 0.90 0.10
#> Sample_45 2 0.2959 0.868 0.00 0.90 0.10
#> Sample_46 2 0.1781 0.880 0.02 0.96 0.02
#> Sample_47 2 0.1781 0.880 0.02 0.96 0.02
#> Sample_48 2 0.0892 0.879 0.02 0.98 0.00
#> Sample_49 2 0.1529 0.875 0.04 0.96 0.00
#> Sample_50 2 0.0892 0.879 0.02 0.98 0.00
#> Sample_51 2 0.0892 0.879 0.02 0.98 0.00
#> Sample_52 2 0.0892 0.879 0.02 0.98 0.00
#> Sample_55 2 0.6192 0.340 0.00 0.58 0.42
#> Sample_56 1 0.2959 0.679 0.90 0.10 0.00
#> Sample_57 1 0.2947 0.706 0.92 0.06 0.02
#> Sample_58 1 0.2947 0.706 0.92 0.06 0.02
#> Sample_59 2 0.6126 0.376 0.40 0.60 0.00
#> Sample_60 1 0.1529 0.719 0.96 0.00 0.04
#> Sample_62 1 0.1529 0.719 0.96 0.00 0.04
#> Sample_63 1 0.1529 0.719 0.96 0.00 0.04
#> Sample_64 1 0.1529 0.719 0.96 0.00 0.04
#> Sample_65 1 0.1529 0.719 0.96 0.00 0.04
#> Sample_66 1 0.1529 0.719 0.96 0.00 0.04
#> Sample_68 1 0.1529 0.719 0.96 0.00 0.04
#> Sample_69 1 0.1529 0.719 0.96 0.00 0.04
#> Sample_74 3 0.6126 0.478 0.40 0.00 0.60
#> Sample_76 1 0.2959 0.679 0.90 0.10 0.00
#> Sample_78 3 0.6302 -0.101 0.48 0.00 0.52
#> Sample_81 1 0.6803 0.573 0.68 0.04 0.28
#> Sample_83 1 0.4002 0.688 0.84 0.00 0.16
#> Sample_84 3 0.5216 0.527 0.26 0.00 0.74
#> Sample_86 2 0.0892 0.880 0.02 0.98 0.00
#> Sample_96 1 0.6984 0.303 0.56 0.02 0.42
#> Sample_97 3 0.5948 0.315 0.36 0.00 0.64
#> Sample_98 3 0.4002 0.645 0.16 0.00 0.84
#> Sample_99 1 0.6302 0.195 0.52 0.00 0.48
#> Sample_102 1 0.4291 0.630 0.82 0.18 0.00
#> Sample_105 1 0.7995 0.239 0.48 0.06 0.46
#> Sample_106 1 0.6192 0.368 0.58 0.00 0.42
#> Sample_108 1 0.8483 0.553 0.60 0.14 0.26
#> Sample_109 3 0.4291 0.629 0.18 0.00 0.82
#> Sample_120 3 0.5948 0.315 0.36 0.00 0.64
#> Sample_124 3 0.4555 0.609 0.20 0.00 0.80
#> Sample_125 3 0.7578 -0.134 0.46 0.04 0.50
#> Sample_127 1 0.5560 0.582 0.70 0.00 0.30
#> Sample_129 3 0.6309 -0.173 0.50 0.00 0.50
#> Sample_132 1 0.5216 0.624 0.74 0.00 0.26
#> Sample_134 1 0.6045 0.458 0.62 0.00 0.38
#> Sample_136 3 0.3415 0.670 0.08 0.02 0.90
#> Sample_137 3 0.2959 0.673 0.10 0.00 0.90
#> Sample_138 3 0.2959 0.673 0.10 0.00 0.90
#> Sample_140 3 0.2959 0.673 0.10 0.00 0.90
#> Sample_141 3 0.3686 0.657 0.14 0.00 0.86
#> Sample_197 3 0.7975 0.441 0.18 0.16 0.66
#> Sample_198 1 0.5706 0.556 0.68 0.00 0.32
#> Sample_199 2 0.1529 0.875 0.04 0.96 0.00
#> Sample_200 2 0.3686 0.798 0.14 0.86 0.00
#> Sample_201 2 0.1529 0.875 0.04 0.96 0.00
#> Sample_202 2 0.1529 0.875 0.04 0.96 0.00
#> Sample_203 2 0.1529 0.875 0.04 0.96 0.00
#> Sample_205 2 0.0892 0.880 0.02 0.98 0.00
#> Sample_206 2 0.1529 0.875 0.04 0.96 0.00
#> Sample_207 2 0.1529 0.875 0.04 0.96 0.00
#> Sample_586 3 0.7277 0.359 0.06 0.28 0.66
#> Sample_587 3 0.2537 0.653 0.08 0.00 0.92
#> Sample_591 3 0.2066 0.657 0.06 0.00 0.94
#> Sample_593 2 0.2959 0.868 0.00 0.90 0.10
#> Sample_607 3 0.4291 0.670 0.18 0.00 0.82
#> Sample_651 2 0.1529 0.879 0.00 0.96 0.04
#> Sample_652 1 0.1529 0.724 0.96 0.00 0.04
#> Sample_653 1 0.5397 0.605 0.72 0.00 0.28
#> Sample_654 1 0.4002 0.651 0.84 0.16 0.00
#> Sample_655 1 0.1529 0.719 0.96 0.00 0.04
#> Sample_656 1 0.5706 0.558 0.68 0.00 0.32
#> Sample_658 2 0.9912 -0.115 0.30 0.40 0.30
#> Sample_659 1 0.4796 0.655 0.78 0.00 0.22
#> Sample_661 1 0.4966 0.691 0.84 0.10 0.06
#> Sample_664 3 0.2959 0.673 0.10 0.00 0.90
#> Sample_671 3 0.2959 0.673 0.10 0.00 0.90
#> Sample_677 2 0.4555 0.720 0.20 0.80 0.00
#> Sample_678 2 0.3686 0.796 0.14 0.86 0.00
#> Sample_679 3 0.4556 0.640 0.06 0.08 0.86
#> Sample_704 1 0.6495 0.677 0.74 0.06 0.20
#> Sample_842 2 0.0892 0.880 0.02 0.98 0.00
#> Sample_843 2 0.1781 0.881 0.02 0.96 0.02
#> Sample_844 1 0.3832 0.687 0.88 0.10 0.02
#> Sample_845 3 0.3340 0.669 0.12 0.00 0.88
#> Sample_846 1 0.4035 0.717 0.88 0.04 0.08
#> Sample_850 1 0.4966 0.691 0.84 0.10 0.06
#> Sample_851 1 0.5835 0.526 0.66 0.00 0.34
#> Sample_853 3 0.6651 0.397 0.34 0.02 0.64
#> Sample_854 1 0.9192 0.451 0.52 0.18 0.30
#> Sample_1519 3 0.4555 0.662 0.20 0.00 0.80
#> Sample_1537 1 0.7948 0.134 0.52 0.06 0.42
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> Sample_2 3 0.8365 0.005649 0.32 0.26 0.40 0.02
#> Sample_3 2 0.6714 0.267131 0.00 0.54 0.10 0.36
#> Sample_5 1 0.4977 -0.029317 0.54 0.46 0.00 0.00
#> Sample_6 1 0.7707 0.082883 0.44 0.24 0.32 0.00
#> Sample_7 3 0.9260 -0.014345 0.32 0.26 0.34 0.08
#> Sample_9 4 0.9036 0.059706 0.06 0.26 0.34 0.34
#> Sample_10 3 0.9243 0.009775 0.30 0.26 0.36 0.08
#> Sample_11 3 0.9816 0.043584 0.20 0.26 0.34 0.20
#> Sample_12 3 0.8738 0.000343 0.32 0.26 0.38 0.04
#> Sample_13 1 0.8754 -0.008571 0.36 0.26 0.34 0.04
#> Sample_14 3 0.8268 0.045587 0.28 0.26 0.44 0.02
#> Sample_15 3 0.8705 0.018531 0.30 0.26 0.40 0.04
#> Sample_16 2 0.6611 0.288941 0.04 0.48 0.02 0.46
#> Sample_24 1 0.6723 0.287056 0.60 0.14 0.26 0.00
#> Sample_26 1 0.5392 0.426356 0.68 0.28 0.04 0.00
#> Sample_29 4 0.0000 0.808489 0.00 0.00 0.00 1.00
#> Sample_32 4 0.0000 0.808489 0.00 0.00 0.00 1.00
#> Sample_33 4 0.0000 0.808489 0.00 0.00 0.00 1.00
#> Sample_34 4 0.2921 0.668804 0.00 0.14 0.00 0.86
#> Sample_35 4 0.0000 0.808489 0.00 0.00 0.00 1.00
#> Sample_36 4 0.0707 0.792892 0.00 0.02 0.00 0.98
#> Sample_37 4 0.0000 0.808489 0.00 0.00 0.00 1.00
#> Sample_38 4 0.0000 0.808489 0.00 0.00 0.00 1.00
#> Sample_39 4 0.0000 0.808489 0.00 0.00 0.00 1.00
#> Sample_40 4 0.1211 0.770864 0.00 0.04 0.00 0.96
#> Sample_41 4 0.0000 0.808489 0.00 0.00 0.00 1.00
#> Sample_42 4 0.0000 0.808489 0.00 0.00 0.00 1.00
#> Sample_44 4 0.0000 0.808489 0.00 0.00 0.00 1.00
#> Sample_45 4 0.0707 0.792892 0.00 0.02 0.00 0.98
#> Sample_46 4 0.4948 -0.169461 0.00 0.44 0.00 0.56
#> Sample_47 4 0.4790 0.050094 0.00 0.38 0.00 0.62
#> Sample_48 2 0.4713 0.633714 0.00 0.64 0.00 0.36
#> Sample_49 2 0.4277 0.709704 0.00 0.72 0.00 0.28
#> Sample_50 2 0.4790 0.601025 0.00 0.62 0.00 0.38
#> Sample_51 2 0.4713 0.632237 0.00 0.64 0.00 0.36
#> Sample_52 2 0.4713 0.633714 0.00 0.64 0.00 0.36
#> Sample_55 4 0.6574 0.142382 0.02 0.04 0.42 0.52
#> Sample_56 1 0.4994 -0.100851 0.52 0.48 0.00 0.00
#> Sample_57 1 0.2345 0.584414 0.90 0.10 0.00 0.00
#> Sample_58 1 0.3853 0.542161 0.82 0.16 0.02 0.00
#> Sample_59 2 0.4134 0.590079 0.26 0.74 0.00 0.00
#> Sample_60 1 0.0707 0.603457 0.98 0.00 0.02 0.00
#> Sample_62 1 0.0000 0.615118 1.00 0.00 0.00 0.00
#> Sample_63 1 0.0000 0.615118 1.00 0.00 0.00 0.00
#> Sample_64 1 0.0000 0.615118 1.00 0.00 0.00 0.00
#> Sample_65 1 0.0000 0.615118 1.00 0.00 0.00 0.00
#> Sample_66 1 0.0000 0.615118 1.00 0.00 0.00 0.00
#> Sample_68 1 0.0000 0.615118 1.00 0.00 0.00 0.00
#> Sample_69 1 0.0000 0.615118 1.00 0.00 0.00 0.00
#> Sample_74 1 0.5291 0.348457 0.74 0.08 0.18 0.00
#> Sample_76 2 0.4624 0.507384 0.34 0.66 0.00 0.00
#> Sample_78 3 0.4713 0.422302 0.36 0.00 0.64 0.00
#> Sample_81 3 0.6262 0.317338 0.40 0.06 0.54 0.00
#> Sample_83 1 0.2921 0.487229 0.86 0.00 0.14 0.00
#> Sample_84 3 0.4855 0.393513 0.40 0.00 0.60 0.00
#> Sample_86 2 0.4624 0.658677 0.00 0.66 0.00 0.34
#> Sample_96 3 0.6089 0.428598 0.28 0.08 0.64 0.00
#> Sample_97 3 0.4522 0.451082 0.32 0.00 0.68 0.00
#> Sample_98 3 0.4522 0.451082 0.32 0.00 0.68 0.00
#> Sample_99 3 0.4790 0.402730 0.38 0.00 0.62 0.00
#> Sample_102 2 0.4134 0.590079 0.26 0.74 0.00 0.00
#> Sample_105 3 0.6656 0.377245 0.22 0.16 0.62 0.00
#> Sample_106 3 0.4855 0.386920 0.40 0.00 0.60 0.00
#> Sample_108 2 0.6201 0.438140 0.08 0.62 0.30 0.00
#> Sample_109 3 0.4855 0.389921 0.40 0.00 0.60 0.00
#> Sample_120 3 0.4855 0.394203 0.40 0.00 0.60 0.00
#> Sample_124 3 0.4406 0.457135 0.30 0.00 0.70 0.00
#> Sample_125 3 0.5619 0.434221 0.32 0.04 0.64 0.00
#> Sample_127 1 0.5000 -0.268381 0.50 0.00 0.50 0.00
#> Sample_129 3 0.4907 0.367105 0.42 0.00 0.58 0.00
#> Sample_132 1 0.4624 0.141970 0.66 0.00 0.34 0.00
#> Sample_134 1 0.4907 -0.108824 0.58 0.00 0.42 0.00
#> Sample_136 3 0.4088 0.422744 0.14 0.00 0.82 0.04
#> Sample_137 3 0.4277 0.459989 0.28 0.00 0.72 0.00
#> Sample_138 3 0.4522 0.451082 0.32 0.00 0.68 0.00
#> Sample_140 3 0.4277 0.459989 0.28 0.00 0.72 0.00
#> Sample_141 3 0.4522 0.451082 0.32 0.00 0.68 0.00
#> Sample_197 3 0.4713 0.187614 0.00 0.36 0.64 0.00
#> Sample_198 3 0.4994 0.255179 0.48 0.00 0.52 0.00
#> Sample_199 2 0.4134 0.717571 0.00 0.74 0.00 0.26
#> Sample_200 2 0.4642 0.720898 0.02 0.74 0.00 0.24
#> Sample_201 2 0.4277 0.708375 0.00 0.72 0.00 0.28
#> Sample_202 2 0.4134 0.717571 0.00 0.74 0.00 0.26
#> Sample_203 2 0.4134 0.717571 0.00 0.74 0.00 0.26
#> Sample_205 2 0.4134 0.717571 0.00 0.74 0.00 0.26
#> Sample_206 2 0.5147 0.718487 0.06 0.74 0.00 0.20
#> Sample_207 2 0.4277 0.708645 0.00 0.72 0.00 0.28
#> Sample_586 3 0.8389 -0.124713 0.02 0.26 0.38 0.34
#> Sample_587 3 0.8991 0.101665 0.18 0.26 0.46 0.10
#> Sample_591 3 0.9194 0.111567 0.12 0.26 0.44 0.18
#> Sample_593 4 0.0000 0.808489 0.00 0.00 0.00 1.00
#> Sample_607 3 0.6471 0.198496 0.08 0.24 0.66 0.02
#> Sample_651 4 0.4855 -0.062097 0.00 0.40 0.00 0.60
#> Sample_652 1 0.0707 0.611882 0.98 0.02 0.00 0.00
#> Sample_653 1 0.3801 0.385137 0.78 0.00 0.22 0.00
#> Sample_654 2 0.4277 0.569513 0.28 0.72 0.00 0.00
#> Sample_655 1 0.0707 0.608092 0.98 0.00 0.02 0.00
#> Sample_656 3 0.4994 0.259771 0.48 0.00 0.52 0.00
#> Sample_658 2 0.6767 0.559299 0.04 0.66 0.22 0.08
#> Sample_659 1 0.4790 0.040812 0.62 0.00 0.38 0.00
#> Sample_661 2 0.4994 0.135088 0.48 0.52 0.00 0.00
#> Sample_664 3 0.4277 0.460007 0.28 0.00 0.72 0.00
#> Sample_671 3 0.4277 0.460007 0.28 0.00 0.72 0.00
#> Sample_677 2 0.5147 0.718502 0.06 0.74 0.00 0.20
#> Sample_678 2 0.5224 0.715722 0.04 0.76 0.02 0.18
#> Sample_679 3 0.6463 0.425755 0.18 0.06 0.70 0.06
#> Sample_704 1 0.7285 0.370406 0.52 0.30 0.18 0.00
#> Sample_842 2 0.4948 0.511629 0.00 0.56 0.00 0.44
#> Sample_843 2 0.4907 0.548089 0.00 0.58 0.00 0.42
#> Sample_844 1 0.5428 0.240672 0.60 0.38 0.02 0.00
#> Sample_845 3 0.6840 0.154556 0.18 0.22 0.60 0.00
#> Sample_846 1 0.5486 0.497698 0.72 0.20 0.08 0.00
#> Sample_850 2 0.4713 0.438061 0.36 0.64 0.00 0.00
#> Sample_851 3 0.4994 0.275836 0.48 0.00 0.52 0.00
#> Sample_853 3 0.6755 0.391666 0.18 0.14 0.66 0.02
#> Sample_854 2 0.6510 0.325406 0.08 0.54 0.38 0.00
#> Sample_1519 3 0.7664 0.153629 0.14 0.24 0.58 0.04
#> Sample_1537 3 0.7537 0.125107 0.10 0.30 0.56 0.04
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
As soon as the classes for columns are determined, the signatures that are significantly different between subgroups can be looked for. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. To get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows (which is done by automatically selecting number of clusters).If there are too many signatures, top_signatures = ... can be set to only show the
signatures with the highest FDRs:
# code only for demonstration
# e.g. to show the top 500 most significant rows
tb = get_signature(res, k = ..., top_signatures = 500)
If the signatures are defined as these which are uniquely high in current group, diff_method argument
can be set to "uniquely_high_in_one_group":
# code only for demonstration
tb = get_signature(res, k = ..., diff_method = "uniquely_high_in_one_group")
UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n_sample age(p-value) cell.type(p-value) k
#> ATC:skmeans 115 0.267 0.3901 2
#> ATC:skmeans 92 0.682 0.0213 3
#> ATC:skmeans 50 NaN 0.3041 4
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
Parent node: Node022. Child nodes: Node02111-leaf , Node02112 , Node02121-leaf , Node02122-leaf , Node02211-leaf , Node02212-leaf , Node02221-leaf , Node02222-leaf , Node03211 , Node03212-leaf , Node03221-leaf , Node03222-leaf .
The object with results only for a single top-value method and a single partitioning method can be extracted as:
res = res_rh["0222"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4.
#> On a matrix with 14719 rows and 100 columns.
#> Top rows (527) are extracted by 'ATC' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 150 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_partitions"
#> [7] "compare_signatures" "consensus_heatmap" "dimension_reduction"
#> [10] "functional_enrichment" "get_anno_col" "get_anno"
#> [13] "get_classes" "get_consensus" "get_matrix"
#> [16] "get_membership" "get_param" "get_signatures"
#> [19] "get_stats" "is_best_k" "is_stable_k"
#> [22] "membership_heatmap" "ncol" "nrow"
#> [25] "plot_ecdf" "predict_classes" "rownames"
#> [28] "select_partition_number" "show" "suggest_best_k"
#> [31] "test_to_known_factors" "top_rows_heatmap"
collect_plots() function collects all the plots made from res for all k (number of subgroups)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, higher 1-PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.817 0.920 0.964 0.504 0.496 0.496
#> 3 3 0.914 0.909 0.958 0.267 0.775 0.584
#> 4 4 0.785 0.855 0.922 0.168 0.808 0.523
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 3
Following is the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall subgroup
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> Sample_4 2 0.827 0.652 0.26 0.74
#> Sample_8 2 0.469 0.876 0.10 0.90
#> Sample_17 2 0.000 0.956 0.00 1.00
#> Sample_18 2 0.881 0.577 0.30 0.70
#> Sample_19 2 0.000 0.956 0.00 1.00
#> Sample_20 1 0.881 0.594 0.70 0.30
#> Sample_21 1 0.827 0.667 0.74 0.26
#> Sample_22 2 0.855 0.616 0.28 0.72
#> Sample_23 1 0.000 0.967 1.00 0.00
#> Sample_25 1 0.680 0.786 0.82 0.18
#> Sample_27 2 0.000 0.956 0.00 1.00
#> Sample_28 2 0.000 0.956 0.00 1.00
#> Sample_30 2 0.000 0.956 0.00 1.00
#> Sample_53 2 0.141 0.942 0.02 0.98
#> Sample_54 1 0.000 0.967 1.00 0.00
#> Sample_61 1 0.000 0.967 1.00 0.00
#> Sample_67 2 0.000 0.956 0.00 1.00
#> Sample_70 2 0.000 0.956 0.00 1.00
#> Sample_71 2 0.402 0.894 0.08 0.92
#> Sample_75 1 0.529 0.857 0.88 0.12
#> Sample_77 1 0.000 0.967 1.00 0.00
#> Sample_79 1 0.000 0.967 1.00 0.00
#> Sample_80 1 0.000 0.967 1.00 0.00
#> Sample_82 1 0.000 0.967 1.00 0.00
#> Sample_85 2 0.000 0.956 0.00 1.00
#> Sample_87 2 0.000 0.956 0.00 1.00
#> Sample_88 2 0.000 0.956 0.00 1.00
#> Sample_89 2 0.000 0.956 0.00 1.00
#> Sample_90 2 0.000 0.956 0.00 1.00
#> Sample_91 2 0.000 0.956 0.00 1.00
#> Sample_92 2 0.000 0.956 0.00 1.00
#> Sample_93 2 0.000 0.956 0.00 1.00
#> Sample_95 1 0.000 0.967 1.00 0.00
#> Sample_100 2 0.000 0.956 0.00 1.00
#> Sample_101 1 0.827 0.673 0.74 0.26
#> Sample_103 2 0.000 0.956 0.00 1.00
#> Sample_104 1 0.000 0.967 1.00 0.00
#> Sample_107 1 0.242 0.934 0.96 0.04
#> Sample_110 1 0.000 0.967 1.00 0.00
#> Sample_112 1 0.402 0.899 0.92 0.08
#> Sample_113 1 0.000 0.967 1.00 0.00
#> Sample_115 1 0.000 0.967 1.00 0.00
#> Sample_116 2 0.000 0.956 0.00 1.00
#> Sample_117 1 0.000 0.967 1.00 0.00
#> Sample_119 1 0.000 0.967 1.00 0.00
#> Sample_122 1 0.000 0.967 1.00 0.00
#> Sample_126 2 0.000 0.956 0.00 1.00
#> Sample_128 2 0.000 0.956 0.00 1.00
#> Sample_130 2 0.904 0.542 0.32 0.68
#> Sample_131 2 0.000 0.956 0.00 1.00
#> Sample_133 2 0.242 0.929 0.04 0.96
#> Sample_135 1 0.000 0.967 1.00 0.00
#> Sample_139 1 0.000 0.967 1.00 0.00
#> Sample_142 1 0.000 0.967 1.00 0.00
#> Sample_143 1 0.000 0.967 1.00 0.00
#> Sample_145 1 0.000 0.967 1.00 0.00
#> Sample_146 2 0.000 0.956 0.00 1.00
#> Sample_596 2 0.000 0.956 0.00 1.00
#> Sample_598 2 0.943 0.473 0.36 0.64
#> Sample_601 2 0.000 0.956 0.00 1.00
#> Sample_603 2 0.000 0.956 0.00 1.00
#> Sample_604 2 0.000 0.956 0.00 1.00
#> Sample_605 2 0.000 0.956 0.00 1.00
#> Sample_606 2 0.000 0.956 0.00 1.00
#> Sample_608 2 0.469 0.875 0.10 0.90
#> Sample_609 2 0.327 0.912 0.06 0.94
#> Sample_610 2 0.722 0.757 0.20 0.80
#> Sample_657 2 0.000 0.956 0.00 1.00
#> Sample_660 1 0.827 0.673 0.74 0.26
#> Sample_662 1 0.000 0.967 1.00 0.00
#> Sample_663 1 0.000 0.967 1.00 0.00
#> Sample_666 1 0.000 0.967 1.00 0.00
#> Sample_668 1 0.000 0.967 1.00 0.00
#> Sample_669 1 0.000 0.967 1.00 0.00
#> Sample_670 1 0.000 0.967 1.00 0.00
#> Sample_672 1 0.000 0.967 1.00 0.00
#> Sample_673 1 0.000 0.967 1.00 0.00
#> Sample_674 1 0.000 0.967 1.00 0.00
#> Sample_675 1 0.000 0.967 1.00 0.00
#> Sample_676 2 0.141 0.942 0.02 0.98
#> Sample_680 1 0.000 0.967 1.00 0.00
#> Sample_682 1 0.000 0.967 1.00 0.00
#> Sample_683 1 0.000 0.967 1.00 0.00
#> Sample_684 1 0.000 0.967 1.00 0.00
#> Sample_689 1 0.000 0.967 1.00 0.00
#> Sample_691 1 0.000 0.967 1.00 0.00
#> Sample_693 1 0.000 0.967 1.00 0.00
#> Sample_694 1 0.000 0.967 1.00 0.00
#> Sample_705 2 0.000 0.956 0.00 1.00
#> Sample_841 2 0.000 0.956 0.00 1.00
#> Sample_847 1 0.000 0.967 1.00 0.00
#> Sample_848 2 0.000 0.956 0.00 1.00
#> Sample_849 1 0.000 0.967 1.00 0.00
#> Sample_852 2 0.000 0.956 0.00 1.00
#> Sample_1517 2 0.000 0.956 0.00 1.00
#> Sample_1518 2 0.000 0.956 0.00 1.00
#> Sample_1535 2 0.000 0.956 0.00 1.00
#> Sample_1536 2 0.000 0.956 0.00 1.00
#> Sample_1554 2 0.000 0.956 0.00 1.00
#> Sample_1588 2 0.000 0.956 0.00 1.00
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> Sample_4 2 0.0000 0.9671 0.00 1.00 0.00
#> Sample_8 2 0.0000 0.9671 0.00 1.00 0.00
#> Sample_17 2 0.0892 0.9726 0.00 0.98 0.02
#> Sample_18 2 0.0000 0.9671 0.00 1.00 0.00
#> Sample_19 2 0.0892 0.9726 0.00 0.98 0.02
#> Sample_20 2 0.2066 0.9211 0.06 0.94 0.00
#> Sample_21 2 0.0000 0.9671 0.00 1.00 0.00
#> Sample_22 2 0.0000 0.9671 0.00 1.00 0.00
#> Sample_23 1 0.1529 0.9026 0.96 0.04 0.00
#> Sample_25 2 0.2066 0.9227 0.06 0.94 0.00
#> Sample_27 2 0.0892 0.9726 0.00 0.98 0.02
#> Sample_28 2 0.0892 0.9726 0.00 0.98 0.02
#> Sample_30 2 0.0892 0.9726 0.00 0.98 0.02
#> Sample_53 2 0.0892 0.9726 0.00 0.98 0.02
#> Sample_54 1 0.0892 0.9108 0.98 0.02 0.00
#> Sample_61 1 0.4291 0.7695 0.82 0.18 0.00
#> Sample_67 2 0.0892 0.9726 0.00 0.98 0.02
#> Sample_70 2 0.0892 0.9726 0.00 0.98 0.02
#> Sample_71 2 0.0892 0.9726 0.00 0.98 0.02
#> Sample_75 1 0.6302 0.1621 0.52 0.48 0.00
#> Sample_77 1 0.0000 0.9189 1.00 0.00 0.00
#> Sample_79 1 0.2414 0.8857 0.94 0.02 0.04
#> Sample_80 1 0.1529 0.9027 0.96 0.04 0.00
#> Sample_82 1 0.0000 0.9189 1.00 0.00 0.00
#> Sample_85 3 0.1529 0.9542 0.00 0.04 0.96
#> Sample_87 2 0.0892 0.9726 0.00 0.98 0.02
#> Sample_88 2 0.0892 0.9726 0.00 0.98 0.02
#> Sample_89 2 0.0892 0.9726 0.00 0.98 0.02
#> Sample_90 2 0.0892 0.9726 0.00 0.98 0.02
#> Sample_91 2 0.0892 0.9726 0.00 0.98 0.02
#> Sample_92 2 0.0892 0.9726 0.00 0.98 0.02
#> Sample_93 2 0.0892 0.9726 0.00 0.98 0.02
#> Sample_95 1 0.2066 0.8833 0.94 0.06 0.00
#> Sample_100 2 0.0892 0.9726 0.00 0.98 0.02
#> Sample_101 2 0.4291 0.7592 0.18 0.82 0.00
#> Sample_103 2 0.0000 0.9671 0.00 1.00 0.00
#> Sample_104 1 0.6309 0.0964 0.50 0.50 0.00
#> Sample_107 2 0.2066 0.9209 0.06 0.94 0.00
#> Sample_110 1 0.0892 0.9108 0.98 0.02 0.00
#> Sample_112 1 0.6280 0.2374 0.54 0.46 0.00
#> Sample_113 1 0.6280 0.2429 0.54 0.46 0.00
#> Sample_115 1 0.2066 0.8889 0.94 0.06 0.00
#> Sample_116 2 0.0892 0.9726 0.00 0.98 0.02
#> Sample_117 1 0.2959 0.8571 0.90 0.10 0.00
#> Sample_119 1 0.0892 0.9108 0.98 0.02 0.00
#> Sample_122 1 0.3340 0.8388 0.88 0.12 0.00
#> Sample_126 2 0.0892 0.9598 0.00 0.98 0.02
#> Sample_128 2 0.2537 0.9257 0.00 0.92 0.08
#> Sample_130 2 0.4862 0.7753 0.16 0.82 0.02
#> Sample_131 2 0.0000 0.9671 0.00 1.00 0.00
#> Sample_133 2 0.0000 0.9671 0.00 1.00 0.00
#> Sample_135 1 0.0000 0.9189 1.00 0.00 0.00
#> Sample_139 1 0.0892 0.9092 0.98 0.02 0.00
#> Sample_142 1 0.0000 0.9189 1.00 0.00 0.00
#> Sample_143 1 0.0000 0.9189 1.00 0.00 0.00
#> Sample_145 1 0.0000 0.9189 1.00 0.00 0.00
#> Sample_146 2 0.0892 0.9726 0.00 0.98 0.02
#> Sample_596 3 0.0000 0.9919 0.00 0.00 1.00
#> Sample_598 3 0.0892 0.9762 0.02 0.00 0.98
#> Sample_601 3 0.0000 0.9919 0.00 0.00 1.00
#> Sample_603 3 0.0000 0.9919 0.00 0.00 1.00
#> Sample_604 3 0.0000 0.9919 0.00 0.00 1.00
#> Sample_605 3 0.0000 0.9919 0.00 0.00 1.00
#> Sample_606 3 0.0000 0.9919 0.00 0.00 1.00
#> Sample_608 3 0.0892 0.9764 0.00 0.02 0.98
#> Sample_609 3 0.0892 0.9764 0.00 0.02 0.98
#> Sample_610 3 0.0000 0.9919 0.00 0.00 1.00
#> Sample_657 2 0.0892 0.9726 0.00 0.98 0.02
#> Sample_660 2 0.0892 0.9558 0.02 0.98 0.00
#> Sample_662 1 0.0000 0.9189 1.00 0.00 0.00
#> Sample_663 1 0.0000 0.9189 1.00 0.00 0.00
#> Sample_666 1 0.0000 0.9189 1.00 0.00 0.00
#> Sample_668 1 0.5016 0.6514 0.76 0.00 0.24
#> Sample_669 1 0.0000 0.9189 1.00 0.00 0.00
#> Sample_670 1 0.0000 0.9189 1.00 0.00 0.00
#> Sample_672 1 0.0000 0.9189 1.00 0.00 0.00
#> Sample_673 1 0.0000 0.9189 1.00 0.00 0.00
#> Sample_674 1 0.0000 0.9189 1.00 0.00 0.00
#> Sample_675 1 0.0000 0.9189 1.00 0.00 0.00
#> Sample_676 2 0.0000 0.9671 0.00 1.00 0.00
#> Sample_680 1 0.0000 0.9189 1.00 0.00 0.00
#> Sample_682 1 0.0000 0.9189 1.00 0.00 0.00
#> Sample_683 1 0.0000 0.9189 1.00 0.00 0.00
#> Sample_684 1 0.0000 0.9189 1.00 0.00 0.00
#> Sample_689 1 0.0000 0.9189 1.00 0.00 0.00
#> Sample_691 1 0.0000 0.9189 1.00 0.00 0.00
#> Sample_693 1 0.0000 0.9189 1.00 0.00 0.00
#> Sample_694 1 0.0000 0.9189 1.00 0.00 0.00
#> Sample_705 2 0.2959 0.9047 0.00 0.90 0.10
#> Sample_841 2 0.0892 0.9726 0.00 0.98 0.02
#> Sample_847 3 0.0892 0.9762 0.02 0.00 0.98
#> Sample_848 2 0.0892 0.9726 0.00 0.98 0.02
#> Sample_849 1 0.0000 0.9189 1.00 0.00 0.00
#> Sample_852 2 0.0000 0.9671 0.00 1.00 0.00
#> Sample_1517 3 0.0000 0.9919 0.00 0.00 1.00
#> Sample_1518 3 0.0000 0.9919 0.00 0.00 1.00
#> Sample_1535 3 0.0000 0.9919 0.00 0.00 1.00
#> Sample_1536 3 0.0000 0.9919 0.00 0.00 1.00
#> Sample_1554 3 0.0000 0.9919 0.00 0.00 1.00
#> Sample_1588 3 0.0000 0.9919 0.00 0.00 1.00
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> Sample_4 4 0.0707 0.821 0.00 0.02 0.00 0.98
#> Sample_8 2 0.0707 0.935 0.00 0.98 0.00 0.02
#> Sample_17 2 0.1637 0.915 0.00 0.94 0.00 0.06
#> Sample_18 4 0.0707 0.821 0.00 0.02 0.00 0.98
#> Sample_19 2 0.3400 0.798 0.00 0.82 0.00 0.18
#> Sample_20 4 0.0707 0.821 0.00 0.02 0.00 0.98
#> Sample_21 4 0.1211 0.813 0.00 0.04 0.00 0.96
#> Sample_22 4 0.0707 0.821 0.00 0.02 0.00 0.98
#> Sample_23 4 0.3975 0.680 0.24 0.00 0.00 0.76
#> Sample_25 4 0.0707 0.821 0.00 0.02 0.00 0.98
#> Sample_27 2 0.0707 0.935 0.00 0.98 0.00 0.02
#> Sample_28 2 0.0000 0.933 0.00 1.00 0.00 0.00
#> Sample_30 2 0.2345 0.881 0.00 0.90 0.00 0.10
#> Sample_53 2 0.0707 0.922 0.00 0.98 0.00 0.02
#> Sample_54 4 0.4790 0.440 0.38 0.00 0.00 0.62
#> Sample_61 4 0.5106 0.650 0.24 0.04 0.00 0.72
#> Sample_67 2 0.0000 0.933 0.00 1.00 0.00 0.00
#> Sample_70 2 0.2011 0.902 0.00 0.92 0.00 0.08
#> Sample_71 2 0.1211 0.915 0.00 0.96 0.00 0.04
#> Sample_75 4 0.2706 0.802 0.08 0.02 0.00 0.90
#> Sample_77 1 0.0000 0.950 1.00 0.00 0.00 0.00
#> Sample_79 1 0.4211 0.811 0.84 0.02 0.04 0.10
#> Sample_80 4 0.1211 0.814 0.04 0.00 0.00 0.96
#> Sample_82 1 0.0000 0.950 1.00 0.00 0.00 0.00
#> Sample_85 3 0.4797 0.622 0.00 0.26 0.72 0.02
#> Sample_87 2 0.0707 0.935 0.00 0.98 0.00 0.02
#> Sample_88 2 0.0707 0.935 0.00 0.98 0.00 0.02
#> Sample_89 2 0.0707 0.935 0.00 0.98 0.00 0.02
#> Sample_90 2 0.0707 0.935 0.00 0.98 0.00 0.02
#> Sample_91 2 0.0707 0.935 0.00 0.98 0.00 0.02
#> Sample_92 2 0.0707 0.935 0.00 0.98 0.00 0.02
#> Sample_93 2 0.0707 0.935 0.00 0.98 0.00 0.02
#> Sample_95 1 0.6286 0.498 0.66 0.14 0.00 0.20
#> Sample_100 2 0.3400 0.778 0.00 0.82 0.00 0.18
#> Sample_101 4 0.6510 0.347 0.08 0.38 0.00 0.54
#> Sample_103 4 0.0707 0.821 0.00 0.02 0.00 0.98
#> Sample_104 4 0.7497 0.458 0.26 0.24 0.00 0.50
#> Sample_107 4 0.0707 0.821 0.00 0.02 0.00 0.98
#> Sample_110 4 0.5606 0.201 0.48 0.02 0.00 0.50
#> Sample_112 2 0.6049 0.576 0.20 0.68 0.00 0.12
#> Sample_113 4 0.5986 0.532 0.06 0.32 0.00 0.62
#> Sample_115 4 0.3606 0.766 0.14 0.02 0.00 0.84
#> Sample_116 2 0.0000 0.933 0.00 1.00 0.00 0.00
#> Sample_117 4 0.6216 0.662 0.22 0.12 0.00 0.66
#> Sample_119 4 0.2011 0.801 0.08 0.00 0.00 0.92
#> Sample_122 4 0.6720 0.554 0.30 0.12 0.00 0.58
#> Sample_126 4 0.0707 0.812 0.00 0.02 0.00 0.98
#> Sample_128 2 0.2335 0.885 0.00 0.92 0.06 0.02
#> Sample_130 4 0.5355 0.477 0.02 0.36 0.00 0.62
#> Sample_131 4 0.2921 0.753 0.00 0.14 0.00 0.86
#> Sample_133 2 0.0707 0.922 0.00 0.98 0.00 0.02
#> Sample_135 1 0.0000 0.950 1.00 0.00 0.00 0.00
#> Sample_139 1 0.3037 0.835 0.88 0.10 0.00 0.02
#> Sample_142 1 0.0707 0.935 0.98 0.00 0.00 0.02
#> Sample_143 1 0.0000 0.950 1.00 0.00 0.00 0.00
#> Sample_145 1 0.1637 0.899 0.94 0.00 0.00 0.06
#> Sample_146 2 0.0000 0.933 0.00 1.00 0.00 0.00
#> Sample_596 3 0.0000 0.982 0.00 0.00 1.00 0.00
#> Sample_598 3 0.0000 0.982 0.00 0.00 1.00 0.00
#> Sample_601 3 0.0000 0.982 0.00 0.00 1.00 0.00
#> Sample_603 3 0.0000 0.982 0.00 0.00 1.00 0.00
#> Sample_604 3 0.0000 0.982 0.00 0.00 1.00 0.00
#> Sample_605 3 0.0000 0.982 0.00 0.00 1.00 0.00
#> Sample_606 3 0.0000 0.982 0.00 0.00 1.00 0.00
#> Sample_608 3 0.0000 0.982 0.00 0.00 1.00 0.00
#> Sample_609 3 0.0000 0.982 0.00 0.00 1.00 0.00
#> Sample_610 3 0.0000 0.982 0.00 0.00 1.00 0.00
#> Sample_657 2 0.0000 0.933 0.00 1.00 0.00 0.00
#> Sample_660 2 0.3037 0.852 0.02 0.88 0.00 0.10
#> Sample_662 1 0.0000 0.950 1.00 0.00 0.00 0.00
#> Sample_663 1 0.0000 0.950 1.00 0.00 0.00 0.00
#> Sample_666 1 0.2011 0.883 0.92 0.00 0.00 0.08
#> Sample_668 1 0.1637 0.901 0.94 0.00 0.06 0.00
#> Sample_669 1 0.0000 0.950 1.00 0.00 0.00 0.00
#> Sample_670 1 0.3610 0.738 0.80 0.00 0.00 0.20
#> Sample_672 1 0.0000 0.950 1.00 0.00 0.00 0.00
#> Sample_673 1 0.0000 0.950 1.00 0.00 0.00 0.00
#> Sample_674 1 0.0000 0.950 1.00 0.00 0.00 0.00
#> Sample_675 1 0.0000 0.950 1.00 0.00 0.00 0.00
#> Sample_676 4 0.0707 0.821 0.00 0.02 0.00 0.98
#> Sample_680 1 0.0000 0.950 1.00 0.00 0.00 0.00
#> Sample_682 1 0.0000 0.950 1.00 0.00 0.00 0.00
#> Sample_683 1 0.0000 0.950 1.00 0.00 0.00 0.00
#> Sample_684 1 0.0000 0.950 1.00 0.00 0.00 0.00
#> Sample_689 1 0.0000 0.950 1.00 0.00 0.00 0.00
#> Sample_691 1 0.0000 0.950 1.00 0.00 0.00 0.00
#> Sample_693 1 0.0000 0.950 1.00 0.00 0.00 0.00
#> Sample_694 1 0.0000 0.950 1.00 0.00 0.00 0.00
#> Sample_705 2 0.5327 0.627 0.00 0.72 0.06 0.22
#> Sample_841 2 0.0707 0.935 0.00 0.98 0.00 0.02
#> Sample_847 3 0.0000 0.982 0.00 0.00 1.00 0.00
#> Sample_848 2 0.0000 0.933 0.00 1.00 0.00 0.00
#> Sample_849 1 0.3172 0.796 0.84 0.00 0.00 0.16
#> Sample_852 4 0.0707 0.821 0.00 0.02 0.00 0.98
#> Sample_1517 3 0.0000 0.982 0.00 0.00 1.00 0.00
#> Sample_1518 3 0.0000 0.982 0.00 0.00 1.00 0.00
#> Sample_1535 3 0.0000 0.982 0.00 0.00 1.00 0.00
#> Sample_1536 3 0.0000 0.982 0.00 0.00 1.00 0.00
#> Sample_1554 3 0.0000 0.982 0.00 0.00 1.00 0.00
#> Sample_1588 3 0.0000 0.982 0.00 0.00 1.00 0.00
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
As soon as the classes for columns are determined, the signatures that are significantly different between subgroups can be looked for. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. To get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows (which is done by automatically selecting number of clusters).If there are too many signatures, top_signatures = ... can be set to only show the
signatures with the highest FDRs:
# code only for demonstration
# e.g. to show the top 500 most significant rows
tb = get_signature(res, k = ..., top_signatures = 500)
If the signatures are defined as these which are uniquely high in current group, diff_method argument
can be set to "uniquely_high_in_one_group":
# code only for demonstration
tb = get_signature(res, k = ..., diff_method = "uniquely_high_in_one_group")
UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n_sample age(p-value) cell.type(p-value) k
#> ATC:skmeans 99 1.34e-03 2.35e-04 2
#> ATC:skmeans 96 1.45e-05 2.62e-16 3
#> ATC:skmeans 94 1.51e-05 1.14e-14 4
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
Parent node: Node02. Child nodes: Node0111-leaf , Node0112-leaf , Node0121-leaf , Node0122-leaf , Node0131-leaf , Node0132-leaf , Node0211 , Node0212 , Node0221 , Node0222 , Node0231-leaf , Node0232-leaf , Node0233-leaf , Node0311-leaf , Node0312-leaf , Node0313-leaf , Node0314-leaf , Node0321 , Node0322 , Node0331-leaf , Node0332-leaf , Node0333-leaf .
The object with results only for a single top-value method and a single partitioning method can be extracted as:
res = res_rh["023"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4.
#> On a matrix with 14735 rows and 75 columns.
#> Top rows (1474) are extracted by 'ATC' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 150 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_partitions"
#> [7] "compare_signatures" "consensus_heatmap" "dimension_reduction"
#> [10] "functional_enrichment" "get_anno_col" "get_anno"
#> [13] "get_classes" "get_consensus" "get_matrix"
#> [16] "get_membership" "get_param" "get_signatures"
#> [19] "get_stats" "is_best_k" "is_stable_k"
#> [22] "membership_heatmap" "ncol" "nrow"
#> [25] "plot_ecdf" "predict_classes" "rownames"
#> [28] "select_partition_number" "show" "suggest_best_k"
#> [31] "test_to_known_factors" "top_rows_heatmap"
collect_plots() function collects all the plots made from res for all k (number of subgroups)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, higher 1-PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.997 0.999 0.5048 0.495 0.495
#> 3 3 1.000 1.000 1.000 0.2914 0.837 0.676
#> 4 4 0.873 0.898 0.929 0.0909 0.939 0.826
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 3
#> attr(,"optional")
#> [1] 2
There is also optional best \(k\) = 2 that is worth to check.
Following is the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall subgroup
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> Sample_31 2 0.000 1.000 0.0 1.0
#> Sample_43 2 0.000 1.000 0.0 1.0
#> Sample_73 2 0.000 1.000 0.0 1.0
#> Sample_94 2 0.000 1.000 0.0 1.0
#> Sample_114 2 0.000 1.000 0.0 1.0
#> Sample_118 2 0.000 1.000 0.0 1.0
#> Sample_123 2 0.000 1.000 0.0 1.0
#> Sample_144 2 0.000 1.000 0.0 1.0
#> Sample_171 2 0.000 1.000 0.0 1.0
#> Sample_173 2 0.000 1.000 0.0 1.0
#> Sample_204 2 0.000 1.000 0.0 1.0
#> Sample_588 1 0.000 0.997 1.0 0.0
#> Sample_592 1 0.000 0.997 1.0 0.0
#> Sample_594 1 0.000 0.997 1.0 0.0
#> Sample_595 1 0.000 0.997 1.0 0.0
#> Sample_597 1 0.000 0.997 1.0 0.0
#> Sample_599 1 0.000 0.997 1.0 0.0
#> Sample_600 1 0.000 0.997 1.0 0.0
#> Sample_602 1 0.000 0.997 1.0 0.0
#> Sample_621 1 0.000 0.997 1.0 0.0
#> Sample_625 1 0.000 0.997 1.0 0.0
#> Sample_627 1 0.000 0.997 1.0 0.0
#> Sample_628 1 0.000 0.997 1.0 0.0
#> Sample_629 1 0.000 0.997 1.0 0.0
#> Sample_631 1 0.000 0.997 1.0 0.0
#> Sample_638 1 0.000 0.997 1.0 0.0
#> Sample_640 1 0.469 0.889 0.9 0.1
#> Sample_642 2 0.000 1.000 0.0 1.0
#> Sample_645 2 0.000 1.000 0.0 1.0
#> Sample_648 2 0.000 1.000 0.0 1.0
#> Sample_649 2 0.000 1.000 0.0 1.0
#> Sample_650 1 0.000 0.997 1.0 0.0
#> Sample_665 2 0.000 1.000 0.0 1.0
#> Sample_667 2 0.000 1.000 0.0 1.0
#> Sample_681 2 0.000 1.000 0.0 1.0
#> Sample_710 2 0.000 1.000 0.0 1.0
#> Sample_720 2 0.000 1.000 0.0 1.0
#> Sample_731 2 0.000 1.000 0.0 1.0
#> Sample_735 2 0.000 1.000 0.0 1.0
#> Sample_738 2 0.000 1.000 0.0 1.0
#> Sample_768 2 0.000 1.000 0.0 1.0
#> Sample_820 2 0.000 1.000 0.0 1.0
#> Sample_836 2 0.000 1.000 0.0 1.0
#> Sample_859 2 0.000 1.000 0.0 1.0
#> Sample_865 2 0.000 1.000 0.0 1.0
#> Sample_882 2 0.000 1.000 0.0 1.0
#> Sample_894 2 0.000 1.000 0.0 1.0
#> Sample_900 2 0.000 1.000 0.0 1.0
#> Sample_1520 2 0.000 1.000 0.0 1.0
#> Sample_1521 2 0.000 1.000 0.0 1.0
#> Sample_1524 2 0.000 1.000 0.0 1.0
#> Sample_1530 2 0.000 1.000 0.0 1.0
#> Sample_1531 2 0.000 1.000 0.0 1.0
#> Sample_1532 2 0.000 1.000 0.0 1.0
#> Sample_1543 2 0.000 1.000 0.0 1.0
#> Sample_1548 1 0.000 0.997 1.0 0.0
#> Sample_1550 1 0.000 0.997 1.0 0.0
#> Sample_1551 1 0.000 0.997 1.0 0.0
#> Sample_1552 1 0.000 0.997 1.0 0.0
#> Sample_1555 1 0.000 0.997 1.0 0.0
#> Sample_1559 1 0.000 0.997 1.0 0.0
#> Sample_1561 1 0.000 0.997 1.0 0.0
#> Sample_1574 1 0.000 0.997 1.0 0.0
#> Sample_1575 1 0.000 0.997 1.0 0.0
#> Sample_1582 1 0.000 0.997 1.0 0.0
#> Sample_1585 1 0.000 0.997 1.0 0.0
#> Sample_1586 1 0.000 0.997 1.0 0.0
#> Sample_1587 1 0.000 0.997 1.0 0.0
#> Sample_1589 1 0.000 0.997 1.0 0.0
#> Sample_1592 1 0.000 0.997 1.0 0.0
#> Sample_1593 1 0.000 0.997 1.0 0.0
#> Sample_1594 1 0.000 0.997 1.0 0.0
#> Sample_1595 1 0.000 0.997 1.0 0.0
#> Sample_1596 2 0.000 1.000 0.0 1.0
#> Sample_1599 2 0.000 1.000 0.0 1.0
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> Sample_31 3 0 1 0 0 1
#> Sample_43 3 0 1 0 0 1
#> Sample_73 3 0 1 0 0 1
#> Sample_94 2 0 1 0 1 0
#> Sample_114 2 0 1 0 1 0
#> Sample_118 2 0 1 0 1 0
#> Sample_123 2 0 1 0 1 0
#> Sample_144 2 0 1 0 1 0
#> Sample_171 2 0 1 0 1 0
#> Sample_173 3 0 1 0 0 1
#> Sample_204 3 0 1 0 0 1
#> Sample_588 1 0 1 1 0 0
#> Sample_592 1 0 1 1 0 0
#> Sample_594 1 0 1 1 0 0
#> Sample_595 1 0 1 1 0 0
#> Sample_597 1 0 1 1 0 0
#> Sample_599 1 0 1 1 0 0
#> Sample_600 1 0 1 1 0 0
#> Sample_602 1 0 1 1 0 0
#> Sample_621 1 0 1 1 0 0
#> Sample_625 1 0 1 1 0 0
#> Sample_627 1 0 1 1 0 0
#> Sample_628 1 0 1 1 0 0
#> Sample_629 1 0 1 1 0 0
#> Sample_631 1 0 1 1 0 0
#> Sample_638 1 0 1 1 0 0
#> Sample_640 2 0 1 0 1 0
#> Sample_642 3 0 1 0 0 1
#> Sample_645 2 0 1 0 1 0
#> Sample_648 2 0 1 0 1 0
#> Sample_649 2 0 1 0 1 0
#> Sample_650 1 0 1 1 0 0
#> Sample_665 2 0 1 0 1 0
#> Sample_667 2 0 1 0 1 0
#> Sample_681 2 0 1 0 1 0
#> Sample_710 2 0 1 0 1 0
#> Sample_720 2 0 1 0 1 0
#> Sample_731 3 0 1 0 0 1
#> Sample_735 3 0 1 0 0 1
#> Sample_738 2 0 1 0 1 0
#> Sample_768 2 0 1 0 1 0
#> Sample_820 3 0 1 0 0 1
#> Sample_836 3 0 1 0 0 1
#> Sample_859 2 0 1 0 1 0
#> Sample_865 3 0 1 0 0 1
#> Sample_882 3 0 1 0 0 1
#> Sample_894 2 0 1 0 1 0
#> Sample_900 3 0 1 0 0 1
#> Sample_1520 3 0 1 0 0 1
#> Sample_1521 2 0 1 0 1 0
#> Sample_1524 3 0 1 0 0 1
#> Sample_1530 3 0 1 0 0 1
#> Sample_1531 2 0 1 0 1 0
#> Sample_1532 2 0 1 0 1 0
#> Sample_1543 3 0 1 0 0 1
#> Sample_1548 1 0 1 1 0 0
#> Sample_1550 1 0 1 1 0 0
#> Sample_1551 1 0 1 1 0 0
#> Sample_1552 1 0 1 1 0 0
#> Sample_1555 1 0 1 1 0 0
#> Sample_1559 1 0 1 1 0 0
#> Sample_1561 1 0 1 1 0 0
#> Sample_1574 1 0 1 1 0 0
#> Sample_1575 1 0 1 1 0 0
#> Sample_1582 1 0 1 1 0 0
#> Sample_1585 1 0 1 1 0 0
#> Sample_1586 1 0 1 1 0 0
#> Sample_1587 1 0 1 1 0 0
#> Sample_1589 1 0 1 1 0 0
#> Sample_1592 1 0 1 1 0 0
#> Sample_1593 1 0 1 1 0 0
#> Sample_1594 1 0 1 1 0 0
#> Sample_1595 1 0 1 1 0 0
#> Sample_1596 2 0 1 0 1 0
#> Sample_1599 3 0 1 0 0 1
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> Sample_31 3 0.0000 0.967 0.00 0.00 1.00 0.00
#> Sample_43 3 0.0000 0.967 0.00 0.00 1.00 0.00
#> Sample_73 3 0.2921 0.904 0.00 0.00 0.86 0.14
#> Sample_94 2 0.2345 0.938 0.00 0.90 0.00 0.10
#> Sample_114 2 0.1637 0.946 0.00 0.94 0.00 0.06
#> Sample_118 2 0.2345 0.938 0.00 0.90 0.00 0.10
#> Sample_123 2 0.2011 0.943 0.00 0.92 0.00 0.08
#> Sample_144 2 0.0707 0.945 0.00 0.98 0.00 0.02
#> Sample_171 2 0.1211 0.939 0.00 0.96 0.00 0.04
#> Sample_173 3 0.0707 0.964 0.00 0.00 0.98 0.02
#> Sample_204 3 0.0000 0.967 0.00 0.00 1.00 0.00
#> Sample_588 1 0.4977 -0.485 0.54 0.00 0.00 0.46
#> Sample_592 4 0.4713 0.914 0.36 0.00 0.00 0.64
#> Sample_594 4 0.4406 0.945 0.30 0.00 0.00 0.70
#> Sample_595 4 0.4277 0.925 0.28 0.00 0.00 0.72
#> Sample_597 4 0.4624 0.932 0.34 0.00 0.00 0.66
#> Sample_599 4 0.4406 0.945 0.30 0.00 0.00 0.70
#> Sample_600 1 0.4994 -0.558 0.52 0.00 0.00 0.48
#> Sample_602 4 0.4713 0.908 0.36 0.00 0.00 0.64
#> Sample_621 1 0.0707 0.920 0.98 0.00 0.00 0.02
#> Sample_625 1 0.0000 0.937 1.00 0.00 0.00 0.00
#> Sample_627 1 0.0000 0.937 1.00 0.00 0.00 0.00
#> Sample_628 1 0.0000 0.937 1.00 0.00 0.00 0.00
#> Sample_629 1 0.0000 0.937 1.00 0.00 0.00 0.00
#> Sample_631 1 0.0707 0.918 0.98 0.00 0.00 0.02
#> Sample_638 1 0.0000 0.937 1.00 0.00 0.00 0.00
#> Sample_640 2 0.4581 0.863 0.08 0.80 0.00 0.12
#> Sample_642 3 0.0000 0.967 0.00 0.00 1.00 0.00
#> Sample_645 2 0.2011 0.943 0.00 0.92 0.00 0.08
#> Sample_648 2 0.1637 0.946 0.00 0.94 0.00 0.06
#> Sample_649 2 0.2011 0.943 0.00 0.92 0.00 0.08
#> Sample_650 1 0.0000 0.937 1.00 0.00 0.00 0.00
#> Sample_665 2 0.2345 0.938 0.00 0.90 0.00 0.10
#> Sample_667 2 0.2345 0.938 0.00 0.90 0.00 0.10
#> Sample_681 2 0.0000 0.943 0.00 1.00 0.00 0.00
#> Sample_710 2 0.1211 0.939 0.00 0.96 0.00 0.04
#> Sample_720 2 0.1211 0.939 0.00 0.96 0.00 0.04
#> Sample_731 3 0.3172 0.892 0.00 0.00 0.84 0.16
#> Sample_735 3 0.0707 0.964 0.00 0.00 0.98 0.02
#> Sample_738 2 0.1211 0.939 0.00 0.96 0.00 0.04
#> Sample_768 2 0.1211 0.939 0.00 0.96 0.00 0.04
#> Sample_820 3 0.0707 0.964 0.00 0.00 0.98 0.02
#> Sample_836 3 0.0707 0.964 0.00 0.00 0.98 0.02
#> Sample_859 2 0.2345 0.903 0.00 0.90 0.00 0.10
#> Sample_865 3 0.3172 0.892 0.00 0.00 0.84 0.16
#> Sample_882 3 0.3172 0.892 0.00 0.00 0.84 0.16
#> Sample_894 2 0.1211 0.939 0.00 0.96 0.00 0.04
#> Sample_900 3 0.0000 0.967 0.00 0.00 1.00 0.00
#> Sample_1520 3 0.0000 0.967 0.00 0.00 1.00 0.00
#> Sample_1521 2 0.2011 0.943 0.00 0.92 0.00 0.08
#> Sample_1524 3 0.0000 0.967 0.00 0.00 1.00 0.00
#> Sample_1530 3 0.0000 0.967 0.00 0.00 1.00 0.00
#> Sample_1531 2 0.1211 0.939 0.00 0.96 0.00 0.04
#> Sample_1532 2 0.1211 0.939 0.00 0.96 0.00 0.04
#> Sample_1543 3 0.0000 0.967 0.00 0.00 1.00 0.00
#> Sample_1548 1 0.0000 0.937 1.00 0.00 0.00 0.00
#> Sample_1550 1 0.0000 0.937 1.00 0.00 0.00 0.00
#> Sample_1551 1 0.1637 0.870 0.94 0.00 0.00 0.06
#> Sample_1552 1 0.0000 0.937 1.00 0.00 0.00 0.00
#> Sample_1555 1 0.0707 0.918 0.98 0.00 0.00 0.02
#> Sample_1559 1 0.0707 0.920 0.98 0.00 0.00 0.02
#> Sample_1561 1 0.1211 0.898 0.96 0.00 0.00 0.04
#> Sample_1574 1 0.0000 0.937 1.00 0.00 0.00 0.00
#> Sample_1575 1 0.0000 0.937 1.00 0.00 0.00 0.00
#> Sample_1582 1 0.0000 0.937 1.00 0.00 0.00 0.00
#> Sample_1585 1 0.0000 0.937 1.00 0.00 0.00 0.00
#> Sample_1586 1 0.0000 0.937 1.00 0.00 0.00 0.00
#> Sample_1587 1 0.0707 0.920 0.98 0.00 0.00 0.02
#> Sample_1589 1 0.0000 0.937 1.00 0.00 0.00 0.00
#> Sample_1592 1 0.0000 0.937 1.00 0.00 0.00 0.00
#> Sample_1593 1 0.0000 0.937 1.00 0.00 0.00 0.00
#> Sample_1594 1 0.0000 0.937 1.00 0.00 0.00 0.00
#> Sample_1595 1 0.0000 0.937 1.00 0.00 0.00 0.00
#> Sample_1596 2 0.2647 0.940 0.00 0.88 0.00 0.12
#> Sample_1599 3 0.0000 0.967 0.00 0.00 1.00 0.00
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
As soon as the classes for columns are determined, the signatures that are significantly different between subgroups can be looked for. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. To get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows (which is done by automatically selecting number of clusters).If there are too many signatures, top_signatures = ... can be set to only show the
signatures with the highest FDRs:
# code only for demonstration
# e.g. to show the top 500 most significant rows
tb = get_signature(res, k = ..., top_signatures = 500)
If the signatures are defined as these which are uniquely high in current group, diff_method argument
can be set to "uniquely_high_in_one_group":
# code only for demonstration
tb = get_signature(res, k = ..., diff_method = "uniquely_high_in_one_group")
UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n_sample age(p-value) cell.type(p-value) k
#> ATC:skmeans 75 0.544 4.86e-14 2
#> ATC:skmeans 75 0.251 9.17e-13 3
#> ATC:skmeans 73 0.257 2.94e-24 4
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
Parent node: Node0. Child nodes: Node011 , Node012 , Node013 , Node021 , Node022 , Node023 , Node031 , Node032 , Node033 .
The object with results only for a single top-value method and a single partitioning method can be extracted as:
res = res_rh["03"]
A summary of res and all the functions that can be applied to it:
res
#> A 'DownSamplingConsensusPartition' object with k = 2, 3, 4.
#> On a matrix with 15527 rows and 500 columns, randomly sampled from 566 columns.
#> Top rows (1241) are extracted by 'ATC' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 150 partitions by row resampling.
#> Best k for subgroups seems to be 4.
#>
#> Following methods can be applied to this 'DownSamplingConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_partitions"
#> [7] "compare_signatures" "consensus_heatmap" "dimension_reduction"
#> [10] "functional_enrichment" "get_anno_col" "get_anno"
#> [13] "get_classes" "get_consensus" "get_matrix"
#> [16] "get_membership" "get_param" "get_signatures"
#> [19] "get_stats" "is_best_k" "is_stable_k"
#> [22] "membership_heatmap" "ncol" "nrow"
#> [25] "plot_ecdf" "predict_classes" "rownames"
#> [28] "select_partition_number" "show" "suggest_best_k"
#> [31] "test_to_known_factors" "top_rows_heatmap"
collect_plots() function collects all the plots made from res for all k (number of subgroups)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, higher 1-PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.997 0.999 0.498 0.503 0.503
#> 3 3 1.000 0.996 0.998 0.167 0.909 0.820
#> 4 4 0.955 0.938 0.972 0.153 0.905 0.775
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 4
#> attr(,"optional")
#> [1] 2 3
There is also optional best \(k\) = 2 3 that is worth to check.
Following is the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall subgroup
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
get_classes(res, k = 2)
#> class p
#> Sample_208 2 0.000
#> Sample_209 2 0.000
#> Sample_210 2 0.000
#> Sample_211 2 0.000
#> Sample_212 2 0.000
#> Sample_213 2 0.000
#> Sample_216 2 0.000
#> Sample_218 2 0.000
#> Sample_219 2 0.000
#> Sample_220 2 0.000
#> Sample_221 2 0.000
#> Sample_222 2 0.000
#> Sample_223 2 0.000
#> Sample_225 2 0.000
#> Sample_227 2 0.000
#> Sample_228 2 0.000
#> Sample_229 2 0.000
#> Sample_230 2 0.000
#> Sample_231 2 0.000
#> Sample_232 2 0.000
#> Sample_233 2 0.000
#> Sample_234 2 0.000
#> Sample_235 2 0.000
#> Sample_237 2 0.000
#> Sample_238 2 0.000
#> Sample_239 2 0.000
#> Sample_240 2 0.000
#> Sample_242 2 0.000
#> Sample_243 2 0.000
#> Sample_244 2 0.000
#> Sample_245 2 0.000
#> Sample_246 2 0.000
#> Sample_247 2 0.000
#> Sample_248 2 0.000
#> Sample_249 2 0.000
#> Sample_250 2 0.000
#> Sample_251 2 0.000
#> Sample_252 2 0.000
#> Sample_253 2 0.000
#> Sample_254 2 0.000
#> Sample_255 2 0.000
#> Sample_256 2 0.000
#> Sample_257 2 0.000
#> Sample_259 2 0.000
#> Sample_260 2 0.000
#> Sample_261 2 0.000
#> Sample_262 2 0.000
#> Sample_263 2 0.000
#> Sample_266 2 0.000
#> Sample_267 2 0.000
#> Sample_268 2 0.000
#> Sample_269 2 0.000
#> Sample_270 2 0.000
#> Sample_271 2 0.000
#> Sample_273 2 0.000
#> Sample_274 2 0.000
#> Sample_277 2 0.000
#> Sample_278 2 0.000
#> Sample_279 2 0.000
#> Sample_280 2 0.000
#> Sample_281 2 0.000
#> Sample_282 2 0.000
#> Sample_283 2 0.000
#> Sample_284 2 0.000
#> Sample_285 2 0.000
#> Sample_286 2 0.000
#> Sample_288 2 0.000
#> Sample_289 2 0.000
#> Sample_290 2 0.000
#> Sample_291 2 0.000
#> Sample_292 2 0.000
#> Sample_293 2 0.000
#> Sample_294 2 0.000
#> Sample_295 2 0.000
#> Sample_296 2 0.000
#> Sample_297 2 0.000
#> Sample_298 2 0.000
#> Sample_299 2 0.000
#> Sample_300 2 0.000
#> Sample_301 2 0.000
#> Sample_306 2 0.000
#> Sample_307 2 0.000
#> Sample_315 2 0.000
#> Sample_317 2 0.000
#> Sample_322 2 0.000
#> Sample_331 2 0.000
#> Sample_332 2 0.000
#> Sample_333 2 0.000
#> Sample_334 2 0.000
#> Sample_335 2 0.000
#> Sample_336 2 0.000
#> Sample_337 2 0.000
#> Sample_338 2 0.000
#> Sample_339 2 0.000
#> Sample_340 2 0.000
#> Sample_341 2 0.000
#> Sample_342 2 0.000
#> Sample_343 2 0.000
#> Sample_344 2 1.000
#> Sample_345 2 0.000
#> Sample_346 2 0.000
#> Sample_347 2 0.000
#> Sample_348 2 0.000
#> Sample_349 2 0.000
#> Sample_353 2 0.000
#> Sample_354 2 0.000
#> Sample_356 2 0.000
#> Sample_357 2 0.000
#> Sample_359 2 0.000
#> Sample_361 2 0.000
#> Sample_363 2 0.000
#> Sample_364 2 0.000
#> Sample_366 2 0.000
#> Sample_367 2 0.000
#> Sample_370 2 0.000
#> Sample_371 2 0.000
#> Sample_374 2 0.000
#> Sample_375 2 0.000
#> Sample_376 2 0.000
#> Sample_378 2 0.000
#> Sample_379 2 0.000
#> Sample_380 2 0.000
#> Sample_382 2 0.000
#> Sample_383 2 0.000
#> Sample_384 2 0.000
#> Sample_387 2 0.000
#> Sample_388 2 0.000
#> Sample_391 2 0.000
#> Sample_392 2 0.000
#> Sample_399 2 0.000
#> Sample_400 2 0.000
#> Sample_401 2 0.000
#> Sample_402 2 0.000
#> Sample_406 2 0.000
#> Sample_407 2 0.000
#> Sample_410 2 0.000
#> Sample_411 2 0.000
#> Sample_413 2 0.000
#> Sample_414 2 0.000
#> Sample_415 2 0.000
#> Sample_417 2 0.000
#> Sample_418 2 0.000
#> Sample_419 2 0.000
#> Sample_420 2 0.000
#> Sample_422 2 0.000
#> Sample_423 2 0.000
#> Sample_426 2 0.000
#> Sample_429 2 0.000
#> Sample_435 2 0.000
#> Sample_436 2 0.000
#> Sample_437 2 0.000
#> Sample_441 2 0.000
#> Sample_444 2 0.000
#> Sample_447 2 0.000
#> Sample_449 2 0.000
#> Sample_450 2 0.000
#> Sample_453 2 0.000
#> Sample_454 2 0.000
#> Sample_458 2 0.000
#> Sample_460 2 0.000
#> Sample_462 2 0.000
#> Sample_467 2 0.000
#> Sample_470 2 0.000
#> Sample_471 2 0.000
#> Sample_473 2 0.000
#> Sample_478 1 0.000
#> Sample_480 1 0.000
#> Sample_481 1 0.000
#> Sample_482 1 0.000
#> Sample_483 1 0.000
#> Sample_484 1 0.000
#> Sample_485 1 0.000
#> Sample_488 1 0.000
#> Sample_489 1 0.000
#> Sample_491 1 0.000
#> Sample_492 1 0.000
#> Sample_493 1 0.000
#> Sample_494 1 0.000
#> Sample_496 1 0.000
#> Sample_497 1 0.000
#> Sample_498 1 0.000
#> Sample_501 1 0.000
#> Sample_502 1 0.000
#> Sample_503 1 0.000
#> Sample_504 1 0.000
#> Sample_508 1 0.000
#> Sample_509 1 0.000
#> Sample_510 1 0.000
#> Sample_512 1 0.000
#> Sample_514 1 0.000
#> Sample_518 1 0.000
#> Sample_519 1 0.000
#> Sample_522 1 0.000
#> Sample_523 1 0.000
#> Sample_524 1 0.000
#> Sample_525 1 0.000
#> Sample_526 1 0.000
#> Sample_528 1 0.000
#> Sample_529 1 0.000
#> Sample_531 1 0.000
#> Sample_534 1 0.000
#> Sample_538 1 0.249
#> Sample_540 1 0.000
#> Sample_541 1 0.000
#> Sample_545 1 0.000
#> Sample_548 1 0.000
#> Sample_549 1 0.000
#> Sample_550 1 0.000
#> Sample_551 1 0.000
#> Sample_552 1 0.000
#> Sample_553 1 0.000
#> Sample_554 1 0.000
#> Sample_555 1 0.000
#> Sample_556 1 0.000
#> Sample_560 1 0.000
#> Sample_561 1 0.000
#> Sample_562 1 0.000
#> Sample_563 1 0.000
#> Sample_564 1 0.000
#> Sample_568 1 0.000
#> Sample_570 2 0.000
#> Sample_571 2 0.000
#> Sample_574 2 0.000
#> Sample_575 2 0.000
#> Sample_576 2 0.000
#> Sample_578 2 0.000
#> Sample_579 2 0.000
#> Sample_580 2 0.000
#> Sample_581 2 0.000
#> Sample_582 2 0.000
#> Sample_583 2 0.000
#> Sample_584 2 0.000
#> Sample_585 2 0.000
#> Sample_589 2 0.000
#> Sample_613 2 0.000
#> Sample_615 2 0.000
#> Sample_623 2 0.000
#> Sample_624 2 0.000
#> Sample_644 2 0.000
#> Sample_948 2 0.000
#> Sample_949 2 0.000
#> Sample_951 2 0.000
#> Sample_952 2 0.000
#> Sample_953 2 0.000
#> Sample_954 2 0.000
#> Sample_955 2 0.000
#> Sample_956 2 0.000
#> Sample_957 2 0.000
#> Sample_958 2 0.000
#> Sample_959 2 0.000
#> Sample_960 2 0.000
#> Sample_961 2 0.000
#> Sample_963 2 0.000
#> Sample_964 2 0.000
#> Sample_965 2 0.000
#> Sample_966 2 0.000
#> Sample_967 2 0.000
#> Sample_969 2 0.000
#> Sample_970 1 1.000
#> Sample_971 2 0.000
#> Sample_972 2 0.000
#> Sample_973 2 0.000
#> Sample_974 2 0.000
#> Sample_975 2 0.000
#> Sample_979 2 0.000
#> Sample_984 2 0.000
#> Sample_995 2 0.000
#> Sample_997 2 0.000
#> Sample_998 2 0.000
#> Sample_1000 2 0.000
#> Sample_1001 2 0.000
#> Sample_1002 2 0.000
#> Sample_1003 2 0.000
#> Sample_1004 2 0.000
#> Sample_1005 2 0.000
#> Sample_1006 2 0.000
#> Sample_1011 2 0.000
#> Sample_1012 2 0.000
#> Sample_1013 2 0.000
#> Sample_1029 1 0.000
#> Sample_1031 1 0.000
#> Sample_1032 1 0.000
#> Sample_1033 1 0.000
#> Sample_1035 1 0.000
#> Sample_1037 1 0.000
#> Sample_1038 1 0.000
#> Sample_1040 1 0.000
#> Sample_1043 1 0.000
#> Sample_1044 1 0.000
#> Sample_1045 1 0.000
#> Sample_1047 1 0.000
#> Sample_1048 1 0.000
#> Sample_1051 1 0.000
#> Sample_1053 2 0.000
#> Sample_1054 2 0.000
#> Sample_1055 2 0.000
#> Sample_1057 2 0.000
#> Sample_1060 2 0.000
#> Sample_1061 2 0.000
#> Sample_1063 1 0.000
#> Sample_1064 1 0.000
#> Sample_1065 1 0.000
#> Sample_1067 1 0.000
#> Sample_1068 1 0.000
#> Sample_1069 1 0.000
#> Sample_1071 1 0.000
#> Sample_1072 1 0.000
#> Sample_1074 1 0.000
#> Sample_1077 1 0.000
#> Sample_1078 1 0.000
#> Sample_1079 1 0.000
#> Sample_1081 1 0.000
#> Sample_1082 1 0.000
#> Sample_1084 1 0.000
#> Sample_1085 1 0.000
#> Sample_1086 1 0.000
#> Sample_1088 1 0.000
#> Sample_1089 1 0.000
#> Sample_1092 1 0.000
#> Sample_1094 1 0.000
#> Sample_1095 1 0.000
#> Sample_1096 1 0.000
#> Sample_1098 1 0.000
#> Sample_1099 1 0.000
#> Sample_1101 1 0.000
#> Sample_1106 1 0.000
#> Sample_1107 1 0.000
#> Sample_1109 1 0.000
#> Sample_1111 1 0.000
#> Sample_1115 1 0.000
#> Sample_1117 1 0.000
#> Sample_1120 1 0.000
#> Sample_1122 1 0.000
#> Sample_1123 1 0.000
#> Sample_1124 1 0.000
#> Sample_1125 1 0.000
#> Sample_1126 1 0.000
#> Sample_1127 1 0.000
#> Sample_1128 1 0.000
#> Sample_1132 1 0.000
#> Sample_1134 1 0.000
#> Sample_1139 1 0.000
#> Sample_1140 1 0.000
#> Sample_1141 1 0.000
#> Sample_1145 1 0.000
#> Sample_1150 1 0.000
#> Sample_1152 1 0.000
#> Sample_1156 1 0.000
#> Sample_1157 1 0.000
#> Sample_1161 1 0.000
#> Sample_1163 1 0.000
#> Sample_1164 1 0.000
#> Sample_1169 1 0.000
#> Sample_1170 1 0.000
#> Sample_1175 1 0.000
#> Sample_1180 1 0.000
#> Sample_1183 1 0.000
#> Sample_1184 1 0.000
#> Sample_1189 2 0.000
#> Sample_1190 2 0.000
#> Sample_1191 2 0.000
#> Sample_1193 2 0.000
#> Sample_1194 2 0.000
#> Sample_1195 2 0.000
#> Sample_1196 2 0.000
#> Sample_1197 2 0.000
#> Sample_1198 2 0.000
#> Sample_1199 2 0.000
#> Sample_1200 2 0.000
#> Sample_1201 2 0.000
#> Sample_1202 2 0.000
#> Sample_1203 2 0.000
#> Sample_1204 2 0.000
#> Sample_1205 2 0.000
#> Sample_1206 2 0.000
#> Sample_1207 2 0.000
#> Sample_1209 2 0.000
#> Sample_1210 2 0.000
#> Sample_1211 2 0.000
#> Sample_1212 2 0.000
#> Sample_1213 2 0.000
#> Sample_1214 2 0.000
#> Sample_1215 2 0.000
#> Sample_1219 2 0.000
#> Sample_1220 2 0.000
#> Sample_1221 2 0.000
#> Sample_1223 1 0.000
#> Sample_1224 1 0.000
#> Sample_1226 1 0.000
#> Sample_1230 1 0.000
#> Sample_1231 1 0.000
#> Sample_1232 1 0.000
#> Sample_1233 1 0.000
#> Sample_1235 1 0.000
#> Sample_1237 1 0.000
#> Sample_1238 1 0.000
#> Sample_1243 1 0.000
#> Sample_1244 1 0.000
#> Sample_1245 1 0.000
#> Sample_1246 1 0.000
#> Sample_1247 1 0.000
#> Sample_1248 1 0.000
#> Sample_1250 1 0.000
#> Sample_1251 1 0.000
#> Sample_1252 1 0.000
#> Sample_1253 1 0.000
#> Sample_1255 1 0.000
#> Sample_1256 1 0.000
#> Sample_1257 1 0.000
#> Sample_1258 1 0.000
#> Sample_1259 1 0.000
#> Sample_1260 1 0.000
#> Sample_1261 1 0.000
#> Sample_1262 1 0.000
#> Sample_1263 1 0.000
#> Sample_1265 1 0.000
#> Sample_1268 1 0.000
#> Sample_1269 1 0.000
#> Sample_1270 1 0.000
#> Sample_1271 1 0.000
#> Sample_1273 1 0.000
#> Sample_1274 1 0.000
#> Sample_1275 1 0.000
#> Sample_1276 1 0.000
#> Sample_1277 1 0.000
#> Sample_1278 1 0.000
#> Sample_1281 1 0.000
#> Sample_1282 1 0.000
#> Sample_1284 1 0.000
#> Sample_1286 1 0.000
#> Sample_1287 1 0.000
#> Sample_1288 1 0.000
#> Sample_1289 1 0.000
#> Sample_1290 1 0.000
#> Sample_1291 1 0.000
#> Sample_1292 1 0.000
#> Sample_1293 1 0.000
#> Sample_1294 1 0.000
#> Sample_1295 1 0.000
#> Sample_1296 1 0.000
#> Sample_1297 1 0.000
#> Sample_1298 1 0.000
#> Sample_1300 1 0.000
#> Sample_1301 1 0.000
#> Sample_1302 1 0.000
#> Sample_1303 1 0.000
#> Sample_1304 1 0.000
#> Sample_1305 1 0.000
#> Sample_1306 1 0.000
#> Sample_1310 1 0.000
#> Sample_1311 1 0.000
#> Sample_1312 1 0.000
#> Sample_1314 1 0.000
#> Sample_1315 1 0.000
#> Sample_1316 1 0.000
#> Sample_1318 1 0.000
#> Sample_1319 1 0.000
#> Sample_1321 1 0.000
#> Sample_1322 1 0.000
#> Sample_1323 1 0.000
#> Sample_1324 1 0.000
#> Sample_1325 1 0.000
#> Sample_1326 1 0.000
#> Sample_1327 1 0.000
#> Sample_1328 1 0.000
#> Sample_1329 1 0.000
#> Sample_1330 1 0.000
#> Sample_1331 1 0.000
#> Sample_1332 1 0.000
#> Sample_1333 1 0.000
#> Sample_1334 1 0.000
#> Sample_1337 1 0.000
#> Sample_1339 1 0.000
#> Sample_1340 1 0.000
#> Sample_1341 1 0.000
#> Sample_1344 1 0.000
#> Sample_1345 1 0.000
#> Sample_1346 1 0.000
#> Sample_1347 1 0.000
#> Sample_1349 1 0.000
#> Sample_1351 1 0.000
#> Sample_1353 1 0.000
#> Sample_1355 1 0.000
#> Sample_1356 1 0.000
#> Sample_1359 1 0.000
#> Sample_1361 1 0.000
#> Sample_1362 1 0.000
#> Sample_1363 1 0.000
#> Sample_1365 1 0.000
#> Sample_1366 1 0.000
#> Sample_1368 1 0.000
#> Sample_1369 1 0.000
#> Sample_1370 1 0.000
#> Sample_1372 1 0.000
#> Sample_1376 1 0.000
#> Sample_1377 1 0.000
#> Sample_1378 1 0.000
#> Sample_1379 1 0.000
#> Sample_1380 1 0.000
#> Sample_1381 1 0.000
#> Sample_1383 1 0.000
#> Sample_1384 1 0.000
#> Sample_1385 1 0.000
#> Sample_1387 1 0.000
#> Sample_1389 1 0.000
#> Sample_1390 1 0.000
#> Sample_1391 1 0.000
#> Sample_1392 1 0.000
#> Sample_1393 1 0.000
#> Sample_1394 1 0.000
#> Sample_1395 1 0.000
#> Sample_1396 1 0.000
#> Sample_1397 1 0.000
#> Sample_1400 1 0.000
#> Sample_1401 1 0.000
#> Sample_1403 1 0.000
#> Sample_1404 1 0.000
#> Sample_1405 1 0.000
#> Sample_1407 1 0.000
#> Sample_1410 1 0.000
#> Sample_1411 1 0.000
#> Sample_1412 1 0.000
#> Sample_1413 1 0.000
#> Sample_1414 1 0.000
#> Sample_1415 1 0.000
#> Sample_1416 1 0.000
#> Sample_1417 1 0.000
#> Sample_1418 1 0.000
#> Sample_1420 1 0.000
#> Sample_1421 1 0.000
#> Sample_1422 1 0.000
#> Sample_1424 1 0.000
#> Sample_1428 1 0.000
#> Sample_1442 1 0.000
#> Sample_1443 1 0.000
#> Sample_1446 1 0.000
#> Sample_1447 1 0.000
#> Sample_1456 1 0.000
#> Sample_1457 1 0.000
#> Sample_1460 1 0.000
#> Sample_1461 1 0.000
#> Sample_1463 1 0.000
#> Sample_1464 1 0.000
#> Sample_1468 1 0.000
#> Sample_1480 1 0.000
#> Sample_1485 1 0.000
#> Sample_1486 1 0.000
#> Sample_1488 1 0.000
#> Sample_1494 1 0.000
#> Sample_1502 1 0.000
#> Sample_1503 1 0.000
#> Sample_1504 1 0.000
#> Sample_1505 1 0.000
#> Sample_1506 1 0.000
#> Sample_1507 1 0.249
#> Sample_1508 1 0.000
#> Sample_1511 1 0.000
#> Sample_1512 1 0.000
#> Sample_1513 1 0.000
#> Sample_1515 1 0.000
#> Sample_1549 2 0.000
#> Sample_1553 2 0.000
#> Sample_1569 1 0.000
#> Sample_1581 2 0.000
#> Sample_1597 2 0.000
#> Sample_1598 1 0.000
get_classes(res, k = 3)
#> class p
#> Sample_208 2 0.000
#> Sample_209 2 0.000
#> Sample_210 2 0.249
#> Sample_211 2 0.000
#> Sample_212 2 0.000
#> Sample_213 2 0.000
#> Sample_216 2 0.000
#> Sample_218 2 0.000
#> Sample_219 2 0.000
#> Sample_220 2 0.000
#> Sample_221 2 0.000
#> Sample_222 2 0.000
#> Sample_223 2 0.000
#> Sample_225 2 0.000
#> Sample_227 2 0.000
#> Sample_228 2 0.000
#> Sample_229 2 0.000
#> Sample_230 2 0.000
#> Sample_231 2 0.000
#> Sample_232 2 0.000
#> Sample_233 3 0.000
#> Sample_234 2 0.000
#> Sample_235 2 0.000
#> Sample_237 2 0.000
#> Sample_238 2 0.000
#> Sample_239 2 0.000
#> Sample_240 2 0.000
#> Sample_242 2 0.000
#> Sample_243 2 0.000
#> Sample_244 2 0.000
#> Sample_245 2 0.000
#> Sample_246 2 0.000
#> Sample_247 2 0.000
#> Sample_248 2 0.000
#> Sample_249 2 0.000
#> Sample_250 2 0.000
#> Sample_251 2 0.000
#> Sample_252 2 0.000
#> Sample_253 2 0.000
#> Sample_254 3 0.000
#> Sample_255 2 0.000
#> Sample_256 2 0.000
#> Sample_257 2 0.000
#> Sample_259 2 0.000
#> Sample_260 2 0.000
#> Sample_261 2 0.000
#> Sample_262 2 0.000
#> Sample_263 2 0.000
#> Sample_266 2 0.000
#> Sample_267 2 0.000
#> Sample_268 2 0.000
#> Sample_269 2 0.000
#> Sample_270 2 0.000
#> Sample_271 3 0.000
#> Sample_273 2 0.000
#> Sample_274 2 0.000
#> Sample_277 2 0.000
#> Sample_278 3 0.000
#> Sample_279 2 0.000
#> Sample_280 2 0.000
#> Sample_281 2 0.000
#> Sample_282 2 0.000
#> Sample_283 2 0.000
#> Sample_284 2 0.000
#> Sample_285 2 0.000
#> Sample_286 2 0.000
#> Sample_288 3 0.498
#> Sample_289 3 0.000
#> Sample_290 2 0.000
#> Sample_291 2 0.000
#> Sample_292 2 0.000
#> Sample_293 2 0.000
#> Sample_294 2 0.000
#> Sample_295 2 0.000
#> Sample_296 2 0.000
#> Sample_297 2 0.000
#> Sample_298 2 0.000
#> Sample_299 2 0.000
#> Sample_300 2 0.000
#> Sample_301 2 0.000
#> Sample_306 2 0.751
#> Sample_307 2 0.000
#> Sample_315 2 0.000
#> Sample_317 2 0.000
#> Sample_322 2 0.000
#> Sample_331 3 0.000
#> Sample_332 2 0.000
#> Sample_333 2 0.000
#> Sample_334 2 0.000
#> Sample_335 2 0.000
#> Sample_336 2 0.000
#> Sample_337 2 0.000
#> Sample_338 2 0.000
#> Sample_339 3 1.000
#> Sample_340 2 0.000
#> Sample_341 2 0.000
#> Sample_342 2 0.000
#> Sample_343 2 0.000
#> Sample_344 2 1.000
#> Sample_345 2 0.000
#> Sample_346 2 0.000
#> Sample_347 2 0.000
#> Sample_348 2 0.000
#> Sample_349 2 0.000
#> Sample_353 2 0.000
#> Sample_354 2 0.000
#> Sample_356 2 0.000
#> Sample_357 2 0.000
#> Sample_359 2 0.751
#> Sample_361 2 0.000
#> Sample_363 2 0.000
#> Sample_364 2 0.000
#> Sample_366 2 0.000
#> Sample_367 2 0.000
#> Sample_370 2 0.000
#> Sample_371 3 0.751
#> Sample_374 2 0.000
#> Sample_375 2 0.000
#> Sample_376 2 0.000
#> Sample_378 2 0.000
#> Sample_379 2 0.000
#> Sample_380 2 0.000
#> Sample_382 2 0.000
#> Sample_383 2 0.000
#> Sample_384 2 0.000
#> Sample_387 2 0.000
#> Sample_388 2 0.000
#> Sample_391 2 0.000
#> Sample_392 2 0.000
#> Sample_399 2 0.000
#> Sample_400 2 0.000
#> Sample_401 2 0.000
#> Sample_402 2 0.000
#> Sample_406 2 0.000
#> Sample_407 2 0.000
#> Sample_410 2 0.000
#> Sample_411 2 0.000
#> Sample_413 2 0.000
#> Sample_414 2 0.000
#> Sample_415 3 0.000
#> Sample_417 2 0.000
#> Sample_418 2 0.000
#> Sample_419 2 0.000
#> Sample_420 3 0.000
#> Sample_422 3 0.000
#> Sample_423 2 0.000
#> Sample_426 2 0.000
#> Sample_429 2 0.249
#> Sample_435 2 0.000
#> Sample_436 3 0.000
#> Sample_437 3 0.000
#> Sample_441 2 0.000
#> Sample_444 2 0.000
#> Sample_447 2 0.000
#> Sample_449 2 0.000
#> Sample_450 2 0.000
#> Sample_453 2 0.000
#> Sample_454 2 0.000
#> Sample_458 2 0.000
#> Sample_460 2 0.000
#> Sample_462 2 0.000
#> Sample_467 2 0.000
#> Sample_470 2 0.000
#> Sample_471 2 0.000
#> Sample_473 2 0.000
#> Sample_478 1 0.000
#> Sample_480 1 0.000
#> Sample_481 1 0.000
#> Sample_482 1 0.000
#> Sample_483 1 0.000
#> Sample_484 1 0.000
#> Sample_485 1 0.000
#> Sample_488 3 0.000
#> Sample_489 1 0.000
#> Sample_491 1 0.000
#> Sample_492 1 0.000
#> Sample_493 1 0.000
#> Sample_494 1 0.000
#> Sample_496 1 0.000
#> Sample_497 1 0.000
#> Sample_498 1 0.000
#> Sample_501 1 0.000
#> Sample_502 1 0.000
#> Sample_503 1 0.000
#> Sample_504 1 0.000
#> Sample_508 1 0.000
#> Sample_509 1 0.000
#> Sample_510 1 0.000
#> Sample_512 3 0.000
#> Sample_514 1 0.000
#> Sample_518 1 0.000
#> Sample_519 1 0.000
#> Sample_522 1 0.000
#> Sample_523 1 0.000
#> Sample_524 1 0.000
#> Sample_525 1 0.000
#> Sample_526 1 0.000
#> Sample_528 1 0.000
#> Sample_529 3 0.000
#> Sample_531 3 0.000
#> Sample_534 1 0.000
#> Sample_538 1 0.000
#> Sample_540 1 0.000
#> Sample_541 1 0.000
#> Sample_545 3 0.000
#> Sample_548 1 0.000
#> Sample_549 1 0.000
#> Sample_550 1 0.000
#> Sample_551 1 0.000
#> Sample_552 1 0.000
#> Sample_553 3 0.000
#> Sample_554 1 0.000
#> Sample_555 1 0.000
#> Sample_556 1 0.000
#> Sample_560 1 0.000
#> Sample_561 1 0.000
#> Sample_562 1 0.000
#> Sample_563 1 0.000
#> Sample_564 1 0.000
#> Sample_568 1 0.000
#> Sample_570 2 0.000
#> Sample_571 2 0.000
#> Sample_574 3 0.249
#> Sample_575 2 0.000
#> Sample_576 2 0.000
#> Sample_578 2 0.000
#> Sample_579 2 0.000
#> Sample_580 2 0.000
#> Sample_581 2 0.000
#> Sample_582 2 0.000
#> Sample_583 2 0.000
#> Sample_584 2 0.000
#> Sample_585 2 0.000
#> Sample_589 2 0.000
#> Sample_613 2 0.000
#> Sample_615 2 0.000
#> Sample_623 2 0.000
#> Sample_624 2 0.000
#> Sample_644 2 0.000
#> Sample_948 2 0.000
#> Sample_949 2 0.000
#> Sample_951 2 0.000
#> Sample_952 2 0.000
#> Sample_953 2 0.000
#> Sample_954 2 0.000
#> Sample_955 2 0.000
#> Sample_956 2 0.000
#> Sample_957 2 0.000
#> Sample_958 2 0.000
#> Sample_959 2 0.000
#> Sample_960 2 0.000
#> Sample_961 2 0.000
#> Sample_963 2 0.000
#> Sample_964 2 0.000
#> Sample_965 2 0.000
#> Sample_966 2 0.000
#> Sample_967 2 0.000
#> Sample_969 2 0.000
#> Sample_970 2 1.000
#> Sample_971 2 0.000
#> Sample_972 2 0.000
#> Sample_973 2 0.000
#> Sample_974 2 0.000
#> Sample_975 3 0.000
#> Sample_979 2 0.000
#> Sample_984 2 0.000
#> Sample_995 2 0.000
#> Sample_997 2 0.000
#> Sample_998 2 0.000
#> Sample_1000 2 0.000
#> Sample_1001 2 0.000
#> Sample_1002 2 0.000
#> Sample_1003 2 0.000
#> Sample_1004 3 0.000
#> Sample_1005 2 0.000
#> Sample_1006 2 0.000
#> Sample_1011 2 0.000
#> Sample_1012 2 0.000
#> Sample_1013 2 0.000
#> Sample_1029 1 0.000
#> Sample_1031 3 0.000
#> Sample_1032 1 0.000
#> Sample_1033 1 0.000
#> Sample_1035 1 0.000
#> Sample_1037 1 0.000
#> Sample_1038 1 0.000
#> Sample_1040 1 0.000
#> Sample_1043 1 0.000
#> Sample_1044 1 0.000
#> Sample_1045 3 0.000
#> Sample_1047 1 0.000
#> Sample_1048 3 0.000
#> Sample_1051 1 0.000
#> Sample_1053 2 0.000
#> Sample_1054 2 0.000
#> Sample_1055 2 0.000
#> Sample_1057 2 0.000
#> Sample_1060 2 0.000
#> Sample_1061 2 0.000
#> Sample_1063 1 0.000
#> Sample_1064 1 0.000
#> Sample_1065 1 0.000
#> Sample_1067 1 0.000
#> Sample_1068 1 0.000
#> Sample_1069 1 0.000
#> Sample_1071 1 0.000
#> Sample_1072 1 0.000
#> Sample_1074 1 0.000
#> Sample_1077 1 0.000
#> Sample_1078 1 0.000
#> Sample_1079 1 0.000
#> Sample_1081 1 0.000
#> Sample_1082 1 0.000
#> Sample_1084 1 0.000
#> Sample_1085 1 0.000
#> Sample_1086 3 0.000
#> Sample_1088 3 0.000
#> Sample_1089 3 0.000
#> Sample_1092 1 0.000
#> Sample_1094 1 0.000
#> Sample_1095 1 0.000
#> Sample_1096 1 0.000
#> Sample_1098 1 0.000
#> Sample_1099 1 0.000
#> Sample_1101 1 0.000
#> Sample_1106 1 0.000
#> Sample_1107 1 0.000
#> Sample_1109 1 0.000
#> Sample_1111 1 0.000
#> Sample_1115 1 0.000
#> Sample_1117 1 0.000
#> Sample_1120 3 0.000
#> Sample_1122 3 0.249
#> Sample_1123 1 0.000
#> Sample_1124 1 0.000
#> Sample_1125 1 0.000
#> Sample_1126 1 0.000
#> Sample_1127 1 0.000
#> Sample_1128 1 0.000
#> Sample_1132 3 0.000
#> Sample_1134 3 0.000
#> Sample_1139 1 0.000
#> Sample_1140 3 0.000
#> Sample_1141 1 0.000
#> Sample_1145 1 0.000
#> Sample_1150 1 0.000
#> Sample_1152 1 0.000
#> Sample_1156 1 0.000
#> Sample_1157 1 0.000
#> Sample_1161 1 0.000
#> Sample_1163 1 0.000
#> Sample_1164 1 0.000
#> Sample_1169 1 0.000
#> Sample_1170 1 0.000
#> Sample_1175 1 0.000
#> Sample_1180 1 0.000
#> Sample_1183 3 0.000
#> Sample_1184 3 0.000
#> Sample_1189 2 0.000
#> Sample_1190 2 0.000
#> Sample_1191 2 0.000
#> Sample_1193 2 0.000
#> Sample_1194 2 0.000
#> Sample_1195 2 0.000
#> Sample_1196 2 0.000
#> Sample_1197 2 0.000
#> Sample_1198 2 0.000
#> Sample_1199 2 0.000
#> Sample_1200 2 0.000
#> Sample_1201 2 0.000
#> Sample_1202 2 0.000
#> Sample_1203 2 0.000
#> Sample_1204 2 0.000
#> Sample_1205 2 0.000
#> Sample_1206 2 0.000
#> Sample_1207 2 0.000
#> Sample_1209 2 0.000
#> Sample_1210 2 0.000
#> Sample_1211 2 0.000
#> Sample_1212 2 0.000
#> Sample_1213 2 0.000
#> Sample_1214 2 0.000
#> Sample_1215 2 0.000
#> Sample_1219 2 0.000
#> Sample_1220 2 0.000
#> Sample_1221 2 0.000
#> Sample_1223 1 0.000
#> Sample_1224 1 0.000
#> Sample_1226 1 0.000
#> Sample_1230 1 0.000
#> Sample_1231 1 0.000
#> Sample_1232 1 0.000
#> Sample_1233 1 0.000
#> Sample_1235 1 0.000
#> Sample_1237 1 0.000
#> Sample_1238 1 0.000
#> Sample_1243 1 0.000
#> Sample_1244 1 0.000
#> Sample_1245 1 0.000
#> Sample_1246 1 0.000
#> Sample_1247 1 0.000
#> Sample_1248 1 0.000
#> Sample_1250 1 0.000
#> Sample_1251 1 0.000
#> Sample_1252 1 0.000
#> Sample_1253 1 0.000
#> Sample_1255 3 0.000
#> Sample_1256 1 0.000
#> Sample_1257 1 0.000
#> Sample_1258 1 0.000
#> Sample_1259 1 0.000
#> Sample_1260 1 0.000
#> Sample_1261 1 0.000
#> Sample_1262 1 0.000
#> Sample_1263 1 0.000
#> Sample_1265 1 0.000
#> Sample_1268 3 0.000
#> Sample_1269 1 0.000
#> Sample_1270 1 0.000
#> Sample_1271 1 0.000
#> Sample_1273 1 0.000
#> Sample_1274 1 0.000
#> Sample_1275 1 0.000
#> Sample_1276 1 0.000
#> Sample_1277 1 0.000
#> Sample_1278 1 0.000
#> Sample_1281 1 0.000
#> Sample_1282 1 0.000
#> Sample_1284 1 0.000
#> Sample_1286 1 0.000
#> Sample_1287 1 0.000
#> Sample_1288 1 0.000
#> Sample_1289 1 0.000
#> Sample_1290 1 0.000
#> Sample_1291 1 0.000
#> Sample_1292 1 0.000
#> Sample_1293 1 0.000
#> Sample_1294 1 0.000
#> Sample_1295 1 0.000
#> Sample_1296 3 0.000
#> Sample_1297 1 0.000
#> Sample_1298 1 0.000
#> Sample_1300 1 0.000
#> Sample_1301 1 0.000
#> Sample_1302 1 0.000
#> Sample_1303 1 0.000
#> Sample_1304 1 0.000
#> Sample_1305 1 0.000
#> Sample_1306 3 0.000
#> Sample_1310 3 0.000
#> Sample_1311 1 0.000
#> Sample_1312 1 0.000
#> Sample_1314 1 0.000
#> Sample_1315 1 0.000
#> Sample_1316 1 0.000
#> Sample_1318 1 0.000
#> Sample_1319 1 0.000
#> Sample_1321 1 0.000
#> Sample_1322 1 0.000
#> Sample_1323 1 0.000
#> Sample_1324 1 0.000
#> Sample_1325 1 0.000
#> Sample_1326 1 0.000
#> Sample_1327 1 0.000
#> Sample_1328 1 0.000
#> Sample_1329 1 0.000
#> Sample_1330 1 0.000
#> Sample_1331 1 0.000
#> Sample_1332 1 0.000
#> Sample_1333 1 0.000
#> Sample_1334 1 0.000
#> Sample_1337 3 0.000
#> Sample_1339 3 0.000
#> Sample_1340 1 0.000
#> Sample_1341 1 0.000
#> Sample_1344 1 0.000
#> Sample_1345 3 0.000
#> Sample_1346 1 0.000
#> Sample_1347 1 0.000
#> Sample_1349 1 0.000
#> Sample_1351 1 0.000
#> Sample_1353 1 0.000
#> Sample_1355 1 0.000
#> Sample_1356 1 0.000
#> Sample_1359 1 0.000
#> Sample_1361 1 0.000
#> Sample_1362 1 0.000
#> Sample_1363 1 0.000
#> Sample_1365 1 0.000
#> Sample_1366 1 0.000
#> Sample_1368 1 0.000
#> Sample_1369 1 0.000
#> Sample_1370 1 0.000
#> Sample_1372 1 0.000
#> Sample_1376 3 0.000
#> Sample_1377 1 0.000
#> Sample_1378 1 0.000
#> Sample_1379 1 0.000
#> Sample_1380 1 0.000
#> Sample_1381 1 0.000
#> Sample_1383 1 0.000
#> Sample_1384 1 0.000
#> Sample_1385 1 0.000
#> Sample_1387 1 0.000
#> Sample_1389 1 0.000
#> Sample_1390 1 0.000
#> Sample_1391 1 0.000
#> Sample_1392 1 0.000
#> Sample_1393 1 0.000
#> Sample_1394 1 0.000
#> Sample_1395 1 0.000
#> Sample_1396 1 0.000
#> Sample_1397 1 0.000
#> Sample_1400 3 0.000
#> Sample_1401 1 0.000
#> Sample_1403 1 0.000
#> Sample_1404 1 0.000
#> Sample_1405 1 0.000
#> Sample_1407 1 0.000
#> Sample_1410 1 0.000
#> Sample_1411 1 0.000
#> Sample_1412 1 0.000
#> Sample_1413 3 0.000
#> Sample_1414 1 0.000
#> Sample_1415 1 0.000
#> Sample_1416 3 0.000
#> Sample_1417 1 0.000
#> Sample_1418 1 0.000
#> Sample_1420 1 0.000
#> Sample_1421 1 0.000
#> Sample_1422 1 0.000
#> Sample_1424 1 0.000
#> Sample_1428 1 0.000
#> Sample_1442 1 0.000
#> Sample_1443 1 0.000
#> Sample_1446 1 0.000
#> Sample_1447 1 0.000
#> Sample_1456 1 0.000
#> Sample_1457 1 0.000
#> Sample_1460 3 0.000
#> Sample_1461 1 0.000
#> Sample_1463 1 0.000
#> Sample_1464 1 0.000
#> Sample_1468 1 0.000
#> Sample_1480 1 0.000
#> Sample_1485 1 0.000
#> Sample_1486 3 0.000
#> Sample_1488 1 0.000
#> Sample_1494 3 0.000
#> Sample_1502 1 0.000
#> Sample_1503 1 0.000
#> Sample_1504 3 0.000
#> Sample_1505 3 0.000
#> Sample_1506 1 0.000
#> Sample_1507 1 0.000
#> Sample_1508 1 0.000
#> Sample_1511 1 0.000
#> Sample_1512 1 0.249
#> Sample_1513 1 0.000
#> Sample_1515 1 0.000
#> Sample_1549 2 0.000
#> Sample_1553 2 0.000
#> Sample_1569 1 0.000
#> Sample_1581 2 0.000
#> Sample_1597 2 0.000
#> Sample_1598 1 0.000
get_classes(res, k = 4)
#> class p
#> Sample_208 2 0.000
#> Sample_209 2 0.000
#> Sample_210 2 0.000
#> Sample_211 2 0.000
#> Sample_212 2 0.000
#> Sample_213 2 0.000
#> Sample_216 2 0.000
#> Sample_218 2 0.000
#> Sample_219 2 0.000
#> Sample_220 2 0.000
#> Sample_221 2 0.000
#> Sample_222 2 0.000
#> Sample_223 2 0.000
#> Sample_225 2 0.000
#> Sample_227 2 0.000
#> Sample_228 2 0.000
#> Sample_229 2 0.000
#> Sample_230 2 0.000
#> Sample_231 2 0.000
#> Sample_232 2 0.000
#> Sample_233 3 0.000
#> Sample_234 2 0.000
#> Sample_235 2 0.000
#> Sample_237 2 0.000
#> Sample_238 2 0.000
#> Sample_239 2 0.000
#> Sample_240 2 0.000
#> Sample_242 2 0.000
#> Sample_243 2 0.000
#> Sample_244 2 0.000
#> Sample_245 2 0.000
#> Sample_246 2 0.000
#> Sample_247 2 0.000
#> Sample_248 2 0.000
#> Sample_249 2 0.000
#> Sample_250 2 0.000
#> Sample_251 2 0.000
#> Sample_252 2 0.000
#> Sample_253 2 0.000
#> Sample_254 3 0.000
#> Sample_255 2 0.000
#> Sample_256 2 0.000
#> Sample_257 2 0.000
#> Sample_259 2 0.000
#> Sample_260 2 0.000
#> Sample_261 2 0.000
#> Sample_262 2 0.000
#> Sample_263 2 0.000
#> Sample_266 2 0.000
#> Sample_267 2 0.000
#> Sample_268 4 0.249
#> Sample_269 4 0.000
#> Sample_270 4 1.000
#> Sample_271 3 0.000
#> Sample_273 4 0.751
#> Sample_274 4 0.000
#> Sample_277 2 0.000
#> Sample_278 3 0.000
#> Sample_279 2 0.000
#> Sample_280 2 0.000
#> Sample_281 2 0.000
#> Sample_282 2 0.000
#> Sample_283 2 0.000
#> Sample_284 2 0.000
#> Sample_285 2 0.000
#> Sample_286 2 0.000
#> Sample_288 2 0.000
#> Sample_289 3 0.000
#> Sample_290 2 0.000
#> Sample_291 2 0.000
#> Sample_292 2 0.000
#> Sample_293 2 0.000
#> Sample_294 2 0.000
#> Sample_295 2 0.000
#> Sample_296 2 0.000
#> Sample_297 2 0.000
#> Sample_298 2 0.000
#> Sample_299 2 0.249
#> Sample_300 2 0.000
#> Sample_301 2 0.000
#> Sample_306 4 0.249
#> Sample_307 4 0.000
#> Sample_315 4 0.000
#> Sample_317 4 0.000
#> Sample_322 4 0.000
#> Sample_331 3 0.000
#> Sample_332 2 0.747
#> Sample_333 4 0.747
#> Sample_334 2 1.000
#> Sample_335 2 1.000
#> Sample_336 2 1.000
#> Sample_337 2 0.000
#> Sample_338 4 0.751
#> Sample_339 2 0.000
#> Sample_340 4 0.000
#> Sample_341 4 0.000
#> Sample_342 4 0.000
#> Sample_343 4 0.502
#> Sample_344 4 1.000
#> Sample_345 4 0.000
#> Sample_346 4 1.000
#> Sample_347 4 0.000
#> Sample_348 4 0.000
#> Sample_349 4 0.000
#> Sample_353 4 0.000
#> Sample_354 2 0.000
#> Sample_356 2 0.000
#> Sample_357 2 0.253
#> Sample_359 2 0.000
#> Sample_361 2 0.751
#> Sample_363 2 0.249
#> Sample_364 2 0.249
#> Sample_366 2 0.000
#> Sample_367 2 0.000
#> Sample_370 2 0.249
#> Sample_371 3 0.000
#> Sample_374 2 0.253
#> Sample_375 4 0.000
#> Sample_376 4 0.000
#> Sample_378 4 0.000
#> Sample_379 4 0.000
#> Sample_380 4 0.000
#> Sample_382 4 0.000
#> Sample_383 4 0.249
#> Sample_384 4 0.000
#> Sample_387 4 0.000
#> Sample_388 4 0.751
#> Sample_391 4 0.000
#> Sample_392 4 0.000
#> Sample_399 4 0.000
#> Sample_400 4 0.000
#> Sample_401 4 0.000
#> Sample_402 4 0.000
#> Sample_406 4 0.000
#> Sample_407 4 0.000
#> Sample_410 4 0.249
#> Sample_411 4 0.000
#> Sample_413 4 0.000
#> Sample_414 4 0.000
#> Sample_415 3 0.000
#> Sample_417 4 0.000
#> Sample_418 4 0.000
#> Sample_419 4 0.000
#> Sample_420 3 0.000
#> Sample_422 3 0.000
#> Sample_423 4 0.000
#> Sample_426 4 0.000
#> Sample_429 4 0.000
#> Sample_435 4 0.000
#> Sample_436 3 0.000
#> Sample_437 3 0.000
#> Sample_441 4 0.000
#> Sample_444 4 0.000
#> Sample_447 4 0.000
#> Sample_449 4 0.000
#> Sample_450 4 0.000
#> Sample_453 4 0.000
#> Sample_454 4 0.000
#> Sample_458 4 0.000
#> Sample_460 4 0.000
#> Sample_462 4 0.000
#> Sample_467 4 0.000
#> Sample_470 4 0.000
#> Sample_471 4 0.000
#> Sample_473 4 0.000
#> Sample_478 1 0.000
#> Sample_480 1 0.000
#> Sample_481 1 0.000
#> Sample_482 1 0.000
#> Sample_483 1 0.000
#> Sample_484 1 0.000
#> Sample_485 1 0.000
#> Sample_488 3 0.000
#> Sample_489 1 0.000
#> Sample_491 1 0.000
#> Sample_492 1 0.000
#> Sample_493 1 0.000
#> Sample_494 1 0.000
#> Sample_496 1 0.000
#> Sample_497 1 0.000
#> Sample_498 1 0.000
#> Sample_501 1 0.000
#> Sample_502 1 0.000
#> Sample_503 1 0.000
#> Sample_504 1 0.000
#> Sample_508 1 0.000
#> Sample_509 1 0.000
#> Sample_510 1 0.000
#> Sample_512 3 0.000
#> Sample_514 1 0.000
#> Sample_518 1 0.000
#> Sample_519 1 0.000
#> Sample_522 1 0.000
#> Sample_523 1 0.000
#> Sample_524 1 0.000
#> Sample_525 1 0.000
#> Sample_526 1 0.000
#> Sample_528 1 0.000
#> Sample_529 3 0.000
#> Sample_531 3 0.000
#> Sample_534 1 0.000
#> Sample_538 1 0.000
#> Sample_540 1 0.000
#> Sample_541 1 0.000
#> Sample_545 3 0.000
#> Sample_548 1 0.000
#> Sample_549 1 0.000
#> Sample_550 1 0.000
#> Sample_551 1 0.000
#> Sample_552 1 0.000
#> Sample_553 3 0.000
#> Sample_554 1 0.000
#> Sample_555 1 0.000
#> Sample_556 1 0.000
#> Sample_560 1 0.000
#> Sample_561 1 0.000
#> Sample_562 1 0.000
#> Sample_563 1 0.000
#> Sample_564 1 0.000
#> Sample_568 1 0.000
#> Sample_570 4 0.000
#> Sample_571 4 0.000
#> Sample_574 3 0.000
#> Sample_575 4 0.000
#> Sample_576 4 0.000
#> Sample_578 2 0.751
#> Sample_579 2 0.498
#> Sample_580 2 0.000
#> Sample_581 4 0.498
#> Sample_582 2 0.000
#> Sample_583 2 0.249
#> Sample_584 2 0.751
#> Sample_585 2 0.000
#> Sample_589 2 0.000
#> Sample_613 2 0.000
#> Sample_615 2 0.000
#> Sample_623 2 0.747
#> Sample_624 2 0.000
#> Sample_644 4 0.000
#> Sample_948 4 0.000
#> Sample_949 4 0.000
#> Sample_951 4 0.249
#> Sample_952 4 0.000
#> Sample_953 4 0.000
#> Sample_954 4 0.000
#> Sample_955 4 0.000
#> Sample_956 4 0.000
#> Sample_957 4 0.751
#> Sample_958 4 0.000
#> Sample_959 4 0.249
#> Sample_960 4 0.249
#> Sample_961 4 0.502
#> Sample_963 4 0.000
#> Sample_964 4 0.000
#> Sample_965 4 0.000
#> Sample_966 4 0.000
#> Sample_967 4 1.000
#> Sample_969 4 0.000
#> Sample_970 4 1.000
#> Sample_971 4 0.498
#> Sample_972 4 0.751
#> Sample_973 4 0.000
#> Sample_974 4 0.000
#> Sample_975 3 0.000
#> Sample_979 4 0.249
#> Sample_984 4 0.000
#> Sample_995 2 0.249
#> Sample_997 2 0.751
#> Sample_998 2 0.000
#> Sample_1000 4 0.000
#> Sample_1001 2 0.000
#> Sample_1002 2 0.000
#> Sample_1003 2 0.000
#> Sample_1004 3 0.000
#> Sample_1005 2 0.498
#> Sample_1006 2 1.000
#> Sample_1011 2 0.000
#> Sample_1012 2 0.249
#> Sample_1013 2 1.000
#> Sample_1029 1 0.000
#> Sample_1031 3 0.000
#> Sample_1032 1 0.000
#> Sample_1033 1 0.000
#> Sample_1035 1 0.000
#> Sample_1037 1 0.000
#> Sample_1038 1 0.000
#> Sample_1040 1 0.000
#> Sample_1043 1 0.000
#> Sample_1044 1 0.000
#> Sample_1045 3 0.000
#> Sample_1047 1 0.000
#> Sample_1048 3 0.000
#> Sample_1051 1 0.000
#> Sample_1053 2 0.000
#> Sample_1054 2 0.000
#> Sample_1055 2 0.249
#> Sample_1057 2 0.000
#> Sample_1060 2 0.498
#> Sample_1061 2 0.249
#> Sample_1063 1 0.000
#> Sample_1064 1 0.000
#> Sample_1065 1 0.000
#> Sample_1067 1 0.000
#> Sample_1068 1 0.000
#> Sample_1069 1 0.000
#> Sample_1071 1 0.000
#> Sample_1072 1 0.000
#> Sample_1074 1 0.000
#> Sample_1077 1 0.000
#> Sample_1078 1 0.000
#> Sample_1079 1 0.000
#> Sample_1081 1 0.000
#> Sample_1082 1 0.000
#> Sample_1084 1 0.000
#> Sample_1085 1 0.000
#> Sample_1086 3 0.000
#> Sample_1088 3 0.000
#> Sample_1089 3 0.000
#> Sample_1092 1 0.000
#> Sample_1094 1 0.000
#> Sample_1095 1 0.000
#> Sample_1096 1 0.000
#> Sample_1098 1 0.000
#> Sample_1099 1 0.000
#> Sample_1101 1 0.000
#> Sample_1106 1 0.000
#> Sample_1107 1 0.000
#> Sample_1109 1 0.000
#> Sample_1111 1 0.000
#> Sample_1115 1 0.000
#> Sample_1117 1 0.000
#> Sample_1120 3 0.000
#> Sample_1122 3 0.249
#> Sample_1123 1 0.000
#> Sample_1124 1 0.000
#> Sample_1125 1 0.000
#> Sample_1126 1 0.000
#> Sample_1127 1 0.000
#> Sample_1128 1 0.000
#> Sample_1132 3 0.000
#> Sample_1134 3 0.000
#> Sample_1139 1 0.000
#> Sample_1140 3 0.000
#> Sample_1141 1 0.000
#> Sample_1145 1 0.000
#> Sample_1150 1 0.000
#> Sample_1152 1 0.000
#> Sample_1156 1 0.000
#> Sample_1157 1 0.000
#> Sample_1161 1 0.000
#> Sample_1163 1 0.000
#> Sample_1164 1 0.000
#> Sample_1169 1 0.000
#> Sample_1170 1 0.000
#> Sample_1175 1 0.000
#> Sample_1180 1 0.000
#> Sample_1183 3 0.000
#> Sample_1184 3 0.000
#> Sample_1189 2 0.000
#> Sample_1190 2 0.000
#> Sample_1191 2 0.000
#> Sample_1193 2 0.253
#> Sample_1194 2 0.253
#> Sample_1195 2 0.000
#> Sample_1196 2 0.000
#> Sample_1197 2 0.249
#> Sample_1198 2 0.249
#> Sample_1199 4 0.000
#> Sample_1200 4 0.000
#> Sample_1201 4 0.000
#> Sample_1202 4 0.000
#> Sample_1203 4 0.000
#> Sample_1204 4 0.000
#> Sample_1205 4 0.000
#> Sample_1206 4 0.000
#> Sample_1207 4 0.000
#> Sample_1209 4 0.000
#> Sample_1210 4 0.000
#> Sample_1211 4 0.000
#> Sample_1212 4 0.000
#> Sample_1213 4 0.249
#> Sample_1214 4 0.000
#> Sample_1215 4 0.000
#> Sample_1219 4 0.000
#> Sample_1220 4 0.000
#> Sample_1221 4 0.000
#> Sample_1223 1 0.000
#> Sample_1224 1 0.000
#> Sample_1226 1 0.000
#> Sample_1230 1 0.000
#> Sample_1231 1 0.000
#> Sample_1232 1 0.000
#> Sample_1233 1 0.000
#> Sample_1235 1 0.000
#> Sample_1237 1 0.000
#> Sample_1238 1 0.000
#> Sample_1243 1 0.000
#> Sample_1244 1 0.000
#> Sample_1245 1 0.000
#> Sample_1246 1 0.000
#> Sample_1247 1 0.000
#> Sample_1248 1 0.000
#> Sample_1250 1 0.000
#> Sample_1251 1 0.000
#> Sample_1252 1 0.000
#> Sample_1253 1 0.000
#> Sample_1255 3 0.000
#> Sample_1256 1 0.000
#> Sample_1257 1 0.000
#> Sample_1258 1 0.000
#> Sample_1259 1 0.000
#> Sample_1260 1 0.000
#> Sample_1261 1 0.000
#> Sample_1262 1 0.000
#> Sample_1263 1 0.000
#> Sample_1265 1 0.000
#> Sample_1268 3 0.000
#> Sample_1269 1 0.000
#> Sample_1270 1 0.000
#> Sample_1271 1 0.000
#> Sample_1273 1 0.000
#> Sample_1274 1 0.000
#> Sample_1275 1 0.000
#> Sample_1276 1 0.000
#> Sample_1277 1 0.000
#> Sample_1278 1 0.000
#> Sample_1281 1 0.000
#> Sample_1282 1 0.000
#> Sample_1284 1 0.000
#> Sample_1286 1 0.000
#> Sample_1287 1 0.000
#> Sample_1288 1 0.000
#> Sample_1289 1 0.000
#> Sample_1290 1 0.000
#> Sample_1291 1 0.000
#> Sample_1292 1 0.000
#> Sample_1293 1 0.000
#> Sample_1294 1 0.000
#> Sample_1295 1 0.000
#> Sample_1296 3 0.000
#> Sample_1297 1 0.000
#> Sample_1298 1 0.000
#> Sample_1300 1 0.000
#> Sample_1301 1 0.000
#> Sample_1302 1 0.000
#> Sample_1303 1 0.000
#> Sample_1304 1 0.000
#> Sample_1305 1 0.000
#> Sample_1306 3 0.000
#> Sample_1310 3 0.000
#> Sample_1311 1 0.000
#> Sample_1312 1 0.000
#> Sample_1314 1 0.000
#> Sample_1315 1 0.000
#> Sample_1316 1 0.000
#> Sample_1318 1 0.000
#> Sample_1319 1 0.000
#> Sample_1321 1 0.000
#> Sample_1322 1 0.000
#> Sample_1323 1 0.000
#> Sample_1324 1 0.000
#> Sample_1325 1 0.000
#> Sample_1326 1 0.000
#> Sample_1327 1 0.000
#> Sample_1328 1 0.000
#> Sample_1329 1 0.000
#> Sample_1330 1 0.000
#> Sample_1331 1 0.000
#> Sample_1332 1 0.000
#> Sample_1333 1 0.000
#> Sample_1334 1 0.000
#> Sample_1337 3 0.000
#> Sample_1339 3 0.000
#> Sample_1340 1 0.000
#> Sample_1341 1 0.000
#> Sample_1344 1 0.000
#> Sample_1345 3 0.000
#> Sample_1346 1 0.000
#> Sample_1347 1 0.000
#> Sample_1349 1 0.000
#> Sample_1351 1 0.000
#> Sample_1353 1 0.000
#> Sample_1355 1 0.000
#> Sample_1356 1 0.000
#> Sample_1359 1 0.000
#> Sample_1361 1 0.000
#> Sample_1362 1 0.000
#> Sample_1363 1 0.000
#> Sample_1365 1 0.000
#> Sample_1366 1 0.000
#> Sample_1368 1 0.000
#> Sample_1369 1 0.000
#> Sample_1370 1 0.000
#> Sample_1372 1 0.000
#> Sample_1376 3 0.000
#> Sample_1377 1 0.000
#> Sample_1378 1 0.000
#> Sample_1379 1 0.000
#> Sample_1380 1 0.000
#> Sample_1381 1 0.000
#> Sample_1383 1 0.000
#> Sample_1384 1 0.000
#> Sample_1385 1 0.000
#> Sample_1387 1 0.000
#> Sample_1389 1 0.000
#> Sample_1390 1 0.000
#> Sample_1391 1 0.000
#> Sample_1392 1 0.000
#> Sample_1393 1 0.000
#> Sample_1394 1 0.000
#> Sample_1395 1 0.000
#> Sample_1396 1 0.000
#> Sample_1397 1 0.000
#> Sample_1400 3 0.000
#> Sample_1401 1 0.000
#> Sample_1403 1 0.000
#> Sample_1404 1 0.000
#> Sample_1405 1 0.000
#> Sample_1407 1 0.000
#> Sample_1410 1 0.000
#> Sample_1411 1 0.000
#> Sample_1412 1 0.000
#> Sample_1413 3 0.000
#> Sample_1414 1 0.000
#> Sample_1415 1 0.000
#> Sample_1416 3 0.000
#> Sample_1417 1 0.000
#> Sample_1418 1 0.000
#> Sample_1420 1 0.000
#> Sample_1421 1 0.000
#> Sample_1422 1 0.000
#> Sample_1424 1 0.000
#> Sample_1428 1 0.000
#> Sample_1442 1 0.000
#> Sample_1443 1 0.000
#> Sample_1446 1 0.000
#> Sample_1447 1 0.000
#> Sample_1456 1 0.000
#> Sample_1457 1 0.000
#> Sample_1460 3 0.000
#> Sample_1461 1 0.000
#> Sample_1463 1 0.000
#> Sample_1464 1 0.000
#> Sample_1468 1 0.000
#> Sample_1480 1 0.000
#> Sample_1485 1 0.000
#> Sample_1486 3 0.000
#> Sample_1488 1 0.000
#> Sample_1494 3 0.000
#> Sample_1502 1 0.000
#> Sample_1503 1 0.000
#> Sample_1504 3 0.000
#> Sample_1505 3 0.000
#> Sample_1506 1 0.000
#> Sample_1507 1 0.000
#> Sample_1508 1 0.000
#> Sample_1511 1 0.000
#> Sample_1512 1 0.000
#> Sample_1513 1 0.000
#> Sample_1515 1 0.000
#> Sample_1549 2 1.000
#> Sample_1553 4 1.000
#> Sample_1569 1 0.000
#> Sample_1581 2 0.000
#> Sample_1597 4 0.000
#> Sample_1598 1 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
As soon as the classes for columns are determined, the signatures that are significantly different between subgroups can be looked for. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. To get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows (which is done by automatically selecting number of clusters).If there are too many signatures, top_signatures = ... can be set to only show the
signatures with the highest FDRs:
# code only for demonstration
# e.g. to show the top 500 most significant rows
tb = get_signature(res, k = ..., top_signatures = 500)
If the signatures are defined as these which are uniquely high in current group, diff_method argument
can be set to "uniquely_high_in_one_group":
# code only for demonstration
tb = get_signature(res, k = ..., diff_method = "uniquely_high_in_one_group")
UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n_sample age(p-value) cell.type(p-value) k
#> ATC:skmeans 562 7.16e-18 6.54e-03 2
#> ATC:skmeans 554 4.03e-14 1.54e-115 3
#> ATC:skmeans 510 1.81e-19 4.89e-98 4
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
Parent node: Node03. Child nodes: Node0111-leaf , Node0112-leaf , Node0121-leaf , Node0122-leaf , Node0131-leaf , Node0132-leaf , Node0211 , Node0212 , Node0221 , Node0222 , Node0231-leaf , Node0232-leaf , Node0233-leaf , Node0311-leaf , Node0312-leaf , Node0313-leaf , Node0314-leaf , Node0321 , Node0322 , Node0331-leaf , Node0332-leaf , Node0333-leaf .
The object with results only for a single top-value method and a single partitioning method can be extracted as:
res = res_rh["031"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4.
#> On a matrix with 14733 rows and 268 columns.
#> Top rows (696) are extracted by 'ATC' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 150 partitions by row resampling.
#> Best k for subgroups seems to be 4.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_partitions"
#> [7] "compare_signatures" "consensus_heatmap" "dimension_reduction"
#> [10] "functional_enrichment" "get_anno_col" "get_anno"
#> [13] "get_classes" "get_consensus" "get_matrix"
#> [16] "get_membership" "get_param" "get_signatures"
#> [19] "get_stats" "is_best_k" "is_stable_k"
#> [22] "membership_heatmap" "ncol" "nrow"
#> [25] "plot_ecdf" "predict_classes" "rownames"
#> [28] "select_partition_number" "show" "suggest_best_k"
#> [31] "test_to_known_factors" "top_rows_heatmap"
collect_plots() function collects all the plots made from res for all k (number of subgroups)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, higher 1-PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.794 0.869 0.946 0.496 0.498 0.498
#> 3 3 0.743 0.864 0.929 0.321 0.799 0.616
#> 4 4 0.967 0.939 0.974 0.143 0.835 0.568
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 4
Following is the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall subgroup
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> Sample_478 2 0.000 0.9585 0.00 1.00
#> Sample_480 2 0.000 0.9585 0.00 1.00
#> Sample_481 2 0.000 0.9585 0.00 1.00
#> Sample_482 2 0.000 0.9585 0.00 1.00
#> Sample_483 2 0.000 0.9585 0.00 1.00
#> Sample_484 2 0.000 0.9585 0.00 1.00
#> Sample_485 2 0.000 0.9585 0.00 1.00
#> Sample_489 2 0.000 0.9585 0.00 1.00
#> Sample_491 2 0.000 0.9585 0.00 1.00
#> Sample_492 2 0.000 0.9585 0.00 1.00
#> Sample_493 2 0.000 0.9585 0.00 1.00
#> Sample_494 2 0.000 0.9585 0.00 1.00
#> Sample_496 2 0.000 0.9585 0.00 1.00
#> Sample_497 2 0.000 0.9585 0.00 1.00
#> Sample_498 2 0.000 0.9585 0.00 1.00
#> Sample_501 2 0.000 0.9585 0.00 1.00
#> Sample_502 2 0.000 0.9585 0.00 1.00
#> Sample_503 2 0.000 0.9585 0.00 1.00
#> Sample_504 2 0.000 0.9585 0.00 1.00
#> Sample_508 2 0.000 0.9585 0.00 1.00
#> Sample_509 2 0.000 0.9585 0.00 1.00
#> Sample_510 2 0.000 0.9585 0.00 1.00
#> Sample_514 2 0.000 0.9585 0.00 1.00
#> Sample_518 2 0.000 0.9585 0.00 1.00
#> Sample_519 2 0.000 0.9585 0.00 1.00
#> Sample_522 2 0.000 0.9585 0.00 1.00
#> Sample_523 2 0.000 0.9585 0.00 1.00
#> Sample_524 2 0.000 0.9585 0.00 1.00
#> Sample_525 2 0.000 0.9585 0.00 1.00
#> Sample_526 2 0.000 0.9585 0.00 1.00
#> Sample_528 2 0.000 0.9585 0.00 1.00
#> Sample_534 2 0.000 0.9585 0.00 1.00
#> Sample_538 2 0.000 0.9585 0.00 1.00
#> Sample_540 2 0.000 0.9585 0.00 1.00
#> Sample_541 2 0.000 0.9585 0.00 1.00
#> Sample_548 2 0.000 0.9585 0.00 1.00
#> Sample_549 2 0.000 0.9585 0.00 1.00
#> Sample_550 2 0.000 0.9585 0.00 1.00
#> Sample_551 2 0.000 0.9585 0.00 1.00
#> Sample_552 2 0.000 0.9585 0.00 1.00
#> Sample_554 2 0.000 0.9585 0.00 1.00
#> Sample_555 2 0.000 0.9585 0.00 1.00
#> Sample_556 2 0.000 0.9585 0.00 1.00
#> Sample_560 2 0.000 0.9585 0.00 1.00
#> Sample_561 2 0.000 0.9585 0.00 1.00
#> Sample_562 2 0.000 0.9585 0.00 1.00
#> Sample_563 2 0.000 0.9585 0.00 1.00
#> Sample_564 2 0.000 0.9585 0.00 1.00
#> Sample_568 2 0.000 0.9585 0.00 1.00
#> Sample_1029 2 0.000 0.9585 0.00 1.00
#> Sample_1032 2 0.000 0.9585 0.00 1.00
#> Sample_1033 2 0.000 0.9585 0.00 1.00
#> Sample_1035 2 0.000 0.9585 0.00 1.00
#> Sample_1037 2 0.000 0.9585 0.00 1.00
#> Sample_1038 2 0.141 0.9412 0.02 0.98
#> Sample_1040 2 0.000 0.9585 0.00 1.00
#> Sample_1043 2 0.000 0.9585 0.00 1.00
#> Sample_1044 2 0.000 0.9585 0.00 1.00
#> Sample_1047 2 0.000 0.9585 0.00 1.00
#> Sample_1051 2 0.000 0.9585 0.00 1.00
#> Sample_1063 2 0.000 0.9585 0.00 1.00
#> Sample_1064 2 0.000 0.9585 0.00 1.00
#> Sample_1065 2 0.000 0.9585 0.00 1.00
#> Sample_1067 2 0.000 0.9585 0.00 1.00
#> Sample_1068 2 0.000 0.9585 0.00 1.00
#> Sample_1069 2 0.000 0.9585 0.00 1.00
#> Sample_1071 2 0.000 0.9585 0.00 1.00
#> Sample_1072 2 0.141 0.9412 0.02 0.98
#> Sample_1074 2 0.000 0.9585 0.00 1.00
#> Sample_1077 2 0.000 0.9585 0.00 1.00
#> Sample_1078 2 0.000 0.9585 0.00 1.00
#> Sample_1079 2 0.000 0.9585 0.00 1.00
#> Sample_1081 2 0.000 0.9585 0.00 1.00
#> Sample_1082 2 0.000 0.9585 0.00 1.00
#> Sample_1084 2 0.000 0.9585 0.00 1.00
#> Sample_1085 2 0.000 0.9585 0.00 1.00
#> Sample_1092 2 0.000 0.9585 0.00 1.00
#> Sample_1094 2 0.000 0.9585 0.00 1.00
#> Sample_1095 2 0.000 0.9585 0.00 1.00
#> Sample_1096 2 0.760 0.7003 0.22 0.78
#> Sample_1098 2 0.000 0.9585 0.00 1.00
#> Sample_1099 2 0.000 0.9585 0.00 1.00
#> Sample_1101 2 0.000 0.9585 0.00 1.00
#> Sample_1106 2 0.000 0.9585 0.00 1.00
#> Sample_1107 2 0.000 0.9585 0.00 1.00
#> Sample_1109 2 0.000 0.9585 0.00 1.00
#> Sample_1111 2 0.141 0.9415 0.02 0.98
#> Sample_1115 2 0.000 0.9585 0.00 1.00
#> Sample_1117 2 0.000 0.9585 0.00 1.00
#> Sample_1123 2 0.584 0.8132 0.14 0.86
#> Sample_1124 2 0.000 0.9585 0.00 1.00
#> Sample_1125 2 0.000 0.9585 0.00 1.00
#> Sample_1126 2 0.000 0.9585 0.00 1.00
#> Sample_1127 2 0.242 0.9237 0.04 0.96
#> Sample_1128 2 0.000 0.9585 0.00 1.00
#> Sample_1139 2 0.000 0.9585 0.00 1.00
#> Sample_1141 2 0.242 0.9253 0.04 0.96
#> Sample_1145 2 0.000 0.9585 0.00 1.00
#> Sample_1150 2 0.000 0.9585 0.00 1.00
#> Sample_1152 2 0.000 0.9585 0.00 1.00
#> Sample_1156 2 0.000 0.9585 0.00 1.00
#> Sample_1157 2 0.000 0.9585 0.00 1.00
#> Sample_1161 2 0.000 0.9585 0.00 1.00
#> Sample_1163 2 0.000 0.9585 0.00 1.00
#> Sample_1164 2 0.000 0.9585 0.00 1.00
#> Sample_1169 2 0.000 0.9585 0.00 1.00
#> Sample_1170 2 0.000 0.9585 0.00 1.00
#> Sample_1175 2 0.000 0.9585 0.00 1.00
#> Sample_1180 2 0.000 0.9585 0.00 1.00
#> Sample_1223 1 0.000 0.9206 1.00 0.00
#> Sample_1224 1 0.000 0.9206 1.00 0.00
#> Sample_1226 1 0.000 0.9206 1.00 0.00
#> Sample_1230 1 0.000 0.9206 1.00 0.00
#> Sample_1231 1 0.000 0.9206 1.00 0.00
#> Sample_1232 1 0.000 0.9206 1.00 0.00
#> Sample_1233 1 0.000 0.9206 1.00 0.00
#> Sample_1235 1 0.000 0.9206 1.00 0.00
#> Sample_1237 1 0.000 0.9206 1.00 0.00
#> Sample_1238 1 0.000 0.9206 1.00 0.00
#> Sample_1243 1 0.000 0.9206 1.00 0.00
#> Sample_1244 1 0.000 0.9206 1.00 0.00
#> Sample_1245 1 0.000 0.9206 1.00 0.00
#> Sample_1246 1 0.000 0.9206 1.00 0.00
#> Sample_1247 1 0.000 0.9206 1.00 0.00
#> Sample_1248 1 0.000 0.9206 1.00 0.00
#> Sample_1250 1 0.000 0.9206 1.00 0.00
#> Sample_1251 1 0.000 0.9206 1.00 0.00
#> Sample_1252 1 0.000 0.9206 1.00 0.00
#> Sample_1253 1 0.000 0.9206 1.00 0.00
#> Sample_1256 1 0.000 0.9206 1.00 0.00
#> Sample_1257 1 0.000 0.9206 1.00 0.00
#> Sample_1258 1 0.000 0.9206 1.00 0.00
#> Sample_1259 1 0.000 0.9206 1.00 0.00
#> Sample_1260 1 0.000 0.9206 1.00 0.00
#> Sample_1261 1 0.000 0.9206 1.00 0.00
#> Sample_1262 1 0.000 0.9206 1.00 0.00
#> Sample_1263 1 0.000 0.9206 1.00 0.00
#> Sample_1265 1 0.000 0.9206 1.00 0.00
#> Sample_1269 1 0.000 0.9206 1.00 0.00
#> Sample_1270 1 0.000 0.9206 1.00 0.00
#> Sample_1271 1 0.000 0.9206 1.00 0.00
#> Sample_1273 1 0.000 0.9206 1.00 0.00
#> Sample_1274 1 0.000 0.9206 1.00 0.00
#> Sample_1275 1 0.000 0.9206 1.00 0.00
#> Sample_1276 1 0.000 0.9206 1.00 0.00
#> Sample_1277 1 0.000 0.9206 1.00 0.00
#> Sample_1278 1 0.000 0.9206 1.00 0.00
#> Sample_1281 1 0.000 0.9206 1.00 0.00
#> Sample_1282 1 0.000 0.9206 1.00 0.00
#> Sample_1284 1 0.000 0.9206 1.00 0.00
#> Sample_1286 1 0.000 0.9206 1.00 0.00
#> Sample_1287 1 0.000 0.9206 1.00 0.00
#> Sample_1288 1 0.000 0.9206 1.00 0.00
#> Sample_1289 1 0.000 0.9206 1.00 0.00
#> Sample_1290 1 0.000 0.9206 1.00 0.00
#> Sample_1291 1 0.000 0.9206 1.00 0.00
#> Sample_1292 1 0.000 0.9206 1.00 0.00
#> Sample_1293 1 0.000 0.9206 1.00 0.00
#> Sample_1294 1 0.000 0.9206 1.00 0.00
#> Sample_1295 1 0.000 0.9206 1.00 0.00
#> Sample_1297 1 0.000 0.9206 1.00 0.00
#> Sample_1298 1 0.000 0.9206 1.00 0.00
#> Sample_1300 1 0.000 0.9206 1.00 0.00
#> Sample_1301 1 0.000 0.9206 1.00 0.00
#> Sample_1302 1 0.000 0.9206 1.00 0.00
#> Sample_1303 1 0.000 0.9206 1.00 0.00
#> Sample_1304 1 0.000 0.9206 1.00 0.00
#> Sample_1305 1 0.000 0.9206 1.00 0.00
#> Sample_1311 1 0.000 0.9206 1.00 0.00
#> Sample_1312 1 0.000 0.9206 1.00 0.00
#> Sample_1314 1 0.000 0.9206 1.00 0.00
#> Sample_1315 1 0.000 0.9206 1.00 0.00
#> Sample_1316 1 0.000 0.9206 1.00 0.00
#> Sample_1318 1 0.000 0.9206 1.00 0.00
#> Sample_1319 1 0.000 0.9206 1.00 0.00
#> Sample_1321 1 0.327 0.8810 0.94 0.06
#> Sample_1322 1 0.000 0.9206 1.00 0.00
#> Sample_1323 1 0.000 0.9206 1.00 0.00
#> Sample_1324 1 0.000 0.9206 1.00 0.00
#> Sample_1325 1 0.000 0.9206 1.00 0.00
#> Sample_1326 1 0.141 0.9086 0.98 0.02
#> Sample_1327 1 0.000 0.9206 1.00 0.00
#> Sample_1328 1 0.584 0.8161 0.86 0.14
#> Sample_1329 2 0.971 0.3102 0.40 0.60
#> Sample_1330 1 0.242 0.8966 0.96 0.04
#> Sample_1331 1 0.000 0.9206 1.00 0.00
#> Sample_1332 1 0.000 0.9206 1.00 0.00
#> Sample_1333 1 0.141 0.9092 0.98 0.02
#> Sample_1334 1 0.000 0.9206 1.00 0.00
#> Sample_1340 1 0.000 0.9206 1.00 0.00
#> Sample_1341 1 0.141 0.9092 0.98 0.02
#> Sample_1344 1 0.242 0.8965 0.96 0.04
#> Sample_1346 1 0.000 0.9206 1.00 0.00
#> Sample_1347 1 0.000 0.9206 1.00 0.00
#> Sample_1349 1 0.327 0.8851 0.94 0.06
#> Sample_1351 1 0.242 0.8966 0.96 0.04
#> Sample_1353 1 0.000 0.9206 1.00 0.00
#> Sample_1355 1 0.402 0.8646 0.92 0.08
#> Sample_1356 1 0.000 0.9206 1.00 0.00
#> Sample_1359 2 0.000 0.9585 0.00 1.00
#> Sample_1361 1 0.242 0.8965 0.96 0.04
#> Sample_1362 1 0.141 0.9092 0.98 0.02
#> Sample_1363 1 0.000 0.9206 1.00 0.00
#> Sample_1365 1 0.000 0.9206 1.00 0.00
#> Sample_1366 1 0.000 0.9206 1.00 0.00
#> Sample_1368 1 0.141 0.9092 0.98 0.02
#> Sample_1369 1 0.000 0.9206 1.00 0.00
#> Sample_1370 2 0.971 0.3111 0.40 0.60
#> Sample_1372 1 0.141 0.9092 0.98 0.02
#> Sample_1377 1 0.000 0.9206 1.00 0.00
#> Sample_1378 1 0.000 0.9206 1.00 0.00
#> Sample_1379 1 0.000 0.9206 1.00 0.00
#> Sample_1380 1 0.000 0.9206 1.00 0.00
#> Sample_1381 1 0.242 0.8966 0.96 0.04
#> Sample_1383 1 0.943 0.5077 0.64 0.36
#> Sample_1384 1 0.925 0.5445 0.66 0.34
#> Sample_1385 2 1.000 -0.1235 0.50 0.50
#> Sample_1387 1 0.943 0.5077 0.64 0.36
#> Sample_1389 1 0.943 0.5077 0.64 0.36
#> Sample_1390 1 0.943 0.5077 0.64 0.36
#> Sample_1391 1 0.943 0.5077 0.64 0.36
#> Sample_1392 2 0.000 0.9585 0.00 1.00
#> Sample_1393 1 0.925 0.5445 0.66 0.34
#> Sample_1394 1 0.680 0.7715 0.82 0.18
#> Sample_1395 1 0.855 0.6419 0.72 0.28
#> Sample_1396 1 0.943 0.5077 0.64 0.36
#> Sample_1397 1 0.943 0.5077 0.64 0.36
#> Sample_1401 1 0.529 0.8325 0.88 0.12
#> Sample_1403 1 0.958 0.4638 0.62 0.38
#> Sample_1404 2 0.141 0.9416 0.02 0.98
#> Sample_1405 1 0.242 0.8968 0.96 0.04
#> Sample_1407 1 0.943 0.5077 0.64 0.36
#> Sample_1410 2 0.327 0.9017 0.06 0.94
#> Sample_1411 2 1.000 -0.1147 0.50 0.50
#> Sample_1412 1 0.971 0.4165 0.60 0.40
#> Sample_1414 2 0.958 0.3155 0.38 0.62
#> Sample_1415 2 0.990 0.1149 0.44 0.56
#> Sample_1417 1 0.943 0.5077 0.64 0.36
#> Sample_1418 2 0.000 0.9585 0.00 1.00
#> Sample_1420 1 0.529 0.8320 0.88 0.12
#> Sample_1421 1 0.981 0.3631 0.58 0.42
#> Sample_1422 1 0.971 0.4160 0.60 0.40
#> Sample_1424 1 0.000 0.9206 1.00 0.00
#> Sample_1428 1 0.943 0.5077 0.64 0.36
#> Sample_1442 2 0.141 0.9416 0.02 0.98
#> Sample_1443 2 0.141 0.9416 0.02 0.98
#> Sample_1446 1 0.990 0.3066 0.56 0.44
#> Sample_1447 2 0.981 0.1861 0.42 0.58
#> Sample_1456 2 0.000 0.9585 0.00 1.00
#> Sample_1457 2 0.634 0.7760 0.16 0.84
#> Sample_1461 1 0.943 0.5077 0.64 0.36
#> Sample_1463 2 0.000 0.9585 0.00 1.00
#> Sample_1464 2 0.000 0.9585 0.00 1.00
#> Sample_1468 2 0.000 0.9585 0.00 1.00
#> Sample_1480 2 0.000 0.9585 0.00 1.00
#> Sample_1485 2 0.242 0.9226 0.04 0.96
#> Sample_1488 2 0.402 0.8800 0.08 0.92
#> Sample_1502 1 0.760 0.7244 0.78 0.22
#> Sample_1503 1 0.634 0.7931 0.84 0.16
#> Sample_1506 1 0.634 0.7933 0.84 0.16
#> Sample_1507 2 0.999 -0.0358 0.48 0.52
#> Sample_1508 1 0.958 0.4638 0.62 0.38
#> Sample_1511 1 0.943 0.5077 0.64 0.36
#> Sample_1512 1 0.795 0.6985 0.76 0.24
#> Sample_1513 2 0.827 0.6060 0.26 0.74
#> Sample_1515 2 0.855 0.5631 0.28 0.72
#> Sample_1569 2 0.000 0.9585 0.00 1.00
#> Sample_1598 1 0.000 0.9206 1.00 0.00
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> Sample_478 2 0.0000 0.916 0.00 1.00 0.00
#> Sample_480 2 0.0000 0.916 0.00 1.00 0.00
#> Sample_481 3 0.3686 0.819 0.00 0.14 0.86
#> Sample_482 2 0.0000 0.916 0.00 1.00 0.00
#> Sample_483 2 0.0000 0.916 0.00 1.00 0.00
#> Sample_484 2 0.4291 0.802 0.00 0.82 0.18
#> Sample_485 2 0.0000 0.916 0.00 1.00 0.00
#> Sample_489 2 0.0000 0.916 0.00 1.00 0.00
#> Sample_491 2 0.1529 0.898 0.04 0.96 0.00
#> Sample_492 2 0.0000 0.916 0.00 1.00 0.00
#> Sample_493 2 0.0000 0.916 0.00 1.00 0.00
#> Sample_494 2 0.0000 0.916 0.00 1.00 0.00
#> Sample_496 2 0.0000 0.916 0.00 1.00 0.00
#> Sample_497 2 0.0000 0.916 0.00 1.00 0.00
#> Sample_498 2 0.0000 0.916 0.00 1.00 0.00
#> Sample_501 2 0.0000 0.916 0.00 1.00 0.00
#> Sample_502 2 0.0000 0.916 0.00 1.00 0.00
#> Sample_503 2 0.0000 0.916 0.00 1.00 0.00
#> Sample_504 2 0.0000 0.916 0.00 1.00 0.00
#> Sample_508 2 0.0000 0.916 0.00 1.00 0.00
#> Sample_509 2 0.0000 0.916 0.00 1.00 0.00
#> Sample_510 2 0.0000 0.916 0.00 1.00 0.00
#> Sample_514 2 0.0000 0.916 0.00 1.00 0.00
#> Sample_518 2 0.0000 0.916 0.00 1.00 0.00
#> Sample_519 2 0.0000 0.916 0.00 1.00 0.00
#> Sample_522 2 0.0000 0.916 0.00 1.00 0.00
#> Sample_523 2 0.0000 0.916 0.00 1.00 0.00
#> Sample_524 2 0.0000 0.916 0.00 1.00 0.00
#> Sample_525 2 0.0000 0.916 0.00 1.00 0.00
#> Sample_526 2 0.0000 0.916 0.00 1.00 0.00
#> Sample_528 2 0.4291 0.856 0.00 0.82 0.18
#> Sample_534 2 0.0000 0.916 0.00 1.00 0.00
#> Sample_538 2 0.0000 0.916 0.00 1.00 0.00
#> Sample_540 2 0.0000 0.916 0.00 1.00 0.00
#> Sample_541 2 0.0000 0.916 0.00 1.00 0.00
#> Sample_548 2 0.0000 0.916 0.00 1.00 0.00
#> Sample_549 2 0.0000 0.916 0.00 1.00 0.00
#> Sample_550 2 0.0000 0.916 0.00 1.00 0.00
#> Sample_551 2 0.0000 0.916 0.00 1.00 0.00
#> Sample_552 2 0.0000 0.916 0.00 1.00 0.00
#> Sample_554 2 0.0000 0.916 0.00 1.00 0.00
#> Sample_555 2 0.0000 0.916 0.00 1.00 0.00
#> Sample_556 2 0.0000 0.916 0.00 1.00 0.00
#> Sample_560 2 0.0000 0.916 0.00 1.00 0.00
#> Sample_561 2 0.0000 0.916 0.00 1.00 0.00
#> Sample_562 2 0.0000 0.916 0.00 1.00 0.00
#> Sample_563 2 0.0000 0.916 0.00 1.00 0.00
#> Sample_564 2 0.0000 0.916 0.00 1.00 0.00
#> Sample_568 2 0.0000 0.916 0.00 1.00 0.00
#> Sample_1029 2 0.1529 0.912 0.00 0.96 0.04
#> Sample_1032 2 0.3340 0.888 0.00 0.88 0.12
#> Sample_1033 2 0.4291 0.856 0.00 0.82 0.18
#> Sample_1035 2 0.3686 0.879 0.00 0.86 0.14
#> Sample_1037 2 0.4291 0.856 0.00 0.82 0.18
#> Sample_1038 2 0.4291 0.856 0.00 0.82 0.18
#> Sample_1040 2 0.1529 0.912 0.00 0.96 0.04
#> Sample_1043 2 0.3340 0.887 0.00 0.88 0.12
#> Sample_1044 2 0.3686 0.879 0.00 0.86 0.14
#> Sample_1047 2 0.4291 0.856 0.00 0.82 0.18
#> Sample_1051 2 0.4291 0.856 0.00 0.82 0.18
#> Sample_1063 2 0.1529 0.912 0.00 0.96 0.04
#> Sample_1064 2 0.1529 0.912 0.00 0.96 0.04
#> Sample_1065 2 0.4291 0.856 0.00 0.82 0.18
#> Sample_1067 2 0.4291 0.856 0.00 0.82 0.18
#> Sample_1068 2 0.4291 0.856 0.00 0.82 0.18
#> Sample_1069 2 0.4291 0.856 0.00 0.82 0.18
#> Sample_1071 2 0.4796 0.816 0.00 0.78 0.22
#> Sample_1072 2 0.4291 0.856 0.00 0.82 0.18
#> Sample_1074 2 0.4291 0.856 0.00 0.82 0.18
#> Sample_1077 2 0.4291 0.856 0.00 0.82 0.18
#> Sample_1078 2 0.4291 0.856 0.00 0.82 0.18
#> Sample_1079 2 0.2066 0.907 0.00 0.94 0.06
#> Sample_1081 2 0.4002 0.868 0.00 0.84 0.16
#> Sample_1082 2 0.0000 0.916 0.00 1.00 0.00
#> Sample_1084 2 0.0000 0.916 0.00 1.00 0.00
#> Sample_1085 2 0.3686 0.879 0.00 0.86 0.14
#> Sample_1092 2 0.4291 0.856 0.00 0.82 0.18
#> Sample_1094 2 0.2066 0.907 0.00 0.94 0.06
#> Sample_1095 2 0.3686 0.879 0.00 0.86 0.14
#> Sample_1096 2 0.8437 0.617 0.20 0.62 0.18
#> Sample_1098 2 0.2537 0.902 0.00 0.92 0.08
#> Sample_1099 2 0.4291 0.856 0.00 0.82 0.18
#> Sample_1101 2 0.2066 0.907 0.00 0.94 0.06
#> Sample_1106 2 0.3686 0.879 0.00 0.86 0.14
#> Sample_1107 2 0.4291 0.856 0.00 0.82 0.18
#> Sample_1109 2 0.4291 0.856 0.00 0.82 0.18
#> Sample_1111 2 0.5016 0.792 0.00 0.76 0.24
#> Sample_1115 2 0.4291 0.856 0.00 0.82 0.18
#> Sample_1117 3 0.6192 0.083 0.00 0.42 0.58
#> Sample_1123 2 0.4291 0.856 0.00 0.82 0.18
#> Sample_1124 2 0.4291 0.856 0.00 0.82 0.18
#> Sample_1125 2 0.4291 0.856 0.00 0.82 0.18
#> Sample_1126 2 0.3686 0.879 0.00 0.86 0.14
#> Sample_1127 3 0.4291 0.711 0.00 0.18 0.82
#> Sample_1128 2 0.4002 0.869 0.00 0.84 0.16
#> Sample_1139 2 0.4002 0.869 0.00 0.84 0.16
#> Sample_1141 2 0.4002 0.869 0.00 0.84 0.16
#> Sample_1145 2 0.1529 0.912 0.00 0.96 0.04
#> Sample_1150 2 0.3686 0.879 0.00 0.86 0.14
#> Sample_1152 2 0.0892 0.914 0.00 0.98 0.02
#> Sample_1156 2 0.0000 0.916 0.00 1.00 0.00
#> Sample_1157 2 0.1529 0.912 0.00 0.96 0.04
#> Sample_1161 2 0.0000 0.916 0.00 1.00 0.00
#> Sample_1163 2 0.0892 0.914 0.00 0.98 0.02
#> Sample_1164 2 0.4291 0.856 0.00 0.82 0.18
#> Sample_1169 2 0.1529 0.912 0.00 0.96 0.04
#> Sample_1170 2 0.0000 0.916 0.00 1.00 0.00
#> Sample_1175 2 0.0000 0.916 0.00 1.00 0.00
#> Sample_1180 2 0.4291 0.856 0.00 0.82 0.18
#> Sample_1223 1 0.0000 0.932 1.00 0.00 0.00
#> Sample_1224 1 0.0000 0.932 1.00 0.00 0.00
#> Sample_1226 1 0.0000 0.932 1.00 0.00 0.00
#> Sample_1230 1 0.0000 0.932 1.00 0.00 0.00
#> Sample_1231 1 0.0000 0.932 1.00 0.00 0.00
#> Sample_1232 1 0.0000 0.932 1.00 0.00 0.00
#> Sample_1233 1 0.0000 0.932 1.00 0.00 0.00
#> Sample_1235 1 0.0000 0.932 1.00 0.00 0.00
#> Sample_1237 1 0.0000 0.932 1.00 0.00 0.00
#> Sample_1238 1 0.0000 0.932 1.00 0.00 0.00
#> Sample_1243 1 0.0000 0.932 1.00 0.00 0.00
#> Sample_1244 1 0.0000 0.932 1.00 0.00 0.00
#> Sample_1245 1 0.0000 0.932 1.00 0.00 0.00
#> Sample_1246 1 0.0000 0.932 1.00 0.00 0.00
#> Sample_1247 1 0.0000 0.932 1.00 0.00 0.00
#> Sample_1248 1 0.0000 0.932 1.00 0.00 0.00
#> Sample_1250 1 0.0000 0.932 1.00 0.00 0.00
#> Sample_1251 1 0.0000 0.932 1.00 0.00 0.00
#> Sample_1252 1 0.0000 0.932 1.00 0.00 0.00
#> Sample_1253 1 0.0000 0.932 1.00 0.00 0.00
#> Sample_1256 1 0.0000 0.932 1.00 0.00 0.00
#> Sample_1257 1 0.0000 0.932 1.00 0.00 0.00
#> Sample_1258 1 0.0000 0.932 1.00 0.00 0.00
#> Sample_1259 1 0.0000 0.932 1.00 0.00 0.00
#> Sample_1260 1 0.0000 0.932 1.00 0.00 0.00
#> Sample_1261 1 0.0000 0.932 1.00 0.00 0.00
#> Sample_1262 1 0.0000 0.932 1.00 0.00 0.00
#> Sample_1263 1 0.0000 0.932 1.00 0.00 0.00
#> Sample_1265 1 0.0000 0.932 1.00 0.00 0.00
#> Sample_1269 1 0.0000 0.932 1.00 0.00 0.00
#> Sample_1270 1 0.0000 0.932 1.00 0.00 0.00
#> Sample_1271 1 0.0000 0.932 1.00 0.00 0.00
#> Sample_1273 1 0.0000 0.932 1.00 0.00 0.00
#> Sample_1274 1 0.0000 0.932 1.00 0.00 0.00
#> Sample_1275 1 0.0000 0.932 1.00 0.00 0.00
#> Sample_1276 1 0.0000 0.932 1.00 0.00 0.00
#> Sample_1277 1 0.0000 0.932 1.00 0.00 0.00
#> Sample_1278 1 0.0000 0.932 1.00 0.00 0.00
#> Sample_1281 1 0.0000 0.932 1.00 0.00 0.00
#> Sample_1282 1 0.0000 0.932 1.00 0.00 0.00
#> Sample_1284 1 0.0000 0.932 1.00 0.00 0.00
#> Sample_1286 1 0.0000 0.932 1.00 0.00 0.00
#> Sample_1287 1 0.0000 0.932 1.00 0.00 0.00
#> Sample_1288 1 0.0000 0.932 1.00 0.00 0.00
#> Sample_1289 1 0.0000 0.932 1.00 0.00 0.00
#> Sample_1290 1 0.0000 0.932 1.00 0.00 0.00
#> Sample_1291 1 0.0000 0.932 1.00 0.00 0.00
#> Sample_1292 1 0.0000 0.932 1.00 0.00 0.00
#> Sample_1293 1 0.0000 0.932 1.00 0.00 0.00
#> Sample_1294 1 0.0000 0.932 1.00 0.00 0.00
#> Sample_1295 1 0.0000 0.932 1.00 0.00 0.00
#> Sample_1297 1 0.0000 0.932 1.00 0.00 0.00
#> Sample_1298 1 0.0000 0.932 1.00 0.00 0.00
#> Sample_1300 1 0.0000 0.932 1.00 0.00 0.00
#> Sample_1301 1 0.0000 0.932 1.00 0.00 0.00
#> Sample_1302 1 0.0000 0.932 1.00 0.00 0.00
#> Sample_1303 1 0.0000 0.932 1.00 0.00 0.00
#> Sample_1304 1 0.0000 0.932 1.00 0.00 0.00
#> Sample_1305 1 0.0000 0.932 1.00 0.00 0.00
#> Sample_1311 1 0.0000 0.932 1.00 0.00 0.00
#> Sample_1312 1 0.0000 0.932 1.00 0.00 0.00
#> Sample_1314 3 0.6192 0.241 0.42 0.00 0.58
#> Sample_1315 3 0.7310 0.359 0.36 0.04 0.60
#> Sample_1316 1 0.5560 0.587 0.70 0.00 0.30
#> Sample_1318 3 0.5216 0.625 0.26 0.00 0.74
#> Sample_1319 1 0.4291 0.769 0.82 0.00 0.18
#> Sample_1321 1 0.7975 0.613 0.66 0.18 0.16
#> Sample_1322 1 0.0000 0.932 1.00 0.00 0.00
#> Sample_1323 1 0.2066 0.887 0.94 0.00 0.06
#> Sample_1324 1 0.5016 0.687 0.76 0.00 0.24
#> Sample_1325 1 0.0000 0.932 1.00 0.00 0.00
#> Sample_1326 1 0.2537 0.871 0.92 0.08 0.00
#> Sample_1327 1 0.0000 0.932 1.00 0.00 0.00
#> Sample_1328 3 0.4209 0.817 0.02 0.12 0.86
#> Sample_1329 3 0.9003 0.489 0.20 0.24 0.56
#> Sample_1330 1 0.4002 0.795 0.84 0.16 0.00
#> Sample_1331 1 0.6192 0.306 0.58 0.00 0.42
#> Sample_1332 1 0.7884 0.554 0.64 0.10 0.26
#> Sample_1333 1 0.9083 0.331 0.52 0.16 0.32
#> Sample_1334 1 0.6280 0.178 0.54 0.00 0.46
#> Sample_1340 1 0.0000 0.932 1.00 0.00 0.00
#> Sample_1341 1 0.7932 0.608 0.66 0.14 0.20
#> Sample_1344 1 0.8733 0.468 0.58 0.16 0.26
#> Sample_1346 1 0.0000 0.932 1.00 0.00 0.00
#> Sample_1347 1 0.4555 0.740 0.80 0.00 0.20
#> Sample_1349 3 0.8576 0.487 0.24 0.16 0.60
#> Sample_1351 1 0.5970 0.753 0.78 0.16 0.06
#> Sample_1353 1 0.0000 0.932 1.00 0.00 0.00
#> Sample_1355 1 0.9267 0.155 0.46 0.16 0.38
#> Sample_1356 3 0.8619 0.071 0.42 0.10 0.48
#> Sample_1359 2 0.0892 0.906 0.00 0.98 0.02
#> Sample_1361 1 0.4035 0.850 0.88 0.08 0.04
#> Sample_1362 1 0.6407 0.734 0.76 0.16 0.08
#> Sample_1363 1 0.3686 0.816 0.86 0.14 0.00
#> Sample_1365 1 0.0000 0.932 1.00 0.00 0.00
#> Sample_1366 3 0.2947 0.868 0.02 0.06 0.92
#> Sample_1368 3 0.4862 0.777 0.02 0.16 0.82
#> Sample_1369 1 0.0000 0.932 1.00 0.00 0.00
#> Sample_1370 2 0.6244 0.103 0.44 0.56 0.00
#> Sample_1372 1 0.7975 0.610 0.66 0.16 0.18
#> Sample_1377 1 0.0000 0.932 1.00 0.00 0.00
#> Sample_1378 1 0.5397 0.623 0.72 0.00 0.28
#> Sample_1379 1 0.0892 0.917 0.98 0.02 0.00
#> Sample_1380 1 0.4449 0.831 0.86 0.04 0.10
#> Sample_1381 1 0.4291 0.775 0.82 0.18 0.00
#> Sample_1383 3 0.0000 0.927 0.00 0.00 1.00
#> Sample_1384 3 0.0000 0.927 0.00 0.00 1.00
#> Sample_1385 3 0.0000 0.927 0.00 0.00 1.00
#> Sample_1387 3 0.0000 0.927 0.00 0.00 1.00
#> Sample_1389 3 0.0000 0.927 0.00 0.00 1.00
#> Sample_1390 3 0.0000 0.927 0.00 0.00 1.00
#> Sample_1391 3 0.0000 0.927 0.00 0.00 1.00
#> Sample_1392 3 0.0892 0.912 0.00 0.02 0.98
#> Sample_1393 3 0.0000 0.927 0.00 0.00 1.00
#> Sample_1394 3 0.0000 0.927 0.00 0.00 1.00
#> Sample_1395 3 0.0000 0.927 0.00 0.00 1.00
#> Sample_1396 3 0.0000 0.927 0.00 0.00 1.00
#> Sample_1397 3 0.0000 0.927 0.00 0.00 1.00
#> Sample_1401 3 0.0000 0.927 0.00 0.00 1.00
#> Sample_1403 3 0.0000 0.927 0.00 0.00 1.00
#> Sample_1404 3 0.0000 0.927 0.00 0.00 1.00
#> Sample_1405 3 0.0000 0.927 0.00 0.00 1.00
#> Sample_1407 3 0.0000 0.927 0.00 0.00 1.00
#> Sample_1410 3 0.0000 0.927 0.00 0.00 1.00
#> Sample_1411 3 0.0000 0.927 0.00 0.00 1.00
#> Sample_1412 3 0.0000 0.927 0.00 0.00 1.00
#> Sample_1414 3 0.0000 0.927 0.00 0.00 1.00
#> Sample_1415 3 0.0000 0.927 0.00 0.00 1.00
#> Sample_1417 3 0.0000 0.927 0.00 0.00 1.00
#> Sample_1418 3 0.0000 0.927 0.00 0.00 1.00
#> Sample_1420 3 0.0000 0.927 0.00 0.00 1.00
#> Sample_1421 3 0.0000 0.927 0.00 0.00 1.00
#> Sample_1422 3 0.0000 0.927 0.00 0.00 1.00
#> Sample_1424 3 0.6192 0.263 0.42 0.00 0.58
#> Sample_1428 3 0.0000 0.927 0.00 0.00 1.00
#> Sample_1442 3 0.0000 0.927 0.00 0.00 1.00
#> Sample_1443 3 0.0000 0.927 0.00 0.00 1.00
#> Sample_1446 3 0.0000 0.927 0.00 0.00 1.00
#> Sample_1447 3 0.0000 0.927 0.00 0.00 1.00
#> Sample_1456 3 0.0000 0.927 0.00 0.00 1.00
#> Sample_1457 3 0.0000 0.927 0.00 0.00 1.00
#> Sample_1461 3 0.0000 0.927 0.00 0.00 1.00
#> Sample_1463 2 0.0000 0.916 0.00 1.00 0.00
#> Sample_1464 2 0.2959 0.846 0.00 0.90 0.10
#> Sample_1468 3 0.0892 0.912 0.00 0.02 0.98
#> Sample_1480 3 0.4555 0.753 0.00 0.20 0.80
#> Sample_1485 3 0.0000 0.927 0.00 0.00 1.00
#> Sample_1488 3 0.0000 0.927 0.00 0.00 1.00
#> Sample_1502 3 0.0000 0.927 0.00 0.00 1.00
#> Sample_1503 3 0.0000 0.927 0.00 0.00 1.00
#> Sample_1506 3 0.0000 0.927 0.00 0.00 1.00
#> Sample_1507 3 0.0000 0.927 0.00 0.00 1.00
#> Sample_1508 3 0.0000 0.927 0.00 0.00 1.00
#> Sample_1511 3 0.0000 0.927 0.00 0.00 1.00
#> Sample_1512 3 0.0000 0.927 0.00 0.00 1.00
#> Sample_1513 3 0.0000 0.927 0.00 0.00 1.00
#> Sample_1515 3 0.0000 0.927 0.00 0.00 1.00
#> Sample_1569 2 0.4002 0.869 0.00 0.84 0.16
#> Sample_1598 1 0.0000 0.932 1.00 0.00 0.00
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> Sample_478 4 0.1637 0.919 0.00 0.06 0.00 0.94
#> Sample_480 4 0.0000 0.966 0.00 0.00 0.00 1.00
#> Sample_481 3 0.3400 0.765 0.00 0.00 0.82 0.18
#> Sample_482 4 0.0000 0.966 0.00 0.00 0.00 1.00
#> Sample_483 4 0.0000 0.966 0.00 0.00 0.00 1.00
#> Sample_484 4 0.2011 0.899 0.00 0.00 0.08 0.92
#> Sample_485 4 0.0000 0.966 0.00 0.00 0.00 1.00
#> Sample_489 4 0.0000 0.966 0.00 0.00 0.00 1.00
#> Sample_491 4 0.0707 0.952 0.00 0.02 0.00 0.98
#> Sample_492 4 0.0000 0.966 0.00 0.00 0.00 1.00
#> Sample_493 4 0.0000 0.966 0.00 0.00 0.00 1.00
#> Sample_494 4 0.0000 0.966 0.00 0.00 0.00 1.00
#> Sample_496 4 0.0000 0.966 0.00 0.00 0.00 1.00
#> Sample_497 4 0.0000 0.966 0.00 0.00 0.00 1.00
#> Sample_498 4 0.0707 0.952 0.00 0.02 0.00 0.98
#> Sample_501 4 0.2921 0.832 0.00 0.14 0.00 0.86
#> Sample_502 4 0.0000 0.966 0.00 0.00 0.00 1.00
#> Sample_503 4 0.0000 0.966 0.00 0.00 0.00 1.00
#> Sample_504 4 0.0000 0.966 0.00 0.00 0.00 1.00
#> Sample_508 4 0.0000 0.966 0.00 0.00 0.00 1.00
#> Sample_509 4 0.0000 0.966 0.00 0.00 0.00 1.00
#> Sample_510 4 0.0000 0.966 0.00 0.00 0.00 1.00
#> Sample_514 4 0.1637 0.918 0.00 0.06 0.00 0.94
#> Sample_518 4 0.0000 0.966 0.00 0.00 0.00 1.00
#> Sample_519 4 0.0000 0.966 0.00 0.00 0.00 1.00
#> Sample_522 4 0.0000 0.966 0.00 0.00 0.00 1.00
#> Sample_523 4 0.0000 0.966 0.00 0.00 0.00 1.00
#> Sample_524 4 0.0000 0.966 0.00 0.00 0.00 1.00
#> Sample_525 4 0.0000 0.966 0.00 0.00 0.00 1.00
#> Sample_526 4 0.0000 0.966 0.00 0.00 0.00 1.00
#> Sample_528 2 0.0707 0.979 0.00 0.98 0.00 0.02
#> Sample_534 4 0.0000 0.966 0.00 0.00 0.00 1.00
#> Sample_538 4 0.0000 0.966 0.00 0.00 0.00 1.00
#> Sample_540 4 0.0000 0.966 0.00 0.00 0.00 1.00
#> Sample_541 4 0.0000 0.966 0.00 0.00 0.00 1.00
#> Sample_548 4 0.0000 0.966 0.00 0.00 0.00 1.00
#> Sample_549 4 0.0000 0.966 0.00 0.00 0.00 1.00
#> Sample_550 4 0.0000 0.966 0.00 0.00 0.00 1.00
#> Sample_551 4 0.0000 0.966 0.00 0.00 0.00 1.00
#> Sample_552 4 0.0000 0.966 0.00 0.00 0.00 1.00
#> Sample_554 4 0.0000 0.966 0.00 0.00 0.00 1.00
#> Sample_555 4 0.0000 0.966 0.00 0.00 0.00 1.00
#> Sample_556 4 0.0000 0.966 0.00 0.00 0.00 1.00
#> Sample_560 4 0.0000 0.966 0.00 0.00 0.00 1.00
#> Sample_561 4 0.0000 0.966 0.00 0.00 0.00 1.00
#> Sample_562 4 0.0000 0.966 0.00 0.00 0.00 1.00
#> Sample_563 4 0.0000 0.966 0.00 0.00 0.00 1.00
#> Sample_564 4 0.0000 0.966 0.00 0.00 0.00 1.00
#> Sample_568 4 0.0000 0.966 0.00 0.00 0.00 1.00
#> Sample_1029 2 0.0000 0.999 0.00 1.00 0.00 0.00
#> Sample_1032 2 0.0000 0.999 0.00 1.00 0.00 0.00
#> Sample_1033 2 0.0000 0.999 0.00 1.00 0.00 0.00
#> Sample_1035 2 0.0000 0.999 0.00 1.00 0.00 0.00
#> Sample_1037 2 0.0000 0.999 0.00 1.00 0.00 0.00
#> Sample_1038 2 0.0000 0.999 0.00 1.00 0.00 0.00
#> Sample_1040 2 0.0000 0.999 0.00 1.00 0.00 0.00
#> Sample_1043 2 0.0000 0.999 0.00 1.00 0.00 0.00
#> Sample_1044 2 0.0000 0.999 0.00 1.00 0.00 0.00
#> Sample_1047 2 0.0000 0.999 0.00 1.00 0.00 0.00
#> Sample_1051 2 0.0000 0.999 0.00 1.00 0.00 0.00
#> Sample_1063 2 0.0000 0.999 0.00 1.00 0.00 0.00
#> Sample_1064 2 0.0000 0.999 0.00 1.00 0.00 0.00
#> Sample_1065 2 0.0000 0.999 0.00 1.00 0.00 0.00
#> Sample_1067 2 0.0000 0.999 0.00 1.00 0.00 0.00
#> Sample_1068 2 0.0000 0.999 0.00 1.00 0.00 0.00
#> Sample_1069 2 0.0000 0.999 0.00 1.00 0.00 0.00
#> Sample_1071 2 0.0000 0.999 0.00 1.00 0.00 0.00
#> Sample_1072 2 0.0000 0.999 0.00 1.00 0.00 0.00
#> Sample_1074 2 0.0000 0.999 0.00 1.00 0.00 0.00
#> Sample_1077 2 0.0000 0.999 0.00 1.00 0.00 0.00
#> Sample_1078 2 0.0000 0.999 0.00 1.00 0.00 0.00
#> Sample_1079 2 0.0000 0.999 0.00 1.00 0.00 0.00
#> Sample_1081 2 0.0000 0.999 0.00 1.00 0.00 0.00
#> Sample_1082 2 0.0000 0.999 0.00 1.00 0.00 0.00
#> Sample_1084 2 0.0000 0.999 0.00 1.00 0.00 0.00
#> Sample_1085 2 0.0000 0.999 0.00 1.00 0.00 0.00
#> Sample_1092 2 0.0000 0.999 0.00 1.00 0.00 0.00
#> Sample_1094 2 0.0000 0.999 0.00 1.00 0.00 0.00
#> Sample_1095 2 0.0000 0.999 0.00 1.00 0.00 0.00
#> Sample_1096 2 0.0000 0.999 0.00 1.00 0.00 0.00
#> Sample_1098 2 0.0000 0.999 0.00 1.00 0.00 0.00
#> Sample_1099 2 0.0000 0.999 0.00 1.00 0.00 0.00
#> Sample_1101 2 0.0000 0.999 0.00 1.00 0.00 0.00
#> Sample_1106 2 0.0000 0.999 0.00 1.00 0.00 0.00
#> Sample_1107 2 0.0000 0.999 0.00 1.00 0.00 0.00
#> Sample_1109 2 0.0000 0.999 0.00 1.00 0.00 0.00
#> Sample_1111 2 0.0000 0.999 0.00 1.00 0.00 0.00
#> Sample_1115 2 0.0000 0.999 0.00 1.00 0.00 0.00
#> Sample_1117 2 0.0000 0.999 0.00 1.00 0.00 0.00
#> Sample_1123 2 0.0000 0.999 0.00 1.00 0.00 0.00
#> Sample_1124 2 0.0000 0.999 0.00 1.00 0.00 0.00
#> Sample_1125 2 0.0000 0.999 0.00 1.00 0.00 0.00
#> Sample_1126 2 0.0000 0.999 0.00 1.00 0.00 0.00
#> Sample_1127 2 0.0000 0.999 0.00 1.00 0.00 0.00
#> Sample_1128 2 0.0000 0.999 0.00 1.00 0.00 0.00
#> Sample_1139 2 0.0000 0.999 0.00 1.00 0.00 0.00
#> Sample_1141 2 0.0000 0.999 0.00 1.00 0.00 0.00
#> Sample_1145 2 0.0000 0.999 0.00 1.00 0.00 0.00
#> Sample_1150 2 0.0000 0.999 0.00 1.00 0.00 0.00
#> Sample_1152 2 0.0000 0.999 0.00 1.00 0.00 0.00
#> Sample_1156 2 0.0000 0.999 0.00 1.00 0.00 0.00
#> Sample_1157 2 0.0000 0.999 0.00 1.00 0.00 0.00
#> Sample_1161 2 0.0707 0.978 0.00 0.98 0.00 0.02
#> Sample_1163 2 0.0000 0.999 0.00 1.00 0.00 0.00
#> Sample_1164 2 0.0000 0.999 0.00 1.00 0.00 0.00
#> Sample_1169 2 0.0000 0.999 0.00 1.00 0.00 0.00
#> Sample_1170 2 0.0000 0.999 0.00 1.00 0.00 0.00
#> Sample_1175 2 0.0000 0.999 0.00 1.00 0.00 0.00
#> Sample_1180 2 0.0000 0.999 0.00 1.00 0.00 0.00
#> Sample_1223 1 0.0000 0.968 1.00 0.00 0.00 0.00
#> Sample_1224 1 0.0000 0.968 1.00 0.00 0.00 0.00
#> Sample_1226 1 0.0000 0.968 1.00 0.00 0.00 0.00
#> Sample_1230 1 0.0000 0.968 1.00 0.00 0.00 0.00
#> Sample_1231 1 0.0000 0.968 1.00 0.00 0.00 0.00
#> Sample_1232 1 0.0000 0.968 1.00 0.00 0.00 0.00
#> Sample_1233 1 0.0000 0.968 1.00 0.00 0.00 0.00
#> Sample_1235 1 0.0000 0.968 1.00 0.00 0.00 0.00
#> Sample_1237 1 0.0000 0.968 1.00 0.00 0.00 0.00
#> Sample_1238 1 0.0000 0.968 1.00 0.00 0.00 0.00
#> Sample_1243 1 0.0000 0.968 1.00 0.00 0.00 0.00
#> Sample_1244 1 0.0000 0.968 1.00 0.00 0.00 0.00
#> Sample_1245 1 0.0000 0.968 1.00 0.00 0.00 0.00
#> Sample_1246 1 0.0000 0.968 1.00 0.00 0.00 0.00
#> Sample_1247 1 0.0000 0.968 1.00 0.00 0.00 0.00
#> Sample_1248 1 0.0000 0.968 1.00 0.00 0.00 0.00
#> Sample_1250 1 0.0000 0.968 1.00 0.00 0.00 0.00
#> Sample_1251 1 0.0000 0.968 1.00 0.00 0.00 0.00
#> Sample_1252 1 0.0000 0.968 1.00 0.00 0.00 0.00
#> Sample_1253 1 0.0000 0.968 1.00 0.00 0.00 0.00
#> Sample_1256 1 0.0000 0.968 1.00 0.00 0.00 0.00
#> Sample_1257 1 0.0000 0.968 1.00 0.00 0.00 0.00
#> Sample_1258 1 0.0000 0.968 1.00 0.00 0.00 0.00
#> Sample_1259 1 0.0000 0.968 1.00 0.00 0.00 0.00
#> Sample_1260 1 0.0000 0.968 1.00 0.00 0.00 0.00
#> Sample_1261 1 0.0000 0.968 1.00 0.00 0.00 0.00
#> Sample_1262 1 0.0000 0.968 1.00 0.00 0.00 0.00
#> Sample_1263 1 0.0000 0.968 1.00 0.00 0.00 0.00
#> Sample_1265 1 0.0000 0.968 1.00 0.00 0.00 0.00
#> Sample_1269 1 0.0000 0.968 1.00 0.00 0.00 0.00
#> Sample_1270 1 0.0000 0.968 1.00 0.00 0.00 0.00
#> Sample_1271 1 0.0000 0.968 1.00 0.00 0.00 0.00
#> Sample_1273 1 0.0000 0.968 1.00 0.00 0.00 0.00
#> Sample_1274 1 0.0000 0.968 1.00 0.00 0.00 0.00
#> Sample_1275 1 0.0000 0.968 1.00 0.00 0.00 0.00
#> Sample_1276 1 0.0000 0.968 1.00 0.00 0.00 0.00
#> Sample_1277 1 0.0000 0.968 1.00 0.00 0.00 0.00
#> Sample_1278 1 0.0000 0.968 1.00 0.00 0.00 0.00
#> Sample_1281 1 0.0000 0.968 1.00 0.00 0.00 0.00
#> Sample_1282 1 0.0000 0.968 1.00 0.00 0.00 0.00
#> Sample_1284 1 0.0000 0.968 1.00 0.00 0.00 0.00
#> Sample_1286 1 0.0000 0.968 1.00 0.00 0.00 0.00
#> Sample_1287 1 0.0000 0.968 1.00 0.00 0.00 0.00
#> Sample_1288 1 0.0000 0.968 1.00 0.00 0.00 0.00
#> Sample_1289 1 0.0000 0.968 1.00 0.00 0.00 0.00
#> Sample_1290 1 0.0000 0.968 1.00 0.00 0.00 0.00
#> Sample_1291 1 0.0000 0.968 1.00 0.00 0.00 0.00
#> Sample_1292 1 0.0000 0.968 1.00 0.00 0.00 0.00
#> Sample_1293 1 0.0000 0.968 1.00 0.00 0.00 0.00
#> Sample_1294 1 0.0000 0.968 1.00 0.00 0.00 0.00
#> Sample_1295 1 0.0000 0.968 1.00 0.00 0.00 0.00
#> Sample_1297 1 0.0000 0.968 1.00 0.00 0.00 0.00
#> Sample_1298 1 0.0000 0.968 1.00 0.00 0.00 0.00
#> Sample_1300 1 0.0000 0.968 1.00 0.00 0.00 0.00
#> Sample_1301 1 0.0000 0.968 1.00 0.00 0.00 0.00
#> Sample_1302 1 0.0000 0.968 1.00 0.00 0.00 0.00
#> Sample_1303 1 0.0000 0.968 1.00 0.00 0.00 0.00
#> Sample_1304 1 0.0000 0.968 1.00 0.00 0.00 0.00
#> Sample_1305 1 0.0000 0.968 1.00 0.00 0.00 0.00
#> Sample_1311 1 0.0000 0.968 1.00 0.00 0.00 0.00
#> Sample_1312 1 0.0000 0.968 1.00 0.00 0.00 0.00
#> Sample_1314 3 0.3400 0.771 0.18 0.00 0.82 0.00
#> Sample_1315 3 0.3606 0.812 0.02 0.00 0.84 0.14
#> Sample_1316 1 0.5570 0.152 0.54 0.00 0.44 0.02
#> Sample_1318 3 0.6617 0.495 0.28 0.12 0.60 0.00
#> Sample_1319 1 0.6477 0.455 0.60 0.30 0.10 0.00
#> Sample_1321 4 0.0000 0.966 0.00 0.00 0.00 1.00
#> Sample_1322 1 0.0000 0.968 1.00 0.00 0.00 0.00
#> Sample_1323 1 0.0707 0.949 0.98 0.00 0.02 0.00
#> Sample_1324 1 0.7707 0.117 0.44 0.00 0.32 0.24
#> Sample_1325 1 0.0000 0.968 1.00 0.00 0.00 0.00
#> Sample_1326 4 0.4624 0.488 0.34 0.00 0.00 0.66
#> Sample_1327 1 0.0000 0.968 1.00 0.00 0.00 0.00
#> Sample_1328 3 0.3172 0.798 0.00 0.00 0.84 0.16
#> Sample_1329 4 0.1411 0.940 0.02 0.00 0.02 0.96
#> Sample_1330 4 0.0000 0.966 0.00 0.00 0.00 1.00
#> Sample_1331 3 0.4948 0.221 0.44 0.00 0.56 0.00
#> Sample_1332 4 0.5151 0.738 0.14 0.00 0.10 0.76
#> Sample_1333 4 0.0000 0.966 0.00 0.00 0.00 1.00
#> Sample_1334 3 0.4790 0.395 0.38 0.00 0.62 0.00
#> Sample_1340 1 0.0000 0.968 1.00 0.00 0.00 0.00
#> Sample_1341 4 0.1637 0.916 0.06 0.00 0.00 0.94
#> Sample_1344 4 0.3247 0.873 0.06 0.00 0.06 0.88
#> Sample_1346 1 0.0000 0.968 1.00 0.00 0.00 0.00
#> Sample_1347 1 0.3610 0.733 0.80 0.00 0.20 0.00
#> Sample_1349 4 0.2345 0.877 0.00 0.00 0.10 0.90
#> Sample_1351 4 0.0000 0.966 0.00 0.00 0.00 1.00
#> Sample_1353 1 0.0000 0.968 1.00 0.00 0.00 0.00
#> Sample_1355 4 0.0000 0.966 0.00 0.00 0.00 1.00
#> Sample_1356 4 0.5987 0.125 0.04 0.00 0.44 0.52
#> Sample_1359 4 0.0000 0.966 0.00 0.00 0.00 1.00
#> Sample_1361 4 0.2345 0.877 0.10 0.00 0.00 0.90
#> Sample_1362 4 0.0000 0.966 0.00 0.00 0.00 1.00
#> Sample_1363 4 0.4134 0.651 0.26 0.00 0.00 0.74
#> Sample_1365 1 0.0000 0.968 1.00 0.00 0.00 0.00
#> Sample_1366 3 0.0707 0.943 0.00 0.00 0.98 0.02
#> Sample_1368 3 0.2011 0.888 0.00 0.00 0.92 0.08
#> Sample_1369 1 0.0000 0.968 1.00 0.00 0.00 0.00
#> Sample_1370 4 0.0707 0.952 0.00 0.02 0.00 0.98
#> Sample_1372 4 0.0000 0.966 0.00 0.00 0.00 1.00
#> Sample_1377 1 0.0000 0.968 1.00 0.00 0.00 0.00
#> Sample_1378 1 0.7653 0.222 0.46 0.00 0.24 0.30
#> Sample_1379 1 0.4292 0.797 0.82 0.00 0.10 0.08
#> Sample_1380 1 0.4949 0.714 0.76 0.00 0.06 0.18
#> Sample_1381 4 0.0000 0.966 0.00 0.00 0.00 1.00
#> Sample_1383 3 0.0000 0.960 0.00 0.00 1.00 0.00
#> Sample_1384 3 0.0000 0.960 0.00 0.00 1.00 0.00
#> Sample_1385 3 0.0000 0.960 0.00 0.00 1.00 0.00
#> Sample_1387 3 0.0000 0.960 0.00 0.00 1.00 0.00
#> Sample_1389 3 0.0000 0.960 0.00 0.00 1.00 0.00
#> Sample_1390 3 0.0000 0.960 0.00 0.00 1.00 0.00
#> Sample_1391 3 0.0000 0.960 0.00 0.00 1.00 0.00
#> Sample_1392 3 0.0000 0.960 0.00 0.00 1.00 0.00
#> Sample_1393 3 0.0000 0.960 0.00 0.00 1.00 0.00
#> Sample_1394 3 0.0000 0.960 0.00 0.00 1.00 0.00
#> Sample_1395 3 0.0000 0.960 0.00 0.00 1.00 0.00
#> Sample_1396 3 0.0000 0.960 0.00 0.00 1.00 0.00
#> Sample_1397 3 0.0000 0.960 0.00 0.00 1.00 0.00
#> Sample_1401 3 0.0000 0.960 0.00 0.00 1.00 0.00
#> Sample_1403 3 0.0000 0.960 0.00 0.00 1.00 0.00
#> Sample_1404 3 0.0000 0.960 0.00 0.00 1.00 0.00
#> Sample_1405 3 0.0000 0.960 0.00 0.00 1.00 0.00
#> Sample_1407 3 0.0000 0.960 0.00 0.00 1.00 0.00
#> Sample_1410 3 0.0000 0.960 0.00 0.00 1.00 0.00
#> Sample_1411 3 0.0000 0.960 0.00 0.00 1.00 0.00
#> Sample_1412 3 0.0000 0.960 0.00 0.00 1.00 0.00
#> Sample_1414 3 0.0000 0.960 0.00 0.00 1.00 0.00
#> Sample_1415 3 0.0000 0.960 0.00 0.00 1.00 0.00
#> Sample_1417 3 0.0000 0.960 0.00 0.00 1.00 0.00
#> Sample_1418 3 0.0000 0.960 0.00 0.00 1.00 0.00
#> Sample_1420 3 0.0000 0.960 0.00 0.00 1.00 0.00
#> Sample_1421 3 0.0000 0.960 0.00 0.00 1.00 0.00
#> Sample_1422 3 0.0000 0.960 0.00 0.00 1.00 0.00
#> Sample_1424 3 0.3801 0.713 0.22 0.00 0.78 0.00
#> Sample_1428 3 0.0000 0.960 0.00 0.00 1.00 0.00
#> Sample_1442 3 0.0000 0.960 0.00 0.00 1.00 0.00
#> Sample_1443 3 0.0000 0.960 0.00 0.00 1.00 0.00
#> Sample_1446 3 0.0000 0.960 0.00 0.00 1.00 0.00
#> Sample_1447 3 0.0000 0.960 0.00 0.00 1.00 0.00
#> Sample_1456 3 0.0000 0.960 0.00 0.00 1.00 0.00
#> Sample_1457 3 0.0000 0.960 0.00 0.00 1.00 0.00
#> Sample_1461 3 0.0000 0.960 0.00 0.00 1.00 0.00
#> Sample_1463 4 0.0000 0.966 0.00 0.00 0.00 1.00
#> Sample_1464 4 0.1211 0.937 0.00 0.00 0.04 0.96
#> Sample_1468 3 0.0000 0.960 0.00 0.00 1.00 0.00
#> Sample_1480 4 0.0707 0.952 0.00 0.00 0.02 0.98
#> Sample_1485 3 0.0000 0.960 0.00 0.00 1.00 0.00
#> Sample_1488 3 0.0000 0.960 0.00 0.00 1.00 0.00
#> Sample_1502 3 0.0000 0.960 0.00 0.00 1.00 0.00
#> Sample_1503 3 0.0000 0.960 0.00 0.00 1.00 0.00
#> Sample_1506 3 0.0000 0.960 0.00 0.00 1.00 0.00
#> Sample_1507 3 0.0000 0.960 0.00 0.00 1.00 0.00
#> Sample_1508 3 0.0000 0.960 0.00 0.00 1.00 0.00
#> Sample_1511 3 0.0000 0.960 0.00 0.00 1.00 0.00
#> Sample_1512 3 0.0000 0.960 0.00 0.00 1.00 0.00
#> Sample_1513 3 0.0000 0.960 0.00 0.00 1.00 0.00
#> Sample_1515 3 0.0000 0.960 0.00 0.00 1.00 0.00
#> Sample_1569 2 0.0000 0.999 0.00 1.00 0.00 0.00
#> Sample_1598 1 0.0000 0.968 1.00 0.00 0.00 0.00
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
As soon as the classes for columns are determined, the signatures that are significantly different between subgroups can be looked for. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. To get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows (which is done by automatically selecting number of clusters).If there are too many signatures, top_signatures = ... can be set to only show the
signatures with the highest FDRs:
# code only for demonstration
# e.g. to show the top 500 most significant rows
tb = get_signature(res, k = ..., top_signatures = 500)
If the signatures are defined as these which are uniquely high in current group, diff_method argument
can be set to "uniquely_high_in_one_group":
# code only for demonstration
tb = get_signature(res, k = ..., diff_method = "uniquely_high_in_one_group")
UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n_sample age(p-value) cell.type(p-value) k
#> ATC:skmeans 254 3.5e-09 1.000 2
#> ATC:skmeans 255 NaN 0.748 3
#> ATC:skmeans 259 NaN 0.609 4
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
Parent node: Node03. Child nodes: Node0111-leaf , Node0112-leaf , Node0121-leaf , Node0122-leaf , Node0131-leaf , Node0132-leaf , Node0211 , Node0212 , Node0221 , Node0222 , Node0231-leaf , Node0232-leaf , Node0233-leaf , Node0311-leaf , Node0312-leaf , Node0313-leaf , Node0314-leaf , Node0321 , Node0322 , Node0331-leaf , Node0332-leaf , Node0333-leaf .
The object with results only for a single top-value method and a single partitioning method can be extracted as:
res = res_rh["032"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4.
#> On a matrix with 14749 rows and 245 columns.
#> Top rows (1215) are extracted by 'ATC' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 150 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_partitions"
#> [7] "compare_signatures" "consensus_heatmap" "dimension_reduction"
#> [10] "functional_enrichment" "get_anno_col" "get_anno"
#> [13] "get_classes" "get_consensus" "get_matrix"
#> [16] "get_membership" "get_param" "get_signatures"
#> [19] "get_stats" "is_best_k" "is_stable_k"
#> [22] "membership_heatmap" "ncol" "nrow"
#> [25] "plot_ecdf" "predict_classes" "rownames"
#> [28] "select_partition_number" "show" "suggest_best_k"
#> [31] "test_to_known_factors" "top_rows_heatmap"
collect_plots() function collects all the plots made from res for all k (number of subgroups)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, higher 1-PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.940 0.937 0.974 0.478 0.523 0.523
#> 3 3 0.760 0.846 0.933 0.365 0.755 0.563
#> 4 4 0.679 0.654 0.843 0.139 0.838 0.584
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 2
Following is the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall subgroup
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> Sample_208 2 0.000 0.9669 0.00 1.00
#> Sample_209 2 0.000 0.9669 0.00 1.00
#> Sample_210 2 0.000 0.9669 0.00 1.00
#> Sample_211 2 0.000 0.9669 0.00 1.00
#> Sample_212 2 0.000 0.9669 0.00 1.00
#> Sample_213 2 0.000 0.9669 0.00 1.00
#> Sample_216 2 0.000 0.9669 0.00 1.00
#> Sample_218 2 0.000 0.9669 0.00 1.00
#> Sample_219 2 0.000 0.9669 0.00 1.00
#> Sample_220 2 0.000 0.9669 0.00 1.00
#> Sample_221 2 0.000 0.9669 0.00 1.00
#> Sample_222 2 0.000 0.9669 0.00 1.00
#> Sample_223 2 0.000 0.9669 0.00 1.00
#> Sample_225 2 0.000 0.9669 0.00 1.00
#> Sample_227 2 0.000 0.9669 0.00 1.00
#> Sample_228 2 0.000 0.9669 0.00 1.00
#> Sample_229 2 0.000 0.9669 0.00 1.00
#> Sample_230 2 0.000 0.9669 0.00 1.00
#> Sample_231 2 0.000 0.9669 0.00 1.00
#> Sample_232 2 0.000 0.9669 0.00 1.00
#> Sample_234 2 0.000 0.9669 0.00 1.00
#> Sample_235 2 0.000 0.9669 0.00 1.00
#> Sample_237 2 0.242 0.9368 0.04 0.96
#> Sample_238 2 0.000 0.9669 0.00 1.00
#> Sample_239 2 0.000 0.9669 0.00 1.00
#> Sample_240 2 0.000 0.9669 0.00 1.00
#> Sample_242 2 0.000 0.9669 0.00 1.00
#> Sample_243 1 0.327 0.9206 0.94 0.06
#> Sample_244 2 0.000 0.9669 0.00 1.00
#> Sample_245 2 0.000 0.9669 0.00 1.00
#> Sample_246 2 0.000 0.9669 0.00 1.00
#> Sample_247 2 0.000 0.9669 0.00 1.00
#> Sample_248 2 0.000 0.9669 0.00 1.00
#> Sample_249 2 0.000 0.9669 0.00 1.00
#> Sample_250 2 0.000 0.9669 0.00 1.00
#> Sample_251 2 0.000 0.9669 0.00 1.00
#> Sample_252 2 0.000 0.9669 0.00 1.00
#> Sample_253 2 0.000 0.9669 0.00 1.00
#> Sample_255 2 0.000 0.9669 0.00 1.00
#> Sample_256 2 0.000 0.9669 0.00 1.00
#> Sample_257 2 0.000 0.9669 0.00 1.00
#> Sample_259 2 0.000 0.9669 0.00 1.00
#> Sample_260 2 0.000 0.9669 0.00 1.00
#> Sample_261 2 0.000 0.9669 0.00 1.00
#> Sample_262 2 0.000 0.9669 0.00 1.00
#> Sample_263 2 0.000 0.9669 0.00 1.00
#> Sample_266 1 0.827 0.6434 0.74 0.26
#> Sample_267 1 0.141 0.9585 0.98 0.02
#> Sample_268 1 0.000 0.9765 1.00 0.00
#> Sample_269 2 0.000 0.9669 0.00 1.00
#> Sample_270 1 0.000 0.9765 1.00 0.00
#> Sample_273 1 0.000 0.9765 1.00 0.00
#> Sample_274 1 0.000 0.9765 1.00 0.00
#> Sample_277 1 0.000 0.9765 1.00 0.00
#> Sample_279 1 0.000 0.9765 1.00 0.00
#> Sample_280 2 0.000 0.9669 0.00 1.00
#> Sample_281 1 0.000 0.9765 1.00 0.00
#> Sample_282 1 0.999 0.0516 0.52 0.48
#> Sample_283 2 0.904 0.5487 0.32 0.68
#> Sample_284 1 0.000 0.9765 1.00 0.00
#> Sample_285 1 0.000 0.9765 1.00 0.00
#> Sample_286 1 0.000 0.9765 1.00 0.00
#> Sample_290 1 0.000 0.9765 1.00 0.00
#> Sample_291 1 0.000 0.9765 1.00 0.00
#> Sample_292 1 0.000 0.9765 1.00 0.00
#> Sample_293 2 0.000 0.9669 0.00 1.00
#> Sample_294 1 0.000 0.9765 1.00 0.00
#> Sample_295 1 0.000 0.9765 1.00 0.00
#> Sample_296 1 0.000 0.9765 1.00 0.00
#> Sample_297 1 0.000 0.9765 1.00 0.00
#> Sample_298 1 0.000 0.9765 1.00 0.00
#> Sample_299 1 0.000 0.9765 1.00 0.00
#> Sample_300 2 0.000 0.9669 0.00 1.00
#> Sample_301 1 0.000 0.9765 1.00 0.00
#> Sample_306 1 0.000 0.9765 1.00 0.00
#> Sample_307 1 0.000 0.9765 1.00 0.00
#> Sample_315 1 0.000 0.9765 1.00 0.00
#> Sample_317 1 0.000 0.9765 1.00 0.00
#> Sample_322 1 0.000 0.9765 1.00 0.00
#> Sample_332 1 0.000 0.9765 1.00 0.00
#> Sample_333 1 0.402 0.8985 0.92 0.08
#> Sample_334 1 0.000 0.9765 1.00 0.00
#> Sample_335 1 0.000 0.9765 1.00 0.00
#> Sample_336 1 0.000 0.9765 1.00 0.00
#> Sample_337 1 0.000 0.9765 1.00 0.00
#> Sample_338 1 0.000 0.9765 1.00 0.00
#> Sample_340 1 0.000 0.9765 1.00 0.00
#> Sample_341 1 0.000 0.9765 1.00 0.00
#> Sample_342 1 0.000 0.9765 1.00 0.00
#> Sample_343 1 0.000 0.9765 1.00 0.00
#> Sample_344 1 0.000 0.9765 1.00 0.00
#> Sample_345 1 0.000 0.9765 1.00 0.00
#> Sample_346 1 0.000 0.9765 1.00 0.00
#> Sample_347 1 0.000 0.9765 1.00 0.00
#> Sample_348 1 0.000 0.9765 1.00 0.00
#> Sample_349 1 0.000 0.9765 1.00 0.00
#> Sample_353 1 0.000 0.9765 1.00 0.00
#> Sample_354 1 0.000 0.9765 1.00 0.00
#> Sample_356 1 0.000 0.9765 1.00 0.00
#> Sample_357 1 0.000 0.9765 1.00 0.00
#> Sample_359 1 0.000 0.9765 1.00 0.00
#> Sample_361 1 0.000 0.9765 1.00 0.00
#> Sample_363 1 0.000 0.9765 1.00 0.00
#> Sample_364 1 0.000 0.9765 1.00 0.00
#> Sample_366 1 0.000 0.9765 1.00 0.00
#> Sample_367 1 0.000 0.9765 1.00 0.00
#> Sample_370 1 0.000 0.9765 1.00 0.00
#> Sample_374 1 0.000 0.9765 1.00 0.00
#> Sample_375 1 0.990 0.1959 0.56 0.44
#> Sample_376 1 0.000 0.9765 1.00 0.00
#> Sample_378 1 0.000 0.9765 1.00 0.00
#> Sample_379 1 0.000 0.9765 1.00 0.00
#> Sample_380 1 0.000 0.9765 1.00 0.00
#> Sample_382 1 0.000 0.9765 1.00 0.00
#> Sample_383 1 0.000 0.9765 1.00 0.00
#> Sample_384 1 0.000 0.9765 1.00 0.00
#> Sample_387 1 0.000 0.9765 1.00 0.00
#> Sample_388 1 0.000 0.9765 1.00 0.00
#> Sample_391 1 0.000 0.9765 1.00 0.00
#> Sample_392 1 0.000 0.9765 1.00 0.00
#> Sample_399 1 0.000 0.9765 1.00 0.00
#> Sample_400 1 0.000 0.9765 1.00 0.00
#> Sample_401 1 0.000 0.9765 1.00 0.00
#> Sample_402 2 0.855 0.6382 0.28 0.72
#> Sample_406 1 0.000 0.9765 1.00 0.00
#> Sample_407 1 0.000 0.9765 1.00 0.00
#> Sample_410 1 0.000 0.9765 1.00 0.00
#> Sample_411 1 0.000 0.9765 1.00 0.00
#> Sample_413 1 0.000 0.9765 1.00 0.00
#> Sample_414 1 0.000 0.9765 1.00 0.00
#> Sample_417 1 0.000 0.9765 1.00 0.00
#> Sample_418 1 0.000 0.9765 1.00 0.00
#> Sample_419 1 0.000 0.9765 1.00 0.00
#> Sample_423 2 0.881 0.5914 0.30 0.70
#> Sample_426 2 0.000 0.9669 0.00 1.00
#> Sample_429 2 0.242 0.9382 0.04 0.96
#> Sample_435 2 0.584 0.8394 0.14 0.86
#> Sample_441 2 0.242 0.9382 0.04 0.96
#> Sample_444 1 0.000 0.9765 1.00 0.00
#> Sample_447 2 0.000 0.9669 0.00 1.00
#> Sample_449 2 0.242 0.9382 0.04 0.96
#> Sample_450 1 0.000 0.9765 1.00 0.00
#> Sample_453 2 0.943 0.4701 0.36 0.64
#> Sample_454 1 0.000 0.9765 1.00 0.00
#> Sample_458 2 0.327 0.9210 0.06 0.94
#> Sample_460 1 0.000 0.9765 1.00 0.00
#> Sample_462 1 0.000 0.9765 1.00 0.00
#> Sample_467 2 0.584 0.8395 0.14 0.86
#> Sample_470 1 0.000 0.9765 1.00 0.00
#> Sample_471 2 0.000 0.9669 0.00 1.00
#> Sample_473 1 0.000 0.9765 1.00 0.00
#> Sample_570 2 0.000 0.9669 0.00 1.00
#> Sample_571 2 0.904 0.5600 0.32 0.68
#> Sample_575 1 0.000 0.9765 1.00 0.00
#> Sample_576 2 0.000 0.9669 0.00 1.00
#> Sample_578 1 0.000 0.9765 1.00 0.00
#> Sample_579 1 0.000 0.9765 1.00 0.00
#> Sample_580 1 0.000 0.9765 1.00 0.00
#> Sample_581 1 0.000 0.9765 1.00 0.00
#> Sample_582 1 0.000 0.9765 1.00 0.00
#> Sample_583 1 0.000 0.9765 1.00 0.00
#> Sample_584 1 0.000 0.9765 1.00 0.00
#> Sample_585 2 0.000 0.9669 0.00 1.00
#> Sample_589 2 0.000 0.9669 0.00 1.00
#> Sample_613 2 0.000 0.9669 0.00 1.00
#> Sample_615 2 0.000 0.9669 0.00 1.00
#> Sample_623 2 0.000 0.9669 0.00 1.00
#> Sample_624 2 0.000 0.9669 0.00 1.00
#> Sample_644 1 0.000 0.9765 1.00 0.00
#> Sample_948 1 0.000 0.9765 1.00 0.00
#> Sample_949 1 0.000 0.9765 1.00 0.00
#> Sample_951 1 0.000 0.9765 1.00 0.00
#> Sample_952 1 0.000 0.9765 1.00 0.00
#> Sample_953 1 0.000 0.9765 1.00 0.00
#> Sample_954 2 0.000 0.9669 0.00 1.00
#> Sample_955 1 0.000 0.9765 1.00 0.00
#> Sample_956 1 0.000 0.9765 1.00 0.00
#> Sample_957 1 0.000 0.9765 1.00 0.00
#> Sample_958 1 0.000 0.9765 1.00 0.00
#> Sample_959 1 0.000 0.9765 1.00 0.00
#> Sample_960 1 0.000 0.9765 1.00 0.00
#> Sample_961 1 0.000 0.9765 1.00 0.00
#> Sample_963 1 0.141 0.9585 0.98 0.02
#> Sample_964 1 0.000 0.9765 1.00 0.00
#> Sample_965 1 0.469 0.8752 0.90 0.10
#> Sample_966 1 0.000 0.9765 1.00 0.00
#> Sample_967 1 0.000 0.9765 1.00 0.00
#> Sample_969 1 0.000 0.9765 1.00 0.00
#> Sample_970 1 0.000 0.9765 1.00 0.00
#> Sample_971 1 0.000 0.9765 1.00 0.00
#> Sample_972 1 0.000 0.9765 1.00 0.00
#> Sample_973 1 0.000 0.9765 1.00 0.00
#> Sample_974 1 0.000 0.9765 1.00 0.00
#> Sample_979 1 0.000 0.9765 1.00 0.00
#> Sample_984 1 0.000 0.9765 1.00 0.00
#> Sample_995 1 0.000 0.9765 1.00 0.00
#> Sample_997 1 0.141 0.9584 0.98 0.02
#> Sample_998 1 0.000 0.9765 1.00 0.00
#> Sample_1000 1 0.999 0.0679 0.52 0.48
#> Sample_1001 1 0.000 0.9765 1.00 0.00
#> Sample_1002 1 0.000 0.9765 1.00 0.00
#> Sample_1003 1 0.000 0.9765 1.00 0.00
#> Sample_1005 1 0.722 0.7474 0.80 0.20
#> Sample_1006 1 0.000 0.9765 1.00 0.00
#> Sample_1011 1 0.000 0.9765 1.00 0.00
#> Sample_1012 2 0.000 0.9669 0.00 1.00
#> Sample_1013 1 0.000 0.9765 1.00 0.00
#> Sample_1053 2 0.000 0.9669 0.00 1.00
#> Sample_1054 1 0.943 0.4337 0.64 0.36
#> Sample_1055 1 0.141 0.9585 0.98 0.02
#> Sample_1057 2 0.000 0.9669 0.00 1.00
#> Sample_1060 1 0.000 0.9765 1.00 0.00
#> Sample_1061 2 0.000 0.9669 0.00 1.00
#> Sample_1189 1 0.000 0.9765 1.00 0.00
#> Sample_1190 2 0.000 0.9669 0.00 1.00
#> Sample_1191 2 0.000 0.9669 0.00 1.00
#> Sample_1193 2 0.000 0.9669 0.00 1.00
#> Sample_1194 2 0.000 0.9669 0.00 1.00
#> Sample_1195 1 0.760 0.7162 0.78 0.22
#> Sample_1196 1 0.000 0.9765 1.00 0.00
#> Sample_1197 2 0.904 0.5441 0.32 0.68
#> Sample_1198 2 0.000 0.9669 0.00 1.00
#> Sample_1199 2 0.000 0.9669 0.00 1.00
#> Sample_1200 1 0.000 0.9765 1.00 0.00
#> Sample_1201 1 0.584 0.8259 0.86 0.14
#> Sample_1202 1 0.000 0.9765 1.00 0.00
#> Sample_1203 2 0.000 0.9669 0.00 1.00
#> Sample_1204 2 0.000 0.9669 0.00 1.00
#> Sample_1205 2 0.760 0.7352 0.22 0.78
#> Sample_1206 1 0.000 0.9765 1.00 0.00
#> Sample_1207 2 0.242 0.9387 0.04 0.96
#> Sample_1209 1 0.000 0.9765 1.00 0.00
#> Sample_1210 2 0.000 0.9669 0.00 1.00
#> Sample_1211 1 0.141 0.9585 0.98 0.02
#> Sample_1212 1 0.000 0.9765 1.00 0.00
#> Sample_1213 1 0.000 0.9765 1.00 0.00
#> Sample_1214 2 0.242 0.9387 0.04 0.96
#> Sample_1215 2 0.327 0.9219 0.06 0.94
#> Sample_1219 2 0.242 0.9382 0.04 0.96
#> Sample_1220 1 0.000 0.9765 1.00 0.00
#> Sample_1221 2 0.795 0.7042 0.24 0.76
#> Sample_1549 2 0.000 0.9669 0.00 1.00
#> Sample_1553 1 0.995 0.1174 0.54 0.46
#> Sample_1581 2 0.000 0.9669 0.00 1.00
#> Sample_1597 2 0.000 0.9669 0.00 1.00
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> Sample_208 2 0.0000 0.9625 0.00 1.00 0.00
#> Sample_209 2 0.0000 0.9625 0.00 1.00 0.00
#> Sample_210 2 0.0000 0.9625 0.00 1.00 0.00
#> Sample_211 2 0.0000 0.9625 0.00 1.00 0.00
#> Sample_212 2 0.0000 0.9625 0.00 1.00 0.00
#> Sample_213 2 0.0000 0.9625 0.00 1.00 0.00
#> Sample_216 2 0.0000 0.9625 0.00 1.00 0.00
#> Sample_218 2 0.0000 0.9625 0.00 1.00 0.00
#> Sample_219 2 0.0000 0.9625 0.00 1.00 0.00
#> Sample_220 2 0.0000 0.9625 0.00 1.00 0.00
#> Sample_221 2 0.0000 0.9625 0.00 1.00 0.00
#> Sample_222 2 0.0000 0.9625 0.00 1.00 0.00
#> Sample_223 2 0.0000 0.9625 0.00 1.00 0.00
#> Sample_225 2 0.0000 0.9625 0.00 1.00 0.00
#> Sample_227 2 0.0000 0.9625 0.00 1.00 0.00
#> Sample_228 2 0.0000 0.9625 0.00 1.00 0.00
#> Sample_229 2 0.0000 0.9625 0.00 1.00 0.00
#> Sample_230 2 0.0000 0.9625 0.00 1.00 0.00
#> Sample_231 2 0.0000 0.9625 0.00 1.00 0.00
#> Sample_232 2 0.0000 0.9625 0.00 1.00 0.00
#> Sample_234 2 0.0000 0.9625 0.00 1.00 0.00
#> Sample_235 2 0.0000 0.9625 0.00 1.00 0.00
#> Sample_237 2 0.0000 0.9625 0.00 1.00 0.00
#> Sample_238 2 0.0000 0.9625 0.00 1.00 0.00
#> Sample_239 2 0.0000 0.9625 0.00 1.00 0.00
#> Sample_240 2 0.0000 0.9625 0.00 1.00 0.00
#> Sample_242 2 0.0000 0.9625 0.00 1.00 0.00
#> Sample_243 1 0.5016 0.6727 0.76 0.24 0.00
#> Sample_244 2 0.0000 0.9625 0.00 1.00 0.00
#> Sample_245 2 0.0000 0.9625 0.00 1.00 0.00
#> Sample_246 2 0.0000 0.9625 0.00 1.00 0.00
#> Sample_247 2 0.0000 0.9625 0.00 1.00 0.00
#> Sample_248 2 0.0000 0.9625 0.00 1.00 0.00
#> Sample_249 2 0.0000 0.9625 0.00 1.00 0.00
#> Sample_250 2 0.0000 0.9625 0.00 1.00 0.00
#> Sample_251 2 0.0000 0.9625 0.00 1.00 0.00
#> Sample_252 2 0.0000 0.9625 0.00 1.00 0.00
#> Sample_253 2 0.0000 0.9625 0.00 1.00 0.00
#> Sample_255 2 0.0000 0.9625 0.00 1.00 0.00
#> Sample_256 2 0.0000 0.9625 0.00 1.00 0.00
#> Sample_257 2 0.0000 0.9625 0.00 1.00 0.00
#> Sample_259 2 0.0000 0.9625 0.00 1.00 0.00
#> Sample_260 2 0.0000 0.9625 0.00 1.00 0.00
#> Sample_261 2 0.0000 0.9625 0.00 1.00 0.00
#> Sample_262 2 0.0000 0.9625 0.00 1.00 0.00
#> Sample_263 2 0.0000 0.9625 0.00 1.00 0.00
#> Sample_266 3 0.7517 0.2332 0.42 0.04 0.54
#> Sample_267 1 0.0000 0.9127 1.00 0.00 0.00
#> Sample_268 1 0.0000 0.9127 1.00 0.00 0.00
#> Sample_269 2 0.0000 0.9625 0.00 1.00 0.00
#> Sample_270 1 0.0000 0.9127 1.00 0.00 0.00
#> Sample_273 1 0.0000 0.9127 1.00 0.00 0.00
#> Sample_274 1 0.0000 0.9127 1.00 0.00 0.00
#> Sample_277 1 0.0000 0.9127 1.00 0.00 0.00
#> Sample_279 1 0.0892 0.9018 0.98 0.00 0.02
#> Sample_280 2 0.0000 0.9625 0.00 1.00 0.00
#> Sample_281 1 0.0000 0.9127 1.00 0.00 0.00
#> Sample_282 1 0.6229 0.5756 0.70 0.28 0.02
#> Sample_283 2 0.6758 0.4160 0.36 0.62 0.02
#> Sample_284 1 0.0000 0.9127 1.00 0.00 0.00
#> Sample_285 1 0.0000 0.9127 1.00 0.00 0.00
#> Sample_286 1 0.1529 0.8908 0.96 0.00 0.04
#> Sample_290 1 0.0000 0.9127 1.00 0.00 0.00
#> Sample_291 1 0.0000 0.9127 1.00 0.00 0.00
#> Sample_292 1 0.0000 0.9127 1.00 0.00 0.00
#> Sample_293 2 0.0000 0.9625 0.00 1.00 0.00
#> Sample_294 1 0.0000 0.9127 1.00 0.00 0.00
#> Sample_295 1 0.0000 0.9127 1.00 0.00 0.00
#> Sample_296 1 0.0000 0.9127 1.00 0.00 0.00
#> Sample_297 1 0.0000 0.9127 1.00 0.00 0.00
#> Sample_298 1 0.0000 0.9127 1.00 0.00 0.00
#> Sample_299 1 0.0000 0.9127 1.00 0.00 0.00
#> Sample_300 2 0.0000 0.9625 0.00 1.00 0.00
#> Sample_301 1 0.0000 0.9127 1.00 0.00 0.00
#> Sample_306 1 0.3340 0.8359 0.88 0.00 0.12
#> Sample_307 1 0.4291 0.7755 0.82 0.00 0.18
#> Sample_315 3 0.0000 0.9084 0.00 0.00 1.00
#> Sample_317 1 0.2959 0.8524 0.90 0.00 0.10
#> Sample_322 1 0.0000 0.9127 1.00 0.00 0.00
#> Sample_332 1 0.0000 0.9127 1.00 0.00 0.00
#> Sample_333 1 0.3340 0.8184 0.88 0.12 0.00
#> Sample_334 1 0.0000 0.9127 1.00 0.00 0.00
#> Sample_335 1 0.0000 0.9127 1.00 0.00 0.00
#> Sample_336 1 0.0000 0.9127 1.00 0.00 0.00
#> Sample_337 1 0.0000 0.9127 1.00 0.00 0.00
#> Sample_338 1 0.0000 0.9127 1.00 0.00 0.00
#> Sample_340 1 0.3340 0.8387 0.88 0.00 0.12
#> Sample_341 1 0.2537 0.8691 0.92 0.00 0.08
#> Sample_342 1 0.3686 0.8163 0.86 0.00 0.14
#> Sample_343 1 0.0000 0.9127 1.00 0.00 0.00
#> Sample_344 1 0.0000 0.9127 1.00 0.00 0.00
#> Sample_345 1 0.5706 0.5554 0.68 0.00 0.32
#> Sample_346 1 0.0000 0.9127 1.00 0.00 0.00
#> Sample_347 1 0.0000 0.9127 1.00 0.00 0.00
#> Sample_348 3 0.0000 0.9084 0.00 0.00 1.00
#> Sample_349 3 0.5560 0.5602 0.30 0.00 0.70
#> Sample_353 3 0.5706 0.5260 0.32 0.00 0.68
#> Sample_354 1 0.0000 0.9127 1.00 0.00 0.00
#> Sample_356 1 0.0000 0.9127 1.00 0.00 0.00
#> Sample_357 1 0.0000 0.9127 1.00 0.00 0.00
#> Sample_359 1 0.0000 0.9127 1.00 0.00 0.00
#> Sample_361 1 0.0000 0.9127 1.00 0.00 0.00
#> Sample_363 1 0.0000 0.9127 1.00 0.00 0.00
#> Sample_364 1 0.0000 0.9127 1.00 0.00 0.00
#> Sample_366 1 0.0000 0.9127 1.00 0.00 0.00
#> Sample_367 1 0.0000 0.9127 1.00 0.00 0.00
#> Sample_370 1 0.0892 0.9027 0.98 0.00 0.02
#> Sample_374 1 0.0000 0.9127 1.00 0.00 0.00
#> Sample_375 3 0.0000 0.9084 0.00 0.00 1.00
#> Sample_376 1 0.5216 0.6700 0.74 0.00 0.26
#> Sample_378 3 0.0000 0.9084 0.00 0.00 1.00
#> Sample_379 3 0.4291 0.7549 0.18 0.00 0.82
#> Sample_380 1 0.4002 0.7981 0.84 0.00 0.16
#> Sample_382 3 0.0000 0.9084 0.00 0.00 1.00
#> Sample_383 1 0.2537 0.8685 0.92 0.00 0.08
#> Sample_384 1 0.4796 0.7307 0.78 0.00 0.22
#> Sample_387 3 0.2959 0.8432 0.10 0.00 0.90
#> Sample_388 1 0.0000 0.9127 1.00 0.00 0.00
#> Sample_391 1 0.5835 0.5179 0.66 0.00 0.34
#> Sample_392 3 0.6126 0.3159 0.40 0.00 0.60
#> Sample_399 3 0.6244 0.1943 0.44 0.00 0.56
#> Sample_400 3 0.4002 0.7796 0.16 0.00 0.84
#> Sample_401 1 0.6302 0.1116 0.52 0.00 0.48
#> Sample_402 3 0.0000 0.9084 0.00 0.00 1.00
#> Sample_406 1 0.4002 0.7983 0.84 0.00 0.16
#> Sample_407 3 0.0000 0.9084 0.00 0.00 1.00
#> Sample_410 1 0.6126 0.3755 0.60 0.00 0.40
#> Sample_411 3 0.0892 0.8993 0.02 0.00 0.98
#> Sample_413 1 0.4002 0.7997 0.84 0.00 0.16
#> Sample_414 1 0.4002 0.7974 0.84 0.00 0.16
#> Sample_417 3 0.0000 0.9084 0.00 0.00 1.00
#> Sample_418 3 0.0000 0.9084 0.00 0.00 1.00
#> Sample_419 1 0.4796 0.7246 0.78 0.00 0.22
#> Sample_423 3 0.2947 0.8605 0.02 0.06 0.92
#> Sample_426 3 0.6192 0.2618 0.00 0.42 0.58
#> Sample_429 3 0.0000 0.9084 0.00 0.00 1.00
#> Sample_435 3 0.0000 0.9084 0.00 0.00 1.00
#> Sample_441 3 0.0000 0.9084 0.00 0.00 1.00
#> Sample_444 3 0.0000 0.9084 0.00 0.00 1.00
#> Sample_447 3 0.0000 0.9084 0.00 0.00 1.00
#> Sample_449 3 0.0000 0.9084 0.00 0.00 1.00
#> Sample_450 3 0.0000 0.9084 0.00 0.00 1.00
#> Sample_453 3 0.0000 0.9084 0.00 0.00 1.00
#> Sample_454 3 0.0000 0.9084 0.00 0.00 1.00
#> Sample_458 3 0.0000 0.9084 0.00 0.00 1.00
#> Sample_460 3 0.0000 0.9084 0.00 0.00 1.00
#> Sample_462 3 0.0000 0.9084 0.00 0.00 1.00
#> Sample_467 3 0.0000 0.9084 0.00 0.00 1.00
#> Sample_470 3 0.0000 0.9084 0.00 0.00 1.00
#> Sample_471 3 0.0000 0.9084 0.00 0.00 1.00
#> Sample_473 3 0.0000 0.9084 0.00 0.00 1.00
#> Sample_570 2 0.6302 0.0724 0.00 0.52 0.48
#> Sample_571 3 0.0000 0.9084 0.00 0.00 1.00
#> Sample_575 1 0.0000 0.9127 1.00 0.00 0.00
#> Sample_576 3 0.0000 0.9084 0.00 0.00 1.00
#> Sample_578 1 0.0000 0.9127 1.00 0.00 0.00
#> Sample_579 1 0.0000 0.9127 1.00 0.00 0.00
#> Sample_580 1 0.0000 0.9127 1.00 0.00 0.00
#> Sample_581 1 0.0000 0.9127 1.00 0.00 0.00
#> Sample_582 1 0.0000 0.9127 1.00 0.00 0.00
#> Sample_583 1 0.0000 0.9127 1.00 0.00 0.00
#> Sample_584 1 0.0000 0.9127 1.00 0.00 0.00
#> Sample_585 2 0.0000 0.9625 0.00 1.00 0.00
#> Sample_589 2 0.0000 0.9625 0.00 1.00 0.00
#> Sample_613 2 0.0000 0.9625 0.00 1.00 0.00
#> Sample_615 2 0.0000 0.9625 0.00 1.00 0.00
#> Sample_623 2 0.2066 0.9052 0.00 0.94 0.06
#> Sample_624 2 0.3686 0.8070 0.14 0.86 0.00
#> Sample_644 1 0.0000 0.9127 1.00 0.00 0.00
#> Sample_948 1 0.0000 0.9127 1.00 0.00 0.00
#> Sample_949 1 0.6192 0.2925 0.58 0.00 0.42
#> Sample_951 1 0.2959 0.8520 0.90 0.00 0.10
#> Sample_952 3 0.5216 0.6497 0.26 0.00 0.74
#> Sample_953 3 0.1529 0.8866 0.04 0.00 0.96
#> Sample_954 2 0.6302 0.0807 0.00 0.52 0.48
#> Sample_955 1 0.0000 0.9127 1.00 0.00 0.00
#> Sample_956 3 0.0000 0.9084 0.00 0.00 1.00
#> Sample_957 1 0.6045 0.3890 0.62 0.00 0.38
#> Sample_958 1 0.5016 0.6926 0.76 0.00 0.24
#> Sample_959 1 0.0892 0.9027 0.98 0.00 0.02
#> Sample_960 1 0.6045 0.4194 0.62 0.00 0.38
#> Sample_961 1 0.0000 0.9127 1.00 0.00 0.00
#> Sample_963 3 0.2537 0.8613 0.08 0.00 0.92
#> Sample_964 1 0.2959 0.8536 0.90 0.00 0.10
#> Sample_965 3 0.1529 0.8887 0.04 0.00 0.96
#> Sample_966 1 0.6045 0.4289 0.62 0.00 0.38
#> Sample_967 1 0.0000 0.9127 1.00 0.00 0.00
#> Sample_969 1 0.0000 0.9127 1.00 0.00 0.00
#> Sample_970 1 0.0000 0.9127 1.00 0.00 0.00
#> Sample_971 1 0.0000 0.9127 1.00 0.00 0.00
#> Sample_972 1 0.0000 0.9127 1.00 0.00 0.00
#> Sample_973 1 0.5560 0.5797 0.70 0.00 0.30
#> Sample_974 1 0.2066 0.8790 0.94 0.00 0.06
#> Sample_979 3 0.3340 0.8248 0.12 0.00 0.88
#> Sample_984 3 0.5016 0.6722 0.24 0.00 0.76
#> Sample_995 1 0.0000 0.9127 1.00 0.00 0.00
#> Sample_997 1 0.3832 0.8446 0.88 0.02 0.10
#> Sample_998 1 0.0000 0.9127 1.00 0.00 0.00
#> Sample_1000 1 0.7517 0.2167 0.54 0.42 0.04
#> Sample_1001 1 0.0000 0.9127 1.00 0.00 0.00
#> Sample_1002 1 0.1529 0.8921 0.96 0.00 0.04
#> Sample_1003 1 0.0000 0.9127 1.00 0.00 0.00
#> Sample_1005 1 0.4291 0.7585 0.82 0.18 0.00
#> Sample_1006 1 0.0000 0.9127 1.00 0.00 0.00
#> Sample_1011 1 0.0000 0.9127 1.00 0.00 0.00
#> Sample_1012 2 0.0000 0.9625 0.00 1.00 0.00
#> Sample_1013 1 0.6045 0.4262 0.62 0.00 0.38
#> Sample_1053 3 0.5706 0.5100 0.00 0.32 0.68
#> Sample_1054 2 0.6244 0.1941 0.44 0.56 0.00
#> Sample_1055 1 0.2537 0.8674 0.92 0.00 0.08
#> Sample_1057 2 0.0000 0.9625 0.00 1.00 0.00
#> Sample_1060 3 0.6302 0.0385 0.48 0.00 0.52
#> Sample_1061 2 0.0000 0.9625 0.00 1.00 0.00
#> Sample_1189 1 0.0000 0.9127 1.00 0.00 0.00
#> Sample_1190 2 0.0000 0.9625 0.00 1.00 0.00
#> Sample_1191 2 0.0000 0.9625 0.00 1.00 0.00
#> Sample_1193 2 0.0892 0.9431 0.02 0.98 0.00
#> Sample_1194 2 0.0000 0.9625 0.00 1.00 0.00
#> Sample_1195 1 0.4291 0.7587 0.82 0.18 0.00
#> Sample_1196 1 0.0000 0.9127 1.00 0.00 0.00
#> Sample_1197 2 0.5706 0.5278 0.32 0.68 0.00
#> Sample_1198 2 0.0000 0.9625 0.00 1.00 0.00
#> Sample_1199 3 0.0000 0.9084 0.00 0.00 1.00
#> Sample_1200 3 0.0000 0.9084 0.00 0.00 1.00
#> Sample_1201 3 0.0000 0.9084 0.00 0.00 1.00
#> Sample_1202 1 0.6126 0.3772 0.60 0.00 0.40
#> Sample_1203 3 0.0000 0.9084 0.00 0.00 1.00
#> Sample_1204 3 0.0000 0.9084 0.00 0.00 1.00
#> Sample_1205 3 0.0000 0.9084 0.00 0.00 1.00
#> Sample_1206 3 0.2537 0.8608 0.08 0.00 0.92
#> Sample_1207 3 0.0000 0.9084 0.00 0.00 1.00
#> Sample_1209 1 0.0000 0.9127 1.00 0.00 0.00
#> Sample_1210 3 0.4291 0.7267 0.00 0.18 0.82
#> Sample_1211 3 0.0000 0.9084 0.00 0.00 1.00
#> Sample_1212 3 0.2959 0.8433 0.10 0.00 0.90
#> Sample_1213 1 0.0000 0.9127 1.00 0.00 0.00
#> Sample_1214 3 0.0000 0.9084 0.00 0.00 1.00
#> Sample_1215 3 0.0000 0.9084 0.00 0.00 1.00
#> Sample_1219 3 0.0000 0.9084 0.00 0.00 1.00
#> Sample_1220 3 0.5216 0.6396 0.26 0.00 0.74
#> Sample_1221 3 0.0000 0.9084 0.00 0.00 1.00
#> Sample_1549 2 0.0892 0.9440 0.00 0.98 0.02
#> Sample_1553 3 0.1529 0.8886 0.04 0.00 0.96
#> Sample_1581 2 0.0000 0.9625 0.00 1.00 0.00
#> Sample_1597 3 0.4291 0.7269 0.00 0.18 0.82
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> Sample_208 2 0.0000 0.97599 0.00 1.00 0.00 0.00
#> Sample_209 2 0.0000 0.97599 0.00 1.00 0.00 0.00
#> Sample_210 2 0.0000 0.97599 0.00 1.00 0.00 0.00
#> Sample_211 2 0.0000 0.97599 0.00 1.00 0.00 0.00
#> Sample_212 2 0.0000 0.97599 0.00 1.00 0.00 0.00
#> Sample_213 2 0.0000 0.97599 0.00 1.00 0.00 0.00
#> Sample_216 2 0.0000 0.97599 0.00 1.00 0.00 0.00
#> Sample_218 2 0.0000 0.97599 0.00 1.00 0.00 0.00
#> Sample_219 2 0.0000 0.97599 0.00 1.00 0.00 0.00
#> Sample_220 2 0.0000 0.97599 0.00 1.00 0.00 0.00
#> Sample_221 2 0.0000 0.97599 0.00 1.00 0.00 0.00
#> Sample_222 2 0.0000 0.97599 0.00 1.00 0.00 0.00
#> Sample_223 2 0.0707 0.95906 0.00 0.98 0.00 0.02
#> Sample_225 2 0.0000 0.97599 0.00 1.00 0.00 0.00
#> Sample_227 2 0.0000 0.97599 0.00 1.00 0.00 0.00
#> Sample_228 2 0.0707 0.95811 0.00 0.98 0.00 0.02
#> Sample_229 2 0.0000 0.97599 0.00 1.00 0.00 0.00
#> Sample_230 2 0.0000 0.97599 0.00 1.00 0.00 0.00
#> Sample_231 2 0.0000 0.97599 0.00 1.00 0.00 0.00
#> Sample_232 2 0.0000 0.97599 0.00 1.00 0.00 0.00
#> Sample_234 2 0.0000 0.97599 0.00 1.00 0.00 0.00
#> Sample_235 2 0.0000 0.97599 0.00 1.00 0.00 0.00
#> Sample_237 2 0.2706 0.87071 0.08 0.90 0.00 0.02
#> Sample_238 2 0.0000 0.97599 0.00 1.00 0.00 0.00
#> Sample_239 2 0.0000 0.97599 0.00 1.00 0.00 0.00
#> Sample_240 2 0.0000 0.97599 0.00 1.00 0.00 0.00
#> Sample_242 2 0.0000 0.97599 0.00 1.00 0.00 0.00
#> Sample_243 4 0.6686 0.43943 0.20 0.18 0.00 0.62
#> Sample_244 2 0.0000 0.97599 0.00 1.00 0.00 0.00
#> Sample_245 2 0.0000 0.97599 0.00 1.00 0.00 0.00
#> Sample_246 2 0.0000 0.97599 0.00 1.00 0.00 0.00
#> Sample_247 2 0.0000 0.97599 0.00 1.00 0.00 0.00
#> Sample_248 2 0.0000 0.97599 0.00 1.00 0.00 0.00
#> Sample_249 2 0.1211 0.93592 0.04 0.96 0.00 0.00
#> Sample_250 2 0.0000 0.97599 0.00 1.00 0.00 0.00
#> Sample_251 2 0.0000 0.97599 0.00 1.00 0.00 0.00
#> Sample_252 2 0.0000 0.97599 0.00 1.00 0.00 0.00
#> Sample_253 2 0.0000 0.97599 0.00 1.00 0.00 0.00
#> Sample_255 2 0.0000 0.97599 0.00 1.00 0.00 0.00
#> Sample_256 2 0.0000 0.97599 0.00 1.00 0.00 0.00
#> Sample_257 2 0.0000 0.97599 0.00 1.00 0.00 0.00
#> Sample_259 2 0.0000 0.97599 0.00 1.00 0.00 0.00
#> Sample_260 2 0.0000 0.97599 0.00 1.00 0.00 0.00
#> Sample_261 2 0.0000 0.97599 0.00 1.00 0.00 0.00
#> Sample_262 2 0.0000 0.97599 0.00 1.00 0.00 0.00
#> Sample_263 2 0.0000 0.97599 0.00 1.00 0.00 0.00
#> Sample_266 4 0.3975 0.54016 0.00 0.00 0.24 0.76
#> Sample_267 4 0.2921 0.62484 0.14 0.00 0.00 0.86
#> Sample_268 1 0.0707 0.65031 0.98 0.00 0.00 0.02
#> Sample_269 2 0.0000 0.97599 0.00 1.00 0.00 0.00
#> Sample_270 1 0.3172 0.63914 0.84 0.00 0.00 0.16
#> Sample_273 1 0.1211 0.65392 0.96 0.00 0.00 0.04
#> Sample_274 1 0.2011 0.65588 0.92 0.00 0.00 0.08
#> Sample_277 4 0.1211 0.70635 0.04 0.00 0.00 0.96
#> Sample_279 4 0.0707 0.71705 0.02 0.00 0.00 0.98
#> Sample_280 2 0.0000 0.97599 0.00 1.00 0.00 0.00
#> Sample_281 4 0.0707 0.71705 0.02 0.00 0.00 0.98
#> Sample_282 4 0.0707 0.71705 0.02 0.00 0.00 0.98
#> Sample_283 4 0.1411 0.70631 0.02 0.02 0.00 0.96
#> Sample_284 4 0.0707 0.71705 0.02 0.00 0.00 0.98
#> Sample_285 4 0.0707 0.71705 0.02 0.00 0.00 0.98
#> Sample_286 4 0.0707 0.71705 0.02 0.00 0.00 0.98
#> Sample_290 4 0.1411 0.70728 0.02 0.00 0.02 0.96
#> Sample_291 4 0.0707 0.71705 0.02 0.00 0.00 0.98
#> Sample_292 4 0.0707 0.71705 0.02 0.00 0.00 0.98
#> Sample_293 2 0.0000 0.97599 0.00 1.00 0.00 0.00
#> Sample_294 4 0.0707 0.71705 0.02 0.00 0.00 0.98
#> Sample_295 4 0.0707 0.71705 0.02 0.00 0.00 0.98
#> Sample_296 4 0.0707 0.71705 0.02 0.00 0.00 0.98
#> Sample_297 4 0.0707 0.71705 0.02 0.00 0.00 0.98
#> Sample_298 4 0.0707 0.71705 0.02 0.00 0.00 0.98
#> Sample_299 4 0.1211 0.70603 0.04 0.00 0.00 0.96
#> Sample_300 2 0.5619 0.48528 0.00 0.64 0.04 0.32
#> Sample_301 4 0.0707 0.71705 0.02 0.00 0.00 0.98
#> Sample_306 1 0.5271 0.26338 0.64 0.00 0.02 0.34
#> Sample_307 1 0.1211 0.63857 0.96 0.00 0.00 0.04
#> Sample_315 3 0.5106 0.63410 0.04 0.00 0.72 0.24
#> Sample_317 1 0.2647 0.64779 0.88 0.00 0.00 0.12
#> Sample_322 1 0.3400 0.63105 0.82 0.00 0.00 0.18
#> Sample_332 1 0.4994 0.17303 0.52 0.00 0.00 0.48
#> Sample_333 4 0.4907 0.09178 0.42 0.00 0.00 0.58
#> Sample_334 1 0.4855 0.36360 0.60 0.00 0.00 0.40
#> Sample_335 1 0.3975 0.59564 0.76 0.00 0.00 0.24
#> Sample_336 1 0.4134 0.57593 0.74 0.00 0.00 0.26
#> Sample_337 1 0.4790 0.40218 0.62 0.00 0.00 0.38
#> Sample_338 1 0.2921 0.64701 0.86 0.00 0.00 0.14
#> Sample_340 1 0.2011 0.61636 0.92 0.00 0.00 0.08
#> Sample_341 4 0.5594 0.05866 0.46 0.00 0.02 0.52
#> Sample_342 4 0.1411 0.70794 0.02 0.00 0.02 0.96
#> Sample_343 4 0.4994 -0.12756 0.48 0.00 0.00 0.52
#> Sample_344 1 0.4948 0.30065 0.56 0.00 0.00 0.44
#> Sample_345 4 0.6649 0.28507 0.34 0.00 0.10 0.56
#> Sample_346 1 0.4907 0.34122 0.58 0.00 0.00 0.42
#> Sample_347 4 0.4790 0.19227 0.38 0.00 0.00 0.62
#> Sample_348 3 0.2830 0.81956 0.04 0.00 0.90 0.06
#> Sample_349 1 0.7497 0.14002 0.50 0.00 0.26 0.24
#> Sample_353 1 0.7653 0.18028 0.46 0.00 0.30 0.24
#> Sample_354 1 0.3610 0.62046 0.80 0.00 0.00 0.20
#> Sample_356 1 0.3975 0.59370 0.76 0.00 0.00 0.24
#> Sample_357 1 0.4134 0.57627 0.74 0.00 0.00 0.26
#> Sample_359 1 0.2345 0.65424 0.90 0.00 0.00 0.10
#> Sample_361 1 0.3400 0.63067 0.82 0.00 0.00 0.18
#> Sample_363 1 0.3975 0.59370 0.76 0.00 0.00 0.24
#> Sample_364 1 0.3975 0.59370 0.76 0.00 0.00 0.24
#> Sample_366 1 0.3975 0.59370 0.76 0.00 0.00 0.24
#> Sample_367 1 0.3975 0.59370 0.76 0.00 0.00 0.24
#> Sample_370 1 0.1211 0.65392 0.96 0.00 0.00 0.04
#> Sample_374 1 0.3975 0.59370 0.76 0.00 0.00 0.24
#> Sample_375 3 0.1211 0.83693 0.00 0.00 0.96 0.04
#> Sample_376 1 0.5327 0.42775 0.72 0.00 0.06 0.22
#> Sample_378 3 0.0707 0.85812 0.02 0.00 0.98 0.00
#> Sample_379 3 0.7816 0.10830 0.34 0.00 0.40 0.26
#> Sample_380 1 0.5915 0.11115 0.56 0.00 0.04 0.40
#> Sample_382 3 0.1411 0.85177 0.02 0.00 0.96 0.02
#> Sample_383 1 0.0000 0.64565 1.00 0.00 0.00 0.00
#> Sample_384 1 0.3247 0.60079 0.88 0.00 0.06 0.06
#> Sample_387 1 0.4713 0.35087 0.64 0.00 0.36 0.00
#> Sample_388 1 0.2921 0.65143 0.86 0.00 0.00 0.14
#> Sample_391 1 0.1211 0.64400 0.96 0.00 0.04 0.00
#> Sample_392 1 0.2647 0.59511 0.88 0.00 0.12 0.00
#> Sample_399 1 0.4332 0.63383 0.80 0.00 0.04 0.16
#> Sample_400 1 0.5355 0.27587 0.62 0.00 0.36 0.02
#> Sample_401 1 0.0000 0.64565 1.00 0.00 0.00 0.00
#> Sample_402 3 0.1637 0.84204 0.06 0.00 0.94 0.00
#> Sample_406 1 0.2921 0.56710 0.86 0.00 0.00 0.14
#> Sample_407 3 0.3172 0.77104 0.16 0.00 0.84 0.00
#> Sample_410 1 0.6720 0.20920 0.58 0.00 0.12 0.30
#> Sample_411 3 0.5383 0.66914 0.10 0.00 0.74 0.16
#> Sample_413 4 0.6011 0.07061 0.48 0.00 0.04 0.48
#> Sample_414 1 0.5606 -0.07377 0.50 0.00 0.02 0.48
#> Sample_417 3 0.3198 0.80775 0.04 0.00 0.88 0.08
#> Sample_418 3 0.3198 0.80755 0.04 0.00 0.88 0.08
#> Sample_419 1 0.6554 0.04753 0.52 0.00 0.08 0.40
#> Sample_423 4 0.6262 0.02987 0.06 0.00 0.40 0.54
#> Sample_426 3 0.5355 0.43612 0.02 0.36 0.62 0.00
#> Sample_429 3 0.0707 0.85812 0.02 0.00 0.98 0.00
#> Sample_435 3 0.0707 0.85812 0.02 0.00 0.98 0.00
#> Sample_441 3 0.0707 0.85812 0.02 0.00 0.98 0.00
#> Sample_444 3 0.1637 0.84204 0.06 0.00 0.94 0.00
#> Sample_447 3 0.0000 0.85408 0.00 0.00 1.00 0.00
#> Sample_449 3 0.0707 0.85812 0.02 0.00 0.98 0.00
#> Sample_450 3 0.1211 0.85091 0.04 0.00 0.96 0.00
#> Sample_453 3 0.0707 0.85812 0.02 0.00 0.98 0.00
#> Sample_454 3 0.0707 0.85812 0.02 0.00 0.98 0.00
#> Sample_458 3 0.0707 0.85812 0.02 0.00 0.98 0.00
#> Sample_460 3 0.0707 0.85812 0.02 0.00 0.98 0.00
#> Sample_462 3 0.0707 0.85812 0.02 0.00 0.98 0.00
#> Sample_467 3 0.0707 0.85812 0.02 0.00 0.98 0.00
#> Sample_470 3 0.0707 0.85812 0.02 0.00 0.98 0.00
#> Sample_471 3 0.0707 0.85812 0.02 0.00 0.98 0.00
#> Sample_473 3 0.0707 0.85812 0.02 0.00 0.98 0.00
#> Sample_570 3 0.4713 0.43528 0.00 0.36 0.64 0.00
#> Sample_571 3 0.0707 0.85812 0.02 0.00 0.98 0.00
#> Sample_575 1 0.0707 0.65031 0.98 0.00 0.00 0.02
#> Sample_576 3 0.0000 0.85408 0.00 0.00 1.00 0.00
#> Sample_578 1 0.4713 0.44009 0.64 0.00 0.00 0.36
#> Sample_579 1 0.4134 0.57591 0.74 0.00 0.00 0.26
#> Sample_580 4 0.3801 0.52388 0.22 0.00 0.00 0.78
#> Sample_581 1 0.3975 0.59370 0.76 0.00 0.00 0.24
#> Sample_582 4 0.4855 0.14895 0.40 0.00 0.00 0.60
#> Sample_583 1 0.4406 0.53861 0.70 0.00 0.00 0.30
#> Sample_584 1 0.4134 0.57597 0.74 0.00 0.00 0.26
#> Sample_585 2 0.0000 0.97599 0.00 1.00 0.00 0.00
#> Sample_589 2 0.0000 0.97599 0.00 1.00 0.00 0.00
#> Sample_613 2 0.0000 0.97599 0.00 1.00 0.00 0.00
#> Sample_615 2 0.0000 0.97599 0.00 1.00 0.00 0.00
#> Sample_623 2 0.4797 0.61579 0.00 0.72 0.26 0.02
#> Sample_624 2 0.7810 0.10066 0.32 0.50 0.02 0.16
#> Sample_644 1 0.0000 0.64565 1.00 0.00 0.00 0.00
#> Sample_948 1 0.2921 0.64671 0.86 0.00 0.00 0.14
#> Sample_949 1 0.3525 0.64320 0.86 0.00 0.04 0.10
#> Sample_951 4 0.5619 0.38473 0.32 0.00 0.04 0.64
#> Sample_952 1 0.5392 0.39268 0.68 0.00 0.28 0.04
#> Sample_953 3 0.4713 0.43623 0.00 0.00 0.64 0.36
#> Sample_954 3 0.6510 0.30661 0.00 0.38 0.54 0.08
#> Sample_955 1 0.4713 0.44126 0.64 0.00 0.00 0.36
#> Sample_956 3 0.3935 0.76113 0.10 0.00 0.84 0.06
#> Sample_957 1 0.7310 0.21602 0.48 0.00 0.16 0.36
#> Sample_958 1 0.0707 0.63803 0.98 0.00 0.00 0.02
#> Sample_959 1 0.4134 0.43930 0.74 0.00 0.00 0.26
#> Sample_960 1 0.3400 0.63118 0.82 0.00 0.00 0.18
#> Sample_961 4 0.4134 0.47987 0.26 0.00 0.00 0.74
#> Sample_963 3 0.4977 0.22740 0.46 0.00 0.54 0.00
#> Sample_964 1 0.1913 0.65312 0.94 0.00 0.02 0.04
#> Sample_965 1 0.5957 0.25381 0.54 0.00 0.42 0.04
#> Sample_966 1 0.1913 0.63429 0.94 0.00 0.04 0.02
#> Sample_967 1 0.1211 0.65392 0.96 0.00 0.00 0.04
#> Sample_969 1 0.2011 0.65739 0.92 0.00 0.00 0.08
#> Sample_970 1 0.0707 0.65031 0.98 0.00 0.00 0.02
#> Sample_971 1 0.4624 0.47228 0.66 0.00 0.00 0.34
#> Sample_972 1 0.4790 0.40413 0.62 0.00 0.00 0.38
#> Sample_973 1 0.4292 0.63225 0.82 0.00 0.10 0.08
#> Sample_974 1 0.2921 0.64732 0.86 0.00 0.00 0.14
#> Sample_979 3 0.7602 -0.15392 0.38 0.00 0.42 0.20
#> Sample_984 1 0.6797 0.42980 0.60 0.00 0.24 0.16
#> Sample_995 4 0.3172 0.58618 0.16 0.00 0.00 0.84
#> Sample_997 1 0.6005 -0.04713 0.50 0.00 0.04 0.46
#> Sample_998 1 0.4977 0.00501 0.54 0.00 0.00 0.46
#> Sample_1000 4 0.6317 0.46675 0.22 0.08 0.02 0.68
#> Sample_1001 4 0.4994 0.08726 0.48 0.00 0.00 0.52
#> Sample_1002 1 0.5000 -0.09907 0.50 0.00 0.00 0.50
#> Sample_1003 4 0.4977 0.05791 0.46 0.00 0.00 0.54
#> Sample_1005 1 0.4088 0.57188 0.82 0.04 0.00 0.14
#> Sample_1006 1 0.4907 0.17122 0.58 0.00 0.00 0.42
#> Sample_1011 4 0.5000 0.06510 0.50 0.00 0.00 0.50
#> Sample_1012 2 0.0000 0.97599 0.00 1.00 0.00 0.00
#> Sample_1013 1 0.6554 0.04499 0.52 0.00 0.08 0.40
#> Sample_1053 3 0.7577 0.52179 0.04 0.20 0.60 0.16
#> Sample_1054 1 0.5956 0.37293 0.68 0.22 0.00 0.10
#> Sample_1055 1 0.7005 -0.04947 0.48 0.04 0.04 0.44
#> Sample_1057 2 0.0707 0.95970 0.00 0.98 0.02 0.00
#> Sample_1060 1 0.6370 0.35523 0.62 0.00 0.28 0.10
#> Sample_1061 2 0.0000 0.97599 0.00 1.00 0.00 0.00
#> Sample_1189 1 0.4713 0.45786 0.64 0.00 0.00 0.36
#> Sample_1190 2 0.0000 0.97599 0.00 1.00 0.00 0.00
#> Sample_1191 2 0.0000 0.97599 0.00 1.00 0.00 0.00
#> Sample_1193 2 0.1411 0.94101 0.02 0.96 0.00 0.02
#> Sample_1194 2 0.0000 0.97599 0.00 1.00 0.00 0.00
#> Sample_1195 4 0.6382 0.30992 0.34 0.08 0.00 0.58
#> Sample_1196 1 0.1637 0.64241 0.94 0.00 0.00 0.06
#> Sample_1197 4 0.8246 0.22916 0.24 0.30 0.02 0.44
#> Sample_1198 2 0.0000 0.97599 0.00 1.00 0.00 0.00
#> Sample_1199 3 0.0000 0.85408 0.00 0.00 1.00 0.00
#> Sample_1200 3 0.1211 0.85097 0.04 0.00 0.96 0.00
#> Sample_1201 3 0.0000 0.85408 0.00 0.00 1.00 0.00
#> Sample_1202 1 0.2335 0.61831 0.92 0.00 0.06 0.02
#> Sample_1203 3 0.0000 0.85408 0.00 0.00 1.00 0.00
#> Sample_1204 3 0.0000 0.85408 0.00 0.00 1.00 0.00
#> Sample_1205 3 0.3400 0.71958 0.00 0.00 0.82 0.18
#> Sample_1206 3 0.7456 0.21489 0.36 0.00 0.46 0.18
#> Sample_1207 3 0.0000 0.85408 0.00 0.00 1.00 0.00
#> Sample_1209 1 0.3801 0.61112 0.78 0.00 0.00 0.22
#> Sample_1210 3 0.2011 0.80230 0.00 0.08 0.92 0.00
#> Sample_1211 3 0.0707 0.85812 0.02 0.00 0.98 0.00
#> Sample_1212 3 0.5606 0.07792 0.48 0.00 0.50 0.02
#> Sample_1213 1 0.2345 0.63979 0.90 0.00 0.00 0.10
#> Sample_1214 3 0.0000 0.85408 0.00 0.00 1.00 0.00
#> Sample_1215 3 0.0000 0.85408 0.00 0.00 1.00 0.00
#> Sample_1219 3 0.0000 0.85408 0.00 0.00 1.00 0.00
#> Sample_1220 1 0.6510 0.14598 0.54 0.00 0.38 0.08
#> Sample_1221 3 0.0000 0.85408 0.00 0.00 1.00 0.00
#> Sample_1549 2 0.2011 0.90027 0.00 0.92 0.08 0.00
#> Sample_1553 3 0.4624 0.48835 0.34 0.00 0.66 0.00
#> Sample_1581 2 0.0000 0.97599 0.00 1.00 0.00 0.00
#> Sample_1597 3 0.1211 0.83216 0.00 0.04 0.96 0.00
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
As soon as the classes for columns are determined, the signatures that are significantly different between subgroups can be looked for. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. To get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows (which is done by automatically selecting number of clusters).If there are too many signatures, top_signatures = ... can be set to only show the
signatures with the highest FDRs:
# code only for demonstration
# e.g. to show the top 500 most significant rows
tb = get_signature(res, k = ..., top_signatures = 500)
If the signatures are defined as these which are uniquely high in current group, diff_method argument
can be set to "uniquely_high_in_one_group":
# code only for demonstration
tb = get_signature(res, k = ..., diff_method = "uniquely_high_in_one_group")
UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n_sample age(p-value) cell.type(p-value) k
#> ATC:skmeans 239 8.05e-06 0.00464 2
#> ATC:skmeans 227 2.03e-06 0.02260 3
#> ATC:skmeans 182 2.83e-05 0.30317 4
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
Parent node: Node032. Child nodes: Node02111-leaf , Node02112 , Node02121-leaf , Node02122-leaf , Node02211-leaf , Node02212-leaf , Node02221-leaf , Node02222-leaf , Node03211 , Node03212-leaf , Node03221-leaf , Node03222-leaf .
The object with results only for a single top-value method and a single partitioning method can be extracted as:
res = res_rh["0321"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4.
#> On a matrix with 14747 rows and 150 columns.
#> Top rows (901) are extracted by 'ATC' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 150 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_partitions"
#> [7] "compare_signatures" "consensus_heatmap" "dimension_reduction"
#> [10] "functional_enrichment" "get_anno_col" "get_anno"
#> [13] "get_classes" "get_consensus" "get_matrix"
#> [16] "get_membership" "get_param" "get_signatures"
#> [19] "get_stats" "is_best_k" "is_stable_k"
#> [22] "membership_heatmap" "ncol" "nrow"
#> [25] "plot_ecdf" "predict_classes" "rownames"
#> [28] "select_partition_number" "show" "suggest_best_k"
#> [31] "test_to_known_factors" "top_rows_heatmap"
collect_plots() function collects all the plots made from res for all k (number of subgroups)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, higher 1-PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.916 0.920 0.968 0.501 0.500 0.500
#> 3 3 0.501 0.672 0.810 0.325 0.720 0.498
#> 4 4 0.651 0.685 0.839 0.125 0.819 0.529
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 2
Following is the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall subgroup
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> Sample_243 2 0.760 0.7202 0.22 0.78
#> Sample_266 1 0.000 0.9722 1.00 0.00
#> Sample_267 1 0.000 0.9722 1.00 0.00
#> Sample_268 1 0.000 0.9722 1.00 0.00
#> Sample_270 1 0.000 0.9722 1.00 0.00
#> Sample_273 2 0.990 0.2409 0.44 0.56
#> Sample_274 1 0.000 0.9722 1.00 0.00
#> Sample_277 1 0.000 0.9722 1.00 0.00
#> Sample_279 1 0.000 0.9722 1.00 0.00
#> Sample_281 1 0.000 0.9722 1.00 0.00
#> Sample_282 1 0.000 0.9722 1.00 0.00
#> Sample_284 1 0.000 0.9722 1.00 0.00
#> Sample_285 1 0.000 0.9722 1.00 0.00
#> Sample_286 1 0.000 0.9722 1.00 0.00
#> Sample_290 1 0.141 0.9546 0.98 0.02
#> Sample_291 1 0.000 0.9722 1.00 0.00
#> Sample_292 1 0.000 0.9722 1.00 0.00
#> Sample_294 1 0.000 0.9722 1.00 0.00
#> Sample_295 1 0.000 0.9722 1.00 0.00
#> Sample_296 1 0.000 0.9722 1.00 0.00
#> Sample_297 1 0.000 0.9722 1.00 0.00
#> Sample_298 1 0.000 0.9722 1.00 0.00
#> Sample_299 1 0.000 0.9722 1.00 0.00
#> Sample_301 1 0.000 0.9722 1.00 0.00
#> Sample_306 2 0.000 0.9615 0.00 1.00
#> Sample_307 2 0.000 0.9615 0.00 1.00
#> Sample_315 2 0.000 0.9615 0.00 1.00
#> Sample_317 2 0.000 0.9615 0.00 1.00
#> Sample_322 1 0.000 0.9722 1.00 0.00
#> Sample_332 1 0.000 0.9722 1.00 0.00
#> Sample_333 1 0.000 0.9722 1.00 0.00
#> Sample_334 1 0.000 0.9722 1.00 0.00
#> Sample_335 1 0.000 0.9722 1.00 0.00
#> Sample_336 1 0.000 0.9722 1.00 0.00
#> Sample_337 1 0.000 0.9722 1.00 0.00
#> Sample_338 1 0.634 0.7963 0.84 0.16
#> Sample_340 2 0.000 0.9615 0.00 1.00
#> Sample_341 2 0.242 0.9283 0.04 0.96
#> Sample_342 1 0.000 0.9722 1.00 0.00
#> Sample_343 1 0.000 0.9722 1.00 0.00
#> Sample_344 2 0.000 0.9615 0.00 1.00
#> Sample_345 2 0.000 0.9615 0.00 1.00
#> Sample_346 2 0.000 0.9615 0.00 1.00
#> Sample_347 1 0.000 0.9722 1.00 0.00
#> Sample_348 2 0.000 0.9615 0.00 1.00
#> Sample_349 2 0.000 0.9615 0.00 1.00
#> Sample_353 1 0.990 0.1962 0.56 0.44
#> Sample_354 1 0.000 0.9722 1.00 0.00
#> Sample_356 1 0.000 0.9722 1.00 0.00
#> Sample_357 1 0.000 0.9722 1.00 0.00
#> Sample_359 1 0.000 0.9722 1.00 0.00
#> Sample_361 1 0.000 0.9722 1.00 0.00
#> Sample_363 1 0.000 0.9722 1.00 0.00
#> Sample_364 1 0.000 0.9722 1.00 0.00
#> Sample_366 1 0.000 0.9722 1.00 0.00
#> Sample_367 1 0.000 0.9722 1.00 0.00
#> Sample_370 2 0.141 0.9459 0.02 0.98
#> Sample_374 1 0.000 0.9722 1.00 0.00
#> Sample_375 2 0.402 0.8887 0.08 0.92
#> Sample_376 2 0.000 0.9615 0.00 1.00
#> Sample_378 2 0.000 0.9615 0.00 1.00
#> Sample_379 2 0.000 0.9615 0.00 1.00
#> Sample_380 2 0.000 0.9615 0.00 1.00
#> Sample_382 2 0.000 0.9615 0.00 1.00
#> Sample_383 2 0.000 0.9615 0.00 1.00
#> Sample_384 2 0.000 0.9615 0.00 1.00
#> Sample_387 2 0.000 0.9615 0.00 1.00
#> Sample_388 1 0.000 0.9722 1.00 0.00
#> Sample_391 2 0.000 0.9615 0.00 1.00
#> Sample_392 2 0.000 0.9615 0.00 1.00
#> Sample_399 2 0.141 0.9458 0.02 0.98
#> Sample_400 2 0.000 0.9615 0.00 1.00
#> Sample_401 2 0.000 0.9615 0.00 1.00
#> Sample_406 2 0.000 0.9615 0.00 1.00
#> Sample_407 2 0.000 0.9615 0.00 1.00
#> Sample_410 2 0.000 0.9615 0.00 1.00
#> Sample_411 2 0.000 0.9615 0.00 1.00
#> Sample_413 2 0.000 0.9615 0.00 1.00
#> Sample_414 2 0.000 0.9615 0.00 1.00
#> Sample_417 2 0.000 0.9615 0.00 1.00
#> Sample_418 2 0.000 0.9615 0.00 1.00
#> Sample_419 2 0.000 0.9615 0.00 1.00
#> Sample_444 2 0.000 0.9615 0.00 1.00
#> Sample_450 2 0.000 0.9615 0.00 1.00
#> Sample_454 2 0.000 0.9615 0.00 1.00
#> Sample_460 2 0.000 0.9615 0.00 1.00
#> Sample_462 2 0.000 0.9615 0.00 1.00
#> Sample_470 2 0.000 0.9615 0.00 1.00
#> Sample_473 2 0.000 0.9615 0.00 1.00
#> Sample_575 2 0.000 0.9615 0.00 1.00
#> Sample_578 1 0.000 0.9722 1.00 0.00
#> Sample_579 1 0.000 0.9722 1.00 0.00
#> Sample_580 1 0.000 0.9722 1.00 0.00
#> Sample_581 1 0.000 0.9722 1.00 0.00
#> Sample_582 1 0.000 0.9722 1.00 0.00
#> Sample_583 1 0.000 0.9722 1.00 0.00
#> Sample_584 1 0.000 0.9722 1.00 0.00
#> Sample_644 2 0.981 0.2804 0.42 0.58
#> Sample_948 2 0.795 0.6863 0.24 0.76
#> Sample_949 2 0.999 0.0884 0.48 0.52
#> Sample_951 1 0.000 0.9722 1.00 0.00
#> Sample_952 2 0.000 0.9615 0.00 1.00
#> Sample_953 1 0.000 0.9722 1.00 0.00
#> Sample_955 1 0.000 0.9722 1.00 0.00
#> Sample_956 2 0.000 0.9615 0.00 1.00
#> Sample_957 1 0.000 0.9722 1.00 0.00
#> Sample_958 2 0.000 0.9615 0.00 1.00
#> Sample_959 2 0.000 0.9615 0.00 1.00
#> Sample_960 2 0.000 0.9615 0.00 1.00
#> Sample_961 1 0.000 0.9722 1.00 0.00
#> Sample_963 2 0.000 0.9615 0.00 1.00
#> Sample_964 1 0.995 0.1063 0.54 0.46
#> Sample_965 2 0.000 0.9615 0.00 1.00
#> Sample_966 2 0.000 0.9615 0.00 1.00
#> Sample_967 1 0.000 0.9722 1.00 0.00
#> Sample_969 1 0.584 0.8273 0.86 0.14
#> Sample_970 2 0.141 0.9460 0.02 0.98
#> Sample_971 1 0.000 0.9722 1.00 0.00
#> Sample_972 1 0.000 0.9722 1.00 0.00
#> Sample_973 2 0.000 0.9615 0.00 1.00
#> Sample_974 1 0.000 0.9722 1.00 0.00
#> Sample_979 2 0.000 0.9615 0.00 1.00
#> Sample_984 2 0.000 0.9615 0.00 1.00
#> Sample_995 1 0.141 0.9547 0.98 0.02
#> Sample_997 2 0.000 0.9615 0.00 1.00
#> Sample_998 2 0.000 0.9615 0.00 1.00
#> Sample_1000 2 0.000 0.9615 0.00 1.00
#> Sample_1001 2 0.141 0.9460 0.02 0.98
#> Sample_1002 2 0.000 0.9615 0.00 1.00
#> Sample_1003 1 0.000 0.9722 1.00 0.00
#> Sample_1005 2 0.000 0.9615 0.00 1.00
#> Sample_1006 1 0.827 0.6467 0.74 0.26
#> Sample_1011 2 0.141 0.9460 0.02 0.98
#> Sample_1013 2 0.000 0.9615 0.00 1.00
#> Sample_1054 2 0.000 0.9615 0.00 1.00
#> Sample_1055 1 0.795 0.6842 0.76 0.24
#> Sample_1060 2 0.000 0.9615 0.00 1.00
#> Sample_1189 1 0.000 0.9722 1.00 0.00
#> Sample_1195 2 0.680 0.7749 0.18 0.82
#> Sample_1196 2 0.971 0.3558 0.40 0.60
#> Sample_1200 2 0.000 0.9615 0.00 1.00
#> Sample_1201 2 0.000 0.9615 0.00 1.00
#> Sample_1202 2 0.000 0.9615 0.00 1.00
#> Sample_1206 2 0.000 0.9615 0.00 1.00
#> Sample_1209 1 0.327 0.9162 0.94 0.06
#> Sample_1211 2 0.000 0.9615 0.00 1.00
#> Sample_1212 2 0.000 0.9615 0.00 1.00
#> Sample_1213 2 0.958 0.4086 0.38 0.62
#> Sample_1220 2 0.000 0.9615 0.00 1.00
#> Sample_1553 2 0.000 0.9615 0.00 1.00
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> Sample_243 3 0.6793 0.72258 0.10 0.16 0.74
#> Sample_266 3 0.6280 0.13617 0.46 0.00 0.54
#> Sample_267 3 0.6126 0.32259 0.40 0.00 0.60
#> Sample_268 1 0.6053 0.60958 0.72 0.02 0.26
#> Sample_270 1 0.4796 0.60902 0.78 0.00 0.22
#> Sample_273 1 0.9657 0.17986 0.46 0.24 0.30
#> Sample_274 1 0.5216 0.63021 0.74 0.00 0.26
#> Sample_277 1 0.2959 0.83408 0.90 0.00 0.10
#> Sample_279 1 0.2959 0.83408 0.90 0.00 0.10
#> Sample_281 1 0.2959 0.83408 0.90 0.00 0.10
#> Sample_282 1 0.2959 0.83408 0.90 0.00 0.10
#> Sample_284 1 0.2959 0.83408 0.90 0.00 0.10
#> Sample_285 1 0.2959 0.83408 0.90 0.00 0.10
#> Sample_286 1 0.2959 0.83408 0.90 0.00 0.10
#> Sample_290 1 0.2959 0.83408 0.90 0.00 0.10
#> Sample_291 1 0.2959 0.83408 0.90 0.00 0.10
#> Sample_292 1 0.2959 0.83408 0.90 0.00 0.10
#> Sample_294 1 0.2959 0.83408 0.90 0.00 0.10
#> Sample_295 1 0.2959 0.83408 0.90 0.00 0.10
#> Sample_296 1 0.2959 0.83408 0.90 0.00 0.10
#> Sample_297 1 0.2959 0.83408 0.90 0.00 0.10
#> Sample_298 1 0.2959 0.83408 0.90 0.00 0.10
#> Sample_299 1 0.2959 0.83408 0.90 0.00 0.10
#> Sample_301 1 0.2959 0.83408 0.90 0.00 0.10
#> Sample_306 2 0.0892 0.76705 0.00 0.98 0.02
#> Sample_307 2 0.0892 0.76705 0.00 0.98 0.02
#> Sample_315 2 0.0000 0.77969 0.00 1.00 0.00
#> Sample_317 3 0.5835 0.66511 0.00 0.34 0.66
#> Sample_322 1 0.4291 0.63689 0.82 0.00 0.18
#> Sample_332 3 0.5835 0.65943 0.34 0.00 0.66
#> Sample_333 3 0.5835 0.65943 0.34 0.00 0.66
#> Sample_334 3 0.5835 0.65943 0.34 0.00 0.66
#> Sample_335 3 0.5706 0.66755 0.32 0.00 0.68
#> Sample_336 3 0.5706 0.66755 0.32 0.00 0.68
#> Sample_337 3 0.5835 0.65943 0.34 0.00 0.66
#> Sample_338 3 0.4555 0.55516 0.20 0.00 0.80
#> Sample_340 2 0.6302 -0.00498 0.00 0.52 0.48
#> Sample_341 3 0.5835 0.66511 0.00 0.34 0.66
#> Sample_342 3 0.7702 0.72290 0.14 0.18 0.68
#> Sample_343 3 0.7277 0.69851 0.28 0.06 0.66
#> Sample_344 3 0.5835 0.66511 0.00 0.34 0.66
#> Sample_345 3 0.6192 0.55523 0.00 0.42 0.58
#> Sample_346 3 0.5835 0.66511 0.00 0.34 0.66
#> Sample_347 3 0.6956 0.68867 0.30 0.04 0.66
#> Sample_348 2 0.0000 0.77969 0.00 1.00 0.00
#> Sample_349 2 0.0000 0.77969 0.00 1.00 0.00
#> Sample_353 1 0.9462 -0.06685 0.42 0.40 0.18
#> Sample_354 1 0.0000 0.82788 1.00 0.00 0.00
#> Sample_356 1 0.0000 0.82788 1.00 0.00 0.00
#> Sample_357 1 0.0000 0.82788 1.00 0.00 0.00
#> Sample_359 1 0.0000 0.82788 1.00 0.00 0.00
#> Sample_361 1 0.0000 0.82788 1.00 0.00 0.00
#> Sample_363 1 0.0000 0.82788 1.00 0.00 0.00
#> Sample_364 1 0.0000 0.82788 1.00 0.00 0.00
#> Sample_366 1 0.0000 0.82788 1.00 0.00 0.00
#> Sample_367 1 0.0000 0.82788 1.00 0.00 0.00
#> Sample_370 1 0.7979 0.09140 0.50 0.44 0.06
#> Sample_374 1 0.0000 0.82788 1.00 0.00 0.00
#> Sample_375 3 0.5835 0.66511 0.00 0.34 0.66
#> Sample_376 2 0.2066 0.73536 0.00 0.94 0.06
#> Sample_378 2 0.0000 0.77969 0.00 1.00 0.00
#> Sample_379 2 0.0000 0.77969 0.00 1.00 0.00
#> Sample_380 2 0.2066 0.73536 0.00 0.94 0.06
#> Sample_382 2 0.5706 0.20395 0.00 0.68 0.32
#> Sample_383 2 0.0892 0.78211 0.00 0.98 0.02
#> Sample_384 2 0.0892 0.76766 0.00 0.98 0.02
#> Sample_387 3 0.5835 0.66511 0.00 0.34 0.66
#> Sample_388 1 0.4796 0.67312 0.78 0.00 0.22
#> Sample_391 3 0.5835 0.66511 0.00 0.34 0.66
#> Sample_392 2 0.2066 0.73371 0.00 0.94 0.06
#> Sample_399 3 0.5948 0.64286 0.00 0.36 0.64
#> Sample_400 2 0.2066 0.78424 0.00 0.94 0.06
#> Sample_401 2 0.2537 0.77177 0.00 0.92 0.08
#> Sample_406 2 0.4555 0.75906 0.00 0.80 0.20
#> Sample_407 2 0.5216 0.38339 0.00 0.74 0.26
#> Sample_410 2 0.3340 0.78270 0.00 0.88 0.12
#> Sample_411 2 0.2537 0.78397 0.00 0.92 0.08
#> Sample_413 2 0.5706 0.70987 0.00 0.68 0.32
#> Sample_414 2 0.5560 0.71886 0.00 0.70 0.30
#> Sample_417 2 0.0000 0.77969 0.00 1.00 0.00
#> Sample_418 2 0.0000 0.77969 0.00 1.00 0.00
#> Sample_419 2 0.3686 0.78069 0.00 0.86 0.14
#> Sample_444 2 0.0000 0.77969 0.00 1.00 0.00
#> Sample_450 2 0.1529 0.75200 0.00 0.96 0.04
#> Sample_454 2 0.1529 0.75200 0.00 0.96 0.04
#> Sample_460 2 0.0000 0.77969 0.00 1.00 0.00
#> Sample_462 2 0.0000 0.77969 0.00 1.00 0.00
#> Sample_470 2 0.0000 0.77969 0.00 1.00 0.00
#> Sample_473 2 0.0000 0.77969 0.00 1.00 0.00
#> Sample_575 2 0.4002 0.77358 0.00 0.84 0.16
#> Sample_578 1 0.2537 0.75523 0.92 0.00 0.08
#> Sample_579 1 0.0000 0.82788 1.00 0.00 0.00
#> Sample_580 1 0.0000 0.82788 1.00 0.00 0.00
#> Sample_581 1 0.0892 0.82309 0.98 0.00 0.02
#> Sample_582 1 0.0000 0.82788 1.00 0.00 0.00
#> Sample_583 1 0.0000 0.82788 1.00 0.00 0.00
#> Sample_584 1 0.0000 0.82788 1.00 0.00 0.00
#> Sample_644 2 0.9953 0.27554 0.32 0.38 0.30
#> Sample_948 1 0.9912 -0.02419 0.40 0.30 0.30
#> Sample_949 3 0.7277 0.69295 0.06 0.28 0.66
#> Sample_951 3 0.6803 0.67825 0.28 0.04 0.68
#> Sample_952 2 0.3340 0.78233 0.00 0.88 0.12
#> Sample_953 3 0.7112 0.69173 0.26 0.06 0.68
#> Sample_955 3 0.5835 0.65943 0.34 0.00 0.66
#> Sample_956 3 0.5835 0.66511 0.00 0.34 0.66
#> Sample_957 3 0.5835 0.65943 0.34 0.00 0.66
#> Sample_958 3 0.5397 0.40761 0.00 0.28 0.72
#> Sample_959 2 0.5835 0.69873 0.00 0.66 0.34
#> Sample_960 3 0.5835 0.66511 0.00 0.34 0.66
#> Sample_961 1 0.2959 0.83408 0.90 0.00 0.10
#> Sample_963 3 0.6280 0.38176 0.00 0.46 0.54
#> Sample_964 3 0.5147 0.68163 0.18 0.02 0.80
#> Sample_965 3 0.5835 0.66511 0.00 0.34 0.66
#> Sample_966 2 0.6302 -0.35848 0.00 0.52 0.48
#> Sample_967 3 0.4555 0.67104 0.20 0.00 0.80
#> Sample_969 3 0.5667 0.56637 0.14 0.06 0.80
#> Sample_970 2 0.8472 0.53802 0.10 0.54 0.36
#> Sample_971 3 0.5835 0.65943 0.34 0.00 0.66
#> Sample_972 3 0.5835 0.65943 0.34 0.00 0.66
#> Sample_973 3 0.6045 0.61836 0.00 0.38 0.62
#> Sample_974 3 0.5706 0.66733 0.32 0.00 0.68
#> Sample_979 3 0.5835 0.66511 0.00 0.34 0.66
#> Sample_984 2 0.0000 0.77969 0.00 1.00 0.00
#> Sample_995 1 0.9863 0.13415 0.40 0.26 0.34
#> Sample_997 2 0.5835 0.69873 0.00 0.66 0.34
#> Sample_998 2 0.5835 0.69873 0.00 0.66 0.34
#> Sample_1000 2 0.5835 0.69873 0.00 0.66 0.34
#> Sample_1001 2 0.5835 0.69873 0.00 0.66 0.34
#> Sample_1002 2 0.5835 0.69873 0.00 0.66 0.34
#> Sample_1003 1 0.6651 0.61073 0.64 0.02 0.34
#> Sample_1005 2 0.5835 0.69873 0.00 0.66 0.34
#> Sample_1006 3 0.6758 -0.28720 0.02 0.36 0.62
#> Sample_1011 2 0.5835 0.69873 0.00 0.66 0.34
#> Sample_1013 2 0.5706 0.70993 0.00 0.68 0.32
#> Sample_1054 2 0.6956 0.69126 0.04 0.66 0.30
#> Sample_1055 2 0.8683 0.58239 0.12 0.54 0.34
#> Sample_1060 2 0.5835 0.69873 0.00 0.66 0.34
#> Sample_1189 1 0.0892 0.83087 0.98 0.00 0.02
#> Sample_1195 2 0.8054 0.63038 0.08 0.58 0.34
#> Sample_1196 2 0.9773 0.39432 0.24 0.42 0.34
#> Sample_1200 2 0.0000 0.77969 0.00 1.00 0.00
#> Sample_1201 2 0.4291 0.55828 0.00 0.82 0.18
#> Sample_1202 2 0.3686 0.77879 0.00 0.86 0.14
#> Sample_1206 2 0.3686 0.77927 0.00 0.86 0.14
#> Sample_1209 3 0.7515 0.71827 0.22 0.10 0.68
#> Sample_1211 2 0.0000 0.77969 0.00 1.00 0.00
#> Sample_1212 2 0.3686 0.77927 0.00 0.86 0.14
#> Sample_1213 3 0.4449 0.60365 0.10 0.04 0.86
#> Sample_1220 2 0.4291 0.76743 0.00 0.82 0.18
#> Sample_1553 2 0.5216 0.73568 0.00 0.74 0.26
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> Sample_243 3 0.6930 0.6398 0.14 0.12 0.68 0.06
#> Sample_266 3 0.4713 0.3360 0.36 0.00 0.64 0.00
#> Sample_267 3 0.2011 0.7296 0.08 0.00 0.92 0.00
#> Sample_268 4 0.1211 0.8422 0.04 0.00 0.00 0.96
#> Sample_270 3 0.7654 0.0671 0.22 0.00 0.44 0.34
#> Sample_273 4 0.1637 0.8337 0.06 0.00 0.00 0.94
#> Sample_274 4 0.2411 0.8219 0.04 0.00 0.04 0.92
#> Sample_277 1 0.1411 0.8053 0.96 0.00 0.02 0.02
#> Sample_279 1 0.1411 0.8053 0.96 0.00 0.02 0.02
#> Sample_281 1 0.1411 0.8053 0.96 0.00 0.02 0.02
#> Sample_282 1 0.1411 0.8053 0.96 0.00 0.02 0.02
#> Sample_284 1 0.1411 0.8053 0.96 0.00 0.02 0.02
#> Sample_285 1 0.1411 0.8053 0.96 0.00 0.02 0.02
#> Sample_286 1 0.1411 0.8053 0.96 0.00 0.02 0.02
#> Sample_290 1 0.1411 0.8053 0.96 0.00 0.02 0.02
#> Sample_291 1 0.1411 0.8053 0.96 0.00 0.02 0.02
#> Sample_292 1 0.1411 0.8053 0.96 0.00 0.02 0.02
#> Sample_294 1 0.7550 0.5236 0.48 0.00 0.30 0.22
#> Sample_295 1 0.7550 0.5236 0.48 0.00 0.30 0.22
#> Sample_296 1 0.7550 0.5236 0.48 0.00 0.30 0.22
#> Sample_297 1 0.7285 0.5561 0.52 0.00 0.30 0.18
#> Sample_298 1 0.7550 0.5236 0.48 0.00 0.30 0.22
#> Sample_299 1 0.7016 0.5596 0.54 0.00 0.32 0.14
#> Sample_301 1 0.7550 0.5236 0.48 0.00 0.30 0.22
#> Sample_306 2 0.0707 0.8272 0.00 0.98 0.00 0.02
#> Sample_307 2 0.0707 0.8200 0.00 0.98 0.02 0.00
#> Sample_315 2 0.0000 0.8290 0.00 1.00 0.00 0.00
#> Sample_317 3 0.4406 0.5777 0.00 0.30 0.70 0.00
#> Sample_322 1 0.6110 0.5287 0.66 0.00 0.24 0.10
#> Sample_332 3 0.0000 0.7826 0.00 0.00 1.00 0.00
#> Sample_333 3 0.0000 0.7826 0.00 0.00 1.00 0.00
#> Sample_334 3 0.0707 0.7798 0.02 0.00 0.98 0.00
#> Sample_335 3 0.1211 0.7723 0.04 0.00 0.96 0.00
#> Sample_336 3 0.1913 0.7634 0.04 0.00 0.94 0.02
#> Sample_337 3 0.0000 0.7826 0.00 0.00 1.00 0.00
#> Sample_338 4 0.5636 0.5563 0.06 0.00 0.26 0.68
#> Sample_340 2 0.8293 0.1970 0.02 0.42 0.24 0.32
#> Sample_341 3 0.4522 0.5499 0.00 0.32 0.68 0.00
#> Sample_342 3 0.0707 0.7833 0.00 0.02 0.98 0.00
#> Sample_343 3 0.0000 0.7826 0.00 0.00 1.00 0.00
#> Sample_344 3 0.4994 0.1819 0.00 0.48 0.52 0.00
#> Sample_345 2 0.4522 0.4347 0.00 0.68 0.32 0.00
#> Sample_346 3 0.4406 0.5829 0.00 0.30 0.70 0.00
#> Sample_347 3 0.0000 0.7826 0.00 0.00 1.00 0.00
#> Sample_348 2 0.0000 0.8290 0.00 1.00 0.00 0.00
#> Sample_349 2 0.1211 0.8217 0.00 0.96 0.00 0.04
#> Sample_353 2 0.5327 0.6037 0.22 0.72 0.06 0.00
#> Sample_354 1 0.1211 0.8011 0.96 0.00 0.00 0.04
#> Sample_356 1 0.1211 0.8011 0.96 0.00 0.00 0.04
#> Sample_357 1 0.1211 0.8011 0.96 0.00 0.00 0.04
#> Sample_359 1 0.1211 0.8011 0.96 0.00 0.00 0.04
#> Sample_361 1 0.1637 0.7903 0.94 0.00 0.00 0.06
#> Sample_363 1 0.1211 0.8011 0.96 0.00 0.00 0.04
#> Sample_364 1 0.1211 0.8011 0.96 0.00 0.00 0.04
#> Sample_366 1 0.1211 0.8011 0.96 0.00 0.00 0.04
#> Sample_367 1 0.1211 0.8011 0.96 0.00 0.00 0.04
#> Sample_370 1 0.5902 0.5552 0.70 0.16 0.00 0.14
#> Sample_374 1 0.1211 0.8011 0.96 0.00 0.00 0.04
#> Sample_375 3 0.3801 0.6789 0.00 0.22 0.78 0.00
#> Sample_376 2 0.0707 0.8200 0.00 0.98 0.02 0.00
#> Sample_378 2 0.0000 0.8290 0.00 1.00 0.00 0.00
#> Sample_379 2 0.0707 0.8272 0.00 0.98 0.00 0.02
#> Sample_380 2 0.0000 0.8290 0.00 1.00 0.00 0.00
#> Sample_382 2 0.1637 0.7926 0.00 0.94 0.06 0.00
#> Sample_383 2 0.1211 0.8130 0.00 0.96 0.00 0.04
#> Sample_384 2 0.0707 0.8270 0.00 0.98 0.00 0.02
#> Sample_387 2 0.4907 0.1435 0.00 0.58 0.42 0.00
#> Sample_388 4 0.5106 0.5890 0.04 0.00 0.24 0.72
#> Sample_391 2 0.6382 0.2348 0.00 0.58 0.34 0.08
#> Sample_392 2 0.0000 0.8290 0.00 1.00 0.00 0.00
#> Sample_399 3 0.4994 0.1932 0.00 0.48 0.52 0.00
#> Sample_400 2 0.2011 0.7979 0.00 0.92 0.00 0.08
#> Sample_401 2 0.3335 0.7853 0.00 0.86 0.02 0.12
#> Sample_406 2 0.4277 0.6341 0.00 0.72 0.00 0.28
#> Sample_407 2 0.1211 0.8064 0.00 0.96 0.04 0.00
#> Sample_410 2 0.3975 0.6561 0.00 0.76 0.00 0.24
#> Sample_411 2 0.2921 0.7541 0.00 0.86 0.00 0.14
#> Sample_413 4 0.4855 0.2671 0.00 0.40 0.00 0.60
#> Sample_414 2 0.4977 0.2028 0.00 0.54 0.00 0.46
#> Sample_417 2 0.0707 0.8270 0.00 0.98 0.00 0.02
#> Sample_418 2 0.0707 0.8270 0.00 0.98 0.00 0.02
#> Sample_419 2 0.3975 0.6531 0.00 0.76 0.00 0.24
#> Sample_444 2 0.0000 0.8290 0.00 1.00 0.00 0.00
#> Sample_450 2 0.0000 0.8290 0.00 1.00 0.00 0.00
#> Sample_454 2 0.0000 0.8290 0.00 1.00 0.00 0.00
#> Sample_460 2 0.0000 0.8290 0.00 1.00 0.00 0.00
#> Sample_462 2 0.0000 0.8290 0.00 1.00 0.00 0.00
#> Sample_470 2 0.0000 0.8290 0.00 1.00 0.00 0.00
#> Sample_473 2 0.0000 0.8290 0.00 1.00 0.00 0.00
#> Sample_575 2 0.4713 0.5236 0.00 0.64 0.00 0.36
#> Sample_578 3 0.4994 -0.2822 0.48 0.00 0.52 0.00
#> Sample_579 1 0.5636 0.6576 0.68 0.00 0.26 0.06
#> Sample_580 1 0.4406 0.6491 0.70 0.00 0.30 0.00
#> Sample_581 1 0.4292 0.7606 0.82 0.00 0.08 0.10
#> Sample_582 1 0.4624 0.6117 0.66 0.00 0.34 0.00
#> Sample_583 1 0.5767 0.6619 0.66 0.00 0.28 0.06
#> Sample_584 1 0.3821 0.7660 0.84 0.00 0.12 0.04
#> Sample_644 4 0.2611 0.8347 0.04 0.02 0.02 0.92
#> Sample_948 4 0.1637 0.8337 0.06 0.00 0.00 0.94
#> Sample_949 3 0.2647 0.7596 0.00 0.12 0.88 0.00
#> Sample_951 3 0.0000 0.7826 0.00 0.00 1.00 0.00
#> Sample_952 2 0.4277 0.6321 0.00 0.72 0.00 0.28
#> Sample_953 3 0.0000 0.7826 0.00 0.00 1.00 0.00
#> Sample_955 3 0.0707 0.7837 0.00 0.02 0.98 0.00
#> Sample_956 3 0.4134 0.6353 0.00 0.26 0.74 0.00
#> Sample_957 3 0.0000 0.7826 0.00 0.00 1.00 0.00
#> Sample_958 4 0.7359 0.3405 0.04 0.08 0.32 0.56
#> Sample_959 4 0.2011 0.8464 0.00 0.08 0.00 0.92
#> Sample_960 3 0.3172 0.7317 0.00 0.16 0.84 0.00
#> Sample_961 1 0.7653 0.4960 0.46 0.00 0.30 0.24
#> Sample_963 2 0.6843 -0.0957 0.00 0.46 0.44 0.10
#> Sample_964 3 0.3106 0.7704 0.02 0.04 0.90 0.04
#> Sample_965 3 0.4134 0.6353 0.00 0.26 0.74 0.00
#> Sample_966 2 0.5636 0.4943 0.00 0.68 0.26 0.06
#> Sample_967 3 0.3525 0.7052 0.04 0.00 0.86 0.10
#> Sample_969 4 0.5957 0.2384 0.04 0.00 0.42 0.54
#> Sample_970 4 0.1211 0.8422 0.04 0.00 0.00 0.96
#> Sample_971 3 0.0707 0.7798 0.02 0.00 0.98 0.00
#> Sample_972 3 0.0000 0.7826 0.00 0.00 1.00 0.00
#> Sample_973 2 0.5957 0.0698 0.00 0.54 0.42 0.04
#> Sample_974 3 0.1913 0.7628 0.02 0.00 0.94 0.04
#> Sample_979 3 0.4994 0.1918 0.00 0.48 0.52 0.00
#> Sample_984 2 0.0000 0.8290 0.00 1.00 0.00 0.00
#> Sample_995 4 0.1913 0.8525 0.04 0.02 0.00 0.94
#> Sample_997 4 0.1637 0.8611 0.00 0.06 0.00 0.94
#> Sample_998 4 0.1637 0.8611 0.00 0.06 0.00 0.94
#> Sample_1000 4 0.1913 0.8610 0.02 0.04 0.00 0.94
#> Sample_1001 4 0.1913 0.8610 0.02 0.04 0.00 0.94
#> Sample_1002 4 0.1637 0.8611 0.00 0.06 0.00 0.94
#> Sample_1003 4 0.1637 0.8406 0.06 0.00 0.00 0.94
#> Sample_1005 4 0.1637 0.8611 0.00 0.06 0.00 0.94
#> Sample_1006 4 0.4491 0.7797 0.00 0.06 0.14 0.80
#> Sample_1011 4 0.1913 0.8610 0.02 0.04 0.00 0.94
#> Sample_1013 4 0.2011 0.8507 0.00 0.08 0.00 0.92
#> Sample_1054 4 0.1913 0.8609 0.02 0.04 0.00 0.94
#> Sample_1055 4 0.1913 0.8610 0.02 0.04 0.00 0.94
#> Sample_1060 4 0.1637 0.8611 0.00 0.06 0.00 0.94
#> Sample_1189 1 0.0707 0.8040 0.98 0.00 0.00 0.02
#> Sample_1195 4 0.1913 0.8574 0.04 0.02 0.00 0.94
#> Sample_1196 4 0.1211 0.8422 0.04 0.00 0.00 0.96
#> Sample_1200 2 0.1211 0.8217 0.00 0.96 0.00 0.04
#> Sample_1201 2 0.1211 0.8064 0.00 0.96 0.04 0.00
#> Sample_1202 2 0.4277 0.6075 0.00 0.72 0.00 0.28
#> Sample_1206 2 0.4134 0.6346 0.00 0.74 0.00 0.26
#> Sample_1209 3 0.0000 0.7826 0.00 0.00 1.00 0.00
#> Sample_1211 2 0.0000 0.8290 0.00 1.00 0.00 0.00
#> Sample_1212 2 0.4277 0.6083 0.00 0.72 0.00 0.28
#> Sample_1213 3 0.7572 0.1169 0.04 0.08 0.48 0.40
#> Sample_1220 2 0.4522 0.5438 0.00 0.68 0.00 0.32
#> Sample_1553 4 0.4936 0.5596 0.00 0.28 0.02 0.70
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
As soon as the classes for columns are determined, the signatures that are significantly different between subgroups can be looked for. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. To get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows (which is done by automatically selecting number of clusters).If there are too many signatures, top_signatures = ... can be set to only show the
signatures with the highest FDRs:
# code only for demonstration
# e.g. to show the top 500 most significant rows
tb = get_signature(res, k = ..., top_signatures = 500)
If the signatures are defined as these which are uniquely high in current group, diff_method argument
can be set to "uniquely_high_in_one_group":
# code only for demonstration
tb = get_signature(res, k = ..., diff_method = "uniquely_high_in_one_group")
UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n_sample age(p-value) cell.type(p-value) k
#> ATC:skmeans 143 0.129051 1.000 2
#> ATC:skmeans 134 0.000368 0.474 3
#> ATC:skmeans 131 0.000584 0.042 4
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
Parent node: Node0321. Child nodes: Node021121-leaf , Node021122-leaf , Node032111-leaf , Node032112-leaf .
The object with results only for a single top-value method and a single partitioning method can be extracted as:
res = res_rh["03211"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4.
#> On a matrix with 14702 rows and 69 columns.
#> Top rows (567) are extracted by 'ATC' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 150 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_partitions"
#> [7] "compare_signatures" "consensus_heatmap" "dimension_reduction"
#> [10] "functional_enrichment" "get_anno_col" "get_anno"
#> [13] "get_classes" "get_consensus" "get_matrix"
#> [16] "get_membership" "get_param" "get_signatures"
#> [19] "get_stats" "is_best_k" "is_stable_k"
#> [22] "membership_heatmap" "ncol" "nrow"
#> [25] "plot_ecdf" "predict_classes" "rownames"
#> [28] "select_partition_number" "show" "suggest_best_k"
#> [31] "test_to_known_factors" "top_rows_heatmap"
collect_plots() function collects all the plots made from res for all k (number of subgroups)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, higher 1-PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.851 0.909 0.961 0.484 0.511 0.511
#> 3 3 0.927 0.894 0.956 0.393 0.714 0.489
#> 4 4 0.885 0.881 0.946 0.104 0.888 0.678
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 3
Following is the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall subgroup
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> Sample_266 2 0.402 0.874 0.08 0.92
#> Sample_267 2 0.881 0.599 0.30 0.70
#> Sample_268 1 0.000 0.976 1.00 0.00
#> Sample_270 1 0.000 0.976 1.00 0.00
#> Sample_274 1 0.000 0.976 1.00 0.00
#> Sample_277 2 0.000 0.930 0.00 1.00
#> Sample_279 2 0.000 0.930 0.00 1.00
#> Sample_281 2 0.000 0.930 0.00 1.00
#> Sample_282 2 0.000 0.930 0.00 1.00
#> Sample_284 2 0.000 0.930 0.00 1.00
#> Sample_285 2 0.000 0.930 0.00 1.00
#> Sample_286 2 0.000 0.930 0.00 1.00
#> Sample_290 2 0.000 0.930 0.00 1.00
#> Sample_291 2 0.000 0.930 0.00 1.00
#> Sample_292 2 0.000 0.930 0.00 1.00
#> Sample_294 2 0.000 0.930 0.00 1.00
#> Sample_295 2 0.000 0.930 0.00 1.00
#> Sample_296 2 0.000 0.930 0.00 1.00
#> Sample_297 2 0.000 0.930 0.00 1.00
#> Sample_298 2 0.000 0.930 0.00 1.00
#> Sample_299 2 0.000 0.930 0.00 1.00
#> Sample_301 2 0.000 0.930 0.00 1.00
#> Sample_322 1 0.000 0.976 1.00 0.00
#> Sample_332 1 0.000 0.976 1.00 0.00
#> Sample_333 1 0.000 0.976 1.00 0.00
#> Sample_334 1 0.000 0.976 1.00 0.00
#> Sample_335 1 0.000 0.976 1.00 0.00
#> Sample_336 1 0.000 0.976 1.00 0.00
#> Sample_337 1 0.000 0.976 1.00 0.00
#> Sample_338 1 0.000 0.976 1.00 0.00
#> Sample_342 1 0.958 0.331 0.62 0.38
#> Sample_343 1 0.529 0.848 0.88 0.12
#> Sample_347 1 0.327 0.920 0.94 0.06
#> Sample_353 2 0.000 0.930 0.00 1.00
#> Sample_354 1 0.141 0.962 0.98 0.02
#> Sample_356 1 0.000 0.976 1.00 0.00
#> Sample_357 1 0.000 0.976 1.00 0.00
#> Sample_359 1 0.141 0.962 0.98 0.02
#> Sample_361 1 0.000 0.976 1.00 0.00
#> Sample_363 1 0.000 0.976 1.00 0.00
#> Sample_364 1 0.141 0.962 0.98 0.02
#> Sample_366 1 0.141 0.962 0.98 0.02
#> Sample_367 1 0.141 0.962 0.98 0.02
#> Sample_374 1 0.000 0.976 1.00 0.00
#> Sample_388 1 0.000 0.976 1.00 0.00
#> Sample_578 1 0.000 0.976 1.00 0.00
#> Sample_579 1 0.000 0.976 1.00 0.00
#> Sample_580 2 0.469 0.857 0.10 0.90
#> Sample_581 1 0.000 0.976 1.00 0.00
#> Sample_582 1 0.000 0.976 1.00 0.00
#> Sample_583 1 0.722 0.728 0.80 0.20
#> Sample_584 1 0.000 0.976 1.00 0.00
#> Sample_951 2 0.971 0.376 0.40 0.60
#> Sample_953 2 0.925 0.525 0.34 0.66
#> Sample_955 1 0.000 0.976 1.00 0.00
#> Sample_957 1 0.000 0.976 1.00 0.00
#> Sample_961 2 0.000 0.930 0.00 1.00
#> Sample_964 1 0.000 0.976 1.00 0.00
#> Sample_967 1 0.000 0.976 1.00 0.00
#> Sample_969 1 0.000 0.976 1.00 0.00
#> Sample_971 1 0.000 0.976 1.00 0.00
#> Sample_972 1 0.000 0.976 1.00 0.00
#> Sample_974 1 0.000 0.976 1.00 0.00
#> Sample_995 2 0.000 0.930 0.00 1.00
#> Sample_1003 2 0.634 0.792 0.16 0.84
#> Sample_1006 1 0.000 0.976 1.00 0.00
#> Sample_1055 2 0.000 0.930 0.00 1.00
#> Sample_1189 2 0.981 0.315 0.42 0.58
#> Sample_1209 1 0.000 0.976 1.00 0.00
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> Sample_266 3 0.0000 0.9791 0.00 0.00 1.00
#> Sample_267 3 0.0000 0.9791 0.00 0.00 1.00
#> Sample_268 1 0.0892 0.9485 0.98 0.02 0.00
#> Sample_270 1 0.0892 0.9460 0.98 0.00 0.02
#> Sample_274 1 0.0892 0.9485 0.98 0.02 0.00
#> Sample_277 2 0.0892 0.9269 0.02 0.98 0.00
#> Sample_279 2 0.0892 0.9269 0.02 0.98 0.00
#> Sample_281 2 0.0892 0.9269 0.02 0.98 0.00
#> Sample_282 2 0.0892 0.9269 0.02 0.98 0.00
#> Sample_284 2 0.0892 0.9269 0.02 0.98 0.00
#> Sample_285 2 0.0892 0.9269 0.02 0.98 0.00
#> Sample_286 2 0.0892 0.9269 0.02 0.98 0.00
#> Sample_290 2 0.0892 0.9269 0.02 0.98 0.00
#> Sample_291 2 0.0892 0.9269 0.02 0.98 0.00
#> Sample_292 2 0.0892 0.9269 0.02 0.98 0.00
#> Sample_294 2 0.0000 0.9251 0.00 1.00 0.00
#> Sample_295 2 0.0000 0.9251 0.00 1.00 0.00
#> Sample_296 2 0.0000 0.9251 0.00 1.00 0.00
#> Sample_297 2 0.0000 0.9251 0.00 1.00 0.00
#> Sample_298 2 0.0000 0.9251 0.00 1.00 0.00
#> Sample_299 2 0.0000 0.9251 0.00 1.00 0.00
#> Sample_301 2 0.0000 0.9251 0.00 1.00 0.00
#> Sample_322 1 0.0892 0.9460 0.98 0.00 0.02
#> Sample_332 3 0.0000 0.9791 0.00 0.00 1.00
#> Sample_333 3 0.0000 0.9791 0.00 0.00 1.00
#> Sample_334 3 0.0000 0.9791 0.00 0.00 1.00
#> Sample_335 3 0.0000 0.9791 0.00 0.00 1.00
#> Sample_336 3 0.0000 0.9791 0.00 0.00 1.00
#> Sample_337 3 0.0000 0.9791 0.00 0.00 1.00
#> Sample_338 1 0.5397 0.6196 0.72 0.00 0.28
#> Sample_342 3 0.0000 0.9791 0.00 0.00 1.00
#> Sample_343 3 0.0000 0.9791 0.00 0.00 1.00
#> Sample_347 3 0.0892 0.9610 0.00 0.02 0.98
#> Sample_353 2 0.4002 0.8039 0.16 0.84 0.00
#> Sample_354 1 0.0000 0.9517 1.00 0.00 0.00
#> Sample_356 1 0.0000 0.9517 1.00 0.00 0.00
#> Sample_357 1 0.0000 0.9517 1.00 0.00 0.00
#> Sample_359 1 0.0000 0.9517 1.00 0.00 0.00
#> Sample_361 1 0.0000 0.9517 1.00 0.00 0.00
#> Sample_363 1 0.0000 0.9517 1.00 0.00 0.00
#> Sample_364 1 0.0000 0.9517 1.00 0.00 0.00
#> Sample_366 1 0.0000 0.9517 1.00 0.00 0.00
#> Sample_367 1 0.0000 0.9517 1.00 0.00 0.00
#> Sample_374 1 0.0000 0.9517 1.00 0.00 0.00
#> Sample_388 1 0.0892 0.9485 0.98 0.02 0.00
#> Sample_578 3 0.0000 0.9791 0.00 0.00 1.00
#> Sample_579 1 0.0892 0.9460 0.98 0.00 0.02
#> Sample_580 2 0.7979 0.0962 0.06 0.50 0.44
#> Sample_581 1 0.0892 0.9485 0.98 0.02 0.00
#> Sample_582 3 0.0000 0.9791 0.00 0.00 1.00
#> Sample_583 1 0.0892 0.9485 0.98 0.02 0.00
#> Sample_584 1 0.0892 0.9485 0.98 0.02 0.00
#> Sample_951 3 0.0892 0.9610 0.00 0.02 0.98
#> Sample_953 3 0.0000 0.9791 0.00 0.00 1.00
#> Sample_955 3 0.0000 0.9791 0.00 0.00 1.00
#> Sample_957 3 0.0000 0.9791 0.00 0.00 1.00
#> Sample_961 2 0.6309 -0.0115 0.50 0.50 0.00
#> Sample_964 3 0.0000 0.9791 0.00 0.00 1.00
#> Sample_967 1 0.0892 0.9460 0.98 0.00 0.02
#> Sample_969 1 0.0892 0.9485 0.98 0.02 0.00
#> Sample_971 3 0.0000 0.9791 0.00 0.00 1.00
#> Sample_972 3 0.0000 0.9791 0.00 0.00 1.00
#> Sample_974 3 0.0000 0.9791 0.00 0.00 1.00
#> Sample_995 2 0.0000 0.9251 0.00 1.00 0.00
#> Sample_1003 1 0.2959 0.8745 0.90 0.10 0.00
#> Sample_1006 3 0.5948 0.3973 0.36 0.00 0.64
#> Sample_1055 2 0.4555 0.7269 0.20 0.80 0.00
#> Sample_1189 1 0.0000 0.9517 1.00 0.00 0.00
#> Sample_1209 1 0.6302 0.0812 0.52 0.00 0.48
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> Sample_266 3 0.4855 0.345 0.00 0.00 0.60 0.40
#> Sample_267 3 0.1637 0.882 0.00 0.00 0.94 0.06
#> Sample_268 1 0.0707 0.937 0.98 0.02 0.00 0.00
#> Sample_270 1 0.0707 0.937 0.98 0.02 0.00 0.00
#> Sample_274 1 0.0707 0.937 0.98 0.02 0.00 0.00
#> Sample_277 4 0.0000 0.982 0.00 0.00 0.00 1.00
#> Sample_279 4 0.0000 0.982 0.00 0.00 0.00 1.00
#> Sample_281 4 0.0000 0.982 0.00 0.00 0.00 1.00
#> Sample_282 4 0.0000 0.982 0.00 0.00 0.00 1.00
#> Sample_284 4 0.0000 0.982 0.00 0.00 0.00 1.00
#> Sample_285 4 0.0000 0.982 0.00 0.00 0.00 1.00
#> Sample_286 4 0.0000 0.982 0.00 0.00 0.00 1.00
#> Sample_290 4 0.0000 0.982 0.00 0.00 0.00 1.00
#> Sample_291 4 0.0000 0.982 0.00 0.00 0.00 1.00
#> Sample_292 4 0.0000 0.982 0.00 0.00 0.00 1.00
#> Sample_294 2 0.0707 0.931 0.00 0.98 0.00 0.02
#> Sample_295 2 0.0707 0.931 0.00 0.98 0.00 0.02
#> Sample_296 2 0.0707 0.931 0.00 0.98 0.00 0.02
#> Sample_297 2 0.0707 0.931 0.00 0.98 0.00 0.02
#> Sample_298 2 0.0707 0.931 0.00 0.98 0.00 0.02
#> Sample_299 2 0.0707 0.931 0.00 0.98 0.00 0.02
#> Sample_301 2 0.0707 0.931 0.00 0.98 0.00 0.02
#> Sample_322 1 0.0000 0.941 1.00 0.00 0.00 0.00
#> Sample_332 3 0.0000 0.924 0.00 0.00 1.00 0.00
#> Sample_333 3 0.0000 0.924 0.00 0.00 1.00 0.00
#> Sample_334 3 0.0000 0.924 0.00 0.00 1.00 0.00
#> Sample_335 3 0.0000 0.924 0.00 0.00 1.00 0.00
#> Sample_336 3 0.0000 0.924 0.00 0.00 1.00 0.00
#> Sample_337 3 0.0000 0.924 0.00 0.00 1.00 0.00
#> Sample_338 1 0.5535 0.253 0.56 0.02 0.42 0.00
#> Sample_342 3 0.0000 0.924 0.00 0.00 1.00 0.00
#> Sample_343 3 0.3172 0.798 0.00 0.16 0.84 0.00
#> Sample_347 3 0.2011 0.874 0.00 0.08 0.92 0.00
#> Sample_353 4 0.4581 0.794 0.08 0.12 0.00 0.80
#> Sample_354 1 0.0000 0.941 1.00 0.00 0.00 0.00
#> Sample_356 1 0.0000 0.941 1.00 0.00 0.00 0.00
#> Sample_357 1 0.3172 0.788 0.84 0.00 0.00 0.16
#> Sample_359 1 0.0000 0.941 1.00 0.00 0.00 0.00
#> Sample_361 1 0.0000 0.941 1.00 0.00 0.00 0.00
#> Sample_363 1 0.0000 0.941 1.00 0.00 0.00 0.00
#> Sample_364 1 0.0000 0.941 1.00 0.00 0.00 0.00
#> Sample_366 1 0.0000 0.941 1.00 0.00 0.00 0.00
#> Sample_367 1 0.0000 0.941 1.00 0.00 0.00 0.00
#> Sample_374 1 0.0000 0.941 1.00 0.00 0.00 0.00
#> Sample_388 2 0.4790 0.358 0.38 0.62 0.00 0.00
#> Sample_578 3 0.0707 0.912 0.02 0.00 0.98 0.00
#> Sample_579 1 0.1411 0.927 0.96 0.02 0.02 0.00
#> Sample_580 2 0.0707 0.917 0.00 0.98 0.02 0.00
#> Sample_581 1 0.0707 0.937 0.98 0.02 0.00 0.00
#> Sample_582 3 0.0000 0.924 0.00 0.00 1.00 0.00
#> Sample_583 2 0.3975 0.667 0.24 0.76 0.00 0.00
#> Sample_584 1 0.0707 0.937 0.98 0.02 0.00 0.00
#> Sample_951 3 0.2921 0.818 0.00 0.14 0.86 0.00
#> Sample_953 3 0.0000 0.924 0.00 0.00 1.00 0.00
#> Sample_955 3 0.0000 0.924 0.00 0.00 1.00 0.00
#> Sample_957 3 0.0000 0.924 0.00 0.00 1.00 0.00
#> Sample_961 2 0.0707 0.911 0.02 0.98 0.00 0.00
#> Sample_964 3 0.0000 0.924 0.00 0.00 1.00 0.00
#> Sample_967 1 0.2335 0.895 0.92 0.02 0.06 0.00
#> Sample_969 1 0.4731 0.746 0.78 0.16 0.06 0.00
#> Sample_971 3 0.0000 0.924 0.00 0.00 1.00 0.00
#> Sample_972 3 0.0000 0.924 0.00 0.00 1.00 0.00
#> Sample_974 3 0.0000 0.924 0.00 0.00 1.00 0.00
#> Sample_995 2 0.0707 0.931 0.00 0.98 0.00 0.02
#> Sample_1003 2 0.0000 0.923 0.00 1.00 0.00 0.00
#> Sample_1006 3 0.6797 0.482 0.16 0.24 0.60 0.00
#> Sample_1055 2 0.0000 0.923 0.00 1.00 0.00 0.00
#> Sample_1189 1 0.2335 0.886 0.92 0.06 0.00 0.02
#> Sample_1209 3 0.5173 0.480 0.32 0.02 0.66 0.00
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
As soon as the classes for columns are determined, the signatures that are significantly different between subgroups can be looked for. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. To get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows (which is done by automatically selecting number of clusters).If there are too many signatures, top_signatures = ... can be set to only show the
signatures with the highest FDRs:
# code only for demonstration
# e.g. to show the top 500 most significant rows
tb = get_signature(res, k = ..., top_signatures = 500)
If the signatures are defined as these which are uniquely high in current group, diff_method argument
can be set to "uniquely_high_in_one_group":
# code only for demonstration
tb = get_signature(res, k = ..., diff_method = "uniquely_high_in_one_group")
UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n_sample age(p-value) cell.type(p-value) k
#> ATC:skmeans 66 0.05181 NA 2
#> ATC:skmeans 65 0.00739 NA 3
#> ATC:skmeans 64 0.00926 NA 4
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
Parent node: Node032. Child nodes: Node02111-leaf , Node02112 , Node02121-leaf , Node02122-leaf , Node02211-leaf , Node02212-leaf , Node02221-leaf , Node02222-leaf , Node03211 , Node03212-leaf , Node03221-leaf , Node03222-leaf .
The object with results only for a single top-value method and a single partitioning method can be extracted as:
res = res_rh["0322"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4.
#> On a matrix with 14740 rows and 95 columns.
#> Top rows (1093) are extracted by 'ATC' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 150 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_partitions"
#> [7] "compare_signatures" "consensus_heatmap" "dimension_reduction"
#> [10] "functional_enrichment" "get_anno_col" "get_anno"
#> [13] "get_classes" "get_consensus" "get_matrix"
#> [16] "get_membership" "get_param" "get_signatures"
#> [19] "get_stats" "is_best_k" "is_stable_k"
#> [22] "membership_heatmap" "ncol" "nrow"
#> [25] "plot_ecdf" "predict_classes" "rownames"
#> [28] "select_partition_number" "show" "suggest_best_k"
#> [31] "test_to_known_factors" "top_rows_heatmap"
collect_plots() function collects all the plots made from res for all k (number of subgroups)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, higher 1-PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.892 0.953 0.978 0.504 0.497 0.497
#> 3 3 0.940 0.912 0.965 0.326 0.765 0.558
#> 4 4 0.755 0.824 0.905 0.105 0.905 0.726
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 3
Following is the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall subgroup
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> Sample_208 2 0.000 0.968 0.00 1.00
#> Sample_209 2 0.000 0.968 0.00 1.00
#> Sample_210 2 0.000 0.968 0.00 1.00
#> Sample_211 2 0.000 0.968 0.00 1.00
#> Sample_212 2 0.000 0.968 0.00 1.00
#> Sample_213 2 0.000 0.968 0.00 1.00
#> Sample_216 2 0.000 0.968 0.00 1.00
#> Sample_218 2 0.000 0.968 0.00 1.00
#> Sample_219 2 0.584 0.837 0.14 0.86
#> Sample_220 2 0.000 0.968 0.00 1.00
#> Sample_221 2 0.925 0.512 0.34 0.66
#> Sample_222 2 0.000 0.968 0.00 1.00
#> Sample_223 2 0.529 0.859 0.12 0.88
#> Sample_225 2 0.000 0.968 0.00 1.00
#> Sample_227 2 0.000 0.968 0.00 1.00
#> Sample_228 2 0.000 0.968 0.00 1.00
#> Sample_229 2 0.000 0.968 0.00 1.00
#> Sample_230 2 0.000 0.968 0.00 1.00
#> Sample_231 2 0.000 0.968 0.00 1.00
#> Sample_232 2 0.000 0.968 0.00 1.00
#> Sample_234 2 0.000 0.968 0.00 1.00
#> Sample_235 2 0.000 0.968 0.00 1.00
#> Sample_237 2 0.827 0.670 0.26 0.74
#> Sample_238 2 0.000 0.968 0.00 1.00
#> Sample_239 2 0.000 0.968 0.00 1.00
#> Sample_240 2 0.000 0.968 0.00 1.00
#> Sample_242 2 0.000 0.968 0.00 1.00
#> Sample_244 2 0.000 0.968 0.00 1.00
#> Sample_245 2 0.000 0.968 0.00 1.00
#> Sample_246 2 0.000 0.968 0.00 1.00
#> Sample_247 2 0.000 0.968 0.00 1.00
#> Sample_248 2 0.000 0.968 0.00 1.00
#> Sample_249 2 0.000 0.968 0.00 1.00
#> Sample_250 2 0.000 0.968 0.00 1.00
#> Sample_251 2 0.000 0.968 0.00 1.00
#> Sample_252 2 0.000 0.968 0.00 1.00
#> Sample_253 2 0.000 0.968 0.00 1.00
#> Sample_255 1 0.469 0.887 0.90 0.10
#> Sample_256 2 0.000 0.968 0.00 1.00
#> Sample_257 2 0.000 0.968 0.00 1.00
#> Sample_259 2 0.000 0.968 0.00 1.00
#> Sample_260 2 0.000 0.968 0.00 1.00
#> Sample_261 2 0.000 0.968 0.00 1.00
#> Sample_262 2 0.000 0.968 0.00 1.00
#> Sample_263 2 0.000 0.968 0.00 1.00
#> Sample_269 1 0.722 0.749 0.80 0.20
#> Sample_280 2 0.722 0.756 0.20 0.80
#> Sample_283 1 0.000 0.988 1.00 0.00
#> Sample_293 2 0.827 0.668 0.26 0.74
#> Sample_300 1 0.000 0.988 1.00 0.00
#> Sample_402 1 0.000 0.988 1.00 0.00
#> Sample_423 1 0.000 0.988 1.00 0.00
#> Sample_426 1 0.000 0.988 1.00 0.00
#> Sample_429 1 0.000 0.988 1.00 0.00
#> Sample_435 1 0.000 0.988 1.00 0.00
#> Sample_441 1 0.000 0.988 1.00 0.00
#> Sample_447 1 0.000 0.988 1.00 0.00
#> Sample_449 1 0.000 0.988 1.00 0.00
#> Sample_453 1 0.000 0.988 1.00 0.00
#> Sample_458 1 0.000 0.988 1.00 0.00
#> Sample_467 1 0.000 0.988 1.00 0.00
#> Sample_471 1 0.000 0.988 1.00 0.00
#> Sample_570 1 0.000 0.988 1.00 0.00
#> Sample_571 1 0.000 0.988 1.00 0.00
#> Sample_576 1 0.000 0.988 1.00 0.00
#> Sample_585 2 0.000 0.968 0.00 1.00
#> Sample_589 2 0.000 0.968 0.00 1.00
#> Sample_613 2 0.000 0.968 0.00 1.00
#> Sample_615 2 0.000 0.968 0.00 1.00
#> Sample_623 1 0.000 0.988 1.00 0.00
#> Sample_624 1 0.000 0.988 1.00 0.00
#> Sample_954 1 0.000 0.988 1.00 0.00
#> Sample_1012 1 0.000 0.988 1.00 0.00
#> Sample_1053 1 0.000 0.988 1.00 0.00
#> Sample_1057 1 0.000 0.988 1.00 0.00
#> Sample_1061 1 0.000 0.988 1.00 0.00
#> Sample_1190 1 0.000 0.988 1.00 0.00
#> Sample_1191 2 0.827 0.668 0.26 0.74
#> Sample_1193 1 0.402 0.910 0.92 0.08
#> Sample_1194 1 0.469 0.888 0.90 0.10
#> Sample_1197 1 0.000 0.988 1.00 0.00
#> Sample_1198 1 0.000 0.988 1.00 0.00
#> Sample_1199 1 0.000 0.988 1.00 0.00
#> Sample_1203 1 0.000 0.988 1.00 0.00
#> Sample_1204 1 0.000 0.988 1.00 0.00
#> Sample_1205 1 0.000 0.988 1.00 0.00
#> Sample_1207 1 0.000 0.988 1.00 0.00
#> Sample_1210 1 0.000 0.988 1.00 0.00
#> Sample_1214 1 0.000 0.988 1.00 0.00
#> Sample_1215 1 0.000 0.988 1.00 0.00
#> Sample_1219 1 0.000 0.988 1.00 0.00
#> Sample_1221 1 0.000 0.988 1.00 0.00
#> Sample_1549 1 0.000 0.988 1.00 0.00
#> Sample_1581 1 0.000 0.988 1.00 0.00
#> Sample_1597 1 0.000 0.988 1.00 0.00
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> Sample_208 2 0.0000 0.957 0.00 1.00 0.00
#> Sample_209 3 0.0000 0.956 0.00 0.00 1.00
#> Sample_210 2 0.0000 0.957 0.00 1.00 0.00
#> Sample_211 2 0.0000 0.957 0.00 1.00 0.00
#> Sample_212 2 0.0000 0.957 0.00 1.00 0.00
#> Sample_213 3 0.0000 0.956 0.00 0.00 1.00
#> Sample_216 3 0.0000 0.956 0.00 0.00 1.00
#> Sample_218 3 0.2066 0.902 0.00 0.06 0.94
#> Sample_219 3 0.0000 0.956 0.00 0.00 1.00
#> Sample_220 3 0.0000 0.956 0.00 0.00 1.00
#> Sample_221 3 0.0000 0.956 0.00 0.00 1.00
#> Sample_222 2 0.0000 0.957 0.00 1.00 0.00
#> Sample_223 3 0.0000 0.956 0.00 0.00 1.00
#> Sample_225 2 0.0000 0.957 0.00 1.00 0.00
#> Sample_227 2 0.0000 0.957 0.00 1.00 0.00
#> Sample_228 3 0.0000 0.956 0.00 0.00 1.00
#> Sample_229 2 0.0000 0.957 0.00 1.00 0.00
#> Sample_230 2 0.2066 0.907 0.00 0.94 0.06
#> Sample_231 2 0.0000 0.957 0.00 1.00 0.00
#> Sample_232 3 0.0000 0.956 0.00 0.00 1.00
#> Sample_234 3 0.0000 0.956 0.00 0.00 1.00
#> Sample_235 2 0.0000 0.957 0.00 1.00 0.00
#> Sample_237 3 0.0000 0.956 0.00 0.00 1.00
#> Sample_238 3 0.0000 0.956 0.00 0.00 1.00
#> Sample_239 2 0.0000 0.957 0.00 1.00 0.00
#> Sample_240 2 0.0000 0.957 0.00 1.00 0.00
#> Sample_242 2 0.0000 0.957 0.00 1.00 0.00
#> Sample_244 2 0.0000 0.957 0.00 1.00 0.00
#> Sample_245 2 0.0000 0.957 0.00 1.00 0.00
#> Sample_246 3 0.0000 0.956 0.00 0.00 1.00
#> Sample_247 2 0.0000 0.957 0.00 1.00 0.00
#> Sample_248 3 0.0000 0.956 0.00 0.00 1.00
#> Sample_249 3 0.0000 0.956 0.00 0.00 1.00
#> Sample_250 2 0.5835 0.494 0.00 0.66 0.34
#> Sample_251 3 0.0000 0.956 0.00 0.00 1.00
#> Sample_252 3 0.0000 0.956 0.00 0.00 1.00
#> Sample_253 3 0.0000 0.956 0.00 0.00 1.00
#> Sample_255 3 0.0892 0.942 0.02 0.00 0.98
#> Sample_256 2 0.0000 0.957 0.00 1.00 0.00
#> Sample_257 2 0.0000 0.957 0.00 1.00 0.00
#> Sample_259 2 0.0000 0.957 0.00 1.00 0.00
#> Sample_260 3 0.0000 0.956 0.00 0.00 1.00
#> Sample_261 2 0.0000 0.957 0.00 1.00 0.00
#> Sample_262 2 0.0000 0.957 0.00 1.00 0.00
#> Sample_263 2 0.1529 0.925 0.00 0.96 0.04
#> Sample_269 2 0.5397 0.604 0.28 0.72 0.00
#> Sample_280 2 0.0000 0.957 0.00 1.00 0.00
#> Sample_283 1 0.1529 0.936 0.96 0.00 0.04
#> Sample_293 2 0.2066 0.901 0.06 0.94 0.00
#> Sample_300 1 0.0000 0.971 1.00 0.00 0.00
#> Sample_402 1 0.0000 0.971 1.00 0.00 0.00
#> Sample_423 1 0.0000 0.971 1.00 0.00 0.00
#> Sample_426 1 0.0000 0.971 1.00 0.00 0.00
#> Sample_429 1 0.0000 0.971 1.00 0.00 0.00
#> Sample_435 1 0.0000 0.971 1.00 0.00 0.00
#> Sample_441 1 0.0000 0.971 1.00 0.00 0.00
#> Sample_447 1 0.0000 0.971 1.00 0.00 0.00
#> Sample_449 1 0.0000 0.971 1.00 0.00 0.00
#> Sample_453 1 0.0000 0.971 1.00 0.00 0.00
#> Sample_458 1 0.0000 0.971 1.00 0.00 0.00
#> Sample_467 1 0.0000 0.971 1.00 0.00 0.00
#> Sample_471 1 0.0000 0.971 1.00 0.00 0.00
#> Sample_570 1 0.0000 0.971 1.00 0.00 0.00
#> Sample_571 1 0.0000 0.971 1.00 0.00 0.00
#> Sample_576 1 0.0000 0.971 1.00 0.00 0.00
#> Sample_585 2 0.0000 0.957 0.00 1.00 0.00
#> Sample_589 2 0.0000 0.957 0.00 1.00 0.00
#> Sample_613 3 0.5948 0.416 0.00 0.36 0.64
#> Sample_615 2 0.0000 0.957 0.00 1.00 0.00
#> Sample_623 1 0.0000 0.971 1.00 0.00 0.00
#> Sample_624 1 0.0892 0.954 0.98 0.00 0.02
#> Sample_954 1 0.0000 0.971 1.00 0.00 0.00
#> Sample_1012 3 0.6244 0.190 0.44 0.00 0.56
#> Sample_1053 1 0.0000 0.971 1.00 0.00 0.00
#> Sample_1057 1 0.0000 0.971 1.00 0.00 0.00
#> Sample_1061 1 0.3686 0.823 0.86 0.00 0.14
#> Sample_1190 3 0.1529 0.926 0.04 0.00 0.96
#> Sample_1191 2 0.7029 0.186 0.02 0.54 0.44
#> Sample_1193 3 0.0000 0.956 0.00 0.00 1.00
#> Sample_1194 3 0.1529 0.926 0.04 0.00 0.96
#> Sample_1197 1 0.6244 0.201 0.56 0.00 0.44
#> Sample_1198 1 0.5948 0.429 0.64 0.00 0.36
#> Sample_1199 1 0.0000 0.971 1.00 0.00 0.00
#> Sample_1203 1 0.0000 0.971 1.00 0.00 0.00
#> Sample_1204 1 0.0000 0.971 1.00 0.00 0.00
#> Sample_1205 1 0.0000 0.971 1.00 0.00 0.00
#> Sample_1207 1 0.0000 0.971 1.00 0.00 0.00
#> Sample_1210 1 0.0000 0.971 1.00 0.00 0.00
#> Sample_1214 1 0.0000 0.971 1.00 0.00 0.00
#> Sample_1215 1 0.0000 0.971 1.00 0.00 0.00
#> Sample_1219 1 0.0000 0.971 1.00 0.00 0.00
#> Sample_1221 1 0.0000 0.971 1.00 0.00 0.00
#> Sample_1549 1 0.0000 0.971 1.00 0.00 0.00
#> Sample_1581 3 0.2959 0.867 0.10 0.00 0.90
#> Sample_1597 1 0.0000 0.971 1.00 0.00 0.00
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> Sample_208 2 0.0000 0.9254 0.00 1.00 0.00 0.00
#> Sample_209 3 0.0000 0.8656 0.00 0.00 1.00 0.00
#> Sample_210 2 0.1637 0.8940 0.00 0.94 0.00 0.06
#> Sample_211 2 0.1211 0.9074 0.00 0.96 0.04 0.00
#> Sample_212 2 0.1211 0.9063 0.00 0.96 0.04 0.00
#> Sample_213 3 0.2345 0.8413 0.00 0.00 0.90 0.10
#> Sample_216 3 0.1211 0.8632 0.00 0.00 0.96 0.04
#> Sample_218 3 0.3611 0.8028 0.00 0.08 0.86 0.06
#> Sample_219 3 0.3610 0.7624 0.00 0.00 0.80 0.20
#> Sample_220 3 0.0707 0.8651 0.00 0.00 0.98 0.02
#> Sample_221 3 0.3610 0.7781 0.00 0.00 0.80 0.20
#> Sample_222 2 0.0000 0.9254 0.00 1.00 0.00 0.00
#> Sample_223 3 0.1637 0.8598 0.00 0.00 0.94 0.06
#> Sample_225 2 0.3335 0.8241 0.00 0.86 0.12 0.02
#> Sample_227 2 0.0000 0.9254 0.00 1.00 0.00 0.00
#> Sample_228 3 0.1211 0.8653 0.00 0.00 0.96 0.04
#> Sample_229 2 0.0000 0.9254 0.00 1.00 0.00 0.00
#> Sample_230 2 0.4522 0.5543 0.00 0.68 0.32 0.00
#> Sample_231 2 0.0000 0.9254 0.00 1.00 0.00 0.00
#> Sample_232 3 0.0707 0.8665 0.00 0.00 0.98 0.02
#> Sample_234 3 0.0000 0.8656 0.00 0.00 1.00 0.00
#> Sample_235 2 0.0000 0.9254 0.00 1.00 0.00 0.00
#> Sample_237 3 0.2011 0.8554 0.00 0.00 0.92 0.08
#> Sample_238 3 0.0707 0.8637 0.00 0.00 0.98 0.02
#> Sample_239 2 0.0000 0.9254 0.00 1.00 0.00 0.00
#> Sample_240 2 0.0000 0.9254 0.00 1.00 0.00 0.00
#> Sample_242 2 0.0000 0.9254 0.00 1.00 0.00 0.00
#> Sample_244 2 0.0000 0.9254 0.00 1.00 0.00 0.00
#> Sample_245 2 0.1637 0.8934 0.00 0.94 0.06 0.00
#> Sample_246 3 0.0000 0.8656 0.00 0.00 1.00 0.00
#> Sample_247 2 0.0000 0.9254 0.00 1.00 0.00 0.00
#> Sample_248 3 0.0707 0.8665 0.00 0.00 0.98 0.02
#> Sample_249 3 0.1211 0.8634 0.00 0.00 0.96 0.04
#> Sample_250 3 0.4977 0.1014 0.00 0.46 0.54 0.00
#> Sample_251 3 0.3172 0.8134 0.00 0.00 0.84 0.16
#> Sample_252 3 0.2011 0.8505 0.00 0.00 0.92 0.08
#> Sample_253 3 0.0000 0.8656 0.00 0.00 1.00 0.00
#> Sample_255 3 0.6049 0.5882 0.12 0.00 0.68 0.20
#> Sample_256 2 0.0000 0.9254 0.00 1.00 0.00 0.00
#> Sample_257 2 0.0000 0.9254 0.00 1.00 0.00 0.00
#> Sample_259 2 0.0000 0.9254 0.00 1.00 0.00 0.00
#> Sample_260 3 0.1211 0.8585 0.00 0.00 0.96 0.04
#> Sample_261 2 0.4088 0.7801 0.00 0.82 0.14 0.04
#> Sample_262 2 0.0707 0.9166 0.00 0.98 0.00 0.02
#> Sample_263 2 0.2345 0.8565 0.00 0.90 0.10 0.00
#> Sample_269 2 0.8363 0.0787 0.28 0.48 0.04 0.20
#> Sample_280 2 0.1211 0.9065 0.00 0.96 0.00 0.04
#> Sample_283 1 0.4841 0.7016 0.78 0.00 0.08 0.14
#> Sample_293 2 0.5486 0.6344 0.08 0.72 0.00 0.20
#> Sample_300 1 0.3610 0.7255 0.80 0.00 0.00 0.20
#> Sample_402 1 0.2011 0.8817 0.92 0.00 0.00 0.08
#> Sample_423 1 0.0707 0.9320 0.98 0.00 0.00 0.02
#> Sample_426 1 0.0000 0.9423 1.00 0.00 0.00 0.00
#> Sample_429 1 0.0707 0.9424 0.98 0.00 0.00 0.02
#> Sample_435 1 0.0707 0.9424 0.98 0.00 0.00 0.02
#> Sample_441 1 0.0707 0.9424 0.98 0.00 0.00 0.02
#> Sample_447 1 0.0707 0.9424 0.98 0.00 0.00 0.02
#> Sample_449 1 0.0707 0.9424 0.98 0.00 0.00 0.02
#> Sample_453 1 0.0707 0.9424 0.98 0.00 0.00 0.02
#> Sample_458 1 0.0707 0.9424 0.98 0.00 0.00 0.02
#> Sample_467 1 0.0707 0.9424 0.98 0.00 0.00 0.02
#> Sample_471 1 0.0707 0.9424 0.98 0.00 0.00 0.02
#> Sample_570 1 0.2345 0.8651 0.90 0.00 0.00 0.10
#> Sample_571 1 0.0707 0.9424 0.98 0.00 0.00 0.02
#> Sample_576 1 0.0000 0.9423 1.00 0.00 0.00 0.00
#> Sample_585 2 0.2647 0.8437 0.00 0.88 0.00 0.12
#> Sample_589 2 0.0000 0.9254 0.00 1.00 0.00 0.00
#> Sample_613 4 0.3606 0.6834 0.00 0.02 0.14 0.84
#> Sample_615 2 0.0000 0.9254 0.00 1.00 0.00 0.00
#> Sample_623 4 0.2647 0.7872 0.12 0.00 0.00 0.88
#> Sample_624 4 0.3610 0.7674 0.20 0.00 0.00 0.80
#> Sample_954 1 0.2647 0.8437 0.88 0.00 0.00 0.12
#> Sample_1012 4 0.4610 0.7337 0.10 0.00 0.10 0.80
#> Sample_1053 4 0.5000 0.2199 0.50 0.00 0.00 0.50
#> Sample_1057 4 0.3610 0.7548 0.20 0.00 0.00 0.80
#> Sample_1061 4 0.4755 0.7525 0.20 0.00 0.04 0.76
#> Sample_1190 3 0.6150 0.3097 0.06 0.00 0.58 0.36
#> Sample_1191 4 0.6122 0.5701 0.00 0.16 0.16 0.68
#> Sample_1193 3 0.3801 0.7226 0.00 0.00 0.78 0.22
#> Sample_1194 3 0.5712 0.5553 0.02 0.02 0.66 0.30
#> Sample_1197 4 0.2411 0.7563 0.04 0.00 0.04 0.92
#> Sample_1198 4 0.5677 0.7161 0.14 0.00 0.14 0.72
#> Sample_1199 1 0.0000 0.9423 1.00 0.00 0.00 0.00
#> Sample_1203 1 0.0000 0.9423 1.00 0.00 0.00 0.00
#> Sample_1204 1 0.0000 0.9423 1.00 0.00 0.00 0.00
#> Sample_1205 1 0.1211 0.9201 0.96 0.00 0.00 0.04
#> Sample_1207 1 0.0707 0.9424 0.98 0.00 0.00 0.02
#> Sample_1210 1 0.0707 0.9314 0.98 0.00 0.00 0.02
#> Sample_1214 1 0.0000 0.9423 1.00 0.00 0.00 0.00
#> Sample_1215 1 0.0000 0.9423 1.00 0.00 0.00 0.00
#> Sample_1219 1 0.0000 0.9423 1.00 0.00 0.00 0.00
#> Sample_1221 1 0.0000 0.9423 1.00 0.00 0.00 0.00
#> Sample_1549 4 0.4406 0.6383 0.30 0.00 0.00 0.70
#> Sample_1581 4 0.2345 0.7226 0.00 0.00 0.10 0.90
#> Sample_1597 1 0.4522 0.4699 0.68 0.00 0.00 0.32
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
As soon as the classes for columns are determined, the signatures that are significantly different between subgroups can be looked for. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. To get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows (which is done by automatically selecting number of clusters).If there are too many signatures, top_signatures = ... can be set to only show the
signatures with the highest FDRs:
# code only for demonstration
# e.g. to show the top 500 most significant rows
tb = get_signature(res, k = ..., top_signatures = 500)
If the signatures are defined as these which are uniquely high in current group, diff_method argument
can be set to "uniquely_high_in_one_group":
# code only for demonstration
tb = get_signature(res, k = ..., diff_method = "uniquely_high_in_one_group")
UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n_sample age(p-value) cell.type(p-value) k
#> ATC:skmeans 95 9.21e-12 2.24e-01 2
#> ATC:skmeans 89 1.27e-09 2.21e-01 3
#> ATC:skmeans 90 1.67e-09 2.21e-05 4
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
Parent node: Node03. Child nodes: Node0111-leaf , Node0112-leaf , Node0121-leaf , Node0122-leaf , Node0131-leaf , Node0132-leaf , Node0211 , Node0212 , Node0221 , Node0222 , Node0231-leaf , Node0232-leaf , Node0233-leaf , Node0311-leaf , Node0312-leaf , Node0313-leaf , Node0314-leaf , Node0321 , Node0322 , Node0331-leaf , Node0332-leaf , Node0333-leaf .
The object with results only for a single top-value method and a single partitioning method can be extracted as:
res = res_rh["033"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4.
#> On a matrix with 14679 rows and 53 columns.
#> Top rows (1366) are extracted by 'ATC' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 150 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_partitions"
#> [7] "compare_signatures" "consensus_heatmap" "dimension_reduction"
#> [10] "functional_enrichment" "get_anno_col" "get_anno"
#> [13] "get_classes" "get_consensus" "get_matrix"
#> [16] "get_membership" "get_param" "get_signatures"
#> [19] "get_stats" "is_best_k" "is_stable_k"
#> [22] "membership_heatmap" "ncol" "nrow"
#> [25] "plot_ecdf" "predict_classes" "rownames"
#> [28] "select_partition_number" "show" "suggest_best_k"
#> [31] "test_to_known_factors" "top_rows_heatmap"
collect_plots() function collects all the plots made from res for all k (number of subgroups)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, higher 1-PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 1.000 1.000 0.5085 0.492 0.492
#> 3 3 1.000 0.982 0.992 0.3334 0.725 0.495
#> 4 4 0.891 0.892 0.938 0.0878 0.928 0.781
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 3
#> attr(,"optional")
#> [1] 2
There is also optional best \(k\) = 2 that is worth to check.
Following is the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall subgroup
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> Sample_233 2 0 1 0 1
#> Sample_254 1 0 1 1 0
#> Sample_271 1 0 1 1 0
#> Sample_278 2 0 1 0 1
#> Sample_288 1 0 1 1 0
#> Sample_289 2 0 1 0 1
#> Sample_331 2 0 1 0 1
#> Sample_339 1 0 1 1 0
#> Sample_371 1 0 1 1 0
#> Sample_415 2 0 1 0 1
#> Sample_420 2 0 1 0 1
#> Sample_422 2 0 1 0 1
#> Sample_436 1 0 1 1 0
#> Sample_437 2 0 1 0 1
#> Sample_488 1 0 1 1 0
#> Sample_512 2 0 1 0 1
#> Sample_529 1 0 1 1 0
#> Sample_531 2 0 1 0 1
#> Sample_545 1 0 1 1 0
#> Sample_553 2 0 1 0 1
#> Sample_574 1 0 1 1 0
#> Sample_975 1 0 1 1 0
#> Sample_1004 2 0 1 0 1
#> Sample_1031 2 0 1 0 1
#> Sample_1045 2 0 1 0 1
#> Sample_1048 1 0 1 1 0
#> Sample_1086 1 0 1 1 0
#> Sample_1088 1 0 1 1 0
#> Sample_1089 2 0 1 0 1
#> Sample_1120 2 0 1 0 1
#> Sample_1122 1 0 1 1 0
#> Sample_1132 2 0 1 0 1
#> Sample_1134 1 0 1 1 0
#> Sample_1140 1 0 1 1 0
#> Sample_1183 1 0 1 1 0
#> Sample_1184 1 0 1 1 0
#> Sample_1255 2 0 1 0 1
#> Sample_1268 2 0 1 0 1
#> Sample_1296 1 0 1 1 0
#> Sample_1306 2 0 1 0 1
#> Sample_1310 2 0 1 0 1
#> Sample_1337 2 0 1 0 1
#> Sample_1339 2 0 1 0 1
#> Sample_1345 2 0 1 0 1
#> Sample_1376 2 0 1 0 1
#> Sample_1400 1 0 1 1 0
#> Sample_1413 1 0 1 1 0
#> Sample_1416 2 0 1 0 1
#> Sample_1460 1 0 1 1 0
#> Sample_1486 1 0 1 1 0
#> Sample_1494 2 0 1 0 1
#> Sample_1504 2 0 1 0 1
#> Sample_1505 1 0 1 1 0
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> Sample_233 3 0.000 0.972 0.00 0.00 1.00
#> Sample_254 3 0.000 0.972 0.00 0.00 1.00
#> Sample_271 3 0.254 0.905 0.08 0.00 0.92
#> Sample_278 3 0.000 0.972 0.00 0.00 1.00
#> Sample_288 3 0.000 0.972 0.00 0.00 1.00
#> Sample_289 3 0.000 0.972 0.00 0.00 1.00
#> Sample_331 3 0.000 0.972 0.00 0.00 1.00
#> Sample_339 3 0.000 0.972 0.00 0.00 1.00
#> Sample_371 3 0.000 0.972 0.00 0.00 1.00
#> Sample_415 3 0.000 0.972 0.00 0.00 1.00
#> Sample_420 3 0.000 0.972 0.00 0.00 1.00
#> Sample_422 3 0.000 0.972 0.00 0.00 1.00
#> Sample_436 3 0.000 0.972 0.00 0.00 1.00
#> Sample_437 3 0.540 0.613 0.00 0.28 0.72
#> Sample_488 1 0.000 1.000 1.00 0.00 0.00
#> Sample_512 2 0.000 1.000 0.00 1.00 0.00
#> Sample_529 1 0.000 1.000 1.00 0.00 0.00
#> Sample_531 2 0.000 1.000 0.00 1.00 0.00
#> Sample_545 1 0.000 1.000 1.00 0.00 0.00
#> Sample_553 2 0.000 1.000 0.00 1.00 0.00
#> Sample_574 3 0.000 0.972 0.00 0.00 1.00
#> Sample_975 3 0.254 0.905 0.08 0.00 0.92
#> Sample_1004 3 0.000 0.972 0.00 0.00 1.00
#> Sample_1031 2 0.000 1.000 0.00 1.00 0.00
#> Sample_1045 2 0.000 1.000 0.00 1.00 0.00
#> Sample_1048 1 0.000 1.000 1.00 0.00 0.00
#> Sample_1086 1 0.000 1.000 1.00 0.00 0.00
#> Sample_1088 1 0.000 1.000 1.00 0.00 0.00
#> Sample_1089 2 0.000 1.000 0.00 1.00 0.00
#> Sample_1120 2 0.000 1.000 0.00 1.00 0.00
#> Sample_1122 1 0.000 1.000 1.00 0.00 0.00
#> Sample_1132 2 0.000 1.000 0.00 1.00 0.00
#> Sample_1134 1 0.000 1.000 1.00 0.00 0.00
#> Sample_1140 1 0.000 1.000 1.00 0.00 0.00
#> Sample_1183 1 0.000 1.000 1.00 0.00 0.00
#> Sample_1184 1 0.000 1.000 1.00 0.00 0.00
#> Sample_1255 2 0.000 1.000 0.00 1.00 0.00
#> Sample_1268 2 0.000 1.000 0.00 1.00 0.00
#> Sample_1296 1 0.000 1.000 1.00 0.00 0.00
#> Sample_1306 2 0.000 1.000 0.00 1.00 0.00
#> Sample_1310 2 0.000 1.000 0.00 1.00 0.00
#> Sample_1337 2 0.000 1.000 0.00 1.00 0.00
#> Sample_1339 2 0.000 1.000 0.00 1.00 0.00
#> Sample_1345 2 0.000 1.000 0.00 1.00 0.00
#> Sample_1376 2 0.000 1.000 0.00 1.00 0.00
#> Sample_1400 1 0.000 1.000 1.00 0.00 0.00
#> Sample_1413 1 0.000 1.000 1.00 0.00 0.00
#> Sample_1416 2 0.000 1.000 0.00 1.00 0.00
#> Sample_1460 1 0.000 1.000 1.00 0.00 0.00
#> Sample_1486 1 0.000 1.000 1.00 0.00 0.00
#> Sample_1494 2 0.000 1.000 0.00 1.00 0.00
#> Sample_1504 2 0.000 1.000 0.00 1.00 0.00
#> Sample_1505 1 0.000 1.000 1.00 0.00 0.00
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> Sample_233 3 0.0707 0.896 0.00 0.00 0.98 0.02
#> Sample_254 4 0.3801 0.774 0.00 0.00 0.22 0.78
#> Sample_271 4 0.3801 0.758 0.22 0.00 0.00 0.78
#> Sample_278 3 0.2011 0.841 0.00 0.00 0.92 0.08
#> Sample_288 4 0.4277 0.706 0.00 0.00 0.28 0.72
#> Sample_289 3 0.1637 0.865 0.00 0.00 0.94 0.06
#> Sample_331 3 0.0000 0.907 0.00 0.00 1.00 0.00
#> Sample_339 4 0.3801 0.774 0.00 0.00 0.22 0.78
#> Sample_371 4 0.0000 0.779 0.00 0.00 0.00 1.00
#> Sample_415 3 0.0000 0.907 0.00 0.00 1.00 0.00
#> Sample_420 3 0.0000 0.907 0.00 0.00 1.00 0.00
#> Sample_422 3 0.0000 0.907 0.00 0.00 1.00 0.00
#> Sample_436 4 0.4894 0.806 0.12 0.00 0.10 0.78
#> Sample_437 3 0.0000 0.907 0.00 0.00 1.00 0.00
#> Sample_488 1 0.2011 0.893 0.92 0.00 0.00 0.08
#> Sample_512 2 0.0000 0.989 0.00 1.00 0.00 0.00
#> Sample_529 1 0.0000 0.924 1.00 0.00 0.00 0.00
#> Sample_531 2 0.0000 0.989 0.00 1.00 0.00 0.00
#> Sample_545 1 0.3801 0.823 0.78 0.00 0.00 0.22
#> Sample_553 2 0.0000 0.989 0.00 1.00 0.00 0.00
#> Sample_574 4 0.0000 0.779 0.00 0.00 0.00 1.00
#> Sample_975 4 0.3172 0.792 0.16 0.00 0.00 0.84
#> Sample_1004 3 0.0000 0.907 0.00 0.00 1.00 0.00
#> Sample_1031 2 0.0000 0.989 0.00 1.00 0.00 0.00
#> Sample_1045 2 0.0000 0.989 0.00 1.00 0.00 0.00
#> Sample_1048 1 0.0000 0.924 1.00 0.00 0.00 0.00
#> Sample_1086 1 0.0000 0.924 1.00 0.00 0.00 0.00
#> Sample_1088 1 0.0000 0.924 1.00 0.00 0.00 0.00
#> Sample_1089 2 0.0000 0.989 0.00 1.00 0.00 0.00
#> Sample_1120 2 0.0000 0.989 0.00 1.00 0.00 0.00
#> Sample_1122 1 0.0000 0.924 1.00 0.00 0.00 0.00
#> Sample_1132 2 0.0000 0.989 0.00 1.00 0.00 0.00
#> Sample_1134 1 0.0000 0.924 1.00 0.00 0.00 0.00
#> Sample_1140 1 0.0000 0.924 1.00 0.00 0.00 0.00
#> Sample_1183 1 0.0000 0.924 1.00 0.00 0.00 0.00
#> Sample_1184 1 0.0000 0.924 1.00 0.00 0.00 0.00
#> Sample_1255 2 0.1637 0.941 0.00 0.94 0.06 0.00
#> Sample_1268 2 0.0000 0.989 0.00 1.00 0.00 0.00
#> Sample_1296 1 0.0000 0.924 1.00 0.00 0.00 0.00
#> Sample_1306 3 0.4907 0.254 0.00 0.42 0.58 0.00
#> Sample_1310 2 0.0000 0.989 0.00 1.00 0.00 0.00
#> Sample_1337 2 0.0000 0.989 0.00 1.00 0.00 0.00
#> Sample_1339 2 0.0000 0.989 0.00 1.00 0.00 0.00
#> Sample_1345 2 0.0000 0.989 0.00 1.00 0.00 0.00
#> Sample_1376 2 0.0000 0.989 0.00 1.00 0.00 0.00
#> Sample_1400 1 0.3801 0.823 0.78 0.00 0.00 0.22
#> Sample_1413 1 0.3801 0.823 0.78 0.00 0.00 0.22
#> Sample_1416 2 0.1637 0.941 0.00 0.94 0.06 0.00
#> Sample_1460 1 0.3801 0.823 0.78 0.00 0.00 0.22
#> Sample_1486 1 0.0000 0.924 1.00 0.00 0.00 0.00
#> Sample_1494 2 0.0000 0.989 0.00 1.00 0.00 0.00
#> Sample_1504 2 0.1637 0.940 0.00 0.94 0.06 0.00
#> Sample_1505 1 0.3801 0.823 0.78 0.00 0.00 0.22
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
As soon as the classes for columns are determined, the signatures that are significantly different between subgroups can be looked for. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. To get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows (which is done by automatically selecting number of clusters).If there are too many signatures, top_signatures = ... can be set to only show the
signatures with the highest FDRs:
# code only for demonstration
# e.g. to show the top 500 most significant rows
tb = get_signature(res, k = ..., top_signatures = 500)
If the signatures are defined as these which are uniquely high in current group, diff_method argument
can be set to "uniquely_high_in_one_group":
# code only for demonstration
tb = get_signature(res, k = ..., diff_method = "uniquely_high_in_one_group")
UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n_sample age(p-value) cell.type(p-value) k
#> ATC:skmeans 53 0.935 1.000 2
#> ATC:skmeans 53 0.238 0.340 3
#> ATC:skmeans 52 0.461 0.181 4
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
sessionInfo()
#> R version 4.1.0 (2021-05-18)
#> Platform: x86_64-pc-linux-gnu (64-bit)
#> Running under: CentOS Linux 7 (Core)
#>
#> Matrix products: default
#> BLAS/LAPACK: /usr/lib64/libopenblas-r0.3.3.so
#>
#> locale:
#> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=en_US.UTF-8
#> [4] LC_COLLATE=en_US.UTF-8 LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
#> [7] LC_PAPER=en_US.UTF-8 LC_NAME=C LC_ADDRESS=C
#> [10] LC_TELEPHONE=C LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
#>
#> attached base packages:
#> [1] grid parallel stats4 stats graphics grDevices utils datasets methods
#> [10] base
#>
#> other attached packages:
#> [1] genefilter_1.74.0 ComplexHeatmap_2.8.0 markdown_1.1
#> [4] knitr_1.33 scRNAseq_2.6.1 SingleCellExperiment_1.14.1
#> [7] SummarizedExperiment_1.22.0 Biobase_2.52.0 GenomicRanges_1.44.0
#> [10] GenomeInfoDb_1.28.1 IRanges_2.26.0 S4Vectors_0.30.0
#> [13] BiocGenerics_0.38.0 MatrixGenerics_1.4.0 matrixStats_0.59.0
#> [16] cola_1.9.4
#>
#> loaded via a namespace (and not attached):
#> [1] circlize_0.4.13 AnnotationHub_3.0.1 BiocFileCache_2.0.0
#> [4] lazyeval_0.2.2 polylabelr_0.2.0 splines_4.1.0
#> [7] Polychrome_1.3.1 BiocParallel_1.26.1 ggplot2_3.3.5
#> [10] digest_0.6.27 foreach_1.5.1 ensembldb_2.16.3
#> [13] htmltools_0.5.1.1 viridis_0.6.1 fansi_0.5.0
#> [16] magrittr_2.0.1 memoise_2.0.0 cluster_2.1.2
#> [19] doParallel_1.0.16 Biostrings_2.60.1 annotate_1.70.0
#> [22] askpass_1.1 prettyunits_1.1.1 colorspace_2.0-2
#> [25] blob_1.2.1 rappdirs_0.3.3 xfun_0.24
#> [28] dplyr_1.0.7 crayon_1.4.1 RCurl_1.98-1.3
#> [31] microbenchmark_1.4-7 jsonlite_1.7.2 impute_1.66.0
#> [34] brew_1.0-6 survival_3.2-11 iterators_1.0.13
#> [37] glue_1.4.2 polyclip_1.10-0 gtable_0.3.0
#> [40] zlibbioc_1.38.0 XVector_0.32.0 GetoptLong_1.0.5
#> [43] DelayedArray_0.18.0 shape_1.4.6 scales_1.1.1
#> [46] data.tree_1.0.0 DBI_1.1.1 Rcpp_1.0.7
#> [49] viridisLite_0.4.0 xtable_1.8-4 progress_1.2.2
#> [52] clue_0.3-59 reticulate_1.20 bit_4.0.4
#> [55] mclust_5.4.7 umap_0.2.7.0 httr_1.4.2
#> [58] RColorBrewer_1.1-2 ellipsis_0.3.2 pkgconfig_2.0.3
#> [61] XML_3.99-0.6 dbplyr_2.1.1 utf8_1.2.1
#> [64] tidyselect_1.1.1 rlang_0.4.11 later_1.2.0
#> [67] AnnotationDbi_1.54.1 munsell_0.5.0 BiocVersion_3.13.1
#> [70] tools_4.1.0 cachem_1.0.5 generics_0.1.0
#> [73] RSQLite_2.2.7 ExperimentHub_2.0.0 evaluate_0.14
#> [76] stringr_1.4.0 fastmap_1.1.0 yaml_2.2.1
#> [79] bit64_4.0.5 purrr_0.3.4 dendextend_1.15.1
#> [82] KEGGREST_1.32.0 AnnotationFilter_1.16.0 mime_0.11
#> [85] slam_0.1-48 xml2_1.3.2 biomaRt_2.48.2
#> [88] compiler_4.1.0 rstudioapi_0.13 filelock_1.0.2
#> [91] curl_4.3.2 png_0.1-7 interactiveDisplayBase_1.30.0
#> [94] tibble_3.1.2 stringi_1.7.3 highr_0.9
#> [97] GenomicFeatures_1.44.0 RSpectra_0.16-0 lattice_0.20-44
#> [100] ProtGenerics_1.24.0 Matrix_1.3-4 vctrs_0.3.8
#> [103] pillar_1.6.1 lifecycle_1.0.0 BiocManager_1.30.16
#> [106] eulerr_6.1.0 GlobalOptions_0.1.2 bitops_1.0-7
#> [109] irlba_2.3.3 httpuv_1.6.1 rtracklayer_1.52.0
#> [112] R6_2.5.0 BiocIO_1.2.0 promises_1.2.0.1
#> [115] gridExtra_2.3 codetools_0.2-18 assertthat_0.2.1
#> [118] openssl_1.4.4 rjson_0.2.20 GenomicAlignments_1.28.0
#> [121] Rsamtools_2.8.0 GenomeInfoDbData_1.2.6 hms_1.1.0
#> [124] skmeans_0.2-13 Cairo_1.5-12.2 scatterplot3d_0.3-41
#> [127] shiny_1.6.0 restfulr_0.0.13