Date: 2021-07-26 10:30:35 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 15681 rows and 288 columns.
#> Performed in total 2550 partitions.
#> There are 10 groups under the following parameters:
#> - min_samples: 6
#> - mean_silhouette_cutoff: 0.9
#> - min_n_signatures: 330 (signatures are selected based on:)
#> - fdr_cutoff: 0.05
#> - group_diff (scaled values): 0.5
#>
#> Hierarchy of the partition:
#> 0, 288 cols
#> |-- 01, 165 cols, 4525 signatures
#> | |-- 011, 64 cols, 696 signatures
#> | | |-- 0111, 34 cols, 13 signatures (c)
#> | | `-- 0112, 30 cols, 76 signatures (c)
#> | |-- 012, 63 cols, 441 signatures
#> | | |-- 0121, 32 cols (a)
#> | | `-- 0122, 31 cols, 4 signatures (c)
#> | `-- 013, 38 cols, 6 signatures (c)
#> `-- 02, 123 cols, 10267 signatures
#> |-- 021, 52 cols, 314 signatures (c)
#> |-- 022, 33 cols, 5620 signatures
#> | |-- 0221, 15 cols, 2339 signatures
#> | | |-- 02211, 9 cols (b)
#> | | `-- 02212, 6 cols (b)
#> | `-- 0222, 18 cols (a)
#> `-- 023, 38 cols, 80 signatures (c)
#> Stop reason:
#> a) Mean silhouette score was too small
#> b) Subgroup had too few columns.
#> c) There were too few signatures.
#>
#> Following methods can be applied to this 'HierarchicalPartition' object:
#> [1] "all_leaves" "all_nodes" "cola_report" "collect_classes"
#> [5] "colnames" "compare_signatures" "dimension_reduction" "functional_enrichment"
#> [9] "get_anno_col" "get_anno" "get_children_nodes" "get_classes"
#> [13] "get_matrix" "get_signatures" "is_leaf_node" "max_depth"
#> [17] "merge_node" "ncol" "node_info" "node_level"
#> [21] "nrow" "rownames" "show" "split_node"
#> [25] "suggest_best_k" "test_to_known_factors" "top_rows_heatmap" "top_rows_overlap"
#>
#> You can get result for a single node by e.g. object["01"]
The call of hierarchical_partition() was:
#> hierarchical_partition(data = lt$mat, anno = lt$anno, subset = 500, cores = 4)
Dimension of the input matrix:
mat = get_matrix(res_rh)
dim(mat)
#> [1] 15681 288
All the methods that were tried:
res_rh@param$combination_method
#> [[1]]
#> [1] "ATC" "skmeans"
The density distribution for each sample is visualized as one column in the following heatmap. The clustering is based on the distance which is the Kolmogorov-Smirnov statistic between two distributions.
library(ComplexHeatmap)
densityHeatmap(mat, ylab = "value", cluster_columns = TRUE, show_column_names = FALSE,
mc.cores = 1)
Some values about the hierarchy:
all_nodes(res_rh)
#> [1] "0" "01" "011" "0111" "0112" "012" "0121" "0122" "013" "02" "021" "022"
#> [13] "0221" "02211" "02212" "0222" "023"
all_leaves(res_rh)
#> [1] "0111" "0112" "0121" "0122" "013" "021" "02211" "02212" "0222" "023"
node_info(res_rh)
#> id best_method depth best_k n_columns n_signatures p_signatures is_leaf
#> 1 0 ATC:skmeans 1 2 288 6601 0.420955 FALSE
#> 2 01 ATC:skmeans 2 3 165 4525 0.288566 FALSE
#> 3 011 ATC:skmeans 3 2 64 696 0.044385 FALSE
#> 4 0111 ATC:skmeans 4 2 34 13 0.000829 TRUE
#> 5 0112 ATC:skmeans 4 3 30 76 0.004847 TRUE
#> 6 012 ATC:skmeans 3 2 63 441 0.028123 FALSE
#> 7 0121 ATC:skmeans 4 2 32 NA NA TRUE
#> 8 0122 ATC:skmeans 4 2 31 4 0.000255 TRUE
#> 9 013 ATC:skmeans 3 2 38 6 0.000383 TRUE
#> 10 02 ATC:skmeans 2 3 123 10267 0.654741 FALSE
#> 11 021 ATC:skmeans 3 3 52 314 0.020024 TRUE
#> 12 022 ATC:skmeans 3 2 33 5620 0.358396 FALSE
#> 13 0221 ATC:skmeans 4 2 15 2339 0.149161 FALSE
#> 14 02211 not applied 5 NA 9 NA NA TRUE
#> 15 02212 not applied 5 NA 6 NA NA TRUE
#> 16 0222 ATC:skmeans 4 2 18 NA NA TRUE
#> 17 023 ATC:skmeans 3 3 38 80 0.005102 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 | 288 | ** | |
| Node01 | ATC:skmeans | 4 | 0.93 | 0.91 | 0.96 | 165 | * | |
| Node011 | ATC:skmeans | 3 | 0.90 | 0.88 | 0.95 | 64 | * | |
| Node0111-leaf | ATC:skmeans | ✓ (c) | 2 | 0.88 | 0.92 | 0.97 | 34 | |
| Node0112-leaf | ATC:skmeans | ✓ (c) | 3 | 1.00 | 0.96 | 0.98 | 30 | ** |
| Node012 | ATC:skmeans | 2 | 1.00 | 0.98 | 0.99 | 63 | ** | |
| Node0121-leaf | ATC:skmeans | ✓ (a) | 2 | 0.51 | 0.87 | 0.92 | 32 | |
| Node0122-leaf | ATC:skmeans | ✓ (c) | 2 | 1.00 | 0.97 | 0.99 | 31 | ** |
| Node013-leaf | ATC:skmeans | ✓ (c) | 2 | 1.00 | 0.96 | 0.98 | 38 | ** |
| Node02 | ATC:skmeans | 3 | 1.00 | 1.00 | 1.00 | 123 | ** | |
| Node021-leaf | ATC:skmeans | ✓ (c) | 3 | 0.92 | 0.91 | 0.96 | 52 | * |
| Node022 | ATC:skmeans | 3 | 1.00 | 0.95 | 0.98 | 33 | ** | |
| Node0221 | ATC:skmeans | 2 | 1.00 | 1.00 | 1.00 | 15 | ** | |
| Node02211-leaf | not applied | ✓ (b) | 9 | |||||
| Node02212-leaf | not applied | ✓ (b) | 6 | |||||
| Node0222-leaf | ATC:skmeans | ✓ (a) | 2 | 0.63 | 0.79 | 0.91 | 18 | |
| Node023-leaf | ATC:skmeans | ✓ (c) | 3 | 1.00 | 0.98 | 0.99 | 38 | ** |
Stop reason: a) Mean silhouette score was too small b) Subgroup had too few columns. c) There were too few signatures.
**: 1-PAC > 0.95, *: 1-PAC > 0.9
The nodes of the hierarchy can be merged by setting the merge_node parameters. Here we
control the hierarchy with the min_n_signatures parameter. The value of min_n_signatures is
from node_info().
collect_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 441))
collect_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 696))
collect_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 2339))
collect_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 4525))
collect_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 5620))
collect_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 6601))
collect_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 10267))
#> 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 = 441))
#> G1_cell1_count G1_cell2_count G1_cell3_count G1_cell4_count G1_cell5_count
#> "023" "023" "023" "0222" "023"
#> G1_cell6_count G1_cell7_count G1_cell8_count G1_cell9_count G1_cell10_count
#> "023" "013" "013" "0112" "021"
#> G1_cell11_count G1_cell12_count G1_cell13_count G1_cell14_count G1_cell15_count
#> "023" "023" "0112" "023" "013"
#> G1_cell16_count G1_cell17_count G1_cell18_count G1_cell19_count G1_cell20_count
#> "013" "013" "023" "023" "013"
#> G1_cell21_count G1_cell22_count G1_cell23_count G1_cell24_count G1_cell25_count
#> "021" "0112" "013" "021" "013"
#> G1_cell26_count G1_cell27_count G1_cell28_count G1_cell29_count G1_cell30_count
#> "013" "023" "0222" "023" "013"
#> G1_cell31_count G1_cell32_count G1_cell33_count G1_cell34_count G1_cell35_count
#> "0222" "021" "023" "021" "0222"
#> G1_cell36_count G1_cell37_count G1_cell38_count G1_cell39_count G1_cell40_count
#> "0112" "021" "023" "013" "023"
#> G1_cell41_count G1_cell42_count G1_cell43_count G1_cell44_count G1_cell45_count
#> "013" "0111" "013" "013" "023"
#> G1_cell46_count G1_cell47_count G1_cell48_count G1_cell49_count G1_cell50_count
#> "023" "013" "0222" "013" "0222"
#> G1_cell51_count G1_cell52_count G1_cell53_count G1_cell54_count G1_cell55_count
#> "023" "023" "023" "013" "013"
#> G1_cell56_count G1_cell57_count G1_cell58_count G1_cell59_count G1_cell60_count
#> "0222" "013" "023" "023" "013"
#> G1_cell61_count G1_cell62_count G1_cell63_count G1_cell64_count G1_cell65_count
#> "013" "0112" "021" "013" "013"
#> G1_cell66_count G1_cell67_count G1_cell68_count G1_cell69_count G1_cell70_count
#> "013" "023" "013" "023" "0111"
#> G1_cell71_count G1_cell72_count G1_cell73_count G1_cell74_count G1_cell75_count
#> "023" "021" "013" "0121" "0112"
#> G1_cell76_count G1_cell77_count G1_cell78_count G1_cell79_count G1_cell80_count
#> "0222" "013" "023" "013" "0122"
#> G1_cell81_count G1_cell82_count G1_cell83_count G1_cell84_count G1_cell85_count
#> "013" "023" "013" "013" "023"
#> G1_cell86_count G1_cell87_count G1_cell88_count G1_cell89_count G1_cell90_count
#> "013" "0112" "02211" "021" "021"
#> G1_cell91_count G1_cell92_count G1_cell93_count G1_cell94_count G1_cell95_count
#> "0111" "023" "013" "0122" "013"
#> G1_cell96_count S_cell1_count S_cell2_count S_cell3_count S_cell4_count
#> "0112" "02211" "0121" "0111" "0121"
#> S_cell5_count S_cell6_count S_cell7_count S_cell8_count S_cell9_count
#> "02211" "0121" "0121" "0111" "0111"
#> S_cell10_count S_cell11_count S_cell12_count S_cell13_count S_cell14_count
#> "0111" "0121" "021" "0111" "013"
#> S_cell15_count S_cell16_count S_cell17_count S_cell18_count S_cell19_count
#> "0111" "0111" "0112" "02211" "0121"
#> S_cell20_count S_cell21_count S_cell22_count S_cell23_count S_cell24_count
#> "0121" "021" "0111" "021" "0111"
#> S_cell25_count S_cell26_count S_cell27_count S_cell28_count S_cell29_count
#> "021" "0112" "0111" "0111" "0111"
#> S_cell30_count S_cell31_count S_cell32_count S_cell33_count S_cell34_count
#> "0112" "02211" "0121" "0121" "0222"
#> S_cell35_count S_cell36_count S_cell37_count S_cell38_count S_cell39_count
#> "0121" "0122" "0111" "021" "0121"
#> S_cell40_count S_cell41_count S_cell42_count S_cell43_count S_cell44_count
#> "0111" "0121" "0111" "0111" "02211"
#> S_cell45_count S_cell46_count S_cell47_count S_cell48_count S_cell49_count
#> "0121" "013" "0121" "0121" "0121"
#> S_cell50_count S_cell51_count S_cell52_count S_cell53_count S_cell54_count
#> "0111" "021" "0111" "0111" "0121"
#> S_cell55_count S_cell56_count S_cell57_count S_cell58_count S_cell59_count
#> "0111" "021" "0112" "02211" "0121"
#> S_cell60_count S_cell61_count S_cell62_count S_cell63_count S_cell64_count
#> "0121" "0121" "0121" "0111" "021"
#> S_cell65_count S_cell66_count S_cell67_count S_cell68_count S_cell69_count
#> "021" "0121" "021" "0121" "0121"
#> S_cell70_count S_cell71_count S_cell72_count S_cell73_count S_cell74_count
#> "0112" "02211" "0121" "0121" "0222"
#> S_cell75_count S_cell76_count S_cell77_count S_cell78_count S_cell79_count
#> "021" "0122" "021" "0222" "0111"
#> S_cell80_count S_cell81_count S_cell82_count S_cell83_count S_cell84_count
#> "0121" "02212" "0121" "0112" "02211"
#> S_cell85_count S_cell86_count S_cell87_count S_cell88_count S_cell89_count
#> "013" "0121" "021" "02212" "0121"
#> S_cell90_count S_cell91_count S_cell92_count S_cell93_count S_cell94_count
#> "02212" "021" "0111" "0121" "02212"
#> S_cell95_count S_cell96_count G2M_cell1_count G2M_cell2_count G2M_cell3_count
#> "02212" "02212" "023" "021" "021"
#> G2M_cell4_count G2M_cell5_count G2M_cell6_count G2M_cell7_count G2M_cell8_count
#> "0222" "0111" "023" "0112" "0111"
#> G2M_cell9_count G2M_cell10_count G2M_cell11_count G2M_cell12_count G2M_cell13_count
#> "0122" "0122" "0122" "0122" "0122"
#> G2M_cell14_count G2M_cell15_count G2M_cell16_count G2M_cell17_count G2M_cell18_count
#> "021" "021" "023" "0122" "0112"
#> G2M_cell19_count G2M_cell20_count G2M_cell21_count G2M_cell22_count G2M_cell23_count
#> "0111" "0112" "0122" "0222" "0112"
#> G2M_cell24_count G2M_cell25_count G2M_cell26_count G2M_cell27_count G2M_cell28_count
#> "021" "0122" "0222" "023" "021"
#> G2M_cell29_count G2M_cell30_count G2M_cell31_count G2M_cell32_count G2M_cell33_count
#> "023" "0222" "023" "0111" "0112"
#> G2M_cell34_count G2M_cell35_count G2M_cell36_count G2M_cell37_count G2M_cell38_count
#> "0122" "013" "021" "0122" "021"
#> G2M_cell39_count G2M_cell40_count G2M_cell41_count G2M_cell42_count G2M_cell43_count
#> "021" "021" "0122" "021" "021"
#> G2M_cell44_count G2M_cell45_count G2M_cell46_count G2M_cell47_count G2M_cell48_count
#> "0112" "0122" "021" "0112" "0112"
#> G2M_cell49_count G2M_cell50_count G2M_cell51_count G2M_cell52_count G2M_cell53_count
#> "0122" "0122" "021" "021" "021"
#> G2M_cell54_count G2M_cell55_count G2M_cell56_count G2M_cell57_count G2M_cell58_count
#> "021" "0122" "021" "0122" "0112"
#> G2M_cell59_count G2M_cell60_count G2M_cell61_count G2M_cell62_count G2M_cell63_count
#> "0111" "0112" "0112" "0122" "0122"
#> G2M_cell64_count G2M_cell65_count G2M_cell66_count G2M_cell67_count G2M_cell68_count
#> "023" "0122" "021" "0222" "0112"
#> G2M_cell69_count G2M_cell70_count G2M_cell71_count G2M_cell72_count G2M_cell73_count
#> "0111" "023" "0112" "023" "0111"
#> G2M_cell74_count G2M_cell75_count G2M_cell76_count G2M_cell77_count G2M_cell78_count
#> "0112" "0122" "0122" "0222" "021"
#> G2M_cell79_count G2M_cell80_count G2M_cell81_count G2M_cell82_count G2M_cell83_count
#> "0122" "0122" "021" "0222" "0122"
#> G2M_cell84_count G2M_cell85_count G2M_cell86_count G2M_cell87_count G2M_cell88_count
#> "0111" "021" "0112" "0122" "0122"
#> G2M_cell89_count G2M_cell90_count G2M_cell91_count G2M_cell92_count G2M_cell93_count
#> "021" "0122" "0112" "021" "021"
#> G2M_cell94_count G2M_cell95_count G2M_cell96_count
#> "021" "021" "021"
get_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 696))
#> G1_cell1_count G1_cell2_count G1_cell3_count G1_cell4_count G1_cell5_count
#> "023" "023" "023" "0222" "023"
#> G1_cell6_count G1_cell7_count G1_cell8_count G1_cell9_count G1_cell10_count
#> "023" "013" "013" "0112" "021"
#> G1_cell11_count G1_cell12_count G1_cell13_count G1_cell14_count G1_cell15_count
#> "023" "023" "0112" "023" "013"
#> G1_cell16_count G1_cell17_count G1_cell18_count G1_cell19_count G1_cell20_count
#> "013" "013" "023" "023" "013"
#> G1_cell21_count G1_cell22_count G1_cell23_count G1_cell24_count G1_cell25_count
#> "021" "0112" "013" "021" "013"
#> G1_cell26_count G1_cell27_count G1_cell28_count G1_cell29_count G1_cell30_count
#> "013" "023" "0222" "023" "013"
#> G1_cell31_count G1_cell32_count G1_cell33_count G1_cell34_count G1_cell35_count
#> "0222" "021" "023" "021" "0222"
#> G1_cell36_count G1_cell37_count G1_cell38_count G1_cell39_count G1_cell40_count
#> "0112" "021" "023" "013" "023"
#> G1_cell41_count G1_cell42_count G1_cell43_count G1_cell44_count G1_cell45_count
#> "013" "0111" "013" "013" "023"
#> G1_cell46_count G1_cell47_count G1_cell48_count G1_cell49_count G1_cell50_count
#> "023" "013" "0222" "013" "0222"
#> G1_cell51_count G1_cell52_count G1_cell53_count G1_cell54_count G1_cell55_count
#> "023" "023" "023" "013" "013"
#> G1_cell56_count G1_cell57_count G1_cell58_count G1_cell59_count G1_cell60_count
#> "0222" "013" "023" "023" "013"
#> G1_cell61_count G1_cell62_count G1_cell63_count G1_cell64_count G1_cell65_count
#> "013" "0112" "021" "013" "013"
#> G1_cell66_count G1_cell67_count G1_cell68_count G1_cell69_count G1_cell70_count
#> "013" "023" "013" "023" "0111"
#> G1_cell71_count G1_cell72_count G1_cell73_count G1_cell74_count G1_cell75_count
#> "023" "021" "013" "012" "0112"
#> G1_cell76_count G1_cell77_count G1_cell78_count G1_cell79_count G1_cell80_count
#> "0222" "013" "023" "013" "012"
#> G1_cell81_count G1_cell82_count G1_cell83_count G1_cell84_count G1_cell85_count
#> "013" "023" "013" "013" "023"
#> G1_cell86_count G1_cell87_count G1_cell88_count G1_cell89_count G1_cell90_count
#> "013" "0112" "02211" "021" "021"
#> G1_cell91_count G1_cell92_count G1_cell93_count G1_cell94_count G1_cell95_count
#> "0111" "023" "013" "012" "013"
#> G1_cell96_count S_cell1_count S_cell2_count S_cell3_count S_cell4_count
#> "0112" "02211" "012" "0111" "012"
#> S_cell5_count S_cell6_count S_cell7_count S_cell8_count S_cell9_count
#> "02211" "012" "012" "0111" "0111"
#> S_cell10_count S_cell11_count S_cell12_count S_cell13_count S_cell14_count
#> "0111" "012" "021" "0111" "013"
#> S_cell15_count S_cell16_count S_cell17_count S_cell18_count S_cell19_count
#> "0111" "0111" "0112" "02211" "012"
#> S_cell20_count S_cell21_count S_cell22_count S_cell23_count S_cell24_count
#> "012" "021" "0111" "021" "0111"
#> S_cell25_count S_cell26_count S_cell27_count S_cell28_count S_cell29_count
#> "021" "0112" "0111" "0111" "0111"
#> S_cell30_count S_cell31_count S_cell32_count S_cell33_count S_cell34_count
#> "0112" "02211" "012" "012" "0222"
#> S_cell35_count S_cell36_count S_cell37_count S_cell38_count S_cell39_count
#> "012" "012" "0111" "021" "012"
#> S_cell40_count S_cell41_count S_cell42_count S_cell43_count S_cell44_count
#> "0111" "012" "0111" "0111" "02211"
#> S_cell45_count S_cell46_count S_cell47_count S_cell48_count S_cell49_count
#> "012" "013" "012" "012" "012"
#> S_cell50_count S_cell51_count S_cell52_count S_cell53_count S_cell54_count
#> "0111" "021" "0111" "0111" "012"
#> S_cell55_count S_cell56_count S_cell57_count S_cell58_count S_cell59_count
#> "0111" "021" "0112" "02211" "012"
#> S_cell60_count S_cell61_count S_cell62_count S_cell63_count S_cell64_count
#> "012" "012" "012" "0111" "021"
#> S_cell65_count S_cell66_count S_cell67_count S_cell68_count S_cell69_count
#> "021" "012" "021" "012" "012"
#> S_cell70_count S_cell71_count S_cell72_count S_cell73_count S_cell74_count
#> "0112" "02211" "012" "012" "0222"
#> S_cell75_count S_cell76_count S_cell77_count S_cell78_count S_cell79_count
#> "021" "012" "021" "0222" "0111"
#> S_cell80_count S_cell81_count S_cell82_count S_cell83_count S_cell84_count
#> "012" "02212" "012" "0112" "02211"
#> S_cell85_count S_cell86_count S_cell87_count S_cell88_count S_cell89_count
#> "013" "012" "021" "02212" "012"
#> S_cell90_count S_cell91_count S_cell92_count S_cell93_count S_cell94_count
#> "02212" "021" "0111" "012" "02212"
#> S_cell95_count S_cell96_count G2M_cell1_count G2M_cell2_count G2M_cell3_count
#> "02212" "02212" "023" "021" "021"
#> G2M_cell4_count G2M_cell5_count G2M_cell6_count G2M_cell7_count G2M_cell8_count
#> "0222" "0111" "023" "0112" "0111"
#> G2M_cell9_count G2M_cell10_count G2M_cell11_count G2M_cell12_count G2M_cell13_count
#> "012" "012" "012" "012" "012"
#> G2M_cell14_count G2M_cell15_count G2M_cell16_count G2M_cell17_count G2M_cell18_count
#> "021" "021" "023" "012" "0112"
#> G2M_cell19_count G2M_cell20_count G2M_cell21_count G2M_cell22_count G2M_cell23_count
#> "0111" "0112" "012" "0222" "0112"
#> G2M_cell24_count G2M_cell25_count G2M_cell26_count G2M_cell27_count G2M_cell28_count
#> "021" "012" "0222" "023" "021"
#> G2M_cell29_count G2M_cell30_count G2M_cell31_count G2M_cell32_count G2M_cell33_count
#> "023" "0222" "023" "0111" "0112"
#> G2M_cell34_count G2M_cell35_count G2M_cell36_count G2M_cell37_count G2M_cell38_count
#> "012" "013" "021" "012" "021"
#> G2M_cell39_count G2M_cell40_count G2M_cell41_count G2M_cell42_count G2M_cell43_count
#> "021" "021" "012" "021" "021"
#> G2M_cell44_count G2M_cell45_count G2M_cell46_count G2M_cell47_count G2M_cell48_count
#> "0112" "012" "021" "0112" "0112"
#> G2M_cell49_count G2M_cell50_count G2M_cell51_count G2M_cell52_count G2M_cell53_count
#> "012" "012" "021" "021" "021"
#> G2M_cell54_count G2M_cell55_count G2M_cell56_count G2M_cell57_count G2M_cell58_count
#> "021" "012" "021" "012" "0112"
#> G2M_cell59_count G2M_cell60_count G2M_cell61_count G2M_cell62_count G2M_cell63_count
#> "0111" "0112" "0112" "012" "012"
#> G2M_cell64_count G2M_cell65_count G2M_cell66_count G2M_cell67_count G2M_cell68_count
#> "023" "012" "021" "0222" "0112"
#> G2M_cell69_count G2M_cell70_count G2M_cell71_count G2M_cell72_count G2M_cell73_count
#> "0111" "023" "0112" "023" "0111"
#> G2M_cell74_count G2M_cell75_count G2M_cell76_count G2M_cell77_count G2M_cell78_count
#> "0112" "012" "012" "0222" "021"
#> G2M_cell79_count G2M_cell80_count G2M_cell81_count G2M_cell82_count G2M_cell83_count
#> "012" "012" "021" "0222" "012"
#> G2M_cell84_count G2M_cell85_count G2M_cell86_count G2M_cell87_count G2M_cell88_count
#> "0111" "021" "0112" "012" "012"
#> G2M_cell89_count G2M_cell90_count G2M_cell91_count G2M_cell92_count G2M_cell93_count
#> "021" "012" "0112" "021" "021"
#> G2M_cell94_count G2M_cell95_count G2M_cell96_count
#> "021" "021" "021"
get_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 2339))
#> G1_cell1_count G1_cell2_count G1_cell3_count G1_cell4_count G1_cell5_count
#> "023" "023" "023" "0222" "023"
#> G1_cell6_count G1_cell7_count G1_cell8_count G1_cell9_count G1_cell10_count
#> "023" "013" "013" "011" "021"
#> G1_cell11_count G1_cell12_count G1_cell13_count G1_cell14_count G1_cell15_count
#> "023" "023" "011" "023" "013"
#> G1_cell16_count G1_cell17_count G1_cell18_count G1_cell19_count G1_cell20_count
#> "013" "013" "023" "023" "013"
#> G1_cell21_count G1_cell22_count G1_cell23_count G1_cell24_count G1_cell25_count
#> "021" "011" "013" "021" "013"
#> G1_cell26_count G1_cell27_count G1_cell28_count G1_cell29_count G1_cell30_count
#> "013" "023" "0222" "023" "013"
#> G1_cell31_count G1_cell32_count G1_cell33_count G1_cell34_count G1_cell35_count
#> "0222" "021" "023" "021" "0222"
#> G1_cell36_count G1_cell37_count G1_cell38_count G1_cell39_count G1_cell40_count
#> "011" "021" "023" "013" "023"
#> G1_cell41_count G1_cell42_count G1_cell43_count G1_cell44_count G1_cell45_count
#> "013" "011" "013" "013" "023"
#> G1_cell46_count G1_cell47_count G1_cell48_count G1_cell49_count G1_cell50_count
#> "023" "013" "0222" "013" "0222"
#> G1_cell51_count G1_cell52_count G1_cell53_count G1_cell54_count G1_cell55_count
#> "023" "023" "023" "013" "013"
#> G1_cell56_count G1_cell57_count G1_cell58_count G1_cell59_count G1_cell60_count
#> "0222" "013" "023" "023" "013"
#> G1_cell61_count G1_cell62_count G1_cell63_count G1_cell64_count G1_cell65_count
#> "013" "011" "021" "013" "013"
#> G1_cell66_count G1_cell67_count G1_cell68_count G1_cell69_count G1_cell70_count
#> "013" "023" "013" "023" "011"
#> G1_cell71_count G1_cell72_count G1_cell73_count G1_cell74_count G1_cell75_count
#> "023" "021" "013" "012" "011"
#> G1_cell76_count G1_cell77_count G1_cell78_count G1_cell79_count G1_cell80_count
#> "0222" "013" "023" "013" "012"
#> G1_cell81_count G1_cell82_count G1_cell83_count G1_cell84_count G1_cell85_count
#> "013" "023" "013" "013" "023"
#> G1_cell86_count G1_cell87_count G1_cell88_count G1_cell89_count G1_cell90_count
#> "013" "011" "02211" "021" "021"
#> G1_cell91_count G1_cell92_count G1_cell93_count G1_cell94_count G1_cell95_count
#> "011" "023" "013" "012" "013"
#> G1_cell96_count S_cell1_count S_cell2_count S_cell3_count S_cell4_count
#> "011" "02211" "012" "011" "012"
#> S_cell5_count S_cell6_count S_cell7_count S_cell8_count S_cell9_count
#> "02211" "012" "012" "011" "011"
#> S_cell10_count S_cell11_count S_cell12_count S_cell13_count S_cell14_count
#> "011" "012" "021" "011" "013"
#> S_cell15_count S_cell16_count S_cell17_count S_cell18_count S_cell19_count
#> "011" "011" "011" "02211" "012"
#> S_cell20_count S_cell21_count S_cell22_count S_cell23_count S_cell24_count
#> "012" "021" "011" "021" "011"
#> S_cell25_count S_cell26_count S_cell27_count S_cell28_count S_cell29_count
#> "021" "011" "011" "011" "011"
#> S_cell30_count S_cell31_count S_cell32_count S_cell33_count S_cell34_count
#> "011" "02211" "012" "012" "0222"
#> S_cell35_count S_cell36_count S_cell37_count S_cell38_count S_cell39_count
#> "012" "012" "011" "021" "012"
#> S_cell40_count S_cell41_count S_cell42_count S_cell43_count S_cell44_count
#> "011" "012" "011" "011" "02211"
#> S_cell45_count S_cell46_count S_cell47_count S_cell48_count S_cell49_count
#> "012" "013" "012" "012" "012"
#> S_cell50_count S_cell51_count S_cell52_count S_cell53_count S_cell54_count
#> "011" "021" "011" "011" "012"
#> S_cell55_count S_cell56_count S_cell57_count S_cell58_count S_cell59_count
#> "011" "021" "011" "02211" "012"
#> S_cell60_count S_cell61_count S_cell62_count S_cell63_count S_cell64_count
#> "012" "012" "012" "011" "021"
#> S_cell65_count S_cell66_count S_cell67_count S_cell68_count S_cell69_count
#> "021" "012" "021" "012" "012"
#> S_cell70_count S_cell71_count S_cell72_count S_cell73_count S_cell74_count
#> "011" "02211" "012" "012" "0222"
#> S_cell75_count S_cell76_count S_cell77_count S_cell78_count S_cell79_count
#> "021" "012" "021" "0222" "011"
#> S_cell80_count S_cell81_count S_cell82_count S_cell83_count S_cell84_count
#> "012" "02212" "012" "011" "02211"
#> S_cell85_count S_cell86_count S_cell87_count S_cell88_count S_cell89_count
#> "013" "012" "021" "02212" "012"
#> S_cell90_count S_cell91_count S_cell92_count S_cell93_count S_cell94_count
#> "02212" "021" "011" "012" "02212"
#> S_cell95_count S_cell96_count G2M_cell1_count G2M_cell2_count G2M_cell3_count
#> "02212" "02212" "023" "021" "021"
#> G2M_cell4_count G2M_cell5_count G2M_cell6_count G2M_cell7_count G2M_cell8_count
#> "0222" "011" "023" "011" "011"
#> G2M_cell9_count G2M_cell10_count G2M_cell11_count G2M_cell12_count G2M_cell13_count
#> "012" "012" "012" "012" "012"
#> G2M_cell14_count G2M_cell15_count G2M_cell16_count G2M_cell17_count G2M_cell18_count
#> "021" "021" "023" "012" "011"
#> G2M_cell19_count G2M_cell20_count G2M_cell21_count G2M_cell22_count G2M_cell23_count
#> "011" "011" "012" "0222" "011"
#> G2M_cell24_count G2M_cell25_count G2M_cell26_count G2M_cell27_count G2M_cell28_count
#> "021" "012" "0222" "023" "021"
#> G2M_cell29_count G2M_cell30_count G2M_cell31_count G2M_cell32_count G2M_cell33_count
#> "023" "0222" "023" "011" "011"
#> G2M_cell34_count G2M_cell35_count G2M_cell36_count G2M_cell37_count G2M_cell38_count
#> "012" "013" "021" "012" "021"
#> G2M_cell39_count G2M_cell40_count G2M_cell41_count G2M_cell42_count G2M_cell43_count
#> "021" "021" "012" "021" "021"
#> G2M_cell44_count G2M_cell45_count G2M_cell46_count G2M_cell47_count G2M_cell48_count
#> "011" "012" "021" "011" "011"
#> G2M_cell49_count G2M_cell50_count G2M_cell51_count G2M_cell52_count G2M_cell53_count
#> "012" "012" "021" "021" "021"
#> G2M_cell54_count G2M_cell55_count G2M_cell56_count G2M_cell57_count G2M_cell58_count
#> "021" "012" "021" "012" "011"
#> G2M_cell59_count G2M_cell60_count G2M_cell61_count G2M_cell62_count G2M_cell63_count
#> "011" "011" "011" "012" "012"
#> G2M_cell64_count G2M_cell65_count G2M_cell66_count G2M_cell67_count G2M_cell68_count
#> "023" "012" "021" "0222" "011"
#> G2M_cell69_count G2M_cell70_count G2M_cell71_count G2M_cell72_count G2M_cell73_count
#> "011" "023" "011" "023" "011"
#> G2M_cell74_count G2M_cell75_count G2M_cell76_count G2M_cell77_count G2M_cell78_count
#> "011" "012" "012" "0222" "021"
#> G2M_cell79_count G2M_cell80_count G2M_cell81_count G2M_cell82_count G2M_cell83_count
#> "012" "012" "021" "0222" "012"
#> G2M_cell84_count G2M_cell85_count G2M_cell86_count G2M_cell87_count G2M_cell88_count
#> "011" "021" "011" "012" "012"
#> G2M_cell89_count G2M_cell90_count G2M_cell91_count G2M_cell92_count G2M_cell93_count
#> "021" "012" "011" "021" "021"
#> G2M_cell94_count G2M_cell95_count G2M_cell96_count
#> "021" "021" "021"
get_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 4525))
#> G1_cell1_count G1_cell2_count G1_cell3_count G1_cell4_count G1_cell5_count
#> "023" "023" "023" "0222" "023"
#> G1_cell6_count G1_cell7_count G1_cell8_count G1_cell9_count G1_cell10_count
#> "023" "013" "013" "011" "021"
#> G1_cell11_count G1_cell12_count G1_cell13_count G1_cell14_count G1_cell15_count
#> "023" "023" "011" "023" "013"
#> G1_cell16_count G1_cell17_count G1_cell18_count G1_cell19_count G1_cell20_count
#> "013" "013" "023" "023" "013"
#> G1_cell21_count G1_cell22_count G1_cell23_count G1_cell24_count G1_cell25_count
#> "021" "011" "013" "021" "013"
#> G1_cell26_count G1_cell27_count G1_cell28_count G1_cell29_count G1_cell30_count
#> "013" "023" "0222" "023" "013"
#> G1_cell31_count G1_cell32_count G1_cell33_count G1_cell34_count G1_cell35_count
#> "0222" "021" "023" "021" "0222"
#> G1_cell36_count G1_cell37_count G1_cell38_count G1_cell39_count G1_cell40_count
#> "011" "021" "023" "013" "023"
#> G1_cell41_count G1_cell42_count G1_cell43_count G1_cell44_count G1_cell45_count
#> "013" "011" "013" "013" "023"
#> G1_cell46_count G1_cell47_count G1_cell48_count G1_cell49_count G1_cell50_count
#> "023" "013" "0222" "013" "0222"
#> G1_cell51_count G1_cell52_count G1_cell53_count G1_cell54_count G1_cell55_count
#> "023" "023" "023" "013" "013"
#> G1_cell56_count G1_cell57_count G1_cell58_count G1_cell59_count G1_cell60_count
#> "0222" "013" "023" "023" "013"
#> G1_cell61_count G1_cell62_count G1_cell63_count G1_cell64_count G1_cell65_count
#> "013" "011" "021" "013" "013"
#> G1_cell66_count G1_cell67_count G1_cell68_count G1_cell69_count G1_cell70_count
#> "013" "023" "013" "023" "011"
#> G1_cell71_count G1_cell72_count G1_cell73_count G1_cell74_count G1_cell75_count
#> "023" "021" "013" "012" "011"
#> G1_cell76_count G1_cell77_count G1_cell78_count G1_cell79_count G1_cell80_count
#> "0222" "013" "023" "013" "012"
#> G1_cell81_count G1_cell82_count G1_cell83_count G1_cell84_count G1_cell85_count
#> "013" "023" "013" "013" "023"
#> G1_cell86_count G1_cell87_count G1_cell88_count G1_cell89_count G1_cell90_count
#> "013" "011" "0221" "021" "021"
#> G1_cell91_count G1_cell92_count G1_cell93_count G1_cell94_count G1_cell95_count
#> "011" "023" "013" "012" "013"
#> G1_cell96_count S_cell1_count S_cell2_count S_cell3_count S_cell4_count
#> "011" "0221" "012" "011" "012"
#> S_cell5_count S_cell6_count S_cell7_count S_cell8_count S_cell9_count
#> "0221" "012" "012" "011" "011"
#> S_cell10_count S_cell11_count S_cell12_count S_cell13_count S_cell14_count
#> "011" "012" "021" "011" "013"
#> S_cell15_count S_cell16_count S_cell17_count S_cell18_count S_cell19_count
#> "011" "011" "011" "0221" "012"
#> S_cell20_count S_cell21_count S_cell22_count S_cell23_count S_cell24_count
#> "012" "021" "011" "021" "011"
#> S_cell25_count S_cell26_count S_cell27_count S_cell28_count S_cell29_count
#> "021" "011" "011" "011" "011"
#> S_cell30_count S_cell31_count S_cell32_count S_cell33_count S_cell34_count
#> "011" "0221" "012" "012" "0222"
#> S_cell35_count S_cell36_count S_cell37_count S_cell38_count S_cell39_count
#> "012" "012" "011" "021" "012"
#> S_cell40_count S_cell41_count S_cell42_count S_cell43_count S_cell44_count
#> "011" "012" "011" "011" "0221"
#> S_cell45_count S_cell46_count S_cell47_count S_cell48_count S_cell49_count
#> "012" "013" "012" "012" "012"
#> S_cell50_count S_cell51_count S_cell52_count S_cell53_count S_cell54_count
#> "011" "021" "011" "011" "012"
#> S_cell55_count S_cell56_count S_cell57_count S_cell58_count S_cell59_count
#> "011" "021" "011" "0221" "012"
#> S_cell60_count S_cell61_count S_cell62_count S_cell63_count S_cell64_count
#> "012" "012" "012" "011" "021"
#> S_cell65_count S_cell66_count S_cell67_count S_cell68_count S_cell69_count
#> "021" "012" "021" "012" "012"
#> S_cell70_count S_cell71_count S_cell72_count S_cell73_count S_cell74_count
#> "011" "0221" "012" "012" "0222"
#> S_cell75_count S_cell76_count S_cell77_count S_cell78_count S_cell79_count
#> "021" "012" "021" "0222" "011"
#> S_cell80_count S_cell81_count S_cell82_count S_cell83_count S_cell84_count
#> "012" "0221" "012" "011" "0221"
#> S_cell85_count S_cell86_count S_cell87_count S_cell88_count S_cell89_count
#> "013" "012" "021" "0221" "012"
#> S_cell90_count S_cell91_count S_cell92_count S_cell93_count S_cell94_count
#> "0221" "021" "011" "012" "0221"
#> S_cell95_count S_cell96_count G2M_cell1_count G2M_cell2_count G2M_cell3_count
#> "0221" "0221" "023" "021" "021"
#> G2M_cell4_count G2M_cell5_count G2M_cell6_count G2M_cell7_count G2M_cell8_count
#> "0222" "011" "023" "011" "011"
#> G2M_cell9_count G2M_cell10_count G2M_cell11_count G2M_cell12_count G2M_cell13_count
#> "012" "012" "012" "012" "012"
#> G2M_cell14_count G2M_cell15_count G2M_cell16_count G2M_cell17_count G2M_cell18_count
#> "021" "021" "023" "012" "011"
#> G2M_cell19_count G2M_cell20_count G2M_cell21_count G2M_cell22_count G2M_cell23_count
#> "011" "011" "012" "0222" "011"
#> G2M_cell24_count G2M_cell25_count G2M_cell26_count G2M_cell27_count G2M_cell28_count
#> "021" "012" "0222" "023" "021"
#> G2M_cell29_count G2M_cell30_count G2M_cell31_count G2M_cell32_count G2M_cell33_count
#> "023" "0222" "023" "011" "011"
#> G2M_cell34_count G2M_cell35_count G2M_cell36_count G2M_cell37_count G2M_cell38_count
#> "012" "013" "021" "012" "021"
#> G2M_cell39_count G2M_cell40_count G2M_cell41_count G2M_cell42_count G2M_cell43_count
#> "021" "021" "012" "021" "021"
#> G2M_cell44_count G2M_cell45_count G2M_cell46_count G2M_cell47_count G2M_cell48_count
#> "011" "012" "021" "011" "011"
#> G2M_cell49_count G2M_cell50_count G2M_cell51_count G2M_cell52_count G2M_cell53_count
#> "012" "012" "021" "021" "021"
#> G2M_cell54_count G2M_cell55_count G2M_cell56_count G2M_cell57_count G2M_cell58_count
#> "021" "012" "021" "012" "011"
#> G2M_cell59_count G2M_cell60_count G2M_cell61_count G2M_cell62_count G2M_cell63_count
#> "011" "011" "011" "012" "012"
#> G2M_cell64_count G2M_cell65_count G2M_cell66_count G2M_cell67_count G2M_cell68_count
#> "023" "012" "021" "0222" "011"
#> G2M_cell69_count G2M_cell70_count G2M_cell71_count G2M_cell72_count G2M_cell73_count
#> "011" "023" "011" "023" "011"
#> G2M_cell74_count G2M_cell75_count G2M_cell76_count G2M_cell77_count G2M_cell78_count
#> "011" "012" "012" "0222" "021"
#> G2M_cell79_count G2M_cell80_count G2M_cell81_count G2M_cell82_count G2M_cell83_count
#> "012" "012" "021" "0222" "012"
#> G2M_cell84_count G2M_cell85_count G2M_cell86_count G2M_cell87_count G2M_cell88_count
#> "011" "021" "011" "012" "012"
#> G2M_cell89_count G2M_cell90_count G2M_cell91_count G2M_cell92_count G2M_cell93_count
#> "021" "012" "011" "021" "021"
#> G2M_cell94_count G2M_cell95_count G2M_cell96_count
#> "021" "021" "021"
get_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 5620))
#> G1_cell1_count G1_cell2_count G1_cell3_count G1_cell4_count G1_cell5_count
#> "023" "023" "023" "0222" "023"
#> G1_cell6_count G1_cell7_count G1_cell8_count G1_cell9_count G1_cell10_count
#> "023" "01" "01" "01" "021"
#> G1_cell11_count G1_cell12_count G1_cell13_count G1_cell14_count G1_cell15_count
#> "023" "023" "01" "023" "01"
#> G1_cell16_count G1_cell17_count G1_cell18_count G1_cell19_count G1_cell20_count
#> "01" "01" "023" "023" "01"
#> G1_cell21_count G1_cell22_count G1_cell23_count G1_cell24_count G1_cell25_count
#> "021" "01" "01" "021" "01"
#> G1_cell26_count G1_cell27_count G1_cell28_count G1_cell29_count G1_cell30_count
#> "01" "023" "0222" "023" "01"
#> G1_cell31_count G1_cell32_count G1_cell33_count G1_cell34_count G1_cell35_count
#> "0222" "021" "023" "021" "0222"
#> G1_cell36_count G1_cell37_count G1_cell38_count G1_cell39_count G1_cell40_count
#> "01" "021" "023" "01" "023"
#> G1_cell41_count G1_cell42_count G1_cell43_count G1_cell44_count G1_cell45_count
#> "01" "01" "01" "01" "023"
#> G1_cell46_count G1_cell47_count G1_cell48_count G1_cell49_count G1_cell50_count
#> "023" "01" "0222" "01" "0222"
#> G1_cell51_count G1_cell52_count G1_cell53_count G1_cell54_count G1_cell55_count
#> "023" "023" "023" "01" "01"
#> G1_cell56_count G1_cell57_count G1_cell58_count G1_cell59_count G1_cell60_count
#> "0222" "01" "023" "023" "01"
#> G1_cell61_count G1_cell62_count G1_cell63_count G1_cell64_count G1_cell65_count
#> "01" "01" "021" "01" "01"
#> G1_cell66_count G1_cell67_count G1_cell68_count G1_cell69_count G1_cell70_count
#> "01" "023" "01" "023" "01"
#> G1_cell71_count G1_cell72_count G1_cell73_count G1_cell74_count G1_cell75_count
#> "023" "021" "01" "01" "01"
#> G1_cell76_count G1_cell77_count G1_cell78_count G1_cell79_count G1_cell80_count
#> "0222" "01" "023" "01" "01"
#> G1_cell81_count G1_cell82_count G1_cell83_count G1_cell84_count G1_cell85_count
#> "01" "023" "01" "01" "023"
#> G1_cell86_count G1_cell87_count G1_cell88_count G1_cell89_count G1_cell90_count
#> "01" "01" "0221" "021" "021"
#> G1_cell91_count G1_cell92_count G1_cell93_count G1_cell94_count G1_cell95_count
#> "01" "023" "01" "01" "01"
#> G1_cell96_count S_cell1_count S_cell2_count S_cell3_count S_cell4_count
#> "01" "0221" "01" "01" "01"
#> S_cell5_count S_cell6_count S_cell7_count S_cell8_count S_cell9_count
#> "0221" "01" "01" "01" "01"
#> S_cell10_count S_cell11_count S_cell12_count S_cell13_count S_cell14_count
#> "01" "01" "021" "01" "01"
#> S_cell15_count S_cell16_count S_cell17_count S_cell18_count S_cell19_count
#> "01" "01" "01" "0221" "01"
#> S_cell20_count S_cell21_count S_cell22_count S_cell23_count S_cell24_count
#> "01" "021" "01" "021" "01"
#> S_cell25_count S_cell26_count S_cell27_count S_cell28_count S_cell29_count
#> "021" "01" "01" "01" "01"
#> S_cell30_count S_cell31_count S_cell32_count S_cell33_count S_cell34_count
#> "01" "0221" "01" "01" "0222"
#> S_cell35_count S_cell36_count S_cell37_count S_cell38_count S_cell39_count
#> "01" "01" "01" "021" "01"
#> S_cell40_count S_cell41_count S_cell42_count S_cell43_count S_cell44_count
#> "01" "01" "01" "01" "0221"
#> S_cell45_count S_cell46_count S_cell47_count S_cell48_count S_cell49_count
#> "01" "01" "01" "01" "01"
#> S_cell50_count S_cell51_count S_cell52_count S_cell53_count S_cell54_count
#> "01" "021" "01" "01" "01"
#> S_cell55_count S_cell56_count S_cell57_count S_cell58_count S_cell59_count
#> "01" "021" "01" "0221" "01"
#> S_cell60_count S_cell61_count S_cell62_count S_cell63_count S_cell64_count
#> "01" "01" "01" "01" "021"
#> S_cell65_count S_cell66_count S_cell67_count S_cell68_count S_cell69_count
#> "021" "01" "021" "01" "01"
#> S_cell70_count S_cell71_count S_cell72_count S_cell73_count S_cell74_count
#> "01" "0221" "01" "01" "0222"
#> S_cell75_count S_cell76_count S_cell77_count S_cell78_count S_cell79_count
#> "021" "01" "021" "0222" "01"
#> S_cell80_count S_cell81_count S_cell82_count S_cell83_count S_cell84_count
#> "01" "0221" "01" "01" "0221"
#> S_cell85_count S_cell86_count S_cell87_count S_cell88_count S_cell89_count
#> "01" "01" "021" "0221" "01"
#> S_cell90_count S_cell91_count S_cell92_count S_cell93_count S_cell94_count
#> "0221" "021" "01" "01" "0221"
#> S_cell95_count S_cell96_count G2M_cell1_count G2M_cell2_count G2M_cell3_count
#> "0221" "0221" "023" "021" "021"
#> G2M_cell4_count G2M_cell5_count G2M_cell6_count G2M_cell7_count G2M_cell8_count
#> "0222" "01" "023" "01" "01"
#> G2M_cell9_count G2M_cell10_count G2M_cell11_count G2M_cell12_count G2M_cell13_count
#> "01" "01" "01" "01" "01"
#> G2M_cell14_count G2M_cell15_count G2M_cell16_count G2M_cell17_count G2M_cell18_count
#> "021" "021" "023" "01" "01"
#> G2M_cell19_count G2M_cell20_count G2M_cell21_count G2M_cell22_count G2M_cell23_count
#> "01" "01" "01" "0222" "01"
#> G2M_cell24_count G2M_cell25_count G2M_cell26_count G2M_cell27_count G2M_cell28_count
#> "021" "01" "0222" "023" "021"
#> G2M_cell29_count G2M_cell30_count G2M_cell31_count G2M_cell32_count G2M_cell33_count
#> "023" "0222" "023" "01" "01"
#> G2M_cell34_count G2M_cell35_count G2M_cell36_count G2M_cell37_count G2M_cell38_count
#> "01" "01" "021" "01" "021"
#> G2M_cell39_count G2M_cell40_count G2M_cell41_count G2M_cell42_count G2M_cell43_count
#> "021" "021" "01" "021" "021"
#> G2M_cell44_count G2M_cell45_count G2M_cell46_count G2M_cell47_count G2M_cell48_count
#> "01" "01" "021" "01" "01"
#> G2M_cell49_count G2M_cell50_count G2M_cell51_count G2M_cell52_count G2M_cell53_count
#> "01" "01" "021" "021" "021"
#> G2M_cell54_count G2M_cell55_count G2M_cell56_count G2M_cell57_count G2M_cell58_count
#> "021" "01" "021" "01" "01"
#> G2M_cell59_count G2M_cell60_count G2M_cell61_count G2M_cell62_count G2M_cell63_count
#> "01" "01" "01" "01" "01"
#> G2M_cell64_count G2M_cell65_count G2M_cell66_count G2M_cell67_count G2M_cell68_count
#> "023" "01" "021" "0222" "01"
#> G2M_cell69_count G2M_cell70_count G2M_cell71_count G2M_cell72_count G2M_cell73_count
#> "01" "023" "01" "023" "01"
#> G2M_cell74_count G2M_cell75_count G2M_cell76_count G2M_cell77_count G2M_cell78_count
#> "01" "01" "01" "0222" "021"
#> G2M_cell79_count G2M_cell80_count G2M_cell81_count G2M_cell82_count G2M_cell83_count
#> "01" "01" "021" "0222" "01"
#> G2M_cell84_count G2M_cell85_count G2M_cell86_count G2M_cell87_count G2M_cell88_count
#> "01" "021" "01" "01" "01"
#> G2M_cell89_count G2M_cell90_count G2M_cell91_count G2M_cell92_count G2M_cell93_count
#> "021" "01" "01" "021" "021"
#> G2M_cell94_count G2M_cell95_count G2M_cell96_count
#> "021" "021" "021"
get_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 6601))
#> G1_cell1_count G1_cell2_count G1_cell3_count G1_cell4_count G1_cell5_count
#> "023" "023" "023" "022" "023"
#> G1_cell6_count G1_cell7_count G1_cell8_count G1_cell9_count G1_cell10_count
#> "023" "01" "01" "01" "021"
#> G1_cell11_count G1_cell12_count G1_cell13_count G1_cell14_count G1_cell15_count
#> "023" "023" "01" "023" "01"
#> G1_cell16_count G1_cell17_count G1_cell18_count G1_cell19_count G1_cell20_count
#> "01" "01" "023" "023" "01"
#> G1_cell21_count G1_cell22_count G1_cell23_count G1_cell24_count G1_cell25_count
#> "021" "01" "01" "021" "01"
#> G1_cell26_count G1_cell27_count G1_cell28_count G1_cell29_count G1_cell30_count
#> "01" "023" "022" "023" "01"
#> G1_cell31_count G1_cell32_count G1_cell33_count G1_cell34_count G1_cell35_count
#> "022" "021" "023" "021" "022"
#> G1_cell36_count G1_cell37_count G1_cell38_count G1_cell39_count G1_cell40_count
#> "01" "021" "023" "01" "023"
#> G1_cell41_count G1_cell42_count G1_cell43_count G1_cell44_count G1_cell45_count
#> "01" "01" "01" "01" "023"
#> G1_cell46_count G1_cell47_count G1_cell48_count G1_cell49_count G1_cell50_count
#> "023" "01" "022" "01" "022"
#> G1_cell51_count G1_cell52_count G1_cell53_count G1_cell54_count G1_cell55_count
#> "023" "023" "023" "01" "01"
#> G1_cell56_count G1_cell57_count G1_cell58_count G1_cell59_count G1_cell60_count
#> "022" "01" "023" "023" "01"
#> G1_cell61_count G1_cell62_count G1_cell63_count G1_cell64_count G1_cell65_count
#> "01" "01" "021" "01" "01"
#> G1_cell66_count G1_cell67_count G1_cell68_count G1_cell69_count G1_cell70_count
#> "01" "023" "01" "023" "01"
#> G1_cell71_count G1_cell72_count G1_cell73_count G1_cell74_count G1_cell75_count
#> "023" "021" "01" "01" "01"
#> G1_cell76_count G1_cell77_count G1_cell78_count G1_cell79_count G1_cell80_count
#> "022" "01" "023" "01" "01"
#> G1_cell81_count G1_cell82_count G1_cell83_count G1_cell84_count G1_cell85_count
#> "01" "023" "01" "01" "023"
#> G1_cell86_count G1_cell87_count G1_cell88_count G1_cell89_count G1_cell90_count
#> "01" "01" "022" "021" "021"
#> G1_cell91_count G1_cell92_count G1_cell93_count G1_cell94_count G1_cell95_count
#> "01" "023" "01" "01" "01"
#> G1_cell96_count S_cell1_count S_cell2_count S_cell3_count S_cell4_count
#> "01" "022" "01" "01" "01"
#> S_cell5_count S_cell6_count S_cell7_count S_cell8_count S_cell9_count
#> "022" "01" "01" "01" "01"
#> S_cell10_count S_cell11_count S_cell12_count S_cell13_count S_cell14_count
#> "01" "01" "021" "01" "01"
#> S_cell15_count S_cell16_count S_cell17_count S_cell18_count S_cell19_count
#> "01" "01" "01" "022" "01"
#> S_cell20_count S_cell21_count S_cell22_count S_cell23_count S_cell24_count
#> "01" "021" "01" "021" "01"
#> S_cell25_count S_cell26_count S_cell27_count S_cell28_count S_cell29_count
#> "021" "01" "01" "01" "01"
#> S_cell30_count S_cell31_count S_cell32_count S_cell33_count S_cell34_count
#> "01" "022" "01" "01" "022"
#> S_cell35_count S_cell36_count S_cell37_count S_cell38_count S_cell39_count
#> "01" "01" "01" "021" "01"
#> S_cell40_count S_cell41_count S_cell42_count S_cell43_count S_cell44_count
#> "01" "01" "01" "01" "022"
#> S_cell45_count S_cell46_count S_cell47_count S_cell48_count S_cell49_count
#> "01" "01" "01" "01" "01"
#> S_cell50_count S_cell51_count S_cell52_count S_cell53_count S_cell54_count
#> "01" "021" "01" "01" "01"
#> S_cell55_count S_cell56_count S_cell57_count S_cell58_count S_cell59_count
#> "01" "021" "01" "022" "01"
#> S_cell60_count S_cell61_count S_cell62_count S_cell63_count S_cell64_count
#> "01" "01" "01" "01" "021"
#> S_cell65_count S_cell66_count S_cell67_count S_cell68_count S_cell69_count
#> "021" "01" "021" "01" "01"
#> S_cell70_count S_cell71_count S_cell72_count S_cell73_count S_cell74_count
#> "01" "022" "01" "01" "022"
#> S_cell75_count S_cell76_count S_cell77_count S_cell78_count S_cell79_count
#> "021" "01" "021" "022" "01"
#> S_cell80_count S_cell81_count S_cell82_count S_cell83_count S_cell84_count
#> "01" "022" "01" "01" "022"
#> S_cell85_count S_cell86_count S_cell87_count S_cell88_count S_cell89_count
#> "01" "01" "021" "022" "01"
#> S_cell90_count S_cell91_count S_cell92_count S_cell93_count S_cell94_count
#> "022" "021" "01" "01" "022"
#> S_cell95_count S_cell96_count G2M_cell1_count G2M_cell2_count G2M_cell3_count
#> "022" "022" "023" "021" "021"
#> G2M_cell4_count G2M_cell5_count G2M_cell6_count G2M_cell7_count G2M_cell8_count
#> "022" "01" "023" "01" "01"
#> G2M_cell9_count G2M_cell10_count G2M_cell11_count G2M_cell12_count G2M_cell13_count
#> "01" "01" "01" "01" "01"
#> G2M_cell14_count G2M_cell15_count G2M_cell16_count G2M_cell17_count G2M_cell18_count
#> "021" "021" "023" "01" "01"
#> G2M_cell19_count G2M_cell20_count G2M_cell21_count G2M_cell22_count G2M_cell23_count
#> "01" "01" "01" "022" "01"
#> G2M_cell24_count G2M_cell25_count G2M_cell26_count G2M_cell27_count G2M_cell28_count
#> "021" "01" "022" "023" "021"
#> G2M_cell29_count G2M_cell30_count G2M_cell31_count G2M_cell32_count G2M_cell33_count
#> "023" "022" "023" "01" "01"
#> G2M_cell34_count G2M_cell35_count G2M_cell36_count G2M_cell37_count G2M_cell38_count
#> "01" "01" "021" "01" "021"
#> G2M_cell39_count G2M_cell40_count G2M_cell41_count G2M_cell42_count G2M_cell43_count
#> "021" "021" "01" "021" "021"
#> G2M_cell44_count G2M_cell45_count G2M_cell46_count G2M_cell47_count G2M_cell48_count
#> "01" "01" "021" "01" "01"
#> G2M_cell49_count G2M_cell50_count G2M_cell51_count G2M_cell52_count G2M_cell53_count
#> "01" "01" "021" "021" "021"
#> G2M_cell54_count G2M_cell55_count G2M_cell56_count G2M_cell57_count G2M_cell58_count
#> "021" "01" "021" "01" "01"
#> G2M_cell59_count G2M_cell60_count G2M_cell61_count G2M_cell62_count G2M_cell63_count
#> "01" "01" "01" "01" "01"
#> G2M_cell64_count G2M_cell65_count G2M_cell66_count G2M_cell67_count G2M_cell68_count
#> "023" "01" "021" "022" "01"
#> G2M_cell69_count G2M_cell70_count G2M_cell71_count G2M_cell72_count G2M_cell73_count
#> "01" "023" "01" "023" "01"
#> G2M_cell74_count G2M_cell75_count G2M_cell76_count G2M_cell77_count G2M_cell78_count
#> "01" "01" "01" "022" "021"
#> G2M_cell79_count G2M_cell80_count G2M_cell81_count G2M_cell82_count G2M_cell83_count
#> "01" "01" "021" "022" "01"
#> G2M_cell84_count G2M_cell85_count G2M_cell86_count G2M_cell87_count G2M_cell88_count
#> "01" "021" "01" "01" "01"
#> G2M_cell89_count G2M_cell90_count G2M_cell91_count G2M_cell92_count G2M_cell93_count
#> "021" "01" "01" "021" "021"
#> G2M_cell94_count G2M_cell95_count G2M_cell96_count
#> "021" "021" "021"
get_classes(res_rh, merge_node = merge_node_param(min_n_signatures = 10267))
#> G1_cell1_count G1_cell2_count G1_cell3_count G1_cell4_count G1_cell5_count
#> NA NA NA NA NA
#> G1_cell6_count G1_cell7_count G1_cell8_count G1_cell9_count G1_cell10_count
#> NA NA NA NA NA
#> G1_cell11_count G1_cell12_count G1_cell13_count G1_cell14_count G1_cell15_count
#> NA NA NA NA NA
#> G1_cell16_count G1_cell17_count G1_cell18_count G1_cell19_count G1_cell20_count
#> NA NA NA NA NA
#> G1_cell21_count G1_cell22_count G1_cell23_count G1_cell24_count G1_cell25_count
#> NA NA NA NA NA
#> G1_cell26_count G1_cell27_count G1_cell28_count G1_cell29_count G1_cell30_count
#> NA NA NA NA NA
#> G1_cell31_count G1_cell32_count G1_cell33_count G1_cell34_count G1_cell35_count
#> NA NA NA NA NA
#> G1_cell36_count G1_cell37_count G1_cell38_count G1_cell39_count G1_cell40_count
#> NA NA NA NA NA
#> G1_cell41_count G1_cell42_count G1_cell43_count G1_cell44_count G1_cell45_count
#> NA NA NA NA NA
#> G1_cell46_count G1_cell47_count G1_cell48_count G1_cell49_count G1_cell50_count
#> NA NA NA NA NA
#> G1_cell51_count G1_cell52_count G1_cell53_count G1_cell54_count G1_cell55_count
#> NA NA NA NA NA
#> G1_cell56_count G1_cell57_count G1_cell58_count G1_cell59_count G1_cell60_count
#> NA NA NA NA NA
#> G1_cell61_count G1_cell62_count G1_cell63_count G1_cell64_count G1_cell65_count
#> NA NA NA NA NA
#> G1_cell66_count G1_cell67_count G1_cell68_count G1_cell69_count G1_cell70_count
#> NA NA NA NA NA
#> G1_cell71_count G1_cell72_count G1_cell73_count G1_cell74_count G1_cell75_count
#> NA NA NA NA NA
#> G1_cell76_count G1_cell77_count G1_cell78_count G1_cell79_count G1_cell80_count
#> NA NA NA NA NA
#> G1_cell81_count G1_cell82_count G1_cell83_count G1_cell84_count G1_cell85_count
#> NA NA NA NA NA
#> G1_cell86_count G1_cell87_count G1_cell88_count G1_cell89_count G1_cell90_count
#> NA NA NA NA NA
#> G1_cell91_count G1_cell92_count G1_cell93_count G1_cell94_count G1_cell95_count
#> NA NA NA NA NA
#> G1_cell96_count S_cell1_count S_cell2_count S_cell3_count S_cell4_count
#> NA NA NA NA NA
#> S_cell5_count S_cell6_count S_cell7_count S_cell8_count S_cell9_count
#> NA NA NA NA NA
#> S_cell10_count S_cell11_count S_cell12_count S_cell13_count S_cell14_count
#> NA NA NA NA NA
#> S_cell15_count S_cell16_count S_cell17_count S_cell18_count S_cell19_count
#> NA NA NA NA NA
#> S_cell20_count S_cell21_count S_cell22_count S_cell23_count S_cell24_count
#> NA NA NA NA NA
#> S_cell25_count S_cell26_count S_cell27_count S_cell28_count S_cell29_count
#> NA NA NA NA NA
#> S_cell30_count S_cell31_count S_cell32_count S_cell33_count S_cell34_count
#> NA NA NA NA NA
#> S_cell35_count S_cell36_count S_cell37_count S_cell38_count S_cell39_count
#> NA NA NA NA NA
#> S_cell40_count S_cell41_count S_cell42_count S_cell43_count S_cell44_count
#> NA NA NA NA NA
#> S_cell45_count S_cell46_count S_cell47_count S_cell48_count S_cell49_count
#> NA NA NA NA NA
#> S_cell50_count S_cell51_count S_cell52_count S_cell53_count S_cell54_count
#> NA NA NA NA NA
#> S_cell55_count S_cell56_count S_cell57_count S_cell58_count S_cell59_count
#> NA NA NA NA NA
#> S_cell60_count S_cell61_count S_cell62_count S_cell63_count S_cell64_count
#> NA NA NA NA NA
#> S_cell65_count S_cell66_count S_cell67_count S_cell68_count S_cell69_count
#> NA NA NA NA NA
#> S_cell70_count S_cell71_count S_cell72_count S_cell73_count S_cell74_count
#> NA NA NA NA NA
#> S_cell75_count S_cell76_count S_cell77_count S_cell78_count S_cell79_count
#> NA NA NA NA NA
#> S_cell80_count S_cell81_count S_cell82_count S_cell83_count S_cell84_count
#> NA NA NA NA NA
#> S_cell85_count S_cell86_count S_cell87_count S_cell88_count S_cell89_count
#> NA NA NA NA NA
#> S_cell90_count S_cell91_count S_cell92_count S_cell93_count S_cell94_count
#> NA NA NA NA NA
#> S_cell95_count S_cell96_count G2M_cell1_count G2M_cell2_count G2M_cell3_count
#> NA NA NA NA NA
#> G2M_cell4_count G2M_cell5_count G2M_cell6_count G2M_cell7_count G2M_cell8_count
#> NA NA NA NA NA
#> G2M_cell9_count G2M_cell10_count G2M_cell11_count G2M_cell12_count G2M_cell13_count
#> NA NA NA NA NA
#> G2M_cell14_count G2M_cell15_count G2M_cell16_count G2M_cell17_count G2M_cell18_count
#> NA NA NA NA NA
#> G2M_cell19_count G2M_cell20_count G2M_cell21_count G2M_cell22_count G2M_cell23_count
#> NA NA NA NA NA
#> G2M_cell24_count G2M_cell25_count G2M_cell26_count G2M_cell27_count G2M_cell28_count
#> NA NA NA NA NA
#> G2M_cell29_count G2M_cell30_count G2M_cell31_count G2M_cell32_count G2M_cell33_count
#> NA NA NA NA NA
#> G2M_cell34_count G2M_cell35_count G2M_cell36_count G2M_cell37_count G2M_cell38_count
#> NA NA NA NA NA
#> G2M_cell39_count G2M_cell40_count G2M_cell41_count G2M_cell42_count G2M_cell43_count
#> NA NA NA NA NA
#> G2M_cell44_count G2M_cell45_count G2M_cell46_count G2M_cell47_count G2M_cell48_count
#> NA NA NA NA NA
#> G2M_cell49_count G2M_cell50_count G2M_cell51_count G2M_cell52_count G2M_cell53_count
#> NA NA NA NA NA
#> G2M_cell54_count G2M_cell55_count G2M_cell56_count G2M_cell57_count G2M_cell58_count
#> NA NA NA NA NA
#> G2M_cell59_count G2M_cell60_count G2M_cell61_count G2M_cell62_count G2M_cell63_count
#> NA NA NA NA NA
#> G2M_cell64_count G2M_cell65_count G2M_cell66_count G2M_cell67_count G2M_cell68_count
#> NA NA NA NA NA
#> G2M_cell69_count G2M_cell70_count G2M_cell71_count G2M_cell72_count G2M_cell73_count
#> NA NA NA NA NA
#> G2M_cell74_count G2M_cell75_count G2M_cell76_count G2M_cell77_count G2M_cell78_count
#> NA NA NA NA NA
#> G2M_cell79_count G2M_cell80_count G2M_cell81_count G2M_cell82_count G2M_cell83_count
#> NA NA NA NA NA
#> G2M_cell84_count G2M_cell85_count G2M_cell86_count G2M_cell87_count G2M_cell88_count
#> NA NA NA NA NA
#> G2M_cell89_count G2M_cell90_count G2M_cell91_count G2M_cell92_count G2M_cell93_count
#> NA NA NA NA NA
#> G2M_cell94_count G2M_cell95_count G2M_cell96_count
#> NA NA NA
Heatmaps of the top rows:
top_rows_heatmap(res_rh)
#> Error in h(simpleError(msg, call)) :
#> error in evaluating the argument 'object' in selecting a method for function 'draw': no applicable method for 'height' applied to an object of class "list"
Top rows on each node:
top_rows_overlap(res_rh, method = "upset")
UMAP plot which shows how samples are separated.
par(mfrow = c(1, 2))
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 441),
method = "UMAP", top_value_method = "SD", top_n = 2000, scale_rows = FALSE)
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 441),
method = "UMAP", top_value_method = "ATC", top_n = 2000, scale_rows = TRUE)
par(mfrow = c(1, 2))
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 696),
method = "UMAP", top_value_method = "SD", top_n = 2000, scale_rows = FALSE)
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 696),
method = "UMAP", top_value_method = "ATC", top_n = 2000, scale_rows = TRUE)
par(mfrow = c(1, 2))
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 2339),
method = "UMAP", top_value_method = "SD", top_n = 2000, scale_rows = FALSE)
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 2339),
method = "UMAP", top_value_method = "ATC", top_n = 2000, scale_rows = TRUE)
par(mfrow = c(1, 2))
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 4525),
method = "UMAP", top_value_method = "SD", top_n = 2000, scale_rows = FALSE)
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 4525),
method = "UMAP", top_value_method = "ATC", top_n = 2000, scale_rows = TRUE)
par(mfrow = c(1, 2))
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 5620),
method = "UMAP", top_value_method = "SD", top_n = 2000, scale_rows = FALSE)
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 5620),
method = "UMAP", top_value_method = "ATC", top_n = 2000, scale_rows = TRUE)
par(mfrow = c(1, 2))
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 6601),
method = "UMAP", top_value_method = "SD", top_n = 2000, scale_rows = FALSE)
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 6601),
method = "UMAP", top_value_method = "ATC", top_n = 2000, scale_rows = TRUE)
par(mfrow = c(1, 2))
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 10267),
method = "UMAP", top_value_method = "SD", top_n = 2000, scale_rows = FALSE)
dimension_reduction(res_rh, merge_node = merge_node_param(min_n_signatures = 10267),
method = "UMAP", top_value_method = "ATC", top_n = 2000, scale_rows = TRUE)
Signatures on the heatmap are the union of all signatures found on every node on the hierarchy. The number of k-means on rows are automatically selected by the function.
get_signatures(res_rh, merge_node = merge_node_param(min_n_signatures = 441))
get_signatures(res_rh, merge_node = merge_node_param(min_n_signatures = 696))
get_signatures(res_rh, merge_node = merge_node_param(min_n_signatures = 2339))
get_signatures(res_rh, merge_node = merge_node_param(min_n_signatures = 4525))
get_signatures(res_rh, merge_node = merge_node_param(min_n_signatures = 5620))
get_signatures(res_rh, merge_node = merge_node_param(min_n_signatures = 6601))
get_signatures(res_rh, merge_node = merge_node_param(min_n_signatures = 10267))
#> Error in names(x) <- value: 'names' attribute [1] must be the same length as the vector [0]
Compare signatures from different nodes:
compare_signatures(res_rh, verbose = FALSE)
If there are too many signatures, top_signatures = ... can be set to only show the
signatures with the highest FDRs. Note it only works on every node and the final signatures
are the union of all signatures of all nodes.
# code only for demonstration
# e.g. to show the top 500 most significant rows on each node.
tb = get_signature(res_rh, top_signatures = 500)
Child nodes: Node01 , Node02 .
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 14880 rows and 288 columns.
#> Top rows (1488) 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 0.983 0.993 0.490 0.509 0.509
#> 3 3 0.793 0.834 0.892 0.198 0.885 0.776
#> 4 4 0.872 0.902 0.958 0.106 0.855 0.680
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
#> G1_cell1_count 2 0.000 0.987 0.00 1.00
#> G1_cell2_count 2 0.000 0.987 0.00 1.00
#> G1_cell3_count 2 0.000 0.987 0.00 1.00
#> G1_cell4_count 2 0.000 0.987 0.00 1.00
#> G1_cell5_count 2 0.000 0.987 0.00 1.00
#> G1_cell6_count 2 0.000 0.987 0.00 1.00
#> G1_cell7_count 1 0.000 0.998 1.00 0.00
#> G1_cell8_count 1 0.000 0.998 1.00 0.00
#> G1_cell9_count 1 0.000 0.998 1.00 0.00
#> G1_cell10_count 2 0.000 0.987 0.00 1.00
#> G1_cell11_count 2 0.000 0.987 0.00 1.00
#> G1_cell12_count 2 0.995 0.161 0.46 0.54
#> G1_cell13_count 1 0.000 0.998 1.00 0.00
#> G1_cell14_count 2 0.000 0.987 0.00 1.00
#> G1_cell15_count 1 0.000 0.998 1.00 0.00
#> G1_cell16_count 1 0.000 0.998 1.00 0.00
#> G1_cell17_count 1 0.000 0.998 1.00 0.00
#> G1_cell18_count 2 0.000 0.987 0.00 1.00
#> G1_cell19_count 2 0.242 0.949 0.04 0.96
#> G1_cell20_count 1 0.000 0.998 1.00 0.00
#> G1_cell21_count 2 0.000 0.987 0.00 1.00
#> G1_cell22_count 1 0.000 0.998 1.00 0.00
#> G1_cell23_count 1 0.000 0.998 1.00 0.00
#> G1_cell24_count 2 0.000 0.987 0.00 1.00
#> G1_cell25_count 1 0.000 0.998 1.00 0.00
#> G1_cell26_count 1 0.000 0.998 1.00 0.00
#> G1_cell27_count 2 0.000 0.987 0.00 1.00
#> G1_cell28_count 2 0.000 0.987 0.00 1.00
#> G1_cell29_count 2 0.000 0.987 0.00 1.00
#> G1_cell30_count 1 0.000 0.998 1.00 0.00
#> G1_cell31_count 2 0.000 0.987 0.00 1.00
#> G1_cell32_count 2 0.000 0.987 0.00 1.00
#> G1_cell33_count 2 0.000 0.987 0.00 1.00
#> G1_cell34_count 2 0.000 0.987 0.00 1.00
#> G1_cell35_count 2 0.000 0.987 0.00 1.00
#> G1_cell36_count 1 0.000 0.998 1.00 0.00
#> G1_cell37_count 2 0.000 0.987 0.00 1.00
#> G1_cell38_count 2 0.000 0.987 0.00 1.00
#> G1_cell39_count 1 0.000 0.998 1.00 0.00
#> G1_cell40_count 2 0.000 0.987 0.00 1.00
#> G1_cell41_count 1 0.000 0.998 1.00 0.00
#> G1_cell42_count 1 0.000 0.998 1.00 0.00
#> G1_cell43_count 1 0.000 0.998 1.00 0.00
#> G1_cell44_count 1 0.000 0.998 1.00 0.00
#> G1_cell45_count 2 0.000 0.987 0.00 1.00
#> G1_cell46_count 2 0.000 0.987 0.00 1.00
#> G1_cell47_count 1 0.000 0.998 1.00 0.00
#> G1_cell48_count 2 0.000 0.987 0.00 1.00
#> G1_cell49_count 1 0.000 0.998 1.00 0.00
#> G1_cell50_count 2 0.000 0.987 0.00 1.00
#> G1_cell51_count 2 0.000 0.987 0.00 1.00
#> G1_cell52_count 2 0.000 0.987 0.00 1.00
#> G1_cell53_count 2 0.000 0.987 0.00 1.00
#> G1_cell54_count 1 0.000 0.998 1.00 0.00
#> G1_cell55_count 1 0.000 0.998 1.00 0.00
#> G1_cell56_count 2 0.000 0.987 0.00 1.00
#> G1_cell57_count 1 0.000 0.998 1.00 0.00
#> G1_cell58_count 2 0.000 0.987 0.00 1.00
#> G1_cell59_count 2 0.000 0.987 0.00 1.00
#> G1_cell60_count 1 0.000 0.998 1.00 0.00
#> G1_cell61_count 1 0.000 0.998 1.00 0.00
#> G1_cell62_count 1 0.000 0.998 1.00 0.00
#> G1_cell63_count 2 0.000 0.987 0.00 1.00
#> G1_cell64_count 1 0.000 0.998 1.00 0.00
#> G1_cell65_count 1 0.000 0.998 1.00 0.00
#> G1_cell66_count 1 0.000 0.998 1.00 0.00
#> G1_cell67_count 2 0.000 0.987 0.00 1.00
#> G1_cell68_count 1 0.000 0.998 1.00 0.00
#> G1_cell69_count 2 0.000 0.987 0.00 1.00
#> G1_cell70_count 1 0.000 0.998 1.00 0.00
#> G1_cell71_count 2 0.000 0.987 0.00 1.00
#> G1_cell72_count 2 0.000 0.987 0.00 1.00
#> G1_cell73_count 1 0.000 0.998 1.00 0.00
#> G1_cell74_count 1 0.000 0.998 1.00 0.00
#> G1_cell75_count 1 0.000 0.998 1.00 0.00
#> G1_cell76_count 2 0.000 0.987 0.00 1.00
#> G1_cell77_count 1 0.000 0.998 1.00 0.00
#> G1_cell78_count 2 0.000 0.987 0.00 1.00
#> G1_cell79_count 1 0.000 0.998 1.00 0.00
#> G1_cell80_count 1 0.000 0.998 1.00 0.00
#> G1_cell81_count 1 0.000 0.998 1.00 0.00
#> G1_cell82_count 2 0.000 0.987 0.00 1.00
#> G1_cell83_count 1 0.000 0.998 1.00 0.00
#> G1_cell84_count 1 0.000 0.998 1.00 0.00
#> G1_cell85_count 2 0.000 0.987 0.00 1.00
#> G1_cell86_count 1 0.000 0.998 1.00 0.00
#> G1_cell87_count 1 0.000 0.998 1.00 0.00
#> G1_cell88_count 2 0.000 0.987 0.00 1.00
#> G1_cell89_count 2 0.000 0.987 0.00 1.00
#> G1_cell90_count 2 0.000 0.987 0.00 1.00
#> G1_cell91_count 1 0.000 0.998 1.00 0.00
#> G1_cell92_count 2 0.000 0.987 0.00 1.00
#> G1_cell93_count 1 0.000 0.998 1.00 0.00
#> G1_cell94_count 1 0.000 0.998 1.00 0.00
#> G1_cell95_count 1 0.000 0.998 1.00 0.00
#> G1_cell96_count 1 0.000 0.998 1.00 0.00
#> S_cell1_count 2 0.000 0.987 0.00 1.00
#> S_cell2_count 1 0.000 0.998 1.00 0.00
#> S_cell3_count 1 0.000 0.998 1.00 0.00
#> S_cell4_count 1 0.000 0.998 1.00 0.00
#> S_cell5_count 2 0.000 0.987 0.00 1.00
#> S_cell6_count 1 0.000 0.998 1.00 0.00
#> S_cell7_count 1 0.000 0.998 1.00 0.00
#> S_cell8_count 1 0.000 0.998 1.00 0.00
#> S_cell9_count 1 0.000 0.998 1.00 0.00
#> S_cell10_count 1 0.000 0.998 1.00 0.00
#> S_cell11_count 1 0.881 0.565 0.70 0.30
#> S_cell12_count 2 0.000 0.987 0.00 1.00
#> S_cell13_count 1 0.000 0.998 1.00 0.00
#> S_cell14_count 1 0.000 0.998 1.00 0.00
#> S_cell15_count 1 0.000 0.998 1.00 0.00
#> S_cell16_count 1 0.000 0.998 1.00 0.00
#> S_cell17_count 1 0.000 0.998 1.00 0.00
#> S_cell18_count 2 0.000 0.987 0.00 1.00
#> S_cell19_count 1 0.000 0.998 1.00 0.00
#> S_cell20_count 1 0.000 0.998 1.00 0.00
#> S_cell21_count 2 0.000 0.987 0.00 1.00
#> S_cell22_count 1 0.000 0.998 1.00 0.00
#> S_cell23_count 2 0.000 0.987 0.00 1.00
#> S_cell24_count 1 0.000 0.998 1.00 0.00
#> S_cell25_count 2 0.000 0.987 0.00 1.00
#> S_cell26_count 1 0.000 0.998 1.00 0.00
#> S_cell27_count 1 0.000 0.998 1.00 0.00
#> S_cell28_count 1 0.000 0.998 1.00 0.00
#> S_cell29_count 1 0.000 0.998 1.00 0.00
#> S_cell30_count 1 0.000 0.998 1.00 0.00
#> S_cell31_count 2 0.000 0.987 0.00 1.00
#> S_cell32_count 1 0.000 0.998 1.00 0.00
#> S_cell33_count 1 0.000 0.998 1.00 0.00
#> S_cell34_count 2 0.000 0.987 0.00 1.00
#> S_cell35_count 1 0.000 0.998 1.00 0.00
#> S_cell36_count 1 0.000 0.998 1.00 0.00
#> S_cell37_count 1 0.000 0.998 1.00 0.00
#> S_cell38_count 2 0.000 0.987 0.00 1.00
#> S_cell39_count 1 0.000 0.998 1.00 0.00
#> S_cell40_count 1 0.000 0.998 1.00 0.00
#> S_cell41_count 1 0.000 0.998 1.00 0.00
#> S_cell42_count 1 0.000 0.998 1.00 0.00
#> S_cell43_count 1 0.000 0.998 1.00 0.00
#> S_cell44_count 2 0.000 0.987 0.00 1.00
#> S_cell45_count 1 0.000 0.998 1.00 0.00
#> S_cell46_count 1 0.000 0.998 1.00 0.00
#> S_cell47_count 1 0.000 0.998 1.00 0.00
#> S_cell48_count 1 0.000 0.998 1.00 0.00
#> S_cell49_count 1 0.000 0.998 1.00 0.00
#> S_cell50_count 1 0.000 0.998 1.00 0.00
#> S_cell51_count 2 0.000 0.987 0.00 1.00
#> S_cell52_count 1 0.000 0.998 1.00 0.00
#> S_cell53_count 1 0.000 0.998 1.00 0.00
#> S_cell54_count 1 0.000 0.998 1.00 0.00
#> S_cell55_count 1 0.000 0.998 1.00 0.00
#> S_cell56_count 2 0.000 0.987 0.00 1.00
#> S_cell57_count 1 0.000 0.998 1.00 0.00
#> S_cell58_count 2 0.000 0.987 0.00 1.00
#> S_cell59_count 1 0.000 0.998 1.00 0.00
#> S_cell60_count 1 0.000 0.998 1.00 0.00
#> S_cell61_count 1 0.000 0.998 1.00 0.00
#> S_cell62_count 1 0.000 0.998 1.00 0.00
#> S_cell63_count 1 0.000 0.998 1.00 0.00
#> S_cell64_count 2 0.000 0.987 0.00 1.00
#> S_cell65_count 2 0.000 0.987 0.00 1.00
#> S_cell66_count 1 0.000 0.998 1.00 0.00
#> S_cell67_count 2 0.000 0.987 0.00 1.00
#> S_cell68_count 1 0.000 0.998 1.00 0.00
#> S_cell69_count 1 0.000 0.998 1.00 0.00
#> S_cell70_count 1 0.000 0.998 1.00 0.00
#> S_cell71_count 2 0.000 0.987 0.00 1.00
#> S_cell72_count 1 0.000 0.998 1.00 0.00
#> S_cell73_count 1 0.000 0.998 1.00 0.00
#> S_cell74_count 2 0.000 0.987 0.00 1.00
#> S_cell75_count 2 0.000 0.987 0.00 1.00
#> S_cell76_count 1 0.000 0.998 1.00 0.00
#> S_cell77_count 2 0.000 0.987 0.00 1.00
#> S_cell78_count 2 0.000 0.987 0.00 1.00
#> S_cell79_count 1 0.000 0.998 1.00 0.00
#> S_cell80_count 1 0.000 0.998 1.00 0.00
#> S_cell81_count 2 0.000 0.987 0.00 1.00
#> S_cell82_count 1 0.000 0.998 1.00 0.00
#> S_cell83_count 1 0.000 0.998 1.00 0.00
#> S_cell84_count 2 0.000 0.987 0.00 1.00
#> S_cell85_count 1 0.000 0.998 1.00 0.00
#> S_cell86_count 1 0.000 0.998 1.00 0.00
#> S_cell87_count 2 0.000 0.987 0.00 1.00
#> S_cell88_count 2 0.000 0.987 0.00 1.00
#> S_cell89_count 1 0.000 0.998 1.00 0.00
#> S_cell90_count 2 0.000 0.987 0.00 1.00
#> S_cell91_count 2 0.000 0.987 0.00 1.00
#> S_cell92_count 1 0.000 0.998 1.00 0.00
#> S_cell93_count 1 0.000 0.998 1.00 0.00
#> S_cell94_count 2 0.000 0.987 0.00 1.00
#> S_cell95_count 2 0.000 0.987 0.00 1.00
#> S_cell96_count 2 0.000 0.987 0.00 1.00
#> G2M_cell1_count 2 0.000 0.987 0.00 1.00
#> G2M_cell2_count 2 0.000 0.987 0.00 1.00
#> G2M_cell3_count 2 0.000 0.987 0.00 1.00
#> G2M_cell4_count 2 0.000 0.987 0.00 1.00
#> G2M_cell5_count 1 0.000 0.998 1.00 0.00
#> G2M_cell6_count 2 0.904 0.538 0.32 0.68
#> G2M_cell7_count 1 0.000 0.998 1.00 0.00
#> G2M_cell8_count 1 0.000 0.998 1.00 0.00
#> G2M_cell9_count 1 0.000 0.998 1.00 0.00
#> G2M_cell10_count 1 0.000 0.998 1.00 0.00
#> G2M_cell11_count 1 0.000 0.998 1.00 0.00
#> G2M_cell12_count 1 0.000 0.998 1.00 0.00
#> G2M_cell13_count 1 0.000 0.998 1.00 0.00
#> G2M_cell14_count 2 0.000 0.987 0.00 1.00
#> G2M_cell15_count 2 0.000 0.987 0.00 1.00
#> G2M_cell16_count 2 0.000 0.987 0.00 1.00
#> G2M_cell17_count 1 0.000 0.998 1.00 0.00
#> G2M_cell18_count 1 0.000 0.998 1.00 0.00
#> G2M_cell19_count 1 0.000 0.998 1.00 0.00
#> G2M_cell20_count 1 0.000 0.998 1.00 0.00
#> G2M_cell21_count 1 0.000 0.998 1.00 0.00
#> G2M_cell22_count 2 0.000 0.987 0.00 1.00
#> G2M_cell23_count 1 0.000 0.998 1.00 0.00
#> G2M_cell24_count 2 0.000 0.987 0.00 1.00
#> G2M_cell25_count 1 0.000 0.998 1.00 0.00
#> G2M_cell26_count 2 0.000 0.987 0.00 1.00
#> G2M_cell27_count 2 0.000 0.987 0.00 1.00
#> G2M_cell28_count 2 0.000 0.987 0.00 1.00
#> G2M_cell29_count 2 0.000 0.987 0.00 1.00
#> G2M_cell30_count 2 0.000 0.987 0.00 1.00
#> G2M_cell31_count 2 0.000 0.987 0.00 1.00
#> G2M_cell32_count 1 0.000 0.998 1.00 0.00
#> G2M_cell33_count 1 0.000 0.998 1.00 0.00
#> G2M_cell34_count 1 0.000 0.998 1.00 0.00
#> G2M_cell35_count 1 0.000 0.998 1.00 0.00
#> G2M_cell36_count 2 0.000 0.987 0.00 1.00
#> G2M_cell37_count 1 0.000 0.998 1.00 0.00
#> G2M_cell38_count 2 0.000 0.987 0.00 1.00
#> G2M_cell39_count 2 0.000 0.987 0.00 1.00
#> G2M_cell40_count 2 0.000 0.987 0.00 1.00
#> G2M_cell41_count 1 0.402 0.911 0.92 0.08
#> G2M_cell42_count 2 0.000 0.987 0.00 1.00
#> G2M_cell43_count 2 0.000 0.987 0.00 1.00
#> G2M_cell44_count 1 0.000 0.998 1.00 0.00
#> G2M_cell45_count 1 0.000 0.998 1.00 0.00
#> G2M_cell46_count 2 0.000 0.987 0.00 1.00
#> G2M_cell47_count 1 0.000 0.998 1.00 0.00
#> G2M_cell48_count 1 0.000 0.998 1.00 0.00
#> G2M_cell49_count 1 0.000 0.998 1.00 0.00
#> G2M_cell50_count 1 0.000 0.998 1.00 0.00
#> G2M_cell51_count 2 0.402 0.906 0.08 0.92
#> G2M_cell52_count 2 0.000 0.987 0.00 1.00
#> G2M_cell53_count 2 0.000 0.987 0.00 1.00
#> G2M_cell54_count 2 0.943 0.447 0.36 0.64
#> G2M_cell55_count 1 0.000 0.998 1.00 0.00
#> G2M_cell56_count 2 0.000 0.987 0.00 1.00
#> G2M_cell57_count 1 0.000 0.998 1.00 0.00
#> G2M_cell58_count 1 0.000 0.998 1.00 0.00
#> G2M_cell59_count 1 0.000 0.998 1.00 0.00
#> G2M_cell60_count 1 0.000 0.998 1.00 0.00
#> G2M_cell61_count 1 0.000 0.998 1.00 0.00
#> G2M_cell62_count 1 0.000 0.998 1.00 0.00
#> G2M_cell63_count 1 0.000 0.998 1.00 0.00
#> G2M_cell64_count 2 0.000 0.987 0.00 1.00
#> G2M_cell65_count 1 0.000 0.998 1.00 0.00
#> G2M_cell66_count 2 0.000 0.987 0.00 1.00
#> G2M_cell67_count 2 0.000 0.987 0.00 1.00
#> G2M_cell68_count 1 0.000 0.998 1.00 0.00
#> G2M_cell69_count 1 0.000 0.998 1.00 0.00
#> G2M_cell70_count 2 0.000 0.987 0.00 1.00
#> G2M_cell71_count 1 0.000 0.998 1.00 0.00
#> G2M_cell72_count 2 0.881 0.579 0.30 0.70
#> G2M_cell73_count 1 0.000 0.998 1.00 0.00
#> G2M_cell74_count 1 0.000 0.998 1.00 0.00
#> G2M_cell75_count 1 0.000 0.998 1.00 0.00
#> G2M_cell76_count 1 0.000 0.998 1.00 0.00
#> G2M_cell77_count 2 0.000 0.987 0.00 1.00
#> G2M_cell78_count 2 0.000 0.987 0.00 1.00
#> G2M_cell79_count 1 0.000 0.998 1.00 0.00
#> G2M_cell80_count 1 0.000 0.998 1.00 0.00
#> G2M_cell81_count 2 0.000 0.987 0.00 1.00
#> G2M_cell82_count 2 0.000 0.987 0.00 1.00
#> G2M_cell83_count 1 0.000 0.998 1.00 0.00
#> G2M_cell84_count 1 0.000 0.998 1.00 0.00
#> G2M_cell85_count 2 0.000 0.987 0.00 1.00
#> G2M_cell86_count 1 0.000 0.998 1.00 0.00
#> G2M_cell87_count 1 0.000 0.998 1.00 0.00
#> G2M_cell88_count 1 0.000 0.998 1.00 0.00
#> G2M_cell89_count 2 0.000 0.987 0.00 1.00
#> G2M_cell90_count 1 0.000 0.998 1.00 0.00
#> G2M_cell91_count 1 0.000 0.998 1.00 0.00
#> G2M_cell92_count 2 0.000 0.987 0.00 1.00
#> G2M_cell93_count 2 0.000 0.987 0.00 1.00
#> G2M_cell94_count 2 0.000 0.987 0.00 1.00
#> G2M_cell95_count 2 0.000 0.987 0.00 1.00
#> G2M_cell96_count 2 0.000 0.987 0.00 1.00
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> G1_cell1_count 2 0.4555 0.877 0.00 0.80 0.20
#> G1_cell2_count 2 0.0000 0.780 0.00 1.00 0.00
#> G1_cell3_count 2 0.0000 0.780 0.00 1.00 0.00
#> G1_cell4_count 2 0.4555 0.877 0.00 0.80 0.20
#> G1_cell5_count 2 0.0000 0.780 0.00 1.00 0.00
#> G1_cell6_count 2 0.4555 0.877 0.00 0.80 0.20
#> G1_cell7_count 1 0.0000 0.969 1.00 0.00 0.00
#> G1_cell8_count 1 0.0000 0.969 1.00 0.00 0.00
#> G1_cell9_count 1 0.0000 0.969 1.00 0.00 0.00
#> G1_cell10_count 3 0.6045 0.466 0.00 0.38 0.62
#> G1_cell11_count 2 0.0000 0.780 0.00 1.00 0.00
#> G1_cell12_count 2 0.0000 0.780 0.00 1.00 0.00
#> G1_cell13_count 1 0.0000 0.969 1.00 0.00 0.00
#> G1_cell14_count 2 0.0000 0.780 0.00 1.00 0.00
#> G1_cell15_count 1 0.0000 0.969 1.00 0.00 0.00
#> G1_cell16_count 1 0.0000 0.969 1.00 0.00 0.00
#> G1_cell17_count 1 0.0000 0.969 1.00 0.00 0.00
#> G1_cell18_count 2 0.0000 0.780 0.00 1.00 0.00
#> G1_cell19_count 2 0.0000 0.780 0.00 1.00 0.00
#> G1_cell20_count 1 0.4002 0.816 0.84 0.16 0.00
#> G1_cell21_count 3 0.6045 0.466 0.00 0.38 0.62
#> G1_cell22_count 1 0.0000 0.969 1.00 0.00 0.00
#> G1_cell23_count 1 0.0000 0.969 1.00 0.00 0.00
#> G1_cell24_count 3 0.6045 0.466 0.00 0.38 0.62
#> G1_cell25_count 1 0.0000 0.969 1.00 0.00 0.00
#> G1_cell26_count 1 0.0000 0.969 1.00 0.00 0.00
#> G1_cell27_count 2 0.0000 0.780 0.00 1.00 0.00
#> G1_cell28_count 2 0.4555 0.877 0.00 0.80 0.20
#> G1_cell29_count 2 0.0892 0.792 0.00 0.98 0.02
#> G1_cell30_count 1 0.0000 0.969 1.00 0.00 0.00
#> G1_cell31_count 2 0.4555 0.877 0.00 0.80 0.20
#> G1_cell32_count 2 0.5835 0.537 0.00 0.66 0.34
#> G1_cell33_count 2 0.2537 0.824 0.00 0.92 0.08
#> G1_cell34_count 2 0.4555 0.877 0.00 0.80 0.20
#> G1_cell35_count 2 0.4555 0.877 0.00 0.80 0.20
#> G1_cell36_count 1 0.0000 0.969 1.00 0.00 0.00
#> G1_cell37_count 2 0.4555 0.877 0.00 0.80 0.20
#> G1_cell38_count 2 0.0000 0.780 0.00 1.00 0.00
#> G1_cell39_count 1 0.0892 0.952 0.98 0.02 0.00
#> G1_cell40_count 2 0.2537 0.824 0.00 0.92 0.08
#> G1_cell41_count 1 0.0000 0.969 1.00 0.00 0.00
#> G1_cell42_count 1 0.0000 0.969 1.00 0.00 0.00
#> G1_cell43_count 1 0.0000 0.969 1.00 0.00 0.00
#> G1_cell44_count 1 0.0000 0.969 1.00 0.00 0.00
#> G1_cell45_count 2 0.0000 0.780 0.00 1.00 0.00
#> G1_cell46_count 2 0.0000 0.780 0.00 1.00 0.00
#> G1_cell47_count 1 0.4555 0.769 0.80 0.20 0.00
#> G1_cell48_count 2 0.4555 0.877 0.00 0.80 0.20
#> G1_cell49_count 1 0.0000 0.969 1.00 0.00 0.00
#> G1_cell50_count 2 0.4555 0.877 0.00 0.80 0.20
#> G1_cell51_count 2 0.2537 0.824 0.00 0.92 0.08
#> G1_cell52_count 2 0.0000 0.780 0.00 1.00 0.00
#> G1_cell53_count 2 0.0000 0.780 0.00 1.00 0.00
#> G1_cell54_count 1 0.0000 0.969 1.00 0.00 0.00
#> G1_cell55_count 1 0.3686 0.838 0.86 0.14 0.00
#> G1_cell56_count 2 0.4555 0.877 0.00 0.80 0.20
#> G1_cell57_count 1 0.4555 0.769 0.80 0.20 0.00
#> G1_cell58_count 2 0.0000 0.780 0.00 1.00 0.00
#> G1_cell59_count 2 0.2537 0.824 0.00 0.92 0.08
#> G1_cell60_count 1 0.0000 0.969 1.00 0.00 0.00
#> G1_cell61_count 1 0.6244 0.351 0.56 0.44 0.00
#> G1_cell62_count 1 0.0000 0.969 1.00 0.00 0.00
#> G1_cell63_count 3 0.6045 0.466 0.00 0.38 0.62
#> G1_cell64_count 1 0.4291 0.793 0.82 0.18 0.00
#> G1_cell65_count 1 0.4555 0.769 0.80 0.20 0.00
#> G1_cell66_count 1 0.0000 0.969 1.00 0.00 0.00
#> G1_cell67_count 2 0.4555 0.877 0.00 0.80 0.20
#> G1_cell68_count 1 0.0000 0.969 1.00 0.00 0.00
#> G1_cell69_count 2 0.0000 0.780 0.00 1.00 0.00
#> G1_cell70_count 1 0.0000 0.969 1.00 0.00 0.00
#> G1_cell71_count 2 0.0892 0.792 0.00 0.98 0.02
#> G1_cell72_count 3 0.6045 0.466 0.00 0.38 0.62
#> G1_cell73_count 1 0.0000 0.969 1.00 0.00 0.00
#> G1_cell74_count 1 0.6126 0.441 0.60 0.00 0.40
#> G1_cell75_count 1 0.0000 0.969 1.00 0.00 0.00
#> G1_cell76_count 2 0.4555 0.877 0.00 0.80 0.20
#> G1_cell77_count 1 0.0000 0.969 1.00 0.00 0.00
#> G1_cell78_count 2 0.0000 0.780 0.00 1.00 0.00
#> G1_cell79_count 1 0.2959 0.879 0.90 0.10 0.00
#> G1_cell80_count 1 0.0000 0.969 1.00 0.00 0.00
#> G1_cell81_count 1 0.0000 0.969 1.00 0.00 0.00
#> G1_cell82_count 2 0.4555 0.877 0.00 0.80 0.20
#> G1_cell83_count 1 0.0000 0.969 1.00 0.00 0.00
#> G1_cell84_count 1 0.0000 0.969 1.00 0.00 0.00
#> G1_cell85_count 2 0.4555 0.877 0.00 0.80 0.20
#> G1_cell86_count 1 0.0000 0.969 1.00 0.00 0.00
#> G1_cell87_count 1 0.0000 0.969 1.00 0.00 0.00
#> G1_cell88_count 2 0.4555 0.877 0.00 0.80 0.20
#> G1_cell89_count 3 0.0892 0.671 0.00 0.02 0.98
#> G1_cell90_count 3 0.6045 0.466 0.00 0.38 0.62
#> G1_cell91_count 1 0.0000 0.969 1.00 0.00 0.00
#> G1_cell92_count 2 0.0000 0.780 0.00 1.00 0.00
#> G1_cell93_count 1 0.0000 0.969 1.00 0.00 0.00
#> G1_cell94_count 1 0.0000 0.969 1.00 0.00 0.00
#> G1_cell95_count 1 0.0000 0.969 1.00 0.00 0.00
#> G1_cell96_count 1 0.0000 0.969 1.00 0.00 0.00
#> S_cell1_count 2 0.4555 0.877 0.00 0.80 0.20
#> S_cell2_count 1 0.6045 0.483 0.62 0.00 0.38
#> S_cell3_count 1 0.0000 0.969 1.00 0.00 0.00
#> S_cell4_count 1 0.0000 0.969 1.00 0.00 0.00
#> S_cell5_count 2 0.4555 0.877 0.00 0.80 0.20
#> S_cell6_count 1 0.0000 0.969 1.00 0.00 0.00
#> S_cell7_count 1 0.0000 0.969 1.00 0.00 0.00
#> S_cell8_count 1 0.0000 0.969 1.00 0.00 0.00
#> S_cell9_count 1 0.0000 0.969 1.00 0.00 0.00
#> S_cell10_count 1 0.0000 0.969 1.00 0.00 0.00
#> S_cell11_count 3 0.0000 0.676 0.00 0.00 1.00
#> S_cell12_count 3 0.0000 0.676 0.00 0.00 1.00
#> S_cell13_count 1 0.0000 0.969 1.00 0.00 0.00
#> S_cell14_count 1 0.0000 0.969 1.00 0.00 0.00
#> S_cell15_count 1 0.0000 0.969 1.00 0.00 0.00
#> S_cell16_count 1 0.0000 0.969 1.00 0.00 0.00
#> S_cell17_count 1 0.0000 0.969 1.00 0.00 0.00
#> S_cell18_count 2 0.4555 0.877 0.00 0.80 0.20
#> S_cell19_count 1 0.0000 0.969 1.00 0.00 0.00
#> S_cell20_count 1 0.6045 0.483 0.62 0.00 0.38
#> S_cell21_count 3 0.0000 0.676 0.00 0.00 1.00
#> S_cell22_count 1 0.0000 0.969 1.00 0.00 0.00
#> S_cell23_count 3 0.6045 0.466 0.00 0.38 0.62
#> S_cell24_count 1 0.0000 0.969 1.00 0.00 0.00
#> S_cell25_count 3 0.6045 0.466 0.00 0.38 0.62
#> S_cell26_count 1 0.0000 0.969 1.00 0.00 0.00
#> S_cell27_count 1 0.0000 0.969 1.00 0.00 0.00
#> S_cell28_count 1 0.0000 0.969 1.00 0.00 0.00
#> S_cell29_count 1 0.0000 0.969 1.00 0.00 0.00
#> S_cell30_count 1 0.0000 0.969 1.00 0.00 0.00
#> S_cell31_count 2 0.4555 0.877 0.00 0.80 0.20
#> S_cell32_count 1 0.0000 0.969 1.00 0.00 0.00
#> S_cell33_count 1 0.0000 0.969 1.00 0.00 0.00
#> S_cell34_count 2 0.4555 0.877 0.00 0.80 0.20
#> S_cell35_count 1 0.0000 0.969 1.00 0.00 0.00
#> S_cell36_count 1 0.0000 0.969 1.00 0.00 0.00
#> S_cell37_count 1 0.0000 0.969 1.00 0.00 0.00
#> S_cell38_count 3 0.0000 0.676 0.00 0.00 1.00
#> S_cell39_count 1 0.0000 0.969 1.00 0.00 0.00
#> S_cell40_count 1 0.0000 0.969 1.00 0.00 0.00
#> S_cell41_count 1 0.0000 0.969 1.00 0.00 0.00
#> S_cell42_count 1 0.0000 0.969 1.00 0.00 0.00
#> S_cell43_count 1 0.0000 0.969 1.00 0.00 0.00
#> S_cell44_count 2 0.4555 0.877 0.00 0.80 0.20
#> S_cell45_count 1 0.0000 0.969 1.00 0.00 0.00
#> S_cell46_count 1 0.0000 0.969 1.00 0.00 0.00
#> S_cell47_count 1 0.0000 0.969 1.00 0.00 0.00
#> S_cell48_count 1 0.0000 0.969 1.00 0.00 0.00
#> S_cell49_count 1 0.3686 0.835 0.86 0.00 0.14
#> S_cell50_count 1 0.0000 0.969 1.00 0.00 0.00
#> S_cell51_count 3 0.6045 0.466 0.00 0.38 0.62
#> S_cell52_count 1 0.0000 0.969 1.00 0.00 0.00
#> S_cell53_count 1 0.0000 0.969 1.00 0.00 0.00
#> S_cell54_count 3 0.6302 -0.141 0.48 0.00 0.52
#> S_cell55_count 1 0.0000 0.969 1.00 0.00 0.00
#> S_cell56_count 3 0.6045 0.466 0.00 0.38 0.62
#> S_cell57_count 1 0.0000 0.969 1.00 0.00 0.00
#> S_cell58_count 2 0.4555 0.877 0.00 0.80 0.20
#> S_cell59_count 1 0.0000 0.969 1.00 0.00 0.00
#> S_cell60_count 1 0.0000 0.969 1.00 0.00 0.00
#> S_cell61_count 1 0.0000 0.969 1.00 0.00 0.00
#> S_cell62_count 1 0.0000 0.969 1.00 0.00 0.00
#> S_cell63_count 1 0.0000 0.969 1.00 0.00 0.00
#> S_cell64_count 3 0.6045 0.466 0.00 0.38 0.62
#> S_cell65_count 2 0.6045 0.499 0.00 0.62 0.38
#> S_cell66_count 1 0.0000 0.969 1.00 0.00 0.00
#> S_cell67_count 3 0.6045 0.466 0.00 0.38 0.62
#> S_cell68_count 1 0.0000 0.969 1.00 0.00 0.00
#> S_cell69_count 1 0.0000 0.969 1.00 0.00 0.00
#> S_cell70_count 1 0.0000 0.969 1.00 0.00 0.00
#> S_cell71_count 2 0.4555 0.877 0.00 0.80 0.20
#> S_cell72_count 1 0.0000 0.969 1.00 0.00 0.00
#> S_cell73_count 1 0.0000 0.969 1.00 0.00 0.00
#> S_cell74_count 2 0.4555 0.877 0.00 0.80 0.20
#> S_cell75_count 3 0.0892 0.671 0.00 0.02 0.98
#> S_cell76_count 1 0.0000 0.969 1.00 0.00 0.00
#> S_cell77_count 3 0.6045 0.466 0.00 0.38 0.62
#> S_cell78_count 2 0.4555 0.877 0.00 0.80 0.20
#> S_cell79_count 1 0.0000 0.969 1.00 0.00 0.00
#> S_cell80_count 1 0.0000 0.969 1.00 0.00 0.00
#> S_cell81_count 2 0.4555 0.877 0.00 0.80 0.20
#> S_cell82_count 1 0.0000 0.969 1.00 0.00 0.00
#> S_cell83_count 1 0.0000 0.969 1.00 0.00 0.00
#> S_cell84_count 2 0.4555 0.877 0.00 0.80 0.20
#> S_cell85_count 1 0.0000 0.969 1.00 0.00 0.00
#> S_cell86_count 1 0.0000 0.969 1.00 0.00 0.00
#> S_cell87_count 3 0.0000 0.676 0.00 0.00 1.00
#> S_cell88_count 2 0.4555 0.877 0.00 0.80 0.20
#> S_cell89_count 1 0.0000 0.969 1.00 0.00 0.00
#> S_cell90_count 2 0.4555 0.877 0.00 0.80 0.20
#> S_cell91_count 3 0.6045 0.466 0.00 0.38 0.62
#> S_cell92_count 1 0.0000 0.969 1.00 0.00 0.00
#> S_cell93_count 1 0.6045 0.483 0.62 0.00 0.38
#> S_cell94_count 2 0.4555 0.877 0.00 0.80 0.20
#> S_cell95_count 2 0.4555 0.877 0.00 0.80 0.20
#> S_cell96_count 2 0.4555 0.877 0.00 0.80 0.20
#> G2M_cell1_count 2 0.0000 0.780 0.00 1.00 0.00
#> G2M_cell2_count 3 0.0000 0.676 0.00 0.00 1.00
#> G2M_cell3_count 3 0.0000 0.676 0.00 0.00 1.00
#> G2M_cell4_count 2 0.4555 0.877 0.00 0.80 0.20
#> G2M_cell5_count 1 0.0000 0.969 1.00 0.00 0.00
#> G2M_cell6_count 2 0.2066 0.689 0.06 0.94 0.00
#> G2M_cell7_count 1 0.0000 0.969 1.00 0.00 0.00
#> G2M_cell8_count 1 0.0000 0.969 1.00 0.00 0.00
#> G2M_cell9_count 1 0.5216 0.683 0.74 0.00 0.26
#> G2M_cell10_count 1 0.0000 0.969 1.00 0.00 0.00
#> G2M_cell11_count 1 0.0000 0.969 1.00 0.00 0.00
#> G2M_cell12_count 3 0.4796 0.478 0.22 0.00 0.78
#> G2M_cell13_count 3 0.4555 0.494 0.20 0.00 0.80
#> G2M_cell14_count 3 0.0000 0.676 0.00 0.00 1.00
#> G2M_cell15_count 3 0.0000 0.676 0.00 0.00 1.00
#> G2M_cell16_count 2 0.4555 0.877 0.00 0.80 0.20
#> G2M_cell17_count 1 0.6045 0.483 0.62 0.00 0.38
#> G2M_cell18_count 1 0.0000 0.969 1.00 0.00 0.00
#> G2M_cell19_count 1 0.0000 0.969 1.00 0.00 0.00
#> G2M_cell20_count 1 0.0000 0.969 1.00 0.00 0.00
#> G2M_cell21_count 1 0.0000 0.969 1.00 0.00 0.00
#> G2M_cell22_count 2 0.4555 0.877 0.00 0.80 0.20
#> G2M_cell23_count 1 0.0000 0.969 1.00 0.00 0.00
#> G2M_cell24_count 3 0.6045 0.466 0.00 0.38 0.62
#> G2M_cell25_count 1 0.0000 0.969 1.00 0.00 0.00
#> G2M_cell26_count 2 0.4555 0.877 0.00 0.80 0.20
#> G2M_cell27_count 2 0.4555 0.877 0.00 0.80 0.20
#> G2M_cell28_count 3 0.0000 0.676 0.00 0.00 1.00
#> G2M_cell29_count 2 0.4555 0.877 0.00 0.80 0.20
#> G2M_cell30_count 2 0.4555 0.877 0.00 0.80 0.20
#> G2M_cell31_count 2 0.4555 0.877 0.00 0.80 0.20
#> G2M_cell32_count 1 0.0000 0.969 1.00 0.00 0.00
#> G2M_cell33_count 1 0.0000 0.969 1.00 0.00 0.00
#> G2M_cell34_count 1 0.0892 0.953 0.98 0.00 0.02
#> G2M_cell35_count 1 0.0000 0.969 1.00 0.00 0.00
#> G2M_cell36_count 3 0.0000 0.676 0.00 0.00 1.00
#> G2M_cell37_count 1 0.0000 0.969 1.00 0.00 0.00
#> G2M_cell38_count 3 0.6045 0.466 0.00 0.38 0.62
#> G2M_cell39_count 2 0.5397 0.749 0.00 0.72 0.28
#> G2M_cell40_count 3 0.6045 0.466 0.00 0.38 0.62
#> G2M_cell41_count 3 0.3340 0.568 0.12 0.00 0.88
#> G2M_cell42_count 3 0.6045 0.466 0.00 0.38 0.62
#> G2M_cell43_count 3 0.6045 0.466 0.00 0.38 0.62
#> G2M_cell44_count 1 0.0000 0.969 1.00 0.00 0.00
#> G2M_cell45_count 1 0.0000 0.969 1.00 0.00 0.00
#> G2M_cell46_count 2 0.4555 0.877 0.00 0.80 0.20
#> G2M_cell47_count 1 0.0000 0.969 1.00 0.00 0.00
#> G2M_cell48_count 1 0.0000 0.969 1.00 0.00 0.00
#> G2M_cell49_count 1 0.0892 0.953 0.98 0.00 0.02
#> G2M_cell50_count 1 0.0000 0.969 1.00 0.00 0.00
#> G2M_cell51_count 3 0.0000 0.676 0.00 0.00 1.00
#> G2M_cell52_count 3 0.0000 0.676 0.00 0.00 1.00
#> G2M_cell53_count 3 0.6045 0.466 0.00 0.38 0.62
#> G2M_cell54_count 3 0.0892 0.660 0.02 0.00 0.98
#> G2M_cell55_count 1 0.6045 0.483 0.62 0.00 0.38
#> G2M_cell56_count 3 0.0000 0.676 0.00 0.00 1.00
#> G2M_cell57_count 1 0.0000 0.969 1.00 0.00 0.00
#> G2M_cell58_count 1 0.0000 0.969 1.00 0.00 0.00
#> G2M_cell59_count 1 0.0000 0.969 1.00 0.00 0.00
#> G2M_cell60_count 1 0.0000 0.969 1.00 0.00 0.00
#> G2M_cell61_count 1 0.0000 0.969 1.00 0.00 0.00
#> G2M_cell62_count 1 0.0000 0.969 1.00 0.00 0.00
#> G2M_cell63_count 1 0.0000 0.969 1.00 0.00 0.00
#> G2M_cell64_count 2 0.4555 0.877 0.00 0.80 0.20
#> G2M_cell65_count 1 0.0000 0.969 1.00 0.00 0.00
#> G2M_cell66_count 3 0.6045 0.466 0.00 0.38 0.62
#> G2M_cell67_count 2 0.4555 0.877 0.00 0.80 0.20
#> G2M_cell68_count 1 0.0000 0.969 1.00 0.00 0.00
#> G2M_cell69_count 1 0.0000 0.969 1.00 0.00 0.00
#> G2M_cell70_count 2 0.4555 0.877 0.00 0.80 0.20
#> G2M_cell71_count 1 0.0000 0.969 1.00 0.00 0.00
#> G2M_cell72_count 2 0.0000 0.780 0.00 1.00 0.00
#> G2M_cell73_count 1 0.0000 0.969 1.00 0.00 0.00
#> G2M_cell74_count 1 0.0000 0.969 1.00 0.00 0.00
#> G2M_cell75_count 1 0.0000 0.969 1.00 0.00 0.00
#> G2M_cell76_count 1 0.0000 0.969 1.00 0.00 0.00
#> G2M_cell77_count 2 0.4555 0.877 0.00 0.80 0.20
#> G2M_cell78_count 3 0.0000 0.676 0.00 0.00 1.00
#> G2M_cell79_count 1 0.5560 0.622 0.70 0.00 0.30
#> G2M_cell80_count 1 0.4796 0.738 0.78 0.00 0.22
#> G2M_cell81_count 3 0.0000 0.676 0.00 0.00 1.00
#> G2M_cell82_count 2 0.4555 0.877 0.00 0.80 0.20
#> G2M_cell83_count 1 0.0000 0.969 1.00 0.00 0.00
#> G2M_cell84_count 1 0.0000 0.969 1.00 0.00 0.00
#> G2M_cell85_count 3 0.0000 0.676 0.00 0.00 1.00
#> G2M_cell86_count 1 0.0000 0.969 1.00 0.00 0.00
#> G2M_cell87_count 1 0.0000 0.969 1.00 0.00 0.00
#> G2M_cell88_count 1 0.0000 0.969 1.00 0.00 0.00
#> G2M_cell89_count 3 0.6045 0.466 0.00 0.38 0.62
#> G2M_cell90_count 3 0.6302 -0.141 0.48 0.00 0.52
#> G2M_cell91_count 1 0.0000 0.969 1.00 0.00 0.00
#> G2M_cell92_count 3 0.6045 0.466 0.00 0.38 0.62
#> G2M_cell93_count 3 0.6045 0.466 0.00 0.38 0.62
#> G2M_cell94_count 3 0.0000 0.676 0.00 0.00 1.00
#> G2M_cell95_count 3 0.6126 0.406 0.00 0.40 0.60
#> G2M_cell96_count 3 0.0000 0.676 0.00 0.00 1.00
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> G1_cell1_count 2 0.0000 0.9394 0.00 1.00 0.00 0.00
#> G1_cell2_count 4 0.0000 0.9460 0.00 0.00 0.00 1.00
#> G1_cell3_count 4 0.0000 0.9460 0.00 0.00 0.00 1.00
#> G1_cell4_count 2 0.0000 0.9394 0.00 1.00 0.00 0.00
#> G1_cell5_count 2 0.3610 0.7249 0.00 0.80 0.00 0.20
#> G1_cell6_count 2 0.0000 0.9394 0.00 1.00 0.00 0.00
#> G1_cell7_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> G1_cell8_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> G1_cell9_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> G1_cell10_count 2 0.1211 0.9237 0.00 0.96 0.04 0.00
#> G1_cell11_count 4 0.0000 0.9460 0.00 0.00 0.00 1.00
#> G1_cell12_count 4 0.0000 0.9460 0.00 0.00 0.00 1.00
#> G1_cell13_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> G1_cell14_count 4 0.0000 0.9460 0.00 0.00 0.00 1.00
#> G1_cell15_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> G1_cell16_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> G1_cell17_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> G1_cell18_count 4 0.0000 0.9460 0.00 0.00 0.00 1.00
#> G1_cell19_count 4 0.0000 0.9460 0.00 0.00 0.00 1.00
#> G1_cell20_count 4 0.1211 0.8921 0.04 0.00 0.00 0.96
#> G1_cell21_count 2 0.2011 0.9045 0.00 0.92 0.08 0.00
#> G1_cell22_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> G1_cell23_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> G1_cell24_count 2 0.2647 0.8819 0.00 0.88 0.12 0.00
#> G1_cell25_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> G1_cell26_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> G1_cell27_count 2 0.4134 0.6298 0.00 0.74 0.00 0.26
#> G1_cell28_count 2 0.0000 0.9394 0.00 1.00 0.00 0.00
#> G1_cell29_count 2 0.2921 0.8102 0.00 0.86 0.00 0.14
#> G1_cell30_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> G1_cell31_count 2 0.0000 0.9394 0.00 1.00 0.00 0.00
#> G1_cell32_count 2 0.1913 0.9177 0.00 0.94 0.04 0.02
#> G1_cell33_count 2 0.0000 0.9394 0.00 1.00 0.00 0.00
#> G1_cell34_count 2 0.0000 0.9394 0.00 1.00 0.00 0.00
#> G1_cell35_count 2 0.0000 0.9394 0.00 1.00 0.00 0.00
#> G1_cell36_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> G1_cell37_count 2 0.0000 0.9394 0.00 1.00 0.00 0.00
#> G1_cell38_count 4 0.3610 0.7202 0.00 0.20 0.00 0.80
#> G1_cell39_count 1 0.4855 0.3433 0.60 0.00 0.00 0.40
#> G1_cell40_count 2 0.0707 0.9262 0.00 0.98 0.00 0.02
#> G1_cell41_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> G1_cell42_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> G1_cell43_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> G1_cell44_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> G1_cell45_count 4 0.0000 0.9460 0.00 0.00 0.00 1.00
#> G1_cell46_count 4 0.0000 0.9460 0.00 0.00 0.00 1.00
#> G1_cell47_count 4 0.0000 0.9460 0.00 0.00 0.00 1.00
#> G1_cell48_count 2 0.0000 0.9394 0.00 1.00 0.00 0.00
#> G1_cell49_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> G1_cell50_count 2 0.0000 0.9394 0.00 1.00 0.00 0.00
#> G1_cell51_count 2 0.0000 0.9394 0.00 1.00 0.00 0.00
#> G1_cell52_count 4 0.0000 0.9460 0.00 0.00 0.00 1.00
#> G1_cell53_count 4 0.0000 0.9460 0.00 0.00 0.00 1.00
#> G1_cell54_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> G1_cell55_count 4 0.0000 0.9460 0.00 0.00 0.00 1.00
#> G1_cell56_count 2 0.0000 0.9394 0.00 1.00 0.00 0.00
#> G1_cell57_count 4 0.0000 0.9460 0.00 0.00 0.00 1.00
#> G1_cell58_count 4 0.0000 0.9460 0.00 0.00 0.00 1.00
#> G1_cell59_count 2 0.0000 0.9394 0.00 1.00 0.00 0.00
#> G1_cell60_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> G1_cell61_count 4 0.0000 0.9460 0.00 0.00 0.00 1.00
#> G1_cell62_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> G1_cell63_count 2 0.2647 0.8819 0.00 0.88 0.12 0.00
#> G1_cell64_count 4 0.0000 0.9460 0.00 0.00 0.00 1.00
#> G1_cell65_count 4 0.0000 0.9460 0.00 0.00 0.00 1.00
#> G1_cell66_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> G1_cell67_count 2 0.0000 0.9394 0.00 1.00 0.00 0.00
#> G1_cell68_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> G1_cell69_count 4 0.0707 0.9279 0.00 0.02 0.00 0.98
#> G1_cell70_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> G1_cell71_count 2 0.4134 0.6254 0.00 0.74 0.00 0.26
#> G1_cell72_count 2 0.2345 0.8937 0.00 0.90 0.10 0.00
#> G1_cell73_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> G1_cell74_count 3 0.2647 0.7302 0.12 0.00 0.88 0.00
#> G1_cell75_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> G1_cell76_count 2 0.0000 0.9394 0.00 1.00 0.00 0.00
#> G1_cell77_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> G1_cell78_count 4 0.3172 0.7767 0.00 0.16 0.00 0.84
#> G1_cell79_count 4 0.2647 0.7589 0.12 0.00 0.00 0.88
#> G1_cell80_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> G1_cell81_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> G1_cell82_count 2 0.0000 0.9394 0.00 1.00 0.00 0.00
#> G1_cell83_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> G1_cell84_count 1 0.2921 0.8285 0.86 0.00 0.00 0.14
#> G1_cell85_count 2 0.0000 0.9394 0.00 1.00 0.00 0.00
#> G1_cell86_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> G1_cell87_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> G1_cell88_count 2 0.0000 0.9394 0.00 1.00 0.00 0.00
#> G1_cell89_count 3 0.4134 0.6109 0.00 0.26 0.74 0.00
#> G1_cell90_count 2 0.2647 0.8819 0.00 0.88 0.12 0.00
#> G1_cell91_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> G1_cell92_count 4 0.4790 0.4241 0.00 0.38 0.00 0.62
#> G1_cell93_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> G1_cell94_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> G1_cell95_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> G1_cell96_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> S_cell1_count 2 0.0000 0.9394 0.00 1.00 0.00 0.00
#> S_cell2_count 3 0.2921 0.7077 0.14 0.00 0.86 0.00
#> S_cell3_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> S_cell4_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> S_cell5_count 2 0.0000 0.9394 0.00 1.00 0.00 0.00
#> S_cell6_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> S_cell7_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> S_cell8_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> S_cell9_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> S_cell10_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> S_cell11_count 3 0.0000 0.8062 0.00 0.00 1.00 0.00
#> S_cell12_count 3 0.0000 0.8062 0.00 0.00 1.00 0.00
#> S_cell13_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> S_cell14_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> S_cell15_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> S_cell16_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> S_cell17_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> S_cell18_count 2 0.0000 0.9394 0.00 1.00 0.00 0.00
#> S_cell19_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> S_cell20_count 3 0.2647 0.7302 0.12 0.00 0.88 0.00
#> S_cell21_count 3 0.0000 0.8062 0.00 0.00 1.00 0.00
#> S_cell22_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> S_cell23_count 2 0.2647 0.8819 0.00 0.88 0.12 0.00
#> S_cell24_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> S_cell25_count 2 0.2647 0.8819 0.00 0.88 0.12 0.00
#> S_cell26_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> S_cell27_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> S_cell28_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> S_cell29_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> S_cell30_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> S_cell31_count 2 0.0000 0.9394 0.00 1.00 0.00 0.00
#> S_cell32_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> S_cell33_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> S_cell34_count 2 0.0000 0.9394 0.00 1.00 0.00 0.00
#> S_cell35_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> S_cell36_count 1 0.0707 0.9615 0.98 0.00 0.00 0.02
#> S_cell37_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> S_cell38_count 3 0.3610 0.6717 0.00 0.20 0.80 0.00
#> S_cell39_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> S_cell40_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> S_cell41_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> S_cell42_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> S_cell43_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> S_cell44_count 2 0.0000 0.9394 0.00 1.00 0.00 0.00
#> S_cell45_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> S_cell46_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> S_cell47_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> S_cell48_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> S_cell49_count 1 0.4277 0.5890 0.72 0.00 0.28 0.00
#> S_cell50_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> S_cell51_count 2 0.2647 0.8819 0.00 0.88 0.12 0.00
#> S_cell52_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> S_cell53_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> S_cell54_count 3 0.2647 0.7302 0.12 0.00 0.88 0.00
#> S_cell55_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> S_cell56_count 2 0.2647 0.8819 0.00 0.88 0.12 0.00
#> S_cell57_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> S_cell58_count 2 0.0000 0.9394 0.00 1.00 0.00 0.00
#> S_cell59_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> S_cell60_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> S_cell61_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> S_cell62_count 1 0.0707 0.9614 0.98 0.00 0.00 0.02
#> S_cell63_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> S_cell64_count 2 0.2647 0.8819 0.00 0.88 0.12 0.00
#> S_cell65_count 2 0.0000 0.9394 0.00 1.00 0.00 0.00
#> S_cell66_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> S_cell67_count 2 0.2647 0.8819 0.00 0.88 0.12 0.00
#> S_cell68_count 1 0.3037 0.8528 0.88 0.00 0.10 0.02
#> S_cell69_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> S_cell70_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> S_cell71_count 2 0.0000 0.9394 0.00 1.00 0.00 0.00
#> S_cell72_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> S_cell73_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> S_cell74_count 2 0.0000 0.9394 0.00 1.00 0.00 0.00
#> S_cell75_count 2 0.5000 0.0562 0.00 0.50 0.50 0.00
#> S_cell76_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> S_cell77_count 2 0.2647 0.8819 0.00 0.88 0.12 0.00
#> S_cell78_count 2 0.0000 0.9394 0.00 1.00 0.00 0.00
#> S_cell79_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> S_cell80_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> S_cell81_count 2 0.0000 0.9394 0.00 1.00 0.00 0.00
#> S_cell82_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> S_cell83_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> S_cell84_count 2 0.0000 0.9394 0.00 1.00 0.00 0.00
#> S_cell85_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> S_cell86_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> S_cell87_count 3 0.3975 0.6331 0.00 0.24 0.76 0.00
#> S_cell88_count 2 0.0000 0.9394 0.00 1.00 0.00 0.00
#> S_cell89_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> S_cell90_count 2 0.0000 0.9394 0.00 1.00 0.00 0.00
#> S_cell91_count 2 0.2647 0.8819 0.00 0.88 0.12 0.00
#> S_cell92_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> S_cell93_count 3 0.2921 0.7076 0.14 0.00 0.86 0.00
#> S_cell94_count 2 0.0000 0.9394 0.00 1.00 0.00 0.00
#> S_cell95_count 2 0.0000 0.9394 0.00 1.00 0.00 0.00
#> S_cell96_count 2 0.0000 0.9394 0.00 1.00 0.00 0.00
#> G2M_cell1_count 2 0.4948 0.1663 0.00 0.56 0.00 0.44
#> G2M_cell2_count 3 0.4624 0.4534 0.00 0.34 0.66 0.00
#> G2M_cell3_count 3 0.0000 0.8062 0.00 0.00 1.00 0.00
#> G2M_cell4_count 2 0.0000 0.9394 0.00 1.00 0.00 0.00
#> G2M_cell5_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> G2M_cell6_count 4 0.1211 0.9125 0.00 0.04 0.00 0.96
#> G2M_cell7_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> G2M_cell8_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> G2M_cell9_count 3 0.4948 0.2287 0.44 0.00 0.56 0.00
#> G2M_cell10_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> G2M_cell11_count 1 0.3610 0.7428 0.80 0.00 0.20 0.00
#> G2M_cell12_count 3 0.0000 0.8062 0.00 0.00 1.00 0.00
#> G2M_cell13_count 3 0.0000 0.8062 0.00 0.00 1.00 0.00
#> G2M_cell14_count 3 0.4134 0.6121 0.00 0.26 0.74 0.00
#> G2M_cell15_count 3 0.4713 0.4033 0.00 0.36 0.64 0.00
#> G2M_cell16_count 2 0.0000 0.9394 0.00 1.00 0.00 0.00
#> G2M_cell17_count 3 0.2647 0.7302 0.12 0.00 0.88 0.00
#> G2M_cell18_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> G2M_cell19_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> G2M_cell20_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> G2M_cell21_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> G2M_cell22_count 2 0.0000 0.9394 0.00 1.00 0.00 0.00
#> G2M_cell23_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> G2M_cell24_count 2 0.2647 0.8819 0.00 0.88 0.12 0.00
#> G2M_cell25_count 1 0.0707 0.9614 0.98 0.00 0.02 0.00
#> G2M_cell26_count 2 0.0000 0.9394 0.00 1.00 0.00 0.00
#> G2M_cell27_count 2 0.0000 0.9394 0.00 1.00 0.00 0.00
#> G2M_cell28_count 3 0.0000 0.8062 0.00 0.00 1.00 0.00
#> G2M_cell29_count 2 0.0000 0.9394 0.00 1.00 0.00 0.00
#> G2M_cell30_count 2 0.0000 0.9394 0.00 1.00 0.00 0.00
#> G2M_cell31_count 2 0.0000 0.9394 0.00 1.00 0.00 0.00
#> G2M_cell32_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> G2M_cell33_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> G2M_cell34_count 1 0.4134 0.6425 0.74 0.00 0.26 0.00
#> G2M_cell35_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> G2M_cell36_count 3 0.3610 0.6720 0.00 0.20 0.80 0.00
#> G2M_cell37_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> G2M_cell38_count 2 0.2647 0.8819 0.00 0.88 0.12 0.00
#> G2M_cell39_count 2 0.0000 0.9394 0.00 1.00 0.00 0.00
#> G2M_cell40_count 2 0.2647 0.8819 0.00 0.88 0.12 0.00
#> G2M_cell41_count 3 0.0000 0.8062 0.00 0.00 1.00 0.00
#> G2M_cell42_count 2 0.2647 0.8819 0.00 0.88 0.12 0.00
#> G2M_cell43_count 2 0.2647 0.8819 0.00 0.88 0.12 0.00
#> G2M_cell44_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> G2M_cell45_count 1 0.2345 0.8760 0.90 0.00 0.10 0.00
#> G2M_cell46_count 2 0.0000 0.9394 0.00 1.00 0.00 0.00
#> G2M_cell47_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> G2M_cell48_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> G2M_cell49_count 1 0.3801 0.7110 0.78 0.00 0.22 0.00
#> G2M_cell50_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> G2M_cell51_count 3 0.0000 0.8062 0.00 0.00 1.00 0.00
#> G2M_cell52_count 3 0.0707 0.7969 0.00 0.02 0.98 0.00
#> G2M_cell53_count 2 0.2345 0.8937 0.00 0.90 0.10 0.00
#> G2M_cell54_count 3 0.0000 0.8062 0.00 0.00 1.00 0.00
#> G2M_cell55_count 3 0.2647 0.7302 0.12 0.00 0.88 0.00
#> G2M_cell56_count 3 0.0000 0.8062 0.00 0.00 1.00 0.00
#> G2M_cell57_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> G2M_cell58_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> G2M_cell59_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> G2M_cell60_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> G2M_cell61_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> G2M_cell62_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> G2M_cell63_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> G2M_cell64_count 2 0.0000 0.9394 0.00 1.00 0.00 0.00
#> G2M_cell65_count 1 0.3610 0.7428 0.80 0.00 0.20 0.00
#> G2M_cell66_count 2 0.2647 0.8819 0.00 0.88 0.12 0.00
#> G2M_cell67_count 2 0.0000 0.9394 0.00 1.00 0.00 0.00
#> G2M_cell68_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> G2M_cell69_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> G2M_cell70_count 2 0.0000 0.9394 0.00 1.00 0.00 0.00
#> G2M_cell71_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> G2M_cell72_count 4 0.0000 0.9460 0.00 0.00 0.00 1.00
#> G2M_cell73_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> G2M_cell74_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> G2M_cell75_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> G2M_cell76_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> G2M_cell77_count 2 0.0000 0.9394 0.00 1.00 0.00 0.00
#> G2M_cell78_count 3 0.0000 0.8062 0.00 0.00 1.00 0.00
#> G2M_cell79_count 3 0.4907 0.2935 0.42 0.00 0.58 0.00
#> G2M_cell80_count 1 0.4948 0.1849 0.56 0.00 0.44 0.00
#> G2M_cell81_count 3 0.0000 0.8062 0.00 0.00 1.00 0.00
#> G2M_cell82_count 2 0.0000 0.9394 0.00 1.00 0.00 0.00
#> G2M_cell83_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> G2M_cell84_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> G2M_cell85_count 3 0.0000 0.8062 0.00 0.00 1.00 0.00
#> G2M_cell86_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> G2M_cell87_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> G2M_cell88_count 1 0.3975 0.6780 0.76 0.00 0.24 0.00
#> G2M_cell89_count 2 0.2647 0.8819 0.00 0.88 0.12 0.00
#> G2M_cell90_count 3 0.2011 0.7600 0.08 0.00 0.92 0.00
#> G2M_cell91_count 1 0.0000 0.9801 1.00 0.00 0.00 0.00
#> G2M_cell92_count 2 0.2647 0.8819 0.00 0.88 0.12 0.00
#> G2M_cell93_count 2 0.2647 0.8819 0.00 0.88 0.12 0.00
#> G2M_cell94_count 3 0.0000 0.8062 0.00 0.00 1.00 0.00
#> G2M_cell95_count 2 0.1637 0.9149 0.00 0.94 0.06 0.00
#> G2M_cell96_count 3 0.3172 0.7035 0.00 0.16 0.84 0.00
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
As soon as the classes for columns are determined, the signatures that are significantly different between subgroups can be looked for. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. To get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows (which is done by automatically selecting number of clusters).If there are too many signatures, top_signatures = ... can be set to only show the
signatures with the highest FDRs:
# code only for demonstration
# e.g. to show the top 500 most significant rows
tb = get_signature(res, k = ..., top_signatures = 500)
If the signatures are defined as these which are uniquely high in current group, diff_method argument
can be set to "uniquely_high_in_one_group":
# code only for demonstration
tb = get_signature(res, k = ..., diff_method = "uniquely_high_in_one_group")
UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
Parent node: Node0. Child nodes: Node011 , Node012 , Node013-leaf , Node021-leaf , Node022 , Node023-leaf .
The object with results only for a single top-value method and a single partitioning method can be extracted as:
res = res_rh["01"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4.
#> On a matrix with 14878 rows and 165 columns.
#> Top rows (1488) are extracted by 'ATC' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 150 partitions by row resampling.
#> Best k for subgroups seems to be 4.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_partitions"
#> [7] "compare_signatures" "consensus_heatmap" "dimension_reduction"
#> [10] "functional_enrichment" "get_anno_col" "get_anno"
#> [13] "get_classes" "get_consensus" "get_matrix"
#> [16] "get_membership" "get_param" "get_signatures"
#> [19] "get_stats" "is_best_k" "is_stable_k"
#> [22] "membership_heatmap" "ncol" "nrow"
#> [25] "plot_ecdf" "predict_classes" "rownames"
#> [28] "select_partition_number" "show" "suggest_best_k"
#> [31] "test_to_known_factors" "top_rows_heatmap"
collect_plots() function collects all the plots made from res for all k (number of subgroups)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, higher 1-PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.991 0.997 0.503 0.497 0.497
#> 3 3 1.000 0.987 0.995 0.305 0.796 0.610
#> 4 4 0.932 0.915 0.959 0.117 0.875 0.663
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 4
#> attr(,"optional")
#> [1] 2 3
There is also optional best \(k\) = 2 3 that is worth to check.
Following is the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall subgroup
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> G1_cell7_count 1 0.000 0.994 1.00 0.00
#> G1_cell8_count 2 0.000 0.999 0.00 1.00
#> G1_cell9_count 1 0.000 0.994 1.00 0.00
#> G1_cell13_count 1 0.000 0.994 1.00 0.00
#> G1_cell15_count 2 0.000 0.999 0.00 1.00
#> G1_cell16_count 2 0.000 0.999 0.00 1.00
#> G1_cell17_count 2 0.000 0.999 0.00 1.00
#> G1_cell20_count 2 0.000 0.999 0.00 1.00
#> G1_cell22_count 1 0.000 0.994 1.00 0.00
#> G1_cell23_count 1 0.995 0.148 0.54 0.46
#> G1_cell25_count 1 0.141 0.974 0.98 0.02
#> G1_cell26_count 2 0.000 0.999 0.00 1.00
#> G1_cell30_count 2 0.000 0.999 0.00 1.00
#> G1_cell36_count 1 0.000 0.994 1.00 0.00
#> G1_cell39_count 2 0.000 0.999 0.00 1.00
#> G1_cell41_count 2 0.000 0.999 0.00 1.00
#> G1_cell42_count 1 0.000 0.994 1.00 0.00
#> G1_cell43_count 1 0.000 0.994 1.00 0.00
#> G1_cell44_count 1 0.000 0.994 1.00 0.00
#> G1_cell47_count 2 0.000 0.999 0.00 1.00
#> G1_cell49_count 1 0.000 0.994 1.00 0.00
#> G1_cell54_count 2 0.000 0.999 0.00 1.00
#> G1_cell55_count 2 0.000 0.999 0.00 1.00
#> G1_cell57_count 2 0.000 0.999 0.00 1.00
#> G1_cell60_count 1 0.000 0.994 1.00 0.00
#> G1_cell61_count 2 0.000 0.999 0.00 1.00
#> G1_cell62_count 1 0.000 0.994 1.00 0.00
#> G1_cell64_count 2 0.000 0.999 0.00 1.00
#> G1_cell65_count 2 0.000 0.999 0.00 1.00
#> G1_cell66_count 2 0.000 0.999 0.00 1.00
#> G1_cell68_count 1 0.000 0.994 1.00 0.00
#> G1_cell70_count 1 0.000 0.994 1.00 0.00
#> G1_cell73_count 1 0.000 0.994 1.00 0.00
#> G1_cell74_count 2 0.000 0.999 0.00 1.00
#> G1_cell75_count 1 0.000 0.994 1.00 0.00
#> G1_cell77_count 2 0.242 0.958 0.04 0.96
#> G1_cell79_count 2 0.000 0.999 0.00 1.00
#> G1_cell80_count 2 0.000 0.999 0.00 1.00
#> G1_cell81_count 1 0.000 0.994 1.00 0.00
#> G1_cell83_count 1 0.000 0.994 1.00 0.00
#> G1_cell84_count 2 0.000 0.999 0.00 1.00
#> G1_cell86_count 1 0.000 0.994 1.00 0.00
#> G1_cell87_count 1 0.000 0.994 1.00 0.00
#> G1_cell91_count 1 0.000 0.994 1.00 0.00
#> G1_cell93_count 1 0.000 0.994 1.00 0.00
#> G1_cell94_count 2 0.000 0.999 0.00 1.00
#> G1_cell95_count 1 0.000 0.994 1.00 0.00
#> G1_cell96_count 1 0.000 0.994 1.00 0.00
#> S_cell2_count 2 0.000 0.999 0.00 1.00
#> S_cell3_count 1 0.000 0.994 1.00 0.00
#> S_cell4_count 2 0.000 0.999 0.00 1.00
#> S_cell6_count 2 0.000 0.999 0.00 1.00
#> S_cell7_count 2 0.000 0.999 0.00 1.00
#> S_cell8_count 1 0.000 0.994 1.00 0.00
#> S_cell9_count 1 0.000 0.994 1.00 0.00
#> S_cell10_count 1 0.000 0.994 1.00 0.00
#> S_cell11_count 2 0.000 0.999 0.00 1.00
#> S_cell13_count 1 0.000 0.994 1.00 0.00
#> S_cell14_count 2 0.000 0.999 0.00 1.00
#> S_cell15_count 1 0.000 0.994 1.00 0.00
#> S_cell16_count 1 0.000 0.994 1.00 0.00
#> S_cell17_count 1 0.000 0.994 1.00 0.00
#> S_cell19_count 2 0.000 0.999 0.00 1.00
#> S_cell20_count 2 0.000 0.999 0.00 1.00
#> S_cell22_count 1 0.000 0.994 1.00 0.00
#> S_cell24_count 1 0.000 0.994 1.00 0.00
#> S_cell26_count 1 0.000 0.994 1.00 0.00
#> S_cell27_count 1 0.000 0.994 1.00 0.00
#> S_cell28_count 1 0.000 0.994 1.00 0.00
#> S_cell29_count 1 0.000 0.994 1.00 0.00
#> S_cell30_count 1 0.000 0.994 1.00 0.00
#> S_cell32_count 2 0.000 0.999 0.00 1.00
#> S_cell33_count 2 0.000 0.999 0.00 1.00
#> S_cell35_count 2 0.000 0.999 0.00 1.00
#> S_cell36_count 2 0.000 0.999 0.00 1.00
#> S_cell37_count 1 0.000 0.994 1.00 0.00
#> S_cell39_count 2 0.000 0.999 0.00 1.00
#> S_cell40_count 1 0.000 0.994 1.00 0.00
#> S_cell41_count 2 0.000 0.999 0.00 1.00
#> S_cell42_count 1 0.000 0.994 1.00 0.00
#> S_cell43_count 1 0.000 0.994 1.00 0.00
#> S_cell45_count 2 0.000 0.999 0.00 1.00
#> S_cell46_count 2 0.000 0.999 0.00 1.00
#> S_cell47_count 2 0.000 0.999 0.00 1.00
#> S_cell48_count 2 0.000 0.999 0.00 1.00
#> S_cell49_count 2 0.000 0.999 0.00 1.00
#> S_cell50_count 1 0.000 0.994 1.00 0.00
#> S_cell52_count 1 0.000 0.994 1.00 0.00
#> S_cell53_count 1 0.000 0.994 1.00 0.00
#> S_cell54_count 2 0.000 0.999 0.00 1.00
#> S_cell55_count 1 0.000 0.994 1.00 0.00
#> S_cell57_count 1 0.000 0.994 1.00 0.00
#> S_cell59_count 2 0.000 0.999 0.00 1.00
#> S_cell60_count 2 0.000 0.999 0.00 1.00
#> S_cell61_count 2 0.000 0.999 0.00 1.00
#> S_cell62_count 2 0.000 0.999 0.00 1.00
#> S_cell63_count 1 0.000 0.994 1.00 0.00
#> S_cell66_count 2 0.000 0.999 0.00 1.00
#> S_cell68_count 2 0.000 0.999 0.00 1.00
#> S_cell69_count 2 0.000 0.999 0.00 1.00
#> S_cell70_count 1 0.000 0.994 1.00 0.00
#> S_cell72_count 2 0.000 0.999 0.00 1.00
#> S_cell73_count 2 0.000 0.999 0.00 1.00
#> S_cell76_count 2 0.000 0.999 0.00 1.00
#> S_cell79_count 1 0.000 0.994 1.00 0.00
#> S_cell80_count 2 0.000 0.999 0.00 1.00
#> S_cell82_count 2 0.000 0.999 0.00 1.00
#> S_cell83_count 1 0.000 0.994 1.00 0.00
#> S_cell85_count 1 0.000 0.994 1.00 0.00
#> S_cell86_count 2 0.141 0.979 0.02 0.98
#> S_cell89_count 2 0.000 0.999 0.00 1.00
#> S_cell92_count 1 0.000 0.994 1.00 0.00
#> S_cell93_count 2 0.000 0.999 0.00 1.00
#> G2M_cell5_count 1 0.000 0.994 1.00 0.00
#> G2M_cell7_count 1 0.000 0.994 1.00 0.00
#> G2M_cell8_count 1 0.000 0.994 1.00 0.00
#> G2M_cell9_count 2 0.000 0.999 0.00 1.00
#> G2M_cell10_count 2 0.000 0.999 0.00 1.00
#> G2M_cell11_count 2 0.000 0.999 0.00 1.00
#> G2M_cell12_count 2 0.000 0.999 0.00 1.00
#> G2M_cell13_count 2 0.000 0.999 0.00 1.00
#> G2M_cell17_count 2 0.000 0.999 0.00 1.00
#> G2M_cell18_count 1 0.000 0.994 1.00 0.00
#> G2M_cell19_count 1 0.000 0.994 1.00 0.00
#> G2M_cell20_count 1 0.000 0.994 1.00 0.00
#> G2M_cell21_count 2 0.000 0.999 0.00 1.00
#> G2M_cell23_count 1 0.000 0.994 1.00 0.00
#> G2M_cell25_count 2 0.000 0.999 0.00 1.00
#> G2M_cell32_count 1 0.000 0.994 1.00 0.00
#> G2M_cell33_count 1 0.000 0.994 1.00 0.00
#> G2M_cell34_count 2 0.000 0.999 0.00 1.00
#> G2M_cell35_count 1 0.000 0.994 1.00 0.00
#> G2M_cell37_count 2 0.000 0.999 0.00 1.00
#> G2M_cell41_count 2 0.000 0.999 0.00 1.00
#> G2M_cell44_count 1 0.000 0.994 1.00 0.00
#> G2M_cell45_count 2 0.000 0.999 0.00 1.00
#> G2M_cell47_count 1 0.000 0.994 1.00 0.00
#> G2M_cell48_count 1 0.000 0.994 1.00 0.00
#> G2M_cell49_count 2 0.000 0.999 0.00 1.00
#> G2M_cell50_count 2 0.000 0.999 0.00 1.00
#> G2M_cell55_count 2 0.000 0.999 0.00 1.00
#> G2M_cell57_count 2 0.000 0.999 0.00 1.00
#> G2M_cell58_count 1 0.000 0.994 1.00 0.00
#> G2M_cell59_count 1 0.000 0.994 1.00 0.00
#> G2M_cell60_count 1 0.000 0.994 1.00 0.00
#> G2M_cell61_count 1 0.000 0.994 1.00 0.00
#> G2M_cell62_count 2 0.000 0.999 0.00 1.00
#> G2M_cell63_count 2 0.000 0.999 0.00 1.00
#> G2M_cell65_count 2 0.000 0.999 0.00 1.00
#> G2M_cell68_count 1 0.000 0.994 1.00 0.00
#> G2M_cell69_count 1 0.000 0.994 1.00 0.00
#> G2M_cell71_count 1 0.000 0.994 1.00 0.00
#> G2M_cell73_count 1 0.000 0.994 1.00 0.00
#> G2M_cell74_count 1 0.000 0.994 1.00 0.00
#> G2M_cell75_count 2 0.000 0.999 0.00 1.00
#> G2M_cell76_count 2 0.000 0.999 0.00 1.00
#> G2M_cell79_count 2 0.000 0.999 0.00 1.00
#> G2M_cell80_count 2 0.000 0.999 0.00 1.00
#> G2M_cell83_count 2 0.000 0.999 0.00 1.00
#> G2M_cell84_count 1 0.000 0.994 1.00 0.00
#> G2M_cell86_count 1 0.000 0.994 1.00 0.00
#> G2M_cell87_count 2 0.000 0.999 0.00 1.00
#> G2M_cell88_count 2 0.000 0.999 0.00 1.00
#> G2M_cell90_count 2 0.000 0.999 0.00 1.00
#> G2M_cell91_count 1 0.000 0.994 1.00 0.00
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> G1_cell7_count 3 0.000 1.0000 0.00 0.00 1.00
#> G1_cell8_count 3 0.000 1.0000 0.00 0.00 1.00
#> G1_cell9_count 1 0.000 0.9962 1.00 0.00 0.00
#> G1_cell13_count 1 0.000 0.9962 1.00 0.00 0.00
#> G1_cell15_count 3 0.000 1.0000 0.00 0.00 1.00
#> G1_cell16_count 3 0.000 1.0000 0.00 0.00 1.00
#> G1_cell17_count 3 0.000 1.0000 0.00 0.00 1.00
#> G1_cell20_count 3 0.000 1.0000 0.00 0.00 1.00
#> G1_cell22_count 1 0.000 0.9962 1.00 0.00 0.00
#> G1_cell23_count 3 0.000 1.0000 0.00 0.00 1.00
#> G1_cell25_count 3 0.000 1.0000 0.00 0.00 1.00
#> G1_cell26_count 3 0.000 1.0000 0.00 0.00 1.00
#> G1_cell30_count 3 0.000 1.0000 0.00 0.00 1.00
#> G1_cell36_count 1 0.000 0.9962 1.00 0.00 0.00
#> G1_cell39_count 3 0.000 1.0000 0.00 0.00 1.00
#> G1_cell41_count 3 0.000 1.0000 0.00 0.00 1.00
#> G1_cell42_count 1 0.153 0.9562 0.96 0.00 0.04
#> G1_cell43_count 3 0.000 1.0000 0.00 0.00 1.00
#> G1_cell44_count 3 0.000 1.0000 0.00 0.00 1.00
#> G1_cell47_count 3 0.000 1.0000 0.00 0.00 1.00
#> G1_cell49_count 3 0.000 1.0000 0.00 0.00 1.00
#> G1_cell54_count 3 0.000 1.0000 0.00 0.00 1.00
#> G1_cell55_count 3 0.000 1.0000 0.00 0.00 1.00
#> G1_cell57_count 3 0.000 1.0000 0.00 0.00 1.00
#> G1_cell60_count 3 0.000 1.0000 0.00 0.00 1.00
#> G1_cell61_count 3 0.000 1.0000 0.00 0.00 1.00
#> G1_cell62_count 1 0.000 0.9962 1.00 0.00 0.00
#> G1_cell64_count 3 0.000 1.0000 0.00 0.00 1.00
#> G1_cell65_count 3 0.000 1.0000 0.00 0.00 1.00
#> G1_cell66_count 3 0.000 1.0000 0.00 0.00 1.00
#> G1_cell68_count 3 0.000 1.0000 0.00 0.00 1.00
#> G1_cell70_count 1 0.000 0.9962 1.00 0.00 0.00
#> G1_cell73_count 3 0.000 1.0000 0.00 0.00 1.00
#> G1_cell74_count 2 0.000 0.9909 0.00 1.00 0.00
#> G1_cell75_count 1 0.000 0.9962 1.00 0.00 0.00
#> G1_cell77_count 3 0.000 1.0000 0.00 0.00 1.00
#> G1_cell79_count 3 0.000 1.0000 0.00 0.00 1.00
#> G1_cell80_count 2 0.000 0.9909 0.00 1.00 0.00
#> G1_cell81_count 3 0.000 1.0000 0.00 0.00 1.00
#> G1_cell83_count 3 0.000 1.0000 0.00 0.00 1.00
#> G1_cell84_count 3 0.000 1.0000 0.00 0.00 1.00
#> G1_cell86_count 3 0.000 1.0000 0.00 0.00 1.00
#> G1_cell87_count 1 0.000 0.9962 1.00 0.00 0.00
#> G1_cell91_count 1 0.000 0.9962 1.00 0.00 0.00
#> G1_cell93_count 3 0.000 1.0000 0.00 0.00 1.00
#> G1_cell94_count 2 0.631 0.0045 0.00 0.50 0.50
#> G1_cell95_count 3 0.000 1.0000 0.00 0.00 1.00
#> G1_cell96_count 1 0.000 0.9962 1.00 0.00 0.00
#> S_cell2_count 2 0.000 0.9909 0.00 1.00 0.00
#> S_cell3_count 1 0.000 0.9962 1.00 0.00 0.00
#> S_cell4_count 2 0.000 0.9909 0.00 1.00 0.00
#> S_cell6_count 2 0.000 0.9909 0.00 1.00 0.00
#> S_cell7_count 2 0.000 0.9909 0.00 1.00 0.00
#> S_cell8_count 1 0.000 0.9962 1.00 0.00 0.00
#> S_cell9_count 1 0.000 0.9962 1.00 0.00 0.00
#> S_cell10_count 1 0.000 0.9962 1.00 0.00 0.00
#> S_cell11_count 2 0.000 0.9909 0.00 1.00 0.00
#> S_cell13_count 1 0.000 0.9962 1.00 0.00 0.00
#> S_cell14_count 3 0.000 1.0000 0.00 0.00 1.00
#> S_cell15_count 1 0.000 0.9962 1.00 0.00 0.00
#> S_cell16_count 1 0.000 0.9962 1.00 0.00 0.00
#> S_cell17_count 1 0.000 0.9962 1.00 0.00 0.00
#> S_cell19_count 2 0.000 0.9909 0.00 1.00 0.00
#> S_cell20_count 2 0.000 0.9909 0.00 1.00 0.00
#> S_cell22_count 1 0.000 0.9962 1.00 0.00 0.00
#> S_cell24_count 1 0.000 0.9962 1.00 0.00 0.00
#> S_cell26_count 1 0.000 0.9962 1.00 0.00 0.00
#> S_cell27_count 1 0.000 0.9962 1.00 0.00 0.00
#> S_cell28_count 1 0.000 0.9962 1.00 0.00 0.00
#> S_cell29_count 1 0.000 0.9962 1.00 0.00 0.00
#> S_cell30_count 1 0.000 0.9962 1.00 0.00 0.00
#> S_cell32_count 2 0.000 0.9909 0.00 1.00 0.00
#> S_cell33_count 2 0.000 0.9909 0.00 1.00 0.00
#> S_cell35_count 2 0.000 0.9909 0.00 1.00 0.00
#> S_cell36_count 2 0.207 0.9293 0.00 0.94 0.06
#> S_cell37_count 1 0.000 0.9962 1.00 0.00 0.00
#> S_cell39_count 2 0.000 0.9909 0.00 1.00 0.00
#> S_cell40_count 1 0.000 0.9962 1.00 0.00 0.00
#> S_cell41_count 2 0.000 0.9909 0.00 1.00 0.00
#> S_cell42_count 1 0.000 0.9962 1.00 0.00 0.00
#> S_cell43_count 1 0.000 0.9962 1.00 0.00 0.00
#> S_cell45_count 2 0.000 0.9909 0.00 1.00 0.00
#> S_cell46_count 3 0.000 1.0000 0.00 0.00 1.00
#> S_cell47_count 2 0.000 0.9909 0.00 1.00 0.00
#> S_cell48_count 2 0.000 0.9909 0.00 1.00 0.00
#> S_cell49_count 2 0.000 0.9909 0.00 1.00 0.00
#> S_cell50_count 1 0.000 0.9962 1.00 0.00 0.00
#> S_cell52_count 1 0.000 0.9962 1.00 0.00 0.00
#> S_cell53_count 1 0.000 0.9962 1.00 0.00 0.00
#> S_cell54_count 2 0.000 0.9909 0.00 1.00 0.00
#> S_cell55_count 1 0.000 0.9962 1.00 0.00 0.00
#> S_cell57_count 1 0.000 0.9962 1.00 0.00 0.00
#> S_cell59_count 2 0.000 0.9909 0.00 1.00 0.00
#> S_cell60_count 2 0.000 0.9909 0.00 1.00 0.00
#> S_cell61_count 2 0.000 0.9909 0.00 1.00 0.00
#> S_cell62_count 2 0.000 0.9909 0.00 1.00 0.00
#> S_cell63_count 1 0.000 0.9962 1.00 0.00 0.00
#> S_cell66_count 2 0.000 0.9909 0.00 1.00 0.00
#> S_cell68_count 2 0.000 0.9909 0.00 1.00 0.00
#> S_cell69_count 2 0.000 0.9909 0.00 1.00 0.00
#> S_cell70_count 1 0.000 0.9962 1.00 0.00 0.00
#> S_cell72_count 2 0.000 0.9909 0.00 1.00 0.00
#> S_cell73_count 2 0.000 0.9909 0.00 1.00 0.00
#> S_cell76_count 2 0.000 0.9909 0.00 1.00 0.00
#> S_cell79_count 1 0.000 0.9962 1.00 0.00 0.00
#> S_cell80_count 2 0.000 0.9909 0.00 1.00 0.00
#> S_cell82_count 2 0.000 0.9909 0.00 1.00 0.00
#> S_cell83_count 1 0.000 0.9962 1.00 0.00 0.00
#> S_cell85_count 3 0.000 1.0000 0.00 0.00 1.00
#> S_cell86_count 2 0.000 0.9909 0.00 1.00 0.00
#> S_cell89_count 2 0.000 0.9909 0.00 1.00 0.00
#> S_cell92_count 1 0.000 0.9962 1.00 0.00 0.00
#> S_cell93_count 2 0.000 0.9909 0.00 1.00 0.00
#> G2M_cell5_count 1 0.000 0.9962 1.00 0.00 0.00
#> G2M_cell7_count 1 0.000 0.9962 1.00 0.00 0.00
#> G2M_cell8_count 1 0.000 0.9962 1.00 0.00 0.00
#> G2M_cell9_count 2 0.000 0.9909 0.00 1.00 0.00
#> G2M_cell10_count 2 0.000 0.9909 0.00 1.00 0.00
#> G2M_cell11_count 2 0.000 0.9909 0.00 1.00 0.00
#> G2M_cell12_count 2 0.000 0.9909 0.00 1.00 0.00
#> G2M_cell13_count 2 0.000 0.9909 0.00 1.00 0.00
#> G2M_cell17_count 2 0.000 0.9909 0.00 1.00 0.00
#> G2M_cell18_count 1 0.000 0.9962 1.00 0.00 0.00
#> G2M_cell19_count 1 0.000 0.9962 1.00 0.00 0.00
#> G2M_cell20_count 1 0.000 0.9962 1.00 0.00 0.00
#> G2M_cell21_count 2 0.000 0.9909 0.00 1.00 0.00
#> G2M_cell23_count 1 0.000 0.9962 1.00 0.00 0.00
#> G2M_cell25_count 2 0.000 0.9909 0.00 1.00 0.00
#> G2M_cell32_count 1 0.000 0.9962 1.00 0.00 0.00
#> G2M_cell33_count 1 0.000 0.9962 1.00 0.00 0.00
#> G2M_cell34_count 2 0.000 0.9909 0.00 1.00 0.00
#> G2M_cell35_count 3 0.000 1.0000 0.00 0.00 1.00
#> G2M_cell37_count 2 0.000 0.9909 0.00 1.00 0.00
#> G2M_cell41_count 2 0.000 0.9909 0.00 1.00 0.00
#> G2M_cell44_count 1 0.000 0.9962 1.00 0.00 0.00
#> G2M_cell45_count 2 0.000 0.9909 0.00 1.00 0.00
#> G2M_cell47_count 1 0.455 0.7509 0.80 0.00 0.20
#> G2M_cell48_count 1 0.000 0.9962 1.00 0.00 0.00
#> G2M_cell49_count 2 0.000 0.9909 0.00 1.00 0.00
#> G2M_cell50_count 2 0.000 0.9909 0.00 1.00 0.00
#> G2M_cell55_count 2 0.000 0.9909 0.00 1.00 0.00
#> G2M_cell57_count 2 0.000 0.9909 0.00 1.00 0.00
#> G2M_cell58_count 1 0.000 0.9962 1.00 0.00 0.00
#> G2M_cell59_count 1 0.000 0.9962 1.00 0.00 0.00
#> G2M_cell60_count 1 0.000 0.9962 1.00 0.00 0.00
#> G2M_cell61_count 1 0.000 0.9962 1.00 0.00 0.00
#> G2M_cell62_count 2 0.000 0.9909 0.00 1.00 0.00
#> G2M_cell63_count 2 0.000 0.9909 0.00 1.00 0.00
#> G2M_cell65_count 2 0.000 0.9909 0.00 1.00 0.00
#> G2M_cell68_count 1 0.000 0.9962 1.00 0.00 0.00
#> G2M_cell69_count 1 0.000 0.9962 1.00 0.00 0.00
#> G2M_cell71_count 1 0.000 0.9962 1.00 0.00 0.00
#> G2M_cell73_count 1 0.000 0.9962 1.00 0.00 0.00
#> G2M_cell74_count 1 0.000 0.9962 1.00 0.00 0.00
#> G2M_cell75_count 2 0.000 0.9909 0.00 1.00 0.00
#> G2M_cell76_count 2 0.000 0.9909 0.00 1.00 0.00
#> G2M_cell79_count 2 0.000 0.9909 0.00 1.00 0.00
#> G2M_cell80_count 2 0.000 0.9909 0.00 1.00 0.00
#> G2M_cell83_count 2 0.000 0.9909 0.00 1.00 0.00
#> G2M_cell84_count 1 0.000 0.9962 1.00 0.00 0.00
#> G2M_cell86_count 1 0.000 0.9962 1.00 0.00 0.00
#> G2M_cell87_count 2 0.000 0.9909 0.00 1.00 0.00
#> G2M_cell88_count 2 0.000 0.9909 0.00 1.00 0.00
#> G2M_cell90_count 2 0.000 0.9909 0.00 1.00 0.00
#> G2M_cell91_count 1 0.000 0.9962 1.00 0.00 0.00
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> G1_cell7_count 3 0.0000 0.948 0.00 0.00 1.00 0.00
#> G1_cell8_count 3 0.0000 0.948 0.00 0.00 1.00 0.00
#> G1_cell9_count 1 0.0000 0.907 1.00 0.00 0.00 0.00
#> G1_cell13_count 1 0.0000 0.907 1.00 0.00 0.00 0.00
#> G1_cell15_count 3 0.0000 0.948 0.00 0.00 1.00 0.00
#> G1_cell16_count 3 0.0000 0.948 0.00 0.00 1.00 0.00
#> G1_cell17_count 3 0.0000 0.948 0.00 0.00 1.00 0.00
#> G1_cell20_count 3 0.0000 0.948 0.00 0.00 1.00 0.00
#> G1_cell22_count 1 0.0000 0.907 1.00 0.00 0.00 0.00
#> G1_cell23_count 3 0.0000 0.948 0.00 0.00 1.00 0.00
#> G1_cell25_count 3 0.0000 0.948 0.00 0.00 1.00 0.00
#> G1_cell26_count 3 0.0000 0.948 0.00 0.00 1.00 0.00
#> G1_cell30_count 3 0.0000 0.948 0.00 0.00 1.00 0.00
#> G1_cell36_count 1 0.0000 0.907 1.00 0.00 0.00 0.00
#> G1_cell39_count 3 0.0000 0.948 0.00 0.00 1.00 0.00
#> G1_cell41_count 3 0.0000 0.948 0.00 0.00 1.00 0.00
#> G1_cell42_count 1 0.3037 0.850 0.88 0.00 0.02 0.10
#> G1_cell43_count 3 0.2011 0.878 0.08 0.00 0.92 0.00
#> G1_cell44_count 3 0.0000 0.948 0.00 0.00 1.00 0.00
#> G1_cell47_count 3 0.0000 0.948 0.00 0.00 1.00 0.00
#> G1_cell49_count 3 0.0000 0.948 0.00 0.00 1.00 0.00
#> G1_cell54_count 3 0.0000 0.948 0.00 0.00 1.00 0.00
#> G1_cell55_count 3 0.0000 0.948 0.00 0.00 1.00 0.00
#> G1_cell57_count 3 0.0000 0.948 0.00 0.00 1.00 0.00
#> G1_cell60_count 3 0.4406 0.586 0.30 0.00 0.70 0.00
#> G1_cell61_count 3 0.0000 0.948 0.00 0.00 1.00 0.00
#> G1_cell62_count 1 0.0000 0.907 1.00 0.00 0.00 0.00
#> G1_cell64_count 3 0.0000 0.948 0.00 0.00 1.00 0.00
#> G1_cell65_count 3 0.0000 0.948 0.00 0.00 1.00 0.00
#> G1_cell66_count 3 0.0000 0.948 0.00 0.00 1.00 0.00
#> G1_cell68_count 1 0.4713 0.407 0.64 0.00 0.36 0.00
#> G1_cell70_count 1 0.0000 0.907 1.00 0.00 0.00 0.00
#> G1_cell73_count 3 0.4948 0.240 0.44 0.00 0.56 0.00
#> G1_cell74_count 2 0.0000 0.994 0.00 1.00 0.00 0.00
#> G1_cell75_count 1 0.0000 0.907 1.00 0.00 0.00 0.00
#> G1_cell77_count 3 0.0000 0.948 0.00 0.00 1.00 0.00
#> G1_cell79_count 3 0.0000 0.948 0.00 0.00 1.00 0.00
#> G1_cell80_count 2 0.0000 0.994 0.00 1.00 0.00 0.00
#> G1_cell81_count 3 0.0000 0.948 0.00 0.00 1.00 0.00
#> G1_cell83_count 1 0.4522 0.497 0.68 0.00 0.32 0.00
#> G1_cell84_count 3 0.0000 0.948 0.00 0.00 1.00 0.00
#> G1_cell86_count 1 0.4855 0.295 0.60 0.00 0.40 0.00
#> G1_cell87_count 1 0.0000 0.907 1.00 0.00 0.00 0.00
#> G1_cell91_count 1 0.4624 0.482 0.66 0.00 0.00 0.34
#> G1_cell93_count 3 0.0707 0.932 0.02 0.00 0.98 0.00
#> G1_cell94_count 3 0.4855 0.315 0.00 0.40 0.60 0.00
#> G1_cell95_count 3 0.4277 0.622 0.28 0.00 0.72 0.00
#> G1_cell96_count 1 0.0000 0.907 1.00 0.00 0.00 0.00
#> S_cell2_count 2 0.0000 0.994 0.00 1.00 0.00 0.00
#> S_cell3_count 4 0.0000 0.976 0.00 0.00 0.00 1.00
#> S_cell4_count 4 0.0707 0.964 0.00 0.02 0.00 0.98
#> S_cell6_count 4 0.1211 0.948 0.00 0.04 0.00 0.96
#> S_cell7_count 2 0.0000 0.994 0.00 1.00 0.00 0.00
#> S_cell8_count 4 0.0000 0.976 0.00 0.00 0.00 1.00
#> S_cell9_count 4 0.0000 0.976 0.00 0.00 0.00 1.00
#> S_cell10_count 4 0.0000 0.976 0.00 0.00 0.00 1.00
#> S_cell11_count 2 0.0000 0.994 0.00 1.00 0.00 0.00
#> S_cell13_count 4 0.0000 0.976 0.00 0.00 0.00 1.00
#> S_cell14_count 3 0.0000 0.948 0.00 0.00 1.00 0.00
#> S_cell15_count 4 0.0000 0.976 0.00 0.00 0.00 1.00
#> S_cell16_count 1 0.2921 0.846 0.86 0.00 0.00 0.14
#> S_cell17_count 1 0.0707 0.911 0.98 0.00 0.00 0.02
#> S_cell19_count 2 0.0000 0.994 0.00 1.00 0.00 0.00
#> S_cell20_count 2 0.0000 0.994 0.00 1.00 0.00 0.00
#> S_cell22_count 4 0.0000 0.976 0.00 0.00 0.00 1.00
#> S_cell24_count 4 0.0000 0.976 0.00 0.00 0.00 1.00
#> S_cell26_count 1 0.0707 0.911 0.98 0.00 0.00 0.02
#> S_cell27_count 1 0.3610 0.790 0.80 0.00 0.00 0.20
#> S_cell28_count 4 0.0000 0.976 0.00 0.00 0.00 1.00
#> S_cell29_count 1 0.3610 0.790 0.80 0.00 0.00 0.20
#> S_cell30_count 1 0.0000 0.907 1.00 0.00 0.00 0.00
#> S_cell32_count 2 0.0000 0.994 0.00 1.00 0.00 0.00
#> S_cell33_count 2 0.0000 0.994 0.00 1.00 0.00 0.00
#> S_cell35_count 2 0.0000 0.994 0.00 1.00 0.00 0.00
#> S_cell36_count 2 0.3975 0.681 0.00 0.76 0.24 0.00
#> S_cell37_count 4 0.1211 0.939 0.04 0.00 0.00 0.96
#> S_cell39_count 2 0.0000 0.994 0.00 1.00 0.00 0.00
#> S_cell40_count 1 0.3400 0.811 0.82 0.00 0.00 0.18
#> S_cell41_count 2 0.0000 0.994 0.00 1.00 0.00 0.00
#> S_cell42_count 4 0.0000 0.976 0.00 0.00 0.00 1.00
#> S_cell43_count 1 0.2011 0.886 0.92 0.00 0.00 0.08
#> S_cell45_count 2 0.0000 0.994 0.00 1.00 0.00 0.00
#> S_cell46_count 3 0.0000 0.948 0.00 0.00 1.00 0.00
#> S_cell47_count 2 0.0000 0.994 0.00 1.00 0.00 0.00
#> S_cell48_count 2 0.0000 0.994 0.00 1.00 0.00 0.00
#> S_cell49_count 2 0.0000 0.994 0.00 1.00 0.00 0.00
#> S_cell50_count 1 0.4522 0.623 0.68 0.00 0.00 0.32
#> S_cell52_count 1 0.3610 0.790 0.80 0.00 0.00 0.20
#> S_cell53_count 1 0.3400 0.811 0.82 0.00 0.00 0.18
#> S_cell54_count 2 0.0000 0.994 0.00 1.00 0.00 0.00
#> S_cell55_count 1 0.2647 0.861 0.88 0.00 0.00 0.12
#> S_cell57_count 1 0.0707 0.911 0.98 0.00 0.00 0.02
#> S_cell59_count 2 0.0000 0.994 0.00 1.00 0.00 0.00
#> S_cell60_count 2 0.0000 0.994 0.00 1.00 0.00 0.00
#> S_cell61_count 4 0.0707 0.964 0.00 0.02 0.00 0.98
#> S_cell62_count 2 0.0000 0.994 0.00 1.00 0.00 0.00
#> S_cell63_count 1 0.2921 0.846 0.86 0.00 0.00 0.14
#> S_cell66_count 2 0.1211 0.954 0.00 0.96 0.00 0.04
#> S_cell68_count 2 0.0000 0.994 0.00 1.00 0.00 0.00
#> S_cell69_count 2 0.0000 0.994 0.00 1.00 0.00 0.00
#> S_cell70_count 1 0.2011 0.886 0.92 0.00 0.00 0.08
#> S_cell72_count 2 0.0000 0.994 0.00 1.00 0.00 0.00
#> S_cell73_count 2 0.0000 0.994 0.00 1.00 0.00 0.00
#> S_cell76_count 2 0.0000 0.994 0.00 1.00 0.00 0.00
#> S_cell79_count 4 0.0000 0.976 0.00 0.00 0.00 1.00
#> S_cell80_count 4 0.1211 0.948 0.00 0.04 0.00 0.96
#> S_cell82_count 4 0.2011 0.904 0.00 0.08 0.00 0.92
#> S_cell83_count 1 0.1637 0.896 0.94 0.00 0.00 0.06
#> S_cell85_count 1 0.4134 0.614 0.74 0.00 0.26 0.00
#> S_cell86_count 4 0.0707 0.964 0.00 0.02 0.00 0.98
#> S_cell89_count 4 0.2921 0.831 0.00 0.14 0.00 0.86
#> S_cell92_count 4 0.0000 0.976 0.00 0.00 0.00 1.00
#> S_cell93_count 2 0.0000 0.994 0.00 1.00 0.00 0.00
#> G2M_cell5_count 1 0.1637 0.896 0.94 0.00 0.00 0.06
#> G2M_cell7_count 1 0.0707 0.911 0.98 0.00 0.00 0.02
#> G2M_cell8_count 4 0.0000 0.976 0.00 0.00 0.00 1.00
#> G2M_cell9_count 2 0.0000 0.994 0.00 1.00 0.00 0.00
#> G2M_cell10_count 2 0.0000 0.994 0.00 1.00 0.00 0.00
#> G2M_cell11_count 2 0.0000 0.994 0.00 1.00 0.00 0.00
#> G2M_cell12_count 2 0.0000 0.994 0.00 1.00 0.00 0.00
#> G2M_cell13_count 2 0.0000 0.994 0.00 1.00 0.00 0.00
#> G2M_cell17_count 2 0.0000 0.994 0.00 1.00 0.00 0.00
#> G2M_cell18_count 1 0.0707 0.911 0.98 0.00 0.00 0.02
#> G2M_cell19_count 1 0.4624 0.583 0.66 0.00 0.00 0.34
#> G2M_cell20_count 1 0.0707 0.911 0.98 0.00 0.00 0.02
#> G2M_cell21_count 2 0.0000 0.994 0.00 1.00 0.00 0.00
#> G2M_cell23_count 1 0.0707 0.911 0.98 0.00 0.00 0.02
#> G2M_cell25_count 2 0.0000 0.994 0.00 1.00 0.00 0.00
#> G2M_cell32_count 4 0.0707 0.960 0.02 0.00 0.00 0.98
#> G2M_cell33_count 1 0.0707 0.911 0.98 0.00 0.00 0.02
#> G2M_cell34_count 2 0.0000 0.994 0.00 1.00 0.00 0.00
#> G2M_cell35_count 3 0.0000 0.948 0.00 0.00 1.00 0.00
#> G2M_cell37_count 2 0.0000 0.994 0.00 1.00 0.00 0.00
#> G2M_cell41_count 2 0.0000 0.994 0.00 1.00 0.00 0.00
#> G2M_cell44_count 1 0.0707 0.911 0.98 0.00 0.00 0.02
#> G2M_cell45_count 2 0.0000 0.994 0.00 1.00 0.00 0.00
#> G2M_cell47_count 1 0.0000 0.907 1.00 0.00 0.00 0.00
#> G2M_cell48_count 1 0.0707 0.911 0.98 0.00 0.00 0.02
#> G2M_cell49_count 2 0.0000 0.994 0.00 1.00 0.00 0.00
#> G2M_cell50_count 2 0.0000 0.994 0.00 1.00 0.00 0.00
#> G2M_cell55_count 2 0.0000 0.994 0.00 1.00 0.00 0.00
#> G2M_cell57_count 2 0.0000 0.994 0.00 1.00 0.00 0.00
#> G2M_cell58_count 1 0.0000 0.907 1.00 0.00 0.00 0.00
#> G2M_cell59_count 1 0.0707 0.911 0.98 0.00 0.00 0.02
#> G2M_cell60_count 1 0.0707 0.911 0.98 0.00 0.00 0.02
#> G2M_cell61_count 1 0.0707 0.911 0.98 0.00 0.00 0.02
#> G2M_cell62_count 2 0.0000 0.994 0.00 1.00 0.00 0.00
#> G2M_cell63_count 2 0.0000 0.994 0.00 1.00 0.00 0.00
#> G2M_cell65_count 2 0.0000 0.994 0.00 1.00 0.00 0.00
#> G2M_cell68_count 1 0.0000 0.907 1.00 0.00 0.00 0.00
#> G2M_cell69_count 1 0.0707 0.911 0.98 0.00 0.00 0.02
#> G2M_cell71_count 1 0.0707 0.911 0.98 0.00 0.00 0.02
#> G2M_cell73_count 1 0.2011 0.886 0.92 0.00 0.00 0.08
#> G2M_cell74_count 1 0.0707 0.911 0.98 0.00 0.00 0.02
#> G2M_cell75_count 2 0.0000 0.994 0.00 1.00 0.00 0.00
#> G2M_cell76_count 2 0.0000 0.994 0.00 1.00 0.00 0.00
#> G2M_cell79_count 2 0.0000 0.994 0.00 1.00 0.00 0.00
#> G2M_cell80_count 2 0.0000 0.994 0.00 1.00 0.00 0.00
#> G2M_cell83_count 2 0.0000 0.994 0.00 1.00 0.00 0.00
#> G2M_cell84_count 4 0.0000 0.976 0.00 0.00 0.00 1.00
#> G2M_cell86_count 1 0.0000 0.907 1.00 0.00 0.00 0.00
#> G2M_cell87_count 2 0.1211 0.954 0.00 0.96 0.00 0.04
#> G2M_cell88_count 2 0.0000 0.994 0.00 1.00 0.00 0.00
#> G2M_cell90_count 2 0.0000 0.994 0.00 1.00 0.00 0.00
#> G2M_cell91_count 1 0.0707 0.911 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)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
Parent node: Node01. Child nodes: Node0111-leaf , Node0112-leaf , Node0121-leaf , Node0122-leaf , Node0221 , Node0222-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 14875 rows and 64 columns.
#> Top rows (1488) are extracted by 'ATC' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 150 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_partitions"
#> [7] "compare_signatures" "consensus_heatmap" "dimension_reduction"
#> [10] "functional_enrichment" "get_anno_col" "get_anno"
#> [13] "get_classes" "get_consensus" "get_matrix"
#> [16] "get_membership" "get_param" "get_signatures"
#> [19] "get_stats" "is_best_k" "is_stable_k"
#> [22] "membership_heatmap" "ncol" "nrow"
#> [25] "plot_ecdf" "predict_classes" "rownames"
#> [28] "select_partition_number" "show" "suggest_best_k"
#> [31] "test_to_known_factors" "top_rows_heatmap"
collect_plots() function collects all the plots made from res for all k (number of subgroups)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, higher 1-PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.974 0.990 0.507 0.494 0.494
#> 3 3 0.903 0.884 0.953 0.308 0.764 0.557
#> 4 4 0.659 0.722 0.846 0.112 0.838 0.571
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
#> G1_cell9_count 2 0.000 1.000 0.00 1.00
#> G1_cell13_count 2 0.000 1.000 0.00 1.00
#> G1_cell22_count 2 0.000 1.000 0.00 1.00
#> G1_cell36_count 2 0.000 1.000 0.00 1.00
#> G1_cell42_count 1 0.000 0.980 1.00 0.00
#> G1_cell62_count 2 0.000 1.000 0.00 1.00
#> G1_cell70_count 1 0.402 0.905 0.92 0.08
#> G1_cell75_count 2 0.000 1.000 0.00 1.00
#> G1_cell87_count 2 0.000 1.000 0.00 1.00
#> G1_cell91_count 1 0.000 0.980 1.00 0.00
#> G1_cell96_count 2 0.000 1.000 0.00 1.00
#> S_cell3_count 1 0.000 0.980 1.00 0.00
#> S_cell8_count 1 0.000 0.980 1.00 0.00
#> S_cell9_count 1 0.000 0.980 1.00 0.00
#> S_cell10_count 1 0.000 0.980 1.00 0.00
#> S_cell13_count 1 0.000 0.980 1.00 0.00
#> S_cell15_count 1 0.000 0.980 1.00 0.00
#> S_cell16_count 1 0.000 0.980 1.00 0.00
#> S_cell17_count 2 0.000 1.000 0.00 1.00
#> S_cell22_count 1 0.000 0.980 1.00 0.00
#> S_cell24_count 1 0.000 0.980 1.00 0.00
#> S_cell26_count 2 0.000 1.000 0.00 1.00
#> S_cell27_count 1 0.000 0.980 1.00 0.00
#> S_cell28_count 1 0.000 0.980 1.00 0.00
#> S_cell29_count 1 0.000 0.980 1.00 0.00
#> S_cell30_count 2 0.000 1.000 0.00 1.00
#> S_cell37_count 1 0.000 0.980 1.00 0.00
#> S_cell40_count 1 0.000 0.980 1.00 0.00
#> S_cell42_count 1 0.000 0.980 1.00 0.00
#> S_cell43_count 1 0.327 0.926 0.94 0.06
#> S_cell50_count 1 0.000 0.980 1.00 0.00
#> S_cell52_count 1 0.000 0.980 1.00 0.00
#> S_cell53_count 1 0.000 0.980 1.00 0.00
#> S_cell55_count 1 0.000 0.980 1.00 0.00
#> S_cell57_count 2 0.000 1.000 0.00 1.00
#> S_cell63_count 1 0.000 0.980 1.00 0.00
#> S_cell70_count 2 0.000 1.000 0.00 1.00
#> S_cell79_count 1 0.000 0.980 1.00 0.00
#> S_cell83_count 2 0.000 1.000 0.00 1.00
#> S_cell92_count 1 0.000 0.980 1.00 0.00
#> G2M_cell5_count 1 0.995 0.163 0.54 0.46
#> G2M_cell7_count 2 0.000 1.000 0.00 1.00
#> G2M_cell8_count 1 0.000 0.980 1.00 0.00
#> G2M_cell18_count 2 0.000 1.000 0.00 1.00
#> G2M_cell19_count 1 0.000 0.980 1.00 0.00
#> G2M_cell20_count 2 0.000 1.000 0.00 1.00
#> G2M_cell23_count 2 0.000 1.000 0.00 1.00
#> G2M_cell32_count 1 0.000 0.980 1.00 0.00
#> G2M_cell33_count 2 0.000 1.000 0.00 1.00
#> G2M_cell44_count 2 0.000 1.000 0.00 1.00
#> G2M_cell47_count 2 0.000 1.000 0.00 1.00
#> G2M_cell48_count 2 0.000 1.000 0.00 1.00
#> G2M_cell58_count 2 0.000 1.000 0.00 1.00
#> G2M_cell59_count 1 0.000 0.980 1.00 0.00
#> G2M_cell60_count 2 0.000 1.000 0.00 1.00
#> G2M_cell61_count 2 0.000 1.000 0.00 1.00
#> G2M_cell68_count 2 0.000 1.000 0.00 1.00
#> G2M_cell69_count 1 0.000 0.980 1.00 0.00
#> G2M_cell71_count 2 0.000 1.000 0.00 1.00
#> G2M_cell73_count 1 0.242 0.946 0.96 0.04
#> G2M_cell74_count 2 0.000 1.000 0.00 1.00
#> G2M_cell84_count 1 0.000 0.980 1.00 0.00
#> G2M_cell86_count 2 0.000 1.000 0.00 1.00
#> G2M_cell91_count 2 0.000 1.000 0.00 1.00
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> G1_cell9_count 2 0.0000 0.970 0.00 1.00 0.00
#> G1_cell13_count 2 0.0000 0.970 0.00 1.00 0.00
#> G1_cell22_count 2 0.0000 0.970 0.00 1.00 0.00
#> G1_cell36_count 2 0.0000 0.970 0.00 1.00 0.00
#> G1_cell42_count 1 0.0000 0.961 1.00 0.00 0.00
#> G1_cell62_count 2 0.0000 0.970 0.00 1.00 0.00
#> G1_cell70_count 3 0.1781 0.876 0.02 0.02 0.96
#> G1_cell75_count 2 0.0000 0.970 0.00 1.00 0.00
#> G1_cell87_count 2 0.0000 0.970 0.00 1.00 0.00
#> G1_cell91_count 1 0.0000 0.961 1.00 0.00 0.00
#> G1_cell96_count 2 0.0000 0.970 0.00 1.00 0.00
#> S_cell3_count 1 0.0000 0.961 1.00 0.00 0.00
#> S_cell8_count 1 0.0000 0.961 1.00 0.00 0.00
#> S_cell9_count 1 0.0000 0.961 1.00 0.00 0.00
#> S_cell10_count 1 0.0000 0.961 1.00 0.00 0.00
#> S_cell13_count 1 0.0000 0.961 1.00 0.00 0.00
#> S_cell15_count 1 0.0000 0.961 1.00 0.00 0.00
#> S_cell16_count 1 0.0000 0.961 1.00 0.00 0.00
#> S_cell17_count 2 0.0000 0.970 0.00 1.00 0.00
#> S_cell22_count 1 0.0000 0.961 1.00 0.00 0.00
#> S_cell24_count 1 0.0000 0.961 1.00 0.00 0.00
#> S_cell26_count 2 0.0000 0.970 0.00 1.00 0.00
#> S_cell27_count 1 0.0000 0.961 1.00 0.00 0.00
#> S_cell28_count 1 0.0000 0.961 1.00 0.00 0.00
#> S_cell29_count 1 0.0000 0.961 1.00 0.00 0.00
#> S_cell30_count 2 0.0000 0.970 0.00 1.00 0.00
#> S_cell37_count 1 0.0000 0.961 1.00 0.00 0.00
#> S_cell40_count 1 0.0000 0.961 1.00 0.00 0.00
#> S_cell42_count 1 0.0000 0.961 1.00 0.00 0.00
#> S_cell43_count 1 0.6651 0.449 0.64 0.34 0.02
#> S_cell50_count 1 0.0000 0.961 1.00 0.00 0.00
#> S_cell52_count 1 0.0000 0.961 1.00 0.00 0.00
#> S_cell53_count 1 0.0000 0.961 1.00 0.00 0.00
#> S_cell55_count 1 0.0000 0.961 1.00 0.00 0.00
#> S_cell57_count 2 0.0000 0.970 0.00 1.00 0.00
#> S_cell63_count 1 0.0000 0.961 1.00 0.00 0.00
#> S_cell70_count 2 0.0000 0.970 0.00 1.00 0.00
#> S_cell79_count 1 0.0000 0.961 1.00 0.00 0.00
#> S_cell83_count 2 0.0892 0.961 0.00 0.98 0.02
#> S_cell92_count 1 0.0000 0.961 1.00 0.00 0.00
#> G2M_cell5_count 3 0.0000 0.893 0.00 0.00 1.00
#> G2M_cell7_count 2 0.0892 0.961 0.00 0.98 0.02
#> G2M_cell8_count 3 0.6192 0.245 0.42 0.00 0.58
#> G2M_cell18_count 3 0.4291 0.747 0.00 0.18 0.82
#> G2M_cell19_count 3 0.6280 0.121 0.46 0.00 0.54
#> G2M_cell20_count 3 0.0892 0.886 0.00 0.02 0.98
#> G2M_cell23_count 3 0.4555 0.720 0.00 0.20 0.80
#> G2M_cell32_count 1 0.5397 0.599 0.72 0.00 0.28
#> G2M_cell33_count 3 0.0892 0.886 0.00 0.02 0.98
#> G2M_cell44_count 3 0.0000 0.893 0.00 0.00 1.00
#> G2M_cell47_count 3 0.0000 0.893 0.00 0.00 1.00
#> G2M_cell48_count 2 0.6126 0.306 0.00 0.60 0.40
#> G2M_cell58_count 2 0.0892 0.961 0.00 0.98 0.02
#> G2M_cell59_count 3 0.0000 0.893 0.00 0.00 1.00
#> G2M_cell60_count 3 0.0000 0.893 0.00 0.00 1.00
#> G2M_cell61_count 2 0.0892 0.961 0.00 0.98 0.02
#> G2M_cell68_count 3 0.0000 0.893 0.00 0.00 1.00
#> G2M_cell69_count 3 0.0000 0.893 0.00 0.00 1.00
#> G2M_cell71_count 3 0.4291 0.747 0.00 0.18 0.82
#> G2M_cell73_count 3 0.0000 0.893 0.00 0.00 1.00
#> G2M_cell74_count 3 0.0000 0.893 0.00 0.00 1.00
#> G2M_cell84_count 1 0.5397 0.599 0.72 0.00 0.28
#> G2M_cell86_count 2 0.0892 0.961 0.00 0.98 0.02
#> G2M_cell91_count 3 0.2066 0.864 0.00 0.06 0.94
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> G1_cell9_count 2 0.0000 0.8425 0.00 1.00 0.00 0.00
#> G1_cell13_count 2 0.0000 0.8425 0.00 1.00 0.00 0.00
#> G1_cell22_count 2 0.0000 0.8425 0.00 1.00 0.00 0.00
#> G1_cell36_count 2 0.0000 0.8425 0.00 1.00 0.00 0.00
#> G1_cell42_count 1 0.0707 0.9163 0.98 0.02 0.00 0.00
#> G1_cell62_count 2 0.0000 0.8425 0.00 1.00 0.00 0.00
#> G1_cell70_count 3 0.6835 0.2928 0.06 0.38 0.54 0.02
#> G1_cell75_count 2 0.0000 0.8425 0.00 1.00 0.00 0.00
#> G1_cell87_count 2 0.0000 0.8425 0.00 1.00 0.00 0.00
#> G1_cell91_count 1 0.2647 0.8362 0.88 0.12 0.00 0.00
#> G1_cell96_count 2 0.0000 0.8425 0.00 1.00 0.00 0.00
#> S_cell3_count 1 0.0000 0.9207 1.00 0.00 0.00 0.00
#> S_cell8_count 1 0.0000 0.9207 1.00 0.00 0.00 0.00
#> S_cell9_count 1 0.2706 0.8671 0.90 0.00 0.08 0.02
#> S_cell10_count 1 0.1411 0.9101 0.96 0.00 0.02 0.02
#> S_cell13_count 1 0.0000 0.9207 1.00 0.00 0.00 0.00
#> S_cell15_count 1 0.1913 0.8986 0.94 0.00 0.04 0.02
#> S_cell16_count 1 0.3400 0.8556 0.82 0.00 0.00 0.18
#> S_cell17_count 2 0.4855 0.2182 0.00 0.60 0.00 0.40
#> S_cell22_count 1 0.1411 0.9098 0.96 0.00 0.02 0.02
#> S_cell24_count 1 0.0000 0.9207 1.00 0.00 0.00 0.00
#> S_cell26_count 4 0.6049 0.4556 0.12 0.20 0.00 0.68
#> S_cell27_count 1 0.2647 0.9035 0.88 0.00 0.00 0.12
#> S_cell28_count 1 0.0000 0.9207 1.00 0.00 0.00 0.00
#> S_cell29_count 1 0.3400 0.8556 0.82 0.00 0.00 0.18
#> S_cell30_count 2 0.2345 0.7584 0.00 0.90 0.00 0.10
#> S_cell37_count 1 0.2011 0.9157 0.92 0.00 0.00 0.08
#> S_cell40_count 1 0.2647 0.8938 0.88 0.00 0.00 0.12
#> S_cell42_count 1 0.0000 0.9207 1.00 0.00 0.00 0.00
#> S_cell43_count 4 0.5810 0.4424 0.20 0.06 0.02 0.72
#> S_cell50_count 1 0.3172 0.8699 0.84 0.00 0.00 0.16
#> S_cell52_count 1 0.2011 0.9100 0.92 0.00 0.00 0.08
#> S_cell53_count 1 0.2011 0.9100 0.92 0.00 0.00 0.08
#> S_cell55_count 1 0.3400 0.8556 0.82 0.00 0.00 0.18
#> S_cell57_count 2 0.4994 -0.0647 0.00 0.52 0.00 0.48
#> S_cell63_count 1 0.2647 0.8944 0.88 0.00 0.00 0.12
#> S_cell70_count 4 0.5489 0.4627 0.06 0.24 0.00 0.70
#> S_cell79_count 1 0.1211 0.9070 0.96 0.00 0.04 0.00
#> S_cell83_count 4 0.2011 0.6690 0.00 0.08 0.00 0.92
#> S_cell92_count 1 0.2335 0.8837 0.92 0.00 0.06 0.02
#> G2M_cell5_count 3 0.2011 0.7233 0.00 0.00 0.92 0.08
#> G2M_cell7_count 4 0.4755 0.6667 0.00 0.20 0.04 0.76
#> G2M_cell8_count 3 0.4755 0.6552 0.20 0.00 0.76 0.04
#> G2M_cell18_count 4 0.4134 0.6380 0.00 0.00 0.26 0.74
#> G2M_cell19_count 3 0.4332 0.6785 0.16 0.00 0.80 0.04
#> G2M_cell20_count 4 0.4406 0.5945 0.00 0.00 0.30 0.70
#> G2M_cell23_count 4 0.4642 0.6593 0.00 0.02 0.24 0.74
#> G2M_cell32_count 3 0.5392 0.5905 0.28 0.00 0.68 0.04
#> G2M_cell33_count 2 0.6723 0.3364 0.00 0.60 0.26 0.14
#> G2M_cell44_count 4 0.4948 0.3254 0.00 0.00 0.44 0.56
#> G2M_cell47_count 3 0.3198 0.7063 0.00 0.04 0.88 0.08
#> G2M_cell48_count 4 0.4731 0.6999 0.00 0.06 0.16 0.78
#> G2M_cell58_count 4 0.5106 0.6421 0.00 0.24 0.04 0.72
#> G2M_cell59_count 3 0.2830 0.7304 0.04 0.00 0.90 0.06
#> G2M_cell60_count 3 0.4277 0.4861 0.00 0.00 0.72 0.28
#> G2M_cell61_count 4 0.5392 0.5955 0.00 0.28 0.04 0.68
#> G2M_cell68_count 3 0.2706 0.7148 0.00 0.02 0.90 0.08
#> G2M_cell69_count 3 0.1211 0.7291 0.00 0.00 0.96 0.04
#> G2M_cell71_count 4 0.3975 0.6433 0.00 0.00 0.24 0.76
#> G2M_cell73_count 3 0.1637 0.7262 0.00 0.00 0.94 0.06
#> G2M_cell74_count 3 0.2921 0.6774 0.00 0.00 0.86 0.14
#> G2M_cell84_count 3 0.5392 0.5905 0.28 0.00 0.68 0.04
#> G2M_cell86_count 4 0.4939 0.6580 0.00 0.22 0.04 0.74
#> G2M_cell91_count 3 0.6150 0.2239 0.00 0.06 0.58 0.36
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
As soon as the classes for columns are determined, the signatures that are significantly different between subgroups can be looked for. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. To get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows (which is done by automatically selecting number of clusters).If there are too many signatures, top_signatures = ... can be set to only show the
signatures with the highest FDRs:
# code only for demonstration
# e.g. to show the top 500 most significant rows
tb = get_signature(res, k = ..., top_signatures = 500)
If the signatures are defined as these which are uniquely high in current group, diff_method argument
can be set to "uniquely_high_in_one_group":
# code only for demonstration
tb = get_signature(res, k = ..., diff_method = "uniquely_high_in_one_group")
UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
Parent node: Node01. Child nodes: Node0111-leaf , Node0112-leaf , Node0121-leaf , Node0122-leaf , Node0221 , Node0222-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 14797 rows and 63 columns.
#> Top rows (1270) 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 0.993 0.508 0.492 0.492
#> 3 3 0.679 0.784 0.894 0.247 0.865 0.732
#> 4 4 0.578 0.580 0.776 0.123 0.903 0.757
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
#> G1_cell74_count 1 0.000 1.000 1.00 0.00
#> G1_cell80_count 2 0.000 0.986 0.00 1.00
#> G1_cell94_count 2 0.000 0.986 0.00 1.00
#> S_cell2_count 1 0.000 1.000 1.00 0.00
#> S_cell4_count 1 0.000 1.000 1.00 0.00
#> S_cell6_count 1 0.000 1.000 1.00 0.00
#> S_cell7_count 1 0.000 1.000 1.00 0.00
#> S_cell11_count 1 0.000 1.000 1.00 0.00
#> S_cell19_count 1 0.000 1.000 1.00 0.00
#> S_cell20_count 1 0.000 1.000 1.00 0.00
#> S_cell32_count 1 0.000 1.000 1.00 0.00
#> S_cell33_count 1 0.000 1.000 1.00 0.00
#> S_cell35_count 1 0.000 1.000 1.00 0.00
#> S_cell36_count 2 0.000 0.986 0.00 1.00
#> S_cell39_count 1 0.000 1.000 1.00 0.00
#> S_cell41_count 1 0.000 1.000 1.00 0.00
#> S_cell45_count 1 0.000 1.000 1.00 0.00
#> S_cell47_count 1 0.000 1.000 1.00 0.00
#> S_cell48_count 1 0.000 1.000 1.00 0.00
#> S_cell49_count 1 0.000 1.000 1.00 0.00
#> S_cell54_count 1 0.000 1.000 1.00 0.00
#> S_cell59_count 1 0.000 1.000 1.00 0.00
#> S_cell60_count 1 0.000 1.000 1.00 0.00
#> S_cell61_count 1 0.000 1.000 1.00 0.00
#> S_cell62_count 1 0.000 1.000 1.00 0.00
#> S_cell66_count 1 0.000 1.000 1.00 0.00
#> S_cell68_count 1 0.000 1.000 1.00 0.00
#> S_cell69_count 1 0.000 1.000 1.00 0.00
#> S_cell72_count 1 0.000 1.000 1.00 0.00
#> S_cell73_count 1 0.000 1.000 1.00 0.00
#> S_cell76_count 2 0.000 0.986 0.00 1.00
#> S_cell80_count 1 0.000 1.000 1.00 0.00
#> S_cell82_count 1 0.000 1.000 1.00 0.00
#> S_cell86_count 1 0.000 1.000 1.00 0.00
#> S_cell89_count 1 0.000 1.000 1.00 0.00
#> S_cell93_count 1 0.000 1.000 1.00 0.00
#> G2M_cell9_count 2 0.000 0.986 0.00 1.00
#> G2M_cell10_count 2 0.000 0.986 0.00 1.00
#> G2M_cell11_count 2 0.000 0.986 0.00 1.00
#> G2M_cell12_count 2 0.000 0.986 0.00 1.00
#> G2M_cell13_count 2 0.000 0.986 0.00 1.00
#> G2M_cell17_count 2 0.000 0.986 0.00 1.00
#> G2M_cell21_count 2 0.000 0.986 0.00 1.00
#> G2M_cell25_count 2 0.000 0.986 0.00 1.00
#> G2M_cell34_count 2 0.981 0.276 0.42 0.58
#> G2M_cell37_count 2 0.000 0.986 0.00 1.00
#> G2M_cell41_count 2 0.000 0.986 0.00 1.00
#> G2M_cell45_count 2 0.000 0.986 0.00 1.00
#> G2M_cell49_count 2 0.000 0.986 0.00 1.00
#> G2M_cell50_count 2 0.000 0.986 0.00 1.00
#> G2M_cell55_count 2 0.000 0.986 0.00 1.00
#> G2M_cell57_count 2 0.000 0.986 0.00 1.00
#> G2M_cell62_count 2 0.000 0.986 0.00 1.00
#> G2M_cell63_count 2 0.000 0.986 0.00 1.00
#> G2M_cell65_count 2 0.000 0.986 0.00 1.00
#> G2M_cell75_count 2 0.000 0.986 0.00 1.00
#> G2M_cell76_count 2 0.000 0.986 0.00 1.00
#> G2M_cell79_count 2 0.000 0.986 0.00 1.00
#> G2M_cell80_count 2 0.000 0.986 0.00 1.00
#> G2M_cell83_count 2 0.000 0.986 0.00 1.00
#> G2M_cell87_count 2 0.000 0.986 0.00 1.00
#> G2M_cell88_count 2 0.000 0.986 0.00 1.00
#> G2M_cell90_count 2 0.000 0.986 0.00 1.00
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> G1_cell74_count 3 0.5706 0.494 0.32 0.00 0.68
#> G1_cell80_count 2 0.6280 0.305 0.00 0.54 0.46
#> G1_cell94_count 2 0.2537 0.883 0.00 0.92 0.08
#> S_cell2_count 1 0.4555 0.761 0.80 0.00 0.20
#> S_cell4_count 1 0.3686 0.808 0.86 0.00 0.14
#> S_cell6_count 1 0.0892 0.899 0.98 0.00 0.02
#> S_cell7_count 1 0.1529 0.885 0.96 0.00 0.04
#> S_cell11_count 1 0.0000 0.898 1.00 0.00 0.00
#> S_cell19_count 1 0.0892 0.899 0.98 0.00 0.02
#> S_cell20_count 1 0.3686 0.829 0.86 0.00 0.14
#> S_cell32_count 1 0.2537 0.877 0.92 0.00 0.08
#> S_cell33_count 1 0.6280 0.146 0.54 0.00 0.46
#> S_cell35_count 1 0.2537 0.876 0.92 0.00 0.08
#> S_cell36_count 2 0.5016 0.704 0.00 0.76 0.24
#> S_cell39_count 1 0.0000 0.898 1.00 0.00 0.00
#> S_cell41_count 3 0.5560 0.507 0.30 0.00 0.70
#> S_cell45_count 1 0.3686 0.809 0.86 0.00 0.14
#> S_cell47_count 1 0.3340 0.824 0.88 0.00 0.12
#> S_cell48_count 1 0.6280 0.144 0.54 0.00 0.46
#> S_cell49_count 1 0.0892 0.894 0.98 0.00 0.02
#> S_cell54_count 3 0.4002 0.646 0.16 0.00 0.84
#> S_cell59_count 1 0.0000 0.898 1.00 0.00 0.00
#> S_cell60_count 1 0.2959 0.840 0.90 0.00 0.10
#> S_cell61_count 1 0.0892 0.899 0.98 0.00 0.02
#> S_cell62_count 1 0.0000 0.898 1.00 0.00 0.00
#> S_cell66_count 1 0.0892 0.899 0.98 0.00 0.02
#> S_cell68_count 1 0.0000 0.898 1.00 0.00 0.00
#> S_cell69_count 1 0.4291 0.767 0.82 0.00 0.18
#> S_cell72_count 1 0.0000 0.898 1.00 0.00 0.00
#> S_cell73_count 3 0.5835 0.421 0.34 0.00 0.66
#> S_cell76_count 3 0.5706 0.334 0.00 0.32 0.68
#> S_cell80_count 1 0.0892 0.899 0.98 0.00 0.02
#> S_cell82_count 1 0.0892 0.899 0.98 0.00 0.02
#> S_cell86_count 1 0.0892 0.899 0.98 0.00 0.02
#> S_cell89_count 1 0.0892 0.899 0.98 0.00 0.02
#> S_cell93_count 3 0.4555 0.616 0.20 0.00 0.80
#> G2M_cell9_count 2 0.4002 0.847 0.00 0.84 0.16
#> G2M_cell10_count 2 0.5216 0.723 0.00 0.74 0.26
#> G2M_cell11_count 2 0.0892 0.914 0.00 0.98 0.02
#> G2M_cell12_count 3 0.3686 0.609 0.00 0.14 0.86
#> G2M_cell13_count 2 0.2537 0.878 0.00 0.92 0.08
#> G2M_cell17_count 2 0.3340 0.875 0.00 0.88 0.12
#> G2M_cell21_count 2 0.3686 0.861 0.00 0.86 0.14
#> G2M_cell25_count 2 0.0000 0.916 0.00 1.00 0.00
#> G2M_cell34_count 3 0.1781 0.669 0.02 0.02 0.96
#> G2M_cell37_count 2 0.0000 0.916 0.00 1.00 0.00
#> G2M_cell41_count 2 0.0000 0.916 0.00 1.00 0.00
#> G2M_cell45_count 2 0.2537 0.896 0.00 0.92 0.08
#> G2M_cell49_count 2 0.0000 0.916 0.00 1.00 0.00
#> G2M_cell50_count 2 0.1529 0.894 0.00 0.96 0.04
#> G2M_cell55_count 2 0.0000 0.916 0.00 1.00 0.00
#> G2M_cell57_count 2 0.0000 0.916 0.00 1.00 0.00
#> G2M_cell62_count 2 0.3340 0.875 0.00 0.88 0.12
#> G2M_cell63_count 2 0.3340 0.876 0.00 0.88 0.12
#> G2M_cell65_count 2 0.0000 0.916 0.00 1.00 0.00
#> G2M_cell75_count 2 0.2959 0.887 0.00 0.90 0.10
#> G2M_cell76_count 2 0.0892 0.914 0.00 0.98 0.02
#> G2M_cell79_count 2 0.0000 0.916 0.00 1.00 0.00
#> G2M_cell80_count 2 0.0000 0.916 0.00 1.00 0.00
#> G2M_cell83_count 2 0.0892 0.914 0.00 0.98 0.02
#> G2M_cell87_count 3 0.6309 -0.250 0.00 0.50 0.50
#> G2M_cell88_count 2 0.0000 0.916 0.00 1.00 0.00
#> G2M_cell90_count 2 0.0000 0.916 0.00 1.00 0.00
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> G1_cell74_count 1 0.7699 -0.39888 0.40 0.00 0.38 0.22
#> G1_cell80_count 3 0.5863 0.52958 0.00 0.18 0.70 0.12
#> G1_cell94_count 2 0.6104 0.56289 0.00 0.68 0.18 0.14
#> S_cell2_count 1 0.5902 0.37339 0.70 0.00 0.14 0.16
#> S_cell4_count 1 0.4642 0.66515 0.74 0.00 0.02 0.24
#> S_cell6_count 1 0.0000 0.79366 1.00 0.00 0.00 0.00
#> S_cell7_count 1 0.3610 0.74295 0.80 0.00 0.00 0.20
#> S_cell11_count 1 0.2345 0.78509 0.90 0.00 0.00 0.10
#> S_cell19_count 1 0.1211 0.79522 0.96 0.00 0.00 0.04
#> S_cell20_count 1 0.5962 0.39619 0.66 0.00 0.08 0.26
#> S_cell32_count 1 0.4134 0.60545 0.74 0.00 0.00 0.26
#> S_cell33_count 4 0.7736 0.52675 0.20 0.06 0.14 0.60
#> S_cell35_count 1 0.1211 0.77728 0.96 0.00 0.00 0.04
#> S_cell36_count 2 0.7414 0.10253 0.00 0.48 0.18 0.34
#> S_cell39_count 1 0.2647 0.78118 0.88 0.00 0.00 0.12
#> S_cell41_count 4 0.4894 0.44771 0.10 0.00 0.12 0.78
#> S_cell45_count 1 0.4472 0.63250 0.76 0.00 0.02 0.22
#> S_cell47_count 4 0.4790 0.18556 0.38 0.00 0.00 0.62
#> S_cell48_count 4 0.5570 0.16121 0.44 0.00 0.02 0.54
#> S_cell49_count 1 0.3172 0.77089 0.84 0.00 0.00 0.16
#> S_cell54_count 4 0.5062 0.34155 0.02 0.00 0.30 0.68
#> S_cell59_count 1 0.2647 0.78031 0.88 0.00 0.00 0.12
#> S_cell60_count 1 0.4948 0.28818 0.56 0.00 0.00 0.44
#> S_cell61_count 1 0.0000 0.79366 1.00 0.00 0.00 0.00
#> S_cell62_count 1 0.3400 0.76306 0.82 0.00 0.00 0.18
#> S_cell66_count 1 0.0707 0.79256 0.98 0.00 0.00 0.02
#> S_cell68_count 1 0.3172 0.76700 0.84 0.00 0.00 0.16
#> S_cell69_count 1 0.3610 0.65440 0.80 0.00 0.00 0.20
#> S_cell72_count 1 0.3172 0.77089 0.84 0.00 0.00 0.16
#> S_cell73_count 4 0.7768 0.45846 0.36 0.00 0.24 0.40
#> S_cell76_count 3 0.7346 0.47967 0.00 0.20 0.52 0.28
#> S_cell80_count 1 0.0000 0.79366 1.00 0.00 0.00 0.00
#> S_cell82_count 1 0.0000 0.79366 1.00 0.00 0.00 0.00
#> S_cell86_count 1 0.0000 0.79366 1.00 0.00 0.00 0.00
#> S_cell89_count 1 0.0000 0.79366 1.00 0.00 0.00 0.00
#> S_cell93_count 4 0.7135 0.48030 0.20 0.00 0.24 0.56
#> G2M_cell9_count 2 0.4790 0.53506 0.00 0.62 0.38 0.00
#> G2M_cell10_count 3 0.5428 0.00831 0.00 0.38 0.60 0.02
#> G2M_cell11_count 2 0.2647 0.73767 0.00 0.88 0.12 0.00
#> G2M_cell12_count 3 0.5489 0.28331 0.00 0.06 0.70 0.24
#> G2M_cell13_count 2 0.2921 0.69492 0.00 0.86 0.14 0.00
#> G2M_cell17_count 2 0.4855 0.38413 0.00 0.60 0.40 0.00
#> G2M_cell21_count 2 0.4907 0.41334 0.00 0.58 0.42 0.00
#> G2M_cell25_count 2 0.0707 0.74766 0.00 0.98 0.02 0.00
#> G2M_cell34_count 3 0.5428 0.01657 0.02 0.00 0.60 0.38
#> G2M_cell37_count 2 0.1211 0.75277 0.00 0.96 0.04 0.00
#> G2M_cell41_count 2 0.0707 0.74766 0.00 0.98 0.02 0.00
#> G2M_cell45_count 2 0.4522 0.59008 0.00 0.68 0.32 0.00
#> G2M_cell49_count 2 0.1211 0.75334 0.00 0.96 0.04 0.00
#> G2M_cell50_count 2 0.2411 0.72536 0.00 0.92 0.04 0.04
#> G2M_cell55_count 2 0.2011 0.74742 0.00 0.92 0.08 0.00
#> G2M_cell57_count 2 0.1211 0.75033 0.00 0.96 0.04 0.00
#> G2M_cell62_count 2 0.4948 0.39616 0.00 0.56 0.44 0.00
#> G2M_cell63_count 2 0.4855 0.45778 0.00 0.60 0.40 0.00
#> G2M_cell65_count 2 0.0000 0.75215 0.00 1.00 0.00 0.00
#> G2M_cell75_count 2 0.4948 0.36933 0.00 0.56 0.44 0.00
#> G2M_cell76_count 2 0.3610 0.68708 0.00 0.80 0.20 0.00
#> G2M_cell79_count 2 0.2345 0.72938 0.00 0.90 0.10 0.00
#> G2M_cell80_count 2 0.2921 0.73220 0.00 0.86 0.14 0.00
#> G2M_cell83_count 2 0.3400 0.72260 0.00 0.82 0.18 0.00
#> G2M_cell87_count 3 0.5594 0.54152 0.00 0.18 0.72 0.10
#> G2M_cell88_count 2 0.0707 0.74766 0.00 0.98 0.02 0.00
#> G2M_cell90_count 2 0.1637 0.74610 0.00 0.94 0.06 0.00
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
As soon as the classes for columns are determined, the signatures that are significantly different between subgroups can be looked for. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. To get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows (which is done by automatically selecting number of clusters).If there are too many signatures, top_signatures = ... can be set to only show the
signatures with the highest FDRs:
# code only for demonstration
# e.g. to show the top 500 most significant rows
tb = get_signature(res, k = ..., top_signatures = 500)
If the signatures are defined as these which are uniquely high in current group, diff_method argument
can be set to "uniquely_high_in_one_group":
# code only for demonstration
tb = get_signature(res, k = ..., diff_method = "uniquely_high_in_one_group")
UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
Parent node: Node0. Child nodes: Node011 , Node012 , Node013-leaf , Node021-leaf , Node022 , Node023-leaf .
The object with results only for a single top-value method and a single partitioning method can be extracted as:
res = res_rh["02"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4.
#> On a matrix with 14830 rows and 123 columns.
#> Top rows (1425) are extracted by 'ATC' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 150 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_partitions"
#> [7] "compare_signatures" "consensus_heatmap" "dimension_reduction"
#> [10] "functional_enrichment" "get_anno_col" "get_anno"
#> [13] "get_classes" "get_consensus" "get_matrix"
#> [16] "get_membership" "get_param" "get_signatures"
#> [19] "get_stats" "is_best_k" "is_stable_k"
#> [22] "membership_heatmap" "ncol" "nrow"
#> [25] "plot_ecdf" "predict_classes" "rownames"
#> [28] "select_partition_number" "show" "suggest_best_k"
#> [31] "test_to_known_factors" "top_rows_heatmap"
collect_plots() function collects all the plots made from res for all k (number of subgroups)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, higher 1-PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.999 0.999 0.4033 0.597 0.597
#> 3 3 1.000 0.995 0.998 0.6352 0.734 0.558
#> 4 4 0.688 0.768 0.887 0.0613 0.982 0.946
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
#> G1_cell1_count 1 0.000 1.000 1.00 0.00
#> G1_cell2_count 1 0.000 1.000 1.00 0.00
#> G1_cell3_count 1 0.000 1.000 1.00 0.00
#> G1_cell4_count 2 0.000 0.998 0.00 1.00
#> G1_cell5_count 1 0.000 1.000 1.00 0.00
#> G1_cell6_count 1 0.000 1.000 1.00 0.00
#> G1_cell10_count 1 0.000 1.000 1.00 0.00
#> G1_cell11_count 1 0.000 1.000 1.00 0.00
#> G1_cell12_count 1 0.000 1.000 1.00 0.00
#> G1_cell14_count 1 0.000 1.000 1.00 0.00
#> G1_cell18_count 1 0.000 1.000 1.00 0.00
#> G1_cell19_count 1 0.000 1.000 1.00 0.00
#> G1_cell21_count 1 0.000 1.000 1.00 0.00
#> G1_cell24_count 1 0.000 1.000 1.00 0.00
#> G1_cell27_count 1 0.000 1.000 1.00 0.00
#> G1_cell28_count 2 0.000 0.998 0.00 1.00
#> G1_cell29_count 1 0.000 1.000 1.00 0.00
#> G1_cell31_count 2 0.000 0.998 0.00 1.00
#> G1_cell32_count 1 0.000 1.000 1.00 0.00
#> G1_cell33_count 1 0.000 1.000 1.00 0.00
#> G1_cell34_count 1 0.000 1.000 1.00 0.00
#> G1_cell35_count 2 0.000 0.998 0.00 1.00
#> G1_cell37_count 1 0.000 1.000 1.00 0.00
#> G1_cell38_count 1 0.000 1.000 1.00 0.00
#> G1_cell40_count 1 0.000 1.000 1.00 0.00
#> G1_cell45_count 1 0.000 1.000 1.00 0.00
#> G1_cell46_count 1 0.000 1.000 1.00 0.00
#> G1_cell48_count 2 0.000 0.998 0.00 1.00
#> G1_cell50_count 2 0.000 0.998 0.00 1.00
#> G1_cell51_count 1 0.000 1.000 1.00 0.00
#> G1_cell52_count 1 0.000 1.000 1.00 0.00
#> G1_cell53_count 1 0.000 1.000 1.00 0.00
#> G1_cell56_count 2 0.000 0.998 0.00 1.00
#> G1_cell58_count 1 0.000 1.000 1.00 0.00
#> G1_cell59_count 1 0.000 1.000 1.00 0.00
#> G1_cell63_count 1 0.000 1.000 1.00 0.00
#> G1_cell67_count 1 0.000 1.000 1.00 0.00
#> G1_cell69_count 1 0.000 1.000 1.00 0.00
#> G1_cell71_count 1 0.000 1.000 1.00 0.00
#> G1_cell72_count 1 0.000 1.000 1.00 0.00
#> G1_cell76_count 2 0.000 0.998 0.00 1.00
#> G1_cell78_count 1 0.000 1.000 1.00 0.00
#> G1_cell82_count 1 0.000 1.000 1.00 0.00
#> G1_cell85_count 1 0.000 1.000 1.00 0.00
#> G1_cell88_count 2 0.000 0.998 0.00 1.00
#> G1_cell89_count 1 0.000 1.000 1.00 0.00
#> G1_cell90_count 1 0.000 1.000 1.00 0.00
#> G1_cell92_count 1 0.000 1.000 1.00 0.00
#> S_cell1_count 2 0.000 0.998 0.00 1.00
#> S_cell5_count 2 0.000 0.998 0.00 1.00
#> S_cell12_count 1 0.000 1.000 1.00 0.00
#> S_cell18_count 2 0.000 0.998 0.00 1.00
#> S_cell21_count 1 0.000 1.000 1.00 0.00
#> S_cell23_count 1 0.000 1.000 1.00 0.00
#> S_cell25_count 1 0.000 1.000 1.00 0.00
#> S_cell31_count 2 0.000 0.998 0.00 1.00
#> S_cell34_count 2 0.000 0.998 0.00 1.00
#> S_cell38_count 1 0.000 1.000 1.00 0.00
#> S_cell44_count 2 0.000 0.998 0.00 1.00
#> S_cell51_count 1 0.000 1.000 1.00 0.00
#> S_cell56_count 1 0.000 1.000 1.00 0.00
#> S_cell58_count 2 0.000 0.998 0.00 1.00
#> S_cell64_count 1 0.000 1.000 1.00 0.00
#> S_cell65_count 1 0.000 1.000 1.00 0.00
#> S_cell67_count 1 0.000 1.000 1.00 0.00
#> S_cell71_count 2 0.000 0.998 0.00 1.00
#> S_cell74_count 2 0.000 0.998 0.00 1.00
#> S_cell75_count 1 0.000 1.000 1.00 0.00
#> S_cell77_count 1 0.000 1.000 1.00 0.00
#> S_cell78_count 2 0.000 0.998 0.00 1.00
#> S_cell81_count 2 0.000 0.998 0.00 1.00
#> S_cell84_count 2 0.000 0.998 0.00 1.00
#> S_cell87_count 1 0.000 1.000 1.00 0.00
#> S_cell88_count 2 0.000 0.998 0.00 1.00
#> S_cell90_count 2 0.000 0.998 0.00 1.00
#> S_cell91_count 1 0.000 1.000 1.00 0.00
#> S_cell94_count 2 0.000 0.998 0.00 1.00
#> S_cell95_count 2 0.000 0.998 0.00 1.00
#> S_cell96_count 2 0.000 0.998 0.00 1.00
#> G2M_cell1_count 1 0.000 1.000 1.00 0.00
#> G2M_cell2_count 1 0.000 1.000 1.00 0.00
#> G2M_cell3_count 1 0.000 1.000 1.00 0.00
#> G2M_cell4_count 2 0.000 0.998 0.00 1.00
#> G2M_cell6_count 1 0.000 1.000 1.00 0.00
#> G2M_cell14_count 1 0.000 1.000 1.00 0.00
#> G2M_cell15_count 1 0.000 1.000 1.00 0.00
#> G2M_cell16_count 1 0.000 1.000 1.00 0.00
#> G2M_cell22_count 2 0.000 0.998 0.00 1.00
#> G2M_cell24_count 1 0.000 1.000 1.00 0.00
#> G2M_cell26_count 2 0.000 0.998 0.00 1.00
#> G2M_cell27_count 1 0.000 1.000 1.00 0.00
#> G2M_cell28_count 1 0.000 1.000 1.00 0.00
#> G2M_cell29_count 1 0.000 1.000 1.00 0.00
#> G2M_cell30_count 2 0.000 0.998 0.00 1.00
#> G2M_cell31_count 1 0.000 1.000 1.00 0.00
#> G2M_cell36_count 1 0.000 1.000 1.00 0.00
#> G2M_cell38_count 1 0.000 1.000 1.00 0.00
#> G2M_cell39_count 1 0.000 1.000 1.00 0.00
#> G2M_cell40_count 1 0.000 1.000 1.00 0.00
#> G2M_cell42_count 1 0.000 1.000 1.00 0.00
#> G2M_cell43_count 1 0.000 1.000 1.00 0.00
#> G2M_cell46_count 1 0.000 1.000 1.00 0.00
#> G2M_cell51_count 1 0.000 1.000 1.00 0.00
#> G2M_cell52_count 1 0.000 1.000 1.00 0.00
#> G2M_cell53_count 1 0.000 1.000 1.00 0.00
#> G2M_cell54_count 1 0.000 1.000 1.00 0.00
#> G2M_cell56_count 1 0.000 1.000 1.00 0.00
#> G2M_cell64_count 1 0.000 1.000 1.00 0.00
#> G2M_cell66_count 1 0.000 1.000 1.00 0.00
#> G2M_cell67_count 2 0.000 0.998 0.00 1.00
#> G2M_cell70_count 2 0.402 0.913 0.08 0.92
#> G2M_cell72_count 1 0.000 1.000 1.00 0.00
#> G2M_cell77_count 2 0.000 0.998 0.00 1.00
#> G2M_cell78_count 1 0.000 1.000 1.00 0.00
#> G2M_cell81_count 1 0.000 1.000 1.00 0.00
#> G2M_cell82_count 2 0.000 0.998 0.00 1.00
#> G2M_cell85_count 1 0.000 1.000 1.00 0.00
#> G2M_cell89_count 1 0.000 1.000 1.00 0.00
#> G2M_cell92_count 1 0.000 1.000 1.00 0.00
#> G2M_cell93_count 1 0.000 1.000 1.00 0.00
#> G2M_cell94_count 1 0.000 1.000 1.00 0.00
#> G2M_cell95_count 1 0.000 1.000 1.00 0.00
#> G2M_cell96_count 1 0.000 1.000 1.00 0.00
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> G1_cell1_count 3 0.0000 0.993 0.00 0.00 1.00
#> G1_cell2_count 3 0.0000 0.993 0.00 0.00 1.00
#> G1_cell3_count 3 0.0000 0.993 0.00 0.00 1.00
#> G1_cell4_count 2 0.0000 1.000 0.00 1.00 0.00
#> G1_cell5_count 3 0.0000 0.993 0.00 0.00 1.00
#> G1_cell6_count 3 0.0000 0.993 0.00 0.00 1.00
#> G1_cell10_count 1 0.0000 1.000 1.00 0.00 0.00
#> G1_cell11_count 3 0.0000 0.993 0.00 0.00 1.00
#> G1_cell12_count 3 0.0000 0.993 0.00 0.00 1.00
#> G1_cell14_count 3 0.0000 0.993 0.00 0.00 1.00
#> G1_cell18_count 3 0.0000 0.993 0.00 0.00 1.00
#> G1_cell19_count 3 0.0000 0.993 0.00 0.00 1.00
#> G1_cell21_count 1 0.0000 1.000 1.00 0.00 0.00
#> G1_cell24_count 1 0.0000 1.000 1.00 0.00 0.00
#> G1_cell27_count 3 0.0000 0.993 0.00 0.00 1.00
#> G1_cell28_count 2 0.0000 1.000 0.00 1.00 0.00
#> G1_cell29_count 3 0.0000 0.993 0.00 0.00 1.00
#> G1_cell31_count 2 0.0000 1.000 0.00 1.00 0.00
#> G1_cell32_count 1 0.0000 1.000 1.00 0.00 0.00
#> G1_cell33_count 3 0.0000 0.993 0.00 0.00 1.00
#> G1_cell34_count 1 0.0000 1.000 1.00 0.00 0.00
#> G1_cell35_count 2 0.0000 1.000 0.00 1.00 0.00
#> G1_cell37_count 1 0.0000 1.000 1.00 0.00 0.00
#> G1_cell38_count 3 0.0000 0.993 0.00 0.00 1.00
#> G1_cell40_count 3 0.0000 0.993 0.00 0.00 1.00
#> G1_cell45_count 3 0.0000 0.993 0.00 0.00 1.00
#> G1_cell46_count 3 0.0000 0.993 0.00 0.00 1.00
#> G1_cell48_count 2 0.0000 1.000 0.00 1.00 0.00
#> G1_cell50_count 2 0.0000 1.000 0.00 1.00 0.00
#> G1_cell51_count 3 0.0892 0.974 0.02 0.00 0.98
#> G1_cell52_count 3 0.0000 0.993 0.00 0.00 1.00
#> G1_cell53_count 3 0.0000 0.993 0.00 0.00 1.00
#> G1_cell56_count 2 0.0000 1.000 0.00 1.00 0.00
#> G1_cell58_count 3 0.0000 0.993 0.00 0.00 1.00
#> G1_cell59_count 3 0.0000 0.993 0.00 0.00 1.00
#> G1_cell63_count 1 0.0000 1.000 1.00 0.00 0.00
#> G1_cell67_count 3 0.0000 0.993 0.00 0.00 1.00
#> G1_cell69_count 3 0.0000 0.993 0.00 0.00 1.00
#> G1_cell71_count 3 0.0000 0.993 0.00 0.00 1.00
#> G1_cell72_count 1 0.0000 1.000 1.00 0.00 0.00
#> G1_cell76_count 2 0.0000 1.000 0.00 1.00 0.00
#> G1_cell78_count 3 0.0000 0.993 0.00 0.00 1.00
#> G1_cell82_count 3 0.0000 0.993 0.00 0.00 1.00
#> G1_cell85_count 3 0.0000 0.993 0.00 0.00 1.00
#> G1_cell88_count 2 0.0000 1.000 0.00 1.00 0.00
#> G1_cell89_count 1 0.0000 1.000 1.00 0.00 0.00
#> G1_cell90_count 1 0.0000 1.000 1.00 0.00 0.00
#> G1_cell92_count 3 0.0000 0.993 0.00 0.00 1.00
#> S_cell1_count 2 0.0000 1.000 0.00 1.00 0.00
#> S_cell5_count 2 0.0000 1.000 0.00 1.00 0.00
#> S_cell12_count 1 0.0000 1.000 1.00 0.00 0.00
#> S_cell18_count 2 0.0000 1.000 0.00 1.00 0.00
#> S_cell21_count 1 0.0000 1.000 1.00 0.00 0.00
#> S_cell23_count 1 0.0000 1.000 1.00 0.00 0.00
#> S_cell25_count 1 0.0000 1.000 1.00 0.00 0.00
#> S_cell31_count 2 0.0000 1.000 0.00 1.00 0.00
#> S_cell34_count 2 0.0000 1.000 0.00 1.00 0.00
#> S_cell38_count 1 0.0000 1.000 1.00 0.00 0.00
#> S_cell44_count 2 0.0000 1.000 0.00 1.00 0.00
#> S_cell51_count 1 0.0000 1.000 1.00 0.00 0.00
#> S_cell56_count 1 0.0892 0.979 0.98 0.00 0.02
#> S_cell58_count 2 0.0000 1.000 0.00 1.00 0.00
#> S_cell64_count 1 0.0000 1.000 1.00 0.00 0.00
#> S_cell65_count 1 0.0000 1.000 1.00 0.00 0.00
#> S_cell67_count 1 0.0000 1.000 1.00 0.00 0.00
#> S_cell71_count 2 0.0000 1.000 0.00 1.00 0.00
#> S_cell74_count 2 0.0000 1.000 0.00 1.00 0.00
#> S_cell75_count 1 0.0000 1.000 1.00 0.00 0.00
#> S_cell77_count 1 0.0000 1.000 1.00 0.00 0.00
#> S_cell78_count 2 0.0000 1.000 0.00 1.00 0.00
#> S_cell81_count 2 0.0000 1.000 0.00 1.00 0.00
#> S_cell84_count 2 0.0000 1.000 0.00 1.00 0.00
#> S_cell87_count 1 0.0000 1.000 1.00 0.00 0.00
#> S_cell88_count 2 0.0000 1.000 0.00 1.00 0.00
#> S_cell90_count 2 0.0000 1.000 0.00 1.00 0.00
#> S_cell91_count 1 0.0000 1.000 1.00 0.00 0.00
#> S_cell94_count 2 0.0000 1.000 0.00 1.00 0.00
#> S_cell95_count 2 0.0000 1.000 0.00 1.00 0.00
#> S_cell96_count 2 0.0000 1.000 0.00 1.00 0.00
#> G2M_cell1_count 3 0.0000 0.993 0.00 0.00 1.00
#> G2M_cell2_count 1 0.0000 1.000 1.00 0.00 0.00
#> G2M_cell3_count 1 0.0000 1.000 1.00 0.00 0.00
#> G2M_cell4_count 2 0.0000 1.000 0.00 1.00 0.00
#> G2M_cell6_count 3 0.0000 0.993 0.00 0.00 1.00
#> G2M_cell14_count 1 0.0000 1.000 1.00 0.00 0.00
#> G2M_cell15_count 1 0.0000 1.000 1.00 0.00 0.00
#> G2M_cell16_count 3 0.1529 0.953 0.04 0.00 0.96
#> G2M_cell22_count 2 0.0000 1.000 0.00 1.00 0.00
#> G2M_cell24_count 1 0.0000 1.000 1.00 0.00 0.00
#> G2M_cell26_count 2 0.0000 1.000 0.00 1.00 0.00
#> G2M_cell27_count 3 0.0000 0.993 0.00 0.00 1.00
#> G2M_cell28_count 1 0.0000 1.000 1.00 0.00 0.00
#> G2M_cell29_count 3 0.0892 0.974 0.02 0.00 0.98
#> G2M_cell30_count 2 0.0000 1.000 0.00 1.00 0.00
#> G2M_cell31_count 3 0.0000 0.993 0.00 0.00 1.00
#> G2M_cell36_count 1 0.0000 1.000 1.00 0.00 0.00
#> G2M_cell38_count 1 0.0000 1.000 1.00 0.00 0.00
#> G2M_cell39_count 1 0.0000 1.000 1.00 0.00 0.00
#> G2M_cell40_count 1 0.0000 1.000 1.00 0.00 0.00
#> G2M_cell42_count 1 0.0000 1.000 1.00 0.00 0.00
#> G2M_cell43_count 1 0.0000 1.000 1.00 0.00 0.00
#> G2M_cell46_count 1 0.0000 1.000 1.00 0.00 0.00
#> G2M_cell51_count 1 0.0000 1.000 1.00 0.00 0.00
#> G2M_cell52_count 1 0.0000 1.000 1.00 0.00 0.00
#> G2M_cell53_count 1 0.0000 1.000 1.00 0.00 0.00
#> G2M_cell54_count 1 0.0000 1.000 1.00 0.00 0.00
#> G2M_cell56_count 1 0.0000 1.000 1.00 0.00 0.00
#> G2M_cell64_count 3 0.0000 0.993 0.00 0.00 1.00
#> G2M_cell66_count 1 0.0000 1.000 1.00 0.00 0.00
#> G2M_cell67_count 2 0.0000 1.000 0.00 1.00 0.00
#> G2M_cell70_count 3 0.4002 0.811 0.00 0.16 0.84
#> G2M_cell72_count 3 0.0000 0.993 0.00 0.00 1.00
#> G2M_cell77_count 2 0.0000 1.000 0.00 1.00 0.00
#> G2M_cell78_count 1 0.0000 1.000 1.00 0.00 0.00
#> G2M_cell81_count 1 0.0000 1.000 1.00 0.00 0.00
#> G2M_cell82_count 2 0.0000 1.000 0.00 1.00 0.00
#> G2M_cell85_count 1 0.0000 1.000 1.00 0.00 0.00
#> G2M_cell89_count 1 0.0000 1.000 1.00 0.00 0.00
#> G2M_cell92_count 1 0.0000 1.000 1.00 0.00 0.00
#> G2M_cell93_count 1 0.0000 1.000 1.00 0.00 0.00
#> G2M_cell94_count 1 0.0000 1.000 1.00 0.00 0.00
#> G2M_cell95_count 1 0.0000 1.000 1.00 0.00 0.00
#> G2M_cell96_count 1 0.0000 1.000 1.00 0.00 0.00
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> G1_cell1_count 3 0.0707 0.7804 0.00 0.00 0.98 0.02
#> G1_cell2_count 3 0.0707 0.7937 0.02 0.00 0.98 0.00
#> G1_cell3_count 3 0.3606 0.7772 0.14 0.00 0.84 0.02
#> G1_cell4_count 2 0.0000 0.9016 0.00 1.00 0.00 0.00
#> G1_cell5_count 3 0.3400 0.6457 0.00 0.00 0.82 0.18
#> G1_cell6_count 4 0.2647 0.5182 0.00 0.00 0.12 0.88
#> G1_cell10_count 1 0.3610 0.7934 0.80 0.00 0.00 0.20
#> G1_cell11_count 3 0.3335 0.7886 0.12 0.00 0.86 0.02
#> G1_cell12_count 3 0.0000 0.7864 0.00 0.00 1.00 0.00
#> G1_cell14_count 3 0.3335 0.7886 0.12 0.00 0.86 0.02
#> G1_cell18_count 3 0.0000 0.7864 0.00 0.00 1.00 0.00
#> G1_cell19_count 3 0.3606 0.7772 0.14 0.00 0.84 0.02
#> G1_cell21_count 1 0.1211 0.8887 0.96 0.00 0.00 0.04
#> G1_cell24_count 1 0.0000 0.9015 1.00 0.00 0.00 0.00
#> G1_cell27_count 3 0.4642 0.6846 0.24 0.00 0.74 0.02
#> G1_cell28_count 2 0.4977 0.0648 0.00 0.54 0.00 0.46
#> G1_cell29_count 3 0.3610 0.6164 0.00 0.00 0.80 0.20
#> G1_cell31_count 2 0.0707 0.8825 0.00 0.98 0.00 0.02
#> G1_cell32_count 1 0.1411 0.8802 0.96 0.00 0.02 0.02
#> G1_cell33_count 3 0.1637 0.7674 0.00 0.00 0.94 0.06
#> G1_cell34_count 1 0.2345 0.8681 0.90 0.00 0.00 0.10
#> G1_cell35_count 2 0.5000 -0.1282 0.00 0.50 0.00 0.50
#> G1_cell37_count 1 0.3935 0.8280 0.84 0.00 0.06 0.10
#> G1_cell38_count 3 0.3821 0.7851 0.12 0.00 0.84 0.04
#> G1_cell40_count 3 0.3853 0.7276 0.02 0.00 0.82 0.16
#> G1_cell45_count 3 0.0000 0.7864 0.00 0.00 1.00 0.00
#> G1_cell46_count 3 0.2706 0.7964 0.08 0.00 0.90 0.02
#> G1_cell48_count 4 0.4907 0.1174 0.00 0.42 0.00 0.58
#> G1_cell50_count 2 0.0000 0.9016 0.00 1.00 0.00 0.00
#> G1_cell51_count 3 0.5606 0.2833 0.48 0.00 0.50 0.02
#> G1_cell52_count 3 0.0707 0.7876 0.00 0.00 0.98 0.02
#> G1_cell53_count 3 0.1211 0.7724 0.00 0.00 0.96 0.04
#> G1_cell56_count 2 0.0000 0.9016 0.00 1.00 0.00 0.00
#> G1_cell58_count 3 0.0000 0.7864 0.00 0.00 1.00 0.00
#> G1_cell59_count 3 0.3821 0.7554 0.04 0.00 0.84 0.12
#> G1_cell63_count 1 0.0707 0.8946 0.98 0.00 0.00 0.02
#> G1_cell67_count 3 0.7004 0.5422 0.20 0.00 0.58 0.22
#> G1_cell69_count 3 0.3335 0.7886 0.12 0.00 0.86 0.02
#> G1_cell71_count 3 0.0707 0.7804 0.00 0.00 0.98 0.02
#> G1_cell72_count 1 0.0000 0.9015 1.00 0.00 0.00 0.00
#> G1_cell76_count 2 0.3801 0.6299 0.00 0.78 0.00 0.22
#> G1_cell78_count 3 0.0000 0.7864 0.00 0.00 1.00 0.00
#> G1_cell82_count 3 0.5077 0.7396 0.16 0.00 0.76 0.08
#> G1_cell85_count 3 0.7028 0.4792 0.28 0.00 0.56 0.16
#> G1_cell88_count 2 0.0000 0.9016 0.00 1.00 0.00 0.00
#> G1_cell89_count 1 0.0000 0.9015 1.00 0.00 0.00 0.00
#> G1_cell90_count 1 0.3975 0.7499 0.76 0.00 0.00 0.24
#> G1_cell92_count 3 0.3335 0.7886 0.12 0.00 0.86 0.02
#> S_cell1_count 2 0.0000 0.9016 0.00 1.00 0.00 0.00
#> S_cell5_count 2 0.0000 0.9016 0.00 1.00 0.00 0.00
#> S_cell12_count 1 0.0000 0.9015 1.00 0.00 0.00 0.00
#> S_cell18_count 2 0.0000 0.9016 0.00 1.00 0.00 0.00
#> S_cell21_count 1 0.0000 0.9015 1.00 0.00 0.00 0.00
#> S_cell23_count 1 0.1211 0.8866 0.96 0.00 0.00 0.04
#> S_cell25_count 1 0.0000 0.9015 1.00 0.00 0.00 0.00
#> S_cell31_count 2 0.0000 0.9016 0.00 1.00 0.00 0.00
#> S_cell34_count 2 0.0000 0.9016 0.00 1.00 0.00 0.00
#> S_cell38_count 1 0.0000 0.9015 1.00 0.00 0.00 0.00
#> S_cell44_count 2 0.0000 0.9016 0.00 1.00 0.00 0.00
#> S_cell51_count 1 0.0000 0.9015 1.00 0.00 0.00 0.00
#> S_cell56_count 1 0.2647 0.8047 0.88 0.00 0.12 0.00
#> S_cell58_count 2 0.0000 0.9016 0.00 1.00 0.00 0.00
#> S_cell64_count 1 0.4522 0.6754 0.68 0.00 0.00 0.32
#> S_cell65_count 1 0.4855 0.5692 0.60 0.00 0.00 0.40
#> S_cell67_count 1 0.2921 0.8356 0.86 0.00 0.00 0.14
#> S_cell71_count 2 0.0000 0.9016 0.00 1.00 0.00 0.00
#> S_cell74_count 2 0.0000 0.9016 0.00 1.00 0.00 0.00
#> S_cell75_count 1 0.0000 0.9015 1.00 0.00 0.00 0.00
#> S_cell77_count 1 0.3400 0.8078 0.82 0.00 0.00 0.18
#> S_cell78_count 2 0.0000 0.9016 0.00 1.00 0.00 0.00
#> S_cell81_count 2 0.0000 0.9016 0.00 1.00 0.00 0.00
#> S_cell84_count 2 0.0000 0.9016 0.00 1.00 0.00 0.00
#> S_cell87_count 1 0.1637 0.8610 0.94 0.00 0.06 0.00
#> S_cell88_count 2 0.0000 0.9016 0.00 1.00 0.00 0.00
#> S_cell90_count 2 0.0000 0.9016 0.00 1.00 0.00 0.00
#> S_cell91_count 1 0.1211 0.8889 0.96 0.00 0.00 0.04
#> S_cell94_count 2 0.0000 0.9016 0.00 1.00 0.00 0.00
#> S_cell95_count 2 0.0000 0.9016 0.00 1.00 0.00 0.00
#> S_cell96_count 2 0.0000 0.9016 0.00 1.00 0.00 0.00
#> G2M_cell1_count 3 0.1211 0.7966 0.04 0.00 0.96 0.00
#> G2M_cell2_count 1 0.0000 0.9015 1.00 0.00 0.00 0.00
#> G2M_cell3_count 1 0.0000 0.9015 1.00 0.00 0.00 0.00
#> G2M_cell4_count 2 0.0000 0.9016 0.00 1.00 0.00 0.00
#> G2M_cell6_count 3 0.0707 0.7804 0.00 0.00 0.98 0.02
#> G2M_cell14_count 1 0.0000 0.9015 1.00 0.00 0.00 0.00
#> G2M_cell15_count 1 0.0000 0.9015 1.00 0.00 0.00 0.00
#> G2M_cell16_count 3 0.5428 0.4895 0.38 0.00 0.60 0.02
#> G2M_cell22_count 2 0.5355 0.3152 0.00 0.62 0.02 0.36
#> G2M_cell24_count 1 0.0000 0.9015 1.00 0.00 0.00 0.00
#> G2M_cell26_count 2 0.0000 0.9016 0.00 1.00 0.00 0.00
#> G2M_cell27_count 3 0.4713 0.2943 0.00 0.00 0.64 0.36
#> G2M_cell28_count 1 0.0000 0.9015 1.00 0.00 0.00 0.00
#> G2M_cell29_count 3 0.5173 0.5830 0.32 0.00 0.66 0.02
#> G2M_cell30_count 2 0.5619 0.3630 0.00 0.64 0.04 0.32
#> G2M_cell31_count 3 0.2411 0.7688 0.04 0.00 0.92 0.04
#> G2M_cell36_count 1 0.0000 0.9015 1.00 0.00 0.00 0.00
#> G2M_cell38_count 1 0.4624 0.6531 0.66 0.00 0.00 0.34
#> G2M_cell39_count 1 0.2921 0.7972 0.86 0.00 0.14 0.00
#> G2M_cell40_count 1 0.3400 0.8054 0.82 0.00 0.00 0.18
#> G2M_cell42_count 1 0.0000 0.9015 1.00 0.00 0.00 0.00
#> G2M_cell43_count 1 0.0707 0.8905 0.98 0.00 0.02 0.00
#> G2M_cell46_count 1 0.0707 0.8920 0.98 0.00 0.00 0.02
#> G2M_cell51_count 1 0.0000 0.9015 1.00 0.00 0.00 0.00
#> G2M_cell52_count 1 0.0000 0.9015 1.00 0.00 0.00 0.00
#> G2M_cell53_count 1 0.4522 0.6714 0.68 0.00 0.00 0.32
#> G2M_cell54_count 1 0.3400 0.7228 0.82 0.00 0.18 0.00
#> G2M_cell56_count 1 0.0000 0.9015 1.00 0.00 0.00 0.00
#> G2M_cell64_count 3 0.3606 0.7610 0.14 0.00 0.84 0.02
#> G2M_cell66_count 1 0.4624 0.6531 0.66 0.00 0.00 0.34
#> G2M_cell67_count 2 0.0000 0.9016 0.00 1.00 0.00 0.00
#> G2M_cell70_count 4 0.6510 0.3053 0.00 0.08 0.38 0.54
#> G2M_cell72_count 3 0.4284 0.7274 0.20 0.00 0.78 0.02
#> G2M_cell77_count 4 0.4134 0.4776 0.00 0.26 0.00 0.74
#> G2M_cell78_count 1 0.0000 0.9015 1.00 0.00 0.00 0.00
#> G2M_cell81_count 1 0.0000 0.9015 1.00 0.00 0.00 0.00
#> G2M_cell82_count 2 0.4948 0.1266 0.00 0.56 0.00 0.44
#> G2M_cell85_count 1 0.0000 0.9015 1.00 0.00 0.00 0.00
#> G2M_cell89_count 1 0.4624 0.6531 0.66 0.00 0.00 0.34
#> G2M_cell92_count 1 0.4624 0.6531 0.66 0.00 0.00 0.34
#> G2M_cell93_count 1 0.4624 0.6531 0.66 0.00 0.00 0.34
#> G2M_cell94_count 1 0.0000 0.9015 1.00 0.00 0.00 0.00
#> G2M_cell95_count 1 0.3606 0.7803 0.84 0.00 0.14 0.02
#> G2M_cell96_count 1 0.0000 0.9015 1.00 0.00 0.00 0.00
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
As soon as the classes for columns are determined, the signatures that are significantly different between subgroups can be looked for. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. To get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows (which is done by automatically selecting number of clusters).If there are too many signatures, top_signatures = ... can be set to only show the
signatures with the highest FDRs:
# code only for demonstration
# e.g. to show the top 500 most significant rows
tb = get_signature(res, k = ..., top_signatures = 500)
If the signatures are defined as these which are uniquely high in current group, diff_method argument
can be set to "uniquely_high_in_one_group":
# code only for demonstration
tb = get_signature(res, k = ..., diff_method = "uniquely_high_in_one_group")
UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
Parent node: Node02. Child nodes: Node0111-leaf , Node0112-leaf , Node0121-leaf , Node0122-leaf , Node0221 , Node0222-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 12745 rows and 33 columns.
#> Top rows (1135) are extracted by 'ATC' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 150 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_partitions"
#> [7] "compare_signatures" "consensus_heatmap" "dimension_reduction"
#> [10] "functional_enrichment" "get_anno_col" "get_anno"
#> [13] "get_classes" "get_consensus" "get_matrix"
#> [16] "get_membership" "get_param" "get_signatures"
#> [19] "get_stats" "is_best_k" "is_stable_k"
#> [22] "membership_heatmap" "ncol" "nrow"
#> [25] "plot_ecdf" "predict_classes" "rownames"
#> [28] "select_partition_number" "show" "suggest_best_k"
#> [31] "test_to_known_factors" "top_rows_heatmap"
collect_plots() function collects all the plots made from res for all k (number of subgroups)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, higher 1-PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 1.00 1.000 0.512 0.489 0.489
#> 3 3 0.996 0.95 0.978 0.143 0.939 0.876
#> 4 4 0.802 0.74 0.901 0.109 0.973 0.938
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
#> G1_cell4_count 2 0 1 0 1
#> G1_cell28_count 2 0 1 0 1
#> G1_cell31_count 2 0 1 0 1
#> G1_cell35_count 2 0 1 0 1
#> G1_cell48_count 2 0 1 0 1
#> G1_cell50_count 2 0 1 0 1
#> G1_cell56_count 2 0 1 0 1
#> G1_cell76_count 2 0 1 0 1
#> G1_cell88_count 1 0 1 1 0
#> S_cell1_count 1 0 1 1 0
#> S_cell5_count 1 0 1 1 0
#> S_cell18_count 1 0 1 1 0
#> S_cell31_count 1 0 1 1 0
#> S_cell34_count 2 0 1 0 1
#> S_cell44_count 1 0 1 1 0
#> S_cell58_count 1 0 1 1 0
#> S_cell71_count 1 0 1 1 0
#> S_cell74_count 2 0 1 0 1
#> S_cell78_count 2 0 1 0 1
#> S_cell81_count 1 0 1 1 0
#> S_cell84_count 1 0 1 1 0
#> S_cell88_count 1 0 1 1 0
#> S_cell90_count 1 0 1 1 0
#> S_cell94_count 1 0 1 1 0
#> S_cell95_count 1 0 1 1 0
#> S_cell96_count 1 0 1 1 0
#> G2M_cell4_count 2 0 1 0 1
#> G2M_cell22_count 2 0 1 0 1
#> G2M_cell26_count 2 0 1 0 1
#> G2M_cell30_count 2 0 1 0 1
#> G2M_cell67_count 2 0 1 0 1
#> G2M_cell77_count 2 0 1 0 1
#> G2M_cell82_count 2 0 1 0 1
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> G1_cell4_count 3 0.0892 0.949 0.00 0.02 0.98
#> G1_cell28_count 2 0.0000 0.965 0.00 1.00 0.00
#> G1_cell31_count 2 0.0000 0.965 0.00 1.00 0.00
#> G1_cell35_count 2 0.0000 0.965 0.00 1.00 0.00
#> G1_cell48_count 2 0.0000 0.965 0.00 1.00 0.00
#> G1_cell50_count 2 0.0000 0.965 0.00 1.00 0.00
#> G1_cell56_count 2 0.2066 0.916 0.00 0.94 0.06
#> G1_cell76_count 2 0.0000 0.965 0.00 1.00 0.00
#> G1_cell88_count 1 0.0000 0.990 1.00 0.00 0.00
#> S_cell1_count 1 0.0000 0.990 1.00 0.00 0.00
#> S_cell5_count 1 0.0000 0.990 1.00 0.00 0.00
#> S_cell18_count 1 0.0000 0.990 1.00 0.00 0.00
#> S_cell31_count 1 0.0000 0.990 1.00 0.00 0.00
#> S_cell34_count 2 0.2066 0.916 0.00 0.94 0.06
#> S_cell44_count 1 0.0000 0.990 1.00 0.00 0.00
#> S_cell58_count 1 0.0000 0.990 1.00 0.00 0.00
#> S_cell71_count 1 0.0000 0.990 1.00 0.00 0.00
#> S_cell74_count 2 0.6045 0.386 0.00 0.62 0.38
#> S_cell78_count 3 0.2066 0.947 0.00 0.06 0.94
#> S_cell81_count 1 0.0000 0.990 1.00 0.00 0.00
#> S_cell84_count 1 0.0000 0.990 1.00 0.00 0.00
#> S_cell88_count 1 0.0000 0.990 1.00 0.00 0.00
#> S_cell90_count 1 0.0000 0.990 1.00 0.00 0.00
#> S_cell94_count 1 0.0000 0.990 1.00 0.00 0.00
#> S_cell95_count 1 0.0000 0.990 1.00 0.00 0.00
#> S_cell96_count 1 0.3686 0.841 0.86 0.00 0.14
#> G2M_cell4_count 2 0.0000 0.965 0.00 1.00 0.00
#> G2M_cell22_count 2 0.0000 0.965 0.00 1.00 0.00
#> G2M_cell26_count 2 0.0000 0.965 0.00 1.00 0.00
#> G2M_cell30_count 2 0.0000 0.965 0.00 1.00 0.00
#> G2M_cell67_count 2 0.0000 0.965 0.00 1.00 0.00
#> G2M_cell77_count 2 0.0000 0.965 0.00 1.00 0.00
#> G2M_cell82_count 2 0.0000 0.965 0.00 1.00 0.00
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> G1_cell4_count 3 0.3606 0.7640 0.00 0.02 0.84 0.14
#> G1_cell28_count 2 0.0000 0.9116 0.00 1.00 0.00 0.00
#> G1_cell31_count 2 0.0000 0.9116 0.00 1.00 0.00 0.00
#> G1_cell35_count 2 0.0000 0.9116 0.00 1.00 0.00 0.00
#> G1_cell48_count 2 0.0000 0.9116 0.00 1.00 0.00 0.00
#> G1_cell50_count 2 0.0000 0.9116 0.00 1.00 0.00 0.00
#> G1_cell56_count 2 0.5383 0.7153 0.00 0.74 0.10 0.16
#> G1_cell76_count 2 0.1913 0.8814 0.00 0.94 0.02 0.04
#> G1_cell88_count 1 0.0000 0.8522 1.00 0.00 0.00 0.00
#> S_cell1_count 1 0.0000 0.8522 1.00 0.00 0.00 0.00
#> S_cell5_count 1 0.0000 0.8522 1.00 0.00 0.00 0.00
#> S_cell18_count 1 0.0000 0.8522 1.00 0.00 0.00 0.00
#> S_cell31_count 1 0.0000 0.8522 1.00 0.00 0.00 0.00
#> S_cell34_count 2 0.6122 0.6404 0.00 0.68 0.16 0.16
#> S_cell44_count 1 0.0000 0.8522 1.00 0.00 0.00 0.00
#> S_cell58_count 1 0.0000 0.8522 1.00 0.00 0.00 0.00
#> S_cell71_count 1 0.0000 0.8522 1.00 0.00 0.00 0.00
#> S_cell74_count 2 0.7028 0.4029 0.00 0.56 0.28 0.16
#> S_cell78_count 3 0.3247 0.7694 0.00 0.06 0.88 0.06
#> S_cell81_count 1 0.4277 0.4850 0.72 0.00 0.00 0.28
#> S_cell84_count 1 0.0000 0.8522 1.00 0.00 0.00 0.00
#> S_cell88_count 1 0.4277 0.4932 0.72 0.00 0.00 0.28
#> S_cell90_count 1 0.4907 0.0125 0.58 0.00 0.00 0.42
#> S_cell94_count 1 0.0000 0.8522 1.00 0.00 0.00 0.00
#> S_cell95_count 1 0.4907 -0.0111 0.58 0.00 0.00 0.42
#> S_cell96_count 4 0.3975 0.0000 0.24 0.00 0.00 0.76
#> G2M_cell4_count 2 0.4949 0.7221 0.00 0.76 0.18 0.06
#> G2M_cell22_count 2 0.0000 0.9116 0.00 1.00 0.00 0.00
#> G2M_cell26_count 2 0.0000 0.9116 0.00 1.00 0.00 0.00
#> G2M_cell30_count 2 0.0000 0.9116 0.00 1.00 0.00 0.00
#> G2M_cell67_count 2 0.0000 0.9116 0.00 1.00 0.00 0.00
#> G2M_cell77_count 2 0.0707 0.9027 0.00 0.98 0.00 0.02
#> G2M_cell82_count 2 0.0000 0.9116 0.00 1.00 0.00 0.00
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
As soon as the classes for columns are determined, the signatures that are significantly different between subgroups can be looked for. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. To get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows (which is done by automatically selecting number of clusters).If there are too many signatures, top_signatures = ... can be set to only show the
signatures with the highest FDRs:
# code only for demonstration
# e.g. to show the top 500 most significant rows
tb = get_signature(res, k = ..., top_signatures = 500)
If the signatures are defined as these which are uniquely high in current group, diff_method argument
can be set to "uniquely_high_in_one_group":
# code only for demonstration
tb = get_signature(res, k = ..., diff_method = "uniquely_high_in_one_group")
UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
Parent node: Node022. Child nodes: Node02211-leaf , Node02212-leaf .
The object with results only for a single top-value method and a single partitioning method can be extracted as:
res = res_rh["0221"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4.
#> On a matrix with 8340 rows and 15 columns.
#> Top rows (834) 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 1.000 1 0.5148 0.486 0.486
#> 3 3 1 0.933 1 0.0924 0.952 0.902
#> 4 4 1 0.867 1 0.0677 0.962 0.913
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
#> G1_cell88_count 1 0 1 1 0
#> S_cell1_count 1 0 1 1 0
#> S_cell5_count 1 0 1 1 0
#> S_cell18_count 1 0 1 1 0
#> S_cell31_count 1 0 1 1 0
#> S_cell44_count 1 0 1 1 0
#> S_cell58_count 1 0 1 1 0
#> S_cell71_count 1 0 1 1 0
#> S_cell81_count 2 0 1 0 1
#> S_cell84_count 1 0 1 1 0
#> S_cell88_count 2 0 1 0 1
#> S_cell90_count 2 0 1 0 1
#> S_cell94_count 2 0 1 0 1
#> S_cell95_count 2 0 1 0 1
#> S_cell96_count 2 0 1 0 1
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> G1_cell88_count 1 0 1 1 0 0
#> S_cell1_count 1 0 1 1 0 0
#> S_cell5_count 1 0 1 1 0 0
#> S_cell18_count 1 0 1 1 0 0
#> S_cell31_count 1 0 1 1 0 0
#> S_cell44_count 1 0 1 1 0 0
#> S_cell58_count 1 0 1 1 0 0
#> S_cell71_count 1 0 1 1 0 0
#> S_cell81_count 2 0 1 0 1 0
#> S_cell84_count 1 0 1 1 0 0
#> S_cell88_count 2 0 1 0 1 0
#> S_cell90_count 2 0 1 0 1 0
#> S_cell94_count 3 0 0 0 0 1
#> S_cell95_count 2 0 1 0 1 0
#> S_cell96_count 2 0 1 0 1 0
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> G1_cell88_count 1 0 1 1 0 0 0
#> S_cell1_count 1 0 1 1 0 0 0
#> S_cell5_count 1 0 1 1 0 0 0
#> S_cell18_count 1 0 1 1 0 0 0
#> S_cell31_count 1 0 1 1 0 0 0
#> S_cell44_count 1 0 1 1 0 0 0
#> S_cell58_count 1 0 1 1 0 0 0
#> S_cell71_count 1 0 1 1 0 0 0
#> S_cell81_count 4 0 0 0 0 0 1
#> S_cell84_count 1 0 1 1 0 0 0
#> S_cell88_count 2 0 1 0 1 0 0
#> S_cell90_count 2 0 1 0 1 0 0
#> S_cell94_count 3 0 0 0 0 1 0
#> S_cell95_count 2 0 1 0 1 0 0
#> S_cell96_count 2 0 1 0 1 0 0
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
As soon as the classes for columns are determined, the signatures that are significantly different between subgroups can be looked for. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. To get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows (which is done by automatically selecting number of clusters).If there are too many signatures, top_signatures = ... can be set to only show the
signatures with the highest FDRs:
# code only for demonstration
# e.g. to show the top 500 most significant rows
tb = get_signature(res, k = ..., top_signatures = 500)
If the signatures are defined as these which are uniquely high in current group, diff_method argument
can be set to "uniquely_high_in_one_group":
# code only for demonstration
tb = get_signature(res, k = ..., diff_method = "uniquely_high_in_one_group")
UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
sessionInfo()
#> R version 4.1.0 (2021-05-18)
#> Platform: x86_64-pc-linux-gnu (64-bit)
#> Running under: CentOS Linux 7 (Core)
#>
#> Matrix products: default
#> BLAS/LAPACK: /usr/lib64/libopenblas-r0.3.3.so
#>
#> 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
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#> [88] compiler_4.1.0 rstudioapi_0.13 filelock_1.0.2
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#> [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
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