Date: 2019-12-25 22:12:05 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 44956 rows and 120 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] 44956 120
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 | 0.970 | 0.987 | ** | |
| ATC:kmeans | 3 | 1.000 | 0.998 | 0.999 | ** | 2 |
| ATC:skmeans | 3 | 1.000 | 0.984 | 0.993 | ** | 2 |
| MAD:skmeans | 2 | 0.915 | 0.947 | 0.977 | * | |
| ATC:pam | 6 | 0.915 | 0.851 | 0.931 | * | 2,3 |
| MAD:NMF | 2 | 0.914 | 0.939 | 0.973 | * | |
| ATC:NMF | 2 | 0.899 | 0.947 | 0.977 | ||
| MAD:pam | 2 | 0.898 | 0.913 | 0.965 | ||
| CV:pam | 2 | 0.897 | 0.896 | 0.962 | ||
| CV:skmeans | 2 | 0.884 | 0.921 | 0.968 | ||
| CV:NMF | 2 | 0.882 | 0.923 | 0.968 | ||
| SD:pam | 2 | 0.881 | 0.921 | 0.966 | ||
| CV:kmeans | 2 | 0.868 | 0.936 | 0.972 | ||
| SD:NMF | 2 | 0.867 | 0.921 | 0.967 | ||
| SD:kmeans | 2 | 0.854 | 0.939 | 0.973 | ||
| SD:skmeans | 2 | 0.853 | 0.929 | 0.968 | ||
| SD:mclust | 5 | 0.796 | 0.836 | 0.882 | ||
| ATC:mclust | 5 | 0.766 | 0.745 | 0.827 | ||
| MAD:mclust | 4 | 0.764 | 0.843 | 0.877 | ||
| CV:mclust | 5 | 0.751 | 0.791 | 0.871 | ||
| MAD:hclust | 2 | 0.618 | 0.832 | 0.920 | ||
| ATC:hclust | 3 | 0.567 | 0.792 | 0.885 | ||
| CV:hclust | 2 | 0.553 | 0.820 | 0.909 | ||
| SD:hclust | 2 | 0.535 | 0.813 | 0.908 |
**: 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 0.867 0.921 0.967 0.496 0.505 0.505
#> CV:NMF 2 0.882 0.923 0.968 0.495 0.507 0.507
#> MAD:NMF 2 0.914 0.939 0.973 0.495 0.510 0.510
#> ATC:NMF 2 0.899 0.947 0.977 0.453 0.546 0.546
#> SD:skmeans 2 0.853 0.929 0.968 0.499 0.503 0.503
#> CV:skmeans 2 0.884 0.921 0.968 0.500 0.501 0.501
#> MAD:skmeans 2 0.915 0.947 0.977 0.499 0.503 0.503
#> ATC:skmeans 2 1.000 0.980 0.993 0.499 0.503 0.503
#> SD:mclust 2 0.357 0.490 0.762 0.353 0.541 0.541
#> CV:mclust 2 0.346 0.815 0.801 0.375 0.532 0.532
#> MAD:mclust 2 0.471 0.754 0.700 0.333 0.507 0.507
#> ATC:mclust 2 0.499 0.934 0.888 0.409 0.552 0.552
#> SD:kmeans 2 0.854 0.939 0.973 0.490 0.513 0.513
#> CV:kmeans 2 0.868 0.936 0.972 0.489 0.513 0.513
#> MAD:kmeans 2 1.000 0.970 0.987 0.488 0.513 0.513
#> ATC:kmeans 2 0.982 0.963 0.985 0.471 0.532 0.532
#> SD:pam 2 0.881 0.921 0.966 0.478 0.513 0.513
#> CV:pam 2 0.897 0.896 0.962 0.473 0.523 0.523
#> MAD:pam 2 0.898 0.913 0.965 0.478 0.510 0.510
#> ATC:pam 2 1.000 0.974 0.989 0.501 0.501 0.501
#> SD:hclust 2 0.535 0.813 0.908 0.475 0.516 0.516
#> CV:hclust 2 0.553 0.820 0.909 0.465 0.519 0.519
#> MAD:hclust 2 0.618 0.832 0.920 0.469 0.541 0.541
#> ATC:hclust 2 0.524 0.860 0.919 0.436 0.583 0.583
get_stats(res_list, k = 3)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 3 0.509 0.529 0.749 0.307 0.804 0.622
#> CV:NMF 3 0.511 0.482 0.672 0.303 0.816 0.654
#> MAD:NMF 3 0.543 0.629 0.810 0.309 0.788 0.603
#> ATC:NMF 3 0.727 0.837 0.902 0.436 0.744 0.549
#> SD:skmeans 3 0.663 0.798 0.883 0.315 0.815 0.641
#> CV:skmeans 3 0.703 0.798 0.889 0.311 0.792 0.603
#> MAD:skmeans 3 0.790 0.887 0.937 0.317 0.815 0.641
#> ATC:skmeans 3 1.000 0.984 0.993 0.220 0.872 0.750
#> SD:mclust 3 0.495 0.642 0.793 0.687 0.569 0.379
#> CV:mclust 3 0.542 0.788 0.846 0.570 0.763 0.605
#> MAD:mclust 3 0.586 0.717 0.843 0.791 0.716 0.539
#> ATC:mclust 3 0.608 0.636 0.799 0.496 0.736 0.539
#> SD:kmeans 3 0.573 0.701 0.829 0.342 0.738 0.526
#> CV:kmeans 3 0.548 0.633 0.795 0.339 0.775 0.585
#> MAD:kmeans 3 0.574 0.674 0.810 0.346 0.736 0.525
#> ATC:kmeans 3 1.000 0.998 0.999 0.411 0.703 0.489
#> SD:pam 3 0.557 0.733 0.853 0.318 0.818 0.664
#> CV:pam 3 0.703 0.822 0.921 0.311 0.811 0.655
#> MAD:pam 3 0.563 0.599 0.790 0.333 0.835 0.690
#> ATC:pam 3 1.000 0.965 0.986 0.316 0.690 0.460
#> SD:hclust 3 0.419 0.618 0.773 0.305 0.843 0.700
#> CV:hclust 3 0.453 0.627 0.800 0.348 0.839 0.696
#> MAD:hclust 3 0.506 0.429 0.717 0.351 0.921 0.858
#> ATC:hclust 3 0.567 0.792 0.885 0.492 0.727 0.540
get_stats(res_list, k = 4)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 4 0.703 0.755 0.886 0.105 0.760 0.442
#> CV:NMF 4 0.698 0.764 0.887 0.106 0.748 0.448
#> MAD:NMF 4 0.753 0.773 0.899 0.100 0.873 0.665
#> ATC:NMF 4 0.590 0.567 0.788 0.107 0.847 0.588
#> SD:skmeans 4 0.761 0.815 0.899 0.115 0.865 0.640
#> CV:skmeans 4 0.710 0.729 0.873 0.114 0.863 0.633
#> MAD:skmeans 4 0.743 0.802 0.880 0.107 0.887 0.692
#> ATC:skmeans 4 0.868 0.869 0.940 0.115 0.944 0.857
#> SD:mclust 4 0.601 0.647 0.828 0.192 0.792 0.532
#> CV:mclust 4 0.495 0.662 0.771 0.171 0.841 0.651
#> MAD:mclust 4 0.764 0.843 0.877 0.179 0.812 0.599
#> ATC:mclust 4 0.598 0.765 0.816 0.158 0.889 0.693
#> SD:kmeans 4 0.540 0.395 0.632 0.120 0.802 0.526
#> CV:kmeans 4 0.516 0.509 0.681 0.122 0.815 0.534
#> MAD:kmeans 4 0.551 0.550 0.733 0.125 0.909 0.741
#> ATC:kmeans 4 0.697 0.614 0.759 0.107 0.885 0.675
#> SD:pam 4 0.657 0.740 0.855 0.166 0.826 0.571
#> CV:pam 4 0.628 0.715 0.812 0.161 0.834 0.586
#> MAD:pam 4 0.687 0.758 0.852 0.149 0.810 0.546
#> ATC:pam 4 0.828 0.777 0.867 0.101 0.907 0.737
#> SD:hclust 4 0.441 0.526 0.718 0.115 0.932 0.818
#> CV:hclust 4 0.505 0.554 0.768 0.105 0.882 0.699
#> MAD:hclust 4 0.477 0.472 0.648 0.116 0.739 0.496
#> ATC:hclust 4 0.617 0.669 0.800 0.113 0.929 0.788
get_stats(res_list, k = 5)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 5 0.601 0.581 0.775 0.0840 0.852 0.551
#> CV:NMF 5 0.646 0.704 0.828 0.0813 0.845 0.540
#> MAD:NMF 5 0.600 0.596 0.779 0.0869 0.835 0.516
#> ATC:NMF 5 0.670 0.701 0.827 0.0729 0.818 0.458
#> SD:skmeans 5 0.721 0.646 0.797 0.0686 0.906 0.681
#> CV:skmeans 5 0.737 0.714 0.831 0.0701 0.904 0.677
#> MAD:skmeans 5 0.710 0.660 0.797 0.0747 0.905 0.684
#> ATC:skmeans 5 0.855 0.836 0.896 0.0650 0.905 0.722
#> SD:mclust 5 0.796 0.836 0.882 0.0641 0.943 0.802
#> CV:mclust 5 0.751 0.791 0.871 0.1040 0.871 0.629
#> MAD:mclust 5 0.779 0.831 0.898 0.0652 0.890 0.668
#> ATC:mclust 5 0.766 0.745 0.827 0.0847 0.895 0.660
#> SD:kmeans 5 0.624 0.467 0.632 0.0720 0.800 0.448
#> CV:kmeans 5 0.622 0.577 0.716 0.0739 0.836 0.480
#> MAD:kmeans 5 0.614 0.487 0.684 0.0704 0.829 0.479
#> ATC:kmeans 5 0.727 0.639 0.793 0.0690 0.858 0.530
#> SD:pam 5 0.637 0.576 0.770 0.0673 0.887 0.604
#> CV:pam 5 0.674 0.711 0.803 0.0801 0.925 0.724
#> MAD:pam 5 0.629 0.534 0.744 0.0788 0.898 0.638
#> ATC:pam 5 0.861 0.834 0.926 0.0727 0.909 0.691
#> SD:hclust 5 0.543 0.489 0.703 0.0929 0.927 0.772
#> CV:hclust 5 0.539 0.494 0.706 0.0965 0.854 0.558
#> MAD:hclust 5 0.569 0.471 0.707 0.0891 0.850 0.544
#> ATC:hclust 5 0.677 0.631 0.774 0.0692 0.896 0.651
get_stats(res_list, k = 6)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 6 0.554 0.355 0.596 0.0486 0.879 0.538
#> CV:NMF 6 0.564 0.400 0.651 0.0536 0.926 0.712
#> MAD:NMF 6 0.561 0.411 0.656 0.0503 0.881 0.563
#> ATC:NMF 6 0.661 0.579 0.768 0.0296 0.939 0.760
#> SD:skmeans 6 0.756 0.755 0.853 0.0484 0.882 0.547
#> CV:skmeans 6 0.797 0.775 0.874 0.0501 0.922 0.684
#> MAD:skmeans 6 0.778 0.750 0.858 0.0506 0.897 0.599
#> ATC:skmeans 6 0.835 0.832 0.912 0.0439 0.974 0.900
#> SD:mclust 6 0.717 0.658 0.788 0.0584 0.874 0.551
#> CV:mclust 6 0.727 0.532 0.752 0.0462 0.854 0.491
#> MAD:mclust 6 0.744 0.688 0.830 0.0752 0.870 0.535
#> ATC:mclust 6 0.867 0.865 0.924 0.0603 0.910 0.642
#> SD:kmeans 6 0.713 0.650 0.745 0.0454 0.897 0.582
#> CV:kmeans 6 0.713 0.663 0.755 0.0449 0.943 0.740
#> MAD:kmeans 6 0.701 0.586 0.760 0.0450 0.905 0.596
#> ATC:kmeans 6 0.738 0.633 0.769 0.0419 0.903 0.587
#> SD:pam 6 0.714 0.634 0.816 0.0504 0.936 0.711
#> CV:pam 6 0.715 0.692 0.779 0.0537 0.913 0.637
#> MAD:pam 6 0.762 0.669 0.817 0.0431 0.873 0.499
#> ATC:pam 6 0.915 0.851 0.931 0.0702 0.915 0.639
#> SD:hclust 6 0.600 0.465 0.662 0.0500 0.877 0.574
#> CV:hclust 6 0.626 0.481 0.714 0.0528 0.887 0.581
#> MAD:hclust 6 0.628 0.498 0.685 0.0424 0.932 0.722
#> ATC:hclust 6 0.730 0.602 0.779 0.0561 0.876 0.528
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 agent(p) other(p) time(p) individual(p) k
#> SD:NMF 117 0.861 0.468 0.650 0.00544 2
#> CV:NMF 117 0.861 0.468 0.650 0.00544 2
#> MAD:NMF 117 0.861 0.468 0.650 0.00814 2
#> ATC:NMF 119 0.575 0.800 0.651 0.05842 2
#> SD:skmeans 117 0.820 0.673 0.788 0.01108 2
#> CV:skmeans 115 1.000 0.739 0.495 0.01084 2
#> MAD:skmeans 117 0.983 0.673 0.634 0.01610 2
#> ATC:skmeans 118 0.940 0.714 0.267 0.01912 2
#> SD:mclust 87 0.935 0.246 1.000 0.02902 2
#> CV:mclust 119 1.000 0.386 0.795 0.00708 2
#> MAD:mclust 104 0.951 0.277 0.808 0.02051 2
#> ATC:mclust 119 0.366 1.000 0.795 0.02087 2
#> SD:kmeans 117 0.896 0.407 0.791 0.00562 2
#> CV:kmeans 116 0.992 0.373 0.705 0.00698 2
#> MAD:kmeans 119 1.000 0.363 0.793 0.00587 2
#> ATC:kmeans 117 0.894 0.539 0.486 0.02305 2
#> SD:pam 115 1.000 0.426 0.648 0.01338 2
#> CV:pam 113 0.911 0.517 0.645 0.01384 2
#> MAD:pam 114 1.000 0.450 0.593 0.01073 2
#> ATC:pam 118 0.804 0.789 0.196 0.01875 2
#> SD:hclust 112 0.879 0.687 1.000 0.00227 2
#> CV:hclust 110 1.000 0.597 1.000 0.00159 2
#> MAD:hclust 110 0.871 0.395 1.000 0.00237 2
#> ATC:hclust 113 0.158 1.000 1.000 0.03397 2
test_to_known_factors(res_list, k = 3)
#> n agent(p) other(p) time(p) individual(p) k
#> SD:NMF 78 0.6435 0.612611 0.1696 1.66e-02 3
#> CV:NMF 79 0.8419 1.000000 1.0000 4.61e-02 3
#> MAD:NMF 102 0.9209 0.588823 0.3497 1.05e-02 3
#> ATC:NMF 117 0.8681 0.715360 0.4275 2.13e-02 3
#> SD:skmeans 113 0.9067 0.456037 0.1802 9.39e-04 3
#> CV:skmeans 114 0.9227 0.533850 0.1169 1.78e-03 3
#> MAD:skmeans 119 0.8418 0.524018 0.2415 1.56e-03 3
#> ATC:skmeans 119 0.7137 0.427559 0.1321 4.84e-02 3
#> SD:mclust 103 0.3177 0.587827 0.6944 1.33e-02 3
#> CV:mclust 116 0.1483 0.866552 0.9411 7.97e-03 3
#> MAD:mclust 104 0.2761 0.595553 0.6285 1.44e-02 3
#> ATC:mclust 84 0.5037 0.815731 0.4919 4.79e-02 3
#> SD:kmeans 107 0.7065 0.788028 0.6377 4.74e-03 3
#> CV:kmeans 91 0.1788 0.409088 0.9963 7.66e-03 3
#> MAD:kmeans 104 0.6407 0.839454 0.6226 7.61e-03 3
#> ATC:kmeans 120 0.2605 0.998819 0.0571 2.32e-02 3
#> SD:pam 107 0.0881 0.001134 0.9913 3.27e-03 3
#> CV:pam 112 0.0897 0.000928 0.9708 5.53e-03 3
#> MAD:pam 89 0.2614 0.760048 0.7639 4.25e-02 3
#> ATC:pam 118 0.1528 0.925174 0.0403 2.69e-02 3
#> SD:hclust 98 0.5025 0.432299 0.8792 2.44e-04 3
#> CV:hclust 94 0.7404 0.187313 0.9768 9.02e-06 3
#> MAD:hclust 38 NA NA NA NA 3
#> ATC:hclust 115 0.2921 0.881812 0.2218 1.40e-02 3
test_to_known_factors(res_list, k = 4)
#> n agent(p) other(p) time(p) individual(p) k
#> SD:NMF 104 0.0845 0.28835 0.1623 1.01e-02 4
#> CV:NMF 107 0.1579 0.19337 0.1647 1.60e-02 4
#> MAD:NMF 104 0.1214 0.42601 0.1154 2.85e-02 4
#> ATC:NMF 86 0.1917 0.81502 0.7263 1.92e-02 4
#> SD:skmeans 112 0.2472 0.70668 0.4778 1.35e-02 4
#> CV:skmeans 96 0.2489 0.48482 0.4673 1.51e-02 4
#> MAD:skmeans 113 0.2904 0.66138 0.6935 5.87e-03 4
#> ATC:skmeans 116 0.3050 0.76007 0.2277 5.55e-02 4
#> SD:mclust 89 0.6028 0.62753 0.6393 2.12e-02 4
#> CV:mclust 98 0.5858 0.76880 0.3742 3.80e-02 4
#> MAD:mclust 116 0.8379 0.88134 0.4963 1.82e-02 4
#> ATC:mclust 115 0.3052 0.98681 0.2473 7.91e-02 4
#> SD:kmeans 47 0.5338 0.58483 0.8032 5.41e-02 4
#> CV:kmeans 85 0.0509 0.94040 0.9883 2.75e-02 4
#> MAD:kmeans 82 0.1195 0.94486 0.9564 4.43e-02 4
#> ATC:kmeans 85 0.0241 0.27191 0.2987 2.39e-02 4
#> SD:pam 103 0.1068 0.00110 0.7946 1.19e-04 4
#> CV:pam 96 0.1319 0.00402 0.5807 5.52e-04 4
#> MAD:pam 104 0.2579 0.01048 0.8846 3.01e-04 4
#> ATC:pam 113 0.3495 0.85584 0.0759 6.39e-02 4
#> SD:hclust 81 0.2123 0.05834 0.9777 1.34e-05 4
#> CV:hclust 77 0.1357 0.03494 0.9837 3.20e-06 4
#> MAD:hclust 73 0.2185 0.09097 0.9578 5.00e-05 4
#> ATC:hclust 96 0.0124 0.15165 0.5087 8.55e-03 4
test_to_known_factors(res_list, k = 5)
#> n agent(p) other(p) time(p) individual(p) k
#> SD:NMF 88 0.29191 0.00571 0.1207 7.89e-03 5
#> CV:NMF 107 0.08934 0.00871 0.0579 5.54e-03 5
#> MAD:NMF 92 0.14574 0.00182 0.1040 2.43e-02 5
#> ATC:NMF 101 0.78108 0.96643 0.5633 1.80e-03 5
#> SD:skmeans 102 0.22972 0.88590 0.6097 1.51e-02 5
#> CV:skmeans 106 0.23119 0.81010 0.4713 3.98e-03 5
#> MAD:skmeans 102 0.14244 0.82122 0.7047 1.10e-02 5
#> ATC:skmeans 112 0.46351 0.98368 0.3517 2.85e-02 5
#> SD:mclust 116 0.56204 0.75771 0.5881 2.64e-03 5
#> CV:mclust 114 0.55636 0.80200 0.6174 3.23e-03 5
#> MAD:mclust 115 0.58197 0.83634 0.5035 6.28e-03 5
#> ATC:mclust 111 0.63393 0.11788 0.3202 2.20e-02 5
#> SD:kmeans 65 0.44375 0.48182 0.6770 7.81e-02 5
#> CV:kmeans 87 0.00942 0.27410 0.3772 1.28e-02 5
#> MAD:kmeans 64 0.84896 0.53563 0.8225 4.49e-02 5
#> ATC:kmeans 86 0.15380 0.11775 0.3891 1.24e-02 5
#> SD:pam 70 0.12139 0.13551 0.9735 2.15e-03 5
#> CV:pam 107 0.17695 0.16046 0.9438 4.25e-05 5
#> MAD:pam 71 0.18845 0.91734 0.9759 2.43e-03 5
#> ATC:pam 111 0.67769 0.28323 0.3202 6.12e-02 5
#> SD:hclust 64 0.08186 0.50098 0.9700 4.52e-05 5
#> CV:hclust 67 0.15349 0.00442 0.9868 1.81e-04 5
#> MAD:hclust 60 0.45969 0.05365 0.9963 2.53e-04 5
#> ATC:hclust 82 0.53405 0.21961 0.8193 2.86e-01 5
test_to_known_factors(res_list, k = 6)
#> n agent(p) other(p) time(p) individual(p) k
#> SD:NMF 40 0.2531 0.221499 0.524 2.18e-02 6
#> CV:NMF 52 0.2010 0.104303 0.508 5.30e-02 6
#> MAD:NMF 56 0.3455 0.654554 0.553 1.00e-01 6
#> ATC:NMF 77 0.6646 0.574145 0.157 2.01e-02 6
#> SD:skmeans 112 0.2346 0.000337 0.744 3.29e-05 6
#> CV:skmeans 110 0.2615 0.000370 0.524 9.72e-06 6
#> MAD:skmeans 106 0.2589 0.000200 0.797 2.39e-05 6
#> ATC:skmeans 109 0.2072 0.460282 0.075 1.01e-02 6
#> SD:mclust 96 0.6625 0.161663 0.709 1.77e-02 6
#> CV:mclust 84 0.5937 0.199636 0.778 2.46e-02 6
#> MAD:mclust 95 0.5042 0.093203 0.673 5.75e-03 6
#> ATC:mclust 117 0.4170 0.034356 0.527 3.72e-02 6
#> SD:kmeans 98 0.7187 0.000299 0.762 1.20e-03 6
#> CV:kmeans 105 0.5384 0.000411 0.577 1.56e-03 6
#> MAD:kmeans 87 0.8795 0.000436 0.789 2.79e-03 6
#> ATC:kmeans 94 0.5150 0.085205 0.643 3.80e-02 6
#> SD:pam 100 0.0190 0.579144 0.800 3.14e-06 6
#> CV:pam 101 0.0444 0.631253 0.674 3.95e-06 6
#> MAD:pam 95 0.0664 0.002323 0.303 8.05e-06 6
#> ATC:pam 110 0.3355 0.351066 0.312 5.02e-03 6
#> SD:hclust 72 0.1442 0.033717 0.998 2.24e-06 6
#> CV:hclust 77 0.2235 0.005293 0.999 9.91e-07 6
#> MAD:hclust 70 0.0741 0.017137 0.988 4.50e-05 6
#> ATC:hclust 77 0.0958 0.062051 0.500 1.70e-02 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 44956 rows and 120 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 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 0.535 0.813 0.908 0.4750 0.516 0.516
#> 3 3 0.419 0.618 0.773 0.3053 0.843 0.700
#> 4 4 0.441 0.526 0.718 0.1153 0.932 0.818
#> 5 5 0.543 0.489 0.703 0.0929 0.927 0.772
#> 6 6 0.600 0.465 0.662 0.0500 0.877 0.574
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
#> GSM1105438 2 0.0000 0.894 0.000 1.000
#> GSM1105486 2 0.1184 0.894 0.016 0.984
#> GSM1105487 1 0.0376 0.894 0.996 0.004
#> GSM1105490 2 0.2778 0.884 0.048 0.952
#> GSM1105491 1 0.8608 0.618 0.716 0.284
#> GSM1105495 2 0.3733 0.871 0.072 0.928
#> GSM1105498 2 0.9393 0.519 0.356 0.644
#> GSM1105499 1 0.0000 0.893 1.000 0.000
#> GSM1105506 2 0.8909 0.592 0.308 0.692
#> GSM1105442 2 0.1414 0.894 0.020 0.980
#> GSM1105511 2 0.2778 0.884 0.048 0.952
#> GSM1105514 2 0.0000 0.894 0.000 1.000
#> GSM1105518 2 0.6048 0.818 0.148 0.852
#> GSM1105522 1 0.0000 0.893 1.000 0.000
#> GSM1105534 1 0.0000 0.893 1.000 0.000
#> GSM1105535 1 0.0000 0.893 1.000 0.000
#> GSM1105538 1 0.7219 0.764 0.800 0.200
#> GSM1105542 2 0.1414 0.894 0.020 0.980
#> GSM1105443 2 0.0672 0.896 0.008 0.992
#> GSM1105551 1 0.6438 0.797 0.836 0.164
#> GSM1105554 1 0.0000 0.893 1.000 0.000
#> GSM1105555 1 0.0376 0.894 0.996 0.004
#> GSM1105447 2 0.0672 0.896 0.008 0.992
#> GSM1105467 2 0.1414 0.894 0.020 0.980
#> GSM1105470 2 0.0000 0.894 0.000 1.000
#> GSM1105471 2 0.3431 0.876 0.064 0.936
#> GSM1105474 2 0.0000 0.894 0.000 1.000
#> GSM1105475 2 0.1414 0.894 0.020 0.980
#> GSM1105440 1 0.0000 0.893 1.000 0.000
#> GSM1105488 2 0.1414 0.894 0.020 0.980
#> GSM1105489 1 0.0376 0.894 0.996 0.004
#> GSM1105492 1 0.0000 0.893 1.000 0.000
#> GSM1105493 1 0.5178 0.845 0.884 0.116
#> GSM1105497 2 0.4298 0.863 0.088 0.912
#> GSM1105500 2 0.9393 0.519 0.356 0.644
#> GSM1105501 2 0.5629 0.831 0.132 0.868
#> GSM1105508 1 0.9686 0.360 0.604 0.396
#> GSM1105444 2 0.0000 0.894 0.000 1.000
#> GSM1105513 2 0.2778 0.884 0.048 0.952
#> GSM1105516 2 0.9850 0.255 0.428 0.572
#> GSM1105520 2 0.6048 0.818 0.148 0.852
#> GSM1105524 1 0.0000 0.893 1.000 0.000
#> GSM1105536 2 0.6343 0.798 0.160 0.840
#> GSM1105537 1 0.0000 0.893 1.000 0.000
#> GSM1105540 1 0.7219 0.764 0.800 0.200
#> GSM1105544 2 0.9522 0.479 0.372 0.628
#> GSM1105445 2 0.0672 0.896 0.008 0.992
#> GSM1105553 1 0.6438 0.797 0.836 0.164
#> GSM1105556 1 0.0000 0.893 1.000 0.000
#> GSM1105557 2 0.2778 0.884 0.048 0.952
#> GSM1105449 2 0.0672 0.896 0.008 0.992
#> GSM1105469 2 0.9491 0.479 0.368 0.632
#> GSM1105472 2 0.0000 0.894 0.000 1.000
#> GSM1105473 1 0.6048 0.820 0.852 0.148
#> GSM1105476 2 0.0000 0.894 0.000 1.000
#> GSM1105477 2 0.1414 0.894 0.020 0.980
#> GSM1105478 2 0.8608 0.637 0.284 0.716
#> GSM1105510 2 0.1414 0.894 0.020 0.980
#> GSM1105530 1 0.1414 0.894 0.980 0.020
#> GSM1105539 1 0.1184 0.894 0.984 0.016
#> GSM1105480 2 0.8608 0.637 0.284 0.716
#> GSM1105512 1 0.0000 0.893 1.000 0.000
#> GSM1105532 1 0.1414 0.894 0.980 0.020
#> GSM1105541 1 0.1184 0.894 0.984 0.016
#> GSM1105439 2 0.0376 0.895 0.004 0.996
#> GSM1105463 1 0.1633 0.893 0.976 0.024
#> GSM1105482 1 0.0376 0.894 0.996 0.004
#> GSM1105483 2 0.9491 0.479 0.368 0.632
#> GSM1105494 2 0.9393 0.519 0.356 0.644
#> GSM1105503 2 0.7139 0.775 0.196 0.804
#> GSM1105507 1 0.8016 0.701 0.756 0.244
#> GSM1105446 2 0.1184 0.895 0.016 0.984
#> GSM1105519 1 0.3733 0.874 0.928 0.072
#> GSM1105526 2 0.0000 0.894 0.000 1.000
#> GSM1105527 2 0.9491 0.479 0.368 0.632
#> GSM1105531 1 0.3879 0.873 0.924 0.076
#> GSM1105543 2 0.0000 0.894 0.000 1.000
#> GSM1105546 1 0.0376 0.894 0.996 0.004
#> GSM1105547 1 0.2778 0.883 0.952 0.048
#> GSM1105455 2 0.0376 0.895 0.004 0.996
#> GSM1105458 2 0.0672 0.896 0.008 0.992
#> GSM1105459 2 0.0000 0.894 0.000 1.000
#> GSM1105462 1 0.3431 0.880 0.936 0.064
#> GSM1105441 2 0.0376 0.895 0.004 0.996
#> GSM1105465 2 0.1414 0.894 0.020 0.980
#> GSM1105484 2 0.0000 0.894 0.000 1.000
#> GSM1105485 2 0.1414 0.894 0.020 0.980
#> GSM1105496 2 0.9393 0.519 0.356 0.644
#> GSM1105505 2 0.7139 0.775 0.196 0.804
#> GSM1105509 1 0.8016 0.701 0.756 0.244
#> GSM1105448 2 0.0000 0.894 0.000 1.000
#> GSM1105521 1 0.3733 0.874 0.928 0.072
#> GSM1105528 2 0.0000 0.894 0.000 1.000
#> GSM1105529 2 0.1414 0.894 0.020 0.980
#> GSM1105533 1 0.0000 0.893 1.000 0.000
#> GSM1105545 2 0.6438 0.793 0.164 0.836
#> GSM1105548 1 0.0376 0.894 0.996 0.004
#> GSM1105549 1 0.2778 0.883 0.952 0.048
#> GSM1105457 2 0.0376 0.895 0.004 0.996
#> GSM1105460 2 0.0672 0.896 0.008 0.992
#> GSM1105461 2 0.0000 0.894 0.000 1.000
#> GSM1105464 1 0.3431 0.880 0.936 0.064
#> GSM1105466 2 0.8909 0.595 0.308 0.692
#> GSM1105479 2 0.2948 0.881 0.052 0.948
#> GSM1105502 1 0.1843 0.892 0.972 0.028
#> GSM1105515 1 0.0000 0.893 1.000 0.000
#> GSM1105523 1 0.9815 0.224 0.580 0.420
#> GSM1105550 1 0.9044 0.560 0.680 0.320
#> GSM1105450 2 0.0000 0.894 0.000 1.000
#> GSM1105451 2 0.0000 0.894 0.000 1.000
#> GSM1105454 2 0.3733 0.871 0.072 0.928
#> GSM1105468 2 0.0000 0.894 0.000 1.000
#> GSM1105481 2 0.3733 0.871 0.072 0.928
#> GSM1105504 1 0.1843 0.892 0.972 0.028
#> GSM1105517 1 0.5842 0.822 0.860 0.140
#> GSM1105525 1 0.9815 0.224 0.580 0.420
#> GSM1105552 1 0.9044 0.560 0.680 0.320
#> GSM1105452 2 0.1414 0.894 0.020 0.980
#> GSM1105453 2 0.0000 0.894 0.000 1.000
#> GSM1105456 2 0.3733 0.871 0.072 0.928
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1105438 2 0.0237 0.754 0.000 0.996 0.004
#> GSM1105486 2 0.1163 0.751 0.000 0.972 0.028
#> GSM1105487 1 0.0747 0.780 0.984 0.000 0.016
#> GSM1105490 2 0.6507 0.535 0.028 0.688 0.284
#> GSM1105491 1 0.8162 0.409 0.644 0.192 0.164
#> GSM1105495 2 0.6912 0.342 0.028 0.628 0.344
#> GSM1105498 3 0.8473 0.670 0.176 0.208 0.616
#> GSM1105499 1 0.4399 0.768 0.812 0.000 0.188
#> GSM1105506 3 0.8800 0.398 0.116 0.396 0.488
#> GSM1105442 2 0.2878 0.719 0.000 0.904 0.096
#> GSM1105511 2 0.6507 0.535 0.028 0.688 0.284
#> GSM1105514 2 0.0000 0.755 0.000 1.000 0.000
#> GSM1105518 3 0.7773 0.466 0.072 0.316 0.612
#> GSM1105522 1 0.4555 0.764 0.800 0.000 0.200
#> GSM1105534 1 0.4399 0.768 0.812 0.000 0.188
#> GSM1105535 1 0.4555 0.764 0.800 0.000 0.200
#> GSM1105538 1 0.8213 0.514 0.632 0.140 0.228
#> GSM1105542 2 0.2878 0.719 0.000 0.904 0.096
#> GSM1105443 2 0.5656 0.583 0.004 0.712 0.284
#> GSM1105551 1 0.5650 0.590 0.688 0.000 0.312
#> GSM1105554 1 0.4399 0.768 0.812 0.000 0.188
#> GSM1105555 1 0.0747 0.780 0.984 0.000 0.016
#> GSM1105447 2 0.5588 0.595 0.004 0.720 0.276
#> GSM1105467 2 0.2096 0.749 0.004 0.944 0.052
#> GSM1105470 2 0.0000 0.755 0.000 1.000 0.000
#> GSM1105471 2 0.6770 0.502 0.044 0.692 0.264
#> GSM1105474 2 0.0000 0.755 0.000 1.000 0.000
#> GSM1105475 2 0.4682 0.664 0.004 0.804 0.192
#> GSM1105440 1 0.4555 0.764 0.800 0.000 0.200
#> GSM1105488 2 0.2796 0.720 0.000 0.908 0.092
#> GSM1105489 1 0.0747 0.780 0.984 0.000 0.016
#> GSM1105492 1 0.4555 0.764 0.800 0.000 0.200
#> GSM1105493 1 0.5053 0.704 0.812 0.024 0.164
#> GSM1105497 2 0.6355 0.457 0.024 0.696 0.280
#> GSM1105500 3 0.8473 0.670 0.176 0.208 0.616
#> GSM1105501 2 0.7644 0.371 0.068 0.624 0.308
#> GSM1105508 3 0.9328 0.192 0.372 0.168 0.460
#> GSM1105444 2 0.0237 0.754 0.000 0.996 0.004
#> GSM1105513 2 0.6507 0.535 0.028 0.688 0.284
#> GSM1105516 2 0.9700 -0.322 0.348 0.428 0.224
#> GSM1105520 3 0.7773 0.466 0.072 0.316 0.612
#> GSM1105524 1 0.4555 0.764 0.800 0.000 0.200
#> GSM1105536 2 0.7533 0.425 0.088 0.668 0.244
#> GSM1105537 1 0.4555 0.764 0.800 0.000 0.200
#> GSM1105540 1 0.8213 0.514 0.632 0.140 0.228
#> GSM1105544 2 0.9676 -0.338 0.220 0.432 0.348
#> GSM1105445 2 0.5656 0.583 0.004 0.712 0.284
#> GSM1105553 1 0.5650 0.590 0.688 0.000 0.312
#> GSM1105556 1 0.4399 0.768 0.812 0.000 0.188
#> GSM1105557 2 0.6507 0.535 0.028 0.688 0.284
#> GSM1105449 2 0.5517 0.603 0.004 0.728 0.268
#> GSM1105469 3 0.8284 0.657 0.148 0.224 0.628
#> GSM1105472 2 0.0000 0.755 0.000 1.000 0.000
#> GSM1105473 1 0.5719 0.694 0.792 0.052 0.156
#> GSM1105476 2 0.0000 0.755 0.000 1.000 0.000
#> GSM1105477 2 0.4682 0.664 0.004 0.804 0.192
#> GSM1105478 3 0.7841 0.630 0.092 0.272 0.636
#> GSM1105510 2 0.2711 0.720 0.000 0.912 0.088
#> GSM1105530 1 0.3038 0.759 0.896 0.000 0.104
#> GSM1105539 1 0.2959 0.761 0.900 0.000 0.100
#> GSM1105480 3 0.7841 0.630 0.092 0.272 0.636
#> GSM1105512 1 0.4399 0.768 0.812 0.000 0.188
#> GSM1105532 1 0.3038 0.759 0.896 0.000 0.104
#> GSM1105541 1 0.2959 0.761 0.900 0.000 0.100
#> GSM1105439 2 0.5016 0.623 0.000 0.760 0.240
#> GSM1105463 1 0.3116 0.759 0.892 0.000 0.108
#> GSM1105482 1 0.3192 0.782 0.888 0.000 0.112
#> GSM1105483 3 0.8284 0.657 0.148 0.224 0.628
#> GSM1105494 3 0.8125 0.669 0.172 0.180 0.648
#> GSM1105503 3 0.8570 0.505 0.120 0.316 0.564
#> GSM1105507 1 0.9054 0.318 0.496 0.144 0.360
#> GSM1105446 2 0.0892 0.748 0.000 0.980 0.020
#> GSM1105519 1 0.6441 0.704 0.696 0.028 0.276
#> GSM1105526 2 0.2261 0.740 0.000 0.932 0.068
#> GSM1105527 3 0.8284 0.657 0.148 0.224 0.628
#> GSM1105531 1 0.4002 0.731 0.840 0.000 0.160
#> GSM1105543 2 0.0237 0.755 0.000 0.996 0.004
#> GSM1105546 1 0.1643 0.784 0.956 0.000 0.044
#> GSM1105547 1 0.3181 0.764 0.912 0.024 0.064
#> GSM1105455 2 0.5058 0.620 0.000 0.756 0.244
#> GSM1105458 2 0.5656 0.583 0.004 0.712 0.284
#> GSM1105459 2 0.0000 0.755 0.000 1.000 0.000
#> GSM1105462 1 0.4137 0.754 0.872 0.032 0.096
#> GSM1105441 2 0.5016 0.623 0.000 0.760 0.240
#> GSM1105465 2 0.2878 0.719 0.000 0.904 0.096
#> GSM1105484 2 0.2066 0.738 0.000 0.940 0.060
#> GSM1105485 2 0.2878 0.719 0.000 0.904 0.096
#> GSM1105496 3 0.8125 0.669 0.172 0.180 0.648
#> GSM1105505 3 0.8570 0.505 0.120 0.316 0.564
#> GSM1105509 1 0.9054 0.318 0.496 0.144 0.360
#> GSM1105448 2 0.0000 0.755 0.000 1.000 0.000
#> GSM1105521 1 0.6441 0.704 0.696 0.028 0.276
#> GSM1105528 2 0.2261 0.740 0.000 0.932 0.068
#> GSM1105529 2 0.2878 0.719 0.000 0.904 0.096
#> GSM1105533 1 0.2066 0.773 0.940 0.000 0.060
#> GSM1105545 2 0.7606 0.412 0.092 0.664 0.244
#> GSM1105548 1 0.1643 0.784 0.956 0.000 0.044
#> GSM1105549 1 0.3181 0.764 0.912 0.024 0.064
#> GSM1105457 2 0.5058 0.620 0.000 0.756 0.244
#> GSM1105460 2 0.5656 0.583 0.004 0.712 0.284
#> GSM1105461 2 0.0000 0.755 0.000 1.000 0.000
#> GSM1105464 1 0.4137 0.754 0.872 0.032 0.096
#> GSM1105466 3 0.8258 0.623 0.112 0.284 0.604
#> GSM1105479 2 0.6224 0.498 0.016 0.688 0.296
#> GSM1105502 1 0.3116 0.760 0.892 0.000 0.108
#> GSM1105515 1 0.4399 0.768 0.812 0.000 0.188
#> GSM1105523 3 0.5621 0.316 0.308 0.000 0.692
#> GSM1105550 1 0.9276 0.173 0.524 0.212 0.264
#> GSM1105450 2 0.0000 0.755 0.000 1.000 0.000
#> GSM1105451 2 0.0000 0.755 0.000 1.000 0.000
#> GSM1105454 3 0.7295 -0.028 0.028 0.480 0.492
#> GSM1105468 2 0.0000 0.755 0.000 1.000 0.000
#> GSM1105481 2 0.6715 0.434 0.028 0.660 0.312
#> GSM1105504 1 0.3116 0.760 0.892 0.000 0.108
#> GSM1105517 1 0.7580 0.585 0.604 0.056 0.340
#> GSM1105525 3 0.5621 0.316 0.308 0.000 0.692
#> GSM1105552 1 0.9276 0.173 0.524 0.212 0.264
#> GSM1105452 2 0.2711 0.721 0.000 0.912 0.088
#> GSM1105453 2 0.0000 0.755 0.000 1.000 0.000
#> GSM1105456 3 0.7295 -0.028 0.028 0.480 0.492
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1105438 2 0.0188 0.7001 0.000 0.996 0.000 0.004
#> GSM1105486 2 0.1118 0.6956 0.000 0.964 0.000 0.036
#> GSM1105487 3 0.3975 0.4697 0.240 0.000 0.760 0.000
#> GSM1105490 2 0.4781 0.4728 0.004 0.660 0.000 0.336
#> GSM1105491 3 0.5847 0.4124 0.208 0.064 0.712 0.016
#> GSM1105495 2 0.6654 0.0173 0.048 0.476 0.016 0.460
#> GSM1105498 4 0.7410 0.6424 0.068 0.184 0.112 0.636
#> GSM1105499 1 0.4372 0.7745 0.728 0.000 0.268 0.004
#> GSM1105506 4 0.8116 0.3799 0.176 0.364 0.024 0.436
#> GSM1105442 2 0.4420 0.5974 0.204 0.776 0.008 0.012
#> GSM1105511 2 0.4781 0.4728 0.004 0.660 0.000 0.336
#> GSM1105514 2 0.0000 0.7007 0.000 1.000 0.000 0.000
#> GSM1105518 4 0.3717 0.5526 0.008 0.132 0.016 0.844
#> GSM1105522 1 0.3907 0.7710 0.768 0.000 0.232 0.000
#> GSM1105534 1 0.4193 0.7735 0.732 0.000 0.268 0.000
#> GSM1105535 1 0.3907 0.7710 0.768 0.000 0.232 0.000
#> GSM1105538 3 0.8932 0.0744 0.212 0.140 0.496 0.152
#> GSM1105542 2 0.4420 0.5974 0.204 0.776 0.008 0.012
#> GSM1105443 2 0.4543 0.5059 0.000 0.676 0.000 0.324
#> GSM1105551 3 0.5537 0.4079 0.056 0.000 0.688 0.256
#> GSM1105554 1 0.4372 0.7745 0.728 0.000 0.268 0.004
#> GSM1105555 3 0.3975 0.4697 0.240 0.000 0.760 0.000
#> GSM1105447 2 0.4677 0.5168 0.004 0.680 0.000 0.316
#> GSM1105467 2 0.1824 0.6931 0.004 0.936 0.000 0.060
#> GSM1105470 2 0.0000 0.7007 0.000 1.000 0.000 0.000
#> GSM1105471 2 0.6112 0.1744 0.004 0.544 0.040 0.412
#> GSM1105474 2 0.0000 0.7007 0.000 1.000 0.000 0.000
#> GSM1105475 2 0.5292 0.5976 0.060 0.724 0.000 0.216
#> GSM1105440 1 0.3907 0.7710 0.768 0.000 0.232 0.000
#> GSM1105488 2 0.4342 0.6021 0.196 0.784 0.008 0.012
#> GSM1105489 3 0.3975 0.4697 0.240 0.000 0.760 0.000
#> GSM1105492 1 0.3907 0.7710 0.768 0.000 0.232 0.000
#> GSM1105493 3 0.3166 0.5602 0.116 0.000 0.868 0.016
#> GSM1105497 2 0.7809 0.1557 0.180 0.472 0.012 0.336
#> GSM1105500 4 0.7410 0.6424 0.068 0.184 0.112 0.636
#> GSM1105501 2 0.6717 0.3469 0.076 0.600 0.016 0.308
#> GSM1105508 4 0.9748 0.1757 0.236 0.160 0.260 0.344
#> GSM1105444 2 0.0188 0.7001 0.000 0.996 0.000 0.004
#> GSM1105513 2 0.4781 0.4728 0.004 0.660 0.000 0.336
#> GSM1105516 2 0.9327 -0.1393 0.112 0.412 0.252 0.224
#> GSM1105520 4 0.3717 0.5526 0.008 0.132 0.016 0.844
#> GSM1105524 1 0.3907 0.7710 0.768 0.000 0.232 0.000
#> GSM1105536 2 0.7367 0.4005 0.064 0.596 0.068 0.272
#> GSM1105537 1 0.3907 0.7710 0.768 0.000 0.232 0.000
#> GSM1105540 3 0.8932 0.0744 0.212 0.140 0.496 0.152
#> GSM1105544 2 0.9587 -0.2685 0.188 0.336 0.148 0.328
#> GSM1105445 2 0.4543 0.5059 0.000 0.676 0.000 0.324
#> GSM1105553 3 0.5537 0.4079 0.056 0.000 0.688 0.256
#> GSM1105556 1 0.4372 0.7745 0.728 0.000 0.268 0.004
#> GSM1105557 2 0.4781 0.4728 0.004 0.660 0.000 0.336
#> GSM1105449 2 0.4746 0.5269 0.008 0.688 0.000 0.304
#> GSM1105469 4 0.7389 0.6287 0.184 0.192 0.024 0.600
#> GSM1105472 2 0.0000 0.7007 0.000 1.000 0.000 0.000
#> GSM1105473 3 0.4615 0.5647 0.020 0.048 0.816 0.116
#> GSM1105476 2 0.0000 0.7007 0.000 1.000 0.000 0.000
#> GSM1105477 2 0.5292 0.5976 0.060 0.724 0.000 0.216
#> GSM1105478 4 0.6648 0.6033 0.120 0.236 0.008 0.636
#> GSM1105510 2 0.4034 0.6156 0.180 0.804 0.004 0.012
#> GSM1105530 3 0.2002 0.6309 0.020 0.000 0.936 0.044
#> GSM1105539 3 0.2111 0.6304 0.024 0.000 0.932 0.044
#> GSM1105480 4 0.6648 0.6033 0.120 0.236 0.008 0.636
#> GSM1105512 1 0.4372 0.7745 0.728 0.000 0.268 0.004
#> GSM1105532 3 0.2002 0.6309 0.020 0.000 0.936 0.044
#> GSM1105541 3 0.2111 0.6304 0.024 0.000 0.932 0.044
#> GSM1105439 2 0.4193 0.5558 0.000 0.732 0.000 0.268
#> GSM1105463 3 0.2335 0.6290 0.020 0.000 0.920 0.060
#> GSM1105482 1 0.4989 0.3758 0.528 0.000 0.472 0.000
#> GSM1105483 4 0.7389 0.6287 0.184 0.192 0.024 0.600
#> GSM1105494 4 0.7020 0.6532 0.064 0.156 0.108 0.672
#> GSM1105503 4 0.4928 0.5682 0.008 0.132 0.072 0.788
#> GSM1105507 1 0.9570 0.2625 0.360 0.132 0.288 0.220
#> GSM1105446 2 0.2469 0.6638 0.108 0.892 0.000 0.000
#> GSM1105519 1 0.7450 0.5261 0.504 0.028 0.376 0.092
#> GSM1105526 2 0.3128 0.6848 0.040 0.884 0.000 0.076
#> GSM1105527 4 0.7389 0.6287 0.184 0.192 0.024 0.600
#> GSM1105531 3 0.2799 0.6106 0.008 0.000 0.884 0.108
#> GSM1105543 2 0.2197 0.6774 0.080 0.916 0.000 0.004
#> GSM1105546 3 0.4454 0.3405 0.308 0.000 0.692 0.000
#> GSM1105547 3 0.5040 0.3281 0.364 0.000 0.628 0.008
#> GSM1105455 2 0.4222 0.5523 0.000 0.728 0.000 0.272
#> GSM1105458 2 0.4522 0.5113 0.000 0.680 0.000 0.320
#> GSM1105459 2 0.0000 0.7007 0.000 1.000 0.000 0.000
#> GSM1105462 3 0.3027 0.6208 0.024 0.032 0.904 0.040
#> GSM1105441 2 0.4193 0.5558 0.000 0.732 0.000 0.268
#> GSM1105465 2 0.4420 0.5974 0.204 0.776 0.008 0.012
#> GSM1105484 2 0.3391 0.6423 0.148 0.844 0.004 0.004
#> GSM1105485 2 0.4420 0.5974 0.204 0.776 0.008 0.012
#> GSM1105496 4 0.7020 0.6532 0.064 0.156 0.108 0.672
#> GSM1105505 4 0.4928 0.5682 0.008 0.132 0.072 0.788
#> GSM1105509 1 0.9570 0.2625 0.360 0.132 0.288 0.220
#> GSM1105448 2 0.0000 0.7007 0.000 1.000 0.000 0.000
#> GSM1105521 1 0.7450 0.5261 0.504 0.028 0.376 0.092
#> GSM1105528 2 0.3128 0.6848 0.040 0.884 0.000 0.076
#> GSM1105529 2 0.4420 0.5974 0.204 0.776 0.008 0.012
#> GSM1105533 3 0.5848 0.2399 0.376 0.000 0.584 0.040
#> GSM1105545 2 0.7389 0.3914 0.064 0.592 0.068 0.276
#> GSM1105548 3 0.4454 0.3405 0.308 0.000 0.692 0.000
#> GSM1105549 3 0.5040 0.3281 0.364 0.000 0.628 0.008
#> GSM1105457 2 0.4222 0.5523 0.000 0.728 0.000 0.272
#> GSM1105460 2 0.4522 0.5113 0.000 0.680 0.000 0.320
#> GSM1105461 2 0.0000 0.7007 0.000 1.000 0.000 0.000
#> GSM1105464 3 0.3027 0.6208 0.024 0.032 0.904 0.040
#> GSM1105466 4 0.7205 0.5816 0.148 0.256 0.012 0.584
#> GSM1105479 2 0.5421 0.1868 0.004 0.548 0.008 0.440
#> GSM1105502 3 0.2483 0.6309 0.032 0.000 0.916 0.052
#> GSM1105515 1 0.4193 0.7735 0.732 0.000 0.268 0.000
#> GSM1105523 4 0.6745 0.4056 0.152 0.000 0.244 0.604
#> GSM1105550 3 0.9468 0.0550 0.152 0.204 0.416 0.228
#> GSM1105450 2 0.0000 0.7007 0.000 1.000 0.000 0.000
#> GSM1105451 2 0.0000 0.7007 0.000 1.000 0.000 0.000
#> GSM1105454 4 0.5223 0.3476 0.008 0.292 0.016 0.684
#> GSM1105468 2 0.0000 0.7007 0.000 1.000 0.000 0.000
#> GSM1105481 2 0.5669 0.1056 0.004 0.516 0.016 0.464
#> GSM1105504 3 0.2483 0.6309 0.032 0.000 0.916 0.052
#> GSM1105517 1 0.8538 0.3779 0.420 0.048 0.356 0.176
#> GSM1105525 4 0.6745 0.4056 0.152 0.000 0.244 0.604
#> GSM1105552 3 0.9468 0.0550 0.152 0.204 0.416 0.228
#> GSM1105452 2 0.3907 0.6168 0.180 0.808 0.004 0.008
#> GSM1105453 2 0.0000 0.7007 0.000 1.000 0.000 0.000
#> GSM1105456 4 0.5223 0.3476 0.008 0.292 0.016 0.684
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM1105438 2 0.0798 0.6443 0.000 0.976 0.000 0.016 0.008
#> GSM1105486 2 0.1638 0.6434 0.000 0.932 0.000 0.064 0.004
#> GSM1105487 3 0.4609 0.5673 0.280 0.000 0.688 0.024 0.008
#> GSM1105490 2 0.5276 0.3219 0.000 0.516 0.000 0.436 0.048
#> GSM1105491 3 0.5419 0.4804 0.004 0.064 0.700 0.028 0.204
#> GSM1105495 5 0.6477 0.2150 0.024 0.444 0.024 0.048 0.460
#> GSM1105498 4 0.6306 0.4550 0.012 0.084 0.088 0.676 0.140
#> GSM1105499 1 0.1638 0.7688 0.932 0.000 0.064 0.004 0.000
#> GSM1105506 4 0.5389 0.4366 0.084 0.176 0.004 0.712 0.024
#> GSM1105442 2 0.4484 0.4878 0.004 0.732 0.016 0.016 0.232
#> GSM1105511 2 0.5276 0.3219 0.000 0.516 0.000 0.436 0.048
#> GSM1105514 2 0.0000 0.6453 0.000 1.000 0.000 0.000 0.000
#> GSM1105518 5 0.4470 0.5272 0.000 0.008 0.008 0.328 0.656
#> GSM1105522 1 0.0794 0.7645 0.972 0.000 0.028 0.000 0.000
#> GSM1105534 1 0.1478 0.7682 0.936 0.000 0.064 0.000 0.000
#> GSM1105535 1 0.0794 0.7645 0.972 0.000 0.028 0.000 0.000
#> GSM1105538 3 0.7882 0.1591 0.208 0.052 0.432 0.292 0.016
#> GSM1105542 2 0.4484 0.4878 0.004 0.732 0.016 0.016 0.232
#> GSM1105443 2 0.6030 0.2879 0.000 0.464 0.000 0.420 0.116
#> GSM1105551 3 0.5833 0.4739 0.024 0.000 0.640 0.244 0.092
#> GSM1105554 1 0.1638 0.7688 0.932 0.000 0.064 0.004 0.000
#> GSM1105555 3 0.4609 0.5673 0.280 0.000 0.688 0.024 0.008
#> GSM1105447 2 0.6062 0.2965 0.000 0.464 0.000 0.416 0.120
#> GSM1105467 2 0.2719 0.6242 0.000 0.852 0.000 0.144 0.004
#> GSM1105470 2 0.0290 0.6469 0.000 0.992 0.000 0.008 0.000
#> GSM1105471 2 0.7101 -0.0897 0.004 0.472 0.032 0.156 0.336
#> GSM1105474 2 0.0000 0.6453 0.000 1.000 0.000 0.000 0.000
#> GSM1105475 2 0.5447 0.5297 0.000 0.640 0.000 0.248 0.112
#> GSM1105440 1 0.0880 0.7648 0.968 0.000 0.032 0.000 0.000
#> GSM1105488 2 0.4357 0.4939 0.004 0.740 0.016 0.012 0.228
#> GSM1105489 3 0.4609 0.5673 0.280 0.000 0.688 0.024 0.008
#> GSM1105492 1 0.0794 0.7645 0.972 0.000 0.028 0.000 0.000
#> GSM1105493 3 0.3291 0.6664 0.016 0.000 0.856 0.028 0.100
#> GSM1105497 5 0.5686 0.0489 0.000 0.428 0.024 0.036 0.512
#> GSM1105500 4 0.6306 0.4550 0.012 0.084 0.088 0.676 0.140
#> GSM1105501 4 0.6722 -0.1187 0.064 0.420 0.008 0.460 0.048
#> GSM1105508 4 0.6348 0.3893 0.144 0.008 0.240 0.596 0.012
#> GSM1105444 2 0.0290 0.6435 0.000 0.992 0.000 0.000 0.008
#> GSM1105513 2 0.5276 0.3219 0.000 0.516 0.000 0.436 0.048
#> GSM1105516 4 0.8375 0.3147 0.116 0.280 0.224 0.372 0.008
#> GSM1105520 5 0.4470 0.5272 0.000 0.008 0.008 0.328 0.656
#> GSM1105524 1 0.0794 0.7645 0.972 0.000 0.028 0.000 0.000
#> GSM1105536 2 0.7199 0.2783 0.008 0.496 0.064 0.332 0.100
#> GSM1105537 1 0.0794 0.7645 0.972 0.000 0.028 0.000 0.000
#> GSM1105540 3 0.7882 0.1591 0.208 0.052 0.432 0.292 0.016
#> GSM1105544 4 0.8821 0.3525 0.064 0.196 0.120 0.440 0.180
#> GSM1105445 2 0.6030 0.2879 0.000 0.464 0.000 0.420 0.116
#> GSM1105553 3 0.5833 0.4739 0.024 0.000 0.640 0.244 0.092
#> GSM1105556 1 0.1638 0.7688 0.932 0.000 0.064 0.004 0.000
#> GSM1105557 2 0.5276 0.3219 0.000 0.516 0.000 0.436 0.048
#> GSM1105449 2 0.6007 0.3346 0.000 0.488 0.000 0.396 0.116
#> GSM1105469 4 0.2925 0.5404 0.084 0.008 0.004 0.880 0.024
#> GSM1105472 2 0.0290 0.6469 0.000 0.992 0.000 0.008 0.000
#> GSM1105473 3 0.4525 0.6612 0.032 0.004 0.796 0.096 0.072
#> GSM1105476 2 0.0000 0.6453 0.000 1.000 0.000 0.000 0.000
#> GSM1105477 2 0.5447 0.5297 0.000 0.640 0.000 0.248 0.112
#> GSM1105478 4 0.3201 0.5097 0.012 0.052 0.008 0.876 0.052
#> GSM1105510 2 0.4460 0.5165 0.000 0.748 0.016 0.032 0.204
#> GSM1105530 3 0.1954 0.7210 0.032 0.000 0.932 0.008 0.028
#> GSM1105539 3 0.2036 0.7209 0.036 0.000 0.928 0.008 0.028
#> GSM1105480 4 0.3201 0.5097 0.012 0.052 0.008 0.876 0.052
#> GSM1105512 1 0.1638 0.7688 0.932 0.000 0.064 0.004 0.000
#> GSM1105532 3 0.1954 0.7210 0.032 0.000 0.932 0.008 0.028
#> GSM1105541 3 0.2036 0.7209 0.036 0.000 0.928 0.008 0.028
#> GSM1105439 2 0.5699 0.4408 0.000 0.584 0.000 0.308 0.108
#> GSM1105463 3 0.2352 0.7191 0.032 0.000 0.912 0.008 0.048
#> GSM1105482 1 0.4268 0.4904 0.708 0.000 0.272 0.016 0.004
#> GSM1105483 4 0.2925 0.5404 0.084 0.008 0.004 0.880 0.024
#> GSM1105494 4 0.6401 0.4084 0.008 0.080 0.080 0.652 0.180
#> GSM1105503 5 0.5462 0.5005 0.000 0.008 0.064 0.316 0.612
#> GSM1105507 1 0.7346 0.1289 0.396 0.016 0.244 0.336 0.008
#> GSM1105446 2 0.2753 0.5871 0.000 0.856 0.000 0.008 0.136
#> GSM1105519 1 0.5977 0.4294 0.592 0.004 0.284 0.116 0.004
#> GSM1105526 2 0.3697 0.6234 0.000 0.820 0.000 0.100 0.080
#> GSM1105527 4 0.2925 0.5404 0.084 0.008 0.004 0.880 0.024
#> GSM1105531 3 0.3164 0.6961 0.044 0.000 0.868 0.012 0.076
#> GSM1105543 2 0.2470 0.6077 0.000 0.884 0.000 0.012 0.104
#> GSM1105546 3 0.5189 0.4706 0.332 0.000 0.620 0.036 0.012
#> GSM1105547 3 0.5880 0.3746 0.360 0.000 0.560 0.028 0.052
#> GSM1105455 2 0.5772 0.4186 0.000 0.564 0.000 0.328 0.108
#> GSM1105458 2 0.6028 0.2957 0.000 0.468 0.000 0.416 0.116
#> GSM1105459 2 0.0404 0.6475 0.000 0.988 0.000 0.012 0.000
#> GSM1105462 3 0.2618 0.7129 0.036 0.000 0.900 0.052 0.012
#> GSM1105441 2 0.5699 0.4408 0.000 0.584 0.000 0.308 0.108
#> GSM1105465 2 0.4484 0.4878 0.004 0.732 0.016 0.016 0.232
#> GSM1105484 2 0.3618 0.5509 0.004 0.808 0.016 0.004 0.168
#> GSM1105485 2 0.4484 0.4878 0.004 0.732 0.016 0.016 0.232
#> GSM1105496 4 0.6401 0.4084 0.008 0.080 0.080 0.652 0.180
#> GSM1105505 5 0.5462 0.5005 0.000 0.008 0.064 0.316 0.612
#> GSM1105509 1 0.7346 0.1289 0.396 0.016 0.244 0.336 0.008
#> GSM1105448 2 0.0000 0.6453 0.000 1.000 0.000 0.000 0.000
#> GSM1105521 1 0.5977 0.4294 0.592 0.004 0.284 0.116 0.004
#> GSM1105528 2 0.3697 0.6234 0.000 0.820 0.000 0.100 0.080
#> GSM1105529 2 0.4484 0.4878 0.004 0.732 0.016 0.016 0.232
#> GSM1105533 1 0.5064 -0.0175 0.552 0.000 0.416 0.004 0.028
#> GSM1105545 2 0.7199 0.2764 0.008 0.496 0.064 0.332 0.100
#> GSM1105548 3 0.5189 0.4706 0.332 0.000 0.620 0.036 0.012
#> GSM1105549 3 0.5880 0.3746 0.360 0.000 0.560 0.028 0.052
#> GSM1105457 2 0.5772 0.4186 0.000 0.564 0.000 0.328 0.108
#> GSM1105460 2 0.6028 0.2957 0.000 0.468 0.000 0.416 0.116
#> GSM1105461 2 0.0404 0.6475 0.000 0.988 0.000 0.012 0.000
#> GSM1105464 3 0.2618 0.7129 0.036 0.000 0.900 0.052 0.012
#> GSM1105466 4 0.3859 0.5122 0.044 0.100 0.004 0.832 0.020
#> GSM1105479 2 0.6161 -0.0418 0.004 0.508 0.008 0.092 0.388
#> GSM1105502 3 0.2390 0.7223 0.044 0.000 0.912 0.012 0.032
#> GSM1105515 1 0.1478 0.7682 0.936 0.000 0.064 0.000 0.000
#> GSM1105523 4 0.5833 0.3642 0.028 0.000 0.256 0.636 0.080
#> GSM1105550 4 0.8254 0.0853 0.144 0.104 0.364 0.368 0.020
#> GSM1105450 2 0.0162 0.6461 0.000 0.996 0.000 0.004 0.000
#> GSM1105451 2 0.0000 0.6453 0.000 1.000 0.000 0.000 0.000
#> GSM1105454 5 0.5171 0.5425 0.024 0.164 0.004 0.076 0.732
#> GSM1105468 2 0.0703 0.6467 0.000 0.976 0.000 0.024 0.000
#> GSM1105481 2 0.5891 -0.1371 0.004 0.492 0.008 0.064 0.432
#> GSM1105504 3 0.2390 0.7223 0.044 0.000 0.912 0.012 0.032
#> GSM1105517 1 0.7268 0.2701 0.468 0.020 0.300 0.200 0.012
#> GSM1105525 4 0.5833 0.3642 0.028 0.000 0.256 0.636 0.080
#> GSM1105552 4 0.8254 0.0853 0.144 0.104 0.364 0.368 0.020
#> GSM1105452 2 0.3942 0.5192 0.004 0.772 0.016 0.004 0.204
#> GSM1105453 2 0.0000 0.6453 0.000 1.000 0.000 0.000 0.000
#> GSM1105456 5 0.5171 0.5425 0.024 0.164 0.004 0.076 0.732
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1105438 2 0.1082 0.6451 0.000 0.956 0.000 0.000 0.040 0.004
#> GSM1105486 2 0.2431 0.5037 0.000 0.860 0.000 0.008 0.132 0.000
#> GSM1105487 3 0.7636 0.2817 0.252 0.000 0.312 0.256 0.000 0.180
#> GSM1105490 5 0.5284 0.7218 0.000 0.388 0.000 0.104 0.508 0.000
#> GSM1105491 3 0.7440 0.3900 0.008 0.060 0.460 0.224 0.220 0.028
#> GSM1105495 6 0.5238 0.0866 0.000 0.408 0.000 0.000 0.096 0.496
#> GSM1105498 4 0.6197 0.3706 0.004 0.036 0.028 0.600 0.240 0.092
#> GSM1105499 1 0.0935 0.7308 0.964 0.000 0.032 0.000 0.004 0.000
#> GSM1105506 5 0.7226 -0.0241 0.076 0.172 0.008 0.360 0.380 0.004
#> GSM1105442 2 0.4018 0.5648 0.000 0.656 0.000 0.020 0.324 0.000
#> GSM1105511 5 0.5284 0.7218 0.000 0.388 0.000 0.104 0.508 0.000
#> GSM1105514 2 0.0146 0.6534 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM1105518 6 0.5066 0.6461 0.000 0.000 0.004 0.248 0.116 0.632
#> GSM1105522 1 0.0260 0.7253 0.992 0.000 0.000 0.000 0.000 0.008
#> GSM1105534 1 0.0790 0.7304 0.968 0.000 0.032 0.000 0.000 0.000
#> GSM1105535 1 0.0260 0.7253 0.992 0.000 0.000 0.000 0.000 0.008
#> GSM1105538 4 0.8334 0.0926 0.224 0.000 0.272 0.296 0.156 0.052
#> GSM1105542 2 0.4018 0.5648 0.000 0.656 0.000 0.020 0.324 0.000
#> GSM1105443 5 0.5060 0.7464 0.000 0.324 0.000 0.016 0.600 0.060
#> GSM1105551 4 0.5703 -0.0234 0.000 0.000 0.244 0.524 0.000 0.232
#> GSM1105554 1 0.0935 0.7308 0.964 0.000 0.032 0.000 0.004 0.000
#> GSM1105555 3 0.7636 0.2817 0.252 0.000 0.312 0.256 0.000 0.180
#> GSM1105447 5 0.5060 0.7426 0.000 0.324 0.000 0.016 0.600 0.060
#> GSM1105467 2 0.3568 0.3677 0.000 0.764 0.000 0.008 0.212 0.016
#> GSM1105470 2 0.0458 0.6475 0.000 0.984 0.000 0.000 0.016 0.000
#> GSM1105471 2 0.6855 -0.0936 0.000 0.432 0.012 0.036 0.216 0.304
#> GSM1105474 2 0.0146 0.6534 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM1105475 2 0.4581 -0.2567 0.000 0.516 0.000 0.036 0.448 0.000
#> GSM1105440 1 0.0363 0.7246 0.988 0.000 0.000 0.000 0.000 0.012
#> GSM1105488 2 0.3986 0.5706 0.000 0.664 0.000 0.020 0.316 0.000
#> GSM1105489 3 0.7636 0.2817 0.252 0.000 0.312 0.256 0.000 0.180
#> GSM1105492 1 0.0260 0.7253 0.992 0.000 0.000 0.000 0.000 0.008
#> GSM1105493 3 0.6297 0.5506 0.044 0.000 0.592 0.224 0.112 0.028
#> GSM1105497 2 0.6031 0.0671 0.000 0.404 0.000 0.000 0.252 0.344
#> GSM1105500 4 0.6197 0.3706 0.004 0.036 0.028 0.600 0.240 0.092
#> GSM1105501 5 0.6378 0.5986 0.064 0.288 0.008 0.104 0.536 0.000
#> GSM1105508 4 0.7904 0.4125 0.156 0.004 0.200 0.392 0.228 0.020
#> GSM1105444 2 0.1219 0.6512 0.000 0.948 0.000 0.000 0.048 0.004
#> GSM1105513 5 0.5284 0.7218 0.000 0.388 0.000 0.104 0.508 0.000
#> GSM1105516 5 0.8623 -0.1001 0.144 0.148 0.188 0.144 0.372 0.004
#> GSM1105520 6 0.5066 0.6461 0.000 0.000 0.004 0.248 0.116 0.632
#> GSM1105524 1 0.0260 0.7253 0.992 0.000 0.000 0.000 0.000 0.008
#> GSM1105536 2 0.6744 -0.3190 0.012 0.416 0.060 0.120 0.392 0.000
#> GSM1105537 1 0.0260 0.7253 0.992 0.000 0.000 0.000 0.000 0.008
#> GSM1105540 4 0.8334 0.0926 0.224 0.000 0.272 0.296 0.156 0.052
#> GSM1105544 5 0.8199 -0.2309 0.064 0.128 0.072 0.312 0.392 0.032
#> GSM1105445 5 0.5060 0.7464 0.000 0.324 0.000 0.016 0.600 0.060
#> GSM1105553 4 0.5703 -0.0234 0.000 0.000 0.244 0.524 0.000 0.232
#> GSM1105556 1 0.0935 0.7308 0.964 0.000 0.032 0.000 0.004 0.000
#> GSM1105557 5 0.5284 0.7218 0.000 0.388 0.000 0.104 0.508 0.000
#> GSM1105449 5 0.4892 0.7336 0.000 0.348 0.000 0.012 0.592 0.048
#> GSM1105469 4 0.5342 0.4341 0.076 0.004 0.008 0.540 0.372 0.000
#> GSM1105472 2 0.0458 0.6475 0.000 0.984 0.000 0.000 0.016 0.000
#> GSM1105473 3 0.5104 0.5946 0.044 0.004 0.748 0.052 0.052 0.100
#> GSM1105476 2 0.0146 0.6534 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM1105477 2 0.4581 -0.2567 0.000 0.516 0.000 0.036 0.448 0.000
#> GSM1105478 4 0.5550 0.3906 0.008 0.024 0.008 0.544 0.376 0.040
#> GSM1105510 2 0.3653 0.5956 0.000 0.692 0.000 0.008 0.300 0.000
#> GSM1105530 3 0.0790 0.6906 0.032 0.000 0.968 0.000 0.000 0.000
#> GSM1105539 3 0.1049 0.6918 0.032 0.000 0.960 0.008 0.000 0.000
#> GSM1105480 4 0.5550 0.3906 0.008 0.024 0.008 0.544 0.376 0.040
#> GSM1105512 1 0.0935 0.7308 0.964 0.000 0.032 0.000 0.004 0.000
#> GSM1105532 3 0.0790 0.6906 0.032 0.000 0.968 0.000 0.000 0.000
#> GSM1105541 3 0.1049 0.6918 0.032 0.000 0.960 0.008 0.000 0.000
#> GSM1105439 5 0.4389 0.6885 0.000 0.448 0.000 0.000 0.528 0.024
#> GSM1105463 3 0.1861 0.6917 0.036 0.000 0.928 0.016 0.000 0.020
#> GSM1105482 1 0.4187 0.5348 0.736 0.000 0.096 0.168 0.000 0.000
#> GSM1105483 4 0.5342 0.4341 0.076 0.004 0.008 0.540 0.372 0.000
#> GSM1105494 4 0.6286 0.3219 0.000 0.036 0.028 0.592 0.212 0.132
#> GSM1105503 6 0.6238 0.6187 0.000 0.000 0.080 0.248 0.112 0.560
#> GSM1105507 1 0.7533 0.0999 0.424 0.004 0.208 0.200 0.160 0.004
#> GSM1105446 2 0.2558 0.6362 0.000 0.840 0.000 0.004 0.156 0.000
#> GSM1105519 1 0.5625 0.4609 0.612 0.000 0.240 0.112 0.036 0.000
#> GSM1105526 2 0.3398 0.4311 0.000 0.740 0.000 0.008 0.252 0.000
#> GSM1105527 4 0.5342 0.4341 0.076 0.004 0.008 0.540 0.372 0.000
#> GSM1105531 3 0.1556 0.6615 0.000 0.000 0.920 0.000 0.000 0.080
#> GSM1105543 2 0.2402 0.6404 0.000 0.856 0.000 0.004 0.140 0.000
#> GSM1105546 3 0.6862 0.1816 0.344 0.000 0.368 0.236 0.000 0.052
#> GSM1105547 1 0.6941 -0.1269 0.392 0.000 0.364 0.180 0.056 0.008
#> GSM1105455 5 0.4498 0.7125 0.000 0.428 0.000 0.004 0.544 0.024
#> GSM1105458 5 0.5073 0.7479 0.000 0.328 0.000 0.016 0.596 0.060
#> GSM1105459 2 0.0713 0.6408 0.000 0.972 0.000 0.000 0.028 0.000
#> GSM1105462 3 0.2990 0.6688 0.036 0.000 0.872 0.048 0.040 0.004
#> GSM1105441 5 0.4389 0.6885 0.000 0.448 0.000 0.000 0.528 0.024
#> GSM1105465 2 0.4018 0.5648 0.000 0.656 0.000 0.020 0.324 0.000
#> GSM1105484 2 0.3398 0.6125 0.000 0.740 0.000 0.008 0.252 0.000
#> GSM1105485 2 0.4018 0.5648 0.000 0.656 0.000 0.020 0.324 0.000
#> GSM1105496 4 0.6286 0.3219 0.000 0.036 0.028 0.592 0.212 0.132
#> GSM1105505 6 0.6238 0.6187 0.000 0.000 0.080 0.248 0.112 0.560
#> GSM1105509 1 0.7533 0.0999 0.424 0.004 0.208 0.200 0.160 0.004
#> GSM1105448 2 0.0260 0.6517 0.000 0.992 0.000 0.000 0.008 0.000
#> GSM1105521 1 0.5625 0.4609 0.612 0.000 0.240 0.112 0.036 0.000
#> GSM1105528 2 0.3398 0.4311 0.000 0.740 0.000 0.008 0.252 0.000
#> GSM1105529 2 0.4018 0.5648 0.000 0.656 0.000 0.020 0.324 0.000
#> GSM1105533 1 0.4184 0.0671 0.556 0.000 0.432 0.008 0.000 0.004
#> GSM1105545 2 0.6769 -0.3157 0.012 0.416 0.060 0.124 0.388 0.000
#> GSM1105548 3 0.6862 0.1816 0.344 0.000 0.368 0.236 0.000 0.052
#> GSM1105549 1 0.6941 -0.1269 0.392 0.000 0.364 0.180 0.056 0.008
#> GSM1105457 5 0.4498 0.7125 0.000 0.428 0.000 0.004 0.544 0.024
#> GSM1105460 5 0.5073 0.7479 0.000 0.328 0.000 0.016 0.596 0.060
#> GSM1105461 2 0.0713 0.6408 0.000 0.972 0.000 0.000 0.028 0.000
#> GSM1105464 3 0.2990 0.6688 0.036 0.000 0.872 0.048 0.040 0.004
#> GSM1105466 4 0.6226 0.2686 0.036 0.092 0.008 0.496 0.364 0.004
#> GSM1105479 2 0.5814 -0.0185 0.000 0.468 0.004 0.000 0.164 0.364
#> GSM1105502 3 0.1908 0.6880 0.044 0.000 0.924 0.012 0.000 0.020
#> GSM1105515 1 0.0790 0.7304 0.968 0.000 0.032 0.000 0.000 0.000
#> GSM1105523 4 0.5067 0.3334 0.004 0.000 0.300 0.612 0.080 0.004
#> GSM1105550 4 0.8308 0.2284 0.164 0.032 0.256 0.328 0.212 0.008
#> GSM1105450 2 0.0363 0.6498 0.000 0.988 0.000 0.000 0.012 0.000
#> GSM1105451 2 0.0146 0.6534 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM1105454 6 0.3655 0.6220 0.000 0.108 0.000 0.004 0.088 0.800
#> GSM1105468 2 0.0937 0.6350 0.000 0.960 0.000 0.000 0.040 0.000
#> GSM1105481 2 0.5682 -0.1029 0.000 0.452 0.004 0.000 0.136 0.408
#> GSM1105504 3 0.1908 0.6880 0.044 0.000 0.924 0.012 0.000 0.020
#> GSM1105517 1 0.7038 0.3014 0.484 0.012 0.252 0.184 0.064 0.004
#> GSM1105525 4 0.5067 0.3334 0.004 0.000 0.300 0.612 0.080 0.004
#> GSM1105552 4 0.8308 0.2284 0.164 0.032 0.256 0.328 0.212 0.008
#> GSM1105452 2 0.3512 0.6004 0.000 0.720 0.000 0.008 0.272 0.000
#> GSM1105453 2 0.0146 0.6534 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM1105456 6 0.3655 0.6220 0.000 0.108 0.000 0.004 0.088 0.800
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 agent(p) other(p) time(p) individual(p) k
#> SD:hclust 112 0.8793 0.6872 1.000 2.27e-03 2
#> SD:hclust 98 0.5025 0.4323 0.879 2.44e-04 3
#> SD:hclust 81 0.2123 0.0583 0.978 1.34e-05 4
#> SD:hclust 64 0.0819 0.5010 0.970 4.52e-05 5
#> SD:hclust 72 0.1442 0.0337 0.998 2.24e-06 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 44956 rows and 120 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 0.854 0.939 0.973 0.4901 0.513 0.513
#> 3 3 0.573 0.701 0.829 0.3419 0.738 0.526
#> 4 4 0.540 0.395 0.632 0.1195 0.802 0.526
#> 5 5 0.624 0.467 0.632 0.0720 0.800 0.448
#> 6 6 0.713 0.650 0.745 0.0454 0.897 0.582
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
#> GSM1105438 2 0.0000 0.966 0.000 1.000
#> GSM1105486 2 0.0000 0.966 0.000 1.000
#> GSM1105487 1 0.0000 0.978 1.000 0.000
#> GSM1105490 2 0.0000 0.966 0.000 1.000
#> GSM1105491 2 0.6438 0.819 0.164 0.836
#> GSM1105495 2 0.6438 0.819 0.164 0.836
#> GSM1105498 2 0.6531 0.814 0.168 0.832
#> GSM1105499 1 0.0000 0.978 1.000 0.000
#> GSM1105506 2 0.0000 0.966 0.000 1.000
#> GSM1105442 2 0.0000 0.966 0.000 1.000
#> GSM1105511 2 0.0000 0.966 0.000 1.000
#> GSM1105514 2 0.0000 0.966 0.000 1.000
#> GSM1105518 2 0.1843 0.946 0.028 0.972
#> GSM1105522 1 0.0000 0.978 1.000 0.000
#> GSM1105534 1 0.0000 0.978 1.000 0.000
#> GSM1105535 1 0.0000 0.978 1.000 0.000
#> GSM1105538 1 0.0000 0.978 1.000 0.000
#> GSM1105542 2 0.0000 0.966 0.000 1.000
#> GSM1105443 2 0.0000 0.966 0.000 1.000
#> GSM1105551 1 0.0000 0.978 1.000 0.000
#> GSM1105554 1 0.0000 0.978 1.000 0.000
#> GSM1105555 1 0.0000 0.978 1.000 0.000
#> GSM1105447 2 0.0000 0.966 0.000 1.000
#> GSM1105467 2 0.0000 0.966 0.000 1.000
#> GSM1105470 2 0.0000 0.966 0.000 1.000
#> GSM1105471 2 0.2043 0.943 0.032 0.968
#> GSM1105474 2 0.0000 0.966 0.000 1.000
#> GSM1105475 2 0.0000 0.966 0.000 1.000
#> GSM1105440 1 0.0000 0.978 1.000 0.000
#> GSM1105488 2 0.0000 0.966 0.000 1.000
#> GSM1105489 1 0.0000 0.978 1.000 0.000
#> GSM1105492 1 0.0000 0.978 1.000 0.000
#> GSM1105493 1 0.0000 0.978 1.000 0.000
#> GSM1105497 2 0.0000 0.966 0.000 1.000
#> GSM1105500 2 0.0000 0.966 0.000 1.000
#> GSM1105501 2 0.0000 0.966 0.000 1.000
#> GSM1105508 1 0.0000 0.978 1.000 0.000
#> GSM1105444 2 0.0000 0.966 0.000 1.000
#> GSM1105513 2 0.0000 0.966 0.000 1.000
#> GSM1105516 1 0.9963 0.155 0.536 0.464
#> GSM1105520 2 0.7219 0.773 0.200 0.800
#> GSM1105524 1 0.0000 0.978 1.000 0.000
#> GSM1105536 2 0.0000 0.966 0.000 1.000
#> GSM1105537 1 0.0000 0.978 1.000 0.000
#> GSM1105540 1 0.0000 0.978 1.000 0.000
#> GSM1105544 2 0.0000 0.966 0.000 1.000
#> GSM1105445 2 0.0000 0.966 0.000 1.000
#> GSM1105553 2 0.6438 0.819 0.164 0.836
#> GSM1105556 1 0.0000 0.978 1.000 0.000
#> GSM1105557 2 0.0000 0.966 0.000 1.000
#> GSM1105449 2 0.0000 0.966 0.000 1.000
#> GSM1105469 1 0.6148 0.813 0.848 0.152
#> GSM1105472 2 0.0000 0.966 0.000 1.000
#> GSM1105473 1 0.0000 0.978 1.000 0.000
#> GSM1105476 2 0.0000 0.966 0.000 1.000
#> GSM1105477 2 0.0000 0.966 0.000 1.000
#> GSM1105478 2 0.4690 0.884 0.100 0.900
#> GSM1105510 2 0.0000 0.966 0.000 1.000
#> GSM1105530 1 0.0000 0.978 1.000 0.000
#> GSM1105539 1 0.0000 0.978 1.000 0.000
#> GSM1105480 2 0.0000 0.966 0.000 1.000
#> GSM1105512 1 0.0000 0.978 1.000 0.000
#> GSM1105532 1 0.0000 0.978 1.000 0.000
#> GSM1105541 1 0.0000 0.978 1.000 0.000
#> GSM1105439 2 0.0000 0.966 0.000 1.000
#> GSM1105463 1 0.0000 0.978 1.000 0.000
#> GSM1105482 1 0.0000 0.978 1.000 0.000
#> GSM1105483 2 0.0672 0.960 0.008 0.992
#> GSM1105494 2 0.0000 0.966 0.000 1.000
#> GSM1105503 2 0.9661 0.408 0.392 0.608
#> GSM1105507 1 0.6438 0.798 0.836 0.164
#> GSM1105446 2 0.0000 0.966 0.000 1.000
#> GSM1105519 1 0.0000 0.978 1.000 0.000
#> GSM1105526 2 0.0000 0.966 0.000 1.000
#> GSM1105527 2 0.0000 0.966 0.000 1.000
#> GSM1105531 1 0.0000 0.978 1.000 0.000
#> GSM1105543 2 0.0000 0.966 0.000 1.000
#> GSM1105546 1 0.0000 0.978 1.000 0.000
#> GSM1105547 1 0.0000 0.978 1.000 0.000
#> GSM1105455 2 0.0000 0.966 0.000 1.000
#> GSM1105458 2 0.0000 0.966 0.000 1.000
#> GSM1105459 2 0.0000 0.966 0.000 1.000
#> GSM1105462 1 0.7528 0.704 0.784 0.216
#> GSM1105441 2 0.0000 0.966 0.000 1.000
#> GSM1105465 2 0.0376 0.963 0.004 0.996
#> GSM1105484 2 0.0000 0.966 0.000 1.000
#> GSM1105485 2 0.0000 0.966 0.000 1.000
#> GSM1105496 2 0.9552 0.448 0.376 0.624
#> GSM1105505 1 0.0000 0.978 1.000 0.000
#> GSM1105509 1 0.0000 0.978 1.000 0.000
#> GSM1105448 2 0.0000 0.966 0.000 1.000
#> GSM1105521 1 0.0000 0.978 1.000 0.000
#> GSM1105528 2 0.0000 0.966 0.000 1.000
#> GSM1105529 2 0.0000 0.966 0.000 1.000
#> GSM1105533 1 0.0000 0.978 1.000 0.000
#> GSM1105545 2 0.0000 0.966 0.000 1.000
#> GSM1105548 1 0.0000 0.978 1.000 0.000
#> GSM1105549 1 0.0000 0.978 1.000 0.000
#> GSM1105457 2 0.0000 0.966 0.000 1.000
#> GSM1105460 2 0.0000 0.966 0.000 1.000
#> GSM1105461 2 0.0000 0.966 0.000 1.000
#> GSM1105464 1 0.0000 0.978 1.000 0.000
#> GSM1105466 2 0.0000 0.966 0.000 1.000
#> GSM1105479 2 0.0000 0.966 0.000 1.000
#> GSM1105502 1 0.0000 0.978 1.000 0.000
#> GSM1105515 1 0.0000 0.978 1.000 0.000
#> GSM1105523 1 0.0000 0.978 1.000 0.000
#> GSM1105550 1 0.0000 0.978 1.000 0.000
#> GSM1105450 2 0.0000 0.966 0.000 1.000
#> GSM1105451 2 0.0000 0.966 0.000 1.000
#> GSM1105454 2 0.6438 0.819 0.164 0.836
#> GSM1105468 2 0.0000 0.966 0.000 1.000
#> GSM1105481 2 0.6531 0.814 0.168 0.832
#> GSM1105504 1 0.0000 0.978 1.000 0.000
#> GSM1105517 1 0.0000 0.978 1.000 0.000
#> GSM1105525 1 0.0000 0.978 1.000 0.000
#> GSM1105552 1 0.0000 0.978 1.000 0.000
#> GSM1105452 2 0.0000 0.966 0.000 1.000
#> GSM1105453 2 0.0000 0.966 0.000 1.000
#> GSM1105456 2 0.6438 0.819 0.164 0.836
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1105438 2 0.0000 0.800 0.000 1.000 0.000
#> GSM1105486 2 0.2356 0.790 0.000 0.928 0.072
#> GSM1105487 1 0.2796 0.902 0.908 0.000 0.092
#> GSM1105490 3 0.6215 0.562 0.000 0.428 0.572
#> GSM1105491 2 0.6483 0.406 0.008 0.600 0.392
#> GSM1105495 2 0.6888 0.335 0.016 0.552 0.432
#> GSM1105498 3 0.1031 0.637 0.000 0.024 0.976
#> GSM1105499 1 0.0000 0.917 1.000 0.000 0.000
#> GSM1105506 3 0.6811 0.582 0.016 0.404 0.580
#> GSM1105442 2 0.4796 0.652 0.000 0.780 0.220
#> GSM1105511 3 0.6811 0.582 0.016 0.404 0.580
#> GSM1105514 2 0.0592 0.799 0.000 0.988 0.012
#> GSM1105518 3 0.2448 0.643 0.000 0.076 0.924
#> GSM1105522 1 0.1163 0.912 0.972 0.000 0.028
#> GSM1105534 1 0.0000 0.917 1.000 0.000 0.000
#> GSM1105535 1 0.0000 0.917 1.000 0.000 0.000
#> GSM1105538 1 0.0000 0.917 1.000 0.000 0.000
#> GSM1105542 2 0.3038 0.768 0.000 0.896 0.104
#> GSM1105443 3 0.6252 0.542 0.000 0.444 0.556
#> GSM1105551 1 0.2878 0.900 0.904 0.000 0.096
#> GSM1105554 1 0.0000 0.917 1.000 0.000 0.000
#> GSM1105555 1 0.2878 0.900 0.904 0.000 0.096
#> GSM1105447 3 0.6154 0.573 0.000 0.408 0.592
#> GSM1105467 2 0.2261 0.793 0.000 0.932 0.068
#> GSM1105470 2 0.2448 0.787 0.000 0.924 0.076
#> GSM1105471 3 0.3686 0.651 0.000 0.140 0.860
#> GSM1105474 2 0.2261 0.793 0.000 0.932 0.068
#> GSM1105475 2 0.6126 -0.114 0.000 0.600 0.400
#> GSM1105440 1 0.0000 0.917 1.000 0.000 0.000
#> GSM1105488 2 0.3038 0.768 0.000 0.896 0.104
#> GSM1105489 1 0.2878 0.900 0.904 0.000 0.096
#> GSM1105492 1 0.0000 0.917 1.000 0.000 0.000
#> GSM1105493 1 0.3116 0.896 0.892 0.000 0.108
#> GSM1105497 2 0.5497 0.559 0.000 0.708 0.292
#> GSM1105500 3 0.5678 0.573 0.000 0.316 0.684
#> GSM1105501 3 0.6836 0.575 0.016 0.412 0.572
#> GSM1105508 1 0.1964 0.900 0.944 0.000 0.056
#> GSM1105444 2 0.0000 0.800 0.000 1.000 0.000
#> GSM1105513 3 0.6192 0.572 0.000 0.420 0.580
#> GSM1105516 1 0.8666 0.359 0.584 0.264 0.152
#> GSM1105520 3 0.2056 0.629 0.024 0.024 0.952
#> GSM1105524 1 0.0000 0.917 1.000 0.000 0.000
#> GSM1105536 2 0.6252 -0.218 0.000 0.556 0.444
#> GSM1105537 1 0.0000 0.917 1.000 0.000 0.000
#> GSM1105540 3 0.6192 0.300 0.420 0.000 0.580
#> GSM1105544 3 0.6208 0.633 0.068 0.164 0.768
#> GSM1105445 3 0.3752 0.655 0.000 0.144 0.856
#> GSM1105553 3 0.2492 0.622 0.016 0.048 0.936
#> GSM1105556 1 0.0000 0.917 1.000 0.000 0.000
#> GSM1105557 3 0.6192 0.572 0.000 0.420 0.580
#> GSM1105449 2 0.2165 0.794 0.000 0.936 0.064
#> GSM1105469 3 0.6704 0.428 0.376 0.016 0.608
#> GSM1105472 2 0.1163 0.799 0.000 0.972 0.028
#> GSM1105473 1 0.4399 0.854 0.812 0.000 0.188
#> GSM1105476 2 0.2448 0.787 0.000 0.924 0.076
#> GSM1105477 2 0.6260 -0.170 0.000 0.552 0.448
#> GSM1105478 3 0.3619 0.656 0.000 0.136 0.864
#> GSM1105510 2 0.3038 0.768 0.000 0.896 0.104
#> GSM1105530 1 0.3482 0.895 0.872 0.000 0.128
#> GSM1105539 1 0.3482 0.895 0.872 0.000 0.128
#> GSM1105480 3 0.6396 0.632 0.016 0.320 0.664
#> GSM1105512 1 0.0424 0.916 0.992 0.000 0.008
#> GSM1105532 1 0.3482 0.895 0.872 0.000 0.128
#> GSM1105541 1 0.3482 0.895 0.872 0.000 0.128
#> GSM1105439 3 0.6260 0.535 0.000 0.448 0.552
#> GSM1105463 1 0.6180 0.559 0.584 0.000 0.416
#> GSM1105482 1 0.0000 0.917 1.000 0.000 0.000
#> GSM1105483 3 0.8040 0.601 0.092 0.300 0.608
#> GSM1105494 3 0.4750 0.645 0.000 0.216 0.784
#> GSM1105503 3 0.2569 0.632 0.032 0.032 0.936
#> GSM1105507 1 0.3207 0.872 0.904 0.012 0.084
#> GSM1105446 2 0.0592 0.798 0.000 0.988 0.012
#> GSM1105519 1 0.1163 0.912 0.972 0.000 0.028
#> GSM1105526 3 0.6180 0.547 0.000 0.416 0.584
#> GSM1105527 3 0.8040 0.601 0.092 0.300 0.608
#> GSM1105531 3 0.5016 0.377 0.240 0.000 0.760
#> GSM1105543 2 0.0424 0.799 0.000 0.992 0.008
#> GSM1105546 1 0.0000 0.917 1.000 0.000 0.000
#> GSM1105547 1 0.0000 0.917 1.000 0.000 0.000
#> GSM1105455 3 0.6280 0.513 0.000 0.460 0.540
#> GSM1105458 3 0.5835 0.610 0.000 0.340 0.660
#> GSM1105459 2 0.2261 0.793 0.000 0.932 0.068
#> GSM1105462 3 0.1643 0.609 0.044 0.000 0.956
#> GSM1105441 2 0.2261 0.793 0.000 0.932 0.068
#> GSM1105465 2 0.5948 0.465 0.000 0.640 0.360
#> GSM1105484 2 0.3038 0.768 0.000 0.896 0.104
#> GSM1105485 2 0.3038 0.768 0.000 0.896 0.104
#> GSM1105496 3 0.2318 0.618 0.028 0.028 0.944
#> GSM1105505 3 0.2796 0.575 0.092 0.000 0.908
#> GSM1105509 1 0.1964 0.900 0.944 0.000 0.056
#> GSM1105448 2 0.0000 0.800 0.000 1.000 0.000
#> GSM1105521 1 0.1163 0.912 0.972 0.000 0.028
#> GSM1105528 2 0.2796 0.773 0.000 0.908 0.092
#> GSM1105529 2 0.3038 0.768 0.000 0.896 0.104
#> GSM1105533 1 0.2959 0.899 0.900 0.000 0.100
#> GSM1105545 3 0.6244 0.544 0.000 0.440 0.560
#> GSM1105548 1 0.3038 0.899 0.896 0.000 0.104
#> GSM1105549 1 0.1411 0.915 0.964 0.000 0.036
#> GSM1105457 3 0.6192 0.572 0.000 0.420 0.580
#> GSM1105460 3 0.6252 0.542 0.000 0.444 0.556
#> GSM1105461 2 0.2261 0.793 0.000 0.932 0.068
#> GSM1105464 1 0.3482 0.895 0.872 0.000 0.128
#> GSM1105466 3 0.6192 0.572 0.000 0.420 0.580
#> GSM1105479 3 0.6045 0.598 0.000 0.380 0.620
#> GSM1105502 1 0.3482 0.895 0.872 0.000 0.128
#> GSM1105515 1 0.0000 0.917 1.000 0.000 0.000
#> GSM1105523 3 0.5529 0.402 0.296 0.000 0.704
#> GSM1105550 3 0.6140 0.339 0.404 0.000 0.596
#> GSM1105450 2 0.2261 0.793 0.000 0.932 0.068
#> GSM1105451 2 0.2261 0.793 0.000 0.932 0.068
#> GSM1105454 3 0.3528 0.632 0.016 0.092 0.892
#> GSM1105468 2 0.2261 0.793 0.000 0.932 0.068
#> GSM1105481 3 0.2902 0.620 0.016 0.064 0.920
#> GSM1105504 3 0.4931 0.393 0.232 0.000 0.768
#> GSM1105517 1 0.2711 0.883 0.912 0.000 0.088
#> GSM1105525 1 0.4002 0.874 0.840 0.000 0.160
#> GSM1105552 1 0.6204 0.548 0.576 0.000 0.424
#> GSM1105452 2 0.2711 0.775 0.000 0.912 0.088
#> GSM1105453 2 0.2261 0.793 0.000 0.932 0.068
#> GSM1105456 3 0.3528 0.632 0.016 0.092 0.892
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1105438 2 0.3356 0.42341 0.000 0.824 0.000 0.176
#> GSM1105486 2 0.0000 0.50745 0.000 1.000 0.000 0.000
#> GSM1105487 1 0.3448 0.78638 0.828 0.000 0.004 0.168
#> GSM1105490 2 0.7806 0.10999 0.000 0.412 0.324 0.264
#> GSM1105491 4 0.7546 0.10688 0.000 0.188 0.400 0.412
#> GSM1105495 3 0.7314 -0.15000 0.000 0.168 0.496 0.336
#> GSM1105498 3 0.3335 0.40639 0.000 0.020 0.860 0.120
#> GSM1105499 1 0.1389 0.80499 0.952 0.000 0.000 0.048
#> GSM1105506 2 0.8127 0.05118 0.008 0.376 0.348 0.268
#> GSM1105442 2 0.7226 0.02223 0.000 0.468 0.144 0.388
#> GSM1105511 2 0.8182 0.00946 0.008 0.360 0.324 0.308
#> GSM1105514 2 0.3356 0.42341 0.000 0.824 0.000 0.176
#> GSM1105518 3 0.2412 0.43072 0.000 0.084 0.908 0.008
#> GSM1105522 1 0.4824 0.74820 0.780 0.000 0.076 0.144
#> GSM1105534 1 0.0188 0.80263 0.996 0.000 0.000 0.004
#> GSM1105535 1 0.1474 0.80471 0.948 0.000 0.000 0.052
#> GSM1105538 1 0.0592 0.80179 0.984 0.000 0.000 0.016
#> GSM1105542 2 0.5326 0.26191 0.000 0.604 0.016 0.380
#> GSM1105443 2 0.6912 0.27969 0.000 0.592 0.192 0.216
#> GSM1105551 1 0.4095 0.77772 0.804 0.000 0.024 0.172
#> GSM1105554 1 0.0188 0.80401 0.996 0.000 0.000 0.004
#> GSM1105555 1 0.4070 0.77584 0.824 0.000 0.044 0.132
#> GSM1105447 2 0.6014 0.21714 0.000 0.588 0.360 0.052
#> GSM1105467 2 0.0000 0.50745 0.000 1.000 0.000 0.000
#> GSM1105470 2 0.0000 0.50745 0.000 1.000 0.000 0.000
#> GSM1105471 3 0.5180 0.34551 0.000 0.196 0.740 0.064
#> GSM1105474 2 0.0000 0.50745 0.000 1.000 0.000 0.000
#> GSM1105475 2 0.6397 0.31499 0.000 0.648 0.144 0.208
#> GSM1105440 1 0.1302 0.80596 0.956 0.000 0.000 0.044
#> GSM1105488 2 0.5326 0.26191 0.000 0.604 0.016 0.380
#> GSM1105489 1 0.3447 0.78531 0.852 0.000 0.020 0.128
#> GSM1105492 1 0.0188 0.80263 0.996 0.000 0.000 0.004
#> GSM1105493 1 0.4907 0.74760 0.764 0.000 0.060 0.176
#> GSM1105497 4 0.7836 0.10016 0.000 0.328 0.272 0.400
#> GSM1105500 4 0.7818 -0.08065 0.000 0.264 0.332 0.404
#> GSM1105501 2 0.8170 0.03011 0.008 0.372 0.312 0.308
#> GSM1105508 1 0.5998 0.64718 0.680 0.000 0.108 0.212
#> GSM1105444 2 0.3400 0.42081 0.000 0.820 0.000 0.180
#> GSM1105513 2 0.7832 0.07237 0.000 0.392 0.344 0.264
#> GSM1105516 4 0.9491 0.02798 0.284 0.136 0.196 0.384
#> GSM1105520 3 0.0657 0.44578 0.000 0.012 0.984 0.004
#> GSM1105524 1 0.1474 0.80471 0.948 0.000 0.000 0.052
#> GSM1105536 2 0.7738 0.06347 0.000 0.424 0.240 0.336
#> GSM1105537 1 0.1474 0.80471 0.948 0.000 0.000 0.052
#> GSM1105540 3 0.7629 -0.01186 0.204 0.000 0.400 0.396
#> GSM1105544 3 0.8301 0.04270 0.104 0.072 0.456 0.368
#> GSM1105445 3 0.5747 0.32567 0.000 0.196 0.704 0.100
#> GSM1105553 3 0.2676 0.42805 0.000 0.012 0.896 0.092
#> GSM1105556 1 0.0592 0.80179 0.984 0.000 0.000 0.016
#> GSM1105557 2 0.7985 0.08760 0.004 0.396 0.336 0.264
#> GSM1105449 2 0.1940 0.47441 0.000 0.924 0.076 0.000
#> GSM1105469 4 0.9312 -0.08531 0.176 0.116 0.352 0.356
#> GSM1105472 2 0.0000 0.50745 0.000 1.000 0.000 0.000
#> GSM1105473 1 0.7268 0.56064 0.516 0.000 0.172 0.312
#> GSM1105476 2 0.0000 0.50745 0.000 1.000 0.000 0.000
#> GSM1105477 2 0.7740 0.03073 0.000 0.404 0.232 0.364
#> GSM1105478 3 0.7002 0.19067 0.000 0.164 0.568 0.268
#> GSM1105510 2 0.5364 0.25014 0.000 0.592 0.016 0.392
#> GSM1105530 1 0.7133 0.61150 0.548 0.000 0.172 0.280
#> GSM1105539 1 0.7028 0.63209 0.568 0.000 0.172 0.260
#> GSM1105480 3 0.7937 0.08342 0.012 0.236 0.480 0.272
#> GSM1105512 1 0.1824 0.80129 0.936 0.000 0.004 0.060
#> GSM1105532 1 0.7133 0.61150 0.548 0.000 0.172 0.280
#> GSM1105541 1 0.7005 0.63433 0.572 0.000 0.172 0.256
#> GSM1105439 2 0.7114 0.25332 0.000 0.560 0.188 0.252
#> GSM1105463 3 0.6976 0.25677 0.136 0.000 0.544 0.320
#> GSM1105482 1 0.1890 0.80463 0.936 0.000 0.008 0.056
#> GSM1105483 3 0.9291 -0.05396 0.092 0.224 0.356 0.328
#> GSM1105494 3 0.6449 0.24830 0.000 0.152 0.644 0.204
#> GSM1105503 3 0.2271 0.44526 0.000 0.008 0.916 0.076
#> GSM1105507 1 0.7501 0.11928 0.472 0.000 0.196 0.332
#> GSM1105446 2 0.3873 0.39217 0.000 0.772 0.000 0.228
#> GSM1105519 1 0.3652 0.77131 0.856 0.000 0.052 0.092
#> GSM1105526 2 0.8128 0.02405 0.008 0.384 0.272 0.336
#> GSM1105527 3 0.9249 -0.02769 0.080 0.256 0.348 0.316
#> GSM1105531 3 0.4936 0.35335 0.008 0.000 0.652 0.340
#> GSM1105543 2 0.3873 0.39217 0.000 0.772 0.000 0.228
#> GSM1105546 1 0.0469 0.80464 0.988 0.000 0.000 0.012
#> GSM1105547 1 0.1256 0.80297 0.964 0.000 0.008 0.028
#> GSM1105455 2 0.6819 0.28799 0.000 0.604 0.188 0.208
#> GSM1105458 2 0.6413 0.09506 0.000 0.516 0.416 0.068
#> GSM1105459 2 0.0000 0.50745 0.000 1.000 0.000 0.000
#> GSM1105462 3 0.4277 0.37958 0.000 0.000 0.720 0.280
#> GSM1105441 2 0.1211 0.49243 0.000 0.960 0.040 0.000
#> GSM1105465 4 0.7830 0.09475 0.000 0.332 0.268 0.400
#> GSM1105484 2 0.5573 0.25770 0.000 0.604 0.028 0.368
#> GSM1105485 2 0.5352 0.25431 0.000 0.596 0.016 0.388
#> GSM1105496 3 0.2530 0.41740 0.000 0.000 0.888 0.112
#> GSM1105505 3 0.4454 0.37092 0.000 0.000 0.692 0.308
#> GSM1105509 1 0.5512 0.65871 0.728 0.000 0.100 0.172
#> GSM1105448 2 0.3400 0.42081 0.000 0.820 0.000 0.180
#> GSM1105521 1 0.3399 0.77816 0.868 0.000 0.040 0.092
#> GSM1105528 2 0.5313 0.26509 0.000 0.608 0.016 0.376
#> GSM1105529 2 0.5326 0.26191 0.000 0.604 0.016 0.380
#> GSM1105533 1 0.5429 0.72958 0.720 0.000 0.072 0.208
#> GSM1105545 2 0.8163 0.02587 0.008 0.376 0.300 0.316
#> GSM1105548 1 0.2843 0.80004 0.892 0.000 0.020 0.088
#> GSM1105549 1 0.2730 0.79336 0.896 0.000 0.016 0.088
#> GSM1105457 2 0.7818 0.09436 0.000 0.404 0.332 0.264
#> GSM1105460 2 0.7145 0.25042 0.000 0.556 0.192 0.252
#> GSM1105461 2 0.0000 0.50745 0.000 1.000 0.000 0.000
#> GSM1105464 1 0.7235 0.60576 0.532 0.000 0.180 0.288
#> GSM1105466 2 0.8117 0.05869 0.008 0.380 0.348 0.264
#> GSM1105479 3 0.7596 0.09416 0.000 0.332 0.456 0.212
#> GSM1105502 1 0.6617 0.67286 0.608 0.000 0.128 0.264
#> GSM1105515 1 0.0592 0.80179 0.984 0.000 0.000 0.016
#> GSM1105523 3 0.5599 0.34543 0.040 0.000 0.644 0.316
#> GSM1105550 4 0.8291 -0.14300 0.140 0.048 0.368 0.444
#> GSM1105450 2 0.0000 0.50745 0.000 1.000 0.000 0.000
#> GSM1105451 2 0.0000 0.50745 0.000 1.000 0.000 0.000
#> GSM1105454 3 0.3542 0.41283 0.000 0.120 0.852 0.028
#> GSM1105468 2 0.0000 0.50745 0.000 1.000 0.000 0.000
#> GSM1105481 3 0.4285 0.41542 0.000 0.104 0.820 0.076
#> GSM1105504 3 0.4917 0.35390 0.008 0.000 0.656 0.336
#> GSM1105517 1 0.7728 0.02761 0.416 0.000 0.232 0.352
#> GSM1105525 3 0.7877 -0.12041 0.308 0.000 0.388 0.304
#> GSM1105552 3 0.6876 0.26188 0.116 0.000 0.532 0.352
#> GSM1105452 2 0.5313 0.26586 0.000 0.608 0.016 0.376
#> GSM1105453 2 0.0000 0.50745 0.000 1.000 0.000 0.000
#> GSM1105456 3 0.3485 0.41434 0.000 0.116 0.856 0.028
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM1105438 2 0.1197 0.641291 0.000 0.952 0.000 0.000 0.048
#> GSM1105486 2 0.2068 0.681967 0.000 0.904 0.004 0.092 0.000
#> GSM1105487 1 0.5011 0.588981 0.660 0.000 0.292 0.012 0.036
#> GSM1105490 4 0.3528 0.686151 0.000 0.084 0.016 0.848 0.052
#> GSM1105491 5 0.4547 0.291213 0.000 0.192 0.072 0.000 0.736
#> GSM1105495 5 0.5825 0.380207 0.000 0.084 0.316 0.012 0.588
#> GSM1105498 4 0.6511 -0.117295 0.000 0.000 0.204 0.460 0.336
#> GSM1105499 1 0.3805 0.689347 0.784 0.000 0.192 0.016 0.008
#> GSM1105506 4 0.3157 0.692187 0.000 0.060 0.016 0.872 0.052
#> GSM1105442 5 0.4557 -0.141268 0.000 0.404 0.012 0.000 0.584
#> GSM1105511 4 0.1648 0.715002 0.000 0.040 0.020 0.940 0.000
#> GSM1105514 2 0.1329 0.647087 0.000 0.956 0.008 0.004 0.032
#> GSM1105518 5 0.6813 0.454712 0.000 0.008 0.344 0.212 0.436
#> GSM1105522 1 0.5961 0.407727 0.548 0.000 0.360 0.076 0.016
#> GSM1105534 1 0.0404 0.757868 0.988 0.000 0.000 0.012 0.000
#> GSM1105535 1 0.4296 0.669699 0.756 0.000 0.204 0.024 0.016
#> GSM1105538 1 0.0404 0.757868 0.988 0.000 0.000 0.012 0.000
#> GSM1105542 2 0.4440 0.303224 0.000 0.528 0.004 0.000 0.468
#> GSM1105443 2 0.6162 0.013878 0.000 0.460 0.012 0.436 0.092
#> GSM1105551 1 0.5189 0.569996 0.644 0.000 0.300 0.012 0.044
#> GSM1105554 1 0.0613 0.757194 0.984 0.000 0.004 0.008 0.004
#> GSM1105555 1 0.4450 0.641100 0.756 0.000 0.188 0.012 0.044
#> GSM1105447 2 0.7162 0.122420 0.000 0.420 0.020 0.252 0.308
#> GSM1105467 2 0.2623 0.676807 0.000 0.884 0.004 0.096 0.016
#> GSM1105470 2 0.2068 0.681967 0.000 0.904 0.004 0.092 0.000
#> GSM1105471 5 0.8123 0.263444 0.000 0.100 0.256 0.300 0.344
#> GSM1105474 2 0.2136 0.682485 0.000 0.904 0.008 0.088 0.000
#> GSM1105475 2 0.5290 0.201421 0.000 0.560 0.004 0.392 0.044
#> GSM1105440 1 0.4086 0.701113 0.796 0.000 0.152 0.024 0.028
#> GSM1105488 2 0.4440 0.303224 0.000 0.528 0.004 0.000 0.468
#> GSM1105489 1 0.3745 0.705090 0.820 0.000 0.132 0.012 0.036
#> GSM1105492 1 0.1701 0.756553 0.944 0.000 0.012 0.028 0.016
#> GSM1105493 1 0.3977 0.592182 0.764 0.000 0.204 0.000 0.032
#> GSM1105497 5 0.4498 0.148351 0.000 0.280 0.032 0.000 0.688
#> GSM1105500 4 0.3556 0.668613 0.000 0.036 0.012 0.836 0.116
#> GSM1105501 4 0.1965 0.714465 0.000 0.052 0.024 0.924 0.000
#> GSM1105508 4 0.7239 -0.089979 0.292 0.000 0.296 0.392 0.020
#> GSM1105444 2 0.1341 0.639198 0.000 0.944 0.000 0.000 0.056
#> GSM1105513 4 0.4112 0.661526 0.000 0.072 0.020 0.812 0.096
#> GSM1105516 4 0.5808 0.447445 0.248 0.008 0.088 0.644 0.012
#> GSM1105520 5 0.6477 0.427388 0.000 0.000 0.392 0.184 0.424
#> GSM1105524 1 0.4296 0.669699 0.756 0.000 0.204 0.024 0.016
#> GSM1105536 4 0.2930 0.703212 0.000 0.076 0.032 0.880 0.012
#> GSM1105537 1 0.4296 0.669699 0.756 0.000 0.204 0.024 0.016
#> GSM1105540 4 0.4741 0.599368 0.104 0.000 0.104 0.768 0.024
#> GSM1105544 4 0.3294 0.670137 0.008 0.000 0.036 0.852 0.104
#> GSM1105445 4 0.8013 -0.258316 0.000 0.084 0.260 0.328 0.328
#> GSM1105553 5 0.6377 0.459778 0.000 0.000 0.336 0.180 0.484
#> GSM1105556 1 0.0854 0.755643 0.976 0.000 0.004 0.012 0.008
#> GSM1105557 4 0.3410 0.689231 0.000 0.076 0.016 0.856 0.052
#> GSM1105449 2 0.4347 0.613110 0.000 0.784 0.008 0.096 0.112
#> GSM1105469 4 0.2248 0.686880 0.012 0.000 0.088 0.900 0.000
#> GSM1105472 2 0.2011 0.682856 0.000 0.908 0.004 0.088 0.000
#> GSM1105473 1 0.5361 0.125387 0.544 0.000 0.412 0.016 0.028
#> GSM1105476 2 0.2136 0.682485 0.000 0.904 0.008 0.088 0.000
#> GSM1105477 4 0.3005 0.702517 0.000 0.068 0.032 0.880 0.020
#> GSM1105478 4 0.4634 0.607643 0.000 0.028 0.044 0.760 0.168
#> GSM1105510 2 0.4443 0.299333 0.000 0.524 0.004 0.000 0.472
#> GSM1105530 3 0.4211 0.186475 0.360 0.000 0.636 0.004 0.000
#> GSM1105539 3 0.4182 0.167502 0.352 0.000 0.644 0.004 0.000
#> GSM1105480 4 0.3166 0.676334 0.000 0.016 0.020 0.860 0.104
#> GSM1105512 1 0.2908 0.698453 0.868 0.000 0.108 0.016 0.008
#> GSM1105532 3 0.4211 0.186475 0.360 0.000 0.636 0.004 0.000
#> GSM1105541 3 0.4225 0.158117 0.364 0.000 0.632 0.004 0.000
#> GSM1105439 2 0.6121 0.000857 0.000 0.464 0.012 0.436 0.088
#> GSM1105463 3 0.4365 0.297878 0.020 0.000 0.748 0.020 0.212
#> GSM1105482 1 0.1267 0.754458 0.960 0.000 0.024 0.004 0.012
#> GSM1105483 4 0.1877 0.703693 0.000 0.012 0.064 0.924 0.000
#> GSM1105494 4 0.6666 0.083719 0.000 0.016 0.156 0.488 0.340
#> GSM1105503 3 0.6377 -0.343957 0.000 0.000 0.484 0.180 0.336
#> GSM1105507 4 0.5732 0.432936 0.224 0.000 0.128 0.640 0.008
#> GSM1105446 2 0.2179 0.606249 0.000 0.888 0.000 0.000 0.112
#> GSM1105519 1 0.3387 0.667569 0.836 0.000 0.132 0.024 0.008
#> GSM1105526 4 0.2278 0.713829 0.000 0.044 0.032 0.916 0.008
#> GSM1105527 4 0.1461 0.715108 0.000 0.016 0.028 0.952 0.004
#> GSM1105531 3 0.4434 0.250927 0.000 0.000 0.736 0.056 0.208
#> GSM1105543 2 0.2488 0.600392 0.000 0.872 0.004 0.000 0.124
#> GSM1105546 1 0.1982 0.753572 0.932 0.000 0.028 0.012 0.028
#> GSM1105547 1 0.1173 0.754567 0.964 0.000 0.020 0.004 0.012
#> GSM1105455 2 0.6108 0.063452 0.000 0.484 0.012 0.416 0.088
#> GSM1105458 2 0.7194 0.101457 0.000 0.404 0.020 0.256 0.320
#> GSM1105459 2 0.1851 0.682705 0.000 0.912 0.000 0.088 0.000
#> GSM1105462 3 0.5322 0.258914 0.000 0.000 0.672 0.140 0.188
#> GSM1105441 2 0.3924 0.625744 0.000 0.816 0.008 0.096 0.080
#> GSM1105465 5 0.4697 0.101371 0.000 0.304 0.036 0.000 0.660
#> GSM1105484 2 0.4287 0.315948 0.000 0.540 0.000 0.000 0.460
#> GSM1105485 2 0.4589 0.293537 0.004 0.520 0.004 0.000 0.472
#> GSM1105496 5 0.6308 0.435531 0.000 0.000 0.388 0.156 0.456
#> GSM1105505 3 0.5331 0.103635 0.000 0.000 0.640 0.092 0.268
#> GSM1105509 1 0.6620 0.103586 0.464 0.000 0.172 0.356 0.008
#> GSM1105448 2 0.0880 0.645377 0.000 0.968 0.000 0.000 0.032
#> GSM1105521 1 0.3342 0.667722 0.836 0.000 0.136 0.020 0.008
#> GSM1105528 2 0.4287 0.315492 0.000 0.540 0.000 0.000 0.460
#> GSM1105529 2 0.4440 0.303224 0.000 0.528 0.004 0.000 0.468
#> GSM1105533 1 0.5218 0.292766 0.516 0.000 0.448 0.008 0.028
#> GSM1105545 4 0.2437 0.711928 0.000 0.060 0.032 0.904 0.004
#> GSM1105548 1 0.2701 0.745423 0.896 0.000 0.048 0.012 0.044
#> GSM1105549 1 0.2125 0.737096 0.920 0.000 0.052 0.004 0.024
#> GSM1105457 4 0.4153 0.661317 0.000 0.076 0.024 0.812 0.088
#> GSM1105460 4 0.6158 0.031292 0.000 0.428 0.012 0.468 0.092
#> GSM1105461 2 0.1851 0.682705 0.000 0.912 0.000 0.088 0.000
#> GSM1105464 3 0.4730 0.087452 0.416 0.000 0.568 0.012 0.004
#> GSM1105466 4 0.3359 0.688073 0.000 0.060 0.016 0.860 0.064
#> GSM1105479 4 0.7791 0.107574 0.000 0.284 0.064 0.384 0.268
#> GSM1105502 3 0.4781 0.038987 0.388 0.000 0.592 0.008 0.012
#> GSM1105515 1 0.0727 0.756600 0.980 0.000 0.004 0.012 0.004
#> GSM1105523 3 0.4782 0.379514 0.012 0.000 0.732 0.196 0.060
#> GSM1105550 4 0.4033 0.583007 0.020 0.000 0.208 0.764 0.008
#> GSM1105450 2 0.2011 0.682856 0.000 0.908 0.004 0.088 0.000
#> GSM1105451 2 0.2011 0.682528 0.000 0.908 0.004 0.088 0.000
#> GSM1105454 5 0.7162 0.470053 0.000 0.052 0.364 0.136 0.448
#> GSM1105468 2 0.2011 0.682856 0.000 0.908 0.004 0.088 0.000
#> GSM1105481 5 0.6875 0.426094 0.000 0.040 0.412 0.116 0.432
#> GSM1105504 3 0.4645 0.259402 0.000 0.000 0.724 0.072 0.204
#> GSM1105517 4 0.6526 0.218431 0.276 0.000 0.212 0.508 0.004
#> GSM1105525 3 0.5566 0.376765 0.172 0.000 0.676 0.140 0.012
#> GSM1105552 3 0.6429 0.403363 0.196 0.000 0.628 0.068 0.108
#> GSM1105452 2 0.4291 0.312055 0.000 0.536 0.000 0.000 0.464
#> GSM1105453 2 0.2011 0.682528 0.000 0.908 0.004 0.088 0.000
#> GSM1105456 5 0.7137 0.468771 0.000 0.052 0.368 0.132 0.448
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1105438 2 0.1471 0.7932 0.000 0.932 0.004 0.000 0.064 0.000
#> GSM1105486 2 0.0260 0.8302 0.000 0.992 0.000 0.000 0.008 0.000
#> GSM1105487 1 0.6686 0.5724 0.548 0.000 0.244 0.024 0.112 0.072
#> GSM1105490 4 0.4213 0.6487 0.000 0.048 0.000 0.708 0.004 0.240
#> GSM1105491 5 0.3264 0.6736 0.000 0.036 0.012 0.008 0.844 0.100
#> GSM1105495 6 0.5792 0.4774 0.000 0.008 0.140 0.004 0.320 0.528
#> GSM1105498 6 0.6430 0.3322 0.000 0.004 0.084 0.352 0.080 0.480
#> GSM1105499 1 0.3481 0.7053 0.792 0.000 0.180 0.012 0.008 0.008
#> GSM1105506 4 0.4314 0.6561 0.000 0.036 0.012 0.716 0.004 0.232
#> GSM1105442 5 0.3752 0.8451 0.000 0.168 0.004 0.000 0.776 0.052
#> GSM1105511 4 0.1390 0.7629 0.000 0.032 0.000 0.948 0.004 0.016
#> GSM1105514 2 0.0937 0.8073 0.000 0.960 0.000 0.000 0.040 0.000
#> GSM1105518 6 0.5373 0.6569 0.000 0.004 0.144 0.060 0.104 0.688
#> GSM1105522 3 0.6726 -0.1203 0.400 0.000 0.428 0.092 0.048 0.032
#> GSM1105534 1 0.0000 0.7888 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1105535 1 0.4785 0.6825 0.724 0.000 0.188 0.024 0.036 0.028
#> GSM1105538 1 0.0260 0.7875 0.992 0.000 0.000 0.008 0.000 0.000
#> GSM1105542 5 0.3426 0.8964 0.000 0.276 0.004 0.000 0.720 0.000
#> GSM1105443 2 0.5610 0.4180 0.000 0.572 0.004 0.156 0.004 0.264
#> GSM1105551 1 0.6738 0.5672 0.540 0.000 0.248 0.024 0.116 0.072
#> GSM1105554 1 0.0146 0.7882 0.996 0.000 0.004 0.000 0.000 0.000
#> GSM1105555 1 0.5621 0.6441 0.672 0.000 0.160 0.012 0.096 0.060
#> GSM1105447 6 0.5335 0.2418 0.000 0.328 0.004 0.048 0.032 0.588
#> GSM1105467 2 0.1511 0.8119 0.000 0.940 0.000 0.004 0.012 0.044
#> GSM1105470 2 0.0260 0.8302 0.000 0.992 0.000 0.000 0.008 0.000
#> GSM1105471 6 0.4611 0.5526 0.000 0.076 0.024 0.128 0.016 0.756
#> GSM1105474 2 0.0363 0.8288 0.000 0.988 0.000 0.000 0.012 0.000
#> GSM1105475 2 0.4267 0.6224 0.000 0.732 0.000 0.152 0.000 0.116
#> GSM1105440 1 0.5256 0.6934 0.712 0.000 0.152 0.028 0.064 0.044
#> GSM1105488 5 0.3426 0.8964 0.000 0.276 0.004 0.000 0.720 0.000
#> GSM1105489 1 0.5098 0.7123 0.728 0.000 0.100 0.012 0.100 0.060
#> GSM1105492 1 0.2170 0.7857 0.920 0.000 0.016 0.020 0.024 0.020
#> GSM1105493 1 0.4230 0.6013 0.740 0.000 0.196 0.000 0.044 0.020
#> GSM1105497 5 0.3462 0.7397 0.000 0.072 0.004 0.008 0.828 0.088
#> GSM1105500 4 0.2421 0.7387 0.000 0.004 0.004 0.896 0.044 0.052
#> GSM1105501 4 0.1464 0.7635 0.000 0.036 0.000 0.944 0.004 0.016
#> GSM1105508 4 0.6643 0.2441 0.136 0.000 0.252 0.540 0.040 0.032
#> GSM1105444 2 0.1531 0.7896 0.000 0.928 0.004 0.000 0.068 0.000
#> GSM1105513 4 0.4870 0.3546 0.000 0.048 0.000 0.512 0.004 0.436
#> GSM1105516 4 0.3018 0.6966 0.112 0.000 0.024 0.848 0.016 0.000
#> GSM1105520 6 0.5347 0.6474 0.000 0.000 0.164 0.056 0.104 0.676
#> GSM1105524 1 0.4785 0.6825 0.724 0.000 0.188 0.024 0.036 0.028
#> GSM1105536 4 0.1082 0.7619 0.000 0.040 0.000 0.956 0.004 0.000
#> GSM1105537 1 0.4785 0.6825 0.724 0.000 0.188 0.024 0.036 0.028
#> GSM1105540 4 0.2805 0.7285 0.012 0.000 0.056 0.884 0.024 0.024
#> GSM1105544 4 0.2154 0.7413 0.000 0.004 0.004 0.908 0.020 0.064
#> GSM1105445 6 0.3182 0.5606 0.000 0.036 0.008 0.124 0.000 0.832
#> GSM1105553 6 0.5219 0.6311 0.000 0.000 0.136 0.040 0.140 0.684
#> GSM1105556 1 0.0291 0.7876 0.992 0.000 0.004 0.000 0.004 0.000
#> GSM1105557 4 0.4290 0.6504 0.000 0.044 0.004 0.708 0.004 0.240
#> GSM1105449 2 0.3270 0.7306 0.000 0.816 0.004 0.004 0.024 0.152
#> GSM1105469 4 0.1635 0.7620 0.004 0.008 0.016 0.944 0.004 0.024
#> GSM1105472 2 0.0260 0.8302 0.000 0.992 0.000 0.000 0.008 0.000
#> GSM1105473 3 0.5895 0.3334 0.384 0.000 0.508 0.048 0.048 0.012
#> GSM1105476 2 0.0363 0.8288 0.000 0.988 0.000 0.000 0.012 0.000
#> GSM1105477 4 0.1196 0.7610 0.000 0.040 0.000 0.952 0.008 0.000
#> GSM1105478 4 0.4880 0.2774 0.000 0.008 0.020 0.488 0.012 0.472
#> GSM1105510 5 0.3541 0.8948 0.000 0.260 0.000 0.012 0.728 0.000
#> GSM1105530 3 0.3178 0.6358 0.176 0.000 0.804 0.016 0.004 0.000
#> GSM1105539 3 0.2946 0.6289 0.176 0.000 0.812 0.000 0.012 0.000
#> GSM1105480 4 0.4240 0.6213 0.000 0.004 0.020 0.680 0.008 0.288
#> GSM1105512 1 0.2213 0.7277 0.888 0.000 0.100 0.004 0.008 0.000
#> GSM1105532 3 0.3178 0.6358 0.176 0.000 0.804 0.016 0.004 0.000
#> GSM1105541 3 0.3014 0.6258 0.184 0.000 0.804 0.000 0.012 0.000
#> GSM1105439 2 0.5679 0.4208 0.000 0.572 0.004 0.192 0.004 0.228
#> GSM1105463 3 0.4424 0.4869 0.000 0.000 0.732 0.016 0.072 0.180
#> GSM1105482 1 0.1773 0.7823 0.932 0.000 0.016 0.000 0.036 0.016
#> GSM1105483 4 0.1908 0.7635 0.004 0.024 0.012 0.932 0.004 0.024
#> GSM1105494 6 0.4624 0.2801 0.000 0.004 0.016 0.300 0.028 0.652
#> GSM1105503 6 0.5638 0.5781 0.000 0.000 0.232 0.060 0.084 0.624
#> GSM1105507 4 0.3142 0.6941 0.092 0.000 0.044 0.848 0.016 0.000
#> GSM1105446 2 0.2669 0.6534 0.000 0.836 0.008 0.000 0.156 0.000
#> GSM1105519 1 0.3745 0.6248 0.792 0.000 0.148 0.044 0.016 0.000
#> GSM1105526 4 0.0972 0.7623 0.000 0.028 0.000 0.964 0.008 0.000
#> GSM1105527 4 0.3500 0.7204 0.000 0.024 0.020 0.816 0.004 0.136
#> GSM1105531 3 0.4807 0.4044 0.000 0.000 0.676 0.020 0.064 0.240
#> GSM1105543 2 0.2595 0.6478 0.000 0.836 0.004 0.000 0.160 0.000
#> GSM1105546 1 0.3573 0.7723 0.836 0.000 0.024 0.012 0.080 0.048
#> GSM1105547 1 0.1577 0.7833 0.940 0.000 0.008 0.000 0.036 0.016
#> GSM1105455 2 0.5550 0.4533 0.000 0.592 0.004 0.172 0.004 0.228
#> GSM1105458 6 0.5689 -0.0314 0.000 0.424 0.004 0.056 0.036 0.480
#> GSM1105459 2 0.0260 0.8302 0.000 0.992 0.000 0.000 0.008 0.000
#> GSM1105462 3 0.5336 0.5172 0.000 0.000 0.664 0.176 0.036 0.124
#> GSM1105441 2 0.2604 0.7516 0.000 0.856 0.004 0.004 0.004 0.132
#> GSM1105465 5 0.3634 0.7916 0.000 0.112 0.004 0.004 0.808 0.072
#> GSM1105484 5 0.3288 0.8959 0.000 0.276 0.000 0.000 0.724 0.000
#> GSM1105485 5 0.3606 0.8961 0.000 0.264 0.004 0.008 0.724 0.000
#> GSM1105496 6 0.5448 0.6275 0.000 0.000 0.160 0.044 0.136 0.660
#> GSM1105505 3 0.5729 0.1660 0.000 0.000 0.548 0.040 0.080 0.332
#> GSM1105509 4 0.5592 0.3504 0.260 0.000 0.148 0.580 0.012 0.000
#> GSM1105448 2 0.1082 0.8077 0.000 0.956 0.004 0.000 0.040 0.000
#> GSM1105521 1 0.3578 0.6338 0.800 0.000 0.152 0.032 0.016 0.000
#> GSM1105528 5 0.3288 0.8959 0.000 0.276 0.000 0.000 0.724 0.000
#> GSM1105529 5 0.3543 0.8974 0.000 0.272 0.004 0.004 0.720 0.000
#> GSM1105533 3 0.5637 0.1965 0.332 0.000 0.564 0.004 0.044 0.056
#> GSM1105545 4 0.1010 0.7623 0.000 0.036 0.000 0.960 0.004 0.000
#> GSM1105548 1 0.4859 0.7312 0.748 0.000 0.056 0.016 0.116 0.064
#> GSM1105549 1 0.2540 0.7694 0.892 0.000 0.044 0.000 0.044 0.020
#> GSM1105457 4 0.4445 0.5935 0.000 0.044 0.000 0.656 0.004 0.296
#> GSM1105460 2 0.6006 0.3427 0.000 0.524 0.004 0.248 0.008 0.216
#> GSM1105461 2 0.0000 0.8294 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105464 3 0.3643 0.6257 0.200 0.000 0.768 0.024 0.008 0.000
#> GSM1105466 4 0.4561 0.6164 0.000 0.036 0.012 0.672 0.004 0.276
#> GSM1105479 6 0.4905 0.4228 0.000 0.164 0.000 0.160 0.004 0.672
#> GSM1105502 3 0.3194 0.6147 0.172 0.000 0.808 0.004 0.012 0.004
#> GSM1105515 1 0.0146 0.7882 0.996 0.000 0.004 0.000 0.000 0.000
#> GSM1105523 3 0.4306 0.5839 0.004 0.000 0.756 0.148 0.012 0.080
#> GSM1105550 4 0.3219 0.6300 0.004 0.000 0.192 0.792 0.012 0.000
#> GSM1105450 2 0.0260 0.8302 0.000 0.992 0.000 0.000 0.008 0.000
#> GSM1105451 2 0.0291 0.8281 0.000 0.992 0.004 0.000 0.000 0.004
#> GSM1105454 6 0.5665 0.6501 0.000 0.028 0.152 0.028 0.124 0.668
#> GSM1105468 2 0.0260 0.8302 0.000 0.992 0.000 0.000 0.008 0.000
#> GSM1105481 6 0.5551 0.6134 0.000 0.016 0.192 0.020 0.120 0.652
#> GSM1105504 3 0.4281 0.4969 0.000 0.000 0.748 0.020 0.060 0.172
#> GSM1105517 4 0.5048 0.4929 0.120 0.000 0.192 0.672 0.016 0.000
#> GSM1105525 3 0.4146 0.6356 0.048 0.000 0.800 0.100 0.020 0.032
#> GSM1105552 3 0.6785 0.5585 0.100 0.000 0.592 0.164 0.076 0.068
#> GSM1105452 5 0.3426 0.8964 0.000 0.276 0.004 0.000 0.720 0.000
#> GSM1105453 2 0.0436 0.8287 0.000 0.988 0.004 0.000 0.004 0.004
#> GSM1105456 6 0.5625 0.6475 0.000 0.024 0.156 0.028 0.124 0.668
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 agent(p) other(p) time(p) individual(p) k
#> SD:kmeans 117 0.896 0.407198 0.791 0.00562 2
#> SD:kmeans 107 0.706 0.788028 0.638 0.00474 3
#> SD:kmeans 47 0.534 0.584835 0.803 0.05414 4
#> SD:kmeans 65 0.444 0.481815 0.677 0.07807 5
#> SD:kmeans 98 0.719 0.000299 0.762 0.00120 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 44956 rows and 120 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 0.853 0.929 0.968 0.4994 0.503 0.503
#> 3 3 0.663 0.798 0.883 0.3154 0.815 0.641
#> 4 4 0.761 0.815 0.899 0.1150 0.865 0.640
#> 5 5 0.721 0.646 0.797 0.0686 0.906 0.681
#> 6 6 0.756 0.755 0.853 0.0484 0.882 0.547
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
#> GSM1105438 2 0.000 0.959 0.000 1.000
#> GSM1105486 2 0.000 0.959 0.000 1.000
#> GSM1105487 1 0.000 0.975 1.000 0.000
#> GSM1105490 2 0.000 0.959 0.000 1.000
#> GSM1105491 2 0.706 0.779 0.192 0.808
#> GSM1105495 2 0.722 0.770 0.200 0.800
#> GSM1105498 2 0.969 0.411 0.396 0.604
#> GSM1105499 1 0.000 0.975 1.000 0.000
#> GSM1105506 2 0.000 0.959 0.000 1.000
#> GSM1105442 2 0.000 0.959 0.000 1.000
#> GSM1105511 2 0.000 0.959 0.000 1.000
#> GSM1105514 2 0.000 0.959 0.000 1.000
#> GSM1105518 2 0.416 0.891 0.084 0.916
#> GSM1105522 1 0.000 0.975 1.000 0.000
#> GSM1105534 1 0.000 0.975 1.000 0.000
#> GSM1105535 1 0.000 0.975 1.000 0.000
#> GSM1105538 1 0.000 0.975 1.000 0.000
#> GSM1105542 2 0.000 0.959 0.000 1.000
#> GSM1105443 2 0.000 0.959 0.000 1.000
#> GSM1105551 1 0.000 0.975 1.000 0.000
#> GSM1105554 1 0.000 0.975 1.000 0.000
#> GSM1105555 1 0.000 0.975 1.000 0.000
#> GSM1105447 2 0.000 0.959 0.000 1.000
#> GSM1105467 2 0.000 0.959 0.000 1.000
#> GSM1105470 2 0.000 0.959 0.000 1.000
#> GSM1105471 2 0.443 0.884 0.092 0.908
#> GSM1105474 2 0.000 0.959 0.000 1.000
#> GSM1105475 2 0.000 0.959 0.000 1.000
#> GSM1105440 1 0.000 0.975 1.000 0.000
#> GSM1105488 2 0.000 0.959 0.000 1.000
#> GSM1105489 1 0.000 0.975 1.000 0.000
#> GSM1105492 1 0.000 0.975 1.000 0.000
#> GSM1105493 1 0.000 0.975 1.000 0.000
#> GSM1105497 2 0.000 0.959 0.000 1.000
#> GSM1105500 2 0.000 0.959 0.000 1.000
#> GSM1105501 2 0.000 0.959 0.000 1.000
#> GSM1105508 1 0.000 0.975 1.000 0.000
#> GSM1105444 2 0.000 0.959 0.000 1.000
#> GSM1105513 2 0.000 0.959 0.000 1.000
#> GSM1105516 1 0.730 0.747 0.796 0.204
#> GSM1105520 2 0.891 0.602 0.308 0.692
#> GSM1105524 1 0.000 0.975 1.000 0.000
#> GSM1105536 2 0.000 0.959 0.000 1.000
#> GSM1105537 1 0.000 0.975 1.000 0.000
#> GSM1105540 1 0.000 0.975 1.000 0.000
#> GSM1105544 1 0.939 0.470 0.644 0.356
#> GSM1105445 2 0.000 0.959 0.000 1.000
#> GSM1105553 2 0.978 0.368 0.412 0.588
#> GSM1105556 1 0.000 0.975 1.000 0.000
#> GSM1105557 2 0.000 0.959 0.000 1.000
#> GSM1105449 2 0.000 0.959 0.000 1.000
#> GSM1105469 1 0.416 0.894 0.916 0.084
#> GSM1105472 2 0.000 0.959 0.000 1.000
#> GSM1105473 1 0.000 0.975 1.000 0.000
#> GSM1105476 2 0.000 0.959 0.000 1.000
#> GSM1105477 2 0.000 0.959 0.000 1.000
#> GSM1105478 2 0.680 0.794 0.180 0.820
#> GSM1105510 2 0.000 0.959 0.000 1.000
#> GSM1105530 1 0.000 0.975 1.000 0.000
#> GSM1105539 1 0.000 0.975 1.000 0.000
#> GSM1105480 2 0.000 0.959 0.000 1.000
#> GSM1105512 1 0.000 0.975 1.000 0.000
#> GSM1105532 1 0.000 0.975 1.000 0.000
#> GSM1105541 1 0.000 0.975 1.000 0.000
#> GSM1105439 2 0.000 0.959 0.000 1.000
#> GSM1105463 1 0.000 0.975 1.000 0.000
#> GSM1105482 1 0.000 0.975 1.000 0.000
#> GSM1105483 1 0.760 0.725 0.780 0.220
#> GSM1105494 2 0.000 0.959 0.000 1.000
#> GSM1105503 1 0.775 0.682 0.772 0.228
#> GSM1105507 1 0.584 0.831 0.860 0.140
#> GSM1105446 2 0.000 0.959 0.000 1.000
#> GSM1105519 1 0.000 0.975 1.000 0.000
#> GSM1105526 2 0.000 0.959 0.000 1.000
#> GSM1105527 2 0.653 0.791 0.168 0.832
#> GSM1105531 1 0.000 0.975 1.000 0.000
#> GSM1105543 2 0.000 0.959 0.000 1.000
#> GSM1105546 1 0.000 0.975 1.000 0.000
#> GSM1105547 1 0.000 0.975 1.000 0.000
#> GSM1105455 2 0.000 0.959 0.000 1.000
#> GSM1105458 2 0.000 0.959 0.000 1.000
#> GSM1105459 2 0.000 0.959 0.000 1.000
#> GSM1105462 1 0.000 0.975 1.000 0.000
#> GSM1105441 2 0.000 0.959 0.000 1.000
#> GSM1105465 2 0.000 0.959 0.000 1.000
#> GSM1105484 2 0.000 0.959 0.000 1.000
#> GSM1105485 2 0.000 0.959 0.000 1.000
#> GSM1105496 1 0.000 0.975 1.000 0.000
#> GSM1105505 1 0.000 0.975 1.000 0.000
#> GSM1105509 1 0.000 0.975 1.000 0.000
#> GSM1105448 2 0.000 0.959 0.000 1.000
#> GSM1105521 1 0.000 0.975 1.000 0.000
#> GSM1105528 2 0.000 0.959 0.000 1.000
#> GSM1105529 2 0.000 0.959 0.000 1.000
#> GSM1105533 1 0.000 0.975 1.000 0.000
#> GSM1105545 2 0.000 0.959 0.000 1.000
#> GSM1105548 1 0.000 0.975 1.000 0.000
#> GSM1105549 1 0.000 0.975 1.000 0.000
#> GSM1105457 2 0.000 0.959 0.000 1.000
#> GSM1105460 2 0.000 0.959 0.000 1.000
#> GSM1105461 2 0.000 0.959 0.000 1.000
#> GSM1105464 1 0.000 0.975 1.000 0.000
#> GSM1105466 2 0.000 0.959 0.000 1.000
#> GSM1105479 2 0.000 0.959 0.000 1.000
#> GSM1105502 1 0.000 0.975 1.000 0.000
#> GSM1105515 1 0.000 0.975 1.000 0.000
#> GSM1105523 1 0.000 0.975 1.000 0.000
#> GSM1105550 1 0.000 0.975 1.000 0.000
#> GSM1105450 2 0.000 0.959 0.000 1.000
#> GSM1105451 2 0.000 0.959 0.000 1.000
#> GSM1105454 2 0.722 0.770 0.200 0.800
#> GSM1105468 2 0.000 0.959 0.000 1.000
#> GSM1105481 2 0.722 0.770 0.200 0.800
#> GSM1105504 1 0.000 0.975 1.000 0.000
#> GSM1105517 1 0.000 0.975 1.000 0.000
#> GSM1105525 1 0.000 0.975 1.000 0.000
#> GSM1105552 1 0.000 0.975 1.000 0.000
#> GSM1105452 2 0.000 0.959 0.000 1.000
#> GSM1105453 2 0.000 0.959 0.000 1.000
#> GSM1105456 2 0.722 0.770 0.200 0.800
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1105438 2 0.0000 0.920 0.000 1.000 0.000
#> GSM1105486 2 0.0000 0.920 0.000 1.000 0.000
#> GSM1105487 1 0.4121 0.852 0.832 0.000 0.168
#> GSM1105490 3 0.5968 0.673 0.000 0.364 0.636
#> GSM1105491 2 0.5968 0.455 0.000 0.636 0.364
#> GSM1105495 2 0.6026 0.439 0.000 0.624 0.376
#> GSM1105498 3 0.0000 0.739 0.000 0.000 1.000
#> GSM1105499 1 0.0000 0.875 1.000 0.000 0.000
#> GSM1105506 3 0.5968 0.673 0.000 0.364 0.636
#> GSM1105442 2 0.3816 0.766 0.000 0.852 0.148
#> GSM1105511 3 0.5968 0.673 0.000 0.364 0.636
#> GSM1105514 2 0.0000 0.920 0.000 1.000 0.000
#> GSM1105518 3 0.0000 0.739 0.000 0.000 1.000
#> GSM1105522 1 0.0000 0.875 1.000 0.000 0.000
#> GSM1105534 1 0.0000 0.875 1.000 0.000 0.000
#> GSM1105535 1 0.0000 0.875 1.000 0.000 0.000
#> GSM1105538 1 0.0000 0.875 1.000 0.000 0.000
#> GSM1105542 2 0.0237 0.918 0.000 0.996 0.004
#> GSM1105443 3 0.5968 0.673 0.000 0.364 0.636
#> GSM1105551 1 0.4235 0.850 0.824 0.000 0.176
#> GSM1105554 1 0.0000 0.875 1.000 0.000 0.000
#> GSM1105555 1 0.4235 0.850 0.824 0.000 0.176
#> GSM1105447 3 0.5016 0.715 0.000 0.240 0.760
#> GSM1105467 2 0.0000 0.920 0.000 1.000 0.000
#> GSM1105470 2 0.0000 0.920 0.000 1.000 0.000
#> GSM1105471 3 0.0237 0.740 0.000 0.004 0.996
#> GSM1105474 2 0.0000 0.920 0.000 1.000 0.000
#> GSM1105475 2 0.0000 0.920 0.000 1.000 0.000
#> GSM1105440 1 0.0000 0.875 1.000 0.000 0.000
#> GSM1105488 2 0.0237 0.918 0.000 0.996 0.004
#> GSM1105489 1 0.4235 0.850 0.824 0.000 0.176
#> GSM1105492 1 0.0000 0.875 1.000 0.000 0.000
#> GSM1105493 1 0.4235 0.850 0.824 0.000 0.176
#> GSM1105497 2 0.4555 0.702 0.000 0.800 0.200
#> GSM1105500 2 0.0237 0.918 0.000 0.996 0.004
#> GSM1105501 2 0.6553 -0.125 0.008 0.580 0.412
#> GSM1105508 1 0.0000 0.875 1.000 0.000 0.000
#> GSM1105444 2 0.0000 0.920 0.000 1.000 0.000
#> GSM1105513 3 0.5968 0.673 0.000 0.364 0.636
#> GSM1105516 1 0.4178 0.698 0.828 0.172 0.000
#> GSM1105520 3 0.0000 0.739 0.000 0.000 1.000
#> GSM1105524 1 0.0000 0.875 1.000 0.000 0.000
#> GSM1105536 2 0.0000 0.920 0.000 1.000 0.000
#> GSM1105537 1 0.0000 0.875 1.000 0.000 0.000
#> GSM1105540 1 0.0000 0.875 1.000 0.000 0.000
#> GSM1105544 1 0.6647 0.161 0.592 0.012 0.396
#> GSM1105445 3 0.0237 0.740 0.000 0.004 0.996
#> GSM1105553 3 0.0000 0.739 0.000 0.000 1.000
#> GSM1105556 1 0.0000 0.875 1.000 0.000 0.000
#> GSM1105557 3 0.5968 0.673 0.000 0.364 0.636
#> GSM1105449 2 0.0592 0.909 0.000 0.988 0.012
#> GSM1105469 3 0.6111 0.421 0.396 0.000 0.604
#> GSM1105472 2 0.0000 0.920 0.000 1.000 0.000
#> GSM1105473 1 0.4235 0.850 0.824 0.000 0.176
#> GSM1105476 2 0.0000 0.920 0.000 1.000 0.000
#> GSM1105477 2 0.0000 0.920 0.000 1.000 0.000
#> GSM1105478 3 0.0237 0.740 0.000 0.004 0.996
#> GSM1105510 2 0.0237 0.918 0.000 0.996 0.004
#> GSM1105530 1 0.4235 0.850 0.824 0.000 0.176
#> GSM1105539 1 0.4235 0.850 0.824 0.000 0.176
#> GSM1105480 3 0.6662 0.718 0.044 0.252 0.704
#> GSM1105512 1 0.0000 0.875 1.000 0.000 0.000
#> GSM1105532 1 0.4235 0.850 0.824 0.000 0.176
#> GSM1105541 1 0.4235 0.850 0.824 0.000 0.176
#> GSM1105439 3 0.5968 0.673 0.000 0.364 0.636
#> GSM1105463 1 0.5948 0.683 0.640 0.000 0.360
#> GSM1105482 1 0.0000 0.875 1.000 0.000 0.000
#> GSM1105483 3 0.8259 0.669 0.216 0.152 0.632
#> GSM1105494 3 0.3340 0.739 0.000 0.120 0.880
#> GSM1105503 3 0.0237 0.737 0.004 0.000 0.996
#> GSM1105507 1 0.0000 0.875 1.000 0.000 0.000
#> GSM1105446 2 0.0000 0.920 0.000 1.000 0.000
#> GSM1105519 1 0.0000 0.875 1.000 0.000 0.000
#> GSM1105526 2 0.0000 0.920 0.000 1.000 0.000
#> GSM1105527 3 0.8220 0.671 0.212 0.152 0.636
#> GSM1105531 1 0.5948 0.683 0.640 0.000 0.360
#> GSM1105543 2 0.0000 0.920 0.000 1.000 0.000
#> GSM1105546 1 0.0000 0.875 1.000 0.000 0.000
#> GSM1105547 1 0.0000 0.875 1.000 0.000 0.000
#> GSM1105455 3 0.5968 0.673 0.000 0.364 0.636
#> GSM1105458 2 0.4235 0.744 0.000 0.824 0.176
#> GSM1105459 2 0.0000 0.920 0.000 1.000 0.000
#> GSM1105462 1 0.5948 0.683 0.640 0.000 0.360
#> GSM1105441 2 0.0000 0.920 0.000 1.000 0.000
#> GSM1105465 2 0.5968 0.455 0.000 0.636 0.364
#> GSM1105484 2 0.0237 0.918 0.000 0.996 0.004
#> GSM1105485 2 0.0237 0.918 0.000 0.996 0.004
#> GSM1105496 3 0.0747 0.725 0.016 0.000 0.984
#> GSM1105505 1 0.5948 0.683 0.640 0.000 0.360
#> GSM1105509 1 0.0000 0.875 1.000 0.000 0.000
#> GSM1105448 2 0.0000 0.920 0.000 1.000 0.000
#> GSM1105521 1 0.0000 0.875 1.000 0.000 0.000
#> GSM1105528 2 0.0237 0.918 0.000 0.996 0.004
#> GSM1105529 2 0.0237 0.918 0.000 0.996 0.004
#> GSM1105533 1 0.4235 0.850 0.824 0.000 0.176
#> GSM1105545 2 0.0000 0.920 0.000 1.000 0.000
#> GSM1105548 1 0.4235 0.850 0.824 0.000 0.176
#> GSM1105549 1 0.2356 0.868 0.928 0.000 0.072
#> GSM1105457 3 0.5968 0.673 0.000 0.364 0.636
#> GSM1105460 2 0.1643 0.874 0.000 0.956 0.044
#> GSM1105461 2 0.0000 0.920 0.000 1.000 0.000
#> GSM1105464 1 0.4235 0.850 0.824 0.000 0.176
#> GSM1105466 3 0.5968 0.673 0.000 0.364 0.636
#> GSM1105479 3 0.5810 0.687 0.000 0.336 0.664
#> GSM1105502 1 0.4235 0.850 0.824 0.000 0.176
#> GSM1105515 1 0.0000 0.875 1.000 0.000 0.000
#> GSM1105523 1 0.6095 0.595 0.608 0.000 0.392
#> GSM1105550 1 0.0000 0.875 1.000 0.000 0.000
#> GSM1105450 2 0.0000 0.920 0.000 1.000 0.000
#> GSM1105451 2 0.0000 0.920 0.000 1.000 0.000
#> GSM1105454 3 0.0000 0.739 0.000 0.000 1.000
#> GSM1105468 2 0.0000 0.920 0.000 1.000 0.000
#> GSM1105481 2 0.6154 0.387 0.000 0.592 0.408
#> GSM1105504 1 0.5948 0.683 0.640 0.000 0.360
#> GSM1105517 1 0.0000 0.875 1.000 0.000 0.000
#> GSM1105525 1 0.4235 0.850 0.824 0.000 0.176
#> GSM1105552 1 0.5016 0.804 0.760 0.000 0.240
#> GSM1105452 2 0.0237 0.918 0.000 0.996 0.004
#> GSM1105453 2 0.0000 0.920 0.000 1.000 0.000
#> GSM1105456 3 0.0000 0.739 0.000 0.000 1.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1105438 2 0.0921 0.8575 0.000 0.972 0.000 0.028
#> GSM1105486 2 0.3444 0.8314 0.000 0.816 0.000 0.184
#> GSM1105487 1 0.1118 0.8935 0.964 0.000 0.036 0.000
#> GSM1105490 4 0.1042 0.8748 0.000 0.008 0.020 0.972
#> GSM1105491 2 0.4925 0.1616 0.000 0.572 0.428 0.000
#> GSM1105495 3 0.1389 0.8921 0.000 0.048 0.952 0.000
#> GSM1105498 3 0.1474 0.9200 0.000 0.000 0.948 0.052
#> GSM1105499 1 0.0000 0.9026 1.000 0.000 0.000 0.000
#> GSM1105506 4 0.0817 0.8751 0.000 0.000 0.024 0.976
#> GSM1105442 2 0.0817 0.8526 0.000 0.976 0.024 0.000
#> GSM1105511 4 0.0707 0.8754 0.000 0.000 0.020 0.980
#> GSM1105514 2 0.0921 0.8575 0.000 0.972 0.000 0.028
#> GSM1105518 3 0.1389 0.9221 0.000 0.000 0.952 0.048
#> GSM1105522 1 0.0000 0.9026 1.000 0.000 0.000 0.000
#> GSM1105534 1 0.0000 0.9026 1.000 0.000 0.000 0.000
#> GSM1105535 1 0.0000 0.9026 1.000 0.000 0.000 0.000
#> GSM1105538 1 0.0000 0.9026 1.000 0.000 0.000 0.000
#> GSM1105542 2 0.0817 0.8526 0.000 0.976 0.024 0.000
#> GSM1105443 4 0.1637 0.8513 0.000 0.060 0.000 0.940
#> GSM1105551 1 0.2921 0.8480 0.860 0.000 0.140 0.000
#> GSM1105554 1 0.0000 0.9026 1.000 0.000 0.000 0.000
#> GSM1105555 1 0.3172 0.8356 0.840 0.000 0.160 0.000
#> GSM1105447 2 0.4998 0.1990 0.000 0.512 0.000 0.488
#> GSM1105467 2 0.3444 0.8314 0.000 0.816 0.000 0.184
#> GSM1105470 2 0.3444 0.8314 0.000 0.816 0.000 0.184
#> GSM1105471 3 0.5000 0.0301 0.000 0.000 0.500 0.500
#> GSM1105474 2 0.3444 0.8314 0.000 0.816 0.000 0.184
#> GSM1105475 4 0.3486 0.7089 0.000 0.188 0.000 0.812
#> GSM1105440 1 0.0000 0.9026 1.000 0.000 0.000 0.000
#> GSM1105488 2 0.0817 0.8526 0.000 0.976 0.024 0.000
#> GSM1105489 1 0.2760 0.8544 0.872 0.000 0.128 0.000
#> GSM1105492 1 0.0000 0.9026 1.000 0.000 0.000 0.000
#> GSM1105493 1 0.3172 0.8356 0.840 0.000 0.160 0.000
#> GSM1105497 2 0.1716 0.8316 0.000 0.936 0.064 0.000
#> GSM1105500 2 0.1191 0.8497 0.004 0.968 0.024 0.004
#> GSM1105501 4 0.0817 0.8657 0.000 0.024 0.000 0.976
#> GSM1105508 1 0.0188 0.9010 0.996 0.000 0.000 0.004
#> GSM1105444 2 0.0817 0.8573 0.000 0.976 0.000 0.024
#> GSM1105513 4 0.1004 0.8754 0.000 0.004 0.024 0.972
#> GSM1105516 1 0.3108 0.8285 0.872 0.016 0.000 0.112
#> GSM1105520 3 0.1389 0.9221 0.000 0.000 0.952 0.048
#> GSM1105524 1 0.0000 0.9026 1.000 0.000 0.000 0.000
#> GSM1105536 4 0.4888 0.1366 0.000 0.412 0.000 0.588
#> GSM1105537 1 0.0000 0.9026 1.000 0.000 0.000 0.000
#> GSM1105540 1 0.0000 0.9026 1.000 0.000 0.000 0.000
#> GSM1105544 1 0.8154 -0.0243 0.420 0.180 0.024 0.376
#> GSM1105445 4 0.4804 0.3203 0.000 0.000 0.384 0.616
#> GSM1105553 3 0.1302 0.9225 0.000 0.000 0.956 0.044
#> GSM1105556 1 0.0000 0.9026 1.000 0.000 0.000 0.000
#> GSM1105557 4 0.0817 0.8751 0.000 0.000 0.024 0.976
#> GSM1105449 2 0.3444 0.8314 0.000 0.816 0.000 0.184
#> GSM1105469 4 0.2670 0.7974 0.072 0.000 0.024 0.904
#> GSM1105472 2 0.3444 0.8314 0.000 0.816 0.000 0.184
#> GSM1105473 1 0.3311 0.8303 0.828 0.000 0.172 0.000
#> GSM1105476 2 0.3444 0.8314 0.000 0.816 0.000 0.184
#> GSM1105477 2 0.3266 0.7381 0.000 0.832 0.000 0.168
#> GSM1105478 4 0.1637 0.8550 0.000 0.000 0.060 0.940
#> GSM1105510 2 0.0817 0.8526 0.000 0.976 0.024 0.000
#> GSM1105530 1 0.3444 0.8233 0.816 0.000 0.184 0.000
#> GSM1105539 1 0.3486 0.8197 0.812 0.000 0.188 0.000
#> GSM1105480 4 0.1022 0.8701 0.000 0.000 0.032 0.968
#> GSM1105512 1 0.0000 0.9026 1.000 0.000 0.000 0.000
#> GSM1105532 1 0.3444 0.8233 0.816 0.000 0.184 0.000
#> GSM1105541 1 0.3444 0.8233 0.816 0.000 0.184 0.000
#> GSM1105439 4 0.0469 0.8700 0.000 0.012 0.000 0.988
#> GSM1105463 3 0.1389 0.9092 0.048 0.000 0.952 0.000
#> GSM1105482 1 0.0000 0.9026 1.000 0.000 0.000 0.000
#> GSM1105483 4 0.0817 0.8751 0.000 0.000 0.024 0.976
#> GSM1105494 4 0.4720 0.5981 0.000 0.016 0.264 0.720
#> GSM1105503 3 0.1389 0.9221 0.000 0.000 0.952 0.048
#> GSM1105507 1 0.2011 0.8602 0.920 0.000 0.000 0.080
#> GSM1105446 2 0.0469 0.8546 0.000 0.988 0.012 0.000
#> GSM1105519 1 0.0000 0.9026 1.000 0.000 0.000 0.000
#> GSM1105526 4 0.4933 0.0643 0.000 0.432 0.000 0.568
#> GSM1105527 4 0.0817 0.8751 0.000 0.000 0.024 0.976
#> GSM1105531 3 0.1302 0.9119 0.044 0.000 0.956 0.000
#> GSM1105543 2 0.0188 0.8560 0.000 0.996 0.000 0.004
#> GSM1105546 1 0.0000 0.9026 1.000 0.000 0.000 0.000
#> GSM1105547 1 0.0000 0.9026 1.000 0.000 0.000 0.000
#> GSM1105455 4 0.0592 0.8697 0.000 0.016 0.000 0.984
#> GSM1105458 2 0.4163 0.8162 0.000 0.792 0.020 0.188
#> GSM1105459 2 0.3444 0.8314 0.000 0.816 0.000 0.184
#> GSM1105462 3 0.1398 0.9140 0.040 0.000 0.956 0.004
#> GSM1105441 2 0.3444 0.8314 0.000 0.816 0.000 0.184
#> GSM1105465 2 0.1867 0.8260 0.000 0.928 0.072 0.000
#> GSM1105484 2 0.0817 0.8526 0.000 0.976 0.024 0.000
#> GSM1105485 2 0.0817 0.8526 0.000 0.976 0.024 0.000
#> GSM1105496 3 0.1516 0.9129 0.008 0.016 0.960 0.016
#> GSM1105505 3 0.1389 0.9092 0.048 0.000 0.952 0.000
#> GSM1105509 1 0.0188 0.9010 0.996 0.000 0.000 0.004
#> GSM1105448 2 0.0921 0.8575 0.000 0.972 0.000 0.028
#> GSM1105521 1 0.0000 0.9026 1.000 0.000 0.000 0.000
#> GSM1105528 2 0.0817 0.8526 0.000 0.976 0.024 0.000
#> GSM1105529 2 0.0817 0.8526 0.000 0.976 0.024 0.000
#> GSM1105533 1 0.3219 0.8339 0.836 0.000 0.164 0.000
#> GSM1105545 4 0.1474 0.8516 0.000 0.052 0.000 0.948
#> GSM1105548 1 0.1389 0.8895 0.952 0.000 0.048 0.000
#> GSM1105549 1 0.0000 0.9026 1.000 0.000 0.000 0.000
#> GSM1105457 4 0.0817 0.8751 0.000 0.000 0.024 0.976
#> GSM1105460 4 0.2081 0.8358 0.000 0.084 0.000 0.916
#> GSM1105461 2 0.3444 0.8314 0.000 0.816 0.000 0.184
#> GSM1105464 1 0.3444 0.8233 0.816 0.000 0.184 0.000
#> GSM1105466 4 0.0817 0.8751 0.000 0.000 0.024 0.976
#> GSM1105479 4 0.2919 0.8258 0.000 0.060 0.044 0.896
#> GSM1105502 1 0.3356 0.8280 0.824 0.000 0.176 0.000
#> GSM1105515 1 0.0000 0.9026 1.000 0.000 0.000 0.000
#> GSM1105523 3 0.1913 0.9078 0.040 0.000 0.940 0.020
#> GSM1105550 1 0.5403 0.4190 0.628 0.000 0.024 0.348
#> GSM1105450 2 0.3444 0.8314 0.000 0.816 0.000 0.184
#> GSM1105451 2 0.3444 0.8314 0.000 0.816 0.000 0.184
#> GSM1105454 3 0.1474 0.9223 0.000 0.000 0.948 0.052
#> GSM1105468 2 0.3444 0.8314 0.000 0.816 0.000 0.184
#> GSM1105481 3 0.1488 0.9118 0.000 0.012 0.956 0.032
#> GSM1105504 3 0.1389 0.9092 0.048 0.000 0.952 0.000
#> GSM1105517 1 0.0817 0.8955 0.976 0.000 0.024 0.000
#> GSM1105525 1 0.4391 0.7466 0.740 0.000 0.252 0.008
#> GSM1105552 1 0.4356 0.6886 0.708 0.000 0.292 0.000
#> GSM1105452 2 0.0817 0.8526 0.000 0.976 0.024 0.000
#> GSM1105453 2 0.3444 0.8314 0.000 0.816 0.000 0.184
#> GSM1105456 3 0.1474 0.9223 0.000 0.000 0.948 0.052
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM1105438 2 0.3999 0.72352 0.000 0.656 0.000 0.000 0.344
#> GSM1105486 2 0.4269 0.77025 0.000 0.684 0.000 0.016 0.300
#> GSM1105487 1 0.3280 0.78756 0.808 0.004 0.184 0.004 0.000
#> GSM1105490 4 0.1478 0.77831 0.000 0.064 0.000 0.936 0.000
#> GSM1105491 5 0.3492 0.63863 0.000 0.188 0.016 0.000 0.796
#> GSM1105495 3 0.5901 0.66825 0.000 0.284 0.596 0.008 0.112
#> GSM1105498 3 0.6402 0.61075 0.000 0.288 0.504 0.208 0.000
#> GSM1105499 1 0.0162 0.83153 0.996 0.000 0.000 0.004 0.000
#> GSM1105506 4 0.1121 0.77724 0.000 0.044 0.000 0.956 0.000
#> GSM1105442 5 0.2516 0.71225 0.000 0.140 0.000 0.000 0.860
#> GSM1105511 4 0.1043 0.77630 0.000 0.040 0.000 0.960 0.000
#> GSM1105514 2 0.4135 0.72826 0.000 0.656 0.000 0.004 0.340
#> GSM1105518 3 0.5579 0.69581 0.000 0.300 0.600 0.100 0.000
#> GSM1105522 1 0.1153 0.82507 0.964 0.004 0.008 0.024 0.000
#> GSM1105534 1 0.0000 0.83126 1.000 0.000 0.000 0.000 0.000
#> GSM1105535 1 0.0324 0.83151 0.992 0.004 0.000 0.004 0.000
#> GSM1105538 1 0.0000 0.83126 1.000 0.000 0.000 0.000 0.000
#> GSM1105542 5 0.0000 0.78306 0.000 0.000 0.000 0.000 1.000
#> GSM1105443 4 0.5176 0.16673 0.000 0.468 0.000 0.492 0.040
#> GSM1105551 1 0.3387 0.78128 0.796 0.004 0.196 0.004 0.000
#> GSM1105554 1 0.0000 0.83126 1.000 0.000 0.000 0.000 0.000
#> GSM1105555 1 0.3366 0.77391 0.784 0.004 0.212 0.000 0.000
#> GSM1105447 2 0.2984 0.40462 0.000 0.860 0.000 0.108 0.032
#> GSM1105467 2 0.4269 0.77025 0.000 0.684 0.000 0.016 0.300
#> GSM1105470 2 0.4339 0.76924 0.000 0.684 0.000 0.020 0.296
#> GSM1105471 2 0.6038 -0.25769 0.000 0.576 0.240 0.184 0.000
#> GSM1105474 2 0.4193 0.76765 0.000 0.684 0.000 0.012 0.304
#> GSM1105475 2 0.5136 0.68425 0.000 0.692 0.000 0.128 0.180
#> GSM1105440 1 0.0324 0.83151 0.992 0.004 0.000 0.004 0.000
#> GSM1105488 5 0.0000 0.78306 0.000 0.000 0.000 0.000 1.000
#> GSM1105489 1 0.3231 0.78162 0.800 0.004 0.196 0.000 0.000
#> GSM1105492 1 0.0162 0.83144 0.996 0.004 0.000 0.000 0.000
#> GSM1105493 1 0.3508 0.75099 0.748 0.000 0.252 0.000 0.000
#> GSM1105497 5 0.3209 0.66126 0.000 0.180 0.008 0.000 0.812
#> GSM1105500 5 0.0566 0.77712 0.000 0.012 0.000 0.004 0.984
#> GSM1105501 4 0.3305 0.66514 0.000 0.224 0.000 0.776 0.000
#> GSM1105508 1 0.0324 0.83151 0.992 0.004 0.000 0.004 0.000
#> GSM1105444 2 0.4030 0.71160 0.000 0.648 0.000 0.000 0.352
#> GSM1105513 4 0.3177 0.69699 0.000 0.208 0.000 0.792 0.000
#> GSM1105516 1 0.3508 0.64917 0.748 0.000 0.000 0.252 0.000
#> GSM1105520 3 0.5506 0.70238 0.000 0.284 0.616 0.100 0.000
#> GSM1105524 1 0.0324 0.83151 0.992 0.004 0.000 0.004 0.000
#> GSM1105536 2 0.5618 0.31307 0.000 0.564 0.000 0.348 0.088
#> GSM1105537 1 0.0324 0.83151 0.992 0.004 0.000 0.004 0.000
#> GSM1105540 1 0.3131 0.76842 0.860 0.008 0.028 0.104 0.000
#> GSM1105544 5 0.6332 0.37662 0.256 0.016 0.000 0.152 0.576
#> GSM1105445 2 0.6655 -0.41210 0.000 0.404 0.228 0.368 0.000
#> GSM1105553 3 0.5892 0.69676 0.000 0.288 0.600 0.100 0.012
#> GSM1105556 1 0.0000 0.83126 1.000 0.000 0.000 0.000 0.000
#> GSM1105557 4 0.1197 0.77806 0.000 0.048 0.000 0.952 0.000
#> GSM1105449 2 0.3861 0.75000 0.000 0.728 0.000 0.008 0.264
#> GSM1105469 4 0.0955 0.73781 0.028 0.004 0.000 0.968 0.000
#> GSM1105472 2 0.4269 0.77025 0.000 0.684 0.000 0.016 0.300
#> GSM1105473 1 0.3876 0.70821 0.684 0.000 0.316 0.000 0.000
#> GSM1105476 2 0.4339 0.76924 0.000 0.684 0.000 0.020 0.296
#> GSM1105477 5 0.5624 -0.13256 0.000 0.388 0.000 0.080 0.532
#> GSM1105478 4 0.2017 0.74247 0.000 0.080 0.008 0.912 0.000
#> GSM1105510 5 0.0000 0.78306 0.000 0.000 0.000 0.000 1.000
#> GSM1105530 1 0.4988 0.59710 0.556 0.004 0.416 0.024 0.000
#> GSM1105539 1 0.4915 0.59554 0.556 0.004 0.420 0.020 0.000
#> GSM1105480 4 0.1341 0.76359 0.000 0.056 0.000 0.944 0.000
#> GSM1105512 1 0.0000 0.83126 1.000 0.000 0.000 0.000 0.000
#> GSM1105532 1 0.4988 0.59710 0.556 0.004 0.416 0.024 0.000
#> GSM1105541 1 0.4908 0.60074 0.560 0.004 0.416 0.020 0.000
#> GSM1105439 4 0.4304 0.23774 0.000 0.484 0.000 0.516 0.000
#> GSM1105463 3 0.0324 0.67500 0.004 0.004 0.992 0.000 0.000
#> GSM1105482 1 0.2280 0.81016 0.880 0.000 0.120 0.000 0.000
#> GSM1105483 4 0.0404 0.76112 0.000 0.012 0.000 0.988 0.000
#> GSM1105494 4 0.6351 0.00731 0.000 0.316 0.184 0.500 0.000
#> GSM1105503 3 0.4493 0.69771 0.000 0.136 0.756 0.108 0.000
#> GSM1105507 1 0.2891 0.70897 0.824 0.000 0.000 0.176 0.000
#> GSM1105446 5 0.3983 0.11929 0.000 0.340 0.000 0.000 0.660
#> GSM1105519 1 0.0000 0.83126 1.000 0.000 0.000 0.000 0.000
#> GSM1105526 4 0.5584 0.36473 0.000 0.324 0.000 0.584 0.092
#> GSM1105527 4 0.0510 0.76419 0.000 0.016 0.000 0.984 0.000
#> GSM1105531 3 0.0162 0.67672 0.000 0.004 0.996 0.000 0.000
#> GSM1105543 5 0.4249 -0.21855 0.000 0.432 0.000 0.000 0.568
#> GSM1105546 1 0.0162 0.83144 0.996 0.004 0.000 0.000 0.000
#> GSM1105547 1 0.0000 0.83126 1.000 0.000 0.000 0.000 0.000
#> GSM1105455 4 0.4648 0.25228 0.000 0.464 0.000 0.524 0.012
#> GSM1105458 2 0.1251 0.52921 0.000 0.956 0.000 0.008 0.036
#> GSM1105459 2 0.4269 0.77025 0.000 0.684 0.000 0.016 0.300
#> GSM1105462 3 0.0703 0.66320 0.000 0.000 0.976 0.024 0.000
#> GSM1105441 2 0.4161 0.76240 0.000 0.704 0.000 0.016 0.280
#> GSM1105465 5 0.3171 0.66626 0.000 0.176 0.008 0.000 0.816
#> GSM1105484 5 0.0000 0.78306 0.000 0.000 0.000 0.000 1.000
#> GSM1105485 5 0.0000 0.78306 0.000 0.000 0.000 0.000 1.000
#> GSM1105496 3 0.5922 0.68128 0.000 0.284 0.604 0.016 0.096
#> GSM1105505 3 0.0703 0.68245 0.000 0.024 0.976 0.000 0.000
#> GSM1105509 1 0.0324 0.83038 0.992 0.000 0.004 0.004 0.000
#> GSM1105448 2 0.3999 0.72352 0.000 0.656 0.000 0.000 0.344
#> GSM1105521 1 0.0162 0.83144 0.996 0.000 0.004 0.000 0.000
#> GSM1105528 5 0.0000 0.78306 0.000 0.000 0.000 0.000 1.000
#> GSM1105529 5 0.0000 0.78306 0.000 0.000 0.000 0.000 1.000
#> GSM1105533 1 0.4236 0.69829 0.664 0.004 0.328 0.004 0.000
#> GSM1105545 4 0.4060 0.44942 0.000 0.360 0.000 0.640 0.000
#> GSM1105548 1 0.2970 0.79393 0.828 0.004 0.168 0.000 0.000
#> GSM1105549 1 0.2660 0.80627 0.864 0.000 0.128 0.000 0.008
#> GSM1105457 4 0.1478 0.77814 0.000 0.064 0.000 0.936 0.000
#> GSM1105460 2 0.4292 0.41974 0.000 0.704 0.000 0.272 0.024
#> GSM1105461 2 0.4269 0.77025 0.000 0.684 0.000 0.016 0.300
#> GSM1105464 1 0.4760 0.60149 0.564 0.000 0.416 0.020 0.000
#> GSM1105466 4 0.1410 0.77776 0.000 0.060 0.000 0.940 0.000
#> GSM1105479 2 0.3837 -0.07115 0.000 0.692 0.000 0.308 0.000
#> GSM1105502 1 0.4389 0.66374 0.624 0.004 0.368 0.004 0.000
#> GSM1105515 1 0.0000 0.83126 1.000 0.000 0.000 0.000 0.000
#> GSM1105523 3 0.3197 0.55947 0.012 0.004 0.832 0.152 0.000
#> GSM1105550 1 0.6987 0.13763 0.372 0.008 0.260 0.360 0.000
#> GSM1105450 2 0.4269 0.77025 0.000 0.684 0.000 0.016 0.300
#> GSM1105451 2 0.4269 0.77025 0.000 0.684 0.000 0.016 0.300
#> GSM1105454 3 0.5579 0.69581 0.000 0.300 0.600 0.100 0.000
#> GSM1105468 2 0.4269 0.77025 0.000 0.684 0.000 0.016 0.300
#> GSM1105481 3 0.3957 0.71726 0.000 0.280 0.712 0.008 0.000
#> GSM1105504 3 0.0000 0.67527 0.000 0.000 1.000 0.000 0.000
#> GSM1105517 1 0.3806 0.74602 0.804 0.004 0.152 0.040 0.000
#> GSM1105525 3 0.6427 -0.36746 0.392 0.004 0.452 0.152 0.000
#> GSM1105552 1 0.4383 0.60488 0.572 0.004 0.424 0.000 0.000
#> GSM1105452 5 0.0000 0.78306 0.000 0.000 0.000 0.000 1.000
#> GSM1105453 2 0.4193 0.76765 0.000 0.684 0.000 0.012 0.304
#> GSM1105456 3 0.5579 0.69581 0.000 0.300 0.600 0.100 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1105438 2 0.0260 0.873 0.000 0.992 0.000 0.000 0.008 0.000
#> GSM1105486 2 0.0000 0.875 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105487 1 0.4353 0.722 0.720 0.000 0.220 0.000 0.032 0.028
#> GSM1105490 4 0.0520 0.855 0.000 0.008 0.000 0.984 0.000 0.008
#> GSM1105491 5 0.2176 0.853 0.000 0.024 0.000 0.000 0.896 0.080
#> GSM1105495 6 0.2882 0.692 0.000 0.000 0.008 0.000 0.180 0.812
#> GSM1105498 6 0.3556 0.731 0.000 0.000 0.028 0.140 0.024 0.808
#> GSM1105499 1 0.0865 0.856 0.964 0.000 0.036 0.000 0.000 0.000
#> GSM1105506 4 0.0291 0.855 0.000 0.000 0.000 0.992 0.004 0.004
#> GSM1105442 5 0.2164 0.862 0.000 0.032 0.000 0.000 0.900 0.068
#> GSM1105511 4 0.0508 0.853 0.000 0.000 0.012 0.984 0.004 0.000
#> GSM1105514 2 0.0146 0.874 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM1105518 6 0.1148 0.811 0.000 0.004 0.016 0.020 0.000 0.960
#> GSM1105522 1 0.3411 0.769 0.804 0.000 0.160 0.000 0.012 0.024
#> GSM1105534 1 0.0000 0.858 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1105535 1 0.2164 0.849 0.908 0.000 0.060 0.000 0.012 0.020
#> GSM1105538 1 0.0291 0.859 0.992 0.000 0.000 0.000 0.004 0.004
#> GSM1105542 5 0.2092 0.906 0.000 0.124 0.000 0.000 0.876 0.000
#> GSM1105443 2 0.4589 0.550 0.000 0.660 0.008 0.288 0.040 0.004
#> GSM1105551 1 0.4637 0.720 0.708 0.000 0.212 0.000 0.040 0.040
#> GSM1105554 1 0.0000 0.858 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1105555 1 0.4430 0.680 0.708 0.000 0.232 0.000 0.032 0.028
#> GSM1105447 2 0.5833 0.390 0.000 0.580 0.008 0.060 0.056 0.296
#> GSM1105467 2 0.0146 0.875 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM1105470 2 0.0000 0.875 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105471 6 0.5701 0.520 0.000 0.260 0.012 0.132 0.008 0.588
#> GSM1105474 2 0.0146 0.874 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM1105475 2 0.0937 0.859 0.000 0.960 0.000 0.040 0.000 0.000
#> GSM1105440 1 0.2358 0.847 0.900 0.000 0.056 0.000 0.016 0.028
#> GSM1105488 5 0.2092 0.906 0.000 0.124 0.000 0.000 0.876 0.000
#> GSM1105489 1 0.4082 0.734 0.752 0.000 0.192 0.000 0.028 0.028
#> GSM1105492 1 0.1053 0.858 0.964 0.000 0.004 0.000 0.012 0.020
#> GSM1105493 1 0.3897 0.539 0.696 0.000 0.280 0.000 0.024 0.000
#> GSM1105497 5 0.2006 0.848 0.000 0.016 0.000 0.000 0.904 0.080
#> GSM1105500 5 0.3605 0.839 0.000 0.096 0.024 0.000 0.820 0.060
#> GSM1105501 4 0.2702 0.789 0.000 0.092 0.036 0.868 0.004 0.000
#> GSM1105508 1 0.2532 0.841 0.884 0.000 0.080 0.000 0.012 0.024
#> GSM1105444 2 0.0363 0.872 0.000 0.988 0.000 0.000 0.012 0.000
#> GSM1105513 4 0.5149 0.612 0.000 0.104 0.008 0.708 0.040 0.140
#> GSM1105516 1 0.4326 0.620 0.716 0.008 0.036 0.232 0.008 0.000
#> GSM1105520 6 0.1092 0.810 0.000 0.000 0.020 0.020 0.000 0.960
#> GSM1105524 1 0.2282 0.846 0.900 0.000 0.068 0.000 0.012 0.020
#> GSM1105536 2 0.4816 0.504 0.000 0.648 0.084 0.264 0.004 0.000
#> GSM1105537 1 0.2282 0.846 0.900 0.000 0.068 0.000 0.012 0.020
#> GSM1105540 1 0.5548 0.556 0.628 0.000 0.268 0.032 0.032 0.040
#> GSM1105544 5 0.7827 0.318 0.204 0.000 0.092 0.148 0.464 0.092
#> GSM1105445 6 0.4950 0.635 0.000 0.028 0.016 0.216 0.044 0.696
#> GSM1105553 6 0.1418 0.793 0.000 0.000 0.032 0.000 0.024 0.944
#> GSM1105556 1 0.0000 0.858 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1105557 4 0.0405 0.855 0.000 0.004 0.000 0.988 0.000 0.008
#> GSM1105449 2 0.1964 0.843 0.000 0.920 0.004 0.012 0.056 0.008
#> GSM1105469 4 0.0972 0.848 0.000 0.000 0.028 0.964 0.008 0.000
#> GSM1105472 2 0.0000 0.875 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105473 3 0.4328 0.263 0.460 0.000 0.520 0.000 0.020 0.000
#> GSM1105476 2 0.0146 0.874 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM1105477 2 0.6006 0.467 0.000 0.604 0.080 0.112 0.204 0.000
#> GSM1105478 4 0.4647 0.552 0.000 0.000 0.024 0.696 0.052 0.228
#> GSM1105510 5 0.2092 0.906 0.000 0.124 0.000 0.000 0.876 0.000
#> GSM1105530 3 0.2135 0.787 0.128 0.000 0.872 0.000 0.000 0.000
#> GSM1105539 3 0.2135 0.787 0.128 0.000 0.872 0.000 0.000 0.000
#> GSM1105480 4 0.3256 0.755 0.000 0.000 0.020 0.836 0.032 0.112
#> GSM1105512 1 0.0547 0.857 0.980 0.000 0.020 0.000 0.000 0.000
#> GSM1105532 3 0.2135 0.787 0.128 0.000 0.872 0.000 0.000 0.000
#> GSM1105541 3 0.2135 0.787 0.128 0.000 0.872 0.000 0.000 0.000
#> GSM1105439 2 0.4464 0.477 0.000 0.624 0.008 0.340 0.028 0.000
#> GSM1105463 3 0.3126 0.648 0.000 0.000 0.752 0.000 0.000 0.248
#> GSM1105482 1 0.2282 0.815 0.888 0.000 0.088 0.000 0.024 0.000
#> GSM1105483 4 0.1010 0.846 0.000 0.000 0.036 0.960 0.004 0.000
#> GSM1105494 6 0.4583 0.540 0.000 0.000 0.032 0.288 0.020 0.660
#> GSM1105503 6 0.2301 0.754 0.000 0.000 0.096 0.020 0.000 0.884
#> GSM1105507 1 0.4610 0.689 0.728 0.000 0.048 0.192 0.016 0.016
#> GSM1105446 2 0.3330 0.534 0.000 0.716 0.000 0.000 0.284 0.000
#> GSM1105519 1 0.0547 0.857 0.980 0.000 0.020 0.000 0.000 0.000
#> GSM1105526 4 0.5283 0.578 0.000 0.236 0.076 0.648 0.040 0.000
#> GSM1105527 4 0.0508 0.853 0.000 0.000 0.012 0.984 0.004 0.000
#> GSM1105531 3 0.3175 0.642 0.000 0.000 0.744 0.000 0.000 0.256
#> GSM1105543 2 0.3076 0.611 0.000 0.760 0.000 0.000 0.240 0.000
#> GSM1105546 1 0.1966 0.855 0.924 0.000 0.024 0.000 0.024 0.028
#> GSM1105547 1 0.0632 0.856 0.976 0.000 0.000 0.000 0.024 0.000
#> GSM1105455 2 0.4315 0.516 0.000 0.648 0.008 0.324 0.016 0.004
#> GSM1105458 2 0.3560 0.772 0.000 0.828 0.008 0.012 0.064 0.088
#> GSM1105459 2 0.0000 0.875 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105462 3 0.2597 0.696 0.000 0.000 0.824 0.000 0.000 0.176
#> GSM1105441 2 0.1511 0.852 0.000 0.940 0.004 0.012 0.044 0.000
#> GSM1105465 5 0.2122 0.855 0.000 0.024 0.000 0.000 0.900 0.076
#> GSM1105484 5 0.2003 0.904 0.000 0.116 0.000 0.000 0.884 0.000
#> GSM1105485 5 0.2092 0.906 0.000 0.124 0.000 0.000 0.876 0.000
#> GSM1105496 6 0.1334 0.800 0.000 0.000 0.032 0.000 0.020 0.948
#> GSM1105505 3 0.3797 0.387 0.000 0.000 0.580 0.000 0.000 0.420
#> GSM1105509 1 0.1349 0.847 0.940 0.000 0.056 0.004 0.000 0.000
#> GSM1105448 2 0.0260 0.873 0.000 0.992 0.000 0.000 0.008 0.000
#> GSM1105521 1 0.0547 0.857 0.980 0.000 0.020 0.000 0.000 0.000
#> GSM1105528 5 0.2092 0.906 0.000 0.124 0.000 0.000 0.876 0.000
#> GSM1105529 5 0.2092 0.906 0.000 0.124 0.000 0.000 0.876 0.000
#> GSM1105533 3 0.4027 0.550 0.308 0.000 0.672 0.000 0.008 0.012
#> GSM1105545 4 0.4640 0.606 0.000 0.232 0.084 0.680 0.004 0.000
#> GSM1105548 1 0.4366 0.748 0.748 0.000 0.168 0.000 0.048 0.036
#> GSM1105549 1 0.2662 0.788 0.856 0.000 0.120 0.000 0.024 0.000
#> GSM1105457 4 0.1312 0.845 0.000 0.004 0.008 0.956 0.020 0.012
#> GSM1105460 2 0.2697 0.810 0.000 0.876 0.008 0.068 0.048 0.000
#> GSM1105461 2 0.0000 0.875 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105464 3 0.2219 0.785 0.136 0.000 0.864 0.000 0.000 0.000
#> GSM1105466 4 0.0951 0.849 0.000 0.000 0.008 0.968 0.020 0.004
#> GSM1105479 6 0.6799 0.311 0.000 0.316 0.008 0.192 0.044 0.440
#> GSM1105502 3 0.3166 0.733 0.184 0.000 0.800 0.000 0.008 0.008
#> GSM1105515 1 0.0000 0.858 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1105523 3 0.2658 0.721 0.000 0.000 0.864 0.036 0.000 0.100
#> GSM1105550 3 0.3660 0.629 0.160 0.000 0.780 0.060 0.000 0.000
#> GSM1105450 2 0.0000 0.875 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105451 2 0.0000 0.875 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105454 6 0.1317 0.811 0.000 0.004 0.016 0.016 0.008 0.956
#> GSM1105468 2 0.0000 0.875 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105481 6 0.2243 0.746 0.000 0.004 0.112 0.000 0.004 0.880
#> GSM1105504 3 0.3050 0.659 0.000 0.000 0.764 0.000 0.000 0.236
#> GSM1105517 3 0.4456 0.136 0.456 0.000 0.520 0.020 0.004 0.000
#> GSM1105525 3 0.2884 0.770 0.072 0.000 0.872 0.036 0.004 0.016
#> GSM1105552 3 0.2631 0.767 0.152 0.000 0.840 0.000 0.008 0.000
#> GSM1105452 5 0.2092 0.906 0.000 0.124 0.000 0.000 0.876 0.000
#> GSM1105453 2 0.0146 0.874 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM1105456 6 0.1317 0.811 0.000 0.004 0.016 0.016 0.008 0.956
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 agent(p) other(p) time(p) individual(p) k
#> SD:skmeans 117 0.820 0.672545 0.788 1.11e-02 2
#> SD:skmeans 113 0.907 0.456037 0.180 9.39e-04 3
#> SD:skmeans 112 0.247 0.706684 0.478 1.35e-02 4
#> SD:skmeans 102 0.230 0.885904 0.610 1.51e-02 5
#> SD:skmeans 112 0.235 0.000337 0.744 3.29e-05 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 44956 rows and 120 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 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 0.881 0.921 0.966 0.4778 0.513 0.513
#> 3 3 0.557 0.733 0.853 0.3184 0.818 0.664
#> 4 4 0.657 0.740 0.855 0.1661 0.826 0.571
#> 5 5 0.637 0.576 0.770 0.0673 0.887 0.604
#> 6 6 0.714 0.634 0.816 0.0504 0.936 0.711
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
#> GSM1105438 2 0.0000 0.9842 0.000 1.000
#> GSM1105486 2 0.0000 0.9842 0.000 1.000
#> GSM1105487 1 0.0000 0.9339 1.000 0.000
#> GSM1105490 2 0.0000 0.9842 0.000 1.000
#> GSM1105491 1 0.9754 0.3943 0.592 0.408
#> GSM1105495 2 0.0000 0.9842 0.000 1.000
#> GSM1105498 2 0.2043 0.9563 0.032 0.968
#> GSM1105499 1 0.0000 0.9339 1.000 0.000
#> GSM1105506 2 0.0000 0.9842 0.000 1.000
#> GSM1105442 2 0.0000 0.9842 0.000 1.000
#> GSM1105511 2 0.0000 0.9842 0.000 1.000
#> GSM1105514 2 0.0000 0.9842 0.000 1.000
#> GSM1105518 2 0.0000 0.9842 0.000 1.000
#> GSM1105522 1 0.0000 0.9339 1.000 0.000
#> GSM1105534 1 0.0000 0.9339 1.000 0.000
#> GSM1105535 1 0.0000 0.9339 1.000 0.000
#> GSM1105538 1 0.0000 0.9339 1.000 0.000
#> GSM1105542 2 0.0000 0.9842 0.000 1.000
#> GSM1105443 2 0.0000 0.9842 0.000 1.000
#> GSM1105551 1 0.0000 0.9339 1.000 0.000
#> GSM1105554 1 0.0000 0.9339 1.000 0.000
#> GSM1105555 1 0.0000 0.9339 1.000 0.000
#> GSM1105447 2 0.0000 0.9842 0.000 1.000
#> GSM1105467 2 0.0000 0.9842 0.000 1.000
#> GSM1105470 2 0.0000 0.9842 0.000 1.000
#> GSM1105471 2 0.0000 0.9842 0.000 1.000
#> GSM1105474 2 0.0000 0.9842 0.000 1.000
#> GSM1105475 2 0.0000 0.9842 0.000 1.000
#> GSM1105440 1 0.0000 0.9339 1.000 0.000
#> GSM1105488 2 0.0000 0.9842 0.000 1.000
#> GSM1105489 1 0.0000 0.9339 1.000 0.000
#> GSM1105492 1 0.0000 0.9339 1.000 0.000
#> GSM1105493 1 0.0000 0.9339 1.000 0.000
#> GSM1105497 2 0.0000 0.9842 0.000 1.000
#> GSM1105500 2 0.6148 0.8027 0.152 0.848
#> GSM1105501 2 0.0672 0.9780 0.008 0.992
#> GSM1105508 1 0.0000 0.9339 1.000 0.000
#> GSM1105444 2 0.0000 0.9842 0.000 1.000
#> GSM1105513 2 0.0000 0.9842 0.000 1.000
#> GSM1105516 1 0.8267 0.6743 0.740 0.260
#> GSM1105520 2 0.1843 0.9590 0.028 0.972
#> GSM1105524 1 0.0000 0.9339 1.000 0.000
#> GSM1105536 2 0.0938 0.9757 0.012 0.988
#> GSM1105537 1 0.0000 0.9339 1.000 0.000
#> GSM1105540 1 0.9754 0.3943 0.592 0.408
#> GSM1105544 1 0.9754 0.3943 0.592 0.408
#> GSM1105445 2 0.0000 0.9842 0.000 1.000
#> GSM1105553 2 0.7453 0.7019 0.212 0.788
#> GSM1105556 1 0.0000 0.9339 1.000 0.000
#> GSM1105557 2 0.0000 0.9842 0.000 1.000
#> GSM1105449 2 0.0000 0.9842 0.000 1.000
#> GSM1105469 2 0.0938 0.9757 0.012 0.988
#> GSM1105472 2 0.0000 0.9842 0.000 1.000
#> GSM1105473 1 0.0000 0.9339 1.000 0.000
#> GSM1105476 2 0.0000 0.9842 0.000 1.000
#> GSM1105477 2 0.0938 0.9757 0.012 0.988
#> GSM1105478 2 0.0000 0.9842 0.000 1.000
#> GSM1105510 2 0.0000 0.9842 0.000 1.000
#> GSM1105530 1 0.0000 0.9339 1.000 0.000
#> GSM1105539 1 0.0000 0.9339 1.000 0.000
#> GSM1105480 2 0.0000 0.9842 0.000 1.000
#> GSM1105512 1 0.0000 0.9339 1.000 0.000
#> GSM1105532 1 0.0000 0.9339 1.000 0.000
#> GSM1105541 1 0.0000 0.9339 1.000 0.000
#> GSM1105439 2 0.0000 0.9842 0.000 1.000
#> GSM1105463 1 0.0000 0.9339 1.000 0.000
#> GSM1105482 1 0.0000 0.9339 1.000 0.000
#> GSM1105483 2 0.0938 0.9757 0.012 0.988
#> GSM1105494 2 0.0000 0.9842 0.000 1.000
#> GSM1105503 2 0.0000 0.9842 0.000 1.000
#> GSM1105507 1 0.6712 0.7798 0.824 0.176
#> GSM1105446 2 0.0000 0.9842 0.000 1.000
#> GSM1105519 1 0.0000 0.9339 1.000 0.000
#> GSM1105526 2 0.0938 0.9757 0.012 0.988
#> GSM1105527 2 0.0938 0.9757 0.012 0.988
#> GSM1105531 1 0.7376 0.7472 0.792 0.208
#> GSM1105543 2 0.0000 0.9842 0.000 1.000
#> GSM1105546 1 0.0000 0.9339 1.000 0.000
#> GSM1105547 1 0.0000 0.9339 1.000 0.000
#> GSM1105455 2 0.0000 0.9842 0.000 1.000
#> GSM1105458 2 0.0000 0.9842 0.000 1.000
#> GSM1105459 2 0.0000 0.9842 0.000 1.000
#> GSM1105462 2 0.0938 0.9757 0.012 0.988
#> GSM1105441 2 0.0000 0.9842 0.000 1.000
#> GSM1105465 2 0.0000 0.9842 0.000 1.000
#> GSM1105484 2 0.0000 0.9842 0.000 1.000
#> GSM1105485 2 0.0938 0.9757 0.012 0.988
#> GSM1105496 1 0.9815 0.3684 0.580 0.420
#> GSM1105505 1 0.8955 0.5934 0.688 0.312
#> GSM1105509 1 0.0000 0.9339 1.000 0.000
#> GSM1105448 2 0.0000 0.9842 0.000 1.000
#> GSM1105521 1 0.0000 0.9339 1.000 0.000
#> GSM1105528 2 0.0000 0.9842 0.000 1.000
#> GSM1105529 2 0.0000 0.9842 0.000 1.000
#> GSM1105533 1 0.0000 0.9339 1.000 0.000
#> GSM1105545 2 0.0938 0.9757 0.012 0.988
#> GSM1105548 1 0.0000 0.9339 1.000 0.000
#> GSM1105549 1 0.0000 0.9339 1.000 0.000
#> GSM1105457 2 0.0000 0.9842 0.000 1.000
#> GSM1105460 2 0.0000 0.9842 0.000 1.000
#> GSM1105461 2 0.0000 0.9842 0.000 1.000
#> GSM1105464 1 0.0000 0.9339 1.000 0.000
#> GSM1105466 2 0.0000 0.9842 0.000 1.000
#> GSM1105479 2 0.0000 0.9842 0.000 1.000
#> GSM1105502 1 0.0000 0.9339 1.000 0.000
#> GSM1105515 1 0.0000 0.9339 1.000 0.000
#> GSM1105523 1 0.7376 0.7472 0.792 0.208
#> GSM1105550 2 0.9963 0.0127 0.464 0.536
#> GSM1105450 2 0.0000 0.9842 0.000 1.000
#> GSM1105451 2 0.0000 0.9842 0.000 1.000
#> GSM1105454 2 0.0000 0.9842 0.000 1.000
#> GSM1105468 2 0.0000 0.9842 0.000 1.000
#> GSM1105481 2 0.0000 0.9842 0.000 1.000
#> GSM1105504 1 0.1843 0.9146 0.972 0.028
#> GSM1105517 1 0.7376 0.7472 0.792 0.208
#> GSM1105525 1 0.0000 0.9339 1.000 0.000
#> GSM1105552 1 0.0000 0.9339 1.000 0.000
#> GSM1105452 2 0.0000 0.9842 0.000 1.000
#> GSM1105453 2 0.0000 0.9842 0.000 1.000
#> GSM1105456 2 0.0000 0.9842 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1105438 2 0.0000 0.7870 0.000 1.000 0.000
#> GSM1105486 2 0.4702 0.8251 0.000 0.788 0.212
#> GSM1105487 1 0.0000 0.8890 1.000 0.000 0.000
#> GSM1105490 2 0.2356 0.8050 0.000 0.928 0.072
#> GSM1105491 3 0.5268 0.7221 0.212 0.012 0.776
#> GSM1105495 3 0.6244 0.4598 0.000 0.440 0.560
#> GSM1105498 3 0.0983 0.6638 0.016 0.004 0.980
#> GSM1105499 1 0.0000 0.8890 1.000 0.000 0.000
#> GSM1105506 2 0.4750 0.8246 0.000 0.784 0.216
#> GSM1105442 2 0.0000 0.7870 0.000 1.000 0.000
#> GSM1105511 2 0.4750 0.8246 0.000 0.784 0.216
#> GSM1105514 2 0.4702 0.8251 0.000 0.788 0.212
#> GSM1105518 2 0.3038 0.6882 0.000 0.896 0.104
#> GSM1105522 1 0.2356 0.8206 0.928 0.000 0.072
#> GSM1105534 1 0.0000 0.8890 1.000 0.000 0.000
#> GSM1105535 1 0.0000 0.8890 1.000 0.000 0.000
#> GSM1105538 1 0.0000 0.8890 1.000 0.000 0.000
#> GSM1105542 2 0.4702 0.8251 0.000 0.788 0.212
#> GSM1105443 2 0.0237 0.7853 0.000 0.996 0.004
#> GSM1105551 1 0.0000 0.8890 1.000 0.000 0.000
#> GSM1105554 1 0.0000 0.8890 1.000 0.000 0.000
#> GSM1105555 1 0.0000 0.8890 1.000 0.000 0.000
#> GSM1105447 2 0.0000 0.7870 0.000 1.000 0.000
#> GSM1105467 2 0.4702 0.8251 0.000 0.788 0.212
#> GSM1105470 2 0.4702 0.8251 0.000 0.788 0.212
#> GSM1105471 2 0.6192 0.6707 0.000 0.580 0.420
#> GSM1105474 2 0.4702 0.8251 0.000 0.788 0.212
#> GSM1105475 2 0.4702 0.8251 0.000 0.788 0.212
#> GSM1105440 1 0.0000 0.8890 1.000 0.000 0.000
#> GSM1105488 2 0.4178 0.8234 0.000 0.828 0.172
#> GSM1105489 1 0.0000 0.8890 1.000 0.000 0.000
#> GSM1105492 1 0.0000 0.8890 1.000 0.000 0.000
#> GSM1105493 1 0.4121 0.7075 0.832 0.000 0.168
#> GSM1105497 2 0.3412 0.6577 0.000 0.876 0.124
#> GSM1105500 2 0.5737 0.7970 0.012 0.732 0.256
#> GSM1105501 2 0.5156 0.8222 0.008 0.776 0.216
#> GSM1105508 1 0.0000 0.8890 1.000 0.000 0.000
#> GSM1105444 2 0.0000 0.7870 0.000 1.000 0.000
#> GSM1105513 2 0.0237 0.7853 0.000 0.996 0.004
#> GSM1105516 1 0.8685 0.1964 0.584 0.260 0.156
#> GSM1105520 3 0.2339 0.6819 0.012 0.048 0.940
#> GSM1105524 1 0.0000 0.8890 1.000 0.000 0.000
#> GSM1105536 2 0.6192 0.6707 0.000 0.580 0.420
#> GSM1105537 1 0.0000 0.8890 1.000 0.000 0.000
#> GSM1105540 3 0.9324 0.4968 0.212 0.272 0.516
#> GSM1105544 2 0.8984 0.3448 0.212 0.564 0.224
#> GSM1105445 2 0.0237 0.7853 0.000 0.996 0.004
#> GSM1105553 2 0.3752 0.6284 0.000 0.856 0.144
#> GSM1105556 1 0.0000 0.8890 1.000 0.000 0.000
#> GSM1105557 2 0.3340 0.8151 0.000 0.880 0.120
#> GSM1105449 2 0.0000 0.7870 0.000 1.000 0.000
#> GSM1105469 2 0.6192 0.6707 0.000 0.580 0.420
#> GSM1105472 2 0.4702 0.8251 0.000 0.788 0.212
#> GSM1105473 1 0.6225 0.0477 0.568 0.000 0.432
#> GSM1105476 2 0.4702 0.8251 0.000 0.788 0.212
#> GSM1105477 2 0.6192 0.6707 0.000 0.580 0.420
#> GSM1105478 2 0.6204 0.6692 0.000 0.576 0.424
#> GSM1105510 2 0.4555 0.8251 0.000 0.800 0.200
#> GSM1105530 3 0.4750 0.7217 0.216 0.000 0.784
#> GSM1105539 3 0.4750 0.7217 0.216 0.000 0.784
#> GSM1105480 2 0.6204 0.6692 0.000 0.576 0.424
#> GSM1105512 1 0.0000 0.8890 1.000 0.000 0.000
#> GSM1105532 3 0.4750 0.7217 0.216 0.000 0.784
#> GSM1105541 1 0.6111 0.2217 0.604 0.000 0.396
#> GSM1105439 2 0.0237 0.7853 0.000 0.996 0.004
#> GSM1105463 3 0.4750 0.7217 0.216 0.000 0.784
#> GSM1105482 1 0.0000 0.8890 1.000 0.000 0.000
#> GSM1105483 2 0.6192 0.6707 0.000 0.580 0.420
#> GSM1105494 2 0.4750 0.8246 0.000 0.784 0.216
#> GSM1105503 3 0.4702 0.6567 0.000 0.212 0.788
#> GSM1105507 1 0.7710 0.4383 0.680 0.176 0.144
#> GSM1105446 2 0.0000 0.7870 0.000 1.000 0.000
#> GSM1105519 1 0.0747 0.8765 0.984 0.000 0.016
#> GSM1105526 2 0.6192 0.6707 0.000 0.580 0.420
#> GSM1105527 2 0.6192 0.6707 0.000 0.580 0.420
#> GSM1105531 3 0.4702 0.7234 0.212 0.000 0.788
#> GSM1105543 2 0.4702 0.8251 0.000 0.788 0.212
#> GSM1105546 1 0.0000 0.8890 1.000 0.000 0.000
#> GSM1105547 1 0.0000 0.8890 1.000 0.000 0.000
#> GSM1105455 2 0.0237 0.7853 0.000 0.996 0.004
#> GSM1105458 2 0.0000 0.7870 0.000 1.000 0.000
#> GSM1105459 2 0.0000 0.7870 0.000 1.000 0.000
#> GSM1105462 3 0.3879 0.4491 0.000 0.152 0.848
#> GSM1105441 2 0.0000 0.7870 0.000 1.000 0.000
#> GSM1105465 2 0.6192 0.6707 0.000 0.580 0.420
#> GSM1105484 2 0.4702 0.8251 0.000 0.788 0.212
#> GSM1105485 2 0.6192 0.6707 0.000 0.580 0.420
#> GSM1105496 3 0.6498 0.5173 0.008 0.396 0.596
#> GSM1105505 3 0.4883 0.7242 0.208 0.004 0.788
#> GSM1105509 1 0.5810 0.3557 0.664 0.000 0.336
#> GSM1105448 2 0.0000 0.7870 0.000 1.000 0.000
#> GSM1105521 1 0.0000 0.8890 1.000 0.000 0.000
#> GSM1105528 2 0.4702 0.8251 0.000 0.788 0.212
#> GSM1105529 2 0.6192 0.6707 0.000 0.580 0.420
#> GSM1105533 1 0.1289 0.8649 0.968 0.000 0.032
#> GSM1105545 2 0.6192 0.6707 0.000 0.580 0.420
#> GSM1105548 1 0.1753 0.8485 0.952 0.000 0.048
#> GSM1105549 1 0.0000 0.8890 1.000 0.000 0.000
#> GSM1105457 2 0.0237 0.7853 0.000 0.996 0.004
#> GSM1105460 2 0.0000 0.7870 0.000 1.000 0.000
#> GSM1105461 2 0.0000 0.7870 0.000 1.000 0.000
#> GSM1105464 3 0.4887 0.7112 0.228 0.000 0.772
#> GSM1105466 2 0.6126 0.6904 0.000 0.600 0.400
#> GSM1105479 2 0.4702 0.8251 0.000 0.788 0.212
#> GSM1105502 1 0.6307 -0.0999 0.512 0.000 0.488
#> GSM1105515 1 0.0000 0.8890 1.000 0.000 0.000
#> GSM1105523 3 0.4702 0.7234 0.212 0.000 0.788
#> GSM1105550 3 0.7026 0.6543 0.152 0.120 0.728
#> GSM1105450 2 0.4702 0.8251 0.000 0.788 0.212
#> GSM1105451 2 0.0000 0.7870 0.000 1.000 0.000
#> GSM1105454 3 0.6244 0.4548 0.000 0.440 0.560
#> GSM1105468 2 0.4702 0.8251 0.000 0.788 0.212
#> GSM1105481 3 0.0237 0.6535 0.000 0.004 0.996
#> GSM1105504 3 0.4931 0.7234 0.212 0.004 0.784
#> GSM1105517 3 0.7851 0.3835 0.412 0.056 0.532
#> GSM1105525 3 0.5835 0.5586 0.340 0.000 0.660
#> GSM1105552 3 0.4750 0.7217 0.216 0.000 0.784
#> GSM1105452 2 0.4702 0.8251 0.000 0.788 0.212
#> GSM1105453 2 0.0000 0.7870 0.000 1.000 0.000
#> GSM1105456 3 0.6192 0.4850 0.000 0.420 0.580
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1105438 2 0.3649 0.8893 0.000 0.796 0.000 0.204
#> GSM1105486 4 0.0000 0.9228 0.000 0.000 0.000 1.000
#> GSM1105487 1 0.0000 0.8316 1.000 0.000 0.000 0.000
#> GSM1105490 2 0.4585 0.3558 0.000 0.668 0.000 0.332
#> GSM1105491 3 0.0921 0.7298 0.000 0.028 0.972 0.000
#> GSM1105495 2 0.4697 0.4783 0.000 0.696 0.296 0.008
#> GSM1105498 3 0.6585 0.4792 0.000 0.104 0.584 0.312
#> GSM1105499 1 0.0000 0.8316 1.000 0.000 0.000 0.000
#> GSM1105506 4 0.3356 0.7872 0.000 0.176 0.000 0.824
#> GSM1105442 2 0.3688 0.8888 0.000 0.792 0.000 0.208
#> GSM1105511 4 0.3356 0.7872 0.000 0.176 0.000 0.824
#> GSM1105514 4 0.0188 0.9206 0.000 0.004 0.000 0.996
#> GSM1105518 2 0.4104 0.8529 0.000 0.808 0.028 0.164
#> GSM1105522 1 0.4661 0.5625 0.652 0.000 0.348 0.000
#> GSM1105534 1 0.0000 0.8316 1.000 0.000 0.000 0.000
#> GSM1105535 1 0.0000 0.8316 1.000 0.000 0.000 0.000
#> GSM1105538 1 0.4500 0.5879 0.684 0.000 0.316 0.000
#> GSM1105542 4 0.0000 0.9228 0.000 0.000 0.000 1.000
#> GSM1105443 2 0.3569 0.8873 0.000 0.804 0.000 0.196
#> GSM1105551 1 0.0707 0.8231 0.980 0.020 0.000 0.000
#> GSM1105554 1 0.0000 0.8316 1.000 0.000 0.000 0.000
#> GSM1105555 1 0.1356 0.8184 0.960 0.008 0.032 0.000
#> GSM1105447 2 0.3688 0.8888 0.000 0.792 0.000 0.208
#> GSM1105467 4 0.0000 0.9228 0.000 0.000 0.000 1.000
#> GSM1105470 4 0.0000 0.9228 0.000 0.000 0.000 1.000
#> GSM1105471 4 0.0000 0.9228 0.000 0.000 0.000 1.000
#> GSM1105474 4 0.0000 0.9228 0.000 0.000 0.000 1.000
#> GSM1105475 4 0.0000 0.9228 0.000 0.000 0.000 1.000
#> GSM1105440 1 0.0000 0.8316 1.000 0.000 0.000 0.000
#> GSM1105488 4 0.4040 0.5093 0.000 0.248 0.000 0.752
#> GSM1105489 1 0.0921 0.8190 0.972 0.028 0.000 0.000
#> GSM1105492 1 0.4500 0.5879 0.684 0.000 0.316 0.000
#> GSM1105493 1 0.2973 0.7165 0.856 0.000 0.144 0.000
#> GSM1105497 2 0.3311 0.8684 0.000 0.828 0.000 0.172
#> GSM1105500 4 0.5407 0.6928 0.000 0.108 0.152 0.740
#> GSM1105501 4 0.3356 0.7872 0.000 0.176 0.000 0.824
#> GSM1105508 1 0.3266 0.7106 0.832 0.168 0.000 0.000
#> GSM1105444 2 0.3649 0.8893 0.000 0.796 0.000 0.204
#> GSM1105513 2 0.3726 0.6072 0.000 0.788 0.000 0.212
#> GSM1105516 1 0.9029 0.2500 0.416 0.172 0.320 0.092
#> GSM1105520 3 0.6229 0.5748 0.000 0.116 0.656 0.228
#> GSM1105524 1 0.0000 0.8316 1.000 0.000 0.000 0.000
#> GSM1105536 4 0.0336 0.9190 0.000 0.008 0.000 0.992
#> GSM1105537 1 0.0000 0.8316 1.000 0.000 0.000 0.000
#> GSM1105540 3 0.3764 0.5941 0.000 0.000 0.784 0.216
#> GSM1105544 4 0.4679 0.4094 0.000 0.000 0.352 0.648
#> GSM1105445 2 0.3610 0.8872 0.000 0.800 0.000 0.200
#> GSM1105553 2 0.3311 0.8684 0.000 0.828 0.000 0.172
#> GSM1105556 1 0.0000 0.8316 1.000 0.000 0.000 0.000
#> GSM1105557 2 0.4996 -0.1738 0.000 0.516 0.000 0.484
#> GSM1105449 2 0.3688 0.8888 0.000 0.792 0.000 0.208
#> GSM1105469 4 0.3266 0.7923 0.000 0.168 0.000 0.832
#> GSM1105472 4 0.0000 0.9228 0.000 0.000 0.000 1.000
#> GSM1105473 3 0.4040 0.4918 0.248 0.000 0.752 0.000
#> GSM1105476 4 0.0000 0.9228 0.000 0.000 0.000 1.000
#> GSM1105477 4 0.0000 0.9228 0.000 0.000 0.000 1.000
#> GSM1105478 4 0.0336 0.9183 0.000 0.008 0.000 0.992
#> GSM1105510 4 0.3074 0.7181 0.000 0.152 0.000 0.848
#> GSM1105530 3 0.0000 0.7277 0.000 0.000 1.000 0.000
#> GSM1105539 3 0.4500 0.4458 0.316 0.000 0.684 0.000
#> GSM1105480 4 0.0336 0.9183 0.000 0.008 0.000 0.992
#> GSM1105512 1 0.4134 0.6483 0.740 0.000 0.260 0.000
#> GSM1105532 3 0.0000 0.7277 0.000 0.000 1.000 0.000
#> GSM1105541 1 0.4605 0.4355 0.664 0.000 0.336 0.000
#> GSM1105439 2 0.3569 0.8873 0.000 0.804 0.000 0.196
#> GSM1105463 3 0.0921 0.7298 0.000 0.028 0.972 0.000
#> GSM1105482 1 0.0000 0.8316 1.000 0.000 0.000 0.000
#> GSM1105483 4 0.3266 0.7923 0.000 0.168 0.000 0.832
#> GSM1105494 4 0.1118 0.8954 0.000 0.036 0.000 0.964
#> GSM1105503 3 0.4643 0.4203 0.000 0.344 0.656 0.000
#> GSM1105507 1 0.7975 0.2848 0.448 0.168 0.364 0.020
#> GSM1105446 2 0.3649 0.8893 0.000 0.796 0.000 0.204
#> GSM1105519 1 0.4500 0.5879 0.684 0.000 0.316 0.000
#> GSM1105526 4 0.0000 0.9228 0.000 0.000 0.000 1.000
#> GSM1105527 4 0.3311 0.7899 0.000 0.172 0.000 0.828
#> GSM1105531 3 0.0921 0.7298 0.000 0.028 0.972 0.000
#> GSM1105543 4 0.0188 0.9206 0.000 0.004 0.000 0.996
#> GSM1105546 1 0.0000 0.8316 1.000 0.000 0.000 0.000
#> GSM1105547 1 0.0000 0.8316 1.000 0.000 0.000 0.000
#> GSM1105455 2 0.3528 0.8859 0.000 0.808 0.000 0.192
#> GSM1105458 2 0.3688 0.8888 0.000 0.792 0.000 0.208
#> GSM1105459 2 0.4193 0.8296 0.000 0.732 0.000 0.268
#> GSM1105462 3 0.4925 0.2915 0.000 0.000 0.572 0.428
#> GSM1105441 2 0.3688 0.8888 0.000 0.792 0.000 0.208
#> GSM1105465 4 0.0000 0.9228 0.000 0.000 0.000 1.000
#> GSM1105484 4 0.0000 0.9228 0.000 0.000 0.000 1.000
#> GSM1105485 4 0.0000 0.9228 0.000 0.000 0.000 1.000
#> GSM1105496 3 0.4761 0.3667 0.000 0.372 0.628 0.000
#> GSM1105505 3 0.1118 0.7293 0.000 0.036 0.964 0.000
#> GSM1105509 3 0.7354 -0.0268 0.352 0.168 0.480 0.000
#> GSM1105448 2 0.3649 0.8893 0.000 0.796 0.000 0.204
#> GSM1105521 1 0.4500 0.5879 0.684 0.000 0.316 0.000
#> GSM1105528 4 0.0000 0.9228 0.000 0.000 0.000 1.000
#> GSM1105529 4 0.0000 0.9228 0.000 0.000 0.000 1.000
#> GSM1105533 1 0.1557 0.8047 0.944 0.000 0.056 0.000
#> GSM1105545 4 0.1792 0.8766 0.000 0.068 0.000 0.932
#> GSM1105548 3 0.5776 -0.1579 0.468 0.028 0.504 0.000
#> GSM1105549 1 0.0000 0.8316 1.000 0.000 0.000 0.000
#> GSM1105457 2 0.1022 0.7202 0.000 0.968 0.000 0.032
#> GSM1105460 2 0.3688 0.8888 0.000 0.792 0.000 0.208
#> GSM1105461 2 0.3649 0.8893 0.000 0.796 0.000 0.204
#> GSM1105464 3 0.4304 0.4913 0.284 0.000 0.716 0.000
#> GSM1105466 4 0.0000 0.9228 0.000 0.000 0.000 1.000
#> GSM1105479 4 0.0000 0.9228 0.000 0.000 0.000 1.000
#> GSM1105502 1 0.4977 0.2744 0.540 0.000 0.460 0.000
#> GSM1105515 1 0.0000 0.8316 1.000 0.000 0.000 0.000
#> GSM1105523 3 0.0000 0.7277 0.000 0.000 1.000 0.000
#> GSM1105550 3 0.2647 0.6821 0.000 0.000 0.880 0.120
#> GSM1105450 4 0.0188 0.9206 0.000 0.004 0.000 0.996
#> GSM1105451 2 0.3649 0.8893 0.000 0.796 0.000 0.204
#> GSM1105454 2 0.3311 0.6557 0.000 0.828 0.172 0.000
#> GSM1105468 4 0.0000 0.9228 0.000 0.000 0.000 1.000
#> GSM1105481 3 0.5343 0.5142 0.000 0.028 0.656 0.316
#> GSM1105504 3 0.0000 0.7277 0.000 0.000 1.000 0.000
#> GSM1105517 3 0.5067 0.6256 0.036 0.164 0.776 0.024
#> GSM1105525 3 0.2408 0.6626 0.104 0.000 0.896 0.000
#> GSM1105552 3 0.0921 0.7298 0.000 0.028 0.972 0.000
#> GSM1105452 4 0.0000 0.9228 0.000 0.000 0.000 1.000
#> GSM1105453 2 0.3649 0.8893 0.000 0.796 0.000 0.204
#> GSM1105456 2 0.3311 0.6557 0.000 0.828 0.172 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM1105438 2 0.2516 0.8573 0.000 0.860 0.000 0.000 0.140
#> GSM1105486 5 0.0703 0.8513 0.000 0.024 0.000 0.000 0.976
#> GSM1105487 1 0.0000 0.8970 1.000 0.000 0.000 0.000 0.000
#> GSM1105490 4 0.6255 0.2324 0.000 0.208 0.000 0.540 0.252
#> GSM1105491 4 0.6862 -0.1903 0.000 0.156 0.360 0.460 0.024
#> GSM1105495 2 0.5660 0.0486 0.000 0.568 0.060 0.360 0.012
#> GSM1105498 4 0.4280 0.2015 0.000 0.140 0.088 0.772 0.000
#> GSM1105499 1 0.0000 0.8970 1.000 0.000 0.000 0.000 0.000
#> GSM1105506 4 0.4287 -0.0409 0.000 0.000 0.000 0.540 0.460
#> GSM1105442 2 0.4504 0.7786 0.000 0.748 0.000 0.084 0.168
#> GSM1105511 4 0.4287 -0.0409 0.000 0.000 0.000 0.540 0.460
#> GSM1105514 5 0.0794 0.8502 0.000 0.028 0.000 0.000 0.972
#> GSM1105518 2 0.3779 0.4800 0.000 0.752 0.012 0.236 0.000
#> GSM1105522 3 0.4101 0.4814 0.184 0.000 0.768 0.048 0.000
#> GSM1105534 1 0.0000 0.8970 1.000 0.000 0.000 0.000 0.000
#> GSM1105535 1 0.0000 0.8970 1.000 0.000 0.000 0.000 0.000
#> GSM1105538 3 0.4201 0.2608 0.408 0.000 0.592 0.000 0.000
#> GSM1105542 5 0.1792 0.8056 0.000 0.000 0.000 0.084 0.916
#> GSM1105443 2 0.2516 0.8573 0.000 0.860 0.000 0.000 0.140
#> GSM1105551 1 0.1965 0.8211 0.904 0.096 0.000 0.000 0.000
#> GSM1105554 1 0.0000 0.8970 1.000 0.000 0.000 0.000 0.000
#> GSM1105555 1 0.3731 0.7108 0.800 0.040 0.160 0.000 0.000
#> GSM1105447 2 0.2561 0.8571 0.000 0.856 0.000 0.000 0.144
#> GSM1105467 5 0.0880 0.8501 0.000 0.032 0.000 0.000 0.968
#> GSM1105470 5 0.0703 0.8513 0.000 0.024 0.000 0.000 0.976
#> GSM1105471 5 0.1018 0.8487 0.000 0.016 0.016 0.000 0.968
#> GSM1105474 5 0.0703 0.8513 0.000 0.024 0.000 0.000 0.976
#> GSM1105475 5 0.0771 0.8491 0.000 0.004 0.000 0.020 0.976
#> GSM1105440 1 0.0000 0.8970 1.000 0.000 0.000 0.000 0.000
#> GSM1105488 5 0.4647 0.6083 0.000 0.184 0.000 0.084 0.732
#> GSM1105489 1 0.2516 0.7726 0.860 0.140 0.000 0.000 0.000
#> GSM1105492 3 0.4201 0.2608 0.408 0.000 0.592 0.000 0.000
#> GSM1105493 1 0.1557 0.8514 0.940 0.008 0.052 0.000 0.000
#> GSM1105497 2 0.2570 0.6691 0.000 0.888 0.000 0.084 0.028
#> GSM1105500 4 0.4278 -0.0918 0.000 0.000 0.000 0.548 0.452
#> GSM1105501 4 0.4126 0.0647 0.000 0.000 0.000 0.620 0.380
#> GSM1105508 1 0.3816 0.5483 0.696 0.000 0.000 0.304 0.000
#> GSM1105444 2 0.2516 0.8573 0.000 0.860 0.000 0.000 0.140
#> GSM1105513 4 0.6000 0.0676 0.000 0.328 0.000 0.540 0.132
#> GSM1105516 3 0.4517 0.3456 0.000 0.000 0.600 0.388 0.012
#> GSM1105520 4 0.5791 0.1104 0.000 0.140 0.260 0.600 0.000
#> GSM1105524 1 0.0000 0.8970 1.000 0.000 0.000 0.000 0.000
#> GSM1105536 5 0.1211 0.8422 0.000 0.000 0.024 0.016 0.960
#> GSM1105537 1 0.0000 0.8970 1.000 0.000 0.000 0.000 0.000
#> GSM1105540 3 0.3039 0.3864 0.000 0.000 0.808 0.000 0.192
#> GSM1105544 3 0.3980 0.3096 0.000 0.000 0.708 0.008 0.284
#> GSM1105445 2 0.2561 0.8571 0.000 0.856 0.000 0.000 0.144
#> GSM1105553 2 0.3550 0.4937 0.000 0.760 0.000 0.236 0.004
#> GSM1105556 1 0.0000 0.8970 1.000 0.000 0.000 0.000 0.000
#> GSM1105557 4 0.5682 0.1013 0.000 0.088 0.000 0.540 0.372
#> GSM1105449 2 0.2561 0.8571 0.000 0.856 0.000 0.000 0.144
#> GSM1105469 5 0.4380 0.4753 0.000 0.000 0.020 0.304 0.676
#> GSM1105472 5 0.0703 0.8513 0.000 0.024 0.000 0.000 0.976
#> GSM1105473 3 0.3109 0.4985 0.200 0.000 0.800 0.000 0.000
#> GSM1105476 5 0.0703 0.8513 0.000 0.024 0.000 0.000 0.976
#> GSM1105477 5 0.2753 0.7814 0.000 0.000 0.008 0.136 0.856
#> GSM1105478 5 0.3826 0.6054 0.000 0.008 0.004 0.236 0.752
#> GSM1105510 5 0.4458 0.6848 0.000 0.120 0.000 0.120 0.760
#> GSM1105530 3 0.4114 0.2666 0.000 0.000 0.624 0.376 0.000
#> GSM1105539 3 0.6615 0.1054 0.216 0.000 0.408 0.376 0.000
#> GSM1105480 5 0.3607 0.6038 0.000 0.000 0.004 0.244 0.752
#> GSM1105512 1 0.4101 0.2753 0.628 0.000 0.372 0.000 0.000
#> GSM1105532 3 0.4114 0.2666 0.000 0.000 0.624 0.376 0.000
#> GSM1105541 1 0.6332 0.2246 0.524 0.000 0.212 0.264 0.000
#> GSM1105439 2 0.3074 0.8260 0.000 0.804 0.000 0.000 0.196
#> GSM1105463 3 0.6431 0.1849 0.000 0.140 0.476 0.376 0.008
#> GSM1105482 1 0.0000 0.8970 1.000 0.000 0.000 0.000 0.000
#> GSM1105483 5 0.4380 0.4753 0.000 0.000 0.020 0.304 0.676
#> GSM1105494 5 0.5752 0.3145 0.000 0.148 0.000 0.240 0.612
#> GSM1105503 4 0.5726 0.1215 0.000 0.140 0.248 0.612 0.000
#> GSM1105507 3 0.4517 0.3456 0.000 0.000 0.600 0.388 0.012
#> GSM1105446 2 0.2605 0.8561 0.000 0.852 0.000 0.000 0.148
#> GSM1105519 3 0.4161 0.2915 0.392 0.000 0.608 0.000 0.000
#> GSM1105526 5 0.1082 0.8449 0.000 0.000 0.008 0.028 0.964
#> GSM1105527 5 0.3913 0.4713 0.000 0.000 0.000 0.324 0.676
#> GSM1105531 3 0.6178 0.1911 0.000 0.140 0.484 0.376 0.000
#> GSM1105543 5 0.0794 0.8502 0.000 0.028 0.000 0.000 0.972
#> GSM1105546 1 0.0000 0.8970 1.000 0.000 0.000 0.000 0.000
#> GSM1105547 1 0.0000 0.8970 1.000 0.000 0.000 0.000 0.000
#> GSM1105455 2 0.3427 0.8250 0.000 0.796 0.000 0.012 0.192
#> GSM1105458 2 0.2561 0.8571 0.000 0.856 0.000 0.000 0.144
#> GSM1105459 2 0.3561 0.7519 0.000 0.740 0.000 0.000 0.260
#> GSM1105462 5 0.5957 0.1981 0.000 0.000 0.280 0.148 0.572
#> GSM1105441 2 0.2516 0.8573 0.000 0.860 0.000 0.000 0.140
#> GSM1105465 5 0.3033 0.7765 0.000 0.052 0.000 0.084 0.864
#> GSM1105484 5 0.0510 0.8484 0.000 0.016 0.000 0.000 0.984
#> GSM1105485 5 0.1908 0.8049 0.000 0.000 0.000 0.092 0.908
#> GSM1105496 4 0.5464 0.1674 0.000 0.152 0.148 0.688 0.012
#> GSM1105505 4 0.6537 -0.1973 0.000 0.140 0.384 0.464 0.012
#> GSM1105509 3 0.4517 0.3456 0.000 0.000 0.600 0.388 0.012
#> GSM1105448 2 0.2605 0.8561 0.000 0.852 0.000 0.000 0.148
#> GSM1105521 3 0.4182 0.2769 0.400 0.000 0.600 0.000 0.000
#> GSM1105528 5 0.2077 0.8043 0.000 0.008 0.000 0.084 0.908
#> GSM1105529 5 0.0290 0.8487 0.000 0.008 0.000 0.000 0.992
#> GSM1105533 1 0.2127 0.8169 0.892 0.000 0.108 0.000 0.000
#> GSM1105545 5 0.2540 0.7844 0.000 0.000 0.024 0.088 0.888
#> GSM1105548 3 0.5607 0.4467 0.228 0.140 0.632 0.000 0.000
#> GSM1105549 1 0.0000 0.8970 1.000 0.000 0.000 0.000 0.000
#> GSM1105457 2 0.4306 0.2674 0.000 0.508 0.000 0.492 0.000
#> GSM1105460 2 0.2561 0.8571 0.000 0.856 0.000 0.000 0.144
#> GSM1105461 2 0.3074 0.8260 0.000 0.804 0.000 0.000 0.196
#> GSM1105464 3 0.6598 0.1244 0.216 0.000 0.428 0.356 0.000
#> GSM1105466 5 0.0880 0.8467 0.000 0.000 0.000 0.032 0.968
#> GSM1105479 5 0.1012 0.8488 0.000 0.012 0.000 0.020 0.968
#> GSM1105502 4 0.6796 -0.2360 0.328 0.000 0.296 0.376 0.000
#> GSM1105515 1 0.0000 0.8970 1.000 0.000 0.000 0.000 0.000
#> GSM1105523 3 0.0000 0.4776 0.000 0.000 1.000 0.000 0.000
#> GSM1105550 3 0.6565 0.1588 0.004 0.000 0.456 0.360 0.180
#> GSM1105450 5 0.0703 0.8513 0.000 0.024 0.000 0.000 0.976
#> GSM1105451 2 0.2605 0.8561 0.000 0.852 0.000 0.000 0.148
#> GSM1105454 2 0.0162 0.7265 0.000 0.996 0.004 0.000 0.000
#> GSM1105468 5 0.0703 0.8513 0.000 0.024 0.000 0.000 0.976
#> GSM1105481 4 0.8275 0.0315 0.000 0.148 0.260 0.376 0.216
#> GSM1105504 3 0.4114 0.2666 0.000 0.000 0.624 0.376 0.000
#> GSM1105517 3 0.3231 0.4376 0.004 0.000 0.800 0.196 0.000
#> GSM1105525 3 0.1043 0.4916 0.040 0.000 0.960 0.000 0.000
#> GSM1105552 3 0.6368 0.1978 0.008 0.132 0.484 0.376 0.000
#> GSM1105452 5 0.0000 0.8492 0.000 0.000 0.000 0.000 1.000
#> GSM1105453 2 0.2605 0.8561 0.000 0.852 0.000 0.000 0.148
#> GSM1105456 2 0.1410 0.6930 0.000 0.940 0.060 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1105438 2 0.0363 0.82755 0.000 0.988 0.000 0.000 0.012 0.000
#> GSM1105486 5 0.0000 0.83155 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1105487 1 0.0632 0.87490 0.976 0.000 0.024 0.000 0.000 0.000
#> GSM1105490 4 0.0000 0.77393 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1105491 6 0.1245 0.43086 0.000 0.016 0.032 0.000 0.000 0.952
#> GSM1105495 6 0.1714 0.42750 0.000 0.092 0.000 0.000 0.000 0.908
#> GSM1105498 4 0.2980 0.71211 0.000 0.000 0.012 0.808 0.000 0.180
#> GSM1105499 1 0.0146 0.87854 0.996 0.000 0.004 0.000 0.000 0.000
#> GSM1105506 4 0.0000 0.77393 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1105442 2 0.5693 0.34098 0.000 0.448 0.000 0.000 0.160 0.392
#> GSM1105511 4 0.0000 0.77393 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1105514 5 0.2454 0.71983 0.000 0.160 0.000 0.000 0.840 0.000
#> GSM1105518 4 0.4734 0.63635 0.000 0.152 0.000 0.680 0.000 0.168
#> GSM1105522 3 0.1649 0.56254 0.000 0.000 0.932 0.036 0.000 0.032
#> GSM1105534 1 0.0146 0.87854 0.996 0.000 0.004 0.000 0.000 0.000
#> GSM1105535 1 0.0632 0.87459 0.976 0.000 0.024 0.000 0.000 0.000
#> GSM1105538 3 0.3288 0.56888 0.276 0.000 0.724 0.000 0.000 0.000
#> GSM1105542 5 0.3872 0.54005 0.000 0.004 0.000 0.000 0.604 0.392
#> GSM1105443 2 0.1663 0.83804 0.000 0.912 0.000 0.000 0.088 0.000
#> GSM1105551 1 0.2053 0.78626 0.888 0.000 0.004 0.000 0.000 0.108
#> GSM1105554 1 0.0000 0.87868 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1105555 1 0.3456 0.69406 0.788 0.000 0.172 0.000 0.000 0.040
#> GSM1105447 2 0.2454 0.82420 0.000 0.840 0.000 0.000 0.160 0.000
#> GSM1105467 5 0.0363 0.83078 0.000 0.012 0.000 0.000 0.988 0.000
#> GSM1105470 5 0.0000 0.83155 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1105471 5 0.0363 0.83078 0.000 0.012 0.000 0.000 0.988 0.000
#> GSM1105474 5 0.0146 0.83144 0.000 0.004 0.000 0.000 0.996 0.000
#> GSM1105475 5 0.0000 0.83155 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1105440 1 0.0363 0.87756 0.988 0.000 0.012 0.000 0.000 0.000
#> GSM1105488 5 0.4619 0.50545 0.000 0.044 0.000 0.000 0.564 0.392
#> GSM1105489 1 0.2558 0.72452 0.840 0.000 0.004 0.000 0.000 0.156
#> GSM1105492 3 0.3330 0.56561 0.284 0.000 0.716 0.000 0.000 0.000
#> GSM1105493 1 0.1007 0.85549 0.956 0.000 0.000 0.000 0.000 0.044
#> GSM1105497 6 0.4913 -0.18787 0.000 0.392 0.000 0.056 0.004 0.548
#> GSM1105500 4 0.2070 0.74437 0.000 0.000 0.012 0.896 0.092 0.000
#> GSM1105501 4 0.0000 0.77393 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1105508 1 0.3853 0.52921 0.680 0.000 0.016 0.304 0.000 0.000
#> GSM1105444 2 0.0146 0.82383 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM1105513 4 0.0000 0.77393 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1105516 3 0.3499 0.53445 0.000 0.000 0.680 0.320 0.000 0.000
#> GSM1105520 4 0.4269 0.54814 0.000 0.000 0.036 0.648 0.000 0.316
#> GSM1105524 1 0.0632 0.87459 0.976 0.000 0.024 0.000 0.000 0.000
#> GSM1105536 5 0.0603 0.82993 0.000 0.000 0.004 0.016 0.980 0.000
#> GSM1105537 1 0.0632 0.87459 0.976 0.000 0.024 0.000 0.000 0.000
#> GSM1105540 3 0.3861 0.50492 0.000 0.000 0.756 0.000 0.184 0.060
#> GSM1105544 3 0.3221 0.45674 0.000 0.000 0.736 0.000 0.264 0.000
#> GSM1105445 2 0.2454 0.82420 0.000 0.840 0.000 0.000 0.160 0.000
#> GSM1105553 4 0.4841 0.63445 0.000 0.160 0.004 0.680 0.000 0.156
#> GSM1105556 1 0.0000 0.87868 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1105557 4 0.0000 0.77393 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1105449 2 0.2454 0.82420 0.000 0.840 0.000 0.000 0.160 0.000
#> GSM1105469 5 0.3499 0.55336 0.000 0.000 0.000 0.320 0.680 0.000
#> GSM1105472 5 0.0000 0.83155 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1105473 3 0.3953 0.58796 0.196 0.000 0.744 0.000 0.000 0.060
#> GSM1105476 5 0.0547 0.82633 0.000 0.020 0.000 0.000 0.980 0.000
#> GSM1105477 5 0.1075 0.81997 0.000 0.000 0.000 0.048 0.952 0.000
#> GSM1105478 4 0.4029 0.57059 0.000 0.000 0.028 0.680 0.292 0.000
#> GSM1105510 5 0.3695 0.67857 0.000 0.164 0.000 0.060 0.776 0.000
#> GSM1105530 3 0.3737 0.01886 0.000 0.000 0.608 0.000 0.000 0.392
#> GSM1105539 6 0.6039 0.22284 0.356 0.000 0.252 0.000 0.000 0.392
#> GSM1105480 4 0.3784 0.55982 0.000 0.000 0.012 0.680 0.308 0.000
#> GSM1105512 1 0.3860 -0.15390 0.528 0.000 0.472 0.000 0.000 0.000
#> GSM1105532 3 0.3737 0.01886 0.000 0.000 0.608 0.000 0.000 0.392
#> GSM1105541 1 0.5879 -0.04280 0.460 0.000 0.216 0.000 0.000 0.324
#> GSM1105439 2 0.2664 0.81370 0.000 0.816 0.000 0.000 0.184 0.000
#> GSM1105463 6 0.1387 0.43805 0.000 0.000 0.068 0.000 0.000 0.932
#> GSM1105482 1 0.0000 0.87868 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1105483 5 0.3499 0.55336 0.000 0.000 0.000 0.320 0.680 0.000
#> GSM1105494 4 0.4976 0.64903 0.000 0.000 0.012 0.680 0.152 0.156
#> GSM1105503 4 0.4127 0.59345 0.000 0.000 0.036 0.680 0.000 0.284
#> GSM1105507 3 0.3499 0.53445 0.000 0.000 0.680 0.320 0.000 0.000
#> GSM1105446 2 0.0363 0.82126 0.000 0.988 0.000 0.000 0.012 0.000
#> GSM1105519 3 0.3371 0.56424 0.292 0.000 0.708 0.000 0.000 0.000
#> GSM1105526 5 0.0713 0.82731 0.000 0.000 0.000 0.028 0.972 0.000
#> GSM1105527 5 0.3531 0.54179 0.000 0.000 0.000 0.328 0.672 0.000
#> GSM1105531 6 0.3737 0.10941 0.000 0.000 0.392 0.000 0.000 0.608
#> GSM1105543 5 0.2135 0.75059 0.000 0.128 0.000 0.000 0.872 0.000
#> GSM1105546 1 0.0146 0.87804 0.996 0.000 0.004 0.000 0.000 0.000
#> GSM1105547 1 0.0000 0.87868 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1105455 2 0.3975 0.53086 0.000 0.716 0.000 0.244 0.040 0.000
#> GSM1105458 2 0.2454 0.82420 0.000 0.840 0.000 0.000 0.160 0.000
#> GSM1105459 2 0.2697 0.78549 0.000 0.812 0.000 0.000 0.188 0.000
#> GSM1105462 5 0.4122 0.51261 0.000 0.000 0.048 0.000 0.704 0.248
#> GSM1105441 2 0.2340 0.82872 0.000 0.852 0.000 0.000 0.148 0.000
#> GSM1105465 5 0.4500 0.51505 0.000 0.036 0.000 0.000 0.572 0.392
#> GSM1105484 5 0.0363 0.83078 0.000 0.012 0.000 0.000 0.988 0.000
#> GSM1105485 5 0.4066 0.54122 0.000 0.000 0.000 0.012 0.596 0.392
#> GSM1105496 6 0.4619 0.00352 0.000 0.012 0.024 0.388 0.000 0.576
#> GSM1105505 6 0.4319 0.11182 0.000 0.000 0.400 0.024 0.000 0.576
#> GSM1105509 3 0.3371 0.55110 0.000 0.000 0.708 0.292 0.000 0.000
#> GSM1105448 2 0.0363 0.82126 0.000 0.988 0.000 0.000 0.012 0.000
#> GSM1105521 3 0.3371 0.56424 0.292 0.000 0.708 0.000 0.000 0.000
#> GSM1105528 5 0.0363 0.83078 0.000 0.012 0.000 0.000 0.988 0.000
#> GSM1105529 5 0.4066 0.53905 0.000 0.012 0.000 0.000 0.596 0.392
#> GSM1105533 1 0.2234 0.78322 0.872 0.000 0.124 0.000 0.000 0.004
#> GSM1105545 5 0.1610 0.79344 0.000 0.000 0.000 0.084 0.916 0.000
#> GSM1105548 3 0.4215 0.52431 0.080 0.000 0.724 0.000 0.000 0.196
#> GSM1105549 1 0.0000 0.87868 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1105457 4 0.0937 0.76611 0.000 0.040 0.000 0.960 0.000 0.000
#> GSM1105460 2 0.2454 0.82420 0.000 0.840 0.000 0.000 0.160 0.000
#> GSM1105461 2 0.0937 0.81796 0.000 0.960 0.000 0.000 0.040 0.000
#> GSM1105464 6 0.5925 0.30076 0.332 0.000 0.224 0.000 0.000 0.444
#> GSM1105466 5 0.0363 0.83069 0.000 0.000 0.000 0.012 0.988 0.000
#> GSM1105479 5 0.0363 0.83078 0.000 0.012 0.000 0.000 0.988 0.000
#> GSM1105502 6 0.6039 0.22799 0.252 0.000 0.356 0.000 0.000 0.392
#> GSM1105515 1 0.0000 0.87868 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1105523 3 0.1610 0.56525 0.000 0.000 0.916 0.000 0.000 0.084
#> GSM1105550 3 0.6067 0.04842 0.008 0.000 0.424 0.000 0.192 0.376
#> GSM1105450 5 0.0146 0.83148 0.000 0.004 0.000 0.000 0.996 0.000
#> GSM1105451 2 0.0363 0.82126 0.000 0.988 0.000 0.000 0.012 0.000
#> GSM1105454 2 0.2454 0.73914 0.000 0.840 0.000 0.000 0.000 0.160
#> GSM1105468 5 0.0146 0.83159 0.000 0.004 0.000 0.000 0.996 0.000
#> GSM1105481 6 0.4491 0.23758 0.000 0.000 0.036 0.000 0.388 0.576
#> GSM1105504 3 0.3843 -0.00942 0.000 0.000 0.548 0.000 0.000 0.452
#> GSM1105517 3 0.4031 0.57758 0.004 0.000 0.748 0.188 0.000 0.060
#> GSM1105525 3 0.1219 0.55164 0.004 0.000 0.948 0.000 0.000 0.048
#> GSM1105552 6 0.4039 0.06718 0.008 0.000 0.424 0.000 0.000 0.568
#> GSM1105452 5 0.3737 0.54247 0.000 0.000 0.000 0.000 0.608 0.392
#> GSM1105453 2 0.0363 0.82126 0.000 0.988 0.000 0.000 0.012 0.000
#> GSM1105456 2 0.2793 0.70786 0.000 0.800 0.000 0.000 0.000 0.200
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 agent(p) other(p) time(p) individual(p) k
#> SD:pam 115 1.0000 0.42629 0.648 1.34e-02 2
#> SD:pam 107 0.0881 0.00113 0.991 3.27e-03 3
#> SD:pam 103 0.1068 0.00110 0.795 1.19e-04 4
#> SD:pam 70 0.1214 0.13551 0.974 2.15e-03 5
#> SD:pam 100 0.0190 0.57914 0.800 3.14e-06 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 44956 rows and 120 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 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 0.357 0.490 0.762 0.3530 0.541 0.541
#> 3 3 0.495 0.642 0.793 0.6866 0.569 0.379
#> 4 4 0.601 0.647 0.828 0.1925 0.792 0.532
#> 5 5 0.796 0.836 0.882 0.0641 0.943 0.802
#> 6 6 0.717 0.658 0.788 0.0584 0.874 0.551
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
#> GSM1105438 2 0.9608 0.7108 0.384 0.616
#> GSM1105486 2 0.0000 0.5207 0.000 1.000
#> GSM1105487 1 0.0000 0.6812 1.000 0.000
#> GSM1105490 2 0.9635 0.7109 0.388 0.612
#> GSM1105491 1 0.9850 0.0381 0.572 0.428
#> GSM1105495 1 0.9850 0.0381 0.572 0.428
#> GSM1105498 1 0.9850 0.0381 0.572 0.428
#> GSM1105499 1 0.0000 0.6812 1.000 0.000
#> GSM1105506 1 0.9850 0.0381 0.572 0.428
#> GSM1105442 1 0.9850 0.0381 0.572 0.428
#> GSM1105511 1 0.9933 -0.1011 0.548 0.452
#> GSM1105514 2 0.9522 0.7082 0.372 0.628
#> GSM1105518 1 0.9850 0.0381 0.572 0.428
#> GSM1105522 1 0.0000 0.6812 1.000 0.000
#> GSM1105534 1 0.0000 0.6812 1.000 0.000
#> GSM1105535 1 0.0000 0.6812 1.000 0.000
#> GSM1105538 1 0.0000 0.6812 1.000 0.000
#> GSM1105542 2 0.9661 0.7045 0.392 0.608
#> GSM1105443 2 0.9635 0.7109 0.388 0.612
#> GSM1105551 1 0.0000 0.6812 1.000 0.000
#> GSM1105554 1 0.0000 0.6812 1.000 0.000
#> GSM1105555 1 0.0000 0.6812 1.000 0.000
#> GSM1105447 2 0.9635 0.7109 0.388 0.612
#> GSM1105467 2 0.2423 0.5309 0.040 0.960
#> GSM1105470 2 0.0000 0.5207 0.000 1.000
#> GSM1105471 1 0.9850 0.0381 0.572 0.428
#> GSM1105474 2 0.0000 0.5207 0.000 1.000
#> GSM1105475 2 0.9635 0.7109 0.388 0.612
#> GSM1105440 1 0.0000 0.6812 1.000 0.000
#> GSM1105488 2 0.9635 0.7109 0.388 0.612
#> GSM1105489 1 0.0000 0.6812 1.000 0.000
#> GSM1105492 1 0.0000 0.6812 1.000 0.000
#> GSM1105493 1 0.0000 0.6812 1.000 0.000
#> GSM1105497 1 0.9850 0.0381 0.572 0.428
#> GSM1105500 2 0.9881 0.5866 0.436 0.564
#> GSM1105501 1 1.0000 -0.3465 0.500 0.500
#> GSM1105508 1 0.0000 0.6812 1.000 0.000
#> GSM1105444 2 0.9635 0.7109 0.388 0.612
#> GSM1105513 2 0.9732 0.6809 0.404 0.596
#> GSM1105516 1 0.9850 0.0381 0.572 0.428
#> GSM1105520 1 0.9850 0.0381 0.572 0.428
#> GSM1105524 1 0.0000 0.6812 1.000 0.000
#> GSM1105536 2 0.9686 0.6974 0.396 0.604
#> GSM1105537 1 0.0000 0.6812 1.000 0.000
#> GSM1105540 1 0.3274 0.6383 0.940 0.060
#> GSM1105544 1 0.9850 0.0381 0.572 0.428
#> GSM1105445 1 0.9850 0.0381 0.572 0.428
#> GSM1105553 1 0.9850 0.0381 0.572 0.428
#> GSM1105556 1 0.0000 0.6812 1.000 0.000
#> GSM1105557 2 0.9775 0.6615 0.412 0.588
#> GSM1105449 2 0.9635 0.7109 0.388 0.612
#> GSM1105469 1 0.2423 0.6545 0.960 0.040
#> GSM1105472 2 0.0000 0.5207 0.000 1.000
#> GSM1105473 1 0.0000 0.6812 1.000 0.000
#> GSM1105476 2 0.9522 0.7082 0.372 0.628
#> GSM1105477 2 0.9635 0.7109 0.388 0.612
#> GSM1105478 1 0.9850 0.0381 0.572 0.428
#> GSM1105510 2 0.9686 0.6974 0.396 0.604
#> GSM1105530 1 0.0000 0.6812 1.000 0.000
#> GSM1105539 1 0.0000 0.6812 1.000 0.000
#> GSM1105480 1 0.9850 0.0381 0.572 0.428
#> GSM1105512 1 0.0000 0.6812 1.000 0.000
#> GSM1105532 1 0.0000 0.6812 1.000 0.000
#> GSM1105541 1 0.0000 0.6812 1.000 0.000
#> GSM1105439 2 0.9635 0.7109 0.388 0.612
#> GSM1105463 1 0.0000 0.6812 1.000 0.000
#> GSM1105482 1 0.0000 0.6812 1.000 0.000
#> GSM1105483 1 0.9850 0.0381 0.572 0.428
#> GSM1105494 1 0.9850 0.0381 0.572 0.428
#> GSM1105503 1 0.9850 0.0381 0.572 0.428
#> GSM1105507 1 0.0000 0.6812 1.000 0.000
#> GSM1105446 2 0.9491 0.7063 0.368 0.632
#> GSM1105519 1 0.0000 0.6812 1.000 0.000
#> GSM1105526 2 0.9775 0.6615 0.412 0.588
#> GSM1105527 1 0.9850 0.0381 0.572 0.428
#> GSM1105531 1 0.0376 0.6789 0.996 0.004
#> GSM1105543 2 0.9491 0.7063 0.368 0.632
#> GSM1105546 1 0.0000 0.6812 1.000 0.000
#> GSM1105547 1 0.0000 0.6812 1.000 0.000
#> GSM1105455 2 0.9635 0.7109 0.388 0.612
#> GSM1105458 1 0.9850 0.0381 0.572 0.428
#> GSM1105459 2 0.0000 0.5207 0.000 1.000
#> GSM1105462 1 0.9850 0.0381 0.572 0.428
#> GSM1105441 2 0.9427 0.6991 0.360 0.640
#> GSM1105465 1 0.9850 0.0381 0.572 0.428
#> GSM1105484 2 0.9635 0.7109 0.388 0.612
#> GSM1105485 2 0.9775 0.6617 0.412 0.588
#> GSM1105496 1 0.9850 0.0381 0.572 0.428
#> GSM1105505 1 0.7950 0.4235 0.760 0.240
#> GSM1105509 1 0.0000 0.6812 1.000 0.000
#> GSM1105448 2 0.9522 0.7082 0.372 0.628
#> GSM1105521 1 0.0000 0.6812 1.000 0.000
#> GSM1105528 2 0.9635 0.7109 0.388 0.612
#> GSM1105529 2 0.9635 0.7109 0.388 0.612
#> GSM1105533 1 0.0000 0.6812 1.000 0.000
#> GSM1105545 2 0.9795 0.6502 0.416 0.584
#> GSM1105548 1 0.0000 0.6812 1.000 0.000
#> GSM1105549 1 0.0000 0.6812 1.000 0.000
#> GSM1105457 1 0.9954 -0.1452 0.540 0.460
#> GSM1105460 2 0.9732 0.6809 0.404 0.596
#> GSM1105461 2 0.0000 0.5207 0.000 1.000
#> GSM1105464 1 0.0000 0.6812 1.000 0.000
#> GSM1105466 1 0.9850 0.0381 0.572 0.428
#> GSM1105479 1 0.9866 0.0152 0.568 0.432
#> GSM1105502 1 0.0000 0.6812 1.000 0.000
#> GSM1105515 1 0.0000 0.6812 1.000 0.000
#> GSM1105523 1 0.0000 0.6812 1.000 0.000
#> GSM1105550 1 0.9850 0.0381 0.572 0.428
#> GSM1105450 2 0.0000 0.5207 0.000 1.000
#> GSM1105451 2 0.0000 0.5207 0.000 1.000
#> GSM1105454 1 0.9850 0.0381 0.572 0.428
#> GSM1105468 2 0.0000 0.5207 0.000 1.000
#> GSM1105481 1 0.9850 0.0381 0.572 0.428
#> GSM1105504 1 0.2043 0.6603 0.968 0.032
#> GSM1105517 1 0.0000 0.6812 1.000 0.000
#> GSM1105525 1 0.0000 0.6812 1.000 0.000
#> GSM1105552 1 0.0000 0.6812 1.000 0.000
#> GSM1105452 2 0.9635 0.7109 0.388 0.612
#> GSM1105453 2 0.0000 0.5207 0.000 1.000
#> GSM1105456 1 0.9850 0.0381 0.572 0.428
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1105438 2 0.6126 -0.3055 0.000 0.600 0.400
#> GSM1105486 3 0.6095 0.7981 0.000 0.392 0.608
#> GSM1105487 1 0.0424 0.8749 0.992 0.000 0.008
#> GSM1105490 2 0.0237 0.6924 0.000 0.996 0.004
#> GSM1105491 3 0.7187 -0.4021 0.024 0.480 0.496
#> GSM1105495 3 0.7075 -0.4097 0.020 0.488 0.492
#> GSM1105498 2 0.5200 0.6135 0.020 0.796 0.184
#> GSM1105499 1 0.0237 0.8763 0.996 0.004 0.000
#> GSM1105506 2 0.0000 0.6934 0.000 1.000 0.000
#> GSM1105442 2 0.5633 0.5288 0.024 0.768 0.208
#> GSM1105511 2 0.0000 0.6934 0.000 1.000 0.000
#> GSM1105514 3 0.5706 0.7560 0.000 0.320 0.680
#> GSM1105518 2 0.5253 0.6116 0.020 0.792 0.188
#> GSM1105522 1 0.0237 0.8763 0.996 0.004 0.000
#> GSM1105534 1 0.0000 0.8751 1.000 0.000 0.000
#> GSM1105535 1 0.0237 0.8763 0.996 0.004 0.000
#> GSM1105538 1 0.0237 0.8763 0.996 0.004 0.000
#> GSM1105542 2 0.5115 0.5047 0.004 0.768 0.228
#> GSM1105443 2 0.0661 0.6937 0.004 0.988 0.008
#> GSM1105551 1 0.4399 0.8271 0.812 0.000 0.188
#> GSM1105554 1 0.0237 0.8763 0.996 0.004 0.000
#> GSM1105555 1 0.4399 0.8271 0.812 0.000 0.188
#> GSM1105447 2 0.1129 0.6924 0.004 0.976 0.020
#> GSM1105467 2 0.4235 0.5051 0.000 0.824 0.176
#> GSM1105470 3 0.5988 0.8228 0.000 0.368 0.632
#> GSM1105471 2 0.5147 0.6160 0.020 0.800 0.180
#> GSM1105474 3 0.5988 0.8228 0.000 0.368 0.632
#> GSM1105475 2 0.1643 0.6724 0.000 0.956 0.044
#> GSM1105440 1 0.0237 0.8763 0.996 0.004 0.000
#> GSM1105488 2 0.5115 0.5047 0.004 0.768 0.228
#> GSM1105489 1 0.4399 0.8271 0.812 0.000 0.188
#> GSM1105492 1 0.0237 0.8763 0.996 0.004 0.000
#> GSM1105493 1 0.4346 0.8275 0.816 0.000 0.184
#> GSM1105497 2 0.6026 0.5160 0.024 0.732 0.244
#> GSM1105500 2 0.0892 0.6954 0.020 0.980 0.000
#> GSM1105501 2 0.0000 0.6934 0.000 1.000 0.000
#> GSM1105508 1 0.0237 0.8763 0.996 0.004 0.000
#> GSM1105444 2 0.5244 0.4830 0.004 0.756 0.240
#> GSM1105513 2 0.0424 0.6954 0.008 0.992 0.000
#> GSM1105516 2 0.3918 0.6268 0.120 0.868 0.012
#> GSM1105520 2 0.6387 0.5327 0.020 0.680 0.300
#> GSM1105524 1 0.0237 0.8763 0.996 0.004 0.000
#> GSM1105536 2 0.0747 0.6872 0.000 0.984 0.016
#> GSM1105537 1 0.0237 0.8763 0.996 0.004 0.000
#> GSM1105540 2 0.6154 0.3524 0.408 0.592 0.000
#> GSM1105544 2 0.0892 0.6954 0.020 0.980 0.000
#> GSM1105445 2 0.4862 0.6298 0.020 0.820 0.160
#> GSM1105553 2 0.6387 0.5327 0.020 0.680 0.300
#> GSM1105556 1 0.0000 0.8751 1.000 0.000 0.000
#> GSM1105557 2 0.0000 0.6934 0.000 1.000 0.000
#> GSM1105449 2 0.3551 0.5853 0.000 0.868 0.132
#> GSM1105469 2 0.4605 0.5670 0.204 0.796 0.000
#> GSM1105472 3 0.5988 0.8228 0.000 0.368 0.632
#> GSM1105473 1 0.9314 0.2261 0.492 0.328 0.180
#> GSM1105476 2 0.4346 0.5138 0.000 0.816 0.184
#> GSM1105477 2 0.1643 0.6688 0.000 0.956 0.044
#> GSM1105478 2 0.4136 0.6534 0.020 0.864 0.116
#> GSM1105510 2 0.4682 0.5327 0.004 0.804 0.192
#> GSM1105530 1 0.4233 0.8372 0.836 0.004 0.160
#> GSM1105539 1 0.4399 0.8271 0.812 0.000 0.188
#> GSM1105480 2 0.0424 0.6954 0.008 0.992 0.000
#> GSM1105512 1 0.0237 0.8763 0.996 0.004 0.000
#> GSM1105532 1 0.4521 0.8294 0.816 0.004 0.180
#> GSM1105541 1 0.4399 0.8271 0.812 0.000 0.188
#> GSM1105439 2 0.0592 0.6879 0.000 0.988 0.012
#> GSM1105463 2 0.9582 0.3392 0.228 0.472 0.300
#> GSM1105482 1 0.0000 0.8751 1.000 0.000 0.000
#> GSM1105483 2 0.0000 0.6934 0.000 1.000 0.000
#> GSM1105494 2 0.0892 0.6954 0.020 0.980 0.000
#> GSM1105503 2 0.6387 0.5327 0.020 0.680 0.300
#> GSM1105507 1 0.6274 -0.0552 0.544 0.456 0.000
#> GSM1105446 3 0.6521 0.4672 0.004 0.492 0.504
#> GSM1105519 1 0.0237 0.8763 0.996 0.004 0.000
#> GSM1105526 2 0.0237 0.6924 0.000 0.996 0.004
#> GSM1105527 2 0.0237 0.6939 0.004 0.996 0.000
#> GSM1105531 2 0.9304 0.3772 0.192 0.508 0.300
#> GSM1105543 3 0.6386 0.7634 0.004 0.412 0.584
#> GSM1105546 1 0.0237 0.8763 0.996 0.004 0.000
#> GSM1105547 1 0.0000 0.8751 1.000 0.000 0.000
#> GSM1105455 2 0.1529 0.6752 0.000 0.960 0.040
#> GSM1105458 2 0.0892 0.6954 0.020 0.980 0.000
#> GSM1105459 3 0.5988 0.8228 0.000 0.368 0.632
#> GSM1105462 2 0.5384 0.6087 0.024 0.788 0.188
#> GSM1105441 3 0.6204 0.7627 0.000 0.424 0.576
#> GSM1105465 2 0.6969 0.4352 0.024 0.596 0.380
#> GSM1105484 2 0.5115 0.5047 0.004 0.768 0.228
#> GSM1105485 2 0.5633 0.5288 0.024 0.768 0.208
#> GSM1105496 2 0.6387 0.5327 0.020 0.680 0.300
#> GSM1105505 2 0.7509 0.4958 0.064 0.636 0.300
#> GSM1105509 1 0.0237 0.8763 0.996 0.004 0.000
#> GSM1105448 2 0.6460 -0.2737 0.004 0.556 0.440
#> GSM1105521 1 0.0237 0.8763 0.996 0.004 0.000
#> GSM1105528 2 0.5115 0.5047 0.004 0.768 0.228
#> GSM1105529 2 0.5115 0.5047 0.004 0.768 0.228
#> GSM1105533 1 0.4399 0.8271 0.812 0.000 0.188
#> GSM1105545 2 0.0424 0.6910 0.000 0.992 0.008
#> GSM1105548 1 0.3941 0.8382 0.844 0.000 0.156
#> GSM1105549 1 0.3267 0.7717 0.884 0.116 0.000
#> GSM1105457 2 0.0000 0.6934 0.000 1.000 0.000
#> GSM1105460 2 0.0237 0.6947 0.004 0.996 0.000
#> GSM1105461 3 0.5988 0.8228 0.000 0.368 0.632
#> GSM1105464 1 0.4521 0.8294 0.816 0.004 0.180
#> GSM1105466 2 0.0000 0.6934 0.000 1.000 0.000
#> GSM1105479 2 0.0892 0.6954 0.020 0.980 0.000
#> GSM1105502 1 0.4399 0.8271 0.812 0.000 0.188
#> GSM1105515 1 0.0000 0.8751 1.000 0.000 0.000
#> GSM1105523 2 0.8562 0.4367 0.208 0.608 0.184
#> GSM1105550 2 0.4654 0.5670 0.208 0.792 0.000
#> GSM1105450 3 0.5988 0.8228 0.000 0.368 0.632
#> GSM1105451 3 0.5988 0.8228 0.000 0.368 0.632
#> GSM1105454 2 0.6387 0.5327 0.020 0.680 0.300
#> GSM1105468 3 0.5988 0.8228 0.000 0.368 0.632
#> GSM1105481 2 0.6387 0.5327 0.020 0.680 0.300
#> GSM1105504 2 0.9231 0.3852 0.184 0.516 0.300
#> GSM1105517 1 0.6302 -0.1457 0.520 0.480 0.000
#> GSM1105525 1 0.5062 0.8193 0.800 0.016 0.184
#> GSM1105552 2 0.9402 0.2623 0.344 0.472 0.184
#> GSM1105452 2 0.5115 0.5047 0.004 0.768 0.228
#> GSM1105453 3 0.5988 0.8228 0.000 0.368 0.632
#> GSM1105456 2 0.6387 0.5327 0.020 0.680 0.300
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1105438 2 0.1637 0.900222 0.000 0.940 0.000 0.060
#> GSM1105486 2 0.1118 0.914155 0.000 0.964 0.000 0.036
#> GSM1105487 1 0.2973 0.868462 0.856 0.000 0.144 0.000
#> GSM1105490 4 0.0000 0.810522 0.000 0.000 0.000 1.000
#> GSM1105491 3 0.3764 0.299146 0.000 0.172 0.816 0.012
#> GSM1105495 3 0.4985 -0.000136 0.000 0.000 0.532 0.468
#> GSM1105498 4 0.3528 0.664577 0.000 0.000 0.192 0.808
#> GSM1105499 1 0.0000 0.870143 1.000 0.000 0.000 0.000
#> GSM1105506 4 0.0000 0.810522 0.000 0.000 0.000 1.000
#> GSM1105442 3 0.4776 0.240215 0.000 0.272 0.712 0.016
#> GSM1105511 4 0.0000 0.810522 0.000 0.000 0.000 1.000
#> GSM1105514 2 0.0921 0.911896 0.000 0.972 0.000 0.028
#> GSM1105518 4 0.4761 0.378321 0.000 0.000 0.372 0.628
#> GSM1105522 1 0.0921 0.873107 0.972 0.000 0.028 0.000
#> GSM1105534 1 0.0000 0.870143 1.000 0.000 0.000 0.000
#> GSM1105535 1 0.0000 0.870143 1.000 0.000 0.000 0.000
#> GSM1105538 1 0.0000 0.870143 1.000 0.000 0.000 0.000
#> GSM1105542 3 0.5712 0.086445 0.000 0.384 0.584 0.032
#> GSM1105443 4 0.1118 0.794391 0.000 0.036 0.000 0.964
#> GSM1105551 1 0.3610 0.858220 0.800 0.000 0.200 0.000
#> GSM1105554 1 0.0000 0.870143 1.000 0.000 0.000 0.000
#> GSM1105555 1 0.3610 0.858220 0.800 0.000 0.200 0.000
#> GSM1105447 4 0.5598 0.495983 0.000 0.220 0.076 0.704
#> GSM1105467 2 0.3486 0.730621 0.000 0.812 0.000 0.188
#> GSM1105470 2 0.1118 0.914155 0.000 0.964 0.000 0.036
#> GSM1105471 4 0.4040 0.594676 0.000 0.000 0.248 0.752
#> GSM1105474 2 0.0921 0.915180 0.000 0.972 0.000 0.028
#> GSM1105475 4 0.3172 0.677141 0.000 0.160 0.000 0.840
#> GSM1105440 1 0.0000 0.870143 1.000 0.000 0.000 0.000
#> GSM1105488 3 0.5712 0.086445 0.000 0.384 0.584 0.032
#> GSM1105489 1 0.3610 0.858220 0.800 0.000 0.200 0.000
#> GSM1105492 1 0.0000 0.870143 1.000 0.000 0.000 0.000
#> GSM1105493 1 0.3610 0.858220 0.800 0.000 0.200 0.000
#> GSM1105497 3 0.5078 0.238193 0.000 0.272 0.700 0.028
#> GSM1105500 4 0.3674 0.706779 0.000 0.036 0.116 0.848
#> GSM1105501 4 0.0000 0.810522 0.000 0.000 0.000 1.000
#> GSM1105508 1 0.0469 0.872116 0.988 0.000 0.012 0.000
#> GSM1105444 2 0.4015 0.719646 0.000 0.832 0.116 0.052
#> GSM1105513 4 0.0000 0.810522 0.000 0.000 0.000 1.000
#> GSM1105516 4 0.2589 0.734815 0.000 0.000 0.116 0.884
#> GSM1105520 3 0.4985 -0.000136 0.000 0.000 0.532 0.468
#> GSM1105524 1 0.0000 0.870143 1.000 0.000 0.000 0.000
#> GSM1105536 4 0.2011 0.766917 0.000 0.000 0.080 0.920
#> GSM1105537 1 0.0000 0.870143 1.000 0.000 0.000 0.000
#> GSM1105540 4 0.6693 0.302179 0.304 0.000 0.116 0.580
#> GSM1105544 4 0.2973 0.725070 0.000 0.000 0.144 0.856
#> GSM1105445 4 0.3569 0.658635 0.000 0.000 0.196 0.804
#> GSM1105553 3 0.4985 -0.000136 0.000 0.000 0.532 0.468
#> GSM1105556 1 0.0000 0.870143 1.000 0.000 0.000 0.000
#> GSM1105557 4 0.0000 0.810522 0.000 0.000 0.000 1.000
#> GSM1105449 2 0.6597 0.101461 0.000 0.540 0.088 0.372
#> GSM1105469 4 0.0000 0.810522 0.000 0.000 0.000 1.000
#> GSM1105472 2 0.0921 0.915180 0.000 0.972 0.000 0.028
#> GSM1105473 1 0.3726 0.849901 0.788 0.000 0.212 0.000
#> GSM1105476 2 0.1637 0.900222 0.000 0.940 0.000 0.060
#> GSM1105477 4 0.4037 0.681788 0.000 0.056 0.112 0.832
#> GSM1105478 4 0.2704 0.740281 0.000 0.000 0.124 0.876
#> GSM1105510 3 0.7275 0.084079 0.000 0.376 0.472 0.152
#> GSM1105530 1 0.3528 0.860680 0.808 0.000 0.192 0.000
#> GSM1105539 1 0.3610 0.858220 0.800 0.000 0.200 0.000
#> GSM1105480 4 0.0000 0.810522 0.000 0.000 0.000 1.000
#> GSM1105512 1 0.0000 0.870143 1.000 0.000 0.000 0.000
#> GSM1105532 1 0.3610 0.858220 0.800 0.000 0.200 0.000
#> GSM1105541 1 0.3610 0.858220 0.800 0.000 0.200 0.000
#> GSM1105439 4 0.0000 0.810522 0.000 0.000 0.000 1.000
#> GSM1105463 1 0.4999 0.476805 0.508 0.000 0.492 0.000
#> GSM1105482 1 0.2973 0.868462 0.856 0.000 0.144 0.000
#> GSM1105483 4 0.0000 0.810522 0.000 0.000 0.000 1.000
#> GSM1105494 4 0.2530 0.751906 0.000 0.000 0.112 0.888
#> GSM1105503 3 0.4992 -0.025936 0.000 0.000 0.524 0.476
#> GSM1105507 1 0.3764 0.747806 0.784 0.000 0.000 0.216
#> GSM1105446 2 0.4799 0.673089 0.000 0.744 0.224 0.032
#> GSM1105519 1 0.0469 0.872122 0.988 0.000 0.012 0.000
#> GSM1105526 4 0.0817 0.801940 0.000 0.000 0.024 0.976
#> GSM1105527 4 0.0000 0.810522 0.000 0.000 0.000 1.000
#> GSM1105531 3 0.4985 -0.000136 0.000 0.000 0.532 0.468
#> GSM1105543 2 0.0817 0.911103 0.000 0.976 0.000 0.024
#> GSM1105546 1 0.0000 0.870143 1.000 0.000 0.000 0.000
#> GSM1105547 1 0.2647 0.870333 0.880 0.000 0.120 0.000
#> GSM1105455 4 0.2647 0.725006 0.000 0.120 0.000 0.880
#> GSM1105458 4 0.6327 0.443238 0.000 0.124 0.228 0.648
#> GSM1105459 2 0.0921 0.915180 0.000 0.972 0.000 0.028
#> GSM1105462 4 0.5000 0.038996 0.000 0.000 0.500 0.500
#> GSM1105441 2 0.1637 0.900222 0.000 0.940 0.000 0.060
#> GSM1105465 3 0.4222 0.238581 0.000 0.272 0.728 0.000
#> GSM1105484 3 0.5712 0.086445 0.000 0.384 0.584 0.032
#> GSM1105485 3 0.5712 0.086445 0.000 0.384 0.584 0.032
#> GSM1105496 3 0.4985 -0.000136 0.000 0.000 0.532 0.468
#> GSM1105505 3 0.4985 -0.000136 0.000 0.000 0.532 0.468
#> GSM1105509 1 0.0188 0.869142 0.996 0.000 0.000 0.004
#> GSM1105448 2 0.1356 0.899280 0.000 0.960 0.008 0.032
#> GSM1105521 1 0.0000 0.870143 1.000 0.000 0.000 0.000
#> GSM1105528 3 0.5712 0.086445 0.000 0.384 0.584 0.032
#> GSM1105529 3 0.5712 0.086445 0.000 0.384 0.584 0.032
#> GSM1105533 1 0.3610 0.858220 0.800 0.000 0.200 0.000
#> GSM1105545 4 0.0000 0.810522 0.000 0.000 0.000 1.000
#> GSM1105548 1 0.3528 0.860717 0.808 0.000 0.192 0.000
#> GSM1105549 1 0.3688 0.832360 0.792 0.000 0.208 0.000
#> GSM1105457 4 0.0000 0.810522 0.000 0.000 0.000 1.000
#> GSM1105460 4 0.0000 0.810522 0.000 0.000 0.000 1.000
#> GSM1105461 2 0.0921 0.915180 0.000 0.972 0.000 0.028
#> GSM1105464 1 0.3610 0.858220 0.800 0.000 0.200 0.000
#> GSM1105466 4 0.0000 0.810522 0.000 0.000 0.000 1.000
#> GSM1105479 4 0.2530 0.751906 0.000 0.000 0.112 0.888
#> GSM1105502 1 0.3610 0.858220 0.800 0.000 0.200 0.000
#> GSM1105515 1 0.0000 0.870143 1.000 0.000 0.000 0.000
#> GSM1105523 4 0.7526 0.142724 0.332 0.000 0.200 0.468
#> GSM1105550 4 0.2647 0.744271 0.000 0.000 0.120 0.880
#> GSM1105450 2 0.0921 0.915180 0.000 0.972 0.000 0.028
#> GSM1105451 2 0.0921 0.915180 0.000 0.972 0.000 0.028
#> GSM1105454 3 0.4985 -0.000136 0.000 0.000 0.532 0.468
#> GSM1105468 2 0.0921 0.915180 0.000 0.972 0.000 0.028
#> GSM1105481 3 0.4985 -0.000136 0.000 0.000 0.532 0.468
#> GSM1105504 3 0.4985 -0.000136 0.000 0.000 0.532 0.468
#> GSM1105517 1 0.5072 0.627615 0.740 0.000 0.052 0.208
#> GSM1105525 1 0.4284 0.842364 0.780 0.000 0.200 0.020
#> GSM1105552 1 0.4936 0.717669 0.672 0.000 0.316 0.012
#> GSM1105452 3 0.5712 0.086445 0.000 0.384 0.584 0.032
#> GSM1105453 2 0.0921 0.915180 0.000 0.972 0.000 0.028
#> GSM1105456 3 0.4985 -0.000136 0.000 0.000 0.532 0.468
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM1105438 2 0.1597 0.892 0.000 0.940 0.000 0.012 0.048
#> GSM1105486 2 0.0162 0.923 0.000 0.996 0.000 0.004 0.000
#> GSM1105487 1 0.4019 0.856 0.792 0.004 0.152 0.000 0.052
#> GSM1105490 4 0.0162 0.879 0.000 0.004 0.000 0.996 0.000
#> GSM1105491 5 0.2304 0.719 0.000 0.000 0.100 0.008 0.892
#> GSM1105495 3 0.2193 0.901 0.000 0.000 0.900 0.008 0.092
#> GSM1105498 4 0.3692 0.787 0.000 0.000 0.136 0.812 0.052
#> GSM1105499 1 0.0000 0.876 1.000 0.000 0.000 0.000 0.000
#> GSM1105506 4 0.0162 0.879 0.000 0.004 0.000 0.996 0.000
#> GSM1105442 5 0.2463 0.900 0.000 0.100 0.008 0.004 0.888
#> GSM1105511 4 0.1041 0.882 0.000 0.004 0.032 0.964 0.000
#> GSM1105514 2 0.0162 0.923 0.000 0.996 0.000 0.004 0.000
#> GSM1105518 4 0.5741 0.318 0.000 0.000 0.360 0.544 0.096
#> GSM1105522 1 0.0324 0.877 0.992 0.000 0.004 0.004 0.000
#> GSM1105534 1 0.0000 0.876 1.000 0.000 0.000 0.000 0.000
#> GSM1105535 1 0.0162 0.876 0.996 0.000 0.000 0.000 0.004
#> GSM1105538 1 0.0000 0.876 1.000 0.000 0.000 0.000 0.000
#> GSM1105542 5 0.2877 0.923 0.000 0.144 0.004 0.004 0.848
#> GSM1105443 4 0.1285 0.870 0.000 0.036 0.004 0.956 0.004
#> GSM1105551 1 0.4060 0.854 0.788 0.004 0.156 0.000 0.052
#> GSM1105554 1 0.0000 0.876 1.000 0.000 0.000 0.000 0.000
#> GSM1105555 1 0.4019 0.855 0.792 0.004 0.152 0.000 0.052
#> GSM1105447 4 0.4949 0.724 0.000 0.148 0.052 0.752 0.048
#> GSM1105467 2 0.4415 0.366 0.000 0.604 0.000 0.388 0.008
#> GSM1105470 2 0.0162 0.923 0.000 0.996 0.000 0.004 0.000
#> GSM1105471 4 0.5611 0.432 0.000 0.004 0.284 0.616 0.096
#> GSM1105474 2 0.0162 0.923 0.000 0.996 0.000 0.004 0.000
#> GSM1105475 4 0.2732 0.763 0.000 0.160 0.000 0.840 0.000
#> GSM1105440 1 0.0162 0.877 0.996 0.000 0.004 0.000 0.000
#> GSM1105488 5 0.2877 0.923 0.000 0.144 0.004 0.004 0.848
#> GSM1105489 1 0.4019 0.855 0.792 0.004 0.152 0.000 0.052
#> GSM1105492 1 0.0000 0.876 1.000 0.000 0.000 0.000 0.000
#> GSM1105493 1 0.4134 0.855 0.792 0.004 0.148 0.004 0.052
#> GSM1105497 5 0.1990 0.827 0.000 0.028 0.040 0.004 0.928
#> GSM1105500 4 0.2875 0.851 0.000 0.056 0.052 0.884 0.008
#> GSM1105501 4 0.1041 0.882 0.000 0.004 0.032 0.964 0.000
#> GSM1105508 1 0.0324 0.877 0.992 0.000 0.004 0.004 0.000
#> GSM1105444 2 0.1830 0.883 0.000 0.932 0.004 0.012 0.052
#> GSM1105513 4 0.0324 0.878 0.000 0.004 0.000 0.992 0.004
#> GSM1105516 1 0.5091 0.518 0.624 0.004 0.044 0.328 0.000
#> GSM1105520 3 0.5137 0.624 0.000 0.000 0.676 0.228 0.096
#> GSM1105524 1 0.0162 0.876 0.996 0.000 0.000 0.000 0.004
#> GSM1105536 4 0.1357 0.878 0.000 0.004 0.048 0.948 0.000
#> GSM1105537 1 0.0162 0.876 0.996 0.000 0.000 0.000 0.004
#> GSM1105540 1 0.4559 0.756 0.748 0.000 0.100 0.152 0.000
#> GSM1105544 4 0.2032 0.869 0.020 0.000 0.052 0.924 0.004
#> GSM1105445 4 0.5053 0.630 0.000 0.000 0.216 0.688 0.096
#> GSM1105553 3 0.2249 0.900 0.000 0.000 0.896 0.008 0.096
#> GSM1105556 1 0.0000 0.876 1.000 0.000 0.000 0.000 0.000
#> GSM1105557 4 0.0162 0.879 0.000 0.004 0.000 0.996 0.000
#> GSM1105449 2 0.4325 0.668 0.000 0.756 0.004 0.192 0.048
#> GSM1105469 4 0.1518 0.877 0.004 0.004 0.048 0.944 0.000
#> GSM1105472 2 0.0162 0.923 0.000 0.996 0.000 0.004 0.000
#> GSM1105473 1 0.4256 0.853 0.788 0.004 0.148 0.008 0.052
#> GSM1105476 2 0.1908 0.838 0.000 0.908 0.000 0.092 0.000
#> GSM1105477 4 0.1597 0.878 0.000 0.012 0.048 0.940 0.000
#> GSM1105478 4 0.1341 0.864 0.000 0.000 0.056 0.944 0.000
#> GSM1105510 5 0.3213 0.921 0.000 0.144 0.004 0.016 0.836
#> GSM1105530 1 0.4060 0.854 0.788 0.004 0.156 0.000 0.052
#> GSM1105539 1 0.4031 0.854 0.788 0.004 0.160 0.000 0.048
#> GSM1105480 4 0.0404 0.882 0.000 0.000 0.012 0.988 0.000
#> GSM1105512 1 0.0000 0.876 1.000 0.000 0.000 0.000 0.000
#> GSM1105532 1 0.4060 0.854 0.788 0.004 0.156 0.000 0.052
#> GSM1105541 1 0.4031 0.854 0.788 0.004 0.160 0.000 0.048
#> GSM1105439 4 0.0290 0.879 0.000 0.008 0.000 0.992 0.000
#> GSM1105463 1 0.4604 0.809 0.732 0.004 0.216 0.004 0.044
#> GSM1105482 1 0.1518 0.875 0.944 0.000 0.048 0.004 0.004
#> GSM1105483 4 0.1282 0.879 0.000 0.004 0.044 0.952 0.000
#> GSM1105494 4 0.1934 0.864 0.000 0.004 0.052 0.928 0.016
#> GSM1105503 3 0.0912 0.854 0.000 0.000 0.972 0.016 0.012
#> GSM1105507 1 0.3333 0.760 0.788 0.004 0.000 0.208 0.000
#> GSM1105446 2 0.3231 0.680 0.000 0.800 0.000 0.004 0.196
#> GSM1105519 1 0.0324 0.877 0.992 0.000 0.004 0.004 0.000
#> GSM1105526 4 0.1282 0.879 0.000 0.004 0.044 0.952 0.000
#> GSM1105527 4 0.0771 0.882 0.000 0.004 0.020 0.976 0.000
#> GSM1105531 3 0.1443 0.810 0.000 0.004 0.948 0.004 0.044
#> GSM1105543 2 0.0162 0.923 0.000 0.996 0.000 0.004 0.000
#> GSM1105546 1 0.0000 0.876 1.000 0.000 0.000 0.000 0.000
#> GSM1105547 1 0.0451 0.878 0.988 0.000 0.008 0.004 0.000
#> GSM1105455 4 0.2648 0.771 0.000 0.152 0.000 0.848 0.000
#> GSM1105458 4 0.4778 0.737 0.000 0.012 0.136 0.752 0.100
#> GSM1105459 2 0.0162 0.923 0.000 0.996 0.000 0.004 0.000
#> GSM1105462 4 0.7917 -0.119 0.264 0.004 0.316 0.356 0.060
#> GSM1105441 2 0.1251 0.903 0.000 0.956 0.000 0.008 0.036
#> GSM1105465 5 0.1768 0.767 0.000 0.000 0.072 0.004 0.924
#> GSM1105484 5 0.3044 0.923 0.000 0.148 0.004 0.008 0.840
#> GSM1105485 5 0.3067 0.924 0.000 0.140 0.004 0.012 0.844
#> GSM1105496 3 0.2249 0.900 0.000 0.000 0.896 0.008 0.096
#> GSM1105505 3 0.2481 0.782 0.056 0.004 0.908 0.008 0.024
#> GSM1105509 1 0.0324 0.877 0.992 0.000 0.004 0.004 0.000
#> GSM1105448 2 0.1205 0.903 0.000 0.956 0.000 0.004 0.040
#> GSM1105521 1 0.0000 0.876 1.000 0.000 0.000 0.000 0.000
#> GSM1105528 5 0.3154 0.920 0.000 0.148 0.004 0.012 0.836
#> GSM1105529 5 0.3111 0.923 0.000 0.144 0.004 0.012 0.840
#> GSM1105533 1 0.4031 0.854 0.788 0.004 0.160 0.000 0.048
#> GSM1105545 4 0.1205 0.880 0.000 0.004 0.040 0.956 0.000
#> GSM1105548 1 0.2823 0.871 0.880 0.004 0.092 0.004 0.020
#> GSM1105549 1 0.1717 0.875 0.936 0.000 0.052 0.004 0.008
#> GSM1105457 4 0.0290 0.879 0.000 0.008 0.000 0.992 0.000
#> GSM1105460 4 0.0486 0.879 0.000 0.004 0.004 0.988 0.004
#> GSM1105461 2 0.0162 0.923 0.000 0.996 0.000 0.004 0.000
#> GSM1105464 1 0.4019 0.855 0.792 0.004 0.152 0.000 0.052
#> GSM1105466 4 0.0162 0.879 0.000 0.004 0.000 0.996 0.000
#> GSM1105479 4 0.1653 0.871 0.000 0.004 0.028 0.944 0.024
#> GSM1105502 1 0.4060 0.854 0.788 0.004 0.156 0.000 0.052
#> GSM1105515 1 0.0000 0.876 1.000 0.000 0.000 0.000 0.000
#> GSM1105523 1 0.4702 0.816 0.740 0.004 0.196 0.008 0.052
#> GSM1105550 4 0.2124 0.850 0.004 0.000 0.096 0.900 0.000
#> GSM1105450 2 0.0162 0.923 0.000 0.996 0.000 0.004 0.000
#> GSM1105451 2 0.0162 0.923 0.000 0.996 0.000 0.004 0.000
#> GSM1105454 3 0.2193 0.901 0.000 0.000 0.900 0.008 0.092
#> GSM1105468 2 0.0162 0.923 0.000 0.996 0.000 0.004 0.000
#> GSM1105481 3 0.2193 0.901 0.000 0.000 0.900 0.008 0.092
#> GSM1105504 1 0.5594 0.536 0.556 0.004 0.384 0.008 0.048
#> GSM1105517 1 0.1012 0.871 0.968 0.000 0.020 0.012 0.000
#> GSM1105525 1 0.4514 0.828 0.752 0.004 0.188 0.004 0.052
#> GSM1105552 1 0.4454 0.840 0.768 0.004 0.168 0.008 0.052
#> GSM1105452 5 0.2763 0.922 0.000 0.148 0.004 0.000 0.848
#> GSM1105453 2 0.0162 0.923 0.000 0.996 0.000 0.004 0.000
#> GSM1105456 3 0.2193 0.901 0.000 0.000 0.900 0.008 0.092
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1105438 2 0.0260 0.9076 0.000 0.992 0.000 0.000 0.008 0.000
#> GSM1105486 2 0.0000 0.9106 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105487 3 0.0508 0.6028 0.012 0.000 0.984 0.000 0.000 0.004
#> GSM1105490 4 0.0363 0.7695 0.012 0.000 0.000 0.988 0.000 0.000
#> GSM1105491 5 0.0260 0.8093 0.000 0.000 0.000 0.000 0.992 0.008
#> GSM1105495 6 0.0000 0.7986 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM1105498 4 0.2969 0.6262 0.000 0.000 0.000 0.776 0.000 0.224
#> GSM1105499 3 0.3515 0.1687 0.324 0.000 0.676 0.000 0.000 0.000
#> GSM1105506 4 0.0363 0.7669 0.012 0.000 0.000 0.988 0.000 0.000
#> GSM1105442 5 0.0260 0.8151 0.000 0.008 0.000 0.000 0.992 0.000
#> GSM1105511 4 0.1643 0.7634 0.068 0.000 0.000 0.924 0.000 0.008
#> GSM1105514 2 0.0146 0.9094 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM1105518 6 0.3482 0.3847 0.000 0.000 0.000 0.316 0.000 0.684
#> GSM1105522 3 0.3565 0.2013 0.304 0.000 0.692 0.004 0.000 0.000
#> GSM1105534 1 0.3547 0.9313 0.668 0.000 0.332 0.000 0.000 0.000
#> GSM1105535 3 0.3499 0.1737 0.320 0.000 0.680 0.000 0.000 0.000
#> GSM1105538 1 0.3547 0.9313 0.668 0.000 0.332 0.000 0.000 0.000
#> GSM1105542 5 0.2491 0.8931 0.000 0.164 0.000 0.000 0.836 0.000
#> GSM1105443 4 0.3368 0.5509 0.012 0.232 0.000 0.756 0.000 0.000
#> GSM1105551 3 0.0405 0.6044 0.008 0.000 0.988 0.000 0.000 0.004
#> GSM1105554 1 0.3547 0.9313 0.668 0.000 0.332 0.000 0.000 0.000
#> GSM1105555 3 0.0146 0.6045 0.000 0.000 0.996 0.000 0.000 0.004
#> GSM1105447 2 0.5634 0.3163 0.012 0.516 0.000 0.372 0.004 0.096
#> GSM1105467 2 0.1556 0.8427 0.000 0.920 0.000 0.080 0.000 0.000
#> GSM1105470 2 0.0000 0.9106 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105471 4 0.3911 0.3129 0.008 0.000 0.000 0.624 0.000 0.368
#> GSM1105474 2 0.0000 0.9106 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105475 2 0.3659 0.5032 0.000 0.636 0.000 0.364 0.000 0.000
#> GSM1105440 3 0.3636 0.1726 0.320 0.000 0.676 0.004 0.000 0.000
#> GSM1105488 5 0.2527 0.8927 0.000 0.168 0.000 0.000 0.832 0.000
#> GSM1105489 3 0.3850 0.0230 0.340 0.000 0.652 0.000 0.004 0.004
#> GSM1105492 1 0.3592 0.9182 0.656 0.000 0.344 0.000 0.000 0.000
#> GSM1105493 3 0.5743 -0.4361 0.404 0.000 0.428 0.000 0.168 0.000
#> GSM1105497 5 0.0260 0.8093 0.000 0.000 0.000 0.000 0.992 0.008
#> GSM1105500 4 0.4903 0.6544 0.068 0.020 0.208 0.696 0.000 0.008
#> GSM1105501 4 0.2562 0.7416 0.172 0.000 0.000 0.828 0.000 0.000
#> GSM1105508 3 0.3672 0.1984 0.304 0.000 0.688 0.008 0.000 0.000
#> GSM1105444 2 0.0260 0.9076 0.000 0.992 0.000 0.000 0.008 0.000
#> GSM1105513 4 0.0363 0.7669 0.012 0.000 0.000 0.988 0.000 0.000
#> GSM1105516 4 0.4422 0.6047 0.068 0.000 0.252 0.680 0.000 0.000
#> GSM1105520 6 0.0865 0.7871 0.000 0.000 0.000 0.036 0.000 0.964
#> GSM1105524 3 0.3499 0.1737 0.320 0.000 0.680 0.000 0.000 0.000
#> GSM1105536 4 0.3446 0.6774 0.308 0.000 0.000 0.692 0.000 0.000
#> GSM1105537 3 0.3499 0.1737 0.320 0.000 0.680 0.000 0.000 0.000
#> GSM1105540 4 0.4388 0.5728 0.056 0.000 0.276 0.668 0.000 0.000
#> GSM1105544 4 0.4448 0.6516 0.068 0.000 0.216 0.708 0.000 0.008
#> GSM1105445 4 0.3852 0.3885 0.012 0.000 0.000 0.664 0.000 0.324
#> GSM1105553 6 0.0632 0.8001 0.000 0.000 0.024 0.000 0.000 0.976
#> GSM1105556 1 0.3547 0.9313 0.668 0.000 0.332 0.000 0.000 0.000
#> GSM1105557 4 0.0363 0.7695 0.012 0.000 0.000 0.988 0.000 0.000
#> GSM1105449 2 0.1655 0.8667 0.012 0.936 0.000 0.044 0.004 0.004
#> GSM1105469 4 0.4222 0.5908 0.032 0.000 0.268 0.692 0.000 0.008
#> GSM1105472 2 0.0000 0.9106 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105473 3 0.4180 -0.0360 0.348 0.000 0.628 0.000 0.024 0.000
#> GSM1105476 2 0.0363 0.9036 0.000 0.988 0.000 0.012 0.000 0.000
#> GSM1105477 4 0.3446 0.6774 0.308 0.000 0.000 0.692 0.000 0.000
#> GSM1105478 4 0.0508 0.7662 0.012 0.000 0.000 0.984 0.000 0.004
#> GSM1105510 5 0.2664 0.8846 0.000 0.184 0.000 0.000 0.816 0.000
#> GSM1105530 3 0.0291 0.6050 0.004 0.000 0.992 0.000 0.000 0.004
#> GSM1105539 3 0.0146 0.6045 0.000 0.000 0.996 0.000 0.000 0.004
#> GSM1105480 4 0.0146 0.7690 0.004 0.000 0.000 0.996 0.000 0.000
#> GSM1105512 1 0.3547 0.9313 0.668 0.000 0.332 0.000 0.000 0.000
#> GSM1105532 3 0.0291 0.6050 0.004 0.000 0.992 0.000 0.000 0.004
#> GSM1105541 3 0.0405 0.6043 0.008 0.000 0.988 0.000 0.000 0.004
#> GSM1105439 4 0.2170 0.7034 0.012 0.100 0.000 0.888 0.000 0.000
#> GSM1105463 3 0.3695 0.3176 0.000 0.000 0.624 0.000 0.000 0.376
#> GSM1105482 1 0.3927 0.9070 0.644 0.000 0.344 0.000 0.012 0.000
#> GSM1105483 4 0.2760 0.7567 0.068 0.000 0.052 0.872 0.000 0.008
#> GSM1105494 4 0.0993 0.7590 0.012 0.000 0.000 0.964 0.000 0.024
#> GSM1105503 6 0.0713 0.7988 0.000 0.000 0.028 0.000 0.000 0.972
#> GSM1105507 4 0.4360 0.5960 0.060 0.000 0.260 0.680 0.000 0.000
#> GSM1105446 2 0.1663 0.8277 0.000 0.912 0.000 0.000 0.088 0.000
#> GSM1105519 1 0.3620 0.9101 0.648 0.000 0.352 0.000 0.000 0.000
#> GSM1105526 4 0.3446 0.6774 0.308 0.000 0.000 0.692 0.000 0.000
#> GSM1105527 4 0.0508 0.7695 0.012 0.000 0.000 0.984 0.000 0.004
#> GSM1105531 6 0.3288 0.5827 0.000 0.000 0.276 0.000 0.000 0.724
#> GSM1105543 2 0.0146 0.9094 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM1105546 1 0.3563 0.9280 0.664 0.000 0.336 0.000 0.000 0.000
#> GSM1105547 1 0.3883 0.9210 0.656 0.000 0.332 0.000 0.012 0.000
#> GSM1105455 2 0.4101 0.4217 0.012 0.580 0.000 0.408 0.000 0.000
#> GSM1105458 6 0.6311 0.1742 0.012 0.256 0.000 0.316 0.000 0.416
#> GSM1105459 2 0.0000 0.9106 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105462 6 0.6277 0.1005 0.008 0.000 0.332 0.268 0.000 0.392
#> GSM1105441 2 0.0146 0.9090 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM1105465 5 0.0260 0.8093 0.000 0.000 0.000 0.000 0.992 0.008
#> GSM1105484 5 0.2491 0.8931 0.000 0.164 0.000 0.000 0.836 0.000
#> GSM1105485 5 0.2527 0.8927 0.000 0.168 0.000 0.000 0.832 0.000
#> GSM1105496 6 0.0632 0.8001 0.000 0.000 0.024 0.000 0.000 0.976
#> GSM1105505 6 0.2300 0.7275 0.000 0.000 0.144 0.000 0.000 0.856
#> GSM1105509 1 0.3975 0.6707 0.544 0.000 0.452 0.004 0.000 0.000
#> GSM1105448 2 0.0146 0.9094 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM1105521 1 0.3547 0.9313 0.668 0.000 0.332 0.000 0.000 0.000
#> GSM1105528 5 0.3446 0.7062 0.000 0.308 0.000 0.000 0.692 0.000
#> GSM1105529 5 0.2697 0.8817 0.000 0.188 0.000 0.000 0.812 0.000
#> GSM1105533 3 0.0146 0.6045 0.000 0.000 0.996 0.000 0.000 0.004
#> GSM1105545 4 0.3446 0.6774 0.308 0.000 0.000 0.692 0.000 0.000
#> GSM1105548 3 0.5737 -0.4030 0.392 0.000 0.440 0.000 0.168 0.000
#> GSM1105549 1 0.6024 0.4460 0.404 0.000 0.348 0.000 0.248 0.000
#> GSM1105457 4 0.0363 0.7669 0.012 0.000 0.000 0.988 0.000 0.000
#> GSM1105460 4 0.1297 0.7489 0.012 0.040 0.000 0.948 0.000 0.000
#> GSM1105461 2 0.0000 0.9106 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105464 3 0.1958 0.5194 0.100 0.000 0.896 0.000 0.000 0.004
#> GSM1105466 4 0.0363 0.7669 0.012 0.000 0.000 0.988 0.000 0.000
#> GSM1105479 4 0.3625 0.6386 0.012 0.052 0.000 0.804 0.000 0.132
#> GSM1105502 3 0.0508 0.6028 0.012 0.000 0.984 0.000 0.000 0.004
#> GSM1105515 1 0.3547 0.9313 0.668 0.000 0.332 0.000 0.000 0.000
#> GSM1105523 3 0.2980 0.4189 0.000 0.000 0.808 0.180 0.000 0.012
#> GSM1105550 4 0.4366 0.6635 0.068 0.000 0.204 0.720 0.000 0.008
#> GSM1105450 2 0.0000 0.9106 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105451 2 0.0000 0.9106 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105454 6 0.0000 0.7986 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM1105468 2 0.0000 0.9106 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105481 6 0.0000 0.7986 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM1105504 3 0.3847 0.0969 0.000 0.000 0.544 0.000 0.000 0.456
#> GSM1105517 4 0.5925 0.1934 0.256 0.000 0.280 0.464 0.000 0.000
#> GSM1105525 3 0.0146 0.6045 0.000 0.000 0.996 0.000 0.000 0.004
#> GSM1105552 3 0.5020 0.0151 0.312 0.000 0.616 0.000 0.044 0.028
#> GSM1105452 5 0.2762 0.8739 0.000 0.196 0.000 0.000 0.804 0.000
#> GSM1105453 2 0.0000 0.9106 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105456 6 0.0000 0.7986 0.000 0.000 0.000 0.000 0.000 1.000
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 agent(p) other(p) time(p) individual(p) k
#> SD:mclust 87 0.935 0.246 1.000 0.02902 2
#> SD:mclust 103 0.318 0.588 0.694 0.01332 3
#> SD:mclust 89 0.603 0.628 0.639 0.02117 4
#> SD:mclust 116 0.562 0.758 0.588 0.00264 5
#> SD:mclust 96 0.662 0.162 0.709 0.01769 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 44956 rows and 120 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 0.867 0.921 0.967 0.4956 0.505 0.505
#> 3 3 0.509 0.529 0.749 0.3071 0.804 0.622
#> 4 4 0.703 0.755 0.886 0.1053 0.760 0.442
#> 5 5 0.601 0.581 0.775 0.0840 0.852 0.551
#> 6 6 0.554 0.355 0.596 0.0486 0.879 0.538
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
#> GSM1105438 2 0.0000 0.9640 0.000 1.000
#> GSM1105486 2 0.0000 0.9640 0.000 1.000
#> GSM1105487 1 0.0000 0.9653 1.000 0.000
#> GSM1105490 2 0.0000 0.9640 0.000 1.000
#> GSM1105491 2 0.5408 0.8541 0.124 0.876
#> GSM1105495 2 0.7219 0.7643 0.200 0.800
#> GSM1105498 2 0.9248 0.5163 0.340 0.660
#> GSM1105499 1 0.0000 0.9653 1.000 0.000
#> GSM1105506 2 0.0000 0.9640 0.000 1.000
#> GSM1105442 2 0.0000 0.9640 0.000 1.000
#> GSM1105511 2 0.0000 0.9640 0.000 1.000
#> GSM1105514 2 0.0000 0.9640 0.000 1.000
#> GSM1105518 2 0.2043 0.9402 0.032 0.968
#> GSM1105522 1 0.0000 0.9653 1.000 0.000
#> GSM1105534 1 0.0000 0.9653 1.000 0.000
#> GSM1105535 1 0.0000 0.9653 1.000 0.000
#> GSM1105538 1 0.0000 0.9653 1.000 0.000
#> GSM1105542 2 0.0000 0.9640 0.000 1.000
#> GSM1105443 2 0.0000 0.9640 0.000 1.000
#> GSM1105551 1 0.0000 0.9653 1.000 0.000
#> GSM1105554 1 0.0000 0.9653 1.000 0.000
#> GSM1105555 1 0.0000 0.9653 1.000 0.000
#> GSM1105447 2 0.0000 0.9640 0.000 1.000
#> GSM1105467 2 0.0000 0.9640 0.000 1.000
#> GSM1105470 2 0.0000 0.9640 0.000 1.000
#> GSM1105471 2 0.2778 0.9269 0.048 0.952
#> GSM1105474 2 0.0000 0.9640 0.000 1.000
#> GSM1105475 2 0.0000 0.9640 0.000 1.000
#> GSM1105440 1 0.0000 0.9653 1.000 0.000
#> GSM1105488 2 0.0000 0.9640 0.000 1.000
#> GSM1105489 1 0.0000 0.9653 1.000 0.000
#> GSM1105492 1 0.0000 0.9653 1.000 0.000
#> GSM1105493 1 0.0000 0.9653 1.000 0.000
#> GSM1105497 2 0.0000 0.9640 0.000 1.000
#> GSM1105500 2 0.0000 0.9640 0.000 1.000
#> GSM1105501 2 0.0000 0.9640 0.000 1.000
#> GSM1105508 1 0.0000 0.9653 1.000 0.000
#> GSM1105444 2 0.0000 0.9640 0.000 1.000
#> GSM1105513 2 0.0000 0.9640 0.000 1.000
#> GSM1105516 1 0.9866 0.2564 0.568 0.432
#> GSM1105520 2 0.9170 0.5339 0.332 0.668
#> GSM1105524 1 0.0000 0.9653 1.000 0.000
#> GSM1105536 2 0.0000 0.9640 0.000 1.000
#> GSM1105537 1 0.0000 0.9653 1.000 0.000
#> GSM1105540 1 0.0000 0.9653 1.000 0.000
#> GSM1105544 2 0.8499 0.6322 0.276 0.724
#> GSM1105445 2 0.0000 0.9640 0.000 1.000
#> GSM1105553 1 0.9988 0.0145 0.520 0.480
#> GSM1105556 1 0.0000 0.9653 1.000 0.000
#> GSM1105557 2 0.0000 0.9640 0.000 1.000
#> GSM1105449 2 0.0000 0.9640 0.000 1.000
#> GSM1105469 1 0.3584 0.9007 0.932 0.068
#> GSM1105472 2 0.0000 0.9640 0.000 1.000
#> GSM1105473 1 0.0000 0.9653 1.000 0.000
#> GSM1105476 2 0.0000 0.9640 0.000 1.000
#> GSM1105477 2 0.0000 0.9640 0.000 1.000
#> GSM1105478 2 0.7219 0.7643 0.200 0.800
#> GSM1105510 2 0.0000 0.9640 0.000 1.000
#> GSM1105530 1 0.0000 0.9653 1.000 0.000
#> GSM1105539 1 0.0000 0.9653 1.000 0.000
#> GSM1105480 2 0.0000 0.9640 0.000 1.000
#> GSM1105512 1 0.0000 0.9653 1.000 0.000
#> GSM1105532 1 0.0000 0.9653 1.000 0.000
#> GSM1105541 1 0.0000 0.9653 1.000 0.000
#> GSM1105439 2 0.0000 0.9640 0.000 1.000
#> GSM1105463 1 0.0000 0.9653 1.000 0.000
#> GSM1105482 1 0.0000 0.9653 1.000 0.000
#> GSM1105483 2 0.4939 0.8642 0.108 0.892
#> GSM1105494 2 0.0000 0.9640 0.000 1.000
#> GSM1105503 1 0.9775 0.2595 0.588 0.412
#> GSM1105507 1 0.6973 0.7534 0.812 0.188
#> GSM1105446 2 0.0000 0.9640 0.000 1.000
#> GSM1105519 1 0.0000 0.9653 1.000 0.000
#> GSM1105526 2 0.0000 0.9640 0.000 1.000
#> GSM1105527 2 0.0672 0.9582 0.008 0.992
#> GSM1105531 1 0.0000 0.9653 1.000 0.000
#> GSM1105543 2 0.0000 0.9640 0.000 1.000
#> GSM1105546 1 0.0000 0.9653 1.000 0.000
#> GSM1105547 1 0.0000 0.9653 1.000 0.000
#> GSM1105455 2 0.0000 0.9640 0.000 1.000
#> GSM1105458 2 0.0000 0.9640 0.000 1.000
#> GSM1105459 2 0.0000 0.9640 0.000 1.000
#> GSM1105462 1 0.2603 0.9256 0.956 0.044
#> GSM1105441 2 0.0000 0.9640 0.000 1.000
#> GSM1105465 2 0.1633 0.9466 0.024 0.976
#> GSM1105484 2 0.0000 0.9640 0.000 1.000
#> GSM1105485 2 0.0000 0.9640 0.000 1.000
#> GSM1105496 1 0.2603 0.9256 0.956 0.044
#> GSM1105505 1 0.0000 0.9653 1.000 0.000
#> GSM1105509 1 0.0000 0.9653 1.000 0.000
#> GSM1105448 2 0.0000 0.9640 0.000 1.000
#> GSM1105521 1 0.0000 0.9653 1.000 0.000
#> GSM1105528 2 0.0000 0.9640 0.000 1.000
#> GSM1105529 2 0.0000 0.9640 0.000 1.000
#> GSM1105533 1 0.0000 0.9653 1.000 0.000
#> GSM1105545 2 0.0000 0.9640 0.000 1.000
#> GSM1105548 1 0.0000 0.9653 1.000 0.000
#> GSM1105549 1 0.0000 0.9653 1.000 0.000
#> GSM1105457 2 0.0000 0.9640 0.000 1.000
#> GSM1105460 2 0.0000 0.9640 0.000 1.000
#> GSM1105461 2 0.0000 0.9640 0.000 1.000
#> GSM1105464 1 0.0000 0.9653 1.000 0.000
#> GSM1105466 2 0.0000 0.9640 0.000 1.000
#> GSM1105479 2 0.0000 0.9640 0.000 1.000
#> GSM1105502 1 0.0000 0.9653 1.000 0.000
#> GSM1105515 1 0.0000 0.9653 1.000 0.000
#> GSM1105523 1 0.0000 0.9653 1.000 0.000
#> GSM1105550 1 0.0000 0.9653 1.000 0.000
#> GSM1105450 2 0.0000 0.9640 0.000 1.000
#> GSM1105451 2 0.0000 0.9640 0.000 1.000
#> GSM1105454 2 0.7219 0.7643 0.200 0.800
#> GSM1105468 2 0.0000 0.9640 0.000 1.000
#> GSM1105481 2 0.7219 0.7643 0.200 0.800
#> GSM1105504 1 0.0000 0.9653 1.000 0.000
#> GSM1105517 1 0.0000 0.9653 1.000 0.000
#> GSM1105525 1 0.0000 0.9653 1.000 0.000
#> GSM1105552 1 0.0000 0.9653 1.000 0.000
#> GSM1105452 2 0.0000 0.9640 0.000 1.000
#> GSM1105453 2 0.0000 0.9640 0.000 1.000
#> GSM1105456 2 0.7219 0.7643 0.200 0.800
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1105438 2 0.4887 0.43626 0.000 0.772 0.228
#> GSM1105486 2 0.6260 0.00202 0.000 0.552 0.448
#> GSM1105487 1 0.4346 0.78334 0.816 0.000 0.184
#> GSM1105490 3 0.5988 0.58093 0.000 0.368 0.632
#> GSM1105491 2 0.4978 0.35314 0.004 0.780 0.216
#> GSM1105495 2 0.6126 0.23365 0.000 0.600 0.400
#> GSM1105498 3 0.1751 0.45177 0.028 0.012 0.960
#> GSM1105499 1 0.0000 0.82157 1.000 0.000 0.000
#> GSM1105506 3 0.7095 0.59997 0.048 0.292 0.660
#> GSM1105442 2 0.0747 0.53847 0.000 0.984 0.016
#> GSM1105511 3 0.7190 0.59269 0.044 0.320 0.636
#> GSM1105514 2 0.4235 0.48462 0.000 0.824 0.176
#> GSM1105518 3 0.3879 0.54606 0.000 0.152 0.848
#> GSM1105522 1 0.2261 0.79657 0.932 0.000 0.068
#> GSM1105534 1 0.0237 0.82115 0.996 0.004 0.000
#> GSM1105535 1 0.0000 0.82157 1.000 0.000 0.000
#> GSM1105538 1 0.0747 0.81806 0.984 0.016 0.000
#> GSM1105542 2 0.0000 0.54821 0.000 1.000 0.000
#> GSM1105443 3 0.5882 0.60615 0.000 0.348 0.652
#> GSM1105551 1 0.5810 0.71484 0.664 0.000 0.336
#> GSM1105554 1 0.0000 0.82157 1.000 0.000 0.000
#> GSM1105555 1 0.4702 0.77290 0.788 0.000 0.212
#> GSM1105447 3 0.5988 0.58090 0.000 0.368 0.632
#> GSM1105467 2 0.6267 -0.01797 0.000 0.548 0.452
#> GSM1105470 2 0.6286 -0.07899 0.000 0.536 0.464
#> GSM1105471 3 0.5815 0.60936 0.004 0.304 0.692
#> GSM1105474 2 0.6062 0.22736 0.000 0.616 0.384
#> GSM1105475 3 0.6302 0.28596 0.000 0.480 0.520
#> GSM1105440 1 0.0000 0.82157 1.000 0.000 0.000
#> GSM1105488 2 0.0000 0.54821 0.000 1.000 0.000
#> GSM1105489 1 0.4702 0.77290 0.788 0.000 0.212
#> GSM1105492 1 0.0000 0.82157 1.000 0.000 0.000
#> GSM1105493 1 0.9673 0.42967 0.400 0.388 0.212
#> GSM1105497 2 0.1163 0.52975 0.000 0.972 0.028
#> GSM1105500 2 0.3116 0.52588 0.000 0.892 0.108
#> GSM1105501 3 0.8390 0.45895 0.100 0.340 0.560
#> GSM1105508 1 0.0747 0.81802 0.984 0.000 0.016
#> GSM1105444 2 0.5988 0.25367 0.000 0.632 0.368
#> GSM1105513 3 0.5859 0.60970 0.000 0.344 0.656
#> GSM1105516 2 0.6521 -0.33233 0.496 0.500 0.004
#> GSM1105520 3 0.0747 0.45160 0.016 0.000 0.984
#> GSM1105524 1 0.0000 0.82157 1.000 0.000 0.000
#> GSM1105536 2 0.5948 0.26871 0.000 0.640 0.360
#> GSM1105537 1 0.0000 0.82157 1.000 0.000 0.000
#> GSM1105540 1 0.0237 0.82088 0.996 0.000 0.004
#> GSM1105544 2 0.9824 -0.03268 0.328 0.416 0.256
#> GSM1105445 3 0.4399 0.56677 0.000 0.188 0.812
#> GSM1105553 3 0.3340 0.35749 0.120 0.000 0.880
#> GSM1105556 1 0.4555 0.71426 0.800 0.200 0.000
#> GSM1105557 3 0.5882 0.60625 0.000 0.348 0.652
#> GSM1105449 3 0.6309 0.22199 0.000 0.496 0.504
#> GSM1105469 1 0.5216 0.61465 0.740 0.000 0.260
#> GSM1105472 2 0.6062 0.22736 0.000 0.616 0.384
#> GSM1105473 1 0.9263 0.49637 0.476 0.360 0.164
#> GSM1105476 2 0.6079 0.21779 0.000 0.612 0.388
#> GSM1105477 2 0.1964 0.54326 0.000 0.944 0.056
#> GSM1105478 3 0.5493 0.58808 0.012 0.232 0.756
#> GSM1105510 2 0.0000 0.54821 0.000 1.000 0.000
#> GSM1105530 1 0.0000 0.82157 1.000 0.000 0.000
#> GSM1105539 1 0.5859 0.71079 0.656 0.000 0.344
#> GSM1105480 3 0.5835 0.61257 0.000 0.340 0.660
#> GSM1105512 1 0.0000 0.82157 1.000 0.000 0.000
#> GSM1105532 1 0.1289 0.81294 0.968 0.000 0.032
#> GSM1105541 1 0.5291 0.75121 0.732 0.000 0.268
#> GSM1105439 3 0.5968 0.58651 0.000 0.364 0.636
#> GSM1105463 1 0.5859 0.71007 0.656 0.000 0.344
#> GSM1105482 1 0.5315 0.69902 0.772 0.216 0.012
#> GSM1105483 3 0.6647 0.30355 0.396 0.012 0.592
#> GSM1105494 3 0.5835 0.61257 0.000 0.340 0.660
#> GSM1105503 3 0.3192 0.36515 0.112 0.000 0.888
#> GSM1105507 1 0.0237 0.82021 0.996 0.000 0.004
#> GSM1105446 2 0.2066 0.54251 0.000 0.940 0.060
#> GSM1105519 1 0.0000 0.82157 1.000 0.000 0.000
#> GSM1105526 2 0.6095 0.20756 0.000 0.608 0.392
#> GSM1105527 3 0.6935 0.37807 0.312 0.036 0.652
#> GSM1105531 1 0.6244 0.63934 0.560 0.000 0.440
#> GSM1105543 2 0.3686 0.50889 0.000 0.860 0.140
#> GSM1105546 1 0.0000 0.82157 1.000 0.000 0.000
#> GSM1105547 1 0.6264 0.51744 0.616 0.380 0.004
#> GSM1105455 3 0.6095 0.53454 0.000 0.392 0.608
#> GSM1105458 3 0.6299 0.29684 0.000 0.476 0.524
#> GSM1105459 2 0.6140 0.17399 0.000 0.596 0.404
#> GSM1105462 1 0.6260 0.62379 0.552 0.000 0.448
#> GSM1105441 3 0.6309 0.22199 0.000 0.496 0.504
#> GSM1105465 2 0.4235 0.39424 0.000 0.824 0.176
#> GSM1105484 2 0.0000 0.54821 0.000 1.000 0.000
#> GSM1105485 2 0.0424 0.54353 0.008 0.992 0.000
#> GSM1105496 1 0.6168 0.66500 0.588 0.000 0.412
#> GSM1105505 1 0.6126 0.67543 0.600 0.000 0.400
#> GSM1105509 1 0.0000 0.82157 1.000 0.000 0.000
#> GSM1105448 2 0.6062 0.22736 0.000 0.616 0.384
#> GSM1105521 1 0.0237 0.82115 0.996 0.004 0.000
#> GSM1105528 2 0.0000 0.54821 0.000 1.000 0.000
#> GSM1105529 2 0.0000 0.54821 0.000 1.000 0.000
#> GSM1105533 1 0.5098 0.75972 0.752 0.000 0.248
#> GSM1105545 3 0.7674 0.19909 0.044 0.472 0.484
#> GSM1105548 1 0.9485 0.54643 0.484 0.304 0.212
#> GSM1105549 1 0.7987 0.39544 0.492 0.448 0.060
#> GSM1105457 3 0.5835 0.61257 0.000 0.340 0.660
#> GSM1105460 3 0.6045 0.55925 0.000 0.380 0.620
#> GSM1105461 2 0.6192 0.11951 0.000 0.580 0.420
#> GSM1105464 1 0.0000 0.82157 1.000 0.000 0.000
#> GSM1105466 3 0.5835 0.61257 0.000 0.340 0.660
#> GSM1105479 3 0.5835 0.61257 0.000 0.340 0.660
#> GSM1105502 1 0.4399 0.78169 0.812 0.000 0.188
#> GSM1105515 1 0.1753 0.80791 0.952 0.048 0.000
#> GSM1105523 1 0.6126 0.56528 0.600 0.000 0.400
#> GSM1105550 1 0.4654 0.68321 0.792 0.000 0.208
#> GSM1105450 2 0.6235 0.05698 0.000 0.564 0.436
#> GSM1105451 2 0.6267 -0.01747 0.000 0.548 0.452
#> GSM1105454 3 0.0237 0.46002 0.004 0.000 0.996
#> GSM1105468 2 0.6225 0.07383 0.000 0.568 0.432
#> GSM1105481 3 0.0237 0.46002 0.004 0.000 0.996
#> GSM1105504 1 0.6045 0.68991 0.620 0.000 0.380
#> GSM1105517 1 0.0000 0.82157 1.000 0.000 0.000
#> GSM1105525 1 0.5905 0.64062 0.648 0.000 0.352
#> GSM1105552 1 0.9985 0.44147 0.360 0.324 0.316
#> GSM1105452 2 0.0000 0.54821 0.000 1.000 0.000
#> GSM1105453 2 0.6140 0.17399 0.000 0.596 0.404
#> GSM1105456 3 0.0237 0.46002 0.004 0.000 0.996
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1105438 4 0.4855 0.2571 0.000 0.400 0.000 0.600
#> GSM1105486 2 0.2814 0.8190 0.000 0.868 0.000 0.132
#> GSM1105487 1 0.1716 0.8669 0.936 0.000 0.064 0.000
#> GSM1105490 2 0.0000 0.8646 0.000 1.000 0.000 0.000
#> GSM1105491 4 0.0000 0.7738 0.000 0.000 0.000 1.000
#> GSM1105495 3 0.0188 0.8830 0.000 0.000 0.996 0.004
#> GSM1105498 2 0.3257 0.7267 0.004 0.844 0.152 0.000
#> GSM1105499 1 0.0188 0.8961 0.996 0.000 0.000 0.004
#> GSM1105506 2 0.0592 0.8591 0.016 0.984 0.000 0.000
#> GSM1105442 4 0.0188 0.7746 0.000 0.004 0.000 0.996
#> GSM1105511 2 0.0469 0.8610 0.012 0.988 0.000 0.000
#> GSM1105514 4 0.4331 0.5368 0.000 0.288 0.000 0.712
#> GSM1105518 2 0.2401 0.8200 0.000 0.904 0.092 0.004
#> GSM1105522 1 0.0376 0.8941 0.992 0.004 0.004 0.000
#> GSM1105534 1 0.1118 0.8864 0.964 0.000 0.000 0.036
#> GSM1105535 1 0.0188 0.8961 0.996 0.000 0.000 0.004
#> GSM1105538 1 0.0921 0.8902 0.972 0.000 0.000 0.028
#> GSM1105542 4 0.0188 0.7746 0.000 0.004 0.000 0.996
#> GSM1105443 2 0.0188 0.8652 0.000 0.996 0.000 0.004
#> GSM1105551 1 0.3801 0.7155 0.780 0.000 0.220 0.000
#> GSM1105554 1 0.0592 0.8943 0.984 0.000 0.000 0.016
#> GSM1105555 1 0.3791 0.7362 0.796 0.000 0.200 0.004
#> GSM1105447 2 0.0895 0.8649 0.000 0.976 0.004 0.020
#> GSM1105467 2 0.1792 0.8528 0.000 0.932 0.000 0.068
#> GSM1105470 2 0.1211 0.8615 0.000 0.960 0.000 0.040
#> GSM1105471 2 0.0376 0.8653 0.000 0.992 0.004 0.004
#> GSM1105474 2 0.4431 0.6191 0.000 0.696 0.000 0.304
#> GSM1105475 2 0.0592 0.8651 0.000 0.984 0.000 0.016
#> GSM1105440 1 0.0000 0.8957 1.000 0.000 0.000 0.000
#> GSM1105488 4 0.0188 0.7746 0.000 0.004 0.000 0.996
#> GSM1105489 1 0.5423 0.4913 0.640 0.000 0.332 0.028
#> GSM1105492 1 0.0336 0.8958 0.992 0.000 0.000 0.008
#> GSM1105493 4 0.4237 0.6396 0.152 0.000 0.040 0.808
#> GSM1105497 4 0.0376 0.7740 0.000 0.004 0.004 0.992
#> GSM1105500 4 0.5013 0.4078 0.004 0.348 0.004 0.644
#> GSM1105501 2 0.1452 0.8499 0.036 0.956 0.000 0.008
#> GSM1105508 1 0.0188 0.8946 0.996 0.004 0.000 0.000
#> GSM1105444 2 0.5050 0.3912 0.000 0.588 0.004 0.408
#> GSM1105513 2 0.0000 0.8646 0.000 1.000 0.000 0.000
#> GSM1105516 4 0.4040 0.5823 0.248 0.000 0.000 0.752
#> GSM1105520 2 0.4972 0.0964 0.000 0.544 0.456 0.000
#> GSM1105524 1 0.0000 0.8957 1.000 0.000 0.000 0.000
#> GSM1105536 2 0.4936 0.4734 0.004 0.624 0.000 0.372
#> GSM1105537 1 0.0000 0.8957 1.000 0.000 0.000 0.000
#> GSM1105540 1 0.0000 0.8957 1.000 0.000 0.000 0.000
#> GSM1105544 2 0.6668 0.3057 0.380 0.528 0.000 0.092
#> GSM1105445 2 0.0469 0.8626 0.000 0.988 0.012 0.000
#> GSM1105553 3 0.0000 0.8847 0.000 0.000 1.000 0.000
#> GSM1105556 1 0.3569 0.7510 0.804 0.000 0.000 0.196
#> GSM1105557 2 0.0188 0.8637 0.004 0.996 0.000 0.000
#> GSM1105449 2 0.1661 0.8583 0.000 0.944 0.004 0.052
#> GSM1105469 1 0.3444 0.7123 0.816 0.184 0.000 0.000
#> GSM1105472 2 0.4277 0.6558 0.000 0.720 0.000 0.280
#> GSM1105473 4 0.4472 0.5958 0.220 0.000 0.020 0.760
#> GSM1105476 2 0.3356 0.7807 0.000 0.824 0.000 0.176
#> GSM1105477 4 0.4252 0.5980 0.004 0.252 0.000 0.744
#> GSM1105478 2 0.0592 0.8591 0.016 0.984 0.000 0.000
#> GSM1105510 4 0.0817 0.7713 0.000 0.024 0.000 0.976
#> GSM1105530 1 0.0336 0.8946 0.992 0.000 0.008 0.000
#> GSM1105539 3 0.4746 0.3335 0.368 0.000 0.632 0.000
#> GSM1105480 2 0.0336 0.8624 0.008 0.992 0.000 0.000
#> GSM1105512 1 0.0469 0.8952 0.988 0.000 0.000 0.012
#> GSM1105532 1 0.0592 0.8933 0.984 0.000 0.016 0.000
#> GSM1105541 1 0.3982 0.7178 0.776 0.000 0.220 0.004
#> GSM1105439 2 0.0000 0.8646 0.000 1.000 0.000 0.000
#> GSM1105463 3 0.0188 0.8837 0.004 0.000 0.996 0.000
#> GSM1105482 1 0.4761 0.4480 0.628 0.000 0.000 0.372
#> GSM1105483 1 0.4804 0.3832 0.616 0.384 0.000 0.000
#> GSM1105494 2 0.0188 0.8649 0.000 0.996 0.004 0.000
#> GSM1105503 3 0.4250 0.6180 0.000 0.276 0.724 0.000
#> GSM1105507 1 0.0000 0.8957 1.000 0.000 0.000 0.000
#> GSM1105446 4 0.3942 0.6197 0.000 0.236 0.000 0.764
#> GSM1105519 1 0.0336 0.8958 0.992 0.000 0.000 0.008
#> GSM1105526 2 0.3870 0.7461 0.004 0.788 0.000 0.208
#> GSM1105527 2 0.4331 0.5190 0.288 0.712 0.000 0.000
#> GSM1105531 3 0.0188 0.8837 0.004 0.000 0.996 0.000
#> GSM1105543 4 0.4585 0.4472 0.000 0.332 0.000 0.668
#> GSM1105546 1 0.0336 0.8958 0.992 0.000 0.000 0.008
#> GSM1105547 4 0.4713 0.3540 0.360 0.000 0.000 0.640
#> GSM1105455 2 0.0188 0.8652 0.000 0.996 0.000 0.004
#> GSM1105458 2 0.1807 0.8580 0.000 0.940 0.008 0.052
#> GSM1105459 2 0.3688 0.7497 0.000 0.792 0.000 0.208
#> GSM1105462 3 0.7536 0.3548 0.284 0.228 0.488 0.000
#> GSM1105441 2 0.1398 0.8614 0.000 0.956 0.004 0.040
#> GSM1105465 4 0.0000 0.7738 0.000 0.000 0.000 1.000
#> GSM1105484 4 0.1474 0.7626 0.000 0.052 0.000 0.948
#> GSM1105485 4 0.0000 0.7738 0.000 0.000 0.000 1.000
#> GSM1105496 3 0.0000 0.8847 0.000 0.000 1.000 0.000
#> GSM1105505 3 0.0000 0.8847 0.000 0.000 1.000 0.000
#> GSM1105509 1 0.0188 0.8961 0.996 0.000 0.000 0.004
#> GSM1105448 2 0.5004 0.4329 0.000 0.604 0.004 0.392
#> GSM1105521 1 0.0817 0.8914 0.976 0.000 0.000 0.024
#> GSM1105528 4 0.2149 0.7467 0.000 0.088 0.000 0.912
#> GSM1105529 4 0.0000 0.7738 0.000 0.000 0.000 1.000
#> GSM1105533 1 0.4632 0.5744 0.688 0.000 0.308 0.004
#> GSM1105545 2 0.2363 0.8418 0.056 0.920 0.000 0.024
#> GSM1105548 4 0.6855 0.3693 0.292 0.000 0.136 0.572
#> GSM1105549 4 0.2216 0.7168 0.092 0.000 0.000 0.908
#> GSM1105457 2 0.0188 0.8637 0.004 0.996 0.000 0.000
#> GSM1105460 2 0.0336 0.8654 0.000 0.992 0.000 0.008
#> GSM1105461 2 0.2921 0.8124 0.000 0.860 0.000 0.140
#> GSM1105464 1 0.0672 0.8955 0.984 0.000 0.008 0.008
#> GSM1105466 2 0.0188 0.8637 0.004 0.996 0.000 0.000
#> GSM1105479 2 0.0188 0.8652 0.000 0.996 0.000 0.004
#> GSM1105502 1 0.3208 0.7940 0.848 0.000 0.148 0.004
#> GSM1105515 1 0.1637 0.8716 0.940 0.000 0.000 0.060
#> GSM1105523 1 0.3529 0.7492 0.836 0.152 0.012 0.000
#> GSM1105550 1 0.2266 0.8322 0.912 0.084 0.004 0.000
#> GSM1105450 2 0.2704 0.8241 0.000 0.876 0.000 0.124
#> GSM1105451 2 0.2334 0.8430 0.000 0.908 0.004 0.088
#> GSM1105454 3 0.0188 0.8830 0.000 0.000 0.996 0.004
#> GSM1105468 2 0.2704 0.8240 0.000 0.876 0.000 0.124
#> GSM1105481 3 0.0188 0.8830 0.000 0.000 0.996 0.004
#> GSM1105504 3 0.0188 0.8837 0.004 0.000 0.996 0.000
#> GSM1105517 1 0.0188 0.8961 0.996 0.000 0.000 0.004
#> GSM1105525 1 0.1767 0.8662 0.944 0.044 0.012 0.000
#> GSM1105552 4 0.6497 0.3647 0.100 0.000 0.304 0.596
#> GSM1105452 4 0.0336 0.7742 0.000 0.008 0.000 0.992
#> GSM1105453 2 0.4103 0.6926 0.000 0.744 0.000 0.256
#> GSM1105456 3 0.0000 0.8847 0.000 0.000 1.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM1105438 2 0.4294 0.3291 0.000 0.532 0.000 0.000 0.468
#> GSM1105486 2 0.4547 0.6759 0.000 0.704 0.000 0.044 0.252
#> GSM1105487 1 0.3184 0.6379 0.852 0.000 0.048 0.100 0.000
#> GSM1105490 2 0.2209 0.7557 0.056 0.912 0.000 0.032 0.000
#> GSM1105491 5 0.0992 0.7876 0.024 0.000 0.008 0.000 0.968
#> GSM1105495 3 0.0671 0.8498 0.000 0.000 0.980 0.004 0.016
#> GSM1105498 2 0.4924 0.5887 0.052 0.740 0.176 0.032 0.000
#> GSM1105499 4 0.4305 -0.0876 0.488 0.000 0.000 0.512 0.000
#> GSM1105506 2 0.4517 0.3453 0.008 0.556 0.000 0.436 0.000
#> GSM1105442 5 0.1012 0.7892 0.020 0.012 0.000 0.000 0.968
#> GSM1105511 2 0.3400 0.7280 0.036 0.828 0.000 0.136 0.000
#> GSM1105514 5 0.3741 0.4888 0.004 0.264 0.000 0.000 0.732
#> GSM1105518 2 0.3433 0.6971 0.008 0.832 0.136 0.024 0.000
#> GSM1105522 4 0.4300 0.0332 0.476 0.000 0.000 0.524 0.000
#> GSM1105534 1 0.3081 0.6424 0.832 0.000 0.000 0.156 0.012
#> GSM1105535 1 0.4249 0.2290 0.568 0.000 0.000 0.432 0.000
#> GSM1105538 1 0.3224 0.6375 0.824 0.000 0.000 0.160 0.016
#> GSM1105542 5 0.1168 0.7896 0.032 0.008 0.000 0.000 0.960
#> GSM1105443 2 0.0609 0.7715 0.000 0.980 0.000 0.020 0.000
#> GSM1105551 1 0.2136 0.6215 0.904 0.000 0.088 0.008 0.000
#> GSM1105554 1 0.4151 0.4377 0.652 0.000 0.000 0.344 0.004
#> GSM1105555 1 0.2349 0.6388 0.900 0.000 0.084 0.012 0.004
#> GSM1105447 2 0.2138 0.7609 0.032 0.928 0.024 0.012 0.004
#> GSM1105467 2 0.4944 0.6934 0.000 0.700 0.000 0.092 0.208
#> GSM1105470 2 0.5060 0.6915 0.000 0.692 0.000 0.104 0.204
#> GSM1105471 2 0.7083 0.4084 0.000 0.504 0.128 0.308 0.060
#> GSM1105474 2 0.3944 0.6621 0.004 0.720 0.000 0.004 0.272
#> GSM1105475 2 0.2344 0.7707 0.000 0.904 0.000 0.064 0.032
#> GSM1105440 1 0.1341 0.6538 0.944 0.000 0.000 0.056 0.000
#> GSM1105488 5 0.1648 0.7894 0.040 0.020 0.000 0.000 0.940
#> GSM1105489 1 0.2700 0.6400 0.884 0.000 0.088 0.024 0.004
#> GSM1105492 1 0.2969 0.6513 0.852 0.000 0.000 0.128 0.020
#> GSM1105493 5 0.4833 0.5243 0.284 0.000 0.016 0.024 0.676
#> GSM1105497 5 0.6985 0.4581 0.336 0.132 0.024 0.012 0.496
#> GSM1105500 1 0.5703 -0.0436 0.516 0.424 0.004 0.012 0.044
#> GSM1105501 4 0.5189 0.2513 0.012 0.300 0.000 0.644 0.044
#> GSM1105508 1 0.4315 0.4881 0.700 0.024 0.000 0.276 0.000
#> GSM1105444 2 0.4632 0.5134 0.004 0.608 0.000 0.012 0.376
#> GSM1105513 2 0.1106 0.7679 0.012 0.964 0.000 0.024 0.000
#> GSM1105516 5 0.5341 0.3794 0.300 0.000 0.000 0.080 0.620
#> GSM1105520 3 0.4316 0.7625 0.008 0.080 0.784 0.128 0.000
#> GSM1105524 1 0.4283 0.1563 0.544 0.000 0.000 0.456 0.000
#> GSM1105536 5 0.3970 0.5419 0.000 0.236 0.000 0.020 0.744
#> GSM1105537 1 0.4273 0.1817 0.552 0.000 0.000 0.448 0.000
#> GSM1105540 1 0.4640 0.2514 0.584 0.016 0.000 0.400 0.000
#> GSM1105544 1 0.3545 0.5180 0.832 0.132 0.008 0.024 0.004
#> GSM1105445 2 0.1597 0.7668 0.008 0.948 0.024 0.020 0.000
#> GSM1105553 1 0.6739 -0.0306 0.440 0.388 0.156 0.016 0.000
#> GSM1105556 1 0.4335 0.6212 0.760 0.000 0.000 0.168 0.072
#> GSM1105557 2 0.1992 0.7601 0.044 0.924 0.000 0.032 0.000
#> GSM1105449 2 0.1153 0.7723 0.000 0.964 0.004 0.008 0.024
#> GSM1105469 4 0.3355 0.6006 0.132 0.036 0.000 0.832 0.000
#> GSM1105472 2 0.4718 0.3822 0.000 0.540 0.000 0.016 0.444
#> GSM1105473 5 0.3970 0.6945 0.016 0.000 0.080 0.084 0.820
#> GSM1105476 2 0.3934 0.6917 0.000 0.740 0.000 0.016 0.244
#> GSM1105477 5 0.2127 0.7395 0.000 0.108 0.000 0.000 0.892
#> GSM1105478 2 0.4613 0.4755 0.020 0.620 0.000 0.360 0.000
#> GSM1105510 5 0.0771 0.7856 0.004 0.020 0.000 0.000 0.976
#> GSM1105530 4 0.2681 0.6122 0.108 0.000 0.012 0.876 0.004
#> GSM1105539 3 0.3844 0.6275 0.004 0.000 0.736 0.256 0.004
#> GSM1105480 2 0.2595 0.7522 0.080 0.888 0.000 0.032 0.000
#> GSM1105512 4 0.4893 0.1479 0.404 0.000 0.000 0.568 0.028
#> GSM1105532 4 0.2595 0.6240 0.080 0.000 0.032 0.888 0.000
#> GSM1105541 4 0.5343 0.4187 0.076 0.000 0.280 0.640 0.004
#> GSM1105439 2 0.0703 0.7717 0.000 0.976 0.000 0.024 0.000
#> GSM1105463 3 0.0771 0.8509 0.000 0.000 0.976 0.020 0.004
#> GSM1105482 1 0.3471 0.6395 0.836 0.000 0.000 0.072 0.092
#> GSM1105483 4 0.3336 0.5784 0.060 0.096 0.000 0.844 0.000
#> GSM1105494 2 0.3182 0.7312 0.092 0.864 0.016 0.028 0.000
#> GSM1105503 3 0.4617 0.7078 0.012 0.184 0.748 0.056 0.000
#> GSM1105507 1 0.4410 0.2178 0.556 0.000 0.000 0.440 0.004
#> GSM1105446 2 0.5055 0.6465 0.072 0.708 0.000 0.012 0.208
#> GSM1105519 4 0.4748 -0.1390 0.492 0.000 0.000 0.492 0.016
#> GSM1105526 5 0.7031 -0.0567 0.008 0.292 0.000 0.328 0.372
#> GSM1105527 4 0.5037 0.2537 0.048 0.336 0.000 0.616 0.000
#> GSM1105531 3 0.1282 0.8490 0.000 0.000 0.952 0.044 0.004
#> GSM1105543 2 0.4557 0.5910 0.012 0.656 0.000 0.008 0.324
#> GSM1105546 1 0.1670 0.6572 0.936 0.000 0.000 0.052 0.012
#> GSM1105547 1 0.3745 0.5688 0.780 0.000 0.000 0.024 0.196
#> GSM1105455 2 0.0693 0.7697 0.008 0.980 0.000 0.012 0.000
#> GSM1105458 2 0.1875 0.7738 0.008 0.940 0.008 0.016 0.028
#> GSM1105459 2 0.4152 0.6357 0.000 0.692 0.000 0.012 0.296
#> GSM1105462 4 0.5787 0.3222 0.016 0.036 0.224 0.676 0.048
#> GSM1105441 2 0.1082 0.7735 0.000 0.964 0.000 0.008 0.028
#> GSM1105465 5 0.2054 0.7823 0.072 0.004 0.008 0.000 0.916
#> GSM1105484 5 0.1197 0.7768 0.000 0.048 0.000 0.000 0.952
#> GSM1105485 5 0.0771 0.7883 0.020 0.004 0.000 0.000 0.976
#> GSM1105496 3 0.6284 0.4355 0.316 0.128 0.544 0.012 0.000
#> GSM1105505 3 0.1043 0.8507 0.000 0.000 0.960 0.040 0.000
#> GSM1105509 4 0.3607 0.4720 0.244 0.000 0.000 0.752 0.004
#> GSM1105448 2 0.4567 0.5443 0.004 0.628 0.000 0.012 0.356
#> GSM1105521 4 0.5940 0.3335 0.140 0.000 0.000 0.568 0.292
#> GSM1105528 5 0.1571 0.7711 0.000 0.060 0.000 0.004 0.936
#> GSM1105529 5 0.1484 0.7893 0.048 0.008 0.000 0.000 0.944
#> GSM1105533 1 0.6031 0.3380 0.520 0.000 0.352 0.128 0.000
#> GSM1105545 2 0.7031 0.3720 0.028 0.420 0.000 0.384 0.168
#> GSM1105548 1 0.2459 0.6198 0.904 0.000 0.052 0.004 0.040
#> GSM1105549 5 0.3916 0.5866 0.256 0.000 0.000 0.012 0.732
#> GSM1105457 2 0.2723 0.7443 0.012 0.864 0.000 0.124 0.000
#> GSM1105460 2 0.5064 0.6672 0.000 0.680 0.000 0.232 0.088
#> GSM1105461 2 0.3819 0.6977 0.000 0.756 0.000 0.016 0.228
#> GSM1105464 4 0.3326 0.6194 0.080 0.000 0.044 0.860 0.016
#> GSM1105466 2 0.3819 0.6669 0.016 0.756 0.000 0.228 0.000
#> GSM1105479 2 0.3093 0.7250 0.000 0.824 0.000 0.168 0.008
#> GSM1105502 4 0.6158 0.0213 0.416 0.000 0.132 0.452 0.000
#> GSM1105515 1 0.3106 0.6496 0.844 0.000 0.000 0.132 0.024
#> GSM1105523 4 0.2980 0.6058 0.024 0.036 0.056 0.884 0.000
#> GSM1105550 4 0.1310 0.6177 0.020 0.024 0.000 0.956 0.000
#> GSM1105450 2 0.3154 0.7449 0.004 0.836 0.000 0.012 0.148
#> GSM1105451 2 0.1525 0.7713 0.004 0.948 0.000 0.012 0.036
#> GSM1105454 3 0.1638 0.8274 0.004 0.064 0.932 0.000 0.000
#> GSM1105468 2 0.3812 0.7133 0.000 0.772 0.000 0.024 0.204
#> GSM1105481 3 0.3798 0.8065 0.000 0.032 0.836 0.088 0.044
#> GSM1105504 3 0.2228 0.8276 0.004 0.000 0.900 0.092 0.004
#> GSM1105517 4 0.2233 0.6191 0.104 0.000 0.000 0.892 0.004
#> GSM1105525 4 0.3134 0.6203 0.120 0.000 0.032 0.848 0.000
#> GSM1105552 5 0.4952 0.5183 0.008 0.000 0.252 0.052 0.688
#> GSM1105452 5 0.3146 0.7461 0.128 0.028 0.000 0.000 0.844
#> GSM1105453 2 0.2464 0.7638 0.004 0.892 0.000 0.012 0.092
#> GSM1105456 3 0.0162 0.8466 0.004 0.000 0.996 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1105438 2 0.5052 0.4454 0.012 0.656 0.000 0.108 0.224 0.000
#> GSM1105486 4 0.5677 0.1477 0.000 0.344 0.004 0.504 0.148 0.000
#> GSM1105487 1 0.2865 0.5314 0.848 0.004 0.128 0.016 0.000 0.004
#> GSM1105490 2 0.3149 0.5165 0.044 0.824 0.000 0.132 0.000 0.000
#> GSM1105491 5 0.1936 0.5892 0.000 0.012 0.016 0.008 0.928 0.036
#> GSM1105495 6 0.3164 0.6059 0.004 0.000 0.000 0.140 0.032 0.824
#> GSM1105498 4 0.7596 0.0889 0.212 0.288 0.008 0.360 0.000 0.132
#> GSM1105499 3 0.4061 0.2662 0.316 0.000 0.664 0.008 0.012 0.000
#> GSM1105506 4 0.5993 0.1992 0.000 0.376 0.232 0.392 0.000 0.000
#> GSM1105442 5 0.4211 0.6502 0.024 0.024 0.000 0.232 0.720 0.000
#> GSM1105511 2 0.5608 0.0638 0.008 0.520 0.124 0.348 0.000 0.000
#> GSM1105514 5 0.5061 0.4391 0.004 0.240 0.000 0.120 0.636 0.000
#> GSM1105518 2 0.5505 0.3054 0.012 0.624 0.000 0.188 0.004 0.172
#> GSM1105522 3 0.5379 0.2681 0.336 0.000 0.536 0.128 0.000 0.000
#> GSM1105534 1 0.4366 0.2194 0.540 0.000 0.440 0.004 0.016 0.000
#> GSM1105535 3 0.4032 0.0715 0.420 0.000 0.572 0.008 0.000 0.000
#> GSM1105538 1 0.4303 0.3609 0.616 0.000 0.360 0.016 0.008 0.000
#> GSM1105542 5 0.3930 0.6558 0.032 0.004 0.000 0.236 0.728 0.000
#> GSM1105443 2 0.1155 0.5828 0.004 0.956 0.004 0.036 0.000 0.000
#> GSM1105551 1 0.2236 0.5105 0.912 0.008 0.036 0.036 0.000 0.008
#> GSM1105554 3 0.4727 0.1288 0.376 0.000 0.580 0.012 0.032 0.000
#> GSM1105555 1 0.5130 0.4938 0.708 0.008 0.172 0.020 0.012 0.080
#> GSM1105447 2 0.1857 0.5847 0.044 0.924 0.000 0.028 0.004 0.000
#> GSM1105467 4 0.4843 0.2539 0.000 0.300 0.000 0.616 0.084 0.000
#> GSM1105470 4 0.5401 0.0554 0.004 0.408 0.012 0.508 0.068 0.000
#> GSM1105471 4 0.5815 0.2881 0.000 0.100 0.064 0.612 0.000 0.224
#> GSM1105474 2 0.6048 -0.0411 0.004 0.416 0.000 0.368 0.212 0.000
#> GSM1105475 4 0.3872 0.1507 0.000 0.392 0.000 0.604 0.004 0.000
#> GSM1105440 1 0.3731 0.4996 0.736 0.004 0.240 0.020 0.000 0.000
#> GSM1105488 5 0.2806 0.6566 0.008 0.008 0.000 0.144 0.840 0.000
#> GSM1105489 1 0.2798 0.5426 0.860 0.000 0.108 0.000 0.012 0.020
#> GSM1105492 1 0.5515 0.3900 0.616 0.000 0.260 0.080 0.044 0.000
#> GSM1105493 5 0.4740 0.3961 0.104 0.000 0.112 0.008 0.744 0.032
#> GSM1105497 5 0.6885 0.2917 0.368 0.052 0.004 0.152 0.416 0.008
#> GSM1105500 2 0.6282 0.1207 0.420 0.420 0.000 0.104 0.056 0.000
#> GSM1105501 2 0.6477 0.1206 0.008 0.500 0.308 0.140 0.044 0.000
#> GSM1105508 1 0.4394 0.0660 0.496 0.004 0.484 0.016 0.000 0.000
#> GSM1105444 2 0.5358 0.3618 0.008 0.616 0.000 0.220 0.156 0.000
#> GSM1105513 2 0.3558 0.4233 0.016 0.736 0.000 0.248 0.000 0.000
#> GSM1105516 5 0.6371 -0.1155 0.096 0.012 0.296 0.060 0.536 0.000
#> GSM1105520 6 0.7327 0.2961 0.004 0.176 0.128 0.236 0.004 0.452
#> GSM1105524 3 0.4168 0.1306 0.400 0.000 0.584 0.016 0.000 0.000
#> GSM1105536 4 0.6396 -0.2362 0.000 0.220 0.020 0.396 0.364 0.000
#> GSM1105537 3 0.4109 0.0972 0.412 0.000 0.576 0.012 0.000 0.000
#> GSM1105540 1 0.6057 0.0943 0.436 0.008 0.192 0.364 0.000 0.000
#> GSM1105544 1 0.3756 0.3953 0.776 0.024 0.004 0.184 0.012 0.000
#> GSM1105445 2 0.2934 0.5491 0.024 0.868 0.000 0.064 0.000 0.044
#> GSM1105553 1 0.6288 0.0522 0.548 0.248 0.000 0.140 0.000 0.064
#> GSM1105556 3 0.6190 0.0391 0.296 0.000 0.440 0.008 0.256 0.000
#> GSM1105557 2 0.3663 0.4805 0.040 0.776 0.004 0.180 0.000 0.000
#> GSM1105449 2 0.1578 0.5900 0.004 0.936 0.000 0.048 0.012 0.000
#> GSM1105469 4 0.5930 0.0248 0.080 0.044 0.428 0.448 0.000 0.000
#> GSM1105472 5 0.6251 0.0575 0.004 0.320 0.000 0.336 0.340 0.000
#> GSM1105473 5 0.6079 0.5397 0.024 0.000 0.068 0.120 0.644 0.144
#> GSM1105476 4 0.5726 0.1801 0.000 0.360 0.000 0.468 0.172 0.000
#> GSM1105477 5 0.5440 0.5300 0.004 0.132 0.004 0.268 0.592 0.000
#> GSM1105478 4 0.5824 0.2086 0.012 0.384 0.104 0.492 0.000 0.008
#> GSM1105510 5 0.2082 0.5924 0.000 0.052 0.020 0.008 0.916 0.004
#> GSM1105530 3 0.2945 0.4709 0.016 0.000 0.864 0.072 0.000 0.048
#> GSM1105539 6 0.4228 0.3918 0.020 0.000 0.316 0.008 0.000 0.656
#> GSM1105480 4 0.6091 0.0558 0.196 0.396 0.008 0.400 0.000 0.000
#> GSM1105512 3 0.5889 0.2753 0.184 0.000 0.560 0.020 0.236 0.000
#> GSM1105532 3 0.3505 0.4574 0.008 0.000 0.812 0.124 0.000 0.056
#> GSM1105541 3 0.4886 0.3625 0.056 0.000 0.660 0.016 0.004 0.264
#> GSM1105439 2 0.1152 0.5823 0.000 0.952 0.004 0.044 0.000 0.000
#> GSM1105463 6 0.0767 0.7051 0.012 0.000 0.008 0.004 0.000 0.976
#> GSM1105482 1 0.5666 0.3966 0.584 0.000 0.236 0.008 0.168 0.004
#> GSM1105483 4 0.5539 0.1575 0.016 0.084 0.436 0.464 0.000 0.000
#> GSM1105494 2 0.6234 -0.0414 0.232 0.404 0.000 0.356 0.004 0.004
#> GSM1105503 6 0.6691 0.3522 0.016 0.168 0.044 0.264 0.000 0.508
#> GSM1105507 3 0.7373 0.2615 0.224 0.028 0.480 0.160 0.108 0.000
#> GSM1105446 2 0.4227 0.5396 0.016 0.764 0.000 0.108 0.112 0.000
#> GSM1105519 3 0.6818 0.3260 0.256 0.000 0.516 0.124 0.092 0.012
#> GSM1105526 4 0.5659 0.3943 0.008 0.096 0.140 0.672 0.084 0.000
#> GSM1105527 4 0.5847 0.3109 0.016 0.132 0.352 0.500 0.000 0.000
#> GSM1105531 6 0.0777 0.7067 0.000 0.000 0.024 0.004 0.000 0.972
#> GSM1105543 2 0.6223 0.1006 0.020 0.444 0.000 0.356 0.180 0.000
#> GSM1105546 1 0.2737 0.5369 0.832 0.000 0.160 0.004 0.004 0.000
#> GSM1105547 1 0.6101 0.2142 0.416 0.000 0.200 0.008 0.376 0.000
#> GSM1105455 2 0.1225 0.5815 0.012 0.952 0.000 0.036 0.000 0.000
#> GSM1105458 2 0.3065 0.5781 0.024 0.852 0.004 0.108 0.008 0.004
#> GSM1105459 2 0.4391 0.4768 0.004 0.720 0.000 0.188 0.088 0.000
#> GSM1105462 4 0.6145 0.0468 0.000 0.000 0.288 0.456 0.008 0.248
#> GSM1105441 2 0.1931 0.5863 0.004 0.916 0.004 0.068 0.008 0.000
#> GSM1105465 5 0.5324 0.6255 0.132 0.004 0.000 0.272 0.592 0.000
#> GSM1105484 5 0.4822 0.5834 0.004 0.072 0.000 0.296 0.628 0.000
#> GSM1105485 5 0.3122 0.6575 0.020 0.000 0.000 0.176 0.804 0.000
#> GSM1105496 6 0.7083 0.3637 0.308 0.180 0.000 0.076 0.008 0.428
#> GSM1105505 6 0.1080 0.7085 0.000 0.004 0.032 0.000 0.004 0.960
#> GSM1105509 3 0.4720 0.4516 0.076 0.000 0.744 0.072 0.108 0.000
#> GSM1105448 2 0.4828 0.4354 0.004 0.676 0.000 0.196 0.124 0.000
#> GSM1105521 3 0.5529 0.3970 0.108 0.000 0.656 0.044 0.188 0.004
#> GSM1105528 5 0.4702 0.6001 0.004 0.076 0.000 0.260 0.660 0.000
#> GSM1105529 5 0.5528 0.5943 0.084 0.024 0.000 0.336 0.556 0.000
#> GSM1105533 1 0.6108 0.0566 0.408 0.000 0.356 0.004 0.000 0.232
#> GSM1105545 4 0.5735 0.4009 0.004 0.172 0.136 0.640 0.048 0.000
#> GSM1105548 1 0.2483 0.5029 0.904 0.000 0.016 0.024 0.036 0.020
#> GSM1105549 5 0.3668 0.4668 0.084 0.000 0.088 0.016 0.812 0.000
#> GSM1105457 2 0.3062 0.5041 0.000 0.816 0.024 0.160 0.000 0.000
#> GSM1105460 2 0.4728 0.5046 0.004 0.724 0.080 0.168 0.024 0.000
#> GSM1105461 2 0.3794 0.5384 0.004 0.788 0.004 0.144 0.060 0.000
#> GSM1105464 3 0.3076 0.4690 0.024 0.000 0.872 0.032 0.020 0.052
#> GSM1105466 2 0.5466 -0.1704 0.000 0.472 0.124 0.404 0.000 0.000
#> GSM1105479 2 0.4758 -0.1419 0.000 0.476 0.048 0.476 0.000 0.000
#> GSM1105502 3 0.5411 0.2828 0.256 0.000 0.588 0.004 0.000 0.152
#> GSM1105515 1 0.4715 0.2485 0.544 0.000 0.416 0.008 0.032 0.000
#> GSM1105523 3 0.5577 -0.0995 0.004 0.008 0.460 0.436 0.000 0.092
#> GSM1105550 3 0.4487 0.2977 0.020 0.028 0.692 0.256 0.000 0.004
#> GSM1105450 2 0.4596 0.3166 0.008 0.616 0.000 0.340 0.036 0.000
#> GSM1105451 2 0.1346 0.5912 0.008 0.952 0.000 0.024 0.016 0.000
#> GSM1105454 6 0.4087 0.5380 0.028 0.276 0.000 0.004 0.000 0.692
#> GSM1105468 2 0.5190 0.1586 0.004 0.524 0.000 0.392 0.080 0.000
#> GSM1105481 6 0.4453 0.4801 0.000 0.000 0.032 0.296 0.012 0.660
#> GSM1105504 6 0.2848 0.6482 0.000 0.000 0.160 0.008 0.004 0.828
#> GSM1105517 3 0.3802 0.4378 0.036 0.000 0.772 0.180 0.012 0.000
#> GSM1105525 3 0.5920 0.1632 0.060 0.000 0.516 0.356 0.000 0.068
#> GSM1105552 5 0.7620 0.4529 0.060 0.004 0.048 0.228 0.444 0.216
#> GSM1105452 5 0.5768 0.6066 0.060 0.064 0.000 0.316 0.560 0.000
#> GSM1105453 2 0.2796 0.5804 0.008 0.868 0.000 0.044 0.080 0.000
#> GSM1105456 6 0.1707 0.6984 0.012 0.056 0.000 0.004 0.000 0.928
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 agent(p) other(p) time(p) individual(p) k
#> SD:NMF 117 0.8612 0.46773 0.650 0.00544 2
#> SD:NMF 78 0.6435 0.61261 0.170 0.01662 3
#> SD:NMF 104 0.0845 0.28835 0.162 0.01009 4
#> SD:NMF 88 0.2919 0.00571 0.121 0.00789 5
#> SD:NMF 40 0.2531 0.22150 0.524 0.02183 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 44956 rows and 120 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 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 0.553 0.820 0.909 0.4646 0.519 0.519
#> 3 3 0.453 0.627 0.800 0.3476 0.839 0.696
#> 4 4 0.505 0.554 0.768 0.1049 0.882 0.699
#> 5 5 0.539 0.494 0.706 0.0965 0.854 0.558
#> 6 6 0.626 0.481 0.714 0.0528 0.887 0.581
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
#> GSM1105438 2 0.0000 0.9209 0.000 1.000
#> GSM1105486 2 0.0000 0.9209 0.000 1.000
#> GSM1105487 1 0.2948 0.8801 0.948 0.052
#> GSM1105490 2 0.1184 0.9187 0.016 0.984
#> GSM1105491 1 0.4690 0.8439 0.900 0.100
#> GSM1105495 2 0.4431 0.8791 0.092 0.908
#> GSM1105498 2 0.9248 0.4764 0.340 0.660
#> GSM1105499 1 0.3114 0.8804 0.944 0.056
#> GSM1105506 2 0.4815 0.8624 0.104 0.896
#> GSM1105442 2 0.0938 0.9206 0.012 0.988
#> GSM1105511 2 0.1184 0.9187 0.016 0.984
#> GSM1105514 2 0.0000 0.9209 0.000 1.000
#> GSM1105518 2 0.6623 0.8200 0.172 0.828
#> GSM1105522 1 0.3114 0.8804 0.944 0.056
#> GSM1105534 1 0.3114 0.8804 0.944 0.056
#> GSM1105535 1 0.3114 0.8804 0.944 0.056
#> GSM1105538 1 0.8144 0.7241 0.748 0.252
#> GSM1105542 2 0.0938 0.9206 0.012 0.988
#> GSM1105443 2 0.0938 0.9208 0.012 0.988
#> GSM1105551 1 1.0000 0.0343 0.500 0.500
#> GSM1105554 1 0.3114 0.8804 0.944 0.056
#> GSM1105555 1 0.2948 0.8801 0.948 0.052
#> GSM1105447 2 0.0938 0.9208 0.012 0.988
#> GSM1105467 2 0.0938 0.9208 0.012 0.988
#> GSM1105470 2 0.0000 0.9209 0.000 1.000
#> GSM1105471 2 0.4815 0.8763 0.104 0.896
#> GSM1105474 2 0.0000 0.9209 0.000 1.000
#> GSM1105475 2 0.0376 0.9212 0.004 0.996
#> GSM1105440 1 0.3114 0.8804 0.944 0.056
#> GSM1105488 2 0.0938 0.9206 0.012 0.988
#> GSM1105489 1 0.2948 0.8801 0.948 0.052
#> GSM1105492 1 0.3114 0.8804 0.944 0.056
#> GSM1105493 1 0.4562 0.8463 0.904 0.096
#> GSM1105497 2 0.4939 0.8718 0.108 0.892
#> GSM1105500 2 0.9248 0.4764 0.340 0.660
#> GSM1105501 2 0.3274 0.8972 0.060 0.940
#> GSM1105508 2 0.8081 0.6687 0.248 0.752
#> GSM1105444 2 0.0000 0.9209 0.000 1.000
#> GSM1105513 2 0.1184 0.9187 0.016 0.984
#> GSM1105516 2 0.9552 0.3085 0.376 0.624
#> GSM1105520 2 0.6623 0.8200 0.172 0.828
#> GSM1105524 1 0.3114 0.8804 0.944 0.056
#> GSM1105536 2 0.6438 0.7878 0.164 0.836
#> GSM1105537 1 0.3114 0.8804 0.944 0.056
#> GSM1105540 1 0.8144 0.7241 0.748 0.252
#> GSM1105544 2 0.9866 0.1706 0.432 0.568
#> GSM1105445 2 0.0938 0.9208 0.012 0.988
#> GSM1105553 1 1.0000 0.0343 0.500 0.500
#> GSM1105556 1 0.3114 0.8804 0.944 0.056
#> GSM1105557 2 0.1184 0.9187 0.016 0.984
#> GSM1105449 2 0.0938 0.9208 0.012 0.988
#> GSM1105469 2 0.5519 0.8387 0.128 0.872
#> GSM1105472 2 0.0000 0.9209 0.000 1.000
#> GSM1105473 1 0.4815 0.8299 0.896 0.104
#> GSM1105476 2 0.0000 0.9209 0.000 1.000
#> GSM1105477 2 0.0376 0.9212 0.004 0.996
#> GSM1105478 2 0.3879 0.8892 0.076 0.924
#> GSM1105510 2 0.1184 0.9189 0.016 0.984
#> GSM1105530 1 0.0376 0.8595 0.996 0.004
#> GSM1105539 1 0.0000 0.8589 1.000 0.000
#> GSM1105480 2 0.3879 0.8892 0.076 0.924
#> GSM1105512 1 0.3114 0.8804 0.944 0.056
#> GSM1105532 1 0.0376 0.8595 0.996 0.004
#> GSM1105541 1 0.0000 0.8589 1.000 0.000
#> GSM1105439 2 0.0000 0.9209 0.000 1.000
#> GSM1105463 1 0.1184 0.8616 0.984 0.016
#> GSM1105482 1 0.2948 0.8801 0.948 0.052
#> GSM1105483 2 0.5519 0.8387 0.128 0.872
#> GSM1105494 2 0.9286 0.4804 0.344 0.656
#> GSM1105503 2 0.7453 0.7734 0.212 0.788
#> GSM1105507 1 0.8861 0.6514 0.696 0.304
#> GSM1105446 2 0.0000 0.9209 0.000 1.000
#> GSM1105519 1 0.7299 0.7769 0.796 0.204
#> GSM1105526 2 0.0376 0.9212 0.004 0.996
#> GSM1105527 2 0.5519 0.8387 0.128 0.872
#> GSM1105531 1 0.1414 0.8627 0.980 0.020
#> GSM1105543 2 0.0376 0.9212 0.004 0.996
#> GSM1105546 1 0.2948 0.8801 0.948 0.052
#> GSM1105547 1 0.3274 0.8795 0.940 0.060
#> GSM1105455 2 0.0000 0.9209 0.000 1.000
#> GSM1105458 2 0.0672 0.9211 0.008 0.992
#> GSM1105459 2 0.0000 0.9209 0.000 1.000
#> GSM1105462 1 0.0672 0.8620 0.992 0.008
#> GSM1105441 2 0.0000 0.9209 0.000 1.000
#> GSM1105465 2 0.1184 0.9198 0.016 0.984
#> GSM1105484 2 0.0000 0.9209 0.000 1.000
#> GSM1105485 2 0.0938 0.9206 0.012 0.988
#> GSM1105496 2 0.9286 0.4804 0.344 0.656
#> GSM1105505 2 0.7453 0.7734 0.212 0.788
#> GSM1105509 1 0.8861 0.6514 0.696 0.304
#> GSM1105448 2 0.0000 0.9209 0.000 1.000
#> GSM1105521 1 0.7299 0.7769 0.796 0.204
#> GSM1105528 2 0.0376 0.9212 0.004 0.996
#> GSM1105529 2 0.0938 0.9206 0.012 0.988
#> GSM1105533 1 0.0000 0.8589 1.000 0.000
#> GSM1105545 2 0.5629 0.8214 0.132 0.868
#> GSM1105548 1 0.2948 0.8801 0.948 0.052
#> GSM1105549 1 0.3274 0.8795 0.940 0.060
#> GSM1105457 2 0.0000 0.9209 0.000 1.000
#> GSM1105460 2 0.0672 0.9211 0.008 0.992
#> GSM1105461 2 0.0000 0.9209 0.000 1.000
#> GSM1105464 1 0.0672 0.8620 0.992 0.008
#> GSM1105466 2 0.3114 0.8972 0.056 0.944
#> GSM1105479 2 0.3584 0.8904 0.068 0.932
#> GSM1105502 1 0.3114 0.8507 0.944 0.056
#> GSM1105515 1 0.3114 0.8804 0.944 0.056
#> GSM1105523 1 0.9998 -0.0517 0.508 0.492
#> GSM1105550 1 0.8555 0.6753 0.720 0.280
#> GSM1105450 2 0.0000 0.9209 0.000 1.000
#> GSM1105451 2 0.0000 0.9209 0.000 1.000
#> GSM1105454 2 0.3879 0.8843 0.076 0.924
#> GSM1105468 2 0.0000 0.9209 0.000 1.000
#> GSM1105481 2 0.3733 0.8872 0.072 0.928
#> GSM1105504 1 0.3114 0.8507 0.944 0.056
#> GSM1105517 1 0.7453 0.7684 0.788 0.212
#> GSM1105525 1 0.9998 -0.0517 0.508 0.492
#> GSM1105552 1 0.8555 0.6753 0.720 0.280
#> GSM1105452 2 0.0376 0.9208 0.004 0.996
#> GSM1105453 2 0.0000 0.9209 0.000 1.000
#> GSM1105456 2 0.3879 0.8843 0.076 0.924
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1105438 2 0.0000 0.75944 0.000 1.000 0.000
#> GSM1105486 2 0.0000 0.75944 0.000 1.000 0.000
#> GSM1105487 1 0.1411 0.83202 0.964 0.000 0.036
#> GSM1105490 2 0.5497 0.55876 0.000 0.708 0.292
#> GSM1105491 1 0.5536 0.76175 0.752 0.012 0.236
#> GSM1105495 2 0.5882 0.34229 0.000 0.652 0.348
#> GSM1105498 3 0.8853 0.63294 0.252 0.176 0.572
#> GSM1105499 1 0.1289 0.83075 0.968 0.000 0.032
#> GSM1105506 2 0.8518 -0.07280 0.092 0.472 0.436
#> GSM1105442 2 0.3846 0.68685 0.016 0.876 0.108
#> GSM1105511 2 0.5497 0.55876 0.000 0.708 0.292
#> GSM1105514 2 0.0000 0.75944 0.000 1.000 0.000
#> GSM1105518 3 0.4953 0.56012 0.016 0.176 0.808
#> GSM1105522 1 0.1289 0.83075 0.968 0.000 0.032
#> GSM1105534 1 0.1289 0.83075 0.968 0.000 0.032
#> GSM1105535 1 0.1289 0.83075 0.968 0.000 0.032
#> GSM1105538 1 0.7360 0.55918 0.692 0.096 0.212
#> GSM1105542 2 0.2492 0.73627 0.016 0.936 0.048
#> GSM1105443 2 0.5536 0.62796 0.012 0.752 0.236
#> GSM1105551 3 0.6298 0.28205 0.388 0.004 0.608
#> GSM1105554 1 0.1289 0.83075 0.968 0.000 0.032
#> GSM1105555 1 0.1411 0.83202 0.964 0.000 0.036
#> GSM1105447 2 0.5450 0.63713 0.012 0.760 0.228
#> GSM1105467 2 0.1015 0.75742 0.008 0.980 0.012
#> GSM1105470 2 0.0000 0.75944 0.000 1.000 0.000
#> GSM1105471 3 0.6935 0.27202 0.024 0.372 0.604
#> GSM1105474 2 0.0000 0.75944 0.000 1.000 0.000
#> GSM1105475 2 0.3686 0.70598 0.000 0.860 0.140
#> GSM1105440 1 0.1289 0.83075 0.968 0.000 0.032
#> GSM1105488 2 0.2492 0.73627 0.016 0.936 0.048
#> GSM1105489 1 0.1411 0.83202 0.964 0.000 0.036
#> GSM1105492 1 0.1289 0.83075 0.968 0.000 0.032
#> GSM1105493 1 0.5378 0.76564 0.756 0.008 0.236
#> GSM1105497 2 0.6381 0.33447 0.012 0.648 0.340
#> GSM1105500 3 0.8853 0.63294 0.252 0.176 0.572
#> GSM1105501 2 0.6798 0.54627 0.048 0.696 0.256
#> GSM1105508 3 0.9211 0.47436 0.176 0.312 0.512
#> GSM1105444 2 0.1964 0.73041 0.000 0.944 0.056
#> GSM1105513 2 0.5497 0.55876 0.000 0.708 0.292
#> GSM1105516 2 0.9613 -0.19345 0.308 0.464 0.228
#> GSM1105520 3 0.4953 0.56012 0.016 0.176 0.808
#> GSM1105524 1 0.1289 0.83075 0.968 0.000 0.032
#> GSM1105536 2 0.7703 0.43025 0.104 0.664 0.232
#> GSM1105537 1 0.1289 0.83075 0.968 0.000 0.032
#> GSM1105540 1 0.7360 0.55918 0.692 0.096 0.212
#> GSM1105544 3 0.9870 0.42527 0.364 0.256 0.380
#> GSM1105445 2 0.5536 0.62796 0.012 0.752 0.236
#> GSM1105553 3 0.6298 0.28205 0.388 0.004 0.608
#> GSM1105556 1 0.1289 0.83075 0.968 0.000 0.032
#> GSM1105557 2 0.5497 0.55876 0.000 0.708 0.292
#> GSM1105449 2 0.5012 0.66122 0.008 0.788 0.204
#> GSM1105469 3 0.8549 0.32261 0.100 0.384 0.516
#> GSM1105472 2 0.0000 0.75944 0.000 1.000 0.000
#> GSM1105473 1 0.5692 0.73147 0.724 0.008 0.268
#> GSM1105476 2 0.0000 0.75944 0.000 1.000 0.000
#> GSM1105477 2 0.3686 0.70598 0.000 0.860 0.140
#> GSM1105478 2 0.7909 0.00118 0.056 0.496 0.448
#> GSM1105510 2 0.2116 0.74094 0.012 0.948 0.040
#> GSM1105530 1 0.4399 0.79681 0.812 0.000 0.188
#> GSM1105539 1 0.4346 0.79912 0.816 0.000 0.184
#> GSM1105480 2 0.7909 0.00118 0.056 0.496 0.448
#> GSM1105512 1 0.1289 0.83075 0.968 0.000 0.032
#> GSM1105532 1 0.4399 0.79681 0.812 0.000 0.188
#> GSM1105541 1 0.4346 0.79912 0.816 0.000 0.184
#> GSM1105439 2 0.4750 0.65295 0.000 0.784 0.216
#> GSM1105463 1 0.4555 0.79147 0.800 0.000 0.200
#> GSM1105482 1 0.1529 0.83131 0.960 0.000 0.040
#> GSM1105483 3 0.8549 0.32261 0.100 0.384 0.516
#> GSM1105494 3 0.8748 0.63694 0.244 0.172 0.584
#> GSM1105503 3 0.5466 0.58434 0.040 0.160 0.800
#> GSM1105507 1 0.8266 0.42430 0.624 0.136 0.240
#> GSM1105446 2 0.0424 0.75755 0.000 0.992 0.008
#> GSM1105519 1 0.5894 0.65725 0.752 0.028 0.220
#> GSM1105526 2 0.0592 0.75797 0.000 0.988 0.012
#> GSM1105527 3 0.8549 0.32261 0.100 0.384 0.516
#> GSM1105531 1 0.4605 0.79047 0.796 0.000 0.204
#> GSM1105543 2 0.0592 0.75820 0.000 0.988 0.012
#> GSM1105546 1 0.1529 0.83200 0.960 0.000 0.040
#> GSM1105547 1 0.1399 0.83184 0.968 0.004 0.028
#> GSM1105455 2 0.4974 0.63555 0.000 0.764 0.236
#> GSM1105458 2 0.5450 0.63467 0.012 0.760 0.228
#> GSM1105459 2 0.0000 0.75944 0.000 1.000 0.000
#> GSM1105462 1 0.4291 0.80111 0.820 0.000 0.180
#> GSM1105441 2 0.4750 0.65295 0.000 0.784 0.216
#> GSM1105465 2 0.4068 0.67440 0.016 0.864 0.120
#> GSM1105484 2 0.2682 0.71912 0.004 0.920 0.076
#> GSM1105485 2 0.2492 0.73627 0.016 0.936 0.048
#> GSM1105496 3 0.8748 0.63694 0.244 0.172 0.584
#> GSM1105505 3 0.5466 0.58434 0.040 0.160 0.800
#> GSM1105509 1 0.8266 0.42430 0.624 0.136 0.240
#> GSM1105448 2 0.0000 0.75944 0.000 1.000 0.000
#> GSM1105521 1 0.5894 0.65725 0.752 0.028 0.220
#> GSM1105528 2 0.0592 0.75797 0.000 0.988 0.012
#> GSM1105529 2 0.2492 0.73627 0.016 0.936 0.048
#> GSM1105533 1 0.3340 0.81055 0.880 0.000 0.120
#> GSM1105545 2 0.7011 0.53316 0.092 0.720 0.188
#> GSM1105548 1 0.1529 0.83200 0.960 0.000 0.040
#> GSM1105549 1 0.1399 0.83184 0.968 0.004 0.028
#> GSM1105457 2 0.4974 0.63555 0.000 0.764 0.236
#> GSM1105460 2 0.5450 0.63467 0.012 0.760 0.228
#> GSM1105461 2 0.0000 0.75944 0.000 1.000 0.000
#> GSM1105464 1 0.4291 0.80111 0.820 0.000 0.180
#> GSM1105466 2 0.7223 0.17051 0.028 0.548 0.424
#> GSM1105479 2 0.6168 0.22826 0.000 0.588 0.412
#> GSM1105502 1 0.5325 0.75468 0.748 0.004 0.248
#> GSM1105515 1 0.1289 0.83075 0.968 0.000 0.032
#> GSM1105523 3 0.5754 0.40818 0.296 0.004 0.700
#> GSM1105550 1 0.8104 0.46328 0.616 0.104 0.280
#> GSM1105450 2 0.0000 0.75944 0.000 1.000 0.000
#> GSM1105451 2 0.0000 0.75944 0.000 1.000 0.000
#> GSM1105454 3 0.6168 0.11938 0.000 0.412 0.588
#> GSM1105468 2 0.0000 0.75944 0.000 1.000 0.000
#> GSM1105481 2 0.6180 0.21801 0.000 0.584 0.416
#> GSM1105504 1 0.5325 0.75468 0.748 0.004 0.248
#> GSM1105517 1 0.5982 0.64731 0.744 0.028 0.228
#> GSM1105525 3 0.5754 0.40818 0.296 0.004 0.700
#> GSM1105552 1 0.8104 0.46328 0.616 0.104 0.280
#> GSM1105452 2 0.1529 0.74469 0.000 0.960 0.040
#> GSM1105453 2 0.0000 0.75944 0.000 1.000 0.000
#> GSM1105456 3 0.6168 0.11938 0.000 0.412 0.588
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1105438 2 0.0000 0.7633 0.000 1.000 0.000 0.000
#> GSM1105486 2 0.0000 0.7633 0.000 1.000 0.000 0.000
#> GSM1105487 1 0.3907 0.6264 0.768 0.000 0.232 0.000
#> GSM1105490 2 0.4837 0.4473 0.000 0.648 0.004 0.348
#> GSM1105491 3 0.1743 0.7831 0.000 0.004 0.940 0.056
#> GSM1105495 2 0.6711 0.1530 0.000 0.576 0.116 0.308
#> GSM1105498 4 0.8354 0.4997 0.092 0.152 0.204 0.552
#> GSM1105499 1 0.0188 0.7395 0.996 0.000 0.004 0.000
#> GSM1105506 4 0.6931 0.2137 0.076 0.412 0.012 0.500
#> GSM1105442 2 0.3734 0.6540 0.000 0.848 0.044 0.108
#> GSM1105511 2 0.4837 0.4473 0.000 0.648 0.004 0.348
#> GSM1105514 2 0.0000 0.7633 0.000 1.000 0.000 0.000
#> GSM1105518 4 0.3947 0.4818 0.004 0.072 0.076 0.848
#> GSM1105522 1 0.0000 0.7397 1.000 0.000 0.000 0.000
#> GSM1105534 1 0.0000 0.7397 1.000 0.000 0.000 0.000
#> GSM1105535 1 0.0000 0.7397 1.000 0.000 0.000 0.000
#> GSM1105538 1 0.9137 -0.0227 0.364 0.092 0.360 0.184
#> GSM1105542 2 0.2313 0.7282 0.000 0.924 0.044 0.032
#> GSM1105443 2 0.4923 0.5344 0.008 0.684 0.004 0.304
#> GSM1105551 4 0.6876 0.1025 0.116 0.000 0.352 0.532
#> GSM1105554 1 0.0188 0.7395 0.996 0.000 0.004 0.000
#> GSM1105555 1 0.3907 0.6264 0.768 0.000 0.232 0.000
#> GSM1105447 2 0.4899 0.5450 0.008 0.688 0.004 0.300
#> GSM1105467 2 0.1109 0.7585 0.004 0.968 0.000 0.028
#> GSM1105470 2 0.0000 0.7633 0.000 1.000 0.000 0.000
#> GSM1105471 4 0.5759 0.4240 0.000 0.268 0.064 0.668
#> GSM1105474 2 0.0000 0.7633 0.000 1.000 0.000 0.000
#> GSM1105475 2 0.3311 0.6719 0.000 0.828 0.000 0.172
#> GSM1105440 1 0.0000 0.7397 1.000 0.000 0.000 0.000
#> GSM1105488 2 0.2313 0.7282 0.000 0.924 0.044 0.032
#> GSM1105489 1 0.3907 0.6264 0.768 0.000 0.232 0.000
#> GSM1105492 1 0.0188 0.7392 0.996 0.000 0.000 0.004
#> GSM1105493 3 0.1743 0.7866 0.004 0.000 0.940 0.056
#> GSM1105497 2 0.6813 0.1525 0.000 0.576 0.132 0.292
#> GSM1105500 4 0.8354 0.4997 0.092 0.152 0.204 0.552
#> GSM1105501 2 0.6141 0.4728 0.040 0.656 0.024 0.280
#> GSM1105508 4 0.8500 0.4637 0.096 0.296 0.112 0.496
#> GSM1105444 2 0.1867 0.7169 0.000 0.928 0.000 0.072
#> GSM1105513 2 0.4837 0.4473 0.000 0.648 0.004 0.348
#> GSM1105516 2 0.9206 -0.1122 0.156 0.464 0.176 0.204
#> GSM1105520 4 0.3947 0.4818 0.004 0.072 0.076 0.848
#> GSM1105524 1 0.0000 0.7397 1.000 0.000 0.000 0.000
#> GSM1105536 2 0.6570 0.4090 0.016 0.652 0.096 0.236
#> GSM1105537 1 0.0000 0.7397 1.000 0.000 0.000 0.000
#> GSM1105540 1 0.9137 -0.0227 0.364 0.092 0.360 0.184
#> GSM1105544 4 0.9627 0.3164 0.132 0.248 0.272 0.348
#> GSM1105445 2 0.4923 0.5344 0.008 0.684 0.004 0.304
#> GSM1105553 4 0.6876 0.1025 0.116 0.000 0.352 0.532
#> GSM1105556 1 0.0188 0.7395 0.996 0.000 0.004 0.000
#> GSM1105557 2 0.4837 0.4473 0.000 0.648 0.004 0.348
#> GSM1105449 2 0.4551 0.5875 0.004 0.724 0.004 0.268
#> GSM1105469 4 0.6490 0.4136 0.080 0.324 0.004 0.592
#> GSM1105472 2 0.0000 0.7633 0.000 1.000 0.000 0.000
#> GSM1105473 3 0.4569 0.7706 0.052 0.004 0.800 0.144
#> GSM1105476 2 0.0000 0.7633 0.000 1.000 0.000 0.000
#> GSM1105477 2 0.3311 0.6719 0.000 0.828 0.000 0.172
#> GSM1105478 4 0.6194 0.2076 0.036 0.428 0.008 0.528
#> GSM1105510 2 0.1722 0.7398 0.000 0.944 0.048 0.008
#> GSM1105530 3 0.2521 0.8481 0.064 0.000 0.912 0.024
#> GSM1105539 3 0.2376 0.8451 0.068 0.000 0.916 0.016
#> GSM1105480 4 0.6194 0.2076 0.036 0.428 0.008 0.528
#> GSM1105512 1 0.0188 0.7395 0.996 0.000 0.004 0.000
#> GSM1105532 3 0.2521 0.8481 0.064 0.000 0.912 0.024
#> GSM1105541 3 0.2376 0.8451 0.068 0.000 0.916 0.016
#> GSM1105439 2 0.4428 0.5741 0.000 0.720 0.004 0.276
#> GSM1105463 3 0.2644 0.8469 0.060 0.000 0.908 0.032
#> GSM1105482 1 0.1867 0.7203 0.928 0.000 0.072 0.000
#> GSM1105483 4 0.6490 0.4136 0.080 0.324 0.004 0.592
#> GSM1105494 4 0.8234 0.5013 0.088 0.148 0.200 0.564
#> GSM1105503 4 0.4734 0.4714 0.004 0.072 0.128 0.796
#> GSM1105507 1 0.9419 0.0946 0.404 0.132 0.268 0.196
#> GSM1105446 2 0.0672 0.7628 0.000 0.984 0.008 0.008
#> GSM1105519 1 0.8012 0.2031 0.500 0.028 0.300 0.172
#> GSM1105526 2 0.0469 0.7615 0.000 0.988 0.000 0.012
#> GSM1105527 4 0.6490 0.4136 0.080 0.324 0.004 0.592
#> GSM1105531 3 0.2660 0.8461 0.056 0.000 0.908 0.036
#> GSM1105543 2 0.0707 0.7604 0.000 0.980 0.000 0.020
#> GSM1105546 1 0.3975 0.6199 0.760 0.000 0.240 0.000
#> GSM1105547 1 0.2773 0.7052 0.880 0.000 0.116 0.004
#> GSM1105455 2 0.4560 0.5498 0.000 0.700 0.004 0.296
#> GSM1105458 2 0.4875 0.5442 0.008 0.692 0.004 0.296
#> GSM1105459 2 0.0188 0.7633 0.000 0.996 0.004 0.000
#> GSM1105462 3 0.2773 0.8461 0.072 0.000 0.900 0.028
#> GSM1105441 2 0.4428 0.5741 0.000 0.720 0.004 0.276
#> GSM1105465 2 0.3934 0.6387 0.000 0.836 0.048 0.116
#> GSM1105484 2 0.2775 0.6975 0.000 0.896 0.020 0.084
#> GSM1105485 2 0.2313 0.7282 0.000 0.924 0.044 0.032
#> GSM1105496 4 0.8234 0.5013 0.088 0.148 0.200 0.564
#> GSM1105505 4 0.4734 0.4714 0.004 0.072 0.128 0.796
#> GSM1105509 1 0.9419 0.0946 0.404 0.132 0.268 0.196
#> GSM1105448 2 0.0000 0.7633 0.000 1.000 0.000 0.000
#> GSM1105521 1 0.8012 0.2031 0.500 0.028 0.300 0.172
#> GSM1105528 2 0.0469 0.7615 0.000 0.988 0.000 0.012
#> GSM1105529 2 0.2313 0.7282 0.000 0.924 0.044 0.032
#> GSM1105533 1 0.4872 0.4337 0.640 0.000 0.356 0.004
#> GSM1105545 2 0.5911 0.5173 0.008 0.708 0.092 0.192
#> GSM1105548 1 0.3975 0.6199 0.760 0.000 0.240 0.000
#> GSM1105549 1 0.2773 0.7052 0.880 0.000 0.116 0.004
#> GSM1105457 2 0.4560 0.5498 0.000 0.700 0.004 0.296
#> GSM1105460 2 0.4875 0.5442 0.008 0.692 0.004 0.296
#> GSM1105461 2 0.0188 0.7633 0.000 0.996 0.004 0.000
#> GSM1105464 3 0.2773 0.8461 0.072 0.000 0.900 0.028
#> GSM1105466 4 0.5607 0.0453 0.020 0.484 0.000 0.496
#> GSM1105479 4 0.5937 0.0514 0.000 0.472 0.036 0.492
#> GSM1105502 3 0.3966 0.8102 0.072 0.000 0.840 0.088
#> GSM1105515 1 0.0000 0.7397 1.000 0.000 0.000 0.000
#> GSM1105523 4 0.6440 0.1654 0.080 0.000 0.356 0.564
#> GSM1105550 3 0.9322 0.0802 0.292 0.100 0.384 0.224
#> GSM1105450 2 0.0000 0.7633 0.000 1.000 0.000 0.000
#> GSM1105451 2 0.0000 0.7633 0.000 1.000 0.000 0.000
#> GSM1105454 4 0.5312 0.3756 0.000 0.268 0.040 0.692
#> GSM1105468 2 0.0188 0.7633 0.000 0.996 0.000 0.004
#> GSM1105481 4 0.5935 0.0603 0.000 0.468 0.036 0.496
#> GSM1105504 3 0.3966 0.8102 0.072 0.000 0.840 0.088
#> GSM1105517 1 0.8099 0.1730 0.484 0.028 0.308 0.180
#> GSM1105525 4 0.6440 0.1654 0.080 0.000 0.356 0.564
#> GSM1105552 3 0.9322 0.0802 0.292 0.100 0.384 0.224
#> GSM1105452 2 0.1452 0.7445 0.000 0.956 0.036 0.008
#> GSM1105453 2 0.0000 0.7633 0.000 1.000 0.000 0.000
#> GSM1105456 4 0.5312 0.3756 0.000 0.268 0.040 0.692
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM1105438 2 0.0000 0.7824 0.000 1.000 0.000 0.000 0.000
#> GSM1105486 2 0.0162 0.7814 0.000 0.996 0.000 0.004 0.000
#> GSM1105487 1 0.4328 0.6116 0.724 0.000 0.248 0.020 0.008
#> GSM1105490 4 0.4537 0.4109 0.000 0.396 0.000 0.592 0.012
#> GSM1105491 3 0.3814 0.6788 0.000 0.004 0.816 0.116 0.064
#> GSM1105495 5 0.6019 0.1677 0.000 0.440 0.064 0.020 0.476
#> GSM1105498 4 0.7424 0.1149 0.024 0.068 0.104 0.536 0.268
#> GSM1105499 1 0.0162 0.7585 0.996 0.000 0.000 0.004 0.000
#> GSM1105506 4 0.4986 0.4239 0.024 0.180 0.000 0.732 0.064
#> GSM1105442 2 0.4450 0.5782 0.000 0.736 0.004 0.044 0.216
#> GSM1105511 4 0.4537 0.4109 0.000 0.396 0.000 0.592 0.012
#> GSM1105514 2 0.0162 0.7812 0.000 0.996 0.000 0.004 0.000
#> GSM1105518 5 0.5162 0.4519 0.000 0.032 0.020 0.300 0.648
#> GSM1105522 1 0.0000 0.7593 1.000 0.000 0.000 0.000 0.000
#> GSM1105534 1 0.0000 0.7593 1.000 0.000 0.000 0.000 0.000
#> GSM1105535 1 0.0000 0.7593 1.000 0.000 0.000 0.000 0.000
#> GSM1105538 3 0.8650 0.0911 0.304 0.032 0.308 0.276 0.080
#> GSM1105542 2 0.3960 0.6771 0.000 0.800 0.004 0.056 0.140
#> GSM1105443 4 0.5271 0.3262 0.000 0.432 0.000 0.520 0.048
#> GSM1105551 4 0.7596 -0.1173 0.044 0.000 0.276 0.376 0.304
#> GSM1105554 1 0.0162 0.7585 0.996 0.000 0.000 0.004 0.000
#> GSM1105555 1 0.4328 0.6116 0.724 0.000 0.248 0.020 0.008
#> GSM1105447 4 0.5341 0.3054 0.000 0.444 0.000 0.504 0.052
#> GSM1105467 2 0.1956 0.7442 0.000 0.916 0.000 0.076 0.008
#> GSM1105470 2 0.0000 0.7824 0.000 1.000 0.000 0.000 0.000
#> GSM1105471 5 0.6814 0.5002 0.000 0.148 0.052 0.232 0.568
#> GSM1105474 2 0.0000 0.7824 0.000 1.000 0.000 0.000 0.000
#> GSM1105475 2 0.4251 0.3395 0.000 0.672 0.000 0.316 0.012
#> GSM1105440 1 0.0000 0.7593 1.000 0.000 0.000 0.000 0.000
#> GSM1105488 2 0.3960 0.6771 0.000 0.800 0.004 0.056 0.140
#> GSM1105489 1 0.4328 0.6116 0.724 0.000 0.248 0.020 0.008
#> GSM1105492 1 0.0162 0.7584 0.996 0.000 0.000 0.004 0.000
#> GSM1105493 3 0.3814 0.6808 0.004 0.000 0.816 0.116 0.064
#> GSM1105497 5 0.6262 0.1644 0.000 0.436 0.072 0.028 0.464
#> GSM1105500 4 0.7424 0.1149 0.024 0.068 0.104 0.536 0.268
#> GSM1105501 4 0.5278 0.3617 0.016 0.408 0.000 0.552 0.024
#> GSM1105508 4 0.6860 0.3334 0.040 0.108 0.060 0.644 0.148
#> GSM1105444 2 0.1608 0.7406 0.000 0.928 0.000 0.000 0.072
#> GSM1105513 4 0.4537 0.4109 0.000 0.396 0.000 0.592 0.012
#> GSM1105516 4 0.9240 0.2700 0.144 0.292 0.152 0.336 0.076
#> GSM1105520 5 0.5162 0.4519 0.000 0.032 0.020 0.300 0.648
#> GSM1105524 1 0.0000 0.7593 1.000 0.000 0.000 0.000 0.000
#> GSM1105536 2 0.6758 0.0283 0.004 0.520 0.100 0.336 0.040
#> GSM1105537 1 0.0000 0.7593 1.000 0.000 0.000 0.000 0.000
#> GSM1105540 3 0.8650 0.0911 0.304 0.032 0.308 0.276 0.080
#> GSM1105544 4 0.9219 0.0963 0.072 0.152 0.212 0.380 0.184
#> GSM1105445 4 0.5271 0.3262 0.000 0.432 0.000 0.520 0.048
#> GSM1105553 4 0.7596 -0.1173 0.044 0.000 0.276 0.376 0.304
#> GSM1105556 1 0.0162 0.7585 0.996 0.000 0.000 0.004 0.000
#> GSM1105557 4 0.4537 0.4109 0.000 0.396 0.000 0.592 0.012
#> GSM1105449 2 0.5195 -0.0101 0.000 0.564 0.000 0.388 0.048
#> GSM1105469 4 0.4577 0.4021 0.024 0.112 0.000 0.780 0.084
#> GSM1105472 2 0.0000 0.7824 0.000 1.000 0.000 0.000 0.000
#> GSM1105473 3 0.3303 0.6690 0.012 0.004 0.840 0.008 0.136
#> GSM1105476 2 0.0000 0.7824 0.000 1.000 0.000 0.000 0.000
#> GSM1105477 2 0.4251 0.3395 0.000 0.672 0.000 0.316 0.012
#> GSM1105478 4 0.5633 0.4349 0.008 0.212 0.004 0.664 0.112
#> GSM1105510 2 0.3822 0.6986 0.000 0.816 0.012 0.040 0.132
#> GSM1105530 3 0.0451 0.7586 0.008 0.000 0.988 0.000 0.004
#> GSM1105539 3 0.0566 0.7578 0.012 0.000 0.984 0.000 0.004
#> GSM1105480 4 0.5633 0.4349 0.008 0.212 0.004 0.664 0.112
#> GSM1105512 1 0.0162 0.7585 0.996 0.000 0.000 0.004 0.000
#> GSM1105532 3 0.0451 0.7586 0.008 0.000 0.988 0.000 0.004
#> GSM1105541 3 0.0566 0.7578 0.012 0.000 0.984 0.000 0.004
#> GSM1105439 2 0.5151 -0.0511 0.000 0.560 0.000 0.396 0.044
#> GSM1105463 3 0.0898 0.7555 0.008 0.000 0.972 0.000 0.020
#> GSM1105482 1 0.2199 0.7349 0.916 0.000 0.060 0.016 0.008
#> GSM1105483 4 0.4577 0.4021 0.024 0.112 0.000 0.780 0.084
#> GSM1105494 4 0.7418 0.1015 0.020 0.068 0.108 0.528 0.276
#> GSM1105503 5 0.6022 0.4302 0.000 0.032 0.072 0.300 0.596
#> GSM1105507 1 0.8947 -0.0602 0.332 0.060 0.256 0.272 0.080
#> GSM1105446 2 0.2408 0.7538 0.000 0.892 0.000 0.016 0.092
#> GSM1105519 1 0.7928 0.0930 0.444 0.008 0.268 0.200 0.080
#> GSM1105526 2 0.3141 0.7227 0.000 0.852 0.000 0.108 0.040
#> GSM1105527 4 0.4577 0.4021 0.024 0.112 0.000 0.780 0.084
#> GSM1105531 3 0.0833 0.7562 0.004 0.000 0.976 0.004 0.016
#> GSM1105543 2 0.3844 0.6976 0.000 0.804 0.000 0.132 0.064
#> GSM1105546 1 0.4380 0.6030 0.716 0.000 0.256 0.020 0.008
#> GSM1105547 1 0.2756 0.7211 0.880 0.000 0.092 0.024 0.004
#> GSM1105455 4 0.5232 0.2876 0.000 0.456 0.000 0.500 0.044
#> GSM1105458 4 0.5238 0.2463 0.000 0.472 0.000 0.484 0.044
#> GSM1105459 2 0.1357 0.7554 0.000 0.948 0.000 0.048 0.004
#> GSM1105462 3 0.0968 0.7601 0.012 0.000 0.972 0.012 0.004
#> GSM1105441 2 0.5151 -0.0511 0.000 0.560 0.000 0.396 0.044
#> GSM1105465 2 0.4538 0.5598 0.000 0.724 0.004 0.044 0.228
#> GSM1105484 2 0.3438 0.6654 0.000 0.808 0.000 0.020 0.172
#> GSM1105485 2 0.3960 0.6771 0.000 0.800 0.004 0.056 0.140
#> GSM1105496 4 0.7418 0.1015 0.020 0.068 0.108 0.528 0.276
#> GSM1105505 5 0.6022 0.4302 0.000 0.032 0.072 0.300 0.596
#> GSM1105509 1 0.8947 -0.0602 0.332 0.060 0.256 0.272 0.080
#> GSM1105448 2 0.0000 0.7824 0.000 1.000 0.000 0.000 0.000
#> GSM1105521 1 0.7928 0.0930 0.444 0.008 0.268 0.200 0.080
#> GSM1105528 2 0.3141 0.7227 0.000 0.852 0.000 0.108 0.040
#> GSM1105529 2 0.3960 0.6771 0.000 0.800 0.004 0.056 0.140
#> GSM1105533 1 0.4341 0.3926 0.592 0.000 0.404 0.000 0.004
#> GSM1105545 2 0.6382 0.2250 0.000 0.580 0.096 0.284 0.040
#> GSM1105548 1 0.4380 0.6030 0.716 0.000 0.256 0.020 0.008
#> GSM1105549 1 0.2756 0.7211 0.880 0.000 0.092 0.024 0.004
#> GSM1105457 4 0.5232 0.2876 0.000 0.456 0.000 0.500 0.044
#> GSM1105460 4 0.5238 0.2463 0.000 0.472 0.000 0.484 0.044
#> GSM1105461 2 0.1357 0.7554 0.000 0.948 0.000 0.048 0.004
#> GSM1105464 3 0.0968 0.7601 0.012 0.000 0.972 0.012 0.004
#> GSM1105466 4 0.5203 0.4652 0.000 0.272 0.000 0.648 0.080
#> GSM1105479 5 0.5580 0.5127 0.000 0.320 0.012 0.064 0.604
#> GSM1105502 3 0.3160 0.7099 0.024 0.000 0.872 0.032 0.072
#> GSM1105515 1 0.0000 0.7593 1.000 0.000 0.000 0.000 0.000
#> GSM1105523 4 0.6906 -0.0874 0.008 0.000 0.356 0.404 0.232
#> GSM1105550 3 0.8743 0.1927 0.232 0.036 0.328 0.312 0.092
#> GSM1105450 2 0.0000 0.7824 0.000 1.000 0.000 0.000 0.000
#> GSM1105451 2 0.0000 0.7824 0.000 1.000 0.000 0.000 0.000
#> GSM1105454 5 0.5183 0.5241 0.000 0.112 0.016 0.152 0.720
#> GSM1105468 2 0.0794 0.7733 0.000 0.972 0.000 0.028 0.000
#> GSM1105481 5 0.5618 0.5152 0.000 0.320 0.016 0.060 0.604
#> GSM1105504 3 0.3160 0.7099 0.024 0.000 0.872 0.032 0.072
#> GSM1105517 1 0.7989 0.0582 0.428 0.008 0.276 0.208 0.080
#> GSM1105525 4 0.6906 -0.0874 0.008 0.000 0.356 0.404 0.232
#> GSM1105552 3 0.8743 0.1927 0.232 0.036 0.328 0.312 0.092
#> GSM1105452 2 0.3433 0.7043 0.000 0.832 0.004 0.032 0.132
#> GSM1105453 2 0.0000 0.7824 0.000 1.000 0.000 0.000 0.000
#> GSM1105456 5 0.5183 0.5241 0.000 0.112 0.016 0.152 0.720
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1105438 2 0.3126 0.7581 0.000 0.752 0.000 0.248 0.000 0.000
#> GSM1105486 2 0.3265 0.7574 0.000 0.748 0.000 0.248 0.004 0.000
#> GSM1105487 1 0.4889 0.5232 0.672 0.000 0.140 0.000 0.184 0.004
#> GSM1105490 4 0.4249 0.6589 0.000 0.116 0.000 0.776 0.048 0.060
#> GSM1105491 3 0.4418 0.6866 0.000 0.044 0.712 0.004 0.228 0.012
#> GSM1105495 2 0.5713 0.1115 0.000 0.564 0.052 0.000 0.068 0.316
#> GSM1105498 6 0.6557 0.2396 0.000 0.020 0.032 0.156 0.276 0.516
#> GSM1105499 1 0.0146 0.6590 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM1105506 4 0.5190 0.3549 0.012 0.000 0.000 0.648 0.204 0.136
#> GSM1105442 2 0.2488 0.6235 0.000 0.880 0.000 0.000 0.044 0.076
#> GSM1105511 4 0.4249 0.6589 0.000 0.116 0.000 0.776 0.048 0.060
#> GSM1105514 2 0.3151 0.7559 0.000 0.748 0.000 0.252 0.000 0.000
#> GSM1105518 6 0.1333 0.4692 0.000 0.000 0.008 0.048 0.000 0.944
#> GSM1105522 1 0.0865 0.6625 0.964 0.000 0.000 0.000 0.036 0.000
#> GSM1105534 1 0.0000 0.6595 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1105535 1 0.0865 0.6625 0.964 0.000 0.000 0.000 0.036 0.000
#> GSM1105538 1 0.8708 -0.8299 0.288 0.020 0.212 0.060 0.288 0.132
#> GSM1105542 2 0.1141 0.6729 0.000 0.948 0.000 0.000 0.052 0.000
#> GSM1105443 4 0.1895 0.6514 0.000 0.072 0.000 0.912 0.000 0.016
#> GSM1105551 6 0.6248 0.0949 0.004 0.000 0.136 0.028 0.376 0.456
#> GSM1105554 1 0.0146 0.6590 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM1105555 1 0.4889 0.5232 0.672 0.000 0.140 0.000 0.184 0.004
#> GSM1105447 4 0.2199 0.6473 0.000 0.088 0.000 0.892 0.000 0.020
#> GSM1105467 2 0.4015 0.6301 0.000 0.616 0.000 0.372 0.000 0.012
#> GSM1105470 2 0.3126 0.7581 0.000 0.752 0.000 0.248 0.000 0.000
#> GSM1105471 6 0.7177 0.3679 0.000 0.076 0.048 0.208 0.144 0.524
#> GSM1105474 2 0.3126 0.7581 0.000 0.752 0.000 0.248 0.000 0.000
#> GSM1105475 4 0.4680 0.1280 0.000 0.384 0.000 0.576 0.028 0.012
#> GSM1105440 1 0.0865 0.6625 0.964 0.000 0.000 0.000 0.036 0.000
#> GSM1105488 2 0.1141 0.6729 0.000 0.948 0.000 0.000 0.052 0.000
#> GSM1105489 1 0.4889 0.5232 0.672 0.000 0.140 0.000 0.184 0.004
#> GSM1105492 1 0.0937 0.6623 0.960 0.000 0.000 0.000 0.040 0.000
#> GSM1105493 3 0.4380 0.6873 0.000 0.040 0.712 0.004 0.232 0.012
#> GSM1105497 2 0.5803 0.1165 0.000 0.568 0.056 0.000 0.076 0.300
#> GSM1105500 6 0.6557 0.2396 0.000 0.020 0.032 0.156 0.276 0.516
#> GSM1105501 4 0.4493 0.5911 0.008 0.076 0.000 0.772 0.048 0.096
#> GSM1105508 4 0.7343 -0.1411 0.028 0.000 0.052 0.396 0.220 0.304
#> GSM1105444 2 0.4588 0.7324 0.000 0.676 0.000 0.248 0.004 0.072
#> GSM1105513 4 0.4249 0.6589 0.000 0.116 0.000 0.776 0.048 0.060
#> GSM1105516 4 0.9382 -0.2245 0.140 0.124 0.148 0.352 0.088 0.148
#> GSM1105520 6 0.1333 0.4692 0.000 0.000 0.008 0.048 0.000 0.944
#> GSM1105524 1 0.0865 0.6625 0.964 0.000 0.000 0.000 0.036 0.000
#> GSM1105536 4 0.7570 0.2499 0.000 0.328 0.096 0.416 0.064 0.096
#> GSM1105537 1 0.0865 0.6625 0.964 0.000 0.000 0.000 0.036 0.000
#> GSM1105540 1 0.8708 -0.8299 0.288 0.020 0.212 0.060 0.288 0.132
#> GSM1105544 6 0.9207 -0.3144 0.052 0.116 0.132 0.128 0.276 0.296
#> GSM1105445 4 0.1895 0.6514 0.000 0.072 0.000 0.912 0.000 0.016
#> GSM1105553 6 0.6248 0.0949 0.004 0.000 0.136 0.028 0.376 0.456
#> GSM1105556 1 0.0146 0.6590 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM1105557 4 0.4249 0.6589 0.000 0.116 0.000 0.776 0.048 0.060
#> GSM1105449 4 0.3617 0.4679 0.000 0.244 0.000 0.736 0.000 0.020
#> GSM1105469 4 0.5637 0.2366 0.012 0.000 0.000 0.584 0.176 0.228
#> GSM1105472 2 0.3126 0.7581 0.000 0.752 0.000 0.248 0.000 0.000
#> GSM1105473 3 0.3149 0.7661 0.008 0.004 0.832 0.004 0.012 0.140
#> GSM1105476 2 0.3126 0.7581 0.000 0.752 0.000 0.248 0.000 0.000
#> GSM1105477 4 0.4680 0.1280 0.000 0.384 0.000 0.576 0.028 0.012
#> GSM1105478 4 0.5261 0.3934 0.000 0.032 0.000 0.656 0.096 0.216
#> GSM1105510 2 0.1788 0.6867 0.000 0.928 0.004 0.028 0.040 0.000
#> GSM1105530 3 0.0146 0.8864 0.004 0.000 0.996 0.000 0.000 0.000
#> GSM1105539 3 0.0935 0.8777 0.004 0.000 0.964 0.000 0.032 0.000
#> GSM1105480 4 0.5261 0.3934 0.000 0.032 0.000 0.656 0.096 0.216
#> GSM1105512 1 0.0146 0.6590 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM1105532 3 0.0146 0.8864 0.004 0.000 0.996 0.000 0.000 0.000
#> GSM1105541 3 0.0935 0.8777 0.004 0.000 0.964 0.000 0.032 0.000
#> GSM1105439 4 0.3337 0.4712 0.000 0.260 0.000 0.736 0.000 0.004
#> GSM1105463 3 0.0665 0.8861 0.004 0.000 0.980 0.000 0.008 0.008
#> GSM1105482 1 0.2046 0.6427 0.908 0.000 0.032 0.000 0.060 0.000
#> GSM1105483 4 0.5637 0.2366 0.012 0.000 0.000 0.584 0.176 0.228
#> GSM1105494 6 0.6590 0.2506 0.000 0.020 0.036 0.156 0.268 0.520
#> GSM1105503 6 0.2442 0.4619 0.000 0.000 0.068 0.048 0.000 0.884
#> GSM1105507 1 0.8751 -0.6775 0.316 0.000 0.192 0.144 0.184 0.164
#> GSM1105446 2 0.2668 0.7410 0.000 0.828 0.000 0.168 0.004 0.000
#> GSM1105519 1 0.7668 -0.5453 0.436 0.000 0.200 0.024 0.188 0.152
#> GSM1105526 2 0.4214 0.6126 0.000 0.652 0.000 0.320 0.024 0.004
#> GSM1105527 4 0.5637 0.2366 0.012 0.000 0.000 0.584 0.176 0.228
#> GSM1105531 3 0.0508 0.8855 0.000 0.000 0.984 0.000 0.004 0.012
#> GSM1105543 2 0.4196 0.5680 0.000 0.640 0.000 0.332 0.028 0.000
#> GSM1105546 1 0.4910 0.5120 0.668 0.000 0.136 0.000 0.192 0.004
#> GSM1105547 1 0.2649 0.6296 0.876 0.000 0.052 0.004 0.068 0.000
#> GSM1105455 4 0.2100 0.6384 0.000 0.112 0.000 0.884 0.000 0.004
#> GSM1105458 4 0.2613 0.6143 0.000 0.140 0.000 0.848 0.000 0.012
#> GSM1105459 2 0.3446 0.7017 0.000 0.692 0.000 0.308 0.000 0.000
#> GSM1105462 3 0.0767 0.8843 0.008 0.000 0.976 0.004 0.012 0.000
#> GSM1105441 4 0.3337 0.4712 0.000 0.260 0.000 0.736 0.000 0.004
#> GSM1105465 2 0.2672 0.6132 0.000 0.868 0.000 0.000 0.052 0.080
#> GSM1105484 2 0.2889 0.6774 0.000 0.868 0.000 0.048 0.016 0.068
#> GSM1105485 2 0.1141 0.6729 0.000 0.948 0.000 0.000 0.052 0.000
#> GSM1105496 6 0.6590 0.2506 0.000 0.020 0.036 0.156 0.268 0.520
#> GSM1105505 6 0.2442 0.4619 0.000 0.000 0.068 0.048 0.000 0.884
#> GSM1105509 1 0.8751 -0.6775 0.316 0.000 0.192 0.144 0.184 0.164
#> GSM1105448 2 0.3126 0.7581 0.000 0.752 0.000 0.248 0.000 0.000
#> GSM1105521 1 0.7668 -0.5453 0.436 0.000 0.200 0.024 0.188 0.152
#> GSM1105528 2 0.4214 0.6126 0.000 0.652 0.000 0.320 0.024 0.004
#> GSM1105529 2 0.1141 0.6729 0.000 0.948 0.000 0.000 0.052 0.000
#> GSM1105533 1 0.4911 0.3278 0.548 0.000 0.384 0.000 0.068 0.000
#> GSM1105545 4 0.7057 0.1498 0.000 0.364 0.092 0.436 0.052 0.056
#> GSM1105548 1 0.4910 0.5120 0.668 0.000 0.136 0.000 0.192 0.004
#> GSM1105549 1 0.2649 0.6296 0.876 0.000 0.052 0.004 0.068 0.000
#> GSM1105457 4 0.2100 0.6384 0.000 0.112 0.000 0.884 0.000 0.004
#> GSM1105460 4 0.2613 0.6143 0.000 0.140 0.000 0.848 0.000 0.012
#> GSM1105461 2 0.3446 0.7017 0.000 0.692 0.000 0.308 0.000 0.000
#> GSM1105464 3 0.0767 0.8843 0.008 0.000 0.976 0.004 0.012 0.000
#> GSM1105466 4 0.5799 0.5129 0.000 0.116 0.000 0.628 0.068 0.188
#> GSM1105479 6 0.6890 0.2450 0.000 0.248 0.000 0.084 0.204 0.464
#> GSM1105502 3 0.2698 0.7971 0.020 0.000 0.872 0.000 0.016 0.092
#> GSM1105515 1 0.0000 0.6595 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1105523 6 0.6537 0.1060 0.000 0.000 0.348 0.028 0.228 0.396
#> GSM1105550 5 0.8944 1.0000 0.216 0.024 0.252 0.068 0.276 0.164
#> GSM1105450 2 0.3126 0.7581 0.000 0.752 0.000 0.248 0.000 0.000
#> GSM1105451 2 0.3126 0.7581 0.000 0.752 0.000 0.248 0.000 0.000
#> GSM1105454 6 0.5090 0.3898 0.000 0.000 0.004 0.096 0.296 0.604
#> GSM1105468 2 0.3390 0.7226 0.000 0.704 0.000 0.296 0.000 0.000
#> GSM1105481 6 0.6979 0.2482 0.000 0.248 0.004 0.080 0.204 0.464
#> GSM1105504 3 0.2698 0.7971 0.020 0.000 0.872 0.000 0.016 0.092
#> GSM1105517 1 0.7742 -0.5826 0.420 0.000 0.208 0.024 0.192 0.156
#> GSM1105525 6 0.6537 0.1060 0.000 0.000 0.348 0.028 0.228 0.396
#> GSM1105552 5 0.8944 1.0000 0.216 0.024 0.252 0.068 0.276 0.164
#> GSM1105452 2 0.1418 0.6942 0.000 0.944 0.000 0.024 0.032 0.000
#> GSM1105453 2 0.3126 0.7581 0.000 0.752 0.000 0.248 0.000 0.000
#> GSM1105456 6 0.5090 0.3898 0.000 0.000 0.004 0.096 0.296 0.604
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 agent(p) other(p) time(p) individual(p) k
#> CV:hclust 110 1.000 0.59679 1.000 1.59e-03 2
#> CV:hclust 94 0.740 0.18731 0.977 9.02e-06 3
#> CV:hclust 77 0.136 0.03494 0.984 3.20e-06 4
#> CV:hclust 67 0.153 0.00442 0.987 1.81e-04 5
#> CV:hclust 77 0.224 0.00529 0.999 9.91e-07 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 44956 rows and 120 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 0.868 0.936 0.972 0.4894 0.513 0.513
#> 3 3 0.548 0.633 0.795 0.3391 0.775 0.585
#> 4 4 0.516 0.509 0.681 0.1223 0.815 0.534
#> 5 5 0.622 0.577 0.716 0.0739 0.836 0.480
#> 6 6 0.713 0.663 0.755 0.0449 0.943 0.740
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
#> GSM1105438 2 0.0000 0.968 0.000 1.000
#> GSM1105486 2 0.0000 0.968 0.000 1.000
#> GSM1105487 1 0.0000 0.974 1.000 0.000
#> GSM1105490 2 0.0000 0.968 0.000 1.000
#> GSM1105491 2 0.6623 0.804 0.172 0.828
#> GSM1105495 2 0.6623 0.804 0.172 0.828
#> GSM1105498 2 0.6887 0.790 0.184 0.816
#> GSM1105499 1 0.0000 0.974 1.000 0.000
#> GSM1105506 2 0.0000 0.968 0.000 1.000
#> GSM1105442 2 0.0000 0.968 0.000 1.000
#> GSM1105511 2 0.0000 0.968 0.000 1.000
#> GSM1105514 2 0.0000 0.968 0.000 1.000
#> GSM1105518 2 0.0672 0.962 0.008 0.992
#> GSM1105522 1 0.0000 0.974 1.000 0.000
#> GSM1105534 1 0.0000 0.974 1.000 0.000
#> GSM1105535 1 0.0000 0.974 1.000 0.000
#> GSM1105538 1 0.0000 0.974 1.000 0.000
#> GSM1105542 2 0.0000 0.968 0.000 1.000
#> GSM1105443 2 0.0000 0.968 0.000 1.000
#> GSM1105551 1 0.0000 0.974 1.000 0.000
#> GSM1105554 1 0.0000 0.974 1.000 0.000
#> GSM1105555 1 0.0000 0.974 1.000 0.000
#> GSM1105447 2 0.0000 0.968 0.000 1.000
#> GSM1105467 2 0.0000 0.968 0.000 1.000
#> GSM1105470 2 0.0000 0.968 0.000 1.000
#> GSM1105471 2 0.0000 0.968 0.000 1.000
#> GSM1105474 2 0.0000 0.968 0.000 1.000
#> GSM1105475 2 0.0000 0.968 0.000 1.000
#> GSM1105440 1 0.0000 0.974 1.000 0.000
#> GSM1105488 2 0.0000 0.968 0.000 1.000
#> GSM1105489 1 0.0000 0.974 1.000 0.000
#> GSM1105492 1 0.0000 0.974 1.000 0.000
#> GSM1105493 1 0.0000 0.974 1.000 0.000
#> GSM1105497 2 0.0000 0.968 0.000 1.000
#> GSM1105500 2 0.0000 0.968 0.000 1.000
#> GSM1105501 2 0.0000 0.968 0.000 1.000
#> GSM1105508 1 0.0000 0.974 1.000 0.000
#> GSM1105444 2 0.0000 0.968 0.000 1.000
#> GSM1105513 2 0.0000 0.968 0.000 1.000
#> GSM1105516 1 0.9922 0.214 0.552 0.448
#> GSM1105520 2 0.7056 0.779 0.192 0.808
#> GSM1105524 1 0.0000 0.974 1.000 0.000
#> GSM1105536 2 0.0000 0.968 0.000 1.000
#> GSM1105537 1 0.0000 0.974 1.000 0.000
#> GSM1105540 1 0.0000 0.974 1.000 0.000
#> GSM1105544 2 0.0000 0.968 0.000 1.000
#> GSM1105445 2 0.0000 0.968 0.000 1.000
#> GSM1105553 2 0.6801 0.795 0.180 0.820
#> GSM1105556 1 0.0000 0.974 1.000 0.000
#> GSM1105557 2 0.0000 0.968 0.000 1.000
#> GSM1105449 2 0.0000 0.968 0.000 1.000
#> GSM1105469 1 0.6712 0.777 0.824 0.176
#> GSM1105472 2 0.0000 0.968 0.000 1.000
#> GSM1105473 1 0.0000 0.974 1.000 0.000
#> GSM1105476 2 0.0000 0.968 0.000 1.000
#> GSM1105477 2 0.0000 0.968 0.000 1.000
#> GSM1105478 2 0.0000 0.968 0.000 1.000
#> GSM1105510 2 0.0000 0.968 0.000 1.000
#> GSM1105530 1 0.0000 0.974 1.000 0.000
#> GSM1105539 1 0.0000 0.974 1.000 0.000
#> GSM1105480 2 0.0000 0.968 0.000 1.000
#> GSM1105512 1 0.0000 0.974 1.000 0.000
#> GSM1105532 1 0.0000 0.974 1.000 0.000
#> GSM1105541 1 0.0000 0.974 1.000 0.000
#> GSM1105439 2 0.0000 0.968 0.000 1.000
#> GSM1105463 1 0.0000 0.974 1.000 0.000
#> GSM1105482 1 0.0000 0.974 1.000 0.000
#> GSM1105483 2 0.0000 0.968 0.000 1.000
#> GSM1105494 2 0.0000 0.968 0.000 1.000
#> GSM1105503 2 0.9358 0.497 0.352 0.648
#> GSM1105507 1 0.6531 0.787 0.832 0.168
#> GSM1105446 2 0.0000 0.968 0.000 1.000
#> GSM1105519 1 0.0000 0.974 1.000 0.000
#> GSM1105526 2 0.0000 0.968 0.000 1.000
#> GSM1105527 2 0.0000 0.968 0.000 1.000
#> GSM1105531 1 0.0000 0.974 1.000 0.000
#> GSM1105543 2 0.0000 0.968 0.000 1.000
#> GSM1105546 1 0.0000 0.974 1.000 0.000
#> GSM1105547 1 0.0000 0.974 1.000 0.000
#> GSM1105455 2 0.0000 0.968 0.000 1.000
#> GSM1105458 2 0.0000 0.968 0.000 1.000
#> GSM1105459 2 0.0000 0.968 0.000 1.000
#> GSM1105462 1 0.9661 0.307 0.608 0.392
#> GSM1105441 2 0.0000 0.968 0.000 1.000
#> GSM1105465 2 0.0376 0.965 0.004 0.996
#> GSM1105484 2 0.0000 0.968 0.000 1.000
#> GSM1105485 2 0.0000 0.968 0.000 1.000
#> GSM1105496 2 0.9460 0.470 0.364 0.636
#> GSM1105505 1 0.0000 0.974 1.000 0.000
#> GSM1105509 1 0.0000 0.974 1.000 0.000
#> GSM1105448 2 0.0000 0.968 0.000 1.000
#> GSM1105521 1 0.0000 0.974 1.000 0.000
#> GSM1105528 2 0.0000 0.968 0.000 1.000
#> GSM1105529 2 0.0000 0.968 0.000 1.000
#> GSM1105533 1 0.0000 0.974 1.000 0.000
#> GSM1105545 2 0.0000 0.968 0.000 1.000
#> GSM1105548 1 0.0000 0.974 1.000 0.000
#> GSM1105549 1 0.0000 0.974 1.000 0.000
#> GSM1105457 2 0.0000 0.968 0.000 1.000
#> GSM1105460 2 0.0000 0.968 0.000 1.000
#> GSM1105461 2 0.0000 0.968 0.000 1.000
#> GSM1105464 1 0.0000 0.974 1.000 0.000
#> GSM1105466 2 0.0000 0.968 0.000 1.000
#> GSM1105479 2 0.0000 0.968 0.000 1.000
#> GSM1105502 1 0.0000 0.974 1.000 0.000
#> GSM1105515 1 0.0000 0.974 1.000 0.000
#> GSM1105523 1 0.0000 0.974 1.000 0.000
#> GSM1105550 1 0.0000 0.974 1.000 0.000
#> GSM1105450 2 0.0000 0.968 0.000 1.000
#> GSM1105451 2 0.0000 0.968 0.000 1.000
#> GSM1105454 2 0.6623 0.804 0.172 0.828
#> GSM1105468 2 0.0000 0.968 0.000 1.000
#> GSM1105481 2 0.6712 0.800 0.176 0.824
#> GSM1105504 1 0.0000 0.974 1.000 0.000
#> GSM1105517 1 0.0000 0.974 1.000 0.000
#> GSM1105525 1 0.0000 0.974 1.000 0.000
#> GSM1105552 1 0.0000 0.974 1.000 0.000
#> GSM1105452 2 0.0000 0.968 0.000 1.000
#> GSM1105453 2 0.0000 0.968 0.000 1.000
#> GSM1105456 2 0.6623 0.804 0.172 0.828
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1105438 2 0.1529 0.6974 0.000 0.960 0.040
#> GSM1105486 2 0.0000 0.7115 0.000 1.000 0.000
#> GSM1105487 1 0.0237 0.9262 0.996 0.000 0.004
#> GSM1105490 2 0.5465 0.5482 0.000 0.712 0.288
#> GSM1105491 3 0.6169 0.2840 0.004 0.360 0.636
#> GSM1105495 3 0.5621 0.3627 0.000 0.308 0.692
#> GSM1105498 3 0.2165 0.6361 0.000 0.064 0.936
#> GSM1105499 1 0.0237 0.9262 0.996 0.000 0.004
#> GSM1105506 2 0.5835 0.4788 0.000 0.660 0.340
#> GSM1105442 2 0.6309 0.0173 0.000 0.504 0.496
#> GSM1105511 2 0.5810 0.4994 0.000 0.664 0.336
#> GSM1105514 2 0.1289 0.7002 0.000 0.968 0.032
#> GSM1105518 3 0.4452 0.5945 0.000 0.192 0.808
#> GSM1105522 1 0.1163 0.9212 0.972 0.000 0.028
#> GSM1105534 1 0.0000 0.9266 1.000 0.000 0.000
#> GSM1105535 1 0.0237 0.9262 0.996 0.000 0.004
#> GSM1105538 1 0.0000 0.9266 1.000 0.000 0.000
#> GSM1105542 2 0.5327 0.5157 0.000 0.728 0.272
#> GSM1105443 2 0.4796 0.6081 0.000 0.780 0.220
#> GSM1105551 1 0.1289 0.9204 0.968 0.000 0.032
#> GSM1105554 1 0.0000 0.9266 1.000 0.000 0.000
#> GSM1105555 1 0.2796 0.8885 0.908 0.000 0.092
#> GSM1105447 2 0.4842 0.6063 0.000 0.776 0.224
#> GSM1105467 2 0.0000 0.7115 0.000 1.000 0.000
#> GSM1105470 2 0.0000 0.7115 0.000 1.000 0.000
#> GSM1105471 3 0.5810 0.4407 0.000 0.336 0.664
#> GSM1105474 2 0.0000 0.7115 0.000 1.000 0.000
#> GSM1105475 2 0.2261 0.6946 0.000 0.932 0.068
#> GSM1105440 1 0.0237 0.9262 0.996 0.000 0.004
#> GSM1105488 2 0.5327 0.5157 0.000 0.728 0.272
#> GSM1105489 1 0.1163 0.9207 0.972 0.000 0.028
#> GSM1105492 1 0.0000 0.9266 1.000 0.000 0.000
#> GSM1105493 1 0.2796 0.8885 0.908 0.000 0.092
#> GSM1105497 3 0.6280 0.0649 0.000 0.460 0.540
#> GSM1105500 3 0.6260 -0.2524 0.000 0.448 0.552
#> GSM1105501 2 0.5591 0.5411 0.000 0.696 0.304
#> GSM1105508 1 0.1860 0.9095 0.948 0.000 0.052
#> GSM1105444 2 0.1529 0.6974 0.000 0.960 0.040
#> GSM1105513 2 0.5810 0.4844 0.000 0.664 0.336
#> GSM1105516 2 0.9816 0.0119 0.356 0.400 0.244
#> GSM1105520 3 0.3896 0.6270 0.008 0.128 0.864
#> GSM1105524 1 0.0237 0.9262 0.996 0.000 0.004
#> GSM1105536 2 0.4842 0.6338 0.000 0.776 0.224
#> GSM1105537 1 0.0237 0.9262 0.996 0.000 0.004
#> GSM1105540 3 0.6308 0.0452 0.492 0.000 0.508
#> GSM1105544 3 0.5692 0.3016 0.008 0.268 0.724
#> GSM1105445 3 0.5810 0.4185 0.000 0.336 0.664
#> GSM1105553 3 0.2096 0.6331 0.004 0.052 0.944
#> GSM1105556 1 0.0000 0.9266 1.000 0.000 0.000
#> GSM1105557 2 0.5678 0.5138 0.000 0.684 0.316
#> GSM1105449 2 0.0000 0.7115 0.000 1.000 0.000
#> GSM1105469 3 0.9870 0.2728 0.364 0.256 0.380
#> GSM1105472 2 0.0747 0.7062 0.000 0.984 0.016
#> GSM1105473 1 0.4887 0.7595 0.772 0.000 0.228
#> GSM1105476 2 0.0424 0.7103 0.000 0.992 0.008
#> GSM1105477 2 0.5138 0.6128 0.000 0.748 0.252
#> GSM1105478 3 0.6062 0.2950 0.000 0.384 0.616
#> GSM1105510 2 0.5327 0.5157 0.000 0.728 0.272
#> GSM1105530 1 0.3412 0.8785 0.876 0.000 0.124
#> GSM1105539 1 0.3412 0.8785 0.876 0.000 0.124
#> GSM1105480 2 0.6291 0.1759 0.000 0.532 0.468
#> GSM1105512 1 0.0000 0.9266 1.000 0.000 0.000
#> GSM1105532 1 0.3412 0.8785 0.876 0.000 0.124
#> GSM1105541 1 0.3412 0.8785 0.876 0.000 0.124
#> GSM1105439 2 0.4750 0.6148 0.000 0.784 0.216
#> GSM1105463 3 0.6180 0.0529 0.416 0.000 0.584
#> GSM1105482 1 0.0000 0.9266 1.000 0.000 0.000
#> GSM1105483 2 0.6008 0.4420 0.000 0.628 0.372
#> GSM1105494 3 0.6079 0.2713 0.000 0.388 0.612
#> GSM1105503 3 0.4353 0.6129 0.008 0.156 0.836
#> GSM1105507 1 0.3607 0.8466 0.880 0.008 0.112
#> GSM1105446 2 0.3340 0.6520 0.000 0.880 0.120
#> GSM1105519 1 0.0892 0.9226 0.980 0.000 0.020
#> GSM1105526 2 0.6204 0.4479 0.000 0.576 0.424
#> GSM1105527 2 0.5968 0.4451 0.000 0.636 0.364
#> GSM1105531 3 0.3340 0.6268 0.120 0.000 0.880
#> GSM1105543 2 0.2959 0.6658 0.000 0.900 0.100
#> GSM1105546 1 0.0000 0.9266 1.000 0.000 0.000
#> GSM1105547 1 0.0000 0.9266 1.000 0.000 0.000
#> GSM1105455 2 0.4750 0.6148 0.000 0.784 0.216
#> GSM1105458 2 0.6026 0.3328 0.000 0.624 0.376
#> GSM1105459 2 0.0000 0.7115 0.000 1.000 0.000
#> GSM1105462 3 0.2743 0.6409 0.052 0.020 0.928
#> GSM1105441 2 0.0000 0.7115 0.000 1.000 0.000
#> GSM1105465 3 0.5988 0.2743 0.000 0.368 0.632
#> GSM1105484 2 0.5291 0.5193 0.000 0.732 0.268
#> GSM1105485 2 0.5553 0.5113 0.004 0.724 0.272
#> GSM1105496 3 0.1315 0.6318 0.008 0.020 0.972
#> GSM1105505 3 0.2356 0.6369 0.072 0.000 0.928
#> GSM1105509 1 0.1753 0.9104 0.952 0.000 0.048
#> GSM1105448 2 0.1529 0.6974 0.000 0.960 0.040
#> GSM1105521 1 0.0892 0.9226 0.980 0.000 0.020
#> GSM1105528 2 0.5178 0.5341 0.000 0.744 0.256
#> GSM1105529 2 0.5327 0.5157 0.000 0.728 0.272
#> GSM1105533 1 0.2959 0.8854 0.900 0.000 0.100
#> GSM1105545 2 0.5497 0.5587 0.000 0.708 0.292
#> GSM1105548 1 0.0000 0.9266 1.000 0.000 0.000
#> GSM1105549 1 0.0000 0.9266 1.000 0.000 0.000
#> GSM1105457 2 0.5810 0.4844 0.000 0.664 0.336
#> GSM1105460 2 0.4504 0.6255 0.000 0.804 0.196
#> GSM1105461 2 0.0000 0.7115 0.000 1.000 0.000
#> GSM1105464 1 0.3340 0.8787 0.880 0.000 0.120
#> GSM1105466 2 0.5810 0.4844 0.000 0.664 0.336
#> GSM1105479 2 0.5810 0.4685 0.000 0.664 0.336
#> GSM1105502 1 0.3412 0.8785 0.876 0.000 0.124
#> GSM1105515 1 0.0000 0.9266 1.000 0.000 0.000
#> GSM1105523 3 0.5216 0.4993 0.260 0.000 0.740
#> GSM1105550 3 0.7876 0.2577 0.424 0.056 0.520
#> GSM1105450 2 0.0000 0.7115 0.000 1.000 0.000
#> GSM1105451 2 0.0000 0.7115 0.000 1.000 0.000
#> GSM1105454 3 0.4796 0.5808 0.000 0.220 0.780
#> GSM1105468 2 0.0000 0.7115 0.000 1.000 0.000
#> GSM1105481 3 0.3267 0.6221 0.000 0.116 0.884
#> GSM1105504 3 0.3412 0.6265 0.124 0.000 0.876
#> GSM1105517 1 0.4121 0.7933 0.832 0.000 0.168
#> GSM1105525 1 0.5905 0.5030 0.648 0.000 0.352
#> GSM1105552 1 0.6307 0.2098 0.512 0.000 0.488
#> GSM1105452 2 0.5178 0.5341 0.000 0.744 0.256
#> GSM1105453 2 0.0000 0.7115 0.000 1.000 0.000
#> GSM1105456 3 0.4796 0.5808 0.000 0.220 0.780
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1105438 2 0.3172 0.6473 0.000 0.840 0.000 0.160
#> GSM1105486 2 0.4164 0.6323 0.000 0.736 0.000 0.264
#> GSM1105487 1 0.3428 0.7800 0.844 0.000 0.144 0.012
#> GSM1105490 4 0.3764 0.5337 0.000 0.216 0.000 0.784
#> GSM1105491 3 0.7335 0.2069 0.000 0.400 0.444 0.156
#> GSM1105495 3 0.6796 0.4327 0.000 0.252 0.596 0.152
#> GSM1105498 4 0.5174 -0.2406 0.000 0.012 0.368 0.620
#> GSM1105499 1 0.1557 0.8014 0.944 0.000 0.056 0.000
#> GSM1105506 4 0.3311 0.5683 0.000 0.172 0.000 0.828
#> GSM1105442 2 0.6563 0.3212 0.000 0.632 0.208 0.160
#> GSM1105511 4 0.4804 0.5784 0.000 0.160 0.064 0.776
#> GSM1105514 2 0.3219 0.6474 0.000 0.836 0.000 0.164
#> GSM1105518 4 0.5570 -0.4674 0.000 0.020 0.440 0.540
#> GSM1105522 1 0.4690 0.7026 0.724 0.000 0.260 0.016
#> GSM1105534 1 0.0000 0.8016 1.000 0.000 0.000 0.000
#> GSM1105535 1 0.1716 0.8003 0.936 0.000 0.064 0.000
#> GSM1105538 1 0.0000 0.8016 1.000 0.000 0.000 0.000
#> GSM1105542 2 0.3790 0.5437 0.000 0.820 0.164 0.016
#> GSM1105443 4 0.5161 -0.0111 0.000 0.476 0.004 0.520
#> GSM1105551 1 0.3377 0.7797 0.848 0.000 0.140 0.012
#> GSM1105554 1 0.0000 0.8016 1.000 0.000 0.000 0.000
#> GSM1105555 1 0.3529 0.7614 0.836 0.000 0.152 0.012
#> GSM1105447 4 0.5080 -0.0406 0.000 0.420 0.004 0.576
#> GSM1105467 2 0.4164 0.6323 0.000 0.736 0.000 0.264
#> GSM1105470 2 0.4164 0.6323 0.000 0.736 0.000 0.264
#> GSM1105471 4 0.6182 0.0127 0.000 0.088 0.276 0.636
#> GSM1105474 2 0.4164 0.6323 0.000 0.736 0.000 0.264
#> GSM1105475 2 0.4933 0.2523 0.000 0.568 0.000 0.432
#> GSM1105440 1 0.1722 0.8018 0.944 0.000 0.048 0.008
#> GSM1105488 2 0.3790 0.5437 0.000 0.820 0.164 0.016
#> GSM1105489 1 0.2928 0.7822 0.880 0.000 0.108 0.012
#> GSM1105492 1 0.0336 0.8013 0.992 0.000 0.008 0.000
#> GSM1105493 1 0.3400 0.7391 0.820 0.000 0.180 0.000
#> GSM1105497 2 0.7304 -0.0175 0.000 0.492 0.344 0.164
#> GSM1105500 4 0.7268 0.3663 0.000 0.372 0.152 0.476
#> GSM1105501 4 0.4820 0.5740 0.000 0.168 0.060 0.772
#> GSM1105508 1 0.7357 0.4317 0.524 0.000 0.260 0.216
#> GSM1105444 2 0.3123 0.6467 0.000 0.844 0.000 0.156
#> GSM1105513 4 0.3583 0.5625 0.000 0.180 0.004 0.816
#> GSM1105516 4 0.9595 0.2155 0.176 0.248 0.184 0.392
#> GSM1105520 3 0.5353 0.5767 0.000 0.012 0.556 0.432
#> GSM1105524 1 0.1716 0.8003 0.936 0.000 0.064 0.000
#> GSM1105536 4 0.6773 0.4118 0.000 0.348 0.108 0.544
#> GSM1105537 1 0.1716 0.8003 0.936 0.000 0.064 0.000
#> GSM1105540 4 0.7704 -0.0283 0.244 0.004 0.264 0.488
#> GSM1105544 4 0.8264 0.2388 0.056 0.244 0.172 0.528
#> GSM1105445 4 0.5907 0.0937 0.000 0.080 0.252 0.668
#> GSM1105553 3 0.6495 0.5556 0.000 0.072 0.492 0.436
#> GSM1105556 1 0.0000 0.8016 1.000 0.000 0.000 0.000
#> GSM1105557 4 0.3444 0.5622 0.000 0.184 0.000 0.816
#> GSM1105449 2 0.4697 0.5232 0.000 0.644 0.000 0.356
#> GSM1105469 4 0.6792 0.2400 0.176 0.004 0.196 0.624
#> GSM1105472 2 0.4134 0.6334 0.000 0.740 0.000 0.260
#> GSM1105473 1 0.5070 0.5500 0.580 0.000 0.416 0.004
#> GSM1105476 2 0.4164 0.6323 0.000 0.736 0.000 0.264
#> GSM1105477 4 0.6867 0.3733 0.000 0.384 0.108 0.508
#> GSM1105478 4 0.2813 0.3964 0.000 0.024 0.080 0.896
#> GSM1105510 2 0.3790 0.5437 0.000 0.820 0.164 0.016
#> GSM1105530 1 0.5143 0.5575 0.540 0.000 0.456 0.004
#> GSM1105539 1 0.5097 0.5842 0.568 0.000 0.428 0.004
#> GSM1105480 4 0.4843 0.5131 0.000 0.112 0.104 0.784
#> GSM1105512 1 0.1489 0.7986 0.952 0.000 0.044 0.004
#> GSM1105532 1 0.5143 0.5575 0.540 0.000 0.456 0.004
#> GSM1105541 1 0.5097 0.5842 0.568 0.000 0.428 0.004
#> GSM1105439 4 0.4999 -0.0505 0.000 0.492 0.000 0.508
#> GSM1105463 3 0.4375 0.5333 0.144 0.008 0.812 0.036
#> GSM1105482 1 0.0469 0.8030 0.988 0.000 0.012 0.000
#> GSM1105483 4 0.5990 0.5387 0.004 0.156 0.136 0.704
#> GSM1105494 4 0.3497 0.3204 0.000 0.024 0.124 0.852
#> GSM1105503 3 0.5112 0.5976 0.000 0.008 0.608 0.384
#> GSM1105507 1 0.7613 0.1827 0.448 0.000 0.212 0.340
#> GSM1105446 2 0.2227 0.6080 0.000 0.928 0.036 0.036
#> GSM1105519 1 0.3208 0.7540 0.848 0.000 0.148 0.004
#> GSM1105526 4 0.6616 0.4721 0.000 0.308 0.108 0.584
#> GSM1105527 4 0.4696 0.5847 0.004 0.148 0.056 0.792
#> GSM1105531 3 0.3727 0.6508 0.004 0.008 0.824 0.164
#> GSM1105543 2 0.2224 0.6060 0.000 0.928 0.040 0.032
#> GSM1105546 1 0.0524 0.8021 0.988 0.000 0.008 0.004
#> GSM1105547 1 0.0000 0.8016 1.000 0.000 0.000 0.000
#> GSM1105455 4 0.4999 -0.0505 0.000 0.492 0.000 0.508
#> GSM1105458 4 0.6023 0.1269 0.000 0.344 0.056 0.600
#> GSM1105459 2 0.4164 0.6323 0.000 0.736 0.000 0.264
#> GSM1105462 3 0.4011 0.6386 0.000 0.008 0.784 0.208
#> GSM1105441 2 0.4431 0.5721 0.000 0.696 0.000 0.304
#> GSM1105465 2 0.7349 -0.0876 0.000 0.472 0.364 0.164
#> GSM1105484 2 0.4532 0.5227 0.000 0.792 0.156 0.052
#> GSM1105485 2 0.4359 0.5302 0.016 0.804 0.164 0.016
#> GSM1105496 3 0.6290 0.5916 0.000 0.068 0.568 0.364
#> GSM1105505 3 0.3681 0.6512 0.000 0.008 0.816 0.176
#> GSM1105509 1 0.6745 0.5085 0.612 0.000 0.212 0.176
#> GSM1105448 2 0.3219 0.6473 0.000 0.836 0.000 0.164
#> GSM1105521 1 0.2999 0.7634 0.864 0.000 0.132 0.004
#> GSM1105528 2 0.3743 0.5454 0.000 0.824 0.160 0.016
#> GSM1105529 2 0.3790 0.5437 0.000 0.820 0.164 0.016
#> GSM1105533 1 0.4576 0.7031 0.728 0.000 0.260 0.012
#> GSM1105545 4 0.5318 0.5493 0.000 0.196 0.072 0.732
#> GSM1105548 1 0.1284 0.8029 0.964 0.000 0.024 0.012
#> GSM1105549 1 0.0592 0.8027 0.984 0.000 0.016 0.000
#> GSM1105457 4 0.3400 0.5629 0.000 0.180 0.000 0.820
#> GSM1105460 4 0.4981 0.0319 0.000 0.464 0.000 0.536
#> GSM1105461 2 0.4164 0.6323 0.000 0.736 0.000 0.264
#> GSM1105464 1 0.5105 0.5686 0.564 0.000 0.432 0.004
#> GSM1105466 4 0.3311 0.5683 0.000 0.172 0.000 0.828
#> GSM1105479 4 0.4956 0.3688 0.000 0.232 0.036 0.732
#> GSM1105502 1 0.5183 0.6166 0.584 0.000 0.408 0.008
#> GSM1105515 1 0.0000 0.8016 1.000 0.000 0.000 0.000
#> GSM1105523 3 0.3942 0.5946 0.000 0.000 0.764 0.236
#> GSM1105550 4 0.7199 0.0520 0.148 0.004 0.304 0.544
#> GSM1105450 2 0.4164 0.6323 0.000 0.736 0.000 0.264
#> GSM1105451 2 0.4164 0.6323 0.000 0.736 0.000 0.264
#> GSM1105454 3 0.6755 0.4200 0.000 0.092 0.460 0.448
#> GSM1105468 2 0.4164 0.6323 0.000 0.736 0.000 0.264
#> GSM1105481 3 0.6170 0.5293 0.000 0.052 0.528 0.420
#> GSM1105504 3 0.3591 0.6513 0.000 0.008 0.824 0.168
#> GSM1105517 1 0.7955 0.0943 0.408 0.004 0.256 0.332
#> GSM1105525 3 0.6655 0.3454 0.184 0.000 0.624 0.192
#> GSM1105552 3 0.5553 0.2974 0.252 0.012 0.700 0.036
#> GSM1105452 2 0.3743 0.5454 0.000 0.824 0.160 0.016
#> GSM1105453 2 0.4164 0.6323 0.000 0.736 0.000 0.264
#> GSM1105456 3 0.6755 0.4200 0.000 0.092 0.460 0.448
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM1105438 2 0.1478 0.7200 0.000 0.936 0.000 0.000 0.064
#> GSM1105486 2 0.0000 0.7852 0.000 1.000 0.000 0.000 0.000
#> GSM1105487 1 0.4650 0.7761 0.760 0.000 0.164 0.024 0.052
#> GSM1105490 4 0.4059 0.6746 0.000 0.172 0.000 0.776 0.052
#> GSM1105491 5 0.3479 0.4897 0.000 0.076 0.056 0.016 0.852
#> GSM1105495 5 0.6861 -0.1543 0.000 0.056 0.360 0.096 0.488
#> GSM1105498 4 0.4171 0.5114 0.000 0.000 0.104 0.784 0.112
#> GSM1105499 1 0.2408 0.8390 0.892 0.000 0.096 0.004 0.008
#> GSM1105506 4 0.3893 0.6832 0.000 0.140 0.004 0.804 0.052
#> GSM1105442 5 0.4088 0.6352 0.000 0.304 0.000 0.008 0.688
#> GSM1105511 4 0.3250 0.6963 0.000 0.128 0.020 0.844 0.008
#> GSM1105514 2 0.0963 0.7486 0.000 0.964 0.000 0.000 0.036
#> GSM1105518 3 0.7052 0.2972 0.000 0.008 0.356 0.320 0.316
#> GSM1105522 1 0.5618 0.5111 0.600 0.000 0.328 0.052 0.020
#> GSM1105534 1 0.0000 0.8496 1.000 0.000 0.000 0.000 0.000
#> GSM1105535 1 0.2674 0.8311 0.888 0.000 0.084 0.008 0.020
#> GSM1105538 1 0.0162 0.8494 0.996 0.000 0.000 0.000 0.004
#> GSM1105542 5 0.4242 0.6381 0.000 0.428 0.000 0.000 0.572
#> GSM1105443 2 0.4905 0.5674 0.000 0.696 0.000 0.224 0.080
#> GSM1105551 1 0.4761 0.7751 0.756 0.000 0.160 0.028 0.056
#> GSM1105554 1 0.0671 0.8478 0.980 0.000 0.016 0.004 0.000
#> GSM1105555 1 0.4393 0.7567 0.780 0.000 0.152 0.024 0.044
#> GSM1105447 2 0.6616 0.2482 0.000 0.456 0.000 0.292 0.252
#> GSM1105467 2 0.0671 0.7792 0.000 0.980 0.000 0.004 0.016
#> GSM1105470 2 0.0000 0.7852 0.000 1.000 0.000 0.000 0.000
#> GSM1105471 4 0.8325 0.0313 0.000 0.172 0.200 0.376 0.252
#> GSM1105474 2 0.0000 0.7852 0.000 1.000 0.000 0.000 0.000
#> GSM1105475 2 0.3565 0.6660 0.000 0.816 0.000 0.144 0.040
#> GSM1105440 1 0.3011 0.8317 0.876 0.000 0.076 0.012 0.036
#> GSM1105488 5 0.4242 0.6381 0.000 0.428 0.000 0.000 0.572
#> GSM1105489 1 0.3513 0.8169 0.852 0.000 0.080 0.024 0.044
#> GSM1105492 1 0.1059 0.8498 0.968 0.000 0.004 0.008 0.020
#> GSM1105493 1 0.4509 0.6467 0.728 0.000 0.232 0.016 0.024
#> GSM1105497 5 0.3605 0.5626 0.000 0.120 0.036 0.012 0.832
#> GSM1105500 4 0.4825 0.6005 0.000 0.028 0.024 0.708 0.240
#> GSM1105501 4 0.3554 0.6959 0.000 0.136 0.020 0.828 0.016
#> GSM1105508 4 0.6928 0.3477 0.216 0.000 0.240 0.516 0.028
#> GSM1105444 2 0.1671 0.7022 0.000 0.924 0.000 0.000 0.076
#> GSM1105513 4 0.4409 0.6663 0.000 0.148 0.004 0.768 0.080
#> GSM1105516 4 0.7866 0.5083 0.168 0.056 0.100 0.560 0.116
#> GSM1105520 3 0.6796 0.3229 0.000 0.000 0.380 0.312 0.308
#> GSM1105524 1 0.2674 0.8311 0.888 0.000 0.084 0.008 0.020
#> GSM1105536 4 0.5693 0.6319 0.000 0.160 0.028 0.684 0.128
#> GSM1105537 1 0.2674 0.8311 0.888 0.000 0.084 0.008 0.020
#> GSM1105540 4 0.5549 0.5555 0.088 0.000 0.188 0.692 0.032
#> GSM1105544 4 0.4699 0.6149 0.004 0.012 0.032 0.724 0.228
#> GSM1105445 4 0.8129 0.0811 0.000 0.140 0.200 0.412 0.248
#> GSM1105553 5 0.6813 -0.3891 0.000 0.000 0.340 0.304 0.356
#> GSM1105556 1 0.0960 0.8455 0.972 0.000 0.016 0.004 0.008
#> GSM1105557 4 0.4019 0.6836 0.000 0.152 0.004 0.792 0.052
#> GSM1105449 2 0.3035 0.7163 0.000 0.856 0.000 0.032 0.112
#> GSM1105469 4 0.4205 0.6585 0.056 0.028 0.088 0.820 0.008
#> GSM1105472 2 0.0000 0.7852 0.000 1.000 0.000 0.000 0.000
#> GSM1105473 3 0.5827 0.0728 0.396 0.000 0.532 0.024 0.048
#> GSM1105476 2 0.0000 0.7852 0.000 1.000 0.000 0.000 0.000
#> GSM1105477 4 0.5818 0.6114 0.000 0.152 0.028 0.672 0.148
#> GSM1105478 4 0.3593 0.6370 0.000 0.052 0.012 0.840 0.096
#> GSM1105510 5 0.4490 0.6467 0.000 0.404 0.004 0.004 0.588
#> GSM1105530 3 0.4063 0.2659 0.280 0.000 0.708 0.012 0.000
#> GSM1105539 3 0.4240 0.2231 0.304 0.000 0.684 0.004 0.008
#> GSM1105480 4 0.2713 0.6790 0.000 0.072 0.004 0.888 0.036
#> GSM1105512 1 0.2796 0.7822 0.868 0.000 0.116 0.008 0.008
#> GSM1105532 3 0.4063 0.2659 0.280 0.000 0.708 0.012 0.000
#> GSM1105541 3 0.4260 0.2137 0.308 0.000 0.680 0.004 0.008
#> GSM1105439 2 0.4905 0.5652 0.000 0.696 0.000 0.224 0.080
#> GSM1105463 3 0.3427 0.5516 0.012 0.000 0.844 0.032 0.112
#> GSM1105482 1 0.1997 0.8444 0.932 0.000 0.024 0.016 0.028
#> GSM1105483 4 0.3955 0.6876 0.004 0.100 0.068 0.820 0.008
#> GSM1105494 4 0.5227 0.4466 0.000 0.024 0.052 0.688 0.236
#> GSM1105503 3 0.6536 0.3657 0.000 0.000 0.468 0.312 0.220
#> GSM1105507 4 0.6714 0.3831 0.280 0.000 0.156 0.536 0.028
#> GSM1105446 2 0.3534 0.3173 0.000 0.744 0.000 0.000 0.256
#> GSM1105519 1 0.3864 0.6881 0.784 0.000 0.188 0.020 0.008
#> GSM1105526 4 0.5420 0.6488 0.000 0.132 0.028 0.712 0.128
#> GSM1105527 4 0.3120 0.6954 0.000 0.116 0.016 0.856 0.012
#> GSM1105531 3 0.3752 0.5440 0.000 0.000 0.812 0.064 0.124
#> GSM1105543 2 0.3561 0.3072 0.000 0.740 0.000 0.000 0.260
#> GSM1105546 1 0.1772 0.8482 0.940 0.000 0.008 0.020 0.032
#> GSM1105547 1 0.1524 0.8465 0.952 0.000 0.016 0.016 0.016
#> GSM1105455 2 0.4847 0.5782 0.000 0.704 0.000 0.216 0.080
#> GSM1105458 2 0.6463 0.3316 0.000 0.496 0.000 0.228 0.276
#> GSM1105459 2 0.0000 0.7852 0.000 1.000 0.000 0.000 0.000
#> GSM1105462 3 0.4169 0.5307 0.000 0.000 0.784 0.116 0.100
#> GSM1105441 2 0.2208 0.7394 0.000 0.908 0.000 0.020 0.072
#> GSM1105465 5 0.3837 0.6041 0.000 0.164 0.024 0.012 0.800
#> GSM1105484 5 0.4383 0.6306 0.000 0.424 0.000 0.004 0.572
#> GSM1105485 5 0.4201 0.6440 0.000 0.408 0.000 0.000 0.592
#> GSM1105496 3 0.6783 0.3364 0.000 0.000 0.372 0.280 0.348
#> GSM1105505 3 0.4054 0.5402 0.000 0.000 0.788 0.072 0.140
#> GSM1105509 4 0.7087 0.1542 0.360 0.000 0.212 0.408 0.020
#> GSM1105448 2 0.1410 0.7227 0.000 0.940 0.000 0.000 0.060
#> GSM1105521 1 0.3805 0.6878 0.784 0.000 0.192 0.016 0.008
#> GSM1105528 5 0.4256 0.6275 0.000 0.436 0.000 0.000 0.564
#> GSM1105529 5 0.4235 0.6407 0.000 0.424 0.000 0.000 0.576
#> GSM1105533 1 0.5214 0.3631 0.540 0.000 0.424 0.012 0.024
#> GSM1105545 4 0.4886 0.6815 0.000 0.160 0.028 0.748 0.064
#> GSM1105548 1 0.2653 0.8395 0.900 0.000 0.020 0.028 0.052
#> GSM1105549 1 0.2082 0.8432 0.928 0.000 0.024 0.016 0.032
#> GSM1105457 4 0.4450 0.6665 0.000 0.152 0.004 0.764 0.080
#> GSM1105460 2 0.5275 0.4598 0.000 0.640 0.000 0.276 0.084
#> GSM1105461 2 0.0000 0.7852 0.000 1.000 0.000 0.000 0.000
#> GSM1105464 3 0.4665 0.2411 0.304 0.000 0.668 0.016 0.012
#> GSM1105466 4 0.4088 0.6799 0.000 0.140 0.004 0.792 0.064
#> GSM1105479 4 0.6911 -0.0187 0.000 0.364 0.008 0.396 0.232
#> GSM1105502 3 0.4135 0.1686 0.340 0.000 0.656 0.004 0.000
#> GSM1105515 1 0.0671 0.8478 0.980 0.000 0.016 0.004 0.000
#> GSM1105523 3 0.3366 0.5061 0.000 0.000 0.828 0.140 0.032
#> GSM1105550 4 0.5377 0.4899 0.044 0.004 0.288 0.648 0.016
#> GSM1105450 2 0.0000 0.7852 0.000 1.000 0.000 0.000 0.000
#> GSM1105451 2 0.0000 0.7852 0.000 1.000 0.000 0.000 0.000
#> GSM1105454 3 0.7636 0.3226 0.000 0.048 0.368 0.260 0.324
#> GSM1105468 2 0.0000 0.7852 0.000 1.000 0.000 0.000 0.000
#> GSM1105481 3 0.7134 0.3668 0.000 0.028 0.444 0.200 0.328
#> GSM1105504 3 0.3359 0.5520 0.000 0.000 0.840 0.052 0.108
#> GSM1105517 4 0.6998 0.3434 0.200 0.000 0.260 0.508 0.032
#> GSM1105525 3 0.3130 0.5132 0.048 0.000 0.856 0.096 0.000
#> GSM1105552 3 0.6652 0.3781 0.228 0.000 0.592 0.060 0.120
#> GSM1105452 5 0.4256 0.6285 0.000 0.436 0.000 0.000 0.564
#> GSM1105453 2 0.0000 0.7852 0.000 1.000 0.000 0.000 0.000
#> GSM1105456 3 0.7636 0.3226 0.000 0.048 0.368 0.260 0.324
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1105438 2 0.0909 0.8173 0.000 0.968 0.000 0.000 0.020 0.012
#> GSM1105486 2 0.0146 0.8303 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM1105487 1 0.6409 0.6158 0.564 0.000 0.240 0.008 0.100 0.088
#> GSM1105490 4 0.4239 0.6437 0.000 0.032 0.008 0.780 0.052 0.128
#> GSM1105491 5 0.3911 0.7090 0.000 0.044 0.004 0.008 0.772 0.172
#> GSM1105495 6 0.5313 0.5298 0.000 0.024 0.112 0.008 0.184 0.672
#> GSM1105498 4 0.5524 0.1737 0.000 0.000 0.064 0.532 0.032 0.372
#> GSM1105499 1 0.3342 0.7435 0.820 0.000 0.140 0.008 0.004 0.028
#> GSM1105506 4 0.4284 0.6373 0.000 0.028 0.008 0.772 0.052 0.140
#> GSM1105442 5 0.3963 0.8433 0.000 0.164 0.000 0.000 0.756 0.080
#> GSM1105511 4 0.0837 0.7370 0.000 0.020 0.004 0.972 0.000 0.004
#> GSM1105514 2 0.0458 0.8192 0.000 0.984 0.000 0.000 0.016 0.000
#> GSM1105518 6 0.3960 0.6956 0.000 0.000 0.104 0.100 0.012 0.784
#> GSM1105522 3 0.6429 -0.1676 0.404 0.000 0.456 0.044 0.040 0.056
#> GSM1105534 1 0.0291 0.7999 0.992 0.000 0.000 0.004 0.004 0.000
#> GSM1105535 1 0.4764 0.7176 0.728 0.000 0.172 0.008 0.036 0.056
#> GSM1105538 1 0.0653 0.7981 0.980 0.000 0.004 0.004 0.012 0.000
#> GSM1105542 5 0.3288 0.8880 0.000 0.276 0.000 0.000 0.724 0.000
#> GSM1105443 2 0.6014 0.5240 0.000 0.612 0.000 0.140 0.080 0.168
#> GSM1105551 1 0.6538 0.6020 0.548 0.000 0.244 0.008 0.112 0.088
#> GSM1105554 1 0.0291 0.7985 0.992 0.000 0.004 0.004 0.000 0.000
#> GSM1105555 1 0.5625 0.6290 0.648 0.000 0.200 0.004 0.088 0.060
#> GSM1105447 6 0.6735 0.1387 0.000 0.344 0.000 0.124 0.092 0.440
#> GSM1105467 2 0.1829 0.8058 0.000 0.928 0.000 0.008 0.028 0.036
#> GSM1105470 2 0.0146 0.8303 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM1105471 6 0.5616 0.5596 0.000 0.080 0.012 0.176 0.064 0.668
#> GSM1105474 2 0.0146 0.8298 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM1105475 2 0.3527 0.7383 0.000 0.828 0.004 0.104 0.040 0.024
#> GSM1105440 1 0.5307 0.7152 0.700 0.000 0.152 0.012 0.052 0.084
#> GSM1105488 5 0.3288 0.8880 0.000 0.276 0.000 0.000 0.724 0.000
#> GSM1105489 1 0.4850 0.7412 0.740 0.000 0.100 0.004 0.096 0.060
#> GSM1105492 1 0.2372 0.7973 0.908 0.000 0.024 0.008 0.024 0.036
#> GSM1105493 1 0.4368 0.5348 0.712 0.000 0.224 0.004 0.056 0.004
#> GSM1105497 5 0.3835 0.7350 0.000 0.060 0.000 0.004 0.772 0.164
#> GSM1105500 4 0.3735 0.7020 0.000 0.004 0.024 0.800 0.144 0.028
#> GSM1105501 4 0.1003 0.7396 0.000 0.028 0.004 0.964 0.004 0.000
#> GSM1105508 4 0.6170 0.4479 0.072 0.000 0.252 0.592 0.024 0.060
#> GSM1105444 2 0.1074 0.8111 0.000 0.960 0.000 0.000 0.028 0.012
#> GSM1105513 4 0.5586 0.4273 0.000 0.032 0.008 0.616 0.080 0.264
#> GSM1105516 4 0.4393 0.6951 0.080 0.004 0.052 0.788 0.068 0.008
#> GSM1105520 6 0.4180 0.6916 0.000 0.000 0.116 0.100 0.016 0.768
#> GSM1105524 1 0.4764 0.7176 0.728 0.000 0.172 0.008 0.036 0.056
#> GSM1105536 4 0.3233 0.7220 0.000 0.036 0.020 0.848 0.092 0.004
#> GSM1105537 1 0.4764 0.7176 0.728 0.000 0.172 0.008 0.036 0.056
#> GSM1105540 4 0.3792 0.7050 0.016 0.000 0.108 0.816 0.040 0.020
#> GSM1105544 4 0.3767 0.7059 0.000 0.004 0.028 0.804 0.132 0.032
#> GSM1105445 6 0.5525 0.5579 0.000 0.056 0.012 0.172 0.084 0.676
#> GSM1105553 6 0.4218 0.6761 0.000 0.000 0.108 0.064 0.048 0.780
#> GSM1105556 1 0.0551 0.7966 0.984 0.000 0.004 0.004 0.008 0.000
#> GSM1105557 4 0.4166 0.6462 0.000 0.028 0.008 0.784 0.052 0.128
#> GSM1105449 2 0.3466 0.7443 0.000 0.816 0.000 0.004 0.084 0.096
#> GSM1105469 4 0.1067 0.7395 0.004 0.004 0.024 0.964 0.000 0.004
#> GSM1105472 2 0.0000 0.8308 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105473 3 0.5819 0.3832 0.360 0.000 0.524 0.024 0.084 0.008
#> GSM1105476 2 0.0146 0.8298 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM1105477 4 0.3728 0.7086 0.000 0.040 0.024 0.816 0.112 0.008
#> GSM1105478 4 0.5729 0.1743 0.000 0.016 0.008 0.520 0.088 0.368
#> GSM1105510 5 0.3368 0.8824 0.000 0.232 0.000 0.012 0.756 0.000
#> GSM1105530 3 0.2726 0.6809 0.136 0.000 0.848 0.008 0.000 0.008
#> GSM1105539 3 0.2624 0.6760 0.148 0.000 0.844 0.000 0.004 0.004
#> GSM1105480 4 0.4172 0.6385 0.000 0.012 0.008 0.764 0.052 0.164
#> GSM1105512 1 0.2214 0.7454 0.892 0.000 0.092 0.004 0.012 0.000
#> GSM1105532 3 0.2726 0.6809 0.136 0.000 0.848 0.008 0.000 0.008
#> GSM1105541 3 0.2624 0.6760 0.148 0.000 0.844 0.000 0.004 0.004
#> GSM1105439 2 0.5879 0.5452 0.000 0.628 0.000 0.152 0.076 0.144
#> GSM1105463 3 0.4153 0.5406 0.000 0.000 0.712 0.020 0.020 0.248
#> GSM1105482 1 0.1668 0.7909 0.928 0.000 0.008 0.000 0.060 0.004
#> GSM1105483 4 0.1148 0.7390 0.000 0.016 0.020 0.960 0.000 0.004
#> GSM1105494 6 0.5702 0.1971 0.000 0.008 0.012 0.376 0.092 0.512
#> GSM1105503 6 0.4829 0.6206 0.000 0.000 0.196 0.096 0.016 0.692
#> GSM1105507 4 0.4542 0.6708 0.092 0.000 0.104 0.764 0.028 0.012
#> GSM1105446 2 0.2964 0.5499 0.000 0.792 0.004 0.000 0.204 0.000
#> GSM1105519 1 0.3920 0.6326 0.784 0.000 0.148 0.040 0.028 0.000
#> GSM1105526 4 0.2544 0.7341 0.000 0.016 0.016 0.888 0.076 0.004
#> GSM1105527 4 0.2878 0.7026 0.000 0.020 0.008 0.876 0.028 0.068
#> GSM1105531 3 0.4513 0.4636 0.000 0.000 0.652 0.020 0.024 0.304
#> GSM1105543 2 0.2964 0.5503 0.000 0.792 0.004 0.000 0.204 0.000
#> GSM1105546 1 0.3332 0.7877 0.848 0.000 0.036 0.004 0.076 0.036
#> GSM1105547 1 0.1349 0.7933 0.940 0.000 0.004 0.000 0.056 0.000
#> GSM1105455 2 0.5711 0.5638 0.000 0.648 0.000 0.144 0.076 0.132
#> GSM1105458 2 0.6597 -0.0215 0.000 0.408 0.000 0.092 0.100 0.400
#> GSM1105459 2 0.0000 0.8308 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105462 3 0.4927 0.5833 0.000 0.000 0.700 0.100 0.028 0.172
#> GSM1105441 2 0.3264 0.7567 0.000 0.840 0.000 0.012 0.072 0.076
#> GSM1105465 5 0.4008 0.7866 0.000 0.100 0.000 0.004 0.768 0.128
#> GSM1105484 5 0.3426 0.8864 0.000 0.276 0.000 0.000 0.720 0.004
#> GSM1105485 5 0.3394 0.8814 0.000 0.236 0.000 0.012 0.752 0.000
#> GSM1105496 6 0.4739 0.6613 0.000 0.000 0.120 0.076 0.064 0.740
#> GSM1105505 3 0.4843 0.3780 0.000 0.000 0.592 0.028 0.024 0.356
#> GSM1105509 4 0.5914 0.4497 0.204 0.000 0.172 0.592 0.028 0.004
#> GSM1105448 2 0.0458 0.8192 0.000 0.984 0.000 0.000 0.016 0.000
#> GSM1105521 1 0.3783 0.6347 0.788 0.000 0.156 0.028 0.028 0.000
#> GSM1105528 5 0.3309 0.8848 0.000 0.280 0.000 0.000 0.720 0.000
#> GSM1105529 5 0.3288 0.8880 0.000 0.276 0.000 0.000 0.724 0.000
#> GSM1105533 3 0.5556 0.2602 0.296 0.000 0.596 0.004 0.040 0.064
#> GSM1105545 4 0.2100 0.7403 0.000 0.032 0.016 0.916 0.036 0.000
#> GSM1105548 1 0.4524 0.7621 0.764 0.000 0.060 0.004 0.112 0.060
#> GSM1105549 1 0.2094 0.7848 0.908 0.000 0.024 0.000 0.064 0.004
#> GSM1105457 4 0.5055 0.5667 0.000 0.032 0.008 0.700 0.076 0.184
#> GSM1105460 2 0.6134 0.4980 0.000 0.596 0.000 0.176 0.080 0.148
#> GSM1105461 2 0.0000 0.8308 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105464 3 0.3122 0.6756 0.160 0.000 0.816 0.020 0.004 0.000
#> GSM1105466 4 0.4827 0.5897 0.000 0.028 0.008 0.724 0.076 0.164
#> GSM1105479 6 0.6661 0.4284 0.000 0.196 0.004 0.188 0.080 0.532
#> GSM1105502 3 0.2967 0.6569 0.136 0.000 0.840 0.008 0.004 0.012
#> GSM1105515 1 0.0551 0.7966 0.984 0.000 0.004 0.004 0.008 0.000
#> GSM1105523 3 0.4384 0.5934 0.000 0.000 0.744 0.112 0.012 0.132
#> GSM1105550 4 0.3627 0.6352 0.000 0.004 0.216 0.760 0.016 0.004
#> GSM1105450 2 0.0000 0.8308 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105451 2 0.0000 0.8308 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105454 6 0.4473 0.6945 0.000 0.020 0.108 0.064 0.032 0.776
#> GSM1105468 2 0.0000 0.8308 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105481 6 0.4509 0.6271 0.000 0.004 0.168 0.040 0.044 0.744
#> GSM1105504 3 0.4125 0.5564 0.000 0.000 0.724 0.028 0.016 0.232
#> GSM1105517 4 0.5169 0.5564 0.084 0.000 0.212 0.672 0.028 0.004
#> GSM1105525 3 0.2658 0.6644 0.004 0.000 0.888 0.052 0.016 0.040
#> GSM1105552 3 0.6418 0.5994 0.112 0.000 0.632 0.128 0.068 0.060
#> GSM1105452 5 0.3309 0.8848 0.000 0.280 0.000 0.000 0.720 0.000
#> GSM1105453 2 0.0000 0.8308 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105456 6 0.4473 0.6945 0.000 0.020 0.108 0.064 0.032 0.776
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 agent(p) other(p) time(p) individual(p) k
#> CV:kmeans 116 0.99217 0.372704 0.705 0.00698 2
#> CV:kmeans 91 0.17880 0.409088 0.996 0.00766 3
#> CV:kmeans 85 0.05088 0.940400 0.988 0.02754 4
#> CV:kmeans 87 0.00942 0.274096 0.377 0.01277 5
#> CV:kmeans 105 0.53843 0.000411 0.577 0.00156 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 44956 rows and 120 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 0.884 0.921 0.968 0.4996 0.501 0.501
#> 3 3 0.703 0.798 0.889 0.3114 0.792 0.603
#> 4 4 0.710 0.729 0.873 0.1141 0.863 0.633
#> 5 5 0.737 0.714 0.831 0.0701 0.904 0.677
#> 6 6 0.797 0.775 0.874 0.0501 0.922 0.684
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 2
Following 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
#> GSM1105438 2 0.0000 0.9663 0.000 1.000
#> GSM1105486 2 0.0000 0.9663 0.000 1.000
#> GSM1105487 1 0.0000 0.9638 1.000 0.000
#> GSM1105490 2 0.0000 0.9663 0.000 1.000
#> GSM1105491 1 0.9998 -0.0152 0.508 0.492
#> GSM1105495 2 0.7219 0.7517 0.200 0.800
#> GSM1105498 2 0.9710 0.3543 0.400 0.600
#> GSM1105499 1 0.0000 0.9638 1.000 0.000
#> GSM1105506 2 0.0000 0.9663 0.000 1.000
#> GSM1105442 2 0.0000 0.9663 0.000 1.000
#> GSM1105511 2 0.0000 0.9663 0.000 1.000
#> GSM1105514 2 0.0000 0.9663 0.000 1.000
#> GSM1105518 2 0.2948 0.9218 0.052 0.948
#> GSM1105522 1 0.0000 0.9638 1.000 0.000
#> GSM1105534 1 0.0000 0.9638 1.000 0.000
#> GSM1105535 1 0.0000 0.9638 1.000 0.000
#> GSM1105538 1 0.0000 0.9638 1.000 0.000
#> GSM1105542 2 0.0000 0.9663 0.000 1.000
#> GSM1105443 2 0.0000 0.9663 0.000 1.000
#> GSM1105551 1 0.0000 0.9638 1.000 0.000
#> GSM1105554 1 0.0000 0.9638 1.000 0.000
#> GSM1105555 1 0.0000 0.9638 1.000 0.000
#> GSM1105447 2 0.0000 0.9663 0.000 1.000
#> GSM1105467 2 0.0000 0.9663 0.000 1.000
#> GSM1105470 2 0.0000 0.9663 0.000 1.000
#> GSM1105471 2 0.0000 0.9663 0.000 1.000
#> GSM1105474 2 0.0000 0.9663 0.000 1.000
#> GSM1105475 2 0.0000 0.9663 0.000 1.000
#> GSM1105440 1 0.0000 0.9638 1.000 0.000
#> GSM1105488 2 0.0000 0.9663 0.000 1.000
#> GSM1105489 1 0.0000 0.9638 1.000 0.000
#> GSM1105492 1 0.0000 0.9638 1.000 0.000
#> GSM1105493 1 0.0000 0.9638 1.000 0.000
#> GSM1105497 2 0.0672 0.9600 0.008 0.992
#> GSM1105500 2 0.0000 0.9663 0.000 1.000
#> GSM1105501 2 0.0000 0.9663 0.000 1.000
#> GSM1105508 1 0.0000 0.9638 1.000 0.000
#> GSM1105444 2 0.0000 0.9663 0.000 1.000
#> GSM1105513 2 0.0000 0.9663 0.000 1.000
#> GSM1105516 1 0.7219 0.7447 0.800 0.200
#> GSM1105520 2 0.9286 0.4943 0.344 0.656
#> GSM1105524 1 0.0000 0.9638 1.000 0.000
#> GSM1105536 2 0.0000 0.9663 0.000 1.000
#> GSM1105537 1 0.0000 0.9638 1.000 0.000
#> GSM1105540 1 0.0000 0.9638 1.000 0.000
#> GSM1105544 1 0.8144 0.6682 0.748 0.252
#> GSM1105445 2 0.0000 0.9663 0.000 1.000
#> GSM1105553 1 0.8499 0.6050 0.724 0.276
#> GSM1105556 1 0.0000 0.9638 1.000 0.000
#> GSM1105557 2 0.0000 0.9663 0.000 1.000
#> GSM1105449 2 0.0000 0.9663 0.000 1.000
#> GSM1105469 1 0.7219 0.7447 0.800 0.200
#> GSM1105472 2 0.0000 0.9663 0.000 1.000
#> GSM1105473 1 0.0000 0.9638 1.000 0.000
#> GSM1105476 2 0.0000 0.9663 0.000 1.000
#> GSM1105477 2 0.0000 0.9663 0.000 1.000
#> GSM1105478 2 0.0000 0.9663 0.000 1.000
#> GSM1105510 2 0.0000 0.9663 0.000 1.000
#> GSM1105530 1 0.0000 0.9638 1.000 0.000
#> GSM1105539 1 0.0000 0.9638 1.000 0.000
#> GSM1105480 2 0.0000 0.9663 0.000 1.000
#> GSM1105512 1 0.0000 0.9638 1.000 0.000
#> GSM1105532 1 0.0000 0.9638 1.000 0.000
#> GSM1105541 1 0.0000 0.9638 1.000 0.000
#> GSM1105439 2 0.0000 0.9663 0.000 1.000
#> GSM1105463 1 0.0000 0.9638 1.000 0.000
#> GSM1105482 1 0.0000 0.9638 1.000 0.000
#> GSM1105483 2 0.9552 0.3718 0.376 0.624
#> GSM1105494 2 0.0000 0.9663 0.000 1.000
#> GSM1105503 1 0.9170 0.4881 0.668 0.332
#> GSM1105507 1 0.2948 0.9169 0.948 0.052
#> GSM1105446 2 0.0000 0.9663 0.000 1.000
#> GSM1105519 1 0.0000 0.9638 1.000 0.000
#> GSM1105526 2 0.0000 0.9663 0.000 1.000
#> GSM1105527 2 0.0000 0.9663 0.000 1.000
#> GSM1105531 1 0.0000 0.9638 1.000 0.000
#> GSM1105543 2 0.0000 0.9663 0.000 1.000
#> GSM1105546 1 0.0000 0.9638 1.000 0.000
#> GSM1105547 1 0.0000 0.9638 1.000 0.000
#> GSM1105455 2 0.0000 0.9663 0.000 1.000
#> GSM1105458 2 0.0000 0.9663 0.000 1.000
#> GSM1105459 2 0.0000 0.9663 0.000 1.000
#> GSM1105462 1 0.0000 0.9638 1.000 0.000
#> GSM1105441 2 0.0000 0.9663 0.000 1.000
#> GSM1105465 2 0.3274 0.9141 0.060 0.940
#> GSM1105484 2 0.0000 0.9663 0.000 1.000
#> GSM1105485 2 0.1843 0.9423 0.028 0.972
#> GSM1105496 1 0.0000 0.9638 1.000 0.000
#> GSM1105505 1 0.0000 0.9638 1.000 0.000
#> GSM1105509 1 0.0000 0.9638 1.000 0.000
#> GSM1105448 2 0.0000 0.9663 0.000 1.000
#> GSM1105521 1 0.0000 0.9638 1.000 0.000
#> GSM1105528 2 0.0000 0.9663 0.000 1.000
#> GSM1105529 2 0.0000 0.9663 0.000 1.000
#> GSM1105533 1 0.0000 0.9638 1.000 0.000
#> GSM1105545 2 0.0000 0.9663 0.000 1.000
#> GSM1105548 1 0.0000 0.9638 1.000 0.000
#> GSM1105549 1 0.0000 0.9638 1.000 0.000
#> GSM1105457 2 0.0000 0.9663 0.000 1.000
#> GSM1105460 2 0.0000 0.9663 0.000 1.000
#> GSM1105461 2 0.0000 0.9663 0.000 1.000
#> GSM1105464 1 0.0000 0.9638 1.000 0.000
#> GSM1105466 2 0.0000 0.9663 0.000 1.000
#> GSM1105479 2 0.0000 0.9663 0.000 1.000
#> GSM1105502 1 0.0000 0.9638 1.000 0.000
#> GSM1105515 1 0.0000 0.9638 1.000 0.000
#> GSM1105523 1 0.0000 0.9638 1.000 0.000
#> GSM1105550 1 0.0000 0.9638 1.000 0.000
#> GSM1105450 2 0.0000 0.9663 0.000 1.000
#> GSM1105451 2 0.0000 0.9663 0.000 1.000
#> GSM1105454 2 0.7219 0.7517 0.200 0.800
#> GSM1105468 2 0.0000 0.9663 0.000 1.000
#> GSM1105481 2 0.7219 0.7517 0.200 0.800
#> GSM1105504 1 0.0000 0.9638 1.000 0.000
#> GSM1105517 1 0.0000 0.9638 1.000 0.000
#> GSM1105525 1 0.0000 0.9638 1.000 0.000
#> GSM1105552 1 0.0000 0.9638 1.000 0.000
#> GSM1105452 2 0.0000 0.9663 0.000 1.000
#> GSM1105453 2 0.0000 0.9663 0.000 1.000
#> GSM1105456 2 0.7219 0.7517 0.200 0.800
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1105438 2 0.0000 0.9213 0.000 1.000 0.000
#> GSM1105486 2 0.0000 0.9213 0.000 1.000 0.000
#> GSM1105487 1 0.0000 0.9130 1.000 0.000 0.000
#> GSM1105490 3 0.6126 0.6290 0.000 0.400 0.600
#> GSM1105491 2 0.6126 0.3556 0.000 0.600 0.400
#> GSM1105495 2 0.6140 0.3494 0.000 0.596 0.404
#> GSM1105498 3 0.0237 0.7017 0.000 0.004 0.996
#> GSM1105499 1 0.0000 0.9130 1.000 0.000 0.000
#> GSM1105506 3 0.6126 0.6290 0.000 0.400 0.600
#> GSM1105442 2 0.5216 0.5818 0.000 0.740 0.260
#> GSM1105511 3 0.6126 0.6290 0.000 0.400 0.600
#> GSM1105514 2 0.0000 0.9213 0.000 1.000 0.000
#> GSM1105518 3 0.0237 0.7017 0.000 0.004 0.996
#> GSM1105522 1 0.0000 0.9130 1.000 0.000 0.000
#> GSM1105534 1 0.0000 0.9130 1.000 0.000 0.000
#> GSM1105535 1 0.0000 0.9130 1.000 0.000 0.000
#> GSM1105538 1 0.0000 0.9130 1.000 0.000 0.000
#> GSM1105542 2 0.0237 0.9203 0.000 0.996 0.004
#> GSM1105443 3 0.6126 0.6290 0.000 0.400 0.600
#> GSM1105551 1 0.2959 0.8862 0.900 0.000 0.100
#> GSM1105554 1 0.0000 0.9130 1.000 0.000 0.000
#> GSM1105555 1 0.3551 0.8746 0.868 0.000 0.132
#> GSM1105447 3 0.6079 0.6353 0.000 0.388 0.612
#> GSM1105467 2 0.0000 0.9213 0.000 1.000 0.000
#> GSM1105470 2 0.0000 0.9213 0.000 1.000 0.000
#> GSM1105471 3 0.0237 0.7017 0.000 0.004 0.996
#> GSM1105474 2 0.0000 0.9213 0.000 1.000 0.000
#> GSM1105475 2 0.0424 0.9145 0.000 0.992 0.008
#> GSM1105440 1 0.0000 0.9130 1.000 0.000 0.000
#> GSM1105488 2 0.0237 0.9203 0.000 0.996 0.004
#> GSM1105489 1 0.2959 0.8862 0.900 0.000 0.100
#> GSM1105492 1 0.0000 0.9130 1.000 0.000 0.000
#> GSM1105493 1 0.3551 0.8746 0.868 0.000 0.132
#> GSM1105497 2 0.6126 0.3556 0.000 0.600 0.400
#> GSM1105500 2 0.0237 0.9203 0.000 0.996 0.004
#> GSM1105501 2 0.4291 0.6351 0.000 0.820 0.180
#> GSM1105508 1 0.0000 0.9130 1.000 0.000 0.000
#> GSM1105444 2 0.0000 0.9213 0.000 1.000 0.000
#> GSM1105513 3 0.6126 0.6290 0.000 0.400 0.600
#> GSM1105516 1 0.2625 0.8440 0.916 0.084 0.000
#> GSM1105520 3 0.0237 0.7017 0.000 0.004 0.996
#> GSM1105524 1 0.0000 0.9130 1.000 0.000 0.000
#> GSM1105536 2 0.0000 0.9213 0.000 1.000 0.000
#> GSM1105537 1 0.0000 0.9130 1.000 0.000 0.000
#> GSM1105540 1 0.0000 0.9130 1.000 0.000 0.000
#> GSM1105544 1 0.5276 0.7488 0.820 0.052 0.128
#> GSM1105445 3 0.0237 0.7017 0.000 0.004 0.996
#> GSM1105553 3 0.0000 0.6997 0.000 0.000 1.000
#> GSM1105556 1 0.0000 0.9130 1.000 0.000 0.000
#> GSM1105557 3 0.6126 0.6290 0.000 0.400 0.600
#> GSM1105449 2 0.0892 0.9024 0.000 0.980 0.020
#> GSM1105469 3 0.6154 0.3165 0.408 0.000 0.592
#> GSM1105472 2 0.0000 0.9213 0.000 1.000 0.000
#> GSM1105473 1 0.3551 0.8746 0.868 0.000 0.132
#> GSM1105476 2 0.0000 0.9213 0.000 1.000 0.000
#> GSM1105477 2 0.0237 0.9203 0.000 0.996 0.004
#> GSM1105478 3 0.0592 0.7021 0.000 0.012 0.988
#> GSM1105510 2 0.0237 0.9203 0.000 0.996 0.004
#> GSM1105530 1 0.3551 0.8746 0.868 0.000 0.132
#> GSM1105539 1 0.3551 0.8746 0.868 0.000 0.132
#> GSM1105480 3 0.5650 0.6638 0.000 0.312 0.688
#> GSM1105512 1 0.0000 0.9130 1.000 0.000 0.000
#> GSM1105532 1 0.3551 0.8746 0.868 0.000 0.132
#> GSM1105541 1 0.3551 0.8746 0.868 0.000 0.132
#> GSM1105439 3 0.6126 0.6290 0.000 0.400 0.600
#> GSM1105463 1 0.6111 0.5853 0.604 0.000 0.396
#> GSM1105482 1 0.0000 0.9130 1.000 0.000 0.000
#> GSM1105483 3 0.7462 0.6340 0.048 0.352 0.600
#> GSM1105494 3 0.3619 0.6905 0.000 0.136 0.864
#> GSM1105503 3 0.0237 0.7017 0.000 0.004 0.996
#> GSM1105507 1 0.0000 0.9130 1.000 0.000 0.000
#> GSM1105446 2 0.0237 0.9203 0.000 0.996 0.004
#> GSM1105519 1 0.0000 0.9130 1.000 0.000 0.000
#> GSM1105526 2 0.0000 0.9213 0.000 1.000 0.000
#> GSM1105527 3 0.6126 0.6290 0.000 0.400 0.600
#> GSM1105531 1 0.6111 0.5853 0.604 0.000 0.396
#> GSM1105543 2 0.0237 0.9203 0.000 0.996 0.004
#> GSM1105546 1 0.0000 0.9130 1.000 0.000 0.000
#> GSM1105547 1 0.0000 0.9130 1.000 0.000 0.000
#> GSM1105455 3 0.6126 0.6290 0.000 0.400 0.600
#> GSM1105458 2 0.3267 0.8007 0.000 0.884 0.116
#> GSM1105459 2 0.0000 0.9213 0.000 1.000 0.000
#> GSM1105462 1 0.6111 0.5853 0.604 0.000 0.396
#> GSM1105441 2 0.0592 0.9107 0.000 0.988 0.012
#> GSM1105465 2 0.6126 0.3556 0.000 0.600 0.400
#> GSM1105484 2 0.0237 0.9203 0.000 0.996 0.004
#> GSM1105485 2 0.0237 0.9203 0.000 0.996 0.004
#> GSM1105496 3 0.0424 0.6959 0.008 0.000 0.992
#> GSM1105505 1 0.6111 0.5853 0.604 0.000 0.396
#> GSM1105509 1 0.0000 0.9130 1.000 0.000 0.000
#> GSM1105448 2 0.0000 0.9213 0.000 1.000 0.000
#> GSM1105521 1 0.0000 0.9130 1.000 0.000 0.000
#> GSM1105528 2 0.0237 0.9203 0.000 0.996 0.004
#> GSM1105529 2 0.0237 0.9203 0.000 0.996 0.004
#> GSM1105533 1 0.3551 0.8746 0.868 0.000 0.132
#> GSM1105545 2 0.0000 0.9213 0.000 1.000 0.000
#> GSM1105548 1 0.0000 0.9130 1.000 0.000 0.000
#> GSM1105549 1 0.0000 0.9130 1.000 0.000 0.000
#> GSM1105457 3 0.6126 0.6290 0.000 0.400 0.600
#> GSM1105460 2 0.1031 0.8979 0.000 0.976 0.024
#> GSM1105461 2 0.0000 0.9213 0.000 1.000 0.000
#> GSM1105464 1 0.3551 0.8746 0.868 0.000 0.132
#> GSM1105466 3 0.6126 0.6290 0.000 0.400 0.600
#> GSM1105479 3 0.5835 0.6534 0.000 0.340 0.660
#> GSM1105502 1 0.3551 0.8746 0.868 0.000 0.132
#> GSM1105515 1 0.0000 0.9130 1.000 0.000 0.000
#> GSM1105523 3 0.4002 0.5260 0.160 0.000 0.840
#> GSM1105550 1 0.0000 0.9130 1.000 0.000 0.000
#> GSM1105450 2 0.0000 0.9213 0.000 1.000 0.000
#> GSM1105451 2 0.0000 0.9213 0.000 1.000 0.000
#> GSM1105454 3 0.0237 0.7017 0.000 0.004 0.996
#> GSM1105468 2 0.0000 0.9213 0.000 1.000 0.000
#> GSM1105481 3 0.6252 -0.0081 0.000 0.444 0.556
#> GSM1105504 1 0.6111 0.5853 0.604 0.000 0.396
#> GSM1105517 1 0.0000 0.9130 1.000 0.000 0.000
#> GSM1105525 1 0.3551 0.8746 0.868 0.000 0.132
#> GSM1105552 1 0.3551 0.8746 0.868 0.000 0.132
#> GSM1105452 2 0.0237 0.9203 0.000 0.996 0.004
#> GSM1105453 2 0.0000 0.9213 0.000 1.000 0.000
#> GSM1105456 3 0.0237 0.7017 0.000 0.004 0.996
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1105438 2 0.3074 0.6509 0.000 0.848 0.000 0.152
#> GSM1105486 2 0.4877 0.4567 0.000 0.592 0.000 0.408
#> GSM1105487 1 0.0336 0.9428 0.992 0.000 0.008 0.000
#> GSM1105490 4 0.0927 0.7951 0.000 0.008 0.016 0.976
#> GSM1105491 2 0.5244 -0.0661 0.000 0.556 0.436 0.008
#> GSM1105495 3 0.1151 0.9344 0.000 0.024 0.968 0.008
#> GSM1105498 3 0.1022 0.9360 0.000 0.000 0.968 0.032
#> GSM1105499 1 0.0000 0.9443 1.000 0.000 0.000 0.000
#> GSM1105506 4 0.0927 0.7951 0.000 0.008 0.016 0.976
#> GSM1105442 2 0.0524 0.6693 0.000 0.988 0.004 0.008
#> GSM1105511 4 0.0927 0.7951 0.000 0.008 0.016 0.976
#> GSM1105514 2 0.2973 0.6543 0.000 0.856 0.000 0.144
#> GSM1105518 3 0.0707 0.9432 0.000 0.000 0.980 0.020
#> GSM1105522 1 0.0000 0.9443 1.000 0.000 0.000 0.000
#> GSM1105534 1 0.0000 0.9443 1.000 0.000 0.000 0.000
#> GSM1105535 1 0.0000 0.9443 1.000 0.000 0.000 0.000
#> GSM1105538 1 0.0000 0.9443 1.000 0.000 0.000 0.000
#> GSM1105542 2 0.0188 0.6726 0.000 0.996 0.004 0.000
#> GSM1105443 4 0.3610 0.6122 0.000 0.200 0.000 0.800
#> GSM1105551 1 0.0707 0.9388 0.980 0.000 0.020 0.000
#> GSM1105554 1 0.0000 0.9443 1.000 0.000 0.000 0.000
#> GSM1105555 1 0.2814 0.8766 0.868 0.000 0.132 0.000
#> GSM1105447 4 0.4817 0.1464 0.000 0.388 0.000 0.612
#> GSM1105467 2 0.4888 0.4486 0.000 0.588 0.000 0.412
#> GSM1105470 2 0.4877 0.4567 0.000 0.592 0.000 0.408
#> GSM1105471 4 0.4866 0.3760 0.000 0.000 0.404 0.596
#> GSM1105474 2 0.4843 0.4772 0.000 0.604 0.000 0.396
#> GSM1105475 4 0.4500 0.3582 0.000 0.316 0.000 0.684
#> GSM1105440 1 0.0000 0.9443 1.000 0.000 0.000 0.000
#> GSM1105488 2 0.0188 0.6726 0.000 0.996 0.004 0.000
#> GSM1105489 1 0.0469 0.9418 0.988 0.000 0.012 0.000
#> GSM1105492 1 0.0000 0.9443 1.000 0.000 0.000 0.000
#> GSM1105493 1 0.2814 0.8766 0.868 0.000 0.132 0.000
#> GSM1105497 2 0.3351 0.5706 0.000 0.844 0.148 0.008
#> GSM1105500 2 0.2944 0.5872 0.000 0.868 0.004 0.128
#> GSM1105501 4 0.0895 0.7874 0.000 0.020 0.004 0.976
#> GSM1105508 1 0.0188 0.9426 0.996 0.000 0.000 0.004
#> GSM1105444 2 0.2921 0.6557 0.000 0.860 0.000 0.140
#> GSM1105513 4 0.1182 0.7941 0.000 0.016 0.016 0.968
#> GSM1105516 1 0.3978 0.7782 0.796 0.012 0.000 0.192
#> GSM1105520 3 0.0592 0.9449 0.000 0.000 0.984 0.016
#> GSM1105524 1 0.0000 0.9443 1.000 0.000 0.000 0.000
#> GSM1105536 2 0.4713 0.4033 0.000 0.640 0.000 0.360
#> GSM1105537 1 0.0000 0.9443 1.000 0.000 0.000 0.000
#> GSM1105540 1 0.0000 0.9443 1.000 0.000 0.000 0.000
#> GSM1105544 2 0.7996 0.0161 0.320 0.400 0.004 0.276
#> GSM1105445 4 0.4431 0.5588 0.000 0.000 0.304 0.696
#> GSM1105553 3 0.0779 0.9438 0.000 0.004 0.980 0.016
#> GSM1105556 1 0.0000 0.9443 1.000 0.000 0.000 0.000
#> GSM1105557 4 0.0927 0.7951 0.000 0.008 0.016 0.976
#> GSM1105449 2 0.4989 0.3169 0.000 0.528 0.000 0.472
#> GSM1105469 4 0.3969 0.5919 0.180 0.000 0.016 0.804
#> GSM1105472 2 0.4843 0.4772 0.000 0.604 0.000 0.396
#> GSM1105473 1 0.2868 0.8744 0.864 0.000 0.136 0.000
#> GSM1105476 2 0.4843 0.4772 0.000 0.604 0.000 0.396
#> GSM1105477 2 0.3219 0.5712 0.000 0.836 0.000 0.164
#> GSM1105478 4 0.2081 0.7611 0.000 0.000 0.084 0.916
#> GSM1105510 2 0.0188 0.6726 0.000 0.996 0.004 0.000
#> GSM1105530 1 0.2973 0.8693 0.856 0.000 0.144 0.000
#> GSM1105539 1 0.3024 0.8656 0.852 0.000 0.148 0.000
#> GSM1105480 4 0.1545 0.7834 0.000 0.008 0.040 0.952
#> GSM1105512 1 0.0000 0.9443 1.000 0.000 0.000 0.000
#> GSM1105532 1 0.2973 0.8693 0.856 0.000 0.144 0.000
#> GSM1105541 1 0.2973 0.8693 0.856 0.000 0.144 0.000
#> GSM1105439 4 0.0592 0.7894 0.000 0.016 0.000 0.984
#> GSM1105463 3 0.0707 0.9401 0.020 0.000 0.980 0.000
#> GSM1105482 1 0.0336 0.9428 0.992 0.000 0.008 0.000
#> GSM1105483 4 0.0927 0.7914 0.008 0.000 0.016 0.976
#> GSM1105494 4 0.3219 0.7026 0.000 0.000 0.164 0.836
#> GSM1105503 3 0.0592 0.9442 0.000 0.000 0.984 0.016
#> GSM1105507 1 0.2408 0.8699 0.896 0.000 0.000 0.104
#> GSM1105446 2 0.0376 0.6730 0.000 0.992 0.004 0.004
#> GSM1105519 1 0.0000 0.9443 1.000 0.000 0.000 0.000
#> GSM1105526 4 0.5155 -0.0801 0.000 0.468 0.004 0.528
#> GSM1105527 4 0.0967 0.7936 0.004 0.004 0.016 0.976
#> GSM1105531 3 0.0707 0.9401 0.020 0.000 0.980 0.000
#> GSM1105543 2 0.0188 0.6729 0.000 0.996 0.000 0.004
#> GSM1105546 1 0.0000 0.9443 1.000 0.000 0.000 0.000
#> GSM1105547 1 0.0000 0.9443 1.000 0.000 0.000 0.000
#> GSM1105455 4 0.0592 0.7894 0.000 0.016 0.000 0.984
#> GSM1105458 4 0.5510 -0.2413 0.000 0.480 0.016 0.504
#> GSM1105459 2 0.4843 0.4772 0.000 0.604 0.000 0.396
#> GSM1105462 3 0.0707 0.9401 0.020 0.000 0.980 0.000
#> GSM1105441 2 0.4981 0.3204 0.000 0.536 0.000 0.464
#> GSM1105465 2 0.3933 0.5174 0.000 0.792 0.200 0.008
#> GSM1105484 2 0.0524 0.6693 0.000 0.988 0.004 0.008
#> GSM1105485 2 0.0188 0.6726 0.000 0.996 0.004 0.000
#> GSM1105496 3 0.0524 0.9444 0.000 0.004 0.988 0.008
#> GSM1105505 3 0.0707 0.9401 0.020 0.000 0.980 0.000
#> GSM1105509 1 0.0188 0.9426 0.996 0.000 0.000 0.004
#> GSM1105448 2 0.2921 0.6557 0.000 0.860 0.000 0.140
#> GSM1105521 1 0.0000 0.9443 1.000 0.000 0.000 0.000
#> GSM1105528 2 0.0000 0.6729 0.000 1.000 0.000 0.000
#> GSM1105529 2 0.0188 0.6726 0.000 0.996 0.004 0.000
#> GSM1105533 1 0.2973 0.8693 0.856 0.000 0.144 0.000
#> GSM1105545 4 0.2216 0.7281 0.000 0.092 0.000 0.908
#> GSM1105548 1 0.0336 0.9428 0.992 0.000 0.008 0.000
#> GSM1105549 1 0.0336 0.9428 0.992 0.000 0.008 0.000
#> GSM1105457 4 0.0927 0.7951 0.000 0.008 0.016 0.976
#> GSM1105460 4 0.4222 0.4912 0.000 0.272 0.000 0.728
#> GSM1105461 2 0.4843 0.4772 0.000 0.604 0.000 0.396
#> GSM1105464 1 0.2973 0.8693 0.856 0.000 0.144 0.000
#> GSM1105466 4 0.0927 0.7951 0.000 0.008 0.016 0.976
#> GSM1105479 4 0.4244 0.6450 0.000 0.160 0.036 0.804
#> GSM1105502 1 0.2973 0.8693 0.856 0.000 0.144 0.000
#> GSM1105515 1 0.0000 0.9443 1.000 0.000 0.000 0.000
#> GSM1105523 3 0.0188 0.9445 0.000 0.000 0.996 0.004
#> GSM1105550 1 0.3032 0.8353 0.868 0.000 0.008 0.124
#> GSM1105450 2 0.4877 0.4567 0.000 0.592 0.000 0.408
#> GSM1105451 2 0.4855 0.4708 0.000 0.600 0.000 0.400
#> GSM1105454 3 0.0817 0.9436 0.000 0.000 0.976 0.024
#> GSM1105468 2 0.4843 0.4772 0.000 0.604 0.000 0.396
#> GSM1105481 3 0.0895 0.9401 0.000 0.004 0.976 0.020
#> GSM1105504 3 0.0707 0.9401 0.020 0.000 0.980 0.000
#> GSM1105517 1 0.0188 0.9435 0.996 0.000 0.004 0.000
#> GSM1105525 3 0.4933 0.1158 0.432 0.000 0.568 0.000
#> GSM1105552 1 0.3172 0.8537 0.840 0.000 0.160 0.000
#> GSM1105452 2 0.0188 0.6726 0.000 0.996 0.004 0.000
#> GSM1105453 2 0.4843 0.4772 0.000 0.604 0.000 0.396
#> GSM1105456 3 0.0817 0.9436 0.000 0.000 0.976 0.024
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM1105438 2 0.0510 0.7675 0.000 0.984 0.000 0.000 0.016
#> GSM1105486 2 0.0162 0.7783 0.000 0.996 0.000 0.004 0.000
#> GSM1105487 1 0.2179 0.8428 0.896 0.000 0.100 0.000 0.004
#> GSM1105490 4 0.1608 0.8670 0.000 0.072 0.000 0.928 0.000
#> GSM1105491 5 0.1364 0.6519 0.000 0.012 0.036 0.000 0.952
#> GSM1105495 3 0.4624 0.7146 0.000 0.000 0.636 0.024 0.340
#> GSM1105498 3 0.6441 0.6114 0.000 0.000 0.504 0.256 0.240
#> GSM1105499 1 0.0162 0.8616 0.996 0.000 0.000 0.004 0.000
#> GSM1105506 4 0.1478 0.8682 0.000 0.064 0.000 0.936 0.000
#> GSM1105442 5 0.2377 0.7899 0.000 0.128 0.000 0.000 0.872
#> GSM1105511 4 0.1478 0.8682 0.000 0.064 0.000 0.936 0.000
#> GSM1105514 2 0.0566 0.7721 0.000 0.984 0.000 0.004 0.012
#> GSM1105518 3 0.5635 0.7437 0.000 0.004 0.636 0.120 0.240
#> GSM1105522 1 0.1356 0.8545 0.956 0.000 0.012 0.028 0.004
#> GSM1105534 1 0.0000 0.8616 1.000 0.000 0.000 0.000 0.000
#> GSM1105535 1 0.0162 0.8617 0.996 0.000 0.000 0.000 0.004
#> GSM1105538 1 0.0000 0.8616 1.000 0.000 0.000 0.000 0.000
#> GSM1105542 5 0.3612 0.8559 0.000 0.268 0.000 0.000 0.732
#> GSM1105443 2 0.3992 0.5363 0.000 0.720 0.000 0.268 0.012
#> GSM1105551 1 0.2411 0.8386 0.884 0.000 0.108 0.000 0.008
#> GSM1105554 1 0.0000 0.8616 1.000 0.000 0.000 0.000 0.000
#> GSM1105555 1 0.3300 0.7911 0.792 0.000 0.204 0.000 0.004
#> GSM1105447 2 0.4680 0.6135 0.000 0.740 0.000 0.128 0.132
#> GSM1105467 2 0.0000 0.7778 0.000 1.000 0.000 0.000 0.000
#> GSM1105470 2 0.0162 0.7783 0.000 0.996 0.000 0.004 0.000
#> GSM1105471 2 0.8141 0.0903 0.000 0.420 0.164 0.180 0.236
#> GSM1105474 2 0.0162 0.7783 0.000 0.996 0.000 0.004 0.000
#> GSM1105475 2 0.1043 0.7653 0.000 0.960 0.000 0.040 0.000
#> GSM1105440 1 0.0162 0.8617 0.996 0.000 0.000 0.000 0.004
#> GSM1105488 5 0.3612 0.8559 0.000 0.268 0.000 0.000 0.732
#> GSM1105489 1 0.2358 0.8403 0.888 0.000 0.104 0.000 0.008
#> GSM1105492 1 0.0000 0.8616 1.000 0.000 0.000 0.000 0.000
#> GSM1105493 1 0.3534 0.7575 0.744 0.000 0.256 0.000 0.000
#> GSM1105497 5 0.1205 0.6996 0.000 0.040 0.004 0.000 0.956
#> GSM1105500 5 0.4141 0.8417 0.000 0.236 0.000 0.028 0.736
#> GSM1105501 4 0.3274 0.7307 0.000 0.220 0.000 0.780 0.000
#> GSM1105508 1 0.0960 0.8594 0.972 0.000 0.008 0.016 0.004
#> GSM1105444 2 0.0794 0.7570 0.000 0.972 0.000 0.000 0.028
#> GSM1105513 4 0.2920 0.8221 0.000 0.132 0.000 0.852 0.016
#> GSM1105516 1 0.3689 0.6615 0.740 0.004 0.000 0.256 0.000
#> GSM1105520 3 0.5484 0.7451 0.000 0.000 0.640 0.120 0.240
#> GSM1105524 1 0.0324 0.8618 0.992 0.000 0.000 0.004 0.004
#> GSM1105536 2 0.6187 0.3210 0.000 0.552 0.000 0.248 0.200
#> GSM1105537 1 0.0162 0.8617 0.996 0.000 0.000 0.000 0.004
#> GSM1105540 1 0.1682 0.8490 0.944 0.000 0.012 0.032 0.012
#> GSM1105544 5 0.5076 0.5706 0.200 0.000 0.000 0.108 0.692
#> GSM1105445 2 0.8369 -0.0794 0.000 0.312 0.144 0.304 0.240
#> GSM1105553 3 0.5531 0.7422 0.000 0.000 0.632 0.120 0.248
#> GSM1105556 1 0.0000 0.8616 1.000 0.000 0.000 0.000 0.000
#> GSM1105557 4 0.1478 0.8682 0.000 0.064 0.000 0.936 0.000
#> GSM1105449 2 0.0798 0.7730 0.000 0.976 0.000 0.008 0.016
#> GSM1105469 4 0.1530 0.8208 0.028 0.008 0.004 0.952 0.008
#> GSM1105472 2 0.0000 0.7778 0.000 1.000 0.000 0.000 0.000
#> GSM1105473 1 0.3730 0.7348 0.712 0.000 0.288 0.000 0.000
#> GSM1105476 2 0.0162 0.7783 0.000 0.996 0.000 0.004 0.000
#> GSM1105477 2 0.5857 -0.3146 0.000 0.460 0.000 0.096 0.444
#> GSM1105478 4 0.3448 0.7511 0.000 0.028 0.036 0.856 0.080
#> GSM1105510 5 0.3612 0.8559 0.000 0.268 0.000 0.000 0.732
#> GSM1105530 1 0.4995 0.6289 0.584 0.000 0.384 0.028 0.004
#> GSM1105539 1 0.5005 0.6233 0.580 0.000 0.388 0.028 0.004
#> GSM1105480 4 0.1195 0.8445 0.000 0.028 0.000 0.960 0.012
#> GSM1105512 1 0.0000 0.8616 1.000 0.000 0.000 0.000 0.000
#> GSM1105532 1 0.4995 0.6289 0.584 0.000 0.384 0.028 0.004
#> GSM1105541 1 0.4995 0.6289 0.584 0.000 0.384 0.028 0.004
#> GSM1105439 2 0.4354 0.3262 0.000 0.624 0.000 0.368 0.008
#> GSM1105463 3 0.0162 0.7239 0.004 0.000 0.996 0.000 0.000
#> GSM1105482 1 0.1478 0.8530 0.936 0.000 0.064 0.000 0.000
#> GSM1105483 4 0.1251 0.8489 0.000 0.036 0.000 0.956 0.008
#> GSM1105494 4 0.5865 0.3537 0.000 0.016 0.112 0.632 0.240
#> GSM1105503 3 0.4411 0.7525 0.000 0.000 0.764 0.120 0.116
#> GSM1105507 1 0.2732 0.7645 0.840 0.000 0.000 0.160 0.000
#> GSM1105446 2 0.4294 -0.3082 0.000 0.532 0.000 0.000 0.468
#> GSM1105519 1 0.0000 0.8616 1.000 0.000 0.000 0.000 0.000
#> GSM1105526 4 0.5475 0.4872 0.000 0.308 0.000 0.604 0.088
#> GSM1105527 4 0.1197 0.8610 0.000 0.048 0.000 0.952 0.000
#> GSM1105531 3 0.0162 0.7271 0.000 0.000 0.996 0.000 0.004
#> GSM1105543 2 0.4161 -0.0488 0.000 0.608 0.000 0.000 0.392
#> GSM1105546 1 0.0162 0.8617 0.996 0.000 0.000 0.000 0.004
#> GSM1105547 1 0.0000 0.8616 1.000 0.000 0.000 0.000 0.000
#> GSM1105455 2 0.4446 0.2599 0.000 0.592 0.000 0.400 0.008
#> GSM1105458 2 0.4083 0.5972 0.000 0.744 0.000 0.028 0.228
#> GSM1105459 2 0.0000 0.7778 0.000 1.000 0.000 0.000 0.000
#> GSM1105462 3 0.0794 0.7118 0.000 0.000 0.972 0.028 0.000
#> GSM1105441 2 0.0693 0.7740 0.000 0.980 0.000 0.008 0.012
#> GSM1105465 5 0.1670 0.7138 0.000 0.052 0.012 0.000 0.936
#> GSM1105484 5 0.3612 0.8559 0.000 0.268 0.000 0.000 0.732
#> GSM1105485 5 0.3612 0.8559 0.000 0.268 0.000 0.000 0.732
#> GSM1105496 3 0.4787 0.7269 0.000 0.000 0.640 0.036 0.324
#> GSM1105505 3 0.0566 0.7303 0.000 0.000 0.984 0.004 0.012
#> GSM1105509 1 0.0404 0.8607 0.988 0.000 0.000 0.012 0.000
#> GSM1105448 2 0.0510 0.7675 0.000 0.984 0.000 0.000 0.016
#> GSM1105521 1 0.0162 0.8616 0.996 0.000 0.000 0.004 0.000
#> GSM1105528 5 0.3612 0.8559 0.000 0.268 0.000 0.000 0.732
#> GSM1105529 5 0.3612 0.8559 0.000 0.268 0.000 0.000 0.732
#> GSM1105533 1 0.4029 0.7126 0.680 0.000 0.316 0.000 0.004
#> GSM1105545 4 0.4327 0.4959 0.000 0.360 0.000 0.632 0.008
#> GSM1105548 1 0.1956 0.8494 0.916 0.000 0.076 0.000 0.008
#> GSM1105549 1 0.1671 0.8508 0.924 0.000 0.076 0.000 0.000
#> GSM1105457 4 0.1830 0.8669 0.000 0.068 0.000 0.924 0.008
#> GSM1105460 2 0.2248 0.7306 0.000 0.900 0.000 0.088 0.012
#> GSM1105461 2 0.0000 0.7778 0.000 1.000 0.000 0.000 0.000
#> GSM1105464 1 0.4846 0.6299 0.588 0.000 0.384 0.028 0.000
#> GSM1105466 4 0.1764 0.8675 0.000 0.064 0.000 0.928 0.008
#> GSM1105479 2 0.6267 0.3279 0.000 0.540 0.000 0.224 0.236
#> GSM1105502 1 0.4530 0.6544 0.612 0.000 0.376 0.008 0.004
#> GSM1105515 1 0.0000 0.8616 1.000 0.000 0.000 0.000 0.000
#> GSM1105523 3 0.2077 0.6808 0.000 0.000 0.908 0.084 0.008
#> GSM1105550 1 0.6609 0.4880 0.520 0.000 0.280 0.188 0.012
#> GSM1105450 2 0.0000 0.7778 0.000 1.000 0.000 0.000 0.000
#> GSM1105451 2 0.0162 0.7783 0.000 0.996 0.000 0.004 0.000
#> GSM1105454 3 0.5709 0.7441 0.000 0.008 0.636 0.116 0.240
#> GSM1105468 2 0.0000 0.7778 0.000 1.000 0.000 0.000 0.000
#> GSM1105481 3 0.4403 0.7623 0.000 0.004 0.724 0.032 0.240
#> GSM1105504 3 0.0000 0.7257 0.000 0.000 1.000 0.000 0.000
#> GSM1105517 1 0.3023 0.8162 0.868 0.000 0.096 0.028 0.008
#> GSM1105525 3 0.4584 0.4757 0.160 0.000 0.752 0.084 0.004
#> GSM1105552 1 0.4264 0.6572 0.620 0.000 0.376 0.000 0.004
#> GSM1105452 5 0.3612 0.8559 0.000 0.268 0.000 0.000 0.732
#> GSM1105453 2 0.0162 0.7783 0.000 0.996 0.000 0.004 0.000
#> GSM1105456 3 0.5709 0.7441 0.000 0.008 0.636 0.116 0.240
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1105438 2 0.0000 0.8782 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105486 2 0.0146 0.8776 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM1105487 1 0.3172 0.8069 0.820 0.000 0.152 0.000 0.016 0.012
#> GSM1105490 4 0.0291 0.8913 0.000 0.004 0.000 0.992 0.000 0.004
#> GSM1105491 5 0.1615 0.9083 0.000 0.004 0.004 0.000 0.928 0.064
#> GSM1105495 6 0.1049 0.8309 0.000 0.000 0.008 0.000 0.032 0.960
#> GSM1105498 6 0.3232 0.7260 0.000 0.000 0.020 0.160 0.008 0.812
#> GSM1105499 1 0.1327 0.8637 0.936 0.000 0.064 0.000 0.000 0.000
#> GSM1105506 4 0.0146 0.8909 0.000 0.000 0.000 0.996 0.000 0.004
#> GSM1105442 5 0.1434 0.9204 0.000 0.012 0.000 0.000 0.940 0.048
#> GSM1105511 4 0.0603 0.8869 0.000 0.000 0.016 0.980 0.004 0.000
#> GSM1105514 2 0.0146 0.8776 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM1105518 6 0.0665 0.8431 0.000 0.004 0.008 0.008 0.000 0.980
#> GSM1105522 1 0.2982 0.7985 0.828 0.000 0.152 0.000 0.012 0.008
#> GSM1105534 1 0.0363 0.8682 0.988 0.000 0.012 0.000 0.000 0.000
#> GSM1105535 1 0.2002 0.8551 0.908 0.000 0.076 0.000 0.012 0.004
#> GSM1105538 1 0.0363 0.8680 0.988 0.000 0.000 0.000 0.012 0.000
#> GSM1105542 5 0.1204 0.9429 0.000 0.056 0.000 0.000 0.944 0.000
#> GSM1105443 2 0.3010 0.7741 0.000 0.836 0.004 0.132 0.028 0.000
#> GSM1105551 1 0.3352 0.7953 0.800 0.000 0.172 0.000 0.016 0.012
#> GSM1105554 1 0.0458 0.8676 0.984 0.000 0.016 0.000 0.000 0.000
#> GSM1105555 1 0.3394 0.6745 0.752 0.000 0.236 0.000 0.000 0.012
#> GSM1105447 2 0.3960 0.7135 0.000 0.784 0.004 0.032 0.028 0.152
#> GSM1105467 2 0.0146 0.8772 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM1105470 2 0.0000 0.8782 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105471 6 0.5406 0.2788 0.000 0.384 0.012 0.084 0.000 0.520
#> GSM1105474 2 0.0146 0.8776 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM1105475 2 0.0291 0.8774 0.000 0.992 0.000 0.004 0.004 0.000
#> GSM1105440 1 0.1858 0.8590 0.924 0.000 0.052 0.000 0.012 0.012
#> GSM1105488 5 0.1204 0.9429 0.000 0.056 0.000 0.000 0.944 0.000
#> GSM1105489 1 0.2758 0.8189 0.860 0.000 0.112 0.000 0.016 0.012
#> GSM1105492 1 0.0146 0.8683 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM1105493 1 0.3717 0.3054 0.616 0.000 0.384 0.000 0.000 0.000
#> GSM1105497 5 0.1349 0.9147 0.000 0.004 0.000 0.000 0.940 0.056
#> GSM1105500 5 0.3116 0.8918 0.000 0.060 0.020 0.032 0.868 0.020
#> GSM1105501 4 0.2789 0.8207 0.000 0.088 0.044 0.864 0.004 0.000
#> GSM1105508 1 0.2326 0.8466 0.888 0.000 0.092 0.000 0.012 0.008
#> GSM1105444 2 0.0146 0.8774 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM1105513 4 0.3934 0.7149 0.000 0.136 0.000 0.788 0.028 0.048
#> GSM1105516 1 0.4424 0.6506 0.720 0.004 0.060 0.208 0.008 0.000
#> GSM1105520 6 0.0603 0.8410 0.000 0.000 0.016 0.004 0.000 0.980
#> GSM1105524 1 0.2002 0.8551 0.908 0.000 0.076 0.000 0.012 0.004
#> GSM1105536 2 0.6695 0.2799 0.000 0.504 0.100 0.256 0.140 0.000
#> GSM1105537 1 0.2002 0.8551 0.908 0.000 0.076 0.000 0.012 0.004
#> GSM1105540 1 0.4634 0.6652 0.696 0.000 0.244 0.016 0.024 0.020
#> GSM1105544 5 0.6255 0.5980 0.136 0.000 0.088 0.100 0.640 0.036
#> GSM1105445 6 0.5583 0.5625 0.000 0.172 0.004 0.156 0.028 0.640
#> GSM1105553 6 0.1036 0.8285 0.008 0.000 0.024 0.000 0.004 0.964
#> GSM1105556 1 0.0458 0.8676 0.984 0.000 0.016 0.000 0.000 0.000
#> GSM1105557 4 0.0291 0.8913 0.000 0.004 0.000 0.992 0.000 0.004
#> GSM1105449 2 0.1003 0.8633 0.000 0.964 0.004 0.004 0.028 0.000
#> GSM1105469 4 0.0508 0.8881 0.000 0.000 0.012 0.984 0.004 0.000
#> GSM1105472 2 0.0000 0.8782 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105473 3 0.3847 0.2703 0.456 0.000 0.544 0.000 0.000 0.000
#> GSM1105476 2 0.0146 0.8776 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM1105477 2 0.6993 -0.0309 0.000 0.404 0.100 0.156 0.340 0.000
#> GSM1105478 4 0.4045 0.6166 0.000 0.004 0.008 0.740 0.032 0.216
#> GSM1105510 5 0.1204 0.9429 0.000 0.056 0.000 0.000 0.944 0.000
#> GSM1105530 3 0.2178 0.7974 0.132 0.000 0.868 0.000 0.000 0.000
#> GSM1105539 3 0.2191 0.8004 0.120 0.000 0.876 0.000 0.000 0.004
#> GSM1105480 4 0.2118 0.8538 0.000 0.004 0.012 0.916 0.020 0.048
#> GSM1105512 1 0.0935 0.8676 0.964 0.000 0.032 0.000 0.004 0.000
#> GSM1105532 3 0.2178 0.7974 0.132 0.000 0.868 0.000 0.000 0.000
#> GSM1105541 3 0.2135 0.7980 0.128 0.000 0.872 0.000 0.000 0.000
#> GSM1105439 2 0.3280 0.7457 0.000 0.808 0.004 0.160 0.028 0.000
#> GSM1105463 3 0.3288 0.6683 0.000 0.000 0.724 0.000 0.000 0.276
#> GSM1105482 1 0.1714 0.8419 0.908 0.000 0.092 0.000 0.000 0.000
#> GSM1105483 4 0.0603 0.8869 0.000 0.000 0.016 0.980 0.004 0.000
#> GSM1105494 6 0.4517 0.4011 0.000 0.004 0.020 0.360 0.008 0.608
#> GSM1105503 6 0.1411 0.8043 0.000 0.000 0.060 0.004 0.000 0.936
#> GSM1105507 1 0.3959 0.7152 0.760 0.000 0.064 0.172 0.004 0.000
#> GSM1105446 2 0.3717 0.3436 0.000 0.616 0.000 0.000 0.384 0.000
#> GSM1105519 1 0.0935 0.8676 0.964 0.000 0.032 0.000 0.004 0.000
#> GSM1105526 4 0.4653 0.7191 0.000 0.136 0.080 0.740 0.044 0.000
#> GSM1105527 4 0.0000 0.8909 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1105531 3 0.3351 0.6563 0.000 0.000 0.712 0.000 0.000 0.288
#> GSM1105543 2 0.3428 0.5152 0.000 0.696 0.000 0.000 0.304 0.000
#> GSM1105546 1 0.1180 0.8651 0.960 0.000 0.016 0.000 0.012 0.012
#> GSM1105547 1 0.0547 0.8675 0.980 0.000 0.020 0.000 0.000 0.000
#> GSM1105455 2 0.2730 0.7352 0.000 0.808 0.000 0.192 0.000 0.000
#> GSM1105458 2 0.2375 0.8184 0.000 0.896 0.004 0.004 0.028 0.068
#> GSM1105459 2 0.0000 0.8782 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105462 3 0.2854 0.7149 0.000 0.000 0.792 0.000 0.000 0.208
#> GSM1105441 2 0.1003 0.8633 0.000 0.964 0.004 0.004 0.028 0.000
#> GSM1105465 5 0.1349 0.9147 0.000 0.004 0.000 0.000 0.940 0.056
#> GSM1105484 5 0.1204 0.9429 0.000 0.056 0.000 0.000 0.944 0.000
#> GSM1105485 5 0.1204 0.9429 0.000 0.056 0.000 0.000 0.944 0.000
#> GSM1105496 6 0.0547 0.8395 0.000 0.000 0.020 0.000 0.000 0.980
#> GSM1105505 3 0.3843 0.3894 0.000 0.000 0.548 0.000 0.000 0.452
#> GSM1105509 1 0.1843 0.8546 0.912 0.000 0.080 0.004 0.004 0.000
#> GSM1105448 2 0.0146 0.8776 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM1105521 1 0.0935 0.8676 0.964 0.000 0.032 0.000 0.004 0.000
#> GSM1105528 5 0.1387 0.9342 0.000 0.068 0.000 0.000 0.932 0.000
#> GSM1105529 5 0.1204 0.9429 0.000 0.056 0.000 0.000 0.944 0.000
#> GSM1105533 3 0.3741 0.5663 0.320 0.000 0.672 0.000 0.000 0.008
#> GSM1105545 4 0.4584 0.6672 0.000 0.196 0.100 0.700 0.004 0.000
#> GSM1105548 1 0.2518 0.8339 0.880 0.000 0.092 0.000 0.016 0.012
#> GSM1105549 1 0.1765 0.8393 0.904 0.000 0.096 0.000 0.000 0.000
#> GSM1105457 4 0.0405 0.8905 0.000 0.004 0.000 0.988 0.000 0.008
#> GSM1105460 2 0.1116 0.8615 0.000 0.960 0.004 0.008 0.028 0.000
#> GSM1105461 2 0.0000 0.8782 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105464 3 0.2219 0.7967 0.136 0.000 0.864 0.000 0.000 0.000
#> GSM1105466 4 0.0820 0.8841 0.000 0.016 0.000 0.972 0.012 0.000
#> GSM1105479 2 0.6105 -0.1401 0.000 0.432 0.004 0.112 0.028 0.424
#> GSM1105502 3 0.2697 0.7623 0.188 0.000 0.812 0.000 0.000 0.000
#> GSM1105515 1 0.0458 0.8676 0.984 0.000 0.016 0.000 0.000 0.000
#> GSM1105523 3 0.2981 0.7318 0.000 0.000 0.820 0.020 0.000 0.160
#> GSM1105550 3 0.2558 0.7018 0.104 0.000 0.868 0.028 0.000 0.000
#> GSM1105450 2 0.0000 0.8782 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105451 2 0.0000 0.8782 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105454 6 0.0653 0.8429 0.000 0.004 0.012 0.004 0.000 0.980
#> GSM1105468 2 0.0000 0.8782 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105481 6 0.0937 0.8272 0.000 0.000 0.040 0.000 0.000 0.960
#> GSM1105504 3 0.3198 0.6818 0.000 0.000 0.740 0.000 0.000 0.260
#> GSM1105517 1 0.4222 0.1857 0.516 0.000 0.472 0.008 0.004 0.000
#> GSM1105525 3 0.3346 0.7725 0.056 0.000 0.840 0.024 0.000 0.080
#> GSM1105552 3 0.2454 0.7742 0.160 0.000 0.840 0.000 0.000 0.000
#> GSM1105452 5 0.1204 0.9429 0.000 0.056 0.000 0.000 0.944 0.000
#> GSM1105453 2 0.0146 0.8776 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM1105456 6 0.0653 0.8429 0.000 0.004 0.012 0.004 0.000 0.980
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 agent(p) other(p) time(p) individual(p) k
#> CV:skmeans 115 1.000 0.73912 0.495 1.08e-02 2
#> CV:skmeans 114 0.923 0.53385 0.117 1.78e-03 3
#> CV:skmeans 96 0.249 0.48482 0.467 1.51e-02 4
#> CV:skmeans 106 0.231 0.81010 0.471 3.98e-03 5
#> CV:skmeans 110 0.262 0.00037 0.524 9.72e-06 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 44956 rows and 120 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 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 0.897 0.896 0.962 0.4728 0.523 0.523
#> 3 3 0.703 0.822 0.921 0.3112 0.811 0.655
#> 4 4 0.628 0.715 0.812 0.1607 0.834 0.586
#> 5 5 0.674 0.711 0.803 0.0801 0.925 0.724
#> 6 6 0.715 0.692 0.779 0.0537 0.913 0.637
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
#> GSM1105438 2 0.000 0.9681 0.000 1.000
#> GSM1105486 2 0.000 0.9681 0.000 1.000
#> GSM1105487 1 0.000 0.9421 1.000 0.000
#> GSM1105490 2 0.000 0.9681 0.000 1.000
#> GSM1105491 2 1.000 -0.0778 0.500 0.500
#> GSM1105495 2 0.000 0.9681 0.000 1.000
#> GSM1105498 2 0.000 0.9681 0.000 1.000
#> GSM1105499 1 0.000 0.9421 1.000 0.000
#> GSM1105506 2 0.000 0.9681 0.000 1.000
#> GSM1105442 2 0.000 0.9681 0.000 1.000
#> GSM1105511 2 0.000 0.9681 0.000 1.000
#> GSM1105514 2 0.000 0.9681 0.000 1.000
#> GSM1105518 2 0.000 0.9681 0.000 1.000
#> GSM1105522 1 0.000 0.9421 1.000 0.000
#> GSM1105534 1 0.000 0.9421 1.000 0.000
#> GSM1105535 1 0.000 0.9421 1.000 0.000
#> GSM1105538 1 0.000 0.9421 1.000 0.000
#> GSM1105542 2 0.000 0.9681 0.000 1.000
#> GSM1105443 2 0.000 0.9681 0.000 1.000
#> GSM1105551 1 0.000 0.9421 1.000 0.000
#> GSM1105554 1 0.000 0.9421 1.000 0.000
#> GSM1105555 1 0.000 0.9421 1.000 0.000
#> GSM1105447 2 0.000 0.9681 0.000 1.000
#> GSM1105467 2 0.000 0.9681 0.000 1.000
#> GSM1105470 2 0.000 0.9681 0.000 1.000
#> GSM1105471 2 0.000 0.9681 0.000 1.000
#> GSM1105474 2 0.000 0.9681 0.000 1.000
#> GSM1105475 2 0.000 0.9681 0.000 1.000
#> GSM1105440 1 0.000 0.9421 1.000 0.000
#> GSM1105488 2 0.000 0.9681 0.000 1.000
#> GSM1105489 1 0.000 0.9421 1.000 0.000
#> GSM1105492 1 0.000 0.9421 1.000 0.000
#> GSM1105493 1 0.000 0.9421 1.000 0.000
#> GSM1105497 2 0.000 0.9681 0.000 1.000
#> GSM1105500 2 0.343 0.9014 0.064 0.936
#> GSM1105501 2 0.000 0.9681 0.000 1.000
#> GSM1105508 1 0.000 0.9421 1.000 0.000
#> GSM1105444 2 0.000 0.9681 0.000 1.000
#> GSM1105513 2 0.000 0.9681 0.000 1.000
#> GSM1105516 1 0.881 0.5831 0.700 0.300
#> GSM1105520 2 0.000 0.9681 0.000 1.000
#> GSM1105524 1 0.000 0.9421 1.000 0.000
#> GSM1105536 2 0.000 0.9681 0.000 1.000
#> GSM1105537 1 0.000 0.9421 1.000 0.000
#> GSM1105540 2 1.000 -0.0778 0.500 0.500
#> GSM1105544 2 1.000 -0.0778 0.500 0.500
#> GSM1105445 2 0.000 0.9681 0.000 1.000
#> GSM1105553 2 0.680 0.7489 0.180 0.820
#> GSM1105556 1 0.000 0.9421 1.000 0.000
#> GSM1105557 2 0.000 0.9681 0.000 1.000
#> GSM1105449 2 0.000 0.9681 0.000 1.000
#> GSM1105469 2 0.000 0.9681 0.000 1.000
#> GSM1105472 2 0.000 0.9681 0.000 1.000
#> GSM1105473 1 0.000 0.9421 1.000 0.000
#> GSM1105476 2 0.000 0.9681 0.000 1.000
#> GSM1105477 2 0.000 0.9681 0.000 1.000
#> GSM1105478 2 0.000 0.9681 0.000 1.000
#> GSM1105510 2 0.000 0.9681 0.000 1.000
#> GSM1105530 1 0.000 0.9421 1.000 0.000
#> GSM1105539 1 0.000 0.9421 1.000 0.000
#> GSM1105480 2 0.000 0.9681 0.000 1.000
#> GSM1105512 1 0.000 0.9421 1.000 0.000
#> GSM1105532 1 0.000 0.9421 1.000 0.000
#> GSM1105541 1 0.000 0.9421 1.000 0.000
#> GSM1105439 2 0.000 0.9681 0.000 1.000
#> GSM1105463 1 0.000 0.9421 1.000 0.000
#> GSM1105482 1 0.000 0.9421 1.000 0.000
#> GSM1105483 2 0.000 0.9681 0.000 1.000
#> GSM1105494 2 0.000 0.9681 0.000 1.000
#> GSM1105503 2 0.000 0.9681 0.000 1.000
#> GSM1105507 1 0.242 0.9097 0.960 0.040
#> GSM1105446 2 0.000 0.9681 0.000 1.000
#> GSM1105519 1 0.000 0.9421 1.000 0.000
#> GSM1105526 2 0.000 0.9681 0.000 1.000
#> GSM1105527 2 0.000 0.9681 0.000 1.000
#> GSM1105531 1 0.929 0.5003 0.656 0.344
#> GSM1105543 2 0.000 0.9681 0.000 1.000
#> GSM1105546 1 0.000 0.9421 1.000 0.000
#> GSM1105547 1 0.000 0.9421 1.000 0.000
#> GSM1105455 2 0.000 0.9681 0.000 1.000
#> GSM1105458 2 0.000 0.9681 0.000 1.000
#> GSM1105459 2 0.000 0.9681 0.000 1.000
#> GSM1105462 2 0.000 0.9681 0.000 1.000
#> GSM1105441 2 0.000 0.9681 0.000 1.000
#> GSM1105465 2 0.000 0.9681 0.000 1.000
#> GSM1105484 2 0.000 0.9681 0.000 1.000
#> GSM1105485 2 0.000 0.9681 0.000 1.000
#> GSM1105496 1 1.000 0.0443 0.500 0.500
#> GSM1105505 1 0.981 0.3114 0.580 0.420
#> GSM1105509 1 0.000 0.9421 1.000 0.000
#> GSM1105448 2 0.000 0.9681 0.000 1.000
#> GSM1105521 1 0.000 0.9421 1.000 0.000
#> GSM1105528 2 0.000 0.9681 0.000 1.000
#> GSM1105529 2 0.000 0.9681 0.000 1.000
#> GSM1105533 1 0.000 0.9421 1.000 0.000
#> GSM1105545 2 0.000 0.9681 0.000 1.000
#> GSM1105548 1 0.000 0.9421 1.000 0.000
#> GSM1105549 1 0.000 0.9421 1.000 0.000
#> GSM1105457 2 0.000 0.9681 0.000 1.000
#> GSM1105460 2 0.000 0.9681 0.000 1.000
#> GSM1105461 2 0.000 0.9681 0.000 1.000
#> GSM1105464 1 0.000 0.9421 1.000 0.000
#> GSM1105466 2 0.000 0.9681 0.000 1.000
#> GSM1105479 2 0.000 0.9681 0.000 1.000
#> GSM1105502 1 0.000 0.9421 1.000 0.000
#> GSM1105515 1 0.000 0.9421 1.000 0.000
#> GSM1105523 1 0.932 0.4919 0.652 0.348
#> GSM1105550 2 0.955 0.3459 0.376 0.624
#> GSM1105450 2 0.000 0.9681 0.000 1.000
#> GSM1105451 2 0.000 0.9681 0.000 1.000
#> GSM1105454 2 0.000 0.9681 0.000 1.000
#> GSM1105468 2 0.000 0.9681 0.000 1.000
#> GSM1105481 2 0.000 0.9681 0.000 1.000
#> GSM1105504 1 0.714 0.7432 0.804 0.196
#> GSM1105517 1 0.886 0.5774 0.696 0.304
#> GSM1105525 1 0.000 0.9421 1.000 0.000
#> GSM1105552 1 0.000 0.9421 1.000 0.000
#> GSM1105452 2 0.000 0.9681 0.000 1.000
#> GSM1105453 2 0.000 0.9681 0.000 1.000
#> GSM1105456 2 0.000 0.9681 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1105438 2 0.0424 0.92696 0.000 0.992 0.008
#> GSM1105486 2 0.0000 0.92750 0.000 1.000 0.000
#> GSM1105487 1 0.0000 0.91828 1.000 0.000 0.000
#> GSM1105490 2 0.0424 0.92696 0.000 0.992 0.008
#> GSM1105491 3 0.0424 0.83082 0.008 0.000 0.992
#> GSM1105495 3 0.4887 0.65711 0.000 0.228 0.772
#> GSM1105498 3 0.2537 0.79591 0.000 0.080 0.920
#> GSM1105499 1 0.0000 0.91828 1.000 0.000 0.000
#> GSM1105506 2 0.0000 0.92750 0.000 1.000 0.000
#> GSM1105442 2 0.0592 0.92512 0.000 0.988 0.012
#> GSM1105511 2 0.0000 0.92750 0.000 1.000 0.000
#> GSM1105514 2 0.0000 0.92750 0.000 1.000 0.000
#> GSM1105518 2 0.5098 0.64661 0.000 0.752 0.248
#> GSM1105522 1 0.5591 0.49634 0.696 0.000 0.304
#> GSM1105534 1 0.0000 0.91828 1.000 0.000 0.000
#> GSM1105535 1 0.0000 0.91828 1.000 0.000 0.000
#> GSM1105538 1 0.0000 0.91828 1.000 0.000 0.000
#> GSM1105542 2 0.0000 0.92750 0.000 1.000 0.000
#> GSM1105443 2 0.0424 0.92696 0.000 0.992 0.008
#> GSM1105551 1 0.0237 0.91614 0.996 0.000 0.004
#> GSM1105554 1 0.0000 0.91828 1.000 0.000 0.000
#> GSM1105555 1 0.0237 0.91614 0.996 0.000 0.004
#> GSM1105447 2 0.0424 0.92696 0.000 0.992 0.008
#> GSM1105467 2 0.0000 0.92750 0.000 1.000 0.000
#> GSM1105470 2 0.0000 0.92750 0.000 1.000 0.000
#> GSM1105471 2 0.4452 0.77917 0.000 0.808 0.192
#> GSM1105474 2 0.0000 0.92750 0.000 1.000 0.000
#> GSM1105475 2 0.0000 0.92750 0.000 1.000 0.000
#> GSM1105440 1 0.0000 0.91828 1.000 0.000 0.000
#> GSM1105488 2 0.0000 0.92750 0.000 1.000 0.000
#> GSM1105489 1 0.0237 0.91614 0.996 0.000 0.004
#> GSM1105492 1 0.0000 0.91828 1.000 0.000 0.000
#> GSM1105493 1 0.3816 0.77049 0.852 0.000 0.148
#> GSM1105497 2 0.4654 0.70456 0.000 0.792 0.208
#> GSM1105500 2 0.3038 0.83813 0.000 0.896 0.104
#> GSM1105501 2 0.0000 0.92750 0.000 1.000 0.000
#> GSM1105508 1 0.1163 0.89708 0.972 0.000 0.028
#> GSM1105444 2 0.0424 0.92696 0.000 0.992 0.008
#> GSM1105513 2 0.0424 0.92696 0.000 0.992 0.008
#> GSM1105516 1 0.9795 -0.00795 0.428 0.316 0.256
#> GSM1105520 3 0.0424 0.82902 0.000 0.008 0.992
#> GSM1105524 1 0.0000 0.91828 1.000 0.000 0.000
#> GSM1105536 2 0.4452 0.77917 0.000 0.808 0.192
#> GSM1105537 1 0.0000 0.91828 1.000 0.000 0.000
#> GSM1105540 3 0.5247 0.66650 0.008 0.224 0.768
#> GSM1105544 2 0.5420 0.71048 0.008 0.752 0.240
#> GSM1105445 2 0.0424 0.92696 0.000 0.992 0.008
#> GSM1105553 2 0.5178 0.63173 0.000 0.744 0.256
#> GSM1105556 1 0.0000 0.91828 1.000 0.000 0.000
#> GSM1105557 2 0.0424 0.92696 0.000 0.992 0.008
#> GSM1105449 2 0.0424 0.92696 0.000 0.992 0.008
#> GSM1105469 2 0.4452 0.77917 0.000 0.808 0.192
#> GSM1105472 2 0.0000 0.92750 0.000 1.000 0.000
#> GSM1105473 3 0.6140 0.31396 0.404 0.000 0.596
#> GSM1105476 2 0.0000 0.92750 0.000 1.000 0.000
#> GSM1105477 2 0.4452 0.77917 0.000 0.808 0.192
#> GSM1105478 2 0.3941 0.81303 0.000 0.844 0.156
#> GSM1105510 2 0.0000 0.92750 0.000 1.000 0.000
#> GSM1105530 3 0.0592 0.83019 0.012 0.000 0.988
#> GSM1105539 3 0.0592 0.83019 0.012 0.000 0.988
#> GSM1105480 2 0.4452 0.77917 0.000 0.808 0.192
#> GSM1105512 1 0.0000 0.91828 1.000 0.000 0.000
#> GSM1105532 3 0.0592 0.83019 0.012 0.000 0.988
#> GSM1105541 1 0.6267 0.15703 0.548 0.000 0.452
#> GSM1105439 2 0.0424 0.92696 0.000 0.992 0.008
#> GSM1105463 3 0.0424 0.83082 0.008 0.000 0.992
#> GSM1105482 1 0.0000 0.91828 1.000 0.000 0.000
#> GSM1105483 2 0.4452 0.77917 0.000 0.808 0.192
#> GSM1105494 2 0.0237 0.92622 0.000 0.996 0.004
#> GSM1105503 3 0.0000 0.82881 0.000 0.000 1.000
#> GSM1105507 1 0.7424 0.21846 0.572 0.040 0.388
#> GSM1105446 2 0.0424 0.92696 0.000 0.992 0.008
#> GSM1105519 1 0.1031 0.89889 0.976 0.000 0.024
#> GSM1105526 2 0.4452 0.77917 0.000 0.808 0.192
#> GSM1105527 2 0.4452 0.77917 0.000 0.808 0.192
#> GSM1105531 3 0.0424 0.83082 0.008 0.000 0.992
#> GSM1105543 2 0.0000 0.92750 0.000 1.000 0.000
#> GSM1105546 1 0.0000 0.91828 1.000 0.000 0.000
#> GSM1105547 1 0.0000 0.91828 1.000 0.000 0.000
#> GSM1105455 2 0.0424 0.92696 0.000 0.992 0.008
#> GSM1105458 2 0.0424 0.92696 0.000 0.992 0.008
#> GSM1105459 2 0.0424 0.92696 0.000 0.992 0.008
#> GSM1105462 3 0.5098 0.62715 0.000 0.248 0.752
#> GSM1105441 2 0.0424 0.92696 0.000 0.992 0.008
#> GSM1105465 2 0.5529 0.63155 0.000 0.704 0.296
#> GSM1105484 2 0.0000 0.92750 0.000 1.000 0.000
#> GSM1105485 2 0.4452 0.77917 0.000 0.808 0.192
#> GSM1105496 3 0.1529 0.81101 0.000 0.040 0.960
#> GSM1105505 3 0.0424 0.83082 0.008 0.000 0.992
#> GSM1105509 3 0.6244 0.21914 0.440 0.000 0.560
#> GSM1105448 2 0.0424 0.92696 0.000 0.992 0.008
#> GSM1105521 1 0.0000 0.91828 1.000 0.000 0.000
#> GSM1105528 2 0.0000 0.92750 0.000 1.000 0.000
#> GSM1105529 2 0.4452 0.77917 0.000 0.808 0.192
#> GSM1105533 1 0.0237 0.91604 0.996 0.000 0.004
#> GSM1105545 2 0.4452 0.77917 0.000 0.808 0.192
#> GSM1105548 1 0.2261 0.85979 0.932 0.000 0.068
#> GSM1105549 1 0.0000 0.91828 1.000 0.000 0.000
#> GSM1105457 2 0.0424 0.92696 0.000 0.992 0.008
#> GSM1105460 2 0.0424 0.92696 0.000 0.992 0.008
#> GSM1105461 2 0.0424 0.92696 0.000 0.992 0.008
#> GSM1105464 3 0.1643 0.81386 0.044 0.000 0.956
#> GSM1105466 2 0.0000 0.92750 0.000 1.000 0.000
#> GSM1105479 2 0.0000 0.92750 0.000 1.000 0.000
#> GSM1105502 3 0.6168 0.21971 0.412 0.000 0.588
#> GSM1105515 1 0.0000 0.91828 1.000 0.000 0.000
#> GSM1105523 3 0.0424 0.83082 0.008 0.000 0.992
#> GSM1105550 3 0.6129 0.47054 0.008 0.324 0.668
#> GSM1105450 2 0.0000 0.92750 0.000 1.000 0.000
#> GSM1105451 2 0.0424 0.92696 0.000 0.992 0.008
#> GSM1105454 3 0.5733 0.54431 0.000 0.324 0.676
#> GSM1105468 2 0.0000 0.92750 0.000 1.000 0.000
#> GSM1105481 3 0.0424 0.82902 0.000 0.008 0.992
#> GSM1105504 3 0.0424 0.83082 0.008 0.000 0.992
#> GSM1105517 3 0.8199 0.57138 0.200 0.160 0.640
#> GSM1105525 3 0.3482 0.75049 0.128 0.000 0.872
#> GSM1105552 3 0.0592 0.83019 0.012 0.000 0.988
#> GSM1105452 2 0.0000 0.92750 0.000 1.000 0.000
#> GSM1105453 2 0.0424 0.92696 0.000 0.992 0.008
#> GSM1105456 3 0.4452 0.68031 0.000 0.192 0.808
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1105438 2 0.4679 0.8711 0.000 0.648 0.000 0.352
#> GSM1105486 4 0.0000 0.9105 0.000 0.000 0.000 1.000
#> GSM1105487 1 0.0000 0.8274 1.000 0.000 0.000 0.000
#> GSM1105490 2 0.4955 0.3495 0.000 0.556 0.000 0.444
#> GSM1105491 3 0.1637 0.6865 0.000 0.060 0.940 0.000
#> GSM1105495 2 0.4730 0.2194 0.000 0.636 0.364 0.000
#> GSM1105498 3 0.7050 0.4807 0.000 0.156 0.552 0.292
#> GSM1105499 1 0.0188 0.8269 0.996 0.004 0.000 0.000
#> GSM1105506 4 0.2973 0.7945 0.000 0.144 0.000 0.856
#> GSM1105442 2 0.4855 0.8680 0.000 0.644 0.004 0.352
#> GSM1105511 4 0.2973 0.7945 0.000 0.144 0.000 0.856
#> GSM1105514 4 0.0000 0.9105 0.000 0.000 0.000 1.000
#> GSM1105518 2 0.6049 0.7726 0.000 0.652 0.084 0.264
#> GSM1105522 1 0.7197 0.2468 0.468 0.140 0.392 0.000
#> GSM1105534 1 0.0000 0.8274 1.000 0.000 0.000 0.000
#> GSM1105535 1 0.0000 0.8274 1.000 0.000 0.000 0.000
#> GSM1105538 1 0.6517 0.4626 0.604 0.108 0.288 0.000
#> GSM1105542 4 0.0000 0.9105 0.000 0.000 0.000 1.000
#> GSM1105443 2 0.4643 0.8686 0.000 0.656 0.000 0.344
#> GSM1105551 1 0.0921 0.8139 0.972 0.028 0.000 0.000
#> GSM1105554 1 0.0188 0.8269 0.996 0.004 0.000 0.000
#> GSM1105555 1 0.2844 0.7824 0.900 0.052 0.048 0.000
#> GSM1105447 2 0.4679 0.8711 0.000 0.648 0.000 0.352
#> GSM1105467 4 0.0000 0.9105 0.000 0.000 0.000 1.000
#> GSM1105470 4 0.0000 0.9105 0.000 0.000 0.000 1.000
#> GSM1105471 4 0.0000 0.9105 0.000 0.000 0.000 1.000
#> GSM1105474 4 0.0000 0.9105 0.000 0.000 0.000 1.000
#> GSM1105475 4 0.0000 0.9105 0.000 0.000 0.000 1.000
#> GSM1105440 1 0.0000 0.8274 1.000 0.000 0.000 0.000
#> GSM1105488 4 0.1211 0.8641 0.000 0.040 0.000 0.960
#> GSM1105489 1 0.1474 0.7978 0.948 0.052 0.000 0.000
#> GSM1105492 1 0.6956 0.4184 0.564 0.148 0.288 0.000
#> GSM1105493 1 0.2530 0.7379 0.888 0.000 0.112 0.000
#> GSM1105497 2 0.4456 0.8228 0.000 0.716 0.004 0.280
#> GSM1105500 4 0.3474 0.8123 0.000 0.068 0.064 0.868
#> GSM1105501 4 0.2973 0.7945 0.000 0.144 0.000 0.856
#> GSM1105508 1 0.2921 0.7197 0.860 0.140 0.000 0.000
#> GSM1105444 2 0.4679 0.8711 0.000 0.648 0.000 0.352
#> GSM1105513 2 0.4543 0.6359 0.000 0.676 0.000 0.324
#> GSM1105516 3 0.9385 0.2500 0.208 0.284 0.392 0.116
#> GSM1105520 3 0.6423 0.5516 0.000 0.156 0.648 0.196
#> GSM1105524 1 0.0000 0.8274 1.000 0.000 0.000 0.000
#> GSM1105536 4 0.2281 0.8383 0.000 0.096 0.000 0.904
#> GSM1105537 1 0.0000 0.8274 1.000 0.000 0.000 0.000
#> GSM1105540 3 0.6275 0.5568 0.000 0.136 0.660 0.204
#> GSM1105544 4 0.7101 -0.0161 0.000 0.136 0.360 0.504
#> GSM1105445 2 0.4643 0.8686 0.000 0.656 0.000 0.344
#> GSM1105553 2 0.4304 0.8256 0.000 0.716 0.000 0.284
#> GSM1105556 1 0.0188 0.8269 0.996 0.004 0.000 0.000
#> GSM1105557 4 0.4994 -0.1010 0.000 0.480 0.000 0.520
#> GSM1105449 2 0.4679 0.8711 0.000 0.648 0.000 0.352
#> GSM1105469 4 0.2868 0.8002 0.000 0.136 0.000 0.864
#> GSM1105472 4 0.0000 0.9105 0.000 0.000 0.000 1.000
#> GSM1105473 3 0.6534 0.4371 0.220 0.148 0.632 0.000
#> GSM1105476 4 0.0000 0.9105 0.000 0.000 0.000 1.000
#> GSM1105477 4 0.0188 0.9091 0.000 0.004 0.000 0.996
#> GSM1105478 4 0.0336 0.9059 0.000 0.008 0.000 0.992
#> GSM1105510 4 0.0188 0.9084 0.000 0.004 0.000 0.996
#> GSM1105530 3 0.0000 0.6833 0.000 0.000 1.000 0.000
#> GSM1105539 3 0.4331 0.4390 0.288 0.000 0.712 0.000
#> GSM1105480 4 0.0336 0.9059 0.000 0.008 0.000 0.992
#> GSM1105512 1 0.4983 0.5730 0.704 0.024 0.272 0.000
#> GSM1105532 3 0.0000 0.6833 0.000 0.000 1.000 0.000
#> GSM1105541 1 0.4907 0.2558 0.580 0.000 0.420 0.000
#> GSM1105439 2 0.4679 0.8711 0.000 0.648 0.000 0.352
#> GSM1105463 3 0.1637 0.6865 0.000 0.060 0.940 0.000
#> GSM1105482 1 0.0000 0.8274 1.000 0.000 0.000 0.000
#> GSM1105483 4 0.2868 0.8002 0.000 0.136 0.000 0.864
#> GSM1105494 4 0.1792 0.8436 0.000 0.068 0.000 0.932
#> GSM1105503 3 0.4679 0.4353 0.000 0.352 0.648 0.000
#> GSM1105507 3 0.8316 0.1102 0.296 0.292 0.396 0.016
#> GSM1105446 2 0.4679 0.8711 0.000 0.648 0.000 0.352
#> GSM1105519 1 0.6975 0.4109 0.560 0.148 0.292 0.000
#> GSM1105526 4 0.0817 0.8965 0.000 0.024 0.000 0.976
#> GSM1105527 4 0.2868 0.8002 0.000 0.136 0.000 0.864
#> GSM1105531 3 0.1637 0.6865 0.000 0.060 0.940 0.000
#> GSM1105543 4 0.0000 0.9105 0.000 0.000 0.000 1.000
#> GSM1105546 1 0.0000 0.8274 1.000 0.000 0.000 0.000
#> GSM1105547 1 0.0188 0.8269 0.996 0.004 0.000 0.000
#> GSM1105455 2 0.4643 0.8686 0.000 0.656 0.000 0.344
#> GSM1105458 2 0.4661 0.8701 0.000 0.652 0.000 0.348
#> GSM1105459 2 0.4877 0.7961 0.000 0.592 0.000 0.408
#> GSM1105462 3 0.4855 0.3516 0.000 0.000 0.600 0.400
#> GSM1105441 2 0.4679 0.8711 0.000 0.648 0.000 0.352
#> GSM1105465 4 0.3280 0.7513 0.000 0.016 0.124 0.860
#> GSM1105484 4 0.0000 0.9105 0.000 0.000 0.000 1.000
#> GSM1105485 4 0.0000 0.9105 0.000 0.000 0.000 1.000
#> GSM1105496 3 0.4564 0.4686 0.000 0.328 0.672 0.000
#> GSM1105505 3 0.1637 0.6865 0.000 0.060 0.940 0.000
#> GSM1105509 3 0.6856 0.4692 0.140 0.284 0.576 0.000
#> GSM1105448 2 0.4679 0.8711 0.000 0.648 0.000 0.352
#> GSM1105521 1 0.6956 0.4184 0.564 0.148 0.288 0.000
#> GSM1105528 4 0.0000 0.9105 0.000 0.000 0.000 1.000
#> GSM1105529 4 0.0000 0.9105 0.000 0.000 0.000 1.000
#> GSM1105533 1 0.1211 0.8089 0.960 0.000 0.040 0.000
#> GSM1105545 4 0.0188 0.9091 0.000 0.004 0.000 0.996
#> GSM1105548 1 0.7591 0.1462 0.432 0.200 0.368 0.000
#> GSM1105549 1 0.0188 0.8269 0.996 0.004 0.000 0.000
#> GSM1105457 2 0.3688 0.7467 0.000 0.792 0.000 0.208
#> GSM1105460 2 0.4679 0.8711 0.000 0.648 0.000 0.352
#> GSM1105461 2 0.4679 0.8711 0.000 0.648 0.000 0.352
#> GSM1105464 3 0.3975 0.5045 0.240 0.000 0.760 0.000
#> GSM1105466 4 0.0000 0.9105 0.000 0.000 0.000 1.000
#> GSM1105479 4 0.0000 0.9105 0.000 0.000 0.000 1.000
#> GSM1105502 3 0.4643 0.2023 0.344 0.000 0.656 0.000
#> GSM1105515 1 0.0000 0.8274 1.000 0.000 0.000 0.000
#> GSM1105523 3 0.3123 0.6445 0.000 0.156 0.844 0.000
#> GSM1105550 3 0.4543 0.4024 0.000 0.000 0.676 0.324
#> GSM1105450 4 0.0000 0.9105 0.000 0.000 0.000 1.000
#> GSM1105451 2 0.4679 0.8711 0.000 0.648 0.000 0.352
#> GSM1105454 2 0.4304 0.3664 0.000 0.716 0.284 0.000
#> GSM1105468 4 0.0000 0.9105 0.000 0.000 0.000 1.000
#> GSM1105481 3 0.5745 0.5304 0.000 0.056 0.656 0.288
#> GSM1105504 3 0.0188 0.6837 0.000 0.004 0.996 0.000
#> GSM1105517 3 0.6751 0.5518 0.060 0.276 0.628 0.036
#> GSM1105525 3 0.4890 0.5895 0.080 0.144 0.776 0.000
#> GSM1105552 3 0.1837 0.6825 0.028 0.028 0.944 0.000
#> GSM1105452 4 0.0000 0.9105 0.000 0.000 0.000 1.000
#> GSM1105453 2 0.4679 0.8711 0.000 0.648 0.000 0.352
#> GSM1105456 2 0.4356 0.3585 0.000 0.708 0.292 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM1105438 2 0.3177 0.8691 0.000 0.792 0.000 0.208 0.000
#> GSM1105486 4 0.0162 0.8344 0.000 0.004 0.000 0.996 0.000
#> GSM1105487 1 0.0510 0.8839 0.984 0.016 0.000 0.000 0.000
#> GSM1105490 4 0.7618 0.1088 0.000 0.348 0.052 0.372 0.228
#> GSM1105491 3 0.3305 0.6667 0.000 0.000 0.776 0.000 0.224
#> GSM1105495 3 0.3684 0.5247 0.000 0.280 0.720 0.000 0.000
#> GSM1105498 3 0.4109 0.6215 0.000 0.012 0.764 0.020 0.204
#> GSM1105499 1 0.0324 0.8871 0.992 0.004 0.000 0.000 0.004
#> GSM1105506 4 0.4898 0.6666 0.000 0.012 0.052 0.708 0.228
#> GSM1105442 2 0.6434 0.6900 0.000 0.588 0.024 0.216 0.172
#> GSM1105511 4 0.4898 0.6666 0.000 0.012 0.052 0.708 0.228
#> GSM1105514 4 0.0404 0.8329 0.000 0.012 0.000 0.988 0.000
#> GSM1105518 2 0.4058 0.6630 0.000 0.740 0.236 0.024 0.000
#> GSM1105522 5 0.6593 0.6087 0.092 0.152 0.128 0.000 0.628
#> GSM1105534 1 0.0000 0.8880 1.000 0.000 0.000 0.000 0.000
#> GSM1105535 1 0.0162 0.8876 0.996 0.004 0.000 0.000 0.000
#> GSM1105538 5 0.4610 0.4326 0.432 0.012 0.000 0.000 0.556
#> GSM1105542 4 0.3527 0.7488 0.000 0.000 0.024 0.804 0.172
#> GSM1105443 2 0.3039 0.8656 0.000 0.808 0.000 0.192 0.000
#> GSM1105551 1 0.1809 0.8424 0.928 0.012 0.060 0.000 0.000
#> GSM1105554 1 0.0324 0.8871 0.992 0.004 0.000 0.000 0.004
#> GSM1105555 1 0.4348 0.7162 0.788 0.016 0.128 0.000 0.068
#> GSM1105447 2 0.3210 0.8684 0.000 0.788 0.000 0.212 0.000
#> GSM1105467 4 0.0162 0.8344 0.000 0.004 0.000 0.996 0.000
#> GSM1105470 4 0.0162 0.8344 0.000 0.004 0.000 0.996 0.000
#> GSM1105471 4 0.0162 0.8346 0.000 0.000 0.004 0.996 0.000
#> GSM1105474 4 0.0162 0.8344 0.000 0.004 0.000 0.996 0.000
#> GSM1105475 4 0.0000 0.8349 0.000 0.000 0.000 1.000 0.000
#> GSM1105440 1 0.0000 0.8880 1.000 0.000 0.000 0.000 0.000
#> GSM1105488 4 0.4015 0.7374 0.000 0.016 0.024 0.788 0.172
#> GSM1105489 1 0.3039 0.7428 0.836 0.012 0.152 0.000 0.000
#> GSM1105492 5 0.4192 0.4981 0.404 0.000 0.000 0.000 0.596
#> GSM1105493 1 0.0798 0.8803 0.976 0.008 0.016 0.000 0.000
#> GSM1105497 2 0.6065 0.5140 0.000 0.616 0.200 0.012 0.172
#> GSM1105500 4 0.6383 0.5609 0.000 0.012 0.156 0.552 0.280
#> GSM1105501 4 0.5673 0.5590 0.000 0.012 0.056 0.544 0.388
#> GSM1105508 1 0.4192 0.6055 0.736 0.000 0.032 0.000 0.232
#> GSM1105444 2 0.3210 0.8684 0.000 0.788 0.000 0.212 0.000
#> GSM1105513 2 0.7459 0.2415 0.000 0.456 0.052 0.264 0.228
#> GSM1105516 5 0.0451 0.5681 0.000 0.004 0.008 0.000 0.988
#> GSM1105520 3 0.0912 0.7313 0.000 0.012 0.972 0.016 0.000
#> GSM1105524 1 0.0000 0.8880 1.000 0.000 0.000 0.000 0.000
#> GSM1105536 4 0.2583 0.7790 0.000 0.000 0.004 0.864 0.132
#> GSM1105537 1 0.0000 0.8880 1.000 0.000 0.000 0.000 0.000
#> GSM1105540 5 0.6265 0.4824 0.000 0.012 0.212 0.188 0.588
#> GSM1105544 5 0.5987 0.4837 0.000 0.012 0.144 0.224 0.620
#> GSM1105445 2 0.3109 0.8666 0.000 0.800 0.000 0.200 0.000
#> GSM1105553 2 0.3720 0.6541 0.000 0.760 0.228 0.012 0.000
#> GSM1105556 1 0.0324 0.8871 0.992 0.004 0.000 0.000 0.004
#> GSM1105557 4 0.7497 0.3098 0.000 0.276 0.052 0.444 0.228
#> GSM1105449 2 0.3210 0.8684 0.000 0.788 0.000 0.212 0.000
#> GSM1105469 4 0.3491 0.7091 0.000 0.000 0.004 0.768 0.228
#> GSM1105472 4 0.0162 0.8344 0.000 0.004 0.000 0.996 0.000
#> GSM1105473 5 0.6049 0.6114 0.188 0.004 0.212 0.000 0.596
#> GSM1105476 4 0.0290 0.8340 0.000 0.008 0.000 0.992 0.000
#> GSM1105477 4 0.3455 0.7472 0.000 0.000 0.008 0.784 0.208
#> GSM1105478 4 0.1670 0.8142 0.000 0.012 0.052 0.936 0.000
#> GSM1105510 4 0.3421 0.7479 0.000 0.000 0.008 0.788 0.204
#> GSM1105530 3 0.4486 0.6807 0.000 0.172 0.748 0.000 0.080
#> GSM1105539 3 0.4585 0.6803 0.076 0.172 0.748 0.000 0.004
#> GSM1105480 4 0.1670 0.8142 0.000 0.012 0.052 0.936 0.000
#> GSM1105512 1 0.4288 0.0837 0.612 0.004 0.000 0.000 0.384
#> GSM1105532 3 0.4698 0.6685 0.000 0.172 0.732 0.000 0.096
#> GSM1105541 1 0.6550 -0.1322 0.436 0.172 0.388 0.000 0.004
#> GSM1105439 2 0.3143 0.8692 0.000 0.796 0.000 0.204 0.000
#> GSM1105463 3 0.1410 0.7317 0.000 0.000 0.940 0.000 0.060
#> GSM1105482 1 0.0404 0.8846 0.988 0.012 0.000 0.000 0.000
#> GSM1105483 4 0.3491 0.7091 0.000 0.000 0.004 0.768 0.228
#> GSM1105494 4 0.3720 0.6698 0.000 0.012 0.228 0.760 0.000
#> GSM1105503 3 0.1124 0.7261 0.000 0.036 0.960 0.004 0.000
#> GSM1105507 5 0.0162 0.5762 0.004 0.000 0.000 0.000 0.996
#> GSM1105446 2 0.3143 0.8692 0.000 0.796 0.000 0.204 0.000
#> GSM1105519 5 0.4666 0.5164 0.388 0.004 0.012 0.000 0.596
#> GSM1105526 4 0.0963 0.8307 0.000 0.000 0.000 0.964 0.036
#> GSM1105527 4 0.3336 0.7092 0.000 0.000 0.000 0.772 0.228
#> GSM1105531 3 0.1671 0.7258 0.000 0.000 0.924 0.000 0.076
#> GSM1105543 4 0.0290 0.8340 0.000 0.008 0.000 0.992 0.000
#> GSM1105546 1 0.0404 0.8846 0.988 0.012 0.000 0.000 0.000
#> GSM1105547 1 0.0324 0.8871 0.992 0.004 0.000 0.000 0.004
#> GSM1105455 2 0.3196 0.8648 0.000 0.804 0.004 0.192 0.000
#> GSM1105458 2 0.3143 0.8681 0.000 0.796 0.000 0.204 0.000
#> GSM1105459 2 0.4182 0.5674 0.000 0.600 0.000 0.400 0.000
#> GSM1105462 4 0.4425 0.3482 0.000 0.008 0.392 0.600 0.000
#> GSM1105441 2 0.3210 0.8684 0.000 0.788 0.000 0.212 0.000
#> GSM1105465 4 0.4313 0.7313 0.000 0.000 0.068 0.760 0.172
#> GSM1105484 4 0.0162 0.8348 0.000 0.000 0.000 0.996 0.004
#> GSM1105485 4 0.3527 0.7488 0.000 0.000 0.024 0.804 0.172
#> GSM1105496 3 0.2852 0.6571 0.000 0.000 0.828 0.000 0.172
#> GSM1105505 3 0.3305 0.6806 0.000 0.000 0.776 0.000 0.224
#> GSM1105509 5 0.0162 0.5762 0.004 0.000 0.000 0.000 0.996
#> GSM1105448 2 0.3143 0.8692 0.000 0.796 0.000 0.204 0.000
#> GSM1105521 5 0.4331 0.4982 0.400 0.004 0.000 0.000 0.596
#> GSM1105528 4 0.3093 0.7596 0.000 0.000 0.008 0.824 0.168
#> GSM1105529 4 0.1211 0.8277 0.000 0.000 0.016 0.960 0.024
#> GSM1105533 1 0.2439 0.7938 0.876 0.120 0.000 0.000 0.004
#> GSM1105545 4 0.0451 0.8349 0.000 0.000 0.004 0.988 0.008
#> GSM1105548 5 0.6252 0.5664 0.224 0.012 0.176 0.000 0.588
#> GSM1105549 1 0.0324 0.8871 0.992 0.004 0.000 0.000 0.004
#> GSM1105457 2 0.4778 0.6469 0.000 0.740 0.052 0.020 0.188
#> GSM1105460 2 0.3210 0.8684 0.000 0.788 0.000 0.212 0.000
#> GSM1105461 2 0.3177 0.8683 0.000 0.792 0.000 0.208 0.000
#> GSM1105464 3 0.5078 0.6623 0.096 0.172 0.720 0.000 0.012
#> GSM1105466 4 0.0000 0.8349 0.000 0.000 0.000 1.000 0.000
#> GSM1105479 4 0.0000 0.8349 0.000 0.000 0.000 1.000 0.000
#> GSM1105502 3 0.7182 0.4893 0.216 0.172 0.540 0.000 0.072
#> GSM1105515 1 0.0000 0.8880 1.000 0.000 0.000 0.000 0.000
#> GSM1105523 5 0.5778 0.5029 0.000 0.132 0.272 0.000 0.596
#> GSM1105550 3 0.5486 0.3173 0.000 0.000 0.572 0.352 0.076
#> GSM1105450 4 0.0162 0.8344 0.000 0.004 0.000 0.996 0.000
#> GSM1105451 2 0.3143 0.8692 0.000 0.796 0.000 0.204 0.000
#> GSM1105454 2 0.3109 0.6697 0.000 0.800 0.200 0.000 0.000
#> GSM1105468 4 0.0162 0.8344 0.000 0.004 0.000 0.996 0.000
#> GSM1105481 3 0.1671 0.7238 0.000 0.000 0.924 0.076 0.000
#> GSM1105504 3 0.4372 0.6855 0.000 0.172 0.756 0.000 0.072
#> GSM1105517 5 0.3563 0.5777 0.012 0.000 0.208 0.000 0.780
#> GSM1105525 5 0.6190 0.5197 0.012 0.168 0.224 0.000 0.596
#> GSM1105552 3 0.3517 0.6961 0.084 0.004 0.840 0.000 0.072
#> GSM1105452 4 0.0671 0.8316 0.000 0.000 0.016 0.980 0.004
#> GSM1105453 2 0.3143 0.8692 0.000 0.796 0.000 0.204 0.000
#> GSM1105456 2 0.3177 0.6662 0.000 0.792 0.208 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1105438 2 0.1863 0.8460 0.000 0.896 0.000 0.000 0.104 0.000
#> GSM1105486 5 0.0000 0.8614 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1105487 1 0.1320 0.9206 0.948 0.036 0.016 0.000 0.000 0.000
#> GSM1105490 4 0.0603 0.7473 0.000 0.016 0.000 0.980 0.004 0.000
#> GSM1105491 6 0.1802 0.4769 0.000 0.000 0.072 0.012 0.000 0.916
#> GSM1105495 6 0.1918 0.4658 0.000 0.088 0.000 0.008 0.000 0.904
#> GSM1105498 4 0.2762 0.6913 0.000 0.000 0.000 0.804 0.000 0.196
#> GSM1105499 1 0.0260 0.9288 0.992 0.000 0.008 0.000 0.000 0.000
#> GSM1105506 4 0.0547 0.7505 0.000 0.000 0.000 0.980 0.020 0.000
#> GSM1105442 2 0.6083 0.5106 0.000 0.480 0.000 0.012 0.200 0.308
#> GSM1105511 4 0.0547 0.7505 0.000 0.000 0.000 0.980 0.020 0.000
#> GSM1105514 5 0.2416 0.7542 0.000 0.156 0.000 0.000 0.844 0.000
#> GSM1105518 4 0.5237 0.6292 0.000 0.144 0.000 0.652 0.016 0.188
#> GSM1105522 3 0.4091 0.5960 0.040 0.036 0.812 0.036 0.000 0.076
#> GSM1105534 1 0.0146 0.9294 0.996 0.004 0.000 0.000 0.000 0.000
#> GSM1105535 1 0.1010 0.9251 0.960 0.036 0.004 0.000 0.000 0.000
#> GSM1105538 3 0.3808 0.6484 0.228 0.036 0.736 0.000 0.000 0.000
#> GSM1105542 5 0.3784 0.6505 0.000 0.000 0.000 0.012 0.680 0.308
#> GSM1105443 2 0.1863 0.8460 0.000 0.896 0.000 0.000 0.104 0.000
#> GSM1105551 1 0.1745 0.8821 0.920 0.000 0.012 0.000 0.000 0.068
#> GSM1105554 1 0.0260 0.9288 0.992 0.000 0.008 0.000 0.000 0.000
#> GSM1105555 1 0.3947 0.7284 0.764 0.000 0.100 0.000 0.000 0.136
#> GSM1105447 2 0.2793 0.8419 0.000 0.800 0.000 0.000 0.200 0.000
#> GSM1105467 5 0.0000 0.8614 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1105470 5 0.0000 0.8614 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1105471 5 0.0000 0.8614 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1105474 5 0.0146 0.8606 0.000 0.004 0.000 0.000 0.996 0.000
#> GSM1105475 5 0.0000 0.8614 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1105440 1 0.0865 0.9245 0.964 0.036 0.000 0.000 0.000 0.000
#> GSM1105488 5 0.3784 0.6505 0.000 0.000 0.000 0.012 0.680 0.308
#> GSM1105489 1 0.2768 0.7771 0.832 0.000 0.012 0.000 0.000 0.156
#> GSM1105492 3 0.3543 0.6722 0.200 0.032 0.768 0.000 0.000 0.000
#> GSM1105493 1 0.0725 0.9254 0.976 0.000 0.012 0.000 0.000 0.012
#> GSM1105497 6 0.5475 -0.3016 0.000 0.428 0.012 0.064 0.008 0.488
#> GSM1105500 4 0.2219 0.7110 0.000 0.000 0.000 0.864 0.136 0.000
#> GSM1105501 4 0.0547 0.7505 0.000 0.000 0.000 0.980 0.020 0.000
#> GSM1105508 1 0.4194 0.5631 0.664 0.020 0.000 0.308 0.000 0.008
#> GSM1105444 2 0.2562 0.8488 0.000 0.828 0.000 0.000 0.172 0.000
#> GSM1105513 4 0.0725 0.7515 0.000 0.012 0.000 0.976 0.012 0.000
#> GSM1105516 3 0.3547 0.5654 0.000 0.000 0.668 0.332 0.000 0.000
#> GSM1105520 4 0.4562 0.4367 0.000 0.000 0.032 0.576 0.004 0.388
#> GSM1105524 1 0.0865 0.9245 0.964 0.036 0.000 0.000 0.000 0.000
#> GSM1105536 5 0.2178 0.7660 0.000 0.000 0.000 0.132 0.868 0.000
#> GSM1105537 1 0.0865 0.9245 0.964 0.036 0.000 0.000 0.000 0.000
#> GSM1105540 3 0.3056 0.5663 0.000 0.004 0.804 0.000 0.184 0.008
#> GSM1105544 3 0.3261 0.5527 0.000 0.000 0.780 0.000 0.204 0.016
#> GSM1105445 2 0.2793 0.8419 0.000 0.800 0.000 0.000 0.200 0.000
#> GSM1105553 4 0.5193 0.6249 0.000 0.156 0.012 0.652 0.000 0.180
#> GSM1105556 1 0.0260 0.9288 0.992 0.000 0.008 0.000 0.000 0.000
#> GSM1105557 4 0.0622 0.7508 0.000 0.008 0.000 0.980 0.012 0.000
#> GSM1105449 2 0.2793 0.8419 0.000 0.800 0.000 0.000 0.200 0.000
#> GSM1105469 5 0.3531 0.5398 0.000 0.000 0.000 0.328 0.672 0.000
#> GSM1105472 5 0.0000 0.8614 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1105473 3 0.3014 0.6737 0.184 0.000 0.804 0.000 0.000 0.012
#> GSM1105476 5 0.0632 0.8546 0.000 0.024 0.000 0.000 0.976 0.000
#> GSM1105477 5 0.0937 0.8514 0.000 0.000 0.000 0.040 0.960 0.000
#> GSM1105478 4 0.3607 0.5284 0.000 0.000 0.000 0.652 0.348 0.000
#> GSM1105510 5 0.1080 0.8530 0.004 0.000 0.004 0.032 0.960 0.000
#> GSM1105530 6 0.4097 0.4191 0.000 0.000 0.492 0.008 0.000 0.500
#> GSM1105539 6 0.5980 0.4531 0.276 0.000 0.216 0.008 0.000 0.500
#> GSM1105480 4 0.3607 0.5284 0.000 0.000 0.000 0.652 0.348 0.000
#> GSM1105512 3 0.3833 0.4109 0.444 0.000 0.556 0.000 0.000 0.000
#> GSM1105532 6 0.4097 0.4191 0.000 0.000 0.492 0.008 0.000 0.500
#> GSM1105541 6 0.6009 0.3074 0.356 0.000 0.184 0.008 0.000 0.452
#> GSM1105439 2 0.2454 0.8500 0.000 0.840 0.000 0.000 0.160 0.000
#> GSM1105463 6 0.1714 0.4901 0.000 0.000 0.092 0.000 0.000 0.908
#> GSM1105482 1 0.0458 0.9277 0.984 0.000 0.016 0.000 0.000 0.000
#> GSM1105483 5 0.3531 0.5398 0.000 0.000 0.000 0.328 0.672 0.000
#> GSM1105494 4 0.4952 0.6345 0.000 0.000 0.000 0.652 0.168 0.180
#> GSM1105503 4 0.4323 0.5608 0.000 0.004 0.032 0.652 0.000 0.312
#> GSM1105507 3 0.3531 0.5673 0.000 0.000 0.672 0.328 0.000 0.000
#> GSM1105446 2 0.1007 0.8223 0.000 0.956 0.000 0.000 0.044 0.000
#> GSM1105519 3 0.2996 0.6697 0.228 0.000 0.772 0.000 0.000 0.000
#> GSM1105526 5 0.1204 0.8387 0.000 0.000 0.000 0.056 0.944 0.000
#> GSM1105527 5 0.3531 0.5398 0.000 0.000 0.000 0.328 0.672 0.000
#> GSM1105531 6 0.3288 0.4716 0.000 0.000 0.276 0.000 0.000 0.724
#> GSM1105543 5 0.0865 0.8484 0.000 0.036 0.000 0.000 0.964 0.000
#> GSM1105546 1 0.0909 0.9268 0.968 0.020 0.012 0.000 0.000 0.000
#> GSM1105547 1 0.0260 0.9288 0.992 0.000 0.008 0.000 0.000 0.000
#> GSM1105455 2 0.3773 0.6145 0.000 0.752 0.000 0.204 0.044 0.000
#> GSM1105458 2 0.2793 0.8419 0.000 0.800 0.000 0.000 0.200 0.000
#> GSM1105459 2 0.3563 0.6090 0.000 0.664 0.000 0.000 0.336 0.000
#> GSM1105462 5 0.3555 0.6547 0.000 0.000 0.040 0.000 0.776 0.184
#> GSM1105441 2 0.2793 0.8419 0.000 0.800 0.000 0.000 0.200 0.000
#> GSM1105465 5 0.3954 0.6053 0.000 0.000 0.000 0.012 0.636 0.352
#> GSM1105484 5 0.0000 0.8614 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1105485 5 0.3784 0.6505 0.000 0.000 0.000 0.012 0.680 0.308
#> GSM1105496 6 0.4332 0.1150 0.000 0.000 0.032 0.352 0.000 0.616
#> GSM1105505 6 0.3841 0.3740 0.000 0.000 0.380 0.004 0.000 0.616
#> GSM1105509 3 0.3136 0.6364 0.004 0.000 0.768 0.228 0.000 0.000
#> GSM1105448 2 0.1007 0.8223 0.000 0.956 0.000 0.000 0.044 0.000
#> GSM1105521 3 0.2996 0.6697 0.228 0.000 0.772 0.000 0.000 0.000
#> GSM1105528 5 0.0146 0.8604 0.000 0.000 0.000 0.004 0.996 0.000
#> GSM1105529 5 0.3690 0.6535 0.000 0.000 0.000 0.008 0.684 0.308
#> GSM1105533 1 0.2346 0.8342 0.868 0.000 0.124 0.008 0.000 0.000
#> GSM1105545 5 0.0458 0.8583 0.000 0.000 0.000 0.016 0.984 0.000
#> GSM1105548 3 0.3551 0.5679 0.036 0.000 0.772 0.000 0.000 0.192
#> GSM1105549 1 0.0260 0.9288 0.992 0.000 0.008 0.000 0.000 0.000
#> GSM1105457 4 0.2121 0.7314 0.000 0.096 0.000 0.892 0.012 0.000
#> GSM1105460 2 0.2793 0.8419 0.000 0.800 0.000 0.000 0.200 0.000
#> GSM1105461 2 0.1141 0.8233 0.000 0.948 0.000 0.000 0.052 0.000
#> GSM1105464 6 0.5950 0.4727 0.232 0.000 0.248 0.008 0.000 0.512
#> GSM1105466 5 0.0000 0.8614 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1105479 5 0.0000 0.8614 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1105502 6 0.5116 0.4262 0.060 0.000 0.432 0.008 0.000 0.500
#> GSM1105515 1 0.0146 0.9290 0.996 0.000 0.004 0.000 0.000 0.000
#> GSM1105523 3 0.1471 0.6006 0.000 0.000 0.932 0.004 0.000 0.064
#> GSM1105550 3 0.6195 -0.0528 0.004 0.000 0.380 0.000 0.348 0.268
#> GSM1105450 5 0.0000 0.8614 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1105451 2 0.1007 0.8223 0.000 0.956 0.000 0.000 0.044 0.000
#> GSM1105454 2 0.2730 0.6929 0.000 0.808 0.000 0.000 0.000 0.192
#> GSM1105468 5 0.0000 0.8614 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1105481 6 0.4332 0.3416 0.000 0.000 0.032 0.000 0.352 0.616
#> GSM1105504 6 0.4183 0.4216 0.000 0.000 0.480 0.012 0.000 0.508
#> GSM1105517 3 0.3231 0.6490 0.012 0.000 0.800 0.180 0.000 0.008
#> GSM1105525 3 0.2009 0.5345 0.008 0.000 0.904 0.004 0.000 0.084
#> GSM1105552 6 0.5093 0.3071 0.084 0.000 0.388 0.000 0.000 0.528
#> GSM1105452 5 0.3690 0.6535 0.000 0.000 0.000 0.008 0.684 0.308
#> GSM1105453 2 0.1007 0.8223 0.000 0.956 0.000 0.000 0.044 0.000
#> GSM1105456 2 0.2730 0.6929 0.000 0.808 0.000 0.000 0.000 0.192
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 agent(p) other(p) time(p) individual(p) k
#> CV:pam 113 0.9110 0.517169 0.645 1.38e-02 2
#> CV:pam 112 0.0897 0.000928 0.971 5.53e-03 3
#> CV:pam 96 0.1319 0.004025 0.581 5.52e-04 4
#> CV:pam 107 0.1769 0.160458 0.944 4.25e-05 5
#> CV:pam 101 0.0444 0.631253 0.674 3.95e-06 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 44956 rows and 120 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 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 0.346 0.815 0.801 0.3753 0.532 0.532
#> 3 3 0.542 0.788 0.846 0.5700 0.763 0.605
#> 4 4 0.495 0.662 0.771 0.1710 0.841 0.651
#> 5 5 0.751 0.791 0.871 0.1040 0.871 0.629
#> 6 6 0.727 0.532 0.752 0.0462 0.854 0.491
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
#> GSM1105438 2 0.0376 0.7981 0.004 0.996
#> GSM1105486 2 0.9170 0.5443 0.332 0.668
#> GSM1105487 1 0.9170 0.9918 0.668 0.332
#> GSM1105490 2 0.0000 0.8000 0.000 1.000
#> GSM1105491 2 0.6343 0.7176 0.160 0.840
#> GSM1105495 2 0.6343 0.7176 0.160 0.840
#> GSM1105498 2 0.6343 0.7176 0.160 0.840
#> GSM1105499 1 0.9170 0.9918 0.668 0.332
#> GSM1105506 2 0.6343 0.7176 0.160 0.840
#> GSM1105442 2 0.6343 0.7176 0.160 0.840
#> GSM1105511 2 0.2948 0.7825 0.052 0.948
#> GSM1105514 2 0.2603 0.7739 0.044 0.956
#> GSM1105518 2 0.6343 0.7176 0.160 0.840
#> GSM1105522 1 0.9170 0.9918 0.668 0.332
#> GSM1105534 1 0.9170 0.9918 0.668 0.332
#> GSM1105535 1 0.9170 0.9918 0.668 0.332
#> GSM1105538 1 0.9170 0.9918 0.668 0.332
#> GSM1105542 2 0.0000 0.8000 0.000 1.000
#> GSM1105443 2 0.0000 0.8000 0.000 1.000
#> GSM1105551 1 0.9170 0.9918 0.668 0.332
#> GSM1105554 1 0.9170 0.9918 0.668 0.332
#> GSM1105555 1 0.9170 0.9918 0.668 0.332
#> GSM1105447 2 0.0000 0.8000 0.000 1.000
#> GSM1105467 2 0.8386 0.5871 0.268 0.732
#> GSM1105470 2 0.9170 0.5443 0.332 0.668
#> GSM1105471 2 0.6343 0.7176 0.160 0.840
#> GSM1105474 2 0.9170 0.5443 0.332 0.668
#> GSM1105475 2 0.2043 0.7824 0.032 0.968
#> GSM1105440 1 0.9170 0.9918 0.668 0.332
#> GSM1105488 2 0.0000 0.8000 0.000 1.000
#> GSM1105489 1 0.9170 0.9918 0.668 0.332
#> GSM1105492 1 0.9170 0.9918 0.668 0.332
#> GSM1105493 1 0.9170 0.9918 0.668 0.332
#> GSM1105497 2 0.6343 0.7176 0.160 0.840
#> GSM1105500 2 0.2778 0.7843 0.048 0.952
#> GSM1105501 2 0.0000 0.8000 0.000 1.000
#> GSM1105508 1 0.9170 0.9918 0.668 0.332
#> GSM1105444 2 0.0000 0.8000 0.000 1.000
#> GSM1105513 2 0.0000 0.8000 0.000 1.000
#> GSM1105516 2 0.6343 0.7176 0.160 0.840
#> GSM1105520 2 0.6343 0.7176 0.160 0.840
#> GSM1105524 1 0.9170 0.9918 0.668 0.332
#> GSM1105536 2 0.0000 0.8000 0.000 1.000
#> GSM1105537 1 0.9170 0.9918 0.668 0.332
#> GSM1105540 1 0.9977 0.6898 0.528 0.472
#> GSM1105544 2 0.6343 0.7176 0.160 0.840
#> GSM1105445 2 0.6343 0.7176 0.160 0.840
#> GSM1105553 2 0.6343 0.7176 0.160 0.840
#> GSM1105556 1 0.9170 0.9918 0.668 0.332
#> GSM1105557 2 0.0000 0.8000 0.000 1.000
#> GSM1105449 2 0.0000 0.8000 0.000 1.000
#> GSM1105469 2 0.6438 0.7106 0.164 0.836
#> GSM1105472 2 0.9170 0.5443 0.332 0.668
#> GSM1105473 1 0.9170 0.9918 0.668 0.332
#> GSM1105476 2 0.2423 0.7768 0.040 0.960
#> GSM1105477 2 0.0000 0.8000 0.000 1.000
#> GSM1105478 2 0.6343 0.7176 0.160 0.840
#> GSM1105510 2 0.0000 0.8000 0.000 1.000
#> GSM1105530 1 0.9170 0.9918 0.668 0.332
#> GSM1105539 1 0.9170 0.9918 0.668 0.332
#> GSM1105480 2 0.6343 0.7176 0.160 0.840
#> GSM1105512 1 0.9170 0.9918 0.668 0.332
#> GSM1105532 1 0.9170 0.9918 0.668 0.332
#> GSM1105541 1 0.9170 0.9918 0.668 0.332
#> GSM1105439 2 0.0000 0.8000 0.000 1.000
#> GSM1105463 1 0.9170 0.9918 0.668 0.332
#> GSM1105482 1 0.9170 0.9918 0.668 0.332
#> GSM1105483 2 0.6343 0.7176 0.160 0.840
#> GSM1105494 2 0.6343 0.7176 0.160 0.840
#> GSM1105503 2 0.6343 0.7176 0.160 0.840
#> GSM1105507 1 0.9170 0.9918 0.668 0.332
#> GSM1105446 2 0.2043 0.7823 0.032 0.968
#> GSM1105519 1 0.9170 0.9918 0.668 0.332
#> GSM1105526 2 0.0000 0.8000 0.000 1.000
#> GSM1105527 2 0.6343 0.7176 0.160 0.840
#> GSM1105531 1 0.9209 0.9855 0.664 0.336
#> GSM1105543 2 0.2603 0.7739 0.044 0.956
#> GSM1105546 1 0.9170 0.9918 0.668 0.332
#> GSM1105547 1 0.9170 0.9918 0.668 0.332
#> GSM1105455 2 0.0000 0.8000 0.000 1.000
#> GSM1105458 2 0.6148 0.7233 0.152 0.848
#> GSM1105459 2 0.9170 0.5443 0.332 0.668
#> GSM1105462 2 0.6343 0.7176 0.160 0.840
#> GSM1105441 2 0.7674 0.6238 0.224 0.776
#> GSM1105465 2 0.6343 0.7176 0.160 0.840
#> GSM1105484 2 0.0000 0.8000 0.000 1.000
#> GSM1105485 2 0.4161 0.7661 0.084 0.916
#> GSM1105496 2 0.6438 0.7106 0.164 0.836
#> GSM1105505 2 0.9427 0.0161 0.360 0.640
#> GSM1105509 1 0.9170 0.9918 0.668 0.332
#> GSM1105448 2 0.2043 0.7823 0.032 0.968
#> GSM1105521 1 0.9170 0.9918 0.668 0.332
#> GSM1105528 2 0.0000 0.8000 0.000 1.000
#> GSM1105529 2 0.0000 0.8000 0.000 1.000
#> GSM1105533 1 0.9170 0.9918 0.668 0.332
#> GSM1105545 2 0.0000 0.8000 0.000 1.000
#> GSM1105548 1 0.9170 0.9918 0.668 0.332
#> GSM1105549 1 0.9170 0.9918 0.668 0.332
#> GSM1105457 2 0.0000 0.8000 0.000 1.000
#> GSM1105460 2 0.0000 0.8000 0.000 1.000
#> GSM1105461 2 0.9170 0.5443 0.332 0.668
#> GSM1105464 1 0.9170 0.9918 0.668 0.332
#> GSM1105466 2 0.2043 0.7904 0.032 0.968
#> GSM1105479 2 0.0376 0.7991 0.004 0.996
#> GSM1105502 1 0.9170 0.9918 0.668 0.332
#> GSM1105515 1 0.9170 0.9918 0.668 0.332
#> GSM1105523 1 0.9170 0.9918 0.668 0.332
#> GSM1105550 2 0.6343 0.7176 0.160 0.840
#> GSM1105450 2 0.9170 0.5443 0.332 0.668
#> GSM1105451 2 0.9170 0.5443 0.332 0.668
#> GSM1105454 2 0.6343 0.7176 0.160 0.840
#> GSM1105468 2 0.9170 0.5443 0.332 0.668
#> GSM1105481 2 0.6343 0.7176 0.160 0.840
#> GSM1105504 1 0.9661 0.8836 0.608 0.392
#> GSM1105517 1 0.9170 0.9918 0.668 0.332
#> GSM1105525 1 0.9170 0.9918 0.668 0.332
#> GSM1105552 1 0.9170 0.9918 0.668 0.332
#> GSM1105452 2 0.0000 0.8000 0.000 1.000
#> GSM1105453 2 0.9170 0.5443 0.332 0.668
#> GSM1105456 2 0.6343 0.7176 0.160 0.840
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1105438 2 0.6192 -0.319 0.000 0.580 0.420
#> GSM1105486 3 0.5529 0.966 0.000 0.296 0.704
#> GSM1105487 1 0.0237 0.917 0.996 0.000 0.004
#> GSM1105490 2 0.0000 0.793 0.000 1.000 0.000
#> GSM1105491 2 0.6473 0.682 0.020 0.668 0.312
#> GSM1105495 2 0.6326 0.689 0.020 0.688 0.292
#> GSM1105498 2 0.3987 0.768 0.020 0.872 0.108
#> GSM1105499 1 0.0000 0.918 1.000 0.000 0.000
#> GSM1105506 2 0.0000 0.793 0.000 1.000 0.000
#> GSM1105442 2 0.4063 0.769 0.020 0.868 0.112
#> GSM1105511 2 0.0000 0.793 0.000 1.000 0.000
#> GSM1105514 3 0.5529 0.966 0.000 0.296 0.704
#> GSM1105518 2 0.5036 0.736 0.020 0.808 0.172
#> GSM1105522 1 0.0000 0.918 1.000 0.000 0.000
#> GSM1105534 1 0.0000 0.918 1.000 0.000 0.000
#> GSM1105535 1 0.0000 0.918 1.000 0.000 0.000
#> GSM1105538 1 0.0000 0.918 1.000 0.000 0.000
#> GSM1105542 2 0.3425 0.761 0.004 0.884 0.112
#> GSM1105443 2 0.1643 0.773 0.000 0.956 0.044
#> GSM1105551 1 0.4062 0.859 0.836 0.000 0.164
#> GSM1105554 1 0.0000 0.918 1.000 0.000 0.000
#> GSM1105555 1 0.4235 0.853 0.824 0.000 0.176
#> GSM1105447 2 0.0747 0.790 0.000 0.984 0.016
#> GSM1105467 2 0.5529 0.262 0.000 0.704 0.296
#> GSM1105470 3 0.5529 0.966 0.000 0.296 0.704
#> GSM1105471 2 0.5147 0.732 0.020 0.800 0.180
#> GSM1105474 3 0.5529 0.966 0.000 0.296 0.704
#> GSM1105475 2 0.4842 0.455 0.000 0.776 0.224
#> GSM1105440 1 0.0000 0.918 1.000 0.000 0.000
#> GSM1105488 2 0.3267 0.759 0.000 0.884 0.116
#> GSM1105489 1 0.4002 0.861 0.840 0.000 0.160
#> GSM1105492 1 0.0000 0.918 1.000 0.000 0.000
#> GSM1105493 1 0.4235 0.853 0.824 0.000 0.176
#> GSM1105497 2 0.4063 0.769 0.020 0.868 0.112
#> GSM1105500 2 0.0747 0.795 0.016 0.984 0.000
#> GSM1105501 2 0.0000 0.793 0.000 1.000 0.000
#> GSM1105508 1 0.0000 0.918 1.000 0.000 0.000
#> GSM1105444 2 0.3340 0.729 0.000 0.880 0.120
#> GSM1105513 2 0.0848 0.794 0.008 0.984 0.008
#> GSM1105516 2 0.2165 0.783 0.064 0.936 0.000
#> GSM1105520 2 0.5899 0.697 0.020 0.736 0.244
#> GSM1105524 1 0.0000 0.918 1.000 0.000 0.000
#> GSM1105536 2 0.1643 0.776 0.000 0.956 0.044
#> GSM1105537 1 0.0000 0.918 1.000 0.000 0.000
#> GSM1105540 2 0.5882 0.551 0.348 0.652 0.000
#> GSM1105544 2 0.0747 0.795 0.016 0.984 0.000
#> GSM1105445 2 0.1482 0.797 0.020 0.968 0.012
#> GSM1105553 2 0.5899 0.697 0.020 0.736 0.244
#> GSM1105556 1 0.0000 0.918 1.000 0.000 0.000
#> GSM1105557 2 0.0000 0.793 0.000 1.000 0.000
#> GSM1105449 2 0.2959 0.733 0.000 0.900 0.100
#> GSM1105469 2 0.4062 0.712 0.164 0.836 0.000
#> GSM1105472 3 0.5529 0.966 0.000 0.296 0.704
#> GSM1105473 1 0.8307 0.601 0.632 0.192 0.176
#> GSM1105476 3 0.6008 0.877 0.000 0.372 0.628
#> GSM1105477 2 0.2066 0.768 0.000 0.940 0.060
#> GSM1105478 2 0.0892 0.796 0.020 0.980 0.000
#> GSM1105510 2 0.2446 0.775 0.012 0.936 0.052
#> GSM1105530 1 0.4178 0.856 0.828 0.000 0.172
#> GSM1105539 1 0.4291 0.850 0.820 0.000 0.180
#> GSM1105480 2 0.0424 0.795 0.008 0.992 0.000
#> GSM1105512 1 0.0000 0.918 1.000 0.000 0.000
#> GSM1105532 1 0.4235 0.853 0.824 0.000 0.176
#> GSM1105541 1 0.4235 0.853 0.824 0.000 0.176
#> GSM1105439 2 0.1860 0.766 0.000 0.948 0.052
#> GSM1105463 2 0.8460 0.592 0.148 0.608 0.244
#> GSM1105482 1 0.0000 0.918 1.000 0.000 0.000
#> GSM1105483 2 0.0000 0.793 0.000 1.000 0.000
#> GSM1105494 2 0.0747 0.795 0.016 0.984 0.000
#> GSM1105503 2 0.5899 0.697 0.020 0.736 0.244
#> GSM1105507 2 0.5948 0.539 0.360 0.640 0.000
#> GSM1105446 2 0.3941 0.655 0.000 0.844 0.156
#> GSM1105519 1 0.0000 0.918 1.000 0.000 0.000
#> GSM1105526 2 0.0000 0.793 0.000 1.000 0.000
#> GSM1105527 2 0.0000 0.793 0.000 1.000 0.000
#> GSM1105531 2 0.7944 0.631 0.112 0.644 0.244
#> GSM1105543 3 0.6008 0.873 0.000 0.372 0.628
#> GSM1105546 1 0.0000 0.918 1.000 0.000 0.000
#> GSM1105547 1 0.0000 0.918 1.000 0.000 0.000
#> GSM1105455 2 0.1964 0.762 0.000 0.944 0.056
#> GSM1105458 2 0.0747 0.795 0.016 0.984 0.000
#> GSM1105459 3 0.5529 0.966 0.000 0.296 0.704
#> GSM1105462 2 0.5687 0.709 0.020 0.756 0.224
#> GSM1105441 3 0.5560 0.963 0.000 0.300 0.700
#> GSM1105465 2 0.6387 0.690 0.020 0.680 0.300
#> GSM1105484 2 0.3267 0.759 0.000 0.884 0.116
#> GSM1105485 2 0.3690 0.768 0.016 0.884 0.100
#> GSM1105496 2 0.5899 0.697 0.020 0.736 0.244
#> GSM1105505 2 0.7381 0.653 0.080 0.676 0.244
#> GSM1105509 1 0.0000 0.918 1.000 0.000 0.000
#> GSM1105448 3 0.6215 0.733 0.000 0.428 0.572
#> GSM1105521 1 0.0000 0.918 1.000 0.000 0.000
#> GSM1105528 2 0.3267 0.759 0.000 0.884 0.116
#> GSM1105529 2 0.3267 0.759 0.000 0.884 0.116
#> GSM1105533 1 0.4235 0.853 0.824 0.000 0.176
#> GSM1105545 2 0.0000 0.793 0.000 1.000 0.000
#> GSM1105548 1 0.0237 0.917 0.996 0.000 0.004
#> GSM1105549 1 0.0237 0.917 0.996 0.000 0.004
#> GSM1105457 2 0.0000 0.793 0.000 1.000 0.000
#> GSM1105460 2 0.0747 0.790 0.000 0.984 0.016
#> GSM1105461 3 0.5529 0.966 0.000 0.296 0.704
#> GSM1105464 1 0.4178 0.856 0.828 0.000 0.172
#> GSM1105466 2 0.0000 0.793 0.000 1.000 0.000
#> GSM1105479 2 0.0747 0.795 0.016 0.984 0.000
#> GSM1105502 1 0.4235 0.853 0.824 0.000 0.176
#> GSM1105515 1 0.0000 0.918 1.000 0.000 0.000
#> GSM1105523 2 0.7633 0.644 0.132 0.684 0.184
#> GSM1105550 2 0.4504 0.698 0.196 0.804 0.000
#> GSM1105450 3 0.5529 0.966 0.000 0.296 0.704
#> GSM1105451 3 0.5529 0.966 0.000 0.296 0.704
#> GSM1105454 2 0.5899 0.697 0.020 0.736 0.244
#> GSM1105468 3 0.5529 0.966 0.000 0.296 0.704
#> GSM1105481 2 0.5899 0.697 0.020 0.736 0.244
#> GSM1105504 2 0.7880 0.634 0.108 0.648 0.244
#> GSM1105517 2 0.5948 0.539 0.360 0.640 0.000
#> GSM1105525 1 0.8792 0.484 0.580 0.244 0.176
#> GSM1105552 2 0.8263 0.598 0.188 0.636 0.176
#> GSM1105452 2 0.3267 0.759 0.000 0.884 0.116
#> GSM1105453 3 0.5529 0.966 0.000 0.296 0.704
#> GSM1105456 2 0.5899 0.697 0.020 0.736 0.244
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1105438 2 0.3688 0.8494 0.000 0.792 0.208 0.000
#> GSM1105486 2 0.2868 0.9212 0.000 0.864 0.136 0.000
#> GSM1105487 1 0.5136 0.8245 0.780 0.012 0.128 0.080
#> GSM1105490 3 0.5213 0.6193 0.000 0.020 0.652 0.328
#> GSM1105491 4 0.5526 0.7175 0.000 0.020 0.416 0.564
#> GSM1105495 3 0.4568 0.4361 0.000 0.124 0.800 0.076
#> GSM1105498 3 0.2480 0.6164 0.000 0.008 0.904 0.088
#> GSM1105499 1 0.0000 0.8342 1.000 0.000 0.000 0.000
#> GSM1105506 3 0.5213 0.6193 0.000 0.020 0.652 0.328
#> GSM1105442 4 0.5592 0.7325 0.000 0.024 0.404 0.572
#> GSM1105511 3 0.5213 0.6193 0.000 0.020 0.652 0.328
#> GSM1105514 2 0.2999 0.9169 0.000 0.864 0.132 0.004
#> GSM1105518 3 0.1743 0.5387 0.000 0.004 0.940 0.056
#> GSM1105522 1 0.2466 0.8371 0.900 0.000 0.096 0.004
#> GSM1105534 1 0.0000 0.8342 1.000 0.000 0.000 0.000
#> GSM1105535 1 0.0000 0.8342 1.000 0.000 0.000 0.000
#> GSM1105538 1 0.0000 0.8342 1.000 0.000 0.000 0.000
#> GSM1105542 4 0.7138 0.8075 0.000 0.164 0.296 0.540
#> GSM1105443 3 0.6407 0.5733 0.000 0.148 0.648 0.204
#> GSM1105551 1 0.5431 0.8177 0.756 0.012 0.152 0.080
#> GSM1105554 1 0.0000 0.8342 1.000 0.000 0.000 0.000
#> GSM1105555 1 0.5431 0.8177 0.756 0.012 0.152 0.080
#> GSM1105447 3 0.3351 0.5123 0.000 0.148 0.844 0.008
#> GSM1105467 2 0.4804 0.7189 0.000 0.708 0.276 0.016
#> GSM1105470 2 0.2868 0.9212 0.000 0.864 0.136 0.000
#> GSM1105471 3 0.2973 0.5817 0.000 0.000 0.856 0.144
#> GSM1105474 2 0.2868 0.9212 0.000 0.864 0.136 0.000
#> GSM1105475 3 0.7485 0.1997 0.000 0.336 0.472 0.192
#> GSM1105440 1 0.0000 0.8342 1.000 0.000 0.000 0.000
#> GSM1105488 4 0.7138 0.8075 0.000 0.164 0.296 0.540
#> GSM1105489 1 0.5366 0.8185 0.760 0.012 0.152 0.076
#> GSM1105492 1 0.0000 0.8342 1.000 0.000 0.000 0.000
#> GSM1105493 1 0.5166 0.8004 0.756 0.012 0.188 0.044
#> GSM1105497 4 0.5691 0.7417 0.000 0.024 0.468 0.508
#> GSM1105500 3 0.3523 0.5874 0.000 0.032 0.856 0.112
#> GSM1105501 3 0.5193 0.6204 0.000 0.020 0.656 0.324
#> GSM1105508 1 0.1474 0.8342 0.948 0.000 0.052 0.000
#> GSM1105444 2 0.5396 0.2373 0.000 0.524 0.464 0.012
#> GSM1105513 3 0.6327 0.5841 0.000 0.132 0.652 0.216
#> GSM1105516 3 0.3902 0.5889 0.012 0.020 0.840 0.128
#> GSM1105520 3 0.4499 0.4395 0.000 0.124 0.804 0.072
#> GSM1105524 1 0.0000 0.8342 1.000 0.000 0.000 0.000
#> GSM1105536 3 0.3763 0.5956 0.000 0.024 0.832 0.144
#> GSM1105537 1 0.0000 0.8342 1.000 0.000 0.000 0.000
#> GSM1105540 3 0.3982 0.3794 0.220 0.000 0.776 0.004
#> GSM1105544 3 0.2563 0.5910 0.000 0.020 0.908 0.072
#> GSM1105445 3 0.4284 0.6269 0.000 0.020 0.780 0.200
#> GSM1105553 3 0.4568 0.4361 0.000 0.124 0.800 0.076
#> GSM1105556 1 0.0000 0.8342 1.000 0.000 0.000 0.000
#> GSM1105557 3 0.5213 0.6193 0.000 0.020 0.652 0.328
#> GSM1105449 3 0.4948 -0.0687 0.000 0.440 0.560 0.000
#> GSM1105469 3 0.5377 0.6236 0.024 0.008 0.684 0.284
#> GSM1105472 2 0.2868 0.9212 0.000 0.864 0.136 0.000
#> GSM1105473 1 0.5931 0.6821 0.660 0.012 0.284 0.044
#> GSM1105476 2 0.3583 0.8826 0.000 0.816 0.180 0.004
#> GSM1105477 3 0.4030 0.5682 0.000 0.072 0.836 0.092
#> GSM1105478 3 0.4323 0.6297 0.000 0.020 0.776 0.204
#> GSM1105510 3 0.7362 -0.6050 0.000 0.164 0.464 0.372
#> GSM1105530 1 0.5431 0.8177 0.756 0.012 0.152 0.080
#> GSM1105539 1 0.5431 0.8177 0.756 0.012 0.152 0.080
#> GSM1105480 3 0.5213 0.6193 0.000 0.020 0.652 0.328
#> GSM1105512 1 0.0000 0.8342 1.000 0.000 0.000 0.000
#> GSM1105532 1 0.5431 0.8177 0.756 0.012 0.152 0.080
#> GSM1105541 1 0.5431 0.8177 0.756 0.012 0.152 0.080
#> GSM1105439 3 0.6386 0.5781 0.000 0.140 0.648 0.212
#> GSM1105463 1 0.8428 0.1866 0.408 0.132 0.400 0.060
#> GSM1105482 1 0.3032 0.8359 0.868 0.000 0.124 0.008
#> GSM1105483 3 0.5193 0.6204 0.000 0.020 0.656 0.324
#> GSM1105494 3 0.4323 0.6297 0.000 0.020 0.776 0.204
#> GSM1105503 3 0.4552 0.4358 0.000 0.128 0.800 0.072
#> GSM1105507 1 0.5432 0.6787 0.740 0.000 0.136 0.124
#> GSM1105446 2 0.6967 0.5155 0.000 0.580 0.244 0.176
#> GSM1105519 1 0.0921 0.8344 0.972 0.000 0.028 0.000
#> GSM1105526 3 0.4121 0.6134 0.000 0.020 0.796 0.184
#> GSM1105527 3 0.5213 0.6193 0.000 0.020 0.652 0.328
#> GSM1105531 3 0.4586 0.4304 0.000 0.136 0.796 0.068
#> GSM1105543 2 0.2868 0.9212 0.000 0.864 0.136 0.000
#> GSM1105546 1 0.0000 0.8342 1.000 0.000 0.000 0.000
#> GSM1105547 1 0.1474 0.8416 0.948 0.000 0.052 0.000
#> GSM1105455 3 0.6407 0.5733 0.000 0.148 0.648 0.204
#> GSM1105458 3 0.0921 0.5635 0.000 0.028 0.972 0.000
#> GSM1105459 2 0.2868 0.9212 0.000 0.864 0.136 0.000
#> GSM1105462 3 0.3934 0.4650 0.000 0.116 0.836 0.048
#> GSM1105441 2 0.3400 0.8851 0.000 0.820 0.180 0.000
#> GSM1105465 4 0.5517 0.7206 0.000 0.020 0.412 0.568
#> GSM1105484 4 0.7272 0.8077 0.000 0.160 0.344 0.496
#> GSM1105485 4 0.7245 0.8153 0.000 0.164 0.324 0.512
#> GSM1105496 3 0.4568 0.4361 0.000 0.124 0.800 0.076
#> GSM1105505 3 0.4514 0.4345 0.000 0.136 0.800 0.064
#> GSM1105509 1 0.1302 0.8327 0.956 0.000 0.044 0.000
#> GSM1105448 2 0.3052 0.9152 0.000 0.860 0.136 0.004
#> GSM1105521 1 0.0188 0.8350 0.996 0.000 0.004 0.000
#> GSM1105528 3 0.7423 -0.6624 0.000 0.168 0.428 0.404
#> GSM1105529 4 0.7314 0.7928 0.000 0.164 0.348 0.488
#> GSM1105533 1 0.5431 0.8177 0.756 0.012 0.152 0.080
#> GSM1105545 3 0.4988 0.6255 0.000 0.020 0.692 0.288
#> GSM1105548 1 0.4462 0.8141 0.792 0.000 0.164 0.044
#> GSM1105549 1 0.4507 0.8120 0.788 0.000 0.168 0.044
#> GSM1105457 3 0.5213 0.6193 0.000 0.020 0.652 0.328
#> GSM1105460 3 0.6440 0.5736 0.000 0.148 0.644 0.208
#> GSM1105461 2 0.2868 0.9212 0.000 0.864 0.136 0.000
#> GSM1105464 1 0.5206 0.8027 0.756 0.012 0.184 0.048
#> GSM1105466 3 0.5213 0.6193 0.000 0.020 0.652 0.328
#> GSM1105479 3 0.4459 0.6306 0.000 0.032 0.780 0.188
#> GSM1105502 1 0.5431 0.8177 0.756 0.012 0.152 0.080
#> GSM1105515 1 0.0000 0.8342 1.000 0.000 0.000 0.000
#> GSM1105523 3 0.5096 0.4214 0.076 0.072 0.804 0.048
#> GSM1105550 3 0.2131 0.5880 0.032 0.000 0.932 0.036
#> GSM1105450 2 0.2868 0.9212 0.000 0.864 0.136 0.000
#> GSM1105451 2 0.2868 0.9212 0.000 0.864 0.136 0.000
#> GSM1105454 3 0.4568 0.4361 0.000 0.124 0.800 0.076
#> GSM1105468 2 0.2868 0.9212 0.000 0.864 0.136 0.000
#> GSM1105481 3 0.4568 0.4361 0.000 0.124 0.800 0.076
#> GSM1105504 3 0.4514 0.4345 0.000 0.136 0.800 0.064
#> GSM1105517 3 0.4776 0.2383 0.376 0.000 0.624 0.000
#> GSM1105525 1 0.6274 0.5950 0.604 0.012 0.336 0.048
#> GSM1105552 1 0.6457 0.3851 0.516 0.012 0.428 0.044
#> GSM1105452 4 0.7138 0.8075 0.000 0.164 0.296 0.540
#> GSM1105453 2 0.2868 0.9212 0.000 0.864 0.136 0.000
#> GSM1105456 3 0.4568 0.4361 0.000 0.124 0.800 0.076
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM1105438 2 0.0566 0.932 0.000 0.984 0.000 0.004 0.012
#> GSM1105486 2 0.0162 0.939 0.000 0.996 0.000 0.004 0.000
#> GSM1105487 1 0.3231 0.825 0.800 0.004 0.196 0.000 0.000
#> GSM1105490 4 0.0000 0.830 0.000 0.000 0.000 1.000 0.000
#> GSM1105491 5 0.0955 0.721 0.000 0.004 0.028 0.000 0.968
#> GSM1105495 3 0.2966 0.807 0.000 0.000 0.816 0.000 0.184
#> GSM1105498 4 0.2648 0.789 0.000 0.000 0.152 0.848 0.000
#> GSM1105499 1 0.0000 0.863 1.000 0.000 0.000 0.000 0.000
#> GSM1105506 4 0.0000 0.830 0.000 0.000 0.000 1.000 0.000
#> GSM1105442 5 0.1357 0.794 0.000 0.048 0.004 0.000 0.948
#> GSM1105511 4 0.1341 0.831 0.000 0.000 0.056 0.944 0.000
#> GSM1105514 2 0.0162 0.939 0.000 0.996 0.000 0.004 0.000
#> GSM1105518 4 0.6416 0.167 0.000 0.000 0.356 0.464 0.180
#> GSM1105522 1 0.0162 0.864 0.996 0.000 0.004 0.000 0.000
#> GSM1105534 1 0.0000 0.863 1.000 0.000 0.000 0.000 0.000
#> GSM1105535 1 0.0162 0.863 0.996 0.000 0.000 0.000 0.004
#> GSM1105538 1 0.0000 0.863 1.000 0.000 0.000 0.000 0.000
#> GSM1105542 5 0.3160 0.885 0.000 0.188 0.004 0.000 0.808
#> GSM1105443 4 0.3398 0.704 0.000 0.216 0.000 0.780 0.004
#> GSM1105551 1 0.3398 0.818 0.780 0.004 0.216 0.000 0.000
#> GSM1105554 1 0.0000 0.863 1.000 0.000 0.000 0.000 0.000
#> GSM1105555 1 0.3366 0.820 0.784 0.004 0.212 0.000 0.000
#> GSM1105447 4 0.5614 0.680 0.000 0.216 0.112 0.660 0.012
#> GSM1105467 2 0.4182 0.367 0.000 0.644 0.000 0.352 0.004
#> GSM1105470 2 0.0162 0.939 0.000 0.996 0.000 0.004 0.000
#> GSM1105471 4 0.5841 0.391 0.000 0.000 0.212 0.608 0.180
#> GSM1105474 2 0.0162 0.939 0.000 0.996 0.000 0.004 0.000
#> GSM1105475 4 0.3274 0.704 0.000 0.220 0.000 0.780 0.000
#> GSM1105440 1 0.0000 0.863 1.000 0.000 0.000 0.000 0.000
#> GSM1105488 5 0.3160 0.885 0.000 0.188 0.004 0.000 0.808
#> GSM1105489 1 0.3300 0.823 0.792 0.004 0.204 0.000 0.000
#> GSM1105492 1 0.0000 0.863 1.000 0.000 0.000 0.000 0.000
#> GSM1105493 1 0.3366 0.820 0.784 0.004 0.212 0.000 0.000
#> GSM1105497 5 0.0451 0.749 0.000 0.008 0.004 0.000 0.988
#> GSM1105500 4 0.2516 0.795 0.000 0.000 0.140 0.860 0.000
#> GSM1105501 4 0.1121 0.832 0.000 0.000 0.044 0.956 0.000
#> GSM1105508 1 0.0324 0.864 0.992 0.000 0.004 0.004 0.000
#> GSM1105444 2 0.0671 0.925 0.000 0.980 0.004 0.000 0.016
#> GSM1105513 4 0.0162 0.831 0.000 0.004 0.000 0.996 0.000
#> GSM1105516 1 0.5771 0.409 0.572 0.000 0.112 0.316 0.000
#> GSM1105520 3 0.4444 0.751 0.000 0.000 0.748 0.072 0.180
#> GSM1105524 1 0.0162 0.863 0.996 0.000 0.000 0.000 0.004
#> GSM1105536 4 0.2230 0.810 0.000 0.000 0.116 0.884 0.000
#> GSM1105537 1 0.0162 0.863 0.996 0.000 0.000 0.000 0.004
#> GSM1105540 1 0.5644 0.529 0.628 0.000 0.144 0.228 0.000
#> GSM1105544 4 0.2953 0.787 0.012 0.000 0.144 0.844 0.000
#> GSM1105445 4 0.5136 0.621 0.000 0.000 0.128 0.692 0.180
#> GSM1105553 3 0.2929 0.807 0.000 0.000 0.820 0.000 0.180
#> GSM1105556 1 0.0000 0.863 1.000 0.000 0.000 0.000 0.000
#> GSM1105557 4 0.0000 0.830 0.000 0.000 0.000 1.000 0.000
#> GSM1105449 2 0.3618 0.666 0.000 0.788 0.004 0.196 0.012
#> GSM1105469 4 0.2230 0.810 0.000 0.000 0.116 0.884 0.000
#> GSM1105472 2 0.0162 0.939 0.000 0.996 0.000 0.004 0.000
#> GSM1105473 1 0.3366 0.820 0.784 0.004 0.212 0.000 0.000
#> GSM1105476 2 0.0290 0.936 0.000 0.992 0.000 0.008 0.000
#> GSM1105477 4 0.4679 0.754 0.000 0.124 0.136 0.740 0.000
#> GSM1105478 4 0.0290 0.831 0.000 0.000 0.008 0.992 0.000
#> GSM1105510 5 0.3317 0.882 0.000 0.188 0.004 0.004 0.804
#> GSM1105530 1 0.3366 0.820 0.784 0.004 0.212 0.000 0.000
#> GSM1105539 1 0.3398 0.818 0.780 0.004 0.216 0.000 0.000
#> GSM1105480 4 0.0510 0.833 0.000 0.000 0.016 0.984 0.000
#> GSM1105512 1 0.0000 0.863 1.000 0.000 0.000 0.000 0.000
#> GSM1105532 1 0.3398 0.818 0.780 0.004 0.216 0.000 0.000
#> GSM1105541 1 0.3398 0.818 0.780 0.004 0.216 0.000 0.000
#> GSM1105439 4 0.3109 0.725 0.000 0.200 0.000 0.800 0.000
#> GSM1105463 1 0.4321 0.602 0.600 0.004 0.396 0.000 0.000
#> GSM1105482 1 0.0162 0.864 0.996 0.000 0.004 0.000 0.000
#> GSM1105483 4 0.1851 0.822 0.000 0.000 0.088 0.912 0.000
#> GSM1105494 4 0.0324 0.831 0.000 0.000 0.004 0.992 0.004
#> GSM1105503 3 0.2970 0.805 0.000 0.000 0.828 0.004 0.168
#> GSM1105507 1 0.3671 0.694 0.756 0.000 0.008 0.236 0.000
#> GSM1105446 2 0.3300 0.651 0.000 0.792 0.000 0.004 0.204
#> GSM1105519 1 0.0162 0.864 0.996 0.000 0.004 0.000 0.000
#> GSM1105526 4 0.2074 0.816 0.000 0.000 0.104 0.896 0.000
#> GSM1105527 4 0.0794 0.833 0.000 0.000 0.028 0.972 0.000
#> GSM1105531 3 0.0000 0.700 0.000 0.000 1.000 0.000 0.000
#> GSM1105543 2 0.0162 0.939 0.000 0.996 0.000 0.004 0.000
#> GSM1105546 1 0.0000 0.863 1.000 0.000 0.000 0.000 0.000
#> GSM1105547 1 0.0162 0.864 0.996 0.000 0.004 0.000 0.000
#> GSM1105455 4 0.3274 0.704 0.000 0.220 0.000 0.780 0.000
#> GSM1105458 4 0.6308 0.626 0.000 0.044 0.144 0.632 0.180
#> GSM1105459 2 0.0162 0.939 0.000 0.996 0.000 0.004 0.000
#> GSM1105462 3 0.8114 0.342 0.168 0.004 0.432 0.260 0.136
#> GSM1105441 2 0.0566 0.932 0.000 0.984 0.000 0.004 0.012
#> GSM1105465 5 0.0865 0.725 0.000 0.004 0.024 0.000 0.972
#> GSM1105484 5 0.3366 0.863 0.000 0.212 0.004 0.000 0.784
#> GSM1105485 5 0.3160 0.885 0.000 0.188 0.004 0.000 0.808
#> GSM1105496 3 0.2929 0.807 0.000 0.000 0.820 0.000 0.180
#> GSM1105505 3 0.0671 0.689 0.016 0.004 0.980 0.000 0.000
#> GSM1105509 1 0.0162 0.864 0.996 0.000 0.004 0.000 0.000
#> GSM1105448 2 0.0451 0.934 0.000 0.988 0.000 0.004 0.008
#> GSM1105521 1 0.0000 0.863 1.000 0.000 0.000 0.000 0.000
#> GSM1105528 5 0.3585 0.848 0.000 0.220 0.004 0.004 0.772
#> GSM1105529 5 0.3317 0.882 0.000 0.188 0.004 0.004 0.804
#> GSM1105533 1 0.3398 0.818 0.780 0.004 0.216 0.000 0.000
#> GSM1105545 4 0.1544 0.828 0.000 0.000 0.068 0.932 0.000
#> GSM1105548 1 0.0880 0.861 0.968 0.000 0.032 0.000 0.000
#> GSM1105549 1 0.0794 0.861 0.972 0.000 0.028 0.000 0.000
#> GSM1105457 4 0.0000 0.830 0.000 0.000 0.000 1.000 0.000
#> GSM1105460 4 0.3554 0.703 0.000 0.216 0.004 0.776 0.004
#> GSM1105461 2 0.0162 0.939 0.000 0.996 0.000 0.004 0.000
#> GSM1105464 1 0.3366 0.820 0.784 0.004 0.212 0.000 0.000
#> GSM1105466 4 0.0000 0.830 0.000 0.000 0.000 1.000 0.000
#> GSM1105479 4 0.3474 0.724 0.000 0.192 0.004 0.796 0.008
#> GSM1105502 1 0.3398 0.818 0.780 0.004 0.216 0.000 0.000
#> GSM1105515 1 0.0000 0.863 1.000 0.000 0.000 0.000 0.000
#> GSM1105523 1 0.4299 0.616 0.608 0.004 0.388 0.000 0.000
#> GSM1105550 4 0.2674 0.794 0.004 0.000 0.140 0.856 0.000
#> GSM1105450 2 0.0162 0.939 0.000 0.996 0.000 0.004 0.000
#> GSM1105451 2 0.0162 0.939 0.000 0.996 0.000 0.004 0.000
#> GSM1105454 3 0.2966 0.807 0.000 0.000 0.816 0.000 0.184
#> GSM1105468 2 0.0162 0.939 0.000 0.996 0.000 0.004 0.000
#> GSM1105481 3 0.2966 0.807 0.000 0.000 0.816 0.000 0.184
#> GSM1105504 3 0.4410 -0.272 0.440 0.004 0.556 0.000 0.000
#> GSM1105517 1 0.1892 0.818 0.916 0.000 0.080 0.004 0.000
#> GSM1105525 1 0.4166 0.676 0.648 0.004 0.348 0.000 0.000
#> GSM1105552 1 0.3906 0.749 0.704 0.004 0.292 0.000 0.000
#> GSM1105452 5 0.3160 0.885 0.000 0.188 0.004 0.000 0.808
#> GSM1105453 2 0.0162 0.939 0.000 0.996 0.000 0.004 0.000
#> GSM1105456 3 0.2966 0.807 0.000 0.000 0.816 0.000 0.184
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1105438 2 0.0000 0.887617 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105486 2 0.0000 0.887617 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105487 3 0.0547 0.650818 0.020 0.000 0.980 0.000 0.000 0.000
#> GSM1105490 4 0.3991 0.677500 0.472 0.000 0.000 0.524 0.000 0.004
#> GSM1105491 5 0.0260 0.786169 0.000 0.000 0.008 0.000 0.992 0.000
#> GSM1105495 6 0.0260 0.830570 0.000 0.000 0.008 0.000 0.000 0.992
#> GSM1105498 4 0.5899 0.553880 0.264 0.000 0.008 0.520 0.000 0.208
#> GSM1105499 3 0.3804 0.223711 0.424 0.000 0.576 0.000 0.000 0.000
#> GSM1105506 4 0.4093 0.674700 0.476 0.000 0.000 0.516 0.000 0.008
#> GSM1105442 5 0.0000 0.791422 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1105511 4 0.3023 0.685746 0.232 0.000 0.000 0.768 0.000 0.000
#> GSM1105514 2 0.0000 0.887617 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105518 6 0.2446 0.700646 0.012 0.000 0.000 0.124 0.000 0.864
#> GSM1105522 3 0.3706 0.288480 0.380 0.000 0.620 0.000 0.000 0.000
#> GSM1105534 1 0.3868 -0.094487 0.508 0.000 0.492 0.000 0.000 0.000
#> GSM1105535 3 0.3804 0.223711 0.424 0.000 0.576 0.000 0.000 0.000
#> GSM1105538 1 0.3868 -0.094487 0.508 0.000 0.492 0.000 0.000 0.000
#> GSM1105542 5 0.2527 0.879241 0.000 0.168 0.000 0.000 0.832 0.000
#> GSM1105443 1 0.5128 -0.425590 0.476 0.456 0.000 0.060 0.000 0.008
#> GSM1105551 3 0.0458 0.647247 0.016 0.000 0.984 0.000 0.000 0.000
#> GSM1105554 1 0.3868 -0.094487 0.508 0.000 0.492 0.000 0.000 0.000
#> GSM1105555 3 0.0146 0.653040 0.004 0.000 0.996 0.000 0.000 0.000
#> GSM1105447 2 0.5935 0.342908 0.376 0.488 0.000 0.032 0.000 0.104
#> GSM1105467 2 0.0551 0.876535 0.004 0.984 0.000 0.004 0.000 0.008
#> GSM1105470 2 0.0000 0.887617 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105471 1 0.5508 -0.362946 0.440 0.000 0.000 0.128 0.000 0.432
#> GSM1105474 2 0.0000 0.887617 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105475 2 0.3993 0.547115 0.300 0.676 0.000 0.024 0.000 0.000
#> GSM1105440 3 0.3804 0.223711 0.424 0.000 0.576 0.000 0.000 0.000
#> GSM1105488 5 0.2562 0.879283 0.000 0.172 0.000 0.000 0.828 0.000
#> GSM1105489 3 0.2135 0.610846 0.128 0.000 0.872 0.000 0.000 0.000
#> GSM1105492 3 0.3868 0.038756 0.496 0.000 0.504 0.000 0.000 0.000
#> GSM1105493 3 0.3671 0.542412 0.208 0.000 0.756 0.000 0.036 0.000
#> GSM1105497 5 0.0000 0.791422 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1105500 4 0.2595 0.629206 0.004 0.000 0.160 0.836 0.000 0.000
#> GSM1105501 4 0.2092 0.663969 0.124 0.000 0.000 0.876 0.000 0.000
#> GSM1105508 3 0.5173 0.323588 0.224 0.000 0.616 0.160 0.000 0.000
#> GSM1105444 2 0.0000 0.887617 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105513 4 0.4093 0.674700 0.476 0.000 0.000 0.516 0.000 0.008
#> GSM1105516 4 0.3151 0.562469 0.000 0.000 0.252 0.748 0.000 0.000
#> GSM1105520 6 0.0806 0.821952 0.000 0.000 0.008 0.020 0.000 0.972
#> GSM1105524 3 0.3804 0.223711 0.424 0.000 0.576 0.000 0.000 0.000
#> GSM1105536 4 0.0000 0.615331 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1105537 3 0.3804 0.223711 0.424 0.000 0.576 0.000 0.000 0.000
#> GSM1105540 4 0.3266 0.540831 0.000 0.000 0.272 0.728 0.000 0.000
#> GSM1105544 4 0.3050 0.584106 0.000 0.000 0.236 0.764 0.000 0.000
#> GSM1105445 1 0.5537 -0.375671 0.476 0.000 0.000 0.136 0.000 0.388
#> GSM1105553 6 0.0458 0.829944 0.000 0.000 0.016 0.000 0.000 0.984
#> GSM1105556 1 0.3868 -0.094487 0.508 0.000 0.492 0.000 0.000 0.000
#> GSM1105557 4 0.3857 0.679686 0.468 0.000 0.000 0.532 0.000 0.000
#> GSM1105449 2 0.0665 0.873394 0.008 0.980 0.000 0.004 0.000 0.008
#> GSM1105469 4 0.3758 0.466364 0.008 0.000 0.324 0.668 0.000 0.000
#> GSM1105472 2 0.0000 0.887617 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105473 3 0.2048 0.614672 0.120 0.000 0.880 0.000 0.000 0.000
#> GSM1105476 2 0.0000 0.887617 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105477 4 0.0865 0.596408 0.000 0.036 0.000 0.964 0.000 0.000
#> GSM1105478 4 0.4093 0.674700 0.476 0.000 0.000 0.516 0.000 0.008
#> GSM1105510 5 0.2562 0.879283 0.000 0.172 0.000 0.000 0.828 0.000
#> GSM1105530 3 0.0000 0.652986 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1105539 3 0.0000 0.652986 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1105480 4 0.3857 0.679686 0.468 0.000 0.000 0.532 0.000 0.000
#> GSM1105512 1 0.3868 -0.094487 0.508 0.000 0.492 0.000 0.000 0.000
#> GSM1105532 3 0.0000 0.652986 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1105541 3 0.0458 0.647247 0.016 0.000 0.984 0.000 0.000 0.000
#> GSM1105439 2 0.4932 0.294784 0.472 0.476 0.000 0.044 0.000 0.008
#> GSM1105463 3 0.1814 0.602932 0.000 0.000 0.900 0.000 0.000 0.100
#> GSM1105482 1 0.3868 -0.094487 0.508 0.000 0.492 0.000 0.000 0.000
#> GSM1105483 4 0.3221 0.688611 0.264 0.000 0.000 0.736 0.000 0.000
#> GSM1105494 4 0.4093 0.674700 0.476 0.000 0.000 0.516 0.000 0.008
#> GSM1105503 6 0.0508 0.830300 0.000 0.000 0.012 0.004 0.000 0.984
#> GSM1105507 4 0.3499 0.469512 0.000 0.000 0.320 0.680 0.000 0.000
#> GSM1105446 2 0.2178 0.741225 0.000 0.868 0.000 0.000 0.132 0.000
#> GSM1105519 3 0.3864 0.087397 0.480 0.000 0.520 0.000 0.000 0.000
#> GSM1105526 4 0.0000 0.615331 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1105527 4 0.3857 0.679686 0.468 0.000 0.000 0.532 0.000 0.000
#> GSM1105531 6 0.3695 0.535418 0.000 0.000 0.376 0.000 0.000 0.624
#> GSM1105543 2 0.0000 0.887617 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105546 1 0.3868 -0.094487 0.508 0.000 0.492 0.000 0.000 0.000
#> GSM1105547 1 0.3868 -0.094487 0.508 0.000 0.492 0.000 0.000 0.000
#> GSM1105455 2 0.4634 0.338007 0.472 0.496 0.000 0.024 0.000 0.008
#> GSM1105458 6 0.6552 0.000214 0.212 0.364 0.000 0.032 0.000 0.392
#> GSM1105459 2 0.0000 0.887617 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105462 3 0.5336 -0.008185 0.000 0.000 0.544 0.124 0.000 0.332
#> GSM1105441 2 0.0000 0.887617 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105465 5 0.0000 0.791422 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1105484 5 0.2527 0.879241 0.000 0.168 0.000 0.000 0.832 0.000
#> GSM1105485 5 0.2562 0.879283 0.000 0.172 0.000 0.000 0.828 0.000
#> GSM1105496 6 0.0713 0.825432 0.000 0.000 0.028 0.000 0.000 0.972
#> GSM1105505 6 0.3727 0.519968 0.000 0.000 0.388 0.000 0.000 0.612
#> GSM1105509 3 0.3930 0.225122 0.420 0.000 0.576 0.004 0.000 0.000
#> GSM1105448 2 0.0000 0.887617 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105521 1 0.3868 -0.094487 0.508 0.000 0.492 0.000 0.000 0.000
#> GSM1105528 5 0.3706 0.531467 0.000 0.380 0.000 0.000 0.620 0.000
#> GSM1105529 5 0.2762 0.860730 0.000 0.196 0.000 0.000 0.804 0.000
#> GSM1105533 3 0.0146 0.653040 0.004 0.000 0.996 0.000 0.000 0.000
#> GSM1105545 4 0.0000 0.615331 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1105548 3 0.3847 0.394798 0.348 0.000 0.644 0.000 0.008 0.000
#> GSM1105549 3 0.5132 0.163509 0.416 0.000 0.500 0.000 0.084 0.000
#> GSM1105457 4 0.4093 0.674700 0.476 0.000 0.000 0.516 0.000 0.008
#> GSM1105460 1 0.5711 -0.335505 0.472 0.392 0.000 0.128 0.000 0.008
#> GSM1105461 2 0.0000 0.887617 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105464 3 0.0000 0.652986 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1105466 4 0.4093 0.674700 0.476 0.000 0.000 0.516 0.000 0.008
#> GSM1105479 1 0.5886 -0.318453 0.476 0.376 0.000 0.132 0.000 0.016
#> GSM1105502 3 0.0458 0.647247 0.016 0.000 0.984 0.000 0.000 0.000
#> GSM1105515 1 0.3868 -0.094487 0.508 0.000 0.492 0.000 0.000 0.000
#> GSM1105523 3 0.2070 0.578985 0.000 0.000 0.896 0.092 0.000 0.012
#> GSM1105550 4 0.3240 0.578390 0.004 0.000 0.244 0.752 0.000 0.000
#> GSM1105450 2 0.0000 0.887617 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105451 2 0.0000 0.887617 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105454 6 0.0260 0.830570 0.000 0.000 0.008 0.000 0.000 0.992
#> GSM1105468 2 0.0000 0.887617 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105481 6 0.0260 0.830570 0.000 0.000 0.008 0.000 0.000 0.992
#> GSM1105504 3 0.3672 0.120466 0.000 0.000 0.632 0.000 0.000 0.368
#> GSM1105517 4 0.5578 0.196096 0.184 0.000 0.276 0.540 0.000 0.000
#> GSM1105525 3 0.0000 0.652986 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1105552 3 0.1610 0.624719 0.084 0.000 0.916 0.000 0.000 0.000
#> GSM1105452 5 0.2793 0.856530 0.000 0.200 0.000 0.000 0.800 0.000
#> GSM1105453 2 0.0000 0.887617 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105456 6 0.0260 0.830570 0.000 0.000 0.008 0.000 0.000 0.992
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 agent(p) other(p) time(p) individual(p) k
#> CV:mclust 119 1.000 0.386 0.795 0.00708 2
#> CV:mclust 116 0.148 0.867 0.941 0.00797 3
#> CV:mclust 98 0.586 0.769 0.374 0.03796 4
#> CV:mclust 114 0.556 0.802 0.617 0.00323 5
#> CV:mclust 84 0.594 0.200 0.778 0.02460 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 44956 rows and 120 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 0.882 0.923 0.968 0.4950 0.507 0.507
#> 3 3 0.511 0.482 0.672 0.3030 0.816 0.654
#> 4 4 0.698 0.764 0.887 0.1064 0.748 0.448
#> 5 5 0.646 0.704 0.828 0.0813 0.845 0.540
#> 6 6 0.564 0.400 0.651 0.0536 0.926 0.712
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
#> GSM1105438 2 0.0000 0.961 0.000 1.000
#> GSM1105486 2 0.0000 0.961 0.000 1.000
#> GSM1105487 1 0.0000 0.972 1.000 0.000
#> GSM1105490 2 0.0000 0.961 0.000 1.000
#> GSM1105491 2 0.9248 0.527 0.340 0.660
#> GSM1105495 2 0.7219 0.761 0.200 0.800
#> GSM1105498 2 0.8909 0.589 0.308 0.692
#> GSM1105499 1 0.0000 0.972 1.000 0.000
#> GSM1105506 2 0.0000 0.961 0.000 1.000
#> GSM1105442 2 0.0000 0.961 0.000 1.000
#> GSM1105511 2 0.0000 0.961 0.000 1.000
#> GSM1105514 2 0.0000 0.961 0.000 1.000
#> GSM1105518 2 0.0672 0.955 0.008 0.992
#> GSM1105522 1 0.0000 0.972 1.000 0.000
#> GSM1105534 1 0.0000 0.972 1.000 0.000
#> GSM1105535 1 0.0000 0.972 1.000 0.000
#> GSM1105538 1 0.0000 0.972 1.000 0.000
#> GSM1105542 2 0.0000 0.961 0.000 1.000
#> GSM1105443 2 0.0000 0.961 0.000 1.000
#> GSM1105551 1 0.0000 0.972 1.000 0.000
#> GSM1105554 1 0.0000 0.972 1.000 0.000
#> GSM1105555 1 0.0000 0.972 1.000 0.000
#> GSM1105447 2 0.0000 0.961 0.000 1.000
#> GSM1105467 2 0.0000 0.961 0.000 1.000
#> GSM1105470 2 0.0000 0.961 0.000 1.000
#> GSM1105471 2 0.0376 0.958 0.004 0.996
#> GSM1105474 2 0.0000 0.961 0.000 1.000
#> GSM1105475 2 0.0000 0.961 0.000 1.000
#> GSM1105440 1 0.0000 0.972 1.000 0.000
#> GSM1105488 2 0.0000 0.961 0.000 1.000
#> GSM1105489 1 0.0000 0.972 1.000 0.000
#> GSM1105492 1 0.0000 0.972 1.000 0.000
#> GSM1105493 1 0.0000 0.972 1.000 0.000
#> GSM1105497 2 0.1184 0.949 0.016 0.984
#> GSM1105500 2 0.0000 0.961 0.000 1.000
#> GSM1105501 2 0.0000 0.961 0.000 1.000
#> GSM1105508 1 0.0000 0.972 1.000 0.000
#> GSM1105444 2 0.0000 0.961 0.000 1.000
#> GSM1105513 2 0.0000 0.961 0.000 1.000
#> GSM1105516 1 0.9866 0.251 0.568 0.432
#> GSM1105520 2 0.8813 0.604 0.300 0.700
#> GSM1105524 1 0.0000 0.972 1.000 0.000
#> GSM1105536 2 0.0000 0.961 0.000 1.000
#> GSM1105537 1 0.0000 0.972 1.000 0.000
#> GSM1105540 1 0.0000 0.972 1.000 0.000
#> GSM1105544 2 0.7528 0.727 0.216 0.784
#> GSM1105445 2 0.0000 0.961 0.000 1.000
#> GSM1105553 1 0.9850 0.188 0.572 0.428
#> GSM1105556 1 0.0000 0.972 1.000 0.000
#> GSM1105557 2 0.0000 0.961 0.000 1.000
#> GSM1105449 2 0.0000 0.961 0.000 1.000
#> GSM1105469 1 0.7602 0.712 0.780 0.220
#> GSM1105472 2 0.0000 0.961 0.000 1.000
#> GSM1105473 1 0.0000 0.972 1.000 0.000
#> GSM1105476 2 0.0000 0.961 0.000 1.000
#> GSM1105477 2 0.0000 0.961 0.000 1.000
#> GSM1105478 2 0.2603 0.927 0.044 0.956
#> GSM1105510 2 0.0000 0.961 0.000 1.000
#> GSM1105530 1 0.0000 0.972 1.000 0.000
#> GSM1105539 1 0.0000 0.972 1.000 0.000
#> GSM1105480 2 0.0000 0.961 0.000 1.000
#> GSM1105512 1 0.0000 0.972 1.000 0.000
#> GSM1105532 1 0.0000 0.972 1.000 0.000
#> GSM1105541 1 0.0000 0.972 1.000 0.000
#> GSM1105439 2 0.0000 0.961 0.000 1.000
#> GSM1105463 1 0.0000 0.972 1.000 0.000
#> GSM1105482 1 0.0000 0.972 1.000 0.000
#> GSM1105483 2 0.0000 0.961 0.000 1.000
#> GSM1105494 2 0.0000 0.961 0.000 1.000
#> GSM1105503 2 0.9850 0.301 0.428 0.572
#> GSM1105507 1 0.6343 0.794 0.840 0.160
#> GSM1105446 2 0.0000 0.961 0.000 1.000
#> GSM1105519 1 0.0000 0.972 1.000 0.000
#> GSM1105526 2 0.0000 0.961 0.000 1.000
#> GSM1105527 2 0.0000 0.961 0.000 1.000
#> GSM1105531 1 0.0000 0.972 1.000 0.000
#> GSM1105543 2 0.0000 0.961 0.000 1.000
#> GSM1105546 1 0.0000 0.972 1.000 0.000
#> GSM1105547 1 0.0000 0.972 1.000 0.000
#> GSM1105455 2 0.0000 0.961 0.000 1.000
#> GSM1105458 2 0.0000 0.961 0.000 1.000
#> GSM1105459 2 0.0000 0.961 0.000 1.000
#> GSM1105462 1 0.3274 0.914 0.940 0.060
#> GSM1105441 2 0.0000 0.961 0.000 1.000
#> GSM1105465 2 0.3733 0.902 0.072 0.928
#> GSM1105484 2 0.0000 0.961 0.000 1.000
#> GSM1105485 2 0.2043 0.935 0.032 0.968
#> GSM1105496 1 0.1414 0.954 0.980 0.020
#> GSM1105505 1 0.0000 0.972 1.000 0.000
#> GSM1105509 1 0.0000 0.972 1.000 0.000
#> GSM1105448 2 0.0000 0.961 0.000 1.000
#> GSM1105521 1 0.0000 0.972 1.000 0.000
#> GSM1105528 2 0.0000 0.961 0.000 1.000
#> GSM1105529 2 0.0000 0.961 0.000 1.000
#> GSM1105533 1 0.0000 0.972 1.000 0.000
#> GSM1105545 2 0.0000 0.961 0.000 1.000
#> GSM1105548 1 0.0000 0.972 1.000 0.000
#> GSM1105549 1 0.0000 0.972 1.000 0.000
#> GSM1105457 2 0.0000 0.961 0.000 1.000
#> GSM1105460 2 0.0000 0.961 0.000 1.000
#> GSM1105461 2 0.0000 0.961 0.000 1.000
#> GSM1105464 1 0.0000 0.972 1.000 0.000
#> GSM1105466 2 0.0000 0.961 0.000 1.000
#> GSM1105479 2 0.0000 0.961 0.000 1.000
#> GSM1105502 1 0.0000 0.972 1.000 0.000
#> GSM1105515 1 0.0000 0.972 1.000 0.000
#> GSM1105523 1 0.0000 0.972 1.000 0.000
#> GSM1105550 1 0.0938 0.962 0.988 0.012
#> GSM1105450 2 0.0000 0.961 0.000 1.000
#> GSM1105451 2 0.0000 0.961 0.000 1.000
#> GSM1105454 2 0.7219 0.761 0.200 0.800
#> GSM1105468 2 0.0000 0.961 0.000 1.000
#> GSM1105481 2 0.7219 0.761 0.200 0.800
#> GSM1105504 1 0.0000 0.972 1.000 0.000
#> GSM1105517 1 0.0000 0.972 1.000 0.000
#> GSM1105525 1 0.0000 0.972 1.000 0.000
#> GSM1105552 1 0.0000 0.972 1.000 0.000
#> GSM1105452 2 0.0000 0.961 0.000 1.000
#> GSM1105453 2 0.0000 0.961 0.000 1.000
#> GSM1105456 2 0.7219 0.761 0.200 0.800
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1105438 2 0.4121 0.7126 0.168 0.832 0.000
#> GSM1105486 2 0.0000 0.7586 0.000 1.000 0.000
#> GSM1105487 1 0.6299 0.5475 0.524 0.000 0.476
#> GSM1105490 2 0.3941 0.7157 0.000 0.844 0.156
#> GSM1105491 1 0.9514 -0.2781 0.468 0.328 0.204
#> GSM1105495 2 0.9589 0.2069 0.200 0.424 0.376
#> GSM1105498 3 0.6062 0.0648 0.000 0.384 0.616
#> GSM1105499 1 0.6168 0.6518 0.588 0.000 0.412
#> GSM1105506 2 0.4931 0.6636 0.000 0.768 0.232
#> GSM1105442 2 0.6168 0.5701 0.412 0.588 0.000
#> GSM1105511 2 0.4473 0.7089 0.008 0.828 0.164
#> GSM1105514 2 0.4931 0.6836 0.232 0.768 0.000
#> GSM1105518 2 0.6215 0.3814 0.000 0.572 0.428
#> GSM1105522 3 0.6260 -0.3683 0.448 0.000 0.552
#> GSM1105534 1 0.6026 0.6422 0.624 0.000 0.376
#> GSM1105535 1 0.6168 0.6518 0.588 0.000 0.412
#> GSM1105538 1 0.5926 0.6304 0.644 0.000 0.356
#> GSM1105542 2 0.6168 0.5701 0.412 0.588 0.000
#> GSM1105443 2 0.4235 0.7041 0.000 0.824 0.176
#> GSM1105551 3 0.4504 0.2558 0.196 0.000 0.804
#> GSM1105554 1 0.6168 0.6518 0.588 0.000 0.412
#> GSM1105555 3 0.6180 -0.3109 0.416 0.000 0.584
#> GSM1105447 2 0.3816 0.7202 0.000 0.852 0.148
#> GSM1105467 2 0.0592 0.7580 0.000 0.988 0.012
#> GSM1105470 2 0.0592 0.7580 0.000 0.988 0.012
#> GSM1105471 2 0.5216 0.6384 0.000 0.740 0.260
#> GSM1105474 2 0.1753 0.7549 0.048 0.952 0.000
#> GSM1105475 2 0.2066 0.7508 0.000 0.940 0.060
#> GSM1105440 1 0.6168 0.6518 0.588 0.000 0.412
#> GSM1105488 2 0.6168 0.5701 0.412 0.588 0.000
#> GSM1105489 3 0.6126 -0.2633 0.400 0.000 0.600
#> GSM1105492 1 0.6168 0.6518 0.588 0.000 0.412
#> GSM1105493 1 0.4605 0.2408 0.796 0.000 0.204
#> GSM1105497 2 0.6553 0.5646 0.412 0.580 0.008
#> GSM1105500 2 0.5363 0.6602 0.276 0.724 0.000
#> GSM1105501 2 0.2939 0.7467 0.012 0.916 0.072
#> GSM1105508 3 0.6299 -0.4382 0.476 0.000 0.524
#> GSM1105444 2 0.2356 0.7490 0.072 0.928 0.000
#> GSM1105513 2 0.4750 0.6763 0.000 0.784 0.216
#> GSM1105516 1 0.2200 0.2829 0.940 0.056 0.004
#> GSM1105520 3 0.6140 0.0115 0.000 0.404 0.596
#> GSM1105524 1 0.6168 0.6518 0.588 0.000 0.412
#> GSM1105536 2 0.4931 0.6841 0.232 0.768 0.000
#> GSM1105537 1 0.6168 0.6518 0.588 0.000 0.412
#> GSM1105540 1 0.6168 0.6518 0.588 0.000 0.412
#> GSM1105544 2 0.8556 0.3502 0.232 0.604 0.164
#> GSM1105445 2 0.5926 0.5120 0.000 0.644 0.356
#> GSM1105553 3 0.3816 0.3966 0.000 0.148 0.852
#> GSM1105556 1 0.4974 0.5343 0.764 0.000 0.236
#> GSM1105557 2 0.4605 0.6853 0.000 0.796 0.204
#> GSM1105449 2 0.1860 0.7523 0.000 0.948 0.052
#> GSM1105469 3 0.7607 -0.0651 0.364 0.052 0.584
#> GSM1105472 2 0.1860 0.7540 0.052 0.948 0.000
#> GSM1105473 1 0.3619 0.4095 0.864 0.000 0.136
#> GSM1105476 2 0.1753 0.7549 0.048 0.952 0.000
#> GSM1105477 2 0.6126 0.5797 0.400 0.600 0.000
#> GSM1105478 2 0.5397 0.6156 0.000 0.720 0.280
#> GSM1105510 2 0.6168 0.5701 0.412 0.588 0.000
#> GSM1105530 1 0.6267 0.5803 0.548 0.000 0.452
#> GSM1105539 3 0.4605 0.2430 0.204 0.000 0.796
#> GSM1105480 2 0.4931 0.6636 0.000 0.768 0.232
#> GSM1105512 1 0.6079 0.6473 0.612 0.000 0.388
#> GSM1105532 3 0.6286 -0.4265 0.464 0.000 0.536
#> GSM1105541 3 0.4842 0.2027 0.224 0.000 0.776
#> GSM1105439 2 0.3941 0.7157 0.000 0.844 0.156
#> GSM1105463 3 0.4605 0.2430 0.204 0.000 0.796
#> GSM1105482 1 0.5465 0.5775 0.712 0.000 0.288
#> GSM1105483 2 0.8868 0.3927 0.196 0.576 0.228
#> GSM1105494 2 0.4931 0.6636 0.000 0.768 0.232
#> GSM1105503 3 0.5733 0.1982 0.000 0.324 0.676
#> GSM1105507 1 0.6168 0.6518 0.588 0.000 0.412
#> GSM1105446 2 0.6095 0.5854 0.392 0.608 0.000
#> GSM1105519 1 0.6168 0.6518 0.588 0.000 0.412
#> GSM1105526 2 0.1753 0.7549 0.048 0.952 0.000
#> GSM1105527 2 0.8683 0.4270 0.172 0.592 0.236
#> GSM1105531 3 0.2066 0.3933 0.060 0.000 0.940
#> GSM1105543 2 0.5431 0.6545 0.284 0.716 0.000
#> GSM1105546 1 0.6168 0.6518 0.588 0.000 0.412
#> GSM1105547 1 0.2165 0.3913 0.936 0.000 0.064
#> GSM1105455 2 0.3551 0.7268 0.000 0.868 0.132
#> GSM1105458 2 0.2066 0.7508 0.000 0.940 0.060
#> GSM1105459 2 0.1163 0.7575 0.028 0.972 0.000
#> GSM1105462 3 0.2280 0.3996 0.052 0.008 0.940
#> GSM1105441 2 0.1529 0.7545 0.000 0.960 0.040
#> GSM1105465 1 0.9464 -0.4023 0.412 0.408 0.180
#> GSM1105484 2 0.6062 0.5906 0.384 0.616 0.000
#> GSM1105485 2 0.6280 0.5215 0.460 0.540 0.000
#> GSM1105496 3 0.3213 0.3812 0.092 0.008 0.900
#> GSM1105505 3 0.3686 0.3311 0.140 0.000 0.860
#> GSM1105509 1 0.6168 0.6518 0.588 0.000 0.412
#> GSM1105448 2 0.2165 0.7512 0.064 0.936 0.000
#> GSM1105521 1 0.6111 0.6495 0.604 0.000 0.396
#> GSM1105528 2 0.6111 0.5825 0.396 0.604 0.000
#> GSM1105529 2 0.6168 0.5701 0.412 0.588 0.000
#> GSM1105533 3 0.5859 -0.1162 0.344 0.000 0.656
#> GSM1105545 2 0.0424 0.7587 0.008 0.992 0.000
#> GSM1105548 1 0.6168 0.3046 0.588 0.000 0.412
#> GSM1105549 1 0.1031 0.3561 0.976 0.000 0.024
#> GSM1105457 2 0.4931 0.6636 0.000 0.768 0.232
#> GSM1105460 2 0.3267 0.7331 0.000 0.884 0.116
#> GSM1105461 2 0.1163 0.7575 0.028 0.972 0.000
#> GSM1105464 1 0.6168 0.6518 0.588 0.000 0.412
#> GSM1105466 2 0.4887 0.6669 0.000 0.772 0.228
#> GSM1105479 2 0.4842 0.6704 0.000 0.776 0.224
#> GSM1105502 3 0.6062 -0.2262 0.384 0.000 0.616
#> GSM1105515 1 0.5948 0.6332 0.640 0.000 0.360
#> GSM1105523 3 0.2793 0.3925 0.044 0.028 0.928
#> GSM1105550 3 0.6648 -0.1617 0.364 0.016 0.620
#> GSM1105450 2 0.0424 0.7587 0.008 0.992 0.000
#> GSM1105451 2 0.0237 0.7587 0.004 0.996 0.000
#> GSM1105454 3 0.6168 -0.0104 0.000 0.412 0.588
#> GSM1105468 2 0.0747 0.7584 0.016 0.984 0.000
#> GSM1105481 3 0.6168 -0.0104 0.000 0.412 0.588
#> GSM1105504 3 0.3482 0.3440 0.128 0.000 0.872
#> GSM1105517 1 0.6168 0.6518 0.588 0.000 0.412
#> GSM1105525 3 0.2537 0.3799 0.080 0.000 0.920
#> GSM1105552 1 0.6260 0.1056 0.552 0.000 0.448
#> GSM1105452 2 0.6168 0.5701 0.412 0.588 0.000
#> GSM1105453 2 0.1643 0.7555 0.044 0.956 0.000
#> GSM1105456 3 0.6168 -0.0104 0.000 0.412 0.588
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1105438 4 0.5000 -0.0745 0.000 0.496 0.000 0.504
#> GSM1105486 2 0.2469 0.8627 0.000 0.892 0.000 0.108
#> GSM1105487 1 0.1109 0.8867 0.968 0.000 0.028 0.004
#> GSM1105490 2 0.0376 0.8794 0.004 0.992 0.000 0.004
#> GSM1105491 4 0.0188 0.7506 0.004 0.000 0.000 0.996
#> GSM1105495 3 0.0000 0.9069 0.000 0.000 1.000 0.000
#> GSM1105498 2 0.3355 0.7301 0.000 0.836 0.160 0.004
#> GSM1105499 1 0.0188 0.8901 0.996 0.000 0.000 0.004
#> GSM1105506 2 0.0376 0.8794 0.004 0.992 0.000 0.004
#> GSM1105442 4 0.0336 0.7522 0.000 0.008 0.000 0.992
#> GSM1105511 2 0.0524 0.8774 0.008 0.988 0.000 0.004
#> GSM1105514 4 0.4855 0.2761 0.000 0.400 0.000 0.600
#> GSM1105518 2 0.1792 0.8568 0.000 0.932 0.068 0.000
#> GSM1105522 1 0.1305 0.8730 0.960 0.036 0.000 0.004
#> GSM1105534 1 0.1474 0.8814 0.948 0.000 0.000 0.052
#> GSM1105535 1 0.0188 0.8901 0.996 0.000 0.000 0.004
#> GSM1105538 1 0.1867 0.8719 0.928 0.000 0.000 0.072
#> GSM1105542 4 0.0336 0.7522 0.000 0.008 0.000 0.992
#> GSM1105443 2 0.0469 0.8838 0.000 0.988 0.000 0.012
#> GSM1105551 1 0.3791 0.7539 0.796 0.000 0.200 0.004
#> GSM1105554 1 0.1211 0.8850 0.960 0.000 0.000 0.040
#> GSM1105555 1 0.4019 0.7502 0.792 0.000 0.196 0.012
#> GSM1105447 2 0.1661 0.8837 0.000 0.944 0.004 0.052
#> GSM1105467 2 0.1940 0.8767 0.000 0.924 0.000 0.076
#> GSM1105470 2 0.1716 0.8806 0.000 0.936 0.000 0.064
#> GSM1105471 2 0.0592 0.8794 0.000 0.984 0.016 0.000
#> GSM1105474 2 0.3837 0.7452 0.000 0.776 0.000 0.224
#> GSM1105475 2 0.1302 0.8840 0.000 0.956 0.000 0.044
#> GSM1105440 1 0.0188 0.8901 0.996 0.000 0.000 0.004
#> GSM1105488 4 0.0336 0.7522 0.000 0.008 0.000 0.992
#> GSM1105489 1 0.5062 0.6197 0.692 0.000 0.284 0.024
#> GSM1105492 1 0.0336 0.8902 0.992 0.000 0.000 0.008
#> GSM1105493 4 0.4010 0.6330 0.156 0.000 0.028 0.816
#> GSM1105497 4 0.0188 0.7517 0.000 0.004 0.000 0.996
#> GSM1105500 4 0.4994 -0.0119 0.000 0.480 0.000 0.520
#> GSM1105501 2 0.0000 0.8818 0.000 1.000 0.000 0.000
#> GSM1105508 1 0.1109 0.8777 0.968 0.028 0.000 0.004
#> GSM1105444 2 0.4477 0.6003 0.000 0.688 0.000 0.312
#> GSM1105513 2 0.0000 0.8818 0.000 1.000 0.000 0.000
#> GSM1105516 4 0.4372 0.5557 0.268 0.004 0.000 0.728
#> GSM1105520 2 0.4981 0.0538 0.000 0.536 0.464 0.000
#> GSM1105524 1 0.0000 0.8897 1.000 0.000 0.000 0.000
#> GSM1105536 2 0.4804 0.4340 0.000 0.616 0.000 0.384
#> GSM1105537 1 0.0000 0.8897 1.000 0.000 0.000 0.000
#> GSM1105540 1 0.0188 0.8888 0.996 0.000 0.000 0.004
#> GSM1105544 2 0.7113 0.3163 0.276 0.552 0.000 0.172
#> GSM1105445 2 0.0707 0.8773 0.000 0.980 0.020 0.000
#> GSM1105553 3 0.0000 0.9069 0.000 0.000 1.000 0.000
#> GSM1105556 1 0.3024 0.8162 0.852 0.000 0.000 0.148
#> GSM1105557 2 0.0376 0.8794 0.004 0.992 0.000 0.004
#> GSM1105449 2 0.1792 0.8797 0.000 0.932 0.000 0.068
#> GSM1105469 1 0.3306 0.7563 0.840 0.156 0.000 0.004
#> GSM1105472 2 0.3649 0.7697 0.000 0.796 0.000 0.204
#> GSM1105473 4 0.4466 0.6173 0.180 0.000 0.036 0.784
#> GSM1105476 2 0.2589 0.8567 0.000 0.884 0.000 0.116
#> GSM1105477 4 0.3726 0.6453 0.000 0.212 0.000 0.788
#> GSM1105478 2 0.0524 0.8779 0.000 0.988 0.008 0.004
#> GSM1105510 4 0.1022 0.7489 0.000 0.032 0.000 0.968
#> GSM1105530 1 0.0657 0.8895 0.984 0.000 0.012 0.004
#> GSM1105539 3 0.4252 0.5808 0.252 0.000 0.744 0.004
#> GSM1105480 2 0.0376 0.8794 0.004 0.992 0.000 0.004
#> GSM1105512 1 0.1389 0.8832 0.952 0.000 0.000 0.048
#> GSM1105532 1 0.1109 0.8869 0.968 0.000 0.028 0.004
#> GSM1105541 1 0.4584 0.6134 0.696 0.000 0.300 0.004
#> GSM1105439 2 0.0188 0.8826 0.000 0.996 0.000 0.004
#> GSM1105463 3 0.0000 0.9069 0.000 0.000 1.000 0.000
#> GSM1105482 1 0.4661 0.5183 0.652 0.000 0.000 0.348
#> GSM1105483 1 0.5088 0.3088 0.572 0.424 0.000 0.004
#> GSM1105494 2 0.0188 0.8809 0.000 0.996 0.004 0.000
#> GSM1105503 3 0.4103 0.6655 0.000 0.256 0.744 0.000
#> GSM1105507 1 0.0188 0.8888 0.996 0.000 0.000 0.004
#> GSM1105446 4 0.3569 0.6644 0.000 0.196 0.000 0.804
#> GSM1105519 1 0.0707 0.8893 0.980 0.000 0.000 0.020
#> GSM1105526 2 0.2868 0.8409 0.000 0.864 0.000 0.136
#> GSM1105527 2 0.3831 0.6541 0.204 0.792 0.000 0.004
#> GSM1105531 3 0.0000 0.9069 0.000 0.000 1.000 0.000
#> GSM1105543 4 0.4817 0.3114 0.000 0.388 0.000 0.612
#> GSM1105546 1 0.0469 0.8901 0.988 0.000 0.000 0.012
#> GSM1105547 4 0.4585 0.4010 0.332 0.000 0.000 0.668
#> GSM1105455 2 0.0592 0.8842 0.000 0.984 0.000 0.016
#> GSM1105458 2 0.1978 0.8805 0.000 0.928 0.004 0.068
#> GSM1105459 2 0.2704 0.8508 0.000 0.876 0.000 0.124
#> GSM1105462 3 0.6851 0.4786 0.132 0.300 0.568 0.000
#> GSM1105441 2 0.1637 0.8816 0.000 0.940 0.000 0.060
#> GSM1105465 4 0.0188 0.7506 0.004 0.000 0.000 0.996
#> GSM1105484 4 0.2281 0.7250 0.000 0.096 0.000 0.904
#> GSM1105485 4 0.0188 0.7506 0.004 0.000 0.000 0.996
#> GSM1105496 3 0.0000 0.9069 0.000 0.000 1.000 0.000
#> GSM1105505 3 0.0000 0.9069 0.000 0.000 1.000 0.000
#> GSM1105509 1 0.0336 0.8902 0.992 0.000 0.000 0.008
#> GSM1105448 2 0.4454 0.6079 0.000 0.692 0.000 0.308
#> GSM1105521 1 0.1389 0.8826 0.952 0.000 0.000 0.048
#> GSM1105528 4 0.2973 0.7015 0.000 0.144 0.000 0.856
#> GSM1105529 4 0.0817 0.7509 0.000 0.024 0.000 0.976
#> GSM1105533 1 0.4889 0.4960 0.636 0.000 0.360 0.004
#> GSM1105545 2 0.1722 0.8844 0.008 0.944 0.000 0.048
#> GSM1105548 4 0.6933 0.3442 0.300 0.000 0.140 0.560
#> GSM1105549 4 0.2589 0.6802 0.116 0.000 0.000 0.884
#> GSM1105457 2 0.0376 0.8794 0.004 0.992 0.000 0.004
#> GSM1105460 2 0.1118 0.8845 0.000 0.964 0.000 0.036
#> GSM1105461 2 0.2530 0.8594 0.000 0.888 0.000 0.112
#> GSM1105464 1 0.2300 0.8731 0.924 0.000 0.048 0.028
#> GSM1105466 2 0.0376 0.8794 0.004 0.992 0.000 0.004
#> GSM1105479 2 0.0376 0.8822 0.000 0.992 0.004 0.004
#> GSM1105502 1 0.3355 0.7925 0.836 0.000 0.160 0.004
#> GSM1105515 1 0.1867 0.8714 0.928 0.000 0.000 0.072
#> GSM1105523 1 0.4232 0.7339 0.804 0.168 0.024 0.004
#> GSM1105550 1 0.2197 0.8423 0.916 0.080 0.000 0.004
#> GSM1105450 2 0.2345 0.8664 0.000 0.900 0.000 0.100
#> GSM1105451 2 0.2281 0.8682 0.000 0.904 0.000 0.096
#> GSM1105454 3 0.0000 0.9069 0.000 0.000 1.000 0.000
#> GSM1105468 2 0.2408 0.8640 0.000 0.896 0.000 0.104
#> GSM1105481 3 0.0000 0.9069 0.000 0.000 1.000 0.000
#> GSM1105504 3 0.0000 0.9069 0.000 0.000 1.000 0.000
#> GSM1105517 1 0.0000 0.8897 1.000 0.000 0.000 0.000
#> GSM1105525 1 0.2310 0.8482 0.920 0.068 0.008 0.004
#> GSM1105552 4 0.6295 0.4772 0.132 0.000 0.212 0.656
#> GSM1105452 4 0.0469 0.7520 0.000 0.012 0.000 0.988
#> GSM1105453 2 0.3649 0.7726 0.000 0.796 0.000 0.204
#> GSM1105456 3 0.0000 0.9069 0.000 0.000 1.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM1105438 2 0.3790 0.6818 0.004 0.724 0.000 0.000 0.272
#> GSM1105486 2 0.3242 0.8256 0.000 0.844 0.000 0.040 0.116
#> GSM1105487 1 0.1399 0.8018 0.952 0.000 0.020 0.028 0.000
#> GSM1105490 2 0.1041 0.8334 0.004 0.964 0.000 0.032 0.000
#> GSM1105491 5 0.0693 0.8223 0.012 0.000 0.008 0.000 0.980
#> GSM1105495 3 0.0566 0.8798 0.000 0.000 0.984 0.004 0.012
#> GSM1105498 2 0.4438 0.6476 0.012 0.748 0.204 0.036 0.000
#> GSM1105499 1 0.4341 0.5412 0.592 0.000 0.000 0.404 0.004
#> GSM1105506 4 0.4283 0.0619 0.000 0.456 0.000 0.544 0.000
#> GSM1105442 5 0.0671 0.8265 0.016 0.004 0.000 0.000 0.980
#> GSM1105511 2 0.3305 0.7073 0.000 0.776 0.000 0.224 0.000
#> GSM1105514 5 0.4126 0.2831 0.000 0.380 0.000 0.000 0.620
#> GSM1105518 2 0.3921 0.7106 0.000 0.784 0.172 0.044 0.000
#> GSM1105522 1 0.4294 0.3761 0.532 0.000 0.000 0.468 0.000
#> GSM1105534 1 0.2236 0.8052 0.908 0.000 0.000 0.068 0.024
#> GSM1105535 1 0.3586 0.7223 0.736 0.000 0.000 0.264 0.000
#> GSM1105538 1 0.1830 0.8000 0.932 0.000 0.000 0.028 0.040
#> GSM1105542 5 0.1041 0.8233 0.032 0.004 0.000 0.000 0.964
#> GSM1105443 2 0.0703 0.8353 0.000 0.976 0.000 0.024 0.000
#> GSM1105551 1 0.1626 0.7849 0.940 0.000 0.044 0.016 0.000
#> GSM1105554 1 0.3318 0.7723 0.800 0.000 0.000 0.192 0.008
#> GSM1105555 1 0.1818 0.7874 0.932 0.000 0.044 0.000 0.024
#> GSM1105447 2 0.1569 0.8353 0.000 0.948 0.012 0.032 0.008
#> GSM1105467 2 0.3301 0.8249 0.000 0.848 0.000 0.072 0.080
#> GSM1105470 2 0.3421 0.8217 0.000 0.840 0.000 0.080 0.080
#> GSM1105471 2 0.6420 0.4653 0.000 0.584 0.124 0.260 0.032
#> GSM1105474 2 0.2561 0.8156 0.000 0.856 0.000 0.000 0.144
#> GSM1105475 2 0.2291 0.8345 0.000 0.908 0.000 0.056 0.036
#> GSM1105440 1 0.0609 0.8006 0.980 0.000 0.000 0.020 0.000
#> GSM1105488 5 0.0579 0.8260 0.008 0.008 0.000 0.000 0.984
#> GSM1105489 1 0.1725 0.7858 0.936 0.000 0.044 0.000 0.020
#> GSM1105492 1 0.2522 0.7997 0.880 0.000 0.000 0.108 0.012
#> GSM1105493 5 0.4421 0.6492 0.220 0.000 0.016 0.024 0.740
#> GSM1105497 5 0.6794 0.5819 0.244 0.164 0.024 0.008 0.560
#> GSM1105500 2 0.5607 0.3661 0.384 0.552 0.000 0.012 0.052
#> GSM1105501 4 0.3970 0.5507 0.000 0.236 0.000 0.744 0.020
#> GSM1105508 1 0.3163 0.7757 0.824 0.012 0.000 0.164 0.000
#> GSM1105444 2 0.3579 0.7179 0.000 0.756 0.000 0.004 0.240
#> GSM1105513 2 0.1121 0.8340 0.000 0.956 0.000 0.044 0.000
#> GSM1105516 5 0.4096 0.6574 0.200 0.000 0.000 0.040 0.760
#> GSM1105520 3 0.4413 0.6658 0.000 0.044 0.724 0.232 0.000
#> GSM1105524 1 0.3796 0.6834 0.700 0.000 0.000 0.300 0.000
#> GSM1105536 5 0.3534 0.6038 0.000 0.256 0.000 0.000 0.744
#> GSM1105537 1 0.3636 0.7124 0.728 0.000 0.000 0.272 0.000
#> GSM1105540 1 0.2694 0.7902 0.864 0.004 0.004 0.128 0.000
#> GSM1105544 1 0.3573 0.6477 0.836 0.124 0.004 0.020 0.016
#> GSM1105445 2 0.0992 0.8347 0.000 0.968 0.008 0.024 0.000
#> GSM1105553 2 0.6741 0.1690 0.396 0.456 0.116 0.032 0.000
#> GSM1105556 1 0.3702 0.7880 0.820 0.000 0.000 0.096 0.084
#> GSM1105557 2 0.0992 0.8338 0.008 0.968 0.000 0.024 0.000
#> GSM1105449 2 0.0865 0.8422 0.000 0.972 0.000 0.004 0.024
#> GSM1105469 4 0.3110 0.6717 0.080 0.060 0.000 0.860 0.000
#> GSM1105472 2 0.4464 0.6574 0.000 0.684 0.000 0.028 0.288
#> GSM1105473 5 0.3733 0.7384 0.016 0.000 0.080 0.068 0.836
#> GSM1105476 2 0.2482 0.8373 0.000 0.892 0.000 0.024 0.084
#> GSM1105477 5 0.1851 0.7970 0.000 0.088 0.000 0.000 0.912
#> GSM1105478 2 0.3612 0.6422 0.000 0.732 0.000 0.268 0.000
#> GSM1105510 5 0.0955 0.8209 0.004 0.028 0.000 0.000 0.968
#> GSM1105530 4 0.3008 0.6646 0.092 0.000 0.036 0.868 0.004
#> GSM1105539 3 0.3550 0.7496 0.020 0.000 0.796 0.184 0.000
#> GSM1105480 2 0.1568 0.8340 0.020 0.944 0.000 0.036 0.000
#> GSM1105512 4 0.5508 -0.3244 0.460 0.000 0.000 0.476 0.064
#> GSM1105532 4 0.3051 0.6655 0.076 0.000 0.060 0.864 0.000
#> GSM1105541 4 0.5717 0.3544 0.096 0.000 0.308 0.592 0.004
#> GSM1105439 2 0.0880 0.8351 0.000 0.968 0.000 0.032 0.000
#> GSM1105463 3 0.0510 0.8814 0.000 0.000 0.984 0.016 0.000
#> GSM1105482 1 0.2482 0.7852 0.892 0.000 0.000 0.024 0.084
#> GSM1105483 4 0.2795 0.6594 0.028 0.100 0.000 0.872 0.000
#> GSM1105494 2 0.1750 0.8268 0.028 0.936 0.000 0.036 0.000
#> GSM1105503 3 0.3110 0.8197 0.000 0.080 0.860 0.060 0.000
#> GSM1105507 1 0.4182 0.6346 0.644 0.000 0.000 0.352 0.004
#> GSM1105446 2 0.3843 0.7648 0.016 0.788 0.000 0.012 0.184
#> GSM1105519 1 0.4470 0.5788 0.616 0.000 0.000 0.372 0.012
#> GSM1105526 4 0.6599 0.2024 0.000 0.272 0.000 0.464 0.264
#> GSM1105527 4 0.3636 0.5183 0.000 0.272 0.000 0.728 0.000
#> GSM1105531 3 0.1043 0.8802 0.000 0.000 0.960 0.040 0.000
#> GSM1105543 2 0.3243 0.7793 0.004 0.812 0.000 0.004 0.180
#> GSM1105546 1 0.0693 0.7978 0.980 0.000 0.000 0.012 0.008
#> GSM1105547 1 0.2771 0.7612 0.860 0.000 0.000 0.012 0.128
#> GSM1105455 2 0.0703 0.8345 0.000 0.976 0.000 0.024 0.000
#> GSM1105458 2 0.1243 0.8426 0.000 0.960 0.008 0.004 0.028
#> GSM1105459 2 0.3284 0.8072 0.000 0.828 0.000 0.024 0.148
#> GSM1105462 4 0.5113 0.4258 0.004 0.028 0.248 0.692 0.028
#> GSM1105441 2 0.1041 0.8420 0.000 0.964 0.000 0.004 0.032
#> GSM1105465 5 0.2331 0.8160 0.064 0.004 0.024 0.000 0.908
#> GSM1105484 5 0.1732 0.8031 0.000 0.080 0.000 0.000 0.920
#> GSM1105485 5 0.0703 0.8211 0.024 0.000 0.000 0.000 0.976
#> GSM1105496 3 0.4756 0.7173 0.152 0.072 0.756 0.020 0.000
#> GSM1105505 3 0.0880 0.8814 0.000 0.000 0.968 0.032 0.000
#> GSM1105509 4 0.3280 0.5435 0.176 0.000 0.000 0.812 0.012
#> GSM1105448 2 0.3398 0.7444 0.000 0.780 0.000 0.004 0.216
#> GSM1105521 4 0.6114 0.1977 0.132 0.000 0.000 0.492 0.376
#> GSM1105528 5 0.1732 0.8015 0.000 0.080 0.000 0.000 0.920
#> GSM1105529 5 0.2376 0.8222 0.052 0.044 0.000 0.000 0.904
#> GSM1105533 1 0.4927 0.5648 0.652 0.000 0.296 0.052 0.000
#> GSM1105545 2 0.5706 0.3922 0.004 0.556 0.000 0.360 0.080
#> GSM1105548 1 0.1471 0.7789 0.952 0.000 0.020 0.004 0.024
#> GSM1105549 5 0.3209 0.7158 0.180 0.000 0.000 0.008 0.812
#> GSM1105457 2 0.1851 0.8197 0.000 0.912 0.000 0.088 0.000
#> GSM1105460 2 0.3994 0.7402 0.000 0.772 0.000 0.188 0.040
#> GSM1105461 2 0.2574 0.8286 0.000 0.876 0.000 0.012 0.112
#> GSM1105464 4 0.3997 0.6524 0.092 0.000 0.068 0.820 0.020
#> GSM1105466 2 0.2773 0.7711 0.000 0.836 0.000 0.164 0.000
#> GSM1105479 2 0.2304 0.8120 0.000 0.892 0.000 0.100 0.008
#> GSM1105502 1 0.6706 0.2850 0.456 0.000 0.224 0.316 0.004
#> GSM1105515 1 0.2077 0.8021 0.920 0.000 0.000 0.040 0.040
#> GSM1105523 4 0.2889 0.6491 0.016 0.020 0.084 0.880 0.000
#> GSM1105550 4 0.0992 0.6797 0.024 0.008 0.000 0.968 0.000
#> GSM1105450 2 0.1774 0.8422 0.000 0.932 0.000 0.016 0.052
#> GSM1105451 2 0.1251 0.8418 0.000 0.956 0.000 0.008 0.036
#> GSM1105454 3 0.1357 0.8613 0.000 0.048 0.948 0.004 0.000
#> GSM1105468 2 0.2540 0.8368 0.000 0.888 0.000 0.024 0.088
#> GSM1105481 3 0.3444 0.8281 0.000 0.024 0.848 0.104 0.024
#> GSM1105504 3 0.2295 0.8563 0.008 0.000 0.900 0.088 0.004
#> GSM1105517 4 0.1502 0.6800 0.056 0.000 0.000 0.940 0.004
#> GSM1105525 4 0.3012 0.6734 0.060 0.008 0.056 0.876 0.000
#> GSM1105552 5 0.5019 0.5028 0.012 0.000 0.280 0.040 0.668
#> GSM1105452 5 0.2522 0.8220 0.052 0.052 0.000 0.000 0.896
#> GSM1105453 2 0.2017 0.8386 0.000 0.912 0.000 0.008 0.080
#> GSM1105456 3 0.0000 0.8774 0.000 0.000 1.000 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1105438 2 0.3703 0.656313 0.000 0.788 0.108 0.000 0.104 0.000
#> GSM1105486 2 0.5847 0.564831 0.000 0.612 0.048 0.184 0.156 0.000
#> GSM1105487 1 0.2149 0.487438 0.888 0.000 0.104 0.004 0.000 0.004
#> GSM1105490 2 0.3361 0.603139 0.000 0.816 0.076 0.108 0.000 0.000
#> GSM1105491 5 0.3731 0.585974 0.000 0.024 0.212 0.000 0.756 0.008
#> GSM1105495 6 0.1951 0.697555 0.000 0.000 0.016 0.000 0.076 0.908
#> GSM1105498 4 0.8197 0.121359 0.040 0.240 0.292 0.316 0.008 0.104
#> GSM1105499 1 0.5861 -0.004559 0.444 0.000 0.356 0.200 0.000 0.000
#> GSM1105506 4 0.4400 0.186436 0.000 0.376 0.032 0.592 0.000 0.000
#> GSM1105442 5 0.2046 0.710697 0.008 0.032 0.044 0.000 0.916 0.000
#> GSM1105511 4 0.4844 -0.010260 0.000 0.440 0.056 0.504 0.000 0.000
#> GSM1105514 5 0.5482 0.438810 0.000 0.292 0.160 0.000 0.548 0.000
#> GSM1105518 2 0.6462 0.301039 0.000 0.560 0.104 0.152 0.000 0.184
#> GSM1105522 4 0.5788 -0.192549 0.276 0.000 0.224 0.500 0.000 0.000
#> GSM1105534 1 0.4053 0.458780 0.744 0.000 0.204 0.040 0.012 0.000
#> GSM1105535 1 0.5328 0.261061 0.560 0.000 0.308 0.132 0.000 0.000
#> GSM1105538 1 0.2876 0.507660 0.844 0.000 0.132 0.008 0.016 0.000
#> GSM1105542 5 0.1167 0.705116 0.012 0.008 0.020 0.000 0.960 0.000
#> GSM1105443 2 0.0806 0.676344 0.000 0.972 0.020 0.008 0.000 0.000
#> GSM1105551 1 0.3144 0.436307 0.808 0.000 0.172 0.004 0.000 0.016
#> GSM1105554 1 0.5182 0.236306 0.556 0.000 0.340 0.104 0.000 0.000
#> GSM1105555 1 0.3370 0.486602 0.828 0.000 0.092 0.008 0.000 0.072
#> GSM1105447 2 0.1285 0.674436 0.000 0.944 0.052 0.004 0.000 0.000
#> GSM1105467 2 0.6783 0.431984 0.000 0.472 0.072 0.252 0.204 0.000
#> GSM1105470 2 0.6136 0.529886 0.000 0.568 0.048 0.208 0.176 0.000
#> GSM1105471 4 0.8293 0.142702 0.000 0.192 0.088 0.384 0.124 0.212
#> GSM1105474 2 0.6142 0.555570 0.000 0.584 0.084 0.112 0.220 0.000
#> GSM1105475 2 0.6206 0.502634 0.000 0.552 0.056 0.252 0.140 0.000
#> GSM1105440 1 0.1625 0.513615 0.928 0.000 0.060 0.012 0.000 0.000
#> GSM1105488 5 0.1594 0.696448 0.000 0.016 0.052 0.000 0.932 0.000
#> GSM1105489 1 0.1644 0.496282 0.932 0.000 0.052 0.000 0.004 0.012
#> GSM1105492 1 0.4067 0.472010 0.756 0.000 0.172 0.064 0.008 0.000
#> GSM1105493 5 0.5817 0.299369 0.144 0.000 0.236 0.000 0.588 0.032
#> GSM1105497 5 0.7137 0.282360 0.312 0.044 0.244 0.000 0.384 0.016
#> GSM1105500 2 0.7083 0.192389 0.252 0.432 0.252 0.016 0.048 0.000
#> GSM1105501 2 0.5555 0.084495 0.000 0.500 0.124 0.372 0.004 0.000
#> GSM1105508 1 0.5470 0.289963 0.584 0.004 0.244 0.168 0.000 0.000
#> GSM1105444 2 0.4065 0.595423 0.000 0.724 0.056 0.000 0.220 0.000
#> GSM1105513 2 0.4223 0.559306 0.000 0.720 0.076 0.204 0.000 0.000
#> GSM1105516 3 0.7015 0.031664 0.148 0.020 0.396 0.056 0.380 0.000
#> GSM1105520 6 0.6590 0.127817 0.000 0.144 0.060 0.384 0.000 0.412
#> GSM1105524 1 0.5536 0.220438 0.540 0.000 0.292 0.168 0.000 0.000
#> GSM1105536 5 0.5740 0.252592 0.000 0.292 0.044 0.088 0.576 0.000
#> GSM1105537 1 0.5351 0.269516 0.568 0.000 0.288 0.144 0.000 0.000
#> GSM1105540 1 0.6853 0.163186 0.424 0.004 0.316 0.204 0.052 0.000
#> GSM1105544 1 0.5201 0.301394 0.644 0.024 0.264 0.008 0.060 0.000
#> GSM1105445 2 0.1620 0.670570 0.000 0.940 0.024 0.024 0.000 0.012
#> GSM1105553 1 0.6667 0.135015 0.484 0.156 0.300 0.008 0.000 0.052
#> GSM1105556 1 0.5322 0.146849 0.520 0.000 0.400 0.020 0.060 0.000
#> GSM1105557 2 0.4023 0.559651 0.000 0.756 0.100 0.144 0.000 0.000
#> GSM1105449 2 0.0405 0.680760 0.000 0.988 0.008 0.000 0.004 0.000
#> GSM1105469 4 0.2825 0.402017 0.028 0.056 0.040 0.876 0.000 0.000
#> GSM1105472 2 0.6223 0.323592 0.000 0.464 0.048 0.112 0.376 0.000
#> GSM1105473 5 0.5066 0.568255 0.036 0.000 0.096 0.016 0.724 0.128
#> GSM1105476 2 0.6370 0.502861 0.000 0.548 0.064 0.204 0.184 0.000
#> GSM1105477 5 0.2443 0.695759 0.000 0.096 0.020 0.004 0.880 0.000
#> GSM1105478 4 0.5278 -0.055249 0.000 0.412 0.100 0.488 0.000 0.000
#> GSM1105510 5 0.3858 0.590332 0.000 0.044 0.216 0.000 0.740 0.000
#> GSM1105530 4 0.5711 -0.125741 0.072 0.000 0.360 0.528 0.000 0.040
#> GSM1105539 6 0.5291 0.442197 0.076 0.000 0.164 0.076 0.000 0.684
#> GSM1105480 2 0.7522 0.129016 0.100 0.376 0.272 0.240 0.012 0.000
#> GSM1105512 3 0.6810 0.166406 0.340 0.000 0.432 0.136 0.092 0.000
#> GSM1105532 4 0.5357 -0.000951 0.032 0.000 0.312 0.592 0.000 0.064
#> GSM1105541 3 0.7575 0.145426 0.152 0.000 0.296 0.280 0.000 0.272
#> GSM1105439 2 0.0993 0.676777 0.000 0.964 0.012 0.024 0.000 0.000
#> GSM1105463 6 0.0146 0.715745 0.000 0.000 0.004 0.000 0.000 0.996
#> GSM1105482 1 0.4374 0.425453 0.732 0.000 0.172 0.008 0.088 0.000
#> GSM1105483 4 0.2964 0.421340 0.004 0.108 0.040 0.848 0.000 0.000
#> GSM1105494 2 0.7517 0.212476 0.104 0.400 0.300 0.180 0.012 0.004
#> GSM1105503 6 0.5773 0.376219 0.000 0.096 0.036 0.312 0.000 0.556
#> GSM1105507 4 0.6600 -0.405320 0.300 0.016 0.328 0.352 0.004 0.000
#> GSM1105446 2 0.3932 0.656115 0.000 0.776 0.108 0.004 0.112 0.000
#> GSM1105519 1 0.6993 -0.188500 0.380 0.000 0.304 0.252 0.064 0.000
#> GSM1105526 4 0.6512 0.201664 0.000 0.120 0.092 0.520 0.268 0.000
#> GSM1105527 4 0.3555 0.420704 0.000 0.176 0.044 0.780 0.000 0.000
#> GSM1105531 6 0.0717 0.716622 0.000 0.000 0.016 0.008 0.000 0.976
#> GSM1105543 2 0.5867 0.472192 0.000 0.556 0.132 0.028 0.284 0.000
#> GSM1105546 1 0.0777 0.504564 0.972 0.000 0.024 0.000 0.004 0.000
#> GSM1105547 1 0.5134 0.267729 0.620 0.000 0.228 0.000 0.152 0.000
#> GSM1105455 2 0.1320 0.671336 0.000 0.948 0.036 0.016 0.000 0.000
#> GSM1105458 2 0.3096 0.681544 0.000 0.860 0.040 0.020 0.076 0.004
#> GSM1105459 2 0.2612 0.680658 0.000 0.868 0.016 0.008 0.108 0.000
#> GSM1105462 4 0.6500 -0.012605 0.000 0.008 0.084 0.504 0.084 0.320
#> GSM1105441 2 0.1074 0.684206 0.000 0.960 0.012 0.000 0.028 0.000
#> GSM1105465 5 0.3388 0.694643 0.048 0.008 0.068 0.000 0.848 0.028
#> GSM1105484 5 0.3233 0.681981 0.000 0.104 0.060 0.004 0.832 0.000
#> GSM1105485 5 0.1285 0.685952 0.004 0.000 0.052 0.000 0.944 0.000
#> GSM1105496 6 0.7102 0.424688 0.188 0.112 0.204 0.008 0.000 0.488
#> GSM1105505 6 0.1334 0.717165 0.000 0.000 0.032 0.020 0.000 0.948
#> GSM1105509 4 0.6443 -0.361696 0.164 0.000 0.324 0.468 0.044 0.000
#> GSM1105448 2 0.4002 0.621864 0.000 0.744 0.068 0.000 0.188 0.000
#> GSM1105521 3 0.7755 0.368474 0.220 0.000 0.312 0.272 0.192 0.004
#> GSM1105528 5 0.3645 0.669179 0.000 0.128 0.056 0.012 0.804 0.000
#> GSM1105529 5 0.3586 0.689508 0.036 0.048 0.080 0.004 0.832 0.000
#> GSM1105533 1 0.5909 0.145732 0.520 0.000 0.156 0.016 0.000 0.308
#> GSM1105545 4 0.6980 0.062090 0.004 0.228 0.088 0.476 0.204 0.000
#> GSM1105548 1 0.3497 0.426206 0.800 0.000 0.156 0.000 0.036 0.008
#> GSM1105549 5 0.5034 0.347277 0.132 0.000 0.240 0.000 0.628 0.000
#> GSM1105457 2 0.2843 0.625916 0.000 0.848 0.036 0.116 0.000 0.000
#> GSM1105460 2 0.4579 0.647046 0.000 0.756 0.076 0.068 0.100 0.000
#> GSM1105461 2 0.2255 0.682934 0.000 0.892 0.016 0.004 0.088 0.000
#> GSM1105464 4 0.6665 -0.222186 0.116 0.000 0.352 0.452 0.004 0.076
#> GSM1105466 2 0.5044 0.314167 0.000 0.548 0.052 0.388 0.012 0.000
#> GSM1105479 2 0.5232 0.499224 0.000 0.636 0.052 0.272 0.036 0.004
#> GSM1105502 1 0.7425 -0.213775 0.348 0.000 0.312 0.148 0.000 0.192
#> GSM1105515 1 0.3844 0.472194 0.764 0.000 0.192 0.028 0.016 0.000
#> GSM1105523 4 0.3846 0.364732 0.000 0.020 0.100 0.800 0.000 0.080
#> GSM1105550 4 0.5098 0.210829 0.028 0.032 0.264 0.660 0.008 0.008
#> GSM1105450 2 0.4961 0.636306 0.000 0.708 0.064 0.060 0.168 0.000
#> GSM1105451 2 0.0865 0.678230 0.000 0.964 0.036 0.000 0.000 0.000
#> GSM1105454 6 0.3454 0.603318 0.000 0.208 0.024 0.000 0.000 0.768
#> GSM1105468 2 0.5684 0.585110 0.000 0.632 0.060 0.104 0.204 0.000
#> GSM1105481 6 0.5566 0.512646 0.000 0.012 0.044 0.156 0.116 0.672
#> GSM1105504 6 0.2595 0.681397 0.000 0.000 0.084 0.044 0.000 0.872
#> GSM1105517 4 0.4693 0.159647 0.032 0.000 0.280 0.660 0.028 0.000
#> GSM1105525 4 0.4265 0.284185 0.020 0.000 0.140 0.760 0.000 0.080
#> GSM1105552 5 0.5702 0.448904 0.096 0.000 0.072 0.000 0.636 0.196
#> GSM1105452 5 0.4370 0.669893 0.072 0.060 0.096 0.000 0.772 0.000
#> GSM1105453 2 0.2579 0.665465 0.000 0.872 0.088 0.000 0.040 0.000
#> GSM1105456 6 0.1434 0.713243 0.000 0.048 0.012 0.000 0.000 0.940
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 agent(p) other(p) time(p) individual(p) k
#> CV:NMF 117 0.8612 0.46773 0.6504 0.00544 2
#> CV:NMF 79 0.8419 1.00000 1.0000 0.04613 3
#> CV:NMF 107 0.1579 0.19337 0.1647 0.01597 4
#> CV:NMF 107 0.0893 0.00871 0.0579 0.00554 5
#> CV:NMF 52 0.2010 0.10430 0.5076 0.05302 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 44956 rows and 120 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 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 0.618 0.832 0.920 0.4687 0.541 0.541
#> 3 3 0.506 0.429 0.717 0.3508 0.921 0.858
#> 4 4 0.477 0.472 0.648 0.1159 0.739 0.496
#> 5 5 0.569 0.471 0.707 0.0891 0.850 0.544
#> 6 6 0.628 0.498 0.685 0.0424 0.932 0.722
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
#> GSM1105438 2 0.0000 0.899 0.000 1.000
#> GSM1105486 2 0.0000 0.899 0.000 1.000
#> GSM1105487 1 0.0376 0.938 0.996 0.004
#> GSM1105490 2 0.2423 0.890 0.040 0.960
#> GSM1105491 2 0.9993 0.106 0.484 0.516
#> GSM1105495 2 0.0672 0.899 0.008 0.992
#> GSM1105498 2 0.7745 0.743 0.228 0.772
#> GSM1105499 1 0.0000 0.937 1.000 0.000
#> GSM1105506 2 0.5519 0.839 0.128 0.872
#> GSM1105442 2 0.1843 0.895 0.028 0.972
#> GSM1105511 2 0.2423 0.890 0.040 0.960
#> GSM1105514 2 0.0000 0.899 0.000 1.000
#> GSM1105518 2 0.4161 0.868 0.084 0.916
#> GSM1105522 1 0.0000 0.937 1.000 0.000
#> GSM1105534 1 0.0000 0.937 1.000 0.000
#> GSM1105535 1 0.0000 0.937 1.000 0.000
#> GSM1105538 1 0.5059 0.865 0.888 0.112
#> GSM1105542 2 0.1843 0.895 0.028 0.972
#> GSM1105443 2 0.0376 0.900 0.004 0.996
#> GSM1105551 2 1.0000 0.174 0.496 0.504
#> GSM1105554 1 0.0000 0.937 1.000 0.000
#> GSM1105555 1 0.0376 0.938 0.996 0.004
#> GSM1105447 2 0.0376 0.900 0.004 0.996
#> GSM1105467 2 0.0000 0.899 0.000 1.000
#> GSM1105470 2 0.0000 0.899 0.000 1.000
#> GSM1105471 2 0.0938 0.899 0.012 0.988
#> GSM1105474 2 0.0000 0.899 0.000 1.000
#> GSM1105475 2 0.0376 0.900 0.004 0.996
#> GSM1105440 1 0.0000 0.937 1.000 0.000
#> GSM1105488 2 0.1843 0.895 0.028 0.972
#> GSM1105489 1 0.0376 0.938 0.996 0.004
#> GSM1105492 1 0.0000 0.937 1.000 0.000
#> GSM1105493 1 0.2423 0.928 0.960 0.040
#> GSM1105497 2 0.2236 0.893 0.036 0.964
#> GSM1105500 2 0.7745 0.743 0.228 0.772
#> GSM1105501 2 0.4431 0.863 0.092 0.908
#> GSM1105508 2 0.9970 0.234 0.468 0.532
#> GSM1105444 2 0.0000 0.899 0.000 1.000
#> GSM1105513 2 0.2423 0.890 0.040 0.960
#> GSM1105516 2 0.9686 0.426 0.396 0.604
#> GSM1105520 2 0.4161 0.868 0.084 0.916
#> GSM1105524 1 0.0000 0.937 1.000 0.000
#> GSM1105536 2 0.7815 0.720 0.232 0.768
#> GSM1105537 1 0.0000 0.937 1.000 0.000
#> GSM1105540 1 0.5059 0.865 0.888 0.112
#> GSM1105544 2 0.9686 0.440 0.396 0.604
#> GSM1105445 2 0.0376 0.900 0.004 0.996
#> GSM1105553 2 1.0000 0.174 0.496 0.504
#> GSM1105556 1 0.0000 0.937 1.000 0.000
#> GSM1105557 2 0.2423 0.890 0.040 0.960
#> GSM1105449 2 0.0376 0.900 0.004 0.996
#> GSM1105469 2 0.7528 0.758 0.216 0.784
#> GSM1105472 2 0.0000 0.899 0.000 1.000
#> GSM1105473 1 0.2603 0.926 0.956 0.044
#> GSM1105476 2 0.0000 0.899 0.000 1.000
#> GSM1105477 2 0.0376 0.900 0.004 0.996
#> GSM1105478 2 0.5408 0.841 0.124 0.876
#> GSM1105510 2 0.2236 0.893 0.036 0.964
#> GSM1105530 1 0.0938 0.938 0.988 0.012
#> GSM1105539 1 0.0672 0.938 0.992 0.008
#> GSM1105480 2 0.5408 0.841 0.124 0.876
#> GSM1105512 1 0.0000 0.937 1.000 0.000
#> GSM1105532 1 0.0938 0.938 0.988 0.012
#> GSM1105541 1 0.0672 0.938 0.992 0.008
#> GSM1105439 2 0.0376 0.900 0.004 0.996
#> GSM1105463 1 0.2423 0.927 0.960 0.040
#> GSM1105482 1 0.1184 0.937 0.984 0.016
#> GSM1105483 2 0.7528 0.758 0.216 0.784
#> GSM1105494 2 0.7745 0.743 0.228 0.772
#> GSM1105503 2 0.6048 0.822 0.148 0.852
#> GSM1105507 1 0.9732 0.263 0.596 0.404
#> GSM1105446 2 0.0000 0.899 0.000 1.000
#> GSM1105519 1 0.2043 0.932 0.968 0.032
#> GSM1105526 2 0.0376 0.899 0.004 0.996
#> GSM1105527 2 0.7528 0.758 0.216 0.784
#> GSM1105531 1 0.4161 0.891 0.916 0.084
#> GSM1105543 2 0.0000 0.899 0.000 1.000
#> GSM1105546 1 0.0376 0.938 0.996 0.004
#> GSM1105547 1 0.1633 0.935 0.976 0.024
#> GSM1105455 2 0.0376 0.900 0.004 0.996
#> GSM1105458 2 0.0376 0.900 0.004 0.996
#> GSM1105459 2 0.0000 0.899 0.000 1.000
#> GSM1105462 1 0.2236 0.930 0.964 0.036
#> GSM1105441 2 0.0376 0.900 0.004 0.996
#> GSM1105465 2 0.1843 0.895 0.028 0.972
#> GSM1105484 2 0.0000 0.899 0.000 1.000
#> GSM1105485 2 0.1843 0.895 0.028 0.972
#> GSM1105496 2 0.7745 0.743 0.228 0.772
#> GSM1105505 2 0.6048 0.822 0.148 0.852
#> GSM1105509 1 0.9732 0.263 0.596 0.404
#> GSM1105448 2 0.0000 0.899 0.000 1.000
#> GSM1105521 1 0.2043 0.932 0.968 0.032
#> GSM1105528 2 0.0376 0.899 0.004 0.996
#> GSM1105529 2 0.1843 0.895 0.028 0.972
#> GSM1105533 1 0.0000 0.937 1.000 0.000
#> GSM1105545 2 0.7815 0.720 0.232 0.768
#> GSM1105548 1 0.0376 0.938 0.996 0.004
#> GSM1105549 1 0.1633 0.935 0.976 0.024
#> GSM1105457 2 0.0376 0.900 0.004 0.996
#> GSM1105460 2 0.0376 0.900 0.004 0.996
#> GSM1105461 2 0.0000 0.899 0.000 1.000
#> GSM1105464 1 0.2236 0.930 0.964 0.036
#> GSM1105466 2 0.6148 0.819 0.152 0.848
#> GSM1105479 2 0.0672 0.899 0.008 0.992
#> GSM1105502 1 0.2948 0.916 0.948 0.052
#> GSM1105515 1 0.0000 0.937 1.000 0.000
#> GSM1105523 2 1.0000 0.172 0.496 0.504
#> GSM1105550 1 0.8144 0.653 0.748 0.252
#> GSM1105450 2 0.0000 0.899 0.000 1.000
#> GSM1105451 2 0.0000 0.899 0.000 1.000
#> GSM1105454 2 0.0672 0.899 0.008 0.992
#> GSM1105468 2 0.0000 0.899 0.000 1.000
#> GSM1105481 2 0.0672 0.899 0.008 0.992
#> GSM1105504 1 0.2948 0.916 0.948 0.052
#> GSM1105517 1 0.6712 0.777 0.824 0.176
#> GSM1105525 2 1.0000 0.172 0.496 0.504
#> GSM1105552 1 0.8081 0.660 0.752 0.248
#> GSM1105452 2 0.1633 0.896 0.024 0.976
#> GSM1105453 2 0.0000 0.899 0.000 1.000
#> GSM1105456 2 0.0672 0.899 0.008 0.992
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1105438 2 0.6008 0.4721 0.000 0.628 0.372
#> GSM1105486 2 0.5988 0.4729 0.000 0.632 0.368
#> GSM1105487 1 0.0747 0.8448 0.984 0.000 0.016
#> GSM1105490 2 0.3295 0.3746 0.008 0.896 0.096
#> GSM1105491 1 0.9692 -0.0871 0.432 0.224 0.344
#> GSM1105495 2 0.6295 0.2962 0.000 0.528 0.472
#> GSM1105498 2 0.8574 -0.1972 0.096 0.472 0.432
#> GSM1105499 1 0.0424 0.8468 0.992 0.000 0.008
#> GSM1105506 2 0.6710 0.1836 0.072 0.732 0.196
#> GSM1105442 2 0.6460 0.4311 0.004 0.556 0.440
#> GSM1105511 2 0.3295 0.3746 0.008 0.896 0.096
#> GSM1105514 2 0.5988 0.4729 0.000 0.632 0.368
#> GSM1105518 2 0.7289 -0.0733 0.028 0.504 0.468
#> GSM1105522 1 0.0424 0.8468 0.992 0.000 0.008
#> GSM1105534 1 0.0424 0.8468 0.992 0.000 0.008
#> GSM1105535 1 0.0424 0.8468 0.992 0.000 0.008
#> GSM1105538 1 0.5585 0.7021 0.812 0.096 0.092
#> GSM1105542 2 0.6398 0.4467 0.004 0.580 0.416
#> GSM1105443 2 0.1964 0.4130 0.000 0.944 0.056
#> GSM1105551 1 0.9956 -0.4587 0.360 0.288 0.352
#> GSM1105554 1 0.0424 0.8468 0.992 0.000 0.008
#> GSM1105555 1 0.0747 0.8448 0.984 0.000 0.016
#> GSM1105447 2 0.1964 0.4173 0.000 0.944 0.056
#> GSM1105467 2 0.6079 0.4675 0.000 0.612 0.388
#> GSM1105470 2 0.5988 0.4729 0.000 0.632 0.368
#> GSM1105471 2 0.6102 0.2809 0.008 0.672 0.320
#> GSM1105474 2 0.5988 0.4729 0.000 0.632 0.368
#> GSM1105475 2 0.5098 0.4594 0.000 0.752 0.248
#> GSM1105440 1 0.0424 0.8468 0.992 0.000 0.008
#> GSM1105488 2 0.6398 0.4467 0.004 0.580 0.416
#> GSM1105489 1 0.0747 0.8448 0.984 0.000 0.016
#> GSM1105492 1 0.0424 0.8468 0.992 0.000 0.008
#> GSM1105493 1 0.2945 0.8153 0.908 0.004 0.088
#> GSM1105497 3 0.6799 -0.5489 0.012 0.456 0.532
#> GSM1105500 2 0.8574 -0.1972 0.096 0.472 0.432
#> GSM1105501 2 0.5634 0.3347 0.056 0.800 0.144
#> GSM1105508 2 0.9827 -0.3698 0.372 0.384 0.244
#> GSM1105444 2 0.6062 0.4669 0.000 0.616 0.384
#> GSM1105513 2 0.3295 0.3746 0.008 0.896 0.096
#> GSM1105516 2 0.9423 -0.1782 0.304 0.492 0.204
#> GSM1105520 2 0.7289 -0.0733 0.028 0.504 0.468
#> GSM1105524 1 0.0424 0.8468 0.992 0.000 0.008
#> GSM1105536 2 0.8556 0.2341 0.164 0.604 0.232
#> GSM1105537 1 0.0424 0.8468 0.992 0.000 0.008
#> GSM1105540 1 0.5585 0.7021 0.812 0.096 0.092
#> GSM1105544 2 0.9901 -0.2730 0.296 0.404 0.300
#> GSM1105445 2 0.1964 0.4130 0.000 0.944 0.056
#> GSM1105553 1 0.9956 -0.4587 0.360 0.288 0.352
#> GSM1105556 1 0.0424 0.8468 0.992 0.000 0.008
#> GSM1105557 2 0.3295 0.3746 0.008 0.896 0.096
#> GSM1105449 2 0.1964 0.4202 0.000 0.944 0.056
#> GSM1105469 2 0.8202 -0.0639 0.092 0.580 0.328
#> GSM1105472 2 0.5988 0.4729 0.000 0.632 0.368
#> GSM1105473 1 0.2947 0.8340 0.920 0.020 0.060
#> GSM1105476 2 0.5988 0.4729 0.000 0.632 0.368
#> GSM1105477 2 0.5098 0.4594 0.000 0.752 0.248
#> GSM1105478 2 0.7104 0.0466 0.032 0.608 0.360
#> GSM1105510 2 0.6745 0.4393 0.012 0.560 0.428
#> GSM1105530 1 0.2096 0.8395 0.944 0.004 0.052
#> GSM1105539 1 0.1989 0.8400 0.948 0.004 0.048
#> GSM1105480 2 0.7104 0.0466 0.032 0.608 0.360
#> GSM1105512 1 0.0424 0.8468 0.992 0.000 0.008
#> GSM1105532 1 0.2096 0.8395 0.944 0.004 0.052
#> GSM1105541 1 0.1989 0.8400 0.948 0.004 0.048
#> GSM1105439 2 0.1031 0.4323 0.000 0.976 0.024
#> GSM1105463 1 0.2772 0.8299 0.916 0.004 0.080
#> GSM1105482 1 0.1129 0.8468 0.976 0.004 0.020
#> GSM1105483 2 0.8202 -0.0639 0.092 0.580 0.328
#> GSM1105494 2 0.8574 -0.1972 0.096 0.472 0.432
#> GSM1105503 2 0.8474 -0.1443 0.092 0.504 0.404
#> GSM1105507 1 0.9277 -0.0372 0.496 0.328 0.176
#> GSM1105446 2 0.5968 0.4734 0.000 0.636 0.364
#> GSM1105519 1 0.2492 0.8309 0.936 0.016 0.048
#> GSM1105526 2 0.6126 0.4724 0.004 0.644 0.352
#> GSM1105527 2 0.8202 -0.0639 0.092 0.580 0.328
#> GSM1105531 1 0.4232 0.7969 0.872 0.044 0.084
#> GSM1105543 2 0.5968 0.4734 0.000 0.636 0.364
#> GSM1105546 1 0.1031 0.8465 0.976 0.000 0.024
#> GSM1105547 1 0.2496 0.8266 0.928 0.004 0.068
#> GSM1105455 2 0.0592 0.4266 0.000 0.988 0.012
#> GSM1105458 2 0.1964 0.4169 0.000 0.944 0.056
#> GSM1105459 2 0.5988 0.4729 0.000 0.632 0.368
#> GSM1105462 1 0.2846 0.8350 0.924 0.020 0.056
#> GSM1105441 2 0.1031 0.4323 0.000 0.976 0.024
#> GSM1105465 2 0.6460 0.4311 0.004 0.556 0.440
#> GSM1105484 2 0.6126 0.4605 0.000 0.600 0.400
#> GSM1105485 2 0.6398 0.4467 0.004 0.580 0.416
#> GSM1105496 2 0.8574 -0.1972 0.096 0.472 0.432
#> GSM1105505 2 0.8474 -0.1443 0.092 0.504 0.404
#> GSM1105509 1 0.9277 -0.0372 0.496 0.328 0.176
#> GSM1105448 2 0.5988 0.4729 0.000 0.632 0.368
#> GSM1105521 1 0.2492 0.8309 0.936 0.016 0.048
#> GSM1105528 2 0.6126 0.4724 0.004 0.644 0.352
#> GSM1105529 2 0.6398 0.4467 0.004 0.580 0.416
#> GSM1105533 1 0.0592 0.8442 0.988 0.000 0.012
#> GSM1105545 2 0.8556 0.2341 0.164 0.604 0.232
#> GSM1105548 1 0.1031 0.8465 0.976 0.000 0.024
#> GSM1105549 1 0.2496 0.8266 0.928 0.004 0.068
#> GSM1105457 2 0.0592 0.4266 0.000 0.988 0.012
#> GSM1105460 2 0.1964 0.4169 0.000 0.944 0.056
#> GSM1105461 2 0.5988 0.4729 0.000 0.632 0.368
#> GSM1105464 1 0.2846 0.8350 0.924 0.020 0.056
#> GSM1105466 2 0.7394 0.0705 0.064 0.652 0.284
#> GSM1105479 2 0.6111 0.3182 0.000 0.604 0.396
#> GSM1105502 1 0.4094 0.7955 0.872 0.028 0.100
#> GSM1105515 1 0.0424 0.8468 0.992 0.000 0.008
#> GSM1105523 3 0.9948 0.3086 0.352 0.284 0.364
#> GSM1105550 1 0.8072 0.4384 0.652 0.184 0.164
#> GSM1105450 2 0.5988 0.4729 0.000 0.632 0.368
#> GSM1105451 2 0.5988 0.4729 0.000 0.632 0.368
#> GSM1105454 2 0.6095 0.1085 0.000 0.608 0.392
#> GSM1105468 2 0.5988 0.4729 0.000 0.632 0.368
#> GSM1105481 2 0.6286 0.2460 0.000 0.536 0.464
#> GSM1105504 1 0.4094 0.7955 0.872 0.028 0.100
#> GSM1105517 1 0.7042 0.5827 0.728 0.132 0.140
#> GSM1105525 3 0.9948 0.3086 0.352 0.284 0.364
#> GSM1105552 1 0.8026 0.4464 0.656 0.180 0.164
#> GSM1105452 2 0.6126 0.4565 0.000 0.600 0.400
#> GSM1105453 2 0.5988 0.4729 0.000 0.632 0.368
#> GSM1105456 2 0.6095 0.1085 0.000 0.608 0.392
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1105438 2 0.0524 0.70964 0.000 0.988 0.004 0.008
#> GSM1105486 2 0.0188 0.70932 0.000 0.996 0.000 0.004
#> GSM1105487 1 0.4605 -0.07449 0.664 0.000 0.336 0.000
#> GSM1105490 4 0.5576 0.26671 0.000 0.444 0.020 0.536
#> GSM1105491 1 0.8120 0.11634 0.440 0.324 0.220 0.016
#> GSM1105495 2 0.7407 0.13233 0.012 0.440 0.116 0.432
#> GSM1105498 4 0.5975 0.65742 0.048 0.096 0.108 0.748
#> GSM1105499 3 0.4804 0.94187 0.384 0.000 0.616 0.000
#> GSM1105506 4 0.6822 0.53718 0.016 0.284 0.092 0.608
#> GSM1105442 2 0.4579 0.60501 0.000 0.768 0.200 0.032
#> GSM1105511 4 0.5576 0.26671 0.000 0.444 0.020 0.536
#> GSM1105514 2 0.0188 0.70864 0.000 0.996 0.004 0.000
#> GSM1105518 4 0.4991 0.61423 0.016 0.100 0.088 0.796
#> GSM1105522 3 0.4790 0.94042 0.380 0.000 0.620 0.000
#> GSM1105534 3 0.4804 0.94187 0.384 0.000 0.616 0.000
#> GSM1105535 3 0.4790 0.94042 0.380 0.000 0.620 0.000
#> GSM1105538 1 0.7189 0.28649 0.596 0.064 0.288 0.052
#> GSM1105542 2 0.4019 0.62051 0.000 0.792 0.196 0.012
#> GSM1105443 2 0.6204 -0.03535 0.000 0.500 0.052 0.448
#> GSM1105551 4 0.6845 0.33789 0.308 0.000 0.128 0.564
#> GSM1105554 3 0.4804 0.94187 0.384 0.000 0.616 0.000
#> GSM1105555 1 0.4605 -0.07449 0.664 0.000 0.336 0.000
#> GSM1105447 2 0.6125 0.00359 0.000 0.516 0.048 0.436
#> GSM1105467 2 0.1022 0.70171 0.000 0.968 0.000 0.032
#> GSM1105470 2 0.0188 0.70932 0.000 0.996 0.000 0.004
#> GSM1105471 4 0.6661 0.31702 0.016 0.376 0.056 0.552
#> GSM1105474 2 0.0336 0.70865 0.000 0.992 0.000 0.008
#> GSM1105475 2 0.4466 0.55240 0.000 0.784 0.036 0.180
#> GSM1105440 3 0.4817 0.92210 0.388 0.000 0.612 0.000
#> GSM1105488 2 0.4019 0.62051 0.000 0.792 0.196 0.012
#> GSM1105489 1 0.4605 -0.07449 0.664 0.000 0.336 0.000
#> GSM1105492 3 0.4790 0.94042 0.380 0.000 0.620 0.000
#> GSM1105493 1 0.4059 0.40458 0.788 0.000 0.200 0.012
#> GSM1105497 2 0.6894 0.48634 0.008 0.624 0.188 0.180
#> GSM1105500 4 0.5975 0.65742 0.048 0.096 0.108 0.748
#> GSM1105501 4 0.6911 0.30316 0.000 0.412 0.108 0.480
#> GSM1105508 4 0.9286 0.37229 0.284 0.152 0.144 0.420
#> GSM1105444 2 0.0927 0.70577 0.000 0.976 0.008 0.016
#> GSM1105513 4 0.5576 0.26671 0.000 0.444 0.020 0.536
#> GSM1105516 4 0.9592 0.39195 0.248 0.256 0.132 0.364
#> GSM1105520 4 0.4991 0.61423 0.016 0.100 0.088 0.796
#> GSM1105524 3 0.4790 0.94042 0.380 0.000 0.620 0.000
#> GSM1105536 2 0.8437 0.08594 0.152 0.520 0.076 0.252
#> GSM1105537 3 0.4790 0.94042 0.380 0.000 0.620 0.000
#> GSM1105540 1 0.7189 0.28649 0.596 0.064 0.288 0.052
#> GSM1105544 4 0.9450 0.41596 0.224 0.216 0.144 0.416
#> GSM1105445 2 0.6204 -0.03535 0.000 0.500 0.052 0.448
#> GSM1105553 4 0.6845 0.33789 0.308 0.000 0.128 0.564
#> GSM1105556 3 0.4804 0.94187 0.384 0.000 0.616 0.000
#> GSM1105557 4 0.5576 0.26671 0.000 0.444 0.020 0.536
#> GSM1105449 2 0.6114 0.02839 0.000 0.524 0.048 0.428
#> GSM1105469 4 0.6273 0.63812 0.020 0.156 0.120 0.704
#> GSM1105472 2 0.0188 0.70932 0.000 0.996 0.000 0.004
#> GSM1105473 1 0.1975 0.53063 0.944 0.012 0.028 0.016
#> GSM1105476 2 0.0336 0.70865 0.000 0.992 0.000 0.008
#> GSM1105477 2 0.4466 0.55240 0.000 0.784 0.036 0.180
#> GSM1105478 4 0.4803 0.63475 0.016 0.176 0.028 0.780
#> GSM1105510 2 0.4875 0.61344 0.008 0.772 0.180 0.040
#> GSM1105530 1 0.0000 0.52791 1.000 0.000 0.000 0.000
#> GSM1105539 1 0.0592 0.52322 0.984 0.000 0.016 0.000
#> GSM1105480 4 0.4803 0.63475 0.016 0.176 0.028 0.780
#> GSM1105512 3 0.4804 0.94187 0.384 0.000 0.616 0.000
#> GSM1105532 1 0.0000 0.52791 1.000 0.000 0.000 0.000
#> GSM1105541 1 0.0592 0.52322 0.984 0.000 0.016 0.000
#> GSM1105439 2 0.6090 0.10988 0.000 0.564 0.052 0.384
#> GSM1105463 1 0.0921 0.52924 0.972 0.000 0.000 0.028
#> GSM1105482 1 0.5119 -0.48585 0.556 0.000 0.440 0.004
#> GSM1105483 4 0.6273 0.63812 0.020 0.156 0.120 0.704
#> GSM1105494 4 0.5975 0.65742 0.048 0.096 0.108 0.748
#> GSM1105503 4 0.6372 0.62634 0.084 0.100 0.088 0.728
#> GSM1105507 1 0.9743 -0.07472 0.312 0.144 0.268 0.276
#> GSM1105446 2 0.1576 0.70009 0.000 0.948 0.048 0.004
#> GSM1105519 3 0.5592 0.60366 0.488 0.008 0.496 0.008
#> GSM1105526 2 0.1833 0.69694 0.000 0.944 0.032 0.024
#> GSM1105527 4 0.6273 0.63812 0.020 0.156 0.120 0.704
#> GSM1105531 1 0.2156 0.51933 0.928 0.008 0.004 0.060
#> GSM1105543 2 0.1576 0.70009 0.000 0.948 0.048 0.004
#> GSM1105546 1 0.4820 0.10976 0.692 0.000 0.296 0.012
#> GSM1105547 1 0.4452 0.31244 0.732 0.000 0.260 0.008
#> GSM1105455 2 0.6139 0.05394 0.000 0.544 0.052 0.404
#> GSM1105458 2 0.6197 -0.00899 0.000 0.508 0.052 0.440
#> GSM1105459 2 0.0804 0.70809 0.000 0.980 0.008 0.012
#> GSM1105462 1 0.1526 0.53120 0.960 0.016 0.012 0.012
#> GSM1105441 2 0.6090 0.10988 0.000 0.564 0.052 0.384
#> GSM1105465 2 0.4579 0.60501 0.000 0.768 0.200 0.032
#> GSM1105484 2 0.3711 0.64878 0.000 0.836 0.140 0.024
#> GSM1105485 2 0.4019 0.62051 0.000 0.792 0.196 0.012
#> GSM1105496 4 0.5975 0.65742 0.048 0.096 0.108 0.748
#> GSM1105505 4 0.6372 0.62634 0.084 0.100 0.088 0.728
#> GSM1105509 1 0.9743 -0.07472 0.312 0.144 0.268 0.276
#> GSM1105448 2 0.0336 0.70828 0.000 0.992 0.008 0.000
#> GSM1105521 3 0.5592 0.60366 0.488 0.008 0.496 0.008
#> GSM1105528 2 0.1833 0.69694 0.000 0.944 0.032 0.024
#> GSM1105529 2 0.4019 0.62051 0.000 0.792 0.196 0.012
#> GSM1105533 1 0.4746 -0.23338 0.632 0.000 0.368 0.000
#> GSM1105545 2 0.8437 0.08594 0.152 0.520 0.076 0.252
#> GSM1105548 1 0.4820 0.10976 0.692 0.000 0.296 0.012
#> GSM1105549 1 0.4452 0.31244 0.732 0.000 0.260 0.008
#> GSM1105457 2 0.6139 0.05394 0.000 0.544 0.052 0.404
#> GSM1105460 2 0.6197 -0.00899 0.000 0.508 0.052 0.440
#> GSM1105461 2 0.0804 0.70809 0.000 0.980 0.008 0.012
#> GSM1105464 1 0.1526 0.53120 0.960 0.016 0.012 0.012
#> GSM1105466 4 0.5843 0.61658 0.016 0.200 0.068 0.716
#> GSM1105479 2 0.6603 -0.04202 0.012 0.500 0.052 0.436
#> GSM1105502 1 0.3004 0.51786 0.892 0.000 0.048 0.060
#> GSM1105515 3 0.4804 0.94187 0.384 0.000 0.616 0.000
#> GSM1105523 4 0.6500 0.34926 0.328 0.000 0.092 0.580
#> GSM1105550 1 0.7798 0.36218 0.596 0.068 0.128 0.208
#> GSM1105450 2 0.0188 0.70932 0.000 0.996 0.000 0.004
#> GSM1105451 2 0.0376 0.70949 0.000 0.992 0.004 0.004
#> GSM1105454 4 0.5710 0.51683 0.012 0.184 0.076 0.728
#> GSM1105468 2 0.0188 0.70932 0.000 0.996 0.000 0.004
#> GSM1105481 4 0.6598 0.10252 0.012 0.432 0.052 0.504
#> GSM1105504 1 0.3004 0.51786 0.892 0.000 0.048 0.060
#> GSM1105517 1 0.7985 0.17299 0.492 0.028 0.320 0.160
#> GSM1105525 4 0.6500 0.34926 0.328 0.000 0.092 0.580
#> GSM1105552 1 0.7734 0.36395 0.600 0.064 0.128 0.208
#> GSM1105452 2 0.3583 0.63317 0.000 0.816 0.180 0.004
#> GSM1105453 2 0.0376 0.70949 0.000 0.992 0.004 0.004
#> GSM1105456 4 0.5710 0.51683 0.012 0.184 0.076 0.728
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM1105438 2 0.0671 0.75821 0.000 0.980 0.000 0.016 0.004
#> GSM1105486 2 0.0671 0.75535 0.000 0.980 0.000 0.016 0.004
#> GSM1105487 1 0.4663 0.41212 0.604 0.000 0.376 0.000 0.020
#> GSM1105490 4 0.4218 0.28069 0.000 0.332 0.000 0.660 0.008
#> GSM1105491 3 0.6891 0.14800 0.004 0.320 0.500 0.024 0.152
#> GSM1105495 2 0.7348 -0.11285 0.000 0.408 0.044 0.184 0.364
#> GSM1105498 4 0.6504 0.01111 0.004 0.040 0.072 0.524 0.360
#> GSM1105499 1 0.0000 0.80540 1.000 0.000 0.000 0.000 0.000
#> GSM1105506 4 0.4479 0.29211 0.052 0.128 0.016 0.792 0.012
#> GSM1105442 2 0.5177 0.62792 0.000 0.720 0.052 0.040 0.188
#> GSM1105511 4 0.4218 0.28069 0.000 0.332 0.000 0.660 0.008
#> GSM1105514 2 0.0162 0.75679 0.000 0.996 0.000 0.004 0.000
#> GSM1105518 5 0.4990 0.54258 0.000 0.056 0.008 0.248 0.688
#> GSM1105522 1 0.0162 0.80500 0.996 0.000 0.000 0.000 0.004
#> GSM1105534 1 0.0000 0.80540 1.000 0.000 0.000 0.000 0.000
#> GSM1105535 1 0.0162 0.80500 0.996 0.000 0.000 0.000 0.004
#> GSM1105538 3 0.7126 0.44258 0.288 0.008 0.528 0.124 0.052
#> GSM1105542 2 0.4813 0.64357 0.000 0.744 0.048 0.028 0.180
#> GSM1105443 4 0.5851 0.17159 0.000 0.340 0.000 0.548 0.112
#> GSM1105551 4 0.7425 0.00973 0.028 0.000 0.312 0.340 0.320
#> GSM1105554 1 0.0000 0.80540 1.000 0.000 0.000 0.000 0.000
#> GSM1105555 1 0.4663 0.41212 0.604 0.000 0.376 0.000 0.020
#> GSM1105447 4 0.5894 0.16469 0.000 0.356 0.000 0.532 0.112
#> GSM1105467 2 0.2723 0.67177 0.000 0.864 0.000 0.124 0.012
#> GSM1105470 2 0.0510 0.75637 0.000 0.984 0.000 0.016 0.000
#> GSM1105471 5 0.7076 0.39133 0.000 0.260 0.012 0.356 0.372
#> GSM1105474 2 0.0510 0.75645 0.000 0.984 0.000 0.016 0.000
#> GSM1105475 2 0.4455 0.44030 0.000 0.692 0.016 0.284 0.008
#> GSM1105440 1 0.0798 0.79819 0.976 0.000 0.016 0.000 0.008
#> GSM1105488 2 0.4813 0.64357 0.000 0.744 0.048 0.028 0.180
#> GSM1105489 1 0.4663 0.41212 0.604 0.000 0.376 0.000 0.020
#> GSM1105492 1 0.0162 0.80500 0.996 0.000 0.000 0.000 0.004
#> GSM1105493 3 0.5102 0.46168 0.288 0.000 0.660 0.020 0.032
#> GSM1105497 2 0.6244 0.46864 0.000 0.584 0.052 0.064 0.300
#> GSM1105500 4 0.6504 0.01111 0.004 0.040 0.072 0.524 0.360
#> GSM1105501 4 0.5542 0.27599 0.044 0.260 0.016 0.664 0.016
#> GSM1105508 4 0.6892 0.23941 0.096 0.008 0.260 0.572 0.064
#> GSM1105444 2 0.1281 0.74898 0.000 0.956 0.000 0.012 0.032
#> GSM1105513 4 0.4218 0.28069 0.000 0.332 0.000 0.660 0.008
#> GSM1105516 4 0.8290 0.24794 0.060 0.140 0.252 0.476 0.072
#> GSM1105520 5 0.4990 0.54258 0.000 0.056 0.008 0.248 0.688
#> GSM1105524 1 0.0162 0.80500 0.996 0.000 0.000 0.000 0.004
#> GSM1105536 2 0.7705 -0.12929 0.004 0.412 0.172 0.344 0.068
#> GSM1105537 1 0.0162 0.80500 0.996 0.000 0.000 0.000 0.004
#> GSM1105540 3 0.7126 0.44258 0.288 0.008 0.528 0.124 0.052
#> GSM1105544 4 0.9004 0.16403 0.064 0.128 0.236 0.400 0.172
#> GSM1105445 4 0.5851 0.17159 0.000 0.340 0.000 0.548 0.112
#> GSM1105553 4 0.7425 0.00973 0.028 0.000 0.312 0.340 0.320
#> GSM1105556 1 0.0000 0.80540 1.000 0.000 0.000 0.000 0.000
#> GSM1105557 4 0.4218 0.28069 0.000 0.332 0.000 0.660 0.008
#> GSM1105449 4 0.5913 0.15920 0.000 0.364 0.000 0.524 0.112
#> GSM1105469 4 0.4461 0.29262 0.052 0.008 0.024 0.796 0.120
#> GSM1105472 2 0.0510 0.75637 0.000 0.984 0.000 0.016 0.000
#> GSM1105473 3 0.3325 0.71990 0.112 0.004 0.852 0.020 0.012
#> GSM1105476 2 0.0510 0.75645 0.000 0.984 0.000 0.016 0.000
#> GSM1105477 2 0.4455 0.44030 0.000 0.692 0.016 0.284 0.008
#> GSM1105478 4 0.4156 0.20637 0.008 0.040 0.016 0.808 0.128
#> GSM1105510 2 0.5042 0.64108 0.000 0.736 0.048 0.044 0.172
#> GSM1105530 3 0.2127 0.71836 0.108 0.000 0.892 0.000 0.000
#> GSM1105539 3 0.2329 0.71668 0.124 0.000 0.876 0.000 0.000
#> GSM1105480 4 0.4156 0.20637 0.008 0.040 0.016 0.808 0.128
#> GSM1105512 1 0.0000 0.80540 1.000 0.000 0.000 0.000 0.000
#> GSM1105532 3 0.2127 0.71836 0.108 0.000 0.892 0.000 0.000
#> GSM1105541 3 0.2329 0.71668 0.124 0.000 0.876 0.000 0.000
#> GSM1105439 2 0.5857 -0.19527 0.000 0.460 0.000 0.444 0.096
#> GSM1105463 3 0.2685 0.71737 0.092 0.000 0.880 0.000 0.028
#> GSM1105482 1 0.3509 0.66767 0.792 0.000 0.196 0.008 0.004
#> GSM1105483 4 0.4461 0.29262 0.052 0.008 0.024 0.796 0.120
#> GSM1105494 4 0.6494 0.01074 0.004 0.040 0.072 0.528 0.356
#> GSM1105503 5 0.6192 0.49993 0.000 0.056 0.084 0.228 0.632
#> GSM1105507 4 0.7961 0.02684 0.208 0.008 0.300 0.412 0.072
#> GSM1105446 2 0.1569 0.75232 0.000 0.948 0.008 0.012 0.032
#> GSM1105519 1 0.3716 0.66961 0.800 0.000 0.172 0.020 0.008
#> GSM1105526 2 0.2507 0.72885 0.000 0.900 0.016 0.072 0.012
#> GSM1105527 4 0.4461 0.29262 0.052 0.008 0.024 0.796 0.120
#> GSM1105531 3 0.3383 0.70184 0.068 0.000 0.860 0.020 0.052
#> GSM1105543 2 0.1885 0.74927 0.000 0.936 0.012 0.020 0.032
#> GSM1105546 1 0.5055 0.20200 0.544 0.000 0.428 0.012 0.016
#> GSM1105547 3 0.5215 0.31049 0.380 0.000 0.580 0.016 0.024
#> GSM1105455 4 0.5847 0.16373 0.000 0.424 0.000 0.480 0.096
#> GSM1105458 4 0.5873 0.16897 0.000 0.348 0.000 0.540 0.112
#> GSM1105459 2 0.0794 0.75225 0.000 0.972 0.000 0.028 0.000
#> GSM1105462 3 0.3106 0.72170 0.116 0.000 0.856 0.020 0.008
#> GSM1105441 2 0.5857 -0.19527 0.000 0.460 0.000 0.444 0.096
#> GSM1105465 2 0.5177 0.62792 0.000 0.720 0.052 0.040 0.188
#> GSM1105484 2 0.4191 0.67884 0.000 0.792 0.040 0.020 0.148
#> GSM1105485 2 0.4813 0.64357 0.000 0.744 0.048 0.028 0.180
#> GSM1105496 4 0.6504 0.01111 0.004 0.040 0.072 0.524 0.360
#> GSM1105505 5 0.6192 0.49993 0.000 0.056 0.084 0.228 0.632
#> GSM1105509 4 0.7961 0.02684 0.208 0.008 0.300 0.412 0.072
#> GSM1105448 2 0.0000 0.75667 0.000 1.000 0.000 0.000 0.000
#> GSM1105521 1 0.3716 0.66961 0.800 0.000 0.172 0.020 0.008
#> GSM1105528 2 0.2507 0.72885 0.000 0.900 0.016 0.072 0.012
#> GSM1105529 2 0.4813 0.64357 0.000 0.744 0.048 0.028 0.180
#> GSM1105533 1 0.3814 0.56390 0.720 0.000 0.276 0.000 0.004
#> GSM1105545 2 0.7695 -0.10906 0.004 0.420 0.172 0.336 0.068
#> GSM1105548 1 0.5055 0.20200 0.544 0.000 0.428 0.012 0.016
#> GSM1105549 3 0.5215 0.31049 0.380 0.000 0.580 0.016 0.024
#> GSM1105457 4 0.5847 0.16373 0.000 0.424 0.000 0.480 0.096
#> GSM1105460 4 0.5873 0.16897 0.000 0.348 0.000 0.540 0.112
#> GSM1105461 2 0.0794 0.75225 0.000 0.972 0.000 0.028 0.000
#> GSM1105464 3 0.3106 0.72170 0.116 0.000 0.856 0.020 0.008
#> GSM1105466 4 0.3305 0.29704 0.036 0.052 0.012 0.876 0.024
#> GSM1105479 5 0.6742 0.40252 0.000 0.388 0.004 0.212 0.396
#> GSM1105502 3 0.4605 0.69013 0.124 0.000 0.780 0.036 0.060
#> GSM1105515 1 0.0000 0.80540 1.000 0.000 0.000 0.000 0.000
#> GSM1105523 4 0.7504 0.07481 0.048 0.000 0.316 0.416 0.220
#> GSM1105550 3 0.7532 0.47779 0.092 0.040 0.568 0.212 0.088
#> GSM1105450 2 0.0404 0.75653 0.000 0.988 0.000 0.012 0.000
#> GSM1105451 2 0.0290 0.75765 0.000 0.992 0.000 0.008 0.000
#> GSM1105454 5 0.5633 0.56455 0.000 0.144 0.004 0.204 0.648
#> GSM1105468 2 0.0794 0.75200 0.000 0.972 0.000 0.028 0.000
#> GSM1105481 5 0.6363 0.44539 0.000 0.384 0.004 0.144 0.468
#> GSM1105504 3 0.4605 0.69013 0.124 0.000 0.780 0.036 0.060
#> GSM1105517 3 0.7827 0.34588 0.304 0.008 0.444 0.164 0.080
#> GSM1105525 4 0.7504 0.07481 0.048 0.000 0.316 0.416 0.220
#> GSM1105552 3 0.7552 0.48450 0.096 0.040 0.568 0.208 0.088
#> GSM1105452 2 0.4118 0.66832 0.000 0.788 0.040 0.012 0.160
#> GSM1105453 2 0.0290 0.75765 0.000 0.992 0.000 0.008 0.000
#> GSM1105456 5 0.5633 0.56455 0.000 0.144 0.004 0.204 0.648
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1105438 2 0.0820 0.7613 0.000 0.972 0.000 0.016 0.012 0.000
#> GSM1105486 2 0.1296 0.7530 0.000 0.952 0.000 0.032 0.012 0.004
#> GSM1105487 1 0.6137 0.5165 0.572 0.000 0.256 0.004 0.108 0.060
#> GSM1105490 4 0.3426 0.5376 0.000 0.276 0.000 0.720 0.004 0.000
#> GSM1105491 3 0.6789 0.0257 0.004 0.316 0.348 0.004 0.308 0.020
#> GSM1105495 6 0.7123 0.1662 0.000 0.324 0.000 0.104 0.180 0.392
#> GSM1105498 5 0.6510 0.7412 0.000 0.020 0.008 0.364 0.416 0.192
#> GSM1105499 1 0.0508 0.8162 0.984 0.000 0.000 0.000 0.004 0.012
#> GSM1105506 4 0.4507 0.4409 0.044 0.120 0.004 0.768 0.060 0.004
#> GSM1105442 2 0.3965 0.5521 0.000 0.604 0.000 0.000 0.388 0.008
#> GSM1105511 4 0.3426 0.5376 0.000 0.276 0.000 0.720 0.004 0.000
#> GSM1105514 2 0.0291 0.7599 0.000 0.992 0.000 0.004 0.004 0.000
#> GSM1105518 6 0.5136 0.2932 0.000 0.008 0.004 0.180 0.144 0.664
#> GSM1105522 1 0.0146 0.8163 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM1105534 1 0.0508 0.8162 0.984 0.000 0.000 0.000 0.004 0.012
#> GSM1105535 1 0.0146 0.8163 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM1105538 3 0.7363 0.3819 0.244 0.000 0.468 0.108 0.156 0.024
#> GSM1105542 2 0.3684 0.5723 0.000 0.628 0.000 0.000 0.372 0.000
#> GSM1105443 4 0.5284 0.4966 0.000 0.268 0.000 0.612 0.012 0.108
#> GSM1105551 5 0.6979 0.6086 0.000 0.000 0.120 0.200 0.480 0.200
#> GSM1105554 1 0.0508 0.8162 0.984 0.000 0.000 0.000 0.004 0.012
#> GSM1105555 1 0.6137 0.5165 0.572 0.000 0.256 0.004 0.108 0.060
#> GSM1105447 4 0.5354 0.4930 0.000 0.284 0.000 0.596 0.012 0.108
#> GSM1105467 2 0.3141 0.6575 0.000 0.828 0.000 0.140 0.012 0.020
#> GSM1105470 2 0.1151 0.7526 0.000 0.956 0.000 0.032 0.012 0.000
#> GSM1105471 6 0.6385 0.3125 0.000 0.212 0.004 0.328 0.016 0.440
#> GSM1105474 2 0.0603 0.7600 0.000 0.980 0.000 0.016 0.004 0.000
#> GSM1105475 2 0.4468 0.2632 0.000 0.640 0.000 0.316 0.040 0.004
#> GSM1105440 1 0.0922 0.8080 0.968 0.000 0.004 0.000 0.024 0.004
#> GSM1105488 2 0.3672 0.5766 0.000 0.632 0.000 0.000 0.368 0.000
#> GSM1105489 1 0.6137 0.5165 0.572 0.000 0.256 0.004 0.108 0.060
#> GSM1105492 1 0.0146 0.8163 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM1105493 3 0.6199 0.4247 0.176 0.000 0.576 0.004 0.196 0.048
#> GSM1105497 2 0.6214 0.3960 0.000 0.504 0.000 0.040 0.316 0.140
#> GSM1105500 5 0.6510 0.7412 0.000 0.020 0.008 0.364 0.416 0.192
#> GSM1105501 4 0.5173 0.5031 0.036 0.228 0.000 0.660 0.076 0.000
#> GSM1105508 4 0.7047 -0.1753 0.068 0.000 0.224 0.532 0.128 0.048
#> GSM1105444 2 0.2588 0.7359 0.000 0.876 0.000 0.024 0.092 0.008
#> GSM1105513 4 0.3426 0.5376 0.000 0.276 0.000 0.720 0.004 0.000
#> GSM1105516 4 0.8005 0.0227 0.052 0.112 0.232 0.452 0.136 0.016
#> GSM1105520 6 0.5136 0.2932 0.000 0.008 0.004 0.180 0.144 0.664
#> GSM1105524 1 0.0146 0.8163 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM1105536 4 0.7497 0.2392 0.004 0.356 0.148 0.364 0.116 0.012
#> GSM1105537 1 0.0146 0.8163 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM1105540 3 0.7363 0.3819 0.244 0.000 0.468 0.108 0.156 0.024
#> GSM1105544 5 0.9009 0.3496 0.056 0.100 0.188 0.292 0.292 0.072
#> GSM1105445 4 0.5284 0.4966 0.000 0.268 0.000 0.612 0.012 0.108
#> GSM1105553 5 0.6979 0.6086 0.000 0.000 0.120 0.200 0.480 0.200
#> GSM1105556 1 0.0508 0.8162 0.984 0.000 0.000 0.000 0.004 0.012
#> GSM1105557 4 0.3426 0.5376 0.000 0.276 0.000 0.720 0.004 0.000
#> GSM1105449 4 0.5387 0.4882 0.000 0.292 0.000 0.588 0.012 0.108
#> GSM1105469 4 0.4856 -0.0574 0.044 0.000 0.008 0.732 0.148 0.068
#> GSM1105472 2 0.1151 0.7526 0.000 0.956 0.000 0.032 0.012 0.000
#> GSM1105473 3 0.3396 0.6362 0.040 0.004 0.856 0.016 0.060 0.024
#> GSM1105476 2 0.0603 0.7600 0.000 0.980 0.000 0.016 0.004 0.000
#> GSM1105477 2 0.4468 0.2632 0.000 0.640 0.000 0.316 0.040 0.004
#> GSM1105478 4 0.4657 -0.0292 0.004 0.020 0.004 0.724 0.192 0.056
#> GSM1105510 2 0.4536 0.5884 0.000 0.652 0.000 0.036 0.300 0.012
#> GSM1105530 3 0.0632 0.6494 0.024 0.000 0.976 0.000 0.000 0.000
#> GSM1105539 3 0.0937 0.6487 0.040 0.000 0.960 0.000 0.000 0.000
#> GSM1105480 4 0.4657 -0.0292 0.004 0.020 0.004 0.724 0.192 0.056
#> GSM1105512 1 0.0508 0.8162 0.984 0.000 0.000 0.000 0.004 0.012
#> GSM1105532 3 0.0632 0.6494 0.024 0.000 0.976 0.000 0.000 0.000
#> GSM1105541 3 0.0937 0.6487 0.040 0.000 0.960 0.000 0.000 0.000
#> GSM1105439 4 0.5539 0.4597 0.000 0.392 0.000 0.504 0.016 0.088
#> GSM1105463 3 0.1313 0.6452 0.016 0.000 0.952 0.000 0.004 0.028
#> GSM1105482 1 0.4231 0.7118 0.776 0.000 0.128 0.004 0.064 0.028
#> GSM1105483 4 0.4856 -0.0574 0.044 0.000 0.008 0.732 0.148 0.068
#> GSM1105494 5 0.6495 0.7374 0.000 0.020 0.008 0.368 0.416 0.188
#> GSM1105503 6 0.6255 0.2308 0.000 0.008 0.080 0.164 0.144 0.604
#> GSM1105507 4 0.7663 -0.1755 0.196 0.000 0.268 0.388 0.132 0.016
#> GSM1105446 2 0.1549 0.7552 0.000 0.936 0.000 0.020 0.044 0.000
#> GSM1105519 1 0.4257 0.6871 0.780 0.000 0.128 0.020 0.056 0.016
#> GSM1105526 2 0.2939 0.7194 0.000 0.860 0.000 0.072 0.060 0.008
#> GSM1105527 4 0.4856 -0.0574 0.044 0.000 0.008 0.732 0.148 0.068
#> GSM1105531 3 0.1531 0.6270 0.000 0.000 0.928 0.004 0.000 0.068
#> GSM1105543 2 0.2006 0.7448 0.000 0.904 0.000 0.016 0.080 0.000
#> GSM1105546 1 0.5924 0.2959 0.512 0.000 0.368 0.008 0.080 0.032
#> GSM1105547 3 0.6540 0.2905 0.268 0.000 0.504 0.004 0.176 0.048
#> GSM1105455 4 0.5472 0.4880 0.000 0.356 0.000 0.540 0.016 0.088
#> GSM1105458 4 0.5320 0.4948 0.000 0.276 0.000 0.604 0.012 0.108
#> GSM1105459 2 0.1594 0.7392 0.000 0.932 0.000 0.052 0.016 0.000
#> GSM1105462 3 0.2554 0.6459 0.032 0.000 0.900 0.024 0.032 0.012
#> GSM1105441 4 0.5539 0.4597 0.000 0.392 0.000 0.504 0.016 0.088
#> GSM1105465 2 0.3965 0.5521 0.000 0.604 0.000 0.000 0.388 0.008
#> GSM1105484 2 0.3653 0.6193 0.000 0.692 0.000 0.000 0.300 0.008
#> GSM1105485 2 0.3672 0.5746 0.000 0.632 0.000 0.000 0.368 0.000
#> GSM1105496 5 0.6510 0.7412 0.000 0.020 0.008 0.364 0.416 0.192
#> GSM1105505 6 0.6255 0.2308 0.000 0.008 0.080 0.164 0.144 0.604
#> GSM1105509 4 0.7663 -0.1755 0.196 0.000 0.268 0.388 0.132 0.016
#> GSM1105448 2 0.0993 0.7546 0.000 0.964 0.000 0.024 0.012 0.000
#> GSM1105521 1 0.4257 0.6871 0.780 0.000 0.128 0.020 0.056 0.016
#> GSM1105528 2 0.2939 0.7194 0.000 0.860 0.000 0.072 0.060 0.008
#> GSM1105529 2 0.3672 0.5746 0.000 0.632 0.000 0.000 0.368 0.000
#> GSM1105533 1 0.3547 0.5839 0.696 0.000 0.300 0.000 0.004 0.000
#> GSM1105545 2 0.7497 -0.2906 0.004 0.364 0.148 0.356 0.116 0.012
#> GSM1105548 1 0.5924 0.2959 0.512 0.000 0.368 0.008 0.080 0.032
#> GSM1105549 3 0.6540 0.2905 0.268 0.000 0.504 0.004 0.176 0.048
#> GSM1105457 4 0.5472 0.4880 0.000 0.356 0.000 0.540 0.016 0.088
#> GSM1105460 4 0.5320 0.4948 0.000 0.276 0.000 0.604 0.012 0.108
#> GSM1105461 2 0.1594 0.7392 0.000 0.932 0.000 0.052 0.016 0.000
#> GSM1105464 3 0.2554 0.6459 0.032 0.000 0.900 0.024 0.032 0.012
#> GSM1105466 4 0.3643 0.2636 0.028 0.036 0.004 0.836 0.084 0.012
#> GSM1105479 6 0.6049 0.3750 0.000 0.340 0.000 0.180 0.012 0.468
#> GSM1105502 3 0.3864 0.6028 0.032 0.000 0.828 0.036 0.060 0.044
#> GSM1105515 1 0.0508 0.8162 0.984 0.000 0.000 0.000 0.004 0.012
#> GSM1105523 3 0.7855 -0.4643 0.032 0.000 0.300 0.300 0.276 0.092
#> GSM1105550 3 0.7280 0.3386 0.060 0.020 0.528 0.188 0.176 0.028
#> GSM1105450 2 0.1245 0.7539 0.000 0.952 0.000 0.032 0.016 0.000
#> GSM1105451 2 0.0405 0.7606 0.000 0.988 0.000 0.008 0.004 0.000
#> GSM1105454 6 0.3248 0.4989 0.000 0.052 0.000 0.116 0.004 0.828
#> GSM1105468 2 0.1594 0.7438 0.000 0.932 0.000 0.052 0.016 0.000
#> GSM1105481 6 0.5542 0.4372 0.000 0.340 0.000 0.108 0.012 0.540
#> GSM1105504 3 0.3864 0.6028 0.032 0.000 0.828 0.036 0.060 0.044
#> GSM1105517 3 0.7673 0.2562 0.276 0.004 0.408 0.144 0.152 0.016
#> GSM1105525 3 0.7855 -0.4643 0.032 0.000 0.300 0.300 0.276 0.092
#> GSM1105552 3 0.7256 0.3432 0.060 0.020 0.532 0.184 0.176 0.028
#> GSM1105452 2 0.3198 0.6404 0.000 0.740 0.000 0.000 0.260 0.000
#> GSM1105453 2 0.0405 0.7606 0.000 0.988 0.000 0.008 0.004 0.000
#> GSM1105456 6 0.3248 0.4989 0.000 0.052 0.000 0.116 0.004 0.828
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 agent(p) other(p) time(p) individual(p) k
#> MAD:hclust 110 0.8710 0.3949 1.000 0.002368 2
#> MAD:hclust 38 NA NA NA NA 3
#> MAD:hclust 73 0.2185 0.0910 0.958 0.000050 4
#> MAD:hclust 60 0.4597 0.0537 0.996 0.000253 5
#> MAD:hclust 70 0.0741 0.0171 0.988 0.000045 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 44956 rows and 120 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 0.970 0.987 0.4880 0.513 0.513
#> 3 3 0.574 0.674 0.810 0.3458 0.736 0.525
#> 4 4 0.551 0.550 0.733 0.1246 0.909 0.741
#> 5 5 0.614 0.487 0.684 0.0704 0.829 0.479
#> 6 6 0.701 0.586 0.760 0.0450 0.905 0.596
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
#> GSM1105438 2 0.0000 0.987 0.000 1.000
#> GSM1105486 2 0.0000 0.987 0.000 1.000
#> GSM1105487 1 0.0000 0.986 1.000 0.000
#> GSM1105490 2 0.0000 0.987 0.000 1.000
#> GSM1105491 2 0.2043 0.961 0.032 0.968
#> GSM1105495 2 0.2043 0.961 0.032 0.968
#> GSM1105498 2 0.2948 0.942 0.052 0.948
#> GSM1105499 1 0.0000 0.986 1.000 0.000
#> GSM1105506 2 0.0000 0.987 0.000 1.000
#> GSM1105442 2 0.0000 0.987 0.000 1.000
#> GSM1105511 2 0.0000 0.987 0.000 1.000
#> GSM1105514 2 0.0000 0.987 0.000 1.000
#> GSM1105518 2 0.0000 0.987 0.000 1.000
#> GSM1105522 1 0.0000 0.986 1.000 0.000
#> GSM1105534 1 0.0000 0.986 1.000 0.000
#> GSM1105535 1 0.0000 0.986 1.000 0.000
#> GSM1105538 1 0.0000 0.986 1.000 0.000
#> GSM1105542 2 0.0000 0.987 0.000 1.000
#> GSM1105443 2 0.0000 0.987 0.000 1.000
#> GSM1105551 1 0.0000 0.986 1.000 0.000
#> GSM1105554 1 0.0000 0.986 1.000 0.000
#> GSM1105555 1 0.0000 0.986 1.000 0.000
#> GSM1105447 2 0.0000 0.987 0.000 1.000
#> GSM1105467 2 0.0000 0.987 0.000 1.000
#> GSM1105470 2 0.0000 0.987 0.000 1.000
#> GSM1105471 2 0.0000 0.987 0.000 1.000
#> GSM1105474 2 0.0000 0.987 0.000 1.000
#> GSM1105475 2 0.0000 0.987 0.000 1.000
#> GSM1105440 1 0.0000 0.986 1.000 0.000
#> GSM1105488 2 0.0000 0.987 0.000 1.000
#> GSM1105489 1 0.0000 0.986 1.000 0.000
#> GSM1105492 1 0.0000 0.986 1.000 0.000
#> GSM1105493 1 0.0000 0.986 1.000 0.000
#> GSM1105497 2 0.0000 0.987 0.000 1.000
#> GSM1105500 2 0.0000 0.987 0.000 1.000
#> GSM1105501 2 0.0000 0.987 0.000 1.000
#> GSM1105508 1 0.0000 0.986 1.000 0.000
#> GSM1105444 2 0.0000 0.987 0.000 1.000
#> GSM1105513 2 0.0000 0.987 0.000 1.000
#> GSM1105516 1 0.9710 0.341 0.600 0.400
#> GSM1105520 2 0.5842 0.841 0.140 0.860
#> GSM1105524 1 0.0000 0.986 1.000 0.000
#> GSM1105536 2 0.0000 0.987 0.000 1.000
#> GSM1105537 1 0.0000 0.986 1.000 0.000
#> GSM1105540 1 0.0000 0.986 1.000 0.000
#> GSM1105544 2 0.0000 0.987 0.000 1.000
#> GSM1105445 2 0.0000 0.987 0.000 1.000
#> GSM1105553 2 0.2778 0.946 0.048 0.952
#> GSM1105556 1 0.0000 0.986 1.000 0.000
#> GSM1105557 2 0.0000 0.987 0.000 1.000
#> GSM1105449 2 0.0000 0.987 0.000 1.000
#> GSM1105469 1 0.0938 0.975 0.988 0.012
#> GSM1105472 2 0.0000 0.987 0.000 1.000
#> GSM1105473 1 0.0000 0.986 1.000 0.000
#> GSM1105476 2 0.0000 0.987 0.000 1.000
#> GSM1105477 2 0.0000 0.987 0.000 1.000
#> GSM1105478 2 0.0000 0.987 0.000 1.000
#> GSM1105510 2 0.0000 0.987 0.000 1.000
#> GSM1105530 1 0.0000 0.986 1.000 0.000
#> GSM1105539 1 0.0000 0.986 1.000 0.000
#> GSM1105480 2 0.0000 0.987 0.000 1.000
#> GSM1105512 1 0.0000 0.986 1.000 0.000
#> GSM1105532 1 0.0000 0.986 1.000 0.000
#> GSM1105541 1 0.0000 0.986 1.000 0.000
#> GSM1105439 2 0.0000 0.987 0.000 1.000
#> GSM1105463 1 0.0000 0.986 1.000 0.000
#> GSM1105482 1 0.0000 0.986 1.000 0.000
#> GSM1105483 2 0.0000 0.987 0.000 1.000
#> GSM1105494 2 0.0000 0.987 0.000 1.000
#> GSM1105503 2 0.8386 0.644 0.268 0.732
#> GSM1105507 1 0.0376 0.982 0.996 0.004
#> GSM1105446 2 0.0000 0.987 0.000 1.000
#> GSM1105519 1 0.0000 0.986 1.000 0.000
#> GSM1105526 2 0.0000 0.987 0.000 1.000
#> GSM1105527 2 0.0000 0.987 0.000 1.000
#> GSM1105531 1 0.0000 0.986 1.000 0.000
#> GSM1105543 2 0.0000 0.987 0.000 1.000
#> GSM1105546 1 0.0000 0.986 1.000 0.000
#> GSM1105547 1 0.0000 0.986 1.000 0.000
#> GSM1105455 2 0.0000 0.987 0.000 1.000
#> GSM1105458 2 0.0000 0.987 0.000 1.000
#> GSM1105459 2 0.0000 0.987 0.000 1.000
#> GSM1105462 1 0.8016 0.668 0.756 0.244
#> GSM1105441 2 0.0000 0.987 0.000 1.000
#> GSM1105465 2 0.0000 0.987 0.000 1.000
#> GSM1105484 2 0.0000 0.987 0.000 1.000
#> GSM1105485 2 0.0000 0.987 0.000 1.000
#> GSM1105496 2 0.8016 0.687 0.244 0.756
#> GSM1105505 1 0.0000 0.986 1.000 0.000
#> GSM1105509 1 0.0000 0.986 1.000 0.000
#> GSM1105448 2 0.0000 0.987 0.000 1.000
#> GSM1105521 1 0.0000 0.986 1.000 0.000
#> GSM1105528 2 0.0000 0.987 0.000 1.000
#> GSM1105529 2 0.0000 0.987 0.000 1.000
#> GSM1105533 1 0.0000 0.986 1.000 0.000
#> GSM1105545 2 0.0000 0.987 0.000 1.000
#> GSM1105548 1 0.0000 0.986 1.000 0.000
#> GSM1105549 1 0.0000 0.986 1.000 0.000
#> GSM1105457 2 0.0000 0.987 0.000 1.000
#> GSM1105460 2 0.0000 0.987 0.000 1.000
#> GSM1105461 2 0.0000 0.987 0.000 1.000
#> GSM1105464 1 0.0000 0.986 1.000 0.000
#> GSM1105466 2 0.0000 0.987 0.000 1.000
#> GSM1105479 2 0.0000 0.987 0.000 1.000
#> GSM1105502 1 0.0000 0.986 1.000 0.000
#> GSM1105515 1 0.0000 0.986 1.000 0.000
#> GSM1105523 1 0.0000 0.986 1.000 0.000
#> GSM1105550 1 0.0000 0.986 1.000 0.000
#> GSM1105450 2 0.0000 0.987 0.000 1.000
#> GSM1105451 2 0.0000 0.987 0.000 1.000
#> GSM1105454 2 0.0672 0.981 0.008 0.992
#> GSM1105468 2 0.0000 0.987 0.000 1.000
#> GSM1105481 2 0.2043 0.961 0.032 0.968
#> GSM1105504 1 0.0000 0.986 1.000 0.000
#> GSM1105517 1 0.0000 0.986 1.000 0.000
#> GSM1105525 1 0.0000 0.986 1.000 0.000
#> GSM1105552 1 0.0000 0.986 1.000 0.000
#> GSM1105452 2 0.0000 0.987 0.000 1.000
#> GSM1105453 2 0.0000 0.987 0.000 1.000
#> GSM1105456 2 0.2043 0.961 0.032 0.968
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1105438 2 0.6154 0.739 0.000 0.592 0.408
#> GSM1105486 2 0.6307 0.707 0.000 0.512 0.488
#> GSM1105487 1 0.0747 0.932 0.984 0.016 0.000
#> GSM1105490 3 0.0237 0.639 0.000 0.004 0.996
#> GSM1105491 2 0.1647 0.487 0.004 0.960 0.036
#> GSM1105495 2 0.2066 0.467 0.000 0.940 0.060
#> GSM1105498 3 0.5948 0.600 0.000 0.360 0.640
#> GSM1105499 1 0.0424 0.931 0.992 0.008 0.000
#> GSM1105506 3 0.0237 0.644 0.000 0.004 0.996
#> GSM1105442 2 0.4002 0.624 0.000 0.840 0.160
#> GSM1105511 3 0.0424 0.644 0.000 0.008 0.992
#> GSM1105514 2 0.6180 0.737 0.000 0.584 0.416
#> GSM1105518 3 0.5591 0.627 0.000 0.304 0.696
#> GSM1105522 1 0.1411 0.929 0.964 0.036 0.000
#> GSM1105534 1 0.0237 0.931 0.996 0.004 0.000
#> GSM1105535 1 0.0424 0.931 0.992 0.008 0.000
#> GSM1105538 1 0.0237 0.931 0.996 0.004 0.000
#> GSM1105542 2 0.5363 0.724 0.000 0.724 0.276
#> GSM1105443 3 0.0747 0.632 0.000 0.016 0.984
#> GSM1105551 1 0.2537 0.918 0.920 0.080 0.000
#> GSM1105554 1 0.0237 0.931 0.996 0.004 0.000
#> GSM1105555 1 0.2796 0.914 0.908 0.092 0.000
#> GSM1105447 3 0.3267 0.654 0.000 0.116 0.884
#> GSM1105467 2 0.6307 0.707 0.000 0.512 0.488
#> GSM1105470 2 0.6309 0.699 0.000 0.504 0.496
#> GSM1105471 3 0.5363 0.636 0.000 0.276 0.724
#> GSM1105474 2 0.6307 0.707 0.000 0.512 0.488
#> GSM1105475 3 0.4291 0.292 0.000 0.180 0.820
#> GSM1105440 1 0.0424 0.931 0.992 0.008 0.000
#> GSM1105488 2 0.5363 0.724 0.000 0.724 0.276
#> GSM1105489 1 0.2448 0.920 0.924 0.076 0.000
#> GSM1105492 1 0.0424 0.931 0.992 0.008 0.000
#> GSM1105493 1 0.3192 0.904 0.888 0.112 0.000
#> GSM1105497 2 0.3116 0.568 0.000 0.892 0.108
#> GSM1105500 3 0.3941 0.609 0.000 0.156 0.844
#> GSM1105501 3 0.0747 0.638 0.000 0.016 0.984
#> GSM1105508 1 0.1647 0.928 0.960 0.036 0.004
#> GSM1105444 2 0.6154 0.739 0.000 0.592 0.408
#> GSM1105513 3 0.0000 0.642 0.000 0.000 1.000
#> GSM1105516 1 0.8355 0.479 0.616 0.140 0.244
#> GSM1105520 3 0.6404 0.607 0.012 0.344 0.644
#> GSM1105524 1 0.0424 0.931 0.992 0.008 0.000
#> GSM1105536 3 0.4702 0.310 0.000 0.212 0.788
#> GSM1105537 1 0.0424 0.931 0.992 0.008 0.000
#> GSM1105540 3 0.8463 0.170 0.444 0.088 0.468
#> GSM1105544 3 0.5939 0.624 0.028 0.224 0.748
#> GSM1105445 3 0.4887 0.649 0.000 0.228 0.772
#> GSM1105553 3 0.6111 0.576 0.000 0.396 0.604
#> GSM1105556 1 0.0237 0.931 0.996 0.004 0.000
#> GSM1105557 3 0.0000 0.642 0.000 0.000 1.000
#> GSM1105449 3 0.6309 -0.707 0.000 0.500 0.500
#> GSM1105469 3 0.7251 0.438 0.348 0.040 0.612
#> GSM1105472 2 0.6260 0.727 0.000 0.552 0.448
#> GSM1105473 1 0.4605 0.845 0.796 0.204 0.000
#> GSM1105476 3 0.6309 -0.706 0.000 0.496 0.504
#> GSM1105477 3 0.5254 0.243 0.000 0.264 0.736
#> GSM1105478 3 0.4702 0.653 0.000 0.212 0.788
#> GSM1105510 2 0.5363 0.724 0.000 0.724 0.276
#> GSM1105530 1 0.3267 0.911 0.884 0.116 0.000
#> GSM1105539 1 0.3340 0.909 0.880 0.120 0.000
#> GSM1105480 3 0.2537 0.665 0.000 0.080 0.920
#> GSM1105512 1 0.0592 0.932 0.988 0.012 0.000
#> GSM1105532 1 0.3267 0.911 0.884 0.116 0.000
#> GSM1105541 1 0.3340 0.909 0.880 0.120 0.000
#> GSM1105439 3 0.0592 0.633 0.000 0.012 0.988
#> GSM1105463 1 0.6079 0.618 0.612 0.388 0.000
#> GSM1105482 1 0.0237 0.931 0.996 0.004 0.000
#> GSM1105483 3 0.3764 0.640 0.068 0.040 0.892
#> GSM1105494 3 0.4346 0.657 0.000 0.184 0.816
#> GSM1105503 3 0.6677 0.608 0.024 0.324 0.652
#> GSM1105507 1 0.2176 0.921 0.948 0.032 0.020
#> GSM1105446 2 0.6045 0.741 0.000 0.620 0.380
#> GSM1105519 1 0.1163 0.930 0.972 0.028 0.000
#> GSM1105526 3 0.3038 0.588 0.000 0.104 0.896
#> GSM1105527 3 0.2918 0.645 0.044 0.032 0.924
#> GSM1105531 2 0.9919 -0.330 0.292 0.396 0.312
#> GSM1105543 2 0.6045 0.741 0.000 0.620 0.380
#> GSM1105546 1 0.0237 0.931 0.996 0.004 0.000
#> GSM1105547 1 0.0237 0.931 0.996 0.004 0.000
#> GSM1105455 3 0.0747 0.629 0.000 0.016 0.984
#> GSM1105458 3 0.3879 0.653 0.000 0.152 0.848
#> GSM1105459 2 0.6307 0.707 0.000 0.512 0.488
#> GSM1105462 3 0.8384 0.518 0.088 0.392 0.520
#> GSM1105441 2 0.6309 0.697 0.000 0.504 0.496
#> GSM1105465 2 0.1643 0.495 0.000 0.956 0.044
#> GSM1105484 2 0.5363 0.724 0.000 0.724 0.276
#> GSM1105485 2 0.5656 0.716 0.008 0.728 0.264
#> GSM1105496 3 0.6798 0.566 0.016 0.400 0.584
#> GSM1105505 3 0.9043 0.465 0.136 0.396 0.468
#> GSM1105509 1 0.1163 0.929 0.972 0.028 0.000
#> GSM1105448 2 0.6154 0.739 0.000 0.592 0.408
#> GSM1105521 1 0.1163 0.930 0.972 0.028 0.000
#> GSM1105528 2 0.5363 0.724 0.000 0.724 0.276
#> GSM1105529 2 0.5363 0.724 0.000 0.724 0.276
#> GSM1105533 1 0.2796 0.913 0.908 0.092 0.000
#> GSM1105545 3 0.1163 0.627 0.000 0.028 0.972
#> GSM1105548 1 0.1860 0.927 0.948 0.052 0.000
#> GSM1105549 1 0.2625 0.913 0.916 0.084 0.000
#> GSM1105457 3 0.0000 0.642 0.000 0.000 1.000
#> GSM1105460 3 0.0892 0.628 0.000 0.020 0.980
#> GSM1105461 2 0.6307 0.707 0.000 0.512 0.488
#> GSM1105464 1 0.3116 0.911 0.892 0.108 0.000
#> GSM1105466 3 0.0000 0.642 0.000 0.000 1.000
#> GSM1105479 3 0.1163 0.637 0.000 0.028 0.972
#> GSM1105502 1 0.3267 0.911 0.884 0.116 0.000
#> GSM1105515 1 0.0237 0.931 0.996 0.004 0.000
#> GSM1105523 3 0.9311 0.222 0.364 0.168 0.468
#> GSM1105550 3 0.8445 0.220 0.424 0.088 0.488
#> GSM1105450 2 0.6307 0.707 0.000 0.512 0.488
#> GSM1105451 2 0.6307 0.707 0.000 0.512 0.488
#> GSM1105454 3 0.5678 0.622 0.000 0.316 0.684
#> GSM1105468 2 0.6307 0.707 0.000 0.512 0.488
#> GSM1105481 3 0.6111 0.578 0.000 0.396 0.604
#> GSM1105504 2 0.9912 -0.338 0.284 0.396 0.320
#> GSM1105517 1 0.1989 0.922 0.948 0.048 0.004
#> GSM1105525 1 0.4636 0.885 0.848 0.116 0.036
#> GSM1105552 1 0.6062 0.625 0.616 0.384 0.000
#> GSM1105452 2 0.5529 0.728 0.000 0.704 0.296
#> GSM1105453 2 0.6307 0.707 0.000 0.512 0.488
#> GSM1105456 3 0.5706 0.621 0.000 0.320 0.680
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1105438 2 0.1661 0.7809 0.000 0.944 0.004 0.052
#> GSM1105486 2 0.3172 0.7642 0.000 0.840 0.000 0.160
#> GSM1105487 1 0.3498 0.7860 0.832 0.000 0.160 0.008
#> GSM1105490 4 0.2868 0.5921 0.000 0.136 0.000 0.864
#> GSM1105491 3 0.6895 -0.0464 0.004 0.412 0.492 0.092
#> GSM1105495 3 0.6649 0.1202 0.000 0.340 0.560 0.100
#> GSM1105498 4 0.4991 0.1004 0.000 0.004 0.388 0.608
#> GSM1105499 1 0.2675 0.7971 0.892 0.000 0.100 0.008
#> GSM1105506 4 0.2469 0.5947 0.000 0.108 0.000 0.892
#> GSM1105442 2 0.5464 0.6136 0.000 0.708 0.228 0.064
#> GSM1105511 4 0.4274 0.5782 0.000 0.108 0.072 0.820
#> GSM1105514 2 0.1978 0.7805 0.000 0.928 0.004 0.068
#> GSM1105518 4 0.4978 0.1038 0.000 0.004 0.384 0.612
#> GSM1105522 1 0.4957 0.6950 0.684 0.000 0.300 0.016
#> GSM1105534 1 0.0000 0.8040 1.000 0.000 0.000 0.000
#> GSM1105535 1 0.2737 0.7960 0.888 0.000 0.104 0.008
#> GSM1105538 1 0.0000 0.8040 1.000 0.000 0.000 0.000
#> GSM1105542 2 0.3688 0.7028 0.000 0.792 0.208 0.000
#> GSM1105443 4 0.4830 0.3347 0.000 0.392 0.000 0.608
#> GSM1105551 1 0.3852 0.7727 0.800 0.000 0.192 0.008
#> GSM1105554 1 0.0188 0.8047 0.996 0.000 0.004 0.000
#> GSM1105555 1 0.2647 0.7887 0.880 0.000 0.120 0.000
#> GSM1105447 4 0.6602 0.1061 0.000 0.436 0.080 0.484
#> GSM1105467 2 0.3172 0.7642 0.000 0.840 0.000 0.160
#> GSM1105470 2 0.3172 0.7642 0.000 0.840 0.000 0.160
#> GSM1105471 4 0.5785 0.3103 0.000 0.064 0.272 0.664
#> GSM1105474 2 0.3123 0.7677 0.000 0.844 0.000 0.156
#> GSM1105475 4 0.4977 0.1667 0.000 0.460 0.000 0.540
#> GSM1105440 1 0.2412 0.8010 0.908 0.000 0.084 0.008
#> GSM1105488 2 0.3688 0.7028 0.000 0.792 0.208 0.000
#> GSM1105489 1 0.1940 0.8038 0.924 0.000 0.076 0.000
#> GSM1105492 1 0.0657 0.8042 0.984 0.000 0.012 0.004
#> GSM1105493 1 0.2530 0.7833 0.888 0.000 0.112 0.000
#> GSM1105497 2 0.6751 0.2347 0.000 0.508 0.396 0.096
#> GSM1105500 4 0.6449 0.4479 0.000 0.140 0.220 0.640
#> GSM1105501 4 0.4344 0.5769 0.000 0.108 0.076 0.816
#> GSM1105508 1 0.6951 0.4911 0.556 0.000 0.304 0.140
#> GSM1105444 2 0.1305 0.7797 0.000 0.960 0.004 0.036
#> GSM1105513 4 0.2011 0.5870 0.000 0.080 0.000 0.920
#> GSM1105516 4 0.8494 -0.0206 0.316 0.032 0.232 0.420
#> GSM1105520 4 0.4989 -0.1181 0.000 0.000 0.472 0.528
#> GSM1105524 1 0.2737 0.7960 0.888 0.000 0.104 0.008
#> GSM1105536 4 0.5850 0.5282 0.000 0.184 0.116 0.700
#> GSM1105537 1 0.2737 0.7960 0.888 0.000 0.104 0.008
#> GSM1105540 4 0.6708 0.1636 0.128 0.000 0.280 0.592
#> GSM1105544 4 0.6521 0.3708 0.044 0.060 0.220 0.676
#> GSM1105445 4 0.5292 0.3974 0.000 0.060 0.216 0.724
#> GSM1105553 4 0.5992 -0.1097 0.000 0.040 0.444 0.516
#> GSM1105556 1 0.0000 0.8040 1.000 0.000 0.000 0.000
#> GSM1105557 4 0.2469 0.5947 0.000 0.108 0.000 0.892
#> GSM1105449 2 0.4103 0.6644 0.000 0.744 0.000 0.256
#> GSM1105469 4 0.5486 0.3858 0.080 0.000 0.200 0.720
#> GSM1105472 2 0.3123 0.7677 0.000 0.844 0.000 0.156
#> GSM1105473 1 0.4776 0.5682 0.624 0.000 0.376 0.000
#> GSM1105476 2 0.3123 0.7677 0.000 0.844 0.000 0.156
#> GSM1105477 4 0.6050 0.5116 0.000 0.212 0.112 0.676
#> GSM1105478 4 0.1807 0.5317 0.000 0.008 0.052 0.940
#> GSM1105510 2 0.3982 0.6907 0.000 0.776 0.220 0.004
#> GSM1105530 1 0.5080 0.6185 0.576 0.000 0.420 0.004
#> GSM1105539 1 0.4964 0.6532 0.616 0.000 0.380 0.004
#> GSM1105480 4 0.2222 0.5398 0.000 0.016 0.060 0.924
#> GSM1105512 1 0.1637 0.7997 0.940 0.000 0.060 0.000
#> GSM1105532 1 0.5080 0.6185 0.576 0.000 0.420 0.004
#> GSM1105541 1 0.4950 0.6559 0.620 0.000 0.376 0.004
#> GSM1105439 4 0.4746 0.3780 0.000 0.368 0.000 0.632
#> GSM1105463 3 0.4881 0.4761 0.196 0.000 0.756 0.048
#> GSM1105482 1 0.1022 0.8055 0.968 0.000 0.032 0.000
#> GSM1105483 4 0.5719 0.4789 0.040 0.052 0.160 0.748
#> GSM1105494 4 0.3157 0.4639 0.000 0.004 0.144 0.852
#> GSM1105503 4 0.4981 -0.1032 0.000 0.000 0.464 0.536
#> GSM1105507 1 0.7606 0.1222 0.468 0.000 0.228 0.304
#> GSM1105446 2 0.1661 0.7667 0.000 0.944 0.052 0.004
#> GSM1105519 1 0.2921 0.7605 0.860 0.000 0.140 0.000
#> GSM1105526 4 0.5515 0.5475 0.000 0.152 0.116 0.732
#> GSM1105527 4 0.4387 0.5651 0.032 0.060 0.068 0.840
#> GSM1105531 3 0.4319 0.5494 0.012 0.000 0.760 0.228
#> GSM1105543 2 0.1743 0.7659 0.000 0.940 0.056 0.004
#> GSM1105546 1 0.0188 0.8047 0.996 0.000 0.004 0.000
#> GSM1105547 1 0.0000 0.8040 1.000 0.000 0.000 0.000
#> GSM1105455 4 0.4843 0.3263 0.000 0.396 0.000 0.604
#> GSM1105458 4 0.7253 0.2448 0.000 0.364 0.152 0.484
#> GSM1105459 2 0.3123 0.7677 0.000 0.844 0.000 0.156
#> GSM1105462 3 0.4647 0.4882 0.008 0.000 0.704 0.288
#> GSM1105441 2 0.3873 0.6789 0.000 0.772 0.000 0.228
#> GSM1105465 2 0.6613 0.3535 0.000 0.560 0.344 0.096
#> GSM1105484 2 0.3831 0.7029 0.000 0.792 0.204 0.004
#> GSM1105485 2 0.4599 0.6729 0.028 0.760 0.212 0.000
#> GSM1105496 3 0.5912 0.1697 0.000 0.036 0.524 0.440
#> GSM1105505 3 0.4482 0.5137 0.008 0.000 0.728 0.264
#> GSM1105509 1 0.5494 0.6205 0.716 0.000 0.208 0.076
#> GSM1105448 2 0.1398 0.7801 0.000 0.956 0.004 0.040
#> GSM1105521 1 0.2814 0.7663 0.868 0.000 0.132 0.000
#> GSM1105528 2 0.3649 0.7049 0.000 0.796 0.204 0.000
#> GSM1105529 2 0.3688 0.7028 0.000 0.792 0.208 0.000
#> GSM1105533 1 0.3982 0.7531 0.776 0.000 0.220 0.004
#> GSM1105545 4 0.4817 0.5712 0.000 0.128 0.088 0.784
#> GSM1105548 1 0.1557 0.8044 0.944 0.000 0.056 0.000
#> GSM1105549 1 0.1211 0.8047 0.960 0.000 0.040 0.000
#> GSM1105457 4 0.2814 0.5924 0.000 0.132 0.000 0.868
#> GSM1105460 4 0.4730 0.3847 0.000 0.364 0.000 0.636
#> GSM1105461 2 0.3123 0.7677 0.000 0.844 0.000 0.156
#> GSM1105464 1 0.4905 0.6367 0.632 0.000 0.364 0.004
#> GSM1105466 4 0.2469 0.5947 0.000 0.108 0.000 0.892
#> GSM1105479 4 0.3984 0.5582 0.000 0.132 0.040 0.828
#> GSM1105502 1 0.4964 0.6648 0.616 0.000 0.380 0.004
#> GSM1105515 1 0.0000 0.8040 1.000 0.000 0.000 0.000
#> GSM1105523 3 0.5712 0.4146 0.048 0.000 0.644 0.308
#> GSM1105550 4 0.6634 0.1637 0.116 0.000 0.292 0.592
#> GSM1105450 2 0.3123 0.7677 0.000 0.844 0.000 0.156
#> GSM1105451 2 0.3123 0.7677 0.000 0.844 0.000 0.156
#> GSM1105454 4 0.6435 0.0848 0.000 0.072 0.396 0.532
#> GSM1105468 2 0.3123 0.7677 0.000 0.844 0.000 0.156
#> GSM1105481 4 0.6003 -0.0407 0.000 0.040 0.456 0.504
#> GSM1105504 3 0.4319 0.5494 0.012 0.000 0.760 0.228
#> GSM1105517 1 0.7654 0.0800 0.464 0.000 0.252 0.284
#> GSM1105525 3 0.7416 0.0150 0.312 0.000 0.496 0.192
#> GSM1105552 3 0.5282 0.3748 0.276 0.000 0.688 0.036
#> GSM1105452 2 0.3610 0.7070 0.000 0.800 0.200 0.000
#> GSM1105453 2 0.3123 0.7677 0.000 0.844 0.000 0.156
#> GSM1105456 4 0.6435 0.0848 0.000 0.072 0.396 0.532
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM1105438 2 0.1671 0.6659 0.000 0.924 0.000 0.000 0.076
#> GSM1105486 2 0.0000 0.7411 0.000 1.000 0.000 0.000 0.000
#> GSM1105487 1 0.5278 0.6165 0.672 0.000 0.252 0.016 0.060
#> GSM1105490 4 0.3055 0.6409 0.000 0.144 0.000 0.840 0.016
#> GSM1105491 5 0.3319 0.4897 0.000 0.100 0.040 0.008 0.852
#> GSM1105495 5 0.6341 0.3000 0.000 0.084 0.208 0.076 0.632
#> GSM1105498 4 0.6592 0.2983 0.000 0.008 0.204 0.512 0.276
#> GSM1105499 1 0.3815 0.7151 0.804 0.000 0.156 0.008 0.032
#> GSM1105506 4 0.3061 0.6444 0.000 0.136 0.000 0.844 0.020
#> GSM1105442 5 0.4387 0.5027 0.000 0.336 0.008 0.004 0.652
#> GSM1105511 4 0.4205 0.6416 0.000 0.124 0.084 0.788 0.004
#> GSM1105514 2 0.1544 0.6747 0.000 0.932 0.000 0.000 0.068
#> GSM1105518 4 0.7159 0.0914 0.000 0.016 0.264 0.380 0.340
#> GSM1105522 1 0.6118 0.2688 0.508 0.000 0.404 0.040 0.048
#> GSM1105534 1 0.0000 0.7770 1.000 0.000 0.000 0.000 0.000
#> GSM1105535 1 0.4178 0.7067 0.788 0.000 0.156 0.016 0.040
#> GSM1105538 1 0.0162 0.7768 0.996 0.000 0.000 0.004 0.000
#> GSM1105542 5 0.4249 0.4771 0.000 0.432 0.000 0.000 0.568
#> GSM1105443 2 0.5352 0.2120 0.000 0.536 0.000 0.408 0.056
#> GSM1105551 1 0.5302 0.6144 0.668 0.000 0.256 0.016 0.060
#> GSM1105554 1 0.0162 0.7768 0.996 0.000 0.004 0.000 0.000
#> GSM1105555 1 0.3799 0.6947 0.812 0.000 0.144 0.012 0.032
#> GSM1105447 2 0.6887 0.1136 0.000 0.432 0.008 0.320 0.240
#> GSM1105467 2 0.0290 0.7382 0.000 0.992 0.000 0.000 0.008
#> GSM1105470 2 0.0000 0.7411 0.000 1.000 0.000 0.000 0.000
#> GSM1105471 4 0.8106 0.2258 0.000 0.144 0.180 0.412 0.264
#> GSM1105474 2 0.0000 0.7411 0.000 1.000 0.000 0.000 0.000
#> GSM1105475 2 0.4130 0.4866 0.000 0.696 0.000 0.292 0.012
#> GSM1105440 1 0.4362 0.7160 0.788 0.000 0.132 0.020 0.060
#> GSM1105488 5 0.4249 0.4771 0.000 0.432 0.000 0.000 0.568
#> GSM1105489 1 0.3154 0.7343 0.860 0.000 0.104 0.012 0.024
#> GSM1105492 1 0.1074 0.7769 0.968 0.000 0.004 0.016 0.012
#> GSM1105493 1 0.3010 0.6735 0.824 0.000 0.172 0.000 0.004
#> GSM1105497 5 0.3556 0.5100 0.000 0.132 0.032 0.008 0.828
#> GSM1105500 4 0.5520 0.5452 0.000 0.024 0.104 0.692 0.180
#> GSM1105501 4 0.4503 0.6387 0.000 0.140 0.084 0.768 0.008
#> GSM1105508 3 0.7668 0.1601 0.264 0.000 0.384 0.300 0.052
#> GSM1105444 2 0.1965 0.6405 0.000 0.904 0.000 0.000 0.096
#> GSM1105513 4 0.3855 0.6242 0.000 0.120 0.008 0.816 0.056
#> GSM1105516 4 0.7417 0.2249 0.216 0.020 0.232 0.504 0.028
#> GSM1105520 4 0.6788 0.0747 0.000 0.000 0.284 0.372 0.344
#> GSM1105524 1 0.4178 0.7067 0.788 0.000 0.156 0.016 0.040
#> GSM1105536 4 0.5546 0.6027 0.000 0.188 0.108 0.684 0.020
#> GSM1105537 1 0.4178 0.7067 0.788 0.000 0.156 0.016 0.040
#> GSM1105540 4 0.5764 0.3942 0.060 0.000 0.296 0.616 0.028
#> GSM1105544 4 0.4899 0.5514 0.016 0.000 0.148 0.744 0.092
#> GSM1105445 4 0.7868 0.2680 0.000 0.116 0.196 0.456 0.232
#> GSM1105553 5 0.6749 -0.1198 0.000 0.000 0.264 0.348 0.388
#> GSM1105556 1 0.0162 0.7768 0.996 0.000 0.004 0.000 0.000
#> GSM1105557 4 0.2966 0.6453 0.000 0.136 0.000 0.848 0.016
#> GSM1105449 2 0.3055 0.6638 0.000 0.864 0.000 0.072 0.064
#> GSM1105469 4 0.4566 0.5603 0.024 0.024 0.196 0.752 0.004
#> GSM1105472 2 0.0000 0.7411 0.000 1.000 0.000 0.000 0.000
#> GSM1105473 3 0.4870 0.1523 0.448 0.000 0.532 0.004 0.016
#> GSM1105476 2 0.0000 0.7411 0.000 1.000 0.000 0.000 0.000
#> GSM1105477 4 0.5822 0.5951 0.000 0.172 0.116 0.676 0.036
#> GSM1105478 4 0.3596 0.6104 0.000 0.052 0.036 0.852 0.060
#> GSM1105510 5 0.4565 0.4847 0.000 0.408 0.012 0.000 0.580
#> GSM1105530 3 0.4474 0.2596 0.332 0.000 0.652 0.004 0.012
#> GSM1105539 3 0.4457 0.1867 0.368 0.000 0.620 0.000 0.012
#> GSM1105480 4 0.1710 0.6363 0.000 0.040 0.004 0.940 0.016
#> GSM1105512 1 0.3129 0.6570 0.832 0.000 0.156 0.008 0.004
#> GSM1105532 3 0.4474 0.2596 0.332 0.000 0.652 0.004 0.012
#> GSM1105541 3 0.4457 0.1867 0.368 0.000 0.620 0.000 0.012
#> GSM1105439 2 0.5256 0.1795 0.000 0.532 0.000 0.420 0.048
#> GSM1105463 3 0.4345 0.4794 0.020 0.000 0.780 0.044 0.156
#> GSM1105482 1 0.0880 0.7733 0.968 0.000 0.032 0.000 0.000
#> GSM1105483 4 0.4565 0.5892 0.008 0.064 0.176 0.752 0.000
#> GSM1105494 4 0.5800 0.4393 0.000 0.020 0.120 0.656 0.204
#> GSM1105503 4 0.6792 0.0900 0.000 0.000 0.324 0.380 0.296
#> GSM1105507 4 0.7147 0.0806 0.264 0.000 0.260 0.452 0.024
#> GSM1105446 2 0.3210 0.4487 0.000 0.788 0.000 0.000 0.212
#> GSM1105519 1 0.3783 0.5609 0.768 0.000 0.216 0.012 0.004
#> GSM1105526 4 0.5304 0.6192 0.000 0.160 0.108 0.712 0.020
#> GSM1105527 4 0.3187 0.6503 0.008 0.096 0.036 0.860 0.000
#> GSM1105531 3 0.4504 0.4390 0.000 0.000 0.748 0.084 0.168
#> GSM1105543 2 0.3242 0.4400 0.000 0.784 0.000 0.000 0.216
#> GSM1105546 1 0.0579 0.7774 0.984 0.000 0.000 0.008 0.008
#> GSM1105547 1 0.0290 0.7764 0.992 0.000 0.008 0.000 0.000
#> GSM1105455 2 0.5165 0.2884 0.000 0.576 0.000 0.376 0.048
#> GSM1105458 2 0.7147 0.1205 0.000 0.432 0.020 0.276 0.272
#> GSM1105459 2 0.0000 0.7411 0.000 1.000 0.000 0.000 0.000
#> GSM1105462 3 0.4316 0.4379 0.000 0.000 0.772 0.120 0.108
#> GSM1105441 2 0.2376 0.6890 0.000 0.904 0.000 0.052 0.044
#> GSM1105465 5 0.4317 0.5157 0.000 0.212 0.032 0.008 0.748
#> GSM1105484 5 0.4256 0.4720 0.000 0.436 0.000 0.000 0.564
#> GSM1105485 5 0.4722 0.4841 0.004 0.412 0.012 0.000 0.572
#> GSM1105496 5 0.6791 -0.1038 0.000 0.000 0.304 0.312 0.384
#> GSM1105505 3 0.4886 0.3961 0.000 0.000 0.712 0.100 0.188
#> GSM1105509 1 0.6477 0.0452 0.516 0.000 0.308 0.168 0.008
#> GSM1105448 2 0.1908 0.6459 0.000 0.908 0.000 0.000 0.092
#> GSM1105521 1 0.3628 0.5639 0.772 0.000 0.216 0.012 0.000
#> GSM1105528 5 0.4249 0.4771 0.000 0.432 0.000 0.000 0.568
#> GSM1105529 5 0.4249 0.4771 0.000 0.432 0.000 0.000 0.568
#> GSM1105533 1 0.5355 0.4362 0.588 0.000 0.360 0.012 0.040
#> GSM1105545 4 0.4863 0.6274 0.000 0.136 0.116 0.740 0.008
#> GSM1105548 1 0.2606 0.7592 0.900 0.000 0.056 0.012 0.032
#> GSM1105549 1 0.1628 0.7603 0.936 0.000 0.056 0.000 0.008
#> GSM1105457 4 0.3752 0.6243 0.000 0.148 0.000 0.804 0.048
#> GSM1105460 2 0.5320 0.1590 0.000 0.524 0.000 0.424 0.052
#> GSM1105461 2 0.0000 0.7411 0.000 1.000 0.000 0.000 0.000
#> GSM1105464 3 0.4251 0.2546 0.372 0.000 0.624 0.004 0.000
#> GSM1105466 4 0.3238 0.6418 0.000 0.136 0.000 0.836 0.028
#> GSM1105479 4 0.7042 0.1343 0.000 0.336 0.028 0.456 0.180
#> GSM1105502 3 0.5302 0.0571 0.392 0.000 0.564 0.012 0.032
#> GSM1105515 1 0.0162 0.7768 0.996 0.000 0.004 0.000 0.000
#> GSM1105523 3 0.3793 0.4554 0.016 0.000 0.800 0.168 0.016
#> GSM1105550 4 0.5041 0.2708 0.028 0.000 0.404 0.564 0.004
#> GSM1105450 2 0.0000 0.7411 0.000 1.000 0.000 0.000 0.000
#> GSM1105451 2 0.0000 0.7411 0.000 1.000 0.000 0.000 0.000
#> GSM1105454 5 0.7946 -0.0824 0.000 0.076 0.268 0.308 0.348
#> GSM1105468 2 0.0000 0.7411 0.000 1.000 0.000 0.000 0.000
#> GSM1105481 3 0.7587 -0.0626 0.000 0.044 0.356 0.256 0.344
#> GSM1105504 3 0.4159 0.4508 0.000 0.000 0.776 0.068 0.156
#> GSM1105517 3 0.6830 0.0760 0.240 0.000 0.396 0.360 0.004
#> GSM1105525 3 0.5610 0.4416 0.144 0.000 0.700 0.120 0.036
#> GSM1105552 3 0.5601 0.4838 0.196 0.000 0.680 0.024 0.100
#> GSM1105452 5 0.4256 0.4700 0.000 0.436 0.000 0.000 0.564
#> GSM1105453 2 0.0000 0.7411 0.000 1.000 0.000 0.000 0.000
#> GSM1105456 5 0.7946 -0.0824 0.000 0.076 0.268 0.308 0.348
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1105438 2 0.0858 0.8125 0.000 0.968 0.004 0.000 0.028 0.000
#> GSM1105486 2 0.0146 0.8304 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM1105487 1 0.6293 0.2558 0.440 0.000 0.412 0.008 0.096 0.044
#> GSM1105490 4 0.6030 0.5323 0.000 0.036 0.080 0.656 0.076 0.152
#> GSM1105491 5 0.4244 0.7112 0.000 0.060 0.012 0.012 0.768 0.148
#> GSM1105495 6 0.4641 0.2933 0.000 0.028 0.016 0.004 0.304 0.648
#> GSM1105498 6 0.4885 0.3961 0.000 0.000 0.060 0.300 0.012 0.628
#> GSM1105499 1 0.4091 0.4889 0.684 0.000 0.292 0.004 0.012 0.008
#> GSM1105506 4 0.6059 0.5235 0.000 0.032 0.080 0.648 0.076 0.164
#> GSM1105442 5 0.3781 0.8761 0.000 0.204 0.004 0.000 0.756 0.036
#> GSM1105511 4 0.1223 0.7203 0.000 0.012 0.004 0.960 0.008 0.016
#> GSM1105514 2 0.0777 0.8121 0.000 0.972 0.004 0.000 0.024 0.000
#> GSM1105518 6 0.2295 0.6456 0.000 0.008 0.016 0.072 0.004 0.900
#> GSM1105522 3 0.5930 0.1458 0.340 0.000 0.548 0.048 0.032 0.032
#> GSM1105534 1 0.0146 0.7093 0.996 0.000 0.004 0.000 0.000 0.000
#> GSM1105535 1 0.4930 0.4637 0.620 0.000 0.320 0.004 0.028 0.028
#> GSM1105538 1 0.0665 0.7071 0.980 0.000 0.008 0.004 0.008 0.000
#> GSM1105542 5 0.3330 0.9047 0.000 0.284 0.000 0.000 0.716 0.000
#> GSM1105443 2 0.7594 0.3387 0.000 0.512 0.092 0.140 0.108 0.148
#> GSM1105551 1 0.6439 0.2418 0.428 0.000 0.408 0.008 0.108 0.048
#> GSM1105554 1 0.0000 0.7090 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1105555 1 0.5376 0.5817 0.676 0.000 0.176 0.004 0.096 0.048
#> GSM1105447 6 0.7289 0.2816 0.000 0.292 0.100 0.036 0.108 0.464
#> GSM1105467 2 0.1321 0.8128 0.000 0.952 0.024 0.000 0.020 0.004
#> GSM1105470 2 0.0146 0.8304 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM1105471 6 0.6659 0.5117 0.000 0.076 0.092 0.120 0.096 0.616
#> GSM1105474 2 0.0146 0.8304 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM1105475 2 0.4705 0.6656 0.000 0.756 0.016 0.116 0.076 0.036
#> GSM1105440 1 0.5667 0.4963 0.612 0.000 0.264 0.008 0.076 0.040
#> GSM1105488 5 0.3351 0.9029 0.000 0.288 0.000 0.000 0.712 0.000
#> GSM1105489 1 0.5223 0.5965 0.692 0.000 0.164 0.004 0.096 0.044
#> GSM1105492 1 0.2633 0.6952 0.892 0.000 0.044 0.004 0.032 0.028
#> GSM1105493 1 0.4073 0.6108 0.780 0.000 0.124 0.004 0.080 0.012
#> GSM1105497 5 0.4136 0.7410 0.000 0.080 0.004 0.004 0.760 0.152
#> GSM1105500 4 0.2623 0.6992 0.000 0.004 0.028 0.892 0.048 0.028
#> GSM1105501 4 0.0976 0.7216 0.000 0.016 0.000 0.968 0.008 0.008
#> GSM1105508 4 0.6398 0.0671 0.084 0.000 0.364 0.488 0.032 0.032
#> GSM1105444 2 0.0935 0.8089 0.000 0.964 0.004 0.000 0.032 0.000
#> GSM1105513 4 0.7306 0.1250 0.000 0.032 0.108 0.432 0.100 0.328
#> GSM1105516 4 0.3356 0.6522 0.116 0.004 0.032 0.832 0.016 0.000
#> GSM1105520 6 0.2009 0.6410 0.000 0.000 0.024 0.068 0.000 0.908
#> GSM1105524 1 0.4930 0.4637 0.620 0.000 0.320 0.004 0.028 0.028
#> GSM1105536 4 0.1232 0.7209 0.000 0.024 0.016 0.956 0.004 0.000
#> GSM1105537 1 0.4930 0.4637 0.620 0.000 0.320 0.004 0.028 0.028
#> GSM1105540 4 0.2063 0.7009 0.008 0.000 0.060 0.912 0.020 0.000
#> GSM1105544 4 0.2345 0.7037 0.000 0.000 0.040 0.904 0.028 0.028
#> GSM1105445 6 0.6363 0.5040 0.000 0.036 0.116 0.112 0.104 0.632
#> GSM1105553 6 0.3483 0.6213 0.000 0.000 0.048 0.068 0.048 0.836
#> GSM1105556 1 0.0405 0.7067 0.988 0.000 0.000 0.004 0.008 0.000
#> GSM1105557 4 0.6030 0.5323 0.000 0.036 0.080 0.656 0.076 0.152
#> GSM1105449 2 0.4335 0.6946 0.000 0.768 0.036 0.000 0.108 0.088
#> GSM1105469 4 0.1363 0.7211 0.004 0.000 0.028 0.952 0.004 0.012
#> GSM1105472 2 0.0146 0.8304 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM1105473 1 0.6504 -0.0612 0.472 0.000 0.376 0.052 0.072 0.028
#> GSM1105476 2 0.0146 0.8304 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM1105477 4 0.1448 0.7195 0.000 0.024 0.016 0.948 0.012 0.000
#> GSM1105478 4 0.7168 0.0634 0.000 0.016 0.116 0.412 0.104 0.352
#> GSM1105510 5 0.3445 0.9030 0.000 0.260 0.000 0.008 0.732 0.000
#> GSM1105530 3 0.3757 0.6048 0.180 0.000 0.776 0.024 0.000 0.020
#> GSM1105539 3 0.3708 0.5932 0.188 0.000 0.776 0.012 0.004 0.020
#> GSM1105480 4 0.5804 0.5186 0.000 0.004 0.096 0.640 0.076 0.184
#> GSM1105512 1 0.2425 0.6409 0.880 0.000 0.100 0.012 0.008 0.000
#> GSM1105532 3 0.3757 0.6048 0.180 0.000 0.776 0.024 0.000 0.020
#> GSM1105541 3 0.3739 0.5910 0.192 0.000 0.772 0.012 0.004 0.020
#> GSM1105439 2 0.7269 0.4134 0.000 0.548 0.088 0.160 0.100 0.104
#> GSM1105463 3 0.5535 0.2707 0.008 0.000 0.508 0.052 0.024 0.408
#> GSM1105482 1 0.1477 0.7041 0.940 0.000 0.008 0.004 0.048 0.000
#> GSM1105483 4 0.1282 0.7216 0.000 0.004 0.024 0.956 0.004 0.012
#> GSM1105494 6 0.6652 0.2338 0.000 0.000 0.120 0.300 0.096 0.484
#> GSM1105503 6 0.2830 0.6251 0.000 0.000 0.064 0.068 0.004 0.864
#> GSM1105507 4 0.3878 0.6066 0.128 0.000 0.060 0.792 0.020 0.000
#> GSM1105446 2 0.2442 0.6627 0.000 0.852 0.004 0.000 0.144 0.000
#> GSM1105519 1 0.3549 0.5749 0.812 0.000 0.128 0.044 0.016 0.000
#> GSM1105526 4 0.1148 0.7215 0.000 0.020 0.016 0.960 0.004 0.000
#> GSM1105527 4 0.3269 0.6750 0.000 0.012 0.052 0.852 0.012 0.072
#> GSM1105531 6 0.5338 -0.2536 0.000 0.000 0.456 0.064 0.016 0.464
#> GSM1105543 2 0.2442 0.6609 0.000 0.852 0.004 0.000 0.144 0.000
#> GSM1105546 1 0.3201 0.6921 0.852 0.000 0.044 0.000 0.072 0.032
#> GSM1105547 1 0.1728 0.6991 0.924 0.000 0.008 0.004 0.064 0.000
#> GSM1105455 2 0.6969 0.4550 0.000 0.580 0.072 0.148 0.100 0.100
#> GSM1105458 6 0.7365 0.0901 0.000 0.372 0.092 0.036 0.112 0.388
#> GSM1105459 2 0.0000 0.8301 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105462 3 0.6111 0.2895 0.000 0.000 0.476 0.164 0.020 0.340
#> GSM1105441 2 0.3861 0.7202 0.000 0.804 0.032 0.000 0.100 0.064
#> GSM1105465 5 0.4172 0.8202 0.000 0.140 0.004 0.004 0.760 0.092
#> GSM1105484 5 0.3351 0.9014 0.000 0.288 0.000 0.000 0.712 0.000
#> GSM1105485 5 0.3360 0.9027 0.000 0.264 0.000 0.004 0.732 0.000
#> GSM1105496 6 0.3224 0.6031 0.000 0.000 0.032 0.084 0.036 0.848
#> GSM1105505 6 0.5613 -0.1490 0.000 0.000 0.392 0.088 0.020 0.500
#> GSM1105509 4 0.5701 0.2150 0.304 0.000 0.140 0.544 0.012 0.000
#> GSM1105448 2 0.0858 0.8094 0.000 0.968 0.004 0.000 0.028 0.000
#> GSM1105521 1 0.3424 0.5780 0.816 0.000 0.136 0.032 0.016 0.000
#> GSM1105528 5 0.3351 0.9014 0.000 0.288 0.000 0.000 0.712 0.000
#> GSM1105529 5 0.3330 0.9047 0.000 0.284 0.000 0.000 0.716 0.000
#> GSM1105533 3 0.5559 0.0365 0.352 0.000 0.548 0.000 0.056 0.044
#> GSM1105545 4 0.1148 0.7215 0.000 0.020 0.016 0.960 0.004 0.000
#> GSM1105548 1 0.4979 0.6458 0.724 0.000 0.092 0.008 0.136 0.040
#> GSM1105549 1 0.3189 0.6679 0.848 0.000 0.060 0.004 0.080 0.008
#> GSM1105457 4 0.6824 0.4137 0.000 0.032 0.096 0.556 0.100 0.216
#> GSM1105460 2 0.7143 0.4392 0.000 0.564 0.080 0.144 0.108 0.104
#> GSM1105461 2 0.0146 0.8301 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM1105464 3 0.4850 0.5401 0.244 0.000 0.680 0.048 0.008 0.020
#> GSM1105466 4 0.6487 0.4764 0.000 0.032 0.096 0.608 0.096 0.168
#> GSM1105479 6 0.8027 0.3586 0.000 0.200 0.112 0.140 0.104 0.444
#> GSM1105502 3 0.3730 0.5363 0.188 0.000 0.772 0.004 0.004 0.032
#> GSM1105515 1 0.0260 0.7077 0.992 0.000 0.000 0.000 0.008 0.000
#> GSM1105523 3 0.4781 0.5243 0.004 0.000 0.696 0.176 0.004 0.120
#> GSM1105550 4 0.3056 0.6113 0.000 0.000 0.160 0.820 0.008 0.012
#> GSM1105450 2 0.0146 0.8304 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM1105451 2 0.0291 0.8294 0.000 0.992 0.004 0.000 0.004 0.000
#> GSM1105454 6 0.2295 0.6418 0.000 0.028 0.008 0.032 0.020 0.912
#> GSM1105468 2 0.0146 0.8304 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM1105481 6 0.3221 0.6015 0.000 0.016 0.048 0.040 0.032 0.864
#> GSM1105504 3 0.5632 0.2409 0.000 0.000 0.484 0.088 0.020 0.408
#> GSM1105517 4 0.5000 0.4808 0.108 0.000 0.168 0.700 0.012 0.012
#> GSM1105525 3 0.4133 0.6006 0.080 0.000 0.796 0.084 0.008 0.032
#> GSM1105552 3 0.8076 0.4109 0.164 0.000 0.432 0.176 0.076 0.152
#> GSM1105452 5 0.3351 0.9029 0.000 0.288 0.000 0.000 0.712 0.000
#> GSM1105453 2 0.0291 0.8294 0.000 0.992 0.004 0.000 0.004 0.000
#> GSM1105456 6 0.2215 0.6414 0.000 0.024 0.008 0.032 0.020 0.916
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 agent(p) other(p) time(p) individual(p) k
#> MAD:kmeans 119 1.000 0.362757 0.793 0.00587 2
#> MAD:kmeans 104 0.641 0.839454 0.623 0.00761 3
#> MAD:kmeans 82 0.120 0.944858 0.956 0.04430 4
#> MAD:kmeans 64 0.849 0.535632 0.823 0.04491 5
#> MAD:kmeans 87 0.880 0.000436 0.789 0.00279 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 44956 rows and 120 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 0.915 0.947 0.977 0.4990 0.503 0.503
#> 3 3 0.790 0.887 0.937 0.3168 0.815 0.641
#> 4 4 0.743 0.802 0.880 0.1070 0.887 0.692
#> 5 5 0.710 0.660 0.797 0.0747 0.905 0.684
#> 6 6 0.778 0.750 0.858 0.0506 0.897 0.599
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
#> GSM1105438 2 0.0000 0.971 0.000 1.000
#> GSM1105486 2 0.0000 0.971 0.000 1.000
#> GSM1105487 1 0.0000 0.982 1.000 0.000
#> GSM1105490 2 0.0000 0.971 0.000 1.000
#> GSM1105491 2 0.6973 0.777 0.188 0.812
#> GSM1105495 2 0.6712 0.792 0.176 0.824
#> GSM1105498 2 0.9580 0.428 0.380 0.620
#> GSM1105499 1 0.0000 0.982 1.000 0.000
#> GSM1105506 2 0.0000 0.971 0.000 1.000
#> GSM1105442 2 0.0000 0.971 0.000 1.000
#> GSM1105511 2 0.0000 0.971 0.000 1.000
#> GSM1105514 2 0.0000 0.971 0.000 1.000
#> GSM1105518 2 0.0000 0.971 0.000 1.000
#> GSM1105522 1 0.0000 0.982 1.000 0.000
#> GSM1105534 1 0.0000 0.982 1.000 0.000
#> GSM1105535 1 0.0000 0.982 1.000 0.000
#> GSM1105538 1 0.0000 0.982 1.000 0.000
#> GSM1105542 2 0.0000 0.971 0.000 1.000
#> GSM1105443 2 0.0000 0.971 0.000 1.000
#> GSM1105551 1 0.0000 0.982 1.000 0.000
#> GSM1105554 1 0.0000 0.982 1.000 0.000
#> GSM1105555 1 0.0000 0.982 1.000 0.000
#> GSM1105447 2 0.0000 0.971 0.000 1.000
#> GSM1105467 2 0.0000 0.971 0.000 1.000
#> GSM1105470 2 0.0000 0.971 0.000 1.000
#> GSM1105471 2 0.0000 0.971 0.000 1.000
#> GSM1105474 2 0.0000 0.971 0.000 1.000
#> GSM1105475 2 0.0000 0.971 0.000 1.000
#> GSM1105440 1 0.0000 0.982 1.000 0.000
#> GSM1105488 2 0.0000 0.971 0.000 1.000
#> GSM1105489 1 0.0000 0.982 1.000 0.000
#> GSM1105492 1 0.0000 0.982 1.000 0.000
#> GSM1105493 1 0.0000 0.982 1.000 0.000
#> GSM1105497 2 0.0000 0.971 0.000 1.000
#> GSM1105500 2 0.0000 0.971 0.000 1.000
#> GSM1105501 2 0.0000 0.971 0.000 1.000
#> GSM1105508 1 0.0000 0.982 1.000 0.000
#> GSM1105444 2 0.0000 0.971 0.000 1.000
#> GSM1105513 2 0.0000 0.971 0.000 1.000
#> GSM1105516 1 0.7219 0.749 0.800 0.200
#> GSM1105520 2 0.8267 0.670 0.260 0.740
#> GSM1105524 1 0.0000 0.982 1.000 0.000
#> GSM1105536 2 0.0000 0.971 0.000 1.000
#> GSM1105537 1 0.0000 0.982 1.000 0.000
#> GSM1105540 1 0.0000 0.982 1.000 0.000
#> GSM1105544 1 0.5294 0.855 0.880 0.120
#> GSM1105445 2 0.0000 0.971 0.000 1.000
#> GSM1105553 2 0.9522 0.447 0.372 0.628
#> GSM1105556 1 0.0000 0.982 1.000 0.000
#> GSM1105557 2 0.0000 0.971 0.000 1.000
#> GSM1105449 2 0.0000 0.971 0.000 1.000
#> GSM1105469 1 0.0376 0.978 0.996 0.004
#> GSM1105472 2 0.0000 0.971 0.000 1.000
#> GSM1105473 1 0.0000 0.982 1.000 0.000
#> GSM1105476 2 0.0000 0.971 0.000 1.000
#> GSM1105477 2 0.0000 0.971 0.000 1.000
#> GSM1105478 2 0.0000 0.971 0.000 1.000
#> GSM1105510 2 0.0000 0.971 0.000 1.000
#> GSM1105530 1 0.0000 0.982 1.000 0.000
#> GSM1105539 1 0.0000 0.982 1.000 0.000
#> GSM1105480 2 0.0000 0.971 0.000 1.000
#> GSM1105512 1 0.0000 0.982 1.000 0.000
#> GSM1105532 1 0.0000 0.982 1.000 0.000
#> GSM1105541 1 0.0000 0.982 1.000 0.000
#> GSM1105439 2 0.0000 0.971 0.000 1.000
#> GSM1105463 1 0.0000 0.982 1.000 0.000
#> GSM1105482 1 0.0000 0.982 1.000 0.000
#> GSM1105483 1 0.7299 0.743 0.796 0.204
#> GSM1105494 2 0.0000 0.971 0.000 1.000
#> GSM1105503 1 0.9427 0.402 0.640 0.360
#> GSM1105507 1 0.0000 0.982 1.000 0.000
#> GSM1105446 2 0.0000 0.971 0.000 1.000
#> GSM1105519 1 0.0000 0.982 1.000 0.000
#> GSM1105526 2 0.0000 0.971 0.000 1.000
#> GSM1105527 2 0.4161 0.893 0.084 0.916
#> GSM1105531 1 0.0000 0.982 1.000 0.000
#> GSM1105543 2 0.0000 0.971 0.000 1.000
#> GSM1105546 1 0.0000 0.982 1.000 0.000
#> GSM1105547 1 0.0000 0.982 1.000 0.000
#> GSM1105455 2 0.0000 0.971 0.000 1.000
#> GSM1105458 2 0.0000 0.971 0.000 1.000
#> GSM1105459 2 0.0000 0.971 0.000 1.000
#> GSM1105462 1 0.0000 0.982 1.000 0.000
#> GSM1105441 2 0.0000 0.971 0.000 1.000
#> GSM1105465 2 0.0000 0.971 0.000 1.000
#> GSM1105484 2 0.0000 0.971 0.000 1.000
#> GSM1105485 2 0.0000 0.971 0.000 1.000
#> GSM1105496 1 0.0000 0.982 1.000 0.000
#> GSM1105505 1 0.0000 0.982 1.000 0.000
#> GSM1105509 1 0.0000 0.982 1.000 0.000
#> GSM1105448 2 0.0000 0.971 0.000 1.000
#> GSM1105521 1 0.0000 0.982 1.000 0.000
#> GSM1105528 2 0.0000 0.971 0.000 1.000
#> GSM1105529 2 0.0000 0.971 0.000 1.000
#> GSM1105533 1 0.0000 0.982 1.000 0.000
#> GSM1105545 2 0.0000 0.971 0.000 1.000
#> GSM1105548 1 0.0000 0.982 1.000 0.000
#> GSM1105549 1 0.0000 0.982 1.000 0.000
#> GSM1105457 2 0.0000 0.971 0.000 1.000
#> GSM1105460 2 0.0000 0.971 0.000 1.000
#> GSM1105461 2 0.0000 0.971 0.000 1.000
#> GSM1105464 1 0.0000 0.982 1.000 0.000
#> GSM1105466 2 0.0000 0.971 0.000 1.000
#> GSM1105479 2 0.0000 0.971 0.000 1.000
#> GSM1105502 1 0.0000 0.982 1.000 0.000
#> GSM1105515 1 0.0000 0.982 1.000 0.000
#> GSM1105523 1 0.0000 0.982 1.000 0.000
#> GSM1105550 1 0.0000 0.982 1.000 0.000
#> GSM1105450 2 0.0000 0.971 0.000 1.000
#> GSM1105451 2 0.0000 0.971 0.000 1.000
#> GSM1105454 2 0.0938 0.961 0.012 0.988
#> GSM1105468 2 0.0000 0.971 0.000 1.000
#> GSM1105481 2 0.7139 0.766 0.196 0.804
#> GSM1105504 1 0.0000 0.982 1.000 0.000
#> GSM1105517 1 0.0000 0.982 1.000 0.000
#> GSM1105525 1 0.0000 0.982 1.000 0.000
#> GSM1105552 1 0.0000 0.982 1.000 0.000
#> GSM1105452 2 0.0000 0.971 0.000 1.000
#> GSM1105453 2 0.0000 0.971 0.000 1.000
#> GSM1105456 2 0.6887 0.782 0.184 0.816
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1105438 2 0.0000 0.9345 0.000 1.000 0.000
#> GSM1105486 2 0.0424 0.9346 0.000 0.992 0.008
#> GSM1105487 1 0.0424 0.9562 0.992 0.000 0.008
#> GSM1105490 3 0.4555 0.8509 0.000 0.200 0.800
#> GSM1105491 2 0.4555 0.7551 0.000 0.800 0.200
#> GSM1105495 2 0.4605 0.7522 0.000 0.796 0.204
#> GSM1105498 3 0.0000 0.8619 0.000 0.000 1.000
#> GSM1105499 1 0.0000 0.9571 1.000 0.000 0.000
#> GSM1105506 3 0.4555 0.8509 0.000 0.200 0.800
#> GSM1105442 2 0.4346 0.7707 0.000 0.816 0.184
#> GSM1105511 3 0.4555 0.8509 0.000 0.200 0.800
#> GSM1105514 2 0.0000 0.9345 0.000 1.000 0.000
#> GSM1105518 3 0.0000 0.8619 0.000 0.000 1.000
#> GSM1105522 1 0.0000 0.9571 1.000 0.000 0.000
#> GSM1105534 1 0.0000 0.9571 1.000 0.000 0.000
#> GSM1105535 1 0.0000 0.9571 1.000 0.000 0.000
#> GSM1105538 1 0.0000 0.9571 1.000 0.000 0.000
#> GSM1105542 2 0.0000 0.9345 0.000 1.000 0.000
#> GSM1105443 3 0.4555 0.8509 0.000 0.200 0.800
#> GSM1105551 1 0.0747 0.9549 0.984 0.000 0.016
#> GSM1105554 1 0.0000 0.9571 1.000 0.000 0.000
#> GSM1105555 1 0.0747 0.9549 0.984 0.000 0.016
#> GSM1105447 3 0.1289 0.8646 0.000 0.032 0.968
#> GSM1105467 2 0.0424 0.9346 0.000 0.992 0.008
#> GSM1105470 2 0.0424 0.9346 0.000 0.992 0.008
#> GSM1105471 3 0.0000 0.8619 0.000 0.000 1.000
#> GSM1105474 2 0.0424 0.9346 0.000 0.992 0.008
#> GSM1105475 2 0.1643 0.9072 0.000 0.956 0.044
#> GSM1105440 1 0.0000 0.9571 1.000 0.000 0.000
#> GSM1105488 2 0.0000 0.9345 0.000 1.000 0.000
#> GSM1105489 1 0.0747 0.9549 0.984 0.000 0.016
#> GSM1105492 1 0.0000 0.9571 1.000 0.000 0.000
#> GSM1105493 1 0.0747 0.9549 0.984 0.000 0.016
#> GSM1105497 2 0.4504 0.7592 0.000 0.804 0.196
#> GSM1105500 2 0.0237 0.9334 0.000 0.996 0.004
#> GSM1105501 2 0.6267 -0.0513 0.000 0.548 0.452
#> GSM1105508 1 0.0000 0.9571 1.000 0.000 0.000
#> GSM1105444 2 0.0000 0.9345 0.000 1.000 0.000
#> GSM1105513 3 0.4555 0.8509 0.000 0.200 0.800
#> GSM1105516 1 0.5397 0.6091 0.720 0.280 0.000
#> GSM1105520 3 0.0000 0.8619 0.000 0.000 1.000
#> GSM1105524 1 0.0000 0.9571 1.000 0.000 0.000
#> GSM1105536 2 0.0424 0.9346 0.000 0.992 0.008
#> GSM1105537 1 0.0000 0.9571 1.000 0.000 0.000
#> GSM1105540 1 0.0000 0.9571 1.000 0.000 0.000
#> GSM1105544 1 0.6200 0.5045 0.676 0.012 0.312
#> GSM1105445 3 0.0000 0.8619 0.000 0.000 1.000
#> GSM1105553 3 0.0424 0.8578 0.000 0.008 0.992
#> GSM1105556 1 0.0000 0.9571 1.000 0.000 0.000
#> GSM1105557 3 0.4555 0.8509 0.000 0.200 0.800
#> GSM1105449 2 0.1529 0.9115 0.000 0.960 0.040
#> GSM1105469 3 0.5216 0.6588 0.260 0.000 0.740
#> GSM1105472 2 0.0424 0.9346 0.000 0.992 0.008
#> GSM1105473 1 0.0747 0.9549 0.984 0.000 0.016
#> GSM1105476 2 0.0424 0.9346 0.000 0.992 0.008
#> GSM1105477 2 0.0000 0.9345 0.000 1.000 0.000
#> GSM1105478 3 0.0000 0.8619 0.000 0.000 1.000
#> GSM1105510 2 0.0000 0.9345 0.000 1.000 0.000
#> GSM1105530 1 0.0747 0.9549 0.984 0.000 0.016
#> GSM1105539 1 0.0747 0.9549 0.984 0.000 0.016
#> GSM1105480 3 0.4504 0.8524 0.000 0.196 0.804
#> GSM1105512 1 0.0000 0.9571 1.000 0.000 0.000
#> GSM1105532 1 0.0747 0.9549 0.984 0.000 0.016
#> GSM1105541 1 0.0747 0.9549 0.984 0.000 0.016
#> GSM1105439 3 0.4555 0.8509 0.000 0.200 0.800
#> GSM1105463 1 0.4555 0.7936 0.800 0.000 0.200
#> GSM1105482 1 0.0000 0.9571 1.000 0.000 0.000
#> GSM1105483 3 0.5200 0.8498 0.020 0.184 0.796
#> GSM1105494 3 0.0747 0.8627 0.000 0.016 0.984
#> GSM1105503 3 0.0000 0.8619 0.000 0.000 1.000
#> GSM1105507 1 0.0000 0.9571 1.000 0.000 0.000
#> GSM1105446 2 0.0000 0.9345 0.000 1.000 0.000
#> GSM1105519 1 0.0000 0.9571 1.000 0.000 0.000
#> GSM1105526 2 0.0424 0.9346 0.000 0.992 0.008
#> GSM1105527 3 0.5062 0.8512 0.016 0.184 0.800
#> GSM1105531 1 0.4555 0.7936 0.800 0.000 0.200
#> GSM1105543 2 0.0000 0.9345 0.000 1.000 0.000
#> GSM1105546 1 0.0000 0.9571 1.000 0.000 0.000
#> GSM1105547 1 0.0000 0.9571 1.000 0.000 0.000
#> GSM1105455 3 0.4555 0.8509 0.000 0.200 0.800
#> GSM1105458 2 0.5560 0.6567 0.000 0.700 0.300
#> GSM1105459 2 0.0424 0.9346 0.000 0.992 0.008
#> GSM1105462 1 0.4555 0.7936 0.800 0.000 0.200
#> GSM1105441 2 0.1163 0.9206 0.000 0.972 0.028
#> GSM1105465 2 0.4555 0.7551 0.000 0.800 0.200
#> GSM1105484 2 0.0000 0.9345 0.000 1.000 0.000
#> GSM1105485 2 0.0000 0.9345 0.000 1.000 0.000
#> GSM1105496 3 0.3213 0.7911 0.092 0.008 0.900
#> GSM1105505 1 0.4555 0.7936 0.800 0.000 0.200
#> GSM1105509 1 0.0000 0.9571 1.000 0.000 0.000
#> GSM1105448 2 0.0000 0.9345 0.000 1.000 0.000
#> GSM1105521 1 0.0000 0.9571 1.000 0.000 0.000
#> GSM1105528 2 0.0000 0.9345 0.000 1.000 0.000
#> GSM1105529 2 0.0000 0.9345 0.000 1.000 0.000
#> GSM1105533 1 0.0747 0.9549 0.984 0.000 0.016
#> GSM1105545 2 0.0424 0.9346 0.000 0.992 0.008
#> GSM1105548 1 0.0747 0.9549 0.984 0.000 0.016
#> GSM1105549 1 0.0424 0.9562 0.992 0.000 0.008
#> GSM1105457 3 0.4555 0.8509 0.000 0.200 0.800
#> GSM1105460 2 0.3482 0.8150 0.000 0.872 0.128
#> GSM1105461 2 0.0424 0.9346 0.000 0.992 0.008
#> GSM1105464 1 0.0747 0.9549 0.984 0.000 0.016
#> GSM1105466 3 0.4555 0.8509 0.000 0.200 0.800
#> GSM1105479 3 0.3482 0.8629 0.000 0.128 0.872
#> GSM1105502 1 0.0747 0.9549 0.984 0.000 0.016
#> GSM1105515 1 0.0000 0.9571 1.000 0.000 0.000
#> GSM1105523 1 0.3816 0.8256 0.852 0.000 0.148
#> GSM1105550 1 0.0000 0.9571 1.000 0.000 0.000
#> GSM1105450 2 0.0424 0.9346 0.000 0.992 0.008
#> GSM1105451 2 0.0424 0.9346 0.000 0.992 0.008
#> GSM1105454 3 0.0000 0.8619 0.000 0.000 1.000
#> GSM1105468 2 0.0424 0.9346 0.000 0.992 0.008
#> GSM1105481 2 0.5397 0.6804 0.000 0.720 0.280
#> GSM1105504 1 0.4555 0.7936 0.800 0.000 0.200
#> GSM1105517 1 0.0000 0.9571 1.000 0.000 0.000
#> GSM1105525 1 0.0747 0.9549 0.984 0.000 0.016
#> GSM1105552 1 0.1031 0.9506 0.976 0.000 0.024
#> GSM1105452 2 0.0000 0.9345 0.000 1.000 0.000
#> GSM1105453 2 0.0424 0.9346 0.000 0.992 0.008
#> GSM1105456 3 0.0000 0.8619 0.000 0.000 1.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1105438 2 0.0707 0.7818 0.000 0.980 0.000 0.020
#> GSM1105486 2 0.3610 0.7405 0.000 0.800 0.000 0.200
#> GSM1105487 1 0.0188 0.9582 0.996 0.000 0.004 0.000
#> GSM1105490 4 0.0336 0.8345 0.000 0.008 0.000 0.992
#> GSM1105491 2 0.5147 0.2644 0.000 0.536 0.460 0.004
#> GSM1105495 3 0.1209 0.7987 0.000 0.032 0.964 0.004
#> GSM1105498 3 0.3024 0.8585 0.000 0.000 0.852 0.148
#> GSM1105499 1 0.0000 0.9589 1.000 0.000 0.000 0.000
#> GSM1105506 4 0.0188 0.8345 0.000 0.004 0.000 0.996
#> GSM1105442 2 0.3105 0.7554 0.000 0.856 0.140 0.004
#> GSM1105511 4 0.0376 0.8339 0.000 0.004 0.004 0.992
#> GSM1105514 2 0.0707 0.7818 0.000 0.980 0.000 0.020
#> GSM1105518 3 0.3024 0.8585 0.000 0.000 0.852 0.148
#> GSM1105522 1 0.0000 0.9589 1.000 0.000 0.000 0.000
#> GSM1105534 1 0.0000 0.9589 1.000 0.000 0.000 0.000
#> GSM1105535 1 0.0000 0.9589 1.000 0.000 0.000 0.000
#> GSM1105538 1 0.0000 0.9589 1.000 0.000 0.000 0.000
#> GSM1105542 2 0.2814 0.7612 0.000 0.868 0.132 0.000
#> GSM1105443 4 0.3311 0.7512 0.000 0.172 0.000 0.828
#> GSM1105551 1 0.0921 0.9503 0.972 0.000 0.028 0.000
#> GSM1105554 1 0.0000 0.9589 1.000 0.000 0.000 0.000
#> GSM1105555 1 0.1022 0.9486 0.968 0.000 0.032 0.000
#> GSM1105447 4 0.5268 0.1003 0.000 0.452 0.008 0.540
#> GSM1105467 2 0.3610 0.7405 0.000 0.800 0.000 0.200
#> GSM1105470 2 0.3610 0.7405 0.000 0.800 0.000 0.200
#> GSM1105471 4 0.6324 0.2732 0.000 0.064 0.400 0.536
#> GSM1105474 2 0.3610 0.7405 0.000 0.800 0.000 0.200
#> GSM1105475 4 0.4955 0.2044 0.000 0.444 0.000 0.556
#> GSM1105440 1 0.0000 0.9589 1.000 0.000 0.000 0.000
#> GSM1105488 2 0.2814 0.7612 0.000 0.868 0.132 0.000
#> GSM1105489 1 0.0707 0.9534 0.980 0.000 0.020 0.000
#> GSM1105492 1 0.0000 0.9589 1.000 0.000 0.000 0.000
#> GSM1105493 1 0.1022 0.9486 0.968 0.000 0.032 0.000
#> GSM1105497 2 0.4535 0.6138 0.000 0.704 0.292 0.004
#> GSM1105500 2 0.2999 0.7598 0.000 0.864 0.132 0.004
#> GSM1105501 4 0.2999 0.7789 0.000 0.132 0.004 0.864
#> GSM1105508 1 0.0000 0.9589 1.000 0.000 0.000 0.000
#> GSM1105444 2 0.0592 0.7817 0.000 0.984 0.000 0.016
#> GSM1105513 4 0.0000 0.8327 0.000 0.000 0.000 1.000
#> GSM1105516 1 0.3829 0.7857 0.828 0.016 0.004 0.152
#> GSM1105520 3 0.3024 0.8585 0.000 0.000 0.852 0.148
#> GSM1105524 1 0.0000 0.9589 1.000 0.000 0.000 0.000
#> GSM1105536 2 0.4936 0.4726 0.000 0.624 0.004 0.372
#> GSM1105537 1 0.0000 0.9589 1.000 0.000 0.000 0.000
#> GSM1105540 1 0.0000 0.9589 1.000 0.000 0.000 0.000
#> GSM1105544 1 0.9316 0.0838 0.420 0.164 0.140 0.276
#> GSM1105445 4 0.4134 0.5666 0.000 0.000 0.260 0.740
#> GSM1105553 3 0.2868 0.8603 0.000 0.000 0.864 0.136
#> GSM1105556 1 0.0000 0.9589 1.000 0.000 0.000 0.000
#> GSM1105557 4 0.0188 0.8345 0.000 0.004 0.000 0.996
#> GSM1105449 2 0.3764 0.7234 0.000 0.784 0.000 0.216
#> GSM1105469 4 0.2401 0.7316 0.092 0.000 0.004 0.904
#> GSM1105472 2 0.3610 0.7405 0.000 0.800 0.000 0.200
#> GSM1105473 1 0.1211 0.9455 0.960 0.000 0.040 0.000
#> GSM1105476 2 0.3610 0.7405 0.000 0.800 0.000 0.200
#> GSM1105477 2 0.2124 0.7809 0.000 0.932 0.040 0.028
#> GSM1105478 4 0.1022 0.8098 0.000 0.000 0.032 0.968
#> GSM1105510 2 0.2814 0.7612 0.000 0.868 0.132 0.000
#> GSM1105530 1 0.1302 0.9432 0.956 0.000 0.044 0.000
#> GSM1105539 1 0.1302 0.9432 0.956 0.000 0.044 0.000
#> GSM1105480 4 0.0707 0.8198 0.000 0.000 0.020 0.980
#> GSM1105512 1 0.0000 0.9589 1.000 0.000 0.000 0.000
#> GSM1105532 1 0.1302 0.9432 0.956 0.000 0.044 0.000
#> GSM1105541 1 0.1302 0.9432 0.956 0.000 0.044 0.000
#> GSM1105439 4 0.2081 0.8149 0.000 0.084 0.000 0.916
#> GSM1105463 3 0.3074 0.8248 0.152 0.000 0.848 0.000
#> GSM1105482 1 0.0188 0.9582 0.996 0.000 0.004 0.000
#> GSM1105483 4 0.0376 0.8325 0.004 0.000 0.004 0.992
#> GSM1105494 4 0.4040 0.5774 0.000 0.000 0.248 0.752
#> GSM1105503 3 0.3024 0.8585 0.000 0.000 0.852 0.148
#> GSM1105507 1 0.3306 0.7968 0.840 0.000 0.004 0.156
#> GSM1105446 2 0.2345 0.7688 0.000 0.900 0.100 0.000
#> GSM1105519 1 0.0000 0.9589 1.000 0.000 0.000 0.000
#> GSM1105526 2 0.5298 0.4564 0.000 0.612 0.016 0.372
#> GSM1105527 4 0.0376 0.8325 0.004 0.000 0.004 0.992
#> GSM1105531 3 0.2999 0.8433 0.132 0.000 0.864 0.004
#> GSM1105543 2 0.1118 0.7783 0.000 0.964 0.036 0.000
#> GSM1105546 1 0.0000 0.9589 1.000 0.000 0.000 0.000
#> GSM1105547 1 0.0000 0.9589 1.000 0.000 0.000 0.000
#> GSM1105455 4 0.2011 0.8169 0.000 0.080 0.000 0.920
#> GSM1105458 2 0.6386 0.5855 0.000 0.640 0.124 0.236
#> GSM1105459 2 0.3610 0.7405 0.000 0.800 0.000 0.200
#> GSM1105462 3 0.2999 0.8433 0.132 0.000 0.864 0.004
#> GSM1105441 2 0.3837 0.7136 0.000 0.776 0.000 0.224
#> GSM1105465 2 0.4535 0.6138 0.000 0.704 0.292 0.004
#> GSM1105484 2 0.2814 0.7612 0.000 0.868 0.132 0.000
#> GSM1105485 2 0.2814 0.7612 0.000 0.868 0.132 0.000
#> GSM1105496 3 0.0921 0.8275 0.000 0.000 0.972 0.028
#> GSM1105505 3 0.2868 0.8406 0.136 0.000 0.864 0.000
#> GSM1105509 1 0.0000 0.9589 1.000 0.000 0.000 0.000
#> GSM1105448 2 0.0707 0.7818 0.000 0.980 0.000 0.020
#> GSM1105521 1 0.0000 0.9589 1.000 0.000 0.000 0.000
#> GSM1105528 2 0.2760 0.7625 0.000 0.872 0.128 0.000
#> GSM1105529 2 0.2814 0.7612 0.000 0.868 0.132 0.000
#> GSM1105533 1 0.1022 0.9486 0.968 0.000 0.032 0.000
#> GSM1105545 4 0.3306 0.7581 0.000 0.156 0.004 0.840
#> GSM1105548 1 0.0188 0.9582 0.996 0.000 0.004 0.000
#> GSM1105549 1 0.0188 0.9582 0.996 0.000 0.004 0.000
#> GSM1105457 4 0.0188 0.8345 0.000 0.004 0.000 0.996
#> GSM1105460 4 0.4406 0.5760 0.000 0.300 0.000 0.700
#> GSM1105461 2 0.3610 0.7405 0.000 0.800 0.000 0.200
#> GSM1105464 1 0.1302 0.9432 0.956 0.000 0.044 0.000
#> GSM1105466 4 0.0188 0.8345 0.000 0.004 0.000 0.996
#> GSM1105479 4 0.4621 0.7526 0.000 0.128 0.076 0.796
#> GSM1105502 1 0.1211 0.9451 0.960 0.000 0.040 0.000
#> GSM1105515 1 0.0000 0.9589 1.000 0.000 0.000 0.000
#> GSM1105523 3 0.5669 0.7228 0.200 0.000 0.708 0.092
#> GSM1105550 1 0.2635 0.8852 0.904 0.000 0.020 0.076
#> GSM1105450 2 0.3610 0.7405 0.000 0.800 0.000 0.200
#> GSM1105451 2 0.3610 0.7405 0.000 0.800 0.000 0.200
#> GSM1105454 3 0.3447 0.8575 0.000 0.020 0.852 0.128
#> GSM1105468 2 0.3610 0.7405 0.000 0.800 0.000 0.200
#> GSM1105481 3 0.3224 0.7985 0.000 0.120 0.864 0.016
#> GSM1105504 3 0.2868 0.8406 0.136 0.000 0.864 0.000
#> GSM1105517 1 0.0469 0.9557 0.988 0.000 0.012 0.000
#> GSM1105525 1 0.3471 0.8583 0.868 0.000 0.060 0.072
#> GSM1105552 1 0.1389 0.9404 0.952 0.000 0.048 0.000
#> GSM1105452 2 0.2814 0.7612 0.000 0.868 0.132 0.000
#> GSM1105453 2 0.3610 0.7405 0.000 0.800 0.000 0.200
#> GSM1105456 3 0.3447 0.8575 0.000 0.020 0.852 0.128
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM1105438 2 0.0963 0.7638 0.000 0.964 0.000 0.000 0.036
#> GSM1105486 2 0.0000 0.7884 0.000 1.000 0.000 0.000 0.000
#> GSM1105487 1 0.3088 0.7994 0.828 0.000 0.164 0.004 0.004
#> GSM1105490 4 0.1952 0.7687 0.000 0.084 0.000 0.912 0.004
#> GSM1105491 5 0.2464 0.7590 0.000 0.096 0.016 0.000 0.888
#> GSM1105495 3 0.5376 0.5268 0.000 0.012 0.520 0.032 0.436
#> GSM1105498 3 0.6667 0.4810 0.000 0.000 0.428 0.328 0.244
#> GSM1105499 1 0.0566 0.8372 0.984 0.000 0.012 0.004 0.000
#> GSM1105506 4 0.1638 0.7677 0.000 0.064 0.000 0.932 0.004
#> GSM1105442 5 0.2929 0.8369 0.000 0.180 0.000 0.000 0.820
#> GSM1105511 4 0.2302 0.7677 0.000 0.080 0.008 0.904 0.008
#> GSM1105514 2 0.0609 0.7759 0.000 0.980 0.000 0.000 0.020
#> GSM1105518 3 0.6641 0.6113 0.000 0.016 0.536 0.204 0.244
#> GSM1105522 1 0.1804 0.8257 0.940 0.000 0.024 0.024 0.012
#> GSM1105534 1 0.0000 0.8358 1.000 0.000 0.000 0.000 0.000
#> GSM1105535 1 0.0613 0.8360 0.984 0.000 0.004 0.008 0.004
#> GSM1105538 1 0.0000 0.8358 1.000 0.000 0.000 0.000 0.000
#> GSM1105542 5 0.3612 0.8762 0.000 0.268 0.000 0.000 0.732
#> GSM1105443 4 0.4451 0.1375 0.000 0.492 0.000 0.504 0.004
#> GSM1105551 1 0.3128 0.7972 0.824 0.000 0.168 0.004 0.004
#> GSM1105554 1 0.0162 0.8364 0.996 0.000 0.004 0.000 0.000
#> GSM1105555 1 0.2891 0.7925 0.824 0.000 0.176 0.000 0.000
#> GSM1105447 2 0.6243 0.2614 0.000 0.544 0.000 0.216 0.240
#> GSM1105467 2 0.0000 0.7884 0.000 1.000 0.000 0.000 0.000
#> GSM1105470 2 0.0000 0.7884 0.000 1.000 0.000 0.000 0.000
#> GSM1105471 2 0.8488 -0.1983 0.000 0.304 0.204 0.296 0.196
#> GSM1105474 2 0.0000 0.7884 0.000 1.000 0.000 0.000 0.000
#> GSM1105475 2 0.2127 0.7107 0.000 0.892 0.000 0.108 0.000
#> GSM1105440 1 0.0486 0.8358 0.988 0.000 0.004 0.004 0.004
#> GSM1105488 5 0.3612 0.8762 0.000 0.268 0.000 0.000 0.732
#> GSM1105489 1 0.2773 0.7979 0.836 0.000 0.164 0.000 0.000
#> GSM1105492 1 0.0324 0.8355 0.992 0.000 0.000 0.004 0.004
#> GSM1105493 1 0.2929 0.7920 0.820 0.000 0.180 0.000 0.000
#> GSM1105497 5 0.2179 0.7644 0.000 0.100 0.004 0.000 0.896
#> GSM1105500 5 0.3398 0.8558 0.000 0.216 0.000 0.004 0.780
#> GSM1105501 4 0.4183 0.5098 0.000 0.324 0.000 0.668 0.008
#> GSM1105508 1 0.0867 0.8350 0.976 0.000 0.008 0.008 0.008
#> GSM1105444 2 0.1121 0.7568 0.000 0.956 0.000 0.000 0.044
#> GSM1105513 4 0.2771 0.7275 0.000 0.128 0.000 0.860 0.012
#> GSM1105516 1 0.2612 0.7782 0.892 0.004 0.004 0.084 0.016
#> GSM1105520 3 0.6166 0.6228 0.000 0.000 0.556 0.200 0.244
#> GSM1105524 1 0.0740 0.8356 0.980 0.000 0.008 0.008 0.004
#> GSM1105536 2 0.3124 0.6815 0.000 0.840 0.008 0.144 0.008
#> GSM1105537 1 0.0740 0.8356 0.980 0.000 0.008 0.008 0.004
#> GSM1105540 1 0.4253 0.7394 0.796 0.000 0.092 0.100 0.012
#> GSM1105544 5 0.5814 0.3997 0.288 0.000 0.000 0.128 0.584
#> GSM1105445 4 0.7673 0.0132 0.000 0.096 0.216 0.480 0.208
#> GSM1105553 3 0.6390 0.6156 0.004 0.000 0.536 0.200 0.260
#> GSM1105556 1 0.0162 0.8364 0.996 0.000 0.004 0.000 0.000
#> GSM1105557 4 0.1768 0.7692 0.000 0.072 0.000 0.924 0.004
#> GSM1105449 2 0.2754 0.7255 0.000 0.880 0.000 0.040 0.080
#> GSM1105469 4 0.2590 0.6999 0.040 0.004 0.028 0.908 0.020
#> GSM1105472 2 0.0000 0.7884 0.000 1.000 0.000 0.000 0.000
#> GSM1105473 1 0.3707 0.7270 0.716 0.000 0.284 0.000 0.000
#> GSM1105476 2 0.0000 0.7884 0.000 1.000 0.000 0.000 0.000
#> GSM1105477 2 0.4801 0.2773 0.000 0.668 0.000 0.048 0.284
#> GSM1105478 4 0.1904 0.7157 0.000 0.020 0.028 0.936 0.016
#> GSM1105510 5 0.3612 0.8762 0.000 0.268 0.000 0.000 0.732
#> GSM1105530 1 0.5255 0.5130 0.496 0.000 0.468 0.024 0.012
#> GSM1105539 1 0.4886 0.5259 0.512 0.000 0.468 0.016 0.004
#> GSM1105480 4 0.0932 0.7396 0.000 0.020 0.004 0.972 0.004
#> GSM1105512 1 0.0451 0.8370 0.988 0.000 0.008 0.004 0.000
#> GSM1105532 1 0.5255 0.5130 0.496 0.000 0.468 0.024 0.012
#> GSM1105541 1 0.4886 0.5259 0.512 0.000 0.468 0.016 0.004
#> GSM1105439 2 0.4449 -0.1565 0.000 0.512 0.000 0.484 0.004
#> GSM1105463 3 0.1082 0.6175 0.028 0.000 0.964 0.000 0.008
#> GSM1105482 1 0.2230 0.8169 0.884 0.000 0.116 0.000 0.000
#> GSM1105483 4 0.2548 0.7334 0.008 0.036 0.024 0.912 0.020
#> GSM1105494 4 0.6131 0.1665 0.000 0.012 0.152 0.600 0.236
#> GSM1105503 3 0.5787 0.6302 0.000 0.000 0.616 0.204 0.180
#> GSM1105507 1 0.1830 0.8106 0.932 0.000 0.004 0.052 0.012
#> GSM1105446 2 0.4030 0.1346 0.000 0.648 0.000 0.000 0.352
#> GSM1105519 1 0.0451 0.8370 0.988 0.000 0.008 0.004 0.000
#> GSM1105526 2 0.5975 0.2544 0.000 0.556 0.008 0.336 0.100
#> GSM1105527 4 0.1892 0.7511 0.004 0.040 0.012 0.936 0.008
#> GSM1105531 3 0.0579 0.6230 0.008 0.000 0.984 0.000 0.008
#> GSM1105543 2 0.3661 0.3678 0.000 0.724 0.000 0.000 0.276
#> GSM1105546 1 0.0000 0.8358 1.000 0.000 0.000 0.000 0.000
#> GSM1105547 1 0.0162 0.8364 0.996 0.000 0.004 0.000 0.000
#> GSM1105455 4 0.4451 0.1396 0.000 0.492 0.000 0.504 0.004
#> GSM1105458 2 0.4462 0.5753 0.000 0.740 0.000 0.064 0.196
#> GSM1105459 2 0.0000 0.7884 0.000 1.000 0.000 0.000 0.000
#> GSM1105462 3 0.1168 0.5993 0.000 0.000 0.960 0.032 0.008
#> GSM1105441 2 0.0955 0.7766 0.000 0.968 0.000 0.028 0.004
#> GSM1105465 5 0.2389 0.7815 0.000 0.116 0.004 0.000 0.880
#> GSM1105484 5 0.3612 0.8762 0.000 0.268 0.000 0.000 0.732
#> GSM1105485 5 0.3612 0.8762 0.000 0.268 0.000 0.000 0.732
#> GSM1105496 3 0.5675 0.6044 0.008 0.000 0.544 0.064 0.384
#> GSM1105505 3 0.1300 0.6285 0.016 0.000 0.956 0.000 0.028
#> GSM1105509 1 0.0451 0.8370 0.988 0.000 0.008 0.004 0.000
#> GSM1105448 2 0.1043 0.7603 0.000 0.960 0.000 0.000 0.040
#> GSM1105521 1 0.0451 0.8370 0.988 0.000 0.008 0.004 0.000
#> GSM1105528 5 0.3612 0.8762 0.000 0.268 0.000 0.000 0.732
#> GSM1105529 5 0.3612 0.8762 0.000 0.268 0.000 0.000 0.732
#> GSM1105533 1 0.3816 0.7109 0.696 0.000 0.304 0.000 0.000
#> GSM1105545 4 0.4990 0.2104 0.000 0.448 0.012 0.528 0.012
#> GSM1105548 1 0.2377 0.8122 0.872 0.000 0.128 0.000 0.000
#> GSM1105549 1 0.2377 0.8139 0.872 0.000 0.128 0.000 0.000
#> GSM1105457 4 0.1831 0.7686 0.000 0.076 0.000 0.920 0.004
#> GSM1105460 2 0.3715 0.4698 0.000 0.736 0.000 0.260 0.004
#> GSM1105461 2 0.0000 0.7884 0.000 1.000 0.000 0.000 0.000
#> GSM1105464 1 0.4886 0.5259 0.512 0.000 0.468 0.016 0.004
#> GSM1105466 4 0.1924 0.7682 0.000 0.064 0.008 0.924 0.004
#> GSM1105479 2 0.6620 -0.0198 0.000 0.452 0.004 0.352 0.192
#> GSM1105502 1 0.4419 0.6773 0.644 0.000 0.344 0.008 0.004
#> GSM1105515 1 0.0162 0.8364 0.996 0.000 0.004 0.000 0.000
#> GSM1105523 3 0.3430 0.5014 0.012 0.000 0.824 0.152 0.012
#> GSM1105550 1 0.6531 0.4005 0.484 0.000 0.360 0.144 0.012
#> GSM1105450 2 0.0000 0.7884 0.000 1.000 0.000 0.000 0.000
#> GSM1105451 2 0.0162 0.7874 0.000 0.996 0.000 0.004 0.000
#> GSM1105454 3 0.6780 0.6145 0.000 0.028 0.540 0.188 0.244
#> GSM1105468 2 0.0000 0.7884 0.000 1.000 0.000 0.000 0.000
#> GSM1105481 3 0.5286 0.6498 0.000 0.036 0.696 0.048 0.220
#> GSM1105504 3 0.0693 0.6217 0.012 0.000 0.980 0.000 0.008
#> GSM1105517 1 0.4622 0.6610 0.700 0.000 0.264 0.024 0.012
#> GSM1105525 3 0.6546 -0.2934 0.352 0.000 0.488 0.148 0.012
#> GSM1105552 1 0.4283 0.5496 0.544 0.000 0.456 0.000 0.000
#> GSM1105452 5 0.3612 0.8762 0.000 0.268 0.000 0.000 0.732
#> GSM1105453 2 0.0000 0.7884 0.000 1.000 0.000 0.000 0.000
#> GSM1105456 3 0.6731 0.6149 0.000 0.024 0.540 0.192 0.244
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1105438 2 0.0405 0.8697 0.000 0.988 0.008 0.000 0.004 0.000
#> GSM1105486 2 0.0146 0.8709 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM1105487 1 0.4008 0.7310 0.736 0.000 0.228 0.008 0.020 0.008
#> GSM1105490 4 0.0820 0.8334 0.000 0.016 0.000 0.972 0.000 0.012
#> GSM1105491 5 0.1713 0.9063 0.000 0.028 0.000 0.000 0.928 0.044
#> GSM1105495 6 0.3259 0.6404 0.000 0.000 0.012 0.000 0.216 0.772
#> GSM1105498 6 0.2699 0.7581 0.000 0.000 0.012 0.124 0.008 0.856
#> GSM1105499 1 0.1556 0.8485 0.920 0.000 0.080 0.000 0.000 0.000
#> GSM1105506 4 0.0692 0.8320 0.000 0.004 0.000 0.976 0.000 0.020
#> GSM1105442 5 0.1594 0.9255 0.000 0.052 0.000 0.000 0.932 0.016
#> GSM1105511 4 0.0622 0.8330 0.000 0.012 0.000 0.980 0.000 0.008
#> GSM1105514 2 0.0146 0.8703 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM1105518 6 0.0862 0.8181 0.000 0.004 0.008 0.016 0.000 0.972
#> GSM1105522 1 0.3927 0.7048 0.748 0.000 0.216 0.008 0.020 0.008
#> GSM1105534 1 0.0363 0.8550 0.988 0.000 0.012 0.000 0.000 0.000
#> GSM1105535 1 0.2819 0.8271 0.864 0.000 0.104 0.008 0.016 0.008
#> GSM1105538 1 0.0520 0.8563 0.984 0.000 0.008 0.000 0.008 0.000
#> GSM1105542 5 0.1387 0.9318 0.000 0.068 0.000 0.000 0.932 0.000
#> GSM1105443 2 0.5110 0.4581 0.000 0.620 0.016 0.312 0.028 0.024
#> GSM1105551 1 0.4130 0.7310 0.736 0.000 0.220 0.012 0.024 0.008
#> GSM1105554 1 0.0363 0.8550 0.988 0.000 0.012 0.000 0.000 0.000
#> GSM1105555 1 0.3480 0.7268 0.776 0.000 0.200 0.000 0.016 0.008
#> GSM1105447 2 0.5852 0.1324 0.000 0.500 0.016 0.060 0.028 0.396
#> GSM1105467 2 0.0146 0.8709 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM1105470 2 0.0146 0.8709 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM1105471 6 0.5941 0.4711 0.000 0.280 0.016 0.120 0.016 0.568
#> GSM1105474 2 0.0146 0.8709 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM1105475 2 0.0713 0.8607 0.000 0.972 0.000 0.028 0.000 0.000
#> GSM1105440 1 0.2107 0.8461 0.916 0.000 0.052 0.008 0.016 0.008
#> GSM1105488 5 0.1387 0.9318 0.000 0.068 0.000 0.000 0.932 0.000
#> GSM1105489 1 0.3073 0.7729 0.824 0.000 0.152 0.000 0.016 0.008
#> GSM1105492 1 0.1026 0.8548 0.968 0.000 0.008 0.004 0.012 0.008
#> GSM1105493 1 0.3081 0.6940 0.776 0.000 0.220 0.000 0.004 0.000
#> GSM1105497 5 0.1644 0.9068 0.000 0.028 0.000 0.000 0.932 0.040
#> GSM1105500 5 0.2696 0.8921 0.000 0.056 0.012 0.004 0.884 0.044
#> GSM1105501 4 0.2778 0.7232 0.000 0.168 0.008 0.824 0.000 0.000
#> GSM1105508 1 0.2912 0.8223 0.856 0.000 0.112 0.008 0.016 0.008
#> GSM1105444 2 0.0405 0.8697 0.000 0.988 0.008 0.000 0.004 0.000
#> GSM1105513 4 0.5072 0.5728 0.000 0.076 0.008 0.692 0.028 0.196
#> GSM1105516 1 0.3221 0.7109 0.792 0.000 0.020 0.188 0.000 0.000
#> GSM1105520 6 0.0909 0.8163 0.000 0.000 0.020 0.012 0.000 0.968
#> GSM1105524 1 0.2819 0.8271 0.864 0.000 0.104 0.008 0.016 0.008
#> GSM1105536 2 0.4814 0.5826 0.000 0.700 0.052 0.216 0.024 0.008
#> GSM1105537 1 0.2819 0.8271 0.864 0.000 0.104 0.008 0.016 0.008
#> GSM1105540 1 0.5986 0.0904 0.468 0.000 0.424 0.052 0.032 0.024
#> GSM1105544 5 0.7614 0.2202 0.308 0.000 0.080 0.108 0.428 0.076
#> GSM1105445 6 0.4939 0.5972 0.000 0.040 0.016 0.224 0.028 0.692
#> GSM1105553 6 0.1323 0.8088 0.008 0.000 0.008 0.008 0.020 0.956
#> GSM1105556 1 0.0363 0.8550 0.988 0.000 0.012 0.000 0.000 0.000
#> GSM1105557 4 0.0820 0.8333 0.000 0.012 0.000 0.972 0.000 0.016
#> GSM1105449 2 0.1975 0.8431 0.000 0.928 0.012 0.012 0.028 0.020
#> GSM1105469 4 0.1410 0.8091 0.004 0.000 0.044 0.944 0.008 0.000
#> GSM1105472 2 0.0146 0.8709 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM1105473 3 0.3997 0.1590 0.488 0.000 0.508 0.000 0.004 0.000
#> GSM1105476 2 0.0146 0.8709 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM1105477 2 0.5648 0.5522 0.000 0.648 0.044 0.104 0.196 0.008
#> GSM1105478 4 0.4351 0.5496 0.000 0.000 0.020 0.704 0.032 0.244
#> GSM1105510 5 0.1387 0.9318 0.000 0.068 0.000 0.000 0.932 0.000
#> GSM1105530 3 0.1714 0.7870 0.092 0.000 0.908 0.000 0.000 0.000
#> GSM1105539 3 0.1765 0.7859 0.096 0.000 0.904 0.000 0.000 0.000
#> GSM1105480 4 0.3046 0.7622 0.000 0.000 0.024 0.852 0.024 0.100
#> GSM1105512 1 0.1007 0.8551 0.956 0.000 0.044 0.000 0.000 0.000
#> GSM1105532 3 0.1714 0.7870 0.092 0.000 0.908 0.000 0.000 0.000
#> GSM1105541 3 0.1863 0.7857 0.104 0.000 0.896 0.000 0.000 0.000
#> GSM1105439 2 0.5055 0.4111 0.000 0.600 0.016 0.340 0.028 0.016
#> GSM1105463 3 0.3076 0.6577 0.000 0.000 0.760 0.000 0.000 0.240
#> GSM1105482 1 0.2191 0.8062 0.876 0.000 0.120 0.000 0.004 0.000
#> GSM1105483 4 0.1226 0.8155 0.000 0.004 0.040 0.952 0.004 0.000
#> GSM1105494 6 0.4281 0.5751 0.000 0.000 0.024 0.272 0.016 0.688
#> GSM1105503 6 0.1572 0.8099 0.000 0.000 0.028 0.036 0.000 0.936
#> GSM1105507 1 0.3831 0.7865 0.800 0.000 0.068 0.116 0.012 0.004
#> GSM1105446 2 0.3528 0.5469 0.000 0.700 0.004 0.000 0.296 0.000
#> GSM1105519 1 0.1007 0.8551 0.956 0.000 0.044 0.000 0.000 0.000
#> GSM1105526 4 0.5757 0.4227 0.000 0.312 0.028 0.568 0.084 0.008
#> GSM1105527 4 0.0810 0.8298 0.000 0.004 0.008 0.976 0.004 0.008
#> GSM1105531 3 0.3126 0.6484 0.000 0.000 0.752 0.000 0.000 0.248
#> GSM1105543 2 0.2730 0.6994 0.000 0.808 0.000 0.000 0.192 0.000
#> GSM1105546 1 0.0862 0.8552 0.972 0.000 0.004 0.000 0.016 0.008
#> GSM1105547 1 0.0508 0.8547 0.984 0.000 0.012 0.000 0.004 0.000
#> GSM1105455 2 0.4944 0.4438 0.000 0.616 0.016 0.328 0.024 0.016
#> GSM1105458 2 0.3816 0.7261 0.000 0.796 0.016 0.012 0.028 0.148
#> GSM1105459 2 0.0146 0.8707 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM1105462 3 0.2340 0.7129 0.000 0.000 0.852 0.000 0.000 0.148
#> GSM1105441 2 0.1793 0.8462 0.000 0.936 0.012 0.012 0.028 0.012
#> GSM1105465 5 0.1720 0.9102 0.000 0.032 0.000 0.000 0.928 0.040
#> GSM1105484 5 0.1501 0.9267 0.000 0.076 0.000 0.000 0.924 0.000
#> GSM1105485 5 0.1387 0.9318 0.000 0.068 0.000 0.000 0.932 0.000
#> GSM1105496 6 0.1313 0.8072 0.000 0.000 0.016 0.004 0.028 0.952
#> GSM1105505 3 0.3774 0.4124 0.000 0.000 0.592 0.000 0.000 0.408
#> GSM1105509 1 0.1387 0.8514 0.932 0.000 0.068 0.000 0.000 0.000
#> GSM1105448 2 0.0405 0.8697 0.000 0.988 0.008 0.000 0.004 0.000
#> GSM1105521 1 0.1007 0.8551 0.956 0.000 0.044 0.000 0.000 0.000
#> GSM1105528 5 0.1556 0.9232 0.000 0.080 0.000 0.000 0.920 0.000
#> GSM1105529 5 0.1387 0.9318 0.000 0.068 0.000 0.000 0.932 0.000
#> GSM1105533 3 0.3923 0.4194 0.372 0.000 0.620 0.000 0.000 0.008
#> GSM1105545 4 0.5079 0.4610 0.000 0.316 0.052 0.612 0.012 0.008
#> GSM1105548 1 0.3184 0.7828 0.828 0.000 0.140 0.004 0.020 0.008
#> GSM1105549 1 0.2402 0.7890 0.856 0.000 0.140 0.000 0.004 0.000
#> GSM1105457 4 0.1856 0.8209 0.000 0.008 0.008 0.932 0.024 0.028
#> GSM1105460 2 0.2587 0.8183 0.000 0.896 0.016 0.048 0.028 0.012
#> GSM1105461 2 0.0146 0.8707 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM1105464 3 0.2003 0.7826 0.116 0.000 0.884 0.000 0.000 0.000
#> GSM1105466 4 0.1508 0.8277 0.000 0.004 0.012 0.948 0.020 0.016
#> GSM1105479 6 0.6556 0.3186 0.000 0.300 0.008 0.196 0.028 0.468
#> GSM1105502 3 0.3134 0.6923 0.208 0.000 0.784 0.004 0.000 0.004
#> GSM1105515 1 0.0363 0.8550 0.988 0.000 0.012 0.000 0.000 0.000
#> GSM1105523 3 0.2213 0.7415 0.000 0.000 0.904 0.044 0.004 0.048
#> GSM1105550 3 0.3856 0.6738 0.140 0.000 0.792 0.052 0.008 0.008
#> GSM1105450 2 0.0146 0.8709 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM1105451 2 0.0405 0.8693 0.000 0.988 0.008 0.004 0.000 0.000
#> GSM1105454 6 0.0767 0.8184 0.000 0.004 0.008 0.012 0.000 0.976
#> GSM1105468 2 0.0146 0.8709 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM1105481 6 0.1918 0.7704 0.000 0.008 0.088 0.000 0.000 0.904
#> GSM1105504 3 0.3050 0.6598 0.000 0.000 0.764 0.000 0.000 0.236
#> GSM1105517 3 0.4480 0.4044 0.368 0.000 0.604 0.012 0.008 0.008
#> GSM1105525 3 0.2619 0.7651 0.056 0.000 0.884 0.048 0.012 0.000
#> GSM1105552 3 0.3089 0.7396 0.188 0.000 0.800 0.000 0.004 0.008
#> GSM1105452 5 0.1387 0.9318 0.000 0.068 0.000 0.000 0.932 0.000
#> GSM1105453 2 0.0260 0.8701 0.000 0.992 0.008 0.000 0.000 0.000
#> GSM1105456 6 0.0767 0.8184 0.000 0.004 0.008 0.012 0.000 0.976
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 agent(p) other(p) time(p) individual(p) k
#> MAD:skmeans 117 0.983 0.6725 0.634 1.61e-02 2
#> MAD:skmeans 119 0.842 0.5240 0.241 1.56e-03 3
#> MAD:skmeans 113 0.290 0.6614 0.694 5.87e-03 4
#> MAD:skmeans 102 0.142 0.8212 0.705 1.10e-02 5
#> MAD:skmeans 106 0.259 0.0002 0.797 2.39e-05 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 44956 rows and 120 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 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 0.898 0.913 0.965 0.4776 0.510 0.510
#> 3 3 0.563 0.599 0.790 0.3329 0.835 0.690
#> 4 4 0.687 0.758 0.852 0.1485 0.810 0.546
#> 5 5 0.629 0.534 0.744 0.0788 0.898 0.638
#> 6 6 0.762 0.669 0.817 0.0431 0.873 0.499
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
#> GSM1105438 2 0.0000 0.991 0.000 1.000
#> GSM1105486 2 0.0000 0.991 0.000 1.000
#> GSM1105487 1 0.0000 0.921 1.000 0.000
#> GSM1105490 2 0.0000 0.991 0.000 1.000
#> GSM1105491 1 1.0000 0.143 0.504 0.496
#> GSM1105495 2 0.0000 0.991 0.000 1.000
#> GSM1105498 2 0.0376 0.987 0.004 0.996
#> GSM1105499 1 0.0000 0.921 1.000 0.000
#> GSM1105506 2 0.0000 0.991 0.000 1.000
#> GSM1105442 2 0.0000 0.991 0.000 1.000
#> GSM1105511 2 0.0000 0.991 0.000 1.000
#> GSM1105514 2 0.0000 0.991 0.000 1.000
#> GSM1105518 2 0.0000 0.991 0.000 1.000
#> GSM1105522 1 0.0000 0.921 1.000 0.000
#> GSM1105534 1 0.0000 0.921 1.000 0.000
#> GSM1105535 1 0.0000 0.921 1.000 0.000
#> GSM1105538 1 0.0000 0.921 1.000 0.000
#> GSM1105542 2 0.0000 0.991 0.000 1.000
#> GSM1105443 2 0.0000 0.991 0.000 1.000
#> GSM1105551 1 0.0000 0.921 1.000 0.000
#> GSM1105554 1 0.0000 0.921 1.000 0.000
#> GSM1105555 1 0.0000 0.921 1.000 0.000
#> GSM1105447 2 0.0000 0.991 0.000 1.000
#> GSM1105467 2 0.0000 0.991 0.000 1.000
#> GSM1105470 2 0.0000 0.991 0.000 1.000
#> GSM1105471 2 0.0000 0.991 0.000 1.000
#> GSM1105474 2 0.0000 0.991 0.000 1.000
#> GSM1105475 2 0.0000 0.991 0.000 1.000
#> GSM1105440 1 0.0000 0.921 1.000 0.000
#> GSM1105488 2 0.0000 0.991 0.000 1.000
#> GSM1105489 1 0.0000 0.921 1.000 0.000
#> GSM1105492 1 0.0000 0.921 1.000 0.000
#> GSM1105493 1 0.0000 0.921 1.000 0.000
#> GSM1105497 2 0.0000 0.991 0.000 1.000
#> GSM1105500 2 0.7139 0.722 0.196 0.804
#> GSM1105501 2 0.0000 0.991 0.000 1.000
#> GSM1105508 1 0.0000 0.921 1.000 0.000
#> GSM1105444 2 0.0000 0.991 0.000 1.000
#> GSM1105513 2 0.0000 0.991 0.000 1.000
#> GSM1105516 1 0.4690 0.840 0.900 0.100
#> GSM1105520 2 0.0000 0.991 0.000 1.000
#> GSM1105524 1 0.0000 0.921 1.000 0.000
#> GSM1105536 2 0.0000 0.991 0.000 1.000
#> GSM1105537 1 0.0000 0.921 1.000 0.000
#> GSM1105540 1 1.0000 0.143 0.504 0.496
#> GSM1105544 1 1.0000 0.143 0.504 0.496
#> GSM1105445 2 0.0000 0.991 0.000 1.000
#> GSM1105553 2 0.9209 0.418 0.336 0.664
#> GSM1105556 1 0.0000 0.921 1.000 0.000
#> GSM1105557 2 0.0000 0.991 0.000 1.000
#> GSM1105449 2 0.0000 0.991 0.000 1.000
#> GSM1105469 2 0.0000 0.991 0.000 1.000
#> GSM1105472 2 0.0000 0.991 0.000 1.000
#> GSM1105473 1 0.0000 0.921 1.000 0.000
#> GSM1105476 2 0.0000 0.991 0.000 1.000
#> GSM1105477 2 0.0000 0.991 0.000 1.000
#> GSM1105478 2 0.0000 0.991 0.000 1.000
#> GSM1105510 2 0.0000 0.991 0.000 1.000
#> GSM1105530 1 0.0000 0.921 1.000 0.000
#> GSM1105539 1 0.0000 0.921 1.000 0.000
#> GSM1105480 2 0.0000 0.991 0.000 1.000
#> GSM1105512 1 0.0000 0.921 1.000 0.000
#> GSM1105532 1 0.0000 0.921 1.000 0.000
#> GSM1105541 1 0.0000 0.921 1.000 0.000
#> GSM1105439 2 0.0000 0.991 0.000 1.000
#> GSM1105463 1 0.0000 0.921 1.000 0.000
#> GSM1105482 1 0.0000 0.921 1.000 0.000
#> GSM1105483 2 0.0000 0.991 0.000 1.000
#> GSM1105494 2 0.0000 0.991 0.000 1.000
#> GSM1105503 2 0.0000 0.991 0.000 1.000
#> GSM1105507 1 0.0000 0.921 1.000 0.000
#> GSM1105446 2 0.0000 0.991 0.000 1.000
#> GSM1105519 1 0.0000 0.921 1.000 0.000
#> GSM1105526 2 0.0000 0.991 0.000 1.000
#> GSM1105527 2 0.0000 0.991 0.000 1.000
#> GSM1105531 1 0.8763 0.608 0.704 0.296
#> GSM1105543 2 0.0000 0.991 0.000 1.000
#> GSM1105546 1 0.0000 0.921 1.000 0.000
#> GSM1105547 1 0.0000 0.921 1.000 0.000
#> GSM1105455 2 0.0000 0.991 0.000 1.000
#> GSM1105458 2 0.0000 0.991 0.000 1.000
#> GSM1105459 2 0.0000 0.991 0.000 1.000
#> GSM1105462 2 0.0000 0.991 0.000 1.000
#> GSM1105441 2 0.0000 0.991 0.000 1.000
#> GSM1105465 2 0.0000 0.991 0.000 1.000
#> GSM1105484 2 0.0000 0.991 0.000 1.000
#> GSM1105485 2 0.0000 0.991 0.000 1.000
#> GSM1105496 1 1.0000 0.143 0.504 0.496
#> GSM1105505 1 0.8763 0.608 0.704 0.296
#> GSM1105509 1 0.0000 0.921 1.000 0.000
#> GSM1105448 2 0.0000 0.991 0.000 1.000
#> GSM1105521 1 0.0000 0.921 1.000 0.000
#> GSM1105528 2 0.0000 0.991 0.000 1.000
#> GSM1105529 2 0.0000 0.991 0.000 1.000
#> GSM1105533 1 0.0000 0.921 1.000 0.000
#> GSM1105545 2 0.0000 0.991 0.000 1.000
#> GSM1105548 1 0.0000 0.921 1.000 0.000
#> GSM1105549 1 0.0000 0.921 1.000 0.000
#> GSM1105457 2 0.0000 0.991 0.000 1.000
#> GSM1105460 2 0.0000 0.991 0.000 1.000
#> GSM1105461 2 0.0000 0.991 0.000 1.000
#> GSM1105464 1 0.0000 0.921 1.000 0.000
#> GSM1105466 2 0.0000 0.991 0.000 1.000
#> GSM1105479 2 0.0000 0.991 0.000 1.000
#> GSM1105502 1 0.0000 0.921 1.000 0.000
#> GSM1105515 1 0.0000 0.921 1.000 0.000
#> GSM1105523 1 0.8608 0.626 0.716 0.284
#> GSM1105550 1 0.9983 0.206 0.524 0.476
#> GSM1105450 2 0.0000 0.991 0.000 1.000
#> GSM1105451 2 0.0000 0.991 0.000 1.000
#> GSM1105454 2 0.0000 0.991 0.000 1.000
#> GSM1105468 2 0.0000 0.991 0.000 1.000
#> GSM1105481 2 0.0000 0.991 0.000 1.000
#> GSM1105504 1 0.0672 0.916 0.992 0.008
#> GSM1105517 1 0.8267 0.661 0.740 0.260
#> GSM1105525 1 0.0000 0.921 1.000 0.000
#> GSM1105552 1 0.0000 0.921 1.000 0.000
#> GSM1105452 2 0.0000 0.991 0.000 1.000
#> GSM1105453 2 0.0000 0.991 0.000 1.000
#> GSM1105456 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
#> GSM1105438 2 0.0000 0.6768 0.000 1.000 0.000
#> GSM1105486 2 0.5706 0.7136 0.000 0.680 0.320
#> GSM1105487 1 0.0000 0.8989 1.000 0.000 0.000
#> GSM1105490 2 0.1411 0.6662 0.000 0.964 0.036
#> GSM1105491 3 0.7308 0.4637 0.296 0.056 0.648
#> GSM1105495 2 0.6307 -0.2488 0.000 0.512 0.488
#> GSM1105498 3 0.0237 0.4376 0.004 0.000 0.996
#> GSM1105499 1 0.0000 0.8989 1.000 0.000 0.000
#> GSM1105506 2 0.5905 0.7070 0.000 0.648 0.352
#> GSM1105442 2 0.0000 0.6768 0.000 1.000 0.000
#> GSM1105511 2 0.5905 0.7070 0.000 0.648 0.352
#> GSM1105514 2 0.5706 0.7136 0.000 0.680 0.320
#> GSM1105518 2 0.4974 0.4061 0.000 0.764 0.236
#> GSM1105522 1 0.0747 0.8874 0.984 0.000 0.016
#> GSM1105534 1 0.0000 0.8989 1.000 0.000 0.000
#> GSM1105535 1 0.0000 0.8989 1.000 0.000 0.000
#> GSM1105538 1 0.0000 0.8989 1.000 0.000 0.000
#> GSM1105542 2 0.5706 0.7136 0.000 0.680 0.320
#> GSM1105443 2 0.1289 0.6639 0.000 0.968 0.032
#> GSM1105551 1 0.1753 0.8617 0.952 0.000 0.048
#> GSM1105554 1 0.0000 0.8989 1.000 0.000 0.000
#> GSM1105555 1 0.0237 0.8964 0.996 0.000 0.004
#> GSM1105447 2 0.1289 0.6639 0.000 0.968 0.032
#> GSM1105467 2 0.5706 0.7136 0.000 0.680 0.320
#> GSM1105470 2 0.5706 0.7136 0.000 0.680 0.320
#> GSM1105471 2 0.6295 0.5841 0.000 0.528 0.472
#> GSM1105474 2 0.5706 0.7136 0.000 0.680 0.320
#> GSM1105475 2 0.5706 0.7136 0.000 0.680 0.320
#> GSM1105440 1 0.0000 0.8989 1.000 0.000 0.000
#> GSM1105488 2 0.3267 0.7007 0.000 0.884 0.116
#> GSM1105489 1 0.1753 0.8620 0.952 0.000 0.048
#> GSM1105492 1 0.0000 0.8989 1.000 0.000 0.000
#> GSM1105493 1 0.0000 0.8989 1.000 0.000 0.000
#> GSM1105497 2 0.5529 0.2784 0.000 0.704 0.296
#> GSM1105500 2 0.7424 0.6778 0.060 0.640 0.300
#> GSM1105501 2 0.5905 0.7070 0.000 0.648 0.352
#> GSM1105508 1 0.0000 0.8989 1.000 0.000 0.000
#> GSM1105444 2 0.0000 0.6768 0.000 1.000 0.000
#> GSM1105513 2 0.1289 0.6639 0.000 0.968 0.032
#> GSM1105516 1 0.4449 0.7185 0.860 0.100 0.040
#> GSM1105520 3 0.0592 0.4374 0.000 0.012 0.988
#> GSM1105524 1 0.0000 0.8989 1.000 0.000 0.000
#> GSM1105536 2 0.6295 0.5841 0.000 0.528 0.472
#> GSM1105537 1 0.0000 0.8989 1.000 0.000 0.000
#> GSM1105540 3 0.9210 0.4403 0.296 0.184 0.520
#> GSM1105544 2 0.9248 0.1353 0.296 0.516 0.188
#> GSM1105445 2 0.1289 0.6639 0.000 0.968 0.032
#> GSM1105553 2 0.4887 0.4172 0.000 0.772 0.228
#> GSM1105556 1 0.0000 0.8989 1.000 0.000 0.000
#> GSM1105557 2 0.3192 0.6897 0.000 0.888 0.112
#> GSM1105449 2 0.0000 0.6768 0.000 1.000 0.000
#> GSM1105469 2 0.6295 0.5841 0.000 0.528 0.472
#> GSM1105472 2 0.5706 0.7136 0.000 0.680 0.320
#> GSM1105473 1 0.5098 0.5589 0.752 0.000 0.248
#> GSM1105476 2 0.5706 0.7136 0.000 0.680 0.320
#> GSM1105477 2 0.6295 0.5841 0.000 0.528 0.472
#> GSM1105478 3 0.6309 -0.5983 0.000 0.496 0.504
#> GSM1105510 2 0.5327 0.7160 0.000 0.728 0.272
#> GSM1105530 3 0.6295 0.1863 0.472 0.000 0.528
#> GSM1105539 3 0.6295 0.1863 0.472 0.000 0.528
#> GSM1105480 3 0.6309 -0.5983 0.000 0.496 0.504
#> GSM1105512 1 0.0000 0.8989 1.000 0.000 0.000
#> GSM1105532 3 0.6295 0.1863 0.472 0.000 0.528
#> GSM1105541 1 0.4121 0.7148 0.832 0.000 0.168
#> GSM1105439 2 0.1289 0.6639 0.000 0.968 0.032
#> GSM1105463 3 0.5905 0.3925 0.352 0.000 0.648
#> GSM1105482 1 0.0000 0.8989 1.000 0.000 0.000
#> GSM1105483 2 0.6295 0.5841 0.000 0.528 0.472
#> GSM1105494 2 0.6295 0.6157 0.000 0.528 0.472
#> GSM1105503 3 0.5706 0.4217 0.000 0.320 0.680
#> GSM1105507 1 0.2261 0.8475 0.932 0.000 0.068
#> GSM1105446 2 0.0000 0.6768 0.000 1.000 0.000
#> GSM1105519 1 0.0000 0.8989 1.000 0.000 0.000
#> GSM1105526 2 0.6295 0.5841 0.000 0.528 0.472
#> GSM1105527 2 0.6299 0.5829 0.000 0.524 0.476
#> GSM1105531 3 0.6714 0.4670 0.296 0.032 0.672
#> GSM1105543 2 0.5706 0.7136 0.000 0.680 0.320
#> GSM1105546 1 0.0000 0.8989 1.000 0.000 0.000
#> GSM1105547 1 0.0000 0.8989 1.000 0.000 0.000
#> GSM1105455 2 0.1289 0.6639 0.000 0.968 0.032
#> GSM1105458 2 0.0747 0.6709 0.000 0.984 0.016
#> GSM1105459 2 0.0000 0.6768 0.000 1.000 0.000
#> GSM1105462 3 0.5859 -0.2863 0.000 0.344 0.656
#> GSM1105441 2 0.0000 0.6768 0.000 1.000 0.000
#> GSM1105465 2 0.6302 0.5771 0.000 0.520 0.480
#> GSM1105484 2 0.5706 0.7136 0.000 0.680 0.320
#> GSM1105485 2 0.6295 0.5841 0.000 0.528 0.472
#> GSM1105496 3 0.6505 0.2369 0.004 0.468 0.528
#> GSM1105505 3 0.6482 0.4541 0.296 0.024 0.680
#> GSM1105509 1 0.1964 0.8531 0.944 0.000 0.056
#> GSM1105448 2 0.0000 0.6768 0.000 1.000 0.000
#> GSM1105521 1 0.0000 0.8989 1.000 0.000 0.000
#> GSM1105528 2 0.5706 0.7136 0.000 0.680 0.320
#> GSM1105529 2 0.6295 0.5841 0.000 0.528 0.472
#> GSM1105533 1 0.0000 0.8989 1.000 0.000 0.000
#> GSM1105545 2 0.6295 0.5841 0.000 0.528 0.472
#> GSM1105548 1 0.3340 0.7792 0.880 0.000 0.120
#> GSM1105549 1 0.0000 0.8989 1.000 0.000 0.000
#> GSM1105457 2 0.1289 0.6639 0.000 0.968 0.032
#> GSM1105460 2 0.0000 0.6768 0.000 1.000 0.000
#> GSM1105461 2 0.0000 0.6768 0.000 1.000 0.000
#> GSM1105464 1 0.6260 0.0196 0.552 0.000 0.448
#> GSM1105466 2 0.6260 0.6096 0.000 0.552 0.448
#> GSM1105479 2 0.5706 0.7136 0.000 0.680 0.320
#> GSM1105502 1 0.5678 0.4328 0.684 0.000 0.316
#> GSM1105515 1 0.0000 0.8989 1.000 0.000 0.000
#> GSM1105523 3 0.5621 0.4547 0.308 0.000 0.692
#> GSM1105550 3 0.8821 0.4475 0.304 0.144 0.552
#> GSM1105450 2 0.5706 0.7136 0.000 0.680 0.320
#> GSM1105451 2 0.0000 0.6768 0.000 1.000 0.000
#> GSM1105454 2 0.6309 -0.2193 0.000 0.504 0.496
#> GSM1105468 2 0.5706 0.7136 0.000 0.680 0.320
#> GSM1105481 3 0.1411 0.4121 0.000 0.036 0.964
#> GSM1105504 3 0.5902 0.4374 0.316 0.004 0.680
#> GSM1105517 1 0.7489 -0.1666 0.496 0.036 0.468
#> GSM1105525 1 0.6291 -0.0551 0.532 0.000 0.468
#> GSM1105552 3 0.6252 0.2472 0.444 0.000 0.556
#> GSM1105452 2 0.5706 0.7136 0.000 0.680 0.320
#> GSM1105453 2 0.0237 0.6755 0.000 0.996 0.004
#> GSM1105456 3 0.6295 0.2307 0.000 0.472 0.528
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1105438 2 0.3444 0.9042 0.000 0.816 0.000 0.184
#> GSM1105486 4 0.0188 0.9267 0.000 0.004 0.000 0.996
#> GSM1105487 1 0.0336 0.8273 0.992 0.000 0.008 0.000
#> GSM1105490 2 0.4605 0.5688 0.000 0.664 0.000 0.336
#> GSM1105491 3 0.2773 0.7255 0.000 0.116 0.880 0.004
#> GSM1105495 2 0.2976 0.6958 0.000 0.872 0.120 0.008
#> GSM1105498 3 0.7332 0.3784 0.000 0.160 0.468 0.372
#> GSM1105499 1 0.0000 0.8300 1.000 0.000 0.000 0.000
#> GSM1105506 4 0.1867 0.8851 0.000 0.072 0.000 0.928
#> GSM1105442 2 0.3610 0.9005 0.000 0.800 0.000 0.200
#> GSM1105511 4 0.1792 0.8839 0.000 0.068 0.000 0.932
#> GSM1105514 4 0.0592 0.9210 0.000 0.016 0.000 0.984
#> GSM1105518 2 0.2179 0.8043 0.000 0.924 0.012 0.064
#> GSM1105522 1 0.4994 0.4338 0.520 0.000 0.480 0.000
#> GSM1105534 1 0.0000 0.8300 1.000 0.000 0.000 0.000
#> GSM1105535 1 0.0000 0.8300 1.000 0.000 0.000 0.000
#> GSM1105538 1 0.4713 0.5607 0.640 0.000 0.360 0.000
#> GSM1105542 4 0.0000 0.9266 0.000 0.000 0.000 1.000
#> GSM1105443 2 0.3356 0.9027 0.000 0.824 0.000 0.176
#> GSM1105551 1 0.1389 0.8033 0.952 0.048 0.000 0.000
#> GSM1105554 1 0.0000 0.8300 1.000 0.000 0.000 0.000
#> GSM1105555 1 0.0524 0.8275 0.988 0.004 0.008 0.000
#> GSM1105447 2 0.3486 0.9018 0.000 0.812 0.000 0.188
#> GSM1105467 4 0.0188 0.9267 0.000 0.004 0.000 0.996
#> GSM1105470 4 0.0188 0.9267 0.000 0.004 0.000 0.996
#> GSM1105471 4 0.0188 0.9263 0.000 0.000 0.004 0.996
#> GSM1105474 4 0.0188 0.9267 0.000 0.004 0.000 0.996
#> GSM1105475 4 0.0188 0.9267 0.000 0.004 0.000 0.996
#> GSM1105440 1 0.0000 0.8300 1.000 0.000 0.000 0.000
#> GSM1105488 4 0.4643 0.2745 0.000 0.344 0.000 0.656
#> GSM1105489 1 0.1389 0.8035 0.952 0.048 0.000 0.000
#> GSM1105492 1 0.4713 0.5607 0.640 0.000 0.360 0.000
#> GSM1105493 1 0.0000 0.8300 1.000 0.000 0.000 0.000
#> GSM1105497 2 0.1940 0.8111 0.000 0.924 0.000 0.076
#> GSM1105500 4 0.5332 0.6455 0.000 0.080 0.184 0.736
#> GSM1105501 4 0.1792 0.8839 0.000 0.068 0.000 0.932
#> GSM1105508 1 0.1661 0.7997 0.944 0.052 0.004 0.000
#> GSM1105444 2 0.3444 0.9042 0.000 0.816 0.000 0.184
#> GSM1105513 2 0.3123 0.8546 0.000 0.844 0.000 0.156
#> GSM1105516 1 0.7662 0.4162 0.512 0.068 0.360 0.060
#> GSM1105520 3 0.7046 0.4508 0.000 0.136 0.524 0.340
#> GSM1105524 1 0.0000 0.8300 1.000 0.000 0.000 0.000
#> GSM1105536 4 0.0188 0.9263 0.000 0.000 0.004 0.996
#> GSM1105537 1 0.0000 0.8300 1.000 0.000 0.000 0.000
#> GSM1105540 3 0.3024 0.6733 0.000 0.000 0.852 0.148
#> GSM1105544 4 0.5137 0.1145 0.000 0.004 0.452 0.544
#> GSM1105445 2 0.3486 0.9018 0.000 0.812 0.000 0.188
#> GSM1105553 2 0.1867 0.8123 0.000 0.928 0.000 0.072
#> GSM1105556 1 0.0000 0.8300 1.000 0.000 0.000 0.000
#> GSM1105557 4 0.4985 0.0395 0.000 0.468 0.000 0.532
#> GSM1105449 2 0.3569 0.9010 0.000 0.804 0.000 0.196
#> GSM1105469 4 0.1824 0.8878 0.000 0.060 0.004 0.936
#> GSM1105472 4 0.0188 0.9267 0.000 0.004 0.000 0.996
#> GSM1105473 3 0.4008 0.5017 0.244 0.000 0.756 0.000
#> GSM1105476 4 0.0188 0.9267 0.000 0.004 0.000 0.996
#> GSM1105477 4 0.0592 0.9202 0.000 0.016 0.000 0.984
#> GSM1105478 4 0.0524 0.9242 0.000 0.008 0.004 0.988
#> GSM1105510 4 0.3024 0.7460 0.000 0.148 0.000 0.852
#> GSM1105530 3 0.0000 0.7244 0.000 0.000 1.000 0.000
#> GSM1105539 3 0.4679 0.3569 0.352 0.000 0.648 0.000
#> GSM1105480 4 0.0524 0.9242 0.000 0.008 0.004 0.988
#> GSM1105512 1 0.3764 0.7003 0.784 0.000 0.216 0.000
#> GSM1105532 3 0.0000 0.7244 0.000 0.000 1.000 0.000
#> GSM1105541 1 0.3873 0.6418 0.772 0.000 0.228 0.000
#> GSM1105439 2 0.3444 0.9027 0.000 0.816 0.000 0.184
#> GSM1105463 3 0.2589 0.7257 0.000 0.116 0.884 0.000
#> GSM1105482 1 0.0000 0.8300 1.000 0.000 0.000 0.000
#> GSM1105483 4 0.1824 0.8878 0.000 0.060 0.004 0.936
#> GSM1105494 4 0.2760 0.7881 0.000 0.128 0.000 0.872
#> GSM1105503 3 0.4992 0.2235 0.000 0.476 0.524 0.000
#> GSM1105507 1 0.6360 0.4918 0.564 0.060 0.372 0.004
#> GSM1105446 2 0.3444 0.9042 0.000 0.816 0.000 0.184
#> GSM1105519 1 0.4713 0.5607 0.640 0.000 0.360 0.000
#> GSM1105526 4 0.0336 0.9246 0.000 0.008 0.000 0.992
#> GSM1105527 4 0.1824 0.8878 0.000 0.060 0.004 0.936
#> GSM1105531 3 0.2589 0.7257 0.000 0.116 0.884 0.000
#> GSM1105543 4 0.0469 0.9236 0.000 0.012 0.000 0.988
#> GSM1105546 1 0.0000 0.8300 1.000 0.000 0.000 0.000
#> GSM1105547 1 0.0000 0.8300 1.000 0.000 0.000 0.000
#> GSM1105455 2 0.3400 0.9033 0.000 0.820 0.000 0.180
#> GSM1105458 2 0.3528 0.9020 0.000 0.808 0.000 0.192
#> GSM1105459 2 0.3569 0.8993 0.000 0.804 0.000 0.196
#> GSM1105462 3 0.5000 0.1105 0.000 0.000 0.500 0.500
#> GSM1105441 2 0.3444 0.9042 0.000 0.816 0.000 0.184
#> GSM1105465 4 0.0336 0.9231 0.000 0.008 0.000 0.992
#> GSM1105484 4 0.0000 0.9266 0.000 0.000 0.000 1.000
#> GSM1105485 4 0.0000 0.9266 0.000 0.000 0.000 1.000
#> GSM1105496 2 0.5132 -0.0704 0.000 0.548 0.448 0.004
#> GSM1105505 3 0.2773 0.7255 0.000 0.116 0.880 0.004
#> GSM1105509 1 0.6295 0.4957 0.568 0.056 0.372 0.004
#> GSM1105448 2 0.3444 0.9042 0.000 0.816 0.000 0.184
#> GSM1105521 1 0.4713 0.5607 0.640 0.000 0.360 0.000
#> GSM1105528 4 0.0000 0.9266 0.000 0.000 0.000 1.000
#> GSM1105529 4 0.0000 0.9266 0.000 0.000 0.000 1.000
#> GSM1105533 1 0.0336 0.8272 0.992 0.000 0.008 0.000
#> GSM1105545 4 0.0524 0.9245 0.000 0.008 0.004 0.988
#> GSM1105548 1 0.6898 0.4208 0.524 0.116 0.360 0.000
#> GSM1105549 1 0.0000 0.8300 1.000 0.000 0.000 0.000
#> GSM1105457 2 0.2921 0.8616 0.000 0.860 0.000 0.140
#> GSM1105460 2 0.3569 0.9010 0.000 0.804 0.000 0.196
#> GSM1105461 2 0.3569 0.8993 0.000 0.804 0.000 0.196
#> GSM1105464 3 0.4431 0.4343 0.304 0.000 0.696 0.000
#> GSM1105466 4 0.0188 0.9263 0.000 0.000 0.004 0.996
#> GSM1105479 4 0.0188 0.9267 0.000 0.004 0.000 0.996
#> GSM1105502 1 0.4500 0.5749 0.684 0.000 0.316 0.000
#> GSM1105515 1 0.0000 0.8300 1.000 0.000 0.000 0.000
#> GSM1105523 3 0.0000 0.7244 0.000 0.000 1.000 0.000
#> GSM1105550 3 0.2799 0.6981 0.008 0.000 0.884 0.108
#> GSM1105450 4 0.0336 0.9255 0.000 0.008 0.000 0.992
#> GSM1105451 2 0.3444 0.9042 0.000 0.816 0.000 0.184
#> GSM1105454 2 0.1867 0.7371 0.000 0.928 0.072 0.000
#> GSM1105468 4 0.0188 0.9267 0.000 0.004 0.000 0.996
#> GSM1105481 3 0.6898 0.4311 0.000 0.116 0.524 0.360
#> GSM1105504 3 0.0000 0.7244 0.000 0.000 1.000 0.000
#> GSM1105517 3 0.4194 0.6000 0.172 0.028 0.800 0.000
#> GSM1105525 3 0.1792 0.6870 0.068 0.000 0.932 0.000
#> GSM1105552 3 0.3015 0.6932 0.092 0.024 0.884 0.000
#> GSM1105452 4 0.0188 0.9260 0.000 0.004 0.000 0.996
#> GSM1105453 2 0.3444 0.9042 0.000 0.816 0.000 0.184
#> GSM1105456 2 0.1940 0.7342 0.000 0.924 0.076 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM1105438 2 0.2179 0.8629 0.000 0.888 0.000 0.000 0.112
#> GSM1105486 5 0.2127 0.7817 0.000 0.108 0.000 0.000 0.892
#> GSM1105487 1 0.0290 0.8437 0.992 0.000 0.008 0.000 0.000
#> GSM1105490 3 0.6896 0.2744 0.000 0.168 0.408 0.404 0.020
#> GSM1105491 3 0.7579 -0.2434 0.000 0.112 0.400 0.380 0.108
#> GSM1105495 2 0.5013 0.5091 0.000 0.700 0.192 0.000 0.108
#> GSM1105498 3 0.6978 0.2630 0.000 0.108 0.524 0.300 0.068
#> GSM1105499 1 0.0000 0.8469 1.000 0.000 0.000 0.000 0.000
#> GSM1105506 3 0.6500 0.2318 0.000 0.000 0.408 0.404 0.188
#> GSM1105442 2 0.6172 0.6643 0.000 0.628 0.068 0.064 0.240
#> GSM1105511 3 0.6519 0.2312 0.000 0.000 0.408 0.400 0.192
#> GSM1105514 5 0.2424 0.7721 0.000 0.132 0.000 0.000 0.868
#> GSM1105518 2 0.4597 0.1497 0.000 0.564 0.424 0.000 0.012
#> GSM1105522 4 0.5799 0.2226 0.324 0.000 0.112 0.564 0.000
#> GSM1105534 1 0.0000 0.8469 1.000 0.000 0.000 0.000 0.000
#> GSM1105535 1 0.0000 0.8469 1.000 0.000 0.000 0.000 0.000
#> GSM1105538 1 0.4210 0.3054 0.588 0.000 0.000 0.412 0.000
#> GSM1105542 5 0.2992 0.7160 0.000 0.000 0.068 0.064 0.868
#> GSM1105443 2 0.2127 0.8627 0.000 0.892 0.000 0.000 0.108
#> GSM1105551 1 0.1331 0.8177 0.952 0.040 0.008 0.000 0.000
#> GSM1105554 1 0.0000 0.8469 1.000 0.000 0.000 0.000 0.000
#> GSM1105555 1 0.0324 0.8443 0.992 0.000 0.004 0.004 0.000
#> GSM1105447 2 0.2424 0.8601 0.000 0.868 0.000 0.000 0.132
#> GSM1105467 5 0.2179 0.7811 0.000 0.112 0.000 0.000 0.888
#> GSM1105470 5 0.2127 0.7817 0.000 0.108 0.000 0.000 0.892
#> GSM1105471 5 0.2608 0.7850 0.000 0.088 0.020 0.004 0.888
#> GSM1105474 5 0.2127 0.7817 0.000 0.108 0.000 0.000 0.892
#> GSM1105475 5 0.2448 0.7699 0.000 0.020 0.088 0.000 0.892
#> GSM1105440 1 0.0000 0.8469 1.000 0.000 0.000 0.000 0.000
#> GSM1105488 5 0.5248 0.5846 0.000 0.128 0.068 0.064 0.740
#> GSM1105489 1 0.1357 0.8146 0.948 0.048 0.004 0.000 0.000
#> GSM1105492 1 0.4210 0.3054 0.588 0.000 0.000 0.412 0.000
#> GSM1105493 1 0.0000 0.8469 1.000 0.000 0.000 0.000 0.000
#> GSM1105497 2 0.5331 0.5669 0.000 0.736 0.080 0.064 0.120
#> GSM1105500 4 0.5505 -0.2876 0.000 0.004 0.412 0.528 0.056
#> GSM1105501 4 0.6121 -0.3082 0.000 0.000 0.408 0.464 0.128
#> GSM1105508 1 0.4150 0.3653 0.612 0.000 0.000 0.388 0.000
#> GSM1105444 2 0.2127 0.8627 0.000 0.892 0.000 0.000 0.108
#> GSM1105513 3 0.6861 0.2730 0.000 0.176 0.408 0.400 0.016
#> GSM1105516 4 0.1124 0.2809 0.036 0.000 0.000 0.960 0.004
#> GSM1105520 3 0.4177 0.2008 0.000 0.116 0.804 0.020 0.060
#> GSM1105524 1 0.0000 0.8469 1.000 0.000 0.000 0.000 0.000
#> GSM1105536 5 0.2519 0.7540 0.000 0.000 0.016 0.100 0.884
#> GSM1105537 1 0.0000 0.8469 1.000 0.000 0.000 0.000 0.000
#> GSM1105540 4 0.6164 0.3662 0.000 0.000 0.328 0.520 0.152
#> GSM1105544 4 0.5598 0.2059 0.000 0.000 0.076 0.524 0.400
#> GSM1105445 2 0.2424 0.8601 0.000 0.868 0.000 0.000 0.132
#> GSM1105553 2 0.4738 0.1482 0.000 0.564 0.420 0.004 0.012
#> GSM1105556 1 0.0000 0.8469 1.000 0.000 0.000 0.000 0.000
#> GSM1105557 3 0.7189 0.2712 0.000 0.136 0.408 0.404 0.052
#> GSM1105449 2 0.2424 0.8601 0.000 0.868 0.000 0.000 0.132
#> GSM1105469 5 0.4450 0.3039 0.000 0.000 0.004 0.488 0.508
#> GSM1105472 5 0.2127 0.7817 0.000 0.108 0.000 0.000 0.892
#> GSM1105473 4 0.6777 0.4011 0.196 0.000 0.288 0.500 0.016
#> GSM1105476 5 0.2127 0.7817 0.000 0.108 0.000 0.000 0.892
#> GSM1105477 5 0.3707 0.6662 0.000 0.000 0.000 0.284 0.716
#> GSM1105478 5 0.4538 0.3566 0.000 0.004 0.428 0.004 0.564
#> GSM1105510 5 0.4686 0.7063 0.000 0.160 0.000 0.104 0.736
#> GSM1105530 3 0.4305 -0.4069 0.000 0.000 0.512 0.488 0.000
#> GSM1105539 3 0.6024 -0.0798 0.364 0.000 0.512 0.124 0.000
#> GSM1105480 5 0.4504 0.3557 0.000 0.000 0.428 0.008 0.564
#> GSM1105512 1 0.2424 0.7400 0.868 0.000 0.000 0.132 0.000
#> GSM1105532 3 0.4307 -0.4155 0.000 0.000 0.500 0.500 0.000
#> GSM1105541 1 0.3562 0.6759 0.788 0.000 0.196 0.016 0.000
#> GSM1105439 2 0.2813 0.8312 0.000 0.832 0.000 0.000 0.168
#> GSM1105463 3 0.7237 -0.2726 0.000 0.108 0.484 0.324 0.084
#> GSM1105482 1 0.0000 0.8469 1.000 0.000 0.000 0.000 0.000
#> GSM1105483 5 0.4305 0.3096 0.000 0.000 0.000 0.488 0.512
#> GSM1105494 5 0.6583 0.1095 0.000 0.112 0.420 0.024 0.444
#> GSM1105503 3 0.3048 0.2170 0.000 0.176 0.820 0.004 0.000
#> GSM1105507 4 0.1117 0.2699 0.020 0.000 0.016 0.964 0.000
#> GSM1105446 2 0.2179 0.8626 0.000 0.888 0.000 0.000 0.112
#> GSM1105519 1 0.4307 0.0837 0.504 0.000 0.000 0.496 0.000
#> GSM1105526 5 0.2732 0.7392 0.000 0.000 0.000 0.160 0.840
#> GSM1105527 5 0.5826 0.2767 0.000 0.000 0.096 0.404 0.500
#> GSM1105531 4 0.6100 0.3255 0.000 0.108 0.416 0.472 0.004
#> GSM1105543 5 0.2329 0.7762 0.000 0.124 0.000 0.000 0.876
#> GSM1105546 1 0.0000 0.8469 1.000 0.000 0.000 0.000 0.000
#> GSM1105547 1 0.0000 0.8469 1.000 0.000 0.000 0.000 0.000
#> GSM1105455 2 0.2848 0.8410 0.000 0.840 0.004 0.000 0.156
#> GSM1105458 2 0.2424 0.8601 0.000 0.868 0.000 0.000 0.132
#> GSM1105459 2 0.2929 0.8207 0.000 0.820 0.000 0.000 0.180
#> GSM1105462 5 0.5861 0.2105 0.000 0.000 0.376 0.104 0.520
#> GSM1105441 2 0.2127 0.8627 0.000 0.892 0.000 0.000 0.108
#> GSM1105465 5 0.3965 0.6947 0.000 0.032 0.076 0.064 0.828
#> GSM1105484 5 0.0162 0.7721 0.000 0.004 0.000 0.000 0.996
#> GSM1105485 5 0.3056 0.7158 0.000 0.000 0.068 0.068 0.864
#> GSM1105496 3 0.4428 0.2303 0.000 0.160 0.756 0.084 0.000
#> GSM1105505 4 0.5865 0.3249 0.000 0.108 0.360 0.532 0.000
#> GSM1105509 4 0.1851 0.2983 0.088 0.000 0.000 0.912 0.000
#> GSM1105448 2 0.2179 0.8626 0.000 0.888 0.000 0.000 0.112
#> GSM1105521 1 0.4307 0.0837 0.504 0.000 0.000 0.496 0.000
#> GSM1105528 5 0.0963 0.7669 0.000 0.000 0.000 0.036 0.964
#> GSM1105529 5 0.1704 0.7471 0.000 0.004 0.068 0.000 0.928
#> GSM1105533 1 0.0290 0.8438 0.992 0.000 0.008 0.000 0.000
#> GSM1105545 5 0.2677 0.7506 0.000 0.000 0.016 0.112 0.872
#> GSM1105548 1 0.6301 0.1066 0.468 0.108 0.012 0.412 0.000
#> GSM1105549 1 0.0000 0.8469 1.000 0.000 0.000 0.000 0.000
#> GSM1105457 3 0.6957 0.2685 0.000 0.220 0.408 0.360 0.012
#> GSM1105460 2 0.2424 0.8601 0.000 0.868 0.000 0.000 0.132
#> GSM1105461 2 0.2891 0.8230 0.000 0.824 0.000 0.000 0.176
#> GSM1105464 3 0.6282 -0.1088 0.340 0.000 0.496 0.164 0.000
#> GSM1105466 5 0.2540 0.7625 0.000 0.000 0.088 0.024 0.888
#> GSM1105479 5 0.2540 0.7697 0.000 0.024 0.088 0.000 0.888
#> GSM1105502 1 0.4490 0.6202 0.724 0.000 0.224 0.052 0.000
#> GSM1105515 1 0.0000 0.8469 1.000 0.000 0.000 0.000 0.000
#> GSM1105523 4 0.4294 0.3692 0.000 0.000 0.468 0.532 0.000
#> GSM1105550 4 0.6234 0.3574 0.008 0.000 0.404 0.476 0.112
#> GSM1105450 5 0.2179 0.7804 0.000 0.112 0.000 0.000 0.888
#> GSM1105451 2 0.2179 0.8626 0.000 0.888 0.000 0.000 0.112
#> GSM1105454 2 0.0703 0.7512 0.000 0.976 0.024 0.000 0.000
#> GSM1105468 5 0.2127 0.7817 0.000 0.108 0.000 0.000 0.892
#> GSM1105481 3 0.7007 -0.0401 0.000 0.192 0.416 0.020 0.372
#> GSM1105504 3 0.4305 -0.4069 0.000 0.000 0.512 0.488 0.000
#> GSM1105517 4 0.5778 0.4268 0.128 0.000 0.280 0.592 0.000
#> GSM1105525 4 0.5447 0.4085 0.064 0.000 0.400 0.536 0.000
#> GSM1105552 4 0.6391 0.3852 0.100 0.020 0.408 0.472 0.000
#> GSM1105452 5 0.2236 0.7385 0.000 0.000 0.068 0.024 0.908
#> GSM1105453 2 0.2179 0.8626 0.000 0.888 0.000 0.000 0.112
#> GSM1105456 2 0.1043 0.7431 0.000 0.960 0.040 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1105438 2 0.1327 0.869 0.000 0.936 0.000 0.000 0.000 0.064
#> GSM1105486 6 0.0260 0.829 0.000 0.008 0.000 0.000 0.000 0.992
#> GSM1105487 1 0.1265 0.852 0.948 0.000 0.008 0.000 0.044 0.000
#> GSM1105490 4 0.0000 0.740 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1105491 5 0.3288 0.369 0.000 0.000 0.276 0.000 0.724 0.000
#> GSM1105495 5 0.3789 0.366 0.000 0.260 0.024 0.000 0.716 0.000
#> GSM1105498 4 0.2848 0.692 0.000 0.000 0.008 0.816 0.176 0.000
#> GSM1105499 1 0.0000 0.871 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1105506 4 0.0000 0.740 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1105442 5 0.5573 0.413 0.000 0.288 0.000 0.000 0.536 0.176
#> GSM1105511 4 0.0000 0.740 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1105514 6 0.2664 0.647 0.000 0.184 0.000 0.000 0.000 0.816
#> GSM1105518 4 0.5377 0.541 0.000 0.216 0.004 0.604 0.176 0.000
#> GSM1105522 3 0.3534 0.604 0.000 0.000 0.716 0.008 0.276 0.000
#> GSM1105534 1 0.0000 0.871 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1105535 1 0.0458 0.867 0.984 0.000 0.000 0.000 0.016 0.000
#> GSM1105538 3 0.3828 0.428 0.440 0.000 0.560 0.000 0.000 0.000
#> GSM1105542 5 0.4111 0.572 0.000 0.004 0.004 0.000 0.536 0.456
#> GSM1105443 2 0.0146 0.891 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM1105551 1 0.1814 0.810 0.900 0.000 0.000 0.000 0.100 0.000
#> GSM1105554 1 0.0000 0.871 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1105555 1 0.0405 0.866 0.988 0.000 0.008 0.000 0.004 0.000
#> GSM1105447 2 0.2597 0.779 0.000 0.824 0.000 0.000 0.000 0.176
#> GSM1105467 6 0.0260 0.829 0.000 0.008 0.000 0.000 0.000 0.992
#> GSM1105470 6 0.0260 0.829 0.000 0.008 0.000 0.000 0.000 0.992
#> GSM1105471 6 0.0260 0.829 0.000 0.008 0.000 0.000 0.000 0.992
#> GSM1105474 6 0.0260 0.829 0.000 0.008 0.000 0.000 0.000 0.992
#> GSM1105475 6 0.0260 0.827 0.000 0.000 0.000 0.008 0.000 0.992
#> GSM1105440 1 0.0547 0.865 0.980 0.000 0.000 0.000 0.020 0.000
#> GSM1105488 5 0.3854 0.567 0.000 0.000 0.000 0.000 0.536 0.464
#> GSM1105489 1 0.1387 0.823 0.932 0.000 0.000 0.000 0.068 0.000
#> GSM1105492 3 0.4051 0.433 0.432 0.000 0.560 0.000 0.008 0.000
#> GSM1105493 1 0.0000 0.871 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1105497 5 0.3468 0.343 0.000 0.284 0.004 0.000 0.712 0.000
#> GSM1105500 4 0.0260 0.737 0.000 0.000 0.000 0.992 0.008 0.000
#> GSM1105501 4 0.0000 0.740 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1105508 1 0.4593 0.395 0.576 0.000 0.000 0.380 0.044 0.000
#> GSM1105444 2 0.0000 0.890 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105513 4 0.0000 0.740 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1105516 3 0.4849 0.459 0.028 0.000 0.560 0.396 0.008 0.008
#> GSM1105520 4 0.5449 0.551 0.000 0.000 0.240 0.572 0.188 0.000
#> GSM1105524 1 0.1007 0.856 0.956 0.000 0.000 0.000 0.044 0.000
#> GSM1105536 6 0.0146 0.827 0.000 0.000 0.000 0.004 0.000 0.996
#> GSM1105537 1 0.1007 0.856 0.956 0.000 0.000 0.000 0.044 0.000
#> GSM1105540 3 0.3141 0.505 0.000 0.000 0.788 0.000 0.012 0.200
#> GSM1105544 3 0.4395 0.250 0.000 0.000 0.568 0.028 0.000 0.404
#> GSM1105445 2 0.2597 0.779 0.000 0.824 0.000 0.000 0.000 0.176
#> GSM1105553 4 0.5265 0.539 0.000 0.220 0.000 0.604 0.176 0.000
#> GSM1105556 1 0.0000 0.871 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1105557 4 0.0000 0.740 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1105449 2 0.2597 0.779 0.000 0.824 0.000 0.000 0.000 0.176
#> GSM1105469 6 0.4379 0.380 0.000 0.000 0.000 0.396 0.028 0.576
#> GSM1105472 6 0.0260 0.829 0.000 0.008 0.000 0.000 0.000 0.992
#> GSM1105473 3 0.3043 0.635 0.200 0.000 0.792 0.000 0.000 0.008
#> GSM1105476 6 0.0260 0.829 0.000 0.008 0.000 0.000 0.000 0.992
#> GSM1105477 6 0.2212 0.738 0.000 0.000 0.000 0.112 0.008 0.880
#> GSM1105478 4 0.3747 0.315 0.000 0.000 0.000 0.604 0.000 0.396
#> GSM1105510 6 0.3147 0.606 0.000 0.160 0.000 0.016 0.008 0.816
#> GSM1105530 3 0.3266 0.603 0.000 0.000 0.728 0.000 0.272 0.000
#> GSM1105539 1 0.5787 0.331 0.504 0.000 0.252 0.000 0.244 0.000
#> GSM1105480 4 0.3747 0.315 0.000 0.000 0.000 0.604 0.000 0.396
#> GSM1105512 1 0.3076 0.493 0.760 0.000 0.240 0.000 0.000 0.000
#> GSM1105532 3 0.3244 0.603 0.000 0.000 0.732 0.000 0.268 0.000
#> GSM1105541 1 0.5565 0.396 0.552 0.000 0.208 0.000 0.240 0.000
#> GSM1105439 2 0.0547 0.889 0.000 0.980 0.000 0.000 0.000 0.020
#> GSM1105463 5 0.3371 0.356 0.000 0.000 0.292 0.000 0.708 0.000
#> GSM1105482 1 0.0000 0.871 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1105483 6 0.3747 0.412 0.000 0.000 0.000 0.396 0.000 0.604
#> GSM1105494 4 0.5265 0.509 0.000 0.000 0.000 0.604 0.176 0.220
#> GSM1105503 4 0.5265 0.577 0.000 0.000 0.220 0.604 0.176 0.000
#> GSM1105507 3 0.4743 0.453 0.000 0.000 0.560 0.396 0.036 0.008
#> GSM1105446 2 0.0000 0.890 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105519 3 0.4051 0.440 0.432 0.000 0.560 0.000 0.000 0.008
#> GSM1105526 6 0.1075 0.802 0.000 0.000 0.000 0.048 0.000 0.952
#> GSM1105527 6 0.4093 0.397 0.000 0.000 0.000 0.404 0.012 0.584
#> GSM1105531 3 0.2996 0.556 0.000 0.000 0.772 0.000 0.228 0.000
#> GSM1105543 6 0.1765 0.757 0.000 0.096 0.000 0.000 0.000 0.904
#> GSM1105546 1 0.0000 0.871 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1105547 1 0.0000 0.871 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1105455 2 0.0717 0.886 0.000 0.976 0.000 0.008 0.000 0.016
#> GSM1105458 2 0.2597 0.779 0.000 0.824 0.000 0.000 0.000 0.176
#> GSM1105459 2 0.0790 0.884 0.000 0.968 0.000 0.000 0.000 0.032
#> GSM1105462 6 0.3266 0.505 0.000 0.000 0.272 0.000 0.000 0.728
#> GSM1105441 2 0.0146 0.891 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM1105465 5 0.4195 0.577 0.000 0.004 0.008 0.000 0.548 0.440
#> GSM1105484 6 0.0260 0.825 0.000 0.000 0.000 0.000 0.008 0.992
#> GSM1105485 5 0.3854 0.567 0.000 0.000 0.000 0.000 0.536 0.464
#> GSM1105496 4 0.5395 0.565 0.000 0.000 0.220 0.584 0.196 0.000
#> GSM1105505 3 0.3152 0.555 0.000 0.000 0.792 0.004 0.196 0.008
#> GSM1105509 3 0.5239 0.491 0.060 0.000 0.560 0.364 0.008 0.008
#> GSM1105448 2 0.0000 0.890 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105521 3 0.4051 0.440 0.432 0.000 0.560 0.000 0.000 0.008
#> GSM1105528 6 0.0458 0.821 0.000 0.000 0.000 0.000 0.016 0.984
#> GSM1105529 5 0.3860 0.556 0.000 0.000 0.000 0.000 0.528 0.472
#> GSM1105533 1 0.0260 0.869 0.992 0.000 0.008 0.000 0.000 0.000
#> GSM1105545 6 0.0458 0.822 0.000 0.000 0.000 0.016 0.000 0.984
#> GSM1105548 3 0.5464 0.560 0.260 0.000 0.564 0.000 0.176 0.000
#> GSM1105549 1 0.0000 0.871 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1105457 4 0.0458 0.737 0.000 0.016 0.000 0.984 0.000 0.000
#> GSM1105460 2 0.2597 0.779 0.000 0.824 0.000 0.000 0.000 0.176
#> GSM1105461 2 0.0632 0.885 0.000 0.976 0.000 0.000 0.000 0.024
#> GSM1105464 3 0.4748 -0.189 0.448 0.000 0.504 0.000 0.048 0.000
#> GSM1105466 6 0.0260 0.827 0.000 0.000 0.000 0.008 0.000 0.992
#> GSM1105479 6 0.0260 0.827 0.000 0.000 0.000 0.008 0.000 0.992
#> GSM1105502 1 0.5963 0.235 0.452 0.000 0.276 0.000 0.272 0.000
#> GSM1105515 1 0.0000 0.871 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1105523 3 0.3076 0.616 0.000 0.000 0.760 0.000 0.240 0.000
#> GSM1105550 3 0.3691 0.547 0.024 0.000 0.796 0.004 0.020 0.156
#> GSM1105450 6 0.0632 0.822 0.000 0.024 0.000 0.000 0.000 0.976
#> GSM1105451 2 0.0000 0.890 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105454 2 0.2597 0.763 0.000 0.824 0.000 0.000 0.176 0.000
#> GSM1105468 6 0.0260 0.829 0.000 0.008 0.000 0.000 0.000 0.992
#> GSM1105481 6 0.5702 0.261 0.000 0.008 0.244 0.000 0.188 0.560
#> GSM1105504 3 0.1219 0.612 0.000 0.000 0.948 0.000 0.048 0.004
#> GSM1105517 3 0.3784 0.647 0.124 0.000 0.792 0.076 0.000 0.008
#> GSM1105525 3 0.3288 0.605 0.000 0.000 0.724 0.000 0.276 0.000
#> GSM1105552 3 0.3645 0.634 0.144 0.000 0.796 0.000 0.052 0.008
#> GSM1105452 5 0.3989 0.560 0.000 0.004 0.000 0.000 0.528 0.468
#> GSM1105453 2 0.0000 0.890 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105456 2 0.2848 0.757 0.000 0.816 0.008 0.000 0.176 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 agent(p) other(p) time(p) individual(p) k
#> MAD:pam 114 1.0000 0.44991 0.593 1.07e-02 2
#> MAD:pam 89 0.2614 0.76005 0.764 4.25e-02 3
#> MAD:pam 104 0.2579 0.01048 0.885 3.01e-04 4
#> MAD:pam 71 0.1885 0.91734 0.976 2.43e-03 5
#> MAD:pam 95 0.0664 0.00232 0.303 8.05e-06 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 44956 rows and 120 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 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 0.471 0.754 0.700 0.3329 0.507 0.507
#> 3 3 0.586 0.717 0.843 0.7912 0.716 0.539
#> 4 4 0.764 0.843 0.877 0.1792 0.812 0.599
#> 5 5 0.779 0.831 0.898 0.0652 0.890 0.668
#> 6 6 0.744 0.688 0.830 0.0752 0.870 0.535
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
#> GSM1105438 2 0.9970 0.814 0.468 0.532
#> GSM1105486 2 0.0000 0.393 0.000 1.000
#> GSM1105487 1 0.0000 0.914 1.000 0.000
#> GSM1105490 2 0.9983 0.820 0.476 0.524
#> GSM1105491 2 0.9996 0.814 0.488 0.512
#> GSM1105495 2 0.9996 0.814 0.488 0.512
#> GSM1105498 2 0.9996 0.814 0.488 0.512
#> GSM1105499 1 0.0000 0.914 1.000 0.000
#> GSM1105506 2 0.9996 0.814 0.488 0.512
#> GSM1105442 2 0.9996 0.814 0.488 0.512
#> GSM1105511 2 0.9996 0.814 0.488 0.512
#> GSM1105514 2 0.9795 0.755 0.416 0.584
#> GSM1105518 2 0.9996 0.814 0.488 0.512
#> GSM1105522 1 0.0000 0.914 1.000 0.000
#> GSM1105534 1 0.0000 0.914 1.000 0.000
#> GSM1105535 1 0.0000 0.914 1.000 0.000
#> GSM1105538 1 0.0000 0.914 1.000 0.000
#> GSM1105542 2 0.9983 0.820 0.476 0.524
#> GSM1105443 2 0.9983 0.820 0.476 0.524
#> GSM1105551 1 0.0000 0.914 1.000 0.000
#> GSM1105554 1 0.0000 0.914 1.000 0.000
#> GSM1105555 1 0.0000 0.914 1.000 0.000
#> GSM1105447 2 0.9983 0.820 0.476 0.524
#> GSM1105467 2 0.4939 0.446 0.108 0.892
#> GSM1105470 2 0.0376 0.395 0.004 0.996
#> GSM1105471 2 0.9996 0.814 0.488 0.512
#> GSM1105474 2 0.0000 0.393 0.000 1.000
#> GSM1105475 2 0.9954 0.806 0.460 0.540
#> GSM1105440 1 0.0000 0.914 1.000 0.000
#> GSM1105488 2 0.9983 0.820 0.476 0.524
#> GSM1105489 1 0.0000 0.914 1.000 0.000
#> GSM1105492 1 0.0000 0.914 1.000 0.000
#> GSM1105493 1 0.0000 0.914 1.000 0.000
#> GSM1105497 2 0.9996 0.814 0.488 0.512
#> GSM1105500 2 0.9996 0.814 0.488 0.512
#> GSM1105501 2 0.9983 0.820 0.476 0.524
#> GSM1105508 1 0.0000 0.914 1.000 0.000
#> GSM1105444 2 0.9983 0.820 0.476 0.524
#> GSM1105513 2 0.9983 0.820 0.476 0.524
#> GSM1105516 2 0.9996 0.814 0.488 0.512
#> GSM1105520 1 0.9983 -0.742 0.524 0.476
#> GSM1105524 1 0.0000 0.914 1.000 0.000
#> GSM1105536 2 0.9983 0.820 0.476 0.524
#> GSM1105537 1 0.0000 0.914 1.000 0.000
#> GSM1105540 1 0.1414 0.886 0.980 0.020
#> GSM1105544 2 0.9996 0.814 0.488 0.512
#> GSM1105445 2 0.9996 0.814 0.488 0.512
#> GSM1105553 2 0.9996 0.814 0.488 0.512
#> GSM1105556 1 0.0000 0.914 1.000 0.000
#> GSM1105557 2 0.9983 0.820 0.476 0.524
#> GSM1105449 2 0.9983 0.820 0.476 0.524
#> GSM1105469 1 0.4690 0.738 0.900 0.100
#> GSM1105472 2 0.0000 0.393 0.000 1.000
#> GSM1105473 1 0.0000 0.914 1.000 0.000
#> GSM1105476 2 0.9933 0.797 0.452 0.548
#> GSM1105477 2 0.9983 0.820 0.476 0.524
#> GSM1105478 2 0.9996 0.814 0.488 0.512
#> GSM1105510 2 0.9983 0.820 0.476 0.524
#> GSM1105530 1 0.0000 0.914 1.000 0.000
#> GSM1105539 1 0.0000 0.914 1.000 0.000
#> GSM1105480 2 0.9996 0.814 0.488 0.512
#> GSM1105512 1 0.0000 0.914 1.000 0.000
#> GSM1105532 1 0.0000 0.914 1.000 0.000
#> GSM1105541 1 0.0000 0.914 1.000 0.000
#> GSM1105439 2 0.9983 0.820 0.476 0.524
#> GSM1105463 1 0.0000 0.914 1.000 0.000
#> GSM1105482 1 0.0000 0.914 1.000 0.000
#> GSM1105483 2 0.9996 0.814 0.488 0.512
#> GSM1105494 2 0.9996 0.814 0.488 0.512
#> GSM1105503 1 0.9635 -0.469 0.612 0.388
#> GSM1105507 1 0.0000 0.914 1.000 0.000
#> GSM1105446 2 0.9815 0.761 0.420 0.580
#> GSM1105519 1 0.0000 0.914 1.000 0.000
#> GSM1105526 2 0.9983 0.820 0.476 0.524
#> GSM1105527 2 0.9996 0.814 0.488 0.512
#> GSM1105531 1 0.0376 0.909 0.996 0.004
#> GSM1105543 2 0.9754 0.746 0.408 0.592
#> GSM1105546 1 0.0000 0.914 1.000 0.000
#> GSM1105547 1 0.0000 0.914 1.000 0.000
#> GSM1105455 2 0.9983 0.820 0.476 0.524
#> GSM1105458 2 0.9996 0.814 0.488 0.512
#> GSM1105459 2 0.0000 0.393 0.000 1.000
#> GSM1105462 1 0.9635 -0.469 0.612 0.388
#> GSM1105441 2 0.9977 0.817 0.472 0.528
#> GSM1105465 2 0.9996 0.814 0.488 0.512
#> GSM1105484 2 0.9983 0.820 0.476 0.524
#> GSM1105485 2 0.9996 0.814 0.488 0.512
#> GSM1105496 1 0.9635 -0.469 0.612 0.388
#> GSM1105505 1 0.6048 0.612 0.852 0.148
#> GSM1105509 1 0.0000 0.914 1.000 0.000
#> GSM1105448 2 0.9833 0.765 0.424 0.576
#> GSM1105521 1 0.0000 0.914 1.000 0.000
#> GSM1105528 2 0.9983 0.820 0.476 0.524
#> GSM1105529 2 0.9983 0.820 0.476 0.524
#> GSM1105533 1 0.0000 0.914 1.000 0.000
#> GSM1105545 2 0.9983 0.820 0.476 0.524
#> GSM1105548 1 0.0000 0.914 1.000 0.000
#> GSM1105549 1 0.0000 0.914 1.000 0.000
#> GSM1105457 2 0.9983 0.820 0.476 0.524
#> GSM1105460 2 0.9983 0.820 0.476 0.524
#> GSM1105461 2 0.0000 0.393 0.000 1.000
#> GSM1105464 1 0.0000 0.914 1.000 0.000
#> GSM1105466 2 0.9996 0.814 0.488 0.512
#> GSM1105479 2 0.9983 0.820 0.476 0.524
#> GSM1105502 1 0.0000 0.914 1.000 0.000
#> GSM1105515 1 0.0000 0.914 1.000 0.000
#> GSM1105523 1 0.0000 0.914 1.000 0.000
#> GSM1105550 1 0.9460 -0.375 0.636 0.364
#> GSM1105450 2 0.0000 0.393 0.000 1.000
#> GSM1105451 2 0.0000 0.393 0.000 1.000
#> GSM1105454 2 0.9996 0.814 0.488 0.512
#> GSM1105468 2 0.0000 0.393 0.000 1.000
#> GSM1105481 2 0.9996 0.814 0.488 0.512
#> GSM1105504 1 0.2236 0.860 0.964 0.036
#> GSM1105517 1 0.0000 0.914 1.000 0.000
#> GSM1105525 1 0.0000 0.914 1.000 0.000
#> GSM1105552 1 0.0000 0.914 1.000 0.000
#> GSM1105452 2 0.9983 0.820 0.476 0.524
#> GSM1105453 2 0.0000 0.393 0.000 1.000
#> GSM1105456 2 0.9996 0.814 0.488 0.512
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1105438 2 0.6302 -0.6801 0.000 0.520 0.480
#> GSM1105486 3 0.6079 0.9724 0.000 0.388 0.612
#> GSM1105487 1 0.0237 0.9799 0.996 0.000 0.004
#> GSM1105490 2 0.0000 0.6914 0.000 1.000 0.000
#> GSM1105491 2 0.6307 0.4750 0.000 0.512 0.488
#> GSM1105495 2 0.6302 0.4829 0.000 0.520 0.480
#> GSM1105498 2 0.3619 0.6578 0.000 0.864 0.136
#> GSM1105499 1 0.0000 0.9805 1.000 0.000 0.000
#> GSM1105506 2 0.0000 0.6914 0.000 1.000 0.000
#> GSM1105442 2 0.4346 0.5969 0.000 0.816 0.184
#> GSM1105511 2 0.0000 0.6914 0.000 1.000 0.000
#> GSM1105514 3 0.5785 0.9024 0.000 0.332 0.668
#> GSM1105518 2 0.5138 0.6023 0.000 0.748 0.252
#> GSM1105522 1 0.0000 0.9805 1.000 0.000 0.000
#> GSM1105534 1 0.0000 0.9805 1.000 0.000 0.000
#> GSM1105535 1 0.0000 0.9805 1.000 0.000 0.000
#> GSM1105538 1 0.0000 0.9805 1.000 0.000 0.000
#> GSM1105542 2 0.4121 0.5923 0.000 0.832 0.168
#> GSM1105443 2 0.0000 0.6914 0.000 1.000 0.000
#> GSM1105551 1 0.0237 0.9799 0.996 0.000 0.004
#> GSM1105554 1 0.0000 0.9805 1.000 0.000 0.000
#> GSM1105555 1 0.0237 0.9799 0.996 0.000 0.004
#> GSM1105447 2 0.0237 0.6913 0.000 0.996 0.004
#> GSM1105467 2 0.4974 0.3284 0.000 0.764 0.236
#> GSM1105470 3 0.6062 0.9772 0.000 0.384 0.616
#> GSM1105471 2 0.4605 0.6276 0.000 0.796 0.204
#> GSM1105474 3 0.6062 0.9772 0.000 0.384 0.616
#> GSM1105475 2 0.2448 0.6229 0.000 0.924 0.076
#> GSM1105440 1 0.0000 0.9805 1.000 0.000 0.000
#> GSM1105488 2 0.4121 0.5923 0.000 0.832 0.168
#> GSM1105489 1 0.0237 0.9799 0.996 0.000 0.004
#> GSM1105492 1 0.0000 0.9805 1.000 0.000 0.000
#> GSM1105493 1 0.0237 0.9799 0.996 0.000 0.004
#> GSM1105497 2 0.5465 0.5627 0.000 0.712 0.288
#> GSM1105500 2 0.0000 0.6914 0.000 1.000 0.000
#> GSM1105501 2 0.0000 0.6914 0.000 1.000 0.000
#> GSM1105508 1 0.0000 0.9805 1.000 0.000 0.000
#> GSM1105444 2 0.4887 0.4912 0.000 0.772 0.228
#> GSM1105513 2 0.0000 0.6914 0.000 1.000 0.000
#> GSM1105516 2 0.4047 0.5978 0.148 0.848 0.004
#> GSM1105520 2 0.5733 0.5589 0.000 0.676 0.324
#> GSM1105524 1 0.0000 0.9805 1.000 0.000 0.000
#> GSM1105536 2 0.1964 0.6581 0.000 0.944 0.056
#> GSM1105537 1 0.0000 0.9805 1.000 0.000 0.000
#> GSM1105540 2 0.6295 0.2519 0.472 0.528 0.000
#> GSM1105544 2 0.0000 0.6914 0.000 1.000 0.000
#> GSM1105445 2 0.4504 0.6317 0.000 0.804 0.196
#> GSM1105553 2 0.5733 0.5589 0.000 0.676 0.324
#> GSM1105556 1 0.0000 0.9805 1.000 0.000 0.000
#> GSM1105557 2 0.0000 0.6914 0.000 1.000 0.000
#> GSM1105449 2 0.3816 0.5551 0.000 0.852 0.148
#> GSM1105469 2 0.4555 0.5702 0.200 0.800 0.000
#> GSM1105472 3 0.6045 0.9727 0.000 0.380 0.620
#> GSM1105473 1 0.3918 0.7922 0.856 0.140 0.004
#> GSM1105476 2 0.4178 0.5168 0.000 0.828 0.172
#> GSM1105477 2 0.2537 0.6384 0.000 0.920 0.080
#> GSM1105478 2 0.0000 0.6914 0.000 1.000 0.000
#> GSM1105510 2 0.4121 0.5923 0.000 0.832 0.168
#> GSM1105530 1 0.0237 0.9799 0.996 0.000 0.004
#> GSM1105539 1 0.0237 0.9799 0.996 0.000 0.004
#> GSM1105480 2 0.0000 0.6914 0.000 1.000 0.000
#> GSM1105512 1 0.0000 0.9805 1.000 0.000 0.000
#> GSM1105532 1 0.0237 0.9799 0.996 0.000 0.004
#> GSM1105541 1 0.0237 0.9799 0.996 0.000 0.004
#> GSM1105439 2 0.0747 0.6795 0.000 0.984 0.016
#> GSM1105463 2 0.9355 0.4286 0.188 0.492 0.320
#> GSM1105482 1 0.0000 0.9805 1.000 0.000 0.000
#> GSM1105483 2 0.0000 0.6914 0.000 1.000 0.000
#> GSM1105494 2 0.0000 0.6914 0.000 1.000 0.000
#> GSM1105503 2 0.5733 0.5589 0.000 0.676 0.324
#> GSM1105507 1 0.5810 0.3692 0.664 0.336 0.000
#> GSM1105446 2 0.5988 -0.0417 0.000 0.632 0.368
#> GSM1105519 1 0.0000 0.9805 1.000 0.000 0.000
#> GSM1105526 2 0.0000 0.6914 0.000 1.000 0.000
#> GSM1105527 2 0.0000 0.6914 0.000 1.000 0.000
#> GSM1105531 2 0.9142 0.4529 0.164 0.512 0.324
#> GSM1105543 3 0.6225 0.8598 0.000 0.432 0.568
#> GSM1105546 1 0.0000 0.9805 1.000 0.000 0.000
#> GSM1105547 1 0.0000 0.9805 1.000 0.000 0.000
#> GSM1105455 2 0.1860 0.6438 0.000 0.948 0.052
#> GSM1105458 2 0.0237 0.6913 0.000 0.996 0.004
#> GSM1105459 3 0.6062 0.9772 0.000 0.384 0.616
#> GSM1105462 2 0.4654 0.6255 0.000 0.792 0.208
#> GSM1105441 3 0.6154 0.9464 0.000 0.408 0.592
#> GSM1105465 2 0.6307 0.4750 0.000 0.512 0.488
#> GSM1105484 2 0.4121 0.5923 0.000 0.832 0.168
#> GSM1105485 2 0.4121 0.5923 0.000 0.832 0.168
#> GSM1105496 2 0.5733 0.5589 0.000 0.676 0.324
#> GSM1105505 2 0.7820 0.5193 0.072 0.604 0.324
#> GSM1105509 1 0.0000 0.9805 1.000 0.000 0.000
#> GSM1105448 2 0.6305 -0.5318 0.000 0.516 0.484
#> GSM1105521 1 0.0000 0.9805 1.000 0.000 0.000
#> GSM1105528 2 0.4121 0.5923 0.000 0.832 0.168
#> GSM1105529 2 0.4121 0.5923 0.000 0.832 0.168
#> GSM1105533 1 0.0237 0.9799 0.996 0.000 0.004
#> GSM1105545 2 0.0000 0.6914 0.000 1.000 0.000
#> GSM1105548 1 0.0237 0.9799 0.996 0.000 0.004
#> GSM1105549 1 0.0237 0.9799 0.996 0.000 0.004
#> GSM1105457 2 0.0000 0.6914 0.000 1.000 0.000
#> GSM1105460 2 0.0000 0.6914 0.000 1.000 0.000
#> GSM1105461 3 0.6062 0.9772 0.000 0.384 0.616
#> GSM1105464 1 0.0237 0.9799 0.996 0.000 0.004
#> GSM1105466 2 0.0000 0.6914 0.000 1.000 0.000
#> GSM1105479 2 0.0000 0.6914 0.000 1.000 0.000
#> GSM1105502 1 0.0237 0.9799 0.996 0.000 0.004
#> GSM1105515 1 0.0000 0.9805 1.000 0.000 0.000
#> GSM1105523 2 0.6373 0.3709 0.408 0.588 0.004
#> GSM1105550 2 0.4750 0.5553 0.216 0.784 0.000
#> GSM1105450 3 0.6062 0.9772 0.000 0.384 0.616
#> GSM1105451 3 0.6062 0.9772 0.000 0.384 0.616
#> GSM1105454 2 0.5733 0.5589 0.000 0.676 0.324
#> GSM1105468 3 0.6062 0.9772 0.000 0.384 0.616
#> GSM1105481 2 0.5733 0.5589 0.000 0.676 0.324
#> GSM1105504 2 0.9142 0.4529 0.164 0.512 0.324
#> GSM1105517 2 0.6307 0.2127 0.488 0.512 0.000
#> GSM1105525 1 0.0829 0.9669 0.984 0.012 0.004
#> GSM1105552 2 0.6520 0.2069 0.488 0.508 0.004
#> GSM1105452 2 0.4121 0.5923 0.000 0.832 0.168
#> GSM1105453 3 0.6062 0.9772 0.000 0.384 0.616
#> GSM1105456 2 0.5733 0.5589 0.000 0.676 0.324
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1105438 2 0.5727 0.941 0.000 0.692 0.080 0.228
#> GSM1105486 2 0.5664 0.946 0.000 0.696 0.076 0.228
#> GSM1105487 1 0.0376 0.972 0.992 0.004 0.000 0.004
#> GSM1105490 3 0.0000 0.805 0.000 0.000 1.000 0.000
#> GSM1105491 4 0.3978 0.736 0.000 0.192 0.012 0.796
#> GSM1105495 3 0.6548 0.685 0.000 0.304 0.592 0.104
#> GSM1105498 3 0.4152 0.782 0.000 0.160 0.808 0.032
#> GSM1105499 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> GSM1105506 3 0.0000 0.805 0.000 0.000 1.000 0.000
#> GSM1105442 4 0.0657 0.902 0.000 0.004 0.012 0.984
#> GSM1105511 3 0.0000 0.805 0.000 0.000 1.000 0.000
#> GSM1105514 2 0.5664 0.946 0.000 0.696 0.076 0.228
#> GSM1105518 3 0.5636 0.733 0.000 0.260 0.680 0.060
#> GSM1105522 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> GSM1105534 1 0.0336 0.971 0.992 0.000 0.000 0.008
#> GSM1105535 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> GSM1105538 1 0.0336 0.971 0.992 0.000 0.000 0.008
#> GSM1105542 4 0.0469 0.904 0.000 0.000 0.012 0.988
#> GSM1105443 3 0.0921 0.801 0.000 0.000 0.972 0.028
#> GSM1105551 1 0.0376 0.972 0.992 0.004 0.000 0.004
#> GSM1105554 1 0.0336 0.971 0.992 0.000 0.000 0.008
#> GSM1105555 1 0.0376 0.972 0.992 0.004 0.000 0.004
#> GSM1105447 3 0.4356 0.643 0.000 0.000 0.708 0.292
#> GSM1105467 2 0.7325 0.687 0.000 0.532 0.236 0.232
#> GSM1105470 2 0.5664 0.946 0.000 0.696 0.076 0.228
#> GSM1105471 3 0.5072 0.758 0.000 0.208 0.740 0.052
#> GSM1105474 2 0.5664 0.946 0.000 0.696 0.076 0.228
#> GSM1105475 3 0.3710 0.688 0.000 0.004 0.804 0.192
#> GSM1105440 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> GSM1105488 4 0.0469 0.904 0.000 0.000 0.012 0.988
#> GSM1105489 1 0.0188 0.972 0.996 0.004 0.000 0.000
#> GSM1105492 1 0.0188 0.972 0.996 0.000 0.000 0.004
#> GSM1105493 1 0.0524 0.969 0.988 0.004 0.008 0.000
#> GSM1105497 4 0.2101 0.862 0.000 0.060 0.012 0.928
#> GSM1105500 3 0.2469 0.791 0.000 0.000 0.892 0.108
#> GSM1105501 3 0.0000 0.805 0.000 0.000 1.000 0.000
#> GSM1105508 1 0.0188 0.972 0.996 0.000 0.004 0.000
#> GSM1105444 2 0.7060 0.568 0.000 0.496 0.128 0.376
#> GSM1105513 3 0.0188 0.806 0.000 0.000 0.996 0.004
#> GSM1105516 3 0.2345 0.793 0.000 0.000 0.900 0.100
#> GSM1105520 3 0.6497 0.687 0.000 0.304 0.596 0.100
#> GSM1105524 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> GSM1105536 3 0.2081 0.797 0.000 0.000 0.916 0.084
#> GSM1105537 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> GSM1105540 3 0.5201 0.449 0.400 0.004 0.592 0.004
#> GSM1105544 3 0.2593 0.792 0.000 0.004 0.892 0.104
#> GSM1105445 3 0.4361 0.767 0.000 0.208 0.772 0.020
#> GSM1105553 3 0.6548 0.685 0.000 0.304 0.592 0.104
#> GSM1105556 1 0.0336 0.971 0.992 0.000 0.000 0.008
#> GSM1105557 3 0.0000 0.805 0.000 0.000 1.000 0.000
#> GSM1105449 3 0.7061 0.332 0.000 0.148 0.540 0.312
#> GSM1105469 3 0.0188 0.805 0.004 0.000 0.996 0.000
#> GSM1105472 2 0.5664 0.946 0.000 0.696 0.076 0.228
#> GSM1105473 1 0.0657 0.965 0.984 0.004 0.012 0.000
#> GSM1105476 2 0.5848 0.929 0.000 0.684 0.088 0.228
#> GSM1105477 3 0.4008 0.700 0.000 0.000 0.756 0.244
#> GSM1105478 3 0.0376 0.807 0.000 0.004 0.992 0.004
#> GSM1105510 4 0.2704 0.749 0.000 0.000 0.124 0.876
#> GSM1105530 1 0.0376 0.972 0.992 0.004 0.000 0.004
#> GSM1105539 1 0.0376 0.972 0.992 0.004 0.000 0.004
#> GSM1105480 3 0.0000 0.805 0.000 0.000 1.000 0.000
#> GSM1105512 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> GSM1105532 1 0.0376 0.972 0.992 0.004 0.000 0.004
#> GSM1105541 1 0.0376 0.972 0.992 0.004 0.000 0.004
#> GSM1105439 3 0.0188 0.805 0.000 0.004 0.996 0.000
#> GSM1105463 1 0.6356 0.494 0.636 0.284 0.012 0.068
#> GSM1105482 1 0.0524 0.971 0.988 0.004 0.000 0.008
#> GSM1105483 3 0.0000 0.805 0.000 0.000 1.000 0.000
#> GSM1105494 3 0.0376 0.807 0.000 0.004 0.992 0.004
#> GSM1105503 3 0.6280 0.695 0.000 0.304 0.612 0.084
#> GSM1105507 1 0.3569 0.753 0.804 0.000 0.196 0.000
#> GSM1105446 2 0.6442 0.625 0.000 0.492 0.068 0.440
#> GSM1105519 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> GSM1105526 3 0.1474 0.801 0.000 0.000 0.948 0.052
#> GSM1105527 3 0.0000 0.805 0.000 0.000 1.000 0.000
#> GSM1105531 3 0.6971 0.674 0.012 0.304 0.580 0.104
#> GSM1105543 2 0.5664 0.946 0.000 0.696 0.076 0.228
#> GSM1105546 1 0.0188 0.972 0.996 0.000 0.000 0.004
#> GSM1105547 1 0.0524 0.971 0.988 0.004 0.000 0.008
#> GSM1105455 3 0.2944 0.745 0.000 0.004 0.868 0.128
#> GSM1105458 3 0.5923 0.735 0.000 0.176 0.696 0.128
#> GSM1105459 2 0.5664 0.946 0.000 0.696 0.076 0.228
#> GSM1105462 3 0.6164 0.721 0.000 0.240 0.656 0.104
#> GSM1105441 2 0.5664 0.946 0.000 0.696 0.076 0.228
#> GSM1105465 4 0.3937 0.738 0.000 0.188 0.012 0.800
#> GSM1105484 4 0.0469 0.904 0.000 0.000 0.012 0.988
#> GSM1105485 4 0.0592 0.903 0.000 0.000 0.016 0.984
#> GSM1105496 3 0.6548 0.685 0.000 0.304 0.592 0.104
#> GSM1105505 3 0.6548 0.685 0.000 0.304 0.592 0.104
#> GSM1105509 1 0.0336 0.970 0.992 0.000 0.008 0.000
#> GSM1105448 2 0.5520 0.922 0.000 0.696 0.060 0.244
#> GSM1105521 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> GSM1105528 4 0.0817 0.896 0.000 0.000 0.024 0.976
#> GSM1105529 4 0.0817 0.896 0.000 0.000 0.024 0.976
#> GSM1105533 1 0.0376 0.972 0.992 0.004 0.000 0.004
#> GSM1105545 3 0.0000 0.805 0.000 0.000 1.000 0.000
#> GSM1105548 1 0.0524 0.969 0.988 0.004 0.008 0.000
#> GSM1105549 1 0.2441 0.894 0.916 0.004 0.012 0.068
#> GSM1105457 3 0.0188 0.806 0.000 0.000 0.996 0.004
#> GSM1105460 3 0.0336 0.806 0.000 0.000 0.992 0.008
#> GSM1105461 2 0.5664 0.946 0.000 0.696 0.076 0.228
#> GSM1105464 1 0.0376 0.971 0.992 0.004 0.004 0.000
#> GSM1105466 3 0.0000 0.805 0.000 0.000 1.000 0.000
#> GSM1105479 3 0.0376 0.807 0.000 0.004 0.992 0.004
#> GSM1105502 1 0.0376 0.972 0.992 0.004 0.000 0.004
#> GSM1105515 1 0.0336 0.971 0.992 0.000 0.000 0.008
#> GSM1105523 3 0.5201 0.449 0.400 0.004 0.592 0.004
#> GSM1105550 3 0.3892 0.690 0.192 0.004 0.800 0.004
#> GSM1105450 2 0.5664 0.946 0.000 0.696 0.076 0.228
#> GSM1105451 2 0.5664 0.946 0.000 0.696 0.076 0.228
#> GSM1105454 3 0.6548 0.685 0.000 0.304 0.592 0.104
#> GSM1105468 2 0.5664 0.946 0.000 0.696 0.076 0.228
#> GSM1105481 3 0.6548 0.685 0.000 0.304 0.592 0.104
#> GSM1105504 3 0.6548 0.685 0.000 0.304 0.592 0.104
#> GSM1105517 1 0.0657 0.965 0.984 0.004 0.012 0.000
#> GSM1105525 1 0.1305 0.940 0.960 0.004 0.036 0.000
#> GSM1105552 1 0.3052 0.847 0.880 0.004 0.012 0.104
#> GSM1105452 4 0.0592 0.903 0.000 0.000 0.016 0.984
#> GSM1105453 2 0.5664 0.946 0.000 0.696 0.076 0.228
#> GSM1105456 3 0.6548 0.685 0.000 0.304 0.592 0.104
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM1105438 2 0.1743 0.8975 0.000 0.940 0.028 0.028 0.004
#> GSM1105486 2 0.0290 0.9430 0.000 0.992 0.000 0.008 0.000
#> GSM1105487 1 0.2027 0.9139 0.928 0.008 0.024 0.000 0.040
#> GSM1105490 4 0.0000 0.8175 0.000 0.000 0.000 1.000 0.000
#> GSM1105491 5 0.1725 0.8956 0.000 0.000 0.044 0.020 0.936
#> GSM1105495 3 0.0703 0.8375 0.000 0.000 0.976 0.024 0.000
#> GSM1105498 4 0.3857 0.6678 0.000 0.000 0.312 0.688 0.000
#> GSM1105499 1 0.0000 0.9194 1.000 0.000 0.000 0.000 0.000
#> GSM1105506 4 0.0000 0.8175 0.000 0.000 0.000 1.000 0.000
#> GSM1105442 5 0.1799 0.9066 0.000 0.012 0.028 0.020 0.940
#> GSM1105511 4 0.2020 0.8343 0.000 0.000 0.100 0.900 0.000
#> GSM1105514 2 0.0290 0.9430 0.000 0.992 0.000 0.008 0.000
#> GSM1105518 3 0.4262 -0.0634 0.000 0.000 0.560 0.440 0.000
#> GSM1105522 1 0.0404 0.9201 0.988 0.000 0.000 0.012 0.000
#> GSM1105534 1 0.0162 0.9189 0.996 0.000 0.004 0.000 0.000
#> GSM1105535 1 0.0000 0.9194 1.000 0.000 0.000 0.000 0.000
#> GSM1105538 1 0.0000 0.9194 1.000 0.000 0.000 0.000 0.000
#> GSM1105542 5 0.1668 0.9071 0.000 0.032 0.028 0.000 0.940
#> GSM1105443 4 0.3351 0.7427 0.000 0.132 0.028 0.836 0.004
#> GSM1105551 1 0.2027 0.9139 0.928 0.008 0.024 0.000 0.040
#> GSM1105554 1 0.0000 0.9194 1.000 0.000 0.000 0.000 0.000
#> GSM1105555 1 0.2027 0.9139 0.928 0.008 0.024 0.000 0.040
#> GSM1105447 4 0.5684 0.6714 0.000 0.156 0.196 0.644 0.004
#> GSM1105467 2 0.4209 0.6544 0.000 0.744 0.028 0.224 0.004
#> GSM1105470 2 0.0290 0.9430 0.000 0.992 0.000 0.008 0.000
#> GSM1105471 4 0.4114 0.3219 0.000 0.000 0.376 0.624 0.000
#> GSM1105474 2 0.0290 0.9430 0.000 0.992 0.000 0.008 0.000
#> GSM1105475 4 0.3305 0.6720 0.000 0.224 0.000 0.776 0.000
#> GSM1105440 1 0.0000 0.9194 1.000 0.000 0.000 0.000 0.000
#> GSM1105488 5 0.2124 0.9137 0.000 0.056 0.028 0.000 0.916
#> GSM1105489 1 0.2036 0.9142 0.928 0.008 0.028 0.000 0.036
#> GSM1105492 1 0.0000 0.9194 1.000 0.000 0.000 0.000 0.000
#> GSM1105493 1 0.2253 0.9058 0.924 0.008 0.036 0.020 0.012
#> GSM1105497 5 0.1648 0.8989 0.000 0.000 0.040 0.020 0.940
#> GSM1105500 4 0.3196 0.7866 0.000 0.000 0.192 0.804 0.004
#> GSM1105501 4 0.2020 0.8343 0.000 0.000 0.100 0.900 0.000
#> GSM1105508 1 0.0609 0.9189 0.980 0.000 0.000 0.020 0.000
#> GSM1105444 2 0.1743 0.8975 0.000 0.940 0.028 0.028 0.004
#> GSM1105513 4 0.1106 0.8208 0.000 0.012 0.024 0.964 0.000
#> GSM1105516 1 0.5253 0.5963 0.676 0.000 0.124 0.200 0.000
#> GSM1105520 3 0.2690 0.7132 0.000 0.000 0.844 0.156 0.000
#> GSM1105524 1 0.0000 0.9194 1.000 0.000 0.000 0.000 0.000
#> GSM1105536 4 0.2719 0.8156 0.000 0.004 0.144 0.852 0.000
#> GSM1105537 1 0.0000 0.9194 1.000 0.000 0.000 0.000 0.000
#> GSM1105540 1 0.4028 0.7381 0.776 0.000 0.176 0.048 0.000
#> GSM1105544 4 0.4238 0.7429 0.052 0.000 0.192 0.756 0.000
#> GSM1105445 4 0.4171 0.4916 0.000 0.000 0.396 0.604 0.000
#> GSM1105553 3 0.0703 0.8375 0.000 0.000 0.976 0.024 0.000
#> GSM1105556 1 0.0162 0.9189 0.996 0.000 0.004 0.000 0.000
#> GSM1105557 4 0.0000 0.8175 0.000 0.000 0.000 1.000 0.000
#> GSM1105449 2 0.3110 0.8162 0.000 0.856 0.028 0.112 0.004
#> GSM1105469 4 0.2798 0.8140 0.008 0.000 0.140 0.852 0.000
#> GSM1105472 2 0.0290 0.9430 0.000 0.992 0.000 0.008 0.000
#> GSM1105473 1 0.2171 0.9061 0.928 0.008 0.032 0.020 0.012
#> GSM1105476 2 0.2074 0.8536 0.000 0.896 0.000 0.104 0.000
#> GSM1105477 4 0.4373 0.7804 0.000 0.080 0.160 0.760 0.000
#> GSM1105478 4 0.1478 0.8323 0.000 0.000 0.064 0.936 0.000
#> GSM1105510 5 0.4044 0.8807 0.000 0.140 0.028 0.028 0.804
#> GSM1105530 1 0.2027 0.9139 0.928 0.008 0.024 0.000 0.040
#> GSM1105539 1 0.2027 0.9139 0.928 0.008 0.024 0.000 0.040
#> GSM1105480 4 0.1908 0.8350 0.000 0.000 0.092 0.908 0.000
#> GSM1105512 1 0.0000 0.9194 1.000 0.000 0.000 0.000 0.000
#> GSM1105532 1 0.2027 0.9139 0.928 0.008 0.024 0.000 0.040
#> GSM1105541 1 0.2027 0.9139 0.928 0.008 0.024 0.000 0.040
#> GSM1105439 4 0.1043 0.8113 0.000 0.040 0.000 0.960 0.000
#> GSM1105463 1 0.5468 0.5260 0.628 0.008 0.312 0.016 0.036
#> GSM1105482 1 0.1012 0.9182 0.968 0.000 0.012 0.020 0.000
#> GSM1105483 4 0.2074 0.8332 0.000 0.000 0.104 0.896 0.000
#> GSM1105494 4 0.2230 0.8320 0.000 0.000 0.116 0.884 0.000
#> GSM1105503 3 0.0703 0.8375 0.000 0.000 0.976 0.024 0.000
#> GSM1105507 1 0.2966 0.7831 0.816 0.000 0.000 0.184 0.000
#> GSM1105446 2 0.3421 0.7000 0.000 0.788 0.000 0.008 0.204
#> GSM1105519 1 0.0510 0.9196 0.984 0.000 0.000 0.016 0.000
#> GSM1105526 4 0.2329 0.8255 0.000 0.000 0.124 0.876 0.000
#> GSM1105527 4 0.1965 0.8348 0.000 0.000 0.096 0.904 0.000
#> GSM1105531 3 0.1498 0.8225 0.000 0.008 0.952 0.024 0.016
#> GSM1105543 2 0.0290 0.9430 0.000 0.992 0.000 0.008 0.000
#> GSM1105546 1 0.0000 0.9194 1.000 0.000 0.000 0.000 0.000
#> GSM1105547 1 0.1399 0.9110 0.952 0.000 0.028 0.020 0.000
#> GSM1105455 4 0.2773 0.7225 0.000 0.164 0.000 0.836 0.000
#> GSM1105458 4 0.4692 0.6387 0.000 0.024 0.320 0.652 0.004
#> GSM1105459 2 0.0290 0.9430 0.000 0.992 0.000 0.008 0.000
#> GSM1105462 3 0.6940 0.2434 0.320 0.008 0.416 0.256 0.000
#> GSM1105441 2 0.0960 0.9297 0.000 0.972 0.008 0.016 0.004
#> GSM1105465 5 0.1648 0.8989 0.000 0.000 0.040 0.020 0.940
#> GSM1105484 5 0.3387 0.9102 0.000 0.100 0.028 0.020 0.852
#> GSM1105485 5 0.2722 0.9180 0.000 0.056 0.028 0.020 0.896
#> GSM1105496 3 0.0703 0.8375 0.000 0.000 0.976 0.024 0.000
#> GSM1105505 3 0.3995 0.6735 0.152 0.008 0.804 0.024 0.012
#> GSM1105509 1 0.0609 0.9189 0.980 0.000 0.000 0.020 0.000
#> GSM1105448 2 0.0451 0.9409 0.000 0.988 0.000 0.008 0.004
#> GSM1105521 1 0.0000 0.9194 1.000 0.000 0.000 0.000 0.000
#> GSM1105528 5 0.4087 0.8769 0.000 0.144 0.028 0.028 0.800
#> GSM1105529 5 0.3824 0.8940 0.000 0.128 0.028 0.024 0.820
#> GSM1105533 1 0.2027 0.9139 0.928 0.008 0.024 0.000 0.040
#> GSM1105545 4 0.2020 0.8343 0.000 0.000 0.100 0.900 0.000
#> GSM1105548 1 0.2058 0.9070 0.932 0.008 0.032 0.020 0.008
#> GSM1105549 1 0.1568 0.9081 0.944 0.000 0.036 0.020 0.000
#> GSM1105457 4 0.0000 0.8175 0.000 0.000 0.000 1.000 0.000
#> GSM1105460 4 0.1943 0.8060 0.000 0.056 0.020 0.924 0.000
#> GSM1105461 2 0.0290 0.9430 0.000 0.992 0.000 0.008 0.000
#> GSM1105464 1 0.2141 0.9148 0.928 0.008 0.020 0.008 0.036
#> GSM1105466 4 0.0000 0.8175 0.000 0.000 0.000 1.000 0.000
#> GSM1105479 4 0.3007 0.7687 0.000 0.104 0.028 0.864 0.004
#> GSM1105502 1 0.2027 0.9139 0.928 0.008 0.024 0.000 0.040
#> GSM1105515 1 0.0162 0.9189 0.996 0.000 0.004 0.000 0.000
#> GSM1105523 1 0.4584 0.7379 0.760 0.008 0.184 0.024 0.024
#> GSM1105550 4 0.4571 0.7314 0.076 0.000 0.188 0.736 0.000
#> GSM1105450 2 0.0290 0.9430 0.000 0.992 0.000 0.008 0.000
#> GSM1105451 2 0.0290 0.9430 0.000 0.992 0.000 0.008 0.000
#> GSM1105454 3 0.0703 0.8375 0.000 0.000 0.976 0.024 0.000
#> GSM1105468 2 0.0290 0.9430 0.000 0.992 0.000 0.008 0.000
#> GSM1105481 3 0.0703 0.8375 0.000 0.000 0.976 0.024 0.000
#> GSM1105504 1 0.5497 0.3392 0.564 0.008 0.388 0.024 0.016
#> GSM1105517 1 0.2813 0.8358 0.868 0.000 0.108 0.024 0.000
#> GSM1105525 1 0.3788 0.8400 0.836 0.008 0.104 0.016 0.036
#> GSM1105552 1 0.3539 0.8348 0.844 0.008 0.112 0.024 0.012
#> GSM1105452 5 0.3283 0.8857 0.000 0.140 0.028 0.000 0.832
#> GSM1105453 2 0.0290 0.9430 0.000 0.992 0.000 0.008 0.000
#> GSM1105456 3 0.0703 0.8375 0.000 0.000 0.976 0.024 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1105438 2 0.0260 0.93548 0.000 0.992 0.000 0.000 0.008 0.000
#> GSM1105486 2 0.0000 0.93741 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105487 3 0.1010 0.64662 0.036 0.000 0.960 0.000 0.000 0.004
#> GSM1105490 4 0.0000 0.78144 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1105491 5 0.0508 0.95329 0.000 0.000 0.004 0.000 0.984 0.012
#> GSM1105495 6 0.0405 0.80808 0.000 0.000 0.008 0.000 0.004 0.988
#> GSM1105498 4 0.3023 0.67124 0.000 0.000 0.004 0.784 0.000 0.212
#> GSM1105499 3 0.3468 0.42230 0.284 0.000 0.712 0.004 0.000 0.000
#> GSM1105506 4 0.0000 0.78144 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1105442 5 0.0508 0.95329 0.000 0.000 0.004 0.000 0.984 0.012
#> GSM1105511 4 0.0865 0.78061 0.036 0.000 0.000 0.964 0.000 0.000
#> GSM1105514 2 0.0146 0.93660 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM1105518 6 0.3988 0.29917 0.012 0.000 0.004 0.324 0.000 0.660
#> GSM1105522 3 0.3240 0.47274 0.244 0.000 0.752 0.004 0.000 0.000
#> GSM1105534 1 0.3126 0.82095 0.752 0.000 0.248 0.000 0.000 0.000
#> GSM1105535 3 0.3448 0.42504 0.280 0.000 0.716 0.000 0.000 0.004
#> GSM1105538 1 0.3175 0.81983 0.744 0.000 0.256 0.000 0.000 0.000
#> GSM1105542 5 0.0146 0.95323 0.000 0.004 0.000 0.000 0.996 0.000
#> GSM1105443 4 0.3795 0.33285 0.000 0.364 0.000 0.632 0.000 0.004
#> GSM1105551 3 0.0146 0.66221 0.000 0.000 0.996 0.000 0.000 0.004
#> GSM1105554 1 0.3126 0.82095 0.752 0.000 0.248 0.000 0.000 0.000
#> GSM1105555 3 0.0520 0.66124 0.008 0.000 0.984 0.000 0.000 0.008
#> GSM1105447 2 0.5909 0.34447 0.012 0.536 0.004 0.292 0.000 0.156
#> GSM1105467 2 0.0790 0.91850 0.000 0.968 0.000 0.032 0.000 0.000
#> GSM1105470 2 0.0146 0.93626 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM1105471 4 0.4226 0.32500 0.012 0.000 0.004 0.580 0.000 0.404
#> GSM1105474 2 0.0000 0.93741 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105475 2 0.3221 0.67762 0.000 0.736 0.000 0.264 0.000 0.000
#> GSM1105440 3 0.3468 0.42230 0.284 0.000 0.712 0.004 0.000 0.000
#> GSM1105488 5 0.0146 0.95323 0.000 0.004 0.000 0.000 0.996 0.000
#> GSM1105489 3 0.4264 -0.45272 0.488 0.000 0.496 0.000 0.000 0.016
#> GSM1105492 1 0.3851 0.44861 0.540 0.000 0.460 0.000 0.000 0.000
#> GSM1105493 1 0.4293 0.51745 0.536 0.000 0.448 0.000 0.004 0.012
#> GSM1105497 5 0.0508 0.95329 0.000 0.000 0.004 0.000 0.984 0.012
#> GSM1105500 4 0.3319 0.71807 0.036 0.000 0.164 0.800 0.000 0.000
#> GSM1105501 4 0.2520 0.75949 0.152 0.000 0.000 0.844 0.000 0.004
#> GSM1105508 3 0.3448 0.42880 0.280 0.000 0.716 0.004 0.000 0.000
#> GSM1105444 2 0.0665 0.92774 0.000 0.980 0.004 0.000 0.008 0.008
#> GSM1105513 4 0.0146 0.78073 0.000 0.000 0.000 0.996 0.000 0.004
#> GSM1105516 4 0.3892 0.64712 0.048 0.000 0.212 0.740 0.000 0.000
#> GSM1105520 6 0.1265 0.78025 0.000 0.000 0.008 0.044 0.000 0.948
#> GSM1105524 3 0.3448 0.42504 0.280 0.000 0.716 0.000 0.000 0.004
#> GSM1105536 4 0.2933 0.74298 0.200 0.000 0.000 0.796 0.000 0.004
#> GSM1105537 3 0.3448 0.42504 0.280 0.000 0.716 0.000 0.000 0.004
#> GSM1105540 4 0.4132 0.63983 0.044 0.000 0.220 0.728 0.000 0.008
#> GSM1105544 4 0.3562 0.70782 0.036 0.000 0.176 0.784 0.000 0.004
#> GSM1105445 4 0.4009 0.40740 0.008 0.000 0.004 0.632 0.000 0.356
#> GSM1105553 6 0.0405 0.80808 0.000 0.000 0.008 0.000 0.004 0.988
#> GSM1105556 1 0.3126 0.82095 0.752 0.000 0.248 0.000 0.000 0.000
#> GSM1105557 4 0.0000 0.78144 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1105449 2 0.1129 0.91863 0.008 0.964 0.004 0.012 0.000 0.012
#> GSM1105469 4 0.3202 0.71080 0.024 0.000 0.176 0.800 0.000 0.000
#> GSM1105472 2 0.0000 0.93741 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105473 1 0.4310 0.46202 0.512 0.000 0.472 0.000 0.004 0.012
#> GSM1105476 2 0.0790 0.91955 0.000 0.968 0.000 0.032 0.000 0.000
#> GSM1105477 4 0.3074 0.74240 0.200 0.004 0.000 0.792 0.000 0.004
#> GSM1105478 4 0.0603 0.77832 0.000 0.000 0.004 0.980 0.000 0.016
#> GSM1105510 5 0.0551 0.95516 0.000 0.004 0.004 0.000 0.984 0.008
#> GSM1105530 3 0.0405 0.66158 0.008 0.000 0.988 0.004 0.000 0.000
#> GSM1105539 3 0.0520 0.66124 0.008 0.000 0.984 0.000 0.000 0.008
#> GSM1105480 4 0.0146 0.78181 0.004 0.000 0.000 0.996 0.000 0.000
#> GSM1105512 1 0.3221 0.81488 0.736 0.000 0.264 0.000 0.000 0.000
#> GSM1105532 3 0.0405 0.66158 0.008 0.000 0.988 0.004 0.000 0.000
#> GSM1105541 3 0.1124 0.65574 0.036 0.000 0.956 0.000 0.000 0.008
#> GSM1105439 4 0.3351 0.50363 0.000 0.288 0.000 0.712 0.000 0.000
#> GSM1105463 3 0.4095 -0.00722 0.008 0.000 0.512 0.000 0.000 0.480
#> GSM1105482 1 0.3265 0.81990 0.748 0.000 0.248 0.000 0.004 0.000
#> GSM1105483 4 0.1492 0.78148 0.036 0.000 0.024 0.940 0.000 0.000
#> GSM1105494 4 0.1668 0.75927 0.008 0.000 0.004 0.928 0.000 0.060
#> GSM1105503 6 0.0363 0.80710 0.000 0.000 0.012 0.000 0.000 0.988
#> GSM1105507 4 0.4634 0.28788 0.044 0.000 0.400 0.556 0.000 0.000
#> GSM1105446 2 0.1075 0.90608 0.000 0.952 0.000 0.000 0.048 0.000
#> GSM1105519 1 0.3672 0.66737 0.632 0.000 0.368 0.000 0.000 0.000
#> GSM1105526 4 0.2933 0.74298 0.200 0.000 0.000 0.796 0.000 0.004
#> GSM1105527 4 0.0146 0.78181 0.004 0.000 0.000 0.996 0.000 0.000
#> GSM1105531 6 0.3050 0.64800 0.000 0.000 0.236 0.000 0.000 0.764
#> GSM1105543 2 0.0146 0.93660 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM1105546 1 0.3221 0.81501 0.736 0.000 0.264 0.000 0.000 0.000
#> GSM1105547 1 0.3265 0.81990 0.748 0.000 0.248 0.000 0.004 0.000
#> GSM1105455 2 0.3499 0.59431 0.000 0.680 0.000 0.320 0.000 0.000
#> GSM1105458 6 0.6048 0.16633 0.012 0.360 0.004 0.152 0.000 0.472
#> GSM1105459 2 0.0000 0.93741 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105462 4 0.6407 0.19652 0.016 0.000 0.316 0.404 0.000 0.264
#> GSM1105441 2 0.0146 0.93626 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM1105465 5 0.0508 0.95329 0.000 0.000 0.004 0.000 0.984 0.012
#> GSM1105484 5 0.0551 0.95516 0.000 0.004 0.004 0.000 0.984 0.008
#> GSM1105485 5 0.0146 0.95323 0.000 0.004 0.000 0.000 0.996 0.000
#> GSM1105496 6 0.0405 0.80808 0.000 0.000 0.008 0.000 0.004 0.988
#> GSM1105505 6 0.3198 0.61160 0.000 0.000 0.260 0.000 0.000 0.740
#> GSM1105509 1 0.3998 0.33696 0.504 0.000 0.492 0.004 0.000 0.000
#> GSM1105448 2 0.0146 0.93660 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM1105521 1 0.3175 0.81983 0.744 0.000 0.256 0.000 0.000 0.000
#> GSM1105528 5 0.2871 0.74992 0.000 0.192 0.000 0.000 0.804 0.004
#> GSM1105529 5 0.0146 0.95323 0.000 0.004 0.000 0.000 0.996 0.000
#> GSM1105533 3 0.0520 0.66124 0.008 0.000 0.984 0.000 0.000 0.008
#> GSM1105545 4 0.2933 0.74298 0.200 0.000 0.000 0.796 0.000 0.004
#> GSM1105548 1 0.4289 0.52462 0.540 0.000 0.444 0.000 0.004 0.012
#> GSM1105549 1 0.5574 0.63946 0.584 0.000 0.256 0.000 0.148 0.012
#> GSM1105457 4 0.0000 0.78144 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1105460 4 0.2462 0.70102 0.000 0.132 0.004 0.860 0.000 0.004
#> GSM1105461 2 0.0000 0.93741 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105464 3 0.2982 0.45313 0.164 0.000 0.820 0.004 0.000 0.012
#> GSM1105466 4 0.0000 0.78144 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1105479 4 0.4336 0.61560 0.012 0.064 0.004 0.744 0.000 0.176
#> GSM1105502 3 0.1010 0.64662 0.036 0.000 0.960 0.000 0.000 0.004
#> GSM1105515 1 0.3126 0.82095 0.752 0.000 0.248 0.000 0.000 0.000
#> GSM1105523 3 0.3667 0.36369 0.008 0.000 0.740 0.240 0.000 0.012
#> GSM1105550 4 0.3604 0.71252 0.036 0.000 0.168 0.788 0.000 0.008
#> GSM1105450 2 0.0000 0.93741 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105451 2 0.0000 0.93741 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105454 6 0.0405 0.80808 0.000 0.000 0.008 0.000 0.004 0.988
#> GSM1105468 2 0.0000 0.93741 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105481 6 0.0405 0.80808 0.000 0.000 0.008 0.000 0.004 0.988
#> GSM1105504 6 0.3851 0.12310 0.000 0.000 0.460 0.000 0.000 0.540
#> GSM1105517 4 0.5719 0.30821 0.248 0.000 0.232 0.520 0.000 0.000
#> GSM1105525 3 0.0767 0.65721 0.008 0.000 0.976 0.004 0.000 0.012
#> GSM1105552 3 0.4524 -0.40053 0.452 0.000 0.520 0.000 0.004 0.024
#> GSM1105452 5 0.1863 0.86218 0.000 0.104 0.000 0.000 0.896 0.000
#> GSM1105453 2 0.0000 0.93741 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105456 6 0.0405 0.80808 0.000 0.000 0.008 0.000 0.004 0.988
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 agent(p) other(p) time(p) individual(p) k
#> MAD:mclust 104 0.951 0.2767 0.808 0.02051 2
#> MAD:mclust 104 0.276 0.5956 0.628 0.01442 3
#> MAD:mclust 116 0.838 0.8813 0.496 0.01823 4
#> MAD:mclust 115 0.582 0.8363 0.504 0.00628 5
#> MAD:mclust 95 0.504 0.0932 0.673 0.00575 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 44956 rows and 120 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 0.914 0.939 0.973 0.4950 0.510 0.510
#> 3 3 0.543 0.629 0.810 0.3092 0.788 0.603
#> 4 4 0.753 0.773 0.899 0.1001 0.873 0.665
#> 5 5 0.600 0.596 0.779 0.0869 0.835 0.516
#> 6 6 0.561 0.411 0.656 0.0503 0.881 0.563
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
#> GSM1105438 2 0.0000 0.959 0.000 1.000
#> GSM1105486 2 0.0000 0.959 0.000 1.000
#> GSM1105487 1 0.0000 0.989 1.000 0.000
#> GSM1105490 2 0.0000 0.959 0.000 1.000
#> GSM1105491 2 0.6623 0.800 0.172 0.828
#> GSM1105495 2 0.6973 0.780 0.188 0.812
#> GSM1105498 2 0.8207 0.686 0.256 0.744
#> GSM1105499 1 0.0000 0.989 1.000 0.000
#> GSM1105506 2 0.0000 0.959 0.000 1.000
#> GSM1105442 2 0.0000 0.959 0.000 1.000
#> GSM1105511 2 0.0000 0.959 0.000 1.000
#> GSM1105514 2 0.0000 0.959 0.000 1.000
#> GSM1105518 2 0.0000 0.959 0.000 1.000
#> GSM1105522 1 0.0000 0.989 1.000 0.000
#> GSM1105534 1 0.0000 0.989 1.000 0.000
#> GSM1105535 1 0.0000 0.989 1.000 0.000
#> GSM1105538 1 0.0000 0.989 1.000 0.000
#> GSM1105542 2 0.0000 0.959 0.000 1.000
#> GSM1105443 2 0.0000 0.959 0.000 1.000
#> GSM1105551 1 0.0000 0.989 1.000 0.000
#> GSM1105554 1 0.0000 0.989 1.000 0.000
#> GSM1105555 1 0.0000 0.989 1.000 0.000
#> GSM1105447 2 0.0000 0.959 0.000 1.000
#> GSM1105467 2 0.0000 0.959 0.000 1.000
#> GSM1105470 2 0.0000 0.959 0.000 1.000
#> GSM1105471 2 0.0000 0.959 0.000 1.000
#> GSM1105474 2 0.0000 0.959 0.000 1.000
#> GSM1105475 2 0.0000 0.959 0.000 1.000
#> GSM1105440 1 0.0000 0.989 1.000 0.000
#> GSM1105488 2 0.0000 0.959 0.000 1.000
#> GSM1105489 1 0.0000 0.989 1.000 0.000
#> GSM1105492 1 0.0000 0.989 1.000 0.000
#> GSM1105493 1 0.0000 0.989 1.000 0.000
#> GSM1105497 2 0.0000 0.959 0.000 1.000
#> GSM1105500 2 0.0000 0.959 0.000 1.000
#> GSM1105501 2 0.0000 0.959 0.000 1.000
#> GSM1105508 1 0.0000 0.989 1.000 0.000
#> GSM1105444 2 0.0000 0.959 0.000 1.000
#> GSM1105513 2 0.0000 0.959 0.000 1.000
#> GSM1105516 1 0.9170 0.494 0.668 0.332
#> GSM1105520 2 0.8207 0.686 0.256 0.744
#> GSM1105524 1 0.0000 0.989 1.000 0.000
#> GSM1105536 2 0.0000 0.959 0.000 1.000
#> GSM1105537 1 0.0000 0.989 1.000 0.000
#> GSM1105540 1 0.0000 0.989 1.000 0.000
#> GSM1105544 2 0.9710 0.361 0.400 0.600
#> GSM1105445 2 0.0000 0.959 0.000 1.000
#> GSM1105553 2 0.9286 0.526 0.344 0.656
#> GSM1105556 1 0.0000 0.989 1.000 0.000
#> GSM1105557 2 0.0000 0.959 0.000 1.000
#> GSM1105449 2 0.0000 0.959 0.000 1.000
#> GSM1105469 1 0.3274 0.930 0.940 0.060
#> GSM1105472 2 0.0000 0.959 0.000 1.000
#> GSM1105473 1 0.0000 0.989 1.000 0.000
#> GSM1105476 2 0.0000 0.959 0.000 1.000
#> GSM1105477 2 0.0000 0.959 0.000 1.000
#> GSM1105478 2 0.1633 0.942 0.024 0.976
#> GSM1105510 2 0.0000 0.959 0.000 1.000
#> GSM1105530 1 0.0000 0.989 1.000 0.000
#> GSM1105539 1 0.0000 0.989 1.000 0.000
#> GSM1105480 2 0.0000 0.959 0.000 1.000
#> GSM1105512 1 0.0000 0.989 1.000 0.000
#> GSM1105532 1 0.0000 0.989 1.000 0.000
#> GSM1105541 1 0.0000 0.989 1.000 0.000
#> GSM1105439 2 0.0000 0.959 0.000 1.000
#> GSM1105463 1 0.0000 0.989 1.000 0.000
#> GSM1105482 1 0.0000 0.989 1.000 0.000
#> GSM1105483 2 0.5737 0.834 0.136 0.864
#> GSM1105494 2 0.0000 0.959 0.000 1.000
#> GSM1105503 2 0.9970 0.194 0.468 0.532
#> GSM1105507 1 0.1414 0.971 0.980 0.020
#> GSM1105446 2 0.0000 0.959 0.000 1.000
#> GSM1105519 1 0.0000 0.989 1.000 0.000
#> GSM1105526 2 0.0000 0.959 0.000 1.000
#> GSM1105527 2 0.0938 0.951 0.012 0.988
#> GSM1105531 1 0.0000 0.989 1.000 0.000
#> GSM1105543 2 0.0000 0.959 0.000 1.000
#> GSM1105546 1 0.0000 0.989 1.000 0.000
#> GSM1105547 1 0.0000 0.989 1.000 0.000
#> GSM1105455 2 0.0000 0.959 0.000 1.000
#> GSM1105458 2 0.0000 0.959 0.000 1.000
#> GSM1105459 2 0.0000 0.959 0.000 1.000
#> GSM1105462 1 0.2948 0.938 0.948 0.052
#> GSM1105441 2 0.0000 0.959 0.000 1.000
#> GSM1105465 2 0.0000 0.959 0.000 1.000
#> GSM1105484 2 0.0000 0.959 0.000 1.000
#> GSM1105485 2 0.0376 0.957 0.004 0.996
#> GSM1105496 1 0.2778 0.942 0.952 0.048
#> GSM1105505 1 0.0000 0.989 1.000 0.000
#> GSM1105509 1 0.0000 0.989 1.000 0.000
#> GSM1105448 2 0.0000 0.959 0.000 1.000
#> GSM1105521 1 0.0000 0.989 1.000 0.000
#> GSM1105528 2 0.0000 0.959 0.000 1.000
#> GSM1105529 2 0.0000 0.959 0.000 1.000
#> GSM1105533 1 0.0000 0.989 1.000 0.000
#> GSM1105545 2 0.0000 0.959 0.000 1.000
#> GSM1105548 1 0.0000 0.989 1.000 0.000
#> GSM1105549 1 0.0000 0.989 1.000 0.000
#> GSM1105457 2 0.0000 0.959 0.000 1.000
#> GSM1105460 2 0.0000 0.959 0.000 1.000
#> GSM1105461 2 0.0000 0.959 0.000 1.000
#> GSM1105464 1 0.0000 0.989 1.000 0.000
#> GSM1105466 2 0.0000 0.959 0.000 1.000
#> GSM1105479 2 0.0000 0.959 0.000 1.000
#> GSM1105502 1 0.0000 0.989 1.000 0.000
#> GSM1105515 1 0.0000 0.989 1.000 0.000
#> GSM1105523 1 0.0000 0.989 1.000 0.000
#> GSM1105550 1 0.0000 0.989 1.000 0.000
#> GSM1105450 2 0.0000 0.959 0.000 1.000
#> GSM1105451 2 0.0000 0.959 0.000 1.000
#> GSM1105454 2 0.4161 0.890 0.084 0.916
#> GSM1105468 2 0.0000 0.959 0.000 1.000
#> GSM1105481 2 0.7219 0.765 0.200 0.800
#> GSM1105504 1 0.0000 0.989 1.000 0.000
#> GSM1105517 1 0.0000 0.989 1.000 0.000
#> GSM1105525 1 0.0000 0.989 1.000 0.000
#> GSM1105552 1 0.0000 0.989 1.000 0.000
#> GSM1105452 2 0.0000 0.959 0.000 1.000
#> GSM1105453 2 0.0000 0.959 0.000 1.000
#> GSM1105456 2 0.7139 0.770 0.196 0.804
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1105438 3 0.5785 -0.0806 0.000 0.332 0.668
#> GSM1105486 2 0.6180 0.6915 0.000 0.584 0.416
#> GSM1105487 1 0.3619 0.8137 0.864 0.136 0.000
#> GSM1105490 2 0.5098 0.7739 0.000 0.752 0.248
#> GSM1105491 3 0.5062 0.5603 0.016 0.184 0.800
#> GSM1105495 3 0.5365 0.5346 0.004 0.252 0.744
#> GSM1105498 2 0.1878 0.5309 0.044 0.952 0.004
#> GSM1105499 1 0.0237 0.8444 0.996 0.000 0.004
#> GSM1105506 2 0.5406 0.7683 0.012 0.764 0.224
#> GSM1105442 3 0.0237 0.6263 0.000 0.004 0.996
#> GSM1105511 2 0.6034 0.7580 0.036 0.752 0.212
#> GSM1105514 3 0.5497 0.0867 0.000 0.292 0.708
#> GSM1105518 2 0.3551 0.7030 0.000 0.868 0.132
#> GSM1105522 1 0.2066 0.8251 0.940 0.060 0.000
#> GSM1105534 1 0.0747 0.8443 0.984 0.000 0.016
#> GSM1105535 1 0.0237 0.8441 0.996 0.004 0.000
#> GSM1105538 1 0.0592 0.8443 0.988 0.000 0.012
#> GSM1105542 3 0.0424 0.6263 0.000 0.008 0.992
#> GSM1105443 2 0.5058 0.7735 0.000 0.756 0.244
#> GSM1105551 1 0.5244 0.7675 0.756 0.240 0.004
#> GSM1105554 1 0.0592 0.8448 0.988 0.000 0.012
#> GSM1105555 1 0.5746 0.7783 0.780 0.180 0.040
#> GSM1105447 2 0.5254 0.7731 0.000 0.736 0.264
#> GSM1105467 2 0.6168 0.6952 0.000 0.588 0.412
#> GSM1105470 2 0.6111 0.7090 0.000 0.604 0.396
#> GSM1105471 2 0.4702 0.7635 0.000 0.788 0.212
#> GSM1105474 2 0.6305 0.5935 0.000 0.516 0.484
#> GSM1105475 2 0.5785 0.7506 0.000 0.668 0.332
#> GSM1105440 1 0.0237 0.8441 0.996 0.004 0.000
#> GSM1105488 3 0.1031 0.6227 0.000 0.024 0.976
#> GSM1105489 1 0.5798 0.7763 0.776 0.184 0.040
#> GSM1105492 1 0.0475 0.8440 0.992 0.004 0.004
#> GSM1105493 3 0.9173 0.1118 0.304 0.176 0.520
#> GSM1105497 3 0.1643 0.6186 0.000 0.044 0.956
#> GSM1105500 3 0.5138 0.2239 0.000 0.252 0.748
#> GSM1105501 2 0.6723 0.7543 0.048 0.704 0.248
#> GSM1105508 1 0.1529 0.8338 0.960 0.040 0.000
#> GSM1105444 3 0.6295 -0.5284 0.000 0.472 0.528
#> GSM1105513 2 0.5016 0.7731 0.000 0.760 0.240
#> GSM1105516 3 0.7129 0.2466 0.392 0.028 0.580
#> GSM1105520 2 0.2339 0.5059 0.048 0.940 0.012
#> GSM1105524 1 0.0237 0.8441 0.996 0.004 0.000
#> GSM1105536 3 0.6286 -0.5051 0.000 0.464 0.536
#> GSM1105537 1 0.0237 0.8441 0.996 0.004 0.000
#> GSM1105540 1 0.0592 0.8437 0.988 0.012 0.000
#> GSM1105544 1 0.9640 -0.0849 0.468 0.252 0.280
#> GSM1105445 2 0.4399 0.7475 0.000 0.812 0.188
#> GSM1105553 2 0.4636 0.3784 0.116 0.848 0.036
#> GSM1105556 1 0.5325 0.6096 0.748 0.004 0.248
#> GSM1105557 2 0.5158 0.7721 0.004 0.764 0.232
#> GSM1105449 2 0.5905 0.7408 0.000 0.648 0.352
#> GSM1105469 1 0.5058 0.6424 0.756 0.244 0.000
#> GSM1105472 2 0.6302 0.6010 0.000 0.520 0.480
#> GSM1105473 3 0.8875 0.0432 0.364 0.128 0.508
#> GSM1105476 2 0.6295 0.6154 0.000 0.528 0.472
#> GSM1105477 3 0.2261 0.5892 0.000 0.068 0.932
#> GSM1105478 2 0.4755 0.7413 0.008 0.808 0.184
#> GSM1105510 3 0.1529 0.6159 0.000 0.040 0.960
#> GSM1105530 1 0.0424 0.8456 0.992 0.008 0.000
#> GSM1105539 1 0.6295 0.7520 0.728 0.236 0.036
#> GSM1105480 2 0.5158 0.7710 0.004 0.764 0.232
#> GSM1105512 1 0.0475 0.8440 0.992 0.004 0.004
#> GSM1105532 1 0.0892 0.8445 0.980 0.020 0.000
#> GSM1105541 1 0.5678 0.7783 0.776 0.192 0.032
#> GSM1105439 2 0.5178 0.7735 0.000 0.744 0.256
#> GSM1105463 1 0.6402 0.7518 0.724 0.236 0.040
#> GSM1105482 1 0.5902 0.5210 0.680 0.004 0.316
#> GSM1105483 2 0.6169 0.3425 0.360 0.636 0.004
#> GSM1105494 2 0.4931 0.7718 0.000 0.768 0.232
#> GSM1105503 2 0.4351 0.3531 0.168 0.828 0.004
#> GSM1105507 1 0.0237 0.8441 0.996 0.004 0.000
#> GSM1105446 3 0.3619 0.4934 0.000 0.136 0.864
#> GSM1105519 1 0.0424 0.8447 0.992 0.000 0.008
#> GSM1105526 2 0.6505 0.6165 0.004 0.528 0.468
#> GSM1105527 2 0.6443 0.5131 0.240 0.720 0.040
#> GSM1105531 1 0.6730 0.7285 0.680 0.284 0.036
#> GSM1105543 3 0.4399 0.3915 0.000 0.188 0.812
#> GSM1105546 1 0.1129 0.8437 0.976 0.004 0.020
#> GSM1105547 3 0.6520 -0.0770 0.488 0.004 0.508
#> GSM1105455 2 0.5254 0.7730 0.000 0.736 0.264
#> GSM1105458 2 0.5905 0.7408 0.000 0.648 0.352
#> GSM1105459 2 0.6267 0.6467 0.000 0.548 0.452
#> GSM1105462 1 0.6823 0.7193 0.668 0.296 0.036
#> GSM1105441 2 0.5905 0.7408 0.000 0.648 0.352
#> GSM1105465 3 0.3619 0.5865 0.000 0.136 0.864
#> GSM1105484 3 0.1529 0.6159 0.000 0.040 0.960
#> GSM1105485 3 0.0848 0.6270 0.008 0.008 0.984
#> GSM1105496 1 0.6852 0.7154 0.664 0.300 0.036
#> GSM1105505 1 0.6452 0.7462 0.712 0.252 0.036
#> GSM1105509 1 0.0475 0.8440 0.992 0.004 0.004
#> GSM1105448 2 0.6308 0.5774 0.000 0.508 0.492
#> GSM1105521 1 0.0747 0.8435 0.984 0.000 0.016
#> GSM1105528 3 0.1529 0.6159 0.000 0.040 0.960
#> GSM1105529 3 0.1411 0.6176 0.000 0.036 0.964
#> GSM1105533 1 0.5798 0.7763 0.776 0.184 0.040
#> GSM1105545 2 0.6696 0.7317 0.020 0.632 0.348
#> GSM1105548 1 0.9488 0.2080 0.424 0.184 0.392
#> GSM1105549 3 0.7250 0.1405 0.396 0.032 0.572
#> GSM1105457 2 0.4931 0.7720 0.000 0.768 0.232
#> GSM1105460 2 0.5254 0.7731 0.000 0.736 0.264
#> GSM1105461 2 0.6225 0.6734 0.000 0.568 0.432
#> GSM1105464 1 0.1647 0.8399 0.960 0.004 0.036
#> GSM1105466 2 0.5201 0.7721 0.004 0.760 0.236
#> GSM1105479 2 0.5016 0.7731 0.000 0.760 0.240
#> GSM1105502 1 0.3983 0.8098 0.852 0.144 0.004
#> GSM1105515 1 0.2165 0.8295 0.936 0.000 0.064
#> GSM1105523 1 0.5327 0.6583 0.728 0.272 0.000
#> GSM1105550 1 0.3879 0.7514 0.848 0.152 0.000
#> GSM1105450 2 0.6204 0.6827 0.000 0.576 0.424
#> GSM1105451 2 0.6180 0.6913 0.000 0.584 0.416
#> GSM1105454 2 0.1015 0.5492 0.008 0.980 0.012
#> GSM1105468 2 0.6235 0.6682 0.000 0.564 0.436
#> GSM1105481 2 0.2339 0.5177 0.012 0.940 0.048
#> GSM1105504 1 0.6375 0.7494 0.720 0.244 0.036
#> GSM1105517 1 0.0475 0.8440 0.992 0.004 0.004
#> GSM1105525 1 0.4555 0.7443 0.800 0.200 0.000
#> GSM1105552 3 0.9364 0.0224 0.332 0.184 0.484
#> GSM1105452 3 0.1529 0.6159 0.000 0.040 0.960
#> GSM1105453 2 0.6267 0.6467 0.000 0.548 0.452
#> GSM1105456 2 0.1482 0.5359 0.012 0.968 0.020
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1105438 4 0.4985 0.0347 0.000 0.468 0.000 0.532
#> GSM1105486 2 0.2149 0.8662 0.000 0.912 0.000 0.088
#> GSM1105487 1 0.0336 0.9146 0.992 0.000 0.008 0.000
#> GSM1105490 2 0.0000 0.8888 0.000 1.000 0.000 0.000
#> GSM1105491 4 0.0188 0.7852 0.004 0.000 0.000 0.996
#> GSM1105495 3 0.1042 0.8460 0.000 0.008 0.972 0.020
#> GSM1105498 2 0.1970 0.8446 0.008 0.932 0.060 0.000
#> GSM1105499 1 0.0188 0.9153 0.996 0.000 0.000 0.004
#> GSM1105506 2 0.0524 0.8849 0.008 0.988 0.004 0.000
#> GSM1105442 4 0.0188 0.7878 0.000 0.004 0.000 0.996
#> GSM1105511 2 0.0524 0.8849 0.008 0.988 0.004 0.000
#> GSM1105514 4 0.4661 0.4088 0.000 0.348 0.000 0.652
#> GSM1105518 2 0.1398 0.8792 0.000 0.956 0.040 0.004
#> GSM1105522 1 0.0895 0.9051 0.976 0.020 0.004 0.000
#> GSM1105534 1 0.0707 0.9119 0.980 0.000 0.000 0.020
#> GSM1105535 1 0.0188 0.9153 0.996 0.000 0.000 0.004
#> GSM1105538 1 0.0707 0.9119 0.980 0.000 0.000 0.020
#> GSM1105542 4 0.0188 0.7878 0.000 0.004 0.000 0.996
#> GSM1105443 2 0.0336 0.8908 0.000 0.992 0.000 0.008
#> GSM1105551 1 0.3764 0.7199 0.784 0.000 0.216 0.000
#> GSM1105554 1 0.0592 0.9133 0.984 0.000 0.000 0.016
#> GSM1105555 1 0.3768 0.7524 0.808 0.000 0.184 0.008
#> GSM1105447 2 0.0921 0.8907 0.000 0.972 0.000 0.028
#> GSM1105467 2 0.1716 0.8802 0.000 0.936 0.000 0.064
#> GSM1105470 2 0.1022 0.8901 0.000 0.968 0.000 0.032
#> GSM1105471 2 0.0336 0.8908 0.000 0.992 0.000 0.008
#> GSM1105474 2 0.3801 0.7394 0.000 0.780 0.000 0.220
#> GSM1105475 2 0.0817 0.8911 0.000 0.976 0.000 0.024
#> GSM1105440 1 0.0000 0.9152 1.000 0.000 0.000 0.000
#> GSM1105488 4 0.0188 0.7878 0.000 0.004 0.000 0.996
#> GSM1105489 1 0.5228 0.5238 0.664 0.000 0.312 0.024
#> GSM1105492 1 0.0336 0.9152 0.992 0.000 0.000 0.008
#> GSM1105493 4 0.2773 0.7048 0.116 0.000 0.004 0.880
#> GSM1105497 4 0.0188 0.7878 0.000 0.004 0.000 0.996
#> GSM1105500 4 0.4955 0.1309 0.000 0.444 0.000 0.556
#> GSM1105501 2 0.0844 0.8855 0.012 0.980 0.004 0.004
#> GSM1105508 1 0.0592 0.9094 0.984 0.016 0.000 0.000
#> GSM1105444 2 0.4817 0.4181 0.000 0.612 0.000 0.388
#> GSM1105513 2 0.0188 0.8901 0.000 0.996 0.000 0.004
#> GSM1105516 4 0.4543 0.4677 0.324 0.000 0.000 0.676
#> GSM1105520 2 0.4941 0.1549 0.000 0.564 0.436 0.000
#> GSM1105524 1 0.0000 0.9152 1.000 0.000 0.000 0.000
#> GSM1105536 2 0.4866 0.3785 0.000 0.596 0.000 0.404
#> GSM1105537 1 0.0000 0.9152 1.000 0.000 0.000 0.000
#> GSM1105540 1 0.0188 0.9145 0.996 0.000 0.004 0.000
#> GSM1105544 2 0.5744 0.2214 0.436 0.536 0.000 0.028
#> GSM1105445 2 0.0000 0.8888 0.000 1.000 0.000 0.000
#> GSM1105553 3 0.0188 0.8625 0.000 0.004 0.996 0.000
#> GSM1105556 1 0.2345 0.8522 0.900 0.000 0.000 0.100
#> GSM1105557 2 0.0376 0.8867 0.004 0.992 0.004 0.000
#> GSM1105449 2 0.1118 0.8892 0.000 0.964 0.000 0.036
#> GSM1105469 1 0.2944 0.7956 0.868 0.128 0.004 0.000
#> GSM1105472 2 0.3907 0.7224 0.000 0.768 0.000 0.232
#> GSM1105473 4 0.4114 0.6822 0.112 0.000 0.060 0.828
#> GSM1105476 2 0.2973 0.8242 0.000 0.856 0.000 0.144
#> GSM1105477 4 0.2921 0.7099 0.000 0.140 0.000 0.860
#> GSM1105478 2 0.0524 0.8849 0.008 0.988 0.004 0.000
#> GSM1105510 4 0.0188 0.7878 0.000 0.004 0.000 0.996
#> GSM1105530 1 0.0188 0.9145 0.996 0.000 0.004 0.000
#> GSM1105539 3 0.4941 0.1478 0.436 0.000 0.564 0.000
#> GSM1105480 2 0.0336 0.8860 0.008 0.992 0.000 0.000
#> GSM1105512 1 0.0336 0.9152 0.992 0.000 0.000 0.008
#> GSM1105532 1 0.0524 0.9125 0.988 0.004 0.008 0.000
#> GSM1105541 1 0.3837 0.7101 0.776 0.000 0.224 0.000
#> GSM1105439 2 0.0336 0.8908 0.000 0.992 0.000 0.008
#> GSM1105463 3 0.0188 0.8631 0.004 0.000 0.996 0.000
#> GSM1105482 1 0.4790 0.4171 0.620 0.000 0.000 0.380
#> GSM1105483 1 0.4720 0.4713 0.672 0.324 0.004 0.000
#> GSM1105494 2 0.0188 0.8901 0.000 0.996 0.000 0.004
#> GSM1105503 3 0.4643 0.4955 0.000 0.344 0.656 0.000
#> GSM1105507 1 0.0188 0.9145 0.996 0.000 0.004 0.000
#> GSM1105446 4 0.3975 0.6123 0.000 0.240 0.000 0.760
#> GSM1105519 1 0.0336 0.9152 0.992 0.000 0.000 0.008
#> GSM1105526 2 0.3837 0.7352 0.000 0.776 0.000 0.224
#> GSM1105527 2 0.4252 0.5769 0.252 0.744 0.004 0.000
#> GSM1105531 3 0.0188 0.8631 0.004 0.000 0.996 0.000
#> GSM1105543 4 0.4500 0.4814 0.000 0.316 0.000 0.684
#> GSM1105546 1 0.0336 0.9152 0.992 0.000 0.000 0.008
#> GSM1105547 4 0.4624 0.4123 0.340 0.000 0.000 0.660
#> GSM1105455 2 0.0469 0.8911 0.000 0.988 0.000 0.012
#> GSM1105458 2 0.1118 0.8892 0.000 0.964 0.000 0.036
#> GSM1105459 2 0.2868 0.8302 0.000 0.864 0.000 0.136
#> GSM1105462 3 0.7520 0.2791 0.340 0.196 0.464 0.000
#> GSM1105441 2 0.1022 0.8901 0.000 0.968 0.000 0.032
#> GSM1105465 4 0.0000 0.7867 0.000 0.000 0.000 1.000
#> GSM1105484 4 0.0817 0.7814 0.000 0.024 0.000 0.976
#> GSM1105485 4 0.0188 0.7852 0.004 0.000 0.000 0.996
#> GSM1105496 3 0.0188 0.8631 0.004 0.000 0.996 0.000
#> GSM1105505 3 0.0188 0.8631 0.004 0.000 0.996 0.000
#> GSM1105509 1 0.0376 0.9153 0.992 0.000 0.004 0.004
#> GSM1105448 2 0.4624 0.5319 0.000 0.660 0.000 0.340
#> GSM1105521 1 0.0592 0.9133 0.984 0.000 0.000 0.016
#> GSM1105528 4 0.0707 0.7834 0.000 0.020 0.000 0.980
#> GSM1105529 4 0.0000 0.7867 0.000 0.000 0.000 1.000
#> GSM1105533 1 0.3837 0.7048 0.776 0.000 0.224 0.000
#> GSM1105545 2 0.1396 0.8910 0.004 0.960 0.004 0.032
#> GSM1105548 4 0.6488 0.4063 0.292 0.000 0.104 0.604
#> GSM1105549 4 0.1716 0.7482 0.064 0.000 0.000 0.936
#> GSM1105457 2 0.0000 0.8888 0.000 1.000 0.000 0.000
#> GSM1105460 2 0.0817 0.8913 0.000 0.976 0.000 0.024
#> GSM1105461 2 0.2081 0.8687 0.000 0.916 0.000 0.084
#> GSM1105464 1 0.0779 0.9141 0.980 0.000 0.004 0.016
#> GSM1105466 2 0.0376 0.8867 0.004 0.992 0.004 0.000
#> GSM1105479 2 0.0336 0.8908 0.000 0.992 0.000 0.008
#> GSM1105502 1 0.1302 0.8949 0.956 0.000 0.044 0.000
#> GSM1105515 1 0.1389 0.8950 0.952 0.000 0.000 0.048
#> GSM1105523 1 0.2611 0.8314 0.896 0.096 0.008 0.000
#> GSM1105550 1 0.1489 0.8872 0.952 0.044 0.004 0.000
#> GSM1105450 2 0.1940 0.8731 0.000 0.924 0.000 0.076
#> GSM1105451 2 0.1716 0.8790 0.000 0.936 0.000 0.064
#> GSM1105454 3 0.0188 0.8625 0.000 0.004 0.996 0.000
#> GSM1105468 2 0.2345 0.8582 0.000 0.900 0.000 0.100
#> GSM1105481 3 0.1022 0.8444 0.000 0.032 0.968 0.000
#> GSM1105504 3 0.0188 0.8631 0.004 0.000 0.996 0.000
#> GSM1105517 1 0.0524 0.9154 0.988 0.000 0.004 0.008
#> GSM1105525 1 0.1452 0.8922 0.956 0.036 0.008 0.000
#> GSM1105552 4 0.5632 0.5581 0.092 0.000 0.196 0.712
#> GSM1105452 4 0.0336 0.7867 0.000 0.008 0.000 0.992
#> GSM1105453 2 0.2973 0.8235 0.000 0.856 0.000 0.144
#> GSM1105456 3 0.0188 0.8625 0.000 0.004 0.996 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM1105438 2 0.4448 0.2798 0.000 0.516 0.000 0.004 0.480
#> GSM1105486 2 0.5018 0.6465 0.000 0.664 0.000 0.068 0.268
#> GSM1105487 1 0.3460 0.7052 0.828 0.000 0.044 0.128 0.000
#> GSM1105490 2 0.1741 0.7449 0.024 0.936 0.000 0.040 0.000
#> GSM1105491 5 0.1341 0.7771 0.056 0.000 0.000 0.000 0.944
#> GSM1105495 3 0.2463 0.7552 0.000 0.008 0.888 0.004 0.100
#> GSM1105498 2 0.3016 0.7181 0.040 0.884 0.032 0.044 0.000
#> GSM1105499 1 0.4262 0.4581 0.560 0.000 0.000 0.440 0.000
#> GSM1105506 2 0.4452 0.1018 0.004 0.500 0.000 0.496 0.000
#> GSM1105442 5 0.1168 0.7779 0.032 0.008 0.000 0.000 0.960
#> GSM1105511 2 0.2920 0.7131 0.016 0.852 0.000 0.132 0.000
#> GSM1105514 5 0.4276 0.1303 0.000 0.380 0.000 0.004 0.616
#> GSM1105518 2 0.2512 0.7439 0.004 0.904 0.060 0.028 0.004
#> GSM1105522 1 0.4302 0.3499 0.520 0.000 0.000 0.480 0.000
#> GSM1105534 1 0.2286 0.7207 0.888 0.000 0.000 0.108 0.004
#> GSM1105535 1 0.3796 0.6305 0.700 0.000 0.000 0.300 0.000
#> GSM1105538 1 0.2848 0.7163 0.840 0.000 0.000 0.156 0.004
#> GSM1105542 5 0.1478 0.7747 0.064 0.000 0.000 0.000 0.936
#> GSM1105443 2 0.0609 0.7579 0.000 0.980 0.000 0.020 0.000
#> GSM1105551 1 0.3053 0.6820 0.872 0.008 0.076 0.044 0.000
#> GSM1105554 1 0.3231 0.7022 0.800 0.000 0.000 0.196 0.004
#> GSM1105555 1 0.1251 0.6926 0.956 0.000 0.036 0.008 0.000
#> GSM1105447 2 0.1932 0.7492 0.020 0.936 0.004 0.032 0.008
#> GSM1105467 2 0.5396 0.6532 0.000 0.656 0.000 0.124 0.220
#> GSM1105470 2 0.5345 0.6687 0.000 0.668 0.000 0.136 0.196
#> GSM1105471 2 0.7285 0.2402 0.000 0.444 0.100 0.368 0.088
#> GSM1105474 2 0.3890 0.6737 0.000 0.736 0.000 0.012 0.252
#> GSM1105475 2 0.3416 0.7510 0.000 0.840 0.000 0.088 0.072
#> GSM1105440 1 0.1830 0.7100 0.924 0.008 0.000 0.068 0.000
#> GSM1105488 5 0.1478 0.7744 0.064 0.000 0.000 0.000 0.936
#> GSM1105489 1 0.2270 0.6812 0.916 0.000 0.052 0.012 0.020
#> GSM1105492 1 0.2068 0.7214 0.904 0.000 0.000 0.092 0.004
#> GSM1105493 5 0.4790 0.4966 0.332 0.000 0.012 0.016 0.640
#> GSM1105497 5 0.6662 0.5332 0.252 0.112 0.040 0.008 0.588
#> GSM1105500 1 0.5903 -0.1719 0.472 0.456 0.000 0.028 0.044
#> GSM1105501 4 0.5726 0.0882 0.004 0.368 0.000 0.548 0.080
#> GSM1105508 1 0.4080 0.6617 0.728 0.020 0.000 0.252 0.000
#> GSM1105444 2 0.4288 0.4948 0.000 0.612 0.000 0.004 0.384
#> GSM1105513 2 0.1168 0.7550 0.008 0.960 0.000 0.032 0.000
#> GSM1105516 1 0.4770 0.4314 0.644 0.000 0.000 0.036 0.320
#> GSM1105520 3 0.4487 0.6939 0.000 0.104 0.756 0.140 0.000
#> GSM1105524 1 0.4101 0.5514 0.628 0.000 0.000 0.372 0.000
#> GSM1105536 5 0.3359 0.6797 0.000 0.108 0.000 0.052 0.840
#> GSM1105537 1 0.4088 0.5587 0.632 0.000 0.000 0.368 0.000
#> GSM1105540 1 0.4430 0.4469 0.540 0.004 0.000 0.456 0.000
#> GSM1105544 1 0.4340 0.5311 0.788 0.152 0.008 0.036 0.016
#> GSM1105445 2 0.1455 0.7512 0.008 0.952 0.008 0.032 0.000
#> GSM1105553 2 0.6982 0.2338 0.260 0.544 0.148 0.044 0.004
#> GSM1105556 1 0.2482 0.7157 0.892 0.000 0.000 0.084 0.024
#> GSM1105557 2 0.1549 0.7504 0.016 0.944 0.000 0.040 0.000
#> GSM1105449 2 0.1041 0.7655 0.000 0.964 0.000 0.004 0.032
#> GSM1105469 4 0.4309 0.6242 0.148 0.084 0.000 0.768 0.000
#> GSM1105472 5 0.5112 -0.2730 0.000 0.468 0.000 0.036 0.496
#> GSM1105473 5 0.4146 0.6924 0.048 0.000 0.068 0.064 0.820
#> GSM1105476 2 0.4150 0.7002 0.000 0.748 0.000 0.036 0.216
#> GSM1105477 5 0.1205 0.7560 0.000 0.040 0.000 0.004 0.956
#> GSM1105478 2 0.4443 0.1656 0.004 0.524 0.000 0.472 0.000
#> GSM1105510 5 0.0771 0.7763 0.020 0.004 0.000 0.000 0.976
#> GSM1105530 4 0.2690 0.5874 0.156 0.000 0.000 0.844 0.000
#> GSM1105539 3 0.4873 0.4266 0.044 0.000 0.644 0.312 0.000
#> GSM1105480 2 0.1740 0.7553 0.012 0.932 0.000 0.056 0.000
#> GSM1105512 1 0.4297 0.3899 0.528 0.000 0.000 0.472 0.000
#> GSM1105532 4 0.2439 0.6310 0.120 0.000 0.004 0.876 0.000
#> GSM1105541 4 0.5875 0.4062 0.152 0.000 0.256 0.592 0.000
#> GSM1105439 2 0.1043 0.7593 0.000 0.960 0.000 0.040 0.000
#> GSM1105463 3 0.0290 0.7880 0.000 0.000 0.992 0.008 0.000
#> GSM1105482 1 0.2124 0.6884 0.916 0.000 0.000 0.028 0.056
#> GSM1105483 4 0.4083 0.6277 0.080 0.132 0.000 0.788 0.000
#> GSM1105494 2 0.2193 0.7368 0.028 0.920 0.008 0.044 0.000
#> GSM1105503 3 0.5731 0.5641 0.016 0.240 0.644 0.100 0.000
#> GSM1105507 1 0.3863 0.6675 0.740 0.012 0.000 0.248 0.000
#> GSM1105446 2 0.5213 0.6267 0.072 0.704 0.000 0.020 0.204
#> GSM1105519 1 0.4264 0.5456 0.620 0.000 0.000 0.376 0.004
#> GSM1105526 5 0.5983 0.1983 0.000 0.116 0.000 0.380 0.504
#> GSM1105527 4 0.4183 0.3729 0.008 0.324 0.000 0.668 0.000
#> GSM1105531 3 0.1197 0.7825 0.000 0.000 0.952 0.048 0.000
#> GSM1105543 2 0.4225 0.5363 0.000 0.632 0.000 0.004 0.364
#> GSM1105546 1 0.1341 0.7178 0.944 0.000 0.000 0.056 0.000
#> GSM1105547 1 0.2873 0.6361 0.856 0.000 0.000 0.016 0.128
#> GSM1105455 2 0.0771 0.7571 0.004 0.976 0.000 0.020 0.000
#> GSM1105458 2 0.1914 0.7671 0.000 0.924 0.000 0.016 0.060
#> GSM1105459 2 0.4562 0.6242 0.000 0.676 0.000 0.032 0.292
#> GSM1105462 4 0.5331 0.4677 0.012 0.036 0.128 0.744 0.080
#> GSM1105441 2 0.1408 0.7664 0.000 0.948 0.000 0.008 0.044
#> GSM1105465 5 0.1197 0.7784 0.048 0.000 0.000 0.000 0.952
#> GSM1105484 5 0.0955 0.7585 0.000 0.028 0.000 0.004 0.968
#> GSM1105485 5 0.1952 0.7638 0.084 0.000 0.000 0.004 0.912
#> GSM1105496 3 0.7076 0.4586 0.296 0.168 0.500 0.032 0.004
#> GSM1105505 3 0.0162 0.7876 0.000 0.000 0.996 0.004 0.000
#> GSM1105509 4 0.4114 0.0215 0.376 0.000 0.000 0.624 0.000
#> GSM1105448 2 0.4235 0.5680 0.000 0.656 0.000 0.008 0.336
#> GSM1105521 4 0.6578 0.0381 0.284 0.000 0.000 0.468 0.248
#> GSM1105528 5 0.0955 0.7581 0.000 0.028 0.000 0.004 0.968
#> GSM1105529 5 0.0963 0.7783 0.036 0.000 0.000 0.000 0.964
#> GSM1105533 1 0.5076 0.6236 0.692 0.000 0.200 0.108 0.000
#> GSM1105545 4 0.6653 0.1158 0.016 0.296 0.000 0.516 0.172
#> GSM1105548 1 0.2026 0.6680 0.928 0.000 0.016 0.012 0.044
#> GSM1105549 5 0.4003 0.5710 0.288 0.000 0.000 0.008 0.704
#> GSM1105457 2 0.3053 0.6923 0.008 0.828 0.000 0.164 0.000
#> GSM1105460 2 0.6195 0.3786 0.000 0.488 0.000 0.368 0.144
#> GSM1105461 2 0.3852 0.6956 0.000 0.760 0.000 0.020 0.220
#> GSM1105464 4 0.2677 0.6436 0.112 0.000 0.016 0.872 0.000
#> GSM1105466 2 0.4151 0.4446 0.000 0.652 0.000 0.344 0.004
#> GSM1105479 2 0.4269 0.6229 0.000 0.732 0.000 0.232 0.036
#> GSM1105502 1 0.5752 0.4028 0.500 0.000 0.088 0.412 0.000
#> GSM1105515 1 0.2189 0.7173 0.904 0.000 0.000 0.084 0.012
#> GSM1105523 4 0.3411 0.6657 0.072 0.036 0.032 0.860 0.000
#> GSM1105550 4 0.1444 0.6668 0.040 0.012 0.000 0.948 0.000
#> GSM1105450 2 0.3495 0.7319 0.000 0.812 0.000 0.028 0.160
#> GSM1105451 2 0.1740 0.7649 0.000 0.932 0.000 0.012 0.056
#> GSM1105454 3 0.2497 0.7522 0.004 0.112 0.880 0.004 0.000
#> GSM1105468 2 0.4547 0.6647 0.000 0.704 0.000 0.044 0.252
#> GSM1105481 3 0.5746 0.6293 0.000 0.044 0.692 0.148 0.116
#> GSM1105504 3 0.2513 0.7498 0.008 0.000 0.876 0.116 0.000
#> GSM1105517 4 0.1792 0.6593 0.084 0.000 0.000 0.916 0.000
#> GSM1105525 4 0.3639 0.6158 0.164 0.008 0.020 0.808 0.000
#> GSM1105552 5 0.5047 0.5721 0.028 0.000 0.192 0.056 0.724
#> GSM1105452 5 0.2873 0.7451 0.120 0.020 0.000 0.000 0.860
#> GSM1105453 2 0.2006 0.7620 0.000 0.916 0.000 0.012 0.072
#> GSM1105456 3 0.0162 0.7869 0.004 0.000 0.996 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1105438 2 0.5130 0.51450 0.000 0.664 0.080 0.032 0.224 0.000
#> GSM1105486 2 0.6832 0.02991 0.000 0.372 0.044 0.292 0.292 0.000
#> GSM1105487 1 0.4321 0.19784 0.580 0.000 0.400 0.008 0.000 0.012
#> GSM1105490 2 0.3449 0.57125 0.000 0.808 0.116 0.076 0.000 0.000
#> GSM1105491 5 0.4547 0.63765 0.008 0.032 0.148 0.060 0.752 0.000
#> GSM1105495 6 0.2975 0.57039 0.000 0.004 0.012 0.004 0.148 0.832
#> GSM1105498 3 0.7341 0.01482 0.008 0.328 0.384 0.196 0.004 0.080
#> GSM1105499 1 0.1984 0.60418 0.912 0.000 0.032 0.056 0.000 0.000
#> GSM1105506 4 0.4342 0.35861 0.008 0.308 0.028 0.656 0.000 0.000
#> GSM1105442 5 0.1088 0.73417 0.000 0.024 0.016 0.000 0.960 0.000
#> GSM1105511 2 0.5540 0.11612 0.036 0.504 0.056 0.404 0.000 0.000
#> GSM1105514 5 0.6043 0.46423 0.000 0.260 0.116 0.056 0.568 0.000
#> GSM1105518 2 0.5257 0.43499 0.000 0.688 0.072 0.080 0.000 0.160
#> GSM1105522 1 0.4288 0.51205 0.716 0.000 0.064 0.216 0.000 0.004
#> GSM1105534 1 0.3126 0.49329 0.752 0.000 0.248 0.000 0.000 0.000
#> GSM1105535 1 0.1408 0.60110 0.944 0.000 0.036 0.020 0.000 0.000
#> GSM1105538 1 0.4539 0.37432 0.644 0.000 0.304 0.004 0.048 0.000
#> GSM1105542 5 0.1958 0.72789 0.000 0.004 0.100 0.000 0.896 0.000
#> GSM1105443 2 0.1780 0.62630 0.000 0.924 0.028 0.048 0.000 0.000
#> GSM1105551 3 0.4481 0.06715 0.416 0.004 0.556 0.000 0.000 0.024
#> GSM1105554 1 0.2214 0.58979 0.888 0.000 0.096 0.016 0.000 0.000
#> GSM1105555 1 0.4970 0.17215 0.540 0.000 0.396 0.004 0.000 0.060
#> GSM1105447 2 0.1759 0.62837 0.000 0.924 0.064 0.004 0.004 0.004
#> GSM1105467 4 0.6852 0.14558 0.000 0.284 0.052 0.404 0.260 0.000
#> GSM1105470 4 0.6695 0.00123 0.000 0.364 0.040 0.372 0.224 0.000
#> GSM1105471 4 0.7551 0.41261 0.000 0.140 0.064 0.508 0.136 0.152
#> GSM1105474 2 0.6667 0.20914 0.000 0.448 0.064 0.160 0.328 0.000
#> GSM1105475 2 0.6488 -0.08704 0.000 0.396 0.036 0.388 0.180 0.000
#> GSM1105440 1 0.3742 0.35259 0.648 0.000 0.348 0.004 0.000 0.000
#> GSM1105488 5 0.2666 0.70647 0.000 0.012 0.112 0.012 0.864 0.000
#> GSM1105489 3 0.4932 -0.12543 0.472 0.000 0.476 0.000 0.008 0.044
#> GSM1105492 1 0.4209 0.28843 0.596 0.000 0.384 0.020 0.000 0.000
#> GSM1105493 5 0.6614 0.38249 0.152 0.000 0.240 0.060 0.536 0.012
#> GSM1105497 3 0.6178 -0.30422 0.000 0.076 0.468 0.024 0.404 0.028
#> GSM1105500 3 0.6623 0.14695 0.080 0.420 0.420 0.032 0.048 0.000
#> GSM1105501 2 0.7262 0.11409 0.072 0.444 0.080 0.336 0.068 0.000
#> GSM1105508 1 0.2767 0.59742 0.868 0.004 0.072 0.056 0.000 0.000
#> GSM1105444 2 0.4428 0.46816 0.000 0.640 0.024 0.012 0.324 0.000
#> GSM1105513 2 0.4054 0.52029 0.000 0.740 0.072 0.188 0.000 0.000
#> GSM1105516 1 0.7229 0.25678 0.452 0.008 0.184 0.108 0.248 0.000
#> GSM1105520 6 0.6216 0.31475 0.004 0.156 0.024 0.320 0.000 0.496
#> GSM1105524 1 0.1644 0.60314 0.932 0.000 0.028 0.040 0.000 0.000
#> GSM1105536 5 0.6336 0.41224 0.020 0.140 0.068 0.164 0.608 0.000
#> GSM1105537 1 0.1572 0.60112 0.936 0.000 0.036 0.028 0.000 0.000
#> GSM1105540 4 0.7462 0.10792 0.328 0.008 0.204 0.372 0.080 0.008
#> GSM1105544 3 0.6053 0.34236 0.212 0.044 0.624 0.032 0.088 0.000
#> GSM1105445 2 0.2144 0.62140 0.000 0.908 0.040 0.048 0.000 0.004
#> GSM1105553 3 0.5283 0.25640 0.004 0.336 0.584 0.012 0.004 0.060
#> GSM1105556 1 0.3885 0.56072 0.756 0.000 0.192 0.048 0.004 0.000
#> GSM1105557 2 0.4125 0.52858 0.000 0.748 0.124 0.128 0.000 0.000
#> GSM1105449 2 0.0622 0.63690 0.000 0.980 0.000 0.012 0.008 0.000
#> GSM1105469 4 0.3813 0.46986 0.152 0.036 0.024 0.788 0.000 0.000
#> GSM1105472 5 0.5589 0.24738 0.000 0.284 0.036 0.088 0.592 0.000
#> GSM1105473 5 0.5276 0.63759 0.032 0.000 0.192 0.020 0.688 0.068
#> GSM1105476 2 0.6885 -0.02784 0.000 0.380 0.052 0.312 0.256 0.000
#> GSM1105477 5 0.2901 0.70259 0.008 0.088 0.020 0.016 0.868 0.000
#> GSM1105478 4 0.4699 0.24550 0.000 0.376 0.036 0.580 0.000 0.008
#> GSM1105510 5 0.5100 0.63393 0.004 0.080 0.144 0.060 0.712 0.000
#> GSM1105530 1 0.5536 0.26520 0.524 0.000 0.052 0.388 0.004 0.032
#> GSM1105539 6 0.5571 0.22648 0.348 0.000 0.040 0.052 0.004 0.556
#> GSM1105480 2 0.5988 0.01126 0.000 0.440 0.200 0.356 0.004 0.000
#> GSM1105512 1 0.5156 0.53764 0.696 0.000 0.136 0.120 0.048 0.000
#> GSM1105532 1 0.5703 0.18115 0.476 0.000 0.048 0.428 0.004 0.044
#> GSM1105541 1 0.6615 0.28488 0.512 0.000 0.056 0.228 0.004 0.200
#> GSM1105439 2 0.1950 0.62613 0.000 0.912 0.024 0.064 0.000 0.000
#> GSM1105463 6 0.0653 0.61857 0.004 0.000 0.012 0.004 0.000 0.980
#> GSM1105482 1 0.4747 0.30071 0.584 0.000 0.356 0.000 0.060 0.000
#> GSM1105483 4 0.4042 0.47638 0.112 0.068 0.024 0.792 0.004 0.000
#> GSM1105494 2 0.6476 0.07076 0.000 0.404 0.308 0.272 0.008 0.008
#> GSM1105503 6 0.6891 0.29016 0.000 0.172 0.092 0.276 0.000 0.460
#> GSM1105507 1 0.4706 0.54910 0.720 0.012 0.112 0.152 0.004 0.000
#> GSM1105446 2 0.3882 0.61740 0.000 0.800 0.084 0.024 0.092 0.000
#> GSM1105519 1 0.4076 0.58587 0.776 0.000 0.060 0.140 0.024 0.000
#> GSM1105526 4 0.5847 0.37825 0.024 0.048 0.040 0.572 0.316 0.000
#> GSM1105527 4 0.3428 0.50266 0.028 0.120 0.028 0.824 0.000 0.000
#> GSM1105531 6 0.1053 0.62109 0.000 0.000 0.012 0.020 0.004 0.964
#> GSM1105543 2 0.5754 0.22930 0.000 0.504 0.072 0.040 0.384 0.000
#> GSM1105546 1 0.3804 0.20624 0.576 0.000 0.424 0.000 0.000 0.000
#> GSM1105547 1 0.5855 0.26034 0.496 0.000 0.380 0.036 0.088 0.000
#> GSM1105455 2 0.1794 0.62736 0.000 0.924 0.036 0.040 0.000 0.000
#> GSM1105458 2 0.1938 0.63484 0.000 0.920 0.008 0.020 0.052 0.000
#> GSM1105459 2 0.3754 0.57758 0.000 0.756 0.016 0.016 0.212 0.000
#> GSM1105462 4 0.6394 0.36809 0.048 0.004 0.036 0.620 0.144 0.148
#> GSM1105441 2 0.1528 0.63753 0.000 0.944 0.012 0.016 0.028 0.000
#> GSM1105465 5 0.2857 0.71594 0.004 0.008 0.132 0.004 0.848 0.004
#> GSM1105484 5 0.2932 0.68917 0.000 0.088 0.040 0.012 0.860 0.000
#> GSM1105485 5 0.2196 0.72221 0.004 0.000 0.108 0.004 0.884 0.000
#> GSM1105496 6 0.6207 0.12565 0.004 0.220 0.384 0.004 0.000 0.388
#> GSM1105505 6 0.1783 0.61961 0.008 0.004 0.036 0.008 0.008 0.936
#> GSM1105509 1 0.5083 0.50989 0.644 0.000 0.088 0.252 0.016 0.000
#> GSM1105448 2 0.4479 0.55087 0.000 0.700 0.028 0.032 0.240 0.000
#> GSM1105521 1 0.5609 0.52198 0.680 0.000 0.088 0.108 0.116 0.008
#> GSM1105528 5 0.1194 0.73009 0.000 0.032 0.008 0.004 0.956 0.000
#> GSM1105529 5 0.3239 0.70684 0.000 0.024 0.164 0.004 0.808 0.000
#> GSM1105533 1 0.3660 0.54033 0.780 0.000 0.060 0.000 0.000 0.160
#> GSM1105545 4 0.6635 0.41753 0.016 0.160 0.060 0.548 0.216 0.000
#> GSM1105548 3 0.4868 0.18293 0.332 0.000 0.608 0.000 0.044 0.016
#> GSM1105549 5 0.6119 0.43808 0.148 0.000 0.212 0.060 0.580 0.000
#> GSM1105457 2 0.3543 0.54184 0.000 0.768 0.032 0.200 0.000 0.000
#> GSM1105460 2 0.4938 0.55011 0.000 0.708 0.032 0.136 0.124 0.000
#> GSM1105461 2 0.3707 0.60094 0.000 0.792 0.024 0.028 0.156 0.000
#> GSM1105464 1 0.5643 0.29437 0.540 0.000 0.060 0.360 0.004 0.036
#> GSM1105466 4 0.4658 0.30612 0.000 0.360 0.036 0.596 0.008 0.000
#> GSM1105479 4 0.6280 0.12026 0.000 0.412 0.032 0.440 0.104 0.012
#> GSM1105502 1 0.3315 0.58815 0.832 0.000 0.008 0.068 0.000 0.092
#> GSM1105515 1 0.3629 0.47600 0.724 0.000 0.260 0.000 0.016 0.000
#> GSM1105523 4 0.4542 0.40630 0.120 0.008 0.032 0.760 0.000 0.080
#> GSM1105550 4 0.5982 0.28785 0.316 0.024 0.060 0.568 0.024 0.008
#> GSM1105450 2 0.5567 0.44097 0.000 0.624 0.044 0.096 0.236 0.000
#> GSM1105451 2 0.0767 0.63718 0.000 0.976 0.012 0.008 0.004 0.000
#> GSM1105454 6 0.4570 0.24129 0.000 0.436 0.036 0.000 0.000 0.528
#> GSM1105468 2 0.6434 0.27446 0.000 0.488 0.044 0.176 0.292 0.000
#> GSM1105481 6 0.6474 0.26429 0.000 0.016 0.040 0.232 0.164 0.548
#> GSM1105504 6 0.4712 0.53006 0.172 0.000 0.040 0.048 0.008 0.732
#> GSM1105517 4 0.5355 -0.17580 0.456 0.000 0.060 0.468 0.008 0.008
#> GSM1105525 4 0.5154 0.25244 0.300 0.000 0.052 0.616 0.000 0.032
#> GSM1105552 5 0.5455 0.60107 0.072 0.000 0.092 0.012 0.696 0.128
#> GSM1105452 5 0.3974 0.67096 0.000 0.036 0.216 0.008 0.740 0.000
#> GSM1105453 2 0.2519 0.63043 0.000 0.892 0.048 0.016 0.044 0.000
#> GSM1105456 6 0.2696 0.59441 0.000 0.116 0.028 0.000 0.000 0.856
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 agent(p) other(p) time(p) individual(p) k
#> MAD:NMF 117 0.861 0.46773 0.650 0.00814 2
#> MAD:NMF 102 0.921 0.58882 0.350 0.01048 3
#> MAD:NMF 104 0.121 0.42601 0.115 0.02852 4
#> MAD:NMF 92 0.146 0.00182 0.104 0.02429 5
#> MAD:NMF 56 0.346 0.65455 0.553 0.10030 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 44956 rows and 120 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 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 0.524 0.860 0.919 0.4357 0.583 0.583
#> 3 3 0.567 0.792 0.885 0.4922 0.727 0.540
#> 4 4 0.617 0.669 0.800 0.1135 0.929 0.788
#> 5 5 0.677 0.631 0.774 0.0692 0.896 0.651
#> 6 6 0.730 0.602 0.779 0.0561 0.876 0.528
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
#> GSM1105438 2 0.0000 0.888 0.000 1.000
#> GSM1105486 2 0.0000 0.888 0.000 1.000
#> GSM1105487 1 0.0000 0.970 1.000 0.000
#> GSM1105490 2 0.0000 0.888 0.000 1.000
#> GSM1105491 2 0.5946 0.857 0.144 0.856
#> GSM1105495 2 0.0000 0.888 0.000 1.000
#> GSM1105498 2 0.6531 0.843 0.168 0.832
#> GSM1105499 1 0.0000 0.970 1.000 0.000
#> GSM1105506 2 0.0000 0.888 0.000 1.000
#> GSM1105442 2 0.5946 0.857 0.144 0.856
#> GSM1105511 2 0.6148 0.853 0.152 0.848
#> GSM1105514 2 0.0000 0.888 0.000 1.000
#> GSM1105518 2 0.5629 0.862 0.132 0.868
#> GSM1105522 1 0.0000 0.970 1.000 0.000
#> GSM1105534 1 0.0000 0.970 1.000 0.000
#> GSM1105535 1 0.0000 0.970 1.000 0.000
#> GSM1105538 1 0.0000 0.970 1.000 0.000
#> GSM1105542 2 0.5946 0.857 0.144 0.856
#> GSM1105443 2 0.0000 0.888 0.000 1.000
#> GSM1105551 1 0.0000 0.970 1.000 0.000
#> GSM1105554 1 0.0000 0.970 1.000 0.000
#> GSM1105555 1 0.0000 0.970 1.000 0.000
#> GSM1105447 2 0.0000 0.888 0.000 1.000
#> GSM1105467 2 0.0000 0.888 0.000 1.000
#> GSM1105470 2 0.0000 0.888 0.000 1.000
#> GSM1105471 2 0.0000 0.888 0.000 1.000
#> GSM1105474 2 0.0000 0.888 0.000 1.000
#> GSM1105475 2 0.0000 0.888 0.000 1.000
#> GSM1105440 1 0.0000 0.970 1.000 0.000
#> GSM1105488 2 0.5946 0.857 0.144 0.856
#> GSM1105489 1 0.0000 0.970 1.000 0.000
#> GSM1105492 1 0.0000 0.970 1.000 0.000
#> GSM1105493 1 0.0000 0.970 1.000 0.000
#> GSM1105497 2 0.5946 0.857 0.144 0.856
#> GSM1105500 2 0.6712 0.837 0.176 0.824
#> GSM1105501 2 0.5946 0.857 0.144 0.856
#> GSM1105508 1 0.7883 0.621 0.764 0.236
#> GSM1105444 2 0.0000 0.888 0.000 1.000
#> GSM1105513 2 0.0000 0.888 0.000 1.000
#> GSM1105516 2 0.9866 0.450 0.432 0.568
#> GSM1105520 2 0.5629 0.862 0.132 0.868
#> GSM1105524 1 0.0000 0.970 1.000 0.000
#> GSM1105536 2 0.7883 0.785 0.236 0.764
#> GSM1105537 1 0.0000 0.970 1.000 0.000
#> GSM1105540 2 0.8016 0.776 0.244 0.756
#> GSM1105544 2 0.7950 0.780 0.240 0.760
#> GSM1105445 2 0.0000 0.888 0.000 1.000
#> GSM1105553 2 0.6712 0.837 0.176 0.824
#> GSM1105556 1 0.0000 0.970 1.000 0.000
#> GSM1105557 2 0.0376 0.887 0.004 0.996
#> GSM1105449 2 0.0000 0.888 0.000 1.000
#> GSM1105469 2 0.6247 0.851 0.156 0.844
#> GSM1105472 2 0.0000 0.888 0.000 1.000
#> GSM1105473 1 0.8713 0.494 0.708 0.292
#> GSM1105476 2 0.0000 0.888 0.000 1.000
#> GSM1105477 2 0.7950 0.780 0.240 0.760
#> GSM1105478 2 0.0000 0.888 0.000 1.000
#> GSM1105510 2 0.5946 0.857 0.144 0.856
#> GSM1105530 1 0.0000 0.970 1.000 0.000
#> GSM1105539 1 0.0000 0.970 1.000 0.000
#> GSM1105480 2 0.0938 0.887 0.012 0.988
#> GSM1105512 1 0.0000 0.970 1.000 0.000
#> GSM1105532 1 0.0000 0.970 1.000 0.000
#> GSM1105541 1 0.0000 0.970 1.000 0.000
#> GSM1105439 2 0.0000 0.888 0.000 1.000
#> GSM1105463 1 0.8713 0.494 0.708 0.292
#> GSM1105482 1 0.0000 0.970 1.000 0.000
#> GSM1105483 2 0.6247 0.851 0.156 0.844
#> GSM1105494 2 0.1184 0.886 0.016 0.984
#> GSM1105503 2 0.5629 0.862 0.132 0.868
#> GSM1105507 2 0.9909 0.420 0.444 0.556
#> GSM1105446 2 0.3274 0.879 0.060 0.940
#> GSM1105519 1 0.0000 0.970 1.000 0.000
#> GSM1105526 2 0.4939 0.869 0.108 0.892
#> GSM1105527 2 0.3733 0.877 0.072 0.928
#> GSM1105531 2 0.8207 0.762 0.256 0.744
#> GSM1105543 2 0.0000 0.888 0.000 1.000
#> GSM1105546 1 0.0000 0.970 1.000 0.000
#> GSM1105547 1 0.0000 0.970 1.000 0.000
#> GSM1105455 2 0.0000 0.888 0.000 1.000
#> GSM1105458 2 0.0000 0.888 0.000 1.000
#> GSM1105459 2 0.0000 0.888 0.000 1.000
#> GSM1105462 2 0.7883 0.785 0.236 0.764
#> GSM1105441 2 0.0000 0.888 0.000 1.000
#> GSM1105465 2 0.5946 0.857 0.144 0.856
#> GSM1105484 2 0.0000 0.888 0.000 1.000
#> GSM1105485 2 0.5946 0.857 0.144 0.856
#> GSM1105496 2 0.6712 0.837 0.176 0.824
#> GSM1105505 2 0.9909 0.420 0.444 0.556
#> GSM1105509 2 0.9909 0.420 0.444 0.556
#> GSM1105448 2 0.0000 0.888 0.000 1.000
#> GSM1105521 1 0.0000 0.970 1.000 0.000
#> GSM1105528 2 0.0000 0.888 0.000 1.000
#> GSM1105529 2 0.5946 0.857 0.144 0.856
#> GSM1105533 1 0.0000 0.970 1.000 0.000
#> GSM1105545 2 0.7883 0.785 0.236 0.764
#> GSM1105548 1 0.0000 0.970 1.000 0.000
#> GSM1105549 1 0.0000 0.970 1.000 0.000
#> GSM1105457 2 0.0000 0.888 0.000 1.000
#> GSM1105460 2 0.0000 0.888 0.000 1.000
#> GSM1105461 2 0.0000 0.888 0.000 1.000
#> GSM1105464 1 0.0938 0.958 0.988 0.012
#> GSM1105466 2 0.0000 0.888 0.000 1.000
#> GSM1105479 2 0.0000 0.888 0.000 1.000
#> GSM1105502 1 0.0000 0.970 1.000 0.000
#> GSM1105515 1 0.0000 0.970 1.000 0.000
#> GSM1105523 2 0.9686 0.535 0.396 0.604
#> GSM1105550 2 0.8081 0.772 0.248 0.752
#> GSM1105450 2 0.0000 0.888 0.000 1.000
#> GSM1105451 2 0.0000 0.888 0.000 1.000
#> GSM1105454 2 0.0000 0.888 0.000 1.000
#> GSM1105468 2 0.0000 0.888 0.000 1.000
#> GSM1105481 2 0.0000 0.888 0.000 1.000
#> GSM1105504 2 0.9909 0.420 0.444 0.556
#> GSM1105517 2 0.8081 0.772 0.248 0.752
#> GSM1105525 2 0.9686 0.535 0.396 0.604
#> GSM1105552 2 0.8144 0.767 0.252 0.748
#> GSM1105452 2 0.5946 0.857 0.144 0.856
#> GSM1105453 2 0.0000 0.888 0.000 1.000
#> GSM1105456 2 0.0000 0.888 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1105438 2 0.2066 0.8731 0.000 0.940 0.060
#> GSM1105486 2 0.4178 0.8431 0.000 0.828 0.172
#> GSM1105487 1 0.0000 0.9146 1.000 0.000 0.000
#> GSM1105490 2 0.4504 0.8172 0.000 0.804 0.196
#> GSM1105491 3 0.2878 0.8174 0.000 0.096 0.904
#> GSM1105495 2 0.5397 0.7243 0.000 0.720 0.280
#> GSM1105498 3 0.2356 0.8229 0.000 0.072 0.928
#> GSM1105499 1 0.0000 0.9146 1.000 0.000 0.000
#> GSM1105506 2 0.3412 0.8656 0.000 0.876 0.124
#> GSM1105442 3 0.2878 0.8174 0.000 0.096 0.904
#> GSM1105511 3 0.4504 0.7386 0.000 0.196 0.804
#> GSM1105514 2 0.2066 0.8731 0.000 0.940 0.060
#> GSM1105518 3 0.5291 0.6435 0.000 0.268 0.732
#> GSM1105522 1 0.5016 0.7695 0.760 0.000 0.240
#> GSM1105534 1 0.0000 0.9146 1.000 0.000 0.000
#> GSM1105535 1 0.0000 0.9146 1.000 0.000 0.000
#> GSM1105538 1 0.0000 0.9146 1.000 0.000 0.000
#> GSM1105542 3 0.2878 0.8174 0.000 0.096 0.904
#> GSM1105443 2 0.0000 0.8721 0.000 1.000 0.000
#> GSM1105551 1 0.1753 0.9048 0.952 0.000 0.048
#> GSM1105554 1 0.0000 0.9146 1.000 0.000 0.000
#> GSM1105555 1 0.1753 0.9048 0.952 0.000 0.048
#> GSM1105447 2 0.4346 0.8291 0.000 0.816 0.184
#> GSM1105467 2 0.4062 0.8479 0.000 0.836 0.164
#> GSM1105470 2 0.0000 0.8721 0.000 1.000 0.000
#> GSM1105471 2 0.3482 0.8628 0.000 0.872 0.128
#> GSM1105474 2 0.4178 0.8431 0.000 0.828 0.172
#> GSM1105475 2 0.3619 0.8604 0.000 0.864 0.136
#> GSM1105440 1 0.1753 0.9048 0.952 0.000 0.048
#> GSM1105488 3 0.2878 0.8174 0.000 0.096 0.904
#> GSM1105489 1 0.0000 0.9146 1.000 0.000 0.000
#> GSM1105492 1 0.0000 0.9146 1.000 0.000 0.000
#> GSM1105493 1 0.0000 0.9146 1.000 0.000 0.000
#> GSM1105497 3 0.2878 0.8174 0.000 0.096 0.904
#> GSM1105500 3 0.2165 0.8236 0.000 0.064 0.936
#> GSM1105501 3 0.4750 0.7152 0.000 0.216 0.784
#> GSM1105508 1 0.6299 0.2414 0.524 0.000 0.476
#> GSM1105444 2 0.0000 0.8721 0.000 1.000 0.000
#> GSM1105513 2 0.4555 0.8142 0.000 0.800 0.200
#> GSM1105516 3 0.4452 0.6526 0.192 0.000 0.808
#> GSM1105520 3 0.5291 0.6435 0.000 0.268 0.732
#> GSM1105524 1 0.0000 0.9146 1.000 0.000 0.000
#> GSM1105536 3 0.0237 0.8198 0.000 0.004 0.996
#> GSM1105537 1 0.0000 0.9146 1.000 0.000 0.000
#> GSM1105540 3 0.0237 0.8183 0.004 0.000 0.996
#> GSM1105544 3 0.0000 0.8186 0.000 0.000 1.000
#> GSM1105445 2 0.2878 0.8723 0.000 0.904 0.096
#> GSM1105553 3 0.2165 0.8236 0.000 0.064 0.936
#> GSM1105556 1 0.0000 0.9146 1.000 0.000 0.000
#> GSM1105557 2 0.5098 0.7536 0.000 0.752 0.248
#> GSM1105449 2 0.0000 0.8721 0.000 1.000 0.000
#> GSM1105469 3 0.4235 0.7601 0.000 0.176 0.824
#> GSM1105472 2 0.0000 0.8721 0.000 1.000 0.000
#> GSM1105473 3 0.6291 -0.0785 0.468 0.000 0.532
#> GSM1105476 2 0.4178 0.8431 0.000 0.828 0.172
#> GSM1105477 3 0.0000 0.8186 0.000 0.000 1.000
#> GSM1105478 2 0.4974 0.7803 0.000 0.764 0.236
#> GSM1105510 3 0.2878 0.8174 0.000 0.096 0.904
#> GSM1105530 1 0.5016 0.7695 0.760 0.000 0.240
#> GSM1105539 1 0.2165 0.8984 0.936 0.000 0.064
#> GSM1105480 2 0.5905 0.5828 0.000 0.648 0.352
#> GSM1105512 1 0.4235 0.8238 0.824 0.000 0.176
#> GSM1105532 1 0.5016 0.7695 0.760 0.000 0.240
#> GSM1105541 1 0.2165 0.8984 0.936 0.000 0.064
#> GSM1105439 2 0.0000 0.8721 0.000 1.000 0.000
#> GSM1105463 3 0.6291 -0.0785 0.468 0.000 0.532
#> GSM1105482 1 0.0000 0.9146 1.000 0.000 0.000
#> GSM1105483 3 0.4235 0.7601 0.000 0.176 0.824
#> GSM1105494 2 0.5785 0.6273 0.000 0.668 0.332
#> GSM1105503 3 0.5291 0.6435 0.000 0.268 0.732
#> GSM1105507 3 0.4605 0.6359 0.204 0.000 0.796
#> GSM1105446 3 0.6215 0.1650 0.000 0.428 0.572
#> GSM1105519 1 0.4931 0.7774 0.768 0.000 0.232
#> GSM1105526 3 0.5138 0.6533 0.000 0.252 0.748
#> GSM1105527 3 0.6140 0.2752 0.000 0.404 0.596
#> GSM1105531 3 0.0747 0.8163 0.016 0.000 0.984
#> GSM1105543 2 0.5058 0.7671 0.000 0.756 0.244
#> GSM1105546 1 0.0000 0.9146 1.000 0.000 0.000
#> GSM1105547 1 0.0000 0.9146 1.000 0.000 0.000
#> GSM1105455 2 0.0000 0.8721 0.000 1.000 0.000
#> GSM1105458 2 0.1643 0.8742 0.000 0.956 0.044
#> GSM1105459 2 0.0000 0.8721 0.000 1.000 0.000
#> GSM1105462 3 0.0237 0.8198 0.000 0.004 0.996
#> GSM1105441 2 0.0000 0.8721 0.000 1.000 0.000
#> GSM1105465 3 0.2878 0.8174 0.000 0.096 0.904
#> GSM1105484 2 0.5058 0.7671 0.000 0.756 0.244
#> GSM1105485 3 0.2878 0.8174 0.000 0.096 0.904
#> GSM1105496 3 0.2165 0.8236 0.000 0.064 0.936
#> GSM1105505 3 0.4605 0.6359 0.204 0.000 0.796
#> GSM1105509 3 0.4605 0.6359 0.204 0.000 0.796
#> GSM1105448 2 0.0000 0.8721 0.000 1.000 0.000
#> GSM1105521 1 0.4931 0.7774 0.768 0.000 0.232
#> GSM1105528 2 0.5397 0.7243 0.000 0.720 0.280
#> GSM1105529 3 0.2878 0.8174 0.000 0.096 0.904
#> GSM1105533 1 0.0000 0.9146 1.000 0.000 0.000
#> GSM1105545 3 0.0237 0.8198 0.000 0.004 0.996
#> GSM1105548 1 0.1753 0.9048 0.952 0.000 0.048
#> GSM1105549 1 0.0000 0.9146 1.000 0.000 0.000
#> GSM1105457 2 0.2878 0.8709 0.000 0.904 0.096
#> GSM1105460 2 0.0000 0.8721 0.000 1.000 0.000
#> GSM1105461 2 0.0000 0.8721 0.000 1.000 0.000
#> GSM1105464 1 0.5138 0.7538 0.748 0.000 0.252
#> GSM1105466 2 0.3482 0.8628 0.000 0.872 0.128
#> GSM1105479 2 0.3482 0.8628 0.000 0.872 0.128
#> GSM1105502 1 0.5016 0.7695 0.760 0.000 0.240
#> GSM1105515 1 0.0000 0.9146 1.000 0.000 0.000
#> GSM1105523 3 0.3941 0.7096 0.156 0.000 0.844
#> GSM1105550 3 0.0424 0.8179 0.008 0.000 0.992
#> GSM1105450 2 0.0000 0.8721 0.000 1.000 0.000
#> GSM1105451 2 0.0000 0.8721 0.000 1.000 0.000
#> GSM1105454 2 0.0000 0.8721 0.000 1.000 0.000
#> GSM1105468 2 0.0000 0.8721 0.000 1.000 0.000
#> GSM1105481 2 0.5397 0.7243 0.000 0.720 0.280
#> GSM1105504 3 0.4605 0.6359 0.204 0.000 0.796
#> GSM1105517 3 0.0424 0.8179 0.008 0.000 0.992
#> GSM1105525 3 0.3941 0.7096 0.156 0.000 0.844
#> GSM1105552 3 0.0592 0.8171 0.012 0.000 0.988
#> GSM1105452 3 0.2878 0.8174 0.000 0.096 0.904
#> GSM1105453 2 0.4654 0.8074 0.000 0.792 0.208
#> GSM1105456 2 0.0000 0.8721 0.000 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1105438 2 0.2281 0.8286 0.000 0.904 0.000 0.096
#> GSM1105486 2 0.4669 0.7854 0.000 0.780 0.052 0.168
#> GSM1105487 1 0.0000 0.8682 1.000 0.000 0.000 0.000
#> GSM1105490 2 0.5159 0.7548 0.000 0.756 0.088 0.156
#> GSM1105491 4 0.0592 0.6252 0.000 0.000 0.016 0.984
#> GSM1105495 2 0.4697 0.6074 0.000 0.644 0.000 0.356
#> GSM1105498 4 0.5295 0.1425 0.000 0.008 0.488 0.504
#> GSM1105499 1 0.0000 0.8682 1.000 0.000 0.000 0.000
#> GSM1105506 2 0.3761 0.8187 0.000 0.852 0.068 0.080
#> GSM1105442 4 0.0592 0.6252 0.000 0.000 0.016 0.984
#> GSM1105511 4 0.6961 0.4069 0.000 0.116 0.388 0.496
#> GSM1105514 2 0.2281 0.8286 0.000 0.904 0.000 0.096
#> GSM1105518 4 0.7386 0.4545 0.000 0.184 0.320 0.496
#> GSM1105522 1 0.4916 0.5442 0.576 0.000 0.424 0.000
#> GSM1105534 1 0.0000 0.8682 1.000 0.000 0.000 0.000
#> GSM1105535 1 0.0000 0.8682 1.000 0.000 0.000 0.000
#> GSM1105538 1 0.0000 0.8682 1.000 0.000 0.000 0.000
#> GSM1105542 4 0.0592 0.6252 0.000 0.000 0.016 0.984
#> GSM1105443 2 0.0188 0.8385 0.000 0.996 0.000 0.004
#> GSM1105551 1 0.1637 0.8495 0.940 0.000 0.060 0.000
#> GSM1105554 1 0.0000 0.8682 1.000 0.000 0.000 0.000
#> GSM1105555 1 0.1637 0.8495 0.940 0.000 0.060 0.000
#> GSM1105447 2 0.3975 0.7574 0.000 0.760 0.000 0.240
#> GSM1105467 2 0.4578 0.7910 0.000 0.788 0.052 0.160
#> GSM1105470 2 0.0188 0.8385 0.000 0.996 0.000 0.004
#> GSM1105471 2 0.3840 0.8154 0.000 0.844 0.052 0.104
#> GSM1105474 2 0.4669 0.7854 0.000 0.780 0.052 0.168
#> GSM1105475 2 0.3958 0.8132 0.000 0.836 0.052 0.112
#> GSM1105440 1 0.1637 0.8495 0.940 0.000 0.060 0.000
#> GSM1105488 4 0.0592 0.6252 0.000 0.000 0.016 0.984
#> GSM1105489 1 0.0000 0.8682 1.000 0.000 0.000 0.000
#> GSM1105492 1 0.0000 0.8682 1.000 0.000 0.000 0.000
#> GSM1105493 1 0.0000 0.8682 1.000 0.000 0.000 0.000
#> GSM1105497 4 0.0592 0.6252 0.000 0.000 0.016 0.984
#> GSM1105500 4 0.4998 0.1378 0.000 0.000 0.488 0.512
#> GSM1105501 4 0.7082 0.4286 0.000 0.132 0.368 0.500
#> GSM1105508 3 0.4624 0.0761 0.340 0.000 0.660 0.000
#> GSM1105444 2 0.0188 0.8385 0.000 0.996 0.000 0.004
#> GSM1105513 2 0.5204 0.7519 0.000 0.752 0.088 0.160
#> GSM1105516 3 0.2759 0.6385 0.052 0.000 0.904 0.044
#> GSM1105520 4 0.7386 0.4545 0.000 0.184 0.320 0.496
#> GSM1105524 1 0.0000 0.8682 1.000 0.000 0.000 0.000
#> GSM1105536 3 0.4679 0.4097 0.000 0.000 0.648 0.352
#> GSM1105537 1 0.0000 0.8682 1.000 0.000 0.000 0.000
#> GSM1105540 3 0.4454 0.4852 0.000 0.000 0.692 0.308
#> GSM1105544 3 0.4643 0.4270 0.000 0.000 0.656 0.344
#> GSM1105445 2 0.3333 0.8303 0.000 0.872 0.040 0.088
#> GSM1105553 4 0.4277 0.4671 0.000 0.000 0.280 0.720
#> GSM1105556 1 0.0000 0.8682 1.000 0.000 0.000 0.000
#> GSM1105557 2 0.5681 0.6909 0.000 0.704 0.088 0.208
#> GSM1105449 2 0.0188 0.8385 0.000 0.996 0.000 0.004
#> GSM1105469 4 0.6799 0.3271 0.000 0.096 0.440 0.464
#> GSM1105472 2 0.0188 0.8385 0.000 0.996 0.000 0.004
#> GSM1105473 3 0.5206 0.2237 0.308 0.000 0.668 0.024
#> GSM1105476 2 0.4669 0.7854 0.000 0.780 0.052 0.168
#> GSM1105477 3 0.4679 0.4127 0.000 0.000 0.648 0.352
#> GSM1105478 2 0.5678 0.7151 0.000 0.716 0.112 0.172
#> GSM1105510 4 0.0592 0.6252 0.000 0.000 0.016 0.984
#> GSM1105530 1 0.4916 0.5442 0.576 0.000 0.424 0.000
#> GSM1105539 1 0.3356 0.7857 0.824 0.000 0.176 0.000
#> GSM1105480 2 0.6716 0.4576 0.000 0.568 0.112 0.320
#> GSM1105512 1 0.4661 0.6369 0.652 0.000 0.348 0.000
#> GSM1105532 1 0.4916 0.5442 0.576 0.000 0.424 0.000
#> GSM1105541 1 0.3356 0.7857 0.824 0.000 0.176 0.000
#> GSM1105439 2 0.0188 0.8385 0.000 0.996 0.000 0.004
#> GSM1105463 3 0.5206 0.2237 0.308 0.000 0.668 0.024
#> GSM1105482 1 0.0000 0.8682 1.000 0.000 0.000 0.000
#> GSM1105483 4 0.6799 0.3271 0.000 0.096 0.440 0.464
#> GSM1105494 2 0.6627 0.5074 0.000 0.588 0.112 0.300
#> GSM1105503 4 0.7386 0.4545 0.000 0.184 0.320 0.496
#> GSM1105507 3 0.2660 0.6369 0.056 0.000 0.908 0.036
#> GSM1105446 4 0.4605 0.1979 0.000 0.336 0.000 0.664
#> GSM1105519 1 0.4888 0.5606 0.588 0.000 0.412 0.000
#> GSM1105526 4 0.7048 0.4870 0.000 0.160 0.284 0.556
#> GSM1105527 4 0.7834 0.3419 0.000 0.320 0.276 0.404
#> GSM1105531 3 0.3801 0.5753 0.000 0.000 0.780 0.220
#> GSM1105543 2 0.4522 0.6565 0.000 0.680 0.000 0.320
#> GSM1105546 1 0.0000 0.8682 1.000 0.000 0.000 0.000
#> GSM1105547 1 0.0000 0.8682 1.000 0.000 0.000 0.000
#> GSM1105455 2 0.0188 0.8385 0.000 0.996 0.000 0.004
#> GSM1105458 2 0.1716 0.8364 0.000 0.936 0.000 0.064
#> GSM1105459 2 0.0188 0.8385 0.000 0.996 0.000 0.004
#> GSM1105462 3 0.4624 0.4326 0.000 0.000 0.660 0.340
#> GSM1105441 2 0.0188 0.8385 0.000 0.996 0.000 0.004
#> GSM1105465 4 0.0592 0.6252 0.000 0.000 0.016 0.984
#> GSM1105484 2 0.4522 0.6565 0.000 0.680 0.000 0.320
#> GSM1105485 4 0.0592 0.6252 0.000 0.000 0.016 0.984
#> GSM1105496 4 0.4277 0.4671 0.000 0.000 0.280 0.720
#> GSM1105505 3 0.2660 0.6369 0.056 0.000 0.908 0.036
#> GSM1105509 3 0.2660 0.6369 0.056 0.000 0.908 0.036
#> GSM1105448 2 0.0188 0.8385 0.000 0.996 0.000 0.004
#> GSM1105521 1 0.4888 0.5606 0.588 0.000 0.412 0.000
#> GSM1105528 2 0.4713 0.6004 0.000 0.640 0.000 0.360
#> GSM1105529 4 0.0592 0.6252 0.000 0.000 0.016 0.984
#> GSM1105533 1 0.0000 0.8682 1.000 0.000 0.000 0.000
#> GSM1105545 3 0.4697 0.3999 0.000 0.000 0.644 0.356
#> GSM1105548 1 0.1637 0.8495 0.940 0.000 0.060 0.000
#> GSM1105549 1 0.0000 0.8682 1.000 0.000 0.000 0.000
#> GSM1105457 2 0.3229 0.8274 0.000 0.880 0.048 0.072
#> GSM1105460 2 0.0000 0.8387 0.000 1.000 0.000 0.000
#> GSM1105461 2 0.0188 0.8385 0.000 0.996 0.000 0.004
#> GSM1105464 1 0.5097 0.5271 0.568 0.000 0.428 0.004
#> GSM1105466 2 0.3840 0.8154 0.000 0.844 0.052 0.104
#> GSM1105479 2 0.3840 0.8154 0.000 0.844 0.052 0.104
#> GSM1105502 1 0.4916 0.5442 0.576 0.000 0.424 0.000
#> GSM1105515 1 0.0000 0.8682 1.000 0.000 0.000 0.000
#> GSM1105523 3 0.1389 0.6285 0.000 0.000 0.952 0.048
#> GSM1105550 3 0.4040 0.5547 0.000 0.000 0.752 0.248
#> GSM1105450 2 0.0188 0.8385 0.000 0.996 0.000 0.004
#> GSM1105451 2 0.0188 0.8385 0.000 0.996 0.000 0.004
#> GSM1105454 2 0.0188 0.8389 0.000 0.996 0.000 0.004
#> GSM1105468 2 0.0188 0.8385 0.000 0.996 0.000 0.004
#> GSM1105481 2 0.4697 0.6074 0.000 0.644 0.000 0.356
#> GSM1105504 3 0.2660 0.6369 0.056 0.000 0.908 0.036
#> GSM1105517 3 0.4040 0.5547 0.000 0.000 0.752 0.248
#> GSM1105525 3 0.1389 0.6285 0.000 0.000 0.952 0.048
#> GSM1105552 3 0.3726 0.5800 0.000 0.000 0.788 0.212
#> GSM1105452 4 0.0592 0.6252 0.000 0.000 0.016 0.984
#> GSM1105453 2 0.4193 0.7194 0.000 0.732 0.000 0.268
#> GSM1105456 2 0.0188 0.8389 0.000 0.996 0.000 0.004
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM1105438 2 0.3012 0.7805 0.000 0.860 0.000 0.104 0.036
#> GSM1105486 2 0.4671 0.6909 0.000 0.640 0.000 0.332 0.028
#> GSM1105487 1 0.0000 0.9382 1.000 0.000 0.000 0.000 0.000
#> GSM1105490 2 0.4114 0.6576 0.000 0.624 0.000 0.376 0.000
#> GSM1105491 5 0.0000 0.8628 0.000 0.000 0.000 0.000 1.000
#> GSM1105495 2 0.5895 0.5926 0.000 0.588 0.000 0.152 0.260
#> GSM1105498 4 0.5810 0.5174 0.000 0.000 0.176 0.612 0.212
#> GSM1105499 1 0.0000 0.9382 1.000 0.000 0.000 0.000 0.000
#> GSM1105506 2 0.3684 0.7365 0.000 0.720 0.000 0.280 0.000
#> GSM1105442 5 0.0000 0.8628 0.000 0.000 0.000 0.000 1.000
#> GSM1105511 4 0.3587 0.5269 0.000 0.012 0.024 0.824 0.140
#> GSM1105514 2 0.3012 0.7805 0.000 0.860 0.000 0.104 0.036
#> GSM1105518 4 0.4693 0.4972 0.000 0.084 0.012 0.756 0.148
#> GSM1105522 3 0.3876 0.4479 0.316 0.000 0.684 0.000 0.000
#> GSM1105534 1 0.0000 0.9382 1.000 0.000 0.000 0.000 0.000
#> GSM1105535 1 0.0000 0.9382 1.000 0.000 0.000 0.000 0.000
#> GSM1105538 1 0.0000 0.9382 1.000 0.000 0.000 0.000 0.000
#> GSM1105542 5 0.0000 0.8628 0.000 0.000 0.000 0.000 1.000
#> GSM1105443 2 0.1124 0.7908 0.000 0.960 0.004 0.036 0.000
#> GSM1105551 1 0.2074 0.8623 0.896 0.000 0.104 0.000 0.000
#> GSM1105554 1 0.0000 0.9382 1.000 0.000 0.000 0.000 0.000
#> GSM1105555 1 0.2074 0.8623 0.896 0.000 0.104 0.000 0.000
#> GSM1105447 2 0.5200 0.7193 0.000 0.688 0.000 0.156 0.156
#> GSM1105467 2 0.4638 0.6951 0.000 0.648 0.000 0.324 0.028
#> GSM1105470 2 0.1041 0.7902 0.000 0.964 0.004 0.032 0.000
#> GSM1105471 2 0.3730 0.7242 0.000 0.712 0.000 0.288 0.000
#> GSM1105474 2 0.4671 0.6894 0.000 0.640 0.000 0.332 0.028
#> GSM1105475 2 0.3730 0.7251 0.000 0.712 0.000 0.288 0.000
#> GSM1105440 1 0.2074 0.8623 0.896 0.000 0.104 0.000 0.000
#> GSM1105488 5 0.0000 0.8628 0.000 0.000 0.000 0.000 1.000
#> GSM1105489 1 0.0000 0.9382 1.000 0.000 0.000 0.000 0.000
#> GSM1105492 1 0.0000 0.9382 1.000 0.000 0.000 0.000 0.000
#> GSM1105493 1 0.0162 0.9363 0.996 0.000 0.004 0.000 0.000
#> GSM1105497 5 0.0000 0.8628 0.000 0.000 0.000 0.000 1.000
#> GSM1105500 4 0.6155 0.4807 0.000 0.000 0.176 0.548 0.276
#> GSM1105501 4 0.3732 0.5194 0.000 0.024 0.016 0.816 0.144
#> GSM1105508 3 0.4247 0.5480 0.132 0.000 0.776 0.092 0.000
#> GSM1105444 2 0.1041 0.7902 0.000 0.964 0.004 0.032 0.000
#> GSM1105513 2 0.4126 0.6573 0.000 0.620 0.000 0.380 0.000
#> GSM1105516 3 0.4455 0.3128 0.000 0.000 0.704 0.260 0.036
#> GSM1105520 4 0.4693 0.4972 0.000 0.084 0.012 0.756 0.148
#> GSM1105524 1 0.0000 0.9382 1.000 0.000 0.000 0.000 0.000
#> GSM1105536 4 0.6514 0.4615 0.000 0.000 0.304 0.476 0.220
#> GSM1105537 1 0.0000 0.9382 1.000 0.000 0.000 0.000 0.000
#> GSM1105540 4 0.6418 0.4158 0.000 0.000 0.344 0.472 0.184
#> GSM1105544 4 0.6506 0.4543 0.000 0.000 0.308 0.476 0.216
#> GSM1105445 2 0.3305 0.7652 0.000 0.776 0.000 0.224 0.000
#> GSM1105553 5 0.5795 0.2029 0.000 0.000 0.136 0.268 0.596
#> GSM1105556 1 0.0404 0.9324 0.988 0.000 0.012 0.000 0.000
#> GSM1105557 2 0.4649 0.6041 0.000 0.580 0.000 0.404 0.016
#> GSM1105449 2 0.1041 0.7902 0.000 0.964 0.004 0.032 0.000
#> GSM1105469 4 0.3527 0.5416 0.000 0.000 0.056 0.828 0.116
#> GSM1105472 2 0.1041 0.7902 0.000 0.964 0.004 0.032 0.000
#> GSM1105473 3 0.4374 0.5425 0.112 0.000 0.792 0.076 0.020
#> GSM1105476 2 0.4671 0.6894 0.000 0.640 0.000 0.332 0.028
#> GSM1105477 4 0.6503 0.4593 0.000 0.000 0.300 0.480 0.220
#> GSM1105478 2 0.4273 0.5811 0.000 0.552 0.000 0.448 0.000
#> GSM1105510 5 0.0000 0.8628 0.000 0.000 0.000 0.000 1.000
#> GSM1105530 3 0.3857 0.4519 0.312 0.000 0.688 0.000 0.000
#> GSM1105539 1 0.4138 0.3771 0.616 0.000 0.384 0.000 0.000
#> GSM1105480 4 0.5478 -0.3838 0.000 0.420 0.000 0.516 0.064
#> GSM1105512 3 0.4227 0.2221 0.420 0.000 0.580 0.000 0.000
#> GSM1105532 3 0.3857 0.4519 0.312 0.000 0.688 0.000 0.000
#> GSM1105541 1 0.4138 0.3771 0.616 0.000 0.384 0.000 0.000
#> GSM1105439 2 0.1041 0.7902 0.000 0.964 0.004 0.032 0.000
#> GSM1105463 3 0.4374 0.5425 0.112 0.000 0.792 0.076 0.020
#> GSM1105482 1 0.0000 0.9382 1.000 0.000 0.000 0.000 0.000
#> GSM1105483 4 0.3527 0.5416 0.000 0.000 0.056 0.828 0.116
#> GSM1105494 4 0.5276 -0.4188 0.000 0.436 0.000 0.516 0.048
#> GSM1105503 4 0.4693 0.4972 0.000 0.084 0.012 0.756 0.148
#> GSM1105507 3 0.4378 0.3342 0.000 0.000 0.716 0.248 0.036
#> GSM1105446 5 0.5834 0.2736 0.000 0.276 0.000 0.136 0.588
#> GSM1105519 3 0.3999 0.4102 0.344 0.000 0.656 0.000 0.000
#> GSM1105526 4 0.5450 0.3981 0.000 0.072 0.016 0.660 0.252
#> GSM1105527 4 0.4393 0.3555 0.000 0.168 0.000 0.756 0.076
#> GSM1105531 4 0.6101 0.2920 0.000 0.000 0.432 0.444 0.124
#> GSM1105543 2 0.5690 0.6383 0.000 0.624 0.000 0.152 0.224
#> GSM1105546 1 0.0000 0.9382 1.000 0.000 0.000 0.000 0.000
#> GSM1105547 1 0.0000 0.9382 1.000 0.000 0.000 0.000 0.000
#> GSM1105455 2 0.1124 0.7908 0.000 0.960 0.004 0.036 0.000
#> GSM1105458 2 0.2408 0.7908 0.000 0.892 0.000 0.092 0.016
#> GSM1105459 2 0.1041 0.7902 0.000 0.964 0.004 0.032 0.000
#> GSM1105462 4 0.6487 0.4547 0.000 0.000 0.316 0.476 0.208
#> GSM1105441 2 0.1041 0.7902 0.000 0.964 0.004 0.032 0.000
#> GSM1105465 5 0.0000 0.8628 0.000 0.000 0.000 0.000 1.000
#> GSM1105484 2 0.5690 0.6383 0.000 0.624 0.000 0.152 0.224
#> GSM1105485 5 0.0000 0.8628 0.000 0.000 0.000 0.000 1.000
#> GSM1105496 5 0.5795 0.2029 0.000 0.000 0.136 0.268 0.596
#> GSM1105505 3 0.4378 0.3342 0.000 0.000 0.716 0.248 0.036
#> GSM1105509 3 0.4378 0.3342 0.000 0.000 0.716 0.248 0.036
#> GSM1105448 2 0.1041 0.7902 0.000 0.964 0.004 0.032 0.000
#> GSM1105521 3 0.3999 0.4102 0.344 0.000 0.656 0.000 0.000
#> GSM1105528 2 0.5915 0.5861 0.000 0.584 0.000 0.152 0.264
#> GSM1105529 5 0.0000 0.8628 0.000 0.000 0.000 0.000 1.000
#> GSM1105533 1 0.0000 0.9382 1.000 0.000 0.000 0.000 0.000
#> GSM1105545 4 0.6503 0.4637 0.000 0.000 0.300 0.480 0.220
#> GSM1105548 1 0.2074 0.8623 0.896 0.000 0.104 0.000 0.000
#> GSM1105549 1 0.0404 0.9324 0.988 0.000 0.012 0.000 0.000
#> GSM1105457 2 0.3305 0.7579 0.000 0.776 0.000 0.224 0.000
#> GSM1105460 2 0.0510 0.7941 0.000 0.984 0.000 0.016 0.000
#> GSM1105461 2 0.1041 0.7902 0.000 0.964 0.004 0.032 0.000
#> GSM1105464 3 0.4142 0.4598 0.308 0.000 0.684 0.004 0.004
#> GSM1105466 2 0.3730 0.7242 0.000 0.712 0.000 0.288 0.000
#> GSM1105479 2 0.3707 0.7257 0.000 0.716 0.000 0.284 0.000
#> GSM1105502 3 0.3876 0.4479 0.316 0.000 0.684 0.000 0.000
#> GSM1105515 1 0.0000 0.9382 1.000 0.000 0.000 0.000 0.000
#> GSM1105523 3 0.4171 0.0552 0.000 0.000 0.604 0.396 0.000
#> GSM1105550 4 0.6180 0.3418 0.000 0.000 0.404 0.460 0.136
#> GSM1105450 2 0.1041 0.7902 0.000 0.964 0.004 0.032 0.000
#> GSM1105451 2 0.1041 0.7902 0.000 0.964 0.004 0.032 0.000
#> GSM1105454 2 0.0880 0.7951 0.000 0.968 0.000 0.032 0.000
#> GSM1105468 2 0.1041 0.7902 0.000 0.964 0.004 0.032 0.000
#> GSM1105481 2 0.5895 0.5926 0.000 0.588 0.000 0.152 0.260
#> GSM1105504 3 0.4378 0.3342 0.000 0.000 0.716 0.248 0.036
#> GSM1105517 4 0.6180 0.3418 0.000 0.000 0.404 0.460 0.136
#> GSM1105525 3 0.4171 0.0552 0.000 0.000 0.604 0.396 0.000
#> GSM1105552 3 0.6132 -0.3370 0.000 0.000 0.440 0.432 0.128
#> GSM1105452 5 0.0000 0.8628 0.000 0.000 0.000 0.000 1.000
#> GSM1105453 2 0.5222 0.6850 0.000 0.680 0.000 0.124 0.196
#> GSM1105456 2 0.0880 0.7951 0.000 0.968 0.000 0.032 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1105438 2 0.4531 0.5394 0.000 0.684 0.020 0.264 0.028 0.004
#> GSM1105486 4 0.4486 0.2506 0.000 0.364 0.012 0.604 0.020 0.000
#> GSM1105487 1 0.0000 0.9519 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1105490 4 0.3563 0.3350 0.000 0.336 0.000 0.664 0.000 0.000
#> GSM1105491 5 0.0363 0.8678 0.000 0.000 0.000 0.000 0.988 0.012
#> GSM1105495 2 0.6568 0.2359 0.000 0.372 0.020 0.368 0.236 0.004
#> GSM1105498 6 0.4787 0.4908 0.000 0.000 0.036 0.236 0.044 0.684
#> GSM1105499 1 0.0000 0.9519 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1105506 4 0.3864 0.0901 0.000 0.480 0.000 0.520 0.000 0.000
#> GSM1105442 5 0.0363 0.8678 0.000 0.000 0.000 0.000 0.988 0.012
#> GSM1105511 4 0.5303 0.2165 0.000 0.000 0.028 0.568 0.056 0.348
#> GSM1105514 2 0.4531 0.5394 0.000 0.684 0.020 0.264 0.028 0.004
#> GSM1105518 4 0.5525 0.3243 0.000 0.016 0.028 0.616 0.060 0.280
#> GSM1105522 3 0.1753 0.8025 0.084 0.000 0.912 0.000 0.000 0.004
#> GSM1105534 1 0.0000 0.9519 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1105535 1 0.0000 0.9519 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1105538 1 0.0632 0.9387 0.976 0.000 0.024 0.000 0.000 0.000
#> GSM1105542 5 0.0363 0.8678 0.000 0.000 0.000 0.000 0.988 0.012
#> GSM1105443 2 0.0260 0.7020 0.000 0.992 0.000 0.008 0.000 0.000
#> GSM1105551 1 0.2491 0.8164 0.836 0.000 0.164 0.000 0.000 0.000
#> GSM1105554 1 0.0000 0.9519 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1105555 1 0.2491 0.8164 0.836 0.000 0.164 0.000 0.000 0.000
#> GSM1105447 2 0.5621 0.1824 0.000 0.460 0.000 0.392 0.148 0.000
#> GSM1105467 4 0.4436 0.2562 0.000 0.380 0.008 0.592 0.020 0.000
#> GSM1105470 2 0.0000 0.7045 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105471 4 0.3851 0.1817 0.000 0.460 0.000 0.540 0.000 0.000
#> GSM1105474 4 0.4416 0.2629 0.000 0.372 0.008 0.600 0.020 0.000
#> GSM1105475 4 0.4080 0.1854 0.000 0.456 0.008 0.536 0.000 0.000
#> GSM1105440 1 0.2491 0.8164 0.836 0.000 0.164 0.000 0.000 0.000
#> GSM1105488 5 0.0363 0.8678 0.000 0.000 0.000 0.000 0.988 0.012
#> GSM1105489 1 0.0000 0.9519 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1105492 1 0.0000 0.9519 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1105493 1 0.0146 0.9501 0.996 0.000 0.004 0.000 0.000 0.000
#> GSM1105497 5 0.0363 0.8678 0.000 0.000 0.000 0.000 0.988 0.012
#> GSM1105500 6 0.5157 0.4952 0.000 0.000 0.028 0.152 0.140 0.680
#> GSM1105501 4 0.5295 0.2494 0.000 0.000 0.028 0.584 0.060 0.328
#> GSM1105508 3 0.3360 0.4347 0.000 0.000 0.732 0.004 0.000 0.264
#> GSM1105444 2 0.0146 0.7031 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM1105513 4 0.3684 0.3374 0.000 0.332 0.004 0.664 0.000 0.000
#> GSM1105516 6 0.3819 0.5306 0.000 0.000 0.372 0.004 0.000 0.624
#> GSM1105520 4 0.5525 0.3243 0.000 0.016 0.028 0.616 0.060 0.280
#> GSM1105524 1 0.0000 0.9519 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1105536 6 0.2009 0.7439 0.000 0.000 0.004 0.008 0.084 0.904
#> GSM1105537 1 0.0000 0.9519 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1105540 6 0.1867 0.7605 0.000 0.000 0.020 0.000 0.064 0.916
#> GSM1105544 6 0.1753 0.7470 0.000 0.000 0.004 0.000 0.084 0.912
#> GSM1105445 2 0.3966 0.1235 0.000 0.552 0.000 0.444 0.004 0.000
#> GSM1105553 5 0.4865 0.0918 0.000 0.000 0.016 0.028 0.488 0.468
#> GSM1105556 1 0.1714 0.8890 0.908 0.000 0.092 0.000 0.000 0.000
#> GSM1105557 4 0.4022 0.3724 0.000 0.300 0.004 0.680 0.012 0.004
#> GSM1105449 2 0.0000 0.7045 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105469 4 0.5103 0.1666 0.000 0.000 0.032 0.556 0.032 0.380
#> GSM1105472 2 0.0000 0.7045 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105473 3 0.3351 0.4457 0.000 0.000 0.712 0.000 0.000 0.288
#> GSM1105476 4 0.4416 0.2629 0.000 0.372 0.008 0.600 0.020 0.000
#> GSM1105477 6 0.1949 0.7430 0.000 0.000 0.004 0.004 0.088 0.904
#> GSM1105478 4 0.3126 0.4183 0.000 0.248 0.000 0.752 0.000 0.000
#> GSM1105510 5 0.0363 0.8678 0.000 0.000 0.000 0.000 0.988 0.012
#> GSM1105530 3 0.1700 0.8014 0.080 0.000 0.916 0.000 0.000 0.004
#> GSM1105539 3 0.3774 0.3638 0.408 0.000 0.592 0.000 0.000 0.000
#> GSM1105480 4 0.3401 0.4698 0.000 0.124 0.008 0.828 0.016 0.024
#> GSM1105512 3 0.2762 0.7480 0.196 0.000 0.804 0.000 0.000 0.000
#> GSM1105532 3 0.1700 0.8014 0.080 0.000 0.916 0.000 0.000 0.004
#> GSM1105541 3 0.3774 0.3638 0.408 0.000 0.592 0.000 0.000 0.000
#> GSM1105439 2 0.0000 0.7045 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105463 3 0.3351 0.4457 0.000 0.000 0.712 0.000 0.000 0.288
#> GSM1105482 1 0.0000 0.9519 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1105483 4 0.5103 0.1666 0.000 0.000 0.032 0.556 0.032 0.380
#> GSM1105494 4 0.3043 0.4648 0.000 0.132 0.008 0.836 0.000 0.024
#> GSM1105503 4 0.5525 0.3243 0.000 0.016 0.028 0.616 0.060 0.280
#> GSM1105507 6 0.3852 0.5123 0.000 0.000 0.384 0.004 0.000 0.612
#> GSM1105446 5 0.5573 0.3061 0.000 0.072 0.028 0.328 0.568 0.004
#> GSM1105519 3 0.2146 0.7979 0.116 0.000 0.880 0.000 0.000 0.004
#> GSM1105526 4 0.6166 0.2788 0.000 0.008 0.020 0.544 0.188 0.240
#> GSM1105527 4 0.3497 0.4519 0.000 0.000 0.036 0.800 0.008 0.156
#> GSM1105531 6 0.1983 0.7609 0.000 0.000 0.072 0.000 0.020 0.908
#> GSM1105543 2 0.6451 0.2739 0.000 0.408 0.020 0.368 0.200 0.004
#> GSM1105546 1 0.0000 0.9519 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1105547 1 0.0000 0.9519 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1105455 2 0.0260 0.7020 0.000 0.992 0.000 0.008 0.000 0.000
#> GSM1105458 2 0.4039 0.5653 0.000 0.720 0.012 0.248 0.016 0.004
#> GSM1105459 2 0.0000 0.7045 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105462 6 0.1845 0.7510 0.000 0.000 0.004 0.008 0.072 0.916
#> GSM1105441 2 0.0000 0.7045 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105465 5 0.0363 0.8678 0.000 0.000 0.000 0.000 0.988 0.012
#> GSM1105484 2 0.6451 0.2739 0.000 0.408 0.020 0.368 0.200 0.004
#> GSM1105485 5 0.0363 0.8678 0.000 0.000 0.000 0.000 0.988 0.012
#> GSM1105496 5 0.4865 0.0918 0.000 0.000 0.016 0.028 0.488 0.468
#> GSM1105505 6 0.3852 0.5123 0.000 0.000 0.384 0.004 0.000 0.612
#> GSM1105509 6 0.3852 0.5123 0.000 0.000 0.384 0.004 0.000 0.612
#> GSM1105448 2 0.0146 0.7031 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM1105521 3 0.2146 0.7979 0.116 0.000 0.880 0.000 0.000 0.004
#> GSM1105528 2 0.6578 0.2307 0.000 0.368 0.020 0.368 0.240 0.004
#> GSM1105529 5 0.0363 0.8678 0.000 0.000 0.000 0.000 0.988 0.012
#> GSM1105533 1 0.0000 0.9519 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1105545 6 0.2110 0.7414 0.000 0.000 0.004 0.012 0.084 0.900
#> GSM1105548 1 0.2491 0.8164 0.836 0.000 0.164 0.000 0.000 0.000
#> GSM1105549 1 0.1714 0.8890 0.908 0.000 0.092 0.000 0.000 0.000
#> GSM1105457 2 0.3843 0.0420 0.000 0.548 0.000 0.452 0.000 0.000
#> GSM1105460 2 0.2631 0.6489 0.000 0.856 0.012 0.128 0.000 0.004
#> GSM1105461 2 0.0000 0.7045 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105464 3 0.1951 0.7970 0.076 0.000 0.908 0.000 0.000 0.016
#> GSM1105466 4 0.3854 0.1779 0.000 0.464 0.000 0.536 0.000 0.000
#> GSM1105479 4 0.3857 0.1705 0.000 0.468 0.000 0.532 0.000 0.000
#> GSM1105502 3 0.1753 0.8025 0.084 0.000 0.912 0.000 0.000 0.004
#> GSM1105515 1 0.0000 0.9519 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1105523 6 0.3349 0.6682 0.000 0.000 0.244 0.008 0.000 0.748
#> GSM1105550 6 0.1549 0.7650 0.000 0.000 0.044 0.000 0.020 0.936
#> GSM1105450 2 0.0000 0.7045 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105451 2 0.0000 0.7045 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105454 2 0.3705 0.5659 0.000 0.740 0.020 0.236 0.004 0.000
#> GSM1105468 2 0.0000 0.7045 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105481 2 0.6568 0.2359 0.000 0.372 0.020 0.368 0.236 0.004
#> GSM1105504 6 0.3852 0.5123 0.000 0.000 0.384 0.004 0.000 0.612
#> GSM1105517 6 0.1549 0.7650 0.000 0.000 0.044 0.000 0.020 0.936
#> GSM1105525 6 0.3349 0.6682 0.000 0.000 0.244 0.008 0.000 0.748
#> GSM1105552 6 0.2095 0.7572 0.000 0.000 0.076 0.004 0.016 0.904
#> GSM1105452 5 0.0363 0.8678 0.000 0.000 0.000 0.000 0.988 0.012
#> GSM1105453 2 0.6432 0.3453 0.000 0.456 0.028 0.336 0.176 0.004
#> GSM1105456 2 0.3705 0.5659 0.000 0.740 0.020 0.236 0.004 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 agent(p) other(p) time(p) individual(p) k
#> ATC:hclust 113 0.1582 1.0000 1.000 0.03397 2
#> ATC:hclust 115 0.2921 0.8818 0.222 0.01398 3
#> ATC:hclust 96 0.0124 0.1517 0.509 0.00855 4
#> ATC:hclust 82 0.5341 0.2196 0.819 0.28592 5
#> ATC:hclust 77 0.0958 0.0621 0.500 0.01695 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 44956 rows and 120 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 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 0.982 0.963 0.985 0.4714 0.532 0.532
#> 3 3 1.000 0.998 0.999 0.4115 0.703 0.489
#> 4 4 0.697 0.614 0.759 0.1073 0.885 0.675
#> 5 5 0.727 0.639 0.793 0.0690 0.858 0.530
#> 6 6 0.738 0.633 0.769 0.0419 0.903 0.587
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
#> GSM1105438 2 0.000 0.983 0.000 1.000
#> GSM1105486 2 0.000 0.983 0.000 1.000
#> GSM1105487 1 0.000 0.987 1.000 0.000
#> GSM1105490 2 0.000 0.983 0.000 1.000
#> GSM1105491 2 0.706 0.762 0.192 0.808
#> GSM1105495 2 0.000 0.983 0.000 1.000
#> GSM1105498 2 0.000 0.983 0.000 1.000
#> GSM1105499 1 0.000 0.987 1.000 0.000
#> GSM1105506 2 0.000 0.983 0.000 1.000
#> GSM1105442 2 0.000 0.983 0.000 1.000
#> GSM1105511 2 0.000 0.983 0.000 1.000
#> GSM1105514 2 0.000 0.983 0.000 1.000
#> GSM1105518 2 0.000 0.983 0.000 1.000
#> GSM1105522 1 0.000 0.987 1.000 0.000
#> GSM1105534 1 0.000 0.987 1.000 0.000
#> GSM1105535 1 0.000 0.987 1.000 0.000
#> GSM1105538 1 0.000 0.987 1.000 0.000
#> GSM1105542 2 0.000 0.983 0.000 1.000
#> GSM1105443 2 0.000 0.983 0.000 1.000
#> GSM1105551 1 0.000 0.987 1.000 0.000
#> GSM1105554 1 0.000 0.987 1.000 0.000
#> GSM1105555 1 0.000 0.987 1.000 0.000
#> GSM1105447 2 0.000 0.983 0.000 1.000
#> GSM1105467 2 0.000 0.983 0.000 1.000
#> GSM1105470 2 0.000 0.983 0.000 1.000
#> GSM1105471 2 0.000 0.983 0.000 1.000
#> GSM1105474 2 0.000 0.983 0.000 1.000
#> GSM1105475 2 0.000 0.983 0.000 1.000
#> GSM1105440 1 0.000 0.987 1.000 0.000
#> GSM1105488 2 0.000 0.983 0.000 1.000
#> GSM1105489 1 0.000 0.987 1.000 0.000
#> GSM1105492 1 0.000 0.987 1.000 0.000
#> GSM1105493 1 0.000 0.987 1.000 0.000
#> GSM1105497 2 0.000 0.983 0.000 1.000
#> GSM1105500 2 0.000 0.983 0.000 1.000
#> GSM1105501 2 0.000 0.983 0.000 1.000
#> GSM1105508 1 0.000 0.987 1.000 0.000
#> GSM1105444 2 0.000 0.983 0.000 1.000
#> GSM1105513 2 0.000 0.983 0.000 1.000
#> GSM1105516 1 0.000 0.987 1.000 0.000
#> GSM1105520 2 0.000 0.983 0.000 1.000
#> GSM1105524 1 0.000 0.987 1.000 0.000
#> GSM1105536 2 0.000 0.983 0.000 1.000
#> GSM1105537 1 0.000 0.987 1.000 0.000
#> GSM1105540 2 0.949 0.429 0.368 0.632
#> GSM1105544 2 0.000 0.983 0.000 1.000
#> GSM1105445 2 0.000 0.983 0.000 1.000
#> GSM1105553 2 0.000 0.983 0.000 1.000
#> GSM1105556 1 0.000 0.987 1.000 0.000
#> GSM1105557 2 0.000 0.983 0.000 1.000
#> GSM1105449 2 0.000 0.983 0.000 1.000
#> GSM1105469 2 0.000 0.983 0.000 1.000
#> GSM1105472 2 0.000 0.983 0.000 1.000
#> GSM1105473 1 0.000 0.987 1.000 0.000
#> GSM1105476 2 0.000 0.983 0.000 1.000
#> GSM1105477 2 0.000 0.983 0.000 1.000
#> GSM1105478 2 0.000 0.983 0.000 1.000
#> GSM1105510 2 0.000 0.983 0.000 1.000
#> GSM1105530 1 0.000 0.987 1.000 0.000
#> GSM1105539 1 0.000 0.987 1.000 0.000
#> GSM1105480 2 0.000 0.983 0.000 1.000
#> GSM1105512 1 0.000 0.987 1.000 0.000
#> GSM1105532 1 0.000 0.987 1.000 0.000
#> GSM1105541 1 0.000 0.987 1.000 0.000
#> GSM1105439 2 0.000 0.983 0.000 1.000
#> GSM1105463 1 0.000 0.987 1.000 0.000
#> GSM1105482 1 0.000 0.987 1.000 0.000
#> GSM1105483 2 0.000 0.983 0.000 1.000
#> GSM1105494 2 0.000 0.983 0.000 1.000
#> GSM1105503 2 0.000 0.983 0.000 1.000
#> GSM1105507 1 0.000 0.987 1.000 0.000
#> GSM1105446 2 0.000 0.983 0.000 1.000
#> GSM1105519 1 0.000 0.987 1.000 0.000
#> GSM1105526 2 0.000 0.983 0.000 1.000
#> GSM1105527 2 0.000 0.983 0.000 1.000
#> GSM1105531 1 0.981 0.248 0.580 0.420
#> GSM1105543 2 0.000 0.983 0.000 1.000
#> GSM1105546 1 0.000 0.987 1.000 0.000
#> GSM1105547 1 0.000 0.987 1.000 0.000
#> GSM1105455 2 0.000 0.983 0.000 1.000
#> GSM1105458 2 0.000 0.983 0.000 1.000
#> GSM1105459 2 0.000 0.983 0.000 1.000
#> GSM1105462 2 0.000 0.983 0.000 1.000
#> GSM1105441 2 0.000 0.983 0.000 1.000
#> GSM1105465 2 0.000 0.983 0.000 1.000
#> GSM1105484 2 0.000 0.983 0.000 1.000
#> GSM1105485 2 0.753 0.726 0.216 0.784
#> GSM1105496 2 0.000 0.983 0.000 1.000
#> GSM1105505 1 0.000 0.987 1.000 0.000
#> GSM1105509 1 0.000 0.987 1.000 0.000
#> GSM1105448 2 0.000 0.983 0.000 1.000
#> GSM1105521 1 0.000 0.987 1.000 0.000
#> GSM1105528 2 0.000 0.983 0.000 1.000
#> GSM1105529 2 0.000 0.983 0.000 1.000
#> GSM1105533 1 0.000 0.987 1.000 0.000
#> GSM1105545 2 0.000 0.983 0.000 1.000
#> GSM1105548 1 0.000 0.987 1.000 0.000
#> GSM1105549 1 0.000 0.987 1.000 0.000
#> GSM1105457 2 0.000 0.983 0.000 1.000
#> GSM1105460 2 0.000 0.983 0.000 1.000
#> GSM1105461 2 0.000 0.983 0.000 1.000
#> GSM1105464 1 0.000 0.987 1.000 0.000
#> GSM1105466 2 0.000 0.983 0.000 1.000
#> GSM1105479 2 0.000 0.983 0.000 1.000
#> GSM1105502 1 0.000 0.987 1.000 0.000
#> GSM1105515 1 0.000 0.987 1.000 0.000
#> GSM1105523 2 0.506 0.867 0.112 0.888
#> GSM1105550 2 0.949 0.429 0.368 0.632
#> GSM1105450 2 0.000 0.983 0.000 1.000
#> GSM1105451 2 0.000 0.983 0.000 1.000
#> GSM1105454 2 0.000 0.983 0.000 1.000
#> GSM1105468 2 0.000 0.983 0.000 1.000
#> GSM1105481 2 0.000 0.983 0.000 1.000
#> GSM1105504 1 0.469 0.879 0.900 0.100
#> GSM1105517 1 0.000 0.987 1.000 0.000
#> GSM1105525 1 0.000 0.987 1.000 0.000
#> GSM1105552 1 0.000 0.987 1.000 0.000
#> GSM1105452 2 0.000 0.983 0.000 1.000
#> GSM1105453 2 0.000 0.983 0.000 1.000
#> GSM1105456 2 0.000 0.983 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1105438 2 0.00 0.998 0 1.000 0.000
#> GSM1105486 2 0.00 0.998 0 1.000 0.000
#> GSM1105487 1 0.00 1.000 1 0.000 0.000
#> GSM1105490 2 0.00 0.998 0 1.000 0.000
#> GSM1105491 3 0.00 1.000 0 0.000 1.000
#> GSM1105495 2 0.00 0.998 0 1.000 0.000
#> GSM1105498 3 0.00 1.000 0 0.000 1.000
#> GSM1105499 1 0.00 1.000 1 0.000 0.000
#> GSM1105506 2 0.00 0.998 0 1.000 0.000
#> GSM1105442 3 0.00 1.000 0 0.000 1.000
#> GSM1105511 3 0.00 1.000 0 0.000 1.000
#> GSM1105514 2 0.00 0.998 0 1.000 0.000
#> GSM1105518 2 0.00 0.998 0 1.000 0.000
#> GSM1105522 1 0.00 1.000 1 0.000 0.000
#> GSM1105534 1 0.00 1.000 1 0.000 0.000
#> GSM1105535 1 0.00 1.000 1 0.000 0.000
#> GSM1105538 1 0.00 1.000 1 0.000 0.000
#> GSM1105542 3 0.00 1.000 0 0.000 1.000
#> GSM1105443 2 0.00 0.998 0 1.000 0.000
#> GSM1105551 1 0.00 1.000 1 0.000 0.000
#> GSM1105554 1 0.00 1.000 1 0.000 0.000
#> GSM1105555 1 0.00 1.000 1 0.000 0.000
#> GSM1105447 2 0.00 0.998 0 1.000 0.000
#> GSM1105467 2 0.00 0.998 0 1.000 0.000
#> GSM1105470 2 0.00 0.998 0 1.000 0.000
#> GSM1105471 2 0.00 0.998 0 1.000 0.000
#> GSM1105474 2 0.00 0.998 0 1.000 0.000
#> GSM1105475 2 0.00 0.998 0 1.000 0.000
#> GSM1105440 1 0.00 1.000 1 0.000 0.000
#> GSM1105488 3 0.00 1.000 0 0.000 1.000
#> GSM1105489 1 0.00 1.000 1 0.000 0.000
#> GSM1105492 1 0.00 1.000 1 0.000 0.000
#> GSM1105493 1 0.00 1.000 1 0.000 0.000
#> GSM1105497 3 0.00 1.000 0 0.000 1.000
#> GSM1105500 3 0.00 1.000 0 0.000 1.000
#> GSM1105501 3 0.00 1.000 0 0.000 1.000
#> GSM1105508 3 0.00 1.000 0 0.000 1.000
#> GSM1105444 2 0.00 0.998 0 1.000 0.000
#> GSM1105513 2 0.00 0.998 0 1.000 0.000
#> GSM1105516 3 0.00 1.000 0 0.000 1.000
#> GSM1105520 3 0.00 1.000 0 0.000 1.000
#> GSM1105524 1 0.00 1.000 1 0.000 0.000
#> GSM1105536 3 0.00 1.000 0 0.000 1.000
#> GSM1105537 1 0.00 1.000 1 0.000 0.000
#> GSM1105540 3 0.00 1.000 0 0.000 1.000
#> GSM1105544 3 0.00 1.000 0 0.000 1.000
#> GSM1105445 2 0.00 0.998 0 1.000 0.000
#> GSM1105553 3 0.00 1.000 0 0.000 1.000
#> GSM1105556 1 0.00 1.000 1 0.000 0.000
#> GSM1105557 2 0.00 0.998 0 1.000 0.000
#> GSM1105449 2 0.00 0.998 0 1.000 0.000
#> GSM1105469 3 0.00 1.000 0 0.000 1.000
#> GSM1105472 2 0.00 0.998 0 1.000 0.000
#> GSM1105473 3 0.00 1.000 0 0.000 1.000
#> GSM1105476 2 0.00 0.998 0 1.000 0.000
#> GSM1105477 3 0.00 1.000 0 0.000 1.000
#> GSM1105478 2 0.00 0.998 0 1.000 0.000
#> GSM1105510 3 0.00 1.000 0 0.000 1.000
#> GSM1105530 1 0.00 1.000 1 0.000 0.000
#> GSM1105539 1 0.00 1.000 1 0.000 0.000
#> GSM1105480 3 0.00 1.000 0 0.000 1.000
#> GSM1105512 1 0.00 1.000 1 0.000 0.000
#> GSM1105532 1 0.00 1.000 1 0.000 0.000
#> GSM1105541 1 0.00 1.000 1 0.000 0.000
#> GSM1105439 2 0.00 0.998 0 1.000 0.000
#> GSM1105463 3 0.00 1.000 0 0.000 1.000
#> GSM1105482 1 0.00 1.000 1 0.000 0.000
#> GSM1105483 3 0.00 1.000 0 0.000 1.000
#> GSM1105494 2 0.00 0.998 0 1.000 0.000
#> GSM1105503 3 0.00 1.000 0 0.000 1.000
#> GSM1105507 3 0.00 1.000 0 0.000 1.000
#> GSM1105446 2 0.28 0.897 0 0.908 0.092
#> GSM1105519 1 0.00 1.000 1 0.000 0.000
#> GSM1105526 3 0.00 1.000 0 0.000 1.000
#> GSM1105527 2 0.00 0.998 0 1.000 0.000
#> GSM1105531 3 0.00 1.000 0 0.000 1.000
#> GSM1105543 2 0.00 0.998 0 1.000 0.000
#> GSM1105546 1 0.00 1.000 1 0.000 0.000
#> GSM1105547 1 0.00 1.000 1 0.000 0.000
#> GSM1105455 2 0.00 0.998 0 1.000 0.000
#> GSM1105458 2 0.00 0.998 0 1.000 0.000
#> GSM1105459 2 0.00 0.998 0 1.000 0.000
#> GSM1105462 3 0.00 1.000 0 0.000 1.000
#> GSM1105441 2 0.00 0.998 0 1.000 0.000
#> GSM1105465 3 0.00 1.000 0 0.000 1.000
#> GSM1105484 2 0.00 0.998 0 1.000 0.000
#> GSM1105485 3 0.00 1.000 0 0.000 1.000
#> GSM1105496 3 0.00 1.000 0 0.000 1.000
#> GSM1105505 3 0.00 1.000 0 0.000 1.000
#> GSM1105509 3 0.00 1.000 0 0.000 1.000
#> GSM1105448 2 0.00 0.998 0 1.000 0.000
#> GSM1105521 1 0.00 1.000 1 0.000 0.000
#> GSM1105528 2 0.00 0.998 0 1.000 0.000
#> GSM1105529 3 0.00 1.000 0 0.000 1.000
#> GSM1105533 1 0.00 1.000 1 0.000 0.000
#> GSM1105545 3 0.00 1.000 0 0.000 1.000
#> GSM1105548 1 0.00 1.000 1 0.000 0.000
#> GSM1105549 1 0.00 1.000 1 0.000 0.000
#> GSM1105457 2 0.00 0.998 0 1.000 0.000
#> GSM1105460 2 0.00 0.998 0 1.000 0.000
#> GSM1105461 2 0.00 0.998 0 1.000 0.000
#> GSM1105464 1 0.00 1.000 1 0.000 0.000
#> GSM1105466 2 0.00 0.998 0 1.000 0.000
#> GSM1105479 2 0.00 0.998 0 1.000 0.000
#> GSM1105502 1 0.00 1.000 1 0.000 0.000
#> GSM1105515 1 0.00 1.000 1 0.000 0.000
#> GSM1105523 3 0.00 1.000 0 0.000 1.000
#> GSM1105550 3 0.00 1.000 0 0.000 1.000
#> GSM1105450 2 0.00 0.998 0 1.000 0.000
#> GSM1105451 2 0.00 0.998 0 1.000 0.000
#> GSM1105454 2 0.00 0.998 0 1.000 0.000
#> GSM1105468 2 0.00 0.998 0 1.000 0.000
#> GSM1105481 2 0.00 0.998 0 1.000 0.000
#> GSM1105504 3 0.00 1.000 0 0.000 1.000
#> GSM1105517 3 0.00 1.000 0 0.000 1.000
#> GSM1105525 3 0.00 1.000 0 0.000 1.000
#> GSM1105552 3 0.00 1.000 0 0.000 1.000
#> GSM1105452 3 0.00 1.000 0 0.000 1.000
#> GSM1105453 2 0.00 0.998 0 1.000 0.000
#> GSM1105456 2 0.00 0.998 0 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1105438 2 0.2081 0.87300 0.000 0.916 0.000 0.084
#> GSM1105486 2 0.2408 0.87316 0.000 0.896 0.000 0.104
#> GSM1105487 1 0.0000 0.93045 1.000 0.000 0.000 0.000
#> GSM1105490 2 0.4679 0.77341 0.000 0.648 0.000 0.352
#> GSM1105491 4 0.4941 0.42960 0.000 0.000 0.436 0.564
#> GSM1105495 4 0.5055 0.00884 0.000 0.368 0.008 0.624
#> GSM1105498 4 0.4967 0.07919 0.000 0.000 0.452 0.548
#> GSM1105499 1 0.0000 0.93045 1.000 0.000 0.000 0.000
#> GSM1105506 2 0.4830 0.73452 0.000 0.608 0.000 0.392
#> GSM1105442 4 0.4877 0.47113 0.000 0.000 0.408 0.592
#> GSM1105511 4 0.4977 0.06727 0.000 0.000 0.460 0.540
#> GSM1105514 2 0.0000 0.86328 0.000 1.000 0.000 0.000
#> GSM1105518 4 0.4992 -0.56686 0.000 0.476 0.000 0.524
#> GSM1105522 3 0.4998 -0.26511 0.488 0.000 0.512 0.000
#> GSM1105534 1 0.0000 0.93045 1.000 0.000 0.000 0.000
#> GSM1105535 1 0.0000 0.93045 1.000 0.000 0.000 0.000
#> GSM1105538 1 0.0188 0.92955 0.996 0.000 0.004 0.000
#> GSM1105542 4 0.4877 0.47113 0.000 0.000 0.408 0.592
#> GSM1105443 2 0.1474 0.86714 0.000 0.948 0.000 0.052
#> GSM1105551 1 0.0188 0.92955 0.996 0.000 0.004 0.000
#> GSM1105554 1 0.0000 0.93045 1.000 0.000 0.000 0.000
#> GSM1105555 1 0.0188 0.92955 0.996 0.000 0.004 0.000
#> GSM1105447 2 0.3764 0.85416 0.000 0.784 0.000 0.216
#> GSM1105467 2 0.2408 0.87316 0.000 0.896 0.000 0.104
#> GSM1105470 2 0.0000 0.86328 0.000 1.000 0.000 0.000
#> GSM1105471 2 0.3400 0.86743 0.000 0.820 0.000 0.180
#> GSM1105474 2 0.2408 0.87316 0.000 0.896 0.000 0.104
#> GSM1105475 2 0.3266 0.87013 0.000 0.832 0.000 0.168
#> GSM1105440 1 0.0188 0.92955 0.996 0.000 0.004 0.000
#> GSM1105488 4 0.4877 0.47113 0.000 0.000 0.408 0.592
#> GSM1105489 1 0.0000 0.93045 1.000 0.000 0.000 0.000
#> GSM1105492 1 0.0000 0.93045 1.000 0.000 0.000 0.000
#> GSM1105493 1 0.0000 0.93045 1.000 0.000 0.000 0.000
#> GSM1105497 4 0.4877 0.47113 0.000 0.000 0.408 0.592
#> GSM1105500 3 0.4761 -0.01345 0.000 0.000 0.628 0.372
#> GSM1105501 4 0.4933 0.10944 0.000 0.000 0.432 0.568
#> GSM1105508 3 0.2737 0.51560 0.104 0.000 0.888 0.008
#> GSM1105444 2 0.0000 0.86328 0.000 1.000 0.000 0.000
#> GSM1105513 2 0.4193 0.83227 0.000 0.732 0.000 0.268
#> GSM1105516 3 0.1389 0.55356 0.000 0.000 0.952 0.048
#> GSM1105520 4 0.4916 0.10549 0.000 0.000 0.424 0.576
#> GSM1105524 1 0.0000 0.93045 1.000 0.000 0.000 0.000
#> GSM1105536 3 0.4961 -0.02535 0.000 0.000 0.552 0.448
#> GSM1105537 1 0.0000 0.93045 1.000 0.000 0.000 0.000
#> GSM1105540 3 0.3907 0.46801 0.000 0.000 0.768 0.232
#> GSM1105544 3 0.4776 -0.01061 0.000 0.000 0.624 0.376
#> GSM1105445 2 0.3801 0.85254 0.000 0.780 0.000 0.220
#> GSM1105553 3 0.4804 -0.04590 0.000 0.000 0.616 0.384
#> GSM1105556 1 0.0000 0.93045 1.000 0.000 0.000 0.000
#> GSM1105557 2 0.4955 0.67356 0.000 0.556 0.000 0.444
#> GSM1105449 2 0.0000 0.86328 0.000 1.000 0.000 0.000
#> GSM1105469 3 0.4564 0.40476 0.000 0.000 0.672 0.328
#> GSM1105472 2 0.0000 0.86328 0.000 1.000 0.000 0.000
#> GSM1105473 3 0.0000 0.55461 0.000 0.000 1.000 0.000
#> GSM1105476 2 0.2408 0.87316 0.000 0.896 0.000 0.104
#> GSM1105477 3 0.4790 -0.00943 0.000 0.000 0.620 0.380
#> GSM1105478 2 0.4830 0.73452 0.000 0.608 0.000 0.392
#> GSM1105510 4 0.4877 0.47113 0.000 0.000 0.408 0.592
#> GSM1105530 1 0.4661 0.60480 0.652 0.000 0.348 0.000
#> GSM1105539 1 0.3266 0.81846 0.832 0.000 0.168 0.000
#> GSM1105480 4 0.4364 0.18718 0.000 0.016 0.220 0.764
#> GSM1105512 1 0.4250 0.70686 0.724 0.000 0.276 0.000
#> GSM1105532 3 0.4998 -0.26511 0.488 0.000 0.512 0.000
#> GSM1105541 1 0.3266 0.81846 0.832 0.000 0.168 0.000
#> GSM1105439 2 0.0817 0.86629 0.000 0.976 0.000 0.024
#> GSM1105463 3 0.0000 0.55461 0.000 0.000 1.000 0.000
#> GSM1105482 1 0.0000 0.93045 1.000 0.000 0.000 0.000
#> GSM1105483 3 0.4790 0.31925 0.000 0.000 0.620 0.380
#> GSM1105494 2 0.4948 0.67884 0.000 0.560 0.000 0.440
#> GSM1105503 4 0.4957 0.16410 0.000 0.016 0.300 0.684
#> GSM1105507 3 0.0524 0.55661 0.008 0.000 0.988 0.004
#> GSM1105446 4 0.5229 0.37832 0.000 0.084 0.168 0.748
#> GSM1105519 1 0.4866 0.50402 0.596 0.000 0.404 0.000
#> GSM1105526 3 0.4999 -0.07486 0.000 0.000 0.508 0.492
#> GSM1105527 2 0.6445 0.56433 0.000 0.488 0.068 0.444
#> GSM1105531 3 0.1867 0.54445 0.000 0.000 0.928 0.072
#> GSM1105543 2 0.3074 0.86591 0.000 0.848 0.000 0.152
#> GSM1105546 1 0.0000 0.93045 1.000 0.000 0.000 0.000
#> GSM1105547 1 0.0000 0.93045 1.000 0.000 0.000 0.000
#> GSM1105455 2 0.1474 0.86714 0.000 0.948 0.000 0.052
#> GSM1105458 2 0.2973 0.87372 0.000 0.856 0.000 0.144
#> GSM1105459 2 0.0000 0.86328 0.000 1.000 0.000 0.000
#> GSM1105462 3 0.4643 0.33601 0.000 0.000 0.656 0.344
#> GSM1105441 2 0.0000 0.86328 0.000 1.000 0.000 0.000
#> GSM1105465 4 0.4877 0.47113 0.000 0.000 0.408 0.592
#> GSM1105484 2 0.4222 0.73501 0.000 0.728 0.000 0.272
#> GSM1105485 4 0.4941 0.42960 0.000 0.000 0.436 0.564
#> GSM1105496 3 0.4804 -0.04590 0.000 0.000 0.616 0.384
#> GSM1105505 3 0.1867 0.54445 0.000 0.000 0.928 0.072
#> GSM1105509 3 0.2530 0.51044 0.112 0.000 0.888 0.000
#> GSM1105448 2 0.0000 0.86328 0.000 1.000 0.000 0.000
#> GSM1105521 1 0.4605 0.62576 0.664 0.000 0.336 0.000
#> GSM1105528 4 0.5244 -0.12015 0.000 0.436 0.008 0.556
#> GSM1105529 4 0.4877 0.47113 0.000 0.000 0.408 0.592
#> GSM1105533 1 0.0000 0.93045 1.000 0.000 0.000 0.000
#> GSM1105545 4 0.4977 0.06727 0.000 0.000 0.460 0.540
#> GSM1105548 1 0.0188 0.92955 0.996 0.000 0.004 0.000
#> GSM1105549 1 0.0469 0.92565 0.988 0.000 0.012 0.000
#> GSM1105457 2 0.4431 0.80993 0.000 0.696 0.000 0.304
#> GSM1105460 2 0.2973 0.87372 0.000 0.856 0.000 0.144
#> GSM1105461 2 0.0000 0.86328 0.000 1.000 0.000 0.000
#> GSM1105464 3 0.4866 0.00270 0.404 0.000 0.596 0.000
#> GSM1105466 2 0.4830 0.73452 0.000 0.608 0.000 0.392
#> GSM1105479 2 0.2868 0.87344 0.000 0.864 0.000 0.136
#> GSM1105502 1 0.3528 0.79769 0.808 0.000 0.192 0.000
#> GSM1105515 1 0.0000 0.93045 1.000 0.000 0.000 0.000
#> GSM1105523 3 0.3486 0.52531 0.000 0.000 0.812 0.188
#> GSM1105550 3 0.3356 0.53290 0.000 0.000 0.824 0.176
#> GSM1105450 2 0.0000 0.86328 0.000 1.000 0.000 0.000
#> GSM1105451 2 0.0000 0.86328 0.000 1.000 0.000 0.000
#> GSM1105454 2 0.3688 0.85731 0.000 0.792 0.000 0.208
#> GSM1105468 2 0.0000 0.86328 0.000 1.000 0.000 0.000
#> GSM1105481 2 0.4500 0.78202 0.000 0.684 0.000 0.316
#> GSM1105504 3 0.1867 0.54445 0.000 0.000 0.928 0.072
#> GSM1105517 3 0.1389 0.55858 0.000 0.000 0.952 0.048
#> GSM1105525 3 0.3032 0.52162 0.008 0.000 0.868 0.124
#> GSM1105552 3 0.0707 0.55717 0.000 0.000 0.980 0.020
#> GSM1105452 4 0.4877 0.47113 0.000 0.000 0.408 0.592
#> GSM1105453 2 0.2530 0.87422 0.000 0.888 0.000 0.112
#> GSM1105456 2 0.3688 0.85731 0.000 0.792 0.000 0.208
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM1105438 2 0.2448 0.8530 0.000 0.892 0.000 0.088 0.020
#> GSM1105486 2 0.2727 0.8489 0.000 0.868 0.000 0.116 0.016
#> GSM1105487 1 0.1124 0.9327 0.960 0.000 0.004 0.036 0.000
#> GSM1105490 4 0.4397 -0.1625 0.000 0.432 0.000 0.564 0.004
#> GSM1105491 5 0.0880 0.7670 0.000 0.000 0.032 0.000 0.968
#> GSM1105495 5 0.3918 0.5686 0.000 0.096 0.000 0.100 0.804
#> GSM1105498 4 0.5728 0.3301 0.000 0.000 0.200 0.624 0.176
#> GSM1105499 1 0.0404 0.9381 0.988 0.000 0.000 0.012 0.000
#> GSM1105506 4 0.4403 -0.1719 0.000 0.436 0.000 0.560 0.004
#> GSM1105442 5 0.0703 0.7709 0.000 0.000 0.024 0.000 0.976
#> GSM1105511 4 0.5759 0.3260 0.000 0.000 0.200 0.620 0.180
#> GSM1105514 2 0.0290 0.8525 0.000 0.992 0.000 0.000 0.008
#> GSM1105518 4 0.2824 0.4842 0.000 0.096 0.000 0.872 0.032
#> GSM1105522 3 0.3214 0.7130 0.120 0.000 0.844 0.036 0.000
#> GSM1105534 1 0.0404 0.9381 0.988 0.000 0.000 0.012 0.000
#> GSM1105535 1 0.0404 0.9381 0.988 0.000 0.000 0.012 0.000
#> GSM1105538 1 0.1845 0.9229 0.928 0.000 0.016 0.056 0.000
#> GSM1105542 5 0.0703 0.7709 0.000 0.000 0.024 0.000 0.976
#> GSM1105443 2 0.1864 0.8429 0.000 0.924 0.004 0.068 0.004
#> GSM1105551 1 0.2012 0.9190 0.920 0.000 0.020 0.060 0.000
#> GSM1105554 1 0.0404 0.9381 0.988 0.000 0.000 0.012 0.000
#> GSM1105555 1 0.1845 0.9229 0.928 0.000 0.016 0.056 0.000
#> GSM1105447 2 0.4314 0.7436 0.000 0.700 0.004 0.280 0.016
#> GSM1105467 2 0.2727 0.8489 0.000 0.868 0.000 0.116 0.016
#> GSM1105470 2 0.0000 0.8523 0.000 1.000 0.000 0.000 0.000
#> GSM1105471 2 0.4065 0.7630 0.000 0.720 0.000 0.264 0.016
#> GSM1105474 2 0.2727 0.8489 0.000 0.868 0.000 0.116 0.016
#> GSM1105475 2 0.3890 0.7778 0.000 0.736 0.000 0.252 0.012
#> GSM1105440 1 0.1845 0.9229 0.928 0.000 0.016 0.056 0.000
#> GSM1105488 5 0.0703 0.7709 0.000 0.000 0.024 0.000 0.976
#> GSM1105489 1 0.0955 0.9337 0.968 0.000 0.004 0.028 0.000
#> GSM1105492 1 0.0955 0.9337 0.968 0.000 0.004 0.028 0.000
#> GSM1105493 1 0.0000 0.9386 1.000 0.000 0.000 0.000 0.000
#> GSM1105497 5 0.0703 0.7709 0.000 0.000 0.024 0.000 0.976
#> GSM1105500 5 0.6348 0.2901 0.000 0.000 0.180 0.324 0.496
#> GSM1105501 4 0.5640 0.3337 0.000 0.000 0.176 0.636 0.188
#> GSM1105508 3 0.0613 0.7642 0.004 0.000 0.984 0.008 0.004
#> GSM1105444 2 0.0162 0.8522 0.000 0.996 0.004 0.000 0.000
#> GSM1105513 4 0.4448 -0.2981 0.000 0.480 0.000 0.516 0.004
#> GSM1105516 3 0.2661 0.7355 0.000 0.000 0.888 0.056 0.056
#> GSM1105520 4 0.4835 0.3846 0.000 0.000 0.120 0.724 0.156
#> GSM1105524 1 0.0404 0.9381 0.988 0.000 0.000 0.012 0.000
#> GSM1105536 4 0.6688 -0.0947 0.000 0.000 0.240 0.404 0.356
#> GSM1105537 1 0.0404 0.9381 0.988 0.000 0.000 0.012 0.000
#> GSM1105540 3 0.5883 0.1734 0.000 0.000 0.524 0.368 0.108
#> GSM1105544 5 0.6706 0.1323 0.000 0.000 0.248 0.348 0.404
#> GSM1105445 2 0.4518 0.6924 0.000 0.660 0.004 0.320 0.016
#> GSM1105553 5 0.6252 0.3002 0.000 0.000 0.164 0.328 0.508
#> GSM1105556 1 0.0000 0.9386 1.000 0.000 0.000 0.000 0.000
#> GSM1105557 4 0.2612 0.4903 0.000 0.124 0.000 0.868 0.008
#> GSM1105449 2 0.0162 0.8522 0.000 0.996 0.004 0.000 0.000
#> GSM1105469 4 0.5613 0.2983 0.000 0.000 0.308 0.592 0.100
#> GSM1105472 2 0.0000 0.8523 0.000 1.000 0.000 0.000 0.000
#> GSM1105473 3 0.0992 0.7609 0.000 0.000 0.968 0.008 0.024
#> GSM1105476 2 0.2727 0.8489 0.000 0.868 0.000 0.116 0.016
#> GSM1105477 5 0.6680 0.1121 0.000 0.000 0.236 0.364 0.400
#> GSM1105478 4 0.4403 -0.1719 0.000 0.436 0.000 0.560 0.004
#> GSM1105510 5 0.0703 0.7709 0.000 0.000 0.024 0.000 0.976
#> GSM1105530 3 0.3772 0.6599 0.172 0.000 0.792 0.036 0.000
#> GSM1105539 1 0.5056 0.3907 0.596 0.000 0.360 0.044 0.000
#> GSM1105480 4 0.2969 0.4278 0.000 0.000 0.020 0.852 0.128
#> GSM1105512 3 0.4734 0.4246 0.312 0.000 0.652 0.036 0.000
#> GSM1105532 3 0.3309 0.7101 0.128 0.000 0.836 0.036 0.000
#> GSM1105541 1 0.5056 0.3907 0.596 0.000 0.360 0.044 0.000
#> GSM1105439 2 0.1285 0.8491 0.000 0.956 0.004 0.036 0.004
#> GSM1105463 3 0.0992 0.7616 0.000 0.000 0.968 0.008 0.024
#> GSM1105482 1 0.0000 0.9386 1.000 0.000 0.000 0.000 0.000
#> GSM1105483 4 0.5640 0.3038 0.000 0.000 0.304 0.592 0.104
#> GSM1105494 4 0.2674 0.4904 0.000 0.120 0.000 0.868 0.012
#> GSM1105503 4 0.3622 0.4238 0.000 0.000 0.056 0.820 0.124
#> GSM1105507 3 0.0992 0.7616 0.000 0.000 0.968 0.008 0.024
#> GSM1105446 5 0.1124 0.7193 0.000 0.004 0.000 0.036 0.960
#> GSM1105519 3 0.3355 0.7041 0.132 0.000 0.832 0.036 0.000
#> GSM1105526 4 0.6615 -0.1116 0.000 0.000 0.216 0.408 0.376
#> GSM1105527 4 0.2116 0.4886 0.000 0.076 0.004 0.912 0.008
#> GSM1105531 3 0.3420 0.7027 0.000 0.000 0.840 0.076 0.084
#> GSM1105543 2 0.3182 0.8432 0.000 0.844 0.000 0.124 0.032
#> GSM1105546 1 0.1124 0.9345 0.960 0.000 0.004 0.036 0.000
#> GSM1105547 1 0.0000 0.9386 1.000 0.000 0.000 0.000 0.000
#> GSM1105455 2 0.1864 0.8429 0.000 0.924 0.004 0.068 0.004
#> GSM1105458 2 0.3795 0.8208 0.000 0.788 0.004 0.184 0.024
#> GSM1105459 2 0.0000 0.8523 0.000 1.000 0.000 0.000 0.000
#> GSM1105462 3 0.6002 -0.0235 0.000 0.000 0.452 0.436 0.112
#> GSM1105441 2 0.0324 0.8516 0.000 0.992 0.004 0.000 0.004
#> GSM1105465 5 0.0703 0.7709 0.000 0.000 0.024 0.000 0.976
#> GSM1105484 2 0.4982 0.6780 0.000 0.700 0.000 0.100 0.200
#> GSM1105485 5 0.0880 0.7670 0.000 0.000 0.032 0.000 0.968
#> GSM1105496 5 0.6241 0.3112 0.000 0.000 0.164 0.324 0.512
#> GSM1105505 3 0.3362 0.7064 0.000 0.000 0.844 0.076 0.080
#> GSM1105509 3 0.1059 0.7616 0.004 0.000 0.968 0.008 0.020
#> GSM1105448 2 0.0162 0.8522 0.000 0.996 0.004 0.000 0.000
#> GSM1105521 3 0.4404 0.5362 0.252 0.000 0.712 0.036 0.000
#> GSM1105528 5 0.4104 0.5436 0.000 0.124 0.000 0.088 0.788
#> GSM1105529 5 0.0703 0.7709 0.000 0.000 0.024 0.000 0.976
#> GSM1105533 1 0.0404 0.9381 0.988 0.000 0.000 0.012 0.000
#> GSM1105545 4 0.5941 0.3004 0.000 0.000 0.228 0.592 0.180
#> GSM1105548 1 0.2012 0.9190 0.920 0.000 0.020 0.060 0.000
#> GSM1105549 1 0.2149 0.8994 0.916 0.000 0.048 0.036 0.000
#> GSM1105457 4 0.4705 -0.3254 0.000 0.484 0.004 0.504 0.008
#> GSM1105460 2 0.3795 0.8208 0.000 0.788 0.004 0.184 0.024
#> GSM1105461 2 0.0162 0.8522 0.000 0.996 0.004 0.000 0.000
#> GSM1105464 3 0.1893 0.7514 0.048 0.000 0.928 0.024 0.000
#> GSM1105466 4 0.4403 -0.1719 0.000 0.436 0.000 0.560 0.004
#> GSM1105479 2 0.3756 0.7765 0.000 0.744 0.000 0.248 0.008
#> GSM1105502 3 0.5096 0.0610 0.444 0.000 0.520 0.036 0.000
#> GSM1105515 1 0.0404 0.9381 0.988 0.000 0.000 0.012 0.000
#> GSM1105523 3 0.5119 0.3097 0.000 0.000 0.592 0.360 0.048
#> GSM1105550 3 0.4689 0.4817 0.000 0.000 0.688 0.264 0.048
#> GSM1105450 2 0.0000 0.8523 0.000 1.000 0.000 0.000 0.000
#> GSM1105451 2 0.0162 0.8522 0.000 0.996 0.004 0.000 0.000
#> GSM1105454 2 0.4314 0.7436 0.000 0.700 0.004 0.280 0.016
#> GSM1105468 2 0.0000 0.8523 0.000 1.000 0.000 0.000 0.000
#> GSM1105481 2 0.5322 0.5029 0.000 0.552 0.000 0.392 0.056
#> GSM1105504 3 0.3420 0.7027 0.000 0.000 0.840 0.076 0.084
#> GSM1105517 3 0.1211 0.7617 0.000 0.000 0.960 0.024 0.016
#> GSM1105525 3 0.0510 0.7625 0.000 0.000 0.984 0.016 0.000
#> GSM1105552 3 0.2149 0.7502 0.000 0.000 0.916 0.048 0.036
#> GSM1105452 5 0.0703 0.7709 0.000 0.000 0.024 0.000 0.976
#> GSM1105453 2 0.2919 0.8517 0.000 0.868 0.004 0.104 0.024
#> GSM1105456 2 0.4314 0.7436 0.000 0.700 0.004 0.280 0.016
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1105438 2 0.3831 0.65038 0.000 0.780 0.020 0.004 0.024 0.172
#> GSM1105486 2 0.3560 0.63627 0.000 0.772 0.012 0.004 0.008 0.204
#> GSM1105487 1 0.3043 0.87755 0.836 0.000 0.024 0.008 0.000 0.132
#> GSM1105490 6 0.5134 0.64439 0.000 0.228 0.000 0.152 0.000 0.620
#> GSM1105491 5 0.1141 0.90753 0.000 0.000 0.000 0.052 0.948 0.000
#> GSM1105495 5 0.5611 0.57810 0.000 0.132 0.032 0.016 0.664 0.156
#> GSM1105498 4 0.4368 0.59797 0.000 0.000 0.000 0.708 0.088 0.204
#> GSM1105499 1 0.1542 0.89847 0.936 0.000 0.000 0.008 0.004 0.052
#> GSM1105506 6 0.5269 0.64355 0.000 0.248 0.000 0.156 0.000 0.596
#> GSM1105442 5 0.0632 0.91569 0.000 0.000 0.000 0.024 0.976 0.000
#> GSM1105511 4 0.4269 0.60924 0.000 0.000 0.000 0.724 0.092 0.184
#> GSM1105514 2 0.1026 0.71631 0.000 0.968 0.012 0.004 0.008 0.008
#> GSM1105518 6 0.5339 0.50909 0.000 0.040 0.016 0.272 0.036 0.636
#> GSM1105522 3 0.1882 0.76360 0.028 0.000 0.928 0.020 0.000 0.024
#> GSM1105534 1 0.1010 0.90256 0.960 0.000 0.000 0.000 0.004 0.036
#> GSM1105535 1 0.1410 0.90065 0.944 0.000 0.000 0.008 0.004 0.044
#> GSM1105538 1 0.4016 0.84563 0.772 0.000 0.088 0.008 0.000 0.132
#> GSM1105542 5 0.0865 0.91951 0.000 0.000 0.000 0.036 0.964 0.000
#> GSM1105443 2 0.3161 0.57222 0.000 0.776 0.008 0.000 0.000 0.216
#> GSM1105551 1 0.4195 0.83714 0.756 0.000 0.100 0.008 0.000 0.136
#> GSM1105554 1 0.1364 0.90004 0.944 0.000 0.000 0.004 0.004 0.048
#> GSM1105555 1 0.4016 0.84563 0.772 0.000 0.088 0.008 0.000 0.132
#> GSM1105447 6 0.4784 -0.00967 0.000 0.464 0.028 0.000 0.012 0.496
#> GSM1105467 2 0.3560 0.63627 0.000 0.772 0.012 0.004 0.008 0.204
#> GSM1105470 2 0.0000 0.71986 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105471 2 0.4341 0.39006 0.000 0.620 0.012 0.004 0.008 0.356
#> GSM1105474 2 0.3560 0.63627 0.000 0.772 0.012 0.004 0.008 0.204
#> GSM1105475 2 0.3872 0.31807 0.000 0.604 0.000 0.000 0.004 0.392
#> GSM1105440 1 0.4055 0.84562 0.768 0.000 0.088 0.008 0.000 0.136
#> GSM1105488 5 0.0865 0.91951 0.000 0.000 0.000 0.036 0.964 0.000
#> GSM1105489 1 0.2163 0.89107 0.892 0.000 0.004 0.008 0.000 0.096
#> GSM1105492 1 0.2488 0.88388 0.864 0.000 0.004 0.008 0.000 0.124
#> GSM1105493 1 0.0551 0.90452 0.984 0.000 0.004 0.004 0.000 0.008
#> GSM1105497 5 0.0865 0.91951 0.000 0.000 0.000 0.036 0.964 0.000
#> GSM1105500 4 0.4078 0.50000 0.000 0.000 0.000 0.640 0.340 0.020
#> GSM1105501 4 0.4376 0.60726 0.000 0.000 0.004 0.724 0.092 0.180
#> GSM1105508 3 0.2896 0.73956 0.000 0.000 0.824 0.160 0.000 0.016
#> GSM1105444 2 0.0458 0.71908 0.000 0.984 0.000 0.000 0.000 0.016
#> GSM1105513 6 0.5103 0.60803 0.000 0.276 0.000 0.120 0.000 0.604
#> GSM1105516 3 0.4406 0.28628 0.000 0.000 0.516 0.464 0.008 0.012
#> GSM1105520 4 0.4718 0.56436 0.000 0.000 0.008 0.684 0.088 0.220
#> GSM1105524 1 0.1410 0.90065 0.944 0.000 0.000 0.008 0.004 0.044
#> GSM1105536 4 0.3533 0.63851 0.000 0.000 0.008 0.776 0.196 0.020
#> GSM1105537 1 0.1410 0.90065 0.944 0.000 0.000 0.008 0.004 0.044
#> GSM1105540 4 0.3294 0.54057 0.000 0.000 0.156 0.812 0.020 0.012
#> GSM1105544 4 0.3560 0.59216 0.000 0.000 0.008 0.732 0.256 0.004
#> GSM1105445 6 0.4470 0.20655 0.000 0.408 0.012 0.004 0.008 0.568
#> GSM1105553 4 0.4199 0.45336 0.000 0.000 0.000 0.600 0.380 0.020
#> GSM1105556 1 0.0748 0.90447 0.976 0.000 0.004 0.004 0.000 0.016
#> GSM1105557 6 0.4831 0.57149 0.000 0.072 0.000 0.256 0.012 0.660
#> GSM1105449 2 0.1204 0.70746 0.000 0.944 0.000 0.000 0.000 0.056
#> GSM1105469 4 0.3054 0.64262 0.000 0.000 0.036 0.828 0.000 0.136
#> GSM1105472 2 0.0260 0.71791 0.000 0.992 0.000 0.000 0.000 0.008
#> GSM1105473 3 0.2882 0.73490 0.000 0.000 0.812 0.180 0.000 0.008
#> GSM1105476 2 0.3560 0.63627 0.000 0.772 0.012 0.004 0.008 0.204
#> GSM1105477 4 0.3691 0.59162 0.000 0.000 0.008 0.724 0.260 0.008
#> GSM1105478 6 0.5249 0.64453 0.000 0.244 0.000 0.156 0.000 0.600
#> GSM1105510 5 0.0865 0.91951 0.000 0.000 0.000 0.036 0.964 0.000
#> GSM1105530 3 0.1887 0.75742 0.048 0.000 0.924 0.012 0.000 0.016
#> GSM1105539 3 0.4648 0.34686 0.320 0.000 0.628 0.008 0.000 0.044
#> GSM1105480 4 0.4923 0.33917 0.000 0.000 0.000 0.560 0.072 0.368
#> GSM1105512 3 0.2265 0.73499 0.076 0.000 0.896 0.004 0.000 0.024
#> GSM1105532 3 0.1794 0.76202 0.028 0.000 0.932 0.024 0.000 0.016
#> GSM1105541 3 0.4648 0.34686 0.320 0.000 0.628 0.008 0.000 0.044
#> GSM1105439 2 0.2814 0.62462 0.000 0.820 0.008 0.000 0.000 0.172
#> GSM1105463 3 0.3230 0.71133 0.000 0.000 0.776 0.212 0.000 0.012
#> GSM1105482 1 0.0291 0.90499 0.992 0.000 0.004 0.004 0.000 0.000
#> GSM1105483 4 0.2983 0.64376 0.000 0.000 0.032 0.832 0.000 0.136
#> GSM1105494 6 0.4936 0.52633 0.000 0.068 0.000 0.288 0.012 0.632
#> GSM1105503 4 0.4795 0.42912 0.000 0.000 0.000 0.604 0.072 0.324
#> GSM1105507 3 0.3606 0.66626 0.000 0.000 0.728 0.256 0.000 0.016
#> GSM1105446 5 0.0779 0.89551 0.000 0.000 0.008 0.008 0.976 0.008
#> GSM1105519 3 0.1882 0.76298 0.028 0.000 0.928 0.020 0.000 0.024
#> GSM1105526 4 0.3792 0.64115 0.000 0.000 0.004 0.764 0.188 0.044
#> GSM1105527 6 0.4790 0.33026 0.000 0.036 0.000 0.376 0.012 0.576
#> GSM1105531 4 0.4551 -0.10923 0.000 0.000 0.436 0.536 0.016 0.012
#> GSM1105543 2 0.4321 0.61435 0.000 0.732 0.020 0.008 0.028 0.212
#> GSM1105546 1 0.2261 0.88917 0.884 0.000 0.004 0.008 0.000 0.104
#> GSM1105547 1 0.0291 0.90499 0.992 0.000 0.004 0.004 0.000 0.000
#> GSM1105455 2 0.3161 0.57222 0.000 0.776 0.008 0.000 0.000 0.216
#> GSM1105458 2 0.5160 0.34019 0.000 0.552 0.040 0.004 0.020 0.384
#> GSM1105459 2 0.0000 0.71986 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105462 4 0.2604 0.60627 0.000 0.000 0.100 0.872 0.020 0.008
#> GSM1105441 2 0.1524 0.70394 0.000 0.932 0.008 0.000 0.000 0.060
#> GSM1105465 5 0.0865 0.91951 0.000 0.000 0.000 0.036 0.964 0.000
#> GSM1105484 2 0.5992 0.40717 0.000 0.588 0.020 0.012 0.204 0.176
#> GSM1105485 5 0.1075 0.91048 0.000 0.000 0.000 0.048 0.952 0.000
#> GSM1105496 4 0.4167 0.45937 0.000 0.000 0.000 0.612 0.368 0.020
#> GSM1105505 4 0.4569 -0.17408 0.000 0.000 0.456 0.516 0.016 0.012
#> GSM1105509 3 0.2402 0.75301 0.000 0.000 0.868 0.120 0.000 0.012
#> GSM1105448 2 0.0458 0.71908 0.000 0.984 0.000 0.000 0.000 0.016
#> GSM1105521 3 0.1951 0.74627 0.060 0.000 0.916 0.004 0.000 0.020
#> GSM1105528 5 0.5201 0.60022 0.000 0.148 0.020 0.012 0.692 0.128
#> GSM1105529 5 0.0865 0.91951 0.000 0.000 0.000 0.036 0.964 0.000
#> GSM1105533 1 0.1410 0.90065 0.944 0.000 0.000 0.008 0.004 0.044
#> GSM1105545 4 0.3150 0.67007 0.000 0.000 0.008 0.844 0.088 0.060
#> GSM1105548 1 0.4111 0.84006 0.764 0.000 0.096 0.008 0.000 0.132
#> GSM1105549 1 0.3329 0.79643 0.792 0.000 0.184 0.004 0.000 0.020
#> GSM1105457 6 0.5152 0.55910 0.000 0.272 0.012 0.072 0.008 0.636
#> GSM1105460 2 0.5160 0.34019 0.000 0.552 0.040 0.004 0.020 0.384
#> GSM1105461 2 0.1075 0.71074 0.000 0.952 0.000 0.000 0.000 0.048
#> GSM1105464 3 0.1913 0.75857 0.000 0.000 0.908 0.080 0.000 0.012
#> GSM1105466 6 0.5269 0.64355 0.000 0.248 0.000 0.156 0.000 0.596
#> GSM1105479 2 0.3717 0.31665 0.000 0.616 0.000 0.000 0.000 0.384
#> GSM1105502 3 0.3194 0.68904 0.132 0.000 0.828 0.008 0.000 0.032
#> GSM1105515 1 0.1155 0.90194 0.956 0.000 0.004 0.000 0.004 0.036
#> GSM1105523 4 0.3341 0.47466 0.000 0.000 0.208 0.776 0.004 0.012
#> GSM1105550 4 0.3608 0.40679 0.000 0.000 0.248 0.736 0.004 0.012
#> GSM1105450 2 0.0000 0.71986 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105451 2 0.1204 0.70746 0.000 0.944 0.000 0.000 0.000 0.056
#> GSM1105454 6 0.4848 -0.04733 0.000 0.468 0.032 0.000 0.012 0.488
#> GSM1105468 2 0.0000 0.71986 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105481 2 0.6096 -0.02925 0.000 0.444 0.036 0.024 0.056 0.440
#> GSM1105504 4 0.4551 -0.10923 0.000 0.000 0.436 0.536 0.016 0.012
#> GSM1105517 3 0.4199 0.42025 0.000 0.000 0.568 0.416 0.000 0.016
#> GSM1105525 3 0.2946 0.73367 0.000 0.000 0.812 0.176 0.000 0.012
#> GSM1105552 3 0.4393 0.32983 0.000 0.000 0.532 0.448 0.008 0.012
#> GSM1105452 5 0.0632 0.91569 0.000 0.000 0.000 0.024 0.976 0.000
#> GSM1105453 2 0.4491 0.63243 0.000 0.716 0.032 0.004 0.028 0.220
#> GSM1105456 6 0.4848 -0.04733 0.000 0.468 0.032 0.000 0.012 0.488
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 agent(p) other(p) time(p) individual(p) k
#> ATC:kmeans 117 0.8941 0.5394 0.4861 0.0231 2
#> ATC:kmeans 120 0.2605 0.9988 0.0571 0.0232 3
#> ATC:kmeans 85 0.0241 0.2719 0.2987 0.0239 4
#> ATC:kmeans 86 0.1538 0.1178 0.3891 0.0124 5
#> ATC:kmeans 94 0.5150 0.0852 0.6426 0.0380 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 44956 rows and 120 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 0.980 0.993 0.4994 0.503 0.503
#> 3 3 1.000 0.984 0.993 0.2203 0.872 0.750
#> 4 4 0.868 0.869 0.940 0.1150 0.944 0.857
#> 5 5 0.855 0.836 0.896 0.0650 0.905 0.722
#> 6 6 0.835 0.832 0.912 0.0439 0.974 0.900
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
#> GSM1105438 2 0.000 0.987 0.000 1.000
#> GSM1105486 2 0.000 0.987 0.000 1.000
#> GSM1105487 1 0.000 1.000 1.000 0.000
#> GSM1105490 2 0.000 0.987 0.000 1.000
#> GSM1105491 1 0.000 1.000 1.000 0.000
#> GSM1105495 2 0.000 0.987 0.000 1.000
#> GSM1105498 2 0.000 0.987 0.000 1.000
#> GSM1105499 1 0.000 1.000 1.000 0.000
#> GSM1105506 2 0.000 0.987 0.000 1.000
#> GSM1105442 2 0.000 0.987 0.000 1.000
#> GSM1105511 2 0.000 0.987 0.000 1.000
#> GSM1105514 2 0.000 0.987 0.000 1.000
#> GSM1105518 2 0.000 0.987 0.000 1.000
#> GSM1105522 1 0.000 1.000 1.000 0.000
#> GSM1105534 1 0.000 1.000 1.000 0.000
#> GSM1105535 1 0.000 1.000 1.000 0.000
#> GSM1105538 1 0.000 1.000 1.000 0.000
#> GSM1105542 2 0.000 0.987 0.000 1.000
#> GSM1105443 2 0.000 0.987 0.000 1.000
#> GSM1105551 1 0.000 1.000 1.000 0.000
#> GSM1105554 1 0.000 1.000 1.000 0.000
#> GSM1105555 1 0.000 1.000 1.000 0.000
#> GSM1105447 2 0.000 0.987 0.000 1.000
#> GSM1105467 2 0.000 0.987 0.000 1.000
#> GSM1105470 2 0.000 0.987 0.000 1.000
#> GSM1105471 2 0.000 0.987 0.000 1.000
#> GSM1105474 2 0.000 0.987 0.000 1.000
#> GSM1105475 2 0.000 0.987 0.000 1.000
#> GSM1105440 1 0.000 1.000 1.000 0.000
#> GSM1105488 2 0.000 0.987 0.000 1.000
#> GSM1105489 1 0.000 1.000 1.000 0.000
#> GSM1105492 1 0.000 1.000 1.000 0.000
#> GSM1105493 1 0.000 1.000 1.000 0.000
#> GSM1105497 2 0.000 0.987 0.000 1.000
#> GSM1105500 1 0.000 1.000 1.000 0.000
#> GSM1105501 2 0.000 0.987 0.000 1.000
#> GSM1105508 1 0.000 1.000 1.000 0.000
#> GSM1105444 2 0.000 0.987 0.000 1.000
#> GSM1105513 2 0.000 0.987 0.000 1.000
#> GSM1105516 1 0.000 1.000 1.000 0.000
#> GSM1105520 2 0.000 0.987 0.000 1.000
#> GSM1105524 1 0.000 1.000 1.000 0.000
#> GSM1105536 2 0.000 0.987 0.000 1.000
#> GSM1105537 1 0.000 1.000 1.000 0.000
#> GSM1105540 1 0.000 1.000 1.000 0.000
#> GSM1105544 1 0.000 1.000 1.000 0.000
#> GSM1105445 2 0.000 0.987 0.000 1.000
#> GSM1105553 2 0.998 0.115 0.472 0.528
#> GSM1105556 1 0.000 1.000 1.000 0.000
#> GSM1105557 2 0.000 0.987 0.000 1.000
#> GSM1105449 2 0.000 0.987 0.000 1.000
#> GSM1105469 1 0.000 1.000 1.000 0.000
#> GSM1105472 2 0.000 0.987 0.000 1.000
#> GSM1105473 1 0.000 1.000 1.000 0.000
#> GSM1105476 2 0.000 0.987 0.000 1.000
#> GSM1105477 2 0.000 0.987 0.000 1.000
#> GSM1105478 2 0.000 0.987 0.000 1.000
#> GSM1105510 2 0.000 0.987 0.000 1.000
#> GSM1105530 1 0.000 1.000 1.000 0.000
#> GSM1105539 1 0.000 1.000 1.000 0.000
#> GSM1105480 2 0.000 0.987 0.000 1.000
#> GSM1105512 1 0.000 1.000 1.000 0.000
#> GSM1105532 1 0.000 1.000 1.000 0.000
#> GSM1105541 1 0.000 1.000 1.000 0.000
#> GSM1105439 2 0.000 0.987 0.000 1.000
#> GSM1105463 1 0.000 1.000 1.000 0.000
#> GSM1105482 1 0.000 1.000 1.000 0.000
#> GSM1105483 2 0.000 0.987 0.000 1.000
#> GSM1105494 2 0.000 0.987 0.000 1.000
#> GSM1105503 2 0.000 0.987 0.000 1.000
#> GSM1105507 1 0.000 1.000 1.000 0.000
#> GSM1105446 2 0.000 0.987 0.000 1.000
#> GSM1105519 1 0.000 1.000 1.000 0.000
#> GSM1105526 2 0.000 0.987 0.000 1.000
#> GSM1105527 2 0.000 0.987 0.000 1.000
#> GSM1105531 1 0.000 1.000 1.000 0.000
#> GSM1105543 2 0.000 0.987 0.000 1.000
#> GSM1105546 1 0.000 1.000 1.000 0.000
#> GSM1105547 1 0.000 1.000 1.000 0.000
#> GSM1105455 2 0.000 0.987 0.000 1.000
#> GSM1105458 2 0.000 0.987 0.000 1.000
#> GSM1105459 2 0.000 0.987 0.000 1.000
#> GSM1105462 2 0.971 0.340 0.400 0.600
#> GSM1105441 2 0.000 0.987 0.000 1.000
#> GSM1105465 2 0.000 0.987 0.000 1.000
#> GSM1105484 2 0.000 0.987 0.000 1.000
#> GSM1105485 1 0.000 1.000 1.000 0.000
#> GSM1105496 1 0.000 1.000 1.000 0.000
#> GSM1105505 1 0.000 1.000 1.000 0.000
#> GSM1105509 1 0.000 1.000 1.000 0.000
#> GSM1105448 2 0.000 0.987 0.000 1.000
#> GSM1105521 1 0.000 1.000 1.000 0.000
#> GSM1105528 2 0.000 0.987 0.000 1.000
#> GSM1105529 2 0.000 0.987 0.000 1.000
#> GSM1105533 1 0.000 1.000 1.000 0.000
#> GSM1105545 2 0.000 0.987 0.000 1.000
#> GSM1105548 1 0.000 1.000 1.000 0.000
#> GSM1105549 1 0.000 1.000 1.000 0.000
#> GSM1105457 2 0.000 0.987 0.000 1.000
#> GSM1105460 2 0.000 0.987 0.000 1.000
#> GSM1105461 2 0.000 0.987 0.000 1.000
#> GSM1105464 1 0.000 1.000 1.000 0.000
#> GSM1105466 2 0.000 0.987 0.000 1.000
#> GSM1105479 2 0.000 0.987 0.000 1.000
#> GSM1105502 1 0.000 1.000 1.000 0.000
#> GSM1105515 1 0.000 1.000 1.000 0.000
#> GSM1105523 1 0.000 1.000 1.000 0.000
#> GSM1105550 1 0.000 1.000 1.000 0.000
#> GSM1105450 2 0.000 0.987 0.000 1.000
#> GSM1105451 2 0.000 0.987 0.000 1.000
#> GSM1105454 2 0.000 0.987 0.000 1.000
#> GSM1105468 2 0.000 0.987 0.000 1.000
#> GSM1105481 2 0.000 0.987 0.000 1.000
#> GSM1105504 1 0.000 1.000 1.000 0.000
#> GSM1105517 1 0.000 1.000 1.000 0.000
#> GSM1105525 1 0.000 1.000 1.000 0.000
#> GSM1105552 1 0.000 1.000 1.000 0.000
#> GSM1105452 2 0.000 0.987 0.000 1.000
#> GSM1105453 2 0.000 0.987 0.000 1.000
#> GSM1105456 2 0.000 0.987 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1105438 2 0.0237 0.990 0.000 0.996 0.004
#> GSM1105486 2 0.0237 0.990 0.000 0.996 0.004
#> GSM1105487 1 0.0000 0.997 1.000 0.000 0.000
#> GSM1105490 2 0.0000 0.990 0.000 1.000 0.000
#> GSM1105491 3 0.0237 0.984 0.004 0.000 0.996
#> GSM1105495 2 0.0424 0.987 0.000 0.992 0.008
#> GSM1105498 2 0.0000 0.990 0.000 1.000 0.000
#> GSM1105499 1 0.0000 0.997 1.000 0.000 0.000
#> GSM1105506 2 0.0000 0.990 0.000 1.000 0.000
#> GSM1105442 3 0.0000 0.985 0.000 0.000 1.000
#> GSM1105511 2 0.0000 0.990 0.000 1.000 0.000
#> GSM1105514 2 0.0237 0.990 0.000 0.996 0.004
#> GSM1105518 2 0.0000 0.990 0.000 1.000 0.000
#> GSM1105522 1 0.0000 0.997 1.000 0.000 0.000
#> GSM1105534 1 0.0000 0.997 1.000 0.000 0.000
#> GSM1105535 1 0.0000 0.997 1.000 0.000 0.000
#> GSM1105538 1 0.0000 0.997 1.000 0.000 0.000
#> GSM1105542 3 0.0000 0.985 0.000 0.000 1.000
#> GSM1105443 2 0.0000 0.990 0.000 1.000 0.000
#> GSM1105551 1 0.0000 0.997 1.000 0.000 0.000
#> GSM1105554 1 0.0000 0.997 1.000 0.000 0.000
#> GSM1105555 1 0.0000 0.997 1.000 0.000 0.000
#> GSM1105447 2 0.0237 0.990 0.000 0.996 0.004
#> GSM1105467 2 0.0237 0.990 0.000 0.996 0.004
#> GSM1105470 2 0.0237 0.990 0.000 0.996 0.004
#> GSM1105471 2 0.0000 0.990 0.000 1.000 0.000
#> GSM1105474 2 0.0237 0.990 0.000 0.996 0.004
#> GSM1105475 2 0.0000 0.990 0.000 1.000 0.000
#> GSM1105440 1 0.0000 0.997 1.000 0.000 0.000
#> GSM1105488 3 0.0000 0.985 0.000 0.000 1.000
#> GSM1105489 1 0.0000 0.997 1.000 0.000 0.000
#> GSM1105492 1 0.0000 0.997 1.000 0.000 0.000
#> GSM1105493 1 0.0000 0.997 1.000 0.000 0.000
#> GSM1105497 3 0.0000 0.985 0.000 0.000 1.000
#> GSM1105500 3 0.0237 0.984 0.004 0.000 0.996
#> GSM1105501 2 0.0000 0.990 0.000 1.000 0.000
#> GSM1105508 1 0.0000 0.997 1.000 0.000 0.000
#> GSM1105444 2 0.0237 0.990 0.000 0.996 0.004
#> GSM1105513 2 0.0000 0.990 0.000 1.000 0.000
#> GSM1105516 1 0.0000 0.997 1.000 0.000 0.000
#> GSM1105520 2 0.0000 0.990 0.000 1.000 0.000
#> GSM1105524 1 0.0000 0.997 1.000 0.000 0.000
#> GSM1105536 2 0.1031 0.972 0.000 0.976 0.024
#> GSM1105537 1 0.0000 0.997 1.000 0.000 0.000
#> GSM1105540 1 0.0000 0.997 1.000 0.000 0.000
#> GSM1105544 3 0.0237 0.984 0.004 0.000 0.996
#> GSM1105445 2 0.0000 0.990 0.000 1.000 0.000
#> GSM1105553 3 0.0000 0.985 0.000 0.000 1.000
#> GSM1105556 1 0.0000 0.997 1.000 0.000 0.000
#> GSM1105557 2 0.0000 0.990 0.000 1.000 0.000
#> GSM1105449 2 0.0237 0.990 0.000 0.996 0.004
#> GSM1105469 1 0.2878 0.870 0.904 0.096 0.000
#> GSM1105472 2 0.0237 0.990 0.000 0.996 0.004
#> GSM1105473 1 0.0000 0.997 1.000 0.000 0.000
#> GSM1105476 2 0.0237 0.990 0.000 0.996 0.004
#> GSM1105477 3 0.0000 0.985 0.000 0.000 1.000
#> GSM1105478 2 0.0000 0.990 0.000 1.000 0.000
#> GSM1105510 3 0.0000 0.985 0.000 0.000 1.000
#> GSM1105530 1 0.0000 0.997 1.000 0.000 0.000
#> GSM1105539 1 0.0000 0.997 1.000 0.000 0.000
#> GSM1105480 2 0.0000 0.990 0.000 1.000 0.000
#> GSM1105512 1 0.0000 0.997 1.000 0.000 0.000
#> GSM1105532 1 0.0000 0.997 1.000 0.000 0.000
#> GSM1105541 1 0.0000 0.997 1.000 0.000 0.000
#> GSM1105439 2 0.0000 0.990 0.000 1.000 0.000
#> GSM1105463 1 0.0000 0.997 1.000 0.000 0.000
#> GSM1105482 1 0.0000 0.997 1.000 0.000 0.000
#> GSM1105483 2 0.0000 0.990 0.000 1.000 0.000
#> GSM1105494 2 0.0000 0.990 0.000 1.000 0.000
#> GSM1105503 2 0.0000 0.990 0.000 1.000 0.000
#> GSM1105507 1 0.0000 0.997 1.000 0.000 0.000
#> GSM1105446 3 0.4346 0.773 0.000 0.184 0.816
#> GSM1105519 1 0.0000 0.997 1.000 0.000 0.000
#> GSM1105526 2 0.0237 0.990 0.000 0.996 0.004
#> GSM1105527 2 0.0000 0.990 0.000 1.000 0.000
#> GSM1105531 1 0.0000 0.997 1.000 0.000 0.000
#> GSM1105543 2 0.0237 0.990 0.000 0.996 0.004
#> GSM1105546 1 0.0000 0.997 1.000 0.000 0.000
#> GSM1105547 1 0.0000 0.997 1.000 0.000 0.000
#> GSM1105455 2 0.0000 0.990 0.000 1.000 0.000
#> GSM1105458 2 0.0237 0.990 0.000 0.996 0.004
#> GSM1105459 2 0.0237 0.990 0.000 0.996 0.004
#> GSM1105462 2 0.5905 0.453 0.352 0.648 0.000
#> GSM1105441 2 0.0237 0.990 0.000 0.996 0.004
#> GSM1105465 3 0.0000 0.985 0.000 0.000 1.000
#> GSM1105484 2 0.0237 0.990 0.000 0.996 0.004
#> GSM1105485 3 0.0237 0.984 0.004 0.000 0.996
#> GSM1105496 3 0.0237 0.984 0.004 0.000 0.996
#> GSM1105505 1 0.0000 0.997 1.000 0.000 0.000
#> GSM1105509 1 0.0000 0.997 1.000 0.000 0.000
#> GSM1105448 2 0.0237 0.990 0.000 0.996 0.004
#> GSM1105521 1 0.0000 0.997 1.000 0.000 0.000
#> GSM1105528 2 0.0592 0.983 0.000 0.988 0.012
#> GSM1105529 3 0.0000 0.985 0.000 0.000 1.000
#> GSM1105533 1 0.0000 0.997 1.000 0.000 0.000
#> GSM1105545 2 0.0000 0.990 0.000 1.000 0.000
#> GSM1105548 1 0.0000 0.997 1.000 0.000 0.000
#> GSM1105549 1 0.0000 0.997 1.000 0.000 0.000
#> GSM1105457 2 0.0000 0.990 0.000 1.000 0.000
#> GSM1105460 2 0.0000 0.990 0.000 1.000 0.000
#> GSM1105461 2 0.0237 0.990 0.000 0.996 0.004
#> GSM1105464 1 0.0000 0.997 1.000 0.000 0.000
#> GSM1105466 2 0.0000 0.990 0.000 1.000 0.000
#> GSM1105479 2 0.0000 0.990 0.000 1.000 0.000
#> GSM1105502 1 0.0000 0.997 1.000 0.000 0.000
#> GSM1105515 1 0.0000 0.997 1.000 0.000 0.000
#> GSM1105523 1 0.0237 0.993 0.996 0.004 0.000
#> GSM1105550 1 0.0000 0.997 1.000 0.000 0.000
#> GSM1105450 2 0.0237 0.990 0.000 0.996 0.004
#> GSM1105451 2 0.0237 0.990 0.000 0.996 0.004
#> GSM1105454 2 0.0237 0.990 0.000 0.996 0.004
#> GSM1105468 2 0.0237 0.990 0.000 0.996 0.004
#> GSM1105481 2 0.0237 0.990 0.000 0.996 0.004
#> GSM1105504 1 0.0000 0.997 1.000 0.000 0.000
#> GSM1105517 1 0.0000 0.997 1.000 0.000 0.000
#> GSM1105525 1 0.0000 0.997 1.000 0.000 0.000
#> GSM1105552 1 0.0000 0.997 1.000 0.000 0.000
#> GSM1105452 3 0.0000 0.985 0.000 0.000 1.000
#> GSM1105453 2 0.0237 0.990 0.000 0.996 0.004
#> GSM1105456 2 0.0237 0.990 0.000 0.996 0.004
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1105438 2 0.0000 0.904 0.000 1.000 0.000 0.000
#> GSM1105486 2 0.0000 0.904 0.000 1.000 0.000 0.000
#> GSM1105487 1 0.0000 0.982 1.000 0.000 0.000 0.000
#> GSM1105490 2 0.2589 0.843 0.000 0.884 0.116 0.000
#> GSM1105491 4 0.0000 0.939 0.000 0.000 0.000 1.000
#> GSM1105495 2 0.0000 0.904 0.000 1.000 0.000 0.000
#> GSM1105498 2 0.4830 0.564 0.000 0.608 0.392 0.000
#> GSM1105499 1 0.0000 0.982 1.000 0.000 0.000 0.000
#> GSM1105506 2 0.4431 0.686 0.000 0.696 0.304 0.000
#> GSM1105442 4 0.0000 0.939 0.000 0.000 0.000 1.000
#> GSM1105511 2 0.4843 0.558 0.000 0.604 0.396 0.000
#> GSM1105514 2 0.0000 0.904 0.000 1.000 0.000 0.000
#> GSM1105518 2 0.3172 0.814 0.000 0.840 0.160 0.000
#> GSM1105522 1 0.0000 0.982 1.000 0.000 0.000 0.000
#> GSM1105534 1 0.0000 0.982 1.000 0.000 0.000 0.000
#> GSM1105535 1 0.0000 0.982 1.000 0.000 0.000 0.000
#> GSM1105538 1 0.0000 0.982 1.000 0.000 0.000 0.000
#> GSM1105542 4 0.0000 0.939 0.000 0.000 0.000 1.000
#> GSM1105443 2 0.0000 0.904 0.000 1.000 0.000 0.000
#> GSM1105551 1 0.0000 0.982 1.000 0.000 0.000 0.000
#> GSM1105554 1 0.0000 0.982 1.000 0.000 0.000 0.000
#> GSM1105555 1 0.0000 0.982 1.000 0.000 0.000 0.000
#> GSM1105447 2 0.0000 0.904 0.000 1.000 0.000 0.000
#> GSM1105467 2 0.0000 0.904 0.000 1.000 0.000 0.000
#> GSM1105470 2 0.0000 0.904 0.000 1.000 0.000 0.000
#> GSM1105471 2 0.0000 0.904 0.000 1.000 0.000 0.000
#> GSM1105474 2 0.0000 0.904 0.000 1.000 0.000 0.000
#> GSM1105475 2 0.0000 0.904 0.000 1.000 0.000 0.000
#> GSM1105440 1 0.0000 0.982 1.000 0.000 0.000 0.000
#> GSM1105488 4 0.0000 0.939 0.000 0.000 0.000 1.000
#> GSM1105489 1 0.0000 0.982 1.000 0.000 0.000 0.000
#> GSM1105492 1 0.0000 0.982 1.000 0.000 0.000 0.000
#> GSM1105493 1 0.0000 0.982 1.000 0.000 0.000 0.000
#> GSM1105497 4 0.0000 0.939 0.000 0.000 0.000 1.000
#> GSM1105500 4 0.0000 0.939 0.000 0.000 0.000 1.000
#> GSM1105501 2 0.2921 0.828 0.000 0.860 0.140 0.000
#> GSM1105508 1 0.0000 0.982 1.000 0.000 0.000 0.000
#> GSM1105444 2 0.0000 0.904 0.000 1.000 0.000 0.000
#> GSM1105513 2 0.2647 0.841 0.000 0.880 0.120 0.000
#> GSM1105516 1 0.0000 0.982 1.000 0.000 0.000 0.000
#> GSM1105520 2 0.4382 0.695 0.000 0.704 0.296 0.000
#> GSM1105524 1 0.0000 0.982 1.000 0.000 0.000 0.000
#> GSM1105536 3 0.4500 0.446 0.000 0.316 0.684 0.000
#> GSM1105537 1 0.0000 0.982 1.000 0.000 0.000 0.000
#> GSM1105540 3 0.4985 0.144 0.468 0.000 0.532 0.000
#> GSM1105544 4 0.2216 0.860 0.000 0.000 0.092 0.908
#> GSM1105445 2 0.0000 0.904 0.000 1.000 0.000 0.000
#> GSM1105553 4 0.0000 0.939 0.000 0.000 0.000 1.000
#> GSM1105556 1 0.0000 0.982 1.000 0.000 0.000 0.000
#> GSM1105557 2 0.4406 0.691 0.000 0.700 0.300 0.000
#> GSM1105449 2 0.0000 0.904 0.000 1.000 0.000 0.000
#> GSM1105469 3 0.0000 0.727 0.000 0.000 1.000 0.000
#> GSM1105472 2 0.0000 0.904 0.000 1.000 0.000 0.000
#> GSM1105473 1 0.0000 0.982 1.000 0.000 0.000 0.000
#> GSM1105476 2 0.0000 0.904 0.000 1.000 0.000 0.000
#> GSM1105477 4 0.4961 0.306 0.000 0.000 0.448 0.552
#> GSM1105478 2 0.4454 0.681 0.000 0.692 0.308 0.000
#> GSM1105510 4 0.0000 0.939 0.000 0.000 0.000 1.000
#> GSM1105530 1 0.0188 0.979 0.996 0.000 0.004 0.000
#> GSM1105539 1 0.0000 0.982 1.000 0.000 0.000 0.000
#> GSM1105480 2 0.4830 0.564 0.000 0.608 0.392 0.000
#> GSM1105512 1 0.0000 0.982 1.000 0.000 0.000 0.000
#> GSM1105532 1 0.0188 0.979 0.996 0.000 0.004 0.000
#> GSM1105541 1 0.0000 0.982 1.000 0.000 0.000 0.000
#> GSM1105439 2 0.0000 0.904 0.000 1.000 0.000 0.000
#> GSM1105463 1 0.0000 0.982 1.000 0.000 0.000 0.000
#> GSM1105482 1 0.0000 0.982 1.000 0.000 0.000 0.000
#> GSM1105483 3 0.0000 0.727 0.000 0.000 1.000 0.000
#> GSM1105494 2 0.4406 0.691 0.000 0.700 0.300 0.000
#> GSM1105503 2 0.4830 0.564 0.000 0.608 0.392 0.000
#> GSM1105507 1 0.0000 0.982 1.000 0.000 0.000 0.000
#> GSM1105446 4 0.3873 0.567 0.000 0.228 0.000 0.772
#> GSM1105519 1 0.0000 0.982 1.000 0.000 0.000 0.000
#> GSM1105526 2 0.2408 0.853 0.000 0.896 0.104 0.000
#> GSM1105527 2 0.4843 0.558 0.000 0.604 0.396 0.000
#> GSM1105531 1 0.0188 0.979 0.996 0.000 0.004 0.000
#> GSM1105543 2 0.0000 0.904 0.000 1.000 0.000 0.000
#> GSM1105546 1 0.0000 0.982 1.000 0.000 0.000 0.000
#> GSM1105547 1 0.0000 0.982 1.000 0.000 0.000 0.000
#> GSM1105455 2 0.0000 0.904 0.000 1.000 0.000 0.000
#> GSM1105458 2 0.0000 0.904 0.000 1.000 0.000 0.000
#> GSM1105459 2 0.0000 0.904 0.000 1.000 0.000 0.000
#> GSM1105462 3 0.0469 0.732 0.012 0.000 0.988 0.000
#> GSM1105441 2 0.0000 0.904 0.000 1.000 0.000 0.000
#> GSM1105465 4 0.0000 0.939 0.000 0.000 0.000 1.000
#> GSM1105484 2 0.0000 0.904 0.000 1.000 0.000 0.000
#> GSM1105485 4 0.0000 0.939 0.000 0.000 0.000 1.000
#> GSM1105496 4 0.0000 0.939 0.000 0.000 0.000 1.000
#> GSM1105505 1 0.0000 0.982 1.000 0.000 0.000 0.000
#> GSM1105509 1 0.0000 0.982 1.000 0.000 0.000 0.000
#> GSM1105448 2 0.0000 0.904 0.000 1.000 0.000 0.000
#> GSM1105521 1 0.0000 0.982 1.000 0.000 0.000 0.000
#> GSM1105528 2 0.0000 0.904 0.000 1.000 0.000 0.000
#> GSM1105529 4 0.0000 0.939 0.000 0.000 0.000 1.000
#> GSM1105533 1 0.0000 0.982 1.000 0.000 0.000 0.000
#> GSM1105545 3 0.1118 0.714 0.000 0.036 0.964 0.000
#> GSM1105548 1 0.0000 0.982 1.000 0.000 0.000 0.000
#> GSM1105549 1 0.0000 0.982 1.000 0.000 0.000 0.000
#> GSM1105457 2 0.3873 0.759 0.000 0.772 0.228 0.000
#> GSM1105460 2 0.0000 0.904 0.000 1.000 0.000 0.000
#> GSM1105461 2 0.0000 0.904 0.000 1.000 0.000 0.000
#> GSM1105464 1 0.0000 0.982 1.000 0.000 0.000 0.000
#> GSM1105466 2 0.4222 0.718 0.000 0.728 0.272 0.000
#> GSM1105479 2 0.0000 0.904 0.000 1.000 0.000 0.000
#> GSM1105502 1 0.0000 0.982 1.000 0.000 0.000 0.000
#> GSM1105515 1 0.0000 0.982 1.000 0.000 0.000 0.000
#> GSM1105523 3 0.0707 0.731 0.020 0.000 0.980 0.000
#> GSM1105550 3 0.4454 0.517 0.308 0.000 0.692 0.000
#> GSM1105450 2 0.0000 0.904 0.000 1.000 0.000 0.000
#> GSM1105451 2 0.0000 0.904 0.000 1.000 0.000 0.000
#> GSM1105454 2 0.0000 0.904 0.000 1.000 0.000 0.000
#> GSM1105468 2 0.0000 0.904 0.000 1.000 0.000 0.000
#> GSM1105481 2 0.0000 0.904 0.000 1.000 0.000 0.000
#> GSM1105504 1 0.0000 0.982 1.000 0.000 0.000 0.000
#> GSM1105517 1 0.3975 0.636 0.760 0.000 0.240 0.000
#> GSM1105525 1 0.4898 0.169 0.584 0.000 0.416 0.000
#> GSM1105552 1 0.0000 0.982 1.000 0.000 0.000 0.000
#> GSM1105452 4 0.0000 0.939 0.000 0.000 0.000 1.000
#> GSM1105453 2 0.0000 0.904 0.000 1.000 0.000 0.000
#> GSM1105456 2 0.0000 0.904 0.000 1.000 0.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM1105438 2 0.0000 0.9349 0.000 1.000 0.000 0.000 0.000
#> GSM1105486 2 0.0000 0.9349 0.000 1.000 0.000 0.000 0.000
#> GSM1105487 1 0.0000 0.9872 1.000 0.000 0.000 0.000 0.000
#> GSM1105490 4 0.4291 0.7809 0.000 0.464 0.000 0.536 0.000
#> GSM1105491 5 0.0000 0.8255 0.000 0.000 0.000 0.000 1.000
#> GSM1105495 2 0.2230 0.7739 0.000 0.884 0.000 0.000 0.116
#> GSM1105498 4 0.0510 0.1746 0.000 0.000 0.016 0.984 0.000
#> GSM1105499 1 0.0000 0.9872 1.000 0.000 0.000 0.000 0.000
#> GSM1105506 4 0.4276 0.8546 0.000 0.380 0.004 0.616 0.000
#> GSM1105442 5 0.0000 0.8255 0.000 0.000 0.000 0.000 1.000
#> GSM1105511 4 0.4585 0.8501 0.000 0.352 0.020 0.628 0.000
#> GSM1105514 2 0.0000 0.9349 0.000 1.000 0.000 0.000 0.000
#> GSM1105518 4 0.4300 0.7594 0.000 0.476 0.000 0.524 0.000
#> GSM1105522 1 0.0000 0.9872 1.000 0.000 0.000 0.000 0.000
#> GSM1105534 1 0.0000 0.9872 1.000 0.000 0.000 0.000 0.000
#> GSM1105535 1 0.0000 0.9872 1.000 0.000 0.000 0.000 0.000
#> GSM1105538 1 0.0000 0.9872 1.000 0.000 0.000 0.000 0.000
#> GSM1105542 5 0.0000 0.8255 0.000 0.000 0.000 0.000 1.000
#> GSM1105443 2 0.1121 0.8954 0.000 0.956 0.000 0.044 0.000
#> GSM1105551 1 0.0000 0.9872 1.000 0.000 0.000 0.000 0.000
#> GSM1105554 1 0.0000 0.9872 1.000 0.000 0.000 0.000 0.000
#> GSM1105555 1 0.0000 0.9872 1.000 0.000 0.000 0.000 0.000
#> GSM1105447 2 0.1043 0.9005 0.000 0.960 0.000 0.040 0.000
#> GSM1105467 2 0.0000 0.9349 0.000 1.000 0.000 0.000 0.000
#> GSM1105470 2 0.0000 0.9349 0.000 1.000 0.000 0.000 0.000
#> GSM1105471 2 0.0000 0.9349 0.000 1.000 0.000 0.000 0.000
#> GSM1105474 2 0.0000 0.9349 0.000 1.000 0.000 0.000 0.000
#> GSM1105475 2 0.0510 0.9234 0.000 0.984 0.000 0.016 0.000
#> GSM1105440 1 0.0000 0.9872 1.000 0.000 0.000 0.000 0.000
#> GSM1105488 5 0.0000 0.8255 0.000 0.000 0.000 0.000 1.000
#> GSM1105489 1 0.0000 0.9872 1.000 0.000 0.000 0.000 0.000
#> GSM1105492 1 0.0000 0.9872 1.000 0.000 0.000 0.000 0.000
#> GSM1105493 1 0.0000 0.9872 1.000 0.000 0.000 0.000 0.000
#> GSM1105497 5 0.0162 0.8240 0.000 0.000 0.000 0.004 0.996
#> GSM1105500 5 0.4886 0.5951 0.000 0.000 0.032 0.372 0.596
#> GSM1105501 2 0.4171 -0.3927 0.000 0.604 0.000 0.396 0.000
#> GSM1105508 1 0.0000 0.9872 1.000 0.000 0.000 0.000 0.000
#> GSM1105444 2 0.0000 0.9349 0.000 1.000 0.000 0.000 0.000
#> GSM1105513 4 0.4297 0.7674 0.000 0.472 0.000 0.528 0.000
#> GSM1105516 1 0.0000 0.9872 1.000 0.000 0.000 0.000 0.000
#> GSM1105520 4 0.4455 0.8426 0.000 0.404 0.008 0.588 0.000
#> GSM1105524 1 0.0000 0.9872 1.000 0.000 0.000 0.000 0.000
#> GSM1105536 3 0.2470 0.6127 0.000 0.104 0.884 0.000 0.012
#> GSM1105537 1 0.0000 0.9872 1.000 0.000 0.000 0.000 0.000
#> GSM1105540 3 0.5668 0.5288 0.144 0.000 0.624 0.232 0.000
#> GSM1105544 5 0.6612 0.3372 0.000 0.000 0.216 0.372 0.412
#> GSM1105445 2 0.3242 0.4968 0.000 0.784 0.000 0.216 0.000
#> GSM1105553 5 0.4886 0.5951 0.000 0.000 0.032 0.372 0.596
#> GSM1105556 1 0.0000 0.9872 1.000 0.000 0.000 0.000 0.000
#> GSM1105557 4 0.4138 0.8534 0.000 0.384 0.000 0.616 0.000
#> GSM1105449 2 0.0000 0.9349 0.000 1.000 0.000 0.000 0.000
#> GSM1105469 3 0.3508 0.6061 0.000 0.000 0.748 0.252 0.000
#> GSM1105472 2 0.0000 0.9349 0.000 1.000 0.000 0.000 0.000
#> GSM1105473 1 0.0000 0.9872 1.000 0.000 0.000 0.000 0.000
#> GSM1105476 2 0.0000 0.9349 0.000 1.000 0.000 0.000 0.000
#> GSM1105477 3 0.6710 -0.1483 0.000 0.000 0.408 0.252 0.340
#> GSM1105478 4 0.4251 0.8545 0.000 0.372 0.004 0.624 0.000
#> GSM1105510 5 0.0000 0.8255 0.000 0.000 0.000 0.000 1.000
#> GSM1105530 1 0.1478 0.9349 0.936 0.000 0.064 0.000 0.000
#> GSM1105539 1 0.1410 0.9385 0.940 0.000 0.060 0.000 0.000
#> GSM1105480 4 0.4232 0.8241 0.000 0.312 0.012 0.676 0.000
#> GSM1105512 1 0.0000 0.9872 1.000 0.000 0.000 0.000 0.000
#> GSM1105532 1 0.1732 0.9187 0.920 0.000 0.080 0.000 0.000
#> GSM1105541 1 0.1410 0.9385 0.940 0.000 0.060 0.000 0.000
#> GSM1105439 2 0.1043 0.9005 0.000 0.960 0.000 0.040 0.000
#> GSM1105463 1 0.0000 0.9872 1.000 0.000 0.000 0.000 0.000
#> GSM1105482 1 0.0000 0.9872 1.000 0.000 0.000 0.000 0.000
#> GSM1105483 3 0.3395 0.6158 0.000 0.000 0.764 0.236 0.000
#> GSM1105494 4 0.3966 0.8390 0.000 0.336 0.000 0.664 0.000
#> GSM1105503 4 0.3779 0.7229 0.000 0.236 0.012 0.752 0.000
#> GSM1105507 1 0.0000 0.9872 1.000 0.000 0.000 0.000 0.000
#> GSM1105446 5 0.4249 0.1203 0.000 0.432 0.000 0.000 0.568
#> GSM1105519 1 0.0000 0.9872 1.000 0.000 0.000 0.000 0.000
#> GSM1105526 2 0.3827 0.6850 0.000 0.816 0.004 0.068 0.112
#> GSM1105527 4 0.4654 0.8482 0.000 0.348 0.024 0.628 0.000
#> GSM1105531 1 0.1965 0.9004 0.904 0.000 0.096 0.000 0.000
#> GSM1105543 2 0.0000 0.9349 0.000 1.000 0.000 0.000 0.000
#> GSM1105546 1 0.0000 0.9872 1.000 0.000 0.000 0.000 0.000
#> GSM1105547 1 0.0000 0.9872 1.000 0.000 0.000 0.000 0.000
#> GSM1105455 2 0.1197 0.8901 0.000 0.952 0.000 0.048 0.000
#> GSM1105458 2 0.0000 0.9349 0.000 1.000 0.000 0.000 0.000
#> GSM1105459 2 0.0000 0.9349 0.000 1.000 0.000 0.000 0.000
#> GSM1105462 3 0.0992 0.6729 0.008 0.000 0.968 0.024 0.000
#> GSM1105441 2 0.0000 0.9349 0.000 1.000 0.000 0.000 0.000
#> GSM1105465 5 0.0000 0.8255 0.000 0.000 0.000 0.000 1.000
#> GSM1105484 2 0.1792 0.8246 0.000 0.916 0.000 0.000 0.084
#> GSM1105485 5 0.0000 0.8255 0.000 0.000 0.000 0.000 1.000
#> GSM1105496 5 0.4886 0.5951 0.000 0.000 0.032 0.372 0.596
#> GSM1105505 1 0.0162 0.9843 0.996 0.000 0.004 0.000 0.000
#> GSM1105509 1 0.0000 0.9872 1.000 0.000 0.000 0.000 0.000
#> GSM1105448 2 0.0000 0.9349 0.000 1.000 0.000 0.000 0.000
#> GSM1105521 1 0.0000 0.9872 1.000 0.000 0.000 0.000 0.000
#> GSM1105528 2 0.2605 0.7206 0.000 0.852 0.000 0.000 0.148
#> GSM1105529 5 0.0000 0.8255 0.000 0.000 0.000 0.000 1.000
#> GSM1105533 1 0.0000 0.9872 1.000 0.000 0.000 0.000 0.000
#> GSM1105545 3 0.2735 0.6552 0.000 0.036 0.880 0.084 0.000
#> GSM1105548 1 0.0000 0.9872 1.000 0.000 0.000 0.000 0.000
#> GSM1105549 1 0.0000 0.9872 1.000 0.000 0.000 0.000 0.000
#> GSM1105457 4 0.4287 0.7873 0.000 0.460 0.000 0.540 0.000
#> GSM1105460 2 0.0000 0.9349 0.000 1.000 0.000 0.000 0.000
#> GSM1105461 2 0.0000 0.9349 0.000 1.000 0.000 0.000 0.000
#> GSM1105464 1 0.1410 0.9385 0.940 0.000 0.060 0.000 0.000
#> GSM1105466 4 0.4256 0.8155 0.000 0.436 0.000 0.564 0.000
#> GSM1105479 2 0.0963 0.9049 0.000 0.964 0.000 0.036 0.000
#> GSM1105502 1 0.0000 0.9872 1.000 0.000 0.000 0.000 0.000
#> GSM1105515 1 0.0000 0.9872 1.000 0.000 0.000 0.000 0.000
#> GSM1105523 3 0.1195 0.6750 0.012 0.000 0.960 0.028 0.000
#> GSM1105550 3 0.1124 0.6711 0.036 0.000 0.960 0.004 0.000
#> GSM1105450 2 0.0000 0.9349 0.000 1.000 0.000 0.000 0.000
#> GSM1105451 2 0.0000 0.9349 0.000 1.000 0.000 0.000 0.000
#> GSM1105454 2 0.0794 0.9128 0.000 0.972 0.000 0.028 0.000
#> GSM1105468 2 0.0000 0.9349 0.000 1.000 0.000 0.000 0.000
#> GSM1105481 2 0.0000 0.9349 0.000 1.000 0.000 0.000 0.000
#> GSM1105504 1 0.1341 0.9421 0.944 0.000 0.056 0.000 0.000
#> GSM1105517 3 0.4305 0.0886 0.488 0.000 0.512 0.000 0.000
#> GSM1105525 3 0.4331 0.3553 0.400 0.000 0.596 0.004 0.000
#> GSM1105552 1 0.0000 0.9872 1.000 0.000 0.000 0.000 0.000
#> GSM1105452 5 0.0000 0.8255 0.000 0.000 0.000 0.000 1.000
#> GSM1105453 2 0.0000 0.9349 0.000 1.000 0.000 0.000 0.000
#> GSM1105456 2 0.0794 0.9128 0.000 0.972 0.000 0.028 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1105438 2 0.0000 0.9213 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105486 2 0.0291 0.9217 0.000 0.992 0.000 0.004 0.000 0.004
#> GSM1105487 1 0.0000 0.9557 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1105490 4 0.3052 0.7986 0.000 0.216 0.000 0.780 0.000 0.004
#> GSM1105491 5 0.0000 1.0000 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1105495 2 0.1219 0.8896 0.000 0.948 0.000 0.000 0.048 0.004
#> GSM1105498 6 0.3765 0.3654 0.000 0.000 0.000 0.404 0.000 0.596
#> GSM1105499 1 0.0291 0.9537 0.992 0.000 0.000 0.004 0.000 0.004
#> GSM1105506 4 0.1714 0.8355 0.000 0.092 0.000 0.908 0.000 0.000
#> GSM1105442 5 0.0000 1.0000 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1105511 4 0.1578 0.8035 0.000 0.048 0.004 0.936 0.000 0.012
#> GSM1105514 2 0.0146 0.9206 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM1105518 4 0.3189 0.7767 0.000 0.236 0.000 0.760 0.000 0.004
#> GSM1105522 1 0.0291 0.9536 0.992 0.000 0.000 0.004 0.000 0.004
#> GSM1105534 1 0.0000 0.9557 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1105535 1 0.0000 0.9557 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1105538 1 0.0000 0.9557 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1105542 5 0.0000 1.0000 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1105443 2 0.2531 0.8301 0.000 0.856 0.000 0.132 0.000 0.012
#> GSM1105551 1 0.0000 0.9557 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1105554 1 0.0000 0.9557 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1105555 1 0.0000 0.9557 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1105447 2 0.2446 0.8386 0.000 0.864 0.000 0.124 0.000 0.012
#> GSM1105467 2 0.0291 0.9217 0.000 0.992 0.000 0.004 0.000 0.004
#> GSM1105470 2 0.0291 0.9217 0.000 0.992 0.000 0.004 0.000 0.004
#> GSM1105471 2 0.0603 0.9195 0.000 0.980 0.000 0.016 0.000 0.004
#> GSM1105474 2 0.0291 0.9217 0.000 0.992 0.000 0.004 0.000 0.004
#> GSM1105475 2 0.1471 0.8916 0.000 0.932 0.000 0.064 0.000 0.004
#> GSM1105440 1 0.0000 0.9557 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1105488 5 0.0000 1.0000 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1105489 1 0.0000 0.9557 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1105492 1 0.0000 0.9557 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1105493 1 0.0146 0.9548 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM1105497 5 0.0000 1.0000 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1105500 6 0.2915 0.7060 0.000 0.000 0.000 0.008 0.184 0.808
#> GSM1105501 4 0.4253 0.3040 0.000 0.460 0.000 0.524 0.000 0.016
#> GSM1105508 1 0.0984 0.9392 0.968 0.000 0.012 0.012 0.000 0.008
#> GSM1105444 2 0.0146 0.9213 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM1105513 4 0.2964 0.8090 0.000 0.204 0.000 0.792 0.000 0.004
#> GSM1105516 1 0.0146 0.9548 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM1105520 4 0.2738 0.8185 0.000 0.176 0.000 0.820 0.000 0.004
#> GSM1105524 1 0.0000 0.9557 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1105536 3 0.4603 0.4223 0.000 0.100 0.740 0.020 0.004 0.136
#> GSM1105537 1 0.0000 0.9557 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1105540 6 0.5112 0.1011 0.084 0.000 0.400 0.000 0.000 0.516
#> GSM1105544 6 0.1707 0.6785 0.000 0.000 0.012 0.004 0.056 0.928
#> GSM1105445 2 0.3867 0.4510 0.000 0.660 0.000 0.328 0.000 0.012
#> GSM1105553 6 0.2848 0.7114 0.000 0.000 0.000 0.008 0.176 0.816
#> GSM1105556 1 0.0000 0.9557 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1105557 4 0.1444 0.8318 0.000 0.072 0.000 0.928 0.000 0.000
#> GSM1105449 2 0.0520 0.9205 0.000 0.984 0.000 0.008 0.000 0.008
#> GSM1105469 3 0.3888 0.4035 0.000 0.000 0.672 0.312 0.000 0.016
#> GSM1105472 2 0.0146 0.9206 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM1105473 1 0.0508 0.9502 0.984 0.000 0.000 0.012 0.000 0.004
#> GSM1105476 2 0.0291 0.9217 0.000 0.992 0.000 0.004 0.000 0.004
#> GSM1105477 6 0.5557 0.4411 0.000 0.000 0.276 0.020 0.116 0.588
#> GSM1105478 4 0.1588 0.8308 0.000 0.072 0.000 0.924 0.000 0.004
#> GSM1105510 5 0.0000 1.0000 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1105530 1 0.3622 0.7769 0.792 0.000 0.164 0.020 0.000 0.024
#> GSM1105539 1 0.3399 0.8054 0.816 0.000 0.140 0.020 0.000 0.024
#> GSM1105480 4 0.1500 0.8130 0.000 0.052 0.000 0.936 0.000 0.012
#> GSM1105512 1 0.0405 0.9521 0.988 0.000 0.000 0.008 0.000 0.004
#> GSM1105532 1 0.3999 0.7083 0.744 0.000 0.212 0.020 0.000 0.024
#> GSM1105541 1 0.3399 0.8054 0.816 0.000 0.140 0.020 0.000 0.024
#> GSM1105439 2 0.2402 0.8430 0.000 0.868 0.000 0.120 0.000 0.012
#> GSM1105463 1 0.1718 0.9191 0.936 0.000 0.020 0.020 0.000 0.024
#> GSM1105482 1 0.0000 0.9557 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1105483 3 0.3717 0.4360 0.000 0.000 0.708 0.276 0.000 0.016
#> GSM1105494 4 0.1701 0.8307 0.000 0.072 0.000 0.920 0.000 0.008
#> GSM1105503 4 0.1334 0.7842 0.000 0.032 0.000 0.948 0.000 0.020
#> GSM1105507 1 0.0000 0.9557 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1105446 2 0.3969 0.5257 0.000 0.652 0.000 0.000 0.332 0.016
#> GSM1105519 1 0.0000 0.9557 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1105526 2 0.4962 0.5647 0.000 0.696 0.004 0.184 0.096 0.020
#> GSM1105527 4 0.1644 0.8083 0.000 0.052 0.004 0.932 0.000 0.012
#> GSM1105531 1 0.4278 0.6801 0.724 0.000 0.220 0.024 0.000 0.032
#> GSM1105543 2 0.0146 0.9206 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM1105546 1 0.0000 0.9557 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1105547 1 0.0000 0.9557 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1105455 2 0.2613 0.8208 0.000 0.848 0.000 0.140 0.000 0.012
#> GSM1105458 2 0.0622 0.9197 0.000 0.980 0.000 0.008 0.000 0.012
#> GSM1105459 2 0.0146 0.9219 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM1105462 3 0.0508 0.5486 0.000 0.000 0.984 0.004 0.000 0.012
#> GSM1105441 2 0.0622 0.9197 0.000 0.980 0.000 0.008 0.000 0.012
#> GSM1105465 5 0.0000 1.0000 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1105484 2 0.0692 0.9095 0.000 0.976 0.000 0.000 0.020 0.004
#> GSM1105485 5 0.0000 1.0000 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1105496 6 0.2814 0.7127 0.000 0.000 0.000 0.008 0.172 0.820
#> GSM1105505 1 0.1679 0.9183 0.936 0.000 0.028 0.008 0.000 0.028
#> GSM1105509 1 0.0405 0.9521 0.988 0.000 0.000 0.008 0.000 0.004
#> GSM1105448 2 0.0146 0.9213 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM1105521 1 0.0405 0.9521 0.988 0.000 0.000 0.008 0.000 0.004
#> GSM1105528 2 0.2668 0.7632 0.000 0.828 0.000 0.000 0.168 0.004
#> GSM1105529 5 0.0000 1.0000 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1105533 1 0.0000 0.9557 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1105545 3 0.4851 0.4593 0.000 0.060 0.728 0.080 0.000 0.132
#> GSM1105548 1 0.0000 0.9557 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1105549 1 0.0146 0.9548 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM1105457 4 0.3012 0.8137 0.000 0.196 0.000 0.796 0.000 0.008
#> GSM1105460 2 0.0622 0.9197 0.000 0.980 0.000 0.008 0.000 0.012
#> GSM1105461 2 0.0520 0.9205 0.000 0.984 0.000 0.008 0.000 0.008
#> GSM1105464 1 0.3550 0.7868 0.800 0.000 0.156 0.020 0.000 0.024
#> GSM1105466 4 0.2823 0.8043 0.000 0.204 0.000 0.796 0.000 0.000
#> GSM1105479 2 0.2320 0.8372 0.000 0.864 0.000 0.132 0.000 0.004
#> GSM1105502 1 0.0146 0.9547 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM1105515 1 0.0000 0.9557 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1105523 3 0.1116 0.5436 0.004 0.000 0.960 0.028 0.000 0.008
#> GSM1105550 3 0.1141 0.5403 0.000 0.000 0.948 0.000 0.000 0.052
#> GSM1105450 2 0.0291 0.9217 0.000 0.992 0.000 0.004 0.000 0.004
#> GSM1105451 2 0.0520 0.9205 0.000 0.984 0.000 0.008 0.000 0.008
#> GSM1105454 2 0.2446 0.8386 0.000 0.864 0.000 0.124 0.000 0.012
#> GSM1105468 2 0.0291 0.9217 0.000 0.992 0.000 0.004 0.000 0.004
#> GSM1105481 2 0.0146 0.9206 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM1105504 1 0.3425 0.8173 0.824 0.000 0.120 0.024 0.000 0.032
#> GSM1105517 3 0.4997 0.0154 0.456 0.000 0.492 0.020 0.000 0.032
#> GSM1105525 3 0.4113 0.3192 0.308 0.000 0.668 0.016 0.000 0.008
#> GSM1105552 1 0.0260 0.9521 0.992 0.000 0.000 0.000 0.000 0.008
#> GSM1105452 5 0.0000 1.0000 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1105453 2 0.0405 0.9206 0.000 0.988 0.000 0.004 0.000 0.008
#> GSM1105456 2 0.2446 0.8386 0.000 0.864 0.000 0.124 0.000 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 agent(p) other(p) time(p) individual(p) k
#> ATC:skmeans 118 0.940 0.714 0.267 0.0191 2
#> ATC:skmeans 119 0.714 0.428 0.132 0.0484 3
#> ATC:skmeans 116 0.305 0.760 0.228 0.0555 4
#> ATC:skmeans 112 0.464 0.984 0.352 0.0285 5
#> ATC:skmeans 109 0.207 0.460 0.075 0.0101 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 44956 rows and 120 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 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 1.000 0.974 0.989 0.5015 0.501 0.501
#> 3 3 1.000 0.965 0.986 0.3159 0.690 0.460
#> 4 4 0.828 0.777 0.867 0.1006 0.907 0.737
#> 5 5 0.861 0.834 0.926 0.0727 0.909 0.691
#> 6 6 0.915 0.851 0.931 0.0702 0.915 0.639
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 6
#> attr(,"optional")
#> [1] 2 3
There is also optional best \(k\) = 2 3 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
#> GSM1105438 2 0.0000 0.980 0.000 1.000
#> GSM1105486 2 0.0000 0.980 0.000 1.000
#> GSM1105487 1 0.0000 1.000 1.000 0.000
#> GSM1105490 2 0.0000 0.980 0.000 1.000
#> GSM1105491 1 0.0000 1.000 1.000 0.000
#> GSM1105495 2 0.0000 0.980 0.000 1.000
#> GSM1105498 2 0.0000 0.980 0.000 1.000
#> GSM1105499 1 0.0000 1.000 1.000 0.000
#> GSM1105506 2 0.0000 0.980 0.000 1.000
#> GSM1105442 2 0.0000 0.980 0.000 1.000
#> GSM1105511 2 0.0000 0.980 0.000 1.000
#> GSM1105514 2 0.0000 0.980 0.000 1.000
#> GSM1105518 2 0.0000 0.980 0.000 1.000
#> GSM1105522 1 0.0000 1.000 1.000 0.000
#> GSM1105534 1 0.0000 1.000 1.000 0.000
#> GSM1105535 1 0.0000 1.000 1.000 0.000
#> GSM1105538 1 0.0000 1.000 1.000 0.000
#> GSM1105542 2 0.7056 0.766 0.192 0.808
#> GSM1105443 2 0.0000 0.980 0.000 1.000
#> GSM1105551 1 0.0000 1.000 1.000 0.000
#> GSM1105554 1 0.0000 1.000 1.000 0.000
#> GSM1105555 1 0.0000 1.000 1.000 0.000
#> GSM1105447 2 0.0000 0.980 0.000 1.000
#> GSM1105467 2 0.0000 0.980 0.000 1.000
#> GSM1105470 2 0.0000 0.980 0.000 1.000
#> GSM1105471 2 0.0000 0.980 0.000 1.000
#> GSM1105474 2 0.0000 0.980 0.000 1.000
#> GSM1105475 2 0.0000 0.980 0.000 1.000
#> GSM1105440 1 0.0000 1.000 1.000 0.000
#> GSM1105488 2 0.0000 0.980 0.000 1.000
#> GSM1105489 1 0.0000 1.000 1.000 0.000
#> GSM1105492 1 0.0000 1.000 1.000 0.000
#> GSM1105493 1 0.0000 1.000 1.000 0.000
#> GSM1105497 2 0.9881 0.245 0.436 0.564
#> GSM1105500 1 0.0000 1.000 1.000 0.000
#> GSM1105501 2 0.0000 0.980 0.000 1.000
#> GSM1105508 1 0.0000 1.000 1.000 0.000
#> GSM1105444 2 0.0000 0.980 0.000 1.000
#> GSM1105513 2 0.0000 0.980 0.000 1.000
#> GSM1105516 1 0.0000 1.000 1.000 0.000
#> GSM1105520 2 0.0000 0.980 0.000 1.000
#> GSM1105524 1 0.0000 1.000 1.000 0.000
#> GSM1105536 2 0.0000 0.980 0.000 1.000
#> GSM1105537 1 0.0000 1.000 1.000 0.000
#> GSM1105540 1 0.0000 1.000 1.000 0.000
#> GSM1105544 1 0.0000 1.000 1.000 0.000
#> GSM1105445 2 0.0000 0.980 0.000 1.000
#> GSM1105553 1 0.0000 1.000 1.000 0.000
#> GSM1105556 1 0.0000 1.000 1.000 0.000
#> GSM1105557 2 0.0000 0.980 0.000 1.000
#> GSM1105449 2 0.0000 0.980 0.000 1.000
#> GSM1105469 2 0.6887 0.776 0.184 0.816
#> GSM1105472 2 0.0000 0.980 0.000 1.000
#> GSM1105473 1 0.0000 1.000 1.000 0.000
#> GSM1105476 2 0.0000 0.980 0.000 1.000
#> GSM1105477 1 0.0000 1.000 1.000 0.000
#> GSM1105478 2 0.0000 0.980 0.000 1.000
#> GSM1105510 2 0.4939 0.873 0.108 0.892
#> GSM1105530 1 0.0000 1.000 1.000 0.000
#> GSM1105539 1 0.0000 1.000 1.000 0.000
#> GSM1105480 2 0.0000 0.980 0.000 1.000
#> GSM1105512 1 0.0000 1.000 1.000 0.000
#> GSM1105532 1 0.0000 1.000 1.000 0.000
#> GSM1105541 1 0.0000 1.000 1.000 0.000
#> GSM1105439 2 0.0000 0.980 0.000 1.000
#> GSM1105463 1 0.0000 1.000 1.000 0.000
#> GSM1105482 1 0.0000 1.000 1.000 0.000
#> GSM1105483 2 0.0000 0.980 0.000 1.000
#> GSM1105494 2 0.0000 0.980 0.000 1.000
#> GSM1105503 2 0.0000 0.980 0.000 1.000
#> GSM1105507 1 0.0000 1.000 1.000 0.000
#> GSM1105446 2 0.0000 0.980 0.000 1.000
#> GSM1105519 1 0.0000 1.000 1.000 0.000
#> GSM1105526 2 0.0000 0.980 0.000 1.000
#> GSM1105527 2 0.0000 0.980 0.000 1.000
#> GSM1105531 1 0.0000 1.000 1.000 0.000
#> GSM1105543 2 0.0000 0.980 0.000 1.000
#> GSM1105546 1 0.0000 1.000 1.000 0.000
#> GSM1105547 1 0.0000 1.000 1.000 0.000
#> GSM1105455 2 0.0000 0.980 0.000 1.000
#> GSM1105458 2 0.0000 0.980 0.000 1.000
#> GSM1105459 2 0.0000 0.980 0.000 1.000
#> GSM1105462 2 0.9491 0.437 0.368 0.632
#> GSM1105441 2 0.0000 0.980 0.000 1.000
#> GSM1105465 2 0.0376 0.977 0.004 0.996
#> GSM1105484 2 0.0000 0.980 0.000 1.000
#> GSM1105485 1 0.0000 1.000 1.000 0.000
#> GSM1105496 1 0.0000 1.000 1.000 0.000
#> GSM1105505 1 0.0000 1.000 1.000 0.000
#> GSM1105509 1 0.0000 1.000 1.000 0.000
#> GSM1105448 2 0.0000 0.980 0.000 1.000
#> GSM1105521 1 0.0000 1.000 1.000 0.000
#> GSM1105528 2 0.0000 0.980 0.000 1.000
#> GSM1105529 2 0.0000 0.980 0.000 1.000
#> GSM1105533 1 0.0000 1.000 1.000 0.000
#> GSM1105545 2 0.0000 0.980 0.000 1.000
#> GSM1105548 1 0.0000 1.000 1.000 0.000
#> GSM1105549 1 0.0000 1.000 1.000 0.000
#> GSM1105457 2 0.0000 0.980 0.000 1.000
#> GSM1105460 2 0.0000 0.980 0.000 1.000
#> GSM1105461 2 0.0000 0.980 0.000 1.000
#> GSM1105464 1 0.0000 1.000 1.000 0.000
#> GSM1105466 2 0.0000 0.980 0.000 1.000
#> GSM1105479 2 0.0000 0.980 0.000 1.000
#> GSM1105502 1 0.0000 1.000 1.000 0.000
#> GSM1105515 1 0.0000 1.000 1.000 0.000
#> GSM1105523 1 0.0000 1.000 1.000 0.000
#> GSM1105550 1 0.0000 1.000 1.000 0.000
#> GSM1105450 2 0.0000 0.980 0.000 1.000
#> GSM1105451 2 0.0000 0.980 0.000 1.000
#> GSM1105454 2 0.0000 0.980 0.000 1.000
#> GSM1105468 2 0.0000 0.980 0.000 1.000
#> GSM1105481 2 0.0000 0.980 0.000 1.000
#> GSM1105504 1 0.0000 1.000 1.000 0.000
#> GSM1105517 1 0.0000 1.000 1.000 0.000
#> GSM1105525 1 0.0000 1.000 1.000 0.000
#> GSM1105552 1 0.0000 1.000 1.000 0.000
#> GSM1105452 2 0.0000 0.980 0.000 1.000
#> GSM1105453 2 0.0000 0.980 0.000 1.000
#> GSM1105456 2 0.0000 0.980 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1105438 2 0.0000 0.990 0.000 1.000 0.000
#> GSM1105486 2 0.0000 0.990 0.000 1.000 0.000
#> GSM1105487 1 0.0000 0.967 1.000 0.000 0.000
#> GSM1105490 2 0.0000 0.990 0.000 1.000 0.000
#> GSM1105491 3 0.0000 0.990 0.000 0.000 1.000
#> GSM1105495 2 0.0000 0.990 0.000 1.000 0.000
#> GSM1105498 3 0.0000 0.990 0.000 0.000 1.000
#> GSM1105499 1 0.0000 0.967 1.000 0.000 0.000
#> GSM1105506 2 0.0000 0.990 0.000 1.000 0.000
#> GSM1105442 3 0.2796 0.889 0.000 0.092 0.908
#> GSM1105511 3 0.0000 0.990 0.000 0.000 1.000
#> GSM1105514 2 0.0000 0.990 0.000 1.000 0.000
#> GSM1105518 2 0.5882 0.465 0.000 0.652 0.348
#> GSM1105522 1 0.5397 0.624 0.720 0.000 0.280
#> GSM1105534 1 0.0000 0.967 1.000 0.000 0.000
#> GSM1105535 1 0.0000 0.967 1.000 0.000 0.000
#> GSM1105538 1 0.0000 0.967 1.000 0.000 0.000
#> GSM1105542 3 0.0000 0.990 0.000 0.000 1.000
#> GSM1105443 2 0.0000 0.990 0.000 1.000 0.000
#> GSM1105551 1 0.0000 0.967 1.000 0.000 0.000
#> GSM1105554 1 0.0000 0.967 1.000 0.000 0.000
#> GSM1105555 1 0.0000 0.967 1.000 0.000 0.000
#> GSM1105447 2 0.0000 0.990 0.000 1.000 0.000
#> GSM1105467 2 0.0000 0.990 0.000 1.000 0.000
#> GSM1105470 2 0.0000 0.990 0.000 1.000 0.000
#> GSM1105471 2 0.0000 0.990 0.000 1.000 0.000
#> GSM1105474 2 0.0000 0.990 0.000 1.000 0.000
#> GSM1105475 2 0.0000 0.990 0.000 1.000 0.000
#> GSM1105440 1 0.0000 0.967 1.000 0.000 0.000
#> GSM1105488 3 0.0000 0.990 0.000 0.000 1.000
#> GSM1105489 1 0.0000 0.967 1.000 0.000 0.000
#> GSM1105492 1 0.0000 0.967 1.000 0.000 0.000
#> GSM1105493 1 0.0000 0.967 1.000 0.000 0.000
#> GSM1105497 3 0.0000 0.990 0.000 0.000 1.000
#> GSM1105500 3 0.0000 0.990 0.000 0.000 1.000
#> GSM1105501 3 0.0000 0.990 0.000 0.000 1.000
#> GSM1105508 3 0.1860 0.940 0.052 0.000 0.948
#> GSM1105444 2 0.0000 0.990 0.000 1.000 0.000
#> GSM1105513 2 0.0000 0.990 0.000 1.000 0.000
#> GSM1105516 3 0.0000 0.990 0.000 0.000 1.000
#> GSM1105520 3 0.0000 0.990 0.000 0.000 1.000
#> GSM1105524 1 0.0000 0.967 1.000 0.000 0.000
#> GSM1105536 3 0.0000 0.990 0.000 0.000 1.000
#> GSM1105537 1 0.0000 0.967 1.000 0.000 0.000
#> GSM1105540 3 0.0000 0.990 0.000 0.000 1.000
#> GSM1105544 3 0.0000 0.990 0.000 0.000 1.000
#> GSM1105445 2 0.0000 0.990 0.000 1.000 0.000
#> GSM1105553 3 0.0000 0.990 0.000 0.000 1.000
#> GSM1105556 1 0.0000 0.967 1.000 0.000 0.000
#> GSM1105557 2 0.0000 0.990 0.000 1.000 0.000
#> GSM1105449 2 0.0000 0.990 0.000 1.000 0.000
#> GSM1105469 3 0.0000 0.990 0.000 0.000 1.000
#> GSM1105472 2 0.0000 0.990 0.000 1.000 0.000
#> GSM1105473 3 0.0000 0.990 0.000 0.000 1.000
#> GSM1105476 2 0.0000 0.990 0.000 1.000 0.000
#> GSM1105477 3 0.0000 0.990 0.000 0.000 1.000
#> GSM1105478 2 0.0000 0.990 0.000 1.000 0.000
#> GSM1105510 3 0.0000 0.990 0.000 0.000 1.000
#> GSM1105530 1 0.4702 0.734 0.788 0.000 0.212
#> GSM1105539 1 0.0000 0.967 1.000 0.000 0.000
#> GSM1105480 3 0.0000 0.990 0.000 0.000 1.000
#> GSM1105512 1 0.0000 0.967 1.000 0.000 0.000
#> GSM1105532 3 0.3412 0.852 0.124 0.000 0.876
#> GSM1105541 1 0.0000 0.967 1.000 0.000 0.000
#> GSM1105439 2 0.0000 0.990 0.000 1.000 0.000
#> GSM1105463 3 0.0000 0.990 0.000 0.000 1.000
#> GSM1105482 1 0.0000 0.967 1.000 0.000 0.000
#> GSM1105483 3 0.0000 0.990 0.000 0.000 1.000
#> GSM1105494 2 0.0892 0.969 0.000 0.980 0.020
#> GSM1105503 3 0.0000 0.990 0.000 0.000 1.000
#> GSM1105507 3 0.0000 0.990 0.000 0.000 1.000
#> GSM1105446 2 0.0000 0.990 0.000 1.000 0.000
#> GSM1105519 1 0.6280 0.178 0.540 0.000 0.460
#> GSM1105526 3 0.0000 0.990 0.000 0.000 1.000
#> GSM1105527 3 0.0424 0.983 0.000 0.008 0.992
#> GSM1105531 3 0.0000 0.990 0.000 0.000 1.000
#> GSM1105543 2 0.0000 0.990 0.000 1.000 0.000
#> GSM1105546 1 0.0000 0.967 1.000 0.000 0.000
#> GSM1105547 1 0.0000 0.967 1.000 0.000 0.000
#> GSM1105455 2 0.0000 0.990 0.000 1.000 0.000
#> GSM1105458 2 0.0000 0.990 0.000 1.000 0.000
#> GSM1105459 2 0.0000 0.990 0.000 1.000 0.000
#> GSM1105462 3 0.0000 0.990 0.000 0.000 1.000
#> GSM1105441 2 0.0000 0.990 0.000 1.000 0.000
#> GSM1105465 3 0.0000 0.990 0.000 0.000 1.000
#> GSM1105484 2 0.0000 0.990 0.000 1.000 0.000
#> GSM1105485 3 0.0000 0.990 0.000 0.000 1.000
#> GSM1105496 3 0.0000 0.990 0.000 0.000 1.000
#> GSM1105505 3 0.0000 0.990 0.000 0.000 1.000
#> GSM1105509 3 0.0000 0.990 0.000 0.000 1.000
#> GSM1105448 2 0.0000 0.990 0.000 1.000 0.000
#> GSM1105521 1 0.0000 0.967 1.000 0.000 0.000
#> GSM1105528 2 0.0000 0.990 0.000 1.000 0.000
#> GSM1105529 3 0.0000 0.990 0.000 0.000 1.000
#> GSM1105533 1 0.0000 0.967 1.000 0.000 0.000
#> GSM1105545 3 0.0000 0.990 0.000 0.000 1.000
#> GSM1105548 1 0.0000 0.967 1.000 0.000 0.000
#> GSM1105549 1 0.0000 0.967 1.000 0.000 0.000
#> GSM1105457 2 0.0000 0.990 0.000 1.000 0.000
#> GSM1105460 2 0.0000 0.990 0.000 1.000 0.000
#> GSM1105461 2 0.0000 0.990 0.000 1.000 0.000
#> GSM1105464 3 0.0000 0.990 0.000 0.000 1.000
#> GSM1105466 2 0.0000 0.990 0.000 1.000 0.000
#> GSM1105479 2 0.0000 0.990 0.000 1.000 0.000
#> GSM1105502 1 0.0000 0.967 1.000 0.000 0.000
#> GSM1105515 1 0.0000 0.967 1.000 0.000 0.000
#> GSM1105523 3 0.0000 0.990 0.000 0.000 1.000
#> GSM1105550 3 0.0000 0.990 0.000 0.000 1.000
#> GSM1105450 2 0.0000 0.990 0.000 1.000 0.000
#> GSM1105451 2 0.0000 0.990 0.000 1.000 0.000
#> GSM1105454 2 0.0000 0.990 0.000 1.000 0.000
#> GSM1105468 2 0.0000 0.990 0.000 1.000 0.000
#> GSM1105481 3 0.3192 0.866 0.000 0.112 0.888
#> GSM1105504 3 0.0000 0.990 0.000 0.000 1.000
#> GSM1105517 3 0.0000 0.990 0.000 0.000 1.000
#> GSM1105525 3 0.0000 0.990 0.000 0.000 1.000
#> GSM1105552 3 0.0000 0.990 0.000 0.000 1.000
#> GSM1105452 3 0.0000 0.990 0.000 0.000 1.000
#> GSM1105453 2 0.0000 0.990 0.000 1.000 0.000
#> GSM1105456 2 0.0000 0.990 0.000 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1105438 2 0.0000 0.974 0.000 1.000 0.000 0.000
#> GSM1105486 2 0.0000 0.974 0.000 1.000 0.000 0.000
#> GSM1105487 1 0.4843 0.829 0.604 0.000 0.000 0.396
#> GSM1105490 2 0.0000 0.974 0.000 1.000 0.000 0.000
#> GSM1105491 4 0.5414 0.877 0.376 0.000 0.020 0.604
#> GSM1105495 2 0.4977 0.183 0.000 0.540 0.000 0.460
#> GSM1105498 3 0.4843 0.650 0.396 0.000 0.604 0.000
#> GSM1105499 1 0.4843 0.829 0.604 0.000 0.000 0.396
#> GSM1105506 2 0.0000 0.974 0.000 1.000 0.000 0.000
#> GSM1105442 4 0.4843 0.899 0.396 0.000 0.000 0.604
#> GSM1105511 3 0.4843 0.650 0.396 0.000 0.604 0.000
#> GSM1105514 2 0.0000 0.974 0.000 1.000 0.000 0.000
#> GSM1105518 2 0.5070 0.612 0.192 0.748 0.060 0.000
#> GSM1105522 3 0.4585 -0.163 0.332 0.000 0.668 0.000
#> GSM1105534 1 0.4843 0.829 0.604 0.000 0.000 0.396
#> GSM1105535 1 0.4843 0.829 0.604 0.000 0.000 0.396
#> GSM1105538 1 0.4843 0.718 0.604 0.000 0.396 0.000
#> GSM1105542 4 0.4843 0.899 0.396 0.000 0.000 0.604
#> GSM1105443 2 0.0000 0.974 0.000 1.000 0.000 0.000
#> GSM1105551 1 0.4843 0.718 0.604 0.000 0.396 0.000
#> GSM1105554 1 0.4843 0.829 0.604 0.000 0.000 0.396
#> GSM1105555 1 0.4843 0.718 0.604 0.000 0.396 0.000
#> GSM1105447 2 0.0000 0.974 0.000 1.000 0.000 0.000
#> GSM1105467 2 0.0000 0.974 0.000 1.000 0.000 0.000
#> GSM1105470 2 0.0000 0.974 0.000 1.000 0.000 0.000
#> GSM1105471 2 0.0000 0.974 0.000 1.000 0.000 0.000
#> GSM1105474 2 0.0000 0.974 0.000 1.000 0.000 0.000
#> GSM1105475 2 0.0000 0.974 0.000 1.000 0.000 0.000
#> GSM1105440 1 0.4843 0.718 0.604 0.000 0.396 0.000
#> GSM1105488 4 0.4843 0.899 0.396 0.000 0.000 0.604
#> GSM1105489 1 0.4843 0.829 0.604 0.000 0.000 0.396
#> GSM1105492 1 0.4843 0.829 0.604 0.000 0.000 0.396
#> GSM1105493 1 0.4843 0.829 0.604 0.000 0.000 0.396
#> GSM1105497 4 0.4843 0.899 0.396 0.000 0.000 0.604
#> GSM1105500 4 0.7236 0.709 0.396 0.000 0.144 0.460
#> GSM1105501 3 0.4843 0.650 0.396 0.000 0.604 0.000
#> GSM1105508 3 0.1474 0.554 0.052 0.000 0.948 0.000
#> GSM1105444 2 0.0000 0.974 0.000 1.000 0.000 0.000
#> GSM1105513 2 0.0000 0.974 0.000 1.000 0.000 0.000
#> GSM1105516 3 0.0188 0.595 0.004 0.000 0.996 0.000
#> GSM1105520 3 0.4843 0.650 0.396 0.000 0.604 0.000
#> GSM1105524 1 0.4843 0.829 0.604 0.000 0.000 0.396
#> GSM1105536 3 0.4843 0.650 0.396 0.000 0.604 0.000
#> GSM1105537 1 0.4843 0.829 0.604 0.000 0.000 0.396
#> GSM1105540 3 0.4843 0.650 0.396 0.000 0.604 0.000
#> GSM1105544 3 0.4843 0.650 0.396 0.000 0.604 0.000
#> GSM1105445 2 0.0000 0.974 0.000 1.000 0.000 0.000
#> GSM1105553 4 0.6652 0.805 0.396 0.000 0.088 0.516
#> GSM1105556 1 0.4843 0.829 0.604 0.000 0.000 0.396
#> GSM1105557 2 0.0000 0.974 0.000 1.000 0.000 0.000
#> GSM1105449 2 0.0000 0.974 0.000 1.000 0.000 0.000
#> GSM1105469 3 0.4843 0.650 0.396 0.000 0.604 0.000
#> GSM1105472 2 0.0000 0.974 0.000 1.000 0.000 0.000
#> GSM1105473 3 0.0469 0.588 0.000 0.000 0.988 0.012
#> GSM1105476 2 0.0000 0.974 0.000 1.000 0.000 0.000
#> GSM1105477 3 0.4843 0.650 0.396 0.000 0.604 0.000
#> GSM1105478 2 0.0000 0.974 0.000 1.000 0.000 0.000
#> GSM1105510 4 0.4843 0.899 0.396 0.000 0.000 0.604
#> GSM1105530 3 0.4843 -0.351 0.396 0.000 0.604 0.000
#> GSM1105539 1 0.6007 0.740 0.604 0.000 0.340 0.056
#> GSM1105480 3 0.4843 0.650 0.396 0.000 0.604 0.000
#> GSM1105512 1 0.4843 0.718 0.604 0.000 0.396 0.000
#> GSM1105532 3 0.0000 0.594 0.000 0.000 1.000 0.000
#> GSM1105541 1 0.5496 0.728 0.604 0.000 0.372 0.024
#> GSM1105439 2 0.0000 0.974 0.000 1.000 0.000 0.000
#> GSM1105463 3 0.5332 0.582 0.184 0.000 0.736 0.080
#> GSM1105482 1 0.4843 0.829 0.604 0.000 0.000 0.396
#> GSM1105483 3 0.4843 0.650 0.396 0.000 0.604 0.000
#> GSM1105494 2 0.1940 0.888 0.000 0.924 0.076 0.000
#> GSM1105503 3 0.4843 0.650 0.396 0.000 0.604 0.000
#> GSM1105507 3 0.0000 0.594 0.000 0.000 1.000 0.000
#> GSM1105446 2 0.3852 0.701 0.192 0.800 0.000 0.008
#> GSM1105519 3 0.2704 0.438 0.124 0.000 0.876 0.000
#> GSM1105526 3 0.5016 0.645 0.396 0.000 0.600 0.004
#> GSM1105527 3 0.6819 0.497 0.208 0.188 0.604 0.000
#> GSM1105531 3 0.4193 0.643 0.268 0.000 0.732 0.000
#> GSM1105543 2 0.0000 0.974 0.000 1.000 0.000 0.000
#> GSM1105546 1 0.4843 0.829 0.604 0.000 0.000 0.396
#> GSM1105547 1 0.4843 0.829 0.604 0.000 0.000 0.396
#> GSM1105455 2 0.0000 0.974 0.000 1.000 0.000 0.000
#> GSM1105458 2 0.0000 0.974 0.000 1.000 0.000 0.000
#> GSM1105459 2 0.0000 0.974 0.000 1.000 0.000 0.000
#> GSM1105462 3 0.4843 0.650 0.396 0.000 0.604 0.000
#> GSM1105441 2 0.0000 0.974 0.000 1.000 0.000 0.000
#> GSM1105465 4 0.4843 0.899 0.396 0.000 0.000 0.604
#> GSM1105484 2 0.0469 0.963 0.000 0.988 0.000 0.012
#> GSM1105485 4 0.4843 0.899 0.396 0.000 0.000 0.604
#> GSM1105496 4 0.6889 0.777 0.396 0.000 0.108 0.496
#> GSM1105505 3 0.4522 0.648 0.320 0.000 0.680 0.000
#> GSM1105509 3 0.0000 0.594 0.000 0.000 1.000 0.000
#> GSM1105448 2 0.0000 0.974 0.000 1.000 0.000 0.000
#> GSM1105521 1 0.4843 0.718 0.604 0.000 0.396 0.000
#> GSM1105528 4 0.4843 0.198 0.000 0.396 0.000 0.604
#> GSM1105529 4 0.4843 0.899 0.396 0.000 0.000 0.604
#> GSM1105533 1 0.4843 0.829 0.604 0.000 0.000 0.396
#> GSM1105545 3 0.4843 0.650 0.396 0.000 0.604 0.000
#> GSM1105548 1 0.4843 0.718 0.604 0.000 0.396 0.000
#> GSM1105549 1 0.5125 0.722 0.604 0.000 0.388 0.008
#> GSM1105457 2 0.0000 0.974 0.000 1.000 0.000 0.000
#> GSM1105460 2 0.0000 0.974 0.000 1.000 0.000 0.000
#> GSM1105461 2 0.0000 0.974 0.000 1.000 0.000 0.000
#> GSM1105464 3 0.0000 0.594 0.000 0.000 1.000 0.000
#> GSM1105466 2 0.0000 0.974 0.000 1.000 0.000 0.000
#> GSM1105479 2 0.0000 0.974 0.000 1.000 0.000 0.000
#> GSM1105502 1 0.4843 0.718 0.604 0.000 0.396 0.000
#> GSM1105515 1 0.4843 0.829 0.604 0.000 0.000 0.396
#> GSM1105523 3 0.4843 0.650 0.396 0.000 0.604 0.000
#> GSM1105550 3 0.4843 0.650 0.396 0.000 0.604 0.000
#> GSM1105450 2 0.0000 0.974 0.000 1.000 0.000 0.000
#> GSM1105451 2 0.0000 0.974 0.000 1.000 0.000 0.000
#> GSM1105454 2 0.0000 0.974 0.000 1.000 0.000 0.000
#> GSM1105468 2 0.0000 0.974 0.000 1.000 0.000 0.000
#> GSM1105481 3 0.7191 0.316 0.156 0.328 0.516 0.000
#> GSM1105504 3 0.4843 0.650 0.396 0.000 0.604 0.000
#> GSM1105517 3 0.2216 0.616 0.092 0.000 0.908 0.000
#> GSM1105525 3 0.0000 0.594 0.000 0.000 1.000 0.000
#> GSM1105552 3 0.0000 0.594 0.000 0.000 1.000 0.000
#> GSM1105452 4 0.4843 0.899 0.396 0.000 0.000 0.604
#> GSM1105453 2 0.0000 0.974 0.000 1.000 0.000 0.000
#> GSM1105456 2 0.0000 0.974 0.000 1.000 0.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM1105438 2 0.0000 0.9549 0.000 1.000 0.000 0.000 0.000
#> GSM1105486 2 0.0404 0.9532 0.000 0.988 0.012 0.000 0.000
#> GSM1105487 1 0.0510 0.8690 0.984 0.000 0.016 0.000 0.000
#> GSM1105490 2 0.2813 0.8060 0.000 0.832 0.000 0.168 0.000
#> GSM1105491 5 0.0000 0.9059 0.000 0.000 0.000 0.000 1.000
#> GSM1105495 5 0.3967 0.6034 0.000 0.264 0.012 0.000 0.724
#> GSM1105498 4 0.0290 0.8398 0.000 0.000 0.000 0.992 0.008
#> GSM1105499 1 0.0000 0.8781 1.000 0.000 0.000 0.000 0.000
#> GSM1105506 2 0.0404 0.9532 0.000 0.988 0.012 0.000 0.000
#> GSM1105442 5 0.0000 0.9059 0.000 0.000 0.000 0.000 1.000
#> GSM1105511 4 0.2605 0.7761 0.000 0.000 0.000 0.852 0.148
#> GSM1105514 2 0.0000 0.9549 0.000 1.000 0.000 0.000 0.000
#> GSM1105518 4 0.4242 0.1397 0.000 0.428 0.000 0.572 0.000
#> GSM1105522 3 0.0451 0.9415 0.004 0.000 0.988 0.008 0.000
#> GSM1105534 1 0.0000 0.8781 1.000 0.000 0.000 0.000 0.000
#> GSM1105535 1 0.0000 0.8781 1.000 0.000 0.000 0.000 0.000
#> GSM1105538 3 0.0404 0.9382 0.012 0.000 0.988 0.000 0.000
#> GSM1105542 5 0.0000 0.9059 0.000 0.000 0.000 0.000 1.000
#> GSM1105443 2 0.0000 0.9549 0.000 1.000 0.000 0.000 0.000
#> GSM1105551 1 0.4294 0.2138 0.532 0.000 0.468 0.000 0.000
#> GSM1105554 1 0.0000 0.8781 1.000 0.000 0.000 0.000 0.000
#> GSM1105555 3 0.0404 0.9382 0.012 0.000 0.988 0.000 0.000
#> GSM1105447 2 0.0000 0.9549 0.000 1.000 0.000 0.000 0.000
#> GSM1105467 2 0.0404 0.9532 0.000 0.988 0.012 0.000 0.000
#> GSM1105470 2 0.0404 0.9532 0.000 0.988 0.012 0.000 0.000
#> GSM1105471 2 0.0404 0.9532 0.000 0.988 0.012 0.000 0.000
#> GSM1105474 2 0.0404 0.9532 0.000 0.988 0.012 0.000 0.000
#> GSM1105475 2 0.0404 0.9532 0.000 0.988 0.012 0.000 0.000
#> GSM1105440 3 0.4256 0.0529 0.436 0.000 0.564 0.000 0.000
#> GSM1105488 5 0.0000 0.9059 0.000 0.000 0.000 0.000 1.000
#> GSM1105489 1 0.0000 0.8781 1.000 0.000 0.000 0.000 0.000
#> GSM1105492 1 0.4101 0.3702 0.628 0.000 0.372 0.000 0.000
#> GSM1105493 1 0.0000 0.8781 1.000 0.000 0.000 0.000 0.000
#> GSM1105497 5 0.0000 0.9059 0.000 0.000 0.000 0.000 1.000
#> GSM1105500 5 0.4150 0.3302 0.000 0.000 0.000 0.388 0.612
#> GSM1105501 4 0.2648 0.7734 0.000 0.000 0.000 0.848 0.152
#> GSM1105508 3 0.0404 0.9425 0.000 0.000 0.988 0.012 0.000
#> GSM1105444 2 0.0000 0.9549 0.000 1.000 0.000 0.000 0.000
#> GSM1105513 2 0.0290 0.9539 0.000 0.992 0.008 0.000 0.000
#> GSM1105516 3 0.0510 0.9402 0.000 0.000 0.984 0.016 0.000
#> GSM1105520 4 0.0000 0.8413 0.000 0.000 0.000 1.000 0.000
#> GSM1105524 1 0.0000 0.8781 1.000 0.000 0.000 0.000 0.000
#> GSM1105536 4 0.0703 0.8378 0.000 0.000 0.000 0.976 0.024
#> GSM1105537 1 0.0000 0.8781 1.000 0.000 0.000 0.000 0.000
#> GSM1105540 4 0.0000 0.8413 0.000 0.000 0.000 1.000 0.000
#> GSM1105544 4 0.0290 0.8400 0.000 0.000 0.000 0.992 0.008
#> GSM1105445 2 0.0000 0.9549 0.000 1.000 0.000 0.000 0.000
#> GSM1105553 5 0.3336 0.7009 0.000 0.000 0.000 0.228 0.772
#> GSM1105556 1 0.0000 0.8781 1.000 0.000 0.000 0.000 0.000
#> GSM1105557 2 0.3366 0.7295 0.000 0.768 0.000 0.232 0.000
#> GSM1105449 2 0.0000 0.9549 0.000 1.000 0.000 0.000 0.000
#> GSM1105469 4 0.0000 0.8413 0.000 0.000 0.000 1.000 0.000
#> GSM1105472 2 0.0404 0.9532 0.000 0.988 0.012 0.000 0.000
#> GSM1105473 3 0.0404 0.9425 0.000 0.000 0.988 0.012 0.000
#> GSM1105476 2 0.0404 0.9532 0.000 0.988 0.012 0.000 0.000
#> GSM1105477 4 0.2561 0.7784 0.000 0.000 0.000 0.856 0.144
#> GSM1105478 2 0.3690 0.7381 0.000 0.764 0.012 0.224 0.000
#> GSM1105510 5 0.0162 0.9037 0.000 0.000 0.000 0.004 0.996
#> GSM1105530 3 0.0404 0.9425 0.000 0.000 0.988 0.012 0.000
#> GSM1105539 1 0.4060 0.4757 0.640 0.000 0.360 0.000 0.000
#> GSM1105480 4 0.0000 0.8413 0.000 0.000 0.000 1.000 0.000
#> GSM1105512 3 0.0404 0.9382 0.012 0.000 0.988 0.000 0.000
#> GSM1105532 3 0.0404 0.9425 0.000 0.000 0.988 0.012 0.000
#> GSM1105541 1 0.4161 0.4140 0.608 0.000 0.392 0.000 0.000
#> GSM1105439 2 0.0000 0.9549 0.000 1.000 0.000 0.000 0.000
#> GSM1105463 4 0.3561 0.6683 0.000 0.000 0.260 0.740 0.000
#> GSM1105482 1 0.0000 0.8781 1.000 0.000 0.000 0.000 0.000
#> GSM1105483 4 0.0000 0.8413 0.000 0.000 0.000 1.000 0.000
#> GSM1105494 2 0.3835 0.7111 0.000 0.744 0.012 0.244 0.000
#> GSM1105503 4 0.0000 0.8413 0.000 0.000 0.000 1.000 0.000
#> GSM1105507 3 0.0404 0.9425 0.000 0.000 0.988 0.012 0.000
#> GSM1105446 2 0.4278 0.1710 0.000 0.548 0.000 0.000 0.452
#> GSM1105519 3 0.0404 0.9425 0.000 0.000 0.988 0.012 0.000
#> GSM1105526 4 0.3983 0.5275 0.000 0.000 0.000 0.660 0.340
#> GSM1105527 4 0.0566 0.8340 0.000 0.000 0.012 0.984 0.004
#> GSM1105531 4 0.3845 0.7262 0.000 0.000 0.208 0.768 0.024
#> GSM1105543 2 0.0404 0.9532 0.000 0.988 0.012 0.000 0.000
#> GSM1105546 1 0.0000 0.8781 1.000 0.000 0.000 0.000 0.000
#> GSM1105547 1 0.0000 0.8781 1.000 0.000 0.000 0.000 0.000
#> GSM1105455 2 0.0000 0.9549 0.000 1.000 0.000 0.000 0.000
#> GSM1105458 2 0.0000 0.9549 0.000 1.000 0.000 0.000 0.000
#> GSM1105459 2 0.0000 0.9549 0.000 1.000 0.000 0.000 0.000
#> GSM1105462 4 0.0510 0.8397 0.000 0.000 0.000 0.984 0.016
#> GSM1105441 2 0.0000 0.9549 0.000 1.000 0.000 0.000 0.000
#> GSM1105465 5 0.0000 0.9059 0.000 0.000 0.000 0.000 1.000
#> GSM1105484 2 0.0912 0.9437 0.000 0.972 0.012 0.000 0.016
#> GSM1105485 5 0.0000 0.9059 0.000 0.000 0.000 0.000 1.000
#> GSM1105496 5 0.3003 0.7200 0.000 0.000 0.000 0.188 0.812
#> GSM1105505 4 0.4322 0.7449 0.000 0.000 0.088 0.768 0.144
#> GSM1105509 3 0.0404 0.9425 0.000 0.000 0.988 0.012 0.000
#> GSM1105448 2 0.0000 0.9549 0.000 1.000 0.000 0.000 0.000
#> GSM1105521 3 0.0404 0.9382 0.012 0.000 0.988 0.000 0.000
#> GSM1105528 5 0.1195 0.8776 0.000 0.028 0.012 0.000 0.960
#> GSM1105529 5 0.0000 0.9059 0.000 0.000 0.000 0.000 1.000
#> GSM1105533 1 0.0000 0.8781 1.000 0.000 0.000 0.000 0.000
#> GSM1105545 4 0.0000 0.8413 0.000 0.000 0.000 1.000 0.000
#> GSM1105548 3 0.0404 0.9382 0.012 0.000 0.988 0.000 0.000
#> GSM1105549 1 0.4201 0.3790 0.592 0.000 0.408 0.000 0.000
#> GSM1105457 2 0.0000 0.9549 0.000 1.000 0.000 0.000 0.000
#> GSM1105460 2 0.0000 0.9549 0.000 1.000 0.000 0.000 0.000
#> GSM1105461 2 0.0000 0.9549 0.000 1.000 0.000 0.000 0.000
#> GSM1105464 3 0.3109 0.7242 0.000 0.000 0.800 0.200 0.000
#> GSM1105466 2 0.3563 0.7575 0.000 0.780 0.012 0.208 0.000
#> GSM1105479 2 0.0404 0.9532 0.000 0.988 0.012 0.000 0.000
#> GSM1105502 3 0.0404 0.9382 0.012 0.000 0.988 0.000 0.000
#> GSM1105515 1 0.0000 0.8781 1.000 0.000 0.000 0.000 0.000
#> GSM1105523 4 0.3143 0.7348 0.000 0.000 0.204 0.796 0.000
#> GSM1105550 4 0.3305 0.7143 0.000 0.000 0.224 0.776 0.000
#> GSM1105450 2 0.0404 0.9532 0.000 0.988 0.012 0.000 0.000
#> GSM1105451 2 0.0000 0.9549 0.000 1.000 0.000 0.000 0.000
#> GSM1105454 2 0.0000 0.9549 0.000 1.000 0.000 0.000 0.000
#> GSM1105468 2 0.0404 0.9532 0.000 0.988 0.012 0.000 0.000
#> GSM1105481 4 0.4607 0.5172 0.000 0.312 0.012 0.664 0.012
#> GSM1105504 4 0.4212 0.7489 0.000 0.000 0.080 0.776 0.144
#> GSM1105517 3 0.2732 0.7747 0.000 0.000 0.840 0.160 0.000
#> GSM1105525 3 0.0404 0.9425 0.000 0.000 0.988 0.012 0.000
#> GSM1105552 3 0.0963 0.9241 0.000 0.000 0.964 0.036 0.000
#> GSM1105452 5 0.0000 0.9059 0.000 0.000 0.000 0.000 1.000
#> GSM1105453 2 0.0000 0.9549 0.000 1.000 0.000 0.000 0.000
#> GSM1105456 2 0.0000 0.9549 0.000 1.000 0.000 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1105438 2 0.1267 0.9018 0.000 0.940 0.000 0.000 0.000 0.060
#> GSM1105486 6 0.1610 0.8723 0.000 0.084 0.000 0.000 0.000 0.916
#> GSM1105487 1 0.0458 0.8672 0.984 0.000 0.016 0.000 0.000 0.000
#> GSM1105490 6 0.3843 0.1653 0.000 0.452 0.000 0.000 0.000 0.548
#> GSM1105491 5 0.0000 0.9558 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1105495 6 0.1753 0.8284 0.000 0.004 0.000 0.000 0.084 0.912
#> GSM1105498 4 0.2119 0.9176 0.000 0.000 0.000 0.904 0.036 0.060
#> GSM1105499 1 0.0000 0.8765 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1105506 6 0.0000 0.8455 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM1105442 5 0.0000 0.9558 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1105511 4 0.2309 0.9099 0.000 0.000 0.000 0.888 0.028 0.084
#> GSM1105514 2 0.1387 0.8943 0.000 0.932 0.000 0.000 0.000 0.068
#> GSM1105518 6 0.4640 0.3218 0.000 0.376 0.000 0.048 0.000 0.576
#> GSM1105522 3 0.0000 0.9312 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1105534 1 0.0000 0.8765 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1105535 1 0.0000 0.8765 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1105538 3 0.0000 0.9312 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1105542 5 0.0000 0.9558 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1105443 2 0.0000 0.9592 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105551 1 0.3857 0.2136 0.532 0.000 0.468 0.000 0.000 0.000
#> GSM1105554 1 0.0000 0.8765 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1105555 3 0.0000 0.9312 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1105447 2 0.0000 0.9592 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105467 6 0.1610 0.8723 0.000 0.084 0.000 0.000 0.000 0.916
#> GSM1105470 6 0.2854 0.7799 0.000 0.208 0.000 0.000 0.000 0.792
#> GSM1105471 6 0.1610 0.8723 0.000 0.084 0.000 0.000 0.000 0.916
#> GSM1105474 6 0.1610 0.8723 0.000 0.084 0.000 0.000 0.000 0.916
#> GSM1105475 6 0.1610 0.8723 0.000 0.084 0.000 0.000 0.000 0.916
#> GSM1105440 3 0.3823 0.0528 0.436 0.000 0.564 0.000 0.000 0.000
#> GSM1105488 5 0.0000 0.9558 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1105489 1 0.0000 0.8765 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1105492 1 0.3717 0.3535 0.616 0.000 0.384 0.000 0.000 0.000
#> GSM1105493 1 0.0000 0.8765 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1105497 5 0.0000 0.9558 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1105500 5 0.3499 0.5668 0.000 0.000 0.000 0.320 0.680 0.000
#> GSM1105501 4 0.2527 0.9023 0.000 0.000 0.000 0.876 0.040 0.084
#> GSM1105508 3 0.0547 0.9183 0.000 0.000 0.980 0.020 0.000 0.000
#> GSM1105444 2 0.0000 0.9592 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105513 6 0.1204 0.8571 0.000 0.056 0.000 0.000 0.000 0.944
#> GSM1105516 3 0.0146 0.9289 0.000 0.000 0.996 0.004 0.000 0.000
#> GSM1105520 4 0.0632 0.9531 0.000 0.000 0.000 0.976 0.000 0.024
#> GSM1105524 1 0.0000 0.8765 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1105536 4 0.0000 0.9620 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1105537 1 0.0000 0.8765 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1105540 4 0.0000 0.9620 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1105544 4 0.0000 0.9620 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1105445 2 0.0146 0.9559 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM1105553 5 0.0935 0.9336 0.000 0.000 0.000 0.032 0.964 0.004
#> GSM1105556 1 0.0000 0.8765 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1105557 6 0.3756 0.3232 0.000 0.400 0.000 0.000 0.000 0.600
#> GSM1105449 2 0.0000 0.9592 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105469 4 0.0000 0.9620 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1105472 6 0.2823 0.7841 0.000 0.204 0.000 0.000 0.000 0.796
#> GSM1105473 3 0.0000 0.9312 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1105476 6 0.1610 0.8723 0.000 0.084 0.000 0.000 0.000 0.916
#> GSM1105477 4 0.0000 0.9620 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1105478 6 0.0000 0.8455 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM1105510 5 0.0000 0.9558 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1105530 3 0.0000 0.9312 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1105539 1 0.3647 0.4741 0.640 0.000 0.360 0.000 0.000 0.000
#> GSM1105480 4 0.1610 0.9224 0.000 0.000 0.000 0.916 0.000 0.084
#> GSM1105512 3 0.0000 0.9312 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1105532 3 0.0000 0.9312 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1105541 1 0.3737 0.4138 0.608 0.000 0.392 0.000 0.000 0.000
#> GSM1105439 2 0.0000 0.9592 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105463 4 0.1204 0.9232 0.000 0.000 0.056 0.944 0.000 0.000
#> GSM1105482 1 0.0000 0.8765 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1105483 4 0.0458 0.9566 0.000 0.000 0.000 0.984 0.000 0.016
#> GSM1105494 6 0.0000 0.8455 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM1105503 4 0.1610 0.9224 0.000 0.000 0.000 0.916 0.000 0.084
#> GSM1105507 3 0.0000 0.9312 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1105446 2 0.3974 0.5505 0.000 0.680 0.000 0.000 0.296 0.024
#> GSM1105519 3 0.0000 0.9312 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1105526 4 0.2697 0.7868 0.000 0.000 0.000 0.812 0.188 0.000
#> GSM1105527 6 0.0000 0.8455 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM1105531 4 0.0000 0.9620 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1105543 6 0.1663 0.8714 0.000 0.088 0.000 0.000 0.000 0.912
#> GSM1105546 1 0.0000 0.8765 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1105547 1 0.0000 0.8765 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1105455 2 0.0000 0.9592 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105458 2 0.0000 0.9592 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105459 2 0.0000 0.9592 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105462 4 0.0000 0.9620 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1105441 2 0.0000 0.9592 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105465 5 0.0000 0.9558 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1105484 6 0.1663 0.8714 0.000 0.088 0.000 0.000 0.000 0.912
#> GSM1105485 5 0.0000 0.9558 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1105496 5 0.2454 0.8161 0.000 0.000 0.000 0.160 0.840 0.000
#> GSM1105505 4 0.0000 0.9620 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1105509 3 0.0000 0.9312 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1105448 2 0.0000 0.9592 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105521 3 0.0000 0.9312 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1105528 6 0.1753 0.8284 0.000 0.004 0.000 0.000 0.084 0.912
#> GSM1105529 5 0.0000 0.9558 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1105533 1 0.0000 0.8765 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1105545 4 0.0000 0.9620 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1105548 3 0.0000 0.9312 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1105549 1 0.3774 0.3789 0.592 0.000 0.408 0.000 0.000 0.000
#> GSM1105457 2 0.1663 0.8724 0.000 0.912 0.000 0.000 0.000 0.088
#> GSM1105460 2 0.0000 0.9592 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105461 2 0.0000 0.9592 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105464 3 0.3717 0.3688 0.000 0.000 0.616 0.384 0.000 0.000
#> GSM1105466 6 0.0000 0.8455 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM1105479 6 0.1910 0.8622 0.000 0.108 0.000 0.000 0.000 0.892
#> GSM1105502 3 0.0000 0.9312 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1105515 1 0.0000 0.8765 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1105523 4 0.0000 0.9620 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1105550 4 0.0000 0.9620 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1105450 2 0.2762 0.6902 0.000 0.804 0.000 0.000 0.000 0.196
#> GSM1105451 2 0.0000 0.9592 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105454 2 0.0000 0.9592 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105468 6 0.3659 0.5473 0.000 0.364 0.000 0.000 0.000 0.636
#> GSM1105481 6 0.2078 0.8578 0.000 0.040 0.000 0.032 0.012 0.916
#> GSM1105504 4 0.0000 0.9620 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1105517 3 0.2378 0.7824 0.000 0.000 0.848 0.152 0.000 0.000
#> GSM1105525 3 0.0713 0.9122 0.000 0.000 0.972 0.028 0.000 0.000
#> GSM1105552 3 0.1327 0.8846 0.000 0.000 0.936 0.064 0.000 0.000
#> GSM1105452 5 0.0000 0.9558 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1105453 2 0.0000 0.9592 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105456 2 0.0000 0.9592 0.000 1.000 0.000 0.000 0.000 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 agent(p) other(p) time(p) individual(p) k
#> ATC:pam 118 0.804 0.789 0.1959 0.01875 2
#> ATC:pam 118 0.153 0.925 0.0403 0.02693 3
#> ATC:pam 113 0.350 0.856 0.0759 0.06389 4
#> ATC:pam 111 0.678 0.283 0.3202 0.06122 5
#> ATC:pam 110 0.335 0.351 0.3121 0.00502 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 44956 rows and 120 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 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 0.499 0.934 0.888 0.4090 0.552 0.552
#> 3 3 0.608 0.636 0.799 0.4958 0.736 0.539
#> 4 4 0.598 0.765 0.816 0.1580 0.889 0.693
#> 5 5 0.766 0.745 0.827 0.0847 0.895 0.660
#> 6 6 0.867 0.865 0.924 0.0603 0.910 0.642
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
#> GSM1105438 2 0.4562 0.893 0.096 0.904
#> GSM1105486 2 0.5737 0.863 0.136 0.864
#> GSM1105487 1 0.6712 0.979 0.824 0.176
#> GSM1105490 2 0.1184 0.942 0.016 0.984
#> GSM1105491 2 0.2423 0.926 0.040 0.960
#> GSM1105495 2 0.0000 0.943 0.000 1.000
#> GSM1105498 2 0.1184 0.942 0.016 0.984
#> GSM1105499 1 0.7056 0.989 0.808 0.192
#> GSM1105506 2 0.0000 0.943 0.000 1.000
#> GSM1105442 2 0.2423 0.926 0.040 0.960
#> GSM1105511 2 0.0000 0.943 0.000 1.000
#> GSM1105514 2 0.5519 0.870 0.128 0.872
#> GSM1105518 2 0.0000 0.943 0.000 1.000
#> GSM1105522 1 0.7056 0.989 0.808 0.192
#> GSM1105534 1 0.7056 0.989 0.808 0.192
#> GSM1105535 1 0.7056 0.989 0.808 0.192
#> GSM1105538 1 0.6887 0.984 0.816 0.184
#> GSM1105542 2 0.2423 0.926 0.040 0.960
#> GSM1105443 2 0.0376 0.943 0.004 0.996
#> GSM1105551 1 0.6712 0.979 0.824 0.176
#> GSM1105554 1 0.7056 0.989 0.808 0.192
#> GSM1105555 1 0.6712 0.979 0.824 0.176
#> GSM1105447 2 0.1184 0.942 0.016 0.984
#> GSM1105467 2 0.5737 0.863 0.136 0.864
#> GSM1105470 2 0.5629 0.867 0.132 0.868
#> GSM1105471 2 0.0000 0.943 0.000 1.000
#> GSM1105474 2 0.5737 0.863 0.136 0.864
#> GSM1105475 2 0.0000 0.943 0.000 1.000
#> GSM1105440 1 0.6712 0.979 0.824 0.176
#> GSM1105488 2 0.2423 0.926 0.040 0.960
#> GSM1105489 1 0.6712 0.979 0.824 0.176
#> GSM1105492 1 0.6712 0.979 0.824 0.176
#> GSM1105493 1 0.7056 0.989 0.808 0.192
#> GSM1105497 2 0.1184 0.942 0.016 0.984
#> GSM1105500 2 0.1184 0.942 0.016 0.984
#> GSM1105501 2 0.0000 0.943 0.000 1.000
#> GSM1105508 1 0.7056 0.989 0.808 0.192
#> GSM1105444 2 0.5519 0.870 0.128 0.872
#> GSM1105513 2 0.1184 0.942 0.016 0.984
#> GSM1105516 2 0.9323 0.249 0.348 0.652
#> GSM1105520 2 0.0000 0.943 0.000 1.000
#> GSM1105524 1 0.7056 0.989 0.808 0.192
#> GSM1105536 2 0.0000 0.943 0.000 1.000
#> GSM1105537 1 0.7056 0.989 0.808 0.192
#> GSM1105540 2 0.1633 0.939 0.024 0.976
#> GSM1105544 2 0.1184 0.942 0.016 0.984
#> GSM1105445 2 0.1184 0.942 0.016 0.984
#> GSM1105553 1 0.9460 0.696 0.636 0.364
#> GSM1105556 1 0.7056 0.989 0.808 0.192
#> GSM1105557 2 0.1184 0.942 0.016 0.984
#> GSM1105449 2 0.5408 0.873 0.124 0.876
#> GSM1105469 2 0.0376 0.942 0.004 0.996
#> GSM1105472 2 0.5737 0.863 0.136 0.864
#> GSM1105473 1 0.7056 0.989 0.808 0.192
#> GSM1105476 2 0.5737 0.863 0.136 0.864
#> GSM1105477 2 0.1184 0.942 0.016 0.984
#> GSM1105478 2 0.1184 0.942 0.016 0.984
#> GSM1105510 2 0.0000 0.943 0.000 1.000
#> GSM1105530 1 0.7056 0.989 0.808 0.192
#> GSM1105539 1 0.7056 0.989 0.808 0.192
#> GSM1105480 2 0.1184 0.942 0.016 0.984
#> GSM1105512 1 0.7056 0.989 0.808 0.192
#> GSM1105532 1 0.7056 0.989 0.808 0.192
#> GSM1105541 1 0.7056 0.989 0.808 0.192
#> GSM1105439 2 0.0376 0.943 0.004 0.996
#> GSM1105463 1 0.7056 0.989 0.808 0.192
#> GSM1105482 1 0.7056 0.989 0.808 0.192
#> GSM1105483 2 0.0376 0.942 0.004 0.996
#> GSM1105494 2 0.1184 0.942 0.016 0.984
#> GSM1105503 2 0.1184 0.942 0.016 0.984
#> GSM1105507 1 0.6712 0.979 0.824 0.176
#> GSM1105446 2 0.1184 0.942 0.016 0.984
#> GSM1105519 1 0.7056 0.989 0.808 0.192
#> GSM1105526 2 0.0000 0.943 0.000 1.000
#> GSM1105527 2 0.0000 0.943 0.000 1.000
#> GSM1105531 2 0.1184 0.935 0.016 0.984
#> GSM1105543 2 0.1184 0.942 0.016 0.984
#> GSM1105546 1 0.6712 0.979 0.824 0.176
#> GSM1105547 1 0.7056 0.989 0.808 0.192
#> GSM1105455 2 0.1184 0.942 0.016 0.984
#> GSM1105458 2 0.5294 0.876 0.120 0.880
#> GSM1105459 2 0.5737 0.863 0.136 0.864
#> GSM1105462 2 0.0376 0.942 0.004 0.996
#> GSM1105441 2 0.5519 0.870 0.128 0.872
#> GSM1105465 2 0.2423 0.926 0.040 0.960
#> GSM1105484 2 0.0000 0.943 0.000 1.000
#> GSM1105485 2 0.0000 0.943 0.000 1.000
#> GSM1105496 2 0.2236 0.930 0.036 0.964
#> GSM1105505 2 0.5178 0.826 0.116 0.884
#> GSM1105509 1 0.7056 0.989 0.808 0.192
#> GSM1105448 2 0.5294 0.876 0.120 0.880
#> GSM1105521 1 0.7056 0.989 0.808 0.192
#> GSM1105528 2 0.0000 0.943 0.000 1.000
#> GSM1105529 2 0.0000 0.943 0.000 1.000
#> GSM1105533 1 0.6973 0.987 0.812 0.188
#> GSM1105545 2 0.0000 0.943 0.000 1.000
#> GSM1105548 1 0.6712 0.979 0.824 0.176
#> GSM1105549 1 0.7056 0.989 0.808 0.192
#> GSM1105457 2 0.0672 0.943 0.008 0.992
#> GSM1105460 2 0.0000 0.943 0.000 1.000
#> GSM1105461 2 0.5737 0.863 0.136 0.864
#> GSM1105464 1 0.7056 0.989 0.808 0.192
#> GSM1105466 2 0.0000 0.943 0.000 1.000
#> GSM1105479 2 0.0000 0.943 0.000 1.000
#> GSM1105502 1 0.7056 0.989 0.808 0.192
#> GSM1105515 1 0.7056 0.989 0.808 0.192
#> GSM1105523 2 0.0376 0.942 0.004 0.996
#> GSM1105550 2 0.0938 0.938 0.012 0.988
#> GSM1105450 2 0.5737 0.863 0.136 0.864
#> GSM1105451 2 0.4939 0.884 0.108 0.892
#> GSM1105454 2 0.1184 0.942 0.016 0.984
#> GSM1105468 2 0.5737 0.863 0.136 0.864
#> GSM1105481 2 0.0000 0.943 0.000 1.000
#> GSM1105504 2 0.0672 0.940 0.008 0.992
#> GSM1105517 1 0.7056 0.989 0.808 0.192
#> GSM1105525 2 0.6531 0.725 0.168 0.832
#> GSM1105552 1 0.7056 0.989 0.808 0.192
#> GSM1105452 2 0.2948 0.922 0.052 0.948
#> GSM1105453 2 0.0376 0.943 0.004 0.996
#> GSM1105456 2 0.1184 0.942 0.016 0.984
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1105438 2 0.4409 0.7261 0.004 0.824 0.172
#> GSM1105486 2 0.0000 0.7790 0.000 1.000 0.000
#> GSM1105487 1 0.0000 0.9311 1.000 0.000 0.000
#> GSM1105490 2 0.6047 0.5511 0.008 0.680 0.312
#> GSM1105491 3 0.1315 0.5271 0.020 0.008 0.972
#> GSM1105495 2 0.6724 0.2551 0.012 0.568 0.420
#> GSM1105498 3 0.7169 0.3861 0.024 0.456 0.520
#> GSM1105499 1 0.0237 0.9301 0.996 0.000 0.004
#> GSM1105506 2 0.5541 0.6596 0.008 0.740 0.252
#> GSM1105442 3 0.1170 0.5259 0.016 0.008 0.976
#> GSM1105511 3 0.7174 0.3817 0.024 0.460 0.516
#> GSM1105514 2 0.0237 0.7793 0.004 0.996 0.000
#> GSM1105518 2 0.6102 0.5250 0.008 0.672 0.320
#> GSM1105522 1 0.0000 0.9311 1.000 0.000 0.000
#> GSM1105534 1 0.0000 0.9311 1.000 0.000 0.000
#> GSM1105535 1 0.0000 0.9311 1.000 0.000 0.000
#> GSM1105538 1 0.0000 0.9311 1.000 0.000 0.000
#> GSM1105542 3 0.1170 0.5259 0.016 0.008 0.976
#> GSM1105443 2 0.2200 0.7763 0.004 0.940 0.056
#> GSM1105551 1 0.0000 0.9311 1.000 0.000 0.000
#> GSM1105554 1 0.0000 0.9311 1.000 0.000 0.000
#> GSM1105555 1 0.0000 0.9311 1.000 0.000 0.000
#> GSM1105447 2 0.5692 0.6426 0.008 0.724 0.268
#> GSM1105467 2 0.0000 0.7790 0.000 1.000 0.000
#> GSM1105470 2 0.0000 0.7790 0.000 1.000 0.000
#> GSM1105471 2 0.0983 0.7806 0.004 0.980 0.016
#> GSM1105474 2 0.0000 0.7790 0.000 1.000 0.000
#> GSM1105475 2 0.3500 0.7600 0.004 0.880 0.116
#> GSM1105440 1 0.0000 0.9311 1.000 0.000 0.000
#> GSM1105488 3 0.1170 0.5259 0.016 0.008 0.976
#> GSM1105489 1 0.0000 0.9311 1.000 0.000 0.000
#> GSM1105492 1 0.0000 0.9311 1.000 0.000 0.000
#> GSM1105493 1 0.0000 0.9311 1.000 0.000 0.000
#> GSM1105497 3 0.1170 0.5259 0.016 0.008 0.976
#> GSM1105500 3 0.7169 0.3861 0.024 0.456 0.520
#> GSM1105501 3 0.7174 0.3817 0.024 0.460 0.516
#> GSM1105508 1 0.6169 0.3948 0.636 0.004 0.360
#> GSM1105444 2 0.0237 0.7793 0.004 0.996 0.000
#> GSM1105513 2 0.5201 0.6847 0.004 0.760 0.236
#> GSM1105516 3 0.6676 0.1070 0.476 0.008 0.516
#> GSM1105520 3 0.7174 0.3817 0.024 0.460 0.516
#> GSM1105524 1 0.0000 0.9311 1.000 0.000 0.000
#> GSM1105536 3 0.7174 0.3817 0.024 0.460 0.516
#> GSM1105537 1 0.0000 0.9311 1.000 0.000 0.000
#> GSM1105540 3 0.7641 0.3990 0.044 0.436 0.520
#> GSM1105544 3 0.7169 0.3861 0.024 0.456 0.520
#> GSM1105445 2 0.5404 0.6629 0.004 0.740 0.256
#> GSM1105553 3 0.7169 0.3861 0.024 0.456 0.520
#> GSM1105556 1 0.0000 0.9311 1.000 0.000 0.000
#> GSM1105557 2 0.6318 0.4251 0.008 0.636 0.356
#> GSM1105449 2 0.0000 0.7790 0.000 1.000 0.000
#> GSM1105469 3 0.7283 0.3820 0.028 0.460 0.512
#> GSM1105472 2 0.0000 0.7790 0.000 1.000 0.000
#> GSM1105473 1 0.5397 0.5839 0.720 0.000 0.280
#> GSM1105476 2 0.0000 0.7790 0.000 1.000 0.000
#> GSM1105477 3 0.7169 0.3861 0.024 0.456 0.520
#> GSM1105478 2 0.5517 0.6448 0.004 0.728 0.268
#> GSM1105510 3 0.1905 0.5258 0.016 0.028 0.956
#> GSM1105530 1 0.0237 0.9301 0.996 0.000 0.004
#> GSM1105539 1 0.0237 0.9301 0.996 0.000 0.004
#> GSM1105480 3 0.6659 0.3369 0.008 0.460 0.532
#> GSM1105512 1 0.0237 0.9301 0.996 0.000 0.004
#> GSM1105532 1 0.0237 0.9301 0.996 0.000 0.004
#> GSM1105541 1 0.0237 0.9301 0.996 0.000 0.004
#> GSM1105439 2 0.1129 0.7784 0.004 0.976 0.020
#> GSM1105463 1 0.6299 0.0406 0.524 0.000 0.476
#> GSM1105482 1 0.0000 0.9311 1.000 0.000 0.000
#> GSM1105483 3 0.7174 0.3817 0.024 0.460 0.516
#> GSM1105494 2 0.6318 0.4251 0.008 0.636 0.356
#> GSM1105503 3 0.6654 0.3490 0.008 0.456 0.536
#> GSM1105507 1 0.6079 0.3286 0.612 0.000 0.388
#> GSM1105446 3 0.3141 0.5229 0.020 0.068 0.912
#> GSM1105519 1 0.0237 0.9301 0.996 0.000 0.004
#> GSM1105526 3 0.7174 0.3817 0.024 0.460 0.516
#> GSM1105527 2 0.6750 0.3939 0.024 0.640 0.336
#> GSM1105531 3 0.7722 0.2237 0.432 0.048 0.520
#> GSM1105543 2 0.5977 0.6246 0.020 0.728 0.252
#> GSM1105546 1 0.0000 0.9311 1.000 0.000 0.000
#> GSM1105547 1 0.0000 0.9311 1.000 0.000 0.000
#> GSM1105455 2 0.2860 0.7704 0.004 0.912 0.084
#> GSM1105458 2 0.0424 0.7805 0.000 0.992 0.008
#> GSM1105459 2 0.0000 0.7790 0.000 1.000 0.000
#> GSM1105462 3 0.7174 0.3817 0.024 0.460 0.516
#> GSM1105441 2 0.0000 0.7790 0.000 1.000 0.000
#> GSM1105465 3 0.1170 0.5259 0.016 0.008 0.976
#> GSM1105484 3 0.6192 -0.0301 0.000 0.420 0.580
#> GSM1105485 3 0.1315 0.5271 0.020 0.008 0.972
#> GSM1105496 3 0.7152 0.3956 0.024 0.444 0.532
#> GSM1105505 3 0.6516 0.0958 0.480 0.004 0.516
#> GSM1105509 1 0.3816 0.7804 0.852 0.000 0.148
#> GSM1105448 2 0.0237 0.7787 0.000 0.996 0.004
#> GSM1105521 1 0.0237 0.9301 0.996 0.000 0.004
#> GSM1105528 3 0.5919 0.2994 0.016 0.260 0.724
#> GSM1105529 3 0.1170 0.5259 0.016 0.008 0.976
#> GSM1105533 1 0.0000 0.9311 1.000 0.000 0.000
#> GSM1105545 3 0.7174 0.3817 0.024 0.460 0.516
#> GSM1105548 1 0.0000 0.9311 1.000 0.000 0.000
#> GSM1105549 1 0.0237 0.9301 0.996 0.000 0.004
#> GSM1105457 2 0.5404 0.6629 0.004 0.740 0.256
#> GSM1105460 2 0.5414 0.6831 0.016 0.772 0.212
#> GSM1105461 2 0.0000 0.7790 0.000 1.000 0.000
#> GSM1105464 1 0.0237 0.9301 0.996 0.000 0.004
#> GSM1105466 2 0.5404 0.6629 0.004 0.740 0.256
#> GSM1105479 2 0.1647 0.7782 0.004 0.960 0.036
#> GSM1105502 1 0.0000 0.9311 1.000 0.000 0.000
#> GSM1105515 1 0.0000 0.9311 1.000 0.000 0.000
#> GSM1105523 3 0.7471 0.3893 0.036 0.448 0.516
#> GSM1105550 3 0.8597 0.4120 0.104 0.380 0.516
#> GSM1105450 2 0.0000 0.7790 0.000 1.000 0.000
#> GSM1105451 2 0.0000 0.7790 0.000 1.000 0.000
#> GSM1105454 2 0.5404 0.6622 0.004 0.740 0.256
#> GSM1105468 2 0.0000 0.7790 0.000 1.000 0.000
#> GSM1105481 2 0.5763 0.5947 0.008 0.716 0.276
#> GSM1105504 3 0.6816 0.1195 0.472 0.012 0.516
#> GSM1105517 3 0.6516 0.0859 0.480 0.004 0.516
#> GSM1105525 1 0.6483 0.1201 0.544 0.004 0.452
#> GSM1105552 3 0.6521 0.0513 0.492 0.004 0.504
#> GSM1105452 3 0.1170 0.5259 0.016 0.008 0.976
#> GSM1105453 2 0.4293 0.7313 0.004 0.832 0.164
#> GSM1105456 2 0.4351 0.7365 0.004 0.828 0.168
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1105438 2 0.1489 0.738 0.000 0.952 0.004 0.044
#> GSM1105486 2 0.0000 0.763 0.000 1.000 0.000 0.000
#> GSM1105487 1 0.1820 0.914 0.944 0.000 0.036 0.020
#> GSM1105490 2 0.6566 0.773 0.000 0.624 0.236 0.140
#> GSM1105491 4 0.3450 0.965 0.000 0.008 0.156 0.836
#> GSM1105495 2 0.6373 0.604 0.000 0.652 0.200 0.148
#> GSM1105498 3 0.3647 0.626 0.000 0.152 0.832 0.016
#> GSM1105499 1 0.0000 0.921 1.000 0.000 0.000 0.000
#> GSM1105506 2 0.6538 0.776 0.000 0.628 0.232 0.140
#> GSM1105442 4 0.3450 0.965 0.000 0.008 0.156 0.836
#> GSM1105511 3 0.3653 0.642 0.000 0.128 0.844 0.028
#> GSM1105514 2 0.0592 0.758 0.000 0.984 0.000 0.016
#> GSM1105518 2 0.6594 0.770 0.000 0.620 0.240 0.140
#> GSM1105522 1 0.4744 0.661 0.704 0.000 0.284 0.012
#> GSM1105534 1 0.0336 0.921 0.992 0.000 0.000 0.008
#> GSM1105535 1 0.0336 0.921 0.992 0.000 0.000 0.008
#> GSM1105538 1 0.1151 0.919 0.968 0.000 0.024 0.008
#> GSM1105542 4 0.3450 0.965 0.000 0.008 0.156 0.836
#> GSM1105443 2 0.6538 0.776 0.000 0.628 0.232 0.140
#> GSM1105551 1 0.1820 0.914 0.944 0.000 0.036 0.020
#> GSM1105554 1 0.0000 0.921 1.000 0.000 0.000 0.000
#> GSM1105555 1 0.1820 0.914 0.944 0.000 0.036 0.020
#> GSM1105447 2 0.6538 0.776 0.000 0.628 0.232 0.140
#> GSM1105467 2 0.0188 0.761 0.000 0.996 0.000 0.004
#> GSM1105470 2 0.0000 0.763 0.000 1.000 0.000 0.000
#> GSM1105471 2 0.3945 0.784 0.000 0.780 0.216 0.004
#> GSM1105474 2 0.0000 0.763 0.000 1.000 0.000 0.000
#> GSM1105475 2 0.6538 0.776 0.000 0.628 0.232 0.140
#> GSM1105440 1 0.1820 0.914 0.944 0.000 0.036 0.020
#> GSM1105488 4 0.3450 0.965 0.000 0.008 0.156 0.836
#> GSM1105489 1 0.1624 0.915 0.952 0.000 0.028 0.020
#> GSM1105492 1 0.1820 0.914 0.944 0.000 0.036 0.020
#> GSM1105493 1 0.0000 0.921 1.000 0.000 0.000 0.000
#> GSM1105497 4 0.3498 0.962 0.000 0.008 0.160 0.832
#> GSM1105500 3 0.5182 0.639 0.048 0.024 0.776 0.152
#> GSM1105501 3 0.3707 0.639 0.000 0.132 0.840 0.028
#> GSM1105508 3 0.5057 0.422 0.340 0.000 0.648 0.012
#> GSM1105444 2 0.0336 0.762 0.000 0.992 0.000 0.008
#> GSM1105513 2 0.6538 0.776 0.000 0.628 0.232 0.140
#> GSM1105516 3 0.4088 0.628 0.232 0.000 0.764 0.004
#> GSM1105520 3 0.3554 0.638 0.000 0.136 0.844 0.020
#> GSM1105524 1 0.0336 0.921 0.992 0.000 0.000 0.008
#> GSM1105536 3 0.3071 0.681 0.000 0.068 0.888 0.044
#> GSM1105537 1 0.0336 0.921 0.992 0.000 0.000 0.008
#> GSM1105540 3 0.2392 0.712 0.036 0.012 0.928 0.024
#> GSM1105544 3 0.4735 0.632 0.032 0.020 0.796 0.152
#> GSM1105445 2 0.6538 0.776 0.000 0.628 0.232 0.140
#> GSM1105553 3 0.5194 0.637 0.056 0.024 0.780 0.140
#> GSM1105556 1 0.0000 0.921 1.000 0.000 0.000 0.000
#> GSM1105557 2 0.6594 0.770 0.000 0.620 0.240 0.140
#> GSM1105449 2 0.0000 0.763 0.000 1.000 0.000 0.000
#> GSM1105469 3 0.0657 0.707 0.000 0.012 0.984 0.004
#> GSM1105472 2 0.0000 0.763 0.000 1.000 0.000 0.000
#> GSM1105473 3 0.4313 0.607 0.260 0.000 0.736 0.004
#> GSM1105476 2 0.0000 0.763 0.000 1.000 0.000 0.000
#> GSM1105477 3 0.3695 0.614 0.000 0.016 0.828 0.156
#> GSM1105478 2 0.6566 0.773 0.000 0.624 0.236 0.140
#> GSM1105510 4 0.3852 0.930 0.000 0.008 0.192 0.800
#> GSM1105530 1 0.3208 0.831 0.848 0.000 0.148 0.004
#> GSM1105539 1 0.2530 0.863 0.888 0.000 0.112 0.000
#> GSM1105480 3 0.4454 0.287 0.000 0.308 0.692 0.000
#> GSM1105512 1 0.2999 0.846 0.864 0.000 0.132 0.004
#> GSM1105532 1 0.4584 0.600 0.696 0.000 0.300 0.004
#> GSM1105541 1 0.2831 0.856 0.876 0.000 0.120 0.004
#> GSM1105439 2 0.6418 0.779 0.000 0.644 0.216 0.140
#> GSM1105463 3 0.4122 0.626 0.236 0.000 0.760 0.004
#> GSM1105482 1 0.0000 0.921 1.000 0.000 0.000 0.000
#> GSM1105483 3 0.0657 0.709 0.004 0.012 0.984 0.000
#> GSM1105494 2 0.6201 0.735 0.000 0.620 0.300 0.080
#> GSM1105503 3 0.3266 0.615 0.000 0.168 0.832 0.000
#> GSM1105507 3 0.4284 0.626 0.224 0.000 0.764 0.012
#> GSM1105446 4 0.6323 0.684 0.000 0.112 0.248 0.640
#> GSM1105519 1 0.3583 0.811 0.816 0.000 0.180 0.004
#> GSM1105526 3 0.3895 0.644 0.000 0.132 0.832 0.036
#> GSM1105527 2 0.5364 0.632 0.000 0.592 0.392 0.016
#> GSM1105531 3 0.3810 0.658 0.188 0.000 0.804 0.008
#> GSM1105543 2 0.5492 0.678 0.000 0.640 0.328 0.032
#> GSM1105546 1 0.1624 0.915 0.952 0.000 0.028 0.020
#> GSM1105547 1 0.0000 0.921 1.000 0.000 0.000 0.000
#> GSM1105455 2 0.6538 0.776 0.000 0.628 0.232 0.140
#> GSM1105458 2 0.2530 0.785 0.000 0.888 0.112 0.000
#> GSM1105459 2 0.0000 0.763 0.000 1.000 0.000 0.000
#> GSM1105462 3 0.1271 0.709 0.008 0.012 0.968 0.012
#> GSM1105441 2 0.0000 0.763 0.000 1.000 0.000 0.000
#> GSM1105465 4 0.3450 0.965 0.000 0.008 0.156 0.836
#> GSM1105484 2 0.4153 0.709 0.000 0.820 0.048 0.132
#> GSM1105485 4 0.3636 0.951 0.000 0.008 0.172 0.820
#> GSM1105496 3 0.5358 0.635 0.068 0.020 0.768 0.144
#> GSM1105505 3 0.4544 0.654 0.192 0.012 0.780 0.016
#> GSM1105509 3 0.5016 0.293 0.396 0.000 0.600 0.004
#> GSM1105448 2 0.0336 0.762 0.000 0.992 0.000 0.008
#> GSM1105521 1 0.2999 0.846 0.864 0.000 0.132 0.004
#> GSM1105528 2 0.7121 0.470 0.000 0.564 0.216 0.220
#> GSM1105529 4 0.3450 0.965 0.000 0.008 0.156 0.836
#> GSM1105533 1 0.0336 0.921 0.992 0.000 0.000 0.008
#> GSM1105545 3 0.3932 0.645 0.004 0.128 0.836 0.032
#> GSM1105548 1 0.1820 0.914 0.944 0.000 0.036 0.020
#> GSM1105549 1 0.0000 0.921 1.000 0.000 0.000 0.000
#> GSM1105457 2 0.6538 0.776 0.000 0.628 0.232 0.140
#> GSM1105460 2 0.4957 0.735 0.000 0.684 0.300 0.016
#> GSM1105461 2 0.0000 0.763 0.000 1.000 0.000 0.000
#> GSM1105464 1 0.4889 0.459 0.636 0.000 0.360 0.004
#> GSM1105466 2 0.6538 0.776 0.000 0.628 0.232 0.140
#> GSM1105479 2 0.6509 0.777 0.000 0.632 0.228 0.140
#> GSM1105502 1 0.1284 0.919 0.964 0.000 0.024 0.012
#> GSM1105515 1 0.0000 0.921 1.000 0.000 0.000 0.000
#> GSM1105523 3 0.1174 0.712 0.020 0.012 0.968 0.000
#> GSM1105550 3 0.1488 0.714 0.032 0.012 0.956 0.000
#> GSM1105450 2 0.0000 0.763 0.000 1.000 0.000 0.000
#> GSM1105451 2 0.0000 0.763 0.000 1.000 0.000 0.000
#> GSM1105454 2 0.6538 0.776 0.000 0.628 0.232 0.140
#> GSM1105468 2 0.0000 0.763 0.000 1.000 0.000 0.000
#> GSM1105481 2 0.5599 0.658 0.000 0.616 0.352 0.032
#> GSM1105504 3 0.3703 0.682 0.140 0.012 0.840 0.008
#> GSM1105517 3 0.4122 0.626 0.236 0.000 0.760 0.004
#> GSM1105525 3 0.4387 0.626 0.236 0.000 0.752 0.012
#> GSM1105552 3 0.4122 0.626 0.236 0.000 0.760 0.004
#> GSM1105452 4 0.3450 0.965 0.000 0.008 0.156 0.836
#> GSM1105453 2 0.2773 0.785 0.000 0.880 0.116 0.004
#> GSM1105456 2 0.6538 0.776 0.000 0.628 0.232 0.140
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM1105438 2 0.0000 0.8780 0.000 1.000 0.000 0.000 0.000
#> GSM1105486 2 0.0000 0.8780 0.000 1.000 0.000 0.000 0.000
#> GSM1105487 1 0.0162 0.9342 0.996 0.000 0.004 0.000 0.000
#> GSM1105490 2 0.4302 0.5273 0.000 0.520 0.000 0.480 0.000
#> GSM1105491 5 0.0404 0.9520 0.000 0.000 0.000 0.012 0.988
#> GSM1105495 2 0.2915 0.8349 0.000 0.860 0.000 0.116 0.024
#> GSM1105498 4 0.3412 0.6656 0.000 0.000 0.152 0.820 0.028
#> GSM1105499 1 0.1270 0.9531 0.948 0.000 0.052 0.000 0.000
#> GSM1105506 2 0.4150 0.6499 0.000 0.612 0.000 0.388 0.000
#> GSM1105442 5 0.0162 0.9537 0.000 0.000 0.000 0.004 0.996
#> GSM1105511 4 0.3766 0.7040 0.000 0.000 0.268 0.728 0.004
#> GSM1105514 2 0.0324 0.8772 0.000 0.992 0.000 0.004 0.004
#> GSM1105518 4 0.4287 -0.4364 0.000 0.460 0.000 0.540 0.000
#> GSM1105522 3 0.2732 0.7452 0.160 0.000 0.840 0.000 0.000
#> GSM1105534 1 0.1270 0.9531 0.948 0.000 0.052 0.000 0.000
#> GSM1105535 1 0.1270 0.9531 0.948 0.000 0.052 0.000 0.000
#> GSM1105538 1 0.1341 0.9512 0.944 0.000 0.056 0.000 0.000
#> GSM1105542 5 0.0162 0.9537 0.000 0.000 0.000 0.004 0.996
#> GSM1105443 2 0.2890 0.8410 0.000 0.836 0.000 0.160 0.004
#> GSM1105551 1 0.0162 0.9342 0.996 0.000 0.004 0.000 0.000
#> GSM1105554 1 0.1270 0.9531 0.948 0.000 0.052 0.000 0.000
#> GSM1105555 1 0.0162 0.9342 0.996 0.000 0.004 0.000 0.000
#> GSM1105447 2 0.4045 0.6859 0.000 0.644 0.000 0.356 0.000
#> GSM1105467 2 0.0000 0.8780 0.000 1.000 0.000 0.000 0.000
#> GSM1105470 2 0.0486 0.8766 0.000 0.988 0.004 0.004 0.004
#> GSM1105471 2 0.1671 0.8692 0.000 0.924 0.000 0.076 0.000
#> GSM1105474 2 0.0000 0.8780 0.000 1.000 0.000 0.000 0.000
#> GSM1105475 2 0.2561 0.8476 0.000 0.856 0.000 0.144 0.000
#> GSM1105440 1 0.0162 0.9342 0.996 0.000 0.004 0.000 0.000
#> GSM1105488 5 0.0162 0.9537 0.000 0.000 0.000 0.004 0.996
#> GSM1105489 1 0.0510 0.9420 0.984 0.000 0.016 0.000 0.000
#> GSM1105492 1 0.0162 0.9342 0.996 0.000 0.004 0.000 0.000
#> GSM1105493 1 0.1410 0.9472 0.940 0.000 0.060 0.000 0.000
#> GSM1105497 5 0.1121 0.9324 0.000 0.000 0.000 0.044 0.956
#> GSM1105500 4 0.5796 0.6698 0.000 0.000 0.284 0.588 0.128
#> GSM1105501 4 0.3280 0.6672 0.000 0.012 0.160 0.824 0.004
#> GSM1105508 3 0.1571 0.6359 0.004 0.000 0.936 0.060 0.000
#> GSM1105444 2 0.0486 0.8766 0.000 0.988 0.004 0.004 0.004
#> GSM1105513 2 0.4114 0.6634 0.000 0.624 0.000 0.376 0.000
#> GSM1105516 3 0.4142 0.0356 0.004 0.000 0.684 0.308 0.004
#> GSM1105520 4 0.2890 0.6646 0.000 0.000 0.160 0.836 0.004
#> GSM1105524 1 0.1270 0.9531 0.948 0.000 0.052 0.000 0.000
#> GSM1105536 4 0.3949 0.7054 0.000 0.000 0.300 0.696 0.004
#> GSM1105537 1 0.1270 0.9531 0.948 0.000 0.052 0.000 0.000
#> GSM1105540 4 0.5613 0.6644 0.000 0.000 0.332 0.576 0.092
#> GSM1105544 4 0.5714 0.6735 0.000 0.000 0.292 0.592 0.116
#> GSM1105445 2 0.4150 0.6499 0.000 0.612 0.000 0.388 0.000
#> GSM1105553 4 0.5934 0.6455 0.000 0.000 0.232 0.592 0.176
#> GSM1105556 1 0.1270 0.9531 0.948 0.000 0.052 0.000 0.000
#> GSM1105557 4 0.4256 -0.3875 0.000 0.436 0.000 0.564 0.000
#> GSM1105449 2 0.0486 0.8766 0.000 0.988 0.004 0.004 0.004
#> GSM1105469 4 0.4066 0.6958 0.000 0.000 0.324 0.672 0.004
#> GSM1105472 2 0.0486 0.8766 0.000 0.988 0.004 0.004 0.004
#> GSM1105473 3 0.1282 0.7246 0.044 0.000 0.952 0.004 0.000
#> GSM1105476 2 0.0000 0.8780 0.000 1.000 0.000 0.000 0.000
#> GSM1105477 4 0.5788 0.6711 0.000 0.000 0.300 0.580 0.120
#> GSM1105478 2 0.4150 0.6499 0.000 0.612 0.000 0.388 0.000
#> GSM1105510 5 0.2370 0.8905 0.000 0.000 0.040 0.056 0.904
#> GSM1105530 3 0.3074 0.7120 0.196 0.000 0.804 0.000 0.000
#> GSM1105539 1 0.4273 0.1173 0.552 0.000 0.448 0.000 0.000
#> GSM1105480 4 0.1331 0.5716 0.000 0.008 0.040 0.952 0.000
#> GSM1105512 3 0.3796 0.5647 0.300 0.000 0.700 0.000 0.000
#> GSM1105532 3 0.2773 0.7410 0.164 0.000 0.836 0.000 0.000
#> GSM1105541 3 0.3949 0.5081 0.332 0.000 0.668 0.000 0.000
#> GSM1105439 2 0.1928 0.8738 0.000 0.920 0.004 0.072 0.004
#> GSM1105463 3 0.0771 0.6860 0.004 0.000 0.976 0.020 0.000
#> GSM1105482 1 0.1270 0.9531 0.948 0.000 0.052 0.000 0.000
#> GSM1105483 4 0.3949 0.7054 0.000 0.000 0.300 0.696 0.004
#> GSM1105494 4 0.4304 -0.4901 0.000 0.484 0.000 0.516 0.000
#> GSM1105503 4 0.2377 0.6450 0.000 0.000 0.128 0.872 0.000
#> GSM1105507 3 0.2674 0.5170 0.004 0.000 0.856 0.140 0.000
#> GSM1105446 5 0.3551 0.8190 0.000 0.012 0.088 0.056 0.844
#> GSM1105519 3 0.2852 0.7342 0.172 0.000 0.828 0.000 0.000
#> GSM1105526 4 0.3949 0.7054 0.000 0.000 0.300 0.696 0.004
#> GSM1105527 4 0.3934 0.2208 0.000 0.276 0.008 0.716 0.000
#> GSM1105531 4 0.4596 0.4842 0.004 0.000 0.492 0.500 0.004
#> GSM1105543 2 0.2605 0.8232 0.000 0.852 0.000 0.148 0.000
#> GSM1105546 1 0.0000 0.9348 1.000 0.000 0.000 0.000 0.000
#> GSM1105547 1 0.1270 0.9531 0.948 0.000 0.052 0.000 0.000
#> GSM1105455 2 0.2629 0.8517 0.000 0.860 0.000 0.136 0.004
#> GSM1105458 2 0.0162 0.8782 0.000 0.996 0.000 0.004 0.000
#> GSM1105459 2 0.0486 0.8766 0.000 0.988 0.004 0.004 0.004
#> GSM1105462 4 0.4084 0.6935 0.000 0.000 0.328 0.668 0.004
#> GSM1105441 2 0.0486 0.8766 0.000 0.988 0.004 0.004 0.004
#> GSM1105465 5 0.0162 0.9537 0.000 0.000 0.000 0.004 0.996
#> GSM1105484 2 0.1544 0.8673 0.000 0.932 0.000 0.068 0.000
#> GSM1105485 5 0.1626 0.9257 0.000 0.000 0.016 0.044 0.940
#> GSM1105496 4 0.5816 0.6680 0.000 0.000 0.280 0.588 0.132
#> GSM1105505 4 0.5971 0.5751 0.004 0.000 0.404 0.496 0.096
#> GSM1105509 3 0.1124 0.7214 0.036 0.000 0.960 0.004 0.000
#> GSM1105448 2 0.0486 0.8766 0.000 0.988 0.004 0.004 0.004
#> GSM1105521 3 0.3774 0.5717 0.296 0.000 0.704 0.000 0.000
#> GSM1105528 2 0.3201 0.8348 0.000 0.852 0.000 0.096 0.052
#> GSM1105529 5 0.0162 0.9537 0.000 0.000 0.000 0.004 0.996
#> GSM1105533 1 0.1270 0.9531 0.948 0.000 0.052 0.000 0.000
#> GSM1105545 4 0.3884 0.7061 0.000 0.000 0.288 0.708 0.004
#> GSM1105548 1 0.0162 0.9342 0.996 0.000 0.004 0.000 0.000
#> GSM1105549 1 0.1671 0.9338 0.924 0.000 0.076 0.000 0.000
#> GSM1105457 2 0.4150 0.6499 0.000 0.612 0.000 0.388 0.000
#> GSM1105460 2 0.1410 0.8695 0.000 0.940 0.000 0.060 0.000
#> GSM1105461 2 0.0486 0.8766 0.000 0.988 0.004 0.004 0.004
#> GSM1105464 3 0.2471 0.7494 0.136 0.000 0.864 0.000 0.000
#> GSM1105466 2 0.4150 0.6499 0.000 0.612 0.000 0.388 0.000
#> GSM1105479 2 0.2648 0.8442 0.000 0.848 0.000 0.152 0.000
#> GSM1105502 3 0.4242 0.2781 0.428 0.000 0.572 0.000 0.000
#> GSM1105515 1 0.1270 0.9531 0.948 0.000 0.052 0.000 0.000
#> GSM1105523 4 0.4135 0.6848 0.000 0.000 0.340 0.656 0.004
#> GSM1105550 4 0.4288 0.6467 0.000 0.000 0.384 0.612 0.004
#> GSM1105450 2 0.0486 0.8766 0.000 0.988 0.004 0.004 0.004
#> GSM1105451 2 0.0486 0.8766 0.000 0.988 0.004 0.004 0.004
#> GSM1105454 2 0.2690 0.8440 0.000 0.844 0.000 0.156 0.000
#> GSM1105468 2 0.0486 0.8766 0.000 0.988 0.004 0.004 0.004
#> GSM1105481 2 0.1270 0.8708 0.000 0.948 0.000 0.052 0.000
#> GSM1105504 4 0.4562 0.5681 0.004 0.000 0.444 0.548 0.004
#> GSM1105517 3 0.0566 0.6921 0.004 0.000 0.984 0.012 0.000
#> GSM1105525 3 0.1704 0.6227 0.004 0.000 0.928 0.068 0.000
#> GSM1105552 3 0.4572 0.3837 0.004 0.000 0.756 0.148 0.092
#> GSM1105452 5 0.0162 0.9537 0.000 0.000 0.000 0.004 0.996
#> GSM1105453 2 0.1965 0.8565 0.000 0.904 0.000 0.096 0.000
#> GSM1105456 2 0.3074 0.8188 0.000 0.804 0.000 0.196 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1105438 2 0.0146 0.9301 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM1105486 2 0.0146 0.9301 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM1105487 1 0.0777 0.9483 0.972 0.000 0.004 0.000 0.000 0.024
#> GSM1105490 6 0.0713 0.8703 0.000 0.028 0.000 0.000 0.000 0.972
#> GSM1105491 5 0.0146 0.9936 0.000 0.000 0.000 0.004 0.996 0.000
#> GSM1105495 2 0.0692 0.9203 0.000 0.976 0.000 0.000 0.020 0.004
#> GSM1105498 4 0.2773 0.7869 0.000 0.000 0.008 0.836 0.004 0.152
#> GSM1105499 1 0.2454 0.8424 0.840 0.000 0.160 0.000 0.000 0.000
#> GSM1105506 6 0.2219 0.8747 0.000 0.136 0.000 0.000 0.000 0.864
#> GSM1105442 5 0.0000 0.9954 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1105511 4 0.1219 0.9034 0.000 0.000 0.000 0.948 0.004 0.048
#> GSM1105514 2 0.0146 0.9301 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM1105518 6 0.2048 0.8855 0.000 0.120 0.000 0.000 0.000 0.880
#> GSM1105522 3 0.3453 0.8039 0.064 0.000 0.804 0.132 0.000 0.000
#> GSM1105534 1 0.0000 0.9530 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1105535 1 0.0000 0.9530 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1105538 1 0.0717 0.9495 0.976 0.000 0.008 0.000 0.000 0.016
#> GSM1105542 5 0.0000 0.9954 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1105443 2 0.3428 0.5513 0.000 0.696 0.000 0.000 0.000 0.304
#> GSM1105551 1 0.0891 0.9473 0.968 0.000 0.008 0.000 0.000 0.024
#> GSM1105554 1 0.1444 0.9130 0.928 0.000 0.072 0.000 0.000 0.000
#> GSM1105555 1 0.0891 0.9473 0.968 0.000 0.008 0.000 0.000 0.024
#> GSM1105447 6 0.2300 0.8231 0.000 0.144 0.000 0.000 0.000 0.856
#> GSM1105467 2 0.0146 0.9301 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM1105470 2 0.0000 0.9298 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105471 2 0.0547 0.9234 0.000 0.980 0.000 0.000 0.000 0.020
#> GSM1105474 2 0.0146 0.9301 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM1105475 2 0.2664 0.7666 0.000 0.816 0.000 0.000 0.000 0.184
#> GSM1105440 1 0.0891 0.9473 0.968 0.000 0.008 0.000 0.000 0.024
#> GSM1105488 5 0.0000 0.9954 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1105489 1 0.0000 0.9530 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1105492 1 0.0632 0.9489 0.976 0.000 0.000 0.000 0.000 0.024
#> GSM1105493 1 0.2340 0.8512 0.852 0.000 0.148 0.000 0.000 0.000
#> GSM1105497 5 0.0000 0.9954 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1105500 4 0.2009 0.8520 0.000 0.000 0.008 0.904 0.004 0.084
#> GSM1105501 4 0.1285 0.9017 0.000 0.000 0.000 0.944 0.004 0.052
#> GSM1105508 3 0.3833 0.3036 0.000 0.000 0.556 0.444 0.000 0.000
#> GSM1105444 2 0.0000 0.9298 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105513 6 0.1765 0.8889 0.000 0.096 0.000 0.000 0.000 0.904
#> GSM1105516 4 0.1075 0.8962 0.000 0.000 0.048 0.952 0.000 0.000
#> GSM1105520 4 0.1152 0.9046 0.000 0.000 0.000 0.952 0.004 0.044
#> GSM1105524 1 0.0000 0.9530 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1105536 4 0.1152 0.9046 0.000 0.000 0.000 0.952 0.004 0.044
#> GSM1105537 1 0.0000 0.9530 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1105540 4 0.0000 0.8988 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1105544 4 0.2213 0.8398 0.000 0.000 0.008 0.888 0.004 0.100
#> GSM1105445 6 0.2048 0.8855 0.000 0.120 0.000 0.000 0.000 0.880
#> GSM1105553 4 0.2656 0.8166 0.000 0.000 0.008 0.860 0.012 0.120
#> GSM1105556 1 0.2260 0.8593 0.860 0.000 0.140 0.000 0.000 0.000
#> GSM1105557 6 0.0713 0.8703 0.000 0.028 0.000 0.000 0.000 0.972
#> GSM1105449 2 0.0146 0.9301 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM1105469 4 0.1168 0.9049 0.000 0.000 0.016 0.956 0.000 0.028
#> GSM1105472 2 0.0000 0.9298 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105473 3 0.2793 0.7668 0.000 0.000 0.800 0.200 0.000 0.000
#> GSM1105476 2 0.0146 0.9301 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM1105477 4 0.0291 0.8976 0.000 0.000 0.004 0.992 0.004 0.000
#> GSM1105478 6 0.0790 0.8725 0.000 0.032 0.000 0.000 0.000 0.968
#> GSM1105510 5 0.0363 0.9863 0.000 0.000 0.000 0.012 0.988 0.000
#> GSM1105530 3 0.0260 0.8384 0.008 0.000 0.992 0.000 0.000 0.000
#> GSM1105539 3 0.0790 0.8239 0.032 0.000 0.968 0.000 0.000 0.000
#> GSM1105480 6 0.2135 0.7851 0.000 0.000 0.000 0.128 0.000 0.872
#> GSM1105512 3 0.0260 0.8384 0.008 0.000 0.992 0.000 0.000 0.000
#> GSM1105532 3 0.0260 0.8388 0.000 0.000 0.992 0.008 0.000 0.000
#> GSM1105541 3 0.0260 0.8384 0.008 0.000 0.992 0.000 0.000 0.000
#> GSM1105439 2 0.1327 0.8856 0.000 0.936 0.000 0.000 0.000 0.064
#> GSM1105463 4 0.3482 0.5089 0.000 0.000 0.316 0.684 0.000 0.000
#> GSM1105482 1 0.0000 0.9530 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1105483 4 0.1007 0.9043 0.000 0.000 0.000 0.956 0.000 0.044
#> GSM1105494 6 0.1498 0.8572 0.000 0.028 0.000 0.032 0.000 0.940
#> GSM1105503 6 0.3592 0.4689 0.000 0.000 0.000 0.344 0.000 0.656
#> GSM1105507 4 0.3244 0.5737 0.000 0.000 0.268 0.732 0.000 0.000
#> GSM1105446 5 0.0622 0.9814 0.000 0.000 0.000 0.012 0.980 0.008
#> GSM1105519 3 0.2653 0.8118 0.012 0.000 0.844 0.144 0.000 0.000
#> GSM1105526 4 0.1152 0.9046 0.000 0.000 0.000 0.952 0.004 0.044
#> GSM1105527 6 0.2462 0.8821 0.000 0.096 0.000 0.028 0.000 0.876
#> GSM1105531 4 0.1075 0.8962 0.000 0.000 0.048 0.952 0.000 0.000
#> GSM1105543 2 0.1492 0.8951 0.000 0.940 0.000 0.000 0.036 0.024
#> GSM1105546 1 0.0000 0.9530 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1105547 1 0.0000 0.9530 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1105455 2 0.2912 0.7088 0.000 0.784 0.000 0.000 0.000 0.216
#> GSM1105458 2 0.0363 0.9273 0.000 0.988 0.000 0.000 0.000 0.012
#> GSM1105459 2 0.0000 0.9298 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105462 4 0.1007 0.9043 0.000 0.000 0.000 0.956 0.000 0.044
#> GSM1105441 2 0.0000 0.9298 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105465 5 0.0000 0.9954 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1105484 2 0.0146 0.9301 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM1105485 5 0.0146 0.9936 0.000 0.000 0.000 0.004 0.996 0.000
#> GSM1105496 4 0.2686 0.8252 0.000 0.000 0.008 0.868 0.024 0.100
#> GSM1105505 4 0.0790 0.9002 0.000 0.000 0.032 0.968 0.000 0.000
#> GSM1105509 3 0.2491 0.7990 0.000 0.000 0.836 0.164 0.000 0.000
#> GSM1105448 2 0.0000 0.9298 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105521 3 0.0260 0.8384 0.008 0.000 0.992 0.000 0.000 0.000
#> GSM1105528 2 0.0692 0.9165 0.000 0.976 0.000 0.000 0.020 0.004
#> GSM1105529 5 0.0000 0.9954 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1105533 1 0.0000 0.9530 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1105545 4 0.1152 0.9046 0.000 0.000 0.000 0.952 0.004 0.044
#> GSM1105548 1 0.0891 0.9473 0.968 0.000 0.008 0.000 0.000 0.024
#> GSM1105549 1 0.3464 0.6337 0.688 0.000 0.312 0.000 0.000 0.000
#> GSM1105457 6 0.2092 0.8832 0.000 0.124 0.000 0.000 0.000 0.876
#> GSM1105460 2 0.0260 0.9288 0.000 0.992 0.000 0.000 0.000 0.008
#> GSM1105461 2 0.0000 0.9298 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105464 3 0.0260 0.8388 0.000 0.000 0.992 0.008 0.000 0.000
#> GSM1105466 6 0.2219 0.8747 0.000 0.136 0.000 0.000 0.000 0.864
#> GSM1105479 2 0.3390 0.5775 0.000 0.704 0.000 0.000 0.000 0.296
#> GSM1105502 3 0.3023 0.6797 0.232 0.000 0.768 0.000 0.000 0.000
#> GSM1105515 1 0.0000 0.9530 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1105523 4 0.1151 0.9022 0.000 0.000 0.032 0.956 0.000 0.012
#> GSM1105550 4 0.1082 0.8994 0.000 0.000 0.040 0.956 0.000 0.004
#> GSM1105450 2 0.0000 0.9298 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105451 2 0.0000 0.9298 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105454 2 0.3266 0.6488 0.000 0.728 0.000 0.000 0.000 0.272
#> GSM1105468 2 0.0000 0.9298 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1105481 2 0.0937 0.9095 0.000 0.960 0.000 0.000 0.000 0.040
#> GSM1105504 4 0.1007 0.8976 0.000 0.000 0.044 0.956 0.000 0.000
#> GSM1105517 4 0.2527 0.7819 0.000 0.000 0.168 0.832 0.000 0.000
#> GSM1105525 3 0.3706 0.5065 0.000 0.000 0.620 0.380 0.000 0.000
#> GSM1105552 4 0.2491 0.7842 0.000 0.000 0.164 0.836 0.000 0.000
#> GSM1105452 5 0.0000 0.9954 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1105453 2 0.0146 0.9301 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM1105456 2 0.3860 0.0778 0.000 0.528 0.000 0.000 0.000 0.472
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 agent(p) other(p) time(p) individual(p) k
#> ATC:mclust 119 0.366 1.0000 0.795 0.0209 2
#> ATC:mclust 84 0.504 0.8157 0.492 0.0479 3
#> ATC:mclust 115 0.305 0.9868 0.247 0.0791 4
#> ATC:mclust 111 0.634 0.1179 0.320 0.0220 5
#> ATC:mclust 117 0.417 0.0344 0.527 0.0372 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 44956 rows and 120 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 0.899 0.947 0.977 0.4532 0.546 0.546
#> 3 3 0.727 0.837 0.902 0.4361 0.744 0.549
#> 4 4 0.590 0.567 0.788 0.1068 0.847 0.588
#> 5 5 0.670 0.701 0.827 0.0729 0.818 0.458
#> 6 6 0.661 0.579 0.768 0.0296 0.939 0.760
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
#> GSM1105438 2 0.000 0.9803 0.000 1.000
#> GSM1105486 2 0.000 0.9803 0.000 1.000
#> GSM1105487 1 0.000 0.9667 1.000 0.000
#> GSM1105490 2 0.000 0.9803 0.000 1.000
#> GSM1105491 2 0.000 0.9803 0.000 1.000
#> GSM1105495 2 0.000 0.9803 0.000 1.000
#> GSM1105498 2 0.000 0.9803 0.000 1.000
#> GSM1105499 1 0.000 0.9667 1.000 0.000
#> GSM1105506 2 0.000 0.9803 0.000 1.000
#> GSM1105442 2 0.000 0.9803 0.000 1.000
#> GSM1105511 2 0.000 0.9803 0.000 1.000
#> GSM1105514 2 0.000 0.9803 0.000 1.000
#> GSM1105518 2 0.000 0.9803 0.000 1.000
#> GSM1105522 1 0.000 0.9667 1.000 0.000
#> GSM1105534 1 0.000 0.9667 1.000 0.000
#> GSM1105535 1 0.000 0.9667 1.000 0.000
#> GSM1105538 1 0.000 0.9667 1.000 0.000
#> GSM1105542 2 0.000 0.9803 0.000 1.000
#> GSM1105443 2 0.000 0.9803 0.000 1.000
#> GSM1105551 1 0.000 0.9667 1.000 0.000
#> GSM1105554 1 0.000 0.9667 1.000 0.000
#> GSM1105555 1 0.000 0.9667 1.000 0.000
#> GSM1105447 2 0.000 0.9803 0.000 1.000
#> GSM1105467 2 0.000 0.9803 0.000 1.000
#> GSM1105470 2 0.000 0.9803 0.000 1.000
#> GSM1105471 2 0.000 0.9803 0.000 1.000
#> GSM1105474 2 0.000 0.9803 0.000 1.000
#> GSM1105475 2 0.000 0.9803 0.000 1.000
#> GSM1105440 1 0.000 0.9667 1.000 0.000
#> GSM1105488 2 0.000 0.9803 0.000 1.000
#> GSM1105489 1 0.000 0.9667 1.000 0.000
#> GSM1105492 1 0.000 0.9667 1.000 0.000
#> GSM1105493 1 0.000 0.9667 1.000 0.000
#> GSM1105497 2 0.000 0.9803 0.000 1.000
#> GSM1105500 2 0.000 0.9803 0.000 1.000
#> GSM1105501 2 0.000 0.9803 0.000 1.000
#> GSM1105508 1 0.358 0.9151 0.932 0.068
#> GSM1105444 2 0.000 0.9803 0.000 1.000
#> GSM1105513 2 0.000 0.9803 0.000 1.000
#> GSM1105516 1 0.891 0.5806 0.692 0.308
#> GSM1105520 2 0.000 0.9803 0.000 1.000
#> GSM1105524 1 0.000 0.9667 1.000 0.000
#> GSM1105536 2 0.000 0.9803 0.000 1.000
#> GSM1105537 1 0.000 0.9667 1.000 0.000
#> GSM1105540 2 0.808 0.6637 0.248 0.752
#> GSM1105544 2 0.000 0.9803 0.000 1.000
#> GSM1105445 2 0.000 0.9803 0.000 1.000
#> GSM1105553 2 0.000 0.9803 0.000 1.000
#> GSM1105556 1 0.000 0.9667 1.000 0.000
#> GSM1105557 2 0.000 0.9803 0.000 1.000
#> GSM1105449 2 0.000 0.9803 0.000 1.000
#> GSM1105469 2 0.000 0.9803 0.000 1.000
#> GSM1105472 2 0.000 0.9803 0.000 1.000
#> GSM1105473 1 0.260 0.9348 0.956 0.044
#> GSM1105476 2 0.000 0.9803 0.000 1.000
#> GSM1105477 2 0.000 0.9803 0.000 1.000
#> GSM1105478 2 0.000 0.9803 0.000 1.000
#> GSM1105510 2 0.000 0.9803 0.000 1.000
#> GSM1105530 1 0.000 0.9667 1.000 0.000
#> GSM1105539 1 0.000 0.9667 1.000 0.000
#> GSM1105480 2 0.000 0.9803 0.000 1.000
#> GSM1105512 1 0.000 0.9667 1.000 0.000
#> GSM1105532 1 0.000 0.9667 1.000 0.000
#> GSM1105541 1 0.000 0.9667 1.000 0.000
#> GSM1105439 2 0.000 0.9803 0.000 1.000
#> GSM1105463 1 0.714 0.7740 0.804 0.196
#> GSM1105482 1 0.000 0.9667 1.000 0.000
#> GSM1105483 2 0.000 0.9803 0.000 1.000
#> GSM1105494 2 0.000 0.9803 0.000 1.000
#> GSM1105503 2 0.000 0.9803 0.000 1.000
#> GSM1105507 1 0.494 0.8778 0.892 0.108
#> GSM1105446 2 0.000 0.9803 0.000 1.000
#> GSM1105519 1 0.000 0.9667 1.000 0.000
#> GSM1105526 2 0.000 0.9803 0.000 1.000
#> GSM1105527 2 0.000 0.9803 0.000 1.000
#> GSM1105531 2 0.584 0.8270 0.140 0.860
#> GSM1105543 2 0.000 0.9803 0.000 1.000
#> GSM1105546 1 0.000 0.9667 1.000 0.000
#> GSM1105547 1 0.000 0.9667 1.000 0.000
#> GSM1105455 2 0.000 0.9803 0.000 1.000
#> GSM1105458 2 0.000 0.9803 0.000 1.000
#> GSM1105459 2 0.000 0.9803 0.000 1.000
#> GSM1105462 2 0.000 0.9803 0.000 1.000
#> GSM1105441 2 0.000 0.9803 0.000 1.000
#> GSM1105465 2 0.000 0.9803 0.000 1.000
#> GSM1105484 2 0.000 0.9803 0.000 1.000
#> GSM1105485 2 0.745 0.7238 0.212 0.788
#> GSM1105496 2 0.000 0.9803 0.000 1.000
#> GSM1105505 2 1.000 -0.0116 0.492 0.508
#> GSM1105509 1 0.000 0.9667 1.000 0.000
#> GSM1105448 2 0.000 0.9803 0.000 1.000
#> GSM1105521 1 0.000 0.9667 1.000 0.000
#> GSM1105528 2 0.000 0.9803 0.000 1.000
#> GSM1105529 2 0.000 0.9803 0.000 1.000
#> GSM1105533 1 0.000 0.9667 1.000 0.000
#> GSM1105545 2 0.000 0.9803 0.000 1.000
#> GSM1105548 1 0.000 0.9667 1.000 0.000
#> GSM1105549 1 0.000 0.9667 1.000 0.000
#> GSM1105457 2 0.000 0.9803 0.000 1.000
#> GSM1105460 2 0.000 0.9803 0.000 1.000
#> GSM1105461 2 0.000 0.9803 0.000 1.000
#> GSM1105464 1 0.000 0.9667 1.000 0.000
#> GSM1105466 2 0.000 0.9803 0.000 1.000
#> GSM1105479 2 0.000 0.9803 0.000 1.000
#> GSM1105502 1 0.000 0.9667 1.000 0.000
#> GSM1105515 1 0.000 0.9667 1.000 0.000
#> GSM1105523 2 0.000 0.9803 0.000 1.000
#> GSM1105550 2 0.469 0.8762 0.100 0.900
#> GSM1105450 2 0.000 0.9803 0.000 1.000
#> GSM1105451 2 0.000 0.9803 0.000 1.000
#> GSM1105454 2 0.000 0.9803 0.000 1.000
#> GSM1105468 2 0.000 0.9803 0.000 1.000
#> GSM1105481 2 0.000 0.9803 0.000 1.000
#> GSM1105504 2 0.839 0.6270 0.268 0.732
#> GSM1105517 1 0.788 0.7134 0.764 0.236
#> GSM1105525 1 0.615 0.8304 0.848 0.152
#> GSM1105552 1 0.662 0.8061 0.828 0.172
#> GSM1105452 2 0.000 0.9803 0.000 1.000
#> GSM1105453 2 0.000 0.9803 0.000 1.000
#> GSM1105456 2 0.000 0.9803 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1105438 2 0.0892 0.799 0.000 0.980 0.020
#> GSM1105486 2 0.5733 0.744 0.000 0.676 0.324
#> GSM1105487 1 0.0000 0.947 1.000 0.000 0.000
#> GSM1105490 3 0.2711 0.867 0.000 0.088 0.912
#> GSM1105491 2 0.0000 0.794 0.000 1.000 0.000
#> GSM1105495 2 0.0237 0.795 0.000 0.996 0.004
#> GSM1105498 3 0.0000 0.918 0.000 0.000 1.000
#> GSM1105499 1 0.0000 0.947 1.000 0.000 0.000
#> GSM1105506 3 0.0000 0.918 0.000 0.000 1.000
#> GSM1105442 2 0.0000 0.794 0.000 1.000 0.000
#> GSM1105511 3 0.0424 0.919 0.000 0.008 0.992
#> GSM1105514 2 0.2356 0.808 0.000 0.928 0.072
#> GSM1105518 3 0.1289 0.912 0.000 0.032 0.968
#> GSM1105522 1 0.0424 0.943 0.992 0.000 0.008
#> GSM1105534 1 0.0000 0.947 1.000 0.000 0.000
#> GSM1105535 1 0.0000 0.947 1.000 0.000 0.000
#> GSM1105538 1 0.0000 0.947 1.000 0.000 0.000
#> GSM1105542 2 0.0000 0.794 0.000 1.000 0.000
#> GSM1105443 3 0.1163 0.914 0.000 0.028 0.972
#> GSM1105551 1 0.0000 0.947 1.000 0.000 0.000
#> GSM1105554 1 0.0000 0.947 1.000 0.000 0.000
#> GSM1105555 1 0.0000 0.947 1.000 0.000 0.000
#> GSM1105447 2 0.6026 0.664 0.000 0.624 0.376
#> GSM1105467 2 0.5678 0.754 0.000 0.684 0.316
#> GSM1105470 2 0.5905 0.704 0.000 0.648 0.352
#> GSM1105471 3 0.1529 0.908 0.000 0.040 0.960
#> GSM1105474 2 0.5397 0.789 0.000 0.720 0.280
#> GSM1105475 3 0.6244 -0.150 0.000 0.440 0.560
#> GSM1105440 1 0.0000 0.947 1.000 0.000 0.000
#> GSM1105488 2 0.0000 0.794 0.000 1.000 0.000
#> GSM1105489 1 0.0000 0.947 1.000 0.000 0.000
#> GSM1105492 1 0.0000 0.947 1.000 0.000 0.000
#> GSM1105493 1 0.0747 0.939 0.984 0.016 0.000
#> GSM1105497 2 0.0000 0.794 0.000 1.000 0.000
#> GSM1105500 2 0.4397 0.804 0.028 0.856 0.116
#> GSM1105501 3 0.4605 0.676 0.000 0.204 0.796
#> GSM1105508 1 0.6280 0.234 0.540 0.000 0.460
#> GSM1105444 2 0.4346 0.814 0.000 0.816 0.184
#> GSM1105513 3 0.2066 0.894 0.000 0.060 0.940
#> GSM1105516 1 0.4172 0.797 0.840 0.156 0.004
#> GSM1105520 3 0.0424 0.919 0.000 0.008 0.992
#> GSM1105524 1 0.0000 0.947 1.000 0.000 0.000
#> GSM1105536 2 0.5254 0.798 0.000 0.736 0.264
#> GSM1105537 1 0.0000 0.947 1.000 0.000 0.000
#> GSM1105540 3 0.5461 0.634 0.244 0.008 0.748
#> GSM1105544 2 0.9125 0.559 0.192 0.540 0.268
#> GSM1105445 3 0.0000 0.918 0.000 0.000 1.000
#> GSM1105553 2 0.4842 0.810 0.000 0.776 0.224
#> GSM1105556 1 0.0000 0.947 1.000 0.000 0.000
#> GSM1105557 3 0.0237 0.919 0.000 0.004 0.996
#> GSM1105449 2 0.5465 0.783 0.000 0.712 0.288
#> GSM1105469 3 0.0237 0.916 0.004 0.000 0.996
#> GSM1105472 2 0.5098 0.804 0.000 0.752 0.248
#> GSM1105473 1 0.2796 0.886 0.908 0.092 0.000
#> GSM1105476 2 0.5397 0.789 0.000 0.720 0.280
#> GSM1105477 2 0.4750 0.812 0.000 0.784 0.216
#> GSM1105478 3 0.0000 0.918 0.000 0.000 1.000
#> GSM1105510 2 0.0000 0.794 0.000 1.000 0.000
#> GSM1105530 1 0.1163 0.931 0.972 0.000 0.028
#> GSM1105539 1 0.0000 0.947 1.000 0.000 0.000
#> GSM1105480 3 0.0237 0.919 0.000 0.004 0.996
#> GSM1105512 1 0.0000 0.947 1.000 0.000 0.000
#> GSM1105532 1 0.4931 0.714 0.768 0.000 0.232
#> GSM1105541 1 0.0000 0.947 1.000 0.000 0.000
#> GSM1105439 3 0.3551 0.807 0.000 0.132 0.868
#> GSM1105463 1 0.0747 0.937 0.984 0.000 0.016
#> GSM1105482 1 0.0000 0.947 1.000 0.000 0.000
#> GSM1105483 3 0.0000 0.918 0.000 0.000 1.000
#> GSM1105494 3 0.2165 0.891 0.000 0.064 0.936
#> GSM1105503 3 0.0000 0.918 0.000 0.000 1.000
#> GSM1105507 1 0.0000 0.947 1.000 0.000 0.000
#> GSM1105446 2 0.0000 0.794 0.000 1.000 0.000
#> GSM1105519 1 0.0000 0.947 1.000 0.000 0.000
#> GSM1105526 2 0.5216 0.800 0.000 0.740 0.260
#> GSM1105527 3 0.0000 0.918 0.000 0.000 1.000
#> GSM1105531 1 0.8505 0.414 0.600 0.144 0.256
#> GSM1105543 2 0.0424 0.796 0.000 0.992 0.008
#> GSM1105546 1 0.0000 0.947 1.000 0.000 0.000
#> GSM1105547 1 0.0237 0.945 0.996 0.004 0.000
#> GSM1105455 3 0.2625 0.872 0.000 0.084 0.916
#> GSM1105458 2 0.5706 0.749 0.000 0.680 0.320
#> GSM1105459 2 0.5178 0.802 0.000 0.744 0.256
#> GSM1105462 3 0.0892 0.917 0.000 0.020 0.980
#> GSM1105441 2 0.5560 0.771 0.000 0.700 0.300
#> GSM1105465 2 0.0000 0.794 0.000 1.000 0.000
#> GSM1105484 2 0.0000 0.794 0.000 1.000 0.000
#> GSM1105485 2 0.0000 0.794 0.000 1.000 0.000
#> GSM1105496 2 0.2774 0.807 0.008 0.920 0.072
#> GSM1105505 1 0.6283 0.696 0.760 0.176 0.064
#> GSM1105509 1 0.0000 0.947 1.000 0.000 0.000
#> GSM1105448 2 0.4605 0.813 0.000 0.796 0.204
#> GSM1105521 1 0.0000 0.947 1.000 0.000 0.000
#> GSM1105528 2 0.0000 0.794 0.000 1.000 0.000
#> GSM1105529 2 0.0000 0.794 0.000 1.000 0.000
#> GSM1105533 1 0.0000 0.947 1.000 0.000 0.000
#> GSM1105545 3 0.1163 0.914 0.000 0.028 0.972
#> GSM1105548 1 0.0592 0.941 0.988 0.012 0.000
#> GSM1105549 1 0.2878 0.882 0.904 0.096 0.000
#> GSM1105457 3 0.0000 0.918 0.000 0.000 1.000
#> GSM1105460 3 0.2625 0.872 0.000 0.084 0.916
#> GSM1105461 2 0.5291 0.796 0.000 0.732 0.268
#> GSM1105464 1 0.1289 0.929 0.968 0.000 0.032
#> GSM1105466 3 0.0000 0.918 0.000 0.000 1.000
#> GSM1105479 3 0.0892 0.917 0.000 0.020 0.980
#> GSM1105502 1 0.0000 0.947 1.000 0.000 0.000
#> GSM1105515 1 0.0000 0.947 1.000 0.000 0.000
#> GSM1105523 3 0.0237 0.916 0.004 0.000 0.996
#> GSM1105550 3 0.0424 0.913 0.008 0.000 0.992
#> GSM1105450 2 0.5465 0.783 0.000 0.712 0.288
#> GSM1105451 2 0.5431 0.786 0.000 0.716 0.284
#> GSM1105454 2 0.6062 0.648 0.000 0.616 0.384
#> GSM1105468 2 0.5465 0.783 0.000 0.712 0.288
#> GSM1105481 2 0.5905 0.706 0.000 0.648 0.352
#> GSM1105504 1 0.7412 0.611 0.700 0.124 0.176
#> GSM1105517 1 0.2625 0.880 0.916 0.000 0.084
#> GSM1105525 3 0.4842 0.622 0.224 0.000 0.776
#> GSM1105552 1 0.0424 0.943 0.992 0.008 0.000
#> GSM1105452 2 0.0000 0.794 0.000 1.000 0.000
#> GSM1105453 2 0.5098 0.804 0.000 0.752 0.248
#> GSM1105456 3 0.3038 0.848 0.000 0.104 0.896
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1105438 2 0.2197 0.6641 0.000 0.916 0.080 0.004
#> GSM1105486 2 0.7551 0.2825 0.000 0.448 0.196 0.356
#> GSM1105487 1 0.0672 0.8902 0.984 0.000 0.008 0.008
#> GSM1105490 4 0.1022 0.6520 0.000 0.000 0.032 0.968
#> GSM1105491 2 0.0188 0.6658 0.000 0.996 0.000 0.004
#> GSM1105495 2 0.1398 0.6704 0.000 0.956 0.040 0.004
#> GSM1105498 4 0.0000 0.6433 0.000 0.000 0.000 1.000
#> GSM1105499 1 0.1389 0.8763 0.952 0.000 0.048 0.000
#> GSM1105506 3 0.3610 0.5223 0.000 0.000 0.800 0.200
#> GSM1105442 2 0.0188 0.6658 0.000 0.996 0.000 0.004
#> GSM1105511 4 0.5408 0.1355 0.000 0.012 0.488 0.500
#> GSM1105514 2 0.4784 0.6383 0.000 0.788 0.100 0.112
#> GSM1105518 4 0.4122 0.6132 0.000 0.004 0.236 0.760
#> GSM1105522 1 0.1833 0.8795 0.944 0.000 0.032 0.024
#> GSM1105534 1 0.0000 0.8906 1.000 0.000 0.000 0.000
#> GSM1105535 1 0.0000 0.8906 1.000 0.000 0.000 0.000
#> GSM1105538 1 0.0592 0.8899 0.984 0.000 0.016 0.000
#> GSM1105542 2 0.1256 0.6523 0.000 0.964 0.008 0.028
#> GSM1105443 4 0.4957 0.5288 0.000 0.012 0.320 0.668
#> GSM1105551 1 0.3695 0.7892 0.828 0.000 0.016 0.156
#> GSM1105554 1 0.0188 0.8903 0.996 0.000 0.004 0.000
#> GSM1105555 1 0.2060 0.8691 0.932 0.000 0.016 0.052
#> GSM1105447 4 0.2500 0.6673 0.000 0.040 0.044 0.916
#> GSM1105467 2 0.7645 0.3667 0.000 0.468 0.264 0.268
#> GSM1105470 3 0.7923 -0.2940 0.000 0.324 0.340 0.336
#> GSM1105471 3 0.4418 0.4897 0.000 0.032 0.784 0.184
#> GSM1105474 2 0.7184 0.3586 0.000 0.492 0.144 0.364
#> GSM1105475 4 0.6869 0.4836 0.000 0.180 0.224 0.596
#> GSM1105440 1 0.2987 0.8346 0.880 0.000 0.016 0.104
#> GSM1105488 2 0.0672 0.6607 0.000 0.984 0.008 0.008
#> GSM1105489 1 0.0592 0.8899 0.984 0.000 0.016 0.000
#> GSM1105492 1 0.0592 0.8899 0.984 0.000 0.016 0.000
#> GSM1105493 1 0.0469 0.8902 0.988 0.000 0.012 0.000
#> GSM1105497 2 0.3108 0.6396 0.000 0.872 0.016 0.112
#> GSM1105500 4 0.3959 0.5756 0.076 0.052 0.016 0.856
#> GSM1105501 3 0.7184 -0.2298 0.000 0.136 0.448 0.416
#> GSM1105508 1 0.3323 0.8334 0.876 0.000 0.064 0.060
#> GSM1105444 2 0.6572 0.5339 0.000 0.608 0.120 0.272
#> GSM1105513 4 0.3401 0.6525 0.000 0.008 0.152 0.840
#> GSM1105516 1 0.2706 0.8457 0.900 0.080 0.020 0.000
#> GSM1105520 3 0.4872 0.1715 0.000 0.004 0.640 0.356
#> GSM1105524 1 0.0188 0.8903 0.996 0.000 0.004 0.000
#> GSM1105536 2 0.6773 0.5085 0.000 0.584 0.132 0.284
#> GSM1105537 1 0.0188 0.8903 0.996 0.000 0.004 0.000
#> GSM1105540 4 0.4018 0.3454 0.224 0.000 0.004 0.772
#> GSM1105544 4 0.2513 0.6115 0.036 0.024 0.016 0.924
#> GSM1105445 4 0.3528 0.6437 0.000 0.000 0.192 0.808
#> GSM1105553 4 0.1843 0.6298 0.008 0.028 0.016 0.948
#> GSM1105556 1 0.0000 0.8906 1.000 0.000 0.000 0.000
#> GSM1105557 4 0.2216 0.6609 0.000 0.000 0.092 0.908
#> GSM1105449 4 0.7184 -0.1589 0.000 0.416 0.136 0.448
#> GSM1105469 3 0.3074 0.5663 0.000 0.000 0.848 0.152
#> GSM1105472 2 0.6673 0.5386 0.000 0.608 0.140 0.252
#> GSM1105473 1 0.5793 0.5056 0.600 0.360 0.040 0.000
#> GSM1105476 2 0.7102 0.4612 0.000 0.540 0.156 0.304
#> GSM1105477 4 0.6343 0.0217 0.036 0.392 0.016 0.556
#> GSM1105478 4 0.4585 0.4685 0.000 0.000 0.332 0.668
#> GSM1105510 2 0.0376 0.6669 0.000 0.992 0.004 0.004
#> GSM1105530 1 0.4843 0.4667 0.604 0.000 0.396 0.000
#> GSM1105539 1 0.4164 0.6852 0.736 0.000 0.264 0.000
#> GSM1105480 4 0.1867 0.6632 0.000 0.000 0.072 0.928
#> GSM1105512 1 0.2760 0.8289 0.872 0.000 0.128 0.000
#> GSM1105532 3 0.5000 -0.2500 0.496 0.000 0.504 0.000
#> GSM1105541 1 0.4382 0.6437 0.704 0.000 0.296 0.000
#> GSM1105439 4 0.6482 0.4221 0.000 0.084 0.352 0.564
#> GSM1105463 1 0.1474 0.8755 0.948 0.000 0.052 0.000
#> GSM1105482 1 0.0592 0.8899 0.984 0.000 0.016 0.000
#> GSM1105483 3 0.1576 0.5971 0.004 0.000 0.948 0.048
#> GSM1105494 4 0.0336 0.6448 0.000 0.000 0.008 0.992
#> GSM1105503 4 0.1940 0.6524 0.000 0.000 0.076 0.924
#> GSM1105507 1 0.2021 0.8690 0.932 0.000 0.012 0.056
#> GSM1105446 2 0.5337 0.3905 0.000 0.564 0.012 0.424
#> GSM1105519 1 0.0000 0.8906 1.000 0.000 0.000 0.000
#> GSM1105526 2 0.4253 0.6156 0.000 0.776 0.208 0.016
#> GSM1105527 3 0.3311 0.5513 0.000 0.000 0.828 0.172
#> GSM1105531 3 0.5644 0.4686 0.220 0.068 0.708 0.004
#> GSM1105543 2 0.5174 0.4659 0.000 0.620 0.012 0.368
#> GSM1105546 1 0.0592 0.8899 0.984 0.000 0.016 0.000
#> GSM1105547 1 0.0779 0.8892 0.980 0.004 0.016 0.000
#> GSM1105455 4 0.4706 0.6219 0.000 0.028 0.224 0.748
#> GSM1105458 4 0.7448 -0.1571 0.000 0.400 0.172 0.428
#> GSM1105459 2 0.6814 0.5150 0.000 0.584 0.140 0.276
#> GSM1105462 3 0.1394 0.5983 0.016 0.008 0.964 0.012
#> GSM1105441 4 0.7221 -0.2092 0.000 0.428 0.140 0.432
#> GSM1105465 2 0.0657 0.6606 0.000 0.984 0.004 0.012
#> GSM1105484 2 0.1109 0.6703 0.000 0.968 0.028 0.004
#> GSM1105485 2 0.0000 0.6638 0.000 1.000 0.000 0.000
#> GSM1105496 4 0.2967 0.5983 0.052 0.028 0.016 0.904
#> GSM1105505 1 0.4941 0.6933 0.760 0.044 0.004 0.192
#> GSM1105509 1 0.1867 0.8655 0.928 0.000 0.072 0.000
#> GSM1105448 2 0.6711 0.4910 0.000 0.576 0.116 0.308
#> GSM1105521 1 0.3754 0.8266 0.852 0.064 0.084 0.000
#> GSM1105528 2 0.1118 0.6671 0.000 0.964 0.036 0.000
#> GSM1105529 2 0.0188 0.6658 0.000 0.996 0.000 0.004
#> GSM1105533 1 0.0000 0.8906 1.000 0.000 0.000 0.000
#> GSM1105545 3 0.1624 0.5952 0.000 0.028 0.952 0.020
#> GSM1105548 1 0.4155 0.8006 0.836 0.032 0.016 0.116
#> GSM1105549 1 0.4220 0.6937 0.748 0.248 0.004 0.000
#> GSM1105457 4 0.4356 0.5475 0.000 0.000 0.292 0.708
#> GSM1105460 3 0.3312 0.5689 0.000 0.052 0.876 0.072
#> GSM1105461 2 0.7072 0.4191 0.000 0.524 0.140 0.336
#> GSM1105464 3 0.4994 -0.2260 0.480 0.000 0.520 0.000
#> GSM1105466 3 0.4925 0.0278 0.000 0.000 0.572 0.428
#> GSM1105479 3 0.5409 -0.2550 0.000 0.012 0.496 0.492
#> GSM1105502 1 0.0000 0.8906 1.000 0.000 0.000 0.000
#> GSM1105515 1 0.0188 0.8907 0.996 0.000 0.004 0.000
#> GSM1105523 3 0.2255 0.5919 0.012 0.000 0.920 0.068
#> GSM1105550 3 0.1975 0.5910 0.048 0.000 0.936 0.016
#> GSM1105450 2 0.7412 0.2502 0.000 0.444 0.168 0.388
#> GSM1105451 4 0.7115 -0.1594 0.000 0.420 0.128 0.452
#> GSM1105454 4 0.4469 0.6517 0.000 0.080 0.112 0.808
#> GSM1105468 2 0.7344 0.2903 0.000 0.460 0.160 0.380
#> GSM1105481 3 0.4844 0.2665 0.000 0.300 0.688 0.012
#> GSM1105504 1 0.6052 0.6397 0.700 0.024 0.216 0.060
#> GSM1105517 1 0.5132 0.3498 0.548 0.004 0.448 0.000
#> GSM1105525 3 0.7322 0.3777 0.224 0.000 0.532 0.244
#> GSM1105552 1 0.1059 0.8872 0.972 0.012 0.016 0.000
#> GSM1105452 2 0.1109 0.6626 0.000 0.968 0.004 0.028
#> GSM1105453 2 0.6949 0.2846 0.000 0.480 0.112 0.408
#> GSM1105456 4 0.3978 0.6447 0.000 0.012 0.192 0.796
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM1105438 2 0.3768 0.7560 0.000 0.808 0.016 0.020 0.156
#> GSM1105486 2 0.4484 0.7441 0.000 0.764 0.176 0.032 0.028
#> GSM1105487 1 0.0324 0.8909 0.992 0.000 0.000 0.004 0.004
#> GSM1105490 2 0.4288 0.5759 0.000 0.664 0.012 0.324 0.000
#> GSM1105491 5 0.2806 0.8168 0.000 0.152 0.000 0.004 0.844
#> GSM1105495 5 0.3427 0.7616 0.000 0.192 0.000 0.012 0.796
#> GSM1105498 4 0.2657 0.7201 0.000 0.024 0.024 0.900 0.052
#> GSM1105499 1 0.1121 0.8784 0.956 0.000 0.044 0.000 0.000
#> GSM1105506 3 0.3983 0.6148 0.000 0.164 0.784 0.052 0.000
#> GSM1105442 5 0.2389 0.8395 0.000 0.116 0.000 0.004 0.880
#> GSM1105511 3 0.5142 0.5369 0.000 0.088 0.668 0.244 0.000
#> GSM1105514 2 0.3201 0.7771 0.000 0.844 0.008 0.016 0.132
#> GSM1105518 2 0.3622 0.7744 0.000 0.820 0.056 0.124 0.000
#> GSM1105522 1 0.6179 0.3691 0.576 0.000 0.132 0.280 0.012
#> GSM1105534 1 0.0162 0.8916 0.996 0.000 0.000 0.000 0.004
#> GSM1105535 1 0.0000 0.8915 1.000 0.000 0.000 0.000 0.000
#> GSM1105538 1 0.0609 0.8905 0.980 0.000 0.000 0.000 0.020
#> GSM1105542 5 0.1818 0.8557 0.000 0.044 0.000 0.024 0.932
#> GSM1105443 2 0.2278 0.8088 0.000 0.908 0.032 0.060 0.000
#> GSM1105551 1 0.4387 0.4528 0.640 0.000 0.000 0.348 0.012
#> GSM1105554 1 0.0324 0.8922 0.992 0.000 0.004 0.000 0.004
#> GSM1105555 1 0.0671 0.8888 0.980 0.000 0.000 0.016 0.004
#> GSM1105447 2 0.2513 0.7975 0.000 0.876 0.008 0.116 0.000
#> GSM1105467 2 0.6460 0.5029 0.000 0.576 0.288 0.056 0.080
#> GSM1105470 2 0.3770 0.7838 0.000 0.824 0.124 0.032 0.020
#> GSM1105471 3 0.5070 0.4741 0.000 0.272 0.668 0.052 0.008
#> GSM1105474 2 0.4121 0.7799 0.000 0.812 0.052 0.028 0.108
#> GSM1105475 2 0.4645 0.7308 0.000 0.736 0.056 0.200 0.008
#> GSM1105440 1 0.0955 0.8832 0.968 0.000 0.000 0.028 0.004
#> GSM1105488 5 0.2260 0.8580 0.000 0.064 0.000 0.028 0.908
#> GSM1105489 1 0.0451 0.8898 0.988 0.000 0.000 0.008 0.004
#> GSM1105492 1 0.0671 0.8906 0.980 0.000 0.000 0.004 0.016
#> GSM1105493 1 0.0566 0.8918 0.984 0.000 0.000 0.004 0.012
#> GSM1105497 5 0.3578 0.7874 0.000 0.048 0.000 0.132 0.820
#> GSM1105500 4 0.3466 0.7194 0.028 0.040 0.000 0.856 0.076
#> GSM1105501 2 0.4297 0.7132 0.000 0.728 0.236 0.036 0.000
#> GSM1105508 1 0.5341 0.4963 0.620 0.000 0.300 0.080 0.000
#> GSM1105444 2 0.2162 0.8069 0.000 0.916 0.008 0.012 0.064
#> GSM1105513 2 0.4908 0.5375 0.000 0.608 0.036 0.356 0.000
#> GSM1105516 1 0.3496 0.7334 0.788 0.012 0.000 0.000 0.200
#> GSM1105520 3 0.3099 0.6464 0.000 0.124 0.848 0.028 0.000
#> GSM1105524 1 0.0162 0.8918 0.996 0.000 0.004 0.000 0.000
#> GSM1105536 2 0.5007 0.6643 0.000 0.704 0.036 0.028 0.232
#> GSM1105537 1 0.0000 0.8915 1.000 0.000 0.000 0.000 0.000
#> GSM1105540 4 0.3572 0.7102 0.024 0.016 0.028 0.860 0.072
#> GSM1105544 4 0.3274 0.7229 0.048 0.020 0.004 0.872 0.056
#> GSM1105445 2 0.3366 0.7802 0.000 0.828 0.032 0.140 0.000
#> GSM1105553 4 0.2844 0.7184 0.012 0.088 0.000 0.880 0.020
#> GSM1105556 1 0.0162 0.8916 0.996 0.000 0.000 0.000 0.004
#> GSM1105557 2 0.4524 0.5617 0.000 0.644 0.020 0.336 0.000
#> GSM1105449 2 0.0771 0.8188 0.000 0.976 0.000 0.004 0.020
#> GSM1105469 3 0.4162 0.6069 0.000 0.056 0.768 0.176 0.000
#> GSM1105472 2 0.3135 0.8009 0.000 0.868 0.024 0.020 0.088
#> GSM1105473 5 0.2388 0.7798 0.072 0.000 0.028 0.000 0.900
#> GSM1105476 2 0.6500 0.1225 0.000 0.452 0.076 0.040 0.432
#> GSM1105477 4 0.6467 0.1983 0.036 0.376 0.000 0.504 0.084
#> GSM1105478 4 0.5777 -0.1641 0.000 0.088 0.444 0.468 0.000
#> GSM1105510 5 0.2701 0.8527 0.000 0.092 0.012 0.012 0.884
#> GSM1105530 3 0.3949 0.4611 0.300 0.000 0.696 0.004 0.000
#> GSM1105539 1 0.2445 0.8407 0.884 0.004 0.108 0.004 0.000
#> GSM1105480 4 0.2592 0.6887 0.000 0.056 0.052 0.892 0.000
#> GSM1105512 1 0.1892 0.8590 0.916 0.000 0.080 0.004 0.000
#> GSM1105532 3 0.3635 0.5259 0.248 0.000 0.748 0.004 0.000
#> GSM1105541 1 0.3400 0.7808 0.808 0.004 0.180 0.004 0.004
#> GSM1105439 2 0.1668 0.8161 0.000 0.940 0.032 0.028 0.000
#> GSM1105463 1 0.5584 0.4984 0.592 0.000 0.312 0.000 0.096
#> GSM1105482 1 0.0609 0.8905 0.980 0.000 0.000 0.000 0.020
#> GSM1105483 3 0.2592 0.6612 0.000 0.056 0.892 0.052 0.000
#> GSM1105494 4 0.2217 0.7134 0.000 0.044 0.024 0.920 0.012
#> GSM1105503 4 0.3410 0.6534 0.000 0.068 0.092 0.840 0.000
#> GSM1105507 4 0.4734 0.4316 0.344 0.000 0.008 0.632 0.016
#> GSM1105446 2 0.2376 0.8001 0.000 0.904 0.000 0.044 0.052
#> GSM1105519 1 0.0451 0.8919 0.988 0.000 0.000 0.004 0.008
#> GSM1105526 5 0.5799 0.4121 0.000 0.056 0.300 0.032 0.612
#> GSM1105527 3 0.4035 0.6215 0.000 0.060 0.784 0.156 0.000
#> GSM1105531 3 0.3783 0.5881 0.012 0.020 0.812 0.004 0.152
#> GSM1105543 2 0.5199 0.4886 0.000 0.636 0.000 0.072 0.292
#> GSM1105546 1 0.0162 0.8912 0.996 0.000 0.000 0.004 0.000
#> GSM1105547 1 0.0510 0.8907 0.984 0.000 0.000 0.000 0.016
#> GSM1105455 2 0.2390 0.8027 0.000 0.896 0.020 0.084 0.000
#> GSM1105458 2 0.1095 0.8205 0.000 0.968 0.012 0.008 0.012
#> GSM1105459 2 0.2151 0.8155 0.000 0.924 0.020 0.016 0.040
#> GSM1105462 3 0.3108 0.6611 0.004 0.048 0.880 0.052 0.016
#> GSM1105441 2 0.0510 0.8187 0.000 0.984 0.000 0.000 0.016
#> GSM1105465 5 0.1493 0.8510 0.000 0.024 0.000 0.028 0.948
#> GSM1105484 5 0.3333 0.7952 0.000 0.164 0.008 0.008 0.820
#> GSM1105485 5 0.1278 0.8503 0.000 0.020 0.016 0.004 0.960
#> GSM1105496 4 0.3134 0.7139 0.012 0.096 0.000 0.864 0.028
#> GSM1105505 4 0.5988 0.2019 0.400 0.020 0.012 0.528 0.040
#> GSM1105509 1 0.3010 0.7898 0.824 0.000 0.172 0.004 0.000
#> GSM1105448 2 0.1484 0.8118 0.000 0.944 0.000 0.008 0.048
#> GSM1105521 1 0.1872 0.8743 0.928 0.000 0.052 0.000 0.020
#> GSM1105528 5 0.2270 0.8537 0.000 0.072 0.016 0.004 0.908
#> GSM1105529 5 0.1568 0.8457 0.000 0.020 0.000 0.036 0.944
#> GSM1105533 1 0.0000 0.8915 1.000 0.000 0.000 0.000 0.000
#> GSM1105545 3 0.3558 0.6379 0.000 0.136 0.824 0.036 0.004
#> GSM1105548 1 0.4394 0.6372 0.732 0.000 0.000 0.220 0.048
#> GSM1105549 1 0.3231 0.7513 0.800 0.000 0.000 0.004 0.196
#> GSM1105457 2 0.4262 0.7535 0.000 0.776 0.100 0.124 0.000
#> GSM1105460 2 0.3880 0.7488 0.000 0.772 0.204 0.020 0.004
#> GSM1105461 2 0.1124 0.8152 0.000 0.960 0.000 0.004 0.036
#> GSM1105464 3 0.4089 0.5132 0.236 0.004 0.744 0.004 0.012
#> GSM1105466 2 0.5876 0.3071 0.000 0.512 0.384 0.104 0.000
#> GSM1105479 2 0.4803 0.6932 0.000 0.712 0.220 0.064 0.004
#> GSM1105502 1 0.0912 0.8901 0.972 0.000 0.012 0.000 0.016
#> GSM1105515 1 0.0510 0.8907 0.984 0.000 0.000 0.000 0.016
#> GSM1105523 3 0.2193 0.6561 0.000 0.028 0.912 0.060 0.000
#> GSM1105550 3 0.7146 0.0459 0.208 0.364 0.404 0.024 0.000
#> GSM1105450 2 0.2026 0.8178 0.000 0.928 0.044 0.012 0.016
#> GSM1105451 2 0.0771 0.8188 0.000 0.976 0.000 0.004 0.020
#> GSM1105454 2 0.2304 0.8040 0.000 0.892 0.008 0.100 0.000
#> GSM1105468 2 0.2269 0.8173 0.000 0.920 0.028 0.020 0.032
#> GSM1105481 3 0.5637 -0.0619 0.000 0.444 0.500 0.028 0.028
#> GSM1105504 1 0.5313 0.6324 0.668 0.020 0.272 0.012 0.028
#> GSM1105517 3 0.4432 0.4490 0.312 0.004 0.672 0.008 0.004
#> GSM1105525 3 0.5123 0.4922 0.032 0.024 0.668 0.276 0.000
#> GSM1105552 5 0.5987 0.3741 0.272 0.000 0.000 0.156 0.572
#> GSM1105452 5 0.2074 0.8518 0.000 0.036 0.000 0.044 0.920
#> GSM1105453 2 0.1205 0.8141 0.000 0.956 0.000 0.004 0.040
#> GSM1105456 2 0.1740 0.8138 0.000 0.932 0.012 0.056 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1105438 2 0.2863 0.78410 0.000 0.864 0.012 0.000 0.088 0.036
#> GSM1105486 2 0.3447 0.78869 0.000 0.836 0.088 0.052 0.004 0.020
#> GSM1105487 1 0.0260 0.88453 0.992 0.000 0.000 0.000 0.000 0.008
#> GSM1105490 2 0.5284 0.50522 0.000 0.604 0.004 0.256 0.000 0.136
#> GSM1105491 5 0.3118 0.74805 0.000 0.072 0.000 0.000 0.836 0.092
#> GSM1105495 5 0.5080 0.52510 0.000 0.256 0.040 0.000 0.652 0.052
#> GSM1105498 4 0.3340 0.00599 0.000 0.000 0.004 0.784 0.016 0.196
#> GSM1105499 1 0.0508 0.88269 0.984 0.000 0.012 0.000 0.000 0.004
#> GSM1105506 3 0.6514 0.09104 0.000 0.316 0.436 0.216 0.000 0.032
#> GSM1105442 5 0.2070 0.77235 0.000 0.044 0.000 0.000 0.908 0.048
#> GSM1105511 4 0.6345 0.33616 0.000 0.140 0.240 0.548 0.000 0.072
#> GSM1105514 2 0.2345 0.80273 0.000 0.900 0.004 0.004 0.056 0.036
#> GSM1105518 2 0.5861 0.55861 0.000 0.608 0.052 0.132 0.000 0.208
#> GSM1105522 4 0.5611 0.25547 0.272 0.000 0.124 0.584 0.000 0.020
#> GSM1105534 1 0.0260 0.88441 0.992 0.000 0.000 0.000 0.000 0.008
#> GSM1105535 1 0.0000 0.88427 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1105538 1 0.0547 0.88371 0.980 0.000 0.000 0.000 0.000 0.020
#> GSM1105542 5 0.2575 0.76230 0.000 0.004 0.000 0.024 0.872 0.100
#> GSM1105443 2 0.1426 0.81721 0.000 0.948 0.016 0.028 0.000 0.008
#> GSM1105551 1 0.3767 0.70515 0.780 0.000 0.000 0.132 0.000 0.088
#> GSM1105554 1 0.0000 0.88427 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1105555 1 0.0865 0.87772 0.964 0.000 0.000 0.000 0.000 0.036
#> GSM1105447 2 0.2798 0.77843 0.000 0.852 0.000 0.036 0.000 0.112
#> GSM1105467 2 0.4770 0.74160 0.000 0.756 0.108 0.076 0.040 0.020
#> GSM1105470 2 0.2262 0.80626 0.000 0.896 0.080 0.016 0.000 0.008
#> GSM1105471 2 0.5692 0.49107 0.000 0.604 0.264 0.096 0.008 0.028
#> GSM1105474 2 0.3440 0.80361 0.000 0.852 0.048 0.048 0.032 0.020
#> GSM1105475 2 0.2588 0.80617 0.000 0.876 0.024 0.092 0.000 0.008
#> GSM1105440 1 0.0972 0.87816 0.964 0.000 0.000 0.008 0.000 0.028
#> GSM1105488 5 0.1429 0.76934 0.000 0.004 0.000 0.004 0.940 0.052
#> GSM1105489 1 0.0713 0.88187 0.972 0.000 0.000 0.000 0.000 0.028
#> GSM1105492 1 0.0405 0.88515 0.988 0.000 0.000 0.004 0.000 0.008
#> GSM1105493 1 0.1010 0.87797 0.960 0.000 0.000 0.000 0.004 0.036
#> GSM1105497 5 0.3978 0.64598 0.000 0.000 0.000 0.084 0.756 0.160
#> GSM1105500 4 0.4591 -0.53702 0.000 0.000 0.000 0.500 0.036 0.464
#> GSM1105501 2 0.6297 0.34197 0.000 0.524 0.272 0.156 0.000 0.048
#> GSM1105508 4 0.7226 0.06774 0.300 0.000 0.304 0.312 0.000 0.084
#> GSM1105444 2 0.1053 0.81244 0.000 0.964 0.004 0.000 0.012 0.020
#> GSM1105513 2 0.4559 0.57086 0.000 0.628 0.004 0.324 0.000 0.044
#> GSM1105516 1 0.6291 0.23452 0.496 0.000 0.052 0.008 0.352 0.092
#> GSM1105520 3 0.4606 0.26728 0.000 0.092 0.692 0.212 0.004 0.000
#> GSM1105524 1 0.0000 0.88427 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1105536 2 0.7252 0.26022 0.000 0.492 0.124 0.024 0.232 0.128
#> GSM1105537 1 0.0000 0.88427 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1105540 4 0.3898 0.06714 0.000 0.000 0.012 0.780 0.060 0.148
#> GSM1105544 6 0.5411 0.43049 0.000 0.000 0.008 0.432 0.088 0.472
#> GSM1105445 2 0.2459 0.81138 0.000 0.896 0.020 0.052 0.000 0.032
#> GSM1105553 6 0.4734 0.67557 0.000 0.028 0.000 0.372 0.016 0.584
#> GSM1105556 1 0.0000 0.88427 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1105557 2 0.5137 0.51772 0.000 0.604 0.004 0.288 0.000 0.104
#> GSM1105449 2 0.0458 0.81417 0.000 0.984 0.000 0.000 0.000 0.016
#> GSM1105469 4 0.4812 0.30840 0.000 0.028 0.372 0.580 0.000 0.020
#> GSM1105472 2 0.2077 0.81192 0.000 0.916 0.056 0.008 0.012 0.008
#> GSM1105473 5 0.3740 0.73276 0.024 0.000 0.060 0.016 0.828 0.072
#> GSM1105476 2 0.4381 0.76163 0.000 0.788 0.052 0.048 0.092 0.020
#> GSM1105477 2 0.7692 0.00751 0.012 0.432 0.020 0.144 0.116 0.276
#> GSM1105478 4 0.5501 0.22281 0.000 0.236 0.200 0.564 0.000 0.000
#> GSM1105510 5 0.2516 0.75385 0.000 0.024 0.004 0.004 0.884 0.084
#> GSM1105530 3 0.2738 0.45517 0.176 0.000 0.820 0.000 0.000 0.004
#> GSM1105539 1 0.4428 0.25385 0.580 0.000 0.388 0.000 0.000 0.032
#> GSM1105480 4 0.2714 0.22645 0.000 0.028 0.016 0.888 0.012 0.056
#> GSM1105512 1 0.1644 0.84217 0.920 0.000 0.076 0.000 0.000 0.004
#> GSM1105532 3 0.3275 0.45165 0.144 0.000 0.816 0.036 0.000 0.004
#> GSM1105541 3 0.4555 0.17710 0.420 0.000 0.548 0.004 0.000 0.028
#> GSM1105439 2 0.1370 0.81652 0.000 0.948 0.012 0.036 0.000 0.004
#> GSM1105463 3 0.6644 0.37863 0.188 0.000 0.580 0.024 0.104 0.104
#> GSM1105482 1 0.0632 0.88263 0.976 0.000 0.000 0.000 0.000 0.024
#> GSM1105483 3 0.5445 0.16885 0.000 0.168 0.564 0.268 0.000 0.000
#> GSM1105494 4 0.2803 0.11962 0.000 0.012 0.000 0.856 0.016 0.116
#> GSM1105503 4 0.4477 -0.26751 0.000 0.004 0.028 0.588 0.000 0.380
#> GSM1105507 4 0.5828 -0.21919 0.120 0.000 0.016 0.560 0.008 0.296
#> GSM1105446 2 0.5416 0.12217 0.000 0.496 0.000 0.024 0.060 0.420
#> GSM1105519 1 0.1036 0.87562 0.964 0.000 0.004 0.000 0.024 0.008
#> GSM1105526 5 0.6788 0.25620 0.000 0.012 0.204 0.244 0.492 0.048
#> GSM1105527 4 0.5115 0.28193 0.000 0.048 0.372 0.560 0.000 0.020
#> GSM1105531 3 0.5342 0.38972 0.008 0.020 0.708 0.032 0.156 0.076
#> GSM1105543 2 0.6382 0.31908 0.000 0.532 0.000 0.060 0.248 0.160
#> GSM1105546 1 0.0632 0.88271 0.976 0.000 0.000 0.000 0.000 0.024
#> GSM1105547 1 0.0865 0.87970 0.964 0.000 0.000 0.000 0.000 0.036
#> GSM1105455 2 0.1692 0.81501 0.000 0.932 0.012 0.048 0.000 0.008
#> GSM1105458 2 0.0405 0.81524 0.000 0.988 0.004 0.000 0.000 0.008
#> GSM1105459 2 0.0665 0.81764 0.000 0.980 0.008 0.000 0.008 0.004
#> GSM1105462 3 0.5558 0.30975 0.000 0.072 0.664 0.204 0.032 0.028
#> GSM1105441 2 0.0547 0.81364 0.000 0.980 0.000 0.000 0.000 0.020
#> GSM1105465 5 0.2908 0.75367 0.000 0.000 0.000 0.048 0.848 0.104
#> GSM1105484 5 0.4805 0.55784 0.000 0.244 0.024 0.008 0.684 0.040
#> GSM1105485 5 0.0862 0.77423 0.000 0.000 0.004 0.008 0.972 0.016
#> GSM1105496 6 0.4721 0.67324 0.000 0.024 0.000 0.364 0.020 0.592
#> GSM1105505 6 0.6131 0.57114 0.036 0.000 0.040 0.260 0.072 0.592
#> GSM1105509 1 0.6125 -0.03771 0.468 0.000 0.416 0.040 0.036 0.040
#> GSM1105448 2 0.0891 0.81167 0.000 0.968 0.000 0.000 0.008 0.024
#> GSM1105521 1 0.2038 0.85262 0.920 0.000 0.028 0.000 0.032 0.020
#> GSM1105528 5 0.2051 0.77069 0.000 0.040 0.008 0.000 0.916 0.036
#> GSM1105529 5 0.2826 0.75575 0.000 0.000 0.000 0.092 0.856 0.052
#> GSM1105533 1 0.0632 0.88314 0.976 0.000 0.000 0.000 0.000 0.024
#> GSM1105545 3 0.5837 0.32807 0.000 0.300 0.584 0.056 0.024 0.036
#> GSM1105548 1 0.5785 0.17295 0.508 0.000 0.000 0.076 0.040 0.376
#> GSM1105549 1 0.4059 0.61723 0.720 0.000 0.000 0.000 0.228 0.052
#> GSM1105457 2 0.3101 0.79486 0.000 0.852 0.032 0.092 0.000 0.024
#> GSM1105460 2 0.2350 0.80446 0.000 0.880 0.100 0.000 0.000 0.020
#> GSM1105461 2 0.0363 0.81420 0.000 0.988 0.000 0.000 0.000 0.012
#> GSM1105464 3 0.3350 0.46602 0.120 0.000 0.828 0.004 0.008 0.040
#> GSM1105466 2 0.4700 0.69852 0.000 0.720 0.164 0.092 0.000 0.024
#> GSM1105479 2 0.2595 0.80154 0.000 0.872 0.084 0.044 0.000 0.000
#> GSM1105502 1 0.1148 0.87505 0.960 0.000 0.004 0.016 0.000 0.020
#> GSM1105515 1 0.0363 0.88402 0.988 0.000 0.000 0.000 0.000 0.012
#> GSM1105523 3 0.4160 0.21334 0.000 0.024 0.684 0.284 0.000 0.008
#> GSM1105550 3 0.6253 0.37144 0.128 0.236 0.576 0.012 0.000 0.048
#> GSM1105450 2 0.1320 0.81594 0.000 0.948 0.036 0.016 0.000 0.000
#> GSM1105451 2 0.0777 0.81382 0.000 0.972 0.000 0.004 0.000 0.024
#> GSM1105454 2 0.2361 0.80333 0.000 0.884 0.000 0.028 0.000 0.088
#> GSM1105468 2 0.1536 0.81543 0.000 0.940 0.040 0.016 0.000 0.004
#> GSM1105481 3 0.5217 -0.00141 0.000 0.440 0.496 0.004 0.044 0.016
#> GSM1105504 3 0.6589 0.37796 0.188 0.000 0.596 0.060 0.052 0.104
#> GSM1105517 3 0.4692 0.41324 0.252 0.000 0.684 0.016 0.008 0.040
#> GSM1105525 4 0.4953 0.21634 0.012 0.016 0.436 0.520 0.000 0.016
#> GSM1105552 5 0.7326 0.20171 0.172 0.000 0.020 0.084 0.428 0.296
#> GSM1105452 5 0.3274 0.74222 0.000 0.000 0.000 0.080 0.824 0.096
#> GSM1105453 2 0.0935 0.81324 0.000 0.964 0.000 0.004 0.000 0.032
#> GSM1105456 2 0.2611 0.79550 0.000 0.876 0.016 0.016 0.000 0.092
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 agent(p) other(p) time(p) individual(p) k
#> ATC:NMF 119 0.575 0.800 0.651 0.0584 2
#> ATC:NMF 117 0.868 0.715 0.427 0.0213 3
#> ATC:NMF 86 0.192 0.815 0.726 0.0192 4
#> ATC:NMF 101 0.781 0.966 0.563 0.0018 5
#> ATC:NMF 77 0.665 0.574 0.157 0.0201 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