Date: 2019-12-25 21:11:50 CET, cola version: 1.3.2
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All available functions which can be applied to this res_list object:
res_list
#> A 'ConsensusPartitionList' object with 24 methods.
#> On a matrix with 21512 rows and 125 columns.
#> Top rows are extracted by 'SD, CV, MAD, ATC' methods.
#> Subgroups are detected by 'hclust, kmeans, skmeans, pam, mclust, NMF' method.
#> Number of partitions are tried for k = 2, 3, 4, 5, 6.
#> Performed in total 30000 partitions by row resampling.
#>
#> Following methods can be applied to this 'ConsensusPartitionList' object:
#> [1] "cola_report" "collect_classes" "collect_plots" "collect_stats"
#> [5] "colnames" "functional_enrichment" "get_anno_col" "get_anno"
#> [9] "get_classes" "get_matrix" "get_membership" "get_stats"
#> [13] "is_best_k" "is_stable_k" "ncol" "nrow"
#> [17] "rownames" "show" "suggest_best_k" "test_to_known_factors"
#> [21] "top_rows_heatmap" "top_rows_overlap"
#>
#> You can get result for a single method by, e.g. object["SD", "hclust"] or object["SD:hclust"]
#> or a subset of methods by object[c("SD", "CV")], c("hclust", "kmeans")]
The call of run_all_consensus_partition_methods() was:
#> run_all_consensus_partition_methods(data = mat, mc.cores = 4, anno = anno)
Dimension of the input matrix:
mat = get_matrix(res_list)
dim(mat)
#> [1] 21512 125
The density distribution for each sample is visualized as in one column in the following heatmap. The clustering is based on the distance which is the Kolmogorov-Smirnov statistic between two distributions.
library(ComplexHeatmap)
densityHeatmap(mat, top_annotation = HeatmapAnnotation(df = get_anno(res_list),
col = get_anno_col(res_list)), ylab = "value", cluster_columns = TRUE, show_column_names = FALSE,
mc.cores = 4)
Folowing table shows the best k (number of partitions) for each combination
of top-value methods and partition methods. Clicking on the method name in
the table goes to the section for a single combination of methods.
The cola vignette explains the definition of the metrics used for determining the best number of partitions.
suggest_best_k(res_list)
| The best k | 1-PAC | Mean silhouette | Concordance | Optional k | ||
|---|---|---|---|---|---|---|
| MAD:kmeans | 2 | 1.000 | 0.986 | 0.992 | ** | |
| MAD:skmeans | 2 | 1.000 | 0.985 | 0.993 | ** | |
| ATC:kmeans | 2 | 1.000 | 0.996 | 0.998 | ** | |
| ATC:NMF | 2 | 1.000 | 0.987 | 0.994 | ** | |
| CV:kmeans | 2 | 1.000 | 0.962 | 0.982 | ** | |
| CV:skmeans | 2 | 0.999 | 0.957 | 0.981 | ** | |
| ATC:skmeans | 3 | 0.985 | 0.923 | 0.958 | ** | 2 |
| ATC:pam | 3 | 0.973 | 0.948 | 0.978 | ** | 2 |
| SD:kmeans | 2 | 0.967 | 0.969 | 0.984 | ** | |
| SD:skmeans | 2 | 0.966 | 0.971 | 0.985 | ** | |
| MAD:NMF | 2 | 0.932 | 0.948 | 0.977 | * | |
| CV:NMF | 2 | 0.915 | 0.937 | 0.973 | * | |
| ATC:mclust | 3 | 0.915 | 0.913 | 0.963 | * | |
| SD:NMF | 2 | 0.886 | 0.932 | 0.971 | ||
| SD:mclust | 6 | 0.833 | 0.769 | 0.894 | ||
| CV:mclust | 6 | 0.827 | 0.797 | 0.904 | ||
| ATC:hclust | 4 | 0.720 | 0.820 | 0.892 | ||
| MAD:mclust | 5 | 0.673 | 0.725 | 0.839 | ||
| SD:pam | 3 | 0.501 | 0.757 | 0.869 | ||
| MAD:pam | 4 | 0.484 | 0.667 | 0.823 | ||
| CV:pam | 3 | 0.311 | 0.725 | 0.823 | ||
| SD:hclust | 5 | 0.306 | 0.589 | 0.761 | ||
| CV:hclust | 4 | 0.185 | 0.601 | 0.771 | ||
| MAD:hclust | 3 | 0.146 | 0.680 | 0.807 |
**: 1-PAC > 0.95, *: 1-PAC > 0.9
Cumulative distribution function curves of consensus matrix for all methods.
collect_plots(res_list, fun = plot_ecdf)
Consensus heatmaps for all methods. (What is a consensus heatmap?)
collect_plots(res_list, k = 2, fun = consensus_heatmap, mc.cores = 4)
collect_plots(res_list, k = 3, fun = consensus_heatmap, mc.cores = 4)
collect_plots(res_list, k = 4, fun = consensus_heatmap, mc.cores = 4)
collect_plots(res_list, k = 5, fun = consensus_heatmap, mc.cores = 4)
collect_plots(res_list, k = 6, fun = consensus_heatmap, mc.cores = 4)
Membership heatmaps for all methods. (What is a membership heatmap?)
collect_plots(res_list, k = 2, fun = membership_heatmap, mc.cores = 4)
collect_plots(res_list, k = 3, fun = membership_heatmap, mc.cores = 4)
collect_plots(res_list, k = 4, fun = membership_heatmap, mc.cores = 4)
collect_plots(res_list, k = 5, fun = membership_heatmap, mc.cores = 4)
collect_plots(res_list, k = 6, fun = membership_heatmap, mc.cores = 4)
Signature heatmaps for all methods. (What is a signature heatmap?)
Note in following heatmaps, rows are scaled.
collect_plots(res_list, k = 2, fun = get_signatures, mc.cores = 4)
collect_plots(res_list, k = 3, fun = get_signatures, mc.cores = 4)
collect_plots(res_list, k = 4, fun = get_signatures, mc.cores = 4)
collect_plots(res_list, k = 5, fun = get_signatures, mc.cores = 4)
collect_plots(res_list, k = 6, fun = get_signatures, mc.cores = 4)
The statistics used for measuring the stability of consensus partitioning. (How are they defined?)
get_stats(res_list, k = 2)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 2 0.886 0.932 0.971 0.5015 0.498 0.498
#> CV:NMF 2 0.915 0.937 0.973 0.5011 0.498 0.498
#> MAD:NMF 2 0.932 0.948 0.977 0.5023 0.497 0.497
#> ATC:NMF 2 1.000 0.987 0.994 0.5041 0.496 0.496
#> SD:skmeans 2 0.966 0.971 0.985 0.5042 0.496 0.496
#> CV:skmeans 2 0.999 0.957 0.981 0.5042 0.496 0.496
#> MAD:skmeans 2 1.000 0.985 0.993 0.5043 0.496 0.496
#> ATC:skmeans 2 1.000 0.994 0.998 0.5043 0.496 0.496
#> SD:mclust 2 0.268 0.782 0.797 0.4485 0.497 0.497
#> CV:mclust 2 0.400 0.849 0.872 0.4437 0.496 0.496
#> MAD:mclust 2 0.423 0.722 0.588 0.3967 0.498 0.498
#> ATC:mclust 2 0.416 0.798 0.882 0.4512 0.567 0.567
#> SD:kmeans 2 0.967 0.969 0.984 0.5039 0.496 0.496
#> CV:kmeans 2 1.000 0.962 0.982 0.5036 0.497 0.497
#> MAD:kmeans 2 1.000 0.986 0.992 0.5043 0.496 0.496
#> ATC:kmeans 2 1.000 0.996 0.998 0.5043 0.496 0.496
#> SD:pam 2 0.567 0.825 0.899 0.4163 0.587 0.587
#> CV:pam 2 0.119 0.469 0.732 0.4013 0.708 0.708
#> MAD:pam 2 0.444 0.799 0.896 0.3856 0.632 0.632
#> ATC:pam 2 1.000 0.975 0.990 0.5014 0.499 0.499
#> SD:hclust 2 0.338 0.854 0.889 0.1795 0.938 0.938
#> CV:hclust 2 0.908 0.917 0.946 0.0977 0.953 0.953
#> MAD:hclust 2 0.211 0.646 0.706 0.3334 0.550 0.550
#> ATC:hclust 2 0.496 0.966 0.857 0.3689 0.496 0.496
get_stats(res_list, k = 3)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 3 0.403 0.543 0.753 0.318 0.741 0.525
#> CV:NMF 3 0.381 0.508 0.702 0.307 0.806 0.632
#> MAD:NMF 3 0.452 0.538 0.734 0.322 0.727 0.505
#> ATC:NMF 3 0.593 0.639 0.818 0.280 0.818 0.646
#> SD:skmeans 3 0.442 0.608 0.720 0.298 0.834 0.673
#> CV:skmeans 3 0.409 0.523 0.696 0.299 0.842 0.691
#> MAD:skmeans 3 0.551 0.763 0.835 0.295 0.835 0.676
#> ATC:skmeans 3 0.985 0.923 0.958 0.261 0.833 0.674
#> SD:mclust 3 0.286 0.623 0.651 0.331 0.668 0.427
#> CV:mclust 3 0.288 0.647 0.739 0.301 0.858 0.730
#> MAD:mclust 3 0.347 0.649 0.762 0.492 0.817 0.663
#> ATC:mclust 3 0.915 0.913 0.963 0.480 0.731 0.539
#> SD:kmeans 3 0.583 0.601 0.779 0.247 0.886 0.777
#> CV:kmeans 3 0.606 0.649 0.772 0.239 0.866 0.734
#> MAD:kmeans 3 0.545 0.460 0.705 0.262 0.925 0.850
#> ATC:kmeans 3 0.693 0.654 0.796 0.244 0.945 0.890
#> SD:pam 3 0.501 0.757 0.869 0.504 0.764 0.610
#> CV:pam 3 0.311 0.725 0.823 0.480 0.643 0.521
#> MAD:pam 3 0.273 0.664 0.800 0.546 0.761 0.625
#> ATC:pam 3 0.973 0.948 0.978 0.279 0.856 0.714
#> SD:hclust 3 0.194 0.569 0.756 1.132 0.656 0.633
#> CV:hclust 3 0.297 0.739 0.839 1.634 0.984 0.984
#> MAD:hclust 3 0.146 0.680 0.807 0.486 0.827 0.715
#> ATC:hclust 3 0.949 0.953 0.982 0.404 0.992 0.984
get_stats(res_list, k = 4)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 4 0.441 0.463 0.700 0.1205 0.847 0.584
#> CV:NMF 4 0.425 0.428 0.660 0.1283 0.756 0.429
#> MAD:NMF 4 0.452 0.459 0.721 0.1168 0.803 0.499
#> ATC:NMF 4 0.458 0.385 0.619 0.1417 0.791 0.496
#> SD:skmeans 4 0.428 0.457 0.695 0.1347 0.827 0.554
#> CV:skmeans 4 0.404 0.410 0.639 0.1345 0.779 0.469
#> MAD:skmeans 4 0.465 0.544 0.725 0.1365 0.875 0.658
#> ATC:skmeans 4 0.728 0.689 0.864 0.1532 0.866 0.646
#> SD:mclust 4 0.423 0.583 0.693 0.1467 0.814 0.521
#> CV:mclust 4 0.486 0.517 0.710 0.1625 0.863 0.688
#> MAD:mclust 4 0.510 0.509 0.689 0.1417 0.839 0.613
#> ATC:mclust 4 0.767 0.824 0.902 0.0618 0.950 0.854
#> SD:kmeans 4 0.617 0.651 0.767 0.1259 0.835 0.628
#> CV:kmeans 4 0.607 0.693 0.781 0.1213 0.904 0.760
#> MAD:kmeans 4 0.560 0.549 0.717 0.1380 0.729 0.432
#> ATC:kmeans 4 0.604 0.644 0.761 0.1361 0.785 0.534
#> SD:pam 4 0.458 0.530 0.779 0.1362 0.917 0.792
#> CV:pam 4 0.371 0.457 0.745 0.1425 0.960 0.906
#> MAD:pam 4 0.484 0.667 0.823 0.1720 0.879 0.710
#> ATC:pam 4 0.885 0.885 0.937 0.1486 0.902 0.730
#> SD:hclust 4 0.157 0.578 0.762 0.3312 0.860 0.775
#> CV:hclust 4 0.185 0.601 0.771 0.7043 0.640 0.616
#> MAD:hclust 4 0.350 0.478 0.759 0.2021 0.966 0.930
#> ATC:hclust 4 0.720 0.820 0.892 0.1445 0.965 0.927
get_stats(res_list, k = 5)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 5 0.460 0.376 0.620 0.0668 0.839 0.474
#> CV:NMF 5 0.455 0.349 0.595 0.0694 0.842 0.475
#> MAD:NMF 5 0.493 0.409 0.606 0.0680 0.875 0.570
#> ATC:NMF 5 0.526 0.484 0.706 0.0593 0.843 0.508
#> SD:skmeans 5 0.463 0.424 0.618 0.0675 0.907 0.670
#> CV:skmeans 5 0.405 0.358 0.571 0.0652 0.933 0.747
#> MAD:skmeans 5 0.484 0.474 0.658 0.0649 0.898 0.632
#> ATC:skmeans 5 0.728 0.733 0.841 0.0646 0.908 0.676
#> SD:mclust 5 0.788 0.768 0.878 0.1382 0.939 0.775
#> CV:mclust 5 0.769 0.793 0.894 0.1527 0.782 0.427
#> MAD:mclust 5 0.673 0.725 0.839 0.1329 0.865 0.563
#> ATC:mclust 5 0.850 0.875 0.923 0.0666 0.901 0.695
#> SD:kmeans 5 0.626 0.733 0.789 0.0821 0.843 0.536
#> CV:kmeans 5 0.604 0.606 0.737 0.0798 0.887 0.664
#> MAD:kmeans 5 0.632 0.730 0.792 0.0687 0.893 0.623
#> ATC:kmeans 5 0.636 0.671 0.758 0.0793 0.876 0.588
#> SD:pam 5 0.482 0.478 0.736 0.0496 0.933 0.798
#> CV:pam 5 0.390 0.430 0.721 0.0303 0.948 0.872
#> MAD:pam 5 0.552 0.621 0.811 0.0679 0.934 0.795
#> ATC:pam 5 0.783 0.659 0.844 0.0617 0.979 0.923
#> SD:hclust 5 0.306 0.589 0.761 0.1596 0.901 0.810
#> CV:hclust 5 0.149 0.566 0.752 0.1915 0.930 0.880
#> MAD:hclust 5 0.379 0.597 0.716 0.0851 0.861 0.706
#> ATC:hclust 5 0.659 0.778 0.865 0.1262 0.899 0.779
get_stats(res_list, k = 6)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 6 0.514 0.377 0.610 0.0438 0.912 0.615
#> CV:NMF 6 0.492 0.302 0.535 0.0410 0.870 0.495
#> MAD:NMF 6 0.533 0.369 0.616 0.0430 0.901 0.587
#> ATC:NMF 6 0.540 0.413 0.640 0.0368 0.905 0.648
#> SD:skmeans 6 0.488 0.317 0.568 0.0401 0.917 0.665
#> CV:skmeans 6 0.450 0.289 0.496 0.0414 0.911 0.635
#> MAD:skmeans 6 0.513 0.386 0.608 0.0402 0.966 0.839
#> ATC:skmeans 6 0.732 0.642 0.801 0.0390 0.941 0.740
#> SD:mclust 6 0.833 0.769 0.894 0.0437 0.927 0.695
#> CV:mclust 6 0.827 0.797 0.904 0.0515 0.952 0.786
#> MAD:mclust 6 0.818 0.766 0.886 0.0699 0.935 0.716
#> ATC:mclust 6 0.892 0.887 0.928 0.0841 0.924 0.694
#> SD:kmeans 6 0.648 0.695 0.776 0.0448 0.977 0.894
#> CV:kmeans 6 0.635 0.658 0.745 0.0474 0.911 0.663
#> MAD:kmeans 6 0.694 0.666 0.752 0.0503 0.975 0.887
#> ATC:kmeans 6 0.700 0.633 0.765 0.0485 0.946 0.761
#> SD:pam 6 0.498 0.471 0.724 0.0179 0.975 0.910
#> CV:pam 6 0.403 0.419 0.719 0.0127 0.999 0.998
#> MAD:pam 6 0.563 0.529 0.779 0.0321 0.990 0.963
#> ATC:pam 6 0.815 0.775 0.888 0.0554 0.900 0.620
#> SD:hclust 6 0.354 0.594 0.775 0.0719 0.886 0.753
#> CV:hclust 6 0.175 0.545 0.736 0.1012 0.904 0.821
#> MAD:hclust 6 0.385 0.540 0.721 0.0776 0.981 0.948
#> ATC:hclust 6 0.634 0.695 0.796 0.0653 0.976 0.934
Following heatmap plots the partition for each combination of methods and the lightness correspond to the silhouette scores for samples in each method. On top the consensus subgroup is inferred from all methods by taking the mean silhouette scores as weight.
collect_stats(res_list, k = 2)
collect_stats(res_list, k = 3)
collect_stats(res_list, k = 4)
collect_stats(res_list, k = 5)
collect_stats(res_list, k = 6)
Collect partitions from all methods:
collect_classes(res_list, k = 2)
collect_classes(res_list, k = 3)
collect_classes(res_list, k = 4)
collect_classes(res_list, k = 5)
collect_classes(res_list, k = 6)
Overlap of top rows from different top-row methods:
top_rows_overlap(res_list, top_n = 1000, method = "euler")
top_rows_overlap(res_list, top_n = 2000, method = "euler")
top_rows_overlap(res_list, top_n = 3000, method = "euler")
top_rows_overlap(res_list, top_n = 4000, method = "euler")
top_rows_overlap(res_list, top_n = 5000, method = "euler")
Also visualize the correspondance of rankings between different top-row methods:
top_rows_overlap(res_list, top_n = 1000, method = "correspondance")
top_rows_overlap(res_list, top_n = 2000, method = "correspondance")
top_rows_overlap(res_list, top_n = 3000, method = "correspondance")
top_rows_overlap(res_list, top_n = 4000, method = "correspondance")
top_rows_overlap(res_list, top_n = 5000, method = "correspondance")
Heatmaps of the top rows:
top_rows_heatmap(res_list, top_n = 1000)
top_rows_heatmap(res_list, top_n = 2000)
top_rows_heatmap(res_list, top_n = 3000)
top_rows_heatmap(res_list, top_n = 4000)
top_rows_heatmap(res_list, top_n = 5000)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res_list, k = 2)
#> n time(p) gender(p) k
#> SD:NMF 122 0.187 0.983 2
#> CV:NMF 122 0.187 0.983 2
#> MAD:NMF 123 0.204 0.974 2
#> ATC:NMF 125 0.294 0.840 2
#> SD:skmeans 125 0.294 0.864 2
#> CV:skmeans 123 0.351 0.717 2
#> MAD:skmeans 125 0.294 0.864 2
#> ATC:skmeans 125 0.294 0.840 2
#> SD:mclust 124 0.251 0.918 2
#> CV:mclust 122 0.221 0.813 2
#> MAD:mclust 105 0.152 0.888 2
#> ATC:mclust 118 0.757 0.493 2
#> SD:kmeans 125 0.294 0.864 2
#> CV:kmeans 124 0.305 0.767 2
#> MAD:kmeans 125 0.294 0.864 2
#> ATC:kmeans 125 0.294 0.840 2
#> SD:pam 117 0.187 0.674 2
#> CV:pam 74 0.296 0.849 2
#> MAD:pam 114 0.475 0.713 2
#> ATC:pam 123 0.187 0.611 2
#> SD:hclust 123 0.017 0.625 2
#> CV:hclust 123 0.230 1.000 2
#> MAD:hclust 105 0.061 0.284 2
#> ATC:hclust 125 0.410 0.586 2
test_to_known_factors(res_list, k = 3)
#> n time(p) gender(p) k
#> SD:NMF 88 0.2971 0.13500 3
#> CV:NMF 79 0.0180 0.61484 3
#> MAD:NMF 84 0.4218 0.21684 3
#> ATC:NMF 99 0.2127 0.88886 3
#> SD:skmeans 103 0.0949 0.52546 3
#> CV:skmeans 81 0.2386 0.44805 3
#> MAD:skmeans 116 0.1462 0.42124 3
#> ATC:skmeans 121 0.3702 0.20330 3
#> SD:mclust 103 0.3108 0.15157 3
#> CV:mclust 106 0.4228 0.10739 3
#> MAD:mclust 112 0.3519 0.06390 3
#> ATC:mclust 120 0.9210 0.56544 3
#> SD:kmeans 89 0.8149 0.26592 3
#> CV:kmeans 85 0.4249 0.17186 3
#> MAD:kmeans 66 0.0459 0.70560 3
#> ATC:kmeans 111 0.0113 0.50252 3
#> SD:pam 111 0.8339 0.00140 3
#> CV:pam 112 0.9423 0.00227 3
#> MAD:pam 108 0.8769 0.01078 3
#> ATC:pam 122 0.2339 0.26808 3
#> SD:hclust 91 0.0746 0.16548 3
#> CV:hclust 117 0.0807 1.00000 3
#> MAD:hclust 105 0.1015 0.40946 3
#> ATC:hclust 124 0.3527 0.50133 3
test_to_known_factors(res_list, k = 4)
#> n time(p) gender(p) k
#> SD:NMF 72 0.4911 0.594910 4
#> CV:NMF 69 0.5889 0.660010 4
#> MAD:NMF 74 0.5090 0.882721 4
#> ATC:NMF 47 0.4064 0.878403 4
#> SD:skmeans 64 0.2892 0.134287 4
#> CV:skmeans 43 0.5188 0.393641 4
#> MAD:skmeans 88 0.1080 0.298711 4
#> ATC:skmeans 104 0.3257 0.583588 4
#> SD:mclust 97 0.5113 0.098201 4
#> CV:mclust 85 0.7019 0.101248 4
#> MAD:mclust 85 0.8486 0.089913 4
#> ATC:mclust 120 0.2480 0.635177 4
#> SD:kmeans 110 0.1713 0.117765 4
#> CV:kmeans 110 0.4348 0.125246 4
#> MAD:kmeans 77 0.1908 0.193359 4
#> ATC:kmeans 96 0.2780 0.707428 4
#> SD:pam 83 0.8489 0.000344 4
#> CV:pam 72 0.9392 0.000563 4
#> MAD:pam 102 0.5526 0.083092 4
#> ATC:pam 118 0.0881 0.440116 4
#> SD:hclust 91 0.1590 0.010107 4
#> CV:hclust 97 0.2204 0.318291 4
#> MAD:hclust 80 0.3296 0.214152 4
#> ATC:hclust 121 0.4526 0.209832 4
test_to_known_factors(res_list, k = 5)
#> n time(p) gender(p) k
#> SD:NMF 38 0.6711 0.006390 5
#> CV:NMF 31 0.9297 0.020622 5
#> MAD:NMF 61 0.3619 0.106234 5
#> ATC:NMF 73 0.4175 0.754588 5
#> SD:skmeans 57 0.4632 0.080870 5
#> CV:skmeans 31 0.9623 0.389385 5
#> MAD:skmeans 67 0.1326 0.468893 5
#> ATC:skmeans 115 0.4521 0.600715 5
#> SD:mclust 112 0.1152 0.240353 5
#> CV:mclust 113 0.4502 0.220560 5
#> MAD:mclust 113 0.1119 0.670623 5
#> ATC:mclust 119 0.1349 0.643813 5
#> SD:kmeans 115 0.0948 0.202733 5
#> CV:kmeans 101 0.6148 0.178639 5
#> MAD:kmeans 113 0.1591 0.337730 5
#> ATC:kmeans 110 0.3298 0.543516 5
#> SD:pam 68 0.6542 0.000115 5
#> CV:pam 65 0.9676 0.000138 5
#> MAD:pam 95 0.4565 0.050487 5
#> ATC:pam 99 0.1276 0.838360 5
#> SD:hclust 93 0.2505 0.011882 5
#> CV:hclust 95 0.4349 0.230826 5
#> MAD:hclust 100 0.5770 0.006428 5
#> ATC:hclust 118 0.4695 0.127073 5
test_to_known_factors(res_list, k = 6)
#> n time(p) gender(p) k
#> SD:NMF 50 0.768 0.000246 6
#> CV:NMF 28 0.344 0.085625 6
#> MAD:NMF 43 0.813 0.002708 6
#> ATC:NMF 59 0.315 0.374447 6
#> SD:skmeans 28 0.697 0.243273 6
#> CV:skmeans 18 0.581 0.409925 6
#> MAD:skmeans 43 0.392 0.621304 6
#> ATC:skmeans 92 0.714 0.351397 6
#> SD:mclust 112 0.298 0.156095 6
#> CV:mclust 116 0.382 0.137336 6
#> MAD:mclust 112 0.114 0.853829 6
#> ATC:mclust 121 0.379 0.538141 6
#> SD:kmeans 109 0.032 0.101524 6
#> CV:kmeans 112 0.469 0.037405 6
#> MAD:kmeans 106 0.198 0.616267 6
#> ATC:kmeans 89 0.364 0.608636 6
#> SD:pam 73 0.436 0.000708 6
#> CV:pam 65 0.968 0.000138 6
#> MAD:pam 87 0.377 0.045498 6
#> ATC:pam 110 0.376 0.348822 6
#> SD:hclust 81 0.441 0.097866 6
#> CV:hclust 91 0.310 0.394331 6
#> MAD:hclust 92 0.854 0.008004 6
#> ATC:hclust 112 0.568 0.152476 6
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["SD", "hclust"]
# you can also extract it by
# res = res_list["SD:hclust"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 21512 rows and 125 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'hclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 5.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
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, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.338 0.854 0.889 0.1795 0.938 0.938
#> 3 3 0.194 0.569 0.756 1.1320 0.656 0.633
#> 4 4 0.157 0.578 0.762 0.3312 0.860 0.775
#> 5 5 0.306 0.589 0.761 0.1596 0.901 0.810
#> 6 6 0.354 0.594 0.775 0.0719 0.886 0.753
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 5
Following shows 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 class
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
#> GSM601872 1 0.7453 0.833 0.788 0.212
#> GSM601882 1 0.7528 0.827 0.784 0.216
#> GSM601887 1 0.2948 0.889 0.948 0.052
#> GSM601892 1 0.2603 0.888 0.956 0.044
#> GSM601897 1 0.5294 0.867 0.880 0.120
#> GSM601902 1 0.6887 0.848 0.816 0.184
#> GSM601912 1 0.2778 0.887 0.952 0.048
#> GSM601927 1 0.0938 0.883 0.988 0.012
#> GSM601932 1 0.6623 0.855 0.828 0.172
#> GSM601937 1 0.9491 0.273 0.632 0.368
#> GSM601942 2 0.9608 0.480 0.384 0.616
#> GSM601947 1 0.5946 0.869 0.856 0.144
#> GSM601957 1 0.1414 0.884 0.980 0.020
#> GSM601972 1 0.6531 0.858 0.832 0.168
#> GSM601977 1 0.7453 0.829 0.788 0.212
#> GSM601987 1 0.7453 0.829 0.788 0.212
#> GSM601877 1 0.0376 0.884 0.996 0.004
#> GSM601907 1 0.7674 0.815 0.776 0.224
#> GSM601917 1 0.5408 0.875 0.876 0.124
#> GSM601922 1 0.5519 0.873 0.872 0.128
#> GSM601952 1 0.5737 0.876 0.864 0.136
#> GSM601962 1 0.1843 0.877 0.972 0.028
#> GSM601967 1 0.1414 0.884 0.980 0.020
#> GSM601982 1 0.4562 0.887 0.904 0.096
#> GSM601992 1 0.7815 0.793 0.768 0.232
#> GSM601873 1 0.7883 0.808 0.764 0.236
#> GSM601883 1 0.7453 0.829 0.788 0.212
#> GSM601888 1 0.2948 0.889 0.948 0.052
#> GSM601893 1 0.2778 0.888 0.952 0.048
#> GSM601898 1 0.1414 0.880 0.980 0.020
#> GSM601903 1 0.6887 0.848 0.816 0.184
#> GSM601913 1 0.1633 0.879 0.976 0.024
#> GSM601928 1 0.0938 0.883 0.988 0.012
#> GSM601933 1 0.7453 0.825 0.788 0.212
#> GSM601938 1 0.7376 0.831 0.792 0.208
#> GSM601943 1 0.7950 0.810 0.760 0.240
#> GSM601948 1 0.4562 0.885 0.904 0.096
#> GSM601958 1 0.1414 0.880 0.980 0.020
#> GSM601973 1 0.6712 0.852 0.824 0.176
#> GSM601978 1 0.7815 0.813 0.768 0.232
#> GSM601988 1 0.6531 0.797 0.832 0.168
#> GSM601878 1 0.0376 0.884 0.996 0.004
#> GSM601908 1 0.7745 0.815 0.772 0.228
#> GSM601918 1 0.5737 0.870 0.864 0.136
#> GSM601923 1 0.0376 0.884 0.996 0.004
#> GSM601953 1 0.7299 0.828 0.796 0.204
#> GSM601963 1 0.2043 0.875 0.968 0.032
#> GSM601968 1 0.1633 0.885 0.976 0.024
#> GSM601983 1 0.1633 0.887 0.976 0.024
#> GSM601993 1 0.7950 0.784 0.760 0.240
#> GSM601874 1 0.7453 0.824 0.788 0.212
#> GSM601884 1 0.7528 0.827 0.784 0.216
#> GSM601889 1 0.1633 0.882 0.976 0.024
#> GSM601894 1 0.1633 0.883 0.976 0.024
#> GSM601899 1 0.3114 0.888 0.944 0.056
#> GSM601904 1 0.5059 0.878 0.888 0.112
#> GSM601914 1 0.2236 0.877 0.964 0.036
#> GSM601929 1 0.1843 0.890 0.972 0.028
#> GSM601934 1 0.7453 0.825 0.788 0.212
#> GSM601939 1 0.1414 0.881 0.980 0.020
#> GSM601944 1 0.7950 0.812 0.760 0.240
#> GSM601949 1 0.4690 0.887 0.900 0.100
#> GSM601959 1 0.1414 0.880 0.980 0.020
#> GSM601974 1 0.5059 0.875 0.888 0.112
#> GSM601979 1 0.7674 0.820 0.776 0.224
#> GSM601989 1 0.1633 0.882 0.976 0.024
#> GSM601879 1 0.0376 0.884 0.996 0.004
#> GSM601909 1 0.2236 0.884 0.964 0.036
#> GSM601919 1 0.5737 0.870 0.864 0.136
#> GSM601924 1 0.0376 0.884 0.996 0.004
#> GSM601954 1 0.5519 0.877 0.872 0.128
#> GSM601964 1 0.2043 0.875 0.968 0.032
#> GSM601969 1 0.3114 0.887 0.944 0.056
#> GSM601984 1 0.1633 0.889 0.976 0.024
#> GSM601994 1 0.7883 0.792 0.764 0.236
#> GSM601875 1 0.7528 0.824 0.784 0.216
#> GSM601885 1 0.7453 0.829 0.788 0.212
#> GSM601890 1 0.3114 0.888 0.944 0.056
#> GSM601895 1 0.2423 0.879 0.960 0.040
#> GSM601900 1 0.2603 0.881 0.956 0.044
#> GSM601905 1 0.5519 0.874 0.872 0.128
#> GSM601915 1 0.1843 0.876 0.972 0.028
#> GSM601930 1 0.0938 0.883 0.988 0.012
#> GSM601935 1 0.6623 0.764 0.828 0.172
#> GSM601940 1 0.1414 0.881 0.980 0.020
#> GSM601945 1 0.7883 0.809 0.764 0.236
#> GSM601950 1 0.1843 0.887 0.972 0.028
#> GSM601960 1 0.2778 0.868 0.952 0.048
#> GSM601975 1 0.6438 0.860 0.836 0.164
#> GSM601980 2 0.6623 0.838 0.172 0.828
#> GSM601990 1 0.2043 0.874 0.968 0.032
#> GSM601880 1 0.0672 0.885 0.992 0.008
#> GSM601910 1 0.2236 0.885 0.964 0.036
#> GSM601920 1 0.5629 0.873 0.868 0.132
#> GSM601925 1 0.0672 0.885 0.992 0.008
#> GSM601955 2 0.7299 0.851 0.204 0.796
#> GSM601965 1 0.2603 0.890 0.956 0.044
#> GSM601970 1 0.1633 0.884 0.976 0.024
#> GSM601985 1 0.0672 0.882 0.992 0.008
#> GSM601995 2 0.7950 0.841 0.240 0.760
#> GSM601876 1 0.1843 0.883 0.972 0.028
#> GSM601886 1 0.4815 0.884 0.896 0.104
#> GSM601891 1 0.3274 0.890 0.940 0.060
#> GSM601896 1 0.1633 0.882 0.976 0.024
#> GSM601901 1 0.6973 0.847 0.812 0.188
#> GSM601906 1 0.5059 0.878 0.888 0.112
#> GSM601916 1 0.6438 0.859 0.836 0.164
#> GSM601931 1 0.1414 0.882 0.980 0.020
#> GSM601936 1 0.6801 0.771 0.820 0.180
#> GSM601941 1 0.6712 0.852 0.824 0.176
#> GSM601946 1 0.1414 0.882 0.980 0.020
#> GSM601951 1 0.5294 0.880 0.880 0.120
#> GSM601961 1 0.6973 0.850 0.812 0.188
#> GSM601976 1 0.6343 0.862 0.840 0.160
#> GSM601981 1 0.7056 0.843 0.808 0.192
#> GSM601991 1 0.2778 0.872 0.952 0.048
#> GSM601881 1 0.0376 0.884 0.996 0.004
#> GSM601911 1 0.3274 0.890 0.940 0.060
#> GSM601921 1 0.5519 0.874 0.872 0.128
#> GSM601926 1 0.0376 0.884 0.996 0.004
#> GSM601956 1 0.7219 0.834 0.800 0.200
#> GSM601966 1 0.6801 0.850 0.820 0.180
#> GSM601971 1 0.2236 0.886 0.964 0.036
#> GSM601986 1 0.3114 0.890 0.944 0.056
#> GSM601996 1 0.7674 0.801 0.776 0.224
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM601872 2 0.7377 0.74950 0.452 0.516 0.032
#> GSM601882 2 0.6587 0.77972 0.424 0.568 0.008
#> GSM601887 1 0.3618 0.68112 0.884 0.104 0.012
#> GSM601892 1 0.3043 0.69478 0.908 0.084 0.008
#> GSM601897 1 0.5730 0.59229 0.796 0.144 0.060
#> GSM601902 1 0.7074 -0.26752 0.500 0.480 0.020
#> GSM601912 1 0.2772 0.72042 0.916 0.080 0.004
#> GSM601927 1 0.1860 0.73283 0.948 0.052 0.000
#> GSM601932 1 0.6925 -0.16527 0.532 0.452 0.016
#> GSM601937 2 0.9702 -0.39493 0.320 0.444 0.236
#> GSM601942 3 0.8716 0.45053 0.172 0.240 0.588
#> GSM601947 1 0.6600 -0.00392 0.604 0.384 0.012
#> GSM601957 1 0.1399 0.73560 0.968 0.028 0.004
#> GSM601972 1 0.6962 -0.05579 0.568 0.412 0.020
#> GSM601977 2 0.7004 0.78702 0.428 0.552 0.020
#> GSM601987 2 0.6617 0.78277 0.436 0.556 0.008
#> GSM601877 1 0.1860 0.73177 0.948 0.052 0.000
#> GSM601907 2 0.6577 0.78661 0.420 0.572 0.008
#> GSM601917 1 0.6161 0.46962 0.708 0.272 0.020
#> GSM601922 1 0.6501 0.35846 0.664 0.316 0.020
#> GSM601952 1 0.6684 0.33894 0.676 0.292 0.032
#> GSM601962 1 0.1337 0.72910 0.972 0.012 0.016
#> GSM601967 1 0.1711 0.73684 0.960 0.032 0.008
#> GSM601982 1 0.6027 0.34574 0.712 0.272 0.016
#> GSM601992 2 0.6255 0.51274 0.300 0.684 0.016
#> GSM601873 2 0.7747 0.73866 0.404 0.544 0.052
#> GSM601883 2 0.6598 0.77914 0.428 0.564 0.008
#> GSM601888 1 0.3618 0.68112 0.884 0.104 0.012
#> GSM601893 1 0.2774 0.70483 0.920 0.072 0.008
#> GSM601898 1 0.0661 0.73135 0.988 0.004 0.008
#> GSM601903 1 0.7074 -0.26752 0.500 0.480 0.020
#> GSM601913 1 0.1015 0.72855 0.980 0.008 0.012
#> GSM601928 1 0.1860 0.73283 0.948 0.052 0.000
#> GSM601933 2 0.6608 0.76500 0.432 0.560 0.008
#> GSM601938 2 0.6608 0.76967 0.432 0.560 0.008
#> GSM601943 2 0.7451 0.75548 0.396 0.564 0.040
#> GSM601948 1 0.5692 0.44173 0.724 0.268 0.008
#> GSM601958 1 0.0848 0.73018 0.984 0.008 0.008
#> GSM601973 1 0.7069 -0.25412 0.508 0.472 0.020
#> GSM601978 2 0.6910 0.78448 0.396 0.584 0.020
#> GSM601988 1 0.7368 0.18198 0.604 0.352 0.044
#> GSM601878 1 0.1860 0.73177 0.948 0.052 0.000
#> GSM601908 2 0.6553 0.78731 0.412 0.580 0.008
#> GSM601918 1 0.6629 0.19100 0.624 0.360 0.016
#> GSM601923 1 0.1860 0.73177 0.948 0.052 0.000
#> GSM601953 2 0.6608 0.77843 0.432 0.560 0.008
#> GSM601963 1 0.1315 0.72448 0.972 0.008 0.020
#> GSM601968 1 0.1999 0.73689 0.952 0.036 0.012
#> GSM601983 1 0.2173 0.73427 0.944 0.048 0.008
#> GSM601993 2 0.6416 0.46146 0.304 0.676 0.020
#> GSM601874 2 0.6398 0.78931 0.416 0.580 0.004
#> GSM601884 2 0.6587 0.77972 0.424 0.568 0.008
#> GSM601889 1 0.0848 0.73500 0.984 0.008 0.008
#> GSM601894 1 0.1015 0.73282 0.980 0.012 0.008
#> GSM601899 1 0.3845 0.66672 0.872 0.116 0.012
#> GSM601904 1 0.5992 0.46955 0.716 0.268 0.016
#> GSM601914 1 0.1482 0.72590 0.968 0.012 0.020
#> GSM601929 1 0.2796 0.72517 0.908 0.092 0.000
#> GSM601934 2 0.6617 0.76427 0.436 0.556 0.008
#> GSM601939 1 0.0983 0.73392 0.980 0.016 0.004
#> GSM601944 2 0.8457 0.62704 0.396 0.512 0.092
#> GSM601949 1 0.5580 0.45814 0.736 0.256 0.008
#> GSM601959 1 0.0848 0.73313 0.984 0.008 0.008
#> GSM601974 1 0.6109 0.55894 0.760 0.192 0.048
#> GSM601979 2 0.6661 0.78618 0.400 0.588 0.012
#> GSM601989 1 0.1585 0.73446 0.964 0.028 0.008
#> GSM601879 1 0.1964 0.73127 0.944 0.056 0.000
#> GSM601909 1 0.2414 0.72813 0.940 0.040 0.020
#> GSM601919 1 0.6629 0.19100 0.624 0.360 0.016
#> GSM601924 1 0.1860 0.73177 0.948 0.052 0.000
#> GSM601954 1 0.6570 0.34927 0.680 0.292 0.028
#> GSM601964 1 0.1315 0.72448 0.972 0.008 0.020
#> GSM601969 1 0.3415 0.71033 0.900 0.080 0.020
#> GSM601984 1 0.3116 0.70441 0.892 0.108 0.000
#> GSM601994 2 0.6224 0.49520 0.296 0.688 0.016
#> GSM601875 2 0.6235 0.77765 0.436 0.564 0.000
#> GSM601885 2 0.6598 0.77914 0.428 0.564 0.008
#> GSM601890 1 0.3129 0.70318 0.904 0.088 0.008
#> GSM601895 1 0.1919 0.72546 0.956 0.020 0.024
#> GSM601900 1 0.2176 0.72806 0.948 0.032 0.020
#> GSM601905 1 0.6161 0.43373 0.696 0.288 0.016
#> GSM601915 1 0.1129 0.72551 0.976 0.004 0.020
#> GSM601930 1 0.1860 0.73283 0.948 0.052 0.000
#> GSM601935 1 0.7101 0.41659 0.704 0.216 0.080
#> GSM601940 1 0.0983 0.73392 0.980 0.016 0.004
#> GSM601945 2 0.7222 0.76003 0.388 0.580 0.032
#> GSM601950 1 0.2496 0.73051 0.928 0.068 0.004
#> GSM601960 1 0.2031 0.72016 0.952 0.016 0.032
#> GSM601975 1 0.6994 -0.05479 0.556 0.424 0.020
#> GSM601980 3 0.2550 0.78872 0.056 0.012 0.932
#> GSM601990 1 0.1315 0.72360 0.972 0.008 0.020
#> GSM601880 1 0.1964 0.73117 0.944 0.056 0.000
#> GSM601910 1 0.2116 0.72759 0.948 0.040 0.012
#> GSM601920 1 0.6294 0.43369 0.692 0.288 0.020
#> GSM601925 1 0.1964 0.73117 0.944 0.056 0.000
#> GSM601955 3 0.3587 0.79950 0.088 0.020 0.892
#> GSM601965 1 0.3715 0.68675 0.868 0.128 0.004
#> GSM601970 1 0.1636 0.73720 0.964 0.020 0.016
#> GSM601985 1 0.0592 0.73412 0.988 0.012 0.000
#> GSM601995 3 0.7558 0.73099 0.188 0.124 0.688
#> GSM601876 1 0.1525 0.73553 0.964 0.032 0.004
#> GSM601886 1 0.5574 0.60252 0.784 0.184 0.032
#> GSM601891 1 0.3532 0.68245 0.884 0.108 0.008
#> GSM601896 1 0.1585 0.73449 0.964 0.028 0.008
#> GSM601901 2 0.6816 0.67225 0.472 0.516 0.012
#> GSM601906 1 0.5956 0.48209 0.720 0.264 0.016
#> GSM601916 1 0.7004 -0.14621 0.552 0.428 0.020
#> GSM601931 1 0.1989 0.73311 0.948 0.048 0.004
#> GSM601936 1 0.7509 0.26398 0.636 0.300 0.064
#> GSM601941 1 0.7072 -0.25930 0.504 0.476 0.020
#> GSM601946 1 0.1399 0.73539 0.968 0.028 0.004
#> GSM601951 1 0.6062 0.39791 0.708 0.276 0.016
#> GSM601961 1 0.6678 -0.60689 0.512 0.480 0.008
#> GSM601976 1 0.6769 0.06854 0.592 0.392 0.016
#> GSM601981 2 0.7121 0.73139 0.428 0.548 0.024
#> GSM601991 1 0.2187 0.72326 0.948 0.028 0.024
#> GSM601881 1 0.1860 0.73177 0.948 0.052 0.000
#> GSM601911 1 0.4682 0.59633 0.804 0.192 0.004
#> GSM601921 1 0.6262 0.44093 0.696 0.284 0.020
#> GSM601926 1 0.1860 0.73177 0.948 0.052 0.000
#> GSM601956 2 0.6763 0.77621 0.436 0.552 0.012
#> GSM601966 1 0.6948 -0.34529 0.512 0.472 0.016
#> GSM601971 1 0.2680 0.72634 0.924 0.068 0.008
#> GSM601986 1 0.4399 0.60830 0.812 0.188 0.000
#> GSM601996 2 0.6369 0.50910 0.316 0.668 0.016
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM601872 2 0.5531 0.5924 0.228 0.716 0.012 0.044
#> GSM601882 2 0.4136 0.6221 0.196 0.788 0.000 0.016
#> GSM601887 1 0.3626 0.7252 0.844 0.136 0.004 0.016
#> GSM601892 1 0.2805 0.7541 0.888 0.100 0.000 0.012
#> GSM601897 1 0.5769 0.6133 0.736 0.180 0.048 0.036
#> GSM601902 2 0.7726 0.0579 0.296 0.444 0.000 0.260
#> GSM601912 1 0.2778 0.7746 0.900 0.080 0.004 0.016
#> GSM601927 1 0.2408 0.7824 0.920 0.044 0.000 0.036
#> GSM601932 2 0.7704 0.0912 0.336 0.432 0.000 0.232
#> GSM601937 4 0.8976 -0.2457 0.180 0.132 0.196 0.492
#> GSM601942 3 0.7186 0.2972 0.084 0.304 0.580 0.032
#> GSM601947 1 0.7374 -0.1509 0.456 0.380 0.000 0.164
#> GSM601957 1 0.1182 0.7892 0.968 0.016 0.000 0.016
#> GSM601972 1 0.7602 -0.1919 0.420 0.380 0.000 0.200
#> GSM601977 2 0.4857 0.6187 0.192 0.764 0.004 0.040
#> GSM601987 2 0.4599 0.6245 0.212 0.760 0.000 0.028
#> GSM601877 1 0.2411 0.7813 0.920 0.040 0.000 0.040
#> GSM601907 2 0.4857 0.6174 0.176 0.772 0.004 0.048
#> GSM601917 1 0.7119 0.4368 0.584 0.200 0.004 0.212
#> GSM601922 1 0.7459 0.3006 0.532 0.252 0.004 0.212
#> GSM601952 1 0.7661 0.1783 0.508 0.284 0.008 0.200
#> GSM601962 1 0.1486 0.7865 0.960 0.008 0.008 0.024
#> GSM601967 1 0.1617 0.7903 0.956 0.024 0.008 0.012
#> GSM601982 1 0.6173 0.2598 0.604 0.340 0.008 0.048
#> GSM601992 4 0.7221 0.6553 0.140 0.428 0.000 0.432
#> GSM601873 2 0.5675 0.5222 0.172 0.744 0.032 0.052
#> GSM601883 2 0.4136 0.6221 0.196 0.788 0.000 0.016
#> GSM601888 1 0.3626 0.7252 0.844 0.136 0.004 0.016
#> GSM601893 1 0.2662 0.7630 0.900 0.084 0.000 0.016
#> GSM601898 1 0.0524 0.7860 0.988 0.004 0.008 0.000
#> GSM601903 2 0.7726 0.0579 0.296 0.444 0.000 0.260
#> GSM601913 1 0.0859 0.7838 0.980 0.008 0.004 0.008
#> GSM601928 1 0.2408 0.7824 0.920 0.044 0.000 0.036
#> GSM601933 2 0.5213 0.6055 0.224 0.724 0.000 0.052
#> GSM601938 2 0.4872 0.6099 0.212 0.752 0.004 0.032
#> GSM601943 2 0.5238 0.5427 0.164 0.768 0.024 0.044
#> GSM601948 1 0.6352 0.4483 0.632 0.260 0.000 0.108
#> GSM601958 1 0.0712 0.7850 0.984 0.004 0.008 0.004
#> GSM601973 2 0.7726 0.0545 0.296 0.444 0.000 0.260
#> GSM601978 2 0.4148 0.5889 0.156 0.816 0.012 0.016
#> GSM601988 1 0.8231 -0.0959 0.464 0.200 0.028 0.308
#> GSM601878 1 0.2411 0.7813 0.920 0.040 0.000 0.040
#> GSM601908 2 0.4936 0.6161 0.176 0.768 0.004 0.052
#> GSM601918 1 0.7520 0.1266 0.492 0.328 0.004 0.176
#> GSM601923 1 0.2411 0.7813 0.920 0.040 0.000 0.040
#> GSM601953 2 0.4996 0.6147 0.192 0.752 0.000 0.056
#> GSM601963 1 0.1471 0.7850 0.960 0.004 0.012 0.024
#> GSM601968 1 0.1943 0.7891 0.944 0.032 0.008 0.016
#> GSM601983 1 0.2245 0.7901 0.932 0.040 0.008 0.020
#> GSM601993 4 0.7324 0.6716 0.144 0.352 0.004 0.500
#> GSM601874 2 0.4375 0.6242 0.180 0.788 0.000 0.032
#> GSM601884 2 0.4095 0.6206 0.192 0.792 0.000 0.016
#> GSM601889 1 0.0779 0.7894 0.980 0.000 0.004 0.016
#> GSM601894 1 0.0937 0.7880 0.976 0.012 0.000 0.012
#> GSM601899 1 0.3737 0.7249 0.840 0.136 0.004 0.020
#> GSM601904 1 0.6723 0.4782 0.616 0.188 0.000 0.196
#> GSM601914 1 0.1509 0.7826 0.960 0.008 0.012 0.020
#> GSM601929 1 0.3421 0.7705 0.868 0.088 0.000 0.044
#> GSM601934 2 0.5136 0.6078 0.224 0.728 0.000 0.048
#> GSM601939 1 0.0859 0.7875 0.980 0.008 0.004 0.008
#> GSM601944 2 0.7645 0.2252 0.144 0.612 0.060 0.184
#> GSM601949 1 0.6081 0.4848 0.652 0.260 0.000 0.088
#> GSM601959 1 0.0712 0.7881 0.984 0.004 0.008 0.004
#> GSM601974 1 0.6318 0.5492 0.684 0.220 0.028 0.068
#> GSM601979 2 0.4093 0.5901 0.156 0.816 0.004 0.024
#> GSM601989 1 0.1362 0.7890 0.964 0.020 0.004 0.012
#> GSM601879 1 0.2500 0.7805 0.916 0.044 0.000 0.040
#> GSM601909 1 0.2553 0.7753 0.916 0.060 0.008 0.016
#> GSM601919 1 0.7490 0.1372 0.496 0.328 0.004 0.172
#> GSM601924 1 0.2411 0.7813 0.920 0.040 0.000 0.040
#> GSM601954 1 0.7512 0.2664 0.536 0.264 0.008 0.192
#> GSM601964 1 0.1471 0.7850 0.960 0.004 0.012 0.024
#> GSM601969 1 0.4198 0.7310 0.828 0.052 0.004 0.116
#> GSM601984 1 0.3934 0.7405 0.836 0.116 0.000 0.048
#> GSM601994 4 0.7202 0.6838 0.140 0.396 0.000 0.464
#> GSM601875 2 0.4323 0.6309 0.204 0.776 0.000 0.020
#> GSM601885 2 0.4136 0.6221 0.196 0.788 0.000 0.016
#> GSM601890 1 0.3102 0.7461 0.872 0.116 0.004 0.008
#> GSM601895 1 0.1770 0.7836 0.952 0.016 0.016 0.016
#> GSM601900 1 0.1985 0.7864 0.944 0.020 0.012 0.024
#> GSM601905 1 0.6886 0.4430 0.596 0.200 0.000 0.204
#> GSM601915 1 0.1247 0.7829 0.968 0.004 0.012 0.016
#> GSM601930 1 0.2408 0.7824 0.920 0.044 0.000 0.036
#> GSM601935 1 0.7327 0.4380 0.632 0.100 0.060 0.208
#> GSM601940 1 0.0859 0.7875 0.980 0.008 0.004 0.008
#> GSM601945 2 0.4804 0.5407 0.152 0.792 0.016 0.040
#> GSM601950 1 0.2892 0.7824 0.896 0.068 0.000 0.036
#> GSM601960 1 0.1985 0.7820 0.944 0.012 0.024 0.020
#> GSM601975 2 0.7683 0.0864 0.384 0.400 0.000 0.216
#> GSM601980 3 0.1690 0.7221 0.032 0.008 0.952 0.008
#> GSM601990 1 0.1362 0.7823 0.964 0.004 0.012 0.020
#> GSM601880 1 0.2589 0.7803 0.912 0.044 0.000 0.044
#> GSM601910 1 0.1985 0.7847 0.940 0.040 0.004 0.016
#> GSM601920 1 0.7208 0.4124 0.572 0.216 0.004 0.208
#> GSM601925 1 0.2589 0.7803 0.912 0.044 0.000 0.044
#> GSM601955 3 0.4324 0.7176 0.056 0.024 0.840 0.080
#> GSM601965 1 0.4199 0.7310 0.824 0.128 0.004 0.044
#> GSM601970 1 0.1516 0.7902 0.960 0.016 0.008 0.016
#> GSM601985 1 0.0707 0.7885 0.980 0.000 0.000 0.020
#> GSM601995 3 0.6396 0.6441 0.088 0.016 0.668 0.228
#> GSM601876 1 0.1396 0.7894 0.960 0.032 0.004 0.004
#> GSM601886 1 0.6381 0.6112 0.696 0.136 0.020 0.148
#> GSM601891 1 0.3612 0.7198 0.840 0.144 0.004 0.012
#> GSM601896 1 0.1509 0.7885 0.960 0.020 0.008 0.012
#> GSM601901 2 0.5918 0.5308 0.276 0.660 0.004 0.060
#> GSM601906 1 0.6650 0.4950 0.624 0.176 0.000 0.200
#> GSM601916 2 0.7729 0.0680 0.372 0.400 0.000 0.228
#> GSM601931 1 0.2499 0.7841 0.920 0.044 0.004 0.032
#> GSM601936 1 0.7764 0.2462 0.560 0.136 0.040 0.264
#> GSM601941 2 0.7732 0.0193 0.288 0.444 0.000 0.268
#> GSM601946 1 0.1369 0.7885 0.964 0.016 0.004 0.016
#> GSM601951 1 0.6896 0.3567 0.596 0.260 0.004 0.140
#> GSM601961 2 0.6214 0.4617 0.332 0.604 0.004 0.060
#> GSM601976 1 0.7502 -0.0464 0.456 0.356 0.000 0.188
#> GSM601981 2 0.6343 0.4889 0.220 0.660 0.004 0.116
#> GSM601991 1 0.2089 0.7839 0.940 0.012 0.020 0.028
#> GSM601881 1 0.2411 0.7813 0.920 0.040 0.000 0.040
#> GSM601911 1 0.5136 0.6100 0.728 0.224 0.000 0.048
#> GSM601921 1 0.7179 0.4188 0.576 0.212 0.004 0.208
#> GSM601926 1 0.2319 0.7814 0.924 0.040 0.000 0.036
#> GSM601956 2 0.4959 0.6149 0.196 0.752 0.000 0.052
#> GSM601966 2 0.7536 0.0921 0.284 0.488 0.000 0.228
#> GSM601971 1 0.3785 0.7560 0.856 0.056 0.004 0.084
#> GSM601986 1 0.4951 0.6278 0.744 0.212 0.000 0.044
#> GSM601996 4 0.7313 0.6381 0.152 0.416 0.000 0.432
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM601872 2 0.3464 0.80218 0.008 0.848 0.108 0.028 0.008
#> GSM601882 2 0.3867 0.81212 0.012 0.824 0.076 0.088 0.000
#> GSM601887 3 0.3354 0.71431 0.004 0.140 0.832 0.024 0.000
#> GSM601892 3 0.2408 0.74668 0.000 0.092 0.892 0.016 0.000
#> GSM601897 3 0.5516 0.57641 0.028 0.192 0.712 0.028 0.040
#> GSM601902 4 0.5556 0.62963 0.008 0.244 0.100 0.648 0.000
#> GSM601912 3 0.3065 0.75373 0.012 0.080 0.876 0.028 0.004
#> GSM601927 3 0.2795 0.74728 0.000 0.028 0.872 0.100 0.000
#> GSM601932 4 0.6176 0.63387 0.016 0.260 0.132 0.592 0.000
#> GSM601937 1 0.8655 0.00000 0.484 0.108 0.092 0.172 0.144
#> GSM601942 5 0.5874 0.10301 0.040 0.352 0.020 0.012 0.576
#> GSM601947 4 0.7099 0.53593 0.012 0.292 0.308 0.388 0.000
#> GSM601957 3 0.0898 0.77566 0.000 0.008 0.972 0.020 0.000
#> GSM601972 4 0.6500 0.61596 0.004 0.216 0.264 0.516 0.000
#> GSM601977 2 0.3579 0.82385 0.008 0.840 0.068 0.084 0.000
#> GSM601987 2 0.4138 0.82174 0.016 0.808 0.092 0.084 0.000
#> GSM601877 3 0.2761 0.74620 0.000 0.024 0.872 0.104 0.000
#> GSM601907 2 0.2734 0.82856 0.008 0.892 0.048 0.052 0.000
#> GSM601917 3 0.6022 -0.13185 0.004 0.084 0.460 0.448 0.004
#> GSM601922 4 0.6610 0.30861 0.012 0.128 0.408 0.448 0.004
#> GSM601952 3 0.7512 -0.38122 0.024 0.252 0.384 0.332 0.008
#> GSM601962 3 0.1461 0.77255 0.028 0.000 0.952 0.016 0.004
#> GSM601967 3 0.1428 0.77610 0.004 0.012 0.956 0.024 0.004
#> GSM601982 3 0.6264 0.15227 0.012 0.336 0.544 0.104 0.004
#> GSM601992 4 0.6930 0.24955 0.124 0.252 0.068 0.556 0.000
#> GSM601873 2 0.3994 0.76444 0.048 0.840 0.068 0.020 0.024
#> GSM601883 2 0.3867 0.81346 0.012 0.824 0.076 0.088 0.000
#> GSM601888 3 0.3354 0.71431 0.004 0.140 0.832 0.024 0.000
#> GSM601893 3 0.2248 0.75366 0.000 0.088 0.900 0.012 0.000
#> GSM601898 3 0.0727 0.77195 0.004 0.000 0.980 0.012 0.004
#> GSM601903 4 0.5556 0.62963 0.008 0.244 0.100 0.648 0.000
#> GSM601913 3 0.0932 0.77239 0.020 0.004 0.972 0.004 0.000
#> GSM601928 3 0.2795 0.74728 0.000 0.028 0.872 0.100 0.000
#> GSM601933 2 0.4524 0.77738 0.008 0.768 0.092 0.132 0.000
#> GSM601938 2 0.4567 0.79333 0.016 0.788 0.080 0.108 0.008
#> GSM601943 2 0.3350 0.78711 0.036 0.872 0.060 0.020 0.012
#> GSM601948 3 0.6163 0.14145 0.000 0.168 0.540 0.292 0.000
#> GSM601958 3 0.0613 0.77109 0.004 0.000 0.984 0.008 0.004
#> GSM601973 4 0.5706 0.62964 0.012 0.244 0.104 0.640 0.000
#> GSM601978 2 0.2550 0.82452 0.012 0.908 0.044 0.032 0.004
#> GSM601988 3 0.8474 -0.22604 0.224 0.116 0.396 0.248 0.016
#> GSM601878 3 0.2761 0.74620 0.000 0.024 0.872 0.104 0.000
#> GSM601908 2 0.3184 0.82889 0.012 0.868 0.052 0.068 0.000
#> GSM601918 4 0.7137 0.47734 0.012 0.232 0.356 0.396 0.004
#> GSM601923 3 0.2761 0.74620 0.000 0.024 0.872 0.104 0.000
#> GSM601953 2 0.2664 0.83004 0.004 0.892 0.064 0.040 0.000
#> GSM601963 3 0.1369 0.77073 0.028 0.000 0.956 0.008 0.008
#> GSM601968 3 0.1725 0.77602 0.004 0.024 0.944 0.024 0.004
#> GSM601983 3 0.2221 0.77453 0.012 0.024 0.924 0.036 0.004
#> GSM601993 4 0.6982 0.08031 0.160 0.184 0.068 0.584 0.004
#> GSM601874 2 0.3275 0.83804 0.008 0.860 0.064 0.068 0.000
#> GSM601884 2 0.3807 0.81209 0.012 0.828 0.072 0.088 0.000
#> GSM601889 3 0.0833 0.77461 0.004 0.000 0.976 0.016 0.004
#> GSM601894 3 0.0854 0.77514 0.012 0.004 0.976 0.008 0.000
#> GSM601899 3 0.3433 0.71552 0.004 0.132 0.832 0.032 0.000
#> GSM601904 3 0.5821 -0.04706 0.004 0.080 0.492 0.424 0.000
#> GSM601914 3 0.1404 0.76939 0.028 0.004 0.956 0.008 0.004
#> GSM601929 3 0.3622 0.72453 0.000 0.048 0.816 0.136 0.000
#> GSM601934 2 0.4480 0.78045 0.008 0.772 0.092 0.128 0.000
#> GSM601939 3 0.1026 0.77406 0.004 0.004 0.968 0.024 0.000
#> GSM601944 2 0.6878 0.28673 0.260 0.576 0.044 0.104 0.016
#> GSM601949 3 0.6043 0.26468 0.000 0.176 0.572 0.252 0.000
#> GSM601959 3 0.0833 0.77380 0.004 0.000 0.976 0.016 0.004
#> GSM601974 3 0.6491 0.41206 0.016 0.196 0.624 0.140 0.024
#> GSM601979 2 0.2438 0.82529 0.008 0.908 0.044 0.040 0.000
#> GSM601989 3 0.1186 0.77601 0.008 0.020 0.964 0.008 0.000
#> GSM601879 3 0.2813 0.74451 0.000 0.024 0.868 0.108 0.000
#> GSM601909 3 0.2368 0.76357 0.012 0.060 0.912 0.012 0.004
#> GSM601919 4 0.7036 0.45874 0.008 0.228 0.364 0.396 0.004
#> GSM601924 3 0.2761 0.74620 0.000 0.024 0.872 0.104 0.000
#> GSM601954 3 0.6956 -0.31684 0.008 0.208 0.420 0.360 0.004
#> GSM601964 3 0.1369 0.77073 0.028 0.000 0.956 0.008 0.008
#> GSM601969 3 0.3915 0.67019 0.004 0.024 0.788 0.180 0.004
#> GSM601984 3 0.4192 0.68199 0.004 0.068 0.784 0.144 0.000
#> GSM601994 4 0.6845 0.17211 0.132 0.232 0.064 0.572 0.000
#> GSM601875 2 0.3618 0.83187 0.004 0.840 0.076 0.076 0.004
#> GSM601885 2 0.3867 0.81346 0.012 0.824 0.076 0.088 0.000
#> GSM601890 3 0.3031 0.73014 0.004 0.128 0.852 0.016 0.000
#> GSM601895 3 0.1805 0.77101 0.020 0.008 0.944 0.012 0.016
#> GSM601900 3 0.2121 0.77280 0.020 0.016 0.932 0.020 0.012
#> GSM601905 3 0.6024 -0.12628 0.008 0.088 0.472 0.432 0.000
#> GSM601915 3 0.1026 0.76964 0.024 0.000 0.968 0.004 0.004
#> GSM601930 3 0.2795 0.74728 0.000 0.028 0.872 0.100 0.000
#> GSM601935 3 0.7286 0.35988 0.192 0.064 0.592 0.116 0.036
#> GSM601940 3 0.1026 0.77406 0.004 0.004 0.968 0.024 0.000
#> GSM601945 2 0.2809 0.79151 0.036 0.896 0.048 0.016 0.004
#> GSM601950 3 0.2708 0.76600 0.000 0.044 0.884 0.072 0.000
#> GSM601960 3 0.1834 0.76855 0.032 0.008 0.940 0.004 0.016
#> GSM601975 4 0.6467 0.64335 0.012 0.228 0.204 0.556 0.000
#> GSM601980 5 0.1554 0.45072 0.024 0.004 0.012 0.008 0.952
#> GSM601990 3 0.1202 0.76822 0.032 0.000 0.960 0.004 0.004
#> GSM601880 3 0.2900 0.74316 0.000 0.028 0.864 0.108 0.000
#> GSM601910 3 0.1731 0.77317 0.012 0.040 0.940 0.008 0.000
#> GSM601920 3 0.6217 -0.18536 0.004 0.104 0.448 0.440 0.004
#> GSM601925 3 0.2900 0.74316 0.000 0.028 0.864 0.108 0.000
#> GSM601955 5 0.4958 0.40904 0.220 0.008 0.028 0.024 0.720
#> GSM601965 3 0.4427 0.67199 0.008 0.088 0.776 0.128 0.000
#> GSM601970 3 0.1347 0.77540 0.008 0.008 0.960 0.020 0.004
#> GSM601985 3 0.0865 0.77437 0.000 0.004 0.972 0.024 0.000
#> GSM601995 5 0.5970 0.15912 0.280 0.008 0.056 0.032 0.624
#> GSM601876 3 0.1739 0.77727 0.004 0.032 0.940 0.024 0.000
#> GSM601886 3 0.6006 0.51156 0.044 0.048 0.656 0.236 0.016
#> GSM601891 3 0.3504 0.69844 0.000 0.160 0.816 0.016 0.008
#> GSM601896 3 0.1340 0.77695 0.004 0.016 0.960 0.016 0.004
#> GSM601901 2 0.5794 0.64761 0.016 0.680 0.148 0.148 0.008
#> GSM601906 3 0.5771 -0.00932 0.004 0.076 0.500 0.420 0.000
#> GSM601916 4 0.6703 0.62728 0.016 0.252 0.208 0.524 0.000
#> GSM601931 3 0.2899 0.75014 0.004 0.028 0.872 0.096 0.000
#> GSM601936 3 0.7825 0.14597 0.212 0.068 0.508 0.188 0.024
#> GSM601941 4 0.5506 0.62969 0.008 0.236 0.100 0.656 0.000
#> GSM601946 3 0.1525 0.77370 0.004 0.012 0.948 0.036 0.000
#> GSM601951 3 0.6579 -0.08708 0.004 0.168 0.492 0.332 0.004
#> GSM601961 2 0.5859 0.42919 0.000 0.628 0.220 0.144 0.008
#> GSM601976 4 0.6552 0.60290 0.004 0.204 0.296 0.496 0.000
#> GSM601981 2 0.5368 0.61594 0.016 0.688 0.088 0.208 0.000
#> GSM601991 3 0.1938 0.76816 0.036 0.008 0.936 0.012 0.008
#> GSM601881 3 0.2761 0.74620 0.000 0.024 0.872 0.104 0.000
#> GSM601911 3 0.5587 0.49512 0.004 0.188 0.656 0.152 0.000
#> GSM601921 3 0.6180 -0.17564 0.004 0.100 0.452 0.440 0.004
#> GSM601926 3 0.2707 0.74685 0.000 0.024 0.876 0.100 0.000
#> GSM601956 2 0.2934 0.83046 0.008 0.884 0.068 0.036 0.004
#> GSM601966 4 0.6576 0.47775 0.028 0.356 0.112 0.504 0.000
#> GSM601971 3 0.3594 0.69801 0.004 0.012 0.804 0.176 0.004
#> GSM601986 3 0.5420 0.52447 0.004 0.172 0.676 0.148 0.000
#> GSM601996 4 0.6866 0.26647 0.112 0.252 0.072 0.564 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM601872 2 0.3002 0.7876 0.064 0.872 0.004 0.040 0.012 0.008
#> GSM601882 2 0.3516 0.8002 0.028 0.812 0.000 0.136 0.024 0.000
#> GSM601887 1 0.3163 0.7463 0.824 0.144 0.000 0.024 0.008 0.000
#> GSM601892 1 0.2264 0.7873 0.888 0.096 0.000 0.012 0.004 0.000
#> GSM601897 1 0.5487 0.5488 0.684 0.200 0.020 0.036 0.044 0.016
#> GSM601902 4 0.3447 0.3297 0.020 0.104 0.004 0.836 0.032 0.004
#> GSM601912 1 0.3201 0.7866 0.852 0.084 0.000 0.040 0.020 0.004
#> GSM601927 1 0.2431 0.7822 0.860 0.000 0.000 0.132 0.008 0.000
#> GSM601932 4 0.4207 0.3676 0.056 0.120 0.008 0.788 0.024 0.004
#> GSM601937 5 0.6553 -0.4044 0.044 0.064 0.136 0.016 0.636 0.104
#> GSM601942 2 0.6643 -0.3415 0.004 0.364 0.300 0.004 0.012 0.316
#> GSM601947 4 0.5992 0.4261 0.236 0.216 0.000 0.532 0.016 0.000
#> GSM601957 1 0.0870 0.8239 0.972 0.004 0.000 0.012 0.012 0.000
#> GSM601972 4 0.5144 0.4673 0.192 0.140 0.000 0.656 0.012 0.000
#> GSM601977 2 0.3101 0.8129 0.036 0.848 0.000 0.104 0.008 0.004
#> GSM601987 2 0.3571 0.8097 0.048 0.816 0.000 0.116 0.020 0.000
#> GSM601877 1 0.2362 0.7807 0.860 0.000 0.000 0.136 0.004 0.000
#> GSM601907 2 0.2257 0.8134 0.008 0.900 0.000 0.076 0.012 0.004
#> GSM601917 4 0.5190 0.4577 0.364 0.016 0.000 0.572 0.032 0.016
#> GSM601922 4 0.5502 0.4892 0.320 0.048 0.000 0.588 0.032 0.012
#> GSM601952 4 0.7672 0.3217 0.308 0.200 0.044 0.396 0.036 0.016
#> GSM601962 1 0.1313 0.8203 0.952 0.000 0.004 0.016 0.028 0.000
#> GSM601967 1 0.1542 0.8250 0.944 0.016 0.000 0.024 0.016 0.000
#> GSM601982 1 0.5982 0.0618 0.512 0.328 0.004 0.140 0.016 0.000
#> GSM601992 4 0.6417 -0.2531 0.036 0.168 0.000 0.432 0.364 0.000
#> GSM601873 2 0.2645 0.7613 0.020 0.900 0.012 0.008 0.024 0.036
#> GSM601883 2 0.3516 0.8005 0.028 0.812 0.000 0.136 0.024 0.000
#> GSM601888 1 0.3163 0.7463 0.824 0.144 0.000 0.024 0.008 0.000
#> GSM601893 1 0.2113 0.7955 0.896 0.092 0.000 0.008 0.004 0.000
#> GSM601898 1 0.0820 0.8212 0.972 0.000 0.000 0.016 0.012 0.000
#> GSM601903 4 0.3447 0.3297 0.020 0.104 0.004 0.836 0.032 0.004
#> GSM601913 1 0.0891 0.8206 0.968 0.000 0.000 0.008 0.024 0.000
#> GSM601928 1 0.2431 0.7822 0.860 0.000 0.000 0.132 0.008 0.000
#> GSM601933 2 0.3671 0.7782 0.040 0.784 0.000 0.168 0.008 0.000
#> GSM601938 2 0.4278 0.7842 0.032 0.768 0.004 0.156 0.036 0.004
#> GSM601943 2 0.1929 0.7721 0.012 0.932 0.004 0.008 0.016 0.028
#> GSM601948 1 0.5587 -0.1294 0.488 0.112 0.000 0.392 0.008 0.000
#> GSM601958 1 0.0725 0.8206 0.976 0.000 0.000 0.012 0.012 0.000
#> GSM601973 4 0.3463 0.3252 0.020 0.096 0.008 0.840 0.032 0.004
#> GSM601978 2 0.2014 0.8125 0.008 0.920 0.000 0.052 0.008 0.012
#> GSM601988 5 0.7476 0.0207 0.364 0.064 0.016 0.180 0.364 0.012
#> GSM601878 1 0.2362 0.7807 0.860 0.000 0.000 0.136 0.004 0.000
#> GSM601908 2 0.2661 0.8145 0.012 0.876 0.000 0.092 0.016 0.004
#> GSM601918 4 0.6303 0.4764 0.260 0.160 0.000 0.540 0.028 0.012
#> GSM601923 1 0.2362 0.7807 0.860 0.000 0.000 0.136 0.004 0.000
#> GSM601953 2 0.2339 0.8118 0.020 0.904 0.000 0.056 0.016 0.004
#> GSM601963 1 0.1230 0.8185 0.956 0.000 0.008 0.008 0.028 0.000
#> GSM601968 1 0.1794 0.8229 0.932 0.028 0.000 0.024 0.016 0.000
#> GSM601983 1 0.2014 0.8210 0.924 0.024 0.004 0.032 0.016 0.000
#> GSM601993 5 0.6327 0.0683 0.032 0.092 0.020 0.412 0.444 0.000
#> GSM601874 2 0.2502 0.8225 0.020 0.884 0.000 0.084 0.012 0.000
#> GSM601884 2 0.3440 0.8002 0.024 0.816 0.000 0.136 0.024 0.000
#> GSM601889 1 0.0972 0.8238 0.964 0.000 0.000 0.028 0.008 0.000
#> GSM601894 1 0.0779 0.8235 0.976 0.008 0.000 0.008 0.008 0.000
#> GSM601899 1 0.3182 0.7491 0.828 0.136 0.000 0.024 0.012 0.000
#> GSM601904 4 0.4974 0.4202 0.408 0.024 0.000 0.544 0.016 0.008
#> GSM601914 1 0.1155 0.8170 0.956 0.000 0.004 0.004 0.036 0.000
#> GSM601929 1 0.3372 0.7424 0.796 0.020 0.000 0.176 0.008 0.000
#> GSM601934 2 0.3637 0.7805 0.040 0.788 0.000 0.164 0.008 0.000
#> GSM601939 1 0.0993 0.8218 0.964 0.000 0.000 0.024 0.012 0.000
#> GSM601944 2 0.6043 0.1503 0.000 0.516 0.000 0.024 0.156 0.304
#> GSM601949 1 0.5601 0.0681 0.532 0.128 0.000 0.332 0.008 0.000
#> GSM601959 1 0.0909 0.8227 0.968 0.000 0.000 0.020 0.012 0.000
#> GSM601974 1 0.6221 0.3010 0.584 0.164 0.008 0.208 0.028 0.008
#> GSM601979 2 0.1925 0.8137 0.008 0.920 0.000 0.060 0.004 0.008
#> GSM601989 1 0.1251 0.8263 0.956 0.024 0.000 0.008 0.012 0.000
#> GSM601879 1 0.2402 0.7783 0.856 0.000 0.000 0.140 0.004 0.000
#> GSM601909 1 0.2280 0.8097 0.904 0.064 0.004 0.012 0.016 0.000
#> GSM601919 4 0.6267 0.4763 0.268 0.160 0.000 0.536 0.024 0.012
#> GSM601924 1 0.2362 0.7807 0.860 0.000 0.000 0.136 0.004 0.000
#> GSM601954 4 0.6927 0.3755 0.356 0.156 0.008 0.428 0.040 0.012
#> GSM601964 1 0.1230 0.8185 0.956 0.000 0.008 0.008 0.028 0.000
#> GSM601969 1 0.3996 0.6413 0.752 0.008 0.000 0.204 0.028 0.008
#> GSM601984 1 0.3824 0.6995 0.780 0.040 0.000 0.164 0.016 0.000
#> GSM601994 4 0.6202 -0.3235 0.032 0.136 0.000 0.420 0.412 0.000
#> GSM601875 2 0.3029 0.8173 0.032 0.852 0.000 0.104 0.008 0.004
#> GSM601885 2 0.3516 0.8005 0.028 0.812 0.000 0.136 0.024 0.000
#> GSM601890 1 0.3024 0.7622 0.840 0.128 0.000 0.016 0.016 0.000
#> GSM601895 1 0.1584 0.8192 0.944 0.004 0.012 0.004 0.032 0.004
#> GSM601900 1 0.1963 0.8195 0.928 0.012 0.012 0.016 0.032 0.000
#> GSM601905 4 0.5090 0.4586 0.392 0.028 0.000 0.552 0.020 0.008
#> GSM601915 1 0.0922 0.8180 0.968 0.000 0.004 0.004 0.024 0.000
#> GSM601930 1 0.2431 0.7822 0.860 0.000 0.000 0.132 0.008 0.000
#> GSM601935 1 0.6253 0.2195 0.568 0.032 0.016 0.056 0.300 0.028
#> GSM601940 1 0.0993 0.8218 0.964 0.000 0.000 0.024 0.012 0.000
#> GSM601945 2 0.1414 0.7771 0.004 0.952 0.000 0.012 0.012 0.020
#> GSM601950 1 0.2763 0.8029 0.868 0.036 0.000 0.088 0.008 0.000
#> GSM601960 1 0.1783 0.8163 0.936 0.004 0.008 0.008 0.036 0.008
#> GSM601975 4 0.4760 0.4525 0.132 0.108 0.008 0.732 0.020 0.000
#> GSM601980 6 0.4531 0.2031 0.000 0.000 0.408 0.000 0.036 0.556
#> GSM601990 1 0.1194 0.8174 0.956 0.000 0.004 0.008 0.032 0.000
#> GSM601880 1 0.2686 0.7758 0.848 0.004 0.000 0.140 0.004 0.004
#> GSM601910 1 0.1820 0.8203 0.928 0.044 0.000 0.012 0.016 0.000
#> GSM601920 4 0.5423 0.4683 0.352 0.032 0.000 0.568 0.036 0.012
#> GSM601925 1 0.2686 0.7758 0.848 0.004 0.000 0.140 0.004 0.004
#> GSM601955 3 0.0547 0.0000 0.020 0.000 0.980 0.000 0.000 0.000
#> GSM601965 1 0.4096 0.6820 0.764 0.060 0.000 0.160 0.016 0.000
#> GSM601970 1 0.1528 0.8245 0.944 0.012 0.000 0.028 0.016 0.000
#> GSM601985 1 0.0790 0.8219 0.968 0.000 0.000 0.032 0.000 0.000
#> GSM601995 6 0.6850 0.2770 0.028 0.000 0.352 0.024 0.176 0.420
#> GSM601876 1 0.1767 0.8250 0.932 0.020 0.000 0.036 0.012 0.000
#> GSM601886 1 0.6147 0.3695 0.600 0.012 0.004 0.228 0.112 0.044
#> GSM601891 1 0.3554 0.7147 0.796 0.168 0.000 0.020 0.008 0.008
#> GSM601896 1 0.1458 0.8270 0.948 0.016 0.000 0.016 0.020 0.000
#> GSM601901 2 0.5185 0.6715 0.096 0.680 0.004 0.196 0.020 0.004
#> GSM601906 4 0.4991 0.3928 0.416 0.020 0.000 0.536 0.020 0.008
#> GSM601916 4 0.5121 0.4102 0.132 0.148 0.000 0.688 0.032 0.000
#> GSM601931 1 0.2431 0.7844 0.860 0.000 0.000 0.132 0.008 0.000
#> GSM601936 1 0.7031 -0.1297 0.476 0.032 0.012 0.124 0.320 0.036
#> GSM601941 4 0.3325 0.3296 0.020 0.088 0.004 0.848 0.036 0.004
#> GSM601946 1 0.1367 0.8205 0.944 0.000 0.000 0.044 0.012 0.000
#> GSM601951 4 0.5808 0.3025 0.432 0.116 0.000 0.436 0.016 0.000
#> GSM601961 2 0.5664 0.4726 0.176 0.612 0.000 0.192 0.012 0.008
#> GSM601976 4 0.5350 0.4766 0.228 0.128 0.000 0.628 0.016 0.000
#> GSM601981 2 0.4752 0.6237 0.040 0.668 0.000 0.268 0.020 0.004
#> GSM601991 1 0.1686 0.8157 0.932 0.000 0.004 0.008 0.052 0.004
#> GSM601881 1 0.2362 0.7807 0.860 0.000 0.000 0.136 0.004 0.000
#> GSM601911 1 0.5396 0.4497 0.632 0.164 0.000 0.188 0.016 0.000
#> GSM601921 4 0.5378 0.4658 0.360 0.028 0.000 0.564 0.036 0.012
#> GSM601926 1 0.2320 0.7819 0.864 0.000 0.000 0.132 0.004 0.000
#> GSM601956 2 0.2389 0.8129 0.020 0.904 0.008 0.052 0.016 0.000
#> GSM601966 4 0.5057 0.2088 0.024 0.244 0.000 0.656 0.076 0.000
#> GSM601971 1 0.3859 0.6566 0.756 0.004 0.000 0.208 0.020 0.012
#> GSM601986 1 0.5242 0.4853 0.652 0.148 0.000 0.184 0.016 0.000
#> GSM601996 4 0.6442 -0.2357 0.040 0.164 0.000 0.440 0.356 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
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)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
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)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, 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.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")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n time(p) gender(p) k
#> SD:hclust 123 0.0170 0.6253 2
#> SD:hclust 91 0.0746 0.1655 3
#> SD:hclust 91 0.1590 0.0101 4
#> SD:hclust 93 0.2505 0.0119 5
#> SD:hclust 81 0.4411 0.0979 6
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.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["SD", "kmeans"]
# you can also extract it by
# res = res_list["SD:kmeans"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 21512 rows and 125 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'kmeans' method.
#> Performed in total 1250 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_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
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, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.967 0.969 0.984 0.5039 0.496 0.496
#> 3 3 0.583 0.601 0.779 0.2471 0.886 0.777
#> 4 4 0.617 0.651 0.767 0.1259 0.835 0.628
#> 5 5 0.626 0.733 0.789 0.0821 0.843 0.536
#> 6 6 0.648 0.695 0.776 0.0448 0.977 0.894
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 2
Following shows 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 class
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
#> GSM601872 2 0.0000 0.986 0.000 1.000
#> GSM601882 2 0.0000 0.986 0.000 1.000
#> GSM601887 1 0.6343 0.828 0.840 0.160
#> GSM601892 1 0.0376 0.982 0.996 0.004
#> GSM601897 1 0.6531 0.817 0.832 0.168
#> GSM601902 2 0.0376 0.985 0.004 0.996
#> GSM601912 1 0.0938 0.977 0.988 0.012
#> GSM601927 1 0.0000 0.982 1.000 0.000
#> GSM601932 2 0.0376 0.985 0.004 0.996
#> GSM601937 2 0.0000 0.986 0.000 1.000
#> GSM601942 2 0.0000 0.986 0.000 1.000
#> GSM601947 2 0.0376 0.985 0.004 0.996
#> GSM601957 1 0.0376 0.982 0.996 0.004
#> GSM601972 2 0.0376 0.985 0.004 0.996
#> GSM601977 2 0.0000 0.986 0.000 1.000
#> GSM601987 2 0.0000 0.986 0.000 1.000
#> GSM601877 1 0.0000 0.982 1.000 0.000
#> GSM601907 2 0.0000 0.986 0.000 1.000
#> GSM601917 2 0.0672 0.983 0.008 0.992
#> GSM601922 2 0.3274 0.935 0.060 0.940
#> GSM601952 2 0.0376 0.985 0.004 0.996
#> GSM601962 1 0.0376 0.982 0.996 0.004
#> GSM601967 1 0.0376 0.982 0.996 0.004
#> GSM601982 2 0.0938 0.977 0.012 0.988
#> GSM601992 2 0.0000 0.986 0.000 1.000
#> GSM601873 2 0.0000 0.986 0.000 1.000
#> GSM601883 2 0.0000 0.986 0.000 1.000
#> GSM601888 1 0.6343 0.828 0.840 0.160
#> GSM601893 1 0.0376 0.982 0.996 0.004
#> GSM601898 1 0.0376 0.982 0.996 0.004
#> GSM601903 2 0.0376 0.985 0.004 0.996
#> GSM601913 1 0.0000 0.982 1.000 0.000
#> GSM601928 1 0.0000 0.982 1.000 0.000
#> GSM601933 2 0.0000 0.986 0.000 1.000
#> GSM601938 2 0.0000 0.986 0.000 1.000
#> GSM601943 2 0.0000 0.986 0.000 1.000
#> GSM601948 1 0.0000 0.982 1.000 0.000
#> GSM601958 1 0.0376 0.982 0.996 0.004
#> GSM601973 2 0.0376 0.985 0.004 0.996
#> GSM601978 2 0.0000 0.986 0.000 1.000
#> GSM601988 2 0.0000 0.986 0.000 1.000
#> GSM601878 1 0.0000 0.982 1.000 0.000
#> GSM601908 2 0.0000 0.986 0.000 1.000
#> GSM601918 2 0.0376 0.985 0.004 0.996
#> GSM601923 1 0.0000 0.982 1.000 0.000
#> GSM601953 2 0.0000 0.986 0.000 1.000
#> GSM601963 1 0.0376 0.982 0.996 0.004
#> GSM601968 1 0.0938 0.977 0.988 0.012
#> GSM601983 1 0.0376 0.982 0.996 0.004
#> GSM601993 2 0.0000 0.986 0.000 1.000
#> GSM601874 2 0.0000 0.986 0.000 1.000
#> GSM601884 2 0.0000 0.986 0.000 1.000
#> GSM601889 1 0.0376 0.982 0.996 0.004
#> GSM601894 1 0.0376 0.982 0.996 0.004
#> GSM601899 1 0.5059 0.883 0.888 0.112
#> GSM601904 2 0.4815 0.886 0.104 0.896
#> GSM601914 1 0.0376 0.982 0.996 0.004
#> GSM601929 1 0.0000 0.982 1.000 0.000
#> GSM601934 2 0.0000 0.986 0.000 1.000
#> GSM601939 1 0.0000 0.982 1.000 0.000
#> GSM601944 2 0.0000 0.986 0.000 1.000
#> GSM601949 1 0.0000 0.982 1.000 0.000
#> GSM601959 1 0.0376 0.982 0.996 0.004
#> GSM601974 2 0.8386 0.628 0.268 0.732
#> GSM601979 2 0.0000 0.986 0.000 1.000
#> GSM601989 1 0.0376 0.982 0.996 0.004
#> GSM601879 1 0.0000 0.982 1.000 0.000
#> GSM601909 1 0.0376 0.982 0.996 0.004
#> GSM601919 2 0.0376 0.985 0.004 0.996
#> GSM601924 1 0.0000 0.982 1.000 0.000
#> GSM601954 2 0.0376 0.985 0.004 0.996
#> GSM601964 1 0.0376 0.982 0.996 0.004
#> GSM601969 1 0.0000 0.982 1.000 0.000
#> GSM601984 1 0.0000 0.982 1.000 0.000
#> GSM601994 2 0.0000 0.986 0.000 1.000
#> GSM601875 2 0.0000 0.986 0.000 1.000
#> GSM601885 2 0.0000 0.986 0.000 1.000
#> GSM601890 1 0.1414 0.971 0.980 0.020
#> GSM601895 1 0.0376 0.982 0.996 0.004
#> GSM601900 1 0.0376 0.982 0.996 0.004
#> GSM601905 2 0.0376 0.985 0.004 0.996
#> GSM601915 1 0.0376 0.982 0.996 0.004
#> GSM601930 1 0.0000 0.982 1.000 0.000
#> GSM601935 1 0.5178 0.874 0.884 0.116
#> GSM601940 1 0.0000 0.982 1.000 0.000
#> GSM601945 2 0.0000 0.986 0.000 1.000
#> GSM601950 1 0.0000 0.982 1.000 0.000
#> GSM601960 1 0.0376 0.982 0.996 0.004
#> GSM601975 2 0.0376 0.985 0.004 0.996
#> GSM601980 2 0.0000 0.986 0.000 1.000
#> GSM601990 1 0.0376 0.982 0.996 0.004
#> GSM601880 1 0.0000 0.982 1.000 0.000
#> GSM601910 1 0.0376 0.982 0.996 0.004
#> GSM601920 2 0.0376 0.985 0.004 0.996
#> GSM601925 1 0.0000 0.982 1.000 0.000
#> GSM601955 2 0.0672 0.981 0.008 0.992
#> GSM601965 1 0.0000 0.982 1.000 0.000
#> GSM601970 1 0.0000 0.982 1.000 0.000
#> GSM601985 1 0.0000 0.982 1.000 0.000
#> GSM601995 2 0.0000 0.986 0.000 1.000
#> GSM601876 1 0.0000 0.982 1.000 0.000
#> GSM601886 2 0.2603 0.951 0.044 0.956
#> GSM601891 1 0.7219 0.772 0.800 0.200
#> GSM601896 1 0.0000 0.982 1.000 0.000
#> GSM601901 2 0.0000 0.986 0.000 1.000
#> GSM601906 1 0.3733 0.921 0.928 0.072
#> GSM601916 2 0.0376 0.985 0.004 0.996
#> GSM601931 1 0.0000 0.982 1.000 0.000
#> GSM601936 2 0.0000 0.986 0.000 1.000
#> GSM601941 2 0.0376 0.985 0.004 0.996
#> GSM601946 1 0.0000 0.982 1.000 0.000
#> GSM601951 1 0.0000 0.982 1.000 0.000
#> GSM601961 2 0.0000 0.986 0.000 1.000
#> GSM601976 2 0.0376 0.985 0.004 0.996
#> GSM601981 2 0.0000 0.986 0.000 1.000
#> GSM601991 1 0.0376 0.982 0.996 0.004
#> GSM601881 1 0.0000 0.982 1.000 0.000
#> GSM601911 2 0.8267 0.651 0.260 0.740
#> GSM601921 2 0.0376 0.985 0.004 0.996
#> GSM601926 1 0.0000 0.982 1.000 0.000
#> GSM601956 2 0.0000 0.986 0.000 1.000
#> GSM601966 2 0.0376 0.985 0.004 0.996
#> GSM601971 1 0.0000 0.982 1.000 0.000
#> GSM601986 1 0.0938 0.975 0.988 0.012
#> GSM601996 2 0.0376 0.985 0.004 0.996
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM601872 2 0.3551 0.4665 0.000 0.868 0.132
#> GSM601882 2 0.0747 0.6103 0.000 0.984 0.016
#> GSM601887 1 0.8762 0.5738 0.576 0.264 0.160
#> GSM601892 1 0.5536 0.8349 0.804 0.052 0.144
#> GSM601897 1 0.9625 0.3193 0.408 0.204 0.388
#> GSM601902 2 0.6302 0.2049 0.000 0.520 0.480
#> GSM601912 1 0.6535 0.7912 0.728 0.052 0.220
#> GSM601927 1 0.0592 0.8623 0.988 0.000 0.012
#> GSM601932 2 0.6302 0.2049 0.000 0.520 0.480
#> GSM601937 3 0.5785 0.5350 0.004 0.300 0.696
#> GSM601942 2 0.6079 -0.0349 0.000 0.612 0.388
#> GSM601947 2 0.6126 0.3959 0.004 0.644 0.352
#> GSM601957 1 0.3340 0.8609 0.880 0.000 0.120
#> GSM601972 2 0.5926 0.3955 0.000 0.644 0.356
#> GSM601977 2 0.0747 0.6044 0.000 0.984 0.016
#> GSM601987 2 0.0747 0.6103 0.000 0.984 0.016
#> GSM601877 1 0.0747 0.8620 0.984 0.000 0.016
#> GSM601907 2 0.0000 0.6076 0.000 1.000 0.000
#> GSM601917 2 0.7295 0.1216 0.028 0.488 0.484
#> GSM601922 3 0.7993 -0.1023 0.060 0.456 0.484
#> GSM601952 2 0.6126 0.2768 0.000 0.600 0.400
#> GSM601962 1 0.5016 0.8098 0.760 0.000 0.240
#> GSM601967 1 0.3482 0.8606 0.872 0.000 0.128
#> GSM601982 2 0.2680 0.5621 0.008 0.924 0.068
#> GSM601992 2 0.5968 0.3263 0.000 0.636 0.364
#> GSM601873 2 0.3752 0.4494 0.000 0.856 0.144
#> GSM601883 2 0.0747 0.6103 0.000 0.984 0.016
#> GSM601888 2 0.9088 -0.2413 0.396 0.464 0.140
#> GSM601893 1 0.8167 0.6753 0.644 0.188 0.168
#> GSM601898 1 0.3482 0.8595 0.872 0.000 0.128
#> GSM601903 2 0.6302 0.2049 0.000 0.520 0.480
#> GSM601913 1 0.3412 0.8609 0.876 0.000 0.124
#> GSM601928 1 0.0592 0.8623 0.988 0.000 0.012
#> GSM601933 2 0.1031 0.6095 0.000 0.976 0.024
#> GSM601938 2 0.2165 0.5948 0.000 0.936 0.064
#> GSM601943 2 0.4605 0.3439 0.000 0.796 0.204
#> GSM601948 1 0.1163 0.8630 0.972 0.000 0.028
#> GSM601958 1 0.3267 0.8614 0.884 0.000 0.116
#> GSM601973 2 0.6302 0.2049 0.000 0.520 0.480
#> GSM601978 2 0.1031 0.5945 0.000 0.976 0.024
#> GSM601988 3 0.5480 0.5979 0.004 0.264 0.732
#> GSM601878 1 0.0747 0.8620 0.984 0.000 0.016
#> GSM601908 2 0.0424 0.6096 0.000 0.992 0.008
#> GSM601918 2 0.5882 0.4046 0.000 0.652 0.348
#> GSM601923 1 0.0747 0.8620 0.984 0.000 0.016
#> GSM601953 2 0.1031 0.5945 0.000 0.976 0.024
#> GSM601963 1 0.4555 0.8340 0.800 0.000 0.200
#> GSM601968 1 0.5115 0.8377 0.796 0.016 0.188
#> GSM601983 1 0.4605 0.8321 0.796 0.000 0.204
#> GSM601993 3 0.5882 0.4399 0.000 0.348 0.652
#> GSM601874 2 0.0237 0.6063 0.000 0.996 0.004
#> GSM601884 2 0.0892 0.6086 0.000 0.980 0.020
#> GSM601889 1 0.3412 0.8603 0.876 0.000 0.124
#> GSM601894 1 0.3686 0.8571 0.860 0.000 0.140
#> GSM601899 1 0.8657 0.6027 0.592 0.244 0.164
#> GSM601904 3 0.9109 0.1099 0.148 0.364 0.488
#> GSM601914 1 0.4654 0.8278 0.792 0.000 0.208
#> GSM601929 1 0.0592 0.8623 0.988 0.000 0.012
#> GSM601934 2 0.0747 0.6103 0.000 0.984 0.016
#> GSM601939 1 0.0424 0.8652 0.992 0.000 0.008
#> GSM601944 2 0.6180 0.0709 0.000 0.584 0.416
#> GSM601949 1 0.1163 0.8630 0.972 0.000 0.028
#> GSM601959 1 0.3340 0.8609 0.880 0.000 0.120
#> GSM601974 3 0.5423 0.5406 0.084 0.096 0.820
#> GSM601979 2 0.0237 0.6064 0.000 0.996 0.004
#> GSM601989 1 0.3192 0.8618 0.888 0.000 0.112
#> GSM601879 1 0.0747 0.8620 0.984 0.000 0.016
#> GSM601909 1 0.4504 0.8394 0.804 0.000 0.196
#> GSM601919 2 0.6867 0.3809 0.028 0.636 0.336
#> GSM601924 1 0.0747 0.8620 0.984 0.000 0.016
#> GSM601954 2 0.5733 0.4162 0.000 0.676 0.324
#> GSM601964 1 0.4555 0.8340 0.800 0.000 0.200
#> GSM601969 1 0.3412 0.8612 0.876 0.000 0.124
#> GSM601984 1 0.0592 0.8631 0.988 0.000 0.012
#> GSM601994 2 0.5968 0.3263 0.000 0.636 0.364
#> GSM601875 2 0.0000 0.6076 0.000 1.000 0.000
#> GSM601885 2 0.0747 0.6103 0.000 0.984 0.016
#> GSM601890 1 0.8767 0.6233 0.588 0.204 0.208
#> GSM601895 1 0.4887 0.8212 0.772 0.000 0.228
#> GSM601900 1 0.4399 0.8411 0.812 0.000 0.188
#> GSM601905 2 0.7187 0.1442 0.024 0.496 0.480
#> GSM601915 1 0.3482 0.8591 0.872 0.000 0.128
#> GSM601930 1 0.0592 0.8623 0.988 0.000 0.012
#> GSM601935 3 0.5291 0.2610 0.268 0.000 0.732
#> GSM601940 1 0.1031 0.8667 0.976 0.000 0.024
#> GSM601945 2 0.0892 0.5974 0.000 0.980 0.020
#> GSM601950 1 0.1163 0.8630 0.972 0.000 0.028
#> GSM601960 1 0.4931 0.8170 0.768 0.000 0.232
#> GSM601975 2 0.6302 0.2049 0.000 0.520 0.480
#> GSM601980 3 0.5588 0.5768 0.004 0.276 0.720
#> GSM601990 1 0.4702 0.8259 0.788 0.000 0.212
#> GSM601880 1 0.0747 0.8620 0.984 0.000 0.016
#> GSM601910 1 0.4702 0.8323 0.788 0.000 0.212
#> GSM601920 3 0.8135 -0.0920 0.068 0.448 0.484
#> GSM601925 1 0.0747 0.8620 0.984 0.000 0.016
#> GSM601955 3 0.5244 0.5938 0.004 0.240 0.756
#> GSM601965 1 0.0747 0.8657 0.984 0.000 0.016
#> GSM601970 1 0.3686 0.8586 0.860 0.000 0.140
#> GSM601985 1 0.0424 0.8654 0.992 0.000 0.008
#> GSM601995 3 0.4883 0.6091 0.004 0.208 0.788
#> GSM601876 1 0.0424 0.8634 0.992 0.000 0.008
#> GSM601886 3 0.5295 0.5633 0.036 0.156 0.808
#> GSM601891 1 0.9329 0.4303 0.488 0.332 0.180
#> GSM601896 1 0.0000 0.8650 1.000 0.000 0.000
#> GSM601901 2 0.3116 0.5786 0.000 0.892 0.108
#> GSM601906 1 0.6750 0.3335 0.640 0.024 0.336
#> GSM601916 2 0.7074 0.1554 0.020 0.500 0.480
#> GSM601931 1 0.0592 0.8623 0.988 0.000 0.012
#> GSM601936 3 0.5529 0.5540 0.000 0.296 0.704
#> GSM601941 2 0.6302 0.2049 0.000 0.520 0.480
#> GSM601946 1 0.0592 0.8623 0.988 0.000 0.012
#> GSM601951 1 0.1031 0.8604 0.976 0.000 0.024
#> GSM601961 2 0.0892 0.5984 0.000 0.980 0.020
#> GSM601976 2 0.6302 0.2028 0.000 0.520 0.480
#> GSM601981 2 0.0747 0.6098 0.000 0.984 0.016
#> GSM601991 1 0.6280 0.5141 0.540 0.000 0.460
#> GSM601881 1 0.0747 0.8620 0.984 0.000 0.016
#> GSM601911 1 0.8561 -0.1320 0.484 0.420 0.096
#> GSM601921 2 0.6302 0.2046 0.000 0.520 0.480
#> GSM601926 1 0.0747 0.8620 0.984 0.000 0.016
#> GSM601956 2 0.1031 0.5945 0.000 0.976 0.024
#> GSM601966 2 0.5650 0.4106 0.000 0.688 0.312
#> GSM601971 1 0.2959 0.8645 0.900 0.000 0.100
#> GSM601986 1 0.1453 0.8550 0.968 0.008 0.024
#> GSM601996 2 0.6225 0.2572 0.000 0.568 0.432
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM601872 2 0.4274 0.7071 0.000 0.808 0.044 0.148
#> GSM601882 2 0.4453 0.7194 0.000 0.744 0.012 0.244
#> GSM601887 2 0.7171 -0.2398 0.352 0.516 0.128 0.004
#> GSM601892 1 0.7113 0.5903 0.532 0.316 0.152 0.000
#> GSM601897 3 0.7748 -0.2015 0.280 0.280 0.440 0.000
#> GSM601902 4 0.1174 0.8666 0.000 0.012 0.020 0.968
#> GSM601912 1 0.7155 0.6357 0.536 0.164 0.300 0.000
#> GSM601927 1 0.1151 0.7419 0.968 0.000 0.008 0.024
#> GSM601932 4 0.1174 0.8666 0.000 0.012 0.020 0.968
#> GSM601937 3 0.4245 0.7099 0.000 0.064 0.820 0.116
#> GSM601942 3 0.4804 0.3639 0.000 0.384 0.616 0.000
#> GSM601947 4 0.3122 0.8372 0.016 0.084 0.012 0.888
#> GSM601957 1 0.6075 0.7250 0.684 0.168 0.148 0.000
#> GSM601972 4 0.2730 0.8405 0.000 0.088 0.016 0.896
#> GSM601977 2 0.3972 0.7338 0.000 0.788 0.008 0.204
#> GSM601987 2 0.4319 0.7289 0.000 0.760 0.012 0.228
#> GSM601877 1 0.1811 0.7360 0.948 0.004 0.020 0.028
#> GSM601907 2 0.3726 0.7349 0.000 0.788 0.000 0.212
#> GSM601917 4 0.1953 0.8314 0.044 0.004 0.012 0.940
#> GSM601922 4 0.2207 0.8198 0.056 0.004 0.012 0.928
#> GSM601952 4 0.5412 0.7165 0.000 0.168 0.096 0.736
#> GSM601962 1 0.7169 0.5970 0.508 0.148 0.344 0.000
#> GSM601967 1 0.6324 0.7240 0.660 0.172 0.168 0.000
#> GSM601982 2 0.4648 0.7214 0.004 0.748 0.016 0.232
#> GSM601992 4 0.6238 0.5568 0.000 0.236 0.112 0.652
#> GSM601873 2 0.4706 0.6953 0.000 0.788 0.072 0.140
#> GSM601883 2 0.4420 0.7215 0.000 0.748 0.012 0.240
#> GSM601888 2 0.5156 0.3209 0.120 0.776 0.096 0.008
#> GSM601893 2 0.7226 -0.3517 0.388 0.468 0.144 0.000
#> GSM601898 1 0.6503 0.7084 0.640 0.164 0.196 0.000
#> GSM601903 4 0.1042 0.8663 0.000 0.008 0.020 0.972
#> GSM601913 1 0.6691 0.6872 0.612 0.152 0.236 0.000
#> GSM601928 1 0.1151 0.7419 0.968 0.000 0.008 0.024
#> GSM601933 2 0.4319 0.7289 0.000 0.760 0.012 0.228
#> GSM601938 2 0.4635 0.6920 0.000 0.720 0.012 0.268
#> GSM601943 2 0.4656 0.6076 0.000 0.792 0.136 0.072
#> GSM601948 1 0.3322 0.7467 0.892 0.036 0.040 0.032
#> GSM601958 1 0.5807 0.7316 0.708 0.160 0.132 0.000
#> GSM601973 4 0.1174 0.8663 0.000 0.012 0.020 0.968
#> GSM601978 2 0.3528 0.7338 0.000 0.808 0.000 0.192
#> GSM601988 3 0.4936 0.6906 0.004 0.052 0.768 0.176
#> GSM601878 1 0.1707 0.7386 0.952 0.004 0.020 0.024
#> GSM601908 2 0.4328 0.7119 0.000 0.748 0.008 0.244
#> GSM601918 4 0.2665 0.8361 0.008 0.088 0.004 0.900
#> GSM601923 1 0.1707 0.7386 0.952 0.004 0.020 0.024
#> GSM601953 2 0.3710 0.7329 0.000 0.804 0.004 0.192
#> GSM601963 1 0.6916 0.6694 0.572 0.148 0.280 0.000
#> GSM601968 1 0.7159 0.6677 0.548 0.180 0.272 0.000
#> GSM601983 1 0.6916 0.6694 0.572 0.148 0.280 0.000
#> GSM601993 3 0.5943 0.4386 0.000 0.048 0.592 0.360
#> GSM601874 2 0.3726 0.7349 0.000 0.788 0.000 0.212
#> GSM601884 2 0.4262 0.7256 0.000 0.756 0.008 0.236
#> GSM601889 1 0.6245 0.7202 0.668 0.168 0.164 0.000
#> GSM601894 1 0.6576 0.7068 0.632 0.168 0.200 0.000
#> GSM601899 2 0.7260 -0.2605 0.356 0.504 0.136 0.004
#> GSM601904 4 0.3852 0.6051 0.180 0.000 0.012 0.808
#> GSM601914 1 0.6975 0.6429 0.560 0.148 0.292 0.000
#> GSM601929 1 0.1256 0.7399 0.964 0.000 0.008 0.028
#> GSM601934 2 0.4284 0.7306 0.000 0.764 0.012 0.224
#> GSM601939 1 0.0779 0.7527 0.980 0.004 0.016 0.000
#> GSM601944 2 0.7871 -0.0902 0.000 0.384 0.284 0.332
#> GSM601949 1 0.2555 0.7521 0.920 0.040 0.032 0.008
#> GSM601959 1 0.6075 0.7250 0.684 0.168 0.148 0.000
#> GSM601974 3 0.7512 0.3283 0.048 0.064 0.468 0.420
#> GSM601979 2 0.3870 0.7353 0.000 0.788 0.004 0.208
#> GSM601989 1 0.5990 0.7277 0.692 0.164 0.144 0.000
#> GSM601879 1 0.1920 0.7341 0.944 0.004 0.024 0.028
#> GSM601909 1 0.7152 0.6656 0.544 0.172 0.284 0.000
#> GSM601919 4 0.3374 0.8255 0.028 0.080 0.012 0.880
#> GSM601924 1 0.1598 0.7402 0.956 0.004 0.020 0.020
#> GSM601954 4 0.3501 0.7988 0.000 0.132 0.020 0.848
#> GSM601964 1 0.6993 0.6550 0.556 0.148 0.296 0.000
#> GSM601969 1 0.7001 0.7224 0.664 0.156 0.136 0.044
#> GSM601984 1 0.2196 0.7529 0.936 0.016 0.032 0.016
#> GSM601994 4 0.6259 0.5637 0.000 0.232 0.116 0.652
#> GSM601875 2 0.4049 0.7352 0.000 0.780 0.008 0.212
#> GSM601885 2 0.4453 0.7194 0.000 0.744 0.012 0.244
#> GSM601890 2 0.7534 -0.3567 0.360 0.448 0.192 0.000
#> GSM601895 1 0.7138 0.6405 0.540 0.164 0.296 0.000
#> GSM601900 1 0.7118 0.6487 0.548 0.168 0.284 0.000
#> GSM601905 4 0.0657 0.8622 0.004 0.000 0.012 0.984
#> GSM601915 1 0.6675 0.6909 0.616 0.156 0.228 0.000
#> GSM601930 1 0.1151 0.7419 0.968 0.000 0.008 0.024
#> GSM601935 3 0.3453 0.6775 0.052 0.000 0.868 0.080
#> GSM601940 1 0.2032 0.7586 0.936 0.036 0.028 0.000
#> GSM601945 2 0.3626 0.7319 0.000 0.812 0.004 0.184
#> GSM601950 1 0.2218 0.7523 0.932 0.028 0.036 0.004
#> GSM601960 1 0.7156 0.6120 0.520 0.152 0.328 0.000
#> GSM601975 4 0.1174 0.8666 0.000 0.012 0.020 0.968
#> GSM601980 3 0.4718 0.7025 0.000 0.116 0.792 0.092
#> GSM601990 1 0.7023 0.6195 0.544 0.144 0.312 0.000
#> GSM601880 1 0.1707 0.7386 0.952 0.004 0.020 0.024
#> GSM601910 1 0.7222 0.6518 0.528 0.172 0.300 0.000
#> GSM601920 4 0.2179 0.8110 0.064 0.000 0.012 0.924
#> GSM601925 1 0.1707 0.7386 0.952 0.004 0.020 0.024
#> GSM601955 3 0.4362 0.7063 0.000 0.096 0.816 0.088
#> GSM601965 1 0.2686 0.7585 0.916 0.032 0.040 0.012
#> GSM601970 1 0.6790 0.7027 0.604 0.168 0.228 0.000
#> GSM601985 1 0.1798 0.7571 0.944 0.016 0.040 0.000
#> GSM601995 3 0.4015 0.7097 0.000 0.052 0.832 0.116
#> GSM601876 1 0.1854 0.7571 0.948 0.020 0.024 0.008
#> GSM601886 3 0.5074 0.5307 0.008 0.004 0.656 0.332
#> GSM601891 2 0.7195 -0.0349 0.276 0.576 0.136 0.012
#> GSM601896 1 0.1920 0.7580 0.944 0.028 0.024 0.004
#> GSM601901 2 0.4907 0.4251 0.000 0.580 0.000 0.420
#> GSM601906 1 0.5483 0.0118 0.536 0.000 0.016 0.448
#> GSM601916 4 0.0927 0.8661 0.000 0.008 0.016 0.976
#> GSM601931 1 0.1151 0.7419 0.968 0.000 0.008 0.024
#> GSM601936 3 0.5524 0.5818 0.000 0.048 0.676 0.276
#> GSM601941 4 0.1042 0.8663 0.000 0.008 0.020 0.972
#> GSM601946 1 0.1059 0.7484 0.972 0.000 0.016 0.012
#> GSM601951 1 0.1109 0.7386 0.968 0.000 0.004 0.028
#> GSM601961 2 0.4399 0.7318 0.000 0.768 0.020 0.212
#> GSM601976 4 0.1059 0.8665 0.000 0.016 0.012 0.972
#> GSM601981 2 0.3975 0.7219 0.000 0.760 0.000 0.240
#> GSM601991 3 0.4804 0.4578 0.160 0.064 0.776 0.000
#> GSM601881 1 0.1707 0.7386 0.952 0.004 0.020 0.024
#> GSM601911 1 0.6785 0.3698 0.632 0.096 0.020 0.252
#> GSM601921 4 0.1339 0.8547 0.024 0.004 0.008 0.964
#> GSM601926 1 0.1707 0.7386 0.952 0.004 0.020 0.024
#> GSM601956 2 0.3626 0.7302 0.000 0.812 0.004 0.184
#> GSM601966 4 0.4399 0.6752 0.000 0.212 0.020 0.768
#> GSM601971 1 0.5651 0.7432 0.740 0.128 0.124 0.008
#> GSM601986 1 0.2841 0.7515 0.912 0.024 0.032 0.032
#> GSM601996 4 0.2623 0.8384 0.000 0.064 0.028 0.908
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM601872 2 0.1774 0.82880 0.000 0.932 0.052 0.000 0.016
#> GSM601882 2 0.3018 0.84096 0.000 0.884 0.044 0.036 0.036
#> GSM601887 3 0.6192 0.53808 0.116 0.284 0.584 0.004 0.012
#> GSM601892 3 0.5484 0.72102 0.204 0.104 0.680 0.004 0.008
#> GSM601897 3 0.6213 0.56487 0.088 0.028 0.652 0.020 0.212
#> GSM601902 4 0.2414 0.85341 0.000 0.080 0.008 0.900 0.012
#> GSM601912 3 0.5809 0.76766 0.224 0.004 0.660 0.024 0.088
#> GSM601927 1 0.1280 0.86773 0.960 0.000 0.024 0.008 0.008
#> GSM601932 4 0.2414 0.85589 0.000 0.080 0.012 0.900 0.008
#> GSM601937 5 0.3126 0.80363 0.000 0.008 0.076 0.048 0.868
#> GSM601942 5 0.5936 0.58041 0.000 0.240 0.096 0.028 0.636
#> GSM601947 4 0.4159 0.83665 0.012 0.124 0.032 0.812 0.020
#> GSM601957 3 0.4235 0.72933 0.336 0.000 0.656 0.008 0.000
#> GSM601972 4 0.2746 0.84930 0.000 0.112 0.008 0.872 0.008
#> GSM601977 2 0.0968 0.85394 0.000 0.972 0.012 0.012 0.004
#> GSM601987 2 0.2342 0.84610 0.000 0.916 0.040 0.020 0.024
#> GSM601877 1 0.0324 0.86791 0.992 0.000 0.004 0.004 0.000
#> GSM601907 2 0.1095 0.85191 0.000 0.968 0.008 0.012 0.012
#> GSM601917 4 0.4369 0.83708 0.032 0.064 0.040 0.824 0.040
#> GSM601922 4 0.4605 0.82938 0.048 0.060 0.040 0.812 0.040
#> GSM601952 4 0.6439 0.70350 0.008 0.208 0.048 0.632 0.104
#> GSM601962 3 0.6436 0.69881 0.332 0.000 0.536 0.028 0.104
#> GSM601967 3 0.4484 0.73608 0.352 0.004 0.636 0.004 0.004
#> GSM601982 2 0.3856 0.82599 0.016 0.848 0.060 0.028 0.048
#> GSM601992 2 0.7968 0.03191 0.000 0.376 0.092 0.316 0.216
#> GSM601873 2 0.1728 0.84119 0.000 0.940 0.020 0.004 0.036
#> GSM601883 2 0.2861 0.84023 0.000 0.892 0.040 0.036 0.032
#> GSM601888 2 0.4848 0.28875 0.008 0.584 0.396 0.004 0.008
#> GSM601893 3 0.5760 0.64738 0.128 0.192 0.664 0.004 0.012
#> GSM601898 3 0.4643 0.75593 0.320 0.000 0.656 0.012 0.012
#> GSM601903 4 0.2414 0.85341 0.000 0.080 0.008 0.900 0.012
#> GSM601913 3 0.6192 0.70045 0.348 0.000 0.548 0.032 0.072
#> GSM601928 1 0.1153 0.86727 0.964 0.000 0.024 0.004 0.008
#> GSM601933 2 0.3229 0.82570 0.000 0.872 0.040 0.032 0.056
#> GSM601938 2 0.3323 0.82774 0.000 0.868 0.044 0.048 0.040
#> GSM601943 2 0.2513 0.81656 0.000 0.904 0.040 0.008 0.048
#> GSM601948 1 0.4683 0.59910 0.728 0.004 0.220 0.040 0.008
#> GSM601958 3 0.4478 0.71668 0.360 0.000 0.628 0.008 0.004
#> GSM601973 4 0.2518 0.85347 0.000 0.080 0.008 0.896 0.016
#> GSM601978 2 0.0566 0.84961 0.000 0.984 0.012 0.004 0.000
#> GSM601988 5 0.3861 0.78860 0.000 0.004 0.088 0.092 0.816
#> GSM601878 1 0.0162 0.86873 0.996 0.000 0.000 0.004 0.000
#> GSM601908 2 0.2436 0.84305 0.000 0.912 0.020 0.032 0.036
#> GSM601918 4 0.4857 0.82308 0.012 0.136 0.040 0.772 0.040
#> GSM601923 1 0.0162 0.86873 0.996 0.000 0.000 0.004 0.000
#> GSM601953 2 0.1195 0.84276 0.000 0.960 0.028 0.000 0.012
#> GSM601963 3 0.6460 0.69669 0.352 0.000 0.524 0.036 0.088
#> GSM601968 3 0.4559 0.77156 0.260 0.004 0.708 0.008 0.020
#> GSM601983 3 0.6354 0.70033 0.348 0.000 0.532 0.028 0.092
#> GSM601993 5 0.5398 0.60810 0.000 0.020 0.088 0.200 0.692
#> GSM601874 2 0.1074 0.85244 0.000 0.968 0.016 0.012 0.004
#> GSM601884 2 0.2761 0.84501 0.000 0.896 0.048 0.028 0.028
#> GSM601889 3 0.4487 0.73999 0.332 0.000 0.652 0.008 0.008
#> GSM601894 3 0.4260 0.75762 0.308 0.000 0.680 0.008 0.004
#> GSM601899 3 0.6054 0.57304 0.116 0.256 0.612 0.004 0.012
#> GSM601904 4 0.3169 0.75724 0.100 0.004 0.020 0.864 0.012
#> GSM601914 3 0.6373 0.73076 0.284 0.000 0.576 0.032 0.108
#> GSM601929 1 0.1588 0.86572 0.948 0.000 0.028 0.016 0.008
#> GSM601934 2 0.2313 0.84648 0.000 0.916 0.032 0.012 0.040
#> GSM601939 1 0.1883 0.85867 0.932 0.000 0.048 0.008 0.012
#> GSM601944 2 0.7773 0.02966 0.000 0.424 0.080 0.208 0.288
#> GSM601949 1 0.4091 0.64390 0.756 0.004 0.220 0.012 0.008
#> GSM601959 3 0.4235 0.73204 0.336 0.000 0.656 0.008 0.000
#> GSM601974 4 0.7300 0.04136 0.016 0.020 0.240 0.492 0.232
#> GSM601979 2 0.0566 0.85245 0.000 0.984 0.004 0.012 0.000
#> GSM601989 3 0.5256 0.73666 0.344 0.000 0.608 0.016 0.032
#> GSM601879 1 0.0727 0.86251 0.980 0.000 0.004 0.012 0.004
#> GSM601909 3 0.4313 0.77751 0.260 0.000 0.716 0.008 0.016
#> GSM601919 4 0.5090 0.81895 0.028 0.124 0.040 0.768 0.040
#> GSM601924 1 0.0000 0.86907 1.000 0.000 0.000 0.000 0.000
#> GSM601954 4 0.4965 0.78353 0.008 0.200 0.032 0.732 0.028
#> GSM601964 3 0.6460 0.69669 0.352 0.000 0.524 0.036 0.088
#> GSM601969 3 0.5626 0.59774 0.388 0.004 0.556 0.028 0.024
#> GSM601984 1 0.1988 0.84299 0.928 0.000 0.048 0.008 0.016
#> GSM601994 2 0.7932 0.00676 0.000 0.368 0.088 0.332 0.212
#> GSM601875 2 0.0566 0.85245 0.000 0.984 0.004 0.012 0.000
#> GSM601885 2 0.2861 0.84023 0.000 0.892 0.040 0.036 0.032
#> GSM601890 3 0.5803 0.63451 0.120 0.196 0.664 0.004 0.016
#> GSM601895 3 0.5485 0.77298 0.232 0.000 0.668 0.016 0.084
#> GSM601900 3 0.4872 0.77934 0.248 0.000 0.692 0.004 0.056
#> GSM601905 4 0.2275 0.85339 0.004 0.068 0.008 0.912 0.008
#> GSM601915 3 0.5880 0.72494 0.336 0.000 0.580 0.032 0.052
#> GSM601930 1 0.1280 0.86773 0.960 0.000 0.024 0.008 0.008
#> GSM601935 5 0.3685 0.76350 0.016 0.000 0.132 0.028 0.824
#> GSM601940 1 0.2505 0.82308 0.888 0.000 0.092 0.000 0.020
#> GSM601945 2 0.0324 0.85189 0.000 0.992 0.000 0.004 0.004
#> GSM601950 1 0.3952 0.64413 0.764 0.004 0.216 0.008 0.008
#> GSM601960 3 0.6352 0.74756 0.240 0.000 0.600 0.032 0.128
#> GSM601975 4 0.2177 0.85486 0.000 0.080 0.004 0.908 0.008
#> GSM601980 5 0.4848 0.79145 0.000 0.032 0.148 0.064 0.756
#> GSM601990 3 0.6358 0.70662 0.312 0.000 0.560 0.032 0.096
#> GSM601880 1 0.0486 0.86828 0.988 0.000 0.004 0.004 0.004
#> GSM601910 3 0.4783 0.77662 0.252 0.000 0.700 0.012 0.036
#> GSM601920 4 0.4508 0.80627 0.068 0.036 0.044 0.816 0.036
#> GSM601925 1 0.0324 0.86791 0.992 0.000 0.004 0.004 0.000
#> GSM601955 5 0.4715 0.79272 0.000 0.020 0.160 0.064 0.756
#> GSM601965 1 0.2908 0.78251 0.868 0.000 0.108 0.008 0.016
#> GSM601970 3 0.4739 0.76769 0.320 0.000 0.652 0.012 0.016
#> GSM601985 1 0.2270 0.82943 0.916 0.000 0.052 0.012 0.020
#> GSM601995 5 0.3807 0.80201 0.000 0.008 0.116 0.056 0.820
#> GSM601876 1 0.2396 0.84180 0.904 0.000 0.068 0.004 0.024
#> GSM601886 5 0.4254 0.68497 0.000 0.000 0.040 0.220 0.740
#> GSM601891 3 0.5608 0.39836 0.048 0.352 0.584 0.004 0.012
#> GSM601896 1 0.2331 0.83345 0.900 0.000 0.080 0.000 0.020
#> GSM601901 2 0.4816 0.51639 0.000 0.680 0.020 0.280 0.020
#> GSM601906 1 0.4757 0.43735 0.648 0.000 0.012 0.324 0.016
#> GSM601916 4 0.2861 0.84932 0.000 0.076 0.016 0.884 0.024
#> GSM601931 1 0.1153 0.86727 0.964 0.000 0.024 0.004 0.008
#> GSM601936 5 0.3681 0.75122 0.000 0.008 0.036 0.136 0.820
#> GSM601941 4 0.2673 0.85494 0.000 0.076 0.016 0.892 0.016
#> GSM601946 1 0.1442 0.86511 0.952 0.000 0.032 0.004 0.012
#> GSM601951 1 0.1597 0.86473 0.948 0.000 0.020 0.024 0.008
#> GSM601961 2 0.1770 0.83294 0.000 0.936 0.048 0.008 0.008
#> GSM601976 4 0.2569 0.85470 0.000 0.076 0.012 0.896 0.016
#> GSM601981 2 0.1484 0.84833 0.000 0.944 0.008 0.048 0.000
#> GSM601991 5 0.5967 0.27340 0.068 0.000 0.340 0.024 0.568
#> GSM601881 1 0.0324 0.86791 0.992 0.000 0.004 0.004 0.000
#> GSM601911 1 0.6104 0.61852 0.712 0.112 0.052 0.048 0.076
#> GSM601921 4 0.4389 0.84354 0.016 0.084 0.044 0.816 0.040
#> GSM601926 1 0.0000 0.86907 1.000 0.000 0.000 0.000 0.000
#> GSM601956 2 0.1653 0.84598 0.000 0.944 0.028 0.004 0.024
#> GSM601966 4 0.6960 0.31293 0.000 0.348 0.084 0.492 0.076
#> GSM601971 1 0.4695 -0.44900 0.524 0.000 0.464 0.008 0.004
#> GSM601986 1 0.2956 0.82363 0.884 0.000 0.060 0.020 0.036
#> GSM601996 4 0.6427 0.62453 0.000 0.112 0.092 0.644 0.152
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM601872 2 0.218 0.8176 0.004 0.912 0.012 0.000 0.020 0.052
#> GSM601882 2 0.331 0.7951 0.004 0.816 0.004 0.028 0.000 0.148
#> GSM601887 3 0.431 0.5463 0.008 0.236 0.712 0.000 0.004 0.040
#> GSM601892 3 0.308 0.6976 0.020 0.080 0.860 0.000 0.004 0.036
#> GSM601897 3 0.545 0.5625 0.008 0.020 0.644 0.000 0.216 0.112
#> GSM601902 4 0.156 0.7581 0.000 0.000 0.000 0.932 0.012 0.056
#> GSM601912 3 0.460 0.7285 0.036 0.000 0.744 0.000 0.096 0.124
#> GSM601927 1 0.196 0.8612 0.888 0.000 0.112 0.000 0.000 0.000
#> GSM601932 4 0.108 0.7764 0.000 0.000 0.000 0.956 0.004 0.040
#> GSM601937 5 0.374 0.5741 0.004 0.000 0.040 0.004 0.780 0.172
#> GSM601942 5 0.531 0.3603 0.032 0.160 0.008 0.000 0.684 0.116
#> GSM601947 4 0.335 0.7695 0.020 0.032 0.004 0.836 0.000 0.108
#> GSM601957 3 0.240 0.7444 0.112 0.000 0.872 0.000 0.000 0.016
#> GSM601972 4 0.137 0.7744 0.000 0.004 0.000 0.948 0.012 0.036
#> GSM601977 2 0.216 0.8431 0.000 0.912 0.012 0.016 0.004 0.056
#> GSM601987 2 0.280 0.8130 0.004 0.852 0.000 0.024 0.000 0.120
#> GSM601877 1 0.209 0.8616 0.900 0.000 0.080 0.000 0.000 0.020
#> GSM601907 2 0.135 0.8401 0.000 0.952 0.012 0.012 0.000 0.024
#> GSM601917 4 0.391 0.7597 0.032 0.004 0.004 0.772 0.008 0.180
#> GSM601922 4 0.388 0.7600 0.032 0.004 0.004 0.776 0.008 0.176
#> GSM601952 4 0.575 0.6478 0.020 0.080 0.004 0.660 0.044 0.192
#> GSM601962 3 0.620 0.6630 0.124 0.000 0.600 0.000 0.128 0.148
#> GSM601967 3 0.285 0.7476 0.132 0.000 0.844 0.000 0.004 0.020
#> GSM601982 2 0.458 0.7693 0.016 0.756 0.048 0.024 0.004 0.152
#> GSM601992 6 0.658 0.6546 0.000 0.224 0.004 0.188 0.060 0.524
#> GSM601873 2 0.322 0.7801 0.016 0.852 0.004 0.004 0.032 0.092
#> GSM601883 2 0.321 0.7938 0.004 0.816 0.000 0.028 0.000 0.152
#> GSM601888 2 0.487 0.2347 0.004 0.536 0.416 0.000 0.004 0.040
#> GSM601893 3 0.340 0.6664 0.012 0.116 0.828 0.000 0.004 0.040
#> GSM601898 3 0.356 0.7685 0.104 0.000 0.816 0.000 0.012 0.068
#> GSM601903 4 0.130 0.7677 0.000 0.000 0.000 0.948 0.012 0.040
#> GSM601913 3 0.581 0.6896 0.156 0.000 0.632 0.000 0.068 0.144
#> GSM601928 1 0.196 0.8612 0.888 0.000 0.112 0.000 0.000 0.000
#> GSM601933 2 0.305 0.8017 0.004 0.832 0.000 0.028 0.000 0.136
#> GSM601938 2 0.342 0.7720 0.004 0.792 0.000 0.028 0.000 0.176
#> GSM601943 2 0.276 0.7919 0.016 0.880 0.004 0.000 0.032 0.068
#> GSM601948 1 0.568 0.4705 0.536 0.000 0.356 0.056 0.000 0.052
#> GSM601958 3 0.335 0.7303 0.160 0.000 0.804 0.000 0.004 0.032
#> GSM601973 4 0.130 0.7682 0.000 0.000 0.000 0.948 0.012 0.040
#> GSM601978 2 0.110 0.8359 0.004 0.964 0.008 0.000 0.004 0.020
#> GSM601988 5 0.577 0.3791 0.004 0.004 0.048 0.052 0.560 0.332
#> GSM601878 1 0.209 0.8616 0.900 0.000 0.080 0.000 0.000 0.020
#> GSM601908 2 0.234 0.8202 0.000 0.896 0.000 0.020 0.012 0.072
#> GSM601918 4 0.416 0.7567 0.020 0.036 0.004 0.776 0.008 0.156
#> GSM601923 1 0.209 0.8616 0.900 0.000 0.080 0.000 0.000 0.020
#> GSM601953 2 0.149 0.8308 0.004 0.948 0.020 0.000 0.008 0.020
#> GSM601963 3 0.612 0.6744 0.128 0.000 0.608 0.000 0.112 0.152
#> GSM601968 3 0.237 0.7509 0.056 0.000 0.900 0.000 0.020 0.024
#> GSM601983 3 0.602 0.6788 0.120 0.000 0.620 0.000 0.112 0.148
#> GSM601993 6 0.602 0.2464 0.000 0.016 0.004 0.152 0.312 0.516
#> GSM601874 2 0.118 0.8417 0.004 0.960 0.004 0.008 0.000 0.024
#> GSM601884 2 0.292 0.8136 0.008 0.844 0.000 0.020 0.000 0.128
#> GSM601889 3 0.293 0.7545 0.124 0.000 0.844 0.000 0.004 0.028
#> GSM601894 3 0.286 0.7602 0.108 0.000 0.856 0.000 0.008 0.028
#> GSM601899 3 0.391 0.6116 0.008 0.180 0.768 0.000 0.004 0.040
#> GSM601904 4 0.314 0.7711 0.048 0.000 0.004 0.844 0.004 0.100
#> GSM601914 3 0.584 0.6866 0.088 0.000 0.636 0.000 0.124 0.152
#> GSM601929 1 0.200 0.8605 0.884 0.000 0.116 0.000 0.000 0.000
#> GSM601934 2 0.277 0.8148 0.004 0.852 0.000 0.020 0.000 0.124
#> GSM601939 1 0.291 0.8505 0.836 0.000 0.136 0.000 0.000 0.028
#> GSM601944 6 0.726 0.4054 0.024 0.292 0.000 0.104 0.124 0.456
#> GSM601949 1 0.422 0.4894 0.556 0.000 0.428 0.000 0.000 0.016
#> GSM601959 3 0.258 0.7388 0.128 0.000 0.856 0.000 0.000 0.016
#> GSM601974 4 0.694 0.2886 0.008 0.004 0.160 0.524 0.208 0.096
#> GSM601979 2 0.087 0.8422 0.000 0.972 0.004 0.012 0.000 0.012
#> GSM601989 3 0.401 0.7471 0.136 0.000 0.772 0.000 0.008 0.084
#> GSM601879 1 0.212 0.8586 0.900 0.000 0.076 0.000 0.000 0.024
#> GSM601909 3 0.283 0.7611 0.056 0.000 0.876 0.000 0.028 0.040
#> GSM601919 4 0.445 0.7475 0.028 0.036 0.004 0.752 0.008 0.172
#> GSM601924 1 0.209 0.8616 0.900 0.000 0.080 0.000 0.000 0.020
#> GSM601954 4 0.460 0.7219 0.012 0.076 0.004 0.748 0.012 0.148
#> GSM601964 3 0.620 0.6673 0.128 0.000 0.600 0.000 0.120 0.152
#> GSM601969 3 0.529 0.6272 0.156 0.000 0.692 0.040 0.008 0.104
#> GSM601984 1 0.398 0.7993 0.768 0.000 0.140 0.004 0.000 0.088
#> GSM601994 6 0.663 0.6634 0.000 0.200 0.004 0.220 0.060 0.516
#> GSM601875 2 0.087 0.8426 0.000 0.972 0.004 0.012 0.000 0.012
#> GSM601885 2 0.321 0.7938 0.004 0.816 0.000 0.028 0.000 0.152
#> GSM601890 3 0.392 0.6494 0.008 0.140 0.792 0.000 0.016 0.044
#> GSM601895 3 0.425 0.7415 0.036 0.000 0.776 0.000 0.096 0.092
#> GSM601900 3 0.363 0.7584 0.036 0.000 0.824 0.000 0.060 0.080
#> GSM601905 4 0.162 0.7765 0.004 0.000 0.000 0.928 0.004 0.064
#> GSM601915 3 0.523 0.7231 0.124 0.000 0.688 0.000 0.048 0.140
#> GSM601930 1 0.196 0.8612 0.888 0.000 0.112 0.000 0.000 0.000
#> GSM601935 5 0.466 0.5513 0.004 0.000 0.100 0.004 0.704 0.188
#> GSM601940 1 0.378 0.7906 0.740 0.000 0.224 0.000 0.000 0.036
#> GSM601945 2 0.171 0.8312 0.012 0.928 0.000 0.000 0.004 0.056
#> GSM601950 1 0.402 0.5308 0.580 0.000 0.412 0.000 0.000 0.008
#> GSM601960 3 0.566 0.6814 0.056 0.000 0.644 0.000 0.156 0.144
#> GSM601975 4 0.130 0.7800 0.004 0.000 0.000 0.952 0.012 0.032
#> GSM601980 5 0.337 0.5797 0.036 0.024 0.028 0.000 0.856 0.056
#> GSM601990 3 0.609 0.6664 0.108 0.000 0.612 0.000 0.128 0.152
#> GSM601880 1 0.212 0.8589 0.900 0.000 0.076 0.000 0.000 0.024
#> GSM601910 3 0.304 0.7596 0.056 0.000 0.864 0.000 0.044 0.036
#> GSM601920 4 0.400 0.7597 0.024 0.008 0.004 0.768 0.012 0.184
#> GSM601925 1 0.220 0.8568 0.896 0.000 0.076 0.000 0.000 0.028
#> GSM601955 5 0.341 0.5819 0.036 0.020 0.028 0.000 0.852 0.064
#> GSM601965 1 0.468 0.7091 0.688 0.000 0.204 0.004 0.000 0.104
#> GSM601970 3 0.352 0.7684 0.132 0.000 0.812 0.000 0.016 0.040
#> GSM601985 1 0.383 0.8046 0.780 0.000 0.140 0.000 0.004 0.076
#> GSM601995 5 0.188 0.5990 0.020 0.000 0.028 0.000 0.928 0.024
#> GSM601876 1 0.374 0.8086 0.764 0.000 0.184 0.000 0.000 0.052
#> GSM601886 5 0.626 0.1122 0.008 0.000 0.012 0.200 0.492 0.288
#> GSM601891 3 0.448 0.4829 0.004 0.288 0.664 0.000 0.004 0.040
#> GSM601896 1 0.377 0.7990 0.752 0.000 0.204 0.000 0.000 0.044
#> GSM601901 2 0.468 0.4149 0.004 0.656 0.000 0.280 0.004 0.056
#> GSM601906 1 0.520 0.4945 0.648 0.000 0.032 0.256 0.004 0.060
#> GSM601916 4 0.217 0.7192 0.000 0.000 0.000 0.888 0.012 0.100
#> GSM601931 1 0.200 0.8604 0.884 0.000 0.116 0.000 0.000 0.000
#> GSM601936 5 0.581 0.2813 0.008 0.012 0.012 0.084 0.564 0.320
#> GSM601941 4 0.191 0.7627 0.000 0.000 0.000 0.908 0.012 0.080
#> GSM601946 1 0.267 0.8543 0.852 0.000 0.128 0.000 0.000 0.020
#> GSM601951 1 0.322 0.8502 0.836 0.000 0.112 0.040 0.000 0.012
#> GSM601961 2 0.256 0.7893 0.000 0.884 0.076 0.004 0.004 0.032
#> GSM601976 4 0.204 0.7686 0.004 0.000 0.000 0.908 0.016 0.072
#> GSM601981 2 0.181 0.8325 0.000 0.920 0.000 0.060 0.000 0.020
#> GSM601991 5 0.631 -0.0639 0.024 0.000 0.376 0.000 0.420 0.180
#> GSM601881 1 0.209 0.8616 0.900 0.000 0.080 0.000 0.000 0.020
#> GSM601911 1 0.688 0.5818 0.576 0.104 0.100 0.048 0.000 0.172
#> GSM601921 4 0.369 0.7671 0.020 0.008 0.004 0.792 0.008 0.168
#> GSM601926 1 0.209 0.8616 0.900 0.000 0.080 0.000 0.000 0.020
#> GSM601956 2 0.172 0.8259 0.004 0.932 0.012 0.000 0.004 0.048
#> GSM601966 4 0.635 -0.5047 0.004 0.204 0.004 0.432 0.008 0.348
#> GSM601971 3 0.427 0.5192 0.316 0.000 0.648 0.000 0.000 0.036
#> GSM601986 1 0.445 0.7825 0.736 0.000 0.144 0.012 0.000 0.108
#> GSM601996 6 0.565 0.4435 0.000 0.072 0.004 0.396 0.024 0.504
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)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
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)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
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)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, 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.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")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n time(p) gender(p) k
#> SD:kmeans 125 0.2945 0.864 2
#> SD:kmeans 89 0.8149 0.266 3
#> SD:kmeans 110 0.1713 0.118 4
#> SD:kmeans 115 0.0948 0.203 5
#> SD:kmeans 109 0.0320 0.102 6
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.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["SD", "skmeans"]
# you can also extract it by
# res = res_list["SD:skmeans"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 21512 rows and 125 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 1250 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_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
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, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.966 0.971 0.985 0.5042 0.496 0.496
#> 3 3 0.442 0.608 0.720 0.2985 0.834 0.673
#> 4 4 0.428 0.457 0.695 0.1347 0.827 0.554
#> 5 5 0.463 0.424 0.618 0.0675 0.907 0.670
#> 6 6 0.488 0.317 0.568 0.0401 0.917 0.665
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 2
Following shows 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 class
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
#> GSM601872 2 0.0000 0.991 0.000 1.000
#> GSM601882 2 0.0000 0.991 0.000 1.000
#> GSM601887 1 0.6343 0.827 0.840 0.160
#> GSM601892 1 0.0000 0.979 1.000 0.000
#> GSM601897 1 0.6048 0.841 0.852 0.148
#> GSM601902 2 0.0000 0.991 0.000 1.000
#> GSM601912 1 0.0672 0.974 0.992 0.008
#> GSM601927 1 0.0000 0.979 1.000 0.000
#> GSM601932 2 0.0000 0.991 0.000 1.000
#> GSM601937 2 0.0000 0.991 0.000 1.000
#> GSM601942 2 0.0000 0.991 0.000 1.000
#> GSM601947 2 0.0000 0.991 0.000 1.000
#> GSM601957 1 0.0000 0.979 1.000 0.000
#> GSM601972 2 0.0000 0.991 0.000 1.000
#> GSM601977 2 0.0000 0.991 0.000 1.000
#> GSM601987 2 0.0000 0.991 0.000 1.000
#> GSM601877 1 0.0000 0.979 1.000 0.000
#> GSM601907 2 0.0000 0.991 0.000 1.000
#> GSM601917 2 0.0672 0.986 0.008 0.992
#> GSM601922 2 0.3114 0.943 0.056 0.944
#> GSM601952 2 0.0000 0.991 0.000 1.000
#> GSM601962 1 0.0000 0.979 1.000 0.000
#> GSM601967 1 0.0000 0.979 1.000 0.000
#> GSM601982 2 0.2043 0.966 0.032 0.968
#> GSM601992 2 0.0000 0.991 0.000 1.000
#> GSM601873 2 0.0000 0.991 0.000 1.000
#> GSM601883 2 0.0000 0.991 0.000 1.000
#> GSM601888 1 0.6973 0.790 0.812 0.188
#> GSM601893 1 0.0376 0.976 0.996 0.004
#> GSM601898 1 0.0000 0.979 1.000 0.000
#> GSM601903 2 0.0000 0.991 0.000 1.000
#> GSM601913 1 0.0000 0.979 1.000 0.000
#> GSM601928 1 0.0000 0.979 1.000 0.000
#> GSM601933 2 0.0000 0.991 0.000 1.000
#> GSM601938 2 0.0000 0.991 0.000 1.000
#> GSM601943 2 0.0000 0.991 0.000 1.000
#> GSM601948 1 0.0000 0.979 1.000 0.000
#> GSM601958 1 0.0000 0.979 1.000 0.000
#> GSM601973 2 0.0000 0.991 0.000 1.000
#> GSM601978 2 0.0000 0.991 0.000 1.000
#> GSM601988 2 0.0000 0.991 0.000 1.000
#> GSM601878 1 0.0000 0.979 1.000 0.000
#> GSM601908 2 0.0000 0.991 0.000 1.000
#> GSM601918 2 0.0000 0.991 0.000 1.000
#> GSM601923 1 0.0000 0.979 1.000 0.000
#> GSM601953 2 0.0000 0.991 0.000 1.000
#> GSM601963 1 0.0000 0.979 1.000 0.000
#> GSM601968 1 0.0672 0.974 0.992 0.008
#> GSM601983 1 0.0000 0.979 1.000 0.000
#> GSM601993 2 0.0000 0.991 0.000 1.000
#> GSM601874 2 0.0000 0.991 0.000 1.000
#> GSM601884 2 0.0000 0.991 0.000 1.000
#> GSM601889 1 0.0000 0.979 1.000 0.000
#> GSM601894 1 0.0000 0.979 1.000 0.000
#> GSM601899 1 0.5178 0.877 0.884 0.116
#> GSM601904 2 0.4431 0.905 0.092 0.908
#> GSM601914 1 0.0000 0.979 1.000 0.000
#> GSM601929 1 0.0000 0.979 1.000 0.000
#> GSM601934 2 0.0000 0.991 0.000 1.000
#> GSM601939 1 0.0000 0.979 1.000 0.000
#> GSM601944 2 0.0000 0.991 0.000 1.000
#> GSM601949 1 0.0000 0.979 1.000 0.000
#> GSM601959 1 0.0000 0.979 1.000 0.000
#> GSM601974 2 0.4815 0.887 0.104 0.896
#> GSM601979 2 0.0000 0.991 0.000 1.000
#> GSM601989 1 0.0000 0.979 1.000 0.000
#> GSM601879 1 0.0000 0.979 1.000 0.000
#> GSM601909 1 0.0000 0.979 1.000 0.000
#> GSM601919 2 0.0000 0.991 0.000 1.000
#> GSM601924 1 0.0000 0.979 1.000 0.000
#> GSM601954 2 0.0000 0.991 0.000 1.000
#> GSM601964 1 0.0000 0.979 1.000 0.000
#> GSM601969 1 0.0000 0.979 1.000 0.000
#> GSM601984 1 0.0000 0.979 1.000 0.000
#> GSM601994 2 0.0000 0.991 0.000 1.000
#> GSM601875 2 0.0000 0.991 0.000 1.000
#> GSM601885 2 0.0000 0.991 0.000 1.000
#> GSM601890 1 0.2043 0.955 0.968 0.032
#> GSM601895 1 0.0000 0.979 1.000 0.000
#> GSM601900 1 0.0000 0.979 1.000 0.000
#> GSM601905 2 0.1843 0.970 0.028 0.972
#> GSM601915 1 0.0000 0.979 1.000 0.000
#> GSM601930 1 0.0000 0.979 1.000 0.000
#> GSM601935 1 0.8608 0.608 0.716 0.284
#> GSM601940 1 0.0000 0.979 1.000 0.000
#> GSM601945 2 0.0000 0.991 0.000 1.000
#> GSM601950 1 0.0000 0.979 1.000 0.000
#> GSM601960 1 0.0000 0.979 1.000 0.000
#> GSM601975 2 0.0000 0.991 0.000 1.000
#> GSM601980 2 0.0000 0.991 0.000 1.000
#> GSM601990 1 0.0000 0.979 1.000 0.000
#> GSM601880 1 0.0000 0.979 1.000 0.000
#> GSM601910 1 0.0938 0.971 0.988 0.012
#> GSM601920 2 0.1184 0.980 0.016 0.984
#> GSM601925 1 0.0000 0.979 1.000 0.000
#> GSM601955 2 0.1414 0.977 0.020 0.980
#> GSM601965 1 0.0000 0.979 1.000 0.000
#> GSM601970 1 0.0000 0.979 1.000 0.000
#> GSM601985 1 0.0000 0.979 1.000 0.000
#> GSM601995 2 0.0000 0.991 0.000 1.000
#> GSM601876 1 0.0000 0.979 1.000 0.000
#> GSM601886 2 0.2236 0.963 0.036 0.964
#> GSM601891 1 0.7376 0.762 0.792 0.208
#> GSM601896 1 0.0000 0.979 1.000 0.000
#> GSM601901 2 0.0000 0.991 0.000 1.000
#> GSM601906 1 0.4815 0.888 0.896 0.104
#> GSM601916 2 0.0672 0.986 0.008 0.992
#> GSM601931 1 0.0000 0.979 1.000 0.000
#> GSM601936 2 0.0376 0.989 0.004 0.996
#> GSM601941 2 0.0000 0.991 0.000 1.000
#> GSM601946 1 0.0000 0.979 1.000 0.000
#> GSM601951 1 0.0000 0.979 1.000 0.000
#> GSM601961 2 0.0000 0.991 0.000 1.000
#> GSM601976 2 0.0376 0.989 0.004 0.996
#> GSM601981 2 0.0000 0.991 0.000 1.000
#> GSM601991 1 0.0000 0.979 1.000 0.000
#> GSM601881 1 0.0000 0.979 1.000 0.000
#> GSM601911 2 0.4815 0.887 0.104 0.896
#> GSM601921 2 0.0000 0.991 0.000 1.000
#> GSM601926 1 0.0000 0.979 1.000 0.000
#> GSM601956 2 0.0000 0.991 0.000 1.000
#> GSM601966 2 0.0000 0.991 0.000 1.000
#> GSM601971 1 0.0000 0.979 1.000 0.000
#> GSM601986 1 0.2948 0.938 0.948 0.052
#> GSM601996 2 0.0000 0.991 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM601872 2 0.6204 0.7110 0.000 0.576 0.424
#> GSM601882 2 0.4654 0.8270 0.000 0.792 0.208
#> GSM601887 3 0.4830 0.4863 0.084 0.068 0.848
#> GSM601892 3 0.4974 0.5827 0.236 0.000 0.764
#> GSM601897 3 0.1877 0.5304 0.032 0.012 0.956
#> GSM601902 2 0.1491 0.8042 0.016 0.968 0.016
#> GSM601912 3 0.4409 0.5867 0.172 0.004 0.824
#> GSM601927 1 0.0237 0.7045 0.996 0.000 0.004
#> GSM601932 2 0.1170 0.8087 0.008 0.976 0.016
#> GSM601937 2 0.5882 0.7010 0.000 0.652 0.348
#> GSM601942 2 0.5760 0.7871 0.000 0.672 0.328
#> GSM601947 2 0.4165 0.8133 0.048 0.876 0.076
#> GSM601957 3 0.6309 0.3618 0.500 0.000 0.500
#> GSM601972 2 0.2096 0.8221 0.004 0.944 0.052
#> GSM601977 2 0.5178 0.8203 0.000 0.744 0.256
#> GSM601987 2 0.4842 0.8220 0.000 0.776 0.224
#> GSM601877 1 0.0661 0.7021 0.988 0.008 0.004
#> GSM601907 2 0.5397 0.8034 0.000 0.720 0.280
#> GSM601917 2 0.6025 0.6211 0.232 0.740 0.028
#> GSM601922 2 0.5723 0.6177 0.240 0.744 0.016
#> GSM601952 2 0.2945 0.8260 0.004 0.908 0.088
#> GSM601962 3 0.6917 0.5469 0.368 0.024 0.608
#> GSM601967 3 0.6305 0.4117 0.484 0.000 0.516
#> GSM601982 2 0.6627 0.7728 0.020 0.644 0.336
#> GSM601992 2 0.1964 0.8189 0.000 0.944 0.056
#> GSM601873 2 0.5591 0.7994 0.000 0.696 0.304
#> GSM601883 2 0.4750 0.8237 0.000 0.784 0.216
#> GSM601888 3 0.5625 0.4371 0.076 0.116 0.808
#> GSM601893 3 0.3784 0.5514 0.132 0.004 0.864
#> GSM601898 3 0.6305 0.4307 0.484 0.000 0.516
#> GSM601903 2 0.1182 0.8061 0.012 0.976 0.012
#> GSM601913 1 0.6192 -0.2012 0.580 0.000 0.420
#> GSM601928 1 0.0237 0.7045 0.996 0.000 0.004
#> GSM601933 2 0.4399 0.8296 0.000 0.812 0.188
#> GSM601938 2 0.4062 0.8311 0.000 0.836 0.164
#> GSM601943 2 0.5905 0.7748 0.000 0.648 0.352
#> GSM601948 1 0.6168 0.5076 0.740 0.036 0.224
#> GSM601958 1 0.6244 -0.2451 0.560 0.000 0.440
#> GSM601973 2 0.1315 0.8046 0.008 0.972 0.020
#> GSM601978 2 0.5560 0.7948 0.000 0.700 0.300
#> GSM601988 2 0.5244 0.7588 0.004 0.756 0.240
#> GSM601878 1 0.0000 0.7045 1.000 0.000 0.000
#> GSM601908 2 0.4750 0.8254 0.000 0.784 0.216
#> GSM601918 2 0.2564 0.8129 0.028 0.936 0.036
#> GSM601923 1 0.0000 0.7045 1.000 0.000 0.000
#> GSM601953 2 0.5650 0.7909 0.000 0.688 0.312
#> GSM601963 3 0.6260 0.5197 0.448 0.000 0.552
#> GSM601968 3 0.5553 0.6123 0.272 0.004 0.724
#> GSM601983 3 0.6192 0.5453 0.420 0.000 0.580
#> GSM601993 2 0.1753 0.8117 0.000 0.952 0.048
#> GSM601874 2 0.5529 0.7966 0.000 0.704 0.296
#> GSM601884 2 0.5397 0.8085 0.000 0.720 0.280
#> GSM601889 1 0.6307 -0.3875 0.512 0.000 0.488
#> GSM601894 3 0.6308 0.4060 0.492 0.000 0.508
#> GSM601899 3 0.3802 0.5232 0.080 0.032 0.888
#> GSM601904 1 0.7289 0.0102 0.504 0.468 0.028
#> GSM601914 3 0.6140 0.5626 0.404 0.000 0.596
#> GSM601929 1 0.1781 0.6946 0.960 0.020 0.020
#> GSM601934 2 0.5363 0.8110 0.000 0.724 0.276
#> GSM601939 1 0.3686 0.6084 0.860 0.000 0.140
#> GSM601944 2 0.3686 0.8318 0.000 0.860 0.140
#> GSM601949 1 0.4963 0.5724 0.792 0.008 0.200
#> GSM601959 1 0.6308 -0.3681 0.508 0.000 0.492
#> GSM601974 3 0.9425 0.0105 0.180 0.368 0.452
#> GSM601979 2 0.5363 0.8052 0.000 0.724 0.276
#> GSM601989 3 0.6302 0.4212 0.480 0.000 0.520
#> GSM601879 1 0.0848 0.7023 0.984 0.008 0.008
#> GSM601909 3 0.5859 0.5976 0.344 0.000 0.656
#> GSM601919 2 0.7213 0.6785 0.212 0.700 0.088
#> GSM601924 1 0.0592 0.7031 0.988 0.000 0.012
#> GSM601954 2 0.4418 0.8223 0.020 0.848 0.132
#> GSM601964 3 0.6180 0.5523 0.416 0.000 0.584
#> GSM601969 1 0.8427 -0.0340 0.500 0.088 0.412
#> GSM601984 1 0.3670 0.6649 0.888 0.020 0.092
#> GSM601994 2 0.0892 0.8127 0.000 0.980 0.020
#> GSM601875 2 0.5497 0.7982 0.000 0.708 0.292
#> GSM601885 2 0.4796 0.8263 0.000 0.780 0.220
#> GSM601890 3 0.2651 0.5397 0.060 0.012 0.928
#> GSM601895 3 0.6154 0.5608 0.408 0.000 0.592
#> GSM601900 3 0.6460 0.5120 0.440 0.004 0.556
#> GSM601905 2 0.5610 0.6690 0.196 0.776 0.028
#> GSM601915 1 0.6307 -0.4150 0.512 0.000 0.488
#> GSM601930 1 0.0424 0.7037 0.992 0.000 0.008
#> GSM601935 3 0.8941 0.4113 0.300 0.156 0.544
#> GSM601940 1 0.4399 0.5485 0.812 0.000 0.188
#> GSM601945 2 0.5291 0.8097 0.000 0.732 0.268
#> GSM601950 1 0.4629 0.5813 0.808 0.004 0.188
#> GSM601960 3 0.6008 0.5827 0.372 0.000 0.628
#> GSM601975 2 0.0848 0.8106 0.008 0.984 0.008
#> GSM601980 2 0.6169 0.7039 0.004 0.636 0.360
#> GSM601990 3 0.6235 0.5220 0.436 0.000 0.564
#> GSM601880 1 0.0592 0.7015 0.988 0.012 0.000
#> GSM601910 3 0.6193 0.6073 0.292 0.016 0.692
#> GSM601920 2 0.6686 0.3647 0.372 0.612 0.016
#> GSM601925 1 0.0424 0.7030 0.992 0.008 0.000
#> GSM601955 2 0.6509 0.4493 0.004 0.524 0.472
#> GSM601965 1 0.7014 0.4728 0.712 0.080 0.208
#> GSM601970 3 0.6235 0.5092 0.436 0.000 0.564
#> GSM601985 1 0.4346 0.5337 0.816 0.000 0.184
#> GSM601995 2 0.5902 0.6272 0.004 0.680 0.316
#> GSM601876 1 0.4062 0.5886 0.836 0.000 0.164
#> GSM601886 2 0.7433 0.6301 0.168 0.700 0.132
#> GSM601891 3 0.3445 0.4652 0.016 0.088 0.896
#> GSM601896 1 0.3412 0.6294 0.876 0.000 0.124
#> GSM601901 2 0.4409 0.8320 0.004 0.824 0.172
#> GSM601906 1 0.5111 0.5277 0.808 0.168 0.024
#> GSM601916 2 0.4677 0.7392 0.132 0.840 0.028
#> GSM601931 1 0.0237 0.7045 0.996 0.000 0.004
#> GSM601936 2 0.5455 0.7770 0.028 0.788 0.184
#> GSM601941 2 0.1315 0.8047 0.008 0.972 0.020
#> GSM601946 1 0.1411 0.6951 0.964 0.000 0.036
#> GSM601951 1 0.2903 0.6757 0.924 0.028 0.048
#> GSM601961 2 0.6330 0.7070 0.004 0.600 0.396
#> GSM601976 2 0.4966 0.7753 0.100 0.840 0.060
#> GSM601981 2 0.4702 0.8268 0.000 0.788 0.212
#> GSM601991 3 0.6686 0.5625 0.372 0.016 0.612
#> GSM601881 1 0.0000 0.7045 1.000 0.000 0.000
#> GSM601911 1 0.8817 0.0880 0.500 0.380 0.120
#> GSM601921 2 0.4453 0.7252 0.152 0.836 0.012
#> GSM601926 1 0.0000 0.7045 1.000 0.000 0.000
#> GSM601956 2 0.5733 0.7863 0.000 0.676 0.324
#> GSM601966 2 0.1289 0.8170 0.000 0.968 0.032
#> GSM601971 1 0.6008 0.0534 0.628 0.000 0.372
#> GSM601986 1 0.5677 0.5651 0.804 0.124 0.072
#> GSM601996 2 0.0747 0.8090 0.000 0.984 0.016
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM601872 2 0.4352 0.568453 0.000 0.816 0.080 0.104
#> GSM601882 2 0.4472 0.576945 0.000 0.760 0.020 0.220
#> GSM601887 3 0.7609 0.298857 0.060 0.392 0.488 0.060
#> GSM601892 3 0.7486 0.485045 0.176 0.156 0.620 0.048
#> GSM601897 3 0.5982 0.565489 0.028 0.108 0.736 0.128
#> GSM601902 4 0.5284 0.460679 0.020 0.308 0.004 0.668
#> GSM601912 3 0.6176 0.574751 0.104 0.076 0.740 0.080
#> GSM601927 1 0.0336 0.743553 0.992 0.000 0.008 0.000
#> GSM601932 4 0.5244 0.377561 0.004 0.372 0.008 0.616
#> GSM601937 4 0.7588 0.272136 0.000 0.220 0.312 0.468
#> GSM601942 2 0.6474 0.367284 0.000 0.624 0.120 0.256
#> GSM601947 2 0.6005 -0.123393 0.040 0.500 0.000 0.460
#> GSM601957 3 0.5915 0.267029 0.400 0.000 0.560 0.040
#> GSM601972 2 0.4998 -0.102352 0.000 0.512 0.000 0.488
#> GSM601977 2 0.4267 0.617555 0.000 0.788 0.024 0.188
#> GSM601987 2 0.2401 0.660193 0.000 0.904 0.004 0.092
#> GSM601877 1 0.1022 0.739418 0.968 0.000 0.000 0.032
#> GSM601907 2 0.0921 0.662522 0.000 0.972 0.000 0.028
#> GSM601917 4 0.7200 0.474278 0.164 0.216 0.016 0.604
#> GSM601922 4 0.7252 0.458353 0.212 0.200 0.008 0.580
#> GSM601952 2 0.6217 0.133796 0.008 0.552 0.040 0.400
#> GSM601962 3 0.6692 0.522374 0.136 0.024 0.672 0.168
#> GSM601967 3 0.6097 0.318590 0.372 0.012 0.584 0.032
#> GSM601982 2 0.5461 0.563621 0.008 0.752 0.096 0.144
#> GSM601992 2 0.4999 -0.062098 0.000 0.508 0.000 0.492
#> GSM601873 2 0.4378 0.622760 0.000 0.796 0.040 0.164
#> GSM601883 2 0.2760 0.649497 0.000 0.872 0.000 0.128
#> GSM601888 2 0.6989 0.033330 0.024 0.532 0.380 0.064
#> GSM601893 3 0.7488 0.462950 0.084 0.288 0.576 0.052
#> GSM601898 3 0.5414 0.384248 0.376 0.000 0.604 0.020
#> GSM601903 4 0.5060 0.475251 0.016 0.288 0.004 0.692
#> GSM601913 3 0.6071 0.239324 0.452 0.000 0.504 0.044
#> GSM601928 1 0.0921 0.741572 0.972 0.000 0.028 0.000
#> GSM601933 2 0.4011 0.610041 0.000 0.784 0.008 0.208
#> GSM601938 2 0.5090 0.431067 0.000 0.660 0.016 0.324
#> GSM601943 2 0.3803 0.616092 0.000 0.836 0.032 0.132
#> GSM601948 1 0.6683 0.491619 0.628 0.008 0.248 0.116
#> GSM601958 1 0.5590 0.000809 0.524 0.000 0.456 0.020
#> GSM601973 4 0.5060 0.481407 0.012 0.284 0.008 0.696
#> GSM601978 2 0.0707 0.660678 0.000 0.980 0.000 0.020
#> GSM601988 4 0.7271 0.318056 0.000 0.244 0.216 0.540
#> GSM601878 1 0.0927 0.742594 0.976 0.000 0.008 0.016
#> GSM601908 2 0.3219 0.616101 0.000 0.836 0.000 0.164
#> GSM601918 2 0.5662 -0.059614 0.016 0.524 0.004 0.456
#> GSM601923 1 0.0779 0.742392 0.980 0.000 0.004 0.016
#> GSM601953 2 0.1820 0.642249 0.000 0.944 0.036 0.020
#> GSM601963 3 0.5732 0.534504 0.264 0.000 0.672 0.064
#> GSM601968 3 0.5214 0.578153 0.128 0.060 0.784 0.028
#> GSM601983 3 0.5576 0.569296 0.184 0.000 0.720 0.096
#> GSM601993 4 0.5716 0.427699 0.000 0.272 0.060 0.668
#> GSM601874 2 0.1489 0.663697 0.000 0.952 0.004 0.044
#> GSM601884 2 0.3307 0.662683 0.000 0.868 0.028 0.104
#> GSM601889 3 0.5467 0.374020 0.364 0.000 0.612 0.024
#> GSM601894 3 0.5968 0.446256 0.328 0.008 0.624 0.040
#> GSM601899 3 0.7675 0.392698 0.072 0.344 0.524 0.060
#> GSM601904 4 0.6612 0.298612 0.372 0.048 0.020 0.560
#> GSM601914 3 0.5056 0.580190 0.164 0.000 0.760 0.076
#> GSM601929 1 0.3745 0.705300 0.852 0.000 0.060 0.088
#> GSM601934 2 0.4307 0.620232 0.000 0.784 0.024 0.192
#> GSM601939 1 0.4284 0.600658 0.764 0.000 0.224 0.012
#> GSM601944 2 0.5649 0.244522 0.004 0.580 0.020 0.396
#> GSM601949 1 0.5911 0.445910 0.656 0.008 0.288 0.048
#> GSM601959 3 0.5908 0.197545 0.432 0.004 0.536 0.028
#> GSM601974 4 0.9066 -0.014596 0.116 0.136 0.368 0.380
#> GSM601979 2 0.0707 0.662699 0.000 0.980 0.000 0.020
#> GSM601989 3 0.5436 0.397227 0.356 0.000 0.620 0.024
#> GSM601879 1 0.2089 0.733571 0.932 0.000 0.020 0.048
#> GSM601909 3 0.4901 0.583606 0.144 0.020 0.792 0.044
#> GSM601919 2 0.7344 -0.167418 0.136 0.452 0.004 0.408
#> GSM601924 1 0.1677 0.741011 0.948 0.000 0.040 0.012
#> GSM601954 2 0.6871 0.124503 0.020 0.524 0.060 0.396
#> GSM601964 3 0.5747 0.563045 0.196 0.000 0.704 0.100
#> GSM601969 3 0.8130 0.100040 0.396 0.052 0.440 0.112
#> GSM601984 1 0.5397 0.645208 0.752 0.004 0.136 0.108
#> GSM601994 4 0.4996 0.091882 0.000 0.484 0.000 0.516
#> GSM601875 2 0.1488 0.662289 0.000 0.956 0.012 0.032
#> GSM601885 2 0.3725 0.640782 0.000 0.812 0.008 0.180
#> GSM601890 3 0.6443 0.502139 0.032 0.252 0.660 0.056
#> GSM601895 3 0.5172 0.582982 0.188 0.000 0.744 0.068
#> GSM601900 3 0.6519 0.520923 0.280 0.012 0.628 0.080
#> GSM601905 4 0.6532 0.497422 0.100 0.256 0.008 0.636
#> GSM601915 3 0.5436 0.430409 0.356 0.000 0.620 0.024
#> GSM601930 1 0.1256 0.742648 0.964 0.000 0.028 0.008
#> GSM601935 3 0.7222 0.302092 0.084 0.024 0.532 0.360
#> GSM601940 1 0.4855 0.523765 0.712 0.000 0.268 0.020
#> GSM601945 2 0.2799 0.665691 0.000 0.884 0.008 0.108
#> GSM601950 1 0.5334 0.492527 0.684 0.004 0.284 0.028
#> GSM601960 3 0.5457 0.576813 0.184 0.000 0.728 0.088
#> GSM601975 4 0.4817 0.362812 0.000 0.388 0.000 0.612
#> GSM601980 4 0.7860 0.175697 0.000 0.312 0.292 0.396
#> GSM601990 3 0.5902 0.552563 0.184 0.000 0.696 0.120
#> GSM601880 1 0.1109 0.740412 0.968 0.000 0.004 0.028
#> GSM601910 3 0.5664 0.592571 0.112 0.056 0.768 0.064
#> GSM601920 4 0.7484 0.424183 0.248 0.200 0.008 0.544
#> GSM601925 1 0.1635 0.736473 0.948 0.000 0.008 0.044
#> GSM601955 3 0.7665 -0.148668 0.000 0.216 0.424 0.360
#> GSM601965 1 0.8909 0.227393 0.488 0.108 0.220 0.184
#> GSM601970 3 0.4661 0.523444 0.256 0.000 0.728 0.016
#> GSM601985 1 0.4838 0.537696 0.724 0.000 0.252 0.024
#> GSM601995 4 0.7362 0.225434 0.000 0.164 0.372 0.464
#> GSM601876 1 0.3852 0.661463 0.808 0.000 0.180 0.012
#> GSM601886 4 0.7389 0.446650 0.096 0.112 0.140 0.652
#> GSM601891 3 0.6658 0.240767 0.008 0.420 0.508 0.064
#> GSM601896 1 0.4012 0.655330 0.800 0.000 0.184 0.016
#> GSM601901 2 0.4472 0.568149 0.000 0.760 0.020 0.220
#> GSM601906 1 0.5733 0.469976 0.648 0.004 0.040 0.308
#> GSM601916 4 0.5867 0.516500 0.084 0.216 0.004 0.696
#> GSM601931 1 0.0817 0.742077 0.976 0.000 0.024 0.000
#> GSM601936 4 0.7568 0.300383 0.016 0.276 0.164 0.544
#> GSM601941 4 0.4799 0.474930 0.004 0.284 0.008 0.704
#> GSM601946 1 0.3157 0.688373 0.852 0.000 0.144 0.004
#> GSM601951 1 0.4898 0.656590 0.780 0.000 0.104 0.116
#> GSM601961 2 0.4743 0.546516 0.008 0.804 0.104 0.084
#> GSM601976 4 0.6905 0.442811 0.064 0.308 0.032 0.596
#> GSM601981 2 0.3764 0.620630 0.000 0.816 0.012 0.172
#> GSM601991 3 0.6075 0.539073 0.112 0.008 0.700 0.180
#> GSM601881 1 0.0804 0.742421 0.980 0.000 0.008 0.012
#> GSM601911 1 0.9025 -0.217743 0.364 0.220 0.068 0.348
#> GSM601921 4 0.6344 0.420600 0.064 0.348 0.004 0.584
#> GSM601926 1 0.1356 0.743003 0.960 0.000 0.032 0.008
#> GSM601956 2 0.2256 0.646625 0.000 0.924 0.020 0.056
#> GSM601966 4 0.5295 0.106100 0.000 0.488 0.008 0.504
#> GSM601971 1 0.5888 0.138135 0.540 0.000 0.424 0.036
#> GSM601986 1 0.7976 0.455664 0.600 0.100 0.156 0.144
#> GSM601996 4 0.5028 0.330325 0.000 0.400 0.004 0.596
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM601872 2 0.448 0.5825 0.000 0.764 0.036 0.024 0.176
#> GSM601882 2 0.567 0.5377 0.000 0.636 0.008 0.248 0.108
#> GSM601887 5 0.757 0.7156 0.040 0.296 0.240 0.004 0.420
#> GSM601892 3 0.766 -0.4229 0.088 0.128 0.408 0.004 0.372
#> GSM601897 3 0.640 0.1638 0.004 0.144 0.620 0.032 0.200
#> GSM601902 4 0.457 0.5752 0.004 0.160 0.004 0.760 0.072
#> GSM601912 3 0.770 0.2355 0.112 0.080 0.548 0.040 0.220
#> GSM601927 1 0.160 0.6969 0.948 0.000 0.024 0.020 0.008
#> GSM601932 4 0.594 0.4946 0.000 0.260 0.008 0.604 0.128
#> GSM601937 3 0.855 -0.2083 0.000 0.204 0.296 0.232 0.268
#> GSM601942 2 0.676 0.4634 0.000 0.596 0.076 0.124 0.204
#> GSM601947 4 0.620 0.3770 0.020 0.332 0.000 0.552 0.096
#> GSM601957 3 0.695 0.1953 0.300 0.004 0.456 0.008 0.232
#> GSM601972 4 0.576 0.2798 0.000 0.384 0.004 0.532 0.080
#> GSM601977 2 0.540 0.6011 0.000 0.712 0.028 0.152 0.108
#> GSM601987 2 0.369 0.6322 0.000 0.812 0.000 0.136 0.052
#> GSM601877 1 0.239 0.6911 0.908 0.000 0.004 0.040 0.048
#> GSM601907 2 0.223 0.6409 0.000 0.912 0.000 0.040 0.048
#> GSM601917 4 0.735 0.5404 0.140 0.092 0.028 0.596 0.144
#> GSM601922 4 0.653 0.5317 0.180 0.096 0.004 0.636 0.084
#> GSM601952 2 0.724 -0.0343 0.012 0.428 0.032 0.392 0.136
#> GSM601962 3 0.626 0.3995 0.092 0.024 0.676 0.044 0.164
#> GSM601967 3 0.745 0.1268 0.284 0.016 0.432 0.016 0.252
#> GSM601982 2 0.742 0.4717 0.016 0.556 0.076 0.144 0.208
#> GSM601992 2 0.671 -0.0133 0.000 0.408 0.008 0.404 0.180
#> GSM601873 2 0.603 0.5949 0.000 0.664 0.048 0.116 0.172
#> GSM601883 2 0.456 0.5944 0.000 0.740 0.000 0.180 0.080
#> GSM601888 5 0.725 0.6302 0.028 0.392 0.148 0.012 0.420
#> GSM601893 5 0.784 0.5817 0.064 0.192 0.324 0.008 0.412
#> GSM601898 3 0.583 0.4040 0.300 0.000 0.576 0.000 0.124
#> GSM601903 4 0.356 0.5774 0.000 0.144 0.000 0.816 0.040
#> GSM601913 3 0.524 0.4077 0.312 0.000 0.620 0.000 0.068
#> GSM601928 1 0.157 0.6921 0.944 0.000 0.044 0.004 0.008
#> GSM601933 2 0.530 0.5793 0.000 0.688 0.004 0.176 0.132
#> GSM601938 2 0.595 0.5080 0.000 0.612 0.008 0.240 0.140
#> GSM601943 2 0.514 0.6223 0.000 0.736 0.040 0.068 0.156
#> GSM601948 1 0.795 0.3308 0.484 0.008 0.168 0.124 0.216
#> GSM601958 3 0.646 0.1967 0.396 0.000 0.444 0.004 0.156
#> GSM601973 4 0.503 0.5708 0.008 0.160 0.012 0.740 0.080
#> GSM601978 2 0.257 0.6408 0.000 0.896 0.004 0.032 0.068
#> GSM601988 4 0.843 0.1921 0.000 0.168 0.264 0.336 0.232
#> GSM601878 1 0.151 0.6977 0.952 0.000 0.012 0.012 0.024
#> GSM601908 2 0.412 0.6268 0.000 0.792 0.004 0.132 0.072
#> GSM601918 4 0.590 0.3975 0.012 0.324 0.000 0.576 0.088
#> GSM601923 1 0.151 0.6965 0.952 0.000 0.012 0.012 0.024
#> GSM601953 2 0.301 0.6081 0.000 0.844 0.000 0.016 0.140
#> GSM601963 3 0.396 0.4912 0.184 0.000 0.776 0.000 0.040
#> GSM601968 3 0.735 -0.0812 0.088 0.080 0.520 0.016 0.296
#> GSM601983 3 0.463 0.4784 0.144 0.000 0.752 0.004 0.100
#> GSM601993 4 0.753 0.2667 0.000 0.256 0.052 0.440 0.252
#> GSM601874 2 0.298 0.6455 0.000 0.868 0.000 0.076 0.056
#> GSM601884 2 0.421 0.6410 0.000 0.788 0.004 0.120 0.088
#> GSM601889 3 0.683 0.3185 0.276 0.012 0.520 0.008 0.184
#> GSM601894 3 0.661 0.3185 0.268 0.000 0.512 0.008 0.212
#> GSM601899 5 0.749 0.7019 0.024 0.268 0.292 0.008 0.408
#> GSM601904 4 0.634 0.4460 0.268 0.020 0.012 0.600 0.100
#> GSM601914 3 0.357 0.4872 0.120 0.000 0.824 0.000 0.056
#> GSM601929 1 0.490 0.6315 0.768 0.004 0.032 0.108 0.088
#> GSM601934 2 0.558 0.5868 0.000 0.676 0.012 0.152 0.160
#> GSM601939 1 0.465 0.5185 0.708 0.000 0.244 0.004 0.044
#> GSM601944 2 0.676 0.3507 0.000 0.520 0.020 0.268 0.192
#> GSM601949 1 0.777 0.2201 0.496 0.032 0.140 0.056 0.276
#> GSM601959 3 0.684 0.2435 0.328 0.000 0.440 0.008 0.224
#> GSM601974 3 0.927 -0.1308 0.060 0.128 0.284 0.272 0.256
#> GSM601979 2 0.257 0.6424 0.000 0.896 0.004 0.068 0.032
#> GSM601989 3 0.650 0.3379 0.260 0.000 0.532 0.008 0.200
#> GSM601879 1 0.275 0.6940 0.896 0.000 0.020 0.044 0.040
#> GSM601909 3 0.582 0.3619 0.136 0.016 0.676 0.008 0.164
#> GSM601919 4 0.723 0.4293 0.104 0.260 0.000 0.528 0.108
#> GSM601924 1 0.220 0.6961 0.920 0.000 0.036 0.008 0.036
#> GSM601954 2 0.731 -0.1050 0.020 0.392 0.008 0.376 0.204
#> GSM601964 3 0.392 0.4799 0.120 0.000 0.808 0.004 0.068
#> GSM601969 5 0.893 0.1455 0.248 0.056 0.256 0.088 0.352
#> GSM601984 1 0.693 0.4340 0.580 0.000 0.204 0.084 0.132
#> GSM601994 2 0.621 0.0401 0.000 0.456 0.000 0.404 0.140
#> GSM601875 2 0.412 0.6338 0.000 0.788 0.000 0.104 0.108
#> GSM601885 2 0.537 0.5570 0.000 0.652 0.000 0.236 0.112
#> GSM601890 5 0.703 0.6168 0.016 0.216 0.360 0.000 0.408
#> GSM601895 3 0.518 0.4257 0.100 0.004 0.716 0.008 0.172
#> GSM601900 3 0.674 0.3479 0.152 0.020 0.600 0.024 0.204
#> GSM601905 4 0.637 0.5729 0.064 0.120 0.004 0.652 0.160
#> GSM601915 3 0.450 0.4667 0.280 0.000 0.688 0.000 0.032
#> GSM601930 1 0.223 0.6942 0.920 0.000 0.040 0.012 0.028
#> GSM601935 3 0.769 0.3004 0.056 0.036 0.532 0.152 0.224
#> GSM601940 1 0.608 0.3399 0.580 0.000 0.280 0.008 0.132
#> GSM601945 2 0.379 0.6491 0.000 0.820 0.004 0.104 0.072
#> GSM601950 1 0.663 0.4058 0.596 0.016 0.180 0.016 0.192
#> GSM601960 3 0.362 0.4821 0.108 0.000 0.824 0.000 0.068
#> GSM601975 4 0.516 0.5145 0.004 0.256 0.000 0.668 0.072
#> GSM601980 2 0.858 -0.0340 0.000 0.276 0.248 0.212 0.264
#> GSM601990 3 0.373 0.4800 0.100 0.000 0.828 0.008 0.064
#> GSM601880 1 0.199 0.6991 0.932 0.000 0.016 0.020 0.032
#> GSM601910 3 0.700 0.2115 0.104 0.048 0.584 0.024 0.240
#> GSM601920 4 0.677 0.5122 0.172 0.072 0.004 0.612 0.140
#> GSM601925 1 0.240 0.6933 0.912 0.000 0.012 0.036 0.040
#> GSM601955 3 0.824 0.0333 0.000 0.168 0.388 0.176 0.268
#> GSM601965 1 0.893 0.0690 0.372 0.052 0.256 0.104 0.216
#> GSM601970 3 0.591 0.3727 0.224 0.000 0.612 0.004 0.160
#> GSM601985 1 0.447 0.4654 0.684 0.000 0.292 0.004 0.020
#> GSM601995 3 0.809 -0.0978 0.000 0.100 0.368 0.272 0.260
#> GSM601876 1 0.571 0.5186 0.660 0.000 0.216 0.020 0.104
#> GSM601886 4 0.897 0.3826 0.080 0.112 0.160 0.416 0.232
#> GSM601891 5 0.734 0.6773 0.016 0.332 0.244 0.008 0.400
#> GSM601896 1 0.553 0.5094 0.656 0.000 0.212 0.004 0.128
#> GSM601901 2 0.610 0.3667 0.004 0.580 0.016 0.312 0.088
#> GSM601906 1 0.680 0.3948 0.564 0.008 0.048 0.280 0.100
#> GSM601916 4 0.616 0.5588 0.064 0.152 0.004 0.672 0.108
#> GSM601931 1 0.146 0.6913 0.952 0.000 0.032 0.008 0.008
#> GSM601936 4 0.873 0.2269 0.020 0.200 0.144 0.348 0.288
#> GSM601941 4 0.458 0.5674 0.004 0.160 0.000 0.752 0.084
#> GSM601946 1 0.342 0.6182 0.816 0.000 0.164 0.004 0.016
#> GSM601951 1 0.561 0.6082 0.716 0.000 0.072 0.120 0.092
#> GSM601961 2 0.579 0.3811 0.000 0.652 0.028 0.088 0.232
#> GSM601976 4 0.761 0.4981 0.084 0.196 0.024 0.552 0.144
#> GSM601981 2 0.560 0.4601 0.000 0.632 0.004 0.256 0.108
#> GSM601991 3 0.501 0.4332 0.044 0.008 0.760 0.048 0.140
#> GSM601881 1 0.148 0.6977 0.952 0.000 0.008 0.012 0.028
#> GSM601911 1 0.953 -0.3126 0.260 0.232 0.068 0.252 0.188
#> GSM601921 4 0.664 0.5311 0.052 0.192 0.004 0.612 0.140
#> GSM601926 1 0.178 0.6987 0.940 0.000 0.028 0.008 0.024
#> GSM601956 2 0.431 0.6342 0.000 0.788 0.012 0.072 0.128
#> GSM601966 4 0.573 0.0455 0.000 0.436 0.000 0.480 0.084
#> GSM601971 1 0.685 0.0484 0.448 0.000 0.356 0.016 0.180
#> GSM601986 1 0.905 0.2722 0.428 0.088 0.132 0.172 0.180
#> GSM601996 4 0.616 0.3371 0.008 0.308 0.000 0.556 0.128
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM601872 2 0.546 0.512939 0.000 0.696 0.040 0.024 0.128 0.112
#> GSM601882 2 0.674 0.326353 0.000 0.484 0.016 0.296 0.160 0.044
#> GSM601887 6 0.575 0.354430 0.024 0.248 0.040 0.004 0.052 0.632
#> GSM601892 6 0.539 0.424504 0.064 0.080 0.108 0.004 0.024 0.720
#> GSM601897 3 0.759 0.098187 0.012 0.132 0.484 0.020 0.192 0.160
#> GSM601902 4 0.492 0.440665 0.008 0.100 0.008 0.732 0.132 0.020
#> GSM601912 3 0.779 0.292322 0.056 0.076 0.500 0.028 0.128 0.212
#> GSM601927 1 0.307 0.655136 0.872 0.000 0.044 0.020 0.020 0.044
#> GSM601932 4 0.604 0.410686 0.008 0.156 0.020 0.608 0.196 0.012
#> GSM601937 3 0.738 -0.517855 0.000 0.080 0.392 0.164 0.340 0.024
#> GSM601942 2 0.706 0.150427 0.000 0.492 0.132 0.092 0.264 0.020
#> GSM601947 4 0.683 0.400175 0.052 0.240 0.008 0.552 0.116 0.032
#> GSM601957 6 0.613 0.409761 0.240 0.004 0.176 0.000 0.028 0.552
#> GSM601972 4 0.493 0.334485 0.000 0.312 0.000 0.600 0.088 0.000
#> GSM601977 2 0.679 0.446860 0.000 0.536 0.044 0.208 0.180 0.032
#> GSM601987 2 0.444 0.558425 0.000 0.724 0.004 0.156 0.116 0.000
#> GSM601877 1 0.216 0.653277 0.916 0.000 0.004 0.016 0.024 0.040
#> GSM601907 2 0.347 0.613222 0.000 0.836 0.000 0.048 0.072 0.044
#> GSM601917 4 0.712 0.338223 0.156 0.052 0.016 0.548 0.192 0.036
#> GSM601922 4 0.814 0.308243 0.184 0.100 0.028 0.464 0.168 0.056
#> GSM601952 2 0.798 -0.087675 0.020 0.332 0.064 0.304 0.252 0.028
#> GSM601962 3 0.548 0.366360 0.072 0.008 0.704 0.016 0.144 0.056
#> GSM601967 6 0.746 0.385737 0.224 0.016 0.192 0.024 0.064 0.480
#> GSM601982 2 0.802 0.395052 0.032 0.504 0.076 0.124 0.164 0.100
#> GSM601992 4 0.661 0.093881 0.004 0.344 0.012 0.368 0.268 0.004
#> GSM601873 2 0.644 0.482290 0.000 0.600 0.060 0.076 0.212 0.052
#> GSM601883 2 0.543 0.478017 0.000 0.628 0.004 0.244 0.104 0.020
#> GSM601888 6 0.532 0.189902 0.004 0.364 0.008 0.000 0.076 0.548
#> GSM601893 6 0.711 0.353711 0.056 0.148 0.124 0.008 0.088 0.576
#> GSM601898 3 0.603 -0.153373 0.172 0.000 0.464 0.000 0.012 0.352
#> GSM601903 4 0.458 0.442189 0.016 0.096 0.016 0.764 0.104 0.004
#> GSM601913 3 0.675 0.057498 0.316 0.000 0.440 0.004 0.048 0.192
#> GSM601928 1 0.280 0.652598 0.884 0.000 0.028 0.012 0.020 0.056
#> GSM601933 2 0.605 0.464175 0.000 0.596 0.020 0.200 0.164 0.020
#> GSM601938 2 0.598 0.428162 0.000 0.556 0.016 0.224 0.200 0.004
#> GSM601943 2 0.610 0.463800 0.000 0.612 0.056 0.044 0.236 0.052
#> GSM601948 1 0.817 0.004567 0.356 0.004 0.088 0.112 0.116 0.324
#> GSM601958 6 0.639 0.309660 0.280 0.000 0.264 0.004 0.012 0.440
#> GSM601973 4 0.485 0.400744 0.000 0.104 0.004 0.700 0.180 0.012
#> GSM601978 2 0.313 0.613066 0.000 0.852 0.000 0.032 0.088 0.028
#> GSM601988 3 0.812 -0.552997 0.008 0.124 0.304 0.284 0.256 0.024
#> GSM601878 1 0.239 0.650597 0.904 0.000 0.008 0.012 0.028 0.048
#> GSM601908 2 0.472 0.567219 0.000 0.720 0.000 0.136 0.124 0.020
#> GSM601918 4 0.635 0.317237 0.020 0.296 0.000 0.512 0.156 0.016
#> GSM601923 1 0.104 0.655859 0.964 0.000 0.004 0.000 0.008 0.024
#> GSM601953 2 0.377 0.593139 0.000 0.820 0.008 0.024 0.080 0.068
#> GSM601963 3 0.550 0.342008 0.156 0.000 0.668 0.004 0.048 0.124
#> GSM601968 6 0.694 0.282644 0.080 0.044 0.284 0.008 0.060 0.524
#> GSM601983 3 0.564 0.386095 0.120 0.000 0.668 0.004 0.080 0.128
#> GSM601993 4 0.717 0.000201 0.004 0.168 0.084 0.404 0.336 0.004
#> GSM601874 2 0.340 0.614104 0.000 0.832 0.000 0.052 0.096 0.020
#> GSM601884 2 0.594 0.574553 0.000 0.648 0.040 0.140 0.144 0.028
#> GSM601889 6 0.634 0.316025 0.184 0.012 0.300 0.004 0.008 0.492
#> GSM601894 6 0.662 0.276843 0.208 0.000 0.324 0.004 0.032 0.432
#> GSM601899 6 0.633 0.364958 0.020 0.228 0.092 0.004 0.060 0.596
#> GSM601904 4 0.793 0.169204 0.284 0.036 0.040 0.404 0.196 0.040
#> GSM601914 3 0.481 0.340106 0.108 0.000 0.724 0.000 0.036 0.132
#> GSM601929 1 0.612 0.552360 0.660 0.000 0.056 0.092 0.068 0.124
#> GSM601934 2 0.618 0.518505 0.000 0.620 0.036 0.128 0.180 0.036
#> GSM601939 1 0.551 0.385336 0.616 0.000 0.144 0.004 0.012 0.224
#> GSM601944 2 0.728 0.136062 0.008 0.412 0.028 0.284 0.240 0.028
#> GSM601949 6 0.652 0.190238 0.348 0.016 0.028 0.052 0.044 0.512
#> GSM601959 6 0.587 0.377646 0.216 0.000 0.212 0.000 0.016 0.556
#> GSM601974 3 0.920 -0.266514 0.056 0.080 0.296 0.188 0.252 0.128
#> GSM601979 2 0.239 0.607828 0.000 0.892 0.000 0.076 0.020 0.012
#> GSM601989 6 0.671 0.208166 0.228 0.000 0.336 0.004 0.032 0.400
#> GSM601879 1 0.380 0.619822 0.824 0.000 0.012 0.032 0.064 0.068
#> GSM601909 6 0.698 0.100353 0.108 0.012 0.384 0.004 0.080 0.412
#> GSM601919 4 0.803 0.292244 0.136 0.268 0.000 0.392 0.144 0.060
#> GSM601924 1 0.375 0.628164 0.812 0.000 0.056 0.004 0.020 0.108
#> GSM601954 2 0.798 -0.099181 0.008 0.336 0.028 0.332 0.180 0.116
#> GSM601964 3 0.494 0.409824 0.120 0.000 0.724 0.000 0.068 0.088
#> GSM601969 6 0.788 0.361390 0.184 0.044 0.072 0.068 0.116 0.516
#> GSM601984 1 0.781 0.293810 0.456 0.004 0.248 0.072 0.120 0.100
#> GSM601994 4 0.653 0.113569 0.000 0.364 0.020 0.412 0.196 0.008
#> GSM601875 2 0.372 0.616681 0.000 0.828 0.008 0.052 0.072 0.040
#> GSM601885 2 0.617 0.494427 0.000 0.596 0.020 0.200 0.152 0.032
#> GSM601890 6 0.694 0.300386 0.020 0.180 0.184 0.004 0.072 0.540
#> GSM601895 3 0.681 0.036881 0.100 0.004 0.452 0.004 0.092 0.348
#> GSM601900 3 0.758 -0.095864 0.152 0.004 0.372 0.028 0.088 0.356
#> GSM601905 4 0.717 0.341315 0.100 0.076 0.016 0.580 0.160 0.068
#> GSM601915 3 0.605 0.063321 0.228 0.000 0.520 0.000 0.016 0.236
#> GSM601930 1 0.252 0.654948 0.892 0.000 0.044 0.000 0.016 0.048
#> GSM601935 3 0.603 0.067294 0.032 0.012 0.620 0.072 0.240 0.024
#> GSM601940 1 0.647 0.170819 0.480 0.000 0.148 0.004 0.044 0.324
#> GSM601945 2 0.465 0.598522 0.000 0.736 0.004 0.076 0.156 0.028
#> GSM601950 6 0.553 0.129027 0.404 0.000 0.032 0.012 0.036 0.516
#> GSM601960 3 0.498 0.334650 0.084 0.000 0.700 0.000 0.040 0.176
#> GSM601975 4 0.558 0.452047 0.016 0.180 0.012 0.660 0.124 0.008
#> GSM601980 5 0.795 0.430122 0.000 0.184 0.272 0.188 0.336 0.020
#> GSM601990 3 0.435 0.422375 0.092 0.000 0.780 0.004 0.052 0.072
#> GSM601880 1 0.196 0.657872 0.928 0.000 0.008 0.012 0.024 0.028
#> GSM601910 3 0.818 -0.041627 0.072 0.064 0.372 0.036 0.112 0.344
#> GSM601920 4 0.820 0.236927 0.196 0.088 0.016 0.404 0.240 0.056
#> GSM601925 1 0.204 0.655696 0.924 0.000 0.008 0.020 0.016 0.032
#> GSM601955 3 0.738 -0.333781 0.004 0.108 0.456 0.080 0.308 0.044
#> GSM601965 1 0.928 -0.006413 0.308 0.052 0.236 0.112 0.156 0.136
#> GSM601970 6 0.627 0.201662 0.160 0.000 0.384 0.004 0.020 0.432
#> GSM601985 1 0.547 0.411902 0.632 0.000 0.224 0.004 0.020 0.120
#> GSM601995 3 0.696 -0.479762 0.000 0.040 0.416 0.180 0.344 0.020
#> GSM601876 1 0.628 0.445952 0.580 0.000 0.160 0.024 0.028 0.208
#> GSM601886 4 0.851 -0.195702 0.044 0.080 0.152 0.384 0.276 0.064
#> GSM601891 6 0.711 0.227742 0.012 0.312 0.084 0.016 0.100 0.476
#> GSM601896 1 0.663 0.230894 0.484 0.000 0.168 0.016 0.032 0.300
#> GSM601901 2 0.655 0.295502 0.000 0.504 0.028 0.292 0.156 0.020
#> GSM601906 1 0.752 0.339620 0.484 0.004 0.060 0.220 0.172 0.060
#> GSM601916 4 0.660 0.372905 0.056 0.128 0.024 0.620 0.148 0.024
#> GSM601931 1 0.262 0.651586 0.888 0.000 0.032 0.008 0.008 0.064
#> GSM601936 5 0.864 0.314273 0.028 0.112 0.224 0.256 0.328 0.052
#> GSM601941 4 0.517 0.432635 0.008 0.160 0.020 0.704 0.100 0.008
#> GSM601946 1 0.532 0.509999 0.676 0.000 0.132 0.008 0.024 0.160
#> GSM601951 1 0.742 0.366553 0.528 0.004 0.056 0.144 0.100 0.168
#> GSM601961 2 0.541 0.432182 0.004 0.656 0.008 0.052 0.044 0.236
#> GSM601976 4 0.839 0.230827 0.068 0.164 0.068 0.452 0.188 0.060
#> GSM601981 2 0.572 0.418322 0.000 0.600 0.004 0.200 0.180 0.016
#> GSM601991 3 0.478 0.386944 0.040 0.004 0.744 0.008 0.144 0.060
#> GSM601881 1 0.148 0.655206 0.944 0.000 0.008 0.000 0.012 0.036
#> GSM601911 1 0.978 -0.271558 0.216 0.212 0.100 0.168 0.192 0.112
#> GSM601921 4 0.761 0.346343 0.088 0.156 0.016 0.464 0.252 0.024
#> GSM601926 1 0.193 0.654733 0.924 0.000 0.028 0.004 0.004 0.040
#> GSM601956 2 0.514 0.587046 0.000 0.712 0.012 0.088 0.148 0.040
#> GSM601966 4 0.602 0.225173 0.008 0.352 0.004 0.472 0.164 0.000
#> GSM601971 6 0.706 0.255069 0.340 0.000 0.248 0.008 0.048 0.356
#> GSM601986 1 0.929 0.175622 0.340 0.064 0.160 0.104 0.188 0.144
#> GSM601996 4 0.607 0.355070 0.004 0.252 0.012 0.524 0.208 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
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)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
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)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, 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.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")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n time(p) gender(p) k
#> SD:skmeans 125 0.2945 0.8638 2
#> SD:skmeans 103 0.0949 0.5255 3
#> SD:skmeans 64 0.2892 0.1343 4
#> SD:skmeans 57 0.4632 0.0809 5
#> SD:skmeans 28 0.6970 0.2433 6
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.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["SD", "pam"]
# you can also extract it by
# res = res_list["SD:pam"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 21512 rows and 125 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'pam' method.
#> Performed in total 1250 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_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
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, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.567 0.825 0.899 0.4163 0.587 0.587
#> 3 3 0.501 0.757 0.869 0.5043 0.764 0.610
#> 4 4 0.458 0.530 0.779 0.1362 0.917 0.792
#> 5 5 0.482 0.478 0.736 0.0496 0.933 0.798
#> 6 6 0.498 0.471 0.724 0.0179 0.975 0.910
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 3
Following shows 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 class
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
#> GSM601872 2 0.3879 0.842 0.076 0.924
#> GSM601882 1 0.4022 0.881 0.920 0.080
#> GSM601887 2 0.4022 0.841 0.080 0.920
#> GSM601892 2 0.9983 0.309 0.476 0.524
#> GSM601897 1 0.6531 0.783 0.832 0.168
#> GSM601902 1 0.3274 0.913 0.940 0.060
#> GSM601912 1 0.0376 0.915 0.996 0.004
#> GSM601927 1 0.0672 0.916 0.992 0.008
#> GSM601932 1 0.2423 0.915 0.960 0.040
#> GSM601937 1 0.2778 0.914 0.952 0.048
#> GSM601942 1 0.4298 0.894 0.912 0.088
#> GSM601947 2 0.8499 0.681 0.276 0.724
#> GSM601957 1 0.9933 -0.055 0.548 0.452
#> GSM601972 1 0.6801 0.767 0.820 0.180
#> GSM601977 1 0.9732 0.198 0.596 0.404
#> GSM601987 2 0.7299 0.806 0.204 0.796
#> GSM601877 1 0.3114 0.910 0.944 0.056
#> GSM601907 2 0.2948 0.845 0.052 0.948
#> GSM601917 1 0.3114 0.910 0.944 0.056
#> GSM601922 1 0.3584 0.910 0.932 0.068
#> GSM601952 1 0.3733 0.909 0.928 0.072
#> GSM601962 1 0.4161 0.902 0.916 0.084
#> GSM601967 1 0.4939 0.888 0.892 0.108
#> GSM601982 1 0.3879 0.890 0.924 0.076
#> GSM601992 1 0.1184 0.915 0.984 0.016
#> GSM601873 2 0.8327 0.731 0.264 0.736
#> GSM601883 1 0.9460 0.290 0.636 0.364
#> GSM601888 2 0.0672 0.837 0.008 0.992
#> GSM601893 1 0.9000 0.462 0.684 0.316
#> GSM601898 1 0.1843 0.918 0.972 0.028
#> GSM601903 1 0.3879 0.903 0.924 0.076
#> GSM601913 1 0.0672 0.917 0.992 0.008
#> GSM601928 1 0.3431 0.912 0.936 0.064
#> GSM601933 2 0.5408 0.839 0.124 0.876
#> GSM601938 2 0.2603 0.843 0.044 0.956
#> GSM601943 2 0.9209 0.654 0.336 0.664
#> GSM601948 2 0.8207 0.690 0.256 0.744
#> GSM601958 1 0.0376 0.915 0.996 0.004
#> GSM601973 1 0.1843 0.917 0.972 0.028
#> GSM601978 2 0.3114 0.844 0.056 0.944
#> GSM601988 1 0.0938 0.913 0.988 0.012
#> GSM601878 1 0.4022 0.903 0.920 0.080
#> GSM601908 2 0.6343 0.825 0.160 0.840
#> GSM601918 2 0.9286 0.560 0.344 0.656
#> GSM601923 1 0.3733 0.904 0.928 0.072
#> GSM601953 2 0.0938 0.838 0.012 0.988
#> GSM601963 1 0.1843 0.915 0.972 0.028
#> GSM601968 2 0.8813 0.689 0.300 0.700
#> GSM601983 1 0.1843 0.918 0.972 0.028
#> GSM601993 1 0.0376 0.916 0.996 0.004
#> GSM601874 2 0.4690 0.844 0.100 0.900
#> GSM601884 1 0.6623 0.790 0.828 0.172
#> GSM601889 1 0.6148 0.799 0.848 0.152
#> GSM601894 1 0.0938 0.913 0.988 0.012
#> GSM601899 2 0.2043 0.844 0.032 0.968
#> GSM601904 1 0.3879 0.907 0.924 0.076
#> GSM601914 1 0.4431 0.905 0.908 0.092
#> GSM601929 1 0.3274 0.911 0.940 0.060
#> GSM601934 2 0.9754 0.463 0.408 0.592
#> GSM601939 1 0.2236 0.914 0.964 0.036
#> GSM601944 1 0.1414 0.915 0.980 0.020
#> GSM601949 2 0.9087 0.634 0.324 0.676
#> GSM601959 1 0.2603 0.908 0.956 0.044
#> GSM601974 1 0.2423 0.915 0.960 0.040
#> GSM601979 2 0.2603 0.843 0.044 0.956
#> GSM601989 1 0.0672 0.914 0.992 0.008
#> GSM601879 1 0.3274 0.910 0.940 0.060
#> GSM601909 2 0.8661 0.674 0.288 0.712
#> GSM601919 2 0.1633 0.834 0.024 0.976
#> GSM601924 1 0.3114 0.910 0.944 0.056
#> GSM601954 2 0.0000 0.833 0.000 1.000
#> GSM601964 1 0.2603 0.915 0.956 0.044
#> GSM601969 2 0.9427 0.571 0.360 0.640
#> GSM601984 1 0.1414 0.916 0.980 0.020
#> GSM601994 1 0.2423 0.908 0.960 0.040
#> GSM601875 2 0.2043 0.844 0.032 0.968
#> GSM601885 2 0.4815 0.843 0.104 0.896
#> GSM601890 2 0.1184 0.838 0.016 0.984
#> GSM601895 1 0.0376 0.915 0.996 0.004
#> GSM601900 1 0.6712 0.766 0.824 0.176
#> GSM601905 1 0.1184 0.918 0.984 0.016
#> GSM601915 1 0.0000 0.915 1.000 0.000
#> GSM601930 1 0.2778 0.911 0.952 0.048
#> GSM601935 1 0.0000 0.915 1.000 0.000
#> GSM601940 1 0.0672 0.916 0.992 0.008
#> GSM601945 2 0.3733 0.842 0.072 0.928
#> GSM601950 1 0.6973 0.791 0.812 0.188
#> GSM601960 1 0.3431 0.909 0.936 0.064
#> GSM601975 1 0.3431 0.907 0.936 0.064
#> GSM601980 2 0.8081 0.734 0.248 0.752
#> GSM601990 1 0.2043 0.916 0.968 0.032
#> GSM601880 1 0.2778 0.912 0.952 0.048
#> GSM601910 1 0.9686 0.331 0.604 0.396
#> GSM601920 1 0.8813 0.576 0.700 0.300
#> GSM601925 1 0.4161 0.902 0.916 0.084
#> GSM601955 1 0.9922 0.162 0.552 0.448
#> GSM601965 1 0.3274 0.909 0.940 0.060
#> GSM601970 1 0.4161 0.908 0.916 0.084
#> GSM601985 1 0.0672 0.916 0.992 0.008
#> GSM601995 1 0.2778 0.902 0.952 0.048
#> GSM601876 1 0.0376 0.915 0.996 0.004
#> GSM601886 1 0.1414 0.917 0.980 0.020
#> GSM601891 2 0.0938 0.838 0.012 0.988
#> GSM601896 1 0.0376 0.915 0.996 0.004
#> GSM601901 1 0.3274 0.904 0.940 0.060
#> GSM601906 1 0.1843 0.917 0.972 0.028
#> GSM601916 1 0.2423 0.918 0.960 0.040
#> GSM601931 1 0.2603 0.910 0.956 0.044
#> GSM601936 1 0.0938 0.913 0.988 0.012
#> GSM601941 1 0.3584 0.909 0.932 0.068
#> GSM601946 1 0.0000 0.915 1.000 0.000
#> GSM601951 1 0.3114 0.915 0.944 0.056
#> GSM601961 2 0.2948 0.844 0.052 0.948
#> GSM601976 1 0.3114 0.899 0.944 0.056
#> GSM601981 2 0.7219 0.809 0.200 0.800
#> GSM601991 1 0.0000 0.915 1.000 0.000
#> GSM601881 1 0.2603 0.912 0.956 0.044
#> GSM601911 1 0.0376 0.916 0.996 0.004
#> GSM601921 2 0.8608 0.667 0.284 0.716
#> GSM601926 1 0.2603 0.910 0.956 0.044
#> GSM601956 2 0.2423 0.843 0.040 0.960
#> GSM601966 1 0.3584 0.896 0.932 0.068
#> GSM601971 1 0.6148 0.854 0.848 0.152
#> GSM601986 1 0.2603 0.910 0.956 0.044
#> GSM601996 1 0.0672 0.914 0.992 0.008
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM601872 3 0.0237 0.8645 0.004 0.000 0.996
#> GSM601882 2 0.2625 0.8340 0.000 0.916 0.084
#> GSM601887 3 0.0661 0.8641 0.008 0.004 0.988
#> GSM601892 3 0.6168 0.2693 0.000 0.412 0.588
#> GSM601897 2 0.4261 0.7847 0.012 0.848 0.140
#> GSM601902 2 0.6008 0.4901 0.372 0.628 0.000
#> GSM601912 2 0.0000 0.8467 0.000 1.000 0.000
#> GSM601927 2 0.6305 -0.0835 0.484 0.516 0.000
#> GSM601932 2 0.1399 0.8506 0.028 0.968 0.004
#> GSM601937 2 0.1636 0.8517 0.016 0.964 0.020
#> GSM601942 2 0.2584 0.8441 0.008 0.928 0.064
#> GSM601947 1 0.0829 0.8711 0.984 0.004 0.012
#> GSM601957 2 0.7446 0.1327 0.036 0.532 0.432
#> GSM601972 2 0.4915 0.7455 0.012 0.804 0.184
#> GSM601977 2 0.6260 0.2469 0.000 0.552 0.448
#> GSM601987 3 0.3482 0.8131 0.000 0.128 0.872
#> GSM601877 1 0.1031 0.8758 0.976 0.024 0.000
#> GSM601907 3 0.0000 0.8643 0.000 0.000 1.000
#> GSM601917 1 0.1647 0.8765 0.960 0.036 0.004
#> GSM601922 1 0.3610 0.8475 0.888 0.096 0.016
#> GSM601952 2 0.4277 0.8135 0.132 0.852 0.016
#> GSM601962 2 0.6235 0.3879 0.436 0.564 0.000
#> GSM601967 1 0.3116 0.8337 0.892 0.108 0.000
#> GSM601982 2 0.5635 0.7659 0.180 0.784 0.036
#> GSM601992 2 0.2443 0.8527 0.032 0.940 0.028
#> GSM601873 3 0.5070 0.7134 0.004 0.224 0.772
#> GSM601883 2 0.6434 0.3609 0.008 0.612 0.380
#> GSM601888 3 0.0592 0.8625 0.012 0.000 0.988
#> GSM601893 2 0.5835 0.5141 0.000 0.660 0.340
#> GSM601898 2 0.4062 0.7741 0.164 0.836 0.000
#> GSM601903 2 0.5047 0.8088 0.140 0.824 0.036
#> GSM601913 2 0.3116 0.8317 0.108 0.892 0.000
#> GSM601928 1 0.4555 0.7666 0.800 0.200 0.000
#> GSM601933 3 0.2096 0.8559 0.004 0.052 0.944
#> GSM601938 3 0.0237 0.8645 0.004 0.000 0.996
#> GSM601943 3 0.6082 0.6003 0.012 0.296 0.692
#> GSM601948 1 0.1015 0.8722 0.980 0.008 0.012
#> GSM601958 2 0.2261 0.8375 0.068 0.932 0.000
#> GSM601973 2 0.1399 0.8501 0.028 0.968 0.004
#> GSM601978 3 0.0000 0.8643 0.000 0.000 1.000
#> GSM601988 2 0.0000 0.8467 0.000 1.000 0.000
#> GSM601878 1 0.0829 0.8748 0.984 0.012 0.004
#> GSM601908 3 0.3784 0.8098 0.004 0.132 0.864
#> GSM601918 1 0.0892 0.8690 0.980 0.000 0.020
#> GSM601923 1 0.0592 0.8754 0.988 0.012 0.000
#> GSM601953 3 0.0000 0.8643 0.000 0.000 1.000
#> GSM601963 2 0.4654 0.7533 0.208 0.792 0.000
#> GSM601968 3 0.7453 0.6514 0.092 0.228 0.680
#> GSM601983 2 0.4235 0.7824 0.176 0.824 0.000
#> GSM601993 2 0.1015 0.8494 0.008 0.980 0.012
#> GSM601874 3 0.1832 0.8594 0.008 0.036 0.956
#> GSM601884 2 0.4291 0.7824 0.008 0.840 0.152
#> GSM601889 2 0.4645 0.7596 0.008 0.816 0.176
#> GSM601894 2 0.0661 0.8494 0.008 0.988 0.004
#> GSM601899 3 0.0592 0.8625 0.012 0.000 0.988
#> GSM601904 2 0.5465 0.6787 0.288 0.712 0.000
#> GSM601914 2 0.5992 0.6954 0.268 0.716 0.016
#> GSM601929 1 0.2860 0.8654 0.912 0.084 0.004
#> GSM601934 3 0.6648 0.4080 0.016 0.364 0.620
#> GSM601939 1 0.5404 0.7128 0.740 0.256 0.004
#> GSM601944 2 0.0829 0.8512 0.004 0.984 0.012
#> GSM601949 1 0.7572 0.6609 0.688 0.128 0.184
#> GSM601959 2 0.1585 0.8514 0.008 0.964 0.028
#> GSM601974 2 0.4291 0.7622 0.180 0.820 0.000
#> GSM601979 3 0.0000 0.8643 0.000 0.000 1.000
#> GSM601989 2 0.0237 0.8475 0.000 0.996 0.004
#> GSM601879 1 0.0592 0.8751 0.988 0.012 0.000
#> GSM601909 1 0.2599 0.8562 0.932 0.016 0.052
#> GSM601919 1 0.0424 0.8703 0.992 0.000 0.008
#> GSM601924 1 0.0829 0.8743 0.984 0.012 0.004
#> GSM601954 3 0.2711 0.8150 0.088 0.000 0.912
#> GSM601964 2 0.3752 0.8061 0.144 0.856 0.000
#> GSM601969 3 0.9292 0.3763 0.272 0.208 0.520
#> GSM601984 2 0.4399 0.7752 0.188 0.812 0.000
#> GSM601994 2 0.2384 0.8483 0.008 0.936 0.056
#> GSM601875 3 0.0592 0.8658 0.000 0.012 0.988
#> GSM601885 3 0.2749 0.8464 0.012 0.064 0.924
#> GSM601890 3 0.0592 0.8625 0.012 0.000 0.988
#> GSM601895 2 0.0237 0.8471 0.004 0.996 0.000
#> GSM601900 2 0.7027 0.7050 0.104 0.724 0.172
#> GSM601905 2 0.0892 0.8503 0.020 0.980 0.000
#> GSM601915 2 0.2625 0.8278 0.084 0.916 0.000
#> GSM601930 1 0.1529 0.8773 0.960 0.040 0.000
#> GSM601935 2 0.0424 0.8473 0.008 0.992 0.000
#> GSM601940 2 0.4178 0.7653 0.172 0.828 0.000
#> GSM601945 3 0.0237 0.8653 0.000 0.004 0.996
#> GSM601950 1 0.5365 0.6971 0.744 0.252 0.004
#> GSM601960 2 0.5431 0.6646 0.284 0.716 0.000
#> GSM601975 2 0.2318 0.8528 0.028 0.944 0.028
#> GSM601980 3 0.7107 0.6815 0.092 0.196 0.712
#> GSM601990 2 0.1860 0.8498 0.052 0.948 0.000
#> GSM601880 1 0.1529 0.8774 0.960 0.040 0.000
#> GSM601910 2 0.7962 0.3889 0.072 0.576 0.352
#> GSM601920 1 0.6351 0.7477 0.760 0.168 0.072
#> GSM601925 1 0.0424 0.8740 0.992 0.008 0.000
#> GSM601955 2 0.9491 0.3164 0.220 0.488 0.292
#> GSM601965 2 0.5785 0.5944 0.332 0.668 0.000
#> GSM601970 1 0.6676 0.0422 0.516 0.476 0.008
#> GSM601985 2 0.1529 0.8504 0.040 0.960 0.000
#> GSM601995 2 0.2200 0.8471 0.004 0.940 0.056
#> GSM601876 2 0.0592 0.8480 0.012 0.988 0.000
#> GSM601886 2 0.0592 0.8481 0.012 0.988 0.000
#> GSM601891 3 0.0829 0.8644 0.012 0.004 0.984
#> GSM601896 2 0.0424 0.8479 0.008 0.992 0.000
#> GSM601901 2 0.1832 0.8506 0.008 0.956 0.036
#> GSM601906 2 0.2165 0.8494 0.064 0.936 0.000
#> GSM601916 2 0.2625 0.8453 0.084 0.916 0.000
#> GSM601931 1 0.2878 0.8609 0.904 0.096 0.000
#> GSM601936 2 0.0475 0.8481 0.004 0.992 0.004
#> GSM601941 2 0.3502 0.8446 0.084 0.896 0.020
#> GSM601946 2 0.0747 0.8495 0.016 0.984 0.000
#> GSM601951 1 0.5285 0.7246 0.752 0.244 0.004
#> GSM601961 3 0.0661 0.8647 0.004 0.008 0.988
#> GSM601976 2 0.2280 0.8481 0.008 0.940 0.052
#> GSM601981 3 0.4912 0.7614 0.008 0.196 0.796
#> GSM601991 2 0.0237 0.8472 0.004 0.996 0.000
#> GSM601881 1 0.1964 0.8736 0.944 0.056 0.000
#> GSM601911 2 0.0424 0.8483 0.008 0.992 0.000
#> GSM601921 1 0.8975 0.1180 0.484 0.132 0.384
#> GSM601926 1 0.1031 0.8755 0.976 0.024 0.000
#> GSM601956 3 0.0592 0.8643 0.012 0.000 0.988
#> GSM601966 2 0.2955 0.8380 0.008 0.912 0.080
#> GSM601971 1 0.0661 0.8734 0.988 0.008 0.004
#> GSM601986 2 0.6204 0.3866 0.424 0.576 0.000
#> GSM601996 2 0.1182 0.8499 0.012 0.976 0.012
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM601872 2 0.0188 0.75440 0.000 0.996 0.000 0.004
#> GSM601882 3 0.6080 -0.29652 0.000 0.044 0.488 0.468
#> GSM601887 2 0.0000 0.75551 0.000 1.000 0.000 0.000
#> GSM601892 2 0.4888 0.16328 0.000 0.588 0.412 0.000
#> GSM601897 3 0.6344 0.49497 0.000 0.128 0.648 0.224
#> GSM601902 3 0.7289 0.33727 0.252 0.000 0.536 0.212
#> GSM601912 3 0.0336 0.67902 0.000 0.000 0.992 0.008
#> GSM601927 3 0.5000 -0.09479 0.496 0.000 0.504 0.000
#> GSM601932 3 0.4819 0.43732 0.004 0.000 0.652 0.344
#> GSM601937 3 0.4262 0.58603 0.000 0.008 0.756 0.236
#> GSM601942 4 0.4635 0.52534 0.000 0.012 0.268 0.720
#> GSM601947 1 0.4999 0.41718 0.508 0.000 0.000 0.492
#> GSM601957 3 0.6671 0.02090 0.024 0.424 0.512 0.040
#> GSM601972 4 0.6188 0.27946 0.000 0.056 0.396 0.548
#> GSM601977 2 0.7871 -0.25264 0.000 0.384 0.284 0.332
#> GSM601987 2 0.6195 0.41688 0.000 0.648 0.100 0.252
#> GSM601877 1 0.0000 0.81151 1.000 0.000 0.000 0.000
#> GSM601907 2 0.0188 0.75450 0.000 0.996 0.000 0.004
#> GSM601917 1 0.1174 0.81465 0.968 0.000 0.012 0.020
#> GSM601922 1 0.5361 0.63968 0.716 0.000 0.060 0.224
#> GSM601952 3 0.5467 0.35529 0.008 0.008 0.584 0.400
#> GSM601962 3 0.6714 0.37718 0.360 0.000 0.540 0.100
#> GSM601967 1 0.2401 0.76972 0.904 0.000 0.092 0.004
#> GSM601982 3 0.7956 -0.03559 0.176 0.020 0.476 0.328
#> GSM601992 3 0.5989 -0.24216 0.024 0.008 0.496 0.472
#> GSM601873 2 0.7114 0.27679 0.004 0.584 0.192 0.220
#> GSM601883 4 0.6626 0.50825 0.008 0.084 0.312 0.596
#> GSM601888 2 0.0000 0.75551 0.000 1.000 0.000 0.000
#> GSM601893 3 0.4624 0.36087 0.000 0.340 0.660 0.000
#> GSM601898 3 0.3862 0.62826 0.152 0.000 0.824 0.024
#> GSM601903 3 0.6125 0.23020 0.048 0.000 0.516 0.436
#> GSM601913 3 0.3342 0.67994 0.100 0.000 0.868 0.032
#> GSM601928 1 0.4756 0.72730 0.772 0.000 0.052 0.176
#> GSM601933 2 0.2983 0.70087 0.000 0.892 0.040 0.068
#> GSM601938 4 0.4933 0.01578 0.000 0.432 0.000 0.568
#> GSM601943 2 0.7472 0.06148 0.000 0.504 0.264 0.232
#> GSM601948 1 0.5095 0.57584 0.624 0.004 0.004 0.368
#> GSM601958 3 0.1792 0.67234 0.068 0.000 0.932 0.000
#> GSM601973 4 0.4978 0.29034 0.004 0.000 0.384 0.612
#> GSM601978 2 0.0188 0.75440 0.000 0.996 0.000 0.004
#> GSM601988 3 0.0707 0.67810 0.000 0.000 0.980 0.020
#> GSM601878 1 0.0000 0.81151 1.000 0.000 0.000 0.000
#> GSM601908 4 0.6792 0.04451 0.000 0.428 0.096 0.476
#> GSM601918 1 0.5360 0.41211 0.552 0.012 0.000 0.436
#> GSM601923 1 0.0376 0.81262 0.992 0.000 0.004 0.004
#> GSM601953 2 0.0000 0.75551 0.000 1.000 0.000 0.000
#> GSM601963 3 0.4464 0.60889 0.208 0.000 0.768 0.024
#> GSM601968 2 0.6189 0.43347 0.092 0.676 0.224 0.008
#> GSM601983 3 0.4553 0.62508 0.180 0.000 0.780 0.040
#> GSM601993 3 0.4679 0.24706 0.000 0.000 0.648 0.352
#> GSM601874 2 0.4936 0.45143 0.000 0.672 0.012 0.316
#> GSM601884 4 0.5436 0.44347 0.000 0.024 0.356 0.620
#> GSM601889 3 0.3636 0.59939 0.000 0.172 0.820 0.008
#> GSM601894 3 0.0336 0.67965 0.000 0.000 0.992 0.008
#> GSM601899 2 0.0188 0.75441 0.000 0.996 0.000 0.004
#> GSM601904 3 0.6508 0.45700 0.104 0.000 0.600 0.296
#> GSM601914 3 0.6362 0.55617 0.176 0.000 0.656 0.168
#> GSM601929 1 0.1978 0.80407 0.928 0.000 0.068 0.004
#> GSM601934 2 0.5818 0.20606 0.004 0.600 0.364 0.032
#> GSM601939 1 0.4252 0.63288 0.744 0.000 0.252 0.004
#> GSM601944 3 0.2179 0.66551 0.000 0.012 0.924 0.064
#> GSM601949 1 0.7446 0.59756 0.636 0.140 0.064 0.160
#> GSM601959 3 0.1733 0.68137 0.000 0.028 0.948 0.024
#> GSM601974 3 0.6323 0.51021 0.164 0.000 0.660 0.176
#> GSM601979 2 0.0000 0.75551 0.000 1.000 0.000 0.000
#> GSM601989 3 0.0000 0.67741 0.000 0.000 1.000 0.000
#> GSM601879 1 0.0000 0.81151 1.000 0.000 0.000 0.000
#> GSM601909 1 0.2967 0.79558 0.904 0.028 0.016 0.052
#> GSM601919 1 0.2345 0.77874 0.900 0.000 0.000 0.100
#> GSM601924 1 0.0000 0.81151 1.000 0.000 0.000 0.000
#> GSM601954 2 0.3168 0.68454 0.060 0.884 0.000 0.056
#> GSM601964 3 0.4514 0.64779 0.064 0.000 0.800 0.136
#> GSM601969 2 0.8628 0.15082 0.216 0.512 0.188 0.084
#> GSM601984 3 0.3810 0.63771 0.188 0.000 0.804 0.008
#> GSM601994 4 0.5755 0.31580 0.000 0.028 0.444 0.528
#> GSM601875 2 0.0188 0.75447 0.000 0.996 0.004 0.000
#> GSM601885 2 0.5614 0.34567 0.000 0.628 0.036 0.336
#> GSM601890 2 0.0000 0.75551 0.000 1.000 0.000 0.000
#> GSM601895 3 0.0336 0.67877 0.000 0.000 0.992 0.008
#> GSM601900 3 0.6159 0.55122 0.088 0.160 0.720 0.032
#> GSM601905 3 0.0707 0.68421 0.020 0.000 0.980 0.000
#> GSM601915 3 0.2542 0.66563 0.084 0.000 0.904 0.012
#> GSM601930 1 0.1059 0.81585 0.972 0.000 0.016 0.012
#> GSM601935 3 0.1716 0.68564 0.000 0.000 0.936 0.064
#> GSM601940 3 0.3219 0.62910 0.164 0.000 0.836 0.000
#> GSM601945 2 0.0000 0.75551 0.000 1.000 0.000 0.000
#> GSM601950 1 0.5798 0.62347 0.696 0.000 0.208 0.096
#> GSM601960 3 0.6473 0.51196 0.188 0.000 0.644 0.168
#> GSM601975 3 0.4548 0.59830 0.008 0.008 0.752 0.232
#> GSM601980 4 0.4929 0.36668 0.008 0.236 0.020 0.736
#> GSM601990 3 0.3577 0.66285 0.012 0.000 0.832 0.156
#> GSM601880 1 0.0895 0.81531 0.976 0.000 0.020 0.004
#> GSM601910 3 0.7997 0.23807 0.028 0.268 0.516 0.188
#> GSM601920 1 0.4819 0.68705 0.784 0.060 0.152 0.004
#> GSM601925 1 0.0336 0.81300 0.992 0.000 0.000 0.008
#> GSM601955 3 0.9087 -0.00762 0.092 0.176 0.388 0.344
#> GSM601965 3 0.4605 0.49444 0.336 0.000 0.664 0.000
#> GSM601970 1 0.7269 0.08245 0.456 0.000 0.396 0.148
#> GSM601985 3 0.1118 0.68680 0.036 0.000 0.964 0.000
#> GSM601995 3 0.5673 0.11971 0.000 0.032 0.596 0.372
#> GSM601876 3 0.1022 0.67957 0.000 0.000 0.968 0.032
#> GSM601886 3 0.1557 0.68306 0.000 0.000 0.944 0.056
#> GSM601891 2 0.0000 0.75551 0.000 1.000 0.000 0.000
#> GSM601896 3 0.0000 0.67741 0.000 0.000 1.000 0.000
#> GSM601901 3 0.2500 0.67994 0.000 0.040 0.916 0.044
#> GSM601906 3 0.4008 0.65451 0.032 0.000 0.820 0.148
#> GSM601916 3 0.3638 0.67015 0.032 0.000 0.848 0.120
#> GSM601931 1 0.1716 0.80528 0.936 0.000 0.064 0.000
#> GSM601936 3 0.0469 0.67936 0.000 0.000 0.988 0.012
#> GSM601941 4 0.3142 0.53106 0.008 0.000 0.132 0.860
#> GSM601946 3 0.1209 0.68118 0.004 0.000 0.964 0.032
#> GSM601951 1 0.6316 0.61985 0.660 0.000 0.184 0.156
#> GSM601961 2 0.0000 0.75551 0.000 1.000 0.000 0.000
#> GSM601976 3 0.2759 0.67796 0.000 0.052 0.904 0.044
#> GSM601981 2 0.4820 0.56418 0.000 0.772 0.168 0.060
#> GSM601991 3 0.0817 0.68118 0.000 0.000 0.976 0.024
#> GSM601881 1 0.1302 0.81184 0.956 0.000 0.044 0.000
#> GSM601911 3 0.0376 0.67993 0.004 0.000 0.992 0.004
#> GSM601921 4 0.8768 0.19104 0.256 0.232 0.060 0.452
#> GSM601926 1 0.0188 0.81289 0.996 0.000 0.004 0.000
#> GSM601956 2 0.1022 0.74281 0.000 0.968 0.000 0.032
#> GSM601966 3 0.6310 -0.14511 0.000 0.060 0.512 0.428
#> GSM601971 1 0.3649 0.73064 0.796 0.000 0.000 0.204
#> GSM601986 3 0.5229 0.36591 0.428 0.000 0.564 0.008
#> GSM601996 3 0.5203 -0.03949 0.008 0.000 0.576 0.416
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM601872 2 0.0162 0.76054 0.000 0.996 0.000 0.000 0.004
#> GSM601882 5 0.5564 0.44643 0.000 0.032 0.456 0.020 0.492
#> GSM601887 2 0.0000 0.76129 0.000 1.000 0.000 0.000 0.000
#> GSM601892 2 0.4210 0.17317 0.000 0.588 0.412 0.000 0.000
#> GSM601897 3 0.7492 0.35091 0.000 0.096 0.496 0.148 0.260
#> GSM601902 3 0.7146 0.22272 0.232 0.000 0.492 0.240 0.036
#> GSM601912 3 0.0992 0.66775 0.000 0.000 0.968 0.008 0.024
#> GSM601927 3 0.4307 -0.09120 0.496 0.000 0.504 0.000 0.000
#> GSM601932 3 0.4883 0.16935 0.004 0.000 0.516 0.464 0.016
#> GSM601937 3 0.5654 0.39900 0.000 0.008 0.592 0.076 0.324
#> GSM601942 5 0.6431 0.28458 0.000 0.008 0.176 0.280 0.536
#> GSM601947 4 0.3700 0.37089 0.240 0.000 0.000 0.752 0.008
#> GSM601957 3 0.7529 -0.07257 0.020 0.336 0.440 0.176 0.028
#> GSM601972 4 0.6996 0.12203 0.000 0.036 0.280 0.508 0.176
#> GSM601977 5 0.7388 0.30820 0.000 0.348 0.272 0.028 0.352
#> GSM601987 2 0.5717 0.37256 0.000 0.608 0.092 0.008 0.292
#> GSM601877 1 0.0000 0.75865 1.000 0.000 0.000 0.000 0.000
#> GSM601907 2 0.0162 0.76064 0.000 0.996 0.000 0.000 0.004
#> GSM601917 1 0.2116 0.73853 0.912 0.000 0.008 0.076 0.004
#> GSM601922 1 0.5807 0.43599 0.664 0.000 0.052 0.220 0.064
#> GSM601952 3 0.4700 0.18388 0.000 0.008 0.516 0.472 0.004
#> GSM601962 3 0.6814 0.39136 0.184 0.000 0.500 0.020 0.296
#> GSM601967 1 0.2295 0.70630 0.900 0.000 0.088 0.004 0.008
#> GSM601982 3 0.6169 -0.25853 0.084 0.016 0.452 0.000 0.448
#> GSM601992 5 0.5146 0.45943 0.016 0.000 0.428 0.016 0.540
#> GSM601873 2 0.6179 0.26366 0.004 0.572 0.180 0.000 0.244
#> GSM601883 5 0.7355 0.42465 0.004 0.060 0.280 0.156 0.500
#> GSM601888 2 0.0000 0.76129 0.000 1.000 0.000 0.000 0.000
#> GSM601893 3 0.3983 0.36444 0.000 0.340 0.660 0.000 0.000
#> GSM601898 3 0.5325 0.56397 0.136 0.000 0.732 0.076 0.056
#> GSM601903 4 0.5847 -0.11445 0.032 0.000 0.452 0.480 0.036
#> GSM601913 3 0.3815 0.66774 0.088 0.000 0.832 0.020 0.060
#> GSM601928 1 0.4118 0.62102 0.772 0.000 0.032 0.188 0.008
#> GSM601933 2 0.3243 0.68933 0.000 0.848 0.032 0.004 0.116
#> GSM601938 5 0.6288 0.11246 0.000 0.372 0.000 0.156 0.472
#> GSM601943 2 0.7441 -0.00601 0.000 0.476 0.256 0.060 0.208
#> GSM601948 4 0.4367 0.01878 0.416 0.004 0.000 0.580 0.000
#> GSM601958 3 0.2102 0.65688 0.068 0.000 0.916 0.004 0.012
#> GSM601973 4 0.6702 -0.09121 0.000 0.000 0.344 0.408 0.248
#> GSM601978 2 0.0404 0.75851 0.000 0.988 0.000 0.000 0.012
#> GSM601988 3 0.1043 0.66016 0.000 0.000 0.960 0.000 0.040
#> GSM601878 1 0.0000 0.75865 1.000 0.000 0.000 0.000 0.000
#> GSM601908 5 0.7043 0.15650 0.000 0.360 0.064 0.104 0.472
#> GSM601918 4 0.5554 0.26993 0.328 0.004 0.000 0.592 0.076
#> GSM601923 1 0.0162 0.75854 0.996 0.000 0.000 0.004 0.000
#> GSM601953 2 0.0000 0.76129 0.000 1.000 0.000 0.000 0.000
#> GSM601963 3 0.5382 0.53573 0.120 0.000 0.656 0.000 0.224
#> GSM601968 2 0.5386 0.45141 0.088 0.676 0.224 0.012 0.000
#> GSM601983 3 0.5551 0.48348 0.104 0.000 0.612 0.000 0.284
#> GSM601993 3 0.4924 -0.01071 0.000 0.000 0.552 0.028 0.420
#> GSM601874 2 0.6373 0.36508 0.000 0.572 0.012 0.204 0.212
#> GSM601884 5 0.4622 0.40911 0.000 0.012 0.164 0.068 0.756
#> GSM601889 3 0.4992 0.52765 0.000 0.168 0.728 0.092 0.012
#> GSM601894 3 0.0290 0.66061 0.000 0.000 0.992 0.008 0.000
#> GSM601899 2 0.1197 0.74260 0.000 0.952 0.000 0.048 0.000
#> GSM601904 3 0.6210 0.45029 0.084 0.000 0.588 0.292 0.036
#> GSM601914 3 0.7291 0.37420 0.080 0.000 0.480 0.120 0.320
#> GSM601929 1 0.1768 0.74338 0.924 0.000 0.072 0.004 0.000
#> GSM601934 2 0.6192 0.13861 0.004 0.536 0.356 0.012 0.092
#> GSM601939 1 0.3783 0.54409 0.740 0.000 0.252 0.008 0.000
#> GSM601944 3 0.2586 0.64453 0.000 0.012 0.892 0.012 0.084
#> GSM601949 1 0.7025 0.26264 0.508 0.120 0.060 0.312 0.000
#> GSM601959 3 0.2523 0.66334 0.000 0.024 0.908 0.040 0.028
#> GSM601974 3 0.5572 0.48671 0.164 0.000 0.644 0.192 0.000
#> GSM601979 2 0.0000 0.76129 0.000 1.000 0.000 0.000 0.000
#> GSM601989 3 0.0162 0.65902 0.000 0.000 0.996 0.004 0.000
#> GSM601879 1 0.0000 0.75865 1.000 0.000 0.000 0.000 0.000
#> GSM601909 1 0.6082 0.38873 0.584 0.016 0.008 0.076 0.316
#> GSM601919 1 0.3756 0.58441 0.744 0.000 0.000 0.248 0.008
#> GSM601924 1 0.0000 0.75865 1.000 0.000 0.000 0.000 0.000
#> GSM601954 2 0.4337 0.59283 0.052 0.744 0.000 0.204 0.000
#> GSM601964 3 0.5600 0.50263 0.032 0.000 0.636 0.048 0.284
#> GSM601969 2 0.8360 -0.06474 0.200 0.376 0.192 0.232 0.000
#> GSM601984 3 0.3953 0.62227 0.188 0.000 0.780 0.008 0.024
#> GSM601994 5 0.6131 0.46231 0.000 0.020 0.356 0.084 0.540
#> GSM601875 2 0.0162 0.76049 0.000 0.996 0.004 0.000 0.000
#> GSM601885 2 0.5047 0.29033 0.000 0.588 0.032 0.004 0.376
#> GSM601890 2 0.0000 0.76129 0.000 1.000 0.000 0.000 0.000
#> GSM601895 3 0.0912 0.66500 0.000 0.000 0.972 0.012 0.016
#> GSM601900 3 0.6301 0.54948 0.084 0.148 0.680 0.020 0.068
#> GSM601905 3 0.0671 0.66623 0.016 0.000 0.980 0.004 0.000
#> GSM601915 3 0.2676 0.64874 0.080 0.000 0.884 0.000 0.036
#> GSM601930 1 0.1200 0.76052 0.964 0.000 0.016 0.008 0.012
#> GSM601935 3 0.3412 0.64699 0.000 0.000 0.820 0.028 0.152
#> GSM601940 3 0.2773 0.62030 0.164 0.000 0.836 0.000 0.000
#> GSM601945 2 0.0290 0.75978 0.000 0.992 0.000 0.000 0.008
#> GSM601950 1 0.6128 0.35035 0.560 0.000 0.188 0.252 0.000
#> GSM601960 3 0.6232 0.49654 0.176 0.000 0.624 0.172 0.028
#> GSM601975 3 0.4692 0.59279 0.004 0.004 0.720 0.228 0.044
#> GSM601980 5 0.5451 0.10284 0.004 0.064 0.000 0.344 0.588
#> GSM601990 3 0.5338 0.50561 0.000 0.000 0.604 0.072 0.324
#> GSM601880 1 0.0798 0.76144 0.976 0.000 0.016 0.008 0.000
#> GSM601910 3 0.8680 0.03906 0.012 0.180 0.364 0.236 0.208
#> GSM601920 1 0.4381 0.60125 0.776 0.060 0.152 0.000 0.012
#> GSM601925 1 0.0290 0.75966 0.992 0.000 0.000 0.000 0.008
#> GSM601955 5 0.8207 -0.02615 0.008 0.096 0.204 0.324 0.368
#> GSM601965 3 0.4235 0.49430 0.336 0.000 0.656 0.000 0.008
#> GSM601970 1 0.8489 -0.15561 0.320 0.000 0.244 0.256 0.180
#> GSM601985 3 0.0963 0.67075 0.036 0.000 0.964 0.000 0.000
#> GSM601995 5 0.4777 0.31248 0.000 0.016 0.356 0.008 0.620
#> GSM601876 3 0.1648 0.66309 0.000 0.000 0.940 0.020 0.040
#> GSM601886 3 0.2171 0.67219 0.000 0.000 0.912 0.064 0.024
#> GSM601891 2 0.0000 0.76129 0.000 1.000 0.000 0.000 0.000
#> GSM601896 3 0.0000 0.65855 0.000 0.000 1.000 0.000 0.000
#> GSM601901 3 0.2688 0.66294 0.000 0.036 0.896 0.056 0.012
#> GSM601906 3 0.3903 0.64326 0.020 0.000 0.800 0.160 0.020
#> GSM601916 3 0.3929 0.66009 0.028 0.000 0.820 0.116 0.036
#> GSM601931 1 0.1410 0.75047 0.940 0.000 0.060 0.000 0.000
#> GSM601936 3 0.0798 0.66234 0.000 0.000 0.976 0.008 0.016
#> GSM601941 4 0.4226 0.32280 0.000 0.000 0.060 0.764 0.176
#> GSM601946 3 0.1630 0.66496 0.004 0.000 0.944 0.016 0.036
#> GSM601951 1 0.6248 0.28821 0.520 0.000 0.172 0.308 0.000
#> GSM601961 2 0.0000 0.76129 0.000 1.000 0.000 0.000 0.000
#> GSM601976 3 0.3238 0.66370 0.000 0.048 0.872 0.032 0.048
#> GSM601981 2 0.5012 0.57848 0.000 0.748 0.136 0.032 0.084
#> GSM601991 3 0.1894 0.66452 0.000 0.000 0.920 0.008 0.072
#> GSM601881 1 0.1121 0.75639 0.956 0.000 0.044 0.000 0.000
#> GSM601911 3 0.1059 0.66644 0.004 0.000 0.968 0.008 0.020
#> GSM601921 4 0.8609 0.30802 0.204 0.184 0.048 0.452 0.112
#> GSM601926 1 0.0162 0.75995 0.996 0.000 0.004 0.000 0.000
#> GSM601956 2 0.1571 0.73989 0.000 0.936 0.000 0.004 0.060
#> GSM601966 5 0.5902 0.34273 0.000 0.044 0.452 0.028 0.476
#> GSM601971 1 0.4318 0.45621 0.644 0.000 0.004 0.348 0.004
#> GSM601986 3 0.4841 0.34664 0.416 0.000 0.560 0.000 0.024
#> GSM601996 3 0.5666 -0.30720 0.008 0.000 0.524 0.060 0.408
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM601872 2 0.0146 0.74010 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM601882 5 0.4559 0.51730 0.000 0.016 0.412 0.008 0.560 0.004
#> GSM601887 2 0.0000 0.74040 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM601892 2 0.3782 0.19265 0.000 0.588 0.412 0.000 0.000 0.000
#> GSM601897 3 0.7488 0.36692 0.000 0.096 0.492 0.140 0.056 0.216
#> GSM601902 3 0.6834 0.17360 0.232 0.000 0.476 0.240 0.024 0.028
#> GSM601912 3 0.0891 0.68248 0.000 0.000 0.968 0.008 0.000 0.024
#> GSM601927 3 0.3868 -0.09549 0.496 0.000 0.504 0.000 0.000 0.000
#> GSM601932 4 0.4393 -0.08674 0.004 0.000 0.480 0.500 0.016 0.000
#> GSM601937 3 0.6168 0.45772 0.000 0.008 0.592 0.064 0.112 0.224
#> GSM601942 5 0.6778 0.34868 0.000 0.000 0.152 0.256 0.492 0.100
#> GSM601947 4 0.2558 0.27122 0.156 0.000 0.000 0.840 0.004 0.000
#> GSM601957 3 0.7127 -0.05927 0.016 0.324 0.416 0.204 0.020 0.020
#> GSM601972 4 0.5902 0.19074 0.000 0.020 0.224 0.560 0.196 0.000
#> GSM601977 5 0.6934 0.31810 0.000 0.332 0.268 0.024 0.360 0.016
#> GSM601987 2 0.5045 0.15834 0.000 0.512 0.076 0.000 0.412 0.000
#> GSM601877 1 0.0000 0.75479 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM601907 2 0.0260 0.73999 0.000 0.992 0.000 0.000 0.008 0.000
#> GSM601917 1 0.2909 0.71217 0.864 0.000 0.008 0.096 0.020 0.012
#> GSM601922 1 0.6322 0.35480 0.584 0.000 0.048 0.240 0.100 0.028
#> GSM601952 3 0.4315 0.04960 0.000 0.004 0.492 0.492 0.012 0.000
#> GSM601962 3 0.5959 0.39065 0.160 0.000 0.496 0.008 0.004 0.332
#> GSM601967 1 0.2062 0.70413 0.900 0.000 0.088 0.004 0.008 0.000
#> GSM601982 3 0.6859 -0.17675 0.056 0.016 0.460 0.000 0.324 0.144
#> GSM601992 5 0.4234 0.56174 0.008 0.000 0.300 0.008 0.672 0.012
#> GSM601873 2 0.5795 0.22706 0.004 0.556 0.168 0.000 0.264 0.008
#> GSM601883 5 0.5640 0.52869 0.000 0.036 0.212 0.116 0.632 0.004
#> GSM601888 2 0.0000 0.74040 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM601893 3 0.3578 0.41014 0.000 0.340 0.660 0.000 0.000 0.000
#> GSM601898 3 0.5140 0.55233 0.132 0.000 0.724 0.076 0.020 0.048
#> GSM601903 4 0.5785 0.00434 0.028 0.000 0.440 0.468 0.036 0.028
#> GSM601913 3 0.3571 0.67708 0.088 0.000 0.832 0.012 0.016 0.052
#> GSM601928 1 0.3730 0.63815 0.772 0.000 0.032 0.188 0.004 0.004
#> GSM601933 2 0.3861 0.59895 0.000 0.744 0.028 0.000 0.220 0.008
#> GSM601938 5 0.5594 0.27202 0.000 0.308 0.000 0.136 0.548 0.008
#> GSM601943 2 0.6919 -0.01919 0.000 0.452 0.244 0.052 0.244 0.008
#> GSM601948 4 0.3769 0.06474 0.356 0.004 0.000 0.640 0.000 0.000
#> GSM601958 3 0.1888 0.67309 0.068 0.000 0.916 0.004 0.000 0.012
#> GSM601973 4 0.6372 0.01301 0.000 0.000 0.340 0.404 0.240 0.016
#> GSM601978 2 0.0790 0.73318 0.000 0.968 0.000 0.000 0.032 0.000
#> GSM601988 3 0.1267 0.67205 0.000 0.000 0.940 0.000 0.060 0.000
#> GSM601878 1 0.0000 0.75479 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM601908 5 0.5607 0.33887 0.000 0.280 0.048 0.064 0.604 0.004
#> GSM601918 4 0.5313 0.23137 0.228 0.004 0.000 0.652 0.088 0.028
#> GSM601923 1 0.0146 0.75472 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM601953 2 0.0000 0.74040 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM601963 3 0.4684 0.55753 0.088 0.000 0.656 0.000 0.000 0.256
#> GSM601968 2 0.4838 0.45234 0.088 0.676 0.224 0.012 0.000 0.000
#> GSM601983 3 0.5173 0.50507 0.084 0.000 0.604 0.000 0.012 0.300
#> GSM601993 3 0.5717 -0.02413 0.000 0.000 0.528 0.016 0.336 0.120
#> GSM601874 2 0.6094 0.31071 0.000 0.524 0.008 0.228 0.232 0.008
#> GSM601884 5 0.6156 0.39018 0.000 0.012 0.128 0.052 0.600 0.208
#> GSM601889 3 0.4660 0.54132 0.000 0.168 0.712 0.108 0.000 0.012
#> GSM601894 3 0.0260 0.67482 0.000 0.000 0.992 0.008 0.000 0.000
#> GSM601899 2 0.1204 0.71830 0.000 0.944 0.000 0.056 0.000 0.000
#> GSM601904 3 0.5851 0.41580 0.076 0.000 0.584 0.292 0.024 0.024
#> GSM601914 3 0.6679 0.34314 0.072 0.000 0.464 0.112 0.008 0.344
#> GSM601929 1 0.1588 0.74097 0.924 0.000 0.072 0.000 0.004 0.000
#> GSM601934 2 0.6232 0.08803 0.000 0.468 0.332 0.000 0.176 0.024
#> GSM601939 1 0.3398 0.55843 0.740 0.000 0.252 0.008 0.000 0.000
#> GSM601944 3 0.3309 0.63764 0.000 0.004 0.816 0.004 0.148 0.028
#> GSM601949 1 0.6308 0.26698 0.476 0.108 0.060 0.356 0.000 0.000
#> GSM601959 3 0.2642 0.67479 0.000 0.024 0.892 0.052 0.008 0.024
#> GSM601974 3 0.5108 0.44411 0.164 0.000 0.628 0.208 0.000 0.000
#> GSM601979 2 0.0000 0.74040 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM601989 3 0.0146 0.67318 0.000 0.000 0.996 0.004 0.000 0.000
#> GSM601879 1 0.0000 0.75479 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM601909 1 0.5906 0.30378 0.548 0.016 0.008 0.092 0.008 0.328
#> GSM601919 1 0.4433 0.50529 0.656 0.000 0.000 0.304 0.016 0.024
#> GSM601924 1 0.0000 0.75479 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM601954 2 0.4204 0.50483 0.052 0.696 0.000 0.252 0.000 0.000
#> GSM601964 3 0.4896 0.52144 0.024 0.000 0.628 0.032 0.004 0.312
#> GSM601969 2 0.7582 -0.08691 0.204 0.344 0.192 0.260 0.000 0.000
#> GSM601984 3 0.3832 0.62826 0.184 0.000 0.772 0.004 0.028 0.012
#> GSM601994 5 0.4872 0.56392 0.000 0.016 0.252 0.052 0.672 0.008
#> GSM601875 2 0.0146 0.73976 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM601885 2 0.5308 0.23873 0.000 0.560 0.028 0.008 0.368 0.036
#> GSM601890 2 0.0000 0.74040 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM601895 3 0.0767 0.67776 0.000 0.000 0.976 0.008 0.004 0.012
#> GSM601900 3 0.5858 0.56993 0.084 0.144 0.680 0.008 0.028 0.056
#> GSM601905 3 0.1007 0.68275 0.016 0.000 0.968 0.004 0.004 0.008
#> GSM601915 3 0.2404 0.66658 0.080 0.000 0.884 0.000 0.000 0.036
#> GSM601930 1 0.1121 0.75614 0.964 0.000 0.016 0.004 0.008 0.008
#> GSM601935 3 0.3338 0.66247 0.000 0.000 0.812 0.024 0.012 0.152
#> GSM601940 3 0.2491 0.62573 0.164 0.000 0.836 0.000 0.000 0.000
#> GSM601945 2 0.0972 0.73123 0.000 0.964 0.000 0.000 0.028 0.008
#> GSM601950 1 0.5607 0.34405 0.532 0.000 0.184 0.284 0.000 0.000
#> GSM601960 3 0.5632 0.48716 0.168 0.000 0.628 0.176 0.004 0.024
#> GSM601975 3 0.4602 0.57834 0.004 0.004 0.716 0.216 0.032 0.028
#> GSM601980 5 0.6586 -0.30260 0.004 0.020 0.000 0.328 0.396 0.252
#> GSM601990 3 0.5046 0.51692 0.000 0.000 0.592 0.044 0.024 0.340
#> GSM601880 1 0.0748 0.75707 0.976 0.000 0.016 0.004 0.004 0.000
#> GSM601910 3 0.8559 -0.09488 0.012 0.172 0.324 0.260 0.052 0.180
#> GSM601920 1 0.4751 0.60162 0.748 0.056 0.148 0.004 0.020 0.024
#> GSM601925 1 0.0291 0.75565 0.992 0.000 0.000 0.000 0.004 0.004
#> GSM601955 6 0.4661 0.00000 0.004 0.016 0.028 0.112 0.076 0.764
#> GSM601965 3 0.3835 0.48749 0.336 0.000 0.656 0.000 0.004 0.004
#> GSM601970 1 0.7755 -0.14970 0.296 0.000 0.228 0.288 0.004 0.184
#> GSM601985 3 0.0865 0.68502 0.036 0.000 0.964 0.000 0.000 0.000
#> GSM601995 3 0.6779 -0.30525 0.000 0.012 0.324 0.016 0.324 0.324
#> GSM601876 3 0.1679 0.67792 0.000 0.000 0.936 0.008 0.028 0.028
#> GSM601886 3 0.2136 0.68400 0.000 0.000 0.908 0.064 0.012 0.016
#> GSM601891 2 0.0000 0.74040 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM601896 3 0.0000 0.67292 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM601901 3 0.2594 0.67793 0.000 0.036 0.888 0.056 0.020 0.000
#> GSM601906 3 0.3649 0.64279 0.016 0.000 0.800 0.156 0.012 0.016
#> GSM601916 3 0.3889 0.66258 0.024 0.000 0.812 0.108 0.036 0.020
#> GSM601931 1 0.1267 0.74702 0.940 0.000 0.060 0.000 0.000 0.000
#> GSM601936 3 0.0862 0.67736 0.000 0.000 0.972 0.004 0.016 0.008
#> GSM601941 4 0.3521 0.22948 0.000 0.000 0.036 0.804 0.148 0.012
#> GSM601946 3 0.1629 0.67988 0.004 0.000 0.940 0.004 0.028 0.024
#> GSM601951 1 0.5713 0.25689 0.476 0.000 0.172 0.352 0.000 0.000
#> GSM601961 2 0.0000 0.74040 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM601976 3 0.3084 0.67935 0.000 0.048 0.872 0.020 0.036 0.024
#> GSM601981 2 0.5104 0.56947 0.000 0.720 0.112 0.020 0.120 0.028
#> GSM601991 3 0.1700 0.68204 0.000 0.000 0.916 0.004 0.000 0.080
#> GSM601881 1 0.1007 0.75309 0.956 0.000 0.044 0.000 0.000 0.000
#> GSM601911 3 0.1225 0.68228 0.004 0.000 0.956 0.004 0.032 0.004
#> GSM601921 4 0.8350 0.12759 0.168 0.156 0.048 0.460 0.116 0.052
#> GSM601926 1 0.0146 0.75590 0.996 0.000 0.004 0.000 0.000 0.000
#> GSM601956 2 0.2239 0.70722 0.000 0.900 0.000 0.008 0.072 0.020
#> GSM601966 5 0.5367 0.36149 0.000 0.032 0.428 0.004 0.500 0.036
#> GSM601971 1 0.4006 0.43264 0.600 0.000 0.004 0.392 0.000 0.004
#> GSM601986 3 0.4536 0.34649 0.408 0.000 0.560 0.000 0.028 0.004
#> GSM601996 5 0.4973 0.45668 0.008 0.000 0.420 0.040 0.528 0.004
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)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
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)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
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)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, 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.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")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n time(p) gender(p) k
#> SD:pam 117 0.187 0.674103 2
#> SD:pam 111 0.834 0.001401 3
#> SD:pam 83 0.849 0.000344 4
#> SD:pam 68 0.654 0.000115 5
#> SD:pam 73 0.436 0.000708 6
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.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["SD", "mclust"]
# you can also extract it by
# res = res_list["SD:mclust"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 21512 rows and 125 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'mclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 6.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
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, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.268 0.782 0.797 0.4485 0.497 0.497
#> 3 3 0.286 0.623 0.651 0.3313 0.668 0.427
#> 4 4 0.423 0.583 0.693 0.1467 0.814 0.521
#> 5 5 0.788 0.768 0.878 0.1382 0.939 0.775
#> 6 6 0.833 0.769 0.894 0.0437 0.927 0.695
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 6
Following shows 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 class
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
#> GSM601872 2 0.961 0.805 0.384 0.616
#> GSM601882 2 0.900 0.853 0.316 0.684
#> GSM601887 1 0.311 0.797 0.944 0.056
#> GSM601892 1 0.295 0.805 0.948 0.052
#> GSM601897 1 0.327 0.793 0.940 0.060
#> GSM601902 2 0.373 0.753 0.072 0.928
#> GSM601912 1 0.260 0.806 0.956 0.044
#> GSM601927 1 0.958 0.647 0.620 0.380
#> GSM601932 2 0.605 0.817 0.148 0.852
#> GSM601937 2 0.961 0.805 0.384 0.616
#> GSM601942 2 0.961 0.805 0.384 0.616
#> GSM601947 2 0.625 0.820 0.156 0.844
#> GSM601957 1 0.163 0.818 0.976 0.024
#> GSM601972 2 0.595 0.813 0.144 0.856
#> GSM601977 2 0.909 0.849 0.324 0.676
#> GSM601987 2 0.895 0.854 0.312 0.688
#> GSM601877 1 0.895 0.689 0.688 0.312
#> GSM601907 2 0.891 0.856 0.308 0.692
#> GSM601917 2 0.343 0.744 0.064 0.936
#> GSM601922 2 0.343 0.744 0.064 0.936
#> GSM601952 2 0.781 0.846 0.232 0.768
#> GSM601962 1 0.260 0.806 0.956 0.044
#> GSM601967 1 0.402 0.807 0.920 0.080
#> GSM601982 2 0.946 0.822 0.364 0.636
#> GSM601992 2 0.753 0.842 0.216 0.784
#> GSM601873 2 0.961 0.805 0.384 0.616
#> GSM601883 2 0.871 0.857 0.292 0.708
#> GSM601888 1 0.327 0.793 0.940 0.060
#> GSM601893 1 0.295 0.800 0.948 0.052
#> GSM601898 1 0.141 0.817 0.980 0.020
#> GSM601903 2 0.373 0.753 0.072 0.928
#> GSM601913 1 0.242 0.819 0.960 0.040
#> GSM601928 1 0.952 0.653 0.628 0.372
#> GSM601933 2 0.881 0.856 0.300 0.700
#> GSM601938 2 0.900 0.853 0.316 0.684
#> GSM601943 2 0.961 0.805 0.384 0.616
#> GSM601948 1 0.662 0.779 0.828 0.172
#> GSM601958 1 0.163 0.818 0.976 0.024
#> GSM601973 2 0.552 0.805 0.128 0.872
#> GSM601978 2 0.961 0.805 0.384 0.616
#> GSM601988 2 0.961 0.805 0.384 0.616
#> GSM601878 1 0.833 0.724 0.736 0.264
#> GSM601908 2 0.850 0.856 0.276 0.724
#> GSM601918 2 0.506 0.790 0.112 0.888
#> GSM601923 1 0.958 0.647 0.620 0.380
#> GSM601953 2 0.946 0.823 0.364 0.636
#> GSM601963 1 0.141 0.817 0.980 0.020
#> GSM601968 1 0.242 0.817 0.960 0.040
#> GSM601983 1 0.141 0.817 0.980 0.020
#> GSM601993 2 0.866 0.858 0.288 0.712
#> GSM601874 2 0.917 0.845 0.332 0.668
#> GSM601884 2 0.955 0.812 0.376 0.624
#> GSM601889 1 0.163 0.818 0.976 0.024
#> GSM601894 1 0.141 0.817 0.980 0.020
#> GSM601899 1 0.295 0.800 0.948 0.052
#> GSM601904 2 0.443 0.765 0.092 0.908
#> GSM601914 1 0.163 0.816 0.976 0.024
#> GSM601929 1 0.839 0.731 0.732 0.268
#> GSM601934 2 0.900 0.853 0.316 0.684
#> GSM601939 1 0.584 0.791 0.860 0.140
#> GSM601944 2 0.876 0.857 0.296 0.704
#> GSM601949 1 0.541 0.795 0.876 0.124
#> GSM601959 1 0.242 0.819 0.960 0.040
#> GSM601974 1 0.998 -0.465 0.528 0.472
#> GSM601979 2 0.904 0.852 0.320 0.680
#> GSM601989 1 0.184 0.819 0.972 0.028
#> GSM601879 1 0.866 0.706 0.712 0.288
#> GSM601909 1 0.141 0.817 0.980 0.020
#> GSM601919 2 0.563 0.805 0.132 0.868
#> GSM601924 1 0.839 0.722 0.732 0.268
#> GSM601954 2 0.753 0.842 0.216 0.784
#> GSM601964 1 0.141 0.817 0.980 0.020
#> GSM601969 1 0.689 0.775 0.816 0.184
#> GSM601984 1 0.738 0.757 0.792 0.208
#> GSM601994 2 0.745 0.843 0.212 0.788
#> GSM601875 2 0.913 0.847 0.328 0.672
#> GSM601885 2 0.881 0.856 0.300 0.700
#> GSM601890 1 0.295 0.800 0.948 0.052
#> GSM601895 1 0.163 0.816 0.976 0.024
#> GSM601900 1 0.224 0.818 0.964 0.036
#> GSM601905 2 0.456 0.778 0.096 0.904
#> GSM601915 1 0.141 0.817 0.980 0.020
#> GSM601930 1 0.955 0.651 0.624 0.376
#> GSM601935 1 0.714 0.542 0.804 0.196
#> GSM601940 1 0.494 0.802 0.892 0.108
#> GSM601945 2 0.909 0.850 0.324 0.676
#> GSM601950 1 0.541 0.796 0.876 0.124
#> GSM601960 1 0.163 0.816 0.976 0.024
#> GSM601975 2 0.518 0.795 0.116 0.884
#> GSM601980 2 0.961 0.805 0.384 0.616
#> GSM601990 1 0.141 0.817 0.980 0.020
#> GSM601880 1 0.958 0.647 0.620 0.380
#> GSM601910 1 0.327 0.810 0.940 0.060
#> GSM601920 2 0.373 0.752 0.072 0.928
#> GSM601925 1 0.958 0.647 0.620 0.380
#> GSM601955 2 0.961 0.805 0.384 0.616
#> GSM601965 1 0.753 0.750 0.784 0.216
#> GSM601970 1 0.141 0.817 0.980 0.020
#> GSM601985 1 0.615 0.786 0.848 0.152
#> GSM601995 2 0.961 0.805 0.384 0.616
#> GSM601876 1 0.653 0.777 0.832 0.168
#> GSM601886 2 0.827 0.853 0.260 0.740
#> GSM601891 1 0.343 0.789 0.936 0.064
#> GSM601896 1 0.482 0.803 0.896 0.104
#> GSM601901 2 0.861 0.857 0.284 0.716
#> GSM601906 1 0.969 0.642 0.604 0.396
#> GSM601916 2 0.574 0.810 0.136 0.864
#> GSM601931 1 0.958 0.647 0.620 0.380
#> GSM601936 2 0.900 0.854 0.316 0.684
#> GSM601941 2 0.595 0.815 0.144 0.856
#> GSM601946 1 0.689 0.769 0.816 0.184
#> GSM601951 1 0.886 0.695 0.696 0.304
#> GSM601961 2 0.904 0.852 0.320 0.680
#> GSM601976 2 0.615 0.819 0.152 0.848
#> GSM601981 2 0.861 0.857 0.284 0.716
#> GSM601991 1 0.295 0.800 0.948 0.052
#> GSM601881 1 0.955 0.650 0.624 0.376
#> GSM601911 2 0.730 0.828 0.204 0.796
#> GSM601921 2 0.343 0.744 0.064 0.936
#> GSM601926 1 0.958 0.647 0.620 0.380
#> GSM601956 2 0.943 0.826 0.360 0.640
#> GSM601966 2 0.574 0.810 0.136 0.864
#> GSM601971 1 0.653 0.777 0.832 0.168
#> GSM601986 1 0.781 0.751 0.768 0.232
#> GSM601996 2 0.615 0.819 0.152 0.848
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM601872 2 0.1643 0.83232 0.000 0.956 0.044
#> GSM601882 2 0.0747 0.84112 0.016 0.984 0.000
#> GSM601887 3 0.7044 0.79999 0.032 0.348 0.620
#> GSM601892 3 0.6794 0.81967 0.028 0.324 0.648
#> GSM601897 3 0.5859 0.73240 0.000 0.344 0.656
#> GSM601902 1 0.5650 0.55050 0.688 0.312 0.000
#> GSM601912 3 0.5845 0.82753 0.004 0.308 0.688
#> GSM601927 1 0.5678 0.55925 0.684 0.000 0.316
#> GSM601932 1 0.5785 0.53195 0.668 0.332 0.000
#> GSM601937 2 0.6372 0.71520 0.068 0.756 0.176
#> GSM601942 2 0.5514 0.75430 0.044 0.800 0.156
#> GSM601947 1 0.6033 0.54426 0.660 0.336 0.004
#> GSM601957 3 0.6908 0.82571 0.036 0.308 0.656
#> GSM601972 1 0.6209 0.50050 0.628 0.368 0.004
#> GSM601977 2 0.0424 0.84007 0.008 0.992 0.000
#> GSM601987 2 0.0237 0.84006 0.004 0.996 0.000
#> GSM601877 1 0.6026 0.53656 0.624 0.000 0.376
#> GSM601907 2 0.0000 0.83906 0.000 1.000 0.000
#> GSM601917 1 0.6082 0.56601 0.692 0.296 0.012
#> GSM601922 1 0.6224 0.56671 0.688 0.296 0.016
#> GSM601952 2 0.4834 0.63814 0.204 0.792 0.004
#> GSM601962 3 0.5958 0.70288 0.008 0.300 0.692
#> GSM601967 3 0.7424 0.81638 0.060 0.300 0.640
#> GSM601982 2 0.4094 0.74878 0.028 0.872 0.100
#> GSM601992 1 0.6299 0.29405 0.524 0.476 0.000
#> GSM601873 2 0.1267 0.83931 0.004 0.972 0.024
#> GSM601883 2 0.1753 0.82201 0.048 0.952 0.000
#> GSM601888 2 0.7278 -0.41281 0.028 0.516 0.456
#> GSM601893 3 0.6931 0.81684 0.032 0.328 0.640
#> GSM601898 3 0.5580 0.82708 0.008 0.256 0.736
#> GSM601903 1 0.5678 0.54699 0.684 0.316 0.000
#> GSM601913 3 0.5551 0.81464 0.020 0.212 0.768
#> GSM601928 1 0.5678 0.55925 0.684 0.000 0.316
#> GSM601933 2 0.1643 0.83282 0.044 0.956 0.000
#> GSM601938 2 0.1989 0.82959 0.048 0.948 0.004
#> GSM601943 2 0.1289 0.83666 0.000 0.968 0.032
#> GSM601948 1 0.8972 0.25857 0.460 0.128 0.412
#> GSM601958 3 0.6451 0.83240 0.024 0.292 0.684
#> GSM601973 1 0.5733 0.54018 0.676 0.324 0.000
#> GSM601978 2 0.0592 0.83554 0.000 0.988 0.012
#> GSM601988 2 0.6093 0.73501 0.068 0.776 0.156
#> GSM601878 1 0.6432 0.50357 0.568 0.004 0.428
#> GSM601908 2 0.2066 0.81134 0.060 0.940 0.000
#> GSM601918 1 0.5859 0.52587 0.656 0.344 0.000
#> GSM601923 1 0.5678 0.55925 0.684 0.000 0.316
#> GSM601953 2 0.0592 0.83583 0.000 0.988 0.012
#> GSM601963 3 0.4963 0.80614 0.008 0.200 0.792
#> GSM601968 3 0.6742 0.82385 0.028 0.316 0.656
#> GSM601983 3 0.5247 0.81965 0.008 0.224 0.768
#> GSM601993 2 0.6144 0.75381 0.132 0.780 0.088
#> GSM601874 2 0.0000 0.83906 0.000 1.000 0.000
#> GSM601884 2 0.0237 0.83821 0.000 0.996 0.004
#> GSM601889 3 0.6744 0.82757 0.032 0.300 0.668
#> GSM601894 3 0.6357 0.83255 0.020 0.296 0.684
#> GSM601899 3 0.7123 0.77988 0.032 0.364 0.604
#> GSM601904 1 0.6387 0.56532 0.680 0.300 0.020
#> GSM601914 3 0.4784 0.80543 0.004 0.200 0.796
#> GSM601929 1 0.8984 0.55579 0.496 0.136 0.368
#> GSM601934 2 0.0424 0.84008 0.008 0.992 0.000
#> GSM601939 1 0.8844 0.19717 0.444 0.116 0.440
#> GSM601944 2 0.2879 0.82249 0.052 0.924 0.024
#> GSM601949 1 0.9862 -0.11474 0.392 0.256 0.352
#> GSM601959 3 0.6927 0.82460 0.040 0.296 0.664
#> GSM601974 2 0.9001 0.13642 0.148 0.520 0.332
#> GSM601979 2 0.0000 0.83906 0.000 1.000 0.000
#> GSM601989 3 0.6984 0.82578 0.040 0.304 0.656
#> GSM601879 1 0.6745 0.50779 0.560 0.012 0.428
#> GSM601909 3 0.6541 0.82920 0.024 0.304 0.672
#> GSM601919 1 0.6510 0.52150 0.624 0.364 0.012
#> GSM601924 1 0.6565 0.50794 0.576 0.008 0.416
#> GSM601954 2 0.6421 -0.05789 0.424 0.572 0.004
#> GSM601964 3 0.4963 0.80365 0.008 0.200 0.792
#> GSM601969 3 0.8876 0.55451 0.220 0.204 0.576
#> GSM601984 1 0.9098 0.52733 0.492 0.148 0.360
#> GSM601994 1 0.6295 0.30951 0.528 0.472 0.000
#> GSM601875 2 0.0000 0.83906 0.000 1.000 0.000
#> GSM601885 2 0.0892 0.83972 0.020 0.980 0.000
#> GSM601890 3 0.7044 0.79972 0.032 0.348 0.620
#> GSM601895 3 0.5244 0.82493 0.004 0.240 0.756
#> GSM601900 3 0.5956 0.83255 0.016 0.264 0.720
#> GSM601905 1 0.5896 0.56711 0.700 0.292 0.008
#> GSM601915 3 0.4963 0.80614 0.008 0.200 0.792
#> GSM601930 1 0.5678 0.55925 0.684 0.000 0.316
#> GSM601935 3 0.7974 0.18088 0.060 0.436 0.504
#> GSM601940 3 0.9502 0.49965 0.272 0.236 0.492
#> GSM601945 2 0.0000 0.83906 0.000 1.000 0.000
#> GSM601950 3 0.9776 0.07312 0.380 0.232 0.388
#> GSM601960 3 0.4963 0.80365 0.008 0.200 0.792
#> GSM601975 1 0.5706 0.54641 0.680 0.320 0.000
#> GSM601980 2 0.6083 0.73041 0.060 0.772 0.168
#> GSM601990 3 0.4963 0.80365 0.008 0.200 0.792
#> GSM601880 1 0.5678 0.55925 0.684 0.000 0.316
#> GSM601910 3 0.6255 0.83190 0.016 0.300 0.684
#> GSM601920 1 0.6051 0.56836 0.696 0.292 0.012
#> GSM601925 1 0.5678 0.55925 0.684 0.000 0.316
#> GSM601955 2 0.6348 0.70846 0.060 0.752 0.188
#> GSM601965 1 0.9602 0.41538 0.460 0.320 0.220
#> GSM601970 3 0.5812 0.83110 0.012 0.264 0.724
#> GSM601985 3 0.8084 -0.09590 0.384 0.072 0.544
#> GSM601995 2 0.6424 0.71052 0.068 0.752 0.180
#> GSM601876 1 0.7841 0.36313 0.480 0.052 0.468
#> GSM601886 2 0.7828 0.18928 0.340 0.592 0.068
#> GSM601891 3 0.7263 0.72368 0.032 0.400 0.568
#> GSM601896 1 0.9537 -0.00798 0.428 0.192 0.380
#> GSM601901 2 0.4784 0.60985 0.200 0.796 0.004
#> GSM601906 1 0.9175 0.59475 0.540 0.216 0.244
#> GSM601916 1 0.6082 0.56675 0.692 0.296 0.012
#> GSM601931 1 0.5678 0.55925 0.684 0.000 0.316
#> GSM601936 2 0.5764 0.76400 0.076 0.800 0.124
#> GSM601941 1 0.5882 0.51414 0.652 0.348 0.000
#> GSM601946 1 0.7549 0.43765 0.524 0.040 0.436
#> GSM601951 1 0.6879 0.51985 0.556 0.016 0.428
#> GSM601961 2 0.5816 0.48157 0.024 0.752 0.224
#> GSM601976 1 0.6543 0.53248 0.640 0.344 0.016
#> GSM601981 2 0.1031 0.83830 0.024 0.976 0.000
#> GSM601991 3 0.6441 0.71165 0.028 0.276 0.696
#> GSM601881 1 0.5678 0.55925 0.684 0.000 0.316
#> GSM601911 1 0.7546 0.43114 0.560 0.396 0.044
#> GSM601921 1 0.5560 0.55779 0.700 0.300 0.000
#> GSM601926 1 0.5678 0.55925 0.684 0.000 0.316
#> GSM601956 2 0.0424 0.83690 0.000 0.992 0.008
#> GSM601966 1 0.5988 0.49101 0.632 0.368 0.000
#> GSM601971 3 0.7533 0.34906 0.244 0.088 0.668
#> GSM601986 1 0.9515 0.50402 0.480 0.304 0.216
#> GSM601996 1 0.5968 0.49317 0.636 0.364 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM601872 2 0.5712 0.4864 0.000 0.644 0.308 0.048
#> GSM601882 2 0.3999 0.6908 0.000 0.824 0.036 0.140
#> GSM601887 3 0.5953 0.6205 0.100 0.172 0.716 0.012
#> GSM601892 3 0.6009 0.6416 0.172 0.104 0.712 0.012
#> GSM601897 3 0.5452 0.6523 0.096 0.116 0.768 0.020
#> GSM601902 4 0.4839 0.8141 0.044 0.200 0.000 0.756
#> GSM601912 3 0.5122 0.6672 0.164 0.080 0.756 0.000
#> GSM601927 1 0.0336 0.7059 0.992 0.000 0.008 0.000
#> GSM601932 4 0.5168 0.8164 0.040 0.248 0.000 0.712
#> GSM601937 3 0.7434 0.2055 0.008 0.156 0.524 0.312
#> GSM601942 3 0.7706 -0.1627 0.008 0.352 0.464 0.176
#> GSM601947 4 0.4957 0.7820 0.016 0.300 0.000 0.684
#> GSM601957 3 0.4617 0.6402 0.204 0.032 0.764 0.000
#> GSM601972 4 0.4836 0.7596 0.008 0.320 0.000 0.672
#> GSM601977 2 0.4236 0.7189 0.000 0.824 0.088 0.088
#> GSM601987 2 0.0921 0.7375 0.000 0.972 0.000 0.028
#> GSM601877 1 0.2266 0.7154 0.912 0.000 0.084 0.004
#> GSM601907 2 0.0000 0.7299 0.000 1.000 0.000 0.000
#> GSM601917 4 0.4877 0.8171 0.044 0.204 0.000 0.752
#> GSM601922 4 0.5184 0.8158 0.056 0.212 0.000 0.732
#> GSM601952 2 0.5660 0.0148 0.000 0.576 0.028 0.396
#> GSM601962 3 0.4762 0.6596 0.112 0.048 0.812 0.028
#> GSM601967 3 0.5343 0.5992 0.240 0.052 0.708 0.000
#> GSM601982 2 0.6188 0.6186 0.020 0.708 0.168 0.104
#> GSM601992 4 0.5699 0.6740 0.032 0.380 0.000 0.588
#> GSM601873 2 0.6229 0.5078 0.000 0.628 0.284 0.088
#> GSM601883 2 0.2197 0.7249 0.000 0.916 0.004 0.080
#> GSM601888 3 0.6676 0.5012 0.072 0.312 0.600 0.016
#> GSM601893 3 0.5926 0.6373 0.132 0.132 0.724 0.012
#> GSM601898 3 0.3708 0.6686 0.148 0.020 0.832 0.000
#> GSM601903 4 0.4839 0.8141 0.044 0.200 0.000 0.756
#> GSM601913 3 0.4260 0.6525 0.180 0.020 0.796 0.004
#> GSM601928 1 0.0336 0.7059 0.992 0.000 0.008 0.000
#> GSM601933 2 0.3583 0.6376 0.000 0.816 0.004 0.180
#> GSM601938 2 0.4741 0.5645 0.000 0.744 0.028 0.228
#> GSM601943 2 0.6720 0.4709 0.000 0.580 0.300 0.120
#> GSM601948 1 0.5478 0.5140 0.628 0.028 0.344 0.000
#> GSM601958 3 0.4225 0.6538 0.184 0.024 0.792 0.000
#> GSM601973 4 0.5056 0.8204 0.044 0.224 0.000 0.732
#> GSM601978 2 0.2647 0.7165 0.000 0.880 0.120 0.000
#> GSM601988 3 0.7587 0.1592 0.008 0.168 0.496 0.328
#> GSM601878 1 0.2868 0.7121 0.864 0.000 0.136 0.000
#> GSM601908 2 0.1004 0.7347 0.000 0.972 0.004 0.024
#> GSM601918 4 0.4792 0.7671 0.008 0.312 0.000 0.680
#> GSM601923 1 0.0336 0.7059 0.992 0.000 0.008 0.000
#> GSM601953 2 0.2704 0.7077 0.000 0.876 0.124 0.000
#> GSM601963 3 0.3088 0.6639 0.128 0.000 0.864 0.008
#> GSM601968 3 0.4842 0.6484 0.192 0.048 0.760 0.000
#> GSM601983 3 0.2814 0.6657 0.132 0.000 0.868 0.000
#> GSM601993 4 0.7846 -0.0484 0.008 0.216 0.308 0.468
#> GSM601874 2 0.0188 0.7321 0.000 0.996 0.004 0.000
#> GSM601884 2 0.3205 0.7280 0.000 0.872 0.104 0.024
#> GSM601889 3 0.4562 0.6353 0.208 0.028 0.764 0.000
#> GSM601894 3 0.4182 0.6579 0.180 0.024 0.796 0.000
#> GSM601899 3 0.5733 0.5969 0.064 0.208 0.716 0.012
#> GSM601904 4 0.5889 0.7918 0.080 0.196 0.012 0.712
#> GSM601914 3 0.3105 0.6624 0.120 0.000 0.868 0.012
#> GSM601929 1 0.4444 0.6880 0.788 0.008 0.184 0.020
#> GSM601934 2 0.2676 0.7275 0.000 0.896 0.012 0.092
#> GSM601939 1 0.5345 0.3646 0.560 0.012 0.428 0.000
#> GSM601944 2 0.6317 0.3952 0.000 0.624 0.096 0.280
#> GSM601949 1 0.6123 0.3971 0.572 0.056 0.372 0.000
#> GSM601959 3 0.4744 0.5939 0.240 0.024 0.736 0.000
#> GSM601974 3 0.8606 0.2511 0.108 0.288 0.492 0.112
#> GSM601979 2 0.0000 0.7299 0.000 1.000 0.000 0.000
#> GSM601989 3 0.4706 0.6175 0.224 0.028 0.748 0.000
#> GSM601879 1 0.2704 0.7147 0.876 0.000 0.124 0.000
#> GSM601909 3 0.4500 0.6513 0.192 0.032 0.776 0.000
#> GSM601919 4 0.5793 0.7089 0.016 0.336 0.020 0.628
#> GSM601924 1 0.2831 0.7151 0.876 0.004 0.120 0.000
#> GSM601954 2 0.6380 -0.1036 0.008 0.536 0.048 0.408
#> GSM601964 3 0.3032 0.6637 0.124 0.000 0.868 0.008
#> GSM601969 3 0.6622 -0.0452 0.444 0.060 0.488 0.008
#> GSM601984 1 0.5830 0.5899 0.672 0.016 0.276 0.036
#> GSM601994 4 0.5686 0.6775 0.032 0.376 0.000 0.592
#> GSM601875 2 0.0592 0.7375 0.000 0.984 0.000 0.016
#> GSM601885 2 0.1824 0.7387 0.000 0.936 0.004 0.060
#> GSM601890 3 0.5878 0.6184 0.092 0.176 0.720 0.012
#> GSM601895 3 0.3501 0.6722 0.132 0.020 0.848 0.000
#> GSM601900 3 0.4004 0.6659 0.164 0.024 0.812 0.000
#> GSM601905 4 0.4877 0.8171 0.044 0.204 0.000 0.752
#> GSM601915 3 0.3428 0.6620 0.144 0.000 0.844 0.012
#> GSM601930 1 0.0336 0.7059 0.992 0.000 0.008 0.000
#> GSM601935 3 0.7230 0.2474 0.008 0.136 0.548 0.308
#> GSM601940 3 0.5731 0.0960 0.428 0.028 0.544 0.000
#> GSM601945 2 0.0657 0.7360 0.000 0.984 0.012 0.004
#> GSM601950 1 0.5847 0.3657 0.560 0.036 0.404 0.000
#> GSM601960 3 0.3047 0.6617 0.116 0.000 0.872 0.012
#> GSM601975 4 0.4877 0.8169 0.044 0.204 0.000 0.752
#> GSM601980 3 0.7346 0.2271 0.008 0.148 0.536 0.308
#> GSM601990 3 0.3161 0.6628 0.124 0.000 0.864 0.012
#> GSM601880 1 0.0524 0.7044 0.988 0.000 0.008 0.004
#> GSM601910 3 0.4898 0.6712 0.156 0.072 0.772 0.000
#> GSM601920 4 0.5321 0.8019 0.064 0.192 0.004 0.740
#> GSM601925 1 0.0524 0.7044 0.988 0.000 0.008 0.004
#> GSM601955 3 0.7346 0.2271 0.008 0.148 0.536 0.308
#> GSM601965 1 0.8399 0.0478 0.424 0.144 0.380 0.052
#> GSM601970 3 0.3991 0.6588 0.172 0.020 0.808 0.000
#> GSM601985 1 0.4898 0.4150 0.584 0.000 0.416 0.000
#> GSM601995 3 0.7324 0.2283 0.008 0.144 0.536 0.312
#> GSM601876 1 0.4877 0.5623 0.664 0.008 0.328 0.000
#> GSM601886 4 0.8387 -0.0843 0.016 0.324 0.320 0.340
#> GSM601891 3 0.5577 0.5810 0.040 0.244 0.704 0.012
#> GSM601896 1 0.5636 0.3490 0.552 0.024 0.424 0.000
#> GSM601901 2 0.4546 0.5132 0.000 0.732 0.012 0.256
#> GSM601906 1 0.7686 0.3787 0.580 0.080 0.076 0.264
#> GSM601916 4 0.4914 0.8178 0.044 0.208 0.000 0.748
#> GSM601931 1 0.0336 0.7059 0.992 0.000 0.008 0.000
#> GSM601936 3 0.7900 -0.0127 0.008 0.204 0.420 0.368
#> GSM601941 4 0.5312 0.8082 0.040 0.268 0.000 0.692
#> GSM601946 1 0.4343 0.6300 0.732 0.004 0.264 0.000
#> GSM601951 1 0.3300 0.7106 0.848 0.000 0.144 0.008
#> GSM601961 2 0.4891 0.6814 0.008 0.792 0.124 0.076
#> GSM601976 4 0.5484 0.8118 0.040 0.248 0.008 0.704
#> GSM601981 2 0.1489 0.7404 0.000 0.952 0.004 0.044
#> GSM601991 3 0.4078 0.5440 0.036 0.004 0.828 0.132
#> GSM601881 1 0.0336 0.7059 0.992 0.000 0.008 0.000
#> GSM601911 4 0.8797 0.3587 0.104 0.344 0.120 0.432
#> GSM601921 4 0.4877 0.8171 0.044 0.204 0.000 0.752
#> GSM601926 1 0.0336 0.7059 0.992 0.000 0.008 0.000
#> GSM601956 2 0.2814 0.7061 0.000 0.868 0.132 0.000
#> GSM601966 4 0.5231 0.7912 0.028 0.296 0.000 0.676
#> GSM601971 1 0.4998 0.2041 0.512 0.000 0.488 0.000
#> GSM601986 1 0.8792 0.3118 0.504 0.184 0.208 0.104
#> GSM601996 4 0.5442 0.7971 0.040 0.288 0.000 0.672
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM601872 2 0.5569 0.2054 0.000 0.556 0.080 0.000 0.364
#> GSM601882 2 0.0898 0.8256 0.000 0.972 0.008 0.020 0.000
#> GSM601887 3 0.1082 0.8374 0.000 0.028 0.964 0.008 0.000
#> GSM601892 3 0.0566 0.8431 0.000 0.012 0.984 0.004 0.000
#> GSM601897 3 0.3266 0.8302 0.000 0.004 0.796 0.000 0.200
#> GSM601902 4 0.0290 0.9190 0.000 0.008 0.000 0.992 0.000
#> GSM601912 3 0.1043 0.8545 0.000 0.000 0.960 0.000 0.040
#> GSM601927 1 0.0000 0.8399 1.000 0.000 0.000 0.000 0.000
#> GSM601932 4 0.0609 0.9183 0.000 0.020 0.000 0.980 0.000
#> GSM601937 5 0.0000 0.8645 0.000 0.000 0.000 0.000 1.000
#> GSM601942 5 0.3177 0.6549 0.000 0.208 0.000 0.000 0.792
#> GSM601947 4 0.1197 0.9086 0.000 0.048 0.000 0.952 0.000
#> GSM601957 3 0.0290 0.8466 0.008 0.000 0.992 0.000 0.000
#> GSM601972 4 0.1270 0.9061 0.000 0.052 0.000 0.948 0.000
#> GSM601977 2 0.0162 0.8303 0.000 0.996 0.004 0.000 0.000
#> GSM601987 2 0.0000 0.8308 0.000 1.000 0.000 0.000 0.000
#> GSM601877 1 0.1341 0.8582 0.944 0.000 0.056 0.000 0.000
#> GSM601907 2 0.0000 0.8308 0.000 1.000 0.000 0.000 0.000
#> GSM601917 4 0.0290 0.9190 0.000 0.008 0.000 0.992 0.000
#> GSM601922 4 0.0451 0.9176 0.004 0.008 0.000 0.988 0.000
#> GSM601952 2 0.3684 0.5895 0.000 0.720 0.000 0.280 0.000
#> GSM601962 3 0.3561 0.7739 0.000 0.000 0.740 0.000 0.260
#> GSM601967 3 0.2284 0.8194 0.096 0.000 0.896 0.004 0.004
#> GSM601982 2 0.3013 0.7047 0.000 0.832 0.160 0.008 0.000
#> GSM601992 4 0.2291 0.8816 0.000 0.036 0.000 0.908 0.056
#> GSM601873 2 0.3123 0.6798 0.000 0.812 0.004 0.000 0.184
#> GSM601883 2 0.3741 0.6012 0.000 0.732 0.004 0.264 0.000
#> GSM601888 3 0.3452 0.5674 0.000 0.244 0.756 0.000 0.000
#> GSM601893 3 0.0798 0.8401 0.000 0.016 0.976 0.008 0.000
#> GSM601898 3 0.2970 0.8429 0.004 0.000 0.828 0.000 0.168
#> GSM601903 4 0.0290 0.9190 0.000 0.008 0.000 0.992 0.000
#> GSM601913 3 0.3370 0.8431 0.028 0.000 0.824 0.000 0.148
#> GSM601928 1 0.0000 0.8399 1.000 0.000 0.000 0.000 0.000
#> GSM601933 2 0.1952 0.7877 0.000 0.912 0.004 0.084 0.000
#> GSM601938 2 0.4605 0.6032 0.000 0.708 0.004 0.248 0.040
#> GSM601943 2 0.4410 0.1719 0.000 0.556 0.004 0.000 0.440
#> GSM601948 1 0.1892 0.8562 0.916 0.000 0.080 0.000 0.004
#> GSM601958 3 0.1851 0.8251 0.088 0.000 0.912 0.000 0.000
#> GSM601973 4 0.0290 0.9190 0.000 0.008 0.000 0.992 0.000
#> GSM601978 2 0.0451 0.8292 0.000 0.988 0.004 0.000 0.008
#> GSM601988 5 0.1851 0.8236 0.000 0.088 0.000 0.000 0.912
#> GSM601878 1 0.1638 0.8591 0.932 0.000 0.064 0.000 0.004
#> GSM601908 2 0.2629 0.7474 0.000 0.860 0.004 0.136 0.000
#> GSM601918 4 0.1197 0.9086 0.000 0.048 0.000 0.952 0.000
#> GSM601923 1 0.0000 0.8399 1.000 0.000 0.000 0.000 0.000
#> GSM601953 2 0.0000 0.8308 0.000 1.000 0.000 0.000 0.000
#> GSM601963 3 0.2966 0.8373 0.000 0.000 0.816 0.000 0.184
#> GSM601968 3 0.0566 0.8496 0.000 0.000 0.984 0.004 0.012
#> GSM601983 3 0.2852 0.8428 0.000 0.000 0.828 0.000 0.172
#> GSM601993 4 0.4456 0.6330 0.000 0.032 0.004 0.716 0.248
#> GSM601874 2 0.0000 0.8308 0.000 1.000 0.000 0.000 0.000
#> GSM601884 2 0.0451 0.8292 0.000 0.988 0.004 0.000 0.008
#> GSM601889 3 0.0162 0.8472 0.004 0.000 0.996 0.000 0.000
#> GSM601894 3 0.0162 0.8480 0.000 0.000 0.996 0.000 0.004
#> GSM601899 3 0.0992 0.8365 0.000 0.024 0.968 0.008 0.000
#> GSM601904 4 0.0566 0.9188 0.004 0.012 0.000 0.984 0.000
#> GSM601914 3 0.2966 0.8372 0.000 0.000 0.816 0.000 0.184
#> GSM601929 1 0.1704 0.8595 0.928 0.000 0.068 0.004 0.000
#> GSM601934 2 0.0324 0.8310 0.000 0.992 0.004 0.004 0.000
#> GSM601939 1 0.4420 0.2618 0.548 0.000 0.448 0.000 0.004
#> GSM601944 2 0.4820 0.5893 0.000 0.700 0.004 0.240 0.056
#> GSM601949 1 0.3048 0.8046 0.820 0.000 0.176 0.000 0.004
#> GSM601959 3 0.1043 0.8427 0.040 0.000 0.960 0.000 0.000
#> GSM601974 3 0.7527 0.0544 0.000 0.348 0.388 0.048 0.216
#> GSM601979 2 0.0000 0.8308 0.000 1.000 0.000 0.000 0.000
#> GSM601989 3 0.0324 0.8483 0.004 0.000 0.992 0.000 0.004
#> GSM601879 1 0.1638 0.8595 0.932 0.000 0.064 0.004 0.000
#> GSM601909 3 0.0671 0.8504 0.000 0.000 0.980 0.004 0.016
#> GSM601919 4 0.1357 0.9082 0.004 0.048 0.000 0.948 0.000
#> GSM601924 1 0.1638 0.8591 0.932 0.000 0.064 0.000 0.004
#> GSM601954 2 0.4446 0.0999 0.000 0.520 0.000 0.476 0.004
#> GSM601964 3 0.2966 0.8373 0.000 0.000 0.816 0.000 0.184
#> GSM601969 1 0.4822 0.2637 0.540 0.004 0.444 0.008 0.004
#> GSM601984 1 0.1928 0.8576 0.920 0.004 0.072 0.000 0.004
#> GSM601994 4 0.2124 0.8874 0.000 0.028 0.000 0.916 0.056
#> GSM601875 2 0.0000 0.8308 0.000 1.000 0.000 0.000 0.000
#> GSM601885 2 0.0324 0.8310 0.000 0.992 0.004 0.004 0.000
#> GSM601890 3 0.0693 0.8417 0.000 0.012 0.980 0.008 0.000
#> GSM601895 3 0.2966 0.8373 0.000 0.000 0.816 0.000 0.184
#> GSM601900 3 0.2930 0.8454 0.004 0.000 0.832 0.000 0.164
#> GSM601905 4 0.0290 0.9190 0.000 0.008 0.000 0.992 0.000
#> GSM601915 3 0.2929 0.8388 0.000 0.000 0.820 0.000 0.180
#> GSM601930 1 0.0000 0.8399 1.000 0.000 0.000 0.000 0.000
#> GSM601935 5 0.1571 0.8263 0.000 0.004 0.060 0.000 0.936
#> GSM601940 1 0.4589 0.2680 0.520 0.000 0.472 0.004 0.004
#> GSM601945 2 0.0162 0.8310 0.000 0.996 0.000 0.004 0.000
#> GSM601950 1 0.2674 0.8308 0.856 0.000 0.140 0.000 0.004
#> GSM601960 3 0.2966 0.8372 0.000 0.000 0.816 0.000 0.184
#> GSM601975 4 0.0404 0.9190 0.000 0.012 0.000 0.988 0.000
#> GSM601980 5 0.0162 0.8654 0.000 0.004 0.000 0.000 0.996
#> GSM601990 3 0.3003 0.8357 0.000 0.000 0.812 0.000 0.188
#> GSM601880 1 0.0000 0.8399 1.000 0.000 0.000 0.000 0.000
#> GSM601910 3 0.2329 0.8527 0.000 0.000 0.876 0.000 0.124
#> GSM601920 4 0.0579 0.9156 0.008 0.008 0.000 0.984 0.000
#> GSM601925 1 0.0000 0.8399 1.000 0.000 0.000 0.000 0.000
#> GSM601955 5 0.0162 0.8654 0.000 0.004 0.000 0.000 0.996
#> GSM601965 1 0.5247 0.7247 0.732 0.040 0.164 0.060 0.004
#> GSM601970 3 0.3269 0.8437 0.056 0.000 0.848 0.000 0.096
#> GSM601985 1 0.4310 0.4183 0.604 0.000 0.392 0.000 0.004
#> GSM601995 5 0.0000 0.8645 0.000 0.000 0.000 0.000 1.000
#> GSM601876 1 0.2011 0.8528 0.908 0.000 0.088 0.000 0.004
#> GSM601886 4 0.6424 0.2377 0.000 0.328 0.004 0.500 0.168
#> GSM601891 3 0.2193 0.8130 0.000 0.092 0.900 0.008 0.000
#> GSM601896 1 0.2964 0.8164 0.840 0.000 0.152 0.004 0.004
#> GSM601901 2 0.4288 0.4122 0.000 0.612 0.004 0.384 0.000
#> GSM601906 1 0.4729 0.6262 0.708 0.004 0.052 0.236 0.000
#> GSM601916 4 0.0290 0.9190 0.000 0.008 0.000 0.992 0.000
#> GSM601931 1 0.0000 0.8399 1.000 0.000 0.000 0.000 0.000
#> GSM601936 5 0.6417 0.2533 0.000 0.336 0.004 0.164 0.496
#> GSM601941 4 0.1331 0.9067 0.000 0.008 0.000 0.952 0.040
#> GSM601946 1 0.1831 0.8571 0.920 0.000 0.076 0.000 0.004
#> GSM601951 1 0.1544 0.8591 0.932 0.000 0.068 0.000 0.000
#> GSM601961 2 0.0671 0.8222 0.000 0.980 0.016 0.000 0.004
#> GSM601976 4 0.1282 0.9014 0.000 0.044 0.004 0.952 0.000
#> GSM601981 2 0.0451 0.8309 0.000 0.988 0.004 0.008 0.000
#> GSM601991 3 0.4114 0.6067 0.000 0.000 0.624 0.000 0.376
#> GSM601881 1 0.0000 0.8399 1.000 0.000 0.000 0.000 0.000
#> GSM601911 4 0.6811 0.3487 0.304 0.092 0.056 0.544 0.004
#> GSM601921 4 0.0290 0.9190 0.000 0.008 0.000 0.992 0.000
#> GSM601926 1 0.0000 0.8399 1.000 0.000 0.000 0.000 0.000
#> GSM601956 2 0.0324 0.8298 0.000 0.992 0.004 0.000 0.004
#> GSM601966 4 0.1197 0.9086 0.000 0.048 0.000 0.952 0.000
#> GSM601971 3 0.4420 0.0746 0.448 0.000 0.548 0.000 0.004
#> GSM601986 1 0.4045 0.7804 0.812 0.008 0.064 0.112 0.004
#> GSM601996 4 0.1943 0.8923 0.000 0.020 0.000 0.924 0.056
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM601872 2 0.1418 0.9103 0.000 0.944 0.000 0.000 0.032 0.024
#> GSM601882 2 0.0632 0.9241 0.000 0.976 0.000 0.024 0.000 0.000
#> GSM601887 6 0.1007 0.7429 0.000 0.000 0.044 0.000 0.000 0.956
#> GSM601892 6 0.1267 0.7434 0.000 0.000 0.060 0.000 0.000 0.940
#> GSM601897 3 0.3958 0.7025 0.000 0.000 0.764 0.000 0.128 0.108
#> GSM601902 4 0.0000 0.9090 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM601912 3 0.2941 0.6985 0.000 0.000 0.780 0.000 0.000 0.220
#> GSM601927 1 0.0000 0.8647 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM601932 4 0.0000 0.9090 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM601937 5 0.0508 0.8798 0.000 0.004 0.012 0.000 0.984 0.000
#> GSM601942 5 0.3215 0.6495 0.000 0.240 0.000 0.000 0.756 0.004
#> GSM601947 4 0.0665 0.9080 0.000 0.008 0.000 0.980 0.008 0.004
#> GSM601957 6 0.5079 0.2258 0.084 0.000 0.380 0.000 0.000 0.536
#> GSM601972 4 0.0363 0.9068 0.000 0.012 0.000 0.988 0.000 0.000
#> GSM601977 2 0.0000 0.9390 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM601987 2 0.0000 0.9390 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM601877 1 0.0458 0.8662 0.984 0.000 0.016 0.000 0.000 0.000
#> GSM601907 2 0.0000 0.9390 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM601917 4 0.0405 0.9086 0.000 0.000 0.000 0.988 0.008 0.004
#> GSM601922 4 0.0405 0.9086 0.000 0.000 0.000 0.988 0.008 0.004
#> GSM601952 2 0.3989 0.0549 0.000 0.528 0.000 0.468 0.000 0.004
#> GSM601962 3 0.0146 0.8223 0.000 0.000 0.996 0.000 0.004 0.000
#> GSM601967 6 0.5885 0.3184 0.228 0.000 0.304 0.000 0.000 0.468
#> GSM601982 2 0.2810 0.7727 0.000 0.832 0.004 0.008 0.000 0.156
#> GSM601992 4 0.0820 0.8991 0.000 0.000 0.000 0.972 0.016 0.012
#> GSM601873 2 0.0692 0.9297 0.000 0.976 0.000 0.000 0.020 0.004
#> GSM601883 2 0.0458 0.9321 0.000 0.984 0.000 0.016 0.000 0.000
#> GSM601888 6 0.1398 0.7426 0.000 0.008 0.052 0.000 0.000 0.940
#> GSM601893 6 0.1007 0.7429 0.000 0.000 0.044 0.000 0.000 0.956
#> GSM601898 3 0.0146 0.8243 0.000 0.000 0.996 0.000 0.000 0.004
#> GSM601903 4 0.0000 0.9090 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM601913 3 0.0291 0.8238 0.004 0.000 0.992 0.000 0.000 0.004
#> GSM601928 1 0.0000 0.8647 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM601933 2 0.2793 0.7021 0.000 0.800 0.000 0.200 0.000 0.000
#> GSM601938 4 0.4178 0.2842 0.000 0.428 0.000 0.560 0.008 0.004
#> GSM601943 2 0.1225 0.9152 0.000 0.952 0.000 0.000 0.036 0.012
#> GSM601948 1 0.2301 0.8148 0.884 0.000 0.020 0.000 0.000 0.096
#> GSM601958 3 0.3650 0.6826 0.116 0.000 0.792 0.000 0.000 0.092
#> GSM601973 4 0.0000 0.9090 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM601978 2 0.0291 0.9376 0.000 0.992 0.000 0.000 0.004 0.004
#> GSM601988 5 0.0767 0.8786 0.000 0.008 0.012 0.000 0.976 0.004
#> GSM601878 1 0.0458 0.8662 0.984 0.000 0.016 0.000 0.000 0.000
#> GSM601908 2 0.0363 0.9356 0.000 0.988 0.000 0.012 0.000 0.000
#> GSM601918 4 0.0665 0.9080 0.000 0.008 0.000 0.980 0.008 0.004
#> GSM601923 1 0.0000 0.8647 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM601953 2 0.0146 0.9386 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM601963 3 0.0000 0.8246 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM601968 6 0.3847 0.0485 0.000 0.000 0.456 0.000 0.000 0.544
#> GSM601983 3 0.0000 0.8246 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM601993 5 0.3111 0.7747 0.000 0.008 0.000 0.156 0.820 0.016
#> GSM601874 2 0.0000 0.9390 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM601884 2 0.0146 0.9384 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM601889 3 0.2912 0.7014 0.000 0.000 0.784 0.000 0.000 0.216
#> GSM601894 3 0.2631 0.7551 0.008 0.000 0.840 0.000 0.000 0.152
#> GSM601899 6 0.1007 0.7429 0.000 0.000 0.044 0.000 0.000 0.956
#> GSM601904 4 0.0436 0.9084 0.004 0.000 0.000 0.988 0.004 0.004
#> GSM601914 3 0.0000 0.8246 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM601929 1 0.0806 0.8655 0.972 0.000 0.020 0.000 0.000 0.008
#> GSM601934 2 0.0000 0.9390 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM601939 1 0.2312 0.7996 0.876 0.000 0.112 0.000 0.000 0.012
#> GSM601944 4 0.3766 0.6502 0.000 0.232 0.000 0.736 0.032 0.000
#> GSM601949 1 0.4105 0.4454 0.632 0.000 0.020 0.000 0.000 0.348
#> GSM601959 3 0.5495 0.2864 0.164 0.000 0.548 0.000 0.000 0.288
#> GSM601974 3 0.6285 0.2337 0.000 0.024 0.480 0.328 0.164 0.004
#> GSM601979 2 0.0000 0.9390 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM601989 3 0.3518 0.6436 0.012 0.000 0.732 0.000 0.000 0.256
#> GSM601879 1 0.0603 0.8660 0.980 0.000 0.016 0.000 0.000 0.004
#> GSM601909 3 0.3647 0.4578 0.000 0.000 0.640 0.000 0.000 0.360
#> GSM601919 4 0.0665 0.9080 0.000 0.008 0.000 0.980 0.008 0.004
#> GSM601924 1 0.0458 0.8662 0.984 0.000 0.016 0.000 0.000 0.000
#> GSM601954 4 0.4211 0.1454 0.000 0.456 0.000 0.532 0.008 0.004
#> GSM601964 3 0.0000 0.8246 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM601969 6 0.4650 -0.0711 0.472 0.000 0.040 0.000 0.000 0.488
#> GSM601984 1 0.1528 0.8531 0.936 0.000 0.048 0.000 0.000 0.016
#> GSM601994 4 0.0820 0.8991 0.000 0.000 0.000 0.972 0.016 0.012
#> GSM601875 2 0.0000 0.9390 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM601885 2 0.0146 0.9383 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM601890 6 0.1152 0.7409 0.000 0.000 0.044 0.000 0.004 0.952
#> GSM601895 3 0.0146 0.8246 0.000 0.000 0.996 0.000 0.000 0.004
#> GSM601900 3 0.0653 0.8222 0.004 0.000 0.980 0.000 0.004 0.012
#> GSM601905 4 0.0291 0.9090 0.000 0.000 0.000 0.992 0.004 0.004
#> GSM601915 3 0.0146 0.8243 0.000 0.000 0.996 0.000 0.000 0.004
#> GSM601930 1 0.0000 0.8647 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM601935 5 0.2527 0.7634 0.000 0.000 0.168 0.000 0.832 0.000
#> GSM601940 1 0.4881 0.5451 0.656 0.000 0.208 0.000 0.000 0.136
#> GSM601945 2 0.0146 0.9384 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM601950 1 0.3487 0.6742 0.756 0.000 0.020 0.000 0.000 0.224
#> GSM601960 3 0.0000 0.8246 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM601975 4 0.0000 0.9090 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM601980 5 0.0508 0.8796 0.000 0.000 0.012 0.000 0.984 0.004
#> GSM601990 3 0.0000 0.8246 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM601880 1 0.0000 0.8647 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM601910 3 0.3446 0.5319 0.000 0.000 0.692 0.000 0.000 0.308
#> GSM601920 4 0.0405 0.9086 0.000 0.000 0.000 0.988 0.008 0.004
#> GSM601925 1 0.0000 0.8647 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM601955 5 0.0508 0.8796 0.000 0.000 0.012 0.000 0.984 0.004
#> GSM601965 1 0.5306 0.4592 0.596 0.000 0.080 0.304 0.000 0.020
#> GSM601970 3 0.2257 0.7699 0.008 0.000 0.876 0.000 0.000 0.116
#> GSM601985 1 0.3833 0.5119 0.648 0.000 0.344 0.000 0.000 0.008
#> GSM601995 5 0.0363 0.8795 0.000 0.000 0.012 0.000 0.988 0.000
#> GSM601876 1 0.1168 0.8612 0.956 0.000 0.028 0.000 0.000 0.016
#> GSM601886 4 0.4072 0.5269 0.004 0.008 0.000 0.688 0.288 0.012
#> GSM601891 6 0.2697 0.6562 0.000 0.092 0.044 0.000 0.000 0.864
#> GSM601896 1 0.1644 0.8522 0.932 0.000 0.028 0.000 0.000 0.040
#> GSM601901 4 0.3578 0.5094 0.000 0.340 0.000 0.660 0.000 0.000
#> GSM601906 1 0.3147 0.7162 0.816 0.000 0.016 0.160 0.000 0.008
#> GSM601916 4 0.0405 0.9086 0.000 0.000 0.000 0.988 0.008 0.004
#> GSM601931 1 0.0000 0.8647 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM601936 5 0.3381 0.7814 0.000 0.028 0.004 0.144 0.816 0.008
#> GSM601941 4 0.0260 0.9074 0.000 0.000 0.000 0.992 0.008 0.000
#> GSM601946 1 0.0993 0.8629 0.964 0.000 0.024 0.000 0.000 0.012
#> GSM601951 1 0.0692 0.8651 0.976 0.000 0.020 0.000 0.000 0.004
#> GSM601961 2 0.1863 0.8537 0.000 0.896 0.000 0.000 0.000 0.104
#> GSM601976 4 0.0146 0.9089 0.000 0.004 0.000 0.996 0.000 0.000
#> GSM601981 2 0.0146 0.9385 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM601991 3 0.2562 0.6735 0.000 0.000 0.828 0.000 0.172 0.000
#> GSM601881 1 0.0000 0.8647 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM601911 4 0.1973 0.8758 0.016 0.016 0.008 0.932 0.008 0.020
#> GSM601921 4 0.0405 0.9086 0.000 0.000 0.000 0.988 0.008 0.004
#> GSM601926 1 0.0000 0.8647 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM601956 2 0.0146 0.9386 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM601966 4 0.0260 0.9082 0.000 0.008 0.000 0.992 0.000 0.000
#> GSM601971 1 0.5167 0.1686 0.500 0.000 0.412 0.000 0.000 0.088
#> GSM601986 1 0.4305 0.5166 0.656 0.000 0.020 0.312 0.000 0.012
#> GSM601996 4 0.0725 0.9041 0.000 0.000 0.000 0.976 0.012 0.012
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)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
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)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
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)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, 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.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")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n time(p) gender(p) k
#> SD:mclust 124 0.251 0.9184 2
#> SD:mclust 103 0.311 0.1516 3
#> SD:mclust 97 0.511 0.0982 4
#> SD:mclust 112 0.115 0.2404 5
#> SD:mclust 112 0.298 0.1561 6
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.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["SD", "NMF"]
# you can also extract it by
# res = res_list["SD:NMF"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 21512 rows and 125 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'NMF' method.
#> Performed in total 1250 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_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
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, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.886 0.932 0.971 0.5015 0.498 0.498
#> 3 3 0.403 0.543 0.753 0.3181 0.741 0.525
#> 4 4 0.441 0.463 0.700 0.1205 0.847 0.584
#> 5 5 0.460 0.376 0.620 0.0668 0.839 0.474
#> 6 6 0.514 0.377 0.610 0.0438 0.912 0.615
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 2
Following shows 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 class
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
#> GSM601872 2 0.0000 0.963 0.000 1.000
#> GSM601882 2 0.0000 0.963 0.000 1.000
#> GSM601887 1 0.6801 0.784 0.820 0.180
#> GSM601892 1 0.0000 0.975 1.000 0.000
#> GSM601897 1 0.2948 0.930 0.948 0.052
#> GSM601902 2 0.0000 0.963 0.000 1.000
#> GSM601912 1 0.0376 0.972 0.996 0.004
#> GSM601927 1 0.0000 0.975 1.000 0.000
#> GSM601932 2 0.0000 0.963 0.000 1.000
#> GSM601937 2 0.0938 0.956 0.012 0.988
#> GSM601942 2 0.0000 0.963 0.000 1.000
#> GSM601947 2 0.0000 0.963 0.000 1.000
#> GSM601957 1 0.0000 0.975 1.000 0.000
#> GSM601972 2 0.0000 0.963 0.000 1.000
#> GSM601977 2 0.0000 0.963 0.000 1.000
#> GSM601987 2 0.0000 0.963 0.000 1.000
#> GSM601877 1 0.0000 0.975 1.000 0.000
#> GSM601907 2 0.0000 0.963 0.000 1.000
#> GSM601917 2 0.4939 0.875 0.108 0.892
#> GSM601922 2 0.7674 0.730 0.224 0.776
#> GSM601952 2 0.0000 0.963 0.000 1.000
#> GSM601962 1 0.0000 0.975 1.000 0.000
#> GSM601967 1 0.0000 0.975 1.000 0.000
#> GSM601982 2 0.3274 0.919 0.060 0.940
#> GSM601992 2 0.0000 0.963 0.000 1.000
#> GSM601873 2 0.0000 0.963 0.000 1.000
#> GSM601883 2 0.0000 0.963 0.000 1.000
#> GSM601888 1 0.8555 0.620 0.720 0.280
#> GSM601893 1 0.0376 0.972 0.996 0.004
#> GSM601898 1 0.0000 0.975 1.000 0.000
#> GSM601903 2 0.0000 0.963 0.000 1.000
#> GSM601913 1 0.0000 0.975 1.000 0.000
#> GSM601928 1 0.0000 0.975 1.000 0.000
#> GSM601933 2 0.0000 0.963 0.000 1.000
#> GSM601938 2 0.0000 0.963 0.000 1.000
#> GSM601943 2 0.0000 0.963 0.000 1.000
#> GSM601948 1 0.0000 0.975 1.000 0.000
#> GSM601958 1 0.0000 0.975 1.000 0.000
#> GSM601973 2 0.0000 0.963 0.000 1.000
#> GSM601978 2 0.0000 0.963 0.000 1.000
#> GSM601988 2 0.0000 0.963 0.000 1.000
#> GSM601878 1 0.0000 0.975 1.000 0.000
#> GSM601908 2 0.0000 0.963 0.000 1.000
#> GSM601918 2 0.0000 0.963 0.000 1.000
#> GSM601923 1 0.0000 0.975 1.000 0.000
#> GSM601953 2 0.0000 0.963 0.000 1.000
#> GSM601963 1 0.0000 0.975 1.000 0.000
#> GSM601968 1 0.0000 0.975 1.000 0.000
#> GSM601983 1 0.0000 0.975 1.000 0.000
#> GSM601993 2 0.0000 0.963 0.000 1.000
#> GSM601874 2 0.0000 0.963 0.000 1.000
#> GSM601884 2 0.0000 0.963 0.000 1.000
#> GSM601889 1 0.0000 0.975 1.000 0.000
#> GSM601894 1 0.0000 0.975 1.000 0.000
#> GSM601899 1 0.4939 0.872 0.892 0.108
#> GSM601904 1 0.9909 0.145 0.556 0.444
#> GSM601914 1 0.0000 0.975 1.000 0.000
#> GSM601929 1 0.0000 0.975 1.000 0.000
#> GSM601934 2 0.0000 0.963 0.000 1.000
#> GSM601939 1 0.0000 0.975 1.000 0.000
#> GSM601944 2 0.0000 0.963 0.000 1.000
#> GSM601949 1 0.0000 0.975 1.000 0.000
#> GSM601959 1 0.0000 0.975 1.000 0.000
#> GSM601974 1 0.6801 0.781 0.820 0.180
#> GSM601979 2 0.0000 0.963 0.000 1.000
#> GSM601989 1 0.0000 0.975 1.000 0.000
#> GSM601879 1 0.0000 0.975 1.000 0.000
#> GSM601909 1 0.0000 0.975 1.000 0.000
#> GSM601919 2 0.1414 0.951 0.020 0.980
#> GSM601924 1 0.0000 0.975 1.000 0.000
#> GSM601954 2 0.0000 0.963 0.000 1.000
#> GSM601964 1 0.0000 0.975 1.000 0.000
#> GSM601969 1 0.0000 0.975 1.000 0.000
#> GSM601984 1 0.0000 0.975 1.000 0.000
#> GSM601994 2 0.0000 0.963 0.000 1.000
#> GSM601875 2 0.0000 0.963 0.000 1.000
#> GSM601885 2 0.0000 0.963 0.000 1.000
#> GSM601890 1 0.0672 0.969 0.992 0.008
#> GSM601895 1 0.0000 0.975 1.000 0.000
#> GSM601900 1 0.0000 0.975 1.000 0.000
#> GSM601905 2 0.4690 0.883 0.100 0.900
#> GSM601915 1 0.0000 0.975 1.000 0.000
#> GSM601930 1 0.0000 0.975 1.000 0.000
#> GSM601935 1 0.1414 0.958 0.980 0.020
#> GSM601940 1 0.0000 0.975 1.000 0.000
#> GSM601945 2 0.0000 0.963 0.000 1.000
#> GSM601950 1 0.0000 0.975 1.000 0.000
#> GSM601960 1 0.0000 0.975 1.000 0.000
#> GSM601975 2 0.0000 0.963 0.000 1.000
#> GSM601980 2 0.1184 0.954 0.016 0.984
#> GSM601990 1 0.0000 0.975 1.000 0.000
#> GSM601880 1 0.0000 0.975 1.000 0.000
#> GSM601910 1 0.0000 0.975 1.000 0.000
#> GSM601920 2 0.6438 0.811 0.164 0.836
#> GSM601925 1 0.0000 0.975 1.000 0.000
#> GSM601955 2 0.9323 0.503 0.348 0.652
#> GSM601965 1 0.0376 0.972 0.996 0.004
#> GSM601970 1 0.0000 0.975 1.000 0.000
#> GSM601985 1 0.0000 0.975 1.000 0.000
#> GSM601995 2 0.6048 0.828 0.148 0.852
#> GSM601876 1 0.0000 0.975 1.000 0.000
#> GSM601886 2 0.9815 0.320 0.420 0.580
#> GSM601891 1 0.8016 0.688 0.756 0.244
#> GSM601896 1 0.0000 0.975 1.000 0.000
#> GSM601901 2 0.0000 0.963 0.000 1.000
#> GSM601906 1 0.0376 0.972 0.996 0.004
#> GSM601916 2 0.1843 0.945 0.028 0.972
#> GSM601931 1 0.0000 0.975 1.000 0.000
#> GSM601936 2 0.2948 0.927 0.052 0.948
#> GSM601941 2 0.0000 0.963 0.000 1.000
#> GSM601946 1 0.0000 0.975 1.000 0.000
#> GSM601951 1 0.0000 0.975 1.000 0.000
#> GSM601961 2 0.0376 0.961 0.004 0.996
#> GSM601976 2 0.0938 0.956 0.012 0.988
#> GSM601981 2 0.0000 0.963 0.000 1.000
#> GSM601991 1 0.0000 0.975 1.000 0.000
#> GSM601881 1 0.0000 0.975 1.000 0.000
#> GSM601911 2 0.9635 0.364 0.388 0.612
#> GSM601921 2 0.0376 0.961 0.004 0.996
#> GSM601926 1 0.0000 0.975 1.000 0.000
#> GSM601956 2 0.0000 0.963 0.000 1.000
#> GSM601966 2 0.0000 0.963 0.000 1.000
#> GSM601971 1 0.0000 0.975 1.000 0.000
#> GSM601986 1 0.2603 0.938 0.956 0.044
#> GSM601996 2 0.0000 0.963 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM601872 3 0.4062 0.4056 0.000 0.164 0.836
#> GSM601882 2 0.3686 0.7624 0.000 0.860 0.140
#> GSM601887 3 0.3816 0.5965 0.148 0.000 0.852
#> GSM601892 3 0.5016 0.5820 0.240 0.000 0.760
#> GSM601897 3 0.3551 0.6140 0.132 0.000 0.868
#> GSM601902 2 0.3784 0.7336 0.132 0.864 0.004
#> GSM601912 3 0.5016 0.6005 0.240 0.000 0.760
#> GSM601927 1 0.0424 0.7246 0.992 0.000 0.008
#> GSM601932 2 0.2680 0.7607 0.068 0.924 0.008
#> GSM601937 3 0.6941 0.0481 0.016 0.464 0.520
#> GSM601942 3 0.5621 0.2511 0.000 0.308 0.692
#> GSM601947 2 0.5730 0.7679 0.060 0.796 0.144
#> GSM601957 1 0.6235 0.0275 0.564 0.000 0.436
#> GSM601972 2 0.3213 0.7788 0.028 0.912 0.060
#> GSM601977 2 0.5859 0.6184 0.000 0.656 0.344
#> GSM601987 2 0.3686 0.7678 0.000 0.860 0.140
#> GSM601877 1 0.2063 0.6995 0.948 0.044 0.008
#> GSM601907 2 0.4931 0.7389 0.000 0.768 0.232
#> GSM601917 2 0.7181 0.2259 0.468 0.508 0.024
#> GSM601922 1 0.7346 -0.0475 0.536 0.432 0.032
#> GSM601952 2 0.4339 0.7810 0.048 0.868 0.084
#> GSM601962 3 0.6422 0.5561 0.324 0.016 0.660
#> GSM601967 1 0.6008 0.3044 0.628 0.000 0.372
#> GSM601982 2 0.6859 0.4449 0.016 0.564 0.420
#> GSM601992 2 0.1482 0.7688 0.012 0.968 0.020
#> GSM601873 3 0.6252 -0.2110 0.000 0.444 0.556
#> GSM601883 2 0.3267 0.7720 0.000 0.884 0.116
#> GSM601888 3 0.5970 0.5078 0.160 0.060 0.780
#> GSM601893 3 0.4235 0.5990 0.176 0.000 0.824
#> GSM601898 3 0.6260 0.3704 0.448 0.000 0.552
#> GSM601903 2 0.4293 0.7136 0.164 0.832 0.004
#> GSM601913 1 0.6215 -0.0503 0.572 0.000 0.428
#> GSM601928 1 0.1289 0.7228 0.968 0.000 0.032
#> GSM601933 2 0.2711 0.7726 0.000 0.912 0.088
#> GSM601938 2 0.2537 0.7633 0.000 0.920 0.080
#> GSM601943 3 0.5178 0.3039 0.000 0.256 0.744
#> GSM601948 1 0.3573 0.6955 0.876 0.004 0.120
#> GSM601958 1 0.6192 0.0351 0.580 0.000 0.420
#> GSM601973 2 0.3644 0.7382 0.124 0.872 0.004
#> GSM601978 2 0.5835 0.6613 0.000 0.660 0.340
#> GSM601988 3 0.7286 0.0413 0.028 0.464 0.508
#> GSM601878 1 0.1753 0.7222 0.952 0.000 0.048
#> GSM601908 2 0.3619 0.7703 0.000 0.864 0.136
#> GSM601918 2 0.5319 0.7608 0.104 0.824 0.072
#> GSM601923 1 0.0592 0.7254 0.988 0.000 0.012
#> GSM601953 2 0.6314 0.5969 0.004 0.604 0.392
#> GSM601963 1 0.6280 -0.1556 0.540 0.000 0.460
#> GSM601968 3 0.6235 0.3254 0.436 0.000 0.564
#> GSM601983 3 0.5988 0.5178 0.368 0.000 0.632
#> GSM601993 2 0.3713 0.7508 0.032 0.892 0.076
#> GSM601874 2 0.5363 0.7161 0.000 0.724 0.276
#> GSM601884 2 0.6111 0.5070 0.000 0.604 0.396
#> GSM601889 3 0.6308 0.2575 0.492 0.000 0.508
#> GSM601894 3 0.6286 0.3276 0.464 0.000 0.536
#> GSM601899 3 0.4002 0.5907 0.160 0.000 0.840
#> GSM601904 1 0.6129 0.3329 0.668 0.324 0.008
#> GSM601914 3 0.5988 0.5165 0.368 0.000 0.632
#> GSM601929 1 0.0983 0.7185 0.980 0.016 0.004
#> GSM601934 2 0.4654 0.7436 0.000 0.792 0.208
#> GSM601939 1 0.3340 0.6734 0.880 0.000 0.120
#> GSM601944 2 0.2066 0.7702 0.000 0.940 0.060
#> GSM601949 1 0.4700 0.6536 0.812 0.008 0.180
#> GSM601959 1 0.6062 0.1907 0.616 0.000 0.384
#> GSM601974 3 0.8064 0.5054 0.328 0.084 0.588
#> GSM601979 2 0.4555 0.7497 0.000 0.800 0.200
#> GSM601989 3 0.6154 0.4566 0.408 0.000 0.592
#> GSM601879 1 0.2443 0.7063 0.940 0.032 0.028
#> GSM601909 3 0.5859 0.5359 0.344 0.000 0.656
#> GSM601919 2 0.8857 0.4865 0.344 0.524 0.132
#> GSM601924 1 0.1643 0.7213 0.956 0.000 0.044
#> GSM601954 2 0.8838 0.6365 0.200 0.580 0.220
#> GSM601964 3 0.6215 0.4221 0.428 0.000 0.572
#> GSM601969 1 0.6322 0.5255 0.700 0.024 0.276
#> GSM601984 1 0.3583 0.6884 0.900 0.056 0.044
#> GSM601994 2 0.1453 0.7680 0.024 0.968 0.008
#> GSM601875 2 0.5623 0.7104 0.004 0.716 0.280
#> GSM601885 2 0.3482 0.7704 0.000 0.872 0.128
#> GSM601890 3 0.3412 0.6015 0.124 0.000 0.876
#> GSM601895 3 0.5810 0.5443 0.336 0.000 0.664
#> GSM601900 3 0.6062 0.4897 0.384 0.000 0.616
#> GSM601905 2 0.6675 0.3840 0.404 0.584 0.012
#> GSM601915 1 0.6260 -0.1152 0.552 0.000 0.448
#> GSM601930 1 0.0829 0.7245 0.984 0.004 0.012
#> GSM601935 3 0.8082 0.4863 0.296 0.096 0.608
#> GSM601940 1 0.4555 0.5875 0.800 0.000 0.200
#> GSM601945 2 0.5178 0.7244 0.000 0.744 0.256
#> GSM601950 1 0.3941 0.6667 0.844 0.000 0.156
#> GSM601960 3 0.6111 0.4817 0.396 0.000 0.604
#> GSM601975 2 0.4136 0.7439 0.116 0.864 0.020
#> GSM601980 3 0.5785 0.2599 0.000 0.332 0.668
#> GSM601990 3 0.6111 0.4794 0.396 0.000 0.604
#> GSM601880 1 0.0424 0.7224 0.992 0.008 0.000
#> GSM601910 3 0.6062 0.4660 0.384 0.000 0.616
#> GSM601920 1 0.7517 -0.0278 0.540 0.420 0.040
#> GSM601925 1 0.1399 0.7129 0.968 0.028 0.004
#> GSM601955 3 0.5180 0.5495 0.032 0.156 0.812
#> GSM601965 1 0.2066 0.7165 0.940 0.000 0.060
#> GSM601970 1 0.6192 0.0752 0.580 0.000 0.420
#> GSM601985 1 0.2878 0.6973 0.904 0.000 0.096
#> GSM601995 3 0.7311 0.2482 0.036 0.384 0.580
#> GSM601876 1 0.2878 0.6944 0.904 0.000 0.096
#> GSM601886 2 0.9887 0.0489 0.336 0.396 0.268
#> GSM601891 3 0.2939 0.5862 0.072 0.012 0.916
#> GSM601896 1 0.5016 0.5090 0.760 0.000 0.240
#> GSM601901 2 0.3965 0.7757 0.008 0.860 0.132
#> GSM601906 1 0.3607 0.6366 0.880 0.112 0.008
#> GSM601916 2 0.5815 0.5675 0.304 0.692 0.004
#> GSM601931 1 0.0747 0.7250 0.984 0.000 0.016
#> GSM601936 2 0.7660 0.4063 0.064 0.612 0.324
#> GSM601941 2 0.2845 0.7600 0.068 0.920 0.012
#> GSM601946 1 0.2448 0.7069 0.924 0.000 0.076
#> GSM601951 1 0.2297 0.7214 0.944 0.020 0.036
#> GSM601961 2 0.7558 0.5642 0.044 0.556 0.400
#> GSM601976 2 0.5899 0.6387 0.244 0.736 0.020
#> GSM601981 2 0.5058 0.7320 0.000 0.756 0.244
#> GSM601991 3 0.6126 0.5838 0.268 0.020 0.712
#> GSM601881 1 0.1267 0.7258 0.972 0.004 0.024
#> GSM601911 1 0.7069 -0.1011 0.508 0.472 0.020
#> GSM601921 2 0.6019 0.5926 0.288 0.700 0.012
#> GSM601926 1 0.0592 0.7256 0.988 0.000 0.012
#> GSM601956 2 0.6126 0.5861 0.000 0.600 0.400
#> GSM601966 2 0.1163 0.7713 0.028 0.972 0.000
#> GSM601971 1 0.4178 0.6270 0.828 0.000 0.172
#> GSM601986 1 0.2945 0.6649 0.908 0.088 0.004
#> GSM601996 2 0.2339 0.7663 0.048 0.940 0.012
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM601872 2 0.551 0.00791 0.000 0.512 0.472 0.016
#> GSM601882 4 0.573 0.32534 0.000 0.344 0.040 0.616
#> GSM601887 2 0.606 0.08759 0.044 0.588 0.364 0.004
#> GSM601892 2 0.761 -0.24203 0.140 0.444 0.404 0.012
#> GSM601897 3 0.267 0.59716 0.008 0.100 0.892 0.000
#> GSM601902 4 0.525 0.63970 0.156 0.060 0.016 0.768
#> GSM601912 3 0.384 0.63793 0.052 0.092 0.852 0.004
#> GSM601927 1 0.322 0.73339 0.892 0.012 0.052 0.044
#> GSM601932 4 0.376 0.66852 0.040 0.052 0.036 0.872
#> GSM601937 3 0.535 0.18160 0.000 0.012 0.560 0.428
#> GSM601942 3 0.612 0.38353 0.000 0.112 0.668 0.220
#> GSM601947 2 0.699 0.17950 0.132 0.532 0.000 0.336
#> GSM601957 1 0.734 0.19499 0.524 0.124 0.340 0.012
#> GSM601972 4 0.612 0.38618 0.060 0.312 0.004 0.624
#> GSM601977 4 0.786 -0.17158 0.000 0.344 0.276 0.380
#> GSM601987 2 0.552 0.25955 0.000 0.568 0.020 0.412
#> GSM601877 1 0.194 0.71865 0.936 0.012 0.000 0.052
#> GSM601907 2 0.343 0.55272 0.012 0.844 0.000 0.144
#> GSM601917 1 0.632 -0.17323 0.500 0.060 0.000 0.440
#> GSM601922 1 0.625 0.14570 0.596 0.060 0.004 0.340
#> GSM601952 4 0.635 0.49934 0.056 0.208 0.044 0.692
#> GSM601962 3 0.391 0.65497 0.088 0.016 0.856 0.040
#> GSM601967 1 0.738 0.15841 0.528 0.148 0.316 0.008
#> GSM601982 2 0.754 0.40956 0.064 0.592 0.084 0.260
#> GSM601992 4 0.198 0.64829 0.004 0.068 0.000 0.928
#> GSM601873 3 0.762 0.01508 0.000 0.244 0.472 0.284
#> GSM601883 4 0.546 -0.07311 0.008 0.484 0.004 0.504
#> GSM601888 2 0.477 0.46974 0.072 0.800 0.120 0.008
#> GSM601893 3 0.679 0.22942 0.072 0.436 0.484 0.008
#> GSM601898 3 0.597 0.45265 0.296 0.032 0.652 0.020
#> GSM601903 4 0.532 0.61801 0.192 0.064 0.004 0.740
#> GSM601913 3 0.592 0.40173 0.328 0.012 0.628 0.032
#> GSM601928 1 0.484 0.70492 0.792 0.012 0.144 0.052
#> GSM601933 4 0.480 0.44795 0.000 0.260 0.020 0.720
#> GSM601938 4 0.410 0.58533 0.000 0.148 0.036 0.816
#> GSM601943 3 0.656 0.21684 0.000 0.296 0.596 0.108
#> GSM601948 1 0.455 0.70269 0.820 0.116 0.040 0.024
#> GSM601958 1 0.675 0.12655 0.512 0.056 0.416 0.016
#> GSM601973 4 0.384 0.66865 0.088 0.032 0.020 0.860
#> GSM601978 2 0.402 0.56291 0.000 0.828 0.044 0.128
#> GSM601988 3 0.557 0.08967 0.004 0.012 0.520 0.464
#> GSM601878 1 0.206 0.73012 0.940 0.032 0.020 0.008
#> GSM601908 2 0.517 0.33443 0.012 0.620 0.000 0.368
#> GSM601918 2 0.734 -0.10246 0.156 0.428 0.000 0.416
#> GSM601923 1 0.180 0.73463 0.944 0.000 0.040 0.016
#> GSM601953 2 0.299 0.56414 0.008 0.900 0.036 0.056
#> GSM601963 3 0.474 0.48602 0.328 0.000 0.668 0.004
#> GSM601968 3 0.778 0.38914 0.308 0.204 0.480 0.008
#> GSM601983 3 0.409 0.64104 0.172 0.024 0.804 0.000
#> GSM601993 4 0.330 0.62211 0.012 0.028 0.076 0.884
#> GSM601874 2 0.265 0.56751 0.004 0.888 0.000 0.108
#> GSM601884 2 0.744 0.27223 0.000 0.492 0.196 0.312
#> GSM601889 3 0.717 0.20729 0.400 0.088 0.496 0.016
#> GSM601894 3 0.654 0.30657 0.372 0.056 0.560 0.012
#> GSM601899 2 0.648 0.07686 0.064 0.580 0.348 0.008
#> GSM601904 1 0.690 -0.00993 0.472 0.016 0.064 0.448
#> GSM601914 3 0.299 0.65831 0.112 0.012 0.876 0.000
#> GSM601929 1 0.320 0.72444 0.892 0.016 0.028 0.064
#> GSM601934 2 0.644 0.15394 0.000 0.488 0.068 0.444
#> GSM601939 1 0.513 0.65886 0.760 0.020 0.188 0.032
#> GSM601944 4 0.368 0.61285 0.000 0.060 0.084 0.856
#> GSM601949 1 0.560 0.61779 0.716 0.228 0.032 0.024
#> GSM601959 1 0.699 0.25845 0.540 0.060 0.372 0.028
#> GSM601974 3 0.541 0.62607 0.072 0.016 0.760 0.152
#> GSM601979 2 0.453 0.50409 0.000 0.744 0.016 0.240
#> GSM601989 3 0.689 0.50264 0.252 0.104 0.624 0.020
#> GSM601879 1 0.204 0.71979 0.936 0.032 0.000 0.032
#> GSM601909 3 0.649 0.57058 0.204 0.156 0.640 0.000
#> GSM601919 2 0.763 0.07348 0.368 0.424 0.000 0.208
#> GSM601924 1 0.212 0.73169 0.932 0.012 0.052 0.004
#> GSM601954 2 0.616 0.42507 0.172 0.676 0.000 0.152
#> GSM601964 3 0.383 0.62213 0.204 0.000 0.792 0.004
#> GSM601969 1 0.725 0.41959 0.588 0.264 0.128 0.020
#> GSM601984 1 0.478 0.69002 0.788 0.000 0.116 0.096
#> GSM601994 4 0.185 0.65632 0.012 0.048 0.000 0.940
#> GSM601875 2 0.292 0.56761 0.004 0.884 0.008 0.104
#> GSM601885 2 0.551 0.23331 0.008 0.568 0.008 0.416
#> GSM601890 3 0.607 0.21703 0.036 0.432 0.528 0.004
#> GSM601895 3 0.287 0.66167 0.072 0.032 0.896 0.000
#> GSM601900 3 0.439 0.63762 0.132 0.024 0.820 0.024
#> GSM601905 4 0.585 0.50586 0.308 0.024 0.020 0.648
#> GSM601915 3 0.572 0.37302 0.344 0.012 0.624 0.020
#> GSM601930 1 0.370 0.72973 0.868 0.012 0.068 0.052
#> GSM601935 3 0.481 0.54539 0.028 0.000 0.736 0.236
#> GSM601940 1 0.544 0.63612 0.740 0.036 0.200 0.024
#> GSM601945 2 0.526 0.50444 0.000 0.700 0.040 0.260
#> GSM601950 1 0.522 0.68110 0.784 0.120 0.072 0.024
#> GSM601960 3 0.310 0.65453 0.116 0.004 0.872 0.008
#> GSM601975 4 0.558 0.61479 0.156 0.104 0.004 0.736
#> GSM601980 3 0.501 0.44579 0.000 0.024 0.700 0.276
#> GSM601990 3 0.375 0.66029 0.088 0.008 0.860 0.044
#> GSM601880 1 0.191 0.72964 0.940 0.000 0.020 0.040
#> GSM601910 3 0.637 0.56145 0.244 0.104 0.648 0.004
#> GSM601920 1 0.583 0.25704 0.632 0.052 0.000 0.316
#> GSM601925 1 0.236 0.72323 0.920 0.000 0.024 0.056
#> GSM601955 3 0.448 0.55642 0.016 0.032 0.812 0.140
#> GSM601965 1 0.436 0.70469 0.816 0.008 0.136 0.040
#> GSM601970 3 0.649 0.23205 0.436 0.060 0.500 0.004
#> GSM601985 1 0.410 0.65209 0.792 0.000 0.192 0.016
#> GSM601995 3 0.535 0.30686 0.004 0.012 0.616 0.368
#> GSM601876 1 0.484 0.68018 0.788 0.024 0.160 0.028
#> GSM601886 4 0.642 0.45305 0.092 0.016 0.224 0.668
#> GSM601891 2 0.619 -0.14567 0.028 0.488 0.472 0.012
#> GSM601896 1 0.568 0.60384 0.716 0.044 0.220 0.020
#> GSM601901 2 0.618 0.07942 0.028 0.508 0.012 0.452
#> GSM601906 1 0.539 0.61954 0.736 0.008 0.056 0.200
#> GSM601916 4 0.589 0.55281 0.260 0.044 0.016 0.680
#> GSM601931 1 0.386 0.72580 0.856 0.012 0.092 0.040
#> GSM601936 4 0.596 0.31107 0.024 0.020 0.328 0.628
#> GSM601941 4 0.231 0.67097 0.040 0.028 0.004 0.928
#> GSM601946 1 0.490 0.67626 0.780 0.016 0.168 0.036
#> GSM601951 1 0.327 0.72718 0.892 0.024 0.028 0.056
#> GSM601961 2 0.362 0.54613 0.052 0.876 0.020 0.052
#> GSM601976 4 0.634 0.59000 0.208 0.060 0.040 0.692
#> GSM601981 2 0.447 0.53585 0.004 0.776 0.020 0.200
#> GSM601991 3 0.268 0.64188 0.028 0.012 0.916 0.044
#> GSM601881 1 0.170 0.73458 0.952 0.004 0.028 0.016
#> GSM601911 1 0.674 0.26742 0.596 0.112 0.004 0.288
#> GSM601921 4 0.642 0.47645 0.336 0.084 0.000 0.580
#> GSM601926 1 0.238 0.73215 0.916 0.000 0.068 0.016
#> GSM601956 2 0.465 0.55862 0.000 0.796 0.084 0.120
#> GSM601966 4 0.393 0.63315 0.040 0.128 0.000 0.832
#> GSM601971 1 0.457 0.61013 0.768 0.016 0.208 0.008
#> GSM601986 1 0.339 0.72939 0.888 0.032 0.028 0.052
#> GSM601996 4 0.276 0.66686 0.044 0.052 0.000 0.904
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM601872 3 0.511 0.1492 0.012 0.420 0.552 0.008 0.008
#> GSM601882 4 0.601 -0.0690 0.008 0.424 0.032 0.504 0.032
#> GSM601887 2 0.656 0.3707 0.152 0.620 0.176 0.004 0.048
#> GSM601892 2 0.726 0.0963 0.396 0.424 0.120 0.004 0.056
#> GSM601897 3 0.363 0.6596 0.052 0.068 0.852 0.024 0.004
#> GSM601902 4 0.588 0.4494 0.028 0.044 0.004 0.572 0.352
#> GSM601912 3 0.645 0.5225 0.292 0.112 0.568 0.024 0.004
#> GSM601927 1 0.477 0.3844 0.680 0.000 0.004 0.040 0.276
#> GSM601932 4 0.665 0.3977 0.040 0.044 0.024 0.496 0.396
#> GSM601937 4 0.613 -0.1051 0.040 0.016 0.456 0.468 0.020
#> GSM601942 3 0.393 0.5998 0.004 0.044 0.832 0.092 0.028
#> GSM601947 5 0.620 0.0894 0.012 0.236 0.000 0.160 0.592
#> GSM601957 1 0.469 0.5458 0.784 0.064 0.092 0.000 0.060
#> GSM601972 4 0.772 0.3343 0.036 0.224 0.012 0.388 0.340
#> GSM601977 2 0.749 0.3398 0.008 0.444 0.228 0.288 0.032
#> GSM601987 2 0.507 0.4800 0.020 0.656 0.004 0.300 0.020
#> GSM601877 5 0.482 0.1825 0.416 0.004 0.000 0.016 0.564
#> GSM601907 2 0.312 0.6029 0.000 0.860 0.000 0.076 0.064
#> GSM601917 5 0.397 0.3983 0.028 0.016 0.004 0.144 0.808
#> GSM601922 5 0.460 0.4774 0.092 0.016 0.000 0.120 0.772
#> GSM601952 5 0.795 -0.0550 0.000 0.140 0.192 0.212 0.456
#> GSM601962 3 0.366 0.6777 0.044 0.004 0.852 0.068 0.032
#> GSM601967 1 0.765 0.3283 0.496 0.084 0.196 0.004 0.220
#> GSM601982 2 0.783 0.4275 0.012 0.512 0.124 0.208 0.144
#> GSM601992 4 0.439 0.5005 0.020 0.092 0.012 0.808 0.068
#> GSM601873 2 0.760 0.2561 0.016 0.388 0.248 0.328 0.020
#> GSM601883 2 0.514 0.3781 0.008 0.592 0.000 0.368 0.032
#> GSM601888 2 0.525 0.5201 0.136 0.736 0.048 0.000 0.080
#> GSM601893 2 0.730 0.1236 0.328 0.452 0.180 0.004 0.036
#> GSM601898 1 0.529 0.2303 0.624 0.012 0.328 0.028 0.008
#> GSM601903 4 0.589 0.3335 0.028 0.028 0.008 0.496 0.440
#> GSM601913 1 0.561 0.3920 0.672 0.000 0.208 0.100 0.020
#> GSM601928 1 0.524 0.3998 0.672 0.000 0.016 0.056 0.256
#> GSM601933 4 0.612 -0.0396 0.048 0.372 0.000 0.536 0.044
#> GSM601938 4 0.515 0.3927 0.032 0.208 0.016 0.720 0.024
#> GSM601943 3 0.540 0.4192 0.016 0.248 0.676 0.052 0.008
#> GSM601948 5 0.590 0.2540 0.328 0.036 0.020 0.020 0.596
#> GSM601958 1 0.388 0.5615 0.824 0.020 0.120 0.004 0.032
#> GSM601973 4 0.543 0.4183 0.016 0.016 0.012 0.568 0.388
#> GSM601978 2 0.319 0.6070 0.000 0.864 0.012 0.088 0.036
#> GSM601988 4 0.619 0.1967 0.064 0.012 0.340 0.564 0.020
#> GSM601878 5 0.474 -0.0107 0.476 0.016 0.000 0.000 0.508
#> GSM601908 2 0.510 0.4633 0.000 0.656 0.000 0.272 0.072
#> GSM601918 5 0.588 0.0804 0.000 0.200 0.000 0.196 0.604
#> GSM601923 5 0.499 0.1904 0.396 0.000 0.016 0.012 0.576
#> GSM601953 2 0.346 0.5777 0.000 0.836 0.032 0.008 0.124
#> GSM601963 3 0.618 0.4392 0.308 0.000 0.580 0.036 0.076
#> GSM601968 3 0.750 0.4815 0.152 0.128 0.524 0.000 0.196
#> GSM601983 3 0.583 0.6147 0.212 0.028 0.680 0.020 0.060
#> GSM601993 4 0.440 0.5251 0.028 0.008 0.088 0.808 0.068
#> GSM601874 2 0.350 0.6011 0.004 0.840 0.000 0.092 0.064
#> GSM601884 2 0.673 0.4409 0.000 0.520 0.224 0.240 0.016
#> GSM601889 1 0.504 0.4101 0.708 0.040 0.228 0.004 0.020
#> GSM601894 1 0.521 0.4128 0.704 0.040 0.224 0.008 0.024
#> GSM601899 2 0.648 0.3494 0.232 0.596 0.132 0.000 0.040
#> GSM601904 5 0.662 0.0143 0.188 0.000 0.004 0.364 0.444
#> GSM601914 3 0.461 0.5934 0.268 0.004 0.700 0.020 0.008
#> GSM601929 1 0.508 0.2714 0.628 0.000 0.000 0.056 0.316
#> GSM601934 2 0.792 0.3241 0.176 0.448 0.040 0.300 0.036
#> GSM601939 1 0.353 0.5508 0.840 0.000 0.032 0.016 0.112
#> GSM601944 4 0.558 0.4606 0.100 0.124 0.020 0.728 0.028
#> GSM601949 1 0.593 0.0793 0.500 0.108 0.000 0.000 0.392
#> GSM601959 1 0.435 0.5632 0.820 0.032 0.084 0.028 0.036
#> GSM601974 3 0.719 0.4553 0.112 0.008 0.580 0.108 0.192
#> GSM601979 2 0.441 0.5709 0.000 0.760 0.004 0.172 0.064
#> GSM601989 1 0.571 0.4256 0.716 0.080 0.148 0.040 0.016
#> GSM601879 5 0.472 0.2684 0.352 0.008 0.004 0.008 0.628
#> GSM601909 3 0.653 0.5823 0.172 0.120 0.628 0.000 0.080
#> GSM601919 5 0.399 0.4346 0.032 0.128 0.000 0.028 0.812
#> GSM601924 1 0.489 0.0699 0.512 0.004 0.016 0.000 0.468
#> GSM601954 5 0.620 0.1734 0.008 0.308 0.012 0.096 0.576
#> GSM601964 3 0.394 0.6739 0.088 0.000 0.824 0.020 0.068
#> GSM601969 5 0.746 0.0750 0.308 0.176 0.044 0.008 0.464
#> GSM601984 1 0.663 0.3492 0.568 0.004 0.020 0.180 0.228
#> GSM601994 4 0.413 0.5182 0.016 0.088 0.004 0.816 0.076
#> GSM601875 2 0.377 0.6004 0.020 0.824 0.000 0.124 0.032
#> GSM601885 2 0.594 0.4481 0.012 0.600 0.020 0.316 0.052
#> GSM601890 3 0.661 0.2090 0.084 0.384 0.488 0.000 0.044
#> GSM601895 3 0.493 0.6026 0.260 0.028 0.692 0.016 0.004
#> GSM601900 1 0.654 0.1976 0.596 0.040 0.272 0.076 0.016
#> GSM601905 4 0.633 0.3523 0.164 0.008 0.000 0.552 0.276
#> GSM601915 1 0.492 0.3138 0.668 0.000 0.288 0.032 0.012
#> GSM601930 1 0.429 0.4314 0.740 0.000 0.004 0.032 0.224
#> GSM601935 3 0.637 0.4741 0.112 0.004 0.572 0.292 0.020
#> GSM601940 1 0.265 0.5699 0.892 0.000 0.036 0.004 0.068
#> GSM601945 2 0.446 0.5616 0.008 0.736 0.008 0.228 0.020
#> GSM601950 1 0.477 0.4631 0.716 0.064 0.000 0.004 0.216
#> GSM601960 3 0.452 0.6596 0.180 0.000 0.760 0.032 0.028
#> GSM601975 4 0.604 0.3895 0.012 0.084 0.000 0.508 0.396
#> GSM601980 3 0.406 0.6028 0.020 0.008 0.820 0.112 0.040
#> GSM601990 3 0.464 0.6504 0.184 0.000 0.744 0.064 0.008
#> GSM601880 5 0.507 0.0757 0.448 0.000 0.008 0.020 0.524
#> GSM601910 3 0.784 0.3474 0.304 0.100 0.460 0.012 0.124
#> GSM601920 5 0.503 0.4646 0.160 0.004 0.000 0.120 0.716
#> GSM601925 5 0.494 0.2300 0.380 0.000 0.008 0.020 0.592
#> GSM601955 3 0.309 0.6444 0.016 0.000 0.876 0.056 0.052
#> GSM601965 1 0.713 0.3371 0.548 0.024 0.024 0.164 0.240
#> GSM601970 3 0.737 0.2873 0.280 0.040 0.472 0.004 0.204
#> GSM601985 1 0.532 0.4601 0.676 0.000 0.060 0.020 0.244
#> GSM601995 3 0.439 0.5475 0.000 0.004 0.748 0.200 0.048
#> GSM601876 1 0.253 0.5570 0.904 0.004 0.004 0.056 0.032
#> GSM601886 4 0.659 0.4597 0.176 0.000 0.108 0.624 0.092
#> GSM601891 2 0.657 0.0616 0.120 0.500 0.360 0.004 0.016
#> GSM601896 1 0.228 0.5627 0.924 0.012 0.012 0.020 0.032
#> GSM601901 2 0.766 0.1734 0.084 0.428 0.004 0.348 0.136
#> GSM601906 1 0.616 0.0675 0.516 0.000 0.000 0.148 0.336
#> GSM601916 4 0.674 0.4163 0.204 0.024 0.000 0.540 0.232
#> GSM601931 1 0.445 0.4439 0.724 0.000 0.004 0.036 0.236
#> GSM601936 4 0.584 0.4369 0.212 0.012 0.080 0.672 0.024
#> GSM601941 4 0.554 0.4911 0.000 0.040 0.028 0.616 0.316
#> GSM601946 1 0.278 0.5580 0.888 0.000 0.012 0.028 0.072
#> GSM601951 1 0.578 0.0221 0.484 0.000 0.016 0.052 0.448
#> GSM601961 2 0.507 0.5813 0.108 0.756 0.000 0.056 0.080
#> GSM601976 4 0.650 0.4746 0.152 0.036 0.000 0.596 0.216
#> GSM601981 2 0.489 0.5707 0.016 0.744 0.004 0.172 0.064
#> GSM601991 3 0.565 0.6404 0.152 0.016 0.700 0.120 0.012
#> GSM601881 1 0.470 0.0206 0.508 0.000 0.008 0.004 0.480
#> GSM601911 1 0.834 0.0108 0.384 0.164 0.004 0.280 0.168
#> GSM601921 5 0.523 0.2065 0.040 0.020 0.000 0.284 0.656
#> GSM601926 1 0.526 0.0578 0.508 0.000 0.020 0.016 0.456
#> GSM601956 2 0.516 0.5543 0.000 0.736 0.152 0.040 0.072
#> GSM601966 4 0.483 0.4664 0.008 0.152 0.000 0.740 0.100
#> GSM601971 5 0.727 0.0375 0.288 0.020 0.208 0.012 0.472
#> GSM601986 1 0.671 0.3579 0.584 0.044 0.008 0.108 0.256
#> GSM601996 4 0.436 0.5403 0.020 0.060 0.008 0.804 0.108
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM601872 3 0.517 0.34542 0.004 0.324 0.604 0.048 0.016 0.004
#> GSM601882 5 0.703 0.18740 0.044 0.284 0.032 0.104 0.516 0.020
#> GSM601887 2 0.560 0.49006 0.016 0.688 0.136 0.048 0.004 0.108
#> GSM601892 2 0.620 0.18709 0.028 0.500 0.048 0.040 0.004 0.380
#> GSM601897 3 0.412 0.65926 0.004 0.064 0.808 0.064 0.008 0.052
#> GSM601902 4 0.626 0.51520 0.088 0.020 0.004 0.576 0.268 0.044
#> GSM601912 3 0.711 0.53872 0.016 0.100 0.528 0.056 0.040 0.260
#> GSM601927 1 0.493 -0.15361 0.480 0.000 0.000 0.032 0.016 0.472
#> GSM601932 4 0.619 0.56412 0.060 0.056 0.016 0.652 0.176 0.040
#> GSM601937 3 0.664 0.31236 0.020 0.008 0.524 0.104 0.300 0.044
#> GSM601942 3 0.452 0.60661 0.008 0.052 0.776 0.100 0.060 0.004
#> GSM601947 4 0.583 0.44556 0.228 0.208 0.000 0.552 0.012 0.000
#> GSM601957 6 0.523 0.57580 0.100 0.092 0.048 0.032 0.000 0.728
#> GSM601972 4 0.720 0.51033 0.088 0.152 0.000 0.532 0.180 0.048
#> GSM601977 2 0.775 0.15499 0.000 0.352 0.220 0.172 0.248 0.008
#> GSM601987 2 0.546 0.17608 0.004 0.492 0.000 0.052 0.428 0.024
#> GSM601877 1 0.407 0.50501 0.780 0.004 0.000 0.032 0.036 0.148
#> GSM601907 2 0.399 0.56828 0.020 0.796 0.000 0.092 0.088 0.004
#> GSM601917 1 0.545 0.28224 0.644 0.012 0.036 0.244 0.064 0.000
#> GSM601922 1 0.448 0.38920 0.724 0.008 0.000 0.168 0.100 0.000
#> GSM601952 4 0.845 0.33341 0.176 0.116 0.168 0.436 0.076 0.028
#> GSM601962 3 0.422 0.67129 0.056 0.000 0.804 0.024 0.060 0.056
#> GSM601967 6 0.786 0.28577 0.268 0.092 0.120 0.060 0.012 0.448
#> GSM601982 5 0.876 -0.00635 0.196 0.292 0.112 0.052 0.308 0.040
#> GSM601992 5 0.256 0.44427 0.028 0.036 0.004 0.028 0.900 0.004
#> GSM601873 5 0.774 -0.07131 0.004 0.316 0.240 0.080 0.336 0.024
#> GSM601883 2 0.599 0.11159 0.008 0.444 0.004 0.104 0.428 0.012
#> GSM601888 2 0.413 0.54592 0.044 0.808 0.016 0.040 0.004 0.088
#> GSM601893 2 0.680 0.39811 0.032 0.580 0.116 0.044 0.020 0.208
#> GSM601898 6 0.398 0.57769 0.028 0.000 0.128 0.040 0.008 0.796
#> GSM601903 4 0.578 0.56316 0.156 0.012 0.008 0.620 0.196 0.008
#> GSM601913 6 0.487 0.56659 0.024 0.004 0.100 0.056 0.056 0.760
#> GSM601928 6 0.545 0.16232 0.420 0.000 0.000 0.084 0.012 0.484
#> GSM601933 5 0.625 0.21503 0.004 0.272 0.004 0.116 0.556 0.048
#> GSM601938 5 0.575 0.34351 0.020 0.076 0.016 0.176 0.676 0.036
#> GSM601943 3 0.615 0.45663 0.000 0.208 0.616 0.080 0.076 0.020
#> GSM601948 1 0.745 0.20414 0.428 0.060 0.040 0.280 0.000 0.192
#> GSM601958 6 0.370 0.61072 0.088 0.020 0.040 0.016 0.004 0.832
#> GSM601973 4 0.564 0.54148 0.100 0.008 0.012 0.628 0.240 0.012
#> GSM601978 2 0.330 0.56979 0.004 0.824 0.008 0.136 0.028 0.000
#> GSM601988 5 0.609 0.35260 0.000 0.008 0.212 0.084 0.608 0.088
#> GSM601878 1 0.364 0.45921 0.776 0.012 0.004 0.008 0.004 0.196
#> GSM601908 2 0.611 0.39545 0.024 0.548 0.000 0.184 0.240 0.004
#> GSM601918 1 0.689 -0.26807 0.416 0.148 0.000 0.344 0.092 0.000
#> GSM601923 1 0.278 0.51373 0.852 0.000 0.004 0.008 0.008 0.128
#> GSM601953 2 0.449 0.55528 0.060 0.776 0.036 0.108 0.020 0.000
#> GSM601963 3 0.605 0.60644 0.124 0.008 0.640 0.020 0.036 0.172
#> GSM601968 3 0.742 0.50516 0.192 0.096 0.528 0.052 0.008 0.124
#> GSM601983 3 0.581 0.65443 0.104 0.020 0.688 0.016 0.048 0.124
#> GSM601993 5 0.369 0.42355 0.028 0.000 0.048 0.076 0.832 0.016
#> GSM601874 2 0.353 0.57771 0.008 0.820 0.000 0.120 0.044 0.008
#> GSM601884 2 0.758 0.25364 0.016 0.412 0.280 0.064 0.212 0.016
#> GSM601889 6 0.369 0.58874 0.024 0.032 0.068 0.040 0.000 0.836
#> GSM601894 6 0.603 0.54990 0.120 0.052 0.124 0.020 0.016 0.668
#> GSM601899 2 0.565 0.48982 0.032 0.692 0.072 0.040 0.008 0.156
#> GSM601904 1 0.721 -0.13487 0.380 0.000 0.000 0.308 0.208 0.104
#> GSM601914 3 0.463 0.63282 0.016 0.012 0.712 0.012 0.020 0.228
#> GSM601929 1 0.566 -0.01762 0.504 0.004 0.000 0.088 0.016 0.388
#> GSM601934 5 0.749 -0.05794 0.004 0.356 0.016 0.104 0.372 0.148
#> GSM601939 6 0.458 0.51065 0.288 0.000 0.020 0.024 0.004 0.664
#> GSM601944 5 0.720 0.22411 0.024 0.072 0.008 0.296 0.476 0.124
#> GSM601949 6 0.695 0.11867 0.336 0.104 0.004 0.124 0.000 0.432
#> GSM601959 6 0.342 0.60169 0.056 0.028 0.020 0.044 0.000 0.852
#> GSM601974 3 0.646 0.41960 0.068 0.004 0.560 0.268 0.016 0.084
#> GSM601979 2 0.463 0.52471 0.004 0.700 0.000 0.180 0.116 0.000
#> GSM601989 6 0.482 0.55750 0.012 0.096 0.036 0.036 0.048 0.772
#> GSM601879 1 0.330 0.50732 0.820 0.008 0.004 0.024 0.000 0.144
#> GSM601909 3 0.637 0.62888 0.132 0.092 0.640 0.028 0.008 0.100
#> GSM601919 1 0.515 0.31328 0.664 0.084 0.000 0.220 0.032 0.000
#> GSM601924 1 0.408 0.37381 0.700 0.008 0.024 0.000 0.000 0.268
#> GSM601954 4 0.764 0.30147 0.236 0.212 0.024 0.444 0.016 0.068
#> GSM601964 3 0.362 0.67364 0.076 0.000 0.836 0.020 0.020 0.048
#> GSM601969 1 0.782 0.09658 0.392 0.116 0.028 0.148 0.008 0.308
#> GSM601984 5 0.725 -0.16104 0.300 0.004 0.016 0.036 0.336 0.308
#> GSM601994 5 0.314 0.43978 0.040 0.040 0.004 0.044 0.868 0.004
#> GSM601875 2 0.439 0.53365 0.008 0.760 0.000 0.052 0.152 0.028
#> GSM601885 2 0.634 0.16205 0.024 0.472 0.004 0.080 0.392 0.028
#> GSM601890 3 0.621 0.15405 0.024 0.400 0.472 0.036 0.000 0.068
#> GSM601895 3 0.536 0.62904 0.012 0.040 0.684 0.024 0.028 0.212
#> GSM601900 6 0.576 0.51999 0.016 0.036 0.104 0.068 0.068 0.708
#> GSM601905 5 0.717 -0.07733 0.244 0.008 0.000 0.220 0.444 0.084
#> GSM601915 6 0.414 0.57649 0.028 0.000 0.128 0.044 0.012 0.788
#> GSM601930 6 0.521 0.31957 0.380 0.000 0.000 0.060 0.016 0.544
#> GSM601935 3 0.694 0.46234 0.008 0.004 0.532 0.112 0.200 0.144
#> GSM601940 6 0.542 0.48386 0.292 0.024 0.008 0.032 0.020 0.624
#> GSM601945 2 0.580 0.45620 0.004 0.604 0.000 0.176 0.192 0.024
#> GSM601950 6 0.535 0.45725 0.276 0.060 0.000 0.044 0.000 0.620
#> GSM601960 3 0.488 0.64434 0.016 0.004 0.708 0.044 0.020 0.208
#> GSM601975 4 0.658 0.57708 0.120 0.056 0.004 0.584 0.208 0.028
#> GSM601980 3 0.454 0.59985 0.024 0.004 0.756 0.148 0.060 0.008
#> GSM601990 3 0.485 0.67486 0.012 0.012 0.736 0.028 0.044 0.168
#> GSM601880 1 0.399 0.48106 0.764 0.000 0.000 0.016 0.044 0.176
#> GSM601910 3 0.818 0.30093 0.184 0.084 0.416 0.028 0.044 0.244
#> GSM601920 1 0.480 0.48849 0.752 0.012 0.004 0.108 0.092 0.032
#> GSM601925 1 0.308 0.51305 0.844 0.000 0.004 0.016 0.016 0.120
#> GSM601955 3 0.325 0.64192 0.028 0.000 0.848 0.096 0.020 0.008
#> GSM601965 6 0.767 0.05444 0.304 0.012 0.016 0.060 0.300 0.308
#> GSM601970 3 0.706 0.34180 0.208 0.008 0.472 0.056 0.008 0.248
#> GSM601985 6 0.590 0.33893 0.348 0.000 0.040 0.036 0.032 0.544
#> GSM601995 3 0.489 0.57819 0.028 0.000 0.724 0.076 0.160 0.012
#> GSM601876 6 0.422 0.58706 0.148 0.008 0.000 0.024 0.048 0.772
#> GSM601886 5 0.737 0.15019 0.040 0.000 0.048 0.236 0.436 0.240
#> GSM601891 2 0.616 0.30573 0.004 0.568 0.252 0.036 0.004 0.136
#> GSM601896 6 0.515 0.56198 0.164 0.032 0.004 0.036 0.044 0.720
#> GSM601901 2 0.794 -0.13108 0.052 0.320 0.000 0.300 0.252 0.076
#> GSM601906 6 0.696 0.16710 0.328 0.000 0.004 0.148 0.088 0.432
#> GSM601916 4 0.742 0.16096 0.044 0.024 0.008 0.404 0.232 0.288
#> GSM601931 6 0.499 0.31269 0.380 0.000 0.000 0.056 0.008 0.556
#> GSM601936 5 0.594 0.34779 0.008 0.008 0.024 0.160 0.620 0.180
#> GSM601941 4 0.594 0.51829 0.060 0.036 0.020 0.604 0.272 0.008
#> GSM601946 6 0.437 0.54595 0.248 0.004 0.000 0.024 0.020 0.704
#> GSM601951 6 0.613 0.19759 0.208 0.004 0.004 0.284 0.004 0.496
#> GSM601961 2 0.460 0.54544 0.016 0.752 0.000 0.144 0.024 0.064
#> GSM601976 5 0.807 -0.18691 0.124 0.032 0.004 0.276 0.344 0.220
#> GSM601981 2 0.632 0.41583 0.004 0.548 0.000 0.256 0.136 0.056
#> GSM601991 3 0.548 0.62104 0.000 0.016 0.664 0.020 0.128 0.172
#> GSM601881 1 0.392 0.40658 0.728 0.008 0.000 0.004 0.016 0.244
#> GSM601911 5 0.751 0.32472 0.176 0.100 0.000 0.048 0.488 0.188
#> GSM601921 1 0.587 0.16703 0.584 0.032 0.000 0.224 0.160 0.000
#> GSM601926 1 0.419 0.39878 0.720 0.000 0.008 0.016 0.016 0.240
#> GSM601956 2 0.603 0.48370 0.012 0.616 0.196 0.128 0.048 0.000
#> GSM601966 5 0.495 0.27743 0.024 0.068 0.000 0.204 0.696 0.008
#> GSM601971 1 0.751 0.07276 0.384 0.000 0.148 0.144 0.012 0.312
#> GSM601986 1 0.726 -0.01375 0.400 0.032 0.000 0.036 0.260 0.272
#> GSM601996 5 0.326 0.42628 0.068 0.024 0.004 0.052 0.852 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
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)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
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)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, 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.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")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n time(p) gender(p) k
#> SD:NMF 122 0.187 0.982701 2
#> SD:NMF 88 0.297 0.135001 3
#> SD:NMF 72 0.491 0.594910 4
#> SD:NMF 38 0.671 0.006390 5
#> SD:NMF 50 0.768 0.000246 6
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.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["CV", "hclust"]
# you can also extract it by
# res = res_list["CV:hclust"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 21512 rows and 125 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#> Subgroups are detected by 'hclust' method.
#> Performed in total 1250 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_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
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, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.908 0.917 0.946 0.0977 0.953 0.953
#> 3 3 0.297 0.739 0.839 1.6340 0.984 0.984
#> 4 4 0.185 0.601 0.771 0.7043 0.640 0.616
#> 5 5 0.149 0.566 0.752 0.1915 0.930 0.880
#> 6 6 0.175 0.545 0.736 0.1012 0.904 0.821
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 4
Following shows 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 class
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
#> GSM601872 1 0.4562 0.906 0.904 0.096
#> GSM601882 1 0.3733 0.923 0.928 0.072
#> GSM601887 1 0.1633 0.948 0.976 0.024
#> GSM601892 1 0.1633 0.948 0.976 0.024
#> GSM601897 1 0.3584 0.933 0.932 0.068
#> GSM601902 1 0.2778 0.939 0.952 0.048
#> GSM601912 1 0.1633 0.948 0.976 0.024
#> GSM601927 1 0.0376 0.947 0.996 0.004
#> GSM601932 1 0.2236 0.943 0.964 0.036
#> GSM601937 1 0.6973 0.754 0.812 0.188
#> GSM601942 2 0.9909 0.623 0.444 0.556
#> GSM601947 1 0.2948 0.939 0.948 0.052
#> GSM601957 1 0.0938 0.945 0.988 0.012
#> GSM601972 1 0.2423 0.943 0.960 0.040
#> GSM601977 1 0.4298 0.917 0.912 0.088
#> GSM601987 1 0.3879 0.922 0.924 0.076
#> GSM601877 1 0.0672 0.946 0.992 0.008
#> GSM601907 1 0.4431 0.909 0.908 0.092
#> GSM601917 1 0.1633 0.945 0.976 0.024
#> GSM601922 1 0.2043 0.946 0.968 0.032
#> GSM601952 1 0.3431 0.933 0.936 0.064
#> GSM601962 1 0.1184 0.944 0.984 0.016
#> GSM601967 1 0.1414 0.946 0.980 0.020
#> GSM601982 1 0.2948 0.943 0.948 0.052
#> GSM601992 1 0.3879 0.923 0.924 0.076
#> GSM601873 1 0.6712 0.788 0.824 0.176
#> GSM601883 1 0.3733 0.923 0.928 0.072
#> GSM601888 1 0.1633 0.948 0.976 0.024
#> GSM601893 1 0.1843 0.948 0.972 0.028
#> GSM601898 1 0.1184 0.944 0.984 0.016
#> GSM601903 1 0.2603 0.940 0.956 0.044
#> GSM601913 1 0.1184 0.945 0.984 0.016
#> GSM601928 1 0.0672 0.946 0.992 0.008
#> GSM601933 1 0.3733 0.926 0.928 0.072
#> GSM601938 1 0.3733 0.923 0.928 0.072
#> GSM601943 1 0.5842 0.851 0.860 0.140
#> GSM601948 1 0.1184 0.948 0.984 0.016
#> GSM601958 1 0.1184 0.944 0.984 0.016
#> GSM601973 1 0.2948 0.938 0.948 0.052
#> GSM601978 1 0.4690 0.902 0.900 0.100
#> GSM601988 1 0.3879 0.922 0.924 0.076
#> GSM601878 1 0.0672 0.946 0.992 0.008
#> GSM601908 1 0.3879 0.921 0.924 0.076
#> GSM601918 1 0.2603 0.939 0.956 0.044
#> GSM601923 1 0.0672 0.946 0.992 0.008
#> GSM601953 1 0.4298 0.910 0.912 0.088
#> GSM601963 1 0.1184 0.944 0.984 0.016
#> GSM601968 1 0.0938 0.945 0.988 0.012
#> GSM601983 1 0.1843 0.948 0.972 0.028
#> GSM601993 1 0.4431 0.900 0.908 0.092
#> GSM601874 1 0.3584 0.926 0.932 0.068
#> GSM601884 1 0.3733 0.923 0.928 0.072
#> GSM601889 1 0.1633 0.944 0.976 0.024
#> GSM601894 1 0.1414 0.947 0.980 0.020
#> GSM601899 1 0.2236 0.947 0.964 0.036
#> GSM601904 1 0.1843 0.947 0.972 0.028
#> GSM601914 1 0.1184 0.944 0.984 0.016
#> GSM601929 1 0.0672 0.948 0.992 0.008
#> GSM601934 1 0.3879 0.927 0.924 0.076
#> GSM601939 1 0.0938 0.945 0.988 0.012
#> GSM601944 1 0.9608 0.072 0.616 0.384
#> GSM601949 1 0.1414 0.948 0.980 0.020
#> GSM601959 1 0.1414 0.948 0.980 0.020
#> GSM601974 1 0.1843 0.947 0.972 0.028
#> GSM601979 1 0.4562 0.906 0.904 0.096
#> GSM601989 1 0.1633 0.945 0.976 0.024
#> GSM601879 1 0.0672 0.946 0.992 0.008
#> GSM601909 1 0.1633 0.947 0.976 0.024
#> GSM601919 1 0.2603 0.939 0.956 0.044
#> GSM601924 1 0.0672 0.946 0.992 0.008
#> GSM601954 1 0.2423 0.944 0.960 0.040
#> GSM601964 1 0.1184 0.944 0.984 0.016
#> GSM601969 1 0.0938 0.947 0.988 0.012
#> GSM601984 1 0.2236 0.948 0.964 0.036
#> GSM601994 1 0.4022 0.916 0.920 0.080
#> GSM601875 1 0.3879 0.926 0.924 0.076
#> GSM601885 1 0.4022 0.923 0.920 0.080
#> GSM601890 1 0.1843 0.947 0.972 0.028
#> GSM601895 1 0.2603 0.931 0.956 0.044
#> GSM601900 1 0.1633 0.943 0.976 0.024
#> GSM601905 1 0.1843 0.945 0.972 0.028
#> GSM601915 1 0.1414 0.944 0.980 0.020
#> GSM601930 1 0.0938 0.947 0.988 0.012
#> GSM601935 1 0.2948 0.939 0.948 0.052
#> GSM601940 1 0.0938 0.945 0.988 0.012
#> GSM601945 1 0.5059 0.887 0.888 0.112
#> GSM601950 1 0.0938 0.947 0.988 0.012
#> GSM601960 1 0.1414 0.943 0.980 0.020
#> GSM601975 1 0.2423 0.942 0.960 0.040
#> GSM601980 2 0.7815 0.810 0.232 0.768
#> GSM601990 1 0.1184 0.944 0.984 0.016
#> GSM601880 1 0.0376 0.946 0.996 0.004
#> GSM601910 1 0.1633 0.945 0.976 0.024
#> GSM601920 1 0.1633 0.946 0.976 0.024
#> GSM601925 1 0.0672 0.946 0.992 0.008
#> GSM601955 2 0.8386 0.832 0.268 0.732
#> GSM601965 1 0.2043 0.948 0.968 0.032
#> GSM601970 1 0.1184 0.946 0.984 0.016
#> GSM601985 1 0.0938 0.945 0.988 0.012
#> GSM601995 1 0.9170 0.192 0.668 0.332
#> GSM601876 1 0.1414 0.945 0.980 0.020
#> GSM601886 1 0.3879 0.899 0.924 0.076
#> GSM601891 1 0.1633 0.948 0.976 0.024
#> GSM601896 1 0.1184 0.944 0.984 0.016
#> GSM601901 1 0.3274 0.931 0.940 0.060
#> GSM601906 1 0.1843 0.946 0.972 0.028
#> GSM601916 1 0.2778 0.940 0.952 0.048
#> GSM601931 1 0.0376 0.946 0.996 0.004
#> GSM601936 1 0.3274 0.930 0.940 0.060
#> GSM601941 1 0.2948 0.938 0.948 0.052
#> GSM601946 1 0.1184 0.944 0.984 0.016
#> GSM601951 1 0.1633 0.948 0.976 0.024
#> GSM601961 1 0.3733 0.934 0.928 0.072
#> GSM601976 1 0.2603 0.943 0.956 0.044
#> GSM601981 1 0.3733 0.924 0.928 0.072
#> GSM601991 1 0.0938 0.945 0.988 0.012
#> GSM601881 1 0.0672 0.946 0.992 0.008
#> GSM601911 1 0.2423 0.945 0.960 0.040
#> GSM601921 1 0.1633 0.946 0.976 0.024
#> GSM601926 1 0.0672 0.946 0.992 0.008
#> GSM601956 1 0.4562 0.905 0.904 0.096
#> GSM601966 1 0.2948 0.937 0.948 0.052
#> GSM601971 1 0.1414 0.945 0.980 0.020
#> GSM601986 1 0.2236 0.947 0.964 0.036
#> GSM601996 1 0.3584 0.928 0.932 0.068
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM601872 1 0.6717 0.607 0.628 0.020 0.352
#> GSM601882 1 0.6229 0.641 0.652 0.008 0.340
#> GSM601887 1 0.2866 0.830 0.916 0.008 0.076
#> GSM601892 1 0.2173 0.831 0.944 0.008 0.048
#> GSM601897 1 0.4172 0.817 0.868 0.028 0.104
#> GSM601902 1 0.5497 0.711 0.708 0.000 0.292
#> GSM601912 1 0.1989 0.836 0.948 0.004 0.048
#> GSM601927 1 0.0892 0.831 0.980 0.000 0.020
#> GSM601932 1 0.5016 0.756 0.760 0.000 0.240
#> GSM601937 1 0.8038 0.341 0.620 0.100 0.280
#> GSM601942 2 0.8167 -0.151 0.212 0.640 0.148
#> GSM601947 1 0.4887 0.764 0.772 0.000 0.228
#> GSM601957 1 0.1453 0.829 0.968 0.008 0.024
#> GSM601972 1 0.4931 0.765 0.768 0.000 0.232
#> GSM601977 1 0.6543 0.620 0.640 0.016 0.344
#> GSM601987 1 0.6381 0.643 0.648 0.012 0.340
#> GSM601877 1 0.0829 0.829 0.984 0.004 0.012
#> GSM601907 1 0.6359 0.606 0.628 0.008 0.364
#> GSM601917 1 0.3116 0.822 0.892 0.000 0.108
#> GSM601922 1 0.3619 0.816 0.864 0.000 0.136
#> GSM601952 1 0.5506 0.760 0.764 0.016 0.220
#> GSM601962 1 0.1015 0.826 0.980 0.008 0.012
#> GSM601967 1 0.1620 0.829 0.964 0.012 0.024
#> GSM601982 1 0.4351 0.798 0.828 0.004 0.168
#> GSM601992 1 0.6229 0.639 0.652 0.008 0.340
#> GSM601873 1 0.8361 0.374 0.544 0.092 0.364
#> GSM601883 1 0.6229 0.641 0.652 0.008 0.340
#> GSM601888 1 0.2866 0.830 0.916 0.008 0.076
#> GSM601893 1 0.2939 0.833 0.916 0.012 0.072
#> GSM601898 1 0.0848 0.824 0.984 0.008 0.008
#> GSM601903 1 0.5591 0.699 0.696 0.000 0.304
#> GSM601913 1 0.1337 0.825 0.972 0.012 0.016
#> GSM601928 1 0.0747 0.830 0.984 0.000 0.016
#> GSM601933 1 0.6102 0.667 0.672 0.008 0.320
#> GSM601938 1 0.6229 0.641 0.652 0.008 0.340
#> GSM601943 1 0.7519 0.450 0.568 0.044 0.388
#> GSM601948 1 0.3272 0.823 0.892 0.004 0.104
#> GSM601958 1 0.1170 0.826 0.976 0.008 0.016
#> GSM601973 1 0.5650 0.692 0.688 0.000 0.312
#> GSM601978 1 0.6667 0.589 0.616 0.016 0.368
#> GSM601988 1 0.5559 0.757 0.780 0.028 0.192
#> GSM601878 1 0.0829 0.829 0.984 0.004 0.012
#> GSM601908 1 0.5988 0.620 0.632 0.000 0.368
#> GSM601918 1 0.4504 0.785 0.804 0.000 0.196
#> GSM601923 1 0.0829 0.829 0.984 0.004 0.012
#> GSM601953 1 0.6275 0.624 0.644 0.008 0.348
#> GSM601963 1 0.0848 0.824 0.984 0.008 0.008
#> GSM601968 1 0.1877 0.831 0.956 0.012 0.032
#> GSM601983 1 0.2096 0.834 0.944 0.004 0.052
#> GSM601993 1 0.6798 0.496 0.584 0.016 0.400
#> GSM601874 1 0.6104 0.634 0.648 0.004 0.348
#> GSM601884 1 0.6229 0.641 0.652 0.008 0.340
#> GSM601889 1 0.1337 0.825 0.972 0.016 0.012
#> GSM601894 1 0.1774 0.828 0.960 0.016 0.024
#> GSM601899 1 0.3043 0.829 0.908 0.008 0.084
#> GSM601904 1 0.3425 0.822 0.884 0.004 0.112
#> GSM601914 1 0.0848 0.824 0.984 0.008 0.008
#> GSM601929 1 0.1289 0.833 0.968 0.000 0.032
#> GSM601934 1 0.6200 0.672 0.676 0.012 0.312
#> GSM601939 1 0.0237 0.828 0.996 0.004 0.000
#> GSM601944 3 0.8162 0.000 0.164 0.192 0.644
#> GSM601949 1 0.2772 0.828 0.916 0.004 0.080
#> GSM601959 1 0.1129 0.830 0.976 0.004 0.020
#> GSM601974 1 0.2486 0.834 0.932 0.008 0.060
#> GSM601979 1 0.6416 0.590 0.616 0.008 0.376
#> GSM601989 1 0.1585 0.833 0.964 0.008 0.028
#> GSM601879 1 0.0829 0.829 0.984 0.004 0.012
#> GSM601909 1 0.3091 0.828 0.912 0.016 0.072
#> GSM601919 1 0.4504 0.785 0.804 0.000 0.196
#> GSM601924 1 0.0592 0.829 0.988 0.000 0.012
#> GSM601954 1 0.4399 0.795 0.812 0.000 0.188
#> GSM601964 1 0.0848 0.824 0.984 0.008 0.008
#> GSM601969 1 0.1399 0.831 0.968 0.004 0.028
#> GSM601984 1 0.2584 0.834 0.928 0.008 0.064
#> GSM601994 1 0.6632 0.532 0.596 0.012 0.392
#> GSM601875 1 0.5956 0.664 0.672 0.004 0.324
#> GSM601885 1 0.6252 0.635 0.648 0.008 0.344
#> GSM601890 1 0.2772 0.829 0.916 0.004 0.080
#> GSM601895 1 0.2443 0.816 0.940 0.032 0.028
#> GSM601900 1 0.1919 0.826 0.956 0.020 0.024
#> GSM601905 1 0.3619 0.814 0.864 0.000 0.136
#> GSM601915 1 0.1015 0.825 0.980 0.012 0.008
#> GSM601930 1 0.0983 0.832 0.980 0.004 0.016
#> GSM601935 1 0.3670 0.804 0.888 0.020 0.092
#> GSM601940 1 0.0475 0.830 0.992 0.004 0.004
#> GSM601945 1 0.6735 0.474 0.564 0.012 0.424
#> GSM601950 1 0.1711 0.831 0.960 0.008 0.032
#> GSM601960 1 0.1015 0.824 0.980 0.008 0.012
#> GSM601975 1 0.5016 0.757 0.760 0.000 0.240
#> GSM601980 2 0.3921 0.535 0.080 0.884 0.036
#> GSM601990 1 0.0848 0.824 0.984 0.008 0.008
#> GSM601880 1 0.1399 0.832 0.968 0.004 0.028
#> GSM601910 1 0.2492 0.829 0.936 0.016 0.048
#> GSM601920 1 0.3192 0.821 0.888 0.000 0.112
#> GSM601925 1 0.0829 0.829 0.984 0.004 0.012
#> GSM601955 2 0.5722 0.544 0.084 0.804 0.112
#> GSM601965 1 0.2584 0.834 0.928 0.008 0.064
#> GSM601970 1 0.1620 0.829 0.964 0.012 0.024
#> GSM601985 1 0.0475 0.827 0.992 0.004 0.004
#> GSM601995 1 0.8479 0.158 0.580 0.300 0.120
#> GSM601876 1 0.0829 0.832 0.984 0.004 0.012
#> GSM601886 1 0.3649 0.782 0.896 0.036 0.068
#> GSM601891 1 0.2860 0.829 0.912 0.004 0.084
#> GSM601896 1 0.0829 0.829 0.984 0.004 0.012
#> GSM601901 1 0.5706 0.674 0.680 0.000 0.320
#> GSM601906 1 0.3267 0.821 0.884 0.000 0.116
#> GSM601916 1 0.4978 0.772 0.780 0.004 0.216
#> GSM601931 1 0.0892 0.831 0.980 0.000 0.020
#> GSM601936 1 0.4799 0.780 0.836 0.032 0.132
#> GSM601941 1 0.6018 0.685 0.684 0.008 0.308
#> GSM601946 1 0.0661 0.829 0.988 0.004 0.008
#> GSM601951 1 0.3272 0.822 0.892 0.004 0.104
#> GSM601961 1 0.5517 0.723 0.728 0.004 0.268
#> GSM601976 1 0.4883 0.779 0.788 0.004 0.208
#> GSM601981 1 0.5650 0.691 0.688 0.000 0.312
#> GSM601991 1 0.1015 0.825 0.980 0.008 0.012
#> GSM601881 1 0.0829 0.829 0.984 0.004 0.012
#> GSM601911 1 0.3695 0.825 0.880 0.012 0.108
#> GSM601921 1 0.3267 0.820 0.884 0.000 0.116
#> GSM601926 1 0.0829 0.829 0.984 0.004 0.012
#> GSM601956 1 0.6490 0.606 0.628 0.012 0.360
#> GSM601966 1 0.5760 0.677 0.672 0.000 0.328
#> GSM601971 1 0.1337 0.824 0.972 0.012 0.016
#> GSM601986 1 0.3043 0.830 0.908 0.008 0.084
#> GSM601996 1 0.6008 0.657 0.664 0.004 0.332
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM601872 2 0.5279 0.7080 0.252 0.712 0.012 0.024
#> GSM601882 2 0.5135 0.7526 0.296 0.684 0.012 0.008
#> GSM601887 1 0.3402 0.7092 0.832 0.164 0.000 0.004
#> GSM601892 1 0.2530 0.7628 0.896 0.100 0.000 0.004
#> GSM601897 1 0.5429 0.5541 0.728 0.220 0.020 0.032
#> GSM601902 1 0.7500 -0.3716 0.416 0.404 0.180 0.000
#> GSM601912 1 0.2271 0.7742 0.916 0.076 0.008 0.000
#> GSM601927 1 0.1978 0.7749 0.928 0.068 0.004 0.000
#> GSM601932 1 0.7060 -0.2150 0.476 0.400 0.124 0.000
#> GSM601937 1 0.9440 -0.1986 0.424 0.220 0.200 0.156
#> GSM601942 4 0.7519 0.1868 0.088 0.192 0.092 0.628
#> GSM601947 1 0.6067 0.0575 0.572 0.376 0.052 0.000
#> GSM601957 1 0.1004 0.7783 0.972 0.024 0.000 0.004
#> GSM601972 1 0.6737 -0.0539 0.532 0.368 0.100 0.000
#> GSM601977 2 0.5243 0.7397 0.276 0.696 0.012 0.016
#> GSM601987 2 0.5339 0.7518 0.312 0.664 0.008 0.016
#> GSM601877 1 0.1847 0.7759 0.940 0.052 0.004 0.004
#> GSM601907 2 0.4978 0.7194 0.256 0.720 0.016 0.008
#> GSM601917 1 0.4839 0.6453 0.756 0.200 0.044 0.000
#> GSM601922 1 0.5374 0.5558 0.704 0.244 0.052 0.000
#> GSM601952 1 0.6781 -0.1577 0.524 0.404 0.048 0.024
#> GSM601962 1 0.1339 0.7783 0.964 0.024 0.008 0.004
#> GSM601967 1 0.1305 0.7783 0.960 0.036 0.000 0.004
#> GSM601982 1 0.4836 0.3762 0.672 0.320 0.008 0.000
#> GSM601992 2 0.7932 0.4839 0.336 0.412 0.248 0.004
#> GSM601873 2 0.7506 0.3536 0.212 0.620 0.092 0.076
#> GSM601883 2 0.5135 0.7526 0.296 0.684 0.012 0.008
#> GSM601888 1 0.3402 0.7092 0.832 0.164 0.000 0.004
#> GSM601893 1 0.3625 0.7141 0.828 0.160 0.000 0.012
#> GSM601898 1 0.0469 0.7741 0.988 0.012 0.000 0.000
#> GSM601903 1 0.7521 -0.3616 0.420 0.396 0.184 0.000
#> GSM601913 1 0.1124 0.7760 0.972 0.012 0.012 0.004
#> GSM601928 1 0.1824 0.7757 0.936 0.060 0.004 0.000
#> GSM601933 2 0.5632 0.7051 0.352 0.620 0.020 0.008
#> GSM601938 2 0.5223 0.7502 0.292 0.684 0.016 0.008
#> GSM601943 2 0.7100 0.4722 0.224 0.640 0.080 0.056
#> GSM601948 1 0.5060 0.6131 0.756 0.188 0.052 0.004
#> GSM601958 1 0.0707 0.7763 0.980 0.020 0.000 0.000
#> GSM601973 2 0.7550 0.4213 0.372 0.436 0.192 0.000
#> GSM601978 2 0.5020 0.6920 0.232 0.736 0.020 0.012
#> GSM601988 1 0.7180 0.3125 0.620 0.232 0.116 0.032
#> GSM601878 1 0.1847 0.7759 0.940 0.052 0.004 0.004
#> GSM601908 2 0.4725 0.7302 0.256 0.728 0.012 0.004
#> GSM601918 1 0.5762 0.2529 0.608 0.352 0.040 0.000
#> GSM601923 1 0.1762 0.7766 0.944 0.048 0.004 0.004
#> GSM601953 2 0.5065 0.7254 0.268 0.708 0.016 0.008
#> GSM601963 1 0.0336 0.7738 0.992 0.000 0.008 0.000
#> GSM601968 1 0.1822 0.7788 0.944 0.044 0.008 0.004
#> GSM601983 1 0.2266 0.7732 0.912 0.084 0.004 0.000
#> GSM601993 2 0.8450 0.3736 0.304 0.364 0.312 0.020
#> GSM601874 2 0.4762 0.7495 0.300 0.692 0.004 0.004
#> GSM601884 2 0.5110 0.7515 0.292 0.688 0.012 0.008
#> GSM601889 1 0.1191 0.7764 0.968 0.024 0.004 0.004
#> GSM601894 1 0.1356 0.7795 0.960 0.032 0.000 0.008
#> GSM601899 1 0.3636 0.6968 0.820 0.172 0.000 0.008
#> GSM601904 1 0.4897 0.6454 0.760 0.196 0.040 0.004
#> GSM601914 1 0.0804 0.7738 0.980 0.012 0.008 0.000
#> GSM601929 1 0.2342 0.7745 0.912 0.080 0.008 0.000
#> GSM601934 2 0.5893 0.6893 0.364 0.600 0.024 0.012
#> GSM601939 1 0.1305 0.7777 0.960 0.036 0.000 0.004
#> GSM601944 3 0.6108 0.0000 0.012 0.316 0.628 0.044
#> GSM601949 1 0.4470 0.6686 0.792 0.172 0.032 0.004
#> GSM601959 1 0.1022 0.7800 0.968 0.032 0.000 0.000
#> GSM601974 1 0.4233 0.7190 0.820 0.140 0.032 0.008
#> GSM601979 2 0.4788 0.6934 0.232 0.744 0.016 0.008
#> GSM601989 1 0.1890 0.7823 0.936 0.056 0.008 0.000
#> GSM601879 1 0.1762 0.7766 0.944 0.048 0.004 0.004
#> GSM601909 1 0.3105 0.7405 0.868 0.120 0.000 0.012
#> GSM601919 1 0.5762 0.2529 0.608 0.352 0.040 0.000
#> GSM601924 1 0.1576 0.7762 0.948 0.048 0.004 0.000
#> GSM601954 1 0.5947 0.1249 0.572 0.384 0.044 0.000
#> GSM601964 1 0.0927 0.7737 0.976 0.016 0.008 0.000
#> GSM601969 1 0.1722 0.7753 0.944 0.048 0.008 0.000
#> GSM601984 1 0.3172 0.7462 0.872 0.112 0.012 0.004
#> GSM601994 2 0.8088 0.3836 0.276 0.412 0.304 0.008
#> GSM601875 2 0.4720 0.7403 0.324 0.672 0.004 0.000
#> GSM601885 2 0.5060 0.7537 0.284 0.696 0.012 0.008
#> GSM601890 1 0.3219 0.7042 0.836 0.164 0.000 0.000
#> GSM601895 1 0.2376 0.7654 0.928 0.040 0.012 0.020
#> GSM601900 1 0.2262 0.7721 0.932 0.040 0.016 0.012
#> GSM601905 1 0.5052 0.5883 0.720 0.244 0.036 0.000
#> GSM601915 1 0.0657 0.7747 0.984 0.004 0.012 0.000
#> GSM601930 1 0.2048 0.7763 0.928 0.064 0.008 0.000
#> GSM601935 1 0.4843 0.6645 0.808 0.096 0.076 0.020
#> GSM601940 1 0.1398 0.7790 0.956 0.040 0.000 0.004
#> GSM601945 2 0.5474 0.4091 0.160 0.756 0.064 0.020
#> GSM601950 1 0.2520 0.7699 0.904 0.088 0.004 0.004
#> GSM601960 1 0.0804 0.7736 0.980 0.008 0.012 0.000
#> GSM601975 1 0.6911 -0.0844 0.504 0.384 0.112 0.000
#> GSM601980 4 0.4466 0.5640 0.040 0.004 0.156 0.800
#> GSM601990 1 0.0524 0.7735 0.988 0.004 0.008 0.000
#> GSM601880 1 0.2238 0.7762 0.920 0.072 0.004 0.004
#> GSM601910 1 0.2530 0.7676 0.912 0.072 0.008 0.008
#> GSM601920 1 0.4842 0.6472 0.760 0.192 0.048 0.000
#> GSM601925 1 0.1847 0.7759 0.940 0.052 0.004 0.004
#> GSM601955 4 0.4146 0.5500 0.024 0.084 0.044 0.848
#> GSM601965 1 0.3113 0.7479 0.876 0.108 0.012 0.004
#> GSM601970 1 0.1191 0.7783 0.968 0.024 0.004 0.004
#> GSM601985 1 0.0895 0.7775 0.976 0.020 0.000 0.004
#> GSM601995 1 0.9031 -0.1249 0.456 0.136 0.132 0.276
#> GSM601876 1 0.1722 0.7828 0.944 0.048 0.008 0.000
#> GSM601886 1 0.4371 0.6617 0.836 0.064 0.080 0.020
#> GSM601891 1 0.3266 0.7008 0.832 0.168 0.000 0.000
#> GSM601896 1 0.1489 0.7830 0.952 0.044 0.004 0.000
#> GSM601901 2 0.5237 0.7071 0.356 0.628 0.016 0.000
#> GSM601906 1 0.4755 0.6494 0.760 0.200 0.040 0.000
#> GSM601916 1 0.6423 0.1049 0.580 0.336 0.084 0.000
#> GSM601931 1 0.1902 0.7753 0.932 0.064 0.004 0.000
#> GSM601936 1 0.6229 0.4750 0.704 0.172 0.104 0.020
#> GSM601941 2 0.7906 0.4181 0.376 0.412 0.204 0.008
#> GSM601946 1 0.1302 0.7797 0.956 0.044 0.000 0.000
#> GSM601951 1 0.4996 0.5900 0.752 0.192 0.056 0.000
#> GSM601961 2 0.5673 0.5023 0.448 0.528 0.024 0.000
#> GSM601976 1 0.6532 0.1367 0.572 0.336 0.092 0.000
#> GSM601981 2 0.5400 0.6694 0.372 0.608 0.020 0.000
#> GSM601991 1 0.0804 0.7755 0.980 0.012 0.008 0.000
#> GSM601881 1 0.1847 0.7759 0.940 0.052 0.004 0.004
#> GSM601911 1 0.4333 0.6573 0.776 0.208 0.008 0.008
#> GSM601921 1 0.4800 0.6446 0.760 0.196 0.044 0.000
#> GSM601926 1 0.1847 0.7759 0.940 0.052 0.004 0.004
#> GSM601956 2 0.5155 0.7094 0.248 0.720 0.016 0.016
#> GSM601966 2 0.7031 0.6116 0.348 0.520 0.132 0.000
#> GSM601971 1 0.1296 0.7786 0.964 0.028 0.004 0.004
#> GSM601986 1 0.3774 0.7038 0.820 0.168 0.008 0.004
#> GSM601996 2 0.7731 0.4909 0.332 0.428 0.240 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM601872 2 0.4100 0.6762 0.004 0.796 0.156 0.028 0.016
#> GSM601882 2 0.4133 0.6921 0.000 0.768 0.180 0.052 0.000
#> GSM601887 3 0.3231 0.7012 0.000 0.196 0.800 0.004 0.000
#> GSM601892 3 0.2561 0.7495 0.000 0.144 0.856 0.000 0.000
#> GSM601897 3 0.5408 0.5635 0.004 0.228 0.688 0.052 0.028
#> GSM601902 4 0.6657 0.4512 0.000 0.352 0.232 0.416 0.000
#> GSM601912 3 0.2305 0.7727 0.000 0.092 0.896 0.012 0.000
#> GSM601927 3 0.2770 0.7612 0.000 0.076 0.880 0.044 0.000
#> GSM601932 2 0.6805 -0.2970 0.000 0.372 0.320 0.308 0.000
#> GSM601937 4 0.9008 -0.0856 0.204 0.144 0.220 0.384 0.048
#> GSM601942 5 0.5185 0.1394 0.016 0.212 0.028 0.028 0.716
#> GSM601947 3 0.6003 -0.1152 0.000 0.440 0.448 0.112 0.000
#> GSM601957 3 0.0963 0.7706 0.000 0.036 0.964 0.000 0.000
#> GSM601972 3 0.6588 -0.2474 0.000 0.392 0.400 0.208 0.000
#> GSM601977 2 0.4260 0.6854 0.000 0.776 0.164 0.052 0.008
#> GSM601987 2 0.4522 0.6714 0.000 0.736 0.196 0.068 0.000
#> GSM601877 3 0.2376 0.7636 0.000 0.052 0.904 0.044 0.000
#> GSM601907 2 0.4297 0.6847 0.004 0.784 0.152 0.052 0.008
#> GSM601917 3 0.5478 0.5457 0.000 0.180 0.656 0.164 0.000
#> GSM601922 3 0.5980 0.4550 0.004 0.220 0.604 0.172 0.000
#> GSM601952 3 0.6750 -0.1790 0.032 0.408 0.444 0.116 0.000
#> GSM601962 3 0.1106 0.7707 0.000 0.024 0.964 0.012 0.000
#> GSM601967 3 0.1168 0.7705 0.000 0.032 0.960 0.008 0.000
#> GSM601982 3 0.4987 0.3998 0.000 0.340 0.616 0.044 0.000
#> GSM601992 4 0.6954 0.5487 0.024 0.312 0.164 0.496 0.004
#> GSM601873 2 0.7111 0.1843 0.048 0.636 0.104 0.112 0.100
#> GSM601883 2 0.4096 0.6923 0.000 0.772 0.176 0.052 0.000
#> GSM601888 3 0.3231 0.7012 0.000 0.196 0.800 0.004 0.000
#> GSM601893 3 0.3422 0.7015 0.000 0.200 0.792 0.004 0.004
#> GSM601898 3 0.0794 0.7670 0.000 0.028 0.972 0.000 0.000
#> GSM601903 4 0.6667 0.4591 0.000 0.364 0.232 0.404 0.000
#> GSM601913 3 0.1018 0.7673 0.000 0.016 0.968 0.016 0.000
#> GSM601928 3 0.2645 0.7617 0.000 0.068 0.888 0.044 0.000
#> GSM601933 2 0.5066 0.5797 0.000 0.676 0.240 0.084 0.000
#> GSM601938 2 0.4372 0.6837 0.000 0.756 0.172 0.072 0.000
#> GSM601943 2 0.6570 0.3366 0.060 0.668 0.120 0.120 0.032
#> GSM601948 3 0.5010 0.5799 0.000 0.224 0.688 0.088 0.000
#> GSM601958 3 0.0880 0.7694 0.000 0.032 0.968 0.000 0.000
#> GSM601973 4 0.6486 0.5092 0.000 0.376 0.188 0.436 0.000
#> GSM601978 2 0.3729 0.6651 0.000 0.824 0.124 0.040 0.012
#> GSM601988 3 0.7524 0.0657 0.032 0.228 0.504 0.212 0.024
#> GSM601878 3 0.2376 0.7636 0.000 0.052 0.904 0.044 0.000
#> GSM601908 2 0.4065 0.6732 0.004 0.796 0.148 0.048 0.004
#> GSM601918 3 0.6164 0.0752 0.000 0.388 0.476 0.136 0.000
#> GSM601923 3 0.2300 0.7646 0.000 0.052 0.908 0.040 0.000
#> GSM601953 2 0.4160 0.6952 0.000 0.780 0.168 0.044 0.008
#> GSM601963 3 0.0579 0.7656 0.000 0.008 0.984 0.008 0.000
#> GSM601968 3 0.1800 0.7700 0.000 0.048 0.932 0.020 0.000
#> GSM601983 3 0.2408 0.7717 0.000 0.092 0.892 0.016 0.000
#> GSM601993 4 0.6460 0.4872 0.040 0.256 0.116 0.588 0.000
#> GSM601874 2 0.3160 0.6995 0.000 0.808 0.188 0.004 0.000
#> GSM601884 2 0.4096 0.6946 0.000 0.772 0.176 0.052 0.000
#> GSM601889 3 0.1211 0.7722 0.000 0.024 0.960 0.016 0.000
#> GSM601894 3 0.1121 0.7713 0.000 0.044 0.956 0.000 0.000
#> GSM601899 3 0.3489 0.6953 0.000 0.208 0.784 0.004 0.004
#> GSM601904 3 0.5602 0.5325 0.000 0.196 0.640 0.164 0.000
#> GSM601914 3 0.0912 0.7654 0.000 0.016 0.972 0.012 0.000
#> GSM601929 3 0.3159 0.7589 0.000 0.088 0.856 0.056 0.000
#> GSM601934 2 0.5158 0.5638 0.000 0.656 0.264 0.080 0.000
#> GSM601939 3 0.1741 0.7714 0.000 0.040 0.936 0.024 0.000
#> GSM601944 1 0.5926 0.0000 0.608 0.268 0.000 0.012 0.112
#> GSM601949 3 0.4302 0.6596 0.000 0.208 0.744 0.048 0.000
#> GSM601959 3 0.1357 0.7735 0.000 0.048 0.948 0.004 0.000
#> GSM601974 3 0.4592 0.6931 0.000 0.140 0.756 0.100 0.004
#> GSM601979 2 0.3619 0.6663 0.000 0.828 0.124 0.040 0.008
#> GSM601989 3 0.2172 0.7765 0.000 0.076 0.908 0.016 0.000
#> GSM601879 3 0.2221 0.7657 0.000 0.052 0.912 0.036 0.000
#> GSM601909 3 0.2929 0.7381 0.004 0.124 0.860 0.008 0.004
#> GSM601919 3 0.6164 0.0752 0.000 0.388 0.476 0.136 0.000
#> GSM601924 3 0.2370 0.7643 0.000 0.056 0.904 0.040 0.000
#> GSM601954 3 0.6314 0.0454 0.008 0.384 0.484 0.124 0.000
#> GSM601964 3 0.1018 0.7648 0.000 0.016 0.968 0.016 0.000
#> GSM601969 3 0.2157 0.7707 0.004 0.040 0.920 0.036 0.000
#> GSM601984 3 0.3365 0.7376 0.000 0.120 0.836 0.044 0.000
#> GSM601994 4 0.6092 0.4677 0.036 0.288 0.076 0.600 0.000
#> GSM601875 2 0.3901 0.6746 0.000 0.776 0.196 0.024 0.004
#> GSM601885 2 0.4184 0.6928 0.000 0.772 0.176 0.048 0.004
#> GSM601890 3 0.3282 0.7053 0.000 0.188 0.804 0.008 0.000
#> GSM601895 3 0.2502 0.7607 0.004 0.040 0.912 0.024 0.020
#> GSM601900 3 0.2264 0.7627 0.004 0.044 0.920 0.024 0.008
#> GSM601905 3 0.5904 0.4563 0.000 0.232 0.596 0.172 0.000
#> GSM601915 3 0.0807 0.7664 0.000 0.012 0.976 0.012 0.000
#> GSM601930 3 0.2782 0.7621 0.000 0.072 0.880 0.048 0.000
#> GSM601935 3 0.5428 0.5800 0.032 0.072 0.728 0.156 0.012
#> GSM601940 3 0.1818 0.7726 0.000 0.044 0.932 0.024 0.000
#> GSM601945 2 0.5054 0.3638 0.048 0.772 0.056 0.108 0.016
#> GSM601950 3 0.2563 0.7600 0.000 0.120 0.872 0.008 0.000
#> GSM601960 3 0.0798 0.7658 0.000 0.008 0.976 0.016 0.000
#> GSM601975 3 0.6819 -0.4253 0.000 0.340 0.348 0.312 0.000
#> GSM601980 5 0.2122 0.4761 0.040 0.008 0.016 0.008 0.928
#> GSM601990 3 0.0693 0.7654 0.000 0.012 0.980 0.008 0.000
#> GSM601880 3 0.2914 0.7604 0.000 0.076 0.872 0.052 0.000
#> GSM601910 3 0.2354 0.7602 0.000 0.076 0.904 0.012 0.008
#> GSM601920 3 0.5519 0.5534 0.004 0.184 0.664 0.148 0.000
#> GSM601925 3 0.2514 0.7622 0.000 0.060 0.896 0.044 0.000
#> GSM601955 5 0.7064 0.4207 0.272 0.056 0.000 0.148 0.524
#> GSM601965 3 0.3090 0.7480 0.000 0.104 0.856 0.040 0.000
#> GSM601970 3 0.1153 0.7693 0.004 0.024 0.964 0.008 0.000
#> GSM601985 3 0.1211 0.7710 0.000 0.024 0.960 0.016 0.000
#> GSM601995 3 0.9011 -0.4050 0.068 0.080 0.336 0.252 0.264
#> GSM601876 3 0.2193 0.7772 0.000 0.060 0.912 0.028 0.000
#> GSM601886 3 0.4760 0.5442 0.024 0.024 0.748 0.192 0.012
#> GSM601891 3 0.3318 0.7031 0.000 0.192 0.800 0.008 0.000
#> GSM601896 3 0.1670 0.7768 0.000 0.052 0.936 0.012 0.000
#> GSM601901 2 0.4370 0.6219 0.000 0.724 0.236 0.040 0.000
#> GSM601906 3 0.5466 0.5613 0.000 0.192 0.656 0.152 0.000
#> GSM601916 3 0.6356 -0.0233 0.000 0.364 0.468 0.168 0.000
#> GSM601931 3 0.2708 0.7613 0.000 0.072 0.884 0.044 0.000
#> GSM601936 3 0.6889 0.1965 0.032 0.140 0.564 0.252 0.012
#> GSM601941 4 0.6465 0.5221 0.000 0.376 0.184 0.440 0.000
#> GSM601946 3 0.1981 0.7713 0.000 0.048 0.924 0.028 0.000
#> GSM601951 3 0.5192 0.5416 0.000 0.244 0.664 0.092 0.000
#> GSM601961 2 0.5085 0.3706 0.000 0.612 0.344 0.040 0.004
#> GSM601976 3 0.6685 -0.1222 0.000 0.356 0.436 0.204 0.004
#> GSM601981 2 0.5191 0.5255 0.000 0.660 0.252 0.088 0.000
#> GSM601991 3 0.0898 0.7672 0.000 0.020 0.972 0.008 0.000
#> GSM601881 3 0.2450 0.7637 0.000 0.052 0.900 0.048 0.000
#> GSM601911 3 0.4584 0.6344 0.000 0.228 0.716 0.056 0.000
#> GSM601921 3 0.5654 0.5260 0.004 0.192 0.648 0.156 0.000
#> GSM601926 3 0.2376 0.7636 0.000 0.052 0.904 0.044 0.000
#> GSM601956 2 0.4267 0.6856 0.004 0.788 0.152 0.044 0.012
#> GSM601966 2 0.6319 -0.1076 0.004 0.540 0.176 0.280 0.000
#> GSM601971 3 0.1372 0.7725 0.004 0.016 0.956 0.024 0.000
#> GSM601986 3 0.4096 0.6905 0.000 0.176 0.772 0.052 0.000
#> GSM601996 4 0.6739 0.5407 0.020 0.372 0.128 0.476 0.004
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM601872 2 0.3536 0.58780 0.092 0.832 0.004 0.040 0.032 0.000
#> GSM601882 2 0.3215 0.59289 0.100 0.828 0.000 0.072 0.000 0.000
#> GSM601887 1 0.3183 0.71073 0.788 0.200 0.000 0.000 0.008 0.004
#> GSM601892 1 0.2573 0.76483 0.856 0.132 0.000 0.004 0.008 0.000
#> GSM601897 1 0.5434 0.53009 0.648 0.248 0.004 0.056 0.032 0.012
#> GSM601902 4 0.5415 0.59145 0.128 0.304 0.000 0.564 0.004 0.000
#> GSM601912 1 0.2648 0.78949 0.876 0.092 0.004 0.020 0.008 0.000
#> GSM601927 1 0.3013 0.76623 0.844 0.068 0.000 0.088 0.000 0.000
#> GSM601932 4 0.6220 0.38661 0.216 0.344 0.000 0.428 0.012 0.000
#> GSM601937 5 0.9512 0.00666 0.112 0.108 0.104 0.260 0.272 0.144
#> GSM601942 5 0.7770 -0.22185 0.016 0.188 0.104 0.024 0.456 0.212
#> GSM601947 2 0.5992 0.05260 0.360 0.436 0.000 0.200 0.004 0.000
#> GSM601957 1 0.0865 0.78888 0.964 0.036 0.000 0.000 0.000 0.000
#> GSM601972 2 0.6389 -0.18036 0.308 0.380 0.000 0.300 0.012 0.000
#> GSM601977 2 0.3867 0.58290 0.096 0.808 0.000 0.072 0.012 0.012
#> GSM601987 2 0.3880 0.58602 0.120 0.780 0.000 0.096 0.004 0.000
#> GSM601877 1 0.2660 0.77021 0.868 0.048 0.000 0.084 0.000 0.000
#> GSM601907 2 0.3716 0.58837 0.080 0.820 0.004 0.072 0.024 0.000
#> GSM601917 1 0.5977 0.43575 0.564 0.180 0.004 0.232 0.020 0.000
#> GSM601922 1 0.6032 0.33114 0.524 0.232 0.000 0.228 0.016 0.000
#> GSM601952 2 0.7152 0.07527 0.360 0.420 0.004 0.136 0.032 0.048
#> GSM601962 1 0.1495 0.78867 0.948 0.020 0.000 0.020 0.008 0.004
#> GSM601967 1 0.1296 0.78918 0.952 0.032 0.000 0.012 0.000 0.004
#> GSM601982 1 0.5065 0.35559 0.576 0.356 0.000 0.056 0.008 0.004
#> GSM601992 4 0.6551 0.53989 0.092 0.260 0.016 0.556 0.072 0.004
#> GSM601873 2 0.7078 0.10804 0.056 0.596 0.044 0.100 0.160 0.044
#> GSM601883 2 0.3112 0.59191 0.096 0.836 0.000 0.068 0.000 0.000
#> GSM601888 1 0.3183 0.71073 0.788 0.200 0.000 0.000 0.008 0.004
#> GSM601893 1 0.3481 0.70463 0.772 0.208 0.000 0.004 0.012 0.004
#> GSM601898 1 0.0692 0.78490 0.976 0.020 0.000 0.004 0.000 0.000
#> GSM601903 4 0.5694 0.59503 0.132 0.296 0.000 0.556 0.016 0.000
#> GSM601913 1 0.1232 0.78751 0.956 0.024 0.004 0.016 0.000 0.000
#> GSM601928 1 0.2905 0.76735 0.852 0.064 0.000 0.084 0.000 0.000
#> GSM601933 2 0.4700 0.50891 0.160 0.704 0.000 0.128 0.008 0.000
#> GSM601938 2 0.3472 0.57868 0.092 0.808 0.000 0.100 0.000 0.000
#> GSM601943 2 0.6631 0.23368 0.060 0.640 0.052 0.112 0.112 0.024
#> GSM601948 1 0.5194 0.52566 0.624 0.232 0.000 0.140 0.004 0.000
#> GSM601958 1 0.0891 0.78716 0.968 0.024 0.000 0.008 0.000 0.000
#> GSM601973 4 0.5128 0.60575 0.076 0.312 0.000 0.600 0.012 0.000
#> GSM601978 2 0.2995 0.57254 0.064 0.868 0.004 0.044 0.020 0.000
#> GSM601988 1 0.7766 -0.08352 0.432 0.204 0.012 0.216 0.120 0.016
#> GSM601878 1 0.2660 0.77021 0.868 0.048 0.000 0.084 0.000 0.000
#> GSM601908 2 0.3636 0.57429 0.076 0.824 0.004 0.076 0.020 0.000
#> GSM601918 2 0.6176 -0.00264 0.380 0.392 0.000 0.220 0.008 0.000
#> GSM601923 1 0.2608 0.77194 0.872 0.048 0.000 0.080 0.000 0.000
#> GSM601953 2 0.3546 0.60059 0.096 0.828 0.004 0.052 0.020 0.000
#> GSM601963 1 0.0976 0.78462 0.968 0.016 0.000 0.008 0.008 0.000
#> GSM601968 1 0.2208 0.78599 0.908 0.052 0.000 0.032 0.004 0.004
#> GSM601983 1 0.2830 0.78645 0.868 0.092 0.000 0.024 0.012 0.004
#> GSM601993 4 0.6551 0.32798 0.060 0.176 0.036 0.612 0.108 0.008
#> GSM601874 2 0.2357 0.60842 0.116 0.872 0.000 0.012 0.000 0.000
#> GSM601884 2 0.3103 0.59510 0.100 0.836 0.000 0.064 0.000 0.000
#> GSM601889 1 0.1377 0.79181 0.952 0.024 0.000 0.016 0.004 0.004
#> GSM601894 1 0.1542 0.78935 0.936 0.052 0.000 0.004 0.008 0.000
#> GSM601899 1 0.3593 0.68648 0.756 0.224 0.000 0.004 0.012 0.004
#> GSM601904 1 0.5686 0.45982 0.580 0.168 0.004 0.240 0.008 0.000
#> GSM601914 1 0.0964 0.78491 0.968 0.016 0.000 0.012 0.004 0.000
#> GSM601929 1 0.3466 0.75983 0.816 0.084 0.000 0.096 0.000 0.004
#> GSM601934 2 0.4761 0.50643 0.184 0.696 0.004 0.112 0.004 0.000
#> GSM601939 1 0.1856 0.78622 0.920 0.032 0.000 0.048 0.000 0.000
#> GSM601944 3 0.2884 0.00000 0.000 0.164 0.824 0.008 0.004 0.000
#> GSM601949 1 0.4586 0.63104 0.692 0.216 0.000 0.088 0.004 0.000
#> GSM601959 1 0.1268 0.79171 0.952 0.036 0.000 0.008 0.000 0.004
#> GSM601974 1 0.4933 0.68952 0.712 0.132 0.004 0.128 0.024 0.000
#> GSM601979 2 0.2849 0.57024 0.060 0.876 0.004 0.044 0.016 0.000
#> GSM601989 1 0.2221 0.79195 0.896 0.072 0.000 0.032 0.000 0.000
#> GSM601879 1 0.2554 0.77331 0.876 0.048 0.000 0.076 0.000 0.000
#> GSM601909 1 0.2951 0.75424 0.844 0.128 0.000 0.004 0.020 0.004
#> GSM601919 2 0.6176 -0.00264 0.380 0.392 0.000 0.220 0.008 0.000
#> GSM601924 1 0.2672 0.77144 0.868 0.052 0.000 0.080 0.000 0.000
#> GSM601954 1 0.6704 -0.19946 0.388 0.368 0.000 0.204 0.036 0.004
#> GSM601964 1 0.1406 0.78523 0.952 0.020 0.000 0.016 0.008 0.004
#> GSM601969 1 0.2726 0.78299 0.884 0.044 0.004 0.052 0.016 0.000
#> GSM601984 1 0.3649 0.73751 0.796 0.132 0.000 0.068 0.004 0.000
#> GSM601994 4 0.5656 0.35130 0.020 0.196 0.036 0.664 0.080 0.004
#> GSM601875 2 0.3249 0.58870 0.128 0.824 0.000 0.044 0.000 0.004
#> GSM601885 2 0.3356 0.58760 0.100 0.824 0.000 0.072 0.000 0.004
#> GSM601890 1 0.3281 0.70878 0.784 0.200 0.000 0.000 0.012 0.004
#> GSM601895 1 0.2533 0.77989 0.904 0.036 0.004 0.020 0.012 0.024
#> GSM601900 1 0.2278 0.78206 0.908 0.052 0.000 0.024 0.008 0.008
#> GSM601905 1 0.5579 0.38697 0.544 0.192 0.000 0.264 0.000 0.000
#> GSM601915 1 0.0964 0.78445 0.968 0.012 0.000 0.016 0.004 0.000
#> GSM601930 1 0.3006 0.76722 0.844 0.064 0.000 0.092 0.000 0.000
#> GSM601935 1 0.5561 0.53007 0.676 0.060 0.008 0.144 0.112 0.000
#> GSM601940 1 0.1930 0.78721 0.916 0.036 0.000 0.048 0.000 0.000
#> GSM601945 2 0.5505 0.31153 0.024 0.712 0.048 0.108 0.100 0.008
#> GSM601950 1 0.2961 0.76813 0.840 0.132 0.000 0.020 0.008 0.000
#> GSM601960 1 0.1026 0.78580 0.968 0.012 0.004 0.008 0.008 0.000
#> GSM601975 4 0.6221 0.32075 0.256 0.312 0.000 0.424 0.008 0.000
#> GSM601980 5 0.5736 -0.33750 0.008 0.004 0.188 0.000 0.576 0.224
#> GSM601990 1 0.0984 0.78379 0.968 0.012 0.000 0.012 0.008 0.000
#> GSM601880 1 0.3308 0.76259 0.828 0.072 0.000 0.096 0.004 0.000
#> GSM601910 1 0.2565 0.77707 0.888 0.072 0.000 0.024 0.012 0.004
#> GSM601920 1 0.5844 0.45251 0.568 0.180 0.000 0.232 0.020 0.000
#> GSM601925 1 0.2776 0.76791 0.860 0.052 0.000 0.088 0.000 0.000
#> GSM601955 6 0.0458 0.00000 0.000 0.016 0.000 0.000 0.000 0.984
#> GSM601965 1 0.3438 0.74921 0.816 0.112 0.000 0.068 0.004 0.000
#> GSM601970 1 0.1307 0.78778 0.952 0.032 0.000 0.008 0.008 0.000
#> GSM601985 1 0.1367 0.78827 0.944 0.012 0.000 0.044 0.000 0.000
#> GSM601995 5 0.8548 0.17409 0.280 0.060 0.024 0.232 0.316 0.088
#> GSM601876 1 0.2197 0.79418 0.900 0.044 0.000 0.056 0.000 0.000
#> GSM601886 1 0.5127 0.52185 0.704 0.016 0.012 0.156 0.108 0.004
#> GSM601891 1 0.3301 0.69999 0.772 0.216 0.000 0.000 0.008 0.004
#> GSM601896 1 0.1719 0.79478 0.932 0.032 0.000 0.032 0.004 0.000
#> GSM601901 2 0.3637 0.54986 0.164 0.780 0.000 0.056 0.000 0.000
#> GSM601906 1 0.5260 0.51037 0.604 0.172 0.000 0.224 0.000 0.000
#> GSM601916 1 0.6351 -0.23379 0.384 0.344 0.000 0.260 0.012 0.000
#> GSM601931 1 0.2962 0.76668 0.848 0.068 0.000 0.084 0.000 0.000
#> GSM601936 1 0.6892 0.03153 0.500 0.092 0.008 0.252 0.148 0.000
#> GSM601941 4 0.5124 0.60480 0.060 0.328 0.000 0.596 0.012 0.004
#> GSM601946 1 0.2129 0.78479 0.904 0.040 0.000 0.056 0.000 0.000
#> GSM601951 1 0.5337 0.48156 0.608 0.224 0.000 0.164 0.004 0.000
#> GSM601961 2 0.5176 0.34310 0.280 0.620 0.000 0.088 0.008 0.004
#> GSM601976 1 0.6414 -0.35030 0.352 0.324 0.000 0.312 0.012 0.000
#> GSM601981 2 0.5220 0.41179 0.172 0.656 0.000 0.156 0.016 0.000
#> GSM601991 1 0.1251 0.78735 0.956 0.024 0.000 0.012 0.008 0.000
#> GSM601881 1 0.2660 0.77098 0.868 0.048 0.000 0.084 0.000 0.000
#> GSM601911 1 0.4792 0.60587 0.672 0.232 0.000 0.088 0.008 0.000
#> GSM601921 1 0.6006 0.41152 0.552 0.212 0.000 0.216 0.016 0.004
#> GSM601926 1 0.2660 0.77021 0.868 0.048 0.000 0.084 0.000 0.000
#> GSM601956 2 0.3458 0.58481 0.072 0.840 0.004 0.056 0.028 0.000
#> GSM601966 2 0.5676 -0.29164 0.092 0.472 0.000 0.416 0.020 0.000
#> GSM601971 1 0.2001 0.79228 0.924 0.028 0.000 0.032 0.012 0.004
#> GSM601986 1 0.4282 0.68147 0.732 0.180 0.000 0.084 0.004 0.000
#> GSM601996 4 0.6265 0.57093 0.080 0.276 0.016 0.564 0.064 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
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)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
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)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, 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.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")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n time(p) gender(p) k
#> CV:hclust 123 0.2302 1.000 2
#> CV:hclust 117 0.0807 1.000 3
#> CV:hclust 97 0.2204 0.318 4
#> CV:hclust 95 0.4349 0.231 5
#> CV:hclust 91 0.3102 0.394 6
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.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["CV", "kmeans"]
# you can also extract it by
# res = res_list["CV:kmeans"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 21512 rows and 125 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#> Subgroups are detected by 'kmeans' method.
#> Performed in total 1250 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_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
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, lower 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 0.962 0.982 0.5036 0.497 0.497
#> 3 3 0.606 0.649 0.772 0.2388 0.866 0.734
#> 4 4 0.607 0.693 0.781 0.1213 0.904 0.760
#> 5 5 0.604 0.606 0.737 0.0798 0.887 0.664
#> 6 6 0.635 0.658 0.745 0.0474 0.911 0.663
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 2
Following shows 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 class
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
#> GSM601872 2 0.0000 0.987 0.000 1.000
#> GSM601882 2 0.0000 0.987 0.000 1.000
#> GSM601887 1 0.2948 0.940 0.948 0.052
#> GSM601892 1 0.0376 0.978 0.996 0.004
#> GSM601897 1 0.6531 0.810 0.832 0.168
#> GSM601902 2 0.0376 0.985 0.004 0.996
#> GSM601912 1 0.0376 0.978 0.996 0.004
#> GSM601927 1 0.0000 0.977 1.000 0.000
#> GSM601932 2 0.0376 0.985 0.004 0.996
#> GSM601937 2 0.0000 0.987 0.000 1.000
#> GSM601942 2 0.0000 0.987 0.000 1.000
#> GSM601947 2 0.1184 0.978 0.016 0.984
#> GSM601957 1 0.0376 0.978 0.996 0.004
#> GSM601972 2 0.0000 0.987 0.000 1.000
#> GSM601977 2 0.0000 0.987 0.000 1.000
#> GSM601987 2 0.0000 0.987 0.000 1.000
#> GSM601877 1 0.0000 0.977 1.000 0.000
#> GSM601907 2 0.0000 0.987 0.000 1.000
#> GSM601917 2 0.0376 0.985 0.004 0.996
#> GSM601922 2 0.4298 0.908 0.088 0.912
#> GSM601952 2 0.0000 0.987 0.000 1.000
#> GSM601962 1 0.0376 0.978 0.996 0.004
#> GSM601967 1 0.0376 0.978 0.996 0.004
#> GSM601982 2 0.4939 0.880 0.108 0.892
#> GSM601992 2 0.0000 0.987 0.000 1.000
#> GSM601873 2 0.0000 0.987 0.000 1.000
#> GSM601883 2 0.0000 0.987 0.000 1.000
#> GSM601888 1 0.1843 0.960 0.972 0.028
#> GSM601893 1 0.1414 0.967 0.980 0.020
#> GSM601898 1 0.0376 0.978 0.996 0.004
#> GSM601903 2 0.0376 0.985 0.004 0.996
#> GSM601913 1 0.0376 0.978 0.996 0.004
#> GSM601928 1 0.0000 0.977 1.000 0.000
#> GSM601933 2 0.0000 0.987 0.000 1.000
#> GSM601938 2 0.0000 0.987 0.000 1.000
#> GSM601943 2 0.0000 0.987 0.000 1.000
#> GSM601948 1 0.0000 0.977 1.000 0.000
#> GSM601958 1 0.0376 0.978 0.996 0.004
#> GSM601973 2 0.0376 0.985 0.004 0.996
#> GSM601978 2 0.0000 0.987 0.000 1.000
#> GSM601988 2 0.0000 0.987 0.000 1.000
#> GSM601878 1 0.0000 0.977 1.000 0.000
#> GSM601908 2 0.0000 0.987 0.000 1.000
#> GSM601918 2 0.0376 0.985 0.004 0.996
#> GSM601923 1 0.0000 0.977 1.000 0.000
#> GSM601953 2 0.0000 0.987 0.000 1.000
#> GSM601963 1 0.0376 0.978 0.996 0.004
#> GSM601968 1 0.0376 0.978 0.996 0.004
#> GSM601983 1 0.0376 0.978 0.996 0.004
#> GSM601993 2 0.0000 0.987 0.000 1.000
#> GSM601874 2 0.0000 0.987 0.000 1.000
#> GSM601884 2 0.0000 0.987 0.000 1.000
#> GSM601889 1 0.0376 0.978 0.996 0.004
#> GSM601894 1 0.0376 0.978 0.996 0.004
#> GSM601899 1 0.1184 0.970 0.984 0.016
#> GSM601904 2 0.1414 0.974 0.020 0.980
#> GSM601914 1 0.0376 0.978 0.996 0.004
#> GSM601929 1 0.0000 0.977 1.000 0.000
#> GSM601934 2 0.1184 0.975 0.016 0.984
#> GSM601939 1 0.0000 0.977 1.000 0.000
#> GSM601944 2 0.0000 0.987 0.000 1.000
#> GSM601949 1 0.0000 0.977 1.000 0.000
#> GSM601959 1 0.0376 0.978 0.996 0.004
#> GSM601974 2 0.9000 0.545 0.316 0.684
#> GSM601979 2 0.0000 0.987 0.000 1.000
#> GSM601989 1 0.0376 0.978 0.996 0.004
#> GSM601879 1 0.0000 0.977 1.000 0.000
#> GSM601909 1 0.0376 0.978 0.996 0.004
#> GSM601919 2 0.2043 0.964 0.032 0.968
#> GSM601924 1 0.0000 0.977 1.000 0.000
#> GSM601954 2 0.0376 0.985 0.004 0.996
#> GSM601964 1 0.0376 0.978 0.996 0.004
#> GSM601969 1 0.0000 0.977 1.000 0.000
#> GSM601984 1 0.0376 0.978 0.996 0.004
#> GSM601994 2 0.0000 0.987 0.000 1.000
#> GSM601875 2 0.0000 0.987 0.000 1.000
#> GSM601885 2 0.0000 0.987 0.000 1.000
#> GSM601890 1 0.0672 0.975 0.992 0.008
#> GSM601895 1 0.0376 0.978 0.996 0.004
#> GSM601900 1 0.3114 0.936 0.944 0.056
#> GSM601905 2 0.1184 0.978 0.016 0.984
#> GSM601915 1 0.0376 0.978 0.996 0.004
#> GSM601930 1 0.0000 0.977 1.000 0.000
#> GSM601935 1 0.8955 0.562 0.688 0.312
#> GSM601940 1 0.0000 0.977 1.000 0.000
#> GSM601945 2 0.0000 0.987 0.000 1.000
#> GSM601950 1 0.0000 0.977 1.000 0.000
#> GSM601960 1 0.0376 0.978 0.996 0.004
#> GSM601975 2 0.0376 0.985 0.004 0.996
#> GSM601980 2 0.0000 0.987 0.000 1.000
#> GSM601990 1 0.0376 0.978 0.996 0.004
#> GSM601880 1 0.0000 0.977 1.000 0.000
#> GSM601910 1 0.0376 0.978 0.996 0.004
#> GSM601920 2 0.0376 0.985 0.004 0.996
#> GSM601925 1 0.0000 0.977 1.000 0.000
#> GSM601955 2 0.0000 0.987 0.000 1.000
#> GSM601965 1 0.0000 0.977 1.000 0.000
#> GSM601970 1 0.0376 0.978 0.996 0.004
#> GSM601985 1 0.0000 0.977 1.000 0.000
#> GSM601995 2 0.0000 0.987 0.000 1.000
#> GSM601876 1 0.0000 0.977 1.000 0.000
#> GSM601886 2 0.4939 0.881 0.108 0.892
#> GSM601891 1 0.3879 0.917 0.924 0.076
#> GSM601896 1 0.0000 0.977 1.000 0.000
#> GSM601901 2 0.0000 0.987 0.000 1.000
#> GSM601906 1 0.7376 0.748 0.792 0.208
#> GSM601916 2 0.0376 0.985 0.004 0.996
#> GSM601931 1 0.0000 0.977 1.000 0.000
#> GSM601936 2 0.0000 0.987 0.000 1.000
#> GSM601941 2 0.0376 0.985 0.004 0.996
#> GSM601946 1 0.0000 0.977 1.000 0.000
#> GSM601951 1 0.0000 0.977 1.000 0.000
#> GSM601961 2 0.0000 0.987 0.000 1.000
#> GSM601976 2 0.0000 0.987 0.000 1.000
#> GSM601981 2 0.0000 0.987 0.000 1.000
#> GSM601991 1 0.0672 0.975 0.992 0.008
#> GSM601881 1 0.0000 0.977 1.000 0.000
#> GSM601911 1 0.9775 0.333 0.588 0.412
#> GSM601921 2 0.0376 0.985 0.004 0.996
#> GSM601926 1 0.0000 0.977 1.000 0.000
#> GSM601956 2 0.0000 0.987 0.000 1.000
#> GSM601966 2 0.0000 0.987 0.000 1.000
#> GSM601971 1 0.0000 0.977 1.000 0.000
#> GSM601986 1 0.0376 0.978 0.996 0.004
#> GSM601996 2 0.0000 0.987 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM601872 2 0.2682 0.6469 0.004 0.920 0.076
#> GSM601882 2 0.3116 0.6718 0.000 0.892 0.108
#> GSM601887 1 0.5595 0.7048 0.756 0.228 0.016
#> GSM601892 1 0.1182 0.9018 0.976 0.012 0.012
#> GSM601897 1 0.8953 0.4175 0.560 0.260 0.180
#> GSM601902 3 0.6274 0.4008 0.000 0.456 0.544
#> GSM601912 1 0.0747 0.9038 0.984 0.000 0.016
#> GSM601927 1 0.3551 0.8854 0.868 0.000 0.132
#> GSM601932 3 0.6280 0.3934 0.000 0.460 0.540
#> GSM601937 3 0.5754 0.3701 0.004 0.296 0.700
#> GSM601942 2 0.6204 0.1185 0.000 0.576 0.424
#> GSM601947 2 0.6026 0.2058 0.000 0.624 0.376
#> GSM601957 1 0.0592 0.9040 0.988 0.000 0.012
#> GSM601972 2 0.5835 0.2731 0.000 0.660 0.340
#> GSM601977 2 0.1753 0.7090 0.000 0.952 0.048
#> GSM601987 2 0.1860 0.7076 0.000 0.948 0.052
#> GSM601877 1 0.3619 0.8832 0.864 0.000 0.136
#> GSM601907 2 0.0237 0.7109 0.000 0.996 0.004
#> GSM601917 3 0.6168 0.4209 0.000 0.412 0.588
#> GSM601922 3 0.6865 0.4116 0.020 0.384 0.596
#> GSM601952 2 0.6180 -0.0308 0.000 0.584 0.416
#> GSM601962 1 0.0592 0.9040 0.988 0.000 0.012
#> GSM601967 1 0.0592 0.9040 0.988 0.000 0.012
#> GSM601982 2 0.3899 0.6620 0.056 0.888 0.056
#> GSM601992 2 0.6235 -0.1210 0.000 0.564 0.436
#> GSM601873 2 0.4002 0.5432 0.000 0.840 0.160
#> GSM601883 2 0.2448 0.6965 0.000 0.924 0.076
#> GSM601888 1 0.6341 0.5767 0.672 0.312 0.016
#> GSM601893 1 0.5643 0.7113 0.760 0.220 0.020
#> GSM601898 1 0.0592 0.9040 0.988 0.000 0.012
#> GSM601903 3 0.6274 0.4008 0.000 0.456 0.544
#> GSM601913 1 0.0000 0.9051 1.000 0.000 0.000
#> GSM601928 1 0.3551 0.8854 0.868 0.000 0.132
#> GSM601933 2 0.3551 0.6462 0.000 0.868 0.132
#> GSM601938 2 0.4235 0.5835 0.000 0.824 0.176
#> GSM601943 2 0.5138 0.3932 0.000 0.748 0.252
#> GSM601948 1 0.3482 0.8939 0.872 0.000 0.128
#> GSM601958 1 0.0592 0.9040 0.988 0.000 0.012
#> GSM601973 3 0.6274 0.4008 0.000 0.456 0.544
#> GSM601978 2 0.0892 0.7023 0.000 0.980 0.020
#> GSM601988 3 0.5529 0.4089 0.000 0.296 0.704
#> GSM601878 1 0.3340 0.8904 0.880 0.000 0.120
#> GSM601908 2 0.0747 0.7148 0.000 0.984 0.016
#> GSM601918 2 0.6008 0.2081 0.000 0.628 0.372
#> GSM601923 1 0.3551 0.8854 0.868 0.000 0.132
#> GSM601953 2 0.0747 0.7050 0.000 0.984 0.016
#> GSM601963 1 0.0592 0.9040 0.988 0.000 0.012
#> GSM601968 1 0.0892 0.9024 0.980 0.000 0.020
#> GSM601983 1 0.0592 0.9040 0.988 0.000 0.012
#> GSM601993 3 0.5560 0.4162 0.000 0.300 0.700
#> GSM601874 2 0.0000 0.7119 0.000 1.000 0.000
#> GSM601884 2 0.1289 0.7139 0.000 0.968 0.032
#> GSM601889 1 0.0747 0.9048 0.984 0.000 0.016
#> GSM601894 1 0.0592 0.9040 0.988 0.000 0.012
#> GSM601899 1 0.5817 0.6914 0.744 0.236 0.020
#> GSM601904 3 0.6427 0.4155 0.012 0.348 0.640
#> GSM601914 1 0.0747 0.9032 0.984 0.000 0.016
#> GSM601929 1 0.3482 0.8873 0.872 0.000 0.128
#> GSM601934 2 0.2496 0.7006 0.004 0.928 0.068
#> GSM601939 1 0.2796 0.8996 0.908 0.000 0.092
#> GSM601944 3 0.6079 0.3123 0.000 0.388 0.612
#> GSM601949 1 0.3267 0.8983 0.884 0.000 0.116
#> GSM601959 1 0.0592 0.9040 0.988 0.000 0.012
#> GSM601974 3 0.8771 0.3060 0.304 0.140 0.556
#> GSM601979 2 0.0237 0.7106 0.000 0.996 0.004
#> GSM601989 1 0.0592 0.9040 0.988 0.000 0.012
#> GSM601879 1 0.3551 0.8854 0.868 0.000 0.132
#> GSM601909 1 0.1267 0.9006 0.972 0.004 0.024
#> GSM601919 2 0.7295 0.1626 0.036 0.584 0.380
#> GSM601924 1 0.3340 0.8904 0.880 0.000 0.120
#> GSM601954 2 0.5650 0.3001 0.000 0.688 0.312
#> GSM601964 1 0.0592 0.9040 0.988 0.000 0.012
#> GSM601969 1 0.1031 0.9059 0.976 0.000 0.024
#> GSM601984 1 0.2625 0.9008 0.916 0.000 0.084
#> GSM601994 2 0.6235 -0.1214 0.000 0.564 0.436
#> GSM601875 2 0.0424 0.7132 0.000 0.992 0.008
#> GSM601885 2 0.2537 0.6939 0.000 0.920 0.080
#> GSM601890 1 0.4748 0.7940 0.832 0.144 0.024
#> GSM601895 1 0.1753 0.8934 0.952 0.000 0.048
#> GSM601900 1 0.3764 0.8588 0.892 0.040 0.068
#> GSM601905 3 0.6225 0.4047 0.000 0.432 0.568
#> GSM601915 1 0.0000 0.9051 1.000 0.000 0.000
#> GSM601930 1 0.3412 0.8889 0.876 0.000 0.124
#> GSM601935 3 0.6539 0.3034 0.288 0.028 0.684
#> GSM601940 1 0.2356 0.9030 0.928 0.000 0.072
#> GSM601945 2 0.0892 0.7031 0.000 0.980 0.020
#> GSM601950 1 0.2796 0.9036 0.908 0.000 0.092
#> GSM601960 1 0.1529 0.8968 0.960 0.000 0.040
#> GSM601975 3 0.6280 0.3945 0.000 0.460 0.540
#> GSM601980 3 0.6033 0.3280 0.004 0.336 0.660
#> GSM601990 1 0.0424 0.9047 0.992 0.000 0.008
#> GSM601880 1 0.3686 0.8808 0.860 0.000 0.140
#> GSM601910 1 0.1163 0.9005 0.972 0.000 0.028
#> GSM601920 3 0.6095 0.4097 0.000 0.392 0.608
#> GSM601925 1 0.3551 0.8854 0.868 0.000 0.132
#> GSM601955 3 0.6081 0.3177 0.004 0.344 0.652
#> GSM601965 1 0.2356 0.9032 0.928 0.000 0.072
#> GSM601970 1 0.0592 0.9040 0.988 0.000 0.012
#> GSM601985 1 0.2261 0.9035 0.932 0.000 0.068
#> GSM601995 3 0.5763 0.3864 0.008 0.276 0.716
#> GSM601876 1 0.2165 0.9042 0.936 0.000 0.064
#> GSM601886 3 0.5069 0.4516 0.044 0.128 0.828
#> GSM601891 1 0.6105 0.6651 0.724 0.252 0.024
#> GSM601896 1 0.2165 0.9042 0.936 0.000 0.064
#> GSM601901 2 0.3116 0.6725 0.000 0.892 0.108
#> GSM601906 3 0.7585 -0.2403 0.476 0.040 0.484
#> GSM601916 3 0.6235 0.4077 0.000 0.436 0.564
#> GSM601931 1 0.3412 0.8893 0.876 0.000 0.124
#> GSM601936 3 0.5254 0.4209 0.000 0.264 0.736
#> GSM601941 3 0.6252 0.4010 0.000 0.444 0.556
#> GSM601946 1 0.2959 0.8973 0.900 0.000 0.100
#> GSM601951 1 0.3412 0.8889 0.876 0.000 0.124
#> GSM601961 2 0.1031 0.7116 0.000 0.976 0.024
#> GSM601976 3 0.6291 0.3702 0.000 0.468 0.532
#> GSM601981 2 0.1289 0.7133 0.000 0.968 0.032
#> GSM601991 1 0.4796 0.7221 0.780 0.000 0.220
#> GSM601881 1 0.3551 0.8857 0.868 0.000 0.132
#> GSM601911 1 0.9017 0.2526 0.516 0.336 0.148
#> GSM601921 3 0.6286 0.3789 0.000 0.464 0.536
#> GSM601926 1 0.3482 0.8873 0.872 0.000 0.128
#> GSM601956 2 0.1163 0.6967 0.000 0.972 0.028
#> GSM601966 2 0.5859 0.1846 0.000 0.656 0.344
#> GSM601971 1 0.1031 0.9062 0.976 0.000 0.024
#> GSM601986 1 0.3030 0.8992 0.904 0.004 0.092
#> GSM601996 2 0.6302 -0.2731 0.000 0.520 0.480
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM601872 2 0.1978 0.8157 0.000 0.928 0.068 0.004
#> GSM601882 2 0.3962 0.7100 0.000 0.820 0.028 0.152
#> GSM601887 1 0.7497 0.4389 0.528 0.308 0.152 0.012
#> GSM601892 1 0.4993 0.7493 0.776 0.048 0.164 0.012
#> GSM601897 1 0.8071 0.2472 0.420 0.180 0.380 0.020
#> GSM601902 4 0.4238 0.7753 0.000 0.176 0.028 0.796
#> GSM601912 1 0.3764 0.7687 0.816 0.000 0.172 0.012
#> GSM601927 1 0.3900 0.7481 0.816 0.000 0.020 0.164
#> GSM601932 4 0.4238 0.7753 0.000 0.176 0.028 0.796
#> GSM601937 3 0.5035 0.7384 0.000 0.056 0.748 0.196
#> GSM601942 3 0.5309 0.6091 0.000 0.256 0.700 0.044
#> GSM601947 4 0.4857 0.7145 0.016 0.284 0.000 0.700
#> GSM601957 1 0.3432 0.7792 0.848 0.004 0.140 0.008
#> GSM601972 4 0.4857 0.6884 0.000 0.324 0.008 0.668
#> GSM601977 2 0.2521 0.8247 0.000 0.912 0.024 0.064
#> GSM601987 2 0.1624 0.8421 0.000 0.952 0.020 0.028
#> GSM601877 1 0.4004 0.7478 0.812 0.000 0.024 0.164
#> GSM601907 2 0.0804 0.8442 0.000 0.980 0.012 0.008
#> GSM601917 4 0.3105 0.7551 0.004 0.140 0.000 0.856
#> GSM601922 4 0.3932 0.7203 0.032 0.128 0.004 0.836
#> GSM601952 4 0.7669 0.3228 0.000 0.328 0.228 0.444
#> GSM601962 1 0.3448 0.7710 0.828 0.000 0.168 0.004
#> GSM601967 1 0.3534 0.7784 0.840 0.004 0.148 0.008
#> GSM601982 2 0.3385 0.8211 0.012 0.884 0.056 0.048
#> GSM601992 4 0.7135 0.4027 0.000 0.400 0.132 0.468
#> GSM601873 2 0.3625 0.7121 0.000 0.828 0.160 0.012
#> GSM601883 2 0.2413 0.8258 0.000 0.916 0.020 0.064
#> GSM601888 2 0.7560 -0.0616 0.388 0.464 0.136 0.012
#> GSM601893 1 0.7502 0.4541 0.536 0.292 0.160 0.012
#> GSM601898 1 0.3351 0.7780 0.844 0.000 0.148 0.008
#> GSM601903 4 0.4281 0.7756 0.000 0.180 0.028 0.792
#> GSM601913 1 0.3208 0.7788 0.848 0.000 0.148 0.004
#> GSM601928 1 0.3946 0.7467 0.812 0.000 0.020 0.168
#> GSM601933 2 0.3399 0.7886 0.000 0.868 0.040 0.092
#> GSM601938 2 0.4636 0.6297 0.000 0.772 0.040 0.188
#> GSM601943 2 0.4360 0.5607 0.000 0.744 0.248 0.008
#> GSM601948 1 0.4046 0.7777 0.828 0.000 0.048 0.124
#> GSM601958 1 0.3432 0.7792 0.848 0.004 0.140 0.008
#> GSM601973 4 0.4281 0.7756 0.000 0.180 0.028 0.792
#> GSM601978 2 0.0657 0.8407 0.000 0.984 0.012 0.004
#> GSM601988 3 0.6248 0.6706 0.000 0.104 0.644 0.252
#> GSM601878 1 0.3910 0.7516 0.820 0.000 0.024 0.156
#> GSM601908 2 0.1661 0.8349 0.000 0.944 0.004 0.052
#> GSM601918 4 0.4673 0.7172 0.008 0.292 0.000 0.700
#> GSM601923 1 0.4004 0.7478 0.812 0.000 0.024 0.164
#> GSM601953 2 0.1022 0.8367 0.000 0.968 0.032 0.000
#> GSM601963 1 0.3266 0.7720 0.832 0.000 0.168 0.000
#> GSM601968 1 0.4119 0.7636 0.796 0.004 0.188 0.012
#> GSM601983 1 0.3311 0.7711 0.828 0.000 0.172 0.000
#> GSM601993 3 0.6432 0.4661 0.000 0.076 0.552 0.372
#> GSM601874 2 0.0524 0.8452 0.000 0.988 0.004 0.008
#> GSM601884 2 0.1610 0.8415 0.000 0.952 0.016 0.032
#> GSM601889 1 0.3326 0.7814 0.856 0.004 0.132 0.008
#> GSM601894 1 0.3632 0.7747 0.832 0.004 0.156 0.008
#> GSM601899 1 0.7536 0.4415 0.528 0.300 0.160 0.012
#> GSM601904 4 0.1771 0.6075 0.012 0.036 0.004 0.948
#> GSM601914 1 0.3486 0.7597 0.812 0.000 0.188 0.000
#> GSM601929 1 0.3900 0.7501 0.816 0.000 0.020 0.164
#> GSM601934 2 0.2383 0.8345 0.004 0.924 0.024 0.048
#> GSM601939 1 0.3335 0.7663 0.860 0.000 0.020 0.120
#> GSM601944 3 0.6750 0.6515 0.000 0.208 0.612 0.180
#> GSM601949 1 0.3890 0.7750 0.836 0.004 0.028 0.132
#> GSM601959 1 0.3484 0.7781 0.844 0.004 0.144 0.008
#> GSM601974 4 0.7947 -0.0460 0.232 0.016 0.256 0.496
#> GSM601979 2 0.0469 0.8451 0.000 0.988 0.000 0.012
#> GSM601989 1 0.3272 0.7823 0.860 0.004 0.128 0.008
#> GSM601879 1 0.4050 0.7470 0.808 0.000 0.024 0.168
#> GSM601909 1 0.4387 0.7598 0.788 0.012 0.188 0.012
#> GSM601919 4 0.5430 0.6994 0.036 0.252 0.008 0.704
#> GSM601924 1 0.3910 0.7516 0.820 0.000 0.024 0.156
#> GSM601954 4 0.5372 0.4664 0.000 0.444 0.012 0.544
#> GSM601964 1 0.3266 0.7720 0.832 0.000 0.168 0.000
#> GSM601969 1 0.4152 0.7803 0.808 0.000 0.160 0.032
#> GSM601984 1 0.3032 0.7725 0.868 0.000 0.008 0.124
#> GSM601994 4 0.7235 0.4372 0.000 0.372 0.148 0.480
#> GSM601875 2 0.0524 0.8452 0.000 0.988 0.004 0.008
#> GSM601885 2 0.2635 0.8176 0.000 0.904 0.020 0.076
#> GSM601890 1 0.7386 0.5252 0.572 0.228 0.188 0.012
#> GSM601895 1 0.4635 0.7007 0.720 0.000 0.268 0.012
#> GSM601900 1 0.5756 0.6933 0.704 0.048 0.232 0.016
#> GSM601905 4 0.3760 0.7695 0.004 0.156 0.012 0.828
#> GSM601915 1 0.2760 0.7824 0.872 0.000 0.128 0.000
#> GSM601930 1 0.3806 0.7519 0.824 0.000 0.020 0.156
#> GSM601935 3 0.5451 0.6641 0.084 0.004 0.740 0.172
#> GSM601940 1 0.2345 0.7763 0.900 0.000 0.000 0.100
#> GSM601945 2 0.1929 0.8367 0.000 0.940 0.036 0.024
#> GSM601950 1 0.3822 0.7780 0.844 0.004 0.032 0.120
#> GSM601960 1 0.4319 0.7347 0.760 0.000 0.228 0.012
#> GSM601975 4 0.4079 0.7762 0.000 0.180 0.020 0.800
#> GSM601980 3 0.4953 0.7332 0.000 0.104 0.776 0.120
#> GSM601990 1 0.3172 0.7733 0.840 0.000 0.160 0.000
#> GSM601880 1 0.4050 0.7463 0.808 0.000 0.024 0.168
#> GSM601910 1 0.4364 0.7456 0.764 0.000 0.220 0.016
#> GSM601920 4 0.3326 0.7441 0.008 0.132 0.004 0.856
#> GSM601925 1 0.4004 0.7478 0.812 0.000 0.024 0.164
#> GSM601955 3 0.4784 0.7298 0.000 0.100 0.788 0.112
#> GSM601965 1 0.2593 0.7759 0.892 0.000 0.004 0.104
#> GSM601970 1 0.3625 0.7776 0.828 0.000 0.160 0.012
#> GSM601985 1 0.2799 0.7742 0.884 0.000 0.008 0.108
#> GSM601995 3 0.4485 0.7431 0.000 0.052 0.796 0.152
#> GSM601876 1 0.2530 0.7770 0.896 0.000 0.004 0.100
#> GSM601886 3 0.5065 0.6887 0.016 0.008 0.708 0.268
#> GSM601891 1 0.7784 0.3281 0.468 0.360 0.156 0.016
#> GSM601896 1 0.2651 0.7783 0.896 0.004 0.004 0.096
#> GSM601901 2 0.3764 0.6986 0.000 0.816 0.012 0.172
#> GSM601906 4 0.5675 0.1632 0.320 0.008 0.028 0.644
#> GSM601916 4 0.4139 0.7756 0.000 0.176 0.024 0.800
#> GSM601931 1 0.3853 0.7503 0.820 0.000 0.020 0.160
#> GSM601936 3 0.5520 0.7001 0.000 0.060 0.696 0.244
#> GSM601941 4 0.4379 0.7719 0.000 0.172 0.036 0.792
#> GSM601946 1 0.3554 0.7601 0.844 0.000 0.020 0.136
#> GSM601951 1 0.3900 0.7513 0.816 0.000 0.020 0.164
#> GSM601961 2 0.1411 0.8385 0.000 0.960 0.020 0.020
#> GSM601976 4 0.5436 0.7289 0.000 0.176 0.092 0.732
#> GSM601981 2 0.1356 0.8437 0.000 0.960 0.008 0.032
#> GSM601991 3 0.5292 -0.2618 0.480 0.000 0.512 0.008
#> GSM601881 1 0.4004 0.7478 0.812 0.000 0.024 0.164
#> GSM601911 1 0.6949 0.5269 0.616 0.208 0.008 0.168
#> GSM601921 4 0.3681 0.7765 0.000 0.176 0.008 0.816
#> GSM601926 1 0.4004 0.7478 0.812 0.000 0.024 0.164
#> GSM601956 2 0.1722 0.8299 0.000 0.944 0.048 0.008
#> GSM601966 2 0.6011 -0.3518 0.000 0.480 0.040 0.480
#> GSM601971 1 0.3606 0.7857 0.844 0.000 0.132 0.024
#> GSM601986 1 0.3272 0.7695 0.860 0.004 0.008 0.128
#> GSM601996 4 0.5966 0.6877 0.000 0.280 0.072 0.648
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM601872 2 0.2964 0.80925 0.068 0.884 0.012 0.004 0.032
#> GSM601882 2 0.4647 0.75301 0.052 0.772 0.004 0.148 0.024
#> GSM601887 3 0.5815 0.45170 0.056 0.276 0.636 0.020 0.012
#> GSM601892 3 0.3941 0.60735 0.064 0.072 0.836 0.016 0.012
#> GSM601897 3 0.6559 0.46864 0.060 0.108 0.660 0.024 0.148
#> GSM601902 4 0.2839 0.74398 0.024 0.048 0.000 0.892 0.036
#> GSM601912 3 0.1310 0.64886 0.000 0.000 0.956 0.020 0.024
#> GSM601927 1 0.4030 0.95148 0.648 0.000 0.352 0.000 0.000
#> GSM601932 4 0.2564 0.74844 0.024 0.052 0.000 0.904 0.020
#> GSM601937 5 0.2661 0.82535 0.004 0.012 0.024 0.060 0.900
#> GSM601942 5 0.5485 0.74911 0.144 0.124 0.004 0.020 0.708
#> GSM601947 4 0.3849 0.72931 0.052 0.136 0.000 0.808 0.004
#> GSM601957 3 0.2340 0.63244 0.068 0.000 0.908 0.012 0.012
#> GSM601972 4 0.3733 0.72615 0.028 0.156 0.000 0.808 0.008
#> GSM601977 2 0.2569 0.84362 0.016 0.896 0.000 0.076 0.012
#> GSM601987 2 0.2765 0.83966 0.044 0.896 0.000 0.036 0.024
#> GSM601877 1 0.3966 0.95465 0.664 0.000 0.336 0.000 0.000
#> GSM601907 2 0.1490 0.84857 0.032 0.952 0.004 0.008 0.004
#> GSM601917 4 0.3154 0.73938 0.088 0.040 0.000 0.864 0.008
#> GSM601922 4 0.3273 0.72395 0.112 0.036 0.000 0.848 0.004
#> GSM601952 4 0.7129 0.36922 0.052 0.288 0.000 0.504 0.156
#> GSM601962 3 0.3441 0.60933 0.092 0.000 0.852 0.020 0.036
#> GSM601967 3 0.2630 0.63066 0.080 0.000 0.892 0.016 0.012
#> GSM601982 2 0.5251 0.79228 0.080 0.768 0.056 0.072 0.024
#> GSM601992 4 0.8140 0.11254 0.112 0.304 0.000 0.360 0.224
#> GSM601873 2 0.4113 0.73780 0.048 0.788 0.000 0.008 0.156
#> GSM601883 2 0.3915 0.80080 0.048 0.824 0.000 0.104 0.024
#> GSM601888 2 0.6268 0.00741 0.056 0.460 0.452 0.020 0.012
#> GSM601893 3 0.5517 0.48258 0.056 0.240 0.676 0.016 0.012
#> GSM601898 3 0.2586 0.62912 0.084 0.000 0.892 0.012 0.012
#> GSM601903 4 0.2839 0.74398 0.024 0.048 0.000 0.892 0.036
#> GSM601913 3 0.3047 0.61379 0.096 0.000 0.868 0.012 0.024
#> GSM601928 1 0.4196 0.94920 0.640 0.000 0.356 0.004 0.000
#> GSM601933 2 0.4626 0.77539 0.064 0.784 0.000 0.108 0.044
#> GSM601938 2 0.5331 0.71759 0.060 0.732 0.000 0.136 0.072
#> GSM601943 2 0.4695 0.67404 0.068 0.744 0.004 0.004 0.180
#> GSM601948 3 0.5520 -0.42071 0.420 0.004 0.532 0.028 0.016
#> GSM601958 3 0.2589 0.62265 0.092 0.000 0.888 0.012 0.008
#> GSM601973 4 0.2673 0.74738 0.024 0.048 0.000 0.900 0.028
#> GSM601978 2 0.1074 0.84947 0.016 0.968 0.000 0.012 0.004
#> GSM601988 5 0.5955 0.74979 0.044 0.040 0.056 0.152 0.708
#> GSM601878 1 0.3983 0.95438 0.660 0.000 0.340 0.000 0.000
#> GSM601908 2 0.1772 0.85012 0.020 0.940 0.000 0.032 0.008
#> GSM601918 4 0.3708 0.72823 0.044 0.136 0.000 0.816 0.004
#> GSM601923 1 0.3966 0.95465 0.664 0.000 0.336 0.000 0.000
#> GSM601953 2 0.2017 0.83768 0.060 0.924 0.004 0.004 0.008
#> GSM601963 3 0.3346 0.60894 0.092 0.000 0.856 0.016 0.036
#> GSM601968 3 0.2437 0.64305 0.040 0.008 0.916 0.016 0.020
#> GSM601983 3 0.3346 0.61554 0.092 0.000 0.856 0.016 0.036
#> GSM601993 5 0.5840 0.61388 0.088 0.028 0.000 0.240 0.644
#> GSM601874 2 0.1278 0.85030 0.020 0.960 0.000 0.016 0.004
#> GSM601884 2 0.3134 0.83176 0.044 0.876 0.000 0.056 0.024
#> GSM601889 3 0.2699 0.61442 0.100 0.000 0.880 0.012 0.008
#> GSM601894 3 0.2047 0.64630 0.040 0.000 0.928 0.012 0.020
#> GSM601899 3 0.5728 0.47358 0.056 0.244 0.664 0.020 0.016
#> GSM601904 4 0.3019 0.70449 0.108 0.012 0.000 0.864 0.016
#> GSM601914 3 0.3152 0.62128 0.064 0.000 0.872 0.016 0.048
#> GSM601929 1 0.4060 0.94874 0.640 0.000 0.360 0.000 0.000
#> GSM601934 2 0.3654 0.83160 0.044 0.856 0.008 0.060 0.032
#> GSM601939 1 0.4192 0.88803 0.596 0.000 0.404 0.000 0.000
#> GSM601944 5 0.5900 0.72939 0.084 0.132 0.000 0.092 0.692
#> GSM601949 3 0.5313 -0.54018 0.452 0.004 0.512 0.020 0.012
#> GSM601959 3 0.2644 0.62145 0.088 0.000 0.888 0.012 0.012
#> GSM601974 4 0.6035 0.22238 0.008 0.004 0.336 0.560 0.092
#> GSM601979 2 0.0566 0.85047 0.000 0.984 0.000 0.012 0.004
#> GSM601989 3 0.1830 0.64205 0.052 0.000 0.932 0.012 0.004
#> GSM601879 1 0.4101 0.94954 0.664 0.000 0.332 0.004 0.000
#> GSM601909 3 0.2064 0.64969 0.028 0.004 0.932 0.016 0.020
#> GSM601919 4 0.4422 0.70775 0.120 0.104 0.000 0.772 0.004
#> GSM601924 1 0.3966 0.95465 0.664 0.000 0.336 0.000 0.000
#> GSM601954 4 0.5261 0.55883 0.044 0.316 0.000 0.628 0.012
#> GSM601964 3 0.3346 0.60894 0.092 0.000 0.856 0.016 0.036
#> GSM601969 3 0.3663 0.61968 0.116 0.004 0.836 0.024 0.020
#> GSM601984 3 0.4452 -0.61977 0.496 0.000 0.500 0.004 0.000
#> GSM601994 4 0.8049 0.10360 0.096 0.276 0.000 0.376 0.252
#> GSM601875 2 0.1012 0.84964 0.020 0.968 0.000 0.012 0.000
#> GSM601885 2 0.3966 0.79741 0.048 0.820 0.000 0.108 0.024
#> GSM601890 3 0.5159 0.53276 0.056 0.160 0.744 0.024 0.016
#> GSM601895 3 0.2196 0.64218 0.004 0.000 0.916 0.024 0.056
#> GSM601900 3 0.3193 0.63728 0.020 0.036 0.884 0.020 0.040
#> GSM601905 4 0.1648 0.74984 0.020 0.040 0.000 0.940 0.000
#> GSM601915 3 0.2967 0.60552 0.104 0.000 0.868 0.012 0.016
#> GSM601930 1 0.4060 0.94754 0.640 0.000 0.360 0.000 0.000
#> GSM601935 5 0.4444 0.72546 0.012 0.000 0.172 0.052 0.764
#> GSM601940 3 0.4443 -0.57829 0.472 0.000 0.524 0.004 0.000
#> GSM601945 2 0.2568 0.84211 0.048 0.904 0.000 0.016 0.032
#> GSM601950 3 0.5302 -0.51384 0.440 0.004 0.524 0.020 0.012
#> GSM601960 3 0.2482 0.64019 0.016 0.000 0.904 0.016 0.064
#> GSM601975 4 0.2722 0.74951 0.020 0.056 0.000 0.896 0.028
#> GSM601980 5 0.4314 0.80367 0.136 0.016 0.020 0.028 0.800
#> GSM601990 3 0.3174 0.61351 0.080 0.000 0.868 0.016 0.036
#> GSM601880 1 0.4101 0.94970 0.664 0.000 0.332 0.000 0.004
#> GSM601910 3 0.2082 0.65002 0.024 0.000 0.928 0.016 0.032
#> GSM601920 4 0.3187 0.73833 0.088 0.036 0.000 0.864 0.012
#> GSM601925 1 0.3966 0.95465 0.664 0.000 0.336 0.000 0.000
#> GSM601955 5 0.4837 0.79967 0.136 0.032 0.028 0.028 0.776
#> GSM601965 3 0.4830 -0.39102 0.420 0.000 0.560 0.016 0.004
#> GSM601970 3 0.2844 0.63280 0.088 0.000 0.880 0.012 0.020
#> GSM601985 1 0.4583 0.70906 0.528 0.000 0.464 0.004 0.004
#> GSM601995 5 0.3519 0.81705 0.080 0.004 0.024 0.036 0.856
#> GSM601876 3 0.4443 -0.57613 0.472 0.000 0.524 0.004 0.000
#> GSM601886 5 0.3640 0.81153 0.024 0.000 0.036 0.100 0.840
#> GSM601891 3 0.5870 0.41653 0.052 0.300 0.616 0.020 0.012
#> GSM601896 3 0.4437 -0.54998 0.464 0.000 0.532 0.004 0.000
#> GSM601901 2 0.4048 0.69834 0.016 0.772 0.000 0.196 0.016
#> GSM601906 4 0.5793 0.13487 0.452 0.004 0.064 0.476 0.004
#> GSM601916 4 0.2627 0.74860 0.044 0.044 0.000 0.900 0.012
#> GSM601931 1 0.4045 0.95065 0.644 0.000 0.356 0.000 0.000
#> GSM601936 5 0.3031 0.80029 0.020 0.004 0.000 0.120 0.856
#> GSM601941 4 0.3152 0.74395 0.044 0.052 0.000 0.876 0.028
#> GSM601946 1 0.4182 0.89591 0.600 0.000 0.400 0.000 0.000
#> GSM601951 1 0.4341 0.93954 0.628 0.000 0.364 0.008 0.000
#> GSM601961 2 0.2721 0.81752 0.036 0.904 0.032 0.020 0.008
#> GSM601976 4 0.3071 0.73956 0.012 0.080 0.000 0.872 0.036
#> GSM601981 2 0.1757 0.84920 0.012 0.936 0.000 0.048 0.004
#> GSM601991 3 0.4532 0.48737 0.020 0.000 0.716 0.016 0.248
#> GSM601881 1 0.3966 0.95465 0.664 0.000 0.336 0.000 0.000
#> GSM601911 3 0.7997 -0.29500 0.364 0.160 0.376 0.092 0.008
#> GSM601921 4 0.2943 0.75045 0.060 0.052 0.000 0.880 0.008
#> GSM601926 1 0.3966 0.95465 0.664 0.000 0.336 0.000 0.000
#> GSM601956 2 0.2556 0.82990 0.068 0.900 0.004 0.004 0.024
#> GSM601966 4 0.7074 0.10145 0.108 0.404 0.000 0.428 0.060
#> GSM601971 3 0.3667 0.56523 0.156 0.000 0.812 0.012 0.020
#> GSM601986 3 0.4747 -0.59504 0.484 0.000 0.500 0.016 0.000
#> GSM601996 4 0.7024 0.44135 0.096 0.168 0.000 0.580 0.156
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM601872 2 0.251 0.7049 0.008 0.884 0.004 0.000 0.016 0.088
#> GSM601882 2 0.483 0.5698 0.000 0.688 0.004 0.116 0.004 0.188
#> GSM601887 3 0.578 0.5887 0.016 0.220 0.572 0.000 0.000 0.192
#> GSM601892 3 0.442 0.7301 0.028 0.048 0.732 0.000 0.000 0.192
#> GSM601897 3 0.493 0.6382 0.012 0.068 0.744 0.000 0.092 0.084
#> GSM601902 4 0.346 0.7385 0.020 0.008 0.000 0.832 0.032 0.108
#> GSM601912 3 0.169 0.7657 0.020 0.000 0.936 0.000 0.012 0.032
#> GSM601927 1 0.276 0.8252 0.804 0.000 0.196 0.000 0.000 0.000
#> GSM601932 4 0.327 0.7471 0.024 0.008 0.000 0.840 0.016 0.112
#> GSM601937 5 0.249 0.6205 0.028 0.000 0.012 0.012 0.900 0.048
#> GSM601942 5 0.590 0.5394 0.048 0.088 0.000 0.004 0.576 0.284
#> GSM601947 4 0.261 0.7506 0.028 0.056 0.000 0.888 0.000 0.028
#> GSM601957 3 0.373 0.7493 0.060 0.000 0.784 0.004 0.000 0.152
#> GSM601972 4 0.429 0.6791 0.028 0.096 0.000 0.768 0.000 0.108
#> GSM601977 2 0.328 0.7215 0.012 0.848 0.000 0.080 0.008 0.052
#> GSM601987 2 0.349 0.6832 0.000 0.804 0.000 0.040 0.008 0.148
#> GSM601877 1 0.274 0.8266 0.820 0.000 0.176 0.000 0.000 0.004
#> GSM601907 2 0.164 0.7366 0.000 0.932 0.004 0.012 0.000 0.052
#> GSM601917 4 0.282 0.7542 0.044 0.012 0.000 0.876 0.004 0.064
#> GSM601922 4 0.330 0.7260 0.084 0.004 0.000 0.836 0.004 0.072
#> GSM601952 4 0.720 0.1070 0.060 0.248 0.000 0.512 0.100 0.080
#> GSM601962 3 0.319 0.6982 0.100 0.000 0.844 0.000 0.020 0.036
#> GSM601967 3 0.383 0.7485 0.072 0.000 0.768 0.000 0.000 0.160
#> GSM601982 2 0.566 0.6092 0.016 0.676 0.060 0.072 0.004 0.172
#> GSM601992 6 0.771 0.8052 0.008 0.200 0.000 0.188 0.232 0.372
#> GSM601873 2 0.514 0.5825 0.048 0.708 0.000 0.004 0.112 0.128
#> GSM601883 2 0.433 0.6356 0.000 0.736 0.000 0.084 0.008 0.172
#> GSM601888 2 0.620 -0.0777 0.016 0.440 0.352 0.000 0.000 0.192
#> GSM601893 3 0.575 0.6209 0.016 0.192 0.596 0.004 0.000 0.192
#> GSM601898 3 0.328 0.7649 0.060 0.000 0.828 0.004 0.000 0.108
#> GSM601903 4 0.299 0.7570 0.020 0.008 0.000 0.864 0.020 0.088
#> GSM601913 3 0.358 0.7017 0.092 0.000 0.828 0.004 0.024 0.052
#> GSM601928 1 0.290 0.8252 0.800 0.000 0.196 0.004 0.000 0.000
#> GSM601933 2 0.471 0.6102 0.000 0.712 0.000 0.052 0.040 0.196
#> GSM601938 2 0.544 0.5366 0.000 0.664 0.000 0.096 0.060 0.180
#> GSM601943 2 0.490 0.5798 0.056 0.732 0.000 0.004 0.124 0.084
#> GSM601948 1 0.625 0.3023 0.436 0.000 0.392 0.036 0.000 0.136
#> GSM601958 3 0.382 0.7436 0.084 0.000 0.784 0.004 0.000 0.128
#> GSM601973 4 0.291 0.7512 0.020 0.008 0.000 0.864 0.012 0.096
#> GSM601978 2 0.126 0.7365 0.004 0.956 0.000 0.008 0.004 0.028
#> GSM601988 5 0.609 0.3783 0.012 0.020 0.068 0.064 0.644 0.192
#> GSM601878 1 0.274 0.8266 0.820 0.000 0.176 0.000 0.000 0.004
#> GSM601908 2 0.205 0.7334 0.008 0.916 0.000 0.032 0.000 0.044
#> GSM601918 4 0.280 0.7415 0.012 0.052 0.000 0.872 0.000 0.064
#> GSM601923 1 0.274 0.8266 0.820 0.000 0.176 0.000 0.000 0.004
#> GSM601953 2 0.203 0.7211 0.012 0.912 0.004 0.004 0.000 0.068
#> GSM601963 3 0.334 0.6996 0.100 0.000 0.836 0.000 0.024 0.040
#> GSM601968 3 0.356 0.7616 0.040 0.000 0.788 0.000 0.004 0.168
#> GSM601983 3 0.333 0.7046 0.084 0.004 0.844 0.000 0.020 0.048
#> GSM601993 5 0.603 -0.0425 0.008 0.028 0.000 0.120 0.552 0.292
#> GSM601874 2 0.109 0.7400 0.000 0.960 0.000 0.016 0.000 0.024
#> GSM601884 2 0.386 0.6694 0.000 0.780 0.000 0.064 0.008 0.148
#> GSM601889 3 0.414 0.7312 0.092 0.000 0.752 0.004 0.000 0.152
#> GSM601894 3 0.338 0.7628 0.052 0.000 0.816 0.004 0.000 0.128
#> GSM601899 3 0.564 0.6189 0.016 0.188 0.596 0.000 0.000 0.200
#> GSM601904 4 0.263 0.7522 0.064 0.004 0.000 0.888 0.020 0.024
#> GSM601914 3 0.310 0.7144 0.060 0.000 0.860 0.000 0.036 0.044
#> GSM601929 1 0.282 0.8261 0.796 0.000 0.204 0.000 0.000 0.000
#> GSM601934 2 0.469 0.6422 0.004 0.736 0.008 0.036 0.040 0.176
#> GSM601939 1 0.344 0.8124 0.752 0.000 0.236 0.004 0.000 0.008
#> GSM601944 5 0.730 0.2962 0.072 0.144 0.000 0.052 0.500 0.232
#> GSM601949 1 0.554 0.4627 0.492 0.000 0.368 0.000 0.000 0.140
#> GSM601959 3 0.380 0.7455 0.068 0.000 0.780 0.004 0.000 0.148
#> GSM601974 4 0.595 0.2166 0.020 0.000 0.368 0.516 0.072 0.024
#> GSM601979 2 0.138 0.7382 0.004 0.952 0.000 0.016 0.004 0.024
#> GSM601989 3 0.241 0.7639 0.052 0.000 0.892 0.004 0.000 0.052
#> GSM601879 1 0.288 0.8248 0.816 0.000 0.176 0.004 0.000 0.004
#> GSM601909 3 0.272 0.7762 0.028 0.000 0.868 0.000 0.008 0.096
#> GSM601919 4 0.366 0.7259 0.068 0.044 0.000 0.824 0.000 0.064
#> GSM601924 1 0.274 0.8266 0.820 0.000 0.176 0.000 0.000 0.004
#> GSM601954 4 0.510 0.5087 0.032 0.212 0.000 0.684 0.008 0.064
#> GSM601964 3 0.309 0.7024 0.092 0.000 0.852 0.000 0.020 0.036
#> GSM601969 3 0.508 0.7183 0.100 0.004 0.688 0.024 0.000 0.184
#> GSM601984 1 0.427 0.6801 0.596 0.000 0.384 0.000 0.004 0.016
#> GSM601994 6 0.784 0.8132 0.012 0.196 0.000 0.200 0.240 0.352
#> GSM601875 2 0.115 0.7385 0.000 0.956 0.000 0.012 0.000 0.032
#> GSM601885 2 0.444 0.6267 0.000 0.724 0.000 0.088 0.008 0.180
#> GSM601890 3 0.499 0.6797 0.016 0.120 0.680 0.000 0.000 0.184
#> GSM601895 3 0.215 0.7533 0.008 0.000 0.908 0.000 0.060 0.024
#> GSM601900 3 0.287 0.7516 0.016 0.004 0.880 0.004 0.052 0.044
#> GSM601905 4 0.257 0.7625 0.012 0.004 0.000 0.888 0.024 0.072
#> GSM601915 3 0.316 0.7166 0.096 0.000 0.844 0.004 0.004 0.052
#> GSM601930 1 0.282 0.8256 0.796 0.000 0.204 0.000 0.000 0.000
#> GSM601935 5 0.517 0.4404 0.016 0.000 0.248 0.020 0.660 0.056
#> GSM601940 1 0.399 0.7026 0.608 0.000 0.384 0.004 0.000 0.004
#> GSM601945 2 0.350 0.7030 0.048 0.840 0.000 0.016 0.016 0.080
#> GSM601950 1 0.558 0.4380 0.476 0.000 0.380 0.000 0.000 0.144
#> GSM601960 3 0.246 0.7494 0.008 0.000 0.892 0.000 0.052 0.048
#> GSM601975 4 0.279 0.7618 0.024 0.008 0.000 0.872 0.008 0.088
#> GSM601980 5 0.483 0.6110 0.048 0.012 0.008 0.008 0.696 0.228
#> GSM601990 3 0.292 0.7018 0.084 0.000 0.864 0.000 0.020 0.032
#> GSM601880 1 0.284 0.8227 0.820 0.000 0.172 0.004 0.000 0.004
#> GSM601910 3 0.257 0.7757 0.020 0.000 0.884 0.000 0.020 0.076
#> GSM601920 4 0.325 0.7470 0.048 0.004 0.000 0.848 0.016 0.084
#> GSM601925 1 0.288 0.8248 0.816 0.000 0.176 0.004 0.000 0.004
#> GSM601955 5 0.529 0.6020 0.064 0.012 0.016 0.004 0.648 0.256
#> GSM601965 1 0.422 0.5467 0.520 0.000 0.468 0.000 0.004 0.008
#> GSM601970 3 0.407 0.7502 0.080 0.000 0.756 0.004 0.000 0.160
#> GSM601985 1 0.454 0.7095 0.620 0.000 0.336 0.004 0.000 0.040
#> GSM601995 5 0.364 0.6345 0.032 0.000 0.016 0.008 0.812 0.132
#> GSM601876 1 0.384 0.7081 0.616 0.000 0.380 0.004 0.000 0.000
#> GSM601886 5 0.388 0.5762 0.020 0.000 0.024 0.076 0.820 0.060
#> GSM601891 3 0.589 0.5406 0.016 0.248 0.548 0.000 0.000 0.188
#> GSM601896 1 0.441 0.6311 0.556 0.000 0.420 0.004 0.000 0.020
#> GSM601901 2 0.433 0.5433 0.008 0.720 0.000 0.220 0.004 0.048
#> GSM601906 1 0.576 0.1610 0.528 0.000 0.040 0.376 0.020 0.036
#> GSM601916 4 0.338 0.7520 0.024 0.008 0.000 0.844 0.036 0.088
#> GSM601931 1 0.282 0.8254 0.796 0.000 0.204 0.000 0.000 0.000
#> GSM601936 5 0.381 0.5648 0.008 0.008 0.020 0.052 0.824 0.088
#> GSM601941 4 0.340 0.7362 0.020 0.008 0.000 0.832 0.024 0.116
#> GSM601946 1 0.341 0.8134 0.756 0.000 0.232 0.004 0.000 0.008
#> GSM601951 1 0.282 0.8249 0.796 0.000 0.204 0.000 0.000 0.000
#> GSM601961 2 0.298 0.6628 0.004 0.848 0.020 0.008 0.000 0.120
#> GSM601976 4 0.422 0.7302 0.020 0.032 0.000 0.796 0.056 0.096
#> GSM601981 2 0.243 0.7345 0.016 0.896 0.000 0.032 0.000 0.056
#> GSM601991 3 0.450 0.5697 0.024 0.004 0.720 0.000 0.212 0.040
#> GSM601881 1 0.274 0.8266 0.820 0.000 0.176 0.000 0.000 0.004
#> GSM601911 1 0.762 0.4456 0.440 0.164 0.276 0.052 0.004 0.064
#> GSM601921 4 0.283 0.7514 0.020 0.020 0.000 0.876 0.008 0.076
#> GSM601926 1 0.274 0.8266 0.820 0.000 0.176 0.000 0.000 0.004
#> GSM601956 2 0.279 0.7016 0.020 0.864 0.004 0.000 0.008 0.104
#> GSM601966 2 0.748 -0.5974 0.016 0.324 0.000 0.300 0.072 0.288
#> GSM601971 3 0.457 0.6829 0.144 0.000 0.700 0.000 0.000 0.156
#> GSM601986 1 0.488 0.6535 0.572 0.004 0.384 0.012 0.004 0.024
#> GSM601996 6 0.742 0.6781 0.008 0.104 0.000 0.340 0.200 0.348
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)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
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)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
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)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, 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.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")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n time(p) gender(p) k
#> CV:kmeans 124 0.305 0.7666 2
#> CV:kmeans 85 0.425 0.1719 3
#> CV:kmeans 110 0.435 0.1252 4
#> CV:kmeans 101 0.615 0.1786 5
#> CV:kmeans 112 0.469 0.0374 6
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.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["CV", "skmeans"]
# you can also extract it by
# res = res_list["CV:skmeans"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 21512 rows and 125 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 1250 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_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
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, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.999 0.957 0.981 0.5042 0.496 0.496
#> 3 3 0.409 0.523 0.696 0.2991 0.842 0.691
#> 4 4 0.404 0.410 0.639 0.1345 0.779 0.469
#> 5 5 0.405 0.358 0.571 0.0652 0.933 0.747
#> 6 6 0.450 0.289 0.496 0.0414 0.911 0.635
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 2
Following shows 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 class
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
#> GSM601872 2 0.0672 0.9707 0.008 0.992
#> GSM601882 2 0.0000 0.9759 0.000 1.000
#> GSM601887 1 0.4022 0.9158 0.920 0.080
#> GSM601892 1 0.0000 0.9855 1.000 0.000
#> GSM601897 1 0.7745 0.7207 0.772 0.228
#> GSM601902 2 0.0000 0.9759 0.000 1.000
#> GSM601912 1 0.0000 0.9855 1.000 0.000
#> GSM601927 1 0.0000 0.9855 1.000 0.000
#> GSM601932 2 0.0000 0.9759 0.000 1.000
#> GSM601937 2 0.0000 0.9759 0.000 1.000
#> GSM601942 2 0.0000 0.9759 0.000 1.000
#> GSM601947 2 0.0938 0.9679 0.012 0.988
#> GSM601957 1 0.0000 0.9855 1.000 0.000
#> GSM601972 2 0.0000 0.9759 0.000 1.000
#> GSM601977 2 0.0000 0.9759 0.000 1.000
#> GSM601987 2 0.0000 0.9759 0.000 1.000
#> GSM601877 1 0.0000 0.9855 1.000 0.000
#> GSM601907 2 0.0000 0.9759 0.000 1.000
#> GSM601917 2 0.0672 0.9708 0.008 0.992
#> GSM601922 2 0.4815 0.8825 0.104 0.896
#> GSM601952 2 0.0000 0.9759 0.000 1.000
#> GSM601962 1 0.0672 0.9801 0.992 0.008
#> GSM601967 1 0.0000 0.9855 1.000 0.000
#> GSM601982 2 0.4022 0.9073 0.080 0.920
#> GSM601992 2 0.0000 0.9759 0.000 1.000
#> GSM601873 2 0.0000 0.9759 0.000 1.000
#> GSM601883 2 0.0000 0.9759 0.000 1.000
#> GSM601888 1 0.2603 0.9514 0.956 0.044
#> GSM601893 1 0.1184 0.9747 0.984 0.016
#> GSM601898 1 0.0000 0.9855 1.000 0.000
#> GSM601903 2 0.0000 0.9759 0.000 1.000
#> GSM601913 1 0.0000 0.9855 1.000 0.000
#> GSM601928 1 0.0000 0.9855 1.000 0.000
#> GSM601933 2 0.0000 0.9759 0.000 1.000
#> GSM601938 2 0.0000 0.9759 0.000 1.000
#> GSM601943 2 0.0000 0.9759 0.000 1.000
#> GSM601948 1 0.0000 0.9855 1.000 0.000
#> GSM601958 1 0.0000 0.9855 1.000 0.000
#> GSM601973 2 0.0000 0.9759 0.000 1.000
#> GSM601978 2 0.0000 0.9759 0.000 1.000
#> GSM601988 2 0.0000 0.9759 0.000 1.000
#> GSM601878 1 0.0000 0.9855 1.000 0.000
#> GSM601908 2 0.0000 0.9759 0.000 1.000
#> GSM601918 2 0.0000 0.9759 0.000 1.000
#> GSM601923 1 0.0000 0.9855 1.000 0.000
#> GSM601953 2 0.0000 0.9759 0.000 1.000
#> GSM601963 1 0.0000 0.9855 1.000 0.000
#> GSM601968 1 0.0376 0.9829 0.996 0.004
#> GSM601983 1 0.0000 0.9855 1.000 0.000
#> GSM601993 2 0.0000 0.9759 0.000 1.000
#> GSM601874 2 0.0000 0.9759 0.000 1.000
#> GSM601884 2 0.0000 0.9759 0.000 1.000
#> GSM601889 1 0.0000 0.9855 1.000 0.000
#> GSM601894 1 0.0000 0.9855 1.000 0.000
#> GSM601899 1 0.2236 0.9586 0.964 0.036
#> GSM601904 2 0.1843 0.9554 0.028 0.972
#> GSM601914 1 0.0000 0.9855 1.000 0.000
#> GSM601929 1 0.0000 0.9855 1.000 0.000
#> GSM601934 2 0.0376 0.9735 0.004 0.996
#> GSM601939 1 0.0000 0.9855 1.000 0.000
#> GSM601944 2 0.0000 0.9759 0.000 1.000
#> GSM601949 1 0.0000 0.9855 1.000 0.000
#> GSM601959 1 0.0000 0.9855 1.000 0.000
#> GSM601974 2 0.7602 0.7310 0.220 0.780
#> GSM601979 2 0.0000 0.9759 0.000 1.000
#> GSM601989 1 0.0000 0.9855 1.000 0.000
#> GSM601879 1 0.0000 0.9855 1.000 0.000
#> GSM601909 1 0.0000 0.9855 1.000 0.000
#> GSM601919 2 0.2423 0.9451 0.040 0.960
#> GSM601924 1 0.0000 0.9855 1.000 0.000
#> GSM601954 2 0.0000 0.9759 0.000 1.000
#> GSM601964 1 0.0000 0.9855 1.000 0.000
#> GSM601969 1 0.0000 0.9855 1.000 0.000
#> GSM601984 1 0.0000 0.9855 1.000 0.000
#> GSM601994 2 0.0000 0.9759 0.000 1.000
#> GSM601875 2 0.0000 0.9759 0.000 1.000
#> GSM601885 2 0.0000 0.9759 0.000 1.000
#> GSM601890 1 0.0000 0.9855 1.000 0.000
#> GSM601895 1 0.0376 0.9829 0.996 0.004
#> GSM601900 1 0.3274 0.9360 0.940 0.060
#> GSM601905 2 0.0938 0.9680 0.012 0.988
#> GSM601915 1 0.0000 0.9855 1.000 0.000
#> GSM601930 1 0.0000 0.9855 1.000 0.000
#> GSM601935 2 0.9993 0.0796 0.484 0.516
#> GSM601940 1 0.0000 0.9855 1.000 0.000
#> GSM601945 2 0.0000 0.9759 0.000 1.000
#> GSM601950 1 0.0000 0.9855 1.000 0.000
#> GSM601960 1 0.0000 0.9855 1.000 0.000
#> GSM601975 2 0.0000 0.9759 0.000 1.000
#> GSM601980 2 0.0000 0.9759 0.000 1.000
#> GSM601990 1 0.0000 0.9855 1.000 0.000
#> GSM601880 1 0.0000 0.9855 1.000 0.000
#> GSM601910 1 0.1184 0.9748 0.984 0.016
#> GSM601920 2 0.0000 0.9759 0.000 1.000
#> GSM601925 1 0.0000 0.9855 1.000 0.000
#> GSM601955 2 0.0000 0.9759 0.000 1.000
#> GSM601965 1 0.0000 0.9855 1.000 0.000
#> GSM601970 1 0.0000 0.9855 1.000 0.000
#> GSM601985 1 0.0000 0.9855 1.000 0.000
#> GSM601995 2 0.0000 0.9759 0.000 1.000
#> GSM601876 1 0.0000 0.9855 1.000 0.000
#> GSM601886 2 0.4022 0.9075 0.080 0.920
#> GSM601891 1 0.5294 0.8711 0.880 0.120
#> GSM601896 1 0.0000 0.9855 1.000 0.000
#> GSM601901 2 0.0000 0.9759 0.000 1.000
#> GSM601906 1 0.7883 0.7028 0.764 0.236
#> GSM601916 2 0.0000 0.9759 0.000 1.000
#> GSM601931 1 0.0000 0.9855 1.000 0.000
#> GSM601936 2 0.0376 0.9735 0.004 0.996
#> GSM601941 2 0.0000 0.9759 0.000 1.000
#> GSM601946 1 0.0000 0.9855 1.000 0.000
#> GSM601951 1 0.0000 0.9855 1.000 0.000
#> GSM601961 2 0.0376 0.9735 0.004 0.996
#> GSM601976 2 0.0000 0.9759 0.000 1.000
#> GSM601981 2 0.0000 0.9759 0.000 1.000
#> GSM601991 1 0.1414 0.9718 0.980 0.020
#> GSM601881 1 0.0000 0.9855 1.000 0.000
#> GSM601911 2 0.9522 0.4128 0.372 0.628
#> GSM601921 2 0.0000 0.9759 0.000 1.000
#> GSM601926 1 0.0000 0.9855 1.000 0.000
#> GSM601956 2 0.0000 0.9759 0.000 1.000
#> GSM601966 2 0.0000 0.9759 0.000 1.000
#> GSM601971 1 0.0000 0.9855 1.000 0.000
#> GSM601986 1 0.1843 0.9648 0.972 0.028
#> GSM601996 2 0.0000 0.9759 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM601872 3 0.6309 -0.6142 0.000 0.496 0.504
#> GSM601882 2 0.4842 0.7976 0.000 0.776 0.224
#> GSM601887 3 0.5159 0.4856 0.140 0.040 0.820
#> GSM601892 3 0.6026 0.2799 0.376 0.000 0.624
#> GSM601897 3 0.4914 0.4614 0.068 0.088 0.844
#> GSM601902 2 0.2269 0.7771 0.040 0.944 0.016
#> GSM601912 3 0.6773 0.3543 0.340 0.024 0.636
#> GSM601927 1 0.0475 0.6318 0.992 0.004 0.004
#> GSM601932 2 0.2269 0.7864 0.016 0.944 0.040
#> GSM601937 2 0.5244 0.7546 0.004 0.756 0.240
#> GSM601942 2 0.5397 0.7812 0.000 0.720 0.280
#> GSM601947 2 0.7155 0.7398 0.128 0.720 0.152
#> GSM601957 1 0.6286 0.1616 0.536 0.000 0.464
#> GSM601972 2 0.2959 0.8010 0.000 0.900 0.100
#> GSM601977 2 0.5325 0.7921 0.004 0.748 0.248
#> GSM601987 2 0.5178 0.7809 0.000 0.744 0.256
#> GSM601877 1 0.1015 0.6258 0.980 0.008 0.012
#> GSM601907 2 0.5859 0.7341 0.000 0.656 0.344
#> GSM601917 2 0.6820 0.5930 0.248 0.700 0.052
#> GSM601922 2 0.7013 0.4320 0.364 0.608 0.028
#> GSM601952 2 0.4805 0.8061 0.012 0.812 0.176
#> GSM601962 3 0.7905 0.1279 0.444 0.056 0.500
#> GSM601967 1 0.6307 0.0933 0.512 0.000 0.488
#> GSM601982 2 0.7379 0.7189 0.048 0.616 0.336
#> GSM601992 2 0.1643 0.7911 0.000 0.956 0.044
#> GSM601873 2 0.5497 0.7771 0.000 0.708 0.292
#> GSM601883 2 0.4978 0.7950 0.004 0.780 0.216
#> GSM601888 3 0.5042 0.4574 0.104 0.060 0.836
#> GSM601893 3 0.4937 0.4928 0.148 0.028 0.824
#> GSM601898 1 0.6286 0.1525 0.536 0.000 0.464
#> GSM601903 2 0.3028 0.7749 0.048 0.920 0.032
#> GSM601913 1 0.6345 0.2732 0.596 0.004 0.400
#> GSM601928 1 0.0747 0.6365 0.984 0.000 0.016
#> GSM601933 2 0.4291 0.8013 0.000 0.820 0.180
#> GSM601938 2 0.3619 0.8043 0.000 0.864 0.136
#> GSM601943 2 0.6126 0.7191 0.000 0.600 0.400
#> GSM601948 1 0.5731 0.5236 0.752 0.020 0.228
#> GSM601958 1 0.6154 0.2898 0.592 0.000 0.408
#> GSM601973 2 0.2527 0.7814 0.020 0.936 0.044
#> GSM601978 2 0.5760 0.7425 0.000 0.672 0.328
#> GSM601988 2 0.4235 0.7718 0.000 0.824 0.176
#> GSM601878 1 0.0237 0.6307 0.996 0.000 0.004
#> GSM601908 2 0.5254 0.7825 0.000 0.736 0.264
#> GSM601918 2 0.3669 0.7914 0.040 0.896 0.064
#> GSM601923 1 0.0000 0.6313 1.000 0.000 0.000
#> GSM601953 2 0.6008 0.7195 0.000 0.628 0.372
#> GSM601963 1 0.6516 0.0644 0.516 0.004 0.480
#> GSM601968 3 0.6702 0.3656 0.328 0.024 0.648
#> GSM601983 3 0.6442 0.1857 0.432 0.004 0.564
#> GSM601993 2 0.2165 0.7799 0.000 0.936 0.064
#> GSM601874 2 0.5706 0.7509 0.000 0.680 0.320
#> GSM601884 2 0.5621 0.7685 0.000 0.692 0.308
#> GSM601889 1 0.6299 0.1267 0.524 0.000 0.476
#> GSM601894 3 0.6659 0.0704 0.460 0.008 0.532
#> GSM601899 3 0.4475 0.4906 0.144 0.016 0.840
#> GSM601904 2 0.7526 0.2908 0.424 0.536 0.040
#> GSM601914 3 0.6295 0.0355 0.472 0.000 0.528
#> GSM601929 1 0.2187 0.6205 0.948 0.028 0.024
#> GSM601934 2 0.6180 0.7616 0.008 0.660 0.332
#> GSM601939 1 0.4291 0.5931 0.820 0.000 0.180
#> GSM601944 2 0.4002 0.8029 0.000 0.840 0.160
#> GSM601949 1 0.4654 0.5658 0.792 0.000 0.208
#> GSM601959 1 0.6654 0.1845 0.536 0.008 0.456
#> GSM601974 3 0.9842 0.0590 0.248 0.368 0.384
#> GSM601979 2 0.5706 0.7469 0.000 0.680 0.320
#> GSM601989 1 0.6204 0.2620 0.576 0.000 0.424
#> GSM601879 1 0.0747 0.6313 0.984 0.000 0.016
#> GSM601909 3 0.6298 0.2408 0.388 0.004 0.608
#> GSM601919 2 0.8770 0.5607 0.272 0.572 0.156
#> GSM601924 1 0.1964 0.6364 0.944 0.000 0.056
#> GSM601954 2 0.7021 0.7699 0.076 0.708 0.216
#> GSM601964 1 0.6509 0.0718 0.524 0.004 0.472
#> GSM601969 1 0.6822 0.0417 0.508 0.012 0.480
#> GSM601984 1 0.4591 0.6088 0.848 0.032 0.120
#> GSM601994 2 0.1878 0.7902 0.004 0.952 0.044
#> GSM601875 2 0.5621 0.7563 0.000 0.692 0.308
#> GSM601885 2 0.5244 0.7912 0.004 0.756 0.240
#> GSM601890 3 0.4353 0.4919 0.156 0.008 0.836
#> GSM601895 3 0.7262 0.1270 0.444 0.028 0.528
#> GSM601900 3 0.7968 0.3172 0.372 0.068 0.560
#> GSM601905 2 0.6226 0.5899 0.252 0.720 0.028
#> GSM601915 1 0.6274 0.1560 0.544 0.000 0.456
#> GSM601930 1 0.0892 0.6356 0.980 0.000 0.020
#> GSM601935 2 0.9920 -0.1921 0.272 0.364 0.364
#> GSM601940 1 0.4974 0.5506 0.764 0.000 0.236
#> GSM601945 2 0.5291 0.7803 0.000 0.732 0.268
#> GSM601950 1 0.5291 0.5171 0.732 0.000 0.268
#> GSM601960 3 0.6489 0.0792 0.456 0.004 0.540
#> GSM601975 2 0.2434 0.7892 0.024 0.940 0.036
#> GSM601980 2 0.5325 0.7512 0.004 0.748 0.248
#> GSM601990 3 0.6516 0.0051 0.480 0.004 0.516
#> GSM601880 1 0.0848 0.6271 0.984 0.008 0.008
#> GSM601910 3 0.6255 0.4104 0.300 0.016 0.684
#> GSM601920 2 0.7189 0.5304 0.292 0.656 0.052
#> GSM601925 1 0.0237 0.6324 0.996 0.000 0.004
#> GSM601955 2 0.6180 0.5824 0.000 0.584 0.416
#> GSM601965 1 0.6496 0.5288 0.736 0.056 0.208
#> GSM601970 1 0.6308 0.0926 0.508 0.000 0.492
#> GSM601985 1 0.4974 0.5385 0.764 0.000 0.236
#> GSM601995 2 0.5098 0.7305 0.000 0.752 0.248
#> GSM601876 1 0.4399 0.5875 0.812 0.000 0.188
#> GSM601886 2 0.8566 0.5140 0.188 0.608 0.204
#> GSM601891 3 0.5428 0.4739 0.120 0.064 0.816
#> GSM601896 1 0.4178 0.6034 0.828 0.000 0.172
#> GSM601901 2 0.4291 0.8020 0.000 0.820 0.180
#> GSM601906 1 0.7265 0.2490 0.684 0.240 0.076
#> GSM601916 2 0.5285 0.7115 0.148 0.812 0.040
#> GSM601931 1 0.0747 0.6349 0.984 0.000 0.016
#> GSM601936 2 0.5631 0.7583 0.044 0.792 0.164
#> GSM601941 2 0.1877 0.7755 0.012 0.956 0.032
#> GSM601946 1 0.2878 0.6284 0.904 0.000 0.096
#> GSM601951 1 0.1877 0.6308 0.956 0.012 0.032
#> GSM601961 3 0.7397 -0.5385 0.032 0.484 0.484
#> GSM601976 2 0.5780 0.7638 0.080 0.800 0.120
#> GSM601981 2 0.5178 0.7823 0.000 0.744 0.256
#> GSM601991 3 0.7285 0.3674 0.320 0.048 0.632
#> GSM601881 1 0.0475 0.6291 0.992 0.004 0.004
#> GSM601911 1 0.9638 -0.1453 0.420 0.372 0.208
#> GSM601921 2 0.4868 0.7459 0.100 0.844 0.056
#> GSM601926 1 0.0747 0.6348 0.984 0.000 0.016
#> GSM601956 2 0.6045 0.7224 0.000 0.620 0.380
#> GSM601966 2 0.1964 0.7967 0.000 0.944 0.056
#> GSM601971 1 0.5905 0.3923 0.648 0.000 0.352
#> GSM601986 1 0.7984 0.2978 0.652 0.132 0.216
#> GSM601996 2 0.1585 0.7894 0.008 0.964 0.028
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM601872 2 0.525 0.4618 0.000 0.752 0.148 0.100
#> GSM601882 2 0.586 0.4189 0.012 0.652 0.036 0.300
#> GSM601887 3 0.728 0.4686 0.092 0.284 0.588 0.036
#> GSM601892 3 0.636 0.5552 0.180 0.108 0.692 0.020
#> GSM601897 3 0.794 0.3729 0.040 0.216 0.556 0.188
#> GSM601902 4 0.592 0.2497 0.008 0.380 0.028 0.584
#> GSM601912 3 0.784 0.4879 0.244 0.080 0.580 0.096
#> GSM601927 1 0.194 0.6996 0.940 0.000 0.032 0.028
#> GSM601932 4 0.620 0.2425 0.020 0.396 0.024 0.560
#> GSM601937 4 0.684 0.3170 0.004 0.308 0.112 0.576
#> GSM601942 2 0.652 0.2797 0.000 0.584 0.096 0.320
#> GSM601947 2 0.758 0.0721 0.088 0.488 0.036 0.388
#> GSM601957 3 0.531 0.4166 0.392 0.004 0.596 0.008
#> GSM601972 2 0.544 0.1295 0.004 0.532 0.008 0.456
#> GSM601977 2 0.581 0.4462 0.004 0.644 0.044 0.308
#> GSM601987 2 0.397 0.5414 0.000 0.788 0.008 0.204
#> GSM601877 1 0.172 0.6996 0.948 0.000 0.020 0.032
#> GSM601907 2 0.234 0.5715 0.000 0.920 0.020 0.060
#> GSM601917 4 0.808 0.3518 0.200 0.224 0.040 0.536
#> GSM601922 4 0.844 0.3300 0.216 0.232 0.052 0.500
#> GSM601952 2 0.712 0.1877 0.024 0.516 0.072 0.388
#> GSM601962 3 0.869 0.3129 0.268 0.052 0.448 0.232
#> GSM601967 3 0.608 0.4457 0.360 0.028 0.596 0.016
#> GSM601982 2 0.720 0.4406 0.036 0.636 0.144 0.184
#> GSM601992 4 0.615 0.0666 0.000 0.460 0.048 0.492
#> GSM601873 2 0.536 0.5111 0.000 0.700 0.048 0.252
#> GSM601883 2 0.451 0.5207 0.000 0.756 0.020 0.224
#> GSM601888 3 0.702 0.3262 0.060 0.384 0.528 0.028
#> GSM601893 3 0.736 0.4987 0.140 0.232 0.600 0.028
#> GSM601898 3 0.571 0.4361 0.384 0.000 0.584 0.032
#> GSM601903 4 0.610 0.3769 0.036 0.304 0.020 0.640
#> GSM601913 1 0.684 -0.3041 0.452 0.000 0.448 0.100
#> GSM601928 1 0.218 0.6957 0.924 0.000 0.064 0.012
#> GSM601933 2 0.515 0.4084 0.000 0.648 0.016 0.336
#> GSM601938 2 0.584 0.2160 0.000 0.564 0.036 0.400
#> GSM601943 2 0.560 0.4484 0.000 0.724 0.116 0.160
#> GSM601948 1 0.727 0.2192 0.540 0.020 0.340 0.100
#> GSM601958 3 0.499 0.3059 0.468 0.000 0.532 0.000
#> GSM601973 4 0.610 0.3344 0.020 0.340 0.028 0.612
#> GSM601978 2 0.213 0.5638 0.000 0.932 0.036 0.032
#> GSM601988 4 0.705 0.3577 0.012 0.300 0.112 0.576
#> GSM601878 1 0.151 0.6984 0.956 0.000 0.028 0.016
#> GSM601908 2 0.406 0.5654 0.000 0.812 0.028 0.160
#> GSM601918 2 0.651 0.0467 0.040 0.492 0.016 0.452
#> GSM601923 1 0.173 0.6983 0.948 0.000 0.028 0.024
#> GSM601953 2 0.329 0.5391 0.000 0.876 0.080 0.044
#> GSM601963 3 0.655 0.4604 0.340 0.004 0.576 0.080
#> GSM601968 3 0.598 0.5811 0.176 0.048 0.728 0.048
#> GSM601983 3 0.728 0.5123 0.276 0.024 0.584 0.116
#> GSM601993 4 0.551 0.3534 0.000 0.300 0.040 0.660
#> GSM601874 2 0.338 0.5746 0.000 0.872 0.052 0.076
#> GSM601884 2 0.468 0.5564 0.000 0.772 0.044 0.184
#> GSM601889 3 0.575 0.3953 0.428 0.008 0.548 0.016
#> GSM601894 3 0.641 0.4967 0.348 0.028 0.592 0.032
#> GSM601899 3 0.668 0.5017 0.068 0.244 0.652 0.036
#> GSM601904 4 0.711 0.3389 0.312 0.096 0.020 0.572
#> GSM601914 3 0.645 0.5382 0.260 0.008 0.640 0.092
#> GSM601929 1 0.499 0.6453 0.796 0.024 0.124 0.056
#> GSM601934 2 0.641 0.4574 0.024 0.664 0.068 0.244
#> GSM601939 1 0.442 0.5027 0.748 0.000 0.240 0.012
#> GSM601944 2 0.586 0.0475 0.000 0.500 0.032 0.468
#> GSM601949 1 0.587 0.2874 0.600 0.008 0.364 0.028
#> GSM601959 3 0.602 0.4920 0.344 0.028 0.612 0.016
#> GSM601974 4 0.935 0.2231 0.120 0.184 0.308 0.388
#> GSM601979 2 0.273 0.5737 0.000 0.896 0.016 0.088
#> GSM601989 3 0.626 0.4686 0.372 0.012 0.576 0.040
#> GSM601879 1 0.241 0.6896 0.920 0.000 0.040 0.040
#> GSM601909 3 0.596 0.5803 0.208 0.036 0.712 0.044
#> GSM601919 2 0.860 -0.1571 0.228 0.368 0.036 0.368
#> GSM601924 1 0.280 0.6848 0.888 0.000 0.100 0.012
#> GSM601954 2 0.789 0.3163 0.084 0.568 0.088 0.260
#> GSM601964 3 0.657 0.4483 0.344 0.004 0.572 0.080
#> GSM601969 3 0.746 0.4362 0.320 0.052 0.556 0.072
#> GSM601984 1 0.629 0.4567 0.648 0.004 0.256 0.092
#> GSM601994 4 0.548 0.1047 0.000 0.448 0.016 0.536
#> GSM601875 2 0.402 0.5659 0.000 0.836 0.068 0.096
#> GSM601885 2 0.517 0.5195 0.000 0.712 0.040 0.248
#> GSM601890 3 0.605 0.5269 0.048 0.176 0.724 0.052
#> GSM601895 3 0.729 0.5042 0.316 0.032 0.564 0.088
#> GSM601900 3 0.854 0.4221 0.284 0.064 0.484 0.168
#> GSM601905 4 0.809 0.3516 0.172 0.264 0.040 0.524
#> GSM601915 3 0.595 0.3839 0.416 0.000 0.544 0.040
#> GSM601930 1 0.264 0.6990 0.904 0.000 0.076 0.020
#> GSM601935 4 0.854 0.1399 0.112 0.096 0.308 0.484
#> GSM601940 1 0.488 0.4302 0.696 0.000 0.288 0.016
#> GSM601945 2 0.444 0.5359 0.000 0.764 0.020 0.216
#> GSM601950 1 0.599 0.2627 0.600 0.016 0.360 0.024
#> GSM601960 3 0.637 0.5492 0.240 0.008 0.656 0.096
#> GSM601975 4 0.642 0.1346 0.032 0.420 0.020 0.528
#> GSM601980 4 0.720 0.2227 0.004 0.364 0.128 0.504
#> GSM601990 3 0.692 0.4595 0.316 0.004 0.564 0.116
#> GSM601880 1 0.232 0.6931 0.924 0.000 0.036 0.040
#> GSM601910 3 0.733 0.5460 0.140 0.088 0.656 0.116
#> GSM601920 4 0.804 0.3395 0.244 0.200 0.032 0.524
#> GSM601925 1 0.232 0.6932 0.924 0.000 0.040 0.036
#> GSM601955 4 0.766 0.1824 0.000 0.332 0.224 0.444
#> GSM601965 1 0.851 0.2194 0.512 0.080 0.256 0.152
#> GSM601970 3 0.565 0.4984 0.344 0.000 0.620 0.036
#> GSM601985 1 0.543 0.3347 0.664 0.000 0.300 0.036
#> GSM601995 4 0.680 0.3592 0.000 0.224 0.172 0.604
#> GSM601876 1 0.547 0.4086 0.668 0.000 0.292 0.040
#> GSM601886 4 0.788 0.3884 0.120 0.148 0.120 0.612
#> GSM601891 3 0.781 0.3887 0.060 0.320 0.532 0.088
#> GSM601896 1 0.475 0.5094 0.716 0.000 0.268 0.016
#> GSM601901 2 0.573 0.4647 0.012 0.664 0.032 0.292
#> GSM601906 1 0.767 0.2849 0.552 0.068 0.072 0.308
#> GSM601916 4 0.714 0.4029 0.100 0.256 0.032 0.612
#> GSM601931 1 0.155 0.6978 0.952 0.000 0.040 0.008
#> GSM601936 4 0.656 0.3860 0.016 0.260 0.084 0.640
#> GSM601941 4 0.538 0.3479 0.008 0.320 0.016 0.656
#> GSM601946 1 0.384 0.6051 0.816 0.000 0.168 0.016
#> GSM601951 1 0.456 0.6426 0.796 0.000 0.140 0.064
#> GSM601961 2 0.557 0.4308 0.004 0.732 0.172 0.092
#> GSM601976 4 0.755 0.2937 0.092 0.328 0.040 0.540
#> GSM601981 2 0.469 0.5496 0.000 0.756 0.032 0.212
#> GSM601991 3 0.773 0.4545 0.176 0.024 0.548 0.252
#> GSM601881 1 0.163 0.6977 0.952 0.000 0.024 0.024
#> GSM601911 2 0.954 -0.1554 0.312 0.332 0.116 0.240
#> GSM601921 4 0.684 0.3890 0.096 0.260 0.020 0.624
#> GSM601926 1 0.209 0.6997 0.932 0.000 0.048 0.020
#> GSM601956 2 0.435 0.5496 0.000 0.816 0.080 0.104
#> GSM601966 2 0.575 0.1534 0.000 0.532 0.028 0.440
#> GSM601971 3 0.586 0.2449 0.476 0.000 0.492 0.032
#> GSM601986 1 0.945 0.0625 0.420 0.152 0.204 0.224
#> GSM601996 4 0.533 0.1869 0.000 0.420 0.012 0.568
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM601872 2 0.613 0.47121 0.000 0.664 0.136 0.060 0.140
#> GSM601882 2 0.657 0.31203 0.000 0.528 0.028 0.324 0.120
#> GSM601887 3 0.765 0.37930 0.096 0.216 0.544 0.024 0.120
#> GSM601892 3 0.599 0.50587 0.160 0.064 0.676 0.000 0.100
#> GSM601897 3 0.845 -0.12857 0.032 0.140 0.376 0.108 0.344
#> GSM601902 4 0.559 0.40221 0.012 0.248 0.004 0.656 0.080
#> GSM601912 3 0.876 0.35216 0.200 0.108 0.388 0.040 0.264
#> GSM601927 1 0.260 0.65857 0.904 0.000 0.040 0.020 0.036
#> GSM601932 4 0.667 0.36688 0.012 0.244 0.012 0.564 0.168
#> GSM601937 5 0.754 0.19870 0.000 0.228 0.056 0.272 0.444
#> GSM601942 2 0.730 0.23930 0.000 0.460 0.040 0.220 0.280
#> GSM601947 4 0.779 0.18369 0.052 0.360 0.036 0.436 0.116
#> GSM601957 3 0.481 0.40200 0.296 0.000 0.664 0.004 0.036
#> GSM601972 2 0.638 0.07708 0.004 0.468 0.008 0.408 0.112
#> GSM601977 2 0.643 0.46767 0.004 0.592 0.016 0.204 0.184
#> GSM601987 2 0.500 0.53252 0.000 0.732 0.012 0.148 0.108
#> GSM601877 1 0.286 0.65140 0.892 0.000 0.040 0.036 0.032
#> GSM601907 2 0.335 0.56280 0.000 0.864 0.036 0.068 0.032
#> GSM601917 4 0.757 0.30620 0.156 0.100 0.036 0.580 0.128
#> GSM601922 4 0.839 0.26678 0.224 0.136 0.020 0.444 0.176
#> GSM601952 2 0.731 0.28763 0.012 0.492 0.028 0.268 0.200
#> GSM601962 5 0.884 -0.12855 0.212 0.052 0.288 0.088 0.360
#> GSM601967 3 0.595 0.39059 0.304 0.016 0.608 0.016 0.056
#> GSM601982 2 0.807 0.32580 0.044 0.512 0.084 0.216 0.144
#> GSM601992 2 0.654 0.00217 0.000 0.408 0.000 0.396 0.196
#> GSM601873 2 0.665 0.46172 0.000 0.584 0.040 0.196 0.180
#> GSM601883 2 0.543 0.45761 0.000 0.636 0.004 0.276 0.084
#> GSM601888 3 0.726 0.16532 0.040 0.380 0.456 0.020 0.104
#> GSM601893 3 0.819 0.33925 0.124 0.248 0.464 0.020 0.144
#> GSM601898 3 0.552 0.43381 0.292 0.000 0.624 0.008 0.076
#> GSM601903 4 0.546 0.44983 0.020 0.172 0.000 0.696 0.112
#> GSM601913 1 0.688 -0.16741 0.424 0.008 0.404 0.012 0.152
#> GSM601928 1 0.294 0.65303 0.884 0.000 0.060 0.016 0.040
#> GSM601933 2 0.618 0.45143 0.000 0.596 0.012 0.228 0.164
#> GSM601938 2 0.600 0.29797 0.000 0.560 0.004 0.316 0.120
#> GSM601943 2 0.587 0.48576 0.000 0.644 0.016 0.136 0.204
#> GSM601948 1 0.831 0.06820 0.412 0.036 0.332 0.112 0.108
#> GSM601958 3 0.572 0.23279 0.408 0.000 0.520 0.008 0.064
#> GSM601973 4 0.600 0.41740 0.008 0.196 0.016 0.652 0.128
#> GSM601978 2 0.262 0.57340 0.000 0.900 0.012 0.048 0.040
#> GSM601988 5 0.761 0.01233 0.008 0.200 0.040 0.372 0.380
#> GSM601878 1 0.181 0.65381 0.936 0.000 0.040 0.004 0.020
#> GSM601908 2 0.482 0.54995 0.000 0.728 0.008 0.192 0.072
#> GSM601918 4 0.651 0.16598 0.044 0.384 0.004 0.504 0.064
#> GSM601923 1 0.285 0.65459 0.888 0.000 0.052 0.012 0.048
#> GSM601953 2 0.454 0.54099 0.004 0.800 0.052 0.084 0.060
#> GSM601963 3 0.701 0.38267 0.288 0.008 0.444 0.004 0.256
#> GSM601968 3 0.651 0.52212 0.132 0.056 0.668 0.028 0.116
#> GSM601983 3 0.734 0.41907 0.184 0.016 0.476 0.024 0.300
#> GSM601993 4 0.694 0.23010 0.004 0.252 0.008 0.468 0.268
#> GSM601874 2 0.476 0.56324 0.000 0.768 0.032 0.128 0.072
#> GSM601884 2 0.582 0.51326 0.000 0.648 0.016 0.208 0.128
#> GSM601889 3 0.622 0.29560 0.392 0.020 0.516 0.008 0.064
#> GSM601894 3 0.652 0.47473 0.244 0.020 0.588 0.008 0.140
#> GSM601899 3 0.745 0.31491 0.052 0.248 0.540 0.028 0.132
#> GSM601904 4 0.780 0.14251 0.236 0.056 0.032 0.508 0.168
#> GSM601914 3 0.641 0.45632 0.232 0.000 0.552 0.008 0.208
#> GSM601929 1 0.616 0.54523 0.688 0.008 0.092 0.112 0.100
#> GSM601934 2 0.663 0.45687 0.004 0.604 0.040 0.172 0.180
#> GSM601939 1 0.446 0.52254 0.740 0.000 0.208 0.004 0.048
#> GSM601944 2 0.710 0.12870 0.000 0.444 0.024 0.320 0.212
#> GSM601949 1 0.661 0.16674 0.512 0.012 0.372 0.032 0.072
#> GSM601959 3 0.613 0.43628 0.264 0.020 0.624 0.016 0.076
#> GSM601974 5 0.934 0.22190 0.092 0.108 0.200 0.280 0.320
#> GSM601979 2 0.262 0.57252 0.000 0.900 0.012 0.052 0.036
#> GSM601989 3 0.616 0.41192 0.300 0.008 0.584 0.012 0.096
#> GSM601879 1 0.386 0.64485 0.844 0.008 0.068 0.032 0.048
#> GSM601909 3 0.682 0.51330 0.176 0.032 0.604 0.020 0.168
#> GSM601919 4 0.850 0.25710 0.164 0.292 0.028 0.404 0.112
#> GSM601924 1 0.383 0.63919 0.816 0.000 0.124 0.008 0.052
#> GSM601954 2 0.821 0.16487 0.032 0.460 0.108 0.276 0.124
#> GSM601964 3 0.721 0.35790 0.292 0.008 0.436 0.012 0.252
#> GSM601969 3 0.761 0.38709 0.268 0.052 0.520 0.036 0.124
#> GSM601984 1 0.791 0.29525 0.524 0.028 0.160 0.096 0.192
#> GSM601994 4 0.686 0.01755 0.004 0.392 0.008 0.412 0.184
#> GSM601875 2 0.438 0.56021 0.000 0.800 0.044 0.104 0.052
#> GSM601885 2 0.651 0.44063 0.008 0.584 0.024 0.264 0.120
#> GSM601890 3 0.677 0.38955 0.044 0.156 0.624 0.020 0.156
#> GSM601895 3 0.758 0.43398 0.248 0.028 0.512 0.040 0.172
#> GSM601900 3 0.824 0.42180 0.224 0.052 0.464 0.052 0.208
#> GSM601905 4 0.779 0.36207 0.124 0.144 0.036 0.560 0.136
#> GSM601915 3 0.602 0.27360 0.364 0.000 0.512 0.000 0.124
#> GSM601930 1 0.331 0.64486 0.848 0.000 0.116 0.012 0.024
#> GSM601935 5 0.823 0.37911 0.064 0.068 0.188 0.176 0.504
#> GSM601940 1 0.625 0.36163 0.592 0.004 0.268 0.016 0.120
#> GSM601945 2 0.564 0.52297 0.000 0.664 0.012 0.196 0.128
#> GSM601950 1 0.605 0.23114 0.548 0.028 0.372 0.008 0.044
#> GSM601960 3 0.648 0.46066 0.208 0.000 0.540 0.008 0.244
#> GSM601975 4 0.647 0.37275 0.024 0.260 0.012 0.596 0.108
#> GSM601980 4 0.772 -0.14599 0.000 0.232 0.060 0.360 0.348
#> GSM601990 3 0.672 0.41122 0.208 0.000 0.476 0.008 0.308
#> GSM601880 1 0.286 0.65832 0.892 0.000 0.040 0.032 0.036
#> GSM601910 3 0.844 0.35154 0.116 0.100 0.476 0.068 0.240
#> GSM601920 4 0.869 0.23415 0.188 0.132 0.048 0.448 0.184
#> GSM601925 1 0.265 0.65261 0.900 0.000 0.036 0.016 0.048
#> GSM601955 5 0.821 0.27022 0.004 0.196 0.128 0.264 0.408
#> GSM601965 1 0.873 0.16540 0.440 0.064 0.196 0.096 0.204
#> GSM601970 3 0.503 0.46873 0.260 0.000 0.668 0.000 0.072
#> GSM601985 1 0.570 0.31259 0.596 0.000 0.304 0.004 0.096
#> GSM601995 5 0.741 0.24275 0.004 0.120 0.076 0.328 0.472
#> GSM601876 1 0.642 0.45137 0.616 0.008 0.220 0.028 0.128
#> GSM601886 5 0.883 0.17099 0.112 0.124 0.068 0.344 0.352
#> GSM601891 3 0.771 0.13337 0.024 0.324 0.456 0.048 0.148
#> GSM601896 1 0.620 0.33797 0.560 0.000 0.292 0.008 0.140
#> GSM601901 2 0.620 0.44329 0.008 0.620 0.012 0.216 0.144
#> GSM601906 1 0.824 0.30285 0.488 0.040 0.092 0.212 0.168
#> GSM601916 4 0.704 0.36693 0.108 0.148 0.016 0.616 0.112
#> GSM601931 1 0.295 0.65302 0.876 0.000 0.068 0.004 0.052
#> GSM601936 5 0.776 0.03719 0.040 0.164 0.024 0.368 0.404
#> GSM601941 4 0.520 0.41641 0.000 0.180 0.012 0.708 0.100
#> GSM601946 1 0.466 0.58621 0.752 0.000 0.168 0.012 0.068
#> GSM601951 1 0.685 0.49735 0.608 0.004 0.180 0.092 0.116
#> GSM601961 2 0.715 0.34695 0.020 0.604 0.172 0.108 0.096
#> GSM601976 4 0.805 0.33892 0.064 0.236 0.048 0.500 0.152
#> GSM601981 2 0.563 0.51607 0.000 0.660 0.016 0.224 0.100
#> GSM601991 5 0.821 -0.19424 0.136 0.056 0.364 0.052 0.392
#> GSM601881 1 0.176 0.65583 0.940 0.000 0.036 0.016 0.008
#> GSM601911 5 0.977 0.06935 0.236 0.228 0.104 0.188 0.244
#> GSM601921 4 0.755 0.40446 0.116 0.188 0.016 0.556 0.124
#> GSM601926 1 0.281 0.65609 0.880 0.000 0.084 0.004 0.032
#> GSM601956 2 0.507 0.54698 0.000 0.744 0.028 0.116 0.112
#> GSM601966 4 0.634 -0.03963 0.004 0.440 0.012 0.448 0.096
#> GSM601971 3 0.593 0.24437 0.400 0.000 0.520 0.020 0.060
#> GSM601986 1 0.935 0.08093 0.384 0.104 0.164 0.148 0.200
#> GSM601996 4 0.641 0.22317 0.000 0.332 0.004 0.500 0.164
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM601872 2 0.664 0.39256 0.004 0.612 0.080 0.044 0.148 0.112
#> GSM601882 2 0.755 0.24344 0.012 0.444 0.052 0.300 0.144 0.048
#> GSM601887 6 0.734 0.26034 0.060 0.184 0.112 0.016 0.076 0.552
#> GSM601892 6 0.491 0.34553 0.076 0.056 0.064 0.000 0.044 0.760
#> GSM601897 5 0.863 -0.05980 0.028 0.164 0.244 0.028 0.272 0.264
#> GSM601902 4 0.631 0.36598 0.028 0.200 0.024 0.608 0.128 0.012
#> GSM601912 3 0.890 0.16460 0.140 0.060 0.344 0.052 0.144 0.260
#> GSM601927 1 0.467 0.61885 0.764 0.000 0.092 0.036 0.020 0.088
#> GSM601932 4 0.681 0.27734 0.008 0.252 0.028 0.508 0.184 0.020
#> GSM601937 5 0.766 0.37679 0.004 0.196 0.136 0.172 0.464 0.028
#> GSM601942 2 0.698 0.15013 0.000 0.460 0.064 0.128 0.324 0.024
#> GSM601947 4 0.773 0.23724 0.108 0.284 0.056 0.452 0.088 0.012
#> GSM601957 6 0.483 0.35693 0.216 0.004 0.068 0.008 0.008 0.696
#> GSM601972 4 0.685 0.07037 0.012 0.348 0.036 0.460 0.128 0.016
#> GSM601977 2 0.682 0.38494 0.012 0.552 0.060 0.192 0.172 0.012
#> GSM601987 2 0.524 0.48021 0.000 0.692 0.016 0.156 0.116 0.020
#> GSM601877 1 0.391 0.62479 0.820 0.000 0.060 0.068 0.016 0.036
#> GSM601907 2 0.474 0.52107 0.000 0.764 0.024 0.088 0.080 0.044
#> GSM601917 4 0.770 0.32509 0.192 0.100 0.076 0.512 0.108 0.012
#> GSM601922 4 0.830 0.28229 0.212 0.088 0.112 0.448 0.116 0.024
#> GSM601952 4 0.800 -0.05206 0.012 0.280 0.096 0.304 0.288 0.020
#> GSM601962 3 0.845 0.33304 0.188 0.072 0.456 0.052 0.108 0.124
#> GSM601967 6 0.666 0.32254 0.220 0.028 0.116 0.016 0.040 0.580
#> GSM601982 2 0.804 0.36884 0.036 0.512 0.108 0.128 0.132 0.084
#> GSM601992 2 0.719 0.04891 0.004 0.340 0.028 0.332 0.276 0.020
#> GSM601873 2 0.614 0.39697 0.000 0.568 0.024 0.112 0.272 0.024
#> GSM601883 2 0.610 0.41830 0.008 0.596 0.036 0.240 0.112 0.008
#> GSM601888 6 0.716 0.18920 0.020 0.304 0.072 0.024 0.088 0.492
#> GSM601893 6 0.773 0.22590 0.072 0.212 0.120 0.008 0.096 0.492
#> GSM601898 6 0.651 0.13540 0.208 0.004 0.232 0.008 0.028 0.520
#> GSM601903 4 0.622 0.33475 0.020 0.120 0.056 0.636 0.160 0.008
#> GSM601913 6 0.754 -0.11798 0.292 0.004 0.292 0.024 0.052 0.336
#> GSM601928 1 0.361 0.63269 0.832 0.000 0.052 0.020 0.012 0.084
#> GSM601933 2 0.659 0.39938 0.000 0.560 0.056 0.156 0.208 0.020
#> GSM601938 2 0.682 0.23548 0.000 0.444 0.028 0.312 0.196 0.020
#> GSM601943 2 0.592 0.36772 0.000 0.600 0.052 0.068 0.264 0.016
#> GSM601948 1 0.815 0.14602 0.440 0.012 0.132 0.108 0.080 0.228
#> GSM601958 6 0.589 0.26583 0.296 0.000 0.108 0.012 0.020 0.564
#> GSM601973 4 0.728 0.29185 0.016 0.240 0.068 0.472 0.196 0.008
#> GSM601978 2 0.377 0.52984 0.000 0.828 0.024 0.044 0.080 0.024
#> GSM601988 5 0.796 0.22543 0.004 0.168 0.168 0.252 0.384 0.024
#> GSM601878 1 0.306 0.63548 0.860 0.000 0.052 0.024 0.000 0.064
#> GSM601908 2 0.594 0.47655 0.004 0.644 0.060 0.160 0.124 0.008
#> GSM601918 4 0.644 0.21138 0.048 0.320 0.048 0.528 0.056 0.000
#> GSM601923 1 0.331 0.63175 0.852 0.000 0.060 0.044 0.004 0.040
#> GSM601953 2 0.586 0.47247 0.000 0.680 0.044 0.068 0.088 0.120
#> GSM601963 3 0.666 0.32532 0.208 0.000 0.428 0.008 0.028 0.328
#> GSM601968 6 0.726 0.20822 0.080 0.032 0.240 0.032 0.080 0.536
#> GSM601983 3 0.716 0.36886 0.156 0.012 0.508 0.020 0.064 0.240
#> GSM601993 5 0.701 0.05369 0.000 0.212 0.052 0.348 0.380 0.008
#> GSM601874 2 0.556 0.50611 0.004 0.704 0.028 0.112 0.104 0.048
#> GSM601884 2 0.594 0.46685 0.000 0.648 0.068 0.160 0.108 0.016
#> GSM601889 6 0.636 0.25890 0.208 0.012 0.156 0.016 0.024 0.584
#> GSM601894 6 0.710 0.20602 0.192 0.020 0.196 0.028 0.036 0.528
#> GSM601899 6 0.715 0.27161 0.036 0.200 0.096 0.032 0.068 0.568
#> GSM601904 4 0.785 0.21053 0.216 0.024 0.100 0.464 0.168 0.028
#> GSM601914 3 0.691 0.28575 0.164 0.004 0.440 0.008 0.052 0.332
#> GSM601929 1 0.624 0.52505 0.648 0.000 0.092 0.116 0.044 0.100
#> GSM601934 2 0.755 0.29488 0.016 0.476 0.096 0.136 0.252 0.024
#> GSM601939 1 0.538 0.46573 0.640 0.000 0.108 0.004 0.020 0.228
#> GSM601944 5 0.696 -0.05810 0.000 0.348 0.060 0.200 0.388 0.004
#> GSM601949 6 0.645 0.08600 0.404 0.008 0.040 0.044 0.040 0.464
#> GSM601959 6 0.649 0.32288 0.204 0.012 0.116 0.040 0.028 0.600
#> GSM601974 4 0.934 -0.10094 0.088 0.068 0.244 0.248 0.240 0.112
#> GSM601979 2 0.425 0.51502 0.000 0.776 0.008 0.108 0.092 0.016
#> GSM601989 6 0.714 0.14386 0.204 0.020 0.252 0.016 0.032 0.476
#> GSM601879 1 0.409 0.62107 0.812 0.000 0.036 0.064 0.028 0.060
#> GSM601909 6 0.728 0.15203 0.140 0.040 0.264 0.008 0.056 0.492
#> GSM601919 4 0.745 0.33321 0.208 0.188 0.060 0.488 0.052 0.004
#> GSM601924 1 0.456 0.59467 0.760 0.000 0.076 0.028 0.012 0.124
#> GSM601954 2 0.876 0.00782 0.052 0.328 0.100 0.276 0.196 0.048
#> GSM601964 3 0.634 0.37573 0.204 0.008 0.508 0.000 0.020 0.260
#> GSM601969 6 0.810 0.28189 0.172 0.048 0.124 0.080 0.076 0.500
#> GSM601984 1 0.834 0.08768 0.416 0.048 0.276 0.064 0.116 0.080
#> GSM601994 2 0.675 0.10464 0.000 0.404 0.032 0.304 0.256 0.004
#> GSM601875 2 0.594 0.47963 0.000 0.668 0.040 0.120 0.116 0.056
#> GSM601885 2 0.639 0.41385 0.000 0.580 0.040 0.204 0.152 0.024
#> GSM601890 6 0.701 0.22132 0.036 0.140 0.224 0.008 0.052 0.540
#> GSM601895 6 0.830 -0.12288 0.136 0.044 0.336 0.036 0.096 0.352
#> GSM601900 6 0.880 -0.04431 0.172 0.052 0.196 0.056 0.136 0.388
#> GSM601905 4 0.798 0.28631 0.100 0.128 0.072 0.508 0.160 0.032
#> GSM601915 6 0.654 -0.02611 0.220 0.000 0.300 0.016 0.012 0.452
#> GSM601930 1 0.438 0.62608 0.792 0.004 0.052 0.024 0.032 0.096
#> GSM601935 5 0.821 0.12922 0.080 0.032 0.328 0.128 0.376 0.056
#> GSM601940 1 0.678 0.26684 0.488 0.004 0.148 0.012 0.048 0.300
#> GSM601945 2 0.579 0.49021 0.000 0.640 0.028 0.108 0.200 0.024
#> GSM601950 6 0.635 0.14967 0.392 0.012 0.076 0.008 0.040 0.472
#> GSM601960 6 0.673 -0.22127 0.116 0.000 0.396 0.020 0.048 0.420
#> GSM601975 4 0.736 0.29062 0.028 0.252 0.052 0.492 0.156 0.020
#> GSM601980 5 0.746 0.34724 0.000 0.236 0.096 0.164 0.468 0.036
#> GSM601990 3 0.696 0.38917 0.148 0.004 0.488 0.012 0.068 0.280
#> GSM601880 1 0.414 0.63204 0.808 0.000 0.060 0.040 0.024 0.068
#> GSM601910 6 0.853 0.00181 0.112 0.048 0.240 0.040 0.168 0.392
#> GSM601920 4 0.842 0.25320 0.172 0.096 0.108 0.460 0.128 0.036
#> GSM601925 1 0.381 0.62894 0.824 0.000 0.076 0.048 0.012 0.040
#> GSM601955 5 0.808 0.36108 0.004 0.168 0.244 0.108 0.412 0.064
#> GSM601965 1 0.903 -0.10377 0.320 0.052 0.284 0.104 0.104 0.136
#> GSM601970 6 0.567 0.27462 0.184 0.000 0.156 0.000 0.036 0.624
#> GSM601985 1 0.636 0.16607 0.508 0.000 0.208 0.004 0.028 0.252
#> GSM601995 5 0.727 0.41852 0.004 0.128 0.164 0.148 0.528 0.028
#> GSM601876 1 0.670 0.33547 0.524 0.000 0.152 0.016 0.056 0.252
#> GSM601886 5 0.848 0.28065 0.044 0.124 0.156 0.216 0.416 0.044
#> GSM601891 6 0.796 0.19367 0.036 0.228 0.128 0.032 0.108 0.468
#> GSM601896 1 0.686 0.25969 0.488 0.000 0.140 0.024 0.052 0.296
#> GSM601901 2 0.703 0.22669 0.004 0.472 0.048 0.316 0.128 0.032
#> GSM601906 1 0.828 0.21043 0.424 0.052 0.084 0.276 0.108 0.056
#> GSM601916 4 0.889 0.22105 0.084 0.172 0.088 0.388 0.204 0.064
#> GSM601931 1 0.322 0.63165 0.856 0.000 0.060 0.012 0.012 0.060
#> GSM601936 5 0.766 0.25673 0.016 0.108 0.136 0.236 0.476 0.028
#> GSM601941 4 0.653 0.30452 0.016 0.208 0.040 0.572 0.156 0.008
#> GSM601946 1 0.583 0.47732 0.640 0.000 0.152 0.024 0.024 0.160
#> GSM601951 1 0.710 0.42897 0.556 0.000 0.080 0.132 0.064 0.168
#> GSM601961 2 0.791 0.23868 0.020 0.476 0.064 0.104 0.104 0.232
#> GSM601976 4 0.845 0.18915 0.056 0.212 0.076 0.384 0.236 0.036
#> GSM601981 2 0.619 0.46218 0.000 0.608 0.032 0.164 0.168 0.028
#> GSM601991 3 0.808 0.32457 0.108 0.020 0.424 0.040 0.156 0.252
#> GSM601881 1 0.236 0.63605 0.904 0.000 0.020 0.008 0.012 0.056
#> GSM601911 3 0.973 -0.16077 0.196 0.192 0.224 0.124 0.184 0.080
#> GSM601921 4 0.776 0.33990 0.072 0.200 0.084 0.488 0.148 0.008
#> GSM601926 1 0.368 0.62769 0.816 0.000 0.056 0.012 0.008 0.108
#> GSM601956 2 0.620 0.47800 0.000 0.648 0.064 0.112 0.128 0.048
#> GSM601966 2 0.709 0.12196 0.000 0.412 0.060 0.320 0.196 0.012
#> GSM601971 6 0.669 0.26568 0.292 0.004 0.148 0.032 0.020 0.504
#> GSM601986 1 0.952 -0.03942 0.292 0.080 0.216 0.160 0.140 0.112
#> GSM601996 4 0.656 0.09050 0.004 0.312 0.024 0.432 0.228 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
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)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
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)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, 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.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")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n time(p) gender(p) k
#> CV:skmeans 123 0.351 0.717 2
#> CV:skmeans 81 0.239 0.448 3
#> CV:skmeans 43 0.519 0.394 4
#> CV:skmeans 31 0.962 0.389 5
#> CV:skmeans 18 0.581 0.410 6
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.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["CV", "pam"]
# you can also extract it by
# res = res_list["CV:pam"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 21512 rows and 125 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#> Subgroups are detected by 'pam' method.
#> Performed in total 1250 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_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
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, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.119 0.469 0.732 0.4013 0.708 0.708
#> 3 3 0.311 0.725 0.823 0.4798 0.643 0.521
#> 4 4 0.371 0.457 0.745 0.1425 0.960 0.906
#> 5 5 0.390 0.430 0.721 0.0303 0.948 0.872
#> 6 6 0.403 0.419 0.719 0.0127 0.999 0.998
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 3
Following shows 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 class
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
#> GSM601872 2 1.0000 0.3687 0.496 0.504
#> GSM601882 1 0.0938 0.6806 0.988 0.012
#> GSM601887 1 0.9996 -0.4040 0.512 0.488
#> GSM601892 1 0.2043 0.6795 0.968 0.032
#> GSM601897 1 0.7056 0.6064 0.808 0.192
#> GSM601902 1 0.9170 0.4608 0.668 0.332
#> GSM601912 1 0.1184 0.6837 0.984 0.016
#> GSM601927 1 0.7219 0.5452 0.800 0.200
#> GSM601932 1 0.5842 0.6302 0.860 0.140
#> GSM601937 1 0.4939 0.6630 0.892 0.108
#> GSM601942 1 0.7219 0.6020 0.800 0.200
#> GSM601947 2 0.6973 0.4766 0.188 0.812
#> GSM601957 1 0.8608 0.4396 0.716 0.284
#> GSM601972 1 0.5408 0.6688 0.876 0.124
#> GSM601977 1 0.6623 0.6100 0.828 0.172
#> GSM601987 1 0.7745 0.3286 0.772 0.228
#> GSM601877 1 0.8909 0.4048 0.692 0.308
#> GSM601907 1 0.9998 -0.4096 0.508 0.492
#> GSM601917 1 0.9661 0.2690 0.608 0.392
#> GSM601922 1 0.9209 0.3788 0.664 0.336
#> GSM601952 1 0.5519 0.6567 0.872 0.128
#> GSM601962 1 0.8267 0.5730 0.740 0.260
#> GSM601967 1 0.8955 0.4702 0.688 0.312
#> GSM601982 1 0.0938 0.6829 0.988 0.012
#> GSM601992 1 0.1184 0.6808 0.984 0.016
#> GSM601873 1 0.9775 0.0963 0.588 0.412
#> GSM601883 1 0.4562 0.6131 0.904 0.096
#> GSM601888 2 0.5519 0.5639 0.128 0.872
#> GSM601893 1 0.1843 0.6844 0.972 0.028
#> GSM601898 1 0.5629 0.6555 0.868 0.132
#> GSM601903 1 0.7528 0.5859 0.784 0.216
#> GSM601913 1 0.6148 0.6551 0.848 0.152
#> GSM601928 1 0.9998 0.1998 0.508 0.492
#> GSM601933 1 0.9815 -0.2431 0.580 0.420
#> GSM601938 2 0.9686 0.4650 0.396 0.604
#> GSM601943 1 0.8813 0.3043 0.700 0.300
#> GSM601948 2 0.5629 0.5219 0.132 0.868
#> GSM601958 1 0.3114 0.6821 0.944 0.056
#> GSM601973 1 0.5408 0.6622 0.876 0.124
#> GSM601978 2 0.9993 0.4085 0.484 0.516
#> GSM601988 1 0.2043 0.6851 0.968 0.032
#> GSM601878 1 0.9996 0.1440 0.512 0.488
#> GSM601908 1 0.9522 -0.1199 0.628 0.372
#> GSM601918 2 0.9661 0.1685 0.392 0.608
#> GSM601923 1 1.0000 0.1534 0.504 0.496
#> GSM601953 2 0.7815 0.5731 0.232 0.768
#> GSM601963 1 0.2236 0.6880 0.964 0.036
#> GSM601968 1 0.8555 0.4528 0.720 0.280
#> GSM601983 1 0.1414 0.6854 0.980 0.020
#> GSM601993 1 0.1633 0.6839 0.976 0.024
#> GSM601874 1 0.9608 -0.1754 0.616 0.384
#> GSM601884 1 0.2236 0.6869 0.964 0.036
#> GSM601889 1 0.4161 0.6665 0.916 0.084
#> GSM601894 1 0.0000 0.6783 1.000 0.000
#> GSM601899 2 0.9522 0.5004 0.372 0.628
#> GSM601904 1 0.8081 0.5758 0.752 0.248
#> GSM601914 1 0.8861 0.5250 0.696 0.304
#> GSM601929 1 0.8813 0.4207 0.700 0.300
#> GSM601934 1 0.4022 0.6432 0.920 0.080
#> GSM601939 1 0.8813 0.4360 0.700 0.300
#> GSM601944 1 0.1843 0.6840 0.972 0.028
#> GSM601949 2 0.9963 0.2262 0.464 0.536
#> GSM601959 1 0.3114 0.6802 0.944 0.056
#> GSM601974 1 0.5629 0.6520 0.868 0.132
#> GSM601979 1 0.9996 -0.4057 0.512 0.488
#> GSM601989 1 0.0672 0.6807 0.992 0.008
#> GSM601879 1 0.9833 0.2556 0.576 0.424
#> GSM601909 2 0.9491 0.1647 0.368 0.632
#> GSM601919 2 0.2948 0.5519 0.052 0.948
#> GSM601924 1 0.9608 0.2755 0.616 0.384
#> GSM601954 2 0.4161 0.5622 0.084 0.916
#> GSM601964 1 0.2423 0.6856 0.960 0.040
#> GSM601969 1 0.9944 -0.0194 0.544 0.456
#> GSM601984 1 0.3733 0.6842 0.928 0.072
#> GSM601994 1 0.2603 0.6834 0.956 0.044
#> GSM601875 1 0.9983 -0.3781 0.524 0.476
#> GSM601885 2 0.9988 0.3661 0.480 0.520
#> GSM601890 2 0.3114 0.5552 0.056 0.944
#> GSM601895 1 0.0938 0.6828 0.988 0.012
#> GSM601900 1 0.5842 0.6694 0.860 0.140
#> GSM601905 1 0.0938 0.6827 0.988 0.012
#> GSM601915 1 0.3114 0.6802 0.944 0.056
#> GSM601930 1 0.9944 0.2574 0.544 0.456
#> GSM601935 1 0.1843 0.6854 0.972 0.028
#> GSM601940 1 0.1633 0.6836 0.976 0.024
#> GSM601945 2 0.9944 0.4229 0.456 0.544
#> GSM601950 1 0.9710 0.2804 0.600 0.400
#> GSM601960 1 0.8327 0.5406 0.736 0.264
#> GSM601975 1 0.7299 0.6049 0.796 0.204
#> GSM601980 1 0.9933 -0.0560 0.548 0.452
#> GSM601990 1 0.4562 0.6766 0.904 0.096
#> GSM601880 1 0.9491 0.3743 0.632 0.368
#> GSM601910 1 0.9710 0.2778 0.600 0.400
#> GSM601920 1 0.9795 0.2089 0.584 0.416
#> GSM601925 1 0.9988 0.2129 0.520 0.480
#> GSM601955 1 0.8763 0.5261 0.704 0.296
#> GSM601965 1 0.4815 0.6709 0.896 0.104
#> GSM601970 1 0.9850 0.2794 0.572 0.428
#> GSM601985 1 0.3879 0.6790 0.924 0.076
#> GSM601995 1 0.5408 0.6605 0.876 0.124
#> GSM601876 1 0.3431 0.6778 0.936 0.064
#> GSM601886 1 0.6531 0.6298 0.832 0.168
#> GSM601891 2 0.6247 0.5773 0.156 0.844
#> GSM601896 1 0.0000 0.6783 1.000 0.000
#> GSM601901 1 0.2043 0.6789 0.968 0.032
#> GSM601906 1 0.6343 0.6397 0.840 0.160
#> GSM601916 1 0.1184 0.6841 0.984 0.016
#> GSM601931 1 0.8909 0.4091 0.692 0.308
#> GSM601936 1 0.1843 0.6867 0.972 0.028
#> GSM601941 1 0.7883 0.5897 0.764 0.236
#> GSM601946 1 0.0376 0.6796 0.996 0.004
#> GSM601951 1 0.9993 0.1825 0.516 0.484
#> GSM601961 2 0.9896 0.4585 0.440 0.560
#> GSM601976 1 0.6887 0.6188 0.816 0.184
#> GSM601981 2 0.9998 0.2942 0.492 0.508
#> GSM601991 1 0.2236 0.6872 0.964 0.036
#> GSM601881 1 0.9427 0.3659 0.640 0.360
#> GSM601911 1 0.0000 0.6783 1.000 0.000
#> GSM601921 2 0.9552 0.2218 0.376 0.624
#> GSM601926 1 0.9248 0.3847 0.660 0.340
#> GSM601956 2 0.9833 0.4326 0.424 0.576
#> GSM601966 1 0.4022 0.6796 0.920 0.080
#> GSM601971 2 0.9896 0.0593 0.440 0.560
#> GSM601986 1 0.3274 0.6867 0.940 0.060
#> GSM601996 1 0.1414 0.6839 0.980 0.020
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM601872 3 0.3425 0.8213 0.004 0.112 0.884
#> GSM601882 2 0.2356 0.8208 0.000 0.928 0.072
#> GSM601887 3 0.4465 0.7898 0.004 0.176 0.820
#> GSM601892 2 0.4110 0.7858 0.004 0.844 0.152
#> GSM601897 2 0.5662 0.8026 0.100 0.808 0.092
#> GSM601902 2 0.7752 -0.0627 0.456 0.496 0.048
#> GSM601912 2 0.0661 0.8270 0.008 0.988 0.004
#> GSM601927 1 0.6513 0.4119 0.520 0.476 0.004
#> GSM601932 2 0.4505 0.8044 0.092 0.860 0.048
#> GSM601937 2 0.4075 0.8247 0.072 0.880 0.048
#> GSM601942 2 0.5965 0.7941 0.108 0.792 0.100
#> GSM601947 1 0.3030 0.7358 0.904 0.004 0.092
#> GSM601957 2 0.7639 0.5825 0.088 0.656 0.256
#> GSM601972 2 0.4095 0.8334 0.064 0.880 0.056
#> GSM601977 2 0.6800 0.5892 0.032 0.660 0.308
#> GSM601987 2 0.6225 0.1313 0.000 0.568 0.432
#> GSM601877 1 0.3879 0.7935 0.848 0.152 0.000
#> GSM601907 3 0.2448 0.8194 0.000 0.076 0.924
#> GSM601917 1 0.3755 0.7980 0.872 0.120 0.008
#> GSM601922 1 0.5656 0.7532 0.728 0.264 0.008
#> GSM601952 2 0.5505 0.8106 0.088 0.816 0.096
#> GSM601962 2 0.6322 0.6849 0.276 0.700 0.024
#> GSM601967 1 0.7425 0.5532 0.620 0.328 0.052
#> GSM601982 2 0.4556 0.8129 0.080 0.860 0.060
#> GSM601992 2 0.2301 0.8351 0.004 0.936 0.060
#> GSM601873 3 0.7451 0.2452 0.040 0.396 0.564
#> GSM601883 2 0.4413 0.7531 0.008 0.832 0.160
#> GSM601888 3 0.3028 0.8081 0.048 0.032 0.920
#> GSM601893 2 0.3832 0.8316 0.036 0.888 0.076
#> GSM601898 2 0.4349 0.7917 0.128 0.852 0.020
#> GSM601903 2 0.7500 0.7291 0.140 0.696 0.164
#> GSM601913 2 0.5413 0.7684 0.164 0.800 0.036
#> GSM601928 1 0.5012 0.7610 0.840 0.080 0.080
#> GSM601933 3 0.5619 0.7098 0.012 0.244 0.744
#> GSM601938 3 0.2866 0.8055 0.008 0.076 0.916
#> GSM601943 2 0.7979 0.0986 0.060 0.500 0.440
#> GSM601948 1 0.2772 0.7453 0.916 0.004 0.080
#> GSM601958 2 0.3349 0.7828 0.108 0.888 0.004
#> GSM601973 2 0.4423 0.8224 0.088 0.864 0.048
#> GSM601978 3 0.2537 0.8190 0.000 0.080 0.920
#> GSM601988 2 0.1491 0.8342 0.016 0.968 0.016
#> GSM601878 1 0.2384 0.7876 0.936 0.056 0.008
#> GSM601908 3 0.6359 0.5620 0.008 0.364 0.628
#> GSM601918 1 0.3207 0.7955 0.904 0.084 0.012
#> GSM601923 1 0.2152 0.7736 0.948 0.036 0.016
#> GSM601953 3 0.1620 0.8039 0.024 0.012 0.964
#> GSM601963 2 0.4068 0.8111 0.120 0.864 0.016
#> GSM601968 2 0.9549 0.0985 0.276 0.484 0.240
#> GSM601983 2 0.3921 0.8067 0.112 0.872 0.016
#> GSM601993 2 0.2313 0.8335 0.024 0.944 0.032
#> GSM601874 3 0.6553 0.4661 0.008 0.412 0.580
#> GSM601884 2 0.2446 0.8371 0.012 0.936 0.052
#> GSM601889 2 0.5092 0.7623 0.020 0.804 0.176
#> GSM601894 2 0.1289 0.8225 0.032 0.968 0.000
#> GSM601899 3 0.3267 0.8160 0.044 0.044 0.912
#> GSM601904 2 0.6968 0.7300 0.204 0.716 0.080
#> GSM601914 2 0.7091 0.6712 0.268 0.676 0.056
#> GSM601929 1 0.4974 0.7758 0.764 0.236 0.000
#> GSM601934 2 0.2955 0.8197 0.008 0.912 0.080
#> GSM601939 1 0.6008 0.6879 0.664 0.332 0.004
#> GSM601944 2 0.1170 0.8306 0.008 0.976 0.016
#> GSM601949 1 0.8196 0.5833 0.560 0.356 0.084
#> GSM601959 2 0.2187 0.8369 0.024 0.948 0.028
#> GSM601974 2 0.5466 0.7879 0.160 0.800 0.040
#> GSM601979 3 0.2711 0.8192 0.000 0.088 0.912
#> GSM601989 2 0.0661 0.8296 0.004 0.988 0.008
#> GSM601879 1 0.2945 0.7929 0.908 0.088 0.004
#> GSM601909 1 0.3856 0.7647 0.888 0.040 0.072
#> GSM601919 1 0.1267 0.7552 0.972 0.004 0.024
#> GSM601924 1 0.3715 0.7953 0.868 0.128 0.004
#> GSM601954 3 0.6252 0.5879 0.268 0.024 0.708
#> GSM601964 2 0.1877 0.8353 0.032 0.956 0.012
#> GSM601969 2 0.9804 -0.0565 0.336 0.416 0.248
#> GSM601984 2 0.3234 0.8270 0.072 0.908 0.020
#> GSM601994 2 0.2564 0.8343 0.028 0.936 0.036
#> GSM601875 3 0.3618 0.8240 0.012 0.104 0.884
#> GSM601885 3 0.5235 0.7634 0.036 0.152 0.812
#> GSM601890 3 0.2772 0.7865 0.080 0.004 0.916
#> GSM601895 2 0.0661 0.8284 0.004 0.988 0.008
#> GSM601900 2 0.5603 0.8012 0.060 0.804 0.136
#> GSM601905 2 0.1399 0.8272 0.028 0.968 0.004
#> GSM601915 2 0.3272 0.7924 0.104 0.892 0.004
#> GSM601930 1 0.2096 0.7908 0.944 0.052 0.004
#> GSM601935 2 0.1337 0.8326 0.016 0.972 0.012
#> GSM601940 2 0.2066 0.8161 0.060 0.940 0.000
#> GSM601945 3 0.2313 0.8182 0.024 0.032 0.944
#> GSM601950 1 0.7129 0.4607 0.580 0.392 0.028
#> GSM601960 2 0.7442 0.4921 0.348 0.604 0.048
#> GSM601975 2 0.5915 0.7879 0.128 0.792 0.080
#> GSM601980 2 0.9377 0.1121 0.172 0.448 0.380
#> GSM601990 2 0.3896 0.8248 0.060 0.888 0.052
#> GSM601880 1 0.4615 0.7965 0.836 0.144 0.020
#> GSM601910 2 0.8649 0.5748 0.172 0.596 0.232
#> GSM601920 1 0.7092 0.7483 0.708 0.208 0.084
#> GSM601925 1 0.1950 0.7857 0.952 0.040 0.008
#> GSM601955 2 0.7899 0.6974 0.192 0.664 0.144
#> GSM601965 2 0.4978 0.7198 0.216 0.780 0.004
#> GSM601970 1 0.7442 0.4529 0.588 0.368 0.044
#> GSM601985 2 0.3769 0.7949 0.104 0.880 0.016
#> GSM601995 2 0.5371 0.8038 0.048 0.812 0.140
#> GSM601876 2 0.3148 0.8252 0.048 0.916 0.036
#> GSM601886 2 0.5428 0.8016 0.120 0.816 0.064
#> GSM601891 3 0.1751 0.8057 0.028 0.012 0.960
#> GSM601896 2 0.0000 0.8247 0.000 1.000 0.000
#> GSM601901 2 0.1877 0.8358 0.012 0.956 0.032
#> GSM601906 2 0.5020 0.8111 0.108 0.836 0.056
#> GSM601916 2 0.0829 0.8292 0.012 0.984 0.004
#> GSM601931 1 0.4178 0.7956 0.828 0.172 0.000
#> GSM601936 2 0.1182 0.8329 0.012 0.976 0.012
#> GSM601941 2 0.5787 0.7962 0.136 0.796 0.068
#> GSM601946 2 0.0000 0.8247 0.000 1.000 0.000
#> GSM601951 1 0.6109 0.7223 0.760 0.192 0.048
#> GSM601961 3 0.3272 0.8253 0.016 0.080 0.904
#> GSM601976 2 0.6222 0.7898 0.092 0.776 0.132
#> GSM601981 3 0.8148 0.5029 0.100 0.296 0.604
#> GSM601991 2 0.2550 0.8347 0.056 0.932 0.012
#> GSM601881 1 0.4531 0.7932 0.824 0.168 0.008
#> GSM601911 2 0.0000 0.8247 0.000 1.000 0.000
#> GSM601921 1 0.9066 0.4126 0.540 0.284 0.176
#> GSM601926 1 0.3619 0.7952 0.864 0.136 0.000
#> GSM601956 3 0.4527 0.7812 0.088 0.052 0.860
#> GSM601966 2 0.4165 0.8282 0.048 0.876 0.076
#> GSM601971 1 0.3850 0.7966 0.884 0.088 0.028
#> GSM601986 2 0.3910 0.8197 0.104 0.876 0.020
#> GSM601996 2 0.1170 0.8314 0.008 0.976 0.016
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM601872 2 0.2010 0.7438 0.004 0.932 0.004 0.060
#> GSM601882 4 0.5056 0.3620 0.000 0.044 0.224 0.732
#> GSM601887 2 0.3501 0.7064 0.000 0.848 0.020 0.132
#> GSM601892 4 0.2814 0.5681 0.000 0.132 0.000 0.868
#> GSM601897 4 0.6206 0.2485 0.020 0.028 0.368 0.584
#> GSM601902 4 0.8181 -0.3352 0.300 0.008 0.340 0.352
#> GSM601912 4 0.0779 0.5967 0.004 0.000 0.016 0.980
#> GSM601927 1 0.5285 0.2293 0.524 0.000 0.008 0.468
#> GSM601932 4 0.4446 0.5048 0.028 0.000 0.196 0.776
#> GSM601937 4 0.4996 0.5113 0.028 0.020 0.184 0.768
#> GSM601942 3 0.6769 -0.0133 0.024 0.044 0.476 0.456
#> GSM601947 1 0.5431 0.6103 0.668 0.028 0.300 0.004
#> GSM601957 4 0.7908 0.0722 0.036 0.172 0.244 0.548
#> GSM601972 4 0.4160 0.5750 0.028 0.012 0.132 0.828
#> GSM601977 4 0.7917 -0.1611 0.004 0.312 0.256 0.428
#> GSM601987 4 0.6206 0.0420 0.000 0.404 0.056 0.540
#> GSM601877 1 0.1118 0.7450 0.964 0.000 0.000 0.036
#> GSM601907 2 0.0188 0.7478 0.000 0.996 0.004 0.000
#> GSM601917 1 0.1109 0.7475 0.968 0.000 0.004 0.028
#> GSM601922 1 0.5781 0.6406 0.708 0.008 0.072 0.212
#> GSM601952 4 0.4447 0.5556 0.036 0.028 0.108 0.828
#> GSM601962 4 0.7164 0.1682 0.240 0.000 0.204 0.556
#> GSM601967 1 0.6848 0.3300 0.592 0.000 0.160 0.248
#> GSM601982 4 0.6892 0.3037 0.080 0.048 0.216 0.656
#> GSM601992 4 0.6815 0.1348 0.012 0.076 0.360 0.552
#> GSM601873 2 0.6907 0.0597 0.008 0.528 0.088 0.376
#> GSM601883 4 0.5820 0.3040 0.000 0.084 0.232 0.684
#> GSM601888 2 0.1305 0.7444 0.004 0.960 0.036 0.000
#> GSM601893 4 0.3110 0.5982 0.004 0.048 0.056 0.892
#> GSM601898 4 0.6111 0.2347 0.056 0.004 0.324 0.616
#> GSM601903 4 0.6908 0.3426 0.044 0.080 0.228 0.648
#> GSM601913 4 0.6377 0.3913 0.108 0.008 0.220 0.664
#> GSM601928 1 0.5169 0.6073 0.696 0.000 0.272 0.032
#> GSM601933 2 0.4840 0.5336 0.000 0.732 0.028 0.240
#> GSM601938 2 0.4608 0.5628 0.000 0.692 0.304 0.004
#> GSM601943 4 0.7538 -0.0648 0.016 0.380 0.124 0.480
#> GSM601948 1 0.5005 0.6403 0.712 0.020 0.264 0.004
#> GSM601958 4 0.4894 0.5180 0.100 0.000 0.120 0.780
#> GSM601973 4 0.5546 0.2924 0.016 0.008 0.356 0.620
#> GSM601978 2 0.0376 0.7484 0.000 0.992 0.004 0.004
#> GSM601988 4 0.1545 0.6014 0.000 0.008 0.040 0.952
#> GSM601878 1 0.0657 0.7423 0.984 0.004 0.000 0.012
#> GSM601908 2 0.7765 0.1361 0.004 0.460 0.220 0.316
#> GSM601918 1 0.4981 0.7034 0.780 0.012 0.156 0.052
#> GSM601923 1 0.1004 0.7389 0.972 0.000 0.024 0.004
#> GSM601953 2 0.0376 0.7482 0.004 0.992 0.004 0.000
#> GSM601963 4 0.3682 0.5897 0.084 0.008 0.044 0.864
#> GSM601968 4 0.7857 -0.1033 0.272 0.272 0.004 0.452
#> GSM601983 4 0.3709 0.5790 0.100 0.004 0.040 0.856
#> GSM601993 4 0.4993 0.4543 0.000 0.028 0.260 0.712
#> GSM601874 2 0.7608 0.0360 0.000 0.408 0.200 0.392
#> GSM601884 4 0.5569 0.3078 0.004 0.040 0.280 0.676
#> GSM601889 4 0.5530 0.4067 0.000 0.212 0.076 0.712
#> GSM601894 4 0.0188 0.5921 0.004 0.000 0.000 0.996
#> GSM601899 2 0.3377 0.7205 0.000 0.848 0.140 0.012
#> GSM601904 4 0.6055 0.0431 0.044 0.000 0.436 0.520
#> GSM601914 4 0.7073 -0.1328 0.108 0.004 0.408 0.480
#> GSM601929 1 0.3591 0.7130 0.824 0.000 0.008 0.168
#> GSM601934 4 0.2413 0.5927 0.000 0.064 0.020 0.916
#> GSM601939 1 0.5110 0.5171 0.656 0.000 0.016 0.328
#> GSM601944 4 0.3400 0.5579 0.000 0.000 0.180 0.820
#> GSM601949 1 0.8564 0.3154 0.452 0.068 0.144 0.336
#> GSM601959 4 0.3695 0.5784 0.000 0.016 0.156 0.828
#> GSM601974 4 0.4621 0.5259 0.076 0.000 0.128 0.796
#> GSM601979 2 0.0779 0.7513 0.000 0.980 0.004 0.016
#> GSM601989 4 0.0524 0.5963 0.000 0.008 0.004 0.988
#> GSM601879 1 0.1584 0.7449 0.952 0.000 0.036 0.012
#> GSM601909 1 0.5428 0.6667 0.736 0.028 0.208 0.028
#> GSM601919 1 0.2654 0.7197 0.888 0.004 0.108 0.000
#> GSM601924 1 0.1022 0.7454 0.968 0.000 0.000 0.032
#> GSM601954 2 0.7482 0.4643 0.212 0.580 0.188 0.020
#> GSM601964 4 0.2101 0.5992 0.012 0.000 0.060 0.928
#> GSM601969 4 0.9641 -0.2548 0.284 0.184 0.168 0.364
#> GSM601984 4 0.5667 0.4069 0.060 0.004 0.240 0.696
#> GSM601994 4 0.5950 0.0901 0.000 0.040 0.416 0.544
#> GSM601875 2 0.1824 0.7434 0.000 0.936 0.004 0.060
#> GSM601885 2 0.6454 0.4140 0.000 0.572 0.344 0.084
#> GSM601890 2 0.2662 0.7373 0.016 0.900 0.084 0.000
#> GSM601895 4 0.2281 0.5921 0.000 0.000 0.096 0.904
#> GSM601900 4 0.6855 0.4169 0.028 0.132 0.180 0.660
#> GSM601905 4 0.1398 0.5985 0.004 0.000 0.040 0.956
#> GSM601915 4 0.2775 0.5813 0.084 0.000 0.020 0.896
#> GSM601930 1 0.1305 0.7427 0.960 0.000 0.036 0.004
#> GSM601935 4 0.3402 0.5616 0.004 0.000 0.164 0.832
#> GSM601940 4 0.1022 0.5964 0.032 0.000 0.000 0.968
#> GSM601945 2 0.1837 0.7513 0.000 0.944 0.028 0.028
#> GSM601950 1 0.7640 0.1557 0.444 0.004 0.180 0.372
#> GSM601960 4 0.6916 0.0811 0.280 0.000 0.148 0.572
#> GSM601975 4 0.5537 0.2180 0.016 0.004 0.392 0.588
#> GSM601980 3 0.5336 0.3855 0.032 0.088 0.784 0.096
#> GSM601990 4 0.5024 0.3184 0.008 0.000 0.360 0.632
#> GSM601880 1 0.4236 0.7288 0.824 0.000 0.088 0.088
#> GSM601910 3 0.7210 0.3224 0.020 0.092 0.536 0.352
#> GSM601920 1 0.6015 0.6696 0.720 0.080 0.024 0.176
#> GSM601925 1 0.1743 0.7413 0.940 0.000 0.056 0.004
#> GSM601955 4 0.7136 -0.1082 0.052 0.036 0.444 0.468
#> GSM601965 4 0.5055 0.4074 0.256 0.000 0.032 0.712
#> GSM601970 1 0.8308 -0.0489 0.412 0.020 0.328 0.240
#> GSM601985 4 0.3266 0.5866 0.084 0.000 0.040 0.876
#> GSM601995 4 0.7172 0.1713 0.012 0.128 0.288 0.572
#> GSM601876 4 0.4632 0.3839 0.004 0.000 0.308 0.688
#> GSM601886 4 0.4898 0.4320 0.024 0.000 0.260 0.716
#> GSM601891 2 0.1807 0.7506 0.000 0.940 0.052 0.008
#> GSM601896 4 0.0000 0.5916 0.000 0.000 0.000 1.000
#> GSM601901 4 0.2089 0.6033 0.000 0.020 0.048 0.932
#> GSM601906 4 0.5055 0.4336 0.032 0.000 0.256 0.712
#> GSM601916 4 0.2773 0.5843 0.004 0.000 0.116 0.880
#> GSM601931 1 0.2281 0.7458 0.904 0.000 0.000 0.096
#> GSM601936 4 0.1305 0.6009 0.000 0.004 0.036 0.960
#> GSM601941 3 0.4800 0.3653 0.004 0.000 0.656 0.340
#> GSM601946 4 0.0817 0.5976 0.000 0.000 0.024 0.976
#> GSM601951 1 0.7274 0.4739 0.540 0.004 0.296 0.160
#> GSM601961 2 0.2275 0.7505 0.004 0.928 0.048 0.020
#> GSM601976 4 0.6228 0.3936 0.008 0.072 0.272 0.648
#> GSM601981 2 0.7770 -0.1690 0.000 0.416 0.336 0.248
#> GSM601991 4 0.4544 0.5620 0.048 0.000 0.164 0.788
#> GSM601881 1 0.3760 0.7266 0.836 0.000 0.028 0.136
#> GSM601911 4 0.0524 0.5951 0.000 0.004 0.008 0.988
#> GSM601921 3 0.8153 0.2751 0.300 0.044 0.504 0.152
#> GSM601926 1 0.1807 0.7495 0.940 0.000 0.008 0.052
#> GSM601956 2 0.5155 0.6467 0.024 0.768 0.172 0.036
#> GSM601966 4 0.6066 0.1695 0.004 0.040 0.392 0.564
#> GSM601971 1 0.4893 0.7110 0.772 0.004 0.172 0.052
#> GSM601986 4 0.4954 0.5323 0.092 0.004 0.120 0.784
#> GSM601996 4 0.2805 0.5940 0.000 0.012 0.100 0.888
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM601872 2 0.1662 0.70172 0.004 0.936 0.056 0.000 0.004
#> GSM601882 3 0.5579 0.38934 0.000 0.024 0.692 0.152 0.132
#> GSM601887 2 0.2612 0.65977 0.000 0.868 0.124 0.008 0.000
#> GSM601892 3 0.2377 0.57682 0.000 0.128 0.872 0.000 0.000
#> GSM601897 3 0.6197 0.29314 0.008 0.028 0.564 0.340 0.060
#> GSM601902 4 0.7409 0.45435 0.276 0.008 0.320 0.380 0.016
#> GSM601912 3 0.0771 0.59204 0.004 0.000 0.976 0.020 0.000
#> GSM601927 1 0.4552 0.11333 0.524 0.000 0.468 0.008 0.000
#> GSM601932 3 0.4901 0.48201 0.008 0.000 0.724 0.188 0.080
#> GSM601937 3 0.5270 0.52961 0.016 0.020 0.744 0.124 0.096
#> GSM601942 3 0.7351 0.02046 0.008 0.024 0.412 0.356 0.200
#> GSM601947 1 0.6109 0.47460 0.568 0.016 0.004 0.328 0.084
#> GSM601957 3 0.6835 -0.11769 0.028 0.144 0.476 0.352 0.000
#> GSM601972 3 0.4585 0.55773 0.016 0.012 0.780 0.144 0.048
#> GSM601977 3 0.8149 -0.03245 0.004 0.272 0.404 0.212 0.108
#> GSM601987 3 0.6310 0.12650 0.000 0.368 0.524 0.036 0.072
#> GSM601877 1 0.0510 0.70579 0.984 0.000 0.016 0.000 0.000
#> GSM601907 2 0.0000 0.70740 0.000 1.000 0.000 0.000 0.000
#> GSM601917 1 0.0771 0.70918 0.976 0.000 0.020 0.004 0.000
#> GSM601922 1 0.5302 0.56274 0.700 0.008 0.204 0.080 0.008
#> GSM601952 3 0.4477 0.54695 0.024 0.028 0.812 0.084 0.052
#> GSM601962 3 0.6777 0.11739 0.224 0.000 0.544 0.204 0.028
#> GSM601967 1 0.5962 0.14936 0.584 0.000 0.248 0.168 0.000
#> GSM601982 3 0.7162 0.35078 0.096 0.032 0.620 0.136 0.116
#> GSM601992 3 0.7431 0.19418 0.016 0.060 0.504 0.300 0.120
#> GSM601873 2 0.6297 0.08174 0.008 0.520 0.372 0.088 0.012
#> GSM601883 3 0.6418 0.33278 0.000 0.060 0.632 0.172 0.136
#> GSM601888 2 0.0771 0.70619 0.004 0.976 0.000 0.020 0.000
#> GSM601893 3 0.2754 0.59348 0.004 0.032 0.884 0.080 0.000
#> GSM601898 3 0.5256 -0.03513 0.048 0.000 0.532 0.420 0.000
#> GSM601903 3 0.6769 0.38963 0.028 0.076 0.640 0.176 0.080
#> GSM601913 3 0.5647 0.34105 0.108 0.008 0.640 0.244 0.000
#> GSM601928 1 0.5497 0.53446 0.676 0.000 0.028 0.228 0.068
#> GSM601933 2 0.4334 0.50447 0.000 0.744 0.220 0.016 0.020
#> GSM601938 2 0.5594 0.30649 0.000 0.632 0.000 0.232 0.136
#> GSM601943 3 0.6984 0.05997 0.000 0.360 0.480 0.084 0.076
#> GSM601948 1 0.6017 0.49314 0.584 0.016 0.004 0.316 0.080
#> GSM601958 3 0.4437 0.49881 0.100 0.000 0.760 0.140 0.000
#> GSM601973 3 0.5875 0.37092 0.004 0.004 0.604 0.280 0.108
#> GSM601978 2 0.0162 0.70807 0.000 0.996 0.004 0.000 0.000
#> GSM601988 3 0.1503 0.59724 0.000 0.008 0.952 0.020 0.020
#> GSM601878 1 0.0324 0.70334 0.992 0.004 0.004 0.000 0.000
#> GSM601908 2 0.8066 -0.00556 0.004 0.408 0.308 0.160 0.120
#> GSM601918 1 0.5019 0.62503 0.732 0.008 0.048 0.192 0.020
#> GSM601923 1 0.1278 0.70331 0.960 0.000 0.004 0.020 0.016
#> GSM601953 2 0.0162 0.70770 0.004 0.996 0.000 0.000 0.000
#> GSM601963 3 0.3355 0.58474 0.076 0.004 0.860 0.052 0.008
#> GSM601968 3 0.6780 -0.13471 0.272 0.276 0.448 0.004 0.000
#> GSM601983 3 0.3502 0.57165 0.104 0.004 0.848 0.028 0.016
#> GSM601993 3 0.5440 0.46330 0.000 0.012 0.664 0.240 0.084
#> GSM601874 3 0.7565 -0.15087 0.000 0.376 0.388 0.168 0.068
#> GSM601884 3 0.6179 0.34172 0.000 0.024 0.624 0.188 0.164
#> GSM601889 3 0.5365 0.36719 0.000 0.204 0.664 0.132 0.000
#> GSM601894 3 0.0162 0.58718 0.004 0.000 0.996 0.000 0.000
#> GSM601899 2 0.3318 0.63531 0.000 0.808 0.012 0.180 0.000
#> GSM601904 3 0.6256 0.01104 0.032 0.000 0.496 0.404 0.068
#> GSM601914 4 0.6703 0.23367 0.096 0.000 0.424 0.440 0.040
#> GSM601929 1 0.3304 0.65412 0.816 0.000 0.168 0.016 0.000
#> GSM601934 3 0.2331 0.59308 0.000 0.068 0.908 0.016 0.008
#> GSM601939 1 0.4558 0.42280 0.652 0.000 0.324 0.024 0.000
#> GSM601944 3 0.4869 0.47239 0.000 0.000 0.712 0.192 0.096
#> GSM601949 1 0.7947 0.11839 0.396 0.052 0.328 0.208 0.016
#> GSM601959 3 0.3427 0.55814 0.000 0.012 0.796 0.192 0.000
#> GSM601974 3 0.4776 0.52012 0.052 0.000 0.776 0.104 0.068
#> GSM601979 2 0.0510 0.71172 0.000 0.984 0.016 0.000 0.000
#> GSM601989 3 0.0451 0.59149 0.000 0.008 0.988 0.004 0.000
#> GSM601879 1 0.1281 0.70758 0.956 0.000 0.012 0.032 0.000
#> GSM601909 1 0.5068 0.57743 0.700 0.024 0.016 0.244 0.016
#> GSM601919 1 0.3250 0.65110 0.820 0.004 0.000 0.168 0.008
#> GSM601924 1 0.0404 0.70577 0.988 0.000 0.012 0.000 0.000
#> GSM601954 2 0.7467 0.15918 0.196 0.500 0.020 0.252 0.032
#> GSM601964 3 0.1907 0.59582 0.000 0.000 0.928 0.044 0.028
#> GSM601969 3 0.8547 -0.34838 0.276 0.124 0.352 0.236 0.012
#> GSM601984 3 0.5159 0.36326 0.060 0.004 0.672 0.260 0.004
#> GSM601994 3 0.6709 0.12314 0.000 0.024 0.484 0.356 0.136
#> GSM601875 2 0.1121 0.70954 0.000 0.956 0.044 0.000 0.000
#> GSM601885 2 0.6988 0.17031 0.000 0.524 0.072 0.300 0.104
#> GSM601890 2 0.2537 0.68663 0.016 0.904 0.000 0.056 0.024
#> GSM601895 3 0.2280 0.58019 0.000 0.000 0.880 0.120 0.000
#> GSM601900 3 0.6123 0.41501 0.032 0.128 0.636 0.204 0.000
#> GSM601905 3 0.1798 0.58720 0.004 0.000 0.928 0.064 0.004
#> GSM601915 3 0.2452 0.57401 0.084 0.000 0.896 0.016 0.004
#> GSM601930 1 0.0865 0.70581 0.972 0.000 0.004 0.024 0.000
#> GSM601935 3 0.3439 0.54216 0.004 0.000 0.800 0.188 0.008
#> GSM601940 3 0.0794 0.59059 0.028 0.000 0.972 0.000 0.000
#> GSM601945 2 0.1364 0.71130 0.000 0.952 0.012 0.036 0.000
#> GSM601950 1 0.7569 -0.07707 0.364 0.000 0.344 0.248 0.044
#> GSM601960 3 0.6889 0.06007 0.232 0.000 0.572 0.120 0.076
#> GSM601975 3 0.5158 0.20900 0.004 0.000 0.568 0.392 0.036
#> GSM601980 5 0.5569 0.00000 0.008 0.044 0.016 0.304 0.628
#> GSM601990 3 0.4801 0.24964 0.008 0.000 0.584 0.396 0.012
#> GSM601880 1 0.3840 0.66579 0.808 0.000 0.076 0.116 0.000
#> GSM601910 4 0.5899 0.46742 0.012 0.072 0.284 0.620 0.012
#> GSM601920 1 0.5368 0.60756 0.716 0.080 0.176 0.020 0.008
#> GSM601925 1 0.1357 0.70334 0.948 0.000 0.004 0.048 0.000
#> GSM601955 4 0.7510 0.09238 0.028 0.012 0.220 0.440 0.300
#> GSM601965 3 0.4503 0.37502 0.268 0.000 0.696 0.036 0.000
#> GSM601970 4 0.7198 0.14765 0.380 0.008 0.180 0.412 0.020
#> GSM601985 3 0.2871 0.57490 0.088 0.000 0.872 0.040 0.000
#> GSM601995 3 0.7395 0.29303 0.004 0.112 0.548 0.200 0.136
#> GSM601876 3 0.4211 0.31941 0.004 0.000 0.636 0.360 0.000
#> GSM601886 3 0.4881 0.42422 0.004 0.000 0.696 0.240 0.060
#> GSM601891 2 0.1365 0.71170 0.000 0.952 0.004 0.040 0.004
#> GSM601896 3 0.0000 0.58696 0.000 0.000 1.000 0.000 0.000
#> GSM601901 3 0.2208 0.59709 0.000 0.020 0.908 0.072 0.000
#> GSM601906 3 0.5266 0.40668 0.020 0.000 0.684 0.236 0.060
#> GSM601916 3 0.2719 0.57069 0.004 0.000 0.852 0.144 0.000
#> GSM601931 1 0.1851 0.70364 0.912 0.000 0.088 0.000 0.000
#> GSM601936 3 0.1329 0.59652 0.000 0.004 0.956 0.032 0.008
#> GSM601941 4 0.4526 0.44533 0.004 0.004 0.260 0.708 0.024
#> GSM601946 3 0.0963 0.59291 0.000 0.000 0.964 0.036 0.000
#> GSM601951 1 0.6988 0.26335 0.460 0.000 0.156 0.352 0.032
#> GSM601961 2 0.1889 0.71090 0.004 0.936 0.020 0.036 0.004
#> GSM601976 3 0.5393 0.38699 0.008 0.064 0.628 0.300 0.000
#> GSM601981 2 0.6759 -0.14616 0.000 0.416 0.220 0.360 0.004
#> GSM601991 3 0.4378 0.54197 0.040 0.000 0.760 0.188 0.012
#> GSM601881 1 0.3193 0.67601 0.840 0.000 0.132 0.028 0.000
#> GSM601911 3 0.0613 0.59159 0.000 0.004 0.984 0.008 0.004
#> GSM601921 4 0.8188 0.24682 0.244 0.036 0.132 0.484 0.104
#> GSM601926 1 0.1251 0.71075 0.956 0.000 0.036 0.008 0.000
#> GSM601956 2 0.5231 0.51786 0.004 0.740 0.032 0.132 0.092
#> GSM601966 3 0.6376 0.17704 0.004 0.024 0.524 0.364 0.084
#> GSM601971 1 0.4898 0.60808 0.708 0.000 0.052 0.228 0.012
#> GSM601986 3 0.4481 0.51761 0.100 0.004 0.776 0.116 0.004
#> GSM601996 3 0.3269 0.58321 0.000 0.000 0.848 0.096 0.056
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM601872 2 0.1493 0.707937 0.004 0.936 0.056 0.000 0.004 0.000
#> GSM601882 3 0.5453 0.371138 0.000 0.016 0.680 0.160 0.112 0.032
#> GSM601887 2 0.2092 0.666300 0.000 0.876 0.124 0.000 0.000 0.000
#> GSM601892 3 0.2092 0.571804 0.000 0.124 0.876 0.000 0.000 0.000
#> GSM601897 3 0.5870 0.304915 0.012 0.032 0.564 0.312 0.080 0.000
#> GSM601902 4 0.6702 0.477530 0.272 0.008 0.308 0.392 0.020 0.000
#> GSM601912 3 0.0692 0.583073 0.004 0.000 0.976 0.020 0.000 0.000
#> GSM601927 1 0.4089 0.091961 0.524 0.000 0.468 0.008 0.000 0.000
#> GSM601932 3 0.4747 0.476162 0.012 0.000 0.716 0.152 0.116 0.004
#> GSM601937 3 0.4623 0.535948 0.016 0.016 0.760 0.108 0.096 0.004
#> GSM601942 3 0.6963 0.026389 0.008 0.016 0.408 0.328 0.220 0.020
#> GSM601947 1 0.5933 0.423958 0.536 0.012 0.004 0.324 0.116 0.008
#> GSM601957 3 0.6157 -0.219904 0.020 0.140 0.444 0.392 0.000 0.004
#> GSM601972 3 0.4685 0.528119 0.020 0.008 0.756 0.144 0.052 0.020
#> GSM601977 3 0.7334 -0.000414 0.004 0.260 0.412 0.208 0.116 0.000
#> GSM601987 3 0.6182 0.136153 0.000 0.348 0.524 0.048 0.048 0.032
#> GSM601877 1 0.0363 0.693005 0.988 0.000 0.012 0.000 0.000 0.000
#> GSM601907 2 0.0000 0.712451 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM601917 1 0.0603 0.697489 0.980 0.000 0.016 0.004 0.000 0.000
#> GSM601922 1 0.4841 0.539840 0.696 0.004 0.204 0.084 0.008 0.004
#> GSM601952 3 0.3990 0.540076 0.024 0.020 0.812 0.064 0.080 0.000
#> GSM601962 3 0.6274 0.123248 0.228 0.000 0.544 0.188 0.036 0.004
#> GSM601967 1 0.5516 0.076485 0.560 0.000 0.244 0.196 0.000 0.000
#> GSM601982 3 0.6323 0.369915 0.096 0.020 0.632 0.128 0.120 0.004
#> GSM601992 3 0.7099 0.171048 0.016 0.052 0.492 0.308 0.100 0.032
#> GSM601873 2 0.5862 0.084097 0.008 0.516 0.372 0.080 0.020 0.004
#> GSM601883 3 0.6369 0.290317 0.000 0.048 0.604 0.192 0.120 0.036
#> GSM601888 2 0.0508 0.711534 0.004 0.984 0.000 0.012 0.000 0.000
#> GSM601893 3 0.2554 0.580978 0.004 0.028 0.876 0.092 0.000 0.000
#> GSM601898 3 0.4685 -0.114070 0.044 0.000 0.520 0.436 0.000 0.000
#> GSM601903 3 0.6263 0.383892 0.032 0.072 0.632 0.148 0.116 0.000
#> GSM601913 3 0.5132 0.319307 0.112 0.008 0.632 0.248 0.000 0.000
#> GSM601928 1 0.5071 0.523954 0.676 0.000 0.028 0.204 0.092 0.000
#> GSM601933 2 0.3921 0.532922 0.000 0.748 0.216 0.016 0.016 0.004
#> GSM601938 2 0.5691 0.323858 0.000 0.600 0.000 0.244 0.124 0.032
#> GSM601943 3 0.6347 0.063765 0.000 0.356 0.476 0.084 0.084 0.000
#> GSM601948 1 0.5698 0.457732 0.568 0.012 0.004 0.304 0.108 0.004
#> GSM601958 3 0.3992 0.485454 0.104 0.000 0.760 0.136 0.000 0.000
#> GSM601973 3 0.5317 0.372186 0.004 0.000 0.604 0.272 0.116 0.004
#> GSM601978 2 0.0146 0.712984 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM601988 3 0.1434 0.589093 0.000 0.008 0.948 0.024 0.020 0.000
#> GSM601878 1 0.0291 0.691760 0.992 0.004 0.004 0.000 0.000 0.000
#> GSM601908 2 0.7633 0.106972 0.000 0.392 0.308 0.172 0.096 0.032
#> GSM601918 1 0.4725 0.591690 0.716 0.008 0.044 0.208 0.016 0.008
#> GSM601923 1 0.0909 0.690597 0.968 0.000 0.000 0.012 0.020 0.000
#> GSM601953 2 0.0146 0.712749 0.004 0.996 0.000 0.000 0.000 0.000
#> GSM601963 3 0.2903 0.579351 0.068 0.004 0.868 0.052 0.008 0.000
#> GSM601968 3 0.6090 -0.133398 0.272 0.276 0.448 0.004 0.000 0.000
#> GSM601983 3 0.2988 0.569482 0.100 0.004 0.860 0.020 0.012 0.004
#> GSM601993 3 0.5292 0.425869 0.000 0.008 0.636 0.268 0.060 0.028
#> GSM601874 3 0.6757 -0.148188 0.000 0.380 0.388 0.168 0.064 0.000
#> GSM601884 3 0.5981 0.335978 0.000 0.016 0.620 0.188 0.140 0.036
#> GSM601889 3 0.5000 0.350266 0.000 0.200 0.656 0.140 0.000 0.004
#> GSM601894 3 0.0146 0.578548 0.004 0.000 0.996 0.000 0.000 0.000
#> GSM601899 2 0.3213 0.617393 0.000 0.784 0.008 0.204 0.000 0.004
#> GSM601904 3 0.5801 0.028685 0.032 0.000 0.496 0.384 0.088 0.000
#> GSM601914 4 0.6330 0.255718 0.100 0.000 0.416 0.428 0.052 0.004
#> GSM601929 1 0.2932 0.642552 0.820 0.000 0.164 0.016 0.000 0.000
#> GSM601934 3 0.2094 0.584572 0.000 0.068 0.908 0.016 0.008 0.000
#> GSM601939 1 0.4094 0.403815 0.652 0.000 0.324 0.024 0.000 0.000
#> GSM601944 3 0.6882 -0.072730 0.000 0.000 0.468 0.236 0.084 0.212
#> GSM601949 1 0.7269 0.056019 0.380 0.048 0.328 0.224 0.016 0.004
#> GSM601959 3 0.3230 0.536451 0.000 0.012 0.776 0.212 0.000 0.000
#> GSM601974 3 0.4322 0.516256 0.056 0.000 0.776 0.084 0.084 0.000
#> GSM601979 2 0.0458 0.716632 0.000 0.984 0.016 0.000 0.000 0.000
#> GSM601989 3 0.0405 0.582910 0.000 0.008 0.988 0.004 0.000 0.000
#> GSM601879 1 0.1367 0.696038 0.944 0.000 0.012 0.044 0.000 0.000
#> GSM601909 1 0.4603 0.566409 0.700 0.024 0.016 0.244 0.012 0.004
#> GSM601919 1 0.2989 0.630207 0.812 0.000 0.000 0.176 0.004 0.008
#> GSM601924 1 0.0260 0.693088 0.992 0.000 0.008 0.000 0.000 0.000
#> GSM601954 2 0.6948 0.158874 0.192 0.488 0.020 0.256 0.040 0.004
#> GSM601964 3 0.1642 0.587648 0.000 0.000 0.936 0.028 0.032 0.004
#> GSM601969 3 0.7893 -0.376945 0.272 0.116 0.348 0.240 0.020 0.004
#> GSM601984 3 0.4505 0.346938 0.056 0.004 0.668 0.272 0.000 0.000
#> GSM601994 3 0.6389 0.092394 0.000 0.016 0.468 0.372 0.112 0.032
#> GSM601875 2 0.0865 0.716448 0.000 0.964 0.036 0.000 0.000 0.000
#> GSM601885 2 0.6359 0.275746 0.000 0.512 0.072 0.304 0.112 0.000
#> GSM601890 2 0.2188 0.692341 0.020 0.912 0.000 0.032 0.036 0.000
#> GSM601895 3 0.2003 0.572490 0.000 0.000 0.884 0.116 0.000 0.000
#> GSM601900 3 0.5641 0.417532 0.032 0.120 0.640 0.200 0.008 0.000
#> GSM601905 3 0.1615 0.576299 0.004 0.000 0.928 0.064 0.004 0.000
#> GSM601915 3 0.2203 0.563644 0.084 0.000 0.896 0.016 0.004 0.000
#> GSM601930 1 0.0837 0.694644 0.972 0.000 0.004 0.020 0.004 0.000
#> GSM601935 3 0.3014 0.535408 0.000 0.000 0.804 0.184 0.012 0.000
#> GSM601940 3 0.0632 0.581847 0.024 0.000 0.976 0.000 0.000 0.000
#> GSM601945 2 0.1010 0.714462 0.000 0.960 0.004 0.036 0.000 0.000
#> GSM601950 1 0.7144 -0.129651 0.352 0.000 0.340 0.236 0.068 0.004
#> GSM601960 3 0.6216 0.064852 0.240 0.000 0.568 0.096 0.096 0.000
#> GSM601975 3 0.4875 0.215820 0.008 0.000 0.568 0.376 0.048 0.000
#> GSM601980 5 0.2537 0.000000 0.008 0.000 0.000 0.088 0.880 0.024
#> GSM601990 3 0.4151 0.230205 0.004 0.000 0.576 0.412 0.008 0.000
#> GSM601880 1 0.3587 0.641369 0.792 0.000 0.068 0.140 0.000 0.000
#> GSM601910 4 0.5088 0.494617 0.008 0.076 0.268 0.640 0.008 0.000
#> GSM601920 1 0.4742 0.596502 0.720 0.076 0.176 0.024 0.004 0.000
#> GSM601925 1 0.1349 0.692538 0.940 0.000 0.004 0.056 0.000 0.000
#> GSM601955 6 0.4654 0.000000 0.004 0.008 0.040 0.180 0.032 0.736
#> GSM601965 3 0.4067 0.380474 0.260 0.000 0.700 0.040 0.000 0.000
#> GSM601970 4 0.6677 0.157796 0.372 0.008 0.172 0.416 0.028 0.004
#> GSM601985 3 0.2579 0.564937 0.088 0.000 0.872 0.040 0.000 0.000
#> GSM601995 3 0.6798 0.284252 0.004 0.104 0.540 0.196 0.152 0.004
#> GSM601876 3 0.3727 0.276791 0.000 0.000 0.612 0.388 0.000 0.000
#> GSM601886 3 0.4533 0.426412 0.004 0.000 0.700 0.208 0.088 0.000
#> GSM601891 2 0.1116 0.716783 0.000 0.960 0.004 0.028 0.008 0.000
#> GSM601896 3 0.0000 0.578397 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM601901 3 0.2147 0.588148 0.000 0.020 0.896 0.084 0.000 0.000
#> GSM601906 3 0.4903 0.404330 0.020 0.000 0.684 0.208 0.088 0.000
#> GSM601916 3 0.2520 0.559990 0.004 0.000 0.844 0.152 0.000 0.000
#> GSM601931 1 0.1610 0.692133 0.916 0.000 0.084 0.000 0.000 0.000
#> GSM601936 3 0.1232 0.587505 0.000 0.004 0.956 0.024 0.016 0.000
#> GSM601941 4 0.4459 0.471469 0.004 0.004 0.240 0.708 0.024 0.020
#> GSM601946 3 0.0937 0.584060 0.000 0.000 0.960 0.040 0.000 0.000
#> GSM601951 1 0.6707 0.200161 0.440 0.000 0.156 0.344 0.056 0.004
#> GSM601961 2 0.1623 0.714928 0.004 0.940 0.020 0.032 0.004 0.000
#> GSM601976 3 0.5067 0.370777 0.008 0.064 0.624 0.296 0.008 0.000
#> GSM601981 2 0.6017 -0.101155 0.004 0.424 0.204 0.368 0.000 0.000
#> GSM601991 3 0.3867 0.532372 0.040 0.000 0.760 0.192 0.008 0.000
#> GSM601881 1 0.2901 0.665043 0.840 0.000 0.128 0.032 0.000 0.000
#> GSM601911 3 0.0508 0.583084 0.000 0.004 0.984 0.012 0.000 0.000
#> GSM601921 4 0.7570 0.340924 0.212 0.036 0.136 0.484 0.128 0.004
#> GSM601926 1 0.1225 0.699477 0.952 0.000 0.036 0.012 0.000 0.000
#> GSM601956 2 0.4698 0.555989 0.008 0.748 0.032 0.108 0.104 0.000
#> GSM601966 3 0.5545 0.155205 0.000 0.016 0.520 0.372 0.092 0.000
#> GSM601971 1 0.4721 0.571861 0.684 0.000 0.052 0.244 0.016 0.004
#> GSM601986 3 0.3968 0.506552 0.100 0.004 0.772 0.124 0.000 0.000
#> GSM601996 3 0.3440 0.564277 0.000 0.000 0.828 0.108 0.028 0.036
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)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
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)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
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)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, 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.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")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n time(p) gender(p) k
#> CV:pam 74 0.296 0.848709 2
#> CV:pam 112 0.942 0.002267 3
#> CV:pam 72 0.939 0.000563 4
#> CV:pam 65 0.968 0.000138 5
#> CV:pam 65 0.968 0.000138 6
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.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["CV", "mclust"]
# you can also extract it by
# res = res_list["CV:mclust"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 21512 rows and 125 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#> Subgroups are detected by 'mclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 6.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
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, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.400 0.849 0.872 0.4437 0.496 0.496
#> 3 3 0.288 0.647 0.739 0.3014 0.858 0.730
#> 4 4 0.486 0.517 0.710 0.1625 0.863 0.688
#> 5 5 0.769 0.793 0.894 0.1527 0.782 0.427
#> 6 6 0.827 0.797 0.904 0.0515 0.952 0.786
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 6
Following shows 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 class
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
#> GSM601872 2 0.0938 0.933 0.012 0.988
#> GSM601882 2 0.0000 0.943 0.000 1.000
#> GSM601887 1 0.9954 0.571 0.540 0.460
#> GSM601892 1 0.8713 0.842 0.708 0.292
#> GSM601897 2 0.9954 -0.361 0.460 0.540
#> GSM601902 2 0.4939 0.858 0.108 0.892
#> GSM601912 1 0.8016 0.878 0.756 0.244
#> GSM601927 1 0.0376 0.763 0.996 0.004
#> GSM601932 2 0.1843 0.929 0.028 0.972
#> GSM601937 2 0.0000 0.943 0.000 1.000
#> GSM601942 2 0.0000 0.943 0.000 1.000
#> GSM601947 2 0.0938 0.938 0.012 0.988
#> GSM601957 1 0.7883 0.882 0.764 0.236
#> GSM601972 2 0.1843 0.929 0.028 0.972
#> GSM601977 2 0.0000 0.943 0.000 1.000
#> GSM601987 2 0.0000 0.943 0.000 1.000
#> GSM601877 1 0.2603 0.794 0.956 0.044
#> GSM601907 2 0.0000 0.943 0.000 1.000
#> GSM601917 2 0.4815 0.863 0.104 0.896
#> GSM601922 2 0.4939 0.858 0.108 0.892
#> GSM601952 2 0.0000 0.943 0.000 1.000
#> GSM601962 1 0.8713 0.839 0.708 0.292
#> GSM601967 1 0.7674 0.884 0.776 0.224
#> GSM601982 2 0.4161 0.842 0.084 0.916
#> GSM601992 2 0.0376 0.941 0.004 0.996
#> GSM601873 2 0.0000 0.943 0.000 1.000
#> GSM601883 2 0.0000 0.943 0.000 1.000
#> GSM601888 1 0.9988 0.522 0.520 0.480
#> GSM601893 1 0.9881 0.624 0.564 0.436
#> GSM601898 1 0.7674 0.884 0.776 0.224
#> GSM601903 2 0.4815 0.863 0.104 0.896
#> GSM601913 1 0.7745 0.884 0.772 0.228
#> GSM601928 1 0.1414 0.776 0.980 0.020
#> GSM601933 2 0.0000 0.943 0.000 1.000
#> GSM601938 2 0.0000 0.943 0.000 1.000
#> GSM601943 2 0.0000 0.943 0.000 1.000
#> GSM601948 1 0.7745 0.884 0.772 0.228
#> GSM601958 1 0.7745 0.884 0.772 0.228
#> GSM601973 2 0.2236 0.923 0.036 0.964
#> GSM601978 2 0.0000 0.943 0.000 1.000
#> GSM601988 2 0.0000 0.943 0.000 1.000
#> GSM601878 1 0.5408 0.845 0.876 0.124
#> GSM601908 2 0.0000 0.943 0.000 1.000
#> GSM601918 2 0.3584 0.895 0.068 0.932
#> GSM601923 1 0.0672 0.767 0.992 0.008
#> GSM601953 2 0.0000 0.943 0.000 1.000
#> GSM601963 1 0.7815 0.883 0.768 0.232
#> GSM601968 1 0.8327 0.867 0.736 0.264
#> GSM601983 1 0.7883 0.882 0.764 0.236
#> GSM601993 2 0.0000 0.943 0.000 1.000
#> GSM601874 2 0.0000 0.943 0.000 1.000
#> GSM601884 2 0.0000 0.943 0.000 1.000
#> GSM601889 1 0.7745 0.884 0.772 0.228
#> GSM601894 1 0.8016 0.878 0.756 0.244
#> GSM601899 1 0.9970 0.552 0.532 0.468
#> GSM601904 2 0.4815 0.868 0.104 0.896
#> GSM601914 1 0.8207 0.871 0.744 0.256
#> GSM601929 1 0.4815 0.833 0.896 0.104
#> GSM601934 2 0.0000 0.943 0.000 1.000
#> GSM601939 1 0.7056 0.877 0.808 0.192
#> GSM601944 2 0.0000 0.943 0.000 1.000
#> GSM601949 1 0.7528 0.884 0.784 0.216
#> GSM601959 1 0.7883 0.882 0.764 0.236
#> GSM601974 2 0.8955 0.298 0.312 0.688
#> GSM601979 2 0.0000 0.943 0.000 1.000
#> GSM601989 1 0.7602 0.884 0.780 0.220
#> GSM601879 1 0.5059 0.838 0.888 0.112
#> GSM601909 1 0.8207 0.873 0.744 0.256
#> GSM601919 2 0.3274 0.902 0.060 0.940
#> GSM601924 1 0.5059 0.838 0.888 0.112
#> GSM601954 2 0.0000 0.943 0.000 1.000
#> GSM601964 1 0.7883 0.882 0.764 0.236
#> GSM601969 1 0.7528 0.884 0.784 0.216
#> GSM601984 1 0.7376 0.883 0.792 0.208
#> GSM601994 2 0.0000 0.943 0.000 1.000
#> GSM601875 2 0.0000 0.943 0.000 1.000
#> GSM601885 2 0.0000 0.943 0.000 1.000
#> GSM601890 1 0.9833 0.648 0.576 0.424
#> GSM601895 1 0.8207 0.871 0.744 0.256
#> GSM601900 1 0.8713 0.844 0.708 0.292
#> GSM601905 2 0.4815 0.863 0.104 0.896
#> GSM601915 1 0.7883 0.882 0.764 0.236
#> GSM601930 1 0.1184 0.773 0.984 0.016
#> GSM601935 2 0.9323 0.129 0.348 0.652
#> GSM601940 1 0.7219 0.880 0.800 0.200
#> GSM601945 2 0.0000 0.943 0.000 1.000
#> GSM601950 1 0.7602 0.884 0.780 0.220
#> GSM601960 1 0.8267 0.870 0.740 0.260
#> GSM601975 2 0.2948 0.911 0.052 0.948
#> GSM601980 2 0.0000 0.943 0.000 1.000
#> GSM601990 1 0.7815 0.883 0.768 0.232
#> GSM601880 1 0.2603 0.783 0.956 0.044
#> GSM601910 1 0.8443 0.859 0.728 0.272
#> GSM601920 2 0.5059 0.858 0.112 0.888
#> GSM601925 1 0.0376 0.763 0.996 0.004
#> GSM601955 2 0.0000 0.943 0.000 1.000
#> GSM601965 1 0.7376 0.882 0.792 0.208
#> GSM601970 1 0.7883 0.882 0.764 0.236
#> GSM601985 1 0.7056 0.877 0.808 0.192
#> GSM601995 2 0.0000 0.943 0.000 1.000
#> GSM601876 1 0.7056 0.877 0.808 0.192
#> GSM601886 2 0.0672 0.937 0.008 0.992
#> GSM601891 1 0.9988 0.522 0.520 0.480
#> GSM601896 1 0.7453 0.883 0.788 0.212
#> GSM601901 2 0.0000 0.943 0.000 1.000
#> GSM601906 1 0.9286 0.592 0.656 0.344
#> GSM601916 2 0.2603 0.923 0.044 0.956
#> GSM601931 1 0.1184 0.773 0.984 0.016
#> GSM601936 2 0.0000 0.943 0.000 1.000
#> GSM601941 2 0.2236 0.923 0.036 0.964
#> GSM601946 1 0.7056 0.877 0.808 0.192
#> GSM601951 1 0.4562 0.828 0.904 0.096
#> GSM601961 2 0.0672 0.937 0.008 0.992
#> GSM601976 2 0.0672 0.940 0.008 0.992
#> GSM601981 2 0.0000 0.943 0.000 1.000
#> GSM601991 1 0.9608 0.715 0.616 0.384
#> GSM601881 1 0.0938 0.770 0.988 0.012
#> GSM601911 2 0.5737 0.756 0.136 0.864
#> GSM601921 2 0.4939 0.858 0.108 0.892
#> GSM601926 1 0.0376 0.763 0.996 0.004
#> GSM601956 2 0.0000 0.943 0.000 1.000
#> GSM601966 2 0.0672 0.940 0.008 0.992
#> GSM601971 1 0.7299 0.881 0.796 0.204
#> GSM601986 1 0.9427 0.754 0.640 0.360
#> GSM601996 2 0.1633 0.931 0.024 0.976
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM601872 2 0.5028 0.7467 0.132 0.828 0.040
#> GSM601882 2 0.4056 0.7805 0.092 0.876 0.032
#> GSM601887 1 0.6967 0.5329 0.668 0.288 0.044
#> GSM601892 1 0.4521 0.6550 0.816 0.180 0.004
#> GSM601897 1 0.6745 0.1422 0.560 0.428 0.012
#> GSM601902 2 0.5706 0.6398 0.000 0.680 0.320
#> GSM601912 1 0.2772 0.7124 0.916 0.080 0.004
#> GSM601927 3 0.5591 0.9278 0.304 0.000 0.696
#> GSM601932 2 0.5848 0.6907 0.012 0.720 0.268
#> GSM601937 2 0.7615 0.7029 0.148 0.688 0.164
#> GSM601942 2 0.7039 0.7328 0.144 0.728 0.128
#> GSM601947 2 0.6416 0.7033 0.032 0.708 0.260
#> GSM601957 1 0.2165 0.7122 0.936 0.064 0.000
#> GSM601972 2 0.6292 0.7349 0.044 0.740 0.216
#> GSM601977 2 0.3933 0.7749 0.092 0.880 0.028
#> GSM601987 2 0.4399 0.7689 0.092 0.864 0.044
#> GSM601877 3 0.5621 0.9286 0.308 0.000 0.692
#> GSM601907 2 0.4399 0.7689 0.092 0.864 0.044
#> GSM601917 2 0.5706 0.6398 0.000 0.680 0.320
#> GSM601922 2 0.6047 0.6400 0.008 0.680 0.312
#> GSM601952 2 0.3042 0.7770 0.040 0.920 0.040
#> GSM601962 1 0.4842 0.5417 0.776 0.224 0.000
#> GSM601967 1 0.2280 0.7040 0.940 0.052 0.008
#> GSM601982 2 0.4723 0.7295 0.160 0.824 0.016
#> GSM601992 2 0.5506 0.7230 0.016 0.764 0.220
#> GSM601873 2 0.4335 0.7721 0.100 0.864 0.036
#> GSM601883 2 0.4232 0.7782 0.084 0.872 0.044
#> GSM601888 2 0.6919 0.2014 0.448 0.536 0.016
#> GSM601893 1 0.6633 0.5670 0.700 0.260 0.040
#> GSM601898 1 0.1289 0.7028 0.968 0.032 0.000
#> GSM601903 2 0.5678 0.6437 0.000 0.684 0.316
#> GSM601913 1 0.0237 0.6868 0.996 0.004 0.000
#> GSM601928 3 0.5621 0.9286 0.308 0.000 0.692
#> GSM601933 2 0.3293 0.7783 0.088 0.900 0.012
#> GSM601938 2 0.4556 0.7832 0.080 0.860 0.060
#> GSM601943 2 0.5319 0.7585 0.104 0.824 0.072
#> GSM601948 1 0.7078 0.3627 0.712 0.088 0.200
#> GSM601958 1 0.1964 0.7086 0.944 0.056 0.000
#> GSM601973 2 0.5884 0.6880 0.012 0.716 0.272
#> GSM601978 2 0.4289 0.7698 0.092 0.868 0.040
#> GSM601988 2 0.6693 0.7410 0.148 0.748 0.104
#> GSM601878 3 0.6763 0.7525 0.436 0.012 0.552
#> GSM601908 2 0.3973 0.7727 0.088 0.880 0.032
#> GSM601918 2 0.5903 0.7169 0.024 0.744 0.232
#> GSM601923 3 0.5591 0.9278 0.304 0.000 0.696
#> GSM601953 2 0.4399 0.7689 0.092 0.864 0.044
#> GSM601963 1 0.0424 0.6900 0.992 0.008 0.000
#> GSM601968 1 0.2772 0.7120 0.916 0.080 0.004
#> GSM601983 1 0.1289 0.7048 0.968 0.032 0.000
#> GSM601993 2 0.5737 0.7547 0.092 0.804 0.104
#> GSM601874 2 0.4399 0.7689 0.092 0.864 0.044
#> GSM601884 2 0.4289 0.7699 0.092 0.868 0.040
#> GSM601889 1 0.2400 0.7110 0.932 0.064 0.004
#> GSM601894 1 0.2496 0.7119 0.928 0.068 0.004
#> GSM601899 1 0.6967 0.5324 0.668 0.288 0.044
#> GSM601904 2 0.6322 0.6768 0.024 0.700 0.276
#> GSM601914 1 0.1163 0.6971 0.972 0.028 0.000
#> GSM601929 1 0.9311 -0.3637 0.452 0.164 0.384
#> GSM601934 2 0.3272 0.7742 0.104 0.892 0.004
#> GSM601939 1 0.5948 -0.1105 0.640 0.000 0.360
#> GSM601944 2 0.5285 0.7763 0.112 0.824 0.064
#> GSM601949 1 0.8241 0.2979 0.636 0.160 0.204
#> GSM601959 1 0.2400 0.7111 0.932 0.064 0.004
#> GSM601974 2 0.8085 0.2779 0.412 0.520 0.068
#> GSM601979 2 0.4289 0.7698 0.092 0.868 0.040
#> GSM601989 1 0.2400 0.7110 0.932 0.064 0.004
#> GSM601879 3 0.6359 0.8270 0.404 0.004 0.592
#> GSM601909 1 0.2945 0.7104 0.908 0.088 0.004
#> GSM601919 2 0.6335 0.7148 0.036 0.724 0.240
#> GSM601924 3 0.6192 0.8012 0.420 0.000 0.580
#> GSM601954 2 0.5191 0.7775 0.060 0.828 0.112
#> GSM601964 1 0.0747 0.6942 0.984 0.016 0.000
#> GSM601969 1 0.4249 0.6836 0.864 0.108 0.028
#> GSM601984 1 0.9410 -0.0507 0.504 0.220 0.276
#> GSM601994 2 0.5939 0.7269 0.028 0.748 0.224
#> GSM601875 2 0.4399 0.7689 0.092 0.864 0.044
#> GSM601885 2 0.3805 0.7771 0.092 0.884 0.024
#> GSM601890 1 0.6562 0.5648 0.700 0.264 0.036
#> GSM601895 1 0.2066 0.7099 0.940 0.060 0.000
#> GSM601900 1 0.1860 0.7112 0.948 0.052 0.000
#> GSM601905 2 0.6047 0.6544 0.008 0.680 0.312
#> GSM601915 1 0.0747 0.6962 0.984 0.016 0.000
#> GSM601930 3 0.5621 0.9286 0.308 0.000 0.692
#> GSM601935 2 0.8211 0.4262 0.404 0.520 0.076
#> GSM601940 1 0.5987 0.4208 0.756 0.036 0.208
#> GSM601945 2 0.4289 0.7698 0.092 0.868 0.040
#> GSM601950 1 0.6936 0.4830 0.732 0.108 0.160
#> GSM601960 1 0.1753 0.7007 0.952 0.048 0.000
#> GSM601975 2 0.5845 0.6536 0.004 0.688 0.308
#> GSM601980 2 0.7615 0.7029 0.148 0.688 0.164
#> GSM601990 1 0.0424 0.6900 0.992 0.008 0.000
#> GSM601880 3 0.6387 0.9042 0.300 0.020 0.680
#> GSM601910 1 0.2796 0.7122 0.908 0.092 0.000
#> GSM601920 2 0.5678 0.6434 0.000 0.684 0.316
#> GSM601925 3 0.5591 0.9278 0.304 0.000 0.696
#> GSM601955 2 0.7615 0.7029 0.148 0.688 0.164
#> GSM601965 1 0.9912 -0.1292 0.396 0.320 0.284
#> GSM601970 1 0.1964 0.7078 0.944 0.056 0.000
#> GSM601985 1 0.5325 0.3033 0.748 0.004 0.248
#> GSM601995 2 0.7670 0.7005 0.152 0.684 0.164
#> GSM601876 1 0.5956 0.0197 0.672 0.004 0.324
#> GSM601886 2 0.7548 0.7162 0.204 0.684 0.112
#> GSM601891 1 0.7186 0.4711 0.624 0.336 0.040
#> GSM601896 1 0.5858 0.3160 0.740 0.020 0.240
#> GSM601901 2 0.5731 0.7804 0.088 0.804 0.108
#> GSM601906 2 0.9301 0.3871 0.212 0.520 0.268
#> GSM601916 2 0.5902 0.6421 0.004 0.680 0.316
#> GSM601931 3 0.5591 0.9278 0.304 0.000 0.696
#> GSM601936 2 0.6470 0.7488 0.148 0.760 0.092
#> GSM601941 2 0.5619 0.7067 0.012 0.744 0.244
#> GSM601946 1 0.6079 -0.2688 0.612 0.000 0.388
#> GSM601951 3 0.6848 0.7965 0.416 0.016 0.568
#> GSM601961 2 0.5551 0.7033 0.212 0.768 0.020
#> GSM601976 2 0.7188 0.7052 0.056 0.664 0.280
#> GSM601981 2 0.4174 0.7706 0.092 0.872 0.036
#> GSM601991 1 0.4750 0.5487 0.784 0.216 0.000
#> GSM601881 3 0.5621 0.9286 0.308 0.000 0.692
#> GSM601911 2 0.8472 0.6549 0.160 0.612 0.228
#> GSM601921 2 0.5706 0.6398 0.000 0.680 0.320
#> GSM601926 3 0.5591 0.9278 0.304 0.000 0.696
#> GSM601956 2 0.4174 0.7710 0.092 0.872 0.036
#> GSM601966 2 0.5756 0.7341 0.028 0.764 0.208
#> GSM601971 1 0.3375 0.6645 0.908 0.048 0.044
#> GSM601986 2 0.9883 0.0460 0.360 0.380 0.260
#> GSM601996 2 0.5643 0.7246 0.020 0.760 0.220
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM601872 2 0.6747 0.7794 0.092 0.556 0.004 0.348
#> GSM601882 4 0.5262 0.0292 0.020 0.304 0.004 0.672
#> GSM601887 1 0.7182 0.3367 0.556 0.340 0.068 0.036
#> GSM601892 1 0.3354 0.6821 0.880 0.084 0.020 0.016
#> GSM601897 1 0.7928 0.3643 0.608 0.140 0.116 0.136
#> GSM601902 4 0.3390 0.5307 0.000 0.132 0.016 0.852
#> GSM601912 1 0.0844 0.7380 0.980 0.004 0.004 0.012
#> GSM601927 3 0.5090 0.9395 0.324 0.000 0.660 0.016
#> GSM601932 4 0.1256 0.5573 0.000 0.028 0.008 0.964
#> GSM601937 4 0.9272 0.1611 0.084 0.272 0.276 0.368
#> GSM601942 4 0.8987 0.0655 0.088 0.220 0.236 0.456
#> GSM601947 4 0.0992 0.5557 0.012 0.008 0.004 0.976
#> GSM601957 1 0.0804 0.7387 0.980 0.012 0.008 0.000
#> GSM601972 4 0.1492 0.5450 0.004 0.036 0.004 0.956
#> GSM601977 4 0.5643 -0.4578 0.016 0.440 0.004 0.540
#> GSM601987 2 0.4950 0.8907 0.004 0.620 0.000 0.376
#> GSM601877 3 0.5090 0.9395 0.324 0.000 0.660 0.016
#> GSM601907 2 0.4819 0.9043 0.004 0.652 0.000 0.344
#> GSM601917 4 0.3606 0.5266 0.000 0.132 0.024 0.844
#> GSM601922 4 0.3736 0.5313 0.004 0.128 0.024 0.844
#> GSM601952 4 0.3878 0.4147 0.016 0.156 0.004 0.824
#> GSM601962 1 0.2002 0.7105 0.936 0.020 0.000 0.044
#> GSM601967 1 0.0672 0.7387 0.984 0.008 0.008 0.000
#> GSM601982 4 0.6991 -0.2980 0.128 0.348 0.000 0.524
#> GSM601992 4 0.1296 0.5487 0.004 0.028 0.004 0.964
#> GSM601873 4 0.7308 -0.5149 0.076 0.416 0.028 0.480
#> GSM601883 2 0.5168 0.6644 0.004 0.504 0.000 0.492
#> GSM601888 1 0.8299 -0.2443 0.400 0.368 0.024 0.208
#> GSM601893 1 0.6769 0.3687 0.588 0.324 0.068 0.020
#> GSM601898 1 0.0188 0.7400 0.996 0.004 0.000 0.000
#> GSM601903 4 0.3217 0.5339 0.000 0.128 0.012 0.860
#> GSM601913 1 0.0188 0.7400 0.996 0.004 0.000 0.000
#> GSM601928 3 0.5090 0.9395 0.324 0.000 0.660 0.016
#> GSM601933 4 0.4997 0.0817 0.012 0.296 0.004 0.688
#> GSM601938 4 0.3105 0.4462 0.004 0.140 0.000 0.856
#> GSM601943 4 0.8427 -0.3794 0.084 0.352 0.104 0.460
#> GSM601948 1 0.3953 0.6379 0.848 0.024 0.108 0.020
#> GSM601958 1 0.0188 0.7402 0.996 0.000 0.004 0.000
#> GSM601973 4 0.1388 0.5581 0.000 0.028 0.012 0.960
#> GSM601978 2 0.5349 0.8924 0.024 0.640 0.000 0.336
#> GSM601988 4 0.9093 0.1917 0.080 0.268 0.236 0.416
#> GSM601878 3 0.5602 0.6971 0.472 0.000 0.508 0.020
#> GSM601908 2 0.5172 0.8417 0.008 0.588 0.000 0.404
#> GSM601918 4 0.0844 0.5551 0.004 0.012 0.004 0.980
#> GSM601923 3 0.5090 0.9395 0.324 0.000 0.660 0.016
#> GSM601953 2 0.5848 0.8644 0.048 0.616 0.000 0.336
#> GSM601963 1 0.0376 0.7396 0.992 0.004 0.004 0.000
#> GSM601968 1 0.0672 0.7394 0.984 0.008 0.008 0.000
#> GSM601983 1 0.0188 0.7400 0.996 0.004 0.000 0.000
#> GSM601993 4 0.7242 0.3977 0.036 0.184 0.148 0.632
#> GSM601874 2 0.4819 0.9043 0.004 0.652 0.000 0.344
#> GSM601884 2 0.5441 0.8509 0.012 0.588 0.004 0.396
#> GSM601889 1 0.0188 0.7400 0.996 0.004 0.000 0.000
#> GSM601894 1 0.0000 0.7402 1.000 0.000 0.000 0.000
#> GSM601899 1 0.7104 0.3559 0.576 0.320 0.068 0.036
#> GSM601904 4 0.2949 0.5461 0.000 0.088 0.024 0.888
#> GSM601914 1 0.0927 0.7348 0.976 0.008 0.016 0.000
#> GSM601929 1 0.5827 -0.0220 0.632 0.000 0.316 0.052
#> GSM601934 4 0.5805 -0.3104 0.036 0.388 0.000 0.576
#> GSM601939 1 0.4126 0.4737 0.776 0.004 0.216 0.004
#> GSM601944 4 0.6882 0.3061 0.040 0.144 0.144 0.672
#> GSM601949 1 0.4937 0.5552 0.788 0.028 0.152 0.032
#> GSM601959 1 0.0672 0.7387 0.984 0.008 0.008 0.000
#> GSM601974 1 0.6678 -0.1193 0.480 0.056 0.012 0.452
#> GSM601979 2 0.4819 0.9043 0.004 0.652 0.000 0.344
#> GSM601989 1 0.0672 0.7387 0.984 0.008 0.008 0.000
#> GSM601879 3 0.5459 0.7897 0.432 0.000 0.552 0.016
#> GSM601909 1 0.0336 0.7399 0.992 0.008 0.000 0.000
#> GSM601919 4 0.1247 0.5543 0.016 0.012 0.004 0.968
#> GSM601924 3 0.5767 0.7766 0.436 0.008 0.540 0.016
#> GSM601954 4 0.3547 0.4407 0.016 0.144 0.000 0.840
#> GSM601964 1 0.0188 0.7400 0.996 0.004 0.000 0.000
#> GSM601969 1 0.2585 0.7096 0.916 0.032 0.048 0.004
#> GSM601984 1 0.6491 0.3195 0.680 0.020 0.188 0.112
#> GSM601994 4 0.1082 0.5521 0.004 0.020 0.004 0.972
#> GSM601875 2 0.4819 0.9043 0.004 0.652 0.000 0.344
#> GSM601885 4 0.5150 -0.3283 0.008 0.396 0.000 0.596
#> GSM601890 1 0.5632 0.5391 0.744 0.168 0.068 0.020
#> GSM601895 1 0.0188 0.7400 0.996 0.004 0.000 0.000
#> GSM601900 1 0.0524 0.7403 0.988 0.008 0.004 0.000
#> GSM601905 4 0.3790 0.5289 0.004 0.132 0.024 0.840
#> GSM601915 1 0.0188 0.7400 0.996 0.004 0.000 0.000
#> GSM601930 3 0.5090 0.9395 0.324 0.000 0.660 0.016
#> GSM601935 4 0.9684 0.1509 0.240 0.232 0.160 0.368
#> GSM601940 1 0.3350 0.6433 0.864 0.016 0.116 0.004
#> GSM601945 2 0.4837 0.9036 0.004 0.648 0.000 0.348
#> GSM601950 1 0.3882 0.6421 0.852 0.028 0.104 0.016
#> GSM601960 1 0.1488 0.7253 0.956 0.012 0.032 0.000
#> GSM601975 4 0.3166 0.5390 0.000 0.116 0.016 0.868
#> GSM601980 4 0.9322 0.1492 0.088 0.276 0.276 0.360
#> GSM601990 1 0.0804 0.7362 0.980 0.008 0.012 0.000
#> GSM601880 3 0.5291 0.9263 0.324 0.000 0.652 0.024
#> GSM601910 1 0.0336 0.7399 0.992 0.008 0.000 0.000
#> GSM601920 4 0.3606 0.5266 0.000 0.132 0.024 0.844
#> GSM601925 3 0.5090 0.9395 0.324 0.000 0.660 0.016
#> GSM601955 4 0.9322 0.1492 0.088 0.276 0.276 0.360
#> GSM601965 1 0.6665 0.3579 0.680 0.028 0.140 0.152
#> GSM601970 1 0.0188 0.7403 0.996 0.004 0.000 0.000
#> GSM601985 1 0.3725 0.5583 0.812 0.008 0.180 0.000
#> GSM601995 4 0.9305 0.1630 0.088 0.268 0.276 0.368
#> GSM601876 1 0.4216 0.5018 0.788 0.008 0.196 0.008
#> GSM601886 4 0.8228 0.2786 0.152 0.128 0.140 0.580
#> GSM601891 1 0.7474 0.3175 0.540 0.340 0.068 0.052
#> GSM601896 1 0.3755 0.5912 0.836 0.008 0.144 0.012
#> GSM601901 4 0.4891 0.0428 0.012 0.308 0.000 0.680
#> GSM601906 4 0.7044 0.2679 0.220 0.012 0.156 0.612
#> GSM601916 4 0.3568 0.5375 0.004 0.116 0.024 0.856
#> GSM601931 3 0.5090 0.9395 0.324 0.000 0.660 0.016
#> GSM601936 4 0.8438 0.3077 0.076 0.228 0.168 0.528
#> GSM601941 4 0.0524 0.5562 0.000 0.004 0.008 0.988
#> GSM601946 1 0.4710 0.3498 0.732 0.008 0.252 0.008
#> GSM601951 1 0.5508 -0.6145 0.508 0.000 0.476 0.016
#> GSM601961 4 0.6248 -0.5850 0.044 0.472 0.004 0.480
#> GSM601976 4 0.3344 0.5556 0.008 0.092 0.024 0.876
#> GSM601981 2 0.5099 0.8852 0.008 0.612 0.000 0.380
#> GSM601991 1 0.3218 0.6736 0.896 0.028 0.032 0.044
#> GSM601881 3 0.5130 0.9328 0.332 0.000 0.652 0.016
#> GSM601911 4 0.6976 0.2964 0.188 0.092 0.056 0.664
#> GSM601921 4 0.3501 0.5290 0.000 0.132 0.020 0.848
#> GSM601926 3 0.5090 0.9395 0.324 0.000 0.660 0.016
#> GSM601956 2 0.6329 0.8453 0.064 0.588 0.004 0.344
#> GSM601966 4 0.0967 0.5508 0.004 0.016 0.004 0.976
#> GSM601971 1 0.1824 0.7058 0.936 0.004 0.060 0.000
#> GSM601986 1 0.7669 0.0921 0.556 0.032 0.136 0.276
#> GSM601996 4 0.0844 0.5549 0.004 0.012 0.004 0.980
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM601872 2 0.3340 0.7281 0.000 0.856 0.044 0.012 0.088
#> GSM601882 2 0.4387 0.4977 0.000 0.640 0.012 0.348 0.000
#> GSM601887 3 0.3906 0.6936 0.000 0.240 0.744 0.000 0.016
#> GSM601892 3 0.0693 0.9361 0.000 0.012 0.980 0.000 0.008
#> GSM601897 3 0.2266 0.8924 0.000 0.016 0.912 0.008 0.064
#> GSM601902 4 0.0000 0.9222 0.000 0.000 0.000 1.000 0.000
#> GSM601912 3 0.0324 0.9430 0.004 0.000 0.992 0.004 0.000
#> GSM601927 1 0.0000 0.8200 1.000 0.000 0.000 0.000 0.000
#> GSM601932 4 0.0404 0.9254 0.000 0.012 0.000 0.988 0.000
#> GSM601937 5 0.1195 0.8346 0.000 0.028 0.000 0.012 0.960
#> GSM601942 5 0.2069 0.8163 0.000 0.076 0.000 0.012 0.912
#> GSM601947 4 0.1121 0.9179 0.000 0.044 0.000 0.956 0.000
#> GSM601957 3 0.0451 0.9440 0.008 0.000 0.988 0.000 0.004
#> GSM601972 4 0.1478 0.9085 0.000 0.064 0.000 0.936 0.000
#> GSM601977 2 0.1121 0.7966 0.000 0.956 0.000 0.044 0.000
#> GSM601987 2 0.0510 0.8018 0.000 0.984 0.000 0.016 0.000
#> GSM601877 1 0.0000 0.8200 1.000 0.000 0.000 0.000 0.000
#> GSM601907 2 0.0162 0.8000 0.000 0.996 0.000 0.004 0.000
#> GSM601917 4 0.0000 0.9222 0.000 0.000 0.000 1.000 0.000
#> GSM601922 4 0.0566 0.9182 0.012 0.004 0.000 0.984 0.000
#> GSM601952 2 0.4697 0.3986 0.000 0.592 0.000 0.388 0.020
#> GSM601962 3 0.0740 0.9393 0.008 0.000 0.980 0.008 0.004
#> GSM601967 3 0.0566 0.9429 0.012 0.000 0.984 0.000 0.004
#> GSM601982 2 0.3424 0.7466 0.008 0.856 0.076 0.056 0.004
#> GSM601992 4 0.3043 0.8574 0.000 0.080 0.000 0.864 0.056
#> GSM601873 2 0.2293 0.7666 0.000 0.900 0.000 0.016 0.084
#> GSM601883 2 0.3143 0.6929 0.000 0.796 0.000 0.204 0.000
#> GSM601888 2 0.4899 0.0528 0.000 0.524 0.456 0.008 0.012
#> GSM601893 3 0.2873 0.8369 0.000 0.128 0.856 0.000 0.016
#> GSM601898 3 0.0290 0.9439 0.008 0.000 0.992 0.000 0.000
#> GSM601903 4 0.0162 0.9241 0.000 0.004 0.000 0.996 0.000
#> GSM601913 3 0.0404 0.9431 0.012 0.000 0.988 0.000 0.000
#> GSM601928 1 0.0000 0.8200 1.000 0.000 0.000 0.000 0.000
#> GSM601933 2 0.3707 0.6124 0.000 0.716 0.000 0.284 0.000
#> GSM601938 4 0.4657 0.5254 0.000 0.296 0.000 0.668 0.036
#> GSM601943 2 0.4511 0.4198 0.000 0.628 0.000 0.016 0.356
#> GSM601948 1 0.3715 0.7122 0.736 0.000 0.260 0.000 0.004
#> GSM601958 3 0.0404 0.9430 0.012 0.000 0.988 0.000 0.000
#> GSM601973 4 0.0566 0.9251 0.000 0.012 0.000 0.984 0.004
#> GSM601978 2 0.0324 0.7996 0.000 0.992 0.000 0.004 0.004
#> GSM601988 5 0.3161 0.8108 0.000 0.092 0.004 0.044 0.860
#> GSM601878 1 0.1121 0.8300 0.956 0.000 0.044 0.000 0.000
#> GSM601908 2 0.0703 0.8009 0.000 0.976 0.000 0.024 0.000
#> GSM601918 4 0.1121 0.9179 0.000 0.044 0.000 0.956 0.000
#> GSM601923 1 0.0000 0.8200 1.000 0.000 0.000 0.000 0.000
#> GSM601953 2 0.0324 0.7996 0.000 0.992 0.000 0.004 0.004
#> GSM601963 3 0.0290 0.9439 0.008 0.000 0.992 0.000 0.000
#> GSM601968 3 0.0324 0.9435 0.004 0.000 0.992 0.000 0.004
#> GSM601983 3 0.0162 0.9437 0.004 0.000 0.996 0.000 0.000
#> GSM601993 5 0.4392 0.4403 0.000 0.008 0.000 0.380 0.612
#> GSM601874 2 0.0162 0.8000 0.000 0.996 0.000 0.004 0.000
#> GSM601884 2 0.0671 0.8000 0.000 0.980 0.000 0.016 0.004
#> GSM601889 3 0.0703 0.9369 0.024 0.000 0.976 0.000 0.000
#> GSM601894 3 0.0162 0.9437 0.004 0.000 0.996 0.000 0.000
#> GSM601899 3 0.3419 0.7767 0.000 0.180 0.804 0.000 0.016
#> GSM601904 4 0.0162 0.9241 0.000 0.004 0.000 0.996 0.000
#> GSM601914 3 0.0324 0.9438 0.004 0.000 0.992 0.000 0.004
#> GSM601929 1 0.1942 0.8239 0.920 0.000 0.068 0.012 0.000
#> GSM601934 2 0.2248 0.7776 0.000 0.900 0.012 0.088 0.000
#> GSM601939 1 0.3636 0.6955 0.728 0.000 0.272 0.000 0.000
#> GSM601944 2 0.6788 0.0260 0.000 0.372 0.000 0.344 0.284
#> GSM601949 1 0.2392 0.8177 0.888 0.004 0.104 0.000 0.004
#> GSM601959 3 0.0324 0.9435 0.004 0.000 0.992 0.000 0.004
#> GSM601974 3 0.4730 0.6995 0.012 0.156 0.768 0.044 0.020
#> GSM601979 2 0.0162 0.8000 0.000 0.996 0.000 0.004 0.000
#> GSM601989 3 0.1043 0.9250 0.040 0.000 0.960 0.000 0.000
#> GSM601879 1 0.1043 0.8298 0.960 0.000 0.040 0.000 0.000
#> GSM601909 3 0.0162 0.9421 0.000 0.000 0.996 0.000 0.004
#> GSM601919 4 0.1121 0.9179 0.000 0.044 0.000 0.956 0.000
#> GSM601924 1 0.1043 0.8298 0.960 0.000 0.040 0.000 0.000
#> GSM601954 2 0.4306 0.1198 0.000 0.508 0.000 0.492 0.000
#> GSM601964 3 0.0290 0.9439 0.008 0.000 0.992 0.000 0.000
#> GSM601969 3 0.3160 0.7363 0.188 0.000 0.808 0.000 0.004
#> GSM601984 1 0.3039 0.7945 0.836 0.000 0.152 0.012 0.000
#> GSM601994 4 0.2922 0.8639 0.000 0.072 0.000 0.872 0.056
#> GSM601875 2 0.0162 0.8000 0.000 0.996 0.000 0.004 0.000
#> GSM601885 2 0.2813 0.7281 0.000 0.832 0.000 0.168 0.000
#> GSM601890 3 0.1117 0.9284 0.000 0.020 0.964 0.000 0.016
#> GSM601895 3 0.0162 0.9437 0.004 0.000 0.996 0.000 0.000
#> GSM601900 3 0.0324 0.9436 0.004 0.000 0.992 0.000 0.004
#> GSM601905 4 0.0162 0.9239 0.000 0.004 0.000 0.996 0.000
#> GSM601915 3 0.0404 0.9430 0.012 0.000 0.988 0.000 0.000
#> GSM601930 1 0.0290 0.8200 0.992 0.000 0.008 0.000 0.000
#> GSM601935 5 0.3982 0.7067 0.000 0.012 0.200 0.016 0.772
#> GSM601940 1 0.4446 0.2489 0.520 0.000 0.476 0.000 0.004
#> GSM601945 2 0.0162 0.8000 0.000 0.996 0.000 0.004 0.000
#> GSM601950 1 0.2890 0.7936 0.836 0.000 0.160 0.000 0.004
#> GSM601960 3 0.0451 0.9435 0.004 0.000 0.988 0.000 0.008
#> GSM601975 4 0.0451 0.9250 0.000 0.008 0.000 0.988 0.004
#> GSM601980 5 0.0404 0.8339 0.000 0.000 0.000 0.012 0.988
#> GSM601990 3 0.0451 0.9437 0.008 0.000 0.988 0.000 0.004
#> GSM601880 1 0.0000 0.8200 1.000 0.000 0.000 0.000 0.000
#> GSM601910 3 0.0324 0.9435 0.004 0.000 0.992 0.000 0.004
#> GSM601920 4 0.0000 0.9222 0.000 0.000 0.000 1.000 0.000
#> GSM601925 1 0.0000 0.8200 1.000 0.000 0.000 0.000 0.000
#> GSM601955 5 0.0404 0.8339 0.000 0.000 0.000 0.012 0.988
#> GSM601965 1 0.4714 0.6008 0.644 0.000 0.324 0.032 0.000
#> GSM601970 3 0.0290 0.9439 0.008 0.000 0.992 0.000 0.000
#> GSM601985 1 0.4060 0.5519 0.640 0.000 0.360 0.000 0.000
#> GSM601995 5 0.0566 0.8352 0.000 0.004 0.000 0.012 0.984
#> GSM601876 1 0.3143 0.7651 0.796 0.000 0.204 0.000 0.000
#> GSM601886 5 0.6676 0.6398 0.004 0.148 0.048 0.196 0.604
#> GSM601891 3 0.2873 0.8388 0.000 0.128 0.856 0.000 0.016
#> GSM601896 1 0.4182 0.4832 0.600 0.000 0.400 0.000 0.000
#> GSM601901 2 0.4138 0.4368 0.000 0.616 0.000 0.384 0.000
#> GSM601906 1 0.4987 0.4308 0.616 0.000 0.044 0.340 0.000
#> GSM601916 4 0.0162 0.9239 0.000 0.004 0.000 0.996 0.000
#> GSM601931 1 0.0000 0.8200 1.000 0.000 0.000 0.000 0.000
#> GSM601936 5 0.4675 0.7507 0.000 0.088 0.004 0.164 0.744
#> GSM601941 4 0.1725 0.9074 0.000 0.020 0.000 0.936 0.044
#> GSM601946 1 0.2074 0.8192 0.896 0.000 0.104 0.000 0.000
#> GSM601951 1 0.1121 0.8300 0.956 0.000 0.044 0.000 0.000
#> GSM601961 2 0.1579 0.7911 0.000 0.944 0.024 0.032 0.000
#> GSM601976 4 0.1502 0.9042 0.000 0.056 0.000 0.940 0.004
#> GSM601981 2 0.0404 0.8008 0.000 0.988 0.000 0.012 0.000
#> GSM601991 3 0.1990 0.8937 0.004 0.000 0.920 0.008 0.068
#> GSM601881 1 0.0000 0.8200 1.000 0.000 0.000 0.000 0.000
#> GSM601911 4 0.5827 0.4934 0.236 0.072 0.040 0.652 0.000
#> GSM601921 4 0.0000 0.9222 0.000 0.000 0.000 1.000 0.000
#> GSM601926 1 0.0000 0.8200 1.000 0.000 0.000 0.000 0.000
#> GSM601956 2 0.0671 0.7971 0.000 0.980 0.000 0.004 0.016
#> GSM601966 4 0.2020 0.8754 0.000 0.100 0.000 0.900 0.000
#> GSM601971 3 0.2773 0.7804 0.164 0.000 0.836 0.000 0.000
#> GSM601986 1 0.5689 0.6337 0.640 0.004 0.212 0.144 0.000
#> GSM601996 4 0.2153 0.8992 0.000 0.040 0.000 0.916 0.044
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM601872 2 0.1866 0.8609 0.000 0.908 0.000 0.000 0.008 0.084
#> GSM601882 2 0.3672 0.5629 0.000 0.688 0.000 0.304 0.000 0.008
#> GSM601887 6 0.0508 0.9096 0.000 0.004 0.012 0.000 0.000 0.984
#> GSM601892 6 0.0713 0.9032 0.000 0.000 0.028 0.000 0.000 0.972
#> GSM601897 3 0.2119 0.8390 0.000 0.000 0.904 0.000 0.060 0.036
#> GSM601902 4 0.0146 0.9104 0.000 0.000 0.000 0.996 0.000 0.004
#> GSM601912 3 0.2092 0.8277 0.000 0.000 0.876 0.000 0.000 0.124
#> GSM601927 1 0.0000 0.8725 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM601932 4 0.0146 0.9104 0.000 0.000 0.000 0.996 0.000 0.004
#> GSM601937 5 0.0000 0.8335 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM601942 5 0.2631 0.7201 0.000 0.152 0.000 0.000 0.840 0.008
#> GSM601947 4 0.0260 0.9092 0.000 0.008 0.000 0.992 0.000 0.000
#> GSM601957 3 0.4312 0.4824 0.028 0.000 0.604 0.000 0.000 0.368
#> GSM601972 4 0.1204 0.8737 0.000 0.056 0.000 0.944 0.000 0.000
#> GSM601977 2 0.0547 0.8949 0.000 0.980 0.000 0.020 0.000 0.000
#> GSM601987 2 0.0146 0.9002 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM601877 1 0.0000 0.8725 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM601907 2 0.0260 0.9000 0.000 0.992 0.000 0.000 0.000 0.008
#> GSM601917 4 0.0146 0.9104 0.000 0.000 0.000 0.996 0.000 0.004
#> GSM601922 4 0.0260 0.9071 0.008 0.000 0.000 0.992 0.000 0.000
#> GSM601952 2 0.4076 0.2653 0.000 0.564 0.000 0.428 0.004 0.004
#> GSM601962 3 0.0291 0.8705 0.004 0.000 0.992 0.000 0.004 0.000
#> GSM601967 3 0.4632 0.2547 0.040 0.000 0.520 0.000 0.000 0.440
#> GSM601982 2 0.2939 0.8423 0.004 0.868 0.016 0.036 0.000 0.076
#> GSM601992 4 0.0717 0.9036 0.000 0.016 0.000 0.976 0.008 0.000
#> GSM601873 2 0.1010 0.8874 0.000 0.960 0.000 0.000 0.036 0.004
#> GSM601883 2 0.0865 0.8880 0.000 0.964 0.000 0.036 0.000 0.000
#> GSM601888 6 0.0767 0.9064 0.000 0.008 0.012 0.000 0.004 0.976
#> GSM601893 6 0.0508 0.9096 0.000 0.004 0.012 0.000 0.000 0.984
#> GSM601898 3 0.0291 0.8723 0.004 0.000 0.992 0.000 0.000 0.004
#> GSM601903 4 0.0146 0.9104 0.000 0.000 0.000 0.996 0.000 0.004
#> GSM601913 3 0.0146 0.8722 0.004 0.000 0.996 0.000 0.000 0.000
#> GSM601928 1 0.0000 0.8725 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM601933 2 0.3163 0.6914 0.000 0.764 0.000 0.232 0.000 0.004
#> GSM601938 4 0.2778 0.7493 0.000 0.168 0.000 0.824 0.008 0.000
#> GSM601943 2 0.2743 0.7799 0.000 0.828 0.000 0.000 0.164 0.008
#> GSM601948 1 0.3707 0.7208 0.784 0.000 0.136 0.000 0.000 0.080
#> GSM601958 3 0.1765 0.8550 0.024 0.000 0.924 0.000 0.000 0.052
#> GSM601973 4 0.0291 0.9099 0.000 0.000 0.000 0.992 0.004 0.004
#> GSM601978 2 0.0363 0.8997 0.000 0.988 0.000 0.000 0.000 0.012
#> GSM601988 5 0.1194 0.8321 0.000 0.008 0.004 0.032 0.956 0.000
#> GSM601878 1 0.0260 0.8728 0.992 0.000 0.008 0.000 0.000 0.000
#> GSM601908 2 0.0000 0.8999 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM601918 4 0.0146 0.9103 0.000 0.004 0.000 0.996 0.000 0.000
#> GSM601923 1 0.0000 0.8725 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM601953 2 0.0603 0.8979 0.000 0.980 0.000 0.000 0.004 0.016
#> GSM601963 3 0.0000 0.8723 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM601968 3 0.3409 0.6371 0.000 0.000 0.700 0.000 0.000 0.300
#> GSM601983 3 0.0000 0.8723 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM601993 5 0.2994 0.7233 0.000 0.000 0.000 0.208 0.788 0.004
#> GSM601874 2 0.0260 0.9000 0.000 0.992 0.000 0.000 0.000 0.008
#> GSM601884 2 0.0260 0.9000 0.000 0.992 0.000 0.000 0.000 0.008
#> GSM601889 3 0.2747 0.8132 0.044 0.000 0.860 0.000 0.000 0.096
#> GSM601894 3 0.0713 0.8687 0.000 0.000 0.972 0.000 0.000 0.028
#> GSM601899 6 0.0508 0.9096 0.000 0.004 0.012 0.000 0.000 0.984
#> GSM601904 4 0.0146 0.9104 0.000 0.000 0.000 0.996 0.000 0.004
#> GSM601914 3 0.0000 0.8723 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM601929 1 0.0363 0.8721 0.988 0.000 0.012 0.000 0.000 0.000
#> GSM601934 2 0.1950 0.8634 0.000 0.912 0.000 0.064 0.000 0.024
#> GSM601939 1 0.2092 0.8094 0.876 0.000 0.124 0.000 0.000 0.000
#> GSM601944 4 0.5029 0.3382 0.000 0.092 0.000 0.580 0.328 0.000
#> GSM601949 1 0.4118 0.4420 0.628 0.000 0.020 0.000 0.000 0.352
#> GSM601959 3 0.3279 0.7764 0.028 0.000 0.796 0.000 0.000 0.176
#> GSM601974 3 0.3665 0.5843 0.000 0.000 0.728 0.252 0.020 0.000
#> GSM601979 2 0.0146 0.9002 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM601989 3 0.3967 0.7441 0.092 0.000 0.760 0.000 0.000 0.148
#> GSM601879 1 0.0363 0.8721 0.988 0.000 0.012 0.000 0.000 0.000
#> GSM601909 3 0.3563 0.5916 0.000 0.000 0.664 0.000 0.000 0.336
#> GSM601919 4 0.0146 0.9103 0.000 0.004 0.000 0.996 0.000 0.000
#> GSM601924 1 0.0260 0.8728 0.992 0.000 0.008 0.000 0.000 0.000
#> GSM601954 4 0.3868 -0.0741 0.000 0.492 0.000 0.508 0.000 0.000
#> GSM601964 3 0.0000 0.8723 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM601969 6 0.5624 0.3637 0.296 0.000 0.180 0.000 0.000 0.524
#> GSM601984 1 0.2003 0.8176 0.884 0.000 0.116 0.000 0.000 0.000
#> GSM601994 4 0.0405 0.9084 0.000 0.004 0.000 0.988 0.008 0.000
#> GSM601875 2 0.0146 0.9002 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM601885 2 0.2234 0.8161 0.000 0.872 0.000 0.124 0.000 0.004
#> GSM601890 6 0.0632 0.9057 0.000 0.000 0.024 0.000 0.000 0.976
#> GSM601895 3 0.0000 0.8723 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM601900 3 0.0146 0.8722 0.000 0.000 0.996 0.000 0.000 0.004
#> GSM601905 4 0.0000 0.9103 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM601915 3 0.0146 0.8719 0.004 0.000 0.996 0.000 0.000 0.000
#> GSM601930 1 0.0146 0.8722 0.996 0.000 0.004 0.000 0.000 0.000
#> GSM601935 5 0.2933 0.6730 0.000 0.000 0.200 0.004 0.796 0.000
#> GSM601940 1 0.4147 0.6423 0.716 0.000 0.224 0.000 0.000 0.060
#> GSM601945 2 0.0000 0.8999 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM601950 1 0.3950 0.5814 0.696 0.000 0.028 0.000 0.000 0.276
#> GSM601960 3 0.0000 0.8723 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM601975 4 0.0291 0.9095 0.000 0.000 0.000 0.992 0.004 0.004
#> GSM601980 5 0.0146 0.8339 0.000 0.000 0.000 0.000 0.996 0.004
#> GSM601990 3 0.0000 0.8723 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM601880 1 0.0000 0.8725 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM601910 3 0.2416 0.7987 0.000 0.000 0.844 0.000 0.000 0.156
#> GSM601920 4 0.0000 0.9103 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM601925 1 0.0000 0.8725 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM601955 5 0.0146 0.8339 0.000 0.000 0.000 0.000 0.996 0.004
#> GSM601965 1 0.4467 0.6496 0.712 0.000 0.092 0.192 0.000 0.004
#> GSM601970 3 0.1387 0.8538 0.000 0.000 0.932 0.000 0.000 0.068
#> GSM601985 1 0.3838 0.2880 0.552 0.000 0.448 0.000 0.000 0.000
#> GSM601995 5 0.0146 0.8339 0.000 0.000 0.000 0.000 0.996 0.004
#> GSM601876 1 0.1411 0.8522 0.936 0.000 0.060 0.000 0.000 0.004
#> GSM601886 5 0.5157 0.5804 0.004 0.000 0.096 0.280 0.616 0.004
#> GSM601891 6 0.0993 0.9019 0.000 0.012 0.024 0.000 0.000 0.964
#> GSM601896 1 0.1895 0.8407 0.912 0.000 0.072 0.000 0.000 0.016
#> GSM601901 4 0.3979 0.1582 0.000 0.456 0.000 0.540 0.000 0.004
#> GSM601906 1 0.3429 0.5983 0.740 0.000 0.004 0.252 0.000 0.004
#> GSM601916 4 0.0000 0.9103 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM601931 1 0.0000 0.8725 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM601936 5 0.2765 0.7742 0.000 0.004 0.004 0.148 0.840 0.004
#> GSM601941 4 0.0405 0.9085 0.000 0.000 0.000 0.988 0.008 0.004
#> GSM601946 1 0.0547 0.8706 0.980 0.000 0.020 0.000 0.000 0.000
#> GSM601951 1 0.0458 0.8715 0.984 0.000 0.016 0.000 0.000 0.000
#> GSM601961 2 0.3265 0.6422 0.000 0.748 0.004 0.000 0.000 0.248
#> GSM601976 4 0.0603 0.9043 0.000 0.016 0.000 0.980 0.004 0.000
#> GSM601981 2 0.0000 0.8999 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM601991 3 0.0363 0.8688 0.000 0.000 0.988 0.000 0.012 0.000
#> GSM601881 1 0.0000 0.8725 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM601911 4 0.3595 0.7055 0.144 0.056 0.004 0.796 0.000 0.000
#> GSM601921 4 0.0146 0.9104 0.000 0.000 0.000 0.996 0.000 0.004
#> GSM601926 1 0.0000 0.8725 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM601956 2 0.0520 0.8988 0.000 0.984 0.000 0.000 0.008 0.008
#> GSM601966 4 0.0692 0.9026 0.000 0.020 0.000 0.976 0.004 0.000
#> GSM601971 3 0.3806 0.6626 0.200 0.000 0.752 0.000 0.000 0.048
#> GSM601986 1 0.4034 0.5812 0.692 0.000 0.024 0.280 0.000 0.004
#> GSM601996 4 0.0405 0.9084 0.000 0.004 0.000 0.988 0.008 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
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)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
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)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, 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.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")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n time(p) gender(p) k
#> CV:mclust 122 0.221 0.813 2
#> CV:mclust 106 0.423 0.107 3
#> CV:mclust 85 0.702 0.101 4
#> CV:mclust 113 0.450 0.221 5
#> CV:mclust 116 0.382 0.137 6
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.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["CV", "NMF"]
# you can also extract it by
# res = res_list["CV:NMF"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 21512 rows and 125 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#> Subgroups are detected by 'NMF' method.
#> Performed in total 1250 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_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
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, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.915 0.937 0.973 0.5011 0.498 0.498
#> 3 3 0.381 0.508 0.702 0.3074 0.806 0.632
#> 4 4 0.425 0.428 0.660 0.1283 0.756 0.429
#> 5 5 0.455 0.349 0.595 0.0694 0.842 0.475
#> 6 6 0.492 0.302 0.535 0.0410 0.870 0.495
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 2
Following shows 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 class
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
#> GSM601872 2 0.0000 0.9650 0.000 1.000
#> GSM601882 2 0.0000 0.9650 0.000 1.000
#> GSM601887 1 0.5737 0.8466 0.864 0.136
#> GSM601892 1 0.0000 0.9787 1.000 0.000
#> GSM601897 1 0.5629 0.8524 0.868 0.132
#> GSM601902 2 0.0000 0.9650 0.000 1.000
#> GSM601912 1 0.0376 0.9761 0.996 0.004
#> GSM601927 1 0.0000 0.9787 1.000 0.000
#> GSM601932 2 0.0000 0.9650 0.000 1.000
#> GSM601937 2 0.0000 0.9650 0.000 1.000
#> GSM601942 2 0.0000 0.9650 0.000 1.000
#> GSM601947 2 0.0000 0.9650 0.000 1.000
#> GSM601957 1 0.0000 0.9787 1.000 0.000
#> GSM601972 2 0.0000 0.9650 0.000 1.000
#> GSM601977 2 0.0000 0.9650 0.000 1.000
#> GSM601987 2 0.0000 0.9650 0.000 1.000
#> GSM601877 1 0.0000 0.9787 1.000 0.000
#> GSM601907 2 0.0000 0.9650 0.000 1.000
#> GSM601917 2 0.2948 0.9291 0.052 0.948
#> GSM601922 2 0.8763 0.6034 0.296 0.704
#> GSM601952 2 0.0000 0.9650 0.000 1.000
#> GSM601962 1 0.0000 0.9787 1.000 0.000
#> GSM601967 1 0.0000 0.9787 1.000 0.000
#> GSM601982 2 0.2778 0.9326 0.048 0.952
#> GSM601992 2 0.0000 0.9650 0.000 1.000
#> GSM601873 2 0.0000 0.9650 0.000 1.000
#> GSM601883 2 0.0000 0.9650 0.000 1.000
#> GSM601888 1 0.3584 0.9219 0.932 0.068
#> GSM601893 1 0.1843 0.9579 0.972 0.028
#> GSM601898 1 0.0000 0.9787 1.000 0.000
#> GSM601903 2 0.0000 0.9650 0.000 1.000
#> GSM601913 1 0.0000 0.9787 1.000 0.000
#> GSM601928 1 0.0000 0.9787 1.000 0.000
#> GSM601933 2 0.0000 0.9650 0.000 1.000
#> GSM601938 2 0.0000 0.9650 0.000 1.000
#> GSM601943 2 0.0000 0.9650 0.000 1.000
#> GSM601948 1 0.0000 0.9787 1.000 0.000
#> GSM601958 1 0.0000 0.9787 1.000 0.000
#> GSM601973 2 0.0000 0.9650 0.000 1.000
#> GSM601978 2 0.0000 0.9650 0.000 1.000
#> GSM601988 2 0.0000 0.9650 0.000 1.000
#> GSM601878 1 0.0000 0.9787 1.000 0.000
#> GSM601908 2 0.0000 0.9650 0.000 1.000
#> GSM601918 2 0.0000 0.9650 0.000 1.000
#> GSM601923 1 0.0000 0.9787 1.000 0.000
#> GSM601953 2 0.0000 0.9650 0.000 1.000
#> GSM601963 1 0.0000 0.9787 1.000 0.000
#> GSM601968 1 0.0000 0.9787 1.000 0.000
#> GSM601983 1 0.0000 0.9787 1.000 0.000
#> GSM601993 2 0.0000 0.9650 0.000 1.000
#> GSM601874 2 0.0000 0.9650 0.000 1.000
#> GSM601884 2 0.0000 0.9650 0.000 1.000
#> GSM601889 1 0.0000 0.9787 1.000 0.000
#> GSM601894 1 0.0000 0.9787 1.000 0.000
#> GSM601899 1 0.2948 0.9371 0.948 0.052
#> GSM601904 1 0.9977 0.0655 0.528 0.472
#> GSM601914 1 0.0000 0.9787 1.000 0.000
#> GSM601929 1 0.0000 0.9787 1.000 0.000
#> GSM601934 2 0.0376 0.9628 0.004 0.996
#> GSM601939 1 0.0000 0.9787 1.000 0.000
#> GSM601944 2 0.0000 0.9650 0.000 1.000
#> GSM601949 1 0.0000 0.9787 1.000 0.000
#> GSM601959 1 0.0000 0.9787 1.000 0.000
#> GSM601974 1 0.5408 0.8535 0.876 0.124
#> GSM601979 2 0.0000 0.9650 0.000 1.000
#> GSM601989 1 0.0000 0.9787 1.000 0.000
#> GSM601879 1 0.0000 0.9787 1.000 0.000
#> GSM601909 1 0.0000 0.9787 1.000 0.000
#> GSM601919 2 0.3114 0.9258 0.056 0.944
#> GSM601924 1 0.0000 0.9787 1.000 0.000
#> GSM601954 2 0.2778 0.9324 0.048 0.952
#> GSM601964 1 0.0000 0.9787 1.000 0.000
#> GSM601969 1 0.0000 0.9787 1.000 0.000
#> GSM601984 1 0.0000 0.9787 1.000 0.000
#> GSM601994 2 0.0000 0.9650 0.000 1.000
#> GSM601875 2 0.0000 0.9650 0.000 1.000
#> GSM601885 2 0.0000 0.9650 0.000 1.000
#> GSM601890 1 0.0000 0.9787 1.000 0.000
#> GSM601895 1 0.0938 0.9704 0.988 0.012
#> GSM601900 1 0.0376 0.9761 0.996 0.004
#> GSM601905 2 0.4815 0.8775 0.104 0.896
#> GSM601915 1 0.0000 0.9787 1.000 0.000
#> GSM601930 1 0.0000 0.9787 1.000 0.000
#> GSM601935 1 0.5294 0.8659 0.880 0.120
#> GSM601940 1 0.0000 0.9787 1.000 0.000
#> GSM601945 2 0.0000 0.9650 0.000 1.000
#> GSM601950 1 0.0000 0.9787 1.000 0.000
#> GSM601960 1 0.0000 0.9787 1.000 0.000
#> GSM601975 2 0.0000 0.9650 0.000 1.000
#> GSM601980 2 0.0938 0.9583 0.012 0.988
#> GSM601990 1 0.0000 0.9787 1.000 0.000
#> GSM601880 1 0.0000 0.9787 1.000 0.000
#> GSM601910 1 0.0000 0.9787 1.000 0.000
#> GSM601920 2 0.6343 0.8078 0.160 0.840
#> GSM601925 1 0.0000 0.9787 1.000 0.000
#> GSM601955 2 0.3114 0.9254 0.056 0.944
#> GSM601965 1 0.0000 0.9787 1.000 0.000
#> GSM601970 1 0.0000 0.9787 1.000 0.000
#> GSM601985 1 0.0000 0.9787 1.000 0.000
#> GSM601995 2 0.1414 0.9533 0.020 0.980
#> GSM601876 1 0.0000 0.9787 1.000 0.000
#> GSM601886 2 0.9988 0.1155 0.480 0.520
#> GSM601891 1 0.5178 0.8708 0.884 0.116
#> GSM601896 1 0.0000 0.9787 1.000 0.000
#> GSM601901 2 0.0000 0.9650 0.000 1.000
#> GSM601906 1 0.2948 0.9370 0.948 0.052
#> GSM601916 2 0.4939 0.8713 0.108 0.892
#> GSM601931 1 0.0000 0.9787 1.000 0.000
#> GSM601936 2 0.0376 0.9628 0.004 0.996
#> GSM601941 2 0.0000 0.9650 0.000 1.000
#> GSM601946 1 0.0000 0.9787 1.000 0.000
#> GSM601951 1 0.0000 0.9787 1.000 0.000
#> GSM601961 2 0.1633 0.9501 0.024 0.976
#> GSM601976 2 0.1184 0.9556 0.016 0.984
#> GSM601981 2 0.0000 0.9650 0.000 1.000
#> GSM601991 1 0.0376 0.9761 0.996 0.004
#> GSM601881 1 0.0000 0.9787 1.000 0.000
#> GSM601911 2 1.0000 -0.0095 0.500 0.500
#> GSM601921 2 0.0000 0.9650 0.000 1.000
#> GSM601926 1 0.0000 0.9787 1.000 0.000
#> GSM601956 2 0.0000 0.9650 0.000 1.000
#> GSM601966 2 0.0000 0.9650 0.000 1.000
#> GSM601971 1 0.0000 0.9787 1.000 0.000
#> GSM601986 1 0.0672 0.9734 0.992 0.008
#> GSM601996 2 0.0000 0.9650 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM601872 3 0.329 0.4450 0.008 0.096 0.896
#> GSM601882 2 0.556 0.5011 0.000 0.700 0.300
#> GSM601887 3 0.540 0.2592 0.280 0.000 0.720
#> GSM601892 3 0.623 -0.2135 0.436 0.000 0.564
#> GSM601897 3 0.585 0.1775 0.268 0.012 0.720
#> GSM601902 2 0.344 0.6258 0.088 0.896 0.016
#> GSM601912 3 0.629 -0.3712 0.464 0.000 0.536
#> GSM601927 1 0.303 0.7166 0.904 0.092 0.004
#> GSM601932 2 0.200 0.6460 0.012 0.952 0.036
#> GSM601937 2 0.606 0.5168 0.016 0.708 0.276
#> GSM601942 3 0.606 0.1133 0.000 0.384 0.616
#> GSM601947 2 0.703 0.5261 0.052 0.676 0.272
#> GSM601957 1 0.588 0.6427 0.652 0.000 0.348
#> GSM601972 2 0.493 0.5620 0.000 0.768 0.232
#> GSM601977 2 0.628 0.2221 0.000 0.540 0.460
#> GSM601987 2 0.623 0.2775 0.000 0.564 0.436
#> GSM601877 1 0.403 0.6754 0.856 0.136 0.008
#> GSM601907 3 0.621 0.0626 0.000 0.428 0.572
#> GSM601917 2 0.636 0.4834 0.280 0.696 0.024
#> GSM601922 2 0.695 0.4098 0.352 0.620 0.028
#> GSM601952 2 0.561 0.5943 0.028 0.776 0.196
#> GSM601962 1 0.694 0.6774 0.652 0.036 0.312
#> GSM601967 1 0.579 0.6421 0.668 0.000 0.332
#> GSM601982 3 0.757 -0.0528 0.040 0.452 0.508
#> GSM601992 2 0.216 0.6371 0.000 0.936 0.064
#> GSM601873 2 0.630 0.2007 0.000 0.524 0.476
#> GSM601883 2 0.562 0.4883 0.000 0.692 0.308
#> GSM601888 3 0.510 0.3262 0.248 0.000 0.752
#> GSM601893 3 0.562 0.1551 0.308 0.000 0.692
#> GSM601898 1 0.569 0.7060 0.708 0.004 0.288
#> GSM601903 2 0.384 0.6072 0.116 0.872 0.012
#> GSM601913 1 0.527 0.7405 0.776 0.012 0.212
#> GSM601928 1 0.309 0.7299 0.912 0.072 0.016
#> GSM601933 2 0.518 0.5355 0.000 0.744 0.256
#> GSM601938 2 0.412 0.5962 0.000 0.832 0.168
#> GSM601943 3 0.497 0.3594 0.000 0.236 0.764
#> GSM601948 1 0.435 0.7430 0.836 0.008 0.156
#> GSM601958 1 0.556 0.6911 0.700 0.000 0.300
#> GSM601973 2 0.199 0.6398 0.048 0.948 0.004
#> GSM601978 3 0.597 0.1957 0.000 0.364 0.636
#> GSM601988 2 0.602 0.5224 0.020 0.724 0.256
#> GSM601878 1 0.304 0.7359 0.920 0.040 0.040
#> GSM601908 2 0.590 0.4280 0.000 0.648 0.352
#> GSM601918 2 0.587 0.6126 0.056 0.784 0.160
#> GSM601923 1 0.286 0.7178 0.912 0.084 0.004
#> GSM601953 3 0.533 0.3269 0.000 0.272 0.728
#> GSM601963 1 0.502 0.7465 0.796 0.012 0.192
#> GSM601968 1 0.636 0.5567 0.592 0.004 0.404
#> GSM601983 1 0.638 0.6743 0.648 0.012 0.340
#> GSM601993 2 0.258 0.6364 0.008 0.928 0.064
#> GSM601874 3 0.573 0.2585 0.000 0.324 0.676
#> GSM601884 3 0.630 -0.0895 0.000 0.480 0.520
#> GSM601889 1 0.568 0.6780 0.684 0.000 0.316
#> GSM601894 1 0.571 0.6794 0.680 0.000 0.320
#> GSM601899 3 0.546 0.2264 0.288 0.000 0.712
#> GSM601904 2 0.745 0.2036 0.436 0.528 0.036
#> GSM601914 1 0.619 0.7045 0.692 0.016 0.292
#> GSM601929 1 0.346 0.7122 0.892 0.096 0.012
#> GSM601934 3 0.631 -0.1508 0.000 0.496 0.504
#> GSM601939 1 0.175 0.7559 0.952 0.000 0.048
#> GSM601944 2 0.406 0.6068 0.000 0.836 0.164
#> GSM601949 1 0.489 0.7144 0.772 0.000 0.228
#> GSM601959 1 0.598 0.6659 0.668 0.004 0.328
#> GSM601974 1 0.848 0.6021 0.600 0.140 0.260
#> GSM601979 2 0.630 0.1839 0.000 0.524 0.476
#> GSM601989 1 0.636 0.5813 0.592 0.004 0.404
#> GSM601879 1 0.409 0.7055 0.872 0.100 0.028
#> GSM601909 1 0.631 0.4155 0.512 0.000 0.488
#> GSM601919 2 0.909 0.4435 0.224 0.552 0.224
#> GSM601924 1 0.200 0.7398 0.952 0.036 0.012
#> GSM601954 2 0.838 0.2341 0.084 0.492 0.424
#> GSM601964 1 0.529 0.7377 0.764 0.008 0.228
#> GSM601969 1 0.614 0.6816 0.720 0.024 0.256
#> GSM601984 1 0.551 0.6649 0.800 0.156 0.044
#> GSM601994 2 0.186 0.6402 0.000 0.948 0.052
#> GSM601875 3 0.614 0.1084 0.000 0.404 0.596
#> GSM601885 2 0.579 0.4577 0.000 0.668 0.332
#> GSM601890 3 0.565 0.1411 0.312 0.000 0.688
#> GSM601895 1 0.656 0.5844 0.576 0.008 0.416
#> GSM601900 1 0.647 0.6747 0.652 0.016 0.332
#> GSM601905 2 0.616 0.4720 0.288 0.696 0.016
#> GSM601915 1 0.502 0.7398 0.776 0.004 0.220
#> GSM601930 1 0.323 0.7323 0.908 0.072 0.020
#> GSM601935 1 0.965 0.3260 0.464 0.288 0.248
#> GSM601940 1 0.304 0.7558 0.896 0.000 0.104
#> GSM601945 2 0.629 0.2071 0.000 0.532 0.468
#> GSM601950 1 0.440 0.7380 0.812 0.000 0.188
#> GSM601960 1 0.613 0.7096 0.700 0.016 0.284
#> GSM601975 2 0.315 0.6432 0.044 0.916 0.040
#> GSM601980 2 0.647 0.3881 0.008 0.604 0.388
#> GSM601990 1 0.610 0.7099 0.704 0.016 0.280
#> GSM601880 1 0.345 0.7025 0.888 0.104 0.008
#> GSM601910 1 0.583 0.6576 0.660 0.000 0.340
#> GSM601920 2 0.708 0.3990 0.356 0.612 0.032
#> GSM601925 1 0.357 0.6939 0.876 0.120 0.004
#> GSM601955 3 0.754 -0.0509 0.040 0.432 0.528
#> GSM601965 1 0.353 0.7387 0.900 0.068 0.032
#> GSM601970 1 0.525 0.7079 0.736 0.000 0.264
#> GSM601985 1 0.290 0.7555 0.920 0.016 0.064
#> GSM601995 2 0.630 0.5190 0.032 0.720 0.248
#> GSM601876 1 0.277 0.7594 0.916 0.004 0.080
#> GSM601886 2 0.845 0.2584 0.372 0.532 0.096
#> GSM601891 3 0.489 0.3253 0.228 0.000 0.772
#> GSM601896 1 0.424 0.7486 0.824 0.000 0.176
#> GSM601901 2 0.593 0.4459 0.000 0.644 0.356
#> GSM601906 1 0.608 0.4775 0.692 0.296 0.012
#> GSM601916 2 0.576 0.5420 0.208 0.764 0.028
#> GSM601931 1 0.220 0.7344 0.940 0.056 0.004
#> GSM601936 2 0.602 0.5656 0.076 0.784 0.140
#> GSM601941 2 0.129 0.6422 0.032 0.968 0.000
#> GSM601946 1 0.217 0.7553 0.944 0.008 0.048
#> GSM601951 1 0.323 0.7263 0.908 0.072 0.020
#> GSM601961 3 0.515 0.4361 0.068 0.100 0.832
#> GSM601976 2 0.509 0.6184 0.112 0.832 0.056
#> GSM601981 3 0.617 0.0939 0.000 0.412 0.588
#> GSM601991 1 0.782 0.5830 0.564 0.060 0.376
#> GSM601881 1 0.304 0.7178 0.908 0.084 0.008
#> GSM601911 1 0.830 0.2346 0.560 0.348 0.092
#> GSM601921 2 0.486 0.5839 0.160 0.820 0.020
#> GSM601926 1 0.280 0.7161 0.908 0.092 0.000
#> GSM601956 3 0.571 0.2648 0.000 0.320 0.680
#> GSM601966 2 0.338 0.6329 0.008 0.892 0.100
#> GSM601971 1 0.462 0.7519 0.836 0.020 0.144
#> GSM601986 1 0.441 0.7081 0.852 0.124 0.024
#> GSM601996 2 0.177 0.6456 0.016 0.960 0.024
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM601872 2 0.504 0.342349 0.000 0.628 0.364 0.008
#> GSM601882 4 0.582 0.356110 0.008 0.332 0.032 0.628
#> GSM601887 2 0.630 0.228968 0.080 0.600 0.320 0.000
#> GSM601892 2 0.748 -0.106842 0.188 0.468 0.344 0.000
#> GSM601897 3 0.343 0.513198 0.008 0.140 0.848 0.004
#> GSM601902 4 0.515 0.568687 0.140 0.072 0.012 0.776
#> GSM601912 3 0.490 0.614124 0.104 0.116 0.780 0.000
#> GSM601927 1 0.275 0.718576 0.904 0.000 0.040 0.056
#> GSM601932 4 0.395 0.586363 0.020 0.084 0.040 0.856
#> GSM601937 4 0.568 0.136166 0.004 0.016 0.488 0.492
#> GSM601942 3 0.681 0.172097 0.000 0.156 0.596 0.248
#> GSM601947 2 0.777 0.000128 0.156 0.476 0.016 0.352
#> GSM601957 1 0.704 -0.063083 0.504 0.128 0.368 0.000
#> GSM601972 4 0.562 0.420164 0.020 0.284 0.020 0.676
#> GSM601977 4 0.786 -0.006786 0.000 0.328 0.280 0.392
#> GSM601987 2 0.550 0.200376 0.000 0.576 0.020 0.404
#> GSM601877 1 0.286 0.695350 0.900 0.012 0.012 0.076
#> GSM601907 2 0.338 0.538752 0.008 0.852 0.004 0.136
#> GSM601917 4 0.701 0.176431 0.452 0.080 0.012 0.456
#> GSM601922 1 0.651 0.123787 0.572 0.056 0.012 0.360
#> GSM601952 4 0.717 0.446325 0.040 0.236 0.100 0.624
#> GSM601962 3 0.533 0.610528 0.188 0.012 0.748 0.052
#> GSM601967 1 0.735 -0.196026 0.456 0.160 0.384 0.000
#> GSM601982 2 0.646 0.473519 0.064 0.688 0.044 0.204
#> GSM601992 4 0.384 0.573926 0.016 0.108 0.024 0.852
#> GSM601873 4 0.786 0.044944 0.000 0.276 0.340 0.384
#> GSM601883 4 0.540 0.017388 0.000 0.468 0.012 0.520
#> GSM601888 2 0.502 0.481389 0.088 0.768 0.144 0.000
#> GSM601893 3 0.651 0.056196 0.072 0.460 0.468 0.000
#> GSM601898 3 0.582 0.548552 0.296 0.048 0.652 0.004
#> GSM601903 4 0.514 0.558325 0.160 0.052 0.016 0.772
#> GSM601913 3 0.552 0.540931 0.292 0.008 0.672 0.028
#> GSM601928 1 0.480 0.667494 0.776 0.000 0.160 0.064
#> GSM601933 4 0.570 0.416497 0.000 0.260 0.064 0.676
#> GSM601938 4 0.422 0.542035 0.000 0.144 0.044 0.812
#> GSM601943 3 0.677 -0.093335 0.000 0.364 0.532 0.104
#> GSM601948 1 0.531 0.637157 0.764 0.096 0.132 0.008
#> GSM601958 3 0.621 0.204319 0.464 0.052 0.484 0.000
#> GSM601973 4 0.322 0.596453 0.060 0.016 0.032 0.892
#> GSM601978 2 0.415 0.552661 0.000 0.824 0.056 0.120
#> GSM601988 3 0.570 0.013181 0.004 0.020 0.560 0.416
#> GSM601878 1 0.191 0.709922 0.940 0.040 0.020 0.000
#> GSM601908 2 0.542 0.248805 0.008 0.600 0.008 0.384
#> GSM601918 4 0.792 0.204064 0.188 0.368 0.012 0.432
#> GSM601923 1 0.222 0.718746 0.932 0.004 0.028 0.036
#> GSM601953 2 0.328 0.560183 0.020 0.892 0.048 0.040
#> GSM601963 3 0.511 0.505349 0.352 0.000 0.636 0.012
#> GSM601968 3 0.744 0.431716 0.328 0.188 0.484 0.000
#> GSM601983 3 0.500 0.620745 0.216 0.036 0.744 0.004
#> GSM601993 4 0.363 0.547548 0.016 0.004 0.136 0.844
#> GSM601874 2 0.363 0.562986 0.000 0.856 0.048 0.096
#> GSM601884 2 0.676 0.213563 0.000 0.524 0.100 0.376
#> GSM601889 3 0.680 0.386420 0.400 0.100 0.500 0.000
#> GSM601894 3 0.635 0.537939 0.296 0.092 0.612 0.000
#> GSM601899 2 0.644 0.175411 0.084 0.576 0.340 0.000
#> GSM601904 4 0.653 0.126922 0.420 0.000 0.076 0.504
#> GSM601914 3 0.393 0.624341 0.200 0.000 0.792 0.008
#> GSM601929 1 0.286 0.714235 0.904 0.004 0.040 0.052
#> GSM601934 4 0.763 -0.092769 0.000 0.392 0.204 0.404
#> GSM601939 1 0.424 0.615839 0.784 0.004 0.200 0.012
#> GSM601944 4 0.508 0.526083 0.000 0.080 0.160 0.760
#> GSM601949 1 0.480 0.606947 0.764 0.188 0.048 0.000
#> GSM601959 3 0.678 0.168354 0.456 0.080 0.460 0.004
#> GSM601974 3 0.621 0.555740 0.136 0.008 0.692 0.164
#> GSM601979 2 0.428 0.484877 0.000 0.764 0.012 0.224
#> GSM601989 3 0.662 0.529553 0.280 0.120 0.600 0.000
#> GSM601879 1 0.264 0.700324 0.916 0.032 0.008 0.044
#> GSM601909 3 0.703 0.526808 0.244 0.184 0.572 0.000
#> GSM601919 1 0.813 -0.163744 0.412 0.336 0.012 0.240
#> GSM601924 1 0.159 0.716166 0.952 0.004 0.040 0.004
#> GSM601954 2 0.679 0.317665 0.124 0.632 0.012 0.232
#> GSM601964 3 0.475 0.584736 0.276 0.004 0.712 0.008
#> GSM601969 1 0.658 0.366246 0.632 0.196 0.172 0.000
#> GSM601984 1 0.550 0.630052 0.744 0.004 0.140 0.112
#> GSM601994 4 0.278 0.583521 0.008 0.072 0.016 0.904
#> GSM601875 2 0.296 0.548310 0.004 0.876 0.004 0.116
#> GSM601885 2 0.541 -0.001736 0.000 0.500 0.012 0.488
#> GSM601890 2 0.669 -0.013904 0.088 0.492 0.420 0.000
#> GSM601895 3 0.376 0.638442 0.116 0.032 0.848 0.004
#> GSM601900 3 0.427 0.634155 0.168 0.020 0.804 0.008
#> GSM601905 4 0.632 0.383664 0.348 0.048 0.012 0.592
#> GSM601915 3 0.556 0.420679 0.392 0.012 0.588 0.008
#> GSM601930 1 0.405 0.694347 0.828 0.000 0.124 0.048
#> GSM601935 3 0.595 0.402259 0.052 0.012 0.672 0.264
#> GSM601940 1 0.436 0.585835 0.784 0.028 0.188 0.000
#> GSM601945 2 0.564 0.398439 0.000 0.648 0.044 0.308
#> GSM601950 1 0.461 0.640286 0.800 0.108 0.092 0.000
#> GSM601960 3 0.385 0.627827 0.192 0.000 0.800 0.008
#> GSM601975 4 0.606 0.566550 0.124 0.104 0.036 0.736
#> GSM601980 3 0.513 0.233418 0.000 0.020 0.668 0.312
#> GSM601990 3 0.462 0.621786 0.164 0.000 0.784 0.052
#> GSM601880 1 0.287 0.714383 0.896 0.000 0.032 0.072
#> GSM601910 3 0.635 0.551495 0.276 0.100 0.624 0.000
#> GSM601920 1 0.679 0.058859 0.544 0.072 0.012 0.372
#> GSM601925 1 0.280 0.711157 0.900 0.000 0.032 0.068
#> GSM601955 3 0.534 0.457971 0.028 0.036 0.756 0.180
#> GSM601965 1 0.476 0.680379 0.800 0.012 0.132 0.056
#> GSM601970 3 0.615 0.219419 0.476 0.048 0.476 0.000
#> GSM601985 1 0.479 0.540530 0.740 0.004 0.236 0.020
#> GSM601995 3 0.596 0.004678 0.012 0.020 0.544 0.424
#> GSM601876 1 0.472 0.572203 0.748 0.020 0.228 0.004
#> GSM601886 4 0.644 0.225085 0.076 0.000 0.376 0.548
#> GSM601891 2 0.649 0.037138 0.060 0.496 0.440 0.004
#> GSM601896 1 0.581 0.356957 0.644 0.056 0.300 0.000
#> GSM601901 2 0.671 0.141883 0.032 0.540 0.036 0.392
#> GSM601906 1 0.534 0.517188 0.688 0.000 0.040 0.272
#> GSM601916 4 0.607 0.510237 0.244 0.028 0.044 0.684
#> GSM601931 1 0.361 0.703557 0.856 0.000 0.100 0.044
#> GSM601936 4 0.554 0.293667 0.016 0.004 0.392 0.588
#> GSM601941 4 0.215 0.596979 0.036 0.020 0.008 0.936
#> GSM601946 1 0.429 0.647615 0.796 0.000 0.172 0.032
#> GSM601951 1 0.406 0.696954 0.840 0.008 0.108 0.044
#> GSM601961 2 0.419 0.534490 0.072 0.848 0.056 0.024
#> GSM601976 4 0.599 0.575338 0.128 0.048 0.080 0.744
#> GSM601981 2 0.556 0.518149 0.000 0.720 0.092 0.188
#> GSM601991 3 0.395 0.608309 0.072 0.008 0.852 0.068
#> GSM601881 1 0.184 0.719525 0.948 0.008 0.016 0.028
#> GSM601911 1 0.636 0.430003 0.664 0.128 0.004 0.204
#> GSM601921 4 0.690 0.443318 0.300 0.100 0.012 0.588
#> GSM601926 1 0.267 0.718115 0.908 0.000 0.052 0.040
#> GSM601956 2 0.521 0.541853 0.000 0.748 0.172 0.080
#> GSM601966 4 0.452 0.546944 0.032 0.164 0.008 0.796
#> GSM601971 1 0.525 0.493000 0.724 0.056 0.220 0.000
#> GSM601986 1 0.351 0.718499 0.884 0.036 0.040 0.040
#> GSM601996 4 0.400 0.581711 0.044 0.104 0.008 0.844
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM601872 2 0.554 0.34847 0.008 0.592 0.044 0.008 0.348
#> GSM601882 4 0.525 0.36029 0.016 0.280 0.000 0.656 0.048
#> GSM601887 2 0.516 0.53858 0.024 0.724 0.168 0.000 0.084
#> GSM601892 2 0.599 0.12980 0.012 0.480 0.432 0.000 0.076
#> GSM601897 5 0.553 0.54690 0.004 0.104 0.196 0.012 0.684
#> GSM601902 4 0.640 0.49251 0.272 0.016 0.056 0.608 0.048
#> GSM601912 3 0.668 -0.06216 0.016 0.128 0.544 0.012 0.300
#> GSM601927 1 0.439 0.33044 0.612 0.000 0.380 0.008 0.000
#> GSM601932 4 0.721 0.54368 0.196 0.048 0.052 0.600 0.104
#> GSM601937 4 0.609 0.09826 0.008 0.004 0.080 0.460 0.448
#> GSM601942 5 0.546 0.43161 0.004 0.088 0.032 0.160 0.716
#> GSM601947 1 0.815 -0.02935 0.452 0.216 0.032 0.240 0.060
#> GSM601957 3 0.651 0.52132 0.184 0.100 0.628 0.000 0.088
#> GSM601972 4 0.692 0.44447 0.116 0.216 0.024 0.600 0.044
#> GSM601977 4 0.755 0.01877 0.012 0.316 0.024 0.408 0.240
#> GSM601987 2 0.579 0.14004 0.012 0.512 0.036 0.428 0.012
#> GSM601877 1 0.375 0.50541 0.780 0.004 0.200 0.016 0.000
#> GSM601907 2 0.286 0.58357 0.016 0.876 0.000 0.096 0.012
#> GSM601917 1 0.539 0.36094 0.720 0.024 0.012 0.176 0.068
#> GSM601922 1 0.390 0.49692 0.820 0.016 0.012 0.132 0.020
#> GSM601952 5 0.860 -0.31571 0.212 0.124 0.016 0.320 0.328
#> GSM601962 5 0.600 0.55739 0.064 0.008 0.192 0.060 0.676
#> GSM601967 3 0.779 0.33496 0.184 0.148 0.488 0.000 0.180
#> GSM601982 2 0.888 0.24388 0.136 0.424 0.064 0.232 0.144
#> GSM601992 4 0.413 0.56963 0.064 0.040 0.032 0.836 0.028
#> GSM601873 4 0.795 0.21667 0.024 0.244 0.060 0.472 0.200
#> GSM601883 4 0.525 -0.02713 0.012 0.456 0.024 0.508 0.000
#> GSM601888 2 0.327 0.60162 0.016 0.852 0.112 0.000 0.020
#> GSM601893 2 0.618 0.34493 0.008 0.560 0.316 0.004 0.112
#> GSM601898 3 0.499 0.36938 0.040 0.020 0.696 0.000 0.244
#> GSM601903 4 0.649 0.25932 0.420 0.004 0.028 0.468 0.080
#> GSM601913 3 0.420 0.40895 0.024 0.000 0.768 0.016 0.192
#> GSM601928 1 0.527 0.15549 0.484 0.000 0.480 0.016 0.020
#> GSM601933 4 0.611 0.37567 0.004 0.228 0.112 0.632 0.024
#> GSM601938 4 0.399 0.56274 0.028 0.064 0.016 0.840 0.052
#> GSM601943 5 0.698 -0.00271 0.004 0.356 0.052 0.100 0.488
#> GSM601948 1 0.685 0.37532 0.588 0.068 0.248 0.012 0.084
#> GSM601958 3 0.495 0.52748 0.088 0.040 0.760 0.000 0.112
#> GSM601973 4 0.592 0.52200 0.240 0.004 0.012 0.632 0.112
#> GSM601978 2 0.292 0.59279 0.004 0.876 0.004 0.092 0.024
#> GSM601988 4 0.663 0.30948 0.012 0.004 0.172 0.536 0.276
#> GSM601878 1 0.442 0.42103 0.700 0.012 0.276 0.000 0.012
#> GSM601908 2 0.539 0.27415 0.024 0.592 0.000 0.356 0.028
#> GSM601918 1 0.741 0.02750 0.508 0.180 0.012 0.256 0.044
#> GSM601923 1 0.360 0.50556 0.796 0.000 0.180 0.000 0.024
#> GSM601953 2 0.346 0.59194 0.040 0.868 0.020 0.016 0.056
#> GSM601963 5 0.634 0.32955 0.104 0.004 0.364 0.012 0.516
#> GSM601968 5 0.788 0.35877 0.144 0.172 0.216 0.000 0.468
#> GSM601983 5 0.636 0.46277 0.068 0.020 0.320 0.020 0.572
#> GSM601993 4 0.473 0.55289 0.028 0.000 0.064 0.764 0.144
#> GSM601874 2 0.324 0.59164 0.008 0.864 0.020 0.096 0.012
#> GSM601884 2 0.667 0.29467 0.016 0.524 0.012 0.332 0.116
#> GSM601889 3 0.512 0.46781 0.040 0.064 0.736 0.000 0.160
#> GSM601894 3 0.613 0.24819 0.048 0.060 0.592 0.000 0.300
#> GSM601899 2 0.544 0.49730 0.008 0.684 0.204 0.004 0.100
#> GSM601904 1 0.663 0.29870 0.580 0.000 0.136 0.240 0.044
#> GSM601914 5 0.510 0.38823 0.012 0.008 0.412 0.008 0.560
#> GSM601929 1 0.452 0.37501 0.616 0.000 0.372 0.008 0.004
#> GSM601934 4 0.795 0.03386 0.012 0.308 0.260 0.372 0.048
#> GSM601939 3 0.480 0.34919 0.328 0.000 0.636 0.000 0.036
#> GSM601944 4 0.619 0.56101 0.032 0.040 0.088 0.688 0.152
#> GSM601949 1 0.633 0.18142 0.520 0.124 0.344 0.000 0.012
#> GSM601959 3 0.582 0.51264 0.096 0.076 0.708 0.004 0.116
#> GSM601974 5 0.600 0.48255 0.108 0.000 0.172 0.052 0.668
#> GSM601979 2 0.397 0.50703 0.008 0.764 0.000 0.212 0.016
#> GSM601989 3 0.430 0.45564 0.016 0.092 0.796 0.000 0.096
#> GSM601879 1 0.305 0.51444 0.832 0.004 0.160 0.004 0.000
#> GSM601909 5 0.697 0.45963 0.060 0.148 0.240 0.000 0.552
#> GSM601919 1 0.542 0.42378 0.732 0.124 0.016 0.108 0.020
#> GSM601924 1 0.454 0.39799 0.684 0.004 0.288 0.000 0.024
#> GSM601954 1 0.850 -0.08470 0.360 0.340 0.024 0.156 0.120
#> GSM601964 5 0.528 0.54500 0.088 0.008 0.188 0.008 0.708
#> GSM601969 1 0.781 0.05819 0.464 0.192 0.232 0.000 0.112
#> GSM601984 3 0.639 0.24713 0.356 0.000 0.516 0.108 0.020
#> GSM601994 4 0.317 0.57675 0.040 0.028 0.012 0.884 0.036
#> GSM601875 2 0.395 0.56747 0.012 0.808 0.024 0.148 0.008
#> GSM601885 4 0.625 -0.01650 0.016 0.428 0.044 0.488 0.024
#> GSM601890 2 0.655 0.22528 0.016 0.532 0.160 0.000 0.292
#> GSM601895 5 0.510 0.39945 0.004 0.016 0.424 0.008 0.548
#> GSM601900 3 0.453 0.33110 0.004 0.016 0.748 0.028 0.204
#> GSM601905 1 0.639 -0.09919 0.448 0.004 0.112 0.428 0.008
#> GSM601915 3 0.473 0.46719 0.076 0.004 0.732 0.000 0.188
#> GSM601930 3 0.481 -0.12559 0.464 0.000 0.520 0.008 0.008
#> GSM601935 5 0.662 0.37720 0.012 0.000 0.244 0.216 0.528
#> GSM601940 3 0.441 0.40160 0.308 0.008 0.676 0.004 0.004
#> GSM601945 2 0.613 0.38613 0.024 0.604 0.036 0.304 0.032
#> GSM601950 3 0.538 0.20320 0.392 0.060 0.548 0.000 0.000
#> GSM601960 5 0.489 0.47370 0.016 0.012 0.360 0.000 0.612
#> GSM601975 4 0.724 0.48732 0.268 0.048 0.048 0.560 0.076
#> GSM601980 5 0.371 0.46650 0.012 0.000 0.032 0.136 0.820
#> GSM601990 5 0.571 0.49661 0.024 0.008 0.312 0.040 0.616
#> GSM601880 1 0.382 0.49312 0.772 0.000 0.208 0.004 0.016
#> GSM601910 5 0.728 0.23554 0.096 0.076 0.388 0.004 0.436
#> GSM601920 1 0.349 0.48061 0.836 0.012 0.008 0.132 0.012
#> GSM601925 1 0.367 0.51008 0.792 0.000 0.188 0.012 0.008
#> GSM601955 5 0.377 0.55462 0.016 0.016 0.048 0.072 0.848
#> GSM601965 3 0.660 0.28489 0.356 0.008 0.512 0.104 0.020
#> GSM601970 5 0.742 0.07386 0.212 0.044 0.316 0.000 0.428
#> GSM601985 3 0.555 0.32510 0.380 0.000 0.552 0.004 0.064
#> GSM601995 5 0.520 0.40482 0.028 0.004 0.040 0.228 0.700
#> GSM601876 3 0.381 0.47525 0.200 0.004 0.780 0.012 0.004
#> GSM601886 4 0.754 0.31173 0.072 0.000 0.188 0.468 0.272
#> GSM601891 2 0.614 0.38684 0.000 0.596 0.200 0.008 0.196
#> GSM601896 3 0.314 0.54256 0.108 0.032 0.856 0.004 0.000
#> GSM601901 2 0.663 0.21289 0.048 0.524 0.056 0.360 0.012
#> GSM601906 1 0.647 0.42325 0.572 0.000 0.256 0.148 0.024
#> GSM601916 4 0.698 0.37811 0.224 0.004 0.244 0.508 0.020
#> GSM601931 1 0.470 0.15794 0.516 0.000 0.472 0.008 0.004
#> GSM601936 4 0.644 0.42299 0.024 0.000 0.264 0.572 0.140
#> GSM601941 4 0.545 0.55490 0.200 0.008 0.004 0.684 0.104
#> GSM601946 3 0.446 0.36981 0.308 0.000 0.672 0.004 0.016
#> GSM601951 1 0.569 0.21240 0.496 0.012 0.452 0.016 0.024
#> GSM601961 2 0.466 0.57817 0.044 0.792 0.116 0.028 0.020
#> GSM601976 4 0.748 0.45008 0.240 0.032 0.172 0.524 0.032
#> GSM601981 2 0.577 0.49908 0.004 0.676 0.064 0.212 0.044
#> GSM601991 3 0.636 -0.37752 0.008 0.008 0.448 0.096 0.440
#> GSM601881 1 0.391 0.42750 0.704 0.000 0.292 0.004 0.000
#> GSM601911 3 0.827 0.07143 0.236 0.136 0.368 0.260 0.000
#> GSM601921 1 0.511 0.24573 0.668 0.020 0.000 0.276 0.036
#> GSM601926 1 0.417 0.45015 0.724 0.000 0.256 0.004 0.016
#> GSM601956 2 0.595 0.48181 0.016 0.624 0.020 0.056 0.284
#> GSM601966 4 0.524 0.54181 0.084 0.112 0.028 0.756 0.020
#> GSM601971 1 0.700 -0.05613 0.468 0.020 0.292 0.000 0.220
#> GSM601986 3 0.668 0.25414 0.348 0.016 0.512 0.112 0.012
#> GSM601996 4 0.445 0.56658 0.112 0.056 0.020 0.800 0.012
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM601872 2 0.552 0.22528 0.008 0.476 0.444 0.012 0.004 0.056
#> GSM601882 5 0.728 0.31518 0.000 0.244 0.076 0.096 0.508 0.076
#> GSM601887 2 0.518 0.53969 0.144 0.720 0.076 0.016 0.004 0.040
#> GSM601892 2 0.662 0.19289 0.380 0.464 0.060 0.016 0.008 0.072
#> GSM601897 3 0.485 0.56421 0.040 0.068 0.752 0.004 0.016 0.120
#> GSM601902 6 0.732 0.44122 0.024 0.032 0.004 0.312 0.300 0.328
#> GSM601912 1 0.783 -0.13006 0.420 0.092 0.304 0.012 0.048 0.124
#> GSM601927 1 0.560 0.17743 0.520 0.004 0.000 0.376 0.016 0.084
#> GSM601932 6 0.715 0.43895 0.008 0.040 0.024 0.180 0.288 0.460
#> GSM601937 3 0.630 0.20862 0.012 0.008 0.504 0.008 0.312 0.156
#> GSM601942 3 0.527 0.47519 0.000 0.092 0.716 0.016 0.060 0.116
#> GSM601947 4 0.684 -0.18815 0.004 0.124 0.008 0.508 0.084 0.272
#> GSM601957 1 0.479 0.51659 0.768 0.068 0.052 0.040 0.000 0.072
#> GSM601972 5 0.762 -0.26747 0.000 0.164 0.004 0.196 0.340 0.296
#> GSM601977 2 0.806 0.09340 0.000 0.328 0.268 0.044 0.252 0.108
#> GSM601987 5 0.529 -0.07861 0.008 0.468 0.008 0.004 0.468 0.044
#> GSM601877 4 0.527 0.24193 0.352 0.000 0.008 0.576 0.036 0.028
#> GSM601907 2 0.362 0.53345 0.000 0.824 0.000 0.040 0.088 0.048
#> GSM601917 4 0.421 0.35839 0.016 0.020 0.012 0.804 0.056 0.092
#> GSM601922 4 0.392 0.42796 0.056 0.008 0.000 0.812 0.088 0.036
#> GSM601952 6 0.857 0.20642 0.000 0.084 0.248 0.236 0.148 0.284
#> GSM601962 3 0.634 0.59359 0.176 0.016 0.636 0.028 0.060 0.084
#> GSM601967 1 0.685 0.38251 0.596 0.088 0.152 0.056 0.004 0.104
#> GSM601982 2 0.931 -0.01669 0.068 0.296 0.156 0.120 0.260 0.100
#> GSM601992 5 0.356 0.40728 0.000 0.044 0.004 0.108 0.824 0.020
#> GSM601873 2 0.736 0.00364 0.004 0.372 0.160 0.012 0.364 0.088
#> GSM601883 2 0.634 -0.05971 0.004 0.436 0.012 0.052 0.428 0.068
#> GSM601888 2 0.394 0.55676 0.132 0.796 0.016 0.012 0.000 0.044
#> GSM601893 2 0.563 0.50430 0.196 0.664 0.076 0.004 0.012 0.048
#> GSM601898 1 0.528 0.39652 0.680 0.020 0.152 0.004 0.004 0.140
#> GSM601903 4 0.710 -0.44815 0.016 0.028 0.012 0.404 0.196 0.344
#> GSM601913 1 0.550 0.40053 0.684 0.008 0.112 0.004 0.044 0.148
#> GSM601928 1 0.652 0.23254 0.484 0.000 0.020 0.256 0.012 0.228
#> GSM601933 5 0.511 0.30304 0.008 0.300 0.008 0.004 0.624 0.056
#> GSM601938 5 0.625 0.35158 0.000 0.084 0.048 0.120 0.648 0.100
#> GSM601943 3 0.603 0.03593 0.004 0.352 0.524 0.004 0.060 0.056
#> GSM601948 4 0.757 0.24943 0.236 0.056 0.036 0.416 0.004 0.252
#> GSM601958 1 0.363 0.53081 0.828 0.032 0.028 0.000 0.012 0.100
#> GSM601973 6 0.730 0.45543 0.004 0.012 0.048 0.296 0.288 0.352
#> GSM601978 2 0.350 0.54940 0.000 0.848 0.028 0.024 0.060 0.040
#> GSM601988 5 0.623 0.27707 0.040 0.004 0.212 0.016 0.600 0.128
#> GSM601878 1 0.558 0.04514 0.468 0.016 0.020 0.456 0.004 0.036
#> GSM601908 2 0.607 0.28998 0.000 0.560 0.004 0.088 0.288 0.060
#> GSM601918 4 0.564 0.13043 0.000 0.084 0.008 0.676 0.124 0.108
#> GSM601923 4 0.500 0.23694 0.340 0.000 0.028 0.596 0.000 0.036
#> GSM601953 2 0.373 0.54836 0.000 0.832 0.032 0.072 0.020 0.044
#> GSM601963 3 0.623 0.44240 0.344 0.004 0.528 0.032 0.032 0.060
#> GSM601968 3 0.730 0.40330 0.272 0.088 0.496 0.060 0.004 0.080
#> GSM601983 3 0.634 0.54991 0.256 0.020 0.596 0.028 0.048 0.052
#> GSM601993 5 0.403 0.35613 0.004 0.000 0.064 0.080 0.804 0.048
#> GSM601874 2 0.365 0.55746 0.008 0.844 0.020 0.020 0.056 0.052
#> GSM601884 2 0.747 0.29154 0.004 0.480 0.188 0.036 0.212 0.080
#> GSM601889 1 0.529 0.46614 0.688 0.044 0.064 0.004 0.008 0.192
#> GSM601894 1 0.605 0.35610 0.616 0.056 0.200 0.012 0.000 0.116
#> GSM601899 2 0.522 0.52746 0.180 0.696 0.060 0.004 0.004 0.056
#> GSM601904 4 0.693 0.06724 0.072 0.000 0.012 0.484 0.160 0.272
#> GSM601914 3 0.592 0.45940 0.336 0.000 0.548 0.016 0.044 0.056
#> GSM601929 1 0.591 0.08919 0.464 0.004 0.000 0.384 0.008 0.140
#> GSM601934 5 0.663 0.00654 0.068 0.388 0.012 0.004 0.448 0.080
#> GSM601939 1 0.386 0.53851 0.804 0.004 0.012 0.116 0.004 0.060
#> GSM601944 5 0.673 0.13434 0.012 0.060 0.056 0.032 0.504 0.336
#> GSM601949 1 0.672 0.26834 0.512 0.092 0.008 0.272 0.000 0.116
#> GSM601959 1 0.523 0.48079 0.668 0.048 0.036 0.008 0.004 0.236
#> GSM601974 3 0.668 0.31710 0.040 0.008 0.516 0.088 0.032 0.316
#> GSM601979 2 0.468 0.48762 0.000 0.740 0.012 0.036 0.164 0.048
#> GSM601989 1 0.541 0.44878 0.712 0.072 0.060 0.000 0.032 0.124
#> GSM601879 4 0.530 0.29569 0.312 0.012 0.016 0.612 0.008 0.040
#> GSM601909 3 0.653 0.52831 0.208 0.092 0.592 0.044 0.000 0.064
#> GSM601919 4 0.449 0.37904 0.036 0.092 0.004 0.788 0.032 0.048
#> GSM601924 1 0.556 0.15716 0.508 0.000 0.032 0.404 0.004 0.052
#> GSM601954 6 0.788 0.13442 0.024 0.224 0.028 0.336 0.052 0.336
#> GSM601964 3 0.540 0.59625 0.160 0.008 0.704 0.048 0.016 0.064
#> GSM601969 1 0.877 0.06746 0.304 0.164 0.080 0.264 0.012 0.176
#> GSM601984 1 0.686 0.34624 0.520 0.000 0.016 0.144 0.240 0.080
#> GSM601994 5 0.314 0.40485 0.000 0.032 0.008 0.088 0.856 0.016
#> GSM601875 2 0.458 0.50491 0.020 0.744 0.000 0.028 0.172 0.036
#> GSM601885 5 0.687 0.09096 0.012 0.372 0.020 0.052 0.452 0.092
#> GSM601890 2 0.641 0.34317 0.120 0.552 0.272 0.024 0.004 0.028
#> GSM601895 3 0.645 0.46534 0.316 0.028 0.532 0.012 0.024 0.088
#> GSM601900 1 0.706 0.26421 0.500 0.044 0.116 0.000 0.064 0.276
#> GSM601905 4 0.712 -0.24438 0.080 0.004 0.000 0.380 0.344 0.192
#> GSM601915 1 0.447 0.45995 0.756 0.000 0.116 0.008 0.016 0.104
#> GSM601930 1 0.578 0.31766 0.568 0.004 0.004 0.288 0.012 0.124
#> GSM601935 3 0.754 0.39361 0.092 0.008 0.484 0.032 0.172 0.212
#> GSM601940 1 0.433 0.52960 0.760 0.000 0.008 0.144 0.012 0.076
#> GSM601945 2 0.612 0.38081 0.008 0.584 0.016 0.012 0.224 0.156
#> GSM601950 1 0.493 0.50764 0.724 0.044 0.004 0.136 0.000 0.092
#> GSM601960 3 0.547 0.56144 0.236 0.004 0.640 0.012 0.012 0.096
#> GSM601975 6 0.729 0.48752 0.020 0.028 0.012 0.280 0.264 0.396
#> GSM601980 3 0.467 0.50522 0.004 0.008 0.748 0.024 0.072 0.144
#> GSM601990 3 0.586 0.55064 0.272 0.000 0.600 0.016 0.060 0.052
#> GSM601880 4 0.547 0.23547 0.336 0.000 0.020 0.580 0.036 0.028
#> GSM601910 1 0.735 -0.17576 0.436 0.056 0.364 0.024 0.052 0.068
#> GSM601920 4 0.468 0.43410 0.084 0.008 0.000 0.760 0.084 0.064
#> GSM601925 4 0.526 0.25889 0.336 0.000 0.016 0.592 0.032 0.024
#> GSM601955 3 0.405 0.54559 0.008 0.008 0.800 0.020 0.044 0.120
#> GSM601965 1 0.680 0.38553 0.556 0.016 0.004 0.128 0.208 0.088
#> GSM601970 3 0.643 0.16805 0.408 0.012 0.440 0.068 0.000 0.072
#> GSM601985 1 0.458 0.52331 0.772 0.000 0.036 0.108 0.028 0.056
#> GSM601995 3 0.540 0.47373 0.012 0.004 0.668 0.016 0.192 0.108
#> GSM601876 1 0.433 0.55621 0.792 0.016 0.004 0.036 0.052 0.100
#> GSM601886 5 0.793 0.03221 0.092 0.000 0.156 0.076 0.416 0.260
#> GSM601891 2 0.623 0.48085 0.124 0.616 0.172 0.004 0.008 0.076
#> GSM601896 1 0.464 0.53166 0.764 0.036 0.000 0.020 0.068 0.112
#> GSM601901 2 0.715 0.23299 0.012 0.492 0.004 0.104 0.248 0.140
#> GSM601906 4 0.769 0.21490 0.260 0.000 0.012 0.352 0.124 0.252
#> GSM601916 6 0.691 0.26374 0.120 0.000 0.000 0.116 0.360 0.404
#> GSM601931 1 0.538 0.35853 0.616 0.008 0.004 0.248 0.000 0.124
#> GSM601936 5 0.612 0.27424 0.076 0.000 0.092 0.016 0.620 0.196
#> GSM601941 5 0.705 -0.39943 0.000 0.016 0.036 0.260 0.396 0.292
#> GSM601946 1 0.397 0.54642 0.796 0.004 0.012 0.068 0.004 0.116
#> GSM601951 1 0.658 0.08961 0.400 0.004 0.008 0.252 0.008 0.328
#> GSM601961 2 0.516 0.52715 0.100 0.716 0.000 0.020 0.032 0.132
#> GSM601976 5 0.743 -0.27678 0.072 0.032 0.004 0.140 0.400 0.352
#> GSM601981 2 0.622 0.41346 0.020 0.560 0.008 0.008 0.152 0.252
#> GSM601991 3 0.757 0.39741 0.276 0.008 0.396 0.004 0.196 0.120
#> GSM601881 1 0.458 0.04370 0.492 0.000 0.000 0.480 0.012 0.016
#> GSM601911 5 0.788 0.17375 0.276 0.096 0.000 0.124 0.420 0.084
#> GSM601921 4 0.416 0.26376 0.004 0.008 0.000 0.760 0.160 0.068
#> GSM601926 1 0.529 0.05245 0.488 0.000 0.024 0.448 0.008 0.032
#> GSM601956 2 0.600 0.38179 0.004 0.536 0.352 0.036 0.020 0.052
#> GSM601966 5 0.593 0.30497 0.012 0.084 0.008 0.100 0.668 0.128
#> GSM601971 1 0.790 0.26183 0.428 0.032 0.160 0.200 0.004 0.176
#> GSM601986 1 0.734 0.28555 0.468 0.028 0.000 0.152 0.256 0.096
#> GSM601996 5 0.345 0.38334 0.000 0.028 0.004 0.124 0.824 0.020
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)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
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)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
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)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, 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.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")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n time(p) gender(p) k
#> CV:NMF 122 0.187 0.9827 2
#> CV:NMF 79 0.018 0.6148 3
#> CV:NMF 69 0.589 0.6600 4
#> CV:NMF 31 0.930 0.0206 5
#> CV:NMF 28 0.344 0.0856 6
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.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["MAD", "hclust"]
# you can also extract it by
# res = res_list["MAD:hclust"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 21512 rows and 125 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'hclust' method.
#> Performed in total 1250 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_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
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, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.211 0.646 0.706 0.3334 0.550 0.550
#> 3 3 0.146 0.680 0.807 0.4859 0.827 0.715
#> 4 4 0.350 0.478 0.759 0.2021 0.966 0.930
#> 5 5 0.379 0.597 0.716 0.0851 0.861 0.706
#> 6 6 0.385 0.540 0.721 0.0776 0.981 0.948
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 3
Following shows 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 class
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
#> GSM601872 2 0.9850 0.75448 0.428 0.572
#> GSM601882 2 0.9815 0.81413 0.420 0.580
#> GSM601887 1 0.7139 0.61847 0.804 0.196
#> GSM601892 1 0.2603 0.78632 0.956 0.044
#> GSM601897 1 0.6887 0.64281 0.816 0.184
#> GSM601902 2 0.9977 0.72549 0.472 0.528
#> GSM601912 1 0.3879 0.77131 0.924 0.076
#> GSM601927 1 0.2423 0.79269 0.960 0.040
#> GSM601932 2 0.9944 0.75608 0.456 0.544
#> GSM601937 2 0.9686 0.53768 0.396 0.604
#> GSM601942 2 0.9323 0.61218 0.348 0.652
#> GSM601947 1 0.9358 -0.02473 0.648 0.352
#> GSM601957 1 0.0672 0.78781 0.992 0.008
#> GSM601972 2 0.9977 0.73742 0.472 0.528
#> GSM601977 2 0.9815 0.81434 0.420 0.580
#> GSM601987 2 0.9795 0.81615 0.416 0.584
#> GSM601877 1 0.2423 0.79269 0.960 0.040
#> GSM601907 2 0.9850 0.81578 0.428 0.572
#> GSM601917 1 0.9044 0.24415 0.680 0.320
#> GSM601922 1 0.9427 0.00182 0.640 0.360
#> GSM601952 2 0.9996 0.69980 0.488 0.512
#> GSM601962 1 0.2603 0.79055 0.956 0.044
#> GSM601967 1 0.0672 0.79168 0.992 0.008
#> GSM601982 1 0.9608 -0.22807 0.616 0.384
#> GSM601992 2 0.9248 0.74086 0.340 0.660
#> GSM601873 2 0.9933 0.74783 0.452 0.548
#> GSM601883 2 0.9795 0.81374 0.416 0.584
#> GSM601888 1 0.7219 0.61164 0.800 0.200
#> GSM601893 1 0.4431 0.76428 0.908 0.092
#> GSM601898 1 0.0376 0.78953 0.996 0.004
#> GSM601903 2 0.9977 0.72549 0.472 0.528
#> GSM601913 1 0.1843 0.78817 0.972 0.028
#> GSM601928 1 0.2423 0.79269 0.960 0.040
#> GSM601933 2 0.9866 0.81436 0.432 0.568
#> GSM601938 2 0.9833 0.81322 0.424 0.576
#> GSM601943 2 0.9850 0.73064 0.428 0.572
#> GSM601948 1 0.4815 0.75077 0.896 0.104
#> GSM601958 1 0.0376 0.78953 0.996 0.004
#> GSM601973 2 0.9983 0.72372 0.476 0.524
#> GSM601978 2 0.9795 0.81501 0.416 0.584
#> GSM601988 1 0.9850 -0.11288 0.572 0.428
#> GSM601878 1 0.2423 0.79269 0.960 0.040
#> GSM601908 2 0.9833 0.81583 0.424 0.576
#> GSM601918 1 0.9661 -0.19531 0.608 0.392
#> GSM601923 1 0.2423 0.79269 0.960 0.040
#> GSM601953 2 0.9833 0.81196 0.424 0.576
#> GSM601963 1 0.2423 0.79421 0.960 0.040
#> GSM601968 1 0.1414 0.79373 0.980 0.020
#> GSM601983 1 0.3274 0.78300 0.940 0.060
#> GSM601993 2 0.9286 0.71594 0.344 0.656
#> GSM601874 2 0.9815 0.81012 0.420 0.580
#> GSM601884 2 0.9815 0.81413 0.420 0.580
#> GSM601889 1 0.0672 0.79221 0.992 0.008
#> GSM601894 1 0.1633 0.78595 0.976 0.024
#> GSM601899 1 0.5737 0.72502 0.864 0.136
#> GSM601904 1 0.7674 0.53867 0.776 0.224
#> GSM601914 1 0.2236 0.78337 0.964 0.036
#> GSM601929 1 0.2948 0.79269 0.948 0.052
#> GSM601934 2 0.9933 0.79824 0.452 0.548
#> GSM601939 1 0.0376 0.78953 0.996 0.004
#> GSM601944 2 0.9983 0.73837 0.476 0.524
#> GSM601949 1 0.4431 0.76178 0.908 0.092
#> GSM601959 1 0.0672 0.78891 0.992 0.008
#> GSM601974 1 0.8661 0.40584 0.712 0.288
#> GSM601979 2 0.9795 0.81501 0.416 0.584
#> GSM601989 1 0.1633 0.79495 0.976 0.024
#> GSM601879 1 0.2423 0.79269 0.960 0.040
#> GSM601909 1 0.3114 0.78435 0.944 0.056
#> GSM601919 1 0.9661 -0.19531 0.608 0.392
#> GSM601924 1 0.2236 0.79362 0.964 0.036
#> GSM601954 1 0.9896 -0.49962 0.560 0.440
#> GSM601964 1 0.2778 0.78995 0.952 0.048
#> GSM601969 1 0.1184 0.79488 0.984 0.016
#> GSM601984 1 0.5294 0.73682 0.880 0.120
#> GSM601994 2 0.9358 0.74010 0.352 0.648
#> GSM601875 2 0.9866 0.81306 0.432 0.568
#> GSM601885 2 0.9833 0.81323 0.424 0.576
#> GSM601890 1 0.7376 0.59950 0.792 0.208
#> GSM601895 1 0.1414 0.79390 0.980 0.020
#> GSM601900 1 0.1843 0.79170 0.972 0.028
#> GSM601905 1 0.8327 0.41738 0.736 0.264
#> GSM601915 1 0.1843 0.78667 0.972 0.028
#> GSM601930 1 0.2778 0.79200 0.952 0.048
#> GSM601935 1 0.8813 0.38434 0.700 0.300
#> GSM601940 1 0.0938 0.79306 0.988 0.012
#> GSM601945 2 0.9881 0.80810 0.436 0.564
#> GSM601950 1 0.2423 0.79585 0.960 0.040
#> GSM601960 1 0.3431 0.77500 0.936 0.064
#> GSM601975 1 0.9909 -0.46863 0.556 0.444
#> GSM601980 2 0.7376 0.38734 0.208 0.792
#> GSM601990 1 0.2236 0.79274 0.964 0.036
#> GSM601880 1 0.2423 0.79269 0.960 0.040
#> GSM601910 1 0.1843 0.79472 0.972 0.028
#> GSM601920 1 0.8499 0.38844 0.724 0.276
#> GSM601925 1 0.2423 0.79269 0.960 0.040
#> GSM601955 2 0.7299 0.35141 0.204 0.796
#> GSM601965 1 0.5178 0.74444 0.884 0.116
#> GSM601970 1 0.0938 0.79052 0.988 0.012
#> GSM601985 1 0.1414 0.78789 0.980 0.020
#> GSM601995 2 0.9491 0.42580 0.368 0.632
#> GSM601876 1 0.2043 0.79646 0.968 0.032
#> GSM601886 1 0.8081 0.52116 0.752 0.248
#> GSM601891 1 0.7299 0.60332 0.796 0.204
#> GSM601896 1 0.1843 0.79507 0.972 0.028
#> GSM601901 2 0.9922 0.79936 0.448 0.552
#> GSM601906 1 0.7528 0.55672 0.784 0.216
#> GSM601916 1 0.9954 -0.54090 0.540 0.460
#> GSM601931 1 0.2236 0.79315 0.964 0.036
#> GSM601936 1 0.9732 -0.05755 0.596 0.404
#> GSM601941 2 0.9954 0.74399 0.460 0.540
#> GSM601946 1 0.1184 0.79367 0.984 0.016
#> GSM601951 1 0.5519 0.72209 0.872 0.128
#> GSM601961 2 0.9983 0.75995 0.476 0.524
#> GSM601976 1 0.9491 -0.10058 0.632 0.368
#> GSM601981 2 0.9983 0.75961 0.476 0.524
#> GSM601991 1 0.5059 0.74661 0.888 0.112
#> GSM601881 1 0.2423 0.79269 0.960 0.040
#> GSM601911 1 0.6887 0.63148 0.816 0.184
#> GSM601921 1 0.8499 0.38844 0.724 0.276
#> GSM601926 1 0.2236 0.79310 0.964 0.036
#> GSM601956 2 0.9815 0.81310 0.420 0.580
#> GSM601966 2 0.9963 0.74061 0.464 0.536
#> GSM601971 1 0.0938 0.79401 0.988 0.012
#> GSM601986 1 0.5178 0.73394 0.884 0.116
#> GSM601996 2 0.9460 0.74315 0.364 0.636
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM601872 2 0.6191 0.6866 0.140 0.776 0.084
#> GSM601882 2 0.3845 0.7462 0.116 0.872 0.012
#> GSM601887 1 0.6414 0.6109 0.716 0.248 0.036
#> GSM601892 1 0.2152 0.8234 0.948 0.036 0.016
#> GSM601897 1 0.7021 0.6133 0.708 0.216 0.076
#> GSM601902 2 0.6354 0.7043 0.196 0.748 0.056
#> GSM601912 1 0.3752 0.8083 0.884 0.096 0.020
#> GSM601927 1 0.3272 0.8180 0.892 0.104 0.004
#> GSM601932 2 0.5901 0.7188 0.176 0.776 0.048
#> GSM601937 2 0.8775 0.2663 0.152 0.568 0.280
#> GSM601942 2 0.8521 -0.1042 0.092 0.468 0.440
#> GSM601947 2 0.7735 0.2793 0.440 0.512 0.048
#> GSM601957 1 0.0661 0.8250 0.988 0.008 0.004
#> GSM601972 2 0.5384 0.7392 0.188 0.788 0.024
#> GSM601977 2 0.4551 0.7489 0.140 0.840 0.020
#> GSM601987 2 0.4209 0.7455 0.120 0.860 0.020
#> GSM601877 1 0.3272 0.8180 0.892 0.104 0.004
#> GSM601907 2 0.4136 0.7400 0.116 0.864 0.020
#> GSM601917 1 0.7841 -0.1017 0.480 0.468 0.052
#> GSM601922 2 0.7883 0.2997 0.428 0.516 0.056
#> GSM601952 2 0.6079 0.7197 0.216 0.748 0.036
#> GSM601962 1 0.2773 0.8265 0.928 0.048 0.024
#> GSM601967 1 0.0592 0.8283 0.988 0.012 0.000
#> GSM601982 2 0.7339 0.4691 0.392 0.572 0.036
#> GSM601992 2 0.5722 0.6389 0.084 0.804 0.112
#> GSM601873 2 0.6299 0.6765 0.132 0.772 0.096
#> GSM601883 2 0.3771 0.7432 0.112 0.876 0.012
#> GSM601888 1 0.6482 0.6097 0.716 0.244 0.040
#> GSM601893 1 0.3933 0.7987 0.880 0.092 0.028
#> GSM601898 1 0.0592 0.8283 0.988 0.012 0.000
#> GSM601903 2 0.6258 0.7057 0.196 0.752 0.052
#> GSM601913 1 0.1337 0.8283 0.972 0.012 0.016
#> GSM601928 1 0.3272 0.8180 0.892 0.104 0.004
#> GSM601933 2 0.4748 0.7535 0.144 0.832 0.024
#> GSM601938 2 0.4209 0.7492 0.120 0.860 0.020
#> GSM601943 2 0.6662 0.6651 0.128 0.752 0.120
#> GSM601948 1 0.5115 0.7585 0.796 0.188 0.016
#> GSM601958 1 0.0424 0.8259 0.992 0.008 0.000
#> GSM601973 2 0.6007 0.7111 0.184 0.768 0.048
#> GSM601978 2 0.4280 0.7412 0.124 0.856 0.020
#> GSM601988 2 0.9506 0.2377 0.360 0.448 0.192
#> GSM601878 1 0.3272 0.8180 0.892 0.104 0.004
#> GSM601908 2 0.4068 0.7419 0.120 0.864 0.016
#> GSM601918 2 0.7784 0.4376 0.388 0.556 0.056
#> GSM601923 1 0.3272 0.8180 0.892 0.104 0.004
#> GSM601953 2 0.4413 0.7368 0.124 0.852 0.024
#> GSM601963 1 0.2152 0.8323 0.948 0.036 0.016
#> GSM601968 1 0.1525 0.8320 0.964 0.032 0.004
#> GSM601983 1 0.3237 0.8198 0.912 0.056 0.032
#> GSM601993 2 0.6561 0.6117 0.100 0.756 0.144
#> GSM601874 2 0.4519 0.7369 0.116 0.852 0.032
#> GSM601884 2 0.3607 0.7441 0.112 0.880 0.008
#> GSM601889 1 0.0592 0.8283 0.988 0.012 0.000
#> GSM601894 1 0.1337 0.8217 0.972 0.016 0.012
#> GSM601899 1 0.4937 0.7560 0.824 0.148 0.028
#> GSM601904 1 0.7424 0.3205 0.592 0.364 0.044
#> GSM601914 1 0.1919 0.8265 0.956 0.020 0.024
#> GSM601929 1 0.3826 0.8094 0.868 0.124 0.008
#> GSM601934 2 0.5178 0.7538 0.164 0.808 0.028
#> GSM601939 1 0.0892 0.8321 0.980 0.020 0.000
#> GSM601944 2 0.6679 0.6803 0.152 0.748 0.100
#> GSM601949 1 0.4749 0.7747 0.816 0.172 0.012
#> GSM601959 1 0.0661 0.8246 0.988 0.008 0.004
#> GSM601974 1 0.8261 0.1815 0.524 0.396 0.080
#> GSM601979 2 0.4280 0.7412 0.124 0.856 0.020
#> GSM601989 1 0.1411 0.8318 0.964 0.036 0.000
#> GSM601879 1 0.3272 0.8180 0.892 0.104 0.004
#> GSM601909 1 0.3129 0.8180 0.904 0.088 0.008
#> GSM601919 2 0.7784 0.4376 0.388 0.556 0.056
#> GSM601924 1 0.3193 0.8193 0.896 0.100 0.004
#> GSM601954 2 0.6988 0.6222 0.320 0.644 0.036
#> GSM601964 1 0.2564 0.8277 0.936 0.036 0.028
#> GSM601969 1 0.1337 0.8316 0.972 0.016 0.012
#> GSM601984 1 0.5200 0.7485 0.796 0.184 0.020
#> GSM601994 2 0.6254 0.6382 0.108 0.776 0.116
#> GSM601875 2 0.4209 0.7451 0.120 0.860 0.020
#> GSM601885 2 0.3918 0.7482 0.120 0.868 0.012
#> GSM601890 1 0.6559 0.5996 0.708 0.252 0.040
#> GSM601895 1 0.1482 0.8303 0.968 0.020 0.012
#> GSM601900 1 0.1482 0.8290 0.968 0.012 0.020
#> GSM601905 1 0.7634 0.0913 0.524 0.432 0.044
#> GSM601915 1 0.1636 0.8266 0.964 0.016 0.020
#> GSM601930 1 0.3532 0.8157 0.884 0.108 0.008
#> GSM601935 1 0.8646 0.3115 0.556 0.320 0.124
#> GSM601940 1 0.1267 0.8334 0.972 0.024 0.004
#> GSM601945 2 0.4848 0.7338 0.128 0.836 0.036
#> GSM601950 1 0.2711 0.8286 0.912 0.088 0.000
#> GSM601960 1 0.3583 0.8111 0.900 0.044 0.056
#> GSM601975 2 0.6897 0.6335 0.292 0.668 0.040
#> GSM601980 3 0.4845 0.7535 0.052 0.104 0.844
#> GSM601990 1 0.2176 0.8292 0.948 0.032 0.020
#> GSM601880 1 0.3272 0.8180 0.892 0.104 0.004
#> GSM601910 1 0.2063 0.8310 0.948 0.044 0.008
#> GSM601920 1 0.7619 0.1138 0.532 0.424 0.044
#> GSM601925 1 0.3272 0.8180 0.892 0.104 0.004
#> GSM601955 3 0.4565 0.7481 0.064 0.076 0.860
#> GSM601965 1 0.5331 0.7456 0.792 0.184 0.024
#> GSM601970 1 0.0829 0.8274 0.984 0.012 0.004
#> GSM601985 1 0.1015 0.8269 0.980 0.008 0.012
#> GSM601995 3 0.9258 0.3838 0.204 0.272 0.524
#> GSM601876 1 0.2400 0.8319 0.932 0.064 0.004
#> GSM601886 1 0.7756 0.2866 0.564 0.380 0.056
#> GSM601891 1 0.6526 0.5890 0.704 0.260 0.036
#> GSM601896 1 0.2066 0.8334 0.940 0.060 0.000
#> GSM601901 2 0.4873 0.7572 0.152 0.824 0.024
#> GSM601906 1 0.7368 0.3586 0.604 0.352 0.044
#> GSM601916 2 0.7213 0.6419 0.272 0.668 0.060
#> GSM601931 1 0.3193 0.8189 0.896 0.100 0.004
#> GSM601936 2 0.9167 0.2705 0.392 0.460 0.148
#> GSM601941 2 0.5743 0.7115 0.172 0.784 0.044
#> GSM601946 1 0.2066 0.8321 0.940 0.060 0.000
#> GSM601951 1 0.5450 0.7027 0.760 0.228 0.012
#> GSM601961 2 0.5470 0.7311 0.168 0.796 0.036
#> GSM601976 2 0.7424 0.4326 0.388 0.572 0.040
#> GSM601981 2 0.5223 0.7521 0.176 0.800 0.024
#> GSM601991 1 0.5093 0.7776 0.836 0.088 0.076
#> GSM601881 1 0.3272 0.8180 0.892 0.104 0.004
#> GSM601911 1 0.6539 0.5741 0.684 0.288 0.028
#> GSM601921 1 0.7619 0.1138 0.532 0.424 0.044
#> GSM601926 1 0.3038 0.8189 0.896 0.104 0.000
#> GSM601956 2 0.4342 0.7378 0.120 0.856 0.024
#> GSM601966 2 0.5743 0.7153 0.172 0.784 0.044
#> GSM601971 1 0.1015 0.8306 0.980 0.012 0.008
#> GSM601986 1 0.5219 0.7304 0.788 0.196 0.016
#> GSM601996 2 0.6462 0.6407 0.120 0.764 0.116
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM601872 2 0.5512 0.4880 0.032 0.772 0.088 0.108
#> GSM601882 2 0.1639 0.5950 0.008 0.952 0.004 0.036
#> GSM601887 1 0.6088 0.4626 0.700 0.212 0.024 0.064
#> GSM601892 1 0.1575 0.7516 0.956 0.028 0.012 0.004
#> GSM601897 1 0.7020 0.4286 0.668 0.172 0.096 0.064
#> GSM601902 2 0.6568 0.2478 0.096 0.572 0.000 0.332
#> GSM601912 1 0.3725 0.7228 0.860 0.076 0.004 0.060
#> GSM601927 1 0.3958 0.7129 0.816 0.024 0.000 0.160
#> GSM601932 2 0.5966 0.3631 0.072 0.648 0.000 0.280
#> GSM601937 4 0.7905 -0.1078 0.040 0.276 0.144 0.540
#> GSM601942 3 0.6655 0.1753 0.000 0.440 0.476 0.084
#> GSM601947 2 0.7808 -0.3338 0.312 0.416 0.000 0.272
#> GSM601957 1 0.0376 0.7539 0.992 0.000 0.004 0.004
#> GSM601972 2 0.5701 0.4424 0.080 0.712 0.004 0.204
#> GSM601977 2 0.2838 0.5922 0.020 0.908 0.016 0.056
#> GSM601987 2 0.1339 0.5966 0.008 0.964 0.004 0.024
#> GSM601877 1 0.3910 0.7155 0.820 0.024 0.000 0.156
#> GSM601907 2 0.2587 0.5797 0.004 0.908 0.012 0.076
#> GSM601917 4 0.7914 0.2781 0.332 0.312 0.000 0.356
#> GSM601922 2 0.7882 -0.3754 0.284 0.368 0.000 0.348
#> GSM601952 2 0.6107 0.4353 0.100 0.708 0.016 0.176
#> GSM601962 1 0.3080 0.7489 0.900 0.028 0.020 0.052
#> GSM601967 1 0.0188 0.7564 0.996 0.000 0.000 0.004
#> GSM601982 2 0.6594 -0.0464 0.308 0.612 0.024 0.056
#> GSM601992 2 0.6049 0.1735 0.028 0.524 0.008 0.440
#> GSM601873 2 0.6118 0.4183 0.024 0.712 0.084 0.180
#> GSM601883 2 0.1492 0.5940 0.004 0.956 0.004 0.036
#> GSM601888 1 0.6086 0.4518 0.696 0.220 0.024 0.060
#> GSM601893 1 0.3617 0.7126 0.876 0.056 0.020 0.048
#> GSM601898 1 0.0469 0.7568 0.988 0.000 0.000 0.012
#> GSM601903 2 0.6519 0.2634 0.096 0.584 0.000 0.320
#> GSM601913 1 0.1545 0.7566 0.952 0.000 0.008 0.040
#> GSM601928 1 0.3958 0.7129 0.816 0.024 0.000 0.160
#> GSM601933 2 0.2820 0.5946 0.020 0.904 0.008 0.068
#> GSM601938 2 0.2380 0.5936 0.008 0.920 0.008 0.064
#> GSM601943 2 0.5953 0.4127 0.016 0.724 0.104 0.156
#> GSM601948 1 0.5678 0.6062 0.716 0.112 0.000 0.172
#> GSM601958 1 0.0188 0.7547 0.996 0.000 0.000 0.004
#> GSM601973 2 0.6435 0.3156 0.080 0.604 0.004 0.312
#> GSM601978 2 0.2587 0.5765 0.004 0.908 0.012 0.076
#> GSM601988 4 0.9406 0.4918 0.244 0.304 0.100 0.352
#> GSM601878 1 0.3910 0.7155 0.820 0.024 0.000 0.156
#> GSM601908 2 0.2528 0.5786 0.004 0.908 0.008 0.080
#> GSM601918 2 0.7724 -0.2571 0.240 0.432 0.000 0.328
#> GSM601923 1 0.3958 0.7129 0.816 0.024 0.000 0.160
#> GSM601953 2 0.2795 0.5755 0.004 0.896 0.012 0.088
#> GSM601963 1 0.2505 0.7564 0.920 0.008 0.020 0.052
#> GSM601968 1 0.1139 0.7569 0.972 0.008 0.008 0.012
#> GSM601983 1 0.3546 0.7395 0.880 0.032 0.028 0.060
#> GSM601993 2 0.6784 0.0490 0.040 0.468 0.028 0.464
#> GSM601874 2 0.2966 0.5839 0.020 0.896 0.008 0.076
#> GSM601884 2 0.1575 0.5961 0.012 0.956 0.004 0.028
#> GSM601889 1 0.0376 0.7566 0.992 0.004 0.000 0.004
#> GSM601894 1 0.1042 0.7561 0.972 0.000 0.008 0.020
#> GSM601899 1 0.4462 0.6548 0.824 0.116 0.024 0.036
#> GSM601904 1 0.7597 -0.1671 0.468 0.224 0.000 0.308
#> GSM601914 1 0.2246 0.7534 0.928 0.004 0.016 0.052
#> GSM601929 1 0.4733 0.6930 0.780 0.044 0.004 0.172
#> GSM601934 2 0.3526 0.5846 0.040 0.872 0.008 0.080
#> GSM601939 1 0.1118 0.7606 0.964 0.000 0.000 0.036
#> GSM601944 2 0.6242 0.3244 0.020 0.624 0.040 0.316
#> GSM601949 1 0.5412 0.6352 0.736 0.096 0.000 0.168
#> GSM601959 1 0.0376 0.7550 0.992 0.000 0.004 0.004
#> GSM601974 1 0.8781 -0.2774 0.440 0.256 0.056 0.248
#> GSM601979 2 0.2587 0.5765 0.004 0.908 0.012 0.076
#> GSM601989 1 0.1182 0.7620 0.968 0.016 0.000 0.016
#> GSM601879 1 0.3910 0.7155 0.820 0.024 0.000 0.156
#> GSM601909 1 0.2982 0.7369 0.900 0.064 0.012 0.024
#> GSM601919 2 0.7724 -0.2571 0.240 0.432 0.000 0.328
#> GSM601924 1 0.3910 0.7160 0.820 0.024 0.000 0.156
#> GSM601954 2 0.7241 0.1583 0.212 0.592 0.012 0.184
#> GSM601964 1 0.2945 0.7506 0.904 0.016 0.024 0.056
#> GSM601969 1 0.1284 0.7611 0.964 0.012 0.000 0.024
#> GSM601984 1 0.5548 0.6323 0.740 0.112 0.004 0.144
#> GSM601994 2 0.6436 0.0973 0.048 0.492 0.008 0.452
#> GSM601875 2 0.2613 0.5926 0.024 0.916 0.008 0.052
#> GSM601885 2 0.1706 0.5956 0.016 0.948 0.000 0.036
#> GSM601890 1 0.6147 0.4567 0.696 0.216 0.028 0.060
#> GSM601895 1 0.1486 0.7593 0.960 0.008 0.008 0.024
#> GSM601900 1 0.1256 0.7581 0.964 0.000 0.008 0.028
#> GSM601905 1 0.7873 -0.4003 0.388 0.292 0.000 0.320
#> GSM601915 1 0.1584 0.7525 0.952 0.000 0.012 0.036
#> GSM601930 1 0.4376 0.7019 0.796 0.028 0.004 0.172
#> GSM601935 1 0.8468 -0.2176 0.456 0.164 0.052 0.328
#> GSM601940 1 0.1398 0.7619 0.956 0.004 0.000 0.040
#> GSM601945 2 0.3560 0.5518 0.004 0.844 0.012 0.140
#> GSM601950 1 0.3441 0.7388 0.856 0.024 0.000 0.120
#> GSM601960 1 0.3641 0.7204 0.868 0.008 0.052 0.072
#> GSM601975 2 0.7153 0.0296 0.160 0.532 0.000 0.308
#> GSM601980 3 0.3103 0.6012 0.024 0.048 0.900 0.028
#> GSM601990 1 0.2604 0.7528 0.916 0.012 0.016 0.056
#> GSM601880 1 0.3958 0.7129 0.816 0.024 0.000 0.160
#> GSM601910 1 0.1920 0.7545 0.944 0.028 0.004 0.024
#> GSM601920 1 0.7856 -0.3935 0.388 0.276 0.000 0.336
#> GSM601925 1 0.3958 0.7129 0.816 0.024 0.000 0.160
#> GSM601955 3 0.2019 0.5892 0.032 0.024 0.940 0.004
#> GSM601965 1 0.5699 0.6237 0.736 0.120 0.008 0.136
#> GSM601970 1 0.0376 0.7556 0.992 0.000 0.004 0.004
#> GSM601985 1 0.1356 0.7590 0.960 0.000 0.008 0.032
#> GSM601995 3 0.8833 0.0593 0.112 0.116 0.432 0.340
#> GSM601876 1 0.2593 0.7565 0.904 0.016 0.000 0.080
#> GSM601886 1 0.8139 -0.2621 0.436 0.224 0.016 0.324
#> GSM601891 1 0.6089 0.4415 0.692 0.224 0.020 0.064
#> GSM601896 1 0.2174 0.7617 0.928 0.020 0.000 0.052
#> GSM601901 2 0.3505 0.5778 0.048 0.864 0.000 0.088
#> GSM601906 1 0.7540 -0.1187 0.480 0.216 0.000 0.304
#> GSM601916 2 0.7714 0.0158 0.160 0.504 0.016 0.320
#> GSM601931 1 0.3806 0.7172 0.824 0.020 0.000 0.156
#> GSM601936 4 0.8759 0.5217 0.244 0.292 0.048 0.416
#> GSM601941 2 0.6106 0.3174 0.064 0.604 0.000 0.332
#> GSM601946 1 0.2198 0.7574 0.920 0.008 0.000 0.072
#> GSM601951 1 0.6405 0.4941 0.660 0.132 0.004 0.204
#> GSM601961 2 0.4123 0.5564 0.068 0.848 0.016 0.068
#> GSM601976 2 0.7948 -0.3044 0.256 0.412 0.004 0.328
#> GSM601981 2 0.4711 0.5588 0.052 0.796 0.008 0.144
#> GSM601991 1 0.5383 0.6618 0.784 0.056 0.052 0.108
#> GSM601881 1 0.3910 0.7155 0.820 0.024 0.000 0.156
#> GSM601911 1 0.6932 0.3053 0.600 0.240 0.004 0.156
#> GSM601921 1 0.7856 -0.3935 0.388 0.276 0.000 0.336
#> GSM601926 1 0.3910 0.7148 0.820 0.024 0.000 0.156
#> GSM601956 2 0.2992 0.5843 0.016 0.892 0.008 0.084
#> GSM601966 2 0.5986 0.3430 0.060 0.620 0.000 0.320
#> GSM601971 1 0.0921 0.7613 0.972 0.000 0.000 0.028
#> GSM601986 1 0.5480 0.6101 0.736 0.124 0.000 0.140
#> GSM601996 2 0.6563 0.0890 0.056 0.488 0.008 0.448
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM601872 2 0.4290 0.6068 0.056 0.828 0.024 0.044 0.048
#> GSM601882 2 0.4064 0.6934 0.000 0.756 0.004 0.216 0.024
#> GSM601887 3 0.5643 0.5355 0.012 0.212 0.688 0.060 0.028
#> GSM601892 3 0.1404 0.7986 0.004 0.028 0.956 0.004 0.008
#> GSM601897 3 0.6491 0.5094 0.064 0.176 0.664 0.044 0.052
#> GSM601902 4 0.5789 0.3284 0.000 0.212 0.044 0.668 0.076
#> GSM601912 3 0.3466 0.7658 0.000 0.048 0.856 0.072 0.024
#> GSM601927 3 0.3686 0.7169 0.000 0.004 0.780 0.204 0.012
#> GSM601932 4 0.6189 0.1949 0.000 0.304 0.032 0.580 0.084
#> GSM601937 5 0.6618 -0.1106 0.064 0.148 0.024 0.108 0.656
#> GSM601942 2 0.6096 -0.1623 0.444 0.472 0.000 0.040 0.044
#> GSM601947 4 0.6597 0.5233 0.000 0.200 0.240 0.544 0.016
#> GSM601957 3 0.0324 0.7965 0.000 0.000 0.992 0.004 0.004
#> GSM601972 2 0.5850 0.1726 0.000 0.468 0.032 0.464 0.036
#> GSM601977 2 0.3893 0.7221 0.008 0.804 0.012 0.160 0.016
#> GSM601987 2 0.3706 0.7100 0.000 0.792 0.004 0.184 0.020
#> GSM601877 3 0.3686 0.7174 0.000 0.004 0.780 0.204 0.012
#> GSM601907 2 0.1956 0.7172 0.000 0.916 0.000 0.076 0.008
#> GSM601917 4 0.6299 0.5388 0.000 0.116 0.252 0.600 0.032
#> GSM601922 4 0.5922 0.5491 0.000 0.116 0.204 0.652 0.028
#> GSM601952 2 0.6417 0.3201 0.012 0.532 0.064 0.364 0.028
#> GSM601962 3 0.2932 0.7876 0.004 0.012 0.888 0.044 0.052
#> GSM601967 3 0.0451 0.7983 0.000 0.000 0.988 0.008 0.004
#> GSM601982 2 0.7748 0.0958 0.016 0.456 0.296 0.180 0.052
#> GSM601992 5 0.6595 0.5456 0.000 0.196 0.004 0.328 0.472
#> GSM601873 2 0.4936 0.5345 0.044 0.780 0.012 0.084 0.080
#> GSM601883 2 0.3877 0.6941 0.000 0.764 0.000 0.212 0.024
#> GSM601888 3 0.5585 0.5241 0.012 0.224 0.684 0.056 0.024
#> GSM601893 3 0.3659 0.7539 0.020 0.048 0.860 0.048 0.024
#> GSM601898 3 0.0693 0.7986 0.000 0.000 0.980 0.008 0.012
#> GSM601903 4 0.5845 0.3269 0.000 0.220 0.044 0.660 0.076
#> GSM601913 3 0.1830 0.8015 0.000 0.000 0.932 0.040 0.028
#> GSM601928 3 0.3686 0.7169 0.000 0.004 0.780 0.204 0.012
#> GSM601933 2 0.4025 0.7060 0.000 0.780 0.012 0.184 0.024
#> GSM601938 2 0.4141 0.6793 0.000 0.736 0.000 0.236 0.028
#> GSM601943 2 0.4617 0.5473 0.064 0.796 0.004 0.080 0.056
#> GSM601948 3 0.5344 0.5787 0.004 0.056 0.676 0.248 0.016
#> GSM601958 3 0.0451 0.7968 0.000 0.000 0.988 0.008 0.004
#> GSM601973 4 0.5780 0.2990 0.000 0.252 0.032 0.644 0.072
#> GSM601978 2 0.1522 0.7128 0.000 0.944 0.000 0.044 0.012
#> GSM601988 5 0.9142 0.2436 0.044 0.152 0.212 0.272 0.320
#> GSM601878 3 0.3686 0.7174 0.000 0.004 0.780 0.204 0.012
#> GSM601908 2 0.1478 0.7151 0.000 0.936 0.000 0.064 0.000
#> GSM601918 4 0.6302 0.5347 0.000 0.196 0.172 0.608 0.024
#> GSM601923 3 0.3719 0.7139 0.000 0.004 0.776 0.208 0.012
#> GSM601953 2 0.1282 0.7094 0.000 0.952 0.000 0.044 0.004
#> GSM601963 3 0.2359 0.7964 0.008 0.000 0.912 0.036 0.044
#> GSM601968 3 0.1248 0.7984 0.004 0.008 0.964 0.008 0.016
#> GSM601983 3 0.3447 0.7791 0.016 0.016 0.868 0.052 0.048
#> GSM601993 5 0.6853 0.5447 0.004 0.144 0.024 0.328 0.500
#> GSM601874 2 0.2912 0.7207 0.004 0.872 0.012 0.104 0.008
#> GSM601884 2 0.4033 0.6961 0.000 0.760 0.004 0.212 0.024
#> GSM601889 3 0.0566 0.7981 0.000 0.000 0.984 0.012 0.004
#> GSM601894 3 0.1186 0.7995 0.008 0.000 0.964 0.020 0.008
#> GSM601899 3 0.4429 0.6985 0.024 0.104 0.808 0.044 0.020
#> GSM601904 4 0.6148 0.3758 0.000 0.060 0.400 0.508 0.032
#> GSM601914 3 0.2308 0.7948 0.004 0.000 0.912 0.036 0.048
#> GSM601929 3 0.3996 0.6921 0.000 0.008 0.752 0.228 0.012
#> GSM601934 2 0.4670 0.6900 0.000 0.748 0.032 0.188 0.032
#> GSM601939 3 0.1205 0.8007 0.000 0.000 0.956 0.040 0.004
#> GSM601944 2 0.6206 0.2974 0.012 0.592 0.000 0.164 0.232
#> GSM601949 3 0.5138 0.6133 0.004 0.048 0.696 0.236 0.016
#> GSM601959 3 0.0579 0.7974 0.000 0.000 0.984 0.008 0.008
#> GSM601974 3 0.8161 -0.2601 0.040 0.120 0.400 0.364 0.076
#> GSM601979 2 0.1522 0.7128 0.000 0.944 0.000 0.044 0.012
#> GSM601989 3 0.1153 0.8029 0.000 0.008 0.964 0.024 0.004
#> GSM601879 3 0.3686 0.7174 0.000 0.004 0.780 0.204 0.012
#> GSM601909 3 0.2950 0.7766 0.004 0.056 0.888 0.036 0.016
#> GSM601919 4 0.6273 0.5362 0.000 0.192 0.172 0.612 0.024
#> GSM601924 3 0.3686 0.7178 0.000 0.004 0.780 0.204 0.012
#> GSM601954 2 0.7167 -0.0145 0.004 0.424 0.168 0.376 0.028
#> GSM601964 3 0.2744 0.7904 0.008 0.004 0.896 0.040 0.052
#> GSM601969 3 0.1412 0.8018 0.000 0.008 0.952 0.036 0.004
#> GSM601984 3 0.5137 0.6568 0.000 0.052 0.720 0.192 0.036
#> GSM601994 5 0.6739 0.5687 0.000 0.156 0.020 0.336 0.488
#> GSM601875 2 0.2777 0.7253 0.000 0.864 0.016 0.120 0.000
#> GSM601885 2 0.4033 0.6985 0.000 0.764 0.008 0.208 0.020
#> GSM601890 3 0.5739 0.5310 0.016 0.212 0.684 0.060 0.028
#> GSM601895 3 0.1106 0.8009 0.000 0.000 0.964 0.024 0.012
#> GSM601900 3 0.1668 0.8001 0.000 0.000 0.940 0.032 0.028
#> GSM601905 4 0.6645 0.5149 0.000 0.112 0.308 0.540 0.040
#> GSM601915 3 0.1648 0.7965 0.000 0.000 0.940 0.020 0.040
#> GSM601930 3 0.4097 0.6989 0.000 0.008 0.756 0.216 0.020
#> GSM601935 3 0.8136 -0.0187 0.020 0.068 0.420 0.208 0.284
#> GSM601940 3 0.1443 0.8018 0.000 0.004 0.948 0.044 0.004
#> GSM601945 2 0.2616 0.6690 0.000 0.880 0.000 0.100 0.020
#> GSM601950 3 0.3362 0.7536 0.000 0.012 0.824 0.156 0.008
#> GSM601960 3 0.3768 0.7533 0.020 0.004 0.840 0.048 0.088
#> GSM601975 4 0.5984 0.4605 0.000 0.224 0.096 0.644 0.036
#> GSM601980 1 0.3642 0.6645 0.856 0.048 0.012 0.020 0.064
#> GSM601990 3 0.2390 0.7937 0.004 0.000 0.908 0.044 0.044
#> GSM601880 3 0.3719 0.7139 0.000 0.004 0.776 0.208 0.012
#> GSM601910 3 0.2087 0.7946 0.000 0.020 0.928 0.032 0.020
#> GSM601920 4 0.6479 0.5176 0.000 0.096 0.308 0.556 0.040
#> GSM601925 3 0.3719 0.7139 0.000 0.004 0.776 0.208 0.012
#> GSM601955 1 0.1460 0.6535 0.956 0.008 0.012 0.004 0.020
#> GSM601965 3 0.5317 0.6561 0.004 0.060 0.720 0.180 0.036
#> GSM601970 3 0.0451 0.7986 0.000 0.000 0.988 0.004 0.008
#> GSM601985 3 0.1399 0.8020 0.000 0.000 0.952 0.020 0.028
#> GSM601995 1 0.8739 0.1330 0.388 0.068 0.088 0.144 0.312
#> GSM601876 3 0.2233 0.7895 0.000 0.004 0.892 0.104 0.000
#> GSM601886 4 0.7474 0.2487 0.000 0.084 0.364 0.424 0.128
#> GSM601891 3 0.5569 0.5175 0.008 0.228 0.680 0.060 0.024
#> GSM601896 3 0.1970 0.8007 0.000 0.012 0.924 0.060 0.004
#> GSM601901 2 0.5065 0.6339 0.000 0.664 0.036 0.284 0.016
#> GSM601906 4 0.6220 0.3257 0.000 0.064 0.416 0.488 0.032
#> GSM601916 4 0.6589 0.3897 0.000 0.236 0.104 0.596 0.064
#> GSM601931 3 0.3496 0.7223 0.000 0.000 0.788 0.200 0.012
#> GSM601936 5 0.8553 0.2109 0.016 0.124 0.196 0.328 0.336
#> GSM601941 4 0.5735 0.2758 0.000 0.236 0.024 0.652 0.088
#> GSM601946 3 0.1908 0.7910 0.000 0.000 0.908 0.092 0.000
#> GSM601951 3 0.5543 0.4261 0.000 0.044 0.612 0.320 0.024
#> GSM601961 2 0.4030 0.6879 0.000 0.808 0.060 0.120 0.012
#> GSM601976 4 0.5908 0.5403 0.000 0.144 0.176 0.656 0.024
#> GSM601981 2 0.4614 0.6256 0.000 0.728 0.032 0.224 0.016
#> GSM601991 3 0.5090 0.7021 0.020 0.036 0.772 0.080 0.092
#> GSM601881 3 0.3686 0.7174 0.000 0.004 0.780 0.204 0.012
#> GSM601911 3 0.6726 0.3280 0.000 0.172 0.564 0.228 0.036
#> GSM601921 4 0.6479 0.5176 0.000 0.096 0.308 0.556 0.040
#> GSM601926 3 0.3686 0.7169 0.000 0.004 0.780 0.204 0.012
#> GSM601956 2 0.2589 0.7181 0.000 0.888 0.012 0.092 0.008
#> GSM601966 4 0.5877 0.2549 0.000 0.276 0.020 0.616 0.088
#> GSM601971 3 0.1195 0.8033 0.000 0.000 0.960 0.028 0.012
#> GSM601986 3 0.5388 0.6284 0.000 0.064 0.700 0.200 0.036
#> GSM601996 5 0.6933 0.5709 0.000 0.160 0.028 0.344 0.468
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM601872 2 0.4618 0.28565 0.016 0.784 0.092 0.024 0.056 0.028
#> GSM601882 2 0.4068 0.64159 0.004 0.788 0.032 0.128 0.048 0.000
#> GSM601887 1 0.5617 0.53056 0.672 0.196 0.052 0.056 0.016 0.008
#> GSM601892 1 0.1375 0.75694 0.952 0.028 0.008 0.008 0.004 0.000
#> GSM601897 1 0.6700 0.49331 0.624 0.164 0.068 0.044 0.072 0.028
#> GSM601902 4 0.6833 0.38286 0.012 0.200 0.060 0.504 0.224 0.000
#> GSM601912 1 0.4187 0.73040 0.804 0.048 0.044 0.080 0.024 0.000
#> GSM601927 1 0.3592 0.61414 0.656 0.000 0.000 0.344 0.000 0.000
#> GSM601932 4 0.7037 0.24121 0.004 0.288 0.064 0.412 0.232 0.000
#> GSM601937 5 0.6052 -0.06086 0.000 0.084 0.248 0.048 0.600 0.020
#> GSM601942 6 0.6459 -0.42940 0.000 0.408 0.124 0.004 0.048 0.416
#> GSM601947 4 0.6431 0.52659 0.132 0.204 0.036 0.588 0.040 0.000
#> GSM601957 1 0.0508 0.75568 0.984 0.000 0.012 0.000 0.004 0.000
#> GSM601972 2 0.6176 0.17595 0.012 0.488 0.028 0.368 0.104 0.000
#> GSM601977 2 0.3652 0.64978 0.004 0.828 0.040 0.096 0.028 0.004
#> GSM601987 2 0.3604 0.65367 0.004 0.820 0.020 0.112 0.044 0.000
#> GSM601877 1 0.3592 0.61480 0.656 0.000 0.000 0.344 0.000 0.000
#> GSM601907 2 0.2342 0.60125 0.000 0.904 0.032 0.040 0.024 0.000
#> GSM601917 4 0.4059 0.53347 0.068 0.076 0.016 0.808 0.032 0.000
#> GSM601922 4 0.4667 0.54486 0.056 0.096 0.020 0.768 0.060 0.000
#> GSM601952 2 0.6814 0.31318 0.024 0.524 0.072 0.300 0.064 0.016
#> GSM601962 1 0.3757 0.73574 0.828 0.012 0.056 0.068 0.036 0.000
#> GSM601967 1 0.0520 0.75676 0.984 0.000 0.008 0.008 0.000 0.000
#> GSM601982 2 0.7410 0.10839 0.268 0.480 0.056 0.128 0.064 0.004
#> GSM601992 5 0.4792 0.55505 0.000 0.148 0.000 0.180 0.672 0.000
#> GSM601873 2 0.5576 -0.00911 0.012 0.700 0.144 0.036 0.084 0.024
#> GSM601883 2 0.3886 0.64135 0.000 0.796 0.032 0.124 0.048 0.000
#> GSM601888 1 0.5573 0.52130 0.668 0.208 0.040 0.060 0.016 0.008
#> GSM601893 1 0.3554 0.72309 0.852 0.048 0.040 0.036 0.012 0.012
#> GSM601898 1 0.0508 0.75680 0.984 0.000 0.012 0.000 0.004 0.000
#> GSM601903 4 0.6855 0.37744 0.012 0.208 0.060 0.500 0.220 0.000
#> GSM601913 1 0.2122 0.75830 0.916 0.000 0.032 0.024 0.028 0.000
#> GSM601928 1 0.3592 0.61414 0.656 0.000 0.000 0.344 0.000 0.000
#> GSM601933 2 0.3733 0.63747 0.008 0.808 0.016 0.128 0.040 0.000
#> GSM601938 2 0.4368 0.62322 0.000 0.756 0.028 0.140 0.076 0.000
#> GSM601943 2 0.5033 0.05503 0.000 0.736 0.120 0.036 0.080 0.028
#> GSM601948 1 0.5048 0.48910 0.580 0.048 0.012 0.356 0.000 0.004
#> GSM601958 1 0.0363 0.75536 0.988 0.000 0.012 0.000 0.000 0.000
#> GSM601973 4 0.7051 0.34803 0.008 0.240 0.060 0.464 0.224 0.004
#> GSM601978 2 0.2039 0.58862 0.000 0.916 0.052 0.020 0.012 0.000
#> GSM601988 5 0.8913 0.22427 0.100 0.128 0.180 0.232 0.336 0.024
#> GSM601878 1 0.3592 0.61480 0.656 0.000 0.000 0.344 0.000 0.000
#> GSM601908 2 0.2081 0.59662 0.000 0.916 0.036 0.036 0.012 0.000
#> GSM601918 4 0.5340 0.54129 0.052 0.180 0.020 0.692 0.056 0.000
#> GSM601923 1 0.3607 0.61037 0.652 0.000 0.000 0.348 0.000 0.000
#> GSM601953 2 0.2189 0.57157 0.000 0.904 0.060 0.032 0.004 0.000
#> GSM601963 1 0.3287 0.74454 0.852 0.004 0.060 0.056 0.028 0.000
#> GSM601968 1 0.1408 0.75685 0.952 0.008 0.024 0.008 0.008 0.000
#> GSM601983 1 0.4119 0.72864 0.812 0.016 0.064 0.064 0.040 0.004
#> GSM601993 5 0.4341 0.56145 0.008 0.088 0.000 0.168 0.736 0.000
#> GSM601874 2 0.3090 0.61270 0.012 0.868 0.036 0.068 0.012 0.004
#> GSM601884 2 0.4027 0.64478 0.004 0.792 0.032 0.124 0.048 0.000
#> GSM601889 1 0.0520 0.75739 0.984 0.000 0.008 0.008 0.000 0.000
#> GSM601894 1 0.1210 0.75828 0.960 0.000 0.020 0.008 0.004 0.008
#> GSM601899 1 0.4228 0.67332 0.800 0.100 0.044 0.036 0.008 0.012
#> GSM601904 4 0.4577 0.44649 0.208 0.040 0.008 0.720 0.024 0.000
#> GSM601914 1 0.2917 0.74638 0.876 0.004 0.048 0.032 0.040 0.000
#> GSM601929 1 0.4077 0.58054 0.620 0.004 0.004 0.368 0.004 0.000
#> GSM601934 2 0.4392 0.61626 0.028 0.776 0.024 0.128 0.044 0.000
#> GSM601939 1 0.2006 0.75126 0.892 0.000 0.004 0.104 0.000 0.000
#> GSM601944 3 0.5083 0.00000 0.000 0.408 0.532 0.036 0.024 0.000
#> GSM601949 1 0.4818 0.52517 0.600 0.040 0.008 0.348 0.000 0.004
#> GSM601959 1 0.0363 0.75569 0.988 0.000 0.012 0.000 0.000 0.000
#> GSM601974 4 0.8460 0.15933 0.280 0.120 0.088 0.392 0.088 0.032
#> GSM601979 2 0.2039 0.58862 0.000 0.916 0.052 0.020 0.012 0.000
#> GSM601989 1 0.1520 0.76348 0.948 0.008 0.008 0.020 0.016 0.000
#> GSM601879 1 0.3592 0.61480 0.656 0.000 0.000 0.344 0.000 0.000
#> GSM601909 1 0.3095 0.73699 0.868 0.052 0.036 0.036 0.008 0.000
#> GSM601919 4 0.5253 0.54188 0.052 0.176 0.020 0.700 0.052 0.000
#> GSM601924 1 0.3592 0.61475 0.656 0.000 0.000 0.344 0.000 0.000
#> GSM601954 2 0.7646 0.07923 0.116 0.412 0.076 0.336 0.052 0.008
#> GSM601964 1 0.3662 0.73522 0.832 0.008 0.060 0.064 0.036 0.000
#> GSM601969 1 0.1578 0.75895 0.936 0.004 0.012 0.048 0.000 0.000
#> GSM601984 1 0.5767 0.59409 0.628 0.052 0.052 0.244 0.024 0.000
#> GSM601994 5 0.4676 0.57917 0.008 0.104 0.000 0.188 0.700 0.000
#> GSM601875 2 0.2454 0.63938 0.008 0.884 0.020 0.088 0.000 0.000
#> GSM601885 2 0.4019 0.64471 0.004 0.788 0.032 0.136 0.040 0.000
#> GSM601890 1 0.5725 0.52131 0.664 0.196 0.052 0.064 0.016 0.008
#> GSM601895 1 0.2007 0.75626 0.920 0.000 0.032 0.036 0.012 0.000
#> GSM601900 1 0.1882 0.75658 0.928 0.000 0.020 0.028 0.024 0.000
#> GSM601905 4 0.4717 0.52193 0.128 0.092 0.012 0.744 0.024 0.000
#> GSM601915 1 0.1930 0.75461 0.924 0.000 0.036 0.012 0.028 0.000
#> GSM601930 1 0.3911 0.58331 0.624 0.000 0.008 0.368 0.000 0.000
#> GSM601935 1 0.8402 -0.23223 0.304 0.052 0.200 0.212 0.232 0.000
#> GSM601940 1 0.2053 0.75220 0.888 0.000 0.004 0.108 0.000 0.000
#> GSM601945 2 0.3414 0.42829 0.000 0.828 0.112 0.032 0.028 0.000
#> GSM601950 1 0.3265 0.68562 0.748 0.004 0.000 0.248 0.000 0.000
#> GSM601960 1 0.4047 0.69888 0.804 0.008 0.072 0.036 0.080 0.000
#> GSM601975 4 0.5823 0.47525 0.004 0.220 0.040 0.612 0.124 0.000
#> GSM601980 6 0.4191 0.48206 0.000 0.020 0.060 0.016 0.116 0.788
#> GSM601990 1 0.3421 0.73880 0.844 0.004 0.060 0.060 0.032 0.000
#> GSM601880 1 0.3620 0.60819 0.648 0.000 0.000 0.352 0.000 0.000
#> GSM601910 1 0.2315 0.75021 0.908 0.016 0.032 0.040 0.004 0.000
#> GSM601920 4 0.4195 0.51673 0.116 0.068 0.008 0.784 0.024 0.000
#> GSM601925 1 0.3607 0.61037 0.652 0.000 0.000 0.348 0.000 0.000
#> GSM601955 6 0.0767 0.45925 0.004 0.000 0.012 0.008 0.000 0.976
#> GSM601965 1 0.5770 0.60490 0.644 0.056 0.048 0.220 0.032 0.000
#> GSM601970 1 0.0881 0.75737 0.972 0.000 0.012 0.008 0.008 0.000
#> GSM601985 1 0.2133 0.76420 0.912 0.000 0.020 0.052 0.016 0.000
#> GSM601995 6 0.8481 0.00935 0.036 0.048 0.140 0.116 0.300 0.360
#> GSM601876 1 0.3087 0.72974 0.808 0.000 0.012 0.176 0.004 0.000
#> GSM601886 4 0.7316 0.14578 0.188 0.060 0.140 0.524 0.088 0.000
#> GSM601891 1 0.5660 0.50951 0.660 0.212 0.044 0.060 0.016 0.008
#> GSM601896 1 0.2680 0.75487 0.876 0.008 0.012 0.092 0.012 0.000
#> GSM601901 2 0.5066 0.57998 0.020 0.692 0.028 0.212 0.048 0.000
#> GSM601906 4 0.4524 0.42997 0.224 0.040 0.008 0.712 0.016 0.000
#> GSM601916 4 0.7390 0.41196 0.040 0.256 0.072 0.480 0.148 0.004
#> GSM601931 1 0.3699 0.62053 0.660 0.000 0.000 0.336 0.004 0.000
#> GSM601936 5 0.8496 0.29064 0.104 0.088 0.180 0.300 0.320 0.008
#> GSM601941 4 0.6693 0.33511 0.000 0.220 0.064 0.484 0.232 0.000
#> GSM601946 1 0.2841 0.73096 0.824 0.000 0.012 0.164 0.000 0.000
#> GSM601951 1 0.5636 0.32243 0.512 0.044 0.032 0.400 0.012 0.000
#> GSM601961 2 0.3674 0.56985 0.052 0.832 0.024 0.076 0.016 0.000
#> GSM601976 4 0.5950 0.54823 0.064 0.144 0.036 0.664 0.092 0.000
#> GSM601981 2 0.4584 0.52258 0.008 0.736 0.036 0.180 0.040 0.000
#> GSM601991 1 0.5719 0.63715 0.704 0.028 0.064 0.084 0.108 0.012
#> GSM601881 1 0.3592 0.61480 0.656 0.000 0.000 0.344 0.000 0.000
#> GSM601911 1 0.6964 0.29734 0.492 0.172 0.048 0.260 0.028 0.000
#> GSM601921 4 0.4042 0.51856 0.112 0.068 0.004 0.792 0.024 0.000
#> GSM601926 1 0.3592 0.61390 0.656 0.000 0.000 0.344 0.000 0.000
#> GSM601956 2 0.3059 0.59290 0.004 0.860 0.072 0.052 0.012 0.000
#> GSM601966 4 0.6790 0.32178 0.000 0.264 0.064 0.456 0.216 0.000
#> GSM601971 1 0.1829 0.76015 0.920 0.000 0.024 0.056 0.000 0.000
#> GSM601986 1 0.5878 0.57272 0.624 0.060 0.044 0.240 0.032 0.000
#> GSM601996 5 0.4924 0.57650 0.008 0.112 0.000 0.212 0.668 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
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)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
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)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, 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.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")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n time(p) gender(p) k
#> MAD:hclust 105 0.061 0.28412 2
#> MAD:hclust 105 0.101 0.40946 3
#> MAD:hclust 80 0.330 0.21415 4
#> MAD:hclust 100 0.577 0.00643 5
#> MAD:hclust 92 0.854 0.00800 6
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.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["MAD", "kmeans"]
# you can also extract it by
# res = res_list["MAD:kmeans"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 21512 rows and 125 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'kmeans' method.
#> Performed in total 1250 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_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
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, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.986 0.992 0.5043 0.496 0.496
#> 3 3 0.545 0.460 0.705 0.2618 0.925 0.850
#> 4 4 0.560 0.549 0.717 0.1380 0.729 0.432
#> 5 5 0.632 0.730 0.792 0.0687 0.893 0.623
#> 6 6 0.694 0.666 0.752 0.0503 0.975 0.887
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 2
Following shows 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 class
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
#> GSM601872 2 0.0376 0.997 0.004 0.996
#> GSM601882 2 0.0000 0.998 0.000 1.000
#> GSM601887 1 0.2948 0.942 0.948 0.052
#> GSM601892 1 0.0000 0.987 1.000 0.000
#> GSM601897 1 0.1184 0.976 0.984 0.016
#> GSM601902 2 0.0000 0.998 0.000 1.000
#> GSM601912 1 0.0000 0.987 1.000 0.000
#> GSM601927 1 0.0376 0.988 0.996 0.004
#> GSM601932 2 0.0000 0.998 0.000 1.000
#> GSM601937 2 0.0672 0.992 0.008 0.992
#> GSM601942 2 0.0376 0.997 0.004 0.996
#> GSM601947 2 0.0000 0.998 0.000 1.000
#> GSM601957 1 0.0000 0.987 1.000 0.000
#> GSM601972 2 0.0000 0.998 0.000 1.000
#> GSM601977 2 0.0376 0.997 0.004 0.996
#> GSM601987 2 0.0376 0.997 0.004 0.996
#> GSM601877 1 0.0376 0.988 0.996 0.004
#> GSM601907 2 0.0376 0.997 0.004 0.996
#> GSM601917 2 0.0000 0.998 0.000 1.000
#> GSM601922 2 0.0000 0.998 0.000 1.000
#> GSM601952 2 0.0376 0.997 0.004 0.996
#> GSM601962 1 0.0376 0.988 0.996 0.004
#> GSM601967 1 0.0000 0.987 1.000 0.000
#> GSM601982 2 0.0000 0.998 0.000 1.000
#> GSM601992 2 0.0000 0.998 0.000 1.000
#> GSM601873 2 0.0376 0.997 0.004 0.996
#> GSM601883 2 0.0000 0.998 0.000 1.000
#> GSM601888 1 0.5629 0.852 0.868 0.132
#> GSM601893 1 0.0000 0.987 1.000 0.000
#> GSM601898 1 0.0000 0.987 1.000 0.000
#> GSM601903 2 0.0000 0.998 0.000 1.000
#> GSM601913 1 0.0376 0.988 0.996 0.004
#> GSM601928 1 0.0376 0.988 0.996 0.004
#> GSM601933 2 0.0000 0.998 0.000 1.000
#> GSM601938 2 0.0000 0.998 0.000 1.000
#> GSM601943 2 0.0376 0.997 0.004 0.996
#> GSM601948 1 0.0376 0.988 0.996 0.004
#> GSM601958 1 0.0000 0.987 1.000 0.000
#> GSM601973 2 0.0000 0.998 0.000 1.000
#> GSM601978 2 0.0376 0.997 0.004 0.996
#> GSM601988 2 0.0000 0.998 0.000 1.000
#> GSM601878 1 0.0376 0.988 0.996 0.004
#> GSM601908 2 0.0376 0.997 0.004 0.996
#> GSM601918 2 0.0000 0.998 0.000 1.000
#> GSM601923 1 0.0376 0.988 0.996 0.004
#> GSM601953 2 0.0376 0.997 0.004 0.996
#> GSM601963 1 0.0376 0.988 0.996 0.004
#> GSM601968 1 0.0000 0.987 1.000 0.000
#> GSM601983 1 0.0376 0.988 0.996 0.004
#> GSM601993 2 0.0000 0.998 0.000 1.000
#> GSM601874 2 0.0376 0.997 0.004 0.996
#> GSM601884 2 0.0376 0.997 0.004 0.996
#> GSM601889 1 0.0000 0.987 1.000 0.000
#> GSM601894 1 0.0000 0.987 1.000 0.000
#> GSM601899 1 0.0938 0.979 0.988 0.012
#> GSM601904 2 0.0376 0.995 0.004 0.996
#> GSM601914 1 0.0000 0.987 1.000 0.000
#> GSM601929 1 0.0376 0.988 0.996 0.004
#> GSM601934 2 0.0376 0.997 0.004 0.996
#> GSM601939 1 0.0376 0.988 0.996 0.004
#> GSM601944 2 0.0376 0.997 0.004 0.996
#> GSM601949 1 0.0000 0.987 1.000 0.000
#> GSM601959 1 0.0000 0.987 1.000 0.000
#> GSM601974 2 0.0938 0.988 0.012 0.988
#> GSM601979 2 0.0376 0.997 0.004 0.996
#> GSM601989 1 0.0000 0.987 1.000 0.000
#> GSM601879 1 0.0376 0.988 0.996 0.004
#> GSM601909 1 0.0000 0.987 1.000 0.000
#> GSM601919 2 0.0000 0.998 0.000 1.000
#> GSM601924 1 0.0376 0.988 0.996 0.004
#> GSM601954 2 0.0376 0.997 0.004 0.996
#> GSM601964 1 0.0376 0.988 0.996 0.004
#> GSM601969 1 0.0000 0.987 1.000 0.000
#> GSM601984 1 0.0376 0.988 0.996 0.004
#> GSM601994 2 0.0000 0.998 0.000 1.000
#> GSM601875 2 0.0376 0.997 0.004 0.996
#> GSM601885 2 0.0000 0.998 0.000 1.000
#> GSM601890 1 0.2236 0.958 0.964 0.036
#> GSM601895 1 0.0000 0.987 1.000 0.000
#> GSM601900 1 0.0000 0.987 1.000 0.000
#> GSM601905 2 0.0000 0.998 0.000 1.000
#> GSM601915 1 0.0376 0.988 0.996 0.004
#> GSM601930 1 0.0376 0.988 0.996 0.004
#> GSM601935 1 0.8081 0.680 0.752 0.248
#> GSM601940 1 0.0376 0.988 0.996 0.004
#> GSM601945 2 0.0376 0.997 0.004 0.996
#> GSM601950 1 0.0000 0.987 1.000 0.000
#> GSM601960 1 0.0000 0.987 1.000 0.000
#> GSM601975 2 0.0000 0.998 0.000 1.000
#> GSM601980 2 0.0376 0.997 0.004 0.996
#> GSM601990 1 0.0376 0.988 0.996 0.004
#> GSM601880 1 0.0376 0.988 0.996 0.004
#> GSM601910 1 0.0000 0.987 1.000 0.000
#> GSM601920 2 0.0000 0.998 0.000 1.000
#> GSM601925 1 0.0376 0.988 0.996 0.004
#> GSM601955 2 0.1414 0.983 0.020 0.980
#> GSM601965 1 0.0376 0.988 0.996 0.004
#> GSM601970 1 0.0000 0.987 1.000 0.000
#> GSM601985 1 0.0376 0.988 0.996 0.004
#> GSM601995 2 0.0000 0.998 0.000 1.000
#> GSM601876 1 0.0376 0.988 0.996 0.004
#> GSM601886 2 0.0000 0.998 0.000 1.000
#> GSM601891 1 0.6623 0.800 0.828 0.172
#> GSM601896 1 0.0376 0.988 0.996 0.004
#> GSM601901 2 0.0000 0.998 0.000 1.000
#> GSM601906 1 0.0938 0.982 0.988 0.012
#> GSM601916 2 0.0000 0.998 0.000 1.000
#> GSM601931 1 0.0376 0.988 0.996 0.004
#> GSM601936 2 0.0000 0.998 0.000 1.000
#> GSM601941 2 0.0000 0.998 0.000 1.000
#> GSM601946 1 0.0376 0.988 0.996 0.004
#> GSM601951 1 0.0376 0.988 0.996 0.004
#> GSM601961 2 0.0376 0.997 0.004 0.996
#> GSM601976 2 0.0000 0.998 0.000 1.000
#> GSM601981 2 0.0376 0.997 0.004 0.996
#> GSM601991 1 0.0376 0.988 0.996 0.004
#> GSM601881 1 0.0376 0.988 0.996 0.004
#> GSM601911 2 0.0000 0.998 0.000 1.000
#> GSM601921 2 0.0000 0.998 0.000 1.000
#> GSM601926 1 0.0376 0.988 0.996 0.004
#> GSM601956 2 0.0376 0.997 0.004 0.996
#> GSM601966 2 0.0000 0.998 0.000 1.000
#> GSM601971 1 0.0000 0.987 1.000 0.000
#> GSM601986 1 0.0672 0.985 0.992 0.008
#> GSM601996 2 0.0000 0.998 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM601872 3 0.6209 0.6407 0.004 0.368 0.628
#> GSM601882 2 0.6079 -0.2217 0.000 0.612 0.388
#> GSM601887 1 0.5967 0.6842 0.752 0.032 0.216
#> GSM601892 1 0.1964 0.8288 0.944 0.000 0.056
#> GSM601897 1 0.5810 0.6124 0.664 0.000 0.336
#> GSM601902 2 0.0000 0.4536 0.000 1.000 0.000
#> GSM601912 1 0.3752 0.7910 0.856 0.000 0.144
#> GSM601927 1 0.5178 0.8153 0.744 0.000 0.256
#> GSM601932 2 0.0237 0.4529 0.000 0.996 0.004
#> GSM601937 2 0.7534 0.0529 0.048 0.584 0.368
#> GSM601942 3 0.5905 0.6395 0.000 0.352 0.648
#> GSM601947 2 0.2796 0.4187 0.000 0.908 0.092
#> GSM601957 1 0.1031 0.8383 0.976 0.000 0.024
#> GSM601972 2 0.3116 0.3980 0.000 0.892 0.108
#> GSM601977 3 0.6309 0.4420 0.000 0.500 0.500
#> GSM601987 2 0.6274 -0.4018 0.000 0.544 0.456
#> GSM601877 1 0.5443 0.8123 0.736 0.004 0.260
#> GSM601907 2 0.6307 -0.4777 0.000 0.512 0.488
#> GSM601917 2 0.2625 0.4208 0.000 0.916 0.084
#> GSM601922 2 0.2945 0.4149 0.004 0.908 0.088
#> GSM601952 2 0.6244 -0.3917 0.000 0.560 0.440
#> GSM601962 1 0.4555 0.7784 0.800 0.000 0.200
#> GSM601967 1 0.0892 0.8363 0.980 0.000 0.020
#> GSM601982 3 0.7069 0.5084 0.020 0.472 0.508
#> GSM601992 2 0.3619 0.3712 0.000 0.864 0.136
#> GSM601873 3 0.6180 0.6010 0.000 0.416 0.584
#> GSM601883 2 0.6215 -0.3273 0.000 0.572 0.428
#> GSM601888 1 0.7481 0.4916 0.640 0.064 0.296
#> GSM601893 1 0.3573 0.7962 0.876 0.004 0.120
#> GSM601898 1 0.0892 0.8357 0.980 0.000 0.020
#> GSM601903 2 0.0000 0.4536 0.000 1.000 0.000
#> GSM601913 1 0.1163 0.8397 0.972 0.000 0.028
#> GSM601928 1 0.5178 0.8153 0.744 0.000 0.256
#> GSM601933 2 0.6008 -0.1764 0.000 0.628 0.372
#> GSM601938 2 0.5560 0.0681 0.000 0.700 0.300
#> GSM601943 3 0.5882 0.6406 0.000 0.348 0.652
#> GSM601948 1 0.5216 0.8187 0.740 0.000 0.260
#> GSM601958 1 0.0892 0.8379 0.980 0.000 0.020
#> GSM601973 2 0.0000 0.4536 0.000 1.000 0.000
#> GSM601978 2 0.6309 -0.5006 0.000 0.504 0.496
#> GSM601988 2 0.6490 0.0938 0.012 0.628 0.360
#> GSM601878 1 0.5216 0.8141 0.740 0.000 0.260
#> GSM601908 2 0.6302 -0.4593 0.000 0.520 0.480
#> GSM601918 2 0.2796 0.4143 0.000 0.908 0.092
#> GSM601923 1 0.5178 0.8153 0.744 0.000 0.256
#> GSM601953 2 0.6309 -0.5006 0.000 0.504 0.496
#> GSM601963 1 0.3482 0.8186 0.872 0.000 0.128
#> GSM601968 1 0.2356 0.8239 0.928 0.000 0.072
#> GSM601983 1 0.3816 0.8095 0.852 0.000 0.148
#> GSM601993 2 0.4887 0.2950 0.000 0.772 0.228
#> GSM601874 2 0.6307 -0.4777 0.000 0.512 0.488
#> GSM601884 2 0.6308 -0.4915 0.000 0.508 0.492
#> GSM601889 1 0.0892 0.8357 0.980 0.000 0.020
#> GSM601894 1 0.0892 0.8370 0.980 0.000 0.020
#> GSM601899 1 0.5506 0.7039 0.764 0.016 0.220
#> GSM601904 2 0.5627 0.2905 0.032 0.780 0.188
#> GSM601914 1 0.3267 0.8078 0.884 0.000 0.116
#> GSM601929 1 0.5404 0.8137 0.740 0.004 0.256
#> GSM601934 2 0.6274 -0.4016 0.000 0.544 0.456
#> GSM601939 1 0.4654 0.8295 0.792 0.000 0.208
#> GSM601944 2 0.6111 -0.2313 0.000 0.604 0.396
#> GSM601949 1 0.4887 0.8262 0.772 0.000 0.228
#> GSM601959 1 0.0892 0.8370 0.980 0.000 0.020
#> GSM601974 2 0.6834 0.1829 0.048 0.692 0.260
#> GSM601979 2 0.6307 -0.4777 0.000 0.512 0.488
#> GSM601989 1 0.1031 0.8350 0.976 0.000 0.024
#> GSM601879 1 0.5443 0.8123 0.736 0.004 0.260
#> GSM601909 1 0.2448 0.8226 0.924 0.000 0.076
#> GSM601919 2 0.3879 0.4092 0.000 0.848 0.152
#> GSM601924 1 0.5178 0.8153 0.744 0.000 0.256
#> GSM601954 2 0.5254 0.0963 0.000 0.736 0.264
#> GSM601964 1 0.3816 0.8095 0.852 0.000 0.148
#> GSM601969 1 0.2448 0.8401 0.924 0.000 0.076
#> GSM601984 1 0.4834 0.8327 0.792 0.004 0.204
#> GSM601994 2 0.3619 0.3712 0.000 0.864 0.136
#> GSM601875 2 0.6307 -0.4777 0.000 0.512 0.488
#> GSM601885 2 0.6192 -0.3068 0.000 0.580 0.420
#> GSM601890 1 0.5656 0.6757 0.728 0.008 0.264
#> GSM601895 1 0.2537 0.8208 0.920 0.000 0.080
#> GSM601900 1 0.2356 0.8252 0.928 0.000 0.072
#> GSM601905 2 0.2590 0.4263 0.004 0.924 0.072
#> GSM601915 1 0.0592 0.8380 0.988 0.000 0.012
#> GSM601930 1 0.5404 0.8137 0.740 0.004 0.256
#> GSM601935 1 0.9800 0.1058 0.412 0.344 0.244
#> GSM601940 1 0.4399 0.8331 0.812 0.000 0.188
#> GSM601945 3 0.6309 0.4330 0.000 0.500 0.500
#> GSM601950 1 0.4796 0.8281 0.780 0.000 0.220
#> GSM601960 1 0.3340 0.8061 0.880 0.000 0.120
#> GSM601975 2 0.0000 0.4536 0.000 1.000 0.000
#> GSM601980 3 0.6897 0.3131 0.016 0.436 0.548
#> GSM601990 1 0.3752 0.8001 0.856 0.000 0.144
#> GSM601880 1 0.5178 0.8153 0.744 0.000 0.256
#> GSM601910 1 0.3686 0.7956 0.860 0.000 0.140
#> GSM601920 2 0.3619 0.3737 0.000 0.864 0.136
#> GSM601925 1 0.5404 0.8137 0.740 0.004 0.256
#> GSM601955 3 0.7839 0.3012 0.060 0.380 0.560
#> GSM601965 1 0.4784 0.8335 0.796 0.004 0.200
#> GSM601970 1 0.1643 0.8308 0.956 0.000 0.044
#> GSM601985 1 0.4399 0.8330 0.812 0.000 0.188
#> GSM601995 2 0.7291 0.0798 0.040 0.604 0.356
#> GSM601876 1 0.4605 0.8297 0.796 0.000 0.204
#> GSM601886 2 0.5122 0.2783 0.012 0.788 0.200
#> GSM601891 1 0.7181 0.5425 0.648 0.048 0.304
#> GSM601896 1 0.4605 0.8296 0.796 0.000 0.204
#> GSM601901 2 0.4178 0.3290 0.000 0.828 0.172
#> GSM601906 2 0.9802 -0.1730 0.312 0.428 0.260
#> GSM601916 2 0.0237 0.4530 0.000 0.996 0.004
#> GSM601931 1 0.5178 0.8153 0.744 0.000 0.256
#> GSM601936 2 0.5623 0.2224 0.004 0.716 0.280
#> GSM601941 2 0.0237 0.4529 0.000 0.996 0.004
#> GSM601946 1 0.5138 0.8167 0.748 0.000 0.252
#> GSM601951 1 0.5618 0.8104 0.732 0.008 0.260
#> GSM601961 2 0.6825 -0.5041 0.012 0.496 0.492
#> GSM601976 2 0.0424 0.4526 0.000 0.992 0.008
#> GSM601981 2 0.6291 -0.4382 0.000 0.532 0.468
#> GSM601991 1 0.5016 0.7275 0.760 0.000 0.240
#> GSM601881 1 0.5178 0.8153 0.744 0.000 0.256
#> GSM601911 2 0.4413 0.3986 0.008 0.832 0.160
#> GSM601921 2 0.2261 0.4305 0.000 0.932 0.068
#> GSM601926 1 0.5178 0.8153 0.744 0.000 0.256
#> GSM601956 2 0.6309 -0.5006 0.000 0.504 0.496
#> GSM601966 2 0.3038 0.4076 0.000 0.896 0.104
#> GSM601971 1 0.2711 0.8401 0.912 0.000 0.088
#> GSM601986 1 0.7983 0.7394 0.648 0.124 0.228
#> GSM601996 2 0.3267 0.3900 0.000 0.884 0.116
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM601872 2 0.2214 0.8284 0.000 0.928 0.028 0.044
#> GSM601882 2 0.3910 0.7338 0.000 0.820 0.024 0.156
#> GSM601887 3 0.7593 0.4350 0.216 0.248 0.528 0.008
#> GSM601892 3 0.6506 0.3122 0.404 0.056 0.532 0.008
#> GSM601897 3 0.6013 0.5060 0.152 0.064 0.736 0.048
#> GSM601902 4 0.3444 0.7866 0.000 0.184 0.000 0.816
#> GSM601912 3 0.5623 0.4874 0.292 0.000 0.660 0.048
#> GSM601927 1 0.0000 0.7588 1.000 0.000 0.000 0.000
#> GSM601932 4 0.3569 0.7838 0.000 0.196 0.000 0.804
#> GSM601937 3 0.7231 -0.2244 0.000 0.144 0.464 0.392
#> GSM601942 2 0.4898 0.6856 0.000 0.780 0.104 0.116
#> GSM601947 4 0.4372 0.7287 0.000 0.268 0.004 0.728
#> GSM601957 1 0.5168 -0.1211 0.504 0.000 0.492 0.004
#> GSM601972 4 0.4632 0.6898 0.000 0.308 0.004 0.688
#> GSM601977 2 0.0524 0.8686 0.000 0.988 0.008 0.004
#> GSM601987 2 0.2313 0.8526 0.000 0.924 0.032 0.044
#> GSM601877 1 0.0000 0.7588 1.000 0.000 0.000 0.000
#> GSM601907 2 0.0592 0.8699 0.000 0.984 0.000 0.016
#> GSM601917 4 0.4044 0.7820 0.024 0.152 0.004 0.820
#> GSM601922 4 0.4095 0.7805 0.028 0.148 0.004 0.820
#> GSM601952 2 0.3554 0.7739 0.000 0.844 0.020 0.136
#> GSM601962 3 0.6194 0.4417 0.288 0.000 0.628 0.084
#> GSM601967 3 0.4999 0.1108 0.492 0.000 0.508 0.000
#> GSM601982 2 0.3320 0.8344 0.000 0.876 0.068 0.056
#> GSM601992 4 0.6903 0.4539 0.000 0.380 0.112 0.508
#> GSM601873 2 0.2722 0.8102 0.000 0.904 0.032 0.064
#> GSM601883 2 0.2742 0.8323 0.000 0.900 0.024 0.076
#> GSM601888 3 0.7598 0.3980 0.168 0.332 0.492 0.008
#> GSM601893 3 0.7340 0.4035 0.316 0.144 0.532 0.008
#> GSM601898 3 0.5168 0.1089 0.492 0.000 0.504 0.004
#> GSM601903 4 0.3400 0.7870 0.000 0.180 0.000 0.820
#> GSM601913 1 0.5764 -0.1394 0.520 0.000 0.452 0.028
#> GSM601928 1 0.0000 0.7588 1.000 0.000 0.000 0.000
#> GSM601933 2 0.3984 0.7538 0.000 0.828 0.040 0.132
#> GSM601938 2 0.5267 0.5123 0.000 0.712 0.048 0.240
#> GSM601943 2 0.3505 0.7708 0.000 0.864 0.048 0.088
#> GSM601948 1 0.1978 0.7162 0.928 0.000 0.068 0.004
#> GSM601958 1 0.5168 -0.1211 0.504 0.000 0.492 0.004
#> GSM601973 4 0.3444 0.7866 0.000 0.184 0.000 0.816
#> GSM601978 2 0.0672 0.8681 0.000 0.984 0.008 0.008
#> GSM601988 3 0.7206 -0.2509 0.000 0.140 0.460 0.400
#> GSM601878 1 0.0000 0.7588 1.000 0.000 0.000 0.000
#> GSM601908 2 0.1256 0.8681 0.000 0.964 0.008 0.028
#> GSM601918 4 0.4428 0.7205 0.000 0.276 0.004 0.720
#> GSM601923 1 0.0000 0.7588 1.000 0.000 0.000 0.000
#> GSM601953 2 0.0804 0.8670 0.000 0.980 0.008 0.012
#> GSM601963 3 0.5613 0.4228 0.380 0.000 0.592 0.028
#> GSM601968 3 0.5024 0.4132 0.360 0.000 0.632 0.008
#> GSM601983 3 0.5869 0.4334 0.360 0.000 0.596 0.044
#> GSM601993 4 0.7028 0.4403 0.000 0.172 0.260 0.568
#> GSM601874 2 0.0927 0.8706 0.000 0.976 0.008 0.016
#> GSM601884 2 0.1733 0.8625 0.000 0.948 0.024 0.028
#> GSM601889 1 0.5168 -0.1331 0.500 0.000 0.496 0.004
#> GSM601894 1 0.5168 -0.1211 0.504 0.000 0.492 0.004
#> GSM601899 3 0.7500 0.4433 0.216 0.232 0.544 0.008
#> GSM601904 4 0.3712 0.6667 0.152 0.012 0.004 0.832
#> GSM601914 3 0.5614 0.4668 0.336 0.000 0.628 0.036
#> GSM601929 1 0.0000 0.7588 1.000 0.000 0.000 0.000
#> GSM601934 2 0.2319 0.8543 0.000 0.924 0.036 0.040
#> GSM601939 1 0.0336 0.7559 0.992 0.000 0.008 0.000
#> GSM601944 2 0.5864 0.5479 0.000 0.664 0.072 0.264
#> GSM601949 1 0.2345 0.6911 0.900 0.000 0.100 0.000
#> GSM601959 1 0.5168 -0.1211 0.504 0.000 0.492 0.004
#> GSM601974 4 0.5733 0.4040 0.008 0.028 0.332 0.632
#> GSM601979 2 0.0592 0.8699 0.000 0.984 0.000 0.016
#> GSM601989 3 0.5167 0.1214 0.488 0.000 0.508 0.004
#> GSM601879 1 0.0000 0.7588 1.000 0.000 0.000 0.000
#> GSM601909 3 0.4643 0.4345 0.344 0.000 0.656 0.000
#> GSM601919 4 0.4601 0.7344 0.008 0.256 0.004 0.732
#> GSM601924 1 0.0000 0.7588 1.000 0.000 0.000 0.000
#> GSM601954 2 0.5126 -0.1200 0.000 0.552 0.004 0.444
#> GSM601964 3 0.5823 0.4404 0.348 0.000 0.608 0.044
#> GSM601969 1 0.5392 -0.0629 0.528 0.000 0.460 0.012
#> GSM601984 1 0.3447 0.6498 0.852 0.000 0.128 0.020
#> GSM601994 4 0.6854 0.4904 0.000 0.360 0.112 0.528
#> GSM601875 2 0.0779 0.8695 0.000 0.980 0.004 0.016
#> GSM601885 2 0.2742 0.8316 0.000 0.900 0.024 0.076
#> GSM601890 3 0.7437 0.4513 0.200 0.240 0.552 0.008
#> GSM601895 3 0.4877 0.4560 0.328 0.000 0.664 0.008
#> GSM601900 3 0.5268 0.3457 0.396 0.000 0.592 0.012
#> GSM601905 4 0.3995 0.7802 0.024 0.148 0.004 0.824
#> GSM601915 1 0.5290 -0.1261 0.516 0.000 0.476 0.008
#> GSM601930 1 0.0000 0.7588 1.000 0.000 0.000 0.000
#> GSM601935 3 0.5887 0.0469 0.036 0.004 0.600 0.360
#> GSM601940 1 0.1004 0.7490 0.972 0.000 0.024 0.004
#> GSM601945 2 0.0524 0.8669 0.000 0.988 0.008 0.004
#> GSM601950 1 0.2345 0.6894 0.900 0.000 0.100 0.000
#> GSM601960 3 0.5512 0.4850 0.300 0.000 0.660 0.040
#> GSM601975 4 0.3668 0.7848 0.000 0.188 0.004 0.808
#> GSM601980 3 0.7760 -0.1165 0.000 0.288 0.436 0.276
#> GSM601990 3 0.5649 0.4803 0.284 0.000 0.664 0.052
#> GSM601880 1 0.0000 0.7588 1.000 0.000 0.000 0.000
#> GSM601910 3 0.4535 0.4846 0.292 0.000 0.704 0.004
#> GSM601920 4 0.4398 0.7470 0.072 0.104 0.004 0.820
#> GSM601925 1 0.0000 0.7588 1.000 0.000 0.000 0.000
#> GSM601955 3 0.7554 -0.0568 0.000 0.244 0.488 0.268
#> GSM601965 1 0.3392 0.6518 0.856 0.000 0.124 0.020
#> GSM601970 3 0.5119 0.2557 0.440 0.000 0.556 0.004
#> GSM601985 1 0.1004 0.7484 0.972 0.000 0.024 0.004
#> GSM601995 3 0.7030 -0.2247 0.000 0.120 0.472 0.408
#> GSM601876 1 0.0895 0.7495 0.976 0.000 0.020 0.004
#> GSM601886 4 0.5013 0.5032 0.000 0.020 0.292 0.688
#> GSM601891 3 0.7471 0.4327 0.172 0.288 0.532 0.008
#> GSM601896 1 0.1109 0.7447 0.968 0.000 0.028 0.004
#> GSM601901 4 0.5168 0.3261 0.000 0.492 0.004 0.504
#> GSM601906 1 0.5070 0.1065 0.580 0.000 0.004 0.416
#> GSM601916 4 0.3626 0.7868 0.000 0.184 0.004 0.812
#> GSM601931 1 0.0000 0.7588 1.000 0.000 0.000 0.000
#> GSM601936 4 0.6664 0.4118 0.000 0.112 0.308 0.580
#> GSM601941 4 0.3486 0.7854 0.000 0.188 0.000 0.812
#> GSM601946 1 0.0000 0.7588 1.000 0.000 0.000 0.000
#> GSM601951 1 0.0000 0.7588 1.000 0.000 0.000 0.000
#> GSM601961 2 0.1520 0.8610 0.000 0.956 0.024 0.020
#> GSM601976 4 0.3494 0.7864 0.000 0.172 0.004 0.824
#> GSM601981 2 0.1305 0.8666 0.000 0.960 0.004 0.036
#> GSM601991 3 0.4852 0.4022 0.072 0.000 0.776 0.152
#> GSM601881 1 0.0000 0.7588 1.000 0.000 0.000 0.000
#> GSM601911 4 0.7651 0.5204 0.092 0.336 0.044 0.528
#> GSM601921 4 0.3988 0.7827 0.020 0.156 0.004 0.820
#> GSM601926 1 0.0000 0.7588 1.000 0.000 0.000 0.000
#> GSM601956 2 0.0672 0.8655 0.000 0.984 0.008 0.008
#> GSM601966 4 0.5311 0.6608 0.000 0.328 0.024 0.648
#> GSM601971 1 0.5080 0.0609 0.576 0.000 0.420 0.004
#> GSM601986 1 0.4882 0.5400 0.776 0.004 0.056 0.164
#> GSM601996 4 0.6637 0.5660 0.000 0.324 0.104 0.572
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM601872 2 0.2338 0.818 0.016 0.916 0.036 0.000 0.032
#> GSM601882 2 0.4887 0.776 0.024 0.756 0.000 0.112 0.108
#> GSM601887 3 0.4300 0.626 0.012 0.232 0.740 0.008 0.008
#> GSM601892 3 0.2678 0.761 0.060 0.036 0.896 0.004 0.004
#> GSM601897 3 0.4518 0.566 0.020 0.016 0.736 0.004 0.224
#> GSM601902 4 0.2673 0.780 0.024 0.048 0.000 0.900 0.028
#> GSM601912 3 0.4405 0.624 0.020 0.000 0.712 0.008 0.260
#> GSM601927 1 0.2813 0.903 0.832 0.000 0.168 0.000 0.000
#> GSM601932 4 0.2564 0.783 0.024 0.052 0.000 0.904 0.020
#> GSM601937 5 0.3418 0.777 0.000 0.016 0.072 0.056 0.856
#> GSM601942 2 0.5219 0.470 0.060 0.636 0.004 0.000 0.300
#> GSM601947 4 0.2270 0.779 0.016 0.072 0.000 0.908 0.004
#> GSM601957 3 0.2690 0.730 0.156 0.000 0.844 0.000 0.000
#> GSM601972 4 0.4000 0.738 0.028 0.136 0.000 0.808 0.028
#> GSM601977 2 0.1862 0.861 0.012 0.940 0.004 0.016 0.028
#> GSM601987 2 0.4014 0.828 0.024 0.820 0.000 0.060 0.096
#> GSM601877 1 0.2732 0.902 0.840 0.000 0.160 0.000 0.000
#> GSM601907 2 0.1347 0.861 0.004 0.960 0.008 0.020 0.008
#> GSM601917 4 0.2640 0.772 0.052 0.032 0.000 0.900 0.016
#> GSM601922 4 0.2536 0.772 0.052 0.032 0.000 0.904 0.012
#> GSM601952 2 0.5084 0.707 0.020 0.736 0.004 0.164 0.076
#> GSM601962 3 0.5546 0.382 0.056 0.000 0.560 0.008 0.376
#> GSM601967 3 0.2877 0.738 0.144 0.004 0.848 0.004 0.000
#> GSM601982 2 0.4447 0.834 0.024 0.812 0.024 0.052 0.088
#> GSM601992 4 0.7825 0.149 0.068 0.264 0.000 0.384 0.284
#> GSM601873 2 0.2956 0.810 0.020 0.872 0.012 0.000 0.096
#> GSM601883 2 0.4207 0.820 0.024 0.808 0.000 0.076 0.092
#> GSM601888 3 0.4722 0.489 0.008 0.324 0.652 0.008 0.008
#> GSM601893 3 0.3326 0.733 0.024 0.104 0.856 0.008 0.008
#> GSM601898 3 0.2722 0.757 0.120 0.000 0.868 0.004 0.008
#> GSM601903 4 0.2342 0.781 0.024 0.040 0.000 0.916 0.020
#> GSM601913 3 0.4744 0.709 0.148 0.000 0.748 0.008 0.096
#> GSM601928 1 0.2813 0.903 0.832 0.000 0.168 0.000 0.000
#> GSM601933 2 0.4877 0.781 0.024 0.752 0.000 0.080 0.144
#> GSM601938 2 0.6641 0.539 0.056 0.600 0.000 0.200 0.144
#> GSM601943 2 0.3694 0.753 0.020 0.824 0.024 0.000 0.132
#> GSM601948 1 0.4145 0.793 0.708 0.004 0.280 0.004 0.004
#> GSM601958 3 0.2806 0.735 0.152 0.000 0.844 0.004 0.000
#> GSM601973 4 0.2492 0.781 0.024 0.048 0.000 0.908 0.020
#> GSM601978 2 0.0932 0.858 0.000 0.972 0.004 0.020 0.004
#> GSM601988 5 0.3739 0.770 0.004 0.020 0.060 0.072 0.844
#> GSM601878 1 0.2773 0.903 0.836 0.000 0.164 0.000 0.000
#> GSM601908 2 0.2040 0.861 0.008 0.928 0.000 0.032 0.032
#> GSM601918 4 0.2332 0.779 0.016 0.076 0.000 0.904 0.004
#> GSM601923 1 0.2773 0.903 0.836 0.000 0.164 0.000 0.000
#> GSM601953 2 0.1278 0.854 0.000 0.960 0.020 0.016 0.004
#> GSM601963 3 0.5052 0.659 0.084 0.000 0.708 0.008 0.200
#> GSM601968 3 0.1604 0.775 0.044 0.004 0.944 0.004 0.004
#> GSM601983 3 0.5092 0.621 0.068 0.000 0.688 0.008 0.236
#> GSM601993 5 0.6206 0.472 0.068 0.056 0.004 0.240 0.632
#> GSM601874 2 0.1460 0.860 0.004 0.956 0.008 0.020 0.012
#> GSM601884 2 0.3644 0.842 0.024 0.844 0.000 0.048 0.084
#> GSM601889 3 0.2377 0.749 0.128 0.000 0.872 0.000 0.000
#> GSM601894 3 0.2561 0.738 0.144 0.000 0.856 0.000 0.000
#> GSM601899 3 0.3682 0.691 0.012 0.160 0.812 0.008 0.008
#> GSM601904 4 0.3332 0.703 0.120 0.008 0.000 0.844 0.028
#> GSM601914 3 0.3965 0.706 0.028 0.000 0.784 0.008 0.180
#> GSM601929 1 0.2732 0.902 0.840 0.000 0.160 0.000 0.000
#> GSM601934 2 0.3948 0.831 0.024 0.824 0.000 0.056 0.096
#> GSM601939 1 0.3648 0.889 0.792 0.000 0.188 0.004 0.016
#> GSM601944 2 0.6584 0.508 0.056 0.584 0.000 0.104 0.256
#> GSM601949 1 0.4438 0.716 0.648 0.004 0.340 0.004 0.004
#> GSM601959 3 0.2605 0.735 0.148 0.000 0.852 0.000 0.000
#> GSM601974 5 0.6934 0.316 0.028 0.016 0.096 0.408 0.452
#> GSM601979 2 0.0932 0.861 0.004 0.972 0.000 0.020 0.004
#> GSM601989 3 0.3857 0.738 0.132 0.000 0.812 0.008 0.048
#> GSM601879 1 0.2732 0.902 0.840 0.000 0.160 0.000 0.000
#> GSM601909 3 0.1372 0.773 0.024 0.000 0.956 0.004 0.016
#> GSM601919 4 0.2679 0.776 0.048 0.056 0.000 0.892 0.004
#> GSM601924 1 0.2773 0.903 0.836 0.000 0.164 0.000 0.000
#> GSM601954 4 0.5329 0.340 0.016 0.400 0.004 0.560 0.020
#> GSM601964 3 0.4990 0.617 0.056 0.000 0.688 0.008 0.248
#> GSM601969 3 0.3831 0.657 0.216 0.004 0.768 0.008 0.004
#> GSM601984 1 0.6158 0.622 0.580 0.000 0.260 0.008 0.152
#> GSM601994 4 0.7742 0.167 0.068 0.228 0.000 0.408 0.296
#> GSM601875 2 0.1278 0.862 0.000 0.960 0.004 0.020 0.016
#> GSM601885 2 0.4254 0.817 0.024 0.804 0.000 0.072 0.100
#> GSM601890 3 0.4149 0.644 0.012 0.212 0.760 0.008 0.008
#> GSM601895 3 0.3666 0.740 0.032 0.000 0.824 0.012 0.132
#> GSM601900 3 0.2238 0.775 0.064 0.000 0.912 0.004 0.020
#> GSM601905 4 0.2696 0.770 0.040 0.032 0.000 0.900 0.028
#> GSM601915 3 0.3812 0.743 0.128 0.000 0.816 0.008 0.048
#> GSM601930 1 0.2813 0.903 0.832 0.000 0.168 0.000 0.000
#> GSM601935 5 0.3477 0.735 0.008 0.004 0.152 0.012 0.824
#> GSM601940 1 0.4052 0.874 0.764 0.000 0.204 0.004 0.028
#> GSM601945 2 0.1173 0.860 0.004 0.964 0.000 0.020 0.012
#> GSM601950 1 0.4236 0.723 0.664 0.004 0.328 0.000 0.004
#> GSM601960 3 0.3806 0.714 0.024 0.000 0.796 0.008 0.172
#> GSM601975 4 0.1893 0.785 0.000 0.048 0.000 0.928 0.024
#> GSM601980 5 0.5276 0.743 0.060 0.112 0.076 0.004 0.748
#> GSM601990 3 0.4723 0.603 0.032 0.000 0.688 0.008 0.272
#> GSM601880 1 0.2773 0.903 0.836 0.000 0.164 0.000 0.000
#> GSM601910 3 0.1285 0.764 0.004 0.000 0.956 0.004 0.036
#> GSM601920 4 0.3137 0.750 0.076 0.024 0.000 0.872 0.028
#> GSM601925 1 0.2732 0.902 0.840 0.000 0.160 0.000 0.000
#> GSM601955 5 0.5332 0.743 0.060 0.112 0.080 0.004 0.744
#> GSM601965 1 0.5904 0.689 0.608 0.000 0.260 0.008 0.124
#> GSM601970 3 0.1952 0.771 0.084 0.000 0.912 0.000 0.004
#> GSM601985 1 0.4638 0.847 0.728 0.000 0.216 0.008 0.048
#> GSM601995 5 0.4509 0.777 0.036 0.028 0.084 0.040 0.812
#> GSM601876 1 0.4375 0.846 0.728 0.000 0.236 0.004 0.032
#> GSM601886 5 0.4329 0.624 0.008 0.008 0.012 0.236 0.736
#> GSM601891 3 0.4713 0.544 0.012 0.280 0.688 0.008 0.012
#> GSM601896 1 0.4288 0.854 0.740 0.000 0.224 0.004 0.032
#> GSM601901 4 0.5175 0.408 0.004 0.372 0.000 0.584 0.040
#> GSM601906 1 0.4065 0.493 0.720 0.000 0.000 0.264 0.016
#> GSM601916 4 0.2931 0.778 0.028 0.040 0.000 0.888 0.044
#> GSM601931 1 0.2813 0.903 0.832 0.000 0.168 0.000 0.000
#> GSM601936 5 0.3953 0.682 0.008 0.024 0.012 0.148 0.808
#> GSM601941 4 0.2758 0.779 0.024 0.048 0.000 0.896 0.032
#> GSM601946 1 0.3612 0.890 0.796 0.000 0.184 0.004 0.016
#> GSM601951 1 0.2930 0.902 0.832 0.000 0.164 0.004 0.000
#> GSM601961 2 0.2102 0.819 0.000 0.916 0.068 0.012 0.004
#> GSM601976 4 0.2499 0.781 0.016 0.040 0.000 0.908 0.036
#> GSM601981 2 0.2427 0.855 0.004 0.912 0.008 0.048 0.028
#> GSM601991 5 0.3975 0.601 0.008 0.000 0.240 0.008 0.744
#> GSM601881 1 0.2773 0.903 0.836 0.000 0.164 0.000 0.000
#> GSM601911 4 0.8634 0.302 0.140 0.272 0.036 0.412 0.140
#> GSM601921 4 0.3054 0.764 0.060 0.032 0.000 0.880 0.028
#> GSM601926 1 0.2773 0.903 0.836 0.000 0.164 0.000 0.000
#> GSM601956 2 0.1692 0.852 0.008 0.948 0.016 0.020 0.008
#> GSM601966 4 0.6062 0.602 0.048 0.176 0.000 0.660 0.116
#> GSM601971 3 0.3884 0.554 0.288 0.004 0.708 0.000 0.000
#> GSM601986 1 0.7007 0.670 0.600 0.008 0.180 0.092 0.120
#> GSM601996 4 0.7470 0.246 0.068 0.172 0.000 0.464 0.296
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM601872 2 0.2980 0.7367 0.000 0.864 0.040 0.000 0.024 NA
#> GSM601882 2 0.5312 0.6791 0.000 0.680 0.000 0.088 0.064 NA
#> GSM601887 3 0.4099 0.6439 0.036 0.132 0.780 0.000 0.000 NA
#> GSM601892 3 0.1983 0.7326 0.060 0.012 0.916 0.000 0.000 NA
#> GSM601897 3 0.5736 0.5337 0.020 0.008 0.608 0.000 0.228 NA
#> GSM601902 4 0.1231 0.8179 0.000 0.012 0.004 0.960 0.012 NA
#> GSM601912 3 0.6806 0.4728 0.056 0.000 0.456 0.004 0.196 NA
#> GSM601927 1 0.0000 0.8650 1.000 0.000 0.000 0.000 0.000 NA
#> GSM601932 4 0.1078 0.8197 0.000 0.012 0.000 0.964 0.008 NA
#> GSM601937 5 0.1364 0.6341 0.000 0.000 0.012 0.016 0.952 NA
#> GSM601942 2 0.6297 0.1692 0.000 0.476 0.028 0.000 0.312 NA
#> GSM601947 4 0.2457 0.8183 0.000 0.036 0.000 0.880 0.000 NA
#> GSM601957 3 0.3355 0.7363 0.132 0.000 0.816 0.004 0.000 NA
#> GSM601972 4 0.2454 0.7604 0.000 0.104 0.000 0.876 0.004 NA
#> GSM601977 2 0.1842 0.7916 0.000 0.932 0.008 0.012 0.012 NA
#> GSM601987 2 0.4393 0.7231 0.000 0.748 0.000 0.036 0.052 NA
#> GSM601877 1 0.0260 0.8647 0.992 0.000 0.000 0.000 0.000 NA
#> GSM601907 2 0.0696 0.7904 0.000 0.980 0.004 0.004 0.008 NA
#> GSM601917 4 0.3437 0.8106 0.004 0.012 0.012 0.808 0.004 NA
#> GSM601922 4 0.3296 0.8105 0.004 0.012 0.012 0.812 0.000 NA
#> GSM601952 2 0.4682 0.6812 0.000 0.740 0.000 0.136 0.060 NA
#> GSM601962 5 0.7248 -0.2387 0.072 0.000 0.296 0.004 0.328 NA
#> GSM601967 3 0.2404 0.7432 0.112 0.000 0.872 0.000 0.000 NA
#> GSM601982 2 0.5796 0.6719 0.000 0.652 0.040 0.028 0.088 NA
#> GSM601992 5 0.7870 0.2124 0.000 0.216 0.008 0.212 0.308 NA
#> GSM601873 2 0.3509 0.7286 0.000 0.824 0.012 0.004 0.052 NA
#> GSM601883 2 0.4678 0.7120 0.000 0.728 0.000 0.052 0.052 NA
#> GSM601888 3 0.4225 0.5436 0.016 0.216 0.728 0.000 0.000 NA
#> GSM601893 3 0.2754 0.7151 0.048 0.048 0.880 0.000 0.000 NA
#> GSM601898 3 0.4092 0.7459 0.104 0.000 0.776 0.004 0.008 NA
#> GSM601903 4 0.1325 0.8194 0.000 0.012 0.004 0.956 0.012 NA
#> GSM601913 3 0.6511 0.6030 0.144 0.000 0.492 0.004 0.052 NA
#> GSM601928 1 0.0000 0.8650 1.000 0.000 0.000 0.000 0.000 NA
#> GSM601933 2 0.4874 0.7005 0.000 0.708 0.000 0.044 0.068 NA
#> GSM601938 2 0.5907 0.5999 0.000 0.612 0.000 0.104 0.076 NA
#> GSM601943 2 0.4681 0.6265 0.000 0.736 0.024 0.004 0.136 NA
#> GSM601948 1 0.3200 0.7090 0.788 0.000 0.196 0.000 0.000 NA
#> GSM601958 3 0.3716 0.7411 0.128 0.000 0.792 0.004 0.000 NA
#> GSM601973 4 0.1129 0.8187 0.000 0.012 0.004 0.964 0.012 NA
#> GSM601978 2 0.0291 0.7893 0.000 0.992 0.004 0.004 0.000 NA
#> GSM601988 5 0.2817 0.6327 0.000 0.012 0.012 0.032 0.880 NA
#> GSM601878 1 0.0260 0.8647 0.992 0.000 0.000 0.000 0.000 NA
#> GSM601908 2 0.1078 0.7903 0.000 0.964 0.000 0.008 0.012 NA
#> GSM601918 4 0.3281 0.8144 0.000 0.036 0.012 0.828 0.000 NA
#> GSM601923 1 0.0146 0.8646 0.996 0.000 0.000 0.000 0.000 NA
#> GSM601953 2 0.1138 0.7839 0.000 0.960 0.012 0.004 0.000 NA
#> GSM601963 3 0.6987 0.4824 0.104 0.000 0.428 0.000 0.156 NA
#> GSM601968 3 0.2668 0.7432 0.060 0.000 0.884 0.000 0.028 NA
#> GSM601983 3 0.7272 0.4138 0.096 0.000 0.388 0.004 0.208 NA
#> GSM601993 5 0.6005 0.5023 0.000 0.044 0.008 0.108 0.588 NA
#> GSM601874 2 0.0912 0.7913 0.000 0.972 0.008 0.004 0.004 NA
#> GSM601884 2 0.4366 0.7237 0.000 0.748 0.000 0.036 0.048 NA
#> GSM601889 3 0.3411 0.7411 0.120 0.000 0.816 0.004 0.000 NA
#> GSM601894 3 0.3411 0.7413 0.120 0.000 0.816 0.004 0.000 NA
#> GSM601899 3 0.3041 0.6988 0.036 0.056 0.864 0.000 0.000 NA
#> GSM601904 4 0.4474 0.7677 0.044 0.004 0.016 0.748 0.012 NA
#> GSM601914 3 0.6645 0.5589 0.064 0.000 0.476 0.004 0.140 NA
#> GSM601929 1 0.0146 0.8646 0.996 0.000 0.000 0.000 0.000 NA
#> GSM601934 2 0.4241 0.7309 0.000 0.760 0.000 0.028 0.056 NA
#> GSM601939 1 0.1285 0.8500 0.944 0.000 0.004 0.000 0.000 NA
#> GSM601944 2 0.6502 0.3829 0.000 0.532 0.008 0.044 0.212 NA
#> GSM601949 1 0.3641 0.6398 0.732 0.000 0.248 0.000 0.000 NA
#> GSM601959 3 0.3454 0.7393 0.124 0.000 0.812 0.004 0.000 NA
#> GSM601974 5 0.6035 0.0859 0.000 0.004 0.016 0.396 0.452 NA
#> GSM601979 2 0.0291 0.7893 0.000 0.992 0.004 0.004 0.000 NA
#> GSM601989 3 0.5356 0.6839 0.116 0.000 0.628 0.004 0.012 NA
#> GSM601879 1 0.0260 0.8647 0.992 0.000 0.000 0.000 0.000 NA
#> GSM601909 3 0.3083 0.7388 0.052 0.000 0.860 0.000 0.028 NA
#> GSM601919 4 0.3483 0.8098 0.000 0.036 0.012 0.808 0.000 NA
#> GSM601924 1 0.0260 0.8647 0.992 0.000 0.000 0.000 0.000 NA
#> GSM601954 4 0.4482 0.3152 0.000 0.384 0.000 0.580 0.000 NA
#> GSM601964 3 0.7275 0.3927 0.092 0.000 0.384 0.004 0.220 NA
#> GSM601969 3 0.3628 0.7091 0.168 0.000 0.784 0.004 0.000 NA
#> GSM601984 1 0.6628 0.4209 0.516 0.000 0.112 0.012 0.072 NA
#> GSM601994 5 0.7841 0.2493 0.000 0.192 0.008 0.224 0.316 NA
#> GSM601875 2 0.1109 0.7909 0.000 0.964 0.004 0.004 0.012 NA
#> GSM601885 2 0.4651 0.7136 0.000 0.728 0.000 0.048 0.052 NA
#> GSM601890 3 0.3994 0.6555 0.036 0.116 0.792 0.000 0.000 NA
#> GSM601895 3 0.5900 0.6133 0.060 0.000 0.580 0.000 0.092 NA
#> GSM601900 3 0.3918 0.7461 0.068 0.000 0.800 0.004 0.020 NA
#> GSM601905 4 0.3657 0.8034 0.000 0.008 0.016 0.792 0.016 NA
#> GSM601915 3 0.5681 0.6643 0.108 0.000 0.576 0.004 0.020 NA
#> GSM601930 1 0.0000 0.8650 1.000 0.000 0.000 0.000 0.000 NA
#> GSM601935 5 0.3645 0.5626 0.000 0.000 0.052 0.008 0.796 NA
#> GSM601940 1 0.3291 0.7814 0.828 0.000 0.064 0.000 0.004 NA
#> GSM601945 2 0.1036 0.7861 0.000 0.964 0.004 0.008 0.000 NA
#> GSM601950 1 0.3259 0.6847 0.772 0.000 0.216 0.000 0.000 NA
#> GSM601960 3 0.6139 0.6305 0.048 0.000 0.564 0.004 0.124 NA
#> GSM601975 4 0.0767 0.8233 0.000 0.012 0.000 0.976 0.004 NA
#> GSM601980 5 0.4615 0.5713 0.000 0.068 0.032 0.000 0.728 NA
#> GSM601990 3 0.6974 0.3872 0.052 0.000 0.396 0.004 0.248 NA
#> GSM601880 1 0.0146 0.8646 0.996 0.000 0.000 0.000 0.000 NA
#> GSM601910 3 0.3481 0.7372 0.052 0.000 0.836 0.000 0.044 NA
#> GSM601920 4 0.3582 0.8001 0.008 0.004 0.012 0.800 0.012 NA
#> GSM601925 1 0.0146 0.8646 0.996 0.000 0.000 0.000 0.000 NA
#> GSM601955 5 0.4650 0.5806 0.000 0.048 0.040 0.000 0.716 NA
#> GSM601965 1 0.6583 0.4362 0.532 0.000 0.128 0.012 0.064 NA
#> GSM601970 3 0.3940 0.7476 0.096 0.000 0.800 0.004 0.020 NA
#> GSM601985 1 0.4112 0.6959 0.724 0.000 0.048 0.004 0.000 NA
#> GSM601995 5 0.2709 0.6267 0.000 0.008 0.020 0.008 0.876 NA
#> GSM601876 1 0.4235 0.7222 0.752 0.000 0.088 0.004 0.004 NA
#> GSM601886 5 0.4391 0.5353 0.000 0.000 0.004 0.188 0.720 NA
#> GSM601891 3 0.4048 0.6032 0.016 0.168 0.764 0.000 0.000 NA
#> GSM601896 1 0.3883 0.7463 0.784 0.000 0.072 0.004 0.004 NA
#> GSM601901 4 0.5009 0.1264 0.000 0.424 0.000 0.516 0.008 NA
#> GSM601906 1 0.4875 0.5839 0.704 0.000 0.016 0.172 0.004 NA
#> GSM601916 4 0.1793 0.8131 0.000 0.008 0.004 0.932 0.016 NA
#> GSM601931 1 0.0000 0.8650 1.000 0.000 0.000 0.000 0.000 NA
#> GSM601936 5 0.4024 0.5924 0.000 0.008 0.000 0.092 0.772 NA
#> GSM601941 4 0.1269 0.8157 0.000 0.012 0.000 0.956 0.012 NA
#> GSM601946 1 0.1285 0.8479 0.944 0.000 0.004 0.000 0.000 NA
#> GSM601951 1 0.0603 0.8627 0.980 0.000 0.000 0.004 0.000 NA
#> GSM601961 2 0.1924 0.7665 0.000 0.920 0.048 0.004 0.000 NA
#> GSM601976 4 0.2424 0.8178 0.000 0.008 0.012 0.892 0.008 NA
#> GSM601981 2 0.1810 0.7863 0.000 0.932 0.008 0.036 0.004 NA
#> GSM601991 5 0.5609 0.2506 0.000 0.000 0.168 0.004 0.552 NA
#> GSM601881 1 0.0260 0.8647 0.992 0.000 0.000 0.000 0.000 NA
#> GSM601911 2 0.8609 -0.0473 0.104 0.292 0.012 0.248 0.076 NA
#> GSM601921 4 0.3680 0.8054 0.004 0.012 0.012 0.796 0.012 NA
#> GSM601926 1 0.0000 0.8650 1.000 0.000 0.000 0.000 0.000 NA
#> GSM601956 2 0.1036 0.7838 0.000 0.964 0.008 0.004 0.000 NA
#> GSM601966 4 0.6040 0.3625 0.000 0.160 0.004 0.604 0.052 NA
#> GSM601971 3 0.4305 0.6408 0.256 0.000 0.692 0.004 0.000 NA
#> GSM601986 1 0.6507 0.5409 0.572 0.000 0.060 0.060 0.056 NA
#> GSM601996 5 0.7744 0.2592 0.000 0.148 0.008 0.252 0.320 NA
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)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
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)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
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)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, 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.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")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n time(p) gender(p) k
#> MAD:kmeans 125 0.2945 0.864 2
#> MAD:kmeans 66 0.0459 0.706 3
#> MAD:kmeans 77 0.1908 0.193 4
#> MAD:kmeans 113 0.1591 0.338 5
#> MAD:kmeans 106 0.1978 0.616 6
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.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["MAD", "skmeans"]
# you can also extract it by
# res = res_list["MAD:skmeans"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 21512 rows and 125 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 1250 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_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
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, lower 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 0.985 0.993 0.5043 0.496 0.496
#> 3 3 0.551 0.763 0.835 0.2950 0.835 0.676
#> 4 4 0.465 0.544 0.725 0.1365 0.875 0.658
#> 5 5 0.484 0.474 0.658 0.0649 0.898 0.632
#> 6 6 0.513 0.386 0.608 0.0402 0.966 0.839
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 2
Following shows 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 class
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
#> GSM601872 2 0.0000 0.999 0.000 1.000
#> GSM601882 2 0.0000 0.999 0.000 1.000
#> GSM601887 1 0.2603 0.950 0.956 0.044
#> GSM601892 1 0.0000 0.987 1.000 0.000
#> GSM601897 1 0.1184 0.975 0.984 0.016
#> GSM601902 2 0.0000 0.999 0.000 1.000
#> GSM601912 1 0.0000 0.987 1.000 0.000
#> GSM601927 1 0.0000 0.987 1.000 0.000
#> GSM601932 2 0.0000 0.999 0.000 1.000
#> GSM601937 2 0.0938 0.988 0.012 0.988
#> GSM601942 2 0.0000 0.999 0.000 1.000
#> GSM601947 2 0.0000 0.999 0.000 1.000
#> GSM601957 1 0.0000 0.987 1.000 0.000
#> GSM601972 2 0.0000 0.999 0.000 1.000
#> GSM601977 2 0.0000 0.999 0.000 1.000
#> GSM601987 2 0.0000 0.999 0.000 1.000
#> GSM601877 1 0.0000 0.987 1.000 0.000
#> GSM601907 2 0.0000 0.999 0.000 1.000
#> GSM601917 2 0.0000 0.999 0.000 1.000
#> GSM601922 2 0.0000 0.999 0.000 1.000
#> GSM601952 2 0.0000 0.999 0.000 1.000
#> GSM601962 1 0.0672 0.981 0.992 0.008
#> GSM601967 1 0.0000 0.987 1.000 0.000
#> GSM601982 2 0.0000 0.999 0.000 1.000
#> GSM601992 2 0.0000 0.999 0.000 1.000
#> GSM601873 2 0.0000 0.999 0.000 1.000
#> GSM601883 2 0.0000 0.999 0.000 1.000
#> GSM601888 1 0.5946 0.838 0.856 0.144
#> GSM601893 1 0.0000 0.987 1.000 0.000
#> GSM601898 1 0.0000 0.987 1.000 0.000
#> GSM601903 2 0.0000 0.999 0.000 1.000
#> GSM601913 1 0.0000 0.987 1.000 0.000
#> GSM601928 1 0.0000 0.987 1.000 0.000
#> GSM601933 2 0.0000 0.999 0.000 1.000
#> GSM601938 2 0.0000 0.999 0.000 1.000
#> GSM601943 2 0.0000 0.999 0.000 1.000
#> GSM601948 1 0.0000 0.987 1.000 0.000
#> GSM601958 1 0.0000 0.987 1.000 0.000
#> GSM601973 2 0.0000 0.999 0.000 1.000
#> GSM601978 2 0.0000 0.999 0.000 1.000
#> GSM601988 2 0.0000 0.999 0.000 1.000
#> GSM601878 1 0.0000 0.987 1.000 0.000
#> GSM601908 2 0.0000 0.999 0.000 1.000
#> GSM601918 2 0.0000 0.999 0.000 1.000
#> GSM601923 1 0.0000 0.987 1.000 0.000
#> GSM601953 2 0.0000 0.999 0.000 1.000
#> GSM601963 1 0.0000 0.987 1.000 0.000
#> GSM601968 1 0.0000 0.987 1.000 0.000
#> GSM601983 1 0.0000 0.987 1.000 0.000
#> GSM601993 2 0.0000 0.999 0.000 1.000
#> GSM601874 2 0.0000 0.999 0.000 1.000
#> GSM601884 2 0.0000 0.999 0.000 1.000
#> GSM601889 1 0.0000 0.987 1.000 0.000
#> GSM601894 1 0.0000 0.987 1.000 0.000
#> GSM601899 1 0.0938 0.978 0.988 0.012
#> GSM601904 2 0.1414 0.980 0.020 0.980
#> GSM601914 1 0.0000 0.987 1.000 0.000
#> GSM601929 1 0.0000 0.987 1.000 0.000
#> GSM601934 2 0.0000 0.999 0.000 1.000
#> GSM601939 1 0.0000 0.987 1.000 0.000
#> GSM601944 2 0.0000 0.999 0.000 1.000
#> GSM601949 1 0.0000 0.987 1.000 0.000
#> GSM601959 1 0.0000 0.987 1.000 0.000
#> GSM601974 2 0.1414 0.980 0.020 0.980
#> GSM601979 2 0.0000 0.999 0.000 1.000
#> GSM601989 1 0.0000 0.987 1.000 0.000
#> GSM601879 1 0.0000 0.987 1.000 0.000
#> GSM601909 1 0.0000 0.987 1.000 0.000
#> GSM601919 2 0.0000 0.999 0.000 1.000
#> GSM601924 1 0.0000 0.987 1.000 0.000
#> GSM601954 2 0.0000 0.999 0.000 1.000
#> GSM601964 1 0.0000 0.987 1.000 0.000
#> GSM601969 1 0.0000 0.987 1.000 0.000
#> GSM601984 1 0.0000 0.987 1.000 0.000
#> GSM601994 2 0.0000 0.999 0.000 1.000
#> GSM601875 2 0.0000 0.999 0.000 1.000
#> GSM601885 2 0.0000 0.999 0.000 1.000
#> GSM601890 1 0.2236 0.957 0.964 0.036
#> GSM601895 1 0.0000 0.987 1.000 0.000
#> GSM601900 1 0.0000 0.987 1.000 0.000
#> GSM601905 2 0.0376 0.995 0.004 0.996
#> GSM601915 1 0.0000 0.987 1.000 0.000
#> GSM601930 1 0.0000 0.987 1.000 0.000
#> GSM601935 1 0.9044 0.535 0.680 0.320
#> GSM601940 1 0.0000 0.987 1.000 0.000
#> GSM601945 2 0.0000 0.999 0.000 1.000
#> GSM601950 1 0.0000 0.987 1.000 0.000
#> GSM601960 1 0.0000 0.987 1.000 0.000
#> GSM601975 2 0.0000 0.999 0.000 1.000
#> GSM601980 2 0.0000 0.999 0.000 1.000
#> GSM601990 1 0.0000 0.987 1.000 0.000
#> GSM601880 1 0.0000 0.987 1.000 0.000
#> GSM601910 1 0.0000 0.987 1.000 0.000
#> GSM601920 2 0.0000 0.999 0.000 1.000
#> GSM601925 1 0.0000 0.987 1.000 0.000
#> GSM601955 2 0.0000 0.999 0.000 1.000
#> GSM601965 1 0.0000 0.987 1.000 0.000
#> GSM601970 1 0.0000 0.987 1.000 0.000
#> GSM601985 1 0.0000 0.987 1.000 0.000
#> GSM601995 2 0.0000 0.999 0.000 1.000
#> GSM601876 1 0.0000 0.987 1.000 0.000
#> GSM601886 2 0.0376 0.995 0.004 0.996
#> GSM601891 1 0.6531 0.806 0.832 0.168
#> GSM601896 1 0.0000 0.987 1.000 0.000
#> GSM601901 2 0.0000 0.999 0.000 1.000
#> GSM601906 1 0.2778 0.945 0.952 0.048
#> GSM601916 2 0.0000 0.999 0.000 1.000
#> GSM601931 1 0.0000 0.987 1.000 0.000
#> GSM601936 2 0.0000 0.999 0.000 1.000
#> GSM601941 2 0.0000 0.999 0.000 1.000
#> GSM601946 1 0.0000 0.987 1.000 0.000
#> GSM601951 1 0.0000 0.987 1.000 0.000
#> GSM601961 2 0.0000 0.999 0.000 1.000
#> GSM601976 2 0.0000 0.999 0.000 1.000
#> GSM601981 2 0.0000 0.999 0.000 1.000
#> GSM601991 1 0.0000 0.987 1.000 0.000
#> GSM601881 1 0.0000 0.987 1.000 0.000
#> GSM601911 2 0.0376 0.995 0.004 0.996
#> GSM601921 2 0.0000 0.999 0.000 1.000
#> GSM601926 1 0.0000 0.987 1.000 0.000
#> GSM601956 2 0.0000 0.999 0.000 1.000
#> GSM601966 2 0.0000 0.999 0.000 1.000
#> GSM601971 1 0.0000 0.987 1.000 0.000
#> GSM601986 1 0.2423 0.953 0.960 0.040
#> GSM601996 2 0.0000 0.999 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM601872 2 0.5098 0.8171 0.000 0.752 0.248
#> GSM601882 2 0.2356 0.8855 0.000 0.928 0.072
#> GSM601887 3 0.3456 0.7243 0.060 0.036 0.904
#> GSM601892 3 0.4883 0.8029 0.208 0.004 0.788
#> GSM601897 3 0.1453 0.7234 0.024 0.008 0.968
#> GSM601902 2 0.1525 0.8650 0.032 0.964 0.004
#> GSM601912 3 0.3682 0.7709 0.116 0.008 0.876
#> GSM601927 1 0.0592 0.8394 0.988 0.000 0.012
#> GSM601932 2 0.0475 0.8747 0.004 0.992 0.004
#> GSM601937 2 0.4504 0.7973 0.000 0.804 0.196
#> GSM601942 2 0.4605 0.8511 0.000 0.796 0.204
#> GSM601947 2 0.2982 0.8644 0.056 0.920 0.024
#> GSM601957 3 0.5650 0.7516 0.312 0.000 0.688
#> GSM601972 2 0.1711 0.8803 0.008 0.960 0.032
#> GSM601977 2 0.3482 0.8826 0.000 0.872 0.128
#> GSM601987 2 0.2878 0.8833 0.000 0.904 0.096
#> GSM601877 1 0.0592 0.8394 0.988 0.000 0.012
#> GSM601907 2 0.3619 0.8751 0.000 0.864 0.136
#> GSM601917 2 0.6057 0.4987 0.340 0.656 0.004
#> GSM601922 2 0.6180 0.5190 0.332 0.660 0.008
#> GSM601952 2 0.2625 0.8860 0.000 0.916 0.084
#> GSM601962 3 0.6988 0.5947 0.320 0.036 0.644
#> GSM601967 3 0.5529 0.7666 0.296 0.000 0.704
#> GSM601982 2 0.4883 0.8442 0.004 0.788 0.208
#> GSM601992 2 0.0661 0.8734 0.004 0.988 0.008
#> GSM601873 2 0.4121 0.8651 0.000 0.832 0.168
#> GSM601883 2 0.3030 0.8847 0.004 0.904 0.092
#> GSM601888 3 0.4399 0.6591 0.044 0.092 0.864
#> GSM601893 3 0.4453 0.7797 0.152 0.012 0.836
#> GSM601898 3 0.5216 0.7952 0.260 0.000 0.740
#> GSM601903 2 0.2200 0.8557 0.056 0.940 0.004
#> GSM601913 3 0.6140 0.6295 0.404 0.000 0.596
#> GSM601928 1 0.0592 0.8394 0.988 0.000 0.012
#> GSM601933 2 0.2261 0.8858 0.000 0.932 0.068
#> GSM601938 2 0.2165 0.8848 0.000 0.936 0.064
#> GSM601943 2 0.4605 0.8485 0.000 0.796 0.204
#> GSM601948 1 0.4346 0.6904 0.816 0.000 0.184
#> GSM601958 3 0.5785 0.7421 0.332 0.000 0.668
#> GSM601973 2 0.0661 0.8711 0.008 0.988 0.004
#> GSM601978 2 0.3816 0.8709 0.000 0.852 0.148
#> GSM601988 2 0.3918 0.8400 0.004 0.856 0.140
#> GSM601878 1 0.0592 0.8394 0.988 0.000 0.012
#> GSM601908 2 0.2959 0.8837 0.000 0.900 0.100
#> GSM601918 2 0.1315 0.8761 0.008 0.972 0.020
#> GSM601923 1 0.0592 0.8394 0.988 0.000 0.012
#> GSM601953 2 0.3752 0.8721 0.000 0.856 0.144
#> GSM601963 3 0.5560 0.7419 0.300 0.000 0.700
#> GSM601968 3 0.4555 0.8069 0.200 0.000 0.800
#> GSM601983 3 0.5678 0.6777 0.316 0.000 0.684
#> GSM601993 2 0.1399 0.8711 0.004 0.968 0.028
#> GSM601874 2 0.3816 0.8709 0.000 0.852 0.148
#> GSM601884 2 0.3551 0.8791 0.000 0.868 0.132
#> GSM601889 3 0.5327 0.7898 0.272 0.000 0.728
#> GSM601894 3 0.5431 0.7813 0.284 0.000 0.716
#> GSM601899 3 0.2446 0.7354 0.052 0.012 0.936
#> GSM601904 1 0.6082 0.5063 0.692 0.296 0.012
#> GSM601914 3 0.4702 0.8013 0.212 0.000 0.788
#> GSM601929 1 0.0983 0.8293 0.980 0.004 0.016
#> GSM601934 2 0.3482 0.8797 0.000 0.872 0.128
#> GSM601939 1 0.4842 0.5910 0.776 0.000 0.224
#> GSM601944 2 0.2625 0.8864 0.000 0.916 0.084
#> GSM601949 1 0.4796 0.6304 0.780 0.000 0.220
#> GSM601959 3 0.5529 0.7707 0.296 0.000 0.704
#> GSM601974 3 0.9641 0.1327 0.228 0.316 0.456
#> GSM601979 2 0.3482 0.8772 0.000 0.872 0.128
#> GSM601989 3 0.5497 0.7837 0.292 0.000 0.708
#> GSM601879 1 0.0592 0.8394 0.988 0.000 0.012
#> GSM601909 3 0.4235 0.8048 0.176 0.000 0.824
#> GSM601919 2 0.6066 0.6664 0.248 0.728 0.024
#> GSM601924 1 0.1163 0.8338 0.972 0.000 0.028
#> GSM601954 2 0.2749 0.8845 0.012 0.924 0.064
#> GSM601964 3 0.5363 0.7497 0.276 0.000 0.724
#> GSM601969 3 0.5948 0.6894 0.360 0.000 0.640
#> GSM601984 1 0.4475 0.7477 0.840 0.016 0.144
#> GSM601994 2 0.0475 0.8745 0.004 0.992 0.004
#> GSM601875 2 0.3412 0.8791 0.000 0.876 0.124
#> GSM601885 2 0.2796 0.8840 0.000 0.908 0.092
#> GSM601890 3 0.1525 0.7268 0.032 0.004 0.964
#> GSM601895 3 0.4842 0.8081 0.224 0.000 0.776
#> GSM601900 3 0.5070 0.8080 0.224 0.004 0.772
#> GSM601905 2 0.6451 0.3887 0.384 0.608 0.008
#> GSM601915 3 0.5431 0.7868 0.284 0.000 0.716
#> GSM601930 1 0.0592 0.8394 0.988 0.000 0.012
#> GSM601935 3 0.9233 0.4111 0.268 0.204 0.528
#> GSM601940 1 0.5678 0.3500 0.684 0.000 0.316
#> GSM601945 2 0.3551 0.8759 0.000 0.868 0.132
#> GSM601950 1 0.5529 0.4484 0.704 0.000 0.296
#> GSM601960 3 0.4702 0.8052 0.212 0.000 0.788
#> GSM601975 2 0.1015 0.8740 0.008 0.980 0.012
#> GSM601980 2 0.4842 0.8158 0.000 0.776 0.224
#> GSM601990 3 0.4796 0.7889 0.220 0.000 0.780
#> GSM601880 1 0.0592 0.8394 0.988 0.000 0.012
#> GSM601910 3 0.3551 0.7939 0.132 0.000 0.868
#> GSM601920 1 0.6804 0.0513 0.528 0.460 0.012
#> GSM601925 1 0.0424 0.8372 0.992 0.000 0.008
#> GSM601955 2 0.6410 0.5793 0.004 0.576 0.420
#> GSM601965 1 0.5987 0.6506 0.756 0.036 0.208
#> GSM601970 3 0.4796 0.8066 0.220 0.000 0.780
#> GSM601985 1 0.5431 0.4315 0.716 0.000 0.284
#> GSM601995 2 0.5070 0.7740 0.004 0.772 0.224
#> GSM601876 1 0.3482 0.7546 0.872 0.000 0.128
#> GSM601886 2 0.8508 0.5084 0.232 0.608 0.160
#> GSM601891 3 0.2804 0.6729 0.016 0.060 0.924
#> GSM601896 1 0.2959 0.7823 0.900 0.000 0.100
#> GSM601901 2 0.1860 0.8839 0.000 0.948 0.052
#> GSM601906 1 0.3375 0.7320 0.892 0.100 0.008
#> GSM601916 2 0.2496 0.8508 0.068 0.928 0.004
#> GSM601931 1 0.0592 0.8394 0.988 0.000 0.012
#> GSM601936 2 0.3043 0.8633 0.008 0.908 0.084
#> GSM601941 2 0.0661 0.8711 0.008 0.988 0.004
#> GSM601946 1 0.2261 0.8074 0.932 0.000 0.068
#> GSM601951 1 0.1399 0.8314 0.968 0.004 0.028
#> GSM601961 2 0.5397 0.7534 0.000 0.720 0.280
#> GSM601976 2 0.5219 0.7307 0.196 0.788 0.016
#> GSM601981 2 0.3116 0.8823 0.000 0.892 0.108
#> GSM601991 3 0.4805 0.7752 0.176 0.012 0.812
#> GSM601881 1 0.0592 0.8394 0.988 0.000 0.012
#> GSM601911 2 0.7838 0.0902 0.460 0.488 0.052
#> GSM601921 2 0.5285 0.6688 0.244 0.752 0.004
#> GSM601926 1 0.0592 0.8394 0.988 0.000 0.012
#> GSM601956 2 0.3752 0.8726 0.000 0.856 0.144
#> GSM601966 2 0.1015 0.8741 0.008 0.980 0.012
#> GSM601971 3 0.6126 0.6315 0.400 0.000 0.600
#> GSM601986 1 0.4288 0.7627 0.872 0.068 0.060
#> GSM601996 2 0.0475 0.8721 0.004 0.992 0.004
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM601872 2 0.4155 0.5475 0.000 0.828 0.072 0.100
#> GSM601882 2 0.4391 0.5995 0.000 0.740 0.008 0.252
#> GSM601887 3 0.5443 0.6375 0.024 0.232 0.720 0.024
#> GSM601892 3 0.4265 0.7250 0.068 0.076 0.840 0.016
#> GSM601897 3 0.4998 0.6787 0.004 0.088 0.780 0.128
#> GSM601902 4 0.5681 0.1930 0.028 0.404 0.000 0.568
#> GSM601912 3 0.7320 0.6159 0.088 0.076 0.640 0.196
#> GSM601927 1 0.0188 0.8257 0.996 0.000 0.004 0.000
#> GSM601932 2 0.5165 0.1078 0.000 0.512 0.004 0.484
#> GSM601937 4 0.7155 0.2748 0.000 0.292 0.168 0.540
#> GSM601942 2 0.4994 0.5431 0.000 0.744 0.048 0.208
#> GSM601947 2 0.6319 0.0683 0.060 0.504 0.000 0.436
#> GSM601957 3 0.4453 0.6588 0.244 0.000 0.744 0.012
#> GSM601972 2 0.4804 0.3547 0.000 0.616 0.000 0.384
#> GSM601977 2 0.3539 0.6635 0.000 0.820 0.004 0.176
#> GSM601987 2 0.2589 0.6767 0.000 0.884 0.000 0.116
#> GSM601877 1 0.0592 0.8208 0.984 0.000 0.000 0.016
#> GSM601907 2 0.0779 0.6687 0.000 0.980 0.004 0.016
#> GSM601917 4 0.7199 0.4106 0.232 0.216 0.000 0.552
#> GSM601922 4 0.7445 0.3695 0.224 0.268 0.000 0.508
#> GSM601952 2 0.4072 0.6062 0.000 0.748 0.000 0.252
#> GSM601962 3 0.8356 0.3115 0.152 0.048 0.452 0.348
#> GSM601967 3 0.4355 0.6988 0.212 0.004 0.772 0.012
#> GSM601982 2 0.4974 0.5721 0.000 0.736 0.040 0.224
#> GSM601992 2 0.4955 0.2576 0.000 0.556 0.000 0.444
#> GSM601873 2 0.3812 0.5928 0.000 0.832 0.028 0.140
#> GSM601883 2 0.3356 0.6466 0.000 0.824 0.000 0.176
#> GSM601888 3 0.5701 0.5073 0.012 0.340 0.628 0.020
#> GSM601893 3 0.5285 0.6998 0.056 0.132 0.780 0.032
#> GSM601898 3 0.3625 0.7254 0.160 0.000 0.828 0.012
#> GSM601903 4 0.5599 0.2808 0.032 0.352 0.000 0.616
#> GSM601913 3 0.6375 0.5508 0.312 0.000 0.600 0.088
#> GSM601928 1 0.0336 0.8257 0.992 0.000 0.008 0.000
#> GSM601933 2 0.4040 0.6191 0.000 0.752 0.000 0.248
#> GSM601938 2 0.4382 0.5424 0.000 0.704 0.000 0.296
#> GSM601943 2 0.4290 0.5661 0.000 0.800 0.036 0.164
#> GSM601948 1 0.5251 0.6128 0.712 0.008 0.252 0.028
#> GSM601958 3 0.4422 0.6575 0.256 0.000 0.736 0.008
#> GSM601973 4 0.4889 0.2530 0.004 0.360 0.000 0.636
#> GSM601978 2 0.0895 0.6699 0.000 0.976 0.004 0.020
#> GSM601988 4 0.6751 0.2893 0.000 0.272 0.136 0.592
#> GSM601878 1 0.0376 0.8254 0.992 0.000 0.004 0.004
#> GSM601908 2 0.2530 0.6722 0.000 0.888 0.000 0.112
#> GSM601918 2 0.5112 0.2174 0.004 0.560 0.000 0.436
#> GSM601923 1 0.0188 0.8257 0.996 0.000 0.004 0.000
#> GSM601953 2 0.0804 0.6639 0.000 0.980 0.008 0.012
#> GSM601963 3 0.6010 0.6191 0.220 0.000 0.676 0.104
#> GSM601968 3 0.3342 0.7420 0.080 0.008 0.880 0.032
#> GSM601983 3 0.6893 0.5270 0.204 0.004 0.612 0.180
#> GSM601993 4 0.5755 0.2256 0.000 0.332 0.044 0.624
#> GSM601874 2 0.1489 0.6740 0.000 0.952 0.004 0.044
#> GSM601884 2 0.2714 0.6798 0.000 0.884 0.004 0.112
#> GSM601889 3 0.4361 0.6960 0.208 0.000 0.772 0.020
#> GSM601894 3 0.4011 0.6933 0.208 0.000 0.784 0.008
#> GSM601899 3 0.5196 0.6756 0.028 0.184 0.760 0.028
#> GSM601904 4 0.6434 0.2261 0.432 0.068 0.000 0.500
#> GSM601914 3 0.4203 0.7087 0.068 0.000 0.824 0.108
#> GSM601929 1 0.1724 0.8137 0.948 0.000 0.020 0.032
#> GSM601934 2 0.3764 0.6413 0.000 0.816 0.012 0.172
#> GSM601939 1 0.4482 0.5891 0.728 0.000 0.264 0.008
#> GSM601944 2 0.4741 0.5158 0.000 0.668 0.004 0.328
#> GSM601949 1 0.5026 0.5067 0.672 0.000 0.312 0.016
#> GSM601959 3 0.4086 0.6860 0.216 0.000 0.776 0.008
#> GSM601974 4 0.9163 0.2564 0.120 0.160 0.292 0.428
#> GSM601979 2 0.1118 0.6745 0.000 0.964 0.000 0.036
#> GSM601989 3 0.5109 0.7120 0.196 0.000 0.744 0.060
#> GSM601879 1 0.0657 0.8252 0.984 0.000 0.004 0.012
#> GSM601909 3 0.2751 0.7407 0.056 0.000 0.904 0.040
#> GSM601919 2 0.7436 -0.1166 0.176 0.460 0.000 0.364
#> GSM601924 1 0.1474 0.8145 0.948 0.000 0.052 0.000
#> GSM601954 2 0.5255 0.5064 0.004 0.696 0.028 0.272
#> GSM601964 3 0.6363 0.5918 0.172 0.000 0.656 0.172
#> GSM601969 3 0.5634 0.5753 0.296 0.008 0.664 0.032
#> GSM601984 1 0.6723 0.5473 0.632 0.004 0.176 0.188
#> GSM601994 2 0.4925 0.2829 0.000 0.572 0.000 0.428
#> GSM601875 2 0.1557 0.6761 0.000 0.944 0.000 0.056
#> GSM601885 2 0.3494 0.6630 0.000 0.824 0.004 0.172
#> GSM601890 3 0.5515 0.6553 0.012 0.208 0.728 0.052
#> GSM601895 3 0.4541 0.7153 0.144 0.000 0.796 0.060
#> GSM601900 3 0.4440 0.7380 0.136 0.000 0.804 0.060
#> GSM601905 4 0.6573 0.4487 0.164 0.184 0.004 0.648
#> GSM601915 3 0.5298 0.6768 0.244 0.000 0.708 0.048
#> GSM601930 1 0.0672 0.8259 0.984 0.000 0.008 0.008
#> GSM601935 4 0.7684 0.0518 0.080 0.056 0.332 0.532
#> GSM601940 1 0.4655 0.5312 0.684 0.000 0.312 0.004
#> GSM601945 2 0.1978 0.6770 0.000 0.928 0.004 0.068
#> GSM601950 1 0.4769 0.5158 0.684 0.000 0.308 0.008
#> GSM601960 3 0.4535 0.7271 0.084 0.000 0.804 0.112
#> GSM601975 4 0.4977 0.0165 0.000 0.460 0.000 0.540
#> GSM601980 2 0.6969 -0.0280 0.000 0.452 0.112 0.436
#> GSM601990 3 0.5609 0.6400 0.088 0.000 0.712 0.200
#> GSM601880 1 0.0000 0.8252 1.000 0.000 0.000 0.000
#> GSM601910 3 0.3237 0.7388 0.040 0.008 0.888 0.064
#> GSM601920 4 0.7165 0.3647 0.372 0.140 0.000 0.488
#> GSM601925 1 0.0927 0.8221 0.976 0.000 0.008 0.016
#> GSM601955 4 0.7576 0.1957 0.000 0.324 0.212 0.464
#> GSM601965 1 0.7973 0.4518 0.552 0.040 0.204 0.204
#> GSM601970 3 0.3105 0.7374 0.120 0.000 0.868 0.012
#> GSM601985 1 0.6023 0.2929 0.600 0.000 0.344 0.056
#> GSM601995 4 0.6513 0.3490 0.000 0.176 0.184 0.640
#> GSM601876 1 0.4638 0.7061 0.776 0.000 0.180 0.044
#> GSM601886 4 0.7545 0.4149 0.120 0.156 0.088 0.636
#> GSM601891 3 0.6216 0.5662 0.016 0.276 0.652 0.056
#> GSM601896 1 0.3900 0.7451 0.816 0.000 0.164 0.020
#> GSM601901 2 0.4477 0.5107 0.000 0.688 0.000 0.312
#> GSM601906 1 0.4287 0.6912 0.804 0.012 0.016 0.168
#> GSM601916 4 0.5856 0.2793 0.036 0.352 0.004 0.608
#> GSM601931 1 0.0469 0.8255 0.988 0.000 0.012 0.000
#> GSM601936 4 0.5785 0.3022 0.008 0.280 0.044 0.668
#> GSM601941 4 0.5060 0.1450 0.004 0.412 0.000 0.584
#> GSM601946 1 0.3447 0.7570 0.852 0.000 0.128 0.020
#> GSM601951 1 0.2670 0.8036 0.908 0.000 0.052 0.040
#> GSM601961 2 0.4538 0.5199 0.004 0.808 0.124 0.064
#> GSM601976 4 0.7083 0.3790 0.120 0.288 0.012 0.580
#> GSM601981 2 0.2704 0.6713 0.000 0.876 0.000 0.124
#> GSM601991 3 0.6416 0.5063 0.056 0.012 0.596 0.336
#> GSM601881 1 0.0188 0.8257 0.996 0.000 0.004 0.000
#> GSM601911 4 0.8974 0.2486 0.292 0.316 0.052 0.340
#> GSM601921 4 0.7008 0.4029 0.156 0.252 0.004 0.588
#> GSM601926 1 0.0336 0.8257 0.992 0.000 0.008 0.000
#> GSM601956 2 0.1209 0.6621 0.000 0.964 0.004 0.032
#> GSM601966 2 0.4941 0.2507 0.000 0.564 0.000 0.436
#> GSM601971 3 0.5060 0.3740 0.412 0.000 0.584 0.004
#> GSM601986 1 0.7099 0.5847 0.648 0.048 0.104 0.200
#> GSM601996 4 0.4981 -0.0303 0.000 0.464 0.000 0.536
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM601872 2 0.4184 0.61113 0.000 0.808 0.048 0.032 0.112
#> GSM601882 2 0.5611 0.41088 0.000 0.584 0.012 0.344 0.060
#> GSM601887 3 0.5399 0.47879 0.008 0.216 0.696 0.020 0.060
#> GSM601892 3 0.3723 0.62775 0.052 0.080 0.844 0.004 0.020
#> GSM601897 3 0.6738 0.23486 0.004 0.144 0.536 0.024 0.292
#> GSM601902 4 0.4377 0.56178 0.008 0.160 0.004 0.776 0.052
#> GSM601912 5 0.7614 0.10973 0.092 0.056 0.356 0.036 0.460
#> GSM601927 1 0.1461 0.76709 0.952 0.000 0.028 0.004 0.016
#> GSM601932 4 0.6186 0.40932 0.000 0.316 0.020 0.564 0.100
#> GSM601937 5 0.7430 0.19938 0.004 0.228 0.040 0.264 0.464
#> GSM601942 2 0.5135 0.55783 0.000 0.704 0.012 0.080 0.204
#> GSM601947 4 0.6343 0.37415 0.028 0.356 0.012 0.544 0.060
#> GSM601957 3 0.4003 0.65148 0.180 0.000 0.780 0.004 0.036
#> GSM601972 4 0.5382 0.02162 0.000 0.476 0.004 0.476 0.044
#> GSM601977 2 0.5438 0.62798 0.000 0.700 0.024 0.176 0.100
#> GSM601987 2 0.4592 0.60012 0.000 0.728 0.012 0.224 0.036
#> GSM601877 1 0.1617 0.76272 0.948 0.000 0.020 0.020 0.012
#> GSM601907 2 0.1934 0.68040 0.000 0.928 0.004 0.052 0.016
#> GSM601917 4 0.6741 0.53336 0.136 0.116 0.012 0.640 0.096
#> GSM601922 4 0.6185 0.53628 0.140 0.088 0.004 0.676 0.092
#> GSM601952 2 0.5862 0.46368 0.000 0.616 0.012 0.264 0.108
#> GSM601962 5 0.7134 0.37282 0.160 0.032 0.176 0.040 0.592
#> GSM601967 3 0.4738 0.64711 0.192 0.016 0.744 0.004 0.044
#> GSM601982 2 0.6616 0.49320 0.004 0.600 0.036 0.192 0.168
#> GSM601992 4 0.6030 -0.00397 0.000 0.424 0.004 0.472 0.100
#> GSM601873 2 0.4976 0.63368 0.000 0.748 0.024 0.124 0.104
#> GSM601883 2 0.5353 0.55225 0.000 0.668 0.024 0.256 0.052
#> GSM601888 3 0.5678 0.36269 0.000 0.300 0.616 0.020 0.064
#> GSM601893 3 0.5684 0.58565 0.052 0.116 0.728 0.016 0.088
#> GSM601898 3 0.5046 0.62404 0.140 0.000 0.704 0.000 0.156
#> GSM601903 4 0.4613 0.57076 0.020 0.124 0.004 0.780 0.072
#> GSM601913 3 0.6961 0.32018 0.312 0.000 0.412 0.008 0.268
#> GSM601928 1 0.0898 0.76533 0.972 0.000 0.020 0.000 0.008
#> GSM601933 2 0.5887 0.45604 0.000 0.588 0.008 0.300 0.104
#> GSM601938 2 0.6075 0.33464 0.000 0.552 0.012 0.336 0.100
#> GSM601943 2 0.4414 0.63134 0.000 0.792 0.024 0.076 0.108
#> GSM601948 1 0.6550 0.43850 0.580 0.004 0.284 0.068 0.064
#> GSM601958 3 0.5115 0.62303 0.232 0.000 0.676 0.000 0.092
#> GSM601973 4 0.5166 0.54000 0.000 0.208 0.012 0.700 0.080
#> GSM601978 2 0.1911 0.68113 0.000 0.932 0.004 0.036 0.028
#> GSM601988 5 0.7377 -0.01894 0.000 0.228 0.036 0.324 0.412
#> GSM601878 1 0.0693 0.76480 0.980 0.000 0.008 0.012 0.000
#> GSM601908 2 0.3548 0.65310 0.000 0.796 0.004 0.188 0.012
#> GSM601918 4 0.5098 0.43104 0.004 0.316 0.008 0.640 0.032
#> GSM601923 1 0.0854 0.76637 0.976 0.000 0.012 0.008 0.004
#> GSM601953 2 0.2095 0.66827 0.000 0.928 0.020 0.028 0.024
#> GSM601963 5 0.6784 -0.15992 0.224 0.000 0.376 0.004 0.396
#> GSM601968 3 0.4414 0.62729 0.044 0.020 0.792 0.008 0.136
#> GSM601983 5 0.7042 0.12141 0.216 0.000 0.288 0.024 0.472
#> GSM601993 4 0.6777 0.29845 0.000 0.220 0.008 0.464 0.308
#> GSM601874 2 0.2732 0.68320 0.000 0.884 0.008 0.088 0.020
#> GSM601884 2 0.4738 0.65618 0.000 0.748 0.012 0.164 0.076
#> GSM601889 3 0.4830 0.64643 0.208 0.000 0.716 0.004 0.072
#> GSM601894 3 0.4627 0.64742 0.188 0.000 0.732 0.000 0.080
#> GSM601899 3 0.5190 0.51726 0.012 0.172 0.736 0.028 0.052
#> GSM601904 4 0.6780 0.22117 0.356 0.012 0.012 0.488 0.132
#> GSM601914 3 0.5853 0.25485 0.084 0.000 0.500 0.004 0.412
#> GSM601929 1 0.4173 0.71163 0.816 0.000 0.040 0.060 0.084
#> GSM601934 2 0.6222 0.51955 0.000 0.604 0.024 0.244 0.128
#> GSM601939 1 0.4495 0.57573 0.712 0.000 0.244 0.000 0.044
#> GSM601944 2 0.6932 0.37179 0.000 0.536 0.048 0.268 0.148
#> GSM601949 1 0.5670 0.32950 0.564 0.004 0.376 0.032 0.024
#> GSM601959 3 0.4558 0.64182 0.208 0.000 0.728 0.000 0.064
#> GSM601974 5 0.9065 0.30722 0.080 0.100 0.192 0.240 0.388
#> GSM601979 2 0.3154 0.67306 0.000 0.860 0.012 0.104 0.024
#> GSM601989 3 0.5800 0.58764 0.156 0.000 0.640 0.008 0.196
#> GSM601879 1 0.2529 0.75789 0.908 0.000 0.036 0.032 0.024
#> GSM601909 3 0.5213 0.60815 0.088 0.008 0.724 0.012 0.168
#> GSM601919 4 0.6961 0.49553 0.100 0.244 0.008 0.576 0.072
#> GSM601924 1 0.2054 0.75237 0.916 0.000 0.072 0.004 0.008
#> GSM601954 2 0.7070 0.22997 0.008 0.512 0.084 0.328 0.068
#> GSM601964 5 0.6397 0.10163 0.164 0.000 0.328 0.004 0.504
#> GSM601969 3 0.5287 0.61083 0.216 0.016 0.708 0.024 0.036
#> GSM601984 1 0.7740 0.18356 0.440 0.000 0.120 0.132 0.308
#> GSM601994 4 0.5944 0.12904 0.000 0.404 0.000 0.488 0.108
#> GSM601875 2 0.3506 0.68424 0.000 0.836 0.016 0.124 0.024
#> GSM601885 2 0.5446 0.54562 0.000 0.632 0.008 0.288 0.072
#> GSM601890 3 0.5649 0.48851 0.008 0.180 0.700 0.032 0.080
#> GSM601895 3 0.6117 0.47462 0.152 0.000 0.588 0.008 0.252
#> GSM601900 3 0.6074 0.60199 0.132 0.020 0.640 0.004 0.204
#> GSM601905 4 0.6707 0.54962 0.108 0.104 0.012 0.644 0.132
#> GSM601915 3 0.6178 0.52133 0.196 0.000 0.576 0.004 0.224
#> GSM601930 1 0.1211 0.76735 0.960 0.000 0.016 0.000 0.024
#> GSM601935 5 0.6654 0.47757 0.048 0.036 0.116 0.144 0.656
#> GSM601940 1 0.5774 0.50029 0.632 0.000 0.248 0.012 0.108
#> GSM601945 2 0.3265 0.68733 0.000 0.856 0.008 0.096 0.040
#> GSM601950 1 0.5267 0.26431 0.560 0.000 0.400 0.020 0.020
#> GSM601960 3 0.6021 0.35321 0.088 0.000 0.536 0.012 0.364
#> GSM601975 4 0.5449 0.44758 0.000 0.328 0.004 0.600 0.068
#> GSM601980 5 0.7183 0.03691 0.000 0.368 0.028 0.200 0.404
#> GSM601990 5 0.5990 0.05402 0.076 0.000 0.372 0.016 0.536
#> GSM601880 1 0.1617 0.76561 0.948 0.000 0.020 0.012 0.020
#> GSM601910 3 0.5800 0.49944 0.020 0.036 0.656 0.032 0.256
#> GSM601920 4 0.7677 0.37981 0.244 0.092 0.024 0.528 0.112
#> GSM601925 1 0.1095 0.76402 0.968 0.000 0.012 0.008 0.012
#> GSM601955 5 0.7595 0.37611 0.000 0.252 0.124 0.132 0.492
#> GSM601965 1 0.8908 0.03646 0.364 0.040 0.160 0.152 0.284
#> GSM601970 3 0.4653 0.65512 0.132 0.000 0.752 0.004 0.112
#> GSM601985 1 0.6003 0.36623 0.592 0.000 0.252 0.004 0.152
#> GSM601995 5 0.6686 0.32646 0.000 0.164 0.040 0.220 0.576
#> GSM601876 1 0.6026 0.56220 0.640 0.000 0.196 0.024 0.140
#> GSM601886 5 0.8311 -0.03628 0.088 0.112 0.048 0.344 0.408
#> GSM601891 3 0.6221 0.35385 0.000 0.276 0.592 0.028 0.104
#> GSM601896 1 0.5173 0.61956 0.704 0.000 0.184 0.008 0.104
#> GSM601901 2 0.6180 0.27050 0.000 0.544 0.028 0.352 0.076
#> GSM601906 1 0.6223 0.53332 0.636 0.000 0.044 0.200 0.120
#> GSM601916 4 0.6754 0.54237 0.052 0.140 0.020 0.632 0.156
#> GSM601931 1 0.1012 0.76612 0.968 0.000 0.020 0.000 0.012
#> GSM601936 5 0.7218 -0.09849 0.004 0.208 0.020 0.372 0.396
#> GSM601941 4 0.5658 0.50781 0.004 0.236 0.008 0.652 0.100
#> GSM601946 1 0.2813 0.74098 0.884 0.000 0.064 0.004 0.048
#> GSM601951 1 0.4454 0.71640 0.800 0.000 0.084 0.060 0.056
#> GSM601961 2 0.5776 0.42736 0.000 0.664 0.220 0.076 0.040
#> GSM601976 4 0.7837 0.47946 0.100 0.172 0.036 0.548 0.144
#> GSM601981 2 0.5252 0.62534 0.000 0.704 0.032 0.208 0.056
#> GSM601991 5 0.5579 0.30657 0.036 0.008 0.268 0.032 0.656
#> GSM601881 1 0.0771 0.76580 0.976 0.000 0.020 0.000 0.004
#> GSM601911 4 0.8989 0.21027 0.196 0.264 0.020 0.288 0.232
#> GSM601921 4 0.6491 0.55168 0.108 0.096 0.012 0.664 0.120
#> GSM601926 1 0.0932 0.76588 0.972 0.000 0.020 0.004 0.004
#> GSM601956 2 0.2820 0.67494 0.000 0.884 0.004 0.056 0.056
#> GSM601966 4 0.5904 0.28219 0.000 0.376 0.008 0.532 0.084
#> GSM601971 3 0.5489 0.29791 0.420 0.000 0.528 0.012 0.040
#> GSM601986 1 0.8823 0.20530 0.424 0.072 0.088 0.216 0.200
#> GSM601996 4 0.5786 0.35347 0.000 0.320 0.008 0.584 0.088
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM601872 2 0.490 0.54333 0.000 0.740 0.064 0.020 0.136 0.040
#> GSM601882 2 0.625 0.22061 0.000 0.508 0.020 0.200 0.268 0.004
#> GSM601887 6 0.715 0.36765 0.028 0.188 0.100 0.008 0.132 0.544
#> GSM601892 6 0.456 0.52309 0.032 0.060 0.032 0.000 0.100 0.776
#> GSM601897 6 0.790 -0.05717 0.000 0.124 0.300 0.044 0.156 0.376
#> GSM601902 4 0.576 0.32705 0.008 0.128 0.028 0.636 0.196 0.004
#> GSM601912 3 0.792 0.28464 0.052 0.064 0.484 0.060 0.096 0.244
#> GSM601927 1 0.264 0.73950 0.896 0.000 0.032 0.016 0.020 0.036
#> GSM601932 4 0.670 0.20339 0.008 0.248 0.036 0.496 0.208 0.004
#> GSM601937 3 0.770 -0.36880 0.000 0.204 0.368 0.116 0.292 0.020
#> GSM601942 2 0.589 0.46204 0.000 0.616 0.112 0.072 0.200 0.000
#> GSM601947 4 0.648 0.33385 0.036 0.292 0.008 0.500 0.164 0.000
#> GSM601957 6 0.342 0.56549 0.116 0.000 0.044 0.000 0.016 0.824
#> GSM601972 4 0.639 0.09813 0.000 0.392 0.020 0.400 0.184 0.004
#> GSM601977 2 0.608 0.49817 0.000 0.632 0.088 0.104 0.164 0.012
#> GSM601987 2 0.471 0.48398 0.000 0.684 0.004 0.104 0.208 0.000
#> GSM601877 1 0.143 0.73452 0.952 0.000 0.008 0.016 0.008 0.016
#> GSM601907 2 0.253 0.59695 0.000 0.888 0.012 0.036 0.064 0.000
#> GSM601917 4 0.622 0.39026 0.112 0.096 0.032 0.644 0.116 0.000
#> GSM601922 4 0.557 0.41411 0.120 0.080 0.012 0.696 0.088 0.004
#> GSM601952 2 0.649 0.36917 0.000 0.532 0.052 0.200 0.212 0.004
#> GSM601962 3 0.724 0.45264 0.096 0.052 0.596 0.040 0.112 0.104
#> GSM601967 6 0.458 0.53759 0.172 0.000 0.072 0.004 0.020 0.732
#> GSM601982 2 0.692 0.32522 0.000 0.508 0.108 0.096 0.268 0.020
#> GSM601992 5 0.671 0.24646 0.000 0.356 0.036 0.252 0.356 0.000
#> GSM601873 2 0.555 0.53747 0.000 0.656 0.064 0.064 0.208 0.008
#> GSM601883 2 0.538 0.42551 0.000 0.620 0.004 0.204 0.168 0.004
#> GSM601888 6 0.639 0.26638 0.012 0.320 0.040 0.000 0.120 0.508
#> GSM601893 6 0.567 0.49053 0.016 0.084 0.104 0.004 0.100 0.692
#> GSM601898 6 0.493 0.50527 0.096 0.000 0.176 0.008 0.016 0.704
#> GSM601903 4 0.533 0.39457 0.020 0.120 0.032 0.700 0.128 0.000
#> GSM601913 6 0.678 0.15092 0.240 0.000 0.316 0.012 0.024 0.408
#> GSM601928 1 0.222 0.73833 0.912 0.000 0.036 0.004 0.012 0.036
#> GSM601933 2 0.633 0.19872 0.000 0.504 0.032 0.164 0.296 0.004
#> GSM601938 2 0.626 0.13878 0.000 0.496 0.024 0.232 0.248 0.000
#> GSM601943 2 0.568 0.51380 0.000 0.660 0.092 0.032 0.188 0.028
#> GSM601948 1 0.678 0.47208 0.568 0.004 0.052 0.064 0.080 0.232
#> GSM601958 6 0.474 0.50272 0.216 0.000 0.092 0.000 0.008 0.684
#> GSM601973 4 0.629 0.28334 0.004 0.164 0.036 0.556 0.236 0.004
#> GSM601978 2 0.276 0.59856 0.000 0.876 0.008 0.040 0.072 0.004
#> GSM601988 5 0.739 0.36687 0.000 0.188 0.312 0.144 0.356 0.000
#> GSM601878 1 0.131 0.73831 0.956 0.000 0.012 0.008 0.004 0.020
#> GSM601908 2 0.409 0.56719 0.000 0.784 0.028 0.108 0.080 0.000
#> GSM601918 4 0.517 0.34982 0.000 0.288 0.012 0.612 0.088 0.000
#> GSM601923 1 0.101 0.73651 0.968 0.000 0.004 0.008 0.004 0.016
#> GSM601953 2 0.290 0.58446 0.000 0.876 0.012 0.020 0.068 0.024
#> GSM601963 3 0.596 0.17129 0.204 0.000 0.500 0.000 0.008 0.288
#> GSM601968 6 0.485 0.53288 0.048 0.020 0.148 0.004 0.036 0.744
#> GSM601983 3 0.628 0.36668 0.132 0.008 0.596 0.008 0.048 0.208
#> GSM601993 5 0.719 0.33506 0.000 0.184 0.116 0.300 0.400 0.000
#> GSM601874 2 0.383 0.59454 0.000 0.804 0.008 0.088 0.092 0.008
#> GSM601884 2 0.577 0.49637 0.000 0.636 0.068 0.092 0.200 0.004
#> GSM601889 6 0.478 0.55369 0.120 0.000 0.088 0.008 0.040 0.744
#> GSM601894 6 0.469 0.54514 0.132 0.000 0.120 0.004 0.016 0.728
#> GSM601899 6 0.594 0.44498 0.008 0.132 0.092 0.004 0.108 0.656
#> GSM601904 4 0.658 0.27425 0.220 0.012 0.064 0.572 0.124 0.008
#> GSM601914 3 0.519 -0.06030 0.052 0.000 0.492 0.000 0.016 0.440
#> GSM601929 1 0.421 0.69293 0.804 0.000 0.044 0.080 0.036 0.036
#> GSM601934 2 0.654 0.36547 0.004 0.552 0.092 0.088 0.256 0.008
#> GSM601939 1 0.546 0.53366 0.636 0.000 0.104 0.008 0.020 0.232
#> GSM601944 2 0.725 0.21336 0.000 0.440 0.100 0.164 0.284 0.012
#> GSM601949 1 0.587 0.26791 0.500 0.004 0.032 0.012 0.052 0.400
#> GSM601959 6 0.447 0.54211 0.156 0.000 0.072 0.008 0.016 0.748
#> GSM601974 3 0.916 -0.08107 0.076 0.100 0.288 0.256 0.216 0.064
#> GSM601979 2 0.303 0.59247 0.000 0.860 0.020 0.072 0.048 0.000
#> GSM601989 6 0.551 0.42408 0.100 0.000 0.284 0.000 0.024 0.592
#> GSM601879 1 0.223 0.73064 0.916 0.000 0.016 0.032 0.012 0.024
#> GSM601909 6 0.510 0.46927 0.040 0.000 0.240 0.016 0.032 0.672
#> GSM601919 4 0.571 0.41841 0.088 0.180 0.012 0.664 0.052 0.004
#> GSM601924 1 0.304 0.72609 0.848 0.000 0.020 0.008 0.008 0.116
#> GSM601954 2 0.747 0.18469 0.008 0.444 0.036 0.264 0.200 0.048
#> GSM601964 3 0.559 0.26393 0.088 0.000 0.592 0.000 0.036 0.284
#> GSM601969 6 0.595 0.53431 0.164 0.028 0.052 0.024 0.056 0.676
#> GSM601984 3 0.840 0.05377 0.316 0.004 0.320 0.096 0.156 0.108
#> GSM601994 5 0.661 0.20020 0.000 0.316 0.024 0.324 0.336 0.000
#> GSM601875 2 0.411 0.59155 0.000 0.788 0.020 0.068 0.116 0.008
#> GSM601885 2 0.634 0.39954 0.000 0.560 0.040 0.160 0.228 0.012
#> GSM601890 6 0.721 0.29807 0.012 0.200 0.152 0.004 0.128 0.504
#> GSM601895 6 0.616 0.27141 0.076 0.004 0.352 0.020 0.028 0.520
#> GSM601900 6 0.595 0.48232 0.092 0.008 0.188 0.016 0.048 0.648
#> GSM601905 4 0.690 0.31833 0.100 0.084 0.064 0.592 0.156 0.004
#> GSM601915 6 0.556 0.36993 0.144 0.000 0.260 0.000 0.012 0.584
#> GSM601930 1 0.310 0.73153 0.872 0.000 0.044 0.020 0.032 0.032
#> GSM601935 3 0.748 0.34394 0.040 0.028 0.528 0.072 0.208 0.124
#> GSM601940 1 0.588 0.59434 0.632 0.000 0.144 0.012 0.040 0.172
#> GSM601945 2 0.379 0.59548 0.000 0.816 0.036 0.048 0.096 0.004
#> GSM601950 1 0.555 0.34360 0.544 0.000 0.040 0.016 0.028 0.372
#> GSM601960 6 0.569 0.26530 0.068 0.000 0.340 0.004 0.036 0.552
#> GSM601975 4 0.590 0.31470 0.004 0.232 0.032 0.592 0.140 0.000
#> GSM601980 2 0.719 -0.08894 0.000 0.352 0.296 0.084 0.268 0.000
#> GSM601990 3 0.541 0.28034 0.048 0.000 0.616 0.016 0.028 0.292
#> GSM601880 1 0.168 0.73812 0.940 0.000 0.004 0.020 0.024 0.012
#> GSM601910 6 0.585 0.36694 0.016 0.020 0.288 0.008 0.076 0.592
#> GSM601920 4 0.630 0.34927 0.180 0.060 0.036 0.620 0.104 0.000
#> GSM601925 1 0.221 0.73829 0.916 0.000 0.028 0.032 0.008 0.016
#> GSM601955 3 0.777 -0.05557 0.000 0.216 0.412 0.072 0.244 0.056
#> GSM601965 1 0.874 -0.05181 0.356 0.028 0.264 0.084 0.140 0.128
#> GSM601970 6 0.447 0.55357 0.096 0.000 0.136 0.004 0.016 0.748
#> GSM601985 1 0.646 0.12712 0.460 0.000 0.172 0.004 0.032 0.332
#> GSM601995 5 0.733 0.24166 0.000 0.128 0.360 0.140 0.364 0.008
#> GSM601876 1 0.673 0.49173 0.548 0.000 0.160 0.028 0.052 0.212
#> GSM601886 5 0.810 0.10441 0.056 0.068 0.180 0.328 0.352 0.016
#> GSM601891 6 0.749 0.17362 0.000 0.296 0.152 0.012 0.140 0.400
#> GSM601896 1 0.611 0.57619 0.612 0.000 0.156 0.020 0.036 0.176
#> GSM601901 2 0.718 0.02388 0.012 0.416 0.040 0.320 0.200 0.012
#> GSM601906 1 0.674 0.39265 0.544 0.000 0.092 0.260 0.056 0.048
#> GSM601916 4 0.738 0.20554 0.020 0.144 0.072 0.472 0.276 0.016
#> GSM601931 1 0.201 0.73871 0.920 0.000 0.032 0.000 0.012 0.036
#> GSM601936 5 0.782 0.32687 0.004 0.148 0.200 0.236 0.396 0.016
#> GSM601941 4 0.613 0.21816 0.004 0.200 0.024 0.548 0.224 0.000
#> GSM601946 1 0.522 0.63810 0.684 0.000 0.096 0.008 0.028 0.184
#> GSM601951 1 0.610 0.60853 0.668 0.000 0.068 0.096 0.084 0.084
#> GSM601961 2 0.646 0.42719 0.008 0.620 0.020 0.080 0.140 0.132
#> GSM601976 4 0.747 0.26410 0.056 0.188 0.036 0.512 0.184 0.024
#> GSM601981 2 0.556 0.53269 0.000 0.656 0.044 0.120 0.176 0.004
#> GSM601991 3 0.559 0.40062 0.008 0.012 0.644 0.012 0.112 0.212
#> GSM601881 1 0.131 0.73870 0.956 0.000 0.004 0.012 0.008 0.020
#> GSM601911 4 0.909 0.00363 0.124 0.220 0.128 0.260 0.248 0.020
#> GSM601921 4 0.640 0.38768 0.092 0.108 0.052 0.636 0.112 0.000
#> GSM601926 1 0.134 0.73710 0.952 0.000 0.008 0.004 0.004 0.032
#> GSM601956 2 0.349 0.59321 0.000 0.828 0.036 0.036 0.100 0.000
#> GSM601966 4 0.672 -0.10118 0.000 0.324 0.032 0.340 0.304 0.000
#> GSM601971 6 0.558 0.23953 0.384 0.004 0.060 0.008 0.016 0.528
#> GSM601986 1 0.913 0.05612 0.352 0.044 0.156 0.136 0.196 0.116
#> GSM601996 5 0.664 0.16936 0.000 0.276 0.028 0.340 0.356 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
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)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
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)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, 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.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")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n time(p) gender(p) k
#> MAD:skmeans 125 0.294 0.864 2
#> MAD:skmeans 116 0.146 0.421 3
#> MAD:skmeans 88 0.108 0.299 4
#> MAD:skmeans 67 0.133 0.469 5
#> MAD:skmeans 43 0.392 0.621 6
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.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["MAD", "pam"]
# you can also extract it by
# res = res_list["MAD:pam"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 21512 rows and 125 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'pam' method.
#> Performed in total 1250 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_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
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, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.444 0.799 0.896 0.3856 0.632 0.632
#> 3 3 0.273 0.664 0.800 0.5456 0.761 0.625
#> 4 4 0.484 0.667 0.823 0.1720 0.879 0.710
#> 5 5 0.552 0.621 0.811 0.0679 0.934 0.795
#> 6 6 0.563 0.529 0.779 0.0321 0.990 0.963
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 4
Following shows 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 class
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
#> GSM601872 2 0.0938 0.812 0.012 0.988
#> GSM601882 1 0.4815 0.846 0.896 0.104
#> GSM601887 2 0.5408 0.819 0.124 0.876
#> GSM601892 2 0.9522 0.598 0.372 0.628
#> GSM601897 1 0.6623 0.746 0.828 0.172
#> GSM601902 1 0.2778 0.890 0.952 0.048
#> GSM601912 1 0.0000 0.902 1.000 0.000
#> GSM601927 1 0.0000 0.902 1.000 0.000
#> GSM601932 1 0.4690 0.857 0.900 0.100
#> GSM601937 1 0.1414 0.898 0.980 0.020
#> GSM601942 1 0.7376 0.766 0.792 0.208
#> GSM601947 1 0.9710 0.285 0.600 0.400
#> GSM601957 2 0.9686 0.541 0.396 0.604
#> GSM601972 1 0.4690 0.857 0.900 0.100
#> GSM601977 1 0.5842 0.813 0.860 0.140
#> GSM601987 2 0.8555 0.672 0.280 0.720
#> GSM601877 1 0.0376 0.901 0.996 0.004
#> GSM601907 2 0.0000 0.807 0.000 1.000
#> GSM601917 1 0.2603 0.892 0.956 0.044
#> GSM601922 1 0.1184 0.900 0.984 0.016
#> GSM601952 1 0.1633 0.898 0.976 0.024
#> GSM601962 1 0.0672 0.901 0.992 0.008
#> GSM601967 1 0.3274 0.877 0.940 0.060
#> GSM601982 1 0.4562 0.852 0.904 0.096
#> GSM601992 1 0.5178 0.840 0.884 0.116
#> GSM601873 2 0.7453 0.757 0.212 0.788
#> GSM601883 1 0.6438 0.800 0.836 0.164
#> GSM601888 2 0.4298 0.824 0.088 0.912
#> GSM601893 1 0.6343 0.782 0.840 0.160
#> GSM601898 1 0.0672 0.900 0.992 0.008
#> GSM601903 1 0.4562 0.863 0.904 0.096
#> GSM601913 1 0.0000 0.902 1.000 0.000
#> GSM601928 1 0.0000 0.902 1.000 0.000
#> GSM601933 2 0.9323 0.585 0.348 0.652
#> GSM601938 2 0.4431 0.810 0.092 0.908
#> GSM601943 2 0.9896 0.408 0.440 0.560
#> GSM601948 1 0.5737 0.826 0.864 0.136
#> GSM601958 1 0.0000 0.902 1.000 0.000
#> GSM601973 1 0.2043 0.895 0.968 0.032
#> GSM601978 2 0.0000 0.807 0.000 1.000
#> GSM601988 1 0.4431 0.854 0.908 0.092
#> GSM601878 1 0.1414 0.896 0.980 0.020
#> GSM601908 2 0.9209 0.573 0.336 0.664
#> GSM601918 1 0.9909 0.159 0.556 0.444
#> GSM601923 1 0.1184 0.899 0.984 0.016
#> GSM601953 2 0.1414 0.814 0.020 0.980
#> GSM601963 1 0.0000 0.902 1.000 0.000
#> GSM601968 2 0.9522 0.608 0.372 0.628
#> GSM601983 1 0.0376 0.901 0.996 0.004
#> GSM601993 1 0.3431 0.872 0.936 0.064
#> GSM601874 2 0.2603 0.825 0.044 0.956
#> GSM601884 1 0.9286 0.495 0.656 0.344
#> GSM601889 1 0.7299 0.723 0.796 0.204
#> GSM601894 1 0.0376 0.901 0.996 0.004
#> GSM601899 2 0.4298 0.823 0.088 0.912
#> GSM601904 1 0.1184 0.899 0.984 0.016
#> GSM601914 1 0.1633 0.895 0.976 0.024
#> GSM601929 1 0.1414 0.896 0.980 0.020
#> GSM601934 1 0.9427 0.394 0.640 0.360
#> GSM601939 1 0.1414 0.896 0.980 0.020
#> GSM601944 1 0.3584 0.869 0.932 0.068
#> GSM601949 1 0.6887 0.747 0.816 0.184
#> GSM601959 1 0.5059 0.839 0.888 0.112
#> GSM601974 1 0.0376 0.901 0.996 0.004
#> GSM601979 2 0.1184 0.812 0.016 0.984
#> GSM601989 1 0.0000 0.902 1.000 0.000
#> GSM601879 1 0.0672 0.900 0.992 0.008
#> GSM601909 2 0.9129 0.658 0.328 0.672
#> GSM601919 2 0.7815 0.755 0.232 0.768
#> GSM601924 1 0.1184 0.898 0.984 0.016
#> GSM601954 2 0.4298 0.824 0.088 0.912
#> GSM601964 1 0.0000 0.902 1.000 0.000
#> GSM601969 2 0.9460 0.602 0.364 0.636
#> GSM601984 1 0.0000 0.902 1.000 0.000
#> GSM601994 1 0.4431 0.854 0.908 0.092
#> GSM601875 2 0.3584 0.826 0.068 0.932
#> GSM601885 2 0.9833 0.417 0.424 0.576
#> GSM601890 2 0.4431 0.823 0.092 0.908
#> GSM601895 1 0.0000 0.902 1.000 0.000
#> GSM601900 1 0.9044 0.472 0.680 0.320
#> GSM601905 1 0.1184 0.901 0.984 0.016
#> GSM601915 1 0.0000 0.902 1.000 0.000
#> GSM601930 1 0.0000 0.902 1.000 0.000
#> GSM601935 1 0.0000 0.902 1.000 0.000
#> GSM601940 1 0.0000 0.902 1.000 0.000
#> GSM601945 2 0.2778 0.824 0.048 0.952
#> GSM601950 1 0.9732 0.166 0.596 0.404
#> GSM601960 1 0.0000 0.902 1.000 0.000
#> GSM601975 1 0.6438 0.783 0.836 0.164
#> GSM601980 1 0.9754 0.209 0.592 0.408
#> GSM601990 1 0.0000 0.902 1.000 0.000
#> GSM601880 1 0.0376 0.901 0.996 0.004
#> GSM601910 1 0.6438 0.776 0.836 0.164
#> GSM601920 1 0.5408 0.828 0.876 0.124
#> GSM601925 1 0.0000 0.902 1.000 0.000
#> GSM601955 1 0.9000 0.468 0.684 0.316
#> GSM601965 1 0.0000 0.902 1.000 0.000
#> GSM601970 1 0.7815 0.663 0.768 0.232
#> GSM601985 1 0.0000 0.902 1.000 0.000
#> GSM601995 1 0.6973 0.740 0.812 0.188
#> GSM601876 1 0.0000 0.902 1.000 0.000
#> GSM601886 1 0.0000 0.902 1.000 0.000
#> GSM601891 2 0.4939 0.823 0.108 0.892
#> GSM601896 1 0.0000 0.902 1.000 0.000
#> GSM601901 1 0.4562 0.865 0.904 0.096
#> GSM601906 1 0.0672 0.900 0.992 0.008
#> GSM601916 1 0.0938 0.901 0.988 0.012
#> GSM601931 1 0.0000 0.902 1.000 0.000
#> GSM601936 1 0.0000 0.902 1.000 0.000
#> GSM601941 1 0.6712 0.803 0.824 0.176
#> GSM601946 1 0.0000 0.902 1.000 0.000
#> GSM601951 1 0.2236 0.893 0.964 0.036
#> GSM601961 2 0.3431 0.826 0.064 0.936
#> GSM601976 1 0.0000 0.902 1.000 0.000
#> GSM601981 2 0.6247 0.797 0.156 0.844
#> GSM601991 1 0.0000 0.902 1.000 0.000
#> GSM601881 1 0.0000 0.902 1.000 0.000
#> GSM601911 1 0.0376 0.901 0.996 0.004
#> GSM601921 1 0.9323 0.428 0.652 0.348
#> GSM601926 1 0.0376 0.901 0.996 0.004
#> GSM601956 2 0.1414 0.812 0.020 0.980
#> GSM601966 1 0.5629 0.837 0.868 0.132
#> GSM601971 1 0.5408 0.826 0.876 0.124
#> GSM601986 1 0.0000 0.902 1.000 0.000
#> GSM601996 1 0.4431 0.854 0.908 0.092
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM601872 3 0.0000 0.7297 0.000 0.000 1.000
#> GSM601882 2 0.7633 0.6263 0.132 0.684 0.184
#> GSM601887 3 0.4805 0.6847 0.012 0.176 0.812
#> GSM601892 3 0.6140 0.4450 0.000 0.404 0.596
#> GSM601897 2 0.4409 0.6762 0.004 0.824 0.172
#> GSM601902 2 0.6379 0.4197 0.368 0.624 0.008
#> GSM601912 2 0.0000 0.7894 0.000 1.000 0.000
#> GSM601927 2 0.3619 0.6798 0.136 0.864 0.000
#> GSM601932 2 0.7453 0.6527 0.148 0.700 0.152
#> GSM601937 2 0.0892 0.7936 0.000 0.980 0.020
#> GSM601942 2 0.8683 0.5180 0.172 0.592 0.236
#> GSM601947 1 0.4232 0.5982 0.872 0.044 0.084
#> GSM601957 3 0.8777 0.5116 0.148 0.288 0.564
#> GSM601972 2 0.7666 0.6284 0.148 0.684 0.168
#> GSM601977 2 0.3682 0.7692 0.008 0.876 0.116
#> GSM601987 3 0.7584 0.5661 0.104 0.220 0.676
#> GSM601877 1 0.5254 0.7891 0.736 0.264 0.000
#> GSM601907 3 0.1529 0.7212 0.040 0.000 0.960
#> GSM601917 1 0.4291 0.7590 0.820 0.180 0.000
#> GSM601922 1 0.6305 0.3719 0.516 0.484 0.000
#> GSM601952 2 0.3682 0.7647 0.116 0.876 0.008
#> GSM601962 2 0.1585 0.7951 0.028 0.964 0.008
#> GSM601967 2 0.6416 0.3519 0.304 0.676 0.020
#> GSM601982 2 0.3377 0.7749 0.012 0.896 0.092
#> GSM601992 2 0.6810 0.6636 0.068 0.720 0.212
#> GSM601873 3 0.5688 0.6660 0.044 0.168 0.788
#> GSM601883 2 0.8221 0.5511 0.128 0.624 0.248
#> GSM601888 3 0.4575 0.6738 0.184 0.004 0.812
#> GSM601893 2 0.4002 0.7302 0.000 0.840 0.160
#> GSM601898 2 0.1163 0.7932 0.028 0.972 0.000
#> GSM601903 2 0.6124 0.6769 0.220 0.744 0.036
#> GSM601913 2 0.2066 0.7757 0.060 0.940 0.000
#> GSM601928 1 0.6267 0.6053 0.548 0.452 0.000
#> GSM601933 3 0.7338 0.5079 0.060 0.288 0.652
#> GSM601938 3 0.6181 0.6631 0.156 0.072 0.772
#> GSM601943 3 0.7558 0.3889 0.044 0.400 0.556
#> GSM601948 1 0.3120 0.6883 0.908 0.080 0.012
#> GSM601958 2 0.5968 -0.0577 0.364 0.636 0.000
#> GSM601973 2 0.3966 0.7717 0.100 0.876 0.024
#> GSM601978 3 0.0892 0.7286 0.020 0.000 0.980
#> GSM601988 2 0.5292 0.7184 0.028 0.800 0.172
#> GSM601878 1 0.4452 0.7704 0.808 0.192 0.000
#> GSM601908 3 0.8779 0.4388 0.164 0.260 0.576
#> GSM601918 1 0.6783 0.3939 0.736 0.088 0.176
#> GSM601923 1 0.4555 0.7794 0.800 0.200 0.000
#> GSM601953 3 0.0237 0.7303 0.004 0.000 0.996
#> GSM601963 2 0.1529 0.7791 0.040 0.960 0.000
#> GSM601968 3 0.6962 0.4426 0.020 0.412 0.568
#> GSM601983 2 0.0983 0.7930 0.016 0.980 0.004
#> GSM601993 2 0.5791 0.7109 0.060 0.792 0.148
#> GSM601874 3 0.4615 0.7255 0.144 0.020 0.836
#> GSM601884 2 0.9067 0.2197 0.140 0.476 0.384
#> GSM601889 2 0.6435 0.6559 0.076 0.756 0.168
#> GSM601894 2 0.0475 0.7922 0.004 0.992 0.004
#> GSM601899 3 0.5235 0.6933 0.152 0.036 0.812
#> GSM601904 2 0.6045 0.2955 0.380 0.620 0.000
#> GSM601914 2 0.4504 0.6961 0.196 0.804 0.000
#> GSM601929 1 0.5327 0.7826 0.728 0.272 0.000
#> GSM601934 2 0.7015 0.3655 0.024 0.584 0.392
#> GSM601939 1 0.6111 0.6247 0.604 0.396 0.000
#> GSM601944 2 0.4982 0.7376 0.036 0.828 0.136
#> GSM601949 1 0.5763 0.7648 0.740 0.244 0.016
#> GSM601959 2 0.4446 0.7540 0.032 0.856 0.112
#> GSM601974 2 0.1964 0.7865 0.056 0.944 0.000
#> GSM601979 3 0.0000 0.7297 0.000 0.000 1.000
#> GSM601989 2 0.0000 0.7894 0.000 1.000 0.000
#> GSM601879 1 0.5291 0.7888 0.732 0.268 0.000
#> GSM601909 3 0.9047 0.3050 0.344 0.148 0.508
#> GSM601919 1 0.2165 0.5970 0.936 0.000 0.064
#> GSM601924 1 0.5138 0.7908 0.748 0.252 0.000
#> GSM601954 3 0.5355 0.6898 0.168 0.032 0.800
#> GSM601964 2 0.0747 0.7927 0.016 0.984 0.000
#> GSM601969 3 0.9048 0.4791 0.268 0.184 0.548
#> GSM601984 2 0.0892 0.7898 0.020 0.980 0.000
#> GSM601994 2 0.6388 0.6801 0.064 0.752 0.184
#> GSM601875 3 0.2537 0.7384 0.000 0.080 0.920
#> GSM601885 3 0.7901 0.2099 0.056 0.440 0.504
#> GSM601890 3 0.4700 0.6760 0.180 0.008 0.812
#> GSM601895 2 0.0000 0.7894 0.000 1.000 0.000
#> GSM601900 2 0.6589 0.5524 0.032 0.688 0.280
#> GSM601905 2 0.1399 0.7933 0.028 0.968 0.004
#> GSM601915 2 0.2625 0.7493 0.084 0.916 0.000
#> GSM601930 1 0.5760 0.7580 0.672 0.328 0.000
#> GSM601935 2 0.0000 0.7894 0.000 1.000 0.000
#> GSM601940 2 0.1289 0.7879 0.032 0.968 0.000
#> GSM601945 3 0.4095 0.7414 0.056 0.064 0.880
#> GSM601950 1 0.4964 0.7210 0.836 0.116 0.048
#> GSM601960 2 0.1753 0.7831 0.048 0.952 0.000
#> GSM601975 2 0.5688 0.7203 0.044 0.788 0.168
#> GSM601980 2 0.9117 0.1947 0.160 0.512 0.328
#> GSM601990 2 0.1163 0.7928 0.028 0.972 0.000
#> GSM601880 1 0.6095 0.6948 0.608 0.392 0.000
#> GSM601910 2 0.7278 0.6058 0.152 0.712 0.136
#> GSM601920 1 0.7175 0.6238 0.592 0.376 0.032
#> GSM601925 1 0.5650 0.7666 0.688 0.312 0.000
#> GSM601955 2 0.7157 0.5026 0.056 0.668 0.276
#> GSM601965 2 0.3340 0.7227 0.120 0.880 0.000
#> GSM601970 1 0.7097 0.7138 0.668 0.280 0.052
#> GSM601985 2 0.0424 0.7902 0.008 0.992 0.000
#> GSM601995 2 0.4521 0.7154 0.004 0.816 0.180
#> GSM601876 2 0.0000 0.7894 0.000 1.000 0.000
#> GSM601886 2 0.0237 0.7908 0.004 0.996 0.000
#> GSM601891 3 0.5357 0.7040 0.064 0.116 0.820
#> GSM601896 2 0.0000 0.7894 0.000 1.000 0.000
#> GSM601901 2 0.6968 0.6884 0.120 0.732 0.148
#> GSM601906 2 0.3619 0.7276 0.136 0.864 0.000
#> GSM601916 2 0.1753 0.7936 0.048 0.952 0.000
#> GSM601931 1 0.6111 0.6938 0.604 0.396 0.000
#> GSM601936 2 0.1170 0.7946 0.016 0.976 0.008
#> GSM601941 2 0.8262 0.5261 0.304 0.592 0.104
#> GSM601946 2 0.0237 0.7900 0.004 0.996 0.000
#> GSM601951 1 0.3482 0.7172 0.872 0.128 0.000
#> GSM601961 3 0.4602 0.7184 0.108 0.040 0.852
#> GSM601976 2 0.0237 0.7907 0.004 0.996 0.000
#> GSM601981 3 0.4818 0.7117 0.048 0.108 0.844
#> GSM601991 2 0.0000 0.7894 0.000 1.000 0.000
#> GSM601881 1 0.5497 0.7823 0.708 0.292 0.000
#> GSM601911 2 0.0000 0.7894 0.000 1.000 0.000
#> GSM601921 1 0.9188 0.1679 0.468 0.380 0.152
#> GSM601926 1 0.5497 0.7835 0.708 0.292 0.000
#> GSM601956 3 0.0237 0.7298 0.004 0.000 0.996
#> GSM601966 2 0.7164 0.6807 0.140 0.720 0.140
#> GSM601971 1 0.4418 0.7397 0.848 0.132 0.020
#> GSM601986 2 0.1031 0.7896 0.024 0.976 0.000
#> GSM601996 2 0.6728 0.6691 0.080 0.736 0.184
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM601872 3 0.2149 0.7264 0.000 0.088 0.912 0.000
#> GSM601882 2 0.2542 0.7651 0.000 0.904 0.012 0.084
#> GSM601887 3 0.0707 0.7489 0.000 0.000 0.980 0.020
#> GSM601892 3 0.4836 0.4886 0.008 0.000 0.672 0.320
#> GSM601897 4 0.4063 0.7039 0.004 0.016 0.172 0.808
#> GSM601902 4 0.6608 0.4156 0.352 0.064 0.012 0.572
#> GSM601912 4 0.0000 0.8046 0.000 0.000 0.000 1.000
#> GSM601927 4 0.3024 0.7375 0.148 0.000 0.000 0.852
#> GSM601932 4 0.5949 -0.0649 0.028 0.484 0.004 0.484
#> GSM601937 4 0.1902 0.7998 0.000 0.064 0.004 0.932
#> GSM601942 2 0.4305 0.7103 0.020 0.808 0.012 0.160
#> GSM601947 1 0.5214 0.7263 0.784 0.128 0.060 0.028
#> GSM601957 3 0.6330 0.4576 0.060 0.008 0.604 0.328
#> GSM601972 2 0.4230 0.7064 0.008 0.776 0.004 0.212
#> GSM601977 4 0.4605 0.7345 0.000 0.072 0.132 0.796
#> GSM601987 2 0.3271 0.6897 0.000 0.856 0.132 0.012
#> GSM601877 1 0.1557 0.8408 0.944 0.000 0.000 0.056
#> GSM601907 3 0.4477 0.4758 0.000 0.312 0.688 0.000
#> GSM601917 1 0.2631 0.8338 0.912 0.016 0.008 0.064
#> GSM601922 1 0.5701 0.4756 0.612 0.028 0.004 0.356
#> GSM601952 4 0.3568 0.7653 0.024 0.116 0.004 0.856
#> GSM601962 4 0.2563 0.7950 0.020 0.072 0.000 0.908
#> GSM601967 4 0.5444 0.2569 0.424 0.000 0.016 0.560
#> GSM601982 4 0.4640 0.7440 0.012 0.112 0.064 0.812
#> GSM601992 2 0.3047 0.7549 0.000 0.872 0.012 0.116
#> GSM601873 2 0.6801 0.0591 0.000 0.452 0.452 0.096
#> GSM601883 2 0.1452 0.7522 0.000 0.956 0.008 0.036
#> GSM601888 3 0.0469 0.7490 0.012 0.000 0.988 0.000
#> GSM601893 4 0.3356 0.7379 0.000 0.000 0.176 0.824
#> GSM601898 4 0.0992 0.8085 0.012 0.004 0.008 0.976
#> GSM601903 4 0.6581 0.6465 0.168 0.112 0.032 0.688
#> GSM601913 4 0.2216 0.7933 0.092 0.000 0.000 0.908
#> GSM601928 1 0.4406 0.6885 0.700 0.000 0.000 0.300
#> GSM601933 2 0.5412 0.6671 0.000 0.736 0.168 0.096
#> GSM601938 2 0.1209 0.7284 0.004 0.964 0.032 0.000
#> GSM601943 3 0.8231 0.2299 0.024 0.204 0.440 0.332
#> GSM601948 1 0.4567 0.7782 0.820 0.104 0.016 0.060
#> GSM601958 4 0.4925 0.0413 0.428 0.000 0.000 0.572
#> GSM601973 4 0.4702 0.7221 0.036 0.172 0.008 0.784
#> GSM601978 3 0.4522 0.4611 0.000 0.320 0.680 0.000
#> GSM601988 4 0.5165 0.0405 0.000 0.484 0.004 0.512
#> GSM601878 1 0.0921 0.8280 0.972 0.000 0.000 0.028
#> GSM601908 2 0.1677 0.7373 0.000 0.948 0.040 0.012
#> GSM601918 2 0.7437 0.1975 0.384 0.504 0.072 0.040
#> GSM601923 1 0.0921 0.8316 0.972 0.000 0.000 0.028
#> GSM601953 3 0.0592 0.7469 0.000 0.016 0.984 0.000
#> GSM601963 4 0.1211 0.8043 0.040 0.000 0.000 0.960
#> GSM601968 3 0.4897 0.4986 0.004 0.004 0.668 0.324
#> GSM601983 4 0.2413 0.7984 0.020 0.064 0.000 0.916
#> GSM601993 2 0.4720 0.6125 0.000 0.672 0.004 0.324
#> GSM601874 3 0.6154 0.4155 0.032 0.312 0.632 0.024
#> GSM601884 2 0.1151 0.7376 0.000 0.968 0.024 0.008
#> GSM601889 4 0.4767 0.6348 0.020 0.000 0.256 0.724
#> GSM601894 4 0.0376 0.8065 0.004 0.000 0.004 0.992
#> GSM601899 3 0.1305 0.7461 0.036 0.000 0.960 0.004
#> GSM601904 4 0.5843 0.3464 0.400 0.028 0.004 0.568
#> GSM601914 4 0.4554 0.7447 0.164 0.028 0.012 0.796
#> GSM601929 1 0.3217 0.8338 0.860 0.000 0.012 0.128
#> GSM601934 4 0.7992 0.1592 0.016 0.248 0.248 0.488
#> GSM601939 1 0.4511 0.6754 0.724 0.000 0.008 0.268
#> GSM601944 4 0.5050 0.1951 0.000 0.408 0.004 0.588
#> GSM601949 1 0.4160 0.7904 0.808 0.008 0.016 0.168
#> GSM601959 4 0.3105 0.7733 0.012 0.000 0.120 0.868
#> GSM601974 4 0.1863 0.8035 0.040 0.012 0.004 0.944
#> GSM601979 3 0.1022 0.7463 0.000 0.032 0.968 0.000
#> GSM601989 4 0.0000 0.8046 0.000 0.000 0.000 1.000
#> GSM601879 1 0.1716 0.8423 0.936 0.000 0.000 0.064
#> GSM601909 3 0.7207 0.2613 0.364 0.008 0.512 0.116
#> GSM601919 1 0.1305 0.8045 0.960 0.036 0.004 0.000
#> GSM601924 1 0.2011 0.8479 0.920 0.000 0.000 0.080
#> GSM601954 3 0.1863 0.7430 0.040 0.012 0.944 0.004
#> GSM601964 4 0.0927 0.8075 0.008 0.016 0.000 0.976
#> GSM601969 3 0.6863 0.5133 0.248 0.004 0.604 0.144
#> GSM601984 4 0.0707 0.8084 0.020 0.000 0.000 0.980
#> GSM601994 2 0.3668 0.7352 0.000 0.808 0.004 0.188
#> GSM601875 3 0.1004 0.7478 0.000 0.024 0.972 0.004
#> GSM601885 3 0.7456 0.2206 0.000 0.236 0.508 0.256
#> GSM601890 3 0.0524 0.7501 0.008 0.000 0.988 0.004
#> GSM601895 4 0.0000 0.8046 0.000 0.000 0.000 1.000
#> GSM601900 4 0.5220 0.4816 0.016 0.000 0.352 0.632
#> GSM601905 4 0.1369 0.8080 0.016 0.016 0.004 0.964
#> GSM601915 4 0.2469 0.7764 0.108 0.000 0.000 0.892
#> GSM601930 1 0.2530 0.8410 0.888 0.000 0.000 0.112
#> GSM601935 4 0.0000 0.8046 0.000 0.000 0.000 1.000
#> GSM601940 4 0.1557 0.8054 0.056 0.000 0.000 0.944
#> GSM601945 3 0.2441 0.7389 0.004 0.068 0.916 0.012
#> GSM601950 1 0.2010 0.8324 0.940 0.008 0.012 0.040
#> GSM601960 4 0.1474 0.8068 0.052 0.000 0.000 0.948
#> GSM601975 4 0.5102 0.7174 0.008 0.096 0.116 0.780
#> GSM601980 4 0.8618 0.0701 0.036 0.252 0.304 0.408
#> GSM601990 4 0.1151 0.8094 0.024 0.008 0.000 0.968
#> GSM601880 1 0.3837 0.7676 0.776 0.000 0.000 0.224
#> GSM601910 4 0.5276 0.6951 0.068 0.012 0.156 0.764
#> GSM601920 1 0.6333 0.6498 0.656 0.040 0.036 0.268
#> GSM601925 1 0.2149 0.8438 0.912 0.000 0.000 0.088
#> GSM601955 4 0.6346 0.5218 0.040 0.032 0.284 0.644
#> GSM601965 4 0.3266 0.7384 0.168 0.000 0.000 0.832
#> GSM601970 1 0.5487 0.7053 0.712 0.016 0.032 0.240
#> GSM601985 4 0.0469 0.8072 0.012 0.000 0.000 0.988
#> GSM601995 4 0.5247 0.7011 0.004 0.112 0.120 0.764
#> GSM601876 4 0.0000 0.8046 0.000 0.000 0.000 1.000
#> GSM601886 4 0.0188 0.8055 0.004 0.000 0.000 0.996
#> GSM601891 3 0.0712 0.7509 0.004 0.004 0.984 0.008
#> GSM601896 4 0.0000 0.8046 0.000 0.000 0.000 1.000
#> GSM601901 4 0.5561 0.5398 0.032 0.284 0.008 0.676
#> GSM601906 4 0.3668 0.7123 0.188 0.000 0.004 0.808
#> GSM601916 4 0.1629 0.8092 0.024 0.024 0.000 0.952
#> GSM601931 1 0.3610 0.7915 0.800 0.000 0.000 0.200
#> GSM601936 4 0.0804 0.8073 0.008 0.012 0.000 0.980
#> GSM601941 2 0.6218 0.6585 0.076 0.708 0.032 0.184
#> GSM601946 4 0.0188 0.8058 0.004 0.000 0.000 0.996
#> GSM601951 1 0.4537 0.7762 0.824 0.088 0.016 0.072
#> GSM601961 3 0.0524 0.7501 0.008 0.000 0.988 0.004
#> GSM601976 4 0.0188 0.8055 0.004 0.000 0.000 0.996
#> GSM601981 3 0.5747 0.5909 0.008 0.224 0.704 0.064
#> GSM601991 4 0.0336 0.8056 0.000 0.008 0.000 0.992
#> GSM601881 1 0.1867 0.8442 0.928 0.000 0.000 0.072
#> GSM601911 4 0.0000 0.8046 0.000 0.000 0.000 1.000
#> GSM601921 4 0.9189 0.0128 0.356 0.128 0.140 0.376
#> GSM601926 1 0.2011 0.8464 0.920 0.000 0.000 0.080
#> GSM601956 3 0.2530 0.7179 0.000 0.112 0.888 0.000
#> GSM601966 2 0.5374 0.6296 0.012 0.708 0.028 0.252
#> GSM601971 1 0.1509 0.8234 0.960 0.008 0.012 0.020
#> GSM601986 4 0.1022 0.8086 0.032 0.000 0.000 0.968
#> GSM601996 2 0.3208 0.7559 0.000 0.848 0.004 0.148
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM601872 2 0.1732 0.7291 0.000 0.920 0.000 0.000 0.080
#> GSM601882 5 0.2376 0.7288 0.000 0.000 0.052 0.044 0.904
#> GSM601887 2 0.0000 0.7491 0.000 1.000 0.000 0.000 0.000
#> GSM601892 2 0.4029 0.4684 0.004 0.680 0.316 0.000 0.000
#> GSM601897 3 0.3527 0.6804 0.000 0.172 0.804 0.024 0.000
#> GSM601902 3 0.7052 -0.3213 0.308 0.000 0.352 0.332 0.008
#> GSM601912 3 0.0290 0.8042 0.000 0.000 0.992 0.008 0.000
#> GSM601927 3 0.2648 0.7215 0.152 0.000 0.848 0.000 0.000
#> GSM601932 4 0.5344 0.6171 0.008 0.000 0.188 0.688 0.116
#> GSM601937 3 0.1915 0.7995 0.000 0.000 0.928 0.032 0.040
#> GSM601942 5 0.5891 0.2633 0.000 0.000 0.120 0.328 0.552
#> GSM601947 4 0.2233 0.6192 0.104 0.000 0.004 0.892 0.000
#> GSM601957 2 0.6077 0.4129 0.012 0.568 0.312 0.108 0.000
#> GSM601972 4 0.4863 0.5313 0.000 0.000 0.088 0.708 0.204
#> GSM601977 3 0.4399 0.7162 0.004 0.136 0.792 0.032 0.036
#> GSM601987 5 0.1518 0.7358 0.000 0.048 0.004 0.004 0.944
#> GSM601877 1 0.0162 0.7720 0.996 0.000 0.004 0.000 0.000
#> GSM601907 2 0.3895 0.4837 0.000 0.680 0.000 0.000 0.320
#> GSM601917 1 0.3732 0.6800 0.792 0.000 0.032 0.176 0.000
#> GSM601922 1 0.6342 0.2105 0.520 0.000 0.272 0.208 0.000
#> GSM601952 4 0.4420 0.2715 0.004 0.000 0.448 0.548 0.000
#> GSM601962 3 0.2955 0.7766 0.004 0.000 0.876 0.060 0.060
#> GSM601967 3 0.4994 0.1528 0.464 0.016 0.512 0.008 0.000
#> GSM601982 3 0.4208 0.7456 0.008 0.048 0.824 0.044 0.076
#> GSM601992 5 0.0162 0.7446 0.000 0.000 0.004 0.000 0.996
#> GSM601873 5 0.5799 0.1039 0.000 0.416 0.092 0.000 0.492
#> GSM601883 5 0.0771 0.7454 0.000 0.000 0.004 0.020 0.976
#> GSM601888 2 0.0000 0.7491 0.000 1.000 0.000 0.000 0.000
#> GSM601893 3 0.2891 0.7225 0.000 0.176 0.824 0.000 0.000
#> GSM601898 3 0.1331 0.8018 0.008 0.000 0.952 0.040 0.000
#> GSM601903 4 0.5602 0.6286 0.072 0.016 0.252 0.656 0.004
#> GSM601913 3 0.2127 0.7762 0.108 0.000 0.892 0.000 0.000
#> GSM601928 1 0.3790 0.5997 0.724 0.000 0.272 0.004 0.000
#> GSM601933 5 0.2914 0.7206 0.000 0.076 0.052 0.000 0.872
#> GSM601938 5 0.2416 0.7089 0.000 0.012 0.000 0.100 0.888
#> GSM601943 2 0.7688 0.2941 0.004 0.444 0.292 0.064 0.196
#> GSM601948 4 0.4382 0.3926 0.288 0.000 0.024 0.688 0.000
#> GSM601958 3 0.4390 0.1003 0.428 0.000 0.568 0.004 0.000
#> GSM601973 4 0.5268 0.4924 0.024 0.000 0.368 0.588 0.020
#> GSM601978 2 0.4238 0.3898 0.000 0.628 0.000 0.004 0.368
#> GSM601988 5 0.4375 0.1477 0.000 0.000 0.420 0.004 0.576
#> GSM601878 1 0.0162 0.7720 0.996 0.000 0.004 0.000 0.000
#> GSM601908 5 0.1197 0.7382 0.000 0.000 0.000 0.048 0.952
#> GSM601918 4 0.3570 0.6113 0.124 0.000 0.004 0.828 0.044
#> GSM601923 1 0.0162 0.7705 0.996 0.000 0.000 0.004 0.000
#> GSM601953 2 0.0000 0.7491 0.000 1.000 0.000 0.000 0.000
#> GSM601963 3 0.1205 0.8019 0.040 0.000 0.956 0.004 0.000
#> GSM601968 2 0.4503 0.4824 0.000 0.664 0.312 0.024 0.000
#> GSM601983 3 0.2665 0.7906 0.020 0.000 0.900 0.032 0.048
#> GSM601993 5 0.4086 0.5397 0.000 0.000 0.240 0.024 0.736
#> GSM601874 2 0.6652 0.3528 0.004 0.532 0.008 0.232 0.224
#> GSM601884 5 0.0609 0.7414 0.000 0.000 0.000 0.020 0.980
#> GSM601889 3 0.4482 0.5857 0.004 0.252 0.712 0.032 0.000
#> GSM601894 3 0.0451 0.8052 0.000 0.004 0.988 0.008 0.000
#> GSM601899 2 0.1408 0.7398 0.008 0.948 0.000 0.044 0.000
#> GSM601904 3 0.6357 0.1812 0.288 0.000 0.512 0.200 0.000
#> GSM601914 3 0.4959 0.6715 0.108 0.000 0.736 0.144 0.012
#> GSM601929 1 0.2740 0.7656 0.876 0.000 0.096 0.028 0.000
#> GSM601934 3 0.6581 0.0727 0.000 0.228 0.456 0.000 0.316
#> GSM601939 1 0.4026 0.5684 0.736 0.000 0.244 0.020 0.000
#> GSM601944 3 0.4287 0.0800 0.000 0.000 0.540 0.000 0.460
#> GSM601949 1 0.4961 0.6688 0.724 0.004 0.132 0.140 0.000
#> GSM601959 3 0.2932 0.7610 0.004 0.112 0.864 0.020 0.000
#> GSM601974 3 0.2416 0.7759 0.012 0.000 0.888 0.100 0.000
#> GSM601979 2 0.0510 0.7479 0.000 0.984 0.000 0.000 0.016
#> GSM601989 3 0.0000 0.8018 0.000 0.000 1.000 0.000 0.000
#> GSM601879 1 0.0404 0.7749 0.988 0.000 0.012 0.000 0.000
#> GSM601909 2 0.7156 0.2622 0.352 0.480 0.076 0.088 0.004
#> GSM601919 1 0.4138 0.3472 0.616 0.000 0.000 0.384 0.000
#> GSM601924 1 0.1357 0.7822 0.948 0.000 0.048 0.004 0.000
#> GSM601954 2 0.2818 0.7010 0.012 0.856 0.000 0.132 0.000
#> GSM601964 3 0.1399 0.8043 0.000 0.000 0.952 0.028 0.020
#> GSM601969 2 0.6992 0.4791 0.164 0.588 0.136 0.112 0.000
#> GSM601984 3 0.0510 0.8054 0.016 0.000 0.984 0.000 0.000
#> GSM601994 5 0.1732 0.7290 0.000 0.000 0.080 0.000 0.920
#> GSM601875 2 0.0324 0.7496 0.000 0.992 0.004 0.000 0.004
#> GSM601885 2 0.7351 0.2569 0.000 0.488 0.252 0.056 0.204
#> GSM601890 2 0.0000 0.7491 0.000 1.000 0.000 0.000 0.000
#> GSM601895 3 0.0000 0.8018 0.000 0.000 1.000 0.000 0.000
#> GSM601900 3 0.4341 0.4708 0.008 0.364 0.628 0.000 0.000
#> GSM601905 3 0.1364 0.8045 0.000 0.000 0.952 0.036 0.012
#> GSM601915 3 0.2230 0.7593 0.116 0.000 0.884 0.000 0.000
#> GSM601930 1 0.1270 0.7812 0.948 0.000 0.052 0.000 0.000
#> GSM601935 3 0.0404 0.8046 0.000 0.000 0.988 0.012 0.000
#> GSM601940 3 0.1341 0.8012 0.056 0.000 0.944 0.000 0.000
#> GSM601945 2 0.2679 0.7146 0.004 0.884 0.004 0.096 0.012
#> GSM601950 1 0.2921 0.7432 0.856 0.000 0.020 0.124 0.000
#> GSM601960 3 0.1557 0.8026 0.052 0.000 0.940 0.008 0.000
#> GSM601975 3 0.5867 0.6117 0.008 0.092 0.708 0.124 0.068
#> GSM601980 4 0.5165 0.6142 0.000 0.072 0.092 0.752 0.084
#> GSM601990 3 0.1830 0.8019 0.012 0.000 0.932 0.052 0.004
#> GSM601880 1 0.2891 0.7106 0.824 0.000 0.176 0.000 0.000
#> GSM601910 3 0.5381 0.6105 0.012 0.144 0.708 0.132 0.004
#> GSM601920 1 0.6511 0.4359 0.588 0.036 0.236 0.140 0.000
#> GSM601925 1 0.0880 0.7812 0.968 0.000 0.032 0.000 0.000
#> GSM601955 3 0.6657 0.2880 0.012 0.204 0.544 0.236 0.004
#> GSM601965 3 0.3003 0.7083 0.188 0.000 0.812 0.000 0.000
#> GSM601970 1 0.6063 0.5788 0.636 0.024 0.196 0.144 0.000
#> GSM601985 3 0.0404 0.8047 0.012 0.000 0.988 0.000 0.000
#> GSM601995 3 0.4979 0.6919 0.000 0.100 0.764 0.072 0.064
#> GSM601876 3 0.0000 0.8018 0.000 0.000 1.000 0.000 0.000
#> GSM601886 3 0.0162 0.8027 0.004 0.000 0.996 0.000 0.000
#> GSM601891 2 0.0162 0.7490 0.000 0.996 0.000 0.000 0.004
#> GSM601896 3 0.0000 0.8018 0.000 0.000 1.000 0.000 0.000
#> GSM601901 3 0.6228 0.3560 0.004 0.004 0.588 0.196 0.208
#> GSM601906 3 0.3530 0.6657 0.204 0.000 0.784 0.012 0.000
#> GSM601916 3 0.2423 0.7796 0.024 0.000 0.896 0.080 0.000
#> GSM601931 1 0.2806 0.7315 0.844 0.000 0.152 0.004 0.000
#> GSM601936 3 0.0693 0.8060 0.000 0.000 0.980 0.008 0.012
#> GSM601941 4 0.3289 0.6342 0.016 0.004 0.040 0.868 0.072
#> GSM601946 3 0.0162 0.8032 0.004 0.000 0.996 0.000 0.000
#> GSM601951 1 0.5039 0.1659 0.512 0.000 0.032 0.456 0.000
#> GSM601961 2 0.0000 0.7491 0.000 1.000 0.000 0.000 0.000
#> GSM601976 3 0.0451 0.8050 0.004 0.000 0.988 0.008 0.000
#> GSM601981 2 0.5443 0.5699 0.000 0.676 0.024 0.068 0.232
#> GSM601991 3 0.0609 0.8040 0.000 0.000 0.980 0.020 0.000
#> GSM601881 1 0.0404 0.7764 0.988 0.000 0.012 0.000 0.000
#> GSM601911 3 0.0162 0.8027 0.000 0.000 0.996 0.004 0.000
#> GSM601921 4 0.6854 0.6151 0.136 0.056 0.176 0.616 0.016
#> GSM601926 1 0.0703 0.7807 0.976 0.000 0.024 0.000 0.000
#> GSM601956 2 0.2612 0.7082 0.000 0.868 0.000 0.008 0.124
#> GSM601966 5 0.6976 -0.0155 0.000 0.020 0.192 0.344 0.444
#> GSM601971 1 0.2416 0.7498 0.888 0.000 0.012 0.100 0.000
#> GSM601986 3 0.0963 0.8056 0.036 0.000 0.964 0.000 0.000
#> GSM601996 5 0.1205 0.7481 0.000 0.000 0.040 0.004 0.956
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM601872 2 0.1610 0.6515 0.000 0.916 0.000 0.000 0.084 0.000
#> GSM601882 5 0.3485 0.7092 0.000 0.000 0.052 0.024 0.828 0.096
#> GSM601887 2 0.0000 0.6704 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM601892 2 0.4220 0.2065 0.004 0.664 0.304 0.000 0.000 0.028
#> GSM601897 3 0.4307 0.5453 0.000 0.164 0.744 0.012 0.000 0.080
#> GSM601902 3 0.6902 -0.2775 0.296 0.000 0.344 0.312 0.000 0.048
#> GSM601912 3 0.0291 0.7391 0.000 0.000 0.992 0.004 0.000 0.004
#> GSM601927 3 0.2340 0.6640 0.148 0.000 0.852 0.000 0.000 0.000
#> GSM601932 4 0.4603 0.5633 0.004 0.000 0.176 0.704 0.116 0.000
#> GSM601937 3 0.2826 0.7131 0.000 0.000 0.856 0.008 0.024 0.112
#> GSM601942 5 0.6814 0.2011 0.000 0.000 0.108 0.300 0.464 0.128
#> GSM601947 4 0.1616 0.5745 0.048 0.000 0.000 0.932 0.000 0.020
#> GSM601957 2 0.7013 -0.0760 0.000 0.452 0.264 0.108 0.000 0.176
#> GSM601972 4 0.4166 0.5506 0.000 0.000 0.076 0.728 0.196 0.000
#> GSM601977 3 0.4430 0.6392 0.000 0.124 0.772 0.028 0.016 0.060
#> GSM601987 5 0.0692 0.7433 0.000 0.020 0.004 0.000 0.976 0.000
#> GSM601877 1 0.0000 0.7392 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM601907 2 0.3835 0.4482 0.000 0.668 0.000 0.000 0.320 0.012
#> GSM601917 1 0.3385 0.6544 0.796 0.000 0.028 0.172 0.000 0.004
#> GSM601922 1 0.5968 0.1943 0.508 0.000 0.248 0.236 0.000 0.008
#> GSM601952 4 0.3797 0.2138 0.000 0.000 0.420 0.580 0.000 0.000
#> GSM601962 3 0.3314 0.6374 0.000 0.000 0.764 0.000 0.012 0.224
#> GSM601967 3 0.5423 0.0602 0.448 0.016 0.476 0.008 0.000 0.052
#> GSM601982 3 0.4137 0.6255 0.004 0.016 0.748 0.004 0.024 0.204
#> GSM601992 5 0.0146 0.7419 0.000 0.000 0.000 0.000 0.996 0.004
#> GSM601873 5 0.5467 0.0939 0.000 0.408 0.096 0.000 0.488 0.008
#> GSM601883 5 0.1296 0.7458 0.000 0.000 0.004 0.012 0.952 0.032
#> GSM601888 2 0.0000 0.6704 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM601893 3 0.3245 0.6337 0.000 0.172 0.800 0.000 0.000 0.028
#> GSM601898 3 0.3781 0.6114 0.004 0.000 0.756 0.036 0.000 0.204
#> GSM601903 4 0.5121 0.5085 0.064 0.012 0.236 0.668 0.000 0.020
#> GSM601913 3 0.2573 0.7131 0.112 0.000 0.864 0.000 0.000 0.024
#> GSM601928 1 0.3490 0.5311 0.724 0.000 0.268 0.008 0.000 0.000
#> GSM601933 5 0.2144 0.7267 0.000 0.048 0.032 0.004 0.912 0.004
#> GSM601938 5 0.2766 0.7140 0.000 0.012 0.000 0.092 0.868 0.028
#> GSM601943 2 0.7586 -0.0517 0.000 0.440 0.284 0.052 0.132 0.092
#> GSM601948 4 0.4886 0.3775 0.232 0.000 0.012 0.668 0.000 0.088
#> GSM601958 3 0.5813 0.0136 0.308 0.000 0.504 0.004 0.000 0.184
#> GSM601973 4 0.5160 0.3473 0.012 0.000 0.340 0.588 0.008 0.052
#> GSM601978 2 0.3862 0.3732 0.000 0.608 0.000 0.004 0.388 0.000
#> GSM601988 5 0.4715 0.0819 0.000 0.000 0.416 0.000 0.536 0.048
#> GSM601878 1 0.0000 0.7392 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM601908 5 0.1657 0.7374 0.000 0.000 0.000 0.056 0.928 0.016
#> GSM601918 4 0.3136 0.5735 0.096 0.000 0.004 0.852 0.028 0.020
#> GSM601923 1 0.0000 0.7392 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM601953 2 0.0000 0.6704 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM601963 3 0.1196 0.7387 0.040 0.000 0.952 0.000 0.000 0.008
#> GSM601968 2 0.4546 0.1995 0.000 0.644 0.312 0.024 0.000 0.020
#> GSM601983 3 0.3321 0.6699 0.016 0.000 0.796 0.000 0.008 0.180
#> GSM601993 5 0.4213 0.5660 0.000 0.000 0.160 0.004 0.744 0.092
#> GSM601874 2 0.6927 0.2525 0.000 0.520 0.008 0.160 0.196 0.116
#> GSM601884 5 0.2165 0.7271 0.000 0.000 0.000 0.008 0.884 0.108
#> GSM601889 3 0.5349 0.4192 0.000 0.204 0.652 0.032 0.000 0.112
#> GSM601894 3 0.2773 0.6806 0.000 0.004 0.836 0.008 0.000 0.152
#> GSM601899 2 0.2573 0.6057 0.000 0.864 0.000 0.024 0.000 0.112
#> GSM601904 3 0.5992 0.1837 0.260 0.000 0.516 0.212 0.000 0.012
#> GSM601914 3 0.5835 0.3747 0.052 0.000 0.556 0.080 0.000 0.312
#> GSM601929 1 0.3319 0.7094 0.836 0.000 0.096 0.016 0.000 0.052
#> GSM601934 3 0.6271 -0.1104 0.000 0.216 0.420 0.004 0.352 0.008
#> GSM601939 1 0.4082 0.4991 0.728 0.000 0.228 0.012 0.000 0.032
#> GSM601944 3 0.4211 0.1150 0.000 0.000 0.532 0.004 0.456 0.008
#> GSM601949 1 0.5882 0.5546 0.644 0.004 0.128 0.136 0.000 0.088
#> GSM601959 3 0.4589 0.5773 0.000 0.088 0.720 0.016 0.000 0.176
#> GSM601974 3 0.2466 0.7146 0.008 0.000 0.872 0.112 0.000 0.008
#> GSM601979 2 0.0508 0.6702 0.000 0.984 0.000 0.004 0.012 0.000
#> GSM601989 3 0.0000 0.7367 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM601879 1 0.0000 0.7392 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM601909 6 0.7373 -0.0375 0.212 0.340 0.040 0.040 0.000 0.368
#> GSM601919 1 0.4310 0.2989 0.580 0.000 0.000 0.396 0.000 0.024
#> GSM601924 1 0.0790 0.7443 0.968 0.000 0.032 0.000 0.000 0.000
#> GSM601954 2 0.3405 0.5705 0.000 0.812 0.000 0.112 0.000 0.076
#> GSM601964 3 0.1528 0.7394 0.000 0.000 0.936 0.016 0.000 0.048
#> GSM601969 2 0.7680 0.0169 0.152 0.504 0.124 0.108 0.000 0.112
#> GSM601984 3 0.0547 0.7413 0.020 0.000 0.980 0.000 0.000 0.000
#> GSM601994 5 0.1141 0.7384 0.000 0.000 0.052 0.000 0.948 0.000
#> GSM601875 2 0.0291 0.6706 0.000 0.992 0.004 0.000 0.004 0.000
#> GSM601885 2 0.7534 -0.0342 0.000 0.452 0.236 0.076 0.192 0.044
#> GSM601890 2 0.0000 0.6704 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM601895 3 0.0790 0.7379 0.000 0.000 0.968 0.000 0.000 0.032
#> GSM601900 3 0.5863 0.1051 0.004 0.328 0.484 0.000 0.000 0.184
#> GSM601905 3 0.1562 0.7422 0.000 0.000 0.940 0.032 0.004 0.024
#> GSM601915 3 0.3978 0.6026 0.064 0.000 0.744 0.000 0.000 0.192
#> GSM601930 1 0.0937 0.7432 0.960 0.000 0.040 0.000 0.000 0.000
#> GSM601935 3 0.0935 0.7410 0.000 0.000 0.964 0.004 0.000 0.032
#> GSM601940 3 0.1204 0.7388 0.056 0.000 0.944 0.000 0.000 0.000
#> GSM601945 2 0.2683 0.6238 0.000 0.868 0.004 0.104 0.020 0.004
#> GSM601950 1 0.4157 0.6591 0.772 0.000 0.020 0.124 0.000 0.084
#> GSM601960 3 0.3884 0.6445 0.052 0.000 0.760 0.004 0.000 0.184
#> GSM601975 3 0.5226 0.5510 0.000 0.072 0.696 0.140 0.092 0.000
#> GSM601980 4 0.5162 0.4385 0.000 0.016 0.032 0.620 0.024 0.308
#> GSM601990 3 0.3410 0.6634 0.008 0.000 0.768 0.008 0.000 0.216
#> GSM601880 1 0.2527 0.6631 0.832 0.000 0.168 0.000 0.000 0.000
#> GSM601910 3 0.6482 0.2481 0.000 0.092 0.528 0.120 0.000 0.260
#> GSM601920 1 0.6233 0.3772 0.568 0.036 0.224 0.164 0.004 0.004
#> GSM601925 1 0.0547 0.7441 0.980 0.000 0.020 0.000 0.000 0.000
#> GSM601955 6 0.6267 0.0644 0.008 0.084 0.332 0.060 0.000 0.516
#> GSM601965 3 0.2762 0.6442 0.196 0.000 0.804 0.000 0.000 0.000
#> GSM601970 1 0.7221 0.1484 0.416 0.008 0.136 0.120 0.000 0.320
#> GSM601985 3 0.0363 0.7401 0.012 0.000 0.988 0.000 0.000 0.000
#> GSM601995 3 0.4777 0.5844 0.000 0.036 0.712 0.024 0.020 0.208
#> GSM601876 3 0.0000 0.7367 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM601886 3 0.0146 0.7380 0.000 0.000 0.996 0.004 0.000 0.000
#> GSM601891 2 0.0363 0.6692 0.000 0.988 0.000 0.000 0.000 0.012
#> GSM601896 3 0.0000 0.7367 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM601901 3 0.5709 0.3189 0.000 0.004 0.568 0.204 0.220 0.004
#> GSM601906 3 0.3328 0.6151 0.192 0.000 0.788 0.012 0.000 0.008
#> GSM601916 3 0.2262 0.7276 0.016 0.000 0.896 0.080 0.000 0.008
#> GSM601931 1 0.2260 0.6901 0.860 0.000 0.140 0.000 0.000 0.000
#> GSM601936 3 0.1086 0.7433 0.000 0.000 0.964 0.012 0.012 0.012
#> GSM601941 4 0.2100 0.6004 0.004 0.000 0.024 0.916 0.048 0.008
#> GSM601946 3 0.0146 0.7381 0.004 0.000 0.996 0.000 0.000 0.000
#> GSM601951 1 0.5837 0.1584 0.456 0.000 0.032 0.424 0.000 0.088
#> GSM601961 2 0.0000 0.6704 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM601976 3 0.0260 0.7391 0.000 0.000 0.992 0.008 0.000 0.000
#> GSM601981 2 0.4891 0.4540 0.000 0.648 0.020 0.044 0.284 0.004
#> GSM601991 3 0.1556 0.7284 0.000 0.000 0.920 0.000 0.000 0.080
#> GSM601881 1 0.0146 0.7409 0.996 0.000 0.004 0.000 0.000 0.000
#> GSM601911 3 0.0146 0.7378 0.000 0.000 0.996 0.000 0.000 0.004
#> GSM601921 4 0.5560 0.4734 0.120 0.048 0.168 0.660 0.004 0.000
#> GSM601926 1 0.0547 0.7446 0.980 0.000 0.020 0.000 0.000 0.000
#> GSM601956 2 0.2793 0.6244 0.000 0.856 0.000 0.004 0.112 0.028
#> GSM601966 5 0.7530 -0.0465 0.000 0.024 0.168 0.344 0.368 0.096
#> GSM601971 1 0.3677 0.6821 0.804 0.000 0.012 0.120 0.000 0.064
#> GSM601986 3 0.0865 0.7417 0.036 0.000 0.964 0.000 0.000 0.000
#> GSM601996 5 0.0692 0.7475 0.000 0.000 0.020 0.000 0.976 0.004
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)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
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)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
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)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, 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.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")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n time(p) gender(p) k
#> MAD:pam 114 0.475 0.7129 2
#> MAD:pam 108 0.877 0.0108 3
#> MAD:pam 102 0.553 0.0831 4
#> MAD:pam 95 0.457 0.0505 5
#> MAD:pam 87 0.377 0.0455 6
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.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["MAD", "mclust"]
# you can also extract it by
# res = res_list["MAD:mclust"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 21512 rows and 125 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'mclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 5.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
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, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.423 0.722 0.588 0.3967 0.498 0.498
#> 3 3 0.347 0.649 0.762 0.4920 0.817 0.663
#> 4 4 0.510 0.509 0.689 0.1417 0.839 0.613
#> 5 5 0.673 0.725 0.839 0.1329 0.865 0.563
#> 6 6 0.818 0.766 0.886 0.0699 0.935 0.716
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 5
Following shows 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 class
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
#> GSM601872 2 0.9996 0.844 0.488 0.512
#> GSM601882 2 0.9970 0.851 0.468 0.532
#> GSM601887 1 0.1843 0.768 0.972 0.028
#> GSM601892 1 0.0376 0.795 0.996 0.004
#> GSM601897 1 0.1414 0.779 0.980 0.020
#> GSM601902 2 0.3114 0.444 0.056 0.944
#> GSM601912 1 0.0376 0.795 0.996 0.004
#> GSM601927 1 0.9996 0.417 0.512 0.488
#> GSM601932 2 0.9754 0.815 0.408 0.592
#> GSM601937 2 0.9996 0.844 0.488 0.512
#> GSM601942 2 0.9996 0.844 0.488 0.512
#> GSM601947 2 0.9850 0.832 0.428 0.572
#> GSM601957 1 0.0000 0.796 1.000 0.000
#> GSM601972 2 0.9922 0.845 0.448 0.552
#> GSM601977 2 0.9983 0.850 0.476 0.524
#> GSM601987 2 0.9983 0.850 0.476 0.524
#> GSM601877 1 0.9970 0.427 0.532 0.468
#> GSM601907 2 0.9988 0.849 0.480 0.520
#> GSM601917 2 0.3114 0.444 0.056 0.944
#> GSM601922 2 0.3114 0.444 0.056 0.944
#> GSM601952 2 0.9963 0.850 0.464 0.536
#> GSM601962 1 0.1184 0.784 0.984 0.016
#> GSM601967 1 0.0376 0.796 0.996 0.004
#> GSM601982 2 0.9996 0.844 0.488 0.512
#> GSM601992 2 0.9944 0.848 0.456 0.544
#> GSM601873 2 0.9996 0.844 0.488 0.512
#> GSM601883 2 0.9954 0.849 0.460 0.540
#> GSM601888 1 0.4431 0.646 0.908 0.092
#> GSM601893 1 0.0376 0.795 0.996 0.004
#> GSM601898 1 0.0000 0.796 1.000 0.000
#> GSM601903 2 0.3114 0.444 0.056 0.944
#> GSM601913 1 0.1184 0.794 0.984 0.016
#> GSM601928 1 0.9993 0.419 0.516 0.484
#> GSM601933 2 0.9983 0.850 0.476 0.524
#> GSM601938 2 0.9983 0.850 0.476 0.524
#> GSM601943 2 0.9996 0.844 0.488 0.512
#> GSM601948 1 0.2043 0.789 0.968 0.032
#> GSM601958 1 0.0000 0.796 1.000 0.000
#> GSM601973 2 0.8443 0.660 0.272 0.728
#> GSM601978 2 0.9996 0.844 0.488 0.512
#> GSM601988 2 0.9996 0.844 0.488 0.512
#> GSM601878 1 0.4298 0.748 0.912 0.088
#> GSM601908 2 0.9963 0.850 0.464 0.536
#> GSM601918 2 0.9754 0.814 0.408 0.592
#> GSM601923 1 0.9983 0.425 0.524 0.476
#> GSM601953 2 0.9996 0.844 0.488 0.512
#> GSM601963 1 0.0376 0.795 0.996 0.004
#> GSM601968 1 0.0376 0.795 0.996 0.004
#> GSM601983 1 0.0376 0.795 0.996 0.004
#> GSM601993 2 0.9988 0.848 0.480 0.520
#> GSM601874 2 0.9996 0.844 0.488 0.512
#> GSM601884 2 0.9996 0.844 0.488 0.512
#> GSM601889 1 0.0000 0.796 1.000 0.000
#> GSM601894 1 0.0000 0.796 1.000 0.000
#> GSM601899 1 0.1414 0.778 0.980 0.020
#> GSM601904 2 0.6148 0.474 0.152 0.848
#> GSM601914 1 0.0376 0.795 0.996 0.004
#> GSM601929 1 0.3584 0.766 0.932 0.068
#> GSM601934 2 0.9993 0.846 0.484 0.516
#> GSM601939 1 0.2236 0.789 0.964 0.036
#> GSM601944 2 0.9970 0.851 0.468 0.532
#> GSM601949 1 0.1843 0.789 0.972 0.028
#> GSM601959 1 0.0000 0.796 1.000 0.000
#> GSM601974 1 0.9933 -0.736 0.548 0.452
#> GSM601979 2 0.9970 0.851 0.468 0.532
#> GSM601989 1 0.0000 0.796 1.000 0.000
#> GSM601879 1 0.5059 0.727 0.888 0.112
#> GSM601909 1 0.0376 0.795 0.996 0.004
#> GSM601919 2 0.9815 0.825 0.420 0.580
#> GSM601924 1 0.3274 0.772 0.940 0.060
#> GSM601954 2 0.9963 0.850 0.464 0.536
#> GSM601964 1 0.0376 0.795 0.996 0.004
#> GSM601969 1 0.1843 0.790 0.972 0.028
#> GSM601984 1 0.2236 0.787 0.964 0.036
#> GSM601994 2 0.9922 0.845 0.448 0.552
#> GSM601875 2 0.9977 0.850 0.472 0.528
#> GSM601885 2 0.9963 0.850 0.464 0.536
#> GSM601890 1 0.2603 0.742 0.956 0.044
#> GSM601895 1 0.0376 0.795 0.996 0.004
#> GSM601900 1 0.0000 0.796 1.000 0.000
#> GSM601905 2 0.5519 0.512 0.128 0.872
#> GSM601915 1 0.0000 0.796 1.000 0.000
#> GSM601930 1 0.9996 0.417 0.512 0.488
#> GSM601935 1 0.5294 0.578 0.880 0.120
#> GSM601940 1 0.2236 0.789 0.964 0.036
#> GSM601945 2 0.9988 0.848 0.480 0.520
#> GSM601950 1 0.2236 0.789 0.964 0.036
#> GSM601960 1 0.0376 0.795 0.996 0.004
#> GSM601975 2 0.9552 0.780 0.376 0.624
#> GSM601980 2 0.9996 0.844 0.488 0.512
#> GSM601990 1 0.0376 0.795 0.996 0.004
#> GSM601880 1 0.9996 0.417 0.512 0.488
#> GSM601910 1 0.0376 0.795 0.996 0.004
#> GSM601920 2 0.3114 0.444 0.056 0.944
#> GSM601925 1 0.9996 0.417 0.512 0.488
#> GSM601955 1 0.9970 -0.777 0.532 0.468
#> GSM601965 1 0.2043 0.787 0.968 0.032
#> GSM601970 1 0.0000 0.796 1.000 0.000
#> GSM601985 1 0.2236 0.789 0.964 0.036
#> GSM601995 2 0.9998 0.840 0.492 0.508
#> GSM601876 1 0.2236 0.789 0.964 0.036
#> GSM601886 2 0.9954 0.849 0.460 0.540
#> GSM601891 1 0.4690 0.627 0.900 0.100
#> GSM601896 1 0.2236 0.789 0.964 0.036
#> GSM601901 2 0.9954 0.849 0.460 0.540
#> GSM601906 1 0.9909 0.432 0.556 0.444
#> GSM601916 2 0.9775 0.819 0.412 0.588
#> GSM601931 1 0.9983 0.426 0.524 0.476
#> GSM601936 2 0.9993 0.846 0.484 0.516
#> GSM601941 2 0.5178 0.500 0.116 0.884
#> GSM601946 1 0.2603 0.784 0.956 0.044
#> GSM601951 1 0.4815 0.735 0.896 0.104
#> GSM601961 2 0.9963 0.850 0.464 0.536
#> GSM601976 2 0.9833 0.828 0.424 0.576
#> GSM601981 2 0.9963 0.850 0.464 0.536
#> GSM601991 1 0.0376 0.795 0.996 0.004
#> GSM601881 1 0.9977 0.428 0.528 0.472
#> GSM601911 2 0.9922 0.845 0.448 0.552
#> GSM601921 2 0.3114 0.444 0.056 0.944
#> GSM601926 1 0.9996 0.417 0.512 0.488
#> GSM601956 2 0.9996 0.844 0.488 0.512
#> GSM601966 2 0.9850 0.832 0.428 0.572
#> GSM601971 1 0.2043 0.790 0.968 0.032
#> GSM601986 1 0.4690 0.697 0.900 0.100
#> GSM601996 2 0.9922 0.845 0.448 0.552
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM601872 2 0.2866 0.7333 0.076 0.916 0.008
#> GSM601882 2 0.6174 0.7336 0.064 0.768 0.168
#> GSM601887 1 0.7770 0.7274 0.628 0.292 0.080
#> GSM601892 1 0.7306 0.7607 0.684 0.236 0.080
#> GSM601897 1 0.5285 0.7369 0.752 0.244 0.004
#> GSM601902 2 0.7023 0.5442 0.032 0.624 0.344
#> GSM601912 1 0.5541 0.7291 0.740 0.252 0.008
#> GSM601927 3 0.0747 0.8422 0.016 0.000 0.984
#> GSM601932 2 0.6141 0.6591 0.032 0.736 0.232
#> GSM601937 2 0.6264 0.6053 0.380 0.616 0.004
#> GSM601942 2 0.3043 0.7353 0.084 0.908 0.008
#> GSM601947 2 0.4887 0.6840 0.000 0.772 0.228
#> GSM601957 1 0.7804 0.7584 0.664 0.216 0.120
#> GSM601972 2 0.5167 0.7102 0.016 0.792 0.192
#> GSM601977 2 0.2200 0.7428 0.056 0.940 0.004
#> GSM601987 2 0.2384 0.7418 0.056 0.936 0.008
#> GSM601877 3 0.1031 0.8389 0.024 0.000 0.976
#> GSM601907 2 0.2384 0.7418 0.056 0.936 0.008
#> GSM601917 2 0.7065 0.5329 0.032 0.616 0.352
#> GSM601922 2 0.7085 0.5276 0.032 0.612 0.356
#> GSM601952 2 0.2384 0.7464 0.056 0.936 0.008
#> GSM601962 1 0.6330 -0.1409 0.600 0.396 0.004
#> GSM601967 1 0.8153 0.7478 0.640 0.216 0.144
#> GSM601982 2 0.3500 0.7135 0.116 0.880 0.004
#> GSM601992 2 0.6349 0.7314 0.080 0.764 0.156
#> GSM601873 2 0.2590 0.7403 0.072 0.924 0.004
#> GSM601883 2 0.4357 0.7528 0.052 0.868 0.080
#> GSM601888 2 0.8228 -0.0987 0.364 0.552 0.084
#> GSM601893 1 0.7306 0.7607 0.684 0.236 0.080
#> GSM601898 1 0.6895 0.7650 0.716 0.212 0.072
#> GSM601903 2 0.7001 0.5494 0.032 0.628 0.340
#> GSM601913 1 0.4865 0.6729 0.832 0.032 0.136
#> GSM601928 3 0.0747 0.8422 0.016 0.000 0.984
#> GSM601933 2 0.3183 0.7522 0.076 0.908 0.016
#> GSM601938 2 0.6283 0.7287 0.064 0.760 0.176
#> GSM601943 2 0.2955 0.7316 0.080 0.912 0.008
#> GSM601948 1 0.7379 0.4539 0.584 0.040 0.376
#> GSM601958 1 0.7330 0.7653 0.692 0.216 0.092
#> GSM601973 2 0.6264 0.6503 0.032 0.724 0.244
#> GSM601978 2 0.2584 0.7389 0.064 0.928 0.008
#> GSM601988 2 0.6169 0.6250 0.360 0.636 0.004
#> GSM601878 3 0.4261 0.7273 0.140 0.012 0.848
#> GSM601908 2 0.2384 0.7418 0.056 0.936 0.008
#> GSM601918 2 0.5061 0.6904 0.008 0.784 0.208
#> GSM601923 3 0.0892 0.8407 0.020 0.000 0.980
#> GSM601953 2 0.2584 0.7389 0.064 0.928 0.008
#> GSM601963 1 0.1525 0.6646 0.964 0.032 0.004
#> GSM601968 1 0.7444 0.7634 0.684 0.220 0.096
#> GSM601983 1 0.1411 0.6638 0.964 0.036 0.000
#> GSM601993 2 0.5873 0.6643 0.312 0.684 0.004
#> GSM601874 2 0.2584 0.7389 0.064 0.928 0.008
#> GSM601884 2 0.2261 0.7426 0.068 0.932 0.000
#> GSM601889 1 0.7865 0.7564 0.660 0.216 0.124
#> GSM601894 1 0.7741 0.7590 0.668 0.216 0.116
#> GSM601899 1 0.7775 0.7192 0.620 0.304 0.076
#> GSM601904 2 0.7141 0.5104 0.032 0.600 0.368
#> GSM601914 1 0.1289 0.6626 0.968 0.032 0.000
#> GSM601929 3 0.9484 0.2235 0.264 0.240 0.496
#> GSM601934 2 0.2400 0.7460 0.064 0.932 0.004
#> GSM601939 1 0.6937 0.3973 0.576 0.020 0.404
#> GSM601944 2 0.4346 0.7330 0.184 0.816 0.000
#> GSM601949 1 0.8834 0.7089 0.580 0.224 0.196
#> GSM601959 1 0.7984 0.7534 0.652 0.216 0.132
#> GSM601974 2 0.9268 0.5265 0.336 0.492 0.172
#> GSM601979 2 0.2384 0.7418 0.056 0.936 0.008
#> GSM601989 1 0.7543 0.7632 0.680 0.216 0.104
#> GSM601879 3 0.3983 0.7318 0.144 0.004 0.852
#> GSM601909 1 0.6897 0.7627 0.712 0.220 0.068
#> GSM601919 2 0.5158 0.6841 0.004 0.764 0.232
#> GSM601924 3 0.5763 0.5510 0.244 0.016 0.740
#> GSM601954 2 0.2879 0.7449 0.052 0.924 0.024
#> GSM601964 1 0.1289 0.6626 0.968 0.032 0.000
#> GSM601969 1 0.8711 0.7207 0.592 0.224 0.184
#> GSM601984 2 0.9877 0.2845 0.296 0.412 0.292
#> GSM601994 2 0.6644 0.7212 0.092 0.748 0.160
#> GSM601875 2 0.2384 0.7418 0.056 0.936 0.008
#> GSM601885 2 0.2982 0.7523 0.056 0.920 0.024
#> GSM601890 1 0.7825 0.7205 0.620 0.300 0.080
#> GSM601895 1 0.3583 0.6964 0.900 0.056 0.044
#> GSM601900 1 0.7568 0.7639 0.680 0.212 0.108
#> GSM601905 2 0.6955 0.5615 0.032 0.636 0.332
#> GSM601915 1 0.1877 0.6663 0.956 0.032 0.012
#> GSM601930 3 0.0747 0.8422 0.016 0.000 0.984
#> GSM601935 1 0.6659 -0.3372 0.532 0.460 0.008
#> GSM601940 1 0.8132 0.5899 0.600 0.096 0.304
#> GSM601945 2 0.2384 0.7418 0.056 0.936 0.008
#> GSM601950 1 0.8767 0.7084 0.588 0.208 0.204
#> GSM601960 1 0.1289 0.6626 0.968 0.032 0.000
#> GSM601975 2 0.5894 0.6695 0.028 0.752 0.220
#> GSM601980 2 0.6247 0.6100 0.376 0.620 0.004
#> GSM601990 1 0.1289 0.6626 0.968 0.032 0.000
#> GSM601880 3 0.0747 0.8422 0.016 0.000 0.984
#> GSM601910 1 0.5360 0.7491 0.768 0.220 0.012
#> GSM601920 2 0.7044 0.5386 0.032 0.620 0.348
#> GSM601925 3 0.0747 0.8422 0.016 0.000 0.984
#> GSM601955 2 0.6467 0.5991 0.388 0.604 0.008
#> GSM601965 2 0.9606 0.3015 0.208 0.440 0.352
#> GSM601970 1 0.7021 0.7652 0.708 0.216 0.076
#> GSM601985 1 0.6303 0.6019 0.720 0.032 0.248
#> GSM601995 2 0.6298 0.5975 0.388 0.608 0.004
#> GSM601876 1 0.7156 0.4063 0.572 0.028 0.400
#> GSM601886 2 0.7966 0.6947 0.220 0.652 0.128
#> GSM601891 1 0.8085 0.6808 0.584 0.332 0.084
#> GSM601896 1 0.7207 0.4379 0.584 0.032 0.384
#> GSM601901 2 0.6000 0.7169 0.040 0.760 0.200
#> GSM601906 3 0.7292 -0.2686 0.028 0.472 0.500
#> GSM601916 2 0.6224 0.6531 0.032 0.728 0.240
#> GSM601931 3 0.0747 0.8422 0.016 0.000 0.984
#> GSM601936 2 0.5845 0.6673 0.308 0.688 0.004
#> GSM601941 2 0.6379 0.6404 0.032 0.712 0.256
#> GSM601946 1 0.6955 0.1768 0.492 0.016 0.492
#> GSM601951 3 0.6247 0.6200 0.212 0.044 0.744
#> GSM601961 2 0.2486 0.7407 0.060 0.932 0.008
#> GSM601976 2 0.5244 0.6810 0.004 0.756 0.240
#> GSM601981 2 0.2384 0.7418 0.056 0.936 0.008
#> GSM601991 1 0.4002 0.5584 0.840 0.160 0.000
#> GSM601881 3 0.0747 0.8422 0.016 0.000 0.984
#> GSM601911 2 0.7383 0.6758 0.084 0.680 0.236
#> GSM601921 2 0.7044 0.5386 0.032 0.620 0.348
#> GSM601926 3 0.0747 0.8422 0.016 0.000 0.984
#> GSM601956 2 0.2584 0.7389 0.064 0.928 0.008
#> GSM601966 2 0.5585 0.6821 0.024 0.772 0.204
#> GSM601971 1 0.8804 0.6985 0.584 0.204 0.212
#> GSM601986 2 0.9282 0.3333 0.164 0.468 0.368
#> GSM601996 2 0.6158 0.7142 0.052 0.760 0.188
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM601872 2 0.2830 0.5321 0.000 0.900 0.040 0.060
#> GSM601882 2 0.5378 0.1916 0.016 0.632 0.004 0.348
#> GSM601887 3 0.3551 0.7146 0.008 0.120 0.856 0.016
#> GSM601892 3 0.3507 0.7466 0.052 0.052 0.880 0.016
#> GSM601897 3 0.4268 0.5865 0.004 0.232 0.760 0.004
#> GSM601902 4 0.6016 0.6196 0.068 0.300 0.000 0.632
#> GSM601912 3 0.4504 0.5963 0.004 0.204 0.772 0.020
#> GSM601927 1 0.0336 0.8416 0.992 0.000 0.008 0.000
#> GSM601932 4 0.6055 0.5993 0.052 0.372 0.000 0.576
#> GSM601937 2 0.7524 -0.0317 0.000 0.408 0.184 0.408
#> GSM601942 2 0.5113 0.4272 0.000 0.712 0.036 0.252
#> GSM601947 4 0.6192 0.5238 0.052 0.436 0.000 0.512
#> GSM601957 3 0.3088 0.7458 0.128 0.008 0.864 0.000
#> GSM601972 2 0.6302 -0.3723 0.048 0.504 0.004 0.444
#> GSM601977 2 0.4088 0.4651 0.000 0.764 0.004 0.232
#> GSM601987 2 0.2868 0.5324 0.000 0.864 0.000 0.136
#> GSM601877 1 0.0336 0.8416 0.992 0.000 0.008 0.000
#> GSM601907 2 0.0524 0.5220 0.000 0.988 0.008 0.004
#> GSM601917 4 0.6016 0.6196 0.068 0.300 0.000 0.632
#> GSM601922 4 0.6016 0.6196 0.068 0.300 0.000 0.632
#> GSM601952 2 0.4889 0.1884 0.000 0.636 0.004 0.360
#> GSM601962 3 0.7558 -0.1616 0.000 0.224 0.480 0.296
#> GSM601967 3 0.3448 0.7221 0.168 0.004 0.828 0.000
#> GSM601982 2 0.6412 0.2506 0.004 0.616 0.084 0.296
#> GSM601992 2 0.5666 -0.2806 0.016 0.520 0.004 0.460
#> GSM601873 2 0.4175 0.4950 0.000 0.784 0.016 0.200
#> GSM601883 2 0.5045 0.3402 0.012 0.680 0.004 0.304
#> GSM601888 3 0.4820 0.5533 0.000 0.296 0.692 0.012
#> GSM601893 3 0.3527 0.7296 0.024 0.088 0.872 0.016
#> GSM601898 3 0.5188 0.7500 0.096 0.000 0.756 0.148
#> GSM601903 4 0.6016 0.6196 0.068 0.300 0.000 0.632
#> GSM601913 3 0.7247 0.6808 0.176 0.008 0.576 0.240
#> GSM601928 1 0.0336 0.8416 0.992 0.000 0.008 0.000
#> GSM601933 2 0.4857 0.3056 0.000 0.668 0.008 0.324
#> GSM601938 2 0.5560 0.1914 0.024 0.628 0.004 0.344
#> GSM601943 2 0.3463 0.5325 0.000 0.864 0.040 0.096
#> GSM601948 3 0.6077 0.5924 0.300 0.036 0.644 0.020
#> GSM601958 3 0.3653 0.7519 0.128 0.000 0.844 0.028
#> GSM601973 4 0.6013 0.6222 0.064 0.312 0.000 0.624
#> GSM601978 2 0.0524 0.5220 0.000 0.988 0.008 0.004
#> GSM601988 4 0.7476 -0.0230 0.000 0.408 0.176 0.416
#> GSM601878 1 0.1211 0.8221 0.960 0.000 0.040 0.000
#> GSM601908 2 0.1174 0.5320 0.000 0.968 0.012 0.020
#> GSM601918 4 0.6187 0.5300 0.052 0.432 0.000 0.516
#> GSM601923 1 0.0336 0.8416 0.992 0.000 0.008 0.000
#> GSM601953 2 0.0524 0.5220 0.000 0.988 0.008 0.004
#> GSM601963 3 0.4475 0.7033 0.004 0.008 0.748 0.240
#> GSM601968 3 0.2847 0.7544 0.084 0.016 0.896 0.004
#> GSM601983 3 0.4295 0.7011 0.000 0.008 0.752 0.240
#> GSM601993 4 0.6213 0.2603 0.000 0.464 0.052 0.484
#> GSM601874 2 0.0524 0.5220 0.000 0.988 0.008 0.004
#> GSM601884 2 0.2760 0.5351 0.000 0.872 0.000 0.128
#> GSM601889 3 0.3224 0.7522 0.120 0.000 0.864 0.016
#> GSM601894 3 0.2611 0.7556 0.096 0.000 0.896 0.008
#> GSM601899 3 0.3551 0.7158 0.008 0.120 0.856 0.016
#> GSM601904 4 0.6217 0.6077 0.084 0.292 0.000 0.624
#> GSM601914 3 0.4228 0.7043 0.000 0.008 0.760 0.232
#> GSM601929 1 0.8033 0.3291 0.572 0.076 0.224 0.128
#> GSM601934 2 0.4647 0.3848 0.000 0.704 0.008 0.288
#> GSM601939 3 0.7566 0.4973 0.356 0.016 0.496 0.132
#> GSM601944 2 0.5024 0.2307 0.000 0.632 0.008 0.360
#> GSM601949 3 0.5527 0.6471 0.256 0.024 0.700 0.020
#> GSM601959 3 0.2921 0.7394 0.140 0.000 0.860 0.000
#> GSM601974 2 0.8276 -0.1191 0.028 0.452 0.204 0.316
#> GSM601979 2 0.0336 0.5241 0.000 0.992 0.008 0.000
#> GSM601989 3 0.4488 0.7637 0.096 0.008 0.820 0.076
#> GSM601879 1 0.1389 0.8159 0.952 0.000 0.048 0.000
#> GSM601909 3 0.1716 0.7551 0.064 0.000 0.936 0.000
#> GSM601919 4 0.6187 0.5300 0.052 0.432 0.000 0.516
#> GSM601924 1 0.3808 0.6674 0.824 0.012 0.160 0.004
#> GSM601954 2 0.5798 -0.2946 0.012 0.524 0.012 0.452
#> GSM601964 3 0.4328 0.7000 0.000 0.008 0.748 0.244
#> GSM601969 3 0.4806 0.6952 0.200 0.028 0.764 0.008
#> GSM601984 4 0.9943 0.1148 0.200 0.256 0.260 0.284
#> GSM601994 4 0.5864 0.3988 0.024 0.480 0.004 0.492
#> GSM601875 2 0.0672 0.5275 0.000 0.984 0.008 0.008
#> GSM601885 2 0.4584 0.3652 0.000 0.696 0.004 0.300
#> GSM601890 3 0.3663 0.7086 0.008 0.128 0.848 0.016
#> GSM601895 3 0.5340 0.7250 0.044 0.008 0.728 0.220
#> GSM601900 3 0.5628 0.7408 0.132 0.000 0.724 0.144
#> GSM601905 4 0.6016 0.6196 0.068 0.300 0.000 0.632
#> GSM601915 3 0.5782 0.7300 0.068 0.008 0.704 0.220
#> GSM601930 1 0.0336 0.8416 0.992 0.000 0.008 0.000
#> GSM601935 4 0.8106 0.0532 0.008 0.336 0.264 0.392
#> GSM601940 3 0.6687 0.6260 0.280 0.016 0.620 0.084
#> GSM601945 2 0.0524 0.5220 0.000 0.988 0.008 0.004
#> GSM601950 3 0.5437 0.6573 0.244 0.020 0.712 0.024
#> GSM601960 3 0.4194 0.7051 0.000 0.008 0.764 0.228
#> GSM601975 4 0.6023 0.6226 0.060 0.328 0.000 0.612
#> GSM601980 2 0.7449 0.0341 0.000 0.464 0.180 0.356
#> GSM601990 3 0.4391 0.6959 0.000 0.008 0.740 0.252
#> GSM601880 1 0.0336 0.8416 0.992 0.000 0.008 0.000
#> GSM601910 3 0.0967 0.7445 0.004 0.016 0.976 0.004
#> GSM601920 4 0.6058 0.6158 0.072 0.296 0.000 0.632
#> GSM601925 1 0.0336 0.8416 0.992 0.000 0.008 0.000
#> GSM601955 2 0.7569 0.0138 0.000 0.436 0.196 0.368
#> GSM601965 4 0.9967 0.1013 0.212 0.260 0.252 0.276
#> GSM601970 3 0.2345 0.7530 0.100 0.000 0.900 0.000
#> GSM601985 3 0.7834 0.5910 0.268 0.016 0.512 0.204
#> GSM601995 4 0.7546 -0.0179 0.000 0.400 0.188 0.412
#> GSM601876 3 0.8115 0.4953 0.340 0.060 0.492 0.108
#> GSM601886 4 0.6190 0.4025 0.016 0.448 0.024 0.512
#> GSM601891 3 0.4458 0.6552 0.008 0.196 0.780 0.016
#> GSM601896 3 0.7643 0.5173 0.344 0.048 0.524 0.084
#> GSM601901 4 0.6026 0.4343 0.032 0.468 0.004 0.496
#> GSM601906 1 0.8791 -0.5298 0.344 0.336 0.040 0.280
#> GSM601916 4 0.5847 0.6223 0.052 0.320 0.000 0.628
#> GSM601931 1 0.0336 0.8416 0.992 0.000 0.008 0.000
#> GSM601936 4 0.6615 0.1654 0.000 0.404 0.084 0.512
#> GSM601941 4 0.6302 0.6025 0.068 0.368 0.000 0.564
#> GSM601946 1 0.7226 -0.1583 0.512 0.016 0.376 0.096
#> GSM601951 1 0.3455 0.7164 0.852 0.012 0.132 0.004
#> GSM601961 2 0.5392 0.4502 0.000 0.724 0.072 0.204
#> GSM601976 4 0.5954 0.6166 0.052 0.344 0.000 0.604
#> GSM601981 2 0.2542 0.5359 0.000 0.904 0.012 0.084
#> GSM601991 3 0.5105 0.6658 0.000 0.028 0.696 0.276
#> GSM601881 1 0.0336 0.8416 0.992 0.000 0.008 0.000
#> GSM601911 4 0.6022 0.4542 0.032 0.460 0.004 0.504
#> GSM601921 4 0.6016 0.6196 0.068 0.300 0.000 0.632
#> GSM601926 1 0.0336 0.8416 0.992 0.000 0.008 0.000
#> GSM601956 2 0.0524 0.5220 0.000 0.988 0.008 0.004
#> GSM601966 4 0.5843 0.4766 0.024 0.448 0.004 0.524
#> GSM601971 3 0.4700 0.6942 0.208 0.016 0.764 0.012
#> GSM601986 2 0.8709 -0.1902 0.236 0.408 0.044 0.312
#> GSM601996 4 0.6074 0.4258 0.028 0.472 0.008 0.492
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM601872 2 0.2677 0.8485 0.000 0.896 0.020 0.020 0.064
#> GSM601882 2 0.5370 0.4106 0.000 0.584 0.000 0.348 0.068
#> GSM601887 3 0.2438 0.7777 0.000 0.060 0.900 0.000 0.040
#> GSM601892 3 0.1074 0.8116 0.004 0.012 0.968 0.000 0.016
#> GSM601897 3 0.3190 0.7953 0.000 0.012 0.840 0.008 0.140
#> GSM601902 4 0.0451 0.8210 0.004 0.000 0.000 0.988 0.008
#> GSM601912 3 0.3863 0.7641 0.000 0.020 0.792 0.012 0.176
#> GSM601927 1 0.0000 0.7525 1.000 0.000 0.000 0.000 0.000
#> GSM601932 4 0.1310 0.8274 0.000 0.020 0.000 0.956 0.024
#> GSM601937 5 0.2124 0.8217 0.000 0.056 0.000 0.028 0.916
#> GSM601942 2 0.3387 0.8158 0.000 0.836 0.008 0.024 0.132
#> GSM601947 4 0.2419 0.8150 0.004 0.064 0.000 0.904 0.028
#> GSM601957 3 0.1671 0.8254 0.076 0.000 0.924 0.000 0.000
#> GSM601972 4 0.3608 0.7532 0.000 0.148 0.000 0.812 0.040
#> GSM601977 2 0.1661 0.8641 0.000 0.940 0.000 0.024 0.036
#> GSM601987 2 0.1568 0.8635 0.000 0.944 0.000 0.020 0.036
#> GSM601877 1 0.0324 0.7536 0.992 0.000 0.004 0.004 0.000
#> GSM601907 2 0.0451 0.8608 0.000 0.988 0.000 0.008 0.004
#> GSM601917 4 0.0451 0.8210 0.004 0.000 0.000 0.988 0.008
#> GSM601922 4 0.0451 0.8210 0.004 0.000 0.000 0.988 0.008
#> GSM601952 2 0.3460 0.8081 0.000 0.828 0.000 0.128 0.044
#> GSM601962 5 0.5365 0.3161 0.000 0.028 0.348 0.024 0.600
#> GSM601967 3 0.1952 0.8238 0.084 0.004 0.912 0.000 0.000
#> GSM601982 2 0.4957 0.7127 0.000 0.724 0.008 0.176 0.092
#> GSM601992 4 0.4793 0.6624 0.000 0.068 0.000 0.700 0.232
#> GSM601873 2 0.2707 0.8506 0.000 0.888 0.008 0.024 0.080
#> GSM601883 2 0.3575 0.8193 0.000 0.824 0.000 0.120 0.056
#> GSM601888 3 0.4081 0.6663 0.000 0.172 0.784 0.012 0.032
#> GSM601893 3 0.1469 0.7991 0.000 0.016 0.948 0.000 0.036
#> GSM601898 3 0.2238 0.8334 0.064 0.000 0.912 0.004 0.020
#> GSM601903 4 0.0451 0.8210 0.004 0.000 0.000 0.988 0.008
#> GSM601913 3 0.4833 0.7420 0.152 0.012 0.752 0.004 0.080
#> GSM601928 1 0.0162 0.7528 0.996 0.000 0.000 0.004 0.000
#> GSM601933 2 0.4676 0.7007 0.000 0.720 0.000 0.208 0.072
#> GSM601938 4 0.6282 0.4362 0.000 0.248 0.000 0.536 0.216
#> GSM601943 2 0.2881 0.8441 0.000 0.876 0.008 0.024 0.092
#> GSM601948 1 0.5322 0.6368 0.664 0.016 0.276 0.012 0.032
#> GSM601958 3 0.1928 0.8296 0.072 0.000 0.920 0.004 0.004
#> GSM601973 4 0.0324 0.8239 0.004 0.000 0.000 0.992 0.004
#> GSM601978 2 0.0613 0.8610 0.000 0.984 0.004 0.008 0.004
#> GSM601988 5 0.2124 0.8217 0.000 0.056 0.000 0.028 0.916
#> GSM601878 1 0.1780 0.7563 0.940 0.000 0.028 0.008 0.024
#> GSM601908 2 0.1211 0.8688 0.000 0.960 0.000 0.024 0.016
#> GSM601918 4 0.2304 0.8159 0.004 0.068 0.000 0.908 0.020
#> GSM601923 1 0.0000 0.7525 1.000 0.000 0.000 0.000 0.000
#> GSM601953 2 0.0854 0.8635 0.000 0.976 0.004 0.008 0.012
#> GSM601963 3 0.3783 0.7501 0.000 0.012 0.768 0.004 0.216
#> GSM601968 3 0.1469 0.8313 0.036 0.000 0.948 0.000 0.016
#> GSM601983 3 0.3905 0.7349 0.000 0.012 0.752 0.004 0.232
#> GSM601993 5 0.4649 0.5184 0.000 0.044 0.004 0.244 0.708
#> GSM601874 2 0.0451 0.8608 0.000 0.988 0.000 0.008 0.004
#> GSM601884 2 0.1725 0.8623 0.000 0.936 0.000 0.020 0.044
#> GSM601889 3 0.1478 0.8296 0.064 0.000 0.936 0.000 0.000
#> GSM601894 3 0.1478 0.8296 0.064 0.000 0.936 0.000 0.000
#> GSM601899 3 0.2228 0.7849 0.000 0.048 0.912 0.000 0.040
#> GSM601904 4 0.0579 0.8238 0.008 0.000 0.000 0.984 0.008
#> GSM601914 3 0.3522 0.7597 0.000 0.004 0.780 0.004 0.212
#> GSM601929 1 0.4610 0.7037 0.736 0.004 0.216 0.032 0.012
#> GSM601934 2 0.2676 0.8470 0.000 0.884 0.000 0.036 0.080
#> GSM601939 1 0.5632 0.5405 0.600 0.016 0.336 0.008 0.040
#> GSM601944 2 0.5831 0.5518 0.000 0.604 0.000 0.236 0.160
#> GSM601949 1 0.5594 0.5669 0.600 0.020 0.340 0.008 0.032
#> GSM601959 3 0.1544 0.8284 0.068 0.000 0.932 0.000 0.000
#> GSM601974 4 0.7852 -0.1977 0.000 0.072 0.236 0.364 0.328
#> GSM601979 2 0.0404 0.8639 0.000 0.988 0.000 0.012 0.000
#> GSM601989 3 0.2948 0.8279 0.064 0.008 0.884 0.004 0.040
#> GSM601879 1 0.1799 0.7566 0.940 0.000 0.028 0.012 0.020
#> GSM601909 3 0.0290 0.8189 0.000 0.000 0.992 0.000 0.008
#> GSM601919 4 0.2238 0.8173 0.004 0.064 0.000 0.912 0.020
#> GSM601924 1 0.4140 0.7393 0.796 0.016 0.152 0.004 0.032
#> GSM601954 2 0.4615 0.6647 0.000 0.700 0.000 0.252 0.048
#> GSM601964 3 0.3815 0.7466 0.000 0.012 0.764 0.004 0.220
#> GSM601969 3 0.3875 0.6719 0.228 0.012 0.756 0.000 0.004
#> GSM601984 1 0.8141 0.3834 0.448 0.028 0.288 0.080 0.156
#> GSM601994 4 0.4666 0.6633 0.000 0.056 0.000 0.704 0.240
#> GSM601875 2 0.0404 0.8641 0.000 0.988 0.000 0.012 0.000
#> GSM601885 2 0.3779 0.7893 0.000 0.804 0.000 0.144 0.052
#> GSM601890 3 0.2300 0.7819 0.000 0.052 0.908 0.000 0.040
#> GSM601895 3 0.3493 0.8024 0.016 0.008 0.828 0.004 0.144
#> GSM601900 3 0.2695 0.8361 0.052 0.004 0.896 0.004 0.044
#> GSM601905 4 0.0324 0.8232 0.004 0.000 0.000 0.992 0.004
#> GSM601915 3 0.3481 0.8153 0.036 0.004 0.840 0.004 0.116
#> GSM601930 1 0.0000 0.7525 1.000 0.000 0.000 0.000 0.000
#> GSM601935 5 0.3343 0.8068 0.000 0.040 0.068 0.028 0.864
#> GSM601940 3 0.5711 0.3125 0.360 0.016 0.576 0.008 0.040
#> GSM601945 2 0.0613 0.8610 0.000 0.984 0.004 0.008 0.004
#> GSM601950 1 0.5560 0.5170 0.588 0.016 0.356 0.008 0.032
#> GSM601960 3 0.3522 0.7597 0.000 0.004 0.780 0.004 0.212
#> GSM601975 4 0.0912 0.8287 0.000 0.016 0.000 0.972 0.012
#> GSM601980 5 0.2754 0.8073 0.000 0.080 0.004 0.032 0.884
#> GSM601990 3 0.4764 0.3354 0.000 0.012 0.548 0.004 0.436
#> GSM601880 1 0.0000 0.7525 1.000 0.000 0.000 0.000 0.000
#> GSM601910 3 0.2228 0.8166 0.000 0.004 0.900 0.004 0.092
#> GSM601920 4 0.0324 0.8232 0.004 0.000 0.000 0.992 0.004
#> GSM601925 1 0.0000 0.7525 1.000 0.000 0.000 0.000 0.000
#> GSM601955 5 0.3426 0.8177 0.000 0.068 0.040 0.032 0.860
#> GSM601965 3 0.8277 0.1223 0.280 0.036 0.428 0.192 0.064
#> GSM601970 3 0.1410 0.8309 0.060 0.000 0.940 0.000 0.000
#> GSM601985 1 0.5711 0.4862 0.576 0.016 0.360 0.008 0.040
#> GSM601995 5 0.2214 0.8222 0.000 0.052 0.004 0.028 0.916
#> GSM601876 1 0.5484 0.6018 0.636 0.016 0.300 0.008 0.040
#> GSM601886 4 0.5837 0.1081 0.000 0.080 0.004 0.460 0.456
#> GSM601891 3 0.2927 0.7572 0.000 0.092 0.868 0.000 0.040
#> GSM601896 1 0.5538 0.5867 0.624 0.016 0.312 0.008 0.040
#> GSM601901 4 0.3970 0.7313 0.000 0.156 0.000 0.788 0.056
#> GSM601906 1 0.6347 0.5586 0.608 0.012 0.108 0.252 0.020
#> GSM601916 4 0.0771 0.8266 0.000 0.004 0.000 0.976 0.020
#> GSM601931 1 0.0000 0.7525 1.000 0.000 0.000 0.000 0.000
#> GSM601936 5 0.3248 0.7806 0.000 0.052 0.004 0.088 0.856
#> GSM601941 4 0.1267 0.8284 0.004 0.024 0.000 0.960 0.012
#> GSM601946 1 0.4916 0.7006 0.728 0.016 0.208 0.008 0.040
#> GSM601951 1 0.3197 0.7527 0.852 0.000 0.116 0.008 0.024
#> GSM601961 2 0.3711 0.7094 0.000 0.820 0.136 0.032 0.012
#> GSM601976 4 0.1117 0.8279 0.000 0.020 0.000 0.964 0.016
#> GSM601981 2 0.1251 0.8683 0.000 0.956 0.000 0.036 0.008
#> GSM601991 5 0.4885 0.3463 0.000 0.016 0.332 0.016 0.636
#> GSM601881 1 0.0000 0.7525 1.000 0.000 0.000 0.000 0.000
#> GSM601911 4 0.4304 0.7542 0.008 0.092 0.024 0.812 0.064
#> GSM601921 4 0.0451 0.8210 0.004 0.000 0.000 0.988 0.008
#> GSM601926 1 0.0000 0.7525 1.000 0.000 0.000 0.000 0.000
#> GSM601956 2 0.0740 0.8618 0.000 0.980 0.004 0.008 0.008
#> GSM601966 4 0.2588 0.8097 0.000 0.060 0.000 0.892 0.048
#> GSM601971 3 0.4548 0.6599 0.228 0.016 0.732 0.004 0.020
#> GSM601986 4 0.8040 0.0952 0.268 0.044 0.192 0.456 0.040
#> GSM601996 4 0.4701 0.6654 0.000 0.060 0.000 0.704 0.236
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM601872 2 0.0405 0.9271 0.000 0.988 0.000 0.004 0.000 0.008
#> GSM601882 2 0.3202 0.7775 0.000 0.800 0.000 0.176 0.024 0.000
#> GSM601887 6 0.0146 0.6863 0.000 0.000 0.004 0.000 0.000 0.996
#> GSM601892 6 0.0458 0.6886 0.000 0.000 0.016 0.000 0.000 0.984
#> GSM601897 3 0.4389 -0.2488 0.000 0.000 0.528 0.000 0.024 0.448
#> GSM601902 4 0.0146 0.8713 0.000 0.000 0.000 0.996 0.004 0.000
#> GSM601912 3 0.2060 0.7569 0.000 0.000 0.900 0.000 0.016 0.084
#> GSM601927 1 0.0000 0.9256 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM601932 4 0.0000 0.8722 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM601937 5 0.0520 0.9405 0.000 0.000 0.008 0.008 0.984 0.000
#> GSM601942 2 0.1010 0.9181 0.000 0.960 0.000 0.004 0.036 0.000
#> GSM601947 4 0.0146 0.8719 0.000 0.000 0.000 0.996 0.004 0.000
#> GSM601957 6 0.4685 0.6464 0.096 0.000 0.240 0.000 0.000 0.664
#> GSM601972 4 0.1757 0.8170 0.000 0.076 0.000 0.916 0.008 0.000
#> GSM601977 2 0.0603 0.9260 0.000 0.980 0.000 0.004 0.016 0.000
#> GSM601987 2 0.0291 0.9282 0.000 0.992 0.000 0.004 0.004 0.000
#> GSM601877 1 0.0000 0.9256 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM601907 2 0.0000 0.9277 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM601917 4 0.0260 0.8700 0.000 0.000 0.000 0.992 0.008 0.000
#> GSM601922 4 0.0260 0.8700 0.000 0.000 0.000 0.992 0.008 0.000
#> GSM601952 2 0.2070 0.8639 0.000 0.892 0.000 0.100 0.008 0.000
#> GSM601962 3 0.1700 0.7754 0.000 0.000 0.916 0.004 0.080 0.000
#> GSM601967 6 0.4723 0.6444 0.140 0.000 0.180 0.000 0.000 0.680
#> GSM601982 2 0.2586 0.8530 0.000 0.868 0.000 0.100 0.032 0.000
#> GSM601992 4 0.4481 0.3179 0.000 0.024 0.000 0.556 0.416 0.004
#> GSM601873 2 0.0508 0.9270 0.000 0.984 0.000 0.004 0.012 0.000
#> GSM601883 2 0.0405 0.9282 0.000 0.988 0.000 0.008 0.004 0.000
#> GSM601888 6 0.0291 0.6842 0.000 0.004 0.004 0.000 0.000 0.992
#> GSM601893 6 0.0260 0.6876 0.000 0.000 0.008 0.000 0.000 0.992
#> GSM601898 3 0.2814 0.6164 0.008 0.000 0.820 0.000 0.000 0.172
#> GSM601903 4 0.0260 0.8700 0.000 0.000 0.000 0.992 0.008 0.000
#> GSM601913 3 0.1444 0.7550 0.072 0.000 0.928 0.000 0.000 0.000
#> GSM601928 1 0.0000 0.9256 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM601933 2 0.3190 0.8086 0.000 0.820 0.000 0.136 0.044 0.000
#> GSM601938 4 0.5480 0.4406 0.000 0.252 0.000 0.580 0.164 0.004
#> GSM601943 2 0.0405 0.9276 0.000 0.988 0.000 0.004 0.008 0.000
#> GSM601948 1 0.1088 0.9075 0.960 0.000 0.016 0.000 0.000 0.024
#> GSM601958 6 0.4806 0.4126 0.052 0.000 0.460 0.000 0.000 0.488
#> GSM601973 4 0.0000 0.8722 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM601978 2 0.0000 0.9277 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM601988 5 0.0405 0.9402 0.000 0.000 0.004 0.008 0.988 0.000
#> GSM601878 1 0.0000 0.9256 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM601908 2 0.0146 0.9282 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM601918 4 0.0146 0.8719 0.000 0.000 0.000 0.996 0.004 0.000
#> GSM601923 1 0.0000 0.9256 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM601953 2 0.0000 0.9277 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM601963 3 0.0547 0.8088 0.000 0.000 0.980 0.000 0.020 0.000
#> GSM601968 6 0.3371 0.6324 0.000 0.000 0.292 0.000 0.000 0.708
#> GSM601983 3 0.0790 0.8055 0.000 0.000 0.968 0.000 0.032 0.000
#> GSM601993 5 0.0748 0.9329 0.000 0.000 0.004 0.016 0.976 0.004
#> GSM601874 2 0.0000 0.9277 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM601884 2 0.0603 0.9260 0.000 0.980 0.000 0.004 0.016 0.000
#> GSM601889 6 0.4634 0.5295 0.044 0.000 0.400 0.000 0.000 0.556
#> GSM601894 6 0.4328 0.4433 0.020 0.000 0.460 0.000 0.000 0.520
#> GSM601899 6 0.0146 0.6863 0.000 0.000 0.004 0.000 0.000 0.996
#> GSM601904 4 0.0146 0.8717 0.004 0.000 0.000 0.996 0.000 0.000
#> GSM601914 3 0.0547 0.8087 0.000 0.000 0.980 0.000 0.020 0.000
#> GSM601929 1 0.0291 0.9239 0.992 0.000 0.004 0.004 0.000 0.000
#> GSM601934 2 0.0993 0.9199 0.000 0.964 0.000 0.012 0.024 0.000
#> GSM601939 1 0.1204 0.8945 0.944 0.000 0.056 0.000 0.000 0.000
#> GSM601944 2 0.4750 0.5834 0.000 0.652 0.000 0.252 0.096 0.000
#> GSM601949 1 0.4052 0.3798 0.628 0.000 0.016 0.000 0.000 0.356
#> GSM601959 6 0.4873 0.5983 0.080 0.000 0.320 0.000 0.000 0.600
#> GSM601974 4 0.4299 0.6138 0.000 0.000 0.188 0.720 0.092 0.000
#> GSM601979 2 0.0000 0.9277 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM601989 3 0.3245 0.5078 0.008 0.000 0.764 0.000 0.000 0.228
#> GSM601879 1 0.0146 0.9247 0.996 0.000 0.004 0.000 0.000 0.000
#> GSM601909 6 0.3717 0.5620 0.000 0.000 0.384 0.000 0.000 0.616
#> GSM601919 4 0.0146 0.8719 0.000 0.000 0.000 0.996 0.004 0.000
#> GSM601924 1 0.0260 0.9230 0.992 0.000 0.008 0.000 0.000 0.000
#> GSM601954 2 0.3266 0.6619 0.000 0.728 0.000 0.272 0.000 0.000
#> GSM601964 3 0.0632 0.8084 0.000 0.000 0.976 0.000 0.024 0.000
#> GSM601969 6 0.3432 0.6050 0.216 0.000 0.020 0.000 0.000 0.764
#> GSM601984 3 0.4265 0.5866 0.208 0.000 0.732 0.036 0.024 0.000
#> GSM601994 4 0.3961 0.3009 0.000 0.000 0.000 0.556 0.440 0.004
#> GSM601875 2 0.0146 0.9282 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM601885 2 0.2631 0.8182 0.000 0.840 0.000 0.152 0.008 0.000
#> GSM601890 6 0.0146 0.6863 0.000 0.000 0.004 0.000 0.000 0.996
#> GSM601895 3 0.0146 0.8025 0.000 0.000 0.996 0.000 0.000 0.004
#> GSM601900 3 0.2404 0.7079 0.016 0.000 0.872 0.000 0.000 0.112
#> GSM601905 4 0.0000 0.8722 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM601915 3 0.0000 0.8038 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM601930 1 0.0000 0.9256 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM601935 5 0.3468 0.6479 0.000 0.000 0.264 0.008 0.728 0.000
#> GSM601940 1 0.4456 0.6307 0.708 0.000 0.180 0.000 0.000 0.112
#> GSM601945 2 0.0000 0.9277 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM601950 1 0.3674 0.5760 0.716 0.000 0.016 0.000 0.000 0.268
#> GSM601960 3 0.0458 0.8085 0.000 0.000 0.984 0.000 0.016 0.000
#> GSM601975 4 0.0146 0.8719 0.000 0.000 0.000 0.996 0.004 0.000
#> GSM601980 5 0.0520 0.9351 0.000 0.008 0.000 0.008 0.984 0.000
#> GSM601990 3 0.0632 0.8084 0.000 0.000 0.976 0.000 0.024 0.000
#> GSM601880 1 0.0000 0.9256 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM601910 6 0.4264 0.3341 0.000 0.000 0.488 0.000 0.016 0.496
#> GSM601920 4 0.0146 0.8713 0.000 0.000 0.000 0.996 0.004 0.000
#> GSM601925 1 0.0000 0.9256 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM601955 5 0.2174 0.8762 0.000 0.008 0.088 0.008 0.896 0.000
#> GSM601965 4 0.5895 0.0611 0.208 0.000 0.356 0.436 0.000 0.000
#> GSM601970 6 0.4250 0.4555 0.016 0.000 0.456 0.000 0.000 0.528
#> GSM601985 3 0.3955 0.1263 0.436 0.000 0.560 0.000 0.000 0.004
#> GSM601995 5 0.0520 0.9405 0.000 0.000 0.008 0.008 0.984 0.000
#> GSM601876 1 0.2632 0.7907 0.832 0.000 0.164 0.004 0.000 0.000
#> GSM601886 4 0.3930 0.3304 0.000 0.000 0.004 0.576 0.420 0.000
#> GSM601891 6 0.0291 0.6858 0.000 0.004 0.004 0.000 0.000 0.992
#> GSM601896 1 0.1806 0.8622 0.908 0.000 0.088 0.004 0.000 0.000
#> GSM601901 4 0.1643 0.8250 0.000 0.068 0.000 0.924 0.008 0.000
#> GSM601906 1 0.2730 0.7260 0.808 0.000 0.000 0.192 0.000 0.000
#> GSM601916 4 0.0000 0.8722 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM601931 1 0.0000 0.9256 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM601936 5 0.0405 0.9402 0.000 0.000 0.004 0.008 0.988 0.000
#> GSM601941 4 0.0000 0.8722 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM601946 1 0.0458 0.9207 0.984 0.000 0.016 0.000 0.000 0.000
#> GSM601951 1 0.0146 0.9247 0.996 0.000 0.004 0.000 0.000 0.000
#> GSM601961 2 0.2333 0.8407 0.000 0.872 0.004 0.004 0.000 0.120
#> GSM601976 4 0.0146 0.8715 0.000 0.000 0.000 0.996 0.004 0.000
#> GSM601981 2 0.0260 0.9283 0.000 0.992 0.000 0.008 0.000 0.000
#> GSM601991 3 0.2854 0.6252 0.000 0.000 0.792 0.000 0.208 0.000
#> GSM601881 1 0.0000 0.9256 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM601911 4 0.0291 0.8703 0.000 0.004 0.000 0.992 0.004 0.000
#> GSM601921 4 0.0260 0.8700 0.000 0.000 0.000 0.992 0.008 0.000
#> GSM601926 1 0.0000 0.9256 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM601956 2 0.0000 0.9277 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM601966 4 0.0260 0.8706 0.000 0.000 0.000 0.992 0.008 0.000
#> GSM601971 6 0.5300 0.3163 0.400 0.000 0.104 0.000 0.000 0.496
#> GSM601986 4 0.3012 0.6690 0.196 0.000 0.008 0.796 0.000 0.000
#> GSM601996 4 0.3508 0.5969 0.000 0.000 0.000 0.704 0.292 0.004
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)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
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)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
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)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, 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.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")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n time(p) gender(p) k
#> MAD:mclust 105 0.152 0.8878 2
#> MAD:mclust 112 0.352 0.0639 3
#> MAD:mclust 85 0.849 0.0899 4
#> MAD:mclust 113 0.112 0.6706 5
#> MAD:mclust 112 0.114 0.8538 6
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.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["MAD", "NMF"]
# you can also extract it by
# res = res_list["MAD:NMF"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 21512 rows and 125 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'NMF' method.
#> Performed in total 1250 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_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
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, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.932 0.948 0.977 0.502 0.497 0.497
#> 3 3 0.452 0.538 0.734 0.322 0.727 0.505
#> 4 4 0.452 0.459 0.721 0.117 0.803 0.499
#> 5 5 0.493 0.409 0.606 0.068 0.875 0.570
#> 6 6 0.533 0.369 0.616 0.043 0.901 0.587
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 2
Following shows 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 class
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
#> GSM601872 2 0.0000 0.970 0.000 1.000
#> GSM601882 2 0.0000 0.970 0.000 1.000
#> GSM601887 1 0.3114 0.933 0.944 0.056
#> GSM601892 1 0.0000 0.981 1.000 0.000
#> GSM601897 1 0.0376 0.978 0.996 0.004
#> GSM601902 2 0.0000 0.970 0.000 1.000
#> GSM601912 1 0.0938 0.972 0.988 0.012
#> GSM601927 1 0.0000 0.981 1.000 0.000
#> GSM601932 2 0.0000 0.970 0.000 1.000
#> GSM601937 2 0.4298 0.903 0.088 0.912
#> GSM601942 2 0.0000 0.970 0.000 1.000
#> GSM601947 2 0.0000 0.970 0.000 1.000
#> GSM601957 1 0.0000 0.981 1.000 0.000
#> GSM601972 2 0.0000 0.970 0.000 1.000
#> GSM601977 2 0.0000 0.970 0.000 1.000
#> GSM601987 2 0.0000 0.970 0.000 1.000
#> GSM601877 1 0.0000 0.981 1.000 0.000
#> GSM601907 2 0.0000 0.970 0.000 1.000
#> GSM601917 2 0.2778 0.938 0.048 0.952
#> GSM601922 2 0.4298 0.903 0.088 0.912
#> GSM601952 2 0.0000 0.970 0.000 1.000
#> GSM601962 1 0.2043 0.956 0.968 0.032
#> GSM601967 1 0.0000 0.981 1.000 0.000
#> GSM601982 2 0.0000 0.970 0.000 1.000
#> GSM601992 2 0.0000 0.970 0.000 1.000
#> GSM601873 2 0.0000 0.970 0.000 1.000
#> GSM601883 2 0.0000 0.970 0.000 1.000
#> GSM601888 1 0.6247 0.818 0.844 0.156
#> GSM601893 1 0.0672 0.975 0.992 0.008
#> GSM601898 1 0.0000 0.981 1.000 0.000
#> GSM601903 2 0.0000 0.970 0.000 1.000
#> GSM601913 1 0.0000 0.981 1.000 0.000
#> GSM601928 1 0.0000 0.981 1.000 0.000
#> GSM601933 2 0.0000 0.970 0.000 1.000
#> GSM601938 2 0.0000 0.970 0.000 1.000
#> GSM601943 2 0.0000 0.970 0.000 1.000
#> GSM601948 1 0.0000 0.981 1.000 0.000
#> GSM601958 1 0.0000 0.981 1.000 0.000
#> GSM601973 2 0.0000 0.970 0.000 1.000
#> GSM601978 2 0.0000 0.970 0.000 1.000
#> GSM601988 2 0.0000 0.970 0.000 1.000
#> GSM601878 1 0.0000 0.981 1.000 0.000
#> GSM601908 2 0.0000 0.970 0.000 1.000
#> GSM601918 2 0.0000 0.970 0.000 1.000
#> GSM601923 1 0.0000 0.981 1.000 0.000
#> GSM601953 2 0.0000 0.970 0.000 1.000
#> GSM601963 1 0.0000 0.981 1.000 0.000
#> GSM601968 1 0.0000 0.981 1.000 0.000
#> GSM601983 1 0.0000 0.981 1.000 0.000
#> GSM601993 2 0.0000 0.970 0.000 1.000
#> GSM601874 2 0.0000 0.970 0.000 1.000
#> GSM601884 2 0.0000 0.970 0.000 1.000
#> GSM601889 1 0.0000 0.981 1.000 0.000
#> GSM601894 1 0.0000 0.981 1.000 0.000
#> GSM601899 1 0.2043 0.956 0.968 0.032
#> GSM601904 2 0.9710 0.370 0.400 0.600
#> GSM601914 1 0.0000 0.981 1.000 0.000
#> GSM601929 1 0.0000 0.981 1.000 0.000
#> GSM601934 2 0.0000 0.970 0.000 1.000
#> GSM601939 1 0.0000 0.981 1.000 0.000
#> GSM601944 2 0.0000 0.970 0.000 1.000
#> GSM601949 1 0.0000 0.981 1.000 0.000
#> GSM601959 1 0.0000 0.981 1.000 0.000
#> GSM601974 1 0.9954 0.101 0.540 0.460
#> GSM601979 2 0.0000 0.970 0.000 1.000
#> GSM601989 1 0.0000 0.981 1.000 0.000
#> GSM601879 1 0.0000 0.981 1.000 0.000
#> GSM601909 1 0.0000 0.981 1.000 0.000
#> GSM601919 2 0.0000 0.970 0.000 1.000
#> GSM601924 1 0.0000 0.981 1.000 0.000
#> GSM601954 2 0.0000 0.970 0.000 1.000
#> GSM601964 1 0.0000 0.981 1.000 0.000
#> GSM601969 1 0.0000 0.981 1.000 0.000
#> GSM601984 1 0.0000 0.981 1.000 0.000
#> GSM601994 2 0.0000 0.970 0.000 1.000
#> GSM601875 2 0.0000 0.970 0.000 1.000
#> GSM601885 2 0.0000 0.970 0.000 1.000
#> GSM601890 1 0.2603 0.945 0.956 0.044
#> GSM601895 1 0.0000 0.981 1.000 0.000
#> GSM601900 1 0.0000 0.981 1.000 0.000
#> GSM601905 2 0.6887 0.791 0.184 0.816
#> GSM601915 1 0.0000 0.981 1.000 0.000
#> GSM601930 1 0.0000 0.981 1.000 0.000
#> GSM601935 1 0.4815 0.876 0.896 0.104
#> GSM601940 1 0.0000 0.981 1.000 0.000
#> GSM601945 2 0.0000 0.970 0.000 1.000
#> GSM601950 1 0.0000 0.981 1.000 0.000
#> GSM601960 1 0.0000 0.981 1.000 0.000
#> GSM601975 2 0.0000 0.970 0.000 1.000
#> GSM601980 2 0.0000 0.970 0.000 1.000
#> GSM601990 1 0.0000 0.981 1.000 0.000
#> GSM601880 1 0.0000 0.981 1.000 0.000
#> GSM601910 1 0.0000 0.981 1.000 0.000
#> GSM601920 2 0.6048 0.832 0.148 0.852
#> GSM601925 1 0.0000 0.981 1.000 0.000
#> GSM601955 2 0.4939 0.884 0.108 0.892
#> GSM601965 1 0.0672 0.975 0.992 0.008
#> GSM601970 1 0.0000 0.981 1.000 0.000
#> GSM601985 1 0.0000 0.981 1.000 0.000
#> GSM601995 2 0.5737 0.853 0.136 0.864
#> GSM601876 1 0.0000 0.981 1.000 0.000
#> GSM601886 2 0.8909 0.584 0.308 0.692
#> GSM601891 1 0.7950 0.692 0.760 0.240
#> GSM601896 1 0.0000 0.981 1.000 0.000
#> GSM601901 2 0.0000 0.970 0.000 1.000
#> GSM601906 1 0.0000 0.981 1.000 0.000
#> GSM601916 2 0.0672 0.966 0.008 0.992
#> GSM601931 1 0.0000 0.981 1.000 0.000
#> GSM601936 2 0.1633 0.956 0.024 0.976
#> GSM601941 2 0.0000 0.970 0.000 1.000
#> GSM601946 1 0.0000 0.981 1.000 0.000
#> GSM601951 1 0.0000 0.981 1.000 0.000
#> GSM601961 2 0.1184 0.961 0.016 0.984
#> GSM601976 2 0.2423 0.944 0.040 0.960
#> GSM601981 2 0.0000 0.970 0.000 1.000
#> GSM601991 1 0.0000 0.981 1.000 0.000
#> GSM601881 1 0.0000 0.981 1.000 0.000
#> GSM601911 2 0.4690 0.886 0.100 0.900
#> GSM601921 2 0.1414 0.958 0.020 0.980
#> GSM601926 1 0.0000 0.981 1.000 0.000
#> GSM601956 2 0.0000 0.970 0.000 1.000
#> GSM601966 2 0.0000 0.970 0.000 1.000
#> GSM601971 1 0.0000 0.981 1.000 0.000
#> GSM601986 1 0.2423 0.947 0.960 0.040
#> GSM601996 2 0.0000 0.970 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM601872 3 0.4062 0.4560 0.000 0.164 0.836
#> GSM601882 2 0.1753 0.7419 0.000 0.952 0.048
#> GSM601887 3 0.2200 0.6197 0.056 0.004 0.940
#> GSM601892 3 0.2959 0.6186 0.100 0.000 0.900
#> GSM601897 3 0.1031 0.6140 0.024 0.000 0.976
#> GSM601902 2 0.5497 0.6096 0.292 0.708 0.000
#> GSM601912 3 0.2537 0.6212 0.080 0.000 0.920
#> GSM601927 1 0.0424 0.7666 0.992 0.000 0.008
#> GSM601932 2 0.2878 0.7471 0.096 0.904 0.000
#> GSM601937 2 0.5450 0.6419 0.012 0.760 0.228
#> GSM601942 3 0.5905 0.0920 0.000 0.352 0.648
#> GSM601947 2 0.4178 0.7129 0.172 0.828 0.000
#> GSM601957 3 0.6192 0.3949 0.420 0.000 0.580
#> GSM601972 2 0.1989 0.7520 0.048 0.948 0.004
#> GSM601977 2 0.6168 0.4206 0.000 0.588 0.412
#> GSM601987 2 0.3482 0.7146 0.000 0.872 0.128
#> GSM601877 1 0.1163 0.7473 0.972 0.028 0.000
#> GSM601907 2 0.5465 0.5898 0.000 0.712 0.288
#> GSM601917 2 0.6267 0.3463 0.452 0.548 0.000
#> GSM601922 2 0.6305 0.2702 0.484 0.516 0.000
#> GSM601952 2 0.3989 0.7206 0.012 0.864 0.124
#> GSM601962 3 0.6521 0.1231 0.496 0.004 0.500
#> GSM601967 3 0.6008 0.4589 0.372 0.000 0.628
#> GSM601982 2 0.5621 0.5782 0.000 0.692 0.308
#> GSM601992 2 0.1529 0.7512 0.040 0.960 0.000
#> GSM601873 3 0.6192 -0.0557 0.000 0.420 0.580
#> GSM601883 2 0.1878 0.7428 0.004 0.952 0.044
#> GSM601888 3 0.1636 0.6065 0.020 0.016 0.964
#> GSM601893 3 0.2165 0.6204 0.064 0.000 0.936
#> GSM601898 3 0.6225 0.3721 0.432 0.000 0.568
#> GSM601903 2 0.5760 0.5665 0.328 0.672 0.000
#> GSM601913 1 0.5591 0.4229 0.696 0.000 0.304
#> GSM601928 1 0.1411 0.7691 0.964 0.000 0.036
#> GSM601933 2 0.1964 0.7394 0.000 0.944 0.056
#> GSM601938 2 0.1482 0.7483 0.012 0.968 0.020
#> GSM601943 3 0.4555 0.4043 0.000 0.200 0.800
#> GSM601948 1 0.3551 0.7079 0.868 0.000 0.132
#> GSM601958 3 0.6291 0.2897 0.468 0.000 0.532
#> GSM601973 2 0.5058 0.6556 0.244 0.756 0.000
#> GSM601978 2 0.6062 0.4655 0.000 0.616 0.384
#> GSM601988 2 0.5335 0.6352 0.008 0.760 0.232
#> GSM601878 1 0.1964 0.7620 0.944 0.000 0.056
#> GSM601908 2 0.3116 0.7227 0.000 0.892 0.108
#> GSM601918 2 0.3267 0.7422 0.116 0.884 0.000
#> GSM601923 1 0.1163 0.7691 0.972 0.000 0.028
#> GSM601953 3 0.6225 -0.0731 0.000 0.432 0.568
#> GSM601963 1 0.6126 0.1649 0.600 0.000 0.400
#> GSM601968 3 0.4121 0.6040 0.168 0.000 0.832
#> GSM601983 3 0.6280 0.2715 0.460 0.000 0.540
#> GSM601993 2 0.2200 0.7516 0.056 0.940 0.004
#> GSM601874 2 0.5968 0.5090 0.000 0.636 0.364
#> GSM601884 2 0.5098 0.6470 0.000 0.752 0.248
#> GSM601889 3 0.6062 0.4532 0.384 0.000 0.616
#> GSM601894 3 0.6168 0.4089 0.412 0.000 0.588
#> GSM601899 3 0.1643 0.6184 0.044 0.000 0.956
#> GSM601904 1 0.6045 0.1109 0.620 0.380 0.000
#> GSM601914 3 0.6154 0.4058 0.408 0.000 0.592
#> GSM601929 1 0.0747 0.7559 0.984 0.016 0.000
#> GSM601934 2 0.4842 0.6566 0.000 0.776 0.224
#> GSM601939 1 0.4002 0.6809 0.840 0.000 0.160
#> GSM601944 2 0.2496 0.7391 0.004 0.928 0.068
#> GSM601949 1 0.5098 0.5411 0.752 0.000 0.248
#> GSM601959 3 0.6299 0.2676 0.476 0.000 0.524
#> GSM601974 2 0.9976 0.0184 0.344 0.356 0.300
#> GSM601979 2 0.4605 0.6659 0.000 0.796 0.204
#> GSM601989 3 0.5650 0.5259 0.312 0.000 0.688
#> GSM601879 1 0.0829 0.7609 0.984 0.012 0.004
#> GSM601909 3 0.4346 0.5972 0.184 0.000 0.816
#> GSM601919 2 0.5650 0.5866 0.312 0.688 0.000
#> GSM601924 1 0.2711 0.7460 0.912 0.000 0.088
#> GSM601954 2 0.6905 0.6016 0.044 0.676 0.280
#> GSM601964 3 0.6309 0.1778 0.496 0.000 0.504
#> GSM601969 3 0.6565 0.3525 0.416 0.008 0.576
#> GSM601984 1 0.3694 0.7341 0.896 0.052 0.052
#> GSM601994 2 0.2066 0.7508 0.060 0.940 0.000
#> GSM601875 2 0.6330 0.4360 0.004 0.600 0.396
#> GSM601885 2 0.2066 0.7387 0.000 0.940 0.060
#> GSM601890 3 0.1832 0.6154 0.036 0.008 0.956
#> GSM601895 3 0.5905 0.4869 0.352 0.000 0.648
#> GSM601900 3 0.5650 0.5282 0.312 0.000 0.688
#> GSM601905 1 0.6286 -0.1604 0.536 0.464 0.000
#> GSM601915 1 0.6126 0.1397 0.600 0.000 0.400
#> GSM601930 1 0.0848 0.7641 0.984 0.008 0.008
#> GSM601935 1 0.8233 0.4543 0.616 0.120 0.264
#> GSM601940 1 0.4605 0.6197 0.796 0.000 0.204
#> GSM601945 2 0.5926 0.5027 0.000 0.644 0.356
#> GSM601950 1 0.5216 0.5152 0.740 0.000 0.260
#> GSM601960 3 0.6079 0.4413 0.388 0.000 0.612
#> GSM601975 2 0.3752 0.7299 0.144 0.856 0.000
#> GSM601980 3 0.6295 -0.2276 0.000 0.472 0.528
#> GSM601990 3 0.6286 0.2779 0.464 0.000 0.536
#> GSM601880 1 0.0661 0.7659 0.988 0.004 0.008
#> GSM601910 3 0.2711 0.6205 0.088 0.000 0.912
#> GSM601920 1 0.6274 -0.1364 0.544 0.456 0.000
#> GSM601925 1 0.0592 0.7580 0.988 0.012 0.000
#> GSM601955 3 0.3941 0.4677 0.000 0.156 0.844
#> GSM601965 1 0.2773 0.7627 0.928 0.024 0.048
#> GSM601970 3 0.5905 0.4903 0.352 0.000 0.648
#> GSM601985 1 0.4002 0.6823 0.840 0.000 0.160
#> GSM601995 2 0.6025 0.6257 0.028 0.740 0.232
#> GSM601876 1 0.2625 0.7500 0.916 0.000 0.084
#> GSM601886 2 0.7075 0.2077 0.484 0.496 0.020
#> GSM601891 3 0.1129 0.5996 0.004 0.020 0.976
#> GSM601896 1 0.3551 0.7132 0.868 0.000 0.132
#> GSM601901 2 0.2313 0.7488 0.024 0.944 0.032
#> GSM601906 1 0.3879 0.6299 0.848 0.152 0.000
#> GSM601916 2 0.5650 0.5855 0.312 0.688 0.000
#> GSM601931 1 0.1643 0.7672 0.956 0.000 0.044
#> GSM601936 2 0.4136 0.7414 0.116 0.864 0.020
#> GSM601941 2 0.3619 0.7340 0.136 0.864 0.000
#> GSM601946 1 0.2448 0.7544 0.924 0.000 0.076
#> GSM601951 1 0.1182 0.7646 0.976 0.012 0.012
#> GSM601961 3 0.6018 0.2467 0.008 0.308 0.684
#> GSM601976 2 0.5988 0.5087 0.368 0.632 0.000
#> GSM601981 2 0.5678 0.5529 0.000 0.684 0.316
#> GSM601991 3 0.5291 0.5554 0.268 0.000 0.732
#> GSM601881 1 0.1411 0.7686 0.964 0.000 0.036
#> GSM601911 2 0.5926 0.5202 0.356 0.644 0.000
#> GSM601921 2 0.6079 0.4746 0.388 0.612 0.000
#> GSM601926 1 0.1411 0.7687 0.964 0.000 0.036
#> GSM601956 3 0.6299 -0.1929 0.000 0.476 0.524
#> GSM601966 2 0.2261 0.7504 0.068 0.932 0.000
#> GSM601971 1 0.5988 0.2398 0.632 0.000 0.368
#> GSM601986 1 0.3879 0.6300 0.848 0.152 0.000
#> GSM601996 2 0.2356 0.7499 0.072 0.928 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM601872 2 0.591 0.2499 0.000 0.584 0.372 0.044
#> GSM601882 4 0.340 0.5773 0.000 0.152 0.008 0.840
#> GSM601887 2 0.551 0.0506 0.020 0.572 0.408 0.000
#> GSM601892 3 0.695 0.4102 0.124 0.348 0.528 0.000
#> GSM601897 3 0.333 0.5911 0.004 0.132 0.856 0.008
#> GSM601902 4 0.506 0.5338 0.256 0.032 0.000 0.712
#> GSM601912 3 0.204 0.6399 0.012 0.048 0.936 0.004
#> GSM601927 1 0.139 0.7375 0.952 0.000 0.048 0.000
#> GSM601932 4 0.448 0.5961 0.084 0.108 0.000 0.808
#> GSM601937 4 0.528 0.1914 0.004 0.004 0.432 0.560
#> GSM601942 3 0.720 0.1985 0.000 0.176 0.536 0.288
#> GSM601947 2 0.744 0.1540 0.284 0.504 0.000 0.212
#> GSM601957 3 0.669 0.2094 0.424 0.088 0.488 0.000
#> GSM601972 4 0.662 0.2664 0.088 0.380 0.000 0.532
#> GSM601977 2 0.768 0.0698 0.000 0.400 0.216 0.384
#> GSM601987 4 0.563 0.2530 0.000 0.384 0.028 0.588
#> GSM601877 1 0.104 0.7276 0.972 0.020 0.000 0.008
#> GSM601907 2 0.265 0.6463 0.004 0.896 0.004 0.096
#> GSM601917 1 0.664 -0.0205 0.524 0.088 0.000 0.388
#> GSM601922 1 0.603 0.0721 0.564 0.048 0.000 0.388
#> GSM601952 4 0.657 0.0808 0.016 0.436 0.044 0.504
#> GSM601962 3 0.463 0.5884 0.032 0.016 0.800 0.152
#> GSM601967 3 0.726 0.3236 0.352 0.156 0.492 0.000
#> GSM601982 4 0.750 0.2630 0.000 0.276 0.228 0.496
#> GSM601992 4 0.193 0.6144 0.000 0.036 0.024 0.940
#> GSM601873 3 0.769 0.0319 0.000 0.256 0.456 0.288
#> GSM601883 4 0.511 0.3033 0.008 0.384 0.000 0.608
#> GSM601888 2 0.423 0.5392 0.052 0.816 0.132 0.000
#> GSM601893 3 0.576 0.3570 0.036 0.372 0.592 0.000
#> GSM601898 3 0.425 0.5873 0.220 0.012 0.768 0.000
#> GSM601903 4 0.545 0.4693 0.320 0.032 0.000 0.648
#> GSM601913 3 0.490 0.5644 0.236 0.000 0.732 0.032
#> GSM601928 1 0.293 0.7186 0.880 0.000 0.108 0.012
#> GSM601933 4 0.367 0.5921 0.000 0.116 0.036 0.848
#> GSM601938 4 0.233 0.6043 0.000 0.088 0.004 0.908
#> GSM601943 3 0.660 0.1972 0.000 0.340 0.564 0.096
#> GSM601948 1 0.396 0.6872 0.832 0.124 0.044 0.000
#> GSM601958 3 0.553 0.2723 0.416 0.020 0.564 0.000
#> GSM601973 4 0.409 0.5839 0.172 0.024 0.000 0.804
#> GSM601978 2 0.311 0.6397 0.000 0.872 0.016 0.112
#> GSM601988 4 0.554 0.1472 0.012 0.004 0.444 0.540
#> GSM601878 1 0.202 0.7311 0.936 0.040 0.024 0.000
#> GSM601908 2 0.451 0.4782 0.008 0.724 0.000 0.268
#> GSM601918 2 0.752 -0.1653 0.184 0.408 0.000 0.408
#> GSM601923 1 0.158 0.7374 0.948 0.004 0.048 0.000
#> GSM601953 2 0.149 0.6454 0.000 0.956 0.012 0.032
#> GSM601963 3 0.487 0.5516 0.244 0.000 0.728 0.028
#> GSM601968 3 0.616 0.5980 0.164 0.160 0.676 0.000
#> GSM601983 3 0.356 0.6465 0.108 0.000 0.856 0.036
#> GSM601993 4 0.311 0.5783 0.016 0.000 0.112 0.872
#> GSM601874 2 0.280 0.6457 0.000 0.892 0.016 0.092
#> GSM601884 4 0.717 0.2543 0.000 0.332 0.152 0.516
#> GSM601889 3 0.590 0.5085 0.280 0.068 0.652 0.000
#> GSM601894 3 0.586 0.4771 0.308 0.056 0.636 0.000
#> GSM601899 2 0.556 -0.0266 0.020 0.544 0.436 0.000
#> GSM601904 1 0.512 0.0648 0.556 0.004 0.000 0.440
#> GSM601914 3 0.254 0.6614 0.084 0.000 0.904 0.012
#> GSM601929 1 0.141 0.7372 0.960 0.020 0.020 0.000
#> GSM601934 4 0.714 0.2671 0.000 0.296 0.164 0.540
#> GSM601939 1 0.425 0.5419 0.724 0.000 0.276 0.000
#> GSM601944 4 0.475 0.5711 0.004 0.124 0.076 0.796
#> GSM601949 1 0.494 0.6436 0.768 0.160 0.072 0.000
#> GSM601959 3 0.627 0.1399 0.452 0.056 0.492 0.000
#> GSM601974 3 0.722 0.2225 0.120 0.008 0.520 0.352
#> GSM601979 2 0.399 0.5844 0.004 0.800 0.008 0.188
#> GSM601989 3 0.395 0.6469 0.136 0.036 0.828 0.000
#> GSM601879 1 0.159 0.7263 0.952 0.040 0.004 0.004
#> GSM601909 3 0.510 0.6326 0.100 0.136 0.764 0.000
#> GSM601919 2 0.745 0.0796 0.412 0.416 0.000 0.172
#> GSM601924 1 0.333 0.7121 0.864 0.024 0.112 0.000
#> GSM601954 2 0.331 0.6082 0.072 0.876 0.000 0.052
#> GSM601964 3 0.355 0.6493 0.096 0.000 0.860 0.044
#> GSM601969 1 0.782 -0.0342 0.416 0.296 0.288 0.000
#> GSM601984 1 0.773 0.2127 0.444 0.000 0.292 0.264
#> GSM601994 4 0.170 0.6141 0.016 0.004 0.028 0.952
#> GSM601875 2 0.357 0.6401 0.004 0.848 0.016 0.132
#> GSM601885 4 0.513 0.2985 0.008 0.388 0.000 0.604
#> GSM601890 3 0.530 0.1032 0.008 0.488 0.504 0.000
#> GSM601895 3 0.216 0.6654 0.068 0.004 0.924 0.004
#> GSM601900 3 0.321 0.6612 0.104 0.024 0.872 0.000
#> GSM601905 4 0.510 0.2376 0.432 0.004 0.000 0.564
#> GSM601915 3 0.445 0.4834 0.308 0.000 0.692 0.000
#> GSM601930 1 0.202 0.7353 0.932 0.000 0.056 0.012
#> GSM601935 3 0.588 0.3494 0.056 0.000 0.632 0.312
#> GSM601940 1 0.458 0.5616 0.728 0.012 0.260 0.000
#> GSM601945 2 0.393 0.6349 0.008 0.832 0.020 0.140
#> GSM601950 1 0.499 0.6359 0.768 0.080 0.152 0.000
#> GSM601960 3 0.247 0.6622 0.080 0.000 0.908 0.012
#> GSM601975 4 0.614 0.5256 0.184 0.140 0.000 0.676
#> GSM601980 3 0.580 0.0791 0.000 0.032 0.548 0.420
#> GSM601990 3 0.361 0.6446 0.060 0.000 0.860 0.080
#> GSM601880 1 0.151 0.7344 0.956 0.000 0.028 0.016
#> GSM601910 3 0.311 0.6463 0.036 0.080 0.884 0.000
#> GSM601920 1 0.558 0.2559 0.636 0.036 0.000 0.328
#> GSM601925 1 0.139 0.7362 0.960 0.000 0.028 0.012
#> GSM601955 3 0.507 0.4873 0.000 0.056 0.744 0.200
#> GSM601965 1 0.596 0.5693 0.676 0.000 0.228 0.096
#> GSM601970 3 0.546 0.5683 0.236 0.060 0.704 0.000
#> GSM601985 1 0.476 0.3527 0.628 0.000 0.372 0.000
#> GSM601995 3 0.541 -0.0390 0.012 0.000 0.500 0.488
#> GSM601876 1 0.447 0.6315 0.768 0.004 0.212 0.016
#> GSM601886 4 0.546 0.5035 0.076 0.000 0.204 0.720
#> GSM601891 3 0.515 0.1453 0.004 0.464 0.532 0.000
#> GSM601896 1 0.437 0.5626 0.728 0.004 0.268 0.000
#> GSM601901 2 0.632 -0.0392 0.060 0.500 0.000 0.440
#> GSM601906 1 0.438 0.6145 0.792 0.000 0.036 0.172
#> GSM601916 4 0.535 0.5310 0.272 0.032 0.004 0.692
#> GSM601931 1 0.228 0.7239 0.904 0.000 0.096 0.000
#> GSM601936 4 0.508 0.4488 0.028 0.000 0.272 0.700
#> GSM601941 4 0.330 0.6127 0.092 0.028 0.004 0.876
#> GSM601946 1 0.387 0.6381 0.788 0.000 0.208 0.004
#> GSM601951 1 0.157 0.7327 0.956 0.028 0.012 0.004
#> GSM601961 2 0.228 0.6232 0.052 0.924 0.024 0.000
#> GSM601976 4 0.598 0.2561 0.432 0.040 0.000 0.528
#> GSM601981 2 0.379 0.6343 0.004 0.836 0.020 0.140
#> GSM601991 3 0.304 0.6188 0.020 0.000 0.880 0.100
#> GSM601881 1 0.173 0.7364 0.948 0.024 0.028 0.000
#> GSM601911 4 0.676 0.3925 0.360 0.104 0.000 0.536
#> GSM601921 1 0.620 -0.1277 0.500 0.052 0.000 0.448
#> GSM601926 1 0.205 0.7326 0.924 0.000 0.072 0.004
#> GSM601956 2 0.362 0.6312 0.000 0.860 0.072 0.068
#> GSM601966 4 0.371 0.5974 0.040 0.112 0.000 0.848
#> GSM601971 1 0.599 0.3834 0.628 0.064 0.308 0.000
#> GSM601986 1 0.495 0.6544 0.776 0.008 0.052 0.164
#> GSM601996 4 0.164 0.6159 0.012 0.012 0.020 0.956
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM601872 2 0.5599 0.1630 0.000 0.492 0.052 0.008 0.448
#> GSM601882 4 0.4338 0.5683 0.000 0.112 0.040 0.800 0.048
#> GSM601887 2 0.5967 0.4936 0.032 0.660 0.160 0.000 0.148
#> GSM601892 2 0.7449 -0.0848 0.104 0.404 0.392 0.000 0.100
#> GSM601897 5 0.3574 0.6313 0.004 0.088 0.072 0.000 0.836
#> GSM601902 4 0.6390 0.4561 0.260 0.012 0.108 0.600 0.020
#> GSM601912 5 0.5302 0.5348 0.008 0.048 0.300 0.004 0.640
#> GSM601927 1 0.2723 0.5286 0.864 0.000 0.124 0.012 0.000
#> GSM601932 4 0.7378 0.5214 0.108 0.060 0.188 0.592 0.052
#> GSM601937 5 0.5684 0.0754 0.000 0.000 0.080 0.432 0.488
#> GSM601942 5 0.4702 0.5748 0.000 0.052 0.060 0.108 0.780
#> GSM601947 1 0.8135 0.0449 0.408 0.256 0.196 0.140 0.000
#> GSM601957 3 0.7210 0.6043 0.300 0.072 0.500 0.000 0.128
#> GSM601972 4 0.7569 0.3188 0.076 0.316 0.144 0.460 0.004
#> GSM601977 2 0.7641 0.0831 0.000 0.400 0.060 0.328 0.212
#> GSM601987 4 0.6289 0.2882 0.000 0.336 0.096 0.544 0.024
#> GSM601877 1 0.0992 0.5804 0.968 0.000 0.024 0.008 0.000
#> GSM601907 2 0.2798 0.5752 0.000 0.852 0.008 0.140 0.000
#> GSM601917 1 0.7198 0.2820 0.564 0.036 0.208 0.168 0.024
#> GSM601922 1 0.6525 0.3118 0.604 0.024 0.160 0.204 0.008
#> GSM601952 4 0.8618 0.1189 0.024 0.328 0.124 0.348 0.176
#> GSM601962 5 0.3775 0.6542 0.016 0.000 0.092 0.060 0.832
#> GSM601967 3 0.8334 0.5037 0.280 0.152 0.352 0.000 0.216
#> GSM601982 4 0.8424 0.1994 0.012 0.244 0.124 0.400 0.220
#> GSM601992 4 0.2228 0.5924 0.000 0.008 0.056 0.916 0.020
#> GSM601873 2 0.8186 0.0444 0.000 0.340 0.140 0.340 0.180
#> GSM601883 4 0.5417 0.2995 0.000 0.372 0.048 0.572 0.008
#> GSM601888 2 0.3458 0.6096 0.016 0.840 0.120 0.000 0.024
#> GSM601893 2 0.7060 0.2237 0.040 0.460 0.352 0.000 0.148
#> GSM601898 3 0.6879 0.4299 0.176 0.020 0.464 0.000 0.340
#> GSM601903 4 0.7450 0.2690 0.324 0.016 0.180 0.452 0.028
#> GSM601913 3 0.6608 0.5679 0.224 0.000 0.544 0.016 0.216
#> GSM601928 1 0.3667 0.4975 0.812 0.000 0.156 0.020 0.012
#> GSM601933 4 0.5414 0.5209 0.000 0.128 0.124 0.716 0.032
#> GSM601938 4 0.3601 0.5976 0.008 0.048 0.048 0.860 0.036
#> GSM601943 5 0.5830 0.3398 0.000 0.264 0.056 0.044 0.636
#> GSM601948 1 0.4841 0.4997 0.748 0.064 0.164 0.000 0.024
#> GSM601958 3 0.6692 0.6130 0.312 0.024 0.516 0.000 0.148
#> GSM601973 4 0.7086 0.4876 0.196 0.012 0.164 0.576 0.052
#> GSM601978 2 0.2800 0.6008 0.000 0.888 0.024 0.072 0.016
#> GSM601988 4 0.6120 0.3091 0.004 0.000 0.172 0.580 0.244
#> GSM601878 1 0.2172 0.5637 0.908 0.016 0.076 0.000 0.000
#> GSM601908 2 0.4546 0.3258 0.000 0.668 0.028 0.304 0.000
#> GSM601918 1 0.8588 -0.1797 0.336 0.240 0.180 0.240 0.004
#> GSM601923 1 0.1251 0.5815 0.956 0.000 0.036 0.000 0.008
#> GSM601953 2 0.1908 0.6177 0.000 0.936 0.024 0.024 0.016
#> GSM601963 5 0.5912 0.3783 0.144 0.004 0.248 0.000 0.604
#> GSM601968 5 0.6945 0.3872 0.080 0.136 0.208 0.000 0.576
#> GSM601983 5 0.4904 0.6062 0.052 0.004 0.208 0.012 0.724
#> GSM601993 4 0.4139 0.5735 0.016 0.000 0.088 0.808 0.088
#> GSM601874 2 0.2392 0.5933 0.000 0.888 0.004 0.104 0.004
#> GSM601884 4 0.7469 0.2921 0.000 0.248 0.060 0.472 0.220
#> GSM601889 3 0.6974 0.5616 0.232 0.020 0.480 0.000 0.268
#> GSM601894 3 0.7076 0.6268 0.248 0.048 0.524 0.000 0.180
#> GSM601899 2 0.6016 0.4879 0.028 0.636 0.224 0.000 0.112
#> GSM601904 1 0.6411 0.2410 0.564 0.000 0.140 0.276 0.020
#> GSM601914 5 0.4919 0.5126 0.040 0.004 0.304 0.000 0.652
#> GSM601929 1 0.2276 0.5790 0.908 0.004 0.076 0.008 0.004
#> GSM601934 4 0.7331 0.1959 0.000 0.296 0.216 0.448 0.040
#> GSM601939 1 0.4909 -0.1810 0.560 0.000 0.412 0.000 0.028
#> GSM601944 4 0.6224 0.5342 0.004 0.104 0.164 0.664 0.064
#> GSM601949 1 0.5389 0.2700 0.660 0.100 0.236 0.000 0.004
#> GSM601959 3 0.6777 0.5901 0.308 0.060 0.536 0.000 0.096
#> GSM601974 5 0.6413 0.5155 0.116 0.016 0.148 0.056 0.664
#> GSM601979 2 0.3675 0.5097 0.000 0.788 0.024 0.188 0.000
#> GSM601989 3 0.6210 0.5586 0.148 0.048 0.648 0.000 0.156
#> GSM601879 1 0.1569 0.5798 0.944 0.008 0.044 0.004 0.000
#> GSM601909 5 0.5894 0.5505 0.044 0.104 0.180 0.000 0.672
#> GSM601919 1 0.7529 0.2224 0.516 0.196 0.176 0.112 0.000
#> GSM601924 1 0.3484 0.4561 0.820 0.004 0.152 0.000 0.024
#> GSM601954 2 0.7362 0.3421 0.092 0.564 0.232 0.088 0.024
#> GSM601964 5 0.2925 0.6570 0.024 0.004 0.084 0.008 0.880
#> GSM601969 3 0.7944 0.4239 0.260 0.268 0.388 0.000 0.084
#> GSM601984 3 0.7369 0.2845 0.224 0.000 0.456 0.276 0.044
#> GSM601994 4 0.2478 0.5935 0.008 0.000 0.060 0.904 0.028
#> GSM601875 2 0.4191 0.5610 0.000 0.780 0.060 0.156 0.004
#> GSM601885 4 0.6793 0.2362 0.012 0.364 0.112 0.492 0.020
#> GSM601890 2 0.6502 0.0728 0.020 0.460 0.112 0.000 0.408
#> GSM601895 5 0.5021 0.5466 0.044 0.012 0.268 0.000 0.676
#> GSM601900 3 0.6498 0.4534 0.116 0.036 0.596 0.004 0.248
#> GSM601905 4 0.5973 0.2461 0.388 0.008 0.088 0.516 0.000
#> GSM601915 3 0.6458 0.5148 0.216 0.000 0.492 0.000 0.292
#> GSM601930 1 0.3238 0.5283 0.836 0.000 0.136 0.028 0.000
#> GSM601935 5 0.6084 0.5170 0.012 0.000 0.144 0.240 0.604
#> GSM601940 1 0.4980 -0.1372 0.576 0.008 0.396 0.000 0.020
#> GSM601945 2 0.3967 0.5753 0.000 0.808 0.040 0.136 0.016
#> GSM601950 1 0.5325 0.0396 0.612 0.044 0.332 0.000 0.012
#> GSM601960 5 0.4250 0.6069 0.016 0.008 0.228 0.004 0.744
#> GSM601975 4 0.7696 0.4479 0.204 0.104 0.184 0.504 0.004
#> GSM601980 5 0.4493 0.5769 0.004 0.012 0.068 0.136 0.780
#> GSM601990 5 0.3697 0.6409 0.008 0.000 0.180 0.016 0.796
#> GSM601880 1 0.1282 0.5764 0.952 0.000 0.044 0.004 0.000
#> GSM601910 5 0.6581 0.2508 0.020 0.104 0.424 0.004 0.448
#> GSM601920 1 0.5834 0.3930 0.648 0.016 0.132 0.204 0.000
#> GSM601925 1 0.1195 0.5813 0.960 0.000 0.028 0.012 0.000
#> GSM601955 5 0.3047 0.6443 0.004 0.020 0.056 0.036 0.884
#> GSM601965 3 0.7090 0.2880 0.332 0.000 0.440 0.204 0.024
#> GSM601970 5 0.6578 0.3539 0.116 0.056 0.232 0.000 0.596
#> GSM601985 3 0.5652 0.3679 0.464 0.000 0.468 0.004 0.064
#> GSM601995 5 0.4827 0.5254 0.004 0.000 0.060 0.232 0.704
#> GSM601876 3 0.5231 0.2752 0.468 0.000 0.496 0.028 0.008
#> GSM601886 4 0.6200 0.4898 0.044 0.004 0.188 0.652 0.112
#> GSM601891 2 0.6124 0.2576 0.000 0.520 0.144 0.000 0.336
#> GSM601896 1 0.4889 -0.3150 0.504 0.000 0.476 0.004 0.016
#> GSM601901 4 0.6034 0.1281 0.016 0.448 0.060 0.472 0.004
#> GSM601906 1 0.5254 0.4899 0.692 0.000 0.200 0.100 0.008
#> GSM601916 4 0.5445 0.5719 0.140 0.020 0.124 0.712 0.004
#> GSM601931 1 0.3439 0.4661 0.800 0.000 0.188 0.008 0.004
#> GSM601936 4 0.5842 0.4761 0.020 0.000 0.216 0.648 0.116
#> GSM601941 4 0.6296 0.5514 0.112 0.012 0.144 0.672 0.060
#> GSM601946 1 0.4680 -0.1824 0.540 0.000 0.448 0.008 0.004
#> GSM601951 1 0.3328 0.5409 0.812 0.004 0.176 0.000 0.008
#> GSM601961 2 0.2630 0.6163 0.012 0.892 0.080 0.016 0.000
#> GSM601976 4 0.6381 0.4552 0.252 0.028 0.132 0.588 0.000
#> GSM601981 2 0.4251 0.5266 0.000 0.756 0.040 0.200 0.004
#> GSM601991 5 0.5532 0.5550 0.000 0.000 0.280 0.104 0.616
#> GSM601881 1 0.1608 0.5663 0.928 0.000 0.072 0.000 0.000
#> GSM601911 4 0.7366 0.4481 0.116 0.100 0.228 0.548 0.008
#> GSM601921 1 0.6830 0.1667 0.532 0.028 0.148 0.288 0.004
#> GSM601926 1 0.1478 0.5695 0.936 0.000 0.064 0.000 0.000
#> GSM601956 2 0.3542 0.6106 0.000 0.856 0.048 0.044 0.052
#> GSM601966 4 0.2523 0.5940 0.008 0.064 0.020 0.904 0.004
#> GSM601971 1 0.7565 -0.3411 0.412 0.052 0.312 0.000 0.224
#> GSM601986 3 0.6969 0.2574 0.300 0.004 0.452 0.236 0.008
#> GSM601996 4 0.1865 0.5983 0.008 0.000 0.032 0.936 0.024
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM601872 3 0.495 0.2670 0.000 0.380 0.568 0.032 0.004 0.016
#> GSM601882 5 0.571 0.3998 0.028 0.132 0.068 0.088 0.684 0.000
#> GSM601887 2 0.625 0.4724 0.036 0.632 0.160 0.064 0.000 0.108
#> GSM601892 2 0.656 0.0798 0.020 0.448 0.080 0.060 0.000 0.392
#> GSM601897 3 0.324 0.6317 0.000 0.040 0.852 0.060 0.000 0.048
#> GSM601902 5 0.670 -0.3713 0.220 0.016 0.008 0.308 0.440 0.008
#> GSM601912 3 0.647 0.5546 0.012 0.056 0.616 0.064 0.048 0.204
#> GSM601927 1 0.409 0.4557 0.668 0.000 0.000 0.020 0.004 0.308
#> GSM601932 4 0.668 0.4571 0.096 0.044 0.008 0.532 0.292 0.028
#> GSM601937 3 0.562 0.1674 0.000 0.000 0.476 0.072 0.424 0.028
#> GSM601942 3 0.379 0.6063 0.000 0.044 0.800 0.136 0.008 0.012
#> GSM601947 1 0.679 -0.2860 0.392 0.184 0.008 0.380 0.032 0.004
#> GSM601957 6 0.376 0.6291 0.040 0.044 0.040 0.040 0.000 0.836
#> GSM601972 4 0.741 0.3728 0.096 0.224 0.008 0.368 0.304 0.000
#> GSM601977 2 0.757 0.1097 0.004 0.348 0.280 0.136 0.232 0.000
#> GSM601987 5 0.511 0.2744 0.008 0.348 0.008 0.044 0.588 0.004
#> GSM601877 1 0.270 0.5766 0.844 0.000 0.000 0.016 0.000 0.140
#> GSM601907 2 0.291 0.5956 0.000 0.844 0.000 0.040 0.116 0.000
#> GSM601917 1 0.503 0.2787 0.640 0.016 0.008 0.284 0.052 0.000
#> GSM601922 1 0.414 0.4121 0.752 0.012 0.000 0.176 0.060 0.000
#> GSM601952 4 0.770 0.3360 0.032 0.224 0.072 0.484 0.160 0.028
#> GSM601962 3 0.526 0.6121 0.048 0.004 0.740 0.060 0.088 0.060
#> GSM601967 6 0.707 0.4974 0.080 0.116 0.140 0.092 0.000 0.572
#> GSM601982 5 0.846 0.0192 0.044 0.272 0.260 0.128 0.284 0.012
#> GSM601992 5 0.214 0.4478 0.000 0.028 0.012 0.048 0.912 0.000
#> GSM601873 2 0.780 0.0409 0.004 0.384 0.112 0.124 0.332 0.044
#> GSM601883 5 0.628 0.1253 0.024 0.388 0.016 0.112 0.460 0.000
#> GSM601888 2 0.406 0.5869 0.008 0.808 0.032 0.064 0.004 0.084
#> GSM601893 2 0.702 0.3611 0.028 0.520 0.096 0.092 0.004 0.260
#> GSM601898 6 0.364 0.5617 0.004 0.004 0.140 0.052 0.000 0.800
#> GSM601903 4 0.652 0.4217 0.304 0.012 0.004 0.428 0.248 0.004
#> GSM601913 6 0.514 0.5936 0.080 0.000 0.076 0.072 0.032 0.740
#> GSM601928 1 0.451 0.4025 0.620 0.000 0.004 0.028 0.004 0.344
#> GSM601933 5 0.514 0.4402 0.008 0.164 0.008 0.056 0.716 0.048
#> GSM601938 5 0.447 0.4259 0.012 0.064 0.072 0.072 0.780 0.000
#> GSM601943 3 0.603 0.4204 0.000 0.236 0.600 0.112 0.020 0.032
#> GSM601948 1 0.684 0.3631 0.500 0.060 0.012 0.188 0.000 0.240
#> GSM601958 6 0.322 0.6310 0.064 0.012 0.040 0.024 0.000 0.860
#> GSM601973 4 0.695 0.4075 0.184 0.016 0.028 0.432 0.332 0.008
#> GSM601978 2 0.351 0.5867 0.020 0.836 0.008 0.084 0.052 0.000
#> GSM601988 5 0.528 0.3627 0.000 0.004 0.200 0.056 0.676 0.064
#> GSM601878 1 0.314 0.5502 0.796 0.000 0.000 0.016 0.000 0.188
#> GSM601908 2 0.553 0.3892 0.012 0.608 0.004 0.136 0.240 0.000
#> GSM601918 1 0.685 -0.1938 0.456 0.156 0.000 0.296 0.092 0.000
#> GSM601923 1 0.257 0.5814 0.856 0.000 0.004 0.008 0.000 0.132
#> GSM601953 2 0.297 0.5948 0.012 0.872 0.012 0.080 0.016 0.008
#> GSM601963 3 0.599 0.4815 0.104 0.004 0.628 0.036 0.020 0.208
#> GSM601968 3 0.632 0.3941 0.008 0.096 0.540 0.064 0.000 0.292
#> GSM601983 3 0.563 0.5975 0.072 0.008 0.708 0.040 0.052 0.120
#> GSM601993 5 0.324 0.4188 0.008 0.000 0.032 0.096 0.848 0.016
#> GSM601874 2 0.335 0.5988 0.004 0.832 0.004 0.076 0.084 0.000
#> GSM601884 3 0.737 -0.1351 0.008 0.296 0.316 0.076 0.304 0.000
#> GSM601889 6 0.405 0.5989 0.012 0.028 0.084 0.072 0.000 0.804
#> GSM601894 6 0.461 0.6261 0.084 0.032 0.068 0.040 0.000 0.776
#> GSM601899 2 0.585 0.5233 0.008 0.664 0.116 0.080 0.004 0.128
#> GSM601904 1 0.622 0.2053 0.524 0.000 0.000 0.288 0.144 0.044
#> GSM601914 3 0.521 0.5173 0.004 0.000 0.624 0.040 0.040 0.292
#> GSM601929 1 0.418 0.5605 0.732 0.000 0.000 0.064 0.004 0.200
#> GSM601934 5 0.700 0.2079 0.012 0.332 0.020 0.080 0.476 0.080
#> GSM601939 6 0.426 0.4435 0.308 0.000 0.012 0.012 0.004 0.664
#> GSM601944 5 0.699 0.0938 0.008 0.092 0.016 0.352 0.452 0.080
#> GSM601949 6 0.667 0.0496 0.400 0.112 0.004 0.076 0.000 0.408
#> GSM601959 6 0.425 0.6164 0.056 0.032 0.024 0.092 0.000 0.796
#> GSM601974 3 0.625 0.3903 0.072 0.008 0.548 0.316 0.020 0.036
#> GSM601979 2 0.392 0.5585 0.004 0.776 0.000 0.096 0.124 0.000
#> GSM601989 6 0.543 0.5806 0.036 0.064 0.064 0.072 0.024 0.740
#> GSM601879 1 0.308 0.5863 0.828 0.000 0.000 0.040 0.000 0.132
#> GSM601909 3 0.488 0.5766 0.020 0.052 0.716 0.024 0.000 0.188
#> GSM601919 1 0.501 0.3019 0.672 0.108 0.000 0.204 0.016 0.000
#> GSM601924 1 0.416 0.3524 0.632 0.000 0.004 0.016 0.000 0.348
#> GSM601954 4 0.718 0.1142 0.076 0.328 0.004 0.448 0.024 0.120
#> GSM601964 3 0.329 0.6472 0.024 0.000 0.860 0.024 0.036 0.056
#> GSM601969 6 0.681 0.4173 0.096 0.132 0.020 0.200 0.000 0.552
#> GSM601984 5 0.751 -0.0707 0.172 0.004 0.032 0.064 0.388 0.340
#> GSM601994 5 0.155 0.4391 0.004 0.008 0.004 0.044 0.940 0.000
#> GSM601875 2 0.419 0.5726 0.008 0.760 0.000 0.036 0.176 0.020
#> GSM601885 5 0.655 0.1304 0.020 0.376 0.016 0.084 0.476 0.028
#> GSM601890 3 0.564 0.1244 0.004 0.412 0.496 0.040 0.000 0.048
#> GSM601895 3 0.620 0.4353 0.044 0.016 0.584 0.048 0.024 0.284
#> GSM601900 6 0.542 0.5301 0.020 0.032 0.088 0.120 0.020 0.720
#> GSM601905 1 0.655 -0.2122 0.392 0.000 0.004 0.192 0.384 0.028
#> GSM601915 6 0.374 0.5881 0.032 0.000 0.136 0.032 0.000 0.800
#> GSM601930 1 0.414 0.4151 0.656 0.000 0.000 0.020 0.004 0.320
#> GSM601935 3 0.719 0.3987 0.004 0.000 0.456 0.172 0.240 0.128
#> GSM601940 6 0.601 0.2602 0.400 0.008 0.028 0.068 0.008 0.488
#> GSM601945 2 0.550 0.5290 0.012 0.676 0.008 0.180 0.100 0.024
#> GSM601950 6 0.529 0.3985 0.284 0.036 0.004 0.052 0.000 0.624
#> GSM601960 3 0.568 0.4878 0.004 0.004 0.576 0.124 0.008 0.284
#> GSM601975 4 0.708 0.4828 0.164 0.080 0.004 0.436 0.312 0.004
#> GSM601980 3 0.416 0.5805 0.000 0.004 0.756 0.180 0.044 0.016
#> GSM601990 3 0.436 0.6339 0.000 0.000 0.768 0.048 0.068 0.116
#> GSM601880 1 0.314 0.5645 0.788 0.000 0.000 0.012 0.000 0.200
#> GSM601910 6 0.700 -0.1463 0.004 0.100 0.356 0.076 0.020 0.444
#> GSM601920 1 0.548 0.3926 0.648 0.004 0.000 0.224 0.072 0.052
#> GSM601925 1 0.267 0.5728 0.836 0.000 0.008 0.000 0.000 0.156
#> GSM601955 3 0.397 0.6019 0.000 0.016 0.768 0.184 0.012 0.020
#> GSM601965 6 0.830 0.2048 0.272 0.008 0.056 0.092 0.264 0.308
#> GSM601970 3 0.592 0.3167 0.020 0.024 0.512 0.068 0.000 0.376
#> GSM601985 6 0.454 0.5755 0.184 0.000 0.028 0.044 0.008 0.736
#> GSM601995 3 0.536 0.4885 0.000 0.000 0.620 0.188 0.184 0.008
#> GSM601876 6 0.549 0.4698 0.260 0.000 0.012 0.044 0.052 0.632
#> GSM601886 5 0.733 0.0831 0.036 0.000 0.068 0.264 0.460 0.172
#> GSM601891 2 0.625 0.2701 0.000 0.528 0.300 0.092 0.000 0.080
#> GSM601896 6 0.583 0.4403 0.284 0.016 0.008 0.064 0.028 0.600
#> GSM601901 2 0.705 -0.1533 0.040 0.372 0.004 0.232 0.344 0.008
#> GSM601906 1 0.703 0.3079 0.436 0.000 0.004 0.200 0.076 0.284
#> GSM601916 5 0.692 -0.1753 0.080 0.036 0.004 0.320 0.492 0.068
#> GSM601931 1 0.466 0.1940 0.540 0.000 0.000 0.028 0.008 0.424
#> GSM601936 5 0.499 0.4057 0.012 0.000 0.044 0.108 0.732 0.104
#> GSM601941 5 0.624 -0.3487 0.076 0.024 0.032 0.408 0.460 0.000
#> GSM601946 6 0.440 0.4559 0.276 0.000 0.008 0.032 0.004 0.680
#> GSM601951 1 0.577 0.2582 0.472 0.004 0.000 0.156 0.000 0.368
#> GSM601961 2 0.373 0.5754 0.004 0.812 0.000 0.112 0.020 0.052
#> GSM601976 5 0.750 -0.2379 0.204 0.044 0.000 0.280 0.416 0.056
#> GSM601981 2 0.543 0.4929 0.004 0.660 0.004 0.176 0.136 0.020
#> GSM601991 3 0.636 0.5511 0.004 0.004 0.584 0.064 0.172 0.172
#> GSM601881 1 0.308 0.5404 0.776 0.000 0.000 0.004 0.000 0.220
#> GSM601911 5 0.733 0.3707 0.096 0.124 0.008 0.088 0.564 0.120
#> GSM601921 1 0.541 0.2529 0.620 0.008 0.000 0.248 0.116 0.008
#> GSM601926 1 0.352 0.5112 0.732 0.000 0.000 0.012 0.000 0.256
#> GSM601956 2 0.491 0.5607 0.012 0.748 0.100 0.100 0.032 0.008
#> GSM601966 5 0.488 0.3580 0.028 0.072 0.008 0.128 0.748 0.016
#> GSM601971 6 0.690 0.4221 0.120 0.020 0.100 0.228 0.000 0.532
#> GSM601986 6 0.778 0.1866 0.268 0.024 0.008 0.072 0.276 0.352
#> GSM601996 5 0.188 0.4387 0.008 0.020 0.004 0.040 0.928 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
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)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
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)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, 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.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")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n time(p) gender(p) k
#> MAD:NMF 123 0.204 0.97448 2
#> MAD:NMF 84 0.422 0.21684 3
#> MAD:NMF 74 0.509 0.88272 4
#> MAD:NMF 61 0.362 0.10623 5
#> MAD:NMF 43 0.813 0.00271 6
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.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["ATC", "hclust"]
# you can also extract it by
# res = res_list["ATC:hclust"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 21512 rows and 125 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'hclust' method.
#> Performed in total 1250 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_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
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, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.496 0.966 0.857 0.3689 0.496 0.496
#> 3 3 0.949 0.953 0.982 0.4037 0.992 0.984
#> 4 4 0.720 0.820 0.892 0.1445 0.965 0.927
#> 5 5 0.659 0.778 0.865 0.1262 0.899 0.779
#> 6 6 0.634 0.695 0.796 0.0653 0.976 0.934
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 4
Following shows 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 class
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
#> GSM601872 2 0.8555 0.987 0.280 0.720
#> GSM601882 2 0.8555 0.987 0.280 0.720
#> GSM601887 1 0.0000 0.976 1.000 0.000
#> GSM601892 1 0.0000 0.976 1.000 0.000
#> GSM601897 1 0.0376 0.973 0.996 0.004
#> GSM601902 2 0.8555 0.987 0.280 0.720
#> GSM601912 1 0.4022 0.891 0.920 0.080
#> GSM601927 1 0.0000 0.976 1.000 0.000
#> GSM601932 2 0.8555 0.987 0.280 0.720
#> GSM601937 2 0.8555 0.987 0.280 0.720
#> GSM601942 2 0.8555 0.987 0.280 0.720
#> GSM601947 2 0.8909 0.952 0.308 0.692
#> GSM601957 1 0.0000 0.976 1.000 0.000
#> GSM601972 2 0.8555 0.987 0.280 0.720
#> GSM601977 2 0.8555 0.987 0.280 0.720
#> GSM601987 2 0.8555 0.987 0.280 0.720
#> GSM601877 1 0.0000 0.976 1.000 0.000
#> GSM601907 2 0.8555 0.987 0.280 0.720
#> GSM601917 2 0.8555 0.987 0.280 0.720
#> GSM601922 2 0.8555 0.987 0.280 0.720
#> GSM601952 2 0.8555 0.987 0.280 0.720
#> GSM601962 1 0.4022 0.891 0.920 0.080
#> GSM601967 1 0.0000 0.976 1.000 0.000
#> GSM601982 2 0.8555 0.987 0.280 0.720
#> GSM601992 2 0.8555 0.987 0.280 0.720
#> GSM601873 2 0.8555 0.987 0.280 0.720
#> GSM601883 2 0.8555 0.987 0.280 0.720
#> GSM601888 1 0.0000 0.976 1.000 0.000
#> GSM601893 1 0.0000 0.976 1.000 0.000
#> GSM601898 1 0.0000 0.976 1.000 0.000
#> GSM601903 2 0.8555 0.987 0.280 0.720
#> GSM601913 1 0.0938 0.967 0.988 0.012
#> GSM601928 1 0.0000 0.976 1.000 0.000
#> GSM601933 2 0.8555 0.987 0.280 0.720
#> GSM601938 2 0.8555 0.987 0.280 0.720
#> GSM601943 2 0.8555 0.987 0.280 0.720
#> GSM601948 1 0.0376 0.973 0.996 0.004
#> GSM601958 1 0.0000 0.976 1.000 0.000
#> GSM601973 2 0.8555 0.987 0.280 0.720
#> GSM601978 2 0.8555 0.987 0.280 0.720
#> GSM601988 2 0.8207 0.960 0.256 0.744
#> GSM601878 1 0.0000 0.976 1.000 0.000
#> GSM601908 2 0.8555 0.987 0.280 0.720
#> GSM601918 2 0.8555 0.987 0.280 0.720
#> GSM601923 1 0.0000 0.976 1.000 0.000
#> GSM601953 2 0.8555 0.987 0.280 0.720
#> GSM601963 1 0.0000 0.976 1.000 0.000
#> GSM601968 1 0.0000 0.976 1.000 0.000
#> GSM601983 1 0.0000 0.976 1.000 0.000
#> GSM601993 2 0.8555 0.987 0.280 0.720
#> GSM601874 2 0.8555 0.987 0.280 0.720
#> GSM601884 2 0.8555 0.987 0.280 0.720
#> GSM601889 1 0.0000 0.976 1.000 0.000
#> GSM601894 1 0.0000 0.976 1.000 0.000
#> GSM601899 1 0.0000 0.976 1.000 0.000
#> GSM601904 2 0.8608 0.983 0.284 0.716
#> GSM601914 1 0.0000 0.976 1.000 0.000
#> GSM601929 1 0.2778 0.931 0.952 0.048
#> GSM601934 2 0.8555 0.987 0.280 0.720
#> GSM601939 1 0.0000 0.976 1.000 0.000
#> GSM601944 2 0.8555 0.987 0.280 0.720
#> GSM601949 1 0.0000 0.976 1.000 0.000
#> GSM601959 1 0.0000 0.976 1.000 0.000
#> GSM601974 2 0.8955 0.947 0.312 0.688
#> GSM601979 2 0.8555 0.987 0.280 0.720
#> GSM601989 1 0.0672 0.970 0.992 0.008
#> GSM601879 1 0.0000 0.976 1.000 0.000
#> GSM601909 1 0.0000 0.976 1.000 0.000
#> GSM601919 2 0.8555 0.987 0.280 0.720
#> GSM601924 1 0.0000 0.976 1.000 0.000
#> GSM601954 2 0.8909 0.952 0.308 0.692
#> GSM601964 1 0.0000 0.976 1.000 0.000
#> GSM601969 1 0.0000 0.976 1.000 0.000
#> GSM601984 1 0.3733 0.901 0.928 0.072
#> GSM601994 2 0.8555 0.987 0.280 0.720
#> GSM601875 2 0.8555 0.987 0.280 0.720
#> GSM601885 2 0.8555 0.987 0.280 0.720
#> GSM601890 1 0.0000 0.976 1.000 0.000
#> GSM601895 1 0.0000 0.976 1.000 0.000
#> GSM601900 1 0.0938 0.967 0.988 0.012
#> GSM601905 2 0.8661 0.979 0.288 0.712
#> GSM601915 1 0.0000 0.976 1.000 0.000
#> GSM601930 1 0.0000 0.976 1.000 0.000
#> GSM601935 2 0.8207 0.960 0.256 0.744
#> GSM601940 1 0.0000 0.976 1.000 0.000
#> GSM601945 2 0.8555 0.987 0.280 0.720
#> GSM601950 1 0.0000 0.976 1.000 0.000
#> GSM601960 1 0.0000 0.976 1.000 0.000
#> GSM601975 2 0.8555 0.987 0.280 0.720
#> GSM601980 2 0.8207 0.960 0.256 0.744
#> GSM601990 1 0.5842 0.789 0.860 0.140
#> GSM601880 1 0.0000 0.976 1.000 0.000
#> GSM601910 1 0.0376 0.973 0.996 0.004
#> GSM601920 2 0.8661 0.979 0.288 0.712
#> GSM601925 1 0.0000 0.976 1.000 0.000
#> GSM601955 2 0.8207 0.960 0.256 0.744
#> GSM601965 1 0.3879 0.896 0.924 0.076
#> GSM601970 1 0.0000 0.976 1.000 0.000
#> GSM601985 1 0.0000 0.976 1.000 0.000
#> GSM601995 2 0.0000 0.631 0.000 1.000
#> GSM601876 1 0.2423 0.940 0.960 0.040
#> GSM601886 2 0.8555 0.987 0.280 0.720
#> GSM601891 1 0.0000 0.976 1.000 0.000
#> GSM601896 1 0.2423 0.940 0.960 0.040
#> GSM601901 2 0.8555 0.987 0.280 0.720
#> GSM601906 2 0.8661 0.979 0.288 0.712
#> GSM601916 2 0.8661 0.979 0.288 0.712
#> GSM601931 1 0.0000 0.976 1.000 0.000
#> GSM601936 2 0.8207 0.960 0.256 0.744
#> GSM601941 2 0.8555 0.987 0.280 0.720
#> GSM601946 1 0.0000 0.976 1.000 0.000
#> GSM601951 1 0.0376 0.973 0.996 0.004
#> GSM601961 1 0.4815 0.848 0.896 0.104
#> GSM601976 2 0.8608 0.983 0.284 0.716
#> GSM601981 2 0.8555 0.987 0.280 0.720
#> GSM601991 1 0.5842 0.789 0.860 0.140
#> GSM601881 1 0.0000 0.976 1.000 0.000
#> GSM601911 1 0.5059 0.842 0.888 0.112
#> GSM601921 2 0.8661 0.979 0.288 0.712
#> GSM601926 1 0.0000 0.976 1.000 0.000
#> GSM601956 2 0.8555 0.987 0.280 0.720
#> GSM601966 2 0.8555 0.987 0.280 0.720
#> GSM601971 1 0.0000 0.976 1.000 0.000
#> GSM601986 1 0.5059 0.842 0.888 0.112
#> GSM601996 2 0.8555 0.987 0.280 0.720
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM601872 2 0.0000 0.980 0.000 1.000 0.000
#> GSM601882 2 0.0000 0.980 0.000 1.000 0.000
#> GSM601887 1 0.0000 0.977 1.000 0.000 0.000
#> GSM601892 1 0.0000 0.977 1.000 0.000 0.000
#> GSM601897 1 0.0237 0.975 0.996 0.004 0.000
#> GSM601902 2 0.0000 0.980 0.000 1.000 0.000
#> GSM601912 1 0.2537 0.899 0.920 0.080 0.000
#> GSM601927 1 0.0000 0.977 1.000 0.000 0.000
#> GSM601932 2 0.0237 0.978 0.000 0.996 0.004
#> GSM601937 2 0.2261 0.927 0.000 0.932 0.068
#> GSM601942 2 0.0000 0.980 0.000 1.000 0.000
#> GSM601947 2 0.3941 0.746 0.156 0.844 0.000
#> GSM601957 1 0.0000 0.977 1.000 0.000 0.000
#> GSM601972 2 0.0000 0.980 0.000 1.000 0.000
#> GSM601977 2 0.0000 0.980 0.000 1.000 0.000
#> GSM601987 2 0.0000 0.980 0.000 1.000 0.000
#> GSM601877 1 0.0000 0.977 1.000 0.000 0.000
#> GSM601907 2 0.0000 0.980 0.000 1.000 0.000
#> GSM601917 2 0.1031 0.964 0.000 0.976 0.024
#> GSM601922 2 0.0000 0.980 0.000 1.000 0.000
#> GSM601952 2 0.0237 0.978 0.000 0.996 0.004
#> GSM601962 1 0.2774 0.903 0.920 0.072 0.008
#> GSM601967 1 0.0000 0.977 1.000 0.000 0.000
#> GSM601982 2 0.0000 0.980 0.000 1.000 0.000
#> GSM601992 2 0.0000 0.980 0.000 1.000 0.000
#> GSM601873 2 0.0000 0.980 0.000 1.000 0.000
#> GSM601883 2 0.0000 0.980 0.000 1.000 0.000
#> GSM601888 1 0.0000 0.977 1.000 0.000 0.000
#> GSM601893 1 0.0000 0.977 1.000 0.000 0.000
#> GSM601898 1 0.0000 0.977 1.000 0.000 0.000
#> GSM601903 2 0.0000 0.980 0.000 1.000 0.000
#> GSM601913 1 0.0592 0.968 0.988 0.012 0.000
#> GSM601928 1 0.0000 0.977 1.000 0.000 0.000
#> GSM601933 2 0.0000 0.980 0.000 1.000 0.000
#> GSM601938 2 0.0000 0.980 0.000 1.000 0.000
#> GSM601943 2 0.0000 0.980 0.000 1.000 0.000
#> GSM601948 1 0.0237 0.974 0.996 0.004 0.000
#> GSM601958 1 0.0000 0.977 1.000 0.000 0.000
#> GSM601973 2 0.0000 0.980 0.000 1.000 0.000
#> GSM601978 2 0.0000 0.980 0.000 1.000 0.000
#> GSM601988 2 0.2625 0.912 0.000 0.916 0.084
#> GSM601878 1 0.0000 0.977 1.000 0.000 0.000
#> GSM601908 2 0.0000 0.980 0.000 1.000 0.000
#> GSM601918 2 0.0000 0.980 0.000 1.000 0.000
#> GSM601923 1 0.0000 0.977 1.000 0.000 0.000
#> GSM601953 2 0.0000 0.980 0.000 1.000 0.000
#> GSM601963 1 0.0000 0.977 1.000 0.000 0.000
#> GSM601968 1 0.0000 0.977 1.000 0.000 0.000
#> GSM601983 1 0.0000 0.977 1.000 0.000 0.000
#> GSM601993 2 0.0000 0.980 0.000 1.000 0.000
#> GSM601874 2 0.0000 0.980 0.000 1.000 0.000
#> GSM601884 2 0.0000 0.980 0.000 1.000 0.000
#> GSM601889 1 0.0000 0.977 1.000 0.000 0.000
#> GSM601894 1 0.0000 0.977 1.000 0.000 0.000
#> GSM601899 1 0.0000 0.977 1.000 0.000 0.000
#> GSM601904 2 0.0237 0.977 0.004 0.996 0.000
#> GSM601914 1 0.0000 0.977 1.000 0.000 0.000
#> GSM601929 1 0.1753 0.934 0.952 0.048 0.000
#> GSM601934 2 0.0000 0.980 0.000 1.000 0.000
#> GSM601939 1 0.0000 0.977 1.000 0.000 0.000
#> GSM601944 2 0.0000 0.980 0.000 1.000 0.000
#> GSM601949 1 0.0000 0.977 1.000 0.000 0.000
#> GSM601959 1 0.0000 0.977 1.000 0.000 0.000
#> GSM601974 2 0.1289 0.947 0.032 0.968 0.000
#> GSM601979 2 0.0000 0.980 0.000 1.000 0.000
#> GSM601989 1 0.0424 0.971 0.992 0.008 0.000
#> GSM601879 1 0.0000 0.977 1.000 0.000 0.000
#> GSM601909 1 0.0000 0.977 1.000 0.000 0.000
#> GSM601919 2 0.0000 0.980 0.000 1.000 0.000
#> GSM601924 1 0.0000 0.977 1.000 0.000 0.000
#> GSM601954 2 0.1411 0.940 0.036 0.964 0.000
#> GSM601964 1 0.0000 0.977 1.000 0.000 0.000
#> GSM601969 1 0.0000 0.977 1.000 0.000 0.000
#> GSM601984 1 0.2448 0.903 0.924 0.076 0.000
#> GSM601994 2 0.0000 0.980 0.000 1.000 0.000
#> GSM601875 2 0.0000 0.980 0.000 1.000 0.000
#> GSM601885 2 0.0000 0.980 0.000 1.000 0.000
#> GSM601890 1 0.0000 0.977 1.000 0.000 0.000
#> GSM601895 1 0.0000 0.977 1.000 0.000 0.000
#> GSM601900 1 0.0592 0.968 0.988 0.012 0.000
#> GSM601905 2 0.0424 0.973 0.008 0.992 0.000
#> GSM601915 1 0.0000 0.977 1.000 0.000 0.000
#> GSM601930 1 0.0000 0.977 1.000 0.000 0.000
#> GSM601935 2 0.3879 0.837 0.000 0.848 0.152
#> GSM601940 1 0.0000 0.977 1.000 0.000 0.000
#> GSM601945 2 0.0000 0.980 0.000 1.000 0.000
#> GSM601950 1 0.0000 0.977 1.000 0.000 0.000
#> GSM601960 1 0.0000 0.977 1.000 0.000 0.000
#> GSM601975 2 0.0000 0.980 0.000 1.000 0.000
#> GSM601980 2 0.2878 0.899 0.000 0.904 0.096
#> GSM601990 1 0.4544 0.842 0.860 0.084 0.056
#> GSM601880 1 0.0000 0.977 1.000 0.000 0.000
#> GSM601910 1 0.0237 0.975 0.996 0.004 0.000
#> GSM601920 2 0.0424 0.973 0.008 0.992 0.000
#> GSM601925 1 0.0000 0.977 1.000 0.000 0.000
#> GSM601955 2 0.4702 0.757 0.000 0.788 0.212
#> GSM601965 1 0.2537 0.899 0.920 0.080 0.000
#> GSM601970 1 0.0000 0.977 1.000 0.000 0.000
#> GSM601985 1 0.0000 0.977 1.000 0.000 0.000
#> GSM601995 3 0.0000 0.000 0.000 0.000 1.000
#> GSM601876 1 0.1643 0.939 0.956 0.044 0.000
#> GSM601886 2 0.0892 0.967 0.000 0.980 0.020
#> GSM601891 1 0.0000 0.977 1.000 0.000 0.000
#> GSM601896 1 0.1643 0.939 0.956 0.044 0.000
#> GSM601901 2 0.0000 0.980 0.000 1.000 0.000
#> GSM601906 2 0.0424 0.973 0.008 0.992 0.000
#> GSM601916 2 0.0424 0.973 0.008 0.992 0.000
#> GSM601931 1 0.0000 0.977 1.000 0.000 0.000
#> GSM601936 2 0.3879 0.837 0.000 0.848 0.152
#> GSM601941 2 0.0000 0.980 0.000 1.000 0.000
#> GSM601946 1 0.0000 0.977 1.000 0.000 0.000
#> GSM601951 1 0.0237 0.974 0.996 0.004 0.000
#> GSM601961 1 0.3038 0.859 0.896 0.104 0.000
#> GSM601976 2 0.0237 0.977 0.004 0.996 0.000
#> GSM601981 2 0.0000 0.980 0.000 1.000 0.000
#> GSM601991 1 0.4544 0.842 0.860 0.084 0.056
#> GSM601881 1 0.0000 0.977 1.000 0.000 0.000
#> GSM601911 1 0.3267 0.851 0.884 0.116 0.000
#> GSM601921 2 0.0424 0.973 0.008 0.992 0.000
#> GSM601926 1 0.0000 0.977 1.000 0.000 0.000
#> GSM601956 2 0.0000 0.980 0.000 1.000 0.000
#> GSM601966 2 0.0000 0.980 0.000 1.000 0.000
#> GSM601971 1 0.0000 0.977 1.000 0.000 0.000
#> GSM601986 1 0.3267 0.851 0.884 0.116 0.000
#> GSM601996 2 0.0000 0.980 0.000 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM601872 2 0.0469 0.869 0.000 0.988 0.000 0.012
#> GSM601882 2 0.0921 0.861 0.000 0.972 0.000 0.028
#> GSM601887 1 0.3266 0.857 0.832 0.000 0.000 0.168
#> GSM601892 1 0.3219 0.859 0.836 0.000 0.000 0.164
#> GSM601897 1 0.1389 0.910 0.952 0.000 0.000 0.048
#> GSM601902 2 0.1940 0.857 0.000 0.924 0.000 0.076
#> GSM601912 1 0.3575 0.869 0.852 0.020 0.004 0.124
#> GSM601927 1 0.0921 0.915 0.972 0.000 0.028 0.000
#> GSM601932 2 0.2011 0.863 0.000 0.920 0.000 0.080
#> GSM601937 4 0.5657 0.722 0.000 0.436 0.024 0.540
#> GSM601942 2 0.0469 0.869 0.000 0.988 0.000 0.012
#> GSM601947 2 0.4122 0.578 0.000 0.760 0.004 0.236
#> GSM601957 1 0.1576 0.912 0.948 0.000 0.048 0.004
#> GSM601972 2 0.1867 0.857 0.000 0.928 0.000 0.072
#> GSM601977 2 0.0469 0.869 0.000 0.988 0.000 0.012
#> GSM601987 2 0.1940 0.855 0.000 0.924 0.000 0.076
#> GSM601877 1 0.0817 0.913 0.976 0.000 0.000 0.024
#> GSM601907 2 0.0188 0.871 0.000 0.996 0.000 0.004
#> GSM601917 2 0.3271 0.794 0.000 0.856 0.012 0.132
#> GSM601922 2 0.1940 0.857 0.000 0.924 0.000 0.076
#> GSM601952 2 0.1302 0.861 0.000 0.956 0.000 0.044
#> GSM601962 1 0.3172 0.877 0.872 0.012 0.004 0.112
#> GSM601967 1 0.2530 0.895 0.896 0.000 0.004 0.100
#> GSM601982 2 0.0817 0.865 0.000 0.976 0.000 0.024
#> GSM601992 2 0.3649 0.598 0.000 0.796 0.000 0.204
#> GSM601873 2 0.0707 0.868 0.000 0.980 0.000 0.020
#> GSM601883 2 0.0921 0.861 0.000 0.972 0.000 0.028
#> GSM601888 1 0.3668 0.843 0.808 0.000 0.004 0.188
#> GSM601893 1 0.0524 0.915 0.988 0.000 0.004 0.008
#> GSM601898 1 0.1722 0.913 0.944 0.000 0.048 0.008
#> GSM601903 2 0.1940 0.857 0.000 0.924 0.000 0.076
#> GSM601913 1 0.1724 0.915 0.948 0.000 0.032 0.020
#> GSM601928 1 0.1389 0.912 0.952 0.000 0.048 0.000
#> GSM601933 2 0.0188 0.871 0.000 0.996 0.000 0.004
#> GSM601938 2 0.4972 -0.514 0.000 0.544 0.000 0.456
#> GSM601943 2 0.0592 0.869 0.000 0.984 0.000 0.016
#> GSM601948 1 0.3908 0.825 0.784 0.000 0.004 0.212
#> GSM601958 1 0.1576 0.912 0.948 0.000 0.048 0.004
#> GSM601973 2 0.2149 0.855 0.000 0.912 0.000 0.088
#> GSM601978 2 0.0188 0.871 0.000 0.996 0.000 0.004
#> GSM601988 4 0.5159 0.764 0.000 0.364 0.012 0.624
#> GSM601878 1 0.0817 0.913 0.976 0.000 0.000 0.024
#> GSM601908 2 0.0188 0.871 0.000 0.996 0.000 0.004
#> GSM601918 2 0.1940 0.857 0.000 0.924 0.000 0.076
#> GSM601923 1 0.1389 0.912 0.952 0.000 0.048 0.000
#> GSM601953 2 0.2011 0.850 0.000 0.920 0.000 0.080
#> GSM601963 1 0.1389 0.912 0.952 0.000 0.048 0.000
#> GSM601968 1 0.2530 0.895 0.896 0.000 0.004 0.100
#> GSM601983 1 0.1398 0.915 0.956 0.000 0.040 0.004
#> GSM601993 2 0.3649 0.598 0.000 0.796 0.000 0.204
#> GSM601874 2 0.0188 0.871 0.000 0.996 0.000 0.004
#> GSM601884 2 0.0921 0.861 0.000 0.972 0.000 0.028
#> GSM601889 1 0.1722 0.913 0.944 0.000 0.048 0.008
#> GSM601894 1 0.1389 0.912 0.952 0.000 0.048 0.000
#> GSM601899 1 0.3400 0.851 0.820 0.000 0.000 0.180
#> GSM601904 2 0.2530 0.833 0.000 0.888 0.000 0.112
#> GSM601914 1 0.1389 0.912 0.952 0.000 0.048 0.000
#> GSM601929 1 0.2597 0.896 0.916 0.040 0.004 0.040
#> GSM601934 2 0.0188 0.871 0.000 0.996 0.000 0.004
#> GSM601939 1 0.1389 0.912 0.952 0.000 0.048 0.000
#> GSM601944 2 0.0592 0.870 0.000 0.984 0.000 0.016
#> GSM601949 1 0.3710 0.840 0.804 0.000 0.004 0.192
#> GSM601959 1 0.1722 0.913 0.944 0.000 0.048 0.008
#> GSM601974 2 0.3326 0.795 0.008 0.856 0.004 0.132
#> GSM601979 2 0.0188 0.871 0.000 0.996 0.000 0.004
#> GSM601989 1 0.1716 0.907 0.936 0.000 0.000 0.064
#> GSM601879 1 0.0817 0.913 0.976 0.000 0.000 0.024
#> GSM601909 1 0.1833 0.917 0.944 0.000 0.032 0.024
#> GSM601919 2 0.1940 0.857 0.000 0.924 0.000 0.076
#> GSM601924 1 0.1389 0.912 0.952 0.000 0.048 0.000
#> GSM601954 2 0.2647 0.810 0.000 0.880 0.000 0.120
#> GSM601964 1 0.1389 0.912 0.952 0.000 0.048 0.000
#> GSM601969 1 0.3710 0.841 0.804 0.000 0.004 0.192
#> GSM601984 1 0.3891 0.858 0.828 0.020 0.004 0.148
#> GSM601994 2 0.3649 0.598 0.000 0.796 0.000 0.204
#> GSM601875 2 0.0188 0.871 0.000 0.996 0.000 0.004
#> GSM601885 2 0.0817 0.865 0.000 0.976 0.000 0.024
#> GSM601890 1 0.3400 0.851 0.820 0.000 0.000 0.180
#> GSM601895 1 0.0376 0.915 0.992 0.000 0.004 0.004
#> GSM601900 1 0.0895 0.915 0.976 0.000 0.004 0.020
#> GSM601905 2 0.2647 0.825 0.000 0.880 0.000 0.120
#> GSM601915 1 0.1389 0.912 0.952 0.000 0.048 0.000
#> GSM601930 1 0.0817 0.916 0.976 0.000 0.024 0.000
#> GSM601935 4 0.5267 0.662 0.000 0.240 0.048 0.712
#> GSM601940 1 0.0817 0.916 0.976 0.000 0.024 0.000
#> GSM601945 2 0.0188 0.871 0.000 0.996 0.000 0.004
#> GSM601950 1 0.3668 0.843 0.808 0.000 0.004 0.188
#> GSM601960 1 0.1389 0.912 0.952 0.000 0.048 0.000
#> GSM601975 2 0.1940 0.859 0.000 0.924 0.000 0.076
#> GSM601980 4 0.5778 0.669 0.000 0.472 0.028 0.500
#> GSM601990 1 0.4165 0.844 0.824 0.012 0.024 0.140
#> GSM601880 1 0.0895 0.916 0.976 0.000 0.020 0.004
#> GSM601910 1 0.1302 0.911 0.956 0.000 0.000 0.044
#> GSM601920 2 0.2647 0.825 0.000 0.880 0.000 0.120
#> GSM601925 1 0.0592 0.914 0.984 0.000 0.000 0.016
#> GSM601955 2 0.7037 -0.612 0.000 0.464 0.120 0.416
#> GSM601965 1 0.3940 0.855 0.824 0.020 0.004 0.152
#> GSM601970 1 0.2089 0.913 0.932 0.000 0.048 0.020
#> GSM601985 1 0.1389 0.912 0.952 0.000 0.048 0.000
#> GSM601995 3 0.1557 0.000 0.000 0.000 0.944 0.056
#> GSM601876 1 0.2909 0.890 0.888 0.020 0.000 0.092
#> GSM601886 2 0.4855 0.138 0.000 0.644 0.004 0.352
#> GSM601891 1 0.3400 0.851 0.820 0.000 0.000 0.180
#> GSM601896 1 0.2909 0.890 0.888 0.020 0.000 0.092
#> GSM601901 2 0.1557 0.867 0.000 0.944 0.000 0.056
#> GSM601906 2 0.2647 0.825 0.000 0.880 0.000 0.120
#> GSM601916 2 0.2281 0.847 0.000 0.904 0.000 0.096
#> GSM601931 1 0.1389 0.912 0.952 0.000 0.048 0.000
#> GSM601936 4 0.5267 0.662 0.000 0.240 0.048 0.712
#> GSM601941 2 0.1940 0.860 0.000 0.924 0.000 0.076
#> GSM601946 1 0.1389 0.912 0.952 0.000 0.048 0.000
#> GSM601951 1 0.3908 0.825 0.784 0.000 0.004 0.212
#> GSM601961 1 0.5161 0.727 0.700 0.024 0.004 0.272
#> GSM601976 2 0.2469 0.836 0.000 0.892 0.000 0.108
#> GSM601981 2 0.0188 0.871 0.000 0.996 0.000 0.004
#> GSM601991 1 0.4165 0.844 0.824 0.012 0.024 0.140
#> GSM601881 1 0.1389 0.912 0.952 0.000 0.048 0.000
#> GSM601911 1 0.4538 0.828 0.800 0.048 0.004 0.148
#> GSM601921 2 0.2647 0.825 0.000 0.880 0.000 0.120
#> GSM601926 1 0.1389 0.912 0.952 0.000 0.048 0.000
#> GSM601956 2 0.0188 0.871 0.000 0.996 0.000 0.004
#> GSM601966 2 0.1389 0.868 0.000 0.952 0.000 0.048
#> GSM601971 1 0.3743 0.856 0.824 0.000 0.016 0.160
#> GSM601986 1 0.4538 0.828 0.800 0.048 0.004 0.148
#> GSM601996 2 0.3649 0.598 0.000 0.796 0.000 0.204
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM601872 2 0.0290 0.89619 0.000 0.992 0.000 0.000 0.008
#> GSM601882 2 0.0963 0.88882 0.000 0.964 0.000 0.000 0.036
#> GSM601887 1 0.3274 0.90597 0.780 0.000 0.220 0.000 0.000
#> GSM601892 1 0.3366 0.89406 0.768 0.000 0.232 0.000 0.000
#> GSM601897 3 0.3719 0.77254 0.208 0.000 0.776 0.012 0.004
#> GSM601902 2 0.2149 0.88544 0.036 0.916 0.000 0.000 0.048
#> GSM601912 3 0.5895 0.62426 0.280 0.012 0.628 0.020 0.060
#> GSM601927 3 0.1331 0.83008 0.040 0.000 0.952 0.008 0.000
#> GSM601932 2 0.2144 0.89150 0.020 0.912 0.000 0.000 0.068
#> GSM601937 5 0.3913 0.60717 0.000 0.324 0.000 0.000 0.676
#> GSM601942 2 0.0290 0.89619 0.000 0.992 0.000 0.000 0.008
#> GSM601947 2 0.4134 0.66841 0.196 0.760 0.000 0.000 0.044
#> GSM601957 3 0.1041 0.82197 0.032 0.000 0.964 0.004 0.000
#> GSM601972 2 0.2067 0.88645 0.032 0.920 0.000 0.000 0.048
#> GSM601977 2 0.0404 0.89607 0.000 0.988 0.000 0.000 0.012
#> GSM601987 2 0.2069 0.88021 0.012 0.912 0.000 0.000 0.076
#> GSM601877 3 0.3612 0.74982 0.228 0.000 0.764 0.008 0.000
#> GSM601907 2 0.0000 0.89791 0.000 1.000 0.000 0.000 0.000
#> GSM601917 2 0.3284 0.81622 0.024 0.828 0.000 0.000 0.148
#> GSM601922 2 0.2149 0.88544 0.036 0.916 0.000 0.000 0.048
#> GSM601952 2 0.1408 0.88816 0.008 0.948 0.000 0.000 0.044
#> GSM601962 3 0.5551 0.66843 0.240 0.000 0.664 0.024 0.072
#> GSM601967 3 0.4294 0.04428 0.468 0.000 0.532 0.000 0.000
#> GSM601982 2 0.0609 0.89330 0.000 0.980 0.000 0.000 0.020
#> GSM601992 2 0.3636 0.54286 0.000 0.728 0.000 0.000 0.272
#> GSM601873 2 0.0510 0.89582 0.000 0.984 0.000 0.000 0.016
#> GSM601883 2 0.0963 0.88882 0.000 0.964 0.000 0.000 0.036
#> GSM601888 1 0.2813 0.92926 0.832 0.000 0.168 0.000 0.000
#> GSM601893 3 0.2843 0.80787 0.144 0.000 0.848 0.008 0.000
#> GSM601898 3 0.1041 0.82323 0.032 0.000 0.964 0.004 0.000
#> GSM601903 2 0.2149 0.88544 0.036 0.916 0.000 0.000 0.048
#> GSM601913 3 0.1830 0.82793 0.052 0.000 0.932 0.004 0.012
#> GSM601928 3 0.0162 0.82302 0.004 0.000 0.996 0.000 0.000
#> GSM601933 2 0.0000 0.89791 0.000 1.000 0.000 0.000 0.000
#> GSM601938 5 0.4249 0.50509 0.000 0.432 0.000 0.000 0.568
#> GSM601943 2 0.0404 0.89690 0.000 0.988 0.000 0.000 0.012
#> GSM601948 1 0.2763 0.90882 0.848 0.000 0.148 0.004 0.000
#> GSM601958 3 0.1041 0.82197 0.032 0.000 0.964 0.004 0.000
#> GSM601973 2 0.2344 0.88419 0.032 0.904 0.000 0.000 0.064
#> GSM601978 2 0.0000 0.89791 0.000 1.000 0.000 0.000 0.000
#> GSM601988 5 0.2813 0.53552 0.000 0.168 0.000 0.000 0.832
#> GSM601878 3 0.3612 0.74982 0.228 0.000 0.764 0.008 0.000
#> GSM601908 2 0.0000 0.89791 0.000 1.000 0.000 0.000 0.000
#> GSM601918 2 0.2149 0.88544 0.036 0.916 0.000 0.000 0.048
#> GSM601923 3 0.1205 0.81890 0.040 0.000 0.956 0.004 0.000
#> GSM601953 2 0.2153 0.87963 0.044 0.916 0.000 0.000 0.040
#> GSM601963 3 0.1608 0.82121 0.072 0.000 0.928 0.000 0.000
#> GSM601968 3 0.4294 0.04428 0.468 0.000 0.532 0.000 0.000
#> GSM601983 3 0.1892 0.82043 0.080 0.000 0.916 0.004 0.000
#> GSM601993 2 0.3636 0.54286 0.000 0.728 0.000 0.000 0.272
#> GSM601874 2 0.0000 0.89791 0.000 1.000 0.000 0.000 0.000
#> GSM601884 2 0.0963 0.88882 0.000 0.964 0.000 0.000 0.036
#> GSM601889 3 0.1041 0.82323 0.032 0.000 0.964 0.004 0.000
#> GSM601894 3 0.0771 0.82373 0.020 0.000 0.976 0.004 0.000
#> GSM601899 1 0.3074 0.92734 0.804 0.000 0.196 0.000 0.000
#> GSM601904 2 0.2850 0.86080 0.036 0.872 0.000 0.000 0.092
#> GSM601914 3 0.0404 0.82356 0.012 0.000 0.988 0.000 0.000
#> GSM601929 3 0.4930 0.69701 0.240 0.040 0.704 0.012 0.004
#> GSM601934 2 0.0000 0.89791 0.000 1.000 0.000 0.000 0.000
#> GSM601939 3 0.0324 0.82215 0.004 0.000 0.992 0.004 0.000
#> GSM601944 2 0.0404 0.89733 0.000 0.988 0.000 0.000 0.012
#> GSM601949 1 0.2813 0.92827 0.832 0.000 0.168 0.000 0.000
#> GSM601959 3 0.1124 0.82251 0.036 0.000 0.960 0.004 0.000
#> GSM601974 2 0.3507 0.83198 0.060 0.844 0.000 0.008 0.088
#> GSM601979 2 0.0000 0.89791 0.000 1.000 0.000 0.000 0.000
#> GSM601989 3 0.3966 0.76014 0.224 0.000 0.756 0.012 0.008
#> GSM601879 3 0.3612 0.74982 0.228 0.000 0.764 0.008 0.000
#> GSM601909 3 0.2488 0.81949 0.124 0.000 0.872 0.004 0.000
#> GSM601919 2 0.2149 0.88544 0.036 0.916 0.000 0.000 0.048
#> GSM601924 3 0.1205 0.81890 0.040 0.000 0.956 0.004 0.000
#> GSM601954 2 0.2813 0.84802 0.084 0.876 0.000 0.000 0.040
#> GSM601964 3 0.1608 0.82121 0.072 0.000 0.928 0.000 0.000
#> GSM601969 1 0.2773 0.92722 0.836 0.000 0.164 0.000 0.000
#> GSM601984 3 0.6107 0.58063 0.300 0.016 0.604 0.024 0.056
#> GSM601994 2 0.3636 0.54286 0.000 0.728 0.000 0.000 0.272
#> GSM601875 2 0.0000 0.89791 0.000 1.000 0.000 0.000 0.000
#> GSM601885 2 0.0609 0.89330 0.000 0.980 0.000 0.000 0.020
#> GSM601890 1 0.3039 0.92836 0.808 0.000 0.192 0.000 0.000
#> GSM601895 3 0.2707 0.81257 0.132 0.000 0.860 0.008 0.000
#> GSM601900 3 0.3163 0.80953 0.128 0.000 0.848 0.012 0.012
#> GSM601905 2 0.2983 0.85435 0.040 0.864 0.000 0.000 0.096
#> GSM601915 3 0.0404 0.82356 0.012 0.000 0.988 0.000 0.000
#> GSM601930 3 0.1484 0.82968 0.048 0.000 0.944 0.008 0.000
#> GSM601935 5 0.1043 0.33625 0.000 0.040 0.000 0.000 0.960
#> GSM601940 3 0.1124 0.83130 0.036 0.000 0.960 0.004 0.000
#> GSM601945 2 0.0000 0.89791 0.000 1.000 0.000 0.000 0.000
#> GSM601950 1 0.2966 0.93080 0.816 0.000 0.184 0.000 0.000
#> GSM601960 3 0.0771 0.82545 0.020 0.000 0.976 0.004 0.000
#> GSM601975 2 0.2139 0.88768 0.032 0.916 0.000 0.000 0.052
#> GSM601980 5 0.4820 0.59838 0.036 0.332 0.000 0.000 0.632
#> GSM601990 3 0.5783 0.65720 0.196 0.000 0.660 0.020 0.124
#> GSM601880 3 0.2612 0.81335 0.124 0.000 0.868 0.008 0.000
#> GSM601910 3 0.3686 0.77496 0.204 0.000 0.780 0.012 0.004
#> GSM601920 2 0.2983 0.85435 0.040 0.864 0.000 0.000 0.096
#> GSM601925 3 0.3388 0.76943 0.200 0.000 0.792 0.008 0.000
#> GSM601955 5 0.6547 0.42243 0.060 0.308 0.000 0.076 0.556
#> GSM601965 3 0.6165 0.57760 0.300 0.016 0.600 0.024 0.060
#> GSM601970 3 0.1430 0.81496 0.052 0.000 0.944 0.004 0.000
#> GSM601985 3 0.0404 0.82356 0.012 0.000 0.988 0.000 0.000
#> GSM601995 4 0.0794 0.00000 0.000 0.000 0.000 0.972 0.028
#> GSM601876 3 0.5079 0.69023 0.248 0.016 0.696 0.012 0.028
#> GSM601886 2 0.4684 -0.00755 0.008 0.536 0.000 0.004 0.452
#> GSM601891 1 0.3074 0.92684 0.804 0.000 0.196 0.000 0.000
#> GSM601896 3 0.5079 0.69023 0.248 0.016 0.696 0.012 0.028
#> GSM601901 2 0.1626 0.89550 0.016 0.940 0.000 0.000 0.044
#> GSM601906 2 0.2983 0.85435 0.040 0.864 0.000 0.000 0.096
#> GSM601916 2 0.2504 0.87823 0.040 0.896 0.000 0.000 0.064
#> GSM601931 3 0.0451 0.82137 0.008 0.000 0.988 0.004 0.000
#> GSM601936 5 0.1043 0.33625 0.000 0.040 0.000 0.000 0.960
#> GSM601941 2 0.2124 0.88885 0.028 0.916 0.000 0.000 0.056
#> GSM601946 3 0.0162 0.82302 0.004 0.000 0.996 0.000 0.000
#> GSM601951 1 0.2763 0.90882 0.848 0.000 0.148 0.004 0.000
#> GSM601961 1 0.3164 0.75343 0.872 0.020 0.068 0.000 0.040
#> GSM601976 2 0.2735 0.86519 0.036 0.880 0.000 0.000 0.084
#> GSM601981 2 0.0000 0.89791 0.000 1.000 0.000 0.000 0.000
#> GSM601991 3 0.5783 0.65720 0.196 0.000 0.660 0.020 0.124
#> GSM601881 3 0.0566 0.82200 0.012 0.000 0.984 0.004 0.000
#> GSM601911 3 0.6612 0.56080 0.276 0.044 0.592 0.020 0.068
#> GSM601921 2 0.2983 0.85435 0.040 0.864 0.000 0.000 0.096
#> GSM601926 3 0.1205 0.81890 0.040 0.000 0.956 0.004 0.000
#> GSM601956 2 0.0000 0.89791 0.000 1.000 0.000 0.000 0.000
#> GSM601966 2 0.1444 0.89644 0.012 0.948 0.000 0.000 0.040
#> GSM601971 1 0.3884 0.78711 0.708 0.000 0.288 0.004 0.000
#> GSM601986 3 0.6612 0.56080 0.276 0.044 0.592 0.020 0.068
#> GSM601996 2 0.3636 0.54286 0.000 0.728 0.000 0.000 0.272
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM601872 2 0.0622 0.805 0.000 0.980 0.000 NA 0.008 0.000
#> GSM601882 2 0.1367 0.793 0.000 0.944 0.000 NA 0.044 0.000
#> GSM601887 6 0.2003 0.823 0.116 0.000 0.000 NA 0.000 0.884
#> GSM601892 6 0.2135 0.813 0.128 0.000 0.000 NA 0.000 0.872
#> GSM601897 1 0.4279 0.746 0.732 0.000 0.000 NA 0.000 0.140
#> GSM601902 2 0.3103 0.779 0.000 0.784 0.000 NA 0.008 0.000
#> GSM601912 1 0.6112 0.585 0.556 0.008 0.000 NA 0.024 0.160
#> GSM601927 1 0.1908 0.814 0.916 0.000 0.000 NA 0.000 0.056
#> GSM601932 2 0.3739 0.777 0.000 0.768 0.000 NA 0.056 0.000
#> GSM601937 5 0.4403 0.541 0.000 0.196 0.000 NA 0.708 0.000
#> GSM601942 2 0.0622 0.805 0.000 0.980 0.000 NA 0.008 0.000
#> GSM601947 2 0.4668 0.652 0.000 0.700 0.000 NA 0.004 0.160
#> GSM601957 1 0.1867 0.800 0.916 0.000 0.000 NA 0.000 0.064
#> GSM601972 2 0.3012 0.784 0.000 0.796 0.000 NA 0.008 0.000
#> GSM601977 2 0.0717 0.805 0.000 0.976 0.000 NA 0.016 0.000
#> GSM601987 2 0.3501 0.746 0.000 0.804 0.000 NA 0.080 0.000
#> GSM601877 1 0.4032 0.729 0.740 0.000 0.000 NA 0.000 0.192
#> GSM601907 2 0.0520 0.806 0.000 0.984 0.000 NA 0.008 0.000
#> GSM601917 2 0.4697 0.699 0.000 0.684 0.000 NA 0.144 0.000
#> GSM601922 2 0.3103 0.779 0.000 0.784 0.000 NA 0.008 0.000
#> GSM601952 2 0.2190 0.798 0.000 0.900 0.000 NA 0.060 0.000
#> GSM601962 1 0.5504 0.625 0.592 0.000 0.000 NA 0.036 0.076
#> GSM601967 6 0.3838 0.127 0.448 0.000 0.000 NA 0.000 0.552
#> GSM601982 2 0.0806 0.803 0.000 0.972 0.000 NA 0.020 0.000
#> GSM601992 2 0.6095 -0.229 0.000 0.384 0.000 NA 0.292 0.000
#> GSM601873 2 0.0820 0.804 0.000 0.972 0.000 NA 0.016 0.000
#> GSM601883 2 0.1367 0.793 0.000 0.944 0.000 NA 0.044 0.000
#> GSM601888 6 0.1267 0.830 0.060 0.000 0.000 NA 0.000 0.940
#> GSM601893 1 0.3473 0.784 0.808 0.000 0.000 NA 0.000 0.096
#> GSM601898 1 0.1807 0.801 0.920 0.000 0.000 NA 0.000 0.060
#> GSM601903 2 0.3103 0.779 0.000 0.784 0.000 NA 0.008 0.000
#> GSM601913 1 0.2398 0.810 0.888 0.000 0.000 NA 0.004 0.028
#> GSM601928 1 0.0972 0.808 0.964 0.000 0.000 NA 0.000 0.028
#> GSM601933 2 0.0622 0.805 0.000 0.980 0.000 NA 0.008 0.000
#> GSM601938 5 0.5027 0.488 0.000 0.304 0.000 NA 0.596 0.000
#> GSM601943 2 0.0725 0.804 0.000 0.976 0.000 NA 0.012 0.000
#> GSM601948 6 0.1745 0.815 0.056 0.000 0.000 NA 0.000 0.924
#> GSM601958 1 0.1867 0.800 0.916 0.000 0.000 NA 0.000 0.064
#> GSM601973 2 0.3543 0.773 0.000 0.768 0.000 NA 0.032 0.000
#> GSM601978 2 0.0520 0.805 0.000 0.984 0.000 NA 0.008 0.000
#> GSM601988 5 0.2509 0.497 0.000 0.088 0.000 NA 0.876 0.000
#> GSM601878 1 0.4032 0.729 0.740 0.000 0.000 NA 0.000 0.192
#> GSM601908 2 0.0520 0.806 0.000 0.984 0.000 NA 0.008 0.000
#> GSM601918 2 0.3103 0.779 0.000 0.784 0.000 NA 0.008 0.000
#> GSM601923 1 0.1686 0.807 0.924 0.000 0.000 NA 0.000 0.064
#> GSM601953 2 0.2006 0.794 0.000 0.904 0.000 NA 0.000 0.016
#> GSM601963 1 0.2112 0.799 0.896 0.000 0.000 NA 0.000 0.016
#> GSM601968 6 0.3838 0.127 0.448 0.000 0.000 NA 0.000 0.552
#> GSM601983 1 0.2311 0.798 0.880 0.000 0.000 NA 0.000 0.016
#> GSM601993 2 0.6095 -0.229 0.000 0.384 0.000 NA 0.292 0.000
#> GSM601874 2 0.0520 0.805 0.000 0.984 0.000 NA 0.008 0.000
#> GSM601884 2 0.1367 0.793 0.000 0.944 0.000 NA 0.044 0.000
#> GSM601889 1 0.1807 0.801 0.920 0.000 0.000 NA 0.000 0.060
#> GSM601894 1 0.1461 0.805 0.940 0.000 0.000 NA 0.000 0.044
#> GSM601899 6 0.1714 0.837 0.092 0.000 0.000 NA 0.000 0.908
#> GSM601904 2 0.4120 0.744 0.000 0.728 0.000 NA 0.068 0.000
#> GSM601914 1 0.0993 0.809 0.964 0.000 0.000 NA 0.000 0.024
#> GSM601929 1 0.5161 0.686 0.680 0.040 0.000 NA 0.000 0.188
#> GSM601934 2 0.0622 0.805 0.000 0.980 0.000 NA 0.008 0.000
#> GSM601939 1 0.1074 0.807 0.960 0.000 0.000 NA 0.000 0.028
#> GSM601944 2 0.0725 0.805 0.000 0.976 0.000 NA 0.012 0.000
#> GSM601949 6 0.1471 0.830 0.064 0.000 0.000 NA 0.000 0.932
#> GSM601959 1 0.1926 0.799 0.912 0.000 0.000 NA 0.000 0.068
#> GSM601974 2 0.4390 0.720 0.000 0.700 0.000 NA 0.064 0.004
#> GSM601979 2 0.0520 0.805 0.000 0.984 0.000 NA 0.008 0.000
#> GSM601989 1 0.4482 0.725 0.708 0.000 0.000 NA 0.000 0.168
#> GSM601879 1 0.4032 0.729 0.740 0.000 0.000 NA 0.000 0.192
#> GSM601909 1 0.3241 0.792 0.824 0.000 0.000 NA 0.000 0.112
#> GSM601919 2 0.3103 0.779 0.000 0.784 0.000 NA 0.008 0.000
#> GSM601924 1 0.1686 0.807 0.924 0.000 0.000 NA 0.000 0.064
#> GSM601954 2 0.3044 0.782 0.000 0.836 0.000 NA 0.000 0.048
#> GSM601964 1 0.2112 0.799 0.896 0.000 0.000 NA 0.000 0.016
#> GSM601969 6 0.1411 0.830 0.060 0.000 0.000 NA 0.000 0.936
#> GSM601984 1 0.6301 0.544 0.528 0.008 0.000 NA 0.024 0.196
#> GSM601994 2 0.6095 -0.229 0.000 0.384 0.000 NA 0.292 0.000
#> GSM601875 2 0.0622 0.805 0.000 0.980 0.000 NA 0.008 0.000
#> GSM601885 2 0.0806 0.803 0.000 0.972 0.000 NA 0.020 0.000
#> GSM601890 6 0.1663 0.838 0.088 0.000 0.000 NA 0.000 0.912
#> GSM601895 1 0.3372 0.788 0.816 0.000 0.000 NA 0.000 0.084
#> GSM601900 1 0.3716 0.788 0.792 0.000 0.000 NA 0.004 0.076
#> GSM601905 2 0.4176 0.737 0.000 0.720 0.000 NA 0.068 0.000
#> GSM601915 1 0.0993 0.809 0.964 0.000 0.000 NA 0.000 0.024
#> GSM601930 1 0.2046 0.814 0.908 0.000 0.000 NA 0.000 0.060
#> GSM601935 5 0.0777 0.372 0.000 0.004 0.000 NA 0.972 0.000
#> GSM601940 1 0.1700 0.816 0.928 0.000 0.000 NA 0.000 0.048
#> GSM601945 2 0.0622 0.805 0.000 0.980 0.000 NA 0.008 0.000
#> GSM601950 6 0.1501 0.836 0.076 0.000 0.000 NA 0.000 0.924
#> GSM601960 1 0.1257 0.810 0.952 0.000 0.000 NA 0.000 0.028
#> GSM601975 2 0.3333 0.781 0.000 0.784 0.000 NA 0.024 0.000
#> GSM601980 5 0.4981 0.504 0.000 0.208 0.000 NA 0.656 0.004
#> GSM601990 1 0.5520 0.610 0.588 0.000 0.000 NA 0.076 0.036
#> GSM601880 1 0.2923 0.795 0.848 0.000 0.000 NA 0.000 0.100
#> GSM601910 1 0.4277 0.746 0.732 0.000 0.000 NA 0.000 0.144
#> GSM601920 2 0.4176 0.737 0.000 0.720 0.000 NA 0.068 0.000
#> GSM601925 1 0.3717 0.756 0.776 0.000 0.000 NA 0.000 0.160
#> GSM601955 5 0.6569 0.138 0.000 0.128 0.048 NA 0.532 0.020
#> GSM601965 1 0.6312 0.539 0.524 0.008 0.000 NA 0.024 0.192
#> GSM601970 1 0.2301 0.776 0.884 0.000 0.000 NA 0.000 0.096
#> GSM601985 1 0.0993 0.809 0.964 0.000 0.000 NA 0.000 0.024
#> GSM601995 3 0.0000 0.000 0.000 0.000 1.000 NA 0.000 0.000
#> GSM601876 1 0.5593 0.648 0.620 0.008 0.000 NA 0.012 0.204
#> GSM601886 5 0.5753 0.134 0.000 0.364 0.000 NA 0.460 0.000
#> GSM601891 6 0.1714 0.837 0.092 0.000 0.000 NA 0.000 0.908
#> GSM601896 1 0.5593 0.648 0.620 0.008 0.000 NA 0.012 0.204
#> GSM601901 2 0.2988 0.796 0.000 0.828 0.000 NA 0.028 0.000
#> GSM601906 2 0.4176 0.737 0.000 0.720 0.000 NA 0.068 0.000
#> GSM601916 2 0.3460 0.771 0.000 0.760 0.000 NA 0.020 0.000
#> GSM601931 1 0.1151 0.806 0.956 0.000 0.000 NA 0.000 0.032
#> GSM601936 5 0.0777 0.372 0.000 0.004 0.000 NA 0.972 0.000
#> GSM601941 2 0.3470 0.780 0.000 0.772 0.000 NA 0.028 0.000
#> GSM601946 1 0.0858 0.809 0.968 0.000 0.000 NA 0.000 0.028
#> GSM601951 6 0.1745 0.815 0.056 0.000 0.000 NA 0.000 0.924
#> GSM601961 6 0.2239 0.671 0.008 0.020 0.000 NA 0.000 0.900
#> GSM601976 2 0.4008 0.751 0.000 0.740 0.000 NA 0.064 0.000
#> GSM601981 2 0.0363 0.805 0.000 0.988 0.000 NA 0.000 0.000
#> GSM601991 1 0.5520 0.610 0.588 0.000 0.000 NA 0.076 0.036
#> GSM601881 1 0.1225 0.806 0.952 0.000 0.000 NA 0.000 0.036
#> GSM601911 1 0.6723 0.530 0.512 0.032 0.000 NA 0.028 0.172
#> GSM601921 2 0.4176 0.737 0.000 0.720 0.000 NA 0.068 0.000
#> GSM601926 1 0.1686 0.807 0.924 0.000 0.000 NA 0.000 0.064
#> GSM601956 2 0.0363 0.805 0.000 0.988 0.000 NA 0.000 0.000
#> GSM601966 2 0.3023 0.794 0.000 0.828 0.000 NA 0.032 0.000
#> GSM601971 6 0.3136 0.706 0.188 0.000 0.000 NA 0.000 0.796
#> GSM601986 1 0.6723 0.530 0.512 0.032 0.000 NA 0.028 0.172
#> GSM601996 2 0.6095 -0.229 0.000 0.384 0.000 NA 0.292 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
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)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
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)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, 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.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")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n time(p) gender(p) k
#> ATC:hclust 125 0.410 0.586 2
#> ATC:hclust 124 0.353 0.501 3
#> ATC:hclust 121 0.453 0.210 4
#> ATC:hclust 118 0.470 0.127 5
#> ATC:hclust 112 0.568 0.152 6
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.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["ATC", "kmeans"]
# you can also extract it by
# res = res_list["ATC:kmeans"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 21512 rows and 125 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'kmeans' method.
#> Performed in total 1250 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_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
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, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.996 0.998 0.5043 0.496 0.496
#> 3 3 0.693 0.654 0.796 0.2437 0.945 0.890
#> 4 4 0.604 0.644 0.761 0.1361 0.785 0.534
#> 5 5 0.636 0.671 0.758 0.0793 0.876 0.588
#> 6 6 0.700 0.633 0.765 0.0485 0.946 0.761
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 2
Following shows 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 class
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
#> GSM601872 2 0.0000 0.997 0.000 1.000
#> GSM601882 2 0.0000 0.997 0.000 1.000
#> GSM601887 1 0.0000 1.000 1.000 0.000
#> GSM601892 1 0.0000 1.000 1.000 0.000
#> GSM601897 1 0.0000 1.000 1.000 0.000
#> GSM601902 2 0.0000 0.997 0.000 1.000
#> GSM601912 1 0.0000 1.000 1.000 0.000
#> GSM601927 1 0.0000 1.000 1.000 0.000
#> GSM601932 2 0.0000 0.997 0.000 1.000
#> GSM601937 2 0.0000 0.997 0.000 1.000
#> GSM601942 2 0.0000 0.997 0.000 1.000
#> GSM601947 2 0.0000 0.997 0.000 1.000
#> GSM601957 1 0.0000 1.000 1.000 0.000
#> GSM601972 2 0.0000 0.997 0.000 1.000
#> GSM601977 2 0.0000 0.997 0.000 1.000
#> GSM601987 2 0.0000 0.997 0.000 1.000
#> GSM601877 1 0.0000 1.000 1.000 0.000
#> GSM601907 2 0.0000 0.997 0.000 1.000
#> GSM601917 2 0.0000 0.997 0.000 1.000
#> GSM601922 2 0.0000 0.997 0.000 1.000
#> GSM601952 2 0.0000 0.997 0.000 1.000
#> GSM601962 1 0.0000 1.000 1.000 0.000
#> GSM601967 1 0.0000 1.000 1.000 0.000
#> GSM601982 2 0.0000 0.997 0.000 1.000
#> GSM601992 2 0.0000 0.997 0.000 1.000
#> GSM601873 2 0.0000 0.997 0.000 1.000
#> GSM601883 2 0.0000 0.997 0.000 1.000
#> GSM601888 1 0.0000 1.000 1.000 0.000
#> GSM601893 1 0.0000 1.000 1.000 0.000
#> GSM601898 1 0.0000 1.000 1.000 0.000
#> GSM601903 2 0.0000 0.997 0.000 1.000
#> GSM601913 1 0.0000 1.000 1.000 0.000
#> GSM601928 1 0.0000 1.000 1.000 0.000
#> GSM601933 2 0.0000 0.997 0.000 1.000
#> GSM601938 2 0.0000 0.997 0.000 1.000
#> GSM601943 2 0.0000 0.997 0.000 1.000
#> GSM601948 1 0.0000 1.000 1.000 0.000
#> GSM601958 1 0.0000 1.000 1.000 0.000
#> GSM601973 2 0.0000 0.997 0.000 1.000
#> GSM601978 2 0.0000 0.997 0.000 1.000
#> GSM601988 2 0.0000 0.997 0.000 1.000
#> GSM601878 1 0.0000 1.000 1.000 0.000
#> GSM601908 2 0.0000 0.997 0.000 1.000
#> GSM601918 2 0.0000 0.997 0.000 1.000
#> GSM601923 1 0.0000 1.000 1.000 0.000
#> GSM601953 2 0.0000 0.997 0.000 1.000
#> GSM601963 1 0.0000 1.000 1.000 0.000
#> GSM601968 1 0.0000 1.000 1.000 0.000
#> GSM601983 1 0.0000 1.000 1.000 0.000
#> GSM601993 2 0.0000 0.997 0.000 1.000
#> GSM601874 2 0.0000 0.997 0.000 1.000
#> GSM601884 2 0.0000 0.997 0.000 1.000
#> GSM601889 1 0.0000 1.000 1.000 0.000
#> GSM601894 1 0.0000 1.000 1.000 0.000
#> GSM601899 1 0.0000 1.000 1.000 0.000
#> GSM601904 2 0.0000 0.997 0.000 1.000
#> GSM601914 1 0.0000 1.000 1.000 0.000
#> GSM601929 1 0.0000 1.000 1.000 0.000
#> GSM601934 2 0.0000 0.997 0.000 1.000
#> GSM601939 1 0.0000 1.000 1.000 0.000
#> GSM601944 2 0.0000 0.997 0.000 1.000
#> GSM601949 1 0.0000 1.000 1.000 0.000
#> GSM601959 1 0.0000 1.000 1.000 0.000
#> GSM601974 2 0.0000 0.997 0.000 1.000
#> GSM601979 2 0.0000 0.997 0.000 1.000
#> GSM601989 1 0.0000 1.000 1.000 0.000
#> GSM601879 1 0.0000 1.000 1.000 0.000
#> GSM601909 1 0.0000 1.000 1.000 0.000
#> GSM601919 2 0.0000 0.997 0.000 1.000
#> GSM601924 1 0.0000 1.000 1.000 0.000
#> GSM601954 2 0.0000 0.997 0.000 1.000
#> GSM601964 1 0.0000 1.000 1.000 0.000
#> GSM601969 1 0.0000 1.000 1.000 0.000
#> GSM601984 1 0.0000 1.000 1.000 0.000
#> GSM601994 2 0.0000 0.997 0.000 1.000
#> GSM601875 2 0.0000 0.997 0.000 1.000
#> GSM601885 2 0.0000 0.997 0.000 1.000
#> GSM601890 1 0.0000 1.000 1.000 0.000
#> GSM601895 1 0.0000 1.000 1.000 0.000
#> GSM601900 1 0.0000 1.000 1.000 0.000
#> GSM601905 2 0.0000 0.997 0.000 1.000
#> GSM601915 1 0.0000 1.000 1.000 0.000
#> GSM601930 1 0.0000 1.000 1.000 0.000
#> GSM601935 2 0.0000 0.997 0.000 1.000
#> GSM601940 1 0.0000 1.000 1.000 0.000
#> GSM601945 2 0.0000 0.997 0.000 1.000
#> GSM601950 1 0.0000 1.000 1.000 0.000
#> GSM601960 1 0.0000 1.000 1.000 0.000
#> GSM601975 2 0.0000 0.997 0.000 1.000
#> GSM601980 2 0.0000 0.997 0.000 1.000
#> GSM601990 1 0.0000 1.000 1.000 0.000
#> GSM601880 1 0.0000 1.000 1.000 0.000
#> GSM601910 1 0.0000 1.000 1.000 0.000
#> GSM601920 2 0.0000 0.997 0.000 1.000
#> GSM601925 1 0.0000 1.000 1.000 0.000
#> GSM601955 2 0.0000 0.997 0.000 1.000
#> GSM601965 1 0.0000 1.000 1.000 0.000
#> GSM601970 1 0.0000 1.000 1.000 0.000
#> GSM601985 1 0.0000 1.000 1.000 0.000
#> GSM601995 2 0.0000 0.997 0.000 1.000
#> GSM601876 1 0.0000 1.000 1.000 0.000
#> GSM601886 2 0.0000 0.997 0.000 1.000
#> GSM601891 1 0.0000 1.000 1.000 0.000
#> GSM601896 1 0.0000 1.000 1.000 0.000
#> GSM601901 2 0.0000 0.997 0.000 1.000
#> GSM601906 2 0.0000 0.997 0.000 1.000
#> GSM601916 2 0.0000 0.997 0.000 1.000
#> GSM601931 1 0.0000 1.000 1.000 0.000
#> GSM601936 2 0.0000 0.997 0.000 1.000
#> GSM601941 2 0.0000 0.997 0.000 1.000
#> GSM601946 1 0.0000 1.000 1.000 0.000
#> GSM601951 1 0.0000 1.000 1.000 0.000
#> GSM601961 2 0.7056 0.762 0.192 0.808
#> GSM601976 2 0.0000 0.997 0.000 1.000
#> GSM601981 2 0.0000 0.997 0.000 1.000
#> GSM601991 1 0.0000 1.000 1.000 0.000
#> GSM601881 1 0.0000 1.000 1.000 0.000
#> GSM601911 2 0.0000 0.997 0.000 1.000
#> GSM601921 2 0.0000 0.997 0.000 1.000
#> GSM601926 1 0.0000 1.000 1.000 0.000
#> GSM601956 2 0.0000 0.997 0.000 1.000
#> GSM601966 2 0.0000 0.997 0.000 1.000
#> GSM601971 1 0.0000 1.000 1.000 0.000
#> GSM601986 2 0.0376 0.993 0.004 0.996
#> GSM601996 2 0.0000 0.997 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM601872 2 0.0000 0.8576 0.000 1.000 0.000
#> GSM601882 2 0.0747 0.8540 0.000 0.984 0.016
#> GSM601887 1 0.6280 0.5948 0.540 0.000 0.460
#> GSM601892 1 0.6280 0.5948 0.540 0.000 0.460
#> GSM601897 1 0.6260 0.5930 0.552 0.000 0.448
#> GSM601902 2 0.2165 0.8452 0.000 0.936 0.064
#> GSM601912 1 0.6008 0.0936 0.628 0.000 0.372
#> GSM601927 1 0.0424 0.6399 0.992 0.000 0.008
#> GSM601932 2 0.2356 0.8486 0.000 0.928 0.072
#> GSM601937 2 0.6274 0.4547 0.000 0.544 0.456
#> GSM601942 2 0.0237 0.8571 0.000 0.996 0.004
#> GSM601947 2 0.2066 0.8460 0.000 0.940 0.060
#> GSM601957 1 0.6045 0.6022 0.620 0.000 0.380
#> GSM601972 2 0.2066 0.8460 0.000 0.940 0.060
#> GSM601977 2 0.0237 0.8571 0.000 0.996 0.004
#> GSM601987 2 0.0592 0.8553 0.000 0.988 0.012
#> GSM601877 1 0.3752 0.6224 0.856 0.000 0.144
#> GSM601907 2 0.0000 0.8576 0.000 1.000 0.000
#> GSM601917 2 0.5988 0.6053 0.000 0.632 0.368
#> GSM601922 2 0.2625 0.8386 0.000 0.916 0.084
#> GSM601952 2 0.1163 0.8565 0.000 0.972 0.028
#> GSM601962 3 0.6302 0.3336 0.480 0.000 0.520
#> GSM601967 1 0.6280 0.5948 0.540 0.000 0.460
#> GSM601982 2 0.0592 0.8573 0.000 0.988 0.012
#> GSM601992 2 0.1411 0.8537 0.000 0.964 0.036
#> GSM601873 2 0.0747 0.8572 0.000 0.984 0.016
#> GSM601883 2 0.0747 0.8540 0.000 0.984 0.016
#> GSM601888 1 0.6295 0.5886 0.528 0.000 0.472
#> GSM601893 1 0.6274 0.5966 0.544 0.000 0.456
#> GSM601898 1 0.5882 0.6050 0.652 0.000 0.348
#> GSM601903 2 0.2356 0.8430 0.000 0.928 0.072
#> GSM601913 1 0.0747 0.6377 0.984 0.000 0.016
#> GSM601928 1 0.0424 0.6399 0.992 0.000 0.008
#> GSM601933 2 0.0000 0.8576 0.000 1.000 0.000
#> GSM601938 2 0.1753 0.8519 0.000 0.952 0.048
#> GSM601943 2 0.0237 0.8571 0.000 0.996 0.004
#> GSM601948 1 0.6302 0.5850 0.520 0.000 0.480
#> GSM601958 1 0.5859 0.6050 0.656 0.000 0.344
#> GSM601973 2 0.3879 0.7993 0.000 0.848 0.152
#> GSM601978 2 0.0000 0.8576 0.000 1.000 0.000
#> GSM601988 2 0.6260 0.4675 0.000 0.552 0.448
#> GSM601878 1 0.4504 0.6431 0.804 0.000 0.196
#> GSM601908 2 0.0000 0.8576 0.000 1.000 0.000
#> GSM601918 2 0.2165 0.8452 0.000 0.936 0.064
#> GSM601923 1 0.0592 0.6420 0.988 0.000 0.012
#> GSM601953 2 0.0000 0.8576 0.000 1.000 0.000
#> GSM601963 1 0.0592 0.6393 0.988 0.000 0.012
#> GSM601968 1 0.6280 0.5948 0.540 0.000 0.460
#> GSM601983 1 0.2959 0.6239 0.900 0.000 0.100
#> GSM601993 2 0.5497 0.6572 0.000 0.708 0.292
#> GSM601874 2 0.0000 0.8576 0.000 1.000 0.000
#> GSM601884 2 0.0000 0.8576 0.000 1.000 0.000
#> GSM601889 1 0.5926 0.6048 0.644 0.000 0.356
#> GSM601894 1 0.5785 0.6081 0.668 0.000 0.332
#> GSM601899 1 0.6280 0.5948 0.540 0.000 0.460
#> GSM601904 2 0.5948 0.5986 0.000 0.640 0.360
#> GSM601914 1 0.0592 0.6393 0.988 0.000 0.012
#> GSM601929 1 0.4178 0.5880 0.828 0.000 0.172
#> GSM601934 2 0.0000 0.8576 0.000 1.000 0.000
#> GSM601939 1 0.1289 0.6494 0.968 0.000 0.032
#> GSM601944 2 0.0000 0.8576 0.000 1.000 0.000
#> GSM601949 1 0.6295 0.5900 0.528 0.000 0.472
#> GSM601959 1 0.6111 0.6015 0.604 0.000 0.396
#> GSM601974 2 0.6735 0.4548 0.012 0.564 0.424
#> GSM601979 2 0.0000 0.8576 0.000 1.000 0.000
#> GSM601989 1 0.6267 0.5976 0.548 0.000 0.452
#> GSM601879 1 0.4121 0.6271 0.832 0.000 0.168
#> GSM601909 1 0.6252 0.5982 0.556 0.000 0.444
#> GSM601919 2 0.2066 0.8460 0.000 0.940 0.060
#> GSM601924 1 0.2356 0.6514 0.928 0.000 0.072
#> GSM601954 2 0.1529 0.8472 0.000 0.960 0.040
#> GSM601964 1 0.0892 0.6362 0.980 0.000 0.020
#> GSM601969 1 0.6295 0.5886 0.528 0.000 0.472
#> GSM601984 1 0.4235 0.5754 0.824 0.000 0.176
#> GSM601994 2 0.0892 0.8524 0.000 0.980 0.020
#> GSM601875 2 0.0000 0.8576 0.000 1.000 0.000
#> GSM601885 2 0.0237 0.8571 0.000 0.996 0.004
#> GSM601890 1 0.6280 0.5948 0.540 0.000 0.460
#> GSM601895 1 0.0237 0.6435 0.996 0.000 0.004
#> GSM601900 1 0.2261 0.6332 0.932 0.000 0.068
#> GSM601905 2 0.5785 0.6306 0.000 0.668 0.332
#> GSM601915 1 0.0592 0.6393 0.988 0.000 0.012
#> GSM601930 1 0.0747 0.6368 0.984 0.000 0.016
#> GSM601935 2 0.6513 0.4137 0.004 0.520 0.476
#> GSM601940 1 0.4002 0.6474 0.840 0.000 0.160
#> GSM601945 2 0.0000 0.8576 0.000 1.000 0.000
#> GSM601950 1 0.6274 0.5966 0.544 0.000 0.456
#> GSM601960 1 0.0592 0.6436 0.988 0.000 0.012
#> GSM601975 2 0.2066 0.8460 0.000 0.940 0.060
#> GSM601980 2 0.5529 0.6524 0.000 0.704 0.296
#> GSM601990 1 0.6308 -0.4222 0.508 0.000 0.492
#> GSM601880 1 0.0592 0.6420 0.988 0.000 0.012
#> GSM601910 1 0.6280 0.5904 0.540 0.000 0.460
#> GSM601920 2 0.5948 0.5986 0.000 0.640 0.360
#> GSM601925 1 0.0747 0.6368 0.984 0.000 0.016
#> GSM601955 2 0.6299 0.4223 0.000 0.524 0.476
#> GSM601965 1 0.5465 0.3407 0.712 0.000 0.288
#> GSM601970 1 0.6045 0.6022 0.620 0.000 0.380
#> GSM601985 1 0.0424 0.6451 0.992 0.000 0.008
#> GSM601995 2 0.6678 0.3965 0.008 0.512 0.480
#> GSM601876 1 0.2537 0.6264 0.920 0.000 0.080
#> GSM601886 2 0.5988 0.5951 0.000 0.632 0.368
#> GSM601891 1 0.6299 0.5868 0.524 0.000 0.476
#> GSM601896 1 0.2625 0.6260 0.916 0.000 0.084
#> GSM601901 2 0.1964 0.8477 0.000 0.944 0.056
#> GSM601906 3 0.9678 0.5531 0.328 0.228 0.444
#> GSM601916 2 0.5785 0.6306 0.000 0.668 0.332
#> GSM601931 1 0.0592 0.6420 0.988 0.000 0.012
#> GSM601936 2 0.6295 0.4294 0.000 0.528 0.472
#> GSM601941 2 0.2261 0.8442 0.000 0.932 0.068
#> GSM601946 1 0.0000 0.6425 1.000 0.000 0.000
#> GSM601951 3 0.6286 -0.5704 0.464 0.000 0.536
#> GSM601961 3 0.7164 0.0761 0.024 0.452 0.524
#> GSM601976 2 0.4842 0.7352 0.000 0.776 0.224
#> GSM601981 2 0.0000 0.8576 0.000 1.000 0.000
#> GSM601991 3 0.6307 0.3485 0.488 0.000 0.512
#> GSM601881 1 0.0592 0.6420 0.988 0.000 0.012
#> GSM601911 3 0.9725 0.5392 0.320 0.240 0.440
#> GSM601921 2 0.5859 0.6170 0.000 0.656 0.344
#> GSM601926 1 0.0592 0.6420 0.988 0.000 0.012
#> GSM601956 2 0.0000 0.8576 0.000 1.000 0.000
#> GSM601966 2 0.0592 0.8560 0.000 0.988 0.012
#> GSM601971 1 0.6180 0.5990 0.584 0.000 0.416
#> GSM601986 3 0.9688 0.5569 0.332 0.228 0.440
#> GSM601996 2 0.1411 0.8537 0.000 0.964 0.036
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM601872 2 0.0336 0.8288 0.000 0.992 0.008 0.000
#> GSM601882 2 0.1489 0.8161 0.000 0.952 0.044 0.004
#> GSM601887 3 0.4356 0.8888 0.292 0.000 0.708 0.000
#> GSM601892 3 0.4356 0.8888 0.292 0.000 0.708 0.000
#> GSM601897 3 0.5184 0.8272 0.304 0.000 0.672 0.024
#> GSM601902 2 0.4914 0.5765 0.000 0.676 0.012 0.312
#> GSM601912 1 0.7202 0.3474 0.464 0.000 0.140 0.396
#> GSM601927 1 0.0672 0.7541 0.984 0.000 0.008 0.008
#> GSM601932 2 0.5228 0.5831 0.000 0.664 0.024 0.312
#> GSM601937 4 0.6992 0.5541 0.000 0.176 0.248 0.576
#> GSM601942 2 0.0927 0.8268 0.000 0.976 0.016 0.008
#> GSM601947 2 0.4483 0.6168 0.000 0.712 0.004 0.284
#> GSM601957 3 0.5183 0.6992 0.408 0.000 0.584 0.008
#> GSM601972 2 0.4690 0.6231 0.000 0.712 0.012 0.276
#> GSM601977 2 0.0779 0.8281 0.000 0.980 0.016 0.004
#> GSM601987 2 0.1302 0.8179 0.000 0.956 0.044 0.000
#> GSM601877 1 0.4868 0.5534 0.748 0.000 0.212 0.040
#> GSM601907 2 0.0000 0.8295 0.000 1.000 0.000 0.000
#> GSM601917 4 0.5799 0.4865 0.000 0.264 0.068 0.668
#> GSM601922 2 0.5244 0.4261 0.000 0.600 0.012 0.388
#> GSM601952 2 0.2924 0.7800 0.000 0.884 0.016 0.100
#> GSM601962 4 0.7588 0.1953 0.320 0.000 0.216 0.464
#> GSM601967 3 0.4356 0.8888 0.292 0.000 0.708 0.000
#> GSM601982 2 0.1109 0.8182 0.000 0.968 0.004 0.028
#> GSM601992 2 0.2919 0.7928 0.000 0.896 0.044 0.060
#> GSM601873 2 0.1059 0.8259 0.000 0.972 0.016 0.012
#> GSM601883 2 0.1489 0.8161 0.000 0.952 0.044 0.004
#> GSM601888 3 0.4222 0.8822 0.272 0.000 0.728 0.000
#> GSM601893 3 0.4608 0.8792 0.304 0.000 0.692 0.004
#> GSM601898 1 0.5295 -0.4672 0.504 0.000 0.488 0.008
#> GSM601903 2 0.5075 0.5206 0.000 0.644 0.012 0.344
#> GSM601913 1 0.2722 0.7533 0.904 0.000 0.032 0.064
#> GSM601928 1 0.0524 0.7545 0.988 0.000 0.004 0.008
#> GSM601933 2 0.0336 0.8290 0.000 0.992 0.008 0.000
#> GSM601938 2 0.3850 0.7527 0.000 0.840 0.044 0.116
#> GSM601943 2 0.0927 0.8268 0.000 0.976 0.016 0.008
#> GSM601948 3 0.4406 0.8644 0.300 0.000 0.700 0.000
#> GSM601958 1 0.5296 -0.4788 0.500 0.000 0.492 0.008
#> GSM601973 2 0.5657 0.3092 0.000 0.540 0.024 0.436
#> GSM601978 2 0.0000 0.8295 0.000 1.000 0.000 0.000
#> GSM601988 4 0.7105 0.5445 0.000 0.184 0.256 0.560
#> GSM601878 1 0.4673 0.3781 0.700 0.000 0.292 0.008
#> GSM601908 2 0.0000 0.8295 0.000 1.000 0.000 0.000
#> GSM601918 2 0.4769 0.5847 0.000 0.684 0.008 0.308
#> GSM601923 1 0.0672 0.7541 0.984 0.000 0.008 0.008
#> GSM601953 2 0.0188 0.8290 0.000 0.996 0.004 0.000
#> GSM601963 1 0.3071 0.7494 0.888 0.000 0.044 0.068
#> GSM601968 3 0.4356 0.8888 0.292 0.000 0.708 0.000
#> GSM601983 1 0.4419 0.7223 0.812 0.000 0.104 0.084
#> GSM601993 2 0.7679 -0.1963 0.000 0.408 0.216 0.376
#> GSM601874 2 0.0000 0.8295 0.000 1.000 0.000 0.000
#> GSM601884 2 0.1305 0.8198 0.000 0.960 0.036 0.004
#> GSM601889 1 0.5296 -0.4788 0.500 0.000 0.492 0.008
#> GSM601894 1 0.5253 0.0124 0.624 0.000 0.360 0.016
#> GSM601899 3 0.4356 0.8888 0.292 0.000 0.708 0.000
#> GSM601904 4 0.4356 0.4848 0.000 0.292 0.000 0.708
#> GSM601914 1 0.3071 0.7494 0.888 0.000 0.044 0.068
#> GSM601929 1 0.5985 0.5419 0.692 0.000 0.168 0.140
#> GSM601934 2 0.0336 0.8290 0.000 0.992 0.008 0.000
#> GSM601939 1 0.2450 0.7402 0.912 0.000 0.072 0.016
#> GSM601944 2 0.0657 0.8287 0.000 0.984 0.012 0.004
#> GSM601949 3 0.4406 0.8713 0.300 0.000 0.700 0.000
#> GSM601959 3 0.4857 0.8494 0.324 0.000 0.668 0.008
#> GSM601974 4 0.5334 0.4893 0.004 0.284 0.028 0.684
#> GSM601979 2 0.0000 0.8295 0.000 1.000 0.000 0.000
#> GSM601989 3 0.4382 0.8796 0.296 0.000 0.704 0.000
#> GSM601879 1 0.5890 0.4344 0.660 0.000 0.268 0.072
#> GSM601909 3 0.4406 0.8855 0.300 0.000 0.700 0.000
#> GSM601919 2 0.4560 0.6024 0.000 0.700 0.004 0.296
#> GSM601924 1 0.2142 0.7229 0.928 0.000 0.056 0.016
#> GSM601954 2 0.4053 0.6638 0.000 0.768 0.004 0.228
#> GSM601964 1 0.3128 0.7503 0.884 0.000 0.040 0.076
#> GSM601969 3 0.4222 0.8822 0.272 0.000 0.728 0.000
#> GSM601984 1 0.6503 0.5371 0.640 0.000 0.164 0.196
#> GSM601994 2 0.2500 0.7984 0.000 0.916 0.044 0.040
#> GSM601875 2 0.0000 0.8295 0.000 1.000 0.000 0.000
#> GSM601885 2 0.1109 0.8204 0.000 0.968 0.028 0.004
#> GSM601890 3 0.4356 0.8888 0.292 0.000 0.708 0.000
#> GSM601895 1 0.3071 0.7494 0.888 0.000 0.044 0.068
#> GSM601900 1 0.3972 0.7364 0.840 0.000 0.080 0.080
#> GSM601905 4 0.4741 0.4346 0.000 0.328 0.004 0.668
#> GSM601915 1 0.3144 0.7487 0.884 0.000 0.044 0.072
#> GSM601930 1 0.1004 0.7516 0.972 0.000 0.004 0.024
#> GSM601935 4 0.6133 0.5841 0.004 0.100 0.220 0.676
#> GSM601940 1 0.4836 0.3991 0.672 0.000 0.320 0.008
#> GSM601945 2 0.0000 0.8295 0.000 1.000 0.000 0.000
#> GSM601950 3 0.4500 0.8742 0.316 0.000 0.684 0.000
#> GSM601960 1 0.3144 0.7487 0.884 0.000 0.044 0.072
#> GSM601975 2 0.4673 0.6057 0.000 0.700 0.008 0.292
#> GSM601980 4 0.7786 0.2418 0.000 0.368 0.244 0.388
#> GSM601990 4 0.7674 0.1536 0.340 0.000 0.224 0.436
#> GSM601880 1 0.0672 0.7541 0.984 0.000 0.008 0.008
#> GSM601910 3 0.4744 0.8678 0.284 0.000 0.704 0.012
#> GSM601920 4 0.4382 0.4831 0.000 0.296 0.000 0.704
#> GSM601925 1 0.1151 0.7515 0.968 0.000 0.008 0.024
#> GSM601955 4 0.6522 0.5792 0.000 0.144 0.224 0.632
#> GSM601965 1 0.6848 0.4818 0.592 0.000 0.160 0.248
#> GSM601970 3 0.5028 0.7212 0.400 0.000 0.596 0.004
#> GSM601985 1 0.2739 0.7550 0.904 0.000 0.036 0.060
#> GSM601995 4 0.5839 0.5825 0.004 0.080 0.220 0.696
#> GSM601876 1 0.3667 0.7341 0.856 0.000 0.088 0.056
#> GSM601886 4 0.4594 0.4981 0.000 0.280 0.008 0.712
#> GSM601891 3 0.4222 0.8822 0.272 0.000 0.728 0.000
#> GSM601896 1 0.3505 0.7340 0.864 0.000 0.088 0.048
#> GSM601901 2 0.4606 0.6366 0.000 0.724 0.012 0.264
#> GSM601906 4 0.6006 0.5297 0.228 0.052 0.024 0.696
#> GSM601916 4 0.4655 0.4572 0.000 0.312 0.004 0.684
#> GSM601931 1 0.0672 0.7541 0.984 0.000 0.008 0.008
#> GSM601936 4 0.6703 0.5701 0.000 0.156 0.232 0.612
#> GSM601941 2 0.4957 0.5697 0.000 0.668 0.012 0.320
#> GSM601946 1 0.2032 0.7582 0.936 0.000 0.028 0.036
#> GSM601951 3 0.6336 0.4235 0.088 0.000 0.608 0.304
#> GSM601961 3 0.5956 0.4247 0.000 0.220 0.680 0.100
#> GSM601976 4 0.5070 0.1949 0.000 0.416 0.004 0.580
#> GSM601981 2 0.0188 0.8290 0.000 0.996 0.004 0.000
#> GSM601991 4 0.7188 0.3610 0.204 0.000 0.244 0.552
#> GSM601881 1 0.0672 0.7541 0.984 0.000 0.008 0.008
#> GSM601911 4 0.6931 0.4287 0.292 0.056 0.044 0.608
#> GSM601921 4 0.4632 0.4642 0.000 0.308 0.004 0.688
#> GSM601926 1 0.0804 0.7533 0.980 0.000 0.008 0.012
#> GSM601956 2 0.0000 0.8295 0.000 1.000 0.000 0.000
#> GSM601966 2 0.0937 0.8278 0.000 0.976 0.012 0.012
#> GSM601971 3 0.4699 0.8717 0.320 0.000 0.676 0.004
#> GSM601986 4 0.6952 0.4221 0.296 0.056 0.044 0.604
#> GSM601996 2 0.2919 0.7928 0.000 0.896 0.044 0.060
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM601872 2 0.0486 0.9213 0.004 0.988 0.000 0.004 0.004
#> GSM601882 2 0.2338 0.8867 0.032 0.916 0.000 0.016 0.036
#> GSM601887 3 0.0404 0.8075 0.012 0.000 0.988 0.000 0.000
#> GSM601892 3 0.0404 0.8075 0.012 0.000 0.988 0.000 0.000
#> GSM601897 3 0.4752 0.6035 0.012 0.000 0.732 0.056 0.200
#> GSM601902 4 0.4499 0.6484 0.004 0.408 0.000 0.584 0.004
#> GSM601912 1 0.8337 0.3799 0.332 0.000 0.152 0.208 0.308
#> GSM601927 1 0.2284 0.7467 0.896 0.000 0.096 0.004 0.004
#> GSM601932 4 0.5229 0.5212 0.008 0.464 0.000 0.500 0.028
#> GSM601937 5 0.5658 0.6926 0.012 0.076 0.000 0.300 0.612
#> GSM601942 2 0.0740 0.9167 0.004 0.980 0.000 0.008 0.008
#> GSM601947 4 0.4410 0.6057 0.000 0.440 0.000 0.556 0.004
#> GSM601957 3 0.3904 0.6904 0.156 0.000 0.792 0.000 0.052
#> GSM601972 4 0.4567 0.5964 0.004 0.448 0.000 0.544 0.004
#> GSM601977 2 0.0486 0.9203 0.004 0.988 0.000 0.004 0.004
#> GSM601987 2 0.2417 0.8798 0.032 0.912 0.000 0.016 0.040
#> GSM601877 1 0.6685 0.5723 0.584 0.000 0.248 0.080 0.088
#> GSM601907 2 0.0162 0.9228 0.000 0.996 0.000 0.000 0.004
#> GSM601917 4 0.4221 0.5039 0.000 0.108 0.000 0.780 0.112
#> GSM601922 4 0.4084 0.6993 0.000 0.328 0.000 0.668 0.004
#> GSM601952 2 0.1788 0.8811 0.004 0.932 0.000 0.056 0.008
#> GSM601962 5 0.6854 -0.1421 0.316 0.000 0.012 0.212 0.460
#> GSM601967 3 0.0404 0.8075 0.012 0.000 0.988 0.000 0.000
#> GSM601982 2 0.1041 0.9046 0.004 0.964 0.000 0.032 0.000
#> GSM601992 2 0.4011 0.8042 0.044 0.828 0.000 0.064 0.064
#> GSM601873 2 0.1267 0.9031 0.004 0.960 0.000 0.024 0.012
#> GSM601883 2 0.2417 0.8835 0.032 0.912 0.000 0.016 0.040
#> GSM601888 3 0.0486 0.8028 0.004 0.000 0.988 0.004 0.004
#> GSM601893 3 0.3413 0.7407 0.044 0.000 0.832 0.000 0.124
#> GSM601898 3 0.5754 0.4213 0.292 0.000 0.588 0.000 0.120
#> GSM601903 4 0.4426 0.6708 0.004 0.380 0.000 0.612 0.004
#> GSM601913 1 0.5382 0.7302 0.640 0.000 0.100 0.000 0.260
#> GSM601928 1 0.1965 0.7463 0.904 0.000 0.096 0.000 0.000
#> GSM601933 2 0.0000 0.9224 0.000 1.000 0.000 0.000 0.000
#> GSM601938 2 0.4806 0.7372 0.044 0.772 0.000 0.108 0.076
#> GSM601943 2 0.0854 0.9163 0.004 0.976 0.000 0.008 0.012
#> GSM601948 3 0.1087 0.7963 0.016 0.000 0.968 0.008 0.008
#> GSM601958 3 0.5691 0.4242 0.296 0.000 0.592 0.000 0.112
#> GSM601973 4 0.4317 0.7002 0.004 0.320 0.000 0.668 0.008
#> GSM601978 2 0.0162 0.9228 0.000 0.996 0.000 0.000 0.004
#> GSM601988 5 0.5752 0.6914 0.016 0.076 0.000 0.300 0.608
#> GSM601878 1 0.4801 0.4057 0.584 0.000 0.396 0.008 0.012
#> GSM601908 2 0.0162 0.9228 0.000 0.996 0.000 0.000 0.004
#> GSM601918 4 0.4557 0.6093 0.004 0.440 0.000 0.552 0.004
#> GSM601923 1 0.1965 0.7463 0.904 0.000 0.096 0.000 0.000
#> GSM601953 2 0.0324 0.9210 0.000 0.992 0.000 0.004 0.004
#> GSM601963 1 0.5382 0.7199 0.644 0.000 0.104 0.000 0.252
#> GSM601968 3 0.0404 0.8075 0.012 0.000 0.988 0.000 0.000
#> GSM601983 1 0.7287 0.6714 0.504 0.000 0.148 0.072 0.276
#> GSM601993 5 0.7355 0.4582 0.044 0.244 0.000 0.248 0.464
#> GSM601874 2 0.0162 0.9228 0.000 0.996 0.000 0.000 0.004
#> GSM601884 2 0.1306 0.9119 0.016 0.960 0.000 0.016 0.008
#> GSM601889 3 0.5637 0.4481 0.284 0.000 0.604 0.000 0.112
#> GSM601894 3 0.6468 0.0792 0.360 0.000 0.452 0.000 0.188
#> GSM601899 3 0.0290 0.8074 0.008 0.000 0.992 0.000 0.000
#> GSM601904 4 0.3333 0.6347 0.008 0.164 0.000 0.820 0.008
#> GSM601914 1 0.5405 0.7176 0.640 0.000 0.104 0.000 0.256
#> GSM601929 1 0.6850 0.5456 0.600 0.000 0.168 0.136 0.096
#> GSM601934 2 0.0000 0.9224 0.000 1.000 0.000 0.000 0.000
#> GSM601939 1 0.5116 0.7186 0.692 0.000 0.120 0.000 0.188
#> GSM601944 2 0.0000 0.9224 0.000 1.000 0.000 0.000 0.000
#> GSM601949 3 0.0740 0.8009 0.008 0.000 0.980 0.004 0.008
#> GSM601959 3 0.2149 0.7855 0.048 0.000 0.916 0.000 0.036
#> GSM601974 4 0.4492 0.5014 0.020 0.100 0.012 0.800 0.068
#> GSM601979 2 0.0162 0.9228 0.000 0.996 0.000 0.000 0.004
#> GSM601989 3 0.4341 0.6848 0.056 0.000 0.788 0.020 0.136
#> GSM601879 1 0.6840 0.5329 0.568 0.000 0.252 0.100 0.080
#> GSM601909 3 0.1818 0.7943 0.024 0.000 0.932 0.000 0.044
#> GSM601919 4 0.4367 0.6414 0.000 0.416 0.000 0.580 0.004
#> GSM601924 1 0.2522 0.7381 0.880 0.000 0.108 0.000 0.012
#> GSM601954 2 0.4425 -0.4278 0.000 0.544 0.000 0.452 0.004
#> GSM601964 1 0.5832 0.7215 0.588 0.000 0.096 0.008 0.308
#> GSM601969 3 0.0486 0.8028 0.004 0.000 0.988 0.004 0.004
#> GSM601984 1 0.7808 0.5452 0.480 0.000 0.180 0.136 0.204
#> GSM601994 2 0.3880 0.8131 0.044 0.836 0.000 0.056 0.064
#> GSM601875 2 0.0162 0.9228 0.000 0.996 0.000 0.000 0.004
#> GSM601885 2 0.0854 0.9186 0.012 0.976 0.000 0.008 0.004
#> GSM601890 3 0.0290 0.8074 0.008 0.000 0.992 0.000 0.000
#> GSM601895 1 0.5844 0.7239 0.612 0.000 0.116 0.008 0.264
#> GSM601900 1 0.7223 0.6847 0.484 0.000 0.144 0.060 0.312
#> GSM601905 4 0.3611 0.6785 0.008 0.208 0.000 0.780 0.004
#> GSM601915 1 0.5382 0.7181 0.644 0.000 0.104 0.000 0.252
#> GSM601930 1 0.3870 0.7370 0.828 0.000 0.092 0.020 0.060
#> GSM601935 5 0.4877 0.7008 0.012 0.024 0.000 0.312 0.652
#> GSM601940 3 0.6017 -0.2633 0.404 0.000 0.480 0.000 0.116
#> GSM601945 2 0.0162 0.9228 0.000 0.996 0.000 0.000 0.004
#> GSM601950 3 0.0771 0.8056 0.020 0.000 0.976 0.000 0.004
#> GSM601960 1 0.5426 0.7184 0.640 0.000 0.108 0.000 0.252
#> GSM601975 4 0.4227 0.6415 0.000 0.420 0.000 0.580 0.000
#> GSM601980 5 0.6435 0.5793 0.016 0.192 0.000 0.220 0.572
#> GSM601990 5 0.6587 -0.1778 0.328 0.000 0.008 0.176 0.488
#> GSM601880 1 0.2284 0.7467 0.896 0.000 0.096 0.004 0.004
#> GSM601910 3 0.3875 0.6937 0.008 0.000 0.808 0.044 0.140
#> GSM601920 4 0.3443 0.6320 0.008 0.164 0.000 0.816 0.012
#> GSM601925 1 0.4024 0.7347 0.820 0.000 0.092 0.024 0.064
#> GSM601955 5 0.5388 0.7053 0.012 0.052 0.000 0.316 0.620
#> GSM601965 1 0.7949 0.5115 0.460 0.000 0.176 0.156 0.208
#> GSM601970 3 0.3669 0.7156 0.128 0.000 0.816 0.000 0.056
#> GSM601985 1 0.5354 0.7220 0.652 0.000 0.108 0.000 0.240
#> GSM601995 5 0.5407 0.6963 0.036 0.024 0.000 0.320 0.620
#> GSM601876 1 0.7055 0.6883 0.552 0.000 0.144 0.072 0.232
#> GSM601886 4 0.4230 0.6186 0.008 0.168 0.000 0.776 0.048
#> GSM601891 3 0.0324 0.8035 0.004 0.000 0.992 0.000 0.004
#> GSM601896 1 0.6780 0.6934 0.596 0.000 0.144 0.072 0.188
#> GSM601901 4 0.4576 0.5822 0.004 0.456 0.000 0.536 0.004
#> GSM601906 4 0.4372 0.3188 0.120 0.008 0.012 0.796 0.064
#> GSM601916 4 0.3611 0.6785 0.008 0.208 0.000 0.780 0.004
#> GSM601931 1 0.1965 0.7463 0.904 0.000 0.096 0.000 0.000
#> GSM601936 5 0.5156 0.7029 0.000 0.060 0.000 0.320 0.620
#> GSM601941 4 0.4871 0.6089 0.008 0.432 0.000 0.548 0.012
#> GSM601946 1 0.5030 0.7376 0.696 0.000 0.104 0.000 0.200
#> GSM601951 3 0.6209 0.1037 0.040 0.000 0.464 0.444 0.052
#> GSM601961 3 0.2984 0.6922 0.008 0.092 0.876 0.012 0.012
#> GSM601976 4 0.3870 0.7048 0.004 0.260 0.000 0.732 0.004
#> GSM601981 2 0.0162 0.9228 0.000 0.996 0.000 0.000 0.004
#> GSM601991 5 0.4272 0.5128 0.052 0.000 0.000 0.196 0.752
#> GSM601881 1 0.1965 0.7463 0.904 0.000 0.096 0.000 0.000
#> GSM601911 4 0.7270 -0.1986 0.300 0.008 0.020 0.452 0.220
#> GSM601921 4 0.3578 0.6758 0.008 0.204 0.000 0.784 0.004
#> GSM601926 1 0.1965 0.7463 0.904 0.000 0.096 0.000 0.000
#> GSM601956 2 0.0162 0.9228 0.000 0.996 0.000 0.000 0.004
#> GSM601966 2 0.2599 0.8664 0.024 0.904 0.000 0.044 0.028
#> GSM601971 3 0.0771 0.8056 0.020 0.000 0.976 0.000 0.004
#> GSM601986 4 0.7289 -0.2147 0.308 0.008 0.020 0.444 0.220
#> GSM601996 2 0.3947 0.8085 0.044 0.832 0.000 0.060 0.064
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM601872 2 0.1180 0.8779 0.000 0.960 0.024 0.008 0.004 0.004
#> GSM601882 2 0.4473 0.7780 0.000 0.752 0.164 0.044 0.028 0.012
#> GSM601887 6 0.1204 0.8209 0.056 0.000 0.000 0.000 0.000 0.944
#> GSM601892 6 0.1204 0.8209 0.056 0.000 0.000 0.000 0.000 0.944
#> GSM601897 3 0.5243 -0.0923 0.080 0.000 0.464 0.000 0.004 0.452
#> GSM601902 4 0.3302 0.7818 0.000 0.232 0.004 0.760 0.004 0.000
#> GSM601912 3 0.5961 0.5361 0.228 0.000 0.624 0.052 0.028 0.068
#> GSM601927 1 0.0458 0.5664 0.984 0.000 0.000 0.000 0.000 0.016
#> GSM601932 4 0.5244 0.7107 0.000 0.232 0.056 0.664 0.040 0.008
#> GSM601937 5 0.2418 0.9045 0.000 0.016 0.008 0.092 0.884 0.000
#> GSM601942 2 0.1465 0.8741 0.000 0.948 0.024 0.004 0.020 0.004
#> GSM601947 4 0.4215 0.7310 0.000 0.300 0.004 0.672 0.016 0.008
#> GSM601957 6 0.5262 0.7077 0.156 0.000 0.048 0.036 0.048 0.712
#> GSM601972 4 0.4139 0.7414 0.000 0.284 0.016 0.688 0.004 0.008
#> GSM601977 2 0.1323 0.8774 0.000 0.956 0.020 0.008 0.008 0.008
#> GSM601987 2 0.4631 0.7483 0.000 0.728 0.188 0.048 0.028 0.008
#> GSM601877 1 0.4973 0.0463 0.640 0.000 0.276 0.008 0.004 0.072
#> GSM601907 2 0.0405 0.8836 0.000 0.988 0.004 0.008 0.000 0.000
#> GSM601917 4 0.3184 0.6683 0.000 0.020 0.004 0.828 0.140 0.008
#> GSM601922 4 0.2643 0.7896 0.000 0.128 0.008 0.856 0.000 0.008
#> GSM601952 2 0.3502 0.7964 0.000 0.836 0.024 0.096 0.032 0.012
#> GSM601962 3 0.6115 0.5323 0.208 0.000 0.592 0.056 0.140 0.004
#> GSM601967 6 0.1204 0.8209 0.056 0.000 0.000 0.000 0.000 0.944
#> GSM601982 2 0.2201 0.8578 0.000 0.912 0.024 0.048 0.004 0.012
#> GSM601992 2 0.5964 0.6183 0.000 0.604 0.216 0.084 0.096 0.000
#> GSM601873 2 0.1971 0.8681 0.000 0.928 0.024 0.016 0.024 0.008
#> GSM601883 2 0.4409 0.7804 0.000 0.756 0.164 0.040 0.028 0.012
#> GSM601888 6 0.1801 0.8142 0.056 0.000 0.016 0.000 0.004 0.924
#> GSM601893 6 0.4448 0.7239 0.084 0.000 0.136 0.012 0.012 0.756
#> GSM601898 6 0.6836 0.4460 0.284 0.000 0.088 0.044 0.064 0.520
#> GSM601903 4 0.2805 0.7905 0.000 0.184 0.000 0.812 0.004 0.000
#> GSM601913 1 0.4629 0.2430 0.560 0.000 0.408 0.020 0.008 0.004
#> GSM601928 1 0.0458 0.5664 0.984 0.000 0.000 0.000 0.000 0.016
#> GSM601933 2 0.0870 0.8832 0.000 0.972 0.004 0.012 0.012 0.000
#> GSM601938 2 0.6316 0.5655 0.000 0.568 0.216 0.096 0.120 0.000
#> GSM601943 2 0.1465 0.8741 0.000 0.948 0.024 0.004 0.020 0.004
#> GSM601948 6 0.2903 0.7953 0.064 0.000 0.028 0.008 0.024 0.876
#> GSM601958 6 0.6754 0.4571 0.284 0.000 0.080 0.044 0.064 0.528
#> GSM601973 4 0.2504 0.7858 0.000 0.136 0.004 0.856 0.004 0.000
#> GSM601978 2 0.0405 0.8836 0.000 0.988 0.004 0.008 0.000 0.000
#> GSM601988 5 0.2816 0.8989 0.000 0.020 0.024 0.088 0.868 0.000
#> GSM601878 1 0.3934 0.2482 0.676 0.000 0.020 0.000 0.000 0.304
#> GSM601908 2 0.0405 0.8836 0.000 0.988 0.004 0.008 0.000 0.000
#> GSM601918 4 0.4330 0.7437 0.000 0.276 0.028 0.684 0.004 0.008
#> GSM601923 1 0.0458 0.5664 0.984 0.000 0.000 0.000 0.000 0.016
#> GSM601953 2 0.0405 0.8836 0.000 0.988 0.004 0.008 0.000 0.000
#> GSM601963 1 0.5444 0.4208 0.600 0.000 0.312 0.036 0.036 0.016
#> GSM601968 6 0.1204 0.8209 0.056 0.000 0.000 0.000 0.000 0.944
#> GSM601983 3 0.4361 0.2681 0.424 0.000 0.552 0.000 0.000 0.024
#> GSM601993 5 0.6183 0.6594 0.000 0.080 0.216 0.124 0.580 0.000
#> GSM601874 2 0.0405 0.8836 0.000 0.988 0.004 0.008 0.000 0.000
#> GSM601884 2 0.2996 0.8501 0.000 0.872 0.064 0.032 0.020 0.012
#> GSM601889 6 0.6740 0.4652 0.280 0.000 0.080 0.044 0.064 0.532
#> GSM601894 1 0.7761 0.0909 0.376 0.000 0.204 0.056 0.064 0.300
#> GSM601899 6 0.1349 0.8207 0.056 0.000 0.000 0.000 0.004 0.940
#> GSM601904 4 0.3362 0.7589 0.000 0.080 0.052 0.840 0.028 0.000
#> GSM601914 1 0.5472 0.4200 0.592 0.000 0.320 0.036 0.036 0.016
#> GSM601929 1 0.5497 -0.0510 0.604 0.000 0.292 0.056 0.004 0.044
#> GSM601934 2 0.0870 0.8832 0.000 0.972 0.004 0.012 0.012 0.000
#> GSM601939 1 0.5768 0.4781 0.656 0.000 0.196 0.056 0.064 0.028
#> GSM601944 2 0.0870 0.8834 0.000 0.972 0.004 0.012 0.012 0.000
#> GSM601949 6 0.2638 0.8053 0.052 0.000 0.028 0.008 0.020 0.892
#> GSM601959 6 0.4288 0.7737 0.092 0.000 0.036 0.032 0.044 0.796
#> GSM601974 4 0.3792 0.6625 0.004 0.032 0.160 0.788 0.016 0.000
#> GSM601979 2 0.0405 0.8836 0.000 0.988 0.004 0.008 0.000 0.000
#> GSM601989 6 0.5052 0.4311 0.088 0.000 0.308 0.000 0.004 0.600
#> GSM601879 1 0.5589 0.0325 0.616 0.000 0.248 0.020 0.008 0.108
#> GSM601909 6 0.3601 0.7925 0.072 0.000 0.024 0.028 0.036 0.840
#> GSM601919 4 0.3421 0.7749 0.000 0.256 0.000 0.736 0.000 0.008
#> GSM601924 1 0.1829 0.5462 0.928 0.000 0.028 0.000 0.008 0.036
#> GSM601954 4 0.4712 0.4524 0.000 0.456 0.008 0.512 0.016 0.008
#> GSM601964 1 0.4577 0.1227 0.528 0.000 0.444 0.020 0.004 0.004
#> GSM601969 6 0.1914 0.8129 0.056 0.000 0.016 0.000 0.008 0.920
#> GSM601984 3 0.5638 0.4537 0.376 0.000 0.528 0.048 0.004 0.044
#> GSM601994 2 0.5784 0.6383 0.000 0.620 0.216 0.072 0.092 0.000
#> GSM601875 2 0.0405 0.8836 0.000 0.988 0.004 0.008 0.000 0.000
#> GSM601885 2 0.2177 0.8708 0.000 0.916 0.044 0.016 0.012 0.012
#> GSM601890 6 0.1349 0.8207 0.056 0.000 0.000 0.000 0.004 0.940
#> GSM601895 1 0.5521 0.3162 0.536 0.000 0.384 0.036 0.020 0.024
#> GSM601900 3 0.4276 0.1991 0.416 0.000 0.564 0.000 0.000 0.020
#> GSM601905 4 0.3237 0.7730 0.000 0.100 0.056 0.836 0.008 0.000
#> GSM601915 1 0.5802 0.4391 0.596 0.000 0.284 0.052 0.052 0.016
#> GSM601930 1 0.1644 0.4874 0.920 0.000 0.076 0.000 0.000 0.004
#> GSM601935 5 0.2800 0.8954 0.000 0.004 0.036 0.100 0.860 0.000
#> GSM601940 6 0.6532 0.1342 0.352 0.000 0.108 0.028 0.032 0.480
#> GSM601945 2 0.0508 0.8834 0.000 0.984 0.004 0.012 0.000 0.000
#> GSM601950 6 0.1349 0.8206 0.056 0.000 0.004 0.000 0.000 0.940
#> GSM601960 1 0.6041 0.4275 0.572 0.000 0.292 0.056 0.064 0.016
#> GSM601975 4 0.3023 0.7844 0.000 0.232 0.000 0.768 0.000 0.000
#> GSM601980 5 0.3837 0.8667 0.000 0.048 0.048 0.084 0.816 0.004
#> GSM601990 3 0.6059 0.4790 0.252 0.000 0.568 0.036 0.140 0.004
#> GSM601880 1 0.0458 0.5664 0.984 0.000 0.000 0.000 0.000 0.016
#> GSM601910 6 0.5138 0.3310 0.072 0.000 0.360 0.000 0.008 0.560
#> GSM601920 4 0.3394 0.7537 0.000 0.068 0.060 0.840 0.032 0.000
#> GSM601925 1 0.1700 0.4828 0.916 0.000 0.080 0.000 0.000 0.004
#> GSM601955 5 0.3365 0.8951 0.000 0.008 0.052 0.104 0.832 0.004
#> GSM601965 3 0.5638 0.4537 0.376 0.000 0.528 0.048 0.004 0.044
#> GSM601970 6 0.4780 0.7367 0.140 0.000 0.048 0.024 0.040 0.748
#> GSM601985 1 0.5768 0.4449 0.604 0.000 0.276 0.052 0.052 0.016
#> GSM601995 5 0.4210 0.8795 0.000 0.004 0.076 0.104 0.784 0.032
#> GSM601876 1 0.4184 -0.2701 0.500 0.000 0.488 0.000 0.000 0.012
#> GSM601886 4 0.3889 0.7412 0.000 0.080 0.052 0.808 0.060 0.000
#> GSM601891 6 0.2036 0.8079 0.064 0.000 0.016 0.000 0.008 0.912
#> GSM601896 1 0.4172 -0.2424 0.528 0.000 0.460 0.000 0.000 0.012
#> GSM601901 4 0.4383 0.7229 0.000 0.296 0.020 0.668 0.008 0.008
#> GSM601906 4 0.4290 0.5809 0.076 0.000 0.168 0.744 0.012 0.000
#> GSM601916 4 0.3200 0.7703 0.000 0.092 0.060 0.840 0.008 0.000
#> GSM601931 1 0.0458 0.5664 0.984 0.000 0.000 0.000 0.000 0.016
#> GSM601936 5 0.2786 0.9035 0.000 0.012 0.024 0.100 0.864 0.000
#> GSM601941 4 0.4702 0.7382 0.000 0.228 0.056 0.696 0.012 0.008
#> GSM601946 1 0.5549 0.4570 0.636 0.000 0.248 0.052 0.052 0.012
#> GSM601951 4 0.6566 0.1648 0.016 0.000 0.156 0.428 0.024 0.376
#> GSM601961 6 0.3301 0.7133 0.000 0.092 0.024 0.012 0.024 0.848
#> GSM601976 4 0.2933 0.7817 0.000 0.108 0.032 0.852 0.008 0.000
#> GSM601981 2 0.0508 0.8819 0.000 0.984 0.004 0.012 0.000 0.000
#> GSM601991 3 0.5183 0.2567 0.028 0.000 0.540 0.040 0.392 0.000
#> GSM601881 1 0.0458 0.5664 0.984 0.000 0.000 0.000 0.000 0.016
#> GSM601911 3 0.6364 0.4641 0.180 0.000 0.508 0.276 0.032 0.004
#> GSM601921 4 0.3249 0.7687 0.000 0.088 0.060 0.840 0.012 0.000
#> GSM601926 1 0.0458 0.5664 0.984 0.000 0.000 0.000 0.000 0.016
#> GSM601956 2 0.0405 0.8836 0.000 0.988 0.004 0.008 0.000 0.000
#> GSM601966 2 0.4075 0.7829 0.000 0.772 0.152 0.060 0.012 0.004
#> GSM601971 6 0.2805 0.8114 0.056 0.000 0.024 0.012 0.024 0.884
#> GSM601986 3 0.6381 0.4687 0.188 0.000 0.508 0.268 0.032 0.004
#> GSM601996 2 0.5879 0.6269 0.000 0.612 0.216 0.084 0.088 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
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)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
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)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, 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.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")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n time(p) gender(p) k
#> ATC:kmeans 125 0.2945 0.840 2
#> ATC:kmeans 111 0.0113 0.503 3
#> ATC:kmeans 96 0.2780 0.707 4
#> ATC:kmeans 110 0.3298 0.544 5
#> ATC:kmeans 89 0.3638 0.609 6
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.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["ATC", "skmeans"]
# you can also extract it by
# res = res_list["ATC:skmeans"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 21512 rows and 125 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 1250 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_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
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, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.994 0.998 0.5043 0.496 0.496
#> 3 3 0.985 0.923 0.958 0.2605 0.833 0.674
#> 4 4 0.728 0.689 0.864 0.1532 0.866 0.646
#> 5 5 0.728 0.733 0.841 0.0646 0.908 0.676
#> 6 6 0.732 0.642 0.801 0.0390 0.941 0.740
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 shows 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 class
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
#> GSM601872 2 0.000 0.995 0.000 1.000
#> GSM601882 2 0.000 0.995 0.000 1.000
#> GSM601887 1 0.000 1.000 1.000 0.000
#> GSM601892 1 0.000 1.000 1.000 0.000
#> GSM601897 1 0.000 1.000 1.000 0.000
#> GSM601902 2 0.000 0.995 0.000 1.000
#> GSM601912 1 0.000 1.000 1.000 0.000
#> GSM601927 1 0.000 1.000 1.000 0.000
#> GSM601932 2 0.000 0.995 0.000 1.000
#> GSM601937 2 0.000 0.995 0.000 1.000
#> GSM601942 2 0.000 0.995 0.000 1.000
#> GSM601947 2 0.000 0.995 0.000 1.000
#> GSM601957 1 0.000 1.000 1.000 0.000
#> GSM601972 2 0.000 0.995 0.000 1.000
#> GSM601977 2 0.000 0.995 0.000 1.000
#> GSM601987 2 0.000 0.995 0.000 1.000
#> GSM601877 1 0.000 1.000 1.000 0.000
#> GSM601907 2 0.000 0.995 0.000 1.000
#> GSM601917 2 0.000 0.995 0.000 1.000
#> GSM601922 2 0.000 0.995 0.000 1.000
#> GSM601952 2 0.000 0.995 0.000 1.000
#> GSM601962 1 0.000 1.000 1.000 0.000
#> GSM601967 1 0.000 1.000 1.000 0.000
#> GSM601982 2 0.000 0.995 0.000 1.000
#> GSM601992 2 0.000 0.995 0.000 1.000
#> GSM601873 2 0.000 0.995 0.000 1.000
#> GSM601883 2 0.000 0.995 0.000 1.000
#> GSM601888 1 0.000 1.000 1.000 0.000
#> GSM601893 1 0.000 1.000 1.000 0.000
#> GSM601898 1 0.000 1.000 1.000 0.000
#> GSM601903 2 0.000 0.995 0.000 1.000
#> GSM601913 1 0.000 1.000 1.000 0.000
#> GSM601928 1 0.000 1.000 1.000 0.000
#> GSM601933 2 0.000 0.995 0.000 1.000
#> GSM601938 2 0.000 0.995 0.000 1.000
#> GSM601943 2 0.000 0.995 0.000 1.000
#> GSM601948 1 0.000 1.000 1.000 0.000
#> GSM601958 1 0.000 1.000 1.000 0.000
#> GSM601973 2 0.000 0.995 0.000 1.000
#> GSM601978 2 0.000 0.995 0.000 1.000
#> GSM601988 2 0.000 0.995 0.000 1.000
#> GSM601878 1 0.000 1.000 1.000 0.000
#> GSM601908 2 0.000 0.995 0.000 1.000
#> GSM601918 2 0.000 0.995 0.000 1.000
#> GSM601923 1 0.000 1.000 1.000 0.000
#> GSM601953 2 0.000 0.995 0.000 1.000
#> GSM601963 1 0.000 1.000 1.000 0.000
#> GSM601968 1 0.000 1.000 1.000 0.000
#> GSM601983 1 0.000 1.000 1.000 0.000
#> GSM601993 2 0.000 0.995 0.000 1.000
#> GSM601874 2 0.000 0.995 0.000 1.000
#> GSM601884 2 0.000 0.995 0.000 1.000
#> GSM601889 1 0.000 1.000 1.000 0.000
#> GSM601894 1 0.000 1.000 1.000 0.000
#> GSM601899 1 0.000 1.000 1.000 0.000
#> GSM601904 2 0.000 0.995 0.000 1.000
#> GSM601914 1 0.000 1.000 1.000 0.000
#> GSM601929 1 0.000 1.000 1.000 0.000
#> GSM601934 2 0.000 0.995 0.000 1.000
#> GSM601939 1 0.000 1.000 1.000 0.000
#> GSM601944 2 0.000 0.995 0.000 1.000
#> GSM601949 1 0.000 1.000 1.000 0.000
#> GSM601959 1 0.000 1.000 1.000 0.000
#> GSM601974 2 0.000 0.995 0.000 1.000
#> GSM601979 2 0.000 0.995 0.000 1.000
#> GSM601989 1 0.000 1.000 1.000 0.000
#> GSM601879 1 0.000 1.000 1.000 0.000
#> GSM601909 1 0.000 1.000 1.000 0.000
#> GSM601919 2 0.000 0.995 0.000 1.000
#> GSM601924 1 0.000 1.000 1.000 0.000
#> GSM601954 2 0.000 0.995 0.000 1.000
#> GSM601964 1 0.000 1.000 1.000 0.000
#> GSM601969 1 0.000 1.000 1.000 0.000
#> GSM601984 1 0.000 1.000 1.000 0.000
#> GSM601994 2 0.000 0.995 0.000 1.000
#> GSM601875 2 0.000 0.995 0.000 1.000
#> GSM601885 2 0.000 0.995 0.000 1.000
#> GSM601890 1 0.000 1.000 1.000 0.000
#> GSM601895 1 0.000 1.000 1.000 0.000
#> GSM601900 1 0.000 1.000 1.000 0.000
#> GSM601905 2 0.000 0.995 0.000 1.000
#> GSM601915 1 0.000 1.000 1.000 0.000
#> GSM601930 1 0.000 1.000 1.000 0.000
#> GSM601935 2 0.000 0.995 0.000 1.000
#> GSM601940 1 0.000 1.000 1.000 0.000
#> GSM601945 2 0.000 0.995 0.000 1.000
#> GSM601950 1 0.000 1.000 1.000 0.000
#> GSM601960 1 0.000 1.000 1.000 0.000
#> GSM601975 2 0.000 0.995 0.000 1.000
#> GSM601980 2 0.000 0.995 0.000 1.000
#> GSM601990 1 0.000 1.000 1.000 0.000
#> GSM601880 1 0.000 1.000 1.000 0.000
#> GSM601910 1 0.000 1.000 1.000 0.000
#> GSM601920 2 0.000 0.995 0.000 1.000
#> GSM601925 1 0.000 1.000 1.000 0.000
#> GSM601955 2 0.000 0.995 0.000 1.000
#> GSM601965 1 0.000 1.000 1.000 0.000
#> GSM601970 1 0.000 1.000 1.000 0.000
#> GSM601985 1 0.000 1.000 1.000 0.000
#> GSM601995 2 0.000 0.995 0.000 1.000
#> GSM601876 1 0.000 1.000 1.000 0.000
#> GSM601886 2 0.000 0.995 0.000 1.000
#> GSM601891 1 0.000 1.000 1.000 0.000
#> GSM601896 1 0.000 1.000 1.000 0.000
#> GSM601901 2 0.000 0.995 0.000 1.000
#> GSM601906 2 0.000 0.995 0.000 1.000
#> GSM601916 2 0.000 0.995 0.000 1.000
#> GSM601931 1 0.000 1.000 1.000 0.000
#> GSM601936 2 0.000 0.995 0.000 1.000
#> GSM601941 2 0.000 0.995 0.000 1.000
#> GSM601946 1 0.000 1.000 1.000 0.000
#> GSM601951 1 0.000 1.000 1.000 0.000
#> GSM601961 2 0.839 0.634 0.268 0.732
#> GSM601976 2 0.000 0.995 0.000 1.000
#> GSM601981 2 0.000 0.995 0.000 1.000
#> GSM601991 1 0.000 1.000 1.000 0.000
#> GSM601881 1 0.000 1.000 1.000 0.000
#> GSM601911 2 0.000 0.995 0.000 1.000
#> GSM601921 2 0.000 0.995 0.000 1.000
#> GSM601926 1 0.000 1.000 1.000 0.000
#> GSM601956 2 0.000 0.995 0.000 1.000
#> GSM601966 2 0.000 0.995 0.000 1.000
#> GSM601971 1 0.000 1.000 1.000 0.000
#> GSM601986 2 0.260 0.951 0.044 0.956
#> GSM601996 2 0.000 0.995 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM601872 2 0.0237 0.9901 0.000 0.996 0.004
#> GSM601882 2 0.0237 0.9901 0.000 0.996 0.004
#> GSM601887 3 0.0592 0.9313 0.012 0.000 0.988
#> GSM601892 3 0.0592 0.9313 0.012 0.000 0.988
#> GSM601897 3 0.0592 0.9313 0.012 0.000 0.988
#> GSM601902 2 0.0424 0.9888 0.000 0.992 0.008
#> GSM601912 3 0.6295 0.1081 0.472 0.000 0.528
#> GSM601927 1 0.1411 0.9439 0.964 0.000 0.036
#> GSM601932 2 0.0424 0.9888 0.000 0.992 0.008
#> GSM601937 2 0.1411 0.9739 0.036 0.964 0.000
#> GSM601942 2 0.0237 0.9901 0.000 0.996 0.004
#> GSM601947 2 0.0424 0.9888 0.000 0.992 0.008
#> GSM601957 3 0.0592 0.9313 0.012 0.000 0.988
#> GSM601972 2 0.0424 0.9888 0.000 0.992 0.008
#> GSM601977 2 0.0237 0.9901 0.000 0.996 0.004
#> GSM601987 2 0.0237 0.9901 0.000 0.996 0.004
#> GSM601877 1 0.4931 0.7085 0.768 0.000 0.232
#> GSM601907 2 0.0237 0.9901 0.000 0.996 0.004
#> GSM601917 2 0.1711 0.9746 0.032 0.960 0.008
#> GSM601922 2 0.0424 0.9888 0.000 0.992 0.008
#> GSM601952 2 0.0000 0.9898 0.000 1.000 0.000
#> GSM601962 1 0.0000 0.9156 1.000 0.000 0.000
#> GSM601967 3 0.0592 0.9313 0.012 0.000 0.988
#> GSM601982 2 0.0237 0.9901 0.000 0.996 0.004
#> GSM601992 2 0.0000 0.9898 0.000 1.000 0.000
#> GSM601873 2 0.0237 0.9901 0.000 0.996 0.004
#> GSM601883 2 0.0237 0.9901 0.000 0.996 0.004
#> GSM601888 3 0.0592 0.9313 0.012 0.000 0.988
#> GSM601893 3 0.1411 0.9184 0.036 0.000 0.964
#> GSM601898 3 0.2066 0.9025 0.060 0.000 0.940
#> GSM601903 2 0.0424 0.9888 0.000 0.992 0.008
#> GSM601913 1 0.1411 0.9439 0.964 0.000 0.036
#> GSM601928 1 0.1411 0.9439 0.964 0.000 0.036
#> GSM601933 2 0.0237 0.9901 0.000 0.996 0.004
#> GSM601938 2 0.0000 0.9898 0.000 1.000 0.000
#> GSM601943 2 0.0237 0.9901 0.000 0.996 0.004
#> GSM601948 3 0.0592 0.9313 0.012 0.000 0.988
#> GSM601958 3 0.1964 0.9057 0.056 0.000 0.944
#> GSM601973 2 0.0424 0.9888 0.000 0.992 0.008
#> GSM601978 2 0.0237 0.9901 0.000 0.996 0.004
#> GSM601988 2 0.1411 0.9739 0.036 0.964 0.000
#> GSM601878 3 0.6154 0.3075 0.408 0.000 0.592
#> GSM601908 2 0.0237 0.9901 0.000 0.996 0.004
#> GSM601918 2 0.0424 0.9888 0.000 0.992 0.008
#> GSM601923 1 0.1411 0.9439 0.964 0.000 0.036
#> GSM601953 2 0.0237 0.9901 0.000 0.996 0.004
#> GSM601963 1 0.1411 0.9439 0.964 0.000 0.036
#> GSM601968 3 0.0592 0.9313 0.012 0.000 0.988
#> GSM601983 1 0.1411 0.9439 0.964 0.000 0.036
#> GSM601993 2 0.0892 0.9828 0.020 0.980 0.000
#> GSM601874 2 0.0237 0.9901 0.000 0.996 0.004
#> GSM601884 2 0.0237 0.9901 0.000 0.996 0.004
#> GSM601889 3 0.1964 0.9057 0.056 0.000 0.944
#> GSM601894 1 0.6302 0.0376 0.520 0.000 0.480
#> GSM601899 3 0.0592 0.9313 0.012 0.000 0.988
#> GSM601904 2 0.1832 0.9720 0.036 0.956 0.008
#> GSM601914 1 0.1411 0.9439 0.964 0.000 0.036
#> GSM601929 1 0.1411 0.9439 0.964 0.000 0.036
#> GSM601934 2 0.0237 0.9901 0.000 0.996 0.004
#> GSM601939 1 0.1411 0.9439 0.964 0.000 0.036
#> GSM601944 2 0.0237 0.9901 0.000 0.996 0.004
#> GSM601949 3 0.0592 0.9313 0.012 0.000 0.988
#> GSM601959 3 0.0592 0.9313 0.012 0.000 0.988
#> GSM601974 2 0.1832 0.9720 0.036 0.956 0.008
#> GSM601979 2 0.0237 0.9901 0.000 0.996 0.004
#> GSM601989 3 0.1860 0.9087 0.052 0.000 0.948
#> GSM601879 3 0.6079 0.3545 0.388 0.000 0.612
#> GSM601909 3 0.0592 0.9313 0.012 0.000 0.988
#> GSM601919 2 0.0424 0.9888 0.000 0.992 0.008
#> GSM601924 1 0.5138 0.6720 0.748 0.000 0.252
#> GSM601954 2 0.0592 0.9888 0.000 0.988 0.012
#> GSM601964 1 0.1411 0.9439 0.964 0.000 0.036
#> GSM601969 3 0.0592 0.9313 0.012 0.000 0.988
#> GSM601984 1 0.1411 0.9439 0.964 0.000 0.036
#> GSM601994 2 0.0000 0.9898 0.000 1.000 0.000
#> GSM601875 2 0.0237 0.9901 0.000 0.996 0.004
#> GSM601885 2 0.0237 0.9901 0.000 0.996 0.004
#> GSM601890 3 0.0592 0.9313 0.012 0.000 0.988
#> GSM601895 1 0.1411 0.9439 0.964 0.000 0.036
#> GSM601900 1 0.2448 0.9081 0.924 0.000 0.076
#> GSM601905 2 0.0848 0.9866 0.008 0.984 0.008
#> GSM601915 1 0.1411 0.9439 0.964 0.000 0.036
#> GSM601930 1 0.1411 0.9439 0.964 0.000 0.036
#> GSM601935 2 0.1411 0.9739 0.036 0.964 0.000
#> GSM601940 3 0.5650 0.5558 0.312 0.000 0.688
#> GSM601945 2 0.0237 0.9901 0.000 0.996 0.004
#> GSM601950 3 0.0592 0.9313 0.012 0.000 0.988
#> GSM601960 1 0.1411 0.9439 0.964 0.000 0.036
#> GSM601975 2 0.0424 0.9888 0.000 0.992 0.008
#> GSM601980 2 0.0892 0.9828 0.020 0.980 0.000
#> GSM601990 1 0.0000 0.9156 1.000 0.000 0.000
#> GSM601880 1 0.1411 0.9439 0.964 0.000 0.036
#> GSM601910 3 0.0592 0.9313 0.012 0.000 0.988
#> GSM601920 2 0.1832 0.9720 0.036 0.956 0.008
#> GSM601925 1 0.1411 0.9439 0.964 0.000 0.036
#> GSM601955 2 0.1411 0.9739 0.036 0.964 0.000
#> GSM601965 1 0.1411 0.9439 0.964 0.000 0.036
#> GSM601970 3 0.0592 0.9313 0.012 0.000 0.988
#> GSM601985 1 0.1411 0.9439 0.964 0.000 0.036
#> GSM601995 2 0.1411 0.9739 0.036 0.964 0.000
#> GSM601876 1 0.1411 0.9439 0.964 0.000 0.036
#> GSM601886 2 0.1832 0.9720 0.036 0.956 0.008
#> GSM601891 3 0.0592 0.9313 0.012 0.000 0.988
#> GSM601896 1 0.1411 0.9439 0.964 0.000 0.036
#> GSM601901 2 0.0237 0.9895 0.000 0.996 0.004
#> GSM601906 1 0.6018 0.5118 0.684 0.308 0.008
#> GSM601916 2 0.0661 0.9879 0.004 0.988 0.008
#> GSM601931 1 0.1411 0.9439 0.964 0.000 0.036
#> GSM601936 2 0.1411 0.9739 0.036 0.964 0.000
#> GSM601941 2 0.0424 0.9888 0.000 0.992 0.008
#> GSM601946 1 0.1411 0.9439 0.964 0.000 0.036
#> GSM601951 3 0.0237 0.9243 0.004 0.000 0.996
#> GSM601961 3 0.1529 0.8797 0.000 0.040 0.960
#> GSM601976 2 0.0424 0.9888 0.000 0.992 0.008
#> GSM601981 2 0.0237 0.9901 0.000 0.996 0.004
#> GSM601991 1 0.0000 0.9156 1.000 0.000 0.000
#> GSM601881 1 0.1411 0.9439 0.964 0.000 0.036
#> GSM601911 1 0.2860 0.8273 0.912 0.084 0.004
#> GSM601921 2 0.1170 0.9832 0.016 0.976 0.008
#> GSM601926 1 0.1411 0.9439 0.964 0.000 0.036
#> GSM601956 2 0.0237 0.9901 0.000 0.996 0.004
#> GSM601966 2 0.0237 0.9901 0.000 0.996 0.004
#> GSM601971 3 0.0592 0.9313 0.012 0.000 0.988
#> GSM601986 1 0.2200 0.8594 0.940 0.056 0.004
#> GSM601996 2 0.0000 0.9898 0.000 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM601872 2 0.0188 0.78737 0.000 0.996 0.000 0.004
#> GSM601882 2 0.0336 0.78599 0.000 0.992 0.000 0.008
#> GSM601887 3 0.0188 0.92822 0.004 0.000 0.996 0.000
#> GSM601892 3 0.0188 0.92822 0.004 0.000 0.996 0.000
#> GSM601897 3 0.0817 0.91731 0.024 0.000 0.976 0.000
#> GSM601902 2 0.4998 -0.03616 0.000 0.512 0.000 0.488
#> GSM601912 1 0.7571 0.23697 0.476 0.008 0.360 0.156
#> GSM601927 1 0.0524 0.89949 0.988 0.000 0.004 0.008
#> GSM601932 2 0.4981 0.01156 0.000 0.536 0.000 0.464
#> GSM601937 4 0.4761 0.50739 0.000 0.332 0.004 0.664
#> GSM601942 2 0.0469 0.78302 0.000 0.988 0.000 0.012
#> GSM601947 2 0.4730 0.30709 0.000 0.636 0.000 0.364
#> GSM601957 3 0.0188 0.92822 0.004 0.000 0.996 0.000
#> GSM601972 2 0.4888 0.20960 0.000 0.588 0.000 0.412
#> GSM601977 2 0.0336 0.78599 0.000 0.992 0.000 0.008
#> GSM601987 2 0.0000 0.78845 0.000 1.000 0.000 0.000
#> GSM601877 1 0.2988 0.81971 0.876 0.000 0.112 0.012
#> GSM601907 2 0.0000 0.78845 0.000 1.000 0.000 0.000
#> GSM601917 4 0.3649 0.63539 0.000 0.204 0.000 0.796
#> GSM601922 4 0.4998 0.06019 0.000 0.488 0.000 0.512
#> GSM601952 2 0.2345 0.71812 0.000 0.900 0.000 0.100
#> GSM601962 1 0.4088 0.73829 0.764 0.000 0.004 0.232
#> GSM601967 3 0.0188 0.92822 0.004 0.000 0.996 0.000
#> GSM601982 2 0.0336 0.78599 0.000 0.992 0.000 0.008
#> GSM601992 2 0.3024 0.65356 0.000 0.852 0.000 0.148
#> GSM601873 2 0.0469 0.78302 0.000 0.988 0.000 0.012
#> GSM601883 2 0.0336 0.78599 0.000 0.992 0.000 0.008
#> GSM601888 3 0.0188 0.92822 0.004 0.000 0.996 0.000
#> GSM601893 3 0.2973 0.82095 0.144 0.000 0.856 0.000
#> GSM601898 3 0.4331 0.62526 0.288 0.000 0.712 0.000
#> GSM601903 4 0.4985 0.13418 0.000 0.468 0.000 0.532
#> GSM601913 1 0.0817 0.89748 0.976 0.000 0.000 0.024
#> GSM601928 1 0.0336 0.89933 0.992 0.000 0.000 0.008
#> GSM601933 2 0.0000 0.78845 0.000 1.000 0.000 0.000
#> GSM601938 2 0.3649 0.57631 0.000 0.796 0.000 0.204
#> GSM601943 2 0.0336 0.78599 0.000 0.992 0.000 0.008
#> GSM601948 3 0.0188 0.92822 0.004 0.000 0.996 0.000
#> GSM601958 3 0.4193 0.65825 0.268 0.000 0.732 0.000
#> GSM601973 4 0.4500 0.52706 0.000 0.316 0.000 0.684
#> GSM601978 2 0.0000 0.78845 0.000 1.000 0.000 0.000
#> GSM601988 4 0.4837 0.49652 0.000 0.348 0.004 0.648
#> GSM601878 1 0.5250 0.16290 0.552 0.000 0.440 0.008
#> GSM601908 2 0.0000 0.78845 0.000 1.000 0.000 0.000
#> GSM601918 2 0.4985 0.03991 0.000 0.532 0.000 0.468
#> GSM601923 1 0.0524 0.89949 0.988 0.000 0.004 0.008
#> GSM601953 2 0.0000 0.78845 0.000 1.000 0.000 0.000
#> GSM601963 1 0.0817 0.89748 0.976 0.000 0.000 0.024
#> GSM601968 3 0.0188 0.92822 0.004 0.000 0.996 0.000
#> GSM601983 1 0.0817 0.89748 0.976 0.000 0.000 0.024
#> GSM601993 2 0.4994 -0.23653 0.000 0.520 0.000 0.480
#> GSM601874 2 0.0000 0.78845 0.000 1.000 0.000 0.000
#> GSM601884 2 0.0336 0.78599 0.000 0.992 0.000 0.008
#> GSM601889 3 0.4008 0.69708 0.244 0.000 0.756 0.000
#> GSM601894 1 0.4250 0.59159 0.724 0.000 0.276 0.000
#> GSM601899 3 0.0188 0.92822 0.004 0.000 0.996 0.000
#> GSM601904 4 0.3266 0.64493 0.000 0.168 0.000 0.832
#> GSM601914 1 0.0817 0.89748 0.976 0.000 0.000 0.024
#> GSM601929 1 0.0657 0.89851 0.984 0.000 0.004 0.012
#> GSM601934 2 0.0000 0.78845 0.000 1.000 0.000 0.000
#> GSM601939 1 0.0188 0.90011 0.996 0.000 0.004 0.000
#> GSM601944 2 0.0188 0.78712 0.000 0.996 0.000 0.004
#> GSM601949 3 0.0188 0.92822 0.004 0.000 0.996 0.000
#> GSM601959 3 0.0188 0.92822 0.004 0.000 0.996 0.000
#> GSM601974 4 0.3400 0.64302 0.000 0.180 0.000 0.820
#> GSM601979 2 0.0000 0.78845 0.000 1.000 0.000 0.000
#> GSM601989 3 0.3266 0.79683 0.168 0.000 0.832 0.000
#> GSM601879 3 0.5366 0.17304 0.440 0.000 0.548 0.012
#> GSM601909 3 0.0188 0.92822 0.004 0.000 0.996 0.000
#> GSM601919 2 0.4941 0.14498 0.000 0.564 0.000 0.436
#> GSM601924 1 0.2480 0.83967 0.904 0.000 0.088 0.008
#> GSM601954 2 0.4193 0.45737 0.000 0.732 0.000 0.268
#> GSM601964 1 0.0817 0.89748 0.976 0.000 0.000 0.024
#> GSM601969 3 0.0188 0.92822 0.004 0.000 0.996 0.000
#> GSM601984 1 0.0188 0.89986 0.996 0.000 0.000 0.004
#> GSM601994 2 0.2216 0.71748 0.000 0.908 0.000 0.092
#> GSM601875 2 0.0000 0.78845 0.000 1.000 0.000 0.000
#> GSM601885 2 0.0336 0.78599 0.000 0.992 0.000 0.008
#> GSM601890 3 0.0188 0.92822 0.004 0.000 0.996 0.000
#> GSM601895 1 0.1284 0.89407 0.964 0.000 0.012 0.024
#> GSM601900 1 0.2635 0.85194 0.904 0.000 0.076 0.020
#> GSM601905 4 0.4250 0.58372 0.000 0.276 0.000 0.724
#> GSM601915 1 0.0817 0.89748 0.976 0.000 0.000 0.024
#> GSM601930 1 0.0336 0.89933 0.992 0.000 0.000 0.008
#> GSM601935 4 0.4634 0.51733 0.004 0.280 0.004 0.712
#> GSM601940 1 0.4877 0.28297 0.592 0.000 0.408 0.000
#> GSM601945 2 0.0000 0.78845 0.000 1.000 0.000 0.000
#> GSM601950 3 0.0188 0.92822 0.004 0.000 0.996 0.000
#> GSM601960 1 0.1004 0.89803 0.972 0.000 0.004 0.024
#> GSM601975 2 0.4916 0.18026 0.000 0.576 0.000 0.424
#> GSM601980 2 0.4898 -0.00655 0.000 0.584 0.000 0.416
#> GSM601990 1 0.3870 0.76212 0.788 0.000 0.004 0.208
#> GSM601880 1 0.0524 0.89949 0.988 0.000 0.004 0.008
#> GSM601910 3 0.0188 0.92822 0.004 0.000 0.996 0.000
#> GSM601920 4 0.3219 0.64362 0.000 0.164 0.000 0.836
#> GSM601925 1 0.0524 0.89949 0.988 0.000 0.004 0.008
#> GSM601955 4 0.4584 0.51931 0.000 0.300 0.004 0.696
#> GSM601965 1 0.0336 0.89997 0.992 0.000 0.000 0.008
#> GSM601970 3 0.0188 0.92822 0.004 0.000 0.996 0.000
#> GSM601985 1 0.0592 0.89886 0.984 0.000 0.000 0.016
#> GSM601995 4 0.4483 0.51840 0.000 0.284 0.004 0.712
#> GSM601876 1 0.0469 0.89932 0.988 0.000 0.000 0.012
#> GSM601886 4 0.3837 0.63512 0.000 0.224 0.000 0.776
#> GSM601891 3 0.0188 0.92822 0.004 0.000 0.996 0.000
#> GSM601896 1 0.0000 0.89988 1.000 0.000 0.000 0.000
#> GSM601901 2 0.4008 0.54140 0.000 0.756 0.000 0.244
#> GSM601906 4 0.3464 0.59676 0.076 0.056 0.000 0.868
#> GSM601916 4 0.4624 0.52801 0.000 0.340 0.000 0.660
#> GSM601931 1 0.0524 0.89949 0.988 0.000 0.004 0.008
#> GSM601936 4 0.4584 0.51931 0.000 0.300 0.004 0.696
#> GSM601941 2 0.5000 -0.07374 0.000 0.504 0.000 0.496
#> GSM601946 1 0.0188 0.89995 0.996 0.000 0.000 0.004
#> GSM601951 3 0.2466 0.84419 0.004 0.000 0.900 0.096
#> GSM601961 3 0.0336 0.92006 0.000 0.008 0.992 0.000
#> GSM601976 4 0.4830 0.37655 0.000 0.392 0.000 0.608
#> GSM601981 2 0.0000 0.78845 0.000 1.000 0.000 0.000
#> GSM601991 1 0.4837 0.58894 0.648 0.000 0.004 0.348
#> GSM601881 1 0.0524 0.89949 0.988 0.000 0.004 0.008
#> GSM601911 4 0.6404 0.00947 0.388 0.060 0.004 0.548
#> GSM601921 4 0.4103 0.60320 0.000 0.256 0.000 0.744
#> GSM601926 1 0.0524 0.89949 0.988 0.000 0.004 0.008
#> GSM601956 2 0.0000 0.78845 0.000 1.000 0.000 0.000
#> GSM601966 2 0.0592 0.78167 0.000 0.984 0.000 0.016
#> GSM601971 3 0.0188 0.92822 0.004 0.000 0.996 0.000
#> GSM601986 1 0.5105 0.41891 0.564 0.000 0.004 0.432
#> GSM601996 2 0.2921 0.66356 0.000 0.860 0.000 0.140
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM601872 2 0.0290 0.8869 0.000 0.992 0.000 0.008 0.000
#> GSM601882 2 0.0671 0.8865 0.000 0.980 0.000 0.016 0.004
#> GSM601887 3 0.0000 0.8750 0.000 0.000 1.000 0.000 0.000
#> GSM601892 3 0.0000 0.8750 0.000 0.000 1.000 0.000 0.000
#> GSM601897 3 0.3446 0.7880 0.048 0.000 0.840 0.004 0.108
#> GSM601902 4 0.3586 0.7671 0.000 0.264 0.000 0.736 0.000
#> GSM601912 5 0.3019 0.5568 0.088 0.000 0.048 0.000 0.864
#> GSM601927 1 0.0162 0.7874 0.996 0.000 0.000 0.000 0.004
#> GSM601932 4 0.4824 0.5413 0.000 0.376 0.000 0.596 0.028
#> GSM601937 5 0.6314 0.5374 0.000 0.184 0.000 0.304 0.512
#> GSM601942 2 0.0451 0.8863 0.000 0.988 0.000 0.008 0.004
#> GSM601947 4 0.4182 0.6016 0.000 0.400 0.000 0.600 0.000
#> GSM601957 3 0.1202 0.8640 0.032 0.000 0.960 0.004 0.004
#> GSM601972 4 0.3966 0.6992 0.000 0.336 0.000 0.664 0.000
#> GSM601977 2 0.0579 0.8865 0.000 0.984 0.000 0.008 0.008
#> GSM601987 2 0.0671 0.8849 0.000 0.980 0.000 0.016 0.004
#> GSM601877 1 0.1444 0.7638 0.948 0.000 0.040 0.000 0.012
#> GSM601907 2 0.0162 0.8869 0.000 0.996 0.000 0.004 0.000
#> GSM601917 4 0.1012 0.6762 0.000 0.012 0.000 0.968 0.020
#> GSM601922 4 0.3231 0.7800 0.000 0.196 0.000 0.800 0.004
#> GSM601952 2 0.2900 0.8073 0.000 0.864 0.000 0.108 0.028
#> GSM601962 5 0.1965 0.5824 0.096 0.000 0.000 0.000 0.904
#> GSM601967 3 0.0000 0.8750 0.000 0.000 1.000 0.000 0.000
#> GSM601982 2 0.0404 0.8876 0.000 0.988 0.000 0.012 0.000
#> GSM601992 2 0.3608 0.7505 0.000 0.812 0.000 0.148 0.040
#> GSM601873 2 0.1300 0.8728 0.000 0.956 0.000 0.028 0.016
#> GSM601883 2 0.0566 0.8874 0.000 0.984 0.000 0.012 0.004
#> GSM601888 3 0.0000 0.8750 0.000 0.000 1.000 0.000 0.000
#> GSM601893 3 0.5039 0.6394 0.188 0.000 0.708 0.004 0.100
#> GSM601898 3 0.5791 0.1549 0.400 0.000 0.516 0.004 0.080
#> GSM601903 4 0.3003 0.7816 0.000 0.188 0.000 0.812 0.000
#> GSM601913 1 0.4009 0.7469 0.684 0.000 0.000 0.004 0.312
#> GSM601928 1 0.0000 0.7892 1.000 0.000 0.000 0.000 0.000
#> GSM601933 2 0.1568 0.8693 0.000 0.944 0.000 0.036 0.020
#> GSM601938 2 0.4010 0.7209 0.000 0.784 0.000 0.160 0.056
#> GSM601943 2 0.0566 0.8855 0.000 0.984 0.000 0.012 0.004
#> GSM601948 3 0.0566 0.8683 0.012 0.000 0.984 0.004 0.000
#> GSM601958 3 0.5657 0.2972 0.360 0.000 0.560 0.004 0.076
#> GSM601973 4 0.2179 0.7628 0.000 0.100 0.000 0.896 0.004
#> GSM601978 2 0.0162 0.8869 0.000 0.996 0.000 0.004 0.000
#> GSM601988 5 0.6422 0.5072 0.000 0.200 0.000 0.308 0.492
#> GSM601878 1 0.2563 0.6974 0.872 0.000 0.120 0.000 0.008
#> GSM601908 2 0.0162 0.8869 0.000 0.996 0.000 0.004 0.000
#> GSM601918 4 0.3876 0.7243 0.000 0.316 0.000 0.684 0.000
#> GSM601923 1 0.0000 0.7892 1.000 0.000 0.000 0.000 0.000
#> GSM601953 2 0.0290 0.8850 0.000 0.992 0.000 0.008 0.000
#> GSM601963 1 0.4280 0.7445 0.676 0.000 0.008 0.004 0.312
#> GSM601968 3 0.0000 0.8750 0.000 0.000 1.000 0.000 0.000
#> GSM601983 1 0.4353 0.7367 0.660 0.000 0.008 0.004 0.328
#> GSM601993 2 0.6695 -0.0487 0.000 0.432 0.000 0.280 0.288
#> GSM601874 2 0.0162 0.8869 0.000 0.996 0.000 0.004 0.000
#> GSM601884 2 0.0290 0.8877 0.000 0.992 0.000 0.008 0.000
#> GSM601889 3 0.5439 0.4354 0.312 0.000 0.612 0.004 0.072
#> GSM601894 1 0.6249 0.5044 0.548 0.000 0.284 0.004 0.164
#> GSM601899 3 0.0000 0.8750 0.000 0.000 1.000 0.000 0.000
#> GSM601904 4 0.0671 0.6882 0.000 0.016 0.000 0.980 0.004
#> GSM601914 1 0.4299 0.7425 0.672 0.000 0.008 0.004 0.316
#> GSM601929 1 0.0510 0.7823 0.984 0.000 0.000 0.000 0.016
#> GSM601934 2 0.1469 0.8715 0.000 0.948 0.000 0.036 0.016
#> GSM601939 1 0.3250 0.7899 0.820 0.000 0.008 0.004 0.168
#> GSM601944 2 0.1469 0.8698 0.000 0.948 0.000 0.036 0.016
#> GSM601949 3 0.0000 0.8750 0.000 0.000 1.000 0.000 0.000
#> GSM601959 3 0.0833 0.8698 0.016 0.000 0.976 0.004 0.004
#> GSM601974 4 0.1924 0.7285 0.004 0.064 0.000 0.924 0.008
#> GSM601979 2 0.0162 0.8869 0.000 0.996 0.000 0.004 0.000
#> GSM601989 3 0.5292 0.5459 0.252 0.000 0.660 0.004 0.084
#> GSM601879 1 0.3642 0.5544 0.760 0.000 0.232 0.000 0.008
#> GSM601909 3 0.1914 0.8519 0.032 0.000 0.932 0.004 0.032
#> GSM601919 4 0.3949 0.7405 0.000 0.300 0.000 0.696 0.004
#> GSM601924 1 0.0703 0.7794 0.976 0.000 0.024 0.000 0.000
#> GSM601954 2 0.3983 0.2197 0.000 0.660 0.000 0.340 0.000
#> GSM601964 1 0.4317 0.7400 0.668 0.000 0.008 0.004 0.320
#> GSM601969 3 0.0000 0.8750 0.000 0.000 1.000 0.000 0.000
#> GSM601984 1 0.3305 0.7376 0.776 0.000 0.000 0.000 0.224
#> GSM601994 2 0.3242 0.7872 0.000 0.844 0.000 0.116 0.040
#> GSM601875 2 0.0000 0.8874 0.000 1.000 0.000 0.000 0.000
#> GSM601885 2 0.0671 0.8870 0.000 0.980 0.000 0.016 0.004
#> GSM601890 3 0.0000 0.8750 0.000 0.000 1.000 0.000 0.000
#> GSM601895 1 0.4830 0.7273 0.648 0.000 0.032 0.004 0.316
#> GSM601900 1 0.5333 0.7089 0.628 0.000 0.068 0.004 0.300
#> GSM601905 4 0.1956 0.7451 0.000 0.076 0.000 0.916 0.008
#> GSM601915 1 0.4280 0.7445 0.676 0.000 0.008 0.004 0.312
#> GSM601930 1 0.0000 0.7892 1.000 0.000 0.000 0.000 0.000
#> GSM601935 5 0.5005 0.6435 0.000 0.064 0.000 0.276 0.660
#> GSM601940 1 0.5832 0.4142 0.560 0.000 0.340 0.004 0.096
#> GSM601945 2 0.0162 0.8869 0.000 0.996 0.000 0.004 0.000
#> GSM601950 3 0.0000 0.8750 0.000 0.000 1.000 0.000 0.000
#> GSM601960 1 0.4385 0.7428 0.672 0.000 0.012 0.004 0.312
#> GSM601975 4 0.3857 0.7280 0.000 0.312 0.000 0.688 0.000
#> GSM601980 2 0.6662 -0.1255 0.000 0.420 0.000 0.236 0.344
#> GSM601990 5 0.2179 0.5661 0.112 0.000 0.000 0.000 0.888
#> GSM601880 1 0.0162 0.7874 0.996 0.000 0.000 0.000 0.004
#> GSM601910 3 0.1970 0.8460 0.012 0.000 0.924 0.004 0.060
#> GSM601920 4 0.1106 0.6769 0.000 0.012 0.000 0.964 0.024
#> GSM601925 1 0.0000 0.7892 1.000 0.000 0.000 0.000 0.000
#> GSM601955 5 0.5797 0.6132 0.000 0.132 0.000 0.276 0.592
#> GSM601965 1 0.3534 0.7203 0.744 0.000 0.000 0.000 0.256
#> GSM601970 3 0.1862 0.8509 0.048 0.000 0.932 0.004 0.016
#> GSM601985 1 0.3928 0.7550 0.700 0.000 0.000 0.004 0.296
#> GSM601995 5 0.4691 0.6473 0.000 0.044 0.000 0.276 0.680
#> GSM601876 1 0.3579 0.7757 0.756 0.000 0.000 0.004 0.240
#> GSM601886 4 0.4959 0.4559 0.000 0.128 0.000 0.712 0.160
#> GSM601891 3 0.0000 0.8750 0.000 0.000 1.000 0.000 0.000
#> GSM601896 1 0.2536 0.7931 0.868 0.000 0.000 0.004 0.128
#> GSM601901 2 0.4108 0.4018 0.000 0.684 0.000 0.308 0.008
#> GSM601906 4 0.3299 0.5553 0.152 0.004 0.000 0.828 0.016
#> GSM601916 4 0.4429 0.7034 0.000 0.192 0.000 0.744 0.064
#> GSM601931 1 0.0000 0.7892 1.000 0.000 0.000 0.000 0.000
#> GSM601936 5 0.5974 0.5930 0.000 0.148 0.000 0.284 0.568
#> GSM601941 4 0.3990 0.7218 0.000 0.308 0.000 0.688 0.004
#> GSM601946 1 0.3430 0.7796 0.776 0.000 0.000 0.004 0.220
#> GSM601951 3 0.3639 0.6985 0.044 0.000 0.812 0.144 0.000
#> GSM601961 3 0.0671 0.8626 0.000 0.016 0.980 0.004 0.000
#> GSM601976 4 0.2690 0.7751 0.000 0.156 0.000 0.844 0.000
#> GSM601981 2 0.0162 0.8869 0.000 0.996 0.000 0.004 0.000
#> GSM601991 5 0.1845 0.6238 0.056 0.000 0.000 0.016 0.928
#> GSM601881 1 0.0000 0.7892 1.000 0.000 0.000 0.000 0.000
#> GSM601911 5 0.4866 0.6471 0.120 0.008 0.000 0.132 0.740
#> GSM601921 4 0.1774 0.7271 0.000 0.052 0.000 0.932 0.016
#> GSM601926 1 0.0000 0.7892 1.000 0.000 0.000 0.000 0.000
#> GSM601956 2 0.0162 0.8869 0.000 0.996 0.000 0.004 0.000
#> GSM601966 2 0.1608 0.8466 0.000 0.928 0.000 0.072 0.000
#> GSM601971 3 0.0000 0.8750 0.000 0.000 1.000 0.000 0.000
#> GSM601986 5 0.5578 0.4564 0.272 0.000 0.000 0.112 0.616
#> GSM601996 2 0.3242 0.7817 0.000 0.844 0.000 0.116 0.040
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM601872 2 0.0458 0.8767 0.000 0.984 0.016 0.000 0.000 0.000
#> GSM601882 2 0.1901 0.8706 0.000 0.924 0.008 0.040 0.028 0.000
#> GSM601887 6 0.0146 0.8325 0.000 0.000 0.004 0.000 0.000 0.996
#> GSM601892 6 0.0405 0.8323 0.000 0.000 0.008 0.000 0.004 0.988
#> GSM601897 6 0.4202 0.6918 0.052 0.000 0.208 0.004 0.004 0.732
#> GSM601902 4 0.2300 0.8170 0.000 0.144 0.000 0.856 0.000 0.000
#> GSM601912 3 0.4453 0.3180 0.024 0.000 0.720 0.008 0.220 0.028
#> GSM601927 1 0.0146 0.7296 0.996 0.000 0.004 0.000 0.000 0.000
#> GSM601932 4 0.5028 0.5969 0.000 0.248 0.004 0.636 0.112 0.000
#> GSM601937 5 0.1970 0.7923 0.000 0.028 0.000 0.060 0.912 0.000
#> GSM601942 2 0.1462 0.8683 0.000 0.936 0.008 0.000 0.056 0.000
#> GSM601947 4 0.3833 0.6992 0.000 0.272 0.016 0.708 0.004 0.000
#> GSM601957 6 0.2604 0.8017 0.064 0.000 0.044 0.004 0.004 0.884
#> GSM601972 4 0.2793 0.7810 0.000 0.200 0.000 0.800 0.000 0.000
#> GSM601977 2 0.1410 0.8747 0.000 0.944 0.004 0.008 0.044 0.000
#> GSM601987 2 0.1826 0.8698 0.000 0.924 0.004 0.052 0.020 0.000
#> GSM601877 1 0.2504 0.6396 0.880 0.000 0.088 0.000 0.004 0.028
#> GSM601907 2 0.0508 0.8781 0.000 0.984 0.012 0.004 0.000 0.000
#> GSM601917 4 0.2723 0.7553 0.000 0.016 0.004 0.852 0.128 0.000
#> GSM601922 4 0.1349 0.8251 0.000 0.056 0.000 0.940 0.004 0.000
#> GSM601952 2 0.3985 0.7019 0.000 0.744 0.008 0.040 0.208 0.000
#> GSM601962 3 0.4310 -0.0483 0.020 0.000 0.540 0.000 0.440 0.000
#> GSM601967 6 0.0260 0.8326 0.000 0.000 0.008 0.000 0.000 0.992
#> GSM601982 2 0.1346 0.8779 0.000 0.952 0.008 0.016 0.024 0.000
#> GSM601992 2 0.5070 0.6177 0.000 0.660 0.008 0.156 0.176 0.000
#> GSM601873 2 0.2149 0.8350 0.000 0.888 0.004 0.004 0.104 0.000
#> GSM601883 2 0.1679 0.8748 0.000 0.936 0.008 0.028 0.028 0.000
#> GSM601888 6 0.0363 0.8294 0.000 0.000 0.012 0.000 0.000 0.988
#> GSM601893 6 0.5246 0.5501 0.144 0.000 0.212 0.004 0.004 0.636
#> GSM601898 6 0.6030 0.2351 0.288 0.000 0.212 0.004 0.004 0.492
#> GSM601903 4 0.1806 0.8293 0.000 0.088 0.000 0.908 0.004 0.000
#> GSM601913 3 0.4184 0.4749 0.432 0.000 0.556 0.000 0.008 0.004
#> GSM601928 1 0.0260 0.7272 0.992 0.000 0.008 0.000 0.000 0.000
#> GSM601933 2 0.1692 0.8714 0.000 0.932 0.008 0.012 0.048 0.000
#> GSM601938 2 0.5280 0.5320 0.000 0.616 0.008 0.128 0.248 0.000
#> GSM601943 2 0.1333 0.8708 0.000 0.944 0.008 0.000 0.048 0.000
#> GSM601948 6 0.2226 0.7942 0.028 0.000 0.044 0.008 0.008 0.912
#> GSM601958 6 0.5945 0.2877 0.280 0.000 0.200 0.004 0.004 0.512
#> GSM601973 4 0.2094 0.8267 0.000 0.080 0.000 0.900 0.020 0.000
#> GSM601978 2 0.0508 0.8781 0.000 0.984 0.012 0.004 0.000 0.000
#> GSM601988 5 0.2365 0.7833 0.000 0.040 0.000 0.072 0.888 0.000
#> GSM601878 1 0.2651 0.6165 0.872 0.000 0.036 0.000 0.004 0.088
#> GSM601908 2 0.0508 0.8781 0.000 0.984 0.012 0.004 0.000 0.000
#> GSM601918 4 0.2823 0.7778 0.000 0.204 0.000 0.796 0.000 0.000
#> GSM601923 1 0.0146 0.7296 0.996 0.000 0.004 0.000 0.000 0.000
#> GSM601953 2 0.0603 0.8774 0.000 0.980 0.016 0.004 0.000 0.000
#> GSM601963 3 0.4184 0.4749 0.432 0.000 0.556 0.000 0.008 0.004
#> GSM601968 6 0.0260 0.8326 0.000 0.000 0.008 0.000 0.000 0.992
#> GSM601983 3 0.4118 0.4765 0.396 0.000 0.592 0.000 0.008 0.004
#> GSM601993 5 0.5456 0.4875 0.000 0.244 0.008 0.152 0.596 0.000
#> GSM601874 2 0.0508 0.8781 0.000 0.984 0.012 0.004 0.000 0.000
#> GSM601884 2 0.1675 0.8751 0.000 0.936 0.008 0.032 0.024 0.000
#> GSM601889 6 0.5839 0.3583 0.252 0.000 0.200 0.004 0.004 0.540
#> GSM601894 3 0.6253 0.2212 0.376 0.000 0.380 0.004 0.004 0.236
#> GSM601899 6 0.0146 0.8320 0.000 0.000 0.004 0.000 0.000 0.996
#> GSM601904 4 0.1857 0.7878 0.000 0.012 0.028 0.928 0.032 0.000
#> GSM601914 3 0.4184 0.4749 0.432 0.000 0.556 0.000 0.008 0.004
#> GSM601929 1 0.1814 0.6503 0.900 0.000 0.100 0.000 0.000 0.000
#> GSM601934 2 0.1873 0.8689 0.000 0.924 0.008 0.020 0.048 0.000
#> GSM601939 1 0.4343 -0.1678 0.584 0.000 0.396 0.004 0.004 0.012
#> GSM601944 2 0.1707 0.8704 0.000 0.928 0.004 0.012 0.056 0.000
#> GSM601949 6 0.1230 0.8168 0.000 0.000 0.028 0.008 0.008 0.956
#> GSM601959 6 0.2415 0.8086 0.040 0.000 0.056 0.004 0.004 0.896
#> GSM601974 4 0.2144 0.7624 0.000 0.004 0.040 0.908 0.048 0.000
#> GSM601979 2 0.0508 0.8781 0.000 0.984 0.012 0.004 0.000 0.000
#> GSM601989 6 0.5670 0.4317 0.184 0.000 0.236 0.004 0.004 0.572
#> GSM601879 1 0.4247 0.4575 0.740 0.000 0.092 0.000 0.004 0.164
#> GSM601909 6 0.3558 0.7536 0.052 0.000 0.132 0.004 0.004 0.808
#> GSM601919 4 0.2357 0.8261 0.000 0.116 0.012 0.872 0.000 0.000
#> GSM601924 1 0.0363 0.7218 0.988 0.000 0.000 0.000 0.000 0.012
#> GSM601954 2 0.4035 0.4563 0.000 0.680 0.020 0.296 0.004 0.000
#> GSM601964 3 0.4262 0.4786 0.424 0.000 0.560 0.000 0.012 0.004
#> GSM601969 6 0.0458 0.8283 0.000 0.000 0.016 0.000 0.000 0.984
#> GSM601984 3 0.4672 0.0267 0.432 0.000 0.532 0.008 0.028 0.000
#> GSM601994 2 0.4814 0.6613 0.000 0.692 0.008 0.148 0.152 0.000
#> GSM601875 2 0.0508 0.8781 0.000 0.984 0.012 0.004 0.000 0.000
#> GSM601885 2 0.1592 0.8765 0.000 0.940 0.008 0.020 0.032 0.000
#> GSM601890 6 0.0000 0.8323 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM601895 3 0.4890 0.4611 0.404 0.000 0.548 0.004 0.008 0.036
#> GSM601900 3 0.4983 0.4373 0.404 0.000 0.532 0.000 0.004 0.060
#> GSM601905 4 0.1666 0.8012 0.000 0.020 0.036 0.936 0.008 0.000
#> GSM601915 3 0.4189 0.4701 0.436 0.000 0.552 0.000 0.008 0.004
#> GSM601930 1 0.0260 0.7272 0.992 0.000 0.008 0.000 0.000 0.000
#> GSM601935 5 0.1332 0.7870 0.000 0.008 0.012 0.028 0.952 0.000
#> GSM601940 1 0.6311 -0.1280 0.380 0.000 0.264 0.004 0.004 0.348
#> GSM601945 2 0.0508 0.8781 0.000 0.984 0.012 0.004 0.000 0.000
#> GSM601950 6 0.0146 0.8318 0.000 0.000 0.004 0.000 0.000 0.996
#> GSM601960 3 0.4512 0.4595 0.436 0.000 0.540 0.004 0.012 0.008
#> GSM601975 4 0.2378 0.8145 0.000 0.152 0.000 0.848 0.000 0.000
#> GSM601980 5 0.4174 0.6617 0.000 0.184 0.000 0.084 0.732 0.000
#> GSM601990 3 0.4864 0.1437 0.064 0.000 0.552 0.000 0.384 0.000
#> GSM601880 1 0.0000 0.7288 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM601910 6 0.3335 0.7528 0.020 0.000 0.168 0.004 0.004 0.804
#> GSM601920 4 0.2257 0.7767 0.000 0.008 0.040 0.904 0.048 0.000
#> GSM601925 1 0.0146 0.7296 0.996 0.000 0.004 0.000 0.000 0.000
#> GSM601955 5 0.1434 0.7889 0.000 0.012 0.012 0.028 0.948 0.000
#> GSM601965 3 0.4670 0.1073 0.380 0.000 0.580 0.012 0.028 0.000
#> GSM601970 6 0.3018 0.7887 0.056 0.000 0.080 0.004 0.004 0.856
#> GSM601985 3 0.4204 0.4519 0.448 0.000 0.540 0.000 0.008 0.004
#> GSM601995 5 0.1401 0.7802 0.000 0.004 0.020 0.028 0.948 0.000
#> GSM601876 1 0.4083 -0.2941 0.532 0.000 0.460 0.000 0.008 0.000
#> GSM601886 5 0.4938 0.3045 0.000 0.052 0.008 0.380 0.560 0.000
#> GSM601891 6 0.0146 0.8320 0.000 0.000 0.004 0.000 0.000 0.996
#> GSM601896 1 0.3489 0.2202 0.708 0.000 0.288 0.000 0.004 0.000
#> GSM601901 2 0.4293 0.0749 0.000 0.536 0.004 0.448 0.012 0.000
#> GSM601906 4 0.4615 0.5785 0.168 0.000 0.056 0.732 0.044 0.000
#> GSM601916 4 0.5735 0.5924 0.000 0.168 0.040 0.620 0.172 0.000
#> GSM601931 1 0.0146 0.7296 0.996 0.000 0.004 0.000 0.000 0.000
#> GSM601936 5 0.1296 0.7913 0.000 0.012 0.004 0.032 0.952 0.000
#> GSM601941 4 0.3514 0.7592 0.000 0.208 0.004 0.768 0.020 0.000
#> GSM601946 1 0.4041 -0.1659 0.584 0.000 0.408 0.004 0.004 0.000
#> GSM601951 6 0.4392 0.6660 0.060 0.000 0.044 0.112 0.008 0.776
#> GSM601961 6 0.1749 0.8041 0.000 0.024 0.036 0.008 0.000 0.932
#> GSM601976 4 0.1578 0.8207 0.000 0.048 0.012 0.936 0.004 0.000
#> GSM601981 2 0.0508 0.8781 0.000 0.984 0.012 0.004 0.000 0.000
#> GSM601991 5 0.3727 0.2256 0.000 0.000 0.388 0.000 0.612 0.000
#> GSM601881 1 0.0000 0.7288 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM601911 3 0.6261 -0.0887 0.084 0.008 0.532 0.064 0.312 0.000
#> GSM601921 4 0.2172 0.7942 0.000 0.020 0.044 0.912 0.024 0.000
#> GSM601926 1 0.0146 0.7296 0.996 0.000 0.004 0.000 0.000 0.000
#> GSM601956 2 0.0508 0.8781 0.000 0.984 0.012 0.004 0.000 0.000
#> GSM601966 2 0.2462 0.8114 0.000 0.860 0.004 0.132 0.004 0.000
#> GSM601971 6 0.0982 0.8276 0.004 0.000 0.020 0.004 0.004 0.968
#> GSM601986 3 0.6398 0.0887 0.148 0.008 0.564 0.060 0.220 0.000
#> GSM601996 2 0.4946 0.6415 0.000 0.676 0.008 0.164 0.152 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
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)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
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)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, 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.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")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n time(p) gender(p) k
#> ATC:skmeans 125 0.294 0.840 2
#> ATC:skmeans 121 0.370 0.203 3
#> ATC:skmeans 104 0.326 0.584 4
#> ATC:skmeans 115 0.452 0.601 5
#> ATC:skmeans 92 0.714 0.351 6
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.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["ATC", "pam"]
# you can also extract it by
# res = res_list["ATC:pam"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 21512 rows and 125 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'pam' method.
#> Performed in total 1250 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_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
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, lower 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 0.975 0.990 0.5014 0.499 0.499
#> 3 3 0.973 0.948 0.978 0.2794 0.856 0.714
#> 4 4 0.885 0.885 0.937 0.1486 0.902 0.730
#> 5 5 0.783 0.659 0.844 0.0617 0.979 0.923
#> 6 6 0.815 0.775 0.888 0.0554 0.900 0.620
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 shows 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 class
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
#> GSM601872 2 0.0000 0.987 0.000 1.000
#> GSM601882 2 0.0000 0.987 0.000 1.000
#> GSM601887 1 0.0000 0.992 1.000 0.000
#> GSM601892 1 0.0000 0.992 1.000 0.000
#> GSM601897 1 0.0000 0.992 1.000 0.000
#> GSM601902 2 0.0000 0.987 0.000 1.000
#> GSM601912 1 0.0938 0.982 0.988 0.012
#> GSM601927 1 0.0000 0.992 1.000 0.000
#> GSM601932 2 0.0000 0.987 0.000 1.000
#> GSM601937 2 0.0000 0.987 0.000 1.000
#> GSM601942 2 0.0000 0.987 0.000 1.000
#> GSM601947 2 0.0000 0.987 0.000 1.000
#> GSM601957 1 0.0000 0.992 1.000 0.000
#> GSM601972 2 0.0000 0.987 0.000 1.000
#> GSM601977 2 0.0000 0.987 0.000 1.000
#> GSM601987 2 0.0000 0.987 0.000 1.000
#> GSM601877 1 0.0000 0.992 1.000 0.000
#> GSM601907 2 0.0000 0.987 0.000 1.000
#> GSM601917 2 0.0000 0.987 0.000 1.000
#> GSM601922 2 0.0000 0.987 0.000 1.000
#> GSM601952 2 0.0000 0.987 0.000 1.000
#> GSM601962 1 0.0000 0.992 1.000 0.000
#> GSM601967 1 0.0000 0.992 1.000 0.000
#> GSM601982 2 0.1633 0.964 0.024 0.976
#> GSM601992 2 0.0000 0.987 0.000 1.000
#> GSM601873 2 0.0000 0.987 0.000 1.000
#> GSM601883 2 0.0000 0.987 0.000 1.000
#> GSM601888 1 0.0000 0.992 1.000 0.000
#> GSM601893 1 0.0000 0.992 1.000 0.000
#> GSM601898 1 0.0000 0.992 1.000 0.000
#> GSM601903 2 0.0000 0.987 0.000 1.000
#> GSM601913 1 0.0000 0.992 1.000 0.000
#> GSM601928 1 0.0000 0.992 1.000 0.000
#> GSM601933 2 0.0000 0.987 0.000 1.000
#> GSM601938 2 0.0000 0.987 0.000 1.000
#> GSM601943 2 0.0000 0.987 0.000 1.000
#> GSM601948 1 0.0000 0.992 1.000 0.000
#> GSM601958 1 0.0000 0.992 1.000 0.000
#> GSM601973 2 0.0000 0.987 0.000 1.000
#> GSM601978 2 0.0000 0.987 0.000 1.000
#> GSM601988 2 0.0000 0.987 0.000 1.000
#> GSM601878 1 0.0000 0.992 1.000 0.000
#> GSM601908 2 0.0000 0.987 0.000 1.000
#> GSM601918 2 0.0000 0.987 0.000 1.000
#> GSM601923 1 0.0000 0.992 1.000 0.000
#> GSM601953 2 0.0000 0.987 0.000 1.000
#> GSM601963 1 0.0000 0.992 1.000 0.000
#> GSM601968 1 0.0000 0.992 1.000 0.000
#> GSM601983 1 0.0000 0.992 1.000 0.000
#> GSM601993 2 0.0000 0.987 0.000 1.000
#> GSM601874 2 0.0000 0.987 0.000 1.000
#> GSM601884 2 0.0000 0.987 0.000 1.000
#> GSM601889 1 0.0000 0.992 1.000 0.000
#> GSM601894 1 0.0000 0.992 1.000 0.000
#> GSM601899 1 0.0000 0.992 1.000 0.000
#> GSM601904 1 0.9732 0.309 0.596 0.404
#> GSM601914 1 0.0000 0.992 1.000 0.000
#> GSM601929 1 0.0000 0.992 1.000 0.000
#> GSM601934 2 0.0000 0.987 0.000 1.000
#> GSM601939 1 0.0000 0.992 1.000 0.000
#> GSM601944 2 0.0000 0.987 0.000 1.000
#> GSM601949 1 0.0000 0.992 1.000 0.000
#> GSM601959 1 0.0000 0.992 1.000 0.000
#> GSM601974 1 0.1184 0.979 0.984 0.016
#> GSM601979 2 0.0000 0.987 0.000 1.000
#> GSM601989 1 0.0000 0.992 1.000 0.000
#> GSM601879 1 0.0000 0.992 1.000 0.000
#> GSM601909 1 0.0000 0.992 1.000 0.000
#> GSM601919 2 0.0000 0.987 0.000 1.000
#> GSM601924 1 0.0000 0.992 1.000 0.000
#> GSM601954 2 0.0000 0.987 0.000 1.000
#> GSM601964 1 0.0000 0.992 1.000 0.000
#> GSM601969 1 0.0000 0.992 1.000 0.000
#> GSM601984 1 0.0000 0.992 1.000 0.000
#> GSM601994 2 0.0000 0.987 0.000 1.000
#> GSM601875 2 0.0000 0.987 0.000 1.000
#> GSM601885 2 0.0000 0.987 0.000 1.000
#> GSM601890 1 0.0000 0.992 1.000 0.000
#> GSM601895 1 0.0000 0.992 1.000 0.000
#> GSM601900 1 0.0000 0.992 1.000 0.000
#> GSM601905 2 0.0000 0.987 0.000 1.000
#> GSM601915 1 0.0000 0.992 1.000 0.000
#> GSM601930 1 0.0000 0.992 1.000 0.000
#> GSM601935 2 0.0000 0.987 0.000 1.000
#> GSM601940 1 0.0000 0.992 1.000 0.000
#> GSM601945 2 0.0000 0.987 0.000 1.000
#> GSM601950 1 0.0000 0.992 1.000 0.000
#> GSM601960 1 0.0000 0.992 1.000 0.000
#> GSM601975 2 0.0000 0.987 0.000 1.000
#> GSM601980 2 0.0000 0.987 0.000 1.000
#> GSM601990 1 0.0000 0.992 1.000 0.000
#> GSM601880 1 0.0000 0.992 1.000 0.000
#> GSM601910 1 0.0000 0.992 1.000 0.000
#> GSM601920 2 0.0000 0.987 0.000 1.000
#> GSM601925 1 0.0000 0.992 1.000 0.000
#> GSM601955 2 0.0000 0.987 0.000 1.000
#> GSM601965 1 0.0000 0.992 1.000 0.000
#> GSM601970 1 0.0000 0.992 1.000 0.000
#> GSM601985 1 0.0000 0.992 1.000 0.000
#> GSM601995 1 0.1633 0.971 0.976 0.024
#> GSM601876 1 0.0000 0.992 1.000 0.000
#> GSM601886 2 0.8861 0.562 0.304 0.696
#> GSM601891 1 0.0000 0.992 1.000 0.000
#> GSM601896 1 0.0000 0.992 1.000 0.000
#> GSM601901 2 0.0000 0.987 0.000 1.000
#> GSM601906 1 0.1633 0.971 0.976 0.024
#> GSM601916 2 0.0000 0.987 0.000 1.000
#> GSM601931 1 0.0000 0.992 1.000 0.000
#> GSM601936 2 0.0000 0.987 0.000 1.000
#> GSM601941 2 0.0000 0.987 0.000 1.000
#> GSM601946 1 0.0000 0.992 1.000 0.000
#> GSM601951 1 0.0000 0.992 1.000 0.000
#> GSM601961 2 0.0672 0.980 0.008 0.992
#> GSM601976 2 0.9552 0.397 0.376 0.624
#> GSM601981 2 0.0000 0.987 0.000 1.000
#> GSM601991 1 0.1184 0.979 0.984 0.016
#> GSM601881 1 0.0000 0.992 1.000 0.000
#> GSM601911 1 0.1184 0.979 0.984 0.016
#> GSM601921 2 0.0000 0.987 0.000 1.000
#> GSM601926 1 0.0000 0.992 1.000 0.000
#> GSM601956 2 0.0000 0.987 0.000 1.000
#> GSM601966 2 0.0000 0.987 0.000 1.000
#> GSM601971 1 0.0000 0.992 1.000 0.000
#> GSM601986 1 0.1184 0.979 0.984 0.016
#> GSM601996 2 0.0000 0.987 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM601872 2 0.0000 0.986 0.000 1.000 0.000
#> GSM601882 2 0.0000 0.986 0.000 1.000 0.000
#> GSM601887 3 0.0000 0.987 0.000 0.000 1.000
#> GSM601892 3 0.0000 0.987 0.000 0.000 1.000
#> GSM601897 3 0.1753 0.948 0.048 0.000 0.952
#> GSM601902 2 0.0000 0.986 0.000 1.000 0.000
#> GSM601912 1 0.0000 0.955 1.000 0.000 0.000
#> GSM601927 1 0.0000 0.955 1.000 0.000 0.000
#> GSM601932 2 0.0000 0.986 0.000 1.000 0.000
#> GSM601937 2 0.0000 0.986 0.000 1.000 0.000
#> GSM601942 2 0.0000 0.986 0.000 1.000 0.000
#> GSM601947 2 0.0000 0.986 0.000 1.000 0.000
#> GSM601957 3 0.0000 0.987 0.000 0.000 1.000
#> GSM601972 2 0.0000 0.986 0.000 1.000 0.000
#> GSM601977 2 0.0000 0.986 0.000 1.000 0.000
#> GSM601987 2 0.0000 0.986 0.000 1.000 0.000
#> GSM601877 1 0.0000 0.955 1.000 0.000 0.000
#> GSM601907 2 0.0000 0.986 0.000 1.000 0.000
#> GSM601917 2 0.0000 0.986 0.000 1.000 0.000
#> GSM601922 2 0.0000 0.986 0.000 1.000 0.000
#> GSM601952 2 0.0000 0.986 0.000 1.000 0.000
#> GSM601962 1 0.0000 0.955 1.000 0.000 0.000
#> GSM601967 3 0.0000 0.987 0.000 0.000 1.000
#> GSM601982 2 0.1031 0.961 0.024 0.976 0.000
#> GSM601992 2 0.0000 0.986 0.000 1.000 0.000
#> GSM601873 2 0.0000 0.986 0.000 1.000 0.000
#> GSM601883 2 0.0000 0.986 0.000 1.000 0.000
#> GSM601888 3 0.0000 0.987 0.000 0.000 1.000
#> GSM601893 3 0.2711 0.906 0.088 0.000 0.912
#> GSM601898 3 0.0000 0.987 0.000 0.000 1.000
#> GSM601903 2 0.0000 0.986 0.000 1.000 0.000
#> GSM601913 1 0.0000 0.955 1.000 0.000 0.000
#> GSM601928 1 0.0000 0.955 1.000 0.000 0.000
#> GSM601933 2 0.0000 0.986 0.000 1.000 0.000
#> GSM601938 2 0.0000 0.986 0.000 1.000 0.000
#> GSM601943 2 0.0000 0.986 0.000 1.000 0.000
#> GSM601948 3 0.0000 0.987 0.000 0.000 1.000
#> GSM601958 3 0.0000 0.987 0.000 0.000 1.000
#> GSM601973 2 0.0000 0.986 0.000 1.000 0.000
#> GSM601978 2 0.0000 0.986 0.000 1.000 0.000
#> GSM601988 2 0.0000 0.986 0.000 1.000 0.000
#> GSM601878 1 0.3879 0.822 0.848 0.000 0.152
#> GSM601908 2 0.0000 0.986 0.000 1.000 0.000
#> GSM601918 2 0.0000 0.986 0.000 1.000 0.000
#> GSM601923 1 0.0000 0.955 1.000 0.000 0.000
#> GSM601953 2 0.0000 0.986 0.000 1.000 0.000
#> GSM601963 1 0.0000 0.955 1.000 0.000 0.000
#> GSM601968 3 0.0000 0.987 0.000 0.000 1.000
#> GSM601983 1 0.0000 0.955 1.000 0.000 0.000
#> GSM601993 2 0.0000 0.986 0.000 1.000 0.000
#> GSM601874 2 0.0000 0.986 0.000 1.000 0.000
#> GSM601884 2 0.0000 0.986 0.000 1.000 0.000
#> GSM601889 3 0.0000 0.987 0.000 0.000 1.000
#> GSM601894 3 0.2165 0.932 0.064 0.000 0.936
#> GSM601899 3 0.0000 0.987 0.000 0.000 1.000
#> GSM601904 1 0.6140 0.319 0.596 0.404 0.000
#> GSM601914 1 0.0000 0.955 1.000 0.000 0.000
#> GSM601929 1 0.0000 0.955 1.000 0.000 0.000
#> GSM601934 2 0.0000 0.986 0.000 1.000 0.000
#> GSM601939 1 0.4399 0.774 0.812 0.000 0.188
#> GSM601944 2 0.0000 0.986 0.000 1.000 0.000
#> GSM601949 3 0.0000 0.987 0.000 0.000 1.000
#> GSM601959 3 0.0000 0.987 0.000 0.000 1.000
#> GSM601974 1 0.0000 0.955 1.000 0.000 0.000
#> GSM601979 2 0.0000 0.986 0.000 1.000 0.000
#> GSM601989 1 0.0000 0.955 1.000 0.000 0.000
#> GSM601879 1 0.0000 0.955 1.000 0.000 0.000
#> GSM601909 3 0.0000 0.987 0.000 0.000 1.000
#> GSM601919 2 0.0000 0.986 0.000 1.000 0.000
#> GSM601924 1 0.4235 0.792 0.824 0.000 0.176
#> GSM601954 2 0.0000 0.986 0.000 1.000 0.000
#> GSM601964 1 0.0000 0.955 1.000 0.000 0.000
#> GSM601969 3 0.0000 0.987 0.000 0.000 1.000
#> GSM601984 1 0.0000 0.955 1.000 0.000 0.000
#> GSM601994 2 0.0000 0.986 0.000 1.000 0.000
#> GSM601875 2 0.0000 0.986 0.000 1.000 0.000
#> GSM601885 2 0.0000 0.986 0.000 1.000 0.000
#> GSM601890 3 0.0000 0.987 0.000 0.000 1.000
#> GSM601895 1 0.3412 0.841 0.876 0.000 0.124
#> GSM601900 1 0.0424 0.950 0.992 0.000 0.008
#> GSM601905 2 0.0000 0.986 0.000 1.000 0.000
#> GSM601915 1 0.0000 0.955 1.000 0.000 0.000
#> GSM601930 1 0.0000 0.955 1.000 0.000 0.000
#> GSM601935 2 0.0000 0.986 0.000 1.000 0.000
#> GSM601940 1 0.6140 0.315 0.596 0.000 0.404
#> GSM601945 2 0.0000 0.986 0.000 1.000 0.000
#> GSM601950 3 0.0000 0.987 0.000 0.000 1.000
#> GSM601960 1 0.1411 0.930 0.964 0.000 0.036
#> GSM601975 2 0.0000 0.986 0.000 1.000 0.000
#> GSM601980 2 0.0000 0.986 0.000 1.000 0.000
#> GSM601990 1 0.0000 0.955 1.000 0.000 0.000
#> GSM601880 1 0.0000 0.955 1.000 0.000 0.000
#> GSM601910 3 0.2959 0.896 0.100 0.000 0.900
#> GSM601920 2 0.0000 0.986 0.000 1.000 0.000
#> GSM601925 1 0.0000 0.955 1.000 0.000 0.000
#> GSM601955 2 0.0000 0.986 0.000 1.000 0.000
#> GSM601965 1 0.0000 0.955 1.000 0.000 0.000
#> GSM601970 3 0.0000 0.987 0.000 0.000 1.000
#> GSM601985 1 0.0000 0.955 1.000 0.000 0.000
#> GSM601995 1 0.0592 0.945 0.988 0.012 0.000
#> GSM601876 1 0.0000 0.955 1.000 0.000 0.000
#> GSM601886 2 0.5591 0.553 0.304 0.696 0.000
#> GSM601891 3 0.0000 0.987 0.000 0.000 1.000
#> GSM601896 1 0.0000 0.955 1.000 0.000 0.000
#> GSM601901 2 0.0000 0.986 0.000 1.000 0.000
#> GSM601906 1 0.0747 0.941 0.984 0.016 0.000
#> GSM601916 2 0.0000 0.986 0.000 1.000 0.000
#> GSM601931 1 0.0000 0.955 1.000 0.000 0.000
#> GSM601936 2 0.0000 0.986 0.000 1.000 0.000
#> GSM601941 2 0.0000 0.986 0.000 1.000 0.000
#> GSM601946 1 0.0000 0.955 1.000 0.000 0.000
#> GSM601951 1 0.4931 0.715 0.768 0.000 0.232
#> GSM601961 3 0.0000 0.987 0.000 0.000 1.000
#> GSM601976 2 0.6026 0.384 0.376 0.624 0.000
#> GSM601981 2 0.0000 0.986 0.000 1.000 0.000
#> GSM601991 1 0.0000 0.955 1.000 0.000 0.000
#> GSM601881 1 0.0000 0.955 1.000 0.000 0.000
#> GSM601911 1 0.0000 0.955 1.000 0.000 0.000
#> GSM601921 2 0.0000 0.986 0.000 1.000 0.000
#> GSM601926 1 0.0000 0.955 1.000 0.000 0.000
#> GSM601956 2 0.0000 0.986 0.000 1.000 0.000
#> GSM601966 2 0.0000 0.986 0.000 1.000 0.000
#> GSM601971 3 0.0000 0.987 0.000 0.000 1.000
#> GSM601986 1 0.0000 0.955 1.000 0.000 0.000
#> GSM601996 2 0.0000 0.986 0.000 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM601872 2 0.2149 0.940 0.000 0.912 0.000 0.088
#> GSM601882 2 0.2149 0.940 0.000 0.912 0.000 0.088
#> GSM601887 3 0.0000 0.977 0.000 0.000 1.000 0.000
#> GSM601892 3 0.0000 0.977 0.000 0.000 1.000 0.000
#> GSM601897 3 0.1389 0.940 0.048 0.000 0.952 0.000
#> GSM601902 4 0.2868 0.831 0.000 0.136 0.000 0.864
#> GSM601912 1 0.0000 0.950 1.000 0.000 0.000 0.000
#> GSM601927 1 0.0817 0.946 0.976 0.024 0.000 0.000
#> GSM601932 4 0.0336 0.904 0.000 0.008 0.000 0.992
#> GSM601937 4 0.0000 0.906 0.000 0.000 0.000 1.000
#> GSM601942 2 0.2973 0.908 0.000 0.856 0.000 0.144
#> GSM601947 2 0.0921 0.908 0.000 0.972 0.000 0.028
#> GSM601957 3 0.0188 0.976 0.000 0.004 0.996 0.000
#> GSM601972 2 0.0921 0.908 0.000 0.972 0.000 0.028
#> GSM601977 2 0.2345 0.937 0.000 0.900 0.000 0.100
#> GSM601987 2 0.2814 0.919 0.000 0.868 0.000 0.132
#> GSM601877 1 0.0592 0.948 0.984 0.016 0.000 0.000
#> GSM601907 2 0.2149 0.940 0.000 0.912 0.000 0.088
#> GSM601917 4 0.1637 0.878 0.000 0.060 0.000 0.940
#> GSM601922 4 0.1637 0.878 0.000 0.060 0.000 0.940
#> GSM601952 4 0.0817 0.898 0.000 0.024 0.000 0.976
#> GSM601962 1 0.0000 0.950 1.000 0.000 0.000 0.000
#> GSM601967 3 0.0000 0.977 0.000 0.000 1.000 0.000
#> GSM601982 2 0.4304 0.725 0.000 0.716 0.000 0.284
#> GSM601992 4 0.0000 0.906 0.000 0.000 0.000 1.000
#> GSM601873 4 0.0000 0.906 0.000 0.000 0.000 1.000
#> GSM601883 2 0.1792 0.933 0.000 0.932 0.000 0.068
#> GSM601888 3 0.0000 0.977 0.000 0.000 1.000 0.000
#> GSM601893 3 0.2149 0.897 0.088 0.000 0.912 0.000
#> GSM601898 3 0.0336 0.975 0.000 0.008 0.992 0.000
#> GSM601903 4 0.1716 0.877 0.000 0.064 0.000 0.936
#> GSM601913 1 0.0000 0.950 1.000 0.000 0.000 0.000
#> GSM601928 1 0.0817 0.946 0.976 0.024 0.000 0.000
#> GSM601933 4 0.0000 0.906 0.000 0.000 0.000 1.000
#> GSM601938 4 0.0000 0.906 0.000 0.000 0.000 1.000
#> GSM601943 4 0.0000 0.906 0.000 0.000 0.000 1.000
#> GSM601948 3 0.0817 0.963 0.000 0.024 0.976 0.000
#> GSM601958 3 0.0469 0.973 0.000 0.012 0.988 0.000
#> GSM601973 4 0.1637 0.878 0.000 0.060 0.000 0.940
#> GSM601978 2 0.2149 0.940 0.000 0.912 0.000 0.088
#> GSM601988 4 0.0000 0.906 0.000 0.000 0.000 1.000
#> GSM601878 1 0.3659 0.834 0.840 0.024 0.136 0.000
#> GSM601908 2 0.3311 0.883 0.000 0.828 0.000 0.172
#> GSM601918 4 0.4933 0.279 0.000 0.432 0.000 0.568
#> GSM601923 1 0.0817 0.946 0.976 0.024 0.000 0.000
#> GSM601953 2 0.2149 0.940 0.000 0.912 0.000 0.088
#> GSM601963 1 0.0000 0.950 1.000 0.000 0.000 0.000
#> GSM601968 3 0.0000 0.977 0.000 0.000 1.000 0.000
#> GSM601983 1 0.0000 0.950 1.000 0.000 0.000 0.000
#> GSM601993 4 0.0000 0.906 0.000 0.000 0.000 1.000
#> GSM601874 2 0.2149 0.940 0.000 0.912 0.000 0.088
#> GSM601884 2 0.1557 0.928 0.000 0.944 0.000 0.056
#> GSM601889 3 0.0188 0.976 0.000 0.004 0.996 0.000
#> GSM601894 3 0.2048 0.919 0.064 0.008 0.928 0.000
#> GSM601899 3 0.0000 0.977 0.000 0.000 1.000 0.000
#> GSM601904 1 0.5582 0.269 0.576 0.024 0.000 0.400
#> GSM601914 1 0.0000 0.950 1.000 0.000 0.000 0.000
#> GSM601929 1 0.0000 0.950 1.000 0.000 0.000 0.000
#> GSM601934 4 0.0000 0.906 0.000 0.000 0.000 1.000
#> GSM601939 1 0.4194 0.785 0.800 0.028 0.172 0.000
#> GSM601944 4 0.0000 0.906 0.000 0.000 0.000 1.000
#> GSM601949 3 0.0000 0.977 0.000 0.000 1.000 0.000
#> GSM601959 3 0.0188 0.976 0.000 0.004 0.996 0.000
#> GSM601974 1 0.0921 0.937 0.972 0.028 0.000 0.000
#> GSM601979 2 0.2149 0.940 0.000 0.912 0.000 0.088
#> GSM601989 1 0.0000 0.950 1.000 0.000 0.000 0.000
#> GSM601879 1 0.0336 0.949 0.992 0.008 0.000 0.000
#> GSM601909 3 0.0000 0.977 0.000 0.000 1.000 0.000
#> GSM601919 2 0.4277 0.619 0.000 0.720 0.000 0.280
#> GSM601924 1 0.3862 0.816 0.824 0.024 0.152 0.000
#> GSM601954 2 0.1637 0.926 0.000 0.940 0.000 0.060
#> GSM601964 1 0.0000 0.950 1.000 0.000 0.000 0.000
#> GSM601969 3 0.0000 0.977 0.000 0.000 1.000 0.000
#> GSM601984 1 0.0000 0.950 1.000 0.000 0.000 0.000
#> GSM601994 4 0.0000 0.906 0.000 0.000 0.000 1.000
#> GSM601875 4 0.0000 0.906 0.000 0.000 0.000 1.000
#> GSM601885 4 0.4955 0.030 0.000 0.444 0.000 0.556
#> GSM601890 3 0.0000 0.977 0.000 0.000 1.000 0.000
#> GSM601895 1 0.2704 0.843 0.876 0.000 0.124 0.000
#> GSM601900 1 0.0188 0.949 0.996 0.000 0.004 0.000
#> GSM601905 4 0.0592 0.902 0.000 0.016 0.000 0.984
#> GSM601915 1 0.0921 0.945 0.972 0.028 0.000 0.000
#> GSM601930 1 0.0469 0.949 0.988 0.012 0.000 0.000
#> GSM601935 4 0.0000 0.906 0.000 0.000 0.000 1.000
#> GSM601940 1 0.4866 0.309 0.596 0.000 0.404 0.000
#> GSM601945 4 0.4624 0.386 0.000 0.340 0.000 0.660
#> GSM601950 3 0.0000 0.977 0.000 0.000 1.000 0.000
#> GSM601960 1 0.1936 0.926 0.940 0.028 0.032 0.000
#> GSM601975 4 0.4624 0.469 0.000 0.340 0.000 0.660
#> GSM601980 4 0.0000 0.906 0.000 0.000 0.000 1.000
#> GSM601990 1 0.0000 0.950 1.000 0.000 0.000 0.000
#> GSM601880 1 0.0817 0.946 0.976 0.024 0.000 0.000
#> GSM601910 3 0.2345 0.888 0.100 0.000 0.900 0.000
#> GSM601920 4 0.0592 0.902 0.000 0.016 0.000 0.984
#> GSM601925 1 0.0000 0.950 1.000 0.000 0.000 0.000
#> GSM601955 4 0.0707 0.899 0.000 0.020 0.000 0.980
#> GSM601965 1 0.0000 0.950 1.000 0.000 0.000 0.000
#> GSM601970 3 0.0000 0.977 0.000 0.000 1.000 0.000
#> GSM601985 1 0.0921 0.945 0.972 0.028 0.000 0.000
#> GSM601995 1 0.0779 0.941 0.980 0.004 0.000 0.016
#> GSM601876 1 0.0000 0.950 1.000 0.000 0.000 0.000
#> GSM601886 4 0.4608 0.542 0.304 0.004 0.000 0.692
#> GSM601891 3 0.0000 0.977 0.000 0.000 1.000 0.000
#> GSM601896 1 0.0000 0.950 1.000 0.000 0.000 0.000
#> GSM601901 4 0.1118 0.884 0.000 0.036 0.000 0.964
#> GSM601906 1 0.0657 0.943 0.984 0.004 0.000 0.012
#> GSM601916 4 0.0000 0.906 0.000 0.000 0.000 1.000
#> GSM601931 1 0.0817 0.946 0.976 0.024 0.000 0.000
#> GSM601936 4 0.0000 0.906 0.000 0.000 0.000 1.000
#> GSM601941 4 0.1557 0.880 0.000 0.056 0.000 0.944
#> GSM601946 1 0.0336 0.949 0.992 0.008 0.000 0.000
#> GSM601951 1 0.4678 0.694 0.744 0.024 0.232 0.000
#> GSM601961 3 0.2704 0.855 0.000 0.124 0.876 0.000
#> GSM601976 4 0.5298 0.393 0.372 0.016 0.000 0.612
#> GSM601981 2 0.2281 0.939 0.000 0.904 0.000 0.096
#> GSM601991 1 0.0000 0.950 1.000 0.000 0.000 0.000
#> GSM601881 1 0.0817 0.946 0.976 0.024 0.000 0.000
#> GSM601911 1 0.0000 0.950 1.000 0.000 0.000 0.000
#> GSM601921 4 0.0592 0.902 0.000 0.016 0.000 0.984
#> GSM601926 1 0.0817 0.946 0.976 0.024 0.000 0.000
#> GSM601956 2 0.2149 0.940 0.000 0.912 0.000 0.088
#> GSM601966 2 0.3801 0.827 0.000 0.780 0.000 0.220
#> GSM601971 3 0.0000 0.977 0.000 0.000 1.000 0.000
#> GSM601986 1 0.0000 0.950 1.000 0.000 0.000 0.000
#> GSM601996 4 0.0000 0.906 0.000 0.000 0.000 1.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM601872 2 0.4420 0.8122 0.000 0.548 0.000 0.004 0.448
#> GSM601882 2 0.4420 0.8117 0.000 0.548 0.000 0.004 0.448
#> GSM601887 3 0.0000 0.9607 0.000 0.000 1.000 0.000 0.000
#> GSM601892 3 0.0000 0.9607 0.000 0.000 1.000 0.000 0.000
#> GSM601897 3 0.1121 0.9367 0.044 0.000 0.956 0.000 0.000
#> GSM601902 4 0.4451 0.5307 0.000 0.492 0.000 0.504 0.004
#> GSM601912 1 0.0000 0.7205 1.000 0.000 0.000 0.000 0.000
#> GSM601927 1 0.4256 0.1779 0.564 0.000 0.000 0.000 0.436
#> GSM601932 4 0.0609 0.8281 0.000 0.020 0.000 0.980 0.000
#> GSM601937 4 0.0000 0.8321 0.000 0.000 0.000 1.000 0.000
#> GSM601942 2 0.5527 0.7778 0.000 0.540 0.000 0.072 0.388
#> GSM601947 2 0.0510 0.5280 0.000 0.984 0.000 0.000 0.016
#> GSM601957 3 0.0510 0.9575 0.000 0.000 0.984 0.000 0.016
#> GSM601972 2 0.0000 0.5153 0.000 1.000 0.000 0.000 0.000
#> GSM601977 2 0.4627 0.8100 0.000 0.544 0.000 0.012 0.444
#> GSM601987 2 0.5450 0.7822 0.000 0.496 0.000 0.060 0.444
#> GSM601877 1 0.4235 0.2096 0.576 0.000 0.000 0.000 0.424
#> GSM601907 2 0.4420 0.8122 0.000 0.548 0.000 0.004 0.448
#> GSM601917 4 0.4538 0.5800 0.000 0.452 0.000 0.540 0.008
#> GSM601922 4 0.4538 0.5800 0.000 0.452 0.000 0.540 0.008
#> GSM601952 4 0.2179 0.7971 0.000 0.112 0.000 0.888 0.000
#> GSM601962 1 0.0000 0.7205 1.000 0.000 0.000 0.000 0.000
#> GSM601967 3 0.0000 0.9607 0.000 0.000 1.000 0.000 0.000
#> GSM601982 5 0.6719 -0.7143 0.004 0.372 0.000 0.208 0.416
#> GSM601992 4 0.0000 0.8321 0.000 0.000 0.000 1.000 0.000
#> GSM601873 4 0.0000 0.8321 0.000 0.000 0.000 1.000 0.000
#> GSM601883 2 0.3607 0.6990 0.000 0.752 0.000 0.004 0.244
#> GSM601888 3 0.0000 0.9607 0.000 0.000 1.000 0.000 0.000
#> GSM601893 3 0.1965 0.8840 0.096 0.000 0.904 0.000 0.000
#> GSM601898 3 0.0880 0.9519 0.000 0.000 0.968 0.000 0.032
#> GSM601903 4 0.4542 0.5754 0.000 0.456 0.000 0.536 0.008
#> GSM601913 1 0.0510 0.7169 0.984 0.000 0.000 0.000 0.016
#> GSM601928 1 0.4300 0.1068 0.524 0.000 0.000 0.000 0.476
#> GSM601933 4 0.0000 0.8321 0.000 0.000 0.000 1.000 0.000
#> GSM601938 4 0.0000 0.8321 0.000 0.000 0.000 1.000 0.000
#> GSM601943 4 0.0000 0.8321 0.000 0.000 0.000 1.000 0.000
#> GSM601948 3 0.3389 0.8404 0.000 0.116 0.836 0.000 0.048
#> GSM601958 3 0.1792 0.9200 0.000 0.000 0.916 0.000 0.084
#> GSM601973 4 0.4538 0.5800 0.000 0.452 0.000 0.540 0.008
#> GSM601978 2 0.4420 0.8122 0.000 0.548 0.000 0.004 0.448
#> GSM601988 4 0.0162 0.8315 0.000 0.004 0.000 0.996 0.000
#> GSM601878 1 0.4907 -0.0169 0.492 0.000 0.024 0.000 0.484
#> GSM601908 2 0.5896 0.7422 0.000 0.452 0.000 0.100 0.448
#> GSM601918 2 0.3366 0.1973 0.000 0.784 0.000 0.212 0.004
#> GSM601923 1 0.4305 0.0594 0.512 0.000 0.000 0.000 0.488
#> GSM601953 2 0.4420 0.8122 0.000 0.548 0.000 0.004 0.448
#> GSM601963 1 0.1341 0.7015 0.944 0.000 0.000 0.000 0.056
#> GSM601968 3 0.0000 0.9607 0.000 0.000 1.000 0.000 0.000
#> GSM601983 1 0.0000 0.7205 1.000 0.000 0.000 0.000 0.000
#> GSM601993 4 0.0000 0.8321 0.000 0.000 0.000 1.000 0.000
#> GSM601874 2 0.4420 0.8122 0.000 0.548 0.000 0.004 0.448
#> GSM601884 2 0.4273 0.8106 0.000 0.552 0.000 0.000 0.448
#> GSM601889 3 0.0510 0.9575 0.000 0.000 0.984 0.000 0.016
#> GSM601894 3 0.3056 0.8709 0.068 0.000 0.864 0.000 0.068
#> GSM601899 3 0.0000 0.9607 0.000 0.000 1.000 0.000 0.000
#> GSM601904 1 0.6632 -0.0105 0.456 0.172 0.000 0.364 0.008
#> GSM601914 1 0.1197 0.7014 0.952 0.000 0.000 0.000 0.048
#> GSM601929 1 0.0000 0.7205 1.000 0.000 0.000 0.000 0.000
#> GSM601934 4 0.0000 0.8321 0.000 0.000 0.000 1.000 0.000
#> GSM601939 5 0.5546 -0.2209 0.436 0.000 0.068 0.000 0.496
#> GSM601944 4 0.0000 0.8321 0.000 0.000 0.000 1.000 0.000
#> GSM601949 3 0.1197 0.9374 0.000 0.000 0.952 0.000 0.048
#> GSM601959 3 0.0510 0.9575 0.000 0.000 0.984 0.000 0.016
#> GSM601974 1 0.3582 0.4706 0.768 0.224 0.000 0.000 0.008
#> GSM601979 2 0.4420 0.8122 0.000 0.548 0.000 0.004 0.448
#> GSM601989 1 0.0162 0.7196 0.996 0.000 0.004 0.000 0.000
#> GSM601879 1 0.3612 0.4920 0.732 0.000 0.000 0.000 0.268
#> GSM601909 3 0.0000 0.9607 0.000 0.000 1.000 0.000 0.000
#> GSM601919 2 0.3177 0.2010 0.000 0.792 0.000 0.208 0.000
#> GSM601924 5 0.5293 -0.2506 0.460 0.000 0.048 0.000 0.492
#> GSM601954 2 0.3759 0.6697 0.000 0.764 0.000 0.016 0.220
#> GSM601964 1 0.0162 0.7197 0.996 0.000 0.000 0.000 0.004
#> GSM601969 3 0.0000 0.9607 0.000 0.000 1.000 0.000 0.000
#> GSM601984 1 0.0000 0.7205 1.000 0.000 0.000 0.000 0.000
#> GSM601994 4 0.0000 0.8321 0.000 0.000 0.000 1.000 0.000
#> GSM601875 4 0.0000 0.8321 0.000 0.000 0.000 1.000 0.000
#> GSM601885 4 0.5953 0.0930 0.000 0.112 0.000 0.504 0.384
#> GSM601890 3 0.0000 0.9607 0.000 0.000 1.000 0.000 0.000
#> GSM601895 1 0.2439 0.6031 0.876 0.000 0.120 0.000 0.004
#> GSM601900 1 0.0290 0.7187 0.992 0.000 0.008 0.000 0.000
#> GSM601905 4 0.2770 0.7893 0.004 0.124 0.000 0.864 0.008
#> GSM601915 1 0.3274 0.5831 0.780 0.000 0.000 0.000 0.220
#> GSM601930 1 0.4088 0.3206 0.632 0.000 0.000 0.000 0.368
#> GSM601935 4 0.0000 0.8321 0.000 0.000 0.000 1.000 0.000
#> GSM601940 1 0.4192 0.2166 0.596 0.000 0.404 0.000 0.000
#> GSM601945 4 0.5612 0.3843 0.000 0.128 0.000 0.624 0.248
#> GSM601950 3 0.0000 0.9607 0.000 0.000 1.000 0.000 0.000
#> GSM601960 1 0.3455 0.5881 0.784 0.000 0.008 0.000 0.208
#> GSM601975 4 0.6133 0.2717 0.000 0.436 0.000 0.436 0.128
#> GSM601980 4 0.0162 0.8316 0.000 0.004 0.000 0.996 0.000
#> GSM601990 1 0.0000 0.7205 1.000 0.000 0.000 0.000 0.000
#> GSM601880 1 0.4304 0.0685 0.516 0.000 0.000 0.000 0.484
#> GSM601910 3 0.1965 0.8895 0.096 0.000 0.904 0.000 0.000
#> GSM601920 4 0.2660 0.7882 0.000 0.128 0.000 0.864 0.008
#> GSM601925 1 0.2127 0.6600 0.892 0.000 0.000 0.000 0.108
#> GSM601955 4 0.3333 0.7426 0.000 0.208 0.000 0.788 0.004
#> GSM601965 1 0.0000 0.7205 1.000 0.000 0.000 0.000 0.000
#> GSM601970 3 0.0162 0.9601 0.000 0.000 0.996 0.000 0.004
#> GSM601985 1 0.3837 0.4708 0.692 0.000 0.000 0.000 0.308
#> GSM601995 1 0.1200 0.7037 0.964 0.016 0.000 0.012 0.008
#> GSM601876 1 0.0000 0.7205 1.000 0.000 0.000 0.000 0.000
#> GSM601886 4 0.4380 0.4574 0.304 0.020 0.000 0.676 0.000
#> GSM601891 3 0.0000 0.9607 0.000 0.000 1.000 0.000 0.000
#> GSM601896 1 0.0162 0.7197 0.996 0.000 0.000 0.000 0.004
#> GSM601901 4 0.1082 0.8146 0.000 0.028 0.000 0.964 0.008
#> GSM601906 1 0.1087 0.7060 0.968 0.016 0.000 0.008 0.008
#> GSM601916 4 0.0324 0.8304 0.004 0.000 0.000 0.992 0.004
#> GSM601931 1 0.4283 0.1337 0.544 0.000 0.000 0.000 0.456
#> GSM601936 4 0.0000 0.8321 0.000 0.000 0.000 1.000 0.000
#> GSM601941 4 0.4256 0.5940 0.000 0.436 0.000 0.564 0.000
#> GSM601946 1 0.1478 0.6976 0.936 0.000 0.000 0.000 0.064
#> GSM601951 1 0.6456 0.2892 0.604 0.116 0.232 0.000 0.048
#> GSM601961 3 0.2773 0.8473 0.000 0.020 0.868 0.000 0.112
#> GSM601976 4 0.6096 0.4356 0.284 0.132 0.000 0.576 0.008
#> GSM601981 2 0.4723 0.8090 0.000 0.536 0.000 0.016 0.448
#> GSM601991 1 0.0000 0.7205 1.000 0.000 0.000 0.000 0.000
#> GSM601881 1 0.4304 0.0685 0.516 0.000 0.000 0.000 0.484
#> GSM601911 1 0.0000 0.7205 1.000 0.000 0.000 0.000 0.000
#> GSM601921 4 0.2660 0.7882 0.000 0.128 0.000 0.864 0.008
#> GSM601926 1 0.4302 0.0782 0.520 0.000 0.000 0.000 0.480
#> GSM601956 2 0.4420 0.8122 0.000 0.548 0.000 0.004 0.448
#> GSM601966 2 0.6132 0.7089 0.000 0.440 0.000 0.128 0.432
#> GSM601971 3 0.1341 0.9330 0.000 0.000 0.944 0.000 0.056
#> GSM601986 1 0.0000 0.7205 1.000 0.000 0.000 0.000 0.000
#> GSM601996 4 0.0000 0.8321 0.000 0.000 0.000 1.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM601872 2 0.0000 0.8712 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM601882 2 0.0146 0.8706 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM601887 6 0.0000 0.9423 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM601892 6 0.0000 0.9423 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM601897 6 0.0937 0.9240 0.000 0.000 0.040 0.000 0.000 0.960
#> GSM601902 4 0.2581 0.7912 0.000 0.016 0.000 0.856 0.128 0.000
#> GSM601912 3 0.0000 0.8526 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM601927 1 0.2664 0.8400 0.816 0.000 0.184 0.000 0.000 0.000
#> GSM601932 5 0.0547 0.8647 0.000 0.000 0.000 0.020 0.980 0.000
#> GSM601937 5 0.0000 0.8754 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM601942 2 0.1387 0.8338 0.000 0.932 0.000 0.000 0.068 0.000
#> GSM601947 4 0.3198 0.5947 0.000 0.260 0.000 0.740 0.000 0.000
#> GSM601957 6 0.0820 0.9364 0.016 0.000 0.000 0.012 0.000 0.972
#> GSM601972 4 0.2416 0.7209 0.000 0.156 0.000 0.844 0.000 0.000
#> GSM601977 2 0.0363 0.8685 0.000 0.988 0.000 0.000 0.012 0.000
#> GSM601987 2 0.1204 0.8445 0.000 0.944 0.000 0.000 0.056 0.000
#> GSM601877 1 0.2135 0.8660 0.872 0.000 0.128 0.000 0.000 0.000
#> GSM601907 2 0.0000 0.8712 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM601917 4 0.1765 0.7959 0.000 0.000 0.000 0.904 0.096 0.000
#> GSM601922 4 0.1765 0.7959 0.000 0.000 0.000 0.904 0.096 0.000
#> GSM601952 5 0.2350 0.7813 0.000 0.020 0.000 0.100 0.880 0.000
#> GSM601962 3 0.0000 0.8526 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM601967 6 0.0000 0.9423 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM601982 2 0.4658 0.5826 0.000 0.700 0.004 0.168 0.128 0.000
#> GSM601992 5 0.0000 0.8754 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM601873 5 0.0000 0.8754 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM601883 2 0.3774 0.2189 0.000 0.592 0.000 0.408 0.000 0.000
#> GSM601888 6 0.0000 0.9423 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM601893 6 0.1714 0.8783 0.000 0.000 0.092 0.000 0.000 0.908
#> GSM601898 6 0.1225 0.9290 0.036 0.000 0.000 0.012 0.000 0.952
#> GSM601903 4 0.1765 0.7959 0.000 0.000 0.000 0.904 0.096 0.000
#> GSM601913 3 0.0891 0.8450 0.008 0.000 0.968 0.024 0.000 0.000
#> GSM601928 1 0.2119 0.8657 0.904 0.000 0.060 0.036 0.000 0.000
#> GSM601933 5 0.0000 0.8754 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM601938 5 0.0000 0.8754 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM601943 5 0.0000 0.8754 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM601948 6 0.4003 0.7507 0.124 0.000 0.000 0.116 0.000 0.760
#> GSM601958 6 0.2966 0.8552 0.076 0.000 0.000 0.076 0.000 0.848
#> GSM601973 4 0.2416 0.7723 0.000 0.000 0.000 0.844 0.156 0.000
#> GSM601978 2 0.0000 0.8712 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM601988 5 0.0547 0.8650 0.000 0.000 0.000 0.020 0.980 0.000
#> GSM601878 1 0.1367 0.8827 0.944 0.000 0.044 0.000 0.000 0.012
#> GSM601908 2 0.1814 0.8075 0.000 0.900 0.000 0.000 0.100 0.000
#> GSM601918 4 0.3370 0.7357 0.000 0.148 0.000 0.804 0.048 0.000
#> GSM601923 1 0.1204 0.8898 0.944 0.000 0.056 0.000 0.000 0.000
#> GSM601953 2 0.0000 0.8712 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM601963 3 0.2376 0.8106 0.044 0.000 0.888 0.068 0.000 0.000
#> GSM601968 6 0.0000 0.9423 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM601983 3 0.0146 0.8519 0.000 0.000 0.996 0.000 0.000 0.004
#> GSM601993 5 0.0000 0.8754 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM601874 2 0.0000 0.8712 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM601884 2 0.0000 0.8712 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM601889 6 0.0914 0.9355 0.016 0.000 0.000 0.016 0.000 0.968
#> GSM601894 6 0.4261 0.7902 0.060 0.000 0.064 0.096 0.000 0.780
#> GSM601899 6 0.0000 0.9423 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM601904 4 0.4897 0.4936 0.000 0.000 0.292 0.616 0.092 0.000
#> GSM601914 3 0.2527 0.8020 0.040 0.000 0.876 0.084 0.000 0.000
#> GSM601929 3 0.0260 0.8501 0.008 0.000 0.992 0.000 0.000 0.000
#> GSM601934 5 0.0000 0.8754 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM601939 1 0.3682 0.7884 0.816 0.000 0.060 0.096 0.000 0.028
#> GSM601944 5 0.0000 0.8754 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM601949 6 0.2048 0.8656 0.120 0.000 0.000 0.000 0.000 0.880
#> GSM601959 6 0.0820 0.9364 0.016 0.000 0.000 0.012 0.000 0.972
#> GSM601974 3 0.3851 0.1947 0.000 0.000 0.540 0.460 0.000 0.000
#> GSM601979 2 0.0000 0.8712 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM601989 3 0.0146 0.8519 0.000 0.000 0.996 0.000 0.000 0.004
#> GSM601879 1 0.3528 0.6725 0.700 0.000 0.296 0.000 0.000 0.004
#> GSM601909 6 0.0000 0.9423 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM601919 4 0.2889 0.7655 0.000 0.108 0.000 0.848 0.044 0.000
#> GSM601924 1 0.1225 0.8544 0.952 0.000 0.012 0.000 0.000 0.036
#> GSM601954 2 0.3802 0.4390 0.000 0.676 0.000 0.312 0.012 0.000
#> GSM601964 3 0.0146 0.8522 0.000 0.000 0.996 0.004 0.000 0.000
#> GSM601969 6 0.0000 0.9423 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM601984 3 0.0000 0.8526 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM601994 5 0.0000 0.8754 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM601875 5 0.0000 0.8754 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM601885 2 0.3869 0.0160 0.000 0.500 0.000 0.000 0.500 0.000
#> GSM601890 6 0.0000 0.9423 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM601895 3 0.2234 0.7546 0.004 0.000 0.872 0.000 0.000 0.124
#> GSM601900 3 0.0260 0.8511 0.000 0.000 0.992 0.000 0.000 0.008
#> GSM601905 5 0.3782 0.3935 0.000 0.000 0.004 0.360 0.636 0.000
#> GSM601915 3 0.4441 0.6543 0.208 0.000 0.700 0.092 0.000 0.000
#> GSM601930 1 0.3221 0.7592 0.736 0.000 0.264 0.000 0.000 0.000
#> GSM601935 5 0.0000 0.8754 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM601940 3 0.3899 0.2902 0.004 0.000 0.592 0.000 0.000 0.404
#> GSM601945 5 0.3737 0.2902 0.000 0.392 0.000 0.000 0.608 0.000
#> GSM601950 6 0.0405 0.9407 0.008 0.000 0.000 0.004 0.000 0.988
#> GSM601960 3 0.4374 0.6693 0.192 0.000 0.712 0.096 0.000 0.000
#> GSM601975 4 0.4466 0.6799 0.000 0.176 0.000 0.708 0.116 0.000
#> GSM601980 5 0.0713 0.8583 0.000 0.000 0.000 0.028 0.972 0.000
#> GSM601990 3 0.0000 0.8526 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM601880 1 0.1204 0.8898 0.944 0.000 0.056 0.000 0.000 0.000
#> GSM601910 6 0.1765 0.8777 0.000 0.000 0.096 0.000 0.000 0.904
#> GSM601920 5 0.3515 0.4746 0.000 0.000 0.000 0.324 0.676 0.000
#> GSM601925 3 0.3782 0.0403 0.412 0.000 0.588 0.000 0.000 0.000
#> GSM601955 5 0.3823 0.0892 0.000 0.000 0.000 0.436 0.564 0.000
#> GSM601965 3 0.0000 0.8526 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM601970 6 0.0146 0.9418 0.004 0.000 0.000 0.000 0.000 0.996
#> GSM601985 3 0.4764 0.5637 0.272 0.000 0.640 0.088 0.000 0.000
#> GSM601995 3 0.2743 0.7413 0.000 0.000 0.828 0.164 0.008 0.000
#> GSM601876 3 0.0000 0.8526 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM601886 5 0.5214 0.2749 0.000 0.000 0.304 0.120 0.576 0.000
#> GSM601891 6 0.0000 0.9423 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM601896 3 0.0146 0.8516 0.004 0.000 0.996 0.000 0.000 0.000
#> GSM601901 5 0.1007 0.8458 0.000 0.044 0.000 0.000 0.956 0.000
#> GSM601906 3 0.2805 0.7248 0.000 0.000 0.812 0.184 0.004 0.000
#> GSM601916 5 0.0692 0.8630 0.000 0.000 0.004 0.020 0.976 0.000
#> GSM601931 1 0.2378 0.8638 0.848 0.000 0.152 0.000 0.000 0.000
#> GSM601936 5 0.0000 0.8754 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM601941 4 0.3330 0.6241 0.000 0.000 0.000 0.716 0.284 0.000
#> GSM601946 3 0.2786 0.7944 0.056 0.000 0.860 0.084 0.000 0.000
#> GSM601951 3 0.6555 0.3704 0.124 0.000 0.540 0.116 0.000 0.220
#> GSM601961 6 0.2135 0.8427 0.000 0.128 0.000 0.000 0.000 0.872
#> GSM601976 4 0.6088 0.1905 0.000 0.000 0.280 0.380 0.340 0.000
#> GSM601981 2 0.0260 0.8694 0.000 0.992 0.000 0.000 0.008 0.000
#> GSM601991 3 0.0000 0.8526 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM601881 1 0.1204 0.8898 0.944 0.000 0.056 0.000 0.000 0.000
#> GSM601911 3 0.0146 0.8520 0.000 0.000 0.996 0.004 0.000 0.000
#> GSM601921 5 0.3499 0.4818 0.000 0.000 0.000 0.320 0.680 0.000
#> GSM601926 1 0.1327 0.8909 0.936 0.000 0.064 0.000 0.000 0.000
#> GSM601956 2 0.0000 0.8712 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM601966 2 0.1714 0.8150 0.000 0.908 0.000 0.000 0.092 0.000
#> GSM601971 6 0.2340 0.8385 0.148 0.000 0.000 0.000 0.000 0.852
#> GSM601986 3 0.0146 0.8521 0.000 0.000 0.996 0.004 0.000 0.000
#> GSM601996 5 0.0000 0.8754 0.000 0.000 0.000 0.000 1.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
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)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
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)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, 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.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")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n time(p) gender(p) k
#> ATC:pam 123 0.1870 0.611 2
#> ATC:pam 122 0.2339 0.268 3
#> ATC:pam 118 0.0881 0.440 4
#> ATC:pam 99 0.1276 0.838 5
#> ATC:pam 110 0.3759 0.349 6
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.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["ATC", "mclust"]
# you can also extract it by
# res = res_list["ATC:mclust"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 21512 rows and 125 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'mclust' method.
#> Performed in total 1250 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_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
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, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.416 0.798 0.882 0.4512 0.567 0.567
#> 3 3 0.915 0.913 0.963 0.4801 0.731 0.539
#> 4 4 0.767 0.824 0.902 0.0618 0.950 0.854
#> 5 5 0.850 0.875 0.923 0.0666 0.901 0.695
#> 6 6 0.892 0.887 0.928 0.0841 0.924 0.694
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 3
Following shows 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 class
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
#> GSM601872 1 0.0000 0.856 1.000 0.000
#> GSM601882 1 0.0000 0.856 1.000 0.000
#> GSM601887 1 0.6343 0.859 0.840 0.160
#> GSM601892 1 0.6343 0.859 0.840 0.160
#> GSM601897 1 0.6343 0.859 0.840 0.160
#> GSM601902 2 0.6343 0.861 0.160 0.840
#> GSM601912 1 0.6343 0.859 0.840 0.160
#> GSM601927 2 0.0000 0.852 0.000 1.000
#> GSM601932 2 0.9866 0.501 0.432 0.568
#> GSM601937 1 0.0000 0.856 1.000 0.000
#> GSM601942 1 0.0000 0.856 1.000 0.000
#> GSM601947 2 0.7139 0.841 0.196 0.804
#> GSM601957 1 0.6343 0.859 0.840 0.160
#> GSM601972 2 0.6343 0.861 0.160 0.840
#> GSM601977 1 0.0000 0.856 1.000 0.000
#> GSM601987 1 0.0000 0.856 1.000 0.000
#> GSM601877 2 0.0000 0.852 0.000 1.000
#> GSM601907 1 0.0000 0.856 1.000 0.000
#> GSM601917 2 0.6343 0.861 0.160 0.840
#> GSM601922 2 0.6343 0.861 0.160 0.840
#> GSM601952 1 0.0000 0.856 1.000 0.000
#> GSM601962 1 0.6343 0.859 0.840 0.160
#> GSM601967 1 0.6343 0.859 0.840 0.160
#> GSM601982 1 0.0000 0.856 1.000 0.000
#> GSM601992 1 0.4022 0.794 0.920 0.080
#> GSM601873 1 0.0000 0.856 1.000 0.000
#> GSM601883 1 0.0000 0.856 1.000 0.000
#> GSM601888 1 0.6343 0.859 0.840 0.160
#> GSM601893 1 0.6343 0.859 0.840 0.160
#> GSM601898 1 0.6343 0.859 0.840 0.160
#> GSM601903 2 0.6343 0.861 0.160 0.840
#> GSM601913 1 0.6343 0.859 0.840 0.160
#> GSM601928 2 0.0672 0.849 0.008 0.992
#> GSM601933 1 0.0000 0.856 1.000 0.000
#> GSM601938 1 0.0000 0.856 1.000 0.000
#> GSM601943 1 0.0000 0.856 1.000 0.000
#> GSM601948 2 0.4161 0.794 0.084 0.916
#> GSM601958 1 0.6343 0.859 0.840 0.160
#> GSM601973 2 0.6343 0.861 0.160 0.840
#> GSM601978 1 0.0000 0.856 1.000 0.000
#> GSM601988 1 0.0000 0.856 1.000 0.000
#> GSM601878 2 0.0000 0.852 0.000 1.000
#> GSM601908 1 0.0000 0.856 1.000 0.000
#> GSM601918 2 0.6343 0.861 0.160 0.840
#> GSM601923 2 0.0000 0.852 0.000 1.000
#> GSM601953 1 0.0000 0.856 1.000 0.000
#> GSM601963 1 0.6343 0.859 0.840 0.160
#> GSM601968 1 0.6343 0.859 0.840 0.160
#> GSM601983 1 0.6343 0.859 0.840 0.160
#> GSM601993 1 0.0000 0.856 1.000 0.000
#> GSM601874 1 0.0000 0.856 1.000 0.000
#> GSM601884 1 0.0000 0.856 1.000 0.000
#> GSM601889 1 0.6343 0.859 0.840 0.160
#> GSM601894 1 0.6343 0.859 0.840 0.160
#> GSM601899 1 0.6343 0.859 0.840 0.160
#> GSM601904 2 0.6343 0.861 0.160 0.840
#> GSM601914 1 0.6343 0.859 0.840 0.160
#> GSM601929 2 0.0000 0.852 0.000 1.000
#> GSM601934 1 0.0000 0.856 1.000 0.000
#> GSM601939 1 0.8661 0.748 0.712 0.288
#> GSM601944 1 0.0000 0.856 1.000 0.000
#> GSM601949 2 0.7219 0.637 0.200 0.800
#> GSM601959 1 0.6343 0.859 0.840 0.160
#> GSM601974 2 0.8386 0.774 0.268 0.732
#> GSM601979 1 0.0000 0.856 1.000 0.000
#> GSM601989 1 0.6343 0.859 0.840 0.160
#> GSM601879 2 0.0000 0.852 0.000 1.000
#> GSM601909 1 0.6343 0.859 0.840 0.160
#> GSM601919 2 0.6343 0.861 0.160 0.840
#> GSM601924 2 0.0000 0.852 0.000 1.000
#> GSM601954 1 0.9993 -0.284 0.516 0.484
#> GSM601964 1 0.6343 0.859 0.840 0.160
#> GSM601969 1 0.6343 0.859 0.840 0.160
#> GSM601984 1 0.9944 0.435 0.544 0.456
#> GSM601994 1 0.0938 0.849 0.988 0.012
#> GSM601875 1 0.0000 0.856 1.000 0.000
#> GSM601885 1 0.0000 0.856 1.000 0.000
#> GSM601890 1 0.6343 0.859 0.840 0.160
#> GSM601895 1 0.6343 0.859 0.840 0.160
#> GSM601900 1 0.6343 0.859 0.840 0.160
#> GSM601905 2 0.6343 0.861 0.160 0.840
#> GSM601915 1 0.6343 0.859 0.840 0.160
#> GSM601930 2 0.0000 0.852 0.000 1.000
#> GSM601935 1 0.0000 0.856 1.000 0.000
#> GSM601940 1 0.6438 0.857 0.836 0.164
#> GSM601945 1 0.0000 0.856 1.000 0.000
#> GSM601950 2 0.8813 0.423 0.300 0.700
#> GSM601960 1 0.6343 0.859 0.840 0.160
#> GSM601975 2 0.6531 0.857 0.168 0.832
#> GSM601980 1 0.0000 0.856 1.000 0.000
#> GSM601990 1 0.6343 0.859 0.840 0.160
#> GSM601880 2 0.0000 0.852 0.000 1.000
#> GSM601910 1 0.6343 0.859 0.840 0.160
#> GSM601920 2 0.6343 0.861 0.160 0.840
#> GSM601925 2 0.0000 0.852 0.000 1.000
#> GSM601955 1 0.0000 0.856 1.000 0.000
#> GSM601965 1 0.8207 0.785 0.744 0.256
#> GSM601970 1 0.6343 0.859 0.840 0.160
#> GSM601985 1 0.6343 0.859 0.840 0.160
#> GSM601995 1 0.0000 0.856 1.000 0.000
#> GSM601876 1 0.7139 0.837 0.804 0.196
#> GSM601886 1 0.9922 -0.136 0.552 0.448
#> GSM601891 1 0.6343 0.859 0.840 0.160
#> GSM601896 1 0.8955 0.716 0.688 0.312
#> GSM601901 1 0.9970 -0.212 0.532 0.468
#> GSM601906 2 0.6247 0.861 0.156 0.844
#> GSM601916 2 0.9000 0.716 0.316 0.684
#> GSM601931 2 0.0000 0.852 0.000 1.000
#> GSM601936 1 0.0000 0.856 1.000 0.000
#> GSM601941 2 0.6343 0.861 0.160 0.840
#> GSM601946 1 0.8555 0.758 0.720 0.280
#> GSM601951 2 0.0000 0.852 0.000 1.000
#> GSM601961 1 0.3274 0.859 0.940 0.060
#> GSM601976 2 0.6343 0.861 0.160 0.840
#> GSM601981 1 0.0000 0.856 1.000 0.000
#> GSM601991 1 0.6343 0.859 0.840 0.160
#> GSM601881 2 0.0000 0.852 0.000 1.000
#> GSM601911 1 0.5294 0.771 0.880 0.120
#> GSM601921 2 0.6343 0.861 0.160 0.840
#> GSM601926 2 0.0000 0.852 0.000 1.000
#> GSM601956 1 0.0000 0.856 1.000 0.000
#> GSM601966 1 0.9970 -0.214 0.532 0.468
#> GSM601971 2 0.8909 0.401 0.308 0.692
#> GSM601986 1 0.7376 0.634 0.792 0.208
#> GSM601996 1 0.5737 0.729 0.864 0.136
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM601872 2 0.0237 0.96975 0.004 0.996 0.000
#> GSM601882 2 0.0237 0.96975 0.004 0.996 0.000
#> GSM601887 1 0.0000 0.95798 1.000 0.000 0.000
#> GSM601892 1 0.0000 0.95798 1.000 0.000 0.000
#> GSM601897 1 0.0000 0.95798 1.000 0.000 0.000
#> GSM601902 3 0.0424 0.95678 0.000 0.008 0.992
#> GSM601912 1 0.0000 0.95798 1.000 0.000 0.000
#> GSM601927 3 0.0237 0.95811 0.004 0.000 0.996
#> GSM601932 2 0.6180 0.26262 0.000 0.584 0.416
#> GSM601937 2 0.0237 0.96975 0.004 0.996 0.000
#> GSM601942 2 0.0237 0.96975 0.004 0.996 0.000
#> GSM601947 3 0.2711 0.88896 0.000 0.088 0.912
#> GSM601957 1 0.0000 0.95798 1.000 0.000 0.000
#> GSM601972 3 0.0747 0.95322 0.000 0.016 0.984
#> GSM601977 2 0.0237 0.96975 0.004 0.996 0.000
#> GSM601987 2 0.0237 0.96975 0.004 0.996 0.000
#> GSM601877 3 0.0237 0.95811 0.004 0.000 0.996
#> GSM601907 2 0.0237 0.96975 0.004 0.996 0.000
#> GSM601917 3 0.0892 0.95088 0.000 0.020 0.980
#> GSM601922 3 0.0592 0.95517 0.000 0.012 0.988
#> GSM601952 2 0.0237 0.96975 0.004 0.996 0.000
#> GSM601962 1 0.0000 0.95798 1.000 0.000 0.000
#> GSM601967 1 0.0000 0.95798 1.000 0.000 0.000
#> GSM601982 2 0.0237 0.96975 0.004 0.996 0.000
#> GSM601992 2 0.0000 0.96668 0.000 1.000 0.000
#> GSM601873 2 0.0237 0.96975 0.004 0.996 0.000
#> GSM601883 2 0.0237 0.96975 0.004 0.996 0.000
#> GSM601888 1 0.0000 0.95798 1.000 0.000 0.000
#> GSM601893 1 0.0000 0.95798 1.000 0.000 0.000
#> GSM601898 1 0.0000 0.95798 1.000 0.000 0.000
#> GSM601903 3 0.0237 0.95777 0.000 0.004 0.996
#> GSM601913 1 0.0000 0.95798 1.000 0.000 0.000
#> GSM601928 3 0.0747 0.95164 0.016 0.000 0.984
#> GSM601933 2 0.0237 0.96975 0.004 0.996 0.000
#> GSM601938 2 0.0237 0.96975 0.004 0.996 0.000
#> GSM601943 2 0.0237 0.96975 0.004 0.996 0.000
#> GSM601948 3 0.3272 0.86467 0.104 0.004 0.892
#> GSM601958 1 0.0000 0.95798 1.000 0.000 0.000
#> GSM601973 3 0.0747 0.95322 0.000 0.016 0.984
#> GSM601978 2 0.0237 0.96975 0.004 0.996 0.000
#> GSM601988 2 0.0237 0.96975 0.004 0.996 0.000
#> GSM601878 3 0.0237 0.95811 0.004 0.000 0.996
#> GSM601908 2 0.0237 0.96975 0.004 0.996 0.000
#> GSM601918 3 0.1163 0.94505 0.000 0.028 0.972
#> GSM601923 3 0.0237 0.95811 0.004 0.000 0.996
#> GSM601953 2 0.0237 0.96975 0.004 0.996 0.000
#> GSM601963 1 0.0000 0.95798 1.000 0.000 0.000
#> GSM601968 1 0.0000 0.95798 1.000 0.000 0.000
#> GSM601983 1 0.0000 0.95798 1.000 0.000 0.000
#> GSM601993 2 0.0237 0.96975 0.004 0.996 0.000
#> GSM601874 2 0.0237 0.96975 0.004 0.996 0.000
#> GSM601884 2 0.0237 0.96975 0.004 0.996 0.000
#> GSM601889 1 0.0000 0.95798 1.000 0.000 0.000
#> GSM601894 1 0.0000 0.95798 1.000 0.000 0.000
#> GSM601899 1 0.0000 0.95798 1.000 0.000 0.000
#> GSM601904 3 0.0237 0.95777 0.000 0.004 0.996
#> GSM601914 1 0.0000 0.95798 1.000 0.000 0.000
#> GSM601929 3 0.0237 0.95811 0.004 0.000 0.996
#> GSM601934 2 0.0237 0.96975 0.004 0.996 0.000
#> GSM601939 1 0.0237 0.95499 0.996 0.004 0.000
#> GSM601944 2 0.0237 0.96975 0.004 0.996 0.000
#> GSM601949 3 0.6520 0.00717 0.488 0.004 0.508
#> GSM601959 1 0.0000 0.95798 1.000 0.000 0.000
#> GSM601974 3 0.3769 0.85926 0.104 0.016 0.880
#> GSM601979 2 0.0237 0.96975 0.004 0.996 0.000
#> GSM601989 1 0.0000 0.95798 1.000 0.000 0.000
#> GSM601879 3 0.0237 0.95811 0.004 0.000 0.996
#> GSM601909 1 0.0000 0.95798 1.000 0.000 0.000
#> GSM601919 3 0.0424 0.95678 0.000 0.008 0.992
#> GSM601924 3 0.0237 0.95811 0.004 0.000 0.996
#> GSM601954 2 0.4399 0.75952 0.000 0.812 0.188
#> GSM601964 1 0.0000 0.95798 1.000 0.000 0.000
#> GSM601969 1 0.0000 0.95798 1.000 0.000 0.000
#> GSM601984 1 0.5815 0.56769 0.692 0.004 0.304
#> GSM601994 2 0.0237 0.96458 0.000 0.996 0.004
#> GSM601875 2 0.0237 0.96975 0.004 0.996 0.000
#> GSM601885 2 0.0237 0.96975 0.004 0.996 0.000
#> GSM601890 1 0.0000 0.95798 1.000 0.000 0.000
#> GSM601895 1 0.0000 0.95798 1.000 0.000 0.000
#> GSM601900 1 0.0000 0.95798 1.000 0.000 0.000
#> GSM601905 3 0.0237 0.95777 0.000 0.004 0.996
#> GSM601915 1 0.0000 0.95798 1.000 0.000 0.000
#> GSM601930 3 0.0237 0.95811 0.004 0.000 0.996
#> GSM601935 2 0.0475 0.96725 0.004 0.992 0.004
#> GSM601940 1 0.0000 0.95798 1.000 0.000 0.000
#> GSM601945 2 0.0237 0.96975 0.004 0.996 0.000
#> GSM601950 1 0.6432 0.22706 0.568 0.004 0.428
#> GSM601960 1 0.0000 0.95798 1.000 0.000 0.000
#> GSM601975 3 0.2066 0.91739 0.000 0.060 0.940
#> GSM601980 2 0.0237 0.96975 0.004 0.996 0.000
#> GSM601990 1 0.0000 0.95798 1.000 0.000 0.000
#> GSM601880 3 0.0237 0.95811 0.004 0.000 0.996
#> GSM601910 1 0.0000 0.95798 1.000 0.000 0.000
#> GSM601920 3 0.0237 0.95777 0.000 0.004 0.996
#> GSM601925 3 0.0237 0.95811 0.004 0.000 0.996
#> GSM601955 2 0.0475 0.96725 0.004 0.992 0.004
#> GSM601965 1 0.2400 0.90096 0.932 0.004 0.064
#> GSM601970 1 0.0000 0.95798 1.000 0.000 0.000
#> GSM601985 1 0.0000 0.95798 1.000 0.000 0.000
#> GSM601995 2 0.0829 0.96055 0.012 0.984 0.004
#> GSM601876 1 0.0237 0.95499 0.996 0.004 0.000
#> GSM601886 2 0.5178 0.65354 0.000 0.744 0.256
#> GSM601891 1 0.0000 0.95798 1.000 0.000 0.000
#> GSM601896 1 0.3573 0.84396 0.876 0.004 0.120
#> GSM601901 2 0.3879 0.81298 0.000 0.848 0.152
#> GSM601906 3 0.0237 0.95777 0.000 0.004 0.996
#> GSM601916 3 0.6095 0.34973 0.000 0.392 0.608
#> GSM601931 3 0.0237 0.95811 0.004 0.000 0.996
#> GSM601936 2 0.0475 0.96725 0.004 0.992 0.004
#> GSM601941 3 0.0892 0.95088 0.000 0.020 0.980
#> GSM601946 1 0.0237 0.95499 0.996 0.004 0.000
#> GSM601951 3 0.0237 0.95811 0.004 0.000 0.996
#> GSM601961 1 0.0237 0.95424 0.996 0.004 0.000
#> GSM601976 3 0.0237 0.95777 0.000 0.004 0.996
#> GSM601981 2 0.0237 0.96975 0.004 0.996 0.000
#> GSM601991 1 0.0237 0.95527 0.996 0.000 0.004
#> GSM601881 3 0.0237 0.95811 0.004 0.000 0.996
#> GSM601911 1 0.4033 0.82272 0.856 0.008 0.136
#> GSM601921 3 0.0237 0.95777 0.000 0.004 0.996
#> GSM601926 3 0.0237 0.95811 0.004 0.000 0.996
#> GSM601956 2 0.0237 0.96975 0.004 0.996 0.000
#> GSM601966 2 0.2959 0.87395 0.000 0.900 0.100
#> GSM601971 1 0.6518 0.02832 0.512 0.004 0.484
#> GSM601986 1 0.5541 0.65803 0.740 0.008 0.252
#> GSM601996 2 0.0747 0.95549 0.000 0.984 0.016
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM601872 2 0.0188 0.931 0.000 0.996 0.004 0.000
#> GSM601882 2 0.0000 0.932 0.000 1.000 0.000 0.000
#> GSM601887 1 0.1489 0.874 0.952 0.000 0.044 0.004
#> GSM601892 1 0.1489 0.874 0.952 0.000 0.044 0.004
#> GSM601897 1 0.0188 0.878 0.996 0.000 0.004 0.000
#> GSM601902 4 0.0707 0.887 0.000 0.020 0.000 0.980
#> GSM601912 1 0.3668 0.751 0.808 0.004 0.188 0.000
#> GSM601927 4 0.2773 0.886 0.004 0.000 0.116 0.880
#> GSM601932 2 0.4974 0.584 0.000 0.736 0.040 0.224
#> GSM601937 3 0.4998 0.351 0.000 0.488 0.512 0.000
#> GSM601942 2 0.0188 0.931 0.000 0.996 0.004 0.000
#> GSM601947 4 0.3311 0.723 0.000 0.172 0.000 0.828
#> GSM601957 1 0.0817 0.878 0.976 0.000 0.024 0.000
#> GSM601972 4 0.1302 0.874 0.000 0.044 0.000 0.956
#> GSM601977 2 0.0188 0.931 0.000 0.996 0.004 0.000
#> GSM601987 2 0.0000 0.932 0.000 1.000 0.000 0.000
#> GSM601877 4 0.2773 0.886 0.004 0.000 0.116 0.880
#> GSM601907 2 0.0188 0.931 0.000 0.996 0.004 0.000
#> GSM601917 4 0.1302 0.873 0.000 0.044 0.000 0.956
#> GSM601922 4 0.0707 0.887 0.000 0.020 0.000 0.980
#> GSM601952 2 0.0000 0.932 0.000 1.000 0.000 0.000
#> GSM601962 1 0.4679 0.559 0.648 0.000 0.352 0.000
#> GSM601967 1 0.1489 0.874 0.952 0.000 0.044 0.004
#> GSM601982 2 0.0188 0.931 0.000 0.996 0.004 0.000
#> GSM601992 2 0.0469 0.923 0.000 0.988 0.000 0.012
#> GSM601873 2 0.0188 0.931 0.000 0.996 0.004 0.000
#> GSM601883 2 0.0000 0.932 0.000 1.000 0.000 0.000
#> GSM601888 1 0.1302 0.875 0.956 0.000 0.044 0.000
#> GSM601893 1 0.0336 0.879 0.992 0.000 0.008 0.000
#> GSM601898 1 0.0000 0.878 1.000 0.000 0.000 0.000
#> GSM601903 4 0.0707 0.887 0.000 0.020 0.000 0.980
#> GSM601913 1 0.3266 0.806 0.832 0.000 0.168 0.000
#> GSM601928 4 0.3278 0.879 0.020 0.000 0.116 0.864
#> GSM601933 2 0.0000 0.932 0.000 1.000 0.000 0.000
#> GSM601938 2 0.0000 0.932 0.000 1.000 0.000 0.000
#> GSM601943 2 0.0188 0.931 0.000 0.996 0.004 0.000
#> GSM601948 4 0.5911 0.648 0.196 0.000 0.112 0.692
#> GSM601958 1 0.0000 0.878 1.000 0.000 0.000 0.000
#> GSM601973 4 0.0921 0.883 0.000 0.028 0.000 0.972
#> GSM601978 2 0.0000 0.932 0.000 1.000 0.000 0.000
#> GSM601988 2 0.2814 0.757 0.000 0.868 0.132 0.000
#> GSM601878 4 0.2773 0.886 0.004 0.000 0.116 0.880
#> GSM601908 2 0.0000 0.932 0.000 1.000 0.000 0.000
#> GSM601918 4 0.2011 0.842 0.000 0.080 0.000 0.920
#> GSM601923 4 0.2773 0.886 0.004 0.000 0.116 0.880
#> GSM601953 2 0.0188 0.931 0.000 0.996 0.004 0.000
#> GSM601963 1 0.3873 0.701 0.772 0.000 0.228 0.000
#> GSM601968 1 0.1489 0.874 0.952 0.000 0.044 0.004
#> GSM601983 1 0.0336 0.877 0.992 0.000 0.008 0.000
#> GSM601993 2 0.0000 0.932 0.000 1.000 0.000 0.000
#> GSM601874 2 0.0000 0.932 0.000 1.000 0.000 0.000
#> GSM601884 2 0.0188 0.931 0.000 0.996 0.004 0.000
#> GSM601889 1 0.0000 0.878 1.000 0.000 0.000 0.000
#> GSM601894 1 0.0336 0.877 0.992 0.000 0.008 0.000
#> GSM601899 1 0.1576 0.873 0.948 0.000 0.048 0.004
#> GSM601904 4 0.0188 0.890 0.000 0.004 0.000 0.996
#> GSM601914 1 0.3528 0.746 0.808 0.000 0.192 0.000
#> GSM601929 4 0.2334 0.890 0.004 0.000 0.088 0.908
#> GSM601934 2 0.0188 0.931 0.000 0.996 0.004 0.000
#> GSM601939 1 0.2831 0.838 0.876 0.000 0.120 0.004
#> GSM601944 2 0.0188 0.931 0.000 0.996 0.004 0.000
#> GSM601949 1 0.6110 0.611 0.680 0.000 0.144 0.176
#> GSM601959 1 0.0921 0.878 0.972 0.000 0.028 0.000
#> GSM601974 4 0.8582 0.123 0.288 0.144 0.080 0.488
#> GSM601979 2 0.0000 0.932 0.000 1.000 0.000 0.000
#> GSM601989 1 0.0469 0.879 0.988 0.000 0.012 0.000
#> GSM601879 4 0.2773 0.886 0.004 0.000 0.116 0.880
#> GSM601909 1 0.0817 0.878 0.976 0.000 0.024 0.000
#> GSM601919 4 0.0817 0.885 0.000 0.024 0.000 0.976
#> GSM601924 4 0.2773 0.886 0.004 0.000 0.116 0.880
#> GSM601954 2 0.3306 0.741 0.000 0.840 0.004 0.156
#> GSM601964 1 0.3688 0.746 0.792 0.000 0.208 0.000
#> GSM601969 1 0.1474 0.874 0.948 0.000 0.052 0.000
#> GSM601984 1 0.5672 0.654 0.712 0.000 0.100 0.188
#> GSM601994 2 0.0469 0.922 0.000 0.988 0.000 0.012
#> GSM601875 2 0.0188 0.931 0.000 0.996 0.004 0.000
#> GSM601885 2 0.0188 0.931 0.000 0.996 0.004 0.000
#> GSM601890 1 0.1576 0.873 0.948 0.000 0.048 0.004
#> GSM601895 1 0.0336 0.877 0.992 0.000 0.008 0.000
#> GSM601900 1 0.0336 0.877 0.992 0.000 0.008 0.000
#> GSM601905 4 0.0188 0.890 0.000 0.004 0.000 0.996
#> GSM601915 1 0.4382 0.597 0.704 0.000 0.296 0.000
#> GSM601930 4 0.2773 0.886 0.004 0.000 0.116 0.880
#> GSM601935 3 0.4328 0.774 0.008 0.244 0.748 0.000
#> GSM601940 1 0.0921 0.879 0.972 0.000 0.028 0.000
#> GSM601945 2 0.0000 0.932 0.000 1.000 0.000 0.000
#> GSM601950 1 0.5314 0.712 0.748 0.000 0.144 0.108
#> GSM601960 1 0.1940 0.849 0.924 0.000 0.076 0.000
#> GSM601975 4 0.4103 0.580 0.000 0.256 0.000 0.744
#> GSM601980 2 0.0188 0.931 0.000 0.996 0.004 0.000
#> GSM601990 1 0.4855 0.462 0.600 0.000 0.400 0.000
#> GSM601880 4 0.2773 0.886 0.004 0.000 0.116 0.880
#> GSM601910 1 0.0000 0.878 1.000 0.000 0.000 0.000
#> GSM601920 4 0.0188 0.890 0.000 0.004 0.000 0.996
#> GSM601925 4 0.2773 0.886 0.004 0.000 0.116 0.880
#> GSM601955 3 0.4391 0.772 0.008 0.252 0.740 0.000
#> GSM601965 1 0.3521 0.824 0.864 0.000 0.084 0.052
#> GSM601970 1 0.0707 0.878 0.980 0.000 0.020 0.000
#> GSM601985 1 0.1867 0.857 0.928 0.000 0.072 0.000
#> GSM601995 3 0.4420 0.773 0.012 0.240 0.748 0.000
#> GSM601876 1 0.2216 0.851 0.908 0.000 0.092 0.000
#> GSM601886 2 0.5397 0.564 0.000 0.720 0.068 0.212
#> GSM601891 1 0.1118 0.876 0.964 0.000 0.036 0.000
#> GSM601896 1 0.3907 0.809 0.836 0.000 0.120 0.044
#> GSM601901 2 0.4482 0.708 0.000 0.804 0.068 0.128
#> GSM601906 4 0.0188 0.889 0.004 0.000 0.000 0.996
#> GSM601916 2 0.6023 0.353 0.000 0.612 0.060 0.328
#> GSM601931 4 0.2773 0.886 0.004 0.000 0.116 0.880
#> GSM601936 3 0.4164 0.763 0.000 0.264 0.736 0.000
#> GSM601941 4 0.1637 0.861 0.000 0.060 0.000 0.940
#> GSM601946 1 0.2773 0.839 0.880 0.000 0.116 0.004
#> GSM601951 4 0.1890 0.892 0.008 0.000 0.056 0.936
#> GSM601961 1 0.2174 0.865 0.928 0.020 0.052 0.000
#> GSM601976 4 0.0336 0.889 0.000 0.008 0.000 0.992
#> GSM601981 2 0.0000 0.932 0.000 1.000 0.000 0.000
#> GSM601991 3 0.5277 -0.224 0.460 0.008 0.532 0.000
#> GSM601881 4 0.2773 0.886 0.004 0.000 0.116 0.880
#> GSM601911 1 0.6084 0.700 0.740 0.052 0.084 0.124
#> GSM601921 4 0.0336 0.889 0.000 0.008 0.000 0.992
#> GSM601926 4 0.2773 0.886 0.004 0.000 0.116 0.880
#> GSM601956 2 0.0188 0.931 0.000 0.996 0.004 0.000
#> GSM601966 2 0.4144 0.741 0.000 0.828 0.068 0.104
#> GSM601971 1 0.5938 0.636 0.696 0.000 0.136 0.168
#> GSM601986 1 0.6002 0.648 0.708 0.016 0.080 0.196
#> GSM601996 2 0.1389 0.885 0.000 0.952 0.000 0.048
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM601872 2 0.0510 0.9754 0.000 0.984 0.000 0.000 0.016
#> GSM601882 2 0.0000 0.9808 0.000 1.000 0.000 0.000 0.000
#> GSM601887 3 0.2116 0.8794 0.076 0.000 0.912 0.004 0.008
#> GSM601892 3 0.2177 0.8774 0.080 0.000 0.908 0.004 0.008
#> GSM601897 3 0.1124 0.9019 0.000 0.000 0.960 0.004 0.036
#> GSM601902 4 0.0451 0.9307 0.004 0.008 0.000 0.988 0.000
#> GSM601912 3 0.1638 0.8971 0.000 0.000 0.932 0.004 0.064
#> GSM601927 1 0.2124 0.9154 0.900 0.000 0.004 0.096 0.000
#> GSM601932 4 0.3751 0.7003 0.004 0.212 0.000 0.772 0.012
#> GSM601937 5 0.4294 0.2227 0.000 0.468 0.000 0.000 0.532
#> GSM601942 2 0.0510 0.9754 0.000 0.984 0.000 0.000 0.016
#> GSM601947 4 0.0955 0.9205 0.004 0.028 0.000 0.968 0.000
#> GSM601957 3 0.1502 0.8908 0.056 0.000 0.940 0.000 0.004
#> GSM601972 4 0.0451 0.9307 0.004 0.008 0.000 0.988 0.000
#> GSM601977 2 0.0162 0.9799 0.000 0.996 0.000 0.000 0.004
#> GSM601987 2 0.0000 0.9808 0.000 1.000 0.000 0.000 0.000
#> GSM601877 1 0.2304 0.9107 0.892 0.000 0.000 0.100 0.008
#> GSM601907 2 0.0000 0.9808 0.000 1.000 0.000 0.000 0.000
#> GSM601917 4 0.0451 0.9307 0.004 0.008 0.000 0.988 0.000
#> GSM601922 4 0.0451 0.9307 0.004 0.008 0.000 0.988 0.000
#> GSM601952 2 0.0290 0.9788 0.000 0.992 0.000 0.000 0.008
#> GSM601962 3 0.1991 0.8929 0.004 0.000 0.916 0.004 0.076
#> GSM601967 3 0.2116 0.8808 0.076 0.000 0.912 0.004 0.008
#> GSM601982 2 0.0404 0.9774 0.000 0.988 0.000 0.000 0.012
#> GSM601992 2 0.1386 0.9438 0.000 0.952 0.000 0.032 0.016
#> GSM601873 2 0.0510 0.9754 0.000 0.984 0.000 0.000 0.016
#> GSM601883 2 0.0000 0.9808 0.000 1.000 0.000 0.000 0.000
#> GSM601888 3 0.2293 0.8793 0.084 0.000 0.900 0.000 0.016
#> GSM601893 3 0.1124 0.9025 0.004 0.000 0.960 0.000 0.036
#> GSM601898 3 0.0451 0.9029 0.004 0.000 0.988 0.000 0.008
#> GSM601903 4 0.0451 0.9307 0.004 0.008 0.000 0.988 0.000
#> GSM601913 3 0.1502 0.8992 0.004 0.000 0.940 0.000 0.056
#> GSM601928 1 0.2249 0.9130 0.896 0.000 0.008 0.096 0.000
#> GSM601933 2 0.0000 0.9808 0.000 1.000 0.000 0.000 0.000
#> GSM601938 2 0.0510 0.9754 0.000 0.984 0.000 0.000 0.016
#> GSM601943 2 0.0510 0.9754 0.000 0.984 0.000 0.000 0.016
#> GSM601948 1 0.3385 0.7899 0.856 0.000 0.084 0.044 0.016
#> GSM601958 3 0.0451 0.9029 0.004 0.000 0.988 0.000 0.008
#> GSM601973 4 0.0451 0.9307 0.004 0.008 0.000 0.988 0.000
#> GSM601978 2 0.0000 0.9808 0.000 1.000 0.000 0.000 0.000
#> GSM601988 2 0.1608 0.9191 0.000 0.928 0.000 0.000 0.072
#> GSM601878 1 0.2124 0.9154 0.900 0.000 0.004 0.096 0.000
#> GSM601908 2 0.0000 0.9808 0.000 1.000 0.000 0.000 0.000
#> GSM601918 4 0.0566 0.9293 0.004 0.012 0.000 0.984 0.000
#> GSM601923 1 0.2124 0.9154 0.900 0.000 0.004 0.096 0.000
#> GSM601953 2 0.0451 0.9776 0.004 0.988 0.000 0.000 0.008
#> GSM601963 3 0.1430 0.9004 0.004 0.000 0.944 0.000 0.052
#> GSM601968 3 0.2237 0.8755 0.084 0.000 0.904 0.004 0.008
#> GSM601983 3 0.1282 0.8992 0.004 0.000 0.952 0.000 0.044
#> GSM601993 2 0.0898 0.9680 0.000 0.972 0.000 0.008 0.020
#> GSM601874 2 0.0000 0.9808 0.000 1.000 0.000 0.000 0.000
#> GSM601884 2 0.0000 0.9808 0.000 1.000 0.000 0.000 0.000
#> GSM601889 3 0.0324 0.9027 0.004 0.000 0.992 0.000 0.004
#> GSM601894 3 0.0290 0.9033 0.000 0.000 0.992 0.000 0.008
#> GSM601899 3 0.2295 0.8736 0.088 0.000 0.900 0.004 0.008
#> GSM601904 4 0.0451 0.9284 0.008 0.004 0.000 0.988 0.000
#> GSM601914 3 0.1502 0.8992 0.004 0.000 0.940 0.000 0.056
#> GSM601929 1 0.2416 0.9095 0.888 0.000 0.000 0.100 0.012
#> GSM601934 2 0.0000 0.9808 0.000 1.000 0.000 0.000 0.000
#> GSM601939 3 0.0968 0.9023 0.012 0.000 0.972 0.004 0.012
#> GSM601944 2 0.0162 0.9800 0.000 0.996 0.000 0.000 0.004
#> GSM601949 1 0.3107 0.7120 0.852 0.000 0.124 0.008 0.016
#> GSM601959 3 0.1628 0.8905 0.056 0.000 0.936 0.000 0.008
#> GSM601974 4 0.2353 0.8681 0.004 0.008 0.044 0.916 0.028
#> GSM601979 2 0.0000 0.9808 0.000 1.000 0.000 0.000 0.000
#> GSM601989 3 0.0324 0.9027 0.004 0.000 0.992 0.000 0.004
#> GSM601879 1 0.2416 0.9095 0.888 0.000 0.000 0.100 0.012
#> GSM601909 3 0.0703 0.9013 0.024 0.000 0.976 0.000 0.000
#> GSM601919 4 0.0451 0.9307 0.004 0.008 0.000 0.988 0.000
#> GSM601924 1 0.2124 0.9154 0.900 0.000 0.004 0.096 0.000
#> GSM601954 4 0.3937 0.7395 0.012 0.184 0.000 0.784 0.020
#> GSM601964 3 0.1502 0.8992 0.004 0.000 0.940 0.000 0.056
#> GSM601969 3 0.2233 0.8803 0.080 0.000 0.904 0.000 0.016
#> GSM601984 3 0.5676 0.4693 0.300 0.000 0.620 0.048 0.032
#> GSM601994 2 0.1774 0.9169 0.000 0.932 0.000 0.052 0.016
#> GSM601875 2 0.0000 0.9808 0.000 1.000 0.000 0.000 0.000
#> GSM601885 2 0.0000 0.9808 0.000 1.000 0.000 0.000 0.000
#> GSM601890 3 0.2237 0.8755 0.084 0.000 0.904 0.004 0.008
#> GSM601895 3 0.1124 0.9011 0.004 0.000 0.960 0.000 0.036
#> GSM601900 3 0.1124 0.9011 0.004 0.000 0.960 0.000 0.036
#> GSM601905 4 0.0451 0.9284 0.008 0.004 0.000 0.988 0.000
#> GSM601915 3 0.1357 0.9031 0.004 0.000 0.948 0.000 0.048
#> GSM601930 1 0.2124 0.9154 0.900 0.000 0.004 0.096 0.000
#> GSM601935 5 0.1544 0.8540 0.000 0.068 0.000 0.000 0.932
#> GSM601940 3 0.0486 0.9030 0.004 0.000 0.988 0.004 0.004
#> GSM601945 2 0.0000 0.9808 0.000 1.000 0.000 0.000 0.000
#> GSM601950 1 0.3183 0.6660 0.828 0.000 0.156 0.000 0.016
#> GSM601960 3 0.0955 0.9031 0.004 0.000 0.968 0.000 0.028
#> GSM601975 4 0.0865 0.9232 0.004 0.024 0.000 0.972 0.000
#> GSM601980 2 0.0609 0.9734 0.000 0.980 0.000 0.000 0.020
#> GSM601990 3 0.2806 0.8475 0.000 0.000 0.844 0.004 0.152
#> GSM601880 1 0.2124 0.9154 0.900 0.000 0.004 0.096 0.000
#> GSM601910 3 0.1251 0.9029 0.008 0.000 0.956 0.000 0.036
#> GSM601920 4 0.0451 0.9284 0.008 0.004 0.000 0.988 0.000
#> GSM601925 1 0.2124 0.9154 0.900 0.000 0.004 0.096 0.000
#> GSM601955 5 0.2020 0.8465 0.000 0.100 0.000 0.000 0.900
#> GSM601965 3 0.2297 0.8893 0.020 0.000 0.912 0.008 0.060
#> GSM601970 3 0.0609 0.9019 0.020 0.000 0.980 0.000 0.000
#> GSM601985 3 0.0566 0.9030 0.004 0.000 0.984 0.000 0.012
#> GSM601995 5 0.1544 0.8540 0.000 0.068 0.000 0.000 0.932
#> GSM601876 3 0.1788 0.8958 0.008 0.000 0.932 0.004 0.056
#> GSM601886 4 0.2880 0.8380 0.004 0.108 0.000 0.868 0.020
#> GSM601891 3 0.2554 0.8894 0.072 0.000 0.892 0.000 0.036
#> GSM601896 3 0.3774 0.7672 0.152 0.000 0.808 0.008 0.032
#> GSM601901 4 0.2570 0.8409 0.004 0.108 0.000 0.880 0.008
#> GSM601906 4 0.0579 0.9219 0.008 0.000 0.000 0.984 0.008
#> GSM601916 4 0.2463 0.8501 0.004 0.100 0.000 0.888 0.008
#> GSM601931 1 0.2124 0.9154 0.900 0.000 0.004 0.096 0.000
#> GSM601936 5 0.1671 0.8544 0.000 0.076 0.000 0.000 0.924
#> GSM601941 4 0.0451 0.9307 0.004 0.008 0.000 0.988 0.000
#> GSM601946 3 0.2396 0.8672 0.068 0.000 0.904 0.004 0.024
#> GSM601951 1 0.3500 0.8419 0.808 0.000 0.004 0.172 0.016
#> GSM601961 3 0.6585 0.0716 0.084 0.428 0.448 0.000 0.040
#> GSM601976 4 0.0451 0.9284 0.008 0.004 0.000 0.988 0.000
#> GSM601981 2 0.0000 0.9808 0.000 1.000 0.000 0.000 0.000
#> GSM601991 5 0.2850 0.7560 0.000 0.036 0.092 0.000 0.872
#> GSM601881 1 0.2124 0.9154 0.900 0.000 0.004 0.096 0.000
#> GSM601911 3 0.7645 0.0481 0.008 0.256 0.432 0.264 0.040
#> GSM601921 4 0.0451 0.9284 0.008 0.004 0.000 0.988 0.000
#> GSM601926 1 0.2124 0.9154 0.900 0.000 0.004 0.096 0.000
#> GSM601956 2 0.0000 0.9808 0.000 1.000 0.000 0.000 0.000
#> GSM601966 4 0.3875 0.6763 0.004 0.228 0.000 0.756 0.012
#> GSM601971 1 0.4270 0.4032 0.656 0.000 0.336 0.004 0.004
#> GSM601986 3 0.5399 0.3123 0.008 0.008 0.560 0.396 0.028
#> GSM601996 2 0.2881 0.7994 0.004 0.860 0.000 0.124 0.012
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM601872 2 0.0363 0.948 0.000 0.988 0.000 0.000 0.012 0.000
#> GSM601882 2 0.0291 0.950 0.000 0.992 0.004 0.000 0.004 0.000
#> GSM601887 6 0.0000 0.944 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM601892 6 0.0000 0.944 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM601897 3 0.1863 0.886 0.000 0.000 0.896 0.000 0.000 0.104
#> GSM601902 4 0.0000 0.944 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM601912 3 0.1918 0.885 0.000 0.000 0.904 0.000 0.008 0.088
#> GSM601927 1 0.0146 0.941 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM601932 4 0.5196 0.408 0.000 0.312 0.020 0.600 0.068 0.000
#> GSM601937 5 0.1327 0.913 0.000 0.064 0.000 0.000 0.936 0.000
#> GSM601942 2 0.2793 0.748 0.000 0.800 0.000 0.000 0.200 0.000
#> GSM601947 4 0.0000 0.944 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM601957 6 0.0632 0.944 0.000 0.000 0.024 0.000 0.000 0.976
#> GSM601972 4 0.0000 0.944 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM601977 2 0.0146 0.951 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM601987 2 0.0717 0.942 0.000 0.976 0.008 0.000 0.016 0.000
#> GSM601877 1 0.1152 0.931 0.952 0.000 0.044 0.004 0.000 0.000
#> GSM601907 2 0.0260 0.950 0.000 0.992 0.000 0.000 0.008 0.000
#> GSM601917 4 0.0000 0.944 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM601922 4 0.0000 0.944 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM601952 2 0.0146 0.951 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM601962 3 0.1926 0.880 0.000 0.000 0.912 0.000 0.020 0.068
#> GSM601967 6 0.0260 0.945 0.000 0.000 0.008 0.000 0.000 0.992
#> GSM601982 2 0.0146 0.951 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM601992 2 0.2828 0.861 0.000 0.872 0.020 0.036 0.072 0.000
#> GSM601873 2 0.2823 0.741 0.000 0.796 0.000 0.000 0.204 0.000
#> GSM601883 2 0.1007 0.930 0.000 0.956 0.000 0.000 0.044 0.000
#> GSM601888 6 0.0260 0.945 0.000 0.000 0.008 0.000 0.000 0.992
#> GSM601893 3 0.2378 0.854 0.000 0.000 0.848 0.000 0.000 0.152
#> GSM601898 6 0.1141 0.931 0.000 0.000 0.052 0.000 0.000 0.948
#> GSM601903 4 0.0000 0.944 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM601913 3 0.1556 0.890 0.000 0.000 0.920 0.000 0.000 0.080
#> GSM601928 1 0.0146 0.941 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM601933 2 0.0000 0.952 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM601938 2 0.1588 0.904 0.000 0.924 0.004 0.000 0.072 0.000
#> GSM601943 2 0.2762 0.753 0.000 0.804 0.000 0.000 0.196 0.000
#> GSM601948 1 0.1625 0.916 0.928 0.000 0.060 0.000 0.000 0.012
#> GSM601958 6 0.1075 0.934 0.000 0.000 0.048 0.000 0.000 0.952
#> GSM601973 4 0.0000 0.944 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM601978 2 0.0260 0.950 0.000 0.992 0.000 0.000 0.008 0.000
#> GSM601988 5 0.2320 0.885 0.000 0.132 0.004 0.000 0.864 0.000
#> GSM601878 1 0.0777 0.936 0.972 0.000 0.024 0.004 0.000 0.000
#> GSM601908 2 0.0260 0.950 0.000 0.992 0.000 0.000 0.008 0.000
#> GSM601918 4 0.0000 0.944 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM601923 1 0.0146 0.941 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM601953 2 0.0146 0.951 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM601963 3 0.1556 0.890 0.000 0.000 0.920 0.000 0.000 0.080
#> GSM601968 6 0.0000 0.944 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM601983 3 0.1663 0.890 0.000 0.000 0.912 0.000 0.000 0.088
#> GSM601993 5 0.3831 0.760 0.000 0.224 0.012 0.020 0.744 0.000
#> GSM601874 2 0.0260 0.950 0.000 0.992 0.000 0.000 0.008 0.000
#> GSM601884 2 0.0000 0.952 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM601889 6 0.1141 0.931 0.000 0.000 0.052 0.000 0.000 0.948
#> GSM601894 6 0.2762 0.767 0.000 0.000 0.196 0.000 0.000 0.804
#> GSM601899 6 0.0000 0.944 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM601904 4 0.0000 0.944 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM601914 3 0.1556 0.890 0.000 0.000 0.920 0.000 0.000 0.080
#> GSM601929 1 0.1219 0.930 0.948 0.000 0.048 0.004 0.000 0.000
#> GSM601934 2 0.0000 0.952 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM601939 6 0.4822 0.422 0.332 0.000 0.072 0.000 0.000 0.596
#> GSM601944 2 0.0000 0.952 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM601949 1 0.3168 0.775 0.804 0.000 0.024 0.000 0.000 0.172
#> GSM601959 6 0.0547 0.945 0.000 0.000 0.020 0.000 0.000 0.980
#> GSM601974 4 0.0922 0.928 0.000 0.004 0.024 0.968 0.004 0.000
#> GSM601979 2 0.0260 0.950 0.000 0.992 0.000 0.000 0.008 0.000
#> GSM601989 6 0.2092 0.843 0.000 0.000 0.124 0.000 0.000 0.876
#> GSM601879 1 0.1219 0.930 0.948 0.000 0.048 0.004 0.000 0.000
#> GSM601909 6 0.0632 0.944 0.000 0.000 0.024 0.000 0.000 0.976
#> GSM601919 4 0.0000 0.944 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM601924 1 0.0146 0.941 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM601954 4 0.1987 0.867 0.004 0.080 0.004 0.908 0.004 0.000
#> GSM601964 3 0.1556 0.890 0.000 0.000 0.920 0.000 0.000 0.080
#> GSM601969 6 0.0603 0.940 0.004 0.000 0.016 0.000 0.000 0.980
#> GSM601984 3 0.5884 0.666 0.084 0.000 0.640 0.160 0.004 0.112
#> GSM601994 2 0.2681 0.869 0.000 0.880 0.020 0.028 0.072 0.000
#> GSM601875 2 0.0000 0.952 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM601885 2 0.0000 0.952 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM601890 6 0.0000 0.944 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM601895 3 0.1556 0.890 0.000 0.000 0.920 0.000 0.000 0.080
#> GSM601900 3 0.1610 0.890 0.000 0.000 0.916 0.000 0.000 0.084
#> GSM601905 4 0.0000 0.944 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM601915 3 0.1556 0.890 0.000 0.000 0.920 0.000 0.000 0.080
#> GSM601930 1 0.0146 0.941 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM601935 5 0.0260 0.899 0.000 0.008 0.000 0.000 0.992 0.000
#> GSM601940 6 0.0865 0.941 0.000 0.000 0.036 0.000 0.000 0.964
#> GSM601945 2 0.0260 0.950 0.000 0.992 0.000 0.000 0.008 0.000
#> GSM601950 1 0.3269 0.762 0.792 0.000 0.024 0.000 0.000 0.184
#> GSM601960 3 0.1556 0.890 0.000 0.000 0.920 0.000 0.000 0.080
#> GSM601975 4 0.0000 0.944 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM601980 5 0.2135 0.887 0.000 0.128 0.000 0.000 0.872 0.000
#> GSM601990 3 0.1812 0.888 0.000 0.000 0.912 0.000 0.008 0.080
#> GSM601880 1 0.0146 0.941 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM601910 3 0.2003 0.882 0.000 0.000 0.884 0.000 0.000 0.116
#> GSM601920 4 0.0000 0.944 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM601925 1 0.0146 0.941 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM601955 5 0.1714 0.900 0.000 0.092 0.000 0.000 0.908 0.000
#> GSM601965 3 0.4353 0.774 0.024 0.000 0.740 0.040 0.004 0.192
#> GSM601970 6 0.0865 0.940 0.000 0.000 0.036 0.000 0.000 0.964
#> GSM601985 3 0.3151 0.762 0.000 0.000 0.748 0.000 0.000 0.252
#> GSM601995 5 0.0260 0.899 0.000 0.008 0.000 0.000 0.992 0.000
#> GSM601876 3 0.2092 0.865 0.000 0.000 0.876 0.000 0.000 0.124
#> GSM601886 4 0.2163 0.848 0.000 0.096 0.008 0.892 0.004 0.000
#> GSM601891 6 0.0260 0.945 0.000 0.000 0.008 0.000 0.000 0.992
#> GSM601896 3 0.4253 0.731 0.064 0.000 0.704 0.000 0.000 0.232
#> GSM601901 4 0.1138 0.919 0.000 0.024 0.012 0.960 0.004 0.000
#> GSM601906 4 0.0363 0.938 0.000 0.000 0.012 0.988 0.000 0.000
#> GSM601916 4 0.0653 0.933 0.000 0.004 0.012 0.980 0.004 0.000
#> GSM601931 1 0.0146 0.941 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM601936 5 0.0713 0.909 0.000 0.028 0.000 0.000 0.972 0.000
#> GSM601941 4 0.0000 0.944 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM601946 3 0.4191 0.724 0.056 0.000 0.704 0.000 0.000 0.240
#> GSM601951 1 0.1528 0.924 0.936 0.000 0.048 0.016 0.000 0.000
#> GSM601961 6 0.1261 0.919 0.004 0.008 0.028 0.000 0.004 0.956
#> GSM601976 4 0.0000 0.944 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM601981 2 0.0000 0.952 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM601991 5 0.2307 0.872 0.000 0.048 0.016 0.000 0.904 0.032
#> GSM601881 1 0.0146 0.941 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM601911 3 0.5126 0.622 0.000 0.012 0.652 0.248 0.008 0.080
#> GSM601921 4 0.0000 0.944 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM601926 1 0.0146 0.941 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM601956 2 0.0260 0.950 0.000 0.992 0.000 0.000 0.008 0.000
#> GSM601966 4 0.4187 0.436 0.000 0.356 0.016 0.624 0.004 0.000
#> GSM601971 1 0.3819 0.616 0.700 0.000 0.020 0.000 0.000 0.280
#> GSM601986 3 0.5377 0.411 0.000 0.008 0.540 0.368 0.004 0.080
#> GSM601996 2 0.2966 0.852 0.000 0.864 0.020 0.044 0.072 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
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)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
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)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, 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.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")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n time(p) gender(p) k
#> ATC:mclust 118 0.757 0.493 2
#> ATC:mclust 120 0.921 0.565 3
#> ATC:mclust 120 0.248 0.635 4
#> ATC:mclust 119 0.135 0.644 5
#> ATC:mclust 121 0.379 0.538 6
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.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["ATC", "NMF"]
# you can also extract it by
# res = res_list["ATC:NMF"]
A summary of res and all the functions that can be applied to it:
res
#> A 'ConsensusPartition' object with k = 2, 3, 4, 5, 6.
#> On a matrix with 21512 rows and 125 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'NMF' method.
#> Performed in total 1250 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_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
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, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.987 0.994 0.5041 0.496 0.496
#> 3 3 0.593 0.639 0.818 0.2802 0.818 0.646
#> 4 4 0.458 0.385 0.619 0.1417 0.791 0.496
#> 5 5 0.526 0.484 0.706 0.0593 0.843 0.508
#> 6 6 0.540 0.413 0.640 0.0368 0.905 0.648
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 2
Following shows 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 class
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
#> GSM601872 2 0.0000 0.991 0.000 1.000
#> GSM601882 2 0.0000 0.991 0.000 1.000
#> GSM601887 1 0.0000 0.996 1.000 0.000
#> GSM601892 1 0.0000 0.996 1.000 0.000
#> GSM601897 1 0.0000 0.996 1.000 0.000
#> GSM601902 2 0.0000 0.991 0.000 1.000
#> GSM601912 1 0.6887 0.773 0.816 0.184
#> GSM601927 1 0.0000 0.996 1.000 0.000
#> GSM601932 2 0.0000 0.991 0.000 1.000
#> GSM601937 2 0.0000 0.991 0.000 1.000
#> GSM601942 2 0.0000 0.991 0.000 1.000
#> GSM601947 2 0.0000 0.991 0.000 1.000
#> GSM601957 1 0.0000 0.996 1.000 0.000
#> GSM601972 2 0.0000 0.991 0.000 1.000
#> GSM601977 2 0.0000 0.991 0.000 1.000
#> GSM601987 2 0.0000 0.991 0.000 1.000
#> GSM601877 1 0.0000 0.996 1.000 0.000
#> GSM601907 2 0.0000 0.991 0.000 1.000
#> GSM601917 2 0.0000 0.991 0.000 1.000
#> GSM601922 2 0.0000 0.991 0.000 1.000
#> GSM601952 2 0.0000 0.991 0.000 1.000
#> GSM601962 1 0.2236 0.961 0.964 0.036
#> GSM601967 1 0.0000 0.996 1.000 0.000
#> GSM601982 2 0.0000 0.991 0.000 1.000
#> GSM601992 2 0.0000 0.991 0.000 1.000
#> GSM601873 2 0.0000 0.991 0.000 1.000
#> GSM601883 2 0.0000 0.991 0.000 1.000
#> GSM601888 1 0.0000 0.996 1.000 0.000
#> GSM601893 1 0.0000 0.996 1.000 0.000
#> GSM601898 1 0.0000 0.996 1.000 0.000
#> GSM601903 2 0.0000 0.991 0.000 1.000
#> GSM601913 1 0.0000 0.996 1.000 0.000
#> GSM601928 1 0.0000 0.996 1.000 0.000
#> GSM601933 2 0.0000 0.991 0.000 1.000
#> GSM601938 2 0.0000 0.991 0.000 1.000
#> GSM601943 2 0.0000 0.991 0.000 1.000
#> GSM601948 1 0.0000 0.996 1.000 0.000
#> GSM601958 1 0.0000 0.996 1.000 0.000
#> GSM601973 2 0.0000 0.991 0.000 1.000
#> GSM601978 2 0.0000 0.991 0.000 1.000
#> GSM601988 2 0.0000 0.991 0.000 1.000
#> GSM601878 1 0.0000 0.996 1.000 0.000
#> GSM601908 2 0.0000 0.991 0.000 1.000
#> GSM601918 2 0.0000 0.991 0.000 1.000
#> GSM601923 1 0.0000 0.996 1.000 0.000
#> GSM601953 2 0.0000 0.991 0.000 1.000
#> GSM601963 1 0.0000 0.996 1.000 0.000
#> GSM601968 1 0.0000 0.996 1.000 0.000
#> GSM601983 1 0.0000 0.996 1.000 0.000
#> GSM601993 2 0.0000 0.991 0.000 1.000
#> GSM601874 2 0.0000 0.991 0.000 1.000
#> GSM601884 2 0.0000 0.991 0.000 1.000
#> GSM601889 1 0.0000 0.996 1.000 0.000
#> GSM601894 1 0.0000 0.996 1.000 0.000
#> GSM601899 1 0.0000 0.996 1.000 0.000
#> GSM601904 2 0.0000 0.991 0.000 1.000
#> GSM601914 1 0.0000 0.996 1.000 0.000
#> GSM601929 1 0.0000 0.996 1.000 0.000
#> GSM601934 2 0.0000 0.991 0.000 1.000
#> GSM601939 1 0.0000 0.996 1.000 0.000
#> GSM601944 2 0.0000 0.991 0.000 1.000
#> GSM601949 1 0.0000 0.996 1.000 0.000
#> GSM601959 1 0.0000 0.996 1.000 0.000
#> GSM601974 2 0.1414 0.973 0.020 0.980
#> GSM601979 2 0.0000 0.991 0.000 1.000
#> GSM601989 1 0.0000 0.996 1.000 0.000
#> GSM601879 1 0.0000 0.996 1.000 0.000
#> GSM601909 1 0.0000 0.996 1.000 0.000
#> GSM601919 2 0.0000 0.991 0.000 1.000
#> GSM601924 1 0.0000 0.996 1.000 0.000
#> GSM601954 2 0.0000 0.991 0.000 1.000
#> GSM601964 1 0.0000 0.996 1.000 0.000
#> GSM601969 1 0.0000 0.996 1.000 0.000
#> GSM601984 1 0.0000 0.996 1.000 0.000
#> GSM601994 2 0.0000 0.991 0.000 1.000
#> GSM601875 2 0.0000 0.991 0.000 1.000
#> GSM601885 2 0.0000 0.991 0.000 1.000
#> GSM601890 1 0.0000 0.996 1.000 0.000
#> GSM601895 1 0.0000 0.996 1.000 0.000
#> GSM601900 1 0.0000 0.996 1.000 0.000
#> GSM601905 2 0.0000 0.991 0.000 1.000
#> GSM601915 1 0.0000 0.996 1.000 0.000
#> GSM601930 1 0.0000 0.996 1.000 0.000
#> GSM601935 2 0.0000 0.991 0.000 1.000
#> GSM601940 1 0.0000 0.996 1.000 0.000
#> GSM601945 2 0.0000 0.991 0.000 1.000
#> GSM601950 1 0.0000 0.996 1.000 0.000
#> GSM601960 1 0.0000 0.996 1.000 0.000
#> GSM601975 2 0.0000 0.991 0.000 1.000
#> GSM601980 2 0.0000 0.991 0.000 1.000
#> GSM601990 1 0.0000 0.996 1.000 0.000
#> GSM601880 1 0.0000 0.996 1.000 0.000
#> GSM601910 1 0.0000 0.996 1.000 0.000
#> GSM601920 2 0.0000 0.991 0.000 1.000
#> GSM601925 1 0.0000 0.996 1.000 0.000
#> GSM601955 2 0.0000 0.991 0.000 1.000
#> GSM601965 1 0.0376 0.992 0.996 0.004
#> GSM601970 1 0.0000 0.996 1.000 0.000
#> GSM601985 1 0.0000 0.996 1.000 0.000
#> GSM601995 2 0.0000 0.991 0.000 1.000
#> GSM601876 1 0.0000 0.996 1.000 0.000
#> GSM601886 2 0.0000 0.991 0.000 1.000
#> GSM601891 1 0.0000 0.996 1.000 0.000
#> GSM601896 1 0.0000 0.996 1.000 0.000
#> GSM601901 2 0.0000 0.991 0.000 1.000
#> GSM601906 2 0.6887 0.782 0.184 0.816
#> GSM601916 2 0.0000 0.991 0.000 1.000
#> GSM601931 1 0.0000 0.996 1.000 0.000
#> GSM601936 2 0.0000 0.991 0.000 1.000
#> GSM601941 2 0.0000 0.991 0.000 1.000
#> GSM601946 1 0.0000 0.996 1.000 0.000
#> GSM601951 1 0.0000 0.996 1.000 0.000
#> GSM601961 2 0.5842 0.843 0.140 0.860
#> GSM601976 2 0.0000 0.991 0.000 1.000
#> GSM601981 2 0.0000 0.991 0.000 1.000
#> GSM601991 1 0.1633 0.973 0.976 0.024
#> GSM601881 1 0.0000 0.996 1.000 0.000
#> GSM601911 2 0.3274 0.934 0.060 0.940
#> GSM601921 2 0.0000 0.991 0.000 1.000
#> GSM601926 1 0.0000 0.996 1.000 0.000
#> GSM601956 2 0.0000 0.991 0.000 1.000
#> GSM601966 2 0.0000 0.991 0.000 1.000
#> GSM601971 1 0.0000 0.996 1.000 0.000
#> GSM601986 2 0.5946 0.838 0.144 0.856
#> GSM601996 2 0.0000 0.991 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM601872 2 0.0983 0.65195 0.004 0.980 0.016
#> GSM601882 2 0.6168 0.30395 0.000 0.588 0.412
#> GSM601887 1 0.3091 0.85737 0.912 0.072 0.016
#> GSM601892 1 0.3445 0.84854 0.896 0.088 0.016
#> GSM601897 1 0.1337 0.88036 0.972 0.016 0.012
#> GSM601902 2 0.4178 0.65046 0.000 0.828 0.172
#> GSM601912 1 0.6937 0.54085 0.680 0.048 0.272
#> GSM601927 1 0.2356 0.87353 0.928 0.000 0.072
#> GSM601932 3 0.6235 0.27600 0.000 0.436 0.564
#> GSM601937 3 0.1964 0.68726 0.000 0.056 0.944
#> GSM601942 2 0.6244 0.21087 0.000 0.560 0.440
#> GSM601947 2 0.0237 0.64038 0.000 0.996 0.004
#> GSM601957 1 0.1337 0.88036 0.972 0.016 0.012
#> GSM601972 2 0.1289 0.66221 0.000 0.968 0.032
#> GSM601977 2 0.6307 -0.00475 0.000 0.512 0.488
#> GSM601987 2 0.4504 0.63975 0.000 0.804 0.196
#> GSM601877 1 0.1337 0.88036 0.972 0.016 0.012
#> GSM601907 2 0.0424 0.64920 0.000 0.992 0.008
#> GSM601917 3 0.3686 0.68283 0.000 0.140 0.860
#> GSM601922 2 0.5216 0.58574 0.000 0.740 0.260
#> GSM601952 3 0.6235 0.27592 0.000 0.436 0.564
#> GSM601962 3 0.2448 0.59541 0.076 0.000 0.924
#> GSM601967 1 0.3359 0.85081 0.900 0.084 0.016
#> GSM601982 2 0.4452 0.64313 0.000 0.808 0.192
#> GSM601992 3 0.6267 0.22296 0.000 0.452 0.548
#> GSM601873 3 0.6154 0.34800 0.000 0.408 0.592
#> GSM601883 2 0.6180 0.29035 0.000 0.584 0.416
#> GSM601888 2 0.6948 -0.25881 0.472 0.512 0.016
#> GSM601893 1 0.0983 0.88158 0.980 0.016 0.004
#> GSM601898 1 0.0000 0.88332 1.000 0.000 0.000
#> GSM601903 2 0.6062 0.37770 0.000 0.616 0.384
#> GSM601913 1 0.6062 0.59214 0.616 0.000 0.384
#> GSM601928 1 0.5363 0.72977 0.724 0.000 0.276
#> GSM601933 2 0.5905 0.44563 0.000 0.648 0.352
#> GSM601938 3 0.6126 0.36528 0.000 0.400 0.600
#> GSM601943 2 0.6026 0.39726 0.000 0.624 0.376
#> GSM601948 1 0.6587 0.57327 0.632 0.352 0.016
#> GSM601958 1 0.0000 0.88332 1.000 0.000 0.000
#> GSM601973 3 0.6095 0.38097 0.000 0.392 0.608
#> GSM601978 2 0.1289 0.66219 0.000 0.968 0.032
#> GSM601988 3 0.2537 0.69089 0.000 0.080 0.920
#> GSM601878 1 0.1337 0.88036 0.972 0.016 0.012
#> GSM601908 2 0.1411 0.66327 0.000 0.964 0.036
#> GSM601918 2 0.4931 0.61254 0.000 0.768 0.232
#> GSM601923 1 0.2165 0.87608 0.936 0.000 0.064
#> GSM601953 2 0.0983 0.62617 0.004 0.980 0.016
#> GSM601963 1 0.5058 0.76256 0.756 0.000 0.244
#> GSM601968 1 0.4068 0.82826 0.864 0.120 0.016
#> GSM601983 1 0.2537 0.87089 0.920 0.000 0.080
#> GSM601993 3 0.4796 0.62468 0.000 0.220 0.780
#> GSM601874 2 0.0000 0.64349 0.000 1.000 0.000
#> GSM601884 2 0.4504 0.64058 0.000 0.804 0.196
#> GSM601889 1 0.0000 0.88332 1.000 0.000 0.000
#> GSM601894 1 0.0592 0.88308 0.988 0.000 0.012
#> GSM601899 1 0.5763 0.71515 0.740 0.244 0.016
#> GSM601904 3 0.3412 0.68728 0.000 0.124 0.876
#> GSM601914 1 0.4702 0.78952 0.788 0.000 0.212
#> GSM601929 1 0.0000 0.88332 1.000 0.000 0.000
#> GSM601934 2 0.6095 0.35966 0.000 0.608 0.392
#> GSM601939 1 0.1643 0.88025 0.956 0.000 0.044
#> GSM601944 2 0.6225 0.24066 0.000 0.568 0.432
#> GSM601949 1 0.6587 0.57269 0.632 0.352 0.016
#> GSM601959 1 0.1337 0.88036 0.972 0.016 0.012
#> GSM601974 2 0.6180 0.28627 0.000 0.584 0.416
#> GSM601979 2 0.1289 0.66219 0.000 0.968 0.032
#> GSM601989 1 0.0237 0.88317 0.996 0.004 0.000
#> GSM601879 1 0.2902 0.86214 0.920 0.064 0.016
#> GSM601909 1 0.1337 0.88036 0.972 0.016 0.012
#> GSM601919 2 0.0892 0.65646 0.000 0.980 0.020
#> GSM601924 1 0.0237 0.88330 0.996 0.000 0.004
#> GSM601954 2 0.1491 0.61477 0.016 0.968 0.016
#> GSM601964 1 0.6045 0.59821 0.620 0.000 0.380
#> GSM601969 1 0.6897 0.41518 0.548 0.436 0.016
#> GSM601984 1 0.1860 0.88063 0.948 0.000 0.052
#> GSM601994 2 0.6309 -0.05342 0.000 0.504 0.496
#> GSM601875 2 0.2796 0.66350 0.000 0.908 0.092
#> GSM601885 2 0.6126 0.33952 0.000 0.600 0.400
#> GSM601890 1 0.5461 0.74546 0.768 0.216 0.016
#> GSM601895 1 0.2066 0.87716 0.940 0.000 0.060
#> GSM601900 1 0.2261 0.87598 0.932 0.000 0.068
#> GSM601905 3 0.6286 0.17748 0.000 0.464 0.536
#> GSM601915 1 0.4796 0.78306 0.780 0.000 0.220
#> GSM601930 1 0.6008 0.60950 0.628 0.000 0.372
#> GSM601935 3 0.0747 0.66737 0.000 0.016 0.984
#> GSM601940 1 0.0000 0.88332 1.000 0.000 0.000
#> GSM601945 2 0.1529 0.66418 0.000 0.960 0.040
#> GSM601950 1 0.2031 0.87443 0.952 0.032 0.016
#> GSM601960 1 0.3192 0.85575 0.888 0.000 0.112
#> GSM601975 2 0.4062 0.65294 0.000 0.836 0.164
#> GSM601980 3 0.4555 0.64354 0.000 0.200 0.800
#> GSM601990 3 0.3752 0.51579 0.144 0.000 0.856
#> GSM601880 1 0.1289 0.88153 0.968 0.000 0.032
#> GSM601910 1 0.1482 0.87936 0.968 0.020 0.012
#> GSM601920 3 0.3816 0.68003 0.000 0.148 0.852
#> GSM601925 1 0.2448 0.87228 0.924 0.000 0.076
#> GSM601955 3 0.1289 0.67806 0.000 0.032 0.968
#> GSM601965 1 0.0424 0.88364 0.992 0.000 0.008
#> GSM601970 1 0.1337 0.88036 0.972 0.016 0.012
#> GSM601985 1 0.3686 0.83975 0.860 0.000 0.140
#> GSM601995 3 0.0829 0.66418 0.004 0.012 0.984
#> GSM601876 1 0.4750 0.78689 0.784 0.000 0.216
#> GSM601886 3 0.3816 0.67984 0.000 0.148 0.852
#> GSM601891 1 0.6543 0.58256 0.640 0.344 0.016
#> GSM601896 1 0.1964 0.87812 0.944 0.000 0.056
#> GSM601901 2 0.5178 0.58994 0.000 0.744 0.256
#> GSM601906 3 0.2063 0.68081 0.008 0.044 0.948
#> GSM601916 3 0.6192 0.31905 0.000 0.420 0.580
#> GSM601931 1 0.2537 0.87114 0.920 0.000 0.080
#> GSM601936 3 0.1163 0.67577 0.000 0.028 0.972
#> GSM601941 2 0.5678 0.50566 0.000 0.684 0.316
#> GSM601946 1 0.5254 0.74248 0.736 0.000 0.264
#> GSM601951 2 0.6798 -0.03983 0.400 0.584 0.016
#> GSM601961 2 0.4723 0.45484 0.160 0.824 0.016
#> GSM601976 2 0.5098 0.59907 0.000 0.752 0.248
#> GSM601981 2 0.1860 0.66506 0.000 0.948 0.052
#> GSM601991 3 0.1964 0.61613 0.056 0.000 0.944
#> GSM601881 1 0.1643 0.88010 0.956 0.000 0.044
#> GSM601911 3 0.2625 0.69242 0.000 0.084 0.916
#> GSM601921 3 0.6267 0.22354 0.000 0.452 0.548
#> GSM601926 1 0.2261 0.87487 0.932 0.000 0.068
#> GSM601956 2 0.1529 0.66418 0.000 0.960 0.040
#> GSM601966 2 0.4346 0.64590 0.000 0.816 0.184
#> GSM601971 1 0.1774 0.87706 0.960 0.024 0.016
#> GSM601986 3 0.2774 0.69073 0.008 0.072 0.920
#> GSM601996 3 0.6309 0.01345 0.000 0.500 0.500
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM601872 2 0.2060 0.44813 0.016 0.932 0.052 0.000
#> GSM601882 2 0.7870 0.11207 0.000 0.392 0.304 0.304
#> GSM601887 1 0.4843 0.38880 0.604 0.396 0.000 0.000
#> GSM601892 1 0.4972 0.29761 0.544 0.456 0.000 0.000
#> GSM601897 1 0.7332 0.29398 0.480 0.356 0.164 0.000
#> GSM601902 4 0.2469 0.56602 0.000 0.108 0.000 0.892
#> GSM601912 3 0.6915 0.38508 0.212 0.196 0.592 0.000
#> GSM601927 1 0.7238 0.38951 0.524 0.000 0.172 0.304
#> GSM601932 4 0.7369 0.21965 0.000 0.292 0.196 0.512
#> GSM601937 3 0.4285 0.56633 0.000 0.156 0.804 0.040
#> GSM601942 3 0.5151 0.29062 0.000 0.464 0.532 0.004
#> GSM601947 4 0.4422 0.43428 0.000 0.256 0.008 0.736
#> GSM601957 1 0.0895 0.68836 0.976 0.020 0.000 0.004
#> GSM601972 4 0.3937 0.50902 0.000 0.188 0.012 0.800
#> GSM601977 3 0.6597 0.28300 0.000 0.372 0.540 0.088
#> GSM601987 2 0.7576 0.11309 0.000 0.452 0.204 0.344
#> GSM601877 4 0.5697 -0.23840 0.468 0.008 0.012 0.512
#> GSM601907 2 0.2179 0.49301 0.000 0.924 0.012 0.064
#> GSM601917 4 0.5463 0.49263 0.000 0.052 0.256 0.692
#> GSM601922 4 0.1247 0.57877 0.004 0.016 0.012 0.968
#> GSM601952 3 0.7878 -0.00786 0.000 0.324 0.384 0.292
#> GSM601962 3 0.3245 0.55532 0.100 0.028 0.872 0.000
#> GSM601967 1 0.4889 0.44436 0.636 0.360 0.000 0.004
#> GSM601982 2 0.5489 0.33826 0.000 0.700 0.240 0.060
#> GSM601992 4 0.7786 0.07858 0.000 0.308 0.268 0.424
#> GSM601873 3 0.4889 0.43619 0.000 0.360 0.636 0.004
#> GSM601883 2 0.7877 0.02282 0.000 0.364 0.280 0.356
#> GSM601888 2 0.6171 0.15714 0.284 0.644 0.008 0.064
#> GSM601893 1 0.5901 0.49816 0.652 0.280 0.068 0.000
#> GSM601898 1 0.0779 0.68749 0.980 0.016 0.004 0.000
#> GSM601903 4 0.1854 0.58261 0.000 0.048 0.012 0.940
#> GSM601913 3 0.4877 -0.09115 0.408 0.000 0.592 0.000
#> GSM601928 1 0.6542 0.52679 0.636 0.000 0.196 0.168
#> GSM601933 2 0.7806 0.05006 0.000 0.392 0.252 0.356
#> GSM601938 4 0.7894 -0.01927 0.000 0.292 0.344 0.364
#> GSM601943 3 0.5163 0.26741 0.000 0.480 0.516 0.004
#> GSM601948 1 0.6820 0.32762 0.528 0.080 0.008 0.384
#> GSM601958 1 0.0524 0.68769 0.988 0.008 0.004 0.000
#> GSM601973 4 0.5209 0.55155 0.000 0.104 0.140 0.756
#> GSM601978 2 0.3486 0.50277 0.000 0.864 0.044 0.092
#> GSM601988 3 0.4452 0.56161 0.000 0.156 0.796 0.048
#> GSM601878 1 0.5250 0.49338 0.640 0.004 0.012 0.344
#> GSM601908 2 0.5256 0.44817 0.000 0.732 0.064 0.204
#> GSM601918 4 0.4898 0.46924 0.000 0.260 0.024 0.716
#> GSM601923 1 0.6856 0.45700 0.576 0.000 0.140 0.284
#> GSM601953 2 0.2924 0.46902 0.016 0.884 0.000 0.100
#> GSM601963 1 0.5060 0.38029 0.584 0.000 0.412 0.004
#> GSM601968 1 0.4992 0.25270 0.524 0.476 0.000 0.000
#> GSM601983 1 0.4661 0.45063 0.652 0.000 0.348 0.000
#> GSM601993 3 0.7658 0.17071 0.000 0.236 0.456 0.308
#> GSM601874 2 0.2480 0.49793 0.000 0.904 0.008 0.088
#> GSM601884 2 0.6816 0.34164 0.000 0.604 0.212 0.184
#> GSM601889 1 0.0779 0.68749 0.980 0.016 0.004 0.000
#> GSM601894 1 0.1489 0.68290 0.952 0.004 0.044 0.000
#> GSM601899 2 0.5137 -0.15512 0.452 0.544 0.004 0.000
#> GSM601904 4 0.4188 0.50750 0.000 0.004 0.244 0.752
#> GSM601914 1 0.5147 0.26661 0.536 0.004 0.460 0.000
#> GSM601929 4 0.6052 -0.08845 0.396 0.000 0.048 0.556
#> GSM601934 2 0.7856 0.07149 0.000 0.388 0.336 0.276
#> GSM601939 1 0.2089 0.68367 0.932 0.000 0.048 0.020
#> GSM601944 2 0.7684 0.06173 0.000 0.420 0.360 0.220
#> GSM601949 1 0.7727 0.26689 0.452 0.364 0.008 0.176
#> GSM601959 1 0.1109 0.68808 0.968 0.028 0.000 0.004
#> GSM601974 4 0.2261 0.58040 0.008 0.036 0.024 0.932
#> GSM601979 2 0.5254 0.42801 0.000 0.724 0.056 0.220
#> GSM601989 1 0.1398 0.68404 0.956 0.040 0.004 0.000
#> GSM601879 4 0.6296 -0.20303 0.452 0.040 0.008 0.500
#> GSM601909 1 0.3768 0.61296 0.808 0.184 0.008 0.000
#> GSM601919 4 0.2918 0.54659 0.000 0.116 0.008 0.876
#> GSM601924 1 0.4225 0.63813 0.792 0.000 0.024 0.184
#> GSM601954 2 0.5272 0.21114 0.004 0.608 0.008 0.380
#> GSM601964 3 0.5070 -0.09115 0.416 0.004 0.580 0.000
#> GSM601969 2 0.5764 -0.06111 0.404 0.568 0.004 0.024
#> GSM601984 1 0.3550 0.66908 0.860 0.000 0.096 0.044
#> GSM601994 4 0.7753 0.07597 0.000 0.312 0.256 0.432
#> GSM601875 2 0.4673 0.46367 0.000 0.792 0.132 0.076
#> GSM601885 2 0.7745 0.06638 0.000 0.412 0.352 0.236
#> GSM601890 2 0.5158 -0.19325 0.472 0.524 0.004 0.000
#> GSM601895 1 0.5339 0.36582 0.600 0.016 0.384 0.000
#> GSM601900 1 0.5459 0.26943 0.552 0.016 0.432 0.000
#> GSM601905 4 0.2480 0.58364 0.000 0.008 0.088 0.904
#> GSM601915 1 0.5143 0.34488 0.540 0.000 0.456 0.004
#> GSM601930 1 0.7449 0.35556 0.500 0.000 0.208 0.292
#> GSM601935 3 0.1284 0.57666 0.000 0.012 0.964 0.024
#> GSM601940 1 0.0336 0.68828 0.992 0.000 0.000 0.008
#> GSM601945 2 0.4235 0.49213 0.000 0.824 0.084 0.092
#> GSM601950 1 0.3001 0.68184 0.896 0.036 0.004 0.064
#> GSM601960 1 0.4193 0.55529 0.732 0.000 0.268 0.000
#> GSM601975 4 0.3351 0.55042 0.000 0.148 0.008 0.844
#> GSM601980 3 0.6432 0.43312 0.000 0.236 0.636 0.128
#> GSM601990 3 0.3182 0.49822 0.132 0.004 0.860 0.004
#> GSM601880 1 0.6532 0.43378 0.572 0.000 0.092 0.336
#> GSM601910 1 0.6926 0.29530 0.496 0.392 0.112 0.000
#> GSM601920 4 0.4198 0.51586 0.004 0.004 0.224 0.768
#> GSM601925 1 0.7410 0.33342 0.488 0.000 0.184 0.328
#> GSM601955 3 0.3257 0.56990 0.000 0.152 0.844 0.004
#> GSM601965 1 0.1486 0.68823 0.960 0.008 0.024 0.008
#> GSM601970 1 0.1824 0.68004 0.936 0.060 0.004 0.000
#> GSM601985 1 0.3583 0.64567 0.816 0.000 0.180 0.004
#> GSM601995 3 0.1677 0.57076 0.000 0.012 0.948 0.040
#> GSM601876 1 0.3486 0.64711 0.812 0.000 0.188 0.000
#> GSM601886 4 0.6658 0.14082 0.000 0.084 0.444 0.472
#> GSM601891 2 0.4877 -0.05285 0.408 0.592 0.000 0.000
#> GSM601896 1 0.3051 0.67441 0.884 0.000 0.088 0.028
#> GSM601901 4 0.6748 0.29008 0.000 0.328 0.112 0.560
#> GSM601906 4 0.4874 0.50428 0.056 0.000 0.180 0.764
#> GSM601916 4 0.6646 0.41116 0.000 0.224 0.156 0.620
#> GSM601931 1 0.6567 0.50211 0.616 0.000 0.128 0.256
#> GSM601936 3 0.2578 0.58355 0.000 0.052 0.912 0.036
#> GSM601941 4 0.5074 0.48259 0.000 0.236 0.040 0.724
#> GSM601946 1 0.4086 0.63277 0.776 0.000 0.216 0.008
#> GSM601951 4 0.6851 0.04693 0.356 0.088 0.008 0.548
#> GSM601961 2 0.5950 0.36104 0.100 0.708 0.008 0.184
#> GSM601976 4 0.1339 0.57595 0.008 0.024 0.004 0.964
#> GSM601981 2 0.5417 0.46664 0.000 0.732 0.088 0.180
#> GSM601991 3 0.2891 0.55766 0.080 0.020 0.896 0.004
#> GSM601881 1 0.6618 0.49054 0.604 0.000 0.124 0.272
#> GSM601911 3 0.6961 0.21941 0.000 0.136 0.548 0.316
#> GSM601921 4 0.2412 0.58379 0.000 0.008 0.084 0.908
#> GSM601926 1 0.6157 0.54569 0.660 0.000 0.108 0.232
#> GSM601956 2 0.3687 0.49104 0.000 0.856 0.080 0.064
#> GSM601966 4 0.6928 0.19121 0.000 0.372 0.116 0.512
#> GSM601971 1 0.2781 0.68393 0.904 0.016 0.008 0.072
#> GSM601986 3 0.6933 -0.08466 0.016 0.068 0.496 0.420
#> GSM601996 4 0.7733 0.09312 0.000 0.304 0.256 0.440
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM601872 1 0.490 0.41783 0.732 0.164 0.000 0.008 0.096
#> GSM601882 2 0.532 0.67456 0.044 0.732 0.000 0.104 0.120
#> GSM601887 3 0.436 0.14184 0.412 0.000 0.584 0.004 0.000
#> GSM601892 3 0.505 0.38393 0.284 0.032 0.668 0.012 0.004
#> GSM601897 3 0.697 -0.12779 0.360 0.008 0.380 0.000 0.252
#> GSM601902 4 0.385 0.57559 0.032 0.172 0.000 0.792 0.004
#> GSM601912 2 0.845 -0.17479 0.172 0.344 0.304 0.004 0.176
#> GSM601927 3 0.570 0.05069 0.008 0.000 0.500 0.432 0.060
#> GSM601932 2 0.618 0.47774 0.040 0.548 0.000 0.352 0.060
#> GSM601937 5 0.517 0.28532 0.004 0.436 0.000 0.032 0.528
#> GSM601942 5 0.561 0.65218 0.192 0.128 0.000 0.012 0.668
#> GSM601947 4 0.488 0.16732 0.444 0.024 0.000 0.532 0.000
#> GSM601957 3 0.160 0.67589 0.048 0.000 0.940 0.012 0.000
#> GSM601972 4 0.491 0.55011 0.204 0.080 0.000 0.712 0.004
#> GSM601977 5 0.600 0.61163 0.120 0.220 0.000 0.024 0.636
#> GSM601987 2 0.365 0.73089 0.036 0.828 0.000 0.124 0.012
#> GSM601877 4 0.485 0.36882 0.028 0.000 0.312 0.652 0.008
#> GSM601907 2 0.486 0.35889 0.364 0.608 0.000 0.024 0.004
#> GSM601917 4 0.558 0.44774 0.020 0.060 0.000 0.628 0.292
#> GSM601922 4 0.260 0.63165 0.060 0.040 0.000 0.896 0.004
#> GSM601952 5 0.668 0.49832 0.084 0.256 0.000 0.080 0.580
#> GSM601962 5 0.206 0.71952 0.008 0.052 0.016 0.000 0.924
#> GSM601967 3 0.384 0.44470 0.280 0.000 0.716 0.004 0.000
#> GSM601982 2 0.603 0.35274 0.344 0.560 0.000 0.024 0.072
#> GSM601992 2 0.354 0.70486 0.008 0.812 0.000 0.164 0.016
#> GSM601873 5 0.462 0.68701 0.148 0.108 0.000 0.000 0.744
#> GSM601883 2 0.430 0.71830 0.020 0.792 0.000 0.132 0.056
#> GSM601888 1 0.543 0.56563 0.708 0.056 0.192 0.040 0.004
#> GSM601893 3 0.542 0.45191 0.244 0.024 0.680 0.008 0.044
#> GSM601898 3 0.184 0.67679 0.052 0.000 0.932 0.008 0.008
#> GSM601903 4 0.246 0.62245 0.008 0.100 0.000 0.888 0.004
#> GSM601913 3 0.520 0.26097 0.012 0.016 0.544 0.004 0.424
#> GSM601928 3 0.616 0.38946 0.012 0.000 0.588 0.260 0.140
#> GSM601933 2 0.305 0.73088 0.044 0.868 0.000 0.084 0.004
#> GSM601938 2 0.383 0.70761 0.000 0.796 0.000 0.156 0.048
#> GSM601943 5 0.593 0.60708 0.208 0.176 0.000 0.004 0.612
#> GSM601948 1 0.586 0.01898 0.496 0.000 0.084 0.416 0.004
#> GSM601958 3 0.133 0.68213 0.032 0.000 0.956 0.004 0.008
#> GSM601973 4 0.353 0.61539 0.004 0.104 0.000 0.836 0.056
#> GSM601978 1 0.511 0.00544 0.564 0.400 0.000 0.032 0.004
#> GSM601988 5 0.481 0.55385 0.000 0.312 0.004 0.032 0.652
#> GSM601878 3 0.511 0.22584 0.024 0.000 0.548 0.420 0.008
#> GSM601908 2 0.341 0.68130 0.144 0.824 0.000 0.032 0.000
#> GSM601918 4 0.530 0.50127 0.120 0.192 0.000 0.684 0.004
#> GSM601923 3 0.549 0.03318 0.008 0.000 0.480 0.468 0.044
#> GSM601953 1 0.375 0.49040 0.820 0.116 0.000 0.060 0.004
#> GSM601963 5 0.369 0.52124 0.004 0.000 0.256 0.000 0.740
#> GSM601968 1 0.465 0.17135 0.520 0.000 0.468 0.012 0.000
#> GSM601983 5 0.451 0.29635 0.016 0.000 0.356 0.000 0.628
#> GSM601993 2 0.439 0.69523 0.004 0.772 0.004 0.160 0.060
#> GSM601874 1 0.511 0.08692 0.588 0.372 0.000 0.036 0.004
#> GSM601884 2 0.702 0.27050 0.412 0.428 0.000 0.064 0.096
#> GSM601889 3 0.144 0.67887 0.044 0.000 0.948 0.004 0.004
#> GSM601894 3 0.175 0.68232 0.028 0.000 0.936 0.000 0.036
#> GSM601899 1 0.491 0.41135 0.612 0.028 0.356 0.000 0.004
#> GSM601904 4 0.453 0.61119 0.008 0.096 0.016 0.792 0.088
#> GSM601914 5 0.403 0.42042 0.004 0.000 0.316 0.000 0.680
#> GSM601929 4 0.441 0.44881 0.008 0.000 0.276 0.700 0.016
#> GSM601934 2 0.207 0.71927 0.044 0.924 0.000 0.028 0.004
#> GSM601939 3 0.106 0.68458 0.004 0.000 0.968 0.008 0.020
#> GSM601944 2 0.254 0.70927 0.056 0.904 0.000 0.020 0.020
#> GSM601949 1 0.586 0.50000 0.632 0.008 0.200 0.160 0.000
#> GSM601959 3 0.238 0.67629 0.060 0.008 0.912 0.008 0.012
#> GSM601974 4 0.502 0.50511 0.212 0.008 0.004 0.712 0.064
#> GSM601979 2 0.472 0.68959 0.180 0.728 0.000 0.092 0.000
#> GSM601989 3 0.223 0.67708 0.076 0.012 0.908 0.000 0.004
#> GSM601879 4 0.493 0.23236 0.024 0.000 0.388 0.584 0.004
#> GSM601909 3 0.275 0.64258 0.112 0.008 0.872 0.008 0.000
#> GSM601919 4 0.379 0.55583 0.192 0.028 0.000 0.780 0.000
#> GSM601924 3 0.361 0.58390 0.000 0.000 0.764 0.228 0.008
#> GSM601954 1 0.473 0.32753 0.676 0.028 0.008 0.288 0.000
#> GSM601964 5 0.261 0.67787 0.020 0.000 0.088 0.004 0.888
#> GSM601969 1 0.557 0.18622 0.492 0.024 0.460 0.020 0.004
#> GSM601984 3 0.468 0.62238 0.020 0.084 0.796 0.076 0.024
#> GSM601994 2 0.307 0.71694 0.004 0.844 0.000 0.140 0.012
#> GSM601875 2 0.340 0.63262 0.168 0.812 0.000 0.020 0.000
#> GSM601885 2 0.389 0.72007 0.056 0.836 0.000 0.060 0.048
#> GSM601890 1 0.453 0.38697 0.612 0.000 0.376 0.008 0.004
#> GSM601895 3 0.372 0.62186 0.024 0.004 0.796 0.000 0.176
#> GSM601900 3 0.462 0.58326 0.028 0.024 0.736 0.000 0.212
#> GSM601905 4 0.436 0.51200 0.004 0.228 0.012 0.740 0.016
#> GSM601915 3 0.452 0.32364 0.004 0.004 0.576 0.000 0.416
#> GSM601930 4 0.624 0.23475 0.012 0.000 0.368 0.512 0.108
#> GSM601935 5 0.191 0.71940 0.000 0.060 0.000 0.016 0.924
#> GSM601940 3 0.127 0.68357 0.024 0.000 0.960 0.004 0.012
#> GSM601945 2 0.347 0.64928 0.148 0.824 0.000 0.020 0.008
#> GSM601950 3 0.228 0.67639 0.044 0.000 0.916 0.032 0.008
#> GSM601960 3 0.423 0.51693 0.012 0.000 0.676 0.000 0.312
#> GSM601975 4 0.373 0.60455 0.056 0.120 0.000 0.820 0.004
#> GSM601980 5 0.531 0.60051 0.040 0.256 0.000 0.032 0.672
#> GSM601990 5 0.405 0.66691 0.004 0.052 0.120 0.012 0.812
#> GSM601880 4 0.510 -0.02380 0.012 0.000 0.476 0.496 0.016
#> GSM601910 3 0.630 0.26540 0.280 0.084 0.600 0.012 0.024
#> GSM601920 4 0.587 0.37190 0.016 0.300 0.032 0.620 0.032
#> GSM601925 4 0.536 0.05755 0.008 0.000 0.460 0.496 0.036
#> GSM601955 5 0.258 0.71712 0.036 0.056 0.000 0.008 0.900
#> GSM601965 3 0.507 0.57887 0.024 0.152 0.756 0.040 0.028
#> GSM601970 3 0.376 0.61761 0.148 0.000 0.812 0.012 0.028
#> GSM601985 3 0.245 0.68031 0.012 0.008 0.904 0.004 0.072
#> GSM601995 5 0.144 0.71696 0.004 0.032 0.000 0.012 0.952
#> GSM601876 3 0.240 0.67818 0.016 0.024 0.920 0.012 0.028
#> GSM601886 4 0.710 0.10853 0.012 0.272 0.004 0.444 0.268
#> GSM601891 1 0.576 0.39044 0.564 0.056 0.364 0.012 0.004
#> GSM601896 3 0.198 0.67999 0.016 0.004 0.936 0.024 0.020
#> GSM601901 2 0.581 0.46342 0.080 0.556 0.000 0.356 0.008
#> GSM601906 4 0.491 0.61608 0.012 0.080 0.060 0.784 0.064
#> GSM601916 2 0.538 0.50301 0.020 0.628 0.012 0.320 0.020
#> GSM601931 3 0.456 0.49349 0.008 0.000 0.696 0.272 0.024
#> GSM601936 5 0.664 0.19487 0.008 0.404 0.020 0.100 0.468
#> GSM601941 4 0.529 0.21655 0.052 0.348 0.000 0.596 0.004
#> GSM601946 3 0.335 0.66821 0.016 0.032 0.876 0.032 0.044
#> GSM601951 4 0.507 0.33875 0.344 0.000 0.048 0.608 0.000
#> GSM601961 1 0.424 0.57178 0.820 0.052 0.052 0.072 0.004
#> GSM601976 4 0.250 0.63519 0.012 0.068 0.012 0.904 0.004
#> GSM601981 2 0.471 0.65350 0.212 0.716 0.000 0.072 0.000
#> GSM601991 5 0.213 0.71658 0.004 0.052 0.024 0.000 0.920
#> GSM601881 3 0.482 0.31772 0.008 0.000 0.608 0.368 0.016
#> GSM601911 2 0.619 0.59655 0.024 0.676 0.084 0.176 0.040
#> GSM601921 4 0.551 -0.01627 0.020 0.436 0.012 0.520 0.012
#> GSM601926 3 0.444 0.52773 0.008 0.000 0.716 0.252 0.024
#> GSM601956 2 0.529 0.21718 0.456 0.504 0.000 0.032 0.008
#> GSM601966 2 0.444 0.66843 0.028 0.724 0.000 0.240 0.008
#> GSM601971 3 0.628 0.08492 0.352 0.000 0.504 0.140 0.004
#> GSM601986 2 0.659 0.53012 0.016 0.628 0.108 0.204 0.044
#> GSM601996 2 0.371 0.69376 0.000 0.784 0.000 0.192 0.024
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM601872 6 0.604 0.3523 0.000 0.060 0.160 0.012 NA 0.628
#> GSM601882 2 0.751 0.3592 0.000 0.476 0.056 0.080 NA 0.156
#> GSM601887 1 0.484 0.3215 0.616 0.000 0.016 0.000 NA 0.324
#> GSM601892 1 0.423 0.4289 0.692 0.000 0.004 0.000 NA 0.264
#> GSM601897 1 0.724 0.0118 0.404 0.008 0.136 0.000 NA 0.332
#> GSM601902 4 0.459 0.4887 0.000 0.232 0.000 0.696 NA 0.020
#> GSM601912 1 0.846 -0.1142 0.352 0.244 0.108 0.000 NA 0.160
#> GSM601927 4 0.561 0.1184 0.428 0.000 0.020 0.468 NA 0.000
#> GSM601932 2 0.646 0.4757 0.000 0.584 0.060 0.188 NA 0.020
#> GSM601937 2 0.571 -0.0468 0.000 0.468 0.420 0.024 NA 0.000
#> GSM601942 3 0.443 0.6374 0.000 0.092 0.768 0.000 NA 0.072
#> GSM601947 4 0.571 0.2862 0.000 0.012 0.000 0.536 NA 0.316
#> GSM601957 1 0.181 0.6320 0.920 0.000 0.000 0.000 NA 0.060
#> GSM601972 4 0.533 0.5470 0.000 0.100 0.000 0.692 NA 0.092
#> GSM601977 3 0.653 0.3385 0.000 0.276 0.512 0.000 NA 0.124
#> GSM601987 2 0.409 0.5628 0.000 0.780 0.000 0.024 NA 0.076
#> GSM601877 4 0.427 0.4977 0.248 0.004 0.000 0.704 NA 0.004
#> GSM601907 6 0.571 0.0595 0.000 0.328 0.008 0.000 NA 0.520
#> GSM601917 4 0.668 0.2654 0.000 0.108 0.292 0.504 NA 0.008
#> GSM601922 4 0.324 0.6060 0.000 0.072 0.004 0.852 NA 0.020
#> GSM601952 3 0.644 0.4271 0.000 0.232 0.576 0.032 NA 0.044
#> GSM601962 3 0.483 0.6544 0.040 0.056 0.732 0.012 NA 0.000
#> GSM601967 1 0.376 0.5059 0.740 0.000 0.004 0.000 NA 0.232
#> GSM601982 2 0.759 0.0904 0.000 0.344 0.056 0.036 NA 0.304
#> GSM601992 2 0.221 0.5928 0.000 0.908 0.012 0.032 NA 0.000
#> GSM601873 3 0.651 0.4549 0.000 0.148 0.560 0.000 NA 0.164
#> GSM601883 2 0.610 0.5023 0.000 0.632 0.040 0.060 NA 0.068
#> GSM601888 6 0.390 0.5300 0.188 0.000 0.000 0.008 NA 0.760
#> GSM601893 1 0.550 0.4154 0.628 0.004 0.040 0.000 NA 0.252
#> GSM601898 1 0.172 0.6496 0.936 0.000 0.020 0.000 NA 0.024
#> GSM601903 4 0.325 0.5836 0.000 0.128 0.000 0.828 NA 0.012
#> GSM601913 1 0.639 0.4214 0.548 0.020 0.220 0.024 NA 0.000
#> GSM601928 1 0.599 0.1597 0.500 0.000 0.028 0.348 NA 0.000
#> GSM601933 2 0.478 0.5494 0.000 0.700 0.008 0.008 NA 0.084
#> GSM601938 2 0.317 0.5842 0.000 0.856 0.028 0.032 NA 0.004
#> GSM601943 3 0.558 0.5767 0.000 0.112 0.664 0.000 NA 0.140
#> GSM601948 4 0.643 0.0462 0.044 0.000 0.000 0.416 NA 0.392
#> GSM601958 1 0.107 0.6431 0.960 0.000 0.000 0.000 NA 0.028
#> GSM601973 4 0.387 0.5464 0.000 0.180 0.004 0.768 NA 0.004
#> GSM601978 6 0.595 0.1914 0.000 0.284 0.012 0.012 NA 0.552
#> GSM601988 2 0.617 0.0233 0.000 0.460 0.328 0.008 NA 0.004
#> GSM601878 1 0.487 -0.0219 0.484 0.000 0.000 0.472 NA 0.016
#> GSM601908 2 0.505 0.5188 0.000 0.688 0.008 0.012 NA 0.176
#> GSM601918 4 0.599 0.3548 0.000 0.252 0.012 0.584 NA 0.028
#> GSM601923 4 0.515 0.1542 0.428 0.000 0.012 0.504 NA 0.000
#> GSM601953 6 0.412 0.4745 0.000 0.068 0.012 0.032 NA 0.800
#> GSM601963 1 0.574 0.1762 0.464 0.000 0.412 0.016 NA 0.000
#> GSM601968 1 0.471 0.0675 0.516 0.000 0.008 0.008 NA 0.452
#> GSM601983 1 0.754 0.2599 0.432 0.016 0.224 0.040 NA 0.032
#> GSM601993 2 0.322 0.5786 0.000 0.848 0.036 0.032 NA 0.000
#> GSM601874 6 0.617 0.2662 0.000 0.240 0.008 0.040 NA 0.572
#> GSM601884 2 0.804 0.0381 0.000 0.320 0.072 0.072 NA 0.316
#> GSM601889 1 0.123 0.6420 0.952 0.000 0.004 0.000 NA 0.040
#> GSM601894 1 0.154 0.6494 0.944 0.000 0.020 0.000 NA 0.012
#> GSM601899 6 0.469 0.3306 0.348 0.000 0.008 0.004 NA 0.608
#> GSM601904 4 0.427 0.5755 0.004 0.096 0.012 0.764 NA 0.000
#> GSM601914 3 0.539 -0.1150 0.436 0.000 0.464 0.004 NA 0.000
#> GSM601929 4 0.381 0.5478 0.200 0.000 0.000 0.752 NA 0.000
#> GSM601934 2 0.481 0.5422 0.000 0.700 0.008 0.004 NA 0.112
#> GSM601939 1 0.181 0.6496 0.932 0.000 0.008 0.008 NA 0.012
#> GSM601944 2 0.422 0.5750 0.000 0.752 0.008 0.000 NA 0.096
#> GSM601949 6 0.680 0.3802 0.152 0.000 0.000 0.196 NA 0.516
#> GSM601959 1 0.297 0.6120 0.848 0.000 0.000 0.000 NA 0.076
#> GSM601974 4 0.496 0.5606 0.000 0.008 0.060 0.736 NA 0.104
#> GSM601979 2 0.528 0.5152 0.000 0.676 0.012 0.020 NA 0.188
#> GSM601989 1 0.326 0.6447 0.856 0.012 0.008 0.004 NA 0.052
#> GSM601879 4 0.496 0.4421 0.304 0.000 0.000 0.624 NA 0.020
#> GSM601909 1 0.237 0.6257 0.888 0.000 0.004 0.000 NA 0.084
#> GSM601919 4 0.502 0.5674 0.000 0.084 0.000 0.720 NA 0.092
#> GSM601924 1 0.390 0.5112 0.736 0.000 0.004 0.232 NA 0.004
#> GSM601954 6 0.673 0.2816 0.008 0.060 0.004 0.212 NA 0.532
#> GSM601964 3 0.375 0.6219 0.104 0.000 0.800 0.004 NA 0.004
#> GSM601969 6 0.502 0.1490 0.428 0.000 0.000 0.000 NA 0.500
#> GSM601984 1 0.776 0.1872 0.380 0.228 0.024 0.092 NA 0.004
#> GSM601994 2 0.205 0.5936 0.000 0.912 0.004 0.028 NA 0.000
#> GSM601875 2 0.530 0.4732 0.000 0.624 0.008 0.000 NA 0.216
#> GSM601885 2 0.593 0.4042 0.000 0.588 0.020 0.008 NA 0.184
#> GSM601890 6 0.512 0.1352 0.420 0.000 0.012 0.004 NA 0.520
#> GSM601895 1 0.344 0.6418 0.836 0.004 0.084 0.004 NA 0.008
#> GSM601900 1 0.573 0.5433 0.660 0.008 0.144 0.004 NA 0.044
#> GSM601905 4 0.464 0.4338 0.000 0.248 0.000 0.664 NA 0.000
#> GSM601915 1 0.554 0.4106 0.568 0.000 0.284 0.008 NA 0.000
#> GSM601930 4 0.581 0.2840 0.336 0.000 0.016 0.516 NA 0.000
#> GSM601935 3 0.350 0.6550 0.000 0.052 0.820 0.016 NA 0.000
#> GSM601940 1 0.107 0.6468 0.960 0.000 0.000 0.000 NA 0.012
#> GSM601945 2 0.531 0.4806 0.004 0.616 0.000 0.000 NA 0.184
#> GSM601950 1 0.313 0.6114 0.848 0.000 0.000 0.012 NA 0.088
#> GSM601960 1 0.427 0.5622 0.700 0.000 0.252 0.000 NA 0.008
#> GSM601975 4 0.502 0.5266 0.000 0.184 0.000 0.696 NA 0.052
#> GSM601980 3 0.545 0.3965 0.000 0.288 0.604 0.016 NA 0.008
#> GSM601990 3 0.622 0.5449 0.152 0.044 0.604 0.020 NA 0.000
#> GSM601880 4 0.511 0.1327 0.436 0.000 0.012 0.500 NA 0.000
#> GSM601910 1 0.556 0.3340 0.612 0.008 0.028 0.000 NA 0.272
#> GSM601920 2 0.648 0.1096 0.004 0.408 0.020 0.360 NA 0.000
#> GSM601925 4 0.566 0.1718 0.416 0.000 0.012 0.464 NA 0.000
#> GSM601955 3 0.225 0.6696 0.000 0.024 0.912 0.008 NA 0.012
#> GSM601965 1 0.533 0.5110 0.668 0.192 0.000 0.024 NA 0.008
#> GSM601970 1 0.383 0.5958 0.792 0.000 0.044 0.000 NA 0.140
#> GSM601985 1 0.281 0.6449 0.864 0.000 0.036 0.008 NA 0.000
#> GSM601995 3 0.210 0.6720 0.000 0.028 0.916 0.016 NA 0.000
#> GSM601876 1 0.218 0.6485 0.908 0.008 0.004 0.016 NA 0.000
#> GSM601886 2 0.788 0.1237 0.008 0.328 0.160 0.264 NA 0.004
#> GSM601891 6 0.505 0.3276 0.336 0.000 0.016 0.000 NA 0.592
#> GSM601896 1 0.384 0.6256 0.800 0.008 0.008 0.068 NA 0.000
#> GSM601901 2 0.595 0.4389 0.000 0.584 0.008 0.276 NA 0.060
#> GSM601906 4 0.520 0.5596 0.044 0.112 0.012 0.712 NA 0.000
#> GSM601916 2 0.457 0.5586 0.000 0.720 0.004 0.112 NA 0.004
#> GSM601931 1 0.485 0.3701 0.632 0.000 0.000 0.272 NA 0.000
#> GSM601936 2 0.656 0.1096 0.000 0.448 0.288 0.036 NA 0.000
#> GSM601941 2 0.586 0.2710 0.000 0.508 0.004 0.348 NA 0.012
#> GSM601946 1 0.397 0.6135 0.796 0.008 0.020 0.052 NA 0.000
#> GSM601951 4 0.538 0.4319 0.012 0.004 0.000 0.632 NA 0.224
#> GSM601961 6 0.411 0.5189 0.064 0.004 0.004 0.028 NA 0.796
#> GSM601976 4 0.355 0.5997 0.000 0.084 0.000 0.828 NA 0.032
#> GSM601981 2 0.611 0.3731 0.000 0.536 0.012 0.024 NA 0.308
#> GSM601991 3 0.472 0.6225 0.040 0.056 0.728 0.004 NA 0.000
#> GSM601881 1 0.518 0.0591 0.516 0.000 0.012 0.412 NA 0.000
#> GSM601911 2 0.499 0.5156 0.124 0.716 0.004 0.036 NA 0.000
#> GSM601921 2 0.633 0.2843 0.000 0.488 0.016 0.292 NA 0.008
#> GSM601926 1 0.482 0.3703 0.636 0.000 0.004 0.284 NA 0.000
#> GSM601956 2 0.630 0.1721 0.000 0.436 0.024 0.008 NA 0.396
#> GSM601966 2 0.406 0.5866 0.000 0.788 0.000 0.096 NA 0.028
#> GSM601971 1 0.760 -0.0348 0.404 0.000 0.024 0.140 NA 0.296
#> GSM601986 2 0.648 0.3794 0.140 0.572 0.008 0.084 NA 0.000
#> GSM601996 2 0.376 0.5772 0.000 0.796 0.000 0.064 NA 0.012
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)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
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)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
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)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, 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.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")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n time(p) gender(p) k
#> ATC:NMF 125 0.294 0.840 2
#> ATC:NMF 99 0.213 0.889 3
#> ATC:NMF 47 0.406 0.878 4
#> ATC:NMF 73 0.418 0.755 5
#> ATC:NMF 59 0.315 0.374 6
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 3.6.0 (2019-04-26)
#> Platform: x86_64-pc-linux-gnu (64-bit)
#> Running under: CentOS Linux 7 (Core)
#>
#> Matrix products: default
#> BLAS: /usr/lib64/libblas.so.3.4.2
#> LAPACK: /usr/lib64/liblapack.so.3.4.2
#>
#> locale:
#> [1] LC_CTYPE=en_GB.UTF-8 LC_NUMERIC=C LC_TIME=en_GB.UTF-8
#> [4] LC_COLLATE=en_GB.UTF-8 LC_MONETARY=en_GB.UTF-8 LC_MESSAGES=en_GB.UTF-8
#> [7] LC_PAPER=en_GB.UTF-8 LC_NAME=C LC_ADDRESS=C
#> [10] LC_TELEPHONE=C LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C
#>
#> attached base packages:
#> [1] grid stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] genefilter_1.66.0 ComplexHeatmap_2.3.1 markdown_1.1 knitr_1.26
#> [5] GetoptLong_0.1.7 cola_1.3.2
#>
#> loaded via a namespace (and not attached):
#> [1] circlize_0.4.8 shape_1.4.4 xfun_0.11 slam_0.1-46
#> [5] lattice_0.20-38 splines_3.6.0 colorspace_1.4-1 vctrs_0.2.0
#> [9] stats4_3.6.0 blob_1.2.0 XML_3.98-1.20 survival_2.44-1.1
#> [13] rlang_0.4.2 pillar_1.4.2 DBI_1.0.0 BiocGenerics_0.30.0
#> [17] bit64_0.9-7 RColorBrewer_1.1-2 matrixStats_0.55.0 stringr_1.4.0
#> [21] GlobalOptions_0.1.1 evaluate_0.14 memoise_1.1.0 Biobase_2.44.0
#> [25] IRanges_2.18.3 parallel_3.6.0 AnnotationDbi_1.46.1 highr_0.8
#> [29] Rcpp_1.0.3 xtable_1.8-4 backports_1.1.5 S4Vectors_0.22.1
#> [33] annotate_1.62.0 skmeans_0.2-11 bit_1.1-14 microbenchmark_1.4-7
#> [37] brew_1.0-6 impute_1.58.0 rjson_0.2.20 png_0.1-7
#> [41] digest_0.6.23 stringi_1.4.3 polyclip_1.10-0 clue_0.3-57
#> [45] tools_3.6.0 bitops_1.0-6 magrittr_1.5 eulerr_6.0.0
#> [49] RCurl_1.95-4.12 RSQLite_2.1.4 tibble_2.1.3 cluster_2.1.0
#> [53] crayon_1.3.4 pkgconfig_2.0.3 zeallot_0.1.0 Matrix_1.2-17
#> [57] xml2_1.2.2 httr_1.4.1 R6_2.4.1 mclust_5.4.5
#> [61] compiler_3.6.0