Date: 2021-07-26 10:57:57 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 17177 rows and 704 columns.
#> Performed in total 7800 partitions.
#> There are 33 groups under the following parameters:
#> - min_samples: 7
#> - mean_silhouette_cutoff: 0.9
#> - min_n_signatures: 312 (signatures are selected based on:)
#> - fdr_cutoff: 0.05
#> - group_diff (scaled values): 0.5
#>
#> Hierarchy of the partition:
#> 0, 704 cols
#> |-- 01, 348 cols, 8673 signatures
#> | |-- 011, 102 cols, 2997 signatures
#> | | |-- 0111, 30 cols, 487 signatures
#> | | | |-- 01111, 9 cols (b)
#> | | | `-- 01112, 21 cols (a)
#> | | |-- 0112, 44 cols, 517 signatures
#> | | | |-- 01121, 35 cols, 35 signatures (c)
#> | | | `-- 01122, 9 cols (b)
#> | | `-- 0113, 28 cols, 2 signatures (c)
#> | |-- 012, 72 cols, 447 signatures
#> | | |-- 0121, 37 cols, 158 signatures (c)
#> | | `-- 0122, 35 cols, 63 signatures (c)
#> | |-- 013, 96 cols, 3417 signatures
#> | | |-- 0131, 35 cols, 74 signatures (c)
#> | | |-- 0132, 37 cols, 27 signatures (c)
#> | | `-- 0133, 24 cols, 29 signatures (c)
#> | `-- 014, 78 cols, 2396 signatures
#> | |-- 0141, 28 cols, 323 signatures
#> | | |-- 01411, 10 cols (b)
#> | | |-- 01412, 10 cols (b)
#> | | `-- 01413, 8 cols (b)
#> | |-- 0142, 28 cols (a)
#> | `-- 0143, 22 cols, 63 signatures (c)
#> |-- 02, 225 cols, 5424 signatures
#> | |-- 021, 104 cols, 682 signatures
#> | | |-- 0211, 54 cols, 466 signatures
#> | | | |-- 02111, 39 cols, 89 signatures (c)
#> | | | `-- 02112, 15 cols, 2 signatures (c)
#> | | `-- 0212, 50 cols, 98 signatures (c)
#> | |-- 022, 93 cols, 4355 signatures
#> | | |-- 0221, 51 cols, 404 signatures
#> | | | |-- 02211, 21 cols, 115 signatures (c)
#> | | | `-- 02212, 30 cols, 29 signatures (c)
#> | | |-- 0222, 24 cols, 342 signatures
#> | | | |-- 02221, 9 cols (b)
#> | | | |-- 02222, 9 cols (b)
#> | | | `-- 02223, 6 cols (b)
#> | | `-- 0223, 18 cols, 183 signatures (c)
#> | `-- 023, 28 cols, 1 signatures (c)
#> `-- 03, 131 cols, 7328 signatures
#> |-- 031, 34 cols, 1173 signatures
#> | |-- 0311, 12 cols (b)
#> | |-- 0312, 13 cols (b)
#> | `-- 0313, 9 cols (b)
#> |-- 032, 42 cols, 423 signatures
#> | |-- 0321, 29 cols, 12 signatures (c)
#> | `-- 0322, 13 cols (b)
#> |-- 033, 32 cols, 430 signatures
#> | |-- 0331, 18 cols, 3 signatures (c)
#> | `-- 0332, 14 cols, 7 signatures (c)
#> `-- 034, 23 cols, 66 signatures (c)
#> 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 = lt$mat, anno = lt$anno, subset = 500, cores = 4)
Dimension of the input matrix:
mat = get_matrix(res_rh)
dim(mat)
#> [1] 17177 704
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, 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" "01111" "01112" "0112" "01121" "01122" "0113" "012" "0121"
#> [13] "0122" "013" "0131" "0132" "0133" "014" "0141" "01411" "01412" "01413" "0142" "0143"
#> [25] "02" "021" "0211" "02111" "02112" "0212" "022" "0221" "02211" "02212" "0222" "02221"
#> [37] "02222" "02223" "0223" "023" "03" "031" "0311" "0312" "0313" "032" "0321" "0322"
#> [49] "033" "0331" "0332" "034"
all_leaves(res_rh)
#> [1] "01111" "01112" "01121" "01122" "0113" "0121" "0122" "0131" "0132" "0133" "01411" "01412"
#> [13] "01413" "0142" "0143" "02111" "02112" "0212" "02211" "02212" "02221" "02222" "02223" "0223"
#> [25] "023" "0311" "0312" "0313" "0321" "0322" "0331" "0332" "034"
node_info(res_rh)
#> id best_method depth best_k n_columns n_signatures p_signatures is_leaf
#> 1 0 ATC:skmeans 1 3 704 6248 3.64e-01 FALSE
#> 2 01 ATC:skmeans 2 4 348 8673 5.05e-01 FALSE
#> 3 011 ATC:skmeans 3 3 102 2997 1.74e-01 FALSE
#> 4 0111 ATC:skmeans 4 2 30 487 2.84e-02 FALSE
#> 5 01111 not applied 5 NA 9 NA NA TRUE
#> 6 01112 ATC:skmeans 5 2 21 NA NA TRUE
#> 7 0112 ATC:skmeans 4 2 44 517 3.01e-02 FALSE
#> 8 01121 ATC:skmeans 5 2 35 35 2.04e-03 TRUE
#> 9 01122 not applied 5 NA 9 NA NA TRUE
#> 10 0113 ATC:skmeans 4 2 28 2 1.16e-04 TRUE
#> 11 012 ATC:skmeans 3 2 72 447 2.60e-02 FALSE
#> 12 0121 ATC:skmeans 4 2 37 158 9.20e-03 TRUE
#> 13 0122 ATC:skmeans 4 2 35 63 3.67e-03 TRUE
#> 14 013 ATC:skmeans 3 3 96 3417 1.99e-01 FALSE
#> 15 0131 ATC:skmeans 4 2 35 74 4.31e-03 TRUE
#> 16 0132 ATC:skmeans 4 2 37 27 1.57e-03 TRUE
#> 17 0133 ATC:skmeans 4 2 24 29 1.69e-03 TRUE
#> 18 014 ATC:skmeans 3 3 78 2396 1.39e-01 FALSE
#> 19 0141 ATC:skmeans 4 3 28 323 1.88e-02 FALSE
#> 20 01411 not applied 5 NA 10 NA NA TRUE
#> 21 01412 not applied 5 NA 10 NA NA TRUE
#> 22 01413 not applied 5 NA 8 NA NA TRUE
#> 23 0142 ATC:skmeans 4 2 28 NA NA TRUE
#> 24 0143 ATC:skmeans 4 2 22 63 3.67e-03 TRUE
#> 25 02 ATC:skmeans 2 3 225 5424 3.16e-01 FALSE
#> 26 021 ATC:skmeans 3 2 104 682 3.97e-02 FALSE
#> 27 0211 ATC:skmeans 4 2 54 466 2.71e-02 FALSE
#> 28 02111 ATC:skmeans 5 2 39 89 5.18e-03 TRUE
#> 29 02112 ATC:skmeans 5 3 15 2 1.16e-04 TRUE
#> 30 0212 ATC:skmeans 4 2 50 98 5.71e-03 TRUE
#> 31 022 ATC:skmeans 3 3 93 4355 2.54e-01 FALSE
#> 32 0221 ATC:skmeans 4 2 51 404 2.35e-02 FALSE
#> 33 02211 ATC:skmeans 5 2 21 115 6.69e-03 TRUE
#> 34 02212 ATC:skmeans 5 2 30 29 1.69e-03 TRUE
#> 35 0222 ATC:skmeans 4 3 24 342 1.99e-02 FALSE
#> 36 02221 not applied 5 NA 9 NA NA TRUE
#> 37 02222 not applied 5 NA 9 NA NA TRUE
#> 38 02223 not applied 5 NA 6 NA NA TRUE
#> 39 0223 ATC:skmeans 4 3 18 183 1.07e-02 TRUE
#> 40 023 ATC:skmeans 3 2 28 1 5.82e-05 TRUE
#> 41 03 ATC:skmeans 2 4 131 7328 4.27e-01 FALSE
#> 42 031 ATC:skmeans 3 3 34 1173 6.83e-02 FALSE
#> 43 0311 not applied 4 NA 12 NA NA TRUE
#> 44 0312 not applied 4 NA 13 NA NA TRUE
#> 45 0313 not applied 4 NA 9 NA NA TRUE
#> 46 032 ATC:skmeans 3 2 42 423 2.46e-02 FALSE
#> 47 0321 ATC:skmeans 4 2 29 12 6.99e-04 TRUE
#> 48 0322 not applied 4 NA 13 NA NA TRUE
#> 49 033 ATC:skmeans 3 2 32 430 2.50e-02 FALSE
#> 50 0331 ATC:skmeans 4 2 18 3 1.75e-04 TRUE
#> 51 0332 ATC:skmeans 4 2 14 7 4.08e-04 TRUE
#> 52 034 ATC:skmeans 3 2 23 66 3.84e-03 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 | 3 | 1.00 | 0.97 | 0.99 | 704 | ** | |
| Node01 | ATC:skmeans | 4 | 1.00 | 0.98 | 0.99 | 348 | ** | |
| Node011 | ATC:skmeans | 3 | 1.00 | 0.97 | 0.99 | 102 | ** | |
| Node0111 | ATC:skmeans | 2 | 1.00 | 1.00 | 1.00 | 30 | ** | |
| Node01111-leaf | not applied | ✓ (b) | 9 | |||||
| Node01112-leaf | ATC:skmeans | ✓ (a) | 2 | 0.55 | 0.78 | 0.90 | 21 | |
| Node0112 | ATC:skmeans | 2 | 1.00 | 0.97 | 0.98 | 44 | ** | |
| Node01121-leaf | ATC:skmeans | ✓ (c) | 2 | 1.00 | 0.98 | 0.99 | 35 | ** |
| Node01122-leaf | not applied | ✓ (b) | 9 | |||||
| Node0113-leaf | ATC:skmeans | ✓ (c) | 2 | 0.92 | 0.95 | 0.97 | 28 | * |
| Node012 | ATC:skmeans | 2 | 1.00 | 0.99 | 0.99 | 72 | ** | |
| Node0121-leaf | ATC:skmeans | ✓ (c) | 2 | 1.00 | 0.97 | 0.99 | 37 | ** |
| Node0122-leaf | ATC:skmeans | ✓ (c) | 2 | 1.00 | 0.95 | 0.98 | 35 | ** |
| Node013 | ATC:skmeans | 3 | 1.00 | 1.00 | 1.00 | 96 | ** | |
| Node0131-leaf | ATC:skmeans | ✓ (c) | 2 | 1.00 | 0.96 | 0.98 | 35 | ** |
| Node0132-leaf | ATC:skmeans | ✓ (c) | 2 | 1.00 | 0.98 | 0.99 | 37 | ** |
| Node0133-leaf | ATC:skmeans | ✓ (c) | 2 | 1.00 | 0.91 | 0.97 | 24 | ** |
| Node014 | ATC:skmeans | 3 | 1.00 | 1.00 | 1.00 | 78 | ** | |
| Node0141 | ATC:skmeans | 3 | 1.00 | 0.98 | 0.99 | 28 | ** | |
| Node01411-leaf | not applied | ✓ (b) | 10 | |||||
| Node01412-leaf | not applied | ✓ (b) | 10 | |||||
| Node01413-leaf | not applied | ✓ (b) | 8 | |||||
| Node0142-leaf | ATC:skmeans | ✓ (a) | 2 | 0.64 | 0.89 | 0.94 | 28 | |
| Node0143-leaf | ATC:skmeans | ✓ (c) | 3 | 0.99 | 0.90 | 0.95 | 22 | ** |
| Node02 | ATC:skmeans | 3 | 1.00 | 0.97 | 0.98 | 225 | ** | |
| Node021 | ATC:skmeans | 2 | 0.98 | 0.95 | 0.98 | 104 | ** | |
| Node0211 | ATC:skmeans | 2 | 1.00 | 1.00 | 1.00 | 54 | ** | |
| Node02111-leaf | ATC:skmeans | ✓ (c) | 2 | 1.00 | 0.97 | 0.99 | 39 | ** |
| Node02112-leaf | ATC:skmeans | ✓ (c) | 3 | 0.91 | 0.97 | 0.99 | 15 | * |
| Node0212-leaf | ATC:skmeans | ✓ (c) | 2 | 0.96 | 0.97 | 0.99 | 50 | ** |
| Node022 | ATC:skmeans | 3 | 1.00 | 0.97 | 0.99 | 93 | ** | |
| Node0221 | ATC:skmeans | 3 | 1.00 | 0.95 | 0.98 | 51 | ** | |
| Node02211-leaf | ATC:skmeans | ✓ (c) | 2 | 1.00 | 0.99 | 0.99 | 21 | ** |
| Node02212-leaf | ATC:skmeans | ✓ (c) | 2 | 0.93 | 0.96 | 0.98 | 30 | * |
| Node0222 | ATC:skmeans | 4 | 0.92 | 0.88 | 0.92 | 24 | * | |
| Node02221-leaf | not applied | ✓ (b) | 9 | |||||
| Node02222-leaf | not applied | ✓ (b) | 9 | |||||
| Node02223-leaf | not applied | ✓ (b) | 6 | |||||
| Node0223-leaf | ATC:skmeans | ✓ (c) | 3 | 0.97 | 0.97 | 0.98 | 18 | ** |
| Node023-leaf | ATC:skmeans | ✓ (c) | 2 | 1.00 | 0.94 | 0.98 | 28 | ** |
| Node03 | ATC:skmeans | 4 | 1.00 | 0.99 | 1.00 | 131 | ** | |
| Node031 | ATC:skmeans | 3 | 1.00 | 1.00 | 1.00 | 34 | ** | |
| Node0311-leaf | not applied | ✓ (b) | 12 | |||||
| Node0312-leaf | not applied | ✓ (b) | 13 | |||||
| Node0313-leaf | not applied | ✓ (b) | 9 | |||||
| Node032 | ATC:skmeans | 3 | 0.93 | 0.81 | 0.93 | 42 | * | |
| Node0321-leaf | ATC:skmeans | ✓ (c) | 2 | 0.93 | 0.93 | 0.97 | 29 | * |
| Node0322-leaf | not applied | ✓ (b) | 13 | |||||
| Node033 | ATC:skmeans | 2 | 1.00 | 1.00 | 1.00 | 32 | ** | |
| Node0331-leaf | ATC:skmeans | ✓ (c) | 2 | 1.00 | 0.99 | 1.00 | 18 | ** |
| Node0332-leaf | ATC:skmeans | ✓ (c) | 2 | 1.00 | 1.00 | 1.00 | 14 | ** |
| Node034-leaf | ATC:skmeans | ✓ (c) | 2 | 1.00 | 1.00 | 1.00 | 23 | ** |
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 = 323))
collect_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 342))
collect_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 404))
collect_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 423))
collect_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 430))
collect_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 447))
collect_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 466))
collect_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 487))
collect_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 517))
collect_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 682))
collect_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 1173))
collect_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 2396))
collect_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 2997))
collect_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 3417))
collect_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 4355))
collect_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 5424))
collect_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 6248))
collect_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 7328))
#> Error in max(children_height): invalid 'type' (list) of argument
collect_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 8673))
#> Error in max(children_height): invalid 'type' (list) of argument
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 = 323))
#> ola_mES_2i_2_1.counts ola_mES_2i_2_10.counts ola_mES_2i_2_11.counts ola_mES_2i_2_12.counts
#> "01112" "0131" "0131" "0131"
#> ola_mES_2i_2_13.counts ola_mES_2i_2_14.counts ola_mES_2i_2_15.counts ola_mES_2i_2_16.counts
#> "0133" "01112" "0133" "0133"
#> ola_mES_2i_2_17.counts ola_mES_2i_2_18.counts ola_mES_2i_2_19.counts ola_mES_2i_2_2.counts
#> "0133" "0131" "0131" "01112"
#> ola_mES_2i_2_20.counts ola_mES_2i_2_21.counts ola_mES_2i_2_22.counts ola_mES_2i_2_23.counts
#> "01112" "0131" "0133" "0131"
#> ola_mES_2i_2_24.counts ola_mES_2i_2_25.counts ola_mES_2i_2_26.counts ola_mES_2i_2_27.counts
#> "0131" "01112" "0133" "01112"
#> ola_mES_2i_2_28.counts ola_mES_2i_2_29.counts ola_mES_2i_2_3.counts ola_mES_2i_2_30.counts
#> "01112" "0133" "01112" "0131"
#> ola_mES_2i_2_31.counts ola_mES_2i_2_32.counts ola_mES_2i_2_33.counts ola_mES_2i_2_34.counts
#> "0131" "0133" "0131" "0131"
#> ola_mES_2i_2_35.counts ola_mES_2i_2_36.counts ola_mES_2i_2_37.counts ola_mES_2i_2_38.counts
#> "0131" "0131" "01112" "01112"
#> ola_mES_2i_2_39.counts ola_mES_2i_2_4.counts ola_mES_2i_2_40.counts ola_mES_2i_2_41.counts
#> "0133" "0131" "0131" "0131"
#> ola_mES_2i_2_42.counts ola_mES_2i_2_43.counts ola_mES_2i_2_44.counts ola_mES_2i_2_46.counts
#> "0131" "01112" "0131" "0131"
#> ola_mES_2i_2_47.counts ola_mES_2i_2_48.counts ola_mES_2i_2_49.counts ola_mES_2i_2_5.counts
#> "0131" "0131" "01112" "0133"
#> ola_mES_2i_2_50.counts ola_mES_2i_2_51.counts ola_mES_2i_2_52.counts ola_mES_2i_2_53.counts
#> "01112" "0131" "0133" "0133"
#> ola_mES_2i_2_54.counts ola_mES_2i_2_55.counts ola_mES_2i_2_56.counts ola_mES_2i_2_57.counts
#> "0131" "0133" "0133" "0133"
#> ola_mES_2i_2_58.counts ola_mES_2i_2_59.counts ola_mES_2i_2_6.counts ola_mES_2i_2_60.counts
#> "0131" "0131" "0133" "0131"
#> ola_mES_2i_2_61.counts ola_mES_2i_2_62.counts ola_mES_2i_2_63.counts ola_mES_2i_2_64.counts
#> "01112" "0322" "01112" "0133"
#> ola_mES_2i_2_66.counts ola_mES_2i_2_67.counts ola_mES_2i_2_68.counts ola_mES_2i_2_69.counts
#> "0131" "0131" "01112" "0131"
#> ola_mES_2i_2_7.counts ola_mES_2i_2_72.counts ola_mES_2i_2_73.counts ola_mES_2i_2_74.counts
#> "0133" "0133" "01112" "0133"
#> ola_mES_2i_2_75.counts ola_mES_2i_2_76.counts ola_mES_2i_2_79.counts ola_mES_2i_2_8.counts
#> "01112" "01112" "0131" "0133"
#> ola_mES_2i_2_80.counts ola_mES_2i_2_81.counts ola_mES_2i_2_85.counts ola_mES_2i_2_86.counts
#> "0133" "0131" "01112" "01112"
#> ola_mES_2i_2_87.counts ola_mES_2i_2_90.counts ola_mES_2i_2_91.counts ola_mES_2i_2_93.counts
#> "0322" "0131" "0133" "0131"
#> ola_mES_2i_2_94.counts ola_mES_2i_2_95.counts ola_mES_2i_3_10.counts ola_mES_2i_3_11.counts
#> "0131" "0133" "02222" "023"
#> ola_mES_2i_3_12.counts ola_mES_2i_3_13.counts ola_mES_2i_3_15.counts ola_mES_2i_3_16.counts
#> "02212" "02212" "02212" "02112"
#> ola_mES_2i_3_17.counts ola_mES_2i_3_18.counts ola_mES_2i_3_19.counts ola_mES_2i_3_2.counts
#> "0322" "0322" "02212" "02212"
#> ola_mES_2i_3_20.counts ola_mES_2i_3_21.counts ola_mES_2i_3_23.counts ola_mES_2i_3_25.counts
#> "02212" "02222" "02112" "023"
#> ola_mES_2i_3_26.counts ola_mES_2i_3_28.counts ola_mES_2i_3_30.counts ola_mES_2i_3_31.counts
#> "02212" "02112" "02212" "0322"
#> ola_mES_2i_3_33.counts ola_mES_2i_3_36.counts ola_mES_2i_3_37.counts ola_mES_2i_3_38.counts
#> "02212" "02112" "02212" "02212"
#> ola_mES_2i_3_39.counts ola_mES_2i_3_4.counts ola_mES_2i_3_41.counts ola_mES_2i_3_42.counts
#> "02223" "02112" "02222" "02212"
#> ola_mES_2i_3_43.counts ola_mES_2i_3_45.counts ola_mES_2i_3_46.counts ola_mES_2i_3_47.counts
#> "02112" "02212" "02212" "02212"
#> ola_mES_2i_3_49.counts ola_mES_2i_3_5.counts ola_mES_2i_3_52.counts ola_mES_2i_3_54.counts
#> "0322" "02212" "02211" "02223"
#> ola_mES_2i_3_56.counts ola_mES_2i_3_57.counts ola_mES_2i_3_58.counts ola_mES_2i_3_6.counts
#> "02212" "02212" "02212" "02222"
#> ola_mES_2i_3_62.counts ola_mES_2i_3_65.counts ola_mES_2i_3_66.counts ola_mES_2i_3_69.counts
#> "0322" "02212" "0322" "02223"
#> ola_mES_2i_3_7.counts ola_mES_2i_3_73.counts ola_mES_2i_3_76.counts ola_mES_2i_3_77.counts
#> "02212" "02212" "02212" "02222"
#> ola_mES_2i_3_78.counts ola_mES_2i_3_79.counts ola_mES_2i_3_8.counts ola_mES_2i_3_80.counts
#> "0322" "02212" "02212" "02212"
#> ola_mES_2i_3_82.counts ola_mES_2i_3_83.counts ola_mES_2i_3_84.counts ola_mES_2i_3_87.counts
#> "02212" "02212" "02112" "02222"
#> ola_mES_2i_3_88.counts ola_mES_2i_3_90.counts ola_mES_2i_3_91.counts ola_mES_2i_3_92.counts
#> "02212" "02112" "02222" "02212"
#> ola_mES_2i_3_96.counts ola_mES_2i_4_1.counts ola_mES_2i_4_10.counts ola_mES_2i_4_11.counts
#> "02223" "0212" "0212" "0212"
#> ola_mES_2i_4_13.counts ola_mES_2i_4_17.counts ola_mES_2i_4_18.counts ola_mES_2i_4_2.counts
#> "02111" "02111" "023" "0132"
#> ola_mES_2i_4_20.counts ola_mES_2i_4_21.counts ola_mES_2i_4_22.counts ola_mES_2i_4_23.counts
#> "02111" "02111" "023" "02211"
#> ola_mES_2i_4_24.counts ola_mES_2i_4_25.counts ola_mES_2i_4_26.counts ola_mES_2i_4_27.counts
#> "02112" "0212" "0212" "023"
#> ola_mES_2i_4_29.counts ola_mES_2i_4_30.counts ola_mES_2i_4_31.counts ola_mES_2i_4_32.counts
#> "0212" "02111" "0212" "02111"
#> ola_mES_2i_4_33.counts ola_mES_2i_4_34.counts ola_mES_2i_4_35.counts ola_mES_2i_4_36.counts
#> "02111" "02111" "0212" "02111"
#> ola_mES_2i_4_37.counts ola_mES_2i_4_38.counts ola_mES_2i_4_39.counts ola_mES_2i_4_40.counts
#> "0322" "01122" "0212" "01122"
#> ola_mES_2i_4_42.counts ola_mES_2i_4_43.counts ola_mES_2i_4_44.counts ola_mES_2i_4_46.counts
#> "0322" "02111" "0212" "02111"
#> ola_mES_2i_4_49.counts ola_mES_2i_4_5.counts ola_mES_2i_4_50.counts ola_mES_2i_4_52.counts
#> "01122" "023" "0212" "0212"
#> ola_mES_2i_4_53.counts ola_mES_2i_4_54.counts ola_mES_2i_4_56.counts ola_mES_2i_4_57.counts
#> "01122" "0322" "02111" "02111"
#> ola_mES_2i_4_58.counts ola_mES_2i_4_59.counts ola_mES_2i_4_6.counts ola_mES_2i_4_60.counts
#> "02111" "02111" "02111" "02211"
#> ola_mES_2i_4_61.counts ola_mES_2i_4_62.counts ola_mES_2i_4_63.counts ola_mES_2i_4_64.counts
#> "02111" "0322" "02211" "0212"
#> ola_mES_2i_4_65.counts ola_mES_2i_4_66.counts ola_mES_2i_4_67.counts ola_mES_2i_4_68.counts
#> "02111" "02111" "01122" "02211"
#> ola_mES_2i_4_69.counts ola_mES_2i_4_7.counts ola_mES_2i_4_70.counts ola_mES_2i_4_71.counts
#> "02111" "023" "02211" "02111"
#> ola_mES_2i_4_72.counts ola_mES_2i_4_73.counts ola_mES_2i_4_75.counts ola_mES_2i_4_77.counts
#> "023" "02111" "02223" "02112"
#> ola_mES_2i_4_78.counts ola_mES_2i_4_80.counts ola_mES_2i_4_81.counts ola_mES_2i_4_83.counts
#> "023" "02111" "02111" "023"
#> ola_mES_2i_4_84.counts ola_mES_2i_4_88.counts ola_mES_2i_4_89.counts ola_mES_2i_4_9.counts
#> "023" "02211" "02211" "023"
#> ola_mES_2i_4_90.counts ola_mES_2i_4_91.counts ola_mES_2i_4_94.counts ola_mES_2i_4_95.counts
#> "02111" "0212" "02211" "02223"
#> ola_mES_2i_4_96.counts ola_mES_2i_5_1.counts ola_mES_2i_5_10.counts ola_mES_2i_5_11.counts
#> "02222" "02211" "023" "023"
#> ola_mES_2i_5_12.counts ola_mES_2i_5_13.counts ola_mES_2i_5_14.counts ola_mES_2i_5_15.counts
#> "023" "023" "0212" "0212"
#> ola_mES_2i_5_16.counts ola_mES_2i_5_17.counts ola_mES_2i_5_18.counts ola_mES_2i_5_19.counts
#> "0212" "0212" "023" "02111"
#> ola_mES_2i_5_2.counts ola_mES_2i_5_20.counts ola_mES_2i_5_21.counts ola_mES_2i_5_23.counts
#> "02211" "0212" "02111" "023"
#> ola_mES_2i_5_24.counts ola_mES_2i_5_25.counts ola_mES_2i_5_26.counts ola_mES_2i_5_27.counts
#> "0212" "02111" "0212" "0212"
#> ola_mES_2i_5_28.counts ola_mES_2i_5_29.counts ola_mES_2i_5_3.counts ola_mES_2i_5_30.counts
#> "02211" "02211" "02211" "0212"
#> ola_mES_2i_5_31.counts ola_mES_2i_5_32.counts ola_mES_2i_5_34.counts ola_mES_2i_5_35.counts
#> "0212" "0212" "0212" "02111"
#> ola_mES_2i_5_36.counts ola_mES_2i_5_38.counts ola_mES_2i_5_39.counts ola_mES_2i_5_4.counts
#> "0212" "0212" "0212" "02211"
#> ola_mES_2i_5_40.counts ola_mES_2i_5_41.counts ola_mES_2i_5_42.counts ola_mES_2i_5_44.counts
#> "02111" "01122" "0212" "0212"
#> ola_mES_2i_5_45.counts ola_mES_2i_5_46.counts ola_mES_2i_5_47.counts ola_mES_2i_5_48.counts
#> "02111" "02111" "02111" "02111"
#> ola_mES_2i_5_49.counts ola_mES_2i_5_5.counts ola_mES_2i_5_51.counts ola_mES_2i_5_52.counts
#> "02111" "023" "0212" "02211"
#> ola_mES_2i_5_53.counts ola_mES_2i_5_54.counts ola_mES_2i_5_55.counts ola_mES_2i_5_56.counts
#> "0212" "02111" "0212" "0212"
#> ola_mES_2i_5_57.counts ola_mES_2i_5_58.counts ola_mES_2i_5_59.counts ola_mES_2i_5_6.counts
#> "0212" "0212" "0212" "02211"
#> ola_mES_2i_5_61.counts ola_mES_2i_5_62.counts ola_mES_2i_5_63.counts ola_mES_2i_5_65.counts
#> "0212" "02211" "02111" "023"
#> ola_mES_2i_5_66.counts ola_mES_2i_5_67.counts ola_mES_2i_5_68.counts ola_mES_2i_5_69.counts
#> "02211" "0212" "0212" "0212"
#> ola_mES_2i_5_7.counts ola_mES_2i_5_70.counts ola_mES_2i_5_72.counts ola_mES_2i_5_73.counts
#> "0212" "02111" "0212" "023"
#> ola_mES_2i_5_74.counts ola_mES_2i_5_75.counts ola_mES_2i_5_76.counts ola_mES_2i_5_77.counts
#> "02111" "023" "023" "023"
#> ola_mES_2i_5_78.counts ola_mES_2i_5_8.counts ola_mES_2i_5_81.counts ola_mES_2i_5_82.counts
#> "0212" "02112" "02211" "0212"
#> ola_mES_2i_5_83.counts ola_mES_2i_5_85.counts ola_mES_2i_5_86.counts ola_mES_2i_5_87.counts
#> "023" "02211" "023" "023"
#> ola_mES_2i_5_88.counts ola_mES_2i_5_9.counts ola_mES_2i_5_90.counts ola_mES_2i_5_92.counts
#> "023" "02222" "02112" "0212"
#> ola_mES_2i_5_94.counts ola_mES_2i_5_95.counts ola_mES_2i_5_96.counts ola_mES_a2i_2_1.counts
#> "0212" "0212" "0212" "0132"
#> ola_mES_a2i_2_10.counts ola_mES_a2i_2_11.counts ola_mES_a2i_2_12.counts ola_mES_a2i_2_13.counts
#> "01121" "0132" "01121" "01111"
#> ola_mES_a2i_2_14.counts ola_mES_a2i_2_15.counts ola_mES_a2i_2_16.counts ola_mES_a2i_2_17.counts
#> "0132" "0132" "0132" "0132"
#> ola_mES_a2i_2_18.counts ola_mES_a2i_2_19.counts ola_mES_a2i_2_2.counts ola_mES_a2i_2_20.counts
#> "0132" "0132" "0132" "01121"
#> ola_mES_a2i_2_21.counts ola_mES_a2i_2_22.counts ola_mES_a2i_2_23.counts ola_mES_a2i_2_24.counts
#> "0132" "0132" "0132" "0132"
#> ola_mES_a2i_2_25.counts ola_mES_a2i_2_26.counts ola_mES_a2i_2_27.counts ola_mES_a2i_2_28.counts
#> "0113" "0321" "01121" "01121"
#> ola_mES_a2i_2_29.counts ola_mES_a2i_2_3.counts ola_mES_a2i_2_30.counts ola_mES_a2i_2_31.counts
#> "01121" "0132" "02221" "0321"
#> ola_mES_a2i_2_32.counts ola_mES_a2i_2_33.counts ola_mES_a2i_2_34.counts ola_mES_a2i_2_35.counts
#> "0321" "01121" "01121" "0312"
#> ola_mES_a2i_2_36.counts ola_mES_a2i_2_37.counts ola_mES_a2i_2_38.counts ola_mES_a2i_2_39.counts
#> "02221" "0321" "01121" "0321"
#> ola_mES_a2i_2_4.counts ola_mES_a2i_2_40.counts ola_mES_a2i_2_41.counts ola_mES_a2i_2_42.counts
#> "01121" "01121" "0132" "0132"
#> ola_mES_a2i_2_43.counts ola_mES_a2i_2_44.counts ola_mES_a2i_2_45.counts ola_mES_a2i_2_46.counts
#> "01111" "0113" "0132" "0132"
#> ola_mES_a2i_2_47.counts ola_mES_a2i_2_48.counts ola_mES_a2i_2_49.counts ola_mES_a2i_2_5.counts
#> "01121" "01121" "0113" "0132"
#> ola_mES_a2i_2_50.counts ola_mES_a2i_2_51.counts ola_mES_a2i_2_52.counts ola_mES_a2i_2_53.counts
#> "0321" "01121" "01121" "0321"
#> ola_mES_a2i_2_54.counts ola_mES_a2i_2_55.counts ola_mES_a2i_2_56.counts ola_mES_a2i_2_57.counts
#> "01121" "0132" "01111" "0132"
#> ola_mES_a2i_2_59.counts ola_mES_a2i_2_6.counts ola_mES_a2i_2_60.counts ola_mES_a2i_2_61.counts
#> "0132" "0321" "01122" "01111"
#> ola_mES_a2i_2_62.counts ola_mES_a2i_2_63.counts ola_mES_a2i_2_64.counts ola_mES_a2i_2_65.counts
#> "01121" "01111" "01121" "01111"
#> ola_mES_a2i_2_66.counts ola_mES_a2i_2_67.counts ola_mES_a2i_2_68.counts ola_mES_a2i_2_69.counts
#> "0132" "01121" "0113" "0321"
#> ola_mES_a2i_2_7.counts ola_mES_a2i_2_70.counts ola_mES_a2i_2_71.counts ola_mES_a2i_2_72.counts
#> "01121" "01121" "0132" "01121"
#> ola_mES_a2i_2_73.counts ola_mES_a2i_2_74.counts ola_mES_a2i_2_75.counts ola_mES_a2i_2_76.counts
#> "0113" "01111" "01121" "01111"
#> ola_mES_a2i_2_77.counts ola_mES_a2i_2_78.counts ola_mES_a2i_2_8.counts ola_mES_a2i_2_80.counts
#> "01121" "0132" "0132" "0113"
#> ola_mES_a2i_2_81.counts ola_mES_a2i_2_82.counts ola_mES_a2i_2_83.counts ola_mES_a2i_2_84.counts
#> "01121" "0132" "0132" "01121"
#> ola_mES_a2i_2_86.counts ola_mES_a2i_2_87.counts ola_mES_a2i_2_88.counts ola_mES_a2i_2_89.counts
#> "0113" "01121" "01121" "0223"
#> ola_mES_a2i_2_9.counts ola_mES_a2i_2_90.counts ola_mES_a2i_2_91.counts ola_mES_a2i_2_92.counts
#> "0113" "0132" "0321" "0321"
#> ola_mES_a2i_2_93.counts ola_mES_a2i_2_94.counts ola_mES_a2i_2_95.counts ola_mES_a2i_2_96.counts
#> "01111" "0132" "0132" "0132"
#> ola_mES_a2i_3_10.counts ola_mES_a2i_3_11.counts ola_mES_a2i_3_13.counts ola_mES_a2i_3_16.counts
#> "02112" "0223" "01121" "0113"
#> ola_mES_a2i_3_17.counts ola_mES_a2i_3_18.counts ola_mES_a2i_3_2.counts ola_mES_a2i_3_20.counts
#> "0321" "0321" "0321" "0321"
#> ola_mES_a2i_3_21.counts ola_mES_a2i_3_22.counts ola_mES_a2i_3_24.counts ola_mES_a2i_3_25.counts
#> "0223" "0223" "0223" "0113"
#> ola_mES_a2i_3_26.counts ola_mES_a2i_3_28.counts ola_mES_a2i_3_29.counts ola_mES_a2i_3_3.counts
#> "0321" "0113" "0113" "02112"
#> ola_mES_a2i_3_30.counts ola_mES_a2i_3_31.counts ola_mES_a2i_3_33.counts ola_mES_a2i_3_34.counts
#> "0321" "0321" "0223" "0113"
#> ola_mES_a2i_3_37.counts ola_mES_a2i_3_38.counts ola_mES_a2i_3_39.counts ola_mES_a2i_3_4.counts
#> "01122" "0113" "0113" "0223"
#> ola_mES_a2i_3_40.counts ola_mES_a2i_3_41.counts ola_mES_a2i_3_42.counts ola_mES_a2i_3_44.counts
#> "0113" "0321" "0321" "0113"
#> ola_mES_a2i_3_45.counts ola_mES_a2i_3_47.counts ola_mES_a2i_3_49.counts ola_mES_a2i_3_50.counts
#> "0321" "0321" "0113" "0113"
#> ola_mES_a2i_3_52.counts ola_mES_a2i_3_54.counts ola_mES_a2i_3_56.counts ola_mES_a2i_3_58.counts
#> "0321" "0113" "0321" "0223"
#> ola_mES_a2i_3_59.counts ola_mES_a2i_3_6.counts ola_mES_a2i_3_62.counts ola_mES_a2i_3_64.counts
#> "0132" "0113" "01121" "01122"
#> ola_mES_a2i_3_65.counts ola_mES_a2i_3_66.counts ola_mES_a2i_3_67.counts ola_mES_a2i_3_68.counts
#> "0132" "0113" "0321" "02221"
#> ola_mES_a2i_3_69.counts ola_mES_a2i_3_7.counts ola_mES_a2i_3_71.counts ola_mES_a2i_3_73.counts
#> "0113" "0321" "0132" "01121"
#> ola_mES_a2i_3_75.counts ola_mES_a2i_3_76.counts ola_mES_a2i_3_77.counts ola_mES_a2i_3_8.counts
#> "0321" "02112" "0113" "0321"
#> ola_mES_a2i_3_80.counts ola_mES_a2i_3_81.counts ola_mES_a2i_3_82.counts ola_mES_a2i_3_83.counts
#> "02221" "0321" "0113" "01121"
#> ola_mES_a2i_3_84.counts ola_mES_a2i_3_85.counts ola_mES_a2i_3_86.counts ola_mES_a2i_3_89.counts
#> "0113" "01121" "0113" "0132"
#> ola_mES_a2i_3_9.counts ola_mES_a2i_3_90.counts ola_mES_a2i_3_91.counts ola_mES_a2i_3_92.counts
#> "0223" "0113" "0223" "01121"
#> ola_mES_a2i_3_94.counts ola_mES_a2i_3_96.counts ola_mES_lif_1_1.counts ola_mES_lif_1_10.counts
#> "0312" "01121" "01413" "0332"
#> ola_mES_lif_1_11.counts ola_mES_lif_1_12.counts ola_mES_lif_1_13.counts ola_mES_lif_1_14.counts
#> "0122" "0332" "01412" "0332"
#> ola_mES_lif_1_15.counts ola_mES_lif_1_17.counts ola_mES_lif_1_18.counts ola_mES_lif_1_19.counts
#> "01413" "0312" "01412" "0142"
#> ola_mES_lif_1_2.counts ola_mES_lif_1_20.counts ola_mES_lif_1_21.counts ola_mES_lif_1_22.counts
#> "0332" "0142" "0332" "0142"
#> ola_mES_lif_1_23.counts ola_mES_lif_1_24.counts ola_mES_lif_1_25.counts ola_mES_lif_1_26.counts
#> "0312" "0142" "0142" "0142"
#> ola_mES_lif_1_28.counts ola_mES_lif_1_29.counts ola_mES_lif_1_3.counts ola_mES_lif_1_30.counts
#> "0142" "0313" "01413" "0142"
#> ola_mES_lif_1_31.counts ola_mES_lif_1_32.counts ola_mES_lif_1_33.counts ola_mES_lif_1_34.counts
#> "0313" "0142" "0142" "01412"
#> ola_mES_lif_1_35.counts ola_mES_lif_1_36.counts ola_mES_lif_1_37.counts ola_mES_lif_1_38.counts
#> "0312" "0142" "0142" "0142"
#> ola_mES_lif_1_39.counts ola_mES_lif_1_4.counts ola_mES_lif_1_41.counts ola_mES_lif_1_42.counts
#> "0142" "0143" "0142" "0142"
#> ola_mES_lif_1_43.counts ola_mES_lif_1_44.counts ola_mES_lif_1_45.counts ola_mES_lif_1_46.counts
#> "0142" "01411" "0142" "0142"
#> ola_mES_lif_1_47.counts ola_mES_lif_1_48.counts ola_mES_lif_1_49.counts ola_mES_lif_1_5.counts
#> "01412" "01411" "01413" "0122"
#> ola_mES_lif_1_50.counts ola_mES_lif_1_51.counts ola_mES_lif_1_52.counts ola_mES_lif_1_53.counts
#> "0142" "0122" "0122" "0223"
#> ola_mES_lif_1_54.counts ola_mES_lif_1_55.counts ola_mES_lif_1_56.counts ola_mES_lif_1_57.counts
#> "01412" "0122" "02221" "0223"
#> ola_mES_lif_1_58.counts ola_mES_lif_1_59.counts ola_mES_lif_1_6.counts ola_mES_lif_1_60.counts
#> "01412" "0122" "0142" "01413"
#> ola_mES_lif_1_61.counts ola_mES_lif_1_62.counts ola_mES_lif_1_63.counts ola_mES_lif_1_64.counts
#> "01412" "0142" "0312" "0312"
#> ola_mES_lif_1_65.counts ola_mES_lif_1_66.counts ola_mES_lif_1_67.counts ola_mES_lif_1_68.counts
#> "0312" "01411" "01411" "0142"
#> ola_mES_lif_1_69.counts ola_mES_lif_1_7.counts ola_mES_lif_1_70.counts ola_mES_lif_1_72.counts
#> "0312" "01413" "0332" "01412"
#> ola_mES_lif_1_73.counts ola_mES_lif_1_74.counts ola_mES_lif_1_75.counts ola_mES_lif_1_79.counts
#> "0142" "0312" "0312" "0332"
#> ola_mES_lif_1_8.counts ola_mES_lif_1_80.counts ola_mES_lif_1_81.counts ola_mES_lif_1_82.counts
#> "0142" "01411" "0142" "0312"
#> ola_mES_lif_1_86.counts ola_mES_lif_1_9.counts ola_mES_lif_1_91.counts ola_mES_lif_1_92.counts
#> "0332" "01413" "0142" "0142"
#> ola_mES_lif_1_93.counts ola_mES_lif_1_95.counts ola_mES_lif_1_96.counts ola_mES_lif_2_1.counts
#> "0332" "01412" "01411" "0331"
#> ola_mES_lif_2_10.counts ola_mES_lif_2_11.counts ola_mES_lif_2_12.counts ola_mES_lif_2_13.counts
#> "0122" "0122" "0223" "0122"
#> ola_mES_lif_2_14.counts ola_mES_lif_2_15.counts ola_mES_lif_2_16.counts ola_mES_lif_2_17.counts
#> "0122" "0121" "0121" "0121"
#> ola_mES_lif_2_18.counts ola_mES_lif_2_19.counts ola_mES_lif_2_2.counts ola_mES_lif_2_20.counts
#> "0121" "0121" "0122" "0331"
#> ola_mES_lif_2_21.counts ola_mES_lif_2_23.counts ola_mES_lif_2_25.counts ola_mES_lif_2_26.counts
#> "0121" "0122" "0331" "0121"
#> ola_mES_lif_2_27.counts ola_mES_lif_2_28.counts ola_mES_lif_2_29.counts ola_mES_lif_2_3.counts
#> "0331" "0121" "0122" "0122"
#> ola_mES_lif_2_30.counts ola_mES_lif_2_31.counts ola_mES_lif_2_32.counts ola_mES_lif_2_33.counts
#> "0121" "0331" "0122" "0331"
#> ola_mES_lif_2_34.counts ola_mES_lif_2_35.counts ola_mES_lif_2_36.counts ola_mES_lif_2_37.counts
#> "0122" "0121" "034" "0331"
#> ola_mES_lif_2_38.counts ola_mES_lif_2_39.counts ola_mES_lif_2_4.counts ola_mES_lif_2_40.counts
#> "0121" "0122" "0121" "0121"
#> ola_mES_lif_2_41.counts ola_mES_lif_2_42.counts ola_mES_lif_2_43.counts ola_mES_lif_2_44.counts
#> "0331" "0121" "0121" "0122"
#> ola_mES_lif_2_45.counts ola_mES_lif_2_46.counts ola_mES_lif_2_47.counts ola_mES_lif_2_48.counts
#> "034" "0121" "0331" "0223"
#> ola_mES_lif_2_49.counts ola_mES_lif_2_5.counts ola_mES_lif_2_50.counts ola_mES_lif_2_51.counts
#> "0122" "0121" "0122" "0122"
#> ola_mES_lif_2_52.counts ola_mES_lif_2_53.counts ola_mES_lif_2_54.counts ola_mES_lif_2_55.counts
#> "0122" "0121" "0121" "0121"
#> ola_mES_lif_2_56.counts ola_mES_lif_2_57.counts ola_mES_lif_2_58.counts ola_mES_lif_2_59.counts
#> "0121" "034" "0121" "0122"
#> ola_mES_lif_2_6.counts ola_mES_lif_2_60.counts ola_mES_lif_2_61.counts ola_mES_lif_2_63.counts
#> "0121" "0331" "01411" "0122"
#> ola_mES_lif_2_64.counts ola_mES_lif_2_65.counts ola_mES_lif_2_66.counts ola_mES_lif_2_67.counts
#> "0121" "0121" "0121" "0121"
#> ola_mES_lif_2_68.counts ola_mES_lif_2_69.counts ola_mES_lif_2_7.counts ola_mES_lif_2_70.counts
#> "0121" "0223" "0122" "0121"
#> ola_mES_lif_2_71.counts ola_mES_lif_2_72.counts ola_mES_lif_2_73.counts ola_mES_lif_2_74.counts
#> "0122" "01413" "0223" "0122"
#> ola_mES_lif_2_75.counts ola_mES_lif_2_76.counts ola_mES_lif_2_77.counts ola_mES_lif_2_79.counts
#> "0121" "0121" "0121" "0331"
#> ola_mES_lif_2_8.counts ola_mES_lif_2_80.counts ola_mES_lif_2_81.counts ola_mES_lif_2_82.counts
#> "0122" "0122" "0331" "0121"
#> ola_mES_lif_2_83.counts ola_mES_lif_2_84.counts ola_mES_lif_2_86.counts ola_mES_lif_2_87.counts
#> "0122" "0121" "0331" "0121"
#> ola_mES_lif_2_89.counts ola_mES_lif_2_9.counts ola_mES_lif_2_90.counts ola_mES_lif_2_91.counts
#> "0122" "0331" "0331" "0331"
#> ola_mES_lif_2_92.counts ola_mES_lif_2_93.counts ola_mES_lif_2_94.counts ola_mES_lif_2_95.counts
#> "0331" "0122" "0121" "0121"
#> ola_mES_lif_2_96.counts ola_mES_lif_3_1.counts ola_mES_lif_3_10.counts ola_mES_lif_3_11.counts
#> "0331" "0143" "0122" "0332"
#> ola_mES_lif_3_12.counts ola_mES_lif_3_13.counts ola_mES_lif_3_14.counts ola_mES_lif_3_15.counts
#> "0223" "0143" "034" "034"
#> ola_mES_lif_3_16.counts ola_mES_lif_3_17.counts ola_mES_lif_3_19.counts ola_mES_lif_3_2.counts
#> "034" "034" "034" "02221"
#> ola_mES_lif_3_20.counts ola_mES_lif_3_21.counts ola_mES_lif_3_22.counts ola_mES_lif_3_23.counts
#> "0311" "02221" "0313" "0311"
#> ola_mES_lif_3_24.counts ola_mES_lif_3_26.counts ola_mES_lif_3_27.counts ola_mES_lif_3_28.counts
#> "0311" "0313" "0143" "034"
#> ola_mES_lif_3_29.counts ola_mES_lif_3_3.counts ola_mES_lif_3_30.counts ola_mES_lif_3_31.counts
#> "0332" "0143" "0143" "034"
#> ola_mES_lif_3_32.counts ola_mES_lif_3_33.counts ola_mES_lif_3_34.counts ola_mES_lif_3_35.counts
#> "0143" "034" "034" "034"
#> ola_mES_lif_3_36.counts ola_mES_lif_3_37.counts ola_mES_lif_3_38.counts ola_mES_lif_3_39.counts
#> "034" "0332" "034" "034"
#> ola_mES_lif_3_4.counts ola_mES_lif_3_40.counts ola_mES_lif_3_42.counts ola_mES_lif_3_45.counts
#> "0143" "0143" "01411" "034"
#> ola_mES_lif_3_46.counts ola_mES_lif_3_47.counts ola_mES_lif_3_48.counts ola_mES_lif_3_49.counts
#> "034" "034" "0332" "0143"
#> ola_mES_lif_3_5.counts ola_mES_lif_3_50.counts ola_mES_lif_3_51.counts ola_mES_lif_3_52.counts
#> "0122" "01412" "0143" "0143"
#> ola_mES_lif_3_53.counts ola_mES_lif_3_54.counts ola_mES_lif_3_56.counts ola_mES_lif_3_57.counts
#> "0143" "01411" "0122" "0332"
#> ola_mES_lif_3_58.counts ola_mES_lif_3_59.counts ola_mES_lif_3_6.counts ola_mES_lif_3_60.counts
#> "0143" "0143" "01411" "0143"
#> ola_mES_lif_3_61.counts ola_mES_lif_3_63.counts ola_mES_lif_3_64.counts ola_mES_lif_3_65.counts
#> "0143" "0143" "0143" "0143"
#> ola_mES_lif_3_69.counts ola_mES_lif_3_70.counts ola_mES_lif_3_71.counts ola_mES_lif_3_72.counts
#> "034" "034" "0143" "034"
#> ola_mES_lif_3_73.counts ola_mES_lif_3_74.counts ola_mES_lif_3_75.counts ola_mES_lif_3_76.counts
#> "0311" "0311" "0313" "0311"
#> ola_mES_lif_3_77.counts ola_mES_lif_3_78.counts ola_mES_lif_3_8.counts ola_mES_lif_3_80.counts
#> "02221" "0311" "0143" "0313"
#> ola_mES_lif_3_82.counts ola_mES_lif_3_83.counts ola_mES_lif_3_84.counts ola_mES_lif_3_87.counts
#> "0313" "0313" "0313" "0312"
#> ola_mES_lif_3_88.counts ola_mES_lif_3_89.counts ola_mES_lif_3_9.counts ola_mES_lif_3_90.counts
#> "0311" "02221" "0223" "034"
#> ola_mES_lif_3_92.counts ola_mES_lif_3_94.counts ola_mES_lif_3_95.counts ola_mES_lif_3_96.counts
#> "0311" "0311" "0311" "0311"
get_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 342))
#> ola_mES_2i_2_1.counts ola_mES_2i_2_10.counts ola_mES_2i_2_11.counts ola_mES_2i_2_12.counts
#> "01112" "0131" "0131" "0131"
#> ola_mES_2i_2_13.counts ola_mES_2i_2_14.counts ola_mES_2i_2_15.counts ola_mES_2i_2_16.counts
#> "0133" "01112" "0133" "0133"
#> ola_mES_2i_2_17.counts ola_mES_2i_2_18.counts ola_mES_2i_2_19.counts ola_mES_2i_2_2.counts
#> "0133" "0131" "0131" "01112"
#> ola_mES_2i_2_20.counts ola_mES_2i_2_21.counts ola_mES_2i_2_22.counts ola_mES_2i_2_23.counts
#> "01112" "0131" "0133" "0131"
#> ola_mES_2i_2_24.counts ola_mES_2i_2_25.counts ola_mES_2i_2_26.counts ola_mES_2i_2_27.counts
#> "0131" "01112" "0133" "01112"
#> ola_mES_2i_2_28.counts ola_mES_2i_2_29.counts ola_mES_2i_2_3.counts ola_mES_2i_2_30.counts
#> "01112" "0133" "01112" "0131"
#> ola_mES_2i_2_31.counts ola_mES_2i_2_32.counts ola_mES_2i_2_33.counts ola_mES_2i_2_34.counts
#> "0131" "0133" "0131" "0131"
#> ola_mES_2i_2_35.counts ola_mES_2i_2_36.counts ola_mES_2i_2_37.counts ola_mES_2i_2_38.counts
#> "0131" "0131" "01112" "01112"
#> ola_mES_2i_2_39.counts ola_mES_2i_2_4.counts ola_mES_2i_2_40.counts ola_mES_2i_2_41.counts
#> "0133" "0131" "0131" "0131"
#> ola_mES_2i_2_42.counts ola_mES_2i_2_43.counts ola_mES_2i_2_44.counts ola_mES_2i_2_46.counts
#> "0131" "01112" "0131" "0131"
#> ola_mES_2i_2_47.counts ola_mES_2i_2_48.counts ola_mES_2i_2_49.counts ola_mES_2i_2_5.counts
#> "0131" "0131" "01112" "0133"
#> ola_mES_2i_2_50.counts ola_mES_2i_2_51.counts ola_mES_2i_2_52.counts ola_mES_2i_2_53.counts
#> "01112" "0131" "0133" "0133"
#> ola_mES_2i_2_54.counts ola_mES_2i_2_55.counts ola_mES_2i_2_56.counts ola_mES_2i_2_57.counts
#> "0131" "0133" "0133" "0133"
#> ola_mES_2i_2_58.counts ola_mES_2i_2_59.counts ola_mES_2i_2_6.counts ola_mES_2i_2_60.counts
#> "0131" "0131" "0133" "0131"
#> ola_mES_2i_2_61.counts ola_mES_2i_2_62.counts ola_mES_2i_2_63.counts ola_mES_2i_2_64.counts
#> "01112" "0322" "01112" "0133"
#> ola_mES_2i_2_66.counts ola_mES_2i_2_67.counts ola_mES_2i_2_68.counts ola_mES_2i_2_69.counts
#> "0131" "0131" "01112" "0131"
#> ola_mES_2i_2_7.counts ola_mES_2i_2_72.counts ola_mES_2i_2_73.counts ola_mES_2i_2_74.counts
#> "0133" "0133" "01112" "0133"
#> ola_mES_2i_2_75.counts ola_mES_2i_2_76.counts ola_mES_2i_2_79.counts ola_mES_2i_2_8.counts
#> "01112" "01112" "0131" "0133"
#> ola_mES_2i_2_80.counts ola_mES_2i_2_81.counts ola_mES_2i_2_85.counts ola_mES_2i_2_86.counts
#> "0133" "0131" "01112" "01112"
#> ola_mES_2i_2_87.counts ola_mES_2i_2_90.counts ola_mES_2i_2_91.counts ola_mES_2i_2_93.counts
#> "0322" "0131" "0133" "0131"
#> ola_mES_2i_2_94.counts ola_mES_2i_2_95.counts ola_mES_2i_3_10.counts ola_mES_2i_3_11.counts
#> "0131" "0133" "02222" "023"
#> ola_mES_2i_3_12.counts ola_mES_2i_3_13.counts ola_mES_2i_3_15.counts ola_mES_2i_3_16.counts
#> "02212" "02212" "02212" "02112"
#> ola_mES_2i_3_17.counts ola_mES_2i_3_18.counts ola_mES_2i_3_19.counts ola_mES_2i_3_2.counts
#> "0322" "0322" "02212" "02212"
#> ola_mES_2i_3_20.counts ola_mES_2i_3_21.counts ola_mES_2i_3_23.counts ola_mES_2i_3_25.counts
#> "02212" "02222" "02112" "023"
#> ola_mES_2i_3_26.counts ola_mES_2i_3_28.counts ola_mES_2i_3_30.counts ola_mES_2i_3_31.counts
#> "02212" "02112" "02212" "0322"
#> ola_mES_2i_3_33.counts ola_mES_2i_3_36.counts ola_mES_2i_3_37.counts ola_mES_2i_3_38.counts
#> "02212" "02112" "02212" "02212"
#> ola_mES_2i_3_39.counts ola_mES_2i_3_4.counts ola_mES_2i_3_41.counts ola_mES_2i_3_42.counts
#> "02223" "02112" "02222" "02212"
#> ola_mES_2i_3_43.counts ola_mES_2i_3_45.counts ola_mES_2i_3_46.counts ola_mES_2i_3_47.counts
#> "02112" "02212" "02212" "02212"
#> ola_mES_2i_3_49.counts ola_mES_2i_3_5.counts ola_mES_2i_3_52.counts ola_mES_2i_3_54.counts
#> "0322" "02212" "02211" "02223"
#> ola_mES_2i_3_56.counts ola_mES_2i_3_57.counts ola_mES_2i_3_58.counts ola_mES_2i_3_6.counts
#> "02212" "02212" "02212" "02222"
#> ola_mES_2i_3_62.counts ola_mES_2i_3_65.counts ola_mES_2i_3_66.counts ola_mES_2i_3_69.counts
#> "0322" "02212" "0322" "02223"
#> ola_mES_2i_3_7.counts ola_mES_2i_3_73.counts ola_mES_2i_3_76.counts ola_mES_2i_3_77.counts
#> "02212" "02212" "02212" "02222"
#> ola_mES_2i_3_78.counts ola_mES_2i_3_79.counts ola_mES_2i_3_8.counts ola_mES_2i_3_80.counts
#> "0322" "02212" "02212" "02212"
#> ola_mES_2i_3_82.counts ola_mES_2i_3_83.counts ola_mES_2i_3_84.counts ola_mES_2i_3_87.counts
#> "02212" "02212" "02112" "02222"
#> ola_mES_2i_3_88.counts ola_mES_2i_3_90.counts ola_mES_2i_3_91.counts ola_mES_2i_3_92.counts
#> "02212" "02112" "02222" "02212"
#> ola_mES_2i_3_96.counts ola_mES_2i_4_1.counts ola_mES_2i_4_10.counts ola_mES_2i_4_11.counts
#> "02223" "0212" "0212" "0212"
#> ola_mES_2i_4_13.counts ola_mES_2i_4_17.counts ola_mES_2i_4_18.counts ola_mES_2i_4_2.counts
#> "02111" "02111" "023" "0132"
#> ola_mES_2i_4_20.counts ola_mES_2i_4_21.counts ola_mES_2i_4_22.counts ola_mES_2i_4_23.counts
#> "02111" "02111" "023" "02211"
#> ola_mES_2i_4_24.counts ola_mES_2i_4_25.counts ola_mES_2i_4_26.counts ola_mES_2i_4_27.counts
#> "02112" "0212" "0212" "023"
#> ola_mES_2i_4_29.counts ola_mES_2i_4_30.counts ola_mES_2i_4_31.counts ola_mES_2i_4_32.counts
#> "0212" "02111" "0212" "02111"
#> ola_mES_2i_4_33.counts ola_mES_2i_4_34.counts ola_mES_2i_4_35.counts ola_mES_2i_4_36.counts
#> "02111" "02111" "0212" "02111"
#> ola_mES_2i_4_37.counts ola_mES_2i_4_38.counts ola_mES_2i_4_39.counts ola_mES_2i_4_40.counts
#> "0322" "01122" "0212" "01122"
#> ola_mES_2i_4_42.counts ola_mES_2i_4_43.counts ola_mES_2i_4_44.counts ola_mES_2i_4_46.counts
#> "0322" "02111" "0212" "02111"
#> ola_mES_2i_4_49.counts ola_mES_2i_4_5.counts ola_mES_2i_4_50.counts ola_mES_2i_4_52.counts
#> "01122" "023" "0212" "0212"
#> ola_mES_2i_4_53.counts ola_mES_2i_4_54.counts ola_mES_2i_4_56.counts ola_mES_2i_4_57.counts
#> "01122" "0322" "02111" "02111"
#> ola_mES_2i_4_58.counts ola_mES_2i_4_59.counts ola_mES_2i_4_6.counts ola_mES_2i_4_60.counts
#> "02111" "02111" "02111" "02211"
#> ola_mES_2i_4_61.counts ola_mES_2i_4_62.counts ola_mES_2i_4_63.counts ola_mES_2i_4_64.counts
#> "02111" "0322" "02211" "0212"
#> ola_mES_2i_4_65.counts ola_mES_2i_4_66.counts ola_mES_2i_4_67.counts ola_mES_2i_4_68.counts
#> "02111" "02111" "01122" "02211"
#> ola_mES_2i_4_69.counts ola_mES_2i_4_7.counts ola_mES_2i_4_70.counts ola_mES_2i_4_71.counts
#> "02111" "023" "02211" "02111"
#> ola_mES_2i_4_72.counts ola_mES_2i_4_73.counts ola_mES_2i_4_75.counts ola_mES_2i_4_77.counts
#> "023" "02111" "02223" "02112"
#> ola_mES_2i_4_78.counts ola_mES_2i_4_80.counts ola_mES_2i_4_81.counts ola_mES_2i_4_83.counts
#> "023" "02111" "02111" "023"
#> ola_mES_2i_4_84.counts ola_mES_2i_4_88.counts ola_mES_2i_4_89.counts ola_mES_2i_4_9.counts
#> "023" "02211" "02211" "023"
#> ola_mES_2i_4_90.counts ola_mES_2i_4_91.counts ola_mES_2i_4_94.counts ola_mES_2i_4_95.counts
#> "02111" "0212" "02211" "02223"
#> ola_mES_2i_4_96.counts ola_mES_2i_5_1.counts ola_mES_2i_5_10.counts ola_mES_2i_5_11.counts
#> "02222" "02211" "023" "023"
#> ola_mES_2i_5_12.counts ola_mES_2i_5_13.counts ola_mES_2i_5_14.counts ola_mES_2i_5_15.counts
#> "023" "023" "0212" "0212"
#> ola_mES_2i_5_16.counts ola_mES_2i_5_17.counts ola_mES_2i_5_18.counts ola_mES_2i_5_19.counts
#> "0212" "0212" "023" "02111"
#> ola_mES_2i_5_2.counts ola_mES_2i_5_20.counts ola_mES_2i_5_21.counts ola_mES_2i_5_23.counts
#> "02211" "0212" "02111" "023"
#> ola_mES_2i_5_24.counts ola_mES_2i_5_25.counts ola_mES_2i_5_26.counts ola_mES_2i_5_27.counts
#> "0212" "02111" "0212" "0212"
#> ola_mES_2i_5_28.counts ola_mES_2i_5_29.counts ola_mES_2i_5_3.counts ola_mES_2i_5_30.counts
#> "02211" "02211" "02211" "0212"
#> ola_mES_2i_5_31.counts ola_mES_2i_5_32.counts ola_mES_2i_5_34.counts ola_mES_2i_5_35.counts
#> "0212" "0212" "0212" "02111"
#> ola_mES_2i_5_36.counts ola_mES_2i_5_38.counts ola_mES_2i_5_39.counts ola_mES_2i_5_4.counts
#> "0212" "0212" "0212" "02211"
#> ola_mES_2i_5_40.counts ola_mES_2i_5_41.counts ola_mES_2i_5_42.counts ola_mES_2i_5_44.counts
#> "02111" "01122" "0212" "0212"
#> ola_mES_2i_5_45.counts ola_mES_2i_5_46.counts ola_mES_2i_5_47.counts ola_mES_2i_5_48.counts
#> "02111" "02111" "02111" "02111"
#> ola_mES_2i_5_49.counts ola_mES_2i_5_5.counts ola_mES_2i_5_51.counts ola_mES_2i_5_52.counts
#> "02111" "023" "0212" "02211"
#> ola_mES_2i_5_53.counts ola_mES_2i_5_54.counts ola_mES_2i_5_55.counts ola_mES_2i_5_56.counts
#> "0212" "02111" "0212" "0212"
#> ola_mES_2i_5_57.counts ola_mES_2i_5_58.counts ola_mES_2i_5_59.counts ola_mES_2i_5_6.counts
#> "0212" "0212" "0212" "02211"
#> ola_mES_2i_5_61.counts ola_mES_2i_5_62.counts ola_mES_2i_5_63.counts ola_mES_2i_5_65.counts
#> "0212" "02211" "02111" "023"
#> ola_mES_2i_5_66.counts ola_mES_2i_5_67.counts ola_mES_2i_5_68.counts ola_mES_2i_5_69.counts
#> "02211" "0212" "0212" "0212"
#> ola_mES_2i_5_7.counts ola_mES_2i_5_70.counts ola_mES_2i_5_72.counts ola_mES_2i_5_73.counts
#> "0212" "02111" "0212" "023"
#> ola_mES_2i_5_74.counts ola_mES_2i_5_75.counts ola_mES_2i_5_76.counts ola_mES_2i_5_77.counts
#> "02111" "023" "023" "023"
#> ola_mES_2i_5_78.counts ola_mES_2i_5_8.counts ola_mES_2i_5_81.counts ola_mES_2i_5_82.counts
#> "0212" "02112" "02211" "0212"
#> ola_mES_2i_5_83.counts ola_mES_2i_5_85.counts ola_mES_2i_5_86.counts ola_mES_2i_5_87.counts
#> "023" "02211" "023" "023"
#> ola_mES_2i_5_88.counts ola_mES_2i_5_9.counts ola_mES_2i_5_90.counts ola_mES_2i_5_92.counts
#> "023" "02222" "02112" "0212"
#> ola_mES_2i_5_94.counts ola_mES_2i_5_95.counts ola_mES_2i_5_96.counts ola_mES_a2i_2_1.counts
#> "0212" "0212" "0212" "0132"
#> ola_mES_a2i_2_10.counts ola_mES_a2i_2_11.counts ola_mES_a2i_2_12.counts ola_mES_a2i_2_13.counts
#> "01121" "0132" "01121" "01111"
#> ola_mES_a2i_2_14.counts ola_mES_a2i_2_15.counts ola_mES_a2i_2_16.counts ola_mES_a2i_2_17.counts
#> "0132" "0132" "0132" "0132"
#> ola_mES_a2i_2_18.counts ola_mES_a2i_2_19.counts ola_mES_a2i_2_2.counts ola_mES_a2i_2_20.counts
#> "0132" "0132" "0132" "01121"
#> ola_mES_a2i_2_21.counts ola_mES_a2i_2_22.counts ola_mES_a2i_2_23.counts ola_mES_a2i_2_24.counts
#> "0132" "0132" "0132" "0132"
#> ola_mES_a2i_2_25.counts ola_mES_a2i_2_26.counts ola_mES_a2i_2_27.counts ola_mES_a2i_2_28.counts
#> "0113" "0321" "01121" "01121"
#> ola_mES_a2i_2_29.counts ola_mES_a2i_2_3.counts ola_mES_a2i_2_30.counts ola_mES_a2i_2_31.counts
#> "01121" "0132" "02221" "0321"
#> ola_mES_a2i_2_32.counts ola_mES_a2i_2_33.counts ola_mES_a2i_2_34.counts ola_mES_a2i_2_35.counts
#> "0321" "01121" "01121" "0312"
#> ola_mES_a2i_2_36.counts ola_mES_a2i_2_37.counts ola_mES_a2i_2_38.counts ola_mES_a2i_2_39.counts
#> "02221" "0321" "01121" "0321"
#> ola_mES_a2i_2_4.counts ola_mES_a2i_2_40.counts ola_mES_a2i_2_41.counts ola_mES_a2i_2_42.counts
#> "01121" "01121" "0132" "0132"
#> ola_mES_a2i_2_43.counts ola_mES_a2i_2_44.counts ola_mES_a2i_2_45.counts ola_mES_a2i_2_46.counts
#> "01111" "0113" "0132" "0132"
#> ola_mES_a2i_2_47.counts ola_mES_a2i_2_48.counts ola_mES_a2i_2_49.counts ola_mES_a2i_2_5.counts
#> "01121" "01121" "0113" "0132"
#> ola_mES_a2i_2_50.counts ola_mES_a2i_2_51.counts ola_mES_a2i_2_52.counts ola_mES_a2i_2_53.counts
#> "0321" "01121" "01121" "0321"
#> ola_mES_a2i_2_54.counts ola_mES_a2i_2_55.counts ola_mES_a2i_2_56.counts ola_mES_a2i_2_57.counts
#> "01121" "0132" "01111" "0132"
#> ola_mES_a2i_2_59.counts ola_mES_a2i_2_6.counts ola_mES_a2i_2_60.counts ola_mES_a2i_2_61.counts
#> "0132" "0321" "01122" "01111"
#> ola_mES_a2i_2_62.counts ola_mES_a2i_2_63.counts ola_mES_a2i_2_64.counts ola_mES_a2i_2_65.counts
#> "01121" "01111" "01121" "01111"
#> ola_mES_a2i_2_66.counts ola_mES_a2i_2_67.counts ola_mES_a2i_2_68.counts ola_mES_a2i_2_69.counts
#> "0132" "01121" "0113" "0321"
#> ola_mES_a2i_2_7.counts ola_mES_a2i_2_70.counts ola_mES_a2i_2_71.counts ola_mES_a2i_2_72.counts
#> "01121" "01121" "0132" "01121"
#> ola_mES_a2i_2_73.counts ola_mES_a2i_2_74.counts ola_mES_a2i_2_75.counts ola_mES_a2i_2_76.counts
#> "0113" "01111" "01121" "01111"
#> ola_mES_a2i_2_77.counts ola_mES_a2i_2_78.counts ola_mES_a2i_2_8.counts ola_mES_a2i_2_80.counts
#> "01121" "0132" "0132" "0113"
#> ola_mES_a2i_2_81.counts ola_mES_a2i_2_82.counts ola_mES_a2i_2_83.counts ola_mES_a2i_2_84.counts
#> "01121" "0132" "0132" "01121"
#> ola_mES_a2i_2_86.counts ola_mES_a2i_2_87.counts ola_mES_a2i_2_88.counts ola_mES_a2i_2_89.counts
#> "0113" "01121" "01121" "0223"
#> ola_mES_a2i_2_9.counts ola_mES_a2i_2_90.counts ola_mES_a2i_2_91.counts ola_mES_a2i_2_92.counts
#> "0113" "0132" "0321" "0321"
#> ola_mES_a2i_2_93.counts ola_mES_a2i_2_94.counts ola_mES_a2i_2_95.counts ola_mES_a2i_2_96.counts
#> "01111" "0132" "0132" "0132"
#> ola_mES_a2i_3_10.counts ola_mES_a2i_3_11.counts ola_mES_a2i_3_13.counts ola_mES_a2i_3_16.counts
#> "02112" "0223" "01121" "0113"
#> ola_mES_a2i_3_17.counts ola_mES_a2i_3_18.counts ola_mES_a2i_3_2.counts ola_mES_a2i_3_20.counts
#> "0321" "0321" "0321" "0321"
#> ola_mES_a2i_3_21.counts ola_mES_a2i_3_22.counts ola_mES_a2i_3_24.counts ola_mES_a2i_3_25.counts
#> "0223" "0223" "0223" "0113"
#> ola_mES_a2i_3_26.counts ola_mES_a2i_3_28.counts ola_mES_a2i_3_29.counts ola_mES_a2i_3_3.counts
#> "0321" "0113" "0113" "02112"
#> ola_mES_a2i_3_30.counts ola_mES_a2i_3_31.counts ola_mES_a2i_3_33.counts ola_mES_a2i_3_34.counts
#> "0321" "0321" "0223" "0113"
#> ola_mES_a2i_3_37.counts ola_mES_a2i_3_38.counts ola_mES_a2i_3_39.counts ola_mES_a2i_3_4.counts
#> "01122" "0113" "0113" "0223"
#> ola_mES_a2i_3_40.counts ola_mES_a2i_3_41.counts ola_mES_a2i_3_42.counts ola_mES_a2i_3_44.counts
#> "0113" "0321" "0321" "0113"
#> ola_mES_a2i_3_45.counts ola_mES_a2i_3_47.counts ola_mES_a2i_3_49.counts ola_mES_a2i_3_50.counts
#> "0321" "0321" "0113" "0113"
#> ola_mES_a2i_3_52.counts ola_mES_a2i_3_54.counts ola_mES_a2i_3_56.counts ola_mES_a2i_3_58.counts
#> "0321" "0113" "0321" "0223"
#> ola_mES_a2i_3_59.counts ola_mES_a2i_3_6.counts ola_mES_a2i_3_62.counts ola_mES_a2i_3_64.counts
#> "0132" "0113" "01121" "01122"
#> ola_mES_a2i_3_65.counts ola_mES_a2i_3_66.counts ola_mES_a2i_3_67.counts ola_mES_a2i_3_68.counts
#> "0132" "0113" "0321" "02221"
#> ola_mES_a2i_3_69.counts ola_mES_a2i_3_7.counts ola_mES_a2i_3_71.counts ola_mES_a2i_3_73.counts
#> "0113" "0321" "0132" "01121"
#> ola_mES_a2i_3_75.counts ola_mES_a2i_3_76.counts ola_mES_a2i_3_77.counts ola_mES_a2i_3_8.counts
#> "0321" "02112" "0113" "0321"
#> ola_mES_a2i_3_80.counts ola_mES_a2i_3_81.counts ola_mES_a2i_3_82.counts ola_mES_a2i_3_83.counts
#> "02221" "0321" "0113" "01121"
#> ola_mES_a2i_3_84.counts ola_mES_a2i_3_85.counts ola_mES_a2i_3_86.counts ola_mES_a2i_3_89.counts
#> "0113" "01121" "0113" "0132"
#> ola_mES_a2i_3_9.counts ola_mES_a2i_3_90.counts ola_mES_a2i_3_91.counts ola_mES_a2i_3_92.counts
#> "0223" "0113" "0223" "01121"
#> ola_mES_a2i_3_94.counts ola_mES_a2i_3_96.counts ola_mES_lif_1_1.counts ola_mES_lif_1_10.counts
#> "0312" "01121" "0141" "0332"
#> ola_mES_lif_1_11.counts ola_mES_lif_1_12.counts ola_mES_lif_1_13.counts ola_mES_lif_1_14.counts
#> "0122" "0332" "0141" "0332"
#> ola_mES_lif_1_15.counts ola_mES_lif_1_17.counts ola_mES_lif_1_18.counts ola_mES_lif_1_19.counts
#> "0141" "0312" "0141" "0142"
#> ola_mES_lif_1_2.counts ola_mES_lif_1_20.counts ola_mES_lif_1_21.counts ola_mES_lif_1_22.counts
#> "0332" "0142" "0332" "0142"
#> ola_mES_lif_1_23.counts ola_mES_lif_1_24.counts ola_mES_lif_1_25.counts ola_mES_lif_1_26.counts
#> "0312" "0142" "0142" "0142"
#> ola_mES_lif_1_28.counts ola_mES_lif_1_29.counts ola_mES_lif_1_3.counts ola_mES_lif_1_30.counts
#> "0142" "0313" "0141" "0142"
#> ola_mES_lif_1_31.counts ola_mES_lif_1_32.counts ola_mES_lif_1_33.counts ola_mES_lif_1_34.counts
#> "0313" "0142" "0142" "0141"
#> ola_mES_lif_1_35.counts ola_mES_lif_1_36.counts ola_mES_lif_1_37.counts ola_mES_lif_1_38.counts
#> "0312" "0142" "0142" "0142"
#> ola_mES_lif_1_39.counts ola_mES_lif_1_4.counts ola_mES_lif_1_41.counts ola_mES_lif_1_42.counts
#> "0142" "0143" "0142" "0142"
#> ola_mES_lif_1_43.counts ola_mES_lif_1_44.counts ola_mES_lif_1_45.counts ola_mES_lif_1_46.counts
#> "0142" "0141" "0142" "0142"
#> ola_mES_lif_1_47.counts ola_mES_lif_1_48.counts ola_mES_lif_1_49.counts ola_mES_lif_1_5.counts
#> "0141" "0141" "0141" "0122"
#> ola_mES_lif_1_50.counts ola_mES_lif_1_51.counts ola_mES_lif_1_52.counts ola_mES_lif_1_53.counts
#> "0142" "0122" "0122" "0223"
#> ola_mES_lif_1_54.counts ola_mES_lif_1_55.counts ola_mES_lif_1_56.counts ola_mES_lif_1_57.counts
#> "0141" "0122" "02221" "0223"
#> ola_mES_lif_1_58.counts ola_mES_lif_1_59.counts ola_mES_lif_1_6.counts ola_mES_lif_1_60.counts
#> "0141" "0122" "0142" "0141"
#> ola_mES_lif_1_61.counts ola_mES_lif_1_62.counts ola_mES_lif_1_63.counts ola_mES_lif_1_64.counts
#> "0141" "0142" "0312" "0312"
#> ola_mES_lif_1_65.counts ola_mES_lif_1_66.counts ola_mES_lif_1_67.counts ola_mES_lif_1_68.counts
#> "0312" "0141" "0141" "0142"
#> ola_mES_lif_1_69.counts ola_mES_lif_1_7.counts ola_mES_lif_1_70.counts ola_mES_lif_1_72.counts
#> "0312" "0141" "0332" "0141"
#> ola_mES_lif_1_73.counts ola_mES_lif_1_74.counts ola_mES_lif_1_75.counts ola_mES_lif_1_79.counts
#> "0142" "0312" "0312" "0332"
#> ola_mES_lif_1_8.counts ola_mES_lif_1_80.counts ola_mES_lif_1_81.counts ola_mES_lif_1_82.counts
#> "0142" "0141" "0142" "0312"
#> ola_mES_lif_1_86.counts ola_mES_lif_1_9.counts ola_mES_lif_1_91.counts ola_mES_lif_1_92.counts
#> "0332" "0141" "0142" "0142"
#> ola_mES_lif_1_93.counts ola_mES_lif_1_95.counts ola_mES_lif_1_96.counts ola_mES_lif_2_1.counts
#> "0332" "0141" "0141" "0331"
#> ola_mES_lif_2_10.counts ola_mES_lif_2_11.counts ola_mES_lif_2_12.counts ola_mES_lif_2_13.counts
#> "0122" "0122" "0223" "0122"
#> ola_mES_lif_2_14.counts ola_mES_lif_2_15.counts ola_mES_lif_2_16.counts ola_mES_lif_2_17.counts
#> "0122" "0121" "0121" "0121"
#> ola_mES_lif_2_18.counts ola_mES_lif_2_19.counts ola_mES_lif_2_2.counts ola_mES_lif_2_20.counts
#> "0121" "0121" "0122" "0331"
#> ola_mES_lif_2_21.counts ola_mES_lif_2_23.counts ola_mES_lif_2_25.counts ola_mES_lif_2_26.counts
#> "0121" "0122" "0331" "0121"
#> ola_mES_lif_2_27.counts ola_mES_lif_2_28.counts ola_mES_lif_2_29.counts ola_mES_lif_2_3.counts
#> "0331" "0121" "0122" "0122"
#> ola_mES_lif_2_30.counts ola_mES_lif_2_31.counts ola_mES_lif_2_32.counts ola_mES_lif_2_33.counts
#> "0121" "0331" "0122" "0331"
#> ola_mES_lif_2_34.counts ola_mES_lif_2_35.counts ola_mES_lif_2_36.counts ola_mES_lif_2_37.counts
#> "0122" "0121" "034" "0331"
#> ola_mES_lif_2_38.counts ola_mES_lif_2_39.counts ola_mES_lif_2_4.counts ola_mES_lif_2_40.counts
#> "0121" "0122" "0121" "0121"
#> ola_mES_lif_2_41.counts ola_mES_lif_2_42.counts ola_mES_lif_2_43.counts ola_mES_lif_2_44.counts
#> "0331" "0121" "0121" "0122"
#> ola_mES_lif_2_45.counts ola_mES_lif_2_46.counts ola_mES_lif_2_47.counts ola_mES_lif_2_48.counts
#> "034" "0121" "0331" "0223"
#> ola_mES_lif_2_49.counts ola_mES_lif_2_5.counts ola_mES_lif_2_50.counts ola_mES_lif_2_51.counts
#> "0122" "0121" "0122" "0122"
#> ola_mES_lif_2_52.counts ola_mES_lif_2_53.counts ola_mES_lif_2_54.counts ola_mES_lif_2_55.counts
#> "0122" "0121" "0121" "0121"
#> ola_mES_lif_2_56.counts ola_mES_lif_2_57.counts ola_mES_lif_2_58.counts ola_mES_lif_2_59.counts
#> "0121" "034" "0121" "0122"
#> ola_mES_lif_2_6.counts ola_mES_lif_2_60.counts ola_mES_lif_2_61.counts ola_mES_lif_2_63.counts
#> "0121" "0331" "0141" "0122"
#> ola_mES_lif_2_64.counts ola_mES_lif_2_65.counts ola_mES_lif_2_66.counts ola_mES_lif_2_67.counts
#> "0121" "0121" "0121" "0121"
#> ola_mES_lif_2_68.counts ola_mES_lif_2_69.counts ola_mES_lif_2_7.counts ola_mES_lif_2_70.counts
#> "0121" "0223" "0122" "0121"
#> ola_mES_lif_2_71.counts ola_mES_lif_2_72.counts ola_mES_lif_2_73.counts ola_mES_lif_2_74.counts
#> "0122" "0141" "0223" "0122"
#> ola_mES_lif_2_75.counts ola_mES_lif_2_76.counts ola_mES_lif_2_77.counts ola_mES_lif_2_79.counts
#> "0121" "0121" "0121" "0331"
#> ola_mES_lif_2_8.counts ola_mES_lif_2_80.counts ola_mES_lif_2_81.counts ola_mES_lif_2_82.counts
#> "0122" "0122" "0331" "0121"
#> ola_mES_lif_2_83.counts ola_mES_lif_2_84.counts ola_mES_lif_2_86.counts ola_mES_lif_2_87.counts
#> "0122" "0121" "0331" "0121"
#> ola_mES_lif_2_89.counts ola_mES_lif_2_9.counts ola_mES_lif_2_90.counts ola_mES_lif_2_91.counts
#> "0122" "0331" "0331" "0331"
#> ola_mES_lif_2_92.counts ola_mES_lif_2_93.counts ola_mES_lif_2_94.counts ola_mES_lif_2_95.counts
#> "0331" "0122" "0121" "0121"
#> ola_mES_lif_2_96.counts ola_mES_lif_3_1.counts ola_mES_lif_3_10.counts ola_mES_lif_3_11.counts
#> "0331" "0143" "0122" "0332"
#> ola_mES_lif_3_12.counts ola_mES_lif_3_13.counts ola_mES_lif_3_14.counts ola_mES_lif_3_15.counts
#> "0223" "0143" "034" "034"
#> ola_mES_lif_3_16.counts ola_mES_lif_3_17.counts ola_mES_lif_3_19.counts ola_mES_lif_3_2.counts
#> "034" "034" "034" "02221"
#> ola_mES_lif_3_20.counts ola_mES_lif_3_21.counts ola_mES_lif_3_22.counts ola_mES_lif_3_23.counts
#> "0311" "02221" "0313" "0311"
#> ola_mES_lif_3_24.counts ola_mES_lif_3_26.counts ola_mES_lif_3_27.counts ola_mES_lif_3_28.counts
#> "0311" "0313" "0143" "034"
#> ola_mES_lif_3_29.counts ola_mES_lif_3_3.counts ola_mES_lif_3_30.counts ola_mES_lif_3_31.counts
#> "0332" "0143" "0143" "034"
#> ola_mES_lif_3_32.counts ola_mES_lif_3_33.counts ola_mES_lif_3_34.counts ola_mES_lif_3_35.counts
#> "0143" "034" "034" "034"
#> ola_mES_lif_3_36.counts ola_mES_lif_3_37.counts ola_mES_lif_3_38.counts ola_mES_lif_3_39.counts
#> "034" "0332" "034" "034"
#> ola_mES_lif_3_4.counts ola_mES_lif_3_40.counts ola_mES_lif_3_42.counts ola_mES_lif_3_45.counts
#> "0143" "0143" "0141" "034"
#> ola_mES_lif_3_46.counts ola_mES_lif_3_47.counts ola_mES_lif_3_48.counts ola_mES_lif_3_49.counts
#> "034" "034" "0332" "0143"
#> ola_mES_lif_3_5.counts ola_mES_lif_3_50.counts ola_mES_lif_3_51.counts ola_mES_lif_3_52.counts
#> "0122" "0141" "0143" "0143"
#> ola_mES_lif_3_53.counts ola_mES_lif_3_54.counts ola_mES_lif_3_56.counts ola_mES_lif_3_57.counts
#> "0143" "0141" "0122" "0332"
#> ola_mES_lif_3_58.counts ola_mES_lif_3_59.counts ola_mES_lif_3_6.counts ola_mES_lif_3_60.counts
#> "0143" "0143" "0141" "0143"
#> ola_mES_lif_3_61.counts ola_mES_lif_3_63.counts ola_mES_lif_3_64.counts ola_mES_lif_3_65.counts
#> "0143" "0143" "0143" "0143"
#> ola_mES_lif_3_69.counts ola_mES_lif_3_70.counts ola_mES_lif_3_71.counts ola_mES_lif_3_72.counts
#> "034" "034" "0143" "034"
#> ola_mES_lif_3_73.counts ola_mES_lif_3_74.counts ola_mES_lif_3_75.counts ola_mES_lif_3_76.counts
#> "0311" "0311" "0313" "0311"
#> ola_mES_lif_3_77.counts ola_mES_lif_3_78.counts ola_mES_lif_3_8.counts ola_mES_lif_3_80.counts
#> "02221" "0311" "0143" "0313"
#> ola_mES_lif_3_82.counts ola_mES_lif_3_83.counts ola_mES_lif_3_84.counts ola_mES_lif_3_87.counts
#> "0313" "0313" "0313" "0312"
#> ola_mES_lif_3_88.counts ola_mES_lif_3_89.counts ola_mES_lif_3_9.counts ola_mES_lif_3_90.counts
#> "0311" "02221" "0223" "034"
#> ola_mES_lif_3_92.counts ola_mES_lif_3_94.counts ola_mES_lif_3_95.counts ola_mES_lif_3_96.counts
#> "0311" "0311" "0311" "0311"
get_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 404))
#> ola_mES_2i_2_1.counts ola_mES_2i_2_10.counts ola_mES_2i_2_11.counts ola_mES_2i_2_12.counts
#> "01112" "0131" "0131" "0131"
#> ola_mES_2i_2_13.counts ola_mES_2i_2_14.counts ola_mES_2i_2_15.counts ola_mES_2i_2_16.counts
#> "0133" "01112" "0133" "0133"
#> ola_mES_2i_2_17.counts ola_mES_2i_2_18.counts ola_mES_2i_2_19.counts ola_mES_2i_2_2.counts
#> "0133" "0131" "0131" "01112"
#> ola_mES_2i_2_20.counts ola_mES_2i_2_21.counts ola_mES_2i_2_22.counts ola_mES_2i_2_23.counts
#> "01112" "0131" "0133" "0131"
#> ola_mES_2i_2_24.counts ola_mES_2i_2_25.counts ola_mES_2i_2_26.counts ola_mES_2i_2_27.counts
#> "0131" "01112" "0133" "01112"
#> ola_mES_2i_2_28.counts ola_mES_2i_2_29.counts ola_mES_2i_2_3.counts ola_mES_2i_2_30.counts
#> "01112" "0133" "01112" "0131"
#> ola_mES_2i_2_31.counts ola_mES_2i_2_32.counts ola_mES_2i_2_33.counts ola_mES_2i_2_34.counts
#> "0131" "0133" "0131" "0131"
#> ola_mES_2i_2_35.counts ola_mES_2i_2_36.counts ola_mES_2i_2_37.counts ola_mES_2i_2_38.counts
#> "0131" "0131" "01112" "01112"
#> ola_mES_2i_2_39.counts ola_mES_2i_2_4.counts ola_mES_2i_2_40.counts ola_mES_2i_2_41.counts
#> "0133" "0131" "0131" "0131"
#> ola_mES_2i_2_42.counts ola_mES_2i_2_43.counts ola_mES_2i_2_44.counts ola_mES_2i_2_46.counts
#> "0131" "01112" "0131" "0131"
#> ola_mES_2i_2_47.counts ola_mES_2i_2_48.counts ola_mES_2i_2_49.counts ola_mES_2i_2_5.counts
#> "0131" "0131" "01112" "0133"
#> ola_mES_2i_2_50.counts ola_mES_2i_2_51.counts ola_mES_2i_2_52.counts ola_mES_2i_2_53.counts
#> "01112" "0131" "0133" "0133"
#> ola_mES_2i_2_54.counts ola_mES_2i_2_55.counts ola_mES_2i_2_56.counts ola_mES_2i_2_57.counts
#> "0131" "0133" "0133" "0133"
#> ola_mES_2i_2_58.counts ola_mES_2i_2_59.counts ola_mES_2i_2_6.counts ola_mES_2i_2_60.counts
#> "0131" "0131" "0133" "0131"
#> ola_mES_2i_2_61.counts ola_mES_2i_2_62.counts ola_mES_2i_2_63.counts ola_mES_2i_2_64.counts
#> "01112" "0322" "01112" "0133"
#> ola_mES_2i_2_66.counts ola_mES_2i_2_67.counts ola_mES_2i_2_68.counts ola_mES_2i_2_69.counts
#> "0131" "0131" "01112" "0131"
#> ola_mES_2i_2_7.counts ola_mES_2i_2_72.counts ola_mES_2i_2_73.counts ola_mES_2i_2_74.counts
#> "0133" "0133" "01112" "0133"
#> ola_mES_2i_2_75.counts ola_mES_2i_2_76.counts ola_mES_2i_2_79.counts ola_mES_2i_2_8.counts
#> "01112" "01112" "0131" "0133"
#> ola_mES_2i_2_80.counts ola_mES_2i_2_81.counts ola_mES_2i_2_85.counts ola_mES_2i_2_86.counts
#> "0133" "0131" "01112" "01112"
#> ola_mES_2i_2_87.counts ola_mES_2i_2_90.counts ola_mES_2i_2_91.counts ola_mES_2i_2_93.counts
#> "0322" "0131" "0133" "0131"
#> ola_mES_2i_2_94.counts ola_mES_2i_2_95.counts ola_mES_2i_3_10.counts ola_mES_2i_3_11.counts
#> "0131" "0133" "0222" "023"
#> ola_mES_2i_3_12.counts ola_mES_2i_3_13.counts ola_mES_2i_3_15.counts ola_mES_2i_3_16.counts
#> "02212" "02212" "02212" "02112"
#> ola_mES_2i_3_17.counts ola_mES_2i_3_18.counts ola_mES_2i_3_19.counts ola_mES_2i_3_2.counts
#> "0322" "0322" "02212" "02212"
#> ola_mES_2i_3_20.counts ola_mES_2i_3_21.counts ola_mES_2i_3_23.counts ola_mES_2i_3_25.counts
#> "02212" "0222" "02112" "023"
#> ola_mES_2i_3_26.counts ola_mES_2i_3_28.counts ola_mES_2i_3_30.counts ola_mES_2i_3_31.counts
#> "02212" "02112" "02212" "0322"
#> ola_mES_2i_3_33.counts ola_mES_2i_3_36.counts ola_mES_2i_3_37.counts ola_mES_2i_3_38.counts
#> "02212" "02112" "02212" "02212"
#> ola_mES_2i_3_39.counts ola_mES_2i_3_4.counts ola_mES_2i_3_41.counts ola_mES_2i_3_42.counts
#> "0222" "02112" "0222" "02212"
#> ola_mES_2i_3_43.counts ola_mES_2i_3_45.counts ola_mES_2i_3_46.counts ola_mES_2i_3_47.counts
#> "02112" "02212" "02212" "02212"
#> ola_mES_2i_3_49.counts ola_mES_2i_3_5.counts ola_mES_2i_3_52.counts ola_mES_2i_3_54.counts
#> "0322" "02212" "02211" "0222"
#> ola_mES_2i_3_56.counts ola_mES_2i_3_57.counts ola_mES_2i_3_58.counts ola_mES_2i_3_6.counts
#> "02212" "02212" "02212" "0222"
#> ola_mES_2i_3_62.counts ola_mES_2i_3_65.counts ola_mES_2i_3_66.counts ola_mES_2i_3_69.counts
#> "0322" "02212" "0322" "0222"
#> ola_mES_2i_3_7.counts ola_mES_2i_3_73.counts ola_mES_2i_3_76.counts ola_mES_2i_3_77.counts
#> "02212" "02212" "02212" "0222"
#> ola_mES_2i_3_78.counts ola_mES_2i_3_79.counts ola_mES_2i_3_8.counts ola_mES_2i_3_80.counts
#> "0322" "02212" "02212" "02212"
#> ola_mES_2i_3_82.counts ola_mES_2i_3_83.counts ola_mES_2i_3_84.counts ola_mES_2i_3_87.counts
#> "02212" "02212" "02112" "0222"
#> ola_mES_2i_3_88.counts ola_mES_2i_3_90.counts ola_mES_2i_3_91.counts ola_mES_2i_3_92.counts
#> "02212" "02112" "0222" "02212"
#> ola_mES_2i_3_96.counts ola_mES_2i_4_1.counts ola_mES_2i_4_10.counts ola_mES_2i_4_11.counts
#> "0222" "0212" "0212" "0212"
#> ola_mES_2i_4_13.counts ola_mES_2i_4_17.counts ola_mES_2i_4_18.counts ola_mES_2i_4_2.counts
#> "02111" "02111" "023" "0132"
#> ola_mES_2i_4_20.counts ola_mES_2i_4_21.counts ola_mES_2i_4_22.counts ola_mES_2i_4_23.counts
#> "02111" "02111" "023" "02211"
#> ola_mES_2i_4_24.counts ola_mES_2i_4_25.counts ola_mES_2i_4_26.counts ola_mES_2i_4_27.counts
#> "02112" "0212" "0212" "023"
#> ola_mES_2i_4_29.counts ola_mES_2i_4_30.counts ola_mES_2i_4_31.counts ola_mES_2i_4_32.counts
#> "0212" "02111" "0212" "02111"
#> ola_mES_2i_4_33.counts ola_mES_2i_4_34.counts ola_mES_2i_4_35.counts ola_mES_2i_4_36.counts
#> "02111" "02111" "0212" "02111"
#> ola_mES_2i_4_37.counts ola_mES_2i_4_38.counts ola_mES_2i_4_39.counts ola_mES_2i_4_40.counts
#> "0322" "01122" "0212" "01122"
#> ola_mES_2i_4_42.counts ola_mES_2i_4_43.counts ola_mES_2i_4_44.counts ola_mES_2i_4_46.counts
#> "0322" "02111" "0212" "02111"
#> ola_mES_2i_4_49.counts ola_mES_2i_4_5.counts ola_mES_2i_4_50.counts ola_mES_2i_4_52.counts
#> "01122" "023" "0212" "0212"
#> ola_mES_2i_4_53.counts ola_mES_2i_4_54.counts ola_mES_2i_4_56.counts ola_mES_2i_4_57.counts
#> "01122" "0322" "02111" "02111"
#> ola_mES_2i_4_58.counts ola_mES_2i_4_59.counts ola_mES_2i_4_6.counts ola_mES_2i_4_60.counts
#> "02111" "02111" "02111" "02211"
#> ola_mES_2i_4_61.counts ola_mES_2i_4_62.counts ola_mES_2i_4_63.counts ola_mES_2i_4_64.counts
#> "02111" "0322" "02211" "0212"
#> ola_mES_2i_4_65.counts ola_mES_2i_4_66.counts ola_mES_2i_4_67.counts ola_mES_2i_4_68.counts
#> "02111" "02111" "01122" "02211"
#> ola_mES_2i_4_69.counts ola_mES_2i_4_7.counts ola_mES_2i_4_70.counts ola_mES_2i_4_71.counts
#> "02111" "023" "02211" "02111"
#> ola_mES_2i_4_72.counts ola_mES_2i_4_73.counts ola_mES_2i_4_75.counts ola_mES_2i_4_77.counts
#> "023" "02111" "0222" "02112"
#> ola_mES_2i_4_78.counts ola_mES_2i_4_80.counts ola_mES_2i_4_81.counts ola_mES_2i_4_83.counts
#> "023" "02111" "02111" "023"
#> ola_mES_2i_4_84.counts ola_mES_2i_4_88.counts ola_mES_2i_4_89.counts ola_mES_2i_4_9.counts
#> "023" "02211" "02211" "023"
#> ola_mES_2i_4_90.counts ola_mES_2i_4_91.counts ola_mES_2i_4_94.counts ola_mES_2i_4_95.counts
#> "02111" "0212" "02211" "0222"
#> ola_mES_2i_4_96.counts ola_mES_2i_5_1.counts ola_mES_2i_5_10.counts ola_mES_2i_5_11.counts
#> "0222" "02211" "023" "023"
#> ola_mES_2i_5_12.counts ola_mES_2i_5_13.counts ola_mES_2i_5_14.counts ola_mES_2i_5_15.counts
#> "023" "023" "0212" "0212"
#> ola_mES_2i_5_16.counts ola_mES_2i_5_17.counts ola_mES_2i_5_18.counts ola_mES_2i_5_19.counts
#> "0212" "0212" "023" "02111"
#> ola_mES_2i_5_2.counts ola_mES_2i_5_20.counts ola_mES_2i_5_21.counts ola_mES_2i_5_23.counts
#> "02211" "0212" "02111" "023"
#> ola_mES_2i_5_24.counts ola_mES_2i_5_25.counts ola_mES_2i_5_26.counts ola_mES_2i_5_27.counts
#> "0212" "02111" "0212" "0212"
#> ola_mES_2i_5_28.counts ola_mES_2i_5_29.counts ola_mES_2i_5_3.counts ola_mES_2i_5_30.counts
#> "02211" "02211" "02211" "0212"
#> ola_mES_2i_5_31.counts ola_mES_2i_5_32.counts ola_mES_2i_5_34.counts ola_mES_2i_5_35.counts
#> "0212" "0212" "0212" "02111"
#> ola_mES_2i_5_36.counts ola_mES_2i_5_38.counts ola_mES_2i_5_39.counts ola_mES_2i_5_4.counts
#> "0212" "0212" "0212" "02211"
#> ola_mES_2i_5_40.counts ola_mES_2i_5_41.counts ola_mES_2i_5_42.counts ola_mES_2i_5_44.counts
#> "02111" "01122" "0212" "0212"
#> ola_mES_2i_5_45.counts ola_mES_2i_5_46.counts ola_mES_2i_5_47.counts ola_mES_2i_5_48.counts
#> "02111" "02111" "02111" "02111"
#> ola_mES_2i_5_49.counts ola_mES_2i_5_5.counts ola_mES_2i_5_51.counts ola_mES_2i_5_52.counts
#> "02111" "023" "0212" "02211"
#> ola_mES_2i_5_53.counts ola_mES_2i_5_54.counts ola_mES_2i_5_55.counts ola_mES_2i_5_56.counts
#> "0212" "02111" "0212" "0212"
#> ola_mES_2i_5_57.counts ola_mES_2i_5_58.counts ola_mES_2i_5_59.counts ola_mES_2i_5_6.counts
#> "0212" "0212" "0212" "02211"
#> ola_mES_2i_5_61.counts ola_mES_2i_5_62.counts ola_mES_2i_5_63.counts ola_mES_2i_5_65.counts
#> "0212" "02211" "02111" "023"
#> ola_mES_2i_5_66.counts ola_mES_2i_5_67.counts ola_mES_2i_5_68.counts ola_mES_2i_5_69.counts
#> "02211" "0212" "0212" "0212"
#> ola_mES_2i_5_7.counts ola_mES_2i_5_70.counts ola_mES_2i_5_72.counts ola_mES_2i_5_73.counts
#> "0212" "02111" "0212" "023"
#> ola_mES_2i_5_74.counts ola_mES_2i_5_75.counts ola_mES_2i_5_76.counts ola_mES_2i_5_77.counts
#> "02111" "023" "023" "023"
#> ola_mES_2i_5_78.counts ola_mES_2i_5_8.counts ola_mES_2i_5_81.counts ola_mES_2i_5_82.counts
#> "0212" "02112" "02211" "0212"
#> ola_mES_2i_5_83.counts ola_mES_2i_5_85.counts ola_mES_2i_5_86.counts ola_mES_2i_5_87.counts
#> "023" "02211" "023" "023"
#> ola_mES_2i_5_88.counts ola_mES_2i_5_9.counts ola_mES_2i_5_90.counts ola_mES_2i_5_92.counts
#> "023" "0222" "02112" "0212"
#> ola_mES_2i_5_94.counts ola_mES_2i_5_95.counts ola_mES_2i_5_96.counts ola_mES_a2i_2_1.counts
#> "0212" "0212" "0212" "0132"
#> ola_mES_a2i_2_10.counts ola_mES_a2i_2_11.counts ola_mES_a2i_2_12.counts ola_mES_a2i_2_13.counts
#> "01121" "0132" "01121" "01111"
#> ola_mES_a2i_2_14.counts ola_mES_a2i_2_15.counts ola_mES_a2i_2_16.counts ola_mES_a2i_2_17.counts
#> "0132" "0132" "0132" "0132"
#> ola_mES_a2i_2_18.counts ola_mES_a2i_2_19.counts ola_mES_a2i_2_2.counts ola_mES_a2i_2_20.counts
#> "0132" "0132" "0132" "01121"
#> ola_mES_a2i_2_21.counts ola_mES_a2i_2_22.counts ola_mES_a2i_2_23.counts ola_mES_a2i_2_24.counts
#> "0132" "0132" "0132" "0132"
#> ola_mES_a2i_2_25.counts ola_mES_a2i_2_26.counts ola_mES_a2i_2_27.counts ola_mES_a2i_2_28.counts
#> "0113" "0321" "01121" "01121"
#> ola_mES_a2i_2_29.counts ola_mES_a2i_2_3.counts ola_mES_a2i_2_30.counts ola_mES_a2i_2_31.counts
#> "01121" "0132" "0222" "0321"
#> ola_mES_a2i_2_32.counts ola_mES_a2i_2_33.counts ola_mES_a2i_2_34.counts ola_mES_a2i_2_35.counts
#> "0321" "01121" "01121" "0312"
#> ola_mES_a2i_2_36.counts ola_mES_a2i_2_37.counts ola_mES_a2i_2_38.counts ola_mES_a2i_2_39.counts
#> "0222" "0321" "01121" "0321"
#> ola_mES_a2i_2_4.counts ola_mES_a2i_2_40.counts ola_mES_a2i_2_41.counts ola_mES_a2i_2_42.counts
#> "01121" "01121" "0132" "0132"
#> ola_mES_a2i_2_43.counts ola_mES_a2i_2_44.counts ola_mES_a2i_2_45.counts ola_mES_a2i_2_46.counts
#> "01111" "0113" "0132" "0132"
#> ola_mES_a2i_2_47.counts ola_mES_a2i_2_48.counts ola_mES_a2i_2_49.counts ola_mES_a2i_2_5.counts
#> "01121" "01121" "0113" "0132"
#> ola_mES_a2i_2_50.counts ola_mES_a2i_2_51.counts ola_mES_a2i_2_52.counts ola_mES_a2i_2_53.counts
#> "0321" "01121" "01121" "0321"
#> ola_mES_a2i_2_54.counts ola_mES_a2i_2_55.counts ola_mES_a2i_2_56.counts ola_mES_a2i_2_57.counts
#> "01121" "0132" "01111" "0132"
#> ola_mES_a2i_2_59.counts ola_mES_a2i_2_6.counts ola_mES_a2i_2_60.counts ola_mES_a2i_2_61.counts
#> "0132" "0321" "01122" "01111"
#> ola_mES_a2i_2_62.counts ola_mES_a2i_2_63.counts ola_mES_a2i_2_64.counts ola_mES_a2i_2_65.counts
#> "01121" "01111" "01121" "01111"
#> ola_mES_a2i_2_66.counts ola_mES_a2i_2_67.counts ola_mES_a2i_2_68.counts ola_mES_a2i_2_69.counts
#> "0132" "01121" "0113" "0321"
#> ola_mES_a2i_2_7.counts ola_mES_a2i_2_70.counts ola_mES_a2i_2_71.counts ola_mES_a2i_2_72.counts
#> "01121" "01121" "0132" "01121"
#> ola_mES_a2i_2_73.counts ola_mES_a2i_2_74.counts ola_mES_a2i_2_75.counts ola_mES_a2i_2_76.counts
#> "0113" "01111" "01121" "01111"
#> ola_mES_a2i_2_77.counts ola_mES_a2i_2_78.counts ola_mES_a2i_2_8.counts ola_mES_a2i_2_80.counts
#> "01121" "0132" "0132" "0113"
#> ola_mES_a2i_2_81.counts ola_mES_a2i_2_82.counts ola_mES_a2i_2_83.counts ola_mES_a2i_2_84.counts
#> "01121" "0132" "0132" "01121"
#> ola_mES_a2i_2_86.counts ola_mES_a2i_2_87.counts ola_mES_a2i_2_88.counts ola_mES_a2i_2_89.counts
#> "0113" "01121" "01121" "0223"
#> ola_mES_a2i_2_9.counts ola_mES_a2i_2_90.counts ola_mES_a2i_2_91.counts ola_mES_a2i_2_92.counts
#> "0113" "0132" "0321" "0321"
#> ola_mES_a2i_2_93.counts ola_mES_a2i_2_94.counts ola_mES_a2i_2_95.counts ola_mES_a2i_2_96.counts
#> "01111" "0132" "0132" "0132"
#> ola_mES_a2i_3_10.counts ola_mES_a2i_3_11.counts ola_mES_a2i_3_13.counts ola_mES_a2i_3_16.counts
#> "02112" "0223" "01121" "0113"
#> ola_mES_a2i_3_17.counts ola_mES_a2i_3_18.counts ola_mES_a2i_3_2.counts ola_mES_a2i_3_20.counts
#> "0321" "0321" "0321" "0321"
#> ola_mES_a2i_3_21.counts ola_mES_a2i_3_22.counts ola_mES_a2i_3_24.counts ola_mES_a2i_3_25.counts
#> "0223" "0223" "0223" "0113"
#> ola_mES_a2i_3_26.counts ola_mES_a2i_3_28.counts ola_mES_a2i_3_29.counts ola_mES_a2i_3_3.counts
#> "0321" "0113" "0113" "02112"
#> ola_mES_a2i_3_30.counts ola_mES_a2i_3_31.counts ola_mES_a2i_3_33.counts ola_mES_a2i_3_34.counts
#> "0321" "0321" "0223" "0113"
#> ola_mES_a2i_3_37.counts ola_mES_a2i_3_38.counts ola_mES_a2i_3_39.counts ola_mES_a2i_3_4.counts
#> "01122" "0113" "0113" "0223"
#> ola_mES_a2i_3_40.counts ola_mES_a2i_3_41.counts ola_mES_a2i_3_42.counts ola_mES_a2i_3_44.counts
#> "0113" "0321" "0321" "0113"
#> ola_mES_a2i_3_45.counts ola_mES_a2i_3_47.counts ola_mES_a2i_3_49.counts ola_mES_a2i_3_50.counts
#> "0321" "0321" "0113" "0113"
#> ola_mES_a2i_3_52.counts ola_mES_a2i_3_54.counts ola_mES_a2i_3_56.counts ola_mES_a2i_3_58.counts
#> "0321" "0113" "0321" "0223"
#> ola_mES_a2i_3_59.counts ola_mES_a2i_3_6.counts ola_mES_a2i_3_62.counts ola_mES_a2i_3_64.counts
#> "0132" "0113" "01121" "01122"
#> ola_mES_a2i_3_65.counts ola_mES_a2i_3_66.counts ola_mES_a2i_3_67.counts ola_mES_a2i_3_68.counts
#> "0132" "0113" "0321" "0222"
#> ola_mES_a2i_3_69.counts ola_mES_a2i_3_7.counts ola_mES_a2i_3_71.counts ola_mES_a2i_3_73.counts
#> "0113" "0321" "0132" "01121"
#> ola_mES_a2i_3_75.counts ola_mES_a2i_3_76.counts ola_mES_a2i_3_77.counts ola_mES_a2i_3_8.counts
#> "0321" "02112" "0113" "0321"
#> ola_mES_a2i_3_80.counts ola_mES_a2i_3_81.counts ola_mES_a2i_3_82.counts ola_mES_a2i_3_83.counts
#> "0222" "0321" "0113" "01121"
#> ola_mES_a2i_3_84.counts ola_mES_a2i_3_85.counts ola_mES_a2i_3_86.counts ola_mES_a2i_3_89.counts
#> "0113" "01121" "0113" "0132"
#> ola_mES_a2i_3_9.counts ola_mES_a2i_3_90.counts ola_mES_a2i_3_91.counts ola_mES_a2i_3_92.counts
#> "0223" "0113" "0223" "01121"
#> ola_mES_a2i_3_94.counts ola_mES_a2i_3_96.counts ola_mES_lif_1_1.counts ola_mES_lif_1_10.counts
#> "0312" "01121" "0141" "0332"
#> ola_mES_lif_1_11.counts ola_mES_lif_1_12.counts ola_mES_lif_1_13.counts ola_mES_lif_1_14.counts
#> "0122" "0332" "0141" "0332"
#> ola_mES_lif_1_15.counts ola_mES_lif_1_17.counts ola_mES_lif_1_18.counts ola_mES_lif_1_19.counts
#> "0141" "0312" "0141" "0142"
#> ola_mES_lif_1_2.counts ola_mES_lif_1_20.counts ola_mES_lif_1_21.counts ola_mES_lif_1_22.counts
#> "0332" "0142" "0332" "0142"
#> ola_mES_lif_1_23.counts ola_mES_lif_1_24.counts ola_mES_lif_1_25.counts ola_mES_lif_1_26.counts
#> "0312" "0142" "0142" "0142"
#> ola_mES_lif_1_28.counts ola_mES_lif_1_29.counts ola_mES_lif_1_3.counts ola_mES_lif_1_30.counts
#> "0142" "0313" "0141" "0142"
#> ola_mES_lif_1_31.counts ola_mES_lif_1_32.counts ola_mES_lif_1_33.counts ola_mES_lif_1_34.counts
#> "0313" "0142" "0142" "0141"
#> ola_mES_lif_1_35.counts ola_mES_lif_1_36.counts ola_mES_lif_1_37.counts ola_mES_lif_1_38.counts
#> "0312" "0142" "0142" "0142"
#> ola_mES_lif_1_39.counts ola_mES_lif_1_4.counts ola_mES_lif_1_41.counts ola_mES_lif_1_42.counts
#> "0142" "0143" "0142" "0142"
#> ola_mES_lif_1_43.counts ola_mES_lif_1_44.counts ola_mES_lif_1_45.counts ola_mES_lif_1_46.counts
#> "0142" "0141" "0142" "0142"
#> ola_mES_lif_1_47.counts ola_mES_lif_1_48.counts ola_mES_lif_1_49.counts ola_mES_lif_1_5.counts
#> "0141" "0141" "0141" "0122"
#> ola_mES_lif_1_50.counts ola_mES_lif_1_51.counts ola_mES_lif_1_52.counts ola_mES_lif_1_53.counts
#> "0142" "0122" "0122" "0223"
#> ola_mES_lif_1_54.counts ola_mES_lif_1_55.counts ola_mES_lif_1_56.counts ola_mES_lif_1_57.counts
#> "0141" "0122" "0222" "0223"
#> ola_mES_lif_1_58.counts ola_mES_lif_1_59.counts ola_mES_lif_1_6.counts ola_mES_lif_1_60.counts
#> "0141" "0122" "0142" "0141"
#> ola_mES_lif_1_61.counts ola_mES_lif_1_62.counts ola_mES_lif_1_63.counts ola_mES_lif_1_64.counts
#> "0141" "0142" "0312" "0312"
#> ola_mES_lif_1_65.counts ola_mES_lif_1_66.counts ola_mES_lif_1_67.counts ola_mES_lif_1_68.counts
#> "0312" "0141" "0141" "0142"
#> ola_mES_lif_1_69.counts ola_mES_lif_1_7.counts ola_mES_lif_1_70.counts ola_mES_lif_1_72.counts
#> "0312" "0141" "0332" "0141"
#> ola_mES_lif_1_73.counts ola_mES_lif_1_74.counts ola_mES_lif_1_75.counts ola_mES_lif_1_79.counts
#> "0142" "0312" "0312" "0332"
#> ola_mES_lif_1_8.counts ola_mES_lif_1_80.counts ola_mES_lif_1_81.counts ola_mES_lif_1_82.counts
#> "0142" "0141" "0142" "0312"
#> ola_mES_lif_1_86.counts ola_mES_lif_1_9.counts ola_mES_lif_1_91.counts ola_mES_lif_1_92.counts
#> "0332" "0141" "0142" "0142"
#> ola_mES_lif_1_93.counts ola_mES_lif_1_95.counts ola_mES_lif_1_96.counts ola_mES_lif_2_1.counts
#> "0332" "0141" "0141" "0331"
#> ola_mES_lif_2_10.counts ola_mES_lif_2_11.counts ola_mES_lif_2_12.counts ola_mES_lif_2_13.counts
#> "0122" "0122" "0223" "0122"
#> ola_mES_lif_2_14.counts ola_mES_lif_2_15.counts ola_mES_lif_2_16.counts ola_mES_lif_2_17.counts
#> "0122" "0121" "0121" "0121"
#> ola_mES_lif_2_18.counts ola_mES_lif_2_19.counts ola_mES_lif_2_2.counts ola_mES_lif_2_20.counts
#> "0121" "0121" "0122" "0331"
#> ola_mES_lif_2_21.counts ola_mES_lif_2_23.counts ola_mES_lif_2_25.counts ola_mES_lif_2_26.counts
#> "0121" "0122" "0331" "0121"
#> ola_mES_lif_2_27.counts ola_mES_lif_2_28.counts ola_mES_lif_2_29.counts ola_mES_lif_2_3.counts
#> "0331" "0121" "0122" "0122"
#> ola_mES_lif_2_30.counts ola_mES_lif_2_31.counts ola_mES_lif_2_32.counts ola_mES_lif_2_33.counts
#> "0121" "0331" "0122" "0331"
#> ola_mES_lif_2_34.counts ola_mES_lif_2_35.counts ola_mES_lif_2_36.counts ola_mES_lif_2_37.counts
#> "0122" "0121" "034" "0331"
#> ola_mES_lif_2_38.counts ola_mES_lif_2_39.counts ola_mES_lif_2_4.counts ola_mES_lif_2_40.counts
#> "0121" "0122" "0121" "0121"
#> ola_mES_lif_2_41.counts ola_mES_lif_2_42.counts ola_mES_lif_2_43.counts ola_mES_lif_2_44.counts
#> "0331" "0121" "0121" "0122"
#> ola_mES_lif_2_45.counts ola_mES_lif_2_46.counts ola_mES_lif_2_47.counts ola_mES_lif_2_48.counts
#> "034" "0121" "0331" "0223"
#> ola_mES_lif_2_49.counts ola_mES_lif_2_5.counts ola_mES_lif_2_50.counts ola_mES_lif_2_51.counts
#> "0122" "0121" "0122" "0122"
#> ola_mES_lif_2_52.counts ola_mES_lif_2_53.counts ola_mES_lif_2_54.counts ola_mES_lif_2_55.counts
#> "0122" "0121" "0121" "0121"
#> ola_mES_lif_2_56.counts ola_mES_lif_2_57.counts ola_mES_lif_2_58.counts ola_mES_lif_2_59.counts
#> "0121" "034" "0121" "0122"
#> ola_mES_lif_2_6.counts ola_mES_lif_2_60.counts ola_mES_lif_2_61.counts ola_mES_lif_2_63.counts
#> "0121" "0331" "0141" "0122"
#> ola_mES_lif_2_64.counts ola_mES_lif_2_65.counts ola_mES_lif_2_66.counts ola_mES_lif_2_67.counts
#> "0121" "0121" "0121" "0121"
#> ola_mES_lif_2_68.counts ola_mES_lif_2_69.counts ola_mES_lif_2_7.counts ola_mES_lif_2_70.counts
#> "0121" "0223" "0122" "0121"
#> ola_mES_lif_2_71.counts ola_mES_lif_2_72.counts ola_mES_lif_2_73.counts ola_mES_lif_2_74.counts
#> "0122" "0141" "0223" "0122"
#> ola_mES_lif_2_75.counts ola_mES_lif_2_76.counts ola_mES_lif_2_77.counts ola_mES_lif_2_79.counts
#> "0121" "0121" "0121" "0331"
#> ola_mES_lif_2_8.counts ola_mES_lif_2_80.counts ola_mES_lif_2_81.counts ola_mES_lif_2_82.counts
#> "0122" "0122" "0331" "0121"
#> ola_mES_lif_2_83.counts ola_mES_lif_2_84.counts ola_mES_lif_2_86.counts ola_mES_lif_2_87.counts
#> "0122" "0121" "0331" "0121"
#> ola_mES_lif_2_89.counts ola_mES_lif_2_9.counts ola_mES_lif_2_90.counts ola_mES_lif_2_91.counts
#> "0122" "0331" "0331" "0331"
#> ola_mES_lif_2_92.counts ola_mES_lif_2_93.counts ola_mES_lif_2_94.counts ola_mES_lif_2_95.counts
#> "0331" "0122" "0121" "0121"
#> ola_mES_lif_2_96.counts ola_mES_lif_3_1.counts ola_mES_lif_3_10.counts ola_mES_lif_3_11.counts
#> "0331" "0143" "0122" "0332"
#> ola_mES_lif_3_12.counts ola_mES_lif_3_13.counts ola_mES_lif_3_14.counts ola_mES_lif_3_15.counts
#> "0223" "0143" "034" "034"
#> ola_mES_lif_3_16.counts ola_mES_lif_3_17.counts ola_mES_lif_3_19.counts ola_mES_lif_3_2.counts
#> "034" "034" "034" "0222"
#> ola_mES_lif_3_20.counts ola_mES_lif_3_21.counts ola_mES_lif_3_22.counts ola_mES_lif_3_23.counts
#> "0311" "0222" "0313" "0311"
#> ola_mES_lif_3_24.counts ola_mES_lif_3_26.counts ola_mES_lif_3_27.counts ola_mES_lif_3_28.counts
#> "0311" "0313" "0143" "034"
#> ola_mES_lif_3_29.counts ola_mES_lif_3_3.counts ola_mES_lif_3_30.counts ola_mES_lif_3_31.counts
#> "0332" "0143" "0143" "034"
#> ola_mES_lif_3_32.counts ola_mES_lif_3_33.counts ola_mES_lif_3_34.counts ola_mES_lif_3_35.counts
#> "0143" "034" "034" "034"
#> ola_mES_lif_3_36.counts ola_mES_lif_3_37.counts ola_mES_lif_3_38.counts ola_mES_lif_3_39.counts
#> "034" "0332" "034" "034"
#> ola_mES_lif_3_4.counts ola_mES_lif_3_40.counts ola_mES_lif_3_42.counts ola_mES_lif_3_45.counts
#> "0143" "0143" "0141" "034"
#> ola_mES_lif_3_46.counts ola_mES_lif_3_47.counts ola_mES_lif_3_48.counts ola_mES_lif_3_49.counts
#> "034" "034" "0332" "0143"
#> ola_mES_lif_3_5.counts ola_mES_lif_3_50.counts ola_mES_lif_3_51.counts ola_mES_lif_3_52.counts
#> "0122" "0141" "0143" "0143"
#> ola_mES_lif_3_53.counts ola_mES_lif_3_54.counts ola_mES_lif_3_56.counts ola_mES_lif_3_57.counts
#> "0143" "0141" "0122" "0332"
#> ola_mES_lif_3_58.counts ola_mES_lif_3_59.counts ola_mES_lif_3_6.counts ola_mES_lif_3_60.counts
#> "0143" "0143" "0141" "0143"
#> ola_mES_lif_3_61.counts ola_mES_lif_3_63.counts ola_mES_lif_3_64.counts ola_mES_lif_3_65.counts
#> "0143" "0143" "0143" "0143"
#> ola_mES_lif_3_69.counts ola_mES_lif_3_70.counts ola_mES_lif_3_71.counts ola_mES_lif_3_72.counts
#> "034" "034" "0143" "034"
#> ola_mES_lif_3_73.counts ola_mES_lif_3_74.counts ola_mES_lif_3_75.counts ola_mES_lif_3_76.counts
#> "0311" "0311" "0313" "0311"
#> ola_mES_lif_3_77.counts ola_mES_lif_3_78.counts ola_mES_lif_3_8.counts ola_mES_lif_3_80.counts
#> "0222" "0311" "0143" "0313"
#> ola_mES_lif_3_82.counts ola_mES_lif_3_83.counts ola_mES_lif_3_84.counts ola_mES_lif_3_87.counts
#> "0313" "0313" "0313" "0312"
#> ola_mES_lif_3_88.counts ola_mES_lif_3_89.counts ola_mES_lif_3_9.counts ola_mES_lif_3_90.counts
#> "0311" "0222" "0223" "034"
#> ola_mES_lif_3_92.counts ola_mES_lif_3_94.counts ola_mES_lif_3_95.counts ola_mES_lif_3_96.counts
#> "0311" "0311" "0311" "0311"
get_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 423))
#> ola_mES_2i_2_1.counts ola_mES_2i_2_10.counts ola_mES_2i_2_11.counts ola_mES_2i_2_12.counts
#> "01112" "0131" "0131" "0131"
#> ola_mES_2i_2_13.counts ola_mES_2i_2_14.counts ola_mES_2i_2_15.counts ola_mES_2i_2_16.counts
#> "0133" "01112" "0133" "0133"
#> ola_mES_2i_2_17.counts ola_mES_2i_2_18.counts ola_mES_2i_2_19.counts ola_mES_2i_2_2.counts
#> "0133" "0131" "0131" "01112"
#> ola_mES_2i_2_20.counts ola_mES_2i_2_21.counts ola_mES_2i_2_22.counts ola_mES_2i_2_23.counts
#> "01112" "0131" "0133" "0131"
#> ola_mES_2i_2_24.counts ola_mES_2i_2_25.counts ola_mES_2i_2_26.counts ola_mES_2i_2_27.counts
#> "0131" "01112" "0133" "01112"
#> ola_mES_2i_2_28.counts ola_mES_2i_2_29.counts ola_mES_2i_2_3.counts ola_mES_2i_2_30.counts
#> "01112" "0133" "01112" "0131"
#> ola_mES_2i_2_31.counts ola_mES_2i_2_32.counts ola_mES_2i_2_33.counts ola_mES_2i_2_34.counts
#> "0131" "0133" "0131" "0131"
#> ola_mES_2i_2_35.counts ola_mES_2i_2_36.counts ola_mES_2i_2_37.counts ola_mES_2i_2_38.counts
#> "0131" "0131" "01112" "01112"
#> ola_mES_2i_2_39.counts ola_mES_2i_2_4.counts ola_mES_2i_2_40.counts ola_mES_2i_2_41.counts
#> "0133" "0131" "0131" "0131"
#> ola_mES_2i_2_42.counts ola_mES_2i_2_43.counts ola_mES_2i_2_44.counts ola_mES_2i_2_46.counts
#> "0131" "01112" "0131" "0131"
#> ola_mES_2i_2_47.counts ola_mES_2i_2_48.counts ola_mES_2i_2_49.counts ola_mES_2i_2_5.counts
#> "0131" "0131" "01112" "0133"
#> ola_mES_2i_2_50.counts ola_mES_2i_2_51.counts ola_mES_2i_2_52.counts ola_mES_2i_2_53.counts
#> "01112" "0131" "0133" "0133"
#> ola_mES_2i_2_54.counts ola_mES_2i_2_55.counts ola_mES_2i_2_56.counts ola_mES_2i_2_57.counts
#> "0131" "0133" "0133" "0133"
#> ola_mES_2i_2_58.counts ola_mES_2i_2_59.counts ola_mES_2i_2_6.counts ola_mES_2i_2_60.counts
#> "0131" "0131" "0133" "0131"
#> ola_mES_2i_2_61.counts ola_mES_2i_2_62.counts ola_mES_2i_2_63.counts ola_mES_2i_2_64.counts
#> "01112" "0322" "01112" "0133"
#> ola_mES_2i_2_66.counts ola_mES_2i_2_67.counts ola_mES_2i_2_68.counts ola_mES_2i_2_69.counts
#> "0131" "0131" "01112" "0131"
#> ola_mES_2i_2_7.counts ola_mES_2i_2_72.counts ola_mES_2i_2_73.counts ola_mES_2i_2_74.counts
#> "0133" "0133" "01112" "0133"
#> ola_mES_2i_2_75.counts ola_mES_2i_2_76.counts ola_mES_2i_2_79.counts ola_mES_2i_2_8.counts
#> "01112" "01112" "0131" "0133"
#> ola_mES_2i_2_80.counts ola_mES_2i_2_81.counts ola_mES_2i_2_85.counts ola_mES_2i_2_86.counts
#> "0133" "0131" "01112" "01112"
#> ola_mES_2i_2_87.counts ola_mES_2i_2_90.counts ola_mES_2i_2_91.counts ola_mES_2i_2_93.counts
#> "0322" "0131" "0133" "0131"
#> ola_mES_2i_2_94.counts ola_mES_2i_2_95.counts ola_mES_2i_3_10.counts ola_mES_2i_3_11.counts
#> "0131" "0133" "0222" "023"
#> ola_mES_2i_3_12.counts ola_mES_2i_3_13.counts ola_mES_2i_3_15.counts ola_mES_2i_3_16.counts
#> "0221" "0221" "0221" "02112"
#> ola_mES_2i_3_17.counts ola_mES_2i_3_18.counts ola_mES_2i_3_19.counts ola_mES_2i_3_2.counts
#> "0322" "0322" "0221" "0221"
#> ola_mES_2i_3_20.counts ola_mES_2i_3_21.counts ola_mES_2i_3_23.counts ola_mES_2i_3_25.counts
#> "0221" "0222" "02112" "023"
#> ola_mES_2i_3_26.counts ola_mES_2i_3_28.counts ola_mES_2i_3_30.counts ola_mES_2i_3_31.counts
#> "0221" "02112" "0221" "0322"
#> ola_mES_2i_3_33.counts ola_mES_2i_3_36.counts ola_mES_2i_3_37.counts ola_mES_2i_3_38.counts
#> "0221" "02112" "0221" "0221"
#> ola_mES_2i_3_39.counts ola_mES_2i_3_4.counts ola_mES_2i_3_41.counts ola_mES_2i_3_42.counts
#> "0222" "02112" "0222" "0221"
#> ola_mES_2i_3_43.counts ola_mES_2i_3_45.counts ola_mES_2i_3_46.counts ola_mES_2i_3_47.counts
#> "02112" "0221" "0221" "0221"
#> ola_mES_2i_3_49.counts ola_mES_2i_3_5.counts ola_mES_2i_3_52.counts ola_mES_2i_3_54.counts
#> "0322" "0221" "0221" "0222"
#> ola_mES_2i_3_56.counts ola_mES_2i_3_57.counts ola_mES_2i_3_58.counts ola_mES_2i_3_6.counts
#> "0221" "0221" "0221" "0222"
#> ola_mES_2i_3_62.counts ola_mES_2i_3_65.counts ola_mES_2i_3_66.counts ola_mES_2i_3_69.counts
#> "0322" "0221" "0322" "0222"
#> ola_mES_2i_3_7.counts ola_mES_2i_3_73.counts ola_mES_2i_3_76.counts ola_mES_2i_3_77.counts
#> "0221" "0221" "0221" "0222"
#> ola_mES_2i_3_78.counts ola_mES_2i_3_79.counts ola_mES_2i_3_8.counts ola_mES_2i_3_80.counts
#> "0322" "0221" "0221" "0221"
#> ola_mES_2i_3_82.counts ola_mES_2i_3_83.counts ola_mES_2i_3_84.counts ola_mES_2i_3_87.counts
#> "0221" "0221" "02112" "0222"
#> ola_mES_2i_3_88.counts ola_mES_2i_3_90.counts ola_mES_2i_3_91.counts ola_mES_2i_3_92.counts
#> "0221" "02112" "0222" "0221"
#> ola_mES_2i_3_96.counts ola_mES_2i_4_1.counts ola_mES_2i_4_10.counts ola_mES_2i_4_11.counts
#> "0222" "0212" "0212" "0212"
#> ola_mES_2i_4_13.counts ola_mES_2i_4_17.counts ola_mES_2i_4_18.counts ola_mES_2i_4_2.counts
#> "02111" "02111" "023" "0132"
#> ola_mES_2i_4_20.counts ola_mES_2i_4_21.counts ola_mES_2i_4_22.counts ola_mES_2i_4_23.counts
#> "02111" "02111" "023" "0221"
#> ola_mES_2i_4_24.counts ola_mES_2i_4_25.counts ola_mES_2i_4_26.counts ola_mES_2i_4_27.counts
#> "02112" "0212" "0212" "023"
#> ola_mES_2i_4_29.counts ola_mES_2i_4_30.counts ola_mES_2i_4_31.counts ola_mES_2i_4_32.counts
#> "0212" "02111" "0212" "02111"
#> ola_mES_2i_4_33.counts ola_mES_2i_4_34.counts ola_mES_2i_4_35.counts ola_mES_2i_4_36.counts
#> "02111" "02111" "0212" "02111"
#> ola_mES_2i_4_37.counts ola_mES_2i_4_38.counts ola_mES_2i_4_39.counts ola_mES_2i_4_40.counts
#> "0322" "01122" "0212" "01122"
#> ola_mES_2i_4_42.counts ola_mES_2i_4_43.counts ola_mES_2i_4_44.counts ola_mES_2i_4_46.counts
#> "0322" "02111" "0212" "02111"
#> ola_mES_2i_4_49.counts ola_mES_2i_4_5.counts ola_mES_2i_4_50.counts ola_mES_2i_4_52.counts
#> "01122" "023" "0212" "0212"
#> ola_mES_2i_4_53.counts ola_mES_2i_4_54.counts ola_mES_2i_4_56.counts ola_mES_2i_4_57.counts
#> "01122" "0322" "02111" "02111"
#> ola_mES_2i_4_58.counts ola_mES_2i_4_59.counts ola_mES_2i_4_6.counts ola_mES_2i_4_60.counts
#> "02111" "02111" "02111" "0221"
#> ola_mES_2i_4_61.counts ola_mES_2i_4_62.counts ola_mES_2i_4_63.counts ola_mES_2i_4_64.counts
#> "02111" "0322" "0221" "0212"
#> ola_mES_2i_4_65.counts ola_mES_2i_4_66.counts ola_mES_2i_4_67.counts ola_mES_2i_4_68.counts
#> "02111" "02111" "01122" "0221"
#> ola_mES_2i_4_69.counts ola_mES_2i_4_7.counts ola_mES_2i_4_70.counts ola_mES_2i_4_71.counts
#> "02111" "023" "0221" "02111"
#> ola_mES_2i_4_72.counts ola_mES_2i_4_73.counts ola_mES_2i_4_75.counts ola_mES_2i_4_77.counts
#> "023" "02111" "0222" "02112"
#> ola_mES_2i_4_78.counts ola_mES_2i_4_80.counts ola_mES_2i_4_81.counts ola_mES_2i_4_83.counts
#> "023" "02111" "02111" "023"
#> ola_mES_2i_4_84.counts ola_mES_2i_4_88.counts ola_mES_2i_4_89.counts ola_mES_2i_4_9.counts
#> "023" "0221" "0221" "023"
#> ola_mES_2i_4_90.counts ola_mES_2i_4_91.counts ola_mES_2i_4_94.counts ola_mES_2i_4_95.counts
#> "02111" "0212" "0221" "0222"
#> ola_mES_2i_4_96.counts ola_mES_2i_5_1.counts ola_mES_2i_5_10.counts ola_mES_2i_5_11.counts
#> "0222" "0221" "023" "023"
#> ola_mES_2i_5_12.counts ola_mES_2i_5_13.counts ola_mES_2i_5_14.counts ola_mES_2i_5_15.counts
#> "023" "023" "0212" "0212"
#> ola_mES_2i_5_16.counts ola_mES_2i_5_17.counts ola_mES_2i_5_18.counts ola_mES_2i_5_19.counts
#> "0212" "0212" "023" "02111"
#> ola_mES_2i_5_2.counts ola_mES_2i_5_20.counts ola_mES_2i_5_21.counts ola_mES_2i_5_23.counts
#> "0221" "0212" "02111" "023"
#> ola_mES_2i_5_24.counts ola_mES_2i_5_25.counts ola_mES_2i_5_26.counts ola_mES_2i_5_27.counts
#> "0212" "02111" "0212" "0212"
#> ola_mES_2i_5_28.counts ola_mES_2i_5_29.counts ola_mES_2i_5_3.counts ola_mES_2i_5_30.counts
#> "0221" "0221" "0221" "0212"
#> ola_mES_2i_5_31.counts ola_mES_2i_5_32.counts ola_mES_2i_5_34.counts ola_mES_2i_5_35.counts
#> "0212" "0212" "0212" "02111"
#> ola_mES_2i_5_36.counts ola_mES_2i_5_38.counts ola_mES_2i_5_39.counts ola_mES_2i_5_4.counts
#> "0212" "0212" "0212" "0221"
#> ola_mES_2i_5_40.counts ola_mES_2i_5_41.counts ola_mES_2i_5_42.counts ola_mES_2i_5_44.counts
#> "02111" "01122" "0212" "0212"
#> ola_mES_2i_5_45.counts ola_mES_2i_5_46.counts ola_mES_2i_5_47.counts ola_mES_2i_5_48.counts
#> "02111" "02111" "02111" "02111"
#> ola_mES_2i_5_49.counts ola_mES_2i_5_5.counts ola_mES_2i_5_51.counts ola_mES_2i_5_52.counts
#> "02111" "023" "0212" "0221"
#> ola_mES_2i_5_53.counts ola_mES_2i_5_54.counts ola_mES_2i_5_55.counts ola_mES_2i_5_56.counts
#> "0212" "02111" "0212" "0212"
#> ola_mES_2i_5_57.counts ola_mES_2i_5_58.counts ola_mES_2i_5_59.counts ola_mES_2i_5_6.counts
#> "0212" "0212" "0212" "0221"
#> ola_mES_2i_5_61.counts ola_mES_2i_5_62.counts ola_mES_2i_5_63.counts ola_mES_2i_5_65.counts
#> "0212" "0221" "02111" "023"
#> ola_mES_2i_5_66.counts ola_mES_2i_5_67.counts ola_mES_2i_5_68.counts ola_mES_2i_5_69.counts
#> "0221" "0212" "0212" "0212"
#> ola_mES_2i_5_7.counts ola_mES_2i_5_70.counts ola_mES_2i_5_72.counts ola_mES_2i_5_73.counts
#> "0212" "02111" "0212" "023"
#> ola_mES_2i_5_74.counts ola_mES_2i_5_75.counts ola_mES_2i_5_76.counts ola_mES_2i_5_77.counts
#> "02111" "023" "023" "023"
#> ola_mES_2i_5_78.counts ola_mES_2i_5_8.counts ola_mES_2i_5_81.counts ola_mES_2i_5_82.counts
#> "0212" "02112" "0221" "0212"
#> ola_mES_2i_5_83.counts ola_mES_2i_5_85.counts ola_mES_2i_5_86.counts ola_mES_2i_5_87.counts
#> "023" "0221" "023" "023"
#> ola_mES_2i_5_88.counts ola_mES_2i_5_9.counts ola_mES_2i_5_90.counts ola_mES_2i_5_92.counts
#> "023" "0222" "02112" "0212"
#> ola_mES_2i_5_94.counts ola_mES_2i_5_95.counts ola_mES_2i_5_96.counts ola_mES_a2i_2_1.counts
#> "0212" "0212" "0212" "0132"
#> ola_mES_a2i_2_10.counts ola_mES_a2i_2_11.counts ola_mES_a2i_2_12.counts ola_mES_a2i_2_13.counts
#> "01121" "0132" "01121" "01111"
#> ola_mES_a2i_2_14.counts ola_mES_a2i_2_15.counts ola_mES_a2i_2_16.counts ola_mES_a2i_2_17.counts
#> "0132" "0132" "0132" "0132"
#> ola_mES_a2i_2_18.counts ola_mES_a2i_2_19.counts ola_mES_a2i_2_2.counts ola_mES_a2i_2_20.counts
#> "0132" "0132" "0132" "01121"
#> ola_mES_a2i_2_21.counts ola_mES_a2i_2_22.counts ola_mES_a2i_2_23.counts ola_mES_a2i_2_24.counts
#> "0132" "0132" "0132" "0132"
#> ola_mES_a2i_2_25.counts ola_mES_a2i_2_26.counts ola_mES_a2i_2_27.counts ola_mES_a2i_2_28.counts
#> "0113" "0321" "01121" "01121"
#> ola_mES_a2i_2_29.counts ola_mES_a2i_2_3.counts ola_mES_a2i_2_30.counts ola_mES_a2i_2_31.counts
#> "01121" "0132" "0222" "0321"
#> ola_mES_a2i_2_32.counts ola_mES_a2i_2_33.counts ola_mES_a2i_2_34.counts ola_mES_a2i_2_35.counts
#> "0321" "01121" "01121" "0312"
#> ola_mES_a2i_2_36.counts ola_mES_a2i_2_37.counts ola_mES_a2i_2_38.counts ola_mES_a2i_2_39.counts
#> "0222" "0321" "01121" "0321"
#> ola_mES_a2i_2_4.counts ola_mES_a2i_2_40.counts ola_mES_a2i_2_41.counts ola_mES_a2i_2_42.counts
#> "01121" "01121" "0132" "0132"
#> ola_mES_a2i_2_43.counts ola_mES_a2i_2_44.counts ola_mES_a2i_2_45.counts ola_mES_a2i_2_46.counts
#> "01111" "0113" "0132" "0132"
#> ola_mES_a2i_2_47.counts ola_mES_a2i_2_48.counts ola_mES_a2i_2_49.counts ola_mES_a2i_2_5.counts
#> "01121" "01121" "0113" "0132"
#> ola_mES_a2i_2_50.counts ola_mES_a2i_2_51.counts ola_mES_a2i_2_52.counts ola_mES_a2i_2_53.counts
#> "0321" "01121" "01121" "0321"
#> ola_mES_a2i_2_54.counts ola_mES_a2i_2_55.counts ola_mES_a2i_2_56.counts ola_mES_a2i_2_57.counts
#> "01121" "0132" "01111" "0132"
#> ola_mES_a2i_2_59.counts ola_mES_a2i_2_6.counts ola_mES_a2i_2_60.counts ola_mES_a2i_2_61.counts
#> "0132" "0321" "01122" "01111"
#> ola_mES_a2i_2_62.counts ola_mES_a2i_2_63.counts ola_mES_a2i_2_64.counts ola_mES_a2i_2_65.counts
#> "01121" "01111" "01121" "01111"
#> ola_mES_a2i_2_66.counts ola_mES_a2i_2_67.counts ola_mES_a2i_2_68.counts ola_mES_a2i_2_69.counts
#> "0132" "01121" "0113" "0321"
#> ola_mES_a2i_2_7.counts ola_mES_a2i_2_70.counts ola_mES_a2i_2_71.counts ola_mES_a2i_2_72.counts
#> "01121" "01121" "0132" "01121"
#> ola_mES_a2i_2_73.counts ola_mES_a2i_2_74.counts ola_mES_a2i_2_75.counts ola_mES_a2i_2_76.counts
#> "0113" "01111" "01121" "01111"
#> ola_mES_a2i_2_77.counts ola_mES_a2i_2_78.counts ola_mES_a2i_2_8.counts ola_mES_a2i_2_80.counts
#> "01121" "0132" "0132" "0113"
#> ola_mES_a2i_2_81.counts ola_mES_a2i_2_82.counts ola_mES_a2i_2_83.counts ola_mES_a2i_2_84.counts
#> "01121" "0132" "0132" "01121"
#> ola_mES_a2i_2_86.counts ola_mES_a2i_2_87.counts ola_mES_a2i_2_88.counts ola_mES_a2i_2_89.counts
#> "0113" "01121" "01121" "0223"
#> ola_mES_a2i_2_9.counts ola_mES_a2i_2_90.counts ola_mES_a2i_2_91.counts ola_mES_a2i_2_92.counts
#> "0113" "0132" "0321" "0321"
#> ola_mES_a2i_2_93.counts ola_mES_a2i_2_94.counts ola_mES_a2i_2_95.counts ola_mES_a2i_2_96.counts
#> "01111" "0132" "0132" "0132"
#> ola_mES_a2i_3_10.counts ola_mES_a2i_3_11.counts ola_mES_a2i_3_13.counts ola_mES_a2i_3_16.counts
#> "02112" "0223" "01121" "0113"
#> ola_mES_a2i_3_17.counts ola_mES_a2i_3_18.counts ola_mES_a2i_3_2.counts ola_mES_a2i_3_20.counts
#> "0321" "0321" "0321" "0321"
#> ola_mES_a2i_3_21.counts ola_mES_a2i_3_22.counts ola_mES_a2i_3_24.counts ola_mES_a2i_3_25.counts
#> "0223" "0223" "0223" "0113"
#> ola_mES_a2i_3_26.counts ola_mES_a2i_3_28.counts ola_mES_a2i_3_29.counts ola_mES_a2i_3_3.counts
#> "0321" "0113" "0113" "02112"
#> ola_mES_a2i_3_30.counts ola_mES_a2i_3_31.counts ola_mES_a2i_3_33.counts ola_mES_a2i_3_34.counts
#> "0321" "0321" "0223" "0113"
#> ola_mES_a2i_3_37.counts ola_mES_a2i_3_38.counts ola_mES_a2i_3_39.counts ola_mES_a2i_3_4.counts
#> "01122" "0113" "0113" "0223"
#> ola_mES_a2i_3_40.counts ola_mES_a2i_3_41.counts ola_mES_a2i_3_42.counts ola_mES_a2i_3_44.counts
#> "0113" "0321" "0321" "0113"
#> ola_mES_a2i_3_45.counts ola_mES_a2i_3_47.counts ola_mES_a2i_3_49.counts ola_mES_a2i_3_50.counts
#> "0321" "0321" "0113" "0113"
#> ola_mES_a2i_3_52.counts ola_mES_a2i_3_54.counts ola_mES_a2i_3_56.counts ola_mES_a2i_3_58.counts
#> "0321" "0113" "0321" "0223"
#> ola_mES_a2i_3_59.counts ola_mES_a2i_3_6.counts ola_mES_a2i_3_62.counts ola_mES_a2i_3_64.counts
#> "0132" "0113" "01121" "01122"
#> ola_mES_a2i_3_65.counts ola_mES_a2i_3_66.counts ola_mES_a2i_3_67.counts ola_mES_a2i_3_68.counts
#> "0132" "0113" "0321" "0222"
#> ola_mES_a2i_3_69.counts ola_mES_a2i_3_7.counts ola_mES_a2i_3_71.counts ola_mES_a2i_3_73.counts
#> "0113" "0321" "0132" "01121"
#> ola_mES_a2i_3_75.counts ola_mES_a2i_3_76.counts ola_mES_a2i_3_77.counts ola_mES_a2i_3_8.counts
#> "0321" "02112" "0113" "0321"
#> ola_mES_a2i_3_80.counts ola_mES_a2i_3_81.counts ola_mES_a2i_3_82.counts ola_mES_a2i_3_83.counts
#> "0222" "0321" "0113" "01121"
#> ola_mES_a2i_3_84.counts ola_mES_a2i_3_85.counts ola_mES_a2i_3_86.counts ola_mES_a2i_3_89.counts
#> "0113" "01121" "0113" "0132"
#> ola_mES_a2i_3_9.counts ola_mES_a2i_3_90.counts ola_mES_a2i_3_91.counts ola_mES_a2i_3_92.counts
#> "0223" "0113" "0223" "01121"
#> ola_mES_a2i_3_94.counts ola_mES_a2i_3_96.counts ola_mES_lif_1_1.counts ola_mES_lif_1_10.counts
#> "0312" "01121" "0141" "0332"
#> ola_mES_lif_1_11.counts ola_mES_lif_1_12.counts ola_mES_lif_1_13.counts ola_mES_lif_1_14.counts
#> "0122" "0332" "0141" "0332"
#> ola_mES_lif_1_15.counts ola_mES_lif_1_17.counts ola_mES_lif_1_18.counts ola_mES_lif_1_19.counts
#> "0141" "0312" "0141" "0142"
#> ola_mES_lif_1_2.counts ola_mES_lif_1_20.counts ola_mES_lif_1_21.counts ola_mES_lif_1_22.counts
#> "0332" "0142" "0332" "0142"
#> ola_mES_lif_1_23.counts ola_mES_lif_1_24.counts ola_mES_lif_1_25.counts ola_mES_lif_1_26.counts
#> "0312" "0142" "0142" "0142"
#> ola_mES_lif_1_28.counts ola_mES_lif_1_29.counts ola_mES_lif_1_3.counts ola_mES_lif_1_30.counts
#> "0142" "0313" "0141" "0142"
#> ola_mES_lif_1_31.counts ola_mES_lif_1_32.counts ola_mES_lif_1_33.counts ola_mES_lif_1_34.counts
#> "0313" "0142" "0142" "0141"
#> ola_mES_lif_1_35.counts ola_mES_lif_1_36.counts ola_mES_lif_1_37.counts ola_mES_lif_1_38.counts
#> "0312" "0142" "0142" "0142"
#> ola_mES_lif_1_39.counts ola_mES_lif_1_4.counts ola_mES_lif_1_41.counts ola_mES_lif_1_42.counts
#> "0142" "0143" "0142" "0142"
#> ola_mES_lif_1_43.counts ola_mES_lif_1_44.counts ola_mES_lif_1_45.counts ola_mES_lif_1_46.counts
#> "0142" "0141" "0142" "0142"
#> ola_mES_lif_1_47.counts ola_mES_lif_1_48.counts ola_mES_lif_1_49.counts ola_mES_lif_1_5.counts
#> "0141" "0141" "0141" "0122"
#> ola_mES_lif_1_50.counts ola_mES_lif_1_51.counts ola_mES_lif_1_52.counts ola_mES_lif_1_53.counts
#> "0142" "0122" "0122" "0223"
#> ola_mES_lif_1_54.counts ola_mES_lif_1_55.counts ola_mES_lif_1_56.counts ola_mES_lif_1_57.counts
#> "0141" "0122" "0222" "0223"
#> ola_mES_lif_1_58.counts ola_mES_lif_1_59.counts ola_mES_lif_1_6.counts ola_mES_lif_1_60.counts
#> "0141" "0122" "0142" "0141"
#> ola_mES_lif_1_61.counts ola_mES_lif_1_62.counts ola_mES_lif_1_63.counts ola_mES_lif_1_64.counts
#> "0141" "0142" "0312" "0312"
#> ola_mES_lif_1_65.counts ola_mES_lif_1_66.counts ola_mES_lif_1_67.counts ola_mES_lif_1_68.counts
#> "0312" "0141" "0141" "0142"
#> ola_mES_lif_1_69.counts ola_mES_lif_1_7.counts ola_mES_lif_1_70.counts ola_mES_lif_1_72.counts
#> "0312" "0141" "0332" "0141"
#> ola_mES_lif_1_73.counts ola_mES_lif_1_74.counts ola_mES_lif_1_75.counts ola_mES_lif_1_79.counts
#> "0142" "0312" "0312" "0332"
#> ola_mES_lif_1_8.counts ola_mES_lif_1_80.counts ola_mES_lif_1_81.counts ola_mES_lif_1_82.counts
#> "0142" "0141" "0142" "0312"
#> ola_mES_lif_1_86.counts ola_mES_lif_1_9.counts ola_mES_lif_1_91.counts ola_mES_lif_1_92.counts
#> "0332" "0141" "0142" "0142"
#> ola_mES_lif_1_93.counts ola_mES_lif_1_95.counts ola_mES_lif_1_96.counts ola_mES_lif_2_1.counts
#> "0332" "0141" "0141" "0331"
#> ola_mES_lif_2_10.counts ola_mES_lif_2_11.counts ola_mES_lif_2_12.counts ola_mES_lif_2_13.counts
#> "0122" "0122" "0223" "0122"
#> ola_mES_lif_2_14.counts ola_mES_lif_2_15.counts ola_mES_lif_2_16.counts ola_mES_lif_2_17.counts
#> "0122" "0121" "0121" "0121"
#> ola_mES_lif_2_18.counts ola_mES_lif_2_19.counts ola_mES_lif_2_2.counts ola_mES_lif_2_20.counts
#> "0121" "0121" "0122" "0331"
#> ola_mES_lif_2_21.counts ola_mES_lif_2_23.counts ola_mES_lif_2_25.counts ola_mES_lif_2_26.counts
#> "0121" "0122" "0331" "0121"
#> ola_mES_lif_2_27.counts ola_mES_lif_2_28.counts ola_mES_lif_2_29.counts ola_mES_lif_2_3.counts
#> "0331" "0121" "0122" "0122"
#> ola_mES_lif_2_30.counts ola_mES_lif_2_31.counts ola_mES_lif_2_32.counts ola_mES_lif_2_33.counts
#> "0121" "0331" "0122" "0331"
#> ola_mES_lif_2_34.counts ola_mES_lif_2_35.counts ola_mES_lif_2_36.counts ola_mES_lif_2_37.counts
#> "0122" "0121" "034" "0331"
#> ola_mES_lif_2_38.counts ola_mES_lif_2_39.counts ola_mES_lif_2_4.counts ola_mES_lif_2_40.counts
#> "0121" "0122" "0121" "0121"
#> ola_mES_lif_2_41.counts ola_mES_lif_2_42.counts ola_mES_lif_2_43.counts ola_mES_lif_2_44.counts
#> "0331" "0121" "0121" "0122"
#> ola_mES_lif_2_45.counts ola_mES_lif_2_46.counts ola_mES_lif_2_47.counts ola_mES_lif_2_48.counts
#> "034" "0121" "0331" "0223"
#> ola_mES_lif_2_49.counts ola_mES_lif_2_5.counts ola_mES_lif_2_50.counts ola_mES_lif_2_51.counts
#> "0122" "0121" "0122" "0122"
#> ola_mES_lif_2_52.counts ola_mES_lif_2_53.counts ola_mES_lif_2_54.counts ola_mES_lif_2_55.counts
#> "0122" "0121" "0121" "0121"
#> ola_mES_lif_2_56.counts ola_mES_lif_2_57.counts ola_mES_lif_2_58.counts ola_mES_lif_2_59.counts
#> "0121" "034" "0121" "0122"
#> ola_mES_lif_2_6.counts ola_mES_lif_2_60.counts ola_mES_lif_2_61.counts ola_mES_lif_2_63.counts
#> "0121" "0331" "0141" "0122"
#> ola_mES_lif_2_64.counts ola_mES_lif_2_65.counts ola_mES_lif_2_66.counts ola_mES_lif_2_67.counts
#> "0121" "0121" "0121" "0121"
#> ola_mES_lif_2_68.counts ola_mES_lif_2_69.counts ola_mES_lif_2_7.counts ola_mES_lif_2_70.counts
#> "0121" "0223" "0122" "0121"
#> ola_mES_lif_2_71.counts ola_mES_lif_2_72.counts ola_mES_lif_2_73.counts ola_mES_lif_2_74.counts
#> "0122" "0141" "0223" "0122"
#> ola_mES_lif_2_75.counts ola_mES_lif_2_76.counts ola_mES_lif_2_77.counts ola_mES_lif_2_79.counts
#> "0121" "0121" "0121" "0331"
#> ola_mES_lif_2_8.counts ola_mES_lif_2_80.counts ola_mES_lif_2_81.counts ola_mES_lif_2_82.counts
#> "0122" "0122" "0331" "0121"
#> ola_mES_lif_2_83.counts ola_mES_lif_2_84.counts ola_mES_lif_2_86.counts ola_mES_lif_2_87.counts
#> "0122" "0121" "0331" "0121"
#> ola_mES_lif_2_89.counts ola_mES_lif_2_9.counts ola_mES_lif_2_90.counts ola_mES_lif_2_91.counts
#> "0122" "0331" "0331" "0331"
#> ola_mES_lif_2_92.counts ola_mES_lif_2_93.counts ola_mES_lif_2_94.counts ola_mES_lif_2_95.counts
#> "0331" "0122" "0121" "0121"
#> ola_mES_lif_2_96.counts ola_mES_lif_3_1.counts ola_mES_lif_3_10.counts ola_mES_lif_3_11.counts
#> "0331" "0143" "0122" "0332"
#> ola_mES_lif_3_12.counts ola_mES_lif_3_13.counts ola_mES_lif_3_14.counts ola_mES_lif_3_15.counts
#> "0223" "0143" "034" "034"
#> ola_mES_lif_3_16.counts ola_mES_lif_3_17.counts ola_mES_lif_3_19.counts ola_mES_lif_3_2.counts
#> "034" "034" "034" "0222"
#> ola_mES_lif_3_20.counts ola_mES_lif_3_21.counts ola_mES_lif_3_22.counts ola_mES_lif_3_23.counts
#> "0311" "0222" "0313" "0311"
#> ola_mES_lif_3_24.counts ola_mES_lif_3_26.counts ola_mES_lif_3_27.counts ola_mES_lif_3_28.counts
#> "0311" "0313" "0143" "034"
#> ola_mES_lif_3_29.counts ola_mES_lif_3_3.counts ola_mES_lif_3_30.counts ola_mES_lif_3_31.counts
#> "0332" "0143" "0143" "034"
#> ola_mES_lif_3_32.counts ola_mES_lif_3_33.counts ola_mES_lif_3_34.counts ola_mES_lif_3_35.counts
#> "0143" "034" "034" "034"
#> ola_mES_lif_3_36.counts ola_mES_lif_3_37.counts ola_mES_lif_3_38.counts ola_mES_lif_3_39.counts
#> "034" "0332" "034" "034"
#> ola_mES_lif_3_4.counts ola_mES_lif_3_40.counts ola_mES_lif_3_42.counts ola_mES_lif_3_45.counts
#> "0143" "0143" "0141" "034"
#> ola_mES_lif_3_46.counts ola_mES_lif_3_47.counts ola_mES_lif_3_48.counts ola_mES_lif_3_49.counts
#> "034" "034" "0332" "0143"
#> ola_mES_lif_3_5.counts ola_mES_lif_3_50.counts ola_mES_lif_3_51.counts ola_mES_lif_3_52.counts
#> "0122" "0141" "0143" "0143"
#> ola_mES_lif_3_53.counts ola_mES_lif_3_54.counts ola_mES_lif_3_56.counts ola_mES_lif_3_57.counts
#> "0143" "0141" "0122" "0332"
#> ola_mES_lif_3_58.counts ola_mES_lif_3_59.counts ola_mES_lif_3_6.counts ola_mES_lif_3_60.counts
#> "0143" "0143" "0141" "0143"
#> ola_mES_lif_3_61.counts ola_mES_lif_3_63.counts ola_mES_lif_3_64.counts ola_mES_lif_3_65.counts
#> "0143" "0143" "0143" "0143"
#> ola_mES_lif_3_69.counts ola_mES_lif_3_70.counts ola_mES_lif_3_71.counts ola_mES_lif_3_72.counts
#> "034" "034" "0143" "034"
#> ola_mES_lif_3_73.counts ola_mES_lif_3_74.counts ola_mES_lif_3_75.counts ola_mES_lif_3_76.counts
#> "0311" "0311" "0313" "0311"
#> ola_mES_lif_3_77.counts ola_mES_lif_3_78.counts ola_mES_lif_3_8.counts ola_mES_lif_3_80.counts
#> "0222" "0311" "0143" "0313"
#> ola_mES_lif_3_82.counts ola_mES_lif_3_83.counts ola_mES_lif_3_84.counts ola_mES_lif_3_87.counts
#> "0313" "0313" "0313" "0312"
#> ola_mES_lif_3_88.counts ola_mES_lif_3_89.counts ola_mES_lif_3_9.counts ola_mES_lif_3_90.counts
#> "0311" "0222" "0223" "034"
#> ola_mES_lif_3_92.counts ola_mES_lif_3_94.counts ola_mES_lif_3_95.counts ola_mES_lif_3_96.counts
#> "0311" "0311" "0311" "0311"
get_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 430))
#> ola_mES_2i_2_1.counts ola_mES_2i_2_10.counts ola_mES_2i_2_11.counts ola_mES_2i_2_12.counts
#> "01112" "0131" "0131" "0131"
#> ola_mES_2i_2_13.counts ola_mES_2i_2_14.counts ola_mES_2i_2_15.counts ola_mES_2i_2_16.counts
#> "0133" "01112" "0133" "0133"
#> ola_mES_2i_2_17.counts ola_mES_2i_2_18.counts ola_mES_2i_2_19.counts ola_mES_2i_2_2.counts
#> "0133" "0131" "0131" "01112"
#> ola_mES_2i_2_20.counts ola_mES_2i_2_21.counts ola_mES_2i_2_22.counts ola_mES_2i_2_23.counts
#> "01112" "0131" "0133" "0131"
#> ola_mES_2i_2_24.counts ola_mES_2i_2_25.counts ola_mES_2i_2_26.counts ola_mES_2i_2_27.counts
#> "0131" "01112" "0133" "01112"
#> ola_mES_2i_2_28.counts ola_mES_2i_2_29.counts ola_mES_2i_2_3.counts ola_mES_2i_2_30.counts
#> "01112" "0133" "01112" "0131"
#> ola_mES_2i_2_31.counts ola_mES_2i_2_32.counts ola_mES_2i_2_33.counts ola_mES_2i_2_34.counts
#> "0131" "0133" "0131" "0131"
#> ola_mES_2i_2_35.counts ola_mES_2i_2_36.counts ola_mES_2i_2_37.counts ola_mES_2i_2_38.counts
#> "0131" "0131" "01112" "01112"
#> ola_mES_2i_2_39.counts ola_mES_2i_2_4.counts ola_mES_2i_2_40.counts ola_mES_2i_2_41.counts
#> "0133" "0131" "0131" "0131"
#> ola_mES_2i_2_42.counts ola_mES_2i_2_43.counts ola_mES_2i_2_44.counts ola_mES_2i_2_46.counts
#> "0131" "01112" "0131" "0131"
#> ola_mES_2i_2_47.counts ola_mES_2i_2_48.counts ola_mES_2i_2_49.counts ola_mES_2i_2_5.counts
#> "0131" "0131" "01112" "0133"
#> ola_mES_2i_2_50.counts ola_mES_2i_2_51.counts ola_mES_2i_2_52.counts ola_mES_2i_2_53.counts
#> "01112" "0131" "0133" "0133"
#> ola_mES_2i_2_54.counts ola_mES_2i_2_55.counts ola_mES_2i_2_56.counts ola_mES_2i_2_57.counts
#> "0131" "0133" "0133" "0133"
#> ola_mES_2i_2_58.counts ola_mES_2i_2_59.counts ola_mES_2i_2_6.counts ola_mES_2i_2_60.counts
#> "0131" "0131" "0133" "0131"
#> ola_mES_2i_2_61.counts ola_mES_2i_2_62.counts ola_mES_2i_2_63.counts ola_mES_2i_2_64.counts
#> "01112" "032" "01112" "0133"
#> ola_mES_2i_2_66.counts ola_mES_2i_2_67.counts ola_mES_2i_2_68.counts ola_mES_2i_2_69.counts
#> "0131" "0131" "01112" "0131"
#> ola_mES_2i_2_7.counts ola_mES_2i_2_72.counts ola_mES_2i_2_73.counts ola_mES_2i_2_74.counts
#> "0133" "0133" "01112" "0133"
#> ola_mES_2i_2_75.counts ola_mES_2i_2_76.counts ola_mES_2i_2_79.counts ola_mES_2i_2_8.counts
#> "01112" "01112" "0131" "0133"
#> ola_mES_2i_2_80.counts ola_mES_2i_2_81.counts ola_mES_2i_2_85.counts ola_mES_2i_2_86.counts
#> "0133" "0131" "01112" "01112"
#> ola_mES_2i_2_87.counts ola_mES_2i_2_90.counts ola_mES_2i_2_91.counts ola_mES_2i_2_93.counts
#> "032" "0131" "0133" "0131"
#> ola_mES_2i_2_94.counts ola_mES_2i_2_95.counts ola_mES_2i_3_10.counts ola_mES_2i_3_11.counts
#> "0131" "0133" "0222" "023"
#> ola_mES_2i_3_12.counts ola_mES_2i_3_13.counts ola_mES_2i_3_15.counts ola_mES_2i_3_16.counts
#> "0221" "0221" "0221" "02112"
#> ola_mES_2i_3_17.counts ola_mES_2i_3_18.counts ola_mES_2i_3_19.counts ola_mES_2i_3_2.counts
#> "032" "032" "0221" "0221"
#> ola_mES_2i_3_20.counts ola_mES_2i_3_21.counts ola_mES_2i_3_23.counts ola_mES_2i_3_25.counts
#> "0221" "0222" "02112" "023"
#> ola_mES_2i_3_26.counts ola_mES_2i_3_28.counts ola_mES_2i_3_30.counts ola_mES_2i_3_31.counts
#> "0221" "02112" "0221" "032"
#> ola_mES_2i_3_33.counts ola_mES_2i_3_36.counts ola_mES_2i_3_37.counts ola_mES_2i_3_38.counts
#> "0221" "02112" "0221" "0221"
#> ola_mES_2i_3_39.counts ola_mES_2i_3_4.counts ola_mES_2i_3_41.counts ola_mES_2i_3_42.counts
#> "0222" "02112" "0222" "0221"
#> ola_mES_2i_3_43.counts ola_mES_2i_3_45.counts ola_mES_2i_3_46.counts ola_mES_2i_3_47.counts
#> "02112" "0221" "0221" "0221"
#> ola_mES_2i_3_49.counts ola_mES_2i_3_5.counts ola_mES_2i_3_52.counts ola_mES_2i_3_54.counts
#> "032" "0221" "0221" "0222"
#> ola_mES_2i_3_56.counts ola_mES_2i_3_57.counts ola_mES_2i_3_58.counts ola_mES_2i_3_6.counts
#> "0221" "0221" "0221" "0222"
#> ola_mES_2i_3_62.counts ola_mES_2i_3_65.counts ola_mES_2i_3_66.counts ola_mES_2i_3_69.counts
#> "032" "0221" "032" "0222"
#> ola_mES_2i_3_7.counts ola_mES_2i_3_73.counts ola_mES_2i_3_76.counts ola_mES_2i_3_77.counts
#> "0221" "0221" "0221" "0222"
#> ola_mES_2i_3_78.counts ola_mES_2i_3_79.counts ola_mES_2i_3_8.counts ola_mES_2i_3_80.counts
#> "032" "0221" "0221" "0221"
#> ola_mES_2i_3_82.counts ola_mES_2i_3_83.counts ola_mES_2i_3_84.counts ola_mES_2i_3_87.counts
#> "0221" "0221" "02112" "0222"
#> ola_mES_2i_3_88.counts ola_mES_2i_3_90.counts ola_mES_2i_3_91.counts ola_mES_2i_3_92.counts
#> "0221" "02112" "0222" "0221"
#> ola_mES_2i_3_96.counts ola_mES_2i_4_1.counts ola_mES_2i_4_10.counts ola_mES_2i_4_11.counts
#> "0222" "0212" "0212" "0212"
#> ola_mES_2i_4_13.counts ola_mES_2i_4_17.counts ola_mES_2i_4_18.counts ola_mES_2i_4_2.counts
#> "02111" "02111" "023" "0132"
#> ola_mES_2i_4_20.counts ola_mES_2i_4_21.counts ola_mES_2i_4_22.counts ola_mES_2i_4_23.counts
#> "02111" "02111" "023" "0221"
#> ola_mES_2i_4_24.counts ola_mES_2i_4_25.counts ola_mES_2i_4_26.counts ola_mES_2i_4_27.counts
#> "02112" "0212" "0212" "023"
#> ola_mES_2i_4_29.counts ola_mES_2i_4_30.counts ola_mES_2i_4_31.counts ola_mES_2i_4_32.counts
#> "0212" "02111" "0212" "02111"
#> ola_mES_2i_4_33.counts ola_mES_2i_4_34.counts ola_mES_2i_4_35.counts ola_mES_2i_4_36.counts
#> "02111" "02111" "0212" "02111"
#> ola_mES_2i_4_37.counts ola_mES_2i_4_38.counts ola_mES_2i_4_39.counts ola_mES_2i_4_40.counts
#> "032" "01122" "0212" "01122"
#> ola_mES_2i_4_42.counts ola_mES_2i_4_43.counts ola_mES_2i_4_44.counts ola_mES_2i_4_46.counts
#> "032" "02111" "0212" "02111"
#> ola_mES_2i_4_49.counts ola_mES_2i_4_5.counts ola_mES_2i_4_50.counts ola_mES_2i_4_52.counts
#> "01122" "023" "0212" "0212"
#> ola_mES_2i_4_53.counts ola_mES_2i_4_54.counts ola_mES_2i_4_56.counts ola_mES_2i_4_57.counts
#> "01122" "032" "02111" "02111"
#> ola_mES_2i_4_58.counts ola_mES_2i_4_59.counts ola_mES_2i_4_6.counts ola_mES_2i_4_60.counts
#> "02111" "02111" "02111" "0221"
#> ola_mES_2i_4_61.counts ola_mES_2i_4_62.counts ola_mES_2i_4_63.counts ola_mES_2i_4_64.counts
#> "02111" "032" "0221" "0212"
#> ola_mES_2i_4_65.counts ola_mES_2i_4_66.counts ola_mES_2i_4_67.counts ola_mES_2i_4_68.counts
#> "02111" "02111" "01122" "0221"
#> ola_mES_2i_4_69.counts ola_mES_2i_4_7.counts ola_mES_2i_4_70.counts ola_mES_2i_4_71.counts
#> "02111" "023" "0221" "02111"
#> ola_mES_2i_4_72.counts ola_mES_2i_4_73.counts ola_mES_2i_4_75.counts ola_mES_2i_4_77.counts
#> "023" "02111" "0222" "02112"
#> ola_mES_2i_4_78.counts ola_mES_2i_4_80.counts ola_mES_2i_4_81.counts ola_mES_2i_4_83.counts
#> "023" "02111" "02111" "023"
#> ola_mES_2i_4_84.counts ola_mES_2i_4_88.counts ola_mES_2i_4_89.counts ola_mES_2i_4_9.counts
#> "023" "0221" "0221" "023"
#> ola_mES_2i_4_90.counts ola_mES_2i_4_91.counts ola_mES_2i_4_94.counts ola_mES_2i_4_95.counts
#> "02111" "0212" "0221" "0222"
#> ola_mES_2i_4_96.counts ola_mES_2i_5_1.counts ola_mES_2i_5_10.counts ola_mES_2i_5_11.counts
#> "0222" "0221" "023" "023"
#> ola_mES_2i_5_12.counts ola_mES_2i_5_13.counts ola_mES_2i_5_14.counts ola_mES_2i_5_15.counts
#> "023" "023" "0212" "0212"
#> ola_mES_2i_5_16.counts ola_mES_2i_5_17.counts ola_mES_2i_5_18.counts ola_mES_2i_5_19.counts
#> "0212" "0212" "023" "02111"
#> ola_mES_2i_5_2.counts ola_mES_2i_5_20.counts ola_mES_2i_5_21.counts ola_mES_2i_5_23.counts
#> "0221" "0212" "02111" "023"
#> ola_mES_2i_5_24.counts ola_mES_2i_5_25.counts ola_mES_2i_5_26.counts ola_mES_2i_5_27.counts
#> "0212" "02111" "0212" "0212"
#> ola_mES_2i_5_28.counts ola_mES_2i_5_29.counts ola_mES_2i_5_3.counts ola_mES_2i_5_30.counts
#> "0221" "0221" "0221" "0212"
#> ola_mES_2i_5_31.counts ola_mES_2i_5_32.counts ola_mES_2i_5_34.counts ola_mES_2i_5_35.counts
#> "0212" "0212" "0212" "02111"
#> ola_mES_2i_5_36.counts ola_mES_2i_5_38.counts ola_mES_2i_5_39.counts ola_mES_2i_5_4.counts
#> "0212" "0212" "0212" "0221"
#> ola_mES_2i_5_40.counts ola_mES_2i_5_41.counts ola_mES_2i_5_42.counts ola_mES_2i_5_44.counts
#> "02111" "01122" "0212" "0212"
#> ola_mES_2i_5_45.counts ola_mES_2i_5_46.counts ola_mES_2i_5_47.counts ola_mES_2i_5_48.counts
#> "02111" "02111" "02111" "02111"
#> ola_mES_2i_5_49.counts ola_mES_2i_5_5.counts ola_mES_2i_5_51.counts ola_mES_2i_5_52.counts
#> "02111" "023" "0212" "0221"
#> ola_mES_2i_5_53.counts ola_mES_2i_5_54.counts ola_mES_2i_5_55.counts ola_mES_2i_5_56.counts
#> "0212" "02111" "0212" "0212"
#> ola_mES_2i_5_57.counts ola_mES_2i_5_58.counts ola_mES_2i_5_59.counts ola_mES_2i_5_6.counts
#> "0212" "0212" "0212" "0221"
#> ola_mES_2i_5_61.counts ola_mES_2i_5_62.counts ola_mES_2i_5_63.counts ola_mES_2i_5_65.counts
#> "0212" "0221" "02111" "023"
#> ola_mES_2i_5_66.counts ola_mES_2i_5_67.counts ola_mES_2i_5_68.counts ola_mES_2i_5_69.counts
#> "0221" "0212" "0212" "0212"
#> ola_mES_2i_5_7.counts ola_mES_2i_5_70.counts ola_mES_2i_5_72.counts ola_mES_2i_5_73.counts
#> "0212" "02111" "0212" "023"
#> ola_mES_2i_5_74.counts ola_mES_2i_5_75.counts ola_mES_2i_5_76.counts ola_mES_2i_5_77.counts
#> "02111" "023" "023" "023"
#> ola_mES_2i_5_78.counts ola_mES_2i_5_8.counts ola_mES_2i_5_81.counts ola_mES_2i_5_82.counts
#> "0212" "02112" "0221" "0212"
#> ola_mES_2i_5_83.counts ola_mES_2i_5_85.counts ola_mES_2i_5_86.counts ola_mES_2i_5_87.counts
#> "023" "0221" "023" "023"
#> ola_mES_2i_5_88.counts ola_mES_2i_5_9.counts ola_mES_2i_5_90.counts ola_mES_2i_5_92.counts
#> "023" "0222" "02112" "0212"
#> ola_mES_2i_5_94.counts ola_mES_2i_5_95.counts ola_mES_2i_5_96.counts ola_mES_a2i_2_1.counts
#> "0212" "0212" "0212" "0132"
#> ola_mES_a2i_2_10.counts ola_mES_a2i_2_11.counts ola_mES_a2i_2_12.counts ola_mES_a2i_2_13.counts
#> "01121" "0132" "01121" "01111"
#> ola_mES_a2i_2_14.counts ola_mES_a2i_2_15.counts ola_mES_a2i_2_16.counts ola_mES_a2i_2_17.counts
#> "0132" "0132" "0132" "0132"
#> ola_mES_a2i_2_18.counts ola_mES_a2i_2_19.counts ola_mES_a2i_2_2.counts ola_mES_a2i_2_20.counts
#> "0132" "0132" "0132" "01121"
#> ola_mES_a2i_2_21.counts ola_mES_a2i_2_22.counts ola_mES_a2i_2_23.counts ola_mES_a2i_2_24.counts
#> "0132" "0132" "0132" "0132"
#> ola_mES_a2i_2_25.counts ola_mES_a2i_2_26.counts ola_mES_a2i_2_27.counts ola_mES_a2i_2_28.counts
#> "0113" "032" "01121" "01121"
#> ola_mES_a2i_2_29.counts ola_mES_a2i_2_3.counts ola_mES_a2i_2_30.counts ola_mES_a2i_2_31.counts
#> "01121" "0132" "0222" "032"
#> ola_mES_a2i_2_32.counts ola_mES_a2i_2_33.counts ola_mES_a2i_2_34.counts ola_mES_a2i_2_35.counts
#> "032" "01121" "01121" "0312"
#> ola_mES_a2i_2_36.counts ola_mES_a2i_2_37.counts ola_mES_a2i_2_38.counts ola_mES_a2i_2_39.counts
#> "0222" "032" "01121" "032"
#> ola_mES_a2i_2_4.counts ola_mES_a2i_2_40.counts ola_mES_a2i_2_41.counts ola_mES_a2i_2_42.counts
#> "01121" "01121" "0132" "0132"
#> ola_mES_a2i_2_43.counts ola_mES_a2i_2_44.counts ola_mES_a2i_2_45.counts ola_mES_a2i_2_46.counts
#> "01111" "0113" "0132" "0132"
#> ola_mES_a2i_2_47.counts ola_mES_a2i_2_48.counts ola_mES_a2i_2_49.counts ola_mES_a2i_2_5.counts
#> "01121" "01121" "0113" "0132"
#> ola_mES_a2i_2_50.counts ola_mES_a2i_2_51.counts ola_mES_a2i_2_52.counts ola_mES_a2i_2_53.counts
#> "032" "01121" "01121" "032"
#> ola_mES_a2i_2_54.counts ola_mES_a2i_2_55.counts ola_mES_a2i_2_56.counts ola_mES_a2i_2_57.counts
#> "01121" "0132" "01111" "0132"
#> ola_mES_a2i_2_59.counts ola_mES_a2i_2_6.counts ola_mES_a2i_2_60.counts ola_mES_a2i_2_61.counts
#> "0132" "032" "01122" "01111"
#> ola_mES_a2i_2_62.counts ola_mES_a2i_2_63.counts ola_mES_a2i_2_64.counts ola_mES_a2i_2_65.counts
#> "01121" "01111" "01121" "01111"
#> ola_mES_a2i_2_66.counts ola_mES_a2i_2_67.counts ola_mES_a2i_2_68.counts ola_mES_a2i_2_69.counts
#> "0132" "01121" "0113" "032"
#> ola_mES_a2i_2_7.counts ola_mES_a2i_2_70.counts ola_mES_a2i_2_71.counts ola_mES_a2i_2_72.counts
#> "01121" "01121" "0132" "01121"
#> ola_mES_a2i_2_73.counts ola_mES_a2i_2_74.counts ola_mES_a2i_2_75.counts ola_mES_a2i_2_76.counts
#> "0113" "01111" "01121" "01111"
#> ola_mES_a2i_2_77.counts ola_mES_a2i_2_78.counts ola_mES_a2i_2_8.counts ola_mES_a2i_2_80.counts
#> "01121" "0132" "0132" "0113"
#> ola_mES_a2i_2_81.counts ola_mES_a2i_2_82.counts ola_mES_a2i_2_83.counts ola_mES_a2i_2_84.counts
#> "01121" "0132" "0132" "01121"
#> ola_mES_a2i_2_86.counts ola_mES_a2i_2_87.counts ola_mES_a2i_2_88.counts ola_mES_a2i_2_89.counts
#> "0113" "01121" "01121" "0223"
#> ola_mES_a2i_2_9.counts ola_mES_a2i_2_90.counts ola_mES_a2i_2_91.counts ola_mES_a2i_2_92.counts
#> "0113" "0132" "032" "032"
#> ola_mES_a2i_2_93.counts ola_mES_a2i_2_94.counts ola_mES_a2i_2_95.counts ola_mES_a2i_2_96.counts
#> "01111" "0132" "0132" "0132"
#> ola_mES_a2i_3_10.counts ola_mES_a2i_3_11.counts ola_mES_a2i_3_13.counts ola_mES_a2i_3_16.counts
#> "02112" "0223" "01121" "0113"
#> ola_mES_a2i_3_17.counts ola_mES_a2i_3_18.counts ola_mES_a2i_3_2.counts ola_mES_a2i_3_20.counts
#> "032" "032" "032" "032"
#> ola_mES_a2i_3_21.counts ola_mES_a2i_3_22.counts ola_mES_a2i_3_24.counts ola_mES_a2i_3_25.counts
#> "0223" "0223" "0223" "0113"
#> ola_mES_a2i_3_26.counts ola_mES_a2i_3_28.counts ola_mES_a2i_3_29.counts ola_mES_a2i_3_3.counts
#> "032" "0113" "0113" "02112"
#> ola_mES_a2i_3_30.counts ola_mES_a2i_3_31.counts ola_mES_a2i_3_33.counts ola_mES_a2i_3_34.counts
#> "032" "032" "0223" "0113"
#> ola_mES_a2i_3_37.counts ola_mES_a2i_3_38.counts ola_mES_a2i_3_39.counts ola_mES_a2i_3_4.counts
#> "01122" "0113" "0113" "0223"
#> ola_mES_a2i_3_40.counts ola_mES_a2i_3_41.counts ola_mES_a2i_3_42.counts ola_mES_a2i_3_44.counts
#> "0113" "032" "032" "0113"
#> ola_mES_a2i_3_45.counts ola_mES_a2i_3_47.counts ola_mES_a2i_3_49.counts ola_mES_a2i_3_50.counts
#> "032" "032" "0113" "0113"
#> ola_mES_a2i_3_52.counts ola_mES_a2i_3_54.counts ola_mES_a2i_3_56.counts ola_mES_a2i_3_58.counts
#> "032" "0113" "032" "0223"
#> ola_mES_a2i_3_59.counts ola_mES_a2i_3_6.counts ola_mES_a2i_3_62.counts ola_mES_a2i_3_64.counts
#> "0132" "0113" "01121" "01122"
#> ola_mES_a2i_3_65.counts ola_mES_a2i_3_66.counts ola_mES_a2i_3_67.counts ola_mES_a2i_3_68.counts
#> "0132" "0113" "032" "0222"
#> ola_mES_a2i_3_69.counts ola_mES_a2i_3_7.counts ola_mES_a2i_3_71.counts ola_mES_a2i_3_73.counts
#> "0113" "032" "0132" "01121"
#> ola_mES_a2i_3_75.counts ola_mES_a2i_3_76.counts ola_mES_a2i_3_77.counts ola_mES_a2i_3_8.counts
#> "032" "02112" "0113" "032"
#> ola_mES_a2i_3_80.counts ola_mES_a2i_3_81.counts ola_mES_a2i_3_82.counts ola_mES_a2i_3_83.counts
#> "0222" "032" "0113" "01121"
#> ola_mES_a2i_3_84.counts ola_mES_a2i_3_85.counts ola_mES_a2i_3_86.counts ola_mES_a2i_3_89.counts
#> "0113" "01121" "0113" "0132"
#> ola_mES_a2i_3_9.counts ola_mES_a2i_3_90.counts ola_mES_a2i_3_91.counts ola_mES_a2i_3_92.counts
#> "0223" "0113" "0223" "01121"
#> ola_mES_a2i_3_94.counts ola_mES_a2i_3_96.counts ola_mES_lif_1_1.counts ola_mES_lif_1_10.counts
#> "0312" "01121" "0141" "0332"
#> ola_mES_lif_1_11.counts ola_mES_lif_1_12.counts ola_mES_lif_1_13.counts ola_mES_lif_1_14.counts
#> "0122" "0332" "0141" "0332"
#> ola_mES_lif_1_15.counts ola_mES_lif_1_17.counts ola_mES_lif_1_18.counts ola_mES_lif_1_19.counts
#> "0141" "0312" "0141" "0142"
#> ola_mES_lif_1_2.counts ola_mES_lif_1_20.counts ola_mES_lif_1_21.counts ola_mES_lif_1_22.counts
#> "0332" "0142" "0332" "0142"
#> ola_mES_lif_1_23.counts ola_mES_lif_1_24.counts ola_mES_lif_1_25.counts ola_mES_lif_1_26.counts
#> "0312" "0142" "0142" "0142"
#> ola_mES_lif_1_28.counts ola_mES_lif_1_29.counts ola_mES_lif_1_3.counts ola_mES_lif_1_30.counts
#> "0142" "0313" "0141" "0142"
#> ola_mES_lif_1_31.counts ola_mES_lif_1_32.counts ola_mES_lif_1_33.counts ola_mES_lif_1_34.counts
#> "0313" "0142" "0142" "0141"
#> ola_mES_lif_1_35.counts ola_mES_lif_1_36.counts ola_mES_lif_1_37.counts ola_mES_lif_1_38.counts
#> "0312" "0142" "0142" "0142"
#> ola_mES_lif_1_39.counts ola_mES_lif_1_4.counts ola_mES_lif_1_41.counts ola_mES_lif_1_42.counts
#> "0142" "0143" "0142" "0142"
#> ola_mES_lif_1_43.counts ola_mES_lif_1_44.counts ola_mES_lif_1_45.counts ola_mES_lif_1_46.counts
#> "0142" "0141" "0142" "0142"
#> ola_mES_lif_1_47.counts ola_mES_lif_1_48.counts ola_mES_lif_1_49.counts ola_mES_lif_1_5.counts
#> "0141" "0141" "0141" "0122"
#> ola_mES_lif_1_50.counts ola_mES_lif_1_51.counts ola_mES_lif_1_52.counts ola_mES_lif_1_53.counts
#> "0142" "0122" "0122" "0223"
#> ola_mES_lif_1_54.counts ola_mES_lif_1_55.counts ola_mES_lif_1_56.counts ola_mES_lif_1_57.counts
#> "0141" "0122" "0222" "0223"
#> ola_mES_lif_1_58.counts ola_mES_lif_1_59.counts ola_mES_lif_1_6.counts ola_mES_lif_1_60.counts
#> "0141" "0122" "0142" "0141"
#> ola_mES_lif_1_61.counts ola_mES_lif_1_62.counts ola_mES_lif_1_63.counts ola_mES_lif_1_64.counts
#> "0141" "0142" "0312" "0312"
#> ola_mES_lif_1_65.counts ola_mES_lif_1_66.counts ola_mES_lif_1_67.counts ola_mES_lif_1_68.counts
#> "0312" "0141" "0141" "0142"
#> ola_mES_lif_1_69.counts ola_mES_lif_1_7.counts ola_mES_lif_1_70.counts ola_mES_lif_1_72.counts
#> "0312" "0141" "0332" "0141"
#> ola_mES_lif_1_73.counts ola_mES_lif_1_74.counts ola_mES_lif_1_75.counts ola_mES_lif_1_79.counts
#> "0142" "0312" "0312" "0332"
#> ola_mES_lif_1_8.counts ola_mES_lif_1_80.counts ola_mES_lif_1_81.counts ola_mES_lif_1_82.counts
#> "0142" "0141" "0142" "0312"
#> ola_mES_lif_1_86.counts ola_mES_lif_1_9.counts ola_mES_lif_1_91.counts ola_mES_lif_1_92.counts
#> "0332" "0141" "0142" "0142"
#> ola_mES_lif_1_93.counts ola_mES_lif_1_95.counts ola_mES_lif_1_96.counts ola_mES_lif_2_1.counts
#> "0332" "0141" "0141" "0331"
#> ola_mES_lif_2_10.counts ola_mES_lif_2_11.counts ola_mES_lif_2_12.counts ola_mES_lif_2_13.counts
#> "0122" "0122" "0223" "0122"
#> ola_mES_lif_2_14.counts ola_mES_lif_2_15.counts ola_mES_lif_2_16.counts ola_mES_lif_2_17.counts
#> "0122" "0121" "0121" "0121"
#> ola_mES_lif_2_18.counts ola_mES_lif_2_19.counts ola_mES_lif_2_2.counts ola_mES_lif_2_20.counts
#> "0121" "0121" "0122" "0331"
#> ola_mES_lif_2_21.counts ola_mES_lif_2_23.counts ola_mES_lif_2_25.counts ola_mES_lif_2_26.counts
#> "0121" "0122" "0331" "0121"
#> ola_mES_lif_2_27.counts ola_mES_lif_2_28.counts ola_mES_lif_2_29.counts ola_mES_lif_2_3.counts
#> "0331" "0121" "0122" "0122"
#> ola_mES_lif_2_30.counts ola_mES_lif_2_31.counts ola_mES_lif_2_32.counts ola_mES_lif_2_33.counts
#> "0121" "0331" "0122" "0331"
#> ola_mES_lif_2_34.counts ola_mES_lif_2_35.counts ola_mES_lif_2_36.counts ola_mES_lif_2_37.counts
#> "0122" "0121" "034" "0331"
#> ola_mES_lif_2_38.counts ola_mES_lif_2_39.counts ola_mES_lif_2_4.counts ola_mES_lif_2_40.counts
#> "0121" "0122" "0121" "0121"
#> ola_mES_lif_2_41.counts ola_mES_lif_2_42.counts ola_mES_lif_2_43.counts ola_mES_lif_2_44.counts
#> "0331" "0121" "0121" "0122"
#> ola_mES_lif_2_45.counts ola_mES_lif_2_46.counts ola_mES_lif_2_47.counts ola_mES_lif_2_48.counts
#> "034" "0121" "0331" "0223"
#> ola_mES_lif_2_49.counts ola_mES_lif_2_5.counts ola_mES_lif_2_50.counts ola_mES_lif_2_51.counts
#> "0122" "0121" "0122" "0122"
#> ola_mES_lif_2_52.counts ola_mES_lif_2_53.counts ola_mES_lif_2_54.counts ola_mES_lif_2_55.counts
#> "0122" "0121" "0121" "0121"
#> ola_mES_lif_2_56.counts ola_mES_lif_2_57.counts ola_mES_lif_2_58.counts ola_mES_lif_2_59.counts
#> "0121" "034" "0121" "0122"
#> ola_mES_lif_2_6.counts ola_mES_lif_2_60.counts ola_mES_lif_2_61.counts ola_mES_lif_2_63.counts
#> "0121" "0331" "0141" "0122"
#> ola_mES_lif_2_64.counts ola_mES_lif_2_65.counts ola_mES_lif_2_66.counts ola_mES_lif_2_67.counts
#> "0121" "0121" "0121" "0121"
#> ola_mES_lif_2_68.counts ola_mES_lif_2_69.counts ola_mES_lif_2_7.counts ola_mES_lif_2_70.counts
#> "0121" "0223" "0122" "0121"
#> ola_mES_lif_2_71.counts ola_mES_lif_2_72.counts ola_mES_lif_2_73.counts ola_mES_lif_2_74.counts
#> "0122" "0141" "0223" "0122"
#> ola_mES_lif_2_75.counts ola_mES_lif_2_76.counts ola_mES_lif_2_77.counts ola_mES_lif_2_79.counts
#> "0121" "0121" "0121" "0331"
#> ola_mES_lif_2_8.counts ola_mES_lif_2_80.counts ola_mES_lif_2_81.counts ola_mES_lif_2_82.counts
#> "0122" "0122" "0331" "0121"
#> ola_mES_lif_2_83.counts ola_mES_lif_2_84.counts ola_mES_lif_2_86.counts ola_mES_lif_2_87.counts
#> "0122" "0121" "0331" "0121"
#> ola_mES_lif_2_89.counts ola_mES_lif_2_9.counts ola_mES_lif_2_90.counts ola_mES_lif_2_91.counts
#> "0122" "0331" "0331" "0331"
#> ola_mES_lif_2_92.counts ola_mES_lif_2_93.counts ola_mES_lif_2_94.counts ola_mES_lif_2_95.counts
#> "0331" "0122" "0121" "0121"
#> ola_mES_lif_2_96.counts ola_mES_lif_3_1.counts ola_mES_lif_3_10.counts ola_mES_lif_3_11.counts
#> "0331" "0143" "0122" "0332"
#> ola_mES_lif_3_12.counts ola_mES_lif_3_13.counts ola_mES_lif_3_14.counts ola_mES_lif_3_15.counts
#> "0223" "0143" "034" "034"
#> ola_mES_lif_3_16.counts ola_mES_lif_3_17.counts ola_mES_lif_3_19.counts ola_mES_lif_3_2.counts
#> "034" "034" "034" "0222"
#> ola_mES_lif_3_20.counts ola_mES_lif_3_21.counts ola_mES_lif_3_22.counts ola_mES_lif_3_23.counts
#> "0311" "0222" "0313" "0311"
#> ola_mES_lif_3_24.counts ola_mES_lif_3_26.counts ola_mES_lif_3_27.counts ola_mES_lif_3_28.counts
#> "0311" "0313" "0143" "034"
#> ola_mES_lif_3_29.counts ola_mES_lif_3_3.counts ola_mES_lif_3_30.counts ola_mES_lif_3_31.counts
#> "0332" "0143" "0143" "034"
#> ola_mES_lif_3_32.counts ola_mES_lif_3_33.counts ola_mES_lif_3_34.counts ola_mES_lif_3_35.counts
#> "0143" "034" "034" "034"
#> ola_mES_lif_3_36.counts ola_mES_lif_3_37.counts ola_mES_lif_3_38.counts ola_mES_lif_3_39.counts
#> "034" "0332" "034" "034"
#> ola_mES_lif_3_4.counts ola_mES_lif_3_40.counts ola_mES_lif_3_42.counts ola_mES_lif_3_45.counts
#> "0143" "0143" "0141" "034"
#> ola_mES_lif_3_46.counts ola_mES_lif_3_47.counts ola_mES_lif_3_48.counts ola_mES_lif_3_49.counts
#> "034" "034" "0332" "0143"
#> ola_mES_lif_3_5.counts ola_mES_lif_3_50.counts ola_mES_lif_3_51.counts ola_mES_lif_3_52.counts
#> "0122" "0141" "0143" "0143"
#> ola_mES_lif_3_53.counts ola_mES_lif_3_54.counts ola_mES_lif_3_56.counts ola_mES_lif_3_57.counts
#> "0143" "0141" "0122" "0332"
#> ola_mES_lif_3_58.counts ola_mES_lif_3_59.counts ola_mES_lif_3_6.counts ola_mES_lif_3_60.counts
#> "0143" "0143" "0141" "0143"
#> ola_mES_lif_3_61.counts ola_mES_lif_3_63.counts ola_mES_lif_3_64.counts ola_mES_lif_3_65.counts
#> "0143" "0143" "0143" "0143"
#> ola_mES_lif_3_69.counts ola_mES_lif_3_70.counts ola_mES_lif_3_71.counts ola_mES_lif_3_72.counts
#> "034" "034" "0143" "034"
#> ola_mES_lif_3_73.counts ola_mES_lif_3_74.counts ola_mES_lif_3_75.counts ola_mES_lif_3_76.counts
#> "0311" "0311" "0313" "0311"
#> ola_mES_lif_3_77.counts ola_mES_lif_3_78.counts ola_mES_lif_3_8.counts ola_mES_lif_3_80.counts
#> "0222" "0311" "0143" "0313"
#> ola_mES_lif_3_82.counts ola_mES_lif_3_83.counts ola_mES_lif_3_84.counts ola_mES_lif_3_87.counts
#> "0313" "0313" "0313" "0312"
#> ola_mES_lif_3_88.counts ola_mES_lif_3_89.counts ola_mES_lif_3_9.counts ola_mES_lif_3_90.counts
#> "0311" "0222" "0223" "034"
#> ola_mES_lif_3_92.counts ola_mES_lif_3_94.counts ola_mES_lif_3_95.counts ola_mES_lif_3_96.counts
#> "0311" "0311" "0311" "0311"
get_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 447))
#> ola_mES_2i_2_1.counts ola_mES_2i_2_10.counts ola_mES_2i_2_11.counts ola_mES_2i_2_12.counts
#> "01112" "0131" "0131" "0131"
#> ola_mES_2i_2_13.counts ola_mES_2i_2_14.counts ola_mES_2i_2_15.counts ola_mES_2i_2_16.counts
#> "0133" "01112" "0133" "0133"
#> ola_mES_2i_2_17.counts ola_mES_2i_2_18.counts ola_mES_2i_2_19.counts ola_mES_2i_2_2.counts
#> "0133" "0131" "0131" "01112"
#> ola_mES_2i_2_20.counts ola_mES_2i_2_21.counts ola_mES_2i_2_22.counts ola_mES_2i_2_23.counts
#> "01112" "0131" "0133" "0131"
#> ola_mES_2i_2_24.counts ola_mES_2i_2_25.counts ola_mES_2i_2_26.counts ola_mES_2i_2_27.counts
#> "0131" "01112" "0133" "01112"
#> ola_mES_2i_2_28.counts ola_mES_2i_2_29.counts ola_mES_2i_2_3.counts ola_mES_2i_2_30.counts
#> "01112" "0133" "01112" "0131"
#> ola_mES_2i_2_31.counts ola_mES_2i_2_32.counts ola_mES_2i_2_33.counts ola_mES_2i_2_34.counts
#> "0131" "0133" "0131" "0131"
#> ola_mES_2i_2_35.counts ola_mES_2i_2_36.counts ola_mES_2i_2_37.counts ola_mES_2i_2_38.counts
#> "0131" "0131" "01112" "01112"
#> ola_mES_2i_2_39.counts ola_mES_2i_2_4.counts ola_mES_2i_2_40.counts ola_mES_2i_2_41.counts
#> "0133" "0131" "0131" "0131"
#> ola_mES_2i_2_42.counts ola_mES_2i_2_43.counts ola_mES_2i_2_44.counts ola_mES_2i_2_46.counts
#> "0131" "01112" "0131" "0131"
#> ola_mES_2i_2_47.counts ola_mES_2i_2_48.counts ola_mES_2i_2_49.counts ola_mES_2i_2_5.counts
#> "0131" "0131" "01112" "0133"
#> ola_mES_2i_2_50.counts ola_mES_2i_2_51.counts ola_mES_2i_2_52.counts ola_mES_2i_2_53.counts
#> "01112" "0131" "0133" "0133"
#> ola_mES_2i_2_54.counts ola_mES_2i_2_55.counts ola_mES_2i_2_56.counts ola_mES_2i_2_57.counts
#> "0131" "0133" "0133" "0133"
#> ola_mES_2i_2_58.counts ola_mES_2i_2_59.counts ola_mES_2i_2_6.counts ola_mES_2i_2_60.counts
#> "0131" "0131" "0133" "0131"
#> ola_mES_2i_2_61.counts ola_mES_2i_2_62.counts ola_mES_2i_2_63.counts ola_mES_2i_2_64.counts
#> "01112" "032" "01112" "0133"
#> ola_mES_2i_2_66.counts ola_mES_2i_2_67.counts ola_mES_2i_2_68.counts ola_mES_2i_2_69.counts
#> "0131" "0131" "01112" "0131"
#> ola_mES_2i_2_7.counts ola_mES_2i_2_72.counts ola_mES_2i_2_73.counts ola_mES_2i_2_74.counts
#> "0133" "0133" "01112" "0133"
#> ola_mES_2i_2_75.counts ola_mES_2i_2_76.counts ola_mES_2i_2_79.counts ola_mES_2i_2_8.counts
#> "01112" "01112" "0131" "0133"
#> ola_mES_2i_2_80.counts ola_mES_2i_2_81.counts ola_mES_2i_2_85.counts ola_mES_2i_2_86.counts
#> "0133" "0131" "01112" "01112"
#> ola_mES_2i_2_87.counts ola_mES_2i_2_90.counts ola_mES_2i_2_91.counts ola_mES_2i_2_93.counts
#> "032" "0131" "0133" "0131"
#> ola_mES_2i_2_94.counts ola_mES_2i_2_95.counts ola_mES_2i_3_10.counts ola_mES_2i_3_11.counts
#> "0131" "0133" "0222" "023"
#> ola_mES_2i_3_12.counts ola_mES_2i_3_13.counts ola_mES_2i_3_15.counts ola_mES_2i_3_16.counts
#> "0221" "0221" "0221" "02112"
#> ola_mES_2i_3_17.counts ola_mES_2i_3_18.counts ola_mES_2i_3_19.counts ola_mES_2i_3_2.counts
#> "032" "032" "0221" "0221"
#> ola_mES_2i_3_20.counts ola_mES_2i_3_21.counts ola_mES_2i_3_23.counts ola_mES_2i_3_25.counts
#> "0221" "0222" "02112" "023"
#> ola_mES_2i_3_26.counts ola_mES_2i_3_28.counts ola_mES_2i_3_30.counts ola_mES_2i_3_31.counts
#> "0221" "02112" "0221" "032"
#> ola_mES_2i_3_33.counts ola_mES_2i_3_36.counts ola_mES_2i_3_37.counts ola_mES_2i_3_38.counts
#> "0221" "02112" "0221" "0221"
#> ola_mES_2i_3_39.counts ola_mES_2i_3_4.counts ola_mES_2i_3_41.counts ola_mES_2i_3_42.counts
#> "0222" "02112" "0222" "0221"
#> ola_mES_2i_3_43.counts ola_mES_2i_3_45.counts ola_mES_2i_3_46.counts ola_mES_2i_3_47.counts
#> "02112" "0221" "0221" "0221"
#> ola_mES_2i_3_49.counts ola_mES_2i_3_5.counts ola_mES_2i_3_52.counts ola_mES_2i_3_54.counts
#> "032" "0221" "0221" "0222"
#> ola_mES_2i_3_56.counts ola_mES_2i_3_57.counts ola_mES_2i_3_58.counts ola_mES_2i_3_6.counts
#> "0221" "0221" "0221" "0222"
#> ola_mES_2i_3_62.counts ola_mES_2i_3_65.counts ola_mES_2i_3_66.counts ola_mES_2i_3_69.counts
#> "032" "0221" "032" "0222"
#> ola_mES_2i_3_7.counts ola_mES_2i_3_73.counts ola_mES_2i_3_76.counts ola_mES_2i_3_77.counts
#> "0221" "0221" "0221" "0222"
#> ola_mES_2i_3_78.counts ola_mES_2i_3_79.counts ola_mES_2i_3_8.counts ola_mES_2i_3_80.counts
#> "032" "0221" "0221" "0221"
#> ola_mES_2i_3_82.counts ola_mES_2i_3_83.counts ola_mES_2i_3_84.counts ola_mES_2i_3_87.counts
#> "0221" "0221" "02112" "0222"
#> ola_mES_2i_3_88.counts ola_mES_2i_3_90.counts ola_mES_2i_3_91.counts ola_mES_2i_3_92.counts
#> "0221" "02112" "0222" "0221"
#> ola_mES_2i_3_96.counts ola_mES_2i_4_1.counts ola_mES_2i_4_10.counts ola_mES_2i_4_11.counts
#> "0222" "0212" "0212" "0212"
#> ola_mES_2i_4_13.counts ola_mES_2i_4_17.counts ola_mES_2i_4_18.counts ola_mES_2i_4_2.counts
#> "02111" "02111" "023" "0132"
#> ola_mES_2i_4_20.counts ola_mES_2i_4_21.counts ola_mES_2i_4_22.counts ola_mES_2i_4_23.counts
#> "02111" "02111" "023" "0221"
#> ola_mES_2i_4_24.counts ola_mES_2i_4_25.counts ola_mES_2i_4_26.counts ola_mES_2i_4_27.counts
#> "02112" "0212" "0212" "023"
#> ola_mES_2i_4_29.counts ola_mES_2i_4_30.counts ola_mES_2i_4_31.counts ola_mES_2i_4_32.counts
#> "0212" "02111" "0212" "02111"
#> ola_mES_2i_4_33.counts ola_mES_2i_4_34.counts ola_mES_2i_4_35.counts ola_mES_2i_4_36.counts
#> "02111" "02111" "0212" "02111"
#> ola_mES_2i_4_37.counts ola_mES_2i_4_38.counts ola_mES_2i_4_39.counts ola_mES_2i_4_40.counts
#> "032" "01122" "0212" "01122"
#> ola_mES_2i_4_42.counts ola_mES_2i_4_43.counts ola_mES_2i_4_44.counts ola_mES_2i_4_46.counts
#> "032" "02111" "0212" "02111"
#> ola_mES_2i_4_49.counts ola_mES_2i_4_5.counts ola_mES_2i_4_50.counts ola_mES_2i_4_52.counts
#> "01122" "023" "0212" "0212"
#> ola_mES_2i_4_53.counts ola_mES_2i_4_54.counts ola_mES_2i_4_56.counts ola_mES_2i_4_57.counts
#> "01122" "032" "02111" "02111"
#> ola_mES_2i_4_58.counts ola_mES_2i_4_59.counts ola_mES_2i_4_6.counts ola_mES_2i_4_60.counts
#> "02111" "02111" "02111" "0221"
#> ola_mES_2i_4_61.counts ola_mES_2i_4_62.counts ola_mES_2i_4_63.counts ola_mES_2i_4_64.counts
#> "02111" "032" "0221" "0212"
#> ola_mES_2i_4_65.counts ola_mES_2i_4_66.counts ola_mES_2i_4_67.counts ola_mES_2i_4_68.counts
#> "02111" "02111" "01122" "0221"
#> ola_mES_2i_4_69.counts ola_mES_2i_4_7.counts ola_mES_2i_4_70.counts ola_mES_2i_4_71.counts
#> "02111" "023" "0221" "02111"
#> ola_mES_2i_4_72.counts ola_mES_2i_4_73.counts ola_mES_2i_4_75.counts ola_mES_2i_4_77.counts
#> "023" "02111" "0222" "02112"
#> ola_mES_2i_4_78.counts ola_mES_2i_4_80.counts ola_mES_2i_4_81.counts ola_mES_2i_4_83.counts
#> "023" "02111" "02111" "023"
#> ola_mES_2i_4_84.counts ola_mES_2i_4_88.counts ola_mES_2i_4_89.counts ola_mES_2i_4_9.counts
#> "023" "0221" "0221" "023"
#> ola_mES_2i_4_90.counts ola_mES_2i_4_91.counts ola_mES_2i_4_94.counts ola_mES_2i_4_95.counts
#> "02111" "0212" "0221" "0222"
#> ola_mES_2i_4_96.counts ola_mES_2i_5_1.counts ola_mES_2i_5_10.counts ola_mES_2i_5_11.counts
#> "0222" "0221" "023" "023"
#> ola_mES_2i_5_12.counts ola_mES_2i_5_13.counts ola_mES_2i_5_14.counts ola_mES_2i_5_15.counts
#> "023" "023" "0212" "0212"
#> ola_mES_2i_5_16.counts ola_mES_2i_5_17.counts ola_mES_2i_5_18.counts ola_mES_2i_5_19.counts
#> "0212" "0212" "023" "02111"
#> ola_mES_2i_5_2.counts ola_mES_2i_5_20.counts ola_mES_2i_5_21.counts ola_mES_2i_5_23.counts
#> "0221" "0212" "02111" "023"
#> ola_mES_2i_5_24.counts ola_mES_2i_5_25.counts ola_mES_2i_5_26.counts ola_mES_2i_5_27.counts
#> "0212" "02111" "0212" "0212"
#> ola_mES_2i_5_28.counts ola_mES_2i_5_29.counts ola_mES_2i_5_3.counts ola_mES_2i_5_30.counts
#> "0221" "0221" "0221" "0212"
#> ola_mES_2i_5_31.counts ola_mES_2i_5_32.counts ola_mES_2i_5_34.counts ola_mES_2i_5_35.counts
#> "0212" "0212" "0212" "02111"
#> ola_mES_2i_5_36.counts ola_mES_2i_5_38.counts ola_mES_2i_5_39.counts ola_mES_2i_5_4.counts
#> "0212" "0212" "0212" "0221"
#> ola_mES_2i_5_40.counts ola_mES_2i_5_41.counts ola_mES_2i_5_42.counts ola_mES_2i_5_44.counts
#> "02111" "01122" "0212" "0212"
#> ola_mES_2i_5_45.counts ola_mES_2i_5_46.counts ola_mES_2i_5_47.counts ola_mES_2i_5_48.counts
#> "02111" "02111" "02111" "02111"
#> ola_mES_2i_5_49.counts ola_mES_2i_5_5.counts ola_mES_2i_5_51.counts ola_mES_2i_5_52.counts
#> "02111" "023" "0212" "0221"
#> ola_mES_2i_5_53.counts ola_mES_2i_5_54.counts ola_mES_2i_5_55.counts ola_mES_2i_5_56.counts
#> "0212" "02111" "0212" "0212"
#> ola_mES_2i_5_57.counts ola_mES_2i_5_58.counts ola_mES_2i_5_59.counts ola_mES_2i_5_6.counts
#> "0212" "0212" "0212" "0221"
#> ola_mES_2i_5_61.counts ola_mES_2i_5_62.counts ola_mES_2i_5_63.counts ola_mES_2i_5_65.counts
#> "0212" "0221" "02111" "023"
#> ola_mES_2i_5_66.counts ola_mES_2i_5_67.counts ola_mES_2i_5_68.counts ola_mES_2i_5_69.counts
#> "0221" "0212" "0212" "0212"
#> ola_mES_2i_5_7.counts ola_mES_2i_5_70.counts ola_mES_2i_5_72.counts ola_mES_2i_5_73.counts
#> "0212" "02111" "0212" "023"
#> ola_mES_2i_5_74.counts ola_mES_2i_5_75.counts ola_mES_2i_5_76.counts ola_mES_2i_5_77.counts
#> "02111" "023" "023" "023"
#> ola_mES_2i_5_78.counts ola_mES_2i_5_8.counts ola_mES_2i_5_81.counts ola_mES_2i_5_82.counts
#> "0212" "02112" "0221" "0212"
#> ola_mES_2i_5_83.counts ola_mES_2i_5_85.counts ola_mES_2i_5_86.counts ola_mES_2i_5_87.counts
#> "023" "0221" "023" "023"
#> ola_mES_2i_5_88.counts ola_mES_2i_5_9.counts ola_mES_2i_5_90.counts ola_mES_2i_5_92.counts
#> "023" "0222" "02112" "0212"
#> ola_mES_2i_5_94.counts ola_mES_2i_5_95.counts ola_mES_2i_5_96.counts ola_mES_a2i_2_1.counts
#> "0212" "0212" "0212" "0132"
#> ola_mES_a2i_2_10.counts ola_mES_a2i_2_11.counts ola_mES_a2i_2_12.counts ola_mES_a2i_2_13.counts
#> "01121" "0132" "01121" "01111"
#> ola_mES_a2i_2_14.counts ola_mES_a2i_2_15.counts ola_mES_a2i_2_16.counts ola_mES_a2i_2_17.counts
#> "0132" "0132" "0132" "0132"
#> ola_mES_a2i_2_18.counts ola_mES_a2i_2_19.counts ola_mES_a2i_2_2.counts ola_mES_a2i_2_20.counts
#> "0132" "0132" "0132" "01121"
#> ola_mES_a2i_2_21.counts ola_mES_a2i_2_22.counts ola_mES_a2i_2_23.counts ola_mES_a2i_2_24.counts
#> "0132" "0132" "0132" "0132"
#> ola_mES_a2i_2_25.counts ola_mES_a2i_2_26.counts ola_mES_a2i_2_27.counts ola_mES_a2i_2_28.counts
#> "0113" "032" "01121" "01121"
#> ola_mES_a2i_2_29.counts ola_mES_a2i_2_3.counts ola_mES_a2i_2_30.counts ola_mES_a2i_2_31.counts
#> "01121" "0132" "0222" "032"
#> ola_mES_a2i_2_32.counts ola_mES_a2i_2_33.counts ola_mES_a2i_2_34.counts ola_mES_a2i_2_35.counts
#> "032" "01121" "01121" "0312"
#> ola_mES_a2i_2_36.counts ola_mES_a2i_2_37.counts ola_mES_a2i_2_38.counts ola_mES_a2i_2_39.counts
#> "0222" "032" "01121" "032"
#> ola_mES_a2i_2_4.counts ola_mES_a2i_2_40.counts ola_mES_a2i_2_41.counts ola_mES_a2i_2_42.counts
#> "01121" "01121" "0132" "0132"
#> ola_mES_a2i_2_43.counts ola_mES_a2i_2_44.counts ola_mES_a2i_2_45.counts ola_mES_a2i_2_46.counts
#> "01111" "0113" "0132" "0132"
#> ola_mES_a2i_2_47.counts ola_mES_a2i_2_48.counts ola_mES_a2i_2_49.counts ola_mES_a2i_2_5.counts
#> "01121" "01121" "0113" "0132"
#> ola_mES_a2i_2_50.counts ola_mES_a2i_2_51.counts ola_mES_a2i_2_52.counts ola_mES_a2i_2_53.counts
#> "032" "01121" "01121" "032"
#> ola_mES_a2i_2_54.counts ola_mES_a2i_2_55.counts ola_mES_a2i_2_56.counts ola_mES_a2i_2_57.counts
#> "01121" "0132" "01111" "0132"
#> ola_mES_a2i_2_59.counts ola_mES_a2i_2_6.counts ola_mES_a2i_2_60.counts ola_mES_a2i_2_61.counts
#> "0132" "032" "01122" "01111"
#> ola_mES_a2i_2_62.counts ola_mES_a2i_2_63.counts ola_mES_a2i_2_64.counts ola_mES_a2i_2_65.counts
#> "01121" "01111" "01121" "01111"
#> ola_mES_a2i_2_66.counts ola_mES_a2i_2_67.counts ola_mES_a2i_2_68.counts ola_mES_a2i_2_69.counts
#> "0132" "01121" "0113" "032"
#> ola_mES_a2i_2_7.counts ola_mES_a2i_2_70.counts ola_mES_a2i_2_71.counts ola_mES_a2i_2_72.counts
#> "01121" "01121" "0132" "01121"
#> ola_mES_a2i_2_73.counts ola_mES_a2i_2_74.counts ola_mES_a2i_2_75.counts ola_mES_a2i_2_76.counts
#> "0113" "01111" "01121" "01111"
#> ola_mES_a2i_2_77.counts ola_mES_a2i_2_78.counts ola_mES_a2i_2_8.counts ola_mES_a2i_2_80.counts
#> "01121" "0132" "0132" "0113"
#> ola_mES_a2i_2_81.counts ola_mES_a2i_2_82.counts ola_mES_a2i_2_83.counts ola_mES_a2i_2_84.counts
#> "01121" "0132" "0132" "01121"
#> ola_mES_a2i_2_86.counts ola_mES_a2i_2_87.counts ola_mES_a2i_2_88.counts ola_mES_a2i_2_89.counts
#> "0113" "01121" "01121" "0223"
#> ola_mES_a2i_2_9.counts ola_mES_a2i_2_90.counts ola_mES_a2i_2_91.counts ola_mES_a2i_2_92.counts
#> "0113" "0132" "032" "032"
#> ola_mES_a2i_2_93.counts ola_mES_a2i_2_94.counts ola_mES_a2i_2_95.counts ola_mES_a2i_2_96.counts
#> "01111" "0132" "0132" "0132"
#> ola_mES_a2i_3_10.counts ola_mES_a2i_3_11.counts ola_mES_a2i_3_13.counts ola_mES_a2i_3_16.counts
#> "02112" "0223" "01121" "0113"
#> ola_mES_a2i_3_17.counts ola_mES_a2i_3_18.counts ola_mES_a2i_3_2.counts ola_mES_a2i_3_20.counts
#> "032" "032" "032" "032"
#> ola_mES_a2i_3_21.counts ola_mES_a2i_3_22.counts ola_mES_a2i_3_24.counts ola_mES_a2i_3_25.counts
#> "0223" "0223" "0223" "0113"
#> ola_mES_a2i_3_26.counts ola_mES_a2i_3_28.counts ola_mES_a2i_3_29.counts ola_mES_a2i_3_3.counts
#> "032" "0113" "0113" "02112"
#> ola_mES_a2i_3_30.counts ola_mES_a2i_3_31.counts ola_mES_a2i_3_33.counts ola_mES_a2i_3_34.counts
#> "032" "032" "0223" "0113"
#> ola_mES_a2i_3_37.counts ola_mES_a2i_3_38.counts ola_mES_a2i_3_39.counts ola_mES_a2i_3_4.counts
#> "01122" "0113" "0113" "0223"
#> ola_mES_a2i_3_40.counts ola_mES_a2i_3_41.counts ola_mES_a2i_3_42.counts ola_mES_a2i_3_44.counts
#> "0113" "032" "032" "0113"
#> ola_mES_a2i_3_45.counts ola_mES_a2i_3_47.counts ola_mES_a2i_3_49.counts ola_mES_a2i_3_50.counts
#> "032" "032" "0113" "0113"
#> ola_mES_a2i_3_52.counts ola_mES_a2i_3_54.counts ola_mES_a2i_3_56.counts ola_mES_a2i_3_58.counts
#> "032" "0113" "032" "0223"
#> ola_mES_a2i_3_59.counts ola_mES_a2i_3_6.counts ola_mES_a2i_3_62.counts ola_mES_a2i_3_64.counts
#> "0132" "0113" "01121" "01122"
#> ola_mES_a2i_3_65.counts ola_mES_a2i_3_66.counts ola_mES_a2i_3_67.counts ola_mES_a2i_3_68.counts
#> "0132" "0113" "032" "0222"
#> ola_mES_a2i_3_69.counts ola_mES_a2i_3_7.counts ola_mES_a2i_3_71.counts ola_mES_a2i_3_73.counts
#> "0113" "032" "0132" "01121"
#> ola_mES_a2i_3_75.counts ola_mES_a2i_3_76.counts ola_mES_a2i_3_77.counts ola_mES_a2i_3_8.counts
#> "032" "02112" "0113" "032"
#> ola_mES_a2i_3_80.counts ola_mES_a2i_3_81.counts ola_mES_a2i_3_82.counts ola_mES_a2i_3_83.counts
#> "0222" "032" "0113" "01121"
#> ola_mES_a2i_3_84.counts ola_mES_a2i_3_85.counts ola_mES_a2i_3_86.counts ola_mES_a2i_3_89.counts
#> "0113" "01121" "0113" "0132"
#> ola_mES_a2i_3_9.counts ola_mES_a2i_3_90.counts ola_mES_a2i_3_91.counts ola_mES_a2i_3_92.counts
#> "0223" "0113" "0223" "01121"
#> ola_mES_a2i_3_94.counts ola_mES_a2i_3_96.counts ola_mES_lif_1_1.counts ola_mES_lif_1_10.counts
#> "0312" "01121" "0141" "033"
#> ola_mES_lif_1_11.counts ola_mES_lif_1_12.counts ola_mES_lif_1_13.counts ola_mES_lif_1_14.counts
#> "0122" "033" "0141" "033"
#> ola_mES_lif_1_15.counts ola_mES_lif_1_17.counts ola_mES_lif_1_18.counts ola_mES_lif_1_19.counts
#> "0141" "0312" "0141" "0142"
#> ola_mES_lif_1_2.counts ola_mES_lif_1_20.counts ola_mES_lif_1_21.counts ola_mES_lif_1_22.counts
#> "033" "0142" "033" "0142"
#> ola_mES_lif_1_23.counts ola_mES_lif_1_24.counts ola_mES_lif_1_25.counts ola_mES_lif_1_26.counts
#> "0312" "0142" "0142" "0142"
#> ola_mES_lif_1_28.counts ola_mES_lif_1_29.counts ola_mES_lif_1_3.counts ola_mES_lif_1_30.counts
#> "0142" "0313" "0141" "0142"
#> ola_mES_lif_1_31.counts ola_mES_lif_1_32.counts ola_mES_lif_1_33.counts ola_mES_lif_1_34.counts
#> "0313" "0142" "0142" "0141"
#> ola_mES_lif_1_35.counts ola_mES_lif_1_36.counts ola_mES_lif_1_37.counts ola_mES_lif_1_38.counts
#> "0312" "0142" "0142" "0142"
#> ola_mES_lif_1_39.counts ola_mES_lif_1_4.counts ola_mES_lif_1_41.counts ola_mES_lif_1_42.counts
#> "0142" "0143" "0142" "0142"
#> ola_mES_lif_1_43.counts ola_mES_lif_1_44.counts ola_mES_lif_1_45.counts ola_mES_lif_1_46.counts
#> "0142" "0141" "0142" "0142"
#> ola_mES_lif_1_47.counts ola_mES_lif_1_48.counts ola_mES_lif_1_49.counts ola_mES_lif_1_5.counts
#> "0141" "0141" "0141" "0122"
#> ola_mES_lif_1_50.counts ola_mES_lif_1_51.counts ola_mES_lif_1_52.counts ola_mES_lif_1_53.counts
#> "0142" "0122" "0122" "0223"
#> ola_mES_lif_1_54.counts ola_mES_lif_1_55.counts ola_mES_lif_1_56.counts ola_mES_lif_1_57.counts
#> "0141" "0122" "0222" "0223"
#> ola_mES_lif_1_58.counts ola_mES_lif_1_59.counts ola_mES_lif_1_6.counts ola_mES_lif_1_60.counts
#> "0141" "0122" "0142" "0141"
#> ola_mES_lif_1_61.counts ola_mES_lif_1_62.counts ola_mES_lif_1_63.counts ola_mES_lif_1_64.counts
#> "0141" "0142" "0312" "0312"
#> ola_mES_lif_1_65.counts ola_mES_lif_1_66.counts ola_mES_lif_1_67.counts ola_mES_lif_1_68.counts
#> "0312" "0141" "0141" "0142"
#> ola_mES_lif_1_69.counts ola_mES_lif_1_7.counts ola_mES_lif_1_70.counts ola_mES_lif_1_72.counts
#> "0312" "0141" "033" "0141"
#> ola_mES_lif_1_73.counts ola_mES_lif_1_74.counts ola_mES_lif_1_75.counts ola_mES_lif_1_79.counts
#> "0142" "0312" "0312" "033"
#> ola_mES_lif_1_8.counts ola_mES_lif_1_80.counts ola_mES_lif_1_81.counts ola_mES_lif_1_82.counts
#> "0142" "0141" "0142" "0312"
#> ola_mES_lif_1_86.counts ola_mES_lif_1_9.counts ola_mES_lif_1_91.counts ola_mES_lif_1_92.counts
#> "033" "0141" "0142" "0142"
#> ola_mES_lif_1_93.counts ola_mES_lif_1_95.counts ola_mES_lif_1_96.counts ola_mES_lif_2_1.counts
#> "033" "0141" "0141" "033"
#> ola_mES_lif_2_10.counts ola_mES_lif_2_11.counts ola_mES_lif_2_12.counts ola_mES_lif_2_13.counts
#> "0122" "0122" "0223" "0122"
#> ola_mES_lif_2_14.counts ola_mES_lif_2_15.counts ola_mES_lif_2_16.counts ola_mES_lif_2_17.counts
#> "0122" "0121" "0121" "0121"
#> ola_mES_lif_2_18.counts ola_mES_lif_2_19.counts ola_mES_lif_2_2.counts ola_mES_lif_2_20.counts
#> "0121" "0121" "0122" "033"
#> ola_mES_lif_2_21.counts ola_mES_lif_2_23.counts ola_mES_lif_2_25.counts ola_mES_lif_2_26.counts
#> "0121" "0122" "033" "0121"
#> ola_mES_lif_2_27.counts ola_mES_lif_2_28.counts ola_mES_lif_2_29.counts ola_mES_lif_2_3.counts
#> "033" "0121" "0122" "0122"
#> ola_mES_lif_2_30.counts ola_mES_lif_2_31.counts ola_mES_lif_2_32.counts ola_mES_lif_2_33.counts
#> "0121" "033" "0122" "033"
#> ola_mES_lif_2_34.counts ola_mES_lif_2_35.counts ola_mES_lif_2_36.counts ola_mES_lif_2_37.counts
#> "0122" "0121" "034" "033"
#> ola_mES_lif_2_38.counts ola_mES_lif_2_39.counts ola_mES_lif_2_4.counts ola_mES_lif_2_40.counts
#> "0121" "0122" "0121" "0121"
#> ola_mES_lif_2_41.counts ola_mES_lif_2_42.counts ola_mES_lif_2_43.counts ola_mES_lif_2_44.counts
#> "033" "0121" "0121" "0122"
#> ola_mES_lif_2_45.counts ola_mES_lif_2_46.counts ola_mES_lif_2_47.counts ola_mES_lif_2_48.counts
#> "034" "0121" "033" "0223"
#> ola_mES_lif_2_49.counts ola_mES_lif_2_5.counts ola_mES_lif_2_50.counts ola_mES_lif_2_51.counts
#> "0122" "0121" "0122" "0122"
#> ola_mES_lif_2_52.counts ola_mES_lif_2_53.counts ola_mES_lif_2_54.counts ola_mES_lif_2_55.counts
#> "0122" "0121" "0121" "0121"
#> ola_mES_lif_2_56.counts ola_mES_lif_2_57.counts ola_mES_lif_2_58.counts ola_mES_lif_2_59.counts
#> "0121" "034" "0121" "0122"
#> ola_mES_lif_2_6.counts ola_mES_lif_2_60.counts ola_mES_lif_2_61.counts ola_mES_lif_2_63.counts
#> "0121" "033" "0141" "0122"
#> ola_mES_lif_2_64.counts ola_mES_lif_2_65.counts ola_mES_lif_2_66.counts ola_mES_lif_2_67.counts
#> "0121" "0121" "0121" "0121"
#> ola_mES_lif_2_68.counts ola_mES_lif_2_69.counts ola_mES_lif_2_7.counts ola_mES_lif_2_70.counts
#> "0121" "0223" "0122" "0121"
#> ola_mES_lif_2_71.counts ola_mES_lif_2_72.counts ola_mES_lif_2_73.counts ola_mES_lif_2_74.counts
#> "0122" "0141" "0223" "0122"
#> ola_mES_lif_2_75.counts ola_mES_lif_2_76.counts ola_mES_lif_2_77.counts ola_mES_lif_2_79.counts
#> "0121" "0121" "0121" "033"
#> ola_mES_lif_2_8.counts ola_mES_lif_2_80.counts ola_mES_lif_2_81.counts ola_mES_lif_2_82.counts
#> "0122" "0122" "033" "0121"
#> ola_mES_lif_2_83.counts ola_mES_lif_2_84.counts ola_mES_lif_2_86.counts ola_mES_lif_2_87.counts
#> "0122" "0121" "033" "0121"
#> ola_mES_lif_2_89.counts ola_mES_lif_2_9.counts ola_mES_lif_2_90.counts ola_mES_lif_2_91.counts
#> "0122" "033" "033" "033"
#> ola_mES_lif_2_92.counts ola_mES_lif_2_93.counts ola_mES_lif_2_94.counts ola_mES_lif_2_95.counts
#> "033" "0122" "0121" "0121"
#> ola_mES_lif_2_96.counts ola_mES_lif_3_1.counts ola_mES_lif_3_10.counts ola_mES_lif_3_11.counts
#> "033" "0143" "0122" "033"
#> ola_mES_lif_3_12.counts ola_mES_lif_3_13.counts ola_mES_lif_3_14.counts ola_mES_lif_3_15.counts
#> "0223" "0143" "034" "034"
#> ola_mES_lif_3_16.counts ola_mES_lif_3_17.counts ola_mES_lif_3_19.counts ola_mES_lif_3_2.counts
#> "034" "034" "034" "0222"
#> ola_mES_lif_3_20.counts ola_mES_lif_3_21.counts ola_mES_lif_3_22.counts ola_mES_lif_3_23.counts
#> "0311" "0222" "0313" "0311"
#> ola_mES_lif_3_24.counts ola_mES_lif_3_26.counts ola_mES_lif_3_27.counts ola_mES_lif_3_28.counts
#> "0311" "0313" "0143" "034"
#> ola_mES_lif_3_29.counts ola_mES_lif_3_3.counts ola_mES_lif_3_30.counts ola_mES_lif_3_31.counts
#> "033" "0143" "0143" "034"
#> ola_mES_lif_3_32.counts ola_mES_lif_3_33.counts ola_mES_lif_3_34.counts ola_mES_lif_3_35.counts
#> "0143" "034" "034" "034"
#> ola_mES_lif_3_36.counts ola_mES_lif_3_37.counts ola_mES_lif_3_38.counts ola_mES_lif_3_39.counts
#> "034" "033" "034" "034"
#> ola_mES_lif_3_4.counts ola_mES_lif_3_40.counts ola_mES_lif_3_42.counts ola_mES_lif_3_45.counts
#> "0143" "0143" "0141" "034"
#> ola_mES_lif_3_46.counts ola_mES_lif_3_47.counts ola_mES_lif_3_48.counts ola_mES_lif_3_49.counts
#> "034" "034" "033" "0143"
#> ola_mES_lif_3_5.counts ola_mES_lif_3_50.counts ola_mES_lif_3_51.counts ola_mES_lif_3_52.counts
#> "0122" "0141" "0143" "0143"
#> ola_mES_lif_3_53.counts ola_mES_lif_3_54.counts ola_mES_lif_3_56.counts ola_mES_lif_3_57.counts
#> "0143" "0141" "0122" "033"
#> ola_mES_lif_3_58.counts ola_mES_lif_3_59.counts ola_mES_lif_3_6.counts ola_mES_lif_3_60.counts
#> "0143" "0143" "0141" "0143"
#> ola_mES_lif_3_61.counts ola_mES_lif_3_63.counts ola_mES_lif_3_64.counts ola_mES_lif_3_65.counts
#> "0143" "0143" "0143" "0143"
#> ola_mES_lif_3_69.counts ola_mES_lif_3_70.counts ola_mES_lif_3_71.counts ola_mES_lif_3_72.counts
#> "034" "034" "0143" "034"
#> ola_mES_lif_3_73.counts ola_mES_lif_3_74.counts ola_mES_lif_3_75.counts ola_mES_lif_3_76.counts
#> "0311" "0311" "0313" "0311"
#> ola_mES_lif_3_77.counts ola_mES_lif_3_78.counts ola_mES_lif_3_8.counts ola_mES_lif_3_80.counts
#> "0222" "0311" "0143" "0313"
#> ola_mES_lif_3_82.counts ola_mES_lif_3_83.counts ola_mES_lif_3_84.counts ola_mES_lif_3_87.counts
#> "0313" "0313" "0313" "0312"
#> ola_mES_lif_3_88.counts ola_mES_lif_3_89.counts ola_mES_lif_3_9.counts ola_mES_lif_3_90.counts
#> "0311" "0222" "0223" "034"
#> ola_mES_lif_3_92.counts ola_mES_lif_3_94.counts ola_mES_lif_3_95.counts ola_mES_lif_3_96.counts
#> "0311" "0311" "0311" "0311"
get_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 466))
#> ola_mES_2i_2_1.counts ola_mES_2i_2_10.counts ola_mES_2i_2_11.counts ola_mES_2i_2_12.counts
#> "01112" "0131" "0131" "0131"
#> ola_mES_2i_2_13.counts ola_mES_2i_2_14.counts ola_mES_2i_2_15.counts ola_mES_2i_2_16.counts
#> "0133" "01112" "0133" "0133"
#> ola_mES_2i_2_17.counts ola_mES_2i_2_18.counts ola_mES_2i_2_19.counts ola_mES_2i_2_2.counts
#> "0133" "0131" "0131" "01112"
#> ola_mES_2i_2_20.counts ola_mES_2i_2_21.counts ola_mES_2i_2_22.counts ola_mES_2i_2_23.counts
#> "01112" "0131" "0133" "0131"
#> ola_mES_2i_2_24.counts ola_mES_2i_2_25.counts ola_mES_2i_2_26.counts ola_mES_2i_2_27.counts
#> "0131" "01112" "0133" "01112"
#> ola_mES_2i_2_28.counts ola_mES_2i_2_29.counts ola_mES_2i_2_3.counts ola_mES_2i_2_30.counts
#> "01112" "0133" "01112" "0131"
#> ola_mES_2i_2_31.counts ola_mES_2i_2_32.counts ola_mES_2i_2_33.counts ola_mES_2i_2_34.counts
#> "0131" "0133" "0131" "0131"
#> ola_mES_2i_2_35.counts ola_mES_2i_2_36.counts ola_mES_2i_2_37.counts ola_mES_2i_2_38.counts
#> "0131" "0131" "01112" "01112"
#> ola_mES_2i_2_39.counts ola_mES_2i_2_4.counts ola_mES_2i_2_40.counts ola_mES_2i_2_41.counts
#> "0133" "0131" "0131" "0131"
#> ola_mES_2i_2_42.counts ola_mES_2i_2_43.counts ola_mES_2i_2_44.counts ola_mES_2i_2_46.counts
#> "0131" "01112" "0131" "0131"
#> ola_mES_2i_2_47.counts ola_mES_2i_2_48.counts ola_mES_2i_2_49.counts ola_mES_2i_2_5.counts
#> "0131" "0131" "01112" "0133"
#> ola_mES_2i_2_50.counts ola_mES_2i_2_51.counts ola_mES_2i_2_52.counts ola_mES_2i_2_53.counts
#> "01112" "0131" "0133" "0133"
#> ola_mES_2i_2_54.counts ola_mES_2i_2_55.counts ola_mES_2i_2_56.counts ola_mES_2i_2_57.counts
#> "0131" "0133" "0133" "0133"
#> ola_mES_2i_2_58.counts ola_mES_2i_2_59.counts ola_mES_2i_2_6.counts ola_mES_2i_2_60.counts
#> "0131" "0131" "0133" "0131"
#> ola_mES_2i_2_61.counts ola_mES_2i_2_62.counts ola_mES_2i_2_63.counts ola_mES_2i_2_64.counts
#> "01112" "032" "01112" "0133"
#> ola_mES_2i_2_66.counts ola_mES_2i_2_67.counts ola_mES_2i_2_68.counts ola_mES_2i_2_69.counts
#> "0131" "0131" "01112" "0131"
#> ola_mES_2i_2_7.counts ola_mES_2i_2_72.counts ola_mES_2i_2_73.counts ola_mES_2i_2_74.counts
#> "0133" "0133" "01112" "0133"
#> ola_mES_2i_2_75.counts ola_mES_2i_2_76.counts ola_mES_2i_2_79.counts ola_mES_2i_2_8.counts
#> "01112" "01112" "0131" "0133"
#> ola_mES_2i_2_80.counts ola_mES_2i_2_81.counts ola_mES_2i_2_85.counts ola_mES_2i_2_86.counts
#> "0133" "0131" "01112" "01112"
#> ola_mES_2i_2_87.counts ola_mES_2i_2_90.counts ola_mES_2i_2_91.counts ola_mES_2i_2_93.counts
#> "032" "0131" "0133" "0131"
#> ola_mES_2i_2_94.counts ola_mES_2i_2_95.counts ola_mES_2i_3_10.counts ola_mES_2i_3_11.counts
#> "0131" "0133" "0222" "023"
#> ola_mES_2i_3_12.counts ola_mES_2i_3_13.counts ola_mES_2i_3_15.counts ola_mES_2i_3_16.counts
#> "0221" "0221" "0221" "02112"
#> ola_mES_2i_3_17.counts ola_mES_2i_3_18.counts ola_mES_2i_3_19.counts ola_mES_2i_3_2.counts
#> "032" "032" "0221" "0221"
#> ola_mES_2i_3_20.counts ola_mES_2i_3_21.counts ola_mES_2i_3_23.counts ola_mES_2i_3_25.counts
#> "0221" "0222" "02112" "023"
#> ola_mES_2i_3_26.counts ola_mES_2i_3_28.counts ola_mES_2i_3_30.counts ola_mES_2i_3_31.counts
#> "0221" "02112" "0221" "032"
#> ola_mES_2i_3_33.counts ola_mES_2i_3_36.counts ola_mES_2i_3_37.counts ola_mES_2i_3_38.counts
#> "0221" "02112" "0221" "0221"
#> ola_mES_2i_3_39.counts ola_mES_2i_3_4.counts ola_mES_2i_3_41.counts ola_mES_2i_3_42.counts
#> "0222" "02112" "0222" "0221"
#> ola_mES_2i_3_43.counts ola_mES_2i_3_45.counts ola_mES_2i_3_46.counts ola_mES_2i_3_47.counts
#> "02112" "0221" "0221" "0221"
#> ola_mES_2i_3_49.counts ola_mES_2i_3_5.counts ola_mES_2i_3_52.counts ola_mES_2i_3_54.counts
#> "032" "0221" "0221" "0222"
#> ola_mES_2i_3_56.counts ola_mES_2i_3_57.counts ola_mES_2i_3_58.counts ola_mES_2i_3_6.counts
#> "0221" "0221" "0221" "0222"
#> ola_mES_2i_3_62.counts ola_mES_2i_3_65.counts ola_mES_2i_3_66.counts ola_mES_2i_3_69.counts
#> "032" "0221" "032" "0222"
#> ola_mES_2i_3_7.counts ola_mES_2i_3_73.counts ola_mES_2i_3_76.counts ola_mES_2i_3_77.counts
#> "0221" "0221" "0221" "0222"
#> ola_mES_2i_3_78.counts ola_mES_2i_3_79.counts ola_mES_2i_3_8.counts ola_mES_2i_3_80.counts
#> "032" "0221" "0221" "0221"
#> ola_mES_2i_3_82.counts ola_mES_2i_3_83.counts ola_mES_2i_3_84.counts ola_mES_2i_3_87.counts
#> "0221" "0221" "02112" "0222"
#> ola_mES_2i_3_88.counts ola_mES_2i_3_90.counts ola_mES_2i_3_91.counts ola_mES_2i_3_92.counts
#> "0221" "02112" "0222" "0221"
#> ola_mES_2i_3_96.counts ola_mES_2i_4_1.counts ola_mES_2i_4_10.counts ola_mES_2i_4_11.counts
#> "0222" "0212" "0212" "0212"
#> ola_mES_2i_4_13.counts ola_mES_2i_4_17.counts ola_mES_2i_4_18.counts ola_mES_2i_4_2.counts
#> "02111" "02111" "023" "0132"
#> ola_mES_2i_4_20.counts ola_mES_2i_4_21.counts ola_mES_2i_4_22.counts ola_mES_2i_4_23.counts
#> "02111" "02111" "023" "0221"
#> ola_mES_2i_4_24.counts ola_mES_2i_4_25.counts ola_mES_2i_4_26.counts ola_mES_2i_4_27.counts
#> "02112" "0212" "0212" "023"
#> ola_mES_2i_4_29.counts ola_mES_2i_4_30.counts ola_mES_2i_4_31.counts ola_mES_2i_4_32.counts
#> "0212" "02111" "0212" "02111"
#> ola_mES_2i_4_33.counts ola_mES_2i_4_34.counts ola_mES_2i_4_35.counts ola_mES_2i_4_36.counts
#> "02111" "02111" "0212" "02111"
#> ola_mES_2i_4_37.counts ola_mES_2i_4_38.counts ola_mES_2i_4_39.counts ola_mES_2i_4_40.counts
#> "032" "01122" "0212" "01122"
#> ola_mES_2i_4_42.counts ola_mES_2i_4_43.counts ola_mES_2i_4_44.counts ola_mES_2i_4_46.counts
#> "032" "02111" "0212" "02111"
#> ola_mES_2i_4_49.counts ola_mES_2i_4_5.counts ola_mES_2i_4_50.counts ola_mES_2i_4_52.counts
#> "01122" "023" "0212" "0212"
#> ola_mES_2i_4_53.counts ola_mES_2i_4_54.counts ola_mES_2i_4_56.counts ola_mES_2i_4_57.counts
#> "01122" "032" "02111" "02111"
#> ola_mES_2i_4_58.counts ola_mES_2i_4_59.counts ola_mES_2i_4_6.counts ola_mES_2i_4_60.counts
#> "02111" "02111" "02111" "0221"
#> ola_mES_2i_4_61.counts ola_mES_2i_4_62.counts ola_mES_2i_4_63.counts ola_mES_2i_4_64.counts
#> "02111" "032" "0221" "0212"
#> ola_mES_2i_4_65.counts ola_mES_2i_4_66.counts ola_mES_2i_4_67.counts ola_mES_2i_4_68.counts
#> "02111" "02111" "01122" "0221"
#> ola_mES_2i_4_69.counts ola_mES_2i_4_7.counts ola_mES_2i_4_70.counts ola_mES_2i_4_71.counts
#> "02111" "023" "0221" "02111"
#> ola_mES_2i_4_72.counts ola_mES_2i_4_73.counts ola_mES_2i_4_75.counts ola_mES_2i_4_77.counts
#> "023" "02111" "0222" "02112"
#> ola_mES_2i_4_78.counts ola_mES_2i_4_80.counts ola_mES_2i_4_81.counts ola_mES_2i_4_83.counts
#> "023" "02111" "02111" "023"
#> ola_mES_2i_4_84.counts ola_mES_2i_4_88.counts ola_mES_2i_4_89.counts ola_mES_2i_4_9.counts
#> "023" "0221" "0221" "023"
#> ola_mES_2i_4_90.counts ola_mES_2i_4_91.counts ola_mES_2i_4_94.counts ola_mES_2i_4_95.counts
#> "02111" "0212" "0221" "0222"
#> ola_mES_2i_4_96.counts ola_mES_2i_5_1.counts ola_mES_2i_5_10.counts ola_mES_2i_5_11.counts
#> "0222" "0221" "023" "023"
#> ola_mES_2i_5_12.counts ola_mES_2i_5_13.counts ola_mES_2i_5_14.counts ola_mES_2i_5_15.counts
#> "023" "023" "0212" "0212"
#> ola_mES_2i_5_16.counts ola_mES_2i_5_17.counts ola_mES_2i_5_18.counts ola_mES_2i_5_19.counts
#> "0212" "0212" "023" "02111"
#> ola_mES_2i_5_2.counts ola_mES_2i_5_20.counts ola_mES_2i_5_21.counts ola_mES_2i_5_23.counts
#> "0221" "0212" "02111" "023"
#> ola_mES_2i_5_24.counts ola_mES_2i_5_25.counts ola_mES_2i_5_26.counts ola_mES_2i_5_27.counts
#> "0212" "02111" "0212" "0212"
#> ola_mES_2i_5_28.counts ola_mES_2i_5_29.counts ola_mES_2i_5_3.counts ola_mES_2i_5_30.counts
#> "0221" "0221" "0221" "0212"
#> ola_mES_2i_5_31.counts ola_mES_2i_5_32.counts ola_mES_2i_5_34.counts ola_mES_2i_5_35.counts
#> "0212" "0212" "0212" "02111"
#> ola_mES_2i_5_36.counts ola_mES_2i_5_38.counts ola_mES_2i_5_39.counts ola_mES_2i_5_4.counts
#> "0212" "0212" "0212" "0221"
#> ola_mES_2i_5_40.counts ola_mES_2i_5_41.counts ola_mES_2i_5_42.counts ola_mES_2i_5_44.counts
#> "02111" "01122" "0212" "0212"
#> ola_mES_2i_5_45.counts ola_mES_2i_5_46.counts ola_mES_2i_5_47.counts ola_mES_2i_5_48.counts
#> "02111" "02111" "02111" "02111"
#> ola_mES_2i_5_49.counts ola_mES_2i_5_5.counts ola_mES_2i_5_51.counts ola_mES_2i_5_52.counts
#> "02111" "023" "0212" "0221"
#> ola_mES_2i_5_53.counts ola_mES_2i_5_54.counts ola_mES_2i_5_55.counts ola_mES_2i_5_56.counts
#> "0212" "02111" "0212" "0212"
#> ola_mES_2i_5_57.counts ola_mES_2i_5_58.counts ola_mES_2i_5_59.counts ola_mES_2i_5_6.counts
#> "0212" "0212" "0212" "0221"
#> ola_mES_2i_5_61.counts ola_mES_2i_5_62.counts ola_mES_2i_5_63.counts ola_mES_2i_5_65.counts
#> "0212" "0221" "02111" "023"
#> ola_mES_2i_5_66.counts ola_mES_2i_5_67.counts ola_mES_2i_5_68.counts ola_mES_2i_5_69.counts
#> "0221" "0212" "0212" "0212"
#> ola_mES_2i_5_7.counts ola_mES_2i_5_70.counts ola_mES_2i_5_72.counts ola_mES_2i_5_73.counts
#> "0212" "02111" "0212" "023"
#> ola_mES_2i_5_74.counts ola_mES_2i_5_75.counts ola_mES_2i_5_76.counts ola_mES_2i_5_77.counts
#> "02111" "023" "023" "023"
#> ola_mES_2i_5_78.counts ola_mES_2i_5_8.counts ola_mES_2i_5_81.counts ola_mES_2i_5_82.counts
#> "0212" "02112" "0221" "0212"
#> ola_mES_2i_5_83.counts ola_mES_2i_5_85.counts ola_mES_2i_5_86.counts ola_mES_2i_5_87.counts
#> "023" "0221" "023" "023"
#> ola_mES_2i_5_88.counts ola_mES_2i_5_9.counts ola_mES_2i_5_90.counts ola_mES_2i_5_92.counts
#> "023" "0222" "02112" "0212"
#> ola_mES_2i_5_94.counts ola_mES_2i_5_95.counts ola_mES_2i_5_96.counts ola_mES_a2i_2_1.counts
#> "0212" "0212" "0212" "0132"
#> ola_mES_a2i_2_10.counts ola_mES_a2i_2_11.counts ola_mES_a2i_2_12.counts ola_mES_a2i_2_13.counts
#> "01121" "0132" "01121" "01111"
#> ola_mES_a2i_2_14.counts ola_mES_a2i_2_15.counts ola_mES_a2i_2_16.counts ola_mES_a2i_2_17.counts
#> "0132" "0132" "0132" "0132"
#> ola_mES_a2i_2_18.counts ola_mES_a2i_2_19.counts ola_mES_a2i_2_2.counts ola_mES_a2i_2_20.counts
#> "0132" "0132" "0132" "01121"
#> ola_mES_a2i_2_21.counts ola_mES_a2i_2_22.counts ola_mES_a2i_2_23.counts ola_mES_a2i_2_24.counts
#> "0132" "0132" "0132" "0132"
#> ola_mES_a2i_2_25.counts ola_mES_a2i_2_26.counts ola_mES_a2i_2_27.counts ola_mES_a2i_2_28.counts
#> "0113" "032" "01121" "01121"
#> ola_mES_a2i_2_29.counts ola_mES_a2i_2_3.counts ola_mES_a2i_2_30.counts ola_mES_a2i_2_31.counts
#> "01121" "0132" "0222" "032"
#> ola_mES_a2i_2_32.counts ola_mES_a2i_2_33.counts ola_mES_a2i_2_34.counts ola_mES_a2i_2_35.counts
#> "032" "01121" "01121" "0312"
#> ola_mES_a2i_2_36.counts ola_mES_a2i_2_37.counts ola_mES_a2i_2_38.counts ola_mES_a2i_2_39.counts
#> "0222" "032" "01121" "032"
#> ola_mES_a2i_2_4.counts ola_mES_a2i_2_40.counts ola_mES_a2i_2_41.counts ola_mES_a2i_2_42.counts
#> "01121" "01121" "0132" "0132"
#> ola_mES_a2i_2_43.counts ola_mES_a2i_2_44.counts ola_mES_a2i_2_45.counts ola_mES_a2i_2_46.counts
#> "01111" "0113" "0132" "0132"
#> ola_mES_a2i_2_47.counts ola_mES_a2i_2_48.counts ola_mES_a2i_2_49.counts ola_mES_a2i_2_5.counts
#> "01121" "01121" "0113" "0132"
#> ola_mES_a2i_2_50.counts ola_mES_a2i_2_51.counts ola_mES_a2i_2_52.counts ola_mES_a2i_2_53.counts
#> "032" "01121" "01121" "032"
#> ola_mES_a2i_2_54.counts ola_mES_a2i_2_55.counts ola_mES_a2i_2_56.counts ola_mES_a2i_2_57.counts
#> "01121" "0132" "01111" "0132"
#> ola_mES_a2i_2_59.counts ola_mES_a2i_2_6.counts ola_mES_a2i_2_60.counts ola_mES_a2i_2_61.counts
#> "0132" "032" "01122" "01111"
#> ola_mES_a2i_2_62.counts ola_mES_a2i_2_63.counts ola_mES_a2i_2_64.counts ola_mES_a2i_2_65.counts
#> "01121" "01111" "01121" "01111"
#> ola_mES_a2i_2_66.counts ola_mES_a2i_2_67.counts ola_mES_a2i_2_68.counts ola_mES_a2i_2_69.counts
#> "0132" "01121" "0113" "032"
#> ola_mES_a2i_2_7.counts ola_mES_a2i_2_70.counts ola_mES_a2i_2_71.counts ola_mES_a2i_2_72.counts
#> "01121" "01121" "0132" "01121"
#> ola_mES_a2i_2_73.counts ola_mES_a2i_2_74.counts ola_mES_a2i_2_75.counts ola_mES_a2i_2_76.counts
#> "0113" "01111" "01121" "01111"
#> ola_mES_a2i_2_77.counts ola_mES_a2i_2_78.counts ola_mES_a2i_2_8.counts ola_mES_a2i_2_80.counts
#> "01121" "0132" "0132" "0113"
#> ola_mES_a2i_2_81.counts ola_mES_a2i_2_82.counts ola_mES_a2i_2_83.counts ola_mES_a2i_2_84.counts
#> "01121" "0132" "0132" "01121"
#> ola_mES_a2i_2_86.counts ola_mES_a2i_2_87.counts ola_mES_a2i_2_88.counts ola_mES_a2i_2_89.counts
#> "0113" "01121" "01121" "0223"
#> ola_mES_a2i_2_9.counts ola_mES_a2i_2_90.counts ola_mES_a2i_2_91.counts ola_mES_a2i_2_92.counts
#> "0113" "0132" "032" "032"
#> ola_mES_a2i_2_93.counts ola_mES_a2i_2_94.counts ola_mES_a2i_2_95.counts ola_mES_a2i_2_96.counts
#> "01111" "0132" "0132" "0132"
#> ola_mES_a2i_3_10.counts ola_mES_a2i_3_11.counts ola_mES_a2i_3_13.counts ola_mES_a2i_3_16.counts
#> "02112" "0223" "01121" "0113"
#> ola_mES_a2i_3_17.counts ola_mES_a2i_3_18.counts ola_mES_a2i_3_2.counts ola_mES_a2i_3_20.counts
#> "032" "032" "032" "032"
#> ola_mES_a2i_3_21.counts ola_mES_a2i_3_22.counts ola_mES_a2i_3_24.counts ola_mES_a2i_3_25.counts
#> "0223" "0223" "0223" "0113"
#> ola_mES_a2i_3_26.counts ola_mES_a2i_3_28.counts ola_mES_a2i_3_29.counts ola_mES_a2i_3_3.counts
#> "032" "0113" "0113" "02112"
#> ola_mES_a2i_3_30.counts ola_mES_a2i_3_31.counts ola_mES_a2i_3_33.counts ola_mES_a2i_3_34.counts
#> "032" "032" "0223" "0113"
#> ola_mES_a2i_3_37.counts ola_mES_a2i_3_38.counts ola_mES_a2i_3_39.counts ola_mES_a2i_3_4.counts
#> "01122" "0113" "0113" "0223"
#> ola_mES_a2i_3_40.counts ola_mES_a2i_3_41.counts ola_mES_a2i_3_42.counts ola_mES_a2i_3_44.counts
#> "0113" "032" "032" "0113"
#> ola_mES_a2i_3_45.counts ola_mES_a2i_3_47.counts ola_mES_a2i_3_49.counts ola_mES_a2i_3_50.counts
#> "032" "032" "0113" "0113"
#> ola_mES_a2i_3_52.counts ola_mES_a2i_3_54.counts ola_mES_a2i_3_56.counts ola_mES_a2i_3_58.counts
#> "032" "0113" "032" "0223"
#> ola_mES_a2i_3_59.counts ola_mES_a2i_3_6.counts ola_mES_a2i_3_62.counts ola_mES_a2i_3_64.counts
#> "0132" "0113" "01121" "01122"
#> ola_mES_a2i_3_65.counts ola_mES_a2i_3_66.counts ola_mES_a2i_3_67.counts ola_mES_a2i_3_68.counts
#> "0132" "0113" "032" "0222"
#> ola_mES_a2i_3_69.counts ola_mES_a2i_3_7.counts ola_mES_a2i_3_71.counts ola_mES_a2i_3_73.counts
#> "0113" "032" "0132" "01121"
#> ola_mES_a2i_3_75.counts ola_mES_a2i_3_76.counts ola_mES_a2i_3_77.counts ola_mES_a2i_3_8.counts
#> "032" "02112" "0113" "032"
#> ola_mES_a2i_3_80.counts ola_mES_a2i_3_81.counts ola_mES_a2i_3_82.counts ola_mES_a2i_3_83.counts
#> "0222" "032" "0113" "01121"
#> ola_mES_a2i_3_84.counts ola_mES_a2i_3_85.counts ola_mES_a2i_3_86.counts ola_mES_a2i_3_89.counts
#> "0113" "01121" "0113" "0132"
#> ola_mES_a2i_3_9.counts ola_mES_a2i_3_90.counts ola_mES_a2i_3_91.counts ola_mES_a2i_3_92.counts
#> "0223" "0113" "0223" "01121"
#> ola_mES_a2i_3_94.counts ola_mES_a2i_3_96.counts ola_mES_lif_1_1.counts ola_mES_lif_1_10.counts
#> "0312" "01121" "0141" "033"
#> ola_mES_lif_1_11.counts ola_mES_lif_1_12.counts ola_mES_lif_1_13.counts ola_mES_lif_1_14.counts
#> "012" "033" "0141" "033"
#> ola_mES_lif_1_15.counts ola_mES_lif_1_17.counts ola_mES_lif_1_18.counts ola_mES_lif_1_19.counts
#> "0141" "0312" "0141" "0142"
#> ola_mES_lif_1_2.counts ola_mES_lif_1_20.counts ola_mES_lif_1_21.counts ola_mES_lif_1_22.counts
#> "033" "0142" "033" "0142"
#> ola_mES_lif_1_23.counts ola_mES_lif_1_24.counts ola_mES_lif_1_25.counts ola_mES_lif_1_26.counts
#> "0312" "0142" "0142" "0142"
#> ola_mES_lif_1_28.counts ola_mES_lif_1_29.counts ola_mES_lif_1_3.counts ola_mES_lif_1_30.counts
#> "0142" "0313" "0141" "0142"
#> ola_mES_lif_1_31.counts ola_mES_lif_1_32.counts ola_mES_lif_1_33.counts ola_mES_lif_1_34.counts
#> "0313" "0142" "0142" "0141"
#> ola_mES_lif_1_35.counts ola_mES_lif_1_36.counts ola_mES_lif_1_37.counts ola_mES_lif_1_38.counts
#> "0312" "0142" "0142" "0142"
#> ola_mES_lif_1_39.counts ola_mES_lif_1_4.counts ola_mES_lif_1_41.counts ola_mES_lif_1_42.counts
#> "0142" "0143" "0142" "0142"
#> ola_mES_lif_1_43.counts ola_mES_lif_1_44.counts ola_mES_lif_1_45.counts ola_mES_lif_1_46.counts
#> "0142" "0141" "0142" "0142"
#> ola_mES_lif_1_47.counts ola_mES_lif_1_48.counts ola_mES_lif_1_49.counts ola_mES_lif_1_5.counts
#> "0141" "0141" "0141" "012"
#> ola_mES_lif_1_50.counts ola_mES_lif_1_51.counts ola_mES_lif_1_52.counts ola_mES_lif_1_53.counts
#> "0142" "012" "012" "0223"
#> ola_mES_lif_1_54.counts ola_mES_lif_1_55.counts ola_mES_lif_1_56.counts ola_mES_lif_1_57.counts
#> "0141" "012" "0222" "0223"
#> ola_mES_lif_1_58.counts ola_mES_lif_1_59.counts ola_mES_lif_1_6.counts ola_mES_lif_1_60.counts
#> "0141" "012" "0142" "0141"
#> ola_mES_lif_1_61.counts ola_mES_lif_1_62.counts ola_mES_lif_1_63.counts ola_mES_lif_1_64.counts
#> "0141" "0142" "0312" "0312"
#> ola_mES_lif_1_65.counts ola_mES_lif_1_66.counts ola_mES_lif_1_67.counts ola_mES_lif_1_68.counts
#> "0312" "0141" "0141" "0142"
#> ola_mES_lif_1_69.counts ola_mES_lif_1_7.counts ola_mES_lif_1_70.counts ola_mES_lif_1_72.counts
#> "0312" "0141" "033" "0141"
#> ola_mES_lif_1_73.counts ola_mES_lif_1_74.counts ola_mES_lif_1_75.counts ola_mES_lif_1_79.counts
#> "0142" "0312" "0312" "033"
#> ola_mES_lif_1_8.counts ola_mES_lif_1_80.counts ola_mES_lif_1_81.counts ola_mES_lif_1_82.counts
#> "0142" "0141" "0142" "0312"
#> ola_mES_lif_1_86.counts ola_mES_lif_1_9.counts ola_mES_lif_1_91.counts ola_mES_lif_1_92.counts
#> "033" "0141" "0142" "0142"
#> ola_mES_lif_1_93.counts ola_mES_lif_1_95.counts ola_mES_lif_1_96.counts ola_mES_lif_2_1.counts
#> "033" "0141" "0141" "033"
#> ola_mES_lif_2_10.counts ola_mES_lif_2_11.counts ola_mES_lif_2_12.counts ola_mES_lif_2_13.counts
#> "012" "012" "0223" "012"
#> ola_mES_lif_2_14.counts ola_mES_lif_2_15.counts ola_mES_lif_2_16.counts ola_mES_lif_2_17.counts
#> "012" "012" "012" "012"
#> ola_mES_lif_2_18.counts ola_mES_lif_2_19.counts ola_mES_lif_2_2.counts ola_mES_lif_2_20.counts
#> "012" "012" "012" "033"
#> ola_mES_lif_2_21.counts ola_mES_lif_2_23.counts ola_mES_lif_2_25.counts ola_mES_lif_2_26.counts
#> "012" "012" "033" "012"
#> ola_mES_lif_2_27.counts ola_mES_lif_2_28.counts ola_mES_lif_2_29.counts ola_mES_lif_2_3.counts
#> "033" "012" "012" "012"
#> ola_mES_lif_2_30.counts ola_mES_lif_2_31.counts ola_mES_lif_2_32.counts ola_mES_lif_2_33.counts
#> "012" "033" "012" "033"
#> ola_mES_lif_2_34.counts ola_mES_lif_2_35.counts ola_mES_lif_2_36.counts ola_mES_lif_2_37.counts
#> "012" "012" "034" "033"
#> ola_mES_lif_2_38.counts ola_mES_lif_2_39.counts ola_mES_lif_2_4.counts ola_mES_lif_2_40.counts
#> "012" "012" "012" "012"
#> ola_mES_lif_2_41.counts ola_mES_lif_2_42.counts ola_mES_lif_2_43.counts ola_mES_lif_2_44.counts
#> "033" "012" "012" "012"
#> ola_mES_lif_2_45.counts ola_mES_lif_2_46.counts ola_mES_lif_2_47.counts ola_mES_lif_2_48.counts
#> "034" "012" "033" "0223"
#> ola_mES_lif_2_49.counts ola_mES_lif_2_5.counts ola_mES_lif_2_50.counts ola_mES_lif_2_51.counts
#> "012" "012" "012" "012"
#> ola_mES_lif_2_52.counts ola_mES_lif_2_53.counts ola_mES_lif_2_54.counts ola_mES_lif_2_55.counts
#> "012" "012" "012" "012"
#> ola_mES_lif_2_56.counts ola_mES_lif_2_57.counts ola_mES_lif_2_58.counts ola_mES_lif_2_59.counts
#> "012" "034" "012" "012"
#> ola_mES_lif_2_6.counts ola_mES_lif_2_60.counts ola_mES_lif_2_61.counts ola_mES_lif_2_63.counts
#> "012" "033" "0141" "012"
#> ola_mES_lif_2_64.counts ola_mES_lif_2_65.counts ola_mES_lif_2_66.counts ola_mES_lif_2_67.counts
#> "012" "012" "012" "012"
#> ola_mES_lif_2_68.counts ola_mES_lif_2_69.counts ola_mES_lif_2_7.counts ola_mES_lif_2_70.counts
#> "012" "0223" "012" "012"
#> ola_mES_lif_2_71.counts ola_mES_lif_2_72.counts ola_mES_lif_2_73.counts ola_mES_lif_2_74.counts
#> "012" "0141" "0223" "012"
#> ola_mES_lif_2_75.counts ola_mES_lif_2_76.counts ola_mES_lif_2_77.counts ola_mES_lif_2_79.counts
#> "012" "012" "012" "033"
#> ola_mES_lif_2_8.counts ola_mES_lif_2_80.counts ola_mES_lif_2_81.counts ola_mES_lif_2_82.counts
#> "012" "012" "033" "012"
#> ola_mES_lif_2_83.counts ola_mES_lif_2_84.counts ola_mES_lif_2_86.counts ola_mES_lif_2_87.counts
#> "012" "012" "033" "012"
#> ola_mES_lif_2_89.counts ola_mES_lif_2_9.counts ola_mES_lif_2_90.counts ola_mES_lif_2_91.counts
#> "012" "033" "033" "033"
#> ola_mES_lif_2_92.counts ola_mES_lif_2_93.counts ola_mES_lif_2_94.counts ola_mES_lif_2_95.counts
#> "033" "012" "012" "012"
#> ola_mES_lif_2_96.counts ola_mES_lif_3_1.counts ola_mES_lif_3_10.counts ola_mES_lif_3_11.counts
#> "033" "0143" "012" "033"
#> ola_mES_lif_3_12.counts ola_mES_lif_3_13.counts ola_mES_lif_3_14.counts ola_mES_lif_3_15.counts
#> "0223" "0143" "034" "034"
#> ola_mES_lif_3_16.counts ola_mES_lif_3_17.counts ola_mES_lif_3_19.counts ola_mES_lif_3_2.counts
#> "034" "034" "034" "0222"
#> ola_mES_lif_3_20.counts ola_mES_lif_3_21.counts ola_mES_lif_3_22.counts ola_mES_lif_3_23.counts
#> "0311" "0222" "0313" "0311"
#> ola_mES_lif_3_24.counts ola_mES_lif_3_26.counts ola_mES_lif_3_27.counts ola_mES_lif_3_28.counts
#> "0311" "0313" "0143" "034"
#> ola_mES_lif_3_29.counts ola_mES_lif_3_3.counts ola_mES_lif_3_30.counts ola_mES_lif_3_31.counts
#> "033" "0143" "0143" "034"
#> ola_mES_lif_3_32.counts ola_mES_lif_3_33.counts ola_mES_lif_3_34.counts ola_mES_lif_3_35.counts
#> "0143" "034" "034" "034"
#> ola_mES_lif_3_36.counts ola_mES_lif_3_37.counts ola_mES_lif_3_38.counts ola_mES_lif_3_39.counts
#> "034" "033" "034" "034"
#> ola_mES_lif_3_4.counts ola_mES_lif_3_40.counts ola_mES_lif_3_42.counts ola_mES_lif_3_45.counts
#> "0143" "0143" "0141" "034"
#> ola_mES_lif_3_46.counts ola_mES_lif_3_47.counts ola_mES_lif_3_48.counts ola_mES_lif_3_49.counts
#> "034" "034" "033" "0143"
#> ola_mES_lif_3_5.counts ola_mES_lif_3_50.counts ola_mES_lif_3_51.counts ola_mES_lif_3_52.counts
#> "012" "0141" "0143" "0143"
#> ola_mES_lif_3_53.counts ola_mES_lif_3_54.counts ola_mES_lif_3_56.counts ola_mES_lif_3_57.counts
#> "0143" "0141" "012" "033"
#> ola_mES_lif_3_58.counts ola_mES_lif_3_59.counts ola_mES_lif_3_6.counts ola_mES_lif_3_60.counts
#> "0143" "0143" "0141" "0143"
#> ola_mES_lif_3_61.counts ola_mES_lif_3_63.counts ola_mES_lif_3_64.counts ola_mES_lif_3_65.counts
#> "0143" "0143" "0143" "0143"
#> ola_mES_lif_3_69.counts ola_mES_lif_3_70.counts ola_mES_lif_3_71.counts ola_mES_lif_3_72.counts
#> "034" "034" "0143" "034"
#> ola_mES_lif_3_73.counts ola_mES_lif_3_74.counts ola_mES_lif_3_75.counts ola_mES_lif_3_76.counts
#> "0311" "0311" "0313" "0311"
#> ola_mES_lif_3_77.counts ola_mES_lif_3_78.counts ola_mES_lif_3_8.counts ola_mES_lif_3_80.counts
#> "0222" "0311" "0143" "0313"
#> ola_mES_lif_3_82.counts ola_mES_lif_3_83.counts ola_mES_lif_3_84.counts ola_mES_lif_3_87.counts
#> "0313" "0313" "0313" "0312"
#> ola_mES_lif_3_88.counts ola_mES_lif_3_89.counts ola_mES_lif_3_9.counts ola_mES_lif_3_90.counts
#> "0311" "0222" "0223" "034"
#> ola_mES_lif_3_92.counts ola_mES_lif_3_94.counts ola_mES_lif_3_95.counts ola_mES_lif_3_96.counts
#> "0311" "0311" "0311" "0311"
get_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 487))
#> ola_mES_2i_2_1.counts ola_mES_2i_2_10.counts ola_mES_2i_2_11.counts ola_mES_2i_2_12.counts
#> "01112" "0131" "0131" "0131"
#> ola_mES_2i_2_13.counts ola_mES_2i_2_14.counts ola_mES_2i_2_15.counts ola_mES_2i_2_16.counts
#> "0133" "01112" "0133" "0133"
#> ola_mES_2i_2_17.counts ola_mES_2i_2_18.counts ola_mES_2i_2_19.counts ola_mES_2i_2_2.counts
#> "0133" "0131" "0131" "01112"
#> ola_mES_2i_2_20.counts ola_mES_2i_2_21.counts ola_mES_2i_2_22.counts ola_mES_2i_2_23.counts
#> "01112" "0131" "0133" "0131"
#> ola_mES_2i_2_24.counts ola_mES_2i_2_25.counts ola_mES_2i_2_26.counts ola_mES_2i_2_27.counts
#> "0131" "01112" "0133" "01112"
#> ola_mES_2i_2_28.counts ola_mES_2i_2_29.counts ola_mES_2i_2_3.counts ola_mES_2i_2_30.counts
#> "01112" "0133" "01112" "0131"
#> ola_mES_2i_2_31.counts ola_mES_2i_2_32.counts ola_mES_2i_2_33.counts ola_mES_2i_2_34.counts
#> "0131" "0133" "0131" "0131"
#> ola_mES_2i_2_35.counts ola_mES_2i_2_36.counts ola_mES_2i_2_37.counts ola_mES_2i_2_38.counts
#> "0131" "0131" "01112" "01112"
#> ola_mES_2i_2_39.counts ola_mES_2i_2_4.counts ola_mES_2i_2_40.counts ola_mES_2i_2_41.counts
#> "0133" "0131" "0131" "0131"
#> ola_mES_2i_2_42.counts ola_mES_2i_2_43.counts ola_mES_2i_2_44.counts ola_mES_2i_2_46.counts
#> "0131" "01112" "0131" "0131"
#> ola_mES_2i_2_47.counts ola_mES_2i_2_48.counts ola_mES_2i_2_49.counts ola_mES_2i_2_5.counts
#> "0131" "0131" "01112" "0133"
#> ola_mES_2i_2_50.counts ola_mES_2i_2_51.counts ola_mES_2i_2_52.counts ola_mES_2i_2_53.counts
#> "01112" "0131" "0133" "0133"
#> ola_mES_2i_2_54.counts ola_mES_2i_2_55.counts ola_mES_2i_2_56.counts ola_mES_2i_2_57.counts
#> "0131" "0133" "0133" "0133"
#> ola_mES_2i_2_58.counts ola_mES_2i_2_59.counts ola_mES_2i_2_6.counts ola_mES_2i_2_60.counts
#> "0131" "0131" "0133" "0131"
#> ola_mES_2i_2_61.counts ola_mES_2i_2_62.counts ola_mES_2i_2_63.counts ola_mES_2i_2_64.counts
#> "01112" "032" "01112" "0133"
#> ola_mES_2i_2_66.counts ola_mES_2i_2_67.counts ola_mES_2i_2_68.counts ola_mES_2i_2_69.counts
#> "0131" "0131" "01112" "0131"
#> ola_mES_2i_2_7.counts ola_mES_2i_2_72.counts ola_mES_2i_2_73.counts ola_mES_2i_2_74.counts
#> "0133" "0133" "01112" "0133"
#> ola_mES_2i_2_75.counts ola_mES_2i_2_76.counts ola_mES_2i_2_79.counts ola_mES_2i_2_8.counts
#> "01112" "01112" "0131" "0133"
#> ola_mES_2i_2_80.counts ola_mES_2i_2_81.counts ola_mES_2i_2_85.counts ola_mES_2i_2_86.counts
#> "0133" "0131" "01112" "01112"
#> ola_mES_2i_2_87.counts ola_mES_2i_2_90.counts ola_mES_2i_2_91.counts ola_mES_2i_2_93.counts
#> "032" "0131" "0133" "0131"
#> ola_mES_2i_2_94.counts ola_mES_2i_2_95.counts ola_mES_2i_3_10.counts ola_mES_2i_3_11.counts
#> "0131" "0133" "0222" "023"
#> ola_mES_2i_3_12.counts ola_mES_2i_3_13.counts ola_mES_2i_3_15.counts ola_mES_2i_3_16.counts
#> "0221" "0221" "0221" "0211"
#> ola_mES_2i_3_17.counts ola_mES_2i_3_18.counts ola_mES_2i_3_19.counts ola_mES_2i_3_2.counts
#> "032" "032" "0221" "0221"
#> ola_mES_2i_3_20.counts ola_mES_2i_3_21.counts ola_mES_2i_3_23.counts ola_mES_2i_3_25.counts
#> "0221" "0222" "0211" "023"
#> ola_mES_2i_3_26.counts ola_mES_2i_3_28.counts ola_mES_2i_3_30.counts ola_mES_2i_3_31.counts
#> "0221" "0211" "0221" "032"
#> ola_mES_2i_3_33.counts ola_mES_2i_3_36.counts ola_mES_2i_3_37.counts ola_mES_2i_3_38.counts
#> "0221" "0211" "0221" "0221"
#> ola_mES_2i_3_39.counts ola_mES_2i_3_4.counts ola_mES_2i_3_41.counts ola_mES_2i_3_42.counts
#> "0222" "0211" "0222" "0221"
#> ola_mES_2i_3_43.counts ola_mES_2i_3_45.counts ola_mES_2i_3_46.counts ola_mES_2i_3_47.counts
#> "0211" "0221" "0221" "0221"
#> ola_mES_2i_3_49.counts ola_mES_2i_3_5.counts ola_mES_2i_3_52.counts ola_mES_2i_3_54.counts
#> "032" "0221" "0221" "0222"
#> ola_mES_2i_3_56.counts ola_mES_2i_3_57.counts ola_mES_2i_3_58.counts ola_mES_2i_3_6.counts
#> "0221" "0221" "0221" "0222"
#> ola_mES_2i_3_62.counts ola_mES_2i_3_65.counts ola_mES_2i_3_66.counts ola_mES_2i_3_69.counts
#> "032" "0221" "032" "0222"
#> ola_mES_2i_3_7.counts ola_mES_2i_3_73.counts ola_mES_2i_3_76.counts ola_mES_2i_3_77.counts
#> "0221" "0221" "0221" "0222"
#> ola_mES_2i_3_78.counts ola_mES_2i_3_79.counts ola_mES_2i_3_8.counts ola_mES_2i_3_80.counts
#> "032" "0221" "0221" "0221"
#> ola_mES_2i_3_82.counts ola_mES_2i_3_83.counts ola_mES_2i_3_84.counts ola_mES_2i_3_87.counts
#> "0221" "0221" "0211" "0222"
#> ola_mES_2i_3_88.counts ola_mES_2i_3_90.counts ola_mES_2i_3_91.counts ola_mES_2i_3_92.counts
#> "0221" "0211" "0222" "0221"
#> ola_mES_2i_3_96.counts ola_mES_2i_4_1.counts ola_mES_2i_4_10.counts ola_mES_2i_4_11.counts
#> "0222" "0212" "0212" "0212"
#> ola_mES_2i_4_13.counts ola_mES_2i_4_17.counts ola_mES_2i_4_18.counts ola_mES_2i_4_2.counts
#> "0211" "0211" "023" "0132"
#> ola_mES_2i_4_20.counts ola_mES_2i_4_21.counts ola_mES_2i_4_22.counts ola_mES_2i_4_23.counts
#> "0211" "0211" "023" "0221"
#> ola_mES_2i_4_24.counts ola_mES_2i_4_25.counts ola_mES_2i_4_26.counts ola_mES_2i_4_27.counts
#> "0211" "0212" "0212" "023"
#> ola_mES_2i_4_29.counts ola_mES_2i_4_30.counts ola_mES_2i_4_31.counts ola_mES_2i_4_32.counts
#> "0212" "0211" "0212" "0211"
#> ola_mES_2i_4_33.counts ola_mES_2i_4_34.counts ola_mES_2i_4_35.counts ola_mES_2i_4_36.counts
#> "0211" "0211" "0212" "0211"
#> ola_mES_2i_4_37.counts ola_mES_2i_4_38.counts ola_mES_2i_4_39.counts ola_mES_2i_4_40.counts
#> "032" "01122" "0212" "01122"
#> ola_mES_2i_4_42.counts ola_mES_2i_4_43.counts ola_mES_2i_4_44.counts ola_mES_2i_4_46.counts
#> "032" "0211" "0212" "0211"
#> ola_mES_2i_4_49.counts ola_mES_2i_4_5.counts ola_mES_2i_4_50.counts ola_mES_2i_4_52.counts
#> "01122" "023" "0212" "0212"
#> ola_mES_2i_4_53.counts ola_mES_2i_4_54.counts ola_mES_2i_4_56.counts ola_mES_2i_4_57.counts
#> "01122" "032" "0211" "0211"
#> ola_mES_2i_4_58.counts ola_mES_2i_4_59.counts ola_mES_2i_4_6.counts ola_mES_2i_4_60.counts
#> "0211" "0211" "0211" "0221"
#> ola_mES_2i_4_61.counts ola_mES_2i_4_62.counts ola_mES_2i_4_63.counts ola_mES_2i_4_64.counts
#> "0211" "032" "0221" "0212"
#> ola_mES_2i_4_65.counts ola_mES_2i_4_66.counts ola_mES_2i_4_67.counts ola_mES_2i_4_68.counts
#> "0211" "0211" "01122" "0221"
#> ola_mES_2i_4_69.counts ola_mES_2i_4_7.counts ola_mES_2i_4_70.counts ola_mES_2i_4_71.counts
#> "0211" "023" "0221" "0211"
#> ola_mES_2i_4_72.counts ola_mES_2i_4_73.counts ola_mES_2i_4_75.counts ola_mES_2i_4_77.counts
#> "023" "0211" "0222" "0211"
#> ola_mES_2i_4_78.counts ola_mES_2i_4_80.counts ola_mES_2i_4_81.counts ola_mES_2i_4_83.counts
#> "023" "0211" "0211" "023"
#> ola_mES_2i_4_84.counts ola_mES_2i_4_88.counts ola_mES_2i_4_89.counts ola_mES_2i_4_9.counts
#> "023" "0221" "0221" "023"
#> ola_mES_2i_4_90.counts ola_mES_2i_4_91.counts ola_mES_2i_4_94.counts ola_mES_2i_4_95.counts
#> "0211" "0212" "0221" "0222"
#> ola_mES_2i_4_96.counts ola_mES_2i_5_1.counts ola_mES_2i_5_10.counts ola_mES_2i_5_11.counts
#> "0222" "0221" "023" "023"
#> ola_mES_2i_5_12.counts ola_mES_2i_5_13.counts ola_mES_2i_5_14.counts ola_mES_2i_5_15.counts
#> "023" "023" "0212" "0212"
#> ola_mES_2i_5_16.counts ola_mES_2i_5_17.counts ola_mES_2i_5_18.counts ola_mES_2i_5_19.counts
#> "0212" "0212" "023" "0211"
#> ola_mES_2i_5_2.counts ola_mES_2i_5_20.counts ola_mES_2i_5_21.counts ola_mES_2i_5_23.counts
#> "0221" "0212" "0211" "023"
#> ola_mES_2i_5_24.counts ola_mES_2i_5_25.counts ola_mES_2i_5_26.counts ola_mES_2i_5_27.counts
#> "0212" "0211" "0212" "0212"
#> ola_mES_2i_5_28.counts ola_mES_2i_5_29.counts ola_mES_2i_5_3.counts ola_mES_2i_5_30.counts
#> "0221" "0221" "0221" "0212"
#> ola_mES_2i_5_31.counts ola_mES_2i_5_32.counts ola_mES_2i_5_34.counts ola_mES_2i_5_35.counts
#> "0212" "0212" "0212" "0211"
#> ola_mES_2i_5_36.counts ola_mES_2i_5_38.counts ola_mES_2i_5_39.counts ola_mES_2i_5_4.counts
#> "0212" "0212" "0212" "0221"
#> ola_mES_2i_5_40.counts ola_mES_2i_5_41.counts ola_mES_2i_5_42.counts ola_mES_2i_5_44.counts
#> "0211" "01122" "0212" "0212"
#> ola_mES_2i_5_45.counts ola_mES_2i_5_46.counts ola_mES_2i_5_47.counts ola_mES_2i_5_48.counts
#> "0211" "0211" "0211" "0211"
#> ola_mES_2i_5_49.counts ola_mES_2i_5_5.counts ola_mES_2i_5_51.counts ola_mES_2i_5_52.counts
#> "0211" "023" "0212" "0221"
#> ola_mES_2i_5_53.counts ola_mES_2i_5_54.counts ola_mES_2i_5_55.counts ola_mES_2i_5_56.counts
#> "0212" "0211" "0212" "0212"
#> ola_mES_2i_5_57.counts ola_mES_2i_5_58.counts ola_mES_2i_5_59.counts ola_mES_2i_5_6.counts
#> "0212" "0212" "0212" "0221"
#> ola_mES_2i_5_61.counts ola_mES_2i_5_62.counts ola_mES_2i_5_63.counts ola_mES_2i_5_65.counts
#> "0212" "0221" "0211" "023"
#> ola_mES_2i_5_66.counts ola_mES_2i_5_67.counts ola_mES_2i_5_68.counts ola_mES_2i_5_69.counts
#> "0221" "0212" "0212" "0212"
#> ola_mES_2i_5_7.counts ola_mES_2i_5_70.counts ola_mES_2i_5_72.counts ola_mES_2i_5_73.counts
#> "0212" "0211" "0212" "023"
#> ola_mES_2i_5_74.counts ola_mES_2i_5_75.counts ola_mES_2i_5_76.counts ola_mES_2i_5_77.counts
#> "0211" "023" "023" "023"
#> ola_mES_2i_5_78.counts ola_mES_2i_5_8.counts ola_mES_2i_5_81.counts ola_mES_2i_5_82.counts
#> "0212" "0211" "0221" "0212"
#> ola_mES_2i_5_83.counts ola_mES_2i_5_85.counts ola_mES_2i_5_86.counts ola_mES_2i_5_87.counts
#> "023" "0221" "023" "023"
#> ola_mES_2i_5_88.counts ola_mES_2i_5_9.counts ola_mES_2i_5_90.counts ola_mES_2i_5_92.counts
#> "023" "0222" "0211" "0212"
#> ola_mES_2i_5_94.counts ola_mES_2i_5_95.counts ola_mES_2i_5_96.counts ola_mES_a2i_2_1.counts
#> "0212" "0212" "0212" "0132"
#> ola_mES_a2i_2_10.counts ola_mES_a2i_2_11.counts ola_mES_a2i_2_12.counts ola_mES_a2i_2_13.counts
#> "01121" "0132" "01121" "01111"
#> ola_mES_a2i_2_14.counts ola_mES_a2i_2_15.counts ola_mES_a2i_2_16.counts ola_mES_a2i_2_17.counts
#> "0132" "0132" "0132" "0132"
#> ola_mES_a2i_2_18.counts ola_mES_a2i_2_19.counts ola_mES_a2i_2_2.counts ola_mES_a2i_2_20.counts
#> "0132" "0132" "0132" "01121"
#> ola_mES_a2i_2_21.counts ola_mES_a2i_2_22.counts ola_mES_a2i_2_23.counts ola_mES_a2i_2_24.counts
#> "0132" "0132" "0132" "0132"
#> ola_mES_a2i_2_25.counts ola_mES_a2i_2_26.counts ola_mES_a2i_2_27.counts ola_mES_a2i_2_28.counts
#> "0113" "032" "01121" "01121"
#> ola_mES_a2i_2_29.counts ola_mES_a2i_2_3.counts ola_mES_a2i_2_30.counts ola_mES_a2i_2_31.counts
#> "01121" "0132" "0222" "032"
#> ola_mES_a2i_2_32.counts ola_mES_a2i_2_33.counts ola_mES_a2i_2_34.counts ola_mES_a2i_2_35.counts
#> "032" "01121" "01121" "0312"
#> ola_mES_a2i_2_36.counts ola_mES_a2i_2_37.counts ola_mES_a2i_2_38.counts ola_mES_a2i_2_39.counts
#> "0222" "032" "01121" "032"
#> ola_mES_a2i_2_4.counts ola_mES_a2i_2_40.counts ola_mES_a2i_2_41.counts ola_mES_a2i_2_42.counts
#> "01121" "01121" "0132" "0132"
#> ola_mES_a2i_2_43.counts ola_mES_a2i_2_44.counts ola_mES_a2i_2_45.counts ola_mES_a2i_2_46.counts
#> "01111" "0113" "0132" "0132"
#> ola_mES_a2i_2_47.counts ola_mES_a2i_2_48.counts ola_mES_a2i_2_49.counts ola_mES_a2i_2_5.counts
#> "01121" "01121" "0113" "0132"
#> ola_mES_a2i_2_50.counts ola_mES_a2i_2_51.counts ola_mES_a2i_2_52.counts ola_mES_a2i_2_53.counts
#> "032" "01121" "01121" "032"
#> ola_mES_a2i_2_54.counts ola_mES_a2i_2_55.counts ola_mES_a2i_2_56.counts ola_mES_a2i_2_57.counts
#> "01121" "0132" "01111" "0132"
#> ola_mES_a2i_2_59.counts ola_mES_a2i_2_6.counts ola_mES_a2i_2_60.counts ola_mES_a2i_2_61.counts
#> "0132" "032" "01122" "01111"
#> ola_mES_a2i_2_62.counts ola_mES_a2i_2_63.counts ola_mES_a2i_2_64.counts ola_mES_a2i_2_65.counts
#> "01121" "01111" "01121" "01111"
#> ola_mES_a2i_2_66.counts ola_mES_a2i_2_67.counts ola_mES_a2i_2_68.counts ola_mES_a2i_2_69.counts
#> "0132" "01121" "0113" "032"
#> ola_mES_a2i_2_7.counts ola_mES_a2i_2_70.counts ola_mES_a2i_2_71.counts ola_mES_a2i_2_72.counts
#> "01121" "01121" "0132" "01121"
#> ola_mES_a2i_2_73.counts ola_mES_a2i_2_74.counts ola_mES_a2i_2_75.counts ola_mES_a2i_2_76.counts
#> "0113" "01111" "01121" "01111"
#> ola_mES_a2i_2_77.counts ola_mES_a2i_2_78.counts ola_mES_a2i_2_8.counts ola_mES_a2i_2_80.counts
#> "01121" "0132" "0132" "0113"
#> ola_mES_a2i_2_81.counts ola_mES_a2i_2_82.counts ola_mES_a2i_2_83.counts ola_mES_a2i_2_84.counts
#> "01121" "0132" "0132" "01121"
#> ola_mES_a2i_2_86.counts ola_mES_a2i_2_87.counts ola_mES_a2i_2_88.counts ola_mES_a2i_2_89.counts
#> "0113" "01121" "01121" "0223"
#> ola_mES_a2i_2_9.counts ola_mES_a2i_2_90.counts ola_mES_a2i_2_91.counts ola_mES_a2i_2_92.counts
#> "0113" "0132" "032" "032"
#> ola_mES_a2i_2_93.counts ola_mES_a2i_2_94.counts ola_mES_a2i_2_95.counts ola_mES_a2i_2_96.counts
#> "01111" "0132" "0132" "0132"
#> ola_mES_a2i_3_10.counts ola_mES_a2i_3_11.counts ola_mES_a2i_3_13.counts ola_mES_a2i_3_16.counts
#> "0211" "0223" "01121" "0113"
#> ola_mES_a2i_3_17.counts ola_mES_a2i_3_18.counts ola_mES_a2i_3_2.counts ola_mES_a2i_3_20.counts
#> "032" "032" "032" "032"
#> ola_mES_a2i_3_21.counts ola_mES_a2i_3_22.counts ola_mES_a2i_3_24.counts ola_mES_a2i_3_25.counts
#> "0223" "0223" "0223" "0113"
#> ola_mES_a2i_3_26.counts ola_mES_a2i_3_28.counts ola_mES_a2i_3_29.counts ola_mES_a2i_3_3.counts
#> "032" "0113" "0113" "0211"
#> ola_mES_a2i_3_30.counts ola_mES_a2i_3_31.counts ola_mES_a2i_3_33.counts ola_mES_a2i_3_34.counts
#> "032" "032" "0223" "0113"
#> ola_mES_a2i_3_37.counts ola_mES_a2i_3_38.counts ola_mES_a2i_3_39.counts ola_mES_a2i_3_4.counts
#> "01122" "0113" "0113" "0223"
#> ola_mES_a2i_3_40.counts ola_mES_a2i_3_41.counts ola_mES_a2i_3_42.counts ola_mES_a2i_3_44.counts
#> "0113" "032" "032" "0113"
#> ola_mES_a2i_3_45.counts ola_mES_a2i_3_47.counts ola_mES_a2i_3_49.counts ola_mES_a2i_3_50.counts
#> "032" "032" "0113" "0113"
#> ola_mES_a2i_3_52.counts ola_mES_a2i_3_54.counts ola_mES_a2i_3_56.counts ola_mES_a2i_3_58.counts
#> "032" "0113" "032" "0223"
#> ola_mES_a2i_3_59.counts ola_mES_a2i_3_6.counts ola_mES_a2i_3_62.counts ola_mES_a2i_3_64.counts
#> "0132" "0113" "01121" "01122"
#> ola_mES_a2i_3_65.counts ola_mES_a2i_3_66.counts ola_mES_a2i_3_67.counts ola_mES_a2i_3_68.counts
#> "0132" "0113" "032" "0222"
#> ola_mES_a2i_3_69.counts ola_mES_a2i_3_7.counts ola_mES_a2i_3_71.counts ola_mES_a2i_3_73.counts
#> "0113" "032" "0132" "01121"
#> ola_mES_a2i_3_75.counts ola_mES_a2i_3_76.counts ola_mES_a2i_3_77.counts ola_mES_a2i_3_8.counts
#> "032" "0211" "0113" "032"
#> ola_mES_a2i_3_80.counts ola_mES_a2i_3_81.counts ola_mES_a2i_3_82.counts ola_mES_a2i_3_83.counts
#> "0222" "032" "0113" "01121"
#> ola_mES_a2i_3_84.counts ola_mES_a2i_3_85.counts ola_mES_a2i_3_86.counts ola_mES_a2i_3_89.counts
#> "0113" "01121" "0113" "0132"
#> ola_mES_a2i_3_9.counts ola_mES_a2i_3_90.counts ola_mES_a2i_3_91.counts ola_mES_a2i_3_92.counts
#> "0223" "0113" "0223" "01121"
#> ola_mES_a2i_3_94.counts ola_mES_a2i_3_96.counts ola_mES_lif_1_1.counts ola_mES_lif_1_10.counts
#> "0312" "01121" "0141" "033"
#> ola_mES_lif_1_11.counts ola_mES_lif_1_12.counts ola_mES_lif_1_13.counts ola_mES_lif_1_14.counts
#> "012" "033" "0141" "033"
#> ola_mES_lif_1_15.counts ola_mES_lif_1_17.counts ola_mES_lif_1_18.counts ola_mES_lif_1_19.counts
#> "0141" "0312" "0141" "0142"
#> ola_mES_lif_1_2.counts ola_mES_lif_1_20.counts ola_mES_lif_1_21.counts ola_mES_lif_1_22.counts
#> "033" "0142" "033" "0142"
#> ola_mES_lif_1_23.counts ola_mES_lif_1_24.counts ola_mES_lif_1_25.counts ola_mES_lif_1_26.counts
#> "0312" "0142" "0142" "0142"
#> ola_mES_lif_1_28.counts ola_mES_lif_1_29.counts ola_mES_lif_1_3.counts ola_mES_lif_1_30.counts
#> "0142" "0313" "0141" "0142"
#> ola_mES_lif_1_31.counts ola_mES_lif_1_32.counts ola_mES_lif_1_33.counts ola_mES_lif_1_34.counts
#> "0313" "0142" "0142" "0141"
#> ola_mES_lif_1_35.counts ola_mES_lif_1_36.counts ola_mES_lif_1_37.counts ola_mES_lif_1_38.counts
#> "0312" "0142" "0142" "0142"
#> ola_mES_lif_1_39.counts ola_mES_lif_1_4.counts ola_mES_lif_1_41.counts ola_mES_lif_1_42.counts
#> "0142" "0143" "0142" "0142"
#> ola_mES_lif_1_43.counts ola_mES_lif_1_44.counts ola_mES_lif_1_45.counts ola_mES_lif_1_46.counts
#> "0142" "0141" "0142" "0142"
#> ola_mES_lif_1_47.counts ola_mES_lif_1_48.counts ola_mES_lif_1_49.counts ola_mES_lif_1_5.counts
#> "0141" "0141" "0141" "012"
#> ola_mES_lif_1_50.counts ola_mES_lif_1_51.counts ola_mES_lif_1_52.counts ola_mES_lif_1_53.counts
#> "0142" "012" "012" "0223"
#> ola_mES_lif_1_54.counts ola_mES_lif_1_55.counts ola_mES_lif_1_56.counts ola_mES_lif_1_57.counts
#> "0141" "012" "0222" "0223"
#> ola_mES_lif_1_58.counts ola_mES_lif_1_59.counts ola_mES_lif_1_6.counts ola_mES_lif_1_60.counts
#> "0141" "012" "0142" "0141"
#> ola_mES_lif_1_61.counts ola_mES_lif_1_62.counts ola_mES_lif_1_63.counts ola_mES_lif_1_64.counts
#> "0141" "0142" "0312" "0312"
#> ola_mES_lif_1_65.counts ola_mES_lif_1_66.counts ola_mES_lif_1_67.counts ola_mES_lif_1_68.counts
#> "0312" "0141" "0141" "0142"
#> ola_mES_lif_1_69.counts ola_mES_lif_1_7.counts ola_mES_lif_1_70.counts ola_mES_lif_1_72.counts
#> "0312" "0141" "033" "0141"
#> ola_mES_lif_1_73.counts ola_mES_lif_1_74.counts ola_mES_lif_1_75.counts ola_mES_lif_1_79.counts
#> "0142" "0312" "0312" "033"
#> ola_mES_lif_1_8.counts ola_mES_lif_1_80.counts ola_mES_lif_1_81.counts ola_mES_lif_1_82.counts
#> "0142" "0141" "0142" "0312"
#> ola_mES_lif_1_86.counts ola_mES_lif_1_9.counts ola_mES_lif_1_91.counts ola_mES_lif_1_92.counts
#> "033" "0141" "0142" "0142"
#> ola_mES_lif_1_93.counts ola_mES_lif_1_95.counts ola_mES_lif_1_96.counts ola_mES_lif_2_1.counts
#> "033" "0141" "0141" "033"
#> ola_mES_lif_2_10.counts ola_mES_lif_2_11.counts ola_mES_lif_2_12.counts ola_mES_lif_2_13.counts
#> "012" "012" "0223" "012"
#> ola_mES_lif_2_14.counts ola_mES_lif_2_15.counts ola_mES_lif_2_16.counts ola_mES_lif_2_17.counts
#> "012" "012" "012" "012"
#> ola_mES_lif_2_18.counts ola_mES_lif_2_19.counts ola_mES_lif_2_2.counts ola_mES_lif_2_20.counts
#> "012" "012" "012" "033"
#> ola_mES_lif_2_21.counts ola_mES_lif_2_23.counts ola_mES_lif_2_25.counts ola_mES_lif_2_26.counts
#> "012" "012" "033" "012"
#> ola_mES_lif_2_27.counts ola_mES_lif_2_28.counts ola_mES_lif_2_29.counts ola_mES_lif_2_3.counts
#> "033" "012" "012" "012"
#> ola_mES_lif_2_30.counts ola_mES_lif_2_31.counts ola_mES_lif_2_32.counts ola_mES_lif_2_33.counts
#> "012" "033" "012" "033"
#> ola_mES_lif_2_34.counts ola_mES_lif_2_35.counts ola_mES_lif_2_36.counts ola_mES_lif_2_37.counts
#> "012" "012" "034" "033"
#> ola_mES_lif_2_38.counts ola_mES_lif_2_39.counts ola_mES_lif_2_4.counts ola_mES_lif_2_40.counts
#> "012" "012" "012" "012"
#> ola_mES_lif_2_41.counts ola_mES_lif_2_42.counts ola_mES_lif_2_43.counts ola_mES_lif_2_44.counts
#> "033" "012" "012" "012"
#> ola_mES_lif_2_45.counts ola_mES_lif_2_46.counts ola_mES_lif_2_47.counts ola_mES_lif_2_48.counts
#> "034" "012" "033" "0223"
#> ola_mES_lif_2_49.counts ola_mES_lif_2_5.counts ola_mES_lif_2_50.counts ola_mES_lif_2_51.counts
#> "012" "012" "012" "012"
#> ola_mES_lif_2_52.counts ola_mES_lif_2_53.counts ola_mES_lif_2_54.counts ola_mES_lif_2_55.counts
#> "012" "012" "012" "012"
#> ola_mES_lif_2_56.counts ola_mES_lif_2_57.counts ola_mES_lif_2_58.counts ola_mES_lif_2_59.counts
#> "012" "034" "012" "012"
#> ola_mES_lif_2_6.counts ola_mES_lif_2_60.counts ola_mES_lif_2_61.counts ola_mES_lif_2_63.counts
#> "012" "033" "0141" "012"
#> ola_mES_lif_2_64.counts ola_mES_lif_2_65.counts ola_mES_lif_2_66.counts ola_mES_lif_2_67.counts
#> "012" "012" "012" "012"
#> ola_mES_lif_2_68.counts ola_mES_lif_2_69.counts ola_mES_lif_2_7.counts ola_mES_lif_2_70.counts
#> "012" "0223" "012" "012"
#> ola_mES_lif_2_71.counts ola_mES_lif_2_72.counts ola_mES_lif_2_73.counts ola_mES_lif_2_74.counts
#> "012" "0141" "0223" "012"
#> ola_mES_lif_2_75.counts ola_mES_lif_2_76.counts ola_mES_lif_2_77.counts ola_mES_lif_2_79.counts
#> "012" "012" "012" "033"
#> ola_mES_lif_2_8.counts ola_mES_lif_2_80.counts ola_mES_lif_2_81.counts ola_mES_lif_2_82.counts
#> "012" "012" "033" "012"
#> ola_mES_lif_2_83.counts ola_mES_lif_2_84.counts ola_mES_lif_2_86.counts ola_mES_lif_2_87.counts
#> "012" "012" "033" "012"
#> ola_mES_lif_2_89.counts ola_mES_lif_2_9.counts ola_mES_lif_2_90.counts ola_mES_lif_2_91.counts
#> "012" "033" "033" "033"
#> ola_mES_lif_2_92.counts ola_mES_lif_2_93.counts ola_mES_lif_2_94.counts ola_mES_lif_2_95.counts
#> "033" "012" "012" "012"
#> ola_mES_lif_2_96.counts ola_mES_lif_3_1.counts ola_mES_lif_3_10.counts ola_mES_lif_3_11.counts
#> "033" "0143" "012" "033"
#> ola_mES_lif_3_12.counts ola_mES_lif_3_13.counts ola_mES_lif_3_14.counts ola_mES_lif_3_15.counts
#> "0223" "0143" "034" "034"
#> ola_mES_lif_3_16.counts ola_mES_lif_3_17.counts ola_mES_lif_3_19.counts ola_mES_lif_3_2.counts
#> "034" "034" "034" "0222"
#> ola_mES_lif_3_20.counts ola_mES_lif_3_21.counts ola_mES_lif_3_22.counts ola_mES_lif_3_23.counts
#> "0311" "0222" "0313" "0311"
#> ola_mES_lif_3_24.counts ola_mES_lif_3_26.counts ola_mES_lif_3_27.counts ola_mES_lif_3_28.counts
#> "0311" "0313" "0143" "034"
#> ola_mES_lif_3_29.counts ola_mES_lif_3_3.counts ola_mES_lif_3_30.counts ola_mES_lif_3_31.counts
#> "033" "0143" "0143" "034"
#> ola_mES_lif_3_32.counts ola_mES_lif_3_33.counts ola_mES_lif_3_34.counts ola_mES_lif_3_35.counts
#> "0143" "034" "034" "034"
#> ola_mES_lif_3_36.counts ola_mES_lif_3_37.counts ola_mES_lif_3_38.counts ola_mES_lif_3_39.counts
#> "034" "033" "034" "034"
#> ola_mES_lif_3_4.counts ola_mES_lif_3_40.counts ola_mES_lif_3_42.counts ola_mES_lif_3_45.counts
#> "0143" "0143" "0141" "034"
#> ola_mES_lif_3_46.counts ola_mES_lif_3_47.counts ola_mES_lif_3_48.counts ola_mES_lif_3_49.counts
#> "034" "034" "033" "0143"
#> ola_mES_lif_3_5.counts ola_mES_lif_3_50.counts ola_mES_lif_3_51.counts ola_mES_lif_3_52.counts
#> "012" "0141" "0143" "0143"
#> ola_mES_lif_3_53.counts ola_mES_lif_3_54.counts ola_mES_lif_3_56.counts ola_mES_lif_3_57.counts
#> "0143" "0141" "012" "033"
#> ola_mES_lif_3_58.counts ola_mES_lif_3_59.counts ola_mES_lif_3_6.counts ola_mES_lif_3_60.counts
#> "0143" "0143" "0141" "0143"
#> ola_mES_lif_3_61.counts ola_mES_lif_3_63.counts ola_mES_lif_3_64.counts ola_mES_lif_3_65.counts
#> "0143" "0143" "0143" "0143"
#> ola_mES_lif_3_69.counts ola_mES_lif_3_70.counts ola_mES_lif_3_71.counts ola_mES_lif_3_72.counts
#> "034" "034" "0143" "034"
#> ola_mES_lif_3_73.counts ola_mES_lif_3_74.counts ola_mES_lif_3_75.counts ola_mES_lif_3_76.counts
#> "0311" "0311" "0313" "0311"
#> ola_mES_lif_3_77.counts ola_mES_lif_3_78.counts ola_mES_lif_3_8.counts ola_mES_lif_3_80.counts
#> "0222" "0311" "0143" "0313"
#> ola_mES_lif_3_82.counts ola_mES_lif_3_83.counts ola_mES_lif_3_84.counts ola_mES_lif_3_87.counts
#> "0313" "0313" "0313" "0312"
#> ola_mES_lif_3_88.counts ola_mES_lif_3_89.counts ola_mES_lif_3_9.counts ola_mES_lif_3_90.counts
#> "0311" "0222" "0223" "034"
#> ola_mES_lif_3_92.counts ola_mES_lif_3_94.counts ola_mES_lif_3_95.counts ola_mES_lif_3_96.counts
#> "0311" "0311" "0311" "0311"
get_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 517))
#> ola_mES_2i_2_1.counts ola_mES_2i_2_10.counts ola_mES_2i_2_11.counts ola_mES_2i_2_12.counts
#> "0111" "0131" "0131" "0131"
#> ola_mES_2i_2_13.counts ola_mES_2i_2_14.counts ola_mES_2i_2_15.counts ola_mES_2i_2_16.counts
#> "0133" "0111" "0133" "0133"
#> ola_mES_2i_2_17.counts ola_mES_2i_2_18.counts ola_mES_2i_2_19.counts ola_mES_2i_2_2.counts
#> "0133" "0131" "0131" "0111"
#> ola_mES_2i_2_20.counts ola_mES_2i_2_21.counts ola_mES_2i_2_22.counts ola_mES_2i_2_23.counts
#> "0111" "0131" "0133" "0131"
#> ola_mES_2i_2_24.counts ola_mES_2i_2_25.counts ola_mES_2i_2_26.counts ola_mES_2i_2_27.counts
#> "0131" "0111" "0133" "0111"
#> ola_mES_2i_2_28.counts ola_mES_2i_2_29.counts ola_mES_2i_2_3.counts ola_mES_2i_2_30.counts
#> "0111" "0133" "0111" "0131"
#> ola_mES_2i_2_31.counts ola_mES_2i_2_32.counts ola_mES_2i_2_33.counts ola_mES_2i_2_34.counts
#> "0131" "0133" "0131" "0131"
#> ola_mES_2i_2_35.counts ola_mES_2i_2_36.counts ola_mES_2i_2_37.counts ola_mES_2i_2_38.counts
#> "0131" "0131" "0111" "0111"
#> ola_mES_2i_2_39.counts ola_mES_2i_2_4.counts ola_mES_2i_2_40.counts ola_mES_2i_2_41.counts
#> "0133" "0131" "0131" "0131"
#> ola_mES_2i_2_42.counts ola_mES_2i_2_43.counts ola_mES_2i_2_44.counts ola_mES_2i_2_46.counts
#> "0131" "0111" "0131" "0131"
#> ola_mES_2i_2_47.counts ola_mES_2i_2_48.counts ola_mES_2i_2_49.counts ola_mES_2i_2_5.counts
#> "0131" "0131" "0111" "0133"
#> ola_mES_2i_2_50.counts ola_mES_2i_2_51.counts ola_mES_2i_2_52.counts ola_mES_2i_2_53.counts
#> "0111" "0131" "0133" "0133"
#> ola_mES_2i_2_54.counts ola_mES_2i_2_55.counts ola_mES_2i_2_56.counts ola_mES_2i_2_57.counts
#> "0131" "0133" "0133" "0133"
#> ola_mES_2i_2_58.counts ola_mES_2i_2_59.counts ola_mES_2i_2_6.counts ola_mES_2i_2_60.counts
#> "0131" "0131" "0133" "0131"
#> ola_mES_2i_2_61.counts ola_mES_2i_2_62.counts ola_mES_2i_2_63.counts ola_mES_2i_2_64.counts
#> "0111" "032" "0111" "0133"
#> ola_mES_2i_2_66.counts ola_mES_2i_2_67.counts ola_mES_2i_2_68.counts ola_mES_2i_2_69.counts
#> "0131" "0131" "0111" "0131"
#> ola_mES_2i_2_7.counts ola_mES_2i_2_72.counts ola_mES_2i_2_73.counts ola_mES_2i_2_74.counts
#> "0133" "0133" "0111" "0133"
#> ola_mES_2i_2_75.counts ola_mES_2i_2_76.counts ola_mES_2i_2_79.counts ola_mES_2i_2_8.counts
#> "0111" "0111" "0131" "0133"
#> ola_mES_2i_2_80.counts ola_mES_2i_2_81.counts ola_mES_2i_2_85.counts ola_mES_2i_2_86.counts
#> "0133" "0131" "0111" "0111"
#> ola_mES_2i_2_87.counts ola_mES_2i_2_90.counts ola_mES_2i_2_91.counts ola_mES_2i_2_93.counts
#> "032" "0131" "0133" "0131"
#> ola_mES_2i_2_94.counts ola_mES_2i_2_95.counts ola_mES_2i_3_10.counts ola_mES_2i_3_11.counts
#> "0131" "0133" "0222" "023"
#> ola_mES_2i_3_12.counts ola_mES_2i_3_13.counts ola_mES_2i_3_15.counts ola_mES_2i_3_16.counts
#> "0221" "0221" "0221" "0211"
#> ola_mES_2i_3_17.counts ola_mES_2i_3_18.counts ola_mES_2i_3_19.counts ola_mES_2i_3_2.counts
#> "032" "032" "0221" "0221"
#> ola_mES_2i_3_20.counts ola_mES_2i_3_21.counts ola_mES_2i_3_23.counts ola_mES_2i_3_25.counts
#> "0221" "0222" "0211" "023"
#> ola_mES_2i_3_26.counts ola_mES_2i_3_28.counts ola_mES_2i_3_30.counts ola_mES_2i_3_31.counts
#> "0221" "0211" "0221" "032"
#> ola_mES_2i_3_33.counts ola_mES_2i_3_36.counts ola_mES_2i_3_37.counts ola_mES_2i_3_38.counts
#> "0221" "0211" "0221" "0221"
#> ola_mES_2i_3_39.counts ola_mES_2i_3_4.counts ola_mES_2i_3_41.counts ola_mES_2i_3_42.counts
#> "0222" "0211" "0222" "0221"
#> ola_mES_2i_3_43.counts ola_mES_2i_3_45.counts ola_mES_2i_3_46.counts ola_mES_2i_3_47.counts
#> "0211" "0221" "0221" "0221"
#> ola_mES_2i_3_49.counts ola_mES_2i_3_5.counts ola_mES_2i_3_52.counts ola_mES_2i_3_54.counts
#> "032" "0221" "0221" "0222"
#> ola_mES_2i_3_56.counts ola_mES_2i_3_57.counts ola_mES_2i_3_58.counts ola_mES_2i_3_6.counts
#> "0221" "0221" "0221" "0222"
#> ola_mES_2i_3_62.counts ola_mES_2i_3_65.counts ola_mES_2i_3_66.counts ola_mES_2i_3_69.counts
#> "032" "0221" "032" "0222"
#> ola_mES_2i_3_7.counts ola_mES_2i_3_73.counts ola_mES_2i_3_76.counts ola_mES_2i_3_77.counts
#> "0221" "0221" "0221" "0222"
#> ola_mES_2i_3_78.counts ola_mES_2i_3_79.counts ola_mES_2i_3_8.counts ola_mES_2i_3_80.counts
#> "032" "0221" "0221" "0221"
#> ola_mES_2i_3_82.counts ola_mES_2i_3_83.counts ola_mES_2i_3_84.counts ola_mES_2i_3_87.counts
#> "0221" "0221" "0211" "0222"
#> ola_mES_2i_3_88.counts ola_mES_2i_3_90.counts ola_mES_2i_3_91.counts ola_mES_2i_3_92.counts
#> "0221" "0211" "0222" "0221"
#> ola_mES_2i_3_96.counts ola_mES_2i_4_1.counts ola_mES_2i_4_10.counts ola_mES_2i_4_11.counts
#> "0222" "0212" "0212" "0212"
#> ola_mES_2i_4_13.counts ola_mES_2i_4_17.counts ola_mES_2i_4_18.counts ola_mES_2i_4_2.counts
#> "0211" "0211" "023" "0132"
#> ola_mES_2i_4_20.counts ola_mES_2i_4_21.counts ola_mES_2i_4_22.counts ola_mES_2i_4_23.counts
#> "0211" "0211" "023" "0221"
#> ola_mES_2i_4_24.counts ola_mES_2i_4_25.counts ola_mES_2i_4_26.counts ola_mES_2i_4_27.counts
#> "0211" "0212" "0212" "023"
#> ola_mES_2i_4_29.counts ola_mES_2i_4_30.counts ola_mES_2i_4_31.counts ola_mES_2i_4_32.counts
#> "0212" "0211" "0212" "0211"
#> ola_mES_2i_4_33.counts ola_mES_2i_4_34.counts ola_mES_2i_4_35.counts ola_mES_2i_4_36.counts
#> "0211" "0211" "0212" "0211"
#> ola_mES_2i_4_37.counts ola_mES_2i_4_38.counts ola_mES_2i_4_39.counts ola_mES_2i_4_40.counts
#> "032" "01122" "0212" "01122"
#> ola_mES_2i_4_42.counts ola_mES_2i_4_43.counts ola_mES_2i_4_44.counts ola_mES_2i_4_46.counts
#> "032" "0211" "0212" "0211"
#> ola_mES_2i_4_49.counts ola_mES_2i_4_5.counts ola_mES_2i_4_50.counts ola_mES_2i_4_52.counts
#> "01122" "023" "0212" "0212"
#> ola_mES_2i_4_53.counts ola_mES_2i_4_54.counts ola_mES_2i_4_56.counts ola_mES_2i_4_57.counts
#> "01122" "032" "0211" "0211"
#> ola_mES_2i_4_58.counts ola_mES_2i_4_59.counts ola_mES_2i_4_6.counts ola_mES_2i_4_60.counts
#> "0211" "0211" "0211" "0221"
#> ola_mES_2i_4_61.counts ola_mES_2i_4_62.counts ola_mES_2i_4_63.counts ola_mES_2i_4_64.counts
#> "0211" "032" "0221" "0212"
#> ola_mES_2i_4_65.counts ola_mES_2i_4_66.counts ola_mES_2i_4_67.counts ola_mES_2i_4_68.counts
#> "0211" "0211" "01122" "0221"
#> ola_mES_2i_4_69.counts ola_mES_2i_4_7.counts ola_mES_2i_4_70.counts ola_mES_2i_4_71.counts
#> "0211" "023" "0221" "0211"
#> ola_mES_2i_4_72.counts ola_mES_2i_4_73.counts ola_mES_2i_4_75.counts ola_mES_2i_4_77.counts
#> "023" "0211" "0222" "0211"
#> ola_mES_2i_4_78.counts ola_mES_2i_4_80.counts ola_mES_2i_4_81.counts ola_mES_2i_4_83.counts
#> "023" "0211" "0211" "023"
#> ola_mES_2i_4_84.counts ola_mES_2i_4_88.counts ola_mES_2i_4_89.counts ola_mES_2i_4_9.counts
#> "023" "0221" "0221" "023"
#> ola_mES_2i_4_90.counts ola_mES_2i_4_91.counts ola_mES_2i_4_94.counts ola_mES_2i_4_95.counts
#> "0211" "0212" "0221" "0222"
#> ola_mES_2i_4_96.counts ola_mES_2i_5_1.counts ola_mES_2i_5_10.counts ola_mES_2i_5_11.counts
#> "0222" "0221" "023" "023"
#> ola_mES_2i_5_12.counts ola_mES_2i_5_13.counts ola_mES_2i_5_14.counts ola_mES_2i_5_15.counts
#> "023" "023" "0212" "0212"
#> ola_mES_2i_5_16.counts ola_mES_2i_5_17.counts ola_mES_2i_5_18.counts ola_mES_2i_5_19.counts
#> "0212" "0212" "023" "0211"
#> ola_mES_2i_5_2.counts ola_mES_2i_5_20.counts ola_mES_2i_5_21.counts ola_mES_2i_5_23.counts
#> "0221" "0212" "0211" "023"
#> ola_mES_2i_5_24.counts ola_mES_2i_5_25.counts ola_mES_2i_5_26.counts ola_mES_2i_5_27.counts
#> "0212" "0211" "0212" "0212"
#> ola_mES_2i_5_28.counts ola_mES_2i_5_29.counts ola_mES_2i_5_3.counts ola_mES_2i_5_30.counts
#> "0221" "0221" "0221" "0212"
#> ola_mES_2i_5_31.counts ola_mES_2i_5_32.counts ola_mES_2i_5_34.counts ola_mES_2i_5_35.counts
#> "0212" "0212" "0212" "0211"
#> ola_mES_2i_5_36.counts ola_mES_2i_5_38.counts ola_mES_2i_5_39.counts ola_mES_2i_5_4.counts
#> "0212" "0212" "0212" "0221"
#> ola_mES_2i_5_40.counts ola_mES_2i_5_41.counts ola_mES_2i_5_42.counts ola_mES_2i_5_44.counts
#> "0211" "01122" "0212" "0212"
#> ola_mES_2i_5_45.counts ola_mES_2i_5_46.counts ola_mES_2i_5_47.counts ola_mES_2i_5_48.counts
#> "0211" "0211" "0211" "0211"
#> ola_mES_2i_5_49.counts ola_mES_2i_5_5.counts ola_mES_2i_5_51.counts ola_mES_2i_5_52.counts
#> "0211" "023" "0212" "0221"
#> ola_mES_2i_5_53.counts ola_mES_2i_5_54.counts ola_mES_2i_5_55.counts ola_mES_2i_5_56.counts
#> "0212" "0211" "0212" "0212"
#> ola_mES_2i_5_57.counts ola_mES_2i_5_58.counts ola_mES_2i_5_59.counts ola_mES_2i_5_6.counts
#> "0212" "0212" "0212" "0221"
#> ola_mES_2i_5_61.counts ola_mES_2i_5_62.counts ola_mES_2i_5_63.counts ola_mES_2i_5_65.counts
#> "0212" "0221" "0211" "023"
#> ola_mES_2i_5_66.counts ola_mES_2i_5_67.counts ola_mES_2i_5_68.counts ola_mES_2i_5_69.counts
#> "0221" "0212" "0212" "0212"
#> ola_mES_2i_5_7.counts ola_mES_2i_5_70.counts ola_mES_2i_5_72.counts ola_mES_2i_5_73.counts
#> "0212" "0211" "0212" "023"
#> ola_mES_2i_5_74.counts ola_mES_2i_5_75.counts ola_mES_2i_5_76.counts ola_mES_2i_5_77.counts
#> "0211" "023" "023" "023"
#> ola_mES_2i_5_78.counts ola_mES_2i_5_8.counts ola_mES_2i_5_81.counts ola_mES_2i_5_82.counts
#> "0212" "0211" "0221" "0212"
#> ola_mES_2i_5_83.counts ola_mES_2i_5_85.counts ola_mES_2i_5_86.counts ola_mES_2i_5_87.counts
#> "023" "0221" "023" "023"
#> ola_mES_2i_5_88.counts ola_mES_2i_5_9.counts ola_mES_2i_5_90.counts ola_mES_2i_5_92.counts
#> "023" "0222" "0211" "0212"
#> ola_mES_2i_5_94.counts ola_mES_2i_5_95.counts ola_mES_2i_5_96.counts ola_mES_a2i_2_1.counts
#> "0212" "0212" "0212" "0132"
#> ola_mES_a2i_2_10.counts ola_mES_a2i_2_11.counts ola_mES_a2i_2_12.counts ola_mES_a2i_2_13.counts
#> "01121" "0132" "01121" "0111"
#> ola_mES_a2i_2_14.counts ola_mES_a2i_2_15.counts ola_mES_a2i_2_16.counts ola_mES_a2i_2_17.counts
#> "0132" "0132" "0132" "0132"
#> ola_mES_a2i_2_18.counts ola_mES_a2i_2_19.counts ola_mES_a2i_2_2.counts ola_mES_a2i_2_20.counts
#> "0132" "0132" "0132" "01121"
#> ola_mES_a2i_2_21.counts ola_mES_a2i_2_22.counts ola_mES_a2i_2_23.counts ola_mES_a2i_2_24.counts
#> "0132" "0132" "0132" "0132"
#> ola_mES_a2i_2_25.counts ola_mES_a2i_2_26.counts ola_mES_a2i_2_27.counts ola_mES_a2i_2_28.counts
#> "0113" "032" "01121" "01121"
#> ola_mES_a2i_2_29.counts ola_mES_a2i_2_3.counts ola_mES_a2i_2_30.counts ola_mES_a2i_2_31.counts
#> "01121" "0132" "0222" "032"
#> ola_mES_a2i_2_32.counts ola_mES_a2i_2_33.counts ola_mES_a2i_2_34.counts ola_mES_a2i_2_35.counts
#> "032" "01121" "01121" "0312"
#> ola_mES_a2i_2_36.counts ola_mES_a2i_2_37.counts ola_mES_a2i_2_38.counts ola_mES_a2i_2_39.counts
#> "0222" "032" "01121" "032"
#> ola_mES_a2i_2_4.counts ola_mES_a2i_2_40.counts ola_mES_a2i_2_41.counts ola_mES_a2i_2_42.counts
#> "01121" "01121" "0132" "0132"
#> ola_mES_a2i_2_43.counts ola_mES_a2i_2_44.counts ola_mES_a2i_2_45.counts ola_mES_a2i_2_46.counts
#> "0111" "0113" "0132" "0132"
#> ola_mES_a2i_2_47.counts ola_mES_a2i_2_48.counts ola_mES_a2i_2_49.counts ola_mES_a2i_2_5.counts
#> "01121" "01121" "0113" "0132"
#> ola_mES_a2i_2_50.counts ola_mES_a2i_2_51.counts ola_mES_a2i_2_52.counts ola_mES_a2i_2_53.counts
#> "032" "01121" "01121" "032"
#> ola_mES_a2i_2_54.counts ola_mES_a2i_2_55.counts ola_mES_a2i_2_56.counts ola_mES_a2i_2_57.counts
#> "01121" "0132" "0111" "0132"
#> ola_mES_a2i_2_59.counts ola_mES_a2i_2_6.counts ola_mES_a2i_2_60.counts ola_mES_a2i_2_61.counts
#> "0132" "032" "01122" "0111"
#> ola_mES_a2i_2_62.counts ola_mES_a2i_2_63.counts ola_mES_a2i_2_64.counts ola_mES_a2i_2_65.counts
#> "01121" "0111" "01121" "0111"
#> ola_mES_a2i_2_66.counts ola_mES_a2i_2_67.counts ola_mES_a2i_2_68.counts ola_mES_a2i_2_69.counts
#> "0132" "01121" "0113" "032"
#> ola_mES_a2i_2_7.counts ola_mES_a2i_2_70.counts ola_mES_a2i_2_71.counts ola_mES_a2i_2_72.counts
#> "01121" "01121" "0132" "01121"
#> ola_mES_a2i_2_73.counts ola_mES_a2i_2_74.counts ola_mES_a2i_2_75.counts ola_mES_a2i_2_76.counts
#> "0113" "0111" "01121" "0111"
#> ola_mES_a2i_2_77.counts ola_mES_a2i_2_78.counts ola_mES_a2i_2_8.counts ola_mES_a2i_2_80.counts
#> "01121" "0132" "0132" "0113"
#> ola_mES_a2i_2_81.counts ola_mES_a2i_2_82.counts ola_mES_a2i_2_83.counts ola_mES_a2i_2_84.counts
#> "01121" "0132" "0132" "01121"
#> ola_mES_a2i_2_86.counts ola_mES_a2i_2_87.counts ola_mES_a2i_2_88.counts ola_mES_a2i_2_89.counts
#> "0113" "01121" "01121" "0223"
#> ola_mES_a2i_2_9.counts ola_mES_a2i_2_90.counts ola_mES_a2i_2_91.counts ola_mES_a2i_2_92.counts
#> "0113" "0132" "032" "032"
#> ola_mES_a2i_2_93.counts ola_mES_a2i_2_94.counts ola_mES_a2i_2_95.counts ola_mES_a2i_2_96.counts
#> "0111" "0132" "0132" "0132"
#> ola_mES_a2i_3_10.counts ola_mES_a2i_3_11.counts ola_mES_a2i_3_13.counts ola_mES_a2i_3_16.counts
#> "0211" "0223" "01121" "0113"
#> ola_mES_a2i_3_17.counts ola_mES_a2i_3_18.counts ola_mES_a2i_3_2.counts ola_mES_a2i_3_20.counts
#> "032" "032" "032" "032"
#> ola_mES_a2i_3_21.counts ola_mES_a2i_3_22.counts ola_mES_a2i_3_24.counts ola_mES_a2i_3_25.counts
#> "0223" "0223" "0223" "0113"
#> ola_mES_a2i_3_26.counts ola_mES_a2i_3_28.counts ola_mES_a2i_3_29.counts ola_mES_a2i_3_3.counts
#> "032" "0113" "0113" "0211"
#> ola_mES_a2i_3_30.counts ola_mES_a2i_3_31.counts ola_mES_a2i_3_33.counts ola_mES_a2i_3_34.counts
#> "032" "032" "0223" "0113"
#> ola_mES_a2i_3_37.counts ola_mES_a2i_3_38.counts ola_mES_a2i_3_39.counts ola_mES_a2i_3_4.counts
#> "01122" "0113" "0113" "0223"
#> ola_mES_a2i_3_40.counts ola_mES_a2i_3_41.counts ola_mES_a2i_3_42.counts ola_mES_a2i_3_44.counts
#> "0113" "032" "032" "0113"
#> ola_mES_a2i_3_45.counts ola_mES_a2i_3_47.counts ola_mES_a2i_3_49.counts ola_mES_a2i_3_50.counts
#> "032" "032" "0113" "0113"
#> ola_mES_a2i_3_52.counts ola_mES_a2i_3_54.counts ola_mES_a2i_3_56.counts ola_mES_a2i_3_58.counts
#> "032" "0113" "032" "0223"
#> ola_mES_a2i_3_59.counts ola_mES_a2i_3_6.counts ola_mES_a2i_3_62.counts ola_mES_a2i_3_64.counts
#> "0132" "0113" "01121" "01122"
#> ola_mES_a2i_3_65.counts ola_mES_a2i_3_66.counts ola_mES_a2i_3_67.counts ola_mES_a2i_3_68.counts
#> "0132" "0113" "032" "0222"
#> ola_mES_a2i_3_69.counts ola_mES_a2i_3_7.counts ola_mES_a2i_3_71.counts ola_mES_a2i_3_73.counts
#> "0113" "032" "0132" "01121"
#> ola_mES_a2i_3_75.counts ola_mES_a2i_3_76.counts ola_mES_a2i_3_77.counts ola_mES_a2i_3_8.counts
#> "032" "0211" "0113" "032"
#> ola_mES_a2i_3_80.counts ola_mES_a2i_3_81.counts ola_mES_a2i_3_82.counts ola_mES_a2i_3_83.counts
#> "0222" "032" "0113" "01121"
#> ola_mES_a2i_3_84.counts ola_mES_a2i_3_85.counts ola_mES_a2i_3_86.counts ola_mES_a2i_3_89.counts
#> "0113" "01121" "0113" "0132"
#> ola_mES_a2i_3_9.counts ola_mES_a2i_3_90.counts ola_mES_a2i_3_91.counts ola_mES_a2i_3_92.counts
#> "0223" "0113" "0223" "01121"
#> ola_mES_a2i_3_94.counts ola_mES_a2i_3_96.counts ola_mES_lif_1_1.counts ola_mES_lif_1_10.counts
#> "0312" "01121" "0141" "033"
#> ola_mES_lif_1_11.counts ola_mES_lif_1_12.counts ola_mES_lif_1_13.counts ola_mES_lif_1_14.counts
#> "012" "033" "0141" "033"
#> ola_mES_lif_1_15.counts ola_mES_lif_1_17.counts ola_mES_lif_1_18.counts ola_mES_lif_1_19.counts
#> "0141" "0312" "0141" "0142"
#> ola_mES_lif_1_2.counts ola_mES_lif_1_20.counts ola_mES_lif_1_21.counts ola_mES_lif_1_22.counts
#> "033" "0142" "033" "0142"
#> ola_mES_lif_1_23.counts ola_mES_lif_1_24.counts ola_mES_lif_1_25.counts ola_mES_lif_1_26.counts
#> "0312" "0142" "0142" "0142"
#> ola_mES_lif_1_28.counts ola_mES_lif_1_29.counts ola_mES_lif_1_3.counts ola_mES_lif_1_30.counts
#> "0142" "0313" "0141" "0142"
#> ola_mES_lif_1_31.counts ola_mES_lif_1_32.counts ola_mES_lif_1_33.counts ola_mES_lif_1_34.counts
#> "0313" "0142" "0142" "0141"
#> ola_mES_lif_1_35.counts ola_mES_lif_1_36.counts ola_mES_lif_1_37.counts ola_mES_lif_1_38.counts
#> "0312" "0142" "0142" "0142"
#> ola_mES_lif_1_39.counts ola_mES_lif_1_4.counts ola_mES_lif_1_41.counts ola_mES_lif_1_42.counts
#> "0142" "0143" "0142" "0142"
#> ola_mES_lif_1_43.counts ola_mES_lif_1_44.counts ola_mES_lif_1_45.counts ola_mES_lif_1_46.counts
#> "0142" "0141" "0142" "0142"
#> ola_mES_lif_1_47.counts ola_mES_lif_1_48.counts ola_mES_lif_1_49.counts ola_mES_lif_1_5.counts
#> "0141" "0141" "0141" "012"
#> ola_mES_lif_1_50.counts ola_mES_lif_1_51.counts ola_mES_lif_1_52.counts ola_mES_lif_1_53.counts
#> "0142" "012" "012" "0223"
#> ola_mES_lif_1_54.counts ola_mES_lif_1_55.counts ola_mES_lif_1_56.counts ola_mES_lif_1_57.counts
#> "0141" "012" "0222" "0223"
#> ola_mES_lif_1_58.counts ola_mES_lif_1_59.counts ola_mES_lif_1_6.counts ola_mES_lif_1_60.counts
#> "0141" "012" "0142" "0141"
#> ola_mES_lif_1_61.counts ola_mES_lif_1_62.counts ola_mES_lif_1_63.counts ola_mES_lif_1_64.counts
#> "0141" "0142" "0312" "0312"
#> ola_mES_lif_1_65.counts ola_mES_lif_1_66.counts ola_mES_lif_1_67.counts ola_mES_lif_1_68.counts
#> "0312" "0141" "0141" "0142"
#> ola_mES_lif_1_69.counts ola_mES_lif_1_7.counts ola_mES_lif_1_70.counts ola_mES_lif_1_72.counts
#> "0312" "0141" "033" "0141"
#> ola_mES_lif_1_73.counts ola_mES_lif_1_74.counts ola_mES_lif_1_75.counts ola_mES_lif_1_79.counts
#> "0142" "0312" "0312" "033"
#> ola_mES_lif_1_8.counts ola_mES_lif_1_80.counts ola_mES_lif_1_81.counts ola_mES_lif_1_82.counts
#> "0142" "0141" "0142" "0312"
#> ola_mES_lif_1_86.counts ola_mES_lif_1_9.counts ola_mES_lif_1_91.counts ola_mES_lif_1_92.counts
#> "033" "0141" "0142" "0142"
#> ola_mES_lif_1_93.counts ola_mES_lif_1_95.counts ola_mES_lif_1_96.counts ola_mES_lif_2_1.counts
#> "033" "0141" "0141" "033"
#> ola_mES_lif_2_10.counts ola_mES_lif_2_11.counts ola_mES_lif_2_12.counts ola_mES_lif_2_13.counts
#> "012" "012" "0223" "012"
#> ola_mES_lif_2_14.counts ola_mES_lif_2_15.counts ola_mES_lif_2_16.counts ola_mES_lif_2_17.counts
#> "012" "012" "012" "012"
#> ola_mES_lif_2_18.counts ola_mES_lif_2_19.counts ola_mES_lif_2_2.counts ola_mES_lif_2_20.counts
#> "012" "012" "012" "033"
#> ola_mES_lif_2_21.counts ola_mES_lif_2_23.counts ola_mES_lif_2_25.counts ola_mES_lif_2_26.counts
#> "012" "012" "033" "012"
#> ola_mES_lif_2_27.counts ola_mES_lif_2_28.counts ola_mES_lif_2_29.counts ola_mES_lif_2_3.counts
#> "033" "012" "012" "012"
#> ola_mES_lif_2_30.counts ola_mES_lif_2_31.counts ola_mES_lif_2_32.counts ola_mES_lif_2_33.counts
#> "012" "033" "012" "033"
#> ola_mES_lif_2_34.counts ola_mES_lif_2_35.counts ola_mES_lif_2_36.counts ola_mES_lif_2_37.counts
#> "012" "012" "034" "033"
#> ola_mES_lif_2_38.counts ola_mES_lif_2_39.counts ola_mES_lif_2_4.counts ola_mES_lif_2_40.counts
#> "012" "012" "012" "012"
#> ola_mES_lif_2_41.counts ola_mES_lif_2_42.counts ola_mES_lif_2_43.counts ola_mES_lif_2_44.counts
#> "033" "012" "012" "012"
#> ola_mES_lif_2_45.counts ola_mES_lif_2_46.counts ola_mES_lif_2_47.counts ola_mES_lif_2_48.counts
#> "034" "012" "033" "0223"
#> ola_mES_lif_2_49.counts ola_mES_lif_2_5.counts ola_mES_lif_2_50.counts ola_mES_lif_2_51.counts
#> "012" "012" "012" "012"
#> ola_mES_lif_2_52.counts ola_mES_lif_2_53.counts ola_mES_lif_2_54.counts ola_mES_lif_2_55.counts
#> "012" "012" "012" "012"
#> ola_mES_lif_2_56.counts ola_mES_lif_2_57.counts ola_mES_lif_2_58.counts ola_mES_lif_2_59.counts
#> "012" "034" "012" "012"
#> ola_mES_lif_2_6.counts ola_mES_lif_2_60.counts ola_mES_lif_2_61.counts ola_mES_lif_2_63.counts
#> "012" "033" "0141" "012"
#> ola_mES_lif_2_64.counts ola_mES_lif_2_65.counts ola_mES_lif_2_66.counts ola_mES_lif_2_67.counts
#> "012" "012" "012" "012"
#> ola_mES_lif_2_68.counts ola_mES_lif_2_69.counts ola_mES_lif_2_7.counts ola_mES_lif_2_70.counts
#> "012" "0223" "012" "012"
#> ola_mES_lif_2_71.counts ola_mES_lif_2_72.counts ola_mES_lif_2_73.counts ola_mES_lif_2_74.counts
#> "012" "0141" "0223" "012"
#> ola_mES_lif_2_75.counts ola_mES_lif_2_76.counts ola_mES_lif_2_77.counts ola_mES_lif_2_79.counts
#> "012" "012" "012" "033"
#> ola_mES_lif_2_8.counts ola_mES_lif_2_80.counts ola_mES_lif_2_81.counts ola_mES_lif_2_82.counts
#> "012" "012" "033" "012"
#> ola_mES_lif_2_83.counts ola_mES_lif_2_84.counts ola_mES_lif_2_86.counts ola_mES_lif_2_87.counts
#> "012" "012" "033" "012"
#> ola_mES_lif_2_89.counts ola_mES_lif_2_9.counts ola_mES_lif_2_90.counts ola_mES_lif_2_91.counts
#> "012" "033" "033" "033"
#> ola_mES_lif_2_92.counts ola_mES_lif_2_93.counts ola_mES_lif_2_94.counts ola_mES_lif_2_95.counts
#> "033" "012" "012" "012"
#> ola_mES_lif_2_96.counts ola_mES_lif_3_1.counts ola_mES_lif_3_10.counts ola_mES_lif_3_11.counts
#> "033" "0143" "012" "033"
#> ola_mES_lif_3_12.counts ola_mES_lif_3_13.counts ola_mES_lif_3_14.counts ola_mES_lif_3_15.counts
#> "0223" "0143" "034" "034"
#> ola_mES_lif_3_16.counts ola_mES_lif_3_17.counts ola_mES_lif_3_19.counts ola_mES_lif_3_2.counts
#> "034" "034" "034" "0222"
#> ola_mES_lif_3_20.counts ola_mES_lif_3_21.counts ola_mES_lif_3_22.counts ola_mES_lif_3_23.counts
#> "0311" "0222" "0313" "0311"
#> ola_mES_lif_3_24.counts ola_mES_lif_3_26.counts ola_mES_lif_3_27.counts ola_mES_lif_3_28.counts
#> "0311" "0313" "0143" "034"
#> ola_mES_lif_3_29.counts ola_mES_lif_3_3.counts ola_mES_lif_3_30.counts ola_mES_lif_3_31.counts
#> "033" "0143" "0143" "034"
#> ola_mES_lif_3_32.counts ola_mES_lif_3_33.counts ola_mES_lif_3_34.counts ola_mES_lif_3_35.counts
#> "0143" "034" "034" "034"
#> ola_mES_lif_3_36.counts ola_mES_lif_3_37.counts ola_mES_lif_3_38.counts ola_mES_lif_3_39.counts
#> "034" "033" "034" "034"
#> ola_mES_lif_3_4.counts ola_mES_lif_3_40.counts ola_mES_lif_3_42.counts ola_mES_lif_3_45.counts
#> "0143" "0143" "0141" "034"
#> ola_mES_lif_3_46.counts ola_mES_lif_3_47.counts ola_mES_lif_3_48.counts ola_mES_lif_3_49.counts
#> "034" "034" "033" "0143"
#> ola_mES_lif_3_5.counts ola_mES_lif_3_50.counts ola_mES_lif_3_51.counts ola_mES_lif_3_52.counts
#> "012" "0141" "0143" "0143"
#> ola_mES_lif_3_53.counts ola_mES_lif_3_54.counts ola_mES_lif_3_56.counts ola_mES_lif_3_57.counts
#> "0143" "0141" "012" "033"
#> ola_mES_lif_3_58.counts ola_mES_lif_3_59.counts ola_mES_lif_3_6.counts ola_mES_lif_3_60.counts
#> "0143" "0143" "0141" "0143"
#> ola_mES_lif_3_61.counts ola_mES_lif_3_63.counts ola_mES_lif_3_64.counts ola_mES_lif_3_65.counts
#> "0143" "0143" "0143" "0143"
#> ola_mES_lif_3_69.counts ola_mES_lif_3_70.counts ola_mES_lif_3_71.counts ola_mES_lif_3_72.counts
#> "034" "034" "0143" "034"
#> ola_mES_lif_3_73.counts ola_mES_lif_3_74.counts ola_mES_lif_3_75.counts ola_mES_lif_3_76.counts
#> "0311" "0311" "0313" "0311"
#> ola_mES_lif_3_77.counts ola_mES_lif_3_78.counts ola_mES_lif_3_8.counts ola_mES_lif_3_80.counts
#> "0222" "0311" "0143" "0313"
#> ola_mES_lif_3_82.counts ola_mES_lif_3_83.counts ola_mES_lif_3_84.counts ola_mES_lif_3_87.counts
#> "0313" "0313" "0313" "0312"
#> ola_mES_lif_3_88.counts ola_mES_lif_3_89.counts ola_mES_lif_3_9.counts ola_mES_lif_3_90.counts
#> "0311" "0222" "0223" "034"
#> ola_mES_lif_3_92.counts ola_mES_lif_3_94.counts ola_mES_lif_3_95.counts ola_mES_lif_3_96.counts
#> "0311" "0311" "0311" "0311"
get_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 682))
#> ola_mES_2i_2_1.counts ola_mES_2i_2_10.counts ola_mES_2i_2_11.counts ola_mES_2i_2_12.counts
#> "0111" "0131" "0131" "0131"
#> ola_mES_2i_2_13.counts ola_mES_2i_2_14.counts ola_mES_2i_2_15.counts ola_mES_2i_2_16.counts
#> "0133" "0111" "0133" "0133"
#> ola_mES_2i_2_17.counts ola_mES_2i_2_18.counts ola_mES_2i_2_19.counts ola_mES_2i_2_2.counts
#> "0133" "0131" "0131" "0111"
#> ola_mES_2i_2_20.counts ola_mES_2i_2_21.counts ola_mES_2i_2_22.counts ola_mES_2i_2_23.counts
#> "0111" "0131" "0133" "0131"
#> ola_mES_2i_2_24.counts ola_mES_2i_2_25.counts ola_mES_2i_2_26.counts ola_mES_2i_2_27.counts
#> "0131" "0111" "0133" "0111"
#> ola_mES_2i_2_28.counts ola_mES_2i_2_29.counts ola_mES_2i_2_3.counts ola_mES_2i_2_30.counts
#> "0111" "0133" "0111" "0131"
#> ola_mES_2i_2_31.counts ola_mES_2i_2_32.counts ola_mES_2i_2_33.counts ola_mES_2i_2_34.counts
#> "0131" "0133" "0131" "0131"
#> ola_mES_2i_2_35.counts ola_mES_2i_2_36.counts ola_mES_2i_2_37.counts ola_mES_2i_2_38.counts
#> "0131" "0131" "0111" "0111"
#> ola_mES_2i_2_39.counts ola_mES_2i_2_4.counts ola_mES_2i_2_40.counts ola_mES_2i_2_41.counts
#> "0133" "0131" "0131" "0131"
#> ola_mES_2i_2_42.counts ola_mES_2i_2_43.counts ola_mES_2i_2_44.counts ola_mES_2i_2_46.counts
#> "0131" "0111" "0131" "0131"
#> ola_mES_2i_2_47.counts ola_mES_2i_2_48.counts ola_mES_2i_2_49.counts ola_mES_2i_2_5.counts
#> "0131" "0131" "0111" "0133"
#> ola_mES_2i_2_50.counts ola_mES_2i_2_51.counts ola_mES_2i_2_52.counts ola_mES_2i_2_53.counts
#> "0111" "0131" "0133" "0133"
#> ola_mES_2i_2_54.counts ola_mES_2i_2_55.counts ola_mES_2i_2_56.counts ola_mES_2i_2_57.counts
#> "0131" "0133" "0133" "0133"
#> ola_mES_2i_2_58.counts ola_mES_2i_2_59.counts ola_mES_2i_2_6.counts ola_mES_2i_2_60.counts
#> "0131" "0131" "0133" "0131"
#> ola_mES_2i_2_61.counts ola_mES_2i_2_62.counts ola_mES_2i_2_63.counts ola_mES_2i_2_64.counts
#> "0111" "032" "0111" "0133"
#> ola_mES_2i_2_66.counts ola_mES_2i_2_67.counts ola_mES_2i_2_68.counts ola_mES_2i_2_69.counts
#> "0131" "0131" "0111" "0131"
#> ola_mES_2i_2_7.counts ola_mES_2i_2_72.counts ola_mES_2i_2_73.counts ola_mES_2i_2_74.counts
#> "0133" "0133" "0111" "0133"
#> ola_mES_2i_2_75.counts ola_mES_2i_2_76.counts ola_mES_2i_2_79.counts ola_mES_2i_2_8.counts
#> "0111" "0111" "0131" "0133"
#> ola_mES_2i_2_80.counts ola_mES_2i_2_81.counts ola_mES_2i_2_85.counts ola_mES_2i_2_86.counts
#> "0133" "0131" "0111" "0111"
#> ola_mES_2i_2_87.counts ola_mES_2i_2_90.counts ola_mES_2i_2_91.counts ola_mES_2i_2_93.counts
#> "032" "0131" "0133" "0131"
#> ola_mES_2i_2_94.counts ola_mES_2i_2_95.counts ola_mES_2i_3_10.counts ola_mES_2i_3_11.counts
#> "0131" "0133" "0222" "023"
#> ola_mES_2i_3_12.counts ola_mES_2i_3_13.counts ola_mES_2i_3_15.counts ola_mES_2i_3_16.counts
#> "0221" "0221" "0221" "0211"
#> ola_mES_2i_3_17.counts ola_mES_2i_3_18.counts ola_mES_2i_3_19.counts ola_mES_2i_3_2.counts
#> "032" "032" "0221" "0221"
#> ola_mES_2i_3_20.counts ola_mES_2i_3_21.counts ola_mES_2i_3_23.counts ola_mES_2i_3_25.counts
#> "0221" "0222" "0211" "023"
#> ola_mES_2i_3_26.counts ola_mES_2i_3_28.counts ola_mES_2i_3_30.counts ola_mES_2i_3_31.counts
#> "0221" "0211" "0221" "032"
#> ola_mES_2i_3_33.counts ola_mES_2i_3_36.counts ola_mES_2i_3_37.counts ola_mES_2i_3_38.counts
#> "0221" "0211" "0221" "0221"
#> ola_mES_2i_3_39.counts ola_mES_2i_3_4.counts ola_mES_2i_3_41.counts ola_mES_2i_3_42.counts
#> "0222" "0211" "0222" "0221"
#> ola_mES_2i_3_43.counts ola_mES_2i_3_45.counts ola_mES_2i_3_46.counts ola_mES_2i_3_47.counts
#> "0211" "0221" "0221" "0221"
#> ola_mES_2i_3_49.counts ola_mES_2i_3_5.counts ola_mES_2i_3_52.counts ola_mES_2i_3_54.counts
#> "032" "0221" "0221" "0222"
#> ola_mES_2i_3_56.counts ola_mES_2i_3_57.counts ola_mES_2i_3_58.counts ola_mES_2i_3_6.counts
#> "0221" "0221" "0221" "0222"
#> ola_mES_2i_3_62.counts ola_mES_2i_3_65.counts ola_mES_2i_3_66.counts ola_mES_2i_3_69.counts
#> "032" "0221" "032" "0222"
#> ola_mES_2i_3_7.counts ola_mES_2i_3_73.counts ola_mES_2i_3_76.counts ola_mES_2i_3_77.counts
#> "0221" "0221" "0221" "0222"
#> ola_mES_2i_3_78.counts ola_mES_2i_3_79.counts ola_mES_2i_3_8.counts ola_mES_2i_3_80.counts
#> "032" "0221" "0221" "0221"
#> ola_mES_2i_3_82.counts ola_mES_2i_3_83.counts ola_mES_2i_3_84.counts ola_mES_2i_3_87.counts
#> "0221" "0221" "0211" "0222"
#> ola_mES_2i_3_88.counts ola_mES_2i_3_90.counts ola_mES_2i_3_91.counts ola_mES_2i_3_92.counts
#> "0221" "0211" "0222" "0221"
#> ola_mES_2i_3_96.counts ola_mES_2i_4_1.counts ola_mES_2i_4_10.counts ola_mES_2i_4_11.counts
#> "0222" "0212" "0212" "0212"
#> ola_mES_2i_4_13.counts ola_mES_2i_4_17.counts ola_mES_2i_4_18.counts ola_mES_2i_4_2.counts
#> "0211" "0211" "023" "0132"
#> ola_mES_2i_4_20.counts ola_mES_2i_4_21.counts ola_mES_2i_4_22.counts ola_mES_2i_4_23.counts
#> "0211" "0211" "023" "0221"
#> ola_mES_2i_4_24.counts ola_mES_2i_4_25.counts ola_mES_2i_4_26.counts ola_mES_2i_4_27.counts
#> "0211" "0212" "0212" "023"
#> ola_mES_2i_4_29.counts ola_mES_2i_4_30.counts ola_mES_2i_4_31.counts ola_mES_2i_4_32.counts
#> "0212" "0211" "0212" "0211"
#> ola_mES_2i_4_33.counts ola_mES_2i_4_34.counts ola_mES_2i_4_35.counts ola_mES_2i_4_36.counts
#> "0211" "0211" "0212" "0211"
#> ola_mES_2i_4_37.counts ola_mES_2i_4_38.counts ola_mES_2i_4_39.counts ola_mES_2i_4_40.counts
#> "032" "0112" "0212" "0112"
#> ola_mES_2i_4_42.counts ola_mES_2i_4_43.counts ola_mES_2i_4_44.counts ola_mES_2i_4_46.counts
#> "032" "0211" "0212" "0211"
#> ola_mES_2i_4_49.counts ola_mES_2i_4_5.counts ola_mES_2i_4_50.counts ola_mES_2i_4_52.counts
#> "0112" "023" "0212" "0212"
#> ola_mES_2i_4_53.counts ola_mES_2i_4_54.counts ola_mES_2i_4_56.counts ola_mES_2i_4_57.counts
#> "0112" "032" "0211" "0211"
#> ola_mES_2i_4_58.counts ola_mES_2i_4_59.counts ola_mES_2i_4_6.counts ola_mES_2i_4_60.counts
#> "0211" "0211" "0211" "0221"
#> ola_mES_2i_4_61.counts ola_mES_2i_4_62.counts ola_mES_2i_4_63.counts ola_mES_2i_4_64.counts
#> "0211" "032" "0221" "0212"
#> ola_mES_2i_4_65.counts ola_mES_2i_4_66.counts ola_mES_2i_4_67.counts ola_mES_2i_4_68.counts
#> "0211" "0211" "0112" "0221"
#> ola_mES_2i_4_69.counts ola_mES_2i_4_7.counts ola_mES_2i_4_70.counts ola_mES_2i_4_71.counts
#> "0211" "023" "0221" "0211"
#> ola_mES_2i_4_72.counts ola_mES_2i_4_73.counts ola_mES_2i_4_75.counts ola_mES_2i_4_77.counts
#> "023" "0211" "0222" "0211"
#> ola_mES_2i_4_78.counts ola_mES_2i_4_80.counts ola_mES_2i_4_81.counts ola_mES_2i_4_83.counts
#> "023" "0211" "0211" "023"
#> ola_mES_2i_4_84.counts ola_mES_2i_4_88.counts ola_mES_2i_4_89.counts ola_mES_2i_4_9.counts
#> "023" "0221" "0221" "023"
#> ola_mES_2i_4_90.counts ola_mES_2i_4_91.counts ola_mES_2i_4_94.counts ola_mES_2i_4_95.counts
#> "0211" "0212" "0221" "0222"
#> ola_mES_2i_4_96.counts ola_mES_2i_5_1.counts ola_mES_2i_5_10.counts ola_mES_2i_5_11.counts
#> "0222" "0221" "023" "023"
#> ola_mES_2i_5_12.counts ola_mES_2i_5_13.counts ola_mES_2i_5_14.counts ola_mES_2i_5_15.counts
#> "023" "023" "0212" "0212"
#> ola_mES_2i_5_16.counts ola_mES_2i_5_17.counts ola_mES_2i_5_18.counts ola_mES_2i_5_19.counts
#> "0212" "0212" "023" "0211"
#> ola_mES_2i_5_2.counts ola_mES_2i_5_20.counts ola_mES_2i_5_21.counts ola_mES_2i_5_23.counts
#> "0221" "0212" "0211" "023"
#> ola_mES_2i_5_24.counts ola_mES_2i_5_25.counts ola_mES_2i_5_26.counts ola_mES_2i_5_27.counts
#> "0212" "0211" "0212" "0212"
#> ola_mES_2i_5_28.counts ola_mES_2i_5_29.counts ola_mES_2i_5_3.counts ola_mES_2i_5_30.counts
#> "0221" "0221" "0221" "0212"
#> ola_mES_2i_5_31.counts ola_mES_2i_5_32.counts ola_mES_2i_5_34.counts ola_mES_2i_5_35.counts
#> "0212" "0212" "0212" "0211"
#> ola_mES_2i_5_36.counts ola_mES_2i_5_38.counts ola_mES_2i_5_39.counts ola_mES_2i_5_4.counts
#> "0212" "0212" "0212" "0221"
#> ola_mES_2i_5_40.counts ola_mES_2i_5_41.counts ola_mES_2i_5_42.counts ola_mES_2i_5_44.counts
#> "0211" "0112" "0212" "0212"
#> ola_mES_2i_5_45.counts ola_mES_2i_5_46.counts ola_mES_2i_5_47.counts ola_mES_2i_5_48.counts
#> "0211" "0211" "0211" "0211"
#> ola_mES_2i_5_49.counts ola_mES_2i_5_5.counts ola_mES_2i_5_51.counts ola_mES_2i_5_52.counts
#> "0211" "023" "0212" "0221"
#> ola_mES_2i_5_53.counts ola_mES_2i_5_54.counts ola_mES_2i_5_55.counts ola_mES_2i_5_56.counts
#> "0212" "0211" "0212" "0212"
#> ola_mES_2i_5_57.counts ola_mES_2i_5_58.counts ola_mES_2i_5_59.counts ola_mES_2i_5_6.counts
#> "0212" "0212" "0212" "0221"
#> ola_mES_2i_5_61.counts ola_mES_2i_5_62.counts ola_mES_2i_5_63.counts ola_mES_2i_5_65.counts
#> "0212" "0221" "0211" "023"
#> ola_mES_2i_5_66.counts ola_mES_2i_5_67.counts ola_mES_2i_5_68.counts ola_mES_2i_5_69.counts
#> "0221" "0212" "0212" "0212"
#> ola_mES_2i_5_7.counts ola_mES_2i_5_70.counts ola_mES_2i_5_72.counts ola_mES_2i_5_73.counts
#> "0212" "0211" "0212" "023"
#> ola_mES_2i_5_74.counts ola_mES_2i_5_75.counts ola_mES_2i_5_76.counts ola_mES_2i_5_77.counts
#> "0211" "023" "023" "023"
#> ola_mES_2i_5_78.counts ola_mES_2i_5_8.counts ola_mES_2i_5_81.counts ola_mES_2i_5_82.counts
#> "0212" "0211" "0221" "0212"
#> ola_mES_2i_5_83.counts ola_mES_2i_5_85.counts ola_mES_2i_5_86.counts ola_mES_2i_5_87.counts
#> "023" "0221" "023" "023"
#> ola_mES_2i_5_88.counts ola_mES_2i_5_9.counts ola_mES_2i_5_90.counts ola_mES_2i_5_92.counts
#> "023" "0222" "0211" "0212"
#> ola_mES_2i_5_94.counts ola_mES_2i_5_95.counts ola_mES_2i_5_96.counts ola_mES_a2i_2_1.counts
#> "0212" "0212" "0212" "0132"
#> ola_mES_a2i_2_10.counts ola_mES_a2i_2_11.counts ola_mES_a2i_2_12.counts ola_mES_a2i_2_13.counts
#> "0112" "0132" "0112" "0111"
#> ola_mES_a2i_2_14.counts ola_mES_a2i_2_15.counts ola_mES_a2i_2_16.counts ola_mES_a2i_2_17.counts
#> "0132" "0132" "0132" "0132"
#> ola_mES_a2i_2_18.counts ola_mES_a2i_2_19.counts ola_mES_a2i_2_2.counts ola_mES_a2i_2_20.counts
#> "0132" "0132" "0132" "0112"
#> ola_mES_a2i_2_21.counts ola_mES_a2i_2_22.counts ola_mES_a2i_2_23.counts ola_mES_a2i_2_24.counts
#> "0132" "0132" "0132" "0132"
#> ola_mES_a2i_2_25.counts ola_mES_a2i_2_26.counts ola_mES_a2i_2_27.counts ola_mES_a2i_2_28.counts
#> "0113" "032" "0112" "0112"
#> ola_mES_a2i_2_29.counts ola_mES_a2i_2_3.counts ola_mES_a2i_2_30.counts ola_mES_a2i_2_31.counts
#> "0112" "0132" "0222" "032"
#> ola_mES_a2i_2_32.counts ola_mES_a2i_2_33.counts ola_mES_a2i_2_34.counts ola_mES_a2i_2_35.counts
#> "032" "0112" "0112" "0312"
#> ola_mES_a2i_2_36.counts ola_mES_a2i_2_37.counts ola_mES_a2i_2_38.counts ola_mES_a2i_2_39.counts
#> "0222" "032" "0112" "032"
#> ola_mES_a2i_2_4.counts ola_mES_a2i_2_40.counts ola_mES_a2i_2_41.counts ola_mES_a2i_2_42.counts
#> "0112" "0112" "0132" "0132"
#> ola_mES_a2i_2_43.counts ola_mES_a2i_2_44.counts ola_mES_a2i_2_45.counts ola_mES_a2i_2_46.counts
#> "0111" "0113" "0132" "0132"
#> ola_mES_a2i_2_47.counts ola_mES_a2i_2_48.counts ola_mES_a2i_2_49.counts ola_mES_a2i_2_5.counts
#> "0112" "0112" "0113" "0132"
#> ola_mES_a2i_2_50.counts ola_mES_a2i_2_51.counts ola_mES_a2i_2_52.counts ola_mES_a2i_2_53.counts
#> "032" "0112" "0112" "032"
#> ola_mES_a2i_2_54.counts ola_mES_a2i_2_55.counts ola_mES_a2i_2_56.counts ola_mES_a2i_2_57.counts
#> "0112" "0132" "0111" "0132"
#> ola_mES_a2i_2_59.counts ola_mES_a2i_2_6.counts ola_mES_a2i_2_60.counts ola_mES_a2i_2_61.counts
#> "0132" "032" "0112" "0111"
#> ola_mES_a2i_2_62.counts ola_mES_a2i_2_63.counts ola_mES_a2i_2_64.counts ola_mES_a2i_2_65.counts
#> "0112" "0111" "0112" "0111"
#> ola_mES_a2i_2_66.counts ola_mES_a2i_2_67.counts ola_mES_a2i_2_68.counts ola_mES_a2i_2_69.counts
#> "0132" "0112" "0113" "032"
#> ola_mES_a2i_2_7.counts ola_mES_a2i_2_70.counts ola_mES_a2i_2_71.counts ola_mES_a2i_2_72.counts
#> "0112" "0112" "0132" "0112"
#> ola_mES_a2i_2_73.counts ola_mES_a2i_2_74.counts ola_mES_a2i_2_75.counts ola_mES_a2i_2_76.counts
#> "0113" "0111" "0112" "0111"
#> ola_mES_a2i_2_77.counts ola_mES_a2i_2_78.counts ola_mES_a2i_2_8.counts ola_mES_a2i_2_80.counts
#> "0112" "0132" "0132" "0113"
#> ola_mES_a2i_2_81.counts ola_mES_a2i_2_82.counts ola_mES_a2i_2_83.counts ola_mES_a2i_2_84.counts
#> "0112" "0132" "0132" "0112"
#> ola_mES_a2i_2_86.counts ola_mES_a2i_2_87.counts ola_mES_a2i_2_88.counts ola_mES_a2i_2_89.counts
#> "0113" "0112" "0112" "0223"
#> ola_mES_a2i_2_9.counts ola_mES_a2i_2_90.counts ola_mES_a2i_2_91.counts ola_mES_a2i_2_92.counts
#> "0113" "0132" "032" "032"
#> ola_mES_a2i_2_93.counts ola_mES_a2i_2_94.counts ola_mES_a2i_2_95.counts ola_mES_a2i_2_96.counts
#> "0111" "0132" "0132" "0132"
#> ola_mES_a2i_3_10.counts ola_mES_a2i_3_11.counts ola_mES_a2i_3_13.counts ola_mES_a2i_3_16.counts
#> "0211" "0223" "0112" "0113"
#> ola_mES_a2i_3_17.counts ola_mES_a2i_3_18.counts ola_mES_a2i_3_2.counts ola_mES_a2i_3_20.counts
#> "032" "032" "032" "032"
#> ola_mES_a2i_3_21.counts ola_mES_a2i_3_22.counts ola_mES_a2i_3_24.counts ola_mES_a2i_3_25.counts
#> "0223" "0223" "0223" "0113"
#> ola_mES_a2i_3_26.counts ola_mES_a2i_3_28.counts ola_mES_a2i_3_29.counts ola_mES_a2i_3_3.counts
#> "032" "0113" "0113" "0211"
#> ola_mES_a2i_3_30.counts ola_mES_a2i_3_31.counts ola_mES_a2i_3_33.counts ola_mES_a2i_3_34.counts
#> "032" "032" "0223" "0113"
#> ola_mES_a2i_3_37.counts ola_mES_a2i_3_38.counts ola_mES_a2i_3_39.counts ola_mES_a2i_3_4.counts
#> "0112" "0113" "0113" "0223"
#> ola_mES_a2i_3_40.counts ola_mES_a2i_3_41.counts ola_mES_a2i_3_42.counts ola_mES_a2i_3_44.counts
#> "0113" "032" "032" "0113"
#> ola_mES_a2i_3_45.counts ola_mES_a2i_3_47.counts ola_mES_a2i_3_49.counts ola_mES_a2i_3_50.counts
#> "032" "032" "0113" "0113"
#> ola_mES_a2i_3_52.counts ola_mES_a2i_3_54.counts ola_mES_a2i_3_56.counts ola_mES_a2i_3_58.counts
#> "032" "0113" "032" "0223"
#> ola_mES_a2i_3_59.counts ola_mES_a2i_3_6.counts ola_mES_a2i_3_62.counts ola_mES_a2i_3_64.counts
#> "0132" "0113" "0112" "0112"
#> ola_mES_a2i_3_65.counts ola_mES_a2i_3_66.counts ola_mES_a2i_3_67.counts ola_mES_a2i_3_68.counts
#> "0132" "0113" "032" "0222"
#> ola_mES_a2i_3_69.counts ola_mES_a2i_3_7.counts ola_mES_a2i_3_71.counts ola_mES_a2i_3_73.counts
#> "0113" "032" "0132" "0112"
#> ola_mES_a2i_3_75.counts ola_mES_a2i_3_76.counts ola_mES_a2i_3_77.counts ola_mES_a2i_3_8.counts
#> "032" "0211" "0113" "032"
#> ola_mES_a2i_3_80.counts ola_mES_a2i_3_81.counts ola_mES_a2i_3_82.counts ola_mES_a2i_3_83.counts
#> "0222" "032" "0113" "0112"
#> ola_mES_a2i_3_84.counts ola_mES_a2i_3_85.counts ola_mES_a2i_3_86.counts ola_mES_a2i_3_89.counts
#> "0113" "0112" "0113" "0132"
#> ola_mES_a2i_3_9.counts ola_mES_a2i_3_90.counts ola_mES_a2i_3_91.counts ola_mES_a2i_3_92.counts
#> "0223" "0113" "0223" "0112"
#> ola_mES_a2i_3_94.counts ola_mES_a2i_3_96.counts ola_mES_lif_1_1.counts ola_mES_lif_1_10.counts
#> "0312" "0112" "0141" "033"
#> ola_mES_lif_1_11.counts ola_mES_lif_1_12.counts ola_mES_lif_1_13.counts ola_mES_lif_1_14.counts
#> "012" "033" "0141" "033"
#> ola_mES_lif_1_15.counts ola_mES_lif_1_17.counts ola_mES_lif_1_18.counts ola_mES_lif_1_19.counts
#> "0141" "0312" "0141" "0142"
#> ola_mES_lif_1_2.counts ola_mES_lif_1_20.counts ola_mES_lif_1_21.counts ola_mES_lif_1_22.counts
#> "033" "0142" "033" "0142"
#> ola_mES_lif_1_23.counts ola_mES_lif_1_24.counts ola_mES_lif_1_25.counts ola_mES_lif_1_26.counts
#> "0312" "0142" "0142" "0142"
#> ola_mES_lif_1_28.counts ola_mES_lif_1_29.counts ola_mES_lif_1_3.counts ola_mES_lif_1_30.counts
#> "0142" "0313" "0141" "0142"
#> ola_mES_lif_1_31.counts ola_mES_lif_1_32.counts ola_mES_lif_1_33.counts ola_mES_lif_1_34.counts
#> "0313" "0142" "0142" "0141"
#> ola_mES_lif_1_35.counts ola_mES_lif_1_36.counts ola_mES_lif_1_37.counts ola_mES_lif_1_38.counts
#> "0312" "0142" "0142" "0142"
#> ola_mES_lif_1_39.counts ola_mES_lif_1_4.counts ola_mES_lif_1_41.counts ola_mES_lif_1_42.counts
#> "0142" "0143" "0142" "0142"
#> ola_mES_lif_1_43.counts ola_mES_lif_1_44.counts ola_mES_lif_1_45.counts ola_mES_lif_1_46.counts
#> "0142" "0141" "0142" "0142"
#> ola_mES_lif_1_47.counts ola_mES_lif_1_48.counts ola_mES_lif_1_49.counts ola_mES_lif_1_5.counts
#> "0141" "0141" "0141" "012"
#> ola_mES_lif_1_50.counts ola_mES_lif_1_51.counts ola_mES_lif_1_52.counts ola_mES_lif_1_53.counts
#> "0142" "012" "012" "0223"
#> ola_mES_lif_1_54.counts ola_mES_lif_1_55.counts ola_mES_lif_1_56.counts ola_mES_lif_1_57.counts
#> "0141" "012" "0222" "0223"
#> ola_mES_lif_1_58.counts ola_mES_lif_1_59.counts ola_mES_lif_1_6.counts ola_mES_lif_1_60.counts
#> "0141" "012" "0142" "0141"
#> ola_mES_lif_1_61.counts ola_mES_lif_1_62.counts ola_mES_lif_1_63.counts ola_mES_lif_1_64.counts
#> "0141" "0142" "0312" "0312"
#> ola_mES_lif_1_65.counts ola_mES_lif_1_66.counts ola_mES_lif_1_67.counts ola_mES_lif_1_68.counts
#> "0312" "0141" "0141" "0142"
#> ola_mES_lif_1_69.counts ola_mES_lif_1_7.counts ola_mES_lif_1_70.counts ola_mES_lif_1_72.counts
#> "0312" "0141" "033" "0141"
#> ola_mES_lif_1_73.counts ola_mES_lif_1_74.counts ola_mES_lif_1_75.counts ola_mES_lif_1_79.counts
#> "0142" "0312" "0312" "033"
#> ola_mES_lif_1_8.counts ola_mES_lif_1_80.counts ola_mES_lif_1_81.counts ola_mES_lif_1_82.counts
#> "0142" "0141" "0142" "0312"
#> ola_mES_lif_1_86.counts ola_mES_lif_1_9.counts ola_mES_lif_1_91.counts ola_mES_lif_1_92.counts
#> "033" "0141" "0142" "0142"
#> ola_mES_lif_1_93.counts ola_mES_lif_1_95.counts ola_mES_lif_1_96.counts ola_mES_lif_2_1.counts
#> "033" "0141" "0141" "033"
#> ola_mES_lif_2_10.counts ola_mES_lif_2_11.counts ola_mES_lif_2_12.counts ola_mES_lif_2_13.counts
#> "012" "012" "0223" "012"
#> ola_mES_lif_2_14.counts ola_mES_lif_2_15.counts ola_mES_lif_2_16.counts ola_mES_lif_2_17.counts
#> "012" "012" "012" "012"
#> ola_mES_lif_2_18.counts ola_mES_lif_2_19.counts ola_mES_lif_2_2.counts ola_mES_lif_2_20.counts
#> "012" "012" "012" "033"
#> ola_mES_lif_2_21.counts ola_mES_lif_2_23.counts ola_mES_lif_2_25.counts ola_mES_lif_2_26.counts
#> "012" "012" "033" "012"
#> ola_mES_lif_2_27.counts ola_mES_lif_2_28.counts ola_mES_lif_2_29.counts ola_mES_lif_2_3.counts
#> "033" "012" "012" "012"
#> ola_mES_lif_2_30.counts ola_mES_lif_2_31.counts ola_mES_lif_2_32.counts ola_mES_lif_2_33.counts
#> "012" "033" "012" "033"
#> ola_mES_lif_2_34.counts ola_mES_lif_2_35.counts ola_mES_lif_2_36.counts ola_mES_lif_2_37.counts
#> "012" "012" "034" "033"
#> ola_mES_lif_2_38.counts ola_mES_lif_2_39.counts ola_mES_lif_2_4.counts ola_mES_lif_2_40.counts
#> "012" "012" "012" "012"
#> ola_mES_lif_2_41.counts ola_mES_lif_2_42.counts ola_mES_lif_2_43.counts ola_mES_lif_2_44.counts
#> "033" "012" "012" "012"
#> ola_mES_lif_2_45.counts ola_mES_lif_2_46.counts ola_mES_lif_2_47.counts ola_mES_lif_2_48.counts
#> "034" "012" "033" "0223"
#> ola_mES_lif_2_49.counts ola_mES_lif_2_5.counts ola_mES_lif_2_50.counts ola_mES_lif_2_51.counts
#> "012" "012" "012" "012"
#> ola_mES_lif_2_52.counts ola_mES_lif_2_53.counts ola_mES_lif_2_54.counts ola_mES_lif_2_55.counts
#> "012" "012" "012" "012"
#> ola_mES_lif_2_56.counts ola_mES_lif_2_57.counts ola_mES_lif_2_58.counts ola_mES_lif_2_59.counts
#> "012" "034" "012" "012"
#> ola_mES_lif_2_6.counts ola_mES_lif_2_60.counts ola_mES_lif_2_61.counts ola_mES_lif_2_63.counts
#> "012" "033" "0141" "012"
#> ola_mES_lif_2_64.counts ola_mES_lif_2_65.counts ola_mES_lif_2_66.counts ola_mES_lif_2_67.counts
#> "012" "012" "012" "012"
#> ola_mES_lif_2_68.counts ola_mES_lif_2_69.counts ola_mES_lif_2_7.counts ola_mES_lif_2_70.counts
#> "012" "0223" "012" "012"
#> ola_mES_lif_2_71.counts ola_mES_lif_2_72.counts ola_mES_lif_2_73.counts ola_mES_lif_2_74.counts
#> "012" "0141" "0223" "012"
#> ola_mES_lif_2_75.counts ola_mES_lif_2_76.counts ola_mES_lif_2_77.counts ola_mES_lif_2_79.counts
#> "012" "012" "012" "033"
#> ola_mES_lif_2_8.counts ola_mES_lif_2_80.counts ola_mES_lif_2_81.counts ola_mES_lif_2_82.counts
#> "012" "012" "033" "012"
#> ola_mES_lif_2_83.counts ola_mES_lif_2_84.counts ola_mES_lif_2_86.counts ola_mES_lif_2_87.counts
#> "012" "012" "033" "012"
#> ola_mES_lif_2_89.counts ola_mES_lif_2_9.counts ola_mES_lif_2_90.counts ola_mES_lif_2_91.counts
#> "012" "033" "033" "033"
#> ola_mES_lif_2_92.counts ola_mES_lif_2_93.counts ola_mES_lif_2_94.counts ola_mES_lif_2_95.counts
#> "033" "012" "012" "012"
#> ola_mES_lif_2_96.counts ola_mES_lif_3_1.counts ola_mES_lif_3_10.counts ola_mES_lif_3_11.counts
#> "033" "0143" "012" "033"
#> ola_mES_lif_3_12.counts ola_mES_lif_3_13.counts ola_mES_lif_3_14.counts ola_mES_lif_3_15.counts
#> "0223" "0143" "034" "034"
#> ola_mES_lif_3_16.counts ola_mES_lif_3_17.counts ola_mES_lif_3_19.counts ola_mES_lif_3_2.counts
#> "034" "034" "034" "0222"
#> ola_mES_lif_3_20.counts ola_mES_lif_3_21.counts ola_mES_lif_3_22.counts ola_mES_lif_3_23.counts
#> "0311" "0222" "0313" "0311"
#> ola_mES_lif_3_24.counts ola_mES_lif_3_26.counts ola_mES_lif_3_27.counts ola_mES_lif_3_28.counts
#> "0311" "0313" "0143" "034"
#> ola_mES_lif_3_29.counts ola_mES_lif_3_3.counts ola_mES_lif_3_30.counts ola_mES_lif_3_31.counts
#> "033" "0143" "0143" "034"
#> ola_mES_lif_3_32.counts ola_mES_lif_3_33.counts ola_mES_lif_3_34.counts ola_mES_lif_3_35.counts
#> "0143" "034" "034" "034"
#> ola_mES_lif_3_36.counts ola_mES_lif_3_37.counts ola_mES_lif_3_38.counts ola_mES_lif_3_39.counts
#> "034" "033" "034" "034"
#> ola_mES_lif_3_4.counts ola_mES_lif_3_40.counts ola_mES_lif_3_42.counts ola_mES_lif_3_45.counts
#> "0143" "0143" "0141" "034"
#> ola_mES_lif_3_46.counts ola_mES_lif_3_47.counts ola_mES_lif_3_48.counts ola_mES_lif_3_49.counts
#> "034" "034" "033" "0143"
#> ola_mES_lif_3_5.counts ola_mES_lif_3_50.counts ola_mES_lif_3_51.counts ola_mES_lif_3_52.counts
#> "012" "0141" "0143" "0143"
#> ola_mES_lif_3_53.counts ola_mES_lif_3_54.counts ola_mES_lif_3_56.counts ola_mES_lif_3_57.counts
#> "0143" "0141" "012" "033"
#> ola_mES_lif_3_58.counts ola_mES_lif_3_59.counts ola_mES_lif_3_6.counts ola_mES_lif_3_60.counts
#> "0143" "0143" "0141" "0143"
#> ola_mES_lif_3_61.counts ola_mES_lif_3_63.counts ola_mES_lif_3_64.counts ola_mES_lif_3_65.counts
#> "0143" "0143" "0143" "0143"
#> ola_mES_lif_3_69.counts ola_mES_lif_3_70.counts ola_mES_lif_3_71.counts ola_mES_lif_3_72.counts
#> "034" "034" "0143" "034"
#> ola_mES_lif_3_73.counts ola_mES_lif_3_74.counts ola_mES_lif_3_75.counts ola_mES_lif_3_76.counts
#> "0311" "0311" "0313" "0311"
#> ola_mES_lif_3_77.counts ola_mES_lif_3_78.counts ola_mES_lif_3_8.counts ola_mES_lif_3_80.counts
#> "0222" "0311" "0143" "0313"
#> ola_mES_lif_3_82.counts ola_mES_lif_3_83.counts ola_mES_lif_3_84.counts ola_mES_lif_3_87.counts
#> "0313" "0313" "0313" "0312"
#> ola_mES_lif_3_88.counts ola_mES_lif_3_89.counts ola_mES_lif_3_9.counts ola_mES_lif_3_90.counts
#> "0311" "0222" "0223" "034"
#> ola_mES_lif_3_92.counts ola_mES_lif_3_94.counts ola_mES_lif_3_95.counts ola_mES_lif_3_96.counts
#> "0311" "0311" "0311" "0311"
get_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 1173))
#> ola_mES_2i_2_1.counts ola_mES_2i_2_10.counts ola_mES_2i_2_11.counts ola_mES_2i_2_12.counts
#> "0111" "0131" "0131" "0131"
#> ola_mES_2i_2_13.counts ola_mES_2i_2_14.counts ola_mES_2i_2_15.counts ola_mES_2i_2_16.counts
#> "0133" "0111" "0133" "0133"
#> ola_mES_2i_2_17.counts ola_mES_2i_2_18.counts ola_mES_2i_2_19.counts ola_mES_2i_2_2.counts
#> "0133" "0131" "0131" "0111"
#> ola_mES_2i_2_20.counts ola_mES_2i_2_21.counts ola_mES_2i_2_22.counts ola_mES_2i_2_23.counts
#> "0111" "0131" "0133" "0131"
#> ola_mES_2i_2_24.counts ola_mES_2i_2_25.counts ola_mES_2i_2_26.counts ola_mES_2i_2_27.counts
#> "0131" "0111" "0133" "0111"
#> ola_mES_2i_2_28.counts ola_mES_2i_2_29.counts ola_mES_2i_2_3.counts ola_mES_2i_2_30.counts
#> "0111" "0133" "0111" "0131"
#> ola_mES_2i_2_31.counts ola_mES_2i_2_32.counts ola_mES_2i_2_33.counts ola_mES_2i_2_34.counts
#> "0131" "0133" "0131" "0131"
#> ola_mES_2i_2_35.counts ola_mES_2i_2_36.counts ola_mES_2i_2_37.counts ola_mES_2i_2_38.counts
#> "0131" "0131" "0111" "0111"
#> ola_mES_2i_2_39.counts ola_mES_2i_2_4.counts ola_mES_2i_2_40.counts ola_mES_2i_2_41.counts
#> "0133" "0131" "0131" "0131"
#> ola_mES_2i_2_42.counts ola_mES_2i_2_43.counts ola_mES_2i_2_44.counts ola_mES_2i_2_46.counts
#> "0131" "0111" "0131" "0131"
#> ola_mES_2i_2_47.counts ola_mES_2i_2_48.counts ola_mES_2i_2_49.counts ola_mES_2i_2_5.counts
#> "0131" "0131" "0111" "0133"
#> ola_mES_2i_2_50.counts ola_mES_2i_2_51.counts ola_mES_2i_2_52.counts ola_mES_2i_2_53.counts
#> "0111" "0131" "0133" "0133"
#> ola_mES_2i_2_54.counts ola_mES_2i_2_55.counts ola_mES_2i_2_56.counts ola_mES_2i_2_57.counts
#> "0131" "0133" "0133" "0133"
#> ola_mES_2i_2_58.counts ola_mES_2i_2_59.counts ola_mES_2i_2_6.counts ola_mES_2i_2_60.counts
#> "0131" "0131" "0133" "0131"
#> ola_mES_2i_2_61.counts ola_mES_2i_2_62.counts ola_mES_2i_2_63.counts ola_mES_2i_2_64.counts
#> "0111" "032" "0111" "0133"
#> ola_mES_2i_2_66.counts ola_mES_2i_2_67.counts ola_mES_2i_2_68.counts ola_mES_2i_2_69.counts
#> "0131" "0131" "0111" "0131"
#> ola_mES_2i_2_7.counts ola_mES_2i_2_72.counts ola_mES_2i_2_73.counts ola_mES_2i_2_74.counts
#> "0133" "0133" "0111" "0133"
#> ola_mES_2i_2_75.counts ola_mES_2i_2_76.counts ola_mES_2i_2_79.counts ola_mES_2i_2_8.counts
#> "0111" "0111" "0131" "0133"
#> ola_mES_2i_2_80.counts ola_mES_2i_2_81.counts ola_mES_2i_2_85.counts ola_mES_2i_2_86.counts
#> "0133" "0131" "0111" "0111"
#> ola_mES_2i_2_87.counts ola_mES_2i_2_90.counts ola_mES_2i_2_91.counts ola_mES_2i_2_93.counts
#> "032" "0131" "0133" "0131"
#> ola_mES_2i_2_94.counts ola_mES_2i_2_95.counts ola_mES_2i_3_10.counts ola_mES_2i_3_11.counts
#> "0131" "0133" "0222" "023"
#> ola_mES_2i_3_12.counts ola_mES_2i_3_13.counts ola_mES_2i_3_15.counts ola_mES_2i_3_16.counts
#> "0221" "0221" "0221" "021"
#> ola_mES_2i_3_17.counts ola_mES_2i_3_18.counts ola_mES_2i_3_19.counts ola_mES_2i_3_2.counts
#> "032" "032" "0221" "0221"
#> ola_mES_2i_3_20.counts ola_mES_2i_3_21.counts ola_mES_2i_3_23.counts ola_mES_2i_3_25.counts
#> "0221" "0222" "021" "023"
#> ola_mES_2i_3_26.counts ola_mES_2i_3_28.counts ola_mES_2i_3_30.counts ola_mES_2i_3_31.counts
#> "0221" "021" "0221" "032"
#> ola_mES_2i_3_33.counts ola_mES_2i_3_36.counts ola_mES_2i_3_37.counts ola_mES_2i_3_38.counts
#> "0221" "021" "0221" "0221"
#> ola_mES_2i_3_39.counts ola_mES_2i_3_4.counts ola_mES_2i_3_41.counts ola_mES_2i_3_42.counts
#> "0222" "021" "0222" "0221"
#> ola_mES_2i_3_43.counts ola_mES_2i_3_45.counts ola_mES_2i_3_46.counts ola_mES_2i_3_47.counts
#> "021" "0221" "0221" "0221"
#> ola_mES_2i_3_49.counts ola_mES_2i_3_5.counts ola_mES_2i_3_52.counts ola_mES_2i_3_54.counts
#> "032" "0221" "0221" "0222"
#> ola_mES_2i_3_56.counts ola_mES_2i_3_57.counts ola_mES_2i_3_58.counts ola_mES_2i_3_6.counts
#> "0221" "0221" "0221" "0222"
#> ola_mES_2i_3_62.counts ola_mES_2i_3_65.counts ola_mES_2i_3_66.counts ola_mES_2i_3_69.counts
#> "032" "0221" "032" "0222"
#> ola_mES_2i_3_7.counts ola_mES_2i_3_73.counts ola_mES_2i_3_76.counts ola_mES_2i_3_77.counts
#> "0221" "0221" "0221" "0222"
#> ola_mES_2i_3_78.counts ola_mES_2i_3_79.counts ola_mES_2i_3_8.counts ola_mES_2i_3_80.counts
#> "032" "0221" "0221" "0221"
#> ola_mES_2i_3_82.counts ola_mES_2i_3_83.counts ola_mES_2i_3_84.counts ola_mES_2i_3_87.counts
#> "0221" "0221" "021" "0222"
#> ola_mES_2i_3_88.counts ola_mES_2i_3_90.counts ola_mES_2i_3_91.counts ola_mES_2i_3_92.counts
#> "0221" "021" "0222" "0221"
#> ola_mES_2i_3_96.counts ola_mES_2i_4_1.counts ola_mES_2i_4_10.counts ola_mES_2i_4_11.counts
#> "0222" "021" "021" "021"
#> ola_mES_2i_4_13.counts ola_mES_2i_4_17.counts ola_mES_2i_4_18.counts ola_mES_2i_4_2.counts
#> "021" "021" "023" "0132"
#> ola_mES_2i_4_20.counts ola_mES_2i_4_21.counts ola_mES_2i_4_22.counts ola_mES_2i_4_23.counts
#> "021" "021" "023" "0221"
#> ola_mES_2i_4_24.counts ola_mES_2i_4_25.counts ola_mES_2i_4_26.counts ola_mES_2i_4_27.counts
#> "021" "021" "021" "023"
#> ola_mES_2i_4_29.counts ola_mES_2i_4_30.counts ola_mES_2i_4_31.counts ola_mES_2i_4_32.counts
#> "021" "021" "021" "021"
#> ola_mES_2i_4_33.counts ola_mES_2i_4_34.counts ola_mES_2i_4_35.counts ola_mES_2i_4_36.counts
#> "021" "021" "021" "021"
#> ola_mES_2i_4_37.counts ola_mES_2i_4_38.counts ola_mES_2i_4_39.counts ola_mES_2i_4_40.counts
#> "032" "0112" "021" "0112"
#> ola_mES_2i_4_42.counts ola_mES_2i_4_43.counts ola_mES_2i_4_44.counts ola_mES_2i_4_46.counts
#> "032" "021" "021" "021"
#> ola_mES_2i_4_49.counts ola_mES_2i_4_5.counts ola_mES_2i_4_50.counts ola_mES_2i_4_52.counts
#> "0112" "023" "021" "021"
#> ola_mES_2i_4_53.counts ola_mES_2i_4_54.counts ola_mES_2i_4_56.counts ola_mES_2i_4_57.counts
#> "0112" "032" "021" "021"
#> ola_mES_2i_4_58.counts ola_mES_2i_4_59.counts ola_mES_2i_4_6.counts ola_mES_2i_4_60.counts
#> "021" "021" "021" "0221"
#> ola_mES_2i_4_61.counts ola_mES_2i_4_62.counts ola_mES_2i_4_63.counts ola_mES_2i_4_64.counts
#> "021" "032" "0221" "021"
#> ola_mES_2i_4_65.counts ola_mES_2i_4_66.counts ola_mES_2i_4_67.counts ola_mES_2i_4_68.counts
#> "021" "021" "0112" "0221"
#> ola_mES_2i_4_69.counts ola_mES_2i_4_7.counts ola_mES_2i_4_70.counts ola_mES_2i_4_71.counts
#> "021" "023" "0221" "021"
#> ola_mES_2i_4_72.counts ola_mES_2i_4_73.counts ola_mES_2i_4_75.counts ola_mES_2i_4_77.counts
#> "023" "021" "0222" "021"
#> ola_mES_2i_4_78.counts ola_mES_2i_4_80.counts ola_mES_2i_4_81.counts ola_mES_2i_4_83.counts
#> "023" "021" "021" "023"
#> ola_mES_2i_4_84.counts ola_mES_2i_4_88.counts ola_mES_2i_4_89.counts ola_mES_2i_4_9.counts
#> "023" "0221" "0221" "023"
#> ola_mES_2i_4_90.counts ola_mES_2i_4_91.counts ola_mES_2i_4_94.counts ola_mES_2i_4_95.counts
#> "021" "021" "0221" "0222"
#> ola_mES_2i_4_96.counts ola_mES_2i_5_1.counts ola_mES_2i_5_10.counts ola_mES_2i_5_11.counts
#> "0222" "0221" "023" "023"
#> ola_mES_2i_5_12.counts ola_mES_2i_5_13.counts ola_mES_2i_5_14.counts ola_mES_2i_5_15.counts
#> "023" "023" "021" "021"
#> ola_mES_2i_5_16.counts ola_mES_2i_5_17.counts ola_mES_2i_5_18.counts ola_mES_2i_5_19.counts
#> "021" "021" "023" "021"
#> ola_mES_2i_5_2.counts ola_mES_2i_5_20.counts ola_mES_2i_5_21.counts ola_mES_2i_5_23.counts
#> "0221" "021" "021" "023"
#> ola_mES_2i_5_24.counts ola_mES_2i_5_25.counts ola_mES_2i_5_26.counts ola_mES_2i_5_27.counts
#> "021" "021" "021" "021"
#> ola_mES_2i_5_28.counts ola_mES_2i_5_29.counts ola_mES_2i_5_3.counts ola_mES_2i_5_30.counts
#> "0221" "0221" "0221" "021"
#> ola_mES_2i_5_31.counts ola_mES_2i_5_32.counts ola_mES_2i_5_34.counts ola_mES_2i_5_35.counts
#> "021" "021" "021" "021"
#> ola_mES_2i_5_36.counts ola_mES_2i_5_38.counts ola_mES_2i_5_39.counts ola_mES_2i_5_4.counts
#> "021" "021" "021" "0221"
#> ola_mES_2i_5_40.counts ola_mES_2i_5_41.counts ola_mES_2i_5_42.counts ola_mES_2i_5_44.counts
#> "021" "0112" "021" "021"
#> ola_mES_2i_5_45.counts ola_mES_2i_5_46.counts ola_mES_2i_5_47.counts ola_mES_2i_5_48.counts
#> "021" "021" "021" "021"
#> ola_mES_2i_5_49.counts ola_mES_2i_5_5.counts ola_mES_2i_5_51.counts ola_mES_2i_5_52.counts
#> "021" "023" "021" "0221"
#> ola_mES_2i_5_53.counts ola_mES_2i_5_54.counts ola_mES_2i_5_55.counts ola_mES_2i_5_56.counts
#> "021" "021" "021" "021"
#> ola_mES_2i_5_57.counts ola_mES_2i_5_58.counts ola_mES_2i_5_59.counts ola_mES_2i_5_6.counts
#> "021" "021" "021" "0221"
#> ola_mES_2i_5_61.counts ola_mES_2i_5_62.counts ola_mES_2i_5_63.counts ola_mES_2i_5_65.counts
#> "021" "0221" "021" "023"
#> ola_mES_2i_5_66.counts ola_mES_2i_5_67.counts ola_mES_2i_5_68.counts ola_mES_2i_5_69.counts
#> "0221" "021" "021" "021"
#> ola_mES_2i_5_7.counts ola_mES_2i_5_70.counts ola_mES_2i_5_72.counts ola_mES_2i_5_73.counts
#> "021" "021" "021" "023"
#> ola_mES_2i_5_74.counts ola_mES_2i_5_75.counts ola_mES_2i_5_76.counts ola_mES_2i_5_77.counts
#> "021" "023" "023" "023"
#> ola_mES_2i_5_78.counts ola_mES_2i_5_8.counts ola_mES_2i_5_81.counts ola_mES_2i_5_82.counts
#> "021" "021" "0221" "021"
#> ola_mES_2i_5_83.counts ola_mES_2i_5_85.counts ola_mES_2i_5_86.counts ola_mES_2i_5_87.counts
#> "023" "0221" "023" "023"
#> ola_mES_2i_5_88.counts ola_mES_2i_5_9.counts ola_mES_2i_5_90.counts ola_mES_2i_5_92.counts
#> "023" "0222" "021" "021"
#> ola_mES_2i_5_94.counts ola_mES_2i_5_95.counts ola_mES_2i_5_96.counts ola_mES_a2i_2_1.counts
#> "021" "021" "021" "0132"
#> ola_mES_a2i_2_10.counts ola_mES_a2i_2_11.counts ola_mES_a2i_2_12.counts ola_mES_a2i_2_13.counts
#> "0112" "0132" "0112" "0111"
#> ola_mES_a2i_2_14.counts ola_mES_a2i_2_15.counts ola_mES_a2i_2_16.counts ola_mES_a2i_2_17.counts
#> "0132" "0132" "0132" "0132"
#> ola_mES_a2i_2_18.counts ola_mES_a2i_2_19.counts ola_mES_a2i_2_2.counts ola_mES_a2i_2_20.counts
#> "0132" "0132" "0132" "0112"
#> ola_mES_a2i_2_21.counts ola_mES_a2i_2_22.counts ola_mES_a2i_2_23.counts ola_mES_a2i_2_24.counts
#> "0132" "0132" "0132" "0132"
#> ola_mES_a2i_2_25.counts ola_mES_a2i_2_26.counts ola_mES_a2i_2_27.counts ola_mES_a2i_2_28.counts
#> "0113" "032" "0112" "0112"
#> ola_mES_a2i_2_29.counts ola_mES_a2i_2_3.counts ola_mES_a2i_2_30.counts ola_mES_a2i_2_31.counts
#> "0112" "0132" "0222" "032"
#> ola_mES_a2i_2_32.counts ola_mES_a2i_2_33.counts ola_mES_a2i_2_34.counts ola_mES_a2i_2_35.counts
#> "032" "0112" "0112" "0312"
#> ola_mES_a2i_2_36.counts ola_mES_a2i_2_37.counts ola_mES_a2i_2_38.counts ola_mES_a2i_2_39.counts
#> "0222" "032" "0112" "032"
#> ola_mES_a2i_2_4.counts ola_mES_a2i_2_40.counts ola_mES_a2i_2_41.counts ola_mES_a2i_2_42.counts
#> "0112" "0112" "0132" "0132"
#> ola_mES_a2i_2_43.counts ola_mES_a2i_2_44.counts ola_mES_a2i_2_45.counts ola_mES_a2i_2_46.counts
#> "0111" "0113" "0132" "0132"
#> ola_mES_a2i_2_47.counts ola_mES_a2i_2_48.counts ola_mES_a2i_2_49.counts ola_mES_a2i_2_5.counts
#> "0112" "0112" "0113" "0132"
#> ola_mES_a2i_2_50.counts ola_mES_a2i_2_51.counts ola_mES_a2i_2_52.counts ola_mES_a2i_2_53.counts
#> "032" "0112" "0112" "032"
#> ola_mES_a2i_2_54.counts ola_mES_a2i_2_55.counts ola_mES_a2i_2_56.counts ola_mES_a2i_2_57.counts
#> "0112" "0132" "0111" "0132"
#> ola_mES_a2i_2_59.counts ola_mES_a2i_2_6.counts ola_mES_a2i_2_60.counts ola_mES_a2i_2_61.counts
#> "0132" "032" "0112" "0111"
#> ola_mES_a2i_2_62.counts ola_mES_a2i_2_63.counts ola_mES_a2i_2_64.counts ola_mES_a2i_2_65.counts
#> "0112" "0111" "0112" "0111"
#> ola_mES_a2i_2_66.counts ola_mES_a2i_2_67.counts ola_mES_a2i_2_68.counts ola_mES_a2i_2_69.counts
#> "0132" "0112" "0113" "032"
#> ola_mES_a2i_2_7.counts ola_mES_a2i_2_70.counts ola_mES_a2i_2_71.counts ola_mES_a2i_2_72.counts
#> "0112" "0112" "0132" "0112"
#> ola_mES_a2i_2_73.counts ola_mES_a2i_2_74.counts ola_mES_a2i_2_75.counts ola_mES_a2i_2_76.counts
#> "0113" "0111" "0112" "0111"
#> ola_mES_a2i_2_77.counts ola_mES_a2i_2_78.counts ola_mES_a2i_2_8.counts ola_mES_a2i_2_80.counts
#> "0112" "0132" "0132" "0113"
#> ola_mES_a2i_2_81.counts ola_mES_a2i_2_82.counts ola_mES_a2i_2_83.counts ola_mES_a2i_2_84.counts
#> "0112" "0132" "0132" "0112"
#> ola_mES_a2i_2_86.counts ola_mES_a2i_2_87.counts ola_mES_a2i_2_88.counts ola_mES_a2i_2_89.counts
#> "0113" "0112" "0112" "0223"
#> ola_mES_a2i_2_9.counts ola_mES_a2i_2_90.counts ola_mES_a2i_2_91.counts ola_mES_a2i_2_92.counts
#> "0113" "0132" "032" "032"
#> ola_mES_a2i_2_93.counts ola_mES_a2i_2_94.counts ola_mES_a2i_2_95.counts ola_mES_a2i_2_96.counts
#> "0111" "0132" "0132" "0132"
#> ola_mES_a2i_3_10.counts ola_mES_a2i_3_11.counts ola_mES_a2i_3_13.counts ola_mES_a2i_3_16.counts
#> "021" "0223" "0112" "0113"
#> ola_mES_a2i_3_17.counts ola_mES_a2i_3_18.counts ola_mES_a2i_3_2.counts ola_mES_a2i_3_20.counts
#> "032" "032" "032" "032"
#> ola_mES_a2i_3_21.counts ola_mES_a2i_3_22.counts ola_mES_a2i_3_24.counts ola_mES_a2i_3_25.counts
#> "0223" "0223" "0223" "0113"
#> ola_mES_a2i_3_26.counts ola_mES_a2i_3_28.counts ola_mES_a2i_3_29.counts ola_mES_a2i_3_3.counts
#> "032" "0113" "0113" "021"
#> ola_mES_a2i_3_30.counts ola_mES_a2i_3_31.counts ola_mES_a2i_3_33.counts ola_mES_a2i_3_34.counts
#> "032" "032" "0223" "0113"
#> ola_mES_a2i_3_37.counts ola_mES_a2i_3_38.counts ola_mES_a2i_3_39.counts ola_mES_a2i_3_4.counts
#> "0112" "0113" "0113" "0223"
#> ola_mES_a2i_3_40.counts ola_mES_a2i_3_41.counts ola_mES_a2i_3_42.counts ola_mES_a2i_3_44.counts
#> "0113" "032" "032" "0113"
#> ola_mES_a2i_3_45.counts ola_mES_a2i_3_47.counts ola_mES_a2i_3_49.counts ola_mES_a2i_3_50.counts
#> "032" "032" "0113" "0113"
#> ola_mES_a2i_3_52.counts ola_mES_a2i_3_54.counts ola_mES_a2i_3_56.counts ola_mES_a2i_3_58.counts
#> "032" "0113" "032" "0223"
#> ola_mES_a2i_3_59.counts ola_mES_a2i_3_6.counts ola_mES_a2i_3_62.counts ola_mES_a2i_3_64.counts
#> "0132" "0113" "0112" "0112"
#> ola_mES_a2i_3_65.counts ola_mES_a2i_3_66.counts ola_mES_a2i_3_67.counts ola_mES_a2i_3_68.counts
#> "0132" "0113" "032" "0222"
#> ola_mES_a2i_3_69.counts ola_mES_a2i_3_7.counts ola_mES_a2i_3_71.counts ola_mES_a2i_3_73.counts
#> "0113" "032" "0132" "0112"
#> ola_mES_a2i_3_75.counts ola_mES_a2i_3_76.counts ola_mES_a2i_3_77.counts ola_mES_a2i_3_8.counts
#> "032" "021" "0113" "032"
#> ola_mES_a2i_3_80.counts ola_mES_a2i_3_81.counts ola_mES_a2i_3_82.counts ola_mES_a2i_3_83.counts
#> "0222" "032" "0113" "0112"
#> ola_mES_a2i_3_84.counts ola_mES_a2i_3_85.counts ola_mES_a2i_3_86.counts ola_mES_a2i_3_89.counts
#> "0113" "0112" "0113" "0132"
#> ola_mES_a2i_3_9.counts ola_mES_a2i_3_90.counts ola_mES_a2i_3_91.counts ola_mES_a2i_3_92.counts
#> "0223" "0113" "0223" "0112"
#> ola_mES_a2i_3_94.counts ola_mES_a2i_3_96.counts ola_mES_lif_1_1.counts ola_mES_lif_1_10.counts
#> "0312" "0112" "0141" "033"
#> ola_mES_lif_1_11.counts ola_mES_lif_1_12.counts ola_mES_lif_1_13.counts ola_mES_lif_1_14.counts
#> "012" "033" "0141" "033"
#> ola_mES_lif_1_15.counts ola_mES_lif_1_17.counts ola_mES_lif_1_18.counts ola_mES_lif_1_19.counts
#> "0141" "0312" "0141" "0142"
#> ola_mES_lif_1_2.counts ola_mES_lif_1_20.counts ola_mES_lif_1_21.counts ola_mES_lif_1_22.counts
#> "033" "0142" "033" "0142"
#> ola_mES_lif_1_23.counts ola_mES_lif_1_24.counts ola_mES_lif_1_25.counts ola_mES_lif_1_26.counts
#> "0312" "0142" "0142" "0142"
#> ola_mES_lif_1_28.counts ola_mES_lif_1_29.counts ola_mES_lif_1_3.counts ola_mES_lif_1_30.counts
#> "0142" "0313" "0141" "0142"
#> ola_mES_lif_1_31.counts ola_mES_lif_1_32.counts ola_mES_lif_1_33.counts ola_mES_lif_1_34.counts
#> "0313" "0142" "0142" "0141"
#> ola_mES_lif_1_35.counts ola_mES_lif_1_36.counts ola_mES_lif_1_37.counts ola_mES_lif_1_38.counts
#> "0312" "0142" "0142" "0142"
#> ola_mES_lif_1_39.counts ola_mES_lif_1_4.counts ola_mES_lif_1_41.counts ola_mES_lif_1_42.counts
#> "0142" "0143" "0142" "0142"
#> ola_mES_lif_1_43.counts ola_mES_lif_1_44.counts ola_mES_lif_1_45.counts ola_mES_lif_1_46.counts
#> "0142" "0141" "0142" "0142"
#> ola_mES_lif_1_47.counts ola_mES_lif_1_48.counts ola_mES_lif_1_49.counts ola_mES_lif_1_5.counts
#> "0141" "0141" "0141" "012"
#> ola_mES_lif_1_50.counts ola_mES_lif_1_51.counts ola_mES_lif_1_52.counts ola_mES_lif_1_53.counts
#> "0142" "012" "012" "0223"
#> ola_mES_lif_1_54.counts ola_mES_lif_1_55.counts ola_mES_lif_1_56.counts ola_mES_lif_1_57.counts
#> "0141" "012" "0222" "0223"
#> ola_mES_lif_1_58.counts ola_mES_lif_1_59.counts ola_mES_lif_1_6.counts ola_mES_lif_1_60.counts
#> "0141" "012" "0142" "0141"
#> ola_mES_lif_1_61.counts ola_mES_lif_1_62.counts ola_mES_lif_1_63.counts ola_mES_lif_1_64.counts
#> "0141" "0142" "0312" "0312"
#> ola_mES_lif_1_65.counts ola_mES_lif_1_66.counts ola_mES_lif_1_67.counts ola_mES_lif_1_68.counts
#> "0312" "0141" "0141" "0142"
#> ola_mES_lif_1_69.counts ola_mES_lif_1_7.counts ola_mES_lif_1_70.counts ola_mES_lif_1_72.counts
#> "0312" "0141" "033" "0141"
#> ola_mES_lif_1_73.counts ola_mES_lif_1_74.counts ola_mES_lif_1_75.counts ola_mES_lif_1_79.counts
#> "0142" "0312" "0312" "033"
#> ola_mES_lif_1_8.counts ola_mES_lif_1_80.counts ola_mES_lif_1_81.counts ola_mES_lif_1_82.counts
#> "0142" "0141" "0142" "0312"
#> ola_mES_lif_1_86.counts ola_mES_lif_1_9.counts ola_mES_lif_1_91.counts ola_mES_lif_1_92.counts
#> "033" "0141" "0142" "0142"
#> ola_mES_lif_1_93.counts ola_mES_lif_1_95.counts ola_mES_lif_1_96.counts ola_mES_lif_2_1.counts
#> "033" "0141" "0141" "033"
#> ola_mES_lif_2_10.counts ola_mES_lif_2_11.counts ola_mES_lif_2_12.counts ola_mES_lif_2_13.counts
#> "012" "012" "0223" "012"
#> ola_mES_lif_2_14.counts ola_mES_lif_2_15.counts ola_mES_lif_2_16.counts ola_mES_lif_2_17.counts
#> "012" "012" "012" "012"
#> ola_mES_lif_2_18.counts ola_mES_lif_2_19.counts ola_mES_lif_2_2.counts ola_mES_lif_2_20.counts
#> "012" "012" "012" "033"
#> ola_mES_lif_2_21.counts ola_mES_lif_2_23.counts ola_mES_lif_2_25.counts ola_mES_lif_2_26.counts
#> "012" "012" "033" "012"
#> ola_mES_lif_2_27.counts ola_mES_lif_2_28.counts ola_mES_lif_2_29.counts ola_mES_lif_2_3.counts
#> "033" "012" "012" "012"
#> ola_mES_lif_2_30.counts ola_mES_lif_2_31.counts ola_mES_lif_2_32.counts ola_mES_lif_2_33.counts
#> "012" "033" "012" "033"
#> ola_mES_lif_2_34.counts ola_mES_lif_2_35.counts ola_mES_lif_2_36.counts ola_mES_lif_2_37.counts
#> "012" "012" "034" "033"
#> ola_mES_lif_2_38.counts ola_mES_lif_2_39.counts ola_mES_lif_2_4.counts ola_mES_lif_2_40.counts
#> "012" "012" "012" "012"
#> ola_mES_lif_2_41.counts ola_mES_lif_2_42.counts ola_mES_lif_2_43.counts ola_mES_lif_2_44.counts
#> "033" "012" "012" "012"
#> ola_mES_lif_2_45.counts ola_mES_lif_2_46.counts ola_mES_lif_2_47.counts ola_mES_lif_2_48.counts
#> "034" "012" "033" "0223"
#> ola_mES_lif_2_49.counts ola_mES_lif_2_5.counts ola_mES_lif_2_50.counts ola_mES_lif_2_51.counts
#> "012" "012" "012" "012"
#> ola_mES_lif_2_52.counts ola_mES_lif_2_53.counts ola_mES_lif_2_54.counts ola_mES_lif_2_55.counts
#> "012" "012" "012" "012"
#> ola_mES_lif_2_56.counts ola_mES_lif_2_57.counts ola_mES_lif_2_58.counts ola_mES_lif_2_59.counts
#> "012" "034" "012" "012"
#> ola_mES_lif_2_6.counts ola_mES_lif_2_60.counts ola_mES_lif_2_61.counts ola_mES_lif_2_63.counts
#> "012" "033" "0141" "012"
#> ola_mES_lif_2_64.counts ola_mES_lif_2_65.counts ola_mES_lif_2_66.counts ola_mES_lif_2_67.counts
#> "012" "012" "012" "012"
#> ola_mES_lif_2_68.counts ola_mES_lif_2_69.counts ola_mES_lif_2_7.counts ola_mES_lif_2_70.counts
#> "012" "0223" "012" "012"
#> ola_mES_lif_2_71.counts ola_mES_lif_2_72.counts ola_mES_lif_2_73.counts ola_mES_lif_2_74.counts
#> "012" "0141" "0223" "012"
#> ola_mES_lif_2_75.counts ola_mES_lif_2_76.counts ola_mES_lif_2_77.counts ola_mES_lif_2_79.counts
#> "012" "012" "012" "033"
#> ola_mES_lif_2_8.counts ola_mES_lif_2_80.counts ola_mES_lif_2_81.counts ola_mES_lif_2_82.counts
#> "012" "012" "033" "012"
#> ola_mES_lif_2_83.counts ola_mES_lif_2_84.counts ola_mES_lif_2_86.counts ola_mES_lif_2_87.counts
#> "012" "012" "033" "012"
#> ola_mES_lif_2_89.counts ola_mES_lif_2_9.counts ola_mES_lif_2_90.counts ola_mES_lif_2_91.counts
#> "012" "033" "033" "033"
#> ola_mES_lif_2_92.counts ola_mES_lif_2_93.counts ola_mES_lif_2_94.counts ola_mES_lif_2_95.counts
#> "033" "012" "012" "012"
#> ola_mES_lif_2_96.counts ola_mES_lif_3_1.counts ola_mES_lif_3_10.counts ola_mES_lif_3_11.counts
#> "033" "0143" "012" "033"
#> ola_mES_lif_3_12.counts ola_mES_lif_3_13.counts ola_mES_lif_3_14.counts ola_mES_lif_3_15.counts
#> "0223" "0143" "034" "034"
#> ola_mES_lif_3_16.counts ola_mES_lif_3_17.counts ola_mES_lif_3_19.counts ola_mES_lif_3_2.counts
#> "034" "034" "034" "0222"
#> ola_mES_lif_3_20.counts ola_mES_lif_3_21.counts ola_mES_lif_3_22.counts ola_mES_lif_3_23.counts
#> "0311" "0222" "0313" "0311"
#> ola_mES_lif_3_24.counts ola_mES_lif_3_26.counts ola_mES_lif_3_27.counts ola_mES_lif_3_28.counts
#> "0311" "0313" "0143" "034"
#> ola_mES_lif_3_29.counts ola_mES_lif_3_3.counts ola_mES_lif_3_30.counts ola_mES_lif_3_31.counts
#> "033" "0143" "0143" "034"
#> ola_mES_lif_3_32.counts ola_mES_lif_3_33.counts ola_mES_lif_3_34.counts ola_mES_lif_3_35.counts
#> "0143" "034" "034" "034"
#> ola_mES_lif_3_36.counts ola_mES_lif_3_37.counts ola_mES_lif_3_38.counts ola_mES_lif_3_39.counts
#> "034" "033" "034" "034"
#> ola_mES_lif_3_4.counts ola_mES_lif_3_40.counts ola_mES_lif_3_42.counts ola_mES_lif_3_45.counts
#> "0143" "0143" "0141" "034"
#> ola_mES_lif_3_46.counts ola_mES_lif_3_47.counts ola_mES_lif_3_48.counts ola_mES_lif_3_49.counts
#> "034" "034" "033" "0143"
#> ola_mES_lif_3_5.counts ola_mES_lif_3_50.counts ola_mES_lif_3_51.counts ola_mES_lif_3_52.counts
#> "012" "0141" "0143" "0143"
#> ola_mES_lif_3_53.counts ola_mES_lif_3_54.counts ola_mES_lif_3_56.counts ola_mES_lif_3_57.counts
#> "0143" "0141" "012" "033"
#> ola_mES_lif_3_58.counts ola_mES_lif_3_59.counts ola_mES_lif_3_6.counts ola_mES_lif_3_60.counts
#> "0143" "0143" "0141" "0143"
#> ola_mES_lif_3_61.counts ola_mES_lif_3_63.counts ola_mES_lif_3_64.counts ola_mES_lif_3_65.counts
#> "0143" "0143" "0143" "0143"
#> ola_mES_lif_3_69.counts ola_mES_lif_3_70.counts ola_mES_lif_3_71.counts ola_mES_lif_3_72.counts
#> "034" "034" "0143" "034"
#> ola_mES_lif_3_73.counts ola_mES_lif_3_74.counts ola_mES_lif_3_75.counts ola_mES_lif_3_76.counts
#> "0311" "0311" "0313" "0311"
#> ola_mES_lif_3_77.counts ola_mES_lif_3_78.counts ola_mES_lif_3_8.counts ola_mES_lif_3_80.counts
#> "0222" "0311" "0143" "0313"
#> ola_mES_lif_3_82.counts ola_mES_lif_3_83.counts ola_mES_lif_3_84.counts ola_mES_lif_3_87.counts
#> "0313" "0313" "0313" "0312"
#> ola_mES_lif_3_88.counts ola_mES_lif_3_89.counts ola_mES_lif_3_9.counts ola_mES_lif_3_90.counts
#> "0311" "0222" "0223" "034"
#> ola_mES_lif_3_92.counts ola_mES_lif_3_94.counts ola_mES_lif_3_95.counts ola_mES_lif_3_96.counts
#> "0311" "0311" "0311" "0311"
get_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 2396))
#> ola_mES_2i_2_1.counts ola_mES_2i_2_10.counts ola_mES_2i_2_11.counts ola_mES_2i_2_12.counts
#> "0111" "0131" "0131" "0131"
#> ola_mES_2i_2_13.counts ola_mES_2i_2_14.counts ola_mES_2i_2_15.counts ola_mES_2i_2_16.counts
#> "0133" "0111" "0133" "0133"
#> ola_mES_2i_2_17.counts ola_mES_2i_2_18.counts ola_mES_2i_2_19.counts ola_mES_2i_2_2.counts
#> "0133" "0131" "0131" "0111"
#> ola_mES_2i_2_20.counts ola_mES_2i_2_21.counts ola_mES_2i_2_22.counts ola_mES_2i_2_23.counts
#> "0111" "0131" "0133" "0131"
#> ola_mES_2i_2_24.counts ola_mES_2i_2_25.counts ola_mES_2i_2_26.counts ola_mES_2i_2_27.counts
#> "0131" "0111" "0133" "0111"
#> ola_mES_2i_2_28.counts ola_mES_2i_2_29.counts ola_mES_2i_2_3.counts ola_mES_2i_2_30.counts
#> "0111" "0133" "0111" "0131"
#> ola_mES_2i_2_31.counts ola_mES_2i_2_32.counts ola_mES_2i_2_33.counts ola_mES_2i_2_34.counts
#> "0131" "0133" "0131" "0131"
#> ola_mES_2i_2_35.counts ola_mES_2i_2_36.counts ola_mES_2i_2_37.counts ola_mES_2i_2_38.counts
#> "0131" "0131" "0111" "0111"
#> ola_mES_2i_2_39.counts ola_mES_2i_2_4.counts ola_mES_2i_2_40.counts ola_mES_2i_2_41.counts
#> "0133" "0131" "0131" "0131"
#> ola_mES_2i_2_42.counts ola_mES_2i_2_43.counts ola_mES_2i_2_44.counts ola_mES_2i_2_46.counts
#> "0131" "0111" "0131" "0131"
#> ola_mES_2i_2_47.counts ola_mES_2i_2_48.counts ola_mES_2i_2_49.counts ola_mES_2i_2_5.counts
#> "0131" "0131" "0111" "0133"
#> ola_mES_2i_2_50.counts ola_mES_2i_2_51.counts ola_mES_2i_2_52.counts ola_mES_2i_2_53.counts
#> "0111" "0131" "0133" "0133"
#> ola_mES_2i_2_54.counts ola_mES_2i_2_55.counts ola_mES_2i_2_56.counts ola_mES_2i_2_57.counts
#> "0131" "0133" "0133" "0133"
#> ola_mES_2i_2_58.counts ola_mES_2i_2_59.counts ola_mES_2i_2_6.counts ola_mES_2i_2_60.counts
#> "0131" "0131" "0133" "0131"
#> ola_mES_2i_2_61.counts ola_mES_2i_2_62.counts ola_mES_2i_2_63.counts ola_mES_2i_2_64.counts
#> "0111" "032" "0111" "0133"
#> ola_mES_2i_2_66.counts ola_mES_2i_2_67.counts ola_mES_2i_2_68.counts ola_mES_2i_2_69.counts
#> "0131" "0131" "0111" "0131"
#> ola_mES_2i_2_7.counts ola_mES_2i_2_72.counts ola_mES_2i_2_73.counts ola_mES_2i_2_74.counts
#> "0133" "0133" "0111" "0133"
#> ola_mES_2i_2_75.counts ola_mES_2i_2_76.counts ola_mES_2i_2_79.counts ola_mES_2i_2_8.counts
#> "0111" "0111" "0131" "0133"
#> ola_mES_2i_2_80.counts ola_mES_2i_2_81.counts ola_mES_2i_2_85.counts ola_mES_2i_2_86.counts
#> "0133" "0131" "0111" "0111"
#> ola_mES_2i_2_87.counts ola_mES_2i_2_90.counts ola_mES_2i_2_91.counts ola_mES_2i_2_93.counts
#> "032" "0131" "0133" "0131"
#> ola_mES_2i_2_94.counts ola_mES_2i_2_95.counts ola_mES_2i_3_10.counts ola_mES_2i_3_11.counts
#> "0131" "0133" "0222" "023"
#> ola_mES_2i_3_12.counts ola_mES_2i_3_13.counts ola_mES_2i_3_15.counts ola_mES_2i_3_16.counts
#> "0221" "0221" "0221" "021"
#> ola_mES_2i_3_17.counts ola_mES_2i_3_18.counts ola_mES_2i_3_19.counts ola_mES_2i_3_2.counts
#> "032" "032" "0221" "0221"
#> ola_mES_2i_3_20.counts ola_mES_2i_3_21.counts ola_mES_2i_3_23.counts ola_mES_2i_3_25.counts
#> "0221" "0222" "021" "023"
#> ola_mES_2i_3_26.counts ola_mES_2i_3_28.counts ola_mES_2i_3_30.counts ola_mES_2i_3_31.counts
#> "0221" "021" "0221" "032"
#> ola_mES_2i_3_33.counts ola_mES_2i_3_36.counts ola_mES_2i_3_37.counts ola_mES_2i_3_38.counts
#> "0221" "021" "0221" "0221"
#> ola_mES_2i_3_39.counts ola_mES_2i_3_4.counts ola_mES_2i_3_41.counts ola_mES_2i_3_42.counts
#> "0222" "021" "0222" "0221"
#> ola_mES_2i_3_43.counts ola_mES_2i_3_45.counts ola_mES_2i_3_46.counts ola_mES_2i_3_47.counts
#> "021" "0221" "0221" "0221"
#> ola_mES_2i_3_49.counts ola_mES_2i_3_5.counts ola_mES_2i_3_52.counts ola_mES_2i_3_54.counts
#> "032" "0221" "0221" "0222"
#> ola_mES_2i_3_56.counts ola_mES_2i_3_57.counts ola_mES_2i_3_58.counts ola_mES_2i_3_6.counts
#> "0221" "0221" "0221" "0222"
#> ola_mES_2i_3_62.counts ola_mES_2i_3_65.counts ola_mES_2i_3_66.counts ola_mES_2i_3_69.counts
#> "032" "0221" "032" "0222"
#> ola_mES_2i_3_7.counts ola_mES_2i_3_73.counts ola_mES_2i_3_76.counts ola_mES_2i_3_77.counts
#> "0221" "0221" "0221" "0222"
#> ola_mES_2i_3_78.counts ola_mES_2i_3_79.counts ola_mES_2i_3_8.counts ola_mES_2i_3_80.counts
#> "032" "0221" "0221" "0221"
#> ola_mES_2i_3_82.counts ola_mES_2i_3_83.counts ola_mES_2i_3_84.counts ola_mES_2i_3_87.counts
#> "0221" "0221" "021" "0222"
#> ola_mES_2i_3_88.counts ola_mES_2i_3_90.counts ola_mES_2i_3_91.counts ola_mES_2i_3_92.counts
#> "0221" "021" "0222" "0221"
#> ola_mES_2i_3_96.counts ola_mES_2i_4_1.counts ola_mES_2i_4_10.counts ola_mES_2i_4_11.counts
#> "0222" "021" "021" "021"
#> ola_mES_2i_4_13.counts ola_mES_2i_4_17.counts ola_mES_2i_4_18.counts ola_mES_2i_4_2.counts
#> "021" "021" "023" "0132"
#> ola_mES_2i_4_20.counts ola_mES_2i_4_21.counts ola_mES_2i_4_22.counts ola_mES_2i_4_23.counts
#> "021" "021" "023" "0221"
#> ola_mES_2i_4_24.counts ola_mES_2i_4_25.counts ola_mES_2i_4_26.counts ola_mES_2i_4_27.counts
#> "021" "021" "021" "023"
#> ola_mES_2i_4_29.counts ola_mES_2i_4_30.counts ola_mES_2i_4_31.counts ola_mES_2i_4_32.counts
#> "021" "021" "021" "021"
#> ola_mES_2i_4_33.counts ola_mES_2i_4_34.counts ola_mES_2i_4_35.counts ola_mES_2i_4_36.counts
#> "021" "021" "021" "021"
#> ola_mES_2i_4_37.counts ola_mES_2i_4_38.counts ola_mES_2i_4_39.counts ola_mES_2i_4_40.counts
#> "032" "0112" "021" "0112"
#> ola_mES_2i_4_42.counts ola_mES_2i_4_43.counts ola_mES_2i_4_44.counts ola_mES_2i_4_46.counts
#> "032" "021" "021" "021"
#> ola_mES_2i_4_49.counts ola_mES_2i_4_5.counts ola_mES_2i_4_50.counts ola_mES_2i_4_52.counts
#> "0112" "023" "021" "021"
#> ola_mES_2i_4_53.counts ola_mES_2i_4_54.counts ola_mES_2i_4_56.counts ola_mES_2i_4_57.counts
#> "0112" "032" "021" "021"
#> ola_mES_2i_4_58.counts ola_mES_2i_4_59.counts ola_mES_2i_4_6.counts ola_mES_2i_4_60.counts
#> "021" "021" "021" "0221"
#> ola_mES_2i_4_61.counts ola_mES_2i_4_62.counts ola_mES_2i_4_63.counts ola_mES_2i_4_64.counts
#> "021" "032" "0221" "021"
#> ola_mES_2i_4_65.counts ola_mES_2i_4_66.counts ola_mES_2i_4_67.counts ola_mES_2i_4_68.counts
#> "021" "021" "0112" "0221"
#> ola_mES_2i_4_69.counts ola_mES_2i_4_7.counts ola_mES_2i_4_70.counts ola_mES_2i_4_71.counts
#> "021" "023" "0221" "021"
#> ola_mES_2i_4_72.counts ola_mES_2i_4_73.counts ola_mES_2i_4_75.counts ola_mES_2i_4_77.counts
#> "023" "021" "0222" "021"
#> ola_mES_2i_4_78.counts ola_mES_2i_4_80.counts ola_mES_2i_4_81.counts ola_mES_2i_4_83.counts
#> "023" "021" "021" "023"
#> ola_mES_2i_4_84.counts ola_mES_2i_4_88.counts ola_mES_2i_4_89.counts ola_mES_2i_4_9.counts
#> "023" "0221" "0221" "023"
#> ola_mES_2i_4_90.counts ola_mES_2i_4_91.counts ola_mES_2i_4_94.counts ola_mES_2i_4_95.counts
#> "021" "021" "0221" "0222"
#> ola_mES_2i_4_96.counts ola_mES_2i_5_1.counts ola_mES_2i_5_10.counts ola_mES_2i_5_11.counts
#> "0222" "0221" "023" "023"
#> ola_mES_2i_5_12.counts ola_mES_2i_5_13.counts ola_mES_2i_5_14.counts ola_mES_2i_5_15.counts
#> "023" "023" "021" "021"
#> ola_mES_2i_5_16.counts ola_mES_2i_5_17.counts ola_mES_2i_5_18.counts ola_mES_2i_5_19.counts
#> "021" "021" "023" "021"
#> ola_mES_2i_5_2.counts ola_mES_2i_5_20.counts ola_mES_2i_5_21.counts ola_mES_2i_5_23.counts
#> "0221" "021" "021" "023"
#> ola_mES_2i_5_24.counts ola_mES_2i_5_25.counts ola_mES_2i_5_26.counts ola_mES_2i_5_27.counts
#> "021" "021" "021" "021"
#> ola_mES_2i_5_28.counts ola_mES_2i_5_29.counts ola_mES_2i_5_3.counts ola_mES_2i_5_30.counts
#> "0221" "0221" "0221" "021"
#> ola_mES_2i_5_31.counts ola_mES_2i_5_32.counts ola_mES_2i_5_34.counts ola_mES_2i_5_35.counts
#> "021" "021" "021" "021"
#> ola_mES_2i_5_36.counts ola_mES_2i_5_38.counts ola_mES_2i_5_39.counts ola_mES_2i_5_4.counts
#> "021" "021" "021" "0221"
#> ola_mES_2i_5_40.counts ola_mES_2i_5_41.counts ola_mES_2i_5_42.counts ola_mES_2i_5_44.counts
#> "021" "0112" "021" "021"
#> ola_mES_2i_5_45.counts ola_mES_2i_5_46.counts ola_mES_2i_5_47.counts ola_mES_2i_5_48.counts
#> "021" "021" "021" "021"
#> ola_mES_2i_5_49.counts ola_mES_2i_5_5.counts ola_mES_2i_5_51.counts ola_mES_2i_5_52.counts
#> "021" "023" "021" "0221"
#> ola_mES_2i_5_53.counts ola_mES_2i_5_54.counts ola_mES_2i_5_55.counts ola_mES_2i_5_56.counts
#> "021" "021" "021" "021"
#> ola_mES_2i_5_57.counts ola_mES_2i_5_58.counts ola_mES_2i_5_59.counts ola_mES_2i_5_6.counts
#> "021" "021" "021" "0221"
#> ola_mES_2i_5_61.counts ola_mES_2i_5_62.counts ola_mES_2i_5_63.counts ola_mES_2i_5_65.counts
#> "021" "0221" "021" "023"
#> ola_mES_2i_5_66.counts ola_mES_2i_5_67.counts ola_mES_2i_5_68.counts ola_mES_2i_5_69.counts
#> "0221" "021" "021" "021"
#> ola_mES_2i_5_7.counts ola_mES_2i_5_70.counts ola_mES_2i_5_72.counts ola_mES_2i_5_73.counts
#> "021" "021" "021" "023"
#> ola_mES_2i_5_74.counts ola_mES_2i_5_75.counts ola_mES_2i_5_76.counts ola_mES_2i_5_77.counts
#> "021" "023" "023" "023"
#> ola_mES_2i_5_78.counts ola_mES_2i_5_8.counts ola_mES_2i_5_81.counts ola_mES_2i_5_82.counts
#> "021" "021" "0221" "021"
#> ola_mES_2i_5_83.counts ola_mES_2i_5_85.counts ola_mES_2i_5_86.counts ola_mES_2i_5_87.counts
#> "023" "0221" "023" "023"
#> ola_mES_2i_5_88.counts ola_mES_2i_5_9.counts ola_mES_2i_5_90.counts ola_mES_2i_5_92.counts
#> "023" "0222" "021" "021"
#> ola_mES_2i_5_94.counts ola_mES_2i_5_95.counts ola_mES_2i_5_96.counts ola_mES_a2i_2_1.counts
#> "021" "021" "021" "0132"
#> ola_mES_a2i_2_10.counts ola_mES_a2i_2_11.counts ola_mES_a2i_2_12.counts ola_mES_a2i_2_13.counts
#> "0112" "0132" "0112" "0111"
#> ola_mES_a2i_2_14.counts ola_mES_a2i_2_15.counts ola_mES_a2i_2_16.counts ola_mES_a2i_2_17.counts
#> "0132" "0132" "0132" "0132"
#> ola_mES_a2i_2_18.counts ola_mES_a2i_2_19.counts ola_mES_a2i_2_2.counts ola_mES_a2i_2_20.counts
#> "0132" "0132" "0132" "0112"
#> ola_mES_a2i_2_21.counts ola_mES_a2i_2_22.counts ola_mES_a2i_2_23.counts ola_mES_a2i_2_24.counts
#> "0132" "0132" "0132" "0132"
#> ola_mES_a2i_2_25.counts ola_mES_a2i_2_26.counts ola_mES_a2i_2_27.counts ola_mES_a2i_2_28.counts
#> "0113" "032" "0112" "0112"
#> ola_mES_a2i_2_29.counts ola_mES_a2i_2_3.counts ola_mES_a2i_2_30.counts ola_mES_a2i_2_31.counts
#> "0112" "0132" "0222" "032"
#> ola_mES_a2i_2_32.counts ola_mES_a2i_2_33.counts ola_mES_a2i_2_34.counts ola_mES_a2i_2_35.counts
#> "032" "0112" "0112" "031"
#> ola_mES_a2i_2_36.counts ola_mES_a2i_2_37.counts ola_mES_a2i_2_38.counts ola_mES_a2i_2_39.counts
#> "0222" "032" "0112" "032"
#> ola_mES_a2i_2_4.counts ola_mES_a2i_2_40.counts ola_mES_a2i_2_41.counts ola_mES_a2i_2_42.counts
#> "0112" "0112" "0132" "0132"
#> ola_mES_a2i_2_43.counts ola_mES_a2i_2_44.counts ola_mES_a2i_2_45.counts ola_mES_a2i_2_46.counts
#> "0111" "0113" "0132" "0132"
#> ola_mES_a2i_2_47.counts ola_mES_a2i_2_48.counts ola_mES_a2i_2_49.counts ola_mES_a2i_2_5.counts
#> "0112" "0112" "0113" "0132"
#> ola_mES_a2i_2_50.counts ola_mES_a2i_2_51.counts ola_mES_a2i_2_52.counts ola_mES_a2i_2_53.counts
#> "032" "0112" "0112" "032"
#> ola_mES_a2i_2_54.counts ola_mES_a2i_2_55.counts ola_mES_a2i_2_56.counts ola_mES_a2i_2_57.counts
#> "0112" "0132" "0111" "0132"
#> ola_mES_a2i_2_59.counts ola_mES_a2i_2_6.counts ola_mES_a2i_2_60.counts ola_mES_a2i_2_61.counts
#> "0132" "032" "0112" "0111"
#> ola_mES_a2i_2_62.counts ola_mES_a2i_2_63.counts ola_mES_a2i_2_64.counts ola_mES_a2i_2_65.counts
#> "0112" "0111" "0112" "0111"
#> ola_mES_a2i_2_66.counts ola_mES_a2i_2_67.counts ola_mES_a2i_2_68.counts ola_mES_a2i_2_69.counts
#> "0132" "0112" "0113" "032"
#> ola_mES_a2i_2_7.counts ola_mES_a2i_2_70.counts ola_mES_a2i_2_71.counts ola_mES_a2i_2_72.counts
#> "0112" "0112" "0132" "0112"
#> ola_mES_a2i_2_73.counts ola_mES_a2i_2_74.counts ola_mES_a2i_2_75.counts ola_mES_a2i_2_76.counts
#> "0113" "0111" "0112" "0111"
#> ola_mES_a2i_2_77.counts ola_mES_a2i_2_78.counts ola_mES_a2i_2_8.counts ola_mES_a2i_2_80.counts
#> "0112" "0132" "0132" "0113"
#> ola_mES_a2i_2_81.counts ola_mES_a2i_2_82.counts ola_mES_a2i_2_83.counts ola_mES_a2i_2_84.counts
#> "0112" "0132" "0132" "0112"
#> ola_mES_a2i_2_86.counts ola_mES_a2i_2_87.counts ola_mES_a2i_2_88.counts ola_mES_a2i_2_89.counts
#> "0113" "0112" "0112" "0223"
#> ola_mES_a2i_2_9.counts ola_mES_a2i_2_90.counts ola_mES_a2i_2_91.counts ola_mES_a2i_2_92.counts
#> "0113" "0132" "032" "032"
#> ola_mES_a2i_2_93.counts ola_mES_a2i_2_94.counts ola_mES_a2i_2_95.counts ola_mES_a2i_2_96.counts
#> "0111" "0132" "0132" "0132"
#> ola_mES_a2i_3_10.counts ola_mES_a2i_3_11.counts ola_mES_a2i_3_13.counts ola_mES_a2i_3_16.counts
#> "021" "0223" "0112" "0113"
#> ola_mES_a2i_3_17.counts ola_mES_a2i_3_18.counts ola_mES_a2i_3_2.counts ola_mES_a2i_3_20.counts
#> "032" "032" "032" "032"
#> ola_mES_a2i_3_21.counts ola_mES_a2i_3_22.counts ola_mES_a2i_3_24.counts ola_mES_a2i_3_25.counts
#> "0223" "0223" "0223" "0113"
#> ola_mES_a2i_3_26.counts ola_mES_a2i_3_28.counts ola_mES_a2i_3_29.counts ola_mES_a2i_3_3.counts
#> "032" "0113" "0113" "021"
#> ola_mES_a2i_3_30.counts ola_mES_a2i_3_31.counts ola_mES_a2i_3_33.counts ola_mES_a2i_3_34.counts
#> "032" "032" "0223" "0113"
#> ola_mES_a2i_3_37.counts ola_mES_a2i_3_38.counts ola_mES_a2i_3_39.counts ola_mES_a2i_3_4.counts
#> "0112" "0113" "0113" "0223"
#> ola_mES_a2i_3_40.counts ola_mES_a2i_3_41.counts ola_mES_a2i_3_42.counts ola_mES_a2i_3_44.counts
#> "0113" "032" "032" "0113"
#> ola_mES_a2i_3_45.counts ola_mES_a2i_3_47.counts ola_mES_a2i_3_49.counts ola_mES_a2i_3_50.counts
#> "032" "032" "0113" "0113"
#> ola_mES_a2i_3_52.counts ola_mES_a2i_3_54.counts ola_mES_a2i_3_56.counts ola_mES_a2i_3_58.counts
#> "032" "0113" "032" "0223"
#> ola_mES_a2i_3_59.counts ola_mES_a2i_3_6.counts ola_mES_a2i_3_62.counts ola_mES_a2i_3_64.counts
#> "0132" "0113" "0112" "0112"
#> ola_mES_a2i_3_65.counts ola_mES_a2i_3_66.counts ola_mES_a2i_3_67.counts ola_mES_a2i_3_68.counts
#> "0132" "0113" "032" "0222"
#> ola_mES_a2i_3_69.counts ola_mES_a2i_3_7.counts ola_mES_a2i_3_71.counts ola_mES_a2i_3_73.counts
#> "0113" "032" "0132" "0112"
#> ola_mES_a2i_3_75.counts ola_mES_a2i_3_76.counts ola_mES_a2i_3_77.counts ola_mES_a2i_3_8.counts
#> "032" "021" "0113" "032"
#> ola_mES_a2i_3_80.counts ola_mES_a2i_3_81.counts ola_mES_a2i_3_82.counts ola_mES_a2i_3_83.counts
#> "0222" "032" "0113" "0112"
#> ola_mES_a2i_3_84.counts ola_mES_a2i_3_85.counts ola_mES_a2i_3_86.counts ola_mES_a2i_3_89.counts
#> "0113" "0112" "0113" "0132"
#> ola_mES_a2i_3_9.counts ola_mES_a2i_3_90.counts ola_mES_a2i_3_91.counts ola_mES_a2i_3_92.counts
#> "0223" "0113" "0223" "0112"
#> ola_mES_a2i_3_94.counts ola_mES_a2i_3_96.counts ola_mES_lif_1_1.counts ola_mES_lif_1_10.counts
#> "031" "0112" "0141" "033"
#> ola_mES_lif_1_11.counts ola_mES_lif_1_12.counts ola_mES_lif_1_13.counts ola_mES_lif_1_14.counts
#> "012" "033" "0141" "033"
#> ola_mES_lif_1_15.counts ola_mES_lif_1_17.counts ola_mES_lif_1_18.counts ola_mES_lif_1_19.counts
#> "0141" "031" "0141" "0142"
#> ola_mES_lif_1_2.counts ola_mES_lif_1_20.counts ola_mES_lif_1_21.counts ola_mES_lif_1_22.counts
#> "033" "0142" "033" "0142"
#> ola_mES_lif_1_23.counts ola_mES_lif_1_24.counts ola_mES_lif_1_25.counts ola_mES_lif_1_26.counts
#> "031" "0142" "0142" "0142"
#> ola_mES_lif_1_28.counts ola_mES_lif_1_29.counts ola_mES_lif_1_3.counts ola_mES_lif_1_30.counts
#> "0142" "031" "0141" "0142"
#> ola_mES_lif_1_31.counts ola_mES_lif_1_32.counts ola_mES_lif_1_33.counts ola_mES_lif_1_34.counts
#> "031" "0142" "0142" "0141"
#> ola_mES_lif_1_35.counts ola_mES_lif_1_36.counts ola_mES_lif_1_37.counts ola_mES_lif_1_38.counts
#> "031" "0142" "0142" "0142"
#> ola_mES_lif_1_39.counts ola_mES_lif_1_4.counts ola_mES_lif_1_41.counts ola_mES_lif_1_42.counts
#> "0142" "0143" "0142" "0142"
#> ola_mES_lif_1_43.counts ola_mES_lif_1_44.counts ola_mES_lif_1_45.counts ola_mES_lif_1_46.counts
#> "0142" "0141" "0142" "0142"
#> ola_mES_lif_1_47.counts ola_mES_lif_1_48.counts ola_mES_lif_1_49.counts ola_mES_lif_1_5.counts
#> "0141" "0141" "0141" "012"
#> ola_mES_lif_1_50.counts ola_mES_lif_1_51.counts ola_mES_lif_1_52.counts ola_mES_lif_1_53.counts
#> "0142" "012" "012" "0223"
#> ola_mES_lif_1_54.counts ola_mES_lif_1_55.counts ola_mES_lif_1_56.counts ola_mES_lif_1_57.counts
#> "0141" "012" "0222" "0223"
#> ola_mES_lif_1_58.counts ola_mES_lif_1_59.counts ola_mES_lif_1_6.counts ola_mES_lif_1_60.counts
#> "0141" "012" "0142" "0141"
#> ola_mES_lif_1_61.counts ola_mES_lif_1_62.counts ola_mES_lif_1_63.counts ola_mES_lif_1_64.counts
#> "0141" "0142" "031" "031"
#> ola_mES_lif_1_65.counts ola_mES_lif_1_66.counts ola_mES_lif_1_67.counts ola_mES_lif_1_68.counts
#> "031" "0141" "0141" "0142"
#> ola_mES_lif_1_69.counts ola_mES_lif_1_7.counts ola_mES_lif_1_70.counts ola_mES_lif_1_72.counts
#> "031" "0141" "033" "0141"
#> ola_mES_lif_1_73.counts ola_mES_lif_1_74.counts ola_mES_lif_1_75.counts ola_mES_lif_1_79.counts
#> "0142" "031" "031" "033"
#> ola_mES_lif_1_8.counts ola_mES_lif_1_80.counts ola_mES_lif_1_81.counts ola_mES_lif_1_82.counts
#> "0142" "0141" "0142" "031"
#> ola_mES_lif_1_86.counts ola_mES_lif_1_9.counts ola_mES_lif_1_91.counts ola_mES_lif_1_92.counts
#> "033" "0141" "0142" "0142"
#> ola_mES_lif_1_93.counts ola_mES_lif_1_95.counts ola_mES_lif_1_96.counts ola_mES_lif_2_1.counts
#> "033" "0141" "0141" "033"
#> ola_mES_lif_2_10.counts ola_mES_lif_2_11.counts ola_mES_lif_2_12.counts ola_mES_lif_2_13.counts
#> "012" "012" "0223" "012"
#> ola_mES_lif_2_14.counts ola_mES_lif_2_15.counts ola_mES_lif_2_16.counts ola_mES_lif_2_17.counts
#> "012" "012" "012" "012"
#> ola_mES_lif_2_18.counts ola_mES_lif_2_19.counts ola_mES_lif_2_2.counts ola_mES_lif_2_20.counts
#> "012" "012" "012" "033"
#> ola_mES_lif_2_21.counts ola_mES_lif_2_23.counts ola_mES_lif_2_25.counts ola_mES_lif_2_26.counts
#> "012" "012" "033" "012"
#> ola_mES_lif_2_27.counts ola_mES_lif_2_28.counts ola_mES_lif_2_29.counts ola_mES_lif_2_3.counts
#> "033" "012" "012" "012"
#> ola_mES_lif_2_30.counts ola_mES_lif_2_31.counts ola_mES_lif_2_32.counts ola_mES_lif_2_33.counts
#> "012" "033" "012" "033"
#> ola_mES_lif_2_34.counts ola_mES_lif_2_35.counts ola_mES_lif_2_36.counts ola_mES_lif_2_37.counts
#> "012" "012" "034" "033"
#> ola_mES_lif_2_38.counts ola_mES_lif_2_39.counts ola_mES_lif_2_4.counts ola_mES_lif_2_40.counts
#> "012" "012" "012" "012"
#> ola_mES_lif_2_41.counts ola_mES_lif_2_42.counts ola_mES_lif_2_43.counts ola_mES_lif_2_44.counts
#> "033" "012" "012" "012"
#> ola_mES_lif_2_45.counts ola_mES_lif_2_46.counts ola_mES_lif_2_47.counts ola_mES_lif_2_48.counts
#> "034" "012" "033" "0223"
#> ola_mES_lif_2_49.counts ola_mES_lif_2_5.counts ola_mES_lif_2_50.counts ola_mES_lif_2_51.counts
#> "012" "012" "012" "012"
#> ola_mES_lif_2_52.counts ola_mES_lif_2_53.counts ola_mES_lif_2_54.counts ola_mES_lif_2_55.counts
#> "012" "012" "012" "012"
#> ola_mES_lif_2_56.counts ola_mES_lif_2_57.counts ola_mES_lif_2_58.counts ola_mES_lif_2_59.counts
#> "012" "034" "012" "012"
#> ola_mES_lif_2_6.counts ola_mES_lif_2_60.counts ola_mES_lif_2_61.counts ola_mES_lif_2_63.counts
#> "012" "033" "0141" "012"
#> ola_mES_lif_2_64.counts ola_mES_lif_2_65.counts ola_mES_lif_2_66.counts ola_mES_lif_2_67.counts
#> "012" "012" "012" "012"
#> ola_mES_lif_2_68.counts ola_mES_lif_2_69.counts ola_mES_lif_2_7.counts ola_mES_lif_2_70.counts
#> "012" "0223" "012" "012"
#> ola_mES_lif_2_71.counts ola_mES_lif_2_72.counts ola_mES_lif_2_73.counts ola_mES_lif_2_74.counts
#> "012" "0141" "0223" "012"
#> ola_mES_lif_2_75.counts ola_mES_lif_2_76.counts ola_mES_lif_2_77.counts ola_mES_lif_2_79.counts
#> "012" "012" "012" "033"
#> ola_mES_lif_2_8.counts ola_mES_lif_2_80.counts ola_mES_lif_2_81.counts ola_mES_lif_2_82.counts
#> "012" "012" "033" "012"
#> ola_mES_lif_2_83.counts ola_mES_lif_2_84.counts ola_mES_lif_2_86.counts ola_mES_lif_2_87.counts
#> "012" "012" "033" "012"
#> ola_mES_lif_2_89.counts ola_mES_lif_2_9.counts ola_mES_lif_2_90.counts ola_mES_lif_2_91.counts
#> "012" "033" "033" "033"
#> ola_mES_lif_2_92.counts ola_mES_lif_2_93.counts ola_mES_lif_2_94.counts ola_mES_lif_2_95.counts
#> "033" "012" "012" "012"
#> ola_mES_lif_2_96.counts ola_mES_lif_3_1.counts ola_mES_lif_3_10.counts ola_mES_lif_3_11.counts
#> "033" "0143" "012" "033"
#> ola_mES_lif_3_12.counts ola_mES_lif_3_13.counts ola_mES_lif_3_14.counts ola_mES_lif_3_15.counts
#> "0223" "0143" "034" "034"
#> ola_mES_lif_3_16.counts ola_mES_lif_3_17.counts ola_mES_lif_3_19.counts ola_mES_lif_3_2.counts
#> "034" "034" "034" "0222"
#> ola_mES_lif_3_20.counts ola_mES_lif_3_21.counts ola_mES_lif_3_22.counts ola_mES_lif_3_23.counts
#> "031" "0222" "031" "031"
#> ola_mES_lif_3_24.counts ola_mES_lif_3_26.counts ola_mES_lif_3_27.counts ola_mES_lif_3_28.counts
#> "031" "031" "0143" "034"
#> ola_mES_lif_3_29.counts ola_mES_lif_3_3.counts ola_mES_lif_3_30.counts ola_mES_lif_3_31.counts
#> "033" "0143" "0143" "034"
#> ola_mES_lif_3_32.counts ola_mES_lif_3_33.counts ola_mES_lif_3_34.counts ola_mES_lif_3_35.counts
#> "0143" "034" "034" "034"
#> ola_mES_lif_3_36.counts ola_mES_lif_3_37.counts ola_mES_lif_3_38.counts ola_mES_lif_3_39.counts
#> "034" "033" "034" "034"
#> ola_mES_lif_3_4.counts ola_mES_lif_3_40.counts ola_mES_lif_3_42.counts ola_mES_lif_3_45.counts
#> "0143" "0143" "0141" "034"
#> ola_mES_lif_3_46.counts ola_mES_lif_3_47.counts ola_mES_lif_3_48.counts ola_mES_lif_3_49.counts
#> "034" "034" "033" "0143"
#> ola_mES_lif_3_5.counts ola_mES_lif_3_50.counts ola_mES_lif_3_51.counts ola_mES_lif_3_52.counts
#> "012" "0141" "0143" "0143"
#> ola_mES_lif_3_53.counts ola_mES_lif_3_54.counts ola_mES_lif_3_56.counts ola_mES_lif_3_57.counts
#> "0143" "0141" "012" "033"
#> ola_mES_lif_3_58.counts ola_mES_lif_3_59.counts ola_mES_lif_3_6.counts ola_mES_lif_3_60.counts
#> "0143" "0143" "0141" "0143"
#> ola_mES_lif_3_61.counts ola_mES_lif_3_63.counts ola_mES_lif_3_64.counts ola_mES_lif_3_65.counts
#> "0143" "0143" "0143" "0143"
#> ola_mES_lif_3_69.counts ola_mES_lif_3_70.counts ola_mES_lif_3_71.counts ola_mES_lif_3_72.counts
#> "034" "034" "0143" "034"
#> ola_mES_lif_3_73.counts ola_mES_lif_3_74.counts ola_mES_lif_3_75.counts ola_mES_lif_3_76.counts
#> "031" "031" "031" "031"
#> ola_mES_lif_3_77.counts ola_mES_lif_3_78.counts ola_mES_lif_3_8.counts ola_mES_lif_3_80.counts
#> "0222" "031" "0143" "031"
#> ola_mES_lif_3_82.counts ola_mES_lif_3_83.counts ola_mES_lif_3_84.counts ola_mES_lif_3_87.counts
#> "031" "031" "031" "031"
#> ola_mES_lif_3_88.counts ola_mES_lif_3_89.counts ola_mES_lif_3_9.counts ola_mES_lif_3_90.counts
#> "031" "0222" "0223" "034"
#> ola_mES_lif_3_92.counts ola_mES_lif_3_94.counts ola_mES_lif_3_95.counts ola_mES_lif_3_96.counts
#> "031" "031" "031" "031"
get_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 2997))
#> ola_mES_2i_2_1.counts ola_mES_2i_2_10.counts ola_mES_2i_2_11.counts ola_mES_2i_2_12.counts
#> "0111" "0131" "0131" "0131"
#> ola_mES_2i_2_13.counts ola_mES_2i_2_14.counts ola_mES_2i_2_15.counts ola_mES_2i_2_16.counts
#> "0133" "0111" "0133" "0133"
#> ola_mES_2i_2_17.counts ola_mES_2i_2_18.counts ola_mES_2i_2_19.counts ola_mES_2i_2_2.counts
#> "0133" "0131" "0131" "0111"
#> ola_mES_2i_2_20.counts ola_mES_2i_2_21.counts ola_mES_2i_2_22.counts ola_mES_2i_2_23.counts
#> "0111" "0131" "0133" "0131"
#> ola_mES_2i_2_24.counts ola_mES_2i_2_25.counts ola_mES_2i_2_26.counts ola_mES_2i_2_27.counts
#> "0131" "0111" "0133" "0111"
#> ola_mES_2i_2_28.counts ola_mES_2i_2_29.counts ola_mES_2i_2_3.counts ola_mES_2i_2_30.counts
#> "0111" "0133" "0111" "0131"
#> ola_mES_2i_2_31.counts ola_mES_2i_2_32.counts ola_mES_2i_2_33.counts ola_mES_2i_2_34.counts
#> "0131" "0133" "0131" "0131"
#> ola_mES_2i_2_35.counts ola_mES_2i_2_36.counts ola_mES_2i_2_37.counts ola_mES_2i_2_38.counts
#> "0131" "0131" "0111" "0111"
#> ola_mES_2i_2_39.counts ola_mES_2i_2_4.counts ola_mES_2i_2_40.counts ola_mES_2i_2_41.counts
#> "0133" "0131" "0131" "0131"
#> ola_mES_2i_2_42.counts ola_mES_2i_2_43.counts ola_mES_2i_2_44.counts ola_mES_2i_2_46.counts
#> "0131" "0111" "0131" "0131"
#> ola_mES_2i_2_47.counts ola_mES_2i_2_48.counts ola_mES_2i_2_49.counts ola_mES_2i_2_5.counts
#> "0131" "0131" "0111" "0133"
#> ola_mES_2i_2_50.counts ola_mES_2i_2_51.counts ola_mES_2i_2_52.counts ola_mES_2i_2_53.counts
#> "0111" "0131" "0133" "0133"
#> ola_mES_2i_2_54.counts ola_mES_2i_2_55.counts ola_mES_2i_2_56.counts ola_mES_2i_2_57.counts
#> "0131" "0133" "0133" "0133"
#> ola_mES_2i_2_58.counts ola_mES_2i_2_59.counts ola_mES_2i_2_6.counts ola_mES_2i_2_60.counts
#> "0131" "0131" "0133" "0131"
#> ola_mES_2i_2_61.counts ola_mES_2i_2_62.counts ola_mES_2i_2_63.counts ola_mES_2i_2_64.counts
#> "0111" "032" "0111" "0133"
#> ola_mES_2i_2_66.counts ola_mES_2i_2_67.counts ola_mES_2i_2_68.counts ola_mES_2i_2_69.counts
#> "0131" "0131" "0111" "0131"
#> ola_mES_2i_2_7.counts ola_mES_2i_2_72.counts ola_mES_2i_2_73.counts ola_mES_2i_2_74.counts
#> "0133" "0133" "0111" "0133"
#> ola_mES_2i_2_75.counts ola_mES_2i_2_76.counts ola_mES_2i_2_79.counts ola_mES_2i_2_8.counts
#> "0111" "0111" "0131" "0133"
#> ola_mES_2i_2_80.counts ola_mES_2i_2_81.counts ola_mES_2i_2_85.counts ola_mES_2i_2_86.counts
#> "0133" "0131" "0111" "0111"
#> ola_mES_2i_2_87.counts ola_mES_2i_2_90.counts ola_mES_2i_2_91.counts ola_mES_2i_2_93.counts
#> "032" "0131" "0133" "0131"
#> ola_mES_2i_2_94.counts ola_mES_2i_2_95.counts ola_mES_2i_3_10.counts ola_mES_2i_3_11.counts
#> "0131" "0133" "0222" "023"
#> ola_mES_2i_3_12.counts ola_mES_2i_3_13.counts ola_mES_2i_3_15.counts ola_mES_2i_3_16.counts
#> "0221" "0221" "0221" "021"
#> ola_mES_2i_3_17.counts ola_mES_2i_3_18.counts ola_mES_2i_3_19.counts ola_mES_2i_3_2.counts
#> "032" "032" "0221" "0221"
#> ola_mES_2i_3_20.counts ola_mES_2i_3_21.counts ola_mES_2i_3_23.counts ola_mES_2i_3_25.counts
#> "0221" "0222" "021" "023"
#> ola_mES_2i_3_26.counts ola_mES_2i_3_28.counts ola_mES_2i_3_30.counts ola_mES_2i_3_31.counts
#> "0221" "021" "0221" "032"
#> ola_mES_2i_3_33.counts ola_mES_2i_3_36.counts ola_mES_2i_3_37.counts ola_mES_2i_3_38.counts
#> "0221" "021" "0221" "0221"
#> ola_mES_2i_3_39.counts ola_mES_2i_3_4.counts ola_mES_2i_3_41.counts ola_mES_2i_3_42.counts
#> "0222" "021" "0222" "0221"
#> ola_mES_2i_3_43.counts ola_mES_2i_3_45.counts ola_mES_2i_3_46.counts ola_mES_2i_3_47.counts
#> "021" "0221" "0221" "0221"
#> ola_mES_2i_3_49.counts ola_mES_2i_3_5.counts ola_mES_2i_3_52.counts ola_mES_2i_3_54.counts
#> "032" "0221" "0221" "0222"
#> ola_mES_2i_3_56.counts ola_mES_2i_3_57.counts ola_mES_2i_3_58.counts ola_mES_2i_3_6.counts
#> "0221" "0221" "0221" "0222"
#> ola_mES_2i_3_62.counts ola_mES_2i_3_65.counts ola_mES_2i_3_66.counts ola_mES_2i_3_69.counts
#> "032" "0221" "032" "0222"
#> ola_mES_2i_3_7.counts ola_mES_2i_3_73.counts ola_mES_2i_3_76.counts ola_mES_2i_3_77.counts
#> "0221" "0221" "0221" "0222"
#> ola_mES_2i_3_78.counts ola_mES_2i_3_79.counts ola_mES_2i_3_8.counts ola_mES_2i_3_80.counts
#> "032" "0221" "0221" "0221"
#> ola_mES_2i_3_82.counts ola_mES_2i_3_83.counts ola_mES_2i_3_84.counts ola_mES_2i_3_87.counts
#> "0221" "0221" "021" "0222"
#> ola_mES_2i_3_88.counts ola_mES_2i_3_90.counts ola_mES_2i_3_91.counts ola_mES_2i_3_92.counts
#> "0221" "021" "0222" "0221"
#> ola_mES_2i_3_96.counts ola_mES_2i_4_1.counts ola_mES_2i_4_10.counts ola_mES_2i_4_11.counts
#> "0222" "021" "021" "021"
#> ola_mES_2i_4_13.counts ola_mES_2i_4_17.counts ola_mES_2i_4_18.counts ola_mES_2i_4_2.counts
#> "021" "021" "023" "0132"
#> ola_mES_2i_4_20.counts ola_mES_2i_4_21.counts ola_mES_2i_4_22.counts ola_mES_2i_4_23.counts
#> "021" "021" "023" "0221"
#> ola_mES_2i_4_24.counts ola_mES_2i_4_25.counts ola_mES_2i_4_26.counts ola_mES_2i_4_27.counts
#> "021" "021" "021" "023"
#> ola_mES_2i_4_29.counts ola_mES_2i_4_30.counts ola_mES_2i_4_31.counts ola_mES_2i_4_32.counts
#> "021" "021" "021" "021"
#> ola_mES_2i_4_33.counts ola_mES_2i_4_34.counts ola_mES_2i_4_35.counts ola_mES_2i_4_36.counts
#> "021" "021" "021" "021"
#> ola_mES_2i_4_37.counts ola_mES_2i_4_38.counts ola_mES_2i_4_39.counts ola_mES_2i_4_40.counts
#> "032" "0112" "021" "0112"
#> ola_mES_2i_4_42.counts ola_mES_2i_4_43.counts ola_mES_2i_4_44.counts ola_mES_2i_4_46.counts
#> "032" "021" "021" "021"
#> ola_mES_2i_4_49.counts ola_mES_2i_4_5.counts ola_mES_2i_4_50.counts ola_mES_2i_4_52.counts
#> "0112" "023" "021" "021"
#> ola_mES_2i_4_53.counts ola_mES_2i_4_54.counts ola_mES_2i_4_56.counts ola_mES_2i_4_57.counts
#> "0112" "032" "021" "021"
#> ola_mES_2i_4_58.counts ola_mES_2i_4_59.counts ola_mES_2i_4_6.counts ola_mES_2i_4_60.counts
#> "021" "021" "021" "0221"
#> ola_mES_2i_4_61.counts ola_mES_2i_4_62.counts ola_mES_2i_4_63.counts ola_mES_2i_4_64.counts
#> "021" "032" "0221" "021"
#> ola_mES_2i_4_65.counts ola_mES_2i_4_66.counts ola_mES_2i_4_67.counts ola_mES_2i_4_68.counts
#> "021" "021" "0112" "0221"
#> ola_mES_2i_4_69.counts ola_mES_2i_4_7.counts ola_mES_2i_4_70.counts ola_mES_2i_4_71.counts
#> "021" "023" "0221" "021"
#> ola_mES_2i_4_72.counts ola_mES_2i_4_73.counts ola_mES_2i_4_75.counts ola_mES_2i_4_77.counts
#> "023" "021" "0222" "021"
#> ola_mES_2i_4_78.counts ola_mES_2i_4_80.counts ola_mES_2i_4_81.counts ola_mES_2i_4_83.counts
#> "023" "021" "021" "023"
#> ola_mES_2i_4_84.counts ola_mES_2i_4_88.counts ola_mES_2i_4_89.counts ola_mES_2i_4_9.counts
#> "023" "0221" "0221" "023"
#> ola_mES_2i_4_90.counts ola_mES_2i_4_91.counts ola_mES_2i_4_94.counts ola_mES_2i_4_95.counts
#> "021" "021" "0221" "0222"
#> ola_mES_2i_4_96.counts ola_mES_2i_5_1.counts ola_mES_2i_5_10.counts ola_mES_2i_5_11.counts
#> "0222" "0221" "023" "023"
#> ola_mES_2i_5_12.counts ola_mES_2i_5_13.counts ola_mES_2i_5_14.counts ola_mES_2i_5_15.counts
#> "023" "023" "021" "021"
#> ola_mES_2i_5_16.counts ola_mES_2i_5_17.counts ola_mES_2i_5_18.counts ola_mES_2i_5_19.counts
#> "021" "021" "023" "021"
#> ola_mES_2i_5_2.counts ola_mES_2i_5_20.counts ola_mES_2i_5_21.counts ola_mES_2i_5_23.counts
#> "0221" "021" "021" "023"
#> ola_mES_2i_5_24.counts ola_mES_2i_5_25.counts ola_mES_2i_5_26.counts ola_mES_2i_5_27.counts
#> "021" "021" "021" "021"
#> ola_mES_2i_5_28.counts ola_mES_2i_5_29.counts ola_mES_2i_5_3.counts ola_mES_2i_5_30.counts
#> "0221" "0221" "0221" "021"
#> ola_mES_2i_5_31.counts ola_mES_2i_5_32.counts ola_mES_2i_5_34.counts ola_mES_2i_5_35.counts
#> "021" "021" "021" "021"
#> ola_mES_2i_5_36.counts ola_mES_2i_5_38.counts ola_mES_2i_5_39.counts ola_mES_2i_5_4.counts
#> "021" "021" "021" "0221"
#> ola_mES_2i_5_40.counts ola_mES_2i_5_41.counts ola_mES_2i_5_42.counts ola_mES_2i_5_44.counts
#> "021" "0112" "021" "021"
#> ola_mES_2i_5_45.counts ola_mES_2i_5_46.counts ola_mES_2i_5_47.counts ola_mES_2i_5_48.counts
#> "021" "021" "021" "021"
#> ola_mES_2i_5_49.counts ola_mES_2i_5_5.counts ola_mES_2i_5_51.counts ola_mES_2i_5_52.counts
#> "021" "023" "021" "0221"
#> ola_mES_2i_5_53.counts ola_mES_2i_5_54.counts ola_mES_2i_5_55.counts ola_mES_2i_5_56.counts
#> "021" "021" "021" "021"
#> ola_mES_2i_5_57.counts ola_mES_2i_5_58.counts ola_mES_2i_5_59.counts ola_mES_2i_5_6.counts
#> "021" "021" "021" "0221"
#> ola_mES_2i_5_61.counts ola_mES_2i_5_62.counts ola_mES_2i_5_63.counts ola_mES_2i_5_65.counts
#> "021" "0221" "021" "023"
#> ola_mES_2i_5_66.counts ola_mES_2i_5_67.counts ola_mES_2i_5_68.counts ola_mES_2i_5_69.counts
#> "0221" "021" "021" "021"
#> ola_mES_2i_5_7.counts ola_mES_2i_5_70.counts ola_mES_2i_5_72.counts ola_mES_2i_5_73.counts
#> "021" "021" "021" "023"
#> ola_mES_2i_5_74.counts ola_mES_2i_5_75.counts ola_mES_2i_5_76.counts ola_mES_2i_5_77.counts
#> "021" "023" "023" "023"
#> ola_mES_2i_5_78.counts ola_mES_2i_5_8.counts ola_mES_2i_5_81.counts ola_mES_2i_5_82.counts
#> "021" "021" "0221" "021"
#> ola_mES_2i_5_83.counts ola_mES_2i_5_85.counts ola_mES_2i_5_86.counts ola_mES_2i_5_87.counts
#> "023" "0221" "023" "023"
#> ola_mES_2i_5_88.counts ola_mES_2i_5_9.counts ola_mES_2i_5_90.counts ola_mES_2i_5_92.counts
#> "023" "0222" "021" "021"
#> ola_mES_2i_5_94.counts ola_mES_2i_5_95.counts ola_mES_2i_5_96.counts ola_mES_a2i_2_1.counts
#> "021" "021" "021" "0132"
#> ola_mES_a2i_2_10.counts ola_mES_a2i_2_11.counts ola_mES_a2i_2_12.counts ola_mES_a2i_2_13.counts
#> "0112" "0132" "0112" "0111"
#> ola_mES_a2i_2_14.counts ola_mES_a2i_2_15.counts ola_mES_a2i_2_16.counts ola_mES_a2i_2_17.counts
#> "0132" "0132" "0132" "0132"
#> ola_mES_a2i_2_18.counts ola_mES_a2i_2_19.counts ola_mES_a2i_2_2.counts ola_mES_a2i_2_20.counts
#> "0132" "0132" "0132" "0112"
#> ola_mES_a2i_2_21.counts ola_mES_a2i_2_22.counts ola_mES_a2i_2_23.counts ola_mES_a2i_2_24.counts
#> "0132" "0132" "0132" "0132"
#> ola_mES_a2i_2_25.counts ola_mES_a2i_2_26.counts ola_mES_a2i_2_27.counts ola_mES_a2i_2_28.counts
#> "0113" "032" "0112" "0112"
#> ola_mES_a2i_2_29.counts ola_mES_a2i_2_3.counts ola_mES_a2i_2_30.counts ola_mES_a2i_2_31.counts
#> "0112" "0132" "0222" "032"
#> ola_mES_a2i_2_32.counts ola_mES_a2i_2_33.counts ola_mES_a2i_2_34.counts ola_mES_a2i_2_35.counts
#> "032" "0112" "0112" "031"
#> ola_mES_a2i_2_36.counts ola_mES_a2i_2_37.counts ola_mES_a2i_2_38.counts ola_mES_a2i_2_39.counts
#> "0222" "032" "0112" "032"
#> ola_mES_a2i_2_4.counts ola_mES_a2i_2_40.counts ola_mES_a2i_2_41.counts ola_mES_a2i_2_42.counts
#> "0112" "0112" "0132" "0132"
#> ola_mES_a2i_2_43.counts ola_mES_a2i_2_44.counts ola_mES_a2i_2_45.counts ola_mES_a2i_2_46.counts
#> "0111" "0113" "0132" "0132"
#> ola_mES_a2i_2_47.counts ola_mES_a2i_2_48.counts ola_mES_a2i_2_49.counts ola_mES_a2i_2_5.counts
#> "0112" "0112" "0113" "0132"
#> ola_mES_a2i_2_50.counts ola_mES_a2i_2_51.counts ola_mES_a2i_2_52.counts ola_mES_a2i_2_53.counts
#> "032" "0112" "0112" "032"
#> ola_mES_a2i_2_54.counts ola_mES_a2i_2_55.counts ola_mES_a2i_2_56.counts ola_mES_a2i_2_57.counts
#> "0112" "0132" "0111" "0132"
#> ola_mES_a2i_2_59.counts ola_mES_a2i_2_6.counts ola_mES_a2i_2_60.counts ola_mES_a2i_2_61.counts
#> "0132" "032" "0112" "0111"
#> ola_mES_a2i_2_62.counts ola_mES_a2i_2_63.counts ola_mES_a2i_2_64.counts ola_mES_a2i_2_65.counts
#> "0112" "0111" "0112" "0111"
#> ola_mES_a2i_2_66.counts ola_mES_a2i_2_67.counts ola_mES_a2i_2_68.counts ola_mES_a2i_2_69.counts
#> "0132" "0112" "0113" "032"
#> ola_mES_a2i_2_7.counts ola_mES_a2i_2_70.counts ola_mES_a2i_2_71.counts ola_mES_a2i_2_72.counts
#> "0112" "0112" "0132" "0112"
#> ola_mES_a2i_2_73.counts ola_mES_a2i_2_74.counts ola_mES_a2i_2_75.counts ola_mES_a2i_2_76.counts
#> "0113" "0111" "0112" "0111"
#> ola_mES_a2i_2_77.counts ola_mES_a2i_2_78.counts ola_mES_a2i_2_8.counts ola_mES_a2i_2_80.counts
#> "0112" "0132" "0132" "0113"
#> ola_mES_a2i_2_81.counts ola_mES_a2i_2_82.counts ola_mES_a2i_2_83.counts ola_mES_a2i_2_84.counts
#> "0112" "0132" "0132" "0112"
#> ola_mES_a2i_2_86.counts ola_mES_a2i_2_87.counts ola_mES_a2i_2_88.counts ola_mES_a2i_2_89.counts
#> "0113" "0112" "0112" "0223"
#> ola_mES_a2i_2_9.counts ola_mES_a2i_2_90.counts ola_mES_a2i_2_91.counts ola_mES_a2i_2_92.counts
#> "0113" "0132" "032" "032"
#> ola_mES_a2i_2_93.counts ola_mES_a2i_2_94.counts ola_mES_a2i_2_95.counts ola_mES_a2i_2_96.counts
#> "0111" "0132" "0132" "0132"
#> ola_mES_a2i_3_10.counts ola_mES_a2i_3_11.counts ola_mES_a2i_3_13.counts ola_mES_a2i_3_16.counts
#> "021" "0223" "0112" "0113"
#> ola_mES_a2i_3_17.counts ola_mES_a2i_3_18.counts ola_mES_a2i_3_2.counts ola_mES_a2i_3_20.counts
#> "032" "032" "032" "032"
#> ola_mES_a2i_3_21.counts ola_mES_a2i_3_22.counts ola_mES_a2i_3_24.counts ola_mES_a2i_3_25.counts
#> "0223" "0223" "0223" "0113"
#> ola_mES_a2i_3_26.counts ola_mES_a2i_3_28.counts ola_mES_a2i_3_29.counts ola_mES_a2i_3_3.counts
#> "032" "0113" "0113" "021"
#> ola_mES_a2i_3_30.counts ola_mES_a2i_3_31.counts ola_mES_a2i_3_33.counts ola_mES_a2i_3_34.counts
#> "032" "032" "0223" "0113"
#> ola_mES_a2i_3_37.counts ola_mES_a2i_3_38.counts ola_mES_a2i_3_39.counts ola_mES_a2i_3_4.counts
#> "0112" "0113" "0113" "0223"
#> ola_mES_a2i_3_40.counts ola_mES_a2i_3_41.counts ola_mES_a2i_3_42.counts ola_mES_a2i_3_44.counts
#> "0113" "032" "032" "0113"
#> ola_mES_a2i_3_45.counts ola_mES_a2i_3_47.counts ola_mES_a2i_3_49.counts ola_mES_a2i_3_50.counts
#> "032" "032" "0113" "0113"
#> ola_mES_a2i_3_52.counts ola_mES_a2i_3_54.counts ola_mES_a2i_3_56.counts ola_mES_a2i_3_58.counts
#> "032" "0113" "032" "0223"
#> ola_mES_a2i_3_59.counts ola_mES_a2i_3_6.counts ola_mES_a2i_3_62.counts ola_mES_a2i_3_64.counts
#> "0132" "0113" "0112" "0112"
#> ola_mES_a2i_3_65.counts ola_mES_a2i_3_66.counts ola_mES_a2i_3_67.counts ola_mES_a2i_3_68.counts
#> "0132" "0113" "032" "0222"
#> ola_mES_a2i_3_69.counts ola_mES_a2i_3_7.counts ola_mES_a2i_3_71.counts ola_mES_a2i_3_73.counts
#> "0113" "032" "0132" "0112"
#> ola_mES_a2i_3_75.counts ola_mES_a2i_3_76.counts ola_mES_a2i_3_77.counts ola_mES_a2i_3_8.counts
#> "032" "021" "0113" "032"
#> ola_mES_a2i_3_80.counts ola_mES_a2i_3_81.counts ola_mES_a2i_3_82.counts ola_mES_a2i_3_83.counts
#> "0222" "032" "0113" "0112"
#> ola_mES_a2i_3_84.counts ola_mES_a2i_3_85.counts ola_mES_a2i_3_86.counts ola_mES_a2i_3_89.counts
#> "0113" "0112" "0113" "0132"
#> ola_mES_a2i_3_9.counts ola_mES_a2i_3_90.counts ola_mES_a2i_3_91.counts ola_mES_a2i_3_92.counts
#> "0223" "0113" "0223" "0112"
#> ola_mES_a2i_3_94.counts ola_mES_a2i_3_96.counts ola_mES_lif_1_1.counts ola_mES_lif_1_10.counts
#> "031" "0112" "014" "033"
#> ola_mES_lif_1_11.counts ola_mES_lif_1_12.counts ola_mES_lif_1_13.counts ola_mES_lif_1_14.counts
#> "012" "033" "014" "033"
#> ola_mES_lif_1_15.counts ola_mES_lif_1_17.counts ola_mES_lif_1_18.counts ola_mES_lif_1_19.counts
#> "014" "031" "014" "014"
#> ola_mES_lif_1_2.counts ola_mES_lif_1_20.counts ola_mES_lif_1_21.counts ola_mES_lif_1_22.counts
#> "033" "014" "033" "014"
#> ola_mES_lif_1_23.counts ola_mES_lif_1_24.counts ola_mES_lif_1_25.counts ola_mES_lif_1_26.counts
#> "031" "014" "014" "014"
#> ola_mES_lif_1_28.counts ola_mES_lif_1_29.counts ola_mES_lif_1_3.counts ola_mES_lif_1_30.counts
#> "014" "031" "014" "014"
#> ola_mES_lif_1_31.counts ola_mES_lif_1_32.counts ola_mES_lif_1_33.counts ola_mES_lif_1_34.counts
#> "031" "014" "014" "014"
#> ola_mES_lif_1_35.counts ola_mES_lif_1_36.counts ola_mES_lif_1_37.counts ola_mES_lif_1_38.counts
#> "031" "014" "014" "014"
#> ola_mES_lif_1_39.counts ola_mES_lif_1_4.counts ola_mES_lif_1_41.counts ola_mES_lif_1_42.counts
#> "014" "014" "014" "014"
#> ola_mES_lif_1_43.counts ola_mES_lif_1_44.counts ola_mES_lif_1_45.counts ola_mES_lif_1_46.counts
#> "014" "014" "014" "014"
#> ola_mES_lif_1_47.counts ola_mES_lif_1_48.counts ola_mES_lif_1_49.counts ola_mES_lif_1_5.counts
#> "014" "014" "014" "012"
#> ola_mES_lif_1_50.counts ola_mES_lif_1_51.counts ola_mES_lif_1_52.counts ola_mES_lif_1_53.counts
#> "014" "012" "012" "0223"
#> ola_mES_lif_1_54.counts ola_mES_lif_1_55.counts ola_mES_lif_1_56.counts ola_mES_lif_1_57.counts
#> "014" "012" "0222" "0223"
#> ola_mES_lif_1_58.counts ola_mES_lif_1_59.counts ola_mES_lif_1_6.counts ola_mES_lif_1_60.counts
#> "014" "012" "014" "014"
#> ola_mES_lif_1_61.counts ola_mES_lif_1_62.counts ola_mES_lif_1_63.counts ola_mES_lif_1_64.counts
#> "014" "014" "031" "031"
#> ola_mES_lif_1_65.counts ola_mES_lif_1_66.counts ola_mES_lif_1_67.counts ola_mES_lif_1_68.counts
#> "031" "014" "014" "014"
#> ola_mES_lif_1_69.counts ola_mES_lif_1_7.counts ola_mES_lif_1_70.counts ola_mES_lif_1_72.counts
#> "031" "014" "033" "014"
#> ola_mES_lif_1_73.counts ola_mES_lif_1_74.counts ola_mES_lif_1_75.counts ola_mES_lif_1_79.counts
#> "014" "031" "031" "033"
#> ola_mES_lif_1_8.counts ola_mES_lif_1_80.counts ola_mES_lif_1_81.counts ola_mES_lif_1_82.counts
#> "014" "014" "014" "031"
#> ola_mES_lif_1_86.counts ola_mES_lif_1_9.counts ola_mES_lif_1_91.counts ola_mES_lif_1_92.counts
#> "033" "014" "014" "014"
#> ola_mES_lif_1_93.counts ola_mES_lif_1_95.counts ola_mES_lif_1_96.counts ola_mES_lif_2_1.counts
#> "033" "014" "014" "033"
#> ola_mES_lif_2_10.counts ola_mES_lif_2_11.counts ola_mES_lif_2_12.counts ola_mES_lif_2_13.counts
#> "012" "012" "0223" "012"
#> ola_mES_lif_2_14.counts ola_mES_lif_2_15.counts ola_mES_lif_2_16.counts ola_mES_lif_2_17.counts
#> "012" "012" "012" "012"
#> ola_mES_lif_2_18.counts ola_mES_lif_2_19.counts ola_mES_lif_2_2.counts ola_mES_lif_2_20.counts
#> "012" "012" "012" "033"
#> ola_mES_lif_2_21.counts ola_mES_lif_2_23.counts ola_mES_lif_2_25.counts ola_mES_lif_2_26.counts
#> "012" "012" "033" "012"
#> ola_mES_lif_2_27.counts ola_mES_lif_2_28.counts ola_mES_lif_2_29.counts ola_mES_lif_2_3.counts
#> "033" "012" "012" "012"
#> ola_mES_lif_2_30.counts ola_mES_lif_2_31.counts ola_mES_lif_2_32.counts ola_mES_lif_2_33.counts
#> "012" "033" "012" "033"
#> ola_mES_lif_2_34.counts ola_mES_lif_2_35.counts ola_mES_lif_2_36.counts ola_mES_lif_2_37.counts
#> "012" "012" "034" "033"
#> ola_mES_lif_2_38.counts ola_mES_lif_2_39.counts ola_mES_lif_2_4.counts ola_mES_lif_2_40.counts
#> "012" "012" "012" "012"
#> ola_mES_lif_2_41.counts ola_mES_lif_2_42.counts ola_mES_lif_2_43.counts ola_mES_lif_2_44.counts
#> "033" "012" "012" "012"
#> ola_mES_lif_2_45.counts ola_mES_lif_2_46.counts ola_mES_lif_2_47.counts ola_mES_lif_2_48.counts
#> "034" "012" "033" "0223"
#> ola_mES_lif_2_49.counts ola_mES_lif_2_5.counts ola_mES_lif_2_50.counts ola_mES_lif_2_51.counts
#> "012" "012" "012" "012"
#> ola_mES_lif_2_52.counts ola_mES_lif_2_53.counts ola_mES_lif_2_54.counts ola_mES_lif_2_55.counts
#> "012" "012" "012" "012"
#> ola_mES_lif_2_56.counts ola_mES_lif_2_57.counts ola_mES_lif_2_58.counts ola_mES_lif_2_59.counts
#> "012" "034" "012" "012"
#> ola_mES_lif_2_6.counts ola_mES_lif_2_60.counts ola_mES_lif_2_61.counts ola_mES_lif_2_63.counts
#> "012" "033" "014" "012"
#> ola_mES_lif_2_64.counts ola_mES_lif_2_65.counts ola_mES_lif_2_66.counts ola_mES_lif_2_67.counts
#> "012" "012" "012" "012"
#> ola_mES_lif_2_68.counts ola_mES_lif_2_69.counts ola_mES_lif_2_7.counts ola_mES_lif_2_70.counts
#> "012" "0223" "012" "012"
#> ola_mES_lif_2_71.counts ola_mES_lif_2_72.counts ola_mES_lif_2_73.counts ola_mES_lif_2_74.counts
#> "012" "014" "0223" "012"
#> ola_mES_lif_2_75.counts ola_mES_lif_2_76.counts ola_mES_lif_2_77.counts ola_mES_lif_2_79.counts
#> "012" "012" "012" "033"
#> ola_mES_lif_2_8.counts ola_mES_lif_2_80.counts ola_mES_lif_2_81.counts ola_mES_lif_2_82.counts
#> "012" "012" "033" "012"
#> ola_mES_lif_2_83.counts ola_mES_lif_2_84.counts ola_mES_lif_2_86.counts ola_mES_lif_2_87.counts
#> "012" "012" "033" "012"
#> ola_mES_lif_2_89.counts ola_mES_lif_2_9.counts ola_mES_lif_2_90.counts ola_mES_lif_2_91.counts
#> "012" "033" "033" "033"
#> ola_mES_lif_2_92.counts ola_mES_lif_2_93.counts ola_mES_lif_2_94.counts ola_mES_lif_2_95.counts
#> "033" "012" "012" "012"
#> ola_mES_lif_2_96.counts ola_mES_lif_3_1.counts ola_mES_lif_3_10.counts ola_mES_lif_3_11.counts
#> "033" "014" "012" "033"
#> ola_mES_lif_3_12.counts ola_mES_lif_3_13.counts ola_mES_lif_3_14.counts ola_mES_lif_3_15.counts
#> "0223" "014" "034" "034"
#> ola_mES_lif_3_16.counts ola_mES_lif_3_17.counts ola_mES_lif_3_19.counts ola_mES_lif_3_2.counts
#> "034" "034" "034" "0222"
#> ola_mES_lif_3_20.counts ola_mES_lif_3_21.counts ola_mES_lif_3_22.counts ola_mES_lif_3_23.counts
#> "031" "0222" "031" "031"
#> ola_mES_lif_3_24.counts ola_mES_lif_3_26.counts ola_mES_lif_3_27.counts ola_mES_lif_3_28.counts
#> "031" "031" "014" "034"
#> ola_mES_lif_3_29.counts ola_mES_lif_3_3.counts ola_mES_lif_3_30.counts ola_mES_lif_3_31.counts
#> "033" "014" "014" "034"
#> ola_mES_lif_3_32.counts ola_mES_lif_3_33.counts ola_mES_lif_3_34.counts ola_mES_lif_3_35.counts
#> "014" "034" "034" "034"
#> ola_mES_lif_3_36.counts ola_mES_lif_3_37.counts ola_mES_lif_3_38.counts ola_mES_lif_3_39.counts
#> "034" "033" "034" "034"
#> ola_mES_lif_3_4.counts ola_mES_lif_3_40.counts ola_mES_lif_3_42.counts ola_mES_lif_3_45.counts
#> "014" "014" "014" "034"
#> ola_mES_lif_3_46.counts ola_mES_lif_3_47.counts ola_mES_lif_3_48.counts ola_mES_lif_3_49.counts
#> "034" "034" "033" "014"
#> ola_mES_lif_3_5.counts ola_mES_lif_3_50.counts ola_mES_lif_3_51.counts ola_mES_lif_3_52.counts
#> "012" "014" "014" "014"
#> ola_mES_lif_3_53.counts ola_mES_lif_3_54.counts ola_mES_lif_3_56.counts ola_mES_lif_3_57.counts
#> "014" "014" "012" "033"
#> ola_mES_lif_3_58.counts ola_mES_lif_3_59.counts ola_mES_lif_3_6.counts ola_mES_lif_3_60.counts
#> "014" "014" "014" "014"
#> ola_mES_lif_3_61.counts ola_mES_lif_3_63.counts ola_mES_lif_3_64.counts ola_mES_lif_3_65.counts
#> "014" "014" "014" "014"
#> ola_mES_lif_3_69.counts ola_mES_lif_3_70.counts ola_mES_lif_3_71.counts ola_mES_lif_3_72.counts
#> "034" "034" "014" "034"
#> ola_mES_lif_3_73.counts ola_mES_lif_3_74.counts ola_mES_lif_3_75.counts ola_mES_lif_3_76.counts
#> "031" "031" "031" "031"
#> ola_mES_lif_3_77.counts ola_mES_lif_3_78.counts ola_mES_lif_3_8.counts ola_mES_lif_3_80.counts
#> "0222" "031" "014" "031"
#> ola_mES_lif_3_82.counts ola_mES_lif_3_83.counts ola_mES_lif_3_84.counts ola_mES_lif_3_87.counts
#> "031" "031" "031" "031"
#> ola_mES_lif_3_88.counts ola_mES_lif_3_89.counts ola_mES_lif_3_9.counts ola_mES_lif_3_90.counts
#> "031" "0222" "0223" "034"
#> ola_mES_lif_3_92.counts ola_mES_lif_3_94.counts ola_mES_lif_3_95.counts ola_mES_lif_3_96.counts
#> "031" "031" "031" "031"
get_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 3417))
#> ola_mES_2i_2_1.counts ola_mES_2i_2_10.counts ola_mES_2i_2_11.counts ola_mES_2i_2_12.counts
#> "011" "0131" "0131" "0131"
#> ola_mES_2i_2_13.counts ola_mES_2i_2_14.counts ola_mES_2i_2_15.counts ola_mES_2i_2_16.counts
#> "0133" "011" "0133" "0133"
#> ola_mES_2i_2_17.counts ola_mES_2i_2_18.counts ola_mES_2i_2_19.counts ola_mES_2i_2_2.counts
#> "0133" "0131" "0131" "011"
#> ola_mES_2i_2_20.counts ola_mES_2i_2_21.counts ola_mES_2i_2_22.counts ola_mES_2i_2_23.counts
#> "011" "0131" "0133" "0131"
#> ola_mES_2i_2_24.counts ola_mES_2i_2_25.counts ola_mES_2i_2_26.counts ola_mES_2i_2_27.counts
#> "0131" "011" "0133" "011"
#> ola_mES_2i_2_28.counts ola_mES_2i_2_29.counts ola_mES_2i_2_3.counts ola_mES_2i_2_30.counts
#> "011" "0133" "011" "0131"
#> ola_mES_2i_2_31.counts ola_mES_2i_2_32.counts ola_mES_2i_2_33.counts ola_mES_2i_2_34.counts
#> "0131" "0133" "0131" "0131"
#> ola_mES_2i_2_35.counts ola_mES_2i_2_36.counts ola_mES_2i_2_37.counts ola_mES_2i_2_38.counts
#> "0131" "0131" "011" "011"
#> ola_mES_2i_2_39.counts ola_mES_2i_2_4.counts ola_mES_2i_2_40.counts ola_mES_2i_2_41.counts
#> "0133" "0131" "0131" "0131"
#> ola_mES_2i_2_42.counts ola_mES_2i_2_43.counts ola_mES_2i_2_44.counts ola_mES_2i_2_46.counts
#> "0131" "011" "0131" "0131"
#> ola_mES_2i_2_47.counts ola_mES_2i_2_48.counts ola_mES_2i_2_49.counts ola_mES_2i_2_5.counts
#> "0131" "0131" "011" "0133"
#> ola_mES_2i_2_50.counts ola_mES_2i_2_51.counts ola_mES_2i_2_52.counts ola_mES_2i_2_53.counts
#> "011" "0131" "0133" "0133"
#> ola_mES_2i_2_54.counts ola_mES_2i_2_55.counts ola_mES_2i_2_56.counts ola_mES_2i_2_57.counts
#> "0131" "0133" "0133" "0133"
#> ola_mES_2i_2_58.counts ola_mES_2i_2_59.counts ola_mES_2i_2_6.counts ola_mES_2i_2_60.counts
#> "0131" "0131" "0133" "0131"
#> ola_mES_2i_2_61.counts ola_mES_2i_2_62.counts ola_mES_2i_2_63.counts ola_mES_2i_2_64.counts
#> "011" "032" "011" "0133"
#> ola_mES_2i_2_66.counts ola_mES_2i_2_67.counts ola_mES_2i_2_68.counts ola_mES_2i_2_69.counts
#> "0131" "0131" "011" "0131"
#> ola_mES_2i_2_7.counts ola_mES_2i_2_72.counts ola_mES_2i_2_73.counts ola_mES_2i_2_74.counts
#> "0133" "0133" "011" "0133"
#> ola_mES_2i_2_75.counts ola_mES_2i_2_76.counts ola_mES_2i_2_79.counts ola_mES_2i_2_8.counts
#> "011" "011" "0131" "0133"
#> ola_mES_2i_2_80.counts ola_mES_2i_2_81.counts ola_mES_2i_2_85.counts ola_mES_2i_2_86.counts
#> "0133" "0131" "011" "011"
#> ola_mES_2i_2_87.counts ola_mES_2i_2_90.counts ola_mES_2i_2_91.counts ola_mES_2i_2_93.counts
#> "032" "0131" "0133" "0131"
#> ola_mES_2i_2_94.counts ola_mES_2i_2_95.counts ola_mES_2i_3_10.counts ola_mES_2i_3_11.counts
#> "0131" "0133" "0222" "023"
#> ola_mES_2i_3_12.counts ola_mES_2i_3_13.counts ola_mES_2i_3_15.counts ola_mES_2i_3_16.counts
#> "0221" "0221" "0221" "021"
#> ola_mES_2i_3_17.counts ola_mES_2i_3_18.counts ola_mES_2i_3_19.counts ola_mES_2i_3_2.counts
#> "032" "032" "0221" "0221"
#> ola_mES_2i_3_20.counts ola_mES_2i_3_21.counts ola_mES_2i_3_23.counts ola_mES_2i_3_25.counts
#> "0221" "0222" "021" "023"
#> ola_mES_2i_3_26.counts ola_mES_2i_3_28.counts ola_mES_2i_3_30.counts ola_mES_2i_3_31.counts
#> "0221" "021" "0221" "032"
#> ola_mES_2i_3_33.counts ola_mES_2i_3_36.counts ola_mES_2i_3_37.counts ola_mES_2i_3_38.counts
#> "0221" "021" "0221" "0221"
#> ola_mES_2i_3_39.counts ola_mES_2i_3_4.counts ola_mES_2i_3_41.counts ola_mES_2i_3_42.counts
#> "0222" "021" "0222" "0221"
#> ola_mES_2i_3_43.counts ola_mES_2i_3_45.counts ola_mES_2i_3_46.counts ola_mES_2i_3_47.counts
#> "021" "0221" "0221" "0221"
#> ola_mES_2i_3_49.counts ola_mES_2i_3_5.counts ola_mES_2i_3_52.counts ola_mES_2i_3_54.counts
#> "032" "0221" "0221" "0222"
#> ola_mES_2i_3_56.counts ola_mES_2i_3_57.counts ola_mES_2i_3_58.counts ola_mES_2i_3_6.counts
#> "0221" "0221" "0221" "0222"
#> ola_mES_2i_3_62.counts ola_mES_2i_3_65.counts ola_mES_2i_3_66.counts ola_mES_2i_3_69.counts
#> "032" "0221" "032" "0222"
#> ola_mES_2i_3_7.counts ola_mES_2i_3_73.counts ola_mES_2i_3_76.counts ola_mES_2i_3_77.counts
#> "0221" "0221" "0221" "0222"
#> ola_mES_2i_3_78.counts ola_mES_2i_3_79.counts ola_mES_2i_3_8.counts ola_mES_2i_3_80.counts
#> "032" "0221" "0221" "0221"
#> ola_mES_2i_3_82.counts ola_mES_2i_3_83.counts ola_mES_2i_3_84.counts ola_mES_2i_3_87.counts
#> "0221" "0221" "021" "0222"
#> ola_mES_2i_3_88.counts ola_mES_2i_3_90.counts ola_mES_2i_3_91.counts ola_mES_2i_3_92.counts
#> "0221" "021" "0222" "0221"
#> ola_mES_2i_3_96.counts ola_mES_2i_4_1.counts ola_mES_2i_4_10.counts ola_mES_2i_4_11.counts
#> "0222" "021" "021" "021"
#> ola_mES_2i_4_13.counts ola_mES_2i_4_17.counts ola_mES_2i_4_18.counts ola_mES_2i_4_2.counts
#> "021" "021" "023" "0132"
#> ola_mES_2i_4_20.counts ola_mES_2i_4_21.counts ola_mES_2i_4_22.counts ola_mES_2i_4_23.counts
#> "021" "021" "023" "0221"
#> ola_mES_2i_4_24.counts ola_mES_2i_4_25.counts ola_mES_2i_4_26.counts ola_mES_2i_4_27.counts
#> "021" "021" "021" "023"
#> ola_mES_2i_4_29.counts ola_mES_2i_4_30.counts ola_mES_2i_4_31.counts ola_mES_2i_4_32.counts
#> "021" "021" "021" "021"
#> ola_mES_2i_4_33.counts ola_mES_2i_4_34.counts ola_mES_2i_4_35.counts ola_mES_2i_4_36.counts
#> "021" "021" "021" "021"
#> ola_mES_2i_4_37.counts ola_mES_2i_4_38.counts ola_mES_2i_4_39.counts ola_mES_2i_4_40.counts
#> "032" "011" "021" "011"
#> ola_mES_2i_4_42.counts ola_mES_2i_4_43.counts ola_mES_2i_4_44.counts ola_mES_2i_4_46.counts
#> "032" "021" "021" "021"
#> ola_mES_2i_4_49.counts ola_mES_2i_4_5.counts ola_mES_2i_4_50.counts ola_mES_2i_4_52.counts
#> "011" "023" "021" "021"
#> ola_mES_2i_4_53.counts ola_mES_2i_4_54.counts ola_mES_2i_4_56.counts ola_mES_2i_4_57.counts
#> "011" "032" "021" "021"
#> ola_mES_2i_4_58.counts ola_mES_2i_4_59.counts ola_mES_2i_4_6.counts ola_mES_2i_4_60.counts
#> "021" "021" "021" "0221"
#> ola_mES_2i_4_61.counts ola_mES_2i_4_62.counts ola_mES_2i_4_63.counts ola_mES_2i_4_64.counts
#> "021" "032" "0221" "021"
#> ola_mES_2i_4_65.counts ola_mES_2i_4_66.counts ola_mES_2i_4_67.counts ola_mES_2i_4_68.counts
#> "021" "021" "011" "0221"
#> ola_mES_2i_4_69.counts ola_mES_2i_4_7.counts ola_mES_2i_4_70.counts ola_mES_2i_4_71.counts
#> "021" "023" "0221" "021"
#> ola_mES_2i_4_72.counts ola_mES_2i_4_73.counts ola_mES_2i_4_75.counts ola_mES_2i_4_77.counts
#> "023" "021" "0222" "021"
#> ola_mES_2i_4_78.counts ola_mES_2i_4_80.counts ola_mES_2i_4_81.counts ola_mES_2i_4_83.counts
#> "023" "021" "021" "023"
#> ola_mES_2i_4_84.counts ola_mES_2i_4_88.counts ola_mES_2i_4_89.counts ola_mES_2i_4_9.counts
#> "023" "0221" "0221" "023"
#> ola_mES_2i_4_90.counts ola_mES_2i_4_91.counts ola_mES_2i_4_94.counts ola_mES_2i_4_95.counts
#> "021" "021" "0221" "0222"
#> ola_mES_2i_4_96.counts ola_mES_2i_5_1.counts ola_mES_2i_5_10.counts ola_mES_2i_5_11.counts
#> "0222" "0221" "023" "023"
#> ola_mES_2i_5_12.counts ola_mES_2i_5_13.counts ola_mES_2i_5_14.counts ola_mES_2i_5_15.counts
#> "023" "023" "021" "021"
#> ola_mES_2i_5_16.counts ola_mES_2i_5_17.counts ola_mES_2i_5_18.counts ola_mES_2i_5_19.counts
#> "021" "021" "023" "021"
#> ola_mES_2i_5_2.counts ola_mES_2i_5_20.counts ola_mES_2i_5_21.counts ola_mES_2i_5_23.counts
#> "0221" "021" "021" "023"
#> ola_mES_2i_5_24.counts ola_mES_2i_5_25.counts ola_mES_2i_5_26.counts ola_mES_2i_5_27.counts
#> "021" "021" "021" "021"
#> ola_mES_2i_5_28.counts ola_mES_2i_5_29.counts ola_mES_2i_5_3.counts ola_mES_2i_5_30.counts
#> "0221" "0221" "0221" "021"
#> ola_mES_2i_5_31.counts ola_mES_2i_5_32.counts ola_mES_2i_5_34.counts ola_mES_2i_5_35.counts
#> "021" "021" "021" "021"
#> ola_mES_2i_5_36.counts ola_mES_2i_5_38.counts ola_mES_2i_5_39.counts ola_mES_2i_5_4.counts
#> "021" "021" "021" "0221"
#> ola_mES_2i_5_40.counts ola_mES_2i_5_41.counts ola_mES_2i_5_42.counts ola_mES_2i_5_44.counts
#> "021" "011" "021" "021"
#> ola_mES_2i_5_45.counts ola_mES_2i_5_46.counts ola_mES_2i_5_47.counts ola_mES_2i_5_48.counts
#> "021" "021" "021" "021"
#> ola_mES_2i_5_49.counts ola_mES_2i_5_5.counts ola_mES_2i_5_51.counts ola_mES_2i_5_52.counts
#> "021" "023" "021" "0221"
#> ola_mES_2i_5_53.counts ola_mES_2i_5_54.counts ola_mES_2i_5_55.counts ola_mES_2i_5_56.counts
#> "021" "021" "021" "021"
#> ola_mES_2i_5_57.counts ola_mES_2i_5_58.counts ola_mES_2i_5_59.counts ola_mES_2i_5_6.counts
#> "021" "021" "021" "0221"
#> ola_mES_2i_5_61.counts ola_mES_2i_5_62.counts ola_mES_2i_5_63.counts ola_mES_2i_5_65.counts
#> "021" "0221" "021" "023"
#> ola_mES_2i_5_66.counts ola_mES_2i_5_67.counts ola_mES_2i_5_68.counts ola_mES_2i_5_69.counts
#> "0221" "021" "021" "021"
#> ola_mES_2i_5_7.counts ola_mES_2i_5_70.counts ola_mES_2i_5_72.counts ola_mES_2i_5_73.counts
#> "021" "021" "021" "023"
#> ola_mES_2i_5_74.counts ola_mES_2i_5_75.counts ola_mES_2i_5_76.counts ola_mES_2i_5_77.counts
#> "021" "023" "023" "023"
#> ola_mES_2i_5_78.counts ola_mES_2i_5_8.counts ola_mES_2i_5_81.counts ola_mES_2i_5_82.counts
#> "021" "021" "0221" "021"
#> ola_mES_2i_5_83.counts ola_mES_2i_5_85.counts ola_mES_2i_5_86.counts ola_mES_2i_5_87.counts
#> "023" "0221" "023" "023"
#> ola_mES_2i_5_88.counts ola_mES_2i_5_9.counts ola_mES_2i_5_90.counts ola_mES_2i_5_92.counts
#> "023" "0222" "021" "021"
#> ola_mES_2i_5_94.counts ola_mES_2i_5_95.counts ola_mES_2i_5_96.counts ola_mES_a2i_2_1.counts
#> "021" "021" "021" "0132"
#> ola_mES_a2i_2_10.counts ola_mES_a2i_2_11.counts ola_mES_a2i_2_12.counts ola_mES_a2i_2_13.counts
#> "011" "0132" "011" "011"
#> ola_mES_a2i_2_14.counts ola_mES_a2i_2_15.counts ola_mES_a2i_2_16.counts ola_mES_a2i_2_17.counts
#> "0132" "0132" "0132" "0132"
#> ola_mES_a2i_2_18.counts ola_mES_a2i_2_19.counts ola_mES_a2i_2_2.counts ola_mES_a2i_2_20.counts
#> "0132" "0132" "0132" "011"
#> ola_mES_a2i_2_21.counts ola_mES_a2i_2_22.counts ola_mES_a2i_2_23.counts ola_mES_a2i_2_24.counts
#> "0132" "0132" "0132" "0132"
#> ola_mES_a2i_2_25.counts ola_mES_a2i_2_26.counts ola_mES_a2i_2_27.counts ola_mES_a2i_2_28.counts
#> "011" "032" "011" "011"
#> ola_mES_a2i_2_29.counts ola_mES_a2i_2_3.counts ola_mES_a2i_2_30.counts ola_mES_a2i_2_31.counts
#> "011" "0132" "0222" "032"
#> ola_mES_a2i_2_32.counts ola_mES_a2i_2_33.counts ola_mES_a2i_2_34.counts ola_mES_a2i_2_35.counts
#> "032" "011" "011" "031"
#> ola_mES_a2i_2_36.counts ola_mES_a2i_2_37.counts ola_mES_a2i_2_38.counts ola_mES_a2i_2_39.counts
#> "0222" "032" "011" "032"
#> ola_mES_a2i_2_4.counts ola_mES_a2i_2_40.counts ola_mES_a2i_2_41.counts ola_mES_a2i_2_42.counts
#> "011" "011" "0132" "0132"
#> ola_mES_a2i_2_43.counts ola_mES_a2i_2_44.counts ola_mES_a2i_2_45.counts ola_mES_a2i_2_46.counts
#> "011" "011" "0132" "0132"
#> ola_mES_a2i_2_47.counts ola_mES_a2i_2_48.counts ola_mES_a2i_2_49.counts ola_mES_a2i_2_5.counts
#> "011" "011" "011" "0132"
#> ola_mES_a2i_2_50.counts ola_mES_a2i_2_51.counts ola_mES_a2i_2_52.counts ola_mES_a2i_2_53.counts
#> "032" "011" "011" "032"
#> ola_mES_a2i_2_54.counts ola_mES_a2i_2_55.counts ola_mES_a2i_2_56.counts ola_mES_a2i_2_57.counts
#> "011" "0132" "011" "0132"
#> ola_mES_a2i_2_59.counts ola_mES_a2i_2_6.counts ola_mES_a2i_2_60.counts ola_mES_a2i_2_61.counts
#> "0132" "032" "011" "011"
#> ola_mES_a2i_2_62.counts ola_mES_a2i_2_63.counts ola_mES_a2i_2_64.counts ola_mES_a2i_2_65.counts
#> "011" "011" "011" "011"
#> ola_mES_a2i_2_66.counts ola_mES_a2i_2_67.counts ola_mES_a2i_2_68.counts ola_mES_a2i_2_69.counts
#> "0132" "011" "011" "032"
#> ola_mES_a2i_2_7.counts ola_mES_a2i_2_70.counts ola_mES_a2i_2_71.counts ola_mES_a2i_2_72.counts
#> "011" "011" "0132" "011"
#> ola_mES_a2i_2_73.counts ola_mES_a2i_2_74.counts ola_mES_a2i_2_75.counts ola_mES_a2i_2_76.counts
#> "011" "011" "011" "011"
#> ola_mES_a2i_2_77.counts ola_mES_a2i_2_78.counts ola_mES_a2i_2_8.counts ola_mES_a2i_2_80.counts
#> "011" "0132" "0132" "011"
#> ola_mES_a2i_2_81.counts ola_mES_a2i_2_82.counts ola_mES_a2i_2_83.counts ola_mES_a2i_2_84.counts
#> "011" "0132" "0132" "011"
#> ola_mES_a2i_2_86.counts ola_mES_a2i_2_87.counts ola_mES_a2i_2_88.counts ola_mES_a2i_2_89.counts
#> "011" "011" "011" "0223"
#> ola_mES_a2i_2_9.counts ola_mES_a2i_2_90.counts ola_mES_a2i_2_91.counts ola_mES_a2i_2_92.counts
#> "011" "0132" "032" "032"
#> ola_mES_a2i_2_93.counts ola_mES_a2i_2_94.counts ola_mES_a2i_2_95.counts ola_mES_a2i_2_96.counts
#> "011" "0132" "0132" "0132"
#> ola_mES_a2i_3_10.counts ola_mES_a2i_3_11.counts ola_mES_a2i_3_13.counts ola_mES_a2i_3_16.counts
#> "021" "0223" "011" "011"
#> ola_mES_a2i_3_17.counts ola_mES_a2i_3_18.counts ola_mES_a2i_3_2.counts ola_mES_a2i_3_20.counts
#> "032" "032" "032" "032"
#> ola_mES_a2i_3_21.counts ola_mES_a2i_3_22.counts ola_mES_a2i_3_24.counts ola_mES_a2i_3_25.counts
#> "0223" "0223" "0223" "011"
#> ola_mES_a2i_3_26.counts ola_mES_a2i_3_28.counts ola_mES_a2i_3_29.counts ola_mES_a2i_3_3.counts
#> "032" "011" "011" "021"
#> ola_mES_a2i_3_30.counts ola_mES_a2i_3_31.counts ola_mES_a2i_3_33.counts ola_mES_a2i_3_34.counts
#> "032" "032" "0223" "011"
#> ola_mES_a2i_3_37.counts ola_mES_a2i_3_38.counts ola_mES_a2i_3_39.counts ola_mES_a2i_3_4.counts
#> "011" "011" "011" "0223"
#> ola_mES_a2i_3_40.counts ola_mES_a2i_3_41.counts ola_mES_a2i_3_42.counts ola_mES_a2i_3_44.counts
#> "011" "032" "032" "011"
#> ola_mES_a2i_3_45.counts ola_mES_a2i_3_47.counts ola_mES_a2i_3_49.counts ola_mES_a2i_3_50.counts
#> "032" "032" "011" "011"
#> ola_mES_a2i_3_52.counts ola_mES_a2i_3_54.counts ola_mES_a2i_3_56.counts ola_mES_a2i_3_58.counts
#> "032" "011" "032" "0223"
#> ola_mES_a2i_3_59.counts ola_mES_a2i_3_6.counts ola_mES_a2i_3_62.counts ola_mES_a2i_3_64.counts
#> "0132" "011" "011" "011"
#> ola_mES_a2i_3_65.counts ola_mES_a2i_3_66.counts ola_mES_a2i_3_67.counts ola_mES_a2i_3_68.counts
#> "0132" "011" "032" "0222"
#> ola_mES_a2i_3_69.counts ola_mES_a2i_3_7.counts ola_mES_a2i_3_71.counts ola_mES_a2i_3_73.counts
#> "011" "032" "0132" "011"
#> ola_mES_a2i_3_75.counts ola_mES_a2i_3_76.counts ola_mES_a2i_3_77.counts ola_mES_a2i_3_8.counts
#> "032" "021" "011" "032"
#> ola_mES_a2i_3_80.counts ola_mES_a2i_3_81.counts ola_mES_a2i_3_82.counts ola_mES_a2i_3_83.counts
#> "0222" "032" "011" "011"
#> ola_mES_a2i_3_84.counts ola_mES_a2i_3_85.counts ola_mES_a2i_3_86.counts ola_mES_a2i_3_89.counts
#> "011" "011" "011" "0132"
#> ola_mES_a2i_3_9.counts ola_mES_a2i_3_90.counts ola_mES_a2i_3_91.counts ola_mES_a2i_3_92.counts
#> "0223" "011" "0223" "011"
#> ola_mES_a2i_3_94.counts ola_mES_a2i_3_96.counts ola_mES_lif_1_1.counts ola_mES_lif_1_10.counts
#> "031" "011" "014" "033"
#> ola_mES_lif_1_11.counts ola_mES_lif_1_12.counts ola_mES_lif_1_13.counts ola_mES_lif_1_14.counts
#> "012" "033" "014" "033"
#> ola_mES_lif_1_15.counts ola_mES_lif_1_17.counts ola_mES_lif_1_18.counts ola_mES_lif_1_19.counts
#> "014" "031" "014" "014"
#> ola_mES_lif_1_2.counts ola_mES_lif_1_20.counts ola_mES_lif_1_21.counts ola_mES_lif_1_22.counts
#> "033" "014" "033" "014"
#> ola_mES_lif_1_23.counts ola_mES_lif_1_24.counts ola_mES_lif_1_25.counts ola_mES_lif_1_26.counts
#> "031" "014" "014" "014"
#> ola_mES_lif_1_28.counts ola_mES_lif_1_29.counts ola_mES_lif_1_3.counts ola_mES_lif_1_30.counts
#> "014" "031" "014" "014"
#> ola_mES_lif_1_31.counts ola_mES_lif_1_32.counts ola_mES_lif_1_33.counts ola_mES_lif_1_34.counts
#> "031" "014" "014" "014"
#> ola_mES_lif_1_35.counts ola_mES_lif_1_36.counts ola_mES_lif_1_37.counts ola_mES_lif_1_38.counts
#> "031" "014" "014" "014"
#> ola_mES_lif_1_39.counts ola_mES_lif_1_4.counts ola_mES_lif_1_41.counts ola_mES_lif_1_42.counts
#> "014" "014" "014" "014"
#> ola_mES_lif_1_43.counts ola_mES_lif_1_44.counts ola_mES_lif_1_45.counts ola_mES_lif_1_46.counts
#> "014" "014" "014" "014"
#> ola_mES_lif_1_47.counts ola_mES_lif_1_48.counts ola_mES_lif_1_49.counts ola_mES_lif_1_5.counts
#> "014" "014" "014" "012"
#> ola_mES_lif_1_50.counts ola_mES_lif_1_51.counts ola_mES_lif_1_52.counts ola_mES_lif_1_53.counts
#> "014" "012" "012" "0223"
#> ola_mES_lif_1_54.counts ola_mES_lif_1_55.counts ola_mES_lif_1_56.counts ola_mES_lif_1_57.counts
#> "014" "012" "0222" "0223"
#> ola_mES_lif_1_58.counts ola_mES_lif_1_59.counts ola_mES_lif_1_6.counts ola_mES_lif_1_60.counts
#> "014" "012" "014" "014"
#> ola_mES_lif_1_61.counts ola_mES_lif_1_62.counts ola_mES_lif_1_63.counts ola_mES_lif_1_64.counts
#> "014" "014" "031" "031"
#> ola_mES_lif_1_65.counts ola_mES_lif_1_66.counts ola_mES_lif_1_67.counts ola_mES_lif_1_68.counts
#> "031" "014" "014" "014"
#> ola_mES_lif_1_69.counts ola_mES_lif_1_7.counts ola_mES_lif_1_70.counts ola_mES_lif_1_72.counts
#> "031" "014" "033" "014"
#> ola_mES_lif_1_73.counts ola_mES_lif_1_74.counts ola_mES_lif_1_75.counts ola_mES_lif_1_79.counts
#> "014" "031" "031" "033"
#> ola_mES_lif_1_8.counts ola_mES_lif_1_80.counts ola_mES_lif_1_81.counts ola_mES_lif_1_82.counts
#> "014" "014" "014" "031"
#> ola_mES_lif_1_86.counts ola_mES_lif_1_9.counts ola_mES_lif_1_91.counts ola_mES_lif_1_92.counts
#> "033" "014" "014" "014"
#> ola_mES_lif_1_93.counts ola_mES_lif_1_95.counts ola_mES_lif_1_96.counts ola_mES_lif_2_1.counts
#> "033" "014" "014" "033"
#> ola_mES_lif_2_10.counts ola_mES_lif_2_11.counts ola_mES_lif_2_12.counts ola_mES_lif_2_13.counts
#> "012" "012" "0223" "012"
#> ola_mES_lif_2_14.counts ola_mES_lif_2_15.counts ola_mES_lif_2_16.counts ola_mES_lif_2_17.counts
#> "012" "012" "012" "012"
#> ola_mES_lif_2_18.counts ola_mES_lif_2_19.counts ola_mES_lif_2_2.counts ola_mES_lif_2_20.counts
#> "012" "012" "012" "033"
#> ola_mES_lif_2_21.counts ola_mES_lif_2_23.counts ola_mES_lif_2_25.counts ola_mES_lif_2_26.counts
#> "012" "012" "033" "012"
#> ola_mES_lif_2_27.counts ola_mES_lif_2_28.counts ola_mES_lif_2_29.counts ola_mES_lif_2_3.counts
#> "033" "012" "012" "012"
#> ola_mES_lif_2_30.counts ola_mES_lif_2_31.counts ola_mES_lif_2_32.counts ola_mES_lif_2_33.counts
#> "012" "033" "012" "033"
#> ola_mES_lif_2_34.counts ola_mES_lif_2_35.counts ola_mES_lif_2_36.counts ola_mES_lif_2_37.counts
#> "012" "012" "034" "033"
#> ola_mES_lif_2_38.counts ola_mES_lif_2_39.counts ola_mES_lif_2_4.counts ola_mES_lif_2_40.counts
#> "012" "012" "012" "012"
#> ola_mES_lif_2_41.counts ola_mES_lif_2_42.counts ola_mES_lif_2_43.counts ola_mES_lif_2_44.counts
#> "033" "012" "012" "012"
#> ola_mES_lif_2_45.counts ola_mES_lif_2_46.counts ola_mES_lif_2_47.counts ola_mES_lif_2_48.counts
#> "034" "012" "033" "0223"
#> ola_mES_lif_2_49.counts ola_mES_lif_2_5.counts ola_mES_lif_2_50.counts ola_mES_lif_2_51.counts
#> "012" "012" "012" "012"
#> ola_mES_lif_2_52.counts ola_mES_lif_2_53.counts ola_mES_lif_2_54.counts ola_mES_lif_2_55.counts
#> "012" "012" "012" "012"
#> ola_mES_lif_2_56.counts ola_mES_lif_2_57.counts ola_mES_lif_2_58.counts ola_mES_lif_2_59.counts
#> "012" "034" "012" "012"
#> ola_mES_lif_2_6.counts ola_mES_lif_2_60.counts ola_mES_lif_2_61.counts ola_mES_lif_2_63.counts
#> "012" "033" "014" "012"
#> ola_mES_lif_2_64.counts ola_mES_lif_2_65.counts ola_mES_lif_2_66.counts ola_mES_lif_2_67.counts
#> "012" "012" "012" "012"
#> ola_mES_lif_2_68.counts ola_mES_lif_2_69.counts ola_mES_lif_2_7.counts ola_mES_lif_2_70.counts
#> "012" "0223" "012" "012"
#> ola_mES_lif_2_71.counts ola_mES_lif_2_72.counts ola_mES_lif_2_73.counts ola_mES_lif_2_74.counts
#> "012" "014" "0223" "012"
#> ola_mES_lif_2_75.counts ola_mES_lif_2_76.counts ola_mES_lif_2_77.counts ola_mES_lif_2_79.counts
#> "012" "012" "012" "033"
#> ola_mES_lif_2_8.counts ola_mES_lif_2_80.counts ola_mES_lif_2_81.counts ola_mES_lif_2_82.counts
#> "012" "012" "033" "012"
#> ola_mES_lif_2_83.counts ola_mES_lif_2_84.counts ola_mES_lif_2_86.counts ola_mES_lif_2_87.counts
#> "012" "012" "033" "012"
#> ola_mES_lif_2_89.counts ola_mES_lif_2_9.counts ola_mES_lif_2_90.counts ola_mES_lif_2_91.counts
#> "012" "033" "033" "033"
#> ola_mES_lif_2_92.counts ola_mES_lif_2_93.counts ola_mES_lif_2_94.counts ola_mES_lif_2_95.counts
#> "033" "012" "012" "012"
#> ola_mES_lif_2_96.counts ola_mES_lif_3_1.counts ola_mES_lif_3_10.counts ola_mES_lif_3_11.counts
#> "033" "014" "012" "033"
#> ola_mES_lif_3_12.counts ola_mES_lif_3_13.counts ola_mES_lif_3_14.counts ola_mES_lif_3_15.counts
#> "0223" "014" "034" "034"
#> ola_mES_lif_3_16.counts ola_mES_lif_3_17.counts ola_mES_lif_3_19.counts ola_mES_lif_3_2.counts
#> "034" "034" "034" "0222"
#> ola_mES_lif_3_20.counts ola_mES_lif_3_21.counts ola_mES_lif_3_22.counts ola_mES_lif_3_23.counts
#> "031" "0222" "031" "031"
#> ola_mES_lif_3_24.counts ola_mES_lif_3_26.counts ola_mES_lif_3_27.counts ola_mES_lif_3_28.counts
#> "031" "031" "014" "034"
#> ola_mES_lif_3_29.counts ola_mES_lif_3_3.counts ola_mES_lif_3_30.counts ola_mES_lif_3_31.counts
#> "033" "014" "014" "034"
#> ola_mES_lif_3_32.counts ola_mES_lif_3_33.counts ola_mES_lif_3_34.counts ola_mES_lif_3_35.counts
#> "014" "034" "034" "034"
#> ola_mES_lif_3_36.counts ola_mES_lif_3_37.counts ola_mES_lif_3_38.counts ola_mES_lif_3_39.counts
#> "034" "033" "034" "034"
#> ola_mES_lif_3_4.counts ola_mES_lif_3_40.counts ola_mES_lif_3_42.counts ola_mES_lif_3_45.counts
#> "014" "014" "014" "034"
#> ola_mES_lif_3_46.counts ola_mES_lif_3_47.counts ola_mES_lif_3_48.counts ola_mES_lif_3_49.counts
#> "034" "034" "033" "014"
#> ola_mES_lif_3_5.counts ola_mES_lif_3_50.counts ola_mES_lif_3_51.counts ola_mES_lif_3_52.counts
#> "012" "014" "014" "014"
#> ola_mES_lif_3_53.counts ola_mES_lif_3_54.counts ola_mES_lif_3_56.counts ola_mES_lif_3_57.counts
#> "014" "014" "012" "033"
#> ola_mES_lif_3_58.counts ola_mES_lif_3_59.counts ola_mES_lif_3_6.counts ola_mES_lif_3_60.counts
#> "014" "014" "014" "014"
#> ola_mES_lif_3_61.counts ola_mES_lif_3_63.counts ola_mES_lif_3_64.counts ola_mES_lif_3_65.counts
#> "014" "014" "014" "014"
#> ola_mES_lif_3_69.counts ola_mES_lif_3_70.counts ola_mES_lif_3_71.counts ola_mES_lif_3_72.counts
#> "034" "034" "014" "034"
#> ola_mES_lif_3_73.counts ola_mES_lif_3_74.counts ola_mES_lif_3_75.counts ola_mES_lif_3_76.counts
#> "031" "031" "031" "031"
#> ola_mES_lif_3_77.counts ola_mES_lif_3_78.counts ola_mES_lif_3_8.counts ola_mES_lif_3_80.counts
#> "0222" "031" "014" "031"
#> ola_mES_lif_3_82.counts ola_mES_lif_3_83.counts ola_mES_lif_3_84.counts ola_mES_lif_3_87.counts
#> "031" "031" "031" "031"
#> ola_mES_lif_3_88.counts ola_mES_lif_3_89.counts ola_mES_lif_3_9.counts ola_mES_lif_3_90.counts
#> "031" "0222" "0223" "034"
#> ola_mES_lif_3_92.counts ola_mES_lif_3_94.counts ola_mES_lif_3_95.counts ola_mES_lif_3_96.counts
#> "031" "031" "031" "031"
get_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 4355))
#> ola_mES_2i_2_1.counts ola_mES_2i_2_10.counts ola_mES_2i_2_11.counts ola_mES_2i_2_12.counts
#> "011" "013" "013" "013"
#> ola_mES_2i_2_13.counts ola_mES_2i_2_14.counts ola_mES_2i_2_15.counts ola_mES_2i_2_16.counts
#> "013" "011" "013" "013"
#> ola_mES_2i_2_17.counts ola_mES_2i_2_18.counts ola_mES_2i_2_19.counts ola_mES_2i_2_2.counts
#> "013" "013" "013" "011"
#> ola_mES_2i_2_20.counts ola_mES_2i_2_21.counts ola_mES_2i_2_22.counts ola_mES_2i_2_23.counts
#> "011" "013" "013" "013"
#> ola_mES_2i_2_24.counts ola_mES_2i_2_25.counts ola_mES_2i_2_26.counts ola_mES_2i_2_27.counts
#> "013" "011" "013" "011"
#> ola_mES_2i_2_28.counts ola_mES_2i_2_29.counts ola_mES_2i_2_3.counts ola_mES_2i_2_30.counts
#> "011" "013" "011" "013"
#> ola_mES_2i_2_31.counts ola_mES_2i_2_32.counts ola_mES_2i_2_33.counts ola_mES_2i_2_34.counts
#> "013" "013" "013" "013"
#> ola_mES_2i_2_35.counts ola_mES_2i_2_36.counts ola_mES_2i_2_37.counts ola_mES_2i_2_38.counts
#> "013" "013" "011" "011"
#> ola_mES_2i_2_39.counts ola_mES_2i_2_4.counts ola_mES_2i_2_40.counts ola_mES_2i_2_41.counts
#> "013" "013" "013" "013"
#> ola_mES_2i_2_42.counts ola_mES_2i_2_43.counts ola_mES_2i_2_44.counts ola_mES_2i_2_46.counts
#> "013" "011" "013" "013"
#> ola_mES_2i_2_47.counts ola_mES_2i_2_48.counts ola_mES_2i_2_49.counts ola_mES_2i_2_5.counts
#> "013" "013" "011" "013"
#> ola_mES_2i_2_50.counts ola_mES_2i_2_51.counts ola_mES_2i_2_52.counts ola_mES_2i_2_53.counts
#> "011" "013" "013" "013"
#> ola_mES_2i_2_54.counts ola_mES_2i_2_55.counts ola_mES_2i_2_56.counts ola_mES_2i_2_57.counts
#> "013" "013" "013" "013"
#> ola_mES_2i_2_58.counts ola_mES_2i_2_59.counts ola_mES_2i_2_6.counts ola_mES_2i_2_60.counts
#> "013" "013" "013" "013"
#> ola_mES_2i_2_61.counts ola_mES_2i_2_62.counts ola_mES_2i_2_63.counts ola_mES_2i_2_64.counts
#> "011" "032" "011" "013"
#> ola_mES_2i_2_66.counts ola_mES_2i_2_67.counts ola_mES_2i_2_68.counts ola_mES_2i_2_69.counts
#> "013" "013" "011" "013"
#> ola_mES_2i_2_7.counts ola_mES_2i_2_72.counts ola_mES_2i_2_73.counts ola_mES_2i_2_74.counts
#> "013" "013" "011" "013"
#> ola_mES_2i_2_75.counts ola_mES_2i_2_76.counts ola_mES_2i_2_79.counts ola_mES_2i_2_8.counts
#> "011" "011" "013" "013"
#> ola_mES_2i_2_80.counts ola_mES_2i_2_81.counts ola_mES_2i_2_85.counts ola_mES_2i_2_86.counts
#> "013" "013" "011" "011"
#> ola_mES_2i_2_87.counts ola_mES_2i_2_90.counts ola_mES_2i_2_91.counts ola_mES_2i_2_93.counts
#> "032" "013" "013" "013"
#> ola_mES_2i_2_94.counts ola_mES_2i_2_95.counts ola_mES_2i_3_10.counts ola_mES_2i_3_11.counts
#> "013" "013" "0222" "023"
#> ola_mES_2i_3_12.counts ola_mES_2i_3_13.counts ola_mES_2i_3_15.counts ola_mES_2i_3_16.counts
#> "0221" "0221" "0221" "021"
#> ola_mES_2i_3_17.counts ola_mES_2i_3_18.counts ola_mES_2i_3_19.counts ola_mES_2i_3_2.counts
#> "032" "032" "0221" "0221"
#> ola_mES_2i_3_20.counts ola_mES_2i_3_21.counts ola_mES_2i_3_23.counts ola_mES_2i_3_25.counts
#> "0221" "0222" "021" "023"
#> ola_mES_2i_3_26.counts ola_mES_2i_3_28.counts ola_mES_2i_3_30.counts ola_mES_2i_3_31.counts
#> "0221" "021" "0221" "032"
#> ola_mES_2i_3_33.counts ola_mES_2i_3_36.counts ola_mES_2i_3_37.counts ola_mES_2i_3_38.counts
#> "0221" "021" "0221" "0221"
#> ola_mES_2i_3_39.counts ola_mES_2i_3_4.counts ola_mES_2i_3_41.counts ola_mES_2i_3_42.counts
#> "0222" "021" "0222" "0221"
#> ola_mES_2i_3_43.counts ola_mES_2i_3_45.counts ola_mES_2i_3_46.counts ola_mES_2i_3_47.counts
#> "021" "0221" "0221" "0221"
#> ola_mES_2i_3_49.counts ola_mES_2i_3_5.counts ola_mES_2i_3_52.counts ola_mES_2i_3_54.counts
#> "032" "0221" "0221" "0222"
#> ola_mES_2i_3_56.counts ola_mES_2i_3_57.counts ola_mES_2i_3_58.counts ola_mES_2i_3_6.counts
#> "0221" "0221" "0221" "0222"
#> ola_mES_2i_3_62.counts ola_mES_2i_3_65.counts ola_mES_2i_3_66.counts ola_mES_2i_3_69.counts
#> "032" "0221" "032" "0222"
#> ola_mES_2i_3_7.counts ola_mES_2i_3_73.counts ola_mES_2i_3_76.counts ola_mES_2i_3_77.counts
#> "0221" "0221" "0221" "0222"
#> ola_mES_2i_3_78.counts ola_mES_2i_3_79.counts ola_mES_2i_3_8.counts ola_mES_2i_3_80.counts
#> "032" "0221" "0221" "0221"
#> ola_mES_2i_3_82.counts ola_mES_2i_3_83.counts ola_mES_2i_3_84.counts ola_mES_2i_3_87.counts
#> "0221" "0221" "021" "0222"
#> ola_mES_2i_3_88.counts ola_mES_2i_3_90.counts ola_mES_2i_3_91.counts ola_mES_2i_3_92.counts
#> "0221" "021" "0222" "0221"
#> ola_mES_2i_3_96.counts ola_mES_2i_4_1.counts ola_mES_2i_4_10.counts ola_mES_2i_4_11.counts
#> "0222" "021" "021" "021"
#> ola_mES_2i_4_13.counts ola_mES_2i_4_17.counts ola_mES_2i_4_18.counts ola_mES_2i_4_2.counts
#> "021" "021" "023" "013"
#> ola_mES_2i_4_20.counts ola_mES_2i_4_21.counts ola_mES_2i_4_22.counts ola_mES_2i_4_23.counts
#> "021" "021" "023" "0221"
#> ola_mES_2i_4_24.counts ola_mES_2i_4_25.counts ola_mES_2i_4_26.counts ola_mES_2i_4_27.counts
#> "021" "021" "021" "023"
#> ola_mES_2i_4_29.counts ola_mES_2i_4_30.counts ola_mES_2i_4_31.counts ola_mES_2i_4_32.counts
#> "021" "021" "021" "021"
#> ola_mES_2i_4_33.counts ola_mES_2i_4_34.counts ola_mES_2i_4_35.counts ola_mES_2i_4_36.counts
#> "021" "021" "021" "021"
#> ola_mES_2i_4_37.counts ola_mES_2i_4_38.counts ola_mES_2i_4_39.counts ola_mES_2i_4_40.counts
#> "032" "011" "021" "011"
#> ola_mES_2i_4_42.counts ola_mES_2i_4_43.counts ola_mES_2i_4_44.counts ola_mES_2i_4_46.counts
#> "032" "021" "021" "021"
#> ola_mES_2i_4_49.counts ola_mES_2i_4_5.counts ola_mES_2i_4_50.counts ola_mES_2i_4_52.counts
#> "011" "023" "021" "021"
#> ola_mES_2i_4_53.counts ola_mES_2i_4_54.counts ola_mES_2i_4_56.counts ola_mES_2i_4_57.counts
#> "011" "032" "021" "021"
#> ola_mES_2i_4_58.counts ola_mES_2i_4_59.counts ola_mES_2i_4_6.counts ola_mES_2i_4_60.counts
#> "021" "021" "021" "0221"
#> ola_mES_2i_4_61.counts ola_mES_2i_4_62.counts ola_mES_2i_4_63.counts ola_mES_2i_4_64.counts
#> "021" "032" "0221" "021"
#> ola_mES_2i_4_65.counts ola_mES_2i_4_66.counts ola_mES_2i_4_67.counts ola_mES_2i_4_68.counts
#> "021" "021" "011" "0221"
#> ola_mES_2i_4_69.counts ola_mES_2i_4_7.counts ola_mES_2i_4_70.counts ola_mES_2i_4_71.counts
#> "021" "023" "0221" "021"
#> ola_mES_2i_4_72.counts ola_mES_2i_4_73.counts ola_mES_2i_4_75.counts ola_mES_2i_4_77.counts
#> "023" "021" "0222" "021"
#> ola_mES_2i_4_78.counts ola_mES_2i_4_80.counts ola_mES_2i_4_81.counts ola_mES_2i_4_83.counts
#> "023" "021" "021" "023"
#> ola_mES_2i_4_84.counts ola_mES_2i_4_88.counts ola_mES_2i_4_89.counts ola_mES_2i_4_9.counts
#> "023" "0221" "0221" "023"
#> ola_mES_2i_4_90.counts ola_mES_2i_4_91.counts ola_mES_2i_4_94.counts ola_mES_2i_4_95.counts
#> "021" "021" "0221" "0222"
#> ola_mES_2i_4_96.counts ola_mES_2i_5_1.counts ola_mES_2i_5_10.counts ola_mES_2i_5_11.counts
#> "0222" "0221" "023" "023"
#> ola_mES_2i_5_12.counts ola_mES_2i_5_13.counts ola_mES_2i_5_14.counts ola_mES_2i_5_15.counts
#> "023" "023" "021" "021"
#> ola_mES_2i_5_16.counts ola_mES_2i_5_17.counts ola_mES_2i_5_18.counts ola_mES_2i_5_19.counts
#> "021" "021" "023" "021"
#> ola_mES_2i_5_2.counts ola_mES_2i_5_20.counts ola_mES_2i_5_21.counts ola_mES_2i_5_23.counts
#> "0221" "021" "021" "023"
#> ola_mES_2i_5_24.counts ola_mES_2i_5_25.counts ola_mES_2i_5_26.counts ola_mES_2i_5_27.counts
#> "021" "021" "021" "021"
#> ola_mES_2i_5_28.counts ola_mES_2i_5_29.counts ola_mES_2i_5_3.counts ola_mES_2i_5_30.counts
#> "0221" "0221" "0221" "021"
#> ola_mES_2i_5_31.counts ola_mES_2i_5_32.counts ola_mES_2i_5_34.counts ola_mES_2i_5_35.counts
#> "021" "021" "021" "021"
#> ola_mES_2i_5_36.counts ola_mES_2i_5_38.counts ola_mES_2i_5_39.counts ola_mES_2i_5_4.counts
#> "021" "021" "021" "0221"
#> ola_mES_2i_5_40.counts ola_mES_2i_5_41.counts ola_mES_2i_5_42.counts ola_mES_2i_5_44.counts
#> "021" "011" "021" "021"
#> ola_mES_2i_5_45.counts ola_mES_2i_5_46.counts ola_mES_2i_5_47.counts ola_mES_2i_5_48.counts
#> "021" "021" "021" "021"
#> ola_mES_2i_5_49.counts ola_mES_2i_5_5.counts ola_mES_2i_5_51.counts ola_mES_2i_5_52.counts
#> "021" "023" "021" "0221"
#> ola_mES_2i_5_53.counts ola_mES_2i_5_54.counts ola_mES_2i_5_55.counts ola_mES_2i_5_56.counts
#> "021" "021" "021" "021"
#> ola_mES_2i_5_57.counts ola_mES_2i_5_58.counts ola_mES_2i_5_59.counts ola_mES_2i_5_6.counts
#> "021" "021" "021" "0221"
#> ola_mES_2i_5_61.counts ola_mES_2i_5_62.counts ola_mES_2i_5_63.counts ola_mES_2i_5_65.counts
#> "021" "0221" "021" "023"
#> ola_mES_2i_5_66.counts ola_mES_2i_5_67.counts ola_mES_2i_5_68.counts ola_mES_2i_5_69.counts
#> "0221" "021" "021" "021"
#> ola_mES_2i_5_7.counts ola_mES_2i_5_70.counts ola_mES_2i_5_72.counts ola_mES_2i_5_73.counts
#> "021" "021" "021" "023"
#> ola_mES_2i_5_74.counts ola_mES_2i_5_75.counts ola_mES_2i_5_76.counts ola_mES_2i_5_77.counts
#> "021" "023" "023" "023"
#> ola_mES_2i_5_78.counts ola_mES_2i_5_8.counts ola_mES_2i_5_81.counts ola_mES_2i_5_82.counts
#> "021" "021" "0221" "021"
#> ola_mES_2i_5_83.counts ola_mES_2i_5_85.counts ola_mES_2i_5_86.counts ola_mES_2i_5_87.counts
#> "023" "0221" "023" "023"
#> ola_mES_2i_5_88.counts ola_mES_2i_5_9.counts ola_mES_2i_5_90.counts ola_mES_2i_5_92.counts
#> "023" "0222" "021" "021"
#> ola_mES_2i_5_94.counts ola_mES_2i_5_95.counts ola_mES_2i_5_96.counts ola_mES_a2i_2_1.counts
#> "021" "021" "021" "013"
#> ola_mES_a2i_2_10.counts ola_mES_a2i_2_11.counts ola_mES_a2i_2_12.counts ola_mES_a2i_2_13.counts
#> "011" "013" "011" "011"
#> ola_mES_a2i_2_14.counts ola_mES_a2i_2_15.counts ola_mES_a2i_2_16.counts ola_mES_a2i_2_17.counts
#> "013" "013" "013" "013"
#> ola_mES_a2i_2_18.counts ola_mES_a2i_2_19.counts ola_mES_a2i_2_2.counts ola_mES_a2i_2_20.counts
#> "013" "013" "013" "011"
#> ola_mES_a2i_2_21.counts ola_mES_a2i_2_22.counts ola_mES_a2i_2_23.counts ola_mES_a2i_2_24.counts
#> "013" "013" "013" "013"
#> ola_mES_a2i_2_25.counts ola_mES_a2i_2_26.counts ola_mES_a2i_2_27.counts ola_mES_a2i_2_28.counts
#> "011" "032" "011" "011"
#> ola_mES_a2i_2_29.counts ola_mES_a2i_2_3.counts ola_mES_a2i_2_30.counts ola_mES_a2i_2_31.counts
#> "011" "013" "0222" "032"
#> ola_mES_a2i_2_32.counts ola_mES_a2i_2_33.counts ola_mES_a2i_2_34.counts ola_mES_a2i_2_35.counts
#> "032" "011" "011" "031"
#> ola_mES_a2i_2_36.counts ola_mES_a2i_2_37.counts ola_mES_a2i_2_38.counts ola_mES_a2i_2_39.counts
#> "0222" "032" "011" "032"
#> ola_mES_a2i_2_4.counts ola_mES_a2i_2_40.counts ola_mES_a2i_2_41.counts ola_mES_a2i_2_42.counts
#> "011" "011" "013" "013"
#> ola_mES_a2i_2_43.counts ola_mES_a2i_2_44.counts ola_mES_a2i_2_45.counts ola_mES_a2i_2_46.counts
#> "011" "011" "013" "013"
#> ola_mES_a2i_2_47.counts ola_mES_a2i_2_48.counts ola_mES_a2i_2_49.counts ola_mES_a2i_2_5.counts
#> "011" "011" "011" "013"
#> ola_mES_a2i_2_50.counts ola_mES_a2i_2_51.counts ola_mES_a2i_2_52.counts ola_mES_a2i_2_53.counts
#> "032" "011" "011" "032"
#> ola_mES_a2i_2_54.counts ola_mES_a2i_2_55.counts ola_mES_a2i_2_56.counts ola_mES_a2i_2_57.counts
#> "011" "013" "011" "013"
#> ola_mES_a2i_2_59.counts ola_mES_a2i_2_6.counts ola_mES_a2i_2_60.counts ola_mES_a2i_2_61.counts
#> "013" "032" "011" "011"
#> ola_mES_a2i_2_62.counts ola_mES_a2i_2_63.counts ola_mES_a2i_2_64.counts ola_mES_a2i_2_65.counts
#> "011" "011" "011" "011"
#> ola_mES_a2i_2_66.counts ola_mES_a2i_2_67.counts ola_mES_a2i_2_68.counts ola_mES_a2i_2_69.counts
#> "013" "011" "011" "032"
#> ola_mES_a2i_2_7.counts ola_mES_a2i_2_70.counts ola_mES_a2i_2_71.counts ola_mES_a2i_2_72.counts
#> "011" "011" "013" "011"
#> ola_mES_a2i_2_73.counts ola_mES_a2i_2_74.counts ola_mES_a2i_2_75.counts ola_mES_a2i_2_76.counts
#> "011" "011" "011" "011"
#> ola_mES_a2i_2_77.counts ola_mES_a2i_2_78.counts ola_mES_a2i_2_8.counts ola_mES_a2i_2_80.counts
#> "011" "013" "013" "011"
#> ola_mES_a2i_2_81.counts ola_mES_a2i_2_82.counts ola_mES_a2i_2_83.counts ola_mES_a2i_2_84.counts
#> "011" "013" "013" "011"
#> ola_mES_a2i_2_86.counts ola_mES_a2i_2_87.counts ola_mES_a2i_2_88.counts ola_mES_a2i_2_89.counts
#> "011" "011" "011" "0223"
#> ola_mES_a2i_2_9.counts ola_mES_a2i_2_90.counts ola_mES_a2i_2_91.counts ola_mES_a2i_2_92.counts
#> "011" "013" "032" "032"
#> ola_mES_a2i_2_93.counts ola_mES_a2i_2_94.counts ola_mES_a2i_2_95.counts ola_mES_a2i_2_96.counts
#> "011" "013" "013" "013"
#> ola_mES_a2i_3_10.counts ola_mES_a2i_3_11.counts ola_mES_a2i_3_13.counts ola_mES_a2i_3_16.counts
#> "021" "0223" "011" "011"
#> ola_mES_a2i_3_17.counts ola_mES_a2i_3_18.counts ola_mES_a2i_3_2.counts ola_mES_a2i_3_20.counts
#> "032" "032" "032" "032"
#> ola_mES_a2i_3_21.counts ola_mES_a2i_3_22.counts ola_mES_a2i_3_24.counts ola_mES_a2i_3_25.counts
#> "0223" "0223" "0223" "011"
#> ola_mES_a2i_3_26.counts ola_mES_a2i_3_28.counts ola_mES_a2i_3_29.counts ola_mES_a2i_3_3.counts
#> "032" "011" "011" "021"
#> ola_mES_a2i_3_30.counts ola_mES_a2i_3_31.counts ola_mES_a2i_3_33.counts ola_mES_a2i_3_34.counts
#> "032" "032" "0223" "011"
#> ola_mES_a2i_3_37.counts ola_mES_a2i_3_38.counts ola_mES_a2i_3_39.counts ola_mES_a2i_3_4.counts
#> "011" "011" "011" "0223"
#> ola_mES_a2i_3_40.counts ola_mES_a2i_3_41.counts ola_mES_a2i_3_42.counts ola_mES_a2i_3_44.counts
#> "011" "032" "032" "011"
#> ola_mES_a2i_3_45.counts ola_mES_a2i_3_47.counts ola_mES_a2i_3_49.counts ola_mES_a2i_3_50.counts
#> "032" "032" "011" "011"
#> ola_mES_a2i_3_52.counts ola_mES_a2i_3_54.counts ola_mES_a2i_3_56.counts ola_mES_a2i_3_58.counts
#> "032" "011" "032" "0223"
#> ola_mES_a2i_3_59.counts ola_mES_a2i_3_6.counts ola_mES_a2i_3_62.counts ola_mES_a2i_3_64.counts
#> "013" "011" "011" "011"
#> ola_mES_a2i_3_65.counts ola_mES_a2i_3_66.counts ola_mES_a2i_3_67.counts ola_mES_a2i_3_68.counts
#> "013" "011" "032" "0222"
#> ola_mES_a2i_3_69.counts ola_mES_a2i_3_7.counts ola_mES_a2i_3_71.counts ola_mES_a2i_3_73.counts
#> "011" "032" "013" "011"
#> ola_mES_a2i_3_75.counts ola_mES_a2i_3_76.counts ola_mES_a2i_3_77.counts ola_mES_a2i_3_8.counts
#> "032" "021" "011" "032"
#> ola_mES_a2i_3_80.counts ola_mES_a2i_3_81.counts ola_mES_a2i_3_82.counts ola_mES_a2i_3_83.counts
#> "0222" "032" "011" "011"
#> ola_mES_a2i_3_84.counts ola_mES_a2i_3_85.counts ola_mES_a2i_3_86.counts ola_mES_a2i_3_89.counts
#> "011" "011" "011" "013"
#> ola_mES_a2i_3_9.counts ola_mES_a2i_3_90.counts ola_mES_a2i_3_91.counts ola_mES_a2i_3_92.counts
#> "0223" "011" "0223" "011"
#> ola_mES_a2i_3_94.counts ola_mES_a2i_3_96.counts ola_mES_lif_1_1.counts ola_mES_lif_1_10.counts
#> "031" "011" "014" "033"
#> ola_mES_lif_1_11.counts ola_mES_lif_1_12.counts ola_mES_lif_1_13.counts ola_mES_lif_1_14.counts
#> "012" "033" "014" "033"
#> ola_mES_lif_1_15.counts ola_mES_lif_1_17.counts ola_mES_lif_1_18.counts ola_mES_lif_1_19.counts
#> "014" "031" "014" "014"
#> ola_mES_lif_1_2.counts ola_mES_lif_1_20.counts ola_mES_lif_1_21.counts ola_mES_lif_1_22.counts
#> "033" "014" "033" "014"
#> ola_mES_lif_1_23.counts ola_mES_lif_1_24.counts ola_mES_lif_1_25.counts ola_mES_lif_1_26.counts
#> "031" "014" "014" "014"
#> ola_mES_lif_1_28.counts ola_mES_lif_1_29.counts ola_mES_lif_1_3.counts ola_mES_lif_1_30.counts
#> "014" "031" "014" "014"
#> ola_mES_lif_1_31.counts ola_mES_lif_1_32.counts ola_mES_lif_1_33.counts ola_mES_lif_1_34.counts
#> "031" "014" "014" "014"
#> ola_mES_lif_1_35.counts ola_mES_lif_1_36.counts ola_mES_lif_1_37.counts ola_mES_lif_1_38.counts
#> "031" "014" "014" "014"
#> ola_mES_lif_1_39.counts ola_mES_lif_1_4.counts ola_mES_lif_1_41.counts ola_mES_lif_1_42.counts
#> "014" "014" "014" "014"
#> ola_mES_lif_1_43.counts ola_mES_lif_1_44.counts ola_mES_lif_1_45.counts ola_mES_lif_1_46.counts
#> "014" "014" "014" "014"
#> ola_mES_lif_1_47.counts ola_mES_lif_1_48.counts ola_mES_lif_1_49.counts ola_mES_lif_1_5.counts
#> "014" "014" "014" "012"
#> ola_mES_lif_1_50.counts ola_mES_lif_1_51.counts ola_mES_lif_1_52.counts ola_mES_lif_1_53.counts
#> "014" "012" "012" "0223"
#> ola_mES_lif_1_54.counts ola_mES_lif_1_55.counts ola_mES_lif_1_56.counts ola_mES_lif_1_57.counts
#> "014" "012" "0222" "0223"
#> ola_mES_lif_1_58.counts ola_mES_lif_1_59.counts ola_mES_lif_1_6.counts ola_mES_lif_1_60.counts
#> "014" "012" "014" "014"
#> ola_mES_lif_1_61.counts ola_mES_lif_1_62.counts ola_mES_lif_1_63.counts ola_mES_lif_1_64.counts
#> "014" "014" "031" "031"
#> ola_mES_lif_1_65.counts ola_mES_lif_1_66.counts ola_mES_lif_1_67.counts ola_mES_lif_1_68.counts
#> "031" "014" "014" "014"
#> ola_mES_lif_1_69.counts ola_mES_lif_1_7.counts ola_mES_lif_1_70.counts ola_mES_lif_1_72.counts
#> "031" "014" "033" "014"
#> ola_mES_lif_1_73.counts ola_mES_lif_1_74.counts ola_mES_lif_1_75.counts ola_mES_lif_1_79.counts
#> "014" "031" "031" "033"
#> ola_mES_lif_1_8.counts ola_mES_lif_1_80.counts ola_mES_lif_1_81.counts ola_mES_lif_1_82.counts
#> "014" "014" "014" "031"
#> ola_mES_lif_1_86.counts ola_mES_lif_1_9.counts ola_mES_lif_1_91.counts ola_mES_lif_1_92.counts
#> "033" "014" "014" "014"
#> ola_mES_lif_1_93.counts ola_mES_lif_1_95.counts ola_mES_lif_1_96.counts ola_mES_lif_2_1.counts
#> "033" "014" "014" "033"
#> ola_mES_lif_2_10.counts ola_mES_lif_2_11.counts ola_mES_lif_2_12.counts ola_mES_lif_2_13.counts
#> "012" "012" "0223" "012"
#> ola_mES_lif_2_14.counts ola_mES_lif_2_15.counts ola_mES_lif_2_16.counts ola_mES_lif_2_17.counts
#> "012" "012" "012" "012"
#> ola_mES_lif_2_18.counts ola_mES_lif_2_19.counts ola_mES_lif_2_2.counts ola_mES_lif_2_20.counts
#> "012" "012" "012" "033"
#> ola_mES_lif_2_21.counts ola_mES_lif_2_23.counts ola_mES_lif_2_25.counts ola_mES_lif_2_26.counts
#> "012" "012" "033" "012"
#> ola_mES_lif_2_27.counts ola_mES_lif_2_28.counts ola_mES_lif_2_29.counts ola_mES_lif_2_3.counts
#> "033" "012" "012" "012"
#> ola_mES_lif_2_30.counts ola_mES_lif_2_31.counts ola_mES_lif_2_32.counts ola_mES_lif_2_33.counts
#> "012" "033" "012" "033"
#> ola_mES_lif_2_34.counts ola_mES_lif_2_35.counts ola_mES_lif_2_36.counts ola_mES_lif_2_37.counts
#> "012" "012" "034" "033"
#> ola_mES_lif_2_38.counts ola_mES_lif_2_39.counts ola_mES_lif_2_4.counts ola_mES_lif_2_40.counts
#> "012" "012" "012" "012"
#> ola_mES_lif_2_41.counts ola_mES_lif_2_42.counts ola_mES_lif_2_43.counts ola_mES_lif_2_44.counts
#> "033" "012" "012" "012"
#> ola_mES_lif_2_45.counts ola_mES_lif_2_46.counts ola_mES_lif_2_47.counts ola_mES_lif_2_48.counts
#> "034" "012" "033" "0223"
#> ola_mES_lif_2_49.counts ola_mES_lif_2_5.counts ola_mES_lif_2_50.counts ola_mES_lif_2_51.counts
#> "012" "012" "012" "012"
#> ola_mES_lif_2_52.counts ola_mES_lif_2_53.counts ola_mES_lif_2_54.counts ola_mES_lif_2_55.counts
#> "012" "012" "012" "012"
#> ola_mES_lif_2_56.counts ola_mES_lif_2_57.counts ola_mES_lif_2_58.counts ola_mES_lif_2_59.counts
#> "012" "034" "012" "012"
#> ola_mES_lif_2_6.counts ola_mES_lif_2_60.counts ola_mES_lif_2_61.counts ola_mES_lif_2_63.counts
#> "012" "033" "014" "012"
#> ola_mES_lif_2_64.counts ola_mES_lif_2_65.counts ola_mES_lif_2_66.counts ola_mES_lif_2_67.counts
#> "012" "012" "012" "012"
#> ola_mES_lif_2_68.counts ola_mES_lif_2_69.counts ola_mES_lif_2_7.counts ola_mES_lif_2_70.counts
#> "012" "0223" "012" "012"
#> ola_mES_lif_2_71.counts ola_mES_lif_2_72.counts ola_mES_lif_2_73.counts ola_mES_lif_2_74.counts
#> "012" "014" "0223" "012"
#> ola_mES_lif_2_75.counts ola_mES_lif_2_76.counts ola_mES_lif_2_77.counts ola_mES_lif_2_79.counts
#> "012" "012" "012" "033"
#> ola_mES_lif_2_8.counts ola_mES_lif_2_80.counts ola_mES_lif_2_81.counts ola_mES_lif_2_82.counts
#> "012" "012" "033" "012"
#> ola_mES_lif_2_83.counts ola_mES_lif_2_84.counts ola_mES_lif_2_86.counts ola_mES_lif_2_87.counts
#> "012" "012" "033" "012"
#> ola_mES_lif_2_89.counts ola_mES_lif_2_9.counts ola_mES_lif_2_90.counts ola_mES_lif_2_91.counts
#> "012" "033" "033" "033"
#> ola_mES_lif_2_92.counts ola_mES_lif_2_93.counts ola_mES_lif_2_94.counts ola_mES_lif_2_95.counts
#> "033" "012" "012" "012"
#> ola_mES_lif_2_96.counts ola_mES_lif_3_1.counts ola_mES_lif_3_10.counts ola_mES_lif_3_11.counts
#> "033" "014" "012" "033"
#> ola_mES_lif_3_12.counts ola_mES_lif_3_13.counts ola_mES_lif_3_14.counts ola_mES_lif_3_15.counts
#> "0223" "014" "034" "034"
#> ola_mES_lif_3_16.counts ola_mES_lif_3_17.counts ola_mES_lif_3_19.counts ola_mES_lif_3_2.counts
#> "034" "034" "034" "0222"
#> ola_mES_lif_3_20.counts ola_mES_lif_3_21.counts ola_mES_lif_3_22.counts ola_mES_lif_3_23.counts
#> "031" "0222" "031" "031"
#> ola_mES_lif_3_24.counts ola_mES_lif_3_26.counts ola_mES_lif_3_27.counts ola_mES_lif_3_28.counts
#> "031" "031" "014" "034"
#> ola_mES_lif_3_29.counts ola_mES_lif_3_3.counts ola_mES_lif_3_30.counts ola_mES_lif_3_31.counts
#> "033" "014" "014" "034"
#> ola_mES_lif_3_32.counts ola_mES_lif_3_33.counts ola_mES_lif_3_34.counts ola_mES_lif_3_35.counts
#> "014" "034" "034" "034"
#> ola_mES_lif_3_36.counts ola_mES_lif_3_37.counts ola_mES_lif_3_38.counts ola_mES_lif_3_39.counts
#> "034" "033" "034" "034"
#> ola_mES_lif_3_4.counts ola_mES_lif_3_40.counts ola_mES_lif_3_42.counts ola_mES_lif_3_45.counts
#> "014" "014" "014" "034"
#> ola_mES_lif_3_46.counts ola_mES_lif_3_47.counts ola_mES_lif_3_48.counts ola_mES_lif_3_49.counts
#> "034" "034" "033" "014"
#> ola_mES_lif_3_5.counts ola_mES_lif_3_50.counts ola_mES_lif_3_51.counts ola_mES_lif_3_52.counts
#> "012" "014" "014" "014"
#> ola_mES_lif_3_53.counts ola_mES_lif_3_54.counts ola_mES_lif_3_56.counts ola_mES_lif_3_57.counts
#> "014" "014" "012" "033"
#> ola_mES_lif_3_58.counts ola_mES_lif_3_59.counts ola_mES_lif_3_6.counts ola_mES_lif_3_60.counts
#> "014" "014" "014" "014"
#> ola_mES_lif_3_61.counts ola_mES_lif_3_63.counts ola_mES_lif_3_64.counts ola_mES_lif_3_65.counts
#> "014" "014" "014" "014"
#> ola_mES_lif_3_69.counts ola_mES_lif_3_70.counts ola_mES_lif_3_71.counts ola_mES_lif_3_72.counts
#> "034" "034" "014" "034"
#> ola_mES_lif_3_73.counts ola_mES_lif_3_74.counts ola_mES_lif_3_75.counts ola_mES_lif_3_76.counts
#> "031" "031" "031" "031"
#> ola_mES_lif_3_77.counts ola_mES_lif_3_78.counts ola_mES_lif_3_8.counts ola_mES_lif_3_80.counts
#> "0222" "031" "014" "031"
#> ola_mES_lif_3_82.counts ola_mES_lif_3_83.counts ola_mES_lif_3_84.counts ola_mES_lif_3_87.counts
#> "031" "031" "031" "031"
#> ola_mES_lif_3_88.counts ola_mES_lif_3_89.counts ola_mES_lif_3_9.counts ola_mES_lif_3_90.counts
#> "031" "0222" "0223" "034"
#> ola_mES_lif_3_92.counts ola_mES_lif_3_94.counts ola_mES_lif_3_95.counts ola_mES_lif_3_96.counts
#> "031" "031" "031" "031"
get_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 5424))
#> ola_mES_2i_2_1.counts ola_mES_2i_2_10.counts ola_mES_2i_2_11.counts ola_mES_2i_2_12.counts
#> "011" "013" "013" "013"
#> ola_mES_2i_2_13.counts ola_mES_2i_2_14.counts ola_mES_2i_2_15.counts ola_mES_2i_2_16.counts
#> "013" "011" "013" "013"
#> ola_mES_2i_2_17.counts ola_mES_2i_2_18.counts ola_mES_2i_2_19.counts ola_mES_2i_2_2.counts
#> "013" "013" "013" "011"
#> ola_mES_2i_2_20.counts ola_mES_2i_2_21.counts ola_mES_2i_2_22.counts ola_mES_2i_2_23.counts
#> "011" "013" "013" "013"
#> ola_mES_2i_2_24.counts ola_mES_2i_2_25.counts ola_mES_2i_2_26.counts ola_mES_2i_2_27.counts
#> "013" "011" "013" "011"
#> ola_mES_2i_2_28.counts ola_mES_2i_2_29.counts ola_mES_2i_2_3.counts ola_mES_2i_2_30.counts
#> "011" "013" "011" "013"
#> ola_mES_2i_2_31.counts ola_mES_2i_2_32.counts ola_mES_2i_2_33.counts ola_mES_2i_2_34.counts
#> "013" "013" "013" "013"
#> ola_mES_2i_2_35.counts ola_mES_2i_2_36.counts ola_mES_2i_2_37.counts ola_mES_2i_2_38.counts
#> "013" "013" "011" "011"
#> ola_mES_2i_2_39.counts ola_mES_2i_2_4.counts ola_mES_2i_2_40.counts ola_mES_2i_2_41.counts
#> "013" "013" "013" "013"
#> ola_mES_2i_2_42.counts ola_mES_2i_2_43.counts ola_mES_2i_2_44.counts ola_mES_2i_2_46.counts
#> "013" "011" "013" "013"
#> ola_mES_2i_2_47.counts ola_mES_2i_2_48.counts ola_mES_2i_2_49.counts ola_mES_2i_2_5.counts
#> "013" "013" "011" "013"
#> ola_mES_2i_2_50.counts ola_mES_2i_2_51.counts ola_mES_2i_2_52.counts ola_mES_2i_2_53.counts
#> "011" "013" "013" "013"
#> ola_mES_2i_2_54.counts ola_mES_2i_2_55.counts ola_mES_2i_2_56.counts ola_mES_2i_2_57.counts
#> "013" "013" "013" "013"
#> ola_mES_2i_2_58.counts ola_mES_2i_2_59.counts ola_mES_2i_2_6.counts ola_mES_2i_2_60.counts
#> "013" "013" "013" "013"
#> ola_mES_2i_2_61.counts ola_mES_2i_2_62.counts ola_mES_2i_2_63.counts ola_mES_2i_2_64.counts
#> "011" "032" "011" "013"
#> ola_mES_2i_2_66.counts ola_mES_2i_2_67.counts ola_mES_2i_2_68.counts ola_mES_2i_2_69.counts
#> "013" "013" "011" "013"
#> ola_mES_2i_2_7.counts ola_mES_2i_2_72.counts ola_mES_2i_2_73.counts ola_mES_2i_2_74.counts
#> "013" "013" "011" "013"
#> ola_mES_2i_2_75.counts ola_mES_2i_2_76.counts ola_mES_2i_2_79.counts ola_mES_2i_2_8.counts
#> "011" "011" "013" "013"
#> ola_mES_2i_2_80.counts ola_mES_2i_2_81.counts ola_mES_2i_2_85.counts ola_mES_2i_2_86.counts
#> "013" "013" "011" "011"
#> ola_mES_2i_2_87.counts ola_mES_2i_2_90.counts ola_mES_2i_2_91.counts ola_mES_2i_2_93.counts
#> "032" "013" "013" "013"
#> ola_mES_2i_2_94.counts ola_mES_2i_2_95.counts ola_mES_2i_3_10.counts ola_mES_2i_3_11.counts
#> "013" "013" "022" "023"
#> ola_mES_2i_3_12.counts ola_mES_2i_3_13.counts ola_mES_2i_3_15.counts ola_mES_2i_3_16.counts
#> "022" "022" "022" "021"
#> ola_mES_2i_3_17.counts ola_mES_2i_3_18.counts ola_mES_2i_3_19.counts ola_mES_2i_3_2.counts
#> "032" "032" "022" "022"
#> ola_mES_2i_3_20.counts ola_mES_2i_3_21.counts ola_mES_2i_3_23.counts ola_mES_2i_3_25.counts
#> "022" "022" "021" "023"
#> ola_mES_2i_3_26.counts ola_mES_2i_3_28.counts ola_mES_2i_3_30.counts ola_mES_2i_3_31.counts
#> "022" "021" "022" "032"
#> ola_mES_2i_3_33.counts ola_mES_2i_3_36.counts ola_mES_2i_3_37.counts ola_mES_2i_3_38.counts
#> "022" "021" "022" "022"
#> ola_mES_2i_3_39.counts ola_mES_2i_3_4.counts ola_mES_2i_3_41.counts ola_mES_2i_3_42.counts
#> "022" "021" "022" "022"
#> ola_mES_2i_3_43.counts ola_mES_2i_3_45.counts ola_mES_2i_3_46.counts ola_mES_2i_3_47.counts
#> "021" "022" "022" "022"
#> ola_mES_2i_3_49.counts ola_mES_2i_3_5.counts ola_mES_2i_3_52.counts ola_mES_2i_3_54.counts
#> "032" "022" "022" "022"
#> ola_mES_2i_3_56.counts ola_mES_2i_3_57.counts ola_mES_2i_3_58.counts ola_mES_2i_3_6.counts
#> "022" "022" "022" "022"
#> ola_mES_2i_3_62.counts ola_mES_2i_3_65.counts ola_mES_2i_3_66.counts ola_mES_2i_3_69.counts
#> "032" "022" "032" "022"
#> ola_mES_2i_3_7.counts ola_mES_2i_3_73.counts ola_mES_2i_3_76.counts ola_mES_2i_3_77.counts
#> "022" "022" "022" "022"
#> ola_mES_2i_3_78.counts ola_mES_2i_3_79.counts ola_mES_2i_3_8.counts ola_mES_2i_3_80.counts
#> "032" "022" "022" "022"
#> ola_mES_2i_3_82.counts ola_mES_2i_3_83.counts ola_mES_2i_3_84.counts ola_mES_2i_3_87.counts
#> "022" "022" "021" "022"
#> ola_mES_2i_3_88.counts ola_mES_2i_3_90.counts ola_mES_2i_3_91.counts ola_mES_2i_3_92.counts
#> "022" "021" "022" "022"
#> ola_mES_2i_3_96.counts ola_mES_2i_4_1.counts ola_mES_2i_4_10.counts ola_mES_2i_4_11.counts
#> "022" "021" "021" "021"
#> ola_mES_2i_4_13.counts ola_mES_2i_4_17.counts ola_mES_2i_4_18.counts ola_mES_2i_4_2.counts
#> "021" "021" "023" "013"
#> ola_mES_2i_4_20.counts ola_mES_2i_4_21.counts ola_mES_2i_4_22.counts ola_mES_2i_4_23.counts
#> "021" "021" "023" "022"
#> ola_mES_2i_4_24.counts ola_mES_2i_4_25.counts ola_mES_2i_4_26.counts ola_mES_2i_4_27.counts
#> "021" "021" "021" "023"
#> ola_mES_2i_4_29.counts ola_mES_2i_4_30.counts ola_mES_2i_4_31.counts ola_mES_2i_4_32.counts
#> "021" "021" "021" "021"
#> ola_mES_2i_4_33.counts ola_mES_2i_4_34.counts ola_mES_2i_4_35.counts ola_mES_2i_4_36.counts
#> "021" "021" "021" "021"
#> ola_mES_2i_4_37.counts ola_mES_2i_4_38.counts ola_mES_2i_4_39.counts ola_mES_2i_4_40.counts
#> "032" "011" "021" "011"
#> ola_mES_2i_4_42.counts ola_mES_2i_4_43.counts ola_mES_2i_4_44.counts ola_mES_2i_4_46.counts
#> "032" "021" "021" "021"
#> ola_mES_2i_4_49.counts ola_mES_2i_4_5.counts ola_mES_2i_4_50.counts ola_mES_2i_4_52.counts
#> "011" "023" "021" "021"
#> ola_mES_2i_4_53.counts ola_mES_2i_4_54.counts ola_mES_2i_4_56.counts ola_mES_2i_4_57.counts
#> "011" "032" "021" "021"
#> ola_mES_2i_4_58.counts ola_mES_2i_4_59.counts ola_mES_2i_4_6.counts ola_mES_2i_4_60.counts
#> "021" "021" "021" "022"
#> ola_mES_2i_4_61.counts ola_mES_2i_4_62.counts ola_mES_2i_4_63.counts ola_mES_2i_4_64.counts
#> "021" "032" "022" "021"
#> ola_mES_2i_4_65.counts ola_mES_2i_4_66.counts ola_mES_2i_4_67.counts ola_mES_2i_4_68.counts
#> "021" "021" "011" "022"
#> ola_mES_2i_4_69.counts ola_mES_2i_4_7.counts ola_mES_2i_4_70.counts ola_mES_2i_4_71.counts
#> "021" "023" "022" "021"
#> ola_mES_2i_4_72.counts ola_mES_2i_4_73.counts ola_mES_2i_4_75.counts ola_mES_2i_4_77.counts
#> "023" "021" "022" "021"
#> ola_mES_2i_4_78.counts ola_mES_2i_4_80.counts ola_mES_2i_4_81.counts ola_mES_2i_4_83.counts
#> "023" "021" "021" "023"
#> ola_mES_2i_4_84.counts ola_mES_2i_4_88.counts ola_mES_2i_4_89.counts ola_mES_2i_4_9.counts
#> "023" "022" "022" "023"
#> ola_mES_2i_4_90.counts ola_mES_2i_4_91.counts ola_mES_2i_4_94.counts ola_mES_2i_4_95.counts
#> "021" "021" "022" "022"
#> ola_mES_2i_4_96.counts ola_mES_2i_5_1.counts ola_mES_2i_5_10.counts ola_mES_2i_5_11.counts
#> "022" "022" "023" "023"
#> ola_mES_2i_5_12.counts ola_mES_2i_5_13.counts ola_mES_2i_5_14.counts ola_mES_2i_5_15.counts
#> "023" "023" "021" "021"
#> ola_mES_2i_5_16.counts ola_mES_2i_5_17.counts ola_mES_2i_5_18.counts ola_mES_2i_5_19.counts
#> "021" "021" "023" "021"
#> ola_mES_2i_5_2.counts ola_mES_2i_5_20.counts ola_mES_2i_5_21.counts ola_mES_2i_5_23.counts
#> "022" "021" "021" "023"
#> ola_mES_2i_5_24.counts ola_mES_2i_5_25.counts ola_mES_2i_5_26.counts ola_mES_2i_5_27.counts
#> "021" "021" "021" "021"
#> ola_mES_2i_5_28.counts ola_mES_2i_5_29.counts ola_mES_2i_5_3.counts ola_mES_2i_5_30.counts
#> "022" "022" "022" "021"
#> ola_mES_2i_5_31.counts ola_mES_2i_5_32.counts ola_mES_2i_5_34.counts ola_mES_2i_5_35.counts
#> "021" "021" "021" "021"
#> ola_mES_2i_5_36.counts ola_mES_2i_5_38.counts ola_mES_2i_5_39.counts ola_mES_2i_5_4.counts
#> "021" "021" "021" "022"
#> ola_mES_2i_5_40.counts ola_mES_2i_5_41.counts ola_mES_2i_5_42.counts ola_mES_2i_5_44.counts
#> "021" "011" "021" "021"
#> ola_mES_2i_5_45.counts ola_mES_2i_5_46.counts ola_mES_2i_5_47.counts ola_mES_2i_5_48.counts
#> "021" "021" "021" "021"
#> ola_mES_2i_5_49.counts ola_mES_2i_5_5.counts ola_mES_2i_5_51.counts ola_mES_2i_5_52.counts
#> "021" "023" "021" "022"
#> ola_mES_2i_5_53.counts ola_mES_2i_5_54.counts ola_mES_2i_5_55.counts ola_mES_2i_5_56.counts
#> "021" "021" "021" "021"
#> ola_mES_2i_5_57.counts ola_mES_2i_5_58.counts ola_mES_2i_5_59.counts ola_mES_2i_5_6.counts
#> "021" "021" "021" "022"
#> ola_mES_2i_5_61.counts ola_mES_2i_5_62.counts ola_mES_2i_5_63.counts ola_mES_2i_5_65.counts
#> "021" "022" "021" "023"
#> ola_mES_2i_5_66.counts ola_mES_2i_5_67.counts ola_mES_2i_5_68.counts ola_mES_2i_5_69.counts
#> "022" "021" "021" "021"
#> ola_mES_2i_5_7.counts ola_mES_2i_5_70.counts ola_mES_2i_5_72.counts ola_mES_2i_5_73.counts
#> "021" "021" "021" "023"
#> ola_mES_2i_5_74.counts ola_mES_2i_5_75.counts ola_mES_2i_5_76.counts ola_mES_2i_5_77.counts
#> "021" "023" "023" "023"
#> ola_mES_2i_5_78.counts ola_mES_2i_5_8.counts ola_mES_2i_5_81.counts ola_mES_2i_5_82.counts
#> "021" "021" "022" "021"
#> ola_mES_2i_5_83.counts ola_mES_2i_5_85.counts ola_mES_2i_5_86.counts ola_mES_2i_5_87.counts
#> "023" "022" "023" "023"
#> ola_mES_2i_5_88.counts ola_mES_2i_5_9.counts ola_mES_2i_5_90.counts ola_mES_2i_5_92.counts
#> "023" "022" "021" "021"
#> ola_mES_2i_5_94.counts ola_mES_2i_5_95.counts ola_mES_2i_5_96.counts ola_mES_a2i_2_1.counts
#> "021" "021" "021" "013"
#> ola_mES_a2i_2_10.counts ola_mES_a2i_2_11.counts ola_mES_a2i_2_12.counts ola_mES_a2i_2_13.counts
#> "011" "013" "011" "011"
#> ola_mES_a2i_2_14.counts ola_mES_a2i_2_15.counts ola_mES_a2i_2_16.counts ola_mES_a2i_2_17.counts
#> "013" "013" "013" "013"
#> ola_mES_a2i_2_18.counts ola_mES_a2i_2_19.counts ola_mES_a2i_2_2.counts ola_mES_a2i_2_20.counts
#> "013" "013" "013" "011"
#> ola_mES_a2i_2_21.counts ola_mES_a2i_2_22.counts ola_mES_a2i_2_23.counts ola_mES_a2i_2_24.counts
#> "013" "013" "013" "013"
#> ola_mES_a2i_2_25.counts ola_mES_a2i_2_26.counts ola_mES_a2i_2_27.counts ola_mES_a2i_2_28.counts
#> "011" "032" "011" "011"
#> ola_mES_a2i_2_29.counts ola_mES_a2i_2_3.counts ola_mES_a2i_2_30.counts ola_mES_a2i_2_31.counts
#> "011" "013" "022" "032"
#> ola_mES_a2i_2_32.counts ola_mES_a2i_2_33.counts ola_mES_a2i_2_34.counts ola_mES_a2i_2_35.counts
#> "032" "011" "011" "031"
#> ola_mES_a2i_2_36.counts ola_mES_a2i_2_37.counts ola_mES_a2i_2_38.counts ola_mES_a2i_2_39.counts
#> "022" "032" "011" "032"
#> ola_mES_a2i_2_4.counts ola_mES_a2i_2_40.counts ola_mES_a2i_2_41.counts ola_mES_a2i_2_42.counts
#> "011" "011" "013" "013"
#> ola_mES_a2i_2_43.counts ola_mES_a2i_2_44.counts ola_mES_a2i_2_45.counts ola_mES_a2i_2_46.counts
#> "011" "011" "013" "013"
#> ola_mES_a2i_2_47.counts ola_mES_a2i_2_48.counts ola_mES_a2i_2_49.counts ola_mES_a2i_2_5.counts
#> "011" "011" "011" "013"
#> ola_mES_a2i_2_50.counts ola_mES_a2i_2_51.counts ola_mES_a2i_2_52.counts ola_mES_a2i_2_53.counts
#> "032" "011" "011" "032"
#> ola_mES_a2i_2_54.counts ola_mES_a2i_2_55.counts ola_mES_a2i_2_56.counts ola_mES_a2i_2_57.counts
#> "011" "013" "011" "013"
#> ola_mES_a2i_2_59.counts ola_mES_a2i_2_6.counts ola_mES_a2i_2_60.counts ola_mES_a2i_2_61.counts
#> "013" "032" "011" "011"
#> ola_mES_a2i_2_62.counts ola_mES_a2i_2_63.counts ola_mES_a2i_2_64.counts ola_mES_a2i_2_65.counts
#> "011" "011" "011" "011"
#> ola_mES_a2i_2_66.counts ola_mES_a2i_2_67.counts ola_mES_a2i_2_68.counts ola_mES_a2i_2_69.counts
#> "013" "011" "011" "032"
#> ola_mES_a2i_2_7.counts ola_mES_a2i_2_70.counts ola_mES_a2i_2_71.counts ola_mES_a2i_2_72.counts
#> "011" "011" "013" "011"
#> ola_mES_a2i_2_73.counts ola_mES_a2i_2_74.counts ola_mES_a2i_2_75.counts ola_mES_a2i_2_76.counts
#> "011" "011" "011" "011"
#> ola_mES_a2i_2_77.counts ola_mES_a2i_2_78.counts ola_mES_a2i_2_8.counts ola_mES_a2i_2_80.counts
#> "011" "013" "013" "011"
#> ola_mES_a2i_2_81.counts ola_mES_a2i_2_82.counts ola_mES_a2i_2_83.counts ola_mES_a2i_2_84.counts
#> "011" "013" "013" "011"
#> ola_mES_a2i_2_86.counts ola_mES_a2i_2_87.counts ola_mES_a2i_2_88.counts ola_mES_a2i_2_89.counts
#> "011" "011" "011" "022"
#> ola_mES_a2i_2_9.counts ola_mES_a2i_2_90.counts ola_mES_a2i_2_91.counts ola_mES_a2i_2_92.counts
#> "011" "013" "032" "032"
#> ola_mES_a2i_2_93.counts ola_mES_a2i_2_94.counts ola_mES_a2i_2_95.counts ola_mES_a2i_2_96.counts
#> "011" "013" "013" "013"
#> ola_mES_a2i_3_10.counts ola_mES_a2i_3_11.counts ola_mES_a2i_3_13.counts ola_mES_a2i_3_16.counts
#> "021" "022" "011" "011"
#> ola_mES_a2i_3_17.counts ola_mES_a2i_3_18.counts ola_mES_a2i_3_2.counts ola_mES_a2i_3_20.counts
#> "032" "032" "032" "032"
#> ola_mES_a2i_3_21.counts ola_mES_a2i_3_22.counts ola_mES_a2i_3_24.counts ola_mES_a2i_3_25.counts
#> "022" "022" "022" "011"
#> ola_mES_a2i_3_26.counts ola_mES_a2i_3_28.counts ola_mES_a2i_3_29.counts ola_mES_a2i_3_3.counts
#> "032" "011" "011" "021"
#> ola_mES_a2i_3_30.counts ola_mES_a2i_3_31.counts ola_mES_a2i_3_33.counts ola_mES_a2i_3_34.counts
#> "032" "032" "022" "011"
#> ola_mES_a2i_3_37.counts ola_mES_a2i_3_38.counts ola_mES_a2i_3_39.counts ola_mES_a2i_3_4.counts
#> "011" "011" "011" "022"
#> ola_mES_a2i_3_40.counts ola_mES_a2i_3_41.counts ola_mES_a2i_3_42.counts ola_mES_a2i_3_44.counts
#> "011" "032" "032" "011"
#> ola_mES_a2i_3_45.counts ola_mES_a2i_3_47.counts ola_mES_a2i_3_49.counts ola_mES_a2i_3_50.counts
#> "032" "032" "011" "011"
#> ola_mES_a2i_3_52.counts ola_mES_a2i_3_54.counts ola_mES_a2i_3_56.counts ola_mES_a2i_3_58.counts
#> "032" "011" "032" "022"
#> ola_mES_a2i_3_59.counts ola_mES_a2i_3_6.counts ola_mES_a2i_3_62.counts ola_mES_a2i_3_64.counts
#> "013" "011" "011" "011"
#> ola_mES_a2i_3_65.counts ola_mES_a2i_3_66.counts ola_mES_a2i_3_67.counts ola_mES_a2i_3_68.counts
#> "013" "011" "032" "022"
#> ola_mES_a2i_3_69.counts ola_mES_a2i_3_7.counts ola_mES_a2i_3_71.counts ola_mES_a2i_3_73.counts
#> "011" "032" "013" "011"
#> ola_mES_a2i_3_75.counts ola_mES_a2i_3_76.counts ola_mES_a2i_3_77.counts ola_mES_a2i_3_8.counts
#> "032" "021" "011" "032"
#> ola_mES_a2i_3_80.counts ola_mES_a2i_3_81.counts ola_mES_a2i_3_82.counts ola_mES_a2i_3_83.counts
#> "022" "032" "011" "011"
#> ola_mES_a2i_3_84.counts ola_mES_a2i_3_85.counts ola_mES_a2i_3_86.counts ola_mES_a2i_3_89.counts
#> "011" "011" "011" "013"
#> ola_mES_a2i_3_9.counts ola_mES_a2i_3_90.counts ola_mES_a2i_3_91.counts ola_mES_a2i_3_92.counts
#> "022" "011" "022" "011"
#> ola_mES_a2i_3_94.counts ola_mES_a2i_3_96.counts ola_mES_lif_1_1.counts ola_mES_lif_1_10.counts
#> "031" "011" "014" "033"
#> ola_mES_lif_1_11.counts ola_mES_lif_1_12.counts ola_mES_lif_1_13.counts ola_mES_lif_1_14.counts
#> "012" "033" "014" "033"
#> ola_mES_lif_1_15.counts ola_mES_lif_1_17.counts ola_mES_lif_1_18.counts ola_mES_lif_1_19.counts
#> "014" "031" "014" "014"
#> ola_mES_lif_1_2.counts ola_mES_lif_1_20.counts ola_mES_lif_1_21.counts ola_mES_lif_1_22.counts
#> "033" "014" "033" "014"
#> ola_mES_lif_1_23.counts ola_mES_lif_1_24.counts ola_mES_lif_1_25.counts ola_mES_lif_1_26.counts
#> "031" "014" "014" "014"
#> ola_mES_lif_1_28.counts ola_mES_lif_1_29.counts ola_mES_lif_1_3.counts ola_mES_lif_1_30.counts
#> "014" "031" "014" "014"
#> ola_mES_lif_1_31.counts ola_mES_lif_1_32.counts ola_mES_lif_1_33.counts ola_mES_lif_1_34.counts
#> "031" "014" "014" "014"
#> ola_mES_lif_1_35.counts ola_mES_lif_1_36.counts ola_mES_lif_1_37.counts ola_mES_lif_1_38.counts
#> "031" "014" "014" "014"
#> ola_mES_lif_1_39.counts ola_mES_lif_1_4.counts ola_mES_lif_1_41.counts ola_mES_lif_1_42.counts
#> "014" "014" "014" "014"
#> ola_mES_lif_1_43.counts ola_mES_lif_1_44.counts ola_mES_lif_1_45.counts ola_mES_lif_1_46.counts
#> "014" "014" "014" "014"
#> ola_mES_lif_1_47.counts ola_mES_lif_1_48.counts ola_mES_lif_1_49.counts ola_mES_lif_1_5.counts
#> "014" "014" "014" "012"
#> ola_mES_lif_1_50.counts ola_mES_lif_1_51.counts ola_mES_lif_1_52.counts ola_mES_lif_1_53.counts
#> "014" "012" "012" "022"
#> ola_mES_lif_1_54.counts ola_mES_lif_1_55.counts ola_mES_lif_1_56.counts ola_mES_lif_1_57.counts
#> "014" "012" "022" "022"
#> ola_mES_lif_1_58.counts ola_mES_lif_1_59.counts ola_mES_lif_1_6.counts ola_mES_lif_1_60.counts
#> "014" "012" "014" "014"
#> ola_mES_lif_1_61.counts ola_mES_lif_1_62.counts ola_mES_lif_1_63.counts ola_mES_lif_1_64.counts
#> "014" "014" "031" "031"
#> ola_mES_lif_1_65.counts ola_mES_lif_1_66.counts ola_mES_lif_1_67.counts ola_mES_lif_1_68.counts
#> "031" "014" "014" "014"
#> ola_mES_lif_1_69.counts ola_mES_lif_1_7.counts ola_mES_lif_1_70.counts ola_mES_lif_1_72.counts
#> "031" "014" "033" "014"
#> ola_mES_lif_1_73.counts ola_mES_lif_1_74.counts ola_mES_lif_1_75.counts ola_mES_lif_1_79.counts
#> "014" "031" "031" "033"
#> ola_mES_lif_1_8.counts ola_mES_lif_1_80.counts ola_mES_lif_1_81.counts ola_mES_lif_1_82.counts
#> "014" "014" "014" "031"
#> ola_mES_lif_1_86.counts ola_mES_lif_1_9.counts ola_mES_lif_1_91.counts ola_mES_lif_1_92.counts
#> "033" "014" "014" "014"
#> ola_mES_lif_1_93.counts ola_mES_lif_1_95.counts ola_mES_lif_1_96.counts ola_mES_lif_2_1.counts
#> "033" "014" "014" "033"
#> ola_mES_lif_2_10.counts ola_mES_lif_2_11.counts ola_mES_lif_2_12.counts ola_mES_lif_2_13.counts
#> "012" "012" "022" "012"
#> ola_mES_lif_2_14.counts ola_mES_lif_2_15.counts ola_mES_lif_2_16.counts ola_mES_lif_2_17.counts
#> "012" "012" "012" "012"
#> ola_mES_lif_2_18.counts ola_mES_lif_2_19.counts ola_mES_lif_2_2.counts ola_mES_lif_2_20.counts
#> "012" "012" "012" "033"
#> ola_mES_lif_2_21.counts ola_mES_lif_2_23.counts ola_mES_lif_2_25.counts ola_mES_lif_2_26.counts
#> "012" "012" "033" "012"
#> ola_mES_lif_2_27.counts ola_mES_lif_2_28.counts ola_mES_lif_2_29.counts ola_mES_lif_2_3.counts
#> "033" "012" "012" "012"
#> ola_mES_lif_2_30.counts ola_mES_lif_2_31.counts ola_mES_lif_2_32.counts ola_mES_lif_2_33.counts
#> "012" "033" "012" "033"
#> ola_mES_lif_2_34.counts ola_mES_lif_2_35.counts ola_mES_lif_2_36.counts ola_mES_lif_2_37.counts
#> "012" "012" "034" "033"
#> ola_mES_lif_2_38.counts ola_mES_lif_2_39.counts ola_mES_lif_2_4.counts ola_mES_lif_2_40.counts
#> "012" "012" "012" "012"
#> ola_mES_lif_2_41.counts ola_mES_lif_2_42.counts ola_mES_lif_2_43.counts ola_mES_lif_2_44.counts
#> "033" "012" "012" "012"
#> ola_mES_lif_2_45.counts ola_mES_lif_2_46.counts ola_mES_lif_2_47.counts ola_mES_lif_2_48.counts
#> "034" "012" "033" "022"
#> ola_mES_lif_2_49.counts ola_mES_lif_2_5.counts ola_mES_lif_2_50.counts ola_mES_lif_2_51.counts
#> "012" "012" "012" "012"
#> ola_mES_lif_2_52.counts ola_mES_lif_2_53.counts ola_mES_lif_2_54.counts ola_mES_lif_2_55.counts
#> "012" "012" "012" "012"
#> ola_mES_lif_2_56.counts ola_mES_lif_2_57.counts ola_mES_lif_2_58.counts ola_mES_lif_2_59.counts
#> "012" "034" "012" "012"
#> ola_mES_lif_2_6.counts ola_mES_lif_2_60.counts ola_mES_lif_2_61.counts ola_mES_lif_2_63.counts
#> "012" "033" "014" "012"
#> ola_mES_lif_2_64.counts ola_mES_lif_2_65.counts ola_mES_lif_2_66.counts ola_mES_lif_2_67.counts
#> "012" "012" "012" "012"
#> ola_mES_lif_2_68.counts ola_mES_lif_2_69.counts ola_mES_lif_2_7.counts ola_mES_lif_2_70.counts
#> "012" "022" "012" "012"
#> ola_mES_lif_2_71.counts ola_mES_lif_2_72.counts ola_mES_lif_2_73.counts ola_mES_lif_2_74.counts
#> "012" "014" "022" "012"
#> ola_mES_lif_2_75.counts ola_mES_lif_2_76.counts ola_mES_lif_2_77.counts ola_mES_lif_2_79.counts
#> "012" "012" "012" "033"
#> ola_mES_lif_2_8.counts ola_mES_lif_2_80.counts ola_mES_lif_2_81.counts ola_mES_lif_2_82.counts
#> "012" "012" "033" "012"
#> ola_mES_lif_2_83.counts ola_mES_lif_2_84.counts ola_mES_lif_2_86.counts ola_mES_lif_2_87.counts
#> "012" "012" "033" "012"
#> ola_mES_lif_2_89.counts ola_mES_lif_2_9.counts ola_mES_lif_2_90.counts ola_mES_lif_2_91.counts
#> "012" "033" "033" "033"
#> ola_mES_lif_2_92.counts ola_mES_lif_2_93.counts ola_mES_lif_2_94.counts ola_mES_lif_2_95.counts
#> "033" "012" "012" "012"
#> ola_mES_lif_2_96.counts ola_mES_lif_3_1.counts ola_mES_lif_3_10.counts ola_mES_lif_3_11.counts
#> "033" "014" "012" "033"
#> ola_mES_lif_3_12.counts ola_mES_lif_3_13.counts ola_mES_lif_3_14.counts ola_mES_lif_3_15.counts
#> "022" "014" "034" "034"
#> ola_mES_lif_3_16.counts ola_mES_lif_3_17.counts ola_mES_lif_3_19.counts ola_mES_lif_3_2.counts
#> "034" "034" "034" "022"
#> ola_mES_lif_3_20.counts ola_mES_lif_3_21.counts ola_mES_lif_3_22.counts ola_mES_lif_3_23.counts
#> "031" "022" "031" "031"
#> ola_mES_lif_3_24.counts ola_mES_lif_3_26.counts ola_mES_lif_3_27.counts ola_mES_lif_3_28.counts
#> "031" "031" "014" "034"
#> ola_mES_lif_3_29.counts ola_mES_lif_3_3.counts ola_mES_lif_3_30.counts ola_mES_lif_3_31.counts
#> "033" "014" "014" "034"
#> ola_mES_lif_3_32.counts ola_mES_lif_3_33.counts ola_mES_lif_3_34.counts ola_mES_lif_3_35.counts
#> "014" "034" "034" "034"
#> ola_mES_lif_3_36.counts ola_mES_lif_3_37.counts ola_mES_lif_3_38.counts ola_mES_lif_3_39.counts
#> "034" "033" "034" "034"
#> ola_mES_lif_3_4.counts ola_mES_lif_3_40.counts ola_mES_lif_3_42.counts ola_mES_lif_3_45.counts
#> "014" "014" "014" "034"
#> ola_mES_lif_3_46.counts ola_mES_lif_3_47.counts ola_mES_lif_3_48.counts ola_mES_lif_3_49.counts
#> "034" "034" "033" "014"
#> ola_mES_lif_3_5.counts ola_mES_lif_3_50.counts ola_mES_lif_3_51.counts ola_mES_lif_3_52.counts
#> "012" "014" "014" "014"
#> ola_mES_lif_3_53.counts ola_mES_lif_3_54.counts ola_mES_lif_3_56.counts ola_mES_lif_3_57.counts
#> "014" "014" "012" "033"
#> ola_mES_lif_3_58.counts ola_mES_lif_3_59.counts ola_mES_lif_3_6.counts ola_mES_lif_3_60.counts
#> "014" "014" "014" "014"
#> ola_mES_lif_3_61.counts ola_mES_lif_3_63.counts ola_mES_lif_3_64.counts ola_mES_lif_3_65.counts
#> "014" "014" "014" "014"
#> ola_mES_lif_3_69.counts ola_mES_lif_3_70.counts ola_mES_lif_3_71.counts ola_mES_lif_3_72.counts
#> "034" "034" "014" "034"
#> ola_mES_lif_3_73.counts ola_mES_lif_3_74.counts ola_mES_lif_3_75.counts ola_mES_lif_3_76.counts
#> "031" "031" "031" "031"
#> ola_mES_lif_3_77.counts ola_mES_lif_3_78.counts ola_mES_lif_3_8.counts ola_mES_lif_3_80.counts
#> "022" "031" "014" "031"
#> ola_mES_lif_3_82.counts ola_mES_lif_3_83.counts ola_mES_lif_3_84.counts ola_mES_lif_3_87.counts
#> "031" "031" "031" "031"
#> ola_mES_lif_3_88.counts ola_mES_lif_3_89.counts ola_mES_lif_3_9.counts ola_mES_lif_3_90.counts
#> "031" "022" "022" "034"
#> ola_mES_lif_3_92.counts ola_mES_lif_3_94.counts ola_mES_lif_3_95.counts ola_mES_lif_3_96.counts
#> "031" "031" "031" "031"
get_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 6248))
#> ola_mES_2i_2_1.counts ola_mES_2i_2_10.counts ola_mES_2i_2_11.counts ola_mES_2i_2_12.counts
#> "011" "013" "013" "013"
#> ola_mES_2i_2_13.counts ola_mES_2i_2_14.counts ola_mES_2i_2_15.counts ola_mES_2i_2_16.counts
#> "013" "011" "013" "013"
#> ola_mES_2i_2_17.counts ola_mES_2i_2_18.counts ola_mES_2i_2_19.counts ola_mES_2i_2_2.counts
#> "013" "013" "013" "011"
#> ola_mES_2i_2_20.counts ola_mES_2i_2_21.counts ola_mES_2i_2_22.counts ola_mES_2i_2_23.counts
#> "011" "013" "013" "013"
#> ola_mES_2i_2_24.counts ola_mES_2i_2_25.counts ola_mES_2i_2_26.counts ola_mES_2i_2_27.counts
#> "013" "011" "013" "011"
#> ola_mES_2i_2_28.counts ola_mES_2i_2_29.counts ola_mES_2i_2_3.counts ola_mES_2i_2_30.counts
#> "011" "013" "011" "013"
#> ola_mES_2i_2_31.counts ola_mES_2i_2_32.counts ola_mES_2i_2_33.counts ola_mES_2i_2_34.counts
#> "013" "013" "013" "013"
#> ola_mES_2i_2_35.counts ola_mES_2i_2_36.counts ola_mES_2i_2_37.counts ola_mES_2i_2_38.counts
#> "013" "013" "011" "011"
#> ola_mES_2i_2_39.counts ola_mES_2i_2_4.counts ola_mES_2i_2_40.counts ola_mES_2i_2_41.counts
#> "013" "013" "013" "013"
#> ola_mES_2i_2_42.counts ola_mES_2i_2_43.counts ola_mES_2i_2_44.counts ola_mES_2i_2_46.counts
#> "013" "011" "013" "013"
#> ola_mES_2i_2_47.counts ola_mES_2i_2_48.counts ola_mES_2i_2_49.counts ola_mES_2i_2_5.counts
#> "013" "013" "011" "013"
#> ola_mES_2i_2_50.counts ola_mES_2i_2_51.counts ola_mES_2i_2_52.counts ola_mES_2i_2_53.counts
#> "011" "013" "013" "013"
#> ola_mES_2i_2_54.counts ola_mES_2i_2_55.counts ola_mES_2i_2_56.counts ola_mES_2i_2_57.counts
#> "013" "013" "013" "013"
#> ola_mES_2i_2_58.counts ola_mES_2i_2_59.counts ola_mES_2i_2_6.counts ola_mES_2i_2_60.counts
#> "013" "013" "013" "013"
#> ola_mES_2i_2_61.counts ola_mES_2i_2_62.counts ola_mES_2i_2_63.counts ola_mES_2i_2_64.counts
#> "011" "032" "011" "013"
#> ola_mES_2i_2_66.counts ola_mES_2i_2_67.counts ola_mES_2i_2_68.counts ola_mES_2i_2_69.counts
#> "013" "013" "011" "013"
#> ola_mES_2i_2_7.counts ola_mES_2i_2_72.counts ola_mES_2i_2_73.counts ola_mES_2i_2_74.counts
#> "013" "013" "011" "013"
#> ola_mES_2i_2_75.counts ola_mES_2i_2_76.counts ola_mES_2i_2_79.counts ola_mES_2i_2_8.counts
#> "011" "011" "013" "013"
#> ola_mES_2i_2_80.counts ola_mES_2i_2_81.counts ola_mES_2i_2_85.counts ola_mES_2i_2_86.counts
#> "013" "013" "011" "011"
#> ola_mES_2i_2_87.counts ola_mES_2i_2_90.counts ola_mES_2i_2_91.counts ola_mES_2i_2_93.counts
#> "032" "013" "013" "013"
#> ola_mES_2i_2_94.counts ola_mES_2i_2_95.counts ola_mES_2i_3_10.counts ola_mES_2i_3_11.counts
#> "013" "013" "02" "02"
#> ola_mES_2i_3_12.counts ola_mES_2i_3_13.counts ola_mES_2i_3_15.counts ola_mES_2i_3_16.counts
#> "02" "02" "02" "02"
#> ola_mES_2i_3_17.counts ola_mES_2i_3_18.counts ola_mES_2i_3_19.counts ola_mES_2i_3_2.counts
#> "032" "032" "02" "02"
#> ola_mES_2i_3_20.counts ola_mES_2i_3_21.counts ola_mES_2i_3_23.counts ola_mES_2i_3_25.counts
#> "02" "02" "02" "02"
#> ola_mES_2i_3_26.counts ola_mES_2i_3_28.counts ola_mES_2i_3_30.counts ola_mES_2i_3_31.counts
#> "02" "02" "02" "032"
#> ola_mES_2i_3_33.counts ola_mES_2i_3_36.counts ola_mES_2i_3_37.counts ola_mES_2i_3_38.counts
#> "02" "02" "02" "02"
#> ola_mES_2i_3_39.counts ola_mES_2i_3_4.counts ola_mES_2i_3_41.counts ola_mES_2i_3_42.counts
#> "02" "02" "02" "02"
#> ola_mES_2i_3_43.counts ola_mES_2i_3_45.counts ola_mES_2i_3_46.counts ola_mES_2i_3_47.counts
#> "02" "02" "02" "02"
#> ola_mES_2i_3_49.counts ola_mES_2i_3_5.counts ola_mES_2i_3_52.counts ola_mES_2i_3_54.counts
#> "032" "02" "02" "02"
#> ola_mES_2i_3_56.counts ola_mES_2i_3_57.counts ola_mES_2i_3_58.counts ola_mES_2i_3_6.counts
#> "02" "02" "02" "02"
#> ola_mES_2i_3_62.counts ola_mES_2i_3_65.counts ola_mES_2i_3_66.counts ola_mES_2i_3_69.counts
#> "032" "02" "032" "02"
#> ola_mES_2i_3_7.counts ola_mES_2i_3_73.counts ola_mES_2i_3_76.counts ola_mES_2i_3_77.counts
#> "02" "02" "02" "02"
#> ola_mES_2i_3_78.counts ola_mES_2i_3_79.counts ola_mES_2i_3_8.counts ola_mES_2i_3_80.counts
#> "032" "02" "02" "02"
#> ola_mES_2i_3_82.counts ola_mES_2i_3_83.counts ola_mES_2i_3_84.counts ola_mES_2i_3_87.counts
#> "02" "02" "02" "02"
#> ola_mES_2i_3_88.counts ola_mES_2i_3_90.counts ola_mES_2i_3_91.counts ola_mES_2i_3_92.counts
#> "02" "02" "02" "02"
#> ola_mES_2i_3_96.counts ola_mES_2i_4_1.counts ola_mES_2i_4_10.counts ola_mES_2i_4_11.counts
#> "02" "02" "02" "02"
#> ola_mES_2i_4_13.counts ola_mES_2i_4_17.counts ola_mES_2i_4_18.counts ola_mES_2i_4_2.counts
#> "02" "02" "02" "013"
#> ola_mES_2i_4_20.counts ola_mES_2i_4_21.counts ola_mES_2i_4_22.counts ola_mES_2i_4_23.counts
#> "02" "02" "02" "02"
#> ola_mES_2i_4_24.counts ola_mES_2i_4_25.counts ola_mES_2i_4_26.counts ola_mES_2i_4_27.counts
#> "02" "02" "02" "02"
#> ola_mES_2i_4_29.counts ola_mES_2i_4_30.counts ola_mES_2i_4_31.counts ola_mES_2i_4_32.counts
#> "02" "02" "02" "02"
#> ola_mES_2i_4_33.counts ola_mES_2i_4_34.counts ola_mES_2i_4_35.counts ola_mES_2i_4_36.counts
#> "02" "02" "02" "02"
#> ola_mES_2i_4_37.counts ola_mES_2i_4_38.counts ola_mES_2i_4_39.counts ola_mES_2i_4_40.counts
#> "032" "011" "02" "011"
#> ola_mES_2i_4_42.counts ola_mES_2i_4_43.counts ola_mES_2i_4_44.counts ola_mES_2i_4_46.counts
#> "032" "02" "02" "02"
#> ola_mES_2i_4_49.counts ola_mES_2i_4_5.counts ola_mES_2i_4_50.counts ola_mES_2i_4_52.counts
#> "011" "02" "02" "02"
#> ola_mES_2i_4_53.counts ola_mES_2i_4_54.counts ola_mES_2i_4_56.counts ola_mES_2i_4_57.counts
#> "011" "032" "02" "02"
#> ola_mES_2i_4_58.counts ola_mES_2i_4_59.counts ola_mES_2i_4_6.counts ola_mES_2i_4_60.counts
#> "02" "02" "02" "02"
#> ola_mES_2i_4_61.counts ola_mES_2i_4_62.counts ola_mES_2i_4_63.counts ola_mES_2i_4_64.counts
#> "02" "032" "02" "02"
#> ola_mES_2i_4_65.counts ola_mES_2i_4_66.counts ola_mES_2i_4_67.counts ola_mES_2i_4_68.counts
#> "02" "02" "011" "02"
#> ola_mES_2i_4_69.counts ola_mES_2i_4_7.counts ola_mES_2i_4_70.counts ola_mES_2i_4_71.counts
#> "02" "02" "02" "02"
#> ola_mES_2i_4_72.counts ola_mES_2i_4_73.counts ola_mES_2i_4_75.counts ola_mES_2i_4_77.counts
#> "02" "02" "02" "02"
#> ola_mES_2i_4_78.counts ola_mES_2i_4_80.counts ola_mES_2i_4_81.counts ola_mES_2i_4_83.counts
#> "02" "02" "02" "02"
#> ola_mES_2i_4_84.counts ola_mES_2i_4_88.counts ola_mES_2i_4_89.counts ola_mES_2i_4_9.counts
#> "02" "02" "02" "02"
#> ola_mES_2i_4_90.counts ola_mES_2i_4_91.counts ola_mES_2i_4_94.counts ola_mES_2i_4_95.counts
#> "02" "02" "02" "02"
#> ola_mES_2i_4_96.counts ola_mES_2i_5_1.counts ola_mES_2i_5_10.counts ola_mES_2i_5_11.counts
#> "02" "02" "02" "02"
#> ola_mES_2i_5_12.counts ola_mES_2i_5_13.counts ola_mES_2i_5_14.counts ola_mES_2i_5_15.counts
#> "02" "02" "02" "02"
#> ola_mES_2i_5_16.counts ola_mES_2i_5_17.counts ola_mES_2i_5_18.counts ola_mES_2i_5_19.counts
#> "02" "02" "02" "02"
#> ola_mES_2i_5_2.counts ola_mES_2i_5_20.counts ola_mES_2i_5_21.counts ola_mES_2i_5_23.counts
#> "02" "02" "02" "02"
#> ola_mES_2i_5_24.counts ola_mES_2i_5_25.counts ola_mES_2i_5_26.counts ola_mES_2i_5_27.counts
#> "02" "02" "02" "02"
#> ola_mES_2i_5_28.counts ola_mES_2i_5_29.counts ola_mES_2i_5_3.counts ola_mES_2i_5_30.counts
#> "02" "02" "02" "02"
#> ola_mES_2i_5_31.counts ola_mES_2i_5_32.counts ola_mES_2i_5_34.counts ola_mES_2i_5_35.counts
#> "02" "02" "02" "02"
#> ola_mES_2i_5_36.counts ola_mES_2i_5_38.counts ola_mES_2i_5_39.counts ola_mES_2i_5_4.counts
#> "02" "02" "02" "02"
#> ola_mES_2i_5_40.counts ola_mES_2i_5_41.counts ola_mES_2i_5_42.counts ola_mES_2i_5_44.counts
#> "02" "011" "02" "02"
#> ola_mES_2i_5_45.counts ola_mES_2i_5_46.counts ola_mES_2i_5_47.counts ola_mES_2i_5_48.counts
#> "02" "02" "02" "02"
#> ola_mES_2i_5_49.counts ola_mES_2i_5_5.counts ola_mES_2i_5_51.counts ola_mES_2i_5_52.counts
#> "02" "02" "02" "02"
#> ola_mES_2i_5_53.counts ola_mES_2i_5_54.counts ola_mES_2i_5_55.counts ola_mES_2i_5_56.counts
#> "02" "02" "02" "02"
#> ola_mES_2i_5_57.counts ola_mES_2i_5_58.counts ola_mES_2i_5_59.counts ola_mES_2i_5_6.counts
#> "02" "02" "02" "02"
#> ola_mES_2i_5_61.counts ola_mES_2i_5_62.counts ola_mES_2i_5_63.counts ola_mES_2i_5_65.counts
#> "02" "02" "02" "02"
#> ola_mES_2i_5_66.counts ola_mES_2i_5_67.counts ola_mES_2i_5_68.counts ola_mES_2i_5_69.counts
#> "02" "02" "02" "02"
#> ola_mES_2i_5_7.counts ola_mES_2i_5_70.counts ola_mES_2i_5_72.counts ola_mES_2i_5_73.counts
#> "02" "02" "02" "02"
#> ola_mES_2i_5_74.counts ola_mES_2i_5_75.counts ola_mES_2i_5_76.counts ola_mES_2i_5_77.counts
#> "02" "02" "02" "02"
#> ola_mES_2i_5_78.counts ola_mES_2i_5_8.counts ola_mES_2i_5_81.counts ola_mES_2i_5_82.counts
#> "02" "02" "02" "02"
#> ola_mES_2i_5_83.counts ola_mES_2i_5_85.counts ola_mES_2i_5_86.counts ola_mES_2i_5_87.counts
#> "02" "02" "02" "02"
#> ola_mES_2i_5_88.counts ola_mES_2i_5_9.counts ola_mES_2i_5_90.counts ola_mES_2i_5_92.counts
#> "02" "02" "02" "02"
#> ola_mES_2i_5_94.counts ola_mES_2i_5_95.counts ola_mES_2i_5_96.counts ola_mES_a2i_2_1.counts
#> "02" "02" "02" "013"
#> ola_mES_a2i_2_10.counts ola_mES_a2i_2_11.counts ola_mES_a2i_2_12.counts ola_mES_a2i_2_13.counts
#> "011" "013" "011" "011"
#> ola_mES_a2i_2_14.counts ola_mES_a2i_2_15.counts ola_mES_a2i_2_16.counts ola_mES_a2i_2_17.counts
#> "013" "013" "013" "013"
#> ola_mES_a2i_2_18.counts ola_mES_a2i_2_19.counts ola_mES_a2i_2_2.counts ola_mES_a2i_2_20.counts
#> "013" "013" "013" "011"
#> ola_mES_a2i_2_21.counts ola_mES_a2i_2_22.counts ola_mES_a2i_2_23.counts ola_mES_a2i_2_24.counts
#> "013" "013" "013" "013"
#> ola_mES_a2i_2_25.counts ola_mES_a2i_2_26.counts ola_mES_a2i_2_27.counts ola_mES_a2i_2_28.counts
#> "011" "032" "011" "011"
#> ola_mES_a2i_2_29.counts ola_mES_a2i_2_3.counts ola_mES_a2i_2_30.counts ola_mES_a2i_2_31.counts
#> "011" "013" "02" "032"
#> ola_mES_a2i_2_32.counts ola_mES_a2i_2_33.counts ola_mES_a2i_2_34.counts ola_mES_a2i_2_35.counts
#> "032" "011" "011" "031"
#> ola_mES_a2i_2_36.counts ola_mES_a2i_2_37.counts ola_mES_a2i_2_38.counts ola_mES_a2i_2_39.counts
#> "02" "032" "011" "032"
#> ola_mES_a2i_2_4.counts ola_mES_a2i_2_40.counts ola_mES_a2i_2_41.counts ola_mES_a2i_2_42.counts
#> "011" "011" "013" "013"
#> ola_mES_a2i_2_43.counts ola_mES_a2i_2_44.counts ola_mES_a2i_2_45.counts ola_mES_a2i_2_46.counts
#> "011" "011" "013" "013"
#> ola_mES_a2i_2_47.counts ola_mES_a2i_2_48.counts ola_mES_a2i_2_49.counts ola_mES_a2i_2_5.counts
#> "011" "011" "011" "013"
#> ola_mES_a2i_2_50.counts ola_mES_a2i_2_51.counts ola_mES_a2i_2_52.counts ola_mES_a2i_2_53.counts
#> "032" "011" "011" "032"
#> ola_mES_a2i_2_54.counts ola_mES_a2i_2_55.counts ola_mES_a2i_2_56.counts ola_mES_a2i_2_57.counts
#> "011" "013" "011" "013"
#> ola_mES_a2i_2_59.counts ola_mES_a2i_2_6.counts ola_mES_a2i_2_60.counts ola_mES_a2i_2_61.counts
#> "013" "032" "011" "011"
#> ola_mES_a2i_2_62.counts ola_mES_a2i_2_63.counts ola_mES_a2i_2_64.counts ola_mES_a2i_2_65.counts
#> "011" "011" "011" "011"
#> ola_mES_a2i_2_66.counts ola_mES_a2i_2_67.counts ola_mES_a2i_2_68.counts ola_mES_a2i_2_69.counts
#> "013" "011" "011" "032"
#> ola_mES_a2i_2_7.counts ola_mES_a2i_2_70.counts ola_mES_a2i_2_71.counts ola_mES_a2i_2_72.counts
#> "011" "011" "013" "011"
#> ola_mES_a2i_2_73.counts ola_mES_a2i_2_74.counts ola_mES_a2i_2_75.counts ola_mES_a2i_2_76.counts
#> "011" "011" "011" "011"
#> ola_mES_a2i_2_77.counts ola_mES_a2i_2_78.counts ola_mES_a2i_2_8.counts ola_mES_a2i_2_80.counts
#> "011" "013" "013" "011"
#> ola_mES_a2i_2_81.counts ola_mES_a2i_2_82.counts ola_mES_a2i_2_83.counts ola_mES_a2i_2_84.counts
#> "011" "013" "013" "011"
#> ola_mES_a2i_2_86.counts ola_mES_a2i_2_87.counts ola_mES_a2i_2_88.counts ola_mES_a2i_2_89.counts
#> "011" "011" "011" "02"
#> ola_mES_a2i_2_9.counts ola_mES_a2i_2_90.counts ola_mES_a2i_2_91.counts ola_mES_a2i_2_92.counts
#> "011" "013" "032" "032"
#> ola_mES_a2i_2_93.counts ola_mES_a2i_2_94.counts ola_mES_a2i_2_95.counts ola_mES_a2i_2_96.counts
#> "011" "013" "013" "013"
#> ola_mES_a2i_3_10.counts ola_mES_a2i_3_11.counts ola_mES_a2i_3_13.counts ola_mES_a2i_3_16.counts
#> "02" "02" "011" "011"
#> ola_mES_a2i_3_17.counts ola_mES_a2i_3_18.counts ola_mES_a2i_3_2.counts ola_mES_a2i_3_20.counts
#> "032" "032" "032" "032"
#> ola_mES_a2i_3_21.counts ola_mES_a2i_3_22.counts ola_mES_a2i_3_24.counts ola_mES_a2i_3_25.counts
#> "02" "02" "02" "011"
#> ola_mES_a2i_3_26.counts ola_mES_a2i_3_28.counts ola_mES_a2i_3_29.counts ola_mES_a2i_3_3.counts
#> "032" "011" "011" "02"
#> ola_mES_a2i_3_30.counts ola_mES_a2i_3_31.counts ola_mES_a2i_3_33.counts ola_mES_a2i_3_34.counts
#> "032" "032" "02" "011"
#> ola_mES_a2i_3_37.counts ola_mES_a2i_3_38.counts ola_mES_a2i_3_39.counts ola_mES_a2i_3_4.counts
#> "011" "011" "011" "02"
#> ola_mES_a2i_3_40.counts ola_mES_a2i_3_41.counts ola_mES_a2i_3_42.counts ola_mES_a2i_3_44.counts
#> "011" "032" "032" "011"
#> ola_mES_a2i_3_45.counts ola_mES_a2i_3_47.counts ola_mES_a2i_3_49.counts ola_mES_a2i_3_50.counts
#> "032" "032" "011" "011"
#> ola_mES_a2i_3_52.counts ola_mES_a2i_3_54.counts ola_mES_a2i_3_56.counts ola_mES_a2i_3_58.counts
#> "032" "011" "032" "02"
#> ola_mES_a2i_3_59.counts ola_mES_a2i_3_6.counts ola_mES_a2i_3_62.counts ola_mES_a2i_3_64.counts
#> "013" "011" "011" "011"
#> ola_mES_a2i_3_65.counts ola_mES_a2i_3_66.counts ola_mES_a2i_3_67.counts ola_mES_a2i_3_68.counts
#> "013" "011" "032" "02"
#> ola_mES_a2i_3_69.counts ola_mES_a2i_3_7.counts ola_mES_a2i_3_71.counts ola_mES_a2i_3_73.counts
#> "011" "032" "013" "011"
#> ola_mES_a2i_3_75.counts ola_mES_a2i_3_76.counts ola_mES_a2i_3_77.counts ola_mES_a2i_3_8.counts
#> "032" "02" "011" "032"
#> ola_mES_a2i_3_80.counts ola_mES_a2i_3_81.counts ola_mES_a2i_3_82.counts ola_mES_a2i_3_83.counts
#> "02" "032" "011" "011"
#> ola_mES_a2i_3_84.counts ola_mES_a2i_3_85.counts ola_mES_a2i_3_86.counts ola_mES_a2i_3_89.counts
#> "011" "011" "011" "013"
#> ola_mES_a2i_3_9.counts ola_mES_a2i_3_90.counts ola_mES_a2i_3_91.counts ola_mES_a2i_3_92.counts
#> "02" "011" "02" "011"
#> ola_mES_a2i_3_94.counts ola_mES_a2i_3_96.counts ola_mES_lif_1_1.counts ola_mES_lif_1_10.counts
#> "031" "011" "014" "033"
#> ola_mES_lif_1_11.counts ola_mES_lif_1_12.counts ola_mES_lif_1_13.counts ola_mES_lif_1_14.counts
#> "012" "033" "014" "033"
#> ola_mES_lif_1_15.counts ola_mES_lif_1_17.counts ola_mES_lif_1_18.counts ola_mES_lif_1_19.counts
#> "014" "031" "014" "014"
#> ola_mES_lif_1_2.counts ola_mES_lif_1_20.counts ola_mES_lif_1_21.counts ola_mES_lif_1_22.counts
#> "033" "014" "033" "014"
#> ola_mES_lif_1_23.counts ola_mES_lif_1_24.counts ola_mES_lif_1_25.counts ola_mES_lif_1_26.counts
#> "031" "014" "014" "014"
#> ola_mES_lif_1_28.counts ola_mES_lif_1_29.counts ola_mES_lif_1_3.counts ola_mES_lif_1_30.counts
#> "014" "031" "014" "014"
#> ola_mES_lif_1_31.counts ola_mES_lif_1_32.counts ola_mES_lif_1_33.counts ola_mES_lif_1_34.counts
#> "031" "014" "014" "014"
#> ola_mES_lif_1_35.counts ola_mES_lif_1_36.counts ola_mES_lif_1_37.counts ola_mES_lif_1_38.counts
#> "031" "014" "014" "014"
#> ola_mES_lif_1_39.counts ola_mES_lif_1_4.counts ola_mES_lif_1_41.counts ola_mES_lif_1_42.counts
#> "014" "014" "014" "014"
#> ola_mES_lif_1_43.counts ola_mES_lif_1_44.counts ola_mES_lif_1_45.counts ola_mES_lif_1_46.counts
#> "014" "014" "014" "014"
#> ola_mES_lif_1_47.counts ola_mES_lif_1_48.counts ola_mES_lif_1_49.counts ola_mES_lif_1_5.counts
#> "014" "014" "014" "012"
#> ola_mES_lif_1_50.counts ola_mES_lif_1_51.counts ola_mES_lif_1_52.counts ola_mES_lif_1_53.counts
#> "014" "012" "012" "02"
#> ola_mES_lif_1_54.counts ola_mES_lif_1_55.counts ola_mES_lif_1_56.counts ola_mES_lif_1_57.counts
#> "014" "012" "02" "02"
#> ola_mES_lif_1_58.counts ola_mES_lif_1_59.counts ola_mES_lif_1_6.counts ola_mES_lif_1_60.counts
#> "014" "012" "014" "014"
#> ola_mES_lif_1_61.counts ola_mES_lif_1_62.counts ola_mES_lif_1_63.counts ola_mES_lif_1_64.counts
#> "014" "014" "031" "031"
#> ola_mES_lif_1_65.counts ola_mES_lif_1_66.counts ola_mES_lif_1_67.counts ola_mES_lif_1_68.counts
#> "031" "014" "014" "014"
#> ola_mES_lif_1_69.counts ola_mES_lif_1_7.counts ola_mES_lif_1_70.counts ola_mES_lif_1_72.counts
#> "031" "014" "033" "014"
#> ola_mES_lif_1_73.counts ola_mES_lif_1_74.counts ola_mES_lif_1_75.counts ola_mES_lif_1_79.counts
#> "014" "031" "031" "033"
#> ola_mES_lif_1_8.counts ola_mES_lif_1_80.counts ola_mES_lif_1_81.counts ola_mES_lif_1_82.counts
#> "014" "014" "014" "031"
#> ola_mES_lif_1_86.counts ola_mES_lif_1_9.counts ola_mES_lif_1_91.counts ola_mES_lif_1_92.counts
#> "033" "014" "014" "014"
#> ola_mES_lif_1_93.counts ola_mES_lif_1_95.counts ola_mES_lif_1_96.counts ola_mES_lif_2_1.counts
#> "033" "014" "014" "033"
#> ola_mES_lif_2_10.counts ola_mES_lif_2_11.counts ola_mES_lif_2_12.counts ola_mES_lif_2_13.counts
#> "012" "012" "02" "012"
#> ola_mES_lif_2_14.counts ola_mES_lif_2_15.counts ola_mES_lif_2_16.counts ola_mES_lif_2_17.counts
#> "012" "012" "012" "012"
#> ola_mES_lif_2_18.counts ola_mES_lif_2_19.counts ola_mES_lif_2_2.counts ola_mES_lif_2_20.counts
#> "012" "012" "012" "033"
#> ola_mES_lif_2_21.counts ola_mES_lif_2_23.counts ola_mES_lif_2_25.counts ola_mES_lif_2_26.counts
#> "012" "012" "033" "012"
#> ola_mES_lif_2_27.counts ola_mES_lif_2_28.counts ola_mES_lif_2_29.counts ola_mES_lif_2_3.counts
#> "033" "012" "012" "012"
#> ola_mES_lif_2_30.counts ola_mES_lif_2_31.counts ola_mES_lif_2_32.counts ola_mES_lif_2_33.counts
#> "012" "033" "012" "033"
#> ola_mES_lif_2_34.counts ola_mES_lif_2_35.counts ola_mES_lif_2_36.counts ola_mES_lif_2_37.counts
#> "012" "012" "034" "033"
#> ola_mES_lif_2_38.counts ola_mES_lif_2_39.counts ola_mES_lif_2_4.counts ola_mES_lif_2_40.counts
#> "012" "012" "012" "012"
#> ola_mES_lif_2_41.counts ola_mES_lif_2_42.counts ola_mES_lif_2_43.counts ola_mES_lif_2_44.counts
#> "033" "012" "012" "012"
#> ola_mES_lif_2_45.counts ola_mES_lif_2_46.counts ola_mES_lif_2_47.counts ola_mES_lif_2_48.counts
#> "034" "012" "033" "02"
#> ola_mES_lif_2_49.counts ola_mES_lif_2_5.counts ola_mES_lif_2_50.counts ola_mES_lif_2_51.counts
#> "012" "012" "012" "012"
#> ola_mES_lif_2_52.counts ola_mES_lif_2_53.counts ola_mES_lif_2_54.counts ola_mES_lif_2_55.counts
#> "012" "012" "012" "012"
#> ola_mES_lif_2_56.counts ola_mES_lif_2_57.counts ola_mES_lif_2_58.counts ola_mES_lif_2_59.counts
#> "012" "034" "012" "012"
#> ola_mES_lif_2_6.counts ola_mES_lif_2_60.counts ola_mES_lif_2_61.counts ola_mES_lif_2_63.counts
#> "012" "033" "014" "012"
#> ola_mES_lif_2_64.counts ola_mES_lif_2_65.counts ola_mES_lif_2_66.counts ola_mES_lif_2_67.counts
#> "012" "012" "012" "012"
#> ola_mES_lif_2_68.counts ola_mES_lif_2_69.counts ola_mES_lif_2_7.counts ola_mES_lif_2_70.counts
#> "012" "02" "012" "012"
#> ola_mES_lif_2_71.counts ola_mES_lif_2_72.counts ola_mES_lif_2_73.counts ola_mES_lif_2_74.counts
#> "012" "014" "02" "012"
#> ola_mES_lif_2_75.counts ola_mES_lif_2_76.counts ola_mES_lif_2_77.counts ola_mES_lif_2_79.counts
#> "012" "012" "012" "033"
#> ola_mES_lif_2_8.counts ola_mES_lif_2_80.counts ola_mES_lif_2_81.counts ola_mES_lif_2_82.counts
#> "012" "012" "033" "012"
#> ola_mES_lif_2_83.counts ola_mES_lif_2_84.counts ola_mES_lif_2_86.counts ola_mES_lif_2_87.counts
#> "012" "012" "033" "012"
#> ola_mES_lif_2_89.counts ola_mES_lif_2_9.counts ola_mES_lif_2_90.counts ola_mES_lif_2_91.counts
#> "012" "033" "033" "033"
#> ola_mES_lif_2_92.counts ola_mES_lif_2_93.counts ola_mES_lif_2_94.counts ola_mES_lif_2_95.counts
#> "033" "012" "012" "012"
#> ola_mES_lif_2_96.counts ola_mES_lif_3_1.counts ola_mES_lif_3_10.counts ola_mES_lif_3_11.counts
#> "033" "014" "012" "033"
#> ola_mES_lif_3_12.counts ola_mES_lif_3_13.counts ola_mES_lif_3_14.counts ola_mES_lif_3_15.counts
#> "02" "014" "034" "034"
#> ola_mES_lif_3_16.counts ola_mES_lif_3_17.counts ola_mES_lif_3_19.counts ola_mES_lif_3_2.counts
#> "034" "034" "034" "02"
#> ola_mES_lif_3_20.counts ola_mES_lif_3_21.counts ola_mES_lif_3_22.counts ola_mES_lif_3_23.counts
#> "031" "02" "031" "031"
#> ola_mES_lif_3_24.counts ola_mES_lif_3_26.counts ola_mES_lif_3_27.counts ola_mES_lif_3_28.counts
#> "031" "031" "014" "034"
#> ola_mES_lif_3_29.counts ola_mES_lif_3_3.counts ola_mES_lif_3_30.counts ola_mES_lif_3_31.counts
#> "033" "014" "014" "034"
#> ola_mES_lif_3_32.counts ola_mES_lif_3_33.counts ola_mES_lif_3_34.counts ola_mES_lif_3_35.counts
#> "014" "034" "034" "034"
#> ola_mES_lif_3_36.counts ola_mES_lif_3_37.counts ola_mES_lif_3_38.counts ola_mES_lif_3_39.counts
#> "034" "033" "034" "034"
#> ola_mES_lif_3_4.counts ola_mES_lif_3_40.counts ola_mES_lif_3_42.counts ola_mES_lif_3_45.counts
#> "014" "014" "014" "034"
#> ola_mES_lif_3_46.counts ola_mES_lif_3_47.counts ola_mES_lif_3_48.counts ola_mES_lif_3_49.counts
#> "034" "034" "033" "014"
#> ola_mES_lif_3_5.counts ola_mES_lif_3_50.counts ola_mES_lif_3_51.counts ola_mES_lif_3_52.counts
#> "012" "014" "014" "014"
#> ola_mES_lif_3_53.counts ola_mES_lif_3_54.counts ola_mES_lif_3_56.counts ola_mES_lif_3_57.counts
#> "014" "014" "012" "033"
#> ola_mES_lif_3_58.counts ola_mES_lif_3_59.counts ola_mES_lif_3_6.counts ola_mES_lif_3_60.counts
#> "014" "014" "014" "014"
#> ola_mES_lif_3_61.counts ola_mES_lif_3_63.counts ola_mES_lif_3_64.counts ola_mES_lif_3_65.counts
#> "014" "014" "014" "014"
#> ola_mES_lif_3_69.counts ola_mES_lif_3_70.counts ola_mES_lif_3_71.counts ola_mES_lif_3_72.counts
#> "034" "034" "014" "034"
#> ola_mES_lif_3_73.counts ola_mES_lif_3_74.counts ola_mES_lif_3_75.counts ola_mES_lif_3_76.counts
#> "031" "031" "031" "031"
#> ola_mES_lif_3_77.counts ola_mES_lif_3_78.counts ola_mES_lif_3_8.counts ola_mES_lif_3_80.counts
#> "02" "031" "014" "031"
#> ola_mES_lif_3_82.counts ola_mES_lif_3_83.counts ola_mES_lif_3_84.counts ola_mES_lif_3_87.counts
#> "031" "031" "031" "031"
#> ola_mES_lif_3_88.counts ola_mES_lif_3_89.counts ola_mES_lif_3_9.counts ola_mES_lif_3_90.counts
#> "031" "02" "02" "034"
#> ola_mES_lif_3_92.counts ola_mES_lif_3_94.counts ola_mES_lif_3_95.counts ola_mES_lif_3_96.counts
#> "031" "031" "031" "031"
get_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 7328))
#> ola_mES_2i_2_1.counts ola_mES_2i_2_10.counts ola_mES_2i_2_11.counts ola_mES_2i_2_12.counts
#> NA NA NA NA
#> ola_mES_2i_2_13.counts ola_mES_2i_2_14.counts ola_mES_2i_2_15.counts ola_mES_2i_2_16.counts
#> NA NA NA NA
#> ola_mES_2i_2_17.counts ola_mES_2i_2_18.counts ola_mES_2i_2_19.counts ola_mES_2i_2_2.counts
#> NA NA NA NA
#> ola_mES_2i_2_20.counts ola_mES_2i_2_21.counts ola_mES_2i_2_22.counts ola_mES_2i_2_23.counts
#> NA NA NA NA
#> ola_mES_2i_2_24.counts ola_mES_2i_2_25.counts ola_mES_2i_2_26.counts ola_mES_2i_2_27.counts
#> NA NA NA NA
#> ola_mES_2i_2_28.counts ola_mES_2i_2_29.counts ola_mES_2i_2_3.counts ola_mES_2i_2_30.counts
#> NA NA NA NA
#> ola_mES_2i_2_31.counts ola_mES_2i_2_32.counts ola_mES_2i_2_33.counts ola_mES_2i_2_34.counts
#> NA NA NA NA
#> ola_mES_2i_2_35.counts ola_mES_2i_2_36.counts ola_mES_2i_2_37.counts ola_mES_2i_2_38.counts
#> NA NA NA NA
#> ola_mES_2i_2_39.counts ola_mES_2i_2_4.counts ola_mES_2i_2_40.counts ola_mES_2i_2_41.counts
#> NA NA NA NA
#> ola_mES_2i_2_42.counts ola_mES_2i_2_43.counts ola_mES_2i_2_44.counts ola_mES_2i_2_46.counts
#> NA NA NA NA
#> ola_mES_2i_2_47.counts ola_mES_2i_2_48.counts ola_mES_2i_2_49.counts ola_mES_2i_2_5.counts
#> NA NA NA NA
#> ola_mES_2i_2_50.counts ola_mES_2i_2_51.counts ola_mES_2i_2_52.counts ola_mES_2i_2_53.counts
#> NA NA NA NA
#> ola_mES_2i_2_54.counts ola_mES_2i_2_55.counts ola_mES_2i_2_56.counts ola_mES_2i_2_57.counts
#> NA NA NA NA
#> ola_mES_2i_2_58.counts ola_mES_2i_2_59.counts ola_mES_2i_2_6.counts ola_mES_2i_2_60.counts
#> NA NA NA NA
#> ola_mES_2i_2_61.counts ola_mES_2i_2_62.counts ola_mES_2i_2_63.counts ola_mES_2i_2_64.counts
#> NA NA NA NA
#> ola_mES_2i_2_66.counts ola_mES_2i_2_67.counts ola_mES_2i_2_68.counts ola_mES_2i_2_69.counts
#> NA NA NA NA
#> ola_mES_2i_2_7.counts ola_mES_2i_2_72.counts ola_mES_2i_2_73.counts ola_mES_2i_2_74.counts
#> NA NA NA NA
#> ola_mES_2i_2_75.counts ola_mES_2i_2_76.counts ola_mES_2i_2_79.counts ola_mES_2i_2_8.counts
#> NA NA NA NA
#> ola_mES_2i_2_80.counts ola_mES_2i_2_81.counts ola_mES_2i_2_85.counts ola_mES_2i_2_86.counts
#> NA NA NA NA
#> ola_mES_2i_2_87.counts ola_mES_2i_2_90.counts ola_mES_2i_2_91.counts ola_mES_2i_2_93.counts
#> NA NA NA NA
#> ola_mES_2i_2_94.counts ola_mES_2i_2_95.counts ola_mES_2i_3_10.counts ola_mES_2i_3_11.counts
#> NA NA NA NA
#> ola_mES_2i_3_12.counts ola_mES_2i_3_13.counts ola_mES_2i_3_15.counts ola_mES_2i_3_16.counts
#> NA NA NA NA
#> ola_mES_2i_3_17.counts ola_mES_2i_3_18.counts ola_mES_2i_3_19.counts ola_mES_2i_3_2.counts
#> NA NA NA NA
#> ola_mES_2i_3_20.counts ola_mES_2i_3_21.counts ola_mES_2i_3_23.counts ola_mES_2i_3_25.counts
#> NA NA NA NA
#> ola_mES_2i_3_26.counts ola_mES_2i_3_28.counts ola_mES_2i_3_30.counts ola_mES_2i_3_31.counts
#> NA NA NA NA
#> ola_mES_2i_3_33.counts ola_mES_2i_3_36.counts ola_mES_2i_3_37.counts ola_mES_2i_3_38.counts
#> NA NA NA NA
#> ola_mES_2i_3_39.counts ola_mES_2i_3_4.counts ola_mES_2i_3_41.counts ola_mES_2i_3_42.counts
#> NA NA NA NA
#> ola_mES_2i_3_43.counts ola_mES_2i_3_45.counts ola_mES_2i_3_46.counts ola_mES_2i_3_47.counts
#> NA NA NA NA
#> ola_mES_2i_3_49.counts ola_mES_2i_3_5.counts ola_mES_2i_3_52.counts ola_mES_2i_3_54.counts
#> NA NA NA NA
#> ola_mES_2i_3_56.counts ola_mES_2i_3_57.counts ola_mES_2i_3_58.counts ola_mES_2i_3_6.counts
#> NA NA NA NA
#> ola_mES_2i_3_62.counts ola_mES_2i_3_65.counts ola_mES_2i_3_66.counts ola_mES_2i_3_69.counts
#> NA NA NA NA
#> ola_mES_2i_3_7.counts ola_mES_2i_3_73.counts ola_mES_2i_3_76.counts ola_mES_2i_3_77.counts
#> NA NA NA NA
#> ola_mES_2i_3_78.counts ola_mES_2i_3_79.counts ola_mES_2i_3_8.counts ola_mES_2i_3_80.counts
#> NA NA NA NA
#> ola_mES_2i_3_82.counts ola_mES_2i_3_83.counts ola_mES_2i_3_84.counts ola_mES_2i_3_87.counts
#> NA NA NA NA
#> ola_mES_2i_3_88.counts ola_mES_2i_3_90.counts ola_mES_2i_3_91.counts ola_mES_2i_3_92.counts
#> NA NA NA NA
#> ola_mES_2i_3_96.counts ola_mES_2i_4_1.counts ola_mES_2i_4_10.counts ola_mES_2i_4_11.counts
#> NA NA NA NA
#> ola_mES_2i_4_13.counts ola_mES_2i_4_17.counts ola_mES_2i_4_18.counts ola_mES_2i_4_2.counts
#> NA NA NA NA
#> ola_mES_2i_4_20.counts ola_mES_2i_4_21.counts ola_mES_2i_4_22.counts ola_mES_2i_4_23.counts
#> NA NA NA NA
#> ola_mES_2i_4_24.counts ola_mES_2i_4_25.counts ola_mES_2i_4_26.counts ola_mES_2i_4_27.counts
#> NA NA NA NA
#> ola_mES_2i_4_29.counts ola_mES_2i_4_30.counts ola_mES_2i_4_31.counts ola_mES_2i_4_32.counts
#> NA NA NA NA
#> ola_mES_2i_4_33.counts ola_mES_2i_4_34.counts ola_mES_2i_4_35.counts ola_mES_2i_4_36.counts
#> NA NA NA NA
#> ola_mES_2i_4_37.counts ola_mES_2i_4_38.counts ola_mES_2i_4_39.counts ola_mES_2i_4_40.counts
#> NA NA NA NA
#> ola_mES_2i_4_42.counts ola_mES_2i_4_43.counts ola_mES_2i_4_44.counts ola_mES_2i_4_46.counts
#> NA NA NA NA
#> ola_mES_2i_4_49.counts ola_mES_2i_4_5.counts ola_mES_2i_4_50.counts ola_mES_2i_4_52.counts
#> NA NA NA NA
#> ola_mES_2i_4_53.counts ola_mES_2i_4_54.counts ola_mES_2i_4_56.counts ola_mES_2i_4_57.counts
#> NA NA NA NA
#> ola_mES_2i_4_58.counts ola_mES_2i_4_59.counts ola_mES_2i_4_6.counts ola_mES_2i_4_60.counts
#> NA NA NA NA
#> ola_mES_2i_4_61.counts ola_mES_2i_4_62.counts ola_mES_2i_4_63.counts ola_mES_2i_4_64.counts
#> NA NA NA NA
#> ola_mES_2i_4_65.counts ola_mES_2i_4_66.counts ola_mES_2i_4_67.counts ola_mES_2i_4_68.counts
#> NA NA NA NA
#> ola_mES_2i_4_69.counts ola_mES_2i_4_7.counts ola_mES_2i_4_70.counts ola_mES_2i_4_71.counts
#> NA NA NA NA
#> ola_mES_2i_4_72.counts ola_mES_2i_4_73.counts ola_mES_2i_4_75.counts ola_mES_2i_4_77.counts
#> NA NA NA NA
#> ola_mES_2i_4_78.counts ola_mES_2i_4_80.counts ola_mES_2i_4_81.counts ola_mES_2i_4_83.counts
#> NA NA NA NA
#> ola_mES_2i_4_84.counts ola_mES_2i_4_88.counts ola_mES_2i_4_89.counts ola_mES_2i_4_9.counts
#> NA NA NA NA
#> ola_mES_2i_4_90.counts ola_mES_2i_4_91.counts ola_mES_2i_4_94.counts ola_mES_2i_4_95.counts
#> NA NA NA NA
#> ola_mES_2i_4_96.counts ola_mES_2i_5_1.counts ola_mES_2i_5_10.counts ola_mES_2i_5_11.counts
#> NA NA NA NA
#> ola_mES_2i_5_12.counts ola_mES_2i_5_13.counts ola_mES_2i_5_14.counts ola_mES_2i_5_15.counts
#> NA NA NA NA
#> ola_mES_2i_5_16.counts ola_mES_2i_5_17.counts ola_mES_2i_5_18.counts ola_mES_2i_5_19.counts
#> NA NA NA NA
#> ola_mES_2i_5_2.counts ola_mES_2i_5_20.counts ola_mES_2i_5_21.counts ola_mES_2i_5_23.counts
#> NA NA NA NA
#> ola_mES_2i_5_24.counts ola_mES_2i_5_25.counts ola_mES_2i_5_26.counts ola_mES_2i_5_27.counts
#> NA NA NA NA
#> ola_mES_2i_5_28.counts ola_mES_2i_5_29.counts ola_mES_2i_5_3.counts ola_mES_2i_5_30.counts
#> NA NA NA NA
#> ola_mES_2i_5_31.counts ola_mES_2i_5_32.counts ola_mES_2i_5_34.counts ola_mES_2i_5_35.counts
#> NA NA NA NA
#> ola_mES_2i_5_36.counts ola_mES_2i_5_38.counts ola_mES_2i_5_39.counts ola_mES_2i_5_4.counts
#> NA NA NA NA
#> ola_mES_2i_5_40.counts ola_mES_2i_5_41.counts ola_mES_2i_5_42.counts ola_mES_2i_5_44.counts
#> NA NA NA NA
#> ola_mES_2i_5_45.counts ola_mES_2i_5_46.counts ola_mES_2i_5_47.counts ola_mES_2i_5_48.counts
#> NA NA NA NA
#> ola_mES_2i_5_49.counts ola_mES_2i_5_5.counts ola_mES_2i_5_51.counts ola_mES_2i_5_52.counts
#> NA NA NA NA
#> ola_mES_2i_5_53.counts ola_mES_2i_5_54.counts ola_mES_2i_5_55.counts ola_mES_2i_5_56.counts
#> NA NA NA NA
#> ola_mES_2i_5_57.counts ola_mES_2i_5_58.counts ola_mES_2i_5_59.counts ola_mES_2i_5_6.counts
#> NA NA NA NA
#> ola_mES_2i_5_61.counts ola_mES_2i_5_62.counts ola_mES_2i_5_63.counts ola_mES_2i_5_65.counts
#> NA NA NA NA
#> ola_mES_2i_5_66.counts ola_mES_2i_5_67.counts ola_mES_2i_5_68.counts ola_mES_2i_5_69.counts
#> NA NA NA NA
#> ola_mES_2i_5_7.counts ola_mES_2i_5_70.counts ola_mES_2i_5_72.counts ola_mES_2i_5_73.counts
#> NA NA NA NA
#> ola_mES_2i_5_74.counts ola_mES_2i_5_75.counts ola_mES_2i_5_76.counts ola_mES_2i_5_77.counts
#> NA NA NA NA
#> ola_mES_2i_5_78.counts ola_mES_2i_5_8.counts ola_mES_2i_5_81.counts ola_mES_2i_5_82.counts
#> NA NA NA NA
#> ola_mES_2i_5_83.counts ola_mES_2i_5_85.counts ola_mES_2i_5_86.counts ola_mES_2i_5_87.counts
#> NA NA NA NA
#> ola_mES_2i_5_88.counts ola_mES_2i_5_9.counts ola_mES_2i_5_90.counts ola_mES_2i_5_92.counts
#> NA NA NA NA
#> ola_mES_2i_5_94.counts ola_mES_2i_5_95.counts ola_mES_2i_5_96.counts ola_mES_a2i_2_1.counts
#> NA NA NA NA
#> ola_mES_a2i_2_10.counts ola_mES_a2i_2_11.counts ola_mES_a2i_2_12.counts ola_mES_a2i_2_13.counts
#> NA NA NA NA
#> ola_mES_a2i_2_14.counts ola_mES_a2i_2_15.counts ola_mES_a2i_2_16.counts ola_mES_a2i_2_17.counts
#> NA NA NA NA
#> ola_mES_a2i_2_18.counts ola_mES_a2i_2_19.counts ola_mES_a2i_2_2.counts ola_mES_a2i_2_20.counts
#> NA NA NA NA
#> ola_mES_a2i_2_21.counts ola_mES_a2i_2_22.counts ola_mES_a2i_2_23.counts ola_mES_a2i_2_24.counts
#> NA NA NA NA
#> ola_mES_a2i_2_25.counts ola_mES_a2i_2_26.counts ola_mES_a2i_2_27.counts ola_mES_a2i_2_28.counts
#> NA NA NA NA
#> ola_mES_a2i_2_29.counts ola_mES_a2i_2_3.counts ola_mES_a2i_2_30.counts ola_mES_a2i_2_31.counts
#> NA NA NA NA
#> ola_mES_a2i_2_32.counts ola_mES_a2i_2_33.counts ola_mES_a2i_2_34.counts ola_mES_a2i_2_35.counts
#> NA NA NA NA
#> ola_mES_a2i_2_36.counts ola_mES_a2i_2_37.counts ola_mES_a2i_2_38.counts ola_mES_a2i_2_39.counts
#> NA NA NA NA
#> ola_mES_a2i_2_4.counts ola_mES_a2i_2_40.counts ola_mES_a2i_2_41.counts ola_mES_a2i_2_42.counts
#> NA NA NA NA
#> ola_mES_a2i_2_43.counts ola_mES_a2i_2_44.counts ola_mES_a2i_2_45.counts ola_mES_a2i_2_46.counts
#> NA NA NA NA
#> ola_mES_a2i_2_47.counts ola_mES_a2i_2_48.counts ola_mES_a2i_2_49.counts ola_mES_a2i_2_5.counts
#> NA NA NA NA
#> ola_mES_a2i_2_50.counts ola_mES_a2i_2_51.counts ola_mES_a2i_2_52.counts ola_mES_a2i_2_53.counts
#> NA NA NA NA
#> ola_mES_a2i_2_54.counts ola_mES_a2i_2_55.counts ola_mES_a2i_2_56.counts ola_mES_a2i_2_57.counts
#> NA NA NA NA
#> ola_mES_a2i_2_59.counts ola_mES_a2i_2_6.counts ola_mES_a2i_2_60.counts ola_mES_a2i_2_61.counts
#> NA NA NA NA
#> ola_mES_a2i_2_62.counts ola_mES_a2i_2_63.counts ola_mES_a2i_2_64.counts ola_mES_a2i_2_65.counts
#> NA NA NA NA
#> ola_mES_a2i_2_66.counts ola_mES_a2i_2_67.counts ola_mES_a2i_2_68.counts ola_mES_a2i_2_69.counts
#> NA NA NA NA
#> ola_mES_a2i_2_7.counts ola_mES_a2i_2_70.counts ola_mES_a2i_2_71.counts ola_mES_a2i_2_72.counts
#> NA NA NA NA
#> ola_mES_a2i_2_73.counts ola_mES_a2i_2_74.counts ola_mES_a2i_2_75.counts ola_mES_a2i_2_76.counts
#> NA NA NA NA
#> ola_mES_a2i_2_77.counts ola_mES_a2i_2_78.counts ola_mES_a2i_2_8.counts ola_mES_a2i_2_80.counts
#> NA NA NA NA
#> ola_mES_a2i_2_81.counts ola_mES_a2i_2_82.counts ola_mES_a2i_2_83.counts ola_mES_a2i_2_84.counts
#> NA NA NA NA
#> ola_mES_a2i_2_86.counts ola_mES_a2i_2_87.counts ola_mES_a2i_2_88.counts ola_mES_a2i_2_89.counts
#> NA NA NA NA
#> ola_mES_a2i_2_9.counts ola_mES_a2i_2_90.counts ola_mES_a2i_2_91.counts ola_mES_a2i_2_92.counts
#> NA NA NA NA
#> ola_mES_a2i_2_93.counts ola_mES_a2i_2_94.counts ola_mES_a2i_2_95.counts ola_mES_a2i_2_96.counts
#> NA NA NA NA
#> ola_mES_a2i_3_10.counts ola_mES_a2i_3_11.counts ola_mES_a2i_3_13.counts ola_mES_a2i_3_16.counts
#> NA NA NA NA
#> ola_mES_a2i_3_17.counts ola_mES_a2i_3_18.counts ola_mES_a2i_3_2.counts ola_mES_a2i_3_20.counts
#> NA NA NA NA
#> ola_mES_a2i_3_21.counts ola_mES_a2i_3_22.counts ola_mES_a2i_3_24.counts ola_mES_a2i_3_25.counts
#> NA NA NA NA
#> ola_mES_a2i_3_26.counts ola_mES_a2i_3_28.counts ola_mES_a2i_3_29.counts ola_mES_a2i_3_3.counts
#> NA NA NA NA
#> ola_mES_a2i_3_30.counts ola_mES_a2i_3_31.counts ola_mES_a2i_3_33.counts ola_mES_a2i_3_34.counts
#> NA NA NA NA
#> ola_mES_a2i_3_37.counts ola_mES_a2i_3_38.counts ola_mES_a2i_3_39.counts ola_mES_a2i_3_4.counts
#> NA NA NA NA
#> ola_mES_a2i_3_40.counts ola_mES_a2i_3_41.counts ola_mES_a2i_3_42.counts ola_mES_a2i_3_44.counts
#> NA NA NA NA
#> ola_mES_a2i_3_45.counts ola_mES_a2i_3_47.counts ola_mES_a2i_3_49.counts ola_mES_a2i_3_50.counts
#> NA NA NA NA
#> ola_mES_a2i_3_52.counts ola_mES_a2i_3_54.counts ola_mES_a2i_3_56.counts ola_mES_a2i_3_58.counts
#> NA NA NA NA
#> ola_mES_a2i_3_59.counts ola_mES_a2i_3_6.counts ola_mES_a2i_3_62.counts ola_mES_a2i_3_64.counts
#> NA NA NA NA
#> ola_mES_a2i_3_65.counts ola_mES_a2i_3_66.counts ola_mES_a2i_3_67.counts ola_mES_a2i_3_68.counts
#> NA NA NA NA
#> ola_mES_a2i_3_69.counts ola_mES_a2i_3_7.counts ola_mES_a2i_3_71.counts ola_mES_a2i_3_73.counts
#> NA NA NA NA
#> ola_mES_a2i_3_75.counts ola_mES_a2i_3_76.counts ola_mES_a2i_3_77.counts ola_mES_a2i_3_8.counts
#> NA NA NA NA
#> ola_mES_a2i_3_80.counts ola_mES_a2i_3_81.counts ola_mES_a2i_3_82.counts ola_mES_a2i_3_83.counts
#> NA NA NA NA
#> ola_mES_a2i_3_84.counts ola_mES_a2i_3_85.counts ola_mES_a2i_3_86.counts ola_mES_a2i_3_89.counts
#> NA NA NA NA
#> ola_mES_a2i_3_9.counts ola_mES_a2i_3_90.counts ola_mES_a2i_3_91.counts ola_mES_a2i_3_92.counts
#> NA NA NA NA
#> ola_mES_a2i_3_94.counts ola_mES_a2i_3_96.counts ola_mES_lif_1_1.counts ola_mES_lif_1_10.counts
#> NA NA NA NA
#> ola_mES_lif_1_11.counts ola_mES_lif_1_12.counts ola_mES_lif_1_13.counts ola_mES_lif_1_14.counts
#> NA NA NA NA
#> ola_mES_lif_1_15.counts ola_mES_lif_1_17.counts ola_mES_lif_1_18.counts ola_mES_lif_1_19.counts
#> NA NA NA NA
#> ola_mES_lif_1_2.counts ola_mES_lif_1_20.counts ola_mES_lif_1_21.counts ola_mES_lif_1_22.counts
#> NA NA NA NA
#> ola_mES_lif_1_23.counts ola_mES_lif_1_24.counts ola_mES_lif_1_25.counts ola_mES_lif_1_26.counts
#> NA NA NA NA
#> ola_mES_lif_1_28.counts ola_mES_lif_1_29.counts ola_mES_lif_1_3.counts ola_mES_lif_1_30.counts
#> NA NA NA NA
#> ola_mES_lif_1_31.counts ola_mES_lif_1_32.counts ola_mES_lif_1_33.counts ola_mES_lif_1_34.counts
#> NA NA NA NA
#> ola_mES_lif_1_35.counts ola_mES_lif_1_36.counts ola_mES_lif_1_37.counts ola_mES_lif_1_38.counts
#> NA NA NA NA
#> ola_mES_lif_1_39.counts ola_mES_lif_1_4.counts ola_mES_lif_1_41.counts ola_mES_lif_1_42.counts
#> NA NA NA NA
#> ola_mES_lif_1_43.counts ola_mES_lif_1_44.counts ola_mES_lif_1_45.counts ola_mES_lif_1_46.counts
#> NA NA NA NA
#> ola_mES_lif_1_47.counts ola_mES_lif_1_48.counts ola_mES_lif_1_49.counts ola_mES_lif_1_5.counts
#> NA NA NA NA
#> ola_mES_lif_1_50.counts ola_mES_lif_1_51.counts ola_mES_lif_1_52.counts ola_mES_lif_1_53.counts
#> NA NA NA NA
#> ola_mES_lif_1_54.counts ola_mES_lif_1_55.counts ola_mES_lif_1_56.counts ola_mES_lif_1_57.counts
#> NA NA NA NA
#> ola_mES_lif_1_58.counts ola_mES_lif_1_59.counts ola_mES_lif_1_6.counts ola_mES_lif_1_60.counts
#> NA NA NA NA
#> ola_mES_lif_1_61.counts ola_mES_lif_1_62.counts ola_mES_lif_1_63.counts ola_mES_lif_1_64.counts
#> NA NA NA NA
#> ola_mES_lif_1_65.counts ola_mES_lif_1_66.counts ola_mES_lif_1_67.counts ola_mES_lif_1_68.counts
#> NA NA NA NA
#> ola_mES_lif_1_69.counts ola_mES_lif_1_7.counts ola_mES_lif_1_70.counts ola_mES_lif_1_72.counts
#> NA NA NA NA
#> ola_mES_lif_1_73.counts ola_mES_lif_1_74.counts ola_mES_lif_1_75.counts ola_mES_lif_1_79.counts
#> NA NA NA NA
#> ola_mES_lif_1_8.counts ola_mES_lif_1_80.counts ola_mES_lif_1_81.counts ola_mES_lif_1_82.counts
#> NA NA NA NA
#> ola_mES_lif_1_86.counts ola_mES_lif_1_9.counts ola_mES_lif_1_91.counts ola_mES_lif_1_92.counts
#> NA NA NA NA
#> ola_mES_lif_1_93.counts ola_mES_lif_1_95.counts ola_mES_lif_1_96.counts ola_mES_lif_2_1.counts
#> NA NA NA NA
#> ola_mES_lif_2_10.counts ola_mES_lif_2_11.counts ola_mES_lif_2_12.counts ola_mES_lif_2_13.counts
#> NA NA NA NA
#> ola_mES_lif_2_14.counts ola_mES_lif_2_15.counts ola_mES_lif_2_16.counts ola_mES_lif_2_17.counts
#> NA NA NA NA
#> ola_mES_lif_2_18.counts ola_mES_lif_2_19.counts ola_mES_lif_2_2.counts ola_mES_lif_2_20.counts
#> NA NA NA NA
#> ola_mES_lif_2_21.counts ola_mES_lif_2_23.counts ola_mES_lif_2_25.counts ola_mES_lif_2_26.counts
#> NA NA NA NA
#> ola_mES_lif_2_27.counts ola_mES_lif_2_28.counts ola_mES_lif_2_29.counts ola_mES_lif_2_3.counts
#> NA NA NA NA
#> ola_mES_lif_2_30.counts ola_mES_lif_2_31.counts ola_mES_lif_2_32.counts ola_mES_lif_2_33.counts
#> NA NA NA NA
#> ola_mES_lif_2_34.counts ola_mES_lif_2_35.counts ola_mES_lif_2_36.counts ola_mES_lif_2_37.counts
#> NA NA NA NA
#> ola_mES_lif_2_38.counts ola_mES_lif_2_39.counts ola_mES_lif_2_4.counts ola_mES_lif_2_40.counts
#> NA NA NA NA
#> ola_mES_lif_2_41.counts ola_mES_lif_2_42.counts ola_mES_lif_2_43.counts ola_mES_lif_2_44.counts
#> NA NA NA NA
#> ola_mES_lif_2_45.counts ola_mES_lif_2_46.counts ola_mES_lif_2_47.counts ola_mES_lif_2_48.counts
#> NA NA NA NA
#> ola_mES_lif_2_49.counts ola_mES_lif_2_5.counts ola_mES_lif_2_50.counts ola_mES_lif_2_51.counts
#> NA NA NA NA
#> ola_mES_lif_2_52.counts ola_mES_lif_2_53.counts ola_mES_lif_2_54.counts ola_mES_lif_2_55.counts
#> NA NA NA NA
#> ola_mES_lif_2_56.counts ola_mES_lif_2_57.counts ola_mES_lif_2_58.counts ola_mES_lif_2_59.counts
#> NA NA NA NA
#> ola_mES_lif_2_6.counts ola_mES_lif_2_60.counts ola_mES_lif_2_61.counts ola_mES_lif_2_63.counts
#> NA NA NA NA
#> ola_mES_lif_2_64.counts ola_mES_lif_2_65.counts ola_mES_lif_2_66.counts ola_mES_lif_2_67.counts
#> NA NA NA NA
#> ola_mES_lif_2_68.counts ola_mES_lif_2_69.counts ola_mES_lif_2_7.counts ola_mES_lif_2_70.counts
#> NA NA NA NA
#> ola_mES_lif_2_71.counts ola_mES_lif_2_72.counts ola_mES_lif_2_73.counts ola_mES_lif_2_74.counts
#> NA NA NA NA
#> ola_mES_lif_2_75.counts ola_mES_lif_2_76.counts ola_mES_lif_2_77.counts ola_mES_lif_2_79.counts
#> NA NA NA NA
#> ola_mES_lif_2_8.counts ola_mES_lif_2_80.counts ola_mES_lif_2_81.counts ola_mES_lif_2_82.counts
#> NA NA NA NA
#> ola_mES_lif_2_83.counts ola_mES_lif_2_84.counts ola_mES_lif_2_86.counts ola_mES_lif_2_87.counts
#> NA NA NA NA
#> ola_mES_lif_2_89.counts ola_mES_lif_2_9.counts ola_mES_lif_2_90.counts ola_mES_lif_2_91.counts
#> NA NA NA NA
#> ola_mES_lif_2_92.counts ola_mES_lif_2_93.counts ola_mES_lif_2_94.counts ola_mES_lif_2_95.counts
#> NA NA NA NA
#> ola_mES_lif_2_96.counts ola_mES_lif_3_1.counts ola_mES_lif_3_10.counts ola_mES_lif_3_11.counts
#> NA NA NA NA
#> ola_mES_lif_3_12.counts ola_mES_lif_3_13.counts ola_mES_lif_3_14.counts ola_mES_lif_3_15.counts
#> NA NA NA NA
#> ola_mES_lif_3_16.counts ola_mES_lif_3_17.counts ola_mES_lif_3_19.counts ola_mES_lif_3_2.counts
#> NA NA NA NA
#> ola_mES_lif_3_20.counts ola_mES_lif_3_21.counts ola_mES_lif_3_22.counts ola_mES_lif_3_23.counts
#> NA NA NA NA
#> ola_mES_lif_3_24.counts ola_mES_lif_3_26.counts ola_mES_lif_3_27.counts ola_mES_lif_3_28.counts
#> NA NA NA NA
#> ola_mES_lif_3_29.counts ola_mES_lif_3_3.counts ola_mES_lif_3_30.counts ola_mES_lif_3_31.counts
#> NA NA NA NA
#> ola_mES_lif_3_32.counts ola_mES_lif_3_33.counts ola_mES_lif_3_34.counts ola_mES_lif_3_35.counts
#> NA NA NA NA
#> ola_mES_lif_3_36.counts ola_mES_lif_3_37.counts ola_mES_lif_3_38.counts ola_mES_lif_3_39.counts
#> NA NA NA NA
#> ola_mES_lif_3_4.counts ola_mES_lif_3_40.counts ola_mES_lif_3_42.counts ola_mES_lif_3_45.counts
#> NA NA NA NA
#> ola_mES_lif_3_46.counts ola_mES_lif_3_47.counts ola_mES_lif_3_48.counts ola_mES_lif_3_49.counts
#> NA NA NA NA
#> ola_mES_lif_3_5.counts ola_mES_lif_3_50.counts ola_mES_lif_3_51.counts ola_mES_lif_3_52.counts
#> NA NA NA NA
#> ola_mES_lif_3_53.counts ola_mES_lif_3_54.counts ola_mES_lif_3_56.counts ola_mES_lif_3_57.counts
#> NA NA NA NA
#> ola_mES_lif_3_58.counts ola_mES_lif_3_59.counts ola_mES_lif_3_6.counts ola_mES_lif_3_60.counts
#> NA NA NA NA
#> ola_mES_lif_3_61.counts ola_mES_lif_3_63.counts ola_mES_lif_3_64.counts ola_mES_lif_3_65.counts
#> NA NA NA NA
#> ola_mES_lif_3_69.counts ola_mES_lif_3_70.counts ola_mES_lif_3_71.counts ola_mES_lif_3_72.counts
#> NA NA NA NA
#> ola_mES_lif_3_73.counts ola_mES_lif_3_74.counts ola_mES_lif_3_75.counts ola_mES_lif_3_76.counts
#> NA NA NA NA
#> ola_mES_lif_3_77.counts ola_mES_lif_3_78.counts ola_mES_lif_3_8.counts ola_mES_lif_3_80.counts
#> NA NA NA NA
#> ola_mES_lif_3_82.counts ola_mES_lif_3_83.counts ola_mES_lif_3_84.counts ola_mES_lif_3_87.counts
#> NA NA NA NA
#> ola_mES_lif_3_88.counts ola_mES_lif_3_89.counts ola_mES_lif_3_9.counts ola_mES_lif_3_90.counts
#> NA NA NA NA
#> ola_mES_lif_3_92.counts ola_mES_lif_3_94.counts ola_mES_lif_3_95.counts ola_mES_lif_3_96.counts
#> NA NA NA NA
get_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 8673))
#> ola_mES_2i_2_1.counts ola_mES_2i_2_10.counts ola_mES_2i_2_11.counts ola_mES_2i_2_12.counts
#> NA NA NA NA
#> ola_mES_2i_2_13.counts ola_mES_2i_2_14.counts ola_mES_2i_2_15.counts ola_mES_2i_2_16.counts
#> NA NA NA NA
#> ola_mES_2i_2_17.counts ola_mES_2i_2_18.counts ola_mES_2i_2_19.counts ola_mES_2i_2_2.counts
#> NA NA NA NA
#> ola_mES_2i_2_20.counts ola_mES_2i_2_21.counts ola_mES_2i_2_22.counts ola_mES_2i_2_23.counts
#> NA NA NA NA
#> ola_mES_2i_2_24.counts ola_mES_2i_2_25.counts ola_mES_2i_2_26.counts ola_mES_2i_2_27.counts
#> NA NA NA NA
#> ola_mES_2i_2_28.counts ola_mES_2i_2_29.counts ola_mES_2i_2_3.counts ola_mES_2i_2_30.counts
#> NA NA NA NA
#> ola_mES_2i_2_31.counts ola_mES_2i_2_32.counts ola_mES_2i_2_33.counts ola_mES_2i_2_34.counts
#> NA NA NA NA
#> ola_mES_2i_2_35.counts ola_mES_2i_2_36.counts ola_mES_2i_2_37.counts ola_mES_2i_2_38.counts
#> NA NA NA NA
#> ola_mES_2i_2_39.counts ola_mES_2i_2_4.counts ola_mES_2i_2_40.counts ola_mES_2i_2_41.counts
#> NA NA NA NA
#> ola_mES_2i_2_42.counts ola_mES_2i_2_43.counts ola_mES_2i_2_44.counts ola_mES_2i_2_46.counts
#> NA NA NA NA
#> ola_mES_2i_2_47.counts ola_mES_2i_2_48.counts ola_mES_2i_2_49.counts ola_mES_2i_2_5.counts
#> NA NA NA NA
#> ola_mES_2i_2_50.counts ola_mES_2i_2_51.counts ola_mES_2i_2_52.counts ola_mES_2i_2_53.counts
#> NA NA NA NA
#> ola_mES_2i_2_54.counts ola_mES_2i_2_55.counts ola_mES_2i_2_56.counts ola_mES_2i_2_57.counts
#> NA NA NA NA
#> ola_mES_2i_2_58.counts ola_mES_2i_2_59.counts ola_mES_2i_2_6.counts ola_mES_2i_2_60.counts
#> NA NA NA NA
#> ola_mES_2i_2_61.counts ola_mES_2i_2_62.counts ola_mES_2i_2_63.counts ola_mES_2i_2_64.counts
#> NA NA NA NA
#> ola_mES_2i_2_66.counts ola_mES_2i_2_67.counts ola_mES_2i_2_68.counts ola_mES_2i_2_69.counts
#> NA NA NA NA
#> ola_mES_2i_2_7.counts ola_mES_2i_2_72.counts ola_mES_2i_2_73.counts ola_mES_2i_2_74.counts
#> NA NA NA NA
#> ola_mES_2i_2_75.counts ola_mES_2i_2_76.counts ola_mES_2i_2_79.counts ola_mES_2i_2_8.counts
#> NA NA NA NA
#> ola_mES_2i_2_80.counts ola_mES_2i_2_81.counts ola_mES_2i_2_85.counts ola_mES_2i_2_86.counts
#> NA NA NA NA
#> ola_mES_2i_2_87.counts ola_mES_2i_2_90.counts ola_mES_2i_2_91.counts ola_mES_2i_2_93.counts
#> NA NA NA NA
#> ola_mES_2i_2_94.counts ola_mES_2i_2_95.counts ola_mES_2i_3_10.counts ola_mES_2i_3_11.counts
#> NA NA NA NA
#> ola_mES_2i_3_12.counts ola_mES_2i_3_13.counts ola_mES_2i_3_15.counts ola_mES_2i_3_16.counts
#> NA NA NA NA
#> ola_mES_2i_3_17.counts ola_mES_2i_3_18.counts ola_mES_2i_3_19.counts ola_mES_2i_3_2.counts
#> NA NA NA NA
#> ola_mES_2i_3_20.counts ola_mES_2i_3_21.counts ola_mES_2i_3_23.counts ola_mES_2i_3_25.counts
#> NA NA NA NA
#> ola_mES_2i_3_26.counts ola_mES_2i_3_28.counts ola_mES_2i_3_30.counts ola_mES_2i_3_31.counts
#> NA NA NA NA
#> ola_mES_2i_3_33.counts ola_mES_2i_3_36.counts ola_mES_2i_3_37.counts ola_mES_2i_3_38.counts
#> NA NA NA NA
#> ola_mES_2i_3_39.counts ola_mES_2i_3_4.counts ola_mES_2i_3_41.counts ola_mES_2i_3_42.counts
#> NA NA NA NA
#> ola_mES_2i_3_43.counts ola_mES_2i_3_45.counts ola_mES_2i_3_46.counts ola_mES_2i_3_47.counts
#> NA NA NA NA
#> ola_mES_2i_3_49.counts ola_mES_2i_3_5.counts ola_mES_2i_3_52.counts ola_mES_2i_3_54.counts
#> NA NA NA NA
#> ola_mES_2i_3_56.counts ola_mES_2i_3_57.counts ola_mES_2i_3_58.counts ola_mES_2i_3_6.counts
#> NA NA NA NA
#> ola_mES_2i_3_62.counts ola_mES_2i_3_65.counts ola_mES_2i_3_66.counts ola_mES_2i_3_69.counts
#> NA NA NA NA
#> ola_mES_2i_3_7.counts ola_mES_2i_3_73.counts ola_mES_2i_3_76.counts ola_mES_2i_3_77.counts
#> NA NA NA NA
#> ola_mES_2i_3_78.counts ola_mES_2i_3_79.counts ola_mES_2i_3_8.counts ola_mES_2i_3_80.counts
#> NA NA NA NA
#> ola_mES_2i_3_82.counts ola_mES_2i_3_83.counts ola_mES_2i_3_84.counts ola_mES_2i_3_87.counts
#> NA NA NA NA
#> ola_mES_2i_3_88.counts ola_mES_2i_3_90.counts ola_mES_2i_3_91.counts ola_mES_2i_3_92.counts
#> NA NA NA NA
#> ola_mES_2i_3_96.counts ola_mES_2i_4_1.counts ola_mES_2i_4_10.counts ola_mES_2i_4_11.counts
#> NA NA NA NA
#> ola_mES_2i_4_13.counts ola_mES_2i_4_17.counts ola_mES_2i_4_18.counts ola_mES_2i_4_2.counts
#> NA NA NA NA
#> ola_mES_2i_4_20.counts ola_mES_2i_4_21.counts ola_mES_2i_4_22.counts ola_mES_2i_4_23.counts
#> NA NA NA NA
#> ola_mES_2i_4_24.counts ola_mES_2i_4_25.counts ola_mES_2i_4_26.counts ola_mES_2i_4_27.counts
#> NA NA NA NA
#> ola_mES_2i_4_29.counts ola_mES_2i_4_30.counts ola_mES_2i_4_31.counts ola_mES_2i_4_32.counts
#> NA NA NA NA
#> ola_mES_2i_4_33.counts ola_mES_2i_4_34.counts ola_mES_2i_4_35.counts ola_mES_2i_4_36.counts
#> NA NA NA NA
#> ola_mES_2i_4_37.counts ola_mES_2i_4_38.counts ola_mES_2i_4_39.counts ola_mES_2i_4_40.counts
#> NA NA NA NA
#> ola_mES_2i_4_42.counts ola_mES_2i_4_43.counts ola_mES_2i_4_44.counts ola_mES_2i_4_46.counts
#> NA NA NA NA
#> ola_mES_2i_4_49.counts ola_mES_2i_4_5.counts ola_mES_2i_4_50.counts ola_mES_2i_4_52.counts
#> NA NA NA NA
#> ola_mES_2i_4_53.counts ola_mES_2i_4_54.counts ola_mES_2i_4_56.counts ola_mES_2i_4_57.counts
#> NA NA NA NA
#> ola_mES_2i_4_58.counts ola_mES_2i_4_59.counts ola_mES_2i_4_6.counts ola_mES_2i_4_60.counts
#> NA NA NA NA
#> ola_mES_2i_4_61.counts ola_mES_2i_4_62.counts ola_mES_2i_4_63.counts ola_mES_2i_4_64.counts
#> NA NA NA NA
#> ola_mES_2i_4_65.counts ola_mES_2i_4_66.counts ola_mES_2i_4_67.counts ola_mES_2i_4_68.counts
#> NA NA NA NA
#> ola_mES_2i_4_69.counts ola_mES_2i_4_7.counts ola_mES_2i_4_70.counts ola_mES_2i_4_71.counts
#> NA NA NA NA
#> ola_mES_2i_4_72.counts ola_mES_2i_4_73.counts ola_mES_2i_4_75.counts ola_mES_2i_4_77.counts
#> NA NA NA NA
#> ola_mES_2i_4_78.counts ola_mES_2i_4_80.counts ola_mES_2i_4_81.counts ola_mES_2i_4_83.counts
#> NA NA NA NA
#> ola_mES_2i_4_84.counts ola_mES_2i_4_88.counts ola_mES_2i_4_89.counts ola_mES_2i_4_9.counts
#> NA NA NA NA
#> ola_mES_2i_4_90.counts ola_mES_2i_4_91.counts ola_mES_2i_4_94.counts ola_mES_2i_4_95.counts
#> NA NA NA NA
#> ola_mES_2i_4_96.counts ola_mES_2i_5_1.counts ola_mES_2i_5_10.counts ola_mES_2i_5_11.counts
#> NA NA NA NA
#> ola_mES_2i_5_12.counts ola_mES_2i_5_13.counts ola_mES_2i_5_14.counts ola_mES_2i_5_15.counts
#> NA NA NA NA
#> ola_mES_2i_5_16.counts ola_mES_2i_5_17.counts ola_mES_2i_5_18.counts ola_mES_2i_5_19.counts
#> NA NA NA NA
#> ola_mES_2i_5_2.counts ola_mES_2i_5_20.counts ola_mES_2i_5_21.counts ola_mES_2i_5_23.counts
#> NA NA NA NA
#> ola_mES_2i_5_24.counts ola_mES_2i_5_25.counts ola_mES_2i_5_26.counts ola_mES_2i_5_27.counts
#> NA NA NA NA
#> ola_mES_2i_5_28.counts ola_mES_2i_5_29.counts ola_mES_2i_5_3.counts ola_mES_2i_5_30.counts
#> NA NA NA NA
#> ola_mES_2i_5_31.counts ola_mES_2i_5_32.counts ola_mES_2i_5_34.counts ola_mES_2i_5_35.counts
#> NA NA NA NA
#> ola_mES_2i_5_36.counts ola_mES_2i_5_38.counts ola_mES_2i_5_39.counts ola_mES_2i_5_4.counts
#> NA NA NA NA
#> ola_mES_2i_5_40.counts ola_mES_2i_5_41.counts ola_mES_2i_5_42.counts ola_mES_2i_5_44.counts
#> NA NA NA NA
#> ola_mES_2i_5_45.counts ola_mES_2i_5_46.counts ola_mES_2i_5_47.counts ola_mES_2i_5_48.counts
#> NA NA NA NA
#> ola_mES_2i_5_49.counts ola_mES_2i_5_5.counts ola_mES_2i_5_51.counts ola_mES_2i_5_52.counts
#> NA NA NA NA
#> ola_mES_2i_5_53.counts ola_mES_2i_5_54.counts ola_mES_2i_5_55.counts ola_mES_2i_5_56.counts
#> NA NA NA NA
#> ola_mES_2i_5_57.counts ola_mES_2i_5_58.counts ola_mES_2i_5_59.counts ola_mES_2i_5_6.counts
#> NA NA NA NA
#> ola_mES_2i_5_61.counts ola_mES_2i_5_62.counts ola_mES_2i_5_63.counts ola_mES_2i_5_65.counts
#> NA NA NA NA
#> ola_mES_2i_5_66.counts ola_mES_2i_5_67.counts ola_mES_2i_5_68.counts ola_mES_2i_5_69.counts
#> NA NA NA NA
#> ola_mES_2i_5_7.counts ola_mES_2i_5_70.counts ola_mES_2i_5_72.counts ola_mES_2i_5_73.counts
#> NA NA NA NA
#> ola_mES_2i_5_74.counts ola_mES_2i_5_75.counts ola_mES_2i_5_76.counts ola_mES_2i_5_77.counts
#> NA NA NA NA
#> ola_mES_2i_5_78.counts ola_mES_2i_5_8.counts ola_mES_2i_5_81.counts ola_mES_2i_5_82.counts
#> NA NA NA NA
#> ola_mES_2i_5_83.counts ola_mES_2i_5_85.counts ola_mES_2i_5_86.counts ola_mES_2i_5_87.counts
#> NA NA NA NA
#> ola_mES_2i_5_88.counts ola_mES_2i_5_9.counts ola_mES_2i_5_90.counts ola_mES_2i_5_92.counts
#> NA NA NA NA
#> ola_mES_2i_5_94.counts ola_mES_2i_5_95.counts ola_mES_2i_5_96.counts ola_mES_a2i_2_1.counts
#> NA NA NA NA
#> ola_mES_a2i_2_10.counts ola_mES_a2i_2_11.counts ola_mES_a2i_2_12.counts ola_mES_a2i_2_13.counts
#> NA NA NA NA
#> ola_mES_a2i_2_14.counts ola_mES_a2i_2_15.counts ola_mES_a2i_2_16.counts ola_mES_a2i_2_17.counts
#> NA NA NA NA
#> ola_mES_a2i_2_18.counts ola_mES_a2i_2_19.counts ola_mES_a2i_2_2.counts ola_mES_a2i_2_20.counts
#> NA NA NA NA
#> ola_mES_a2i_2_21.counts ola_mES_a2i_2_22.counts ola_mES_a2i_2_23.counts ola_mES_a2i_2_24.counts
#> NA NA NA NA
#> ola_mES_a2i_2_25.counts ola_mES_a2i_2_26.counts ola_mES_a2i_2_27.counts ola_mES_a2i_2_28.counts
#> NA NA NA NA
#> ola_mES_a2i_2_29.counts ola_mES_a2i_2_3.counts ola_mES_a2i_2_30.counts ola_mES_a2i_2_31.counts
#> NA NA NA NA
#> ola_mES_a2i_2_32.counts ola_mES_a2i_2_33.counts ola_mES_a2i_2_34.counts ola_mES_a2i_2_35.counts
#> NA NA NA NA
#> ola_mES_a2i_2_36.counts ola_mES_a2i_2_37.counts ola_mES_a2i_2_38.counts ola_mES_a2i_2_39.counts
#> NA NA NA NA
#> ola_mES_a2i_2_4.counts ola_mES_a2i_2_40.counts ola_mES_a2i_2_41.counts ola_mES_a2i_2_42.counts
#> NA NA NA NA
#> ola_mES_a2i_2_43.counts ola_mES_a2i_2_44.counts ola_mES_a2i_2_45.counts ola_mES_a2i_2_46.counts
#> NA NA NA NA
#> ola_mES_a2i_2_47.counts ola_mES_a2i_2_48.counts ola_mES_a2i_2_49.counts ola_mES_a2i_2_5.counts
#> NA NA NA NA
#> ola_mES_a2i_2_50.counts ola_mES_a2i_2_51.counts ola_mES_a2i_2_52.counts ola_mES_a2i_2_53.counts
#> NA NA NA NA
#> ola_mES_a2i_2_54.counts ola_mES_a2i_2_55.counts ola_mES_a2i_2_56.counts ola_mES_a2i_2_57.counts
#> NA NA NA NA
#> ola_mES_a2i_2_59.counts ola_mES_a2i_2_6.counts ola_mES_a2i_2_60.counts ola_mES_a2i_2_61.counts
#> NA NA NA NA
#> ola_mES_a2i_2_62.counts ola_mES_a2i_2_63.counts ola_mES_a2i_2_64.counts ola_mES_a2i_2_65.counts
#> NA NA NA NA
#> ola_mES_a2i_2_66.counts ola_mES_a2i_2_67.counts ola_mES_a2i_2_68.counts ola_mES_a2i_2_69.counts
#> NA NA NA NA
#> ola_mES_a2i_2_7.counts ola_mES_a2i_2_70.counts ola_mES_a2i_2_71.counts ola_mES_a2i_2_72.counts
#> NA NA NA NA
#> ola_mES_a2i_2_73.counts ola_mES_a2i_2_74.counts ola_mES_a2i_2_75.counts ola_mES_a2i_2_76.counts
#> NA NA NA NA
#> ola_mES_a2i_2_77.counts ola_mES_a2i_2_78.counts ola_mES_a2i_2_8.counts ola_mES_a2i_2_80.counts
#> NA NA NA NA
#> ola_mES_a2i_2_81.counts ola_mES_a2i_2_82.counts ola_mES_a2i_2_83.counts ola_mES_a2i_2_84.counts
#> NA NA NA NA
#> ola_mES_a2i_2_86.counts ola_mES_a2i_2_87.counts ola_mES_a2i_2_88.counts ola_mES_a2i_2_89.counts
#> NA NA NA NA
#> ola_mES_a2i_2_9.counts ola_mES_a2i_2_90.counts ola_mES_a2i_2_91.counts ola_mES_a2i_2_92.counts
#> NA NA NA NA
#> ola_mES_a2i_2_93.counts ola_mES_a2i_2_94.counts ola_mES_a2i_2_95.counts ola_mES_a2i_2_96.counts
#> NA NA NA NA
#> ola_mES_a2i_3_10.counts ola_mES_a2i_3_11.counts ola_mES_a2i_3_13.counts ola_mES_a2i_3_16.counts
#> NA NA NA NA
#> ola_mES_a2i_3_17.counts ola_mES_a2i_3_18.counts ola_mES_a2i_3_2.counts ola_mES_a2i_3_20.counts
#> NA NA NA NA
#> ola_mES_a2i_3_21.counts ola_mES_a2i_3_22.counts ola_mES_a2i_3_24.counts ola_mES_a2i_3_25.counts
#> NA NA NA NA
#> ola_mES_a2i_3_26.counts ola_mES_a2i_3_28.counts ola_mES_a2i_3_29.counts ola_mES_a2i_3_3.counts
#> NA NA NA NA
#> ola_mES_a2i_3_30.counts ola_mES_a2i_3_31.counts ola_mES_a2i_3_33.counts ola_mES_a2i_3_34.counts
#> NA NA NA NA
#> ola_mES_a2i_3_37.counts ola_mES_a2i_3_38.counts ola_mES_a2i_3_39.counts ola_mES_a2i_3_4.counts
#> NA NA NA NA
#> ola_mES_a2i_3_40.counts ola_mES_a2i_3_41.counts ola_mES_a2i_3_42.counts ola_mES_a2i_3_44.counts
#> NA NA NA NA
#> ola_mES_a2i_3_45.counts ola_mES_a2i_3_47.counts ola_mES_a2i_3_49.counts ola_mES_a2i_3_50.counts
#> NA NA NA NA
#> ola_mES_a2i_3_52.counts ola_mES_a2i_3_54.counts ola_mES_a2i_3_56.counts ola_mES_a2i_3_58.counts
#> NA NA NA NA
#> ola_mES_a2i_3_59.counts ola_mES_a2i_3_6.counts ola_mES_a2i_3_62.counts ola_mES_a2i_3_64.counts
#> NA NA NA NA
#> ola_mES_a2i_3_65.counts ola_mES_a2i_3_66.counts ola_mES_a2i_3_67.counts ola_mES_a2i_3_68.counts
#> NA NA NA NA
#> ola_mES_a2i_3_69.counts ola_mES_a2i_3_7.counts ola_mES_a2i_3_71.counts ola_mES_a2i_3_73.counts
#> NA NA NA NA
#> ola_mES_a2i_3_75.counts ola_mES_a2i_3_76.counts ola_mES_a2i_3_77.counts ola_mES_a2i_3_8.counts
#> NA NA NA NA
#> ola_mES_a2i_3_80.counts ola_mES_a2i_3_81.counts ola_mES_a2i_3_82.counts ola_mES_a2i_3_83.counts
#> NA NA NA NA
#> ola_mES_a2i_3_84.counts ola_mES_a2i_3_85.counts ola_mES_a2i_3_86.counts ola_mES_a2i_3_89.counts
#> NA NA NA NA
#> ola_mES_a2i_3_9.counts ola_mES_a2i_3_90.counts ola_mES_a2i_3_91.counts ola_mES_a2i_3_92.counts
#> NA NA NA NA
#> ola_mES_a2i_3_94.counts ola_mES_a2i_3_96.counts ola_mES_lif_1_1.counts ola_mES_lif_1_10.counts
#> NA NA NA NA
#> ola_mES_lif_1_11.counts ola_mES_lif_1_12.counts ola_mES_lif_1_13.counts ola_mES_lif_1_14.counts
#> NA NA NA NA
#> ola_mES_lif_1_15.counts ola_mES_lif_1_17.counts ola_mES_lif_1_18.counts ola_mES_lif_1_19.counts
#> NA NA NA NA
#> ola_mES_lif_1_2.counts ola_mES_lif_1_20.counts ola_mES_lif_1_21.counts ola_mES_lif_1_22.counts
#> NA NA NA NA
#> ola_mES_lif_1_23.counts ola_mES_lif_1_24.counts ola_mES_lif_1_25.counts ola_mES_lif_1_26.counts
#> NA NA NA NA
#> ola_mES_lif_1_28.counts ola_mES_lif_1_29.counts ola_mES_lif_1_3.counts ola_mES_lif_1_30.counts
#> NA NA NA NA
#> ola_mES_lif_1_31.counts ola_mES_lif_1_32.counts ola_mES_lif_1_33.counts ola_mES_lif_1_34.counts
#> NA NA NA NA
#> ola_mES_lif_1_35.counts ola_mES_lif_1_36.counts ola_mES_lif_1_37.counts ola_mES_lif_1_38.counts
#> NA NA NA NA
#> ola_mES_lif_1_39.counts ola_mES_lif_1_4.counts ola_mES_lif_1_41.counts ola_mES_lif_1_42.counts
#> NA NA NA NA
#> ola_mES_lif_1_43.counts ola_mES_lif_1_44.counts ola_mES_lif_1_45.counts ola_mES_lif_1_46.counts
#> NA NA NA NA
#> ola_mES_lif_1_47.counts ola_mES_lif_1_48.counts ola_mES_lif_1_49.counts ola_mES_lif_1_5.counts
#> NA NA NA NA
#> ola_mES_lif_1_50.counts ola_mES_lif_1_51.counts ola_mES_lif_1_52.counts ola_mES_lif_1_53.counts
#> NA NA NA NA
#> ola_mES_lif_1_54.counts ola_mES_lif_1_55.counts ola_mES_lif_1_56.counts ola_mES_lif_1_57.counts
#> NA NA NA NA
#> ola_mES_lif_1_58.counts ola_mES_lif_1_59.counts ola_mES_lif_1_6.counts ola_mES_lif_1_60.counts
#> NA NA NA NA
#> ola_mES_lif_1_61.counts ola_mES_lif_1_62.counts ola_mES_lif_1_63.counts ola_mES_lif_1_64.counts
#> NA NA NA NA
#> ola_mES_lif_1_65.counts ola_mES_lif_1_66.counts ola_mES_lif_1_67.counts ola_mES_lif_1_68.counts
#> NA NA NA NA
#> ola_mES_lif_1_69.counts ola_mES_lif_1_7.counts ola_mES_lif_1_70.counts ola_mES_lif_1_72.counts
#> NA NA NA NA
#> ola_mES_lif_1_73.counts ola_mES_lif_1_74.counts ola_mES_lif_1_75.counts ola_mES_lif_1_79.counts
#> NA NA NA NA
#> ola_mES_lif_1_8.counts ola_mES_lif_1_80.counts ola_mES_lif_1_81.counts ola_mES_lif_1_82.counts
#> NA NA NA NA
#> ola_mES_lif_1_86.counts ola_mES_lif_1_9.counts ola_mES_lif_1_91.counts ola_mES_lif_1_92.counts
#> NA NA NA NA
#> ola_mES_lif_1_93.counts ola_mES_lif_1_95.counts ola_mES_lif_1_96.counts ola_mES_lif_2_1.counts
#> NA NA NA NA
#> ola_mES_lif_2_10.counts ola_mES_lif_2_11.counts ola_mES_lif_2_12.counts ola_mES_lif_2_13.counts
#> NA NA NA NA
#> ola_mES_lif_2_14.counts ola_mES_lif_2_15.counts ola_mES_lif_2_16.counts ola_mES_lif_2_17.counts
#> NA NA NA NA
#> ola_mES_lif_2_18.counts ola_mES_lif_2_19.counts ola_mES_lif_2_2.counts ola_mES_lif_2_20.counts
#> NA NA NA NA
#> ola_mES_lif_2_21.counts ola_mES_lif_2_23.counts ola_mES_lif_2_25.counts ola_mES_lif_2_26.counts
#> NA NA NA NA
#> ola_mES_lif_2_27.counts ola_mES_lif_2_28.counts ola_mES_lif_2_29.counts ola_mES_lif_2_3.counts
#> NA NA NA NA
#> ola_mES_lif_2_30.counts ola_mES_lif_2_31.counts ola_mES_lif_2_32.counts ola_mES_lif_2_33.counts
#> NA NA NA NA
#> ola_mES_lif_2_34.counts ola_mES_lif_2_35.counts ola_mES_lif_2_36.counts ola_mES_lif_2_37.counts
#> NA NA NA NA
#> ola_mES_lif_2_38.counts ola_mES_lif_2_39.counts ola_mES_lif_2_4.counts ola_mES_lif_2_40.counts
#> NA NA NA NA
#> ola_mES_lif_2_41.counts ola_mES_lif_2_42.counts ola_mES_lif_2_43.counts ola_mES_lif_2_44.counts
#> NA NA NA NA
#> ola_mES_lif_2_45.counts ola_mES_lif_2_46.counts ola_mES_lif_2_47.counts ola_mES_lif_2_48.counts
#> NA NA NA NA
#> ola_mES_lif_2_49.counts ola_mES_lif_2_5.counts ola_mES_lif_2_50.counts ola_mES_lif_2_51.counts
#> NA NA NA NA
#> ola_mES_lif_2_52.counts ola_mES_lif_2_53.counts ola_mES_lif_2_54.counts ola_mES_lif_2_55.counts
#> NA NA NA NA
#> ola_mES_lif_2_56.counts ola_mES_lif_2_57.counts ola_mES_lif_2_58.counts ola_mES_lif_2_59.counts
#> NA NA NA NA
#> ola_mES_lif_2_6.counts ola_mES_lif_2_60.counts ola_mES_lif_2_61.counts ola_mES_lif_2_63.counts
#> NA NA NA NA
#> ola_mES_lif_2_64.counts ola_mES_lif_2_65.counts ola_mES_lif_2_66.counts ola_mES_lif_2_67.counts
#> NA NA NA NA
#> ola_mES_lif_2_68.counts ola_mES_lif_2_69.counts ola_mES_lif_2_7.counts ola_mES_lif_2_70.counts
#> NA NA NA NA
#> ola_mES_lif_2_71.counts ola_mES_lif_2_72.counts ola_mES_lif_2_73.counts ola_mES_lif_2_74.counts
#> NA NA NA NA
#> ola_mES_lif_2_75.counts ola_mES_lif_2_76.counts ola_mES_lif_2_77.counts ola_mES_lif_2_79.counts
#> NA NA NA NA
#> ola_mES_lif_2_8.counts ola_mES_lif_2_80.counts ola_mES_lif_2_81.counts ola_mES_lif_2_82.counts
#> NA NA NA NA
#> ola_mES_lif_2_83.counts ola_mES_lif_2_84.counts ola_mES_lif_2_86.counts ola_mES_lif_2_87.counts
#> NA NA NA NA
#> ola_mES_lif_2_89.counts ola_mES_lif_2_9.counts ola_mES_lif_2_90.counts ola_mES_lif_2_91.counts
#> NA NA NA NA
#> ola_mES_lif_2_92.counts ola_mES_lif_2_93.counts ola_mES_lif_2_94.counts ola_mES_lif_2_95.counts
#> NA NA NA NA
#> ola_mES_lif_2_96.counts ola_mES_lif_3_1.counts ola_mES_lif_3_10.counts ola_mES_lif_3_11.counts
#> NA NA NA NA
#> ola_mES_lif_3_12.counts ola_mES_lif_3_13.counts ola_mES_lif_3_14.counts ola_mES_lif_3_15.counts
#> NA NA NA NA
#> ola_mES_lif_3_16.counts ola_mES_lif_3_17.counts ola_mES_lif_3_19.counts ola_mES_lif_3_2.counts
#> NA NA NA NA
#> ola_mES_lif_3_20.counts ola_mES_lif_3_21.counts ola_mES_lif_3_22.counts ola_mES_lif_3_23.counts
#> NA NA NA NA
#> ola_mES_lif_3_24.counts ola_mES_lif_3_26.counts ola_mES_lif_3_27.counts ola_mES_lif_3_28.counts
#> NA NA NA NA
#> ola_mES_lif_3_29.counts ola_mES_lif_3_3.counts ola_mES_lif_3_30.counts ola_mES_lif_3_31.counts
#> NA NA NA NA
#> ola_mES_lif_3_32.counts ola_mES_lif_3_33.counts ola_mES_lif_3_34.counts ola_mES_lif_3_35.counts
#> NA NA NA NA
#> ola_mES_lif_3_36.counts ola_mES_lif_3_37.counts ola_mES_lif_3_38.counts ola_mES_lif_3_39.counts
#> NA NA NA NA
#> ola_mES_lif_3_4.counts ola_mES_lif_3_40.counts ola_mES_lif_3_42.counts ola_mES_lif_3_45.counts
#> NA NA NA NA
#> ola_mES_lif_3_46.counts ola_mES_lif_3_47.counts ola_mES_lif_3_48.counts ola_mES_lif_3_49.counts
#> NA NA NA NA
#> ola_mES_lif_3_5.counts ola_mES_lif_3_50.counts ola_mES_lif_3_51.counts ola_mES_lif_3_52.counts
#> NA NA NA NA
#> ola_mES_lif_3_53.counts ola_mES_lif_3_54.counts ola_mES_lif_3_56.counts ola_mES_lif_3_57.counts
#> NA NA NA NA
#> ola_mES_lif_3_58.counts ola_mES_lif_3_59.counts ola_mES_lif_3_6.counts ola_mES_lif_3_60.counts
#> NA NA NA NA
#> ola_mES_lif_3_61.counts ola_mES_lif_3_63.counts ola_mES_lif_3_64.counts ola_mES_lif_3_65.counts
#> NA NA NA NA
#> ola_mES_lif_3_69.counts ola_mES_lif_3_70.counts ola_mES_lif_3_71.counts ola_mES_lif_3_72.counts
#> NA NA NA NA
#> ola_mES_lif_3_73.counts ola_mES_lif_3_74.counts ola_mES_lif_3_75.counts ola_mES_lif_3_76.counts
#> NA NA NA NA
#> ola_mES_lif_3_77.counts ola_mES_lif_3_78.counts ola_mES_lif_3_8.counts ola_mES_lif_3_80.counts
#> NA NA NA NA
#> ola_mES_lif_3_82.counts ola_mES_lif_3_83.counts ola_mES_lif_3_84.counts ola_mES_lif_3_87.counts
#> NA NA NA NA
#> ola_mES_lif_3_88.counts ola_mES_lif_3_89.counts ola_mES_lif_3_9.counts ola_mES_lif_3_90.counts
#> NA NA NA NA
#> ola_mES_lif_3_92.counts ola_mES_lif_3_94.counts ola_mES_lif_3_95.counts ola_mES_lif_3_96.counts
#> NA NA NA NA
Heatmaps of the top rows:
top_rows_heatmap(res_rh)
#> Error in h(simpleError(msg, call)) :
#> error in evaluating the argument 'object' in selecting a method for function 'draw': no applicable method for 'height' applied to an object of class "list"
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 = 323),
method = "UMAP", top_value_method = "SD", top_n = 2000, scale_rows = FALSE)
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 323),
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 = 342),
method = "UMAP", top_value_method = "SD", top_n = 2000, scale_rows = FALSE)
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 342),
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 = 404),
method = "UMAP", top_value_method = "SD", top_n = 2000, scale_rows = FALSE)
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 404),
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 = 423),
method = "UMAP", top_value_method = "SD", top_n = 2000, scale_rows = FALSE)
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 423),
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 = 430),
method = "UMAP", top_value_method = "SD", top_n = 2000, scale_rows = FALSE)
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 430),
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 = 447),
method = "UMAP", top_value_method = "SD", top_n = 2000, scale_rows = FALSE)
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 447),
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 = 466),
method = "UMAP", top_value_method = "SD", top_n = 2000, scale_rows = FALSE)
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 466),
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 = 487),
method = "UMAP", top_value_method = "SD", top_n = 2000, scale_rows = FALSE)
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 487),
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 = 517),
method = "UMAP", top_value_method = "SD", top_n = 2000, scale_rows = FALSE)
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 517),
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 = 682),
method = "UMAP", top_value_method = "SD", top_n = 2000, scale_rows = FALSE)
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 682),
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 = 1173),
method = "UMAP", top_value_method = "SD", top_n = 2000, scale_rows = FALSE)
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 1173),
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 = 2396),
method = "UMAP", top_value_method = "SD", top_n = 2000, scale_rows = FALSE)
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 2396),
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 = 2997),
method = "UMAP", top_value_method = "SD", top_n = 2000, scale_rows = FALSE)
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 2997),
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 = 3417),
method = "UMAP", top_value_method = "SD", top_n = 2000, scale_rows = FALSE)
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 3417),
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 = 4355),
method = "UMAP", top_value_method = "SD", top_n = 2000, scale_rows = FALSE)
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 4355),
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 = 5424),
method = "UMAP", top_value_method = "SD", top_n = 2000, scale_rows = FALSE)
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 5424),
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 = 6248),
method = "UMAP", top_value_method = "SD", top_n = 2000, scale_rows = FALSE)
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 6248),
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 = 7328),
method = "UMAP", top_value_method = "SD", top_n = 2000, scale_rows = FALSE)
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 7328),
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 = 8673),
method = "UMAP", top_value_method = "SD", top_n = 2000, scale_rows = FALSE)
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 8673),
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 = 323))
get_signatures(res_rh, merge_node = merge_node_param(min_n_signatures = 342))
get_signatures(res_rh, merge_node = merge_node_param(min_n_signatures = 404))
get_signatures(res_rh, merge_node = merge_node_param(min_n_signatures = 423))
get_signatures(res_rh, merge_node = merge_node_param(min_n_signatures = 430))
get_signatures(res_rh, merge_node = merge_node_param(min_n_signatures = 447))
get_signatures(res_rh, merge_node = merge_node_param(min_n_signatures = 466))
get_signatures(res_rh, merge_node = merge_node_param(min_n_signatures = 487))
get_signatures(res_rh, merge_node = merge_node_param(min_n_signatures = 517))
get_signatures(res_rh, merge_node = merge_node_param(min_n_signatures = 682))
get_signatures(res_rh, merge_node = merge_node_param(min_n_signatures = 1173))
get_signatures(res_rh, merge_node = merge_node_param(min_n_signatures = 2396))
get_signatures(res_rh, merge_node = merge_node_param(min_n_signatures = 2997))
get_signatures(res_rh, merge_node = merge_node_param(min_n_signatures = 3417))
get_signatures(res_rh, merge_node = merge_node_param(min_n_signatures = 4355))
get_signatures(res_rh, merge_node = merge_node_param(min_n_signatures = 5424))
get_signatures(res_rh, merge_node = merge_node_param(min_n_signatures = 6248))
get_signatures(res_rh, merge_node = merge_node_param(min_n_signatures = 7328))
#> 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 = 8673))
#> 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)
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 17177 rows and 500 columns, randomly sampled from 704 columns.
#> Top rows (1628) 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 0.997 0.999 0.500 0.500 0.500
#> 3 3 1.000 0.970 0.986 0.217 0.873 0.750
#> 4 4 0.807 0.898 0.938 0.135 0.855 0.653
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
#> ola_mES_2i_2_1.counts 1 0.000
#> ola_mES_2i_2_10.counts 1 0.000
#> ola_mES_2i_2_11.counts 1 0.000
#> ola_mES_2i_2_12.counts 1 0.000
#> ola_mES_2i_2_13.counts 1 0.000
#> ola_mES_2i_2_14.counts 1 0.000
#> ola_mES_2i_2_15.counts 1 0.000
#> ola_mES_2i_2_16.counts 1 0.000
#> ola_mES_2i_2_17.counts 1 0.000
#> ola_mES_2i_2_18.counts 1 0.000
#> ola_mES_2i_2_19.counts 1 0.000
#> ola_mES_2i_2_2.counts 1 0.000
#> ola_mES_2i_2_20.counts 1 0.000
#> ola_mES_2i_2_21.counts 1 0.000
#> ola_mES_2i_2_22.counts 1 0.000
#> ola_mES_2i_2_23.counts 1 0.000
#> ola_mES_2i_2_24.counts 1 0.000
#> ola_mES_2i_2_25.counts 1 0.000
#> ola_mES_2i_2_26.counts 1 0.000
#> ola_mES_2i_2_27.counts 1 0.000
#> ola_mES_2i_2_28.counts 1 0.000
#> ola_mES_2i_2_29.counts 1 0.000
#> ola_mES_2i_2_3.counts 1 0.000
#> ola_mES_2i_2_30.counts 1 0.000
#> ola_mES_2i_2_31.counts 1 0.000
#> ola_mES_2i_2_32.counts 1 0.000
#> ola_mES_2i_2_33.counts 1 0.000
#> ola_mES_2i_2_34.counts 1 0.000
#> ola_mES_2i_2_35.counts 1 0.000
#> ola_mES_2i_2_36.counts 1 0.000
#> ola_mES_2i_2_37.counts 1 0.000
#> ola_mES_2i_2_38.counts 1 0.000
#> ola_mES_2i_2_39.counts 1 0.000
#> ola_mES_2i_2_4.counts 1 0.000
#> ola_mES_2i_2_40.counts 1 0.000
#> ola_mES_2i_2_41.counts 1 0.000
#> ola_mES_2i_2_42.counts 1 0.000
#> ola_mES_2i_2_43.counts 1 0.000
#> ola_mES_2i_2_44.counts 1 0.000
#> ola_mES_2i_2_46.counts 1 0.000
#> ola_mES_2i_2_47.counts 1 0.000
#> ola_mES_2i_2_48.counts 1 0.000
#> ola_mES_2i_2_49.counts 1 0.000
#> ola_mES_2i_2_5.counts 1 0.000
#> ola_mES_2i_2_50.counts 1 0.000
#> ola_mES_2i_2_51.counts 1 0.000
#> ola_mES_2i_2_52.counts 1 0.000
#> ola_mES_2i_2_53.counts 1 0.000
#> ola_mES_2i_2_54.counts 1 0.000
#> ola_mES_2i_2_55.counts 1 0.000
#> ola_mES_2i_2_56.counts 1 0.000
#> ola_mES_2i_2_57.counts 1 0.000
#> ola_mES_2i_2_58.counts 1 0.000
#> ola_mES_2i_2_59.counts 1 0.000
#> ola_mES_2i_2_6.counts 1 0.000
#> ola_mES_2i_2_60.counts 1 0.000
#> ola_mES_2i_2_61.counts 1 0.000
#> ola_mES_2i_2_62.counts 1 0.000
#> ola_mES_2i_2_63.counts 1 0.000
#> ola_mES_2i_2_64.counts 1 0.000
#> ola_mES_2i_2_66.counts 1 0.000
#> ola_mES_2i_2_67.counts 1 0.000
#> ola_mES_2i_2_68.counts 1 0.000
#> ola_mES_2i_2_69.counts 1 0.000
#> ola_mES_2i_2_7.counts 1 0.000
#> ola_mES_2i_2_72.counts 1 0.000
#> ola_mES_2i_2_73.counts 1 0.000
#> ola_mES_2i_2_74.counts 1 0.000
#> ola_mES_2i_2_75.counts 1 0.000
#> ola_mES_2i_2_76.counts 1 0.000
#> ola_mES_2i_2_79.counts 1 0.000
#> ola_mES_2i_2_8.counts 1 0.000
#> ola_mES_2i_2_80.counts 1 0.000
#> ola_mES_2i_2_81.counts 1 0.000
#> ola_mES_2i_2_85.counts 1 0.000
#> ola_mES_2i_2_86.counts 1 0.498
#> ola_mES_2i_2_87.counts 1 0.000
#> ola_mES_2i_2_90.counts 1 0.000
#> ola_mES_2i_2_91.counts 1 0.751
#> ola_mES_2i_2_93.counts 1 0.000
#> ola_mES_2i_2_94.counts 1 0.000
#> ola_mES_2i_2_95.counts 1 0.000
#> ola_mES_2i_3_10.counts 2 1.000
#> ola_mES_2i_3_11.counts 2 0.000
#> ola_mES_2i_3_12.counts 2 0.000
#> ola_mES_2i_3_13.counts 2 0.000
#> ola_mES_2i_3_15.counts 2 0.000
#> ola_mES_2i_3_16.counts 2 0.000
#> ola_mES_2i_3_17.counts 2 1.000
#> ola_mES_2i_3_18.counts 1 0.000
#> ola_mES_2i_3_19.counts 2 0.000
#> ola_mES_2i_3_2.counts 2 0.502
#> ola_mES_2i_3_20.counts 2 0.000
#> ola_mES_2i_3_21.counts 2 1.000
#> ola_mES_2i_3_23.counts 2 0.253
#> ola_mES_2i_3_25.counts 2 0.253
#> ola_mES_2i_3_26.counts 2 0.000
#> ola_mES_2i_3_28.counts 2 0.000
#> ola_mES_2i_3_30.counts 2 0.000
#> ola_mES_2i_3_31.counts 2 0.751
#> ola_mES_2i_3_33.counts 2 0.249
#> ola_mES_2i_3_36.counts 2 0.000
#> ola_mES_2i_3_37.counts 2 0.751
#> ola_mES_2i_3_38.counts 2 0.253
#> ola_mES_2i_3_39.counts 2 0.751
#> ola_mES_2i_3_4.counts 2 0.751
#> ola_mES_2i_3_41.counts 2 0.000
#> ola_mES_2i_3_42.counts 2 0.498
#> ola_mES_2i_3_43.counts 2 0.253
#> ola_mES_2i_3_45.counts 2 0.000
#> ola_mES_2i_3_46.counts 2 0.000
#> ola_mES_2i_3_47.counts 2 0.253
#> ola_mES_2i_3_49.counts 2 0.000
#> ola_mES_2i_3_5.counts 2 0.000
#> ola_mES_2i_3_52.counts 2 0.000
#> ola_mES_2i_3_54.counts 2 0.751
#> ola_mES_2i_3_56.counts 2 0.000
#> ola_mES_2i_3_57.counts 2 0.000
#> ola_mES_2i_3_58.counts 2 0.249
#> ola_mES_2i_3_6.counts 2 0.000
#> ola_mES_2i_3_62.counts 2 1.000
#> ola_mES_2i_3_65.counts 2 0.000
#> ola_mES_2i_3_66.counts 2 1.000
#> ola_mES_2i_3_69.counts 2 0.249
#> ola_mES_2i_3_7.counts 2 0.000
#> ola_mES_2i_3_73.counts 2 0.000
#> ola_mES_2i_3_76.counts 2 0.253
#> ola_mES_2i_3_77.counts 2 0.000
#> ola_mES_2i_3_78.counts 2 1.000
#> ola_mES_2i_3_79.counts 2 0.000
#> ola_mES_2i_3_8.counts 2 0.000
#> ola_mES_2i_3_80.counts 2 0.751
#> ola_mES_2i_3_82.counts 2 0.000
#> ola_mES_2i_3_83.counts 2 0.498
#> ola_mES_2i_3_84.counts 2 0.000
#> ola_mES_2i_3_87.counts 2 0.000
#> ola_mES_2i_3_88.counts 2 1.000
#> ola_mES_2i_3_90.counts 2 1.000
#> ola_mES_2i_3_91.counts 2 0.000
#> ola_mES_2i_3_92.counts 2 0.000
#> ola_mES_2i_3_96.counts 2 0.000
#> ola_mES_2i_4_1.counts 2 0.000
#> ola_mES_2i_4_10.counts 2 0.000
#> ola_mES_2i_4_11.counts 2 0.249
#> ola_mES_2i_4_13.counts 2 0.000
#> ola_mES_2i_4_17.counts 2 0.000
#> ola_mES_2i_4_18.counts 2 0.000
#> ola_mES_2i_4_2.counts 1 0.751
#> ola_mES_2i_4_20.counts 2 0.000
#> ola_mES_2i_4_21.counts 2 1.000
#> ola_mES_2i_4_22.counts 2 0.000
#> ola_mES_2i_4_23.counts 2 0.000
#> ola_mES_2i_4_24.counts 2 0.000
#> ola_mES_2i_4_25.counts 2 0.751
#> ola_mES_2i_4_26.counts 2 0.253
#> ola_mES_2i_4_27.counts 2 0.000
#> ola_mES_2i_4_29.counts 2 0.249
#> ola_mES_2i_4_30.counts 2 0.000
#> ola_mES_2i_4_31.counts 2 1.000
#> ola_mES_2i_4_32.counts 2 1.000
#> ola_mES_2i_4_33.counts 2 1.000
#> ola_mES_2i_4_34.counts 2 0.000
#> ola_mES_2i_4_35.counts 2 0.751
#> ola_mES_2i_4_36.counts 2 0.000
#> ola_mES_2i_4_37.counts 1 0.249
#> ola_mES_2i_4_38.counts 1 0.249
#> ola_mES_2i_4_39.counts 2 0.751
#> ola_mES_2i_4_40.counts 1 0.498
#> ola_mES_2i_4_42.counts 2 0.253
#> ola_mES_2i_4_43.counts 2 0.751
#> ola_mES_2i_4_44.counts 2 1.000
#> ola_mES_2i_4_46.counts 2 0.000
#> ola_mES_2i_4_49.counts 1 0.000
#> ola_mES_2i_4_5.counts 2 0.000
#> ola_mES_2i_4_50.counts 2 0.000
#> ola_mES_2i_4_52.counts 2 1.000
#> ola_mES_2i_4_53.counts 1 0.249
#> ola_mES_2i_4_54.counts 2 0.253
#> ola_mES_2i_4_56.counts 2 0.253
#> ola_mES_2i_4_57.counts 2 0.000
#> ola_mES_2i_4_58.counts 2 0.253
#> ola_mES_2i_4_59.counts 2 0.000
#> ola_mES_2i_4_6.counts 2 0.000
#> ola_mES_2i_4_60.counts 2 0.000
#> ola_mES_2i_4_61.counts 2 0.000
#> ola_mES_2i_4_62.counts 1 0.498
#> ola_mES_2i_4_63.counts 2 0.000
#> ola_mES_2i_4_64.counts 2 0.502
#> ola_mES_2i_4_65.counts 2 0.000
#> ola_mES_2i_4_66.counts 2 0.253
#> ola_mES_2i_4_67.counts 1 0.751
#> ola_mES_2i_4_68.counts 2 0.249
#> ola_mES_2i_4_69.counts 2 0.000
#> ola_mES_2i_4_7.counts 2 0.000
#> ola_mES_2i_4_70.counts 2 0.000
#> ola_mES_2i_4_71.counts 2 0.000
#> ola_mES_2i_4_72.counts 2 0.000
#> ola_mES_2i_4_73.counts 2 0.751
#> ola_mES_2i_4_75.counts 2 1.000
#> ola_mES_2i_4_77.counts 2 0.000
#> ola_mES_2i_4_78.counts 2 0.000
#> ola_mES_2i_4_80.counts 2 1.000
#> ola_mES_2i_4_81.counts 2 0.000
#> ola_mES_2i_4_83.counts 2 0.000
#> ola_mES_2i_4_84.counts 2 0.000
#> ola_mES_2i_4_88.counts 2 0.000
#> ola_mES_2i_4_89.counts 2 0.000
#> ola_mES_2i_4_9.counts 2 0.000
#> ola_mES_2i_4_90.counts 2 0.249
#> ola_mES_2i_4_91.counts 2 1.000
#> ola_mES_2i_4_94.counts 2 0.000
#> ola_mES_2i_4_95.counts 2 1.000
#> ola_mES_2i_4_96.counts 2 0.000
#> ola_mES_2i_5_1.counts 2 0.249
#> ola_mES_2i_5_10.counts 2 0.000
#> ola_mES_2i_5_11.counts 2 0.000
#> ola_mES_2i_5_12.counts 2 0.000
#> ola_mES_2i_5_13.counts 2 0.000
#> ola_mES_2i_5_14.counts 2 0.249
#> ola_mES_2i_5_15.counts 2 0.000
#> ola_mES_2i_5_16.counts 2 0.000
#> ola_mES_2i_5_17.counts 1 0.253
#> ola_mES_2i_5_18.counts 2 0.000
#> ola_mES_2i_5_19.counts 2 0.000
#> ola_mES_2i_5_2.counts 2 0.000
#> ola_mES_2i_5_20.counts 2 0.000
#> ola_mES_2i_5_21.counts 2 0.000
#> ola_mES_2i_5_23.counts 2 0.000
#> ola_mES_2i_5_24.counts 2 0.000
#> ola_mES_2i_5_25.counts 2 0.000
#> ola_mES_2i_5_26.counts 2 0.249
#> ola_mES_2i_5_27.counts 2 0.000
#> ola_mES_2i_5_28.counts 2 0.000
#> ola_mES_2i_5_29.counts 2 0.000
#> ola_mES_2i_5_3.counts 2 0.000
#> ola_mES_2i_5_30.counts 2 0.000
#> ola_mES_2i_5_31.counts 2 0.249
#> ola_mES_2i_5_32.counts 2 0.751
#> ola_mES_2i_5_34.counts 2 0.000
#> ola_mES_2i_5_35.counts 2 0.000
#> ola_mES_2i_5_36.counts 2 1.000
#> ola_mES_2i_5_38.counts 2 0.249
#> ola_mES_2i_5_39.counts 2 0.000
#> ola_mES_2i_5_4.counts 2 0.000
#> ola_mES_2i_5_40.counts 2 0.000
#> ola_mES_2i_5_41.counts 1 0.498
#> ola_mES_2i_5_42.counts 2 0.000
#> ola_mES_2i_5_44.counts 2 0.000
#> ola_mES_2i_5_45.counts 2 1.000
#> ola_mES_2i_5_46.counts 2 0.000
#> ola_mES_2i_5_47.counts 2 0.000
#> ola_mES_2i_5_48.counts 2 0.000
#> ola_mES_2i_5_49.counts 2 0.000
#> ola_mES_2i_5_5.counts 2 0.000
#> ola_mES_2i_5_51.counts 2 0.249
#> ola_mES_2i_5_52.counts 2 1.000
#> ola_mES_2i_5_53.counts 2 0.498
#> ola_mES_2i_5_54.counts 2 0.000
#> ola_mES_2i_5_55.counts 2 0.502
#> ola_mES_2i_5_56.counts 2 0.747
#> ola_mES_2i_5_57.counts 1 0.751
#> ola_mES_2i_5_58.counts 2 0.000
#> ola_mES_2i_5_59.counts 2 0.751
#> ola_mES_2i_5_6.counts 2 0.000
#> ola_mES_2i_5_61.counts 2 0.000
#> ola_mES_2i_5_62.counts 2 0.000
#> ola_mES_2i_5_63.counts 2 0.000
#> ola_mES_2i_5_65.counts 2 0.249
#> ola_mES_2i_5_66.counts 2 0.000
#> ola_mES_2i_5_67.counts 2 0.000
#> ola_mES_2i_5_68.counts 2 0.000
#> ola_mES_2i_5_69.counts 2 1.000
#> ola_mES_2i_5_7.counts 2 0.498
#> ola_mES_2i_5_70.counts 2 0.000
#> ola_mES_2i_5_72.counts 2 0.000
#> ola_mES_2i_5_73.counts 2 0.000
#> ola_mES_2i_5_74.counts 2 0.249
#> ola_mES_2i_5_75.counts 2 0.000
#> ola_mES_2i_5_76.counts 2 0.000
#> ola_mES_2i_5_77.counts 2 0.000
#> ola_mES_2i_5_78.counts 2 0.249
#> ola_mES_2i_5_8.counts 2 0.000
#> ola_mES_2i_5_81.counts 2 0.000
#> ola_mES_2i_5_82.counts 2 0.000
#> ola_mES_2i_5_83.counts 2 0.000
#> ola_mES_2i_5_85.counts 2 0.000
#> ola_mES_2i_5_86.counts 2 0.000
#> ola_mES_2i_5_87.counts 2 0.000
#> ola_mES_2i_5_88.counts 2 0.000
#> ola_mES_2i_5_9.counts 2 0.000
#> ola_mES_2i_5_90.counts 2 0.000
#> ola_mES_2i_5_92.counts 2 0.249
#> ola_mES_2i_5_94.counts 2 0.249
#> ola_mES_2i_5_95.counts 1 0.000
#> ola_mES_2i_5_96.counts 2 0.000
#> ola_mES_a2i_2_1.counts 1 0.249
#> ola_mES_a2i_2_10.counts 1 0.249
#> ola_mES_a2i_2_11.counts 1 0.000
#> ola_mES_a2i_2_12.counts 1 0.249
#> ola_mES_a2i_2_13.counts 1 0.000
#> ola_mES_a2i_2_14.counts 1 0.000
#> ola_mES_a2i_2_15.counts 1 0.000
#> ola_mES_a2i_2_16.counts 1 1.000
#> ola_mES_a2i_2_17.counts 1 0.000
#> ola_mES_a2i_2_18.counts 1 0.000
#> ola_mES_a2i_2_19.counts 1 0.000
#> ola_mES_a2i_2_2.counts 1 0.000
#> ola_mES_a2i_2_20.counts 1 1.000
#> ola_mES_a2i_2_21.counts 1 0.000
#> ola_mES_a2i_2_22.counts 1 0.000
#> ola_mES_a2i_2_23.counts 1 0.000
#> ola_mES_a2i_2_24.counts 1 0.000
#> ola_mES_a2i_2_25.counts 1 0.000
#> ola_mES_a2i_2_26.counts 1 0.000
#> ola_mES_a2i_2_27.counts 1 1.000
#> ola_mES_a2i_2_28.counts 1 0.000
#> ola_mES_a2i_2_29.counts 1 0.502
#> ola_mES_a2i_2_3.counts 1 0.000
#> ola_mES_a2i_2_30.counts 2 0.000
#> ola_mES_a2i_2_31.counts 2 0.253
#> ola_mES_a2i_2_32.counts 2 0.249
#> ola_mES_a2i_2_33.counts 1 1.000
#> ola_mES_a2i_2_34.counts 1 1.000
#> ola_mES_a2i_2_35.counts 2 0.000
#> ola_mES_a2i_2_36.counts 2 0.000
#> ola_mES_a2i_2_37.counts 2 0.000
#> ola_mES_a2i_2_38.counts 1 0.498
#> ola_mES_a2i_2_39.counts 1 0.000
#> ola_mES_a2i_2_4.counts 1 0.498
#> ola_mES_a2i_2_40.counts 1 0.502
#> ola_mES_a2i_2_41.counts 1 0.000
#> ola_mES_a2i_2_42.counts 1 0.249
#> ola_mES_a2i_2_43.counts 1 0.000
#> ola_mES_a2i_2_44.counts 1 0.502
#> ola_mES_a2i_2_45.counts 1 0.000
#> ola_mES_a2i_2_46.counts 1 0.000
#> ola_mES_a2i_2_47.counts 1 0.000
#> ola_mES_a2i_2_48.counts 1 0.751
#> ola_mES_a2i_2_49.counts 1 0.000
#> ola_mES_a2i_2_5.counts 1 0.502
#> ola_mES_a2i_2_50.counts 1 0.000
#> ola_mES_a2i_2_51.counts 1 0.000
#> ola_mES_a2i_2_52.counts 1 0.253
#> ola_mES_a2i_2_53.counts 1 0.000
#> ola_mES_a2i_2_54.counts 1 0.000
#> ola_mES_a2i_2_55.counts 1 0.000
#> ola_mES_a2i_2_56.counts 1 0.000
#> ola_mES_a2i_2_57.counts 1 0.000
#> ola_mES_a2i_2_59.counts 1 0.000
#> ola_mES_a2i_2_6.counts 2 1.000
#> ola_mES_a2i_2_60.counts 1 0.000
#> ola_mES_a2i_2_61.counts 1 0.498
#> ola_mES_a2i_2_62.counts 1 0.000
#> ola_mES_a2i_2_63.counts 1 0.502
#> ola_mES_a2i_2_64.counts 1 1.000
#> ola_mES_a2i_2_65.counts 1 0.000
#> ola_mES_a2i_2_66.counts 1 0.000
#> ola_mES_a2i_2_67.counts 1 0.000
#> ola_mES_a2i_2_68.counts 1 0.000
#> ola_mES_a2i_2_69.counts 2 0.249
#> ola_mES_a2i_2_7.counts 1 1.000
#> ola_mES_a2i_2_70.counts 1 0.000
#> ola_mES_a2i_2_71.counts 1 0.498
#> ola_mES_a2i_2_72.counts 1 0.000
#> ola_mES_a2i_2_73.counts 1 0.000
#> ola_mES_a2i_2_74.counts 1 0.000
#> ola_mES_a2i_2_75.counts 1 0.000
#> ola_mES_a2i_2_76.counts 1 0.253
#> ola_mES_a2i_2_77.counts 1 0.000
#> ola_mES_a2i_2_78.counts 1 0.000
#> ola_mES_a2i_2_8.counts 1 0.000
#> ola_mES_a2i_2_80.counts 1 0.000
#> ola_mES_a2i_2_81.counts 1 0.249
#> ola_mES_a2i_2_82.counts 1 0.000
#> ola_mES_a2i_2_83.counts 1 0.000
#> ola_mES_a2i_2_84.counts 1 0.747
#> ola_mES_a2i_2_86.counts 1 0.249
#> ola_mES_a2i_2_87.counts 1 0.000
#> ola_mES_a2i_2_88.counts 1 0.249
#> ola_mES_a2i_2_89.counts 2 0.498
#> ola_mES_a2i_2_9.counts 1 0.747
#> ola_mES_a2i_2_90.counts 1 0.000
#> ola_mES_a2i_2_91.counts 2 0.249
#> ola_mES_a2i_2_92.counts 2 0.000
#> ola_mES_a2i_2_93.counts 1 0.000
#> ola_mES_a2i_2_94.counts 1 0.000
#> ola_mES_a2i_2_95.counts 1 0.000
#> ola_mES_a2i_2_96.counts 1 0.000
#> ola_mES_a2i_3_10.counts 2 0.000
#> ola_mES_a2i_3_11.counts 2 0.249
#> ola_mES_a2i_3_13.counts 1 0.751
#> ola_mES_a2i_3_16.counts 1 0.751
#> ola_mES_a2i_3_17.counts 2 0.751
#> ola_mES_a2i_3_18.counts 2 0.249
#> ola_mES_a2i_3_2.counts 2 0.498
#> ola_mES_a2i_3_20.counts 2 0.249
#> ola_mES_a2i_3_21.counts 2 0.000
#> ola_mES_a2i_3_22.counts 2 0.253
#> ola_mES_a2i_3_24.counts 2 0.249
#> ola_mES_a2i_3_25.counts 1 1.000
#> ola_mES_a2i_3_26.counts 2 0.249
#> ola_mES_a2i_3_28.counts 1 0.249
#> ola_mES_a2i_3_29.counts 1 0.000
#> ola_mES_a2i_3_3.counts 2 0.000
#> ola_mES_a2i_3_30.counts 2 0.502
#> ola_mES_a2i_3_31.counts 2 0.000
#> ola_mES_a2i_3_33.counts 2 0.249
#> ola_mES_a2i_3_34.counts 1 0.000
#> ola_mES_a2i_3_37.counts 1 0.000
#> ola_mES_a2i_3_38.counts 1 0.253
#> ola_mES_a2i_3_39.counts 1 0.000
#> ola_mES_a2i_3_4.counts 2 0.000
#> ola_mES_a2i_3_40.counts 1 0.000
#> ola_mES_a2i_3_41.counts 1 0.249
#> ola_mES_a2i_3_42.counts 2 0.502
#> ola_mES_a2i_3_44.counts 1 0.000
#> ola_mES_a2i_3_45.counts 2 0.000
#> ola_mES_a2i_3_47.counts 1 0.000
#> ola_mES_a2i_3_49.counts 1 0.249
#> ola_mES_a2i_3_50.counts 1 1.000
#> ola_mES_a2i_3_52.counts 1 0.000
#> ola_mES_a2i_3_54.counts 1 0.000
#> ola_mES_a2i_3_56.counts 1 0.498
#> ola_mES_a2i_3_58.counts 2 0.000
#> ola_mES_a2i_3_59.counts 1 0.000
#> ola_mES_a2i_3_6.counts 1 0.249
#> ola_mES_a2i_3_62.counts 1 0.000
#> ola_mES_a2i_3_64.counts 1 0.249
#> ola_mES_a2i_3_65.counts 1 0.000
#> ola_mES_a2i_3_66.counts 1 0.000
#> ola_mES_a2i_3_67.counts 2 1.000
#> ola_mES_a2i_3_68.counts 2 0.000
#> ola_mES_a2i_3_69.counts 1 0.000
#> ola_mES_a2i_3_7.counts 2 1.000
#> ola_mES_a2i_3_71.counts 1 0.000
#> ola_mES_a2i_3_73.counts 1 0.000
#> ola_mES_a2i_3_75.counts 2 1.000
#> ola_mES_a2i_3_76.counts 2 0.249
#> ola_mES_a2i_3_77.counts 1 0.000
#> ola_mES_a2i_3_8.counts 2 0.000
#> ola_mES_a2i_3_80.counts 2 0.000
#> ola_mES_a2i_3_81.counts 2 0.249
#> ola_mES_a2i_3_82.counts 1 0.249
#> ola_mES_a2i_3_83.counts 1 0.000
#> ola_mES_a2i_3_84.counts 1 0.000
#> ola_mES_a2i_3_85.counts 1 0.000
#> ola_mES_a2i_3_86.counts 1 0.249
#> ola_mES_a2i_3_89.counts 1 0.000
#> ola_mES_a2i_3_9.counts 2 0.249
#> ola_mES_a2i_3_90.counts 1 0.000
#> ola_mES_a2i_3_91.counts 2 0.249
#> ola_mES_a2i_3_92.counts 1 0.498
#> ola_mES_a2i_3_94.counts 1 0.498
#> ola_mES_a2i_3_96.counts 1 1.000
#> ola_mES_lif_1_1.counts 1 0.000
#> ola_mES_lif_1_10.counts 2 0.249
#> ola_mES_lif_1_11.counts 1 0.249
#> ola_mES_lif_1_12.counts 1 1.000
#> ola_mES_lif_1_13.counts 1 0.751
#> ola_mES_lif_1_14.counts 2 0.000
#> ola_mES_lif_1_15.counts 1 1.000
#> ola_mES_lif_1_17.counts 2 0.000
#> ola_mES_lif_1_18.counts 1 0.751
#> ola_mES_lif_1_19.counts 1 1.000
#> ola_mES_lif_1_2.counts 2 0.498
#> ola_mES_lif_1_20.counts 1 1.000
#> ola_mES_lif_1_21.counts 1 1.000
#> ola_mES_lif_1_22.counts 1 1.000
#> ola_mES_lif_1_23.counts 2 0.000
#> ola_mES_lif_1_24.counts 1 1.000
#> ola_mES_lif_1_25.counts 1 1.000
#> ola_mES_lif_1_26.counts 1 1.000
#> ola_mES_lif_1_28.counts 1 1.000
#> ola_mES_lif_1_29.counts 2 0.000
#> ola_mES_lif_1_3.counts 1 0.253
#> ola_mES_lif_1_30.counts 1 1.000
#> ola_mES_lif_1_31.counts 2 0.000
#> ola_mES_lif_1_32.counts 1 1.000
#> ola_mES_lif_1_33.counts 1 1.000
#> ola_mES_lif_1_34.counts 1 1.000
#> ola_mES_lif_1_35.counts 2 0.000
#> ola_mES_lif_1_36.counts 1 1.000
#> ola_mES_lif_1_37.counts 1 1.000
#> ola_mES_lif_1_38.counts 1 1.000
#> ola_mES_lif_1_39.counts 1 1.000
#> ola_mES_lif_1_4.counts 1 0.000
#> ola_mES_lif_1_41.counts 1 1.000
#> ola_mES_lif_1_42.counts 1 1.000
#> ola_mES_lif_1_43.counts 1 0.000
#> ola_mES_lif_1_44.counts 1 1.000
#> ola_mES_lif_1_45.counts 1 1.000
#> ola_mES_lif_1_46.counts 1 1.000
#> ola_mES_lif_1_47.counts 1 0.751
#> ola_mES_lif_1_48.counts 1 1.000
#> ola_mES_lif_1_49.counts 1 0.000
#> ola_mES_lif_1_5.counts 1 0.000
#> ola_mES_lif_1_50.counts 1 0.751
#> ola_mES_lif_1_51.counts 1 1.000
#> ola_mES_lif_1_52.counts 1 0.502
#> ola_mES_lif_1_53.counts 2 0.000
#> ola_mES_lif_1_54.counts 1 0.000
#> ola_mES_lif_1_55.counts 1 0.000
#> ola_mES_lif_1_56.counts 2 0.000
#> ola_mES_lif_1_57.counts 2 0.000
#> ola_mES_lif_1_58.counts 1 0.000
#> ola_mES_lif_1_59.counts 1 0.502
#> ola_mES_lif_1_6.counts 1 0.000
#> ola_mES_lif_1_60.counts 1 1.000
#> ola_mES_lif_1_61.counts 1 0.000
#> ola_mES_lif_1_62.counts 1 1.000
#> ola_mES_lif_1_63.counts 2 0.000
#> ola_mES_lif_1_64.counts 2 0.000
#> ola_mES_lif_1_65.counts 2 0.000
#> ola_mES_lif_1_66.counts 1 0.249
#> ola_mES_lif_1_67.counts 1 0.000
#> ola_mES_lif_1_68.counts 1 1.000
#> ola_mES_lif_1_69.counts 2 0.000
#> ola_mES_lif_1_7.counts 1 0.000
#> ola_mES_lif_1_70.counts 1 1.000
#> ola_mES_lif_1_72.counts 1 0.000
#> ola_mES_lif_1_73.counts 1 0.751
#> ola_mES_lif_1_74.counts 2 0.000
#> ola_mES_lif_1_75.counts 2 0.000
#> ola_mES_lif_1_79.counts 2 0.000
#> ola_mES_lif_1_8.counts 1 0.249
#> ola_mES_lif_1_80.counts 1 0.000
#> ola_mES_lif_1_81.counts 1 1.000
#> ola_mES_lif_1_82.counts 2 0.000
#> ola_mES_lif_1_86.counts 1 0.751
#> ola_mES_lif_1_9.counts 1 0.000
#> ola_mES_lif_1_91.counts 1 0.751
#> ola_mES_lif_1_92.counts 1 0.000
#> ola_mES_lif_1_93.counts 2 0.000
#> ola_mES_lif_1_95.counts 1 0.249
#> ola_mES_lif_1_96.counts 1 0.000
#> ola_mES_lif_2_1.counts 2 0.249
#> ola_mES_lif_2_10.counts 1 0.000
#> ola_mES_lif_2_11.counts 1 0.000
#> ola_mES_lif_2_12.counts 2 0.000
#> ola_mES_lif_2_13.counts 1 0.000
#> ola_mES_lif_2_14.counts 1 0.000
#> ola_mES_lif_2_15.counts 1 0.000
#> ola_mES_lif_2_16.counts 1 0.000
#> ola_mES_lif_2_17.counts 1 0.000
#> ola_mES_lif_2_18.counts 1 0.000
#> ola_mES_lif_2_19.counts 1 0.000
#> ola_mES_lif_2_2.counts 1 0.000
#> ola_mES_lif_2_20.counts 2 1.000
#> ola_mES_lif_2_21.counts 1 0.000
#> ola_mES_lif_2_23.counts 1 0.000
#> ola_mES_lif_2_25.counts 2 1.000
#> ola_mES_lif_2_26.counts 1 0.000
#> ola_mES_lif_2_27.counts 1 0.000
#> ola_mES_lif_2_28.counts 1 0.000
#> ola_mES_lif_2_29.counts 1 0.000
#> ola_mES_lif_2_3.counts 1 0.502
#> ola_mES_lif_2_30.counts 1 0.000
#> ola_mES_lif_2_31.counts 2 1.000
#> ola_mES_lif_2_32.counts 1 0.000
#> ola_mES_lif_2_33.counts 2 1.000
#> ola_mES_lif_2_34.counts 1 0.000
#> ola_mES_lif_2_35.counts 1 0.000
#> ola_mES_lif_2_36.counts 2 1.000
#> ola_mES_lif_2_37.counts 2 1.000
#> ola_mES_lif_2_38.counts 1 0.000
#> ola_mES_lif_2_39.counts 1 0.000
#> ola_mES_lif_2_4.counts 1 0.000
#> ola_mES_lif_2_40.counts 1 0.000
#> ola_mES_lif_2_41.counts 2 1.000
#> ola_mES_lif_2_42.counts 1 0.000
#> ola_mES_lif_2_43.counts 1 0.000
#> ola_mES_lif_2_44.counts 1 0.000
#> ola_mES_lif_2_45.counts 2 1.000
#> ola_mES_lif_2_46.counts 1 0.000
#> ola_mES_lif_2_47.counts 2 1.000
#> ola_mES_lif_2_48.counts 2 0.498
#> ola_mES_lif_2_49.counts 1 0.249
#> ola_mES_lif_2_5.counts 1 0.000
#> ola_mES_lif_2_50.counts 1 0.000
#> ola_mES_lif_2_51.counts 1 0.000
#> ola_mES_lif_2_52.counts 1 0.000
#> ola_mES_lif_2_53.counts 1 0.000
#> ola_mES_lif_2_54.counts 1 0.000
#> ola_mES_lif_2_55.counts 1 0.000
#> ola_mES_lif_2_56.counts 1 0.000
#> ola_mES_lif_2_57.counts 2 1.000
#> ola_mES_lif_2_58.counts 1 0.000
#> ola_mES_lif_2_59.counts 1 0.000
#> ola_mES_lif_2_6.counts 1 0.000
#> ola_mES_lif_2_60.counts 2 1.000
#> ola_mES_lif_2_61.counts 1 0.000
#> ola_mES_lif_2_63.counts 1 0.000
#> ola_mES_lif_2_64.counts 1 0.000
#> ola_mES_lif_2_65.counts 1 0.000
#> ola_mES_lif_2_66.counts 1 0.000
#> ola_mES_lif_2_67.counts 1 0.000
#> ola_mES_lif_2_68.counts 1 0.000
#> ola_mES_lif_2_69.counts 2 0.000
#> ola_mES_lif_2_7.counts 1 0.000
#> ola_mES_lif_2_70.counts 1 0.000
#> ola_mES_lif_2_71.counts 1 0.000
#> ola_mES_lif_2_72.counts 1 0.000
#> ola_mES_lif_2_73.counts 2 0.000
#> ola_mES_lif_2_74.counts 1 0.000
#> ola_mES_lif_2_75.counts 1 0.000
#> ola_mES_lif_2_76.counts 1 0.000
#> ola_mES_lif_2_77.counts 1 0.000
#> ola_mES_lif_2_79.counts 1 0.000
#> ola_mES_lif_2_8.counts 1 0.000
#> ola_mES_lif_2_80.counts 1 0.000
#> ola_mES_lif_2_81.counts 2 1.000
#> ola_mES_lif_2_82.counts 1 0.000
#> ola_mES_lif_2_83.counts 1 0.000
#> ola_mES_lif_2_84.counts 1 0.000
#> ola_mES_lif_2_86.counts 2 0.498
#> ola_mES_lif_2_87.counts 1 0.000
#> ola_mES_lif_2_89.counts 1 0.000
#> ola_mES_lif_2_9.counts 2 1.000
#> ola_mES_lif_2_90.counts 2 1.000
#> ola_mES_lif_2_91.counts 2 0.751
#> ola_mES_lif_2_92.counts 2 1.000
#> ola_mES_lif_2_93.counts 1 0.000
#> ola_mES_lif_2_94.counts 1 0.000
#> ola_mES_lif_2_95.counts 1 0.000
#> ola_mES_lif_2_96.counts 1 0.000
#> ola_mES_lif_3_1.counts 1 1.000
#> ola_mES_lif_3_10.counts 1 1.000
#> ola_mES_lif_3_11.counts 1 1.000
#> ola_mES_lif_3_12.counts 2 0.000
#> ola_mES_lif_3_13.counts 1 1.000
#> ola_mES_lif_3_14.counts 2 0.000
#> ola_mES_lif_3_15.counts 2 0.000
#> ola_mES_lif_3_16.counts 2 0.000
#> ola_mES_lif_3_17.counts 2 0.000
#> ola_mES_lif_3_19.counts 2 0.000
#> ola_mES_lif_3_2.counts 2 0.000
#> ola_mES_lif_3_20.counts 2 0.000
#> ola_mES_lif_3_21.counts 2 0.000
#> ola_mES_lif_3_22.counts 2 0.000
#> ola_mES_lif_3_23.counts 2 0.000
#> ola_mES_lif_3_24.counts 2 0.000
#> ola_mES_lif_3_26.counts 2 0.000
#> ola_mES_lif_3_27.counts 1 1.000
#> ola_mES_lif_3_28.counts 1 1.000
#> ola_mES_lif_3_29.counts 1 1.000
#> ola_mES_lif_3_3.counts 1 1.000
#> ola_mES_lif_3_30.counts 1 1.000
#> ola_mES_lif_3_31.counts 1 1.000
#> ola_mES_lif_3_32.counts 1 0.502
#> ola_mES_lif_3_33.counts 2 0.000
#> ola_mES_lif_3_34.counts 2 0.000
#> ola_mES_lif_3_35.counts 2 0.000
#> ola_mES_lif_3_36.counts 2 0.000
#> ola_mES_lif_3_37.counts 2 0.000
#> ola_mES_lif_3_38.counts 1 1.000
#> ola_mES_lif_3_39.counts 1 1.000
#> ola_mES_lif_3_4.counts 1 1.000
#> ola_mES_lif_3_40.counts 1 1.000
#> ola_mES_lif_3_42.counts 1 0.000
#> ola_mES_lif_3_45.counts 2 0.000
#> ola_mES_lif_3_46.counts 2 0.000
#> ola_mES_lif_3_47.counts 1 1.000
#> ola_mES_lif_3_48.counts 2 0.000
#> ola_mES_lif_3_49.counts 1 1.000
#> ola_mES_lif_3_5.counts 1 1.000
#> ola_mES_lif_3_50.counts 1 0.000
#> ola_mES_lif_3_51.counts 1 0.000
#> ola_mES_lif_3_52.counts 1 0.249
#> ola_mES_lif_3_53.counts 1 0.747
#> ola_mES_lif_3_54.counts 1 0.000
#> ola_mES_lif_3_56.counts 1 1.000
#> ola_mES_lif_3_57.counts 1 0.751
#> ola_mES_lif_3_58.counts 1 1.000
#> ola_mES_lif_3_59.counts 1 0.000
#> ola_mES_lif_3_6.counts 1 1.000
#> ola_mES_lif_3_60.counts 1 0.747
#> ola_mES_lif_3_61.counts 1 1.000
#> ola_mES_lif_3_63.counts 1 1.000
#> ola_mES_lif_3_64.counts 1 1.000
#> ola_mES_lif_3_65.counts 1 1.000
#> ola_mES_lif_3_69.counts 2 0.000
#> ola_mES_lif_3_70.counts 1 1.000
#> ola_mES_lif_3_71.counts 1 1.000
#> ola_mES_lif_3_72.counts 2 0.000
#> ola_mES_lif_3_73.counts 2 0.000
#> ola_mES_lif_3_74.counts 2 0.000
#> ola_mES_lif_3_75.counts 2 0.000
#> ola_mES_lif_3_76.counts 2 0.000
#> ola_mES_lif_3_77.counts 2 0.000
#> ola_mES_lif_3_78.counts 2 0.000
#> ola_mES_lif_3_8.counts 1 1.000
#> ola_mES_lif_3_80.counts 2 0.000
#> ola_mES_lif_3_82.counts 2 0.000
#> ola_mES_lif_3_83.counts 2 0.000
#> ola_mES_lif_3_84.counts 2 0.000
#> ola_mES_lif_3_87.counts 2 0.000
#> ola_mES_lif_3_88.counts 2 0.000
#> ola_mES_lif_3_89.counts 2 0.000
#> ola_mES_lif_3_9.counts 2 0.000
#> ola_mES_lif_3_90.counts 2 0.000
#> ola_mES_lif_3_92.counts 2 0.000
#> ola_mES_lif_3_94.counts 2 0.000
#> ola_mES_lif_3_95.counts 2 0.000
#> ola_mES_lif_3_96.counts 2 0.000
get_classes(res, k = 3)
#> class p
#> ola_mES_2i_2_1.counts 1 0.000
#> ola_mES_2i_2_10.counts 1 0.000
#> ola_mES_2i_2_11.counts 1 0.000
#> ola_mES_2i_2_12.counts 1 0.000
#> ola_mES_2i_2_13.counts 1 0.000
#> ola_mES_2i_2_14.counts 1 0.000
#> ola_mES_2i_2_15.counts 1 0.000
#> ola_mES_2i_2_16.counts 1 0.000
#> ola_mES_2i_2_17.counts 1 0.000
#> ola_mES_2i_2_18.counts 1 0.000
#> ola_mES_2i_2_19.counts 1 0.000
#> ola_mES_2i_2_2.counts 1 0.000
#> ola_mES_2i_2_20.counts 1 0.000
#> ola_mES_2i_2_21.counts 1 0.000
#> ola_mES_2i_2_22.counts 1 0.000
#> ola_mES_2i_2_23.counts 1 0.000
#> ola_mES_2i_2_24.counts 1 0.000
#> ola_mES_2i_2_25.counts 1 0.000
#> ola_mES_2i_2_26.counts 1 0.000
#> ola_mES_2i_2_27.counts 1 0.000
#> ola_mES_2i_2_28.counts 1 0.000
#> ola_mES_2i_2_29.counts 1 0.000
#> ola_mES_2i_2_3.counts 1 0.000
#> ola_mES_2i_2_30.counts 1 0.000
#> ola_mES_2i_2_31.counts 1 0.000
#> ola_mES_2i_2_32.counts 1 0.000
#> ola_mES_2i_2_33.counts 1 0.000
#> ola_mES_2i_2_34.counts 1 0.000
#> ola_mES_2i_2_35.counts 1 0.000
#> ola_mES_2i_2_36.counts 1 0.000
#> ola_mES_2i_2_37.counts 1 0.000
#> ola_mES_2i_2_38.counts 1 0.000
#> ola_mES_2i_2_39.counts 1 0.000
#> ola_mES_2i_2_4.counts 1 0.000
#> ola_mES_2i_2_40.counts 1 0.000
#> ola_mES_2i_2_41.counts 1 0.000
#> ola_mES_2i_2_42.counts 1 0.000
#> ola_mES_2i_2_43.counts 1 0.000
#> ola_mES_2i_2_44.counts 1 0.000
#> ola_mES_2i_2_46.counts 1 0.000
#> ola_mES_2i_2_47.counts 1 0.000
#> ola_mES_2i_2_48.counts 1 0.000
#> ola_mES_2i_2_49.counts 1 0.000
#> ola_mES_2i_2_5.counts 1 0.000
#> ola_mES_2i_2_50.counts 1 0.000
#> ola_mES_2i_2_51.counts 1 0.000
#> ola_mES_2i_2_52.counts 1 0.000
#> ola_mES_2i_2_53.counts 1 0.000
#> ola_mES_2i_2_54.counts 1 0.000
#> ola_mES_2i_2_55.counts 1 0.000
#> ola_mES_2i_2_56.counts 1 0.000
#> ola_mES_2i_2_57.counts 1 0.000
#> ola_mES_2i_2_58.counts 1 0.000
#> ola_mES_2i_2_59.counts 1 0.000
#> ola_mES_2i_2_6.counts 1 0.000
#> ola_mES_2i_2_60.counts 1 0.000
#> ola_mES_2i_2_61.counts 1 0.000
#> ola_mES_2i_2_62.counts 3 1.000
#> ola_mES_2i_2_63.counts 1 0.000
#> ola_mES_2i_2_64.counts 1 0.000
#> ola_mES_2i_2_66.counts 1 0.000
#> ola_mES_2i_2_67.counts 1 0.000
#> ola_mES_2i_2_68.counts 1 0.000
#> ola_mES_2i_2_69.counts 1 0.000
#> ola_mES_2i_2_7.counts 1 0.000
#> ola_mES_2i_2_72.counts 1 0.000
#> ola_mES_2i_2_73.counts 1 0.000
#> ola_mES_2i_2_74.counts 1 0.000
#> ola_mES_2i_2_75.counts 1 0.000
#> ola_mES_2i_2_76.counts 1 0.000
#> ola_mES_2i_2_79.counts 1 0.000
#> ola_mES_2i_2_8.counts 1 0.000
#> ola_mES_2i_2_80.counts 1 0.000
#> ola_mES_2i_2_81.counts 1 0.000
#> ola_mES_2i_2_85.counts 1 0.000
#> ola_mES_2i_2_86.counts 1 0.000
#> ola_mES_2i_2_87.counts 3 1.000
#> ola_mES_2i_2_90.counts 1 0.000
#> ola_mES_2i_2_91.counts 1 0.000
#> ola_mES_2i_2_93.counts 1 0.000
#> ola_mES_2i_2_94.counts 1 0.000
#> ola_mES_2i_2_95.counts 1 0.000
#> ola_mES_2i_3_10.counts 2 0.000
#> ola_mES_2i_3_11.counts 2 0.000
#> ola_mES_2i_3_12.counts 2 1.000
#> ola_mES_2i_3_13.counts 2 1.000
#> ola_mES_2i_3_15.counts 2 1.000
#> ola_mES_2i_3_16.counts 2 0.000
#> ola_mES_2i_3_17.counts 3 1.000
#> ola_mES_2i_3_18.counts 3 1.000
#> ola_mES_2i_3_19.counts 2 1.000
#> ola_mES_2i_3_2.counts 2 0.000
#> ola_mES_2i_3_20.counts 2 1.000
#> ola_mES_2i_3_21.counts 2 0.000
#> ola_mES_2i_3_23.counts 2 0.000
#> ola_mES_2i_3_25.counts 2 0.000
#> ola_mES_2i_3_26.counts 2 0.253
#> ola_mES_2i_3_28.counts 2 0.000
#> ola_mES_2i_3_30.counts 2 1.000
#> ola_mES_2i_3_31.counts 3 1.000
#> ola_mES_2i_3_33.counts 2 1.000
#> ola_mES_2i_3_36.counts 2 0.000
#> ola_mES_2i_3_37.counts 2 0.000
#> ola_mES_2i_3_38.counts 2 0.000
#> ola_mES_2i_3_39.counts 2 1.000
#> ola_mES_2i_3_4.counts 2 0.000
#> ola_mES_2i_3_41.counts 2 1.000
#> ola_mES_2i_3_42.counts 2 0.000
#> ola_mES_2i_3_43.counts 2 0.000
#> ola_mES_2i_3_45.counts 2 0.000
#> ola_mES_2i_3_46.counts 2 0.000
#> ola_mES_2i_3_47.counts 2 1.000
#> ola_mES_2i_3_49.counts 3 1.000
#> ola_mES_2i_3_5.counts 2 1.000
#> ola_mES_2i_3_52.counts 2 1.000
#> ola_mES_2i_3_54.counts 2 0.000
#> ola_mES_2i_3_56.counts 2 1.000
#> ola_mES_2i_3_57.counts 2 0.249
#> ola_mES_2i_3_58.counts 2 0.502
#> ola_mES_2i_3_6.counts 2 1.000
#> ola_mES_2i_3_62.counts 3 1.000
#> ola_mES_2i_3_65.counts 2 1.000
#> ola_mES_2i_3_66.counts 3 1.000
#> ola_mES_2i_3_69.counts 2 1.000
#> ola_mES_2i_3_7.counts 2 0.751
#> ola_mES_2i_3_73.counts 2 1.000
#> ola_mES_2i_3_76.counts 2 0.249
#> ola_mES_2i_3_77.counts 2 1.000
#> ola_mES_2i_3_78.counts 3 1.000
#> ola_mES_2i_3_79.counts 2 0.751
#> ola_mES_2i_3_8.counts 2 1.000
#> ola_mES_2i_3_80.counts 2 0.000
#> ola_mES_2i_3_82.counts 2 0.498
#> ola_mES_2i_3_83.counts 2 0.249
#> ola_mES_2i_3_84.counts 2 0.000
#> ola_mES_2i_3_87.counts 2 1.000
#> ola_mES_2i_3_88.counts 2 0.000
#> ola_mES_2i_3_90.counts 2 0.000
#> ola_mES_2i_3_91.counts 2 1.000
#> ola_mES_2i_3_92.counts 2 0.747
#> ola_mES_2i_3_96.counts 2 1.000
#> ola_mES_2i_4_1.counts 2 1.000
#> ola_mES_2i_4_10.counts 2 0.000
#> ola_mES_2i_4_11.counts 2 0.000
#> ola_mES_2i_4_13.counts 2 0.000
#> ola_mES_2i_4_17.counts 2 0.000
#> ola_mES_2i_4_18.counts 2 0.000
#> ola_mES_2i_4_2.counts 1 0.000
#> ola_mES_2i_4_20.counts 2 0.000
#> ola_mES_2i_4_21.counts 2 0.000
#> ola_mES_2i_4_22.counts 2 0.000
#> ola_mES_2i_4_23.counts 2 0.000
#> ola_mES_2i_4_24.counts 2 0.000
#> ola_mES_2i_4_25.counts 2 0.000
#> ola_mES_2i_4_26.counts 2 0.000
#> ola_mES_2i_4_27.counts 2 0.000
#> ola_mES_2i_4_29.counts 2 0.000
#> ola_mES_2i_4_30.counts 2 0.000
#> ola_mES_2i_4_31.counts 2 1.000
#> ola_mES_2i_4_32.counts 2 0.000
#> ola_mES_2i_4_33.counts 2 0.000
#> ola_mES_2i_4_34.counts 2 0.000
#> ola_mES_2i_4_35.counts 2 0.253
#> ola_mES_2i_4_36.counts 2 0.000
#> ola_mES_2i_4_37.counts 3 1.000
#> ola_mES_2i_4_38.counts 1 0.000
#> ola_mES_2i_4_39.counts 2 0.751
#> ola_mES_2i_4_40.counts 1 0.000
#> ola_mES_2i_4_42.counts 3 1.000
#> ola_mES_2i_4_43.counts 2 0.000
#> ola_mES_2i_4_44.counts 2 0.253
#> ola_mES_2i_4_46.counts 2 0.000
#> ola_mES_2i_4_49.counts 1 0.000
#> ola_mES_2i_4_5.counts 2 0.000
#> ola_mES_2i_4_50.counts 2 0.000
#> ola_mES_2i_4_52.counts 2 1.000
#> ola_mES_2i_4_53.counts 1 0.000
#> ola_mES_2i_4_54.counts 3 1.000
#> ola_mES_2i_4_56.counts 2 0.000
#> ola_mES_2i_4_57.counts 2 0.000
#> ola_mES_2i_4_58.counts 2 0.000
#> ola_mES_2i_4_59.counts 2 0.000
#> ola_mES_2i_4_6.counts 2 0.000
#> ola_mES_2i_4_60.counts 2 0.751
#> ola_mES_2i_4_61.counts 2 0.000
#> ola_mES_2i_4_62.counts 3 1.000
#> ola_mES_2i_4_63.counts 2 1.000
#> ola_mES_2i_4_64.counts 2 0.000
#> ola_mES_2i_4_65.counts 2 0.000
#> ola_mES_2i_4_66.counts 2 0.000
#> ola_mES_2i_4_67.counts 1 0.000
#> ola_mES_2i_4_68.counts 2 0.000
#> ola_mES_2i_4_69.counts 2 0.000
#> ola_mES_2i_4_7.counts 2 0.000
#> ola_mES_2i_4_70.counts 2 0.000
#> ola_mES_2i_4_71.counts 2 0.000
#> ola_mES_2i_4_72.counts 2 0.000
#> ola_mES_2i_4_73.counts 2 0.000
#> ola_mES_2i_4_75.counts 2 0.498
#> ola_mES_2i_4_77.counts 2 0.000
#> ola_mES_2i_4_78.counts 2 1.000
#> ola_mES_2i_4_80.counts 2 0.000
#> ola_mES_2i_4_81.counts 2 0.000
#> ola_mES_2i_4_83.counts 2 0.253
#> ola_mES_2i_4_84.counts 2 0.000
#> ola_mES_2i_4_88.counts 2 0.498
#> ola_mES_2i_4_89.counts 2 1.000
#> ola_mES_2i_4_9.counts 2 0.000
#> ola_mES_2i_4_90.counts 2 0.000
#> ola_mES_2i_4_91.counts 2 0.000
#> ola_mES_2i_4_94.counts 2 0.000
#> ola_mES_2i_4_95.counts 2 1.000
#> ola_mES_2i_4_96.counts 2 1.000
#> ola_mES_2i_5_1.counts 2 0.000
#> ola_mES_2i_5_10.counts 2 0.000
#> ola_mES_2i_5_11.counts 2 0.000
#> ola_mES_2i_5_12.counts 2 0.000
#> ola_mES_2i_5_13.counts 2 0.000
#> ola_mES_2i_5_14.counts 2 0.000
#> ola_mES_2i_5_15.counts 2 0.000
#> ola_mES_2i_5_16.counts 2 0.000
#> ola_mES_2i_5_17.counts 2 1.000
#> ola_mES_2i_5_18.counts 2 0.000
#> ola_mES_2i_5_19.counts 2 0.000
#> ola_mES_2i_5_2.counts 2 0.000
#> ola_mES_2i_5_20.counts 2 0.000
#> ola_mES_2i_5_21.counts 2 0.000
#> ola_mES_2i_5_23.counts 2 0.000
#> ola_mES_2i_5_24.counts 2 0.000
#> ola_mES_2i_5_25.counts 2 0.000
#> ola_mES_2i_5_26.counts 2 0.000
#> ola_mES_2i_5_27.counts 2 0.000
#> ola_mES_2i_5_28.counts 2 0.000
#> ola_mES_2i_5_29.counts 2 0.000
#> ola_mES_2i_5_3.counts 2 0.000
#> ola_mES_2i_5_30.counts 2 0.000
#> ola_mES_2i_5_31.counts 2 0.000
#> ola_mES_2i_5_32.counts 2 0.000
#> ola_mES_2i_5_34.counts 2 0.000
#> ola_mES_2i_5_35.counts 2 0.000
#> ola_mES_2i_5_36.counts 2 0.000
#> ola_mES_2i_5_38.counts 2 0.000
#> ola_mES_2i_5_39.counts 2 0.000
#> ola_mES_2i_5_4.counts 2 0.000
#> ola_mES_2i_5_40.counts 2 0.000
#> ola_mES_2i_5_41.counts 1 1.000
#> ola_mES_2i_5_42.counts 2 0.000
#> ola_mES_2i_5_44.counts 2 0.000
#> ola_mES_2i_5_45.counts 2 0.000
#> ola_mES_2i_5_46.counts 2 0.000
#> ola_mES_2i_5_47.counts 2 0.000
#> ola_mES_2i_5_48.counts 2 0.000
#> ola_mES_2i_5_49.counts 2 0.000
#> ola_mES_2i_5_5.counts 2 0.000
#> ola_mES_2i_5_51.counts 2 0.000
#> ola_mES_2i_5_52.counts 2 0.000
#> ola_mES_2i_5_53.counts 2 0.000
#> ola_mES_2i_5_54.counts 2 0.000
#> ola_mES_2i_5_55.counts 2 0.000
#> ola_mES_2i_5_56.counts 2 0.000
#> ola_mES_2i_5_57.counts 2 1.000
#> ola_mES_2i_5_58.counts 2 0.000
#> ola_mES_2i_5_59.counts 2 0.000
#> ola_mES_2i_5_6.counts 2 0.000
#> ola_mES_2i_5_61.counts 2 0.000
#> ola_mES_2i_5_62.counts 2 0.000
#> ola_mES_2i_5_63.counts 2 0.000
#> ola_mES_2i_5_65.counts 2 0.000
#> ola_mES_2i_5_66.counts 2 0.000
#> ola_mES_2i_5_67.counts 2 0.000
#> ola_mES_2i_5_68.counts 2 0.000
#> ola_mES_2i_5_69.counts 2 0.000
#> ola_mES_2i_5_7.counts 2 0.000
#> ola_mES_2i_5_70.counts 2 0.000
#> ola_mES_2i_5_72.counts 2 0.000
#> ola_mES_2i_5_73.counts 2 0.000
#> ola_mES_2i_5_74.counts 2 0.000
#> ola_mES_2i_5_75.counts 2 0.000
#> ola_mES_2i_5_76.counts 2 0.000
#> ola_mES_2i_5_77.counts 2 0.000
#> ola_mES_2i_5_78.counts 2 0.000
#> ola_mES_2i_5_8.counts 2 0.000
#> ola_mES_2i_5_81.counts 2 0.751
#> ola_mES_2i_5_82.counts 2 0.000
#> ola_mES_2i_5_83.counts 2 0.000
#> ola_mES_2i_5_85.counts 2 0.000
#> ola_mES_2i_5_86.counts 2 0.000
#> ola_mES_2i_5_87.counts 2 0.000
#> ola_mES_2i_5_88.counts 2 0.000
#> ola_mES_2i_5_9.counts 2 0.000
#> ola_mES_2i_5_90.counts 2 0.000
#> ola_mES_2i_5_92.counts 2 0.000
#> ola_mES_2i_5_94.counts 2 0.000
#> ola_mES_2i_5_95.counts 2 1.000
#> ola_mES_2i_5_96.counts 2 0.000
#> ola_mES_a2i_2_1.counts 1 0.000
#> ola_mES_a2i_2_10.counts 1 1.000
#> ola_mES_a2i_2_11.counts 1 0.000
#> ola_mES_a2i_2_12.counts 1 0.000
#> ola_mES_a2i_2_13.counts 1 0.249
#> ola_mES_a2i_2_14.counts 1 0.498
#> ola_mES_a2i_2_15.counts 1 0.000
#> ola_mES_a2i_2_16.counts 1 0.000
#> ola_mES_a2i_2_17.counts 1 0.000
#> ola_mES_a2i_2_18.counts 1 0.000
#> ola_mES_a2i_2_19.counts 1 0.000
#> ola_mES_a2i_2_2.counts 1 0.249
#> ola_mES_a2i_2_20.counts 1 0.751
#> ola_mES_a2i_2_21.counts 1 0.000
#> ola_mES_a2i_2_22.counts 1 0.000
#> ola_mES_a2i_2_23.counts 1 0.000
#> ola_mES_a2i_2_24.counts 1 0.000
#> ola_mES_a2i_2_25.counts 1 0.000
#> ola_mES_a2i_2_26.counts 3 0.000
#> ola_mES_a2i_2_27.counts 1 0.502
#> ola_mES_a2i_2_28.counts 1 0.000
#> ola_mES_a2i_2_29.counts 1 0.000
#> ola_mES_a2i_2_3.counts 1 0.000
#> ola_mES_a2i_2_30.counts 2 1.000
#> ola_mES_a2i_2_31.counts 3 0.000
#> ola_mES_a2i_2_32.counts 3 0.000
#> ola_mES_a2i_2_33.counts 1 0.502
#> ola_mES_a2i_2_34.counts 1 0.249
#> ola_mES_a2i_2_35.counts 3 1.000
#> ola_mES_a2i_2_36.counts 2 1.000
#> ola_mES_a2i_2_37.counts 3 0.000
#> ola_mES_a2i_2_38.counts 1 1.000
#> ola_mES_a2i_2_39.counts 3 1.000
#> ola_mES_a2i_2_4.counts 1 0.000
#> ola_mES_a2i_2_40.counts 1 1.000
#> ola_mES_a2i_2_41.counts 1 0.000
#> ola_mES_a2i_2_42.counts 1 0.000
#> ola_mES_a2i_2_43.counts 1 1.000
#> ola_mES_a2i_2_44.counts 1 0.249
#> ola_mES_a2i_2_45.counts 1 0.000
#> ola_mES_a2i_2_46.counts 1 0.000
#> ola_mES_a2i_2_47.counts 1 0.000
#> ola_mES_a2i_2_48.counts 1 1.000
#> ola_mES_a2i_2_49.counts 1 0.000
#> ola_mES_a2i_2_5.counts 1 0.000
#> ola_mES_a2i_2_50.counts 3 1.000
#> ola_mES_a2i_2_51.counts 1 0.000
#> ola_mES_a2i_2_52.counts 1 0.249
#> ola_mES_a2i_2_53.counts 3 0.502
#> ola_mES_a2i_2_54.counts 1 0.000
#> ola_mES_a2i_2_55.counts 1 0.000
#> ola_mES_a2i_2_56.counts 1 0.498
#> ola_mES_a2i_2_57.counts 1 0.000
#> ola_mES_a2i_2_59.counts 1 0.000
#> ola_mES_a2i_2_6.counts 3 0.000
#> ola_mES_a2i_2_60.counts 1 0.000
#> ola_mES_a2i_2_61.counts 1 0.751
#> ola_mES_a2i_2_62.counts 1 0.000
#> ola_mES_a2i_2_63.counts 1 1.000
#> ola_mES_a2i_2_64.counts 1 1.000
#> ola_mES_a2i_2_65.counts 1 0.000
#> ola_mES_a2i_2_66.counts 1 0.000
#> ola_mES_a2i_2_67.counts 1 0.000
#> ola_mES_a2i_2_68.counts 1 0.000
#> ola_mES_a2i_2_69.counts 3 1.000
#> ola_mES_a2i_2_7.counts 1 1.000
#> ola_mES_a2i_2_70.counts 1 0.000
#> ola_mES_a2i_2_71.counts 1 0.000
#> ola_mES_a2i_2_72.counts 1 0.000
#> ola_mES_a2i_2_73.counts 1 0.000
#> ola_mES_a2i_2_74.counts 1 0.502
#> ola_mES_a2i_2_75.counts 1 0.000
#> ola_mES_a2i_2_76.counts 1 1.000
#> ola_mES_a2i_2_77.counts 1 0.000
#> ola_mES_a2i_2_78.counts 1 0.000
#> ola_mES_a2i_2_8.counts 1 0.000
#> ola_mES_a2i_2_80.counts 1 0.000
#> ola_mES_a2i_2_81.counts 1 0.000
#> ola_mES_a2i_2_82.counts 1 0.000
#> ola_mES_a2i_2_83.counts 1 0.000
#> ola_mES_a2i_2_84.counts 1 0.000
#> ola_mES_a2i_2_86.counts 1 0.502
#> ola_mES_a2i_2_87.counts 1 0.000
#> ola_mES_a2i_2_88.counts 1 0.000
#> ola_mES_a2i_2_89.counts 2 1.000
#> ola_mES_a2i_2_9.counts 1 1.000
#> ola_mES_a2i_2_90.counts 1 0.000
#> ola_mES_a2i_2_91.counts 3 0.000
#> ola_mES_a2i_2_92.counts 3 0.000
#> ola_mES_a2i_2_93.counts 1 1.000
#> ola_mES_a2i_2_94.counts 1 0.000
#> ola_mES_a2i_2_95.counts 1 0.000
#> ola_mES_a2i_2_96.counts 1 0.000
#> ola_mES_a2i_3_10.counts 2 0.498
#> ola_mES_a2i_3_11.counts 2 1.000
#> ola_mES_a2i_3_13.counts 1 0.000
#> ola_mES_a2i_3_16.counts 1 0.000
#> ola_mES_a2i_3_17.counts 3 1.000
#> ola_mES_a2i_3_18.counts 3 1.000
#> ola_mES_a2i_3_2.counts 3 1.000
#> ola_mES_a2i_3_20.counts 3 1.000
#> ola_mES_a2i_3_21.counts 2 1.000
#> ola_mES_a2i_3_22.counts 2 0.000
#> ola_mES_a2i_3_24.counts 2 1.000
#> ola_mES_a2i_3_25.counts 1 0.000
#> ola_mES_a2i_3_26.counts 3 0.502
#> ola_mES_a2i_3_28.counts 1 0.000
#> ola_mES_a2i_3_29.counts 1 0.000
#> ola_mES_a2i_3_3.counts 2 1.000
#> ola_mES_a2i_3_30.counts 3 1.000
#> ola_mES_a2i_3_31.counts 3 0.751
#> ola_mES_a2i_3_33.counts 2 1.000
#> ola_mES_a2i_3_34.counts 1 0.000
#> ola_mES_a2i_3_37.counts 1 0.000
#> ola_mES_a2i_3_38.counts 1 0.000
#> ola_mES_a2i_3_39.counts 1 0.751
#> ola_mES_a2i_3_4.counts 2 0.498
#> ola_mES_a2i_3_40.counts 1 0.000
#> ola_mES_a2i_3_41.counts 3 1.000
#> ola_mES_a2i_3_42.counts 3 1.000
#> ola_mES_a2i_3_44.counts 1 0.000
#> ola_mES_a2i_3_45.counts 3 0.751
#> ola_mES_a2i_3_47.counts 3 1.000
#> ola_mES_a2i_3_49.counts 1 0.000
#> ola_mES_a2i_3_50.counts 1 0.000
#> ola_mES_a2i_3_52.counts 3 1.000
#> ola_mES_a2i_3_54.counts 1 0.000
#> ola_mES_a2i_3_56.counts 3 0.000
#> ola_mES_a2i_3_58.counts 2 1.000
#> ola_mES_a2i_3_59.counts 1 0.000
#> ola_mES_a2i_3_6.counts 1 0.000
#> ola_mES_a2i_3_62.counts 1 0.000
#> ola_mES_a2i_3_64.counts 1 0.000
#> ola_mES_a2i_3_65.counts 1 0.000
#> ola_mES_a2i_3_66.counts 1 0.000
#> ola_mES_a2i_3_67.counts 3 1.000
#> ola_mES_a2i_3_68.counts 2 1.000
#> ola_mES_a2i_3_69.counts 1 0.000
#> ola_mES_a2i_3_7.counts 3 1.000
#> ola_mES_a2i_3_71.counts 1 0.000
#> ola_mES_a2i_3_73.counts 1 0.000
#> ola_mES_a2i_3_75.counts 3 1.000
#> ola_mES_a2i_3_76.counts 2 1.000
#> ola_mES_a2i_3_77.counts 1 0.000
#> ola_mES_a2i_3_8.counts 3 1.000
#> ola_mES_a2i_3_80.counts 2 1.000
#> ola_mES_a2i_3_81.counts 3 0.000
#> ola_mES_a2i_3_82.counts 1 0.000
#> ola_mES_a2i_3_83.counts 1 0.000
#> ola_mES_a2i_3_84.counts 1 1.000
#> ola_mES_a2i_3_85.counts 1 0.498
#> ola_mES_a2i_3_86.counts 1 0.502
#> ola_mES_a2i_3_89.counts 1 0.000
#> ola_mES_a2i_3_9.counts 2 0.249
#> ola_mES_a2i_3_90.counts 1 0.000
#> ola_mES_a2i_3_91.counts 2 1.000
#> ola_mES_a2i_3_92.counts 1 0.000
#> ola_mES_a2i_3_94.counts 3 0.751
#> ola_mES_a2i_3_96.counts 1 0.502
#> ola_mES_lif_1_1.counts 1 0.000
#> ola_mES_lif_1_10.counts 3 0.000
#> ola_mES_lif_1_11.counts 1 1.000
#> ola_mES_lif_1_12.counts 3 0.000
#> ola_mES_lif_1_13.counts 1 1.000
#> ola_mES_lif_1_14.counts 3 0.000
#> ola_mES_lif_1_15.counts 1 1.000
#> ola_mES_lif_1_17.counts 3 0.000
#> ola_mES_lif_1_18.counts 1 1.000
#> ola_mES_lif_1_19.counts 1 1.000
#> ola_mES_lif_1_2.counts 3 0.000
#> ola_mES_lif_1_20.counts 1 1.000
#> ola_mES_lif_1_21.counts 3 1.000
#> ola_mES_lif_1_22.counts 1 1.000
#> ola_mES_lif_1_23.counts 3 1.000
#> ola_mES_lif_1_24.counts 1 1.000
#> ola_mES_lif_1_25.counts 1 1.000
#> ola_mES_lif_1_26.counts 1 1.000
#> ola_mES_lif_1_28.counts 1 1.000
#> ola_mES_lif_1_29.counts 3 1.000
#> ola_mES_lif_1_3.counts 1 0.000
#> ola_mES_lif_1_30.counts 1 1.000
#> ola_mES_lif_1_31.counts 3 1.000
#> ola_mES_lif_1_32.counts 1 1.000
#> ola_mES_lif_1_33.counts 1 1.000
#> ola_mES_lif_1_34.counts 1 1.000
#> ola_mES_lif_1_35.counts 3 0.000
#> ola_mES_lif_1_36.counts 1 1.000
#> ola_mES_lif_1_37.counts 1 1.000
#> ola_mES_lif_1_38.counts 1 1.000
#> ola_mES_lif_1_39.counts 1 1.000
#> ola_mES_lif_1_4.counts 1 0.000
#> ola_mES_lif_1_41.counts 1 1.000
#> ola_mES_lif_1_42.counts 1 1.000
#> ola_mES_lif_1_43.counts 1 1.000
#> ola_mES_lif_1_44.counts 1 1.000
#> ola_mES_lif_1_45.counts 1 1.000
#> ola_mES_lif_1_46.counts 1 1.000
#> ola_mES_lif_1_47.counts 1 1.000
#> ola_mES_lif_1_48.counts 1 1.000
#> ola_mES_lif_1_49.counts 1 0.000
#> ola_mES_lif_1_5.counts 1 1.000
#> ola_mES_lif_1_50.counts 1 1.000
#> ola_mES_lif_1_51.counts 1 1.000
#> ola_mES_lif_1_52.counts 1 1.000
#> ola_mES_lif_1_53.counts 2 1.000
#> ola_mES_lif_1_54.counts 1 0.000
#> ola_mES_lif_1_55.counts 1 0.498
#> ola_mES_lif_1_56.counts 2 1.000
#> ola_mES_lif_1_57.counts 2 1.000
#> ola_mES_lif_1_58.counts 1 0.000
#> ola_mES_lif_1_59.counts 1 0.000
#> ola_mES_lif_1_6.counts 1 0.751
#> ola_mES_lif_1_60.counts 1 1.000
#> ola_mES_lif_1_61.counts 1 1.000
#> ola_mES_lif_1_62.counts 1 1.000
#> ola_mES_lif_1_63.counts 3 0.000
#> ola_mES_lif_1_64.counts 3 0.000
#> ola_mES_lif_1_65.counts 3 0.000
#> ola_mES_lif_1_66.counts 1 1.000
#> ola_mES_lif_1_67.counts 1 1.000
#> ola_mES_lif_1_68.counts 1 1.000
#> ola_mES_lif_1_69.counts 3 0.000
#> ola_mES_lif_1_7.counts 1 0.502
#> ola_mES_lif_1_70.counts 3 1.000
#> ola_mES_lif_1_72.counts 1 1.000
#> ola_mES_lif_1_73.counts 1 1.000
#> ola_mES_lif_1_74.counts 3 0.253
#> ola_mES_lif_1_75.counts 3 0.000
#> ola_mES_lif_1_79.counts 3 0.000
#> ola_mES_lif_1_8.counts 1 1.000
#> ola_mES_lif_1_80.counts 1 1.000
#> ola_mES_lif_1_81.counts 1 1.000
#> ola_mES_lif_1_82.counts 3 0.000
#> ola_mES_lif_1_86.counts 3 0.000
#> ola_mES_lif_1_9.counts 1 1.000
#> ola_mES_lif_1_91.counts 1 1.000
#> ola_mES_lif_1_92.counts 1 1.000
#> ola_mES_lif_1_93.counts 3 0.000
#> ola_mES_lif_1_95.counts 1 1.000
#> ola_mES_lif_1_96.counts 1 0.000
#> ola_mES_lif_2_1.counts 3 0.249
#> ola_mES_lif_2_10.counts 1 0.000
#> ola_mES_lif_2_11.counts 1 0.000
#> ola_mES_lif_2_12.counts 2 0.253
#> ola_mES_lif_2_13.counts 1 0.000
#> ola_mES_lif_2_14.counts 1 0.000
#> ola_mES_lif_2_15.counts 1 0.000
#> ola_mES_lif_2_16.counts 1 0.000
#> ola_mES_lif_2_17.counts 1 0.000
#> ola_mES_lif_2_18.counts 1 0.000
#> ola_mES_lif_2_19.counts 1 0.000
#> ola_mES_lif_2_2.counts 1 0.000
#> ola_mES_lif_2_20.counts 3 1.000
#> ola_mES_lif_2_21.counts 1 0.000
#> ola_mES_lif_2_23.counts 1 0.000
#> ola_mES_lif_2_25.counts 3 0.253
#> ola_mES_lif_2_26.counts 1 0.000
#> ola_mES_lif_2_27.counts 3 1.000
#> ola_mES_lif_2_28.counts 1 0.000
#> ola_mES_lif_2_29.counts 1 0.000
#> ola_mES_lif_2_3.counts 1 0.000
#> ola_mES_lif_2_30.counts 1 0.000
#> ola_mES_lif_2_31.counts 3 1.000
#> ola_mES_lif_2_32.counts 1 0.000
#> ola_mES_lif_2_33.counts 3 1.000
#> ola_mES_lif_2_34.counts 1 0.000
#> ola_mES_lif_2_35.counts 1 0.000
#> ola_mES_lif_2_36.counts 3 0.000
#> ola_mES_lif_2_37.counts 3 1.000
#> ola_mES_lif_2_38.counts 1 0.000
#> ola_mES_lif_2_39.counts 1 0.000
#> ola_mES_lif_2_4.counts 1 0.000
#> ola_mES_lif_2_40.counts 1 0.000
#> ola_mES_lif_2_41.counts 3 0.498
#> ola_mES_lif_2_42.counts 1 0.000
#> ola_mES_lif_2_43.counts 1 0.000
#> ola_mES_lif_2_44.counts 1 0.000
#> ola_mES_lif_2_45.counts 3 0.751
#> ola_mES_lif_2_46.counts 1 0.000
#> ola_mES_lif_2_47.counts 3 0.000
#> ola_mES_lif_2_48.counts 2 1.000
#> ola_mES_lif_2_49.counts 1 0.000
#> ola_mES_lif_2_5.counts 1 0.000
#> ola_mES_lif_2_50.counts 1 0.000
#> ola_mES_lif_2_51.counts 1 0.751
#> ola_mES_lif_2_52.counts 1 0.000
#> ola_mES_lif_2_53.counts 1 0.000
#> ola_mES_lif_2_54.counts 1 0.000
#> ola_mES_lif_2_55.counts 1 0.000
#> ola_mES_lif_2_56.counts 1 0.000
#> ola_mES_lif_2_57.counts 3 0.249
#> ola_mES_lif_2_58.counts 1 0.000
#> ola_mES_lif_2_59.counts 1 0.000
#> ola_mES_lif_2_6.counts 1 0.000
#> ola_mES_lif_2_60.counts 3 0.249
#> ola_mES_lif_2_61.counts 1 0.751
#> ola_mES_lif_2_63.counts 1 0.000
#> ola_mES_lif_2_64.counts 1 0.000
#> ola_mES_lif_2_65.counts 1 0.000
#> ola_mES_lif_2_66.counts 1 0.000
#> ola_mES_lif_2_67.counts 1 0.000
#> ola_mES_lif_2_68.counts 1 0.000
#> ola_mES_lif_2_69.counts 2 1.000
#> ola_mES_lif_2_7.counts 1 0.000
#> ola_mES_lif_2_70.counts 1 0.000
#> ola_mES_lif_2_71.counts 1 0.000
#> ola_mES_lif_2_72.counts 1 0.000
#> ola_mES_lif_2_73.counts 2 1.000
#> ola_mES_lif_2_74.counts 1 0.000
#> ola_mES_lif_2_75.counts 1 0.000
#> ola_mES_lif_2_76.counts 1 0.000
#> ola_mES_lif_2_77.counts 1 0.000
#> ola_mES_lif_2_79.counts 3 1.000
#> ola_mES_lif_2_8.counts 1 0.000
#> ola_mES_lif_2_80.counts 1 0.000
#> ola_mES_lif_2_81.counts 3 1.000
#> ola_mES_lif_2_82.counts 1 0.000
#> ola_mES_lif_2_83.counts 1 0.000
#> ola_mES_lif_2_84.counts 1 0.000
#> ola_mES_lif_2_86.counts 3 0.000
#> ola_mES_lif_2_87.counts 1 0.000
#> ola_mES_lif_2_89.counts 1 0.000
#> ola_mES_lif_2_9.counts 3 0.000
#> ola_mES_lif_2_90.counts 3 1.000
#> ola_mES_lif_2_91.counts 3 1.000
#> ola_mES_lif_2_92.counts 3 0.249
#> ola_mES_lif_2_93.counts 1 0.000
#> ola_mES_lif_2_94.counts 1 0.000
#> ola_mES_lif_2_95.counts 1 0.000
#> ola_mES_lif_2_96.counts 3 1.000
#> ola_mES_lif_3_1.counts 1 1.000
#> ola_mES_lif_3_10.counts 1 1.000
#> ola_mES_lif_3_11.counts 3 1.000
#> ola_mES_lif_3_12.counts 2 1.000
#> ola_mES_lif_3_13.counts 1 1.000
#> ola_mES_lif_3_14.counts 3 0.000
#> ola_mES_lif_3_15.counts 3 0.000
#> ola_mES_lif_3_16.counts 3 0.000
#> ola_mES_lif_3_17.counts 3 0.000
#> ola_mES_lif_3_19.counts 3 0.000
#> ola_mES_lif_3_2.counts 2 1.000
#> ola_mES_lif_3_20.counts 3 0.000
#> ola_mES_lif_3_21.counts 2 1.000
#> ola_mES_lif_3_22.counts 3 0.000
#> ola_mES_lif_3_23.counts 3 0.000
#> ola_mES_lif_3_24.counts 3 1.000
#> ola_mES_lif_3_26.counts 3 0.751
#> ola_mES_lif_3_27.counts 1 1.000
#> ola_mES_lif_3_28.counts 3 0.000
#> ola_mES_lif_3_29.counts 3 1.000
#> ola_mES_lif_3_3.counts 1 1.000
#> ola_mES_lif_3_30.counts 1 1.000
#> ola_mES_lif_3_31.counts 3 0.000
#> ola_mES_lif_3_32.counts 1 1.000
#> ola_mES_lif_3_33.counts 3 0.000
#> ola_mES_lif_3_34.counts 3 0.000
#> ola_mES_lif_3_35.counts 3 0.000
#> ola_mES_lif_3_36.counts 3 0.000
#> ola_mES_lif_3_37.counts 3 0.000
#> ola_mES_lif_3_38.counts 3 1.000
#> ola_mES_lif_3_39.counts 3 0.502
#> ola_mES_lif_3_4.counts 1 1.000
#> ola_mES_lif_3_40.counts 1 1.000
#> ola_mES_lif_3_42.counts 1 1.000
#> ola_mES_lif_3_45.counts 3 0.000
#> ola_mES_lif_3_46.counts 3 0.000
#> ola_mES_lif_3_47.counts 3 0.000
#> ola_mES_lif_3_48.counts 3 0.000
#> ola_mES_lif_3_49.counts 1 1.000
#> ola_mES_lif_3_5.counts 1 1.000
#> ola_mES_lif_3_50.counts 1 1.000
#> ola_mES_lif_3_51.counts 1 1.000
#> ola_mES_lif_3_52.counts 1 1.000
#> ola_mES_lif_3_53.counts 1 1.000
#> ola_mES_lif_3_54.counts 1 1.000
#> ola_mES_lif_3_56.counts 1 1.000
#> ola_mES_lif_3_57.counts 3 0.000
#> ola_mES_lif_3_58.counts 1 1.000
#> ola_mES_lif_3_59.counts 1 1.000
#> ola_mES_lif_3_6.counts 1 1.000
#> ola_mES_lif_3_60.counts 1 1.000
#> ola_mES_lif_3_61.counts 1 1.000
#> ola_mES_lif_3_63.counts 1 1.000
#> ola_mES_lif_3_64.counts 1 1.000
#> ola_mES_lif_3_65.counts 1 1.000
#> ola_mES_lif_3_69.counts 3 0.000
#> ola_mES_lif_3_70.counts 3 0.000
#> ola_mES_lif_3_71.counts 1 1.000
#> ola_mES_lif_3_72.counts 3 0.000
#> ola_mES_lif_3_73.counts 3 0.000
#> ola_mES_lif_3_74.counts 3 0.000
#> ola_mES_lif_3_75.counts 3 0.000
#> ola_mES_lif_3_76.counts 3 0.000
#> ola_mES_lif_3_77.counts 2 1.000
#> ola_mES_lif_3_78.counts 3 0.000
#> ola_mES_lif_3_8.counts 1 1.000
#> ola_mES_lif_3_80.counts 3 1.000
#> ola_mES_lif_3_82.counts 3 1.000
#> ola_mES_lif_3_83.counts 3 0.000
#> ola_mES_lif_3_84.counts 3 1.000
#> ola_mES_lif_3_87.counts 3 0.000
#> ola_mES_lif_3_88.counts 3 0.000
#> ola_mES_lif_3_89.counts 2 1.000
#> ola_mES_lif_3_9.counts 2 1.000
#> ola_mES_lif_3_90.counts 3 0.000
#> ola_mES_lif_3_92.counts 3 0.000
#> ola_mES_lif_3_94.counts 3 0.000
#> ola_mES_lif_3_95.counts 3 0.000
#> ola_mES_lif_3_96.counts 3 1.000
get_classes(res, k = 4)
#> class p
#> ola_mES_2i_2_1.counts 1 0.000
#> ola_mES_2i_2_10.counts 1 0.000
#> ola_mES_2i_2_11.counts 1 0.000
#> ola_mES_2i_2_12.counts 1 0.000
#> ola_mES_2i_2_13.counts 1 0.000
#> ola_mES_2i_2_14.counts 1 0.000
#> ola_mES_2i_2_15.counts 1 0.000
#> ola_mES_2i_2_16.counts 1 0.000
#> ola_mES_2i_2_17.counts 1 0.000
#> ola_mES_2i_2_18.counts 1 0.000
#> ola_mES_2i_2_19.counts 1 0.000
#> ola_mES_2i_2_2.counts 4 1.000
#> ola_mES_2i_2_20.counts 1 0.000
#> ola_mES_2i_2_21.counts 1 0.000
#> ola_mES_2i_2_22.counts 1 0.000
#> ola_mES_2i_2_23.counts 1 0.000
#> ola_mES_2i_2_24.counts 1 0.000
#> ola_mES_2i_2_25.counts 1 0.000
#> ola_mES_2i_2_26.counts 1 0.000
#> ola_mES_2i_2_27.counts 1 0.000
#> ola_mES_2i_2_28.counts 1 0.000
#> ola_mES_2i_2_29.counts 1 0.000
#> ola_mES_2i_2_3.counts 1 0.000
#> ola_mES_2i_2_30.counts 1 0.000
#> ola_mES_2i_2_31.counts 1 0.000
#> ola_mES_2i_2_32.counts 1 0.000
#> ola_mES_2i_2_33.counts 1 0.000
#> ola_mES_2i_2_34.counts 1 0.000
#> ola_mES_2i_2_35.counts 1 0.000
#> ola_mES_2i_2_36.counts 1 0.000
#> ola_mES_2i_2_37.counts 1 0.000
#> ola_mES_2i_2_38.counts 1 0.000
#> ola_mES_2i_2_39.counts 1 0.000
#> ola_mES_2i_2_4.counts 1 0.000
#> ola_mES_2i_2_40.counts 1 0.000
#> ola_mES_2i_2_41.counts 1 0.000
#> ola_mES_2i_2_42.counts 1 0.000
#> ola_mES_2i_2_43.counts 1 0.000
#> ola_mES_2i_2_44.counts 1 0.000
#> ola_mES_2i_2_46.counts 1 0.000
#> ola_mES_2i_2_47.counts 1 0.000
#> ola_mES_2i_2_48.counts 1 0.000
#> ola_mES_2i_2_49.counts 1 0.000
#> ola_mES_2i_2_5.counts 1 0.000
#> ola_mES_2i_2_50.counts 1 0.000
#> ola_mES_2i_2_51.counts 1 0.000
#> ola_mES_2i_2_52.counts 1 0.000
#> ola_mES_2i_2_53.counts 1 0.000
#> ola_mES_2i_2_54.counts 1 0.000
#> ola_mES_2i_2_55.counts 1 0.000
#> ola_mES_2i_2_56.counts 1 0.000
#> ola_mES_2i_2_57.counts 1 0.000
#> ola_mES_2i_2_58.counts 1 0.000
#> ola_mES_2i_2_59.counts 1 0.000
#> ola_mES_2i_2_6.counts 1 0.000
#> ola_mES_2i_2_60.counts 1 0.000
#> ola_mES_2i_2_61.counts 1 0.000
#> ola_mES_2i_2_62.counts 4 1.000
#> ola_mES_2i_2_63.counts 1 0.000
#> ola_mES_2i_2_64.counts 1 0.000
#> ola_mES_2i_2_66.counts 1 0.000
#> ola_mES_2i_2_67.counts 1 0.000
#> ola_mES_2i_2_68.counts 1 0.000
#> ola_mES_2i_2_69.counts 1 0.000
#> ola_mES_2i_2_7.counts 1 0.000
#> ola_mES_2i_2_72.counts 1 0.000
#> ola_mES_2i_2_73.counts 1 0.000
#> ola_mES_2i_2_74.counts 1 0.000
#> ola_mES_2i_2_75.counts 4 1.000
#> ola_mES_2i_2_76.counts 1 0.000
#> ola_mES_2i_2_79.counts 1 0.000
#> ola_mES_2i_2_8.counts 1 0.000
#> ola_mES_2i_2_80.counts 1 0.000
#> ola_mES_2i_2_81.counts 1 0.000
#> ola_mES_2i_2_85.counts 4 1.000
#> ola_mES_2i_2_86.counts 1 0.000
#> ola_mES_2i_2_87.counts 4 1.000
#> ola_mES_2i_2_90.counts 1 0.000
#> ola_mES_2i_2_91.counts 1 0.000
#> ola_mES_2i_2_93.counts 1 0.000
#> ola_mES_2i_2_94.counts 1 0.000
#> ola_mES_2i_2_95.counts 1 0.000
#> ola_mES_2i_3_10.counts 3 0.000
#> ola_mES_2i_3_11.counts 2 0.498
#> ola_mES_2i_3_12.counts 3 0.000
#> ola_mES_2i_3_13.counts 3 0.249
#> ola_mES_2i_3_15.counts 3 0.249
#> ola_mES_2i_3_16.counts 2 0.000
#> ola_mES_2i_3_17.counts 2 0.000
#> ola_mES_2i_3_18.counts 4 1.000
#> ola_mES_2i_3_19.counts 3 0.000
#> ola_mES_2i_3_2.counts 3 0.498
#> ola_mES_2i_3_20.counts 3 0.000
#> ola_mES_2i_3_21.counts 3 0.000
#> ola_mES_2i_3_23.counts 2 0.000
#> ola_mES_2i_3_25.counts 2 0.000
#> ola_mES_2i_3_26.counts 3 0.249
#> ola_mES_2i_3_28.counts 2 0.000
#> ola_mES_2i_3_30.counts 3 0.249
#> ola_mES_2i_3_31.counts 2 0.000
#> ola_mES_2i_3_33.counts 3 0.000
#> ola_mES_2i_3_36.counts 2 0.000
#> ola_mES_2i_3_37.counts 2 0.249
#> ola_mES_2i_3_38.counts 3 0.747
#> ola_mES_2i_3_39.counts 3 0.000
#> ola_mES_2i_3_4.counts 2 0.000
#> ola_mES_2i_3_41.counts 3 0.000
#> ola_mES_2i_3_42.counts 2 0.498
#> ola_mES_2i_3_43.counts 2 0.000
#> ola_mES_2i_3_45.counts 3 0.249
#> ola_mES_2i_3_46.counts 3 0.249
#> ola_mES_2i_3_47.counts 3 0.000
#> ola_mES_2i_3_49.counts 2 0.000
#> ola_mES_2i_3_5.counts 3 0.498
#> ola_mES_2i_3_52.counts 3 0.000
#> ola_mES_2i_3_54.counts 3 0.000
#> ola_mES_2i_3_56.counts 3 0.000
#> ola_mES_2i_3_57.counts 3 0.000
#> ola_mES_2i_3_58.counts 3 0.000
#> ola_mES_2i_3_6.counts 3 0.000
#> ola_mES_2i_3_62.counts 2 1.000
#> ola_mES_2i_3_65.counts 3 0.000
#> ola_mES_2i_3_66.counts 2 0.000
#> ola_mES_2i_3_69.counts 3 0.000
#> ola_mES_2i_3_7.counts 3 0.000
#> ola_mES_2i_3_73.counts 3 0.000
#> ola_mES_2i_3_76.counts 3 0.502
#> ola_mES_2i_3_77.counts 3 0.000
#> ola_mES_2i_3_78.counts 2 0.000
#> ola_mES_2i_3_79.counts 3 0.000
#> ola_mES_2i_3_8.counts 3 0.000
#> ola_mES_2i_3_80.counts 3 0.000
#> ola_mES_2i_3_82.counts 3 0.249
#> ola_mES_2i_3_83.counts 3 0.747
#> ola_mES_2i_3_84.counts 2 0.498
#> ola_mES_2i_3_87.counts 3 0.000
#> ola_mES_2i_3_88.counts 3 0.751
#> ola_mES_2i_3_90.counts 2 0.253
#> ola_mES_2i_3_91.counts 3 0.000
#> ola_mES_2i_3_92.counts 3 0.000
#> ola_mES_2i_3_96.counts 3 0.000
#> ola_mES_2i_4_1.counts 2 0.000
#> ola_mES_2i_4_10.counts 2 0.000
#> ola_mES_2i_4_11.counts 2 0.000
#> ola_mES_2i_4_13.counts 2 0.000
#> ola_mES_2i_4_17.counts 2 0.000
#> ola_mES_2i_4_18.counts 2 0.253
#> ola_mES_2i_4_2.counts 1 0.000
#> ola_mES_2i_4_20.counts 2 0.000
#> ola_mES_2i_4_21.counts 2 0.000
#> ola_mES_2i_4_22.counts 2 0.000
#> ola_mES_2i_4_23.counts 2 1.000
#> ola_mES_2i_4_24.counts 2 0.000
#> ola_mES_2i_4_25.counts 2 0.000
#> ola_mES_2i_4_26.counts 2 0.000
#> ola_mES_2i_4_27.counts 2 0.000
#> ola_mES_2i_4_29.counts 2 0.000
#> ola_mES_2i_4_30.counts 2 0.000
#> ola_mES_2i_4_31.counts 2 1.000
#> ola_mES_2i_4_32.counts 2 0.000
#> ola_mES_2i_4_33.counts 2 0.000
#> ola_mES_2i_4_34.counts 2 0.000
#> ola_mES_2i_4_35.counts 2 0.000
#> ola_mES_2i_4_36.counts 2 0.000
#> ola_mES_2i_4_37.counts 4 1.000
#> ola_mES_2i_4_38.counts 1 0.000
#> ola_mES_2i_4_39.counts 2 0.000
#> ola_mES_2i_4_40.counts 1 0.000
#> ola_mES_2i_4_42.counts 2 1.000
#> ola_mES_2i_4_43.counts 2 0.000
#> ola_mES_2i_4_44.counts 2 0.000
#> ola_mES_2i_4_46.counts 2 0.000
#> ola_mES_2i_4_49.counts 1 0.000
#> ola_mES_2i_4_5.counts 2 0.249
#> ola_mES_2i_4_50.counts 2 0.000
#> ola_mES_2i_4_52.counts 2 0.000
#> ola_mES_2i_4_53.counts 1 0.000
#> ola_mES_2i_4_54.counts 2 0.249
#> ola_mES_2i_4_56.counts 2 0.000
#> ola_mES_2i_4_57.counts 2 0.000
#> ola_mES_2i_4_58.counts 2 0.000
#> ola_mES_2i_4_59.counts 2 0.000
#> ola_mES_2i_4_6.counts 2 0.000
#> ola_mES_2i_4_60.counts 3 1.000
#> ola_mES_2i_4_61.counts 2 0.000
#> ola_mES_2i_4_62.counts 4 1.000
#> ola_mES_2i_4_63.counts 3 1.000
#> ola_mES_2i_4_64.counts 2 0.000
#> ola_mES_2i_4_65.counts 2 0.000
#> ola_mES_2i_4_66.counts 2 0.000
#> ola_mES_2i_4_67.counts 1 0.751
#> ola_mES_2i_4_68.counts 2 0.502
#> ola_mES_2i_4_69.counts 2 0.000
#> ola_mES_2i_4_7.counts 2 0.000
#> ola_mES_2i_4_70.counts 2 0.502
#> ola_mES_2i_4_71.counts 2 0.000
#> ola_mES_2i_4_72.counts 2 0.000
#> ola_mES_2i_4_73.counts 2 0.000
#> ola_mES_2i_4_75.counts 3 0.000
#> ola_mES_2i_4_77.counts 2 0.000
#> ola_mES_2i_4_78.counts 3 1.000
#> ola_mES_2i_4_80.counts 2 0.000
#> ola_mES_2i_4_81.counts 2 0.000
#> ola_mES_2i_4_83.counts 2 1.000
#> ola_mES_2i_4_84.counts 2 0.249
#> ola_mES_2i_4_88.counts 3 0.751
#> ola_mES_2i_4_89.counts 3 1.000
#> ola_mES_2i_4_9.counts 2 0.000
#> ola_mES_2i_4_90.counts 2 0.000
#> ola_mES_2i_4_91.counts 2 0.000
#> ola_mES_2i_4_94.counts 2 0.498
#> ola_mES_2i_4_95.counts 3 0.000
#> ola_mES_2i_4_96.counts 3 0.000
#> ola_mES_2i_5_1.counts 2 0.000
#> ola_mES_2i_5_10.counts 2 0.000
#> ola_mES_2i_5_11.counts 2 0.000
#> ola_mES_2i_5_12.counts 2 0.000
#> ola_mES_2i_5_13.counts 2 0.000
#> ola_mES_2i_5_14.counts 2 0.000
#> ola_mES_2i_5_15.counts 2 0.000
#> ola_mES_2i_5_16.counts 2 0.000
#> ola_mES_2i_5_17.counts 2 1.000
#> ola_mES_2i_5_18.counts 2 0.000
#> ola_mES_2i_5_19.counts 2 0.000
#> ola_mES_2i_5_2.counts 2 1.000
#> ola_mES_2i_5_20.counts 2 0.000
#> ola_mES_2i_5_21.counts 2 0.000
#> ola_mES_2i_5_23.counts 2 0.000
#> ola_mES_2i_5_24.counts 2 0.000
#> ola_mES_2i_5_25.counts 2 0.000
#> ola_mES_2i_5_26.counts 2 0.000
#> ola_mES_2i_5_27.counts 2 0.000
#> ola_mES_2i_5_28.counts 2 0.249
#> ola_mES_2i_5_29.counts 2 0.000
#> ola_mES_2i_5_3.counts 2 0.000
#> ola_mES_2i_5_30.counts 2 0.000
#> ola_mES_2i_5_31.counts 2 0.000
#> ola_mES_2i_5_32.counts 2 0.000
#> ola_mES_2i_5_34.counts 2 0.000
#> ola_mES_2i_5_35.counts 2 0.000
#> ola_mES_2i_5_36.counts 2 0.000
#> ola_mES_2i_5_38.counts 2 0.000
#> ola_mES_2i_5_39.counts 2 0.000
#> ola_mES_2i_5_4.counts 2 1.000
#> ola_mES_2i_5_40.counts 2 0.000
#> ola_mES_2i_5_41.counts 1 1.000
#> ola_mES_2i_5_42.counts 2 0.000
#> ola_mES_2i_5_44.counts 2 0.000
#> ola_mES_2i_5_45.counts 2 0.000
#> ola_mES_2i_5_46.counts 2 0.000
#> ola_mES_2i_5_47.counts 2 0.000
#> ola_mES_2i_5_48.counts 2 0.000
#> ola_mES_2i_5_49.counts 2 0.000
#> ola_mES_2i_5_5.counts 2 0.000
#> ola_mES_2i_5_51.counts 2 0.000
#> ola_mES_2i_5_52.counts 2 0.249
#> ola_mES_2i_5_53.counts 2 0.000
#> ola_mES_2i_5_54.counts 2 0.000
#> ola_mES_2i_5_55.counts 2 0.000
#> ola_mES_2i_5_56.counts 2 0.000
#> ola_mES_2i_5_57.counts 1 1.000
#> ola_mES_2i_5_58.counts 2 0.000
#> ola_mES_2i_5_59.counts 2 0.000
#> ola_mES_2i_5_6.counts 2 1.000
#> ola_mES_2i_5_61.counts 2 0.000
#> ola_mES_2i_5_62.counts 2 0.000
#> ola_mES_2i_5_63.counts 2 0.000
#> ola_mES_2i_5_65.counts 2 0.000
#> ola_mES_2i_5_66.counts 2 0.000
#> ola_mES_2i_5_67.counts 2 0.000
#> ola_mES_2i_5_68.counts 2 0.000
#> ola_mES_2i_5_69.counts 2 0.000
#> ola_mES_2i_5_7.counts 2 0.000
#> ola_mES_2i_5_70.counts 2 0.000
#> ola_mES_2i_5_72.counts 2 0.000
#> ola_mES_2i_5_73.counts 2 0.000
#> ola_mES_2i_5_74.counts 2 0.000
#> ola_mES_2i_5_75.counts 2 0.000
#> ola_mES_2i_5_76.counts 2 0.000
#> ola_mES_2i_5_77.counts 2 0.000
#> ola_mES_2i_5_78.counts 2 0.000
#> ola_mES_2i_5_8.counts 2 0.000
#> ola_mES_2i_5_81.counts 3 1.000
#> ola_mES_2i_5_82.counts 2 0.000
#> ola_mES_2i_5_83.counts 2 0.000
#> ola_mES_2i_5_85.counts 2 0.000
#> ola_mES_2i_5_86.counts 2 0.000
#> ola_mES_2i_5_87.counts 2 0.000
#> ola_mES_2i_5_88.counts 2 0.000
#> ola_mES_2i_5_9.counts 3 1.000
#> ola_mES_2i_5_90.counts 2 0.000
#> ola_mES_2i_5_92.counts 2 0.000
#> ola_mES_2i_5_94.counts 2 0.000
#> ola_mES_2i_5_95.counts 1 1.000
#> ola_mES_2i_5_96.counts 2 0.000
#> ola_mES_a2i_2_1.counts 1 0.000
#> ola_mES_a2i_2_10.counts 1 0.747
#> ola_mES_a2i_2_11.counts 1 0.000
#> ola_mES_a2i_2_12.counts 1 0.000
#> ola_mES_a2i_2_13.counts 4 0.249
#> ola_mES_a2i_2_14.counts 1 0.000
#> ola_mES_a2i_2_15.counts 1 0.000
#> ola_mES_a2i_2_16.counts 1 0.000
#> ola_mES_a2i_2_17.counts 1 0.000
#> ola_mES_a2i_2_18.counts 1 0.000
#> ola_mES_a2i_2_19.counts 1 0.000
#> ola_mES_a2i_2_2.counts 1 0.498
#> ola_mES_a2i_2_20.counts 1 1.000
#> ola_mES_a2i_2_21.counts 1 0.000
#> ola_mES_a2i_2_22.counts 1 0.000
#> ola_mES_a2i_2_23.counts 1 0.000
#> ola_mES_a2i_2_24.counts 1 0.000
#> ola_mES_a2i_2_25.counts 1 0.000
#> ola_mES_a2i_2_26.counts 4 0.000
#> ola_mES_a2i_2_27.counts 4 1.000
#> ola_mES_a2i_2_28.counts 1 0.000
#> ola_mES_a2i_2_29.counts 1 0.000
#> ola_mES_a2i_2_3.counts 1 0.000
#> ola_mES_a2i_2_30.counts 3 0.000
#> ola_mES_a2i_2_31.counts 3 0.000
#> ola_mES_a2i_2_32.counts 4 0.498
#> ola_mES_a2i_2_33.counts 4 1.000
#> ola_mES_a2i_2_34.counts 1 0.000
#> ola_mES_a2i_2_35.counts 3 0.000
#> ola_mES_a2i_2_36.counts 3 0.000
#> ola_mES_a2i_2_37.counts 4 0.000
#> ola_mES_a2i_2_38.counts 4 0.000
#> ola_mES_a2i_2_39.counts 4 0.000
#> ola_mES_a2i_2_4.counts 1 0.000
#> ola_mES_a2i_2_40.counts 4 0.249
#> ola_mES_a2i_2_41.counts 1 0.000
#> ola_mES_a2i_2_42.counts 1 0.000
#> ola_mES_a2i_2_43.counts 4 0.000
#> ola_mES_a2i_2_44.counts 1 0.000
#> ola_mES_a2i_2_45.counts 1 0.000
#> ola_mES_a2i_2_46.counts 1 0.000
#> ola_mES_a2i_2_47.counts 1 0.000
#> ola_mES_a2i_2_48.counts 4 0.253
#> ola_mES_a2i_2_49.counts 1 0.000
#> ola_mES_a2i_2_5.counts 1 0.000
#> ola_mES_a2i_2_50.counts 4 1.000
#> ola_mES_a2i_2_51.counts 1 0.502
#> ola_mES_a2i_2_52.counts 4 1.000
#> ola_mES_a2i_2_53.counts 4 0.249
#> ola_mES_a2i_2_54.counts 1 0.000
#> ola_mES_a2i_2_55.counts 1 0.000
#> ola_mES_a2i_2_56.counts 4 0.498
#> ola_mES_a2i_2_57.counts 1 0.000
#> ola_mES_a2i_2_59.counts 1 0.000
#> ola_mES_a2i_2_6.counts 3 0.249
#> ola_mES_a2i_2_60.counts 1 0.000
#> ola_mES_a2i_2_61.counts 4 0.751
#> ola_mES_a2i_2_62.counts 1 0.000
#> ola_mES_a2i_2_63.counts 4 0.000
#> ola_mES_a2i_2_64.counts 4 1.000
#> ola_mES_a2i_2_65.counts 4 0.498
#> ola_mES_a2i_2_66.counts 1 0.000
#> ola_mES_a2i_2_67.counts 4 1.000
#> ola_mES_a2i_2_68.counts 1 0.498
#> ola_mES_a2i_2_69.counts 2 0.000
#> ola_mES_a2i_2_7.counts 1 1.000
#> ola_mES_a2i_2_70.counts 1 0.000
#> ola_mES_a2i_2_71.counts 1 0.000
#> ola_mES_a2i_2_72.counts 1 0.502
#> ola_mES_a2i_2_73.counts 1 0.000
#> ola_mES_a2i_2_74.counts 4 0.502
#> ola_mES_a2i_2_75.counts 1 0.502
#> ola_mES_a2i_2_76.counts 4 1.000
#> ola_mES_a2i_2_77.counts 1 0.000
#> ola_mES_a2i_2_78.counts 1 0.000
#> ola_mES_a2i_2_8.counts 1 0.000
#> ola_mES_a2i_2_80.counts 1 0.000
#> ola_mES_a2i_2_81.counts 1 0.000
#> ola_mES_a2i_2_82.counts 1 0.000
#> ola_mES_a2i_2_83.counts 1 0.000
#> ola_mES_a2i_2_84.counts 1 0.000
#> ola_mES_a2i_2_86.counts 1 0.000
#> ola_mES_a2i_2_87.counts 1 0.000
#> ola_mES_a2i_2_88.counts 1 0.000
#> ola_mES_a2i_2_89.counts 3 0.000
#> ola_mES_a2i_2_9.counts 4 0.249
#> ola_mES_a2i_2_90.counts 1 0.000
#> ola_mES_a2i_2_91.counts 3 0.253
#> ola_mES_a2i_2_92.counts 4 1.000
#> ola_mES_a2i_2_93.counts 4 0.000
#> ola_mES_a2i_2_94.counts 1 0.000
#> ola_mES_a2i_2_95.counts 1 0.000
#> ola_mES_a2i_2_96.counts 1 0.000
#> ola_mES_a2i_3_10.counts 2 0.000
#> ola_mES_a2i_3_11.counts 3 0.000
#> ola_mES_a2i_3_13.counts 4 1.000
#> ola_mES_a2i_3_16.counts 1 0.000
#> ola_mES_a2i_3_17.counts 4 1.000
#> ola_mES_a2i_3_18.counts 2 1.000
#> ola_mES_a2i_3_2.counts 2 0.000
#> ola_mES_a2i_3_20.counts 4 1.000
#> ola_mES_a2i_3_21.counts 3 0.000
#> ola_mES_a2i_3_22.counts 3 0.751
#> ola_mES_a2i_3_24.counts 3 0.000
#> ola_mES_a2i_3_25.counts 1 0.000
#> ola_mES_a2i_3_26.counts 3 1.000
#> ola_mES_a2i_3_28.counts 4 1.000
#> ola_mES_a2i_3_29.counts 4 1.000
#> ola_mES_a2i_3_3.counts 2 0.000
#> ola_mES_a2i_3_30.counts 2 1.000
#> ola_mES_a2i_3_31.counts 2 1.000
#> ola_mES_a2i_3_33.counts 3 0.000
#> ola_mES_a2i_3_34.counts 1 0.000
#> ola_mES_a2i_3_37.counts 1 0.000
#> ola_mES_a2i_3_38.counts 4 1.000
#> ola_mES_a2i_3_39.counts 4 0.502
#> ola_mES_a2i_3_4.counts 3 0.249
#> ola_mES_a2i_3_40.counts 1 0.000
#> ola_mES_a2i_3_41.counts 4 0.747
#> ola_mES_a2i_3_42.counts 4 1.000
#> ola_mES_a2i_3_44.counts 1 0.000
#> ola_mES_a2i_3_45.counts 2 0.502
#> ola_mES_a2i_3_47.counts 4 1.000
#> ola_mES_a2i_3_49.counts 1 0.000
#> ola_mES_a2i_3_50.counts 4 0.751
#> ola_mES_a2i_3_52.counts 4 0.249
#> ola_mES_a2i_3_54.counts 1 0.000
#> ola_mES_a2i_3_56.counts 4 0.253
#> ola_mES_a2i_3_58.counts 3 0.000
#> ola_mES_a2i_3_59.counts 1 0.000
#> ola_mES_a2i_3_6.counts 1 0.000
#> ola_mES_a2i_3_62.counts 1 1.000
#> ola_mES_a2i_3_64.counts 1 0.000
#> ola_mES_a2i_3_65.counts 1 0.000
#> ola_mES_a2i_3_66.counts 1 0.000
#> ola_mES_a2i_3_67.counts 4 1.000
#> ola_mES_a2i_3_68.counts 3 0.000
#> ola_mES_a2i_3_69.counts 1 0.751
#> ola_mES_a2i_3_7.counts 2 0.000
#> ola_mES_a2i_3_71.counts 1 0.000
#> ola_mES_a2i_3_73.counts 1 0.000
#> ola_mES_a2i_3_75.counts 3 0.000
#> ola_mES_a2i_3_76.counts 2 0.000
#> ola_mES_a2i_3_77.counts 1 0.000
#> ola_mES_a2i_3_8.counts 3 0.747
#> ola_mES_a2i_3_80.counts 3 0.000
#> ola_mES_a2i_3_81.counts 3 0.000
#> ola_mES_a2i_3_82.counts 1 0.000
#> ola_mES_a2i_3_83.counts 1 0.498
#> ola_mES_a2i_3_84.counts 4 0.000
#> ola_mES_a2i_3_85.counts 1 0.498
#> ola_mES_a2i_3_86.counts 4 0.000
#> ola_mES_a2i_3_89.counts 1 0.000
#> ola_mES_a2i_3_9.counts 3 0.502
#> ola_mES_a2i_3_90.counts 1 0.000
#> ola_mES_a2i_3_91.counts 3 0.000
#> ola_mES_a2i_3_92.counts 1 0.000
#> ola_mES_a2i_3_94.counts 4 0.000
#> ola_mES_a2i_3_96.counts 4 0.000
#> ola_mES_lif_1_1.counts 1 0.000
#> ola_mES_lif_1_10.counts 4 0.000
#> ola_mES_lif_1_11.counts 4 0.000
#> ola_mES_lif_1_12.counts 4 0.000
#> ola_mES_lif_1_13.counts 1 1.000
#> ola_mES_lif_1_14.counts 4 0.000
#> ola_mES_lif_1_15.counts 1 1.000
#> ola_mES_lif_1_17.counts 3 0.000
#> ola_mES_lif_1_18.counts 1 1.000
#> ola_mES_lif_1_19.counts 4 1.000
#> ola_mES_lif_1_2.counts 3 0.000
#> ola_mES_lif_1_20.counts 4 0.000
#> ola_mES_lif_1_21.counts 4 0.000
#> ola_mES_lif_1_22.counts 4 0.000
#> ola_mES_lif_1_23.counts 3 0.000
#> ola_mES_lif_1_24.counts 4 0.000
#> ola_mES_lif_1_25.counts 4 0.000
#> ola_mES_lif_1_26.counts 4 0.751
#> ola_mES_lif_1_28.counts 4 0.000
#> ola_mES_lif_1_29.counts 3 0.000
#> ola_mES_lif_1_3.counts 1 1.000
#> ola_mES_lif_1_30.counts 4 1.000
#> ola_mES_lif_1_31.counts 3 0.000
#> ola_mES_lif_1_32.counts 4 0.000
#> ola_mES_lif_1_33.counts 4 0.000
#> ola_mES_lif_1_34.counts 1 1.000
#> ola_mES_lif_1_35.counts 4 0.000
#> ola_mES_lif_1_36.counts 4 0.000
#> ola_mES_lif_1_37.counts 1 1.000
#> ola_mES_lif_1_38.counts 4 0.000
#> ola_mES_lif_1_39.counts 4 0.000
#> ola_mES_lif_1_4.counts 1 1.000
#> ola_mES_lif_1_41.counts 1 1.000
#> ola_mES_lif_1_42.counts 4 0.000
#> ola_mES_lif_1_43.counts 1 1.000
#> ola_mES_lif_1_44.counts 1 1.000
#> ola_mES_lif_1_45.counts 4 1.000
#> ola_mES_lif_1_46.counts 4 1.000
#> ola_mES_lif_1_47.counts 1 1.000
#> ola_mES_lif_1_48.counts 1 1.000
#> ola_mES_lif_1_49.counts 1 0.751
#> ola_mES_lif_1_5.counts 4 0.000
#> ola_mES_lif_1_50.counts 4 0.000
#> ola_mES_lif_1_51.counts 4 0.000
#> ola_mES_lif_1_52.counts 4 0.000
#> ola_mES_lif_1_53.counts 3 0.000
#> ola_mES_lif_1_54.counts 1 0.000
#> ola_mES_lif_1_55.counts 4 0.000
#> ola_mES_lif_1_56.counts 3 0.000
#> ola_mES_lif_1_57.counts 3 0.000
#> ola_mES_lif_1_58.counts 1 0.747
#> ola_mES_lif_1_59.counts 4 0.000
#> ola_mES_lif_1_6.counts 1 1.000
#> ola_mES_lif_1_60.counts 1 1.000
#> ola_mES_lif_1_61.counts 1 1.000
#> ola_mES_lif_1_62.counts 4 1.000
#> ola_mES_lif_1_63.counts 3 0.000
#> ola_mES_lif_1_64.counts 4 0.000
#> ola_mES_lif_1_65.counts 3 0.000
#> ola_mES_lif_1_66.counts 1 1.000
#> ola_mES_lif_1_67.counts 1 1.000
#> ola_mES_lif_1_68.counts 4 0.000
#> ola_mES_lif_1_69.counts 3 0.000
#> ola_mES_lif_1_7.counts 1 1.000
#> ola_mES_lif_1_70.counts 4 0.000
#> ola_mES_lif_1_72.counts 1 1.000
#> ola_mES_lif_1_73.counts 1 1.000
#> ola_mES_lif_1_74.counts 3 0.000
#> ola_mES_lif_1_75.counts 3 0.000
#> ola_mES_lif_1_79.counts 3 0.000
#> ola_mES_lif_1_8.counts 1 1.000
#> ola_mES_lif_1_80.counts 1 1.000
#> ola_mES_lif_1_81.counts 4 0.000
#> ola_mES_lif_1_82.counts 3 0.000
#> ola_mES_lif_1_86.counts 4 0.000
#> ola_mES_lif_1_9.counts 1 1.000
#> ola_mES_lif_1_91.counts 1 1.000
#> ola_mES_lif_1_92.counts 4 1.000
#> ola_mES_lif_1_93.counts 3 0.000
#> ola_mES_lif_1_95.counts 1 1.000
#> ola_mES_lif_1_96.counts 1 0.000
#> ola_mES_lif_2_1.counts 3 0.502
#> ola_mES_lif_2_10.counts 4 1.000
#> ola_mES_lif_2_11.counts 1 0.000
#> ola_mES_lif_2_12.counts 3 1.000
#> ola_mES_lif_2_13.counts 1 0.000
#> ola_mES_lif_2_14.counts 4 0.249
#> ola_mES_lif_2_15.counts 1 0.000
#> ola_mES_lif_2_16.counts 1 0.000
#> ola_mES_lif_2_17.counts 1 0.000
#> ola_mES_lif_2_18.counts 1 0.000
#> ola_mES_lif_2_19.counts 1 0.000
#> ola_mES_lif_2_2.counts 1 1.000
#> ola_mES_lif_2_20.counts 4 0.000
#> ola_mES_lif_2_21.counts 1 0.502
#> ola_mES_lif_2_23.counts 1 1.000
#> ola_mES_lif_2_25.counts 3 1.000
#> ola_mES_lif_2_26.counts 1 0.000
#> ola_mES_lif_2_27.counts 4 1.000
#> ola_mES_lif_2_28.counts 1 0.000
#> ola_mES_lif_2_29.counts 4 1.000
#> ola_mES_lif_2_3.counts 4 0.751
#> ola_mES_lif_2_30.counts 1 0.000
#> ola_mES_lif_2_31.counts 4 0.498
#> ola_mES_lif_2_32.counts 1 1.000
#> ola_mES_lif_2_33.counts 2 0.498
#> ola_mES_lif_2_34.counts 4 1.000
#> ola_mES_lif_2_35.counts 1 0.000
#> ola_mES_lif_2_36.counts 3 0.751
#> ola_mES_lif_2_37.counts 3 0.000
#> ola_mES_lif_2_38.counts 1 0.000
#> ola_mES_lif_2_39.counts 4 1.000
#> ola_mES_lif_2_4.counts 1 0.000
#> ola_mES_lif_2_40.counts 1 0.000
#> ola_mES_lif_2_41.counts 4 0.000
#> ola_mES_lif_2_42.counts 1 0.000
#> ola_mES_lif_2_43.counts 1 0.000
#> ola_mES_lif_2_44.counts 1 0.000
#> ola_mES_lif_2_45.counts 2 1.000
#> ola_mES_lif_2_46.counts 1 0.000
#> ola_mES_lif_2_47.counts 3 0.253
#> ola_mES_lif_2_48.counts 3 0.000
#> ola_mES_lif_2_49.counts 4 1.000
#> ola_mES_lif_2_5.counts 1 0.000
#> ola_mES_lif_2_50.counts 1 0.000
#> ola_mES_lif_2_51.counts 4 0.000
#> ola_mES_lif_2_52.counts 1 0.000
#> ola_mES_lif_2_53.counts 1 0.000
#> ola_mES_lif_2_54.counts 1 0.000
#> ola_mES_lif_2_55.counts 1 0.000
#> ola_mES_lif_2_56.counts 4 0.751
#> ola_mES_lif_2_57.counts 3 0.000
#> ola_mES_lif_2_58.counts 1 0.000
#> ola_mES_lif_2_59.counts 4 1.000
#> ola_mES_lif_2_6.counts 1 0.000
#> ola_mES_lif_2_60.counts 4 0.249
#> ola_mES_lif_2_61.counts 1 1.000
#> ola_mES_lif_2_63.counts 1 0.000
#> ola_mES_lif_2_64.counts 1 0.000
#> ola_mES_lif_2_65.counts 1 0.000
#> ola_mES_lif_2_66.counts 1 0.000
#> ola_mES_lif_2_67.counts 1 0.000
#> ola_mES_lif_2_68.counts 1 0.000
#> ola_mES_lif_2_69.counts 3 0.000
#> ola_mES_lif_2_7.counts 1 0.000
#> ola_mES_lif_2_70.counts 1 0.000
#> ola_mES_lif_2_71.counts 4 1.000
#> ola_mES_lif_2_72.counts 1 0.000
#> ola_mES_lif_2_73.counts 3 0.000
#> ola_mES_lif_2_74.counts 4 1.000
#> ola_mES_lif_2_75.counts 4 1.000
#> ola_mES_lif_2_76.counts 1 0.000
#> ola_mES_lif_2_77.counts 1 0.000
#> ola_mES_lif_2_79.counts 4 1.000
#> ola_mES_lif_2_8.counts 1 1.000
#> ola_mES_lif_2_80.counts 1 0.000
#> ola_mES_lif_2_81.counts 4 0.000
#> ola_mES_lif_2_82.counts 1 0.000
#> ola_mES_lif_2_83.counts 1 0.000
#> ola_mES_lif_2_84.counts 1 0.000
#> ola_mES_lif_2_86.counts 3 0.498
#> ola_mES_lif_2_87.counts 1 0.000
#> ola_mES_lif_2_89.counts 1 0.000
#> ola_mES_lif_2_9.counts 3 0.747
#> ola_mES_lif_2_90.counts 4 0.249
#> ola_mES_lif_2_91.counts 4 0.498
#> ola_mES_lif_2_92.counts 4 0.000
#> ola_mES_lif_2_93.counts 1 0.000
#> ola_mES_lif_2_94.counts 1 0.000
#> ola_mES_lif_2_95.counts 1 0.000
#> ola_mES_lif_2_96.counts 4 1.000
#> ola_mES_lif_3_1.counts 4 1.000
#> ola_mES_lif_3_10.counts 4 0.000
#> ola_mES_lif_3_11.counts 4 0.000
#> ola_mES_lif_3_12.counts 3 0.000
#> ola_mES_lif_3_13.counts 4 0.000
#> ola_mES_lif_3_14.counts 4 0.000
#> ola_mES_lif_3_15.counts 4 0.000
#> ola_mES_lif_3_16.counts 3 0.000
#> ola_mES_lif_3_17.counts 3 0.000
#> ola_mES_lif_3_19.counts 4 0.000
#> ola_mES_lif_3_2.counts 3 0.000
#> ola_mES_lif_3_20.counts 3 0.000
#> ola_mES_lif_3_21.counts 3 0.000
#> ola_mES_lif_3_22.counts 3 0.000
#> ola_mES_lif_3_23.counts 3 0.000
#> ola_mES_lif_3_24.counts 3 0.000
#> ola_mES_lif_3_26.counts 3 0.000
#> ola_mES_lif_3_27.counts 4 0.000
#> ola_mES_lif_3_28.counts 4 0.000
#> ola_mES_lif_3_29.counts 4 0.000
#> ola_mES_lif_3_3.counts 4 1.000
#> ola_mES_lif_3_30.counts 4 0.000
#> ola_mES_lif_3_31.counts 4 0.000
#> ola_mES_lif_3_32.counts 4 0.000
#> ola_mES_lif_3_33.counts 3 0.000
#> ola_mES_lif_3_34.counts 3 0.000
#> ola_mES_lif_3_35.counts 4 0.000
#> ola_mES_lif_3_36.counts 4 0.000
#> ola_mES_lif_3_37.counts 3 0.000
#> ola_mES_lif_3_38.counts 4 0.000
#> ola_mES_lif_3_39.counts 4 0.000
#> ola_mES_lif_3_4.counts 4 0.000
#> ola_mES_lif_3_40.counts 4 0.000
#> ola_mES_lif_3_42.counts 1 1.000
#> ola_mES_lif_3_45.counts 3 0.000
#> ola_mES_lif_3_46.counts 3 0.000
#> ola_mES_lif_3_47.counts 4 0.000
#> ola_mES_lif_3_48.counts 4 0.000
#> ola_mES_lif_3_49.counts 4 0.000
#> ola_mES_lif_3_5.counts 4 0.000
#> ola_mES_lif_3_50.counts 1 1.000
#> ola_mES_lif_3_51.counts 4 1.000
#> ola_mES_lif_3_52.counts 1 1.000
#> ola_mES_lif_3_53.counts 1 1.000
#> ola_mES_lif_3_54.counts 1 1.000
#> ola_mES_lif_3_56.counts 4 0.000
#> ola_mES_lif_3_57.counts 4 0.000
#> ola_mES_lif_3_58.counts 1 1.000
#> ola_mES_lif_3_59.counts 4 1.000
#> ola_mES_lif_3_6.counts 1 1.000
#> ola_mES_lif_3_60.counts 4 0.000
#> ola_mES_lif_3_61.counts 4 1.000
#> ola_mES_lif_3_63.counts 4 0.000
#> ola_mES_lif_3_64.counts 4 0.000
#> ola_mES_lif_3_65.counts 4 0.000
#> ola_mES_lif_3_69.counts 3 0.000
#> ola_mES_lif_3_70.counts 4 0.000
#> ola_mES_lif_3_71.counts 4 0.000
#> ola_mES_lif_3_72.counts 3 0.000
#> ola_mES_lif_3_73.counts 3 0.000
#> ola_mES_lif_3_74.counts 3 0.000
#> ola_mES_lif_3_75.counts 3 0.000
#> ola_mES_lif_3_76.counts 3 0.000
#> ola_mES_lif_3_77.counts 3 0.000
#> ola_mES_lif_3_78.counts 3 0.000
#> ola_mES_lif_3_8.counts 4 1.000
#> ola_mES_lif_3_80.counts 3 0.000
#> ola_mES_lif_3_82.counts 3 0.000
#> ola_mES_lif_3_83.counts 3 0.000
#> ola_mES_lif_3_84.counts 3 0.000
#> ola_mES_lif_3_87.counts 3 0.000
#> ola_mES_lif_3_88.counts 3 0.000
#> ola_mES_lif_3_89.counts 3 0.000
#> ola_mES_lif_3_9.counts 3 0.000
#> ola_mES_lif_3_90.counts 3 0.000
#> ola_mES_lif_3_92.counts 3 0.000
#> ola_mES_lif_3_94.counts 3 0.000
#> ola_mES_lif_3_95.counts 3 0.000
#> ola_mES_lif_3_96.counts 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)
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 , Node014 , Node021 , Node022 , Node023-leaf , Node031 , Node032 , Node033 , Node034-leaf .
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 16285 rows and 348 columns.
#> Top rows (1628) 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 0.976 0.983 0.493 0.507 0.507
#> 3 3 1 0.982 0.993 0.342 0.799 0.616
#> 4 4 1 0.978 0.992 0.129 0.871 0.642
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
#> ola_mES_2i_2_1.counts 1 0.242 0.966 0.96 0.04
#> ola_mES_2i_2_10.counts 1 0.000 0.978 1.00 0.00
#> ola_mES_2i_2_11.counts 1 0.000 0.978 1.00 0.00
#> ola_mES_2i_2_12.counts 1 0.000 0.978 1.00 0.00
#> ola_mES_2i_2_13.counts 1 0.000 0.978 1.00 0.00
#> ola_mES_2i_2_14.counts 1 0.000 0.978 1.00 0.00
#> ola_mES_2i_2_15.counts 1 0.000 0.978 1.00 0.00
#> ola_mES_2i_2_16.counts 1 0.000 0.978 1.00 0.00
#> ola_mES_2i_2_17.counts 1 0.000 0.978 1.00 0.00
#> ola_mES_2i_2_18.counts 1 0.000 0.978 1.00 0.00
#> ola_mES_2i_2_19.counts 1 0.000 0.978 1.00 0.00
#> ola_mES_2i_2_2.counts 1 0.327 0.959 0.94 0.06
#> ola_mES_2i_2_20.counts 1 0.000 0.978 1.00 0.00
#> ola_mES_2i_2_21.counts 1 0.000 0.978 1.00 0.00
#> ola_mES_2i_2_22.counts 1 0.000 0.978 1.00 0.00
#> ola_mES_2i_2_23.counts 1 0.000 0.978 1.00 0.00
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#> ola_mES_lif_2_21.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_2_23.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_2_26.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_2_28.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_2_29.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_2_3.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_2_30.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_2_32.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_2_34.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_2_35.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_2_38.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_2_39.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_2_4.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_2_40.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_2_42.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_2_43.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_2_44.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_2_46.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_2_49.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_2_5.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_2_50.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_2_51.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_2_52.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_2_53.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_2_54.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_2_55.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_2_56.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_2_58.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_2_59.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_2_6.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_2_61.counts 2 0.327 0.947 0.06 0.94
#> ola_mES_lif_2_63.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_2_64.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_2_65.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_2_66.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_2_67.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_2_68.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_2_7.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_2_70.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_2_71.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_2_72.counts 2 0.327 0.947 0.06 0.94
#> ola_mES_lif_2_74.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_2_75.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_2_76.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_2_77.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_2_8.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_2_80.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_2_82.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_2_83.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_2_84.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_2_87.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_2_89.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_2_93.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_2_94.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_2_95.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_3_1.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_3_10.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_3_13.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_3_27.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_3_3.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_3_30.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_3_32.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_3_4.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_3_40.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_3_42.counts 2 0.327 0.947 0.06 0.94
#> ola_mES_lif_3_49.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_3_5.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_3_50.counts 2 0.327 0.947 0.06 0.94
#> ola_mES_lif_3_51.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_3_52.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_3_53.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_3_54.counts 2 0.327 0.947 0.06 0.94
#> ola_mES_lif_3_56.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_3_58.counts 2 0.327 0.947 0.06 0.94
#> ola_mES_lif_3_59.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_3_6.counts 2 0.327 0.947 0.06 0.94
#> ola_mES_lif_3_60.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_3_61.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_3_63.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_3_64.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_3_65.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_3_71.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_3_8.counts 2 0.000 0.989 0.00 1.00
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> ola_mES_2i_2_1.counts 1 0.0000 0.985 1.00 0.00 0.00
#> ola_mES_2i_2_10.counts 3 0.0000 0.996 0.00 0.00 1.00
#> ola_mES_2i_2_11.counts 3 0.0000 0.996 0.00 0.00 1.00
#> ola_mES_2i_2_12.counts 3 0.0000 0.996 0.00 0.00 1.00
#> ola_mES_2i_2_13.counts 3 0.0000 0.996 0.00 0.00 1.00
#> ola_mES_2i_2_14.counts 1 0.0000 0.985 1.00 0.00 0.00
#> ola_mES_2i_2_15.counts 3 0.0000 0.996 0.00 0.00 1.00
#> ola_mES_2i_2_16.counts 3 0.0000 0.996 0.00 0.00 1.00
#> ola_mES_2i_2_17.counts 1 0.6192 0.289 0.58 0.00 0.42
#> ola_mES_2i_2_18.counts 3 0.0000 0.996 0.00 0.00 1.00
#> ola_mES_2i_2_19.counts 3 0.0000 0.996 0.00 0.00 1.00
#> ola_mES_2i_2_2.counts 1 0.0000 0.985 1.00 0.00 0.00
#> ola_mES_2i_2_20.counts 1 0.0000 0.985 1.00 0.00 0.00
#> ola_mES_2i_2_21.counts 3 0.0000 0.996 0.00 0.00 1.00
#> ola_mES_2i_2_22.counts 3 0.0000 0.996 0.00 0.00 1.00
#> ola_mES_2i_2_23.counts 3 0.0000 0.996 0.00 0.00 1.00
#> ola_mES_2i_2_24.counts 3 0.0000 0.996 0.00 0.00 1.00
#> ola_mES_2i_2_25.counts 1 0.0000 0.985 1.00 0.00 0.00
#> ola_mES_2i_2_26.counts 3 0.5016 0.678 0.24 0.00 0.76
#> ola_mES_2i_2_27.counts 1 0.0000 0.985 1.00 0.00 0.00
#> ola_mES_2i_2_28.counts 1 0.0000 0.985 1.00 0.00 0.00
#> ola_mES_2i_2_29.counts 3 0.0000 0.996 0.00 0.00 1.00
#> ola_mES_2i_2_3.counts 1 0.0000 0.985 1.00 0.00 0.00
#> ola_mES_2i_2_30.counts 3 0.0000 0.996 0.00 0.00 1.00
#> ola_mES_2i_2_31.counts 3 0.0000 0.996 0.00 0.00 1.00
#> ola_mES_2i_2_32.counts 1 0.2066 0.928 0.94 0.00 0.06
#> ola_mES_2i_2_33.counts 3 0.0000 0.996 0.00 0.00 1.00
#> ola_mES_2i_2_34.counts 3 0.0000 0.996 0.00 0.00 1.00
#> ola_mES_2i_2_35.counts 3 0.0000 0.996 0.00 0.00 1.00
#> ola_mES_2i_2_36.counts 3 0.0000 0.996 0.00 0.00 1.00
#> ola_mES_2i_2_37.counts 1 0.0000 0.985 1.00 0.00 0.00
#> ola_mES_2i_2_38.counts 1 0.0000 0.985 1.00 0.00 0.00
#> ola_mES_2i_2_39.counts 3 0.0000 0.996 0.00 0.00 1.00
#> ola_mES_2i_2_4.counts 3 0.0000 0.996 0.00 0.00 1.00
#> ola_mES_2i_2_40.counts 3 0.0000 0.996 0.00 0.00 1.00
#> ola_mES_2i_2_41.counts 3 0.0000 0.996 0.00 0.00 1.00
#> ola_mES_2i_2_42.counts 3 0.0000 0.996 0.00 0.00 1.00
#> ola_mES_2i_2_43.counts 1 0.0000 0.985 1.00 0.00 0.00
#> ola_mES_2i_2_44.counts 3 0.0000 0.996 0.00 0.00 1.00
#> ola_mES_2i_2_46.counts 3 0.0000 0.996 0.00 0.00 1.00
#> ola_mES_2i_2_47.counts 3 0.0000 0.996 0.00 0.00 1.00
#> ola_mES_2i_2_48.counts 3 0.0000 0.996 0.00 0.00 1.00
#> ola_mES_2i_2_49.counts 1 0.0000 0.985 1.00 0.00 0.00
#> ola_mES_2i_2_5.counts 3 0.0000 0.996 0.00 0.00 1.00
#> ola_mES_2i_2_50.counts 1 0.0000 0.985 1.00 0.00 0.00
#> ola_mES_2i_2_51.counts 3 0.0000 0.996 0.00 0.00 1.00
#> ola_mES_2i_2_52.counts 3 0.0000 0.996 0.00 0.00 1.00
#> ola_mES_2i_2_53.counts 3 0.0000 0.996 0.00 0.00 1.00
#> ola_mES_2i_2_54.counts 3 0.0000 0.996 0.00 0.00 1.00
#> ola_mES_2i_2_55.counts 3 0.0000 0.996 0.00 0.00 1.00
#> ola_mES_2i_2_56.counts 3 0.0000 0.996 0.00 0.00 1.00
#> ola_mES_2i_2_57.counts 3 0.0000 0.996 0.00 0.00 1.00
#> ola_mES_2i_2_58.counts 3 0.0000 0.996 0.00 0.00 1.00
#> ola_mES_2i_2_59.counts 3 0.0000 0.996 0.00 0.00 1.00
#> ola_mES_2i_2_6.counts 3 0.0000 0.996 0.00 0.00 1.00
#> ola_mES_2i_2_60.counts 3 0.0000 0.996 0.00 0.00 1.00
#> ola_mES_2i_2_61.counts 1 0.0000 0.985 1.00 0.00 0.00
#> ola_mES_2i_2_63.counts 1 0.0000 0.985 1.00 0.00 0.00
#> ola_mES_2i_2_64.counts 3 0.0000 0.996 0.00 0.00 1.00
#> ola_mES_2i_2_66.counts 3 0.0000 0.996 0.00 0.00 1.00
#> ola_mES_2i_2_67.counts 3 0.0000 0.996 0.00 0.00 1.00
#> ola_mES_2i_2_68.counts 1 0.0000 0.985 1.00 0.00 0.00
#> ola_mES_2i_2_69.counts 3 0.0000 0.996 0.00 0.00 1.00
#> ola_mES_2i_2_7.counts 3 0.0000 0.996 0.00 0.00 1.00
#> ola_mES_2i_2_72.counts 3 0.0000 0.996 0.00 0.00 1.00
#> ola_mES_2i_2_73.counts 1 0.0000 0.985 1.00 0.00 0.00
#> ola_mES_2i_2_74.counts 3 0.0000 0.996 0.00 0.00 1.00
#> ola_mES_2i_2_75.counts 1 0.0000 0.985 1.00 0.00 0.00
#> ola_mES_2i_2_76.counts 1 0.0000 0.985 1.00 0.00 0.00
#> ola_mES_2i_2_79.counts 3 0.0000 0.996 0.00 0.00 1.00
#> ola_mES_2i_2_8.counts 3 0.0000 0.996 0.00 0.00 1.00
#> ola_mES_2i_2_80.counts 3 0.0000 0.996 0.00 0.00 1.00
#> ola_mES_2i_2_81.counts 3 0.0000 0.996 0.00 0.00 1.00
#> ola_mES_2i_2_85.counts 1 0.0000 0.985 1.00 0.00 0.00
#> ola_mES_2i_2_86.counts 1 0.0000 0.985 1.00 0.00 0.00
#> ola_mES_2i_2_90.counts 3 0.0000 0.996 0.00 0.00 1.00
#> ola_mES_2i_2_91.counts 3 0.0000 0.996 0.00 0.00 1.00
#> ola_mES_2i_2_93.counts 3 0.0000 0.996 0.00 0.00 1.00
#> ola_mES_2i_2_94.counts 3 0.0000 0.996 0.00 0.00 1.00
#> ola_mES_2i_2_95.counts 3 0.0000 0.996 0.00 0.00 1.00
#> ola_mES_2i_4_2.counts 3 0.0000 0.996 0.00 0.00 1.00
#> ola_mES_2i_4_38.counts 1 0.0000 0.985 1.00 0.00 0.00
#> ola_mES_2i_4_40.counts 1 0.0000 0.985 1.00 0.00 0.00
#> ola_mES_2i_4_49.counts 1 0.0000 0.985 1.00 0.00 0.00
#> ola_mES_2i_4_53.counts 1 0.0000 0.985 1.00 0.00 0.00
#> ola_mES_2i_4_67.counts 1 0.0000 0.985 1.00 0.00 0.00
#> ola_mES_2i_5_41.counts 1 0.0000 0.985 1.00 0.00 0.00
#> ola_mES_a2i_2_1.counts 3 0.0000 0.996 0.00 0.00 1.00
#> ola_mES_a2i_2_10.counts 1 0.0000 0.985 1.00 0.00 0.00
#> ola_mES_a2i_2_11.counts 3 0.0000 0.996 0.00 0.00 1.00
#> ola_mES_a2i_2_12.counts 1 0.0000 0.985 1.00 0.00 0.00
#> ola_mES_a2i_2_13.counts 1 0.0000 0.985 1.00 0.00 0.00
#> ola_mES_a2i_2_14.counts 1 0.5016 0.691 0.76 0.00 0.24
#> ola_mES_a2i_2_15.counts 3 0.0000 0.996 0.00 0.00 1.00
#> ola_mES_a2i_2_16.counts 3 0.0000 0.996 0.00 0.00 1.00
#> ola_mES_a2i_2_17.counts 3 0.0000 0.996 0.00 0.00 1.00
#> ola_mES_a2i_2_18.counts 3 0.0000 0.996 0.00 0.00 1.00
#> ola_mES_a2i_2_19.counts 3 0.0000 0.996 0.00 0.00 1.00
#> ola_mES_a2i_2_2.counts 3 0.0000 0.996 0.00 0.00 1.00
#> ola_mES_a2i_2_20.counts 1 0.0000 0.985 1.00 0.00 0.00
#> ola_mES_a2i_2_21.counts 3 0.0000 0.996 0.00 0.00 1.00
#> ola_mES_a2i_2_22.counts 3 0.0000 0.996 0.00 0.00 1.00
#> ola_mES_a2i_2_23.counts 3 0.0000 0.996 0.00 0.00 1.00
#> ola_mES_a2i_2_24.counts 3 0.0000 0.996 0.00 0.00 1.00
#> ola_mES_a2i_2_25.counts 1 0.0000 0.985 1.00 0.00 0.00
#> ola_mES_a2i_2_27.counts 1 0.0000 0.985 1.00 0.00 0.00
#> ola_mES_a2i_2_28.counts 1 0.0000 0.985 1.00 0.00 0.00
#> ola_mES_a2i_2_29.counts 1 0.0000 0.985 1.00 0.00 0.00
#> ola_mES_a2i_2_3.counts 3 0.0000 0.996 0.00 0.00 1.00
#> ola_mES_a2i_2_33.counts 1 0.0000 0.985 1.00 0.00 0.00
#> ola_mES_a2i_2_34.counts 1 0.0000 0.985 1.00 0.00 0.00
#> ola_mES_a2i_2_38.counts 1 0.0000 0.985 1.00 0.00 0.00
#> ola_mES_a2i_2_4.counts 1 0.0000 0.985 1.00 0.00 0.00
#> ola_mES_a2i_2_40.counts 1 0.0000 0.985 1.00 0.00 0.00
#> ola_mES_a2i_2_41.counts 1 0.3686 0.838 0.86 0.00 0.14
#> ola_mES_a2i_2_42.counts 3 0.0000 0.996 0.00 0.00 1.00
#> ola_mES_a2i_2_43.counts 1 0.0000 0.985 1.00 0.00 0.00
#> ola_mES_a2i_2_44.counts 1 0.0000 0.985 1.00 0.00 0.00
#> ola_mES_a2i_2_45.counts 1 0.3340 0.863 0.88 0.00 0.12
#> ola_mES_a2i_2_46.counts 3 0.0000 0.996 0.00 0.00 1.00
#> ola_mES_a2i_2_47.counts 1 0.2959 0.886 0.90 0.00 0.10
#> ola_mES_a2i_2_48.counts 1 0.0000 0.985 1.00 0.00 0.00
#> ola_mES_a2i_2_49.counts 1 0.0000 0.985 1.00 0.00 0.00
#> ola_mES_a2i_2_5.counts 3 0.0000 0.996 0.00 0.00 1.00
#> ola_mES_a2i_2_51.counts 1 0.0000 0.985 1.00 0.00 0.00
#> ola_mES_a2i_2_52.counts 1 0.0000 0.985 1.00 0.00 0.00
#> ola_mES_a2i_2_54.counts 1 0.0000 0.985 1.00 0.00 0.00
#> ola_mES_a2i_2_55.counts 3 0.0000 0.996 0.00 0.00 1.00
#> ola_mES_a2i_2_56.counts 1 0.0000 0.985 1.00 0.00 0.00
#> ola_mES_a2i_2_57.counts 3 0.0000 0.996 0.00 0.00 1.00
#> ola_mES_a2i_2_59.counts 3 0.0000 0.996 0.00 0.00 1.00
#> ola_mES_a2i_2_60.counts 1 0.0000 0.985 1.00 0.00 0.00
#> ola_mES_a2i_2_61.counts 1 0.0000 0.985 1.00 0.00 0.00
#> ola_mES_a2i_2_62.counts 1 0.0000 0.985 1.00 0.00 0.00
#> ola_mES_a2i_2_63.counts 1 0.0000 0.985 1.00 0.00 0.00
#> ola_mES_a2i_2_64.counts 1 0.0000 0.985 1.00 0.00 0.00
#> ola_mES_a2i_2_65.counts 1 0.0000 0.985 1.00 0.00 0.00
#> ola_mES_a2i_2_66.counts 3 0.0000 0.996 0.00 0.00 1.00
#> ola_mES_a2i_2_67.counts 1 0.0000 0.985 1.00 0.00 0.00
#> ola_mES_a2i_2_68.counts 1 0.0000 0.985 1.00 0.00 0.00
#> ola_mES_a2i_2_7.counts 1 0.0000 0.985 1.00 0.00 0.00
#> ola_mES_a2i_2_70.counts 1 0.0000 0.985 1.00 0.00 0.00
#> ola_mES_a2i_2_71.counts 3 0.0000 0.996 0.00 0.00 1.00
#> ola_mES_a2i_2_72.counts 1 0.0000 0.985 1.00 0.00 0.00
#> ola_mES_a2i_2_73.counts 1 0.0000 0.985 1.00 0.00 0.00
#> ola_mES_a2i_2_74.counts 1 0.0000 0.985 1.00 0.00 0.00
#> ola_mES_a2i_2_75.counts 1 0.0000 0.985 1.00 0.00 0.00
#> ola_mES_a2i_2_76.counts 1 0.0000 0.985 1.00 0.00 0.00
#> ola_mES_a2i_2_77.counts 1 0.0000 0.985 1.00 0.00 0.00
#> ola_mES_a2i_2_78.counts 3 0.0000 0.996 0.00 0.00 1.00
#> ola_mES_a2i_2_8.counts 3 0.0000 0.996 0.00 0.00 1.00
#> ola_mES_a2i_2_80.counts 1 0.0000 0.985 1.00 0.00 0.00
#> ola_mES_a2i_2_81.counts 1 0.0000 0.985 1.00 0.00 0.00
#> ola_mES_a2i_2_82.counts 3 0.0000 0.996 0.00 0.00 1.00
#> ola_mES_a2i_2_83.counts 3 0.0000 0.996 0.00 0.00 1.00
#> ola_mES_a2i_2_84.counts 1 0.0000 0.985 1.00 0.00 0.00
#> ola_mES_a2i_2_86.counts 1 0.0000 0.985 1.00 0.00 0.00
#> ola_mES_a2i_2_87.counts 1 0.0000 0.985 1.00 0.00 0.00
#> ola_mES_a2i_2_88.counts 1 0.0000 0.985 1.00 0.00 0.00
#> ola_mES_a2i_2_9.counts 1 0.0000 0.985 1.00 0.00 0.00
#> ola_mES_a2i_2_90.counts 3 0.0000 0.996 0.00 0.00 1.00
#> ola_mES_a2i_2_93.counts 1 0.0000 0.985 1.00 0.00 0.00
#> ola_mES_a2i_2_94.counts 3 0.0000 0.996 0.00 0.00 1.00
#> ola_mES_a2i_2_95.counts 3 0.0000 0.996 0.00 0.00 1.00
#> ola_mES_a2i_2_96.counts 1 0.2959 0.886 0.90 0.00 0.10
#> ola_mES_a2i_3_13.counts 1 0.0000 0.985 1.00 0.00 0.00
#> ola_mES_a2i_3_16.counts 1 0.0000 0.985 1.00 0.00 0.00
#> ola_mES_a2i_3_25.counts 1 0.0000 0.985 1.00 0.00 0.00
#> ola_mES_a2i_3_28.counts 1 0.0000 0.985 1.00 0.00 0.00
#> ola_mES_a2i_3_29.counts 1 0.0000 0.985 1.00 0.00 0.00
#> ola_mES_a2i_3_34.counts 1 0.0000 0.985 1.00 0.00 0.00
#> ola_mES_a2i_3_37.counts 1 0.0000 0.985 1.00 0.00 0.00
#> ola_mES_a2i_3_38.counts 1 0.0000 0.985 1.00 0.00 0.00
#> ola_mES_a2i_3_39.counts 1 0.0000 0.985 1.00 0.00 0.00
#> ola_mES_a2i_3_40.counts 1 0.0000 0.985 1.00 0.00 0.00
#> ola_mES_a2i_3_44.counts 1 0.0000 0.985 1.00 0.00 0.00
#> ola_mES_a2i_3_49.counts 1 0.0000 0.985 1.00 0.00 0.00
#> ola_mES_a2i_3_50.counts 1 0.0000 0.985 1.00 0.00 0.00
#> ola_mES_a2i_3_54.counts 1 0.0000 0.985 1.00 0.00 0.00
#> ola_mES_a2i_3_59.counts 3 0.0000 0.996 0.00 0.00 1.00
#> ola_mES_a2i_3_6.counts 1 0.0000 0.985 1.00 0.00 0.00
#> ola_mES_a2i_3_62.counts 1 0.0000 0.985 1.00 0.00 0.00
#> ola_mES_a2i_3_64.counts 1 0.0000 0.985 1.00 0.00 0.00
#> ola_mES_a2i_3_65.counts 3 0.0000 0.996 0.00 0.00 1.00
#> ola_mES_a2i_3_66.counts 1 0.0000 0.985 1.00 0.00 0.00
#> ola_mES_a2i_3_69.counts 1 0.0000 0.985 1.00 0.00 0.00
#> ola_mES_a2i_3_71.counts 3 0.0000 0.996 0.00 0.00 1.00
#> ola_mES_a2i_3_73.counts 1 0.0000 0.985 1.00 0.00 0.00
#> ola_mES_a2i_3_77.counts 1 0.0000 0.985 1.00 0.00 0.00
#> ola_mES_a2i_3_82.counts 1 0.0000 0.985 1.00 0.00 0.00
#> ola_mES_a2i_3_83.counts 1 0.0000 0.985 1.00 0.00 0.00
#> ola_mES_a2i_3_84.counts 1 0.0000 0.985 1.00 0.00 0.00
#> ola_mES_a2i_3_85.counts 1 0.0000 0.985 1.00 0.00 0.00
#> ola_mES_a2i_3_86.counts 1 0.0000 0.985 1.00 0.00 0.00
#> ola_mES_a2i_3_89.counts 1 0.6280 0.167 0.54 0.00 0.46
#> ola_mES_a2i_3_90.counts 1 0.0000 0.985 1.00 0.00 0.00
#> ola_mES_a2i_3_92.counts 1 0.0000 0.985 1.00 0.00 0.00
#> ola_mES_a2i_3_96.counts 1 0.0000 0.985 1.00 0.00 0.00
#> ola_mES_lif_1_1.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_1_11.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_1_13.counts 2 0.4555 0.753 0.00 0.80 0.20
#> ola_mES_lif_1_15.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_1_18.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_1_19.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_1_20.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_1_22.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_1_24.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_1_25.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_1_26.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_1_28.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_1_3.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_1_30.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_1_32.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_1_33.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_1_34.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_1_36.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_1_37.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_1_38.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_1_39.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_1_4.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_1_41.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_1_42.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_1_43.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_1_44.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_1_45.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_1_46.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_1_47.counts 3 0.2537 0.907 0.00 0.08 0.92
#> ola_mES_lif_1_48.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_1_49.counts 2 0.5397 0.615 0.00 0.72 0.28
#> ola_mES_lif_1_5.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_1_50.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_1_51.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_1_52.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_1_54.counts 3 0.0000 0.996 0.00 0.00 1.00
#> ola_mES_lif_1_55.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_1_58.counts 2 0.3340 0.863 0.00 0.88 0.12
#> ola_mES_lif_1_59.counts 2 0.0892 0.976 0.02 0.98 0.00
#> ola_mES_lif_1_6.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_1_60.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_1_61.counts 3 0.0000 0.996 0.00 0.00 1.00
#> ola_mES_lif_1_62.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_1_66.counts 3 0.0000 0.996 0.00 0.00 1.00
#> ola_mES_lif_1_67.counts 3 0.0892 0.975 0.00 0.02 0.98
#> ola_mES_lif_1_68.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_1_7.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_1_72.counts 3 0.0000 0.996 0.00 0.00 1.00
#> ola_mES_lif_1_73.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_1_8.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_1_80.counts 3 0.0000 0.996 0.00 0.00 1.00
#> ola_mES_lif_1_81.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_1_9.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_1_91.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_1_92.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_1_95.counts 3 0.0000 0.996 0.00 0.00 1.00
#> ola_mES_lif_1_96.counts 3 0.0000 0.996 0.00 0.00 1.00
#> ola_mES_lif_2_10.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_2_11.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_2_13.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_2_14.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_2_15.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_2_16.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_2_17.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_2_18.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_2_19.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_2_2.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_2_21.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_2_23.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_2_26.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_2_28.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_2_29.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_2_3.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_2_30.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_2_32.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_2_34.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_2_35.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_2_38.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_2_39.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_2_4.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_2_40.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_2_42.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_2_43.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_2_44.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_2_46.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_2_49.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_2_5.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_2_50.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_2_51.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_2_52.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_2_53.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_2_54.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_2_55.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_2_56.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_2_58.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_2_59.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_2_6.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_2_61.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_2_63.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_2_64.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_2_65.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_2_66.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_2_67.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_2_68.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_2_7.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_2_70.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_2_71.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_2_72.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_2_74.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_2_75.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_2_76.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_2_77.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_2_8.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_2_80.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_2_82.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_2_83.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_2_84.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_2_87.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_2_89.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_2_93.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_2_94.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_2_95.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_3_1.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_3_10.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_3_13.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_3_27.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_3_3.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_3_30.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_3_32.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_3_4.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_3_40.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_3_42.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_3_49.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_3_5.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_3_50.counts 3 0.0000 0.996 0.00 0.00 1.00
#> ola_mES_lif_3_51.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_3_52.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_3_53.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_3_54.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_3_56.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_3_58.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_3_59.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_3_6.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_3_60.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_3_61.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_3_63.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_3_64.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_3_65.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_3_71.counts 2 0.0000 0.996 0.00 1.00 0.00
#> ola_mES_lif_3_8.counts 2 0.0000 0.996 0.00 1.00 0.00
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> ola_mES_2i_2_1.counts 1 0.0000 0.9854 1.00 0.00 0.00 0.00
#> ola_mES_2i_2_10.counts 3 0.0000 0.9837 0.00 0.00 1.00 0.00
#> ola_mES_2i_2_11.counts 3 0.0000 0.9837 0.00 0.00 1.00 0.00
#> ola_mES_2i_2_12.counts 3 0.0000 0.9837 0.00 0.00 1.00 0.00
#> ola_mES_2i_2_13.counts 3 0.0000 0.9837 0.00 0.00 1.00 0.00
#> ola_mES_2i_2_14.counts 1 0.0000 0.9854 1.00 0.00 0.00 0.00
#> ola_mES_2i_2_15.counts 3 0.0000 0.9837 0.00 0.00 1.00 0.00
#> ola_mES_2i_2_16.counts 3 0.0000 0.9837 0.00 0.00 1.00 0.00
#> ola_mES_2i_2_17.counts 3 0.0000 0.9837 0.00 0.00 1.00 0.00
#> ola_mES_2i_2_18.counts 3 0.0000 0.9837 0.00 0.00 1.00 0.00
#> ola_mES_2i_2_19.counts 3 0.0000 0.9837 0.00 0.00 1.00 0.00
#> ola_mES_2i_2_2.counts 1 0.0000 0.9854 1.00 0.00 0.00 0.00
#> ola_mES_2i_2_20.counts 1 0.1637 0.9257 0.94 0.00 0.06 0.00
#> ola_mES_2i_2_21.counts 3 0.0000 0.9837 0.00 0.00 1.00 0.00
#> ola_mES_2i_2_22.counts 3 0.0000 0.9837 0.00 0.00 1.00 0.00
#> ola_mES_2i_2_23.counts 3 0.0000 0.9837 0.00 0.00 1.00 0.00
#> ola_mES_2i_2_24.counts 3 0.0000 0.9837 0.00 0.00 1.00 0.00
#> ola_mES_2i_2_25.counts 1 0.0000 0.9854 1.00 0.00 0.00 0.00
#> ola_mES_2i_2_26.counts 3 0.0000 0.9837 0.00 0.00 1.00 0.00
#> ola_mES_2i_2_27.counts 1 0.0000 0.9854 1.00 0.00 0.00 0.00
#> ola_mES_2i_2_28.counts 1 0.0000 0.9854 1.00 0.00 0.00 0.00
#> ola_mES_2i_2_29.counts 3 0.0000 0.9837 0.00 0.00 1.00 0.00
#> ola_mES_2i_2_3.counts 1 0.0000 0.9854 1.00 0.00 0.00 0.00
#> ola_mES_2i_2_30.counts 3 0.0000 0.9837 0.00 0.00 1.00 0.00
#> ola_mES_2i_2_31.counts 3 0.0000 0.9837 0.00 0.00 1.00 0.00
#> ola_mES_2i_2_32.counts 3 0.0000 0.9837 0.00 0.00 1.00 0.00
#> ola_mES_2i_2_33.counts 3 0.0000 0.9837 0.00 0.00 1.00 0.00
#> ola_mES_2i_2_34.counts 3 0.0000 0.9837 0.00 0.00 1.00 0.00
#> ola_mES_2i_2_35.counts 3 0.0000 0.9837 0.00 0.00 1.00 0.00
#> ola_mES_2i_2_36.counts 3 0.0000 0.9837 0.00 0.00 1.00 0.00
#> ola_mES_2i_2_37.counts 1 0.0000 0.9854 1.00 0.00 0.00 0.00
#> ola_mES_2i_2_38.counts 1 0.0000 0.9854 1.00 0.00 0.00 0.00
#> ola_mES_2i_2_39.counts 3 0.0000 0.9837 0.00 0.00 1.00 0.00
#> ola_mES_2i_2_4.counts 3 0.0000 0.9837 0.00 0.00 1.00 0.00
#> ola_mES_2i_2_40.counts 3 0.0000 0.9837 0.00 0.00 1.00 0.00
#> ola_mES_2i_2_41.counts 3 0.0000 0.9837 0.00 0.00 1.00 0.00
#> ola_mES_2i_2_42.counts 3 0.0000 0.9837 0.00 0.00 1.00 0.00
#> ola_mES_2i_2_43.counts 1 0.4713 0.4356 0.64 0.00 0.36 0.00
#> ola_mES_2i_2_44.counts 3 0.0000 0.9837 0.00 0.00 1.00 0.00
#> ola_mES_2i_2_46.counts 3 0.0000 0.9837 0.00 0.00 1.00 0.00
#> ola_mES_2i_2_47.counts 3 0.0000 0.9837 0.00 0.00 1.00 0.00
#> ola_mES_2i_2_48.counts 3 0.0000 0.9837 0.00 0.00 1.00 0.00
#> ola_mES_2i_2_49.counts 1 0.0000 0.9854 1.00 0.00 0.00 0.00
#> ola_mES_2i_2_5.counts 3 0.0000 0.9837 0.00 0.00 1.00 0.00
#> ola_mES_2i_2_50.counts 1 0.0000 0.9854 1.00 0.00 0.00 0.00
#> ola_mES_2i_2_51.counts 3 0.0000 0.9837 0.00 0.00 1.00 0.00
#> ola_mES_2i_2_52.counts 3 0.0000 0.9837 0.00 0.00 1.00 0.00
#> ola_mES_2i_2_53.counts 3 0.0000 0.9837 0.00 0.00 1.00 0.00
#> ola_mES_2i_2_54.counts 3 0.0000 0.9837 0.00 0.00 1.00 0.00
#> ola_mES_2i_2_55.counts 3 0.0000 0.9837 0.00 0.00 1.00 0.00
#> ola_mES_2i_2_56.counts 3 0.0000 0.9837 0.00 0.00 1.00 0.00
#> ola_mES_2i_2_57.counts 3 0.0000 0.9837 0.00 0.00 1.00 0.00
#> ola_mES_2i_2_58.counts 3 0.0000 0.9837 0.00 0.00 1.00 0.00
#> ola_mES_2i_2_59.counts 3 0.0000 0.9837 0.00 0.00 1.00 0.00
#> ola_mES_2i_2_6.counts 3 0.0000 0.9837 0.00 0.00 1.00 0.00
#> ola_mES_2i_2_60.counts 3 0.0000 0.9837 0.00 0.00 1.00 0.00
#> ola_mES_2i_2_61.counts 1 0.2011 0.9040 0.92 0.00 0.08 0.00
#> ola_mES_2i_2_63.counts 1 0.0000 0.9854 1.00 0.00 0.00 0.00
#> ola_mES_2i_2_64.counts 3 0.0000 0.9837 0.00 0.00 1.00 0.00
#> ola_mES_2i_2_66.counts 3 0.0000 0.9837 0.00 0.00 1.00 0.00
#> ola_mES_2i_2_67.counts 3 0.0000 0.9837 0.00 0.00 1.00 0.00
#> ola_mES_2i_2_68.counts 1 0.0000 0.9854 1.00 0.00 0.00 0.00
#> ola_mES_2i_2_69.counts 3 0.0000 0.9837 0.00 0.00 1.00 0.00
#> ola_mES_2i_2_7.counts 3 0.0000 0.9837 0.00 0.00 1.00 0.00
#> ola_mES_2i_2_72.counts 3 0.0000 0.9837 0.00 0.00 1.00 0.00
#> ola_mES_2i_2_73.counts 1 0.0000 0.9854 1.00 0.00 0.00 0.00
#> ola_mES_2i_2_74.counts 3 0.0000 0.9837 0.00 0.00 1.00 0.00
#> ola_mES_2i_2_75.counts 1 0.0000 0.9854 1.00 0.00 0.00 0.00
#> ola_mES_2i_2_76.counts 1 0.0000 0.9854 1.00 0.00 0.00 0.00
#> ola_mES_2i_2_79.counts 3 0.0000 0.9837 0.00 0.00 1.00 0.00
#> ola_mES_2i_2_8.counts 3 0.0000 0.9837 0.00 0.00 1.00 0.00
#> ola_mES_2i_2_80.counts 3 0.0000 0.9837 0.00 0.00 1.00 0.00
#> ola_mES_2i_2_81.counts 3 0.0000 0.9837 0.00 0.00 1.00 0.00
#> ola_mES_2i_2_85.counts 1 0.0000 0.9854 1.00 0.00 0.00 0.00
#> ola_mES_2i_2_86.counts 1 0.0000 0.9854 1.00 0.00 0.00 0.00
#> ola_mES_2i_2_90.counts 3 0.0000 0.9837 0.00 0.00 1.00 0.00
#> ola_mES_2i_2_91.counts 3 0.0000 0.9837 0.00 0.00 1.00 0.00
#> ola_mES_2i_2_93.counts 3 0.0000 0.9837 0.00 0.00 1.00 0.00
#> ola_mES_2i_2_94.counts 3 0.0000 0.9837 0.00 0.00 1.00 0.00
#> ola_mES_2i_2_95.counts 3 0.0000 0.9837 0.00 0.00 1.00 0.00
#> ola_mES_2i_4_2.counts 3 0.0000 0.9837 0.00 0.00 1.00 0.00
#> ola_mES_2i_4_38.counts 1 0.0000 0.9854 1.00 0.00 0.00 0.00
#> ola_mES_2i_4_40.counts 1 0.0000 0.9854 1.00 0.00 0.00 0.00
#> ola_mES_2i_4_49.counts 1 0.0000 0.9854 1.00 0.00 0.00 0.00
#> ola_mES_2i_4_53.counts 1 0.4790 0.3815 0.62 0.00 0.38 0.00
#> ola_mES_2i_4_67.counts 1 0.0000 0.9854 1.00 0.00 0.00 0.00
#> ola_mES_2i_5_41.counts 1 0.0000 0.9854 1.00 0.00 0.00 0.00
#> ola_mES_a2i_2_1.counts 3 0.0000 0.9837 0.00 0.00 1.00 0.00
#> ola_mES_a2i_2_10.counts 1 0.0000 0.9854 1.00 0.00 0.00 0.00
#> ola_mES_a2i_2_11.counts 3 0.0000 0.9837 0.00 0.00 1.00 0.00
#> ola_mES_a2i_2_12.counts 1 0.0000 0.9854 1.00 0.00 0.00 0.00
#> ola_mES_a2i_2_13.counts 1 0.0000 0.9854 1.00 0.00 0.00 0.00
#> ola_mES_a2i_2_14.counts 3 0.3975 0.6874 0.24 0.00 0.76 0.00
#> ola_mES_a2i_2_15.counts 3 0.0000 0.9837 0.00 0.00 1.00 0.00
#> ola_mES_a2i_2_16.counts 3 0.0000 0.9837 0.00 0.00 1.00 0.00
#> ola_mES_a2i_2_17.counts 3 0.0000 0.9837 0.00 0.00 1.00 0.00
#> ola_mES_a2i_2_18.counts 3 0.0000 0.9837 0.00 0.00 1.00 0.00
#> ola_mES_a2i_2_19.counts 3 0.0000 0.9837 0.00 0.00 1.00 0.00
#> ola_mES_a2i_2_2.counts 3 0.0000 0.9837 0.00 0.00 1.00 0.00
#> ola_mES_a2i_2_20.counts 1 0.0000 0.9854 1.00 0.00 0.00 0.00
#> ola_mES_a2i_2_21.counts 3 0.0000 0.9837 0.00 0.00 1.00 0.00
#> ola_mES_a2i_2_22.counts 3 0.0000 0.9837 0.00 0.00 1.00 0.00
#> ola_mES_a2i_2_23.counts 3 0.0000 0.9837 0.00 0.00 1.00 0.00
#> ola_mES_a2i_2_24.counts 3 0.0000 0.9837 0.00 0.00 1.00 0.00
#> ola_mES_a2i_2_25.counts 1 0.0000 0.9854 1.00 0.00 0.00 0.00
#> ola_mES_a2i_2_27.counts 1 0.0000 0.9854 1.00 0.00 0.00 0.00
#> ola_mES_a2i_2_28.counts 1 0.0000 0.9854 1.00 0.00 0.00 0.00
#> ola_mES_a2i_2_29.counts 1 0.0000 0.9854 1.00 0.00 0.00 0.00
#> ola_mES_a2i_2_3.counts 3 0.0000 0.9837 0.00 0.00 1.00 0.00
#> ola_mES_a2i_2_33.counts 1 0.0000 0.9854 1.00 0.00 0.00 0.00
#> ola_mES_a2i_2_34.counts 1 0.0000 0.9854 1.00 0.00 0.00 0.00
#> ola_mES_a2i_2_38.counts 1 0.0000 0.9854 1.00 0.00 0.00 0.00
#> ola_mES_a2i_2_4.counts 1 0.0000 0.9854 1.00 0.00 0.00 0.00
#> ola_mES_a2i_2_40.counts 1 0.0000 0.9854 1.00 0.00 0.00 0.00
#> ola_mES_a2i_2_41.counts 3 0.3172 0.8058 0.16 0.00 0.84 0.00
#> ola_mES_a2i_2_42.counts 3 0.0000 0.9837 0.00 0.00 1.00 0.00
#> ola_mES_a2i_2_43.counts 1 0.0000 0.9854 1.00 0.00 0.00 0.00
#> ola_mES_a2i_2_44.counts 1 0.0000 0.9854 1.00 0.00 0.00 0.00
#> ola_mES_a2i_2_45.counts 3 0.4624 0.4926 0.34 0.00 0.66 0.00
#> ola_mES_a2i_2_46.counts 3 0.0000 0.9837 0.00 0.00 1.00 0.00
#> ola_mES_a2i_2_47.counts 1 0.5000 -0.0138 0.50 0.00 0.50 0.00
#> ola_mES_a2i_2_48.counts 1 0.0000 0.9854 1.00 0.00 0.00 0.00
#> ola_mES_a2i_2_49.counts 1 0.0000 0.9854 1.00 0.00 0.00 0.00
#> ola_mES_a2i_2_5.counts 3 0.0000 0.9837 0.00 0.00 1.00 0.00
#> ola_mES_a2i_2_51.counts 1 0.0000 0.9854 1.00 0.00 0.00 0.00
#> ola_mES_a2i_2_52.counts 1 0.0000 0.9854 1.00 0.00 0.00 0.00
#> ola_mES_a2i_2_54.counts 1 0.0000 0.9854 1.00 0.00 0.00 0.00
#> ola_mES_a2i_2_55.counts 3 0.0000 0.9837 0.00 0.00 1.00 0.00
#> ola_mES_a2i_2_56.counts 1 0.0000 0.9854 1.00 0.00 0.00 0.00
#> ola_mES_a2i_2_57.counts 3 0.0000 0.9837 0.00 0.00 1.00 0.00
#> ola_mES_a2i_2_59.counts 3 0.0000 0.9837 0.00 0.00 1.00 0.00
#> ola_mES_a2i_2_60.counts 1 0.0000 0.9854 1.00 0.00 0.00 0.00
#> ola_mES_a2i_2_61.counts 1 0.0000 0.9854 1.00 0.00 0.00 0.00
#> ola_mES_a2i_2_62.counts 1 0.0000 0.9854 1.00 0.00 0.00 0.00
#> ola_mES_a2i_2_63.counts 1 0.0000 0.9854 1.00 0.00 0.00 0.00
#> ola_mES_a2i_2_64.counts 1 0.0000 0.9854 1.00 0.00 0.00 0.00
#> ola_mES_a2i_2_65.counts 1 0.0000 0.9854 1.00 0.00 0.00 0.00
#> ola_mES_a2i_2_66.counts 3 0.0000 0.9837 0.00 0.00 1.00 0.00
#> ola_mES_a2i_2_67.counts 1 0.0000 0.9854 1.00 0.00 0.00 0.00
#> ola_mES_a2i_2_68.counts 1 0.0000 0.9854 1.00 0.00 0.00 0.00
#> ola_mES_a2i_2_7.counts 1 0.0000 0.9854 1.00 0.00 0.00 0.00
#> ola_mES_a2i_2_70.counts 1 0.0000 0.9854 1.00 0.00 0.00 0.00
#> ola_mES_a2i_2_71.counts 3 0.0000 0.9837 0.00 0.00 1.00 0.00
#> ola_mES_a2i_2_72.counts 1 0.0000 0.9854 1.00 0.00 0.00 0.00
#> ola_mES_a2i_2_73.counts 1 0.0000 0.9854 1.00 0.00 0.00 0.00
#> ola_mES_a2i_2_74.counts 1 0.0000 0.9854 1.00 0.00 0.00 0.00
#> ola_mES_a2i_2_75.counts 1 0.0000 0.9854 1.00 0.00 0.00 0.00
#> ola_mES_a2i_2_76.counts 1 0.0000 0.9854 1.00 0.00 0.00 0.00
#> ola_mES_a2i_2_77.counts 1 0.0000 0.9854 1.00 0.00 0.00 0.00
#> ola_mES_a2i_2_78.counts 3 0.0000 0.9837 0.00 0.00 1.00 0.00
#> ola_mES_a2i_2_8.counts 3 0.0000 0.9837 0.00 0.00 1.00 0.00
#> ola_mES_a2i_2_80.counts 1 0.0000 0.9854 1.00 0.00 0.00 0.00
#> ola_mES_a2i_2_81.counts 1 0.0000 0.9854 1.00 0.00 0.00 0.00
#> ola_mES_a2i_2_82.counts 3 0.0000 0.9837 0.00 0.00 1.00 0.00
#> ola_mES_a2i_2_83.counts 3 0.0000 0.9837 0.00 0.00 1.00 0.00
#> ola_mES_a2i_2_84.counts 1 0.0000 0.9854 1.00 0.00 0.00 0.00
#> ola_mES_a2i_2_86.counts 1 0.0000 0.9854 1.00 0.00 0.00 0.00
#> ola_mES_a2i_2_87.counts 1 0.0000 0.9854 1.00 0.00 0.00 0.00
#> ola_mES_a2i_2_88.counts 1 0.0000 0.9854 1.00 0.00 0.00 0.00
#> ola_mES_a2i_2_9.counts 1 0.0000 0.9854 1.00 0.00 0.00 0.00
#> ola_mES_a2i_2_90.counts 3 0.0000 0.9837 0.00 0.00 1.00 0.00
#> ola_mES_a2i_2_93.counts 1 0.0000 0.9854 1.00 0.00 0.00 0.00
#> ola_mES_a2i_2_94.counts 3 0.0000 0.9837 0.00 0.00 1.00 0.00
#> ola_mES_a2i_2_95.counts 3 0.0000 0.9837 0.00 0.00 1.00 0.00
#> ola_mES_a2i_2_96.counts 3 0.4713 0.4440 0.36 0.00 0.64 0.00
#> ola_mES_a2i_3_13.counts 1 0.0000 0.9854 1.00 0.00 0.00 0.00
#> ola_mES_a2i_3_16.counts 1 0.0000 0.9854 1.00 0.00 0.00 0.00
#> ola_mES_a2i_3_25.counts 1 0.0000 0.9854 1.00 0.00 0.00 0.00
#> ola_mES_a2i_3_28.counts 1 0.0000 0.9854 1.00 0.00 0.00 0.00
#> ola_mES_a2i_3_29.counts 1 0.0000 0.9854 1.00 0.00 0.00 0.00
#> ola_mES_a2i_3_34.counts 1 0.0000 0.9854 1.00 0.00 0.00 0.00
#> ola_mES_a2i_3_37.counts 1 0.0000 0.9854 1.00 0.00 0.00 0.00
#> ola_mES_a2i_3_38.counts 1 0.0000 0.9854 1.00 0.00 0.00 0.00
#> ola_mES_a2i_3_39.counts 1 0.0000 0.9854 1.00 0.00 0.00 0.00
#> ola_mES_a2i_3_40.counts 1 0.0000 0.9854 1.00 0.00 0.00 0.00
#> ola_mES_a2i_3_44.counts 1 0.0000 0.9854 1.00 0.00 0.00 0.00
#> ola_mES_a2i_3_49.counts 1 0.0000 0.9854 1.00 0.00 0.00 0.00
#> ola_mES_a2i_3_50.counts 1 0.0000 0.9854 1.00 0.00 0.00 0.00
#> ola_mES_a2i_3_54.counts 1 0.0000 0.9854 1.00 0.00 0.00 0.00
#> ola_mES_a2i_3_59.counts 3 0.0000 0.9837 0.00 0.00 1.00 0.00
#> ola_mES_a2i_3_6.counts 1 0.0000 0.9854 1.00 0.00 0.00 0.00
#> ola_mES_a2i_3_62.counts 1 0.0000 0.9854 1.00 0.00 0.00 0.00
#> ola_mES_a2i_3_64.counts 1 0.0000 0.9854 1.00 0.00 0.00 0.00
#> ola_mES_a2i_3_65.counts 3 0.0000 0.9837 0.00 0.00 1.00 0.00
#> ola_mES_a2i_3_66.counts 1 0.0000 0.9854 1.00 0.00 0.00 0.00
#> ola_mES_a2i_3_69.counts 1 0.0000 0.9854 1.00 0.00 0.00 0.00
#> ola_mES_a2i_3_71.counts 3 0.0000 0.9837 0.00 0.00 1.00 0.00
#> ola_mES_a2i_3_73.counts 1 0.0000 0.9854 1.00 0.00 0.00 0.00
#> ola_mES_a2i_3_77.counts 1 0.0000 0.9854 1.00 0.00 0.00 0.00
#> ola_mES_a2i_3_82.counts 1 0.0000 0.9854 1.00 0.00 0.00 0.00
#> ola_mES_a2i_3_83.counts 1 0.0000 0.9854 1.00 0.00 0.00 0.00
#> ola_mES_a2i_3_84.counts 1 0.0000 0.9854 1.00 0.00 0.00 0.00
#> ola_mES_a2i_3_85.counts 1 0.0707 0.9658 0.98 0.00 0.02 0.00
#> ola_mES_a2i_3_86.counts 1 0.0000 0.9854 1.00 0.00 0.00 0.00
#> ola_mES_a2i_3_89.counts 3 0.4624 0.4912 0.34 0.00 0.66 0.00
#> ola_mES_a2i_3_90.counts 1 0.0000 0.9854 1.00 0.00 0.00 0.00
#> ola_mES_a2i_3_92.counts 1 0.0000 0.9854 1.00 0.00 0.00 0.00
#> ola_mES_a2i_3_96.counts 1 0.0000 0.9854 1.00 0.00 0.00 0.00
#> ola_mES_lif_1_1.counts 4 0.0000 1.0000 0.00 0.00 0.00 1.00
#> ola_mES_lif_1_11.counts 2 0.0000 0.9992 0.00 1.00 0.00 0.00
#> ola_mES_lif_1_13.counts 4 0.0000 1.0000 0.00 0.00 0.00 1.00
#> ola_mES_lif_1_15.counts 4 0.0000 1.0000 0.00 0.00 0.00 1.00
#> ola_mES_lif_1_18.counts 4 0.0000 1.0000 0.00 0.00 0.00 1.00
#> ola_mES_lif_1_19.counts 4 0.0000 1.0000 0.00 0.00 0.00 1.00
#> ola_mES_lif_1_20.counts 4 0.0000 1.0000 0.00 0.00 0.00 1.00
#> ola_mES_lif_1_22.counts 4 0.0000 1.0000 0.00 0.00 0.00 1.00
#> ola_mES_lif_1_24.counts 4 0.0000 1.0000 0.00 0.00 0.00 1.00
#> ola_mES_lif_1_25.counts 4 0.0000 1.0000 0.00 0.00 0.00 1.00
#> ola_mES_lif_1_26.counts 4 0.0000 1.0000 0.00 0.00 0.00 1.00
#> ola_mES_lif_1_28.counts 4 0.0000 1.0000 0.00 0.00 0.00 1.00
#> ola_mES_lif_1_3.counts 4 0.0000 1.0000 0.00 0.00 0.00 1.00
#> ola_mES_lif_1_30.counts 4 0.0000 1.0000 0.00 0.00 0.00 1.00
#> ola_mES_lif_1_32.counts 4 0.0000 1.0000 0.00 0.00 0.00 1.00
#> ola_mES_lif_1_33.counts 4 0.0000 1.0000 0.00 0.00 0.00 1.00
#> ola_mES_lif_1_34.counts 4 0.0000 1.0000 0.00 0.00 0.00 1.00
#> ola_mES_lif_1_36.counts 4 0.0000 1.0000 0.00 0.00 0.00 1.00
#> ola_mES_lif_1_37.counts 4 0.0000 1.0000 0.00 0.00 0.00 1.00
#> ola_mES_lif_1_38.counts 4 0.0000 1.0000 0.00 0.00 0.00 1.00
#> ola_mES_lif_1_39.counts 4 0.0000 1.0000 0.00 0.00 0.00 1.00
#> ola_mES_lif_1_4.counts 4 0.0000 1.0000 0.00 0.00 0.00 1.00
#> ola_mES_lif_1_41.counts 4 0.0000 1.0000 0.00 0.00 0.00 1.00
#> ola_mES_lif_1_42.counts 4 0.0000 1.0000 0.00 0.00 0.00 1.00
#> ola_mES_lif_1_43.counts 4 0.0000 1.0000 0.00 0.00 0.00 1.00
#> ola_mES_lif_1_44.counts 4 0.0000 1.0000 0.00 0.00 0.00 1.00
#> ola_mES_lif_1_45.counts 4 0.0000 1.0000 0.00 0.00 0.00 1.00
#> ola_mES_lif_1_46.counts 4 0.0000 1.0000 0.00 0.00 0.00 1.00
#> ola_mES_lif_1_47.counts 4 0.0000 1.0000 0.00 0.00 0.00 1.00
#> ola_mES_lif_1_48.counts 4 0.0000 1.0000 0.00 0.00 0.00 1.00
#> ola_mES_lif_1_49.counts 4 0.0000 1.0000 0.00 0.00 0.00 1.00
#> ola_mES_lif_1_5.counts 2 0.0000 0.9992 0.00 1.00 0.00 0.00
#> ola_mES_lif_1_50.counts 4 0.0000 1.0000 0.00 0.00 0.00 1.00
#> ola_mES_lif_1_51.counts 2 0.0000 0.9992 0.00 1.00 0.00 0.00
#> ola_mES_lif_1_52.counts 2 0.0000 0.9992 0.00 1.00 0.00 0.00
#> ola_mES_lif_1_54.counts 4 0.0000 1.0000 0.00 0.00 0.00 1.00
#> ola_mES_lif_1_55.counts 2 0.0000 0.9992 0.00 1.00 0.00 0.00
#> ola_mES_lif_1_58.counts 4 0.0000 1.0000 0.00 0.00 0.00 1.00
#> ola_mES_lif_1_59.counts 2 0.0000 0.9992 0.00 1.00 0.00 0.00
#> ola_mES_lif_1_6.counts 4 0.0000 1.0000 0.00 0.00 0.00 1.00
#> ola_mES_lif_1_60.counts 4 0.0000 1.0000 0.00 0.00 0.00 1.00
#> ola_mES_lif_1_61.counts 4 0.0000 1.0000 0.00 0.00 0.00 1.00
#> ola_mES_lif_1_62.counts 4 0.0000 1.0000 0.00 0.00 0.00 1.00
#> ola_mES_lif_1_66.counts 4 0.0000 1.0000 0.00 0.00 0.00 1.00
#> ola_mES_lif_1_67.counts 4 0.0000 1.0000 0.00 0.00 0.00 1.00
#> ola_mES_lif_1_68.counts 4 0.0000 1.0000 0.00 0.00 0.00 1.00
#> ola_mES_lif_1_7.counts 4 0.0000 1.0000 0.00 0.00 0.00 1.00
#> ola_mES_lif_1_72.counts 4 0.0000 1.0000 0.00 0.00 0.00 1.00
#> ola_mES_lif_1_73.counts 4 0.0000 1.0000 0.00 0.00 0.00 1.00
#> ola_mES_lif_1_8.counts 4 0.0000 1.0000 0.00 0.00 0.00 1.00
#> ola_mES_lif_1_80.counts 4 0.0000 1.0000 0.00 0.00 0.00 1.00
#> ola_mES_lif_1_81.counts 4 0.0000 1.0000 0.00 0.00 0.00 1.00
#> ola_mES_lif_1_9.counts 4 0.0000 1.0000 0.00 0.00 0.00 1.00
#> ola_mES_lif_1_91.counts 4 0.0000 1.0000 0.00 0.00 0.00 1.00
#> ola_mES_lif_1_92.counts 4 0.0000 1.0000 0.00 0.00 0.00 1.00
#> ola_mES_lif_1_95.counts 4 0.0000 1.0000 0.00 0.00 0.00 1.00
#> ola_mES_lif_1_96.counts 4 0.0000 1.0000 0.00 0.00 0.00 1.00
#> ola_mES_lif_2_10.counts 2 0.0000 0.9992 0.00 1.00 0.00 0.00
#> ola_mES_lif_2_11.counts 2 0.0000 0.9992 0.00 1.00 0.00 0.00
#> ola_mES_lif_2_13.counts 2 0.0000 0.9992 0.00 1.00 0.00 0.00
#> ola_mES_lif_2_14.counts 2 0.0000 0.9992 0.00 1.00 0.00 0.00
#> ola_mES_lif_2_15.counts 2 0.0000 0.9992 0.00 1.00 0.00 0.00
#> ola_mES_lif_2_16.counts 2 0.0000 0.9992 0.00 1.00 0.00 0.00
#> ola_mES_lif_2_17.counts 2 0.0000 0.9992 0.00 1.00 0.00 0.00
#> ola_mES_lif_2_18.counts 2 0.0000 0.9992 0.00 1.00 0.00 0.00
#> ola_mES_lif_2_19.counts 2 0.0000 0.9992 0.00 1.00 0.00 0.00
#> ola_mES_lif_2_2.counts 2 0.0000 0.9992 0.00 1.00 0.00 0.00
#> ola_mES_lif_2_21.counts 2 0.0000 0.9992 0.00 1.00 0.00 0.00
#> ola_mES_lif_2_23.counts 2 0.0000 0.9992 0.00 1.00 0.00 0.00
#> ola_mES_lif_2_26.counts 2 0.0000 0.9992 0.00 1.00 0.00 0.00
#> ola_mES_lif_2_28.counts 2 0.0000 0.9992 0.00 1.00 0.00 0.00
#> ola_mES_lif_2_29.counts 2 0.0000 0.9992 0.00 1.00 0.00 0.00
#> ola_mES_lif_2_3.counts 2 0.0000 0.9992 0.00 1.00 0.00 0.00
#> ola_mES_lif_2_30.counts 2 0.0000 0.9992 0.00 1.00 0.00 0.00
#> ola_mES_lif_2_32.counts 2 0.0000 0.9992 0.00 1.00 0.00 0.00
#> ola_mES_lif_2_34.counts 2 0.0000 0.9992 0.00 1.00 0.00 0.00
#> ola_mES_lif_2_35.counts 2 0.0000 0.9992 0.00 1.00 0.00 0.00
#> ola_mES_lif_2_38.counts 2 0.0000 0.9992 0.00 1.00 0.00 0.00
#> ola_mES_lif_2_39.counts 2 0.0000 0.9992 0.00 1.00 0.00 0.00
#> ola_mES_lif_2_4.counts 2 0.0000 0.9992 0.00 1.00 0.00 0.00
#> ola_mES_lif_2_40.counts 2 0.0000 0.9992 0.00 1.00 0.00 0.00
#> ola_mES_lif_2_42.counts 2 0.0000 0.9992 0.00 1.00 0.00 0.00
#> ola_mES_lif_2_43.counts 2 0.0000 0.9992 0.00 1.00 0.00 0.00
#> ola_mES_lif_2_44.counts 2 0.0000 0.9992 0.00 1.00 0.00 0.00
#> ola_mES_lif_2_46.counts 2 0.0000 0.9992 0.00 1.00 0.00 0.00
#> ola_mES_lif_2_49.counts 2 0.0000 0.9992 0.00 1.00 0.00 0.00
#> ola_mES_lif_2_5.counts 2 0.0000 0.9992 0.00 1.00 0.00 0.00
#> ola_mES_lif_2_50.counts 2 0.0000 0.9992 0.00 1.00 0.00 0.00
#> ola_mES_lif_2_51.counts 2 0.0000 0.9992 0.00 1.00 0.00 0.00
#> ola_mES_lif_2_52.counts 2 0.0000 0.9992 0.00 1.00 0.00 0.00
#> ola_mES_lif_2_53.counts 2 0.0000 0.9992 0.00 1.00 0.00 0.00
#> ola_mES_lif_2_54.counts 2 0.0000 0.9992 0.00 1.00 0.00 0.00
#> ola_mES_lif_2_55.counts 2 0.0000 0.9992 0.00 1.00 0.00 0.00
#> ola_mES_lif_2_56.counts 2 0.0000 0.9992 0.00 1.00 0.00 0.00
#> ola_mES_lif_2_58.counts 2 0.0000 0.9992 0.00 1.00 0.00 0.00
#> ola_mES_lif_2_59.counts 2 0.0000 0.9992 0.00 1.00 0.00 0.00
#> ola_mES_lif_2_6.counts 2 0.0000 0.9992 0.00 1.00 0.00 0.00
#> ola_mES_lif_2_61.counts 4 0.0000 1.0000 0.00 0.00 0.00 1.00
#> ola_mES_lif_2_63.counts 2 0.0000 0.9992 0.00 1.00 0.00 0.00
#> ola_mES_lif_2_64.counts 2 0.0000 0.9992 0.00 1.00 0.00 0.00
#> ola_mES_lif_2_65.counts 2 0.0000 0.9992 0.00 1.00 0.00 0.00
#> ola_mES_lif_2_66.counts 2 0.0000 0.9992 0.00 1.00 0.00 0.00
#> ola_mES_lif_2_67.counts 2 0.0000 0.9992 0.00 1.00 0.00 0.00
#> ola_mES_lif_2_68.counts 2 0.0000 0.9992 0.00 1.00 0.00 0.00
#> ola_mES_lif_2_7.counts 2 0.0000 0.9992 0.00 1.00 0.00 0.00
#> ola_mES_lif_2_70.counts 2 0.0000 0.9992 0.00 1.00 0.00 0.00
#> ola_mES_lif_2_71.counts 2 0.0000 0.9992 0.00 1.00 0.00 0.00
#> ola_mES_lif_2_72.counts 4 0.0000 1.0000 0.00 0.00 0.00 1.00
#> ola_mES_lif_2_74.counts 2 0.0000 0.9992 0.00 1.00 0.00 0.00
#> ola_mES_lif_2_75.counts 2 0.0000 0.9992 0.00 1.00 0.00 0.00
#> ola_mES_lif_2_76.counts 2 0.0000 0.9992 0.00 1.00 0.00 0.00
#> ola_mES_lif_2_77.counts 2 0.0000 0.9992 0.00 1.00 0.00 0.00
#> ola_mES_lif_2_8.counts 2 0.0000 0.9992 0.00 1.00 0.00 0.00
#> ola_mES_lif_2_80.counts 2 0.0000 0.9992 0.00 1.00 0.00 0.00
#> ola_mES_lif_2_82.counts 2 0.0000 0.9992 0.00 1.00 0.00 0.00
#> ola_mES_lif_2_83.counts 2 0.0000 0.9992 0.00 1.00 0.00 0.00
#> ola_mES_lif_2_84.counts 2 0.0000 0.9992 0.00 1.00 0.00 0.00
#> ola_mES_lif_2_87.counts 2 0.0000 0.9992 0.00 1.00 0.00 0.00
#> ola_mES_lif_2_89.counts 2 0.0000 0.9992 0.00 1.00 0.00 0.00
#> ola_mES_lif_2_93.counts 2 0.0000 0.9992 0.00 1.00 0.00 0.00
#> ola_mES_lif_2_94.counts 2 0.0000 0.9992 0.00 1.00 0.00 0.00
#> ola_mES_lif_2_95.counts 2 0.0000 0.9992 0.00 1.00 0.00 0.00
#> ola_mES_lif_3_1.counts 4 0.0000 1.0000 0.00 0.00 0.00 1.00
#> ola_mES_lif_3_10.counts 2 0.1637 0.9362 0.00 0.94 0.00 0.06
#> ola_mES_lif_3_13.counts 4 0.0000 1.0000 0.00 0.00 0.00 1.00
#> ola_mES_lif_3_27.counts 4 0.0000 1.0000 0.00 0.00 0.00 1.00
#> ola_mES_lif_3_3.counts 4 0.0000 1.0000 0.00 0.00 0.00 1.00
#> ola_mES_lif_3_30.counts 4 0.0000 1.0000 0.00 0.00 0.00 1.00
#> ola_mES_lif_3_32.counts 4 0.0000 1.0000 0.00 0.00 0.00 1.00
#> ola_mES_lif_3_4.counts 4 0.0000 1.0000 0.00 0.00 0.00 1.00
#> ola_mES_lif_3_40.counts 4 0.0000 1.0000 0.00 0.00 0.00 1.00
#> ola_mES_lif_3_42.counts 4 0.0000 1.0000 0.00 0.00 0.00 1.00
#> ola_mES_lif_3_49.counts 4 0.0000 1.0000 0.00 0.00 0.00 1.00
#> ola_mES_lif_3_5.counts 2 0.0000 0.9992 0.00 1.00 0.00 0.00
#> ola_mES_lif_3_50.counts 4 0.0000 1.0000 0.00 0.00 0.00 1.00
#> ola_mES_lif_3_51.counts 4 0.0000 1.0000 0.00 0.00 0.00 1.00
#> ola_mES_lif_3_52.counts 4 0.0000 1.0000 0.00 0.00 0.00 1.00
#> ola_mES_lif_3_53.counts 4 0.0000 1.0000 0.00 0.00 0.00 1.00
#> ola_mES_lif_3_54.counts 4 0.0000 1.0000 0.00 0.00 0.00 1.00
#> ola_mES_lif_3_56.counts 2 0.0000 0.9992 0.00 1.00 0.00 0.00
#> ola_mES_lif_3_58.counts 4 0.0000 1.0000 0.00 0.00 0.00 1.00
#> ola_mES_lif_3_59.counts 4 0.0000 1.0000 0.00 0.00 0.00 1.00
#> ola_mES_lif_3_6.counts 4 0.0000 1.0000 0.00 0.00 0.00 1.00
#> ola_mES_lif_3_60.counts 4 0.0000 1.0000 0.00 0.00 0.00 1.00
#> ola_mES_lif_3_61.counts 4 0.0000 1.0000 0.00 0.00 0.00 1.00
#> ola_mES_lif_3_63.counts 4 0.0000 1.0000 0.00 0.00 0.00 1.00
#> ola_mES_lif_3_64.counts 4 0.0000 1.0000 0.00 0.00 0.00 1.00
#> ola_mES_lif_3_65.counts 4 0.0000 1.0000 0.00 0.00 0.00 1.00
#> ola_mES_lif_3_71.counts 4 0.0000 1.0000 0.00 0.00 0.00 1.00
#> ola_mES_lif_3_8.counts 4 0.0000 1.0000 0.00 0.00 0.00 1.00
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)
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 , Node0112 , Node0113-leaf , Node0121-leaf , Node0122-leaf , Node0131-leaf , Node0132-leaf , Node0133-leaf , Node0141 , Node0142-leaf , Node0143-leaf , Node0211 , Node0212-leaf , Node0221 , Node0222 , Node0223-leaf , Node0311-leaf , Node0312-leaf , Node0313-leaf , Node0321-leaf , Node0322-leaf , Node0331-leaf , Node0332-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 16132 rows and 102 columns.
#> Top rows (1519) 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 0.899 0.920 0.968 0.474 0.523 0.523
#> 3 3 1.000 0.967 0.987 0.389 0.729 0.522
#> 4 4 0.897 0.876 0.945 0.133 0.875 0.652
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
#> ola_mES_2i_2_1.counts 1 0.000 0.945 1.00 0.00
#> ola_mES_2i_2_14.counts 1 0.000 0.945 1.00 0.00
#> ola_mES_2i_2_2.counts 1 0.000 0.945 1.00 0.00
#> ola_mES_2i_2_20.counts 1 0.000 0.945 1.00 0.00
#> ola_mES_2i_2_25.counts 1 0.000 0.945 1.00 0.00
#> ola_mES_2i_2_27.counts 1 0.000 0.945 1.00 0.00
#> ola_mES_2i_2_28.counts 1 0.000 0.945 1.00 0.00
#> ola_mES_2i_2_3.counts 1 0.000 0.945 1.00 0.00
#> ola_mES_2i_2_37.counts 1 0.000 0.945 1.00 0.00
#> ola_mES_2i_2_38.counts 1 0.000 0.945 1.00 0.00
#> ola_mES_2i_2_43.counts 1 0.000 0.945 1.00 0.00
#> ola_mES_2i_2_49.counts 1 0.000 0.945 1.00 0.00
#> ola_mES_2i_2_50.counts 1 0.000 0.945 1.00 0.00
#> ola_mES_2i_2_61.counts 1 0.000 0.945 1.00 0.00
#> ola_mES_2i_2_63.counts 1 0.000 0.945 1.00 0.00
#> ola_mES_2i_2_68.counts 1 0.000 0.945 1.00 0.00
#> ola_mES_2i_2_73.counts 1 0.000 0.945 1.00 0.00
#> ola_mES_2i_2_75.counts 1 0.000 0.945 1.00 0.00
#> ola_mES_2i_2_76.counts 1 0.000 0.945 1.00 0.00
#> ola_mES_2i_2_85.counts 1 0.000 0.945 1.00 0.00
#> ola_mES_2i_2_86.counts 1 0.000 0.945 1.00 0.00
#> ola_mES_2i_4_38.counts 2 0.000 0.977 0.00 1.00
#> ola_mES_2i_4_40.counts 2 0.000 0.977 0.00 1.00
#> ola_mES_2i_4_49.counts 2 0.000 0.977 0.00 1.00
#> ola_mES_2i_4_53.counts 2 0.000 0.977 0.00 1.00
#> ola_mES_2i_4_67.counts 2 0.000 0.977 0.00 1.00
#> ola_mES_2i_5_41.counts 2 0.000 0.977 0.00 1.00
#> ola_mES_a2i_2_10.counts 2 0.000 0.977 0.00 1.00
#> ola_mES_a2i_2_12.counts 2 0.000 0.977 0.00 1.00
#> ola_mES_a2i_2_13.counts 1 0.000 0.945 1.00 0.00
#> ola_mES_a2i_2_20.counts 2 0.000 0.977 0.00 1.00
#> ola_mES_a2i_2_25.counts 2 0.242 0.938 0.04 0.96
#> ola_mES_a2i_2_27.counts 2 0.904 0.513 0.32 0.68
#> ola_mES_a2i_2_28.counts 2 0.000 0.977 0.00 1.00
#> ola_mES_a2i_2_29.counts 2 0.000 0.977 0.00 1.00
#> ola_mES_a2i_2_33.counts 2 0.000 0.977 0.00 1.00
#> ola_mES_a2i_2_34.counts 2 0.000 0.977 0.00 1.00
#> ola_mES_a2i_2_38.counts 1 0.981 0.293 0.58 0.42
#> ola_mES_a2i_2_4.counts 2 0.000 0.977 0.00 1.00
#> ola_mES_a2i_2_40.counts 2 0.000 0.977 0.00 1.00
#> ola_mES_a2i_2_43.counts 1 0.000 0.945 1.00 0.00
#> ola_mES_a2i_2_44.counts 2 0.000 0.977 0.00 1.00
#> ola_mES_a2i_2_47.counts 2 0.000 0.977 0.00 1.00
#> ola_mES_a2i_2_48.counts 2 0.634 0.792 0.16 0.84
#> ola_mES_a2i_2_49.counts 2 0.000 0.977 0.00 1.00
#> ola_mES_a2i_2_51.counts 2 0.000 0.977 0.00 1.00
#> ola_mES_a2i_2_52.counts 2 0.904 0.513 0.32 0.68
#> ola_mES_a2i_2_54.counts 2 0.000 0.977 0.00 1.00
#> ola_mES_a2i_2_56.counts 1 0.000 0.945 1.00 0.00
#> ola_mES_a2i_2_60.counts 2 0.000 0.977 0.00 1.00
#> ola_mES_a2i_2_61.counts 1 0.000 0.945 1.00 0.00
#> ola_mES_a2i_2_62.counts 2 0.000 0.977 0.00 1.00
#> ola_mES_a2i_2_63.counts 1 0.000 0.945 1.00 0.00
#> ola_mES_a2i_2_64.counts 2 0.000 0.977 0.00 1.00
#> ola_mES_a2i_2_65.counts 1 0.000 0.945 1.00 0.00
#> ola_mES_a2i_2_67.counts 1 0.722 0.734 0.80 0.20
#> ola_mES_a2i_2_68.counts 2 0.000 0.977 0.00 1.00
#> ola_mES_a2i_2_7.counts 2 0.000 0.977 0.00 1.00
#> ola_mES_a2i_2_70.counts 2 0.000 0.977 0.00 1.00
#> ola_mES_a2i_2_72.counts 2 0.402 0.895 0.08 0.92
#> ola_mES_a2i_2_73.counts 2 0.000 0.977 0.00 1.00
#> ola_mES_a2i_2_74.counts 1 0.000 0.945 1.00 0.00
#> ola_mES_a2i_2_75.counts 2 0.000 0.977 0.00 1.00
#> ola_mES_a2i_2_76.counts 1 0.000 0.945 1.00 0.00
#> ola_mES_a2i_2_77.counts 2 0.000 0.977 0.00 1.00
#> ola_mES_a2i_2_80.counts 2 0.000 0.977 0.00 1.00
#> ola_mES_a2i_2_81.counts 2 0.000 0.977 0.00 1.00
#> ola_mES_a2i_2_84.counts 2 0.000 0.977 0.00 1.00
#> ola_mES_a2i_2_86.counts 1 0.904 0.557 0.68 0.32
#> ola_mES_a2i_2_87.counts 2 0.000 0.977 0.00 1.00
#> ola_mES_a2i_2_88.counts 2 0.000 0.977 0.00 1.00
#> ola_mES_a2i_2_9.counts 1 0.000 0.945 1.00 0.00
#> ola_mES_a2i_2_93.counts 1 0.000 0.945 1.00 0.00
#> ola_mES_a2i_3_13.counts 2 0.000 0.977 0.00 1.00
#> ola_mES_a2i_3_16.counts 2 0.000 0.977 0.00 1.00
#> ola_mES_a2i_3_25.counts 2 0.000 0.977 0.00 1.00
#> ola_mES_a2i_3_28.counts 2 0.000 0.977 0.00 1.00
#> ola_mES_a2i_3_29.counts 2 0.000 0.977 0.00 1.00
#> ola_mES_a2i_3_34.counts 2 0.958 0.332 0.38 0.62
#> ola_mES_a2i_3_37.counts 2 0.000 0.977 0.00 1.00
#> ola_mES_a2i_3_38.counts 1 0.990 0.269 0.56 0.44
#> ola_mES_a2i_3_39.counts 1 0.000 0.945 1.00 0.00
#> ola_mES_a2i_3_40.counts 2 0.000 0.977 0.00 1.00
#> ola_mES_a2i_3_44.counts 2 0.000 0.977 0.00 1.00
#> ola_mES_a2i_3_49.counts 2 0.000 0.977 0.00 1.00
#> ola_mES_a2i_3_50.counts 2 0.000 0.977 0.00 1.00
#> ola_mES_a2i_3_54.counts 2 0.000 0.977 0.00 1.00
#> ola_mES_a2i_3_6.counts 2 0.000 0.977 0.00 1.00
#> ola_mES_a2i_3_62.counts 2 0.000 0.977 0.00 1.00
#> ola_mES_a2i_3_64.counts 2 0.000 0.977 0.00 1.00
#> ola_mES_a2i_3_66.counts 2 0.000 0.977 0.00 1.00
#> ola_mES_a2i_3_69.counts 1 0.000 0.945 1.00 0.00
#> ola_mES_a2i_3_73.counts 2 0.000 0.977 0.00 1.00
#> ola_mES_a2i_3_77.counts 2 0.000 0.977 0.00 1.00
#> ola_mES_a2i_3_82.counts 2 0.000 0.977 0.00 1.00
#> ola_mES_a2i_3_83.counts 2 0.000 0.977 0.00 1.00
#> ola_mES_a2i_3_84.counts 2 0.000 0.977 0.00 1.00
#> ola_mES_a2i_3_85.counts 2 0.000 0.977 0.00 1.00
#> ola_mES_a2i_3_86.counts 1 0.904 0.557 0.68 0.32
#> ola_mES_a2i_3_90.counts 1 0.881 0.593 0.70 0.30
#> ola_mES_a2i_3_92.counts 2 0.000 0.977 0.00 1.00
#> ola_mES_a2i_3_96.counts 2 0.000 0.977 0.00 1.00
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> ola_mES_2i_2_1.counts 1 0.0000 0.991 1.00 0.00 0.00
#> ola_mES_2i_2_14.counts 1 0.0000 0.991 1.00 0.00 0.00
#> ola_mES_2i_2_2.counts 1 0.0000 0.991 1.00 0.00 0.00
#> ola_mES_2i_2_20.counts 1 0.0000 0.991 1.00 0.00 0.00
#> ola_mES_2i_2_25.counts 1 0.0000 0.991 1.00 0.00 0.00
#> ola_mES_2i_2_27.counts 1 0.0000 0.991 1.00 0.00 0.00
#> ola_mES_2i_2_28.counts 1 0.0000 0.991 1.00 0.00 0.00
#> ola_mES_2i_2_3.counts 1 0.0000 0.991 1.00 0.00 0.00
#> ola_mES_2i_2_37.counts 1 0.0000 0.991 1.00 0.00 0.00
#> ola_mES_2i_2_38.counts 1 0.0000 0.991 1.00 0.00 0.00
#> ola_mES_2i_2_43.counts 1 0.0000 0.991 1.00 0.00 0.00
#> ola_mES_2i_2_49.counts 1 0.0000 0.991 1.00 0.00 0.00
#> ola_mES_2i_2_50.counts 1 0.0000 0.991 1.00 0.00 0.00
#> ola_mES_2i_2_61.counts 1 0.0000 0.991 1.00 0.00 0.00
#> ola_mES_2i_2_63.counts 1 0.0000 0.991 1.00 0.00 0.00
#> ola_mES_2i_2_68.counts 1 0.0000 0.991 1.00 0.00 0.00
#> ola_mES_2i_2_73.counts 1 0.0000 0.991 1.00 0.00 0.00
#> ola_mES_2i_2_75.counts 1 0.0000 0.991 1.00 0.00 0.00
#> ola_mES_2i_2_76.counts 1 0.0000 0.991 1.00 0.00 0.00
#> ola_mES_2i_2_85.counts 1 0.0000 0.991 1.00 0.00 0.00
#> ola_mES_2i_2_86.counts 1 0.0000 0.991 1.00 0.00 0.00
#> ola_mES_2i_4_38.counts 2 0.0000 0.986 0.00 1.00 0.00
#> ola_mES_2i_4_40.counts 2 0.0000 0.986 0.00 1.00 0.00
#> ola_mES_2i_4_49.counts 2 0.0000 0.986 0.00 1.00 0.00
#> ola_mES_2i_4_53.counts 2 0.0000 0.986 0.00 1.00 0.00
#> ola_mES_2i_4_67.counts 2 0.0000 0.986 0.00 1.00 0.00
#> ola_mES_2i_5_41.counts 2 0.0000 0.986 0.00 1.00 0.00
#> ola_mES_a2i_2_10.counts 2 0.0000 0.986 0.00 1.00 0.00
#> ola_mES_a2i_2_12.counts 2 0.0000 0.986 0.00 1.00 0.00
#> ola_mES_a2i_2_13.counts 1 0.0000 0.991 1.00 0.00 0.00
#> ola_mES_a2i_2_20.counts 2 0.0000 0.986 0.00 1.00 0.00
#> ola_mES_a2i_2_25.counts 3 0.0000 0.978 0.00 0.00 1.00
#> ola_mES_a2i_2_27.counts 2 0.0000 0.986 0.00 1.00 0.00
#> ola_mES_a2i_2_28.counts 2 0.0000 0.986 0.00 1.00 0.00
#> ola_mES_a2i_2_29.counts 2 0.0000 0.986 0.00 1.00 0.00
#> ola_mES_a2i_2_33.counts 2 0.0000 0.986 0.00 1.00 0.00
#> ola_mES_a2i_2_34.counts 2 0.0000 0.986 0.00 1.00 0.00
#> ola_mES_a2i_2_38.counts 2 0.8399 0.512 0.22 0.62 0.16
#> ola_mES_a2i_2_4.counts 2 0.0000 0.986 0.00 1.00 0.00
#> ola_mES_a2i_2_40.counts 2 0.0000 0.986 0.00 1.00 0.00
#> ola_mES_a2i_2_43.counts 1 0.0000 0.991 1.00 0.00 0.00
#> ola_mES_a2i_2_44.counts 3 0.0000 0.978 0.00 0.00 1.00
#> ola_mES_a2i_2_47.counts 2 0.0000 0.986 0.00 1.00 0.00
#> ola_mES_a2i_2_48.counts 2 0.0000 0.986 0.00 1.00 0.00
#> ola_mES_a2i_2_49.counts 3 0.0000 0.978 0.00 0.00 1.00
#> ola_mES_a2i_2_51.counts 2 0.0000 0.986 0.00 1.00 0.00
#> ola_mES_a2i_2_52.counts 2 0.0000 0.986 0.00 1.00 0.00
#> ola_mES_a2i_2_54.counts 2 0.0000 0.986 0.00 1.00 0.00
#> ola_mES_a2i_2_56.counts 1 0.1529 0.954 0.96 0.04 0.00
#> ola_mES_a2i_2_60.counts 2 0.0000 0.986 0.00 1.00 0.00
#> ola_mES_a2i_2_61.counts 1 0.2066 0.933 0.94 0.06 0.00
#> ola_mES_a2i_2_62.counts 2 0.4291 0.774 0.00 0.82 0.18
#> ola_mES_a2i_2_63.counts 1 0.0000 0.991 1.00 0.00 0.00
#> ola_mES_a2i_2_64.counts 2 0.0000 0.986 0.00 1.00 0.00
#> ola_mES_a2i_2_65.counts 1 0.0892 0.973 0.98 0.02 0.00
#> ola_mES_a2i_2_67.counts 2 0.0000 0.986 0.00 1.00 0.00
#> ola_mES_a2i_2_68.counts 3 0.6280 0.138 0.00 0.46 0.54
#> ola_mES_a2i_2_7.counts 2 0.0000 0.986 0.00 1.00 0.00
#> ola_mES_a2i_2_70.counts 2 0.0000 0.986 0.00 1.00 0.00
#> ola_mES_a2i_2_72.counts 2 0.0000 0.986 0.00 1.00 0.00
#> ola_mES_a2i_2_73.counts 3 0.1529 0.939 0.00 0.04 0.96
#> ola_mES_a2i_2_74.counts 1 0.0000 0.991 1.00 0.00 0.00
#> ola_mES_a2i_2_75.counts 2 0.0000 0.986 0.00 1.00 0.00
#> ola_mES_a2i_2_76.counts 1 0.3340 0.863 0.88 0.12 0.00
#> ola_mES_a2i_2_77.counts 2 0.0000 0.986 0.00 1.00 0.00
#> ola_mES_a2i_2_80.counts 3 0.0000 0.978 0.00 0.00 1.00
#> ola_mES_a2i_2_81.counts 2 0.0000 0.986 0.00 1.00 0.00
#> ola_mES_a2i_2_84.counts 2 0.0000 0.986 0.00 1.00 0.00
#> ola_mES_a2i_2_86.counts 3 0.0000 0.978 0.00 0.00 1.00
#> ola_mES_a2i_2_87.counts 2 0.0000 0.986 0.00 1.00 0.00
#> ola_mES_a2i_2_88.counts 2 0.0000 0.986 0.00 1.00 0.00
#> ola_mES_a2i_2_9.counts 3 0.0000 0.978 0.00 0.00 1.00
#> ola_mES_a2i_2_93.counts 1 0.0000 0.991 1.00 0.00 0.00
#> ola_mES_a2i_3_13.counts 2 0.0000 0.986 0.00 1.00 0.00
#> ola_mES_a2i_3_16.counts 3 0.0000 0.978 0.00 0.00 1.00
#> ola_mES_a2i_3_25.counts 3 0.0000 0.978 0.00 0.00 1.00
#> ola_mES_a2i_3_28.counts 3 0.0000 0.978 0.00 0.00 1.00
#> ola_mES_a2i_3_29.counts 3 0.0000 0.978 0.00 0.00 1.00
#> ola_mES_a2i_3_34.counts 3 0.0000 0.978 0.00 0.00 1.00
#> ola_mES_a2i_3_37.counts 2 0.0000 0.986 0.00 1.00 0.00
#> ola_mES_a2i_3_38.counts 3 0.0000 0.978 0.00 0.00 1.00
#> ola_mES_a2i_3_39.counts 3 0.0000 0.978 0.00 0.00 1.00
#> ola_mES_a2i_3_40.counts 3 0.0000 0.978 0.00 0.00 1.00
#> ola_mES_a2i_3_44.counts 3 0.0000 0.978 0.00 0.00 1.00
#> ola_mES_a2i_3_49.counts 3 0.0000 0.978 0.00 0.00 1.00
#> ola_mES_a2i_3_50.counts 3 0.0000 0.978 0.00 0.00 1.00
#> ola_mES_a2i_3_54.counts 3 0.0000 0.978 0.00 0.00 1.00
#> ola_mES_a2i_3_6.counts 3 0.0000 0.978 0.00 0.00 1.00
#> ola_mES_a2i_3_62.counts 2 0.1529 0.947 0.00 0.96 0.04
#> ola_mES_a2i_3_64.counts 2 0.0000 0.986 0.00 1.00 0.00
#> ola_mES_a2i_3_66.counts 3 0.0000 0.978 0.00 0.00 1.00
#> ola_mES_a2i_3_69.counts 3 0.0000 0.978 0.00 0.00 1.00
#> ola_mES_a2i_3_73.counts 2 0.0000 0.986 0.00 1.00 0.00
#> ola_mES_a2i_3_77.counts 3 0.0000 0.978 0.00 0.00 1.00
#> ola_mES_a2i_3_82.counts 3 0.0000 0.978 0.00 0.00 1.00
#> ola_mES_a2i_3_83.counts 2 0.0000 0.986 0.00 1.00 0.00
#> ola_mES_a2i_3_84.counts 3 0.0892 0.960 0.00 0.02 0.98
#> ola_mES_a2i_3_85.counts 2 0.0000 0.986 0.00 1.00 0.00
#> ola_mES_a2i_3_86.counts 3 0.0000 0.978 0.00 0.00 1.00
#> ola_mES_a2i_3_90.counts 3 0.0000 0.978 0.00 0.00 1.00
#> ola_mES_a2i_3_92.counts 2 0.0000 0.986 0.00 1.00 0.00
#> ola_mES_a2i_3_96.counts 2 0.0000 0.986 0.00 1.00 0.00
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> ola_mES_2i_2_1.counts 1 0.0000 0.9964 1.00 0.00 0.00 0.00
#> ola_mES_2i_2_14.counts 1 0.0000 0.9964 1.00 0.00 0.00 0.00
#> ola_mES_2i_2_2.counts 1 0.0000 0.9964 1.00 0.00 0.00 0.00
#> ola_mES_2i_2_20.counts 1 0.0000 0.9964 1.00 0.00 0.00 0.00
#> ola_mES_2i_2_25.counts 1 0.0000 0.9964 1.00 0.00 0.00 0.00
#> ola_mES_2i_2_27.counts 1 0.0000 0.9964 1.00 0.00 0.00 0.00
#> ola_mES_2i_2_28.counts 1 0.0000 0.9964 1.00 0.00 0.00 0.00
#> ola_mES_2i_2_3.counts 1 0.0000 0.9964 1.00 0.00 0.00 0.00
#> ola_mES_2i_2_37.counts 1 0.0000 0.9964 1.00 0.00 0.00 0.00
#> ola_mES_2i_2_38.counts 1 0.0000 0.9964 1.00 0.00 0.00 0.00
#> ola_mES_2i_2_43.counts 1 0.0000 0.9964 1.00 0.00 0.00 0.00
#> ola_mES_2i_2_49.counts 1 0.0000 0.9964 1.00 0.00 0.00 0.00
#> ola_mES_2i_2_50.counts 1 0.0000 0.9964 1.00 0.00 0.00 0.00
#> ola_mES_2i_2_61.counts 1 0.0000 0.9964 1.00 0.00 0.00 0.00
#> ola_mES_2i_2_63.counts 1 0.0000 0.9964 1.00 0.00 0.00 0.00
#> ola_mES_2i_2_68.counts 1 0.0000 0.9964 1.00 0.00 0.00 0.00
#> ola_mES_2i_2_73.counts 1 0.0000 0.9964 1.00 0.00 0.00 0.00
#> ola_mES_2i_2_75.counts 1 0.0000 0.9964 1.00 0.00 0.00 0.00
#> ola_mES_2i_2_76.counts 1 0.0000 0.9964 1.00 0.00 0.00 0.00
#> ola_mES_2i_2_85.counts 1 0.0000 0.9964 1.00 0.00 0.00 0.00
#> ola_mES_2i_2_86.counts 1 0.0000 0.9964 1.00 0.00 0.00 0.00
#> ola_mES_2i_4_38.counts 2 0.0000 0.9167 0.00 1.00 0.00 0.00
#> ola_mES_2i_4_40.counts 2 0.0000 0.9167 0.00 1.00 0.00 0.00
#> ola_mES_2i_4_49.counts 2 0.0000 0.9167 0.00 1.00 0.00 0.00
#> ola_mES_2i_4_53.counts 2 0.0000 0.9167 0.00 1.00 0.00 0.00
#> ola_mES_2i_4_67.counts 2 0.0000 0.9167 0.00 1.00 0.00 0.00
#> ola_mES_2i_5_41.counts 2 0.0000 0.9167 0.00 1.00 0.00 0.00
#> ola_mES_a2i_2_10.counts 2 0.0000 0.9167 0.00 1.00 0.00 0.00
#> ola_mES_a2i_2_12.counts 2 0.2345 0.8545 0.00 0.90 0.00 0.10
#> ola_mES_a2i_2_13.counts 1 0.1211 0.9613 0.96 0.00 0.00 0.04
#> ola_mES_a2i_2_20.counts 2 0.0000 0.9167 0.00 1.00 0.00 0.00
#> ola_mES_a2i_2_25.counts 3 0.0000 0.9640 0.00 0.00 1.00 0.00
#> ola_mES_a2i_2_27.counts 4 0.0707 0.8602 0.00 0.02 0.00 0.98
#> ola_mES_a2i_2_28.counts 2 0.2011 0.8703 0.00 0.92 0.00 0.08
#> ola_mES_a2i_2_29.counts 2 0.0000 0.9167 0.00 1.00 0.00 0.00
#> ola_mES_a2i_2_33.counts 2 0.4713 0.4429 0.00 0.64 0.00 0.36
#> ola_mES_a2i_2_34.counts 2 0.1211 0.8964 0.00 0.96 0.00 0.04
#> ola_mES_a2i_2_38.counts 4 0.0000 0.8696 0.00 0.00 0.00 1.00
#> ola_mES_a2i_2_4.counts 2 0.0000 0.9167 0.00 1.00 0.00 0.00
#> ola_mES_a2i_2_40.counts 2 0.4994 0.0867 0.00 0.52 0.00 0.48
#> ola_mES_a2i_2_43.counts 4 0.1637 0.8312 0.06 0.00 0.00 0.94
#> ola_mES_a2i_2_44.counts 3 0.4406 0.5875 0.00 0.30 0.70 0.00
#> ola_mES_a2i_2_47.counts 2 0.0000 0.9167 0.00 1.00 0.00 0.00
#> ola_mES_a2i_2_48.counts 4 0.0000 0.8696 0.00 0.00 0.00 1.00
#> ola_mES_a2i_2_49.counts 3 0.0000 0.9640 0.00 0.00 1.00 0.00
#> ola_mES_a2i_2_51.counts 4 0.2345 0.8062 0.00 0.10 0.00 0.90
#> ola_mES_a2i_2_52.counts 4 0.0000 0.8696 0.00 0.00 0.00 1.00
#> ola_mES_a2i_2_54.counts 2 0.0000 0.9167 0.00 1.00 0.00 0.00
#> ola_mES_a2i_2_56.counts 4 0.0000 0.8696 0.00 0.00 0.00 1.00
#> ola_mES_a2i_2_60.counts 2 0.0707 0.9071 0.00 0.98 0.00 0.02
#> ola_mES_a2i_2_61.counts 4 0.0000 0.8696 0.00 0.00 0.00 1.00
#> ola_mES_a2i_2_62.counts 2 0.1637 0.8670 0.00 0.94 0.06 0.00
#> ola_mES_a2i_2_63.counts 4 0.0000 0.8696 0.00 0.00 0.00 1.00
#> ola_mES_a2i_2_64.counts 4 0.2921 0.7704 0.00 0.14 0.00 0.86
#> ola_mES_a2i_2_65.counts 4 0.0000 0.8696 0.00 0.00 0.00 1.00
#> ola_mES_a2i_2_67.counts 4 0.0000 0.8696 0.00 0.00 0.00 1.00
#> ola_mES_a2i_2_68.counts 4 0.7855 0.2580 0.00 0.30 0.30 0.40
#> ola_mES_a2i_2_7.counts 2 0.2011 0.8703 0.00 0.92 0.00 0.08
#> ola_mES_a2i_2_70.counts 4 0.4948 0.1736 0.00 0.44 0.00 0.56
#> ola_mES_a2i_2_72.counts 4 0.0000 0.8696 0.00 0.00 0.00 1.00
#> ola_mES_a2i_2_73.counts 3 0.4731 0.7307 0.00 0.16 0.78 0.06
#> ola_mES_a2i_2_74.counts 4 0.1637 0.8313 0.06 0.00 0.00 0.94
#> ola_mES_a2i_2_75.counts 2 0.4134 0.6617 0.00 0.74 0.00 0.26
#> ola_mES_a2i_2_76.counts 4 0.0000 0.8696 0.00 0.00 0.00 1.00
#> ola_mES_a2i_2_77.counts 2 0.4406 0.5867 0.00 0.70 0.00 0.30
#> ola_mES_a2i_2_80.counts 3 0.0000 0.9640 0.00 0.00 1.00 0.00
#> ola_mES_a2i_2_81.counts 2 0.3610 0.7462 0.00 0.80 0.00 0.20
#> ola_mES_a2i_2_84.counts 2 0.1211 0.8973 0.00 0.96 0.00 0.04
#> ola_mES_a2i_2_86.counts 3 0.0000 0.9640 0.00 0.00 1.00 0.00
#> ola_mES_a2i_2_87.counts 2 0.0000 0.9167 0.00 1.00 0.00 0.00
#> ola_mES_a2i_2_88.counts 2 0.4713 0.4597 0.00 0.64 0.00 0.36
#> ola_mES_a2i_2_9.counts 3 0.0000 0.9640 0.00 0.00 1.00 0.00
#> ola_mES_a2i_2_93.counts 1 0.1211 0.9613 0.96 0.00 0.00 0.04
#> ola_mES_a2i_3_13.counts 2 0.0000 0.9167 0.00 1.00 0.00 0.00
#> ola_mES_a2i_3_16.counts 3 0.0000 0.9640 0.00 0.00 1.00 0.00
#> ola_mES_a2i_3_25.counts 3 0.0000 0.9640 0.00 0.00 1.00 0.00
#> ola_mES_a2i_3_28.counts 3 0.0000 0.9640 0.00 0.00 1.00 0.00
#> ola_mES_a2i_3_29.counts 3 0.0000 0.9640 0.00 0.00 1.00 0.00
#> ola_mES_a2i_3_34.counts 3 0.0000 0.9640 0.00 0.00 1.00 0.00
#> ola_mES_a2i_3_37.counts 2 0.0000 0.9167 0.00 1.00 0.00 0.00
#> ola_mES_a2i_3_38.counts 3 0.0000 0.9640 0.00 0.00 1.00 0.00
#> ola_mES_a2i_3_39.counts 3 0.0000 0.9640 0.00 0.00 1.00 0.00
#> ola_mES_a2i_3_40.counts 3 0.0000 0.9640 0.00 0.00 1.00 0.00
#> ola_mES_a2i_3_44.counts 3 0.0000 0.9640 0.00 0.00 1.00 0.00
#> ola_mES_a2i_3_49.counts 3 0.0000 0.9640 0.00 0.00 1.00 0.00
#> ola_mES_a2i_3_50.counts 3 0.0707 0.9470 0.00 0.02 0.98 0.00
#> ola_mES_a2i_3_54.counts 3 0.0000 0.9640 0.00 0.00 1.00 0.00
#> ola_mES_a2i_3_6.counts 3 0.0000 0.9640 0.00 0.00 1.00 0.00
#> ola_mES_a2i_3_62.counts 4 0.6988 0.2981 0.00 0.38 0.12 0.50
#> ola_mES_a2i_3_64.counts 2 0.0000 0.9167 0.00 1.00 0.00 0.00
#> ola_mES_a2i_3_66.counts 3 0.0000 0.9640 0.00 0.00 1.00 0.00
#> ola_mES_a2i_3_69.counts 3 0.0000 0.9640 0.00 0.00 1.00 0.00
#> ola_mES_a2i_3_73.counts 2 0.0000 0.9167 0.00 1.00 0.00 0.00
#> ola_mES_a2i_3_77.counts 3 0.1211 0.9283 0.00 0.04 0.96 0.00
#> ola_mES_a2i_3_82.counts 3 0.0000 0.9640 0.00 0.00 1.00 0.00
#> ola_mES_a2i_3_83.counts 2 0.0000 0.9167 0.00 1.00 0.00 0.00
#> ola_mES_a2i_3_84.counts 3 0.5147 0.6815 0.00 0.20 0.74 0.06
#> ola_mES_a2i_3_85.counts 2 0.0000 0.9167 0.00 1.00 0.00 0.00
#> ola_mES_a2i_3_86.counts 3 0.0000 0.9640 0.00 0.00 1.00 0.00
#> ola_mES_a2i_3_90.counts 3 0.0000 0.9640 0.00 0.00 1.00 0.00
#> ola_mES_a2i_3_92.counts 2 0.0000 0.9167 0.00 1.00 0.00 0.00
#> ola_mES_a2i_3_96.counts 4 0.4713 0.4080 0.00 0.36 0.00 0.64
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)
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: Node011. Child nodes: Node01111-leaf , Node01112-leaf , Node01121-leaf , Node01122-leaf , Node01411-leaf , Node01412-leaf , Node01413-leaf , Node02111-leaf , Node02112-leaf , Node02211-leaf , Node02212-leaf , Node02221-leaf , Node02222-leaf , Node02223-leaf .
The object with results only for a single top-value method and a single partitioning method can be extracted as:
res = res_rh["0111"]
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 15429 rows and 30 columns.
#> Top rows (1073) 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 0.435 0.566 0.566
#> 3 3 0.725 0.837 0.925 0.462 0.816 0.675
#> 4 4 0.688 0.687 0.856 0.159 0.871 0.671
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
#> ola_mES_2i_2_1.counts 2 0 1 0 1
#> ola_mES_2i_2_14.counts 2 0 1 0 1
#> ola_mES_2i_2_2.counts 2 0 1 0 1
#> ola_mES_2i_2_20.counts 2 0 1 0 1
#> ola_mES_2i_2_25.counts 2 0 1 0 1
#> ola_mES_2i_2_27.counts 2 0 1 0 1
#> ola_mES_2i_2_28.counts 2 0 1 0 1
#> ola_mES_2i_2_3.counts 2 0 1 0 1
#> ola_mES_2i_2_37.counts 2 0 1 0 1
#> ola_mES_2i_2_38.counts 2 0 1 0 1
#> ola_mES_2i_2_43.counts 2 0 1 0 1
#> ola_mES_2i_2_49.counts 2 0 1 0 1
#> ola_mES_2i_2_50.counts 2 0 1 0 1
#> ola_mES_2i_2_61.counts 2 0 1 0 1
#> ola_mES_2i_2_63.counts 2 0 1 0 1
#> ola_mES_2i_2_68.counts 2 0 1 0 1
#> ola_mES_2i_2_73.counts 2 0 1 0 1
#> ola_mES_2i_2_75.counts 2 0 1 0 1
#> ola_mES_2i_2_76.counts 2 0 1 0 1
#> ola_mES_2i_2_85.counts 2 0 1 0 1
#> ola_mES_2i_2_86.counts 2 0 1 0 1
#> ola_mES_a2i_2_13.counts 1 0 1 1 0
#> ola_mES_a2i_2_43.counts 1 0 1 1 0
#> ola_mES_a2i_2_56.counts 1 0 1 1 0
#> ola_mES_a2i_2_61.counts 1 0 1 1 0
#> ola_mES_a2i_2_63.counts 1 0 1 1 0
#> ola_mES_a2i_2_65.counts 1 0 1 1 0
#> ola_mES_a2i_2_74.counts 1 0 1 1 0
#> ola_mES_a2i_2_76.counts 1 0 1 1 0
#> ola_mES_a2i_2_93.counts 1 0 1 1 0
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> ola_mES_2i_2_1.counts 2 0.2537 0.843 0 0.92 0.08
#> ola_mES_2i_2_14.counts 3 0.6192 0.260 0 0.42 0.58
#> ola_mES_2i_2_2.counts 2 0.2537 0.847 0 0.92 0.08
#> ola_mES_2i_2_20.counts 2 0.6192 0.168 0 0.58 0.42
#> ola_mES_2i_2_25.counts 2 0.0000 0.873 0 1.00 0.00
#> ola_mES_2i_2_27.counts 2 0.0000 0.873 0 1.00 0.00
#> ola_mES_2i_2_28.counts 3 0.0892 0.822 0 0.02 0.98
#> ola_mES_2i_2_3.counts 2 0.0000 0.873 0 1.00 0.00
#> ola_mES_2i_2_37.counts 2 0.4002 0.804 0 0.84 0.16
#> ola_mES_2i_2_38.counts 3 0.2066 0.818 0 0.06 0.94
#> ola_mES_2i_2_43.counts 2 0.0000 0.873 0 1.00 0.00
#> ola_mES_2i_2_49.counts 2 0.5016 0.730 0 0.76 0.24
#> ola_mES_2i_2_50.counts 3 0.3340 0.793 0 0.12 0.88
#> ola_mES_2i_2_61.counts 2 0.4555 0.726 0 0.80 0.20
#> ola_mES_2i_2_63.counts 2 0.4796 0.716 0 0.78 0.22
#> ola_mES_2i_2_68.counts 2 0.0000 0.873 0 1.00 0.00
#> ola_mES_2i_2_73.counts 2 0.3340 0.821 0 0.88 0.12
#> ola_mES_2i_2_75.counts 2 0.0000 0.873 0 1.00 0.00
#> ola_mES_2i_2_76.counts 2 0.0892 0.869 0 0.98 0.02
#> ola_mES_2i_2_85.counts 2 0.2959 0.835 0 0.90 0.10
#> ola_mES_2i_2_86.counts 3 0.0000 0.822 0 0.00 1.00
#> ola_mES_a2i_2_13.counts 1 0.0000 1.000 1 0.00 0.00
#> ola_mES_a2i_2_43.counts 1 0.0000 1.000 1 0.00 0.00
#> ola_mES_a2i_2_56.counts 1 0.0000 1.000 1 0.00 0.00
#> ola_mES_a2i_2_61.counts 1 0.0000 1.000 1 0.00 0.00
#> ola_mES_a2i_2_63.counts 1 0.0000 1.000 1 0.00 0.00
#> ola_mES_a2i_2_65.counts 1 0.0000 1.000 1 0.00 0.00
#> ola_mES_a2i_2_74.counts 1 0.0000 1.000 1 0.00 0.00
#> ola_mES_a2i_2_76.counts 1 0.0000 1.000 1 0.00 0.00
#> ola_mES_a2i_2_93.counts 1 0.0000 1.000 1 0.00 0.00
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> ola_mES_2i_2_1.counts 4 0.2011 0.5736 0 0.08 0.00 0.92
#> ola_mES_2i_2_14.counts 4 0.3198 0.5276 0 0.04 0.08 0.88
#> ola_mES_2i_2_2.counts 2 0.6089 0.4771 0 0.64 0.28 0.08
#> ola_mES_2i_2_20.counts 4 0.1211 0.5729 0 0.04 0.00 0.96
#> ola_mES_2i_2_25.counts 2 0.1211 0.7826 0 0.96 0.00 0.04
#> ola_mES_2i_2_27.counts 2 0.0707 0.7844 0 0.98 0.00 0.02
#> ola_mES_2i_2_28.counts 3 0.1637 0.6057 0 0.00 0.94 0.06
#> ola_mES_2i_2_3.counts 2 0.1211 0.7827 0 0.96 0.00 0.04
#> ola_mES_2i_2_37.counts 2 0.5327 0.6032 0 0.72 0.22 0.06
#> ola_mES_2i_2_38.counts 3 0.6805 0.0635 0 0.10 0.50 0.40
#> ola_mES_2i_2_43.counts 2 0.0707 0.7847 0 0.98 0.00 0.02
#> ola_mES_2i_2_49.counts 2 0.7344 0.0292 0 0.46 0.16 0.38
#> ola_mES_2i_2_50.counts 3 0.6299 0.3023 0 0.08 0.60 0.32
#> ola_mES_2i_2_61.counts 4 0.6941 0.3149 0 0.36 0.12 0.52
#> ola_mES_2i_2_63.counts 4 0.7474 0.2698 0 0.28 0.22 0.50
#> ola_mES_2i_2_68.counts 2 0.2830 0.7627 0 0.90 0.04 0.06
#> ola_mES_2i_2_73.counts 2 0.4088 0.7145 0 0.82 0.14 0.04
#> ola_mES_2i_2_75.counts 2 0.2011 0.7736 0 0.92 0.00 0.08
#> ola_mES_2i_2_76.counts 2 0.5784 0.5233 0 0.70 0.10 0.20
#> ola_mES_2i_2_85.counts 2 0.2411 0.7747 0 0.92 0.04 0.04
#> ola_mES_2i_2_86.counts 3 0.2830 0.5951 0 0.04 0.90 0.06
#> ola_mES_a2i_2_13.counts 1 0.0000 1.0000 1 0.00 0.00 0.00
#> ola_mES_a2i_2_43.counts 1 0.0000 1.0000 1 0.00 0.00 0.00
#> ola_mES_a2i_2_56.counts 1 0.0000 1.0000 1 0.00 0.00 0.00
#> ola_mES_a2i_2_61.counts 1 0.0000 1.0000 1 0.00 0.00 0.00
#> ola_mES_a2i_2_63.counts 1 0.0000 1.0000 1 0.00 0.00 0.00
#> ola_mES_a2i_2_65.counts 1 0.0000 1.0000 1 0.00 0.00 0.00
#> ola_mES_a2i_2_74.counts 1 0.0000 1.0000 1 0.00 0.00 0.00
#> ola_mES_a2i_2_76.counts 1 0.0000 1.0000 1 0.00 0.00 0.00
#> ola_mES_a2i_2_93.counts 1 0.0000 1.0000 1 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)
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: Node011. Child nodes: Node01111-leaf , Node01112-leaf , Node01121-leaf , Node01122-leaf , Node01411-leaf , Node01412-leaf , Node01413-leaf , Node02111-leaf , Node02112-leaf , Node02211-leaf , Node02212-leaf , Node02221-leaf , Node02222-leaf , Node02223-leaf .
The object with results only for a single top-value method and a single partitioning method can be extracted as:
res = res_rh["0112"]
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 15715 rows and 44 columns.
#> Top rows (1425) 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 0.999 0.966 0.985 0.326 0.667 0.667
#> 3 3 0.620 0.795 0.907 0.931 0.630 0.472
#> 4 4 0.495 0.626 0.795 0.163 0.823 0.559
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
#> ola_mES_2i_4_38.counts 2 0.000 0.941 0.00 1.00
#> ola_mES_2i_4_40.counts 2 0.000 0.941 0.00 1.00
#> ola_mES_2i_4_49.counts 2 0.000 0.941 0.00 1.00
#> ola_mES_2i_4_53.counts 2 0.000 0.941 0.00 1.00
#> ola_mES_2i_4_67.counts 2 0.000 0.941 0.00 1.00
#> ola_mES_2i_5_41.counts 2 0.000 0.941 0.00 1.00
#> ola_mES_a2i_2_10.counts 1 0.000 0.994 1.00 0.00
#> ola_mES_a2i_2_12.counts 1 0.000 0.994 1.00 0.00
#> ola_mES_a2i_2_20.counts 1 0.242 0.956 0.96 0.04
#> ola_mES_a2i_2_27.counts 1 0.000 0.994 1.00 0.00
#> ola_mES_a2i_2_28.counts 1 0.000 0.994 1.00 0.00
#> ola_mES_a2i_2_29.counts 1 0.000 0.994 1.00 0.00
#> ola_mES_a2i_2_33.counts 1 0.000 0.994 1.00 0.00
#> ola_mES_a2i_2_34.counts 1 0.000 0.994 1.00 0.00
#> ola_mES_a2i_2_38.counts 1 0.000 0.994 1.00 0.00
#> ola_mES_a2i_2_4.counts 1 0.000 0.994 1.00 0.00
#> ola_mES_a2i_2_40.counts 1 0.000 0.994 1.00 0.00
#> ola_mES_a2i_2_47.counts 1 0.000 0.994 1.00 0.00
#> ola_mES_a2i_2_48.counts 1 0.000 0.994 1.00 0.00
#> ola_mES_a2i_2_51.counts 1 0.000 0.994 1.00 0.00
#> ola_mES_a2i_2_52.counts 1 0.000 0.994 1.00 0.00
#> ola_mES_a2i_2_54.counts 1 0.327 0.933 0.94 0.06
#> ola_mES_a2i_2_60.counts 2 0.925 0.501 0.34 0.66
#> ola_mES_a2i_2_62.counts 1 0.000 0.994 1.00 0.00
#> ola_mES_a2i_2_64.counts 1 0.000 0.994 1.00 0.00
#> ola_mES_a2i_2_67.counts 1 0.000 0.994 1.00 0.00
#> ola_mES_a2i_2_7.counts 1 0.000 0.994 1.00 0.00
#> ola_mES_a2i_2_70.counts 1 0.000 0.994 1.00 0.00
#> ola_mES_a2i_2_72.counts 1 0.000 0.994 1.00 0.00
#> ola_mES_a2i_2_75.counts 1 0.000 0.994 1.00 0.00
#> ola_mES_a2i_2_77.counts 1 0.000 0.994 1.00 0.00
#> ola_mES_a2i_2_81.counts 1 0.000 0.994 1.00 0.00
#> ola_mES_a2i_2_84.counts 1 0.000 0.994 1.00 0.00
#> ola_mES_a2i_2_87.counts 1 0.000 0.994 1.00 0.00
#> ola_mES_a2i_2_88.counts 1 0.000 0.994 1.00 0.00
#> ola_mES_a2i_3_13.counts 1 0.000 0.994 1.00 0.00
#> ola_mES_a2i_3_37.counts 2 0.469 0.869 0.10 0.90
#> ola_mES_a2i_3_62.counts 1 0.000 0.994 1.00 0.00
#> ola_mES_a2i_3_64.counts 2 0.141 0.932 0.02 0.98
#> ola_mES_a2i_3_73.counts 1 0.000 0.994 1.00 0.00
#> ola_mES_a2i_3_83.counts 1 0.469 0.884 0.90 0.10
#> ola_mES_a2i_3_85.counts 1 0.000 0.994 1.00 0.00
#> ola_mES_a2i_3_92.counts 1 0.000 0.994 1.00 0.00
#> ola_mES_a2i_3_96.counts 1 0.000 0.994 1.00 0.00
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> ola_mES_2i_4_38.counts 2 0.0000 0.926 0.00 1.00 0.00
#> ola_mES_2i_4_40.counts 2 0.0000 0.926 0.00 1.00 0.00
#> ola_mES_2i_4_49.counts 2 0.0000 0.926 0.00 1.00 0.00
#> ola_mES_2i_4_53.counts 2 0.0000 0.926 0.00 1.00 0.00
#> ola_mES_2i_4_67.counts 2 0.0000 0.926 0.00 1.00 0.00
#> ola_mES_2i_5_41.counts 2 0.0000 0.926 0.00 1.00 0.00
#> ola_mES_a2i_2_10.counts 3 0.0000 0.839 0.00 0.00 1.00
#> ola_mES_a2i_2_12.counts 3 0.3340 0.806 0.12 0.00 0.88
#> ola_mES_a2i_2_20.counts 3 0.0892 0.836 0.00 0.02 0.98
#> ola_mES_a2i_2_27.counts 1 0.0892 0.906 0.98 0.00 0.02
#> ola_mES_a2i_2_28.counts 1 0.6244 0.132 0.56 0.00 0.44
#> ola_mES_a2i_2_29.counts 3 0.0000 0.839 0.00 0.00 1.00
#> ola_mES_a2i_2_33.counts 1 0.1529 0.899 0.96 0.00 0.04
#> ola_mES_a2i_2_34.counts 3 0.5560 0.605 0.30 0.00 0.70
#> ola_mES_a2i_2_38.counts 1 0.0000 0.914 1.00 0.00 0.00
#> ola_mES_a2i_2_4.counts 3 0.1529 0.839 0.04 0.00 0.96
#> ola_mES_a2i_2_40.counts 1 0.1529 0.897 0.96 0.00 0.04
#> ola_mES_a2i_2_47.counts 3 0.0000 0.839 0.00 0.00 1.00
#> ola_mES_a2i_2_48.counts 1 0.0000 0.914 1.00 0.00 0.00
#> ola_mES_a2i_2_51.counts 1 0.0000 0.914 1.00 0.00 0.00
#> ola_mES_a2i_2_52.counts 1 0.0000 0.914 1.00 0.00 0.00
#> ola_mES_a2i_2_54.counts 3 0.0892 0.832 0.00 0.02 0.98
#> ola_mES_a2i_2_60.counts 3 0.8953 0.457 0.26 0.18 0.56
#> ola_mES_a2i_2_62.counts 3 0.2066 0.837 0.06 0.00 0.94
#> ola_mES_a2i_2_64.counts 1 0.0000 0.914 1.00 0.00 0.00
#> ola_mES_a2i_2_67.counts 1 0.0000 0.914 1.00 0.00 0.00
#> ola_mES_a2i_2_7.counts 3 0.2959 0.820 0.10 0.00 0.90
#> ola_mES_a2i_2_70.counts 1 0.0000 0.914 1.00 0.00 0.00
#> ola_mES_a2i_2_72.counts 1 0.0000 0.914 1.00 0.00 0.00
#> ola_mES_a2i_2_75.counts 1 0.4796 0.705 0.78 0.00 0.22
#> ola_mES_a2i_2_77.counts 1 0.0000 0.914 1.00 0.00 0.00
#> ola_mES_a2i_2_81.counts 1 0.5835 0.444 0.66 0.00 0.34
#> ola_mES_a2i_2_84.counts 1 0.3340 0.834 0.88 0.00 0.12
#> ola_mES_a2i_2_87.counts 3 0.2537 0.828 0.08 0.00 0.92
#> ola_mES_a2i_2_88.counts 1 0.1529 0.895 0.96 0.00 0.04
#> ola_mES_a2i_3_13.counts 3 0.6045 0.418 0.38 0.00 0.62
#> ola_mES_a2i_3_37.counts 3 0.2066 0.812 0.00 0.06 0.94
#> ola_mES_a2i_3_62.counts 1 0.0000 0.914 1.00 0.00 0.00
#> ola_mES_a2i_3_64.counts 2 0.8939 0.290 0.14 0.52 0.34
#> ola_mES_a2i_3_73.counts 3 0.0000 0.839 0.00 0.00 1.00
#> ola_mES_a2i_3_83.counts 3 0.6758 0.448 0.36 0.02 0.62
#> ola_mES_a2i_3_85.counts 3 0.0892 0.842 0.02 0.00 0.98
#> ola_mES_a2i_3_92.counts 3 0.5016 0.712 0.24 0.00 0.76
#> ola_mES_a2i_3_96.counts 1 0.2959 0.853 0.90 0.00 0.10
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> ola_mES_2i_4_38.counts 2 0.0000 1.000 0.00 1.00 0.00 0.00
#> ola_mES_2i_4_40.counts 2 0.0000 1.000 0.00 1.00 0.00 0.00
#> ola_mES_2i_4_49.counts 2 0.0000 1.000 0.00 1.00 0.00 0.00
#> ola_mES_2i_4_53.counts 2 0.0000 1.000 0.00 1.00 0.00 0.00
#> ola_mES_2i_4_67.counts 2 0.0000 1.000 0.00 1.00 0.00 0.00
#> ola_mES_2i_5_41.counts 2 0.0000 1.000 0.00 1.00 0.00 0.00
#> ola_mES_a2i_2_10.counts 3 0.0707 0.673 0.00 0.00 0.98 0.02
#> ola_mES_a2i_2_12.counts 3 0.4227 0.637 0.06 0.00 0.82 0.12
#> ola_mES_a2i_2_20.counts 3 0.5535 0.335 0.00 0.02 0.56 0.42
#> ola_mES_a2i_2_27.counts 1 0.2830 0.807 0.90 0.00 0.04 0.06
#> ola_mES_a2i_2_28.counts 3 0.5986 0.207 0.32 0.00 0.62 0.06
#> ola_mES_a2i_2_29.counts 3 0.2345 0.677 0.00 0.00 0.90 0.10
#> ola_mES_a2i_2_33.counts 1 0.6104 0.613 0.68 0.00 0.14 0.18
#> ola_mES_a2i_2_34.counts 4 0.7775 0.163 0.24 0.00 0.38 0.38
#> ola_mES_a2i_2_38.counts 1 0.1637 0.820 0.94 0.00 0.00 0.06
#> ola_mES_a2i_2_4.counts 3 0.2335 0.666 0.02 0.00 0.92 0.06
#> ola_mES_a2i_2_40.counts 1 0.5677 0.634 0.72 0.00 0.14 0.14
#> ola_mES_a2i_2_47.counts 3 0.4855 0.407 0.00 0.00 0.60 0.40
#> ola_mES_a2i_2_48.counts 1 0.0000 0.826 1.00 0.00 0.00 0.00
#> ola_mES_a2i_2_51.counts 1 0.3972 0.765 0.84 0.00 0.08 0.08
#> ola_mES_a2i_2_52.counts 1 0.0000 0.826 1.00 0.00 0.00 0.00
#> ola_mES_a2i_2_54.counts 3 0.4713 0.414 0.00 0.00 0.64 0.36
#> ola_mES_a2i_2_60.counts 3 0.7146 0.448 0.12 0.04 0.64 0.20
#> ola_mES_a2i_2_62.counts 4 0.4939 0.369 0.04 0.00 0.22 0.74
#> ola_mES_a2i_2_64.counts 1 0.1211 0.828 0.96 0.00 0.00 0.04
#> ola_mES_a2i_2_67.counts 1 0.2335 0.823 0.92 0.00 0.02 0.06
#> ola_mES_a2i_2_7.counts 3 0.3525 0.649 0.04 0.00 0.86 0.10
#> ola_mES_a2i_2_70.counts 1 0.0707 0.825 0.98 0.00 0.00 0.02
#> ola_mES_a2i_2_72.counts 1 0.0000 0.826 1.00 0.00 0.00 0.00
#> ola_mES_a2i_2_75.counts 4 0.5062 0.501 0.30 0.00 0.02 0.68
#> ola_mES_a2i_2_77.counts 1 0.3525 0.783 0.86 0.00 0.10 0.04
#> ola_mES_a2i_2_81.counts 4 0.5915 0.390 0.40 0.00 0.04 0.56
#> ola_mES_a2i_2_84.counts 1 0.7497 0.162 0.50 0.00 0.24 0.26
#> ola_mES_a2i_2_87.counts 4 0.4642 0.390 0.02 0.00 0.24 0.74
#> ola_mES_a2i_2_88.counts 1 0.3821 0.759 0.84 0.00 0.04 0.12
#> ola_mES_a2i_3_13.counts 4 0.6122 0.461 0.16 0.00 0.16 0.68
#> ola_mES_a2i_3_37.counts 3 0.1637 0.671 0.00 0.00 0.94 0.06
#> ola_mES_a2i_3_62.counts 1 0.2011 0.802 0.92 0.00 0.00 0.08
#> ola_mES_a2i_3_64.counts 4 0.9907 0.190 0.24 0.22 0.22 0.32
#> ola_mES_a2i_3_73.counts 3 0.4406 0.524 0.00 0.00 0.70 0.30
#> ola_mES_a2i_3_83.counts 4 0.6216 0.480 0.22 0.00 0.12 0.66
#> ola_mES_a2i_3_85.counts 4 0.3400 0.393 0.00 0.00 0.18 0.82
#> ola_mES_a2i_3_92.counts 4 0.7855 0.194 0.30 0.00 0.30 0.40
#> ola_mES_a2i_3_96.counts 1 0.5147 0.623 0.74 0.00 0.20 0.06
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)
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 , Node0112 , Node0113-leaf , Node0121-leaf , Node0122-leaf , Node0131-leaf , Node0132-leaf , Node0133-leaf , Node0141 , Node0142-leaf , Node0143-leaf , Node0211 , Node0212-leaf , Node0221 , Node0222 , Node0223-leaf , Node0311-leaf , Node0312-leaf , Node0313-leaf , Node0321-leaf , Node0322-leaf , Node0331-leaf , Node0332-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 15965 rows and 72 columns.
#> Top rows (1531) 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 0.986 0.993 0.507 0.493 0.493
#> 3 3 0.877 0.920 0.966 0.302 0.753 0.544
#> 4 4 0.716 0.691 0.864 0.123 0.885 0.684
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
#> ola_mES_lif_1_11.counts 2 0.000 0.986 0.00 1.00
#> ola_mES_lif_1_5.counts 2 0.000 0.986 0.00 1.00
#> ola_mES_lif_1_51.counts 2 0.000 0.986 0.00 1.00
#> ola_mES_lif_1_52.counts 2 0.000 0.986 0.00 1.00
#> ola_mES_lif_1_55.counts 2 0.000 0.986 0.00 1.00
#> ola_mES_lif_1_59.counts 2 0.000 0.986 0.00 1.00
#> ola_mES_lif_2_10.counts 2 0.000 0.986 0.00 1.00
#> ola_mES_lif_2_11.counts 2 0.634 0.817 0.16 0.84
#> ola_mES_lif_2_13.counts 2 0.000 0.986 0.00 1.00
#> ola_mES_lif_2_14.counts 2 0.000 0.986 0.00 1.00
#> ola_mES_lif_2_15.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_lif_2_16.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_lif_2_17.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_lif_2_18.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_lif_2_19.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_lif_2_2.counts 2 0.000 0.986 0.00 1.00
#> ola_mES_lif_2_21.counts 1 0.242 0.957 0.96 0.04
#> ola_mES_lif_2_23.counts 2 0.000 0.986 0.00 1.00
#> ola_mES_lif_2_26.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_lif_2_28.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_lif_2_29.counts 2 0.000 0.986 0.00 1.00
#> ola_mES_lif_2_3.counts 2 0.000 0.986 0.00 1.00
#> ola_mES_lif_2_30.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_lif_2_32.counts 2 0.000 0.986 0.00 1.00
#> ola_mES_lif_2_34.counts 2 0.000 0.986 0.00 1.00
#> ola_mES_lif_2_35.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_lif_2_38.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_lif_2_39.counts 2 0.000 0.986 0.00 1.00
#> ola_mES_lif_2_4.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_lif_2_40.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_lif_2_42.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_lif_2_43.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_lif_2_44.counts 2 0.000 0.986 0.00 1.00
#> ola_mES_lif_2_46.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_lif_2_49.counts 2 0.000 0.986 0.00 1.00
#> ola_mES_lif_2_5.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_lif_2_50.counts 2 0.000 0.986 0.00 1.00
#> ola_mES_lif_2_51.counts 2 0.000 0.986 0.00 1.00
#> ola_mES_lif_2_52.counts 2 0.000 0.986 0.00 1.00
#> ola_mES_lif_2_53.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_lif_2_54.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_lif_2_55.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_lif_2_56.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_lif_2_58.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_lif_2_59.counts 2 0.000 0.986 0.00 1.00
#> ola_mES_lif_2_6.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_lif_2_63.counts 2 0.000 0.986 0.00 1.00
#> ola_mES_lif_2_64.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_lif_2_65.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_lif_2_66.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_lif_2_67.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_lif_2_68.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_lif_2_7.counts 2 0.000 0.986 0.00 1.00
#> ola_mES_lif_2_70.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_lif_2_71.counts 2 0.000 0.986 0.00 1.00
#> ola_mES_lif_2_74.counts 2 0.000 0.986 0.00 1.00
#> ola_mES_lif_2_75.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_lif_2_76.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_lif_2_77.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_lif_2_8.counts 2 0.000 0.986 0.00 1.00
#> ola_mES_lif_2_80.counts 2 0.000 0.986 0.00 1.00
#> ola_mES_lif_2_82.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_lif_2_83.counts 2 0.680 0.789 0.18 0.82
#> ola_mES_lif_2_84.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_lif_2_87.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_lif_2_89.counts 2 0.000 0.986 0.00 1.00
#> ola_mES_lif_2_93.counts 2 0.529 0.868 0.12 0.88
#> ola_mES_lif_2_94.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_lif_2_95.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_lif_3_10.counts 2 0.000 0.986 0.00 1.00
#> ola_mES_lif_3_5.counts 2 0.000 0.986 0.00 1.00
#> ola_mES_lif_3_56.counts 2 0.000 0.986 0.00 1.00
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> ola_mES_lif_1_11.counts 2 0.000 0.959 0.00 1.00 0.00
#> ola_mES_lif_1_5.counts 2 0.000 0.959 0.00 1.00 0.00
#> ola_mES_lif_1_51.counts 2 0.000 0.959 0.00 1.00 0.00
#> ola_mES_lif_1_52.counts 2 0.000 0.959 0.00 1.00 0.00
#> ola_mES_lif_1_55.counts 2 0.000 0.959 0.00 1.00 0.00
#> ola_mES_lif_1_59.counts 2 0.000 0.959 0.00 1.00 0.00
#> ola_mES_lif_2_10.counts 2 0.000 0.959 0.00 1.00 0.00
#> ola_mES_lif_2_11.counts 1 0.296 0.863 0.90 0.10 0.00
#> ola_mES_lif_2_13.counts 2 0.502 0.687 0.00 0.76 0.24
#> ola_mES_lif_2_14.counts 3 0.000 0.965 0.00 0.00 1.00
#> ola_mES_lif_2_15.counts 3 0.000 0.965 0.00 0.00 1.00
#> ola_mES_lif_2_16.counts 1 0.000 0.962 1.00 0.00 0.00
#> ola_mES_lif_2_17.counts 1 0.000 0.962 1.00 0.00 0.00
#> ola_mES_lif_2_18.counts 1 0.000 0.962 1.00 0.00 0.00
#> ola_mES_lif_2_19.counts 1 0.000 0.962 1.00 0.00 0.00
#> ola_mES_lif_2_2.counts 2 0.000 0.959 0.00 1.00 0.00
#> ola_mES_lif_2_21.counts 3 0.000 0.965 0.00 0.00 1.00
#> ola_mES_lif_2_23.counts 2 0.000 0.959 0.00 1.00 0.00
#> ola_mES_lif_2_26.counts 1 0.000 0.962 1.00 0.00 0.00
#> ola_mES_lif_2_28.counts 1 0.583 0.478 0.66 0.00 0.34
#> ola_mES_lif_2_29.counts 2 0.000 0.959 0.00 1.00 0.00
#> ola_mES_lif_2_3.counts 2 0.000 0.959 0.00 1.00 0.00
#> ola_mES_lif_2_30.counts 3 0.000 0.965 0.00 0.00 1.00
#> ola_mES_lif_2_32.counts 2 0.000 0.959 0.00 1.00 0.00
#> ola_mES_lif_2_34.counts 3 0.000 0.965 0.00 0.00 1.00
#> ola_mES_lif_2_35.counts 1 0.000 0.962 1.00 0.00 0.00
#> ola_mES_lif_2_38.counts 1 0.000 0.962 1.00 0.00 0.00
#> ola_mES_lif_2_39.counts 2 0.000 0.959 0.00 1.00 0.00
#> ola_mES_lif_2_4.counts 1 0.000 0.962 1.00 0.00 0.00
#> ola_mES_lif_2_40.counts 3 0.000 0.965 0.00 0.00 1.00
#> ola_mES_lif_2_42.counts 1 0.000 0.962 1.00 0.00 0.00
#> ola_mES_lif_2_43.counts 1 0.000 0.962 1.00 0.00 0.00
#> ola_mES_lif_2_44.counts 2 0.000 0.959 0.00 1.00 0.00
#> ola_mES_lif_2_46.counts 1 0.000 0.962 1.00 0.00 0.00
#> ola_mES_lif_2_49.counts 2 0.604 0.388 0.00 0.62 0.38
#> ola_mES_lif_2_5.counts 3 0.000 0.965 0.00 0.00 1.00
#> ola_mES_lif_2_50.counts 2 0.429 0.767 0.18 0.82 0.00
#> ola_mES_lif_2_51.counts 2 0.000 0.959 0.00 1.00 0.00
#> ola_mES_lif_2_52.counts 2 0.000 0.959 0.00 1.00 0.00
#> ola_mES_lif_2_53.counts 3 0.000 0.965 0.00 0.00 1.00
#> ola_mES_lif_2_54.counts 1 0.000 0.962 1.00 0.00 0.00
#> ola_mES_lif_2_55.counts 3 0.000 0.965 0.00 0.00 1.00
#> ola_mES_lif_2_56.counts 3 0.000 0.965 0.00 0.00 1.00
#> ola_mES_lif_2_58.counts 3 0.000 0.965 0.00 0.00 1.00
#> ola_mES_lif_2_59.counts 2 0.000 0.959 0.00 1.00 0.00
#> ola_mES_lif_2_6.counts 1 0.000 0.962 1.00 0.00 0.00
#> ola_mES_lif_2_63.counts 3 0.480 0.710 0.00 0.22 0.78
#> ola_mES_lif_2_64.counts 1 0.000 0.962 1.00 0.00 0.00
#> ola_mES_lif_2_65.counts 1 0.000 0.962 1.00 0.00 0.00
#> ola_mES_lif_2_66.counts 1 0.000 0.962 1.00 0.00 0.00
#> ola_mES_lif_2_67.counts 1 0.000 0.962 1.00 0.00 0.00
#> ola_mES_lif_2_68.counts 3 0.455 0.738 0.20 0.00 0.80
#> ola_mES_lif_2_7.counts 2 0.000 0.959 0.00 1.00 0.00
#> ola_mES_lif_2_70.counts 1 0.000 0.962 1.00 0.00 0.00
#> ola_mES_lif_2_71.counts 3 0.254 0.897 0.00 0.08 0.92
#> ola_mES_lif_2_74.counts 2 0.000 0.959 0.00 1.00 0.00
#> ola_mES_lif_2_75.counts 3 0.000 0.965 0.00 0.00 1.00
#> ola_mES_lif_2_76.counts 3 0.000 0.965 0.00 0.00 1.00
#> ola_mES_lif_2_77.counts 1 0.000 0.962 1.00 0.00 0.00
#> ola_mES_lif_2_8.counts 2 0.000 0.959 0.00 1.00 0.00
#> ola_mES_lif_2_80.counts 2 0.000 0.959 0.00 1.00 0.00
#> ola_mES_lif_2_82.counts 1 0.000 0.962 1.00 0.00 0.00
#> ola_mES_lif_2_83.counts 1 0.486 0.780 0.82 0.16 0.02
#> ola_mES_lif_2_84.counts 1 0.000 0.962 1.00 0.00 0.00
#> ola_mES_lif_2_87.counts 1 0.000 0.962 1.00 0.00 0.00
#> ola_mES_lif_2_89.counts 2 0.486 0.778 0.16 0.82 0.02
#> ola_mES_lif_2_93.counts 1 0.571 0.533 0.68 0.32 0.00
#> ola_mES_lif_2_94.counts 1 0.000 0.962 1.00 0.00 0.00
#> ola_mES_lif_2_95.counts 1 0.000 0.962 1.00 0.00 0.00
#> ola_mES_lif_3_10.counts 2 0.000 0.959 0.00 1.00 0.00
#> ola_mES_lif_3_5.counts 2 0.241 0.914 0.02 0.94 0.04
#> ola_mES_lif_3_56.counts 2 0.000 0.959 0.00 1.00 0.00
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> ola_mES_lif_1_11.counts 2 0.5594 -0.1817 0.00 0.52 0.02 0.46
#> ola_mES_lif_1_5.counts 2 0.0000 0.7443 0.00 1.00 0.00 0.00
#> ola_mES_lif_1_51.counts 2 0.0000 0.7443 0.00 1.00 0.00 0.00
#> ola_mES_lif_1_52.counts 2 0.0000 0.7443 0.00 1.00 0.00 0.00
#> ola_mES_lif_1_55.counts 2 0.0000 0.7443 0.00 1.00 0.00 0.00
#> ola_mES_lif_1_59.counts 2 0.0000 0.7443 0.00 1.00 0.00 0.00
#> ola_mES_lif_2_10.counts 4 0.6011 0.0603 0.00 0.48 0.04 0.48
#> ola_mES_lif_2_11.counts 1 0.6720 0.3554 0.58 0.12 0.00 0.30
#> ola_mES_lif_2_13.counts 2 0.7274 0.2191 0.00 0.54 0.24 0.22
#> ola_mES_lif_2_14.counts 3 0.6611 -0.0936 0.00 0.08 0.46 0.46
#> ola_mES_lif_2_15.counts 3 0.1411 0.8737 0.02 0.00 0.96 0.02
#> ola_mES_lif_2_16.counts 1 0.0000 0.9152 1.00 0.00 0.00 0.00
#> ola_mES_lif_2_17.counts 1 0.0707 0.9066 0.98 0.00 0.00 0.02
#> ola_mES_lif_2_18.counts 1 0.0000 0.9152 1.00 0.00 0.00 0.00
#> ola_mES_lif_2_19.counts 1 0.0000 0.9152 1.00 0.00 0.00 0.00
#> ola_mES_lif_2_2.counts 4 0.2647 0.7132 0.00 0.12 0.00 0.88
#> ola_mES_lif_2_21.counts 3 0.0707 0.8904 0.00 0.00 0.98 0.02
#> ola_mES_lif_2_23.counts 2 0.4977 0.2087 0.00 0.54 0.00 0.46
#> ola_mES_lif_2_26.counts 1 0.0000 0.9152 1.00 0.00 0.00 0.00
#> ola_mES_lif_2_28.counts 1 0.7028 0.4140 0.56 0.00 0.28 0.16
#> ola_mES_lif_2_29.counts 4 0.3610 0.6979 0.00 0.20 0.00 0.80
#> ola_mES_lif_2_3.counts 4 0.2011 0.7115 0.00 0.08 0.00 0.92
#> ola_mES_lif_2_30.counts 3 0.0000 0.9019 0.00 0.00 1.00 0.00
#> ola_mES_lif_2_32.counts 2 0.4406 0.4878 0.00 0.70 0.00 0.30
#> ola_mES_lif_2_34.counts 3 0.0707 0.8913 0.00 0.00 0.98 0.02
#> ola_mES_lif_2_35.counts 1 0.0000 0.9152 1.00 0.00 0.00 0.00
#> ola_mES_lif_2_38.counts 1 0.4088 0.7788 0.82 0.00 0.14 0.04
#> ola_mES_lif_2_39.counts 4 0.4855 0.4348 0.00 0.40 0.00 0.60
#> ola_mES_lif_2_4.counts 1 0.0000 0.9152 1.00 0.00 0.00 0.00
#> ola_mES_lif_2_40.counts 3 0.0000 0.9019 0.00 0.00 1.00 0.00
#> ola_mES_lif_2_42.counts 1 0.0707 0.9044 0.98 0.00 0.00 0.02
#> ola_mES_lif_2_43.counts 1 0.0000 0.9152 1.00 0.00 0.00 0.00
#> ola_mES_lif_2_44.counts 2 0.0000 0.7443 0.00 1.00 0.00 0.00
#> ola_mES_lif_2_46.counts 1 0.0000 0.9152 1.00 0.00 0.00 0.00
#> ola_mES_lif_2_49.counts 4 0.5151 0.6664 0.00 0.14 0.10 0.76
#> ola_mES_lif_2_5.counts 3 0.0707 0.8913 0.00 0.00 0.98 0.02
#> ola_mES_lif_2_50.counts 2 0.5962 0.4896 0.08 0.66 0.00 0.26
#> ola_mES_lif_2_51.counts 2 0.2647 0.6865 0.00 0.88 0.00 0.12
#> ola_mES_lif_2_52.counts 2 0.1637 0.7224 0.00 0.94 0.00 0.06
#> ola_mES_lif_2_53.counts 3 0.0000 0.9019 0.00 0.00 1.00 0.00
#> ola_mES_lif_2_54.counts 1 0.1211 0.8969 0.96 0.00 0.00 0.04
#> ola_mES_lif_2_55.counts 3 0.0000 0.9019 0.00 0.00 1.00 0.00
#> ola_mES_lif_2_56.counts 3 0.0000 0.9019 0.00 0.00 1.00 0.00
#> ola_mES_lif_2_58.counts 3 0.0000 0.9019 0.00 0.00 1.00 0.00
#> ola_mES_lif_2_59.counts 4 0.4907 0.4284 0.00 0.42 0.00 0.58
#> ola_mES_lif_2_6.counts 1 0.0000 0.9152 1.00 0.00 0.00 0.00
#> ola_mES_lif_2_63.counts 4 0.6320 0.5817 0.00 0.16 0.18 0.66
#> ola_mES_lif_2_64.counts 1 0.0000 0.9152 1.00 0.00 0.00 0.00
#> ola_mES_lif_2_65.counts 1 0.0000 0.9152 1.00 0.00 0.00 0.00
#> ola_mES_lif_2_66.counts 1 0.0000 0.9152 1.00 0.00 0.00 0.00
#> ola_mES_lif_2_67.counts 1 0.0000 0.9152 1.00 0.00 0.00 0.00
#> ola_mES_lif_2_68.counts 1 0.8718 0.0612 0.42 0.06 0.34 0.18
#> ola_mES_lif_2_7.counts 2 0.4624 0.4578 0.00 0.66 0.00 0.34
#> ola_mES_lif_2_70.counts 1 0.1211 0.8908 0.96 0.00 0.04 0.00
#> ola_mES_lif_2_71.counts 3 0.7179 -0.0224 0.00 0.14 0.48 0.38
#> ola_mES_lif_2_74.counts 2 0.3400 0.6296 0.00 0.82 0.00 0.18
#> ola_mES_lif_2_75.counts 3 0.0000 0.9019 0.00 0.00 1.00 0.00
#> ola_mES_lif_2_76.counts 3 0.0000 0.9019 0.00 0.00 1.00 0.00
#> ola_mES_lif_2_77.counts 1 0.2921 0.8176 0.86 0.00 0.00 0.14
#> ola_mES_lif_2_8.counts 4 0.3172 0.7070 0.00 0.16 0.00 0.84
#> ola_mES_lif_2_80.counts 2 0.2011 0.7155 0.00 0.92 0.00 0.08
#> ola_mES_lif_2_82.counts 1 0.0000 0.9152 1.00 0.00 0.00 0.00
#> ola_mES_lif_2_83.counts 4 0.1637 0.6800 0.06 0.00 0.00 0.94
#> ola_mES_lif_2_84.counts 1 0.0000 0.9152 1.00 0.00 0.00 0.00
#> ola_mES_lif_2_87.counts 1 0.1211 0.8966 0.96 0.00 0.00 0.04
#> ola_mES_lif_2_89.counts 2 0.7604 0.3144 0.18 0.56 0.02 0.24
#> ola_mES_lif_2_93.counts 4 0.3198 0.6689 0.08 0.04 0.00 0.88
#> ola_mES_lif_2_94.counts 1 0.0000 0.9152 1.00 0.00 0.00 0.00
#> ola_mES_lif_2_95.counts 1 0.4406 0.6246 0.70 0.00 0.00 0.30
#> ola_mES_lif_3_10.counts 2 0.0000 0.7443 0.00 1.00 0.00 0.00
#> ola_mES_lif_3_5.counts 2 0.6323 -0.1246 0.00 0.50 0.06 0.44
#> ola_mES_lif_3_56.counts 2 0.0000 0.7443 0.00 1.00 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)
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 , Node0112 , Node0113-leaf , Node0121-leaf , Node0122-leaf , Node0131-leaf , Node0132-leaf , Node0133-leaf , Node0141 , Node0142-leaf , Node0143-leaf , Node0211 , Node0212-leaf , Node0221 , Node0222 , Node0223-leaf , Node0311-leaf , Node0312-leaf , Node0313-leaf , Node0321-leaf , Node0322-leaf , Node0331-leaf , Node0332-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 16100 rows and 96 columns.
#> Top rows (1610) 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 0.999 0.999 0.5057 0.495 0.495
#> 3 3 1.000 1.000 1.000 0.3115 0.780 0.581
#> 4 4 0.877 0.806 0.898 0.0964 0.934 0.806
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
#> ola_mES_2i_2_10.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_2i_2_11.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_2i_2_12.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_2i_2_13.counts 2 0.000 1.000 0.00 1.00
#> ola_mES_2i_2_15.counts 2 0.000 1.000 0.00 1.00
#> ola_mES_2i_2_16.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_2i_2_17.counts 2 0.000 1.000 0.00 1.00
#> ola_mES_2i_2_18.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_2i_2_19.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_2i_2_21.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_2i_2_22.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_2i_2_23.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_2i_2_24.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_2i_2_26.counts 2 0.000 1.000 0.00 1.00
#> ola_mES_2i_2_29.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_2i_2_30.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_2i_2_31.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_2i_2_32.counts 2 0.000 1.000 0.00 1.00
#> ola_mES_2i_2_33.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_2i_2_34.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_2i_2_35.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_2i_2_36.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_2i_2_39.counts 2 0.000 1.000 0.00 1.00
#> ola_mES_2i_2_4.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_2i_2_40.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_2i_2_41.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_2i_2_42.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_2i_2_44.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_2i_2_46.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_2i_2_47.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_2i_2_48.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_2i_2_5.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_2i_2_51.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_2i_2_52.counts 2 0.141 0.980 0.02 0.98
#> ola_mES_2i_2_53.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_2i_2_54.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_2i_2_55.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_2i_2_56.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_2i_2_57.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_2i_2_58.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_2i_2_59.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_2i_2_6.counts 1 0.242 0.958 0.96 0.04
#> ola_mES_2i_2_60.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_2i_2_64.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_2i_2_66.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_2i_2_67.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_2i_2_69.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_2i_2_7.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_2i_2_72.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_2i_2_74.counts 2 0.000 1.000 0.00 1.00
#> ola_mES_2i_2_79.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_2i_2_8.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_2i_2_80.counts 2 0.000 1.000 0.00 1.00
#> ola_mES_2i_2_81.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_2i_2_90.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_2i_2_91.counts 2 0.000 1.000 0.00 1.00
#> ola_mES_2i_2_93.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_2i_2_94.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_2i_2_95.counts 2 0.000 1.000 0.00 1.00
#> ola_mES_2i_4_2.counts 2 0.000 1.000 0.00 1.00
#> ola_mES_a2i_2_1.counts 2 0.000 1.000 0.00 1.00
#> ola_mES_a2i_2_11.counts 2 0.000 1.000 0.00 1.00
#> ola_mES_a2i_2_14.counts 2 0.000 1.000 0.00 1.00
#> ola_mES_a2i_2_15.counts 2 0.000 1.000 0.00 1.00
#> ola_mES_a2i_2_16.counts 2 0.000 1.000 0.00 1.00
#> ola_mES_a2i_2_17.counts 2 0.000 1.000 0.00 1.00
#> ola_mES_a2i_2_18.counts 2 0.000 1.000 0.00 1.00
#> ola_mES_a2i_2_19.counts 2 0.000 1.000 0.00 1.00
#> ola_mES_a2i_2_2.counts 2 0.000 1.000 0.00 1.00
#> ola_mES_a2i_2_21.counts 2 0.000 1.000 0.00 1.00
#> ola_mES_a2i_2_22.counts 2 0.000 1.000 0.00 1.00
#> ola_mES_a2i_2_23.counts 2 0.000 1.000 0.00 1.00
#> ola_mES_a2i_2_24.counts 2 0.000 1.000 0.00 1.00
#> ola_mES_a2i_2_3.counts 2 0.000 1.000 0.00 1.00
#> ola_mES_a2i_2_41.counts 2 0.000 1.000 0.00 1.00
#> ola_mES_a2i_2_42.counts 2 0.000 1.000 0.00 1.00
#> ola_mES_a2i_2_45.counts 2 0.000 1.000 0.00 1.00
#> ola_mES_a2i_2_46.counts 2 0.000 1.000 0.00 1.00
#> ola_mES_a2i_2_5.counts 2 0.000 1.000 0.00 1.00
#> ola_mES_a2i_2_55.counts 2 0.000 1.000 0.00 1.00
#> ola_mES_a2i_2_57.counts 2 0.000 1.000 0.00 1.00
#> ola_mES_a2i_2_59.counts 2 0.000 1.000 0.00 1.00
#> ola_mES_a2i_2_66.counts 2 0.000 1.000 0.00 1.00
#> ola_mES_a2i_2_71.counts 2 0.000 1.000 0.00 1.00
#> ola_mES_a2i_2_78.counts 2 0.000 1.000 0.00 1.00
#> ola_mES_a2i_2_8.counts 2 0.000 1.000 0.00 1.00
#> ola_mES_a2i_2_82.counts 2 0.000 1.000 0.00 1.00
#> ola_mES_a2i_2_83.counts 2 0.000 1.000 0.00 1.00
#> ola_mES_a2i_2_90.counts 2 0.000 1.000 0.00 1.00
#> ola_mES_a2i_2_94.counts 2 0.000 1.000 0.00 1.00
#> ola_mES_a2i_2_95.counts 2 0.000 1.000 0.00 1.00
#> ola_mES_a2i_2_96.counts 2 0.000 1.000 0.00 1.00
#> ola_mES_a2i_3_59.counts 2 0.000 1.000 0.00 1.00
#> ola_mES_a2i_3_65.counts 2 0.000 1.000 0.00 1.00
#> ola_mES_a2i_3_71.counts 2 0.000 1.000 0.00 1.00
#> ola_mES_a2i_3_89.counts 2 0.000 1.000 0.00 1.00
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> ola_mES_2i_2_10.counts 1 0 1 1 0 0
#> ola_mES_2i_2_11.counts 1 0 1 1 0 0
#> ola_mES_2i_2_12.counts 1 0 1 1 0 0
#> ola_mES_2i_2_13.counts 3 0 1 0 0 1
#> ola_mES_2i_2_15.counts 3 0 1 0 0 1
#> ola_mES_2i_2_16.counts 3 0 1 0 0 1
#> ola_mES_2i_2_17.counts 3 0 1 0 0 1
#> ola_mES_2i_2_18.counts 1 0 1 1 0 0
#> ola_mES_2i_2_19.counts 1 0 1 1 0 0
#> ola_mES_2i_2_21.counts 1 0 1 1 0 0
#> ola_mES_2i_2_22.counts 3 0 1 0 0 1
#> ola_mES_2i_2_23.counts 1 0 1 1 0 0
#> ola_mES_2i_2_24.counts 1 0 1 1 0 0
#> ola_mES_2i_2_26.counts 3 0 1 0 0 1
#> ola_mES_2i_2_29.counts 3 0 1 0 0 1
#> ola_mES_2i_2_30.counts 1 0 1 1 0 0
#> ola_mES_2i_2_31.counts 1 0 1 1 0 0
#> ola_mES_2i_2_32.counts 3 0 1 0 0 1
#> ola_mES_2i_2_33.counts 1 0 1 1 0 0
#> ola_mES_2i_2_34.counts 1 0 1 1 0 0
#> ola_mES_2i_2_35.counts 1 0 1 1 0 0
#> ola_mES_2i_2_36.counts 1 0 1 1 0 0
#> ola_mES_2i_2_39.counts 3 0 1 0 0 1
#> ola_mES_2i_2_4.counts 1 0 1 1 0 0
#> ola_mES_2i_2_40.counts 1 0 1 1 0 0
#> ola_mES_2i_2_41.counts 1 0 1 1 0 0
#> ola_mES_2i_2_42.counts 1 0 1 1 0 0
#> ola_mES_2i_2_44.counts 1 0 1 1 0 0
#> ola_mES_2i_2_46.counts 1 0 1 1 0 0
#> ola_mES_2i_2_47.counts 1 0 1 1 0 0
#> ola_mES_2i_2_48.counts 1 0 1 1 0 0
#> ola_mES_2i_2_5.counts 3 0 1 0 0 1
#> ola_mES_2i_2_51.counts 1 0 1 1 0 0
#> ola_mES_2i_2_52.counts 3 0 1 0 0 1
#> ola_mES_2i_2_53.counts 3 0 1 0 0 1
#> ola_mES_2i_2_54.counts 1 0 1 1 0 0
#> ola_mES_2i_2_55.counts 3 0 1 0 0 1
#> ola_mES_2i_2_56.counts 3 0 1 0 0 1
#> ola_mES_2i_2_57.counts 3 0 1 0 0 1
#> ola_mES_2i_2_58.counts 1 0 1 1 0 0
#> ola_mES_2i_2_59.counts 1 0 1 1 0 0
#> ola_mES_2i_2_6.counts 3 0 1 0 0 1
#> ola_mES_2i_2_60.counts 1 0 1 1 0 0
#> ola_mES_2i_2_64.counts 3 0 1 0 0 1
#> ola_mES_2i_2_66.counts 1 0 1 1 0 0
#> ola_mES_2i_2_67.counts 1 0 1 1 0 0
#> ola_mES_2i_2_69.counts 1 0 1 1 0 0
#> ola_mES_2i_2_7.counts 3 0 1 0 0 1
#> ola_mES_2i_2_72.counts 3 0 1 0 0 1
#> ola_mES_2i_2_74.counts 3 0 1 0 0 1
#> ola_mES_2i_2_79.counts 1 0 1 1 0 0
#> ola_mES_2i_2_8.counts 3 0 1 0 0 1
#> ola_mES_2i_2_80.counts 3 0 1 0 0 1
#> ola_mES_2i_2_81.counts 1 0 1 1 0 0
#> ola_mES_2i_2_90.counts 1 0 1 1 0 0
#> ola_mES_2i_2_91.counts 3 0 1 0 0 1
#> ola_mES_2i_2_93.counts 1 0 1 1 0 0
#> ola_mES_2i_2_94.counts 1 0 1 1 0 0
#> ola_mES_2i_2_95.counts 3 0 1 0 0 1
#> ola_mES_2i_4_2.counts 2 0 1 0 1 0
#> ola_mES_a2i_2_1.counts 2 0 1 0 1 0
#> ola_mES_a2i_2_11.counts 2 0 1 0 1 0
#> ola_mES_a2i_2_14.counts 2 0 1 0 1 0
#> ola_mES_a2i_2_15.counts 2 0 1 0 1 0
#> ola_mES_a2i_2_16.counts 2 0 1 0 1 0
#> ola_mES_a2i_2_17.counts 2 0 1 0 1 0
#> ola_mES_a2i_2_18.counts 2 0 1 0 1 0
#> ola_mES_a2i_2_19.counts 2 0 1 0 1 0
#> ola_mES_a2i_2_2.counts 2 0 1 0 1 0
#> ola_mES_a2i_2_21.counts 2 0 1 0 1 0
#> ola_mES_a2i_2_22.counts 2 0 1 0 1 0
#> ola_mES_a2i_2_23.counts 2 0 1 0 1 0
#> ola_mES_a2i_2_24.counts 2 0 1 0 1 0
#> ola_mES_a2i_2_3.counts 2 0 1 0 1 0
#> ola_mES_a2i_2_41.counts 2 0 1 0 1 0
#> ola_mES_a2i_2_42.counts 2 0 1 0 1 0
#> ola_mES_a2i_2_45.counts 2 0 1 0 1 0
#> ola_mES_a2i_2_46.counts 2 0 1 0 1 0
#> ola_mES_a2i_2_5.counts 2 0 1 0 1 0
#> ola_mES_a2i_2_55.counts 2 0 1 0 1 0
#> ola_mES_a2i_2_57.counts 2 0 1 0 1 0
#> ola_mES_a2i_2_59.counts 2 0 1 0 1 0
#> ola_mES_a2i_2_66.counts 2 0 1 0 1 0
#> ola_mES_a2i_2_71.counts 2 0 1 0 1 0
#> ola_mES_a2i_2_78.counts 2 0 1 0 1 0
#> ola_mES_a2i_2_8.counts 2 0 1 0 1 0
#> ola_mES_a2i_2_82.counts 2 0 1 0 1 0
#> ola_mES_a2i_2_83.counts 2 0 1 0 1 0
#> ola_mES_a2i_2_90.counts 2 0 1 0 1 0
#> ola_mES_a2i_2_94.counts 2 0 1 0 1 0
#> ola_mES_a2i_2_95.counts 2 0 1 0 1 0
#> ola_mES_a2i_2_96.counts 2 0 1 0 1 0
#> ola_mES_a2i_3_59.counts 2 0 1 0 1 0
#> ola_mES_a2i_3_65.counts 2 0 1 0 1 0
#> ola_mES_a2i_3_71.counts 2 0 1 0 1 0
#> ola_mES_a2i_3_89.counts 2 0 1 0 1 0
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> ola_mES_2i_2_10.counts 1 0.0707 0.9810 0.98 0.00 0.00 0.02
#> ola_mES_2i_2_11.counts 1 0.0000 0.9887 1.00 0.00 0.00 0.00
#> ola_mES_2i_2_12.counts 1 0.0000 0.9887 1.00 0.00 0.00 0.00
#> ola_mES_2i_2_13.counts 3 0.0000 0.9728 0.00 0.00 1.00 0.00
#> ola_mES_2i_2_15.counts 3 0.2345 0.9116 0.00 0.00 0.90 0.10
#> ola_mES_2i_2_16.counts 3 0.0707 0.9738 0.00 0.00 0.98 0.02
#> ola_mES_2i_2_17.counts 3 0.0000 0.9728 0.00 0.00 1.00 0.00
#> ola_mES_2i_2_18.counts 1 0.0000 0.9887 1.00 0.00 0.00 0.00
#> ola_mES_2i_2_19.counts 1 0.1211 0.9699 0.96 0.00 0.00 0.04
#> ola_mES_2i_2_21.counts 1 0.1637 0.9574 0.94 0.00 0.00 0.06
#> ola_mES_2i_2_22.counts 3 0.2345 0.9401 0.00 0.00 0.90 0.10
#> ola_mES_2i_2_23.counts 1 0.0000 0.9887 1.00 0.00 0.00 0.00
#> ola_mES_2i_2_24.counts 1 0.0707 0.9810 0.98 0.00 0.00 0.02
#> ola_mES_2i_2_26.counts 3 0.0707 0.9675 0.00 0.00 0.98 0.02
#> ola_mES_2i_2_29.counts 3 0.1211 0.9712 0.00 0.00 0.96 0.04
#> ola_mES_2i_2_30.counts 1 0.0707 0.9810 0.98 0.00 0.00 0.02
#> ola_mES_2i_2_31.counts 1 0.0707 0.9810 0.98 0.00 0.00 0.02
#> ola_mES_2i_2_32.counts 3 0.0000 0.9728 0.00 0.00 1.00 0.00
#> ola_mES_2i_2_33.counts 1 0.0000 0.9887 1.00 0.00 0.00 0.00
#> ola_mES_2i_2_34.counts 1 0.2011 0.9392 0.92 0.00 0.00 0.08
#> ola_mES_2i_2_35.counts 1 0.0000 0.9887 1.00 0.00 0.00 0.00
#> ola_mES_2i_2_36.counts 1 0.0000 0.9887 1.00 0.00 0.00 0.00
#> ola_mES_2i_2_39.counts 3 0.0000 0.9728 0.00 0.00 1.00 0.00
#> ola_mES_2i_2_4.counts 1 0.0000 0.9887 1.00 0.00 0.00 0.00
#> ola_mES_2i_2_40.counts 1 0.0000 0.9887 1.00 0.00 0.00 0.00
#> ola_mES_2i_2_41.counts 1 0.1637 0.9561 0.94 0.00 0.00 0.06
#> ola_mES_2i_2_42.counts 1 0.0000 0.9887 1.00 0.00 0.00 0.00
#> ola_mES_2i_2_44.counts 1 0.0000 0.9887 1.00 0.00 0.00 0.00
#> ola_mES_2i_2_46.counts 1 0.0707 0.9810 0.98 0.00 0.00 0.02
#> ola_mES_2i_2_47.counts 1 0.0000 0.9887 1.00 0.00 0.00 0.00
#> ola_mES_2i_2_48.counts 1 0.0000 0.9887 1.00 0.00 0.00 0.00
#> ola_mES_2i_2_5.counts 3 0.0707 0.9689 0.00 0.00 0.98 0.02
#> ola_mES_2i_2_51.counts 1 0.0000 0.9887 1.00 0.00 0.00 0.00
#> ola_mES_2i_2_52.counts 3 0.1211 0.9612 0.00 0.00 0.96 0.04
#> ola_mES_2i_2_53.counts 3 0.1211 0.9712 0.00 0.00 0.96 0.04
#> ola_mES_2i_2_54.counts 1 0.0000 0.9887 1.00 0.00 0.00 0.00
#> ola_mES_2i_2_55.counts 3 0.1211 0.9712 0.00 0.00 0.96 0.04
#> ola_mES_2i_2_56.counts 3 0.0707 0.9736 0.00 0.00 0.98 0.02
#> ola_mES_2i_2_57.counts 3 0.1211 0.9712 0.00 0.00 0.96 0.04
#> ola_mES_2i_2_58.counts 1 0.2011 0.9392 0.92 0.00 0.00 0.08
#> ola_mES_2i_2_59.counts 1 0.0000 0.9887 1.00 0.00 0.00 0.00
#> ola_mES_2i_2_6.counts 3 0.0707 0.9738 0.00 0.00 0.98 0.02
#> ola_mES_2i_2_60.counts 1 0.0000 0.9887 1.00 0.00 0.00 0.00
#> ola_mES_2i_2_64.counts 3 0.1211 0.9712 0.00 0.00 0.96 0.04
#> ola_mES_2i_2_66.counts 1 0.0000 0.9887 1.00 0.00 0.00 0.00
#> ola_mES_2i_2_67.counts 1 0.0000 0.9887 1.00 0.00 0.00 0.00
#> ola_mES_2i_2_69.counts 1 0.0000 0.9887 1.00 0.00 0.00 0.00
#> ola_mES_2i_2_7.counts 3 0.1211 0.9712 0.00 0.00 0.96 0.04
#> ola_mES_2i_2_72.counts 3 0.2011 0.9484 0.00 0.00 0.92 0.08
#> ola_mES_2i_2_74.counts 3 0.0707 0.9675 0.00 0.00 0.98 0.02
#> ola_mES_2i_2_79.counts 1 0.0000 0.9887 1.00 0.00 0.00 0.00
#> ola_mES_2i_2_8.counts 3 0.1637 0.9662 0.00 0.00 0.94 0.06
#> ola_mES_2i_2_80.counts 3 0.0000 0.9728 0.00 0.00 1.00 0.00
#> ola_mES_2i_2_81.counts 1 0.1211 0.9699 0.96 0.00 0.00 0.04
#> ola_mES_2i_2_90.counts 1 0.0000 0.9887 1.00 0.00 0.00 0.00
#> ola_mES_2i_2_91.counts 3 0.0707 0.9675 0.00 0.00 0.98 0.02
#> ola_mES_2i_2_93.counts 1 0.0000 0.9887 1.00 0.00 0.00 0.00
#> ola_mES_2i_2_94.counts 1 0.0000 0.9887 1.00 0.00 0.00 0.00
#> ola_mES_2i_2_95.counts 2 0.5606 -0.0722 0.00 0.50 0.48 0.02
#> ola_mES_2i_4_2.counts 4 0.4624 0.4847 0.00 0.34 0.00 0.66
#> ola_mES_a2i_2_1.counts 4 0.3801 0.7446 0.00 0.22 0.00 0.78
#> ola_mES_a2i_2_11.counts 2 0.4994 0.2386 0.00 0.52 0.00 0.48
#> ola_mES_a2i_2_14.counts 2 0.4277 0.5639 0.00 0.72 0.00 0.28
#> ola_mES_a2i_2_15.counts 2 0.5000 -0.2440 0.00 0.50 0.00 0.50
#> ola_mES_a2i_2_16.counts 2 0.4907 0.4031 0.00 0.58 0.00 0.42
#> ola_mES_a2i_2_17.counts 4 0.2647 0.8658 0.00 0.12 0.00 0.88
#> ola_mES_a2i_2_18.counts 4 0.2921 0.8507 0.00 0.14 0.00 0.86
#> ola_mES_a2i_2_19.counts 2 0.1637 0.6657 0.00 0.94 0.00 0.06
#> ola_mES_a2i_2_2.counts 2 0.2921 0.5798 0.00 0.86 0.00 0.14
#> ola_mES_a2i_2_21.counts 2 0.4522 0.5151 0.00 0.68 0.00 0.32
#> ola_mES_a2i_2_22.counts 4 0.2647 0.8658 0.00 0.12 0.00 0.88
#> ola_mES_a2i_2_23.counts 2 0.4907 0.3340 0.00 0.58 0.00 0.42
#> ola_mES_a2i_2_24.counts 4 0.2647 0.8658 0.00 0.12 0.00 0.88
#> ola_mES_a2i_2_3.counts 2 0.4790 0.4483 0.00 0.62 0.00 0.38
#> ola_mES_a2i_2_41.counts 2 0.1637 0.6652 0.00 0.94 0.00 0.06
#> ola_mES_a2i_2_42.counts 2 0.4624 0.5225 0.00 0.66 0.00 0.34
#> ola_mES_a2i_2_45.counts 2 0.2921 0.6555 0.00 0.86 0.00 0.14
#> ola_mES_a2i_2_46.counts 2 0.0000 0.6505 0.00 1.00 0.00 0.00
#> ola_mES_a2i_2_5.counts 4 0.2647 0.8658 0.00 0.12 0.00 0.88
#> ola_mES_a2i_2_55.counts 2 0.2345 0.6640 0.00 0.90 0.00 0.10
#> ola_mES_a2i_2_57.counts 2 0.3975 0.6236 0.00 0.76 0.00 0.24
#> ola_mES_a2i_2_59.counts 2 0.4977 0.2603 0.00 0.54 0.00 0.46
#> ola_mES_a2i_2_66.counts 2 0.4522 0.2716 0.00 0.68 0.00 0.32
#> ola_mES_a2i_2_71.counts 4 0.4907 0.3693 0.00 0.42 0.00 0.58
#> ola_mES_a2i_2_78.counts 2 0.4994 0.2119 0.00 0.52 0.00 0.48
#> ola_mES_a2i_2_8.counts 2 0.4134 0.4867 0.00 0.74 0.00 0.26
#> ola_mES_a2i_2_82.counts 2 0.1637 0.6456 0.00 0.94 0.00 0.06
#> ola_mES_a2i_2_83.counts 2 0.1211 0.6476 0.00 0.96 0.00 0.04
#> ola_mES_a2i_2_90.counts 2 0.4790 0.4469 0.00 0.62 0.00 0.38
#> ola_mES_a2i_2_94.counts 2 0.3400 0.6387 0.00 0.82 0.00 0.18
#> ola_mES_a2i_2_95.counts 4 0.2647 0.8658 0.00 0.12 0.00 0.88
#> ola_mES_a2i_2_96.counts 2 0.3801 0.6187 0.00 0.78 0.00 0.22
#> ola_mES_a2i_3_59.counts 2 0.1637 0.6456 0.00 0.94 0.00 0.06
#> ola_mES_a2i_3_65.counts 2 0.0000 0.6505 0.00 1.00 0.00 0.00
#> ola_mES_a2i_3_71.counts 2 0.0000 0.6505 0.00 1.00 0.00 0.00
#> ola_mES_a2i_3_89.counts 2 0.0000 0.6505 0.00 1.00 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)
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 , Node0112 , Node0113-leaf , Node0121-leaf , Node0122-leaf , Node0131-leaf , Node0132-leaf , Node0133-leaf , Node0141 , Node0142-leaf , Node0143-leaf , Node0211 , Node0212-leaf , Node0221 , Node0222 , Node0223-leaf , Node0311-leaf , Node0312-leaf , Node0313-leaf , Node0321-leaf , Node0322-leaf , Node0331-leaf , Node0332-leaf .
The object with results only for a single top-value method and a single partitioning method can be extracted as:
res = res_rh["014"]
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 16129 rows and 78 columns.
#> Top rows (1613) 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 0.947 0.960 0.983 0.5027 0.497 0.497
#> 3 3 1.000 1.000 1.000 0.3361 0.726 0.501
#> 4 4 0.841 0.779 0.899 0.0894 0.947 0.840
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
#> ola_mES_lif_1_1.counts 1 0.000 0.973 1.00 0.00
#> ola_mES_lif_1_13.counts 1 0.000 0.973 1.00 0.00
#> ola_mES_lif_1_15.counts 1 0.000 0.973 1.00 0.00
#> ola_mES_lif_1_18.counts 1 0.000 0.973 1.00 0.00
#> ola_mES_lif_1_19.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_1_20.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_1_22.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_1_24.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_1_25.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_1_26.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_1_28.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_1_3.counts 1 0.000 0.973 1.00 0.00
#> ola_mES_lif_1_30.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_1_32.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_1_33.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_1_34.counts 1 0.000 0.973 1.00 0.00
#> ola_mES_lif_1_36.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_1_37.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_1_38.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_1_39.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_1_4.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_1_41.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_1_42.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_1_43.counts 1 0.529 0.857 0.88 0.12
#> ola_mES_lif_1_44.counts 1 0.000 0.973 1.00 0.00
#> ola_mES_lif_1_45.counts 1 0.760 0.729 0.78 0.22
#> ola_mES_lif_1_46.counts 1 0.795 0.698 0.76 0.24
#> ola_mES_lif_1_47.counts 1 0.000 0.973 1.00 0.00
#> ola_mES_lif_1_48.counts 1 0.000 0.973 1.00 0.00
#> ola_mES_lif_1_49.counts 1 0.000 0.973 1.00 0.00
#> ola_mES_lif_1_50.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_1_54.counts 1 0.000 0.973 1.00 0.00
#> ola_mES_lif_1_58.counts 1 0.000 0.973 1.00 0.00
#> ola_mES_lif_1_6.counts 2 0.855 0.598 0.28 0.72
#> ola_mES_lif_1_60.counts 1 0.000 0.973 1.00 0.00
#> ola_mES_lif_1_61.counts 1 0.000 0.973 1.00 0.00
#> ola_mES_lif_1_62.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_1_66.counts 1 0.000 0.973 1.00 0.00
#> ola_mES_lif_1_67.counts 1 0.000 0.973 1.00 0.00
#> ola_mES_lif_1_68.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_1_7.counts 1 0.000 0.973 1.00 0.00
#> ola_mES_lif_1_72.counts 1 0.000 0.973 1.00 0.00
#> ola_mES_lif_1_73.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_1_8.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_1_80.counts 1 0.000 0.973 1.00 0.00
#> ola_mES_lif_1_81.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_1_9.counts 1 0.000 0.973 1.00 0.00
#> ola_mES_lif_1_91.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_1_92.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_1_95.counts 1 0.000 0.973 1.00 0.00
#> ola_mES_lif_1_96.counts 1 0.000 0.973 1.00 0.00
#> ola_mES_lif_2_61.counts 1 0.000 0.973 1.00 0.00
#> ola_mES_lif_2_72.counts 1 0.000 0.973 1.00 0.00
#> ola_mES_lif_3_1.counts 1 0.000 0.973 1.00 0.00
#> ola_mES_lif_3_13.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_3_27.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_3_3.counts 1 0.000 0.973 1.00 0.00
#> ola_mES_lif_3_30.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_3_32.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_3_4.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_3_40.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_3_42.counts 1 0.000 0.973 1.00 0.00
#> ola_mES_lif_3_49.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_3_50.counts 1 0.000 0.973 1.00 0.00
#> ola_mES_lif_3_51.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_3_52.counts 1 0.925 0.489 0.66 0.34
#> ola_mES_lif_3_53.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_3_54.counts 1 0.000 0.973 1.00 0.00
#> ola_mES_lif_3_58.counts 1 0.000 0.973 1.00 0.00
#> ola_mES_lif_3_59.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_3_6.counts 1 0.000 0.973 1.00 0.00
#> ola_mES_lif_3_60.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_3_61.counts 2 0.584 0.830 0.14 0.86
#> ola_mES_lif_3_63.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_3_64.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_3_65.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_3_71.counts 2 0.000 0.989 0.00 1.00
#> ola_mES_lif_3_8.counts 1 0.000 0.973 1.00 0.00
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> ola_mES_lif_1_1.counts 1 0 1 1 0 0
#> ola_mES_lif_1_13.counts 1 0 1 1 0 0
#> ola_mES_lif_1_15.counts 1 0 1 1 0 0
#> ola_mES_lif_1_18.counts 1 0 1 1 0 0
#> ola_mES_lif_1_19.counts 2 0 1 0 1 0
#> ola_mES_lif_1_20.counts 2 0 1 0 1 0
#> ola_mES_lif_1_22.counts 2 0 1 0 1 0
#> ola_mES_lif_1_24.counts 2 0 1 0 1 0
#> ola_mES_lif_1_25.counts 2 0 1 0 1 0
#> ola_mES_lif_1_26.counts 2 0 1 0 1 0
#> ola_mES_lif_1_28.counts 2 0 1 0 1 0
#> ola_mES_lif_1_3.counts 1 0 1 1 0 0
#> ola_mES_lif_1_30.counts 2 0 1 0 1 0
#> ola_mES_lif_1_32.counts 2 0 1 0 1 0
#> ola_mES_lif_1_33.counts 2 0 1 0 1 0
#> ola_mES_lif_1_34.counts 1 0 1 1 0 0
#> ola_mES_lif_1_36.counts 2 0 1 0 1 0
#> ola_mES_lif_1_37.counts 2 0 1 0 1 0
#> ola_mES_lif_1_38.counts 2 0 1 0 1 0
#> ola_mES_lif_1_39.counts 2 0 1 0 1 0
#> ola_mES_lif_1_4.counts 3 0 1 0 0 1
#> ola_mES_lif_1_41.counts 2 0 1 0 1 0
#> ola_mES_lif_1_42.counts 2 0 1 0 1 0
#> ola_mES_lif_1_43.counts 2 0 1 0 1 0
#> ola_mES_lif_1_44.counts 1 0 1 1 0 0
#> ola_mES_lif_1_45.counts 2 0 1 0 1 0
#> ola_mES_lif_1_46.counts 2 0 1 0 1 0
#> ola_mES_lif_1_47.counts 1 0 1 1 0 0
#> ola_mES_lif_1_48.counts 1 0 1 1 0 0
#> ola_mES_lif_1_49.counts 1 0 1 1 0 0
#> ola_mES_lif_1_50.counts 2 0 1 0 1 0
#> ola_mES_lif_1_54.counts 1 0 1 1 0 0
#> ola_mES_lif_1_58.counts 1 0 1 1 0 0
#> ola_mES_lif_1_6.counts 2 0 1 0 1 0
#> ola_mES_lif_1_60.counts 1 0 1 1 0 0
#> ola_mES_lif_1_61.counts 1 0 1 1 0 0
#> ola_mES_lif_1_62.counts 2 0 1 0 1 0
#> ola_mES_lif_1_66.counts 1 0 1 1 0 0
#> ola_mES_lif_1_67.counts 1 0 1 1 0 0
#> ola_mES_lif_1_68.counts 2 0 1 0 1 0
#> ola_mES_lif_1_7.counts 1 0 1 1 0 0
#> ola_mES_lif_1_72.counts 1 0 1 1 0 0
#> ola_mES_lif_1_73.counts 2 0 1 0 1 0
#> ola_mES_lif_1_8.counts 2 0 1 0 1 0
#> ola_mES_lif_1_80.counts 1 0 1 1 0 0
#> ola_mES_lif_1_81.counts 2 0 1 0 1 0
#> ola_mES_lif_1_9.counts 1 0 1 1 0 0
#> ola_mES_lif_1_91.counts 2 0 1 0 1 0
#> ola_mES_lif_1_92.counts 2 0 1 0 1 0
#> ola_mES_lif_1_95.counts 1 0 1 1 0 0
#> ola_mES_lif_1_96.counts 1 0 1 1 0 0
#> ola_mES_lif_2_61.counts 1 0 1 1 0 0
#> ola_mES_lif_2_72.counts 1 0 1 1 0 0
#> ola_mES_lif_3_1.counts 3 0 1 0 0 1
#> ola_mES_lif_3_13.counts 3 0 1 0 0 1
#> ola_mES_lif_3_27.counts 3 0 1 0 0 1
#> ola_mES_lif_3_3.counts 3 0 1 0 0 1
#> ola_mES_lif_3_30.counts 3 0 1 0 0 1
#> ola_mES_lif_3_32.counts 3 0 1 0 0 1
#> ola_mES_lif_3_4.counts 3 0 1 0 0 1
#> ola_mES_lif_3_40.counts 3 0 1 0 0 1
#> ola_mES_lif_3_42.counts 1 0 1 1 0 0
#> ola_mES_lif_3_49.counts 3 0 1 0 0 1
#> ola_mES_lif_3_50.counts 1 0 1 1 0 0
#> ola_mES_lif_3_51.counts 3 0 1 0 0 1
#> ola_mES_lif_3_52.counts 3 0 1 0 0 1
#> ola_mES_lif_3_53.counts 3 0 1 0 0 1
#> ola_mES_lif_3_54.counts 1 0 1 1 0 0
#> ola_mES_lif_3_58.counts 3 0 1 0 0 1
#> ola_mES_lif_3_59.counts 3 0 1 0 0 1
#> ola_mES_lif_3_6.counts 1 0 1 1 0 0
#> ola_mES_lif_3_60.counts 3 0 1 0 0 1
#> ola_mES_lif_3_61.counts 3 0 1 0 0 1
#> ola_mES_lif_3_63.counts 3 0 1 0 0 1
#> ola_mES_lif_3_64.counts 3 0 1 0 0 1
#> ola_mES_lif_3_65.counts 3 0 1 0 0 1
#> ola_mES_lif_3_71.counts 3 0 1 0 0 1
#> ola_mES_lif_3_8.counts 3 0 1 0 0 1
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> ola_mES_lif_1_1.counts 1 0.3400 0.7063 0.82 0.00 0.00 0.18
#> ola_mES_lif_1_13.counts 1 0.4624 0.4362 0.66 0.00 0.00 0.34
#> ola_mES_lif_1_15.counts 4 0.4522 0.3355 0.32 0.00 0.00 0.68
#> ola_mES_lif_1_18.counts 1 0.4624 0.4574 0.66 0.00 0.00 0.34
#> ola_mES_lif_1_19.counts 2 0.0707 0.9238 0.00 0.98 0.00 0.02
#> ola_mES_lif_1_20.counts 2 0.0000 0.9391 0.00 1.00 0.00 0.00
#> ola_mES_lif_1_22.counts 2 0.0000 0.9391 0.00 1.00 0.00 0.00
#> ola_mES_lif_1_24.counts 2 0.0000 0.9391 0.00 1.00 0.00 0.00
#> ola_mES_lif_1_25.counts 2 0.0000 0.9391 0.00 1.00 0.00 0.00
#> ola_mES_lif_1_26.counts 2 0.0000 0.9391 0.00 1.00 0.00 0.00
#> ola_mES_lif_1_28.counts 2 0.0000 0.9391 0.00 1.00 0.00 0.00
#> ola_mES_lif_1_3.counts 1 0.4994 0.0652 0.52 0.00 0.00 0.48
#> ola_mES_lif_1_30.counts 2 0.0000 0.9391 0.00 1.00 0.00 0.00
#> ola_mES_lif_1_32.counts 2 0.0000 0.9391 0.00 1.00 0.00 0.00
#> ola_mES_lif_1_33.counts 2 0.0000 0.9391 0.00 1.00 0.00 0.00
#> ola_mES_lif_1_34.counts 1 0.4994 0.0504 0.52 0.00 0.00 0.48
#> ola_mES_lif_1_36.counts 2 0.0000 0.9391 0.00 1.00 0.00 0.00
#> ola_mES_lif_1_37.counts 2 0.4522 0.4142 0.00 0.68 0.00 0.32
#> ola_mES_lif_1_38.counts 2 0.0000 0.9391 0.00 1.00 0.00 0.00
#> ola_mES_lif_1_39.counts 2 0.0000 0.9391 0.00 1.00 0.00 0.00
#> ola_mES_lif_1_4.counts 3 0.3172 0.8561 0.00 0.00 0.84 0.16
#> ola_mES_lif_1_41.counts 2 0.0000 0.9391 0.00 1.00 0.00 0.00
#> ola_mES_lif_1_42.counts 2 0.0000 0.9391 0.00 1.00 0.00 0.00
#> ola_mES_lif_1_43.counts 4 0.5000 0.0497 0.00 0.50 0.00 0.50
#> ola_mES_lif_1_44.counts 1 0.4713 0.3917 0.64 0.00 0.00 0.36
#> ola_mES_lif_1_45.counts 2 0.4994 -0.1872 0.00 0.52 0.00 0.48
#> ola_mES_lif_1_46.counts 4 0.4994 0.1141 0.00 0.48 0.00 0.52
#> ola_mES_lif_1_47.counts 1 0.0000 0.8678 1.00 0.00 0.00 0.00
#> ola_mES_lif_1_48.counts 1 0.0000 0.8678 1.00 0.00 0.00 0.00
#> ola_mES_lif_1_49.counts 1 0.0000 0.8678 1.00 0.00 0.00 0.00
#> ola_mES_lif_1_50.counts 2 0.0000 0.9391 0.00 1.00 0.00 0.00
#> ola_mES_lif_1_54.counts 1 0.0000 0.8678 1.00 0.00 0.00 0.00
#> ola_mES_lif_1_58.counts 1 0.0000 0.8678 1.00 0.00 0.00 0.00
#> ola_mES_lif_1_6.counts 4 0.3610 0.5161 0.00 0.20 0.00 0.80
#> ola_mES_lif_1_60.counts 1 0.3172 0.7382 0.84 0.00 0.00 0.16
#> ola_mES_lif_1_61.counts 1 0.0000 0.8678 1.00 0.00 0.00 0.00
#> ola_mES_lif_1_62.counts 2 0.0707 0.9244 0.00 0.98 0.00 0.02
#> ola_mES_lif_1_66.counts 1 0.0000 0.8678 1.00 0.00 0.00 0.00
#> ola_mES_lif_1_67.counts 1 0.0000 0.8678 1.00 0.00 0.00 0.00
#> ola_mES_lif_1_68.counts 2 0.0000 0.9391 0.00 1.00 0.00 0.00
#> ola_mES_lif_1_7.counts 4 0.4790 0.2360 0.38 0.00 0.00 0.62
#> ola_mES_lif_1_72.counts 1 0.0000 0.8678 1.00 0.00 0.00 0.00
#> ola_mES_lif_1_73.counts 2 0.1637 0.8867 0.00 0.94 0.00 0.06
#> ola_mES_lif_1_8.counts 2 0.2921 0.7880 0.00 0.86 0.00 0.14
#> ola_mES_lif_1_80.counts 1 0.0000 0.8678 1.00 0.00 0.00 0.00
#> ola_mES_lif_1_81.counts 2 0.0000 0.9391 0.00 1.00 0.00 0.00
#> ola_mES_lif_1_9.counts 4 0.4624 0.3204 0.34 0.00 0.00 0.66
#> ola_mES_lif_1_91.counts 2 0.2011 0.8706 0.00 0.92 0.00 0.08
#> ola_mES_lif_1_92.counts 2 0.1211 0.9092 0.00 0.96 0.00 0.04
#> ola_mES_lif_1_95.counts 1 0.0000 0.8678 1.00 0.00 0.00 0.00
#> ola_mES_lif_1_96.counts 1 0.0000 0.8678 1.00 0.00 0.00 0.00
#> ola_mES_lif_2_61.counts 1 0.0707 0.8606 0.98 0.00 0.00 0.02
#> ola_mES_lif_2_72.counts 1 0.1211 0.8499 0.96 0.00 0.00 0.04
#> ola_mES_lif_3_1.counts 3 0.4624 0.6746 0.00 0.00 0.66 0.34
#> ola_mES_lif_3_13.counts 3 0.1211 0.9217 0.00 0.00 0.96 0.04
#> ola_mES_lif_3_27.counts 3 0.0000 0.9243 0.00 0.00 1.00 0.00
#> ola_mES_lif_3_3.counts 3 0.5271 0.6484 0.02 0.00 0.64 0.34
#> ola_mES_lif_3_30.counts 3 0.1211 0.9217 0.00 0.00 0.96 0.04
#> ola_mES_lif_3_32.counts 3 0.0000 0.9243 0.00 0.00 1.00 0.00
#> ola_mES_lif_3_4.counts 3 0.0000 0.9243 0.00 0.00 1.00 0.00
#> ola_mES_lif_3_40.counts 3 0.0000 0.9243 0.00 0.00 1.00 0.00
#> ola_mES_lif_3_42.counts 1 0.1211 0.8499 0.96 0.00 0.00 0.04
#> ola_mES_lif_3_49.counts 3 0.1211 0.9217 0.00 0.00 0.96 0.04
#> ola_mES_lif_3_50.counts 1 0.0707 0.8606 0.98 0.00 0.00 0.02
#> ola_mES_lif_3_51.counts 3 0.0000 0.9243 0.00 0.00 1.00 0.00
#> ola_mES_lif_3_52.counts 3 0.2345 0.8835 0.00 0.00 0.90 0.10
#> ola_mES_lif_3_53.counts 3 0.1211 0.9217 0.00 0.00 0.96 0.04
#> ola_mES_lif_3_54.counts 1 0.1211 0.8499 0.96 0.00 0.00 0.04
#> ola_mES_lif_3_58.counts 3 0.3172 0.8743 0.00 0.00 0.84 0.16
#> ola_mES_lif_3_59.counts 3 0.0000 0.9243 0.00 0.00 1.00 0.00
#> ola_mES_lif_3_6.counts 1 0.2011 0.8149 0.92 0.00 0.00 0.08
#> ola_mES_lif_3_60.counts 3 0.0000 0.9243 0.00 0.00 1.00 0.00
#> ola_mES_lif_3_61.counts 3 0.2921 0.8875 0.00 0.00 0.86 0.14
#> ola_mES_lif_3_63.counts 3 0.1211 0.9217 0.00 0.00 0.96 0.04
#> ola_mES_lif_3_64.counts 3 0.0000 0.9243 0.00 0.00 1.00 0.00
#> ola_mES_lif_3_65.counts 3 0.1211 0.9217 0.00 0.00 0.96 0.04
#> ola_mES_lif_3_71.counts 3 0.1211 0.9217 0.00 0.00 0.96 0.04
#> ola_mES_lif_3_8.counts 3 0.4624 0.6746 0.00 0.00 0.66 0.34
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)
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: Node014. Child nodes: Node01111-leaf , Node01112-leaf , Node01121-leaf , Node01122-leaf , Node01411-leaf , Node01412-leaf , Node01413-leaf , Node02111-leaf , Node02112-leaf , Node02211-leaf , Node02212-leaf , Node02221-leaf , Node02222-leaf , Node02223-leaf .
The object with results only for a single top-value method and a single partitioning method can be extracted as:
res = res_rh["0141"]
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 15706 rows and 28 columns.
#> Top rows (1257) 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 0.984 0.993 0.498 0.505 0.505
#> 3 3 1.000 0.980 0.991 0.383 0.759 0.545
#> 4 4 0.809 0.682 0.849 0.097 0.950 0.840
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
#> ola_mES_lif_1_1.counts 1 0.000 0.987 1.0 0.0
#> ola_mES_lif_1_13.counts 2 0.000 1.000 0.0 1.0
#> ola_mES_lif_1_15.counts 1 0.000 0.987 1.0 0.0
#> ola_mES_lif_1_18.counts 2 0.000 1.000 0.0 1.0
#> ola_mES_lif_1_3.counts 1 0.000 0.987 1.0 0.0
#> ola_mES_lif_1_34.counts 2 0.000 1.000 0.0 1.0
#> ola_mES_lif_1_44.counts 1 0.000 0.987 1.0 0.0
#> ola_mES_lif_1_47.counts 2 0.000 1.000 0.0 1.0
#> ola_mES_lif_1_48.counts 1 0.000 0.987 1.0 0.0
#> ola_mES_lif_1_49.counts 1 0.000 0.987 1.0 0.0
#> ola_mES_lif_1_54.counts 2 0.000 1.000 0.0 1.0
#> ola_mES_lif_1_58.counts 2 0.000 1.000 0.0 1.0
#> ola_mES_lif_1_60.counts 1 0.000 0.987 1.0 0.0
#> ola_mES_lif_1_61.counts 2 0.000 1.000 0.0 1.0
#> ola_mES_lif_1_66.counts 1 0.722 0.750 0.8 0.2
#> ola_mES_lif_1_67.counts 1 0.000 0.987 1.0 0.0
#> ola_mES_lif_1_7.counts 1 0.000 0.987 1.0 0.0
#> ola_mES_lif_1_72.counts 2 0.000 1.000 0.0 1.0
#> ola_mES_lif_1_80.counts 1 0.000 0.987 1.0 0.0
#> ola_mES_lif_1_9.counts 1 0.000 0.987 1.0 0.0
#> ola_mES_lif_1_95.counts 2 0.000 1.000 0.0 1.0
#> ola_mES_lif_1_96.counts 2 0.000 1.000 0.0 1.0
#> ola_mES_lif_2_61.counts 1 0.000 0.987 1.0 0.0
#> ola_mES_lif_2_72.counts 1 0.000 0.987 1.0 0.0
#> ola_mES_lif_3_42.counts 1 0.000 0.987 1.0 0.0
#> ola_mES_lif_3_50.counts 2 0.000 1.000 0.0 1.0
#> ola_mES_lif_3_54.counts 1 0.000 0.987 1.0 0.0
#> ola_mES_lif_3_6.counts 1 0.000 0.987 1.0 0.0
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> ola_mES_lif_1_1.counts 3 0.0000 1.000 0.00 0.00 1.00
#> ola_mES_lif_1_13.counts 2 0.0000 0.998 0.00 1.00 0.00
#> ola_mES_lif_1_15.counts 3 0.0000 1.000 0.00 0.00 1.00
#> ola_mES_lif_1_18.counts 2 0.0000 0.998 0.00 1.00 0.00
#> ola_mES_lif_1_3.counts 3 0.0000 1.000 0.00 0.00 1.00
#> ola_mES_lif_1_34.counts 2 0.0000 0.998 0.00 1.00 0.00
#> ola_mES_lif_1_44.counts 1 0.4796 0.718 0.78 0.00 0.22
#> ola_mES_lif_1_47.counts 2 0.0000 0.998 0.00 1.00 0.00
#> ola_mES_lif_1_48.counts 1 0.0000 0.975 1.00 0.00 0.00
#> ola_mES_lif_1_49.counts 3 0.0000 1.000 0.00 0.00 1.00
#> ola_mES_lif_1_54.counts 2 0.0000 0.998 0.00 1.00 0.00
#> ola_mES_lif_1_58.counts 2 0.0000 0.998 0.00 1.00 0.00
#> ola_mES_lif_1_60.counts 3 0.0000 1.000 0.00 0.00 1.00
#> ola_mES_lif_1_61.counts 2 0.0000 0.998 0.00 1.00 0.00
#> ola_mES_lif_1_66.counts 1 0.0000 0.975 1.00 0.00 0.00
#> ola_mES_lif_1_67.counts 1 0.0000 0.975 1.00 0.00 0.00
#> ola_mES_lif_1_7.counts 3 0.0000 1.000 0.00 0.00 1.00
#> ola_mES_lif_1_72.counts 2 0.0892 0.979 0.02 0.98 0.00
#> ola_mES_lif_1_80.counts 1 0.0000 0.975 1.00 0.00 0.00
#> ola_mES_lif_1_9.counts 3 0.0000 1.000 0.00 0.00 1.00
#> ola_mES_lif_1_95.counts 2 0.0000 0.998 0.00 1.00 0.00
#> ola_mES_lif_1_96.counts 1 0.0000 0.975 1.00 0.00 0.00
#> ola_mES_lif_2_61.counts 1 0.0000 0.975 1.00 0.00 0.00
#> ola_mES_lif_2_72.counts 3 0.0000 1.000 0.00 0.00 1.00
#> ola_mES_lif_3_42.counts 1 0.0000 0.975 1.00 0.00 0.00
#> ola_mES_lif_3_50.counts 2 0.0000 0.998 0.00 1.00 0.00
#> ola_mES_lif_3_54.counts 1 0.0000 0.975 1.00 0.00 0.00
#> ola_mES_lif_3_6.counts 1 0.0000 0.975 1.00 0.00 0.00
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> ola_mES_lif_1_1.counts 3 0.0000 0.9465 0.00 0.00 1.00 0.00
#> ola_mES_lif_1_13.counts 2 0.4790 0.6828 0.00 0.62 0.00 0.38
#> ola_mES_lif_1_15.counts 3 0.0000 0.9465 0.00 0.00 1.00 0.00
#> ola_mES_lif_1_18.counts 2 0.4790 0.6828 0.00 0.62 0.00 0.38
#> ola_mES_lif_1_3.counts 3 0.0000 0.9465 0.00 0.00 1.00 0.00
#> ola_mES_lif_1_34.counts 2 0.4855 0.6758 0.00 0.60 0.00 0.40
#> ola_mES_lif_1_44.counts 1 0.5986 0.3966 0.62 0.00 0.32 0.06
#> ola_mES_lif_1_47.counts 2 0.1637 0.6789 0.00 0.94 0.00 0.06
#> ola_mES_lif_1_48.counts 1 0.5489 0.5390 0.70 0.00 0.06 0.24
#> ola_mES_lif_1_49.counts 3 0.2335 0.8810 0.06 0.00 0.92 0.02
#> ola_mES_lif_1_54.counts 2 0.0707 0.6915 0.00 0.98 0.00 0.02
#> ola_mES_lif_1_58.counts 2 0.4907 0.6660 0.00 0.58 0.00 0.42
#> ola_mES_lif_1_60.counts 3 0.0000 0.9465 0.00 0.00 1.00 0.00
#> ola_mES_lif_1_61.counts 2 0.2011 0.6198 0.00 0.92 0.00 0.08
#> ola_mES_lif_1_66.counts 1 0.5000 -0.2544 0.50 0.00 0.00 0.50
#> ola_mES_lif_1_67.counts 1 0.1211 0.7861 0.96 0.00 0.00 0.04
#> ola_mES_lif_1_7.counts 3 0.0000 0.9465 0.00 0.00 1.00 0.00
#> ola_mES_lif_1_72.counts 4 0.4977 0.2377 0.00 0.46 0.00 0.54
#> ola_mES_lif_1_80.counts 1 0.0707 0.7915 0.98 0.00 0.00 0.02
#> ola_mES_lif_1_9.counts 3 0.0000 0.9465 0.00 0.00 1.00 0.00
#> ola_mES_lif_1_95.counts 2 0.0000 0.6981 0.00 1.00 0.00 0.00
#> ola_mES_lif_1_96.counts 4 0.6805 0.0852 0.40 0.10 0.00 0.50
#> ola_mES_lif_2_61.counts 1 0.0000 0.7979 1.00 0.00 0.00 0.00
#> ola_mES_lif_2_72.counts 3 0.5602 0.6669 0.04 0.02 0.72 0.22
#> ola_mES_lif_3_42.counts 1 0.0000 0.7979 1.00 0.00 0.00 0.00
#> ola_mES_lif_3_50.counts 2 0.1211 0.6891 0.00 0.96 0.00 0.04
#> ola_mES_lif_3_54.counts 1 0.0000 0.7979 1.00 0.00 0.00 0.00
#> ola_mES_lif_3_6.counts 1 0.0000 0.7979 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)
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 , Node014 , Node021 , Node022 , Node023-leaf , Node031 , Node032 , Node033 , Node034-leaf .
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 'ConsensusPartition' object with k = 2, 3, 4.
#> On a matrix with 16235 rows and 225 columns.
#> Top rows (1624) 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 0.988 0.996 0.5009 0.499 0.499
#> 3 3 0.999 0.967 0.980 0.1954 0.882 0.768
#> 4 4 0.892 0.853 0.933 0.0863 0.935 0.840
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
#> ola_mES_2i_3_10.counts 2 0.000 0.9939 0.00 1.00
#> ola_mES_2i_3_11.counts 2 0.141 0.9745 0.02 0.98
#> ola_mES_2i_3_12.counts 2 0.000 0.9939 0.00 1.00
#> ola_mES_2i_3_13.counts 2 0.000 0.9939 0.00 1.00
#> ola_mES_2i_3_15.counts 2 0.000 0.9939 0.00 1.00
#> ola_mES_2i_3_16.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_3_19.counts 2 0.000 0.9939 0.00 1.00
#> ola_mES_2i_3_2.counts 2 0.000 0.9939 0.00 1.00
#> ola_mES_2i_3_20.counts 2 0.000 0.9939 0.00 1.00
#> ola_mES_2i_3_21.counts 2 0.000 0.9939 0.00 1.00
#> ola_mES_2i_3_23.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_3_25.counts 1 0.925 0.4809 0.66 0.34
#> ola_mES_2i_3_26.counts 2 0.000 0.9939 0.00 1.00
#> ola_mES_2i_3_28.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_3_30.counts 2 0.000 0.9939 0.00 1.00
#> ola_mES_2i_3_33.counts 2 0.000 0.9939 0.00 1.00
#> ola_mES_2i_3_36.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_3_37.counts 2 0.000 0.9939 0.00 1.00
#> ola_mES_2i_3_38.counts 2 0.000 0.9939 0.00 1.00
#> ola_mES_2i_3_39.counts 2 0.000 0.9939 0.00 1.00
#> ola_mES_2i_3_4.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_3_41.counts 2 0.000 0.9939 0.00 1.00
#> ola_mES_2i_3_42.counts 2 0.000 0.9939 0.00 1.00
#> ola_mES_2i_3_43.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_3_45.counts 2 0.000 0.9939 0.00 1.00
#> ola_mES_2i_3_46.counts 2 0.000 0.9939 0.00 1.00
#> ola_mES_2i_3_47.counts 2 0.000 0.9939 0.00 1.00
#> ola_mES_2i_3_5.counts 2 0.000 0.9939 0.00 1.00
#> ola_mES_2i_3_52.counts 2 0.000 0.9939 0.00 1.00
#> ola_mES_2i_3_54.counts 2 0.000 0.9939 0.00 1.00
#> ola_mES_2i_3_56.counts 2 0.000 0.9939 0.00 1.00
#> ola_mES_2i_3_57.counts 2 0.000 0.9939 0.00 1.00
#> ola_mES_2i_3_58.counts 2 0.000 0.9939 0.00 1.00
#> ola_mES_2i_3_6.counts 2 0.000 0.9939 0.00 1.00
#> ola_mES_2i_3_65.counts 2 0.000 0.9939 0.00 1.00
#> ola_mES_2i_3_69.counts 2 0.000 0.9939 0.00 1.00
#> ola_mES_2i_3_7.counts 2 0.000 0.9939 0.00 1.00
#> ola_mES_2i_3_73.counts 2 0.000 0.9939 0.00 1.00
#> ola_mES_2i_3_76.counts 2 0.000 0.9939 0.00 1.00
#> ola_mES_2i_3_77.counts 2 0.000 0.9939 0.00 1.00
#> ola_mES_2i_3_79.counts 2 0.000 0.9939 0.00 1.00
#> ola_mES_2i_3_8.counts 2 0.000 0.9939 0.00 1.00
#> ola_mES_2i_3_80.counts 2 0.000 0.9939 0.00 1.00
#> ola_mES_2i_3_82.counts 2 0.000 0.9939 0.00 1.00
#> ola_mES_2i_3_83.counts 2 0.000 0.9939 0.00 1.00
#> ola_mES_2i_3_84.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_3_87.counts 2 0.000 0.9939 0.00 1.00
#> ola_mES_2i_3_88.counts 2 0.000 0.9939 0.00 1.00
#> ola_mES_2i_3_90.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_3_91.counts 2 0.000 0.9939 0.00 1.00
#> ola_mES_2i_3_92.counts 2 0.000 0.9939 0.00 1.00
#> ola_mES_2i_3_96.counts 2 0.000 0.9939 0.00 1.00
#> ola_mES_2i_4_1.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_4_10.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_4_11.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_4_13.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_4_17.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_4_18.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_4_20.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_4_21.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_4_22.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_4_23.counts 2 0.000 0.9939 0.00 1.00
#> ola_mES_2i_4_24.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_4_25.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_4_26.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_4_27.counts 2 0.999 0.0742 0.48 0.52
#> ola_mES_2i_4_29.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_4_30.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_4_31.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_4_32.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_4_33.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_4_34.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_4_35.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_4_36.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_4_39.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_4_43.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_4_44.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_4_46.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_4_5.counts 2 0.242 0.9542 0.04 0.96
#> ola_mES_2i_4_50.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_4_52.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_4_56.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_4_57.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_4_58.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_4_59.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_4_6.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_4_60.counts 2 0.000 0.9939 0.00 1.00
#> ola_mES_2i_4_61.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_4_63.counts 2 0.000 0.9939 0.00 1.00
#> ola_mES_2i_4_64.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_4_65.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_4_66.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_4_68.counts 2 0.000 0.9939 0.00 1.00
#> ola_mES_2i_4_69.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_4_7.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_4_70.counts 2 0.000 0.9939 0.00 1.00
#> ola_mES_2i_4_71.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_4_72.counts 2 0.000 0.9939 0.00 1.00
#> ola_mES_2i_4_73.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_4_75.counts 2 0.000 0.9939 0.00 1.00
#> ola_mES_2i_4_77.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_4_78.counts 2 0.000 0.9939 0.00 1.00
#> ola_mES_2i_4_80.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_4_81.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_4_83.counts 2 0.000 0.9939 0.00 1.00
#> ola_mES_2i_4_84.counts 2 0.469 0.8860 0.10 0.90
#> ola_mES_2i_4_88.counts 2 0.000 0.9939 0.00 1.00
#> ola_mES_2i_4_89.counts 2 0.000 0.9939 0.00 1.00
#> ola_mES_2i_4_9.counts 2 0.000 0.9939 0.00 1.00
#> ola_mES_2i_4_90.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_4_91.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_4_94.counts 2 0.000 0.9939 0.00 1.00
#> ola_mES_2i_4_95.counts 2 0.000 0.9939 0.00 1.00
#> ola_mES_2i_4_96.counts 2 0.000 0.9939 0.00 1.00
#> ola_mES_2i_5_1.counts 2 0.000 0.9939 0.00 1.00
#> ola_mES_2i_5_10.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_5_11.counts 2 0.000 0.9939 0.00 1.00
#> ola_mES_2i_5_12.counts 2 0.000 0.9939 0.00 1.00
#> ola_mES_2i_5_13.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_5_14.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_5_15.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_5_16.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_5_17.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_5_18.counts 2 0.000 0.9939 0.00 1.00
#> ola_mES_2i_5_19.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_5_2.counts 2 0.000 0.9939 0.00 1.00
#> ola_mES_2i_5_20.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_5_21.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_5_23.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_5_24.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_5_25.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_5_26.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_5_27.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_5_28.counts 2 0.000 0.9939 0.00 1.00
#> ola_mES_2i_5_29.counts 2 0.000 0.9939 0.00 1.00
#> ola_mES_2i_5_3.counts 2 0.000 0.9939 0.00 1.00
#> ola_mES_2i_5_30.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_5_31.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_5_32.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_5_34.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_5_35.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_5_36.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_5_38.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_5_39.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_5_4.counts 2 0.000 0.9939 0.00 1.00
#> ola_mES_2i_5_40.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_5_42.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_5_44.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_5_45.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_5_46.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_5_47.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_5_48.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_5_49.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_5_5.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_5_51.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_5_52.counts 2 0.000 0.9939 0.00 1.00
#> ola_mES_2i_5_53.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_5_54.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_5_55.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_5_56.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_5_57.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_5_58.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_5_59.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_5_6.counts 2 0.000 0.9939 0.00 1.00
#> ola_mES_2i_5_61.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_5_62.counts 2 0.000 0.9939 0.00 1.00
#> ola_mES_2i_5_63.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_5_65.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_5_66.counts 2 0.000 0.9939 0.00 1.00
#> ola_mES_2i_5_67.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_5_68.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_5_69.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_5_7.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_5_70.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_5_72.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_5_73.counts 2 0.000 0.9939 0.00 1.00
#> ola_mES_2i_5_74.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_5_75.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_5_76.counts 2 0.000 0.9939 0.00 1.00
#> ola_mES_2i_5_77.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_5_78.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_5_8.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_5_81.counts 2 0.000 0.9939 0.00 1.00
#> ola_mES_2i_5_82.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_5_83.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_5_85.counts 2 0.000 0.9939 0.00 1.00
#> ola_mES_2i_5_86.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_5_87.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_5_88.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_5_9.counts 2 0.000 0.9939 0.00 1.00
#> ola_mES_2i_5_90.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_5_92.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_5_94.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_5_95.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_2i_5_96.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_a2i_2_30.counts 2 0.000 0.9939 0.00 1.00
#> ola_mES_a2i_2_36.counts 2 0.000 0.9939 0.00 1.00
#> ola_mES_a2i_2_89.counts 2 0.000 0.9939 0.00 1.00
#> ola_mES_a2i_3_10.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_a2i_3_11.counts 2 0.000 0.9939 0.00 1.00
#> ola_mES_a2i_3_21.counts 2 0.000 0.9939 0.00 1.00
#> ola_mES_a2i_3_22.counts 2 0.000 0.9939 0.00 1.00
#> ola_mES_a2i_3_24.counts 2 0.000 0.9939 0.00 1.00
#> ola_mES_a2i_3_3.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_a2i_3_33.counts 2 0.000 0.9939 0.00 1.00
#> ola_mES_a2i_3_4.counts 2 0.000 0.9939 0.00 1.00
#> ola_mES_a2i_3_58.counts 2 0.000 0.9939 0.00 1.00
#> ola_mES_a2i_3_68.counts 2 0.000 0.9939 0.00 1.00
#> ola_mES_a2i_3_76.counts 1 0.000 0.9971 1.00 0.00
#> ola_mES_a2i_3_80.counts 2 0.000 0.9939 0.00 1.00
#> ola_mES_a2i_3_9.counts 2 0.000 0.9939 0.00 1.00
#> ola_mES_a2i_3_91.counts 2 0.000 0.9939 0.00 1.00
#> ola_mES_lif_1_53.counts 2 0.000 0.9939 0.00 1.00
#> ola_mES_lif_1_56.counts 2 0.000 0.9939 0.00 1.00
#> ola_mES_lif_1_57.counts 2 0.000 0.9939 0.00 1.00
#> ola_mES_lif_2_12.counts 2 0.000 0.9939 0.00 1.00
#> ola_mES_lif_2_48.counts 2 0.000 0.9939 0.00 1.00
#> ola_mES_lif_2_69.counts 2 0.000 0.9939 0.00 1.00
#> ola_mES_lif_2_73.counts 2 0.000 0.9939 0.00 1.00
#> ola_mES_lif_3_12.counts 2 0.000 0.9939 0.00 1.00
#> ola_mES_lif_3_2.counts 2 0.000 0.9939 0.00 1.00
#> ola_mES_lif_3_21.counts 2 0.000 0.9939 0.00 1.00
#> ola_mES_lif_3_77.counts 2 0.000 0.9939 0.00 1.00
#> ola_mES_lif_3_89.counts 2 0.000 0.9939 0.00 1.00
#> ola_mES_lif_3_9.counts 2 0.000 0.9939 0.00 1.00
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> ola_mES_2i_3_10.counts 2 0.0000 0.983 0.00 1.00 0.00
#> ola_mES_2i_3_11.counts 3 0.6192 0.272 0.00 0.42 0.58
#> ola_mES_2i_3_12.counts 2 0.0892 0.973 0.00 0.98 0.02
#> ola_mES_2i_3_13.counts 2 0.0000 0.983 0.00 1.00 0.00
#> ola_mES_2i_3_15.counts 2 0.0000 0.983 0.00 1.00 0.00
#> ola_mES_2i_3_16.counts 1 0.0000 0.999 1.00 0.00 0.00
#> ola_mES_2i_3_19.counts 2 0.4291 0.807 0.00 0.82 0.18
#> ola_mES_2i_3_2.counts 2 0.0000 0.983 0.00 1.00 0.00
#> ola_mES_2i_3_20.counts 2 0.0000 0.983 0.00 1.00 0.00
#> ola_mES_2i_3_21.counts 2 0.0000 0.983 0.00 1.00 0.00
#> ola_mES_2i_3_23.counts 1 0.0000 0.999 1.00 0.00 0.00
#> ola_mES_2i_3_25.counts 3 0.7447 0.749 0.16 0.14 0.70
#> ola_mES_2i_3_26.counts 2 0.0892 0.974 0.00 0.98 0.02
#> ola_mES_2i_3_28.counts 1 0.0000 0.999 1.00 0.00 0.00
#> ola_mES_2i_3_30.counts 2 0.2066 0.937 0.00 0.94 0.06
#> ola_mES_2i_3_33.counts 2 0.0000 0.983 0.00 1.00 0.00
#> ola_mES_2i_3_36.counts 1 0.0000 0.999 1.00 0.00 0.00
#> ola_mES_2i_3_37.counts 2 0.0000 0.983 0.00 1.00 0.00
#> ola_mES_2i_3_38.counts 2 0.0000 0.983 0.00 1.00 0.00
#> ola_mES_2i_3_39.counts 2 0.0000 0.983 0.00 1.00 0.00
#> ola_mES_2i_3_4.counts 1 0.0000 0.999 1.00 0.00 0.00
#> ola_mES_2i_3_41.counts 2 0.0000 0.983 0.00 1.00 0.00
#> ola_mES_2i_3_42.counts 2 0.0000 0.983 0.00 1.00 0.00
#> ola_mES_2i_3_43.counts 1 0.0000 0.999 1.00 0.00 0.00
#> ola_mES_2i_3_45.counts 2 0.0000 0.983 0.00 1.00 0.00
#> ola_mES_2i_3_46.counts 2 0.0000 0.983 0.00 1.00 0.00
#> ola_mES_2i_3_47.counts 2 0.0000 0.983 0.00 1.00 0.00
#> ola_mES_2i_3_5.counts 2 0.0892 0.973 0.00 0.98 0.02
#> ola_mES_2i_3_52.counts 2 0.0000 0.983 0.00 1.00 0.00
#> ola_mES_2i_3_54.counts 2 0.0000 0.983 0.00 1.00 0.00
#> ola_mES_2i_3_56.counts 2 0.0000 0.983 0.00 1.00 0.00
#> ola_mES_2i_3_57.counts 2 0.0000 0.983 0.00 1.00 0.00
#> ola_mES_2i_3_58.counts 2 0.0000 0.983 0.00 1.00 0.00
#> ola_mES_2i_3_6.counts 2 0.0000 0.983 0.00 1.00 0.00
#> ola_mES_2i_3_65.counts 2 0.0000 0.983 0.00 1.00 0.00
#> ola_mES_2i_3_69.counts 2 0.0000 0.983 0.00 1.00 0.00
#> ola_mES_2i_3_7.counts 2 0.0000 0.983 0.00 1.00 0.00
#> ola_mES_2i_3_73.counts 2 0.0000 0.983 0.00 1.00 0.00
#> ola_mES_2i_3_76.counts 2 0.0000 0.983 0.00 1.00 0.00
#> ola_mES_2i_3_77.counts 2 0.0000 0.983 0.00 1.00 0.00
#> ola_mES_2i_3_79.counts 2 0.0000 0.983 0.00 1.00 0.00
#> ola_mES_2i_3_8.counts 2 0.0000 0.983 0.00 1.00 0.00
#> ola_mES_2i_3_80.counts 2 0.0000 0.983 0.00 1.00 0.00
#> ola_mES_2i_3_82.counts 2 0.0000 0.983 0.00 1.00 0.00
#> ola_mES_2i_3_83.counts 2 0.0000 0.983 0.00 1.00 0.00
#> ola_mES_2i_3_84.counts 1 0.0000 0.999 1.00 0.00 0.00
#> ola_mES_2i_3_87.counts 2 0.0000 0.983 0.00 1.00 0.00
#> ola_mES_2i_3_88.counts 2 0.0000 0.983 0.00 1.00 0.00
#> ola_mES_2i_3_90.counts 1 0.0000 0.999 1.00 0.00 0.00
#> ola_mES_2i_3_91.counts 2 0.0000 0.983 0.00 1.00 0.00
#> ola_mES_2i_3_92.counts 2 0.0000 0.983 0.00 1.00 0.00
#> ola_mES_2i_3_96.counts 2 0.0000 0.983 0.00 1.00 0.00
#> ola_mES_2i_4_1.counts 1 0.0000 0.999 1.00 0.00 0.00
#> ola_mES_2i_4_10.counts 1 0.0000 0.999 1.00 0.00 0.00
#> ola_mES_2i_4_11.counts 1 0.0000 0.999 1.00 0.00 0.00
#> ola_mES_2i_4_13.counts 1 0.0000 0.999 1.00 0.00 0.00
#> ola_mES_2i_4_17.counts 1 0.0000 0.999 1.00 0.00 0.00
#> ola_mES_2i_4_18.counts 3 0.0892 0.903 0.02 0.00 0.98
#> ola_mES_2i_4_20.counts 1 0.0000 0.999 1.00 0.00 0.00
#> ola_mES_2i_4_21.counts 1 0.0000 0.999 1.00 0.00 0.00
#> ola_mES_2i_4_22.counts 3 0.0892 0.903 0.02 0.00 0.98
#> ola_mES_2i_4_23.counts 2 0.2066 0.946 0.00 0.94 0.06
#> ola_mES_2i_4_24.counts 1 0.0000 0.999 1.00 0.00 0.00
#> ola_mES_2i_4_25.counts 1 0.0000 0.999 1.00 0.00 0.00
#> ola_mES_2i_4_26.counts 1 0.0000 0.999 1.00 0.00 0.00
#> ola_mES_2i_4_27.counts 3 0.0892 0.903 0.02 0.00 0.98
#> ola_mES_2i_4_29.counts 1 0.0000 0.999 1.00 0.00 0.00
#> ola_mES_2i_4_30.counts 1 0.0000 0.999 1.00 0.00 0.00
#> ola_mES_2i_4_31.counts 1 0.0000 0.999 1.00 0.00 0.00
#> ola_mES_2i_4_32.counts 1 0.0000 0.999 1.00 0.00 0.00
#> ola_mES_2i_4_33.counts 1 0.0000 0.999 1.00 0.00 0.00
#> ola_mES_2i_4_34.counts 1 0.0000 0.999 1.00 0.00 0.00
#> ola_mES_2i_4_35.counts 1 0.0000 0.999 1.00 0.00 0.00
#> ola_mES_2i_4_36.counts 1 0.0000 0.999 1.00 0.00 0.00
#> ola_mES_2i_4_39.counts 1 0.0000 0.999 1.00 0.00 0.00
#> ola_mES_2i_4_43.counts 1 0.0000 0.999 1.00 0.00 0.00
#> ola_mES_2i_4_44.counts 1 0.0000 0.999 1.00 0.00 0.00
#> ola_mES_2i_4_46.counts 1 0.0000 0.999 1.00 0.00 0.00
#> ola_mES_2i_4_5.counts 3 0.0892 0.900 0.00 0.02 0.98
#> ola_mES_2i_4_50.counts 1 0.0000 0.999 1.00 0.00 0.00
#> ola_mES_2i_4_52.counts 1 0.0000 0.999 1.00 0.00 0.00
#> ola_mES_2i_4_56.counts 1 0.0000 0.999 1.00 0.00 0.00
#> ola_mES_2i_4_57.counts 1 0.0000 0.999 1.00 0.00 0.00
#> ola_mES_2i_4_58.counts 1 0.0000 0.999 1.00 0.00 0.00
#> ola_mES_2i_4_59.counts 1 0.0000 0.999 1.00 0.00 0.00
#> ola_mES_2i_4_6.counts 1 0.0000 0.999 1.00 0.00 0.00
#> ola_mES_2i_4_60.counts 2 0.1529 0.960 0.00 0.96 0.04
#> ola_mES_2i_4_61.counts 1 0.0000 0.999 1.00 0.00 0.00
#> ola_mES_2i_4_63.counts 2 0.2066 0.946 0.00 0.94 0.06
#> ola_mES_2i_4_64.counts 1 0.0000 0.999 1.00 0.00 0.00
#> ola_mES_2i_4_65.counts 1 0.0000 0.999 1.00 0.00 0.00
#> ola_mES_2i_4_66.counts 1 0.0000 0.999 1.00 0.00 0.00
#> ola_mES_2i_4_68.counts 2 0.0892 0.973 0.00 0.98 0.02
#> ola_mES_2i_4_69.counts 1 0.0000 0.999 1.00 0.00 0.00
#> ola_mES_2i_4_7.counts 3 0.5016 0.737 0.24 0.00 0.76
#> ola_mES_2i_4_70.counts 2 0.2066 0.946 0.00 0.94 0.06
#> ola_mES_2i_4_71.counts 1 0.0000 0.999 1.00 0.00 0.00
#> ola_mES_2i_4_72.counts 3 0.0892 0.900 0.00 0.02 0.98
#> ola_mES_2i_4_73.counts 1 0.0000 0.999 1.00 0.00 0.00
#> ola_mES_2i_4_75.counts 2 0.0000 0.983 0.00 1.00 0.00
#> ola_mES_2i_4_77.counts 1 0.0000 0.999 1.00 0.00 0.00
#> ola_mES_2i_4_78.counts 3 0.0892 0.900 0.00 0.02 0.98
#> ola_mES_2i_4_80.counts 1 0.0000 0.999 1.00 0.00 0.00
#> ola_mES_2i_4_81.counts 1 0.0000 0.999 1.00 0.00 0.00
#> ola_mES_2i_4_83.counts 3 0.0892 0.900 0.00 0.02 0.98
#> ola_mES_2i_4_84.counts 3 0.0892 0.900 0.00 0.02 0.98
#> ola_mES_2i_4_88.counts 2 0.0892 0.973 0.00 0.98 0.02
#> ola_mES_2i_4_89.counts 2 0.2066 0.946 0.00 0.94 0.06
#> ola_mES_2i_4_9.counts 3 0.0892 0.900 0.00 0.02 0.98
#> ola_mES_2i_4_90.counts 1 0.0000 0.999 1.00 0.00 0.00
#> ola_mES_2i_4_91.counts 1 0.0000 0.999 1.00 0.00 0.00
#> ola_mES_2i_4_94.counts 2 0.2066 0.946 0.00 0.94 0.06
#> ola_mES_2i_4_95.counts 2 0.0000 0.983 0.00 1.00 0.00
#> ola_mES_2i_4_96.counts 2 0.0000 0.983 0.00 1.00 0.00
#> ola_mES_2i_5_1.counts 2 0.0000 0.983 0.00 1.00 0.00
#> ola_mES_2i_5_10.counts 3 0.5948 0.529 0.36 0.00 0.64
#> ola_mES_2i_5_11.counts 3 0.0892 0.900 0.00 0.02 0.98
#> ola_mES_2i_5_12.counts 3 0.0892 0.900 0.00 0.02 0.98
#> ola_mES_2i_5_13.counts 3 0.4291 0.808 0.18 0.00 0.82
#> ola_mES_2i_5_14.counts 1 0.0000 0.999 1.00 0.00 0.00
#> ola_mES_2i_5_15.counts 1 0.0000 0.999 1.00 0.00 0.00
#> ola_mES_2i_5_16.counts 1 0.0000 0.999 1.00 0.00 0.00
#> ola_mES_2i_5_17.counts 1 0.0000 0.999 1.00 0.00 0.00
#> ola_mES_2i_5_18.counts 3 0.0892 0.900 0.00 0.02 0.98
#> ola_mES_2i_5_19.counts 1 0.0000 0.999 1.00 0.00 0.00
#> ola_mES_2i_5_2.counts 2 0.2066 0.946 0.00 0.94 0.06
#> ola_mES_2i_5_20.counts 1 0.0000 0.999 1.00 0.00 0.00
#> ola_mES_2i_5_21.counts 1 0.0000 0.999 1.00 0.00 0.00
#> ola_mES_2i_5_23.counts 3 0.1529 0.900 0.04 0.00 0.96
#> ola_mES_2i_5_24.counts 1 0.0000 0.999 1.00 0.00 0.00
#> ola_mES_2i_5_25.counts 1 0.0000 0.999 1.00 0.00 0.00
#> ola_mES_2i_5_26.counts 1 0.0000 0.999 1.00 0.00 0.00
#> ola_mES_2i_5_27.counts 1 0.0000 0.999 1.00 0.00 0.00
#> ola_mES_2i_5_28.counts 2 0.0892 0.973 0.00 0.98 0.02
#> ola_mES_2i_5_29.counts 2 0.0892 0.973 0.00 0.98 0.02
#> ola_mES_2i_5_3.counts 2 0.2066 0.946 0.00 0.94 0.06
#> ola_mES_2i_5_30.counts 1 0.0000 0.999 1.00 0.00 0.00
#> ola_mES_2i_5_31.counts 1 0.0000 0.999 1.00 0.00 0.00
#> ola_mES_2i_5_32.counts 1 0.0000 0.999 1.00 0.00 0.00
#> ola_mES_2i_5_34.counts 1 0.0000 0.999 1.00 0.00 0.00
#> ola_mES_2i_5_35.counts 1 0.0000 0.999 1.00 0.00 0.00
#> ola_mES_2i_5_36.counts 1 0.0000 0.999 1.00 0.00 0.00
#> ola_mES_2i_5_38.counts 1 0.0000 0.999 1.00 0.00 0.00
#> ola_mES_2i_5_39.counts 1 0.0000 0.999 1.00 0.00 0.00
#> ola_mES_2i_5_4.counts 2 0.2066 0.946 0.00 0.94 0.06
#> ola_mES_2i_5_40.counts 1 0.0000 0.999 1.00 0.00 0.00
#> ola_mES_2i_5_42.counts 1 0.0000 0.999 1.00 0.00 0.00
#> ola_mES_2i_5_44.counts 1 0.0000 0.999 1.00 0.00 0.00
#> ola_mES_2i_5_45.counts 1 0.0000 0.999 1.00 0.00 0.00
#> ola_mES_2i_5_46.counts 1 0.0000 0.999 1.00 0.00 0.00
#> ola_mES_2i_5_47.counts 1 0.0000 0.999 1.00 0.00 0.00
#> ola_mES_2i_5_48.counts 1 0.0000 0.999 1.00 0.00 0.00
#> ola_mES_2i_5_49.counts 1 0.0000 0.999 1.00 0.00 0.00
#> ola_mES_2i_5_5.counts 3 0.2066 0.894 0.06 0.00 0.94
#> ola_mES_2i_5_51.counts 1 0.0000 0.999 1.00 0.00 0.00
#> ola_mES_2i_5_52.counts 2 0.0000 0.983 0.00 1.00 0.00
#> ola_mES_2i_5_53.counts 1 0.0000 0.999 1.00 0.00 0.00
#> ola_mES_2i_5_54.counts 1 0.0000 0.999 1.00 0.00 0.00
#> ola_mES_2i_5_55.counts 1 0.0000 0.999 1.00 0.00 0.00
#> ola_mES_2i_5_56.counts 1 0.0000 0.999 1.00 0.00 0.00
#> ola_mES_2i_5_57.counts 1 0.0000 0.999 1.00 0.00 0.00
#> ola_mES_2i_5_58.counts 1 0.0000 0.999 1.00 0.00 0.00
#> ola_mES_2i_5_59.counts 1 0.0000 0.999 1.00 0.00 0.00
#> ola_mES_2i_5_6.counts 2 0.2066 0.946 0.00 0.94 0.06
#> ola_mES_2i_5_61.counts 1 0.0000 0.999 1.00 0.00 0.00
#> ola_mES_2i_5_62.counts 2 0.2066 0.946 0.00 0.94 0.06
#> ola_mES_2i_5_63.counts 1 0.0000 0.999 1.00 0.00 0.00
#> ola_mES_2i_5_65.counts 3 0.3686 0.844 0.14 0.00 0.86
#> ola_mES_2i_5_66.counts 2 0.0892 0.974 0.00 0.98 0.02
#> ola_mES_2i_5_67.counts 1 0.0000 0.999 1.00 0.00 0.00
#> ola_mES_2i_5_68.counts 1 0.0000 0.999 1.00 0.00 0.00
#> ola_mES_2i_5_69.counts 1 0.0000 0.999 1.00 0.00 0.00
#> ola_mES_2i_5_7.counts 1 0.0000 0.999 1.00 0.00 0.00
#> ola_mES_2i_5_70.counts 1 0.0000 0.999 1.00 0.00 0.00
#> ola_mES_2i_5_72.counts 1 0.0000 0.999 1.00 0.00 0.00
#> ola_mES_2i_5_73.counts 3 0.0892 0.900 0.00 0.02 0.98
#> ola_mES_2i_5_74.counts 1 0.0000 0.999 1.00 0.00 0.00
#> ola_mES_2i_5_75.counts 3 0.2959 0.872 0.10 0.00 0.90
#> ola_mES_2i_5_76.counts 3 0.0892 0.900 0.00 0.02 0.98
#> ola_mES_2i_5_77.counts 3 0.4796 0.763 0.22 0.00 0.78
#> ola_mES_2i_5_78.counts 1 0.0000 0.999 1.00 0.00 0.00
#> ola_mES_2i_5_8.counts 1 0.0000 0.999 1.00 0.00 0.00
#> ola_mES_2i_5_81.counts 2 0.2066 0.946 0.00 0.94 0.06
#> ola_mES_2i_5_82.counts 1 0.0000 0.999 1.00 0.00 0.00
#> ola_mES_2i_5_83.counts 3 0.0892 0.903 0.02 0.00 0.98
#> ola_mES_2i_5_85.counts 2 0.4291 0.806 0.00 0.82 0.18
#> ola_mES_2i_5_86.counts 3 0.5706 0.611 0.32 0.00 0.68
#> ola_mES_2i_5_87.counts 3 0.2066 0.894 0.06 0.00 0.94
#> ola_mES_2i_5_88.counts 3 0.1529 0.900 0.04 0.00 0.96
#> ola_mES_2i_5_9.counts 2 0.0892 0.973 0.00 0.98 0.02
#> ola_mES_2i_5_90.counts 1 0.0000 0.999 1.00 0.00 0.00
#> ola_mES_2i_5_92.counts 1 0.0000 0.999 1.00 0.00 0.00
#> ola_mES_2i_5_94.counts 1 0.0000 0.999 1.00 0.00 0.00
#> ola_mES_2i_5_95.counts 1 0.0000 0.999 1.00 0.00 0.00
#> ola_mES_2i_5_96.counts 1 0.0000 0.999 1.00 0.00 0.00
#> ola_mES_a2i_2_30.counts 2 0.0000 0.983 0.00 1.00 0.00
#> ola_mES_a2i_2_36.counts 2 0.0000 0.983 0.00 1.00 0.00
#> ola_mES_a2i_2_89.counts 2 0.0000 0.983 0.00 1.00 0.00
#> ola_mES_a2i_3_10.counts 1 0.0892 0.978 0.98 0.00 0.02
#> ola_mES_a2i_3_11.counts 2 0.0892 0.974 0.00 0.98 0.02
#> ola_mES_a2i_3_21.counts 2 0.0892 0.974 0.00 0.98 0.02
#> ola_mES_a2i_3_22.counts 2 0.0892 0.974 0.00 0.98 0.02
#> ola_mES_a2i_3_24.counts 2 0.0892 0.974 0.00 0.98 0.02
#> ola_mES_a2i_3_3.counts 1 0.0892 0.978 0.98 0.00 0.02
#> ola_mES_a2i_3_33.counts 2 0.0892 0.974 0.00 0.98 0.02
#> ola_mES_a2i_3_4.counts 2 0.0892 0.974 0.00 0.98 0.02
#> ola_mES_a2i_3_58.counts 2 0.0892 0.974 0.00 0.98 0.02
#> ola_mES_a2i_3_68.counts 2 0.0000 0.983 0.00 1.00 0.00
#> ola_mES_a2i_3_76.counts 1 0.0892 0.978 0.98 0.00 0.02
#> ola_mES_a2i_3_80.counts 2 0.0000 0.983 0.00 1.00 0.00
#> ola_mES_a2i_3_9.counts 2 0.0892 0.974 0.00 0.98 0.02
#> ola_mES_a2i_3_91.counts 2 0.0892 0.974 0.00 0.98 0.02
#> ola_mES_lif_1_53.counts 2 0.0892 0.974 0.00 0.98 0.02
#> ola_mES_lif_1_56.counts 2 0.0000 0.983 0.00 1.00 0.00
#> ola_mES_lif_1_57.counts 2 0.0892 0.974 0.00 0.98 0.02
#> ola_mES_lif_2_12.counts 2 0.0892 0.974 0.00 0.98 0.02
#> ola_mES_lif_2_48.counts 2 0.0892 0.974 0.00 0.98 0.02
#> ola_mES_lif_2_69.counts 2 0.0892 0.974 0.00 0.98 0.02
#> ola_mES_lif_2_73.counts 2 0.0892 0.974 0.00 0.98 0.02
#> ola_mES_lif_3_12.counts 2 0.0892 0.974 0.00 0.98 0.02
#> ola_mES_lif_3_2.counts 2 0.0000 0.983 0.00 1.00 0.00
#> ola_mES_lif_3_21.counts 2 0.0000 0.983 0.00 1.00 0.00
#> ola_mES_lif_3_77.counts 2 0.0000 0.983 0.00 1.00 0.00
#> ola_mES_lif_3_89.counts 2 0.0000 0.983 0.00 1.00 0.00
#> ola_mES_lif_3_9.counts 2 0.0000 0.983 0.00 1.00 0.00
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> ola_mES_2i_3_10.counts 2 0.0000 0.9474 0.00 1.00 0.00 0.00
#> ola_mES_2i_3_11.counts 3 0.7877 -0.1680 0.00 0.28 0.36 0.36
#> ola_mES_2i_3_12.counts 2 0.1411 0.9257 0.00 0.96 0.02 0.02
#> ola_mES_2i_3_13.counts 2 0.0707 0.9379 0.00 0.98 0.00 0.02
#> ola_mES_2i_3_15.counts 2 0.0707 0.9379 0.00 0.98 0.00 0.02
#> ola_mES_2i_3_16.counts 1 0.5392 0.5729 0.68 0.00 0.04 0.28
#> ola_mES_2i_3_19.counts 2 0.6500 0.3579 0.00 0.62 0.12 0.26
#> ola_mES_2i_3_2.counts 2 0.0000 0.9474 0.00 1.00 0.00 0.00
#> ola_mES_2i_3_20.counts 2 0.0000 0.9474 0.00 1.00 0.00 0.00
#> ola_mES_2i_3_21.counts 2 0.0000 0.9474 0.00 1.00 0.00 0.00
#> ola_mES_2i_3_23.counts 1 0.5256 0.6042 0.70 0.00 0.04 0.26
#> ola_mES_2i_3_25.counts 3 0.9723 -0.0733 0.14 0.26 0.30 0.30
#> ola_mES_2i_3_26.counts 2 0.5256 0.4817 0.00 0.70 0.04 0.26
#> ola_mES_2i_3_28.counts 1 0.5619 0.5102 0.64 0.00 0.04 0.32
#> ola_mES_2i_3_30.counts 4 0.6299 0.2607 0.00 0.42 0.06 0.52
#> ola_mES_2i_3_33.counts 2 0.0000 0.9474 0.00 1.00 0.00 0.00
#> ola_mES_2i_3_36.counts 1 0.5512 0.5422 0.66 0.00 0.04 0.30
#> ola_mES_2i_3_37.counts 2 0.4284 0.6483 0.00 0.78 0.02 0.20
#> ola_mES_2i_3_38.counts 2 0.0000 0.9474 0.00 1.00 0.00 0.00
#> ola_mES_2i_3_39.counts 2 0.0000 0.9474 0.00 1.00 0.00 0.00
#> ola_mES_2i_3_4.counts 1 0.5619 0.5102 0.64 0.00 0.04 0.32
#> ola_mES_2i_3_41.counts 2 0.0000 0.9474 0.00 1.00 0.00 0.00
#> ola_mES_2i_3_42.counts 2 0.3525 0.7848 0.00 0.86 0.04 0.10
#> ola_mES_2i_3_43.counts 1 0.5512 0.5422 0.66 0.00 0.04 0.30
#> ola_mES_2i_3_45.counts 2 0.0000 0.9474 0.00 1.00 0.00 0.00
#> ola_mES_2i_3_46.counts 2 0.0000 0.9474 0.00 1.00 0.00 0.00
#> ola_mES_2i_3_47.counts 2 0.0000 0.9474 0.00 1.00 0.00 0.00
#> ola_mES_2i_3_5.counts 2 0.1411 0.9257 0.00 0.96 0.02 0.02
#> ola_mES_2i_3_52.counts 2 0.0000 0.9474 0.00 1.00 0.00 0.00
#> ola_mES_2i_3_54.counts 2 0.0000 0.9474 0.00 1.00 0.00 0.00
#> ola_mES_2i_3_56.counts 2 0.0000 0.9474 0.00 1.00 0.00 0.00
#> ola_mES_2i_3_57.counts 2 0.0000 0.9474 0.00 1.00 0.00 0.00
#> ola_mES_2i_3_58.counts 2 0.0000 0.9474 0.00 1.00 0.00 0.00
#> ola_mES_2i_3_6.counts 2 0.0000 0.9474 0.00 1.00 0.00 0.00
#> ola_mES_2i_3_65.counts 2 0.0000 0.9474 0.00 1.00 0.00 0.00
#> ola_mES_2i_3_69.counts 2 0.0000 0.9474 0.00 1.00 0.00 0.00
#> ola_mES_2i_3_7.counts 2 0.0000 0.9474 0.00 1.00 0.00 0.00
#> ola_mES_2i_3_73.counts 2 0.0000 0.9474 0.00 1.00 0.00 0.00
#> ola_mES_2i_3_76.counts 2 0.0000 0.9474 0.00 1.00 0.00 0.00
#> ola_mES_2i_3_77.counts 2 0.0000 0.9474 0.00 1.00 0.00 0.00
#> ola_mES_2i_3_79.counts 2 0.0000 0.9474 0.00 1.00 0.00 0.00
#> ola_mES_2i_3_8.counts 2 0.0000 0.9474 0.00 1.00 0.00 0.00
#> ola_mES_2i_3_80.counts 2 0.0000 0.9474 0.00 1.00 0.00 0.00
#> ola_mES_2i_3_82.counts 2 0.0000 0.9474 0.00 1.00 0.00 0.00
#> ola_mES_2i_3_83.counts 2 0.0707 0.9379 0.00 0.98 0.00 0.02
#> ola_mES_2i_3_84.counts 1 0.5619 0.5102 0.64 0.00 0.04 0.32
#> ola_mES_2i_3_87.counts 2 0.0000 0.9474 0.00 1.00 0.00 0.00
#> ola_mES_2i_3_88.counts 2 0.0000 0.9474 0.00 1.00 0.00 0.00
#> ola_mES_2i_3_90.counts 1 0.5619 0.5102 0.64 0.00 0.04 0.32
#> ola_mES_2i_3_91.counts 2 0.0000 0.9474 0.00 1.00 0.00 0.00
#> ola_mES_2i_3_92.counts 2 0.0000 0.9474 0.00 1.00 0.00 0.00
#> ola_mES_2i_3_96.counts 2 0.0000 0.9474 0.00 1.00 0.00 0.00
#> ola_mES_2i_4_1.counts 1 0.0000 0.9574 1.00 0.00 0.00 0.00
#> ola_mES_2i_4_10.counts 1 0.0000 0.9574 1.00 0.00 0.00 0.00
#> ola_mES_2i_4_11.counts 1 0.0000 0.9574 1.00 0.00 0.00 0.00
#> ola_mES_2i_4_13.counts 1 0.0000 0.9574 1.00 0.00 0.00 0.00
#> ola_mES_2i_4_17.counts 1 0.0000 0.9574 1.00 0.00 0.00 0.00
#> ola_mES_2i_4_18.counts 3 0.1637 0.7581 0.06 0.00 0.94 0.00
#> ola_mES_2i_4_20.counts 1 0.0707 0.9414 0.98 0.00 0.02 0.00
#> ola_mES_2i_4_21.counts 1 0.0000 0.9574 1.00 0.00 0.00 0.00
#> ola_mES_2i_4_22.counts 3 0.1211 0.7620 0.04 0.00 0.96 0.00
#> ola_mES_2i_4_23.counts 2 0.1913 0.9108 0.00 0.94 0.04 0.02
#> ola_mES_2i_4_24.counts 1 0.0000 0.9574 1.00 0.00 0.00 0.00
#> ola_mES_2i_4_25.counts 1 0.0000 0.9574 1.00 0.00 0.00 0.00
#> ola_mES_2i_4_26.counts 1 0.0000 0.9574 1.00 0.00 0.00 0.00
#> ola_mES_2i_4_27.counts 3 0.1211 0.7621 0.00 0.04 0.96 0.00
#> ola_mES_2i_4_29.counts 1 0.0000 0.9574 1.00 0.00 0.00 0.00
#> ola_mES_2i_4_30.counts 1 0.0000 0.9574 1.00 0.00 0.00 0.00
#> ola_mES_2i_4_31.counts 1 0.0000 0.9574 1.00 0.00 0.00 0.00
#> ola_mES_2i_4_32.counts 1 0.0000 0.9574 1.00 0.00 0.00 0.00
#> ola_mES_2i_4_33.counts 1 0.0000 0.9574 1.00 0.00 0.00 0.00
#> ola_mES_2i_4_34.counts 1 0.0000 0.9574 1.00 0.00 0.00 0.00
#> ola_mES_2i_4_35.counts 1 0.0000 0.9574 1.00 0.00 0.00 0.00
#> ola_mES_2i_4_36.counts 1 0.0000 0.9574 1.00 0.00 0.00 0.00
#> ola_mES_2i_4_39.counts 1 0.0000 0.9574 1.00 0.00 0.00 0.00
#> ola_mES_2i_4_43.counts 1 0.0707 0.9414 0.98 0.00 0.02 0.00
#> ola_mES_2i_4_44.counts 1 0.0000 0.9574 1.00 0.00 0.00 0.00
#> ola_mES_2i_4_46.counts 1 0.0000 0.9574 1.00 0.00 0.00 0.00
#> ola_mES_2i_4_5.counts 3 0.1913 0.7661 0.00 0.04 0.94 0.02
#> ola_mES_2i_4_50.counts 1 0.0000 0.9574 1.00 0.00 0.00 0.00
#> ola_mES_2i_4_52.counts 1 0.0000 0.9574 1.00 0.00 0.00 0.00
#> ola_mES_2i_4_56.counts 1 0.0000 0.9574 1.00 0.00 0.00 0.00
#> ola_mES_2i_4_57.counts 1 0.0000 0.9574 1.00 0.00 0.00 0.00
#> ola_mES_2i_4_58.counts 1 0.0000 0.9574 1.00 0.00 0.00 0.00
#> ola_mES_2i_4_59.counts 1 0.0000 0.9574 1.00 0.00 0.00 0.00
#> ola_mES_2i_4_6.counts 1 0.0000 0.9574 1.00 0.00 0.00 0.00
#> ola_mES_2i_4_60.counts 2 0.1913 0.9108 0.00 0.94 0.04 0.02
#> ola_mES_2i_4_61.counts 1 0.0707 0.9414 0.98 0.00 0.02 0.00
#> ola_mES_2i_4_63.counts 2 0.1913 0.9108 0.00 0.94 0.04 0.02
#> ola_mES_2i_4_64.counts 1 0.0000 0.9574 1.00 0.00 0.00 0.00
#> ola_mES_2i_4_65.counts 1 0.0000 0.9574 1.00 0.00 0.00 0.00
#> ola_mES_2i_4_66.counts 1 0.0000 0.9574 1.00 0.00 0.00 0.00
#> ola_mES_2i_4_68.counts 2 0.0707 0.9379 0.00 0.98 0.02 0.00
#> ola_mES_2i_4_69.counts 1 0.0707 0.9414 0.98 0.00 0.02 0.00
#> ola_mES_2i_4_7.counts 3 0.4948 0.2908 0.44 0.00 0.56 0.00
#> ola_mES_2i_4_70.counts 2 0.1913 0.9108 0.00 0.94 0.04 0.02
#> ola_mES_2i_4_71.counts 1 0.0000 0.9574 1.00 0.00 0.00 0.00
#> ola_mES_2i_4_72.counts 3 0.1913 0.7661 0.00 0.04 0.94 0.02
#> ola_mES_2i_4_73.counts 1 0.0000 0.9574 1.00 0.00 0.00 0.00
#> ola_mES_2i_4_75.counts 2 0.0000 0.9474 0.00 1.00 0.00 0.00
#> ola_mES_2i_4_77.counts 1 0.0000 0.9574 1.00 0.00 0.00 0.00
#> ola_mES_2i_4_78.counts 3 0.1913 0.7661 0.00 0.04 0.94 0.02
#> ola_mES_2i_4_80.counts 1 0.0000 0.9574 1.00 0.00 0.00 0.00
#> ola_mES_2i_4_81.counts 1 0.0000 0.9574 1.00 0.00 0.00 0.00
#> ola_mES_2i_4_83.counts 3 0.2335 0.7446 0.00 0.06 0.92 0.02
#> ola_mES_2i_4_84.counts 3 0.1913 0.7661 0.00 0.04 0.94 0.02
#> ola_mES_2i_4_88.counts 2 0.1411 0.9257 0.00 0.96 0.02 0.02
#> ola_mES_2i_4_89.counts 2 0.1913 0.9108 0.00 0.94 0.04 0.02
#> ola_mES_2i_4_9.counts 3 0.1913 0.7661 0.00 0.04 0.94 0.02
#> ola_mES_2i_4_90.counts 1 0.0000 0.9574 1.00 0.00 0.00 0.00
#> ola_mES_2i_4_91.counts 1 0.0000 0.9574 1.00 0.00 0.00 0.00
#> ola_mES_2i_4_94.counts 2 0.1913 0.9108 0.00 0.94 0.04 0.02
#> ola_mES_2i_4_95.counts 2 0.0000 0.9474 0.00 1.00 0.00 0.00
#> ola_mES_2i_4_96.counts 2 0.0000 0.9474 0.00 1.00 0.00 0.00
#> ola_mES_2i_5_1.counts 2 0.1211 0.9154 0.00 0.96 0.00 0.04
#> ola_mES_2i_5_10.counts 1 0.4994 -0.0767 0.52 0.00 0.48 0.00
#> ola_mES_2i_5_11.counts 3 0.1913 0.7661 0.00 0.04 0.94 0.02
#> ola_mES_2i_5_12.counts 3 0.1913 0.7661 0.00 0.04 0.94 0.02
#> ola_mES_2i_5_13.counts 3 0.4790 0.4548 0.38 0.00 0.62 0.00
#> ola_mES_2i_5_14.counts 1 0.0000 0.9574 1.00 0.00 0.00 0.00
#> ola_mES_2i_5_15.counts 1 0.0000 0.9574 1.00 0.00 0.00 0.00
#> ola_mES_2i_5_16.counts 1 0.0000 0.9574 1.00 0.00 0.00 0.00
#> ola_mES_2i_5_17.counts 1 0.0000 0.9574 1.00 0.00 0.00 0.00
#> ola_mES_2i_5_18.counts 3 0.1913 0.7661 0.00 0.04 0.94 0.02
#> ola_mES_2i_5_19.counts 1 0.0000 0.9574 1.00 0.00 0.00 0.00
#> ola_mES_2i_5_2.counts 2 0.1913 0.9108 0.00 0.94 0.04 0.02
#> ola_mES_2i_5_20.counts 1 0.0000 0.9574 1.00 0.00 0.00 0.00
#> ola_mES_2i_5_21.counts 1 0.0000 0.9574 1.00 0.00 0.00 0.00
#> ola_mES_2i_5_23.counts 3 0.2011 0.7490 0.08 0.00 0.92 0.00
#> ola_mES_2i_5_24.counts 1 0.0000 0.9574 1.00 0.00 0.00 0.00
#> ola_mES_2i_5_25.counts 1 0.0000 0.9574 1.00 0.00 0.00 0.00
#> ola_mES_2i_5_26.counts 1 0.0000 0.9574 1.00 0.00 0.00 0.00
#> ola_mES_2i_5_27.counts 1 0.0000 0.9574 1.00 0.00 0.00 0.00
#> ola_mES_2i_5_28.counts 2 0.0000 0.9474 0.00 1.00 0.00 0.00
#> ola_mES_2i_5_29.counts 2 0.0707 0.9377 0.00 0.98 0.02 0.00
#> ola_mES_2i_5_3.counts 2 0.1913 0.9108 0.00 0.94 0.04 0.02
#> ola_mES_2i_5_30.counts 1 0.0000 0.9574 1.00 0.00 0.00 0.00
#> ola_mES_2i_5_31.counts 1 0.0000 0.9574 1.00 0.00 0.00 0.00
#> ola_mES_2i_5_32.counts 1 0.0000 0.9574 1.00 0.00 0.00 0.00
#> ola_mES_2i_5_34.counts 1 0.0000 0.9574 1.00 0.00 0.00 0.00
#> ola_mES_2i_5_35.counts 1 0.0707 0.9414 0.98 0.00 0.02 0.00
#> ola_mES_2i_5_36.counts 1 0.0000 0.9574 1.00 0.00 0.00 0.00
#> ola_mES_2i_5_38.counts 1 0.0000 0.9574 1.00 0.00 0.00 0.00
#> ola_mES_2i_5_39.counts 1 0.0000 0.9574 1.00 0.00 0.00 0.00
#> ola_mES_2i_5_4.counts 2 0.1913 0.9108 0.00 0.94 0.04 0.02
#> ola_mES_2i_5_40.counts 1 0.0707 0.9414 0.98 0.00 0.02 0.00
#> ola_mES_2i_5_42.counts 1 0.0000 0.9574 1.00 0.00 0.00 0.00
#> ola_mES_2i_5_44.counts 1 0.0000 0.9574 1.00 0.00 0.00 0.00
#> ola_mES_2i_5_45.counts 1 0.0000 0.9574 1.00 0.00 0.00 0.00
#> ola_mES_2i_5_46.counts 1 0.0000 0.9574 1.00 0.00 0.00 0.00
#> ola_mES_2i_5_47.counts 1 0.0707 0.9414 0.98 0.00 0.02 0.00
#> ola_mES_2i_5_48.counts 1 0.0000 0.9574 1.00 0.00 0.00 0.00
#> ola_mES_2i_5_49.counts 1 0.0000 0.9574 1.00 0.00 0.00 0.00
#> ola_mES_2i_5_5.counts 3 0.2011 0.7490 0.08 0.00 0.92 0.00
#> ola_mES_2i_5_51.counts 1 0.0000 0.9574 1.00 0.00 0.00 0.00
#> ola_mES_2i_5_52.counts 2 0.0000 0.9474 0.00 1.00 0.00 0.00
#> ola_mES_2i_5_53.counts 1 0.0000 0.9574 1.00 0.00 0.00 0.00
#> ola_mES_2i_5_54.counts 1 0.0707 0.9414 0.98 0.00 0.02 0.00
#> ola_mES_2i_5_55.counts 1 0.0000 0.9574 1.00 0.00 0.00 0.00
#> ola_mES_2i_5_56.counts 1 0.0000 0.9574 1.00 0.00 0.00 0.00
#> ola_mES_2i_5_57.counts 1 0.0000 0.9574 1.00 0.00 0.00 0.00
#> ola_mES_2i_5_58.counts 1 0.0000 0.9574 1.00 0.00 0.00 0.00
#> ola_mES_2i_5_59.counts 1 0.0000 0.9574 1.00 0.00 0.00 0.00
#> ola_mES_2i_5_6.counts 2 0.1913 0.9108 0.00 0.94 0.04 0.02
#> ola_mES_2i_5_61.counts 1 0.0000 0.9574 1.00 0.00 0.00 0.00
#> ola_mES_2i_5_62.counts 2 0.1913 0.9108 0.00 0.94 0.04 0.02
#> ola_mES_2i_5_63.counts 1 0.0000 0.9574 1.00 0.00 0.00 0.00
#> ola_mES_2i_5_65.counts 3 0.4134 0.5823 0.26 0.00 0.74 0.00
#> ola_mES_2i_5_66.counts 2 0.2345 0.8449 0.00 0.90 0.00 0.10
#> ola_mES_2i_5_67.counts 1 0.0000 0.9574 1.00 0.00 0.00 0.00
#> ola_mES_2i_5_68.counts 1 0.0000 0.9574 1.00 0.00 0.00 0.00
#> ola_mES_2i_5_69.counts 1 0.0000 0.9574 1.00 0.00 0.00 0.00
#> ola_mES_2i_5_7.counts 1 0.0000 0.9574 1.00 0.00 0.00 0.00
#> ola_mES_2i_5_70.counts 1 0.0000 0.9574 1.00 0.00 0.00 0.00
#> ola_mES_2i_5_72.counts 1 0.0000 0.9574 1.00 0.00 0.00 0.00
#> ola_mES_2i_5_73.counts 3 0.1913 0.7661 0.00 0.04 0.94 0.02
#> ola_mES_2i_5_74.counts 1 0.0707 0.9414 0.98 0.00 0.02 0.00
#> ola_mES_2i_5_75.counts 3 0.4134 0.5827 0.26 0.00 0.74 0.00
#> ola_mES_2i_5_76.counts 3 0.1913 0.7661 0.00 0.04 0.94 0.02
#> ola_mES_2i_5_77.counts 3 0.4977 0.2495 0.46 0.00 0.54 0.00
#> ola_mES_2i_5_78.counts 1 0.0000 0.9574 1.00 0.00 0.00 0.00
#> ola_mES_2i_5_8.counts 1 0.1211 0.9221 0.96 0.00 0.04 0.00
#> ola_mES_2i_5_81.counts 2 0.1913 0.9108 0.00 0.94 0.04 0.02
#> ola_mES_2i_5_82.counts 1 0.0000 0.9574 1.00 0.00 0.00 0.00
#> ola_mES_2i_5_83.counts 3 0.1211 0.7620 0.04 0.00 0.96 0.00
#> ola_mES_2i_5_85.counts 2 0.3853 0.7502 0.00 0.82 0.16 0.02
#> ola_mES_2i_5_86.counts 1 0.6336 -0.1624 0.48 0.00 0.46 0.06
#> ola_mES_2i_5_87.counts 3 0.2011 0.7490 0.08 0.00 0.92 0.00
#> ola_mES_2i_5_88.counts 3 0.2011 0.7490 0.08 0.00 0.92 0.00
#> ola_mES_2i_5_9.counts 2 0.0707 0.9379 0.00 0.98 0.00 0.02
#> ola_mES_2i_5_90.counts 1 0.0000 0.9574 1.00 0.00 0.00 0.00
#> ola_mES_2i_5_92.counts 1 0.0000 0.9574 1.00 0.00 0.00 0.00
#> ola_mES_2i_5_94.counts 1 0.0000 0.9574 1.00 0.00 0.00 0.00
#> ola_mES_2i_5_95.counts 1 0.0000 0.9574 1.00 0.00 0.00 0.00
#> ola_mES_2i_5_96.counts 1 0.0000 0.9574 1.00 0.00 0.00 0.00
#> ola_mES_a2i_2_30.counts 2 0.0000 0.9474 0.00 1.00 0.00 0.00
#> ola_mES_a2i_2_36.counts 2 0.0707 0.9338 0.00 0.98 0.00 0.02
#> ola_mES_a2i_2_89.counts 2 0.0707 0.9338 0.00 0.98 0.00 0.02
#> ola_mES_a2i_3_10.counts 4 0.2830 0.5469 0.06 0.00 0.04 0.90
#> ola_mES_a2i_3_11.counts 4 0.4522 0.7549 0.00 0.32 0.00 0.68
#> ola_mES_a2i_3_21.counts 2 0.2345 0.8441 0.00 0.90 0.00 0.10
#> ola_mES_a2i_3_22.counts 4 0.4522 0.7549 0.00 0.32 0.00 0.68
#> ola_mES_a2i_3_24.counts 4 0.4522 0.7549 0.00 0.32 0.00 0.68
#> ola_mES_a2i_3_3.counts 4 0.1637 0.5659 0.06 0.00 0.00 0.94
#> ola_mES_a2i_3_33.counts 4 0.4522 0.7549 0.00 0.32 0.00 0.68
#> ola_mES_a2i_3_4.counts 4 0.3975 0.7329 0.00 0.24 0.00 0.76
#> ola_mES_a2i_3_58.counts 2 0.1637 0.8945 0.00 0.94 0.00 0.06
#> ola_mES_a2i_3_68.counts 2 0.0000 0.9474 0.00 1.00 0.00 0.00
#> ola_mES_a2i_3_76.counts 4 0.0707 0.5995 0.02 0.00 0.00 0.98
#> ola_mES_a2i_3_80.counts 2 0.0000 0.9474 0.00 1.00 0.00 0.00
#> ola_mES_a2i_3_9.counts 4 0.0707 0.6209 0.00 0.02 0.00 0.98
#> ola_mES_a2i_3_91.counts 4 0.0707 0.6209 0.00 0.02 0.00 0.98
#> ola_mES_lif_1_53.counts 4 0.4522 0.7549 0.00 0.32 0.00 0.68
#> ola_mES_lif_1_56.counts 2 0.0000 0.9474 0.00 1.00 0.00 0.00
#> ola_mES_lif_1_57.counts 2 0.4522 0.3495 0.00 0.68 0.00 0.32
#> ola_mES_lif_2_12.counts 4 0.0707 0.6209 0.00 0.02 0.00 0.98
#> ola_mES_lif_2_48.counts 4 0.4522 0.7549 0.00 0.32 0.00 0.68
#> ola_mES_lif_2_69.counts 4 0.4522 0.7549 0.00 0.32 0.00 0.68
#> ola_mES_lif_2_73.counts 4 0.4522 0.7549 0.00 0.32 0.00 0.68
#> ola_mES_lif_3_12.counts 2 0.4624 0.2797 0.00 0.66 0.00 0.34
#> ola_mES_lif_3_2.counts 2 0.0707 0.9338 0.00 0.98 0.00 0.02
#> ola_mES_lif_3_21.counts 2 0.0000 0.9474 0.00 1.00 0.00 0.00
#> ola_mES_lif_3_77.counts 2 0.0000 0.9474 0.00 1.00 0.00 0.00
#> ola_mES_lif_3_89.counts 2 0.0000 0.9474 0.00 1.00 0.00 0.00
#> ola_mES_lif_3_9.counts 2 0.0000 0.9474 0.00 1.00 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)
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 , Node0112 , Node0113-leaf , Node0121-leaf , Node0122-leaf , Node0131-leaf , Node0132-leaf , Node0133-leaf , Node0141 , Node0142-leaf , Node0143-leaf , Node0211 , Node0212-leaf , Node0221 , Node0222 , Node0223-leaf , Node0311-leaf , Node0312-leaf , Node0313-leaf , Node0321-leaf , Node0322-leaf , Node0331-leaf , Node0332-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 16101 rows and 104 columns.
#> Top rows (1584) 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 0.979 0.948 0.980 0.505 0.496 0.496
#> 3 3 0.730 0.828 0.920 0.287 0.801 0.622
#> 4 4 0.633 0.675 0.839 0.139 0.781 0.478
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
#> ola_mES_2i_3_16.counts 1 0.000 0.9767 1.00 0.00
#> ola_mES_2i_3_23.counts 1 0.000 0.9767 1.00 0.00
#> ola_mES_2i_3_28.counts 1 0.000 0.9767 1.00 0.00
#> ola_mES_2i_3_36.counts 1 0.000 0.9767 1.00 0.00
#> ola_mES_2i_3_4.counts 1 0.000 0.9767 1.00 0.00
#> ola_mES_2i_3_43.counts 1 0.000 0.9767 1.00 0.00
#> ola_mES_2i_3_84.counts 1 0.000 0.9767 1.00 0.00
#> ola_mES_2i_3_90.counts 1 0.000 0.9767 1.00 0.00
#> ola_mES_2i_4_1.counts 2 0.000 0.9810 0.00 1.00
#> ola_mES_2i_4_10.counts 2 0.000 0.9810 0.00 1.00
#> ola_mES_2i_4_11.counts 2 0.000 0.9810 0.00 1.00
#> ola_mES_2i_4_13.counts 1 0.000 0.9767 1.00 0.00
#> ola_mES_2i_4_17.counts 1 0.000 0.9767 1.00 0.00
#> ola_mES_2i_4_20.counts 1 0.000 0.9767 1.00 0.00
#> ola_mES_2i_4_21.counts 1 0.000 0.9767 1.00 0.00
#> ola_mES_2i_4_24.counts 1 0.000 0.9767 1.00 0.00
#> ola_mES_2i_4_25.counts 2 0.000 0.9810 0.00 1.00
#> ola_mES_2i_4_26.counts 2 0.000 0.9810 0.00 1.00
#> ola_mES_2i_4_29.counts 2 0.000 0.9810 0.00 1.00
#> ola_mES_2i_4_30.counts 1 0.141 0.9598 0.98 0.02
#> ola_mES_2i_4_31.counts 2 0.000 0.9810 0.00 1.00
#> ola_mES_2i_4_32.counts 1 0.999 0.0834 0.52 0.48
#> ola_mES_2i_4_33.counts 1 0.000 0.9767 1.00 0.00
#> ola_mES_2i_4_34.counts 1 0.000 0.9767 1.00 0.00
#> ola_mES_2i_4_35.counts 2 0.000 0.9810 0.00 1.00
#> ola_mES_2i_4_36.counts 1 0.000 0.9767 1.00 0.00
#> ola_mES_2i_4_39.counts 2 0.000 0.9810 0.00 1.00
#> ola_mES_2i_4_43.counts 1 0.000 0.9767 1.00 0.00
#> ola_mES_2i_4_44.counts 2 0.000 0.9810 0.00 1.00
#> ola_mES_2i_4_46.counts 1 0.000 0.9767 1.00 0.00
#> ola_mES_2i_4_50.counts 2 0.000 0.9810 0.00 1.00
#> ola_mES_2i_4_52.counts 2 0.000 0.9810 0.00 1.00
#> ola_mES_2i_4_56.counts 1 0.000 0.9767 1.00 0.00
#> ola_mES_2i_4_57.counts 1 0.981 0.2814 0.58 0.42
#> ola_mES_2i_4_58.counts 1 0.000 0.9767 1.00 0.00
#> ola_mES_2i_4_59.counts 1 0.327 0.9218 0.94 0.06
#> ola_mES_2i_4_6.counts 1 0.000 0.9767 1.00 0.00
#> ola_mES_2i_4_61.counts 1 0.000 0.9767 1.00 0.00
#> ola_mES_2i_4_64.counts 2 0.000 0.9810 0.00 1.00
#> ola_mES_2i_4_65.counts 1 0.000 0.9767 1.00 0.00
#> ola_mES_2i_4_66.counts 1 0.000 0.9767 1.00 0.00
#> ola_mES_2i_4_69.counts 1 0.000 0.9767 1.00 0.00
#> ola_mES_2i_4_71.counts 1 0.402 0.9002 0.92 0.08
#> ola_mES_2i_4_73.counts 1 0.000 0.9767 1.00 0.00
#> ola_mES_2i_4_77.counts 1 0.000 0.9767 1.00 0.00
#> ola_mES_2i_4_80.counts 1 0.000 0.9767 1.00 0.00
#> ola_mES_2i_4_81.counts 1 0.000 0.9767 1.00 0.00
#> ola_mES_2i_4_90.counts 1 0.141 0.9598 0.98 0.02
#> ola_mES_2i_4_91.counts 2 0.000 0.9810 0.00 1.00
#> ola_mES_2i_5_14.counts 2 0.000 0.9810 0.00 1.00
#> ola_mES_2i_5_15.counts 2 0.000 0.9810 0.00 1.00
#> ola_mES_2i_5_16.counts 2 0.995 0.1378 0.46 0.54
#> ola_mES_2i_5_17.counts 2 0.000 0.9810 0.00 1.00
#> ola_mES_2i_5_19.counts 1 0.000 0.9767 1.00 0.00
#> ola_mES_2i_5_20.counts 2 0.000 0.9810 0.00 1.00
#> ola_mES_2i_5_21.counts 1 0.000 0.9767 1.00 0.00
#> ola_mES_2i_5_24.counts 2 0.000 0.9810 0.00 1.00
#> ola_mES_2i_5_25.counts 1 0.000 0.9767 1.00 0.00
#> ola_mES_2i_5_26.counts 2 0.000 0.9810 0.00 1.00
#> ola_mES_2i_5_27.counts 2 0.000 0.9810 0.00 1.00
#> ola_mES_2i_5_30.counts 2 0.000 0.9810 0.00 1.00
#> ola_mES_2i_5_31.counts 2 0.000 0.9810 0.00 1.00
#> ola_mES_2i_5_32.counts 2 0.000 0.9810 0.00 1.00
#> ola_mES_2i_5_34.counts 2 0.000 0.9810 0.00 1.00
#> ola_mES_2i_5_35.counts 1 0.000 0.9767 1.00 0.00
#> ola_mES_2i_5_36.counts 2 0.000 0.9810 0.00 1.00
#> ola_mES_2i_5_38.counts 2 0.000 0.9810 0.00 1.00
#> ola_mES_2i_5_39.counts 2 0.000 0.9810 0.00 1.00
#> ola_mES_2i_5_40.counts 1 0.000 0.9767 1.00 0.00
#> ola_mES_2i_5_42.counts 2 0.000 0.9810 0.00 1.00
#> ola_mES_2i_5_44.counts 2 0.000 0.9810 0.00 1.00
#> ola_mES_2i_5_45.counts 1 0.000 0.9767 1.00 0.00
#> ola_mES_2i_5_46.counts 1 0.000 0.9767 1.00 0.00
#> ola_mES_2i_5_47.counts 1 0.000 0.9767 1.00 0.00
#> ola_mES_2i_5_48.counts 1 0.000 0.9767 1.00 0.00
#> ola_mES_2i_5_49.counts 1 0.000 0.9767 1.00 0.00
#> ola_mES_2i_5_51.counts 2 0.000 0.9810 0.00 1.00
#> ola_mES_2i_5_53.counts 2 0.000 0.9810 0.00 1.00
#> ola_mES_2i_5_54.counts 1 0.000 0.9767 1.00 0.00
#> ola_mES_2i_5_55.counts 2 0.000 0.9810 0.00 1.00
#> ola_mES_2i_5_56.counts 2 0.000 0.9810 0.00 1.00
#> ola_mES_2i_5_57.counts 2 0.000 0.9810 0.00 1.00
#> ola_mES_2i_5_58.counts 2 0.000 0.9810 0.00 1.00
#> ola_mES_2i_5_59.counts 2 0.141 0.9629 0.02 0.98
#> ola_mES_2i_5_61.counts 2 0.000 0.9810 0.00 1.00
#> ola_mES_2i_5_63.counts 1 0.000 0.9767 1.00 0.00
#> ola_mES_2i_5_67.counts 2 0.000 0.9810 0.00 1.00
#> ola_mES_2i_5_68.counts 2 0.000 0.9810 0.00 1.00
#> ola_mES_2i_5_69.counts 2 0.000 0.9810 0.00 1.00
#> ola_mES_2i_5_7.counts 2 0.000 0.9810 0.00 1.00
#> ola_mES_2i_5_70.counts 1 0.000 0.9767 1.00 0.00
#> ola_mES_2i_5_72.counts 2 0.722 0.7427 0.20 0.80
#> ola_mES_2i_5_74.counts 1 0.000 0.9767 1.00 0.00
#> ola_mES_2i_5_78.counts 2 0.000 0.9810 0.00 1.00
#> ola_mES_2i_5_8.counts 1 0.000 0.9767 1.00 0.00
#> ola_mES_2i_5_82.counts 2 0.469 0.8805 0.10 0.90
#> ola_mES_2i_5_90.counts 1 0.529 0.8507 0.88 0.12
#> ola_mES_2i_5_92.counts 2 0.529 0.8570 0.12 0.88
#> ola_mES_2i_5_94.counts 2 0.000 0.9810 0.00 1.00
#> ola_mES_2i_5_95.counts 2 0.000 0.9810 0.00 1.00
#> ola_mES_2i_5_96.counts 2 0.000 0.9810 0.00 1.00
#> ola_mES_a2i_3_10.counts 1 0.000 0.9767 1.00 0.00
#> ola_mES_a2i_3_3.counts 1 0.000 0.9767 1.00 0.00
#> ola_mES_a2i_3_76.counts 1 0.000 0.9767 1.00 0.00
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> ola_mES_2i_3_16.counts 3 0.0000 0.895 0.00 0.00 1.00
#> ola_mES_2i_3_23.counts 3 0.0000 0.895 0.00 0.00 1.00
#> ola_mES_2i_3_28.counts 3 0.0000 0.895 0.00 0.00 1.00
#> ola_mES_2i_3_36.counts 3 0.0000 0.895 0.00 0.00 1.00
#> ola_mES_2i_3_4.counts 3 0.0000 0.895 0.00 0.00 1.00
#> ola_mES_2i_3_43.counts 3 0.0000 0.895 0.00 0.00 1.00
#> ola_mES_2i_3_84.counts 3 0.0000 0.895 0.00 0.00 1.00
#> ola_mES_2i_3_90.counts 3 0.0000 0.895 0.00 0.00 1.00
#> ola_mES_2i_4_1.counts 2 0.0000 0.947 0.00 1.00 0.00
#> ola_mES_2i_4_10.counts 2 0.0000 0.947 0.00 1.00 0.00
#> ola_mES_2i_4_11.counts 2 0.1529 0.928 0.04 0.96 0.00
#> ola_mES_2i_4_13.counts 1 0.0000 0.870 1.00 0.00 0.00
#> ola_mES_2i_4_17.counts 1 0.4796 0.762 0.78 0.00 0.22
#> ola_mES_2i_4_20.counts 1 0.1529 0.871 0.96 0.00 0.04
#> ola_mES_2i_4_21.counts 1 0.0000 0.870 1.00 0.00 0.00
#> ola_mES_2i_4_24.counts 3 0.6302 -0.188 0.48 0.00 0.52
#> ola_mES_2i_4_25.counts 2 0.3340 0.879 0.12 0.88 0.00
#> ola_mES_2i_4_26.counts 2 0.2959 0.894 0.10 0.90 0.00
#> ola_mES_2i_4_29.counts 2 0.2537 0.906 0.08 0.92 0.00
#> ola_mES_2i_4_30.counts 1 0.0000 0.870 1.00 0.00 0.00
#> ola_mES_2i_4_31.counts 2 0.2959 0.894 0.10 0.90 0.00
#> ola_mES_2i_4_32.counts 1 0.0000 0.870 1.00 0.00 0.00
#> ola_mES_2i_4_33.counts 1 0.0000 0.870 1.00 0.00 0.00
#> ola_mES_2i_4_34.counts 1 0.0000 0.870 1.00 0.00 0.00
#> ola_mES_2i_4_35.counts 2 0.2066 0.918 0.06 0.94 0.00
#> ola_mES_2i_4_36.counts 1 0.3686 0.836 0.86 0.00 0.14
#> ola_mES_2i_4_39.counts 2 0.3340 0.879 0.12 0.88 0.00
#> ola_mES_2i_4_43.counts 1 0.0000 0.870 1.00 0.00 0.00
#> ola_mES_2i_4_44.counts 2 0.3340 0.879 0.12 0.88 0.00
#> ola_mES_2i_4_46.counts 1 0.0000 0.870 1.00 0.00 0.00
#> ola_mES_2i_4_50.counts 2 0.3340 0.879 0.12 0.88 0.00
#> ola_mES_2i_4_52.counts 2 0.3340 0.879 0.12 0.88 0.00
#> ola_mES_2i_4_56.counts 1 0.0000 0.870 1.00 0.00 0.00
#> ola_mES_2i_4_57.counts 1 0.0000 0.870 1.00 0.00 0.00
#> ola_mES_2i_4_58.counts 1 0.5948 0.565 0.64 0.00 0.36
#> ola_mES_2i_4_59.counts 1 0.3042 0.822 0.92 0.04 0.04
#> ola_mES_2i_4_6.counts 1 0.6126 0.481 0.60 0.00 0.40
#> ola_mES_2i_4_61.counts 1 0.0000 0.870 1.00 0.00 0.00
#> ola_mES_2i_4_64.counts 2 0.5416 0.830 0.10 0.82 0.08
#> ola_mES_2i_4_65.counts 1 0.2959 0.862 0.90 0.00 0.10
#> ola_mES_2i_4_66.counts 1 0.0000 0.870 1.00 0.00 0.00
#> ola_mES_2i_4_69.counts 1 0.2959 0.862 0.90 0.00 0.10
#> ola_mES_2i_4_71.counts 1 0.8733 0.369 0.58 0.16 0.26
#> ola_mES_2i_4_73.counts 1 0.2537 0.866 0.92 0.00 0.08
#> ola_mES_2i_4_77.counts 3 0.0000 0.895 0.00 0.00 1.00
#> ola_mES_2i_4_80.counts 1 0.2537 0.866 0.92 0.00 0.08
#> ola_mES_2i_4_81.counts 1 0.0892 0.872 0.98 0.00 0.02
#> ola_mES_2i_4_90.counts 1 0.6232 0.598 0.74 0.04 0.22
#> ola_mES_2i_4_91.counts 2 0.2959 0.894 0.10 0.90 0.00
#> ola_mES_2i_5_14.counts 2 0.0000 0.947 0.00 1.00 0.00
#> ola_mES_2i_5_15.counts 2 0.0000 0.947 0.00 1.00 0.00
#> ola_mES_2i_5_16.counts 3 0.2066 0.841 0.00 0.06 0.94
#> ola_mES_2i_5_17.counts 2 0.0000 0.947 0.00 1.00 0.00
#> ola_mES_2i_5_19.counts 1 0.6045 0.522 0.62 0.00 0.38
#> ola_mES_2i_5_20.counts 2 0.0000 0.947 0.00 1.00 0.00
#> ola_mES_2i_5_21.counts 3 0.0892 0.876 0.02 0.00 0.98
#> ola_mES_2i_5_24.counts 2 0.0000 0.947 0.00 1.00 0.00
#> ola_mES_2i_5_25.counts 1 0.3340 0.849 0.88 0.00 0.12
#> ola_mES_2i_5_26.counts 2 0.0000 0.947 0.00 1.00 0.00
#> ola_mES_2i_5_27.counts 2 0.0000 0.947 0.00 1.00 0.00
#> ola_mES_2i_5_30.counts 2 0.0000 0.947 0.00 1.00 0.00
#> ola_mES_2i_5_31.counts 2 0.0000 0.947 0.00 1.00 0.00
#> ola_mES_2i_5_32.counts 2 0.0000 0.947 0.00 1.00 0.00
#> ola_mES_2i_5_34.counts 2 0.0000 0.947 0.00 1.00 0.00
#> ola_mES_2i_5_35.counts 1 0.2959 0.862 0.90 0.00 0.10
#> ola_mES_2i_5_36.counts 2 0.0000 0.947 0.00 1.00 0.00
#> ola_mES_2i_5_38.counts 2 0.0000 0.947 0.00 1.00 0.00
#> ola_mES_2i_5_39.counts 2 0.0000 0.947 0.00 1.00 0.00
#> ola_mES_2i_5_40.counts 1 0.6280 0.326 0.54 0.00 0.46
#> ola_mES_2i_5_42.counts 2 0.0000 0.947 0.00 1.00 0.00
#> ola_mES_2i_5_44.counts 2 0.0000 0.947 0.00 1.00 0.00
#> ola_mES_2i_5_45.counts 1 0.2959 0.862 0.90 0.00 0.10
#> ola_mES_2i_5_46.counts 1 0.2959 0.862 0.90 0.00 0.10
#> ola_mES_2i_5_47.counts 1 0.2959 0.862 0.90 0.00 0.10
#> ola_mES_2i_5_48.counts 1 0.0892 0.871 0.98 0.00 0.02
#> ola_mES_2i_5_49.counts 1 0.2959 0.862 0.90 0.00 0.10
#> ola_mES_2i_5_51.counts 2 0.0000 0.947 0.00 1.00 0.00
#> ola_mES_2i_5_53.counts 2 0.0000 0.947 0.00 1.00 0.00
#> ola_mES_2i_5_54.counts 1 0.2959 0.862 0.90 0.00 0.10
#> ola_mES_2i_5_55.counts 2 0.0000 0.947 0.00 1.00 0.00
#> ola_mES_2i_5_56.counts 2 0.0000 0.947 0.00 1.00 0.00
#> ola_mES_2i_5_57.counts 2 0.0000 0.947 0.00 1.00 0.00
#> ola_mES_2i_5_58.counts 2 0.0000 0.947 0.00 1.00 0.00
#> ola_mES_2i_5_59.counts 1 0.0000 0.870 1.00 0.00 0.00
#> ola_mES_2i_5_61.counts 1 0.4291 0.721 0.82 0.18 0.00
#> ola_mES_2i_5_63.counts 1 0.6045 0.524 0.62 0.00 0.38
#> ola_mES_2i_5_67.counts 2 0.6045 0.351 0.38 0.62 0.00
#> ola_mES_2i_5_68.counts 2 0.0000 0.947 0.00 1.00 0.00
#> ola_mES_2i_5_69.counts 2 0.6280 0.124 0.00 0.54 0.46
#> ola_mES_2i_5_7.counts 2 0.1529 0.918 0.00 0.96 0.04
#> ola_mES_2i_5_70.counts 3 0.6302 -0.182 0.48 0.00 0.52
#> ola_mES_2i_5_72.counts 1 0.0000 0.870 1.00 0.00 0.00
#> ola_mES_2i_5_74.counts 1 0.2959 0.862 0.90 0.00 0.10
#> ola_mES_2i_5_78.counts 2 0.0000 0.947 0.00 1.00 0.00
#> ola_mES_2i_5_8.counts 3 0.0000 0.895 0.00 0.00 1.00
#> ola_mES_2i_5_82.counts 3 0.4796 0.672 0.00 0.22 0.78
#> ola_mES_2i_5_90.counts 3 0.0000 0.895 0.00 0.00 1.00
#> ola_mES_2i_5_92.counts 3 0.5835 0.443 0.00 0.34 0.66
#> ola_mES_2i_5_94.counts 2 0.0000 0.947 0.00 1.00 0.00
#> ola_mES_2i_5_95.counts 2 0.0000 0.947 0.00 1.00 0.00
#> ola_mES_2i_5_96.counts 2 0.0000 0.947 0.00 1.00 0.00
#> ola_mES_a2i_3_10.counts 3 0.0000 0.895 0.00 0.00 1.00
#> ola_mES_a2i_3_3.counts 3 0.0000 0.895 0.00 0.00 1.00
#> ola_mES_a2i_3_76.counts 3 0.0000 0.895 0.00 0.00 1.00
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> ola_mES_2i_3_16.counts 3 0.0000 0.90573 0.00 0.00 1.00 0.00
#> ola_mES_2i_3_23.counts 3 0.0000 0.90573 0.00 0.00 1.00 0.00
#> ola_mES_2i_3_28.counts 3 0.0000 0.90573 0.00 0.00 1.00 0.00
#> ola_mES_2i_3_36.counts 3 0.0000 0.90573 0.00 0.00 1.00 0.00
#> ola_mES_2i_3_4.counts 3 0.0000 0.90573 0.00 0.00 1.00 0.00
#> ola_mES_2i_3_43.counts 3 0.0000 0.90573 0.00 0.00 1.00 0.00
#> ola_mES_2i_3_84.counts 3 0.0000 0.90573 0.00 0.00 1.00 0.00
#> ola_mES_2i_3_90.counts 3 0.0000 0.90573 0.00 0.00 1.00 0.00
#> ola_mES_2i_4_1.counts 2 0.4406 0.52577 0.00 0.70 0.00 0.30
#> ola_mES_2i_4_10.counts 2 0.4134 0.60292 0.00 0.74 0.00 0.26
#> ola_mES_2i_4_11.counts 4 0.5535 0.19292 0.02 0.42 0.00 0.56
#> ola_mES_2i_4_13.counts 4 0.4994 -0.00445 0.48 0.00 0.00 0.52
#> ola_mES_2i_4_17.counts 1 0.5256 0.63351 0.70 0.00 0.26 0.04
#> ola_mES_2i_4_20.counts 1 0.5636 0.55961 0.68 0.00 0.06 0.26
#> ola_mES_2i_4_21.counts 4 0.4790 0.25824 0.38 0.00 0.00 0.62
#> ola_mES_2i_4_24.counts 1 0.6323 0.37123 0.50 0.00 0.44 0.06
#> ola_mES_2i_4_25.counts 4 0.2921 0.71366 0.00 0.14 0.00 0.86
#> ola_mES_2i_4_26.counts 4 0.2647 0.71230 0.00 0.12 0.00 0.88
#> ola_mES_2i_4_29.counts 4 0.4134 0.61665 0.00 0.26 0.00 0.74
#> ola_mES_2i_4_30.counts 4 0.1211 0.67773 0.04 0.00 0.00 0.96
#> ola_mES_2i_4_31.counts 4 0.3400 0.69560 0.00 0.18 0.00 0.82
#> ola_mES_2i_4_32.counts 4 0.1211 0.67773 0.04 0.00 0.00 0.96
#> ola_mES_2i_4_33.counts 4 0.4977 0.06481 0.46 0.00 0.00 0.54
#> ola_mES_2i_4_34.counts 1 0.4907 0.29254 0.58 0.00 0.00 0.42
#> ola_mES_2i_4_35.counts 4 0.4277 0.58733 0.00 0.28 0.00 0.72
#> ola_mES_2i_4_36.counts 1 0.5594 0.67052 0.72 0.00 0.18 0.10
#> ola_mES_2i_4_39.counts 4 0.2921 0.71366 0.00 0.14 0.00 0.86
#> ola_mES_2i_4_43.counts 4 0.5000 -0.07602 0.50 0.00 0.00 0.50
#> ola_mES_2i_4_44.counts 4 0.2921 0.71366 0.00 0.14 0.00 0.86
#> ola_mES_2i_4_46.counts 4 0.4948 0.12521 0.44 0.00 0.00 0.56
#> ola_mES_2i_4_50.counts 4 0.2921 0.71366 0.00 0.14 0.00 0.86
#> ola_mES_2i_4_52.counts 4 0.2921 0.71366 0.00 0.14 0.00 0.86
#> ola_mES_2i_4_56.counts 4 0.4907 0.17969 0.42 0.00 0.00 0.58
#> ola_mES_2i_4_57.counts 4 0.1211 0.67773 0.04 0.00 0.00 0.96
#> ola_mES_2i_4_58.counts 1 0.6262 0.45807 0.54 0.00 0.40 0.06
#> ola_mES_2i_4_59.counts 4 0.3611 0.64872 0.06 0.00 0.08 0.86
#> ola_mES_2i_4_6.counts 1 0.5987 0.37828 0.52 0.00 0.44 0.04
#> ola_mES_2i_4_61.counts 1 0.4855 0.33032 0.60 0.00 0.00 0.40
#> ola_mES_2i_4_64.counts 4 0.4949 0.65419 0.00 0.18 0.06 0.76
#> ola_mES_2i_4_65.counts 1 0.5383 0.64418 0.74 0.00 0.10 0.16
#> ola_mES_2i_4_66.counts 4 0.3975 0.50685 0.24 0.00 0.00 0.76
#> ola_mES_2i_4_69.counts 1 0.4894 0.67491 0.78 0.00 0.10 0.12
#> ola_mES_2i_4_71.counts 4 0.4211 0.64844 0.04 0.02 0.10 0.84
#> ola_mES_2i_4_73.counts 1 0.6933 0.42399 0.56 0.00 0.14 0.30
#> ola_mES_2i_4_77.counts 3 0.0000 0.90573 0.00 0.00 1.00 0.00
#> ola_mES_2i_4_80.counts 1 0.6449 0.56464 0.64 0.00 0.14 0.22
#> ola_mES_2i_4_81.counts 1 0.5915 0.29635 0.56 0.00 0.04 0.40
#> ola_mES_2i_4_90.counts 4 0.2830 0.66507 0.04 0.00 0.06 0.90
#> ola_mES_2i_4_91.counts 4 0.4134 0.61652 0.00 0.26 0.00 0.74
#> ola_mES_2i_5_14.counts 2 0.0000 0.93082 0.00 1.00 0.00 0.00
#> ola_mES_2i_5_15.counts 2 0.0000 0.93082 0.00 1.00 0.00 0.00
#> ola_mES_2i_5_16.counts 3 0.6208 0.65509 0.16 0.08 0.72 0.04
#> ola_mES_2i_5_17.counts 2 0.0000 0.93082 0.00 1.00 0.00 0.00
#> ola_mES_2i_5_19.counts 1 0.3606 0.65149 0.84 0.00 0.14 0.02
#> ola_mES_2i_5_20.counts 2 0.0000 0.93082 0.00 1.00 0.00 0.00
#> ola_mES_2i_5_21.counts 1 0.5173 0.37950 0.66 0.00 0.32 0.02
#> ola_mES_2i_5_24.counts 2 0.0000 0.93082 0.00 1.00 0.00 0.00
#> ola_mES_2i_5_25.counts 1 0.0000 0.71428 1.00 0.00 0.00 0.00
#> ola_mES_2i_5_26.counts 2 0.0707 0.92219 0.02 0.98 0.00 0.00
#> ola_mES_2i_5_27.counts 2 0.0000 0.93082 0.00 1.00 0.00 0.00
#> ola_mES_2i_5_30.counts 2 0.1411 0.91153 0.02 0.96 0.00 0.02
#> ola_mES_2i_5_31.counts 2 0.0707 0.91843 0.00 0.98 0.00 0.02
#> ola_mES_2i_5_32.counts 2 0.0000 0.93082 0.00 1.00 0.00 0.00
#> ola_mES_2i_5_34.counts 2 0.0000 0.93082 0.00 1.00 0.00 0.00
#> ola_mES_2i_5_35.counts 1 0.1637 0.71675 0.94 0.00 0.00 0.06
#> ola_mES_2i_5_36.counts 2 0.0000 0.93082 0.00 1.00 0.00 0.00
#> ola_mES_2i_5_38.counts 2 0.0000 0.93082 0.00 1.00 0.00 0.00
#> ola_mES_2i_5_39.counts 2 0.0707 0.92219 0.02 0.98 0.00 0.00
#> ola_mES_2i_5_40.counts 1 0.4472 0.59290 0.76 0.00 0.22 0.02
#> ola_mES_2i_5_42.counts 2 0.0000 0.93082 0.00 1.00 0.00 0.00
#> ola_mES_2i_5_44.counts 2 0.0000 0.93082 0.00 1.00 0.00 0.00
#> ola_mES_2i_5_45.counts 1 0.2335 0.71522 0.92 0.00 0.02 0.06
#> ola_mES_2i_5_46.counts 1 0.0707 0.71702 0.98 0.00 0.00 0.02
#> ola_mES_2i_5_47.counts 1 0.2345 0.70393 0.90 0.00 0.00 0.10
#> ola_mES_2i_5_48.counts 1 0.1211 0.71336 0.96 0.00 0.00 0.04
#> ola_mES_2i_5_49.counts 1 0.1637 0.71675 0.94 0.00 0.00 0.06
#> ola_mES_2i_5_51.counts 2 0.0000 0.93082 0.00 1.00 0.00 0.00
#> ola_mES_2i_5_53.counts 2 0.0000 0.93082 0.00 1.00 0.00 0.00
#> ola_mES_2i_5_54.counts 1 0.1211 0.71760 0.96 0.00 0.00 0.04
#> ola_mES_2i_5_55.counts 2 0.0000 0.93082 0.00 1.00 0.00 0.00
#> ola_mES_2i_5_56.counts 2 0.2830 0.86546 0.04 0.90 0.00 0.06
#> ola_mES_2i_5_57.counts 2 0.0000 0.93082 0.00 1.00 0.00 0.00
#> ola_mES_2i_5_58.counts 2 0.0707 0.92255 0.00 0.98 0.00 0.02
#> ola_mES_2i_5_59.counts 1 0.5271 0.38107 0.64 0.02 0.00 0.34
#> ola_mES_2i_5_61.counts 1 0.1637 0.69710 0.94 0.00 0.00 0.06
#> ola_mES_2i_5_63.counts 1 0.3606 0.65030 0.84 0.00 0.14 0.02
#> ola_mES_2i_5_67.counts 1 0.5428 0.22355 0.60 0.38 0.00 0.02
#> ola_mES_2i_5_68.counts 2 0.2411 0.88555 0.04 0.92 0.00 0.04
#> ola_mES_2i_5_69.counts 2 0.4472 0.68353 0.00 0.76 0.22 0.02
#> ola_mES_2i_5_7.counts 2 0.2411 0.88519 0.04 0.92 0.00 0.04
#> ola_mES_2i_5_70.counts 1 0.4284 0.58851 0.78 0.00 0.20 0.02
#> ola_mES_2i_5_72.counts 1 0.0707 0.70781 0.98 0.00 0.00 0.02
#> ola_mES_2i_5_74.counts 1 0.1637 0.71675 0.94 0.00 0.00 0.06
#> ola_mES_2i_5_78.counts 2 0.1913 0.89847 0.02 0.94 0.00 0.04
#> ola_mES_2i_5_8.counts 1 0.5535 0.11521 0.56 0.00 0.42 0.02
#> ola_mES_2i_5_82.counts 2 0.8277 0.23549 0.20 0.50 0.26 0.04
#> ola_mES_2i_5_90.counts 3 0.4790 0.38399 0.38 0.00 0.62 0.00
#> ola_mES_2i_5_92.counts 3 0.7539 0.21526 0.08 0.38 0.50 0.04
#> ola_mES_2i_5_94.counts 2 0.0707 0.92255 0.00 0.98 0.00 0.02
#> ola_mES_2i_5_95.counts 2 0.0000 0.93082 0.00 1.00 0.00 0.00
#> ola_mES_2i_5_96.counts 2 0.4079 0.74668 0.18 0.80 0.00 0.02
#> ola_mES_a2i_3_10.counts 3 0.0000 0.90573 0.00 0.00 1.00 0.00
#> ola_mES_a2i_3_3.counts 3 0.0000 0.90573 0.00 0.00 1.00 0.00
#> ola_mES_a2i_3_76.counts 3 0.0000 0.90573 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)
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: Node01111-leaf , Node01112-leaf , Node01121-leaf , Node01122-leaf , Node01411-leaf , Node01412-leaf , Node01413-leaf , Node02111-leaf , Node02112-leaf , Node02211-leaf , Node02212-leaf , Node02221-leaf , Node02222-leaf , Node02223-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 15754 rows and 54 columns.
#> Top rows (1279) 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 0.998 0.999 0.410 0.591 0.591
#> 3 3 0.725 0.819 0.920 0.591 0.684 0.494
#> 4 4 0.540 0.535 0.721 0.136 0.884 0.687
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
#> ola_mES_2i_3_16.counts 2 0.000 1.000 0.00 1.00
#> ola_mES_2i_3_23.counts 2 0.000 1.000 0.00 1.00
#> ola_mES_2i_3_28.counts 2 0.000 1.000 0.00 1.00
#> ola_mES_2i_3_36.counts 2 0.000 1.000 0.00 1.00
#> ola_mES_2i_3_4.counts 2 0.000 1.000 0.00 1.00
#> ola_mES_2i_3_43.counts 2 0.000 1.000 0.00 1.00
#> ola_mES_2i_3_84.counts 2 0.000 1.000 0.00 1.00
#> ola_mES_2i_3_90.counts 2 0.000 1.000 0.00 1.00
#> ola_mES_2i_4_13.counts 1 0.000 0.998 1.00 0.00
#> ola_mES_2i_4_17.counts 1 0.000 0.998 1.00 0.00
#> ola_mES_2i_4_20.counts 1 0.000 0.998 1.00 0.00
#> ola_mES_2i_4_21.counts 1 0.000 0.998 1.00 0.00
#> ola_mES_2i_4_24.counts 2 0.000 1.000 0.00 1.00
#> ola_mES_2i_4_30.counts 1 0.000 0.998 1.00 0.00
#> ola_mES_2i_4_32.counts 1 0.000 0.998 1.00 0.00
#> ola_mES_2i_4_33.counts 1 0.000 0.998 1.00 0.00
#> ola_mES_2i_4_34.counts 1 0.000 0.998 1.00 0.00
#> ola_mES_2i_4_36.counts 1 0.000 0.998 1.00 0.00
#> ola_mES_2i_4_43.counts 1 0.000 0.998 1.00 0.00
#> ola_mES_2i_4_46.counts 1 0.000 0.998 1.00 0.00
#> ola_mES_2i_4_56.counts 1 0.000 0.998 1.00 0.00
#> ola_mES_2i_4_57.counts 1 0.000 0.998 1.00 0.00
#> ola_mES_2i_4_58.counts 1 0.327 0.936 0.94 0.06
#> ola_mES_2i_4_59.counts 1 0.000 0.998 1.00 0.00
#> ola_mES_2i_4_6.counts 1 0.000 0.998 1.00 0.00
#> ola_mES_2i_4_61.counts 1 0.000 0.998 1.00 0.00
#> ola_mES_2i_4_65.counts 1 0.000 0.998 1.00 0.00
#> ola_mES_2i_4_66.counts 1 0.000 0.998 1.00 0.00
#> ola_mES_2i_4_69.counts 1 0.000 0.998 1.00 0.00
#> ola_mES_2i_4_71.counts 1 0.000 0.998 1.00 0.00
#> ola_mES_2i_4_73.counts 1 0.000 0.998 1.00 0.00
#> ola_mES_2i_4_77.counts 2 0.000 1.000 0.00 1.00
#> ola_mES_2i_4_80.counts 1 0.000 0.998 1.00 0.00
#> ola_mES_2i_4_81.counts 1 0.000 0.998 1.00 0.00
#> ola_mES_2i_4_90.counts 1 0.000 0.998 1.00 0.00
#> ola_mES_2i_5_19.counts 1 0.000 0.998 1.00 0.00
#> ola_mES_2i_5_21.counts 1 0.000 0.998 1.00 0.00
#> ola_mES_2i_5_25.counts 1 0.000 0.998 1.00 0.00
#> ola_mES_2i_5_35.counts 1 0.000 0.998 1.00 0.00
#> ola_mES_2i_5_40.counts 1 0.000 0.998 1.00 0.00
#> ola_mES_2i_5_45.counts 1 0.000 0.998 1.00 0.00
#> ola_mES_2i_5_46.counts 1 0.000 0.998 1.00 0.00
#> ola_mES_2i_5_47.counts 1 0.000 0.998 1.00 0.00
#> ola_mES_2i_5_48.counts 1 0.000 0.998 1.00 0.00
#> ola_mES_2i_5_49.counts 1 0.000 0.998 1.00 0.00
#> ola_mES_2i_5_54.counts 1 0.000 0.998 1.00 0.00
#> ola_mES_2i_5_63.counts 1 0.000 0.998 1.00 0.00
#> ola_mES_2i_5_70.counts 1 0.000 0.998 1.00 0.00
#> ola_mES_2i_5_74.counts 1 0.000 0.998 1.00 0.00
#> ola_mES_2i_5_8.counts 2 0.000 1.000 0.00 1.00
#> ola_mES_2i_5_90.counts 2 0.000 1.000 0.00 1.00
#> ola_mES_a2i_3_10.counts 2 0.000 1.000 0.00 1.00
#> ola_mES_a2i_3_3.counts 2 0.000 1.000 0.00 1.00
#> ola_mES_a2i_3_76.counts 2 0.000 1.000 0.00 1.00
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> ola_mES_2i_3_16.counts 2 0.0000 0.986 0.00 1.00 0.00
#> ola_mES_2i_3_23.counts 2 0.0000 0.986 0.00 1.00 0.00
#> ola_mES_2i_3_28.counts 2 0.0000 0.986 0.00 1.00 0.00
#> ola_mES_2i_3_36.counts 2 0.0000 0.986 0.00 1.00 0.00
#> ola_mES_2i_3_4.counts 2 0.0000 0.986 0.00 1.00 0.00
#> ola_mES_2i_3_43.counts 2 0.0000 0.986 0.00 1.00 0.00
#> ola_mES_2i_3_84.counts 2 0.0000 0.986 0.00 1.00 0.00
#> ola_mES_2i_3_90.counts 2 0.0000 0.986 0.00 1.00 0.00
#> ola_mES_2i_4_13.counts 1 0.0000 0.932 1.00 0.00 0.00
#> ola_mES_2i_4_17.counts 3 0.1781 0.805 0.02 0.02 0.96
#> ola_mES_2i_4_20.counts 1 0.0000 0.932 1.00 0.00 0.00
#> ola_mES_2i_4_21.counts 1 0.0000 0.932 1.00 0.00 0.00
#> ola_mES_2i_4_24.counts 3 0.4796 0.625 0.00 0.22 0.78
#> ola_mES_2i_4_30.counts 1 0.0000 0.932 1.00 0.00 0.00
#> ola_mES_2i_4_32.counts 1 0.0000 0.932 1.00 0.00 0.00
#> ola_mES_2i_4_33.counts 1 0.0000 0.932 1.00 0.00 0.00
#> ola_mES_2i_4_34.counts 1 0.2066 0.890 0.94 0.00 0.06
#> ola_mES_2i_4_36.counts 3 0.1529 0.806 0.04 0.00 0.96
#> ola_mES_2i_4_43.counts 1 0.0000 0.932 1.00 0.00 0.00
#> ola_mES_2i_4_46.counts 1 0.0000 0.932 1.00 0.00 0.00
#> ola_mES_2i_4_56.counts 1 0.0000 0.932 1.00 0.00 0.00
#> ola_mES_2i_4_57.counts 1 0.0000 0.932 1.00 0.00 0.00
#> ola_mES_2i_4_58.counts 3 0.0000 0.811 0.00 0.00 1.00
#> ola_mES_2i_4_59.counts 1 0.0000 0.932 1.00 0.00 0.00
#> ola_mES_2i_4_6.counts 3 0.1781 0.805 0.02 0.02 0.96
#> ola_mES_2i_4_61.counts 1 0.0000 0.932 1.00 0.00 0.00
#> ola_mES_2i_4_65.counts 1 0.2537 0.872 0.92 0.00 0.08
#> ola_mES_2i_4_66.counts 1 0.0000 0.932 1.00 0.00 0.00
#> ola_mES_2i_4_69.counts 1 0.5016 0.658 0.76 0.00 0.24
#> ola_mES_2i_4_71.counts 1 0.5560 0.537 0.70 0.00 0.30
#> ola_mES_2i_4_73.counts 1 0.0000 0.932 1.00 0.00 0.00
#> ola_mES_2i_4_77.counts 2 0.3686 0.830 0.00 0.86 0.14
#> ola_mES_2i_4_80.counts 1 0.0892 0.917 0.98 0.00 0.02
#> ola_mES_2i_4_81.counts 1 0.0000 0.932 1.00 0.00 0.00
#> ola_mES_2i_4_90.counts 1 0.0000 0.932 1.00 0.00 0.00
#> ola_mES_2i_5_19.counts 3 0.0000 0.811 0.00 0.00 1.00
#> ola_mES_2i_5_21.counts 3 0.0000 0.811 0.00 0.00 1.00
#> ola_mES_2i_5_25.counts 3 0.0000 0.811 0.00 0.00 1.00
#> ola_mES_2i_5_35.counts 3 0.6045 0.453 0.38 0.00 0.62
#> ola_mES_2i_5_40.counts 3 0.0000 0.811 0.00 0.00 1.00
#> ola_mES_2i_5_45.counts 1 0.5016 0.645 0.76 0.00 0.24
#> ola_mES_2i_5_46.counts 3 0.2959 0.783 0.10 0.00 0.90
#> ola_mES_2i_5_47.counts 1 0.5835 0.422 0.66 0.00 0.34
#> ola_mES_2i_5_48.counts 3 0.6280 0.232 0.46 0.00 0.54
#> ola_mES_2i_5_49.counts 3 0.6126 0.414 0.40 0.00 0.60
#> ola_mES_2i_5_54.counts 3 0.5835 0.525 0.34 0.00 0.66
#> ola_mES_2i_5_63.counts 3 0.1529 0.810 0.04 0.00 0.96
#> ola_mES_2i_5_70.counts 3 0.0000 0.811 0.00 0.00 1.00
#> ola_mES_2i_5_74.counts 3 0.5706 0.560 0.32 0.00 0.68
#> ola_mES_2i_5_8.counts 3 0.3340 0.737 0.00 0.12 0.88
#> ola_mES_2i_5_90.counts 3 0.6126 0.250 0.00 0.40 0.60
#> ola_mES_a2i_3_10.counts 2 0.0000 0.986 0.00 1.00 0.00
#> ola_mES_a2i_3_3.counts 2 0.0000 0.986 0.00 1.00 0.00
#> ola_mES_a2i_3_76.counts 2 0.0000 0.986 0.00 1.00 0.00
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> ola_mES_2i_3_16.counts 2 0.0000 0.9647 0.00 1.00 0.00 0.00
#> ola_mES_2i_3_23.counts 2 0.0000 0.9647 0.00 1.00 0.00 0.00
#> ola_mES_2i_3_28.counts 2 0.0000 0.9647 0.00 1.00 0.00 0.00
#> ola_mES_2i_3_36.counts 2 0.0000 0.9647 0.00 1.00 0.00 0.00
#> ola_mES_2i_3_4.counts 2 0.0000 0.9647 0.00 1.00 0.00 0.00
#> ola_mES_2i_3_43.counts 2 0.0000 0.9647 0.00 1.00 0.00 0.00
#> ola_mES_2i_3_84.counts 2 0.0000 0.9647 0.00 1.00 0.00 0.00
#> ola_mES_2i_3_90.counts 2 0.0000 0.9647 0.00 1.00 0.00 0.00
#> ola_mES_2i_4_13.counts 1 0.1913 0.7462 0.94 0.00 0.04 0.02
#> ola_mES_2i_4_17.counts 3 0.4855 0.0487 0.00 0.00 0.60 0.40
#> ola_mES_2i_4_20.counts 1 0.2335 0.7440 0.92 0.00 0.02 0.06
#> ola_mES_2i_4_21.counts 1 0.3172 0.7233 0.84 0.00 0.00 0.16
#> ola_mES_2i_4_24.counts 4 0.6617 0.2715 0.00 0.12 0.28 0.60
#> ola_mES_2i_4_30.counts 1 0.3400 0.7142 0.82 0.00 0.00 0.18
#> ola_mES_2i_4_32.counts 1 0.1637 0.7463 0.94 0.00 0.00 0.06
#> ola_mES_2i_4_33.counts 1 0.1637 0.7367 0.94 0.00 0.06 0.00
#> ola_mES_2i_4_34.counts 1 0.6320 0.5187 0.66 0.00 0.16 0.18
#> ola_mES_2i_4_36.counts 3 0.7414 0.0561 0.18 0.00 0.48 0.34
#> ola_mES_2i_4_43.counts 1 0.2830 0.7490 0.90 0.00 0.04 0.06
#> ola_mES_2i_4_46.counts 1 0.3037 0.7497 0.88 0.00 0.02 0.10
#> ola_mES_2i_4_56.counts 1 0.0000 0.7489 1.00 0.00 0.00 0.00
#> ola_mES_2i_4_57.counts 1 0.0707 0.7482 0.98 0.00 0.00 0.02
#> ola_mES_2i_4_58.counts 4 0.5355 0.2872 0.02 0.00 0.36 0.62
#> ola_mES_2i_4_59.counts 1 0.4797 0.6475 0.72 0.00 0.02 0.26
#> ola_mES_2i_4_6.counts 4 0.6150 0.2834 0.06 0.00 0.36 0.58
#> ola_mES_2i_4_61.counts 1 0.3335 0.6934 0.86 0.00 0.12 0.02
#> ola_mES_2i_4_65.counts 4 0.7485 0.1260 0.38 0.00 0.18 0.44
#> ola_mES_2i_4_66.counts 1 0.2011 0.7391 0.92 0.00 0.00 0.08
#> ola_mES_2i_4_69.counts 1 0.7610 0.1168 0.46 0.00 0.32 0.22
#> ola_mES_2i_4_71.counts 4 0.6110 0.3419 0.24 0.00 0.10 0.66
#> ola_mES_2i_4_73.counts 1 0.6150 0.4633 0.58 0.00 0.06 0.36
#> ola_mES_2i_4_77.counts 2 0.6248 0.4353 0.00 0.64 0.10 0.26
#> ola_mES_2i_4_80.counts 1 0.5428 0.4920 0.60 0.00 0.02 0.38
#> ola_mES_2i_4_81.counts 1 0.2921 0.7203 0.86 0.00 0.00 0.14
#> ola_mES_2i_4_90.counts 1 0.5173 0.5744 0.66 0.00 0.02 0.32
#> ola_mES_2i_5_19.counts 3 0.3400 0.4249 0.00 0.00 0.82 0.18
#> ola_mES_2i_5_21.counts 3 0.4790 0.1234 0.00 0.00 0.62 0.38
#> ola_mES_2i_5_25.counts 3 0.2011 0.4669 0.00 0.00 0.92 0.08
#> ola_mES_2i_5_35.counts 3 0.5883 0.3232 0.30 0.00 0.64 0.06
#> ola_mES_2i_5_40.counts 3 0.3400 0.3720 0.00 0.00 0.82 0.18
#> ola_mES_2i_5_45.counts 1 0.7485 0.0251 0.44 0.00 0.38 0.18
#> ola_mES_2i_5_46.counts 3 0.4292 0.4429 0.08 0.00 0.82 0.10
#> ola_mES_2i_5_47.counts 3 0.7040 0.0417 0.42 0.00 0.46 0.12
#> ola_mES_2i_5_48.counts 1 0.6336 -0.0378 0.48 0.00 0.46 0.06
#> ola_mES_2i_5_49.counts 3 0.6570 0.2870 0.32 0.00 0.58 0.10
#> ola_mES_2i_5_54.counts 3 0.4284 0.4246 0.20 0.00 0.78 0.02
#> ola_mES_2i_5_63.counts 3 0.3198 0.4671 0.04 0.00 0.88 0.08
#> ola_mES_2i_5_70.counts 3 0.2647 0.4620 0.00 0.00 0.88 0.12
#> ola_mES_2i_5_74.counts 3 0.5784 0.3686 0.10 0.00 0.70 0.20
#> ola_mES_2i_5_8.counts 3 0.5987 0.0222 0.00 0.04 0.52 0.44
#> ola_mES_2i_5_90.counts 3 0.7904 -0.1749 0.00 0.30 0.36 0.34
#> ola_mES_a2i_3_10.counts 2 0.0000 0.9647 0.00 1.00 0.00 0.00
#> ola_mES_a2i_3_3.counts 2 0.0000 0.9647 0.00 1.00 0.00 0.00
#> ola_mES_a2i_3_76.counts 2 0.0000 0.9647 0.00 1.00 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)
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 , Node0112 , Node0113-leaf , Node0121-leaf , Node0122-leaf , Node0131-leaf , Node0132-leaf , Node0133-leaf , Node0141 , Node0142-leaf , Node0143-leaf , Node0211 , Node0212-leaf , Node0221 , Node0222 , Node0223-leaf , Node0311-leaf , Node0312-leaf , Node0313-leaf , Node0321-leaf , Node0322-leaf , Node0331-leaf , Node0332-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 16017 rows and 93 columns.
#> Top rows (1602) 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 0.979 0.991 0.468 0.531 0.531
#> 3 3 1.000 0.972 0.989 0.289 0.820 0.676
#> 4 4 0.877 0.814 0.921 0.245 0.842 0.609
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
#> ola_mES_2i_3_10.counts 2 0.000 0.983 0.00 1.00
#> ola_mES_2i_3_12.counts 1 0.000 0.995 1.00 0.00
#> ola_mES_2i_3_13.counts 1 0.000 0.995 1.00 0.00
#> ola_mES_2i_3_15.counts 1 0.000 0.995 1.00 0.00
#> ola_mES_2i_3_19.counts 1 0.000 0.995 1.00 0.00
#> ola_mES_2i_3_2.counts 1 0.000 0.995 1.00 0.00
#> ola_mES_2i_3_20.counts 1 0.141 0.981 0.98 0.02
#> ola_mES_2i_3_21.counts 2 0.000 0.983 0.00 1.00
#> ola_mES_2i_3_26.counts 1 0.000 0.995 1.00 0.00
#> ola_mES_2i_3_30.counts 1 0.000 0.995 1.00 0.00
#> ola_mES_2i_3_33.counts 1 0.141 0.981 0.98 0.02
#> ola_mES_2i_3_37.counts 1 0.000 0.995 1.00 0.00
#> ola_mES_2i_3_38.counts 1 0.000 0.995 1.00 0.00
#> ola_mES_2i_3_39.counts 2 0.000 0.983 0.00 1.00
#> ola_mES_2i_3_41.counts 2 0.000 0.983 0.00 1.00
#> ola_mES_2i_3_42.counts 1 0.000 0.995 1.00 0.00
#> ola_mES_2i_3_45.counts 1 0.000 0.995 1.00 0.00
#> ola_mES_2i_3_46.counts 1 0.000 0.995 1.00 0.00
#> ola_mES_2i_3_47.counts 1 0.000 0.995 1.00 0.00
#> ola_mES_2i_3_5.counts 1 0.000 0.995 1.00 0.00
#> ola_mES_2i_3_52.counts 1 0.141 0.981 0.98 0.02
#> ola_mES_2i_3_54.counts 2 0.000 0.983 0.00 1.00
#> ola_mES_2i_3_56.counts 1 0.000 0.995 1.00 0.00
#> ola_mES_2i_3_57.counts 1 0.141 0.981 0.98 0.02
#> ola_mES_2i_3_58.counts 1 0.141 0.981 0.98 0.02
#> ola_mES_2i_3_6.counts 2 0.000 0.983 0.00 1.00
#> ola_mES_2i_3_65.counts 1 0.000 0.995 1.00 0.00
#> ola_mES_2i_3_69.counts 2 0.000 0.983 0.00 1.00
#> ola_mES_2i_3_7.counts 1 0.141 0.981 0.98 0.02
#> ola_mES_2i_3_73.counts 1 0.000 0.995 1.00 0.00
#> ola_mES_2i_3_76.counts 1 0.000 0.995 1.00 0.00
#> ola_mES_2i_3_77.counts 2 0.000 0.983 0.00 1.00
#> ola_mES_2i_3_79.counts 1 0.141 0.981 0.98 0.02
#> ola_mES_2i_3_8.counts 1 0.141 0.981 0.98 0.02
#> ola_mES_2i_3_80.counts 1 0.000 0.995 1.00 0.00
#> ola_mES_2i_3_82.counts 1 0.000 0.995 1.00 0.00
#> ola_mES_2i_3_83.counts 1 0.000 0.995 1.00 0.00
#> ola_mES_2i_3_87.counts 2 0.000 0.983 0.00 1.00
#> ola_mES_2i_3_88.counts 1 0.000 0.995 1.00 0.00
#> ola_mES_2i_3_91.counts 2 0.000 0.983 0.00 1.00
#> ola_mES_2i_3_92.counts 1 0.141 0.981 0.98 0.02
#> ola_mES_2i_3_96.counts 2 0.000 0.983 0.00 1.00
#> ola_mES_2i_4_23.counts 1 0.000 0.995 1.00 0.00
#> ola_mES_2i_4_60.counts 1 0.000 0.995 1.00 0.00
#> ola_mES_2i_4_63.counts 1 0.000 0.995 1.00 0.00
#> ola_mES_2i_4_68.counts 1 0.000 0.995 1.00 0.00
#> ola_mES_2i_4_70.counts 1 0.000 0.995 1.00 0.00
#> ola_mES_2i_4_75.counts 2 0.000 0.983 0.00 1.00
#> ola_mES_2i_4_88.counts 1 0.000 0.995 1.00 0.00
#> ola_mES_2i_4_89.counts 1 0.000 0.995 1.00 0.00
#> ola_mES_2i_4_94.counts 1 0.000 0.995 1.00 0.00
#> ola_mES_2i_4_95.counts 2 0.000 0.983 0.00 1.00
#> ola_mES_2i_4_96.counts 2 0.000 0.983 0.00 1.00
#> ola_mES_2i_5_1.counts 1 0.000 0.995 1.00 0.00
#> ola_mES_2i_5_2.counts 1 0.000 0.995 1.00 0.00
#> ola_mES_2i_5_28.counts 1 0.000 0.995 1.00 0.00
#> ola_mES_2i_5_29.counts 1 0.000 0.995 1.00 0.00
#> ola_mES_2i_5_3.counts 1 0.000 0.995 1.00 0.00
#> ola_mES_2i_5_4.counts 1 0.000 0.995 1.00 0.00
#> ola_mES_2i_5_52.counts 1 0.141 0.981 0.98 0.02
#> ola_mES_2i_5_6.counts 1 0.000 0.995 1.00 0.00
#> ola_mES_2i_5_62.counts 1 0.000 0.995 1.00 0.00
#> ola_mES_2i_5_66.counts 1 0.000 0.995 1.00 0.00
#> ola_mES_2i_5_81.counts 1 0.000 0.995 1.00 0.00
#> ola_mES_2i_5_85.counts 1 0.000 0.995 1.00 0.00
#> ola_mES_2i_5_9.counts 1 0.469 0.896 0.90 0.10
#> ola_mES_a2i_2_30.counts 2 0.000 0.983 0.00 1.00
#> ola_mES_a2i_2_36.counts 2 0.000 0.983 0.00 1.00
#> ola_mES_a2i_2_89.counts 2 0.000 0.983 0.00 1.00
#> ola_mES_a2i_3_11.counts 1 0.000 0.995 1.00 0.00
#> ola_mES_a2i_3_21.counts 2 0.141 0.970 0.02 0.98
#> ola_mES_a2i_3_22.counts 1 0.000 0.995 1.00 0.00
#> ola_mES_a2i_3_24.counts 1 0.000 0.995 1.00 0.00
#> ola_mES_a2i_3_33.counts 2 0.990 0.229 0.44 0.56
#> ola_mES_a2i_3_4.counts 1 0.000 0.995 1.00 0.00
#> ola_mES_a2i_3_58.counts 2 0.141 0.970 0.02 0.98
#> ola_mES_a2i_3_68.counts 2 0.000 0.983 0.00 1.00
#> ola_mES_a2i_3_80.counts 2 0.000 0.983 0.00 1.00
#> ola_mES_a2i_3_9.counts 1 0.000 0.995 1.00 0.00
#> ola_mES_a2i_3_91.counts 1 0.000 0.995 1.00 0.00
#> ola_mES_lif_1_53.counts 2 0.141 0.970 0.02 0.98
#> ola_mES_lif_1_56.counts 2 0.000 0.983 0.00 1.00
#> ola_mES_lif_1_57.counts 2 0.000 0.983 0.00 1.00
#> ola_mES_lif_2_12.counts 1 0.000 0.995 1.00 0.00
#> ola_mES_lif_2_48.counts 2 0.141 0.970 0.02 0.98
#> ola_mES_lif_2_69.counts 2 0.141 0.970 0.02 0.98
#> ola_mES_lif_2_73.counts 2 0.141 0.970 0.02 0.98
#> ola_mES_lif_3_12.counts 2 0.000 0.983 0.00 1.00
#> ola_mES_lif_3_2.counts 2 0.000 0.983 0.00 1.00
#> ola_mES_lif_3_21.counts 2 0.000 0.983 0.00 1.00
#> ola_mES_lif_3_77.counts 2 0.000 0.983 0.00 1.00
#> ola_mES_lif_3_89.counts 2 0.000 0.983 0.00 1.00
#> ola_mES_lif_3_9.counts 2 0.000 0.983 0.00 1.00
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> ola_mES_2i_3_10.counts 2 0.000 0.977 0.00 1.00 0
#> ola_mES_2i_3_12.counts 1 0.000 0.988 1.00 0.00 0
#> ola_mES_2i_3_13.counts 1 0.000 0.988 1.00 0.00 0
#> ola_mES_2i_3_15.counts 1 0.000 0.988 1.00 0.00 0
#> ola_mES_2i_3_19.counts 1 0.000 0.988 1.00 0.00 0
#> ola_mES_2i_3_2.counts 1 0.000 0.988 1.00 0.00 0
#> ola_mES_2i_3_20.counts 1 0.000 0.988 1.00 0.00 0
#> ola_mES_2i_3_21.counts 2 0.000 0.977 0.00 1.00 0
#> ola_mES_2i_3_26.counts 1 0.000 0.988 1.00 0.00 0
#> ola_mES_2i_3_30.counts 1 0.000 0.988 1.00 0.00 0
#> ola_mES_2i_3_33.counts 1 0.583 0.478 0.66 0.34 0
#> ola_mES_2i_3_37.counts 1 0.000 0.988 1.00 0.00 0
#> ola_mES_2i_3_38.counts 1 0.000 0.988 1.00 0.00 0
#> ola_mES_2i_3_39.counts 2 0.000 0.977 0.00 1.00 0
#> ola_mES_2i_3_41.counts 2 0.000 0.977 0.00 1.00 0
#> ola_mES_2i_3_42.counts 1 0.000 0.988 1.00 0.00 0
#> ola_mES_2i_3_45.counts 1 0.000 0.988 1.00 0.00 0
#> ola_mES_2i_3_46.counts 1 0.000 0.988 1.00 0.00 0
#> ola_mES_2i_3_47.counts 1 0.000 0.988 1.00 0.00 0
#> ola_mES_2i_3_5.counts 1 0.000 0.988 1.00 0.00 0
#> ola_mES_2i_3_52.counts 1 0.000 0.988 1.00 0.00 0
#> ola_mES_2i_3_54.counts 2 0.000 0.977 0.00 1.00 0
#> ola_mES_2i_3_56.counts 1 0.000 0.988 1.00 0.00 0
#> ola_mES_2i_3_57.counts 1 0.000 0.988 1.00 0.00 0
#> ola_mES_2i_3_58.counts 1 0.000 0.988 1.00 0.00 0
#> ola_mES_2i_3_6.counts 2 0.000 0.977 0.00 1.00 0
#> ola_mES_2i_3_65.counts 1 0.000 0.988 1.00 0.00 0
#> ola_mES_2i_3_69.counts 2 0.000 0.977 0.00 1.00 0
#> ola_mES_2i_3_7.counts 1 0.000 0.988 1.00 0.00 0
#> ola_mES_2i_3_73.counts 1 0.000 0.988 1.00 0.00 0
#> ola_mES_2i_3_76.counts 1 0.000 0.988 1.00 0.00 0
#> ola_mES_2i_3_77.counts 2 0.000 0.977 0.00 1.00 0
#> ola_mES_2i_3_79.counts 1 0.000 0.988 1.00 0.00 0
#> ola_mES_2i_3_8.counts 1 0.000 0.988 1.00 0.00 0
#> ola_mES_2i_3_80.counts 1 0.000 0.988 1.00 0.00 0
#> ola_mES_2i_3_82.counts 1 0.000 0.988 1.00 0.00 0
#> ola_mES_2i_3_83.counts 1 0.000 0.988 1.00 0.00 0
#> ola_mES_2i_3_87.counts 2 0.000 0.977 0.00 1.00 0
#> ola_mES_2i_3_88.counts 1 0.000 0.988 1.00 0.00 0
#> ola_mES_2i_3_91.counts 2 0.000 0.977 0.00 1.00 0
#> ola_mES_2i_3_92.counts 1 0.502 0.681 0.76 0.24 0
#> ola_mES_2i_3_96.counts 2 0.000 0.977 0.00 1.00 0
#> ola_mES_2i_4_23.counts 1 0.000 0.988 1.00 0.00 0
#> ola_mES_2i_4_60.counts 1 0.000 0.988 1.00 0.00 0
#> ola_mES_2i_4_63.counts 1 0.000 0.988 1.00 0.00 0
#> ola_mES_2i_4_68.counts 1 0.000 0.988 1.00 0.00 0
#> ola_mES_2i_4_70.counts 1 0.000 0.988 1.00 0.00 0
#> ola_mES_2i_4_75.counts 2 0.000 0.977 0.00 1.00 0
#> ola_mES_2i_4_88.counts 1 0.000 0.988 1.00 0.00 0
#> ola_mES_2i_4_89.counts 1 0.000 0.988 1.00 0.00 0
#> ola_mES_2i_4_94.counts 1 0.000 0.988 1.00 0.00 0
#> ola_mES_2i_4_95.counts 2 0.000 0.977 0.00 1.00 0
#> ola_mES_2i_4_96.counts 2 0.000 0.977 0.00 1.00 0
#> ola_mES_2i_5_1.counts 1 0.000 0.988 1.00 0.00 0
#> ola_mES_2i_5_2.counts 1 0.000 0.988 1.00 0.00 0
#> ola_mES_2i_5_28.counts 1 0.000 0.988 1.00 0.00 0
#> ola_mES_2i_5_29.counts 1 0.000 0.988 1.00 0.00 0
#> ola_mES_2i_5_3.counts 1 0.000 0.988 1.00 0.00 0
#> ola_mES_2i_5_4.counts 1 0.000 0.988 1.00 0.00 0
#> ola_mES_2i_5_52.counts 1 0.000 0.988 1.00 0.00 0
#> ola_mES_2i_5_6.counts 1 0.000 0.988 1.00 0.00 0
#> ola_mES_2i_5_62.counts 1 0.000 0.988 1.00 0.00 0
#> ola_mES_2i_5_66.counts 1 0.000 0.988 1.00 0.00 0
#> ola_mES_2i_5_81.counts 1 0.000 0.988 1.00 0.00 0
#> ola_mES_2i_5_85.counts 1 0.000 0.988 1.00 0.00 0
#> ola_mES_2i_5_9.counts 2 0.613 0.319 0.40 0.60 0
#> ola_mES_a2i_2_30.counts 2 0.000 0.977 0.00 1.00 0
#> ola_mES_a2i_2_36.counts 2 0.000 0.977 0.00 1.00 0
#> ola_mES_a2i_2_89.counts 3 0.000 1.000 0.00 0.00 1
#> ola_mES_a2i_3_11.counts 3 0.000 1.000 0.00 0.00 1
#> ola_mES_a2i_3_21.counts 3 0.000 1.000 0.00 0.00 1
#> ola_mES_a2i_3_22.counts 3 0.000 1.000 0.00 0.00 1
#> ola_mES_a2i_3_24.counts 3 0.000 1.000 0.00 0.00 1
#> ola_mES_a2i_3_33.counts 3 0.000 1.000 0.00 0.00 1
#> ola_mES_a2i_3_4.counts 3 0.000 1.000 0.00 0.00 1
#> ola_mES_a2i_3_58.counts 3 0.000 1.000 0.00 0.00 1
#> ola_mES_a2i_3_68.counts 2 0.000 0.977 0.00 1.00 0
#> ola_mES_a2i_3_80.counts 2 0.000 0.977 0.00 1.00 0
#> ola_mES_a2i_3_9.counts 3 0.000 1.000 0.00 0.00 1
#> ola_mES_a2i_3_91.counts 3 0.000 1.000 0.00 0.00 1
#> ola_mES_lif_1_53.counts 3 0.000 1.000 0.00 0.00 1
#> ola_mES_lif_1_56.counts 2 0.000 0.977 0.00 1.00 0
#> ola_mES_lif_1_57.counts 3 0.000 1.000 0.00 0.00 1
#> ola_mES_lif_2_12.counts 3 0.000 1.000 0.00 0.00 1
#> ola_mES_lif_2_48.counts 3 0.000 1.000 0.00 0.00 1
#> ola_mES_lif_2_69.counts 3 0.000 1.000 0.00 0.00 1
#> ola_mES_lif_2_73.counts 3 0.000 1.000 0.00 0.00 1
#> ola_mES_lif_3_12.counts 3 0.000 1.000 0.00 0.00 1
#> ola_mES_lif_3_2.counts 2 0.000 0.977 0.00 1.00 0
#> ola_mES_lif_3_21.counts 2 0.000 0.977 0.00 1.00 0
#> ola_mES_lif_3_77.counts 2 0.000 0.977 0.00 1.00 0
#> ola_mES_lif_3_89.counts 2 0.000 0.977 0.00 1.00 0
#> ola_mES_lif_3_9.counts 3 0.000 1.000 0.00 0.00 1
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> ola_mES_2i_3_10.counts 2 0.0707 0.979 0.02 0.98 0 0.00
#> ola_mES_2i_3_12.counts 1 0.0707 0.810 0.98 0.00 0 0.02
#> ola_mES_2i_3_13.counts 1 0.0707 0.810 0.98 0.00 0 0.02
#> ola_mES_2i_3_15.counts 1 0.0707 0.810 0.98 0.00 0 0.02
#> ola_mES_2i_3_19.counts 1 0.0707 0.810 0.98 0.00 0 0.02
#> ola_mES_2i_3_2.counts 1 0.0000 0.808 1.00 0.00 0 0.00
#> ola_mES_2i_3_20.counts 1 0.4907 0.412 0.58 0.00 0 0.42
#> ola_mES_2i_3_21.counts 2 0.0000 0.999 0.00 1.00 0 0.00
#> ola_mES_2i_3_26.counts 1 0.0707 0.810 0.98 0.00 0 0.02
#> ola_mES_2i_3_30.counts 1 0.0707 0.810 0.98 0.00 0 0.02
#> ola_mES_2i_3_33.counts 4 0.5767 0.364 0.28 0.06 0 0.66
#> ola_mES_2i_3_37.counts 1 0.0707 0.810 0.98 0.00 0 0.02
#> ola_mES_2i_3_38.counts 1 0.0000 0.808 1.00 0.00 0 0.00
#> ola_mES_2i_3_39.counts 2 0.0000 0.999 0.00 1.00 0 0.00
#> ola_mES_2i_3_41.counts 2 0.0000 0.999 0.00 1.00 0 0.00
#> ola_mES_2i_3_42.counts 1 0.0000 0.808 1.00 0.00 0 0.00
#> ola_mES_2i_3_45.counts 1 0.0000 0.808 1.00 0.00 0 0.00
#> ola_mES_2i_3_46.counts 1 0.0707 0.810 0.98 0.00 0 0.02
#> ola_mES_2i_3_47.counts 1 0.0000 0.808 1.00 0.00 0 0.00
#> ola_mES_2i_3_5.counts 1 0.0707 0.810 0.98 0.00 0 0.02
#> ola_mES_2i_3_52.counts 4 0.4713 0.206 0.36 0.00 0 0.64
#> ola_mES_2i_3_54.counts 2 0.0000 0.999 0.00 1.00 0 0.00
#> ola_mES_2i_3_56.counts 1 0.4790 0.477 0.62 0.00 0 0.38
#> ola_mES_2i_3_57.counts 1 0.4790 0.483 0.62 0.00 0 0.38
#> ola_mES_2i_3_58.counts 1 0.4790 0.483 0.62 0.00 0 0.38
#> ola_mES_2i_3_6.counts 2 0.0000 0.999 0.00 1.00 0 0.00
#> ola_mES_2i_3_65.counts 1 0.4277 0.591 0.72 0.00 0 0.28
#> ola_mES_2i_3_69.counts 2 0.0000 0.999 0.00 1.00 0 0.00
#> ola_mES_2i_3_7.counts 1 0.4790 0.483 0.62 0.00 0 0.38
#> ola_mES_2i_3_73.counts 1 0.0707 0.801 0.98 0.00 0 0.02
#> ola_mES_2i_3_76.counts 1 0.0707 0.810 0.98 0.00 0 0.02
#> ola_mES_2i_3_77.counts 2 0.0000 0.999 0.00 1.00 0 0.00
#> ola_mES_2i_3_79.counts 1 0.4790 0.483 0.62 0.00 0 0.38
#> ola_mES_2i_3_8.counts 1 0.4790 0.483 0.62 0.00 0 0.38
#> ola_mES_2i_3_80.counts 1 0.0000 0.808 1.00 0.00 0 0.00
#> ola_mES_2i_3_82.counts 1 0.0000 0.808 1.00 0.00 0 0.00
#> ola_mES_2i_3_83.counts 1 0.0707 0.810 0.98 0.00 0 0.02
#> ola_mES_2i_3_87.counts 2 0.0000 0.999 0.00 1.00 0 0.00
#> ola_mES_2i_3_88.counts 1 0.0707 0.810 0.98 0.00 0 0.02
#> ola_mES_2i_3_91.counts 2 0.0000 0.999 0.00 1.00 0 0.00
#> ola_mES_2i_3_92.counts 1 0.6510 0.379 0.54 0.08 0 0.38
#> ola_mES_2i_3_96.counts 2 0.0000 0.999 0.00 1.00 0 0.00
#> ola_mES_2i_4_23.counts 4 0.0000 0.804 0.00 0.00 0 1.00
#> ola_mES_2i_4_60.counts 4 0.0000 0.804 0.00 0.00 0 1.00
#> ola_mES_2i_4_63.counts 4 0.0000 0.804 0.00 0.00 0 1.00
#> ola_mES_2i_4_68.counts 1 0.4994 -0.223 0.52 0.00 0 0.48
#> ola_mES_2i_4_70.counts 4 0.4790 0.479 0.38 0.00 0 0.62
#> ola_mES_2i_4_75.counts 2 0.0000 0.999 0.00 1.00 0 0.00
#> ola_mES_2i_4_88.counts 4 0.4713 0.506 0.36 0.00 0 0.64
#> ola_mES_2i_4_89.counts 4 0.0000 0.804 0.00 0.00 0 1.00
#> ola_mES_2i_4_94.counts 4 0.4713 0.508 0.36 0.00 0 0.64
#> ola_mES_2i_4_95.counts 2 0.0000 0.999 0.00 1.00 0 0.00
#> ola_mES_2i_4_96.counts 2 0.0000 0.999 0.00 1.00 0 0.00
#> ola_mES_2i_5_1.counts 4 0.4994 0.278 0.48 0.00 0 0.52
#> ola_mES_2i_5_2.counts 4 0.0000 0.804 0.00 0.00 0 1.00
#> ola_mES_2i_5_28.counts 4 0.0000 0.804 0.00 0.00 0 1.00
#> ola_mES_2i_5_29.counts 4 0.0000 0.804 0.00 0.00 0 1.00
#> ola_mES_2i_5_3.counts 4 0.0000 0.804 0.00 0.00 0 1.00
#> ola_mES_2i_5_4.counts 4 0.0000 0.804 0.00 0.00 0 1.00
#> ola_mES_2i_5_52.counts 4 0.0707 0.789 0.02 0.00 0 0.98
#> ola_mES_2i_5_6.counts 4 0.0000 0.804 0.00 0.00 0 1.00
#> ola_mES_2i_5_62.counts 4 0.2011 0.761 0.08 0.00 0 0.92
#> ola_mES_2i_5_66.counts 4 0.4790 0.479 0.38 0.00 0 0.62
#> ola_mES_2i_5_81.counts 4 0.0000 0.804 0.00 0.00 0 1.00
#> ola_mES_2i_5_85.counts 4 0.4713 0.508 0.36 0.00 0 0.64
#> ola_mES_2i_5_9.counts 4 0.0707 0.789 0.00 0.02 0 0.98
#> ola_mES_a2i_2_30.counts 2 0.0000 0.999 0.00 1.00 0 0.00
#> ola_mES_a2i_2_36.counts 2 0.0000 0.999 0.00 1.00 0 0.00
#> ola_mES_a2i_2_89.counts 3 0.0000 1.000 0.00 0.00 1 0.00
#> ola_mES_a2i_3_11.counts 3 0.0000 1.000 0.00 0.00 1 0.00
#> ola_mES_a2i_3_21.counts 3 0.0000 1.000 0.00 0.00 1 0.00
#> ola_mES_a2i_3_22.counts 3 0.0000 1.000 0.00 0.00 1 0.00
#> ola_mES_a2i_3_24.counts 3 0.0000 1.000 0.00 0.00 1 0.00
#> ola_mES_a2i_3_33.counts 3 0.0000 1.000 0.00 0.00 1 0.00
#> ola_mES_a2i_3_4.counts 3 0.0000 1.000 0.00 0.00 1 0.00
#> ola_mES_a2i_3_58.counts 3 0.0000 1.000 0.00 0.00 1 0.00
#> ola_mES_a2i_3_68.counts 2 0.0000 0.999 0.00 1.00 0 0.00
#> ola_mES_a2i_3_80.counts 2 0.0000 0.999 0.00 1.00 0 0.00
#> ola_mES_a2i_3_9.counts 3 0.0000 1.000 0.00 0.00 1 0.00
#> ola_mES_a2i_3_91.counts 3 0.0000 1.000 0.00 0.00 1 0.00
#> ola_mES_lif_1_53.counts 3 0.0000 1.000 0.00 0.00 1 0.00
#> ola_mES_lif_1_56.counts 2 0.0000 0.999 0.00 1.00 0 0.00
#> ola_mES_lif_1_57.counts 3 0.0000 1.000 0.00 0.00 1 0.00
#> ola_mES_lif_2_12.counts 3 0.0000 1.000 0.00 0.00 1 0.00
#> ola_mES_lif_2_48.counts 3 0.0000 1.000 0.00 0.00 1 0.00
#> ola_mES_lif_2_69.counts 3 0.0000 1.000 0.00 0.00 1 0.00
#> ola_mES_lif_2_73.counts 3 0.0000 1.000 0.00 0.00 1 0.00
#> ola_mES_lif_3_12.counts 3 0.0000 1.000 0.00 0.00 1 0.00
#> ola_mES_lif_3_2.counts 2 0.0000 0.999 0.00 1.00 0 0.00
#> ola_mES_lif_3_21.counts 2 0.0000 0.999 0.00 1.00 0 0.00
#> ola_mES_lif_3_77.counts 2 0.0000 0.999 0.00 1.00 0 0.00
#> ola_mES_lif_3_89.counts 2 0.0000 0.999 0.00 1.00 0 0.00
#> ola_mES_lif_3_9.counts 3 0.0000 1.000 0.00 0.00 1 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)
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: Node01111-leaf , Node01112-leaf , Node01121-leaf , Node01122-leaf , Node01411-leaf , Node01412-leaf , Node01413-leaf , Node02111-leaf , Node02112-leaf , Node02211-leaf , Node02212-leaf , Node02221-leaf , Node02222-leaf , Node02223-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 15602 rows and 51 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 0.4952 0.506 0.506
#> 3 3 1.000 0.952 0.978 0.3550 0.824 0.653
#> 4 4 0.729 0.796 0.855 0.0888 0.947 0.838
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
#> ola_mES_2i_3_12.counts 2 0.000 0.997 0.0 1.0
#> ola_mES_2i_3_13.counts 2 0.000 0.997 0.0 1.0
#> ola_mES_2i_3_15.counts 2 0.000 0.997 0.0 1.0
#> ola_mES_2i_3_19.counts 2 0.000 0.997 0.0 1.0
#> ola_mES_2i_3_2.counts 2 0.000 0.997 0.0 1.0
#> ola_mES_2i_3_20.counts 2 0.000 0.997 0.0 1.0
#> ola_mES_2i_3_26.counts 2 0.000 0.997 0.0 1.0
#> ola_mES_2i_3_30.counts 2 0.000 0.997 0.0 1.0
#> ola_mES_2i_3_33.counts 2 0.469 0.889 0.1 0.9
#> ola_mES_2i_3_37.counts 2 0.000 0.997 0.0 1.0
#> ola_mES_2i_3_38.counts 2 0.000 0.997 0.0 1.0
#> ola_mES_2i_3_42.counts 2 0.000 0.997 0.0 1.0
#> ola_mES_2i_3_45.counts 2 0.000 0.997 0.0 1.0
#> ola_mES_2i_3_46.counts 2 0.000 0.997 0.0 1.0
#> ola_mES_2i_3_47.counts 2 0.000 0.997 0.0 1.0
#> ola_mES_2i_3_5.counts 2 0.000 0.997 0.0 1.0
#> ola_mES_2i_3_52.counts 1 0.000 1.000 1.0 0.0
#> ola_mES_2i_3_56.counts 2 0.000 0.997 0.0 1.0
#> ola_mES_2i_3_57.counts 2 0.000 0.997 0.0 1.0
#> ola_mES_2i_3_58.counts 2 0.000 0.997 0.0 1.0
#> ola_mES_2i_3_65.counts 2 0.000 0.997 0.0 1.0
#> ola_mES_2i_3_7.counts 2 0.000 0.997 0.0 1.0
#> ola_mES_2i_3_73.counts 2 0.000 0.997 0.0 1.0
#> ola_mES_2i_3_76.counts 2 0.000 0.997 0.0 1.0
#> ola_mES_2i_3_79.counts 2 0.000 0.997 0.0 1.0
#> ola_mES_2i_3_8.counts 2 0.000 0.997 0.0 1.0
#> ola_mES_2i_3_80.counts 2 0.000 0.997 0.0 1.0
#> ola_mES_2i_3_82.counts 2 0.000 0.997 0.0 1.0
#> ola_mES_2i_3_83.counts 2 0.000 0.997 0.0 1.0
#> ola_mES_2i_3_88.counts 2 0.000 0.997 0.0 1.0
#> ola_mES_2i_3_92.counts 2 0.000 0.997 0.0 1.0
#> ola_mES_2i_4_23.counts 1 0.000 1.000 1.0 0.0
#> ola_mES_2i_4_60.counts 1 0.000 1.000 1.0 0.0
#> ola_mES_2i_4_63.counts 1 0.000 1.000 1.0 0.0
#> ola_mES_2i_4_68.counts 1 0.000 1.000 1.0 0.0
#> ola_mES_2i_4_70.counts 1 0.000 1.000 1.0 0.0
#> ola_mES_2i_4_88.counts 1 0.000 1.000 1.0 0.0
#> ola_mES_2i_4_89.counts 1 0.000 1.000 1.0 0.0
#> ola_mES_2i_4_94.counts 1 0.000 1.000 1.0 0.0
#> ola_mES_2i_5_1.counts 1 0.000 1.000 1.0 0.0
#> ola_mES_2i_5_2.counts 1 0.000 1.000 1.0 0.0
#> ola_mES_2i_5_28.counts 1 0.000 1.000 1.0 0.0
#> ola_mES_2i_5_29.counts 1 0.000 1.000 1.0 0.0
#> ola_mES_2i_5_3.counts 1 0.000 1.000 1.0 0.0
#> ola_mES_2i_5_4.counts 1 0.000 1.000 1.0 0.0
#> ola_mES_2i_5_52.counts 1 0.000 1.000 1.0 0.0
#> ola_mES_2i_5_6.counts 1 0.000 1.000 1.0 0.0
#> ola_mES_2i_5_62.counts 1 0.000 1.000 1.0 0.0
#> ola_mES_2i_5_66.counts 1 0.000 1.000 1.0 0.0
#> ola_mES_2i_5_81.counts 1 0.000 1.000 1.0 0.0
#> ola_mES_2i_5_85.counts 1 0.000 1.000 1.0 0.0
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> ola_mES_2i_3_12.counts 3 0.0000 0.973 0.00 0.00 1.00
#> ola_mES_2i_3_13.counts 3 0.0000 0.973 0.00 0.00 1.00
#> ola_mES_2i_3_15.counts 3 0.0000 0.973 0.00 0.00 1.00
#> ola_mES_2i_3_19.counts 3 0.0000 0.973 0.00 0.00 1.00
#> ola_mES_2i_3_2.counts 2 0.0892 0.961 0.00 0.98 0.02
#> ola_mES_2i_3_20.counts 3 0.0892 0.967 0.00 0.02 0.98
#> ola_mES_2i_3_26.counts 2 0.0892 0.961 0.00 0.98 0.02
#> ola_mES_2i_3_30.counts 3 0.0000 0.973 0.00 0.00 1.00
#> ola_mES_2i_3_33.counts 3 0.1781 0.953 0.02 0.02 0.96
#> ola_mES_2i_3_37.counts 3 0.4796 0.698 0.00 0.22 0.78
#> ola_mES_2i_3_38.counts 2 0.0000 0.964 0.00 1.00 0.00
#> ola_mES_2i_3_42.counts 2 0.0892 0.961 0.00 0.98 0.02
#> ola_mES_2i_3_45.counts 2 0.0000 0.964 0.00 1.00 0.00
#> ola_mES_2i_3_46.counts 2 0.0892 0.961 0.00 0.98 0.02
#> ola_mES_2i_3_47.counts 2 0.0000 0.964 0.00 1.00 0.00
#> ola_mES_2i_3_5.counts 3 0.0000 0.973 0.00 0.00 1.00
#> ola_mES_2i_3_52.counts 1 0.5016 0.684 0.76 0.00 0.24
#> ola_mES_2i_3_56.counts 3 0.0892 0.967 0.00 0.02 0.98
#> ola_mES_2i_3_57.counts 2 0.0000 0.964 0.00 1.00 0.00
#> ola_mES_2i_3_58.counts 2 0.0000 0.964 0.00 1.00 0.00
#> ola_mES_2i_3_65.counts 2 0.0892 0.953 0.00 0.98 0.02
#> ola_mES_2i_3_7.counts 2 0.0000 0.964 0.00 1.00 0.00
#> ola_mES_2i_3_73.counts 2 0.0000 0.964 0.00 1.00 0.00
#> ola_mES_2i_3_76.counts 3 0.0000 0.973 0.00 0.00 1.00
#> ola_mES_2i_3_79.counts 3 0.0892 0.967 0.00 0.02 0.98
#> ola_mES_2i_3_8.counts 3 0.0892 0.967 0.00 0.02 0.98
#> ola_mES_2i_3_80.counts 2 0.0892 0.961 0.00 0.98 0.02
#> ola_mES_2i_3_82.counts 2 0.0892 0.961 0.00 0.98 0.02
#> ola_mES_2i_3_83.counts 3 0.0000 0.973 0.00 0.00 1.00
#> ola_mES_2i_3_88.counts 2 0.6045 0.397 0.00 0.62 0.38
#> ola_mES_2i_3_92.counts 2 0.0000 0.964 0.00 1.00 0.00
#> ola_mES_2i_4_23.counts 1 0.0000 0.988 1.00 0.00 0.00
#> ola_mES_2i_4_60.counts 1 0.0000 0.988 1.00 0.00 0.00
#> ola_mES_2i_4_63.counts 1 0.0000 0.988 1.00 0.00 0.00
#> ola_mES_2i_4_68.counts 1 0.0000 0.988 1.00 0.00 0.00
#> ola_mES_2i_4_70.counts 1 0.0000 0.988 1.00 0.00 0.00
#> ola_mES_2i_4_88.counts 1 0.0000 0.988 1.00 0.00 0.00
#> ola_mES_2i_4_89.counts 1 0.0000 0.988 1.00 0.00 0.00
#> ola_mES_2i_4_94.counts 1 0.0000 0.988 1.00 0.00 0.00
#> ola_mES_2i_5_1.counts 1 0.0000 0.988 1.00 0.00 0.00
#> ola_mES_2i_5_2.counts 1 0.0000 0.988 1.00 0.00 0.00
#> ola_mES_2i_5_28.counts 1 0.0000 0.988 1.00 0.00 0.00
#> ola_mES_2i_5_29.counts 1 0.0000 0.988 1.00 0.00 0.00
#> ola_mES_2i_5_3.counts 1 0.0000 0.988 1.00 0.00 0.00
#> ola_mES_2i_5_4.counts 1 0.0000 0.988 1.00 0.00 0.00
#> ola_mES_2i_5_52.counts 1 0.0000 0.988 1.00 0.00 0.00
#> ola_mES_2i_5_6.counts 1 0.0000 0.988 1.00 0.00 0.00
#> ola_mES_2i_5_62.counts 1 0.0000 0.988 1.00 0.00 0.00
#> ola_mES_2i_5_66.counts 1 0.0000 0.988 1.00 0.00 0.00
#> ola_mES_2i_5_81.counts 1 0.0000 0.988 1.00 0.00 0.00
#> ola_mES_2i_5_85.counts 1 0.0000 0.988 1.00 0.00 0.00
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> ola_mES_2i_3_12.counts 3 0.588 0.800 0.00 0.06 0.64 0.30
#> ola_mES_2i_3_13.counts 3 0.551 0.794 0.00 0.04 0.66 0.30
#> ola_mES_2i_3_15.counts 3 0.657 0.801 0.00 0.10 0.58 0.32
#> ola_mES_2i_3_19.counts 3 0.657 0.801 0.00 0.10 0.58 0.32
#> ola_mES_2i_3_2.counts 2 0.000 0.879 0.00 1.00 0.00 0.00
#> ola_mES_2i_3_20.counts 3 0.000 0.694 0.00 0.00 1.00 0.00
#> ola_mES_2i_3_26.counts 2 0.000 0.879 0.00 1.00 0.00 0.00
#> ola_mES_2i_3_30.counts 3 0.657 0.801 0.00 0.10 0.58 0.32
#> ola_mES_2i_3_33.counts 3 0.320 0.607 0.04 0.00 0.88 0.08
#> ola_mES_2i_3_37.counts 3 0.787 0.499 0.00 0.34 0.38 0.28
#> ola_mES_2i_3_38.counts 2 0.265 0.852 0.00 0.88 0.00 0.12
#> ola_mES_2i_3_42.counts 2 0.141 0.862 0.00 0.96 0.02 0.02
#> ola_mES_2i_3_45.counts 2 0.121 0.874 0.00 0.96 0.00 0.04
#> ola_mES_2i_3_46.counts 2 0.000 0.879 0.00 1.00 0.00 0.00
#> ola_mES_2i_3_47.counts 2 0.495 0.748 0.00 0.76 0.18 0.06
#> ola_mES_2i_3_5.counts 3 0.657 0.801 0.00 0.10 0.58 0.32
#> ola_mES_2i_3_52.counts 1 0.591 0.119 0.56 0.00 0.40 0.04
#> ola_mES_2i_3_56.counts 3 0.164 0.684 0.00 0.00 0.94 0.06
#> ola_mES_2i_3_57.counts 2 0.317 0.822 0.00 0.84 0.00 0.16
#> ola_mES_2i_3_58.counts 2 0.265 0.846 0.00 0.88 0.00 0.12
#> ola_mES_2i_3_65.counts 2 0.494 0.709 0.00 0.74 0.22 0.04
#> ola_mES_2i_3_7.counts 2 0.283 0.864 0.00 0.90 0.06 0.04
#> ola_mES_2i_3_73.counts 2 0.283 0.850 0.00 0.90 0.06 0.04
#> ola_mES_2i_3_76.counts 3 0.630 0.801 0.00 0.08 0.60 0.32
#> ola_mES_2i_3_79.counts 3 0.000 0.694 0.00 0.00 1.00 0.00
#> ola_mES_2i_3_8.counts 3 0.394 0.588 0.00 0.10 0.84 0.06
#> ola_mES_2i_3_80.counts 2 0.000 0.879 0.00 1.00 0.00 0.00
#> ola_mES_2i_3_82.counts 2 0.000 0.879 0.00 1.00 0.00 0.00
#> ola_mES_2i_3_83.counts 3 0.657 0.801 0.00 0.10 0.58 0.32
#> ola_mES_2i_3_88.counts 2 0.538 0.594 0.00 0.74 0.16 0.10
#> ola_mES_2i_3_92.counts 2 0.485 0.534 0.00 0.60 0.00 0.40
#> ola_mES_2i_4_23.counts 1 0.000 0.896 1.00 0.00 0.00 0.00
#> ola_mES_2i_4_60.counts 1 0.121 0.844 0.96 0.00 0.00 0.04
#> ola_mES_2i_4_63.counts 1 0.000 0.896 1.00 0.00 0.00 0.00
#> ola_mES_2i_4_68.counts 1 0.559 0.442 0.72 0.00 0.18 0.10
#> ola_mES_2i_4_70.counts 1 0.000 0.896 1.00 0.00 0.00 0.00
#> ola_mES_2i_4_88.counts 1 0.164 0.829 0.94 0.00 0.00 0.06
#> ola_mES_2i_4_89.counts 1 0.000 0.896 1.00 0.00 0.00 0.00
#> ola_mES_2i_4_94.counts 1 0.000 0.896 1.00 0.00 0.00 0.00
#> ola_mES_2i_5_1.counts 4 0.495 0.926 0.44 0.00 0.00 0.56
#> ola_mES_2i_5_2.counts 1 0.000 0.896 1.00 0.00 0.00 0.00
#> ola_mES_2i_5_28.counts 4 0.491 0.962 0.42 0.00 0.00 0.58
#> ola_mES_2i_5_29.counts 4 0.491 0.962 0.42 0.00 0.00 0.58
#> ola_mES_2i_5_3.counts 1 0.000 0.896 1.00 0.00 0.00 0.00
#> ola_mES_2i_5_4.counts 1 0.000 0.896 1.00 0.00 0.00 0.00
#> ola_mES_2i_5_52.counts 4 0.485 0.949 0.40 0.00 0.00 0.60
#> ola_mES_2i_5_6.counts 1 0.000 0.896 1.00 0.00 0.00 0.00
#> ola_mES_2i_5_62.counts 1 0.000 0.896 1.00 0.00 0.00 0.00
#> ola_mES_2i_5_66.counts 1 0.265 0.716 0.88 0.00 0.00 0.12
#> ola_mES_2i_5_81.counts 1 0.000 0.896 1.00 0.00 0.00 0.00
#> ola_mES_2i_5_85.counts 1 0.000 0.896 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)
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: Node01111-leaf , Node01112-leaf , Node01121-leaf , Node01122-leaf , Node01411-leaf , Node01412-leaf , Node01413-leaf , Node02111-leaf , Node02112-leaf , Node02211-leaf , Node02212-leaf , Node02221-leaf , Node02222-leaf , Node02223-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 14992 rows and 24 columns.
#> Top rows (1499) 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 1.000 1.000 0.5186 0.482 0.482
#> 3 3 1.000 0.982 0.987 0.3165 0.775 0.560
#> 4 4 0.918 0.880 0.921 0.0843 0.924 0.764
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
#> ola_mES_2i_3_10.counts 2 0 1 0 1
#> ola_mES_2i_3_21.counts 2 0 1 0 1
#> ola_mES_2i_3_39.counts 2 0 1 0 1
#> ola_mES_2i_3_41.counts 2 0 1 0 1
#> ola_mES_2i_3_54.counts 2 0 1 0 1
#> ola_mES_2i_3_6.counts 2 0 1 0 1
#> ola_mES_2i_3_69.counts 1 0 1 1 0
#> ola_mES_2i_3_77.counts 2 0 1 0 1
#> ola_mES_2i_3_87.counts 2 0 1 0 1
#> ola_mES_2i_3_91.counts 2 0 1 0 1
#> ola_mES_2i_3_96.counts 1 0 1 1 0
#> ola_mES_2i_4_75.counts 1 0 1 1 0
#> ola_mES_2i_4_95.counts 1 0 1 1 0
#> ola_mES_2i_4_96.counts 2 0 1 0 1
#> ola_mES_2i_5_9.counts 2 0 1 0 1
#> ola_mES_a2i_2_30.counts 1 0 1 1 0
#> ola_mES_a2i_2_36.counts 1 0 1 1 0
#> ola_mES_a2i_3_68.counts 1 0 1 1 0
#> ola_mES_a2i_3_80.counts 1 0 1 1 0
#> ola_mES_lif_1_56.counts 1 0 1 1 0
#> ola_mES_lif_3_2.counts 1 0 1 1 0
#> ola_mES_lif_3_21.counts 1 0 1 1 0
#> ola_mES_lif_3_77.counts 1 0 1 1 0
#> ola_mES_lif_3_89.counts 1 0 1 1 0
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> ola_mES_2i_3_10.counts 2 0.153 0.965 0.00 0.96 0.04
#> ola_mES_2i_3_21.counts 2 0.207 0.949 0.00 0.94 0.06
#> ola_mES_2i_3_39.counts 3 0.000 0.958 0.00 0.00 1.00
#> ola_mES_2i_3_41.counts 2 0.000 0.989 0.00 1.00 0.00
#> ola_mES_2i_3_54.counts 3 0.000 0.958 0.00 0.00 1.00
#> ola_mES_2i_3_6.counts 2 0.000 0.989 0.00 1.00 0.00
#> ola_mES_2i_3_69.counts 3 0.153 0.966 0.04 0.00 0.96
#> ola_mES_2i_3_77.counts 2 0.000 0.989 0.00 1.00 0.00
#> ola_mES_2i_3_87.counts 2 0.000 0.989 0.00 1.00 0.00
#> ola_mES_2i_3_91.counts 2 0.000 0.989 0.00 1.00 0.00
#> ola_mES_2i_3_96.counts 3 0.153 0.966 0.04 0.00 0.96
#> ola_mES_2i_4_75.counts 3 0.207 0.953 0.06 0.00 0.94
#> ola_mES_2i_4_95.counts 3 0.295 0.934 0.02 0.06 0.92
#> ola_mES_2i_4_96.counts 2 0.000 0.989 0.00 1.00 0.00
#> ola_mES_2i_5_9.counts 2 0.000 0.989 0.00 1.00 0.00
#> ola_mES_a2i_2_30.counts 1 0.000 1.000 1.00 0.00 0.00
#> ola_mES_a2i_2_36.counts 1 0.000 1.000 1.00 0.00 0.00
#> ola_mES_a2i_3_68.counts 1 0.000 1.000 1.00 0.00 0.00
#> ola_mES_a2i_3_80.counts 1 0.000 1.000 1.00 0.00 0.00
#> ola_mES_lif_1_56.counts 1 0.000 1.000 1.00 0.00 0.00
#> ola_mES_lif_3_2.counts 1 0.000 1.000 1.00 0.00 0.00
#> ola_mES_lif_3_21.counts 1 0.000 1.000 1.00 0.00 0.00
#> ola_mES_lif_3_77.counts 1 0.000 1.000 1.00 0.00 0.00
#> ola_mES_lif_3_89.counts 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
#> ola_mES_2i_3_10.counts 4 0.4406 0.749 0.00 0.30 0.00 0.70
#> ola_mES_2i_3_21.counts 4 0.4406 0.749 0.00 0.30 0.00 0.70
#> ola_mES_2i_3_39.counts 3 0.3610 0.574 0.00 0.00 0.80 0.20
#> ola_mES_2i_3_41.counts 2 0.0000 0.979 0.00 1.00 0.00 0.00
#> ola_mES_2i_3_54.counts 4 0.4406 0.417 0.00 0.00 0.30 0.70
#> ola_mES_2i_3_6.counts 2 0.0000 0.979 0.00 1.00 0.00 0.00
#> ola_mES_2i_3_69.counts 3 0.0000 0.780 0.00 0.00 1.00 0.00
#> ola_mES_2i_3_77.counts 2 0.1211 0.948 0.00 0.96 0.00 0.04
#> ola_mES_2i_3_87.counts 2 0.1637 0.917 0.00 0.94 0.00 0.06
#> ola_mES_2i_3_91.counts 2 0.0000 0.979 0.00 1.00 0.00 0.00
#> ola_mES_2i_3_96.counts 3 0.0000 0.780 0.00 0.00 1.00 0.00
#> ola_mES_2i_4_75.counts 3 0.4277 0.734 0.00 0.00 0.72 0.28
#> ola_mES_2i_4_95.counts 3 0.5062 0.713 0.00 0.02 0.68 0.30
#> ola_mES_2i_4_96.counts 2 0.0000 0.979 0.00 1.00 0.00 0.00
#> ola_mES_2i_5_9.counts 2 0.0000 0.979 0.00 1.00 0.00 0.00
#> ola_mES_a2i_2_30.counts 1 0.1211 0.971 0.96 0.00 0.00 0.04
#> ola_mES_a2i_2_36.counts 1 0.0707 0.981 0.98 0.00 0.00 0.02
#> ola_mES_a2i_3_68.counts 1 0.0000 0.989 1.00 0.00 0.00 0.00
#> ola_mES_a2i_3_80.counts 1 0.1211 0.971 0.96 0.00 0.00 0.04
#> ola_mES_lif_1_56.counts 1 0.0000 0.989 1.00 0.00 0.00 0.00
#> ola_mES_lif_3_2.counts 1 0.0000 0.989 1.00 0.00 0.00 0.00
#> ola_mES_lif_3_21.counts 1 0.0000 0.989 1.00 0.00 0.00 0.00
#> ola_mES_lif_3_77.counts 1 0.0000 0.989 1.00 0.00 0.00 0.00
#> ola_mES_lif_3_89.counts 1 0.0000 0.989 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)
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 , Node014 , Node021 , Node022 , Node023-leaf , Node031 , Node032 , Node033 , Node034-leaf .
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 'ConsensusPartition' object with k = 2, 3, 4.
#> On a matrix with 16189 rows and 131 columns.
#> Top rows (1619) 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 0.988 0.995 0.490 0.512 0.512
#> 3 3 1 0.985 0.993 0.370 0.719 0.499
#> 4 4 1 0.994 0.997 0.109 0.896 0.697
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
#> ola_mES_2i_2_62.counts 2 0.000 0.992 0.00 1.00
#> ola_mES_2i_2_87.counts 2 0.000 0.992 0.00 1.00
#> ola_mES_2i_3_17.counts 2 0.000 0.992 0.00 1.00
#> ola_mES_2i_3_18.counts 2 0.000 0.992 0.00 1.00
#> ola_mES_2i_3_31.counts 2 0.000 0.992 0.00 1.00
#> ola_mES_2i_3_49.counts 2 0.000 0.992 0.00 1.00
#> ola_mES_2i_3_62.counts 2 0.000 0.992 0.00 1.00
#> ola_mES_2i_3_66.counts 2 0.000 0.992 0.00 1.00
#> ola_mES_2i_3_78.counts 2 0.000 0.992 0.00 1.00
#> ola_mES_2i_4_37.counts 2 0.000 0.992 0.00 1.00
#> ola_mES_2i_4_42.counts 2 0.000 0.992 0.00 1.00
#> ola_mES_2i_4_54.counts 2 0.000 0.992 0.00 1.00
#> ola_mES_2i_4_62.counts 2 0.000 0.992 0.00 1.00
#> ola_mES_a2i_2_26.counts 2 0.000 0.992 0.00 1.00
#> ola_mES_a2i_2_31.counts 2 0.000 0.992 0.00 1.00
#> ola_mES_a2i_2_32.counts 2 0.000 0.992 0.00 1.00
#> ola_mES_a2i_2_35.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_a2i_2_37.counts 2 0.000 0.992 0.00 1.00
#> ola_mES_a2i_2_39.counts 2 0.000 0.992 0.00 1.00
#> ola_mES_a2i_2_50.counts 2 0.000 0.992 0.00 1.00
#> ola_mES_a2i_2_53.counts 2 0.000 0.992 0.00 1.00
#> ola_mES_a2i_2_6.counts 2 0.000 0.992 0.00 1.00
#> ola_mES_a2i_2_69.counts 2 0.000 0.992 0.00 1.00
#> ola_mES_a2i_2_91.counts 2 0.000 0.992 0.00 1.00
#> ola_mES_a2i_2_92.counts 2 0.000 0.992 0.00 1.00
#> ola_mES_a2i_3_17.counts 2 0.000 0.992 0.00 1.00
#> ola_mES_a2i_3_18.counts 2 0.000 0.992 0.00 1.00
#> ola_mES_a2i_3_2.counts 2 0.000 0.992 0.00 1.00
#> ola_mES_a2i_3_20.counts 2 0.000 0.992 0.00 1.00
#> ola_mES_a2i_3_26.counts 2 0.000 0.992 0.00 1.00
#> ola_mES_a2i_3_30.counts 2 0.000 0.992 0.00 1.00
#> ola_mES_a2i_3_31.counts 2 0.000 0.992 0.00 1.00
#> ola_mES_a2i_3_41.counts 2 0.000 0.992 0.00 1.00
#> ola_mES_a2i_3_42.counts 2 0.000 0.992 0.00 1.00
#> ola_mES_a2i_3_45.counts 2 0.000 0.992 0.00 1.00
#> ola_mES_a2i_3_47.counts 2 0.000 0.992 0.00 1.00
#> ola_mES_a2i_3_52.counts 2 0.000 0.992 0.00 1.00
#> ola_mES_a2i_3_56.counts 2 0.000 0.992 0.00 1.00
#> ola_mES_a2i_3_67.counts 2 0.000 0.992 0.00 1.00
#> ola_mES_a2i_3_7.counts 2 0.000 0.992 0.00 1.00
#> ola_mES_a2i_3_75.counts 2 0.000 0.992 0.00 1.00
#> ola_mES_a2i_3_8.counts 2 0.000 0.992 0.00 1.00
#> ola_mES_a2i_3_81.counts 2 0.000 0.992 0.00 1.00
#> ola_mES_a2i_3_94.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_lif_1_10.counts 2 0.000 0.992 0.00 1.00
#> ola_mES_lif_1_12.counts 2 0.000 0.992 0.00 1.00
#> ola_mES_lif_1_14.counts 2 0.000 0.992 0.00 1.00
#> ola_mES_lif_1_17.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_lif_1_2.counts 2 0.000 0.992 0.00 1.00
#> ola_mES_lif_1_21.counts 2 0.000 0.992 0.00 1.00
#> ola_mES_lif_1_23.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_lif_1_29.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_lif_1_31.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_lif_1_35.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_lif_1_63.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_lif_1_64.counts 2 0.827 0.653 0.26 0.74
#> ola_mES_lif_1_65.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_lif_1_69.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_lif_1_70.counts 2 0.000 0.992 0.00 1.00
#> ola_mES_lif_1_74.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_lif_1_75.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_lif_1_79.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_lif_1_82.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_lif_1_86.counts 2 0.000 0.992 0.00 1.00
#> ola_mES_lif_1_93.counts 2 0.000 0.992 0.00 1.00
#> ola_mES_lif_2_1.counts 2 0.000 0.992 0.00 1.00
#> ola_mES_lif_2_20.counts 2 0.000 0.992 0.00 1.00
#> ola_mES_lif_2_25.counts 2 0.000 0.992 0.00 1.00
#> ola_mES_lif_2_27.counts 2 0.000 0.992 0.00 1.00
#> ola_mES_lif_2_31.counts 2 0.000 0.992 0.00 1.00
#> ola_mES_lif_2_33.counts 2 0.000 0.992 0.00 1.00
#> ola_mES_lif_2_36.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_lif_2_37.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_lif_2_41.counts 2 0.000 0.992 0.00 1.00
#> ola_mES_lif_2_45.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_lif_2_47.counts 2 0.000 0.992 0.00 1.00
#> ola_mES_lif_2_57.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_lif_2_60.counts 2 0.000 0.992 0.00 1.00
#> ola_mES_lif_2_79.counts 2 0.000 0.992 0.00 1.00
#> ola_mES_lif_2_81.counts 2 0.000 0.992 0.00 1.00
#> ola_mES_lif_2_86.counts 2 0.000 0.992 0.00 1.00
#> ola_mES_lif_2_9.counts 2 0.000 0.992 0.00 1.00
#> ola_mES_lif_2_90.counts 2 0.000 0.992 0.00 1.00
#> ola_mES_lif_2_91.counts 2 0.000 0.992 0.00 1.00
#> ola_mES_lif_2_92.counts 2 0.000 0.992 0.00 1.00
#> ola_mES_lif_2_96.counts 2 0.000 0.992 0.00 1.00
#> ola_mES_lif_3_11.counts 2 0.000 0.992 0.00 1.00
#> ola_mES_lif_3_14.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_lif_3_15.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_lif_3_16.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_lif_3_17.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_lif_3_19.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_lif_3_20.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_lif_3_22.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_lif_3_23.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_lif_3_24.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_lif_3_26.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_lif_3_28.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_lif_3_29.counts 2 0.000 0.992 0.00 1.00
#> ola_mES_lif_3_31.counts 2 0.000 0.992 0.00 1.00
#> ola_mES_lif_3_33.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_lif_3_34.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_lif_3_35.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_lif_3_36.counts 2 0.881 0.577 0.30 0.70
#> ola_mES_lif_3_37.counts 2 0.402 0.909 0.08 0.92
#> ola_mES_lif_3_38.counts 2 0.000 0.992 0.00 1.00
#> ola_mES_lif_3_39.counts 2 0.000 0.992 0.00 1.00
#> ola_mES_lif_3_45.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_lif_3_46.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_lif_3_47.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_lif_3_48.counts 2 0.000 0.992 0.00 1.00
#> ola_mES_lif_3_57.counts 2 0.000 0.992 0.00 1.00
#> ola_mES_lif_3_69.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_lif_3_70.counts 1 0.242 0.958 0.96 0.04
#> ola_mES_lif_3_72.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_lif_3_73.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_lif_3_74.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_lif_3_75.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_lif_3_76.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_lif_3_78.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_lif_3_80.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_lif_3_82.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_lif_3_83.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_lif_3_84.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_lif_3_87.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_lif_3_88.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_lif_3_90.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_lif_3_92.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_lif_3_94.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_lif_3_95.counts 1 0.000 0.999 1.00 0.00
#> ola_mES_lif_3_96.counts 1 0.000 0.999 1.00 0.00
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> ola_mES_2i_2_62.counts 2 0.000 1.000 0.00 1.00 0.00
#> ola_mES_2i_2_87.counts 2 0.000 1.000 0.00 1.00 0.00
#> ola_mES_2i_3_17.counts 2 0.000 1.000 0.00 1.00 0.00
#> ola_mES_2i_3_18.counts 2 0.000 1.000 0.00 1.00 0.00
#> ola_mES_2i_3_31.counts 2 0.000 1.000 0.00 1.00 0.00
#> ola_mES_2i_3_49.counts 2 0.000 1.000 0.00 1.00 0.00
#> ola_mES_2i_3_62.counts 2 0.000 1.000 0.00 1.00 0.00
#> ola_mES_2i_3_66.counts 2 0.000 1.000 0.00 1.00 0.00
#> ola_mES_2i_3_78.counts 2 0.000 1.000 0.00 1.00 0.00
#> ola_mES_2i_4_37.counts 2 0.000 1.000 0.00 1.00 0.00
#> ola_mES_2i_4_42.counts 2 0.000 1.000 0.00 1.00 0.00
#> ola_mES_2i_4_54.counts 2 0.000 1.000 0.00 1.00 0.00
#> ola_mES_2i_4_62.counts 2 0.000 1.000 0.00 1.00 0.00
#> ola_mES_a2i_2_26.counts 2 0.000 1.000 0.00 1.00 0.00
#> ola_mES_a2i_2_31.counts 2 0.000 1.000 0.00 1.00 0.00
#> ola_mES_a2i_2_32.counts 2 0.000 1.000 0.00 1.00 0.00
#> ola_mES_a2i_2_35.counts 1 0.000 0.996 1.00 0.00 0.00
#> ola_mES_a2i_2_37.counts 2 0.000 1.000 0.00 1.00 0.00
#> ola_mES_a2i_2_39.counts 2 0.000 1.000 0.00 1.00 0.00
#> ola_mES_a2i_2_50.counts 2 0.000 1.000 0.00 1.00 0.00
#> ola_mES_a2i_2_53.counts 2 0.000 1.000 0.00 1.00 0.00
#> ola_mES_a2i_2_6.counts 2 0.000 1.000 0.00 1.00 0.00
#> ola_mES_a2i_2_69.counts 2 0.000 1.000 0.00 1.00 0.00
#> ola_mES_a2i_2_91.counts 2 0.000 1.000 0.00 1.00 0.00
#> ola_mES_a2i_2_92.counts 2 0.000 1.000 0.00 1.00 0.00
#> ola_mES_a2i_3_17.counts 2 0.000 1.000 0.00 1.00 0.00
#> ola_mES_a2i_3_18.counts 2 0.000 1.000 0.00 1.00 0.00
#> ola_mES_a2i_3_2.counts 2 0.000 1.000 0.00 1.00 0.00
#> ola_mES_a2i_3_20.counts 2 0.000 1.000 0.00 1.00 0.00
#> ola_mES_a2i_3_26.counts 2 0.000 1.000 0.00 1.00 0.00
#> ola_mES_a2i_3_30.counts 2 0.000 1.000 0.00 1.00 0.00
#> ola_mES_a2i_3_31.counts 2 0.000 1.000 0.00 1.00 0.00
#> ola_mES_a2i_3_41.counts 2 0.000 1.000 0.00 1.00 0.00
#> ola_mES_a2i_3_42.counts 2 0.000 1.000 0.00 1.00 0.00
#> ola_mES_a2i_3_45.counts 2 0.000 1.000 0.00 1.00 0.00
#> ola_mES_a2i_3_47.counts 2 0.000 1.000 0.00 1.00 0.00
#> ola_mES_a2i_3_52.counts 2 0.000 1.000 0.00 1.00 0.00
#> ola_mES_a2i_3_56.counts 2 0.000 1.000 0.00 1.00 0.00
#> ola_mES_a2i_3_67.counts 2 0.000 1.000 0.00 1.00 0.00
#> ola_mES_a2i_3_7.counts 2 0.000 1.000 0.00 1.00 0.00
#> ola_mES_a2i_3_75.counts 2 0.000 1.000 0.00 1.00 0.00
#> ola_mES_a2i_3_8.counts 2 0.000 1.000 0.00 1.00 0.00
#> ola_mES_a2i_3_81.counts 2 0.000 1.000 0.00 1.00 0.00
#> ola_mES_a2i_3_94.counts 1 0.000 0.996 1.00 0.00 0.00
#> ola_mES_lif_1_10.counts 3 0.000 0.984 0.00 0.00 1.00
#> ola_mES_lif_1_12.counts 3 0.000 0.984 0.00 0.00 1.00
#> ola_mES_lif_1_14.counts 3 0.000 0.984 0.00 0.00 1.00
#> ola_mES_lif_1_17.counts 1 0.000 0.996 1.00 0.00 0.00
#> ola_mES_lif_1_2.counts 3 0.000 0.984 0.00 0.00 1.00
#> ola_mES_lif_1_21.counts 3 0.000 0.984 0.00 0.00 1.00
#> ola_mES_lif_1_23.counts 1 0.000 0.996 1.00 0.00 0.00
#> ola_mES_lif_1_29.counts 1 0.000 0.996 1.00 0.00 0.00
#> ola_mES_lif_1_31.counts 1 0.000 0.996 1.00 0.00 0.00
#> ola_mES_lif_1_35.counts 1 0.000 0.996 1.00 0.00 0.00
#> ola_mES_lif_1_63.counts 1 0.000 0.996 1.00 0.00 0.00
#> ola_mES_lif_1_64.counts 1 0.429 0.779 0.82 0.18 0.00
#> ola_mES_lif_1_65.counts 1 0.000 0.996 1.00 0.00 0.00
#> ola_mES_lif_1_69.counts 1 0.000 0.996 1.00 0.00 0.00
#> ola_mES_lif_1_70.counts 3 0.000 0.984 0.00 0.00 1.00
#> ola_mES_lif_1_74.counts 1 0.000 0.996 1.00 0.00 0.00
#> ola_mES_lif_1_75.counts 1 0.000 0.996 1.00 0.00 0.00
#> ola_mES_lif_1_79.counts 3 0.296 0.885 0.10 0.00 0.90
#> ola_mES_lif_1_82.counts 1 0.000 0.996 1.00 0.00 0.00
#> ola_mES_lif_1_86.counts 3 0.000 0.984 0.00 0.00 1.00
#> ola_mES_lif_1_93.counts 3 0.000 0.984 0.00 0.00 1.00
#> ola_mES_lif_2_1.counts 3 0.000 0.984 0.00 0.00 1.00
#> ola_mES_lif_2_20.counts 3 0.000 0.984 0.00 0.00 1.00
#> ola_mES_lif_2_25.counts 3 0.000 0.984 0.00 0.00 1.00
#> ola_mES_lif_2_27.counts 3 0.000 0.984 0.00 0.00 1.00
#> ola_mES_lif_2_31.counts 3 0.000 0.984 0.00 0.00 1.00
#> ola_mES_lif_2_33.counts 3 0.000 0.984 0.00 0.00 1.00
#> ola_mES_lif_2_36.counts 3 0.000 0.984 0.00 0.00 1.00
#> ola_mES_lif_2_37.counts 3 0.000 0.984 0.00 0.00 1.00
#> ola_mES_lif_2_41.counts 3 0.000 0.984 0.00 0.00 1.00
#> ola_mES_lif_2_45.counts 3 0.000 0.984 0.00 0.00 1.00
#> ola_mES_lif_2_47.counts 3 0.000 0.984 0.00 0.00 1.00
#> ola_mES_lif_2_57.counts 3 0.595 0.453 0.36 0.00 0.64
#> ola_mES_lif_2_60.counts 3 0.000 0.984 0.00 0.00 1.00
#> ola_mES_lif_2_79.counts 3 0.000 0.984 0.00 0.00 1.00
#> ola_mES_lif_2_81.counts 3 0.000 0.984 0.00 0.00 1.00
#> ola_mES_lif_2_86.counts 3 0.000 0.984 0.00 0.00 1.00
#> ola_mES_lif_2_9.counts 3 0.000 0.984 0.00 0.00 1.00
#> ola_mES_lif_2_90.counts 3 0.000 0.984 0.00 0.00 1.00
#> ola_mES_lif_2_91.counts 3 0.000 0.984 0.00 0.00 1.00
#> ola_mES_lif_2_92.counts 3 0.000 0.984 0.00 0.00 1.00
#> ola_mES_lif_2_96.counts 3 0.000 0.984 0.00 0.00 1.00
#> ola_mES_lif_3_11.counts 3 0.000 0.984 0.00 0.00 1.00
#> ola_mES_lif_3_14.counts 3 0.400 0.813 0.16 0.00 0.84
#> ola_mES_lif_3_15.counts 3 0.254 0.909 0.08 0.00 0.92
#> ola_mES_lif_3_16.counts 1 0.000 0.996 1.00 0.00 0.00
#> ola_mES_lif_3_17.counts 1 0.000 0.996 1.00 0.00 0.00
#> ola_mES_lif_3_19.counts 1 0.000 0.996 1.00 0.00 0.00
#> ola_mES_lif_3_20.counts 1 0.000 0.996 1.00 0.00 0.00
#> ola_mES_lif_3_22.counts 1 0.000 0.996 1.00 0.00 0.00
#> ola_mES_lif_3_23.counts 1 0.000 0.996 1.00 0.00 0.00
#> ola_mES_lif_3_24.counts 1 0.000 0.996 1.00 0.00 0.00
#> ola_mES_lif_3_26.counts 1 0.000 0.996 1.00 0.00 0.00
#> ola_mES_lif_3_28.counts 3 0.000 0.984 0.00 0.00 1.00
#> ola_mES_lif_3_29.counts 3 0.000 0.984 0.00 0.00 1.00
#> ola_mES_lif_3_31.counts 3 0.000 0.984 0.00 0.00 1.00
#> ola_mES_lif_3_33.counts 1 0.000 0.996 1.00 0.00 0.00
#> ola_mES_lif_3_34.counts 1 0.000 0.996 1.00 0.00 0.00
#> ola_mES_lif_3_35.counts 1 0.000 0.996 1.00 0.00 0.00
#> ola_mES_lif_3_36.counts 3 0.000 0.984 0.00 0.00 1.00
#> ola_mES_lif_3_37.counts 3 0.000 0.984 0.00 0.00 1.00
#> ola_mES_lif_3_38.counts 3 0.000 0.984 0.00 0.00 1.00
#> ola_mES_lif_3_39.counts 3 0.000 0.984 0.00 0.00 1.00
#> ola_mES_lif_3_45.counts 1 0.000 0.996 1.00 0.00 0.00
#> ola_mES_lif_3_46.counts 1 0.000 0.996 1.00 0.00 0.00
#> ola_mES_lif_3_47.counts 3 0.000 0.984 0.00 0.00 1.00
#> ola_mES_lif_3_48.counts 3 0.000 0.984 0.00 0.00 1.00
#> ola_mES_lif_3_57.counts 3 0.000 0.984 0.00 0.00 1.00
#> ola_mES_lif_3_69.counts 1 0.000 0.996 1.00 0.00 0.00
#> ola_mES_lif_3_70.counts 3 0.000 0.984 0.00 0.00 1.00
#> ola_mES_lif_3_72.counts 3 0.000 0.984 0.00 0.00 1.00
#> ola_mES_lif_3_73.counts 1 0.000 0.996 1.00 0.00 0.00
#> ola_mES_lif_3_74.counts 1 0.000 0.996 1.00 0.00 0.00
#> ola_mES_lif_3_75.counts 1 0.000 0.996 1.00 0.00 0.00
#> ola_mES_lif_3_76.counts 1 0.000 0.996 1.00 0.00 0.00
#> ola_mES_lif_3_78.counts 1 0.000 0.996 1.00 0.00 0.00
#> ola_mES_lif_3_80.counts 1 0.000 0.996 1.00 0.00 0.00
#> ola_mES_lif_3_82.counts 1 0.000 0.996 1.00 0.00 0.00
#> ola_mES_lif_3_83.counts 1 0.000 0.996 1.00 0.00 0.00
#> ola_mES_lif_3_84.counts 1 0.000 0.996 1.00 0.00 0.00
#> ola_mES_lif_3_87.counts 1 0.000 0.996 1.00 0.00 0.00
#> ola_mES_lif_3_88.counts 1 0.000 0.996 1.00 0.00 0.00
#> ola_mES_lif_3_90.counts 1 0.000 0.996 1.00 0.00 0.00
#> ola_mES_lif_3_92.counts 1 0.000 0.996 1.00 0.00 0.00
#> ola_mES_lif_3_94.counts 1 0.000 0.996 1.00 0.00 0.00
#> ola_mES_lif_3_95.counts 1 0.000 0.996 1.00 0.00 0.00
#> ola_mES_lif_3_96.counts 1 0.000 0.996 1.00 0.00 0.00
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> ola_mES_2i_2_62.counts 2 0.0000 1.000 0.00 1 0.00 0.00
#> ola_mES_2i_2_87.counts 2 0.0000 1.000 0.00 1 0.00 0.00
#> ola_mES_2i_3_17.counts 2 0.0000 1.000 0.00 1 0.00 0.00
#> ola_mES_2i_3_18.counts 2 0.0000 1.000 0.00 1 0.00 0.00
#> ola_mES_2i_3_31.counts 2 0.0000 1.000 0.00 1 0.00 0.00
#> ola_mES_2i_3_49.counts 2 0.0000 1.000 0.00 1 0.00 0.00
#> ola_mES_2i_3_62.counts 2 0.0000 1.000 0.00 1 0.00 0.00
#> ola_mES_2i_3_66.counts 2 0.0000 1.000 0.00 1 0.00 0.00
#> ola_mES_2i_3_78.counts 2 0.0000 1.000 0.00 1 0.00 0.00
#> ola_mES_2i_4_37.counts 2 0.0000 1.000 0.00 1 0.00 0.00
#> ola_mES_2i_4_42.counts 2 0.0000 1.000 0.00 1 0.00 0.00
#> ola_mES_2i_4_54.counts 2 0.0000 1.000 0.00 1 0.00 0.00
#> ola_mES_2i_4_62.counts 2 0.0000 1.000 0.00 1 0.00 0.00
#> ola_mES_a2i_2_26.counts 2 0.0000 1.000 0.00 1 0.00 0.00
#> ola_mES_a2i_2_31.counts 2 0.0000 1.000 0.00 1 0.00 0.00
#> ola_mES_a2i_2_32.counts 2 0.0000 1.000 0.00 1 0.00 0.00
#> ola_mES_a2i_2_35.counts 1 0.0000 0.999 1.00 0 0.00 0.00
#> ola_mES_a2i_2_37.counts 2 0.0000 1.000 0.00 1 0.00 0.00
#> ola_mES_a2i_2_39.counts 2 0.0000 1.000 0.00 1 0.00 0.00
#> ola_mES_a2i_2_50.counts 2 0.0000 1.000 0.00 1 0.00 0.00
#> ola_mES_a2i_2_53.counts 2 0.0000 1.000 0.00 1 0.00 0.00
#> ola_mES_a2i_2_6.counts 2 0.0000 1.000 0.00 1 0.00 0.00
#> ola_mES_a2i_2_69.counts 2 0.0000 1.000 0.00 1 0.00 0.00
#> ola_mES_a2i_2_91.counts 2 0.0000 1.000 0.00 1 0.00 0.00
#> ola_mES_a2i_2_92.counts 2 0.0000 1.000 0.00 1 0.00 0.00
#> ola_mES_a2i_3_17.counts 2 0.0000 1.000 0.00 1 0.00 0.00
#> ola_mES_a2i_3_18.counts 2 0.0000 1.000 0.00 1 0.00 0.00
#> ola_mES_a2i_3_2.counts 2 0.0000 1.000 0.00 1 0.00 0.00
#> ola_mES_a2i_3_20.counts 2 0.0000 1.000 0.00 1 0.00 0.00
#> ola_mES_a2i_3_26.counts 2 0.0000 1.000 0.00 1 0.00 0.00
#> ola_mES_a2i_3_30.counts 2 0.0000 1.000 0.00 1 0.00 0.00
#> ola_mES_a2i_3_31.counts 2 0.0000 1.000 0.00 1 0.00 0.00
#> ola_mES_a2i_3_41.counts 2 0.0000 1.000 0.00 1 0.00 0.00
#> ola_mES_a2i_3_42.counts 2 0.0000 1.000 0.00 1 0.00 0.00
#> ola_mES_a2i_3_45.counts 2 0.0000 1.000 0.00 1 0.00 0.00
#> ola_mES_a2i_3_47.counts 2 0.0000 1.000 0.00 1 0.00 0.00
#> ola_mES_a2i_3_52.counts 2 0.0000 1.000 0.00 1 0.00 0.00
#> ola_mES_a2i_3_56.counts 2 0.0000 1.000 0.00 1 0.00 0.00
#> ola_mES_a2i_3_67.counts 2 0.0000 1.000 0.00 1 0.00 0.00
#> ola_mES_a2i_3_7.counts 2 0.0000 1.000 0.00 1 0.00 0.00
#> ola_mES_a2i_3_75.counts 2 0.0000 1.000 0.00 1 0.00 0.00
#> ola_mES_a2i_3_8.counts 2 0.0000 1.000 0.00 1 0.00 0.00
#> ola_mES_a2i_3_81.counts 2 0.0000 1.000 0.00 1 0.00 0.00
#> ola_mES_a2i_3_94.counts 1 0.0000 0.999 1.00 0 0.00 0.00
#> ola_mES_lif_1_10.counts 3 0.0000 0.998 0.00 0 1.00 0.00
#> ola_mES_lif_1_12.counts 3 0.0000 0.998 0.00 0 1.00 0.00
#> ola_mES_lif_1_14.counts 3 0.0000 0.998 0.00 0 1.00 0.00
#> ola_mES_lif_1_17.counts 1 0.0000 0.999 1.00 0 0.00 0.00
#> ola_mES_lif_1_2.counts 3 0.0000 0.998 0.00 0 1.00 0.00
#> ola_mES_lif_1_21.counts 3 0.0000 0.998 0.00 0 1.00 0.00
#> ola_mES_lif_1_23.counts 1 0.0000 0.999 1.00 0 0.00 0.00
#> ola_mES_lif_1_29.counts 1 0.0000 0.999 1.00 0 0.00 0.00
#> ola_mES_lif_1_31.counts 1 0.0000 0.999 1.00 0 0.00 0.00
#> ola_mES_lif_1_35.counts 1 0.0000 0.999 1.00 0 0.00 0.00
#> ola_mES_lif_1_63.counts 1 0.0000 0.999 1.00 0 0.00 0.00
#> ola_mES_lif_1_64.counts 1 0.0707 0.978 0.98 0 0.02 0.00
#> ola_mES_lif_1_65.counts 1 0.0000 0.999 1.00 0 0.00 0.00
#> ola_mES_lif_1_69.counts 1 0.0000 0.999 1.00 0 0.00 0.00
#> ola_mES_lif_1_70.counts 3 0.0000 0.998 0.00 0 1.00 0.00
#> ola_mES_lif_1_74.counts 1 0.0000 0.999 1.00 0 0.00 0.00
#> ola_mES_lif_1_75.counts 1 0.0000 0.999 1.00 0 0.00 0.00
#> ola_mES_lif_1_79.counts 3 0.0000 0.998 0.00 0 1.00 0.00
#> ola_mES_lif_1_82.counts 1 0.0000 0.999 1.00 0 0.00 0.00
#> ola_mES_lif_1_86.counts 3 0.0000 0.998 0.00 0 1.00 0.00
#> ola_mES_lif_1_93.counts 3 0.0000 0.998 0.00 0 1.00 0.00
#> ola_mES_lif_2_1.counts 3 0.0000 0.998 0.00 0 1.00 0.00
#> ola_mES_lif_2_20.counts 3 0.0000 0.998 0.00 0 1.00 0.00
#> ola_mES_lif_2_25.counts 3 0.0000 0.998 0.00 0 1.00 0.00
#> ola_mES_lif_2_27.counts 3 0.0000 0.998 0.00 0 1.00 0.00
#> ola_mES_lif_2_31.counts 3 0.1211 0.959 0.00 0 0.96 0.04
#> ola_mES_lif_2_33.counts 3 0.0000 0.998 0.00 0 1.00 0.00
#> ola_mES_lif_2_36.counts 4 0.0000 0.988 0.00 0 0.00 1.00
#> ola_mES_lif_2_37.counts 3 0.0000 0.998 0.00 0 1.00 0.00
#> ola_mES_lif_2_41.counts 3 0.0000 0.998 0.00 0 1.00 0.00
#> ola_mES_lif_2_45.counts 4 0.0000 0.988 0.00 0 0.00 1.00
#> ola_mES_lif_2_47.counts 3 0.0000 0.998 0.00 0 1.00 0.00
#> ola_mES_lif_2_57.counts 4 0.0000 0.988 0.00 0 0.00 1.00
#> ola_mES_lif_2_60.counts 3 0.0000 0.998 0.00 0 1.00 0.00
#> ola_mES_lif_2_79.counts 3 0.0000 0.998 0.00 0 1.00 0.00
#> ola_mES_lif_2_81.counts 3 0.0000 0.998 0.00 0 1.00 0.00
#> ola_mES_lif_2_86.counts 3 0.0000 0.998 0.00 0 1.00 0.00
#> ola_mES_lif_2_9.counts 3 0.0000 0.998 0.00 0 1.00 0.00
#> ola_mES_lif_2_90.counts 3 0.0000 0.998 0.00 0 1.00 0.00
#> ola_mES_lif_2_91.counts 3 0.0707 0.979 0.00 0 0.98 0.02
#> ola_mES_lif_2_92.counts 3 0.0000 0.998 0.00 0 1.00 0.00
#> ola_mES_lif_2_96.counts 3 0.0000 0.998 0.00 0 1.00 0.00
#> ola_mES_lif_3_11.counts 3 0.0000 0.998 0.00 0 1.00 0.00
#> ola_mES_lif_3_14.counts 4 0.0000 0.988 0.00 0 0.00 1.00
#> ola_mES_lif_3_15.counts 4 0.0000 0.988 0.00 0 0.00 1.00
#> ola_mES_lif_3_16.counts 4 0.0000 0.988 0.00 0 0.00 1.00
#> ola_mES_lif_3_17.counts 4 0.0000 0.988 0.00 0 0.00 1.00
#> ola_mES_lif_3_19.counts 4 0.1211 0.951 0.04 0 0.00 0.96
#> ola_mES_lif_3_20.counts 1 0.0000 0.999 1.00 0 0.00 0.00
#> ola_mES_lif_3_22.counts 1 0.0000 0.999 1.00 0 0.00 0.00
#> ola_mES_lif_3_23.counts 1 0.0000 0.999 1.00 0 0.00 0.00
#> ola_mES_lif_3_24.counts 1 0.0000 0.999 1.00 0 0.00 0.00
#> ola_mES_lif_3_26.counts 1 0.0000 0.999 1.00 0 0.00 0.00
#> ola_mES_lif_3_28.counts 4 0.0000 0.988 0.00 0 0.00 1.00
#> ola_mES_lif_3_29.counts 3 0.0000 0.998 0.00 0 1.00 0.00
#> ola_mES_lif_3_31.counts 4 0.0000 0.988 0.00 0 0.00 1.00
#> ola_mES_lif_3_33.counts 4 0.0000 0.988 0.00 0 0.00 1.00
#> ola_mES_lif_3_34.counts 4 0.0000 0.988 0.00 0 0.00 1.00
#> ola_mES_lif_3_35.counts 4 0.0000 0.988 0.00 0 0.00 1.00
#> ola_mES_lif_3_36.counts 4 0.3801 0.717 0.00 0 0.22 0.78
#> ola_mES_lif_3_37.counts 3 0.0000 0.998 0.00 0 1.00 0.00
#> ola_mES_lif_3_38.counts 4 0.0000 0.988 0.00 0 0.00 1.00
#> ola_mES_lif_3_39.counts 4 0.0000 0.988 0.00 0 0.00 1.00
#> ola_mES_lif_3_45.counts 4 0.0000 0.988 0.00 0 0.00 1.00
#> ola_mES_lif_3_46.counts 4 0.0000 0.988 0.00 0 0.00 1.00
#> ola_mES_lif_3_47.counts 4 0.0000 0.988 0.00 0 0.00 1.00
#> ola_mES_lif_3_48.counts 3 0.0000 0.998 0.00 0 1.00 0.00
#> ola_mES_lif_3_57.counts 3 0.0000 0.998 0.00 0 1.00 0.00
#> ola_mES_lif_3_69.counts 4 0.0000 0.988 0.00 0 0.00 1.00
#> ola_mES_lif_3_70.counts 4 0.0000 0.988 0.00 0 0.00 1.00
#> ola_mES_lif_3_72.counts 4 0.0000 0.988 0.00 0 0.00 1.00
#> ola_mES_lif_3_73.counts 1 0.0000 0.999 1.00 0 0.00 0.00
#> ola_mES_lif_3_74.counts 1 0.0000 0.999 1.00 0 0.00 0.00
#> ola_mES_lif_3_75.counts 1 0.0000 0.999 1.00 0 0.00 0.00
#> ola_mES_lif_3_76.counts 1 0.0000 0.999 1.00 0 0.00 0.00
#> ola_mES_lif_3_78.counts 1 0.0000 0.999 1.00 0 0.00 0.00
#> ola_mES_lif_3_80.counts 1 0.0000 0.999 1.00 0 0.00 0.00
#> ola_mES_lif_3_82.counts 1 0.0000 0.999 1.00 0 0.00 0.00
#> ola_mES_lif_3_83.counts 1 0.0000 0.999 1.00 0 0.00 0.00
#> ola_mES_lif_3_84.counts 1 0.0000 0.999 1.00 0 0.00 0.00
#> ola_mES_lif_3_87.counts 1 0.0000 0.999 1.00 0 0.00 0.00
#> ola_mES_lif_3_88.counts 1 0.0000 0.999 1.00 0 0.00 0.00
#> ola_mES_lif_3_90.counts 4 0.0000 0.988 0.00 0 0.00 1.00
#> ola_mES_lif_3_92.counts 1 0.0000 0.999 1.00 0 0.00 0.00
#> ola_mES_lif_3_94.counts 1 0.0000 0.999 1.00 0 0.00 0.00
#> ola_mES_lif_3_95.counts 1 0.0000 0.999 1.00 0 0.00 0.00
#> ola_mES_lif_3_96.counts 1 0.0000 0.999 1.00 0 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)
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 , Node0112 , Node0113-leaf , Node0121-leaf , Node0122-leaf , Node0131-leaf , Node0132-leaf , Node0133-leaf , Node0141 , Node0142-leaf , Node0143-leaf , Node0211 , Node0212-leaf , Node0221 , Node0222 , Node0223-leaf , Node0311-leaf , Node0312-leaf , Node0313-leaf , Node0321-leaf , Node0322-leaf , Node0331-leaf , Node0332-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 15421 rows and 34 columns.
#> Top rows (1542) 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 0.5139 0.487 0.487
#> 3 3 1.000 1.000 1.000 0.3223 0.770 0.557
#> 4 4 0.806 0.708 0.862 0.0976 0.895 0.693
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
#> ola_mES_a2i_2_35.counts 2 0 1 0 1
#> ola_mES_a2i_3_94.counts 2 0 1 0 1
#> ola_mES_lif_1_17.counts 2 0 1 0 1
#> ola_mES_lif_1_23.counts 2 0 1 0 1
#> ola_mES_lif_1_29.counts 2 0 1 0 1
#> ola_mES_lif_1_31.counts 2 0 1 0 1
#> ola_mES_lif_1_35.counts 2 0 1 0 1
#> ola_mES_lif_1_63.counts 2 0 1 0 1
#> ola_mES_lif_1_64.counts 2 0 1 0 1
#> ola_mES_lif_1_65.counts 2 0 1 0 1
#> ola_mES_lif_1_69.counts 2 0 1 0 1
#> ola_mES_lif_1_74.counts 2 0 1 0 1
#> ola_mES_lif_1_75.counts 2 0 1 0 1
#> ola_mES_lif_1_82.counts 2 0 1 0 1
#> ola_mES_lif_3_20.counts 1 0 1 1 0
#> ola_mES_lif_3_22.counts 1 0 1 1 0
#> ola_mES_lif_3_23.counts 1 0 1 1 0
#> ola_mES_lif_3_24.counts 1 0 1 1 0
#> ola_mES_lif_3_26.counts 1 0 1 1 0
#> ola_mES_lif_3_73.counts 1 0 1 1 0
#> ola_mES_lif_3_74.counts 1 0 1 1 0
#> ola_mES_lif_3_75.counts 1 0 1 1 0
#> ola_mES_lif_3_76.counts 1 0 1 1 0
#> ola_mES_lif_3_78.counts 1 0 1 1 0
#> ola_mES_lif_3_80.counts 1 0 1 1 0
#> ola_mES_lif_3_82.counts 1 0 1 1 0
#> ola_mES_lif_3_83.counts 2 0 1 0 1
#> ola_mES_lif_3_84.counts 1 0 1 1 0
#> ola_mES_lif_3_87.counts 2 0 1 0 1
#> ola_mES_lif_3_88.counts 1 0 1 1 0
#> ola_mES_lif_3_92.counts 1 0 1 1 0
#> ola_mES_lif_3_94.counts 1 0 1 1 0
#> ola_mES_lif_3_95.counts 1 0 1 1 0
#> ola_mES_lif_3_96.counts 1 0 1 1 0
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> ola_mES_a2i_2_35.counts 2 0 1 0 1 0
#> ola_mES_a2i_3_94.counts 2 0 1 0 1 0
#> ola_mES_lif_1_17.counts 2 0 1 0 1 0
#> ola_mES_lif_1_23.counts 2 0 1 0 1 0
#> ola_mES_lif_1_29.counts 3 0 1 0 0 1
#> ola_mES_lif_1_31.counts 3 0 1 0 0 1
#> ola_mES_lif_1_35.counts 2 0 1 0 1 0
#> ola_mES_lif_1_63.counts 2 0 1 0 1 0
#> ola_mES_lif_1_64.counts 2 0 1 0 1 0
#> ola_mES_lif_1_65.counts 2 0 1 0 1 0
#> ola_mES_lif_1_69.counts 2 0 1 0 1 0
#> ola_mES_lif_1_74.counts 2 0 1 0 1 0
#> ola_mES_lif_1_75.counts 2 0 1 0 1 0
#> ola_mES_lif_1_82.counts 2 0 1 0 1 0
#> ola_mES_lif_3_20.counts 1 0 1 1 0 0
#> ola_mES_lif_3_22.counts 3 0 1 0 0 1
#> ola_mES_lif_3_23.counts 1 0 1 1 0 0
#> ola_mES_lif_3_24.counts 1 0 1 1 0 0
#> ola_mES_lif_3_26.counts 3 0 1 0 0 1
#> ola_mES_lif_3_73.counts 1 0 1 1 0 0
#> ola_mES_lif_3_74.counts 1 0 1 1 0 0
#> ola_mES_lif_3_75.counts 3 0 1 0 0 1
#> ola_mES_lif_3_76.counts 1 0 1 1 0 0
#> ola_mES_lif_3_78.counts 1 0 1 1 0 0
#> ola_mES_lif_3_80.counts 3 0 1 0 0 1
#> ola_mES_lif_3_82.counts 3 0 1 0 0 1
#> ola_mES_lif_3_83.counts 3 0 1 0 0 1
#> ola_mES_lif_3_84.counts 3 0 1 0 0 1
#> ola_mES_lif_3_87.counts 2 0 1 0 1 0
#> ola_mES_lif_3_88.counts 1 0 1 1 0 0
#> ola_mES_lif_3_92.counts 1 0 1 1 0 0
#> ola_mES_lif_3_94.counts 1 0 1 1 0 0
#> ola_mES_lif_3_95.counts 1 0 1 1 0 0
#> ola_mES_lif_3_96.counts 1 0 1 1 0 0
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> ola_mES_a2i_2_35.counts 2 0.2011 0.9239 0.00 0.92 0.00 0.08
#> ola_mES_a2i_3_94.counts 2 0.2011 0.9181 0.00 0.92 0.00 0.08
#> ola_mES_lif_1_17.counts 2 0.0707 0.9542 0.00 0.98 0.00 0.02
#> ola_mES_lif_1_23.counts 2 0.1211 0.9471 0.00 0.96 0.00 0.04
#> ola_mES_lif_1_29.counts 3 0.0000 0.9531 0.00 0.00 1.00 0.00
#> ola_mES_lif_1_31.counts 3 0.0000 0.9531 0.00 0.00 1.00 0.00
#> ola_mES_lif_1_35.counts 2 0.0707 0.9542 0.00 0.98 0.00 0.02
#> ola_mES_lif_1_63.counts 2 0.0707 0.9501 0.00 0.98 0.00 0.02
#> ola_mES_lif_1_64.counts 2 0.0000 0.9564 0.00 1.00 0.00 0.00
#> ola_mES_lif_1_65.counts 2 0.0000 0.9564 0.00 1.00 0.00 0.00
#> ola_mES_lif_1_69.counts 2 0.0000 0.9564 0.00 1.00 0.00 0.00
#> ola_mES_lif_1_74.counts 4 0.4610 0.5807 0.10 0.10 0.00 0.80
#> ola_mES_lif_1_75.counts 2 0.0000 0.9564 0.00 1.00 0.00 0.00
#> ola_mES_lif_1_82.counts 2 0.0707 0.9542 0.00 0.98 0.00 0.02
#> ola_mES_lif_3_20.counts 4 0.4994 0.3907 0.48 0.00 0.00 0.52
#> ola_mES_lif_3_22.counts 3 0.2335 0.9266 0.02 0.00 0.92 0.06
#> ola_mES_lif_3_23.counts 4 0.4277 0.7341 0.28 0.00 0.00 0.72
#> ola_mES_lif_3_24.counts 1 0.1211 0.6142 0.96 0.00 0.00 0.04
#> ola_mES_lif_3_26.counts 3 0.2011 0.9246 0.00 0.00 0.92 0.08
#> ola_mES_lif_3_73.counts 1 0.0000 0.6390 1.00 0.00 0.00 0.00
#> ola_mES_lif_3_74.counts 1 0.0000 0.6390 1.00 0.00 0.00 0.00
#> ola_mES_lif_3_75.counts 3 0.0000 0.9531 0.00 0.00 1.00 0.00
#> ola_mES_lif_3_76.counts 1 0.0000 0.6390 1.00 0.00 0.00 0.00
#> ola_mES_lif_3_78.counts 1 0.2921 0.5489 0.86 0.00 0.00 0.14
#> ola_mES_lif_3_80.counts 1 0.4994 -0.0739 0.52 0.00 0.48 0.00
#> ola_mES_lif_3_82.counts 3 0.0000 0.9531 0.00 0.00 1.00 0.00
#> ola_mES_lif_3_83.counts 3 0.4472 0.7987 0.00 0.02 0.76 0.22
#> ola_mES_lif_3_84.counts 3 0.0000 0.9531 0.00 0.00 1.00 0.00
#> ola_mES_lif_3_87.counts 2 0.4277 0.7216 0.00 0.72 0.00 0.28
#> ola_mES_lif_3_88.counts 4 0.4406 0.7293 0.30 0.00 0.00 0.70
#> ola_mES_lif_3_92.counts 1 0.4977 -0.4103 0.54 0.00 0.00 0.46
#> ola_mES_lif_3_94.counts 1 0.4522 0.0776 0.68 0.00 0.00 0.32
#> ola_mES_lif_3_95.counts 1 0.4855 -0.2109 0.60 0.00 0.00 0.40
#> ola_mES_lif_3_96.counts 1 0.1211 0.6221 0.96 0.00 0.04 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)
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 , Node0112 , Node0113-leaf , Node0121-leaf , Node0122-leaf , Node0131-leaf , Node0132-leaf , Node0133-leaf , Node0141 , Node0142-leaf , Node0143-leaf , Node0211 , Node0212-leaf , Node0221 , Node0222 , Node0223-leaf , Node0311-leaf , Node0312-leaf , Node0313-leaf , Node0321-leaf , Node0322-leaf , Node0331-leaf , Node0332-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 15517 rows and 42 columns.
#> Top rows (1120) 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 0.994 0.997 0.4396 0.562 0.562
#> 3 3 0.926 0.812 0.932 0.5265 0.743 0.554
#> 4 4 0.713 0.787 0.874 0.0849 0.941 0.824
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
#> ola_mES_2i_2_62.counts 2 0.000 0.998 0.00 1.00
#> ola_mES_2i_2_87.counts 2 0.000 0.998 0.00 1.00
#> ola_mES_2i_3_17.counts 2 0.000 0.998 0.00 1.00
#> ola_mES_2i_3_18.counts 2 0.000 0.998 0.00 1.00
#> ola_mES_2i_3_31.counts 2 0.000 0.998 0.00 1.00
#> ola_mES_2i_3_49.counts 2 0.000 0.998 0.00 1.00
#> ola_mES_2i_3_62.counts 2 0.000 0.998 0.00 1.00
#> ola_mES_2i_3_66.counts 2 0.141 0.980 0.02 0.98
#> ola_mES_2i_3_78.counts 2 0.000 0.998 0.00 1.00
#> ola_mES_2i_4_37.counts 2 0.000 0.998 0.00 1.00
#> ola_mES_2i_4_42.counts 2 0.000 0.998 0.00 1.00
#> ola_mES_2i_4_54.counts 2 0.000 0.998 0.00 1.00
#> ola_mES_2i_4_62.counts 2 0.000 0.998 0.00 1.00
#> ola_mES_a2i_2_26.counts 1 0.141 0.983 0.98 0.02
#> ola_mES_a2i_2_31.counts 1 0.000 0.996 1.00 0.00
#> ola_mES_a2i_2_32.counts 1 0.141 0.983 0.98 0.02
#> ola_mES_a2i_2_37.counts 1 0.141 0.983 0.98 0.02
#> ola_mES_a2i_2_39.counts 1 0.000 0.996 1.00 0.00
#> ola_mES_a2i_2_50.counts 1 0.000 0.996 1.00 0.00
#> ola_mES_a2i_2_53.counts 1 0.141 0.983 0.98 0.02
#> ola_mES_a2i_2_6.counts 1 0.141 0.983 0.98 0.02
#> ola_mES_a2i_2_69.counts 1 0.000 0.996 1.00 0.00
#> ola_mES_a2i_2_91.counts 1 0.000 0.996 1.00 0.00
#> ola_mES_a2i_2_92.counts 1 0.141 0.983 0.98 0.02
#> ola_mES_a2i_3_17.counts 1 0.000 0.996 1.00 0.00
#> ola_mES_a2i_3_18.counts 1 0.000 0.996 1.00 0.00
#> ola_mES_a2i_3_2.counts 1 0.000 0.996 1.00 0.00
#> ola_mES_a2i_3_20.counts 1 0.000 0.996 1.00 0.00
#> ola_mES_a2i_3_26.counts 1 0.000 0.996 1.00 0.00
#> ola_mES_a2i_3_30.counts 1 0.000 0.996 1.00 0.00
#> ola_mES_a2i_3_31.counts 1 0.000 0.996 1.00 0.00
#> ola_mES_a2i_3_41.counts 1 0.000 0.996 1.00 0.00
#> ola_mES_a2i_3_42.counts 1 0.000 0.996 1.00 0.00
#> ola_mES_a2i_3_45.counts 1 0.000 0.996 1.00 0.00
#> ola_mES_a2i_3_47.counts 1 0.000 0.996 1.00 0.00
#> ola_mES_a2i_3_52.counts 1 0.000 0.996 1.00 0.00
#> ola_mES_a2i_3_56.counts 1 0.000 0.996 1.00 0.00
#> ola_mES_a2i_3_67.counts 1 0.000 0.996 1.00 0.00
#> ola_mES_a2i_3_7.counts 1 0.000 0.996 1.00 0.00
#> ola_mES_a2i_3_75.counts 1 0.000 0.996 1.00 0.00
#> ola_mES_a2i_3_8.counts 1 0.000 0.996 1.00 0.00
#> ola_mES_a2i_3_81.counts 1 0.000 0.996 1.00 0.00
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> ola_mES_2i_2_62.counts 2 0.4555 -0.4471 0.00 0.80 0.20
#> ola_mES_2i_2_87.counts 2 0.0000 0.0946 0.00 1.00 0.00
#> ola_mES_2i_3_17.counts 2 0.6302 0.8824 0.00 0.52 0.48
#> ola_mES_2i_3_18.counts 2 0.6302 0.8824 0.00 0.52 0.48
#> ola_mES_2i_3_31.counts 2 0.6302 0.8824 0.00 0.52 0.48
#> ola_mES_2i_3_49.counts 2 0.6302 0.8824 0.00 0.52 0.48
#> ola_mES_2i_3_62.counts 2 0.6302 0.8824 0.00 0.52 0.48
#> ola_mES_2i_3_66.counts 3 0.8635 -0.8026 0.10 0.44 0.46
#> ola_mES_2i_3_78.counts 2 0.6302 0.8824 0.00 0.52 0.48
#> ola_mES_2i_4_37.counts 2 0.6302 0.8824 0.00 0.52 0.48
#> ola_mES_2i_4_42.counts 2 0.6302 0.8824 0.00 0.52 0.48
#> ola_mES_2i_4_54.counts 2 0.6302 0.8824 0.00 0.52 0.48
#> ola_mES_2i_4_62.counts 2 0.6302 0.8824 0.00 0.52 0.48
#> ola_mES_a2i_2_26.counts 3 0.6302 0.9032 0.00 0.48 0.52
#> ola_mES_a2i_2_31.counts 1 0.0000 0.9588 1.00 0.00 0.00
#> ola_mES_a2i_2_32.counts 3 0.6302 0.9032 0.00 0.48 0.52
#> ola_mES_a2i_2_37.counts 3 0.6302 0.9032 0.00 0.48 0.52
#> ola_mES_a2i_2_39.counts 3 0.6302 0.9032 0.00 0.48 0.52
#> ola_mES_a2i_2_50.counts 3 0.6302 0.9032 0.00 0.48 0.52
#> ola_mES_a2i_2_53.counts 3 0.6302 0.9032 0.00 0.48 0.52
#> ola_mES_a2i_2_6.counts 3 0.6302 0.9032 0.00 0.48 0.52
#> ola_mES_a2i_2_69.counts 3 0.6302 0.9032 0.00 0.48 0.52
#> ola_mES_a2i_2_91.counts 3 0.6302 0.9032 0.00 0.48 0.52
#> ola_mES_a2i_2_92.counts 3 0.6302 0.9032 0.00 0.48 0.52
#> ola_mES_a2i_3_17.counts 1 0.0000 0.9588 1.00 0.00 0.00
#> ola_mES_a2i_3_18.counts 1 0.0892 0.9479 0.98 0.00 0.02
#> ola_mES_a2i_3_2.counts 1 0.0000 0.9588 1.00 0.00 0.00
#> ola_mES_a2i_3_20.counts 1 0.0000 0.9588 1.00 0.00 0.00
#> ola_mES_a2i_3_26.counts 1 0.0000 0.9588 1.00 0.00 0.00
#> ola_mES_a2i_3_30.counts 1 0.0000 0.9588 1.00 0.00 0.00
#> ola_mES_a2i_3_31.counts 3 0.6302 0.9032 0.00 0.48 0.52
#> ola_mES_a2i_3_41.counts 1 0.0000 0.9588 1.00 0.00 0.00
#> ola_mES_a2i_3_42.counts 1 0.0000 0.9588 1.00 0.00 0.00
#> ola_mES_a2i_3_45.counts 1 0.0000 0.9588 1.00 0.00 0.00
#> ola_mES_a2i_3_47.counts 1 0.1529 0.9289 0.96 0.00 0.04
#> ola_mES_a2i_3_52.counts 1 0.7138 0.6342 0.72 0.16 0.12
#> ola_mES_a2i_3_56.counts 1 0.0892 0.9472 0.98 0.02 0.00
#> ola_mES_a2i_3_67.counts 1 0.0000 0.9588 1.00 0.00 0.00
#> ola_mES_a2i_3_7.counts 1 0.0892 0.9479 0.98 0.00 0.02
#> ola_mES_a2i_3_75.counts 1 0.0000 0.9588 1.00 0.00 0.00
#> ola_mES_a2i_3_8.counts 1 0.0000 0.9588 1.00 0.00 0.00
#> ola_mES_a2i_3_81.counts 1 0.7398 0.5983 0.70 0.18 0.12
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> ola_mES_2i_2_62.counts 4 0.7084 0.4002 0.00 0.14 0.34 0.52
#> ola_mES_2i_2_87.counts 4 0.7004 0.4852 0.00 0.22 0.20 0.58
#> ola_mES_2i_3_17.counts 4 0.3172 0.5945 0.00 0.16 0.00 0.84
#> ola_mES_2i_3_18.counts 4 0.4948 0.0596 0.00 0.44 0.00 0.56
#> ola_mES_2i_3_31.counts 2 0.4907 0.3175 0.00 0.58 0.00 0.42
#> ola_mES_2i_3_49.counts 2 0.4134 0.6344 0.00 0.74 0.00 0.26
#> ola_mES_2i_3_62.counts 2 0.3975 0.6545 0.00 0.76 0.00 0.24
#> ola_mES_2i_3_66.counts 4 0.3972 0.5719 0.08 0.08 0.00 0.84
#> ola_mES_2i_3_78.counts 4 0.3610 0.5690 0.00 0.20 0.00 0.80
#> ola_mES_2i_4_37.counts 2 0.0000 0.7920 0.00 1.00 0.00 0.00
#> ola_mES_2i_4_42.counts 2 0.0000 0.7920 0.00 1.00 0.00 0.00
#> ola_mES_2i_4_54.counts 2 0.0000 0.7920 0.00 1.00 0.00 0.00
#> ola_mES_2i_4_62.counts 2 0.0000 0.7920 0.00 1.00 0.00 0.00
#> ola_mES_a2i_2_26.counts 3 0.0000 0.9744 0.00 0.00 1.00 0.00
#> ola_mES_a2i_2_31.counts 1 0.1637 0.8769 0.94 0.00 0.00 0.06
#> ola_mES_a2i_2_32.counts 3 0.0000 0.9744 0.00 0.00 1.00 0.00
#> ola_mES_a2i_2_37.counts 3 0.0000 0.9744 0.00 0.00 1.00 0.00
#> ola_mES_a2i_2_39.counts 3 0.0000 0.9744 0.00 0.00 1.00 0.00
#> ola_mES_a2i_2_50.counts 3 0.1211 0.9498 0.00 0.00 0.96 0.04
#> ola_mES_a2i_2_53.counts 3 0.0000 0.9744 0.00 0.00 1.00 0.00
#> ola_mES_a2i_2_6.counts 3 0.0000 0.9744 0.00 0.00 1.00 0.00
#> ola_mES_a2i_2_69.counts 3 0.2335 0.9181 0.02 0.00 0.92 0.06
#> ola_mES_a2i_2_91.counts 3 0.0000 0.9744 0.00 0.00 1.00 0.00
#> ola_mES_a2i_2_92.counts 3 0.0000 0.9744 0.00 0.00 1.00 0.00
#> ola_mES_a2i_3_17.counts 1 0.2335 0.8768 0.92 0.00 0.02 0.06
#> ola_mES_a2i_3_18.counts 1 0.4227 0.8355 0.82 0.00 0.06 0.12
#> ola_mES_a2i_3_2.counts 1 0.0707 0.8811 0.98 0.00 0.00 0.02
#> ola_mES_a2i_3_20.counts 1 0.2647 0.8623 0.88 0.00 0.00 0.12
#> ola_mES_a2i_3_26.counts 1 0.1637 0.8769 0.94 0.00 0.00 0.06
#> ola_mES_a2i_3_30.counts 1 0.0707 0.8824 0.98 0.00 0.00 0.02
#> ola_mES_a2i_3_31.counts 3 0.3198 0.8746 0.04 0.00 0.88 0.08
#> ola_mES_a2i_3_41.counts 1 0.1637 0.8779 0.94 0.00 0.00 0.06
#> ola_mES_a2i_3_42.counts 1 0.2011 0.8719 0.92 0.00 0.00 0.08
#> ola_mES_a2i_3_45.counts 1 0.2011 0.8719 0.92 0.00 0.00 0.08
#> ola_mES_a2i_3_47.counts 1 0.4332 0.7937 0.80 0.04 0.00 0.16
#> ola_mES_a2i_3_52.counts 1 0.6248 0.6231 0.64 0.00 0.26 0.10
#> ola_mES_a2i_3_56.counts 1 0.4841 0.8083 0.78 0.00 0.08 0.14
#> ola_mES_a2i_3_67.counts 1 0.1637 0.8831 0.94 0.00 0.00 0.06
#> ola_mES_a2i_3_7.counts 1 0.3037 0.8633 0.88 0.00 0.02 0.10
#> ola_mES_a2i_3_75.counts 1 0.2706 0.8796 0.90 0.00 0.02 0.08
#> ola_mES_a2i_3_8.counts 1 0.2921 0.8368 0.86 0.00 0.00 0.14
#> ola_mES_a2i_3_81.counts 1 0.5256 0.6546 0.70 0.00 0.26 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)
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 , Node0112 , Node0113-leaf , Node0121-leaf , Node0122-leaf , Node0131-leaf , Node0132-leaf , Node0133-leaf , Node0141 , Node0142-leaf , Node0143-leaf , Node0211 , Node0212-leaf , Node0221 , Node0222 , Node0223-leaf , Node0311-leaf , Node0312-leaf , Node0313-leaf , Node0321-leaf , Node0322-leaf , Node0331-leaf , Node0332-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 15367 rows and 32 columns.
#> Top rows (1220) 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 0.509 0.492 0.492
#> 3 3 0.768 0.931 0.923 0.201 0.909 0.816
#> 4 4 0.648 0.724 0.849 0.193 0.855 0.638
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
#> ola_mES_lif_1_10.counts 2 0 1 0 1
#> ola_mES_lif_1_12.counts 2 0 1 0 1
#> ola_mES_lif_1_14.counts 2 0 1 0 1
#> ola_mES_lif_1_2.counts 2 0 1 0 1
#> ola_mES_lif_1_21.counts 2 0 1 0 1
#> ola_mES_lif_1_70.counts 2 0 1 0 1
#> ola_mES_lif_1_79.counts 2 0 1 0 1
#> ola_mES_lif_1_86.counts 2 0 1 0 1
#> ola_mES_lif_1_93.counts 2 0 1 0 1
#> ola_mES_lif_2_1.counts 1 0 1 1 0
#> ola_mES_lif_2_20.counts 1 0 1 1 0
#> ola_mES_lif_2_25.counts 1 0 1 1 0
#> ola_mES_lif_2_27.counts 1 0 1 1 0
#> ola_mES_lif_2_31.counts 1 0 1 1 0
#> ola_mES_lif_2_33.counts 1 0 1 1 0
#> ola_mES_lif_2_37.counts 1 0 1 1 0
#> ola_mES_lif_2_41.counts 1 0 1 1 0
#> ola_mES_lif_2_47.counts 1 0 1 1 0
#> ola_mES_lif_2_60.counts 1 0 1 1 0
#> ola_mES_lif_2_79.counts 1 0 1 1 0
#> ola_mES_lif_2_81.counts 1 0 1 1 0
#> ola_mES_lif_2_86.counts 1 0 1 1 0
#> ola_mES_lif_2_9.counts 1 0 1 1 0
#> ola_mES_lif_2_90.counts 1 0 1 1 0
#> ola_mES_lif_2_91.counts 1 0 1 1 0
#> ola_mES_lif_2_92.counts 1 0 1 1 0
#> ola_mES_lif_2_96.counts 1 0 1 1 0
#> ola_mES_lif_3_11.counts 2 0 1 0 1
#> ola_mES_lif_3_29.counts 2 0 1 0 1
#> ola_mES_lif_3_37.counts 2 0 1 0 1
#> ola_mES_lif_3_48.counts 2 0 1 0 1
#> ola_mES_lif_3_57.counts 2 0 1 0 1
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> ola_mES_lif_1_10.counts 2 0.0892 0.911 0.00 0.98 0.02
#> ola_mES_lif_1_12.counts 2 0.2537 0.900 0.00 0.92 0.08
#> ola_mES_lif_1_14.counts 2 0.2066 0.914 0.00 0.94 0.06
#> ola_mES_lif_1_2.counts 2 0.3340 0.795 0.00 0.88 0.12
#> ola_mES_lif_1_21.counts 2 0.3340 0.866 0.00 0.88 0.12
#> ola_mES_lif_1_70.counts 2 0.2537 0.904 0.00 0.92 0.08
#> ola_mES_lif_1_79.counts 2 0.0000 0.921 0.00 1.00 0.00
#> ola_mES_lif_1_86.counts 2 0.0892 0.923 0.00 0.98 0.02
#> ola_mES_lif_1_93.counts 2 0.0000 0.921 0.00 1.00 0.00
#> ola_mES_lif_2_1.counts 1 0.0000 0.946 1.00 0.00 0.00
#> ola_mES_lif_2_20.counts 1 0.0892 0.943 0.98 0.00 0.02
#> ola_mES_lif_2_25.counts 1 0.0000 0.946 1.00 0.00 0.00
#> ola_mES_lif_2_27.counts 1 0.4002 0.895 0.84 0.00 0.16
#> ola_mES_lif_2_31.counts 1 0.0892 0.943 0.98 0.00 0.02
#> ola_mES_lif_2_33.counts 1 0.0000 0.946 1.00 0.00 0.00
#> ola_mES_lif_2_37.counts 1 0.2066 0.936 0.94 0.00 0.06
#> ola_mES_lif_2_41.counts 1 0.0892 0.943 0.98 0.00 0.02
#> ola_mES_lif_2_47.counts 1 0.2066 0.936 0.94 0.00 0.06
#> ola_mES_lif_2_60.counts 1 0.0892 0.943 0.98 0.00 0.02
#> ola_mES_lif_2_79.counts 1 0.3686 0.904 0.86 0.00 0.14
#> ola_mES_lif_2_81.counts 1 0.1529 0.941 0.96 0.00 0.04
#> ola_mES_lif_2_86.counts 1 0.4002 0.895 0.84 0.00 0.16
#> ola_mES_lif_2_9.counts 1 0.0000 0.946 1.00 0.00 0.00
#> ola_mES_lif_2_90.counts 1 0.4002 0.895 0.84 0.00 0.16
#> ola_mES_lif_2_91.counts 1 0.0892 0.943 0.98 0.00 0.02
#> ola_mES_lif_2_92.counts 1 0.4002 0.895 0.84 0.00 0.16
#> ola_mES_lif_2_96.counts 1 0.0892 0.943 0.98 0.00 0.02
#> ola_mES_lif_3_11.counts 3 0.4291 1.000 0.00 0.18 0.82
#> ola_mES_lif_3_29.counts 3 0.4291 1.000 0.00 0.18 0.82
#> ola_mES_lif_3_37.counts 3 0.4291 1.000 0.00 0.18 0.82
#> ola_mES_lif_3_48.counts 3 0.4291 1.000 0.00 0.18 0.82
#> ola_mES_lif_3_57.counts 3 0.4291 1.000 0.00 0.18 0.82
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> ola_mES_lif_1_10.counts 2 0.201 0.891 0.00 0.92 0.00 0.08
#> ola_mES_lif_1_12.counts 2 0.234 0.912 0.00 0.90 0.10 0.00
#> ola_mES_lif_1_14.counts 2 0.164 0.931 0.00 0.94 0.06 0.00
#> ola_mES_lif_1_2.counts 2 0.201 0.884 0.00 0.92 0.00 0.08
#> ola_mES_lif_1_21.counts 2 0.265 0.904 0.00 0.88 0.12 0.00
#> ola_mES_lif_1_70.counts 2 0.292 0.887 0.00 0.86 0.14 0.00
#> ola_mES_lif_1_79.counts 2 0.000 0.918 0.00 1.00 0.00 0.00
#> ola_mES_lif_1_86.counts 2 0.164 0.931 0.00 0.94 0.06 0.00
#> ola_mES_lif_1_93.counts 2 0.121 0.930 0.00 0.96 0.04 0.00
#> ola_mES_lif_2_1.counts 1 0.413 0.613 0.74 0.00 0.00 0.26
#> ola_mES_lif_2_20.counts 1 0.191 0.670 0.94 0.00 0.04 0.02
#> ola_mES_lif_2_25.counts 1 0.441 0.576 0.70 0.00 0.00 0.30
#> ola_mES_lif_2_27.counts 4 0.234 0.729 0.10 0.00 0.00 0.90
#> ola_mES_lif_2_31.counts 1 0.121 0.658 0.96 0.00 0.04 0.00
#> ola_mES_lif_2_33.counts 1 0.479 0.419 0.62 0.00 0.00 0.38
#> ola_mES_lif_2_37.counts 4 0.500 -0.215 0.50 0.00 0.00 0.50
#> ola_mES_lif_2_41.counts 1 0.320 0.670 0.88 0.00 0.04 0.08
#> ola_mES_lif_2_47.counts 1 0.499 0.072 0.52 0.00 0.00 0.48
#> ola_mES_lif_2_60.counts 1 0.380 0.634 0.78 0.00 0.00 0.22
#> ola_mES_lif_2_79.counts 4 0.452 0.598 0.32 0.00 0.00 0.68
#> ola_mES_lif_2_81.counts 1 0.485 0.377 0.60 0.00 0.00 0.40
#> ola_mES_lif_2_86.counts 4 0.317 0.744 0.16 0.00 0.00 0.84
#> ola_mES_lif_2_9.counts 1 0.380 0.642 0.78 0.00 0.00 0.22
#> ola_mES_lif_2_90.counts 4 0.265 0.725 0.12 0.00 0.00 0.88
#> ola_mES_lif_2_91.counts 1 0.121 0.658 0.96 0.00 0.04 0.00
#> ola_mES_lif_2_92.counts 4 0.292 0.744 0.14 0.00 0.00 0.86
#> ola_mES_lif_2_96.counts 1 0.191 0.669 0.94 0.00 0.04 0.02
#> ola_mES_lif_3_11.counts 3 0.121 1.000 0.00 0.04 0.96 0.00
#> ola_mES_lif_3_29.counts 3 0.121 1.000 0.00 0.04 0.96 0.00
#> ola_mES_lif_3_37.counts 3 0.121 1.000 0.00 0.04 0.96 0.00
#> ola_mES_lif_3_48.counts 3 0.121 1.000 0.00 0.04 0.96 0.00
#> ola_mES_lif_3_57.counts 3 0.121 1.000 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)
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
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#> locale:
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#> [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
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#> other attached packages:
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