Date: 2021-07-26 10:36:12 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 13689 rows and 460 columns.
#> Performed in total 3000 partitions.
#> There are 11 groups under the following parameters:
#> - min_samples: 6
#> - mean_silhouette_cutoff: 0.9
#> - min_n_signatures: 70 (signatures are selected based on:)
#> - fdr_cutoff: 0.05
#> - group_diff (scaled values): 0.5
#>
#> Hierarchy of the partition:
#> 0, 460 cols
#> |-- 01, 221 cols, 1886 signatures
#> | |-- 011, 149 cols, 450 signatures
#> | | |-- 0111, 82 cols, 198 signatures
#> | | | |-- 01111, 46 cols, 62 signatures (c)
#> | | | `-- 01112, 36 cols (a)
#> | | `-- 0112, 67 cols, 200 signatures
#> | | |-- 01121, 32 cols, 14 signatures (c)
#> | | `-- 01122, 35 cols, 12 signatures (c)
#> | `-- 012, 72 cols, 1688 signatures
#> | |-- 0121, 44 cols, 16 signatures (c)
#> | `-- 0122, 28 cols, 16 signatures (c)
#> `-- 02, 239 cols, 1358 signatures
#> |-- 021, 79 cols, 46 signatures (c)
#> |-- 022, 80 cols, 112 signatures
#> | |-- 0221, 39 cols, 9 signatures (c)
#> | `-- 0222, 41 cols, 16 signatures (c)
#> `-- 023, 80 cols, 94 signatures
#> |-- 0231, 45 cols, 3 signatures (c)
#> `-- 0232, 35 cols, 0 signatures (c)
#> Stop reason:
#> a) Mean silhouette score was too small
#> c) There were too few signatures.
#>
#> Following methods can be applied to this 'HierarchicalPartition' object:
#> [1] "all_leaves" "all_nodes" "cola_report" "collect_classes"
#> [5] "colnames" "compare_signatures" "dimension_reduction" "functional_enrichment"
#> [9] "get_anno_col" "get_anno" "get_children_nodes" "get_classes"
#> [13] "get_matrix" "get_signatures" "is_leaf_node" "max_depth"
#> [17] "merge_node" "ncol" "node_info" "node_level"
#> [21] "nrow" "rownames" "show" "split_node"
#> [25] "suggest_best_k" "test_to_known_factors" "top_rows_heatmap" "top_rows_overlap"
#>
#> You can get result for a single node by e.g. object["01"]
The call of hierarchical_partition() was:
#> hierarchical_partition(data = mat, anno = anno, subset = 500, cores = 4)
Dimension of the input matrix:
mat = get_matrix(res_rh)
dim(mat)
#> [1] 13689 460
All the methods that were tried:
res_rh@param$combination_method
#> [[1]]
#> [1] "ATC" "skmeans"
The density distribution for each sample is visualized as one column in the following heatmap. The clustering is based on the distance which is the Kolmogorov-Smirnov statistic between two distributions.
library(ComplexHeatmap)
densityHeatmap(mat, top_annotation = HeatmapAnnotation(df = get_anno(res_rh),
col = get_anno_col(res_rh)), ylab = "value", cluster_columns = TRUE, show_column_names = FALSE,
mc.cores = 1)
Some values about the hierarchy:
all_nodes(res_rh)
#> [1] "0" "01" "011" "0111" "01111" "01112" "0112" "01121" "01122" "012" "0121" "0122"
#> [13] "02" "021" "022" "0221" "0222" "023" "0231" "0232"
all_leaves(res_rh)
#> [1] "01111" "01112" "01121" "01122" "0121" "0122" "021" "0221" "0222" "0231" "0232"
node_info(res_rh)
#> id best_method depth best_k n_columns n_signatures p_signatures is_leaf
#> 1 0 ATC:skmeans 1 2 460 1418 0.103587 FALSE
#> 2 01 ATC:skmeans 2 2 221 1886 0.137775 FALSE
#> 3 011 ATC:skmeans 3 2 149 450 0.032873 FALSE
#> 4 0111 ATC:skmeans 4 2 82 198 0.014464 FALSE
#> 5 01111 ATC:skmeans 5 3 46 62 0.004529 TRUE
#> 6 01112 ATC:skmeans 5 2 36 NA NA TRUE
#> 7 0112 ATC:skmeans 4 2 67 200 0.014610 FALSE
#> 8 01121 ATC:skmeans 5 2 32 14 0.001023 TRUE
#> 9 01122 ATC:skmeans 5 2 35 12 0.000877 TRUE
#> 10 012 ATC:skmeans 3 2 72 1688 0.123311 FALSE
#> 11 0121 ATC:skmeans 4 2 44 16 0.001169 TRUE
#> 12 0122 ATC:skmeans 4 2 28 16 0.001169 TRUE
#> 13 02 ATC:skmeans 2 3 239 1358 0.099204 FALSE
#> 14 021 ATC:skmeans 3 2 79 46 0.003360 TRUE
#> 15 022 ATC:skmeans 3 2 80 112 0.008182 FALSE
#> 16 0221 ATC:skmeans 4 2 39 9 0.000657 TRUE
#> 17 0222 ATC:skmeans 4 3 41 16 0.001169 TRUE
#> 18 023 ATC:skmeans 3 2 80 94 0.006867 FALSE
#> 19 0231 ATC:skmeans 4 2 45 3 0.000219 TRUE
#> 20 0232 ATC:skmeans 4 2 35 0 0.000000 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 | 2 | 1.00 | 0.98 | 0.99 | 460 | ** | |
| Node01 | ATC:skmeans | 2 | 1.00 | 0.99 | 0.99 | 221 | ** | |
| Node011 | ATC:skmeans | 3 | 0.93 | 0.94 | 0.97 | 149 | * | |
| Node0111 | ATC:skmeans | 2 | 1.00 | 0.95 | 0.98 | 82 | ** | |
| Node01111-leaf | ATC:skmeans | ✓ (c) | 3 | 0.94 | 0.93 | 0.97 | 46 | * |
| Node01112-leaf | ATC:skmeans | ✓ (a) | 2 | 0.83 | 0.90 | 0.95 | 36 | |
| Node0112 | ATC:skmeans | 2 | 1.00 | 0.97 | 0.99 | 67 | ** | |
| Node01121-leaf | ATC:skmeans | ✓ (c) | 2 | 1.00 | 0.99 | 1.00 | 32 | ** |
| Node01122-leaf | ATC:skmeans | ✓ (c) | 3 | 0.92 | 0.94 | 0.96 | 35 | * |
| Node012 | ATC:skmeans | 2 | 1.00 | 0.98 | 0.99 | 72 | ** | |
| Node0121-leaf | ATC:skmeans | ✓ (c) | 2 | 1.00 | 0.93 | 0.97 | 44 | ** |
| Node0122-leaf | ATC:skmeans | ✓ (c) | 3 | 0.95 | 0.93 | 0.97 | 28 | ** |
| Node02 | ATC:skmeans | 3 | 0.91 | 0.93 | 0.97 | 239 | * | |
| Node021-leaf | ATC:skmeans | ✓ (c) | 3 | 0.91 | 0.92 | 0.96 | 79 | * |
| Node022 | ATC:skmeans | 2 | 0.90 | 0.94 | 0.98 | 80 | ||
| Node0221-leaf | ATC:skmeans | ✓ (c) | 2 | 0.95 | 0.94 | 0.97 | 39 | * |
| Node0222-leaf | ATC:skmeans | ✓ (c) | 3 | 1.00 | 0.98 | 0.99 | 41 | ** |
| Node023 | ATC:skmeans | 2 | 0.97 | 0.96 | 0.98 | 80 | ** | |
| Node0231-leaf | ATC:skmeans | ✓ (c) | 2 | 0.94 | 0.92 | 0.97 | 45 | * |
| Node0232-leaf | ATC:skmeans | ✓ (c) | 2 | 0.87 | 0.91 | 0.96 | 35 |
Stop reason: a) Mean silhouette score was too small 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 = 94))
collect_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 112))
collect_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 198))
collect_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 200))
collect_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 450))
collect_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 1358))
collect_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 1418))
collect_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 1688))
#> Error in lt[[hierarchy[i, 1]]]$AddChildNode({: attempt to apply non-function
collect_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 1886))
#> 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 = 94))
#> H1_Exp1.001 H1_Exp1.002 H1_Exp1.003 H1_Exp1.004 H1_Exp1.006 H1_Exp1.007 H1_Exp1.008 H1_Exp1.009
#> "021" "01112" "01122" "01111" "01112" "01121" "0222" "0121"
#> H1_Exp1.010 H1_Exp1.011 H1_Exp1.012 H1_Exp1.014 H1_Exp1.015 H1_Exp1.016 H1_Exp1.017 H1_Exp1.018
#> "01121" "01112" "01112" "01112" "01122" "01121" "01121" "01122"
#> H1_Exp1.019 H1_Exp1.020 H1_Exp1.021 H1_Exp1.022 H1_Exp1.023 H1_Exp1.024 H1_Exp1.025 H1_Exp1.026
#> "0121" "01121" "0222" "01122" "01121" "01121" "01122" "01122"
#> H1_Exp1.027 H1_Exp1.029 H1_Exp1.030 H1_Exp1.031 H1_Exp1.032 H1_Exp1.033 H1_Exp1.035 H1_Exp1.036
#> "0232" "01122" "0232" "0232" "0232" "021" "01121" "0232"
#> H1_Exp1.038 H1_Exp1.039 H1_Exp1.040 H1_Exp1.041 H1_Exp1.042 H1_Exp1.043 H1_Exp1.044 H1_Exp1.045
#> "01121" "01122" "01122" "01122" "0231" "0231" "0221" "0221"
#> H1_Exp1.047 H1_Exp1.048 H1_Exp1.049 H1_Exp1.050 H1_Exp1.051 H1_Exp1.052 H1_Exp1.053 H1_Exp1.054
#> "0222" "0221" "01121" "0121" "01122" "0121" "01122" "0121"
#> H1_Exp1.055 H1_Exp1.057 H1_Exp1.058 H1_Exp1.059 H1_Exp1.060 H1_Exp1.061 H1_Exp1.062 H1_Exp1.063
#> "0121" "0121" "0232" "0122" "0221" "01121" "01122" "01122"
#> H1_Exp1.064 H1_Exp1.065 H1_Exp1.066 H1_Exp1.068 H1_Exp1.069 H1_Exp1.070 H1_Exp2.073 H1_Exp2.074
#> "01121" "021" "01122" "0232" "0122" "01121" "01111" "01112"
#> H1_Exp2.075 H1_Exp2.076 H1_Exp2.077 H1_Exp2.078 H1_Exp2.079 H1_Exp2.080 H1_Exp2.081 H1_Exp2.082
#> "01112" "021" "021" "021" "01112" "021" "01112" "0121"
#> H1_Exp2.083 H1_Exp2.084 H1_Exp2.085 H1_Exp2.086 H1_Exp2.087 H1_Exp2.088 H1_Exp2.089 H1_Exp2.090
#> "01112" "01112" "01112" "021" "01112" "01112" "01112" "021"
#> H1_Exp2.091 H1_Exp2.092 H1_Exp2.093 H1_Exp2.094 H1_Exp2.096 H1_Exp2.097 H1_Exp2.098 H1_Exp2.099
#> "01112" "01112" "0121" "01112" "01122" "01122" "01112" "0222"
#> H1_Exp2.100 H1_Exp2.101 H1_Exp2.102 H1_Exp2.103 H1_Exp2.104 H1_Exp2.105 H1_Exp2.106 H1_Exp2.107
#> "01112" "01112" "021" "021" "021" "01112" "01122" "0222"
#> H1_Exp2.108 H1_Exp2.109 H1_Exp2.110 H1_Exp2.111 H1_Exp2.112 H1_Exp2.113 H1_Exp2.114 H1_Exp2.115
#> "01112" "021" "021" "021" "0121" "0221" "021" "0232"
#> H1_Exp2.116 H1_Exp2.117 H1_Exp2.118 H1_Exp2.120 H1_Exp2.121 H1_Exp2.122 H1_Exp2.123 H1_Exp2.124
#> "0121" "0232" "01121" "0232" "01121" "021" "0122" "021"
#> H1_Exp2.125 H1_Exp2.126 H1_Exp2.127 H1_Exp2.128 H1_Exp2.129 H1_Exp2.130 H1_Exp2.132 H1_Exp2.133
#> "0232" "0231" "01122" "021" "0221" "0121" "0232" "0121"
#> H1_Exp2.134 H1_Exp2.135 H1_Exp2.136 H1_Exp2.137 H1_Exp2.138 H1_Exp2.139 H1_Exp2.140 H1_Exp2.141
#> "0222" "0122" "01112" "0231" "01112" "0121" "0121" "021"
#> H1_Exp2.142 H1_Exp2.144 H1_Exp2.145 H1_Exp2.146 H1_Exp2.147 H1_Exp2.148 H1_Exp2.149 H1_Exp2.150
#> "021" "01112" "021" "0122" "0222" "01121" "0231" "0122"
#> H1_Exp2.151 H1_Exp2.152 H1_Exp2.153 H1_Exp2.154 H1_Exp3.218 H1_Exp3.219 H1_Exp3.220 H1_Exp3.221
#> "0222" "01111" "021" "0121" "0222" "021" "0122" "021"
#> H1_Exp3.222 H1_Exp3.223 H1_Exp3.224 H1_Exp3.225 H1_Exp3.226 H1_Exp3.227 H1_Exp3.228 H1_Exp3.229
#> "021" "01121" "0232" "021" "0222" "021" "021" "021"
#> H1_Exp3.230 H1_Exp3.231 H1_Exp3.232 H1_Exp3.233 H1_Exp3.234 H1_Exp3.235 H1_Exp3.236 H1_Exp3.237
#> "021" "021" "01122" "0121" "0221" "0222" "021" "0222"
#> H1_Exp3.238 H1_Exp3.239 H1_Exp3.240 H1_Exp3.241 H1_Exp3.242 H1_Exp3.243 H1_Exp3.244 H1_Exp3.246
#> "0232" "021" "0221" "01122" "0222" "0222" "0232" "0222"
#> H1_Exp3.247 H1_Exp3.248 H1_Exp3.249 H1_Exp3.250 H1_Exp3.251 H1_Exp3.252 H1_Exp3.253 H1_Exp3.254
#> "0232" "0121" "0222" "0222" "0222" "0221" "0232" "0232"
#> H1_Exp3.255 H1_Exp3.256 H1_Exp3.257 H1_Exp3.258 H1_Exp3.259 H1_Exp3.260 H1_Exp3.261 H1_Exp3.262
#> "0222" "0232" "0232" "0221" "0222" "0232" "0232" "0232"
#> H1_Exp3.263 H1_Exp3.264 H1_Exp3.265 H1_Exp3.266 H1_Exp3.267 H1_Exp3.268 H1_Exp3.269 H1_Exp3.270
#> "0232" "0231" "0222" "0232" "0222" "0232" "0221" "0232"
#> H1_Exp3.271 H1_Exp3.272 H1_Exp3.273 H1_Exp3.274 H1_Exp3.275 H1_Exp3.276 H1_Exp3.277 H1_Exp3.278
#> "0232" "0231" "0222" "0221" "0221" "0222" "0121" "0231"
#> H1_Exp3.279 H1_Exp3.280 H1_Exp3.281 H1_Exp3.282 H1_Exp3.283 H1_Exp3.284 H1_Exp3.285 H1_Exp3.286
#> "0232" "0222" "0232" "0232" "0121" "0121" "0221" "0222"
#> H1_Exp3.287 H1_Exp3.288 H1_Exp3.289 H1_Exp3.290 H1_Exp3.291 G2_Exp1.059 G2_Exp1.069 G2_Exp1.075
#> "0222" "0232" "0232" "021" "0221" "0231" "01121" "0221"
#> G2_Exp1.063 G2_Exp1.029 G2_Exp1.076 G2_Exp1.013 G2_Exp1.037 G2_Exp1.057 G2_Exp1.018 G2_Exp1.015
#> "0221" "0221" "01121" "01112" "021" "0231" "0221" "01111"
#> G2_Exp1.019 G2_Exp1.050 G2_Exp1.004 G2_Exp1.061 G2_Exp1.042 G2_Exp1.060 G2_Exp1.058 G2_Exp1.065
#> "01121" "0121" "0221" "021" "0121" "0221" "0221" "0221"
#> G2_Exp1.002 G2_Exp1.044 G2_Exp1.051 G2_Exp1.073 G2_Exp1.030 G2_Exp1.028 G2_Exp1.022 G2_Exp1.034
#> "021" "0231" "0221" "0221" "01121" "021" "0121" "021"
#> G2_Exp1.017 G2_Exp1.047 G2_Exp1.072 G2_Exp1.074 G2_Exp1.054 G2_Exp1.024 G2_Exp1.032 G2_Exp1.020
#> "01112" "01122" "0121" "0121" "0231" "021" "01121" "0222"
#> G2_Exp1.064 G2_Exp1.045 G2_Exp1.038 G2_Exp1.001 G2_Exp1.049 G2_Exp1.031 G2_Exp1.039 G2_Exp1.070
#> "01121" "01121" "01122" "01111" "01122" "01121" "0221" "0121"
#> G2_Exp1.007 G2_Exp1.021 G2_Exp1.036 G2_Exp1.046 G2_Exp1.040 G2_Exp1.068 G2_Exp1.077 G2_Exp1.026
#> "021" "0121" "01121" "01121" "01121" "01121" "01122" "0221"
#> G2_Exp1.011 G2_Exp1.053 G2_Exp1.008 G2_Exp1.006 G2_Exp1.016 G2_Exp1.010 G2_Exp1.014 G2_Exp1.005
#> "021" "0221" "0121" "021" "021" "021" "01111" "01121"
#> G2_Exp1.048 G2_Exp1.027 G2_Exp1.067 G2_Exp1.009 G2_Exp1.062 G2_Exp1.025 G2_Exp1.056 G2_Exp1.055
#> "0122" "0221" "0221" "021" "0121" "021" "01122" "0221"
#> G2_Exp1.041 G2_Exp1.012 G2_Exp1.066 G2_Exp1.023 G2_Exp1.033 G2_Exp1.043 G2_Exp1.052 G2_Exp1.071
#> "01122" "01112" "0121" "0121" "0232" "0221" "0221" "0221"
#> G2_Exp1.003 S_Exp1.071 S_Exp1.074 S_Exp1.031 S_Exp1.032 S_Exp1.035 S_Exp1.030 S_Exp1.056
#> "01112" "0122" "021" "01111" "0121" "0122" "021" "01111"
#> S_Exp1.011 S_Exp1.016 S_Exp1.013 S_Exp1.063 S_Exp1.038 S_Exp1.065 S_Exp1.048 S_Exp1.051
#> "01111" "01111" "0222" "0122" "01111" "021" "01111" "01112"
#> S_Exp1.078 S_Exp1.023 S_Exp1.022 S_Exp1.001 S_Exp1.046 S_Exp1.061 S_Exp1.080 S_Exp1.008
#> "0122" "0122" "01111" "01111" "0122" "01112" "01111" "0122"
#> S_Exp1.069 S_Exp1.076 S_Exp1.019 S_Exp1.003 S_Exp1.006 S_Exp1.029 S_Exp1.025 S_Exp1.064
#> "01111" "021" "01111" "01111" "0122" "01111" "01111" "0122"
#> S_Exp1.041 S_Exp1.057 S_Exp1.068 S_Exp1.073 S_Exp1.014 S_Exp1.039 S_Exp1.055 S_Exp1.033
#> "01111" "01111" "021" "0122" "0122" "0122" "01111" "01111"
#> S_Exp1.081 S_Exp1.004 S_Exp1.045 S_Exp1.009 S_Exp1.054 S_Exp1.018 S_Exp1.034 S_Exp1.042
#> "01111" "01111" "01111" "021" "0122" "01111" "01111" "021"
#> S_Exp1.067 S_Exp1.012 S_Exp1.002 S_Exp1.037 S_Exp1.070 S_Exp1.053 S_Exp1.036 S_Exp1.021
#> "01112" "0122" "021" "01112" "0122" "01111" "01111" "01111"
#> S_Exp1.020 S_Exp1.060 S_Exp1.015 S_Exp1.059 S_Exp1.066 S_Exp1.062 S_Exp1.040 S_Exp1.028
#> "0121" "01111" "0122" "021" "0122" "01111" "0122" "01112"
#> S_Exp1.079 S_Exp1.047 S_Exp1.044 S_Exp1.026 S_Exp1.077 S_Exp1.052 S_Exp1.007 S_Exp1.027
#> "01111" "01111" "021" "0121" "01111" "01111" "01111" "01111"
#> S_Exp1.017 S_Exp1.075 S_Exp1.049 S_Exp1.072 S_Exp1.005 S_Exp1.058 S_Exp1.010 S_Exp1.043
#> "0122" "01111" "01112" "01111" "01111" "01111" "0122" "01111"
#> S_Exp1.050 G1_Exp1.048 G1_Exp1.090 G1_Exp1.061 G1_Exp1.025 G1_Exp1.033 G1_Exp1.081 G1_Exp1.013
#> "01111" "0231" "0222" "0231" "0231" "01122" "0231" "01122"
#> G1_Exp1.024 G1_Exp1.056 G1_Exp1.026 G1_Exp1.064 G1_Exp1.053 G1_Exp1.070 G1_Exp1.087 G1_Exp1.067
#> "021" "0222" "01122" "0221" "0231" "0231" "0231" "0222"
#> G1_Exp1.003 G1_Exp1.052 G1_Exp1.060 G1_Exp1.071 G1_Exp1.045 G1_Exp1.040 G1_Exp1.016 G1_Exp1.027
#> "021" "0231" "0221" "0222" "0222" "0232" "021" "01121"
#> G1_Exp1.059 G1_Exp1.002 G1_Exp1.021 G1_Exp1.015 G1_Exp1.042 G1_Exp1.058 G1_Exp1.075 G1_Exp1.019
#> "0121" "021" "021" "021" "0231" "0231" "0221" "01122"
#> G1_Exp1.074 G1_Exp1.079 G1_Exp1.038 G1_Exp1.035 G1_Exp1.012 G1_Exp1.032 G1_Exp1.009 G1_Exp1.043
#> "0231" "0231" "0231" "021" "021" "0231" "021" "0231"
#> G1_Exp1.089 G1_Exp1.080 G1_Exp1.007 G1_Exp1.062 G1_Exp1.069 G1_Exp1.004 G1_Exp1.010 G1_Exp1.044
#> "0231" "0231" "021" "0231" "0231" "0221" "0222" "0231"
#> G1_Exp1.065 G1_Exp1.029 G1_Exp1.086 G1_Exp1.084 G1_Exp1.088 G1_Exp1.051 G1_Exp1.085 G1_Exp1.066
#> "0222" "021" "021" "0231" "0222" "0231" "0121" "0221"
#> G1_Exp1.047 G1_Exp1.072 G1_Exp1.057 G1_Exp1.054 G1_Exp1.082 G1_Exp1.031 G1_Exp1.077 G1_Exp1.091
#> "0231" "0222" "0231" "0222" "021" "0121" "0231" "021"
#> G1_Exp1.078 G1_Exp1.073 G1_Exp1.068 G1_Exp1.001 G1_Exp1.023 G1_Exp1.022 G1_Exp1.034 G1_Exp1.005
#> "0121" "01122" "0231" "021" "01122" "01121" "0121" "021"
#> G1_Exp1.039 G1_Exp1.037 G1_Exp1.014 G1_Exp1.017 G1_Exp1.020 G1_Exp1.028 G1_Exp1.036 G1_Exp1.049
#> "01122" "0231" "021" "021" "0121" "021" "0231" "0231"
#> G1_Exp1.041 G1_Exp1.006 G1_Exp1.008 G1_Exp1.055 G1_Exp1.050 G1_Exp1.076 G1_Exp1.011 G1_Exp1.063
#> "0222" "021" "021" "0231" "0222" "0231" "0121" "0121"
#> G1_Exp1.083 G1_Exp1.030 G1_Exp1.018 G1_Exp1.046
#> "0231" "0231" "01122" "0121"
get_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 112))
#> H1_Exp1.001 H1_Exp1.002 H1_Exp1.003 H1_Exp1.004 H1_Exp1.006 H1_Exp1.007 H1_Exp1.008 H1_Exp1.009
#> "021" "01112" "01122" "01111" "01112" "01121" "0222" "0121"
#> H1_Exp1.010 H1_Exp1.011 H1_Exp1.012 H1_Exp1.014 H1_Exp1.015 H1_Exp1.016 H1_Exp1.017 H1_Exp1.018
#> "01121" "01112" "01112" "01112" "01122" "01121" "01121" "01122"
#> H1_Exp1.019 H1_Exp1.020 H1_Exp1.021 H1_Exp1.022 H1_Exp1.023 H1_Exp1.024 H1_Exp1.025 H1_Exp1.026
#> "0121" "01121" "0222" "01122" "01121" "01121" "01122" "01122"
#> H1_Exp1.027 H1_Exp1.029 H1_Exp1.030 H1_Exp1.031 H1_Exp1.032 H1_Exp1.033 H1_Exp1.035 H1_Exp1.036
#> "023" "01122" "023" "023" "023" "021" "01121" "023"
#> H1_Exp1.038 H1_Exp1.039 H1_Exp1.040 H1_Exp1.041 H1_Exp1.042 H1_Exp1.043 H1_Exp1.044 H1_Exp1.045
#> "01121" "01122" "01122" "01122" "023" "023" "0221" "0221"
#> H1_Exp1.047 H1_Exp1.048 H1_Exp1.049 H1_Exp1.050 H1_Exp1.051 H1_Exp1.052 H1_Exp1.053 H1_Exp1.054
#> "0222" "0221" "01121" "0121" "01122" "0121" "01122" "0121"
#> H1_Exp1.055 H1_Exp1.057 H1_Exp1.058 H1_Exp1.059 H1_Exp1.060 H1_Exp1.061 H1_Exp1.062 H1_Exp1.063
#> "0121" "0121" "023" "0122" "0221" "01121" "01122" "01122"
#> H1_Exp1.064 H1_Exp1.065 H1_Exp1.066 H1_Exp1.068 H1_Exp1.069 H1_Exp1.070 H1_Exp2.073 H1_Exp2.074
#> "01121" "021" "01122" "023" "0122" "01121" "01111" "01112"
#> H1_Exp2.075 H1_Exp2.076 H1_Exp2.077 H1_Exp2.078 H1_Exp2.079 H1_Exp2.080 H1_Exp2.081 H1_Exp2.082
#> "01112" "021" "021" "021" "01112" "021" "01112" "0121"
#> H1_Exp2.083 H1_Exp2.084 H1_Exp2.085 H1_Exp2.086 H1_Exp2.087 H1_Exp2.088 H1_Exp2.089 H1_Exp2.090
#> "01112" "01112" "01112" "021" "01112" "01112" "01112" "021"
#> H1_Exp2.091 H1_Exp2.092 H1_Exp2.093 H1_Exp2.094 H1_Exp2.096 H1_Exp2.097 H1_Exp2.098 H1_Exp2.099
#> "01112" "01112" "0121" "01112" "01122" "01122" "01112" "0222"
#> H1_Exp2.100 H1_Exp2.101 H1_Exp2.102 H1_Exp2.103 H1_Exp2.104 H1_Exp2.105 H1_Exp2.106 H1_Exp2.107
#> "01112" "01112" "021" "021" "021" "01112" "01122" "0222"
#> H1_Exp2.108 H1_Exp2.109 H1_Exp2.110 H1_Exp2.111 H1_Exp2.112 H1_Exp2.113 H1_Exp2.114 H1_Exp2.115
#> "01112" "021" "021" "021" "0121" "0221" "021" "023"
#> H1_Exp2.116 H1_Exp2.117 H1_Exp2.118 H1_Exp2.120 H1_Exp2.121 H1_Exp2.122 H1_Exp2.123 H1_Exp2.124
#> "0121" "023" "01121" "023" "01121" "021" "0122" "021"
#> H1_Exp2.125 H1_Exp2.126 H1_Exp2.127 H1_Exp2.128 H1_Exp2.129 H1_Exp2.130 H1_Exp2.132 H1_Exp2.133
#> "023" "023" "01122" "021" "0221" "0121" "023" "0121"
#> H1_Exp2.134 H1_Exp2.135 H1_Exp2.136 H1_Exp2.137 H1_Exp2.138 H1_Exp2.139 H1_Exp2.140 H1_Exp2.141
#> "0222" "0122" "01112" "023" "01112" "0121" "0121" "021"
#> H1_Exp2.142 H1_Exp2.144 H1_Exp2.145 H1_Exp2.146 H1_Exp2.147 H1_Exp2.148 H1_Exp2.149 H1_Exp2.150
#> "021" "01112" "021" "0122" "0222" "01121" "023" "0122"
#> H1_Exp2.151 H1_Exp2.152 H1_Exp2.153 H1_Exp2.154 H1_Exp3.218 H1_Exp3.219 H1_Exp3.220 H1_Exp3.221
#> "0222" "01111" "021" "0121" "0222" "021" "0122" "021"
#> H1_Exp3.222 H1_Exp3.223 H1_Exp3.224 H1_Exp3.225 H1_Exp3.226 H1_Exp3.227 H1_Exp3.228 H1_Exp3.229
#> "021" "01121" "023" "021" "0222" "021" "021" "021"
#> H1_Exp3.230 H1_Exp3.231 H1_Exp3.232 H1_Exp3.233 H1_Exp3.234 H1_Exp3.235 H1_Exp3.236 H1_Exp3.237
#> "021" "021" "01122" "0121" "0221" "0222" "021" "0222"
#> H1_Exp3.238 H1_Exp3.239 H1_Exp3.240 H1_Exp3.241 H1_Exp3.242 H1_Exp3.243 H1_Exp3.244 H1_Exp3.246
#> "023" "021" "0221" "01122" "0222" "0222" "023" "0222"
#> H1_Exp3.247 H1_Exp3.248 H1_Exp3.249 H1_Exp3.250 H1_Exp3.251 H1_Exp3.252 H1_Exp3.253 H1_Exp3.254
#> "023" "0121" "0222" "0222" "0222" "0221" "023" "023"
#> H1_Exp3.255 H1_Exp3.256 H1_Exp3.257 H1_Exp3.258 H1_Exp3.259 H1_Exp3.260 H1_Exp3.261 H1_Exp3.262
#> "0222" "023" "023" "0221" "0222" "023" "023" "023"
#> H1_Exp3.263 H1_Exp3.264 H1_Exp3.265 H1_Exp3.266 H1_Exp3.267 H1_Exp3.268 H1_Exp3.269 H1_Exp3.270
#> "023" "023" "0222" "023" "0222" "023" "0221" "023"
#> H1_Exp3.271 H1_Exp3.272 H1_Exp3.273 H1_Exp3.274 H1_Exp3.275 H1_Exp3.276 H1_Exp3.277 H1_Exp3.278
#> "023" "023" "0222" "0221" "0221" "0222" "0121" "023"
#> H1_Exp3.279 H1_Exp3.280 H1_Exp3.281 H1_Exp3.282 H1_Exp3.283 H1_Exp3.284 H1_Exp3.285 H1_Exp3.286
#> "023" "0222" "023" "023" "0121" "0121" "0221" "0222"
#> H1_Exp3.287 H1_Exp3.288 H1_Exp3.289 H1_Exp3.290 H1_Exp3.291 G2_Exp1.059 G2_Exp1.069 G2_Exp1.075
#> "0222" "023" "023" "021" "0221" "023" "01121" "0221"
#> G2_Exp1.063 G2_Exp1.029 G2_Exp1.076 G2_Exp1.013 G2_Exp1.037 G2_Exp1.057 G2_Exp1.018 G2_Exp1.015
#> "0221" "0221" "01121" "01112" "021" "023" "0221" "01111"
#> G2_Exp1.019 G2_Exp1.050 G2_Exp1.004 G2_Exp1.061 G2_Exp1.042 G2_Exp1.060 G2_Exp1.058 G2_Exp1.065
#> "01121" "0121" "0221" "021" "0121" "0221" "0221" "0221"
#> G2_Exp1.002 G2_Exp1.044 G2_Exp1.051 G2_Exp1.073 G2_Exp1.030 G2_Exp1.028 G2_Exp1.022 G2_Exp1.034
#> "021" "023" "0221" "0221" "01121" "021" "0121" "021"
#> G2_Exp1.017 G2_Exp1.047 G2_Exp1.072 G2_Exp1.074 G2_Exp1.054 G2_Exp1.024 G2_Exp1.032 G2_Exp1.020
#> "01112" "01122" "0121" "0121" "023" "021" "01121" "0222"
#> G2_Exp1.064 G2_Exp1.045 G2_Exp1.038 G2_Exp1.001 G2_Exp1.049 G2_Exp1.031 G2_Exp1.039 G2_Exp1.070
#> "01121" "01121" "01122" "01111" "01122" "01121" "0221" "0121"
#> G2_Exp1.007 G2_Exp1.021 G2_Exp1.036 G2_Exp1.046 G2_Exp1.040 G2_Exp1.068 G2_Exp1.077 G2_Exp1.026
#> "021" "0121" "01121" "01121" "01121" "01121" "01122" "0221"
#> G2_Exp1.011 G2_Exp1.053 G2_Exp1.008 G2_Exp1.006 G2_Exp1.016 G2_Exp1.010 G2_Exp1.014 G2_Exp1.005
#> "021" "0221" "0121" "021" "021" "021" "01111" "01121"
#> G2_Exp1.048 G2_Exp1.027 G2_Exp1.067 G2_Exp1.009 G2_Exp1.062 G2_Exp1.025 G2_Exp1.056 G2_Exp1.055
#> "0122" "0221" "0221" "021" "0121" "021" "01122" "0221"
#> G2_Exp1.041 G2_Exp1.012 G2_Exp1.066 G2_Exp1.023 G2_Exp1.033 G2_Exp1.043 G2_Exp1.052 G2_Exp1.071
#> "01122" "01112" "0121" "0121" "023" "0221" "0221" "0221"
#> G2_Exp1.003 S_Exp1.071 S_Exp1.074 S_Exp1.031 S_Exp1.032 S_Exp1.035 S_Exp1.030 S_Exp1.056
#> "01112" "0122" "021" "01111" "0121" "0122" "021" "01111"
#> S_Exp1.011 S_Exp1.016 S_Exp1.013 S_Exp1.063 S_Exp1.038 S_Exp1.065 S_Exp1.048 S_Exp1.051
#> "01111" "01111" "0222" "0122" "01111" "021" "01111" "01112"
#> S_Exp1.078 S_Exp1.023 S_Exp1.022 S_Exp1.001 S_Exp1.046 S_Exp1.061 S_Exp1.080 S_Exp1.008
#> "0122" "0122" "01111" "01111" "0122" "01112" "01111" "0122"
#> S_Exp1.069 S_Exp1.076 S_Exp1.019 S_Exp1.003 S_Exp1.006 S_Exp1.029 S_Exp1.025 S_Exp1.064
#> "01111" "021" "01111" "01111" "0122" "01111" "01111" "0122"
#> S_Exp1.041 S_Exp1.057 S_Exp1.068 S_Exp1.073 S_Exp1.014 S_Exp1.039 S_Exp1.055 S_Exp1.033
#> "01111" "01111" "021" "0122" "0122" "0122" "01111" "01111"
#> S_Exp1.081 S_Exp1.004 S_Exp1.045 S_Exp1.009 S_Exp1.054 S_Exp1.018 S_Exp1.034 S_Exp1.042
#> "01111" "01111" "01111" "021" "0122" "01111" "01111" "021"
#> S_Exp1.067 S_Exp1.012 S_Exp1.002 S_Exp1.037 S_Exp1.070 S_Exp1.053 S_Exp1.036 S_Exp1.021
#> "01112" "0122" "021" "01112" "0122" "01111" "01111" "01111"
#> S_Exp1.020 S_Exp1.060 S_Exp1.015 S_Exp1.059 S_Exp1.066 S_Exp1.062 S_Exp1.040 S_Exp1.028
#> "0121" "01111" "0122" "021" "0122" "01111" "0122" "01112"
#> S_Exp1.079 S_Exp1.047 S_Exp1.044 S_Exp1.026 S_Exp1.077 S_Exp1.052 S_Exp1.007 S_Exp1.027
#> "01111" "01111" "021" "0121" "01111" "01111" "01111" "01111"
#> S_Exp1.017 S_Exp1.075 S_Exp1.049 S_Exp1.072 S_Exp1.005 S_Exp1.058 S_Exp1.010 S_Exp1.043
#> "0122" "01111" "01112" "01111" "01111" "01111" "0122" "01111"
#> S_Exp1.050 G1_Exp1.048 G1_Exp1.090 G1_Exp1.061 G1_Exp1.025 G1_Exp1.033 G1_Exp1.081 G1_Exp1.013
#> "01111" "023" "0222" "023" "023" "01122" "023" "01122"
#> G1_Exp1.024 G1_Exp1.056 G1_Exp1.026 G1_Exp1.064 G1_Exp1.053 G1_Exp1.070 G1_Exp1.087 G1_Exp1.067
#> "021" "0222" "01122" "0221" "023" "023" "023" "0222"
#> G1_Exp1.003 G1_Exp1.052 G1_Exp1.060 G1_Exp1.071 G1_Exp1.045 G1_Exp1.040 G1_Exp1.016 G1_Exp1.027
#> "021" "023" "0221" "0222" "0222" "023" "021" "01121"
#> G1_Exp1.059 G1_Exp1.002 G1_Exp1.021 G1_Exp1.015 G1_Exp1.042 G1_Exp1.058 G1_Exp1.075 G1_Exp1.019
#> "0121" "021" "021" "021" "023" "023" "0221" "01122"
#> G1_Exp1.074 G1_Exp1.079 G1_Exp1.038 G1_Exp1.035 G1_Exp1.012 G1_Exp1.032 G1_Exp1.009 G1_Exp1.043
#> "023" "023" "023" "021" "021" "023" "021" "023"
#> G1_Exp1.089 G1_Exp1.080 G1_Exp1.007 G1_Exp1.062 G1_Exp1.069 G1_Exp1.004 G1_Exp1.010 G1_Exp1.044
#> "023" "023" "021" "023" "023" "0221" "0222" "023"
#> G1_Exp1.065 G1_Exp1.029 G1_Exp1.086 G1_Exp1.084 G1_Exp1.088 G1_Exp1.051 G1_Exp1.085 G1_Exp1.066
#> "0222" "021" "021" "023" "0222" "023" "0121" "0221"
#> G1_Exp1.047 G1_Exp1.072 G1_Exp1.057 G1_Exp1.054 G1_Exp1.082 G1_Exp1.031 G1_Exp1.077 G1_Exp1.091
#> "023" "0222" "023" "0222" "021" "0121" "023" "021"
#> G1_Exp1.078 G1_Exp1.073 G1_Exp1.068 G1_Exp1.001 G1_Exp1.023 G1_Exp1.022 G1_Exp1.034 G1_Exp1.005
#> "0121" "01122" "023" "021" "01122" "01121" "0121" "021"
#> G1_Exp1.039 G1_Exp1.037 G1_Exp1.014 G1_Exp1.017 G1_Exp1.020 G1_Exp1.028 G1_Exp1.036 G1_Exp1.049
#> "01122" "023" "021" "021" "0121" "021" "023" "023"
#> G1_Exp1.041 G1_Exp1.006 G1_Exp1.008 G1_Exp1.055 G1_Exp1.050 G1_Exp1.076 G1_Exp1.011 G1_Exp1.063
#> "0222" "021" "021" "023" "0222" "023" "0121" "0121"
#> G1_Exp1.083 G1_Exp1.030 G1_Exp1.018 G1_Exp1.046
#> "023" "023" "01122" "0121"
get_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 198))
#> H1_Exp1.001 H1_Exp1.002 H1_Exp1.003 H1_Exp1.004 H1_Exp1.006 H1_Exp1.007 H1_Exp1.008 H1_Exp1.009
#> "021" "01112" "01122" "01111" "01112" "01121" "022" "0121"
#> H1_Exp1.010 H1_Exp1.011 H1_Exp1.012 H1_Exp1.014 H1_Exp1.015 H1_Exp1.016 H1_Exp1.017 H1_Exp1.018
#> "01121" "01112" "01112" "01112" "01122" "01121" "01121" "01122"
#> H1_Exp1.019 H1_Exp1.020 H1_Exp1.021 H1_Exp1.022 H1_Exp1.023 H1_Exp1.024 H1_Exp1.025 H1_Exp1.026
#> "0121" "01121" "022" "01122" "01121" "01121" "01122" "01122"
#> H1_Exp1.027 H1_Exp1.029 H1_Exp1.030 H1_Exp1.031 H1_Exp1.032 H1_Exp1.033 H1_Exp1.035 H1_Exp1.036
#> "023" "01122" "023" "023" "023" "021" "01121" "023"
#> H1_Exp1.038 H1_Exp1.039 H1_Exp1.040 H1_Exp1.041 H1_Exp1.042 H1_Exp1.043 H1_Exp1.044 H1_Exp1.045
#> "01121" "01122" "01122" "01122" "023" "023" "022" "022"
#> H1_Exp1.047 H1_Exp1.048 H1_Exp1.049 H1_Exp1.050 H1_Exp1.051 H1_Exp1.052 H1_Exp1.053 H1_Exp1.054
#> "022" "022" "01121" "0121" "01122" "0121" "01122" "0121"
#> H1_Exp1.055 H1_Exp1.057 H1_Exp1.058 H1_Exp1.059 H1_Exp1.060 H1_Exp1.061 H1_Exp1.062 H1_Exp1.063
#> "0121" "0121" "023" "0122" "022" "01121" "01122" "01122"
#> H1_Exp1.064 H1_Exp1.065 H1_Exp1.066 H1_Exp1.068 H1_Exp1.069 H1_Exp1.070 H1_Exp2.073 H1_Exp2.074
#> "01121" "021" "01122" "023" "0122" "01121" "01111" "01112"
#> H1_Exp2.075 H1_Exp2.076 H1_Exp2.077 H1_Exp2.078 H1_Exp2.079 H1_Exp2.080 H1_Exp2.081 H1_Exp2.082
#> "01112" "021" "021" "021" "01112" "021" "01112" "0121"
#> H1_Exp2.083 H1_Exp2.084 H1_Exp2.085 H1_Exp2.086 H1_Exp2.087 H1_Exp2.088 H1_Exp2.089 H1_Exp2.090
#> "01112" "01112" "01112" "021" "01112" "01112" "01112" "021"
#> H1_Exp2.091 H1_Exp2.092 H1_Exp2.093 H1_Exp2.094 H1_Exp2.096 H1_Exp2.097 H1_Exp2.098 H1_Exp2.099
#> "01112" "01112" "0121" "01112" "01122" "01122" "01112" "022"
#> H1_Exp2.100 H1_Exp2.101 H1_Exp2.102 H1_Exp2.103 H1_Exp2.104 H1_Exp2.105 H1_Exp2.106 H1_Exp2.107
#> "01112" "01112" "021" "021" "021" "01112" "01122" "022"
#> H1_Exp2.108 H1_Exp2.109 H1_Exp2.110 H1_Exp2.111 H1_Exp2.112 H1_Exp2.113 H1_Exp2.114 H1_Exp2.115
#> "01112" "021" "021" "021" "0121" "022" "021" "023"
#> H1_Exp2.116 H1_Exp2.117 H1_Exp2.118 H1_Exp2.120 H1_Exp2.121 H1_Exp2.122 H1_Exp2.123 H1_Exp2.124
#> "0121" "023" "01121" "023" "01121" "021" "0122" "021"
#> H1_Exp2.125 H1_Exp2.126 H1_Exp2.127 H1_Exp2.128 H1_Exp2.129 H1_Exp2.130 H1_Exp2.132 H1_Exp2.133
#> "023" "023" "01122" "021" "022" "0121" "023" "0121"
#> H1_Exp2.134 H1_Exp2.135 H1_Exp2.136 H1_Exp2.137 H1_Exp2.138 H1_Exp2.139 H1_Exp2.140 H1_Exp2.141
#> "022" "0122" "01112" "023" "01112" "0121" "0121" "021"
#> H1_Exp2.142 H1_Exp2.144 H1_Exp2.145 H1_Exp2.146 H1_Exp2.147 H1_Exp2.148 H1_Exp2.149 H1_Exp2.150
#> "021" "01112" "021" "0122" "022" "01121" "023" "0122"
#> H1_Exp2.151 H1_Exp2.152 H1_Exp2.153 H1_Exp2.154 H1_Exp3.218 H1_Exp3.219 H1_Exp3.220 H1_Exp3.221
#> "022" "01111" "021" "0121" "022" "021" "0122" "021"
#> H1_Exp3.222 H1_Exp3.223 H1_Exp3.224 H1_Exp3.225 H1_Exp3.226 H1_Exp3.227 H1_Exp3.228 H1_Exp3.229
#> "021" "01121" "023" "021" "022" "021" "021" "021"
#> H1_Exp3.230 H1_Exp3.231 H1_Exp3.232 H1_Exp3.233 H1_Exp3.234 H1_Exp3.235 H1_Exp3.236 H1_Exp3.237
#> "021" "021" "01122" "0121" "022" "022" "021" "022"
#> H1_Exp3.238 H1_Exp3.239 H1_Exp3.240 H1_Exp3.241 H1_Exp3.242 H1_Exp3.243 H1_Exp3.244 H1_Exp3.246
#> "023" "021" "022" "01122" "022" "022" "023" "022"
#> H1_Exp3.247 H1_Exp3.248 H1_Exp3.249 H1_Exp3.250 H1_Exp3.251 H1_Exp3.252 H1_Exp3.253 H1_Exp3.254
#> "023" "0121" "022" "022" "022" "022" "023" "023"
#> H1_Exp3.255 H1_Exp3.256 H1_Exp3.257 H1_Exp3.258 H1_Exp3.259 H1_Exp3.260 H1_Exp3.261 H1_Exp3.262
#> "022" "023" "023" "022" "022" "023" "023" "023"
#> H1_Exp3.263 H1_Exp3.264 H1_Exp3.265 H1_Exp3.266 H1_Exp3.267 H1_Exp3.268 H1_Exp3.269 H1_Exp3.270
#> "023" "023" "022" "023" "022" "023" "022" "023"
#> H1_Exp3.271 H1_Exp3.272 H1_Exp3.273 H1_Exp3.274 H1_Exp3.275 H1_Exp3.276 H1_Exp3.277 H1_Exp3.278
#> "023" "023" "022" "022" "022" "022" "0121" "023"
#> H1_Exp3.279 H1_Exp3.280 H1_Exp3.281 H1_Exp3.282 H1_Exp3.283 H1_Exp3.284 H1_Exp3.285 H1_Exp3.286
#> "023" "022" "023" "023" "0121" "0121" "022" "022"
#> H1_Exp3.287 H1_Exp3.288 H1_Exp3.289 H1_Exp3.290 H1_Exp3.291 G2_Exp1.059 G2_Exp1.069 G2_Exp1.075
#> "022" "023" "023" "021" "022" "023" "01121" "022"
#> G2_Exp1.063 G2_Exp1.029 G2_Exp1.076 G2_Exp1.013 G2_Exp1.037 G2_Exp1.057 G2_Exp1.018 G2_Exp1.015
#> "022" "022" "01121" "01112" "021" "023" "022" "01111"
#> G2_Exp1.019 G2_Exp1.050 G2_Exp1.004 G2_Exp1.061 G2_Exp1.042 G2_Exp1.060 G2_Exp1.058 G2_Exp1.065
#> "01121" "0121" "022" "021" "0121" "022" "022" "022"
#> G2_Exp1.002 G2_Exp1.044 G2_Exp1.051 G2_Exp1.073 G2_Exp1.030 G2_Exp1.028 G2_Exp1.022 G2_Exp1.034
#> "021" "023" "022" "022" "01121" "021" "0121" "021"
#> G2_Exp1.017 G2_Exp1.047 G2_Exp1.072 G2_Exp1.074 G2_Exp1.054 G2_Exp1.024 G2_Exp1.032 G2_Exp1.020
#> "01112" "01122" "0121" "0121" "023" "021" "01121" "022"
#> G2_Exp1.064 G2_Exp1.045 G2_Exp1.038 G2_Exp1.001 G2_Exp1.049 G2_Exp1.031 G2_Exp1.039 G2_Exp1.070
#> "01121" "01121" "01122" "01111" "01122" "01121" "022" "0121"
#> G2_Exp1.007 G2_Exp1.021 G2_Exp1.036 G2_Exp1.046 G2_Exp1.040 G2_Exp1.068 G2_Exp1.077 G2_Exp1.026
#> "021" "0121" "01121" "01121" "01121" "01121" "01122" "022"
#> G2_Exp1.011 G2_Exp1.053 G2_Exp1.008 G2_Exp1.006 G2_Exp1.016 G2_Exp1.010 G2_Exp1.014 G2_Exp1.005
#> "021" "022" "0121" "021" "021" "021" "01111" "01121"
#> G2_Exp1.048 G2_Exp1.027 G2_Exp1.067 G2_Exp1.009 G2_Exp1.062 G2_Exp1.025 G2_Exp1.056 G2_Exp1.055
#> "0122" "022" "022" "021" "0121" "021" "01122" "022"
#> G2_Exp1.041 G2_Exp1.012 G2_Exp1.066 G2_Exp1.023 G2_Exp1.033 G2_Exp1.043 G2_Exp1.052 G2_Exp1.071
#> "01122" "01112" "0121" "0121" "023" "022" "022" "022"
#> G2_Exp1.003 S_Exp1.071 S_Exp1.074 S_Exp1.031 S_Exp1.032 S_Exp1.035 S_Exp1.030 S_Exp1.056
#> "01112" "0122" "021" "01111" "0121" "0122" "021" "01111"
#> S_Exp1.011 S_Exp1.016 S_Exp1.013 S_Exp1.063 S_Exp1.038 S_Exp1.065 S_Exp1.048 S_Exp1.051
#> "01111" "01111" "022" "0122" "01111" "021" "01111" "01112"
#> S_Exp1.078 S_Exp1.023 S_Exp1.022 S_Exp1.001 S_Exp1.046 S_Exp1.061 S_Exp1.080 S_Exp1.008
#> "0122" "0122" "01111" "01111" "0122" "01112" "01111" "0122"
#> S_Exp1.069 S_Exp1.076 S_Exp1.019 S_Exp1.003 S_Exp1.006 S_Exp1.029 S_Exp1.025 S_Exp1.064
#> "01111" "021" "01111" "01111" "0122" "01111" "01111" "0122"
#> S_Exp1.041 S_Exp1.057 S_Exp1.068 S_Exp1.073 S_Exp1.014 S_Exp1.039 S_Exp1.055 S_Exp1.033
#> "01111" "01111" "021" "0122" "0122" "0122" "01111" "01111"
#> S_Exp1.081 S_Exp1.004 S_Exp1.045 S_Exp1.009 S_Exp1.054 S_Exp1.018 S_Exp1.034 S_Exp1.042
#> "01111" "01111" "01111" "021" "0122" "01111" "01111" "021"
#> S_Exp1.067 S_Exp1.012 S_Exp1.002 S_Exp1.037 S_Exp1.070 S_Exp1.053 S_Exp1.036 S_Exp1.021
#> "01112" "0122" "021" "01112" "0122" "01111" "01111" "01111"
#> S_Exp1.020 S_Exp1.060 S_Exp1.015 S_Exp1.059 S_Exp1.066 S_Exp1.062 S_Exp1.040 S_Exp1.028
#> "0121" "01111" "0122" "021" "0122" "01111" "0122" "01112"
#> S_Exp1.079 S_Exp1.047 S_Exp1.044 S_Exp1.026 S_Exp1.077 S_Exp1.052 S_Exp1.007 S_Exp1.027
#> "01111" "01111" "021" "0121" "01111" "01111" "01111" "01111"
#> S_Exp1.017 S_Exp1.075 S_Exp1.049 S_Exp1.072 S_Exp1.005 S_Exp1.058 S_Exp1.010 S_Exp1.043
#> "0122" "01111" "01112" "01111" "01111" "01111" "0122" "01111"
#> S_Exp1.050 G1_Exp1.048 G1_Exp1.090 G1_Exp1.061 G1_Exp1.025 G1_Exp1.033 G1_Exp1.081 G1_Exp1.013
#> "01111" "023" "022" "023" "023" "01122" "023" "01122"
#> G1_Exp1.024 G1_Exp1.056 G1_Exp1.026 G1_Exp1.064 G1_Exp1.053 G1_Exp1.070 G1_Exp1.087 G1_Exp1.067
#> "021" "022" "01122" "022" "023" "023" "023" "022"
#> G1_Exp1.003 G1_Exp1.052 G1_Exp1.060 G1_Exp1.071 G1_Exp1.045 G1_Exp1.040 G1_Exp1.016 G1_Exp1.027
#> "021" "023" "022" "022" "022" "023" "021" "01121"
#> G1_Exp1.059 G1_Exp1.002 G1_Exp1.021 G1_Exp1.015 G1_Exp1.042 G1_Exp1.058 G1_Exp1.075 G1_Exp1.019
#> "0121" "021" "021" "021" "023" "023" "022" "01122"
#> G1_Exp1.074 G1_Exp1.079 G1_Exp1.038 G1_Exp1.035 G1_Exp1.012 G1_Exp1.032 G1_Exp1.009 G1_Exp1.043
#> "023" "023" "023" "021" "021" "023" "021" "023"
#> G1_Exp1.089 G1_Exp1.080 G1_Exp1.007 G1_Exp1.062 G1_Exp1.069 G1_Exp1.004 G1_Exp1.010 G1_Exp1.044
#> "023" "023" "021" "023" "023" "022" "022" "023"
#> G1_Exp1.065 G1_Exp1.029 G1_Exp1.086 G1_Exp1.084 G1_Exp1.088 G1_Exp1.051 G1_Exp1.085 G1_Exp1.066
#> "022" "021" "021" "023" "022" "023" "0121" "022"
#> G1_Exp1.047 G1_Exp1.072 G1_Exp1.057 G1_Exp1.054 G1_Exp1.082 G1_Exp1.031 G1_Exp1.077 G1_Exp1.091
#> "023" "022" "023" "022" "021" "0121" "023" "021"
#> G1_Exp1.078 G1_Exp1.073 G1_Exp1.068 G1_Exp1.001 G1_Exp1.023 G1_Exp1.022 G1_Exp1.034 G1_Exp1.005
#> "0121" "01122" "023" "021" "01122" "01121" "0121" "021"
#> G1_Exp1.039 G1_Exp1.037 G1_Exp1.014 G1_Exp1.017 G1_Exp1.020 G1_Exp1.028 G1_Exp1.036 G1_Exp1.049
#> "01122" "023" "021" "021" "0121" "021" "023" "023"
#> G1_Exp1.041 G1_Exp1.006 G1_Exp1.008 G1_Exp1.055 G1_Exp1.050 G1_Exp1.076 G1_Exp1.011 G1_Exp1.063
#> "022" "021" "021" "023" "022" "023" "0121" "0121"
#> G1_Exp1.083 G1_Exp1.030 G1_Exp1.018 G1_Exp1.046
#> "023" "023" "01122" "0121"
get_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 200))
#> H1_Exp1.001 H1_Exp1.002 H1_Exp1.003 H1_Exp1.004 H1_Exp1.006 H1_Exp1.007 H1_Exp1.008 H1_Exp1.009
#> "021" "0111" "01122" "0111" "0111" "01121" "022" "0121"
#> H1_Exp1.010 H1_Exp1.011 H1_Exp1.012 H1_Exp1.014 H1_Exp1.015 H1_Exp1.016 H1_Exp1.017 H1_Exp1.018
#> "01121" "0111" "0111" "0111" "01122" "01121" "01121" "01122"
#> H1_Exp1.019 H1_Exp1.020 H1_Exp1.021 H1_Exp1.022 H1_Exp1.023 H1_Exp1.024 H1_Exp1.025 H1_Exp1.026
#> "0121" "01121" "022" "01122" "01121" "01121" "01122" "01122"
#> H1_Exp1.027 H1_Exp1.029 H1_Exp1.030 H1_Exp1.031 H1_Exp1.032 H1_Exp1.033 H1_Exp1.035 H1_Exp1.036
#> "023" "01122" "023" "023" "023" "021" "01121" "023"
#> H1_Exp1.038 H1_Exp1.039 H1_Exp1.040 H1_Exp1.041 H1_Exp1.042 H1_Exp1.043 H1_Exp1.044 H1_Exp1.045
#> "01121" "01122" "01122" "01122" "023" "023" "022" "022"
#> H1_Exp1.047 H1_Exp1.048 H1_Exp1.049 H1_Exp1.050 H1_Exp1.051 H1_Exp1.052 H1_Exp1.053 H1_Exp1.054
#> "022" "022" "01121" "0121" "01122" "0121" "01122" "0121"
#> H1_Exp1.055 H1_Exp1.057 H1_Exp1.058 H1_Exp1.059 H1_Exp1.060 H1_Exp1.061 H1_Exp1.062 H1_Exp1.063
#> "0121" "0121" "023" "0122" "022" "01121" "01122" "01122"
#> H1_Exp1.064 H1_Exp1.065 H1_Exp1.066 H1_Exp1.068 H1_Exp1.069 H1_Exp1.070 H1_Exp2.073 H1_Exp2.074
#> "01121" "021" "01122" "023" "0122" "01121" "0111" "0111"
#> H1_Exp2.075 H1_Exp2.076 H1_Exp2.077 H1_Exp2.078 H1_Exp2.079 H1_Exp2.080 H1_Exp2.081 H1_Exp2.082
#> "0111" "021" "021" "021" "0111" "021" "0111" "0121"
#> H1_Exp2.083 H1_Exp2.084 H1_Exp2.085 H1_Exp2.086 H1_Exp2.087 H1_Exp2.088 H1_Exp2.089 H1_Exp2.090
#> "0111" "0111" "0111" "021" "0111" "0111" "0111" "021"
#> H1_Exp2.091 H1_Exp2.092 H1_Exp2.093 H1_Exp2.094 H1_Exp2.096 H1_Exp2.097 H1_Exp2.098 H1_Exp2.099
#> "0111" "0111" "0121" "0111" "01122" "01122" "0111" "022"
#> H1_Exp2.100 H1_Exp2.101 H1_Exp2.102 H1_Exp2.103 H1_Exp2.104 H1_Exp2.105 H1_Exp2.106 H1_Exp2.107
#> "0111" "0111" "021" "021" "021" "0111" "01122" "022"
#> H1_Exp2.108 H1_Exp2.109 H1_Exp2.110 H1_Exp2.111 H1_Exp2.112 H1_Exp2.113 H1_Exp2.114 H1_Exp2.115
#> "0111" "021" "021" "021" "0121" "022" "021" "023"
#> H1_Exp2.116 H1_Exp2.117 H1_Exp2.118 H1_Exp2.120 H1_Exp2.121 H1_Exp2.122 H1_Exp2.123 H1_Exp2.124
#> "0121" "023" "01121" "023" "01121" "021" "0122" "021"
#> H1_Exp2.125 H1_Exp2.126 H1_Exp2.127 H1_Exp2.128 H1_Exp2.129 H1_Exp2.130 H1_Exp2.132 H1_Exp2.133
#> "023" "023" "01122" "021" "022" "0121" "023" "0121"
#> H1_Exp2.134 H1_Exp2.135 H1_Exp2.136 H1_Exp2.137 H1_Exp2.138 H1_Exp2.139 H1_Exp2.140 H1_Exp2.141
#> "022" "0122" "0111" "023" "0111" "0121" "0121" "021"
#> H1_Exp2.142 H1_Exp2.144 H1_Exp2.145 H1_Exp2.146 H1_Exp2.147 H1_Exp2.148 H1_Exp2.149 H1_Exp2.150
#> "021" "0111" "021" "0122" "022" "01121" "023" "0122"
#> H1_Exp2.151 H1_Exp2.152 H1_Exp2.153 H1_Exp2.154 H1_Exp3.218 H1_Exp3.219 H1_Exp3.220 H1_Exp3.221
#> "022" "0111" "021" "0121" "022" "021" "0122" "021"
#> H1_Exp3.222 H1_Exp3.223 H1_Exp3.224 H1_Exp3.225 H1_Exp3.226 H1_Exp3.227 H1_Exp3.228 H1_Exp3.229
#> "021" "01121" "023" "021" "022" "021" "021" "021"
#> H1_Exp3.230 H1_Exp3.231 H1_Exp3.232 H1_Exp3.233 H1_Exp3.234 H1_Exp3.235 H1_Exp3.236 H1_Exp3.237
#> "021" "021" "01122" "0121" "022" "022" "021" "022"
#> H1_Exp3.238 H1_Exp3.239 H1_Exp3.240 H1_Exp3.241 H1_Exp3.242 H1_Exp3.243 H1_Exp3.244 H1_Exp3.246
#> "023" "021" "022" "01122" "022" "022" "023" "022"
#> H1_Exp3.247 H1_Exp3.248 H1_Exp3.249 H1_Exp3.250 H1_Exp3.251 H1_Exp3.252 H1_Exp3.253 H1_Exp3.254
#> "023" "0121" "022" "022" "022" "022" "023" "023"
#> H1_Exp3.255 H1_Exp3.256 H1_Exp3.257 H1_Exp3.258 H1_Exp3.259 H1_Exp3.260 H1_Exp3.261 H1_Exp3.262
#> "022" "023" "023" "022" "022" "023" "023" "023"
#> H1_Exp3.263 H1_Exp3.264 H1_Exp3.265 H1_Exp3.266 H1_Exp3.267 H1_Exp3.268 H1_Exp3.269 H1_Exp3.270
#> "023" "023" "022" "023" "022" "023" "022" "023"
#> H1_Exp3.271 H1_Exp3.272 H1_Exp3.273 H1_Exp3.274 H1_Exp3.275 H1_Exp3.276 H1_Exp3.277 H1_Exp3.278
#> "023" "023" "022" "022" "022" "022" "0121" "023"
#> H1_Exp3.279 H1_Exp3.280 H1_Exp3.281 H1_Exp3.282 H1_Exp3.283 H1_Exp3.284 H1_Exp3.285 H1_Exp3.286
#> "023" "022" "023" "023" "0121" "0121" "022" "022"
#> H1_Exp3.287 H1_Exp3.288 H1_Exp3.289 H1_Exp3.290 H1_Exp3.291 G2_Exp1.059 G2_Exp1.069 G2_Exp1.075
#> "022" "023" "023" "021" "022" "023" "01121" "022"
#> G2_Exp1.063 G2_Exp1.029 G2_Exp1.076 G2_Exp1.013 G2_Exp1.037 G2_Exp1.057 G2_Exp1.018 G2_Exp1.015
#> "022" "022" "01121" "0111" "021" "023" "022" "0111"
#> G2_Exp1.019 G2_Exp1.050 G2_Exp1.004 G2_Exp1.061 G2_Exp1.042 G2_Exp1.060 G2_Exp1.058 G2_Exp1.065
#> "01121" "0121" "022" "021" "0121" "022" "022" "022"
#> G2_Exp1.002 G2_Exp1.044 G2_Exp1.051 G2_Exp1.073 G2_Exp1.030 G2_Exp1.028 G2_Exp1.022 G2_Exp1.034
#> "021" "023" "022" "022" "01121" "021" "0121" "021"
#> G2_Exp1.017 G2_Exp1.047 G2_Exp1.072 G2_Exp1.074 G2_Exp1.054 G2_Exp1.024 G2_Exp1.032 G2_Exp1.020
#> "0111" "01122" "0121" "0121" "023" "021" "01121" "022"
#> G2_Exp1.064 G2_Exp1.045 G2_Exp1.038 G2_Exp1.001 G2_Exp1.049 G2_Exp1.031 G2_Exp1.039 G2_Exp1.070
#> "01121" "01121" "01122" "0111" "01122" "01121" "022" "0121"
#> G2_Exp1.007 G2_Exp1.021 G2_Exp1.036 G2_Exp1.046 G2_Exp1.040 G2_Exp1.068 G2_Exp1.077 G2_Exp1.026
#> "021" "0121" "01121" "01121" "01121" "01121" "01122" "022"
#> G2_Exp1.011 G2_Exp1.053 G2_Exp1.008 G2_Exp1.006 G2_Exp1.016 G2_Exp1.010 G2_Exp1.014 G2_Exp1.005
#> "021" "022" "0121" "021" "021" "021" "0111" "01121"
#> G2_Exp1.048 G2_Exp1.027 G2_Exp1.067 G2_Exp1.009 G2_Exp1.062 G2_Exp1.025 G2_Exp1.056 G2_Exp1.055
#> "0122" "022" "022" "021" "0121" "021" "01122" "022"
#> G2_Exp1.041 G2_Exp1.012 G2_Exp1.066 G2_Exp1.023 G2_Exp1.033 G2_Exp1.043 G2_Exp1.052 G2_Exp1.071
#> "01122" "0111" "0121" "0121" "023" "022" "022" "022"
#> G2_Exp1.003 S_Exp1.071 S_Exp1.074 S_Exp1.031 S_Exp1.032 S_Exp1.035 S_Exp1.030 S_Exp1.056
#> "0111" "0122" "021" "0111" "0121" "0122" "021" "0111"
#> S_Exp1.011 S_Exp1.016 S_Exp1.013 S_Exp1.063 S_Exp1.038 S_Exp1.065 S_Exp1.048 S_Exp1.051
#> "0111" "0111" "022" "0122" "0111" "021" "0111" "0111"
#> S_Exp1.078 S_Exp1.023 S_Exp1.022 S_Exp1.001 S_Exp1.046 S_Exp1.061 S_Exp1.080 S_Exp1.008
#> "0122" "0122" "0111" "0111" "0122" "0111" "0111" "0122"
#> S_Exp1.069 S_Exp1.076 S_Exp1.019 S_Exp1.003 S_Exp1.006 S_Exp1.029 S_Exp1.025 S_Exp1.064
#> "0111" "021" "0111" "0111" "0122" "0111" "0111" "0122"
#> S_Exp1.041 S_Exp1.057 S_Exp1.068 S_Exp1.073 S_Exp1.014 S_Exp1.039 S_Exp1.055 S_Exp1.033
#> "0111" "0111" "021" "0122" "0122" "0122" "0111" "0111"
#> S_Exp1.081 S_Exp1.004 S_Exp1.045 S_Exp1.009 S_Exp1.054 S_Exp1.018 S_Exp1.034 S_Exp1.042
#> "0111" "0111" "0111" "021" "0122" "0111" "0111" "021"
#> S_Exp1.067 S_Exp1.012 S_Exp1.002 S_Exp1.037 S_Exp1.070 S_Exp1.053 S_Exp1.036 S_Exp1.021
#> "0111" "0122" "021" "0111" "0122" "0111" "0111" "0111"
#> S_Exp1.020 S_Exp1.060 S_Exp1.015 S_Exp1.059 S_Exp1.066 S_Exp1.062 S_Exp1.040 S_Exp1.028
#> "0121" "0111" "0122" "021" "0122" "0111" "0122" "0111"
#> S_Exp1.079 S_Exp1.047 S_Exp1.044 S_Exp1.026 S_Exp1.077 S_Exp1.052 S_Exp1.007 S_Exp1.027
#> "0111" "0111" "021" "0121" "0111" "0111" "0111" "0111"
#> S_Exp1.017 S_Exp1.075 S_Exp1.049 S_Exp1.072 S_Exp1.005 S_Exp1.058 S_Exp1.010 S_Exp1.043
#> "0122" "0111" "0111" "0111" "0111" "0111" "0122" "0111"
#> S_Exp1.050 G1_Exp1.048 G1_Exp1.090 G1_Exp1.061 G1_Exp1.025 G1_Exp1.033 G1_Exp1.081 G1_Exp1.013
#> "0111" "023" "022" "023" "023" "01122" "023" "01122"
#> G1_Exp1.024 G1_Exp1.056 G1_Exp1.026 G1_Exp1.064 G1_Exp1.053 G1_Exp1.070 G1_Exp1.087 G1_Exp1.067
#> "021" "022" "01122" "022" "023" "023" "023" "022"
#> G1_Exp1.003 G1_Exp1.052 G1_Exp1.060 G1_Exp1.071 G1_Exp1.045 G1_Exp1.040 G1_Exp1.016 G1_Exp1.027
#> "021" "023" "022" "022" "022" "023" "021" "01121"
#> G1_Exp1.059 G1_Exp1.002 G1_Exp1.021 G1_Exp1.015 G1_Exp1.042 G1_Exp1.058 G1_Exp1.075 G1_Exp1.019
#> "0121" "021" "021" "021" "023" "023" "022" "01122"
#> G1_Exp1.074 G1_Exp1.079 G1_Exp1.038 G1_Exp1.035 G1_Exp1.012 G1_Exp1.032 G1_Exp1.009 G1_Exp1.043
#> "023" "023" "023" "021" "021" "023" "021" "023"
#> G1_Exp1.089 G1_Exp1.080 G1_Exp1.007 G1_Exp1.062 G1_Exp1.069 G1_Exp1.004 G1_Exp1.010 G1_Exp1.044
#> "023" "023" "021" "023" "023" "022" "022" "023"
#> G1_Exp1.065 G1_Exp1.029 G1_Exp1.086 G1_Exp1.084 G1_Exp1.088 G1_Exp1.051 G1_Exp1.085 G1_Exp1.066
#> "022" "021" "021" "023" "022" "023" "0121" "022"
#> G1_Exp1.047 G1_Exp1.072 G1_Exp1.057 G1_Exp1.054 G1_Exp1.082 G1_Exp1.031 G1_Exp1.077 G1_Exp1.091
#> "023" "022" "023" "022" "021" "0121" "023" "021"
#> G1_Exp1.078 G1_Exp1.073 G1_Exp1.068 G1_Exp1.001 G1_Exp1.023 G1_Exp1.022 G1_Exp1.034 G1_Exp1.005
#> "0121" "01122" "023" "021" "01122" "01121" "0121" "021"
#> G1_Exp1.039 G1_Exp1.037 G1_Exp1.014 G1_Exp1.017 G1_Exp1.020 G1_Exp1.028 G1_Exp1.036 G1_Exp1.049
#> "01122" "023" "021" "021" "0121" "021" "023" "023"
#> G1_Exp1.041 G1_Exp1.006 G1_Exp1.008 G1_Exp1.055 G1_Exp1.050 G1_Exp1.076 G1_Exp1.011 G1_Exp1.063
#> "022" "021" "021" "023" "022" "023" "0121" "0121"
#> G1_Exp1.083 G1_Exp1.030 G1_Exp1.018 G1_Exp1.046
#> "023" "023" "01122" "0121"
get_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 450))
#> H1_Exp1.001 H1_Exp1.002 H1_Exp1.003 H1_Exp1.004 H1_Exp1.006 H1_Exp1.007 H1_Exp1.008 H1_Exp1.009
#> "021" "0111" "0112" "0111" "0111" "0112" "022" "0121"
#> H1_Exp1.010 H1_Exp1.011 H1_Exp1.012 H1_Exp1.014 H1_Exp1.015 H1_Exp1.016 H1_Exp1.017 H1_Exp1.018
#> "0112" "0111" "0111" "0111" "0112" "0112" "0112" "0112"
#> H1_Exp1.019 H1_Exp1.020 H1_Exp1.021 H1_Exp1.022 H1_Exp1.023 H1_Exp1.024 H1_Exp1.025 H1_Exp1.026
#> "0121" "0112" "022" "0112" "0112" "0112" "0112" "0112"
#> H1_Exp1.027 H1_Exp1.029 H1_Exp1.030 H1_Exp1.031 H1_Exp1.032 H1_Exp1.033 H1_Exp1.035 H1_Exp1.036
#> "023" "0112" "023" "023" "023" "021" "0112" "023"
#> H1_Exp1.038 H1_Exp1.039 H1_Exp1.040 H1_Exp1.041 H1_Exp1.042 H1_Exp1.043 H1_Exp1.044 H1_Exp1.045
#> "0112" "0112" "0112" "0112" "023" "023" "022" "022"
#> H1_Exp1.047 H1_Exp1.048 H1_Exp1.049 H1_Exp1.050 H1_Exp1.051 H1_Exp1.052 H1_Exp1.053 H1_Exp1.054
#> "022" "022" "0112" "0121" "0112" "0121" "0112" "0121"
#> H1_Exp1.055 H1_Exp1.057 H1_Exp1.058 H1_Exp1.059 H1_Exp1.060 H1_Exp1.061 H1_Exp1.062 H1_Exp1.063
#> "0121" "0121" "023" "0122" "022" "0112" "0112" "0112"
#> H1_Exp1.064 H1_Exp1.065 H1_Exp1.066 H1_Exp1.068 H1_Exp1.069 H1_Exp1.070 H1_Exp2.073 H1_Exp2.074
#> "0112" "021" "0112" "023" "0122" "0112" "0111" "0111"
#> H1_Exp2.075 H1_Exp2.076 H1_Exp2.077 H1_Exp2.078 H1_Exp2.079 H1_Exp2.080 H1_Exp2.081 H1_Exp2.082
#> "0111" "021" "021" "021" "0111" "021" "0111" "0121"
#> H1_Exp2.083 H1_Exp2.084 H1_Exp2.085 H1_Exp2.086 H1_Exp2.087 H1_Exp2.088 H1_Exp2.089 H1_Exp2.090
#> "0111" "0111" "0111" "021" "0111" "0111" "0111" "021"
#> H1_Exp2.091 H1_Exp2.092 H1_Exp2.093 H1_Exp2.094 H1_Exp2.096 H1_Exp2.097 H1_Exp2.098 H1_Exp2.099
#> "0111" "0111" "0121" "0111" "0112" "0112" "0111" "022"
#> H1_Exp2.100 H1_Exp2.101 H1_Exp2.102 H1_Exp2.103 H1_Exp2.104 H1_Exp2.105 H1_Exp2.106 H1_Exp2.107
#> "0111" "0111" "021" "021" "021" "0111" "0112" "022"
#> H1_Exp2.108 H1_Exp2.109 H1_Exp2.110 H1_Exp2.111 H1_Exp2.112 H1_Exp2.113 H1_Exp2.114 H1_Exp2.115
#> "0111" "021" "021" "021" "0121" "022" "021" "023"
#> H1_Exp2.116 H1_Exp2.117 H1_Exp2.118 H1_Exp2.120 H1_Exp2.121 H1_Exp2.122 H1_Exp2.123 H1_Exp2.124
#> "0121" "023" "0112" "023" "0112" "021" "0122" "021"
#> H1_Exp2.125 H1_Exp2.126 H1_Exp2.127 H1_Exp2.128 H1_Exp2.129 H1_Exp2.130 H1_Exp2.132 H1_Exp2.133
#> "023" "023" "0112" "021" "022" "0121" "023" "0121"
#> H1_Exp2.134 H1_Exp2.135 H1_Exp2.136 H1_Exp2.137 H1_Exp2.138 H1_Exp2.139 H1_Exp2.140 H1_Exp2.141
#> "022" "0122" "0111" "023" "0111" "0121" "0121" "021"
#> H1_Exp2.142 H1_Exp2.144 H1_Exp2.145 H1_Exp2.146 H1_Exp2.147 H1_Exp2.148 H1_Exp2.149 H1_Exp2.150
#> "021" "0111" "021" "0122" "022" "0112" "023" "0122"
#> H1_Exp2.151 H1_Exp2.152 H1_Exp2.153 H1_Exp2.154 H1_Exp3.218 H1_Exp3.219 H1_Exp3.220 H1_Exp3.221
#> "022" "0111" "021" "0121" "022" "021" "0122" "021"
#> H1_Exp3.222 H1_Exp3.223 H1_Exp3.224 H1_Exp3.225 H1_Exp3.226 H1_Exp3.227 H1_Exp3.228 H1_Exp3.229
#> "021" "0112" "023" "021" "022" "021" "021" "021"
#> H1_Exp3.230 H1_Exp3.231 H1_Exp3.232 H1_Exp3.233 H1_Exp3.234 H1_Exp3.235 H1_Exp3.236 H1_Exp3.237
#> "021" "021" "0112" "0121" "022" "022" "021" "022"
#> H1_Exp3.238 H1_Exp3.239 H1_Exp3.240 H1_Exp3.241 H1_Exp3.242 H1_Exp3.243 H1_Exp3.244 H1_Exp3.246
#> "023" "021" "022" "0112" "022" "022" "023" "022"
#> H1_Exp3.247 H1_Exp3.248 H1_Exp3.249 H1_Exp3.250 H1_Exp3.251 H1_Exp3.252 H1_Exp3.253 H1_Exp3.254
#> "023" "0121" "022" "022" "022" "022" "023" "023"
#> H1_Exp3.255 H1_Exp3.256 H1_Exp3.257 H1_Exp3.258 H1_Exp3.259 H1_Exp3.260 H1_Exp3.261 H1_Exp3.262
#> "022" "023" "023" "022" "022" "023" "023" "023"
#> H1_Exp3.263 H1_Exp3.264 H1_Exp3.265 H1_Exp3.266 H1_Exp3.267 H1_Exp3.268 H1_Exp3.269 H1_Exp3.270
#> "023" "023" "022" "023" "022" "023" "022" "023"
#> H1_Exp3.271 H1_Exp3.272 H1_Exp3.273 H1_Exp3.274 H1_Exp3.275 H1_Exp3.276 H1_Exp3.277 H1_Exp3.278
#> "023" "023" "022" "022" "022" "022" "0121" "023"
#> H1_Exp3.279 H1_Exp3.280 H1_Exp3.281 H1_Exp3.282 H1_Exp3.283 H1_Exp3.284 H1_Exp3.285 H1_Exp3.286
#> "023" "022" "023" "023" "0121" "0121" "022" "022"
#> H1_Exp3.287 H1_Exp3.288 H1_Exp3.289 H1_Exp3.290 H1_Exp3.291 G2_Exp1.059 G2_Exp1.069 G2_Exp1.075
#> "022" "023" "023" "021" "022" "023" "0112" "022"
#> G2_Exp1.063 G2_Exp1.029 G2_Exp1.076 G2_Exp1.013 G2_Exp1.037 G2_Exp1.057 G2_Exp1.018 G2_Exp1.015
#> "022" "022" "0112" "0111" "021" "023" "022" "0111"
#> G2_Exp1.019 G2_Exp1.050 G2_Exp1.004 G2_Exp1.061 G2_Exp1.042 G2_Exp1.060 G2_Exp1.058 G2_Exp1.065
#> "0112" "0121" "022" "021" "0121" "022" "022" "022"
#> G2_Exp1.002 G2_Exp1.044 G2_Exp1.051 G2_Exp1.073 G2_Exp1.030 G2_Exp1.028 G2_Exp1.022 G2_Exp1.034
#> "021" "023" "022" "022" "0112" "021" "0121" "021"
#> G2_Exp1.017 G2_Exp1.047 G2_Exp1.072 G2_Exp1.074 G2_Exp1.054 G2_Exp1.024 G2_Exp1.032 G2_Exp1.020
#> "0111" "0112" "0121" "0121" "023" "021" "0112" "022"
#> G2_Exp1.064 G2_Exp1.045 G2_Exp1.038 G2_Exp1.001 G2_Exp1.049 G2_Exp1.031 G2_Exp1.039 G2_Exp1.070
#> "0112" "0112" "0112" "0111" "0112" "0112" "022" "0121"
#> G2_Exp1.007 G2_Exp1.021 G2_Exp1.036 G2_Exp1.046 G2_Exp1.040 G2_Exp1.068 G2_Exp1.077 G2_Exp1.026
#> "021" "0121" "0112" "0112" "0112" "0112" "0112" "022"
#> G2_Exp1.011 G2_Exp1.053 G2_Exp1.008 G2_Exp1.006 G2_Exp1.016 G2_Exp1.010 G2_Exp1.014 G2_Exp1.005
#> "021" "022" "0121" "021" "021" "021" "0111" "0112"
#> G2_Exp1.048 G2_Exp1.027 G2_Exp1.067 G2_Exp1.009 G2_Exp1.062 G2_Exp1.025 G2_Exp1.056 G2_Exp1.055
#> "0122" "022" "022" "021" "0121" "021" "0112" "022"
#> G2_Exp1.041 G2_Exp1.012 G2_Exp1.066 G2_Exp1.023 G2_Exp1.033 G2_Exp1.043 G2_Exp1.052 G2_Exp1.071
#> "0112" "0111" "0121" "0121" "023" "022" "022" "022"
#> G2_Exp1.003 S_Exp1.071 S_Exp1.074 S_Exp1.031 S_Exp1.032 S_Exp1.035 S_Exp1.030 S_Exp1.056
#> "0111" "0122" "021" "0111" "0121" "0122" "021" "0111"
#> S_Exp1.011 S_Exp1.016 S_Exp1.013 S_Exp1.063 S_Exp1.038 S_Exp1.065 S_Exp1.048 S_Exp1.051
#> "0111" "0111" "022" "0122" "0111" "021" "0111" "0111"
#> S_Exp1.078 S_Exp1.023 S_Exp1.022 S_Exp1.001 S_Exp1.046 S_Exp1.061 S_Exp1.080 S_Exp1.008
#> "0122" "0122" "0111" "0111" "0122" "0111" "0111" "0122"
#> S_Exp1.069 S_Exp1.076 S_Exp1.019 S_Exp1.003 S_Exp1.006 S_Exp1.029 S_Exp1.025 S_Exp1.064
#> "0111" "021" "0111" "0111" "0122" "0111" "0111" "0122"
#> S_Exp1.041 S_Exp1.057 S_Exp1.068 S_Exp1.073 S_Exp1.014 S_Exp1.039 S_Exp1.055 S_Exp1.033
#> "0111" "0111" "021" "0122" "0122" "0122" "0111" "0111"
#> S_Exp1.081 S_Exp1.004 S_Exp1.045 S_Exp1.009 S_Exp1.054 S_Exp1.018 S_Exp1.034 S_Exp1.042
#> "0111" "0111" "0111" "021" "0122" "0111" "0111" "021"
#> S_Exp1.067 S_Exp1.012 S_Exp1.002 S_Exp1.037 S_Exp1.070 S_Exp1.053 S_Exp1.036 S_Exp1.021
#> "0111" "0122" "021" "0111" "0122" "0111" "0111" "0111"
#> S_Exp1.020 S_Exp1.060 S_Exp1.015 S_Exp1.059 S_Exp1.066 S_Exp1.062 S_Exp1.040 S_Exp1.028
#> "0121" "0111" "0122" "021" "0122" "0111" "0122" "0111"
#> S_Exp1.079 S_Exp1.047 S_Exp1.044 S_Exp1.026 S_Exp1.077 S_Exp1.052 S_Exp1.007 S_Exp1.027
#> "0111" "0111" "021" "0121" "0111" "0111" "0111" "0111"
#> S_Exp1.017 S_Exp1.075 S_Exp1.049 S_Exp1.072 S_Exp1.005 S_Exp1.058 S_Exp1.010 S_Exp1.043
#> "0122" "0111" "0111" "0111" "0111" "0111" "0122" "0111"
#> S_Exp1.050 G1_Exp1.048 G1_Exp1.090 G1_Exp1.061 G1_Exp1.025 G1_Exp1.033 G1_Exp1.081 G1_Exp1.013
#> "0111" "023" "022" "023" "023" "0112" "023" "0112"
#> G1_Exp1.024 G1_Exp1.056 G1_Exp1.026 G1_Exp1.064 G1_Exp1.053 G1_Exp1.070 G1_Exp1.087 G1_Exp1.067
#> "021" "022" "0112" "022" "023" "023" "023" "022"
#> G1_Exp1.003 G1_Exp1.052 G1_Exp1.060 G1_Exp1.071 G1_Exp1.045 G1_Exp1.040 G1_Exp1.016 G1_Exp1.027
#> "021" "023" "022" "022" "022" "023" "021" "0112"
#> G1_Exp1.059 G1_Exp1.002 G1_Exp1.021 G1_Exp1.015 G1_Exp1.042 G1_Exp1.058 G1_Exp1.075 G1_Exp1.019
#> "0121" "021" "021" "021" "023" "023" "022" "0112"
#> G1_Exp1.074 G1_Exp1.079 G1_Exp1.038 G1_Exp1.035 G1_Exp1.012 G1_Exp1.032 G1_Exp1.009 G1_Exp1.043
#> "023" "023" "023" "021" "021" "023" "021" "023"
#> G1_Exp1.089 G1_Exp1.080 G1_Exp1.007 G1_Exp1.062 G1_Exp1.069 G1_Exp1.004 G1_Exp1.010 G1_Exp1.044
#> "023" "023" "021" "023" "023" "022" "022" "023"
#> G1_Exp1.065 G1_Exp1.029 G1_Exp1.086 G1_Exp1.084 G1_Exp1.088 G1_Exp1.051 G1_Exp1.085 G1_Exp1.066
#> "022" "021" "021" "023" "022" "023" "0121" "022"
#> G1_Exp1.047 G1_Exp1.072 G1_Exp1.057 G1_Exp1.054 G1_Exp1.082 G1_Exp1.031 G1_Exp1.077 G1_Exp1.091
#> "023" "022" "023" "022" "021" "0121" "023" "021"
#> G1_Exp1.078 G1_Exp1.073 G1_Exp1.068 G1_Exp1.001 G1_Exp1.023 G1_Exp1.022 G1_Exp1.034 G1_Exp1.005
#> "0121" "0112" "023" "021" "0112" "0112" "0121" "021"
#> G1_Exp1.039 G1_Exp1.037 G1_Exp1.014 G1_Exp1.017 G1_Exp1.020 G1_Exp1.028 G1_Exp1.036 G1_Exp1.049
#> "0112" "023" "021" "021" "0121" "021" "023" "023"
#> G1_Exp1.041 G1_Exp1.006 G1_Exp1.008 G1_Exp1.055 G1_Exp1.050 G1_Exp1.076 G1_Exp1.011 G1_Exp1.063
#> "022" "021" "021" "023" "022" "023" "0121" "0121"
#> G1_Exp1.083 G1_Exp1.030 G1_Exp1.018 G1_Exp1.046
#> "023" "023" "0112" "0121"
get_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 1358))
#> H1_Exp1.001 H1_Exp1.002 H1_Exp1.003 H1_Exp1.004 H1_Exp1.006 H1_Exp1.007 H1_Exp1.008 H1_Exp1.009
#> "021" "011" "011" "011" "011" "011" "022" "0121"
#> H1_Exp1.010 H1_Exp1.011 H1_Exp1.012 H1_Exp1.014 H1_Exp1.015 H1_Exp1.016 H1_Exp1.017 H1_Exp1.018
#> "011" "011" "011" "011" "011" "011" "011" "011"
#> H1_Exp1.019 H1_Exp1.020 H1_Exp1.021 H1_Exp1.022 H1_Exp1.023 H1_Exp1.024 H1_Exp1.025 H1_Exp1.026
#> "0121" "011" "022" "011" "011" "011" "011" "011"
#> H1_Exp1.027 H1_Exp1.029 H1_Exp1.030 H1_Exp1.031 H1_Exp1.032 H1_Exp1.033 H1_Exp1.035 H1_Exp1.036
#> "023" "011" "023" "023" "023" "021" "011" "023"
#> H1_Exp1.038 H1_Exp1.039 H1_Exp1.040 H1_Exp1.041 H1_Exp1.042 H1_Exp1.043 H1_Exp1.044 H1_Exp1.045
#> "011" "011" "011" "011" "023" "023" "022" "022"
#> H1_Exp1.047 H1_Exp1.048 H1_Exp1.049 H1_Exp1.050 H1_Exp1.051 H1_Exp1.052 H1_Exp1.053 H1_Exp1.054
#> "022" "022" "011" "0121" "011" "0121" "011" "0121"
#> H1_Exp1.055 H1_Exp1.057 H1_Exp1.058 H1_Exp1.059 H1_Exp1.060 H1_Exp1.061 H1_Exp1.062 H1_Exp1.063
#> "0121" "0121" "023" "0122" "022" "011" "011" "011"
#> H1_Exp1.064 H1_Exp1.065 H1_Exp1.066 H1_Exp1.068 H1_Exp1.069 H1_Exp1.070 H1_Exp2.073 H1_Exp2.074
#> "011" "021" "011" "023" "0122" "011" "011" "011"
#> H1_Exp2.075 H1_Exp2.076 H1_Exp2.077 H1_Exp2.078 H1_Exp2.079 H1_Exp2.080 H1_Exp2.081 H1_Exp2.082
#> "011" "021" "021" "021" "011" "021" "011" "0121"
#> H1_Exp2.083 H1_Exp2.084 H1_Exp2.085 H1_Exp2.086 H1_Exp2.087 H1_Exp2.088 H1_Exp2.089 H1_Exp2.090
#> "011" "011" "011" "021" "011" "011" "011" "021"
#> H1_Exp2.091 H1_Exp2.092 H1_Exp2.093 H1_Exp2.094 H1_Exp2.096 H1_Exp2.097 H1_Exp2.098 H1_Exp2.099
#> "011" "011" "0121" "011" "011" "011" "011" "022"
#> H1_Exp2.100 H1_Exp2.101 H1_Exp2.102 H1_Exp2.103 H1_Exp2.104 H1_Exp2.105 H1_Exp2.106 H1_Exp2.107
#> "011" "011" "021" "021" "021" "011" "011" "022"
#> H1_Exp2.108 H1_Exp2.109 H1_Exp2.110 H1_Exp2.111 H1_Exp2.112 H1_Exp2.113 H1_Exp2.114 H1_Exp2.115
#> "011" "021" "021" "021" "0121" "022" "021" "023"
#> H1_Exp2.116 H1_Exp2.117 H1_Exp2.118 H1_Exp2.120 H1_Exp2.121 H1_Exp2.122 H1_Exp2.123 H1_Exp2.124
#> "0121" "023" "011" "023" "011" "021" "0122" "021"
#> H1_Exp2.125 H1_Exp2.126 H1_Exp2.127 H1_Exp2.128 H1_Exp2.129 H1_Exp2.130 H1_Exp2.132 H1_Exp2.133
#> "023" "023" "011" "021" "022" "0121" "023" "0121"
#> H1_Exp2.134 H1_Exp2.135 H1_Exp2.136 H1_Exp2.137 H1_Exp2.138 H1_Exp2.139 H1_Exp2.140 H1_Exp2.141
#> "022" "0122" "011" "023" "011" "0121" "0121" "021"
#> H1_Exp2.142 H1_Exp2.144 H1_Exp2.145 H1_Exp2.146 H1_Exp2.147 H1_Exp2.148 H1_Exp2.149 H1_Exp2.150
#> "021" "011" "021" "0122" "022" "011" "023" "0122"
#> H1_Exp2.151 H1_Exp2.152 H1_Exp2.153 H1_Exp2.154 H1_Exp3.218 H1_Exp3.219 H1_Exp3.220 H1_Exp3.221
#> "022" "011" "021" "0121" "022" "021" "0122" "021"
#> H1_Exp3.222 H1_Exp3.223 H1_Exp3.224 H1_Exp3.225 H1_Exp3.226 H1_Exp3.227 H1_Exp3.228 H1_Exp3.229
#> "021" "011" "023" "021" "022" "021" "021" "021"
#> H1_Exp3.230 H1_Exp3.231 H1_Exp3.232 H1_Exp3.233 H1_Exp3.234 H1_Exp3.235 H1_Exp3.236 H1_Exp3.237
#> "021" "021" "011" "0121" "022" "022" "021" "022"
#> H1_Exp3.238 H1_Exp3.239 H1_Exp3.240 H1_Exp3.241 H1_Exp3.242 H1_Exp3.243 H1_Exp3.244 H1_Exp3.246
#> "023" "021" "022" "011" "022" "022" "023" "022"
#> H1_Exp3.247 H1_Exp3.248 H1_Exp3.249 H1_Exp3.250 H1_Exp3.251 H1_Exp3.252 H1_Exp3.253 H1_Exp3.254
#> "023" "0121" "022" "022" "022" "022" "023" "023"
#> H1_Exp3.255 H1_Exp3.256 H1_Exp3.257 H1_Exp3.258 H1_Exp3.259 H1_Exp3.260 H1_Exp3.261 H1_Exp3.262
#> "022" "023" "023" "022" "022" "023" "023" "023"
#> H1_Exp3.263 H1_Exp3.264 H1_Exp3.265 H1_Exp3.266 H1_Exp3.267 H1_Exp3.268 H1_Exp3.269 H1_Exp3.270
#> "023" "023" "022" "023" "022" "023" "022" "023"
#> H1_Exp3.271 H1_Exp3.272 H1_Exp3.273 H1_Exp3.274 H1_Exp3.275 H1_Exp3.276 H1_Exp3.277 H1_Exp3.278
#> "023" "023" "022" "022" "022" "022" "0121" "023"
#> H1_Exp3.279 H1_Exp3.280 H1_Exp3.281 H1_Exp3.282 H1_Exp3.283 H1_Exp3.284 H1_Exp3.285 H1_Exp3.286
#> "023" "022" "023" "023" "0121" "0121" "022" "022"
#> H1_Exp3.287 H1_Exp3.288 H1_Exp3.289 H1_Exp3.290 H1_Exp3.291 G2_Exp1.059 G2_Exp1.069 G2_Exp1.075
#> "022" "023" "023" "021" "022" "023" "011" "022"
#> G2_Exp1.063 G2_Exp1.029 G2_Exp1.076 G2_Exp1.013 G2_Exp1.037 G2_Exp1.057 G2_Exp1.018 G2_Exp1.015
#> "022" "022" "011" "011" "021" "023" "022" "011"
#> G2_Exp1.019 G2_Exp1.050 G2_Exp1.004 G2_Exp1.061 G2_Exp1.042 G2_Exp1.060 G2_Exp1.058 G2_Exp1.065
#> "011" "0121" "022" "021" "0121" "022" "022" "022"
#> G2_Exp1.002 G2_Exp1.044 G2_Exp1.051 G2_Exp1.073 G2_Exp1.030 G2_Exp1.028 G2_Exp1.022 G2_Exp1.034
#> "021" "023" "022" "022" "011" "021" "0121" "021"
#> G2_Exp1.017 G2_Exp1.047 G2_Exp1.072 G2_Exp1.074 G2_Exp1.054 G2_Exp1.024 G2_Exp1.032 G2_Exp1.020
#> "011" "011" "0121" "0121" "023" "021" "011" "022"
#> G2_Exp1.064 G2_Exp1.045 G2_Exp1.038 G2_Exp1.001 G2_Exp1.049 G2_Exp1.031 G2_Exp1.039 G2_Exp1.070
#> "011" "011" "011" "011" "011" "011" "022" "0121"
#> G2_Exp1.007 G2_Exp1.021 G2_Exp1.036 G2_Exp1.046 G2_Exp1.040 G2_Exp1.068 G2_Exp1.077 G2_Exp1.026
#> "021" "0121" "011" "011" "011" "011" "011" "022"
#> G2_Exp1.011 G2_Exp1.053 G2_Exp1.008 G2_Exp1.006 G2_Exp1.016 G2_Exp1.010 G2_Exp1.014 G2_Exp1.005
#> "021" "022" "0121" "021" "021" "021" "011" "011"
#> G2_Exp1.048 G2_Exp1.027 G2_Exp1.067 G2_Exp1.009 G2_Exp1.062 G2_Exp1.025 G2_Exp1.056 G2_Exp1.055
#> "0122" "022" "022" "021" "0121" "021" "011" "022"
#> G2_Exp1.041 G2_Exp1.012 G2_Exp1.066 G2_Exp1.023 G2_Exp1.033 G2_Exp1.043 G2_Exp1.052 G2_Exp1.071
#> "011" "011" "0121" "0121" "023" "022" "022" "022"
#> G2_Exp1.003 S_Exp1.071 S_Exp1.074 S_Exp1.031 S_Exp1.032 S_Exp1.035 S_Exp1.030 S_Exp1.056
#> "011" "0122" "021" "011" "0121" "0122" "021" "011"
#> S_Exp1.011 S_Exp1.016 S_Exp1.013 S_Exp1.063 S_Exp1.038 S_Exp1.065 S_Exp1.048 S_Exp1.051
#> "011" "011" "022" "0122" "011" "021" "011" "011"
#> S_Exp1.078 S_Exp1.023 S_Exp1.022 S_Exp1.001 S_Exp1.046 S_Exp1.061 S_Exp1.080 S_Exp1.008
#> "0122" "0122" "011" "011" "0122" "011" "011" "0122"
#> S_Exp1.069 S_Exp1.076 S_Exp1.019 S_Exp1.003 S_Exp1.006 S_Exp1.029 S_Exp1.025 S_Exp1.064
#> "011" "021" "011" "011" "0122" "011" "011" "0122"
#> S_Exp1.041 S_Exp1.057 S_Exp1.068 S_Exp1.073 S_Exp1.014 S_Exp1.039 S_Exp1.055 S_Exp1.033
#> "011" "011" "021" "0122" "0122" "0122" "011" "011"
#> S_Exp1.081 S_Exp1.004 S_Exp1.045 S_Exp1.009 S_Exp1.054 S_Exp1.018 S_Exp1.034 S_Exp1.042
#> "011" "011" "011" "021" "0122" "011" "011" "021"
#> S_Exp1.067 S_Exp1.012 S_Exp1.002 S_Exp1.037 S_Exp1.070 S_Exp1.053 S_Exp1.036 S_Exp1.021
#> "011" "0122" "021" "011" "0122" "011" "011" "011"
#> S_Exp1.020 S_Exp1.060 S_Exp1.015 S_Exp1.059 S_Exp1.066 S_Exp1.062 S_Exp1.040 S_Exp1.028
#> "0121" "011" "0122" "021" "0122" "011" "0122" "011"
#> S_Exp1.079 S_Exp1.047 S_Exp1.044 S_Exp1.026 S_Exp1.077 S_Exp1.052 S_Exp1.007 S_Exp1.027
#> "011" "011" "021" "0121" "011" "011" "011" "011"
#> S_Exp1.017 S_Exp1.075 S_Exp1.049 S_Exp1.072 S_Exp1.005 S_Exp1.058 S_Exp1.010 S_Exp1.043
#> "0122" "011" "011" "011" "011" "011" "0122" "011"
#> S_Exp1.050 G1_Exp1.048 G1_Exp1.090 G1_Exp1.061 G1_Exp1.025 G1_Exp1.033 G1_Exp1.081 G1_Exp1.013
#> "011" "023" "022" "023" "023" "011" "023" "011"
#> G1_Exp1.024 G1_Exp1.056 G1_Exp1.026 G1_Exp1.064 G1_Exp1.053 G1_Exp1.070 G1_Exp1.087 G1_Exp1.067
#> "021" "022" "011" "022" "023" "023" "023" "022"
#> G1_Exp1.003 G1_Exp1.052 G1_Exp1.060 G1_Exp1.071 G1_Exp1.045 G1_Exp1.040 G1_Exp1.016 G1_Exp1.027
#> "021" "023" "022" "022" "022" "023" "021" "011"
#> G1_Exp1.059 G1_Exp1.002 G1_Exp1.021 G1_Exp1.015 G1_Exp1.042 G1_Exp1.058 G1_Exp1.075 G1_Exp1.019
#> "0121" "021" "021" "021" "023" "023" "022" "011"
#> G1_Exp1.074 G1_Exp1.079 G1_Exp1.038 G1_Exp1.035 G1_Exp1.012 G1_Exp1.032 G1_Exp1.009 G1_Exp1.043
#> "023" "023" "023" "021" "021" "023" "021" "023"
#> G1_Exp1.089 G1_Exp1.080 G1_Exp1.007 G1_Exp1.062 G1_Exp1.069 G1_Exp1.004 G1_Exp1.010 G1_Exp1.044
#> "023" "023" "021" "023" "023" "022" "022" "023"
#> G1_Exp1.065 G1_Exp1.029 G1_Exp1.086 G1_Exp1.084 G1_Exp1.088 G1_Exp1.051 G1_Exp1.085 G1_Exp1.066
#> "022" "021" "021" "023" "022" "023" "0121" "022"
#> G1_Exp1.047 G1_Exp1.072 G1_Exp1.057 G1_Exp1.054 G1_Exp1.082 G1_Exp1.031 G1_Exp1.077 G1_Exp1.091
#> "023" "022" "023" "022" "021" "0121" "023" "021"
#> G1_Exp1.078 G1_Exp1.073 G1_Exp1.068 G1_Exp1.001 G1_Exp1.023 G1_Exp1.022 G1_Exp1.034 G1_Exp1.005
#> "0121" "011" "023" "021" "011" "011" "0121" "021"
#> G1_Exp1.039 G1_Exp1.037 G1_Exp1.014 G1_Exp1.017 G1_Exp1.020 G1_Exp1.028 G1_Exp1.036 G1_Exp1.049
#> "011" "023" "021" "021" "0121" "021" "023" "023"
#> G1_Exp1.041 G1_Exp1.006 G1_Exp1.008 G1_Exp1.055 G1_Exp1.050 G1_Exp1.076 G1_Exp1.011 G1_Exp1.063
#> "022" "021" "021" "023" "022" "023" "0121" "0121"
#> G1_Exp1.083 G1_Exp1.030 G1_Exp1.018 G1_Exp1.046
#> "023" "023" "011" "0121"
get_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 1418))
#> H1_Exp1.001 H1_Exp1.002 H1_Exp1.003 H1_Exp1.004 H1_Exp1.006 H1_Exp1.007 H1_Exp1.008 H1_Exp1.009
#> "02" "011" "011" "011" "011" "011" "02" "0121"
#> H1_Exp1.010 H1_Exp1.011 H1_Exp1.012 H1_Exp1.014 H1_Exp1.015 H1_Exp1.016 H1_Exp1.017 H1_Exp1.018
#> "011" "011" "011" "011" "011" "011" "011" "011"
#> H1_Exp1.019 H1_Exp1.020 H1_Exp1.021 H1_Exp1.022 H1_Exp1.023 H1_Exp1.024 H1_Exp1.025 H1_Exp1.026
#> "0121" "011" "02" "011" "011" "011" "011" "011"
#> H1_Exp1.027 H1_Exp1.029 H1_Exp1.030 H1_Exp1.031 H1_Exp1.032 H1_Exp1.033 H1_Exp1.035 H1_Exp1.036
#> "02" "011" "02" "02" "02" "02" "011" "02"
#> H1_Exp1.038 H1_Exp1.039 H1_Exp1.040 H1_Exp1.041 H1_Exp1.042 H1_Exp1.043 H1_Exp1.044 H1_Exp1.045
#> "011" "011" "011" "011" "02" "02" "02" "02"
#> H1_Exp1.047 H1_Exp1.048 H1_Exp1.049 H1_Exp1.050 H1_Exp1.051 H1_Exp1.052 H1_Exp1.053 H1_Exp1.054
#> "02" "02" "011" "0121" "011" "0121" "011" "0121"
#> H1_Exp1.055 H1_Exp1.057 H1_Exp1.058 H1_Exp1.059 H1_Exp1.060 H1_Exp1.061 H1_Exp1.062 H1_Exp1.063
#> "0121" "0121" "02" "0122" "02" "011" "011" "011"
#> H1_Exp1.064 H1_Exp1.065 H1_Exp1.066 H1_Exp1.068 H1_Exp1.069 H1_Exp1.070 H1_Exp2.073 H1_Exp2.074
#> "011" "02" "011" "02" "0122" "011" "011" "011"
#> H1_Exp2.075 H1_Exp2.076 H1_Exp2.077 H1_Exp2.078 H1_Exp2.079 H1_Exp2.080 H1_Exp2.081 H1_Exp2.082
#> "011" "02" "02" "02" "011" "02" "011" "0121"
#> H1_Exp2.083 H1_Exp2.084 H1_Exp2.085 H1_Exp2.086 H1_Exp2.087 H1_Exp2.088 H1_Exp2.089 H1_Exp2.090
#> "011" "011" "011" "02" "011" "011" "011" "02"
#> H1_Exp2.091 H1_Exp2.092 H1_Exp2.093 H1_Exp2.094 H1_Exp2.096 H1_Exp2.097 H1_Exp2.098 H1_Exp2.099
#> "011" "011" "0121" "011" "011" "011" "011" "02"
#> H1_Exp2.100 H1_Exp2.101 H1_Exp2.102 H1_Exp2.103 H1_Exp2.104 H1_Exp2.105 H1_Exp2.106 H1_Exp2.107
#> "011" "011" "02" "02" "02" "011" "011" "02"
#> H1_Exp2.108 H1_Exp2.109 H1_Exp2.110 H1_Exp2.111 H1_Exp2.112 H1_Exp2.113 H1_Exp2.114 H1_Exp2.115
#> "011" "02" "02" "02" "0121" "02" "02" "02"
#> H1_Exp2.116 H1_Exp2.117 H1_Exp2.118 H1_Exp2.120 H1_Exp2.121 H1_Exp2.122 H1_Exp2.123 H1_Exp2.124
#> "0121" "02" "011" "02" "011" "02" "0122" "02"
#> H1_Exp2.125 H1_Exp2.126 H1_Exp2.127 H1_Exp2.128 H1_Exp2.129 H1_Exp2.130 H1_Exp2.132 H1_Exp2.133
#> "02" "02" "011" "02" "02" "0121" "02" "0121"
#> H1_Exp2.134 H1_Exp2.135 H1_Exp2.136 H1_Exp2.137 H1_Exp2.138 H1_Exp2.139 H1_Exp2.140 H1_Exp2.141
#> "02" "0122" "011" "02" "011" "0121" "0121" "02"
#> H1_Exp2.142 H1_Exp2.144 H1_Exp2.145 H1_Exp2.146 H1_Exp2.147 H1_Exp2.148 H1_Exp2.149 H1_Exp2.150
#> "02" "011" "02" "0122" "02" "011" "02" "0122"
#> H1_Exp2.151 H1_Exp2.152 H1_Exp2.153 H1_Exp2.154 H1_Exp3.218 H1_Exp3.219 H1_Exp3.220 H1_Exp3.221
#> "02" "011" "02" "0121" "02" "02" "0122" "02"
#> H1_Exp3.222 H1_Exp3.223 H1_Exp3.224 H1_Exp3.225 H1_Exp3.226 H1_Exp3.227 H1_Exp3.228 H1_Exp3.229
#> "02" "011" "02" "02" "02" "02" "02" "02"
#> H1_Exp3.230 H1_Exp3.231 H1_Exp3.232 H1_Exp3.233 H1_Exp3.234 H1_Exp3.235 H1_Exp3.236 H1_Exp3.237
#> "02" "02" "011" "0121" "02" "02" "02" "02"
#> H1_Exp3.238 H1_Exp3.239 H1_Exp3.240 H1_Exp3.241 H1_Exp3.242 H1_Exp3.243 H1_Exp3.244 H1_Exp3.246
#> "02" "02" "02" "011" "02" "02" "02" "02"
#> H1_Exp3.247 H1_Exp3.248 H1_Exp3.249 H1_Exp3.250 H1_Exp3.251 H1_Exp3.252 H1_Exp3.253 H1_Exp3.254
#> "02" "0121" "02" "02" "02" "02" "02" "02"
#> H1_Exp3.255 H1_Exp3.256 H1_Exp3.257 H1_Exp3.258 H1_Exp3.259 H1_Exp3.260 H1_Exp3.261 H1_Exp3.262
#> "02" "02" "02" "02" "02" "02" "02" "02"
#> H1_Exp3.263 H1_Exp3.264 H1_Exp3.265 H1_Exp3.266 H1_Exp3.267 H1_Exp3.268 H1_Exp3.269 H1_Exp3.270
#> "02" "02" "02" "02" "02" "02" "02" "02"
#> H1_Exp3.271 H1_Exp3.272 H1_Exp3.273 H1_Exp3.274 H1_Exp3.275 H1_Exp3.276 H1_Exp3.277 H1_Exp3.278
#> "02" "02" "02" "02" "02" "02" "0121" "02"
#> H1_Exp3.279 H1_Exp3.280 H1_Exp3.281 H1_Exp3.282 H1_Exp3.283 H1_Exp3.284 H1_Exp3.285 H1_Exp3.286
#> "02" "02" "02" "02" "0121" "0121" "02" "02"
#> H1_Exp3.287 H1_Exp3.288 H1_Exp3.289 H1_Exp3.290 H1_Exp3.291 G2_Exp1.059 G2_Exp1.069 G2_Exp1.075
#> "02" "02" "02" "02" "02" "02" "011" "02"
#> G2_Exp1.063 G2_Exp1.029 G2_Exp1.076 G2_Exp1.013 G2_Exp1.037 G2_Exp1.057 G2_Exp1.018 G2_Exp1.015
#> "02" "02" "011" "011" "02" "02" "02" "011"
#> G2_Exp1.019 G2_Exp1.050 G2_Exp1.004 G2_Exp1.061 G2_Exp1.042 G2_Exp1.060 G2_Exp1.058 G2_Exp1.065
#> "011" "0121" "02" "02" "0121" "02" "02" "02"
#> G2_Exp1.002 G2_Exp1.044 G2_Exp1.051 G2_Exp1.073 G2_Exp1.030 G2_Exp1.028 G2_Exp1.022 G2_Exp1.034
#> "02" "02" "02" "02" "011" "02" "0121" "02"
#> G2_Exp1.017 G2_Exp1.047 G2_Exp1.072 G2_Exp1.074 G2_Exp1.054 G2_Exp1.024 G2_Exp1.032 G2_Exp1.020
#> "011" "011" "0121" "0121" "02" "02" "011" "02"
#> G2_Exp1.064 G2_Exp1.045 G2_Exp1.038 G2_Exp1.001 G2_Exp1.049 G2_Exp1.031 G2_Exp1.039 G2_Exp1.070
#> "011" "011" "011" "011" "011" "011" "02" "0121"
#> G2_Exp1.007 G2_Exp1.021 G2_Exp1.036 G2_Exp1.046 G2_Exp1.040 G2_Exp1.068 G2_Exp1.077 G2_Exp1.026
#> "02" "0121" "011" "011" "011" "011" "011" "02"
#> G2_Exp1.011 G2_Exp1.053 G2_Exp1.008 G2_Exp1.006 G2_Exp1.016 G2_Exp1.010 G2_Exp1.014 G2_Exp1.005
#> "02" "02" "0121" "02" "02" "02" "011" "011"
#> G2_Exp1.048 G2_Exp1.027 G2_Exp1.067 G2_Exp1.009 G2_Exp1.062 G2_Exp1.025 G2_Exp1.056 G2_Exp1.055
#> "0122" "02" "02" "02" "0121" "02" "011" "02"
#> G2_Exp1.041 G2_Exp1.012 G2_Exp1.066 G2_Exp1.023 G2_Exp1.033 G2_Exp1.043 G2_Exp1.052 G2_Exp1.071
#> "011" "011" "0121" "0121" "02" "02" "02" "02"
#> G2_Exp1.003 S_Exp1.071 S_Exp1.074 S_Exp1.031 S_Exp1.032 S_Exp1.035 S_Exp1.030 S_Exp1.056
#> "011" "0122" "02" "011" "0121" "0122" "02" "011"
#> S_Exp1.011 S_Exp1.016 S_Exp1.013 S_Exp1.063 S_Exp1.038 S_Exp1.065 S_Exp1.048 S_Exp1.051
#> "011" "011" "02" "0122" "011" "02" "011" "011"
#> S_Exp1.078 S_Exp1.023 S_Exp1.022 S_Exp1.001 S_Exp1.046 S_Exp1.061 S_Exp1.080 S_Exp1.008
#> "0122" "0122" "011" "011" "0122" "011" "011" "0122"
#> S_Exp1.069 S_Exp1.076 S_Exp1.019 S_Exp1.003 S_Exp1.006 S_Exp1.029 S_Exp1.025 S_Exp1.064
#> "011" "02" "011" "011" "0122" "011" "011" "0122"
#> S_Exp1.041 S_Exp1.057 S_Exp1.068 S_Exp1.073 S_Exp1.014 S_Exp1.039 S_Exp1.055 S_Exp1.033
#> "011" "011" "02" "0122" "0122" "0122" "011" "011"
#> S_Exp1.081 S_Exp1.004 S_Exp1.045 S_Exp1.009 S_Exp1.054 S_Exp1.018 S_Exp1.034 S_Exp1.042
#> "011" "011" "011" "02" "0122" "011" "011" "02"
#> S_Exp1.067 S_Exp1.012 S_Exp1.002 S_Exp1.037 S_Exp1.070 S_Exp1.053 S_Exp1.036 S_Exp1.021
#> "011" "0122" "02" "011" "0122" "011" "011" "011"
#> S_Exp1.020 S_Exp1.060 S_Exp1.015 S_Exp1.059 S_Exp1.066 S_Exp1.062 S_Exp1.040 S_Exp1.028
#> "0121" "011" "0122" "02" "0122" "011" "0122" "011"
#> S_Exp1.079 S_Exp1.047 S_Exp1.044 S_Exp1.026 S_Exp1.077 S_Exp1.052 S_Exp1.007 S_Exp1.027
#> "011" "011" "02" "0121" "011" "011" "011" "011"
#> S_Exp1.017 S_Exp1.075 S_Exp1.049 S_Exp1.072 S_Exp1.005 S_Exp1.058 S_Exp1.010 S_Exp1.043
#> "0122" "011" "011" "011" "011" "011" "0122" "011"
#> S_Exp1.050 G1_Exp1.048 G1_Exp1.090 G1_Exp1.061 G1_Exp1.025 G1_Exp1.033 G1_Exp1.081 G1_Exp1.013
#> "011" "02" "02" "02" "02" "011" "02" "011"
#> G1_Exp1.024 G1_Exp1.056 G1_Exp1.026 G1_Exp1.064 G1_Exp1.053 G1_Exp1.070 G1_Exp1.087 G1_Exp1.067
#> "02" "02" "011" "02" "02" "02" "02" "02"
#> G1_Exp1.003 G1_Exp1.052 G1_Exp1.060 G1_Exp1.071 G1_Exp1.045 G1_Exp1.040 G1_Exp1.016 G1_Exp1.027
#> "02" "02" "02" "02" "02" "02" "02" "011"
#> G1_Exp1.059 G1_Exp1.002 G1_Exp1.021 G1_Exp1.015 G1_Exp1.042 G1_Exp1.058 G1_Exp1.075 G1_Exp1.019
#> "0121" "02" "02" "02" "02" "02" "02" "011"
#> G1_Exp1.074 G1_Exp1.079 G1_Exp1.038 G1_Exp1.035 G1_Exp1.012 G1_Exp1.032 G1_Exp1.009 G1_Exp1.043
#> "02" "02" "02" "02" "02" "02" "02" "02"
#> G1_Exp1.089 G1_Exp1.080 G1_Exp1.007 G1_Exp1.062 G1_Exp1.069 G1_Exp1.004 G1_Exp1.010 G1_Exp1.044
#> "02" "02" "02" "02" "02" "02" "02" "02"
#> G1_Exp1.065 G1_Exp1.029 G1_Exp1.086 G1_Exp1.084 G1_Exp1.088 G1_Exp1.051 G1_Exp1.085 G1_Exp1.066
#> "02" "02" "02" "02" "02" "02" "0121" "02"
#> G1_Exp1.047 G1_Exp1.072 G1_Exp1.057 G1_Exp1.054 G1_Exp1.082 G1_Exp1.031 G1_Exp1.077 G1_Exp1.091
#> "02" "02" "02" "02" "02" "0121" "02" "02"
#> G1_Exp1.078 G1_Exp1.073 G1_Exp1.068 G1_Exp1.001 G1_Exp1.023 G1_Exp1.022 G1_Exp1.034 G1_Exp1.005
#> "0121" "011" "02" "02" "011" "011" "0121" "02"
#> G1_Exp1.039 G1_Exp1.037 G1_Exp1.014 G1_Exp1.017 G1_Exp1.020 G1_Exp1.028 G1_Exp1.036 G1_Exp1.049
#> "011" "02" "02" "02" "0121" "02" "02" "02"
#> G1_Exp1.041 G1_Exp1.006 G1_Exp1.008 G1_Exp1.055 G1_Exp1.050 G1_Exp1.076 G1_Exp1.011 G1_Exp1.063
#> "02" "02" "02" "02" "02" "02" "0121" "0121"
#> G1_Exp1.083 G1_Exp1.030 G1_Exp1.018 G1_Exp1.046
#> "02" "02" "011" "0121"
get_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 1688))
#> H1_Exp1.001 H1_Exp1.002 H1_Exp1.003 H1_Exp1.004 H1_Exp1.006 H1_Exp1.007 H1_Exp1.008 H1_Exp1.009
#> NA NA NA NA NA NA NA "0121"
#> H1_Exp1.010 H1_Exp1.011 H1_Exp1.012 H1_Exp1.014 H1_Exp1.015 H1_Exp1.016 H1_Exp1.017 H1_Exp1.018
#> NA NA NA NA NA NA NA NA
#> H1_Exp1.019 H1_Exp1.020 H1_Exp1.021 H1_Exp1.022 H1_Exp1.023 H1_Exp1.024 H1_Exp1.025 H1_Exp1.026
#> "0121" NA NA NA NA NA NA NA
#> H1_Exp1.027 H1_Exp1.029 H1_Exp1.030 H1_Exp1.031 H1_Exp1.032 H1_Exp1.033 H1_Exp1.035 H1_Exp1.036
#> NA NA NA NA NA NA NA NA
#> H1_Exp1.038 H1_Exp1.039 H1_Exp1.040 H1_Exp1.041 H1_Exp1.042 H1_Exp1.043 H1_Exp1.044 H1_Exp1.045
#> NA NA NA NA NA NA NA NA
#> H1_Exp1.047 H1_Exp1.048 H1_Exp1.049 H1_Exp1.050 H1_Exp1.051 H1_Exp1.052 H1_Exp1.053 H1_Exp1.054
#> NA NA NA "0121" NA "0121" NA "0121"
#> H1_Exp1.055 H1_Exp1.057 H1_Exp1.058 H1_Exp1.059 H1_Exp1.060 H1_Exp1.061 H1_Exp1.062 H1_Exp1.063
#> "0121" "0121" NA "0122" NA NA NA NA
#> H1_Exp1.064 H1_Exp1.065 H1_Exp1.066 H1_Exp1.068 H1_Exp1.069 H1_Exp1.070 H1_Exp2.073 H1_Exp2.074
#> NA NA NA NA "0122" NA NA NA
#> H1_Exp2.075 H1_Exp2.076 H1_Exp2.077 H1_Exp2.078 H1_Exp2.079 H1_Exp2.080 H1_Exp2.081 H1_Exp2.082
#> NA NA NA NA NA NA NA "0121"
#> H1_Exp2.083 H1_Exp2.084 H1_Exp2.085 H1_Exp2.086 H1_Exp2.087 H1_Exp2.088 H1_Exp2.089 H1_Exp2.090
#> NA NA NA NA NA NA NA NA
#> H1_Exp2.091 H1_Exp2.092 H1_Exp2.093 H1_Exp2.094 H1_Exp2.096 H1_Exp2.097 H1_Exp2.098 H1_Exp2.099
#> NA NA "0121" NA NA NA NA NA
#> H1_Exp2.100 H1_Exp2.101 H1_Exp2.102 H1_Exp2.103 H1_Exp2.104 H1_Exp2.105 H1_Exp2.106 H1_Exp2.107
#> NA NA NA NA NA NA NA NA
#> H1_Exp2.108 H1_Exp2.109 H1_Exp2.110 H1_Exp2.111 H1_Exp2.112 H1_Exp2.113 H1_Exp2.114 H1_Exp2.115
#> NA NA NA NA "0121" NA NA NA
#> H1_Exp2.116 H1_Exp2.117 H1_Exp2.118 H1_Exp2.120 H1_Exp2.121 H1_Exp2.122 H1_Exp2.123 H1_Exp2.124
#> "0121" NA NA NA NA NA "0122" NA
#> H1_Exp2.125 H1_Exp2.126 H1_Exp2.127 H1_Exp2.128 H1_Exp2.129 H1_Exp2.130 H1_Exp2.132 H1_Exp2.133
#> NA NA NA NA NA "0121" NA "0121"
#> H1_Exp2.134 H1_Exp2.135 H1_Exp2.136 H1_Exp2.137 H1_Exp2.138 H1_Exp2.139 H1_Exp2.140 H1_Exp2.141
#> NA "0122" NA NA NA "0121" "0121" NA
#> H1_Exp2.142 H1_Exp2.144 H1_Exp2.145 H1_Exp2.146 H1_Exp2.147 H1_Exp2.148 H1_Exp2.149 H1_Exp2.150
#> NA NA NA "0122" NA NA NA "0122"
#> H1_Exp2.151 H1_Exp2.152 H1_Exp2.153 H1_Exp2.154 H1_Exp3.218 H1_Exp3.219 H1_Exp3.220 H1_Exp3.221
#> NA NA NA "0121" NA NA "0122" NA
#> H1_Exp3.222 H1_Exp3.223 H1_Exp3.224 H1_Exp3.225 H1_Exp3.226 H1_Exp3.227 H1_Exp3.228 H1_Exp3.229
#> NA NA NA NA NA NA NA NA
#> H1_Exp3.230 H1_Exp3.231 H1_Exp3.232 H1_Exp3.233 H1_Exp3.234 H1_Exp3.235 H1_Exp3.236 H1_Exp3.237
#> NA NA NA "0121" NA NA NA NA
#> H1_Exp3.238 H1_Exp3.239 H1_Exp3.240 H1_Exp3.241 H1_Exp3.242 H1_Exp3.243 H1_Exp3.244 H1_Exp3.246
#> NA NA NA NA NA NA NA NA
#> H1_Exp3.247 H1_Exp3.248 H1_Exp3.249 H1_Exp3.250 H1_Exp3.251 H1_Exp3.252 H1_Exp3.253 H1_Exp3.254
#> NA "0121" NA NA NA NA NA NA
#> H1_Exp3.255 H1_Exp3.256 H1_Exp3.257 H1_Exp3.258 H1_Exp3.259 H1_Exp3.260 H1_Exp3.261 H1_Exp3.262
#> NA NA NA NA NA NA NA NA
#> H1_Exp3.263 H1_Exp3.264 H1_Exp3.265 H1_Exp3.266 H1_Exp3.267 H1_Exp3.268 H1_Exp3.269 H1_Exp3.270
#> NA NA NA NA NA NA NA NA
#> H1_Exp3.271 H1_Exp3.272 H1_Exp3.273 H1_Exp3.274 H1_Exp3.275 H1_Exp3.276 H1_Exp3.277 H1_Exp3.278
#> NA NA NA NA NA NA "0121" NA
#> H1_Exp3.279 H1_Exp3.280 H1_Exp3.281 H1_Exp3.282 H1_Exp3.283 H1_Exp3.284 H1_Exp3.285 H1_Exp3.286
#> NA NA NA NA "0121" "0121" NA NA
#> H1_Exp3.287 H1_Exp3.288 H1_Exp3.289 H1_Exp3.290 H1_Exp3.291 G2_Exp1.059 G2_Exp1.069 G2_Exp1.075
#> NA NA NA NA NA NA NA NA
#> G2_Exp1.063 G2_Exp1.029 G2_Exp1.076 G2_Exp1.013 G2_Exp1.037 G2_Exp1.057 G2_Exp1.018 G2_Exp1.015
#> NA NA NA NA NA NA NA NA
#> G2_Exp1.019 G2_Exp1.050 G2_Exp1.004 G2_Exp1.061 G2_Exp1.042 G2_Exp1.060 G2_Exp1.058 G2_Exp1.065
#> NA "0121" NA NA "0121" NA NA NA
#> G2_Exp1.002 G2_Exp1.044 G2_Exp1.051 G2_Exp1.073 G2_Exp1.030 G2_Exp1.028 G2_Exp1.022 G2_Exp1.034
#> NA NA NA NA NA NA "0121" NA
#> G2_Exp1.017 G2_Exp1.047 G2_Exp1.072 G2_Exp1.074 G2_Exp1.054 G2_Exp1.024 G2_Exp1.032 G2_Exp1.020
#> NA NA "0121" "0121" NA NA NA NA
#> G2_Exp1.064 G2_Exp1.045 G2_Exp1.038 G2_Exp1.001 G2_Exp1.049 G2_Exp1.031 G2_Exp1.039 G2_Exp1.070
#> NA NA NA NA NA NA NA "0121"
#> G2_Exp1.007 G2_Exp1.021 G2_Exp1.036 G2_Exp1.046 G2_Exp1.040 G2_Exp1.068 G2_Exp1.077 G2_Exp1.026
#> NA "0121" NA NA NA NA NA NA
#> G2_Exp1.011 G2_Exp1.053 G2_Exp1.008 G2_Exp1.006 G2_Exp1.016 G2_Exp1.010 G2_Exp1.014 G2_Exp1.005
#> NA NA "0121" NA NA NA NA NA
#> G2_Exp1.048 G2_Exp1.027 G2_Exp1.067 G2_Exp1.009 G2_Exp1.062 G2_Exp1.025 G2_Exp1.056 G2_Exp1.055
#> "0122" NA NA NA "0121" NA NA NA
#> G2_Exp1.041 G2_Exp1.012 G2_Exp1.066 G2_Exp1.023 G2_Exp1.033 G2_Exp1.043 G2_Exp1.052 G2_Exp1.071
#> NA NA "0121" "0121" NA NA NA NA
#> G2_Exp1.003 S_Exp1.071 S_Exp1.074 S_Exp1.031 S_Exp1.032 S_Exp1.035 S_Exp1.030 S_Exp1.056
#> NA "0122" NA NA "0121" "0122" NA NA
#> S_Exp1.011 S_Exp1.016 S_Exp1.013 S_Exp1.063 S_Exp1.038 S_Exp1.065 S_Exp1.048 S_Exp1.051
#> NA NA NA "0122" NA NA NA NA
#> S_Exp1.078 S_Exp1.023 S_Exp1.022 S_Exp1.001 S_Exp1.046 S_Exp1.061 S_Exp1.080 S_Exp1.008
#> "0122" "0122" NA NA "0122" NA NA "0122"
#> S_Exp1.069 S_Exp1.076 S_Exp1.019 S_Exp1.003 S_Exp1.006 S_Exp1.029 S_Exp1.025 S_Exp1.064
#> NA NA NA NA "0122" NA NA "0122"
#> S_Exp1.041 S_Exp1.057 S_Exp1.068 S_Exp1.073 S_Exp1.014 S_Exp1.039 S_Exp1.055 S_Exp1.033
#> NA NA NA "0122" "0122" "0122" NA NA
#> S_Exp1.081 S_Exp1.004 S_Exp1.045 S_Exp1.009 S_Exp1.054 S_Exp1.018 S_Exp1.034 S_Exp1.042
#> NA NA NA NA "0122" NA NA NA
#> S_Exp1.067 S_Exp1.012 S_Exp1.002 S_Exp1.037 S_Exp1.070 S_Exp1.053 S_Exp1.036 S_Exp1.021
#> NA "0122" NA NA "0122" NA NA NA
#> S_Exp1.020 S_Exp1.060 S_Exp1.015 S_Exp1.059 S_Exp1.066 S_Exp1.062 S_Exp1.040 S_Exp1.028
#> "0121" NA "0122" NA "0122" NA "0122" NA
#> S_Exp1.079 S_Exp1.047 S_Exp1.044 S_Exp1.026 S_Exp1.077 S_Exp1.052 S_Exp1.007 S_Exp1.027
#> NA NA NA "0121" NA NA NA NA
#> S_Exp1.017 S_Exp1.075 S_Exp1.049 S_Exp1.072 S_Exp1.005 S_Exp1.058 S_Exp1.010 S_Exp1.043
#> "0122" NA NA NA NA NA "0122" NA
#> S_Exp1.050 G1_Exp1.048 G1_Exp1.090 G1_Exp1.061 G1_Exp1.025 G1_Exp1.033 G1_Exp1.081 G1_Exp1.013
#> NA NA NA NA NA NA NA NA
#> G1_Exp1.024 G1_Exp1.056 G1_Exp1.026 G1_Exp1.064 G1_Exp1.053 G1_Exp1.070 G1_Exp1.087 G1_Exp1.067
#> NA NA NA NA NA NA NA NA
#> G1_Exp1.003 G1_Exp1.052 G1_Exp1.060 G1_Exp1.071 G1_Exp1.045 G1_Exp1.040 G1_Exp1.016 G1_Exp1.027
#> NA NA NA NA NA NA NA NA
#> G1_Exp1.059 G1_Exp1.002 G1_Exp1.021 G1_Exp1.015 G1_Exp1.042 G1_Exp1.058 G1_Exp1.075 G1_Exp1.019
#> "0121" NA NA NA NA NA NA NA
#> G1_Exp1.074 G1_Exp1.079 G1_Exp1.038 G1_Exp1.035 G1_Exp1.012 G1_Exp1.032 G1_Exp1.009 G1_Exp1.043
#> NA NA NA NA NA NA NA NA
#> G1_Exp1.089 G1_Exp1.080 G1_Exp1.007 G1_Exp1.062 G1_Exp1.069 G1_Exp1.004 G1_Exp1.010 G1_Exp1.044
#> NA NA NA NA NA NA NA NA
#> G1_Exp1.065 G1_Exp1.029 G1_Exp1.086 G1_Exp1.084 G1_Exp1.088 G1_Exp1.051 G1_Exp1.085 G1_Exp1.066
#> NA NA NA NA NA NA "0121" NA
#> G1_Exp1.047 G1_Exp1.072 G1_Exp1.057 G1_Exp1.054 G1_Exp1.082 G1_Exp1.031 G1_Exp1.077 G1_Exp1.091
#> NA NA NA NA NA "0121" NA NA
#> G1_Exp1.078 G1_Exp1.073 G1_Exp1.068 G1_Exp1.001 G1_Exp1.023 G1_Exp1.022 G1_Exp1.034 G1_Exp1.005
#> "0121" NA NA NA NA NA "0121" NA
#> G1_Exp1.039 G1_Exp1.037 G1_Exp1.014 G1_Exp1.017 G1_Exp1.020 G1_Exp1.028 G1_Exp1.036 G1_Exp1.049
#> NA NA NA NA "0121" NA NA NA
#> G1_Exp1.041 G1_Exp1.006 G1_Exp1.008 G1_Exp1.055 G1_Exp1.050 G1_Exp1.076 G1_Exp1.011 G1_Exp1.063
#> NA NA NA NA NA NA "0121" "0121"
#> G1_Exp1.083 G1_Exp1.030 G1_Exp1.018 G1_Exp1.046
#> NA NA NA "0121"
get_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 1886))
#> H1_Exp1.001 H1_Exp1.002 H1_Exp1.003 H1_Exp1.004 H1_Exp1.006 H1_Exp1.007 H1_Exp1.008 H1_Exp1.009
#> NA NA NA NA NA NA NA NA
#> H1_Exp1.010 H1_Exp1.011 H1_Exp1.012 H1_Exp1.014 H1_Exp1.015 H1_Exp1.016 H1_Exp1.017 H1_Exp1.018
#> NA NA NA NA NA NA NA NA
#> H1_Exp1.019 H1_Exp1.020 H1_Exp1.021 H1_Exp1.022 H1_Exp1.023 H1_Exp1.024 H1_Exp1.025 H1_Exp1.026
#> NA NA NA NA NA NA NA NA
#> H1_Exp1.027 H1_Exp1.029 H1_Exp1.030 H1_Exp1.031 H1_Exp1.032 H1_Exp1.033 H1_Exp1.035 H1_Exp1.036
#> NA NA NA NA NA NA NA NA
#> H1_Exp1.038 H1_Exp1.039 H1_Exp1.040 H1_Exp1.041 H1_Exp1.042 H1_Exp1.043 H1_Exp1.044 H1_Exp1.045
#> NA NA NA NA NA NA NA NA
#> H1_Exp1.047 H1_Exp1.048 H1_Exp1.049 H1_Exp1.050 H1_Exp1.051 H1_Exp1.052 H1_Exp1.053 H1_Exp1.054
#> NA NA NA NA NA NA NA NA
#> H1_Exp1.055 H1_Exp1.057 H1_Exp1.058 H1_Exp1.059 H1_Exp1.060 H1_Exp1.061 H1_Exp1.062 H1_Exp1.063
#> NA NA NA NA NA NA NA NA
#> H1_Exp1.064 H1_Exp1.065 H1_Exp1.066 H1_Exp1.068 H1_Exp1.069 H1_Exp1.070 H1_Exp2.073 H1_Exp2.074
#> NA NA NA NA NA NA NA NA
#> H1_Exp2.075 H1_Exp2.076 H1_Exp2.077 H1_Exp2.078 H1_Exp2.079 H1_Exp2.080 H1_Exp2.081 H1_Exp2.082
#> NA NA NA NA NA NA NA NA
#> H1_Exp2.083 H1_Exp2.084 H1_Exp2.085 H1_Exp2.086 H1_Exp2.087 H1_Exp2.088 H1_Exp2.089 H1_Exp2.090
#> NA NA NA NA NA NA NA NA
#> H1_Exp2.091 H1_Exp2.092 H1_Exp2.093 H1_Exp2.094 H1_Exp2.096 H1_Exp2.097 H1_Exp2.098 H1_Exp2.099
#> NA NA NA NA NA NA NA NA
#> H1_Exp2.100 H1_Exp2.101 H1_Exp2.102 H1_Exp2.103 H1_Exp2.104 H1_Exp2.105 H1_Exp2.106 H1_Exp2.107
#> NA NA NA NA NA NA NA NA
#> H1_Exp2.108 H1_Exp2.109 H1_Exp2.110 H1_Exp2.111 H1_Exp2.112 H1_Exp2.113 H1_Exp2.114 H1_Exp2.115
#> NA NA NA NA NA NA NA NA
#> H1_Exp2.116 H1_Exp2.117 H1_Exp2.118 H1_Exp2.120 H1_Exp2.121 H1_Exp2.122 H1_Exp2.123 H1_Exp2.124
#> NA NA NA NA NA NA NA NA
#> H1_Exp2.125 H1_Exp2.126 H1_Exp2.127 H1_Exp2.128 H1_Exp2.129 H1_Exp2.130 H1_Exp2.132 H1_Exp2.133
#> NA NA NA NA NA NA NA NA
#> H1_Exp2.134 H1_Exp2.135 H1_Exp2.136 H1_Exp2.137 H1_Exp2.138 H1_Exp2.139 H1_Exp2.140 H1_Exp2.141
#> NA NA NA NA NA NA NA NA
#> H1_Exp2.142 H1_Exp2.144 H1_Exp2.145 H1_Exp2.146 H1_Exp2.147 H1_Exp2.148 H1_Exp2.149 H1_Exp2.150
#> NA NA NA NA NA NA NA NA
#> H1_Exp2.151 H1_Exp2.152 H1_Exp2.153 H1_Exp2.154 H1_Exp3.218 H1_Exp3.219 H1_Exp3.220 H1_Exp3.221
#> NA NA NA NA NA NA NA NA
#> H1_Exp3.222 H1_Exp3.223 H1_Exp3.224 H1_Exp3.225 H1_Exp3.226 H1_Exp3.227 H1_Exp3.228 H1_Exp3.229
#> NA NA NA NA NA NA NA NA
#> H1_Exp3.230 H1_Exp3.231 H1_Exp3.232 H1_Exp3.233 H1_Exp3.234 H1_Exp3.235 H1_Exp3.236 H1_Exp3.237
#> NA NA NA NA NA NA NA NA
#> H1_Exp3.238 H1_Exp3.239 H1_Exp3.240 H1_Exp3.241 H1_Exp3.242 H1_Exp3.243 H1_Exp3.244 H1_Exp3.246
#> NA NA NA NA NA NA NA NA
#> H1_Exp3.247 H1_Exp3.248 H1_Exp3.249 H1_Exp3.250 H1_Exp3.251 H1_Exp3.252 H1_Exp3.253 H1_Exp3.254
#> NA NA NA NA NA NA NA NA
#> H1_Exp3.255 H1_Exp3.256 H1_Exp3.257 H1_Exp3.258 H1_Exp3.259 H1_Exp3.260 H1_Exp3.261 H1_Exp3.262
#> NA NA NA NA NA NA NA NA
#> H1_Exp3.263 H1_Exp3.264 H1_Exp3.265 H1_Exp3.266 H1_Exp3.267 H1_Exp3.268 H1_Exp3.269 H1_Exp3.270
#> NA NA NA NA NA NA NA NA
#> H1_Exp3.271 H1_Exp3.272 H1_Exp3.273 H1_Exp3.274 H1_Exp3.275 H1_Exp3.276 H1_Exp3.277 H1_Exp3.278
#> NA NA NA NA NA NA NA NA
#> H1_Exp3.279 H1_Exp3.280 H1_Exp3.281 H1_Exp3.282 H1_Exp3.283 H1_Exp3.284 H1_Exp3.285 H1_Exp3.286
#> NA NA NA NA NA NA NA NA
#> H1_Exp3.287 H1_Exp3.288 H1_Exp3.289 H1_Exp3.290 H1_Exp3.291 G2_Exp1.059 G2_Exp1.069 G2_Exp1.075
#> NA NA NA NA NA NA NA NA
#> G2_Exp1.063 G2_Exp1.029 G2_Exp1.076 G2_Exp1.013 G2_Exp1.037 G2_Exp1.057 G2_Exp1.018 G2_Exp1.015
#> NA NA NA NA NA NA NA NA
#> G2_Exp1.019 G2_Exp1.050 G2_Exp1.004 G2_Exp1.061 G2_Exp1.042 G2_Exp1.060 G2_Exp1.058 G2_Exp1.065
#> NA NA NA NA NA NA NA NA
#> G2_Exp1.002 G2_Exp1.044 G2_Exp1.051 G2_Exp1.073 G2_Exp1.030 G2_Exp1.028 G2_Exp1.022 G2_Exp1.034
#> NA NA NA NA NA NA NA NA
#> G2_Exp1.017 G2_Exp1.047 G2_Exp1.072 G2_Exp1.074 G2_Exp1.054 G2_Exp1.024 G2_Exp1.032 G2_Exp1.020
#> NA NA NA NA NA NA NA NA
#> G2_Exp1.064 G2_Exp1.045 G2_Exp1.038 G2_Exp1.001 G2_Exp1.049 G2_Exp1.031 G2_Exp1.039 G2_Exp1.070
#> NA NA NA NA NA NA NA NA
#> G2_Exp1.007 G2_Exp1.021 G2_Exp1.036 G2_Exp1.046 G2_Exp1.040 G2_Exp1.068 G2_Exp1.077 G2_Exp1.026
#> NA NA NA NA NA NA NA NA
#> G2_Exp1.011 G2_Exp1.053 G2_Exp1.008 G2_Exp1.006 G2_Exp1.016 G2_Exp1.010 G2_Exp1.014 G2_Exp1.005
#> NA NA NA NA NA NA NA NA
#> G2_Exp1.048 G2_Exp1.027 G2_Exp1.067 G2_Exp1.009 G2_Exp1.062 G2_Exp1.025 G2_Exp1.056 G2_Exp1.055
#> NA NA NA NA NA NA NA NA
#> G2_Exp1.041 G2_Exp1.012 G2_Exp1.066 G2_Exp1.023 G2_Exp1.033 G2_Exp1.043 G2_Exp1.052 G2_Exp1.071
#> NA NA NA NA NA NA NA NA
#> G2_Exp1.003 S_Exp1.071 S_Exp1.074 S_Exp1.031 S_Exp1.032 S_Exp1.035 S_Exp1.030 S_Exp1.056
#> NA NA NA NA NA NA NA NA
#> S_Exp1.011 S_Exp1.016 S_Exp1.013 S_Exp1.063 S_Exp1.038 S_Exp1.065 S_Exp1.048 S_Exp1.051
#> NA NA NA NA NA NA NA NA
#> S_Exp1.078 S_Exp1.023 S_Exp1.022 S_Exp1.001 S_Exp1.046 S_Exp1.061 S_Exp1.080 S_Exp1.008
#> NA NA NA NA NA NA NA NA
#> S_Exp1.069 S_Exp1.076 S_Exp1.019 S_Exp1.003 S_Exp1.006 S_Exp1.029 S_Exp1.025 S_Exp1.064
#> NA NA NA NA NA NA NA NA
#> S_Exp1.041 S_Exp1.057 S_Exp1.068 S_Exp1.073 S_Exp1.014 S_Exp1.039 S_Exp1.055 S_Exp1.033
#> NA NA NA NA NA NA NA NA
#> S_Exp1.081 S_Exp1.004 S_Exp1.045 S_Exp1.009 S_Exp1.054 S_Exp1.018 S_Exp1.034 S_Exp1.042
#> NA NA NA NA NA NA NA NA
#> S_Exp1.067 S_Exp1.012 S_Exp1.002 S_Exp1.037 S_Exp1.070 S_Exp1.053 S_Exp1.036 S_Exp1.021
#> NA NA NA NA NA NA NA NA
#> S_Exp1.020 S_Exp1.060 S_Exp1.015 S_Exp1.059 S_Exp1.066 S_Exp1.062 S_Exp1.040 S_Exp1.028
#> NA NA NA NA NA NA NA NA
#> S_Exp1.079 S_Exp1.047 S_Exp1.044 S_Exp1.026 S_Exp1.077 S_Exp1.052 S_Exp1.007 S_Exp1.027
#> NA NA NA NA NA NA NA NA
#> S_Exp1.017 S_Exp1.075 S_Exp1.049 S_Exp1.072 S_Exp1.005 S_Exp1.058 S_Exp1.010 S_Exp1.043
#> NA NA NA NA NA NA NA NA
#> S_Exp1.050 G1_Exp1.048 G1_Exp1.090 G1_Exp1.061 G1_Exp1.025 G1_Exp1.033 G1_Exp1.081 G1_Exp1.013
#> NA NA NA NA NA NA NA NA
#> G1_Exp1.024 G1_Exp1.056 G1_Exp1.026 G1_Exp1.064 G1_Exp1.053 G1_Exp1.070 G1_Exp1.087 G1_Exp1.067
#> NA NA NA NA NA NA NA NA
#> G1_Exp1.003 G1_Exp1.052 G1_Exp1.060 G1_Exp1.071 G1_Exp1.045 G1_Exp1.040 G1_Exp1.016 G1_Exp1.027
#> NA NA NA NA NA NA NA NA
#> G1_Exp1.059 G1_Exp1.002 G1_Exp1.021 G1_Exp1.015 G1_Exp1.042 G1_Exp1.058 G1_Exp1.075 G1_Exp1.019
#> NA NA NA NA NA NA NA NA
#> G1_Exp1.074 G1_Exp1.079 G1_Exp1.038 G1_Exp1.035 G1_Exp1.012 G1_Exp1.032 G1_Exp1.009 G1_Exp1.043
#> NA NA NA NA NA NA NA NA
#> G1_Exp1.089 G1_Exp1.080 G1_Exp1.007 G1_Exp1.062 G1_Exp1.069 G1_Exp1.004 G1_Exp1.010 G1_Exp1.044
#> NA NA NA NA NA NA NA NA
#> G1_Exp1.065 G1_Exp1.029 G1_Exp1.086 G1_Exp1.084 G1_Exp1.088 G1_Exp1.051 G1_Exp1.085 G1_Exp1.066
#> NA NA NA NA NA NA NA NA
#> G1_Exp1.047 G1_Exp1.072 G1_Exp1.057 G1_Exp1.054 G1_Exp1.082 G1_Exp1.031 G1_Exp1.077 G1_Exp1.091
#> NA NA NA NA NA NA NA NA
#> G1_Exp1.078 G1_Exp1.073 G1_Exp1.068 G1_Exp1.001 G1_Exp1.023 G1_Exp1.022 G1_Exp1.034 G1_Exp1.005
#> NA NA NA NA NA NA NA NA
#> G1_Exp1.039 G1_Exp1.037 G1_Exp1.014 G1_Exp1.017 G1_Exp1.020 G1_Exp1.028 G1_Exp1.036 G1_Exp1.049
#> NA NA NA NA NA NA NA NA
#> G1_Exp1.041 G1_Exp1.006 G1_Exp1.008 G1_Exp1.055 G1_Exp1.050 G1_Exp1.076 G1_Exp1.011 G1_Exp1.063
#> NA NA NA NA NA NA NA NA
#> G1_Exp1.083 G1_Exp1.030 G1_Exp1.018 G1_Exp1.046
#> NA NA NA NA
Heatmaps of the top rows:
top_rows_heatmap(res_rh)
Top rows on each node:
top_rows_overlap(res_rh, method = "upset")
UMAP plot which shows how samples are separated.
par(mfrow = c(1, 2))
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 94),
method = "UMAP", top_value_method = "SD", top_n = 1400, scale_rows = FALSE)
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 94),
method = "UMAP", top_value_method = "ATC", top_n = 1400, scale_rows = TRUE)
par(mfrow = c(1, 2))
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 112),
method = "UMAP", top_value_method = "SD", top_n = 1400, scale_rows = FALSE)
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 112),
method = "UMAP", top_value_method = "ATC", top_n = 1400, scale_rows = TRUE)
par(mfrow = c(1, 2))
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 198),
method = "UMAP", top_value_method = "SD", top_n = 1400, scale_rows = FALSE)
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 198),
method = "UMAP", top_value_method = "ATC", top_n = 1400, scale_rows = TRUE)
par(mfrow = c(1, 2))
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 200),
method = "UMAP", top_value_method = "SD", top_n = 1400, scale_rows = FALSE)
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 200),
method = "UMAP", top_value_method = "ATC", top_n = 1400, scale_rows = TRUE)
par(mfrow = c(1, 2))
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 450),
method = "UMAP", top_value_method = "SD", top_n = 1400, scale_rows = FALSE)
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 450),
method = "UMAP", top_value_method = "ATC", top_n = 1400, scale_rows = TRUE)
par(mfrow = c(1, 2))
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 1358),
method = "UMAP", top_value_method = "SD", top_n = 1400, scale_rows = FALSE)
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 1358),
method = "UMAP", top_value_method = "ATC", top_n = 1400, scale_rows = TRUE)
par(mfrow = c(1, 2))
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 1418),
method = "UMAP", top_value_method = "SD", top_n = 1400, scale_rows = FALSE)
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 1418),
method = "UMAP", top_value_method = "ATC", top_n = 1400, scale_rows = TRUE)
par(mfrow = c(1, 2))
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 1688),
method = "UMAP", top_value_method = "SD", top_n = 1400, scale_rows = FALSE)
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 1688),
method = "UMAP", top_value_method = "ATC", top_n = 1400, scale_rows = TRUE)
par(mfrow = c(1, 2))
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 1886),
method = "UMAP", top_value_method = "SD", top_n = 1400, scale_rows = FALSE)
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 1886),
method = "UMAP", top_value_method = "ATC", top_n = 1400, 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 = 94))
get_signatures(res_rh, merge_node = merge_node_param(min_n_signatures = 112))
get_signatures(res_rh, merge_node = merge_node_param(min_n_signatures = 198))
get_signatures(res_rh, merge_node = merge_node_param(min_n_signatures = 200))
get_signatures(res_rh, merge_node = merge_node_param(min_n_signatures = 450))
get_signatures(res_rh, merge_node = merge_node_param(min_n_signatures = 1358))
get_signatures(res_rh, merge_node = merge_node_param(min_n_signatures = 1418))
get_signatures(res_rh, merge_node = merge_node_param(min_n_signatures = 1688))
#> Error in lt[[hierarchy[i, 1]]]$AddChildNode({: attempt to apply non-function
get_signatures(res_rh, merge_node = merge_node_param(min_n_signatures = 1886))
#> Error in names(x) <- value: 'names' attribute [1] must be the same length as the vector [0]
Compare signatures from different nodes:
compare_signatures(res_rh, verbose = FALSE)
If there are too many signatures, top_signatures = ... can be set to only show the
signatures with the highest FDRs. Note it only works on every node and the final signatures
are the union of all signatures of all nodes.
# code only for demonstration
# e.g. to show the top 500 most significant rows on each node.
tb = get_signature(res_rh, top_signatures = 500)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res_rh, merge_node = merge_node_param(min_n_signatures = 94))
#> CellLine Experiment Phase
#> class 2.24e-17 1e-26 2.86e-23
test_to_known_factors(res_rh, merge_node = merge_node_param(min_n_signatures = 112))
#> CellLine Experiment Phase
#> class 9.46e-09 1.3e-18 7.23e-24
test_to_known_factors(res_rh, merge_node = merge_node_param(min_n_signatures = 198))
#> CellLine Experiment Phase
#> class 5e-08 1.44e-17 1.79e-24
test_to_known_factors(res_rh, merge_node = merge_node_param(min_n_signatures = 200))
#> CellLine Experiment Phase
#> class 0.0432 3.12e-10 3.96e-25
test_to_known_factors(res_rh, merge_node = merge_node_param(min_n_signatures = 450))
#> CellLine Experiment Phase
#> class 0.0279 5.25e-11 8.07e-26
test_to_known_factors(res_rh, merge_node = merge_node_param(min_n_signatures = 1358))
#> CellLine Experiment Phase
#> class 0.192 2.57e-10 1.42e-19
test_to_known_factors(res_rh, merge_node = merge_node_param(min_n_signatures = 1418))
#> CellLine Experiment Phase
#> class 0.104 1.59e-08 1.59e-19
test_to_known_factors(res_rh, merge_node = merge_node_param(min_n_signatures = 1688))
#> CellLine Experiment Phase
#> class 0.0929 0.357 3.15e-05
test_to_known_factors(res_rh, merge_node = merge_node_param(min_n_signatures = 1886))
#> CellLine Experiment Phase
#> class NA NA NA
Child nodes: Node01 , Node02 .
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 'ConsensusPartition' object with k = 2, 3, 4.
#> On a matrix with 13001 rows and 460 columns.
#> Top rows (1300) 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 0.984 0.994 0.501 0.500 0.500
#> 3 3 0.849 0.919 0.941 0.205 0.883 0.769
#> 4 4 0.792 0.860 0.922 0.094 0.926 0.819
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
#> H1_Exp1.001 2 0.971 0.3410 0.40 0.60
#> H1_Exp1.002 1 0.000 0.9966 1.00 0.00
#> H1_Exp1.003 1 0.141 0.9768 0.98 0.02
#> H1_Exp1.004 1 0.000 0.9966 1.00 0.00
#> H1_Exp1.006 1 0.000 0.9966 1.00 0.00
#> H1_Exp1.007 1 0.000 0.9966 1.00 0.00
#> H1_Exp1.008 2 0.000 0.9906 0.00 1.00
#> H1_Exp1.009 1 0.000 0.9966 1.00 0.00
#> H1_Exp1.010 1 0.000 0.9966 1.00 0.00
#> H1_Exp1.011 1 0.000 0.9966 1.00 0.00
#> H1_Exp1.012 1 0.000 0.9966 1.00 0.00
#> H1_Exp1.014 1 0.000 0.9966 1.00 0.00
#> H1_Exp1.015 1 0.000 0.9966 1.00 0.00
#> H1_Exp1.016 1 0.000 0.9966 1.00 0.00
#> H1_Exp1.017 1 0.000 0.9966 1.00 0.00
#> H1_Exp1.018 1 0.000 0.9966 1.00 0.00
#> H1_Exp1.019 1 0.000 0.9966 1.00 0.00
#> H1_Exp1.020 1 0.000 0.9966 1.00 0.00
#> H1_Exp1.021 2 0.000 0.9906 0.00 1.00
#> H1_Exp1.022 1 0.855 0.6089 0.72 0.28
#> H1_Exp1.023 1 0.000 0.9966 1.00 0.00
#> H1_Exp1.024 1 0.000 0.9966 1.00 0.00
#> H1_Exp1.025 1 0.469 0.8874 0.90 0.10
#> H1_Exp1.026 1 0.000 0.9966 1.00 0.00
#> H1_Exp1.027 2 0.000 0.9906 0.00 1.00
#> H1_Exp1.029 1 0.000 0.9966 1.00 0.00
#> H1_Exp1.030 2 0.402 0.9090 0.08 0.92
#> H1_Exp1.031 2 0.000 0.9906 0.00 1.00
#> H1_Exp1.032 2 0.000 0.9906 0.00 1.00
#> H1_Exp1.033 2 0.000 0.9906 0.00 1.00
#> H1_Exp1.035 1 0.000 0.9966 1.00 0.00
#> H1_Exp1.036 2 0.000 0.9906 0.00 1.00
#> H1_Exp1.038 1 0.000 0.9966 1.00 0.00
#> H1_Exp1.039 1 0.000 0.9966 1.00 0.00
#> H1_Exp1.040 1 0.000 0.9966 1.00 0.00
#> H1_Exp1.041 1 0.000 0.9966 1.00 0.00
#> H1_Exp1.042 2 0.000 0.9906 0.00 1.00
#> H1_Exp1.043 2 0.529 0.8618 0.12 0.88
#> H1_Exp1.044 2 0.000 0.9906 0.00 1.00
#> H1_Exp1.045 2 0.000 0.9906 0.00 1.00
#> H1_Exp1.047 2 0.000 0.9906 0.00 1.00
#> H1_Exp1.048 2 0.000 0.9906 0.00 1.00
#> H1_Exp1.049 1 0.000 0.9966 1.00 0.00
#> H1_Exp1.050 1 0.000 0.9966 1.00 0.00
#> H1_Exp1.051 1 0.000 0.9966 1.00 0.00
#> H1_Exp1.052 1 0.000 0.9966 1.00 0.00
#> H1_Exp1.053 1 0.000 0.9966 1.00 0.00
#> H1_Exp1.054 1 0.000 0.9966 1.00 0.00
#> H1_Exp1.055 1 0.000 0.9966 1.00 0.00
#> H1_Exp1.057 1 0.000 0.9966 1.00 0.00
#> H1_Exp1.058 2 0.000 0.9906 0.00 1.00
#> H1_Exp1.059 1 0.000 0.9966 1.00 0.00
#> H1_Exp1.060 2 0.000 0.9906 0.00 1.00
#> H1_Exp1.061 1 0.000 0.9966 1.00 0.00
#> H1_Exp1.062 1 0.000 0.9966 1.00 0.00
#> H1_Exp1.063 1 0.000 0.9966 1.00 0.00
#> H1_Exp1.064 1 0.000 0.9966 1.00 0.00
#> H1_Exp1.065 2 0.000 0.9906 0.00 1.00
#> H1_Exp1.066 1 0.000 0.9966 1.00 0.00
#> H1_Exp1.068 2 0.000 0.9906 0.00 1.00
#> H1_Exp1.069 1 0.000 0.9966 1.00 0.00
#> H1_Exp1.070 1 0.000 0.9966 1.00 0.00
#> H1_Exp2.073 1 0.000 0.9966 1.00 0.00
#> H1_Exp2.074 1 0.000 0.9966 1.00 0.00
#> H1_Exp2.075 1 0.000 0.9966 1.00 0.00
#> H1_Exp2.076 2 0.999 0.0853 0.48 0.52
#> H1_Exp2.077 2 0.000 0.9906 0.00 1.00
#> H1_Exp2.078 2 0.000 0.9906 0.00 1.00
#> H1_Exp2.079 1 0.000 0.9966 1.00 0.00
#> H1_Exp2.080 2 0.000 0.9906 0.00 1.00
#> H1_Exp2.081 1 0.000 0.9966 1.00 0.00
#> H1_Exp2.082 1 0.000 0.9966 1.00 0.00
#> H1_Exp2.083 1 0.000 0.9966 1.00 0.00
#> H1_Exp2.084 1 0.000 0.9966 1.00 0.00
#> H1_Exp2.085 1 0.000 0.9966 1.00 0.00
#> H1_Exp2.086 2 0.000 0.9906 0.00 1.00
#> H1_Exp2.087 1 0.141 0.9768 0.98 0.02
#> H1_Exp2.088 1 0.000 0.9966 1.00 0.00
#> H1_Exp2.089 1 0.000 0.9966 1.00 0.00
#> H1_Exp2.090 2 0.000 0.9906 0.00 1.00
#> H1_Exp2.091 1 0.000 0.9966 1.00 0.00
#> H1_Exp2.092 1 0.000 0.9966 1.00 0.00
#> H1_Exp2.093 1 0.000 0.9966 1.00 0.00
#> H1_Exp2.094 1 0.000 0.9966 1.00 0.00
#> H1_Exp2.096 1 0.000 0.9966 1.00 0.00
#> H1_Exp2.097 1 0.327 0.9343 0.94 0.06
#> H1_Exp2.098 1 0.000 0.9966 1.00 0.00
#> H1_Exp2.099 2 0.000 0.9906 0.00 1.00
#> H1_Exp2.100 1 0.000 0.9966 1.00 0.00
#> H1_Exp2.101 1 0.000 0.9966 1.00 0.00
#> H1_Exp2.102 2 0.000 0.9906 0.00 1.00
#> H1_Exp2.103 2 0.000 0.9906 0.00 1.00
#> H1_Exp2.104 2 0.000 0.9906 0.00 1.00
#> H1_Exp2.105 1 0.000 0.9966 1.00 0.00
#> H1_Exp2.106 1 0.000 0.9966 1.00 0.00
#> H1_Exp2.107 2 0.000 0.9906 0.00 1.00
#> H1_Exp2.108 1 0.000 0.9966 1.00 0.00
#> H1_Exp2.109 2 0.000 0.9906 0.00 1.00
#> H1_Exp2.110 2 0.000 0.9906 0.00 1.00
#> H1_Exp2.111 2 0.000 0.9906 0.00 1.00
#> H1_Exp2.112 1 0.000 0.9966 1.00 0.00
#> H1_Exp2.113 2 0.000 0.9906 0.00 1.00
#> H1_Exp2.114 2 0.000 0.9906 0.00 1.00
#> H1_Exp2.115 2 0.000 0.9906 0.00 1.00
#> H1_Exp2.116 1 0.000 0.9966 1.00 0.00
#> H1_Exp2.117 2 0.000 0.9906 0.00 1.00
#> H1_Exp2.118 1 0.000 0.9966 1.00 0.00
#> H1_Exp2.120 2 0.000 0.9906 0.00 1.00
#> H1_Exp2.121 1 0.000 0.9966 1.00 0.00
#> H1_Exp2.122 2 0.000 0.9906 0.00 1.00
#> H1_Exp2.123 1 0.000 0.9966 1.00 0.00
#> H1_Exp2.124 2 0.000 0.9906 0.00 1.00
#> H1_Exp2.125 2 0.000 0.9906 0.00 1.00
#> H1_Exp2.126 2 0.000 0.9906 0.00 1.00
#> H1_Exp2.127 1 0.000 0.9966 1.00 0.00
#> H1_Exp2.128 2 0.000 0.9906 0.00 1.00
#> H1_Exp2.129 2 0.000 0.9906 0.00 1.00
#> H1_Exp2.130 1 0.000 0.9966 1.00 0.00
#> H1_Exp2.132 2 0.242 0.9519 0.04 0.96
#> H1_Exp2.133 1 0.000 0.9966 1.00 0.00
#> H1_Exp2.134 2 0.000 0.9906 0.00 1.00
#> H1_Exp2.135 1 0.000 0.9966 1.00 0.00
#> H1_Exp2.136 1 0.000 0.9966 1.00 0.00
#> H1_Exp2.137 2 0.000 0.9906 0.00 1.00
#> H1_Exp2.138 1 0.000 0.9966 1.00 0.00
#> H1_Exp2.139 1 0.000 0.9966 1.00 0.00
#> H1_Exp2.140 1 0.000 0.9966 1.00 0.00
#> H1_Exp2.141 2 0.000 0.9906 0.00 1.00
#> H1_Exp2.142 2 0.000 0.9906 0.00 1.00
#> H1_Exp2.144 1 0.000 0.9966 1.00 0.00
#> H1_Exp2.145 2 0.000 0.9906 0.00 1.00
#> H1_Exp2.146 1 0.000 0.9966 1.00 0.00
#> H1_Exp2.147 2 0.000 0.9906 0.00 1.00
#> H1_Exp2.148 1 0.000 0.9966 1.00 0.00
#> H1_Exp2.149 2 0.000 0.9906 0.00 1.00
#> H1_Exp2.150 1 0.000 0.9966 1.00 0.00
#> H1_Exp2.151 2 0.000 0.9906 0.00 1.00
#> H1_Exp2.152 1 0.000 0.9966 1.00 0.00
#> H1_Exp2.153 2 0.904 0.5342 0.32 0.68
#> H1_Exp2.154 1 0.000 0.9966 1.00 0.00
#> H1_Exp3.218 2 0.000 0.9906 0.00 1.00
#> H1_Exp3.219 2 0.971 0.3415 0.40 0.60
#> H1_Exp3.220 1 0.000 0.9966 1.00 0.00
#> H1_Exp3.221 2 0.000 0.9906 0.00 1.00
#> H1_Exp3.222 2 0.000 0.9906 0.00 1.00
#> H1_Exp3.223 1 0.000 0.9966 1.00 0.00
#> H1_Exp3.224 2 0.000 0.9906 0.00 1.00
#> H1_Exp3.225 2 0.000 0.9906 0.00 1.00
#> H1_Exp3.226 2 0.000 0.9906 0.00 1.00
#> H1_Exp3.227 2 0.000 0.9906 0.00 1.00
#> H1_Exp3.228 2 0.000 0.9906 0.00 1.00
#> H1_Exp3.229 2 0.000 0.9906 0.00 1.00
#> H1_Exp3.230 2 0.000 0.9906 0.00 1.00
#> H1_Exp3.231 2 0.000 0.9906 0.00 1.00
#> H1_Exp3.232 1 0.000 0.9966 1.00 0.00
#> H1_Exp3.233 1 0.000 0.9966 1.00 0.00
#> H1_Exp3.234 2 0.000 0.9906 0.00 1.00
#> H1_Exp3.235 2 0.000 0.9906 0.00 1.00
#> H1_Exp3.236 2 0.000 0.9906 0.00 1.00
#> H1_Exp3.237 2 0.000 0.9906 0.00 1.00
#> H1_Exp3.238 2 0.000 0.9906 0.00 1.00
#> H1_Exp3.239 2 0.000 0.9906 0.00 1.00
#> H1_Exp3.240 2 0.000 0.9906 0.00 1.00
#> H1_Exp3.241 1 0.000 0.9966 1.00 0.00
#> H1_Exp3.242 2 0.000 0.9906 0.00 1.00
#> H1_Exp3.243 2 0.000 0.9906 0.00 1.00
#> H1_Exp3.244 2 0.000 0.9906 0.00 1.00
#> H1_Exp3.246 2 0.000 0.9906 0.00 1.00
#> H1_Exp3.247 2 0.000 0.9906 0.00 1.00
#> H1_Exp3.248 1 0.000 0.9966 1.00 0.00
#> H1_Exp3.249 2 0.000 0.9906 0.00 1.00
#> H1_Exp3.250 2 0.000 0.9906 0.00 1.00
#> H1_Exp3.251 2 0.000 0.9906 0.00 1.00
#> H1_Exp3.252 2 0.000 0.9906 0.00 1.00
#> H1_Exp3.253 2 0.000 0.9906 0.00 1.00
#> H1_Exp3.254 2 0.000 0.9906 0.00 1.00
#> H1_Exp3.255 2 0.000 0.9906 0.00 1.00
#> H1_Exp3.256 2 0.469 0.8864 0.10 0.90
#> H1_Exp3.257 2 0.000 0.9906 0.00 1.00
#> H1_Exp3.258 2 0.000 0.9906 0.00 1.00
#> H1_Exp3.259 2 0.000 0.9906 0.00 1.00
#> H1_Exp3.260 2 0.000 0.9906 0.00 1.00
#> H1_Exp3.261 2 0.000 0.9906 0.00 1.00
#> H1_Exp3.262 2 0.000 0.9906 0.00 1.00
#> H1_Exp3.263 2 0.000 0.9906 0.00 1.00
#> H1_Exp3.264 2 0.000 0.9906 0.00 1.00
#> H1_Exp3.265 2 0.000 0.9906 0.00 1.00
#> H1_Exp3.266 2 0.000 0.9906 0.00 1.00
#> H1_Exp3.267 2 0.000 0.9906 0.00 1.00
#> H1_Exp3.268 2 0.000 0.9906 0.00 1.00
#> H1_Exp3.269 2 0.000 0.9906 0.00 1.00
#> H1_Exp3.270 2 0.000 0.9906 0.00 1.00
#> H1_Exp3.271 2 0.000 0.9906 0.00 1.00
#> H1_Exp3.272 2 0.000 0.9906 0.00 1.00
#> H1_Exp3.273 2 0.000 0.9906 0.00 1.00
#> H1_Exp3.274 2 0.000 0.9906 0.00 1.00
#> H1_Exp3.275 2 0.000 0.9906 0.00 1.00
#> H1_Exp3.276 2 0.000 0.9906 0.00 1.00
#> H1_Exp3.277 1 0.000 0.9966 1.00 0.00
#> H1_Exp3.278 2 0.000 0.9906 0.00 1.00
#> H1_Exp3.279 2 0.000 0.9906 0.00 1.00
#> H1_Exp3.280 2 0.000 0.9906 0.00 1.00
#> H1_Exp3.281 2 0.000 0.9906 0.00 1.00
#> H1_Exp3.282 2 0.000 0.9906 0.00 1.00
#> H1_Exp3.283 1 0.000 0.9966 1.00 0.00
#> H1_Exp3.284 1 0.000 0.9966 1.00 0.00
#> H1_Exp3.285 2 0.000 0.9906 0.00 1.00
#> H1_Exp3.286 2 0.000 0.9906 0.00 1.00
#> H1_Exp3.287 2 0.000 0.9906 0.00 1.00
#> H1_Exp3.288 2 0.000 0.9906 0.00 1.00
#> H1_Exp3.289 2 0.000 0.9906 0.00 1.00
#> H1_Exp3.290 2 0.000 0.9906 0.00 1.00
#> H1_Exp3.291 2 0.000 0.9906 0.00 1.00
#> G2_Exp1.059 2 0.000 0.9906 0.00 1.00
#> G2_Exp1.069 1 0.000 0.9966 1.00 0.00
#> G2_Exp1.075 2 0.000 0.9906 0.00 1.00
#> G2_Exp1.063 2 0.000 0.9906 0.00 1.00
#> G2_Exp1.029 2 0.000 0.9906 0.00 1.00
#> G2_Exp1.076 1 0.000 0.9966 1.00 0.00
#> G2_Exp1.013 1 0.000 0.9966 1.00 0.00
#> G2_Exp1.037 2 0.000 0.9906 0.00 1.00
#> G2_Exp1.057 2 0.000 0.9906 0.00 1.00
#> G2_Exp1.018 2 0.000 0.9906 0.00 1.00
#> G2_Exp1.015 1 0.000 0.9966 1.00 0.00
#> G2_Exp1.019 1 0.000 0.9966 1.00 0.00
#> G2_Exp1.050 1 0.000 0.9966 1.00 0.00
#> G2_Exp1.004 2 0.000 0.9906 0.00 1.00
#> G2_Exp1.061 2 0.000 0.9906 0.00 1.00
#> G2_Exp1.042 1 0.000 0.9966 1.00 0.00
#> G2_Exp1.060 2 0.000 0.9906 0.00 1.00
#> G2_Exp1.058 2 0.000 0.9906 0.00 1.00
#> G2_Exp1.065 2 0.000 0.9906 0.00 1.00
#> G2_Exp1.002 2 0.000 0.9906 0.00 1.00
#> G2_Exp1.044 2 0.000 0.9906 0.00 1.00
#> G2_Exp1.051 2 0.000 0.9906 0.00 1.00
#> G2_Exp1.073 2 0.000 0.9906 0.00 1.00
#> G2_Exp1.030 1 0.000 0.9966 1.00 0.00
#> G2_Exp1.028 2 0.000 0.9906 0.00 1.00
#> G2_Exp1.022 1 0.000 0.9966 1.00 0.00
#> G2_Exp1.034 2 0.000 0.9906 0.00 1.00
#> G2_Exp1.017 1 0.000 0.9966 1.00 0.00
#> G2_Exp1.047 1 0.000 0.9966 1.00 0.00
#> G2_Exp1.072 1 0.000 0.9966 1.00 0.00
#> G2_Exp1.074 1 0.000 0.9966 1.00 0.00
#> G2_Exp1.054 2 0.000 0.9906 0.00 1.00
#> G2_Exp1.024 2 0.000 0.9906 0.00 1.00
#> G2_Exp1.032 1 0.000 0.9966 1.00 0.00
#> G2_Exp1.020 2 0.000 0.9906 0.00 1.00
#> G2_Exp1.064 1 0.000 0.9966 1.00 0.00
#> G2_Exp1.045 1 0.000 0.9966 1.00 0.00
#> G2_Exp1.038 1 0.000 0.9966 1.00 0.00
#> G2_Exp1.001 1 0.000 0.9966 1.00 0.00
#> G2_Exp1.049 1 0.000 0.9966 1.00 0.00
#> G2_Exp1.031 1 0.000 0.9966 1.00 0.00
#> G2_Exp1.039 2 0.000 0.9906 0.00 1.00
#> G2_Exp1.070 1 0.000 0.9966 1.00 0.00
#> G2_Exp1.007 2 0.000 0.9906 0.00 1.00
#> G2_Exp1.021 1 0.000 0.9966 1.00 0.00
#> G2_Exp1.036 1 0.000 0.9966 1.00 0.00
#> G2_Exp1.046 1 0.000 0.9966 1.00 0.00
#> G2_Exp1.040 1 0.000 0.9966 1.00 0.00
#> G2_Exp1.068 1 0.000 0.9966 1.00 0.00
#> G2_Exp1.077 1 0.000 0.9966 1.00 0.00
#> G2_Exp1.026 2 0.000 0.9906 0.00 1.00
#> G2_Exp1.011 2 0.000 0.9906 0.00 1.00
#> G2_Exp1.053 2 0.000 0.9906 0.00 1.00
#> G2_Exp1.008 1 0.000 0.9966 1.00 0.00
#> G2_Exp1.006 2 0.000 0.9906 0.00 1.00
#> G2_Exp1.016 2 0.000 0.9906 0.00 1.00
#> G2_Exp1.010 2 0.000 0.9906 0.00 1.00
#> G2_Exp1.014 1 0.000 0.9966 1.00 0.00
#> G2_Exp1.005 1 0.000 0.9966 1.00 0.00
#> G2_Exp1.048 1 0.000 0.9966 1.00 0.00
#> G2_Exp1.027 2 0.000 0.9906 0.00 1.00
#> G2_Exp1.067 2 0.000 0.9906 0.00 1.00
#> G2_Exp1.009 2 0.000 0.9906 0.00 1.00
#> G2_Exp1.062 1 0.000 0.9966 1.00 0.00
#> G2_Exp1.025 2 0.000 0.9906 0.00 1.00
#> G2_Exp1.056 1 0.000 0.9966 1.00 0.00
#> G2_Exp1.055 2 0.000 0.9906 0.00 1.00
#> G2_Exp1.041 1 0.000 0.9966 1.00 0.00
#> G2_Exp1.012 1 0.000 0.9966 1.00 0.00
#> G2_Exp1.066 1 0.000 0.9966 1.00 0.00
#> G2_Exp1.023 1 0.000 0.9966 1.00 0.00
#> G2_Exp1.033 2 0.000 0.9906 0.00 1.00
#> G2_Exp1.043 2 0.000 0.9906 0.00 1.00
#> G2_Exp1.052 2 0.000 0.9906 0.00 1.00
#> G2_Exp1.071 2 0.000 0.9906 0.00 1.00
#> G2_Exp1.003 1 0.000 0.9966 1.00 0.00
#> S_Exp1.071 1 0.000 0.9966 1.00 0.00
#> S_Exp1.074 2 0.000 0.9906 0.00 1.00
#> S_Exp1.031 1 0.000 0.9966 1.00 0.00
#> S_Exp1.032 1 0.000 0.9966 1.00 0.00
#> S_Exp1.035 1 0.000 0.9966 1.00 0.00
#> S_Exp1.030 2 0.000 0.9906 0.00 1.00
#> S_Exp1.056 1 0.000 0.9966 1.00 0.00
#> S_Exp1.011 1 0.000 0.9966 1.00 0.00
#> S_Exp1.016 1 0.000 0.9966 1.00 0.00
#> S_Exp1.013 2 0.000 0.9906 0.00 1.00
#> S_Exp1.063 1 0.000 0.9966 1.00 0.00
#> S_Exp1.038 1 0.000 0.9966 1.00 0.00
#> S_Exp1.065 2 0.584 0.8366 0.14 0.86
#> S_Exp1.048 1 0.000 0.9966 1.00 0.00
#> S_Exp1.051 1 0.000 0.9966 1.00 0.00
#> S_Exp1.078 1 0.000 0.9966 1.00 0.00
#> S_Exp1.023 1 0.000 0.9966 1.00 0.00
#> S_Exp1.022 1 0.000 0.9966 1.00 0.00
#> S_Exp1.001 1 0.000 0.9966 1.00 0.00
#> S_Exp1.046 1 0.000 0.9966 1.00 0.00
#> S_Exp1.061 1 0.000 0.9966 1.00 0.00
#> S_Exp1.080 1 0.000 0.9966 1.00 0.00
#> S_Exp1.008 1 0.000 0.9966 1.00 0.00
#> S_Exp1.069 1 0.000 0.9966 1.00 0.00
#> S_Exp1.076 2 0.000 0.9906 0.00 1.00
#> S_Exp1.019 1 0.000 0.9966 1.00 0.00
#> S_Exp1.003 1 0.000 0.9966 1.00 0.00
#> S_Exp1.006 1 0.000 0.9966 1.00 0.00
#> S_Exp1.029 1 0.000 0.9966 1.00 0.00
#> S_Exp1.025 1 0.000 0.9966 1.00 0.00
#> S_Exp1.064 1 0.000 0.9966 1.00 0.00
#> S_Exp1.041 1 0.000 0.9966 1.00 0.00
#> S_Exp1.057 1 0.000 0.9966 1.00 0.00
#> S_Exp1.068 2 0.000 0.9906 0.00 1.00
#> S_Exp1.073 1 0.000 0.9966 1.00 0.00
#> S_Exp1.014 1 0.000 0.9966 1.00 0.00
#> S_Exp1.039 1 0.000 0.9966 1.00 0.00
#> S_Exp1.055 1 0.000 0.9966 1.00 0.00
#> S_Exp1.033 1 0.000 0.9966 1.00 0.00
#> S_Exp1.081 1 0.000 0.9966 1.00 0.00
#> S_Exp1.004 1 0.000 0.9966 1.00 0.00
#> S_Exp1.045 1 0.000 0.9966 1.00 0.00
#> S_Exp1.009 2 0.000 0.9906 0.00 1.00
#> S_Exp1.054 1 0.000 0.9966 1.00 0.00
#> S_Exp1.018 1 0.000 0.9966 1.00 0.00
#> S_Exp1.034 1 0.000 0.9966 1.00 0.00
#> S_Exp1.042 2 0.000 0.9906 0.00 1.00
#> S_Exp1.067 1 0.000 0.9966 1.00 0.00
#> S_Exp1.012 1 0.000 0.9966 1.00 0.00
#> S_Exp1.002 2 0.000 0.9906 0.00 1.00
#> S_Exp1.037 1 0.000 0.9966 1.00 0.00
#> S_Exp1.070 1 0.000 0.9966 1.00 0.00
#> S_Exp1.053 1 0.000 0.9966 1.00 0.00
#> S_Exp1.036 1 0.000 0.9966 1.00 0.00
#> S_Exp1.021 1 0.000 0.9966 1.00 0.00
#> S_Exp1.020 1 0.000 0.9966 1.00 0.00
#> S_Exp1.060 1 0.000 0.9966 1.00 0.00
#> S_Exp1.015 1 0.000 0.9966 1.00 0.00
#> S_Exp1.059 2 0.000 0.9906 0.00 1.00
#> S_Exp1.066 1 0.000 0.9966 1.00 0.00
#> S_Exp1.062 1 0.000 0.9966 1.00 0.00
#> S_Exp1.040 1 0.000 0.9966 1.00 0.00
#> S_Exp1.028 1 0.000 0.9966 1.00 0.00
#> S_Exp1.079 1 0.000 0.9966 1.00 0.00
#> S_Exp1.047 1 0.000 0.9966 1.00 0.00
#> S_Exp1.044 2 0.000 0.9906 0.00 1.00
#> S_Exp1.026 1 0.000 0.9966 1.00 0.00
#> S_Exp1.077 1 0.000 0.9966 1.00 0.00
#> S_Exp1.052 1 0.000 0.9966 1.00 0.00
#> S_Exp1.007 1 0.000 0.9966 1.00 0.00
#> S_Exp1.027 1 0.000 0.9966 1.00 0.00
#> S_Exp1.017 1 0.000 0.9966 1.00 0.00
#> S_Exp1.075 1 0.000 0.9966 1.00 0.00
#> S_Exp1.049 1 0.000 0.9966 1.00 0.00
#> S_Exp1.072 1 0.000 0.9966 1.00 0.00
#> S_Exp1.005 1 0.000 0.9966 1.00 0.00
#> S_Exp1.058 1 0.000 0.9966 1.00 0.00
#> S_Exp1.010 1 0.000 0.9966 1.00 0.00
#> S_Exp1.043 1 0.000 0.9966 1.00 0.00
#> S_Exp1.050 1 0.000 0.9966 1.00 0.00
#> G1_Exp1.048 2 0.000 0.9906 0.00 1.00
#> G1_Exp1.090 2 0.000 0.9906 0.00 1.00
#> G1_Exp1.061 2 0.000 0.9906 0.00 1.00
#> G1_Exp1.025 2 0.000 0.9906 0.00 1.00
#> G1_Exp1.033 1 0.000 0.9966 1.00 0.00
#> G1_Exp1.081 2 0.000 0.9906 0.00 1.00
#> G1_Exp1.013 1 0.000 0.9966 1.00 0.00
#> G1_Exp1.024 2 0.000 0.9906 0.00 1.00
#> G1_Exp1.056 2 0.000 0.9906 0.00 1.00
#> G1_Exp1.026 1 0.000 0.9966 1.00 0.00
#> G1_Exp1.064 2 0.000 0.9906 0.00 1.00
#> G1_Exp1.053 2 0.000 0.9906 0.00 1.00
#> G1_Exp1.070 2 0.000 0.9906 0.00 1.00
#> G1_Exp1.087 2 0.000 0.9906 0.00 1.00
#> G1_Exp1.067 2 0.000 0.9906 0.00 1.00
#> G1_Exp1.003 2 0.000 0.9906 0.00 1.00
#> G1_Exp1.052 2 0.000 0.9906 0.00 1.00
#> G1_Exp1.060 2 0.000 0.9906 0.00 1.00
#> G1_Exp1.071 2 0.000 0.9906 0.00 1.00
#> G1_Exp1.045 2 0.000 0.9906 0.00 1.00
#> G1_Exp1.040 2 0.000 0.9906 0.00 1.00
#> G1_Exp1.016 2 0.000 0.9906 0.00 1.00
#> G1_Exp1.027 1 0.000 0.9966 1.00 0.00
#> G1_Exp1.059 1 0.000 0.9966 1.00 0.00
#> G1_Exp1.002 2 0.000 0.9906 0.00 1.00
#> G1_Exp1.021 2 0.000 0.9906 0.00 1.00
#> G1_Exp1.015 2 0.000 0.9906 0.00 1.00
#> G1_Exp1.042 2 0.000 0.9906 0.00 1.00
#> G1_Exp1.058 2 0.000 0.9906 0.00 1.00
#> G1_Exp1.075 2 0.000 0.9906 0.00 1.00
#> G1_Exp1.019 1 0.000 0.9966 1.00 0.00
#> G1_Exp1.074 2 0.000 0.9906 0.00 1.00
#> G1_Exp1.079 2 0.000 0.9906 0.00 1.00
#> G1_Exp1.038 2 0.000 0.9906 0.00 1.00
#> G1_Exp1.035 2 0.000 0.9906 0.00 1.00
#> G1_Exp1.012 2 0.000 0.9906 0.00 1.00
#> G1_Exp1.032 2 0.000 0.9906 0.00 1.00
#> G1_Exp1.009 2 0.000 0.9906 0.00 1.00
#> G1_Exp1.043 2 0.000 0.9906 0.00 1.00
#> G1_Exp1.089 2 0.000 0.9906 0.00 1.00
#> G1_Exp1.080 2 0.000 0.9906 0.00 1.00
#> G1_Exp1.007 2 0.000 0.9906 0.00 1.00
#> G1_Exp1.062 2 0.000 0.9906 0.00 1.00
#> G1_Exp1.069 2 0.000 0.9906 0.00 1.00
#> G1_Exp1.004 2 0.000 0.9906 0.00 1.00
#> G1_Exp1.010 2 0.000 0.9906 0.00 1.00
#> G1_Exp1.044 2 0.000 0.9906 0.00 1.00
#> G1_Exp1.065 2 0.000 0.9906 0.00 1.00
#> G1_Exp1.029 2 0.000 0.9906 0.00 1.00
#> G1_Exp1.086 2 0.141 0.9716 0.02 0.98
#> G1_Exp1.084 2 0.000 0.9906 0.00 1.00
#> G1_Exp1.088 2 0.000 0.9906 0.00 1.00
#> G1_Exp1.051 2 0.000 0.9906 0.00 1.00
#> G1_Exp1.085 1 0.000 0.9966 1.00 0.00
#> G1_Exp1.066 2 0.000 0.9906 0.00 1.00
#> G1_Exp1.047 2 0.000 0.9906 0.00 1.00
#> G1_Exp1.072 2 0.000 0.9906 0.00 1.00
#> G1_Exp1.057 2 0.000 0.9906 0.00 1.00
#> G1_Exp1.054 2 0.000 0.9906 0.00 1.00
#> G1_Exp1.082 2 0.000 0.9906 0.00 1.00
#> G1_Exp1.031 1 0.827 0.6472 0.74 0.26
#> G1_Exp1.077 2 0.000 0.9906 0.00 1.00
#> G1_Exp1.091 2 0.000 0.9906 0.00 1.00
#> G1_Exp1.078 1 0.000 0.9966 1.00 0.00
#> G1_Exp1.073 1 0.000 0.9966 1.00 0.00
#> G1_Exp1.068 2 0.000 0.9906 0.00 1.00
#> G1_Exp1.001 2 0.000 0.9906 0.00 1.00
#> G1_Exp1.023 1 0.000 0.9966 1.00 0.00
#> G1_Exp1.022 1 0.000 0.9966 1.00 0.00
#> G1_Exp1.034 1 0.000 0.9966 1.00 0.00
#> G1_Exp1.005 2 0.000 0.9906 0.00 1.00
#> G1_Exp1.039 1 0.000 0.9966 1.00 0.00
#> G1_Exp1.037 2 0.000 0.9906 0.00 1.00
#> G1_Exp1.014 2 0.000 0.9906 0.00 1.00
#> G1_Exp1.017 2 0.000 0.9906 0.00 1.00
#> G1_Exp1.020 1 0.000 0.9966 1.00 0.00
#> G1_Exp1.028 2 0.000 0.9906 0.00 1.00
#> G1_Exp1.036 2 0.000 0.9906 0.00 1.00
#> G1_Exp1.049 2 0.000 0.9906 0.00 1.00
#> G1_Exp1.041 2 0.000 0.9906 0.00 1.00
#> G1_Exp1.006 2 0.000 0.9906 0.00 1.00
#> G1_Exp1.008 2 0.529 0.8617 0.12 0.88
#> G1_Exp1.055 2 0.000 0.9906 0.00 1.00
#> G1_Exp1.050 2 0.000 0.9906 0.00 1.00
#> G1_Exp1.076 2 0.000 0.9906 0.00 1.00
#> G1_Exp1.011 1 0.000 0.9966 1.00 0.00
#> G1_Exp1.063 1 0.000 0.9966 1.00 0.00
#> G1_Exp1.083 2 0.000 0.9906 0.00 1.00
#> G1_Exp1.030 2 0.000 0.9906 0.00 1.00
#> G1_Exp1.018 1 0.000 0.9966 1.00 0.00
#> G1_Exp1.046 1 0.000 0.9966 1.00 0.00
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> H1_Exp1.001 1 0.2066 0.7274 0.94 0.06 0.00
#> H1_Exp1.002 1 0.0000 0.7989 1.00 0.00 0.00
#> H1_Exp1.003 1 0.0000 0.7989 1.00 0.00 0.00
#> H1_Exp1.004 1 0.4555 0.9377 0.80 0.00 0.20
#> H1_Exp1.006 1 0.0000 0.7989 1.00 0.00 0.00
#> H1_Exp1.007 1 0.4555 0.9377 0.80 0.00 0.20
#> H1_Exp1.008 2 0.0000 0.9780 0.00 1.00 0.00
#> H1_Exp1.009 1 0.4555 0.9377 0.80 0.00 0.20
#> H1_Exp1.010 1 0.4555 0.9377 0.80 0.00 0.20
#> H1_Exp1.011 1 0.4555 0.9377 0.80 0.00 0.20
#> H1_Exp1.012 1 0.4002 0.9265 0.84 0.00 0.16
#> H1_Exp1.014 1 0.4002 0.9265 0.84 0.00 0.16
#> H1_Exp1.015 1 0.3340 0.9005 0.88 0.00 0.12
#> H1_Exp1.016 1 0.4555 0.9377 0.80 0.00 0.20
#> H1_Exp1.017 1 0.4555 0.9377 0.80 0.00 0.20
#> H1_Exp1.018 1 0.0000 0.7989 1.00 0.00 0.00
#> H1_Exp1.019 1 0.4555 0.9377 0.80 0.00 0.20
#> H1_Exp1.020 1 0.4555 0.9377 0.80 0.00 0.20
#> H1_Exp1.021 2 0.0000 0.9780 0.00 1.00 0.00
#> H1_Exp1.022 1 0.2066 0.7269 0.94 0.06 0.00
#> H1_Exp1.023 1 0.4555 0.9377 0.80 0.00 0.20
#> H1_Exp1.024 1 0.4555 0.9377 0.80 0.00 0.20
#> H1_Exp1.025 3 0.7344 0.6923 0.24 0.08 0.68
#> H1_Exp1.026 1 0.4002 0.9265 0.84 0.00 0.16
#> H1_Exp1.027 2 0.0000 0.9780 0.00 1.00 0.00
#> H1_Exp1.029 1 0.4555 0.9377 0.80 0.00 0.20
#> H1_Exp1.030 2 0.7683 0.4942 0.08 0.64 0.28
#> H1_Exp1.031 2 0.0000 0.9780 0.00 1.00 0.00
#> H1_Exp1.032 2 0.0000 0.9780 0.00 1.00 0.00
#> H1_Exp1.033 2 0.0000 0.9780 0.00 1.00 0.00
#> H1_Exp1.035 1 0.4555 0.9377 0.80 0.00 0.20
#> H1_Exp1.036 2 0.0000 0.9780 0.00 1.00 0.00
#> H1_Exp1.038 1 0.4555 0.9377 0.80 0.00 0.20
#> H1_Exp1.039 1 0.4555 0.9377 0.80 0.00 0.20
#> H1_Exp1.040 1 0.4002 0.9265 0.84 0.00 0.16
#> H1_Exp1.041 1 0.4555 0.9377 0.80 0.00 0.20
#> H1_Exp1.042 2 0.0000 0.9780 0.00 1.00 0.00
#> H1_Exp1.043 2 0.5216 0.6810 0.26 0.74 0.00
#> H1_Exp1.044 2 0.0000 0.9780 0.00 1.00 0.00
#> H1_Exp1.045 2 0.0000 0.9780 0.00 1.00 0.00
#> H1_Exp1.047 2 0.0000 0.9780 0.00 1.00 0.00
#> H1_Exp1.048 2 0.0000 0.9780 0.00 1.00 0.00
#> H1_Exp1.049 1 0.4555 0.9377 0.80 0.00 0.20
#> H1_Exp1.050 1 0.4555 0.9377 0.80 0.00 0.20
#> H1_Exp1.051 1 0.4555 0.9377 0.80 0.00 0.20
#> H1_Exp1.052 1 0.4555 0.9377 0.80 0.00 0.20
#> H1_Exp1.053 3 0.0000 0.9236 0.00 0.00 1.00
#> H1_Exp1.054 3 0.5016 0.5736 0.24 0.00 0.76
#> H1_Exp1.055 1 0.4555 0.9377 0.80 0.00 0.20
#> H1_Exp1.057 3 0.0000 0.9236 0.00 0.00 1.00
#> H1_Exp1.058 2 0.0000 0.9780 0.00 1.00 0.00
#> H1_Exp1.059 3 0.0000 0.9236 0.00 0.00 1.00
#> H1_Exp1.060 2 0.0000 0.9780 0.00 1.00 0.00
#> H1_Exp1.061 1 0.4555 0.9377 0.80 0.00 0.20
#> H1_Exp1.062 1 0.3686 0.9122 0.86 0.00 0.14
#> H1_Exp1.063 1 0.4555 0.9377 0.80 0.00 0.20
#> H1_Exp1.064 1 0.4555 0.9377 0.80 0.00 0.20
#> H1_Exp1.065 2 0.2066 0.9269 0.06 0.94 0.00
#> H1_Exp1.066 1 0.3340 0.9004 0.88 0.00 0.12
#> H1_Exp1.068 2 0.0000 0.9780 0.00 1.00 0.00
#> H1_Exp1.069 3 0.0000 0.9236 0.00 0.00 1.00
#> H1_Exp1.070 1 0.4555 0.9377 0.80 0.00 0.20
#> H1_Exp2.073 1 0.4555 0.9377 0.80 0.00 0.20
#> H1_Exp2.074 1 0.0892 0.8178 0.98 0.00 0.02
#> H1_Exp2.075 1 0.0000 0.7989 1.00 0.00 0.00
#> H1_Exp2.076 1 0.2959 0.6647 0.90 0.10 0.00
#> H1_Exp2.077 2 0.0000 0.9780 0.00 1.00 0.00
#> H1_Exp2.078 2 0.0000 0.9780 0.00 1.00 0.00
#> H1_Exp2.079 1 0.1529 0.8360 0.96 0.00 0.04
#> H1_Exp2.080 2 0.4555 0.7823 0.20 0.80 0.00
#> H1_Exp2.081 1 0.0000 0.7989 1.00 0.00 0.00
#> H1_Exp2.082 3 0.0892 0.9071 0.02 0.00 0.98
#> H1_Exp2.083 1 0.0892 0.8178 0.98 0.00 0.02
#> H1_Exp2.084 1 0.4555 0.9377 0.80 0.00 0.20
#> H1_Exp2.085 1 0.0000 0.7989 1.00 0.00 0.00
#> H1_Exp2.086 2 0.0000 0.9780 0.00 1.00 0.00
#> H1_Exp2.087 1 0.0000 0.7989 1.00 0.00 0.00
#> H1_Exp2.088 1 0.4555 0.9377 0.80 0.00 0.20
#> H1_Exp2.089 1 0.0000 0.7989 1.00 0.00 0.00
#> H1_Exp2.090 2 0.6244 0.3674 0.44 0.56 0.00
#> H1_Exp2.091 1 0.5706 0.7867 0.68 0.00 0.32
#> H1_Exp2.092 1 0.0000 0.7989 1.00 0.00 0.00
#> H1_Exp2.093 3 0.3340 0.8173 0.12 0.00 0.88
#> H1_Exp2.094 1 0.4555 0.9377 0.80 0.00 0.20
#> H1_Exp2.096 1 0.4555 0.9377 0.80 0.00 0.20
#> H1_Exp2.097 3 0.4002 0.7802 0.16 0.00 0.84
#> H1_Exp2.098 1 0.4002 0.9265 0.84 0.00 0.16
#> H1_Exp2.099 2 0.0000 0.9780 0.00 1.00 0.00
#> H1_Exp2.100 1 0.0000 0.7989 1.00 0.00 0.00
#> H1_Exp2.101 1 0.0892 0.8175 0.98 0.00 0.02
#> H1_Exp2.102 2 0.4002 0.8278 0.16 0.84 0.00
#> H1_Exp2.103 2 0.0000 0.9780 0.00 1.00 0.00
#> H1_Exp2.104 2 0.0000 0.9780 0.00 1.00 0.00
#> H1_Exp2.105 1 0.4002 0.9265 0.84 0.00 0.16
#> H1_Exp2.106 1 0.4555 0.9377 0.80 0.00 0.20
#> H1_Exp2.107 2 0.0000 0.9780 0.00 1.00 0.00
#> H1_Exp2.108 1 0.4555 0.9377 0.80 0.00 0.20
#> H1_Exp2.109 2 0.0000 0.9780 0.00 1.00 0.00
#> H1_Exp2.110 2 0.0000 0.9780 0.00 1.00 0.00
#> H1_Exp2.111 2 0.0000 0.9780 0.00 1.00 0.00
#> H1_Exp2.112 3 0.0000 0.9236 0.00 0.00 1.00
#> H1_Exp2.113 2 0.0000 0.9780 0.00 1.00 0.00
#> H1_Exp2.114 2 0.0000 0.9780 0.00 1.00 0.00
#> H1_Exp2.115 2 0.0000 0.9780 0.00 1.00 0.00
#> H1_Exp2.116 3 0.0000 0.9236 0.00 0.00 1.00
#> H1_Exp2.117 2 0.0000 0.9780 0.00 1.00 0.00
#> H1_Exp2.118 1 0.4555 0.9377 0.80 0.00 0.20
#> H1_Exp2.120 2 0.0000 0.9780 0.00 1.00 0.00
#> H1_Exp2.121 1 0.4555 0.9377 0.80 0.00 0.20
#> H1_Exp2.122 2 0.0000 0.9780 0.00 1.00 0.00
#> H1_Exp2.123 3 0.0000 0.9236 0.00 0.00 1.00
#> H1_Exp2.124 2 0.0000 0.9780 0.00 1.00 0.00
#> H1_Exp2.125 2 0.0000 0.9780 0.00 1.00 0.00
#> H1_Exp2.126 2 0.0000 0.9780 0.00 1.00 0.00
#> H1_Exp2.127 1 0.4555 0.9377 0.80 0.00 0.20
#> H1_Exp2.128 2 0.0000 0.9780 0.00 1.00 0.00
#> H1_Exp2.129 2 0.0000 0.9780 0.00 1.00 0.00
#> H1_Exp2.130 3 0.0000 0.9236 0.00 0.00 1.00
#> H1_Exp2.132 2 0.4966 0.8315 0.10 0.84 0.06
#> H1_Exp2.133 3 0.0000 0.9236 0.00 0.00 1.00
#> H1_Exp2.134 2 0.0000 0.9780 0.00 1.00 0.00
#> H1_Exp2.135 3 0.0000 0.9236 0.00 0.00 1.00
#> H1_Exp2.136 1 0.4555 0.9377 0.80 0.00 0.20
#> H1_Exp2.137 2 0.0000 0.9780 0.00 1.00 0.00
#> H1_Exp2.138 1 0.4555 0.9377 0.80 0.00 0.20
#> H1_Exp2.139 3 0.0000 0.9236 0.00 0.00 1.00
#> H1_Exp2.140 3 0.6192 -0.0513 0.42 0.00 0.58
#> H1_Exp2.141 2 0.0000 0.9780 0.00 1.00 0.00
#> H1_Exp2.142 2 0.0000 0.9780 0.00 1.00 0.00
#> H1_Exp2.144 1 0.4555 0.9377 0.80 0.00 0.20
#> H1_Exp2.145 2 0.0000 0.9780 0.00 1.00 0.00
#> H1_Exp2.146 3 0.0000 0.9236 0.00 0.00 1.00
#> H1_Exp2.147 2 0.0000 0.9780 0.00 1.00 0.00
#> H1_Exp2.148 1 0.4555 0.9377 0.80 0.00 0.20
#> H1_Exp2.149 2 0.0000 0.9780 0.00 1.00 0.00
#> H1_Exp2.150 3 0.0000 0.9236 0.00 0.00 1.00
#> H1_Exp2.151 2 0.0000 0.9780 0.00 1.00 0.00
#> H1_Exp2.152 1 0.4555 0.9377 0.80 0.00 0.20
#> H1_Exp2.153 2 0.8635 0.1230 0.44 0.46 0.10
#> H1_Exp2.154 1 0.4555 0.9377 0.80 0.00 0.20
#> H1_Exp3.218 2 0.0000 0.9780 0.00 1.00 0.00
#> H1_Exp3.219 3 0.6793 0.7154 0.16 0.10 0.74
#> H1_Exp3.220 3 0.0000 0.9236 0.00 0.00 1.00
#> H1_Exp3.221 2 0.0000 0.9780 0.00 1.00 0.00
#> H1_Exp3.222 2 0.0000 0.9780 0.00 1.00 0.00
#> H1_Exp3.223 1 0.4555 0.9377 0.80 0.00 0.20
#> H1_Exp3.224 2 0.0000 0.9780 0.00 1.00 0.00
#> H1_Exp3.225 2 0.0000 0.9780 0.00 1.00 0.00
#> H1_Exp3.226 2 0.0000 0.9780 0.00 1.00 0.00
#> H1_Exp3.227 2 0.0000 0.9780 0.00 1.00 0.00
#> H1_Exp3.228 2 0.0000 0.9780 0.00 1.00 0.00
#> H1_Exp3.229 2 0.0000 0.9780 0.00 1.00 0.00
#> H1_Exp3.230 2 0.0000 0.9780 0.00 1.00 0.00
#> H1_Exp3.231 2 0.0000 0.9780 0.00 1.00 0.00
#> H1_Exp3.232 1 0.4555 0.9377 0.80 0.00 0.20
#> H1_Exp3.233 3 0.0000 0.9236 0.00 0.00 1.00
#> H1_Exp3.234 2 0.0000 0.9780 0.00 1.00 0.00
#> H1_Exp3.235 2 0.0000 0.9780 0.00 1.00 0.00
#> H1_Exp3.236 2 0.0000 0.9780 0.00 1.00 0.00
#> H1_Exp3.237 2 0.0000 0.9780 0.00 1.00 0.00
#> H1_Exp3.238 3 0.4796 0.6684 0.00 0.22 0.78
#> H1_Exp3.239 2 0.0000 0.9780 0.00 1.00 0.00
#> H1_Exp3.240 2 0.0000 0.9780 0.00 1.00 0.00
#> H1_Exp3.241 1 0.4002 0.9265 0.84 0.00 0.16
#> H1_Exp3.242 2 0.0000 0.9780 0.00 1.00 0.00
#> H1_Exp3.243 2 0.0000 0.9780 0.00 1.00 0.00
#> H1_Exp3.244 2 0.0000 0.9780 0.00 1.00 0.00
#> H1_Exp3.246 2 0.0000 0.9780 0.00 1.00 0.00
#> H1_Exp3.247 2 0.0000 0.9780 0.00 1.00 0.00
#> H1_Exp3.248 3 0.0892 0.9070 0.02 0.00 0.98
#> H1_Exp3.249 2 0.0000 0.9780 0.00 1.00 0.00
#> H1_Exp3.250 2 0.0000 0.9780 0.00 1.00 0.00
#> H1_Exp3.251 2 0.0000 0.9780 0.00 1.00 0.00
#> H1_Exp3.252 2 0.0000 0.9780 0.00 1.00 0.00
#> H1_Exp3.253 2 0.0000 0.9780 0.00 1.00 0.00
#> H1_Exp3.254 2 0.0000 0.9780 0.00 1.00 0.00
#> H1_Exp3.255 2 0.0000 0.9780 0.00 1.00 0.00
#> H1_Exp3.256 3 0.4002 0.7390 0.00 0.16 0.84
#> H1_Exp3.257 2 0.0000 0.9780 0.00 1.00 0.00
#> H1_Exp3.258 2 0.0000 0.9780 0.00 1.00 0.00
#> H1_Exp3.259 2 0.0000 0.9780 0.00 1.00 0.00
#> H1_Exp3.260 2 0.0000 0.9780 0.00 1.00 0.00
#> H1_Exp3.261 2 0.3686 0.8349 0.00 0.86 0.14
#> H1_Exp3.262 2 0.0000 0.9780 0.00 1.00 0.00
#> H1_Exp3.263 2 0.0000 0.9780 0.00 1.00 0.00
#> H1_Exp3.264 2 0.0000 0.9780 0.00 1.00 0.00
#> H1_Exp3.265 2 0.0000 0.9780 0.00 1.00 0.00
#> H1_Exp3.266 2 0.1529 0.9422 0.00 0.96 0.04
#> H1_Exp3.267 2 0.0000 0.9780 0.00 1.00 0.00
#> H1_Exp3.268 2 0.0000 0.9780 0.00 1.00 0.00
#> H1_Exp3.269 2 0.0000 0.9780 0.00 1.00 0.00
#> H1_Exp3.270 2 0.0000 0.9780 0.00 1.00 0.00
#> H1_Exp3.271 2 0.0000 0.9780 0.00 1.00 0.00
#> H1_Exp3.272 2 0.0000 0.9780 0.00 1.00 0.00
#> H1_Exp3.273 2 0.0000 0.9780 0.00 1.00 0.00
#> H1_Exp3.274 2 0.0000 0.9780 0.00 1.00 0.00
#> H1_Exp3.275 2 0.0000 0.9780 0.00 1.00 0.00
#> H1_Exp3.276 2 0.0000 0.9780 0.00 1.00 0.00
#> H1_Exp3.277 3 0.0000 0.9236 0.00 0.00 1.00
#> H1_Exp3.278 2 0.0000 0.9780 0.00 1.00 0.00
#> H1_Exp3.279 2 0.0000 0.9780 0.00 1.00 0.00
#> H1_Exp3.280 2 0.0000 0.9780 0.00 1.00 0.00
#> H1_Exp3.281 2 0.0000 0.9780 0.00 1.00 0.00
#> H1_Exp3.282 2 0.0000 0.9780 0.00 1.00 0.00
#> H1_Exp3.283 3 0.0000 0.9236 0.00 0.00 1.00
#> H1_Exp3.284 3 0.0000 0.9236 0.00 0.00 1.00
#> H1_Exp3.285 2 0.0000 0.9780 0.00 1.00 0.00
#> H1_Exp3.286 2 0.0000 0.9780 0.00 1.00 0.00
#> H1_Exp3.287 2 0.0000 0.9780 0.00 1.00 0.00
#> H1_Exp3.288 2 0.0000 0.9780 0.00 1.00 0.00
#> H1_Exp3.289 2 0.3340 0.8587 0.00 0.88 0.12
#> H1_Exp3.290 2 0.0000 0.9780 0.00 1.00 0.00
#> H1_Exp3.291 2 0.0000 0.9780 0.00 1.00 0.00
#> G2_Exp1.059 2 0.0000 0.9780 0.00 1.00 0.00
#> G2_Exp1.069 1 0.4555 0.9377 0.80 0.00 0.20
#> G2_Exp1.075 2 0.0000 0.9780 0.00 1.00 0.00
#> G2_Exp1.063 2 0.0000 0.9780 0.00 1.00 0.00
#> G2_Exp1.029 2 0.0000 0.9780 0.00 1.00 0.00
#> G2_Exp1.076 1 0.4555 0.9377 0.80 0.00 0.20
#> G2_Exp1.013 1 0.4291 0.9327 0.82 0.00 0.18
#> G2_Exp1.037 2 0.0000 0.9780 0.00 1.00 0.00
#> G2_Exp1.057 2 0.0000 0.9780 0.00 1.00 0.00
#> G2_Exp1.018 2 0.0000 0.9780 0.00 1.00 0.00
#> G2_Exp1.015 1 0.4002 0.9265 0.84 0.00 0.16
#> G2_Exp1.019 1 0.4555 0.9377 0.80 0.00 0.20
#> G2_Exp1.050 3 0.0000 0.9236 0.00 0.00 1.00
#> G2_Exp1.004 2 0.0000 0.9780 0.00 1.00 0.00
#> G2_Exp1.061 2 0.0000 0.9780 0.00 1.00 0.00
#> G2_Exp1.042 3 0.0000 0.9236 0.00 0.00 1.00
#> G2_Exp1.060 2 0.0000 0.9780 0.00 1.00 0.00
#> G2_Exp1.058 2 0.0000 0.9780 0.00 1.00 0.00
#> G2_Exp1.065 2 0.0000 0.9780 0.00 1.00 0.00
#> G2_Exp1.002 2 0.6045 0.4916 0.38 0.62 0.00
#> G2_Exp1.044 2 0.0000 0.9780 0.00 1.00 0.00
#> G2_Exp1.051 2 0.0000 0.9780 0.00 1.00 0.00
#> G2_Exp1.073 2 0.0000 0.9780 0.00 1.00 0.00
#> G2_Exp1.030 1 0.4555 0.9377 0.80 0.00 0.20
#> G2_Exp1.028 2 0.0892 0.9617 0.02 0.98 0.00
#> G2_Exp1.022 1 0.4555 0.9377 0.80 0.00 0.20
#> G2_Exp1.034 2 0.0000 0.9780 0.00 1.00 0.00
#> G2_Exp1.017 1 0.0000 0.7989 1.00 0.00 0.00
#> G2_Exp1.047 1 0.4002 0.9265 0.84 0.00 0.16
#> G2_Exp1.072 3 0.0000 0.9236 0.00 0.00 1.00
#> G2_Exp1.074 3 0.0000 0.9236 0.00 0.00 1.00
#> G2_Exp1.054 2 0.0000 0.9780 0.00 1.00 0.00
#> G2_Exp1.024 2 0.0000 0.9780 0.00 1.00 0.00
#> G2_Exp1.032 1 0.4555 0.9377 0.80 0.00 0.20
#> G2_Exp1.020 2 0.0000 0.9780 0.00 1.00 0.00
#> G2_Exp1.064 1 0.4555 0.9377 0.80 0.00 0.20
#> G2_Exp1.045 1 0.4555 0.9377 0.80 0.00 0.20
#> G2_Exp1.038 1 0.4555 0.9377 0.80 0.00 0.20
#> G2_Exp1.001 1 0.4555 0.9377 0.80 0.00 0.20
#> G2_Exp1.049 1 0.4555 0.9377 0.80 0.00 0.20
#> G2_Exp1.031 1 0.4555 0.9377 0.80 0.00 0.20
#> G2_Exp1.039 2 0.0000 0.9780 0.00 1.00 0.00
#> G2_Exp1.070 3 0.0000 0.9236 0.00 0.00 1.00
#> G2_Exp1.007 2 0.0000 0.9780 0.00 1.00 0.00
#> G2_Exp1.021 1 0.4555 0.9377 0.80 0.00 0.20
#> G2_Exp1.036 1 0.4555 0.9377 0.80 0.00 0.20
#> G2_Exp1.046 1 0.4555 0.9377 0.80 0.00 0.20
#> G2_Exp1.040 1 0.4555 0.9377 0.80 0.00 0.20
#> G2_Exp1.068 1 0.4555 0.9377 0.80 0.00 0.20
#> G2_Exp1.077 1 0.5216 0.8735 0.74 0.00 0.26
#> G2_Exp1.026 2 0.0000 0.9780 0.00 1.00 0.00
#> G2_Exp1.011 2 0.0000 0.9780 0.00 1.00 0.00
#> G2_Exp1.053 2 0.0000 0.9780 0.00 1.00 0.00
#> G2_Exp1.008 3 0.4555 0.6575 0.20 0.00 0.80
#> G2_Exp1.006 2 0.0000 0.9780 0.00 1.00 0.00
#> G2_Exp1.016 2 0.0000 0.9780 0.00 1.00 0.00
#> G2_Exp1.010 2 0.4796 0.7570 0.22 0.78 0.00
#> G2_Exp1.014 1 0.4002 0.9265 0.84 0.00 0.16
#> G2_Exp1.005 1 0.4555 0.9377 0.80 0.00 0.20
#> G2_Exp1.048 3 0.6045 0.1371 0.38 0.00 0.62
#> G2_Exp1.027 2 0.0000 0.9780 0.00 1.00 0.00
#> G2_Exp1.067 2 0.0000 0.9780 0.00 1.00 0.00
#> G2_Exp1.009 2 0.0000 0.9780 0.00 1.00 0.00
#> G2_Exp1.062 3 0.4555 0.6572 0.20 0.00 0.80
#> G2_Exp1.025 2 0.0000 0.9780 0.00 1.00 0.00
#> G2_Exp1.056 1 0.3686 0.9145 0.86 0.00 0.14
#> G2_Exp1.055 2 0.0000 0.9780 0.00 1.00 0.00
#> G2_Exp1.041 1 0.4002 0.9265 0.84 0.00 0.16
#> G2_Exp1.012 1 0.4555 0.9377 0.80 0.00 0.20
#> G2_Exp1.066 1 0.6244 0.5363 0.56 0.00 0.44
#> G2_Exp1.023 1 0.4555 0.9377 0.80 0.00 0.20
#> G2_Exp1.033 2 0.4555 0.7528 0.00 0.80 0.20
#> G2_Exp1.043 2 0.0000 0.9780 0.00 1.00 0.00
#> G2_Exp1.052 2 0.0000 0.9780 0.00 1.00 0.00
#> G2_Exp1.071 2 0.0000 0.9780 0.00 1.00 0.00
#> G2_Exp1.003 1 0.4002 0.9265 0.84 0.00 0.16
#> S_Exp1.071 3 0.0000 0.9236 0.00 0.00 1.00
#> S_Exp1.074 2 0.0000 0.9780 0.00 1.00 0.00
#> S_Exp1.031 1 0.4002 0.9265 0.84 0.00 0.16
#> S_Exp1.032 3 0.0000 0.9236 0.00 0.00 1.00
#> S_Exp1.035 1 0.4555 0.9377 0.80 0.00 0.20
#> S_Exp1.030 2 0.3686 0.8492 0.14 0.86 0.00
#> S_Exp1.056 1 0.4555 0.9377 0.80 0.00 0.20
#> S_Exp1.011 1 0.4002 0.9265 0.84 0.00 0.16
#> S_Exp1.016 1 0.4555 0.9377 0.80 0.00 0.20
#> S_Exp1.013 2 0.0000 0.9780 0.00 1.00 0.00
#> S_Exp1.063 3 0.0000 0.9236 0.00 0.00 1.00
#> S_Exp1.038 1 0.4555 0.9377 0.80 0.00 0.20
#> S_Exp1.065 2 0.8202 0.5006 0.26 0.62 0.12
#> S_Exp1.048 1 0.4555 0.9377 0.80 0.00 0.20
#> S_Exp1.051 1 0.0000 0.7989 1.00 0.00 0.00
#> S_Exp1.078 3 0.0000 0.9236 0.00 0.00 1.00
#> S_Exp1.023 1 0.4555 0.9377 0.80 0.00 0.20
#> S_Exp1.022 1 0.4555 0.9377 0.80 0.00 0.20
#> S_Exp1.001 1 0.4555 0.9377 0.80 0.00 0.20
#> S_Exp1.046 3 0.0000 0.9236 0.00 0.00 1.00
#> S_Exp1.061 1 0.0000 0.7989 1.00 0.00 0.00
#> S_Exp1.080 1 0.1529 0.8356 0.96 0.00 0.04
#> S_Exp1.008 3 0.0000 0.9236 0.00 0.00 1.00
#> S_Exp1.069 1 0.4555 0.9377 0.80 0.00 0.20
#> S_Exp1.076 2 0.0000 0.9780 0.00 1.00 0.00
#> S_Exp1.019 1 0.3686 0.9145 0.86 0.00 0.14
#> S_Exp1.003 1 0.4555 0.9377 0.80 0.00 0.20
#> S_Exp1.006 3 0.0000 0.9236 0.00 0.00 1.00
#> S_Exp1.029 1 0.4291 0.9327 0.82 0.00 0.18
#> S_Exp1.025 1 0.4002 0.9265 0.84 0.00 0.16
#> S_Exp1.064 3 0.0000 0.9236 0.00 0.00 1.00
#> S_Exp1.041 1 0.0000 0.7989 1.00 0.00 0.00
#> S_Exp1.057 1 0.4002 0.9265 0.84 0.00 0.16
#> S_Exp1.068 2 0.0000 0.9780 0.00 1.00 0.00
#> S_Exp1.073 3 0.0000 0.9236 0.00 0.00 1.00
#> S_Exp1.014 3 0.0000 0.9236 0.00 0.00 1.00
#> S_Exp1.039 3 0.0000 0.9236 0.00 0.00 1.00
#> S_Exp1.055 1 0.4291 0.9327 0.82 0.00 0.18
#> S_Exp1.033 1 0.0000 0.7989 1.00 0.00 0.00
#> S_Exp1.081 1 0.4002 0.9265 0.84 0.00 0.16
#> S_Exp1.004 1 0.4002 0.9265 0.84 0.00 0.16
#> S_Exp1.045 1 0.4002 0.9265 0.84 0.00 0.16
#> S_Exp1.009 2 0.4555 0.7823 0.20 0.80 0.00
#> S_Exp1.054 3 0.0000 0.9236 0.00 0.00 1.00
#> S_Exp1.018 1 0.4555 0.9377 0.80 0.00 0.20
#> S_Exp1.034 1 0.4555 0.9377 0.80 0.00 0.20
#> S_Exp1.042 2 0.0000 0.9780 0.00 1.00 0.00
#> S_Exp1.067 1 0.0000 0.7989 1.00 0.00 0.00
#> S_Exp1.012 3 0.0000 0.9236 0.00 0.00 1.00
#> S_Exp1.002 2 0.0000 0.9780 0.00 1.00 0.00
#> S_Exp1.037 1 0.1529 0.8355 0.96 0.00 0.04
#> S_Exp1.070 1 0.4555 0.9377 0.80 0.00 0.20
#> S_Exp1.053 1 0.4555 0.9377 0.80 0.00 0.20
#> S_Exp1.036 1 0.4291 0.9327 0.82 0.00 0.18
#> S_Exp1.021 1 0.4002 0.9265 0.84 0.00 0.16
#> S_Exp1.020 3 0.0000 0.9236 0.00 0.00 1.00
#> S_Exp1.060 1 0.4555 0.9377 0.80 0.00 0.20
#> S_Exp1.015 3 0.0000 0.9236 0.00 0.00 1.00
#> S_Exp1.059 2 0.0000 0.9780 0.00 1.00 0.00
#> S_Exp1.066 3 0.0000 0.9236 0.00 0.00 1.00
#> S_Exp1.062 1 0.4002 0.9265 0.84 0.00 0.16
#> S_Exp1.040 3 0.0000 0.9236 0.00 0.00 1.00
#> S_Exp1.028 1 0.2066 0.8525 0.94 0.00 0.06
#> S_Exp1.079 1 0.4555 0.9377 0.80 0.00 0.20
#> S_Exp1.047 1 0.4002 0.9265 0.84 0.00 0.16
#> S_Exp1.044 2 0.0000 0.9780 0.00 1.00 0.00
#> S_Exp1.026 3 0.2066 0.8631 0.06 0.00 0.94
#> S_Exp1.077 1 0.0000 0.7989 1.00 0.00 0.00
#> S_Exp1.052 1 0.4555 0.9377 0.80 0.00 0.20
#> S_Exp1.007 1 0.4002 0.9265 0.84 0.00 0.16
#> S_Exp1.027 1 0.4555 0.9377 0.80 0.00 0.20
#> S_Exp1.017 3 0.0000 0.9236 0.00 0.00 1.00
#> S_Exp1.075 1 0.4555 0.9377 0.80 0.00 0.20
#> S_Exp1.049 1 0.4291 0.9326 0.82 0.00 0.18
#> S_Exp1.072 1 0.4002 0.9265 0.84 0.00 0.16
#> S_Exp1.005 1 0.4002 0.9265 0.84 0.00 0.16
#> S_Exp1.058 1 0.4555 0.9377 0.80 0.00 0.20
#> S_Exp1.010 3 0.0000 0.9236 0.00 0.00 1.00
#> S_Exp1.043 1 0.4002 0.9265 0.84 0.00 0.16
#> S_Exp1.050 1 0.4555 0.9377 0.80 0.00 0.20
#> G1_Exp1.048 2 0.0000 0.9780 0.00 1.00 0.00
#> G1_Exp1.090 2 0.0000 0.9780 0.00 1.00 0.00
#> G1_Exp1.061 2 0.0000 0.9780 0.00 1.00 0.00
#> G1_Exp1.025 2 0.0000 0.9780 0.00 1.00 0.00
#> G1_Exp1.033 1 0.4555 0.9377 0.80 0.00 0.20
#> G1_Exp1.081 2 0.0000 0.9780 0.00 1.00 0.00
#> G1_Exp1.013 1 0.4002 0.9256 0.84 0.00 0.16
#> G1_Exp1.024 2 0.4555 0.7823 0.20 0.80 0.00
#> G1_Exp1.056 2 0.0000 0.9780 0.00 1.00 0.00
#> G1_Exp1.026 1 0.0000 0.7989 1.00 0.00 0.00
#> G1_Exp1.064 2 0.0000 0.9780 0.00 1.00 0.00
#> G1_Exp1.053 2 0.0000 0.9780 0.00 1.00 0.00
#> G1_Exp1.070 2 0.0000 0.9780 0.00 1.00 0.00
#> G1_Exp1.087 2 0.2414 0.9279 0.02 0.94 0.04
#> G1_Exp1.067 2 0.0000 0.9780 0.00 1.00 0.00
#> G1_Exp1.003 2 0.0000 0.9780 0.00 1.00 0.00
#> G1_Exp1.052 2 0.0000 0.9780 0.00 1.00 0.00
#> G1_Exp1.060 2 0.0000 0.9780 0.00 1.00 0.00
#> G1_Exp1.071 2 0.0000 0.9780 0.00 1.00 0.00
#> G1_Exp1.045 2 0.0000 0.9780 0.00 1.00 0.00
#> G1_Exp1.040 2 0.0000 0.9780 0.00 1.00 0.00
#> G1_Exp1.016 2 0.0000 0.9780 0.00 1.00 0.00
#> G1_Exp1.027 1 0.4555 0.9377 0.80 0.00 0.20
#> G1_Exp1.059 3 0.0000 0.9236 0.00 0.00 1.00
#> G1_Exp1.002 2 0.0000 0.9780 0.00 1.00 0.00
#> G1_Exp1.021 2 0.0000 0.9780 0.00 1.00 0.00
#> G1_Exp1.015 2 0.0000 0.9780 0.00 1.00 0.00
#> G1_Exp1.042 2 0.2537 0.9031 0.00 0.92 0.08
#> G1_Exp1.058 2 0.0000 0.9780 0.00 1.00 0.00
#> G1_Exp1.075 2 0.0000 0.9780 0.00 1.00 0.00
#> G1_Exp1.019 3 0.5706 0.3557 0.32 0.00 0.68
#> G1_Exp1.074 2 0.0000 0.9780 0.00 1.00 0.00
#> G1_Exp1.079 2 0.0000 0.9780 0.00 1.00 0.00
#> G1_Exp1.038 2 0.0000 0.9780 0.00 1.00 0.00
#> G1_Exp1.035 2 0.0000 0.9780 0.00 1.00 0.00
#> G1_Exp1.012 2 0.0000 0.9780 0.00 1.00 0.00
#> G1_Exp1.032 2 0.0000 0.9780 0.00 1.00 0.00
#> G1_Exp1.009 2 0.4291 0.7816 0.00 0.82 0.18
#> G1_Exp1.043 2 0.0000 0.9780 0.00 1.00 0.00
#> G1_Exp1.089 2 0.0000 0.9780 0.00 1.00 0.00
#> G1_Exp1.080 2 0.0000 0.9780 0.00 1.00 0.00
#> G1_Exp1.007 2 0.0000 0.9780 0.00 1.00 0.00
#> G1_Exp1.062 2 0.0000 0.9780 0.00 1.00 0.00
#> G1_Exp1.069 2 0.0000 0.9780 0.00 1.00 0.00
#> G1_Exp1.004 2 0.0000 0.9780 0.00 1.00 0.00
#> G1_Exp1.010 2 0.0000 0.9780 0.00 1.00 0.00
#> G1_Exp1.044 2 0.0000 0.9780 0.00 1.00 0.00
#> G1_Exp1.065 2 0.0000 0.9780 0.00 1.00 0.00
#> G1_Exp1.029 2 0.0000 0.9780 0.00 1.00 0.00
#> G1_Exp1.086 2 0.5397 0.6632 0.28 0.72 0.00
#> G1_Exp1.084 2 0.0000 0.9780 0.00 1.00 0.00
#> G1_Exp1.088 2 0.0000 0.9780 0.00 1.00 0.00
#> G1_Exp1.051 2 0.0000 0.9780 0.00 1.00 0.00
#> G1_Exp1.085 3 0.0000 0.9236 0.00 0.00 1.00
#> G1_Exp1.066 2 0.0000 0.9780 0.00 1.00 0.00
#> G1_Exp1.047 2 0.4002 0.8076 0.00 0.84 0.16
#> G1_Exp1.072 2 0.0000 0.9780 0.00 1.00 0.00
#> G1_Exp1.057 2 0.0000 0.9780 0.00 1.00 0.00
#> G1_Exp1.054 2 0.0000 0.9780 0.00 1.00 0.00
#> G1_Exp1.082 2 0.0000 0.9780 0.00 1.00 0.00
#> G1_Exp1.031 3 0.0892 0.9018 0.00 0.02 0.98
#> G1_Exp1.077 3 0.4555 0.6928 0.00 0.20 0.80
#> G1_Exp1.091 2 0.0000 0.9780 0.00 1.00 0.00
#> G1_Exp1.078 3 0.0000 0.9236 0.00 0.00 1.00
#> G1_Exp1.073 1 0.1529 0.8357 0.96 0.00 0.04
#> G1_Exp1.068 2 0.0000 0.9780 0.00 1.00 0.00
#> G1_Exp1.001 2 0.0000 0.9780 0.00 1.00 0.00
#> G1_Exp1.023 1 0.4002 0.9265 0.84 0.00 0.16
#> G1_Exp1.022 1 0.4555 0.9377 0.80 0.00 0.20
#> G1_Exp1.034 3 0.0000 0.9236 0.00 0.00 1.00
#> G1_Exp1.005 2 0.0000 0.9780 0.00 1.00 0.00
#> G1_Exp1.039 1 0.4555 0.9377 0.80 0.00 0.20
#> G1_Exp1.037 2 0.0000 0.9780 0.00 1.00 0.00
#> G1_Exp1.014 2 0.0000 0.9780 0.00 1.00 0.00
#> G1_Exp1.017 2 0.0000 0.9780 0.00 1.00 0.00
#> G1_Exp1.020 3 0.3686 0.7594 0.14 0.00 0.86
#> G1_Exp1.028 2 0.0000 0.9780 0.00 1.00 0.00
#> G1_Exp1.036 2 0.0000 0.9780 0.00 1.00 0.00
#> G1_Exp1.049 2 0.0000 0.9780 0.00 1.00 0.00
#> G1_Exp1.041 2 0.0000 0.9780 0.00 1.00 0.00
#> G1_Exp1.006 2 0.4002 0.8278 0.16 0.84 0.00
#> G1_Exp1.008 3 0.8321 0.5746 0.14 0.24 0.62
#> G1_Exp1.055 2 0.0000 0.9780 0.00 1.00 0.00
#> G1_Exp1.050 2 0.0000 0.9780 0.00 1.00 0.00
#> G1_Exp1.076 2 0.0000 0.9780 0.00 1.00 0.00
#> G1_Exp1.011 3 0.0000 0.9236 0.00 0.00 1.00
#> G1_Exp1.063 3 0.0000 0.9236 0.00 0.00 1.00
#> G1_Exp1.083 2 0.0000 0.9780 0.00 1.00 0.00
#> G1_Exp1.030 2 0.0000 0.9780 0.00 1.00 0.00
#> G1_Exp1.018 3 0.6045 0.5855 0.38 0.00 0.62
#> G1_Exp1.046 3 0.0000 0.9236 0.00 0.00 1.00
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> H1_Exp1.001 4 0.2647 0.7218 0.12 0.00 0.00 0.88
#> H1_Exp1.002 4 0.3610 0.7074 0.20 0.00 0.00 0.80
#> H1_Exp1.003 4 0.3172 0.7187 0.16 0.00 0.00 0.84
#> H1_Exp1.004 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> H1_Exp1.006 4 0.3610 0.7057 0.20 0.00 0.00 0.80
#> H1_Exp1.007 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> H1_Exp1.008 2 0.0707 0.9426 0.00 0.98 0.00 0.02
#> H1_Exp1.009 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> H1_Exp1.010 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> H1_Exp1.011 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> H1_Exp1.012 1 0.2345 0.8436 0.90 0.00 0.00 0.10
#> H1_Exp1.014 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> H1_Exp1.015 1 0.2921 0.7984 0.86 0.00 0.00 0.14
#> H1_Exp1.016 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> H1_Exp1.017 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> H1_Exp1.018 4 0.4406 0.6224 0.30 0.00 0.00 0.70
#> H1_Exp1.019 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> H1_Exp1.020 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> H1_Exp1.021 2 0.0000 0.9501 0.00 1.00 0.00 0.00
#> H1_Exp1.022 4 0.3725 0.7055 0.10 0.02 0.02 0.86
#> H1_Exp1.023 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> H1_Exp1.024 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> H1_Exp1.025 3 0.8093 0.1605 0.20 0.02 0.46 0.32
#> H1_Exp1.026 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> H1_Exp1.027 2 0.0000 0.9501 0.00 1.00 0.00 0.00
#> H1_Exp1.029 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> H1_Exp1.030 4 0.7544 0.3094 0.00 0.20 0.34 0.46
#> H1_Exp1.031 2 0.1211 0.9354 0.00 0.96 0.00 0.04
#> H1_Exp1.032 2 0.0000 0.9501 0.00 1.00 0.00 0.00
#> H1_Exp1.033 2 0.0000 0.9501 0.00 1.00 0.00 0.00
#> H1_Exp1.035 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> H1_Exp1.036 2 0.1211 0.9354 0.00 0.96 0.00 0.04
#> H1_Exp1.038 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> H1_Exp1.039 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> H1_Exp1.040 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> H1_Exp1.041 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> H1_Exp1.042 2 0.0707 0.9438 0.00 0.98 0.00 0.02
#> H1_Exp1.043 4 0.7598 0.2225 0.08 0.44 0.04 0.44
#> H1_Exp1.044 2 0.0000 0.9501 0.00 1.00 0.00 0.00
#> H1_Exp1.045 2 0.0000 0.9501 0.00 1.00 0.00 0.00
#> H1_Exp1.047 2 0.0000 0.9501 0.00 1.00 0.00 0.00
#> H1_Exp1.048 2 0.0000 0.9501 0.00 1.00 0.00 0.00
#> H1_Exp1.049 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> H1_Exp1.050 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> H1_Exp1.051 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> H1_Exp1.052 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> H1_Exp1.053 3 0.1411 0.7628 0.02 0.00 0.96 0.02
#> H1_Exp1.054 1 0.3610 0.6915 0.80 0.00 0.20 0.00
#> H1_Exp1.055 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> H1_Exp1.057 3 0.3172 0.8931 0.16 0.00 0.84 0.00
#> H1_Exp1.058 2 0.0707 0.9438 0.00 0.98 0.00 0.02
#> H1_Exp1.059 3 0.1637 0.8163 0.06 0.00 0.94 0.00
#> H1_Exp1.060 2 0.0000 0.9501 0.00 1.00 0.00 0.00
#> H1_Exp1.061 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> H1_Exp1.062 1 0.1211 0.9101 0.96 0.00 0.00 0.04
#> H1_Exp1.063 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> H1_Exp1.064 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> H1_Exp1.065 4 0.4907 0.3144 0.00 0.42 0.00 0.58
#> H1_Exp1.066 1 0.3172 0.7758 0.84 0.00 0.00 0.16
#> H1_Exp1.068 2 0.0000 0.9501 0.00 1.00 0.00 0.00
#> H1_Exp1.069 3 0.2647 0.8681 0.12 0.00 0.88 0.00
#> H1_Exp1.070 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> H1_Exp2.073 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> H1_Exp2.074 1 0.3975 0.6411 0.76 0.00 0.00 0.24
#> H1_Exp2.075 1 0.4855 0.2551 0.60 0.00 0.00 0.40
#> H1_Exp2.076 4 0.3198 0.7269 0.08 0.04 0.00 0.88
#> H1_Exp2.077 2 0.1211 0.9323 0.00 0.96 0.00 0.04
#> H1_Exp2.078 2 0.1637 0.9195 0.00 0.94 0.00 0.06
#> H1_Exp2.079 1 0.3975 0.6438 0.76 0.00 0.00 0.24
#> H1_Exp2.080 4 0.2647 0.7019 0.00 0.12 0.00 0.88
#> H1_Exp2.081 4 0.3400 0.7148 0.18 0.00 0.00 0.82
#> H1_Exp2.082 3 0.3606 0.8759 0.14 0.00 0.84 0.02
#> H1_Exp2.083 1 0.3975 0.6459 0.76 0.00 0.00 0.24
#> H1_Exp2.084 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> H1_Exp2.085 4 0.3801 0.6951 0.22 0.00 0.00 0.78
#> H1_Exp2.086 2 0.1637 0.9195 0.00 0.94 0.00 0.06
#> H1_Exp2.087 4 0.2647 0.7218 0.12 0.00 0.00 0.88
#> H1_Exp2.088 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> H1_Exp2.089 4 0.4134 0.6674 0.26 0.00 0.00 0.74
#> H1_Exp2.090 4 0.3037 0.7133 0.02 0.10 0.00 0.88
#> H1_Exp2.091 1 0.3821 0.7739 0.84 0.00 0.12 0.04
#> H1_Exp2.092 4 0.4134 0.6668 0.26 0.00 0.00 0.74
#> H1_Exp2.093 3 0.5657 0.7380 0.12 0.00 0.72 0.16
#> H1_Exp2.094 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> H1_Exp2.096 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> H1_Exp2.097 3 0.5000 -0.0735 0.00 0.00 0.50 0.50
#> H1_Exp2.098 1 0.0707 0.9244 0.98 0.00 0.00 0.02
#> H1_Exp2.099 2 0.0000 0.9501 0.00 1.00 0.00 0.00
#> H1_Exp2.100 4 0.3400 0.7133 0.18 0.00 0.00 0.82
#> H1_Exp2.101 4 0.4797 0.6657 0.26 0.00 0.02 0.72
#> H1_Exp2.102 4 0.3037 0.6958 0.00 0.10 0.02 0.88
#> H1_Exp2.103 2 0.2011 0.9039 0.00 0.92 0.00 0.08
#> H1_Exp2.104 2 0.2011 0.9039 0.00 0.92 0.00 0.08
#> H1_Exp2.105 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> H1_Exp2.106 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> H1_Exp2.107 2 0.0000 0.9501 0.00 1.00 0.00 0.00
#> H1_Exp2.108 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> H1_Exp2.109 2 0.0000 0.9501 0.00 1.00 0.00 0.00
#> H1_Exp2.110 2 0.2335 0.9132 0.00 0.92 0.02 0.06
#> H1_Exp2.111 2 0.0000 0.9501 0.00 1.00 0.00 0.00
#> H1_Exp2.112 3 0.0000 0.7482 0.00 0.00 1.00 0.00
#> H1_Exp2.113 2 0.0000 0.9501 0.00 1.00 0.00 0.00
#> H1_Exp2.114 2 0.0000 0.9501 0.00 1.00 0.00 0.00
#> H1_Exp2.115 2 0.0000 0.9501 0.00 1.00 0.00 0.00
#> H1_Exp2.116 3 0.3172 0.8931 0.16 0.00 0.84 0.00
#> H1_Exp2.117 2 0.0000 0.9501 0.00 1.00 0.00 0.00
#> H1_Exp2.118 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> H1_Exp2.120 2 0.0000 0.9501 0.00 1.00 0.00 0.00
#> H1_Exp2.121 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> H1_Exp2.122 2 0.0000 0.9501 0.00 1.00 0.00 0.00
#> H1_Exp2.123 3 0.3172 0.8931 0.16 0.00 0.84 0.00
#> H1_Exp2.124 2 0.0000 0.9501 0.00 1.00 0.00 0.00
#> H1_Exp2.125 2 0.1411 0.9338 0.00 0.96 0.02 0.02
#> H1_Exp2.126 2 0.4079 0.8114 0.00 0.80 0.02 0.18
#> H1_Exp2.127 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> H1_Exp2.128 2 0.0707 0.9426 0.00 0.98 0.00 0.02
#> H1_Exp2.129 2 0.0000 0.9501 0.00 1.00 0.00 0.00
#> H1_Exp2.130 3 0.3172 0.8931 0.16 0.00 0.84 0.00
#> H1_Exp2.132 4 0.4731 0.6343 0.00 0.16 0.06 0.78
#> H1_Exp2.133 3 0.3172 0.8931 0.16 0.00 0.84 0.00
#> H1_Exp2.134 2 0.0000 0.9501 0.00 1.00 0.00 0.00
#> H1_Exp2.135 3 0.3172 0.8931 0.16 0.00 0.84 0.00
#> H1_Exp2.136 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> H1_Exp2.137 2 0.1637 0.9316 0.00 0.94 0.00 0.06
#> H1_Exp2.138 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> H1_Exp2.139 3 0.2345 0.8524 0.10 0.00 0.90 0.00
#> H1_Exp2.140 1 0.4277 0.5234 0.72 0.00 0.28 0.00
#> H1_Exp2.141 2 0.2411 0.9106 0.00 0.92 0.04 0.04
#> H1_Exp2.142 2 0.1211 0.9323 0.00 0.96 0.00 0.04
#> H1_Exp2.144 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> H1_Exp2.145 2 0.0707 0.9426 0.00 0.98 0.00 0.02
#> H1_Exp2.146 3 0.3172 0.8931 0.16 0.00 0.84 0.00
#> H1_Exp2.147 2 0.0000 0.9501 0.00 1.00 0.00 0.00
#> H1_Exp2.148 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> H1_Exp2.149 2 0.2335 0.9102 0.00 0.92 0.02 0.06
#> H1_Exp2.150 3 0.3172 0.8931 0.16 0.00 0.84 0.00
#> H1_Exp2.151 2 0.0000 0.9501 0.00 1.00 0.00 0.00
#> H1_Exp2.152 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> H1_Exp2.153 4 0.5095 0.6907 0.06 0.04 0.10 0.80
#> H1_Exp2.154 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> H1_Exp3.218 2 0.0000 0.9501 0.00 1.00 0.00 0.00
#> H1_Exp3.219 3 0.4855 0.2196 0.00 0.00 0.60 0.40
#> H1_Exp3.220 3 0.3172 0.8931 0.16 0.00 0.84 0.00
#> H1_Exp3.221 2 0.0000 0.9501 0.00 1.00 0.00 0.00
#> H1_Exp3.222 2 0.0707 0.9426 0.00 0.98 0.00 0.02
#> H1_Exp3.223 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> H1_Exp3.224 2 0.3611 0.8610 0.00 0.86 0.06 0.08
#> H1_Exp3.225 2 0.0707 0.9437 0.00 0.98 0.00 0.02
#> H1_Exp3.226 2 0.0000 0.9501 0.00 1.00 0.00 0.00
#> H1_Exp3.227 2 0.0707 0.9426 0.00 0.98 0.00 0.02
#> H1_Exp3.228 2 0.0000 0.9501 0.00 1.00 0.00 0.00
#> H1_Exp3.229 2 0.1211 0.9323 0.00 0.96 0.00 0.04
#> H1_Exp3.230 2 0.0000 0.9501 0.00 1.00 0.00 0.00
#> H1_Exp3.231 2 0.0000 0.9501 0.00 1.00 0.00 0.00
#> H1_Exp3.232 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> H1_Exp3.233 3 0.3400 0.8706 0.18 0.00 0.82 0.00
#> H1_Exp3.234 2 0.0000 0.9501 0.00 1.00 0.00 0.00
#> H1_Exp3.235 2 0.0000 0.9501 0.00 1.00 0.00 0.00
#> H1_Exp3.236 2 0.1637 0.9195 0.00 0.94 0.00 0.06
#> H1_Exp3.237 2 0.0000 0.9501 0.00 1.00 0.00 0.00
#> H1_Exp3.238 3 0.3037 0.6474 0.00 0.02 0.88 0.10
#> H1_Exp3.239 2 0.0000 0.9501 0.00 1.00 0.00 0.00
#> H1_Exp3.240 2 0.0000 0.9501 0.00 1.00 0.00 0.00
#> H1_Exp3.241 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> H1_Exp3.242 2 0.0000 0.9501 0.00 1.00 0.00 0.00
#> H1_Exp3.243 2 0.0000 0.9501 0.00 1.00 0.00 0.00
#> H1_Exp3.244 2 0.0707 0.9438 0.00 0.98 0.00 0.02
#> H1_Exp3.246 2 0.0000 0.9501 0.00 1.00 0.00 0.00
#> H1_Exp3.247 2 0.0707 0.9438 0.00 0.98 0.00 0.02
#> H1_Exp3.248 3 0.1211 0.7962 0.04 0.00 0.96 0.00
#> H1_Exp3.249 2 0.0000 0.9501 0.00 1.00 0.00 0.00
#> H1_Exp3.250 2 0.0000 0.9501 0.00 1.00 0.00 0.00
#> H1_Exp3.251 2 0.0000 0.9501 0.00 1.00 0.00 0.00
#> H1_Exp3.252 2 0.0000 0.9501 0.00 1.00 0.00 0.00
#> H1_Exp3.253 2 0.1211 0.9356 0.00 0.96 0.00 0.04
#> H1_Exp3.254 2 0.0707 0.9438 0.00 0.98 0.00 0.02
#> H1_Exp3.255 2 0.0000 0.9501 0.00 1.00 0.00 0.00
#> H1_Exp3.256 3 0.0707 0.7370 0.00 0.00 0.98 0.02
#> H1_Exp3.257 2 0.3611 0.8562 0.00 0.86 0.08 0.06
#> H1_Exp3.258 2 0.0000 0.9501 0.00 1.00 0.00 0.00
#> H1_Exp3.259 2 0.0000 0.9501 0.00 1.00 0.00 0.00
#> H1_Exp3.260 2 0.1913 0.9251 0.00 0.94 0.04 0.02
#> H1_Exp3.261 2 0.6500 0.5087 0.00 0.62 0.26 0.12
#> H1_Exp3.262 2 0.0000 0.9501 0.00 1.00 0.00 0.00
#> H1_Exp3.263 2 0.0000 0.9501 0.00 1.00 0.00 0.00
#> H1_Exp3.264 2 0.3198 0.8770 0.00 0.88 0.04 0.08
#> H1_Exp3.265 2 0.0000 0.9501 0.00 1.00 0.00 0.00
#> H1_Exp3.266 2 0.6513 0.5493 0.00 0.64 0.18 0.18
#> H1_Exp3.267 2 0.0000 0.9501 0.00 1.00 0.00 0.00
#> H1_Exp3.268 2 0.0707 0.9438 0.00 0.98 0.00 0.02
#> H1_Exp3.269 2 0.0000 0.9501 0.00 1.00 0.00 0.00
#> H1_Exp3.270 2 0.0707 0.9438 0.00 0.98 0.00 0.02
#> H1_Exp3.271 2 0.2335 0.9100 0.00 0.92 0.02 0.06
#> H1_Exp3.272 2 0.0707 0.9438 0.00 0.98 0.00 0.02
#> H1_Exp3.273 2 0.0000 0.9501 0.00 1.00 0.00 0.00
#> H1_Exp3.274 2 0.0000 0.9501 0.00 1.00 0.00 0.00
#> H1_Exp3.275 2 0.0000 0.9501 0.00 1.00 0.00 0.00
#> H1_Exp3.276 2 0.0000 0.9501 0.00 1.00 0.00 0.00
#> H1_Exp3.277 3 0.3400 0.8708 0.18 0.00 0.82 0.00
#> H1_Exp3.278 2 0.0000 0.9501 0.00 1.00 0.00 0.00
#> H1_Exp3.279 2 0.1913 0.9280 0.00 0.94 0.02 0.04
#> H1_Exp3.280 2 0.0000 0.9501 0.00 1.00 0.00 0.00
#> H1_Exp3.281 2 0.1411 0.9363 0.00 0.96 0.02 0.02
#> H1_Exp3.282 2 0.0000 0.9501 0.00 1.00 0.00 0.00
#> H1_Exp3.283 3 0.2345 0.8524 0.10 0.00 0.90 0.00
#> H1_Exp3.284 3 0.3172 0.8931 0.16 0.00 0.84 0.00
#> H1_Exp3.285 2 0.0000 0.9501 0.00 1.00 0.00 0.00
#> H1_Exp3.286 2 0.0000 0.9501 0.00 1.00 0.00 0.00
#> H1_Exp3.287 2 0.0000 0.9501 0.00 1.00 0.00 0.00
#> H1_Exp3.288 2 0.3198 0.8782 0.00 0.88 0.04 0.08
#> H1_Exp3.289 2 0.5883 0.5207 0.00 0.64 0.30 0.06
#> H1_Exp3.290 2 0.0707 0.9425 0.00 0.98 0.00 0.02
#> H1_Exp3.291 2 0.0000 0.9501 0.00 1.00 0.00 0.00
#> G2_Exp1.059 2 0.2411 0.9099 0.00 0.92 0.04 0.04
#> G2_Exp1.069 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> G2_Exp1.075 2 0.0000 0.9501 0.00 1.00 0.00 0.00
#> G2_Exp1.063 2 0.0000 0.9501 0.00 1.00 0.00 0.00
#> G2_Exp1.029 2 0.0000 0.9501 0.00 1.00 0.00 0.00
#> G2_Exp1.076 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> G2_Exp1.013 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> G2_Exp1.037 2 0.0000 0.9501 0.00 1.00 0.00 0.00
#> G2_Exp1.057 2 0.3611 0.8552 0.00 0.86 0.08 0.06
#> G2_Exp1.018 2 0.0000 0.9501 0.00 1.00 0.00 0.00
#> G2_Exp1.015 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> G2_Exp1.019 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> G2_Exp1.050 3 0.0000 0.7482 0.00 0.00 1.00 0.00
#> G2_Exp1.004 2 0.0000 0.9501 0.00 1.00 0.00 0.00
#> G2_Exp1.061 2 0.1637 0.9195 0.00 0.94 0.00 0.06
#> G2_Exp1.042 3 0.3172 0.8931 0.16 0.00 0.84 0.00
#> G2_Exp1.060 2 0.0000 0.9501 0.00 1.00 0.00 0.00
#> G2_Exp1.058 2 0.0000 0.9501 0.00 1.00 0.00 0.00
#> G2_Exp1.065 2 0.1211 0.9323 0.00 0.96 0.00 0.04
#> G2_Exp1.002 4 0.2647 0.7019 0.00 0.12 0.00 0.88
#> G2_Exp1.044 2 0.0707 0.9438 0.00 0.98 0.00 0.02
#> G2_Exp1.051 2 0.0000 0.9501 0.00 1.00 0.00 0.00
#> G2_Exp1.073 2 0.0000 0.9501 0.00 1.00 0.00 0.00
#> G2_Exp1.030 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> G2_Exp1.028 2 0.5915 0.2584 0.00 0.56 0.04 0.40
#> G2_Exp1.022 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> G2_Exp1.034 2 0.0000 0.9501 0.00 1.00 0.00 0.00
#> G2_Exp1.017 1 0.3610 0.7046 0.80 0.00 0.00 0.20
#> G2_Exp1.047 1 0.0707 0.9244 0.98 0.00 0.00 0.02
#> G2_Exp1.072 3 0.3172 0.8931 0.16 0.00 0.84 0.00
#> G2_Exp1.074 3 0.2921 0.8818 0.14 0.00 0.86 0.00
#> G2_Exp1.054 2 0.0000 0.9501 0.00 1.00 0.00 0.00
#> G2_Exp1.024 2 0.1211 0.9323 0.00 0.96 0.00 0.04
#> G2_Exp1.032 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> G2_Exp1.020 2 0.0000 0.9501 0.00 1.00 0.00 0.00
#> G2_Exp1.064 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> G2_Exp1.045 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> G2_Exp1.038 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> G2_Exp1.001 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> G2_Exp1.049 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> G2_Exp1.031 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> G2_Exp1.039 2 0.0707 0.9438 0.00 0.98 0.00 0.02
#> G2_Exp1.070 3 0.3172 0.8931 0.16 0.00 0.84 0.00
#> G2_Exp1.007 2 0.1211 0.9323 0.00 0.96 0.00 0.04
#> G2_Exp1.021 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> G2_Exp1.036 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> G2_Exp1.046 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> G2_Exp1.040 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> G2_Exp1.068 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> G2_Exp1.077 1 0.1637 0.8842 0.94 0.00 0.06 0.00
#> G2_Exp1.026 2 0.0000 0.9501 0.00 1.00 0.00 0.00
#> G2_Exp1.011 2 0.1637 0.9195 0.00 0.94 0.00 0.06
#> G2_Exp1.053 2 0.0000 0.9501 0.00 1.00 0.00 0.00
#> G2_Exp1.008 1 0.4713 0.3036 0.64 0.00 0.36 0.00
#> G2_Exp1.006 2 0.1211 0.9323 0.00 0.96 0.00 0.04
#> G2_Exp1.016 2 0.1637 0.9195 0.00 0.94 0.00 0.06
#> G2_Exp1.010 4 0.2647 0.7019 0.00 0.12 0.00 0.88
#> G2_Exp1.014 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> G2_Exp1.005 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> G2_Exp1.048 1 0.2921 0.7790 0.86 0.00 0.14 0.00
#> G2_Exp1.027 2 0.0000 0.9501 0.00 1.00 0.00 0.00
#> G2_Exp1.067 2 0.0000 0.9501 0.00 1.00 0.00 0.00
#> G2_Exp1.009 2 0.1211 0.9323 0.00 0.96 0.00 0.04
#> G2_Exp1.062 1 0.4406 0.4744 0.70 0.00 0.30 0.00
#> G2_Exp1.025 2 0.0000 0.9501 0.00 1.00 0.00 0.00
#> G2_Exp1.056 1 0.2647 0.8226 0.88 0.00 0.00 0.12
#> G2_Exp1.055 2 0.0000 0.9501 0.00 1.00 0.00 0.00
#> G2_Exp1.041 1 0.1211 0.9051 0.96 0.00 0.00 0.04
#> G2_Exp1.012 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> G2_Exp1.066 1 0.3400 0.7253 0.82 0.00 0.18 0.00
#> G2_Exp1.023 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> G2_Exp1.033 2 0.6500 0.5106 0.00 0.62 0.26 0.12
#> G2_Exp1.043 2 0.0000 0.9501 0.00 1.00 0.00 0.00
#> G2_Exp1.052 2 0.0000 0.9501 0.00 1.00 0.00 0.00
#> G2_Exp1.071 2 0.0000 0.9501 0.00 1.00 0.00 0.00
#> G2_Exp1.003 1 0.0707 0.9244 0.98 0.00 0.00 0.02
#> S_Exp1.071 3 0.3172 0.8931 0.16 0.00 0.84 0.00
#> S_Exp1.074 2 0.1637 0.9195 0.00 0.94 0.00 0.06
#> S_Exp1.031 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> S_Exp1.032 3 0.3400 0.8698 0.18 0.00 0.82 0.00
#> S_Exp1.035 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> S_Exp1.030 4 0.2921 0.6891 0.00 0.14 0.00 0.86
#> S_Exp1.056 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> S_Exp1.011 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> S_Exp1.016 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> S_Exp1.013 2 0.0000 0.9501 0.00 1.00 0.00 0.00
#> S_Exp1.063 3 0.3172 0.8931 0.16 0.00 0.84 0.00
#> S_Exp1.038 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> S_Exp1.065 4 0.6449 0.4827 0.00 0.22 0.14 0.64
#> S_Exp1.048 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> S_Exp1.051 4 0.3172 0.7187 0.16 0.00 0.00 0.84
#> S_Exp1.078 3 0.3172 0.8931 0.16 0.00 0.84 0.00
#> S_Exp1.023 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> S_Exp1.022 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> S_Exp1.001 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> S_Exp1.046 3 0.3172 0.8931 0.16 0.00 0.84 0.00
#> S_Exp1.061 4 0.4134 0.6682 0.26 0.00 0.00 0.74
#> S_Exp1.080 1 0.2921 0.7827 0.86 0.00 0.00 0.14
#> S_Exp1.008 3 0.3172 0.8931 0.16 0.00 0.84 0.00
#> S_Exp1.069 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> S_Exp1.076 2 0.2011 0.9111 0.00 0.92 0.00 0.08
#> S_Exp1.019 1 0.2011 0.8613 0.92 0.00 0.00 0.08
#> S_Exp1.003 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> S_Exp1.006 3 0.3172 0.8931 0.16 0.00 0.84 0.00
#> S_Exp1.029 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> S_Exp1.025 1 0.2011 0.8613 0.92 0.00 0.00 0.08
#> S_Exp1.064 3 0.3172 0.8931 0.16 0.00 0.84 0.00
#> S_Exp1.041 1 0.4406 0.5163 0.70 0.00 0.00 0.30
#> S_Exp1.057 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> S_Exp1.068 2 0.2647 0.8638 0.00 0.88 0.00 0.12
#> S_Exp1.073 3 0.3172 0.8931 0.16 0.00 0.84 0.00
#> S_Exp1.014 3 0.3172 0.8931 0.16 0.00 0.84 0.00
#> S_Exp1.039 3 0.3172 0.8931 0.16 0.00 0.84 0.00
#> S_Exp1.055 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> S_Exp1.033 4 0.4948 0.3095 0.44 0.00 0.00 0.56
#> S_Exp1.081 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> S_Exp1.004 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> S_Exp1.045 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> S_Exp1.009 4 0.2647 0.7019 0.00 0.12 0.00 0.88
#> S_Exp1.054 3 0.3172 0.8931 0.16 0.00 0.84 0.00
#> S_Exp1.018 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> S_Exp1.034 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> S_Exp1.042 2 0.1211 0.9323 0.00 0.96 0.00 0.04
#> S_Exp1.067 1 0.4907 0.1837 0.58 0.00 0.00 0.42
#> S_Exp1.012 3 0.3172 0.8931 0.16 0.00 0.84 0.00
#> S_Exp1.002 2 0.1637 0.9195 0.00 0.94 0.00 0.06
#> S_Exp1.037 1 0.4855 0.2521 0.60 0.00 0.00 0.40
#> S_Exp1.070 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> S_Exp1.053 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> S_Exp1.036 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> S_Exp1.021 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> S_Exp1.020 3 0.3172 0.8931 0.16 0.00 0.84 0.00
#> S_Exp1.060 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> S_Exp1.015 3 0.3172 0.8931 0.16 0.00 0.84 0.00
#> S_Exp1.059 2 0.1211 0.9323 0.00 0.96 0.00 0.04
#> S_Exp1.066 3 0.3172 0.8931 0.16 0.00 0.84 0.00
#> S_Exp1.062 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> S_Exp1.040 3 0.3172 0.8931 0.16 0.00 0.84 0.00
#> S_Exp1.028 1 0.4134 0.6062 0.74 0.00 0.00 0.26
#> S_Exp1.079 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> S_Exp1.047 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> S_Exp1.044 2 0.2647 0.8638 0.00 0.88 0.00 0.12
#> S_Exp1.026 3 0.4994 0.3049 0.48 0.00 0.52 0.00
#> S_Exp1.077 4 0.4790 0.4661 0.38 0.00 0.00 0.62
#> S_Exp1.052 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> S_Exp1.007 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> S_Exp1.027 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> S_Exp1.017 3 0.3172 0.8931 0.16 0.00 0.84 0.00
#> S_Exp1.075 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> S_Exp1.049 1 0.0707 0.9259 0.98 0.00 0.00 0.02
#> S_Exp1.072 1 0.0707 0.9244 0.98 0.00 0.00 0.02
#> S_Exp1.005 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> S_Exp1.058 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> S_Exp1.010 3 0.3172 0.8931 0.16 0.00 0.84 0.00
#> S_Exp1.043 1 0.1211 0.9051 0.96 0.00 0.00 0.04
#> S_Exp1.050 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> G1_Exp1.048 2 0.0707 0.9438 0.00 0.98 0.00 0.02
#> G1_Exp1.090 2 0.0000 0.9501 0.00 1.00 0.00 0.00
#> G1_Exp1.061 2 0.5657 0.6859 0.00 0.72 0.16 0.12
#> G1_Exp1.025 2 0.3525 0.8704 0.00 0.86 0.04 0.10
#> G1_Exp1.033 1 0.1411 0.9126 0.96 0.00 0.02 0.02
#> G1_Exp1.081 2 0.4227 0.8181 0.00 0.82 0.06 0.12
#> G1_Exp1.013 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> G1_Exp1.024 4 0.2345 0.7021 0.00 0.10 0.00 0.90
#> G1_Exp1.056 2 0.0000 0.9501 0.00 1.00 0.00 0.00
#> G1_Exp1.026 1 0.4855 0.2546 0.60 0.00 0.00 0.40
#> G1_Exp1.064 2 0.0707 0.9438 0.00 0.98 0.00 0.02
#> G1_Exp1.053 2 0.3821 0.8387 0.00 0.84 0.04 0.12
#> G1_Exp1.070 2 0.0707 0.9438 0.00 0.98 0.00 0.02
#> G1_Exp1.087 2 0.7004 0.4357 0.00 0.58 0.20 0.22
#> G1_Exp1.067 2 0.0000 0.9501 0.00 1.00 0.00 0.00
#> G1_Exp1.003 2 0.2921 0.8679 0.00 0.86 0.00 0.14
#> G1_Exp1.052 2 0.3198 0.8770 0.00 0.88 0.04 0.08
#> G1_Exp1.060 2 0.0000 0.9501 0.00 1.00 0.00 0.00
#> G1_Exp1.071 2 0.0000 0.9501 0.00 1.00 0.00 0.00
#> G1_Exp1.045 2 0.0000 0.9501 0.00 1.00 0.00 0.00
#> G1_Exp1.040 2 0.0707 0.9438 0.00 0.98 0.00 0.02
#> G1_Exp1.016 2 0.0707 0.9426 0.00 0.98 0.00 0.02
#> G1_Exp1.027 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> G1_Exp1.059 3 0.1211 0.7960 0.04 0.00 0.96 0.00
#> G1_Exp1.002 2 0.1211 0.9323 0.00 0.96 0.00 0.04
#> G1_Exp1.021 2 0.1637 0.9195 0.00 0.94 0.00 0.06
#> G1_Exp1.015 2 0.1211 0.9323 0.00 0.96 0.00 0.04
#> G1_Exp1.042 2 0.6049 0.6230 0.00 0.68 0.20 0.12
#> G1_Exp1.058 2 0.0707 0.9438 0.00 0.98 0.00 0.02
#> G1_Exp1.075 2 0.0000 0.9501 0.00 1.00 0.00 0.00
#> G1_Exp1.019 1 0.4790 0.2321 0.62 0.00 0.38 0.00
#> G1_Exp1.074 2 0.1211 0.9358 0.00 0.96 0.00 0.04
#> G1_Exp1.079 2 0.0707 0.9438 0.00 0.98 0.00 0.02
#> G1_Exp1.038 2 0.2706 0.8955 0.00 0.90 0.02 0.08
#> G1_Exp1.035 2 0.2921 0.8699 0.00 0.86 0.00 0.14
#> G1_Exp1.012 2 0.2011 0.9148 0.00 0.92 0.00 0.08
#> G1_Exp1.032 2 0.2706 0.8941 0.00 0.90 0.02 0.08
#> G1_Exp1.009 2 0.5428 0.7140 0.00 0.74 0.14 0.12
#> G1_Exp1.043 2 0.3821 0.8387 0.00 0.84 0.04 0.12
#> G1_Exp1.089 2 0.4227 0.8172 0.00 0.82 0.06 0.12
#> G1_Exp1.080 2 0.4227 0.8177 0.00 0.82 0.06 0.12
#> G1_Exp1.007 2 0.0000 0.9501 0.00 1.00 0.00 0.00
#> G1_Exp1.062 2 0.0707 0.9438 0.00 0.98 0.00 0.02
#> G1_Exp1.069 2 0.0707 0.9438 0.00 0.98 0.00 0.02
#> G1_Exp1.004 2 0.0000 0.9501 0.00 1.00 0.00 0.00
#> G1_Exp1.010 2 0.0000 0.9501 0.00 1.00 0.00 0.00
#> G1_Exp1.044 2 0.0707 0.9438 0.00 0.98 0.00 0.02
#> G1_Exp1.065 2 0.0707 0.9438 0.00 0.98 0.00 0.02
#> G1_Exp1.029 2 0.2011 0.9148 0.00 0.92 0.00 0.08
#> G1_Exp1.086 4 0.5677 0.5556 0.00 0.14 0.14 0.72
#> G1_Exp1.084 2 0.5175 0.7435 0.00 0.76 0.12 0.12
#> G1_Exp1.088 2 0.0000 0.9501 0.00 1.00 0.00 0.00
#> G1_Exp1.051 2 0.3525 0.8587 0.00 0.86 0.04 0.10
#> G1_Exp1.085 3 0.3172 0.8931 0.16 0.00 0.84 0.00
#> G1_Exp1.066 2 0.0707 0.9438 0.00 0.98 0.00 0.02
#> G1_Exp1.047 2 0.6366 0.5527 0.00 0.64 0.24 0.12
#> G1_Exp1.072 2 0.0707 0.9438 0.00 0.98 0.00 0.02
#> G1_Exp1.057 2 0.1211 0.9356 0.00 0.96 0.00 0.04
#> G1_Exp1.054 2 0.0000 0.9501 0.00 1.00 0.00 0.00
#> G1_Exp1.082 2 0.0000 0.9501 0.00 1.00 0.00 0.00
#> G1_Exp1.031 3 0.0707 0.7359 0.00 0.00 0.98 0.02
#> G1_Exp1.077 3 0.2647 0.6509 0.00 0.00 0.88 0.12
#> G1_Exp1.091 2 0.2345 0.9032 0.00 0.90 0.00 0.10
#> G1_Exp1.078 3 0.3172 0.8931 0.16 0.00 0.84 0.00
#> G1_Exp1.073 1 0.6074 0.2849 0.60 0.00 0.06 0.34
#> G1_Exp1.068 2 0.0707 0.9438 0.00 0.98 0.00 0.02
#> G1_Exp1.001 2 0.0707 0.9426 0.00 0.98 0.00 0.02
#> G1_Exp1.023 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> G1_Exp1.022 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> G1_Exp1.034 3 0.3400 0.8711 0.18 0.00 0.82 0.00
#> G1_Exp1.005 2 0.3975 0.7130 0.00 0.76 0.00 0.24
#> G1_Exp1.039 1 0.0000 0.9418 1.00 0.00 0.00 0.00
#> G1_Exp1.037 2 0.4894 0.7697 0.00 0.78 0.10 0.12
#> G1_Exp1.014 2 0.1637 0.9195 0.00 0.94 0.00 0.06
#> G1_Exp1.017 2 0.0000 0.9501 0.00 1.00 0.00 0.00
#> G1_Exp1.020 1 0.4977 -0.0979 0.54 0.00 0.46 0.00
#> G1_Exp1.028 2 0.1211 0.9323 0.00 0.96 0.00 0.04
#> G1_Exp1.036 2 0.2335 0.9102 0.00 0.92 0.02 0.06
#> G1_Exp1.049 2 0.5657 0.6859 0.00 0.72 0.16 0.12
#> G1_Exp1.041 2 0.0000 0.9501 0.00 1.00 0.00 0.00
#> G1_Exp1.006 4 0.2706 0.6937 0.00 0.08 0.02 0.90
#> G1_Exp1.008 4 0.6605 0.1495 0.00 0.08 0.44 0.48
#> G1_Exp1.055 2 0.0707 0.9438 0.00 0.98 0.00 0.02
#> G1_Exp1.050 2 0.0000 0.9501 0.00 1.00 0.00 0.00
#> G1_Exp1.076 2 0.0000 0.9501 0.00 1.00 0.00 0.00
#> G1_Exp1.011 3 0.2921 0.8818 0.14 0.00 0.86 0.00
#> G1_Exp1.063 3 0.0707 0.7732 0.02 0.00 0.98 0.00
#> G1_Exp1.083 2 0.0000 0.9501 0.00 1.00 0.00 0.00
#> G1_Exp1.030 2 0.1637 0.9316 0.00 0.94 0.00 0.06
#> G1_Exp1.018 3 0.7832 -0.0271 0.26 0.00 0.38 0.36
#> G1_Exp1.046 3 0.1211 0.7234 0.00 0.00 0.96 0.04
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
As soon as the classes for columns are determined, the signatures that are significantly different between subgroups can be looked for. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. To get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows (which is done by automatically selecting number of clusters).If there are too many signatures, top_signatures = ... can be set to only show the
signatures with the highest FDRs:
# code only for demonstration
# e.g. to show the top 500 most significant rows
tb = get_signature(res, k = ..., top_signatures = 500)
If the signatures are defined as these which are uniquely high in current group, diff_method argument
can be set to "uniquely_high_in_one_group":
# code only for demonstration
tb = get_signature(res, k = ..., diff_method = "uniquely_high_in_one_group")
UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n_sample CellLine(p-value) Experiment(p-value) Phase(p-value) k
#> ATC:skmeans 457 0.2555 2.75e-10 5.25e-17 2
#> ATC:skmeans 453 0.5542 8.96e-09 7.98e-17 3
#> ATC:skmeans 437 0.0658 6.45e-12 1.50e-16 4
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
Parent node: Node0. Child nodes: Node011 , Node012 , Node021-leaf , Node022 , Node023 .
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 13001 rows and 221 columns.
#> Top rows (1300) 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 0.987 0.994 0.441 0.559 0.559
#> 3 3 0.898 0.898 0.959 0.452 0.747 0.567
#> 4 4 0.638 0.638 0.818 0.112 0.916 0.775
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
#> H1_Exp1.002 1 0.000 0.996 1.00 0.00
#> H1_Exp1.003 1 0.000 0.996 1.00 0.00
#> H1_Exp1.004 1 0.000 0.996 1.00 0.00
#> H1_Exp1.006 1 0.000 0.996 1.00 0.00
#> H1_Exp1.007 1 0.000 0.996 1.00 0.00
#> H1_Exp1.009 2 0.000 0.990 0.00 1.00
#> H1_Exp1.010 1 0.000 0.996 1.00 0.00
#> H1_Exp1.011 1 0.000 0.996 1.00 0.00
#> H1_Exp1.012 1 0.000 0.996 1.00 0.00
#> H1_Exp1.014 1 0.000 0.996 1.00 0.00
#> H1_Exp1.015 1 0.000 0.996 1.00 0.00
#> H1_Exp1.016 1 0.000 0.996 1.00 0.00
#> H1_Exp1.017 1 0.000 0.996 1.00 0.00
#> H1_Exp1.018 1 0.000 0.996 1.00 0.00
#> H1_Exp1.019 2 0.000 0.990 0.00 1.00
#> H1_Exp1.020 1 0.000 0.996 1.00 0.00
#> H1_Exp1.022 1 0.000 0.996 1.00 0.00
#> H1_Exp1.023 1 0.000 0.996 1.00 0.00
#> H1_Exp1.024 1 0.000 0.996 1.00 0.00
#> H1_Exp1.025 1 0.000 0.996 1.00 0.00
#> H1_Exp1.026 1 0.000 0.996 1.00 0.00
#> H1_Exp1.029 1 0.000 0.996 1.00 0.00
#> H1_Exp1.035 1 0.000 0.996 1.00 0.00
#> H1_Exp1.038 1 0.000 0.996 1.00 0.00
#> H1_Exp1.039 1 0.000 0.996 1.00 0.00
#> H1_Exp1.040 1 0.000 0.996 1.00 0.00
#> H1_Exp1.041 1 0.000 0.996 1.00 0.00
#> H1_Exp1.049 1 0.000 0.996 1.00 0.00
#> H1_Exp1.050 2 0.000 0.990 0.00 1.00
#> H1_Exp1.051 1 0.000 0.996 1.00 0.00
#> H1_Exp1.052 2 0.529 0.864 0.12 0.88
#> H1_Exp1.053 1 0.584 0.835 0.86 0.14
#> H1_Exp1.054 2 0.000 0.990 0.00 1.00
#> H1_Exp1.055 2 0.000 0.990 0.00 1.00
#> H1_Exp1.057 2 0.000 0.990 0.00 1.00
#> H1_Exp1.059 2 0.000 0.990 0.00 1.00
#> H1_Exp1.061 1 0.000 0.996 1.00 0.00
#> H1_Exp1.062 1 0.000 0.996 1.00 0.00
#> H1_Exp1.063 1 0.000 0.996 1.00 0.00
#> H1_Exp1.064 1 0.000 0.996 1.00 0.00
#> H1_Exp1.066 1 0.000 0.996 1.00 0.00
#> H1_Exp1.069 2 0.000 0.990 0.00 1.00
#> H1_Exp1.070 1 0.000 0.996 1.00 0.00
#> H1_Exp2.073 1 0.000 0.996 1.00 0.00
#> H1_Exp2.074 1 0.000 0.996 1.00 0.00
#> H1_Exp2.075 1 0.000 0.996 1.00 0.00
#> H1_Exp2.079 1 0.000 0.996 1.00 0.00
#> H1_Exp2.081 1 0.000 0.996 1.00 0.00
#> H1_Exp2.082 2 0.000 0.990 0.00 1.00
#> H1_Exp2.083 1 0.000 0.996 1.00 0.00
#> H1_Exp2.084 1 0.000 0.996 1.00 0.00
#> H1_Exp2.085 1 0.000 0.996 1.00 0.00
#> H1_Exp2.087 1 0.000 0.996 1.00 0.00
#> H1_Exp2.088 1 0.000 0.996 1.00 0.00
#> H1_Exp2.089 1 0.000 0.996 1.00 0.00
#> H1_Exp2.091 1 0.141 0.976 0.98 0.02
#> H1_Exp2.092 1 0.000 0.996 1.00 0.00
#> H1_Exp2.093 2 0.000 0.990 0.00 1.00
#> H1_Exp2.094 1 0.000 0.996 1.00 0.00
#> H1_Exp2.096 1 0.000 0.996 1.00 0.00
#> H1_Exp2.097 1 0.000 0.996 1.00 0.00
#> H1_Exp2.098 1 0.000 0.996 1.00 0.00
#> H1_Exp2.100 1 0.000 0.996 1.00 0.00
#> H1_Exp2.101 1 0.000 0.996 1.00 0.00
#> H1_Exp2.105 1 0.000 0.996 1.00 0.00
#> H1_Exp2.106 1 0.000 0.996 1.00 0.00
#> H1_Exp2.108 1 0.000 0.996 1.00 0.00
#> H1_Exp2.112 2 0.000 0.990 0.00 1.00
#> H1_Exp2.116 2 0.000 0.990 0.00 1.00
#> H1_Exp2.118 1 0.000 0.996 1.00 0.00
#> H1_Exp2.121 1 0.000 0.996 1.00 0.00
#> H1_Exp2.123 2 0.000 0.990 0.00 1.00
#> H1_Exp2.127 1 0.000 0.996 1.00 0.00
#> H1_Exp2.130 2 0.000 0.990 0.00 1.00
#> H1_Exp2.133 2 0.000 0.990 0.00 1.00
#> H1_Exp2.135 2 0.000 0.990 0.00 1.00
#> H1_Exp2.136 1 0.000 0.996 1.00 0.00
#> H1_Exp2.138 1 0.000 0.996 1.00 0.00
#> H1_Exp2.139 2 0.000 0.990 0.00 1.00
#> H1_Exp2.140 2 0.000 0.990 0.00 1.00
#> H1_Exp2.144 1 0.000 0.996 1.00 0.00
#> H1_Exp2.146 2 0.000 0.990 0.00 1.00
#> H1_Exp2.148 1 0.000 0.996 1.00 0.00
#> H1_Exp2.150 2 0.000 0.990 0.00 1.00
#> H1_Exp2.152 1 0.000 0.996 1.00 0.00
#> H1_Exp2.154 2 0.000 0.990 0.00 1.00
#> H1_Exp3.220 2 0.000 0.990 0.00 1.00
#> H1_Exp3.223 1 0.000 0.996 1.00 0.00
#> H1_Exp3.232 1 0.000 0.996 1.00 0.00
#> H1_Exp3.233 2 0.000 0.990 0.00 1.00
#> H1_Exp3.241 1 0.000 0.996 1.00 0.00
#> H1_Exp3.248 2 0.000 0.990 0.00 1.00
#> H1_Exp3.277 2 0.000 0.990 0.00 1.00
#> H1_Exp3.283 2 0.000 0.990 0.00 1.00
#> H1_Exp3.284 2 0.000 0.990 0.00 1.00
#> G2_Exp1.069 1 0.000 0.996 1.00 0.00
#> G2_Exp1.076 1 0.000 0.996 1.00 0.00
#> G2_Exp1.013 1 0.000 0.996 1.00 0.00
#> G2_Exp1.015 1 0.000 0.996 1.00 0.00
#> G2_Exp1.019 1 0.000 0.996 1.00 0.00
#> G2_Exp1.050 2 0.000 0.990 0.00 1.00
#> G2_Exp1.042 2 0.000 0.990 0.00 1.00
#> G2_Exp1.030 1 0.000 0.996 1.00 0.00
#> G2_Exp1.022 2 0.000 0.990 0.00 1.00
#> G2_Exp1.017 1 0.000 0.996 1.00 0.00
#> G2_Exp1.047 1 0.000 0.996 1.00 0.00
#> G2_Exp1.072 2 0.000 0.990 0.00 1.00
#> G2_Exp1.074 2 0.000 0.990 0.00 1.00
#> G2_Exp1.032 1 0.000 0.996 1.00 0.00
#> G2_Exp1.064 1 0.000 0.996 1.00 0.00
#> G2_Exp1.045 1 0.000 0.996 1.00 0.00
#> G2_Exp1.038 1 0.000 0.996 1.00 0.00
#> G2_Exp1.001 1 0.000 0.996 1.00 0.00
#> G2_Exp1.049 1 0.000 0.996 1.00 0.00
#> G2_Exp1.031 1 0.000 0.996 1.00 0.00
#> G2_Exp1.070 2 0.000 0.990 0.00 1.00
#> G2_Exp1.021 2 0.760 0.724 0.22 0.78
#> G2_Exp1.036 1 0.000 0.996 1.00 0.00
#> G2_Exp1.046 1 0.000 0.996 1.00 0.00
#> G2_Exp1.040 1 0.000 0.996 1.00 0.00
#> G2_Exp1.068 1 0.000 0.996 1.00 0.00
#> G2_Exp1.077 1 0.000 0.996 1.00 0.00
#> G2_Exp1.008 2 0.827 0.655 0.26 0.74
#> G2_Exp1.014 1 0.000 0.996 1.00 0.00
#> G2_Exp1.005 1 0.000 0.996 1.00 0.00
#> G2_Exp1.048 2 0.000 0.990 0.00 1.00
#> G2_Exp1.062 2 0.000 0.990 0.00 1.00
#> G2_Exp1.056 1 0.000 0.996 1.00 0.00
#> G2_Exp1.041 1 0.000 0.996 1.00 0.00
#> G2_Exp1.012 1 0.000 0.996 1.00 0.00
#> G2_Exp1.066 2 0.000 0.990 0.00 1.00
#> G2_Exp1.023 2 0.242 0.953 0.04 0.96
#> G2_Exp1.003 1 0.000 0.996 1.00 0.00
#> S_Exp1.071 2 0.000 0.990 0.00 1.00
#> S_Exp1.031 1 0.000 0.996 1.00 0.00
#> S_Exp1.032 2 0.000 0.990 0.00 1.00
#> S_Exp1.035 2 0.000 0.990 0.00 1.00
#> S_Exp1.056 1 0.000 0.996 1.00 0.00
#> S_Exp1.011 1 0.000 0.996 1.00 0.00
#> S_Exp1.016 1 0.000 0.996 1.00 0.00
#> S_Exp1.063 2 0.000 0.990 0.00 1.00
#> S_Exp1.038 1 0.000 0.996 1.00 0.00
#> S_Exp1.048 1 0.000 0.996 1.00 0.00
#> S_Exp1.051 1 0.000 0.996 1.00 0.00
#> S_Exp1.078 2 0.000 0.990 0.00 1.00
#> S_Exp1.023 2 0.141 0.973 0.02 0.98
#> S_Exp1.022 1 0.000 0.996 1.00 0.00
#> S_Exp1.001 1 0.000 0.996 1.00 0.00
#> S_Exp1.046 2 0.000 0.990 0.00 1.00
#> S_Exp1.061 1 0.000 0.996 1.00 0.00
#> S_Exp1.080 1 0.000 0.996 1.00 0.00
#> S_Exp1.008 2 0.000 0.990 0.00 1.00
#> S_Exp1.069 1 0.000 0.996 1.00 0.00
#> S_Exp1.019 1 0.000 0.996 1.00 0.00
#> S_Exp1.003 1 0.000 0.996 1.00 0.00
#> S_Exp1.006 2 0.000 0.990 0.00 1.00
#> S_Exp1.029 1 0.000 0.996 1.00 0.00
#> S_Exp1.025 1 0.000 0.996 1.00 0.00
#> S_Exp1.064 2 0.000 0.990 0.00 1.00
#> S_Exp1.041 1 0.000 0.996 1.00 0.00
#> S_Exp1.057 1 0.000 0.996 1.00 0.00
#> S_Exp1.073 2 0.000 0.990 0.00 1.00
#> S_Exp1.014 2 0.000 0.990 0.00 1.00
#> S_Exp1.039 2 0.000 0.990 0.00 1.00
#> S_Exp1.055 1 0.000 0.996 1.00 0.00
#> S_Exp1.033 1 0.000 0.996 1.00 0.00
#> S_Exp1.081 1 0.000 0.996 1.00 0.00
#> S_Exp1.004 1 0.000 0.996 1.00 0.00
#> S_Exp1.045 1 0.000 0.996 1.00 0.00
#> S_Exp1.054 2 0.000 0.990 0.00 1.00
#> S_Exp1.018 1 0.000 0.996 1.00 0.00
#> S_Exp1.034 1 0.000 0.996 1.00 0.00
#> S_Exp1.067 1 0.000 0.996 1.00 0.00
#> S_Exp1.012 2 0.000 0.990 0.00 1.00
#> S_Exp1.037 1 0.000 0.996 1.00 0.00
#> S_Exp1.070 2 0.000 0.990 0.00 1.00
#> S_Exp1.053 1 0.000 0.996 1.00 0.00
#> S_Exp1.036 1 0.000 0.996 1.00 0.00
#> S_Exp1.021 1 0.000 0.996 1.00 0.00
#> S_Exp1.020 2 0.000 0.990 0.00 1.00
#> S_Exp1.060 1 0.000 0.996 1.00 0.00
#> S_Exp1.015 2 0.000 0.990 0.00 1.00
#> S_Exp1.066 2 0.000 0.990 0.00 1.00
#> S_Exp1.062 1 0.000 0.996 1.00 0.00
#> S_Exp1.040 2 0.000 0.990 0.00 1.00
#> S_Exp1.028 1 0.000 0.996 1.00 0.00
#> S_Exp1.079 1 0.000 0.996 1.00 0.00
#> S_Exp1.047 1 0.000 0.996 1.00 0.00
#> S_Exp1.026 2 0.000 0.990 0.00 1.00
#> S_Exp1.077 1 0.000 0.996 1.00 0.00
#> S_Exp1.052 1 0.000 0.996 1.00 0.00
#> S_Exp1.007 1 0.000 0.996 1.00 0.00
#> S_Exp1.027 1 0.000 0.996 1.00 0.00
#> S_Exp1.017 2 0.000 0.990 0.00 1.00
#> S_Exp1.075 1 0.000 0.996 1.00 0.00
#> S_Exp1.049 1 0.000 0.996 1.00 0.00
#> S_Exp1.072 1 0.000 0.996 1.00 0.00
#> S_Exp1.005 1 0.000 0.996 1.00 0.00
#> S_Exp1.058 1 0.000 0.996 1.00 0.00
#> S_Exp1.010 2 0.000 0.990 0.00 1.00
#> S_Exp1.043 1 0.000 0.996 1.00 0.00
#> S_Exp1.050 1 0.000 0.996 1.00 0.00
#> G1_Exp1.033 1 0.000 0.996 1.00 0.00
#> G1_Exp1.013 1 0.000 0.996 1.00 0.00
#> G1_Exp1.026 1 0.000 0.996 1.00 0.00
#> G1_Exp1.027 1 0.000 0.996 1.00 0.00
#> G1_Exp1.059 2 0.000 0.990 0.00 1.00
#> G1_Exp1.019 1 0.958 0.380 0.62 0.38
#> G1_Exp1.085 2 0.000 0.990 0.00 1.00
#> G1_Exp1.031 2 0.000 0.990 0.00 1.00
#> G1_Exp1.078 2 0.000 0.990 0.00 1.00
#> G1_Exp1.073 1 0.000 0.996 1.00 0.00
#> G1_Exp1.023 1 0.000 0.996 1.00 0.00
#> G1_Exp1.022 1 0.000 0.996 1.00 0.00
#> G1_Exp1.034 2 0.000 0.990 0.00 1.00
#> G1_Exp1.039 1 0.000 0.996 1.00 0.00
#> G1_Exp1.020 2 0.141 0.973 0.02 0.98
#> G1_Exp1.011 2 0.000 0.990 0.00 1.00
#> G1_Exp1.063 2 0.000 0.990 0.00 1.00
#> G1_Exp1.018 1 0.000 0.996 1.00 0.00
#> G1_Exp1.046 2 0.000 0.990 0.00 1.00
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> H1_Exp1.002 1 0.0000 0.97072 1.00 0.00 0.00
#> H1_Exp1.003 1 0.0000 0.97072 1.00 0.00 0.00
#> H1_Exp1.004 1 0.0000 0.97072 1.00 0.00 0.00
#> H1_Exp1.006 1 0.0000 0.97072 1.00 0.00 0.00
#> H1_Exp1.007 1 0.0000 0.97072 1.00 0.00 0.00
#> H1_Exp1.009 2 0.3340 0.83060 0.12 0.88 0.00
#> H1_Exp1.010 1 0.0892 0.95198 0.98 0.00 0.02
#> H1_Exp1.011 1 0.0000 0.97072 1.00 0.00 0.00
#> H1_Exp1.012 1 0.0000 0.97072 1.00 0.00 0.00
#> H1_Exp1.014 1 0.0000 0.97072 1.00 0.00 0.00
#> H1_Exp1.015 3 0.6302 0.15604 0.48 0.00 0.52
#> H1_Exp1.016 3 0.6302 0.14406 0.48 0.00 0.52
#> H1_Exp1.017 3 0.4796 0.72700 0.22 0.00 0.78
#> H1_Exp1.018 3 0.1529 0.88983 0.04 0.00 0.96
#> H1_Exp1.019 3 0.6244 0.17125 0.00 0.44 0.56
#> H1_Exp1.020 3 0.0000 0.90638 0.00 0.00 1.00
#> H1_Exp1.022 3 0.4796 0.73141 0.22 0.00 0.78
#> H1_Exp1.023 3 0.0000 0.90638 0.00 0.00 1.00
#> H1_Exp1.024 3 0.0000 0.90638 0.00 0.00 1.00
#> H1_Exp1.025 3 0.0892 0.90037 0.02 0.00 0.98
#> H1_Exp1.026 1 0.0000 0.97072 1.00 0.00 0.00
#> H1_Exp1.029 3 0.6302 0.15062 0.48 0.00 0.52
#> H1_Exp1.035 3 0.0000 0.90638 0.00 0.00 1.00
#> H1_Exp1.038 3 0.0000 0.90638 0.00 0.00 1.00
#> H1_Exp1.039 3 0.0000 0.90638 0.00 0.00 1.00
#> H1_Exp1.040 3 0.1529 0.88920 0.04 0.00 0.96
#> H1_Exp1.041 3 0.0000 0.90638 0.00 0.00 1.00
#> H1_Exp1.049 3 0.0000 0.90638 0.00 0.00 1.00
#> H1_Exp1.050 2 0.3686 0.83056 0.00 0.86 0.14
#> H1_Exp1.051 3 0.0000 0.90638 0.00 0.00 1.00
#> H1_Exp1.052 3 0.0000 0.90638 0.00 0.00 1.00
#> H1_Exp1.053 3 0.0000 0.90638 0.00 0.00 1.00
#> H1_Exp1.054 3 0.0000 0.90638 0.00 0.00 1.00
#> H1_Exp1.055 3 0.0000 0.90638 0.00 0.00 1.00
#> H1_Exp1.057 2 0.0000 0.97010 0.00 1.00 0.00
#> H1_Exp1.059 2 0.0000 0.97010 0.00 1.00 0.00
#> H1_Exp1.061 3 0.0000 0.90638 0.00 0.00 1.00
#> H1_Exp1.062 3 0.0000 0.90638 0.00 0.00 1.00
#> H1_Exp1.063 3 0.0892 0.89987 0.02 0.00 0.98
#> H1_Exp1.064 3 0.0000 0.90638 0.00 0.00 1.00
#> H1_Exp1.066 3 0.0000 0.90638 0.00 0.00 1.00
#> H1_Exp1.069 2 0.0000 0.97010 0.00 1.00 0.00
#> H1_Exp1.070 1 0.4555 0.72614 0.80 0.00 0.20
#> H1_Exp2.073 1 0.0000 0.97072 1.00 0.00 0.00
#> H1_Exp2.074 1 0.0000 0.97072 1.00 0.00 0.00
#> H1_Exp2.075 1 0.0000 0.97072 1.00 0.00 0.00
#> H1_Exp2.079 1 0.0000 0.97072 1.00 0.00 0.00
#> H1_Exp2.081 1 0.0000 0.97072 1.00 0.00 0.00
#> H1_Exp2.082 2 0.0000 0.97010 0.00 1.00 0.00
#> H1_Exp2.083 1 0.0000 0.97072 1.00 0.00 0.00
#> H1_Exp2.084 1 0.0000 0.97072 1.00 0.00 0.00
#> H1_Exp2.085 1 0.0000 0.97072 1.00 0.00 0.00
#> H1_Exp2.087 1 0.0000 0.97072 1.00 0.00 0.00
#> H1_Exp2.088 1 0.0000 0.97072 1.00 0.00 0.00
#> H1_Exp2.089 1 0.0000 0.97072 1.00 0.00 0.00
#> H1_Exp2.091 1 0.0000 0.97072 1.00 0.00 0.00
#> H1_Exp2.092 1 0.0000 0.97072 1.00 0.00 0.00
#> H1_Exp2.093 2 0.0000 0.97010 0.00 1.00 0.00
#> H1_Exp2.094 1 0.0000 0.97072 1.00 0.00 0.00
#> H1_Exp2.096 1 0.0000 0.97072 1.00 0.00 0.00
#> H1_Exp2.097 1 0.0000 0.97072 1.00 0.00 0.00
#> H1_Exp2.098 1 0.0000 0.97072 1.00 0.00 0.00
#> H1_Exp2.100 1 0.0000 0.97072 1.00 0.00 0.00
#> H1_Exp2.101 1 0.0000 0.97072 1.00 0.00 0.00
#> H1_Exp2.105 1 0.0000 0.97072 1.00 0.00 0.00
#> H1_Exp2.106 1 0.0000 0.97072 1.00 0.00 0.00
#> H1_Exp2.108 1 0.0000 0.97072 1.00 0.00 0.00
#> H1_Exp2.112 2 0.0000 0.97010 0.00 1.00 0.00
#> H1_Exp2.116 2 0.0000 0.97010 0.00 1.00 0.00
#> H1_Exp2.118 3 0.5216 0.67197 0.26 0.00 0.74
#> H1_Exp2.121 3 0.5016 0.70000 0.24 0.00 0.76
#> H1_Exp2.123 2 0.0000 0.97010 0.00 1.00 0.00
#> H1_Exp2.127 3 0.6126 0.39272 0.40 0.00 0.60
#> H1_Exp2.130 2 0.0000 0.97010 0.00 1.00 0.00
#> H1_Exp2.133 2 0.0000 0.97010 0.00 1.00 0.00
#> H1_Exp2.135 2 0.0000 0.97010 0.00 1.00 0.00
#> H1_Exp2.136 1 0.5706 0.49919 0.68 0.00 0.32
#> H1_Exp2.138 1 0.0000 0.97072 1.00 0.00 0.00
#> H1_Exp2.139 2 0.0000 0.97010 0.00 1.00 0.00
#> H1_Exp2.140 2 0.1529 0.92956 0.04 0.96 0.00
#> H1_Exp2.144 1 0.0000 0.97072 1.00 0.00 0.00
#> H1_Exp2.146 2 0.0000 0.97010 0.00 1.00 0.00
#> H1_Exp2.148 1 0.0000 0.97072 1.00 0.00 0.00
#> H1_Exp2.150 2 0.0000 0.97010 0.00 1.00 0.00
#> H1_Exp2.152 1 0.0000 0.97072 1.00 0.00 0.00
#> H1_Exp2.154 2 0.0892 0.95054 0.02 0.98 0.00
#> H1_Exp3.220 2 0.0000 0.97010 0.00 1.00 0.00
#> H1_Exp3.223 1 0.0000 0.97072 1.00 0.00 0.00
#> H1_Exp3.232 1 0.0000 0.97072 1.00 0.00 0.00
#> H1_Exp3.233 2 0.2414 0.92276 0.02 0.94 0.04
#> H1_Exp3.241 3 0.0892 0.90037 0.02 0.00 0.98
#> H1_Exp3.248 2 0.0000 0.97010 0.00 1.00 0.00
#> H1_Exp3.277 2 0.0000 0.97010 0.00 1.00 0.00
#> H1_Exp3.283 2 0.0000 0.97010 0.00 1.00 0.00
#> H1_Exp3.284 2 0.0000 0.97010 0.00 1.00 0.00
#> G2_Exp1.069 1 0.6309 -0.09400 0.50 0.00 0.50
#> G2_Exp1.076 3 0.0892 0.89966 0.02 0.00 0.98
#> G2_Exp1.013 1 0.0000 0.97072 1.00 0.00 0.00
#> G2_Exp1.015 1 0.0000 0.97072 1.00 0.00 0.00
#> G2_Exp1.019 1 0.6280 0.07984 0.54 0.00 0.46
#> G2_Exp1.050 3 0.0892 0.89674 0.00 0.02 0.98
#> G2_Exp1.042 3 0.0892 0.89676 0.00 0.02 0.98
#> G2_Exp1.030 3 0.5397 0.63817 0.28 0.00 0.72
#> G2_Exp1.022 2 0.0000 0.97010 0.00 1.00 0.00
#> G2_Exp1.017 1 0.0000 0.97072 1.00 0.00 0.00
#> G2_Exp1.047 3 0.0000 0.90638 0.00 0.00 1.00
#> G2_Exp1.072 2 0.0000 0.97010 0.00 1.00 0.00
#> G2_Exp1.074 3 0.0892 0.89660 0.00 0.02 0.98
#> G2_Exp1.032 3 0.3340 0.82722 0.12 0.00 0.88
#> G2_Exp1.064 3 0.0000 0.90638 0.00 0.00 1.00
#> G2_Exp1.045 3 0.0000 0.90638 0.00 0.00 1.00
#> G2_Exp1.038 3 0.0000 0.90638 0.00 0.00 1.00
#> G2_Exp1.001 1 0.0000 0.97072 1.00 0.00 0.00
#> G2_Exp1.049 3 0.0000 0.90638 0.00 0.00 1.00
#> G2_Exp1.031 1 0.0000 0.97072 1.00 0.00 0.00
#> G2_Exp1.070 2 0.0000 0.97010 0.00 1.00 0.00
#> G2_Exp1.021 3 0.0892 0.89618 0.00 0.02 0.98
#> G2_Exp1.036 3 0.0892 0.90037 0.02 0.00 0.98
#> G2_Exp1.046 3 0.0000 0.90638 0.00 0.00 1.00
#> G2_Exp1.040 3 0.0000 0.90638 0.00 0.00 1.00
#> G2_Exp1.068 3 0.0000 0.90638 0.00 0.00 1.00
#> G2_Exp1.077 3 0.0000 0.90638 0.00 0.00 1.00
#> G2_Exp1.008 1 0.4002 0.79023 0.84 0.16 0.00
#> G2_Exp1.014 1 0.0000 0.97072 1.00 0.00 0.00
#> G2_Exp1.005 1 0.0000 0.97072 1.00 0.00 0.00
#> G2_Exp1.048 2 0.0000 0.97010 0.00 1.00 0.00
#> G2_Exp1.062 2 0.4796 0.71510 0.00 0.78 0.22
#> G2_Exp1.056 3 0.0892 0.90037 0.02 0.00 0.98
#> G2_Exp1.041 3 0.0000 0.90638 0.00 0.00 1.00
#> G2_Exp1.012 1 0.0000 0.97072 1.00 0.00 0.00
#> G2_Exp1.066 3 0.2959 0.82755 0.00 0.10 0.90
#> G2_Exp1.023 3 0.0892 0.89634 0.00 0.02 0.98
#> G2_Exp1.003 1 0.0000 0.97072 1.00 0.00 0.00
#> S_Exp1.071 2 0.0000 0.97010 0.00 1.00 0.00
#> S_Exp1.031 1 0.0000 0.97072 1.00 0.00 0.00
#> S_Exp1.032 2 0.0000 0.97010 0.00 1.00 0.00
#> S_Exp1.035 2 0.0000 0.97010 0.00 1.00 0.00
#> S_Exp1.056 1 0.0000 0.97072 1.00 0.00 0.00
#> S_Exp1.011 1 0.0000 0.97072 1.00 0.00 0.00
#> S_Exp1.016 1 0.0000 0.97072 1.00 0.00 0.00
#> S_Exp1.063 2 0.0000 0.97010 0.00 1.00 0.00
#> S_Exp1.038 1 0.1529 0.93380 0.96 0.00 0.04
#> S_Exp1.048 1 0.0000 0.97072 1.00 0.00 0.00
#> S_Exp1.051 1 0.0000 0.97072 1.00 0.00 0.00
#> S_Exp1.078 2 0.0000 0.97010 0.00 1.00 0.00
#> S_Exp1.023 2 0.3415 0.87012 0.08 0.90 0.02
#> S_Exp1.022 1 0.0000 0.97072 1.00 0.00 0.00
#> S_Exp1.001 1 0.0000 0.97072 1.00 0.00 0.00
#> S_Exp1.046 2 0.0000 0.97010 0.00 1.00 0.00
#> S_Exp1.061 1 0.0000 0.97072 1.00 0.00 0.00
#> S_Exp1.080 1 0.0000 0.97072 1.00 0.00 0.00
#> S_Exp1.008 2 0.0000 0.97010 0.00 1.00 0.00
#> S_Exp1.069 1 0.0000 0.97072 1.00 0.00 0.00
#> S_Exp1.019 1 0.0000 0.97072 1.00 0.00 0.00
#> S_Exp1.003 1 0.0000 0.97072 1.00 0.00 0.00
#> S_Exp1.006 2 0.0000 0.97010 0.00 1.00 0.00
#> S_Exp1.029 1 0.6244 0.14929 0.56 0.00 0.44
#> S_Exp1.025 1 0.0000 0.97072 1.00 0.00 0.00
#> S_Exp1.064 2 0.0000 0.97010 0.00 1.00 0.00
#> S_Exp1.041 1 0.0000 0.97072 1.00 0.00 0.00
#> S_Exp1.057 1 0.0000 0.97072 1.00 0.00 0.00
#> S_Exp1.073 2 0.0000 0.97010 0.00 1.00 0.00
#> S_Exp1.014 2 0.0000 0.97010 0.00 1.00 0.00
#> S_Exp1.039 2 0.0000 0.97010 0.00 1.00 0.00
#> S_Exp1.055 1 0.0000 0.97072 1.00 0.00 0.00
#> S_Exp1.033 1 0.0000 0.97072 1.00 0.00 0.00
#> S_Exp1.081 1 0.0000 0.97072 1.00 0.00 0.00
#> S_Exp1.004 1 0.0000 0.97072 1.00 0.00 0.00
#> S_Exp1.045 1 0.0000 0.97072 1.00 0.00 0.00
#> S_Exp1.054 2 0.0000 0.97010 0.00 1.00 0.00
#> S_Exp1.018 1 0.0000 0.97072 1.00 0.00 0.00
#> S_Exp1.034 1 0.4002 0.79274 0.84 0.00 0.16
#> S_Exp1.067 1 0.0000 0.97072 1.00 0.00 0.00
#> S_Exp1.012 2 0.0000 0.97010 0.00 1.00 0.00
#> S_Exp1.037 1 0.0000 0.97072 1.00 0.00 0.00
#> S_Exp1.070 2 0.0000 0.97010 0.00 1.00 0.00
#> S_Exp1.053 1 0.0000 0.97072 1.00 0.00 0.00
#> S_Exp1.036 1 0.0000 0.97072 1.00 0.00 0.00
#> S_Exp1.021 1 0.0000 0.97072 1.00 0.00 0.00
#> S_Exp1.020 2 0.0000 0.97010 0.00 1.00 0.00
#> S_Exp1.060 1 0.0000 0.97072 1.00 0.00 0.00
#> S_Exp1.015 2 0.0000 0.97010 0.00 1.00 0.00
#> S_Exp1.066 2 0.0000 0.97010 0.00 1.00 0.00
#> S_Exp1.062 1 0.0000 0.97072 1.00 0.00 0.00
#> S_Exp1.040 2 0.0000 0.97010 0.00 1.00 0.00
#> S_Exp1.028 1 0.0000 0.97072 1.00 0.00 0.00
#> S_Exp1.079 1 0.0000 0.97072 1.00 0.00 0.00
#> S_Exp1.047 1 0.0000 0.97072 1.00 0.00 0.00
#> S_Exp1.026 2 0.1529 0.93614 0.00 0.96 0.04
#> S_Exp1.077 1 0.0000 0.97072 1.00 0.00 0.00
#> S_Exp1.052 1 0.2066 0.91460 0.94 0.00 0.06
#> S_Exp1.007 1 0.0000 0.97072 1.00 0.00 0.00
#> S_Exp1.027 1 0.0892 0.95190 0.98 0.00 0.02
#> S_Exp1.017 2 0.0000 0.97010 0.00 1.00 0.00
#> S_Exp1.075 1 0.0000 0.97072 1.00 0.00 0.00
#> S_Exp1.049 1 0.0000 0.97072 1.00 0.00 0.00
#> S_Exp1.072 1 0.0000 0.97072 1.00 0.00 0.00
#> S_Exp1.005 1 0.0000 0.97072 1.00 0.00 0.00
#> S_Exp1.058 1 0.1529 0.93438 0.96 0.00 0.04
#> S_Exp1.010 2 0.0000 0.97010 0.00 1.00 0.00
#> S_Exp1.043 1 0.0000 0.97072 1.00 0.00 0.00
#> S_Exp1.050 1 0.0000 0.97072 1.00 0.00 0.00
#> G1_Exp1.033 1 0.5835 0.44865 0.66 0.00 0.34
#> G1_Exp1.013 1 0.0000 0.97072 1.00 0.00 0.00
#> G1_Exp1.026 3 0.1529 0.88947 0.04 0.00 0.96
#> G1_Exp1.027 3 0.5397 0.64023 0.28 0.00 0.72
#> G1_Exp1.059 2 0.6192 0.27666 0.00 0.58 0.42
#> G1_Exp1.019 3 0.0892 0.90000 0.02 0.00 0.98
#> G1_Exp1.085 2 0.0000 0.97010 0.00 1.00 0.00
#> G1_Exp1.031 2 0.0000 0.97010 0.00 1.00 0.00
#> G1_Exp1.078 2 0.0000 0.97010 0.00 1.00 0.00
#> G1_Exp1.073 3 0.0000 0.90638 0.00 0.00 1.00
#> G1_Exp1.023 1 0.0000 0.97072 1.00 0.00 0.00
#> G1_Exp1.022 1 0.0000 0.97072 1.00 0.00 0.00
#> G1_Exp1.034 2 0.0000 0.97010 0.00 1.00 0.00
#> G1_Exp1.039 3 0.0892 0.90037 0.02 0.00 0.98
#> G1_Exp1.020 2 0.6309 -0.00389 0.00 0.50 0.50
#> G1_Exp1.011 2 0.0000 0.97010 0.00 1.00 0.00
#> G1_Exp1.063 2 0.0000 0.97010 0.00 1.00 0.00
#> G1_Exp1.018 1 0.0000 0.97072 1.00 0.00 0.00
#> G1_Exp1.046 3 0.4291 0.73650 0.00 0.18 0.82
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> H1_Exp1.002 1 0.0000 0.78944 1.00 0.00 0.00 0.00
#> H1_Exp1.003 1 0.0000 0.78944 1.00 0.00 0.00 0.00
#> H1_Exp1.004 1 0.4907 0.14962 0.58 0.00 0.00 0.42
#> H1_Exp1.006 1 0.0000 0.78944 1.00 0.00 0.00 0.00
#> H1_Exp1.007 1 0.4994 -0.07474 0.52 0.00 0.00 0.48
#> H1_Exp1.009 2 0.5957 0.54447 0.04 0.54 0.00 0.42
#> H1_Exp1.010 4 0.6150 0.48053 0.36 0.00 0.06 0.58
#> H1_Exp1.011 1 0.1637 0.77201 0.94 0.00 0.00 0.06
#> H1_Exp1.012 1 0.0000 0.78944 1.00 0.00 0.00 0.00
#> H1_Exp1.014 1 0.2647 0.72358 0.88 0.00 0.00 0.12
#> H1_Exp1.015 1 0.4522 0.31427 0.68 0.00 0.32 0.00
#> H1_Exp1.016 4 0.7684 0.36719 0.22 0.00 0.36 0.42
#> H1_Exp1.017 4 0.6382 0.33086 0.08 0.00 0.34 0.58
#> H1_Exp1.018 3 0.4406 0.50952 0.30 0.00 0.70 0.00
#> H1_Exp1.019 2 0.7550 0.18686 0.00 0.48 0.22 0.30
#> H1_Exp1.020 3 0.4522 0.53683 0.00 0.00 0.68 0.32
#> H1_Exp1.022 3 0.4977 0.19059 0.46 0.00 0.54 0.00
#> H1_Exp1.023 3 0.4227 0.71050 0.12 0.00 0.82 0.06
#> H1_Exp1.024 3 0.2647 0.74779 0.00 0.00 0.88 0.12
#> H1_Exp1.025 3 0.5291 0.62627 0.18 0.00 0.74 0.08
#> H1_Exp1.026 1 0.0000 0.78944 1.00 0.00 0.00 0.00
#> H1_Exp1.029 1 0.5619 0.23757 0.64 0.00 0.32 0.04
#> H1_Exp1.035 3 0.2011 0.76861 0.00 0.00 0.92 0.08
#> H1_Exp1.038 3 0.3172 0.72144 0.00 0.00 0.84 0.16
#> H1_Exp1.039 3 0.0000 0.78328 0.00 0.00 1.00 0.00
#> H1_Exp1.040 3 0.4406 0.50724 0.30 0.00 0.70 0.00
#> H1_Exp1.041 3 0.0000 0.78328 0.00 0.00 1.00 0.00
#> H1_Exp1.049 3 0.1637 0.77403 0.00 0.00 0.94 0.06
#> H1_Exp1.050 2 0.6286 0.55798 0.00 0.66 0.20 0.14
#> H1_Exp1.051 3 0.1211 0.77924 0.00 0.00 0.96 0.04
#> H1_Exp1.052 3 0.1211 0.77850 0.00 0.00 0.96 0.04
#> H1_Exp1.053 3 0.0000 0.78328 0.00 0.00 1.00 0.00
#> H1_Exp1.054 3 0.2411 0.76712 0.00 0.04 0.92 0.04
#> H1_Exp1.055 3 0.4841 0.66892 0.00 0.14 0.78 0.08
#> H1_Exp1.057 2 0.0707 0.86235 0.00 0.98 0.02 0.00
#> H1_Exp1.059 2 0.0000 0.87318 0.00 1.00 0.00 0.00
#> H1_Exp1.061 3 0.1637 0.77604 0.00 0.00 0.94 0.06
#> H1_Exp1.062 3 0.0000 0.78328 0.00 0.00 1.00 0.00
#> H1_Exp1.063 3 0.5173 0.46429 0.32 0.00 0.66 0.02
#> H1_Exp1.064 3 0.4134 0.61931 0.00 0.00 0.74 0.26
#> H1_Exp1.066 3 0.1211 0.77727 0.04 0.00 0.96 0.00
#> H1_Exp1.069 2 0.0000 0.87318 0.00 1.00 0.00 0.00
#> H1_Exp1.070 4 0.6477 0.54403 0.30 0.00 0.10 0.60
#> H1_Exp2.073 1 0.4790 0.28320 0.62 0.00 0.00 0.38
#> H1_Exp2.074 1 0.0000 0.78944 1.00 0.00 0.00 0.00
#> H1_Exp2.075 1 0.0000 0.78944 1.00 0.00 0.00 0.00
#> H1_Exp2.079 1 0.1637 0.75335 0.94 0.00 0.00 0.06
#> H1_Exp2.081 1 0.0000 0.78944 1.00 0.00 0.00 0.00
#> H1_Exp2.082 2 0.4855 0.63239 0.00 0.60 0.00 0.40
#> H1_Exp2.083 1 0.1637 0.75324 0.94 0.00 0.00 0.06
#> H1_Exp2.084 1 0.4907 0.18388 0.58 0.00 0.00 0.42
#> H1_Exp2.085 1 0.0000 0.78944 1.00 0.00 0.00 0.00
#> H1_Exp2.087 1 0.0000 0.78944 1.00 0.00 0.00 0.00
#> H1_Exp2.088 1 0.3400 0.65706 0.82 0.00 0.00 0.18
#> H1_Exp2.089 1 0.0000 0.78944 1.00 0.00 0.00 0.00
#> H1_Exp2.091 1 0.4713 0.29940 0.64 0.00 0.00 0.36
#> H1_Exp2.092 1 0.0000 0.78944 1.00 0.00 0.00 0.00
#> H1_Exp2.093 2 0.4907 0.61261 0.00 0.58 0.00 0.42
#> H1_Exp2.094 1 0.1211 0.76768 0.96 0.00 0.00 0.04
#> H1_Exp2.096 1 0.1211 0.77686 0.96 0.00 0.00 0.04
#> H1_Exp2.097 1 0.5428 0.22565 0.60 0.00 0.02 0.38
#> H1_Exp2.098 1 0.0000 0.78944 1.00 0.00 0.00 0.00
#> H1_Exp2.100 1 0.0000 0.78944 1.00 0.00 0.00 0.00
#> H1_Exp2.101 1 0.1211 0.76768 0.96 0.00 0.00 0.04
#> H1_Exp2.105 1 0.0000 0.78944 1.00 0.00 0.00 0.00
#> H1_Exp2.106 1 0.3972 0.64707 0.84 0.00 0.08 0.08
#> H1_Exp2.108 1 0.4522 0.43967 0.68 0.00 0.00 0.32
#> H1_Exp2.112 2 0.4855 0.63239 0.00 0.60 0.00 0.40
#> H1_Exp2.116 2 0.0000 0.87318 0.00 1.00 0.00 0.00
#> H1_Exp2.118 4 0.7179 0.06647 0.14 0.00 0.38 0.48
#> H1_Exp2.121 4 0.7016 0.41309 0.14 0.00 0.32 0.54
#> H1_Exp2.123 2 0.0000 0.87318 0.00 1.00 0.00 0.00
#> H1_Exp2.127 3 0.7602 0.01774 0.38 0.00 0.42 0.20
#> H1_Exp2.130 2 0.5987 0.55175 0.00 0.52 0.04 0.44
#> H1_Exp2.133 2 0.0000 0.87318 0.00 1.00 0.00 0.00
#> H1_Exp2.135 2 0.0000 0.87318 0.00 1.00 0.00 0.00
#> H1_Exp2.136 1 0.6976 0.19710 0.58 0.00 0.18 0.24
#> H1_Exp2.138 1 0.2011 0.73508 0.92 0.00 0.00 0.08
#> H1_Exp2.139 2 0.0000 0.87318 0.00 1.00 0.00 0.00
#> H1_Exp2.140 2 0.6554 0.52895 0.08 0.52 0.00 0.40
#> H1_Exp2.144 1 0.2011 0.73510 0.92 0.00 0.00 0.08
#> H1_Exp2.146 2 0.0000 0.87318 0.00 1.00 0.00 0.00
#> H1_Exp2.148 4 0.6150 0.48047 0.36 0.00 0.06 0.58
#> H1_Exp2.150 2 0.0000 0.87318 0.00 1.00 0.00 0.00
#> H1_Exp2.152 1 0.4977 0.00700 0.54 0.00 0.00 0.46
#> H1_Exp2.154 2 0.5355 0.64587 0.02 0.62 0.00 0.36
#> H1_Exp3.220 2 0.0000 0.87318 0.00 1.00 0.00 0.00
#> H1_Exp3.223 1 0.4134 0.54458 0.74 0.00 0.00 0.26
#> H1_Exp3.232 4 0.5606 -0.04775 0.48 0.00 0.02 0.50
#> H1_Exp3.233 2 0.7909 0.43571 0.06 0.44 0.08 0.42
#> H1_Exp3.241 3 0.2921 0.71341 0.14 0.00 0.86 0.00
#> H1_Exp3.248 2 0.4134 0.74119 0.00 0.74 0.00 0.26
#> H1_Exp3.277 2 0.2706 0.83606 0.00 0.90 0.02 0.08
#> H1_Exp3.283 2 0.5535 0.59608 0.00 0.56 0.02 0.42
#> H1_Exp3.284 2 0.4855 0.63239 0.00 0.60 0.00 0.40
#> G2_Exp1.069 1 0.6299 -0.01483 0.52 0.00 0.42 0.06
#> G2_Exp1.076 3 0.4088 0.70732 0.04 0.00 0.82 0.14
#> G2_Exp1.013 1 0.1211 0.77777 0.96 0.00 0.00 0.04
#> G2_Exp1.015 1 0.1637 0.76970 0.94 0.00 0.00 0.06
#> G2_Exp1.019 4 0.6797 0.58067 0.24 0.00 0.16 0.60
#> G2_Exp1.050 3 0.4472 0.64217 0.00 0.02 0.76 0.22
#> G2_Exp1.042 3 0.3611 0.73099 0.00 0.08 0.86 0.06
#> G2_Exp1.030 4 0.7365 0.27465 0.16 0.00 0.40 0.44
#> G2_Exp1.022 2 0.2647 0.78894 0.00 0.88 0.00 0.12
#> G2_Exp1.017 1 0.0000 0.78944 1.00 0.00 0.00 0.00
#> G2_Exp1.047 3 0.0000 0.78328 0.00 0.00 1.00 0.00
#> G2_Exp1.072 2 0.0707 0.86624 0.00 0.98 0.00 0.02
#> G2_Exp1.074 3 0.3611 0.72759 0.00 0.06 0.86 0.08
#> G2_Exp1.032 3 0.7135 0.18667 0.20 0.00 0.56 0.24
#> G2_Exp1.064 3 0.0707 0.78167 0.00 0.00 0.98 0.02
#> G2_Exp1.045 3 0.0000 0.78328 0.00 0.00 1.00 0.00
#> G2_Exp1.038 3 0.0000 0.78328 0.00 0.00 1.00 0.00
#> G2_Exp1.001 1 0.4134 0.54653 0.74 0.00 0.00 0.26
#> G2_Exp1.049 3 0.0000 0.78328 0.00 0.00 1.00 0.00
#> G2_Exp1.031 4 0.5535 0.36038 0.42 0.00 0.02 0.56
#> G2_Exp1.070 2 0.0000 0.87318 0.00 1.00 0.00 0.00
#> G2_Exp1.021 3 0.4088 0.71785 0.00 0.04 0.82 0.14
#> G2_Exp1.036 3 0.4332 0.67547 0.16 0.00 0.80 0.04
#> G2_Exp1.046 3 0.0707 0.78167 0.00 0.00 0.98 0.02
#> G2_Exp1.040 3 0.0000 0.78328 0.00 0.00 1.00 0.00
#> G2_Exp1.068 3 0.1211 0.77945 0.00 0.00 0.96 0.04
#> G2_Exp1.077 3 0.0707 0.78269 0.02 0.00 0.98 0.00
#> G2_Exp1.008 4 0.6049 0.27145 0.12 0.20 0.00 0.68
#> G2_Exp1.014 1 0.1637 0.76789 0.94 0.00 0.00 0.06
#> G2_Exp1.005 1 0.6212 0.08700 0.56 0.00 0.06 0.38
#> G2_Exp1.048 2 0.0000 0.87318 0.00 1.00 0.00 0.00
#> G2_Exp1.062 2 0.6049 0.61494 0.00 0.68 0.20 0.12
#> G2_Exp1.056 3 0.4642 0.59371 0.24 0.00 0.74 0.02
#> G2_Exp1.041 3 0.1211 0.77727 0.04 0.00 0.96 0.00
#> G2_Exp1.012 1 0.1637 0.75311 0.94 0.00 0.00 0.06
#> G2_Exp1.066 3 0.4939 0.62137 0.00 0.04 0.74 0.22
#> G2_Exp1.023 3 0.4292 0.70045 0.00 0.10 0.82 0.08
#> G2_Exp1.003 1 0.0000 0.78944 1.00 0.00 0.00 0.00
#> S_Exp1.071 2 0.0000 0.87318 0.00 1.00 0.00 0.00
#> S_Exp1.031 1 0.2345 0.73930 0.90 0.00 0.00 0.10
#> S_Exp1.032 2 0.5355 0.64792 0.02 0.62 0.00 0.36
#> S_Exp1.035 2 0.2921 0.77104 0.00 0.86 0.00 0.14
#> S_Exp1.056 1 0.4522 0.42842 0.68 0.00 0.00 0.32
#> S_Exp1.011 1 0.2345 0.73506 0.90 0.00 0.00 0.10
#> S_Exp1.016 1 0.4855 0.22477 0.60 0.00 0.00 0.40
#> S_Exp1.063 2 0.0000 0.87318 0.00 1.00 0.00 0.00
#> S_Exp1.038 4 0.4907 0.36059 0.42 0.00 0.00 0.58
#> S_Exp1.048 1 0.0707 0.77766 0.98 0.00 0.02 0.00
#> S_Exp1.051 1 0.0000 0.78944 1.00 0.00 0.00 0.00
#> S_Exp1.078 2 0.0000 0.87318 0.00 1.00 0.00 0.00
#> S_Exp1.023 4 0.4907 0.10739 0.00 0.42 0.00 0.58
#> S_Exp1.022 1 0.3975 0.57923 0.76 0.00 0.00 0.24
#> S_Exp1.001 1 0.5000 -0.15474 0.50 0.00 0.00 0.50
#> S_Exp1.046 2 0.0000 0.87318 0.00 1.00 0.00 0.00
#> S_Exp1.061 1 0.0000 0.78944 1.00 0.00 0.00 0.00
#> S_Exp1.080 1 0.0000 0.78944 1.00 0.00 0.00 0.00
#> S_Exp1.008 2 0.0000 0.87318 0.00 1.00 0.00 0.00
#> S_Exp1.069 1 0.4522 0.43276 0.68 0.00 0.00 0.32
#> S_Exp1.019 1 0.0000 0.78944 1.00 0.00 0.00 0.00
#> S_Exp1.003 1 0.4948 0.09053 0.56 0.00 0.00 0.44
#> S_Exp1.006 2 0.0000 0.87318 0.00 1.00 0.00 0.00
#> S_Exp1.029 1 0.4522 0.33201 0.68 0.00 0.32 0.00
#> S_Exp1.025 1 0.0000 0.78944 1.00 0.00 0.00 0.00
#> S_Exp1.064 2 0.0000 0.87318 0.00 1.00 0.00 0.00
#> S_Exp1.041 1 0.0000 0.78944 1.00 0.00 0.00 0.00
#> S_Exp1.057 1 0.0000 0.78944 1.00 0.00 0.00 0.00
#> S_Exp1.073 2 0.0000 0.87318 0.00 1.00 0.00 0.00
#> S_Exp1.014 2 0.0000 0.87318 0.00 1.00 0.00 0.00
#> S_Exp1.039 2 0.0000 0.87318 0.00 1.00 0.00 0.00
#> S_Exp1.055 1 0.0707 0.78473 0.98 0.00 0.00 0.02
#> S_Exp1.033 1 0.0000 0.78944 1.00 0.00 0.00 0.00
#> S_Exp1.081 1 0.0707 0.78473 0.98 0.00 0.00 0.02
#> S_Exp1.004 1 0.4406 0.47148 0.70 0.00 0.00 0.30
#> S_Exp1.045 1 0.0000 0.78944 1.00 0.00 0.00 0.00
#> S_Exp1.054 2 0.0000 0.87318 0.00 1.00 0.00 0.00
#> S_Exp1.018 1 0.0707 0.78522 0.98 0.00 0.00 0.02
#> S_Exp1.034 4 0.6570 0.52992 0.32 0.00 0.10 0.58
#> S_Exp1.067 1 0.0000 0.78944 1.00 0.00 0.00 0.00
#> S_Exp1.012 2 0.0000 0.87318 0.00 1.00 0.00 0.00
#> S_Exp1.037 1 0.2011 0.73563 0.92 0.00 0.00 0.08
#> S_Exp1.070 2 0.0707 0.86235 0.00 0.98 0.00 0.02
#> S_Exp1.053 1 0.0000 0.78944 1.00 0.00 0.00 0.00
#> S_Exp1.036 1 0.3172 0.68182 0.84 0.00 0.00 0.16
#> S_Exp1.021 1 0.1211 0.77740 0.96 0.00 0.00 0.04
#> S_Exp1.020 2 0.0000 0.87318 0.00 1.00 0.00 0.00
#> S_Exp1.060 1 0.0707 0.78473 0.98 0.00 0.00 0.02
#> S_Exp1.015 2 0.0000 0.87318 0.00 1.00 0.00 0.00
#> S_Exp1.066 2 0.0000 0.87318 0.00 1.00 0.00 0.00
#> S_Exp1.062 1 0.0707 0.78467 0.98 0.00 0.00 0.02
#> S_Exp1.040 2 0.0000 0.87318 0.00 1.00 0.00 0.00
#> S_Exp1.028 1 0.0707 0.77982 0.98 0.00 0.00 0.02
#> S_Exp1.079 4 0.5000 0.09819 0.50 0.00 0.00 0.50
#> S_Exp1.047 1 0.3975 0.58134 0.76 0.00 0.00 0.24
#> S_Exp1.026 2 0.2830 0.81102 0.00 0.90 0.06 0.04
#> S_Exp1.077 1 0.0000 0.78944 1.00 0.00 0.00 0.00
#> S_Exp1.052 1 0.4610 0.62615 0.80 0.00 0.10 0.10
#> S_Exp1.007 1 0.3610 0.62738 0.80 0.00 0.00 0.20
#> S_Exp1.027 4 0.4907 0.36059 0.42 0.00 0.00 0.58
#> S_Exp1.017 2 0.0000 0.87318 0.00 1.00 0.00 0.00
#> S_Exp1.075 1 0.2647 0.72158 0.88 0.00 0.00 0.12
#> S_Exp1.049 1 0.0707 0.78072 0.98 0.00 0.00 0.02
#> S_Exp1.072 1 0.0000 0.78944 1.00 0.00 0.00 0.00
#> S_Exp1.005 1 0.4134 0.54479 0.74 0.00 0.00 0.26
#> S_Exp1.058 1 0.5487 0.19189 0.58 0.00 0.02 0.40
#> S_Exp1.010 2 0.0000 0.87318 0.00 1.00 0.00 0.00
#> S_Exp1.043 1 0.0707 0.78473 0.98 0.00 0.00 0.02
#> S_Exp1.050 1 0.4977 -0.00319 0.54 0.00 0.00 0.46
#> G1_Exp1.033 1 0.5175 0.53303 0.76 0.00 0.12 0.12
#> G1_Exp1.013 1 0.2011 0.76396 0.92 0.00 0.00 0.08
#> G1_Exp1.026 3 0.4406 0.50592 0.30 0.00 0.70 0.00
#> G1_Exp1.027 4 0.5383 0.48483 0.10 0.00 0.16 0.74
#> G1_Exp1.059 3 0.7310 0.12601 0.00 0.36 0.48 0.16
#> G1_Exp1.019 3 0.6836 0.47017 0.14 0.00 0.58 0.28
#> G1_Exp1.085 2 0.0000 0.87318 0.00 1.00 0.00 0.00
#> G1_Exp1.031 2 0.2011 0.84323 0.00 0.92 0.00 0.08
#> G1_Exp1.078 2 0.0000 0.87318 0.00 1.00 0.00 0.00
#> G1_Exp1.073 3 0.0707 0.78251 0.02 0.00 0.98 0.00
#> G1_Exp1.023 1 0.0000 0.78944 1.00 0.00 0.00 0.00
#> G1_Exp1.022 1 0.4994 -0.07587 0.52 0.00 0.00 0.48
#> G1_Exp1.034 2 0.4713 0.66736 0.00 0.64 0.00 0.36
#> G1_Exp1.039 3 0.3610 0.65299 0.20 0.00 0.80 0.00
#> G1_Exp1.020 4 0.8959 -0.20445 0.06 0.28 0.26 0.40
#> G1_Exp1.011 2 0.4406 0.71135 0.00 0.70 0.00 0.30
#> G1_Exp1.063 2 0.1211 0.85884 0.00 0.96 0.00 0.04
#> G1_Exp1.018 1 0.3975 0.51614 0.76 0.00 0.00 0.24
#> G1_Exp1.046 3 0.7372 0.22915 0.00 0.16 0.42 0.42
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
As soon as the classes for columns are determined, the signatures that are significantly different between subgroups can be looked for. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. To get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows (which is done by automatically selecting number of clusters).If there are too many signatures, top_signatures = ... can be set to only show the
signatures with the highest FDRs:
# code only for demonstration
# e.g. to show the top 500 most significant rows
tb = get_signature(res, k = ..., top_signatures = 500)
If the signatures are defined as these which are uniquely high in current group, diff_method argument
can be set to "uniquely_high_in_one_group":
# code only for demonstration
tb = get_signature(res, k = ..., diff_method = "uniquely_high_in_one_group")
UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n_sample CellLine(p-value) Experiment(p-value) Phase(p-value) k
#> ATC:skmeans 220 0.452 0.084735 3.02e-01 2
#> ATC:skmeans 209 0.534 0.000656 4.31e-07 3
#> ATC:skmeans 173 0.708 0.002570 1.96e-03 4
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
Parent node: Node01. Child nodes: Node0111 , Node0112 , Node0121-leaf , Node0122-leaf , Node0221-leaf , Node0222-leaf , Node0231-leaf , Node0232-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 13002 rows and 149 columns.
#> Top rows (1196) 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 0.986 0.965 0.985 0.500 0.502 0.502
#> 3 3 0.934 0.937 0.972 0.341 0.733 0.514
#> 4 4 0.650 0.591 0.797 0.117 0.819 0.527
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
#> H1_Exp1.002 1 0.000 0.981 1.00 0.00
#> H1_Exp1.003 2 0.000 0.989 0.00 1.00
#> H1_Exp1.004 1 0.000 0.981 1.00 0.00
#> H1_Exp1.006 1 0.000 0.981 1.00 0.00
#> H1_Exp1.007 2 0.469 0.888 0.10 0.90
#> H1_Exp1.010 2 0.327 0.932 0.06 0.94
#> H1_Exp1.011 1 0.000 0.981 1.00 0.00
#> H1_Exp1.012 1 0.904 0.539 0.68 0.32
#> H1_Exp1.014 1 0.000 0.981 1.00 0.00
#> H1_Exp1.015 2 0.000 0.989 0.00 1.00
#> H1_Exp1.016 2 0.000 0.989 0.00 1.00
#> H1_Exp1.017 2 0.000 0.989 0.00 1.00
#> H1_Exp1.018 2 0.000 0.989 0.00 1.00
#> H1_Exp1.020 2 0.000 0.989 0.00 1.00
#> H1_Exp1.022 2 0.000 0.989 0.00 1.00
#> H1_Exp1.023 2 0.000 0.989 0.00 1.00
#> H1_Exp1.024 2 0.000 0.989 0.00 1.00
#> H1_Exp1.025 2 0.000 0.989 0.00 1.00
#> H1_Exp1.026 2 0.000 0.989 0.00 1.00
#> H1_Exp1.029 2 0.000 0.989 0.00 1.00
#> H1_Exp1.035 2 0.000 0.989 0.00 1.00
#> H1_Exp1.038 2 0.000 0.989 0.00 1.00
#> H1_Exp1.039 2 0.000 0.989 0.00 1.00
#> H1_Exp1.040 2 0.000 0.989 0.00 1.00
#> H1_Exp1.041 2 0.000 0.989 0.00 1.00
#> H1_Exp1.049 2 0.000 0.989 0.00 1.00
#> H1_Exp1.051 2 0.000 0.989 0.00 1.00
#> H1_Exp1.053 2 0.000 0.989 0.00 1.00
#> H1_Exp1.061 2 0.000 0.989 0.00 1.00
#> H1_Exp1.062 2 0.000 0.989 0.00 1.00
#> H1_Exp1.063 2 0.000 0.989 0.00 1.00
#> H1_Exp1.064 2 0.000 0.989 0.00 1.00
#> H1_Exp1.066 2 0.000 0.989 0.00 1.00
#> H1_Exp1.070 2 0.000 0.989 0.00 1.00
#> H1_Exp2.073 1 0.000 0.981 1.00 0.00
#> H1_Exp2.074 1 0.000 0.981 1.00 0.00
#> H1_Exp2.075 1 0.000 0.981 1.00 0.00
#> H1_Exp2.079 1 0.000 0.981 1.00 0.00
#> H1_Exp2.081 1 0.000 0.981 1.00 0.00
#> H1_Exp2.083 1 0.000 0.981 1.00 0.00
#> H1_Exp2.084 1 0.000 0.981 1.00 0.00
#> H1_Exp2.085 1 0.000 0.981 1.00 0.00
#> H1_Exp2.087 1 0.000 0.981 1.00 0.00
#> H1_Exp2.088 1 0.000 0.981 1.00 0.00
#> H1_Exp2.089 1 0.000 0.981 1.00 0.00
#> H1_Exp2.091 1 0.000 0.981 1.00 0.00
#> H1_Exp2.092 1 0.000 0.981 1.00 0.00
#> H1_Exp2.094 1 0.000 0.981 1.00 0.00
#> H1_Exp2.096 2 0.722 0.752 0.20 0.80
#> H1_Exp2.097 2 0.855 0.608 0.28 0.72
#> H1_Exp2.098 1 0.000 0.981 1.00 0.00
#> H1_Exp2.100 1 0.634 0.811 0.84 0.16
#> H1_Exp2.101 1 0.141 0.963 0.98 0.02
#> H1_Exp2.105 1 0.000 0.981 1.00 0.00
#> H1_Exp2.106 2 0.000 0.989 0.00 1.00
#> H1_Exp2.108 1 0.000 0.981 1.00 0.00
#> H1_Exp2.118 2 0.000 0.989 0.00 1.00
#> H1_Exp2.121 2 0.000 0.989 0.00 1.00
#> H1_Exp2.127 2 0.000 0.989 0.00 1.00
#> H1_Exp2.136 1 0.990 0.221 0.56 0.44
#> H1_Exp2.138 1 0.000 0.981 1.00 0.00
#> H1_Exp2.144 1 0.141 0.963 0.98 0.02
#> H1_Exp2.148 2 0.000 0.989 0.00 1.00
#> H1_Exp2.152 1 0.795 0.688 0.76 0.24
#> H1_Exp3.223 2 0.000 0.989 0.00 1.00
#> H1_Exp3.232 2 0.000 0.989 0.00 1.00
#> H1_Exp3.241 2 0.000 0.989 0.00 1.00
#> G2_Exp1.069 2 0.000 0.989 0.00 1.00
#> G2_Exp1.076 2 0.000 0.989 0.00 1.00
#> G2_Exp1.013 1 0.000 0.981 1.00 0.00
#> G2_Exp1.015 1 0.722 0.753 0.80 0.20
#> G2_Exp1.019 2 0.000 0.989 0.00 1.00
#> G2_Exp1.030 2 0.000 0.989 0.00 1.00
#> G2_Exp1.017 1 0.000 0.981 1.00 0.00
#> G2_Exp1.047 2 0.000 0.989 0.00 1.00
#> G2_Exp1.032 2 0.000 0.989 0.00 1.00
#> G2_Exp1.064 2 0.000 0.989 0.00 1.00
#> G2_Exp1.045 2 0.000 0.989 0.00 1.00
#> G2_Exp1.038 2 0.000 0.989 0.00 1.00
#> G2_Exp1.001 1 0.000 0.981 1.00 0.00
#> G2_Exp1.049 2 0.000 0.989 0.00 1.00
#> G2_Exp1.031 2 0.327 0.932 0.06 0.94
#> G2_Exp1.036 2 0.000 0.989 0.00 1.00
#> G2_Exp1.046 2 0.000 0.989 0.00 1.00
#> G2_Exp1.040 2 0.000 0.989 0.00 1.00
#> G2_Exp1.068 2 0.000 0.989 0.00 1.00
#> G2_Exp1.077 2 0.000 0.989 0.00 1.00
#> G2_Exp1.014 1 0.000 0.981 1.00 0.00
#> G2_Exp1.005 2 0.000 0.989 0.00 1.00
#> G2_Exp1.056 2 0.000 0.989 0.00 1.00
#> G2_Exp1.041 2 0.000 0.989 0.00 1.00
#> G2_Exp1.012 1 0.000 0.981 1.00 0.00
#> G2_Exp1.003 1 0.000 0.981 1.00 0.00
#> S_Exp1.031 1 0.000 0.981 1.00 0.00
#> S_Exp1.056 1 0.000 0.981 1.00 0.00
#> S_Exp1.011 1 0.000 0.981 1.00 0.00
#> S_Exp1.016 1 0.000 0.981 1.00 0.00
#> S_Exp1.038 1 0.000 0.981 1.00 0.00
#> S_Exp1.048 1 0.000 0.981 1.00 0.00
#> S_Exp1.051 1 0.000 0.981 1.00 0.00
#> S_Exp1.022 1 0.000 0.981 1.00 0.00
#> S_Exp1.001 1 0.000 0.981 1.00 0.00
#> S_Exp1.061 1 0.000 0.981 1.00 0.00
#> S_Exp1.080 1 0.000 0.981 1.00 0.00
#> S_Exp1.069 1 0.000 0.981 1.00 0.00
#> S_Exp1.019 1 0.000 0.981 1.00 0.00
#> S_Exp1.003 1 0.000 0.981 1.00 0.00
#> S_Exp1.029 1 0.584 0.837 0.86 0.14
#> S_Exp1.025 1 0.000 0.981 1.00 0.00
#> S_Exp1.041 1 0.000 0.981 1.00 0.00
#> S_Exp1.057 1 0.000 0.981 1.00 0.00
#> S_Exp1.055 1 0.000 0.981 1.00 0.00
#> S_Exp1.033 1 0.000 0.981 1.00 0.00
#> S_Exp1.081 1 0.000 0.981 1.00 0.00
#> S_Exp1.004 1 0.000 0.981 1.00 0.00
#> S_Exp1.045 1 0.000 0.981 1.00 0.00
#> S_Exp1.018 1 0.000 0.981 1.00 0.00
#> S_Exp1.034 1 0.000 0.981 1.00 0.00
#> S_Exp1.067 1 0.000 0.981 1.00 0.00
#> S_Exp1.037 1 0.000 0.981 1.00 0.00
#> S_Exp1.053 1 0.000 0.981 1.00 0.00
#> S_Exp1.036 1 0.000 0.981 1.00 0.00
#> S_Exp1.021 1 0.000 0.981 1.00 0.00
#> S_Exp1.060 1 0.000 0.981 1.00 0.00
#> S_Exp1.062 1 0.000 0.981 1.00 0.00
#> S_Exp1.028 1 0.000 0.981 1.00 0.00
#> S_Exp1.079 1 0.000 0.981 1.00 0.00
#> S_Exp1.047 1 0.000 0.981 1.00 0.00
#> S_Exp1.077 1 0.000 0.981 1.00 0.00
#> S_Exp1.052 1 0.000 0.981 1.00 0.00
#> S_Exp1.007 1 0.000 0.981 1.00 0.00
#> S_Exp1.027 1 0.000 0.981 1.00 0.00
#> S_Exp1.075 1 0.000 0.981 1.00 0.00
#> S_Exp1.049 1 0.000 0.981 1.00 0.00
#> S_Exp1.072 1 0.000 0.981 1.00 0.00
#> S_Exp1.005 1 0.000 0.981 1.00 0.00
#> S_Exp1.058 1 0.000 0.981 1.00 0.00
#> S_Exp1.043 1 0.000 0.981 1.00 0.00
#> S_Exp1.050 1 0.000 0.981 1.00 0.00
#> G1_Exp1.033 2 0.000 0.989 0.00 1.00
#> G1_Exp1.013 2 0.141 0.971 0.02 0.98
#> G1_Exp1.026 2 0.000 0.989 0.00 1.00
#> G1_Exp1.027 2 0.000 0.989 0.00 1.00
#> G1_Exp1.019 2 0.000 0.989 0.00 1.00
#> G1_Exp1.073 2 0.000 0.989 0.00 1.00
#> G1_Exp1.023 2 0.000 0.989 0.00 1.00
#> G1_Exp1.022 2 0.000 0.989 0.00 1.00
#> G1_Exp1.039 2 0.000 0.989 0.00 1.00
#> G1_Exp1.018 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
#> H1_Exp1.002 1 0.0000 0.976 1.00 0.00 0.00
#> H1_Exp1.003 2 0.5706 0.546 0.32 0.68 0.00
#> H1_Exp1.004 3 0.0000 0.961 0.00 0.00 1.00
#> H1_Exp1.006 1 0.0000 0.976 1.00 0.00 0.00
#> H1_Exp1.007 3 0.0000 0.961 0.00 0.00 1.00
#> H1_Exp1.010 3 0.0000 0.961 0.00 0.00 1.00
#> H1_Exp1.011 1 0.0000 0.976 1.00 0.00 0.00
#> H1_Exp1.012 1 0.0000 0.976 1.00 0.00 0.00
#> H1_Exp1.014 3 0.1529 0.931 0.04 0.00 0.96
#> H1_Exp1.015 2 0.0000 0.973 0.00 1.00 0.00
#> H1_Exp1.016 2 0.0000 0.973 0.00 1.00 0.00
#> H1_Exp1.017 3 0.1529 0.928 0.00 0.04 0.96
#> H1_Exp1.018 2 0.0000 0.973 0.00 1.00 0.00
#> H1_Exp1.020 2 0.0000 0.973 0.00 1.00 0.00
#> H1_Exp1.022 2 0.2959 0.884 0.10 0.90 0.00
#> H1_Exp1.023 2 0.0000 0.973 0.00 1.00 0.00
#> H1_Exp1.024 2 0.0000 0.973 0.00 1.00 0.00
#> H1_Exp1.025 2 0.4291 0.786 0.18 0.82 0.00
#> H1_Exp1.026 1 0.1529 0.942 0.96 0.04 0.00
#> H1_Exp1.029 2 0.0000 0.973 0.00 1.00 0.00
#> H1_Exp1.035 2 0.0000 0.973 0.00 1.00 0.00
#> H1_Exp1.038 2 0.0000 0.973 0.00 1.00 0.00
#> H1_Exp1.039 2 0.0000 0.973 0.00 1.00 0.00
#> H1_Exp1.040 2 0.0000 0.973 0.00 1.00 0.00
#> H1_Exp1.041 2 0.0000 0.973 0.00 1.00 0.00
#> H1_Exp1.049 2 0.0000 0.973 0.00 1.00 0.00
#> H1_Exp1.051 2 0.0000 0.973 0.00 1.00 0.00
#> H1_Exp1.053 2 0.0000 0.973 0.00 1.00 0.00
#> H1_Exp1.061 2 0.0000 0.973 0.00 1.00 0.00
#> H1_Exp1.062 2 0.0000 0.973 0.00 1.00 0.00
#> H1_Exp1.063 2 0.0000 0.973 0.00 1.00 0.00
#> H1_Exp1.064 2 0.0000 0.973 0.00 1.00 0.00
#> H1_Exp1.066 2 0.0000 0.973 0.00 1.00 0.00
#> H1_Exp1.070 2 0.0000 0.973 0.00 1.00 0.00
#> H1_Exp2.073 3 0.0000 0.961 0.00 0.00 1.00
#> H1_Exp2.074 1 0.0000 0.976 1.00 0.00 0.00
#> H1_Exp2.075 1 0.0000 0.976 1.00 0.00 0.00
#> H1_Exp2.079 1 0.0000 0.976 1.00 0.00 0.00
#> H1_Exp2.081 1 0.0000 0.976 1.00 0.00 0.00
#> H1_Exp2.083 1 0.0000 0.976 1.00 0.00 0.00
#> H1_Exp2.084 3 0.0000 0.961 0.00 0.00 1.00
#> H1_Exp2.085 1 0.0000 0.976 1.00 0.00 0.00
#> H1_Exp2.087 1 0.0000 0.976 1.00 0.00 0.00
#> H1_Exp2.088 3 0.5948 0.455 0.36 0.00 0.64
#> H1_Exp2.089 1 0.0000 0.976 1.00 0.00 0.00
#> H1_Exp2.091 1 0.0000 0.976 1.00 0.00 0.00
#> H1_Exp2.092 1 0.0000 0.976 1.00 0.00 0.00
#> H1_Exp2.094 1 0.0000 0.976 1.00 0.00 0.00
#> H1_Exp2.096 1 0.0000 0.976 1.00 0.00 0.00
#> H1_Exp2.097 1 0.0000 0.976 1.00 0.00 0.00
#> H1_Exp2.098 1 0.0000 0.976 1.00 0.00 0.00
#> H1_Exp2.100 1 0.0000 0.976 1.00 0.00 0.00
#> H1_Exp2.101 1 0.0000 0.976 1.00 0.00 0.00
#> H1_Exp2.105 1 0.0000 0.976 1.00 0.00 0.00
#> H1_Exp2.106 2 0.1529 0.942 0.04 0.96 0.00
#> H1_Exp2.108 3 0.0000 0.961 0.00 0.00 1.00
#> H1_Exp2.118 2 0.0000 0.973 0.00 1.00 0.00
#> H1_Exp2.121 2 0.0000 0.973 0.00 1.00 0.00
#> H1_Exp2.127 2 0.0000 0.973 0.00 1.00 0.00
#> H1_Exp2.136 1 0.2959 0.880 0.90 0.10 0.00
#> H1_Exp2.138 1 0.0000 0.976 1.00 0.00 0.00
#> H1_Exp2.144 1 0.0000 0.976 1.00 0.00 0.00
#> H1_Exp2.148 3 0.0000 0.961 0.00 0.00 1.00
#> H1_Exp2.152 3 0.0000 0.961 0.00 0.00 1.00
#> H1_Exp3.223 2 0.5706 0.535 0.00 0.68 0.32
#> H1_Exp3.232 2 0.0000 0.973 0.00 1.00 0.00
#> H1_Exp3.241 2 0.0000 0.973 0.00 1.00 0.00
#> G2_Exp1.069 2 0.0000 0.973 0.00 1.00 0.00
#> G2_Exp1.076 2 0.0000 0.973 0.00 1.00 0.00
#> G2_Exp1.013 1 0.0000 0.976 1.00 0.00 0.00
#> G2_Exp1.015 3 0.0000 0.961 0.00 0.00 1.00
#> G2_Exp1.019 3 0.0000 0.961 0.00 0.00 1.00
#> G2_Exp1.030 2 0.2537 0.901 0.00 0.92 0.08
#> G2_Exp1.017 1 0.0000 0.976 1.00 0.00 0.00
#> G2_Exp1.047 2 0.0000 0.973 0.00 1.00 0.00
#> G2_Exp1.032 2 0.0000 0.973 0.00 1.00 0.00
#> G2_Exp1.064 2 0.0000 0.973 0.00 1.00 0.00
#> G2_Exp1.045 2 0.0000 0.973 0.00 1.00 0.00
#> G2_Exp1.038 2 0.0000 0.973 0.00 1.00 0.00
#> G2_Exp1.001 3 0.0000 0.961 0.00 0.00 1.00
#> G2_Exp1.049 2 0.0000 0.973 0.00 1.00 0.00
#> G2_Exp1.031 3 0.0000 0.961 0.00 0.00 1.00
#> G2_Exp1.036 2 0.0000 0.973 0.00 1.00 0.00
#> G2_Exp1.046 2 0.0000 0.973 0.00 1.00 0.00
#> G2_Exp1.040 2 0.0000 0.973 0.00 1.00 0.00
#> G2_Exp1.068 2 0.0000 0.973 0.00 1.00 0.00
#> G2_Exp1.077 2 0.0000 0.973 0.00 1.00 0.00
#> G2_Exp1.014 3 0.0000 0.961 0.00 0.00 1.00
#> G2_Exp1.005 3 0.0000 0.961 0.00 0.00 1.00
#> G2_Exp1.056 2 0.2066 0.925 0.06 0.94 0.00
#> G2_Exp1.041 2 0.0000 0.973 0.00 1.00 0.00
#> G2_Exp1.012 1 0.0000 0.976 1.00 0.00 0.00
#> G2_Exp1.003 3 0.5397 0.623 0.28 0.00 0.72
#> S_Exp1.031 3 0.0000 0.961 0.00 0.00 1.00
#> S_Exp1.056 3 0.0000 0.961 0.00 0.00 1.00
#> S_Exp1.011 3 0.0000 0.961 0.00 0.00 1.00
#> S_Exp1.016 3 0.0000 0.961 0.00 0.00 1.00
#> S_Exp1.038 3 0.0000 0.961 0.00 0.00 1.00
#> S_Exp1.048 1 0.4796 0.713 0.78 0.00 0.22
#> S_Exp1.051 1 0.0000 0.976 1.00 0.00 0.00
#> S_Exp1.022 3 0.0000 0.961 0.00 0.00 1.00
#> S_Exp1.001 3 0.0000 0.961 0.00 0.00 1.00
#> S_Exp1.061 1 0.0000 0.976 1.00 0.00 0.00
#> S_Exp1.080 1 0.0000 0.976 1.00 0.00 0.00
#> S_Exp1.069 3 0.0000 0.961 0.00 0.00 1.00
#> S_Exp1.019 3 0.4002 0.804 0.16 0.00 0.84
#> S_Exp1.003 3 0.0000 0.961 0.00 0.00 1.00
#> S_Exp1.029 1 0.2537 0.902 0.92 0.08 0.00
#> S_Exp1.025 3 0.0000 0.961 0.00 0.00 1.00
#> S_Exp1.041 1 0.0000 0.976 1.00 0.00 0.00
#> S_Exp1.057 1 0.0000 0.976 1.00 0.00 0.00
#> S_Exp1.055 1 0.5397 0.603 0.72 0.00 0.28
#> S_Exp1.033 1 0.0000 0.976 1.00 0.00 0.00
#> S_Exp1.081 3 0.0000 0.961 0.00 0.00 1.00
#> S_Exp1.004 3 0.0000 0.961 0.00 0.00 1.00
#> S_Exp1.045 1 0.0000 0.976 1.00 0.00 0.00
#> S_Exp1.018 3 0.6045 0.403 0.38 0.00 0.62
#> S_Exp1.034 3 0.0000 0.961 0.00 0.00 1.00
#> S_Exp1.067 1 0.0000 0.976 1.00 0.00 0.00
#> S_Exp1.037 1 0.0000 0.976 1.00 0.00 0.00
#> S_Exp1.053 1 0.0000 0.976 1.00 0.00 0.00
#> S_Exp1.036 3 0.0000 0.961 0.00 0.00 1.00
#> S_Exp1.021 1 0.5216 0.637 0.74 0.00 0.26
#> S_Exp1.060 3 0.0892 0.946 0.02 0.00 0.98
#> S_Exp1.062 3 0.2959 0.874 0.10 0.00 0.90
#> S_Exp1.028 1 0.0000 0.976 1.00 0.00 0.00
#> S_Exp1.079 3 0.0000 0.961 0.00 0.00 1.00
#> S_Exp1.047 3 0.0000 0.961 0.00 0.00 1.00
#> S_Exp1.077 1 0.0000 0.976 1.00 0.00 0.00
#> S_Exp1.052 3 0.5216 0.657 0.26 0.00 0.74
#> S_Exp1.007 3 0.0000 0.961 0.00 0.00 1.00
#> S_Exp1.027 3 0.0000 0.961 0.00 0.00 1.00
#> S_Exp1.075 3 0.0000 0.961 0.00 0.00 1.00
#> S_Exp1.049 1 0.0000 0.976 1.00 0.00 0.00
#> S_Exp1.072 1 0.0892 0.959 0.98 0.00 0.02
#> S_Exp1.005 3 0.0000 0.961 0.00 0.00 1.00
#> S_Exp1.058 3 0.0000 0.961 0.00 0.00 1.00
#> S_Exp1.043 3 0.0000 0.961 0.00 0.00 1.00
#> S_Exp1.050 3 0.0000 0.961 0.00 0.00 1.00
#> G1_Exp1.033 2 0.2537 0.906 0.08 0.92 0.00
#> G1_Exp1.013 1 0.1529 0.941 0.96 0.04 0.00
#> G1_Exp1.026 2 0.0000 0.973 0.00 1.00 0.00
#> G1_Exp1.027 2 0.4555 0.749 0.00 0.80 0.20
#> G1_Exp1.019 2 0.0000 0.973 0.00 1.00 0.00
#> G1_Exp1.073 2 0.0000 0.973 0.00 1.00 0.00
#> G1_Exp1.023 2 0.1529 0.943 0.04 0.96 0.00
#> G1_Exp1.022 3 0.2537 0.890 0.00 0.08 0.92
#> G1_Exp1.039 2 0.0000 0.973 0.00 1.00 0.00
#> G1_Exp1.018 1 0.0000 0.976 1.00 0.00 0.00
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> H1_Exp1.002 1 0.0000 0.87191 1.00 0.00 0.00 0.00
#> H1_Exp1.003 2 0.4907 0.25984 0.42 0.58 0.00 0.00
#> H1_Exp1.004 4 0.4790 -0.17337 0.00 0.00 0.38 0.62
#> H1_Exp1.006 1 0.0000 0.87191 1.00 0.00 0.00 0.00
#> H1_Exp1.007 4 0.0000 0.62775 0.00 0.00 0.00 1.00
#> H1_Exp1.010 4 0.0000 0.62775 0.00 0.00 0.00 1.00
#> H1_Exp1.011 1 0.0000 0.87191 1.00 0.00 0.00 0.00
#> H1_Exp1.012 1 0.2345 0.80904 0.90 0.10 0.00 0.00
#> H1_Exp1.014 3 0.6941 0.47856 0.12 0.00 0.52 0.36
#> H1_Exp1.015 2 0.0707 0.80346 0.02 0.98 0.00 0.00
#> H1_Exp1.016 4 0.4134 0.50642 0.00 0.26 0.00 0.74
#> H1_Exp1.017 4 0.0000 0.62775 0.00 0.00 0.00 1.00
#> H1_Exp1.018 2 0.0000 0.81204 0.00 1.00 0.00 0.00
#> H1_Exp1.020 4 0.4406 0.44352 0.00 0.30 0.00 0.70
#> H1_Exp1.022 2 0.4624 0.44808 0.34 0.66 0.00 0.00
#> H1_Exp1.023 2 0.4406 0.54023 0.00 0.70 0.00 0.30
#> H1_Exp1.024 2 0.4907 0.30113 0.00 0.58 0.00 0.42
#> H1_Exp1.025 2 0.3172 0.69541 0.16 0.84 0.00 0.00
#> H1_Exp1.026 1 0.2345 0.80909 0.90 0.10 0.00 0.00
#> H1_Exp1.029 2 0.0707 0.80372 0.02 0.98 0.00 0.00
#> H1_Exp1.035 2 0.4855 0.34986 0.00 0.60 0.00 0.40
#> H1_Exp1.038 4 0.4994 -0.03682 0.00 0.48 0.00 0.52
#> H1_Exp1.039 2 0.0000 0.81204 0.00 1.00 0.00 0.00
#> H1_Exp1.040 2 0.2011 0.77668 0.00 0.92 0.00 0.08
#> H1_Exp1.041 2 0.0000 0.81204 0.00 1.00 0.00 0.00
#> H1_Exp1.049 4 0.4977 0.04379 0.00 0.46 0.00 0.54
#> H1_Exp1.051 2 0.1637 0.78907 0.00 0.94 0.00 0.06
#> H1_Exp1.053 2 0.0000 0.81204 0.00 1.00 0.00 0.00
#> H1_Exp1.061 2 0.4790 0.39328 0.00 0.62 0.00 0.38
#> H1_Exp1.062 2 0.0000 0.81204 0.00 1.00 0.00 0.00
#> H1_Exp1.063 2 0.0000 0.81204 0.00 1.00 0.00 0.00
#> H1_Exp1.064 4 0.4522 0.40835 0.00 0.32 0.00 0.68
#> H1_Exp1.066 2 0.0000 0.81204 0.00 1.00 0.00 0.00
#> H1_Exp1.070 4 0.3801 0.55478 0.00 0.22 0.00 0.78
#> H1_Exp2.073 4 0.4977 -0.36220 0.00 0.00 0.46 0.54
#> H1_Exp2.074 1 0.0000 0.87191 1.00 0.00 0.00 0.00
#> H1_Exp2.075 1 0.0000 0.87191 1.00 0.00 0.00 0.00
#> H1_Exp2.079 1 0.0000 0.87191 1.00 0.00 0.00 0.00
#> H1_Exp2.081 1 0.0000 0.87191 1.00 0.00 0.00 0.00
#> H1_Exp2.083 1 0.0000 0.87191 1.00 0.00 0.00 0.00
#> H1_Exp2.084 3 0.5915 0.52334 0.04 0.00 0.56 0.40
#> H1_Exp2.085 1 0.0707 0.86521 0.98 0.00 0.02 0.00
#> H1_Exp2.087 1 0.0000 0.87191 1.00 0.00 0.00 0.00
#> H1_Exp2.088 1 0.5820 0.55360 0.68 0.00 0.08 0.24
#> H1_Exp2.089 1 0.0000 0.87191 1.00 0.00 0.00 0.00
#> H1_Exp2.091 1 0.0000 0.87191 1.00 0.00 0.00 0.00
#> H1_Exp2.092 1 0.0000 0.87191 1.00 0.00 0.00 0.00
#> H1_Exp2.094 1 0.0000 0.87191 1.00 0.00 0.00 0.00
#> H1_Exp2.096 1 0.1211 0.85250 0.96 0.04 0.00 0.00
#> H1_Exp2.097 1 0.0707 0.86362 0.98 0.02 0.00 0.00
#> H1_Exp2.098 1 0.2611 0.83500 0.92 0.02 0.02 0.04
#> H1_Exp2.100 1 0.0707 0.86362 0.98 0.02 0.00 0.00
#> H1_Exp2.101 1 0.0000 0.87191 1.00 0.00 0.00 0.00
#> H1_Exp2.105 1 0.0000 0.87191 1.00 0.00 0.00 0.00
#> H1_Exp2.106 2 0.3037 0.75623 0.10 0.88 0.00 0.02
#> H1_Exp2.108 4 0.4977 -0.35582 0.00 0.00 0.46 0.54
#> H1_Exp2.118 2 0.4994 0.12022 0.00 0.52 0.00 0.48
#> H1_Exp2.121 4 0.4790 0.27958 0.00 0.38 0.00 0.62
#> H1_Exp2.127 2 0.0000 0.81204 0.00 1.00 0.00 0.00
#> H1_Exp2.136 1 0.7382 0.45687 0.52 0.26 0.22 0.00
#> H1_Exp2.138 1 0.0707 0.86524 0.98 0.00 0.02 0.00
#> H1_Exp2.144 1 0.0000 0.87191 1.00 0.00 0.00 0.00
#> H1_Exp2.148 4 0.0000 0.62775 0.00 0.00 0.00 1.00
#> H1_Exp2.152 4 0.2011 0.53909 0.00 0.00 0.08 0.92
#> H1_Exp3.223 4 0.2011 0.64139 0.00 0.08 0.00 0.92
#> H1_Exp3.232 2 0.6714 0.34305 0.36 0.54 0.00 0.10
#> H1_Exp3.241 2 0.0000 0.81204 0.00 1.00 0.00 0.00
#> G2_Exp1.069 2 0.3610 0.67088 0.00 0.80 0.00 0.20
#> G2_Exp1.076 2 0.4624 0.47094 0.00 0.66 0.00 0.34
#> G2_Exp1.013 1 0.2011 0.82840 0.92 0.00 0.08 0.00
#> G2_Exp1.015 4 0.3975 0.26473 0.00 0.00 0.24 0.76
#> G2_Exp1.019 4 0.0000 0.62775 0.00 0.00 0.00 1.00
#> G2_Exp1.030 4 0.4134 0.50774 0.00 0.26 0.00 0.74
#> G2_Exp1.017 1 0.0000 0.87191 1.00 0.00 0.00 0.00
#> G2_Exp1.047 2 0.0000 0.81204 0.00 1.00 0.00 0.00
#> G2_Exp1.032 2 0.4977 0.18608 0.00 0.54 0.00 0.46
#> G2_Exp1.064 2 0.2011 0.77779 0.00 0.92 0.00 0.08
#> G2_Exp1.045 2 0.2921 0.73156 0.00 0.86 0.00 0.14
#> G2_Exp1.038 2 0.0000 0.81204 0.00 1.00 0.00 0.00
#> G2_Exp1.001 3 0.4948 0.50411 0.00 0.00 0.56 0.44
#> G2_Exp1.049 2 0.0000 0.81204 0.00 1.00 0.00 0.00
#> G2_Exp1.031 4 0.1211 0.59857 0.00 0.00 0.04 0.96
#> G2_Exp1.036 2 0.2345 0.76393 0.00 0.90 0.00 0.10
#> G2_Exp1.046 2 0.1637 0.78816 0.00 0.94 0.00 0.06
#> G2_Exp1.040 2 0.4406 0.53841 0.00 0.70 0.00 0.30
#> G2_Exp1.068 4 0.4790 0.29116 0.00 0.38 0.00 0.62
#> G2_Exp1.077 2 0.0000 0.81204 0.00 1.00 0.00 0.00
#> G2_Exp1.014 3 0.5355 0.53316 0.02 0.00 0.62 0.36
#> G2_Exp1.005 4 0.0000 0.62775 0.00 0.00 0.00 1.00
#> G2_Exp1.056 2 0.0707 0.80287 0.00 0.98 0.02 0.00
#> G2_Exp1.041 2 0.0000 0.81204 0.00 1.00 0.00 0.00
#> G2_Exp1.012 1 0.0707 0.86531 0.98 0.00 0.02 0.00
#> G2_Exp1.003 1 0.7544 0.10735 0.46 0.00 0.20 0.34
#> S_Exp1.031 3 0.4855 0.54840 0.00 0.00 0.60 0.40
#> S_Exp1.056 3 0.3975 0.64248 0.00 0.00 0.76 0.24
#> S_Exp1.011 3 0.3610 0.64750 0.00 0.00 0.80 0.20
#> S_Exp1.016 3 0.4855 0.54840 0.00 0.00 0.60 0.40
#> S_Exp1.038 3 0.4994 0.43628 0.00 0.00 0.52 0.48
#> S_Exp1.048 3 0.2011 0.58904 0.08 0.00 0.92 0.00
#> S_Exp1.051 1 0.4790 0.52124 0.62 0.00 0.38 0.00
#> S_Exp1.022 3 0.3801 0.64636 0.00 0.00 0.78 0.22
#> S_Exp1.001 3 0.4855 0.54840 0.00 0.00 0.60 0.40
#> S_Exp1.061 1 0.3975 0.69000 0.76 0.00 0.24 0.00
#> S_Exp1.080 3 0.4277 0.31316 0.28 0.00 0.72 0.00
#> S_Exp1.069 3 0.4855 0.54840 0.00 0.00 0.60 0.40
#> S_Exp1.019 3 0.0707 0.62885 0.00 0.00 0.98 0.02
#> S_Exp1.003 3 0.4907 0.52795 0.00 0.00 0.58 0.42
#> S_Exp1.029 2 0.5987 0.24046 0.04 0.52 0.44 0.00
#> S_Exp1.025 3 0.3801 0.64698 0.00 0.00 0.78 0.22
#> S_Exp1.041 3 0.6005 -0.26200 0.46 0.04 0.50 0.00
#> S_Exp1.057 3 0.4855 -0.00863 0.40 0.00 0.60 0.00
#> S_Exp1.055 3 0.2011 0.58763 0.08 0.00 0.92 0.00
#> S_Exp1.033 1 0.5987 0.37370 0.52 0.04 0.44 0.00
#> S_Exp1.081 3 0.3975 0.64293 0.00 0.00 0.76 0.24
#> S_Exp1.004 3 0.4855 0.54840 0.00 0.00 0.60 0.40
#> S_Exp1.045 3 0.4790 0.05779 0.38 0.00 0.62 0.00
#> S_Exp1.018 3 0.0000 0.62360 0.00 0.00 1.00 0.00
#> S_Exp1.034 4 0.5000 -0.43303 0.00 0.00 0.50 0.50
#> S_Exp1.067 1 0.4907 0.45561 0.58 0.00 0.42 0.00
#> S_Exp1.037 1 0.2921 0.78607 0.86 0.00 0.14 0.00
#> S_Exp1.053 3 0.4713 0.12364 0.36 0.00 0.64 0.00
#> S_Exp1.036 3 0.0707 0.62885 0.00 0.00 0.98 0.02
#> S_Exp1.021 3 0.1211 0.61031 0.04 0.00 0.96 0.00
#> S_Exp1.060 3 0.0000 0.62360 0.00 0.00 1.00 0.00
#> S_Exp1.062 3 0.0000 0.62360 0.00 0.00 1.00 0.00
#> S_Exp1.028 1 0.4855 0.48887 0.60 0.00 0.40 0.00
#> S_Exp1.079 3 0.4948 0.49936 0.00 0.00 0.56 0.44
#> S_Exp1.047 3 0.3975 0.64021 0.00 0.00 0.76 0.24
#> S_Exp1.077 3 0.4790 0.05876 0.38 0.00 0.62 0.00
#> S_Exp1.052 3 0.0707 0.61664 0.02 0.00 0.98 0.00
#> S_Exp1.007 3 0.4277 0.62740 0.00 0.00 0.72 0.28
#> S_Exp1.027 3 0.4977 0.46774 0.00 0.00 0.54 0.46
#> S_Exp1.075 3 0.3801 0.64711 0.00 0.00 0.78 0.22
#> S_Exp1.049 1 0.4790 0.52124 0.62 0.00 0.38 0.00
#> S_Exp1.072 3 0.3610 0.46760 0.20 0.00 0.80 0.00
#> S_Exp1.005 3 0.4406 0.61748 0.00 0.00 0.70 0.30
#> S_Exp1.058 3 0.4907 0.52731 0.00 0.00 0.58 0.42
#> S_Exp1.043 3 0.0000 0.62360 0.00 0.00 1.00 0.00
#> S_Exp1.050 3 0.4994 0.43628 0.00 0.00 0.52 0.48
#> G1_Exp1.033 2 0.3610 0.65814 0.20 0.80 0.00 0.00
#> G1_Exp1.013 1 0.3400 0.72101 0.82 0.18 0.00 0.00
#> G1_Exp1.026 2 0.0000 0.81204 0.00 1.00 0.00 0.00
#> G1_Exp1.027 4 0.2921 0.62814 0.00 0.14 0.00 0.86
#> G1_Exp1.019 2 0.1637 0.77953 0.06 0.94 0.00 0.00
#> G1_Exp1.073 2 0.0000 0.81204 0.00 1.00 0.00 0.00
#> G1_Exp1.023 2 0.4797 0.57660 0.26 0.72 0.00 0.02
#> G1_Exp1.022 4 0.1411 0.62629 0.00 0.02 0.02 0.96
#> G1_Exp1.039 2 0.0000 0.81204 0.00 1.00 0.00 0.00
#> G1_Exp1.018 1 0.2011 0.82585 0.92 0.08 0.00 0.00
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
As soon as the classes for columns are determined, the signatures that are significantly different between subgroups can be looked for. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. To get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows (which is done by automatically selecting number of clusters).If there are too many signatures, top_signatures = ... can be set to only show the
signatures with the highest FDRs:
# code only for demonstration
# e.g. to show the top 500 most significant rows
tb = get_signature(res, k = ..., top_signatures = 500)
If the signatures are defined as these which are uniquely high in current group, diff_method argument
can be set to "uniquely_high_in_one_group":
# code only for demonstration
tb = get_signature(res, k = ..., diff_method = "uniquely_high_in_one_group")
UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n_sample CellLine(p-value) Experiment(p-value) Phase(p-value) k
#> ATC:skmeans 148 1.13e-02 8.42e-03 2.10e-12 2
#> ATC:skmeans 147 1.02e-03 2.70e-05 1.24e-08 3
#> ATC:skmeans 111 4.53e-07 3.05e-07 3.40e-07 4
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
Parent node: Node011. Child nodes: Node01111-leaf , Node01112-leaf , Node01121-leaf , Node01122-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 13002 rows and 82 columns.
#> Top rows (1133) 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 0.998 0.953 0.980 0.496 0.501 0.501
#> 3 3 0.725 0.805 0.913 0.351 0.740 0.526
#> 4 4 0.675 0.708 0.852 0.119 0.870 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
#> H1_Exp1.002 2 0.000 0.964 0.00 1.00
#> H1_Exp1.004 1 0.000 0.991 1.00 0.00
#> H1_Exp1.006 2 0.000 0.964 0.00 1.00
#> H1_Exp1.011 2 0.000 0.964 0.00 1.00
#> H1_Exp1.012 2 0.000 0.964 0.00 1.00
#> H1_Exp1.014 2 0.971 0.358 0.40 0.60
#> H1_Exp2.073 1 0.000 0.991 1.00 0.00
#> H1_Exp2.074 2 0.000 0.964 0.00 1.00
#> H1_Exp2.075 2 0.000 0.964 0.00 1.00
#> H1_Exp2.079 2 0.000 0.964 0.00 1.00
#> H1_Exp2.081 2 0.000 0.964 0.00 1.00
#> H1_Exp2.083 2 0.000 0.964 0.00 1.00
#> H1_Exp2.084 2 0.000 0.964 0.00 1.00
#> H1_Exp2.085 2 0.000 0.964 0.00 1.00
#> H1_Exp2.087 2 0.000 0.964 0.00 1.00
#> H1_Exp2.088 2 0.000 0.964 0.00 1.00
#> H1_Exp2.089 2 0.000 0.964 0.00 1.00
#> H1_Exp2.091 2 0.000 0.964 0.00 1.00
#> H1_Exp2.092 2 0.000 0.964 0.00 1.00
#> H1_Exp2.094 2 0.000 0.964 0.00 1.00
#> H1_Exp2.098 2 0.000 0.964 0.00 1.00
#> H1_Exp2.100 2 0.000 0.964 0.00 1.00
#> H1_Exp2.101 2 0.000 0.964 0.00 1.00
#> H1_Exp2.105 2 0.000 0.964 0.00 1.00
#> H1_Exp2.108 2 0.327 0.915 0.06 0.94
#> H1_Exp2.136 2 0.000 0.964 0.00 1.00
#> H1_Exp2.138 2 0.000 0.964 0.00 1.00
#> H1_Exp2.144 2 0.000 0.964 0.00 1.00
#> H1_Exp2.152 1 0.000 0.991 1.00 0.00
#> G2_Exp1.013 2 0.000 0.964 0.00 1.00
#> G2_Exp1.015 1 0.000 0.991 1.00 0.00
#> G2_Exp1.017 2 0.000 0.964 0.00 1.00
#> G2_Exp1.001 1 0.000 0.991 1.00 0.00
#> G2_Exp1.014 1 0.000 0.991 1.00 0.00
#> G2_Exp1.012 2 0.000 0.964 0.00 1.00
#> G2_Exp1.003 2 0.327 0.915 0.06 0.94
#> S_Exp1.031 1 0.000 0.991 1.00 0.00
#> S_Exp1.056 1 0.000 0.991 1.00 0.00
#> S_Exp1.011 1 0.000 0.991 1.00 0.00
#> S_Exp1.016 1 0.000 0.991 1.00 0.00
#> S_Exp1.038 1 0.000 0.991 1.00 0.00
#> S_Exp1.048 1 0.000 0.991 1.00 0.00
#> S_Exp1.051 2 0.760 0.718 0.22 0.78
#> S_Exp1.022 1 0.000 0.991 1.00 0.00
#> S_Exp1.001 1 0.000 0.991 1.00 0.00
#> S_Exp1.061 2 0.000 0.964 0.00 1.00
#> S_Exp1.080 1 0.000 0.991 1.00 0.00
#> S_Exp1.069 1 0.000 0.991 1.00 0.00
#> S_Exp1.019 1 0.000 0.991 1.00 0.00
#> S_Exp1.003 1 0.000 0.991 1.00 0.00
#> S_Exp1.029 1 0.000 0.991 1.00 0.00
#> S_Exp1.025 1 0.000 0.991 1.00 0.00
#> S_Exp1.041 1 0.000 0.991 1.00 0.00
#> S_Exp1.057 1 0.469 0.883 0.90 0.10
#> S_Exp1.055 1 0.000 0.991 1.00 0.00
#> S_Exp1.033 1 0.827 0.639 0.74 0.26
#> S_Exp1.081 1 0.000 0.991 1.00 0.00
#> S_Exp1.004 1 0.000 0.991 1.00 0.00
#> S_Exp1.045 1 0.000 0.991 1.00 0.00
#> S_Exp1.018 1 0.000 0.991 1.00 0.00
#> S_Exp1.034 1 0.000 0.991 1.00 0.00
#> S_Exp1.067 2 0.981 0.291 0.42 0.58
#> S_Exp1.037 2 0.000 0.964 0.00 1.00
#> S_Exp1.053 1 0.242 0.951 0.96 0.04
#> S_Exp1.036 1 0.000 0.991 1.00 0.00
#> S_Exp1.021 1 0.000 0.991 1.00 0.00
#> S_Exp1.060 1 0.000 0.991 1.00 0.00
#> S_Exp1.062 1 0.000 0.991 1.00 0.00
#> S_Exp1.028 2 0.327 0.914 0.06 0.94
#> S_Exp1.079 1 0.000 0.991 1.00 0.00
#> S_Exp1.047 1 0.000 0.991 1.00 0.00
#> S_Exp1.077 1 0.000 0.991 1.00 0.00
#> S_Exp1.052 1 0.000 0.991 1.00 0.00
#> S_Exp1.007 1 0.000 0.991 1.00 0.00
#> S_Exp1.027 1 0.000 0.991 1.00 0.00
#> S_Exp1.075 1 0.000 0.991 1.00 0.00
#> S_Exp1.049 2 0.000 0.964 0.00 1.00
#> S_Exp1.072 1 0.000 0.991 1.00 0.00
#> S_Exp1.005 1 0.000 0.991 1.00 0.00
#> S_Exp1.058 1 0.000 0.991 1.00 0.00
#> S_Exp1.043 1 0.000 0.991 1.00 0.00
#> S_Exp1.050 1 0.000 0.991 1.00 0.00
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> H1_Exp1.002 2 0.0000 0.9268 0.00 1.00 0.00
#> H1_Exp1.004 1 0.0000 0.8539 1.00 0.00 0.00
#> H1_Exp1.006 2 0.0000 0.9268 0.00 1.00 0.00
#> H1_Exp1.011 2 0.0000 0.9268 0.00 1.00 0.00
#> H1_Exp1.012 2 0.2066 0.8849 0.00 0.94 0.06
#> H1_Exp1.014 1 0.5397 0.5762 0.72 0.28 0.00
#> H1_Exp2.073 1 0.0000 0.8539 1.00 0.00 0.00
#> H1_Exp2.074 2 0.0000 0.9268 0.00 1.00 0.00
#> H1_Exp2.075 2 0.0892 0.9143 0.02 0.98 0.00
#> H1_Exp2.079 2 0.0000 0.9268 0.00 1.00 0.00
#> H1_Exp2.081 2 0.0000 0.9268 0.00 1.00 0.00
#> H1_Exp2.083 2 0.0000 0.9268 0.00 1.00 0.00
#> H1_Exp2.084 1 0.6309 -0.0347 0.50 0.50 0.00
#> H1_Exp2.085 2 0.0000 0.9268 0.00 1.00 0.00
#> H1_Exp2.087 2 0.0000 0.9268 0.00 1.00 0.00
#> H1_Exp2.088 2 0.0892 0.9143 0.02 0.98 0.00
#> H1_Exp2.089 2 0.0000 0.9268 0.00 1.00 0.00
#> H1_Exp2.091 2 0.0000 0.9268 0.00 1.00 0.00
#> H1_Exp2.092 2 0.0000 0.9268 0.00 1.00 0.00
#> H1_Exp2.094 2 0.0000 0.9268 0.00 1.00 0.00
#> H1_Exp2.098 2 0.0000 0.9268 0.00 1.00 0.00
#> H1_Exp2.100 2 0.0000 0.9268 0.00 1.00 0.00
#> H1_Exp2.101 2 0.0000 0.9268 0.00 1.00 0.00
#> H1_Exp2.105 2 0.0000 0.9268 0.00 1.00 0.00
#> H1_Exp2.108 1 0.5948 0.4037 0.64 0.36 0.00
#> H1_Exp2.136 2 0.6244 0.2766 0.00 0.56 0.44
#> H1_Exp2.138 2 0.0000 0.9268 0.00 1.00 0.00
#> H1_Exp2.144 2 0.0000 0.9268 0.00 1.00 0.00
#> H1_Exp2.152 1 0.0000 0.8539 1.00 0.00 0.00
#> G2_Exp1.013 2 0.3340 0.8317 0.00 0.88 0.12
#> G2_Exp1.015 1 0.1529 0.8435 0.96 0.00 0.04
#> G2_Exp1.017 2 0.3686 0.8087 0.00 0.86 0.14
#> G2_Exp1.001 1 0.0000 0.8539 1.00 0.00 0.00
#> G2_Exp1.014 1 0.6387 0.5583 0.68 0.02 0.30
#> G2_Exp1.012 2 0.0000 0.9268 0.00 1.00 0.00
#> G2_Exp1.003 2 0.6280 0.1297 0.46 0.54 0.00
#> S_Exp1.031 1 0.0892 0.8537 0.98 0.00 0.02
#> S_Exp1.056 1 0.3686 0.7938 0.86 0.00 0.14
#> S_Exp1.011 1 0.0000 0.8539 1.00 0.00 0.00
#> S_Exp1.016 1 0.0000 0.8539 1.00 0.00 0.00
#> S_Exp1.038 1 0.0892 0.8537 0.98 0.00 0.02
#> S_Exp1.048 3 0.0000 0.9345 0.00 0.00 1.00
#> S_Exp1.051 3 0.0892 0.9179 0.00 0.02 0.98
#> S_Exp1.022 1 0.5016 0.7046 0.76 0.00 0.24
#> S_Exp1.001 1 0.0000 0.8539 1.00 0.00 0.00
#> S_Exp1.061 3 0.4555 0.6887 0.00 0.20 0.80
#> S_Exp1.080 3 0.0000 0.9345 0.00 0.00 1.00
#> S_Exp1.069 1 0.0892 0.8537 0.98 0.00 0.02
#> S_Exp1.019 1 0.5706 0.5887 0.68 0.00 0.32
#> S_Exp1.003 1 0.0000 0.8539 1.00 0.00 0.00
#> S_Exp1.029 3 0.0000 0.9345 0.00 0.00 1.00
#> S_Exp1.025 1 0.5948 0.5167 0.64 0.00 0.36
#> S_Exp1.041 3 0.0000 0.9345 0.00 0.00 1.00
#> S_Exp1.057 3 0.0000 0.9345 0.00 0.00 1.00
#> S_Exp1.055 3 0.0892 0.9228 0.02 0.00 0.98
#> S_Exp1.033 3 0.0000 0.9345 0.00 0.00 1.00
#> S_Exp1.081 1 0.4796 0.7235 0.78 0.00 0.22
#> S_Exp1.004 1 0.0000 0.8539 1.00 0.00 0.00
#> S_Exp1.045 3 0.0000 0.9345 0.00 0.00 1.00
#> S_Exp1.018 3 0.1529 0.9093 0.04 0.00 0.96
#> S_Exp1.034 1 0.3340 0.8055 0.88 0.00 0.12
#> S_Exp1.067 3 0.0000 0.9345 0.00 0.00 1.00
#> S_Exp1.037 2 0.5016 0.6824 0.00 0.76 0.24
#> S_Exp1.053 3 0.0000 0.9345 0.00 0.00 1.00
#> S_Exp1.036 1 0.6244 0.3248 0.56 0.00 0.44
#> S_Exp1.021 3 0.4291 0.7464 0.18 0.00 0.82
#> S_Exp1.060 3 0.5706 0.4690 0.32 0.00 0.68
#> S_Exp1.062 3 0.2537 0.8717 0.08 0.00 0.92
#> S_Exp1.028 3 0.0000 0.9345 0.00 0.00 1.00
#> S_Exp1.079 1 0.0892 0.8537 0.98 0.00 0.02
#> S_Exp1.047 1 0.5216 0.6801 0.74 0.00 0.26
#> S_Exp1.077 3 0.0000 0.9345 0.00 0.00 1.00
#> S_Exp1.052 3 0.0000 0.9345 0.00 0.00 1.00
#> S_Exp1.007 1 0.0000 0.8539 1.00 0.00 0.00
#> S_Exp1.027 1 0.0892 0.8537 0.98 0.00 0.02
#> S_Exp1.075 1 0.5560 0.6259 0.70 0.00 0.30
#> S_Exp1.049 2 0.6280 0.2179 0.00 0.54 0.46
#> S_Exp1.072 3 0.0892 0.9225 0.02 0.00 0.98
#> S_Exp1.005 1 0.0000 0.8539 1.00 0.00 0.00
#> S_Exp1.058 1 0.2066 0.8388 0.94 0.00 0.06
#> S_Exp1.043 3 0.4555 0.7134 0.20 0.00 0.80
#> S_Exp1.050 1 0.0892 0.8537 0.98 0.00 0.02
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> H1_Exp1.002 2 0.0000 0.8771 0.00 1.00 0.00 0.00
#> H1_Exp1.004 4 0.4936 0.4886 0.28 0.02 0.00 0.70
#> H1_Exp1.006 2 0.1211 0.8720 0.04 0.96 0.00 0.00
#> H1_Exp1.011 2 0.0000 0.8771 0.00 1.00 0.00 0.00
#> H1_Exp1.012 2 0.1211 0.8720 0.04 0.96 0.00 0.00
#> H1_Exp1.014 1 0.4971 0.7362 0.80 0.08 0.02 0.10
#> H1_Exp2.073 1 0.2011 0.8064 0.92 0.00 0.00 0.08
#> H1_Exp2.074 2 0.0000 0.8771 0.00 1.00 0.00 0.00
#> H1_Exp2.075 2 0.4994 0.1864 0.48 0.52 0.00 0.00
#> H1_Exp2.079 2 0.0000 0.8771 0.00 1.00 0.00 0.00
#> H1_Exp2.081 2 0.0000 0.8771 0.00 1.00 0.00 0.00
#> H1_Exp2.083 2 0.0000 0.8771 0.00 1.00 0.00 0.00
#> H1_Exp2.084 1 0.4472 0.6435 0.76 0.22 0.00 0.02
#> H1_Exp2.085 2 0.1913 0.8563 0.04 0.94 0.02 0.00
#> H1_Exp2.087 2 0.0707 0.8724 0.02 0.98 0.00 0.00
#> H1_Exp2.088 2 0.3172 0.7636 0.16 0.84 0.00 0.00
#> H1_Exp2.089 2 0.0000 0.8771 0.00 1.00 0.00 0.00
#> H1_Exp2.091 2 0.0000 0.8771 0.00 1.00 0.00 0.00
#> H1_Exp2.092 2 0.0707 0.8724 0.02 0.98 0.00 0.00
#> H1_Exp2.094 2 0.0707 0.8744 0.02 0.98 0.00 0.00
#> H1_Exp2.098 2 0.3606 0.7795 0.14 0.84 0.02 0.00
#> H1_Exp2.100 2 0.2335 0.8455 0.02 0.92 0.06 0.00
#> H1_Exp2.101 2 0.0707 0.8744 0.02 0.98 0.00 0.00
#> H1_Exp2.105 2 0.0000 0.8771 0.00 1.00 0.00 0.00
#> H1_Exp2.108 4 0.7139 0.1493 0.36 0.14 0.00 0.50
#> H1_Exp2.136 2 0.8335 0.0861 0.02 0.42 0.28 0.28
#> H1_Exp2.138 2 0.0707 0.8744 0.02 0.98 0.00 0.00
#> H1_Exp2.144 2 0.0707 0.8744 0.02 0.98 0.00 0.00
#> H1_Exp2.152 1 0.4855 0.3988 0.60 0.00 0.00 0.40
#> G2_Exp1.013 2 0.7198 0.4044 0.18 0.54 0.28 0.00
#> G2_Exp1.015 1 0.1913 0.7977 0.94 0.00 0.02 0.04
#> G2_Exp1.017 2 0.7550 0.2885 0.30 0.48 0.22 0.00
#> G2_Exp1.001 1 0.1637 0.8077 0.94 0.00 0.00 0.06
#> G2_Exp1.014 1 0.3525 0.7570 0.86 0.00 0.10 0.04
#> G2_Exp1.012 2 0.0707 0.8744 0.02 0.98 0.00 0.00
#> G2_Exp1.003 1 0.1913 0.7673 0.94 0.04 0.02 0.00
#> S_Exp1.031 4 0.3975 0.5498 0.24 0.00 0.00 0.76
#> S_Exp1.056 4 0.0000 0.7951 0.00 0.00 0.00 1.00
#> S_Exp1.011 1 0.1211 0.8035 0.96 0.00 0.00 0.04
#> S_Exp1.016 1 0.4277 0.6855 0.72 0.00 0.00 0.28
#> S_Exp1.038 4 0.0707 0.7918 0.02 0.00 0.00 0.98
#> S_Exp1.048 3 0.4713 0.4282 0.00 0.00 0.64 0.36
#> S_Exp1.051 3 0.0707 0.8466 0.02 0.00 0.98 0.00
#> S_Exp1.022 4 0.1637 0.7921 0.00 0.00 0.06 0.94
#> S_Exp1.001 1 0.3172 0.8021 0.84 0.00 0.00 0.16
#> S_Exp1.061 3 0.3172 0.7110 0.00 0.16 0.84 0.00
#> S_Exp1.080 3 0.0707 0.8596 0.00 0.00 0.98 0.02
#> S_Exp1.069 4 0.1211 0.7827 0.04 0.00 0.00 0.96
#> S_Exp1.019 1 0.7474 0.3701 0.50 0.00 0.22 0.28
#> S_Exp1.003 1 0.3172 0.8021 0.84 0.00 0.00 0.16
#> S_Exp1.029 3 0.0707 0.8596 0.00 0.00 0.98 0.02
#> S_Exp1.025 1 0.7748 0.2090 0.44 0.00 0.28 0.28
#> S_Exp1.041 3 0.0707 0.8596 0.00 0.00 0.98 0.02
#> S_Exp1.057 3 0.0707 0.8596 0.00 0.00 0.98 0.02
#> S_Exp1.055 3 0.4907 0.2533 0.00 0.00 0.58 0.42
#> S_Exp1.033 3 0.0707 0.8596 0.00 0.00 0.98 0.02
#> S_Exp1.081 4 0.3935 0.7318 0.10 0.00 0.06 0.84
#> S_Exp1.004 1 0.3172 0.8021 0.84 0.00 0.00 0.16
#> S_Exp1.045 3 0.0707 0.8596 0.00 0.00 0.98 0.02
#> S_Exp1.018 3 0.3611 0.7631 0.06 0.00 0.86 0.08
#> S_Exp1.034 4 0.0707 0.7961 0.00 0.00 0.02 0.98
#> S_Exp1.067 3 0.0707 0.8466 0.02 0.00 0.98 0.00
#> S_Exp1.037 2 0.4642 0.6575 0.02 0.74 0.24 0.00
#> S_Exp1.053 3 0.4134 0.6249 0.00 0.00 0.74 0.26
#> S_Exp1.036 4 0.4949 0.6893 0.06 0.00 0.18 0.76
#> S_Exp1.021 3 0.7346 0.2200 0.28 0.00 0.52 0.20
#> S_Exp1.060 4 0.3172 0.7296 0.00 0.00 0.16 0.84
#> S_Exp1.062 4 0.4522 0.5209 0.00 0.00 0.32 0.68
#> S_Exp1.028 3 0.0000 0.8540 0.00 0.00 1.00 0.00
#> S_Exp1.079 4 0.0707 0.7918 0.02 0.00 0.00 0.98
#> S_Exp1.047 4 0.1211 0.7928 0.00 0.00 0.04 0.96
#> S_Exp1.077 3 0.0707 0.8596 0.00 0.00 0.98 0.02
#> S_Exp1.052 4 0.4855 0.2618 0.00 0.00 0.40 0.60
#> S_Exp1.007 1 0.2647 0.8096 0.88 0.00 0.00 0.12
#> S_Exp1.027 4 0.0707 0.7918 0.02 0.00 0.00 0.98
#> S_Exp1.075 4 0.4841 0.7205 0.08 0.00 0.14 0.78
#> S_Exp1.049 2 0.5487 0.3569 0.02 0.58 0.40 0.00
#> S_Exp1.072 3 0.0707 0.8473 0.02 0.00 0.98 0.00
#> S_Exp1.005 1 0.3172 0.8021 0.84 0.00 0.00 0.16
#> S_Exp1.058 4 0.0707 0.7961 0.00 0.00 0.02 0.98
#> S_Exp1.043 4 0.6605 0.0986 0.08 0.00 0.44 0.48
#> S_Exp1.050 4 0.0707 0.7918 0.02 0.00 0.00 0.98
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
As soon as the classes for columns are determined, the signatures that are significantly different between subgroups can be looked for. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. To get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows (which is done by automatically selecting number of clusters).If there are too many signatures, top_signatures = ... can be set to only show the
signatures with the highest FDRs:
# code only for demonstration
# e.g. to show the top 500 most significant rows
tb = get_signature(res, k = ..., top_signatures = 500)
If the signatures are defined as these which are uniquely high in current group, diff_method argument
can be set to "uniquely_high_in_one_group":
# code only for demonstration
tb = get_signature(res, k = ..., diff_method = "uniquely_high_in_one_group")
UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n_sample CellLine(p-value) Experiment(p-value) Phase(p-value) k
#> ATC:skmeans 80 2.31e-09 8.35e-08 NA 2
#> ATC:skmeans 75 1.84e-10 8.54e-09 NA 3
#> ATC:skmeans 67 1.44e-10 5.24e-08 NA 4
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
Parent node: Node011. Child nodes: Node01111-leaf , Node01112-leaf , Node01121-leaf , Node01122-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 13002 rows and 67 columns.
#> Top rows (1040) 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 0.998 0.969 0.986 0.507 0.493 0.493
#> 3 3 0.748 0.777 0.907 0.307 0.797 0.608
#> 4 4 0.770 0.809 0.905 0.134 0.814 0.518
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
#> H1_Exp1.003 2 0.000 0.977 0.00 1.00
#> H1_Exp1.007 1 0.000 0.994 1.00 0.00
#> H1_Exp1.010 1 0.000 0.994 1.00 0.00
#> H1_Exp1.015 2 0.000 0.977 0.00 1.00
#> H1_Exp1.016 1 0.000 0.994 1.00 0.00
#> H1_Exp1.017 1 0.000 0.994 1.00 0.00
#> H1_Exp1.018 2 0.000 0.977 0.00 1.00
#> H1_Exp1.020 1 0.000 0.994 1.00 0.00
#> H1_Exp1.022 2 0.000 0.977 0.00 1.00
#> H1_Exp1.023 1 0.000 0.994 1.00 0.00
#> H1_Exp1.024 1 0.000 0.994 1.00 0.00
#> H1_Exp1.025 2 0.000 0.977 0.00 1.00
#> H1_Exp1.026 2 0.000 0.977 0.00 1.00
#> H1_Exp1.029 2 0.000 0.977 0.00 1.00
#> H1_Exp1.035 1 0.000 0.994 1.00 0.00
#> H1_Exp1.038 1 0.000 0.994 1.00 0.00
#> H1_Exp1.039 2 0.000 0.977 0.00 1.00
#> H1_Exp1.040 2 0.529 0.862 0.12 0.88
#> H1_Exp1.041 2 0.000 0.977 0.00 1.00
#> H1_Exp1.049 1 0.000 0.994 1.00 0.00
#> H1_Exp1.051 2 0.881 0.589 0.30 0.70
#> H1_Exp1.053 2 0.000 0.977 0.00 1.00
#> H1_Exp1.061 1 0.000 0.994 1.00 0.00
#> H1_Exp1.062 2 0.000 0.977 0.00 1.00
#> H1_Exp1.063 2 0.000 0.977 0.00 1.00
#> H1_Exp1.064 1 0.000 0.994 1.00 0.00
#> H1_Exp1.066 2 0.000 0.977 0.00 1.00
#> H1_Exp1.070 1 0.000 0.994 1.00 0.00
#> H1_Exp2.096 2 0.000 0.977 0.00 1.00
#> H1_Exp2.097 2 0.000 0.977 0.00 1.00
#> H1_Exp2.106 2 0.141 0.961 0.02 0.98
#> H1_Exp2.118 1 0.000 0.994 1.00 0.00
#> H1_Exp2.121 1 0.000 0.994 1.00 0.00
#> H1_Exp2.127 2 0.000 0.977 0.00 1.00
#> H1_Exp2.148 1 0.000 0.994 1.00 0.00
#> H1_Exp3.223 1 0.000 0.994 1.00 0.00
#> H1_Exp3.232 2 0.000 0.977 0.00 1.00
#> H1_Exp3.241 2 0.827 0.660 0.26 0.74
#> G2_Exp1.069 1 0.529 0.861 0.88 0.12
#> G2_Exp1.076 1 0.000 0.994 1.00 0.00
#> G2_Exp1.019 1 0.000 0.994 1.00 0.00
#> G2_Exp1.030 1 0.000 0.994 1.00 0.00
#> G2_Exp1.047 2 0.000 0.977 0.00 1.00
#> G2_Exp1.032 1 0.000 0.994 1.00 0.00
#> G2_Exp1.064 1 0.000 0.994 1.00 0.00
#> G2_Exp1.045 1 0.000 0.994 1.00 0.00
#> G2_Exp1.038 2 0.000 0.977 0.00 1.00
#> G2_Exp1.049 2 0.000 0.977 0.00 1.00
#> G2_Exp1.031 1 0.000 0.994 1.00 0.00
#> G2_Exp1.036 1 0.141 0.976 0.98 0.02
#> G2_Exp1.046 1 0.242 0.956 0.96 0.04
#> G2_Exp1.040 1 0.000 0.994 1.00 0.00
#> G2_Exp1.068 1 0.000 0.994 1.00 0.00
#> G2_Exp1.077 2 0.000 0.977 0.00 1.00
#> G2_Exp1.005 1 0.000 0.994 1.00 0.00
#> G2_Exp1.056 2 0.000 0.977 0.00 1.00
#> G2_Exp1.041 2 0.000 0.977 0.00 1.00
#> G1_Exp1.033 2 0.000 0.977 0.00 1.00
#> G1_Exp1.013 2 0.000 0.977 0.00 1.00
#> G1_Exp1.026 2 0.000 0.977 0.00 1.00
#> G1_Exp1.027 1 0.000 0.994 1.00 0.00
#> G1_Exp1.019 2 0.000 0.977 0.00 1.00
#> G1_Exp1.073 2 0.000 0.977 0.00 1.00
#> G1_Exp1.023 2 0.402 0.905 0.08 0.92
#> G1_Exp1.022 1 0.000 0.994 1.00 0.00
#> G1_Exp1.039 2 0.000 0.977 0.00 1.00
#> G1_Exp1.018 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
#> H1_Exp1.003 3 0.0000 0.8958 0.00 0.00 1.00
#> H1_Exp1.007 1 0.0000 0.9107 1.00 0.00 0.00
#> H1_Exp1.010 1 0.0000 0.9107 1.00 0.00 0.00
#> H1_Exp1.015 3 0.0892 0.8813 0.00 0.02 0.98
#> H1_Exp1.016 1 0.0000 0.9107 1.00 0.00 0.00
#> H1_Exp1.017 1 0.0000 0.9107 1.00 0.00 0.00
#> H1_Exp1.018 3 0.0000 0.8958 0.00 0.00 1.00
#> H1_Exp1.020 1 0.0000 0.9107 1.00 0.00 0.00
#> H1_Exp1.022 3 0.5835 0.4834 0.00 0.34 0.66
#> H1_Exp1.023 3 0.6244 0.1843 0.44 0.00 0.56
#> H1_Exp1.024 1 0.0000 0.9107 1.00 0.00 0.00
#> H1_Exp1.025 3 0.0000 0.8958 0.00 0.00 1.00
#> H1_Exp1.026 3 0.0000 0.8958 0.00 0.00 1.00
#> H1_Exp1.029 3 0.0000 0.8958 0.00 0.00 1.00
#> H1_Exp1.035 1 0.0000 0.9107 1.00 0.00 0.00
#> H1_Exp1.038 1 0.0000 0.9107 1.00 0.00 0.00
#> H1_Exp1.039 3 0.0000 0.8958 0.00 0.00 1.00
#> H1_Exp1.040 3 0.0000 0.8958 0.00 0.00 1.00
#> H1_Exp1.041 3 0.0000 0.8958 0.00 0.00 1.00
#> H1_Exp1.049 1 0.0000 0.9107 1.00 0.00 0.00
#> H1_Exp1.051 3 0.1529 0.8632 0.04 0.00 0.96
#> H1_Exp1.053 3 0.0000 0.8958 0.00 0.00 1.00
#> H1_Exp1.061 1 0.0000 0.9107 1.00 0.00 0.00
#> H1_Exp1.062 3 0.0000 0.8958 0.00 0.00 1.00
#> H1_Exp1.063 3 0.0000 0.8958 0.00 0.00 1.00
#> H1_Exp1.064 1 0.0000 0.9107 1.00 0.00 0.00
#> H1_Exp1.066 3 0.0000 0.8958 0.00 0.00 1.00
#> H1_Exp1.070 1 0.0000 0.9107 1.00 0.00 0.00
#> H1_Exp2.096 2 0.0892 0.8619 0.00 0.98 0.02
#> H1_Exp2.097 2 0.2537 0.8269 0.00 0.92 0.08
#> H1_Exp2.106 3 0.5970 0.7126 0.06 0.16 0.78
#> H1_Exp2.118 1 0.5147 0.7042 0.80 0.02 0.18
#> H1_Exp2.121 1 0.0000 0.9107 1.00 0.00 0.00
#> H1_Exp2.127 2 0.3340 0.8040 0.00 0.88 0.12
#> H1_Exp2.148 1 0.0000 0.9107 1.00 0.00 0.00
#> H1_Exp3.223 1 0.0000 0.9107 1.00 0.00 0.00
#> H1_Exp3.232 2 0.0000 0.8683 0.00 1.00 0.00
#> H1_Exp3.241 2 0.5780 0.7747 0.08 0.80 0.12
#> G2_Exp1.069 1 0.9953 -0.0315 0.38 0.30 0.32
#> G2_Exp1.076 1 0.2066 0.8670 0.94 0.06 0.00
#> G2_Exp1.019 1 0.0000 0.9107 1.00 0.00 0.00
#> G2_Exp1.030 1 0.0000 0.9107 1.00 0.00 0.00
#> G2_Exp1.047 2 0.5560 0.6067 0.00 0.70 0.30
#> G2_Exp1.032 1 0.0892 0.8968 0.98 0.02 0.00
#> G2_Exp1.064 1 0.9659 0.0196 0.44 0.34 0.22
#> G2_Exp1.045 1 0.8350 0.3926 0.60 0.28 0.12
#> G2_Exp1.038 2 0.6192 0.3570 0.00 0.58 0.42
#> G2_Exp1.049 2 0.3340 0.8231 0.00 0.88 0.12
#> G2_Exp1.031 1 0.0000 0.9107 1.00 0.00 0.00
#> G2_Exp1.036 2 0.7948 0.1654 0.42 0.52 0.06
#> G2_Exp1.046 1 0.9267 0.0306 0.46 0.38 0.16
#> G2_Exp1.040 1 0.2066 0.8651 0.94 0.06 0.00
#> G2_Exp1.068 1 0.0000 0.9107 1.00 0.00 0.00
#> G2_Exp1.077 2 0.2959 0.8360 0.00 0.90 0.10
#> G2_Exp1.005 1 0.0000 0.9107 1.00 0.00 0.00
#> G2_Exp1.056 3 0.6280 -0.0515 0.00 0.46 0.54
#> G2_Exp1.041 2 0.3340 0.8218 0.00 0.88 0.12
#> G1_Exp1.033 2 0.0000 0.8683 0.00 1.00 0.00
#> G1_Exp1.013 2 0.0000 0.8683 0.00 1.00 0.00
#> G1_Exp1.026 2 0.0000 0.8683 0.00 1.00 0.00
#> G1_Exp1.027 1 0.0000 0.9107 1.00 0.00 0.00
#> G1_Exp1.019 2 0.0000 0.8683 0.00 1.00 0.00
#> G1_Exp1.073 2 0.2066 0.8531 0.00 0.94 0.06
#> G1_Exp1.023 2 0.0000 0.8683 0.00 1.00 0.00
#> G1_Exp1.022 2 0.4555 0.6945 0.20 0.80 0.00
#> G1_Exp1.039 2 0.0000 0.8683 0.00 1.00 0.00
#> G1_Exp1.018 2 0.0000 0.8683 0.00 1.00 0.00
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> H1_Exp1.003 3 0.1211 0.9376 0.00 0.00 0.96 0.04
#> H1_Exp1.007 1 0.0000 0.9067 1.00 0.00 0.00 0.00
#> H1_Exp1.010 1 0.0000 0.9067 1.00 0.00 0.00 0.00
#> H1_Exp1.015 3 0.0707 0.9357 0.00 0.02 0.98 0.00
#> H1_Exp1.016 1 0.0000 0.9067 1.00 0.00 0.00 0.00
#> H1_Exp1.017 1 0.0707 0.9068 0.98 0.00 0.02 0.00
#> H1_Exp1.018 3 0.0707 0.9357 0.00 0.02 0.98 0.00
#> H1_Exp1.020 1 0.0707 0.9068 0.98 0.00 0.02 0.00
#> H1_Exp1.022 2 0.3801 0.6944 0.00 0.78 0.22 0.00
#> H1_Exp1.023 1 0.4522 0.5407 0.68 0.00 0.32 0.00
#> H1_Exp1.024 1 0.0707 0.9068 0.98 0.00 0.02 0.00
#> H1_Exp1.025 3 0.0707 0.9357 0.00 0.02 0.98 0.00
#> H1_Exp1.026 3 0.0707 0.9357 0.00 0.02 0.98 0.00
#> H1_Exp1.029 3 0.0707 0.9416 0.00 0.00 0.98 0.02
#> H1_Exp1.035 1 0.0707 0.9068 0.98 0.00 0.02 0.00
#> H1_Exp1.038 1 0.0707 0.9068 0.98 0.00 0.02 0.00
#> H1_Exp1.039 3 0.1637 0.9291 0.00 0.00 0.94 0.06
#> H1_Exp1.040 3 0.0707 0.9255 0.02 0.00 0.98 0.00
#> H1_Exp1.041 3 0.1211 0.9376 0.00 0.00 0.96 0.04
#> H1_Exp1.049 1 0.0707 0.9068 0.98 0.00 0.02 0.00
#> H1_Exp1.051 3 0.2647 0.8293 0.12 0.00 0.88 0.00
#> H1_Exp1.053 3 0.0707 0.9416 0.00 0.00 0.98 0.02
#> H1_Exp1.061 1 0.1211 0.8968 0.96 0.00 0.04 0.00
#> H1_Exp1.062 3 0.1637 0.9264 0.00 0.00 0.94 0.06
#> H1_Exp1.063 3 0.0707 0.9416 0.00 0.00 0.98 0.02
#> H1_Exp1.064 1 0.0707 0.9068 0.98 0.00 0.02 0.00
#> H1_Exp1.066 3 0.1211 0.9376 0.00 0.00 0.96 0.04
#> H1_Exp1.070 1 0.0000 0.9067 1.00 0.00 0.00 0.00
#> H1_Exp2.096 2 0.0707 0.8805 0.00 0.98 0.02 0.00
#> H1_Exp2.097 2 0.0707 0.8805 0.00 0.98 0.02 0.00
#> H1_Exp2.106 3 0.7667 0.5099 0.06 0.22 0.60 0.12
#> H1_Exp2.118 1 0.4905 0.7761 0.80 0.06 0.12 0.02
#> H1_Exp2.121 1 0.0000 0.9067 1.00 0.00 0.00 0.00
#> H1_Exp2.127 2 0.7544 0.2514 0.00 0.46 0.20 0.34
#> H1_Exp2.148 1 0.0707 0.8993 0.98 0.02 0.00 0.00
#> H1_Exp3.223 1 0.2335 0.8672 0.92 0.02 0.00 0.06
#> H1_Exp3.232 2 0.0707 0.8805 0.00 0.98 0.02 0.00
#> H1_Exp3.241 4 0.0707 0.8443 0.00 0.02 0.00 0.98
#> G2_Exp1.069 4 0.4610 0.7537 0.10 0.00 0.10 0.80
#> G2_Exp1.076 4 0.4332 0.7371 0.16 0.04 0.00 0.80
#> G2_Exp1.019 1 0.3335 0.8152 0.86 0.02 0.00 0.12
#> G2_Exp1.030 1 0.4642 0.6570 0.74 0.02 0.00 0.24
#> G2_Exp1.047 4 0.0000 0.8509 0.00 0.00 0.00 1.00
#> G2_Exp1.032 4 0.4797 0.6053 0.26 0.02 0.00 0.72
#> G2_Exp1.064 4 0.0000 0.8509 0.00 0.00 0.00 1.00
#> G2_Exp1.045 4 0.0000 0.8509 0.00 0.00 0.00 1.00
#> G2_Exp1.038 4 0.0000 0.8509 0.00 0.00 0.00 1.00
#> G2_Exp1.049 4 0.0707 0.8413 0.00 0.02 0.00 0.98
#> G2_Exp1.031 1 0.3853 0.7734 0.82 0.02 0.00 0.16
#> G2_Exp1.036 4 0.0707 0.8451 0.00 0.02 0.00 0.98
#> G2_Exp1.046 4 0.0000 0.8509 0.00 0.00 0.00 1.00
#> G2_Exp1.040 4 0.1913 0.8314 0.04 0.02 0.00 0.94
#> G2_Exp1.068 4 0.5535 0.2204 0.42 0.02 0.00 0.56
#> G2_Exp1.077 4 0.2647 0.7697 0.00 0.12 0.00 0.88
#> G2_Exp1.005 1 0.5271 0.4510 0.64 0.02 0.00 0.34
#> G2_Exp1.056 4 0.7206 0.0415 0.00 0.14 0.40 0.46
#> G2_Exp1.041 4 0.1211 0.8345 0.00 0.04 0.00 0.96
#> G1_Exp1.033 2 0.0707 0.8805 0.00 0.98 0.02 0.00
#> G1_Exp1.013 2 0.0707 0.8805 0.00 0.98 0.02 0.00
#> G1_Exp1.026 2 0.3801 0.7078 0.00 0.78 0.00 0.22
#> G1_Exp1.027 1 0.2706 0.8628 0.90 0.08 0.00 0.02
#> G1_Exp1.019 2 0.0707 0.8805 0.00 0.98 0.02 0.00
#> G1_Exp1.073 2 0.4948 0.3024 0.00 0.56 0.00 0.44
#> G1_Exp1.023 2 0.1211 0.8567 0.00 0.96 0.00 0.04
#> G1_Exp1.022 2 0.1411 0.8498 0.02 0.96 0.00 0.02
#> G1_Exp1.039 2 0.0707 0.8715 0.00 0.98 0.00 0.02
#> G1_Exp1.018 2 0.0707 0.8805 0.00 0.98 0.02 0.00
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
As soon as the classes for columns are determined, the signatures that are significantly different between subgroups can be looked for. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. To get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows (which is done by automatically selecting number of clusters).If there are too many signatures, top_signatures = ... can be set to only show the
signatures with the highest FDRs:
# code only for demonstration
# e.g. to show the top 500 most significant rows
tb = get_signature(res, k = ..., top_signatures = 500)
If the signatures are defined as these which are uniquely high in current group, diff_method argument
can be set to "uniquely_high_in_one_group":
# code only for demonstration
tb = get_signature(res, k = ..., diff_method = "uniquely_high_in_one_group")
UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n_sample CellLine(p-value) Experiment(p-value) Phase(p-value) k
#> ATC:skmeans 67 7.49e-01 0.835 NA 2
#> ATC:skmeans 58 1.12e-04 0.525 NA 3
#> ATC:skmeans 62 2.40e-07 0.657 NA 4
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
Parent node: Node01. Child nodes: Node0111 , Node0112 , Node0121-leaf , Node0122-leaf , Node0221-leaf , Node0222-leaf , Node0231-leaf , Node0232-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 12999 rows and 72 columns.
#> Top rows (1300) 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 0.982 0.992 0.481 0.518 0.518
#> 3 3 0.895 0.915 0.966 0.320 0.792 0.616
#> 4 4 0.656 0.670 0.829 0.114 0.934 0.821
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
#> H1_Exp1.009 1 0.000 0.995 1.00 0.00
#> H1_Exp1.019 1 0.469 0.890 0.90 0.10
#> H1_Exp1.050 1 0.000 0.995 1.00 0.00
#> H1_Exp1.052 1 0.000 0.995 1.00 0.00
#> H1_Exp1.054 1 0.000 0.995 1.00 0.00
#> H1_Exp1.055 1 0.000 0.995 1.00 0.00
#> H1_Exp1.057 1 0.000 0.995 1.00 0.00
#> H1_Exp1.059 2 0.000 0.986 0.00 1.00
#> H1_Exp1.069 2 0.000 0.986 0.00 1.00
#> H1_Exp2.082 1 0.000 0.995 1.00 0.00
#> H1_Exp2.093 1 0.000 0.995 1.00 0.00
#> H1_Exp2.112 1 0.000 0.995 1.00 0.00
#> H1_Exp2.116 1 0.000 0.995 1.00 0.00
#> H1_Exp2.123 2 0.000 0.986 0.00 1.00
#> H1_Exp2.130 1 0.000 0.995 1.00 0.00
#> H1_Exp2.133 1 0.000 0.995 1.00 0.00
#> H1_Exp2.135 2 0.402 0.906 0.08 0.92
#> H1_Exp2.139 1 0.000 0.995 1.00 0.00
#> H1_Exp2.140 1 0.000 0.995 1.00 0.00
#> H1_Exp2.146 2 0.000 0.986 0.00 1.00
#> H1_Exp2.150 2 0.000 0.986 0.00 1.00
#> H1_Exp2.154 1 0.000 0.995 1.00 0.00
#> H1_Exp3.220 2 0.000 0.986 0.00 1.00
#> H1_Exp3.233 1 0.000 0.995 1.00 0.00
#> H1_Exp3.248 1 0.000 0.995 1.00 0.00
#> H1_Exp3.277 1 0.000 0.995 1.00 0.00
#> H1_Exp3.283 1 0.000 0.995 1.00 0.00
#> H1_Exp3.284 1 0.000 0.995 1.00 0.00
#> G2_Exp1.050 1 0.000 0.995 1.00 0.00
#> G2_Exp1.042 1 0.000 0.995 1.00 0.00
#> G2_Exp1.022 1 0.469 0.890 0.90 0.10
#> G2_Exp1.072 1 0.000 0.995 1.00 0.00
#> G2_Exp1.074 1 0.000 0.995 1.00 0.00
#> G2_Exp1.070 1 0.000 0.995 1.00 0.00
#> G2_Exp1.021 1 0.000 0.995 1.00 0.00
#> G2_Exp1.008 1 0.000 0.995 1.00 0.00
#> G2_Exp1.048 2 0.000 0.986 0.00 1.00
#> G2_Exp1.062 1 0.000 0.995 1.00 0.00
#> G2_Exp1.066 1 0.000 0.995 1.00 0.00
#> G2_Exp1.023 1 0.000 0.995 1.00 0.00
#> S_Exp1.071 2 0.000 0.986 0.00 1.00
#> S_Exp1.032 1 0.000 0.995 1.00 0.00
#> S_Exp1.035 2 0.881 0.568 0.30 0.70
#> S_Exp1.063 2 0.000 0.986 0.00 1.00
#> S_Exp1.078 2 0.000 0.986 0.00 1.00
#> S_Exp1.023 2 0.000 0.986 0.00 1.00
#> S_Exp1.046 2 0.000 0.986 0.00 1.00
#> S_Exp1.008 2 0.000 0.986 0.00 1.00
#> S_Exp1.006 2 0.000 0.986 0.00 1.00
#> S_Exp1.064 2 0.000 0.986 0.00 1.00
#> S_Exp1.073 2 0.000 0.986 0.00 1.00
#> S_Exp1.014 2 0.000 0.986 0.00 1.00
#> S_Exp1.039 2 0.000 0.986 0.00 1.00
#> S_Exp1.054 2 0.000 0.986 0.00 1.00
#> S_Exp1.012 2 0.000 0.986 0.00 1.00
#> S_Exp1.070 2 0.000 0.986 0.00 1.00
#> S_Exp1.020 1 0.000 0.995 1.00 0.00
#> S_Exp1.015 2 0.000 0.986 0.00 1.00
#> S_Exp1.066 2 0.000 0.986 0.00 1.00
#> S_Exp1.040 2 0.000 0.986 0.00 1.00
#> S_Exp1.026 1 0.000 0.995 1.00 0.00
#> S_Exp1.017 2 0.000 0.986 0.00 1.00
#> S_Exp1.010 2 0.000 0.986 0.00 1.00
#> G1_Exp1.059 1 0.000 0.995 1.00 0.00
#> G1_Exp1.085 1 0.000 0.995 1.00 0.00
#> G1_Exp1.031 1 0.000 0.995 1.00 0.00
#> G1_Exp1.078 1 0.000 0.995 1.00 0.00
#> G1_Exp1.034 1 0.000 0.995 1.00 0.00
#> G1_Exp1.020 1 0.000 0.995 1.00 0.00
#> G1_Exp1.011 1 0.000 0.995 1.00 0.00
#> G1_Exp1.063 1 0.000 0.995 1.00 0.00
#> G1_Exp1.046 1 0.000 0.995 1.00 0.00
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> H1_Exp1.009 1 0.0000 0.9886 1.00 0.00 0.00
#> H1_Exp1.019 3 0.0000 0.8744 0.00 0.00 1.00
#> H1_Exp1.050 3 0.0000 0.8744 0.00 0.00 1.00
#> H1_Exp1.052 3 0.6126 0.3800 0.40 0.00 0.60
#> H1_Exp1.054 3 0.2959 0.8330 0.10 0.00 0.90
#> H1_Exp1.055 3 0.0000 0.8744 0.00 0.00 1.00
#> H1_Exp1.057 1 0.0892 0.9686 0.98 0.00 0.02
#> H1_Exp1.059 2 0.0000 0.9717 0.00 1.00 0.00
#> H1_Exp1.069 2 0.0000 0.9717 0.00 1.00 0.00
#> H1_Exp2.082 1 0.0000 0.9886 1.00 0.00 0.00
#> H1_Exp2.093 1 0.0000 0.9886 1.00 0.00 0.00
#> H1_Exp2.112 1 0.0000 0.9886 1.00 0.00 0.00
#> H1_Exp2.116 1 0.0000 0.9886 1.00 0.00 0.00
#> H1_Exp2.123 2 0.0000 0.9717 0.00 1.00 0.00
#> H1_Exp2.130 1 0.0000 0.9886 1.00 0.00 0.00
#> H1_Exp2.133 1 0.0000 0.9886 1.00 0.00 0.00
#> H1_Exp2.135 2 0.5016 0.6324 0.24 0.76 0.00
#> H1_Exp2.139 1 0.0000 0.9886 1.00 0.00 0.00
#> H1_Exp2.140 1 0.0000 0.9886 1.00 0.00 0.00
#> H1_Exp2.146 2 0.0000 0.9717 0.00 1.00 0.00
#> H1_Exp2.150 2 0.0000 0.9717 0.00 1.00 0.00
#> H1_Exp2.154 1 0.0000 0.9886 1.00 0.00 0.00
#> H1_Exp3.220 2 0.0000 0.9717 0.00 1.00 0.00
#> H1_Exp3.233 1 0.0000 0.9886 1.00 0.00 0.00
#> H1_Exp3.248 1 0.0000 0.9886 1.00 0.00 0.00
#> H1_Exp3.277 1 0.0000 0.9886 1.00 0.00 0.00
#> H1_Exp3.283 1 0.0000 0.9886 1.00 0.00 0.00
#> H1_Exp3.284 1 0.0000 0.9886 1.00 0.00 0.00
#> G2_Exp1.050 1 0.0000 0.9886 1.00 0.00 0.00
#> G2_Exp1.042 1 0.0000 0.9886 1.00 0.00 0.00
#> G2_Exp1.022 3 0.0000 0.8744 0.00 0.00 1.00
#> G2_Exp1.072 1 0.0000 0.9886 1.00 0.00 0.00
#> G2_Exp1.074 1 0.0000 0.9886 1.00 0.00 0.00
#> G2_Exp1.070 1 0.0892 0.9688 0.98 0.00 0.02
#> G2_Exp1.021 3 0.0000 0.8744 0.00 0.00 1.00
#> G2_Exp1.008 1 0.0000 0.9886 1.00 0.00 0.00
#> G2_Exp1.048 3 0.0000 0.8744 0.00 0.00 1.00
#> G2_Exp1.062 3 0.5216 0.6634 0.26 0.00 0.74
#> G2_Exp1.066 1 0.0000 0.9886 1.00 0.00 0.00
#> G2_Exp1.023 3 0.3340 0.8223 0.12 0.00 0.88
#> S_Exp1.071 2 0.0000 0.9717 0.00 1.00 0.00
#> S_Exp1.032 1 0.0000 0.9886 1.00 0.00 0.00
#> S_Exp1.035 3 0.0000 0.8744 0.00 0.00 1.00
#> S_Exp1.063 2 0.0000 0.9717 0.00 1.00 0.00
#> S_Exp1.078 2 0.0000 0.9717 0.00 1.00 0.00
#> S_Exp1.023 3 0.0000 0.8744 0.00 0.00 1.00
#> S_Exp1.046 2 0.0000 0.9717 0.00 1.00 0.00
#> S_Exp1.008 2 0.0000 0.9717 0.00 1.00 0.00
#> S_Exp1.006 2 0.0000 0.9717 0.00 1.00 0.00
#> S_Exp1.064 2 0.0000 0.9717 0.00 1.00 0.00
#> S_Exp1.073 2 0.0000 0.9717 0.00 1.00 0.00
#> S_Exp1.014 2 0.0000 0.9717 0.00 1.00 0.00
#> S_Exp1.039 2 0.0000 0.9717 0.00 1.00 0.00
#> S_Exp1.054 3 0.3340 0.7796 0.00 0.12 0.88
#> S_Exp1.012 2 0.0000 0.9717 0.00 1.00 0.00
#> S_Exp1.070 3 0.0000 0.8744 0.00 0.00 1.00
#> S_Exp1.020 1 0.0000 0.9886 1.00 0.00 0.00
#> S_Exp1.015 2 0.5216 0.6358 0.00 0.74 0.26
#> S_Exp1.066 3 0.6309 -0.0667 0.00 0.50 0.50
#> S_Exp1.040 2 0.0000 0.9717 0.00 1.00 0.00
#> S_Exp1.026 3 0.3686 0.8084 0.14 0.00 0.86
#> S_Exp1.017 2 0.0000 0.9717 0.00 1.00 0.00
#> S_Exp1.010 2 0.0000 0.9717 0.00 1.00 0.00
#> G1_Exp1.059 1 0.0000 0.9886 1.00 0.00 0.00
#> G1_Exp1.085 1 0.0000 0.9886 1.00 0.00 0.00
#> G1_Exp1.031 1 0.0000 0.9886 1.00 0.00 0.00
#> G1_Exp1.078 1 0.5560 0.5212 0.70 0.00 0.30
#> G1_Exp1.034 1 0.0000 0.9886 1.00 0.00 0.00
#> G1_Exp1.020 1 0.0000 0.9886 1.00 0.00 0.00
#> G1_Exp1.011 1 0.0000 0.9886 1.00 0.00 0.00
#> G1_Exp1.063 1 0.0000 0.9886 1.00 0.00 0.00
#> G1_Exp1.046 1 0.0000 0.9886 1.00 0.00 0.00
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> H1_Exp1.009 1 0.3975 0.6471 0.76 0.00 0.00 0.24
#> H1_Exp1.019 3 0.0000 0.7078 0.00 0.00 1.00 0.00
#> H1_Exp1.050 3 0.1637 0.6941 0.00 0.00 0.94 0.06
#> H1_Exp1.052 4 0.7775 0.4443 0.38 0.00 0.24 0.38
#> H1_Exp1.054 3 0.6714 0.1314 0.10 0.00 0.54 0.36
#> H1_Exp1.055 3 0.3172 0.6347 0.00 0.00 0.84 0.16
#> H1_Exp1.057 1 0.5392 0.4763 0.68 0.00 0.04 0.28
#> H1_Exp1.059 2 0.1637 0.9480 0.00 0.94 0.00 0.06
#> H1_Exp1.069 2 0.1637 0.9480 0.00 0.94 0.00 0.06
#> H1_Exp2.082 1 0.2647 0.7196 0.88 0.00 0.00 0.12
#> H1_Exp2.093 1 0.2921 0.7076 0.86 0.00 0.00 0.14
#> H1_Exp2.112 1 0.2011 0.7411 0.92 0.00 0.00 0.08
#> H1_Exp2.116 4 0.5487 0.1419 0.40 0.00 0.02 0.58
#> H1_Exp2.123 2 0.3172 0.8724 0.00 0.84 0.00 0.16
#> H1_Exp2.130 1 0.1637 0.7687 0.94 0.00 0.00 0.06
#> H1_Exp2.133 1 0.4277 0.5812 0.72 0.00 0.00 0.28
#> H1_Exp2.135 4 0.7653 0.1242 0.24 0.30 0.00 0.46
#> H1_Exp2.139 1 0.4977 0.1620 0.54 0.00 0.00 0.46
#> H1_Exp2.140 1 0.2011 0.7654 0.92 0.00 0.00 0.08
#> H1_Exp2.146 2 0.1637 0.9480 0.00 0.94 0.00 0.06
#> H1_Exp2.150 2 0.0000 0.9564 0.00 1.00 0.00 0.00
#> H1_Exp2.154 1 0.5962 0.4152 0.66 0.00 0.08 0.26
#> H1_Exp3.220 2 0.4277 0.7333 0.00 0.72 0.00 0.28
#> H1_Exp3.233 1 0.1637 0.7639 0.94 0.00 0.00 0.06
#> H1_Exp3.248 1 0.1211 0.7679 0.96 0.00 0.00 0.04
#> H1_Exp3.277 1 0.3801 0.6454 0.78 0.00 0.00 0.22
#> H1_Exp3.283 1 0.0707 0.7685 0.98 0.00 0.00 0.02
#> H1_Exp3.284 1 0.0000 0.7666 1.00 0.00 0.00 0.00
#> G2_Exp1.050 1 0.1637 0.7611 0.94 0.00 0.00 0.06
#> G2_Exp1.042 1 0.4522 0.4180 0.68 0.00 0.00 0.32
#> G2_Exp1.022 3 0.1637 0.6955 0.00 0.00 0.94 0.06
#> G2_Exp1.072 1 0.3172 0.7000 0.84 0.00 0.00 0.16
#> G2_Exp1.074 1 0.2921 0.7135 0.86 0.00 0.00 0.14
#> G2_Exp1.070 1 0.5594 0.0293 0.52 0.00 0.02 0.46
#> G2_Exp1.021 3 0.6500 0.2649 0.12 0.00 0.62 0.26
#> G2_Exp1.008 1 0.3610 0.6564 0.80 0.00 0.00 0.20
#> G2_Exp1.048 3 0.1211 0.7011 0.00 0.04 0.96 0.00
#> G2_Exp1.062 4 0.7775 0.4768 0.38 0.00 0.24 0.38
#> G2_Exp1.066 1 0.2345 0.7469 0.90 0.00 0.00 0.10
#> G2_Exp1.023 3 0.7707 -0.3176 0.24 0.00 0.44 0.32
#> S_Exp1.071 2 0.0707 0.9549 0.00 0.98 0.00 0.02
#> S_Exp1.032 1 0.2647 0.7254 0.88 0.00 0.00 0.12
#> S_Exp1.035 3 0.0000 0.7078 0.00 0.00 1.00 0.00
#> S_Exp1.063 2 0.0000 0.9564 0.00 1.00 0.00 0.00
#> S_Exp1.078 2 0.0000 0.9564 0.00 1.00 0.00 0.00
#> S_Exp1.023 3 0.0000 0.7078 0.00 0.00 1.00 0.00
#> S_Exp1.046 2 0.0707 0.9497 0.00 0.98 0.00 0.02
#> S_Exp1.008 2 0.0000 0.9564 0.00 1.00 0.00 0.00
#> S_Exp1.006 2 0.1637 0.9480 0.00 0.94 0.00 0.06
#> S_Exp1.064 2 0.0000 0.9564 0.00 1.00 0.00 0.00
#> S_Exp1.073 2 0.1211 0.9518 0.00 0.96 0.00 0.04
#> S_Exp1.014 2 0.0000 0.9564 0.00 1.00 0.00 0.00
#> S_Exp1.039 2 0.1637 0.9480 0.00 0.94 0.00 0.06
#> S_Exp1.054 3 0.5657 0.5756 0.00 0.16 0.72 0.12
#> S_Exp1.012 2 0.0000 0.9564 0.00 1.00 0.00 0.00
#> S_Exp1.070 3 0.1637 0.6932 0.00 0.06 0.94 0.00
#> S_Exp1.020 1 0.4134 0.5696 0.74 0.00 0.00 0.26
#> S_Exp1.015 3 0.4977 0.1985 0.00 0.46 0.54 0.00
#> S_Exp1.066 3 0.4522 0.4780 0.00 0.32 0.68 0.00
#> S_Exp1.040 2 0.1637 0.9480 0.00 0.94 0.00 0.06
#> S_Exp1.026 4 0.7581 0.2370 0.20 0.00 0.36 0.44
#> S_Exp1.017 2 0.0000 0.9564 0.00 1.00 0.00 0.00
#> S_Exp1.010 2 0.0000 0.9564 0.00 1.00 0.00 0.00
#> G1_Exp1.059 1 0.2345 0.7477 0.90 0.00 0.00 0.10
#> G1_Exp1.085 1 0.4624 0.4077 0.66 0.00 0.00 0.34
#> G1_Exp1.031 1 0.1211 0.7571 0.96 0.00 0.00 0.04
#> G1_Exp1.078 4 0.7544 0.4892 0.34 0.00 0.20 0.46
#> G1_Exp1.034 1 0.2647 0.7573 0.88 0.00 0.00 0.12
#> G1_Exp1.020 1 0.2647 0.7334 0.88 0.00 0.00 0.12
#> G1_Exp1.011 1 0.2345 0.7538 0.90 0.00 0.00 0.10
#> G1_Exp1.063 1 0.2647 0.7273 0.88 0.00 0.00 0.12
#> G1_Exp1.046 1 0.0707 0.7682 0.98 0.00 0.00 0.02
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
As soon as the classes for columns are determined, the signatures that are significantly different between subgroups can be looked for. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. To get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows (which is done by automatically selecting number of clusters).If there are too many signatures, top_signatures = ... can be set to only show the
signatures with the highest FDRs:
# code only for demonstration
# e.g. to show the top 500 most significant rows
tb = get_signature(res, k = ..., top_signatures = 500)
If the signatures are defined as these which are uniquely high in current group, diff_method argument
can be set to "uniquely_high_in_one_group":
# code only for demonstration
tb = get_signature(res, k = ..., diff_method = "uniquely_high_in_one_group")
UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n_sample CellLine(p-value) Experiment(p-value) Phase(p-value) k
#> ATC:skmeans 72 0.0929 0.3569 3.15e-05 2
#> ATC:skmeans 70 0.3592 0.0552 9.84e-06 3
#> ATC:skmeans 55 0.5074 0.1181 7.87e-05 4
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
Parent node: Node0. Child nodes: Node011 , Node012 , Node021-leaf , Node022 , Node023 .
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 13002 rows and 239 columns.
#> Top rows (1300) 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 0.871 0.913 0.959 0.498 0.503 0.503
#> 3 3 0.908 0.929 0.969 0.345 0.707 0.480
#> 4 4 0.865 0.865 0.941 0.120 0.846 0.580
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
#> H1_Exp1.001 1 0.000 0.959 1.00 0.00
#> H1_Exp1.008 2 0.141 0.950 0.02 0.98
#> H1_Exp1.021 2 0.141 0.950 0.02 0.98
#> H1_Exp1.027 2 0.760 0.730 0.22 0.78
#> H1_Exp1.030 1 0.000 0.959 1.00 0.00
#> H1_Exp1.031 1 0.402 0.910 0.92 0.08
#> H1_Exp1.032 2 0.402 0.899 0.08 0.92
#> H1_Exp1.033 1 0.141 0.948 0.98 0.02
#> H1_Exp1.036 2 0.722 0.759 0.20 0.80
#> H1_Exp1.042 2 0.000 0.955 0.00 1.00
#> H1_Exp1.043 1 0.141 0.955 0.98 0.02
#> H1_Exp1.044 2 0.722 0.772 0.20 0.80
#> H1_Exp1.045 2 0.000 0.955 0.00 1.00
#> H1_Exp1.047 2 0.000 0.955 0.00 1.00
#> H1_Exp1.048 2 0.000 0.955 0.00 1.00
#> H1_Exp1.058 2 0.402 0.899 0.08 0.92
#> H1_Exp1.060 2 0.000 0.955 0.00 1.00
#> H1_Exp1.065 1 0.000 0.959 1.00 0.00
#> H1_Exp1.068 1 0.242 0.944 0.96 0.04
#> H1_Exp2.076 1 0.000 0.959 1.00 0.00
#> H1_Exp2.077 1 0.000 0.959 1.00 0.00
#> H1_Exp2.078 1 0.000 0.959 1.00 0.00
#> H1_Exp2.080 1 0.000 0.959 1.00 0.00
#> H1_Exp2.086 1 0.000 0.959 1.00 0.00
#> H1_Exp2.090 1 0.000 0.959 1.00 0.00
#> H1_Exp2.099 2 0.141 0.950 0.02 0.98
#> H1_Exp2.102 1 0.000 0.959 1.00 0.00
#> H1_Exp2.103 1 0.000 0.959 1.00 0.00
#> H1_Exp2.104 1 0.000 0.959 1.00 0.00
#> H1_Exp2.107 2 0.141 0.950 0.02 0.98
#> H1_Exp2.109 2 0.402 0.910 0.08 0.92
#> H1_Exp2.110 1 0.000 0.959 1.00 0.00
#> H1_Exp2.111 1 0.855 0.609 0.72 0.28
#> H1_Exp2.113 2 0.141 0.950 0.02 0.98
#> H1_Exp2.114 1 0.000 0.959 1.00 0.00
#> H1_Exp2.115 1 0.141 0.955 0.98 0.02
#> H1_Exp2.117 1 0.141 0.949 0.98 0.02
#> H1_Exp2.120 1 0.000 0.959 1.00 0.00
#> H1_Exp2.122 1 0.000 0.959 1.00 0.00
#> H1_Exp2.124 1 0.402 0.896 0.92 0.08
#> H1_Exp2.125 1 0.141 0.955 0.98 0.02
#> H1_Exp2.126 1 0.000 0.959 1.00 0.00
#> H1_Exp2.128 1 0.000 0.959 1.00 0.00
#> H1_Exp2.129 2 0.141 0.950 0.02 0.98
#> H1_Exp2.132 1 0.000 0.959 1.00 0.00
#> H1_Exp2.134 2 0.000 0.955 0.00 1.00
#> H1_Exp2.137 1 0.000 0.959 1.00 0.00
#> H1_Exp2.141 1 0.000 0.959 1.00 0.00
#> H1_Exp2.142 1 0.000 0.959 1.00 0.00
#> H1_Exp2.145 1 0.000 0.959 1.00 0.00
#> H1_Exp2.147 2 0.242 0.941 0.04 0.96
#> H1_Exp2.149 1 0.141 0.955 0.98 0.02
#> H1_Exp2.151 2 0.141 0.950 0.02 0.98
#> H1_Exp2.153 1 0.000 0.959 1.00 0.00
#> H1_Exp3.218 2 0.000 0.955 0.00 1.00
#> H1_Exp3.219 1 0.000 0.959 1.00 0.00
#> H1_Exp3.221 1 0.000 0.959 1.00 0.00
#> H1_Exp3.222 2 0.141 0.950 0.02 0.98
#> H1_Exp3.224 1 0.141 0.955 0.98 0.02
#> H1_Exp3.225 1 0.000 0.959 1.00 0.00
#> H1_Exp3.226 2 0.141 0.950 0.02 0.98
#> H1_Exp3.227 1 0.000 0.959 1.00 0.00
#> H1_Exp3.228 1 0.680 0.773 0.82 0.18
#> H1_Exp3.229 1 0.000 0.959 1.00 0.00
#> H1_Exp3.230 1 0.000 0.959 1.00 0.00
#> H1_Exp3.231 1 0.000 0.959 1.00 0.00
#> H1_Exp3.234 2 0.141 0.950 0.02 0.98
#> H1_Exp3.235 2 0.000 0.955 0.00 1.00
#> H1_Exp3.236 1 0.000 0.959 1.00 0.00
#> H1_Exp3.237 2 0.000 0.955 0.00 1.00
#> H1_Exp3.238 1 0.402 0.910 0.92 0.08
#> H1_Exp3.239 2 0.990 0.246 0.44 0.56
#> H1_Exp3.240 2 0.141 0.950 0.02 0.98
#> H1_Exp3.242 2 0.000 0.955 0.00 1.00
#> H1_Exp3.243 2 0.000 0.955 0.00 1.00
#> H1_Exp3.244 1 0.327 0.927 0.94 0.06
#> H1_Exp3.246 2 0.000 0.955 0.00 1.00
#> H1_Exp3.247 1 0.402 0.910 0.92 0.08
#> H1_Exp3.249 2 0.000 0.955 0.00 1.00
#> H1_Exp3.250 2 0.000 0.955 0.00 1.00
#> H1_Exp3.251 2 0.000 0.955 0.00 1.00
#> H1_Exp3.252 2 0.000 0.955 0.00 1.00
#> H1_Exp3.253 1 0.141 0.955 0.98 0.02
#> H1_Exp3.254 2 0.469 0.880 0.10 0.90
#> H1_Exp3.255 2 0.000 0.955 0.00 1.00
#> H1_Exp3.256 1 0.402 0.910 0.92 0.08
#> H1_Exp3.257 1 0.995 0.160 0.54 0.46
#> H1_Exp3.258 2 0.000 0.955 0.00 1.00
#> H1_Exp3.259 2 0.000 0.955 0.00 1.00
#> H1_Exp3.260 2 0.327 0.916 0.06 0.94
#> H1_Exp3.261 1 0.141 0.955 0.98 0.02
#> H1_Exp3.262 2 0.000 0.955 0.00 1.00
#> H1_Exp3.263 2 0.141 0.945 0.02 0.98
#> H1_Exp3.264 1 0.827 0.665 0.74 0.26
#> H1_Exp3.265 2 0.000 0.955 0.00 1.00
#> H1_Exp3.266 1 0.141 0.955 0.98 0.02
#> H1_Exp3.267 2 0.000 0.955 0.00 1.00
#> H1_Exp3.268 2 0.971 0.336 0.40 0.60
#> H1_Exp3.269 2 0.000 0.955 0.00 1.00
#> H1_Exp3.270 2 0.402 0.899 0.08 0.92
#> H1_Exp3.271 1 0.141 0.955 0.98 0.02
#> H1_Exp3.272 1 0.141 0.955 0.98 0.02
#> H1_Exp3.273 2 0.000 0.955 0.00 1.00
#> H1_Exp3.274 2 0.000 0.955 0.00 1.00
#> H1_Exp3.275 2 0.000 0.955 0.00 1.00
#> H1_Exp3.276 2 0.000 0.955 0.00 1.00
#> H1_Exp3.278 2 0.680 0.785 0.18 0.82
#> H1_Exp3.279 1 0.141 0.955 0.98 0.02
#> H1_Exp3.280 2 0.000 0.955 0.00 1.00
#> H1_Exp3.281 1 0.141 0.955 0.98 0.02
#> H1_Exp3.282 2 0.529 0.860 0.12 0.88
#> H1_Exp3.285 2 0.141 0.950 0.02 0.98
#> H1_Exp3.286 2 0.000 0.955 0.00 1.00
#> H1_Exp3.287 2 0.000 0.955 0.00 1.00
#> H1_Exp3.288 1 0.402 0.910 0.92 0.08
#> H1_Exp3.289 1 0.141 0.955 0.98 0.02
#> H1_Exp3.290 1 0.000 0.959 1.00 0.00
#> H1_Exp3.291 2 0.141 0.950 0.02 0.98
#> G2_Exp1.059 1 0.141 0.955 0.98 0.02
#> G2_Exp1.075 2 0.141 0.950 0.02 0.98
#> G2_Exp1.063 2 0.000 0.955 0.00 1.00
#> G2_Exp1.029 2 0.141 0.950 0.02 0.98
#> G2_Exp1.037 1 0.000 0.959 1.00 0.00
#> G2_Exp1.057 1 0.141 0.955 0.98 0.02
#> G2_Exp1.018 2 0.141 0.950 0.02 0.98
#> G2_Exp1.004 2 0.141 0.950 0.02 0.98
#> G2_Exp1.061 1 0.000 0.959 1.00 0.00
#> G2_Exp1.060 2 0.000 0.955 0.00 1.00
#> G2_Exp1.058 2 0.000 0.955 0.00 1.00
#> G2_Exp1.065 2 0.141 0.950 0.02 0.98
#> G2_Exp1.002 1 0.000 0.959 1.00 0.00
#> G2_Exp1.044 1 0.141 0.955 0.98 0.02
#> G2_Exp1.051 2 0.000 0.955 0.00 1.00
#> G2_Exp1.073 2 0.141 0.950 0.02 0.98
#> G2_Exp1.028 1 0.000 0.959 1.00 0.00
#> G2_Exp1.034 1 0.000 0.959 1.00 0.00
#> G2_Exp1.054 1 0.000 0.959 1.00 0.00
#> G2_Exp1.024 1 0.141 0.948 0.98 0.02
#> G2_Exp1.020 2 0.141 0.950 0.02 0.98
#> G2_Exp1.039 2 0.000 0.955 0.00 1.00
#> G2_Exp1.007 1 0.000 0.959 1.00 0.00
#> G2_Exp1.026 2 0.141 0.950 0.02 0.98
#> G2_Exp1.011 1 0.000 0.959 1.00 0.00
#> G2_Exp1.053 2 0.000 0.955 0.00 1.00
#> G2_Exp1.006 1 0.402 0.895 0.92 0.08
#> G2_Exp1.016 1 0.000 0.959 1.00 0.00
#> G2_Exp1.010 1 0.000 0.959 1.00 0.00
#> G2_Exp1.027 2 0.141 0.950 0.02 0.98
#> G2_Exp1.067 2 0.141 0.950 0.02 0.98
#> G2_Exp1.009 2 0.402 0.906 0.08 0.92
#> G2_Exp1.025 1 0.000 0.959 1.00 0.00
#> G2_Exp1.055 2 0.000 0.955 0.00 1.00
#> G2_Exp1.033 1 0.141 0.955 0.98 0.02
#> G2_Exp1.043 2 0.000 0.955 0.00 1.00
#> G2_Exp1.052 2 0.000 0.955 0.00 1.00
#> G2_Exp1.071 2 0.141 0.950 0.02 0.98
#> S_Exp1.074 1 0.469 0.874 0.90 0.10
#> S_Exp1.030 1 0.000 0.959 1.00 0.00
#> S_Exp1.013 2 0.141 0.950 0.02 0.98
#> S_Exp1.065 1 0.000 0.959 1.00 0.00
#> S_Exp1.076 1 0.000 0.959 1.00 0.00
#> S_Exp1.068 1 0.000 0.959 1.00 0.00
#> S_Exp1.009 1 0.000 0.959 1.00 0.00
#> S_Exp1.042 2 0.327 0.923 0.06 0.94
#> S_Exp1.002 1 0.141 0.948 0.98 0.02
#> S_Exp1.059 1 0.327 0.916 0.94 0.06
#> S_Exp1.044 1 0.000 0.959 1.00 0.00
#> G1_Exp1.048 2 0.881 0.576 0.30 0.70
#> G1_Exp1.090 2 0.000 0.955 0.00 1.00
#> G1_Exp1.061 1 0.141 0.955 0.98 0.02
#> G1_Exp1.025 1 0.141 0.955 0.98 0.02
#> G1_Exp1.081 1 0.402 0.910 0.92 0.08
#> G1_Exp1.024 1 0.000 0.959 1.00 0.00
#> G1_Exp1.056 2 0.000 0.955 0.00 1.00
#> G1_Exp1.064 2 0.000 0.955 0.00 1.00
#> G1_Exp1.053 1 0.141 0.955 0.98 0.02
#> G1_Exp1.070 2 0.000 0.955 0.00 1.00
#> G1_Exp1.087 1 0.141 0.955 0.98 0.02
#> G1_Exp1.067 2 0.000 0.955 0.00 1.00
#> G1_Exp1.003 1 0.000 0.959 1.00 0.00
#> G1_Exp1.052 1 0.141 0.955 0.98 0.02
#> G1_Exp1.060 2 0.000 0.955 0.00 1.00
#> G1_Exp1.071 2 0.000 0.955 0.00 1.00
#> G1_Exp1.045 2 0.000 0.955 0.00 1.00
#> G1_Exp1.040 1 0.402 0.910 0.92 0.08
#> G1_Exp1.016 2 0.760 0.743 0.22 0.78
#> G1_Exp1.002 1 0.990 0.198 0.56 0.44
#> G1_Exp1.021 1 0.000 0.959 1.00 0.00
#> G1_Exp1.015 1 0.000 0.959 1.00 0.00
#> G1_Exp1.042 1 0.141 0.955 0.98 0.02
#> G1_Exp1.058 2 0.529 0.860 0.12 0.88
#> G1_Exp1.075 2 0.000 0.955 0.00 1.00
#> G1_Exp1.074 2 0.242 0.931 0.04 0.96
#> G1_Exp1.079 2 0.855 0.623 0.28 0.72
#> G1_Exp1.038 1 0.402 0.910 0.92 0.08
#> G1_Exp1.035 1 0.000 0.959 1.00 0.00
#> G1_Exp1.012 1 0.000 0.959 1.00 0.00
#> G1_Exp1.032 1 0.141 0.955 0.98 0.02
#> G1_Exp1.009 1 0.000 0.959 1.00 0.00
#> G1_Exp1.043 1 0.141 0.955 0.98 0.02
#> G1_Exp1.089 1 0.141 0.955 0.98 0.02
#> G1_Exp1.080 1 0.141 0.955 0.98 0.02
#> G1_Exp1.007 1 0.999 0.045 0.52 0.48
#> G1_Exp1.062 1 0.971 0.352 0.60 0.40
#> G1_Exp1.069 2 0.000 0.955 0.00 1.00
#> G1_Exp1.004 2 0.141 0.950 0.02 0.98
#> G1_Exp1.010 2 0.141 0.950 0.02 0.98
#> G1_Exp1.044 1 0.242 0.943 0.96 0.04
#> G1_Exp1.065 2 0.000 0.955 0.00 1.00
#> G1_Exp1.029 1 0.000 0.959 1.00 0.00
#> G1_Exp1.086 1 0.000 0.959 1.00 0.00
#> G1_Exp1.084 1 0.141 0.955 0.98 0.02
#> G1_Exp1.088 2 0.000 0.955 0.00 1.00
#> G1_Exp1.051 1 0.141 0.955 0.98 0.02
#> G1_Exp1.066 2 0.000 0.955 0.00 1.00
#> G1_Exp1.047 1 0.141 0.955 0.98 0.02
#> G1_Exp1.072 2 0.000 0.955 0.00 1.00
#> G1_Exp1.057 2 0.469 0.880 0.10 0.90
#> G1_Exp1.054 2 0.000 0.955 0.00 1.00
#> G1_Exp1.082 1 0.995 0.122 0.54 0.46
#> G1_Exp1.077 1 0.141 0.955 0.98 0.02
#> G1_Exp1.091 1 0.000 0.959 1.00 0.00
#> G1_Exp1.068 1 0.327 0.927 0.94 0.06
#> G1_Exp1.001 1 0.000 0.959 1.00 0.00
#> G1_Exp1.005 1 0.000 0.959 1.00 0.00
#> G1_Exp1.037 1 0.141 0.955 0.98 0.02
#> G1_Exp1.014 1 0.000 0.959 1.00 0.00
#> G1_Exp1.017 1 0.000 0.959 1.00 0.00
#> G1_Exp1.028 1 0.722 0.746 0.80 0.20
#> G1_Exp1.036 1 0.141 0.955 0.98 0.02
#> G1_Exp1.049 1 0.141 0.955 0.98 0.02
#> G1_Exp1.041 2 0.000 0.955 0.00 1.00
#> G1_Exp1.006 1 0.000 0.959 1.00 0.00
#> G1_Exp1.008 1 0.000 0.959 1.00 0.00
#> G1_Exp1.055 1 0.141 0.955 0.98 0.02
#> G1_Exp1.050 2 0.000 0.955 0.00 1.00
#> G1_Exp1.076 2 0.680 0.779 0.18 0.82
#> G1_Exp1.083 2 0.943 0.438 0.36 0.64
#> G1_Exp1.030 1 0.000 0.959 1.00 0.00
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> H1_Exp1.001 1 0.0000 0.9634 1.00 0.00 0.00
#> H1_Exp1.008 2 0.0000 0.9711 0.00 1.00 0.00
#> H1_Exp1.021 2 0.0000 0.9711 0.00 1.00 0.00
#> H1_Exp1.027 3 0.0000 0.9682 0.00 0.00 1.00
#> H1_Exp1.030 3 0.2066 0.9163 0.06 0.00 0.94
#> H1_Exp1.031 3 0.0000 0.9682 0.00 0.00 1.00
#> H1_Exp1.032 3 0.0000 0.9682 0.00 0.00 1.00
#> H1_Exp1.033 1 0.0000 0.9634 1.00 0.00 0.00
#> H1_Exp1.036 3 0.0000 0.9682 0.00 0.00 1.00
#> H1_Exp1.042 3 0.0000 0.9682 0.00 0.00 1.00
#> H1_Exp1.043 3 0.0000 0.9682 0.00 0.00 1.00
#> H1_Exp1.044 2 0.5216 0.6402 0.26 0.74 0.00
#> H1_Exp1.045 2 0.0000 0.9711 0.00 1.00 0.00
#> H1_Exp1.047 2 0.0000 0.9711 0.00 1.00 0.00
#> H1_Exp1.048 2 0.0000 0.9711 0.00 1.00 0.00
#> H1_Exp1.058 3 0.0000 0.9682 0.00 0.00 1.00
#> H1_Exp1.060 2 0.0000 0.9711 0.00 1.00 0.00
#> H1_Exp1.065 1 0.0000 0.9634 1.00 0.00 0.00
#> H1_Exp1.068 3 0.0000 0.9682 0.00 0.00 1.00
#> H1_Exp2.076 1 0.0000 0.9634 1.00 0.00 0.00
#> H1_Exp2.077 1 0.0000 0.9634 1.00 0.00 0.00
#> H1_Exp2.078 1 0.0000 0.9634 1.00 0.00 0.00
#> H1_Exp2.080 1 0.0000 0.9634 1.00 0.00 0.00
#> H1_Exp2.086 1 0.0000 0.9634 1.00 0.00 0.00
#> H1_Exp2.090 1 0.0000 0.9634 1.00 0.00 0.00
#> H1_Exp2.099 2 0.0000 0.9711 0.00 1.00 0.00
#> H1_Exp2.102 1 0.0000 0.9634 1.00 0.00 0.00
#> H1_Exp2.103 1 0.0000 0.9634 1.00 0.00 0.00
#> H1_Exp2.104 1 0.0000 0.9634 1.00 0.00 0.00
#> H1_Exp2.107 2 0.0000 0.9711 0.00 1.00 0.00
#> H1_Exp2.109 1 0.4555 0.7450 0.80 0.20 0.00
#> H1_Exp2.110 1 0.1529 0.9311 0.96 0.00 0.04
#> H1_Exp2.111 1 0.0000 0.9634 1.00 0.00 0.00
#> H1_Exp2.113 2 0.0000 0.9711 0.00 1.00 0.00
#> H1_Exp2.114 1 0.4002 0.8054 0.84 0.00 0.16
#> H1_Exp2.115 3 0.0892 0.9520 0.02 0.00 0.98
#> H1_Exp2.117 3 0.5835 0.4858 0.34 0.00 0.66
#> H1_Exp2.120 3 0.1529 0.9345 0.04 0.00 0.96
#> H1_Exp2.122 1 0.0000 0.9634 1.00 0.00 0.00
#> H1_Exp2.124 1 0.0000 0.9634 1.00 0.00 0.00
#> H1_Exp2.125 3 0.0000 0.9682 0.00 0.00 1.00
#> H1_Exp2.126 3 0.0000 0.9682 0.00 0.00 1.00
#> H1_Exp2.128 1 0.0000 0.9634 1.00 0.00 0.00
#> H1_Exp2.129 2 0.0000 0.9711 0.00 1.00 0.00
#> H1_Exp2.132 3 0.4555 0.7467 0.20 0.00 0.80
#> H1_Exp2.134 2 0.0000 0.9711 0.00 1.00 0.00
#> H1_Exp2.137 3 0.5835 0.4851 0.34 0.00 0.66
#> H1_Exp2.141 1 0.5397 0.6189 0.72 0.00 0.28
#> H1_Exp2.142 1 0.0000 0.9634 1.00 0.00 0.00
#> H1_Exp2.145 1 0.0000 0.9634 1.00 0.00 0.00
#> H1_Exp2.147 2 0.6309 -0.0224 0.50 0.50 0.00
#> H1_Exp2.149 3 0.0000 0.9682 0.00 0.00 1.00
#> H1_Exp2.151 2 0.0000 0.9711 0.00 1.00 0.00
#> H1_Exp2.153 1 0.6045 0.3964 0.62 0.00 0.38
#> H1_Exp3.218 2 0.0892 0.9553 0.00 0.98 0.02
#> H1_Exp3.219 1 0.0000 0.9634 1.00 0.00 0.00
#> H1_Exp3.221 1 0.0000 0.9634 1.00 0.00 0.00
#> H1_Exp3.222 1 0.5560 0.5746 0.70 0.30 0.00
#> H1_Exp3.224 3 0.0000 0.9682 0.00 0.00 1.00
#> H1_Exp3.225 1 0.0000 0.9634 1.00 0.00 0.00
#> H1_Exp3.226 2 0.0000 0.9711 0.00 1.00 0.00
#> H1_Exp3.227 1 0.0000 0.9634 1.00 0.00 0.00
#> H1_Exp3.228 1 0.1781 0.9362 0.96 0.02 0.02
#> H1_Exp3.229 1 0.0000 0.9634 1.00 0.00 0.00
#> H1_Exp3.230 1 0.0000 0.9634 1.00 0.00 0.00
#> H1_Exp3.231 1 0.0000 0.9634 1.00 0.00 0.00
#> H1_Exp3.234 2 0.0000 0.9711 0.00 1.00 0.00
#> H1_Exp3.235 2 0.0000 0.9711 0.00 1.00 0.00
#> H1_Exp3.236 1 0.0000 0.9634 1.00 0.00 0.00
#> H1_Exp3.237 2 0.0000 0.9711 0.00 1.00 0.00
#> H1_Exp3.238 3 0.0000 0.9682 0.00 0.00 1.00
#> H1_Exp3.239 1 0.3686 0.8253 0.86 0.14 0.00
#> H1_Exp3.240 2 0.0000 0.9711 0.00 1.00 0.00
#> H1_Exp3.242 2 0.1529 0.9376 0.00 0.96 0.04
#> H1_Exp3.243 2 0.0000 0.9711 0.00 1.00 0.00
#> H1_Exp3.244 3 0.0000 0.9682 0.00 0.00 1.00
#> H1_Exp3.246 2 0.0000 0.9711 0.00 1.00 0.00
#> H1_Exp3.247 3 0.0000 0.9682 0.00 0.00 1.00
#> H1_Exp3.249 2 0.0000 0.9711 0.00 1.00 0.00
#> H1_Exp3.250 2 0.0000 0.9711 0.00 1.00 0.00
#> H1_Exp3.251 2 0.0000 0.9711 0.00 1.00 0.00
#> H1_Exp3.252 2 0.0000 0.9711 0.00 1.00 0.00
#> H1_Exp3.253 3 0.0000 0.9682 0.00 0.00 1.00
#> H1_Exp3.254 3 0.0000 0.9682 0.00 0.00 1.00
#> H1_Exp3.255 2 0.2959 0.8774 0.00 0.90 0.10
#> H1_Exp3.256 3 0.0000 0.9682 0.00 0.00 1.00
#> H1_Exp3.257 3 0.0000 0.9682 0.00 0.00 1.00
#> H1_Exp3.258 2 0.0000 0.9711 0.00 1.00 0.00
#> H1_Exp3.259 2 0.1529 0.9377 0.00 0.96 0.04
#> H1_Exp3.260 3 0.0000 0.9682 0.00 0.00 1.00
#> H1_Exp3.261 3 0.0000 0.9682 0.00 0.00 1.00
#> H1_Exp3.262 3 0.4555 0.7446 0.00 0.20 0.80
#> H1_Exp3.263 3 0.0892 0.9523 0.00 0.02 0.98
#> H1_Exp3.264 3 0.0000 0.9682 0.00 0.00 1.00
#> H1_Exp3.265 2 0.0000 0.9711 0.00 1.00 0.00
#> H1_Exp3.266 3 0.0000 0.9682 0.00 0.00 1.00
#> H1_Exp3.267 2 0.0000 0.9711 0.00 1.00 0.00
#> H1_Exp3.268 3 0.0000 0.9682 0.00 0.00 1.00
#> H1_Exp3.269 2 0.0000 0.9711 0.00 1.00 0.00
#> H1_Exp3.270 3 0.0000 0.9682 0.00 0.00 1.00
#> H1_Exp3.271 3 0.0000 0.9682 0.00 0.00 1.00
#> H1_Exp3.272 3 0.0000 0.9682 0.00 0.00 1.00
#> H1_Exp3.273 2 0.5216 0.6548 0.00 0.74 0.26
#> H1_Exp3.274 2 0.0000 0.9711 0.00 1.00 0.00
#> H1_Exp3.275 2 0.0000 0.9711 0.00 1.00 0.00
#> H1_Exp3.276 2 0.0000 0.9711 0.00 1.00 0.00
#> H1_Exp3.278 3 0.0000 0.9682 0.00 0.00 1.00
#> H1_Exp3.279 3 0.0000 0.9682 0.00 0.00 1.00
#> H1_Exp3.280 2 0.0000 0.9711 0.00 1.00 0.00
#> H1_Exp3.281 3 0.0000 0.9682 0.00 0.00 1.00
#> H1_Exp3.282 3 0.5406 0.7321 0.02 0.20 0.78
#> H1_Exp3.285 2 0.0000 0.9711 0.00 1.00 0.00
#> H1_Exp3.286 2 0.0000 0.9711 0.00 1.00 0.00
#> H1_Exp3.287 2 0.0000 0.9711 0.00 1.00 0.00
#> H1_Exp3.288 3 0.0000 0.9682 0.00 0.00 1.00
#> H1_Exp3.289 3 0.0000 0.9682 0.00 0.00 1.00
#> H1_Exp3.290 1 0.0000 0.9634 1.00 0.00 0.00
#> H1_Exp3.291 2 0.0000 0.9711 0.00 1.00 0.00
#> G2_Exp1.059 3 0.0000 0.9682 0.00 0.00 1.00
#> G2_Exp1.075 2 0.0892 0.9540 0.02 0.98 0.00
#> G2_Exp1.063 2 0.0000 0.9711 0.00 1.00 0.00
#> G2_Exp1.029 2 0.0000 0.9711 0.00 1.00 0.00
#> G2_Exp1.037 1 0.0000 0.9634 1.00 0.00 0.00
#> G2_Exp1.057 3 0.0000 0.9682 0.00 0.00 1.00
#> G2_Exp1.018 2 0.0000 0.9711 0.00 1.00 0.00
#> G2_Exp1.004 2 0.0000 0.9711 0.00 1.00 0.00
#> G2_Exp1.061 1 0.0000 0.9634 1.00 0.00 0.00
#> G2_Exp1.060 2 0.0000 0.9711 0.00 1.00 0.00
#> G2_Exp1.058 2 0.0000 0.9711 0.00 1.00 0.00
#> G2_Exp1.065 2 0.0000 0.9711 0.00 1.00 0.00
#> G2_Exp1.002 1 0.0000 0.9634 1.00 0.00 0.00
#> G2_Exp1.044 3 0.0000 0.9682 0.00 0.00 1.00
#> G2_Exp1.051 2 0.4796 0.7201 0.00 0.78 0.22
#> G2_Exp1.073 2 0.0000 0.9711 0.00 1.00 0.00
#> G2_Exp1.028 1 0.0000 0.9634 1.00 0.00 0.00
#> G2_Exp1.034 1 0.0000 0.9634 1.00 0.00 0.00
#> G2_Exp1.054 3 0.5706 0.5319 0.32 0.00 0.68
#> G2_Exp1.024 1 0.0000 0.9634 1.00 0.00 0.00
#> G2_Exp1.020 2 0.0000 0.9711 0.00 1.00 0.00
#> G2_Exp1.039 2 0.0000 0.9711 0.00 1.00 0.00
#> G2_Exp1.007 1 0.0000 0.9634 1.00 0.00 0.00
#> G2_Exp1.026 2 0.0000 0.9711 0.00 1.00 0.00
#> G2_Exp1.011 1 0.0000 0.9634 1.00 0.00 0.00
#> G2_Exp1.053 2 0.0000 0.9711 0.00 1.00 0.00
#> G2_Exp1.006 1 0.0000 0.9634 1.00 0.00 0.00
#> G2_Exp1.016 1 0.0000 0.9634 1.00 0.00 0.00
#> G2_Exp1.010 1 0.0000 0.9634 1.00 0.00 0.00
#> G2_Exp1.027 2 0.0000 0.9711 0.00 1.00 0.00
#> G2_Exp1.067 2 0.0000 0.9711 0.00 1.00 0.00
#> G2_Exp1.009 1 0.0000 0.9634 1.00 0.00 0.00
#> G2_Exp1.025 1 0.0000 0.9634 1.00 0.00 0.00
#> G2_Exp1.055 2 0.0000 0.9711 0.00 1.00 0.00
#> G2_Exp1.033 3 0.0000 0.9682 0.00 0.00 1.00
#> G2_Exp1.043 2 0.0000 0.9711 0.00 1.00 0.00
#> G2_Exp1.052 2 0.0000 0.9711 0.00 1.00 0.00
#> G2_Exp1.071 2 0.0000 0.9711 0.00 1.00 0.00
#> S_Exp1.074 1 0.0000 0.9634 1.00 0.00 0.00
#> S_Exp1.030 1 0.0000 0.9634 1.00 0.00 0.00
#> S_Exp1.013 2 0.0000 0.9711 0.00 1.00 0.00
#> S_Exp1.065 1 0.4796 0.7220 0.78 0.00 0.22
#> S_Exp1.076 1 0.0000 0.9634 1.00 0.00 0.00
#> S_Exp1.068 1 0.0000 0.9634 1.00 0.00 0.00
#> S_Exp1.009 1 0.0000 0.9634 1.00 0.00 0.00
#> S_Exp1.042 1 0.2537 0.8937 0.92 0.08 0.00
#> S_Exp1.002 1 0.0000 0.9634 1.00 0.00 0.00
#> S_Exp1.059 1 0.0000 0.9634 1.00 0.00 0.00
#> S_Exp1.044 1 0.0000 0.9634 1.00 0.00 0.00
#> G1_Exp1.048 3 0.0000 0.9682 0.00 0.00 1.00
#> G1_Exp1.090 2 0.0892 0.9551 0.00 0.98 0.02
#> G1_Exp1.061 3 0.0000 0.9682 0.00 0.00 1.00
#> G1_Exp1.025 3 0.0000 0.9682 0.00 0.00 1.00
#> G1_Exp1.081 3 0.0000 0.9682 0.00 0.00 1.00
#> G1_Exp1.024 1 0.0000 0.9634 1.00 0.00 0.00
#> G1_Exp1.056 2 0.0000 0.9711 0.00 1.00 0.00
#> G1_Exp1.064 2 0.5560 0.5799 0.00 0.70 0.30
#> G1_Exp1.053 3 0.0000 0.9682 0.00 0.00 1.00
#> G1_Exp1.070 3 0.4002 0.7999 0.00 0.16 0.84
#> G1_Exp1.087 3 0.0000 0.9682 0.00 0.00 1.00
#> G1_Exp1.067 2 0.0000 0.9711 0.00 1.00 0.00
#> G1_Exp1.003 1 0.0000 0.9634 1.00 0.00 0.00
#> G1_Exp1.052 3 0.0000 0.9682 0.00 0.00 1.00
#> G1_Exp1.060 2 0.3686 0.8323 0.00 0.86 0.14
#> G1_Exp1.071 2 0.0000 0.9711 0.00 1.00 0.00
#> G1_Exp1.045 2 0.0000 0.9711 0.00 1.00 0.00
#> G1_Exp1.040 3 0.0000 0.9682 0.00 0.00 1.00
#> G1_Exp1.016 1 0.0000 0.9634 1.00 0.00 0.00
#> G1_Exp1.002 1 0.0000 0.9634 1.00 0.00 0.00
#> G1_Exp1.021 1 0.0000 0.9634 1.00 0.00 0.00
#> G1_Exp1.015 1 0.0000 0.9634 1.00 0.00 0.00
#> G1_Exp1.042 3 0.0000 0.9682 0.00 0.00 1.00
#> G1_Exp1.058 3 0.0000 0.9682 0.00 0.00 1.00
#> G1_Exp1.075 2 0.0000 0.9711 0.00 1.00 0.00
#> G1_Exp1.074 3 0.0000 0.9682 0.00 0.00 1.00
#> G1_Exp1.079 3 0.0000 0.9682 0.00 0.00 1.00
#> G1_Exp1.038 3 0.0000 0.9682 0.00 0.00 1.00
#> G1_Exp1.035 1 0.0892 0.9477 0.98 0.00 0.02
#> G1_Exp1.012 1 0.0000 0.9634 1.00 0.00 0.00
#> G1_Exp1.032 3 0.0000 0.9682 0.00 0.00 1.00
#> G1_Exp1.009 1 0.5216 0.6579 0.74 0.00 0.26
#> G1_Exp1.043 3 0.0000 0.9682 0.00 0.00 1.00
#> G1_Exp1.089 3 0.0000 0.9682 0.00 0.00 1.00
#> G1_Exp1.080 3 0.0000 0.9682 0.00 0.00 1.00
#> G1_Exp1.007 1 0.0000 0.9634 1.00 0.00 0.00
#> G1_Exp1.062 3 0.0000 0.9682 0.00 0.00 1.00
#> G1_Exp1.069 3 0.0892 0.9520 0.00 0.02 0.98
#> G1_Exp1.004 2 0.0000 0.9711 0.00 1.00 0.00
#> G1_Exp1.010 2 0.0000 0.9711 0.00 1.00 0.00
#> G1_Exp1.044 3 0.0000 0.9682 0.00 0.00 1.00
#> G1_Exp1.065 2 0.0000 0.9711 0.00 1.00 0.00
#> G1_Exp1.029 1 0.0000 0.9634 1.00 0.00 0.00
#> G1_Exp1.086 1 0.2537 0.8935 0.92 0.00 0.08
#> G1_Exp1.084 3 0.0000 0.9682 0.00 0.00 1.00
#> G1_Exp1.088 2 0.0000 0.9711 0.00 1.00 0.00
#> G1_Exp1.051 3 0.0000 0.9682 0.00 0.00 1.00
#> G1_Exp1.066 2 0.5560 0.5824 0.00 0.70 0.30
#> G1_Exp1.047 3 0.0000 0.9682 0.00 0.00 1.00
#> G1_Exp1.072 2 0.0000 0.9711 0.00 1.00 0.00
#> G1_Exp1.057 3 0.0000 0.9682 0.00 0.00 1.00
#> G1_Exp1.054 2 0.0000 0.9711 0.00 1.00 0.00
#> G1_Exp1.082 1 0.5397 0.6090 0.72 0.28 0.00
#> G1_Exp1.077 3 0.0000 0.9682 0.00 0.00 1.00
#> G1_Exp1.091 1 0.5397 0.6177 0.72 0.00 0.28
#> G1_Exp1.068 3 0.0000 0.9682 0.00 0.00 1.00
#> G1_Exp1.001 1 0.0000 0.9634 1.00 0.00 0.00
#> G1_Exp1.005 1 0.0000 0.9634 1.00 0.00 0.00
#> G1_Exp1.037 3 0.0000 0.9682 0.00 0.00 1.00
#> G1_Exp1.014 1 0.0000 0.9634 1.00 0.00 0.00
#> G1_Exp1.017 1 0.0000 0.9634 1.00 0.00 0.00
#> G1_Exp1.028 1 0.0000 0.9634 1.00 0.00 0.00
#> G1_Exp1.036 3 0.0000 0.9682 0.00 0.00 1.00
#> G1_Exp1.049 3 0.0000 0.9682 0.00 0.00 1.00
#> G1_Exp1.041 2 0.0000 0.9711 0.00 1.00 0.00
#> G1_Exp1.006 1 0.0000 0.9634 1.00 0.00 0.00
#> G1_Exp1.008 1 0.0000 0.9634 1.00 0.00 0.00
#> G1_Exp1.055 3 0.0000 0.9682 0.00 0.00 1.00
#> G1_Exp1.050 2 0.0000 0.9711 0.00 1.00 0.00
#> G1_Exp1.076 3 0.2959 0.8737 0.00 0.10 0.90
#> G1_Exp1.083 3 0.3832 0.8639 0.02 0.10 0.88
#> G1_Exp1.030 3 0.5216 0.6464 0.26 0.00 0.74
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> H1_Exp1.001 1 0.0000 0.9533 1.00 0.00 0.00 0.00
#> H1_Exp1.008 2 0.0707 0.9313 0.00 0.98 0.00 0.02
#> H1_Exp1.021 2 0.0000 0.9444 0.00 1.00 0.00 0.00
#> H1_Exp1.027 4 0.0000 0.8836 0.00 0.00 0.00 1.00
#> H1_Exp1.030 4 0.5428 0.7185 0.14 0.00 0.12 0.74
#> H1_Exp1.031 4 0.0707 0.8777 0.00 0.00 0.02 0.98
#> H1_Exp1.032 4 0.0000 0.8836 0.00 0.00 0.00 1.00
#> H1_Exp1.033 1 0.0707 0.9384 0.98 0.00 0.02 0.00
#> H1_Exp1.036 4 0.1637 0.8606 0.00 0.00 0.06 0.94
#> H1_Exp1.042 3 0.0000 0.9552 0.00 0.00 1.00 0.00
#> H1_Exp1.043 3 0.0000 0.9552 0.00 0.00 1.00 0.00
#> H1_Exp1.044 2 0.6808 0.3747 0.12 0.56 0.32 0.00
#> H1_Exp1.045 2 0.0000 0.9444 0.00 1.00 0.00 0.00
#> H1_Exp1.047 2 0.0000 0.9444 0.00 1.00 0.00 0.00
#> H1_Exp1.048 2 0.0000 0.9444 0.00 1.00 0.00 0.00
#> H1_Exp1.058 4 0.2011 0.8472 0.00 0.00 0.08 0.92
#> H1_Exp1.060 2 0.0000 0.9444 0.00 1.00 0.00 0.00
#> H1_Exp1.065 1 0.0000 0.9533 1.00 0.00 0.00 0.00
#> H1_Exp1.068 4 0.0000 0.8836 0.00 0.00 0.00 1.00
#> H1_Exp2.076 1 0.0000 0.9533 1.00 0.00 0.00 0.00
#> H1_Exp2.077 1 0.0000 0.9533 1.00 0.00 0.00 0.00
#> H1_Exp2.078 1 0.0000 0.9533 1.00 0.00 0.00 0.00
#> H1_Exp2.080 1 0.0000 0.9533 1.00 0.00 0.00 0.00
#> H1_Exp2.086 1 0.0000 0.9533 1.00 0.00 0.00 0.00
#> H1_Exp2.090 1 0.0000 0.9533 1.00 0.00 0.00 0.00
#> H1_Exp2.099 2 0.0000 0.9444 0.00 1.00 0.00 0.00
#> H1_Exp2.102 1 0.0000 0.9533 1.00 0.00 0.00 0.00
#> H1_Exp2.103 1 0.0000 0.9533 1.00 0.00 0.00 0.00
#> H1_Exp2.104 1 0.0000 0.9533 1.00 0.00 0.00 0.00
#> H1_Exp2.107 2 0.0000 0.9444 0.00 1.00 0.00 0.00
#> H1_Exp2.109 1 0.6500 0.4726 0.62 0.12 0.00 0.26
#> H1_Exp2.110 4 0.4624 0.4832 0.34 0.00 0.00 0.66
#> H1_Exp2.111 1 0.0707 0.9383 0.98 0.02 0.00 0.00
#> H1_Exp2.113 2 0.0000 0.9444 0.00 1.00 0.00 0.00
#> H1_Exp2.114 4 0.1637 0.8609 0.06 0.00 0.00 0.94
#> H1_Exp2.115 4 0.3610 0.7363 0.00 0.00 0.20 0.80
#> H1_Exp2.117 4 0.1211 0.8706 0.04 0.00 0.00 0.96
#> H1_Exp2.120 4 0.2011 0.8474 0.00 0.00 0.08 0.92
#> H1_Exp2.122 1 0.1211 0.9212 0.96 0.00 0.04 0.00
#> H1_Exp2.124 1 0.0707 0.9378 0.98 0.02 0.00 0.00
#> H1_Exp2.125 4 0.0000 0.8836 0.00 0.00 0.00 1.00
#> H1_Exp2.126 4 0.6299 0.2314 0.06 0.00 0.42 0.52
#> H1_Exp2.128 1 0.0000 0.9533 1.00 0.00 0.00 0.00
#> H1_Exp2.129 2 0.0000 0.9444 0.00 1.00 0.00 0.00
#> H1_Exp2.132 4 0.2011 0.8465 0.08 0.00 0.00 0.92
#> H1_Exp2.134 2 0.0000 0.9444 0.00 1.00 0.00 0.00
#> H1_Exp2.137 3 0.7493 0.2733 0.32 0.00 0.48 0.20
#> H1_Exp2.141 4 0.1637 0.8592 0.06 0.00 0.00 0.94
#> H1_Exp2.142 1 0.3610 0.7337 0.80 0.00 0.00 0.20
#> H1_Exp2.145 1 0.0000 0.9533 1.00 0.00 0.00 0.00
#> H1_Exp2.147 4 0.0000 0.8836 0.00 0.00 0.00 1.00
#> H1_Exp2.149 3 0.4855 0.2730 0.00 0.00 0.60 0.40
#> H1_Exp2.151 2 0.0000 0.9444 0.00 1.00 0.00 0.00
#> H1_Exp2.153 4 0.4522 0.5229 0.32 0.00 0.00 0.68
#> H1_Exp3.218 4 0.2647 0.8168 0.00 0.12 0.00 0.88
#> H1_Exp3.219 1 0.2011 0.8829 0.92 0.00 0.00 0.08
#> H1_Exp3.221 4 0.0707 0.8780 0.02 0.00 0.00 0.98
#> H1_Exp3.222 4 0.7310 0.2595 0.36 0.16 0.00 0.48
#> H1_Exp3.224 4 0.0000 0.8836 0.00 0.00 0.00 1.00
#> H1_Exp3.225 1 0.0000 0.9533 1.00 0.00 0.00 0.00
#> H1_Exp3.226 2 0.4713 0.4227 0.00 0.64 0.00 0.36
#> H1_Exp3.227 1 0.0000 0.9533 1.00 0.00 0.00 0.00
#> H1_Exp3.228 1 0.6074 0.3735 0.60 0.06 0.00 0.34
#> H1_Exp3.229 1 0.1637 0.9027 0.94 0.00 0.00 0.06
#> H1_Exp3.230 4 0.2345 0.8345 0.10 0.00 0.00 0.90
#> H1_Exp3.231 1 0.4994 0.0532 0.52 0.00 0.00 0.48
#> H1_Exp3.234 2 0.0000 0.9444 0.00 1.00 0.00 0.00
#> H1_Exp3.235 4 0.4713 0.4371 0.00 0.36 0.00 0.64
#> H1_Exp3.236 1 0.0000 0.9533 1.00 0.00 0.00 0.00
#> H1_Exp3.237 4 0.1211 0.8700 0.00 0.04 0.00 0.96
#> H1_Exp3.238 4 0.0000 0.8836 0.00 0.00 0.00 1.00
#> H1_Exp3.239 1 0.4522 0.5297 0.68 0.32 0.00 0.00
#> H1_Exp3.240 2 0.0000 0.9444 0.00 1.00 0.00 0.00
#> H1_Exp3.242 4 0.1637 0.8601 0.00 0.06 0.00 0.94
#> H1_Exp3.243 2 0.1211 0.9159 0.00 0.96 0.00 0.04
#> H1_Exp3.244 4 0.0000 0.8836 0.00 0.00 0.00 1.00
#> H1_Exp3.246 2 0.0000 0.9444 0.00 1.00 0.00 0.00
#> H1_Exp3.247 4 0.0000 0.8836 0.00 0.00 0.00 1.00
#> H1_Exp3.249 4 0.2345 0.8320 0.00 0.10 0.00 0.90
#> H1_Exp3.250 2 0.0000 0.9444 0.00 1.00 0.00 0.00
#> H1_Exp3.251 2 0.3975 0.6715 0.00 0.76 0.00 0.24
#> H1_Exp3.252 2 0.0000 0.9444 0.00 1.00 0.00 0.00
#> H1_Exp3.253 4 0.0707 0.8777 0.00 0.00 0.02 0.98
#> H1_Exp3.254 4 0.0000 0.8836 0.00 0.00 0.00 1.00
#> H1_Exp3.255 4 0.4713 0.4341 0.00 0.36 0.00 0.64
#> H1_Exp3.256 4 0.0000 0.8836 0.00 0.00 0.00 1.00
#> H1_Exp3.257 4 0.0000 0.8836 0.00 0.00 0.00 1.00
#> H1_Exp3.258 2 0.0000 0.9444 0.00 1.00 0.00 0.00
#> H1_Exp3.259 4 0.2011 0.8492 0.00 0.08 0.00 0.92
#> H1_Exp3.260 4 0.0000 0.8836 0.00 0.00 0.00 1.00
#> H1_Exp3.261 4 0.4713 0.4695 0.00 0.00 0.36 0.64
#> H1_Exp3.262 4 0.4642 0.6532 0.00 0.24 0.02 0.74
#> H1_Exp3.263 4 0.0000 0.8836 0.00 0.00 0.00 1.00
#> H1_Exp3.264 3 0.1211 0.9205 0.00 0.00 0.96 0.04
#> H1_Exp3.265 4 0.0707 0.8779 0.00 0.02 0.00 0.98
#> H1_Exp3.266 4 0.3172 0.7788 0.00 0.00 0.16 0.84
#> H1_Exp3.267 2 0.0707 0.9313 0.00 0.98 0.00 0.02
#> H1_Exp3.268 4 0.0000 0.8836 0.00 0.00 0.00 1.00
#> H1_Exp3.269 2 0.0000 0.9444 0.00 1.00 0.00 0.00
#> H1_Exp3.270 4 0.0000 0.8836 0.00 0.00 0.00 1.00
#> H1_Exp3.271 4 0.0000 0.8836 0.00 0.00 0.00 1.00
#> H1_Exp3.272 3 0.1211 0.9210 0.00 0.00 0.96 0.04
#> H1_Exp3.273 4 0.3610 0.7267 0.00 0.20 0.00 0.80
#> H1_Exp3.274 2 0.0000 0.9444 0.00 1.00 0.00 0.00
#> H1_Exp3.275 2 0.0000 0.9444 0.00 1.00 0.00 0.00
#> H1_Exp3.276 2 0.0707 0.9313 0.00 0.98 0.00 0.02
#> H1_Exp3.278 4 0.3172 0.7825 0.00 0.00 0.16 0.84
#> H1_Exp3.279 4 0.0000 0.8836 0.00 0.00 0.00 1.00
#> H1_Exp3.280 2 0.0000 0.9444 0.00 1.00 0.00 0.00
#> H1_Exp3.281 4 0.0707 0.8777 0.00 0.00 0.02 0.98
#> H1_Exp3.282 4 0.0000 0.8836 0.00 0.00 0.00 1.00
#> H1_Exp3.285 2 0.0000 0.9444 0.00 1.00 0.00 0.00
#> H1_Exp3.286 2 0.4855 0.3194 0.00 0.60 0.00 0.40
#> H1_Exp3.287 2 0.0000 0.9444 0.00 1.00 0.00 0.00
#> H1_Exp3.288 4 0.0000 0.8836 0.00 0.00 0.00 1.00
#> H1_Exp3.289 4 0.0000 0.8836 0.00 0.00 0.00 1.00
#> H1_Exp3.290 1 0.0000 0.9533 1.00 0.00 0.00 0.00
#> H1_Exp3.291 2 0.2345 0.8594 0.00 0.90 0.00 0.10
#> G2_Exp1.059 3 0.0000 0.9552 0.00 0.00 1.00 0.00
#> G2_Exp1.075 2 0.4755 0.7076 0.04 0.76 0.20 0.00
#> G2_Exp1.063 2 0.0000 0.9444 0.00 1.00 0.00 0.00
#> G2_Exp1.029 2 0.0000 0.9444 0.00 1.00 0.00 0.00
#> G2_Exp1.037 1 0.0707 0.9390 0.98 0.00 0.02 0.00
#> G2_Exp1.057 3 0.0000 0.9552 0.00 0.00 1.00 0.00
#> G2_Exp1.018 2 0.0000 0.9444 0.00 1.00 0.00 0.00
#> G2_Exp1.004 2 0.0000 0.9444 0.00 1.00 0.00 0.00
#> G2_Exp1.061 1 0.0000 0.9533 1.00 0.00 0.00 0.00
#> G2_Exp1.060 2 0.0000 0.9444 0.00 1.00 0.00 0.00
#> G2_Exp1.058 2 0.0000 0.9444 0.00 1.00 0.00 0.00
#> G2_Exp1.065 2 0.1211 0.9103 0.04 0.96 0.00 0.00
#> G2_Exp1.002 1 0.0000 0.9533 1.00 0.00 0.00 0.00
#> G2_Exp1.044 3 0.0000 0.9552 0.00 0.00 1.00 0.00
#> G2_Exp1.051 2 0.5570 0.2091 0.00 0.54 0.44 0.02
#> G2_Exp1.073 2 0.0000 0.9444 0.00 1.00 0.00 0.00
#> G2_Exp1.028 1 0.0000 0.9533 1.00 0.00 0.00 0.00
#> G2_Exp1.034 1 0.4134 0.6400 0.74 0.00 0.26 0.00
#> G2_Exp1.054 3 0.0000 0.9552 0.00 0.00 1.00 0.00
#> G2_Exp1.024 1 0.0000 0.9533 1.00 0.00 0.00 0.00
#> G2_Exp1.020 2 0.0000 0.9444 0.00 1.00 0.00 0.00
#> G2_Exp1.039 2 0.4994 0.1114 0.00 0.52 0.48 0.00
#> G2_Exp1.007 1 0.0000 0.9533 1.00 0.00 0.00 0.00
#> G2_Exp1.026 2 0.0000 0.9444 0.00 1.00 0.00 0.00
#> G2_Exp1.011 1 0.0000 0.9533 1.00 0.00 0.00 0.00
#> G2_Exp1.053 2 0.0000 0.9444 0.00 1.00 0.00 0.00
#> G2_Exp1.006 1 0.0000 0.9533 1.00 0.00 0.00 0.00
#> G2_Exp1.016 1 0.0000 0.9533 1.00 0.00 0.00 0.00
#> G2_Exp1.010 1 0.0000 0.9533 1.00 0.00 0.00 0.00
#> G2_Exp1.027 2 0.0000 0.9444 0.00 1.00 0.00 0.00
#> G2_Exp1.067 2 0.0000 0.9444 0.00 1.00 0.00 0.00
#> G2_Exp1.009 1 0.1211 0.9193 0.96 0.04 0.00 0.00
#> G2_Exp1.025 1 0.0000 0.9533 1.00 0.00 0.00 0.00
#> G2_Exp1.055 2 0.1211 0.9145 0.00 0.96 0.04 0.00
#> G2_Exp1.033 3 0.0000 0.9552 0.00 0.00 1.00 0.00
#> G2_Exp1.043 2 0.2345 0.8620 0.00 0.90 0.10 0.00
#> G2_Exp1.052 2 0.2011 0.8805 0.00 0.92 0.08 0.00
#> G2_Exp1.071 2 0.0000 0.9444 0.00 1.00 0.00 0.00
#> S_Exp1.074 1 0.0000 0.9533 1.00 0.00 0.00 0.00
#> S_Exp1.030 1 0.0000 0.9533 1.00 0.00 0.00 0.00
#> S_Exp1.013 2 0.0000 0.9444 0.00 1.00 0.00 0.00
#> S_Exp1.065 1 0.4406 0.5626 0.70 0.00 0.30 0.00
#> S_Exp1.076 1 0.0000 0.9533 1.00 0.00 0.00 0.00
#> S_Exp1.068 1 0.0000 0.9533 1.00 0.00 0.00 0.00
#> S_Exp1.009 1 0.0000 0.9533 1.00 0.00 0.00 0.00
#> S_Exp1.042 1 0.3610 0.7394 0.80 0.20 0.00 0.00
#> S_Exp1.002 1 0.0000 0.9533 1.00 0.00 0.00 0.00
#> S_Exp1.059 1 0.0000 0.9533 1.00 0.00 0.00 0.00
#> S_Exp1.044 1 0.0000 0.9533 1.00 0.00 0.00 0.00
#> G1_Exp1.048 3 0.0000 0.9552 0.00 0.00 1.00 0.00
#> G1_Exp1.090 2 0.3821 0.8087 0.00 0.84 0.04 0.12
#> G1_Exp1.061 3 0.0000 0.9552 0.00 0.00 1.00 0.00
#> G1_Exp1.025 3 0.0000 0.9552 0.00 0.00 1.00 0.00
#> G1_Exp1.081 3 0.0000 0.9552 0.00 0.00 1.00 0.00
#> G1_Exp1.024 1 0.0000 0.9533 1.00 0.00 0.00 0.00
#> G1_Exp1.056 2 0.2345 0.8616 0.00 0.90 0.00 0.10
#> G1_Exp1.064 3 0.0707 0.9378 0.00 0.02 0.98 0.00
#> G1_Exp1.053 3 0.0000 0.9552 0.00 0.00 1.00 0.00
#> G1_Exp1.070 3 0.0000 0.9552 0.00 0.00 1.00 0.00
#> G1_Exp1.087 3 0.0000 0.9552 0.00 0.00 1.00 0.00
#> G1_Exp1.067 2 0.0000 0.9444 0.00 1.00 0.00 0.00
#> G1_Exp1.003 1 0.0000 0.9533 1.00 0.00 0.00 0.00
#> G1_Exp1.052 3 0.0000 0.9552 0.00 0.00 1.00 0.00
#> G1_Exp1.060 3 0.1211 0.9189 0.00 0.04 0.96 0.00
#> G1_Exp1.071 2 0.0000 0.9444 0.00 1.00 0.00 0.00
#> G1_Exp1.045 2 0.0000 0.9444 0.00 1.00 0.00 0.00
#> G1_Exp1.040 4 0.4907 0.3182 0.00 0.00 0.42 0.58
#> G1_Exp1.016 1 0.0707 0.9374 0.98 0.02 0.00 0.00
#> G1_Exp1.002 1 0.0000 0.9533 1.00 0.00 0.00 0.00
#> G1_Exp1.021 1 0.0000 0.9533 1.00 0.00 0.00 0.00
#> G1_Exp1.015 1 0.0000 0.9533 1.00 0.00 0.00 0.00
#> G1_Exp1.042 3 0.0000 0.9552 0.00 0.00 1.00 0.00
#> G1_Exp1.058 4 0.4855 0.3731 0.00 0.00 0.40 0.60
#> G1_Exp1.075 2 0.0000 0.9444 0.00 1.00 0.00 0.00
#> G1_Exp1.074 3 0.0000 0.9552 0.00 0.00 1.00 0.00
#> G1_Exp1.079 3 0.2011 0.8793 0.00 0.00 0.92 0.08
#> G1_Exp1.038 3 0.0000 0.9552 0.00 0.00 1.00 0.00
#> G1_Exp1.035 3 0.1637 0.8984 0.06 0.00 0.94 0.00
#> G1_Exp1.012 1 0.0000 0.9533 1.00 0.00 0.00 0.00
#> G1_Exp1.032 3 0.0000 0.9552 0.00 0.00 1.00 0.00
#> G1_Exp1.009 3 0.4134 0.6454 0.26 0.00 0.74 0.00
#> G1_Exp1.043 3 0.0000 0.9552 0.00 0.00 1.00 0.00
#> G1_Exp1.089 3 0.0000 0.9552 0.00 0.00 1.00 0.00
#> G1_Exp1.080 3 0.0000 0.9552 0.00 0.00 1.00 0.00
#> G1_Exp1.007 1 0.0000 0.9533 1.00 0.00 0.00 0.00
#> G1_Exp1.062 3 0.0000 0.9552 0.00 0.00 1.00 0.00
#> G1_Exp1.069 3 0.0000 0.9552 0.00 0.00 1.00 0.00
#> G1_Exp1.004 2 0.0000 0.9444 0.00 1.00 0.00 0.00
#> G1_Exp1.010 2 0.0000 0.9444 0.00 1.00 0.00 0.00
#> G1_Exp1.044 3 0.0000 0.9552 0.00 0.00 1.00 0.00
#> G1_Exp1.065 2 0.0000 0.9444 0.00 1.00 0.00 0.00
#> G1_Exp1.029 1 0.0000 0.9533 1.00 0.00 0.00 0.00
#> G1_Exp1.086 3 0.4855 0.3374 0.40 0.00 0.60 0.00
#> G1_Exp1.084 3 0.0000 0.9552 0.00 0.00 1.00 0.00
#> G1_Exp1.088 2 0.0000 0.9444 0.00 1.00 0.00 0.00
#> G1_Exp1.051 3 0.0000 0.9552 0.00 0.00 1.00 0.00
#> G1_Exp1.066 3 0.2921 0.8063 0.00 0.14 0.86 0.00
#> G1_Exp1.047 3 0.0000 0.9552 0.00 0.00 1.00 0.00
#> G1_Exp1.072 2 0.2647 0.8436 0.00 0.88 0.12 0.00
#> G1_Exp1.057 3 0.0000 0.9552 0.00 0.00 1.00 0.00
#> G1_Exp1.054 2 0.0000 0.9444 0.00 1.00 0.00 0.00
#> G1_Exp1.082 3 0.0707 0.9381 0.02 0.00 0.98 0.00
#> G1_Exp1.077 3 0.0000 0.9552 0.00 0.00 1.00 0.00
#> G1_Exp1.091 3 0.0000 0.9552 0.00 0.00 1.00 0.00
#> G1_Exp1.068 3 0.0000 0.9552 0.00 0.00 1.00 0.00
#> G1_Exp1.001 1 0.0000 0.9533 1.00 0.00 0.00 0.00
#> G1_Exp1.005 1 0.0000 0.9533 1.00 0.00 0.00 0.00
#> G1_Exp1.037 3 0.0000 0.9552 0.00 0.00 1.00 0.00
#> G1_Exp1.014 1 0.0000 0.9533 1.00 0.00 0.00 0.00
#> G1_Exp1.017 1 0.0707 0.9386 0.98 0.00 0.02 0.00
#> G1_Exp1.028 1 0.0000 0.9533 1.00 0.00 0.00 0.00
#> G1_Exp1.036 3 0.0000 0.9552 0.00 0.00 1.00 0.00
#> G1_Exp1.049 3 0.0000 0.9552 0.00 0.00 1.00 0.00
#> G1_Exp1.041 2 0.0000 0.9444 0.00 1.00 0.00 0.00
#> G1_Exp1.006 1 0.0000 0.9533 1.00 0.00 0.00 0.00
#> G1_Exp1.008 1 0.0000 0.9533 1.00 0.00 0.00 0.00
#> G1_Exp1.055 3 0.0000 0.9552 0.00 0.00 1.00 0.00
#> G1_Exp1.050 2 0.0000 0.9444 0.00 1.00 0.00 0.00
#> G1_Exp1.076 3 0.0000 0.9552 0.00 0.00 1.00 0.00
#> G1_Exp1.083 3 0.0000 0.9552 0.00 0.00 1.00 0.00
#> G1_Exp1.030 3 0.0000 0.9552 0.00 0.00 1.00 0.00
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
As soon as the classes for columns are determined, the signatures that are significantly different between subgroups can be looked for. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. To get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows (which is done by automatically selecting number of clusters).If there are too many signatures, top_signatures = ... can be set to only show the
signatures with the highest FDRs:
# code only for demonstration
# e.g. to show the top 500 most significant rows
tb = get_signature(res, k = ..., top_signatures = 500)
If the signatures are defined as these which are uniquely high in current group, diff_method argument
can be set to "uniquely_high_in_one_group":
# code only for demonstration
tb = get_signature(res, k = ..., diff_method = "uniquely_high_in_one_group")
UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n_sample CellLine(p-value) Experiment(p-value) Phase(p-value) k
#> ATC:skmeans 231 3.94e-01 2.83e-03 3.26e-01 2
#> ATC:skmeans 235 5.52e-01 1.53e-03 3.60e-04 3
#> ATC:skmeans 220 1.01e-17 2.10e-15 4.03e-05 4
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
Parent node: Node02. Child nodes: Node0111 , Node0112 , Node0121-leaf , Node0122-leaf , Node0221-leaf , Node0222-leaf , Node0231-leaf , Node0232-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 13000 rows and 80 columns.
#> Top rows (947) 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 0.898 0.945 0.976 0.506 0.494 0.494
#> 3 3 0.676 0.687 0.827 0.296 0.776 0.580
#> 4 4 0.763 0.817 0.909 0.129 0.859 0.622
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
#> H1_Exp1.008 2 0.000 0.968 0.00 1.00
#> H1_Exp1.021 2 0.904 0.541 0.32 0.68
#> H1_Exp1.044 1 0.000 0.981 1.00 0.00
#> H1_Exp1.045 1 0.000 0.981 1.00 0.00
#> H1_Exp1.047 2 0.881 0.599 0.30 0.70
#> H1_Exp1.048 1 0.000 0.981 1.00 0.00
#> H1_Exp1.060 1 0.000 0.981 1.00 0.00
#> H1_Exp2.099 2 0.000 0.968 0.00 1.00
#> H1_Exp2.107 2 0.000 0.968 0.00 1.00
#> H1_Exp2.113 1 0.000 0.981 1.00 0.00
#> H1_Exp2.129 1 0.000 0.981 1.00 0.00
#> H1_Exp2.134 2 0.000 0.968 0.00 1.00
#> H1_Exp2.147 2 0.000 0.968 0.00 1.00
#> H1_Exp2.151 2 0.000 0.968 0.00 1.00
#> H1_Exp3.218 2 0.000 0.968 0.00 1.00
#> H1_Exp3.226 2 0.000 0.968 0.00 1.00
#> H1_Exp3.234 1 0.000 0.981 1.00 0.00
#> H1_Exp3.235 2 0.000 0.968 0.00 1.00
#> H1_Exp3.237 2 0.000 0.968 0.00 1.00
#> H1_Exp3.240 1 0.000 0.981 1.00 0.00
#> H1_Exp3.242 2 0.000 0.968 0.00 1.00
#> H1_Exp3.243 2 0.000 0.968 0.00 1.00
#> H1_Exp3.246 2 0.000 0.968 0.00 1.00
#> H1_Exp3.249 2 0.795 0.688 0.24 0.76
#> H1_Exp3.250 2 0.000 0.968 0.00 1.00
#> H1_Exp3.251 2 0.000 0.968 0.00 1.00
#> H1_Exp3.252 1 0.634 0.809 0.84 0.16
#> H1_Exp3.255 2 0.000 0.968 0.00 1.00
#> H1_Exp3.258 1 0.000 0.981 1.00 0.00
#> H1_Exp3.259 2 0.000 0.968 0.00 1.00
#> H1_Exp3.265 2 0.000 0.968 0.00 1.00
#> H1_Exp3.267 2 0.000 0.968 0.00 1.00
#> H1_Exp3.269 1 0.000 0.981 1.00 0.00
#> H1_Exp3.273 2 0.000 0.968 0.00 1.00
#> H1_Exp3.274 1 0.000 0.981 1.00 0.00
#> H1_Exp3.275 1 0.000 0.981 1.00 0.00
#> H1_Exp3.276 2 0.000 0.968 0.00 1.00
#> H1_Exp3.280 2 0.000 0.968 0.00 1.00
#> H1_Exp3.285 1 0.680 0.779 0.82 0.18
#> H1_Exp3.286 2 0.000 0.968 0.00 1.00
#> H1_Exp3.287 2 0.000 0.968 0.00 1.00
#> H1_Exp3.291 1 0.943 0.440 0.64 0.36
#> G2_Exp1.075 1 0.000 0.981 1.00 0.00
#> G2_Exp1.063 1 0.000 0.981 1.00 0.00
#> G2_Exp1.029 1 0.000 0.981 1.00 0.00
#> G2_Exp1.018 1 0.000 0.981 1.00 0.00
#> G2_Exp1.004 1 0.000 0.981 1.00 0.00
#> G2_Exp1.060 1 0.000 0.981 1.00 0.00
#> G2_Exp1.058 1 0.000 0.981 1.00 0.00
#> G2_Exp1.065 1 0.000 0.981 1.00 0.00
#> G2_Exp1.051 1 0.000 0.981 1.00 0.00
#> G2_Exp1.073 1 0.000 0.981 1.00 0.00
#> G2_Exp1.020 2 0.000 0.968 0.00 1.00
#> G2_Exp1.039 1 0.000 0.981 1.00 0.00
#> G2_Exp1.026 1 0.000 0.981 1.00 0.00
#> G2_Exp1.053 1 0.000 0.981 1.00 0.00
#> G2_Exp1.027 1 0.000 0.981 1.00 0.00
#> G2_Exp1.067 1 0.000 0.981 1.00 0.00
#> G2_Exp1.055 1 0.000 0.981 1.00 0.00
#> G2_Exp1.043 1 0.000 0.981 1.00 0.00
#> G2_Exp1.052 1 0.000 0.981 1.00 0.00
#> G2_Exp1.071 1 0.000 0.981 1.00 0.00
#> S_Exp1.013 2 0.000 0.968 0.00 1.00
#> G1_Exp1.090 2 0.000 0.968 0.00 1.00
#> G1_Exp1.056 2 0.000 0.968 0.00 1.00
#> G1_Exp1.064 1 0.000 0.981 1.00 0.00
#> G1_Exp1.067 2 0.402 0.902 0.08 0.92
#> G1_Exp1.060 1 0.000 0.981 1.00 0.00
#> G1_Exp1.071 2 0.000 0.968 0.00 1.00
#> G1_Exp1.045 2 0.000 0.968 0.00 1.00
#> G1_Exp1.075 1 0.000 0.981 1.00 0.00
#> G1_Exp1.004 1 0.000 0.981 1.00 0.00
#> G1_Exp1.010 2 0.000 0.968 0.00 1.00
#> G1_Exp1.065 2 0.680 0.788 0.18 0.82
#> G1_Exp1.088 2 0.469 0.881 0.10 0.90
#> G1_Exp1.066 1 0.000 0.981 1.00 0.00
#> G1_Exp1.072 2 0.000 0.968 0.00 1.00
#> G1_Exp1.054 2 0.242 0.936 0.04 0.96
#> G1_Exp1.041 2 0.000 0.968 0.00 1.00
#> G1_Exp1.050 2 0.000 0.968 0.00 1.00
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> H1_Exp1.008 2 0.6192 0.668 0.00 0.58 0.42
#> H1_Exp1.021 2 0.9460 0.135 0.24 0.50 0.26
#> H1_Exp1.044 1 0.0000 0.905 1.00 0.00 0.00
#> H1_Exp1.045 1 0.0000 0.905 1.00 0.00 0.00
#> H1_Exp1.047 3 0.6443 0.586 0.24 0.04 0.72
#> H1_Exp1.048 1 0.0000 0.905 1.00 0.00 0.00
#> H1_Exp1.060 1 0.0000 0.905 1.00 0.00 0.00
#> H1_Exp2.099 2 0.6192 0.668 0.00 0.58 0.42
#> H1_Exp2.107 2 0.6192 0.668 0.00 0.58 0.42
#> H1_Exp2.113 1 0.1529 0.875 0.96 0.04 0.00
#> H1_Exp2.129 1 0.0000 0.905 1.00 0.00 0.00
#> H1_Exp2.134 2 0.6192 0.668 0.00 0.58 0.42
#> H1_Exp2.147 2 0.6192 0.668 0.00 0.58 0.42
#> H1_Exp2.151 2 0.6192 0.668 0.00 0.58 0.42
#> H1_Exp3.218 2 0.0000 0.629 0.00 1.00 0.00
#> H1_Exp3.226 2 0.6192 0.668 0.00 0.58 0.42
#> H1_Exp3.234 1 0.0000 0.905 1.00 0.00 0.00
#> H1_Exp3.235 2 0.6192 0.668 0.00 0.58 0.42
#> H1_Exp3.237 2 0.6192 0.668 0.00 0.58 0.42
#> H1_Exp3.240 1 0.4291 0.747 0.82 0.18 0.00
#> H1_Exp3.242 2 0.0000 0.629 0.00 1.00 0.00
#> H1_Exp3.243 2 0.5706 0.666 0.00 0.68 0.32
#> H1_Exp3.246 2 0.0000 0.629 0.00 1.00 0.00
#> H1_Exp3.249 2 0.0000 0.629 0.00 1.00 0.00
#> H1_Exp3.250 2 0.4291 0.565 0.00 0.82 0.18
#> H1_Exp3.251 2 0.2537 0.588 0.00 0.92 0.08
#> H1_Exp3.252 2 0.4291 0.445 0.18 0.82 0.00
#> H1_Exp3.255 2 0.6244 0.649 0.00 0.56 0.44
#> H1_Exp3.258 1 0.6302 0.369 0.52 0.48 0.00
#> H1_Exp3.259 2 0.2066 0.601 0.00 0.94 0.06
#> H1_Exp3.265 2 0.6045 0.645 0.00 0.62 0.38
#> H1_Exp3.267 2 0.6280 0.622 0.00 0.54 0.46
#> H1_Exp3.269 1 0.6192 0.482 0.58 0.42 0.00
#> H1_Exp3.273 3 0.6244 -0.227 0.00 0.44 0.56
#> H1_Exp3.274 1 0.6192 0.482 0.58 0.42 0.00
#> H1_Exp3.275 1 0.6192 0.482 0.58 0.42 0.00
#> H1_Exp3.276 2 0.0000 0.629 0.00 1.00 0.00
#> H1_Exp3.280 2 0.1529 0.615 0.00 0.96 0.04
#> H1_Exp3.285 2 0.1529 0.601 0.04 0.96 0.00
#> H1_Exp3.286 2 0.6244 0.649 0.00 0.56 0.44
#> H1_Exp3.287 2 0.6192 0.668 0.00 0.58 0.42
#> H1_Exp3.291 2 0.0000 0.629 0.00 1.00 0.00
#> G2_Exp1.075 1 0.0000 0.905 1.00 0.00 0.00
#> G2_Exp1.063 1 0.0000 0.905 1.00 0.00 0.00
#> G2_Exp1.029 1 0.0000 0.905 1.00 0.00 0.00
#> G2_Exp1.018 1 0.0000 0.905 1.00 0.00 0.00
#> G2_Exp1.004 1 0.0000 0.905 1.00 0.00 0.00
#> G2_Exp1.060 1 0.0892 0.888 0.98 0.00 0.02
#> G2_Exp1.058 1 0.0000 0.905 1.00 0.00 0.00
#> G2_Exp1.065 1 0.0000 0.905 1.00 0.00 0.00
#> G2_Exp1.051 1 0.0000 0.905 1.00 0.00 0.00
#> G2_Exp1.073 1 0.0000 0.905 1.00 0.00 0.00
#> G2_Exp1.020 2 0.6192 0.668 0.00 0.58 0.42
#> G2_Exp1.039 1 0.0000 0.905 1.00 0.00 0.00
#> G2_Exp1.026 1 0.0000 0.905 1.00 0.00 0.00
#> G2_Exp1.053 1 0.0000 0.905 1.00 0.00 0.00
#> G2_Exp1.027 1 0.0000 0.905 1.00 0.00 0.00
#> G2_Exp1.067 1 0.0000 0.905 1.00 0.00 0.00
#> G2_Exp1.055 1 0.0000 0.905 1.00 0.00 0.00
#> G2_Exp1.043 1 0.0000 0.905 1.00 0.00 0.00
#> G2_Exp1.052 1 0.0000 0.905 1.00 0.00 0.00
#> G2_Exp1.071 1 0.0000 0.905 1.00 0.00 0.00
#> S_Exp1.013 2 0.6192 0.668 0.00 0.58 0.42
#> G1_Exp1.090 3 0.2537 0.633 0.00 0.08 0.92
#> G1_Exp1.056 3 0.0892 0.718 0.00 0.02 0.98
#> G1_Exp1.064 3 0.6302 0.129 0.48 0.00 0.52
#> G1_Exp1.067 3 0.0000 0.737 0.00 0.00 1.00
#> G1_Exp1.060 1 0.4796 0.628 0.78 0.00 0.22
#> G1_Exp1.071 3 0.0000 0.737 0.00 0.00 1.00
#> G1_Exp1.045 3 0.0000 0.737 0.00 0.00 1.00
#> G1_Exp1.075 3 0.6244 0.243 0.44 0.00 0.56
#> G1_Exp1.004 1 0.6192 0.137 0.58 0.00 0.42
#> G1_Exp1.010 3 0.1529 0.703 0.00 0.04 0.96
#> G1_Exp1.065 3 0.6407 0.586 0.08 0.16 0.76
#> G1_Exp1.088 3 0.2066 0.720 0.06 0.00 0.94
#> G1_Exp1.066 3 0.6126 0.337 0.40 0.00 0.60
#> G1_Exp1.072 3 0.0000 0.737 0.00 0.00 1.00
#> G1_Exp1.054 3 0.0892 0.735 0.02 0.00 0.98
#> G1_Exp1.041 3 0.0000 0.737 0.00 0.00 1.00
#> G1_Exp1.050 3 0.0000 0.737 0.00 0.00 1.00
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> H1_Exp1.008 2 0.0707 0.91976 0.00 0.98 0.02 0.00
#> H1_Exp1.021 3 0.9971 -0.00513 0.22 0.24 0.28 0.26
#> H1_Exp1.044 1 0.0000 0.93795 1.00 0.00 0.00 0.00
#> H1_Exp1.045 1 0.0000 0.93795 1.00 0.00 0.00 0.00
#> H1_Exp1.047 3 0.3886 0.77065 0.08 0.02 0.86 0.04
#> H1_Exp1.048 1 0.0000 0.93795 1.00 0.00 0.00 0.00
#> H1_Exp1.060 1 0.0000 0.93795 1.00 0.00 0.00 0.00
#> H1_Exp2.099 2 0.0707 0.91755 0.00 0.98 0.00 0.02
#> H1_Exp2.107 2 0.0000 0.92981 0.00 1.00 0.00 0.00
#> H1_Exp2.113 1 0.4079 0.75188 0.80 0.00 0.02 0.18
#> H1_Exp2.129 1 0.4211 0.81383 0.84 0.04 0.02 0.10
#> H1_Exp2.134 2 0.0000 0.92981 0.00 1.00 0.00 0.00
#> H1_Exp2.147 2 0.0707 0.91976 0.00 0.98 0.02 0.00
#> H1_Exp2.151 2 0.0000 0.92981 0.00 1.00 0.00 0.00
#> H1_Exp3.218 4 0.2921 0.85036 0.00 0.14 0.00 0.86
#> H1_Exp3.226 2 0.0000 0.92981 0.00 1.00 0.00 0.00
#> H1_Exp3.234 1 0.1637 0.89804 0.94 0.00 0.00 0.06
#> H1_Exp3.235 2 0.0000 0.92981 0.00 1.00 0.00 0.00
#> H1_Exp3.237 2 0.0000 0.92981 0.00 1.00 0.00 0.00
#> H1_Exp3.240 1 0.3610 0.74756 0.80 0.00 0.00 0.20
#> H1_Exp3.242 4 0.2921 0.84607 0.00 0.14 0.00 0.86
#> H1_Exp3.243 2 0.1637 0.88757 0.00 0.94 0.00 0.06
#> H1_Exp3.246 4 0.3610 0.78209 0.00 0.20 0.00 0.80
#> H1_Exp3.249 4 0.0000 0.87739 0.00 0.00 0.00 1.00
#> H1_Exp3.250 4 0.3335 0.84770 0.00 0.12 0.02 0.86
#> H1_Exp3.251 4 0.0000 0.87739 0.00 0.00 0.00 1.00
#> H1_Exp3.252 4 0.6477 0.53347 0.10 0.30 0.00 0.60
#> H1_Exp3.255 2 0.3525 0.82051 0.00 0.86 0.10 0.04
#> H1_Exp3.258 4 0.0707 0.87874 0.02 0.00 0.00 0.98
#> H1_Exp3.259 4 0.1211 0.88246 0.00 0.04 0.00 0.96
#> H1_Exp3.265 2 0.4755 0.68696 0.00 0.76 0.04 0.20
#> H1_Exp3.267 2 0.1211 0.90440 0.00 0.96 0.04 0.00
#> H1_Exp3.269 4 0.3172 0.75620 0.16 0.00 0.00 0.84
#> H1_Exp3.273 3 0.7738 0.24355 0.00 0.26 0.44 0.30
#> H1_Exp3.274 4 0.0707 0.87855 0.02 0.00 0.00 0.98
#> H1_Exp3.275 4 0.1211 0.87255 0.04 0.00 0.00 0.96
#> H1_Exp3.276 4 0.3172 0.82035 0.00 0.16 0.00 0.84
#> H1_Exp3.280 4 0.2011 0.87810 0.00 0.08 0.00 0.92
#> H1_Exp3.285 4 0.1913 0.88360 0.02 0.04 0.00 0.94
#> H1_Exp3.286 2 0.1211 0.90506 0.00 0.96 0.04 0.00
#> H1_Exp3.287 2 0.0000 0.92981 0.00 1.00 0.00 0.00
#> H1_Exp3.291 4 0.1211 0.88503 0.00 0.04 0.00 0.96
#> G2_Exp1.075 1 0.0000 0.93795 1.00 0.00 0.00 0.00
#> G2_Exp1.063 1 0.0000 0.93795 1.00 0.00 0.00 0.00
#> G2_Exp1.029 1 0.0000 0.93795 1.00 0.00 0.00 0.00
#> G2_Exp1.018 1 0.0000 0.93795 1.00 0.00 0.00 0.00
#> G2_Exp1.004 1 0.0000 0.93795 1.00 0.00 0.00 0.00
#> G2_Exp1.060 1 0.1211 0.91666 0.96 0.00 0.04 0.00
#> G2_Exp1.058 1 0.0000 0.93795 1.00 0.00 0.00 0.00
#> G2_Exp1.065 1 0.0000 0.93795 1.00 0.00 0.00 0.00
#> G2_Exp1.051 1 0.2411 0.89188 0.92 0.00 0.04 0.04
#> G2_Exp1.073 1 0.0707 0.92889 0.98 0.00 0.02 0.00
#> G2_Exp1.020 2 0.0000 0.92981 0.00 1.00 0.00 0.00
#> G2_Exp1.039 1 0.0000 0.93795 1.00 0.00 0.00 0.00
#> G2_Exp1.026 1 0.0000 0.93795 1.00 0.00 0.00 0.00
#> G2_Exp1.053 1 0.0707 0.92889 0.98 0.00 0.02 0.00
#> G2_Exp1.027 1 0.0000 0.93795 1.00 0.00 0.00 0.00
#> G2_Exp1.067 1 0.0000 0.93795 1.00 0.00 0.00 0.00
#> G2_Exp1.055 1 0.0000 0.93795 1.00 0.00 0.00 0.00
#> G2_Exp1.043 1 0.0000 0.93795 1.00 0.00 0.00 0.00
#> G2_Exp1.052 1 0.0707 0.92889 0.98 0.00 0.02 0.00
#> G2_Exp1.071 1 0.0000 0.93795 1.00 0.00 0.00 0.00
#> S_Exp1.013 2 0.0000 0.92981 0.00 1.00 0.00 0.00
#> G1_Exp1.090 3 0.4977 0.18770 0.00 0.46 0.54 0.00
#> G1_Exp1.056 3 0.3821 0.78305 0.00 0.12 0.84 0.04
#> G1_Exp1.064 3 0.3400 0.70346 0.18 0.00 0.82 0.00
#> G1_Exp1.067 3 0.0707 0.82479 0.00 0.02 0.98 0.00
#> G1_Exp1.060 1 0.4855 0.35637 0.60 0.00 0.40 0.00
#> G1_Exp1.071 3 0.2011 0.81981 0.00 0.08 0.92 0.00
#> G1_Exp1.045 3 0.1211 0.82750 0.00 0.04 0.96 0.00
#> G1_Exp1.075 3 0.2647 0.76269 0.12 0.00 0.88 0.00
#> G1_Exp1.004 1 0.5594 0.10504 0.52 0.02 0.46 0.00
#> G1_Exp1.010 2 0.4994 -0.05738 0.00 0.52 0.48 0.00
#> G1_Exp1.065 3 0.2411 0.82171 0.00 0.04 0.92 0.04
#> G1_Exp1.088 3 0.0000 0.81874 0.00 0.00 1.00 0.00
#> G1_Exp1.066 3 0.0707 0.81524 0.02 0.00 0.98 0.00
#> G1_Exp1.072 3 0.1211 0.82750 0.00 0.04 0.96 0.00
#> G1_Exp1.054 3 0.1211 0.82750 0.00 0.04 0.96 0.00
#> G1_Exp1.041 3 0.3172 0.76432 0.00 0.16 0.84 0.00
#> G1_Exp1.050 3 0.2345 0.80817 0.00 0.10 0.90 0.00
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
As soon as the classes for columns are determined, the signatures that are significantly different between subgroups can be looked for. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. To get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows (which is done by automatically selecting number of clusters).If there are too many signatures, top_signatures = ... can be set to only show the
signatures with the highest FDRs:
# code only for demonstration
# e.g. to show the top 500 most significant rows
tb = get_signature(res, k = ..., top_signatures = 500)
If the signatures are defined as these which are uniquely high in current group, diff_method argument
can be set to "uniquely_high_in_one_group":
# code only for demonstration
tb = get_signature(res, k = ..., diff_method = "uniquely_high_in_one_group")
UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n_sample CellLine(p-value) Experiment(p-value) Phase(p-value) k
#> ATC:skmeans 79 1.86e-02 1.53e-02 1.000000 2
#> ATC:skmeans 69 2.49e-08 4.12e-09 0.000912 3
#> ATC:skmeans 74 8.40e-09 8.51e-12 0.084182 4
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
Parent node: Node02. Child nodes: Node0111 , Node0112 , Node0121-leaf , Node0122-leaf , Node0221-leaf , Node0222-leaf , Node0231-leaf , Node0232-leaf .
The object with results only for a single top-value method and a single partitioning method can be extracted as:
res = res_rh["023"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4.
#> On a matrix with 13000 rows and 80 columns.
#> Top rows (1022) 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 0.973 0.959 0.983 0.499 0.502 0.502
#> 3 3 0.540 0.723 0.856 0.281 0.877 0.759
#> 4 4 0.473 0.525 0.748 0.118 0.943 0.857
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
#> H1_Exp1.027 2 0.000 0.981 0.00 1.00
#> H1_Exp1.030 2 0.000 0.981 0.00 1.00
#> H1_Exp1.031 2 0.000 0.981 0.00 1.00
#> H1_Exp1.032 2 0.000 0.981 0.00 1.00
#> H1_Exp1.036 2 0.000 0.981 0.00 1.00
#> H1_Exp1.042 1 0.000 0.983 1.00 0.00
#> H1_Exp1.043 1 0.000 0.983 1.00 0.00
#> H1_Exp1.058 2 0.000 0.981 0.00 1.00
#> H1_Exp1.068 2 0.000 0.981 0.00 1.00
#> H1_Exp2.115 2 0.925 0.486 0.34 0.66
#> H1_Exp2.117 2 0.327 0.924 0.06 0.94
#> H1_Exp2.120 2 0.000 0.981 0.00 1.00
#> H1_Exp2.125 2 0.000 0.981 0.00 1.00
#> H1_Exp2.126 1 0.242 0.946 0.96 0.04
#> H1_Exp2.132 2 0.000 0.981 0.00 1.00
#> H1_Exp2.137 1 0.000 0.983 1.00 0.00
#> H1_Exp2.149 1 0.469 0.882 0.90 0.10
#> H1_Exp3.224 2 0.000 0.981 0.00 1.00
#> H1_Exp3.238 2 0.000 0.981 0.00 1.00
#> H1_Exp3.244 2 0.000 0.981 0.00 1.00
#> H1_Exp3.247 2 0.000 0.981 0.00 1.00
#> H1_Exp3.253 2 0.000 0.981 0.00 1.00
#> H1_Exp3.254 2 0.000 0.981 0.00 1.00
#> H1_Exp3.256 2 0.000 0.981 0.00 1.00
#> H1_Exp3.257 2 0.000 0.981 0.00 1.00
#> H1_Exp3.260 2 0.000 0.981 0.00 1.00
#> H1_Exp3.261 2 0.000 0.981 0.00 1.00
#> H1_Exp3.262 2 0.000 0.981 0.00 1.00
#> H1_Exp3.263 2 0.000 0.981 0.00 1.00
#> H1_Exp3.264 1 0.760 0.716 0.78 0.22
#> H1_Exp3.266 2 0.000 0.981 0.00 1.00
#> H1_Exp3.268 2 0.000 0.981 0.00 1.00
#> H1_Exp3.270 2 0.000 0.981 0.00 1.00
#> H1_Exp3.271 2 0.000 0.981 0.00 1.00
#> H1_Exp3.272 1 0.000 0.983 1.00 0.00
#> H1_Exp3.278 1 0.000 0.983 1.00 0.00
#> H1_Exp3.279 2 0.000 0.981 0.00 1.00
#> H1_Exp3.281 2 0.000 0.981 0.00 1.00
#> H1_Exp3.282 2 0.000 0.981 0.00 1.00
#> H1_Exp3.288 2 0.000 0.981 0.00 1.00
#> H1_Exp3.289 2 0.000 0.981 0.00 1.00
#> G2_Exp1.059 1 0.000 0.983 1.00 0.00
#> G2_Exp1.057 1 0.000 0.983 1.00 0.00
#> G2_Exp1.044 1 0.000 0.983 1.00 0.00
#> G2_Exp1.054 1 0.000 0.983 1.00 0.00
#> G2_Exp1.033 2 0.000 0.981 0.00 1.00
#> G1_Exp1.048 1 0.000 0.983 1.00 0.00
#> G1_Exp1.061 1 0.000 0.983 1.00 0.00
#> G1_Exp1.025 1 0.000 0.983 1.00 0.00
#> G1_Exp1.081 1 0.000 0.983 1.00 0.00
#> G1_Exp1.053 1 0.000 0.983 1.00 0.00
#> G1_Exp1.070 1 0.000 0.983 1.00 0.00
#> G1_Exp1.087 1 0.000 0.983 1.00 0.00
#> G1_Exp1.052 1 0.000 0.983 1.00 0.00
#> G1_Exp1.040 2 0.795 0.685 0.24 0.76
#> G1_Exp1.042 1 0.000 0.983 1.00 0.00
#> G1_Exp1.058 1 0.943 0.426 0.64 0.36
#> G1_Exp1.074 1 0.000 0.983 1.00 0.00
#> G1_Exp1.079 1 0.000 0.983 1.00 0.00
#> G1_Exp1.038 1 0.000 0.983 1.00 0.00
#> G1_Exp1.032 1 0.000 0.983 1.00 0.00
#> G1_Exp1.043 1 0.000 0.983 1.00 0.00
#> G1_Exp1.089 1 0.000 0.983 1.00 0.00
#> G1_Exp1.080 1 0.000 0.983 1.00 0.00
#> G1_Exp1.062 1 0.000 0.983 1.00 0.00
#> G1_Exp1.069 1 0.000 0.983 1.00 0.00
#> G1_Exp1.044 1 0.000 0.983 1.00 0.00
#> G1_Exp1.084 1 0.000 0.983 1.00 0.00
#> G1_Exp1.051 1 0.000 0.983 1.00 0.00
#> G1_Exp1.047 1 0.000 0.983 1.00 0.00
#> G1_Exp1.057 1 0.000 0.983 1.00 0.00
#> G1_Exp1.077 1 0.000 0.983 1.00 0.00
#> G1_Exp1.068 1 0.000 0.983 1.00 0.00
#> G1_Exp1.037 1 0.000 0.983 1.00 0.00
#> G1_Exp1.036 1 0.000 0.983 1.00 0.00
#> G1_Exp1.049 1 0.000 0.983 1.00 0.00
#> G1_Exp1.055 1 0.000 0.983 1.00 0.00
#> G1_Exp1.076 1 0.000 0.983 1.00 0.00
#> G1_Exp1.083 1 0.000 0.983 1.00 0.00
#> G1_Exp1.030 1 0.000 0.983 1.00 0.00
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> H1_Exp1.027 3 0.6280 0.18321 0.00 0.46 0.54
#> H1_Exp1.030 3 0.7059 -0.08573 0.02 0.46 0.52
#> H1_Exp1.031 2 0.2959 0.83933 0.00 0.90 0.10
#> H1_Exp1.032 2 0.2537 0.84656 0.00 0.92 0.08
#> H1_Exp1.036 2 0.5216 0.66493 0.00 0.74 0.26
#> H1_Exp1.042 1 0.5706 0.51768 0.68 0.00 0.32
#> H1_Exp1.043 1 0.5016 0.66909 0.76 0.00 0.24
#> H1_Exp1.058 2 0.6045 0.38650 0.00 0.62 0.38
#> H1_Exp1.068 2 0.5835 0.51877 0.00 0.66 0.34
#> H1_Exp2.115 3 0.4556 0.70836 0.08 0.06 0.86
#> H1_Exp2.117 3 0.3042 0.70421 0.04 0.04 0.92
#> H1_Exp2.120 3 0.5159 0.67778 0.04 0.14 0.82
#> H1_Exp2.125 2 0.0892 0.87661 0.00 0.98 0.02
#> H1_Exp2.126 3 0.4002 0.61903 0.16 0.00 0.84
#> H1_Exp2.132 2 0.6126 0.37286 0.00 0.60 0.40
#> H1_Exp2.137 1 0.6192 0.39032 0.58 0.00 0.42
#> H1_Exp2.149 1 0.9899 -0.26449 0.40 0.28 0.32
#> H1_Exp3.224 2 0.0000 0.88510 0.00 1.00 0.00
#> H1_Exp3.238 2 0.0000 0.88510 0.00 1.00 0.00
#> H1_Exp3.244 2 0.0000 0.88510 0.00 1.00 0.00
#> H1_Exp3.247 2 0.0892 0.87810 0.00 0.98 0.02
#> H1_Exp3.253 3 0.6232 0.57545 0.04 0.22 0.74
#> H1_Exp3.254 2 0.0000 0.88510 0.00 1.00 0.00
#> H1_Exp3.256 2 0.0000 0.88510 0.00 1.00 0.00
#> H1_Exp3.257 2 0.0000 0.88510 0.00 1.00 0.00
#> H1_Exp3.260 2 0.0000 0.88510 0.00 1.00 0.00
#> H1_Exp3.261 2 0.5948 0.44124 0.00 0.64 0.36
#> H1_Exp3.262 2 0.0000 0.88510 0.00 1.00 0.00
#> H1_Exp3.263 2 0.1529 0.86937 0.00 0.96 0.04
#> H1_Exp3.264 3 0.7398 0.66808 0.12 0.18 0.70
#> H1_Exp3.266 2 0.4291 0.73935 0.00 0.82 0.18
#> H1_Exp3.268 2 0.2959 0.82678 0.00 0.90 0.10
#> H1_Exp3.270 2 0.0892 0.87764 0.00 0.98 0.02
#> H1_Exp3.271 2 0.0000 0.88510 0.00 1.00 0.00
#> H1_Exp3.272 1 0.4796 0.72726 0.78 0.00 0.22
#> H1_Exp3.278 1 0.3340 0.78824 0.88 0.00 0.12
#> H1_Exp3.279 2 0.0892 0.87816 0.00 0.98 0.02
#> H1_Exp3.281 2 0.5560 0.58656 0.00 0.70 0.30
#> H1_Exp3.282 2 0.0000 0.88510 0.00 1.00 0.00
#> H1_Exp3.288 2 0.0000 0.88510 0.00 1.00 0.00
#> H1_Exp3.289 2 0.0000 0.88510 0.00 1.00 0.00
#> G2_Exp1.059 1 0.3686 0.77002 0.86 0.00 0.14
#> G2_Exp1.057 1 0.6245 0.66959 0.76 0.06 0.18
#> G2_Exp1.044 1 0.2066 0.81995 0.94 0.00 0.06
#> G2_Exp1.054 1 0.0892 0.83405 0.98 0.00 0.02
#> G2_Exp1.033 2 0.0892 0.87435 0.00 0.98 0.02
#> G1_Exp1.048 1 0.0000 0.83900 1.00 0.00 0.00
#> G1_Exp1.061 1 0.2959 0.82676 0.90 0.00 0.10
#> G1_Exp1.025 1 0.4002 0.79429 0.84 0.00 0.16
#> G1_Exp1.081 1 0.4555 0.77249 0.80 0.00 0.20
#> G1_Exp1.053 1 0.3340 0.81780 0.88 0.00 0.12
#> G1_Exp1.070 1 0.5706 0.62880 0.68 0.00 0.32
#> G1_Exp1.087 1 0.4555 0.77426 0.80 0.00 0.20
#> G1_Exp1.052 1 0.2537 0.83242 0.92 0.00 0.08
#> G1_Exp1.040 3 0.7138 0.69061 0.12 0.16 0.72
#> G1_Exp1.042 1 0.4002 0.80077 0.84 0.00 0.16
#> G1_Exp1.058 3 0.7725 0.67129 0.16 0.16 0.68
#> G1_Exp1.074 1 0.3686 0.80973 0.86 0.00 0.14
#> G1_Exp1.079 1 0.5016 0.71754 0.76 0.00 0.24
#> G1_Exp1.038 1 0.2537 0.83250 0.92 0.00 0.08
#> G1_Exp1.032 1 0.6849 0.44939 0.60 0.02 0.38
#> G1_Exp1.043 1 0.0000 0.83900 1.00 0.00 0.00
#> G1_Exp1.089 1 0.0000 0.83900 1.00 0.00 0.00
#> G1_Exp1.080 1 0.0000 0.83900 1.00 0.00 0.00
#> G1_Exp1.062 1 0.5835 0.58991 0.66 0.00 0.34
#> G1_Exp1.069 1 0.2959 0.82771 0.90 0.00 0.10
#> G1_Exp1.044 1 0.0000 0.83900 1.00 0.00 0.00
#> G1_Exp1.084 1 0.0892 0.83836 0.98 0.00 0.02
#> G1_Exp1.051 1 0.1529 0.83771 0.96 0.00 0.04
#> G1_Exp1.047 1 0.0000 0.83900 1.00 0.00 0.00
#> G1_Exp1.057 3 0.6192 -0.00378 0.42 0.00 0.58
#> G1_Exp1.077 1 0.5216 0.73173 0.74 0.00 0.26
#> G1_Exp1.068 1 0.5397 0.68346 0.72 0.00 0.28
#> G1_Exp1.037 1 0.1529 0.83771 0.96 0.00 0.04
#> G1_Exp1.036 1 0.0000 0.83900 1.00 0.00 0.00
#> G1_Exp1.049 1 0.0000 0.83900 1.00 0.00 0.00
#> G1_Exp1.055 1 0.0000 0.83900 1.00 0.00 0.00
#> G1_Exp1.076 1 0.0000 0.83900 1.00 0.00 0.00
#> G1_Exp1.083 1 0.0892 0.83836 0.98 0.00 0.02
#> G1_Exp1.030 1 0.3686 0.81738 0.86 0.00 0.14
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> H1_Exp1.027 3 0.6323 -0.04775 0.00 0.44 0.50 0.06
#> H1_Exp1.030 4 0.8172 0.15547 0.04 0.14 0.38 0.44
#> H1_Exp1.031 2 0.4949 0.62459 0.00 0.76 0.06 0.18
#> H1_Exp1.032 2 0.5657 0.58706 0.00 0.72 0.12 0.16
#> H1_Exp1.036 4 0.7869 0.17790 0.00 0.34 0.28 0.38
#> H1_Exp1.042 1 0.7869 -0.20150 0.38 0.00 0.28 0.34
#> H1_Exp1.043 1 0.5619 0.47842 0.64 0.00 0.04 0.32
#> H1_Exp1.058 2 0.7357 0.00529 0.00 0.50 0.32 0.18
#> H1_Exp1.068 2 0.7913 -0.33863 0.00 0.36 0.32 0.32
#> H1_Exp2.115 3 0.3821 0.40194 0.04 0.00 0.84 0.12
#> H1_Exp2.117 3 0.3030 0.43863 0.02 0.02 0.90 0.06
#> H1_Exp2.120 3 0.4227 0.36465 0.00 0.06 0.82 0.12
#> H1_Exp2.125 2 0.1913 0.75256 0.00 0.94 0.02 0.04
#> H1_Exp2.126 3 0.4971 0.45678 0.10 0.02 0.80 0.08
#> H1_Exp2.132 4 0.6933 0.39197 0.00 0.30 0.14 0.56
#> H1_Exp2.137 1 0.6286 0.60151 0.66 0.00 0.20 0.14
#> H1_Exp2.149 1 0.9557 -0.22338 0.34 0.24 0.30 0.12
#> H1_Exp3.224 2 0.0000 0.76383 0.00 1.00 0.00 0.00
#> H1_Exp3.238 2 0.0000 0.76383 0.00 1.00 0.00 0.00
#> H1_Exp3.244 2 0.2830 0.74977 0.00 0.90 0.06 0.04
#> H1_Exp3.247 2 0.3198 0.74388 0.00 0.88 0.04 0.08
#> H1_Exp3.253 3 0.8149 -0.16707 0.02 0.22 0.46 0.30
#> H1_Exp3.254 2 0.0000 0.76383 0.00 1.00 0.00 0.00
#> H1_Exp3.256 2 0.0000 0.76383 0.00 1.00 0.00 0.00
#> H1_Exp3.257 2 0.2706 0.75467 0.00 0.90 0.02 0.08
#> H1_Exp3.260 2 0.0000 0.76383 0.00 1.00 0.00 0.00
#> H1_Exp3.261 2 0.6881 0.15042 0.00 0.54 0.34 0.12
#> H1_Exp3.262 2 0.2411 0.75430 0.00 0.92 0.04 0.04
#> H1_Exp3.263 2 0.2706 0.74879 0.00 0.90 0.02 0.08
#> H1_Exp3.264 3 0.9470 0.08693 0.14 0.18 0.38 0.30
#> H1_Exp3.266 2 0.5619 0.41861 0.00 0.64 0.04 0.32
#> H1_Exp3.268 2 0.4581 0.69878 0.00 0.80 0.08 0.12
#> H1_Exp3.270 2 0.3853 0.71094 0.00 0.82 0.02 0.16
#> H1_Exp3.271 2 0.4079 0.63566 0.00 0.80 0.02 0.18
#> H1_Exp3.272 1 0.7310 0.21567 0.48 0.00 0.16 0.36
#> H1_Exp3.278 1 0.5570 0.26271 0.54 0.00 0.02 0.44
#> H1_Exp3.279 2 0.4642 0.59538 0.00 0.74 0.02 0.24
#> H1_Exp3.281 4 0.6808 0.34877 0.00 0.32 0.12 0.56
#> H1_Exp3.282 2 0.3172 0.69073 0.00 0.84 0.00 0.16
#> H1_Exp3.288 2 0.1637 0.76038 0.00 0.94 0.00 0.06
#> H1_Exp3.289 2 0.0000 0.76383 0.00 1.00 0.00 0.00
#> G2_Exp1.059 1 0.5271 0.47790 0.64 0.00 0.02 0.34
#> G2_Exp1.057 4 0.5993 0.01269 0.36 0.02 0.02 0.60
#> G2_Exp1.044 1 0.3801 0.64186 0.78 0.00 0.00 0.22
#> G2_Exp1.054 1 0.2647 0.71545 0.88 0.00 0.00 0.12
#> G2_Exp1.033 2 0.6150 0.22249 0.00 0.58 0.06 0.36
#> G1_Exp1.048 1 0.0000 0.74929 1.00 0.00 0.00 0.00
#> G1_Exp1.061 1 0.6122 0.64530 0.68 0.00 0.16 0.16
#> G1_Exp1.025 1 0.6976 0.49248 0.58 0.00 0.18 0.24
#> G1_Exp1.081 1 0.6049 0.64916 0.68 0.00 0.12 0.20
#> G1_Exp1.053 1 0.5902 0.66114 0.70 0.00 0.14 0.16
#> G1_Exp1.070 1 0.7135 0.52899 0.56 0.00 0.20 0.24
#> G1_Exp1.087 1 0.3972 0.73306 0.84 0.00 0.08 0.08
#> G1_Exp1.052 1 0.5151 0.70750 0.76 0.00 0.10 0.14
#> G1_Exp1.040 3 0.8939 0.32931 0.14 0.18 0.50 0.18
#> G1_Exp1.042 1 0.5383 0.68618 0.74 0.00 0.10 0.16
#> G1_Exp1.058 3 0.6969 0.35645 0.04 0.12 0.66 0.18
#> G1_Exp1.074 1 0.4227 0.73367 0.82 0.00 0.06 0.12
#> G1_Exp1.079 1 0.6449 0.56417 0.64 0.00 0.22 0.14
#> G1_Exp1.038 1 0.3935 0.74145 0.84 0.00 0.06 0.10
#> G1_Exp1.032 3 0.8272 0.07097 0.38 0.02 0.38 0.22
#> G1_Exp1.043 1 0.0000 0.74929 1.00 0.00 0.00 0.00
#> G1_Exp1.089 1 0.0707 0.74556 0.98 0.00 0.00 0.02
#> G1_Exp1.080 1 0.0000 0.74929 1.00 0.00 0.00 0.00
#> G1_Exp1.062 1 0.6477 0.44033 0.60 0.00 0.30 0.10
#> G1_Exp1.069 1 0.3525 0.74482 0.86 0.00 0.04 0.10
#> G1_Exp1.044 1 0.0000 0.74929 1.00 0.00 0.00 0.00
#> G1_Exp1.084 1 0.2335 0.75111 0.92 0.00 0.02 0.06
#> G1_Exp1.051 1 0.1411 0.74981 0.96 0.00 0.02 0.02
#> G1_Exp1.047 1 0.1211 0.75025 0.96 0.00 0.00 0.04
#> G1_Exp1.057 3 0.7198 0.31128 0.28 0.00 0.54 0.18
#> G1_Exp1.077 1 0.6941 0.40804 0.52 0.00 0.12 0.36
#> G1_Exp1.068 1 0.6286 0.62437 0.66 0.00 0.20 0.14
#> G1_Exp1.037 1 0.2706 0.75110 0.90 0.00 0.02 0.08
#> G1_Exp1.036 1 0.0000 0.74929 1.00 0.00 0.00 0.00
#> G1_Exp1.049 1 0.3335 0.74435 0.86 0.00 0.02 0.12
#> G1_Exp1.055 1 0.0000 0.74929 1.00 0.00 0.00 0.00
#> G1_Exp1.076 1 0.0000 0.74929 1.00 0.00 0.00 0.00
#> G1_Exp1.083 1 0.3247 0.74395 0.88 0.00 0.06 0.06
#> G1_Exp1.030 1 0.5863 0.67869 0.70 0.00 0.12 0.18
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
As soon as the classes for columns are determined, the signatures that are significantly different between subgroups can be looked for. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. To get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows (which is done by automatically selecting number of clusters).If there are too many signatures, top_signatures = ... can be set to only show the
signatures with the highest FDRs:
# code only for demonstration
# e.g. to show the top 500 most significant rows
tb = get_signature(res, k = ..., top_signatures = 500)
If the signatures are defined as these which are uniquely high in current group, diff_method argument
can be set to "uniquely_high_in_one_group":
# code only for demonstration
tb = get_signature(res, k = ..., diff_method = "uniquely_high_in_one_group")
UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n_sample CellLine(p-value) Experiment(p-value) Phase(p-value) k
#> ATC:skmeans 78 1.32e-10 2.09e-07 NA 2
#> ATC:skmeans 71 6.88e-11 6.15e-13 NA 3
#> ATC:skmeans 50 8.46e-11 1.94e-09 NA 4
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
sessionInfo()
#> R version 4.1.0 (2021-05-18)
#> Platform: x86_64-pc-linux-gnu (64-bit)
#> Running under: CentOS Linux 7 (Core)
#>
#> Matrix products: default
#> BLAS/LAPACK: /usr/lib64/libopenblas-r0.3.3.so
#>
#> locale:
#> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=en_US.UTF-8
#> [4] LC_COLLATE=en_US.UTF-8 LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
#> [7] LC_PAPER=en_US.UTF-8 LC_NAME=C LC_ADDRESS=C
#> [10] LC_TELEPHONE=C LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
#>
#> attached base packages:
#> [1] grid parallel stats4 stats graphics grDevices utils datasets methods
#> [10] base
#>
#> other attached packages:
#> [1] genefilter_1.74.0 ComplexHeatmap_2.8.0 markdown_1.1
#> [4] knitr_1.33 scRNAseq_2.6.1 SingleCellExperiment_1.14.1
#> [7] SummarizedExperiment_1.22.0 Biobase_2.52.0 GenomicRanges_1.44.0
#> [10] GenomeInfoDb_1.28.1 IRanges_2.26.0 S4Vectors_0.30.0
#> [13] BiocGenerics_0.38.0 MatrixGenerics_1.4.0 matrixStats_0.59.0
#> [16] cola_1.9.4
#>
#> loaded via a namespace (and not attached):
#> [1] circlize_0.4.13 AnnotationHub_3.0.1 BiocFileCache_2.0.0
#> [4] lazyeval_0.2.2 polylabelr_0.2.0 splines_4.1.0
#> [7] Polychrome_1.3.1 BiocParallel_1.26.1 ggplot2_3.3.5
#> [10] digest_0.6.27 foreach_1.5.1 ensembldb_2.16.3
#> [13] htmltools_0.5.1.1 viridis_0.6.1 fansi_0.5.0
#> [16] magrittr_2.0.1 memoise_2.0.0 cluster_2.1.2
#> [19] doParallel_1.0.16 Biostrings_2.60.1 annotate_1.70.0
#> [22] askpass_1.1 prettyunits_1.1.1 colorspace_2.0-2
#> [25] blob_1.2.1 rappdirs_0.3.3 xfun_0.24
#> [28] dplyr_1.0.7 crayon_1.4.1 RCurl_1.98-1.3
#> [31] microbenchmark_1.4-7 jsonlite_1.7.2 impute_1.66.0
#> [34] brew_1.0-6 survival_3.2-11 iterators_1.0.13
#> [37] glue_1.4.2 polyclip_1.10-0 gtable_0.3.0
#> [40] zlibbioc_1.38.0 XVector_0.32.0 GetoptLong_1.0.5
#> [43] DelayedArray_0.18.0 shape_1.4.6 scales_1.1.1
#> [46] data.tree_1.0.0 DBI_1.1.1 Rcpp_1.0.7
#> [49] viridisLite_0.4.0 xtable_1.8-4 progress_1.2.2
#> [52] clue_0.3-59 reticulate_1.20 bit_4.0.4
#> [55] mclust_5.4.7 umap_0.2.7.0 httr_1.4.2
#> [58] RColorBrewer_1.1-2 ellipsis_0.3.2 pkgconfig_2.0.3
#> [61] XML_3.99-0.6 dbplyr_2.1.1 utf8_1.2.1
#> [64] tidyselect_1.1.1 rlang_0.4.11 later_1.2.0
#> [67] AnnotationDbi_1.54.1 munsell_0.5.0 BiocVersion_3.13.1
#> [70] tools_4.1.0 cachem_1.0.5 generics_0.1.0
#> [73] RSQLite_2.2.7 ExperimentHub_2.0.0 evaluate_0.14
#> [76] stringr_1.4.0 fastmap_1.1.0 yaml_2.2.1
#> [79] bit64_4.0.5 purrr_0.3.4 dendextend_1.15.1
#> [82] KEGGREST_1.32.0 AnnotationFilter_1.16.0 mime_0.11
#> [85] slam_0.1-48 xml2_1.3.2 biomaRt_2.48.2
#> [88] compiler_4.1.0 rstudioapi_0.13 filelock_1.0.2
#> [91] curl_4.3.2 png_0.1-7 interactiveDisplayBase_1.30.0
#> [94] tibble_3.1.2 stringi_1.7.3 highr_0.9
#> [97] GenomicFeatures_1.44.0 RSpectra_0.16-0 lattice_0.20-44
#> [100] ProtGenerics_1.24.0 Matrix_1.3-4 vctrs_0.3.8
#> [103] pillar_1.6.1 lifecycle_1.0.0 BiocManager_1.30.16
#> [106] eulerr_6.1.0 GlobalOptions_0.1.2 bitops_1.0-7
#> [109] irlba_2.3.3 httpuv_1.6.1 rtracklayer_1.52.0
#> [112] R6_2.5.0 BiocIO_1.2.0 promises_1.2.0.1
#> [115] gridExtra_2.3 codetools_0.2-18 assertthat_0.2.1
#> [118] openssl_1.4.4 rjson_0.2.20 GenomicAlignments_1.28.0
#> [121] Rsamtools_2.8.0 GenomeInfoDbData_1.2.6 hms_1.1.0
#> [124] skmeans_0.2-13 Cairo_1.5-12.2 scatterplot3d_0.3-41
#> [127] shiny_1.6.0 restfulr_0.0.13