Date: 2019-12-25 21:47:21 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 48983 rows and 91 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] 48983 91
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 | ||
|---|---|---|---|---|---|---|
| ATC:pam | 3 | 0.939 | 0.932 | 0.974 | * | 2 |
| ATC:skmeans | 2 | 0.932 | 0.927 | 0.972 | * | |
| SD:kmeans | 2 | 0.899 | 0.894 | 0.942 | ||
| MAD:kmeans | 2 | 0.870 | 0.929 | 0.969 | ||
| CV:NMF | 6 | 0.869 | 0.871 | 0.921 | ||
| MAD:skmeans | 2 | 0.869 | 0.935 | 0.970 | ||
| SD:skmeans | 2 | 0.866 | 0.897 | 0.959 | ||
| MAD:mclust | 2 | 0.853 | 0.952 | 0.976 | ||
| MAD:NMF | 2 | 0.844 | 0.926 | 0.966 | ||
| SD:NMF | 2 | 0.803 | 0.878 | 0.948 | ||
| CV:skmeans | 2 | 0.787 | 0.880 | 0.952 | ||
| MAD:pam | 3 | 0.760 | 0.888 | 0.934 | ||
| SD:pam | 4 | 0.756 | 0.883 | 0.923 | ||
| CV:mclust | 5 | 0.727 | 0.812 | 0.866 | ||
| CV:pam | 4 | 0.723 | 0.837 | 0.910 | ||
| ATC:mclust | 2 | 0.721 | 0.944 | 0.956 | ||
| ATC:NMF | 2 | 0.720 | 0.878 | 0.943 | ||
| SD:mclust | 3 | 0.704 | 0.872 | 0.932 | ||
| CV:kmeans | 3 | 0.676 | 0.744 | 0.855 | ||
| ATC:kmeans | 3 | 0.559 | 0.769 | 0.868 | ||
| ATC:hclust | 4 | 0.518 | 0.776 | 0.864 | ||
| SD:hclust | 3 | 0.319 | 0.679 | 0.804 | ||
| MAD:hclust | 3 | 0.310 | 0.691 | 0.836 | ||
| CV:hclust | 4 | 0.282 | 0.632 | 0.749 |
**: 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 0.803 0.878 0.948 0.494 0.499 0.499
#> CV:NMF 2 0.670 0.852 0.937 0.500 0.502 0.502
#> MAD:NMF 2 0.844 0.926 0.966 0.495 0.505 0.505
#> ATC:NMF 2 0.720 0.878 0.943 0.494 0.499 0.499
#> SD:skmeans 2 0.866 0.897 0.959 0.491 0.512 0.512
#> CV:skmeans 2 0.787 0.880 0.952 0.491 0.508 0.508
#> MAD:skmeans 2 0.869 0.935 0.970 0.494 0.508 0.508
#> ATC:skmeans 2 0.932 0.927 0.972 0.496 0.505 0.505
#> SD:mclust 2 0.491 0.690 0.851 0.390 0.693 0.693
#> CV:mclust 2 0.507 0.817 0.891 0.364 0.693 0.693
#> MAD:mclust 2 0.853 0.952 0.976 0.456 0.546 0.546
#> ATC:mclust 2 0.721 0.944 0.956 0.401 0.561 0.561
#> SD:kmeans 2 0.899 0.894 0.942 0.466 0.516 0.516
#> CV:kmeans 2 0.426 0.842 0.855 0.428 0.546 0.546
#> MAD:kmeans 2 0.870 0.929 0.969 0.481 0.512 0.512
#> ATC:kmeans 2 0.651 0.816 0.859 0.364 0.666 0.666
#> SD:pam 2 0.689 0.816 0.907 0.369 0.587 0.587
#> CV:pam 2 0.669 0.873 0.917 0.360 0.597 0.597
#> MAD:pam 2 0.655 0.770 0.909 0.357 0.707 0.707
#> ATC:pam 2 0.977 0.945 0.978 0.271 0.752 0.752
#> SD:hclust 2 0.627 0.884 0.938 0.240 0.785 0.785
#> CV:hclust 2 0.447 0.788 0.889 0.261 0.820 0.820
#> MAD:hclust 2 0.432 0.832 0.907 0.248 0.785 0.785
#> ATC:hclust 2 0.548 0.845 0.919 0.308 0.752 0.752
get_stats(res_list, k = 3)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 3 0.637 0.783 0.902 0.239 0.792 0.621
#> CV:NMF 3 0.588 0.724 0.867 0.247 0.805 0.639
#> MAD:NMF 3 0.879 0.919 0.965 0.235 0.766 0.583
#> ATC:NMF 3 0.682 0.799 0.910 0.290 0.795 0.615
#> SD:skmeans 3 0.499 0.600 0.816 0.321 0.780 0.598
#> CV:skmeans 3 0.590 0.661 0.843 0.322 0.763 0.560
#> MAD:skmeans 3 0.564 0.511 0.778 0.326 0.762 0.565
#> ATC:skmeans 3 0.807 0.780 0.917 0.271 0.738 0.531
#> SD:mclust 3 0.704 0.872 0.932 0.570 0.670 0.532
#> CV:mclust 3 0.396 0.789 0.857 0.599 0.636 0.486
#> MAD:mclust 3 0.619 0.725 0.841 0.261 0.892 0.807
#> ATC:mclust 3 0.665 0.825 0.887 0.358 0.827 0.711
#> SD:kmeans 3 0.546 0.609 0.814 0.301 0.896 0.805
#> CV:kmeans 3 0.676 0.744 0.855 0.405 0.839 0.713
#> MAD:kmeans 3 0.611 0.682 0.788 0.291 0.882 0.775
#> ATC:kmeans 3 0.559 0.769 0.868 0.616 0.673 0.536
#> SD:pam 3 0.723 0.795 0.876 0.428 0.889 0.815
#> CV:pam 3 0.733 0.800 0.883 0.426 0.855 0.758
#> MAD:pam 3 0.760 0.888 0.934 0.493 0.754 0.658
#> ATC:pam 3 0.939 0.932 0.974 1.119 0.629 0.523
#> SD:hclust 3 0.319 0.679 0.804 1.267 0.717 0.640
#> CV:hclust 3 0.190 0.449 0.729 0.701 0.840 0.804
#> MAD:hclust 3 0.310 0.691 0.836 1.164 0.660 0.579
#> ATC:hclust 3 0.585 0.780 0.876 0.187 0.993 0.990
get_stats(res_list, k = 4)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 4 0.727 0.763 0.887 0.1452 0.753 0.478
#> CV:NMF 4 0.695 0.823 0.906 0.1432 0.746 0.460
#> MAD:NMF 4 0.887 0.869 0.943 0.2028 0.790 0.509
#> ATC:NMF 4 0.805 0.833 0.918 0.1317 0.807 0.536
#> SD:skmeans 4 0.747 0.791 0.893 0.1607 0.839 0.584
#> CV:skmeans 4 0.759 0.810 0.902 0.1552 0.814 0.517
#> MAD:skmeans 4 0.853 0.856 0.931 0.1451 0.779 0.458
#> ATC:skmeans 4 0.807 0.836 0.917 0.1146 0.898 0.728
#> SD:mclust 4 0.708 0.765 0.867 0.0714 0.932 0.836
#> CV:mclust 4 0.555 0.741 0.817 0.1124 0.904 0.764
#> MAD:mclust 4 0.541 0.462 0.746 0.2206 0.847 0.678
#> ATC:mclust 4 0.824 0.872 0.919 0.2548 0.743 0.507
#> SD:kmeans 4 0.618 0.611 0.809 0.1707 0.786 0.557
#> CV:kmeans 4 0.605 0.645 0.786 0.1701 0.879 0.718
#> MAD:kmeans 4 0.693 0.832 0.881 0.1729 0.754 0.471
#> ATC:kmeans 4 0.603 0.425 0.648 0.1724 0.801 0.547
#> SD:pam 4 0.756 0.883 0.923 0.2280 0.868 0.738
#> CV:pam 4 0.723 0.837 0.910 0.2279 0.847 0.698
#> MAD:pam 4 0.763 0.771 0.910 0.3169 0.815 0.621
#> ATC:pam 4 0.805 0.890 0.936 0.1425 0.907 0.788
#> SD:hclust 4 0.369 0.632 0.760 0.1820 0.852 0.711
#> CV:hclust 4 0.282 0.632 0.749 0.3181 0.730 0.603
#> MAD:hclust 4 0.380 0.418 0.726 0.1967 0.958 0.915
#> ATC:hclust 4 0.518 0.776 0.864 0.5851 0.649 0.533
get_stats(res_list, k = 5)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 5 0.848 0.833 0.911 0.1149 0.820 0.492
#> CV:NMF 5 0.765 0.828 0.903 0.0969 0.796 0.442
#> MAD:NMF 5 0.825 0.765 0.892 0.0606 0.917 0.705
#> ATC:NMF 5 0.751 0.780 0.882 0.0720 0.889 0.647
#> SD:skmeans 5 0.679 0.663 0.810 0.0593 0.910 0.666
#> CV:skmeans 5 0.709 0.751 0.837 0.0639 0.920 0.698
#> MAD:skmeans 5 0.698 0.612 0.791 0.0610 0.977 0.909
#> ATC:skmeans 5 0.798 0.798 0.905 0.0994 0.897 0.670
#> SD:mclust 5 0.718 0.798 0.868 0.1496 0.841 0.592
#> CV:mclust 5 0.727 0.812 0.866 0.1371 0.901 0.724
#> MAD:mclust 5 0.562 0.510 0.734 0.0932 0.785 0.470
#> ATC:mclust 5 0.807 0.843 0.842 0.1451 0.865 0.589
#> SD:kmeans 5 0.642 0.682 0.766 0.0827 0.823 0.496
#> CV:kmeans 5 0.595 0.637 0.758 0.0907 0.821 0.505
#> MAD:kmeans 5 0.676 0.543 0.721 0.0747 0.899 0.661
#> ATC:kmeans 5 0.677 0.730 0.834 0.0988 0.802 0.434
#> SD:pam 5 0.762 0.742 0.894 0.1357 0.889 0.707
#> CV:pam 5 0.708 0.821 0.863 0.1497 0.882 0.716
#> MAD:pam 5 0.833 0.849 0.921 0.0844 0.925 0.756
#> ATC:pam 5 0.798 0.864 0.926 0.1176 0.906 0.737
#> SD:hclust 5 0.397 0.572 0.718 0.0882 0.886 0.718
#> CV:hclust 5 0.436 0.700 0.784 0.1321 0.953 0.892
#> MAD:hclust 5 0.421 0.379 0.649 0.1011 0.868 0.713
#> ATC:hclust 5 0.578 0.600 0.809 0.1483 0.936 0.845
get_stats(res_list, k = 6)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 6 0.805 0.725 0.865 0.0447 0.945 0.759
#> CV:NMF 6 0.869 0.871 0.921 0.0580 0.932 0.707
#> MAD:NMF 6 0.731 0.525 0.747 0.0482 0.931 0.715
#> ATC:NMF 6 0.761 0.728 0.865 0.0599 0.889 0.579
#> SD:skmeans 6 0.717 0.570 0.748 0.0418 0.893 0.545
#> CV:skmeans 6 0.728 0.644 0.794 0.0417 0.908 0.601
#> MAD:skmeans 6 0.683 0.537 0.753 0.0395 0.915 0.652
#> ATC:skmeans 6 0.781 0.694 0.832 0.0430 0.963 0.845
#> SD:mclust 6 0.744 0.750 0.845 0.0426 0.931 0.733
#> CV:mclust 6 0.780 0.644 0.820 0.0449 0.979 0.920
#> MAD:mclust 6 0.818 0.834 0.901 0.0459 0.892 0.619
#> ATC:mclust 6 0.865 0.864 0.907 0.0537 0.921 0.647
#> SD:kmeans 6 0.705 0.644 0.767 0.0550 0.930 0.704
#> CV:kmeans 6 0.665 0.574 0.765 0.0553 0.929 0.709
#> MAD:kmeans 6 0.718 0.630 0.791 0.0478 0.904 0.619
#> ATC:kmeans 6 0.748 0.692 0.803 0.0607 0.894 0.594
#> SD:pam 6 0.721 0.776 0.865 0.0797 0.927 0.740
#> CV:pam 6 0.745 0.732 0.862 0.0912 0.873 0.596
#> MAD:pam 6 0.792 0.735 0.850 0.0643 0.954 0.811
#> ATC:pam 6 0.723 0.792 0.880 0.0320 0.990 0.964
#> SD:hclust 6 0.482 0.487 0.675 0.0701 0.809 0.499
#> CV:hclust 6 0.526 0.553 0.717 0.1098 0.996 0.991
#> MAD:hclust 6 0.433 0.346 0.626 0.0452 0.904 0.725
#> ATC:hclust 6 0.673 0.692 0.827 0.1019 0.892 0.711
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 tissue(p) k
#> SD:NMF 84 1.17e-01 2
#> CV:NMF 84 7.76e-03 2
#> MAD:NMF 90 5.72e-01 2
#> ATC:NMF 86 4.93e-01 2
#> SD:skmeans 85 2.64e-01 2
#> CV:skmeans 83 9.38e-02 2
#> MAD:skmeans 89 4.88e-01 2
#> ATC:skmeans 87 4.17e-01 2
#> SD:mclust 84 5.32e-16 2
#> CV:mclust 87 2.44e-15 2
#> MAD:mclust 91 2.24e-01 2
#> ATC:mclust 91 3.80e-01 2
#> SD:kmeans 84 9.23e-02 2
#> CV:kmeans 86 8.31e-02 2
#> MAD:kmeans 89 3.83e-01 2
#> ATC:kmeans 87 1.46e-01 2
#> SD:pam 85 1.30e-01 2
#> CV:pam 88 1.01e-01 2
#> MAD:pam 74 3.68e-01 2
#> ATC:pam 87 2.44e-01 2
#> SD:hclust 89 1.49e-01 2
#> CV:hclust 86 6.87e-02 2
#> MAD:hclust 89 8.14e-02 2
#> ATC:hclust 88 1.92e-01 2
test_to_known_factors(res_list, k = 3)
#> n tissue(p) k
#> SD:NMF 82 1.14e-03 3
#> CV:NMF 85 2.09e-03 3
#> MAD:NMF 90 3.05e-02 3
#> ATC:NMF 83 2.91e-01 3
#> SD:skmeans 72 6.46e-02 3
#> CV:skmeans 74 5.17e-12 3
#> MAD:skmeans 65 4.17e-02 3
#> ATC:skmeans 82 5.71e-02 3
#> SD:mclust 86 2.14e-13 3
#> CV:mclust 85 6.01e-14 3
#> MAD:mclust 83 5.40e-02 3
#> ATC:mclust 88 3.06e-01 3
#> SD:kmeans 64 6.27e-02 3
#> CV:kmeans 80 1.15e-09 3
#> MAD:kmeans 86 1.13e-01 3
#> ATC:kmeans 81 1.74e-01 3
#> SD:pam 86 1.01e-01 3
#> CV:pam 81 1.24e-09 3
#> MAD:pam 88 7.64e-02 3
#> ATC:pam 89 5.07e-02 3
#> SD:hclust 76 1.83e-01 3
#> CV:hclust 56 4.59e-02 3
#> MAD:hclust 79 7.34e-02 3
#> ATC:hclust 87 1.56e-01 3
test_to_known_factors(res_list, k = 4)
#> n tissue(p) k
#> SD:NMF 83 1.47e-13 4
#> CV:NMF 88 2.63e-12 4
#> MAD:NMF 86 1.66e-02 4
#> ATC:NMF 86 2.88e-01 4
#> SD:skmeans 86 2.15e-05 4
#> CV:skmeans 87 7.43e-06 4
#> MAD:skmeans 85 4.72e-02 4
#> ATC:skmeans 85 2.73e-01 4
#> SD:mclust 81 8.62e-16 4
#> CV:mclust 79 7.15e-12 4
#> MAD:mclust 51 1.22e-04 4
#> ATC:mclust 89 5.02e-02 4
#> SD:kmeans 69 7.43e-10 4
#> CV:kmeans 73 3.96e-08 4
#> MAD:kmeans 86 3.71e-02 4
#> ATC:kmeans 35 2.31e-01 4
#> SD:pam 90 7.47e-09 4
#> CV:pam 84 1.06e-08 4
#> MAD:pam 76 3.51e-04 4
#> ATC:pam 88 1.60e-01 4
#> SD:hclust 76 6.47e-06 4
#> CV:hclust 71 1.41e-07 4
#> MAD:hclust 44 3.52e-02 4
#> ATC:hclust 88 1.19e-01 4
test_to_known_factors(res_list, k = 5)
#> n tissue(p) k
#> SD:NMF 85 2.68e-07 5
#> CV:NMF 86 7.94e-09 5
#> MAD:NMF 79 5.23e-02 5
#> ATC:NMF 84 5.68e-02 5
#> SD:skmeans 75 9.57e-07 5
#> CV:skmeans 83 2.69e-07 5
#> MAD:skmeans 68 1.20e-02 5
#> ATC:skmeans 83 4.58e-01 5
#> SD:mclust 87 1.01e-07 5
#> CV:mclust 85 5.74e-10 5
#> MAD:mclust 63 4.33e-04 5
#> ATC:mclust 88 3.26e-02 5
#> SD:kmeans 76 4.33e-06 5
#> CV:kmeans 71 1.20e-05 5
#> MAD:kmeans 64 6.83e-02 5
#> ATC:kmeans 79 3.01e-01 5
#> SD:pam 76 3.91e-07 5
#> CV:pam 89 2.60e-07 5
#> MAD:pam 88 3.22e-04 5
#> ATC:pam 89 2.92e-02 5
#> SD:hclust 70 1.04e-06 5
#> CV:hclust 79 5.09e-05 5
#> MAD:hclust 45 1.11e-01 5
#> ATC:hclust 71 3.28e-02 5
test_to_known_factors(res_list, k = 6)
#> n tissue(p) k
#> SD:NMF 72 4.02e-04 6
#> CV:NMF 86 8.13e-05 6
#> MAD:NMF 53 2.93e-01 6
#> ATC:NMF 79 4.65e-02 6
#> SD:skmeans 58 6.40e-07 6
#> CV:skmeans 71 2.04e-05 6
#> MAD:skmeans 57 1.51e-01 6
#> ATC:skmeans 77 3.93e-01 6
#> SD:mclust 82 9.95e-07 6
#> CV:mclust 71 6.17e-11 6
#> MAD:mclust 87 3.48e-02 6
#> ATC:mclust 87 1.41e-01 6
#> SD:kmeans 71 2.65e-04 6
#> CV:kmeans 50 1.44e-03 6
#> MAD:kmeans 69 7.35e-03 6
#> ATC:kmeans 75 1.33e-01 6
#> SD:pam 83 9.01e-07 6
#> CV:pam 79 3.31e-06 6
#> MAD:pam 79 3.93e-04 6
#> ATC:pam 86 1.94e-02 6
#> SD:hclust 43 4.30e-03 6
#> CV:hclust 59 4.87e-07 6
#> MAD:hclust 39 1.12e-01 6
#> ATC:hclust 77 1.68e-01 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 48983 rows and 91 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 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 0.627 0.884 0.938 0.2397 0.785 0.785
#> 3 3 0.319 0.679 0.804 1.2669 0.717 0.640
#> 4 4 0.369 0.632 0.760 0.1820 0.852 0.711
#> 5 5 0.397 0.572 0.718 0.0882 0.886 0.718
#> 6 6 0.482 0.487 0.675 0.0701 0.809 0.499
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
#> GSM1124891 1 0.8327 0.8682 0.736 0.264
#> GSM1124888 1 0.8327 0.8682 0.736 0.264
#> GSM1124890 2 0.5059 0.8386 0.112 0.888
#> GSM1124904 2 0.0000 0.9419 0.000 1.000
#> GSM1124927 2 0.0000 0.9419 0.000 1.000
#> GSM1124953 2 0.9795 0.0761 0.416 0.584
#> GSM1124869 2 0.0000 0.9419 0.000 1.000
#> GSM1124870 2 0.0000 0.9419 0.000 1.000
#> GSM1124882 2 0.0672 0.9400 0.008 0.992
#> GSM1124884 2 0.0000 0.9419 0.000 1.000
#> GSM1124898 2 0.0000 0.9419 0.000 1.000
#> GSM1124903 2 0.0000 0.9419 0.000 1.000
#> GSM1124905 2 0.0000 0.9419 0.000 1.000
#> GSM1124910 2 0.1633 0.9318 0.024 0.976
#> GSM1124919 2 0.5059 0.8386 0.112 0.888
#> GSM1124932 2 0.0376 0.9411 0.004 0.996
#> GSM1124933 1 0.6973 0.8640 0.812 0.188
#> GSM1124867 2 0.0672 0.9405 0.008 0.992
#> GSM1124868 2 0.4161 0.8753 0.084 0.916
#> GSM1124878 2 0.4022 0.8790 0.080 0.920
#> GSM1124895 2 0.6973 0.7571 0.188 0.812
#> GSM1124897 2 0.4161 0.8753 0.084 0.916
#> GSM1124902 2 0.6973 0.7571 0.188 0.812
#> GSM1124908 2 0.6973 0.7571 0.188 0.812
#> GSM1124921 2 0.6973 0.7571 0.188 0.812
#> GSM1124939 2 0.6973 0.7571 0.188 0.812
#> GSM1124944 2 0.6973 0.7571 0.188 0.812
#> GSM1124945 1 0.0672 0.7903 0.992 0.008
#> GSM1124946 2 0.6973 0.7571 0.188 0.812
#> GSM1124947 2 0.6973 0.7571 0.188 0.812
#> GSM1124951 1 0.0672 0.7903 0.992 0.008
#> GSM1124952 2 0.6973 0.7571 0.188 0.812
#> GSM1124957 1 0.0672 0.7903 0.992 0.008
#> GSM1124900 2 0.0672 0.9400 0.008 0.992
#> GSM1124914 2 0.0000 0.9419 0.000 1.000
#> GSM1124871 2 0.0000 0.9419 0.000 1.000
#> GSM1124874 2 0.0000 0.9419 0.000 1.000
#> GSM1124875 2 0.1184 0.9358 0.016 0.984
#> GSM1124880 2 0.1184 0.9370 0.016 0.984
#> GSM1124881 2 0.0376 0.9411 0.004 0.996
#> GSM1124885 2 0.0000 0.9419 0.000 1.000
#> GSM1124886 2 0.6438 0.7471 0.164 0.836
#> GSM1124887 2 0.3584 0.8891 0.068 0.932
#> GSM1124894 2 0.0376 0.9407 0.004 0.996
#> GSM1124896 2 0.0000 0.9419 0.000 1.000
#> GSM1124899 2 0.0000 0.9419 0.000 1.000
#> GSM1124901 2 0.0000 0.9419 0.000 1.000
#> GSM1124906 2 0.0000 0.9419 0.000 1.000
#> GSM1124907 2 0.1414 0.9333 0.020 0.980
#> GSM1124911 2 0.0000 0.9419 0.000 1.000
#> GSM1124912 2 0.0672 0.9400 0.008 0.992
#> GSM1124915 2 0.0000 0.9419 0.000 1.000
#> GSM1124917 2 0.0938 0.9389 0.012 0.988
#> GSM1124918 2 0.1184 0.9358 0.016 0.984
#> GSM1124920 1 0.8443 0.8621 0.728 0.272
#> GSM1124922 2 0.0000 0.9419 0.000 1.000
#> GSM1124924 2 0.1184 0.9374 0.016 0.984
#> GSM1124926 2 0.0000 0.9419 0.000 1.000
#> GSM1124928 2 0.1633 0.9318 0.024 0.976
#> GSM1124930 2 0.1414 0.9333 0.020 0.980
#> GSM1124931 2 0.0376 0.9411 0.004 0.996
#> GSM1124935 2 0.0000 0.9419 0.000 1.000
#> GSM1124936 1 0.8555 0.8524 0.720 0.280
#> GSM1124938 2 0.1184 0.9360 0.016 0.984
#> GSM1124940 2 0.0000 0.9419 0.000 1.000
#> GSM1124941 2 0.0000 0.9419 0.000 1.000
#> GSM1124942 2 0.1414 0.9333 0.020 0.980
#> GSM1124943 2 0.1414 0.9333 0.020 0.980
#> GSM1124948 2 0.0938 0.9390 0.012 0.988
#> GSM1124949 2 0.1184 0.9360 0.016 0.984
#> GSM1124950 2 0.0376 0.9411 0.004 0.996
#> GSM1124954 1 0.8327 0.8682 0.736 0.264
#> GSM1124955 2 0.0000 0.9419 0.000 1.000
#> GSM1124956 2 0.0000 0.9419 0.000 1.000
#> GSM1124872 2 0.0376 0.9411 0.004 0.996
#> GSM1124873 2 0.0376 0.9411 0.004 0.996
#> GSM1124876 1 0.6973 0.8640 0.812 0.188
#> GSM1124877 2 0.0000 0.9419 0.000 1.000
#> GSM1124879 2 0.1184 0.9359 0.016 0.984
#> GSM1124883 2 0.0000 0.9419 0.000 1.000
#> GSM1124889 2 0.0000 0.9419 0.000 1.000
#> GSM1124892 2 0.9522 0.1807 0.372 0.628
#> GSM1124893 2 0.0000 0.9419 0.000 1.000
#> GSM1124909 2 0.0376 0.9411 0.004 0.996
#> GSM1124913 2 0.0000 0.9419 0.000 1.000
#> GSM1124916 2 0.0376 0.9411 0.004 0.996
#> GSM1124923 2 0.5059 0.8386 0.112 0.888
#> GSM1124925 2 0.0000 0.9419 0.000 1.000
#> GSM1124929 2 0.1184 0.9360 0.016 0.984
#> GSM1124934 1 0.8499 0.8567 0.724 0.276
#> GSM1124937 2 0.1414 0.9338 0.020 0.980
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1124891 3 0.596 0.8506 0.264 0.016 0.720
#> GSM1124888 3 0.573 0.8529 0.272 0.008 0.720
#> GSM1124890 2 0.460 0.7502 0.040 0.852 0.108
#> GSM1124904 2 0.116 0.7704 0.028 0.972 0.000
#> GSM1124927 2 0.604 0.4291 0.380 0.620 0.000
#> GSM1124953 2 0.640 0.3309 0.004 0.580 0.416
#> GSM1124869 1 0.254 0.8099 0.920 0.080 0.000
#> GSM1124870 2 0.604 0.4291 0.380 0.620 0.000
#> GSM1124882 1 0.259 0.8039 0.924 0.072 0.004
#> GSM1124884 2 0.388 0.7614 0.152 0.848 0.000
#> GSM1124898 2 0.245 0.7820 0.076 0.924 0.000
#> GSM1124903 2 0.116 0.7704 0.028 0.972 0.000
#> GSM1124905 2 0.626 0.2244 0.448 0.552 0.000
#> GSM1124910 1 0.468 0.7140 0.804 0.192 0.004
#> GSM1124919 2 0.460 0.7502 0.040 0.852 0.108
#> GSM1124932 2 0.468 0.7359 0.192 0.804 0.004
#> GSM1124933 3 0.429 0.8464 0.180 0.000 0.820
#> GSM1124867 2 0.648 0.2385 0.452 0.544 0.004
#> GSM1124868 2 0.395 0.7376 0.040 0.884 0.076
#> GSM1124878 2 0.386 0.7391 0.040 0.888 0.072
#> GSM1124895 2 0.642 0.6647 0.068 0.752 0.180
#> GSM1124897 2 0.395 0.7376 0.040 0.884 0.076
#> GSM1124902 2 0.642 0.6647 0.068 0.752 0.180
#> GSM1124908 2 0.642 0.6647 0.068 0.752 0.180
#> GSM1124921 2 0.642 0.6647 0.068 0.752 0.180
#> GSM1124939 2 0.642 0.6647 0.068 0.752 0.180
#> GSM1124944 2 0.642 0.6647 0.068 0.752 0.180
#> GSM1124945 3 0.000 0.7628 0.000 0.000 1.000
#> GSM1124946 2 0.642 0.6647 0.068 0.752 0.180
#> GSM1124947 2 0.642 0.6647 0.068 0.752 0.180
#> GSM1124951 3 0.000 0.7628 0.000 0.000 1.000
#> GSM1124952 2 0.776 0.6340 0.144 0.676 0.180
#> GSM1124957 3 0.000 0.7628 0.000 0.000 1.000
#> GSM1124900 2 0.636 0.3696 0.404 0.592 0.004
#> GSM1124914 2 0.236 0.7817 0.072 0.928 0.000
#> GSM1124871 2 0.175 0.7821 0.048 0.952 0.000
#> GSM1124874 2 0.196 0.7843 0.056 0.944 0.000
#> GSM1124875 2 0.517 0.7230 0.192 0.792 0.016
#> GSM1124880 1 0.633 0.3007 0.600 0.396 0.004
#> GSM1124881 2 0.518 0.6670 0.256 0.744 0.000
#> GSM1124885 2 0.116 0.7704 0.028 0.972 0.000
#> GSM1124886 1 0.603 0.6074 0.780 0.068 0.152
#> GSM1124887 2 0.367 0.7690 0.040 0.896 0.064
#> GSM1124894 2 0.644 0.2715 0.432 0.564 0.004
#> GSM1124896 1 0.254 0.8099 0.920 0.080 0.000
#> GSM1124899 2 0.319 0.7821 0.112 0.888 0.000
#> GSM1124901 2 0.245 0.7827 0.076 0.924 0.000
#> GSM1124906 2 0.418 0.7479 0.172 0.828 0.000
#> GSM1124907 2 0.323 0.7802 0.072 0.908 0.020
#> GSM1124911 2 0.263 0.7797 0.084 0.916 0.000
#> GSM1124912 1 0.259 0.8039 0.924 0.072 0.004
#> GSM1124915 2 0.164 0.7836 0.044 0.956 0.000
#> GSM1124917 2 0.537 0.6699 0.252 0.744 0.004
#> GSM1124918 2 0.517 0.7230 0.192 0.792 0.016
#> GSM1124920 3 0.592 0.8486 0.276 0.012 0.712
#> GSM1124922 2 0.388 0.7614 0.152 0.848 0.000
#> GSM1124924 1 0.648 0.1112 0.548 0.448 0.004
#> GSM1124926 2 0.319 0.7821 0.112 0.888 0.000
#> GSM1124928 1 0.596 0.4925 0.672 0.324 0.004
#> GSM1124930 2 0.359 0.7794 0.088 0.892 0.020
#> GSM1124931 2 0.488 0.7168 0.208 0.788 0.004
#> GSM1124935 2 0.245 0.7820 0.076 0.924 0.000
#> GSM1124936 3 0.602 0.8389 0.288 0.012 0.700
#> GSM1124938 2 0.369 0.7791 0.100 0.884 0.016
#> GSM1124940 1 0.254 0.8099 0.920 0.080 0.000
#> GSM1124941 2 0.418 0.7479 0.172 0.828 0.000
#> GSM1124942 2 0.359 0.7794 0.088 0.892 0.020
#> GSM1124943 2 0.359 0.7794 0.088 0.892 0.020
#> GSM1124948 2 0.651 0.1497 0.472 0.524 0.004
#> GSM1124949 1 0.277 0.7996 0.920 0.072 0.008
#> GSM1124950 2 0.627 0.2290 0.456 0.544 0.000
#> GSM1124954 3 0.576 0.8521 0.276 0.008 0.716
#> GSM1124955 1 0.254 0.8099 0.920 0.080 0.000
#> GSM1124956 2 0.263 0.7797 0.084 0.916 0.000
#> GSM1124872 2 0.627 0.2290 0.456 0.544 0.000
#> GSM1124873 2 0.518 0.6670 0.256 0.744 0.000
#> GSM1124876 3 0.429 0.8464 0.180 0.000 0.820
#> GSM1124877 1 0.254 0.8099 0.920 0.080 0.000
#> GSM1124879 1 0.254 0.7988 0.920 0.080 0.000
#> GSM1124883 2 0.116 0.7704 0.028 0.972 0.000
#> GSM1124889 2 0.164 0.7836 0.044 0.956 0.000
#> GSM1124892 1 0.684 -0.0515 0.624 0.024 0.352
#> GSM1124893 1 0.254 0.8099 0.920 0.080 0.000
#> GSM1124909 2 0.518 0.6670 0.256 0.744 0.000
#> GSM1124913 2 0.116 0.7704 0.028 0.972 0.000
#> GSM1124916 2 0.518 0.6670 0.256 0.744 0.000
#> GSM1124923 2 0.460 0.7502 0.040 0.852 0.108
#> GSM1124925 1 0.254 0.8099 0.920 0.080 0.000
#> GSM1124929 1 0.277 0.7996 0.920 0.072 0.008
#> GSM1124934 3 0.586 0.8404 0.288 0.008 0.704
#> GSM1124937 1 0.604 0.3697 0.620 0.380 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1124891 3 0.6564 0.83727 0.248 0.024 0.652 0.076
#> GSM1124888 3 0.6418 0.84030 0.256 0.016 0.652 0.076
#> GSM1124890 2 0.4235 0.63594 0.020 0.840 0.096 0.044
#> GSM1124904 2 0.2976 0.64181 0.008 0.872 0.000 0.120
#> GSM1124927 2 0.7591 0.29979 0.352 0.444 0.000 0.204
#> GSM1124953 2 0.5984 0.23431 0.008 0.560 0.404 0.028
#> GSM1124869 1 0.0804 0.72623 0.980 0.012 0.000 0.008
#> GSM1124870 2 0.7591 0.29979 0.352 0.444 0.000 0.204
#> GSM1124882 1 0.0524 0.72011 0.988 0.004 0.000 0.008
#> GSM1124884 2 0.6404 0.58147 0.136 0.644 0.000 0.220
#> GSM1124898 2 0.3934 0.69880 0.048 0.836 0.000 0.116
#> GSM1124903 2 0.2976 0.64181 0.008 0.872 0.000 0.120
#> GSM1124905 1 0.7429 0.13828 0.492 0.192 0.000 0.316
#> GSM1124910 1 0.4820 0.61617 0.772 0.168 0.000 0.060
#> GSM1124919 2 0.4235 0.63594 0.020 0.840 0.096 0.044
#> GSM1124932 2 0.6360 0.61020 0.180 0.656 0.000 0.164
#> GSM1124933 3 0.3400 0.82927 0.180 0.000 0.820 0.000
#> GSM1124867 2 0.6957 0.26085 0.416 0.472 0.000 0.112
#> GSM1124868 2 0.4053 0.55433 0.004 0.768 0.000 0.228
#> GSM1124878 2 0.3870 0.54785 0.004 0.788 0.000 0.208
#> GSM1124895 4 0.3791 0.95057 0.004 0.200 0.000 0.796
#> GSM1124897 2 0.4053 0.55433 0.004 0.768 0.000 0.228
#> GSM1124902 4 0.3791 0.95057 0.004 0.200 0.000 0.796
#> GSM1124908 4 0.3751 0.94323 0.004 0.196 0.000 0.800
#> GSM1124921 4 0.3751 0.94323 0.004 0.196 0.000 0.800
#> GSM1124939 4 0.3791 0.95057 0.004 0.200 0.000 0.796
#> GSM1124944 4 0.3791 0.95057 0.004 0.200 0.000 0.796
#> GSM1124945 3 0.0000 0.74586 0.000 0.000 1.000 0.000
#> GSM1124946 4 0.3710 0.93892 0.004 0.192 0.000 0.804
#> GSM1124947 4 0.3791 0.95057 0.004 0.200 0.000 0.796
#> GSM1124951 3 0.0000 0.74586 0.000 0.000 1.000 0.000
#> GSM1124952 4 0.6536 0.59205 0.096 0.324 0.000 0.580
#> GSM1124957 3 0.0000 0.74586 0.000 0.000 1.000 0.000
#> GSM1124900 2 0.7485 0.27255 0.380 0.440 0.000 0.180
#> GSM1124914 2 0.3850 0.69744 0.044 0.840 0.000 0.116
#> GSM1124871 2 0.3308 0.70174 0.036 0.872 0.000 0.092
#> GSM1124874 2 0.3840 0.70561 0.052 0.844 0.000 0.104
#> GSM1124875 2 0.5074 0.68151 0.168 0.764 0.004 0.064
#> GSM1124880 1 0.6383 0.16817 0.568 0.356 0.000 0.076
#> GSM1124881 2 0.5790 0.64066 0.236 0.684 0.000 0.080
#> GSM1124885 2 0.2976 0.64181 0.008 0.872 0.000 0.120
#> GSM1124886 1 0.4411 0.53386 0.824 0.012 0.112 0.052
#> GSM1124887 2 0.3928 0.65542 0.020 0.860 0.052 0.068
#> GSM1124894 1 0.7660 0.10533 0.476 0.200 0.004 0.320
#> GSM1124896 1 0.0804 0.72623 0.980 0.012 0.000 0.008
#> GSM1124899 2 0.4856 0.69832 0.084 0.780 0.000 0.136
#> GSM1124901 2 0.3899 0.70278 0.052 0.840 0.000 0.108
#> GSM1124906 2 0.5515 0.67812 0.152 0.732 0.000 0.116
#> GSM1124907 2 0.2920 0.69787 0.044 0.904 0.008 0.044
#> GSM1124911 2 0.4791 0.68379 0.080 0.784 0.000 0.136
#> GSM1124912 1 0.0524 0.72011 0.988 0.004 0.000 0.008
#> GSM1124915 2 0.3286 0.70905 0.044 0.876 0.000 0.080
#> GSM1124917 2 0.5997 0.64678 0.232 0.680 0.004 0.084
#> GSM1124918 2 0.5074 0.68151 0.168 0.764 0.004 0.064
#> GSM1124920 3 0.6471 0.83658 0.264 0.016 0.644 0.076
#> GSM1124922 2 0.5434 0.68038 0.128 0.740 0.000 0.132
#> GSM1124924 1 0.6527 -0.04647 0.508 0.416 0.000 0.076
#> GSM1124926 2 0.4856 0.69832 0.084 0.780 0.000 0.136
#> GSM1124928 1 0.6016 0.36432 0.632 0.300 0.000 0.068
#> GSM1124930 2 0.2877 0.70112 0.060 0.904 0.008 0.028
#> GSM1124931 2 0.6852 0.51724 0.192 0.600 0.000 0.208
#> GSM1124935 2 0.3818 0.70220 0.048 0.844 0.000 0.108
#> GSM1124936 3 0.6581 0.82669 0.272 0.016 0.632 0.080
#> GSM1124938 2 0.2958 0.70416 0.072 0.896 0.004 0.028
#> GSM1124940 1 0.0804 0.72623 0.980 0.012 0.000 0.008
#> GSM1124941 2 0.5515 0.67812 0.152 0.732 0.000 0.116
#> GSM1124942 2 0.2877 0.70112 0.060 0.904 0.008 0.028
#> GSM1124943 2 0.2877 0.70112 0.060 0.904 0.008 0.028
#> GSM1124948 2 0.6247 0.28567 0.428 0.516 0.000 0.056
#> GSM1124949 1 0.1059 0.71797 0.972 0.012 0.000 0.016
#> GSM1124950 2 0.7044 0.22175 0.428 0.452 0.000 0.120
#> GSM1124954 3 0.6539 0.83895 0.256 0.016 0.644 0.084
#> GSM1124955 1 0.0804 0.72623 0.980 0.012 0.000 0.008
#> GSM1124956 2 0.4791 0.68379 0.080 0.784 0.000 0.136
#> GSM1124872 2 0.7044 0.22175 0.428 0.452 0.000 0.120
#> GSM1124873 2 0.5790 0.64066 0.236 0.684 0.000 0.080
#> GSM1124876 3 0.3400 0.82927 0.180 0.000 0.820 0.000
#> GSM1124877 1 0.0937 0.72390 0.976 0.012 0.000 0.012
#> GSM1124879 1 0.3081 0.68487 0.888 0.048 0.000 0.064
#> GSM1124883 2 0.2976 0.64181 0.008 0.872 0.000 0.120
#> GSM1124889 2 0.3505 0.70991 0.048 0.864 0.000 0.088
#> GSM1124892 1 0.6126 -0.00345 0.632 0.004 0.300 0.064
#> GSM1124893 1 0.0804 0.72623 0.980 0.012 0.000 0.008
#> GSM1124909 2 0.5759 0.64291 0.232 0.688 0.000 0.080
#> GSM1124913 2 0.2976 0.64181 0.008 0.872 0.000 0.120
#> GSM1124916 2 0.5759 0.64291 0.232 0.688 0.000 0.080
#> GSM1124923 2 0.4235 0.63594 0.020 0.840 0.096 0.044
#> GSM1124925 1 0.0804 0.72623 0.980 0.012 0.000 0.008
#> GSM1124929 1 0.1059 0.71797 0.972 0.012 0.000 0.016
#> GSM1124934 3 0.6531 0.83120 0.264 0.016 0.640 0.080
#> GSM1124937 1 0.6362 0.16982 0.560 0.368 0.000 0.072
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM1124891 5 0.6284 0.9481 0.188 0.008 0.232 0.000 0.572
#> GSM1124888 5 0.6076 0.9549 0.196 0.000 0.232 0.000 0.572
#> GSM1124890 2 0.6524 0.5678 0.008 0.620 0.080 0.064 0.228
#> GSM1124904 2 0.3141 0.6053 0.000 0.852 0.000 0.040 0.108
#> GSM1124927 1 0.8472 0.1052 0.300 0.268 0.000 0.268 0.164
#> GSM1124953 3 0.6791 0.2139 0.000 0.312 0.384 0.000 0.304
#> GSM1124869 1 0.0162 0.5363 0.996 0.004 0.000 0.000 0.000
#> GSM1124870 1 0.8472 0.1052 0.300 0.268 0.000 0.268 0.164
#> GSM1124882 1 0.0510 0.5283 0.984 0.000 0.000 0.000 0.016
#> GSM1124884 2 0.6267 0.5615 0.136 0.560 0.000 0.292 0.012
#> GSM1124898 2 0.3935 0.6903 0.040 0.828 0.000 0.092 0.040
#> GSM1124903 2 0.3141 0.6053 0.000 0.852 0.000 0.040 0.108
#> GSM1124905 1 0.7001 0.0735 0.452 0.048 0.000 0.380 0.120
#> GSM1124910 1 0.5546 0.5136 0.720 0.100 0.000 0.064 0.116
#> GSM1124919 2 0.6524 0.5678 0.008 0.620 0.080 0.064 0.228
#> GSM1124932 2 0.7630 0.4970 0.160 0.496 0.000 0.228 0.116
#> GSM1124933 3 0.3203 0.4773 0.168 0.000 0.820 0.000 0.012
#> GSM1124867 1 0.8139 0.0821 0.372 0.316 0.000 0.172 0.140
#> GSM1124868 2 0.5153 0.5229 0.000 0.684 0.000 0.204 0.112
#> GSM1124878 2 0.4454 0.5313 0.000 0.760 0.000 0.128 0.112
#> GSM1124895 4 0.0963 0.9339 0.000 0.036 0.000 0.964 0.000
#> GSM1124897 2 0.5153 0.5229 0.000 0.684 0.000 0.204 0.112
#> GSM1124902 4 0.0963 0.9339 0.000 0.036 0.000 0.964 0.000
#> GSM1124908 4 0.1341 0.9255 0.000 0.056 0.000 0.944 0.000
#> GSM1124921 4 0.1341 0.9255 0.000 0.056 0.000 0.944 0.000
#> GSM1124939 4 0.0963 0.9339 0.000 0.036 0.000 0.964 0.000
#> GSM1124944 4 0.0963 0.9339 0.000 0.036 0.000 0.964 0.000
#> GSM1124945 3 0.0000 0.6720 0.000 0.000 1.000 0.000 0.000
#> GSM1124946 4 0.1410 0.9204 0.000 0.060 0.000 0.940 0.000
#> GSM1124947 4 0.0963 0.9339 0.000 0.036 0.000 0.964 0.000
#> GSM1124951 3 0.0000 0.6720 0.000 0.000 1.000 0.000 0.000
#> GSM1124952 4 0.5215 0.4640 0.096 0.240 0.000 0.664 0.000
#> GSM1124957 3 0.0000 0.6720 0.000 0.000 1.000 0.000 0.000
#> GSM1124900 1 0.8453 0.1118 0.312 0.280 0.000 0.244 0.164
#> GSM1124914 2 0.4083 0.6942 0.044 0.820 0.000 0.092 0.044
#> GSM1124871 2 0.4026 0.7045 0.040 0.824 0.000 0.088 0.048
#> GSM1124874 2 0.4461 0.7010 0.048 0.784 0.000 0.136 0.032
#> GSM1124875 2 0.6455 0.6341 0.152 0.636 0.000 0.140 0.072
#> GSM1124880 1 0.7551 0.3723 0.496 0.248 0.000 0.104 0.152
#> GSM1124881 2 0.6693 0.5576 0.216 0.584 0.000 0.152 0.048
#> GSM1124885 2 0.3141 0.6053 0.000 0.852 0.000 0.040 0.108
#> GSM1124886 1 0.3048 0.3207 0.820 0.004 0.000 0.000 0.176
#> GSM1124887 2 0.5500 0.6062 0.008 0.696 0.036 0.048 0.212
#> GSM1124894 1 0.7099 0.0461 0.436 0.048 0.000 0.384 0.132
#> GSM1124896 1 0.0579 0.5402 0.984 0.008 0.000 0.000 0.008
#> GSM1124899 2 0.5274 0.6858 0.080 0.724 0.000 0.160 0.036
#> GSM1124901 2 0.3986 0.6957 0.044 0.824 0.000 0.096 0.036
#> GSM1124906 2 0.5828 0.6456 0.148 0.660 0.000 0.172 0.020
#> GSM1124907 2 0.4908 0.6640 0.028 0.748 0.000 0.068 0.156
#> GSM1124911 2 0.5254 0.6771 0.072 0.712 0.000 0.188 0.028
#> GSM1124912 1 0.0510 0.5283 0.984 0.000 0.000 0.000 0.016
#> GSM1124915 2 0.3606 0.7113 0.044 0.840 0.000 0.100 0.016
#> GSM1124917 2 0.6949 0.5776 0.208 0.572 0.000 0.148 0.072
#> GSM1124918 2 0.6455 0.6341 0.152 0.636 0.000 0.140 0.072
#> GSM1124920 5 0.6155 0.9535 0.212 0.000 0.228 0.000 0.560
#> GSM1124922 2 0.5922 0.6598 0.116 0.652 0.000 0.204 0.028
#> GSM1124924 1 0.7810 0.3007 0.444 0.276 0.000 0.104 0.176
#> GSM1124926 2 0.5274 0.6858 0.080 0.724 0.000 0.160 0.036
#> GSM1124928 1 0.7048 0.4519 0.560 0.216 0.000 0.076 0.148
#> GSM1124930 2 0.5209 0.6651 0.044 0.732 0.000 0.068 0.156
#> GSM1124931 2 0.8121 0.3364 0.160 0.400 0.000 0.284 0.156
#> GSM1124935 2 0.3853 0.6989 0.044 0.832 0.000 0.092 0.032
#> GSM1124936 5 0.6203 0.9416 0.224 0.000 0.224 0.000 0.552
#> GSM1124938 2 0.5371 0.6682 0.056 0.724 0.000 0.068 0.152
#> GSM1124940 1 0.0162 0.5363 0.996 0.004 0.000 0.000 0.000
#> GSM1124941 2 0.5828 0.6456 0.148 0.660 0.000 0.172 0.020
#> GSM1124942 2 0.5209 0.6651 0.044 0.732 0.000 0.068 0.156
#> GSM1124943 2 0.5209 0.6651 0.044 0.732 0.000 0.068 0.156
#> GSM1124948 2 0.7069 0.0737 0.404 0.432 0.000 0.092 0.072
#> GSM1124949 1 0.0955 0.5241 0.968 0.004 0.000 0.000 0.028
#> GSM1124950 1 0.8280 0.1797 0.372 0.280 0.000 0.180 0.168
#> GSM1124954 5 0.5820 0.9477 0.196 0.000 0.192 0.000 0.612
#> GSM1124955 1 0.0579 0.5402 0.984 0.008 0.000 0.000 0.008
#> GSM1124956 2 0.5254 0.6771 0.072 0.712 0.000 0.188 0.028
#> GSM1124872 1 0.8280 0.1797 0.372 0.280 0.000 0.180 0.168
#> GSM1124873 2 0.6754 0.5541 0.216 0.580 0.000 0.152 0.052
#> GSM1124876 3 0.3203 0.4773 0.168 0.000 0.820 0.000 0.012
#> GSM1124877 1 0.0807 0.5399 0.976 0.012 0.000 0.000 0.012
#> GSM1124879 1 0.3079 0.5221 0.876 0.016 0.000 0.064 0.044
#> GSM1124883 2 0.3141 0.6053 0.000 0.852 0.000 0.040 0.108
#> GSM1124889 2 0.3781 0.7108 0.048 0.828 0.000 0.108 0.016
#> GSM1124892 1 0.5174 -0.2583 0.604 0.000 0.056 0.000 0.340
#> GSM1124893 1 0.0324 0.5357 0.992 0.004 0.000 0.000 0.004
#> GSM1124909 2 0.6693 0.5575 0.216 0.584 0.000 0.152 0.048
#> GSM1124913 2 0.3141 0.6053 0.000 0.852 0.000 0.040 0.108
#> GSM1124916 2 0.6693 0.5575 0.216 0.584 0.000 0.152 0.048
#> GSM1124923 2 0.6499 0.5671 0.008 0.624 0.080 0.064 0.224
#> GSM1124925 1 0.0579 0.5402 0.984 0.008 0.000 0.000 0.008
#> GSM1124929 1 0.0955 0.5241 0.968 0.004 0.000 0.000 0.028
#> GSM1124934 5 0.5969 0.9398 0.200 0.004 0.188 0.000 0.608
#> GSM1124937 1 0.6720 0.2607 0.528 0.328 0.000 0.076 0.068
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1124891 6 0.3375 0.9222 0.112 0.008 0.056 0.000 0.000 0.824
#> GSM1124888 6 0.3352 0.9255 0.120 0.004 0.056 0.000 0.000 0.820
#> GSM1124890 2 0.5257 0.1657 0.000 0.724 0.080 0.020 0.088 0.088
#> GSM1124904 5 0.3189 0.7292 0.000 0.236 0.000 0.000 0.760 0.004
#> GSM1124927 2 0.8090 0.3618 0.228 0.380 0.000 0.160 0.188 0.044
#> GSM1124953 3 0.7356 0.3192 0.000 0.364 0.380 0.020 0.120 0.116
#> GSM1124869 1 0.0405 0.7343 0.988 0.004 0.000 0.000 0.000 0.008
#> GSM1124870 2 0.8090 0.3618 0.228 0.380 0.000 0.160 0.188 0.044
#> GSM1124882 1 0.0865 0.7218 0.964 0.000 0.000 0.000 0.000 0.036
#> GSM1124884 2 0.6437 0.3292 0.088 0.572 0.000 0.184 0.152 0.004
#> GSM1124898 5 0.5155 0.5345 0.008 0.424 0.000 0.064 0.504 0.000
#> GSM1124903 5 0.3189 0.7292 0.000 0.236 0.000 0.000 0.760 0.004
#> GSM1124905 1 0.7868 -0.0167 0.364 0.056 0.000 0.328 0.156 0.096
#> GSM1124910 1 0.5891 0.5090 0.656 0.168 0.000 0.024 0.088 0.064
#> GSM1124919 2 0.5257 0.1657 0.000 0.724 0.080 0.020 0.088 0.088
#> GSM1124932 2 0.7423 0.3428 0.128 0.444 0.000 0.140 0.268 0.020
#> GSM1124933 3 0.3703 0.6468 0.104 0.000 0.788 0.000 0.000 0.108
#> GSM1124867 2 0.7235 0.4020 0.292 0.456 0.000 0.076 0.148 0.028
#> GSM1124868 5 0.5184 0.6434 0.000 0.176 0.000 0.148 0.660 0.016
#> GSM1124878 5 0.4492 0.6555 0.000 0.164 0.000 0.088 0.732 0.016
#> GSM1124895 4 0.0937 0.9225 0.000 0.040 0.000 0.960 0.000 0.000
#> GSM1124897 5 0.5242 0.6412 0.000 0.184 0.000 0.148 0.652 0.016
#> GSM1124902 4 0.0937 0.9225 0.000 0.040 0.000 0.960 0.000 0.000
#> GSM1124908 4 0.1257 0.9137 0.000 0.028 0.000 0.952 0.020 0.000
#> GSM1124921 4 0.1257 0.9137 0.000 0.028 0.000 0.952 0.020 0.000
#> GSM1124939 4 0.0937 0.9225 0.000 0.040 0.000 0.960 0.000 0.000
#> GSM1124944 4 0.0937 0.9225 0.000 0.040 0.000 0.960 0.000 0.000
#> GSM1124945 3 0.0458 0.7606 0.000 0.000 0.984 0.000 0.000 0.016
#> GSM1124946 4 0.1261 0.9085 0.000 0.024 0.000 0.952 0.024 0.000
#> GSM1124947 4 0.0937 0.9225 0.000 0.040 0.000 0.960 0.000 0.000
#> GSM1124951 3 0.0458 0.7606 0.000 0.000 0.984 0.000 0.000 0.016
#> GSM1124952 4 0.6380 0.3369 0.080 0.204 0.000 0.572 0.140 0.004
#> GSM1124957 3 0.0458 0.7606 0.000 0.000 0.984 0.000 0.000 0.016
#> GSM1124900 2 0.8087 0.3621 0.232 0.396 0.000 0.144 0.172 0.056
#> GSM1124914 5 0.5464 0.5347 0.012 0.416 0.000 0.064 0.500 0.008
#> GSM1124871 5 0.4761 0.3734 0.008 0.468 0.000 0.032 0.492 0.000
#> GSM1124874 2 0.5336 -0.2105 0.012 0.520 0.000 0.076 0.392 0.000
#> GSM1124875 2 0.5948 0.3916 0.128 0.652 0.008 0.048 0.152 0.012
#> GSM1124880 1 0.7235 -0.1303 0.400 0.360 0.000 0.028 0.144 0.068
#> GSM1124881 2 0.5839 0.4293 0.156 0.624 0.000 0.060 0.160 0.000
#> GSM1124885 5 0.3076 0.7270 0.000 0.240 0.000 0.000 0.760 0.000
#> GSM1124886 1 0.3171 0.5583 0.812 0.008 0.000 0.004 0.008 0.168
#> GSM1124887 2 0.5706 -0.0211 0.000 0.624 0.036 0.020 0.252 0.068
#> GSM1124894 1 0.8000 -0.0516 0.344 0.060 0.000 0.328 0.164 0.104
#> GSM1124896 1 0.0405 0.7354 0.988 0.008 0.000 0.000 0.000 0.004
#> GSM1124899 2 0.5860 0.1278 0.036 0.564 0.000 0.116 0.284 0.000
#> GSM1124901 5 0.5254 0.4906 0.012 0.440 0.000 0.064 0.484 0.000
#> GSM1124906 2 0.5960 0.3428 0.100 0.616 0.000 0.096 0.188 0.000
#> GSM1124907 2 0.2695 0.2844 0.012 0.884 0.012 0.000 0.072 0.020
#> GSM1124911 2 0.5929 0.1299 0.036 0.556 0.000 0.124 0.284 0.000
#> GSM1124912 1 0.0865 0.7218 0.964 0.000 0.000 0.000 0.000 0.036
#> GSM1124915 2 0.5025 -0.2162 0.012 0.532 0.000 0.048 0.408 0.000
#> GSM1124917 2 0.7020 0.4012 0.156 0.544 0.000 0.060 0.188 0.052
#> GSM1124918 2 0.5948 0.3916 0.128 0.652 0.008 0.048 0.152 0.012
#> GSM1124920 6 0.3878 0.9067 0.176 0.004 0.056 0.000 0.000 0.764
#> GSM1124922 2 0.6093 0.3372 0.076 0.608 0.000 0.120 0.192 0.004
#> GSM1124924 2 0.7231 0.1844 0.352 0.396 0.000 0.028 0.168 0.056
#> GSM1124926 2 0.5860 0.1278 0.036 0.564 0.000 0.116 0.284 0.000
#> GSM1124928 1 0.6939 0.0799 0.472 0.312 0.000 0.024 0.132 0.060
#> GSM1124930 2 0.2674 0.3002 0.028 0.892 0.012 0.000 0.048 0.020
#> GSM1124931 2 0.7870 0.3259 0.124 0.400 0.000 0.176 0.260 0.040
#> GSM1124935 5 0.5303 0.4510 0.012 0.452 0.000 0.068 0.468 0.000
#> GSM1124936 6 0.4332 0.8836 0.192 0.004 0.052 0.008 0.004 0.740
#> GSM1124938 2 0.2427 0.3094 0.032 0.904 0.008 0.000 0.040 0.016
#> GSM1124940 1 0.0405 0.7343 0.988 0.004 0.000 0.000 0.000 0.008
#> GSM1124941 2 0.5960 0.3428 0.100 0.616 0.000 0.096 0.188 0.000
#> GSM1124942 2 0.2674 0.3002 0.028 0.892 0.012 0.000 0.048 0.020
#> GSM1124943 2 0.2674 0.3002 0.028 0.892 0.012 0.000 0.048 0.020
#> GSM1124948 2 0.5451 0.4126 0.328 0.580 0.000 0.016 0.064 0.012
#> GSM1124949 1 0.1210 0.7271 0.960 0.008 0.000 0.004 0.008 0.020
#> GSM1124950 2 0.7677 0.3458 0.288 0.408 0.000 0.084 0.176 0.044
#> GSM1124954 6 0.2333 0.9211 0.120 0.004 0.004 0.000 0.000 0.872
#> GSM1124955 1 0.0405 0.7354 0.988 0.008 0.000 0.000 0.000 0.004
#> GSM1124956 2 0.5929 0.1299 0.036 0.556 0.000 0.124 0.284 0.000
#> GSM1124872 2 0.7677 0.3458 0.288 0.408 0.000 0.084 0.176 0.044
#> GSM1124873 2 0.5927 0.4281 0.156 0.612 0.000 0.060 0.172 0.000
#> GSM1124876 3 0.3703 0.6468 0.104 0.000 0.788 0.000 0.000 0.108
#> GSM1124877 1 0.0717 0.7347 0.976 0.016 0.000 0.000 0.000 0.008
#> GSM1124879 1 0.3751 0.6492 0.828 0.084 0.000 0.024 0.040 0.024
#> GSM1124883 5 0.3189 0.7292 0.000 0.236 0.000 0.000 0.760 0.004
#> GSM1124889 2 0.5198 -0.1824 0.016 0.528 0.000 0.056 0.400 0.000
#> GSM1124892 1 0.4363 -0.0174 0.580 0.004 0.000 0.008 0.008 0.400
#> GSM1124893 1 0.0405 0.7336 0.988 0.004 0.000 0.000 0.000 0.008
#> GSM1124909 2 0.5869 0.4317 0.156 0.620 0.000 0.060 0.164 0.000
#> GSM1124913 5 0.3189 0.7292 0.000 0.236 0.000 0.000 0.760 0.004
#> GSM1124916 2 0.5869 0.4317 0.156 0.620 0.000 0.060 0.164 0.000
#> GSM1124923 2 0.5383 0.1505 0.000 0.712 0.080 0.020 0.108 0.080
#> GSM1124925 1 0.0405 0.7354 0.988 0.008 0.000 0.000 0.000 0.004
#> GSM1124929 1 0.1210 0.7271 0.960 0.008 0.000 0.004 0.008 0.020
#> GSM1124934 6 0.2402 0.9161 0.120 0.012 0.000 0.000 0.000 0.868
#> GSM1124937 2 0.6047 0.1458 0.440 0.444 0.000 0.024 0.068 0.024
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 tissue(p) k
#> SD:hclust 89 1.49e-01 2
#> SD:hclust 76 1.83e-01 3
#> SD:hclust 76 6.47e-06 4
#> SD:hclust 70 1.04e-06 5
#> SD:hclust 43 4.30e-03 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 48983 rows and 91 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 0.899 0.894 0.942 0.4661 0.516 0.516
#> 3 3 0.546 0.609 0.814 0.3007 0.896 0.805
#> 4 4 0.618 0.611 0.809 0.1707 0.786 0.557
#> 5 5 0.642 0.682 0.766 0.0827 0.823 0.496
#> 6 6 0.705 0.644 0.767 0.0550 0.930 0.704
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
#> GSM1124891 1 0.1843 0.911 0.972 0.028
#> GSM1124888 1 0.1843 0.911 0.972 0.028
#> GSM1124890 1 0.9909 0.336 0.556 0.444
#> GSM1124904 2 0.0000 0.968 0.000 1.000
#> GSM1124927 2 0.4022 0.895 0.080 0.920
#> GSM1124953 2 0.1843 0.952 0.028 0.972
#> GSM1124869 1 0.3114 0.917 0.944 0.056
#> GSM1124870 1 0.3733 0.907 0.928 0.072
#> GSM1124882 1 0.3114 0.917 0.944 0.056
#> GSM1124884 2 0.0938 0.967 0.012 0.988
#> GSM1124898 2 0.0000 0.968 0.000 1.000
#> GSM1124903 2 0.0376 0.967 0.004 0.996
#> GSM1124905 1 0.3114 0.917 0.944 0.056
#> GSM1124910 1 0.2043 0.912 0.968 0.032
#> GSM1124919 2 0.1633 0.955 0.024 0.976
#> GSM1124932 2 0.0938 0.967 0.012 0.988
#> GSM1124933 1 0.1843 0.911 0.972 0.028
#> GSM1124867 2 0.0938 0.967 0.012 0.988
#> GSM1124868 2 0.1843 0.955 0.028 0.972
#> GSM1124878 2 0.1843 0.955 0.028 0.972
#> GSM1124895 2 0.1843 0.955 0.028 0.972
#> GSM1124897 2 0.1843 0.955 0.028 0.972
#> GSM1124902 2 0.1843 0.955 0.028 0.972
#> GSM1124908 2 0.2043 0.953 0.032 0.968
#> GSM1124921 2 0.2043 0.953 0.032 0.968
#> GSM1124939 2 0.1843 0.955 0.028 0.972
#> GSM1124944 2 0.2043 0.953 0.032 0.968
#> GSM1124945 1 0.9795 0.334 0.584 0.416
#> GSM1124946 2 0.2043 0.953 0.032 0.968
#> GSM1124947 2 0.1843 0.955 0.028 0.972
#> GSM1124951 1 0.9795 0.334 0.584 0.416
#> GSM1124952 2 0.1843 0.955 0.028 0.972
#> GSM1124957 1 0.0938 0.887 0.988 0.012
#> GSM1124900 1 0.3733 0.907 0.928 0.072
#> GSM1124914 2 0.0000 0.968 0.000 1.000
#> GSM1124871 2 0.0000 0.968 0.000 1.000
#> GSM1124874 2 0.0938 0.967 0.012 0.988
#> GSM1124875 2 0.0000 0.968 0.000 1.000
#> GSM1124880 1 0.3114 0.917 0.944 0.056
#> GSM1124881 2 0.0938 0.967 0.012 0.988
#> GSM1124885 2 0.0000 0.968 0.000 1.000
#> GSM1124886 1 0.2043 0.912 0.968 0.032
#> GSM1124887 2 0.0376 0.967 0.004 0.996
#> GSM1124894 2 0.8955 0.486 0.312 0.688
#> GSM1124896 1 0.3114 0.917 0.944 0.056
#> GSM1124899 2 0.0938 0.967 0.012 0.988
#> GSM1124901 2 0.0000 0.968 0.000 1.000
#> GSM1124906 2 0.0938 0.967 0.012 0.988
#> GSM1124907 2 0.0376 0.967 0.004 0.996
#> GSM1124911 2 0.0938 0.967 0.012 0.988
#> GSM1124912 1 0.3114 0.917 0.944 0.056
#> GSM1124915 2 0.0000 0.968 0.000 1.000
#> GSM1124917 2 0.0000 0.968 0.000 1.000
#> GSM1124918 2 0.0672 0.967 0.008 0.992
#> GSM1124920 1 0.1843 0.911 0.972 0.028
#> GSM1124922 2 0.0938 0.967 0.012 0.988
#> GSM1124924 1 0.5519 0.849 0.872 0.128
#> GSM1124926 2 0.0938 0.967 0.012 0.988
#> GSM1124928 1 0.3114 0.917 0.944 0.056
#> GSM1124930 2 0.1414 0.958 0.020 0.980
#> GSM1124931 2 0.1184 0.964 0.016 0.984
#> GSM1124935 2 0.0000 0.968 0.000 1.000
#> GSM1124936 1 0.1843 0.911 0.972 0.028
#> GSM1124938 1 0.9866 0.352 0.568 0.432
#> GSM1124940 1 0.3114 0.917 0.944 0.056
#> GSM1124941 2 0.0938 0.967 0.012 0.988
#> GSM1124942 2 0.0376 0.967 0.004 0.996
#> GSM1124943 2 0.9795 0.149 0.416 0.584
#> GSM1124948 1 0.9909 0.319 0.556 0.444
#> GSM1124949 1 0.3114 0.917 0.944 0.056
#> GSM1124950 2 0.0938 0.967 0.012 0.988
#> GSM1124954 1 0.1843 0.911 0.972 0.028
#> GSM1124955 1 0.3114 0.917 0.944 0.056
#> GSM1124956 2 0.0938 0.967 0.012 0.988
#> GSM1124872 2 0.0938 0.967 0.012 0.988
#> GSM1124873 2 0.0938 0.967 0.012 0.988
#> GSM1124876 1 0.1843 0.911 0.972 0.028
#> GSM1124877 1 0.3114 0.917 0.944 0.056
#> GSM1124879 1 0.3114 0.917 0.944 0.056
#> GSM1124883 2 0.0000 0.968 0.000 1.000
#> GSM1124889 2 0.0938 0.967 0.012 0.988
#> GSM1124892 1 0.2043 0.912 0.968 0.032
#> GSM1124893 1 0.3114 0.917 0.944 0.056
#> GSM1124909 2 0.0938 0.967 0.012 0.988
#> GSM1124913 2 0.0000 0.968 0.000 1.000
#> GSM1124916 2 0.0938 0.967 0.012 0.988
#> GSM1124923 2 0.1843 0.952 0.028 0.972
#> GSM1124925 1 0.3274 0.915 0.940 0.060
#> GSM1124929 1 0.3114 0.917 0.944 0.056
#> GSM1124934 1 0.2043 0.912 0.968 0.032
#> GSM1124937 1 0.6973 0.800 0.812 0.188
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1124891 1 0.6309 -0.0598 0.500 0.000 0.500
#> GSM1124888 3 0.6309 -0.0782 0.500 0.000 0.500
#> GSM1124890 3 0.8676 0.5546 0.112 0.368 0.520
#> GSM1124904 2 0.4121 0.7257 0.000 0.832 0.168
#> GSM1124927 2 0.3454 0.6912 0.104 0.888 0.008
#> GSM1124953 3 0.6275 0.4653 0.008 0.348 0.644
#> GSM1124869 1 0.0237 0.8034 0.996 0.004 0.000
#> GSM1124870 1 0.4861 0.6045 0.800 0.192 0.008
#> GSM1124882 1 0.0237 0.8034 0.996 0.004 0.000
#> GSM1124884 2 0.0592 0.7788 0.012 0.988 0.000
#> GSM1124898 2 0.1163 0.7746 0.000 0.972 0.028
#> GSM1124903 2 0.4178 0.7235 0.000 0.828 0.172
#> GSM1124905 1 0.1399 0.7943 0.968 0.028 0.004
#> GSM1124910 1 0.0829 0.7984 0.984 0.004 0.012
#> GSM1124919 2 0.6148 0.3341 0.004 0.640 0.356
#> GSM1124932 2 0.1267 0.7762 0.024 0.972 0.004
#> GSM1124933 3 0.6309 -0.0656 0.496 0.000 0.504
#> GSM1124867 2 0.1399 0.7743 0.028 0.968 0.004
#> GSM1124868 2 0.5948 0.5784 0.000 0.640 0.360
#> GSM1124878 2 0.5859 0.5933 0.000 0.656 0.344
#> GSM1124895 2 0.6309 0.4410 0.000 0.504 0.496
#> GSM1124897 2 0.5882 0.5905 0.000 0.652 0.348
#> GSM1124902 2 0.6309 0.4410 0.000 0.504 0.496
#> GSM1124908 2 0.6309 0.4410 0.000 0.504 0.496
#> GSM1124921 2 0.6309 0.4410 0.000 0.504 0.496
#> GSM1124939 2 0.6309 0.4410 0.000 0.504 0.496
#> GSM1124944 2 0.6309 0.4410 0.000 0.504 0.496
#> GSM1124945 3 0.3112 0.4869 0.096 0.004 0.900
#> GSM1124946 2 0.6309 0.4410 0.000 0.504 0.496
#> GSM1124947 2 0.6309 0.4410 0.000 0.504 0.496
#> GSM1124951 3 0.2772 0.4871 0.080 0.004 0.916
#> GSM1124952 2 0.6309 0.4410 0.000 0.504 0.496
#> GSM1124957 3 0.4235 0.4417 0.176 0.000 0.824
#> GSM1124900 1 0.4912 0.5982 0.796 0.196 0.008
#> GSM1124914 2 0.2878 0.7565 0.000 0.904 0.096
#> GSM1124871 2 0.0237 0.7785 0.004 0.996 0.000
#> GSM1124874 2 0.1031 0.7772 0.024 0.976 0.000
#> GSM1124875 2 0.1031 0.7761 0.000 0.976 0.024
#> GSM1124880 1 0.4531 0.6353 0.824 0.168 0.008
#> GSM1124881 2 0.1267 0.7762 0.024 0.972 0.004
#> GSM1124885 2 0.3816 0.7359 0.000 0.852 0.148
#> GSM1124886 1 0.0661 0.7987 0.988 0.004 0.008
#> GSM1124887 2 0.4235 0.7219 0.000 0.824 0.176
#> GSM1124894 2 0.8264 0.2325 0.356 0.556 0.088
#> GSM1124896 1 0.1411 0.7894 0.964 0.036 0.000
#> GSM1124899 2 0.1031 0.7772 0.024 0.976 0.000
#> GSM1124901 2 0.1289 0.7745 0.000 0.968 0.032
#> GSM1124906 2 0.1267 0.7762 0.024 0.972 0.004
#> GSM1124907 2 0.3038 0.7545 0.000 0.896 0.104
#> GSM1124911 2 0.1031 0.7772 0.024 0.976 0.000
#> GSM1124912 1 0.0237 0.8034 0.996 0.004 0.000
#> GSM1124915 2 0.1289 0.7749 0.000 0.968 0.032
#> GSM1124917 2 0.0475 0.7781 0.004 0.992 0.004
#> GSM1124918 2 0.0829 0.7782 0.012 0.984 0.004
#> GSM1124920 1 0.6192 0.1787 0.580 0.000 0.420
#> GSM1124922 2 0.1031 0.7772 0.024 0.976 0.000
#> GSM1124924 3 0.9914 0.4073 0.272 0.348 0.380
#> GSM1124926 2 0.1267 0.7778 0.024 0.972 0.004
#> GSM1124928 1 0.1129 0.7983 0.976 0.020 0.004
#> GSM1124930 2 0.4002 0.6996 0.000 0.840 0.160
#> GSM1124931 2 0.1453 0.7742 0.024 0.968 0.008
#> GSM1124935 2 0.0000 0.7781 0.000 1.000 0.000
#> GSM1124936 1 0.6168 0.1928 0.588 0.000 0.412
#> GSM1124938 3 0.8474 0.5179 0.092 0.404 0.504
#> GSM1124940 1 0.0237 0.8034 0.996 0.004 0.000
#> GSM1124941 2 0.1267 0.7762 0.024 0.972 0.004
#> GSM1124942 2 0.3038 0.7520 0.000 0.896 0.104
#> GSM1124943 3 0.8157 0.4885 0.072 0.412 0.516
#> GSM1124948 2 0.8363 -0.3697 0.084 0.504 0.412
#> GSM1124949 1 0.0237 0.8034 0.996 0.004 0.000
#> GSM1124950 2 0.1267 0.7762 0.024 0.972 0.004
#> GSM1124954 1 0.6235 0.1403 0.564 0.000 0.436
#> GSM1124955 1 0.1411 0.7894 0.964 0.036 0.000
#> GSM1124956 2 0.1031 0.7772 0.024 0.976 0.000
#> GSM1124872 2 0.1267 0.7762 0.024 0.972 0.004
#> GSM1124873 2 0.1267 0.7762 0.024 0.972 0.004
#> GSM1124876 1 0.6309 -0.0598 0.500 0.000 0.500
#> GSM1124877 1 0.0237 0.8034 0.996 0.004 0.000
#> GSM1124879 1 0.0592 0.8014 0.988 0.012 0.000
#> GSM1124883 2 0.4002 0.7301 0.000 0.840 0.160
#> GSM1124889 2 0.0892 0.7781 0.020 0.980 0.000
#> GSM1124892 1 0.0592 0.7920 0.988 0.000 0.012
#> GSM1124893 1 0.0237 0.8034 0.996 0.004 0.000
#> GSM1124909 2 0.1267 0.7762 0.024 0.972 0.004
#> GSM1124913 2 0.4121 0.7257 0.000 0.832 0.168
#> GSM1124916 2 0.1267 0.7762 0.024 0.972 0.004
#> GSM1124923 2 0.6518 0.0737 0.004 0.512 0.484
#> GSM1124925 1 0.1529 0.7859 0.960 0.040 0.000
#> GSM1124929 1 0.0237 0.8034 0.996 0.004 0.000
#> GSM1124934 1 0.5070 0.5800 0.772 0.004 0.224
#> GSM1124937 1 0.5591 0.3324 0.696 0.304 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1124891 3 0.3975 0.6148 0.240 0.000 0.760 0.000
#> GSM1124888 3 0.3942 0.6173 0.236 0.000 0.764 0.000
#> GSM1124890 3 0.3847 0.5849 0.020 0.108 0.852 0.020
#> GSM1124904 2 0.7469 0.0359 0.000 0.432 0.176 0.392
#> GSM1124927 2 0.2363 0.7298 0.024 0.920 0.056 0.000
#> GSM1124953 3 0.2032 0.5961 0.000 0.036 0.936 0.028
#> GSM1124869 1 0.0188 0.8720 0.996 0.004 0.000 0.000
#> GSM1124870 1 0.6206 0.3313 0.540 0.404 0.056 0.000
#> GSM1124882 1 0.0188 0.8720 0.996 0.004 0.000 0.000
#> GSM1124884 2 0.0336 0.7504 0.000 0.992 0.000 0.008
#> GSM1124898 2 0.5690 0.5933 0.000 0.716 0.168 0.116
#> GSM1124903 2 0.7416 0.0571 0.000 0.440 0.168 0.392
#> GSM1124905 1 0.2744 0.8138 0.912 0.052 0.024 0.012
#> GSM1124910 1 0.0000 0.8687 1.000 0.000 0.000 0.000
#> GSM1124919 3 0.6928 -0.1043 0.000 0.372 0.512 0.116
#> GSM1124932 2 0.1389 0.7450 0.000 0.952 0.048 0.000
#> GSM1124933 3 0.3626 0.6349 0.184 0.000 0.812 0.004
#> GSM1124867 2 0.2021 0.7402 0.000 0.932 0.056 0.012
#> GSM1124868 4 0.5681 0.6204 0.000 0.208 0.088 0.704
#> GSM1124878 4 0.7321 0.2587 0.000 0.328 0.172 0.500
#> GSM1124895 4 0.1211 0.8388 0.000 0.040 0.000 0.960
#> GSM1124897 4 0.7321 0.2587 0.000 0.328 0.172 0.500
#> GSM1124902 4 0.1211 0.8388 0.000 0.040 0.000 0.960
#> GSM1124908 4 0.1118 0.8376 0.000 0.036 0.000 0.964
#> GSM1124921 4 0.0817 0.8335 0.000 0.024 0.000 0.976
#> GSM1124939 4 0.1211 0.8388 0.000 0.040 0.000 0.960
#> GSM1124944 4 0.0817 0.8335 0.000 0.024 0.000 0.976
#> GSM1124945 3 0.4391 0.5438 0.008 0.000 0.740 0.252
#> GSM1124946 4 0.0817 0.8335 0.000 0.024 0.000 0.976
#> GSM1124947 4 0.0817 0.8335 0.000 0.024 0.000 0.976
#> GSM1124951 3 0.4155 0.5499 0.004 0.000 0.756 0.240
#> GSM1124952 4 0.1211 0.8388 0.000 0.040 0.000 0.960
#> GSM1124957 3 0.4361 0.5736 0.020 0.000 0.772 0.208
#> GSM1124900 1 0.6206 0.3313 0.540 0.404 0.056 0.000
#> GSM1124914 2 0.7015 0.3725 0.000 0.568 0.168 0.264
#> GSM1124871 2 0.0804 0.7490 0.000 0.980 0.008 0.012
#> GSM1124874 2 0.0927 0.7482 0.000 0.976 0.016 0.008
#> GSM1124875 2 0.4418 0.6794 0.000 0.784 0.184 0.032
#> GSM1124880 2 0.6395 -0.1813 0.460 0.476 0.064 0.000
#> GSM1124881 2 0.1398 0.7476 0.000 0.956 0.040 0.004
#> GSM1124885 2 0.7412 0.0698 0.000 0.444 0.168 0.388
#> GSM1124886 1 0.0000 0.8687 1.000 0.000 0.000 0.000
#> GSM1124887 2 0.7472 0.0402 0.000 0.428 0.176 0.396
#> GSM1124894 2 0.9042 0.1236 0.248 0.456 0.100 0.196
#> GSM1124896 1 0.0524 0.8687 0.988 0.008 0.000 0.004
#> GSM1124899 2 0.0000 0.7517 0.000 1.000 0.000 0.000
#> GSM1124901 2 0.5842 0.5800 0.000 0.704 0.168 0.128
#> GSM1124906 2 0.0188 0.7519 0.000 0.996 0.004 0.000
#> GSM1124907 2 0.6967 0.4374 0.000 0.580 0.176 0.244
#> GSM1124911 2 0.0336 0.7504 0.000 0.992 0.000 0.008
#> GSM1124912 1 0.0188 0.8720 0.996 0.004 0.000 0.000
#> GSM1124915 2 0.5742 0.5892 0.000 0.712 0.168 0.120
#> GSM1124917 2 0.4095 0.6891 0.000 0.792 0.192 0.016
#> GSM1124918 2 0.2060 0.7463 0.000 0.932 0.052 0.016
#> GSM1124920 3 0.4996 0.2548 0.484 0.000 0.516 0.000
#> GSM1124922 2 0.0000 0.7517 0.000 1.000 0.000 0.000
#> GSM1124924 2 0.6211 0.4414 0.072 0.676 0.236 0.016
#> GSM1124926 2 0.0524 0.7503 0.000 0.988 0.004 0.008
#> GSM1124928 1 0.2399 0.8147 0.920 0.048 0.032 0.000
#> GSM1124930 2 0.6790 0.5126 0.000 0.576 0.296 0.128
#> GSM1124931 2 0.1557 0.7435 0.000 0.944 0.056 0.000
#> GSM1124935 2 0.4010 0.6777 0.000 0.816 0.156 0.028
#> GSM1124936 3 0.4996 0.2626 0.484 0.000 0.516 0.000
#> GSM1124938 3 0.5437 0.5060 0.024 0.244 0.712 0.020
#> GSM1124940 1 0.0188 0.8720 0.996 0.004 0.000 0.000
#> GSM1124941 2 0.0188 0.7519 0.000 0.996 0.004 0.000
#> GSM1124942 2 0.5757 0.6172 0.000 0.684 0.240 0.076
#> GSM1124943 3 0.4709 0.5028 0.008 0.200 0.768 0.024
#> GSM1124948 2 0.5066 0.5251 0.016 0.740 0.224 0.020
#> GSM1124949 1 0.0188 0.8720 0.996 0.004 0.000 0.000
#> GSM1124950 2 0.1557 0.7435 0.000 0.944 0.056 0.000
#> GSM1124954 3 0.5387 0.3991 0.400 0.000 0.584 0.016
#> GSM1124955 1 0.0336 0.8700 0.992 0.008 0.000 0.000
#> GSM1124956 2 0.0336 0.7504 0.000 0.992 0.000 0.008
#> GSM1124872 2 0.1557 0.7435 0.000 0.944 0.056 0.000
#> GSM1124873 2 0.1389 0.7458 0.000 0.952 0.048 0.000
#> GSM1124876 3 0.4088 0.6177 0.232 0.000 0.764 0.004
#> GSM1124877 1 0.0188 0.8720 0.996 0.004 0.000 0.000
#> GSM1124879 1 0.0188 0.8720 0.996 0.004 0.000 0.000
#> GSM1124883 2 0.7416 0.0571 0.000 0.440 0.168 0.392
#> GSM1124889 2 0.0336 0.7504 0.000 0.992 0.000 0.008
#> GSM1124892 1 0.0000 0.8687 1.000 0.000 0.000 0.000
#> GSM1124893 1 0.0188 0.8720 0.996 0.004 0.000 0.000
#> GSM1124909 2 0.1854 0.7431 0.000 0.940 0.048 0.012
#> GSM1124913 2 0.7416 0.0571 0.000 0.440 0.168 0.392
#> GSM1124916 2 0.1854 0.7431 0.000 0.940 0.048 0.012
#> GSM1124923 3 0.6397 0.2895 0.000 0.208 0.648 0.144
#> GSM1124925 1 0.0336 0.8700 0.992 0.008 0.000 0.000
#> GSM1124929 1 0.0188 0.8720 0.996 0.004 0.000 0.000
#> GSM1124934 1 0.5157 0.3714 0.676 0.004 0.304 0.016
#> GSM1124937 1 0.5937 0.3887 0.608 0.340 0.052 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM1124891 3 0.1981 0.7497 0.064 0.000 0.920 0.000 0.016
#> GSM1124888 3 0.1981 0.7510 0.064 0.000 0.920 0.000 0.016
#> GSM1124890 3 0.4900 0.4086 0.000 0.024 0.512 0.000 0.464
#> GSM1124904 5 0.5638 0.7000 0.000 0.152 0.000 0.216 0.632
#> GSM1124927 2 0.1444 0.7111 0.000 0.948 0.040 0.000 0.012
#> GSM1124953 3 0.4511 0.5639 0.000 0.016 0.628 0.000 0.356
#> GSM1124869 1 0.0162 0.9323 0.996 0.004 0.000 0.000 0.000
#> GSM1124870 2 0.5019 0.4591 0.280 0.668 0.040 0.000 0.012
#> GSM1124882 1 0.0162 0.9323 0.996 0.004 0.000 0.000 0.000
#> GSM1124884 2 0.3697 0.6688 0.000 0.796 0.008 0.016 0.180
#> GSM1124898 5 0.4570 0.6173 0.000 0.348 0.000 0.020 0.632
#> GSM1124903 5 0.5707 0.7003 0.000 0.160 0.000 0.216 0.624
#> GSM1124905 1 0.7066 0.4803 0.572 0.224 0.100 0.004 0.100
#> GSM1124910 1 0.2507 0.8602 0.908 0.020 0.044 0.000 0.028
#> GSM1124919 5 0.4490 0.4758 0.000 0.072 0.168 0.004 0.756
#> GSM1124932 2 0.1243 0.7261 0.000 0.960 0.008 0.004 0.028
#> GSM1124933 3 0.2124 0.7451 0.028 0.000 0.916 0.000 0.056
#> GSM1124867 2 0.2054 0.7032 0.000 0.920 0.028 0.000 0.052
#> GSM1124868 4 0.5928 -0.0363 0.000 0.124 0.000 0.548 0.328
#> GSM1124878 5 0.5887 0.6620 0.000 0.156 0.000 0.252 0.592
#> GSM1124895 4 0.0162 0.9288 0.000 0.004 0.000 0.996 0.000
#> GSM1124897 5 0.5854 0.6615 0.000 0.152 0.000 0.252 0.596
#> GSM1124902 4 0.0162 0.9288 0.000 0.004 0.000 0.996 0.000
#> GSM1124908 4 0.0613 0.9256 0.000 0.004 0.004 0.984 0.008
#> GSM1124921 4 0.0727 0.9251 0.000 0.004 0.004 0.980 0.012
#> GSM1124939 4 0.0162 0.9288 0.000 0.004 0.000 0.996 0.000
#> GSM1124944 4 0.0324 0.9275 0.000 0.004 0.000 0.992 0.004
#> GSM1124945 3 0.3697 0.7011 0.000 0.000 0.820 0.100 0.080
#> GSM1124946 4 0.0727 0.9251 0.000 0.004 0.004 0.980 0.012
#> GSM1124947 4 0.0324 0.9275 0.000 0.004 0.000 0.992 0.004
#> GSM1124951 3 0.3622 0.7135 0.000 0.000 0.820 0.056 0.124
#> GSM1124952 4 0.0162 0.9288 0.000 0.004 0.000 0.996 0.000
#> GSM1124957 3 0.2592 0.7259 0.000 0.000 0.892 0.052 0.056
#> GSM1124900 2 0.5019 0.4591 0.280 0.668 0.040 0.000 0.012
#> GSM1124914 5 0.5541 0.7039 0.000 0.236 0.000 0.128 0.636
#> GSM1124871 2 0.3718 0.6517 0.000 0.784 0.004 0.016 0.196
#> GSM1124874 2 0.3421 0.6884 0.000 0.824 0.008 0.016 0.152
#> GSM1124875 5 0.3949 0.5613 0.000 0.300 0.004 0.000 0.696
#> GSM1124880 2 0.5591 0.5619 0.140 0.712 0.088 0.000 0.060
#> GSM1124881 2 0.2583 0.7115 0.000 0.864 0.000 0.004 0.132
#> GSM1124885 5 0.5740 0.7025 0.000 0.164 0.000 0.216 0.620
#> GSM1124886 1 0.0162 0.9291 0.996 0.000 0.000 0.000 0.004
#> GSM1124887 5 0.4901 0.7025 0.000 0.116 0.000 0.168 0.716
#> GSM1124894 2 0.7280 0.4233 0.044 0.600 0.184 0.064 0.108
#> GSM1124896 1 0.0960 0.9221 0.972 0.004 0.008 0.000 0.016
#> GSM1124899 2 0.3544 0.6485 0.000 0.788 0.004 0.008 0.200
#> GSM1124901 5 0.4819 0.6113 0.000 0.352 0.004 0.024 0.620
#> GSM1124906 2 0.3053 0.6882 0.000 0.828 0.008 0.000 0.164
#> GSM1124907 5 0.3731 0.6799 0.000 0.160 0.000 0.040 0.800
#> GSM1124911 2 0.3538 0.6728 0.000 0.804 0.004 0.016 0.176
#> GSM1124912 1 0.0162 0.9323 0.996 0.004 0.000 0.000 0.000
#> GSM1124915 5 0.4696 0.6018 0.000 0.360 0.000 0.024 0.616
#> GSM1124917 5 0.4138 0.4562 0.000 0.384 0.000 0.000 0.616
#> GSM1124918 2 0.4268 0.5063 0.000 0.648 0.008 0.000 0.344
#> GSM1124920 3 0.4575 0.5289 0.328 0.000 0.648 0.000 0.024
#> GSM1124922 2 0.3585 0.6213 0.000 0.772 0.004 0.004 0.220
#> GSM1124924 2 0.5578 0.5343 0.020 0.680 0.108 0.000 0.192
#> GSM1124926 2 0.3718 0.6498 0.000 0.784 0.004 0.016 0.196
#> GSM1124928 1 0.5835 0.5100 0.624 0.276 0.072 0.000 0.028
#> GSM1124930 5 0.4150 0.6178 0.000 0.180 0.044 0.004 0.772
#> GSM1124931 2 0.1569 0.7150 0.000 0.944 0.044 0.004 0.008
#> GSM1124935 5 0.4604 0.4825 0.000 0.428 0.000 0.012 0.560
#> GSM1124936 3 0.4639 0.4769 0.368 0.000 0.612 0.000 0.020
#> GSM1124938 3 0.6318 0.3329 0.000 0.168 0.488 0.000 0.344
#> GSM1124940 1 0.0162 0.9323 0.996 0.004 0.000 0.000 0.000
#> GSM1124941 2 0.3053 0.6882 0.000 0.828 0.008 0.000 0.164
#> GSM1124942 5 0.4240 0.6145 0.000 0.240 0.024 0.004 0.732
#> GSM1124943 5 0.5389 -0.1968 0.000 0.056 0.436 0.000 0.508
#> GSM1124948 2 0.4960 0.5392 0.000 0.688 0.080 0.000 0.232
#> GSM1124949 1 0.0162 0.9323 0.996 0.004 0.000 0.000 0.000
#> GSM1124950 2 0.1522 0.7142 0.000 0.944 0.044 0.000 0.012
#> GSM1124954 3 0.4747 0.6609 0.184 0.000 0.732 0.004 0.080
#> GSM1124955 1 0.0566 0.9298 0.984 0.004 0.000 0.000 0.012
#> GSM1124956 2 0.3538 0.6728 0.000 0.804 0.004 0.016 0.176
#> GSM1124872 2 0.1331 0.7151 0.000 0.952 0.040 0.000 0.008
#> GSM1124873 2 0.1952 0.7206 0.000 0.912 0.000 0.004 0.084
#> GSM1124876 3 0.2588 0.7495 0.060 0.000 0.892 0.000 0.048
#> GSM1124877 1 0.0451 0.9309 0.988 0.004 0.000 0.000 0.008
#> GSM1124879 1 0.0566 0.9298 0.984 0.004 0.000 0.000 0.012
#> GSM1124883 5 0.5707 0.7003 0.000 0.160 0.000 0.216 0.624
#> GSM1124889 2 0.3575 0.6692 0.000 0.800 0.004 0.016 0.180
#> GSM1124892 1 0.0290 0.9273 0.992 0.000 0.000 0.000 0.008
#> GSM1124893 1 0.0162 0.9323 0.996 0.004 0.000 0.000 0.000
#> GSM1124909 2 0.1952 0.7166 0.000 0.912 0.004 0.000 0.084
#> GSM1124913 5 0.5707 0.7003 0.000 0.160 0.000 0.216 0.624
#> GSM1124916 2 0.1952 0.7166 0.000 0.912 0.004 0.000 0.084
#> GSM1124923 5 0.4256 0.4254 0.000 0.044 0.192 0.004 0.760
#> GSM1124925 1 0.0566 0.9298 0.984 0.004 0.000 0.000 0.012
#> GSM1124929 1 0.0162 0.9323 0.996 0.004 0.000 0.000 0.000
#> GSM1124934 3 0.6671 0.4241 0.304 0.036 0.548 0.004 0.108
#> GSM1124937 2 0.6602 0.2176 0.324 0.540 0.060 0.000 0.076
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1124891 3 0.3465 0.70337 0.024 0.000 0.804 0.000 0.156 0.016
#> GSM1124888 3 0.3313 0.70744 0.024 0.000 0.820 0.000 0.140 0.016
#> GSM1124890 5 0.6063 0.29861 0.000 0.012 0.316 0.000 0.480 0.192
#> GSM1124904 6 0.3013 0.77979 0.000 0.024 0.004 0.116 0.008 0.848
#> GSM1124927 2 0.2738 0.64782 0.000 0.820 0.000 0.000 0.176 0.004
#> GSM1124953 3 0.5532 -0.00867 0.000 0.012 0.520 0.000 0.368 0.100
#> GSM1124869 1 0.0000 0.93480 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124870 2 0.3771 0.60356 0.056 0.764 0.000 0.000 0.180 0.000
#> GSM1124882 1 0.0000 0.93480 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124884 2 0.3309 0.70136 0.000 0.788 0.004 0.000 0.016 0.192
#> GSM1124898 6 0.2613 0.69893 0.000 0.140 0.000 0.000 0.012 0.848
#> GSM1124903 6 0.2902 0.78121 0.000 0.024 0.004 0.116 0.004 0.852
#> GSM1124905 5 0.6643 -0.27104 0.384 0.160 0.012 0.004 0.416 0.024
#> GSM1124910 1 0.2730 0.74108 0.808 0.000 0.000 0.000 0.192 0.000
#> GSM1124919 5 0.6038 0.38691 0.000 0.020 0.140 0.000 0.432 0.408
#> GSM1124932 2 0.1720 0.71588 0.000 0.928 0.000 0.000 0.032 0.040
#> GSM1124933 3 0.0964 0.70100 0.000 0.004 0.968 0.000 0.016 0.012
#> GSM1124867 2 0.2703 0.64934 0.000 0.824 0.000 0.000 0.172 0.004
#> GSM1124868 6 0.4477 0.43227 0.000 0.020 0.004 0.384 0.004 0.588
#> GSM1124878 6 0.3310 0.77284 0.000 0.024 0.008 0.124 0.012 0.832
#> GSM1124895 4 0.0146 0.99283 0.000 0.004 0.000 0.996 0.000 0.000
#> GSM1124897 6 0.3747 0.76328 0.000 0.024 0.004 0.124 0.040 0.808
#> GSM1124902 4 0.0146 0.99283 0.000 0.004 0.000 0.996 0.000 0.000
#> GSM1124908 4 0.0665 0.98561 0.000 0.000 0.008 0.980 0.004 0.008
#> GSM1124921 4 0.0665 0.98561 0.000 0.000 0.008 0.980 0.004 0.008
#> GSM1124939 4 0.0146 0.99283 0.000 0.004 0.000 0.996 0.000 0.000
#> GSM1124944 4 0.0146 0.99283 0.000 0.004 0.000 0.996 0.000 0.000
#> GSM1124945 3 0.3411 0.63393 0.000 0.000 0.836 0.044 0.088 0.032
#> GSM1124946 4 0.0665 0.98561 0.000 0.000 0.008 0.980 0.004 0.008
#> GSM1124947 4 0.0146 0.99283 0.000 0.004 0.000 0.996 0.000 0.000
#> GSM1124951 3 0.3098 0.62341 0.000 0.000 0.844 0.004 0.088 0.064
#> GSM1124952 4 0.0146 0.99283 0.000 0.004 0.000 0.996 0.000 0.000
#> GSM1124957 3 0.0767 0.70081 0.000 0.000 0.976 0.008 0.004 0.012
#> GSM1124900 2 0.3771 0.60356 0.056 0.764 0.000 0.000 0.180 0.000
#> GSM1124914 6 0.2926 0.72212 0.000 0.124 0.000 0.028 0.004 0.844
#> GSM1124871 2 0.3341 0.69278 0.000 0.776 0.004 0.000 0.012 0.208
#> GSM1124874 2 0.3453 0.71124 0.000 0.788 0.004 0.000 0.028 0.180
#> GSM1124875 5 0.5943 0.26240 0.000 0.216 0.000 0.000 0.404 0.380
#> GSM1124880 2 0.3876 0.53618 0.024 0.700 0.000 0.000 0.276 0.000
#> GSM1124881 2 0.2306 0.71516 0.000 0.888 0.004 0.000 0.016 0.092
#> GSM1124885 6 0.2871 0.77999 0.000 0.024 0.000 0.116 0.008 0.852
#> GSM1124886 1 0.0405 0.92964 0.988 0.000 0.000 0.000 0.008 0.004
#> GSM1124887 6 0.3303 0.67447 0.000 0.020 0.004 0.040 0.092 0.844
#> GSM1124894 2 0.6277 0.27807 0.008 0.472 0.064 0.024 0.404 0.028
#> GSM1124896 1 0.0862 0.92473 0.972 0.000 0.000 0.004 0.016 0.008
#> GSM1124899 2 0.3622 0.68152 0.000 0.760 0.004 0.000 0.024 0.212
#> GSM1124901 6 0.3452 0.65454 0.000 0.176 0.000 0.008 0.024 0.792
#> GSM1124906 2 0.3176 0.71058 0.000 0.812 0.000 0.000 0.032 0.156
#> GSM1124907 6 0.4803 -0.21288 0.000 0.044 0.004 0.000 0.424 0.528
#> GSM1124911 2 0.3309 0.70226 0.000 0.788 0.004 0.000 0.016 0.192
#> GSM1124912 1 0.0000 0.93480 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124915 6 0.2573 0.71781 0.000 0.132 0.000 0.008 0.004 0.856
#> GSM1124917 2 0.6069 -0.36838 0.000 0.368 0.000 0.000 0.368 0.264
#> GSM1124918 2 0.5353 -0.12399 0.000 0.472 0.000 0.000 0.420 0.108
#> GSM1124920 3 0.5754 0.59393 0.240 0.000 0.572 0.000 0.172 0.016
#> GSM1124922 2 0.3852 0.66524 0.000 0.740 0.004 0.000 0.032 0.224
#> GSM1124924 5 0.4111 -0.13962 0.000 0.456 0.004 0.000 0.536 0.004
#> GSM1124926 2 0.3213 0.69707 0.000 0.784 0.004 0.000 0.008 0.204
#> GSM1124928 1 0.6009 0.16382 0.432 0.268 0.000 0.000 0.300 0.000
#> GSM1124930 5 0.5710 0.38785 0.000 0.120 0.012 0.000 0.496 0.372
#> GSM1124931 2 0.2830 0.68221 0.000 0.836 0.000 0.000 0.144 0.020
#> GSM1124935 6 0.3898 0.41568 0.000 0.336 0.000 0.000 0.012 0.652
#> GSM1124936 3 0.5335 0.50181 0.336 0.000 0.568 0.000 0.080 0.016
#> GSM1124938 5 0.6408 0.38553 0.000 0.136 0.216 0.000 0.556 0.092
#> GSM1124940 1 0.0000 0.93480 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124941 2 0.3176 0.71058 0.000 0.812 0.000 0.000 0.032 0.156
#> GSM1124942 5 0.6201 0.38643 0.000 0.132 0.036 0.000 0.464 0.368
#> GSM1124943 5 0.6443 0.45427 0.000 0.048 0.212 0.000 0.512 0.228
#> GSM1124948 5 0.4318 0.26154 0.000 0.340 0.008 0.000 0.632 0.020
#> GSM1124949 1 0.0000 0.93480 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124950 2 0.2260 0.66795 0.000 0.860 0.000 0.000 0.140 0.000
#> GSM1124954 3 0.5574 0.62683 0.096 0.000 0.584 0.000 0.292 0.028
#> GSM1124955 1 0.0551 0.93157 0.984 0.000 0.000 0.004 0.004 0.008
#> GSM1124956 2 0.3309 0.70226 0.000 0.788 0.004 0.000 0.016 0.192
#> GSM1124872 2 0.2178 0.66884 0.000 0.868 0.000 0.000 0.132 0.000
#> GSM1124873 2 0.1584 0.71946 0.000 0.928 0.000 0.000 0.008 0.064
#> GSM1124876 3 0.1257 0.71360 0.020 0.000 0.952 0.000 0.028 0.000
#> GSM1124877 1 0.0551 0.93157 0.984 0.000 0.000 0.004 0.004 0.008
#> GSM1124879 1 0.0405 0.93261 0.988 0.000 0.000 0.004 0.000 0.008
#> GSM1124883 6 0.2902 0.78121 0.000 0.024 0.004 0.116 0.004 0.852
#> GSM1124889 2 0.2902 0.70304 0.000 0.800 0.004 0.000 0.000 0.196
#> GSM1124892 1 0.0767 0.92170 0.976 0.000 0.004 0.000 0.008 0.012
#> GSM1124893 1 0.0000 0.93480 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124909 2 0.1838 0.69203 0.000 0.916 0.000 0.000 0.068 0.016
#> GSM1124913 6 0.2902 0.78121 0.000 0.024 0.004 0.116 0.004 0.852
#> GSM1124916 2 0.1779 0.69242 0.000 0.920 0.000 0.000 0.064 0.016
#> GSM1124923 5 0.6005 0.38671 0.000 0.012 0.160 0.000 0.428 0.400
#> GSM1124925 1 0.0551 0.93157 0.984 0.000 0.000 0.004 0.004 0.008
#> GSM1124929 1 0.0000 0.93480 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124934 3 0.6202 0.54763 0.124 0.000 0.480 0.004 0.360 0.032
#> GSM1124937 2 0.5911 0.25700 0.192 0.540 0.000 0.004 0.256 0.008
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 tissue(p) k
#> SD:kmeans 84 9.23e-02 2
#> SD:kmeans 64 6.27e-02 3
#> SD:kmeans 69 7.43e-10 4
#> SD:kmeans 76 4.33e-06 5
#> SD:kmeans 71 2.65e-04 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 48983 rows and 91 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 0.866 0.897 0.959 0.4905 0.512 0.512
#> 3 3 0.499 0.600 0.816 0.3207 0.780 0.598
#> 4 4 0.747 0.791 0.893 0.1607 0.839 0.584
#> 5 5 0.679 0.663 0.810 0.0593 0.910 0.666
#> 6 6 0.717 0.570 0.748 0.0418 0.893 0.545
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
#> GSM1124891 1 0.0000 0.953 1.000 0.000
#> GSM1124888 1 0.0000 0.953 1.000 0.000
#> GSM1124890 1 0.7376 0.743 0.792 0.208
#> GSM1124904 2 0.0000 0.956 0.000 1.000
#> GSM1124927 2 0.9988 0.118 0.480 0.520
#> GSM1124953 2 0.4298 0.870 0.088 0.912
#> GSM1124869 1 0.0000 0.953 1.000 0.000
#> GSM1124870 1 0.0000 0.953 1.000 0.000
#> GSM1124882 1 0.0000 0.953 1.000 0.000
#> GSM1124884 2 0.0000 0.956 0.000 1.000
#> GSM1124898 2 0.0000 0.956 0.000 1.000
#> GSM1124903 2 0.0000 0.956 0.000 1.000
#> GSM1124905 1 0.0000 0.953 1.000 0.000
#> GSM1124910 1 0.0000 0.953 1.000 0.000
#> GSM1124919 2 0.0000 0.956 0.000 1.000
#> GSM1124932 2 0.9000 0.540 0.316 0.684
#> GSM1124933 1 0.0000 0.953 1.000 0.000
#> GSM1124867 2 0.9491 0.369 0.368 0.632
#> GSM1124868 2 0.0000 0.956 0.000 1.000
#> GSM1124878 2 0.0000 0.956 0.000 1.000
#> GSM1124895 2 0.0000 0.956 0.000 1.000
#> GSM1124897 2 0.0000 0.956 0.000 1.000
#> GSM1124902 2 0.0000 0.956 0.000 1.000
#> GSM1124908 2 0.0000 0.956 0.000 1.000
#> GSM1124921 2 0.0000 0.956 0.000 1.000
#> GSM1124939 2 0.0000 0.956 0.000 1.000
#> GSM1124944 2 0.0000 0.956 0.000 1.000
#> GSM1124945 1 0.7219 0.754 0.800 0.200
#> GSM1124946 2 0.0000 0.956 0.000 1.000
#> GSM1124947 2 0.0000 0.956 0.000 1.000
#> GSM1124951 1 0.9460 0.455 0.636 0.364
#> GSM1124952 2 0.0000 0.956 0.000 1.000
#> GSM1124957 1 0.0000 0.953 1.000 0.000
#> GSM1124900 1 0.0000 0.953 1.000 0.000
#> GSM1124914 2 0.0000 0.956 0.000 1.000
#> GSM1124871 2 0.0000 0.956 0.000 1.000
#> GSM1124874 2 0.0000 0.956 0.000 1.000
#> GSM1124875 2 0.0000 0.956 0.000 1.000
#> GSM1124880 1 0.0000 0.953 1.000 0.000
#> GSM1124881 2 0.0000 0.956 0.000 1.000
#> GSM1124885 2 0.0000 0.956 0.000 1.000
#> GSM1124886 1 0.0000 0.953 1.000 0.000
#> GSM1124887 2 0.0000 0.956 0.000 1.000
#> GSM1124894 2 0.9988 0.118 0.480 0.520
#> GSM1124896 1 0.0000 0.953 1.000 0.000
#> GSM1124899 2 0.0000 0.956 0.000 1.000
#> GSM1124901 2 0.0000 0.956 0.000 1.000
#> GSM1124906 2 0.0000 0.956 0.000 1.000
#> GSM1124907 2 0.0000 0.956 0.000 1.000
#> GSM1124911 2 0.0000 0.956 0.000 1.000
#> GSM1124912 1 0.0000 0.953 1.000 0.000
#> GSM1124915 2 0.0000 0.956 0.000 1.000
#> GSM1124917 2 0.0000 0.956 0.000 1.000
#> GSM1124918 2 0.0000 0.956 0.000 1.000
#> GSM1124920 1 0.0000 0.953 1.000 0.000
#> GSM1124922 2 0.0000 0.956 0.000 1.000
#> GSM1124924 1 0.0000 0.953 1.000 0.000
#> GSM1124926 2 0.0000 0.956 0.000 1.000
#> GSM1124928 1 0.0000 0.953 1.000 0.000
#> GSM1124930 2 0.0000 0.956 0.000 1.000
#> GSM1124931 2 0.9608 0.394 0.384 0.616
#> GSM1124935 2 0.0000 0.956 0.000 1.000
#> GSM1124936 1 0.0000 0.953 1.000 0.000
#> GSM1124938 1 0.7299 0.749 0.796 0.204
#> GSM1124940 1 0.0000 0.953 1.000 0.000
#> GSM1124941 2 0.0000 0.956 0.000 1.000
#> GSM1124942 2 0.0000 0.956 0.000 1.000
#> GSM1124943 1 0.9732 0.355 0.596 0.404
#> GSM1124948 1 0.6801 0.778 0.820 0.180
#> GSM1124949 1 0.0000 0.953 1.000 0.000
#> GSM1124950 2 0.1633 0.935 0.024 0.976
#> GSM1124954 1 0.0000 0.953 1.000 0.000
#> GSM1124955 1 0.0000 0.953 1.000 0.000
#> GSM1124956 2 0.0000 0.956 0.000 1.000
#> GSM1124872 2 0.2603 0.917 0.044 0.956
#> GSM1124873 2 0.0000 0.956 0.000 1.000
#> GSM1124876 1 0.0000 0.953 1.000 0.000
#> GSM1124877 1 0.0000 0.953 1.000 0.000
#> GSM1124879 1 0.0000 0.953 1.000 0.000
#> GSM1124883 2 0.0000 0.956 0.000 1.000
#> GSM1124889 2 0.0000 0.956 0.000 1.000
#> GSM1124892 1 0.0000 0.953 1.000 0.000
#> GSM1124893 1 0.0000 0.953 1.000 0.000
#> GSM1124909 2 0.0938 0.946 0.012 0.988
#> GSM1124913 2 0.0000 0.956 0.000 1.000
#> GSM1124916 2 0.0000 0.956 0.000 1.000
#> GSM1124923 2 0.0000 0.956 0.000 1.000
#> GSM1124925 1 0.0000 0.953 1.000 0.000
#> GSM1124929 1 0.0000 0.953 1.000 0.000
#> GSM1124934 1 0.0000 0.953 1.000 0.000
#> GSM1124937 1 0.0000 0.953 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1124891 3 0.6286 0.0523 0.464 0.000 0.536
#> GSM1124888 3 0.6286 0.0523 0.464 0.000 0.536
#> GSM1124890 3 0.3896 0.6202 0.008 0.128 0.864
#> GSM1124904 2 0.5706 0.6171 0.000 0.680 0.320
#> GSM1124927 1 0.5948 0.4656 0.640 0.360 0.000
#> GSM1124953 3 0.0747 0.6064 0.000 0.016 0.984
#> GSM1124869 1 0.0000 0.8782 1.000 0.000 0.000
#> GSM1124870 1 0.3816 0.7603 0.852 0.148 0.000
#> GSM1124882 1 0.0000 0.8782 1.000 0.000 0.000
#> GSM1124884 2 0.0000 0.7354 0.000 1.000 0.000
#> GSM1124898 2 0.3482 0.7263 0.000 0.872 0.128
#> GSM1124903 2 0.5254 0.6665 0.000 0.736 0.264
#> GSM1124905 1 0.0000 0.8782 1.000 0.000 0.000
#> GSM1124910 1 0.0000 0.8782 1.000 0.000 0.000
#> GSM1124919 3 0.3482 0.6177 0.000 0.128 0.872
#> GSM1124932 2 0.2165 0.6930 0.064 0.936 0.000
#> GSM1124933 3 0.6286 0.0523 0.464 0.000 0.536
#> GSM1124867 2 0.9188 0.0244 0.380 0.468 0.152
#> GSM1124868 2 0.6140 0.5800 0.000 0.596 0.404
#> GSM1124878 2 0.6140 0.5800 0.000 0.596 0.404
#> GSM1124895 2 0.6286 0.5142 0.000 0.536 0.464
#> GSM1124897 2 0.6286 0.5142 0.000 0.536 0.464
#> GSM1124902 2 0.6286 0.5142 0.000 0.536 0.464
#> GSM1124908 2 0.6299 0.4956 0.000 0.524 0.476
#> GSM1124921 2 0.6309 0.4598 0.000 0.504 0.496
#> GSM1124939 2 0.6286 0.5142 0.000 0.536 0.464
#> GSM1124944 3 0.6307 -0.4700 0.000 0.488 0.512
#> GSM1124945 3 0.0000 0.6010 0.000 0.000 1.000
#> GSM1124946 2 0.6308 0.4675 0.000 0.508 0.492
#> GSM1124947 3 0.6309 -0.4912 0.000 0.500 0.500
#> GSM1124951 3 0.0000 0.6010 0.000 0.000 1.000
#> GSM1124952 2 0.6295 0.5021 0.000 0.528 0.472
#> GSM1124957 3 0.3482 0.5645 0.128 0.000 0.872
#> GSM1124900 1 0.3412 0.7851 0.876 0.124 0.000
#> GSM1124914 2 0.5178 0.6706 0.000 0.744 0.256
#> GSM1124871 2 0.0000 0.7354 0.000 1.000 0.000
#> GSM1124874 2 0.0000 0.7354 0.000 1.000 0.000
#> GSM1124875 2 0.5216 0.6624 0.000 0.740 0.260
#> GSM1124880 1 0.3340 0.7885 0.880 0.120 0.000
#> GSM1124881 2 0.0000 0.7354 0.000 1.000 0.000
#> GSM1124885 2 0.5216 0.6691 0.000 0.740 0.260
#> GSM1124886 1 0.0000 0.8782 1.000 0.000 0.000
#> GSM1124887 2 0.5988 0.5643 0.000 0.632 0.368
#> GSM1124894 1 0.5986 0.5361 0.736 0.240 0.024
#> GSM1124896 1 0.0000 0.8782 1.000 0.000 0.000
#> GSM1124899 2 0.0000 0.7354 0.000 1.000 0.000
#> GSM1124901 2 0.3267 0.7297 0.000 0.884 0.116
#> GSM1124906 2 0.0000 0.7354 0.000 1.000 0.000
#> GSM1124907 3 0.6299 -0.2758 0.000 0.476 0.524
#> GSM1124911 2 0.0000 0.7354 0.000 1.000 0.000
#> GSM1124912 1 0.0000 0.8782 1.000 0.000 0.000
#> GSM1124915 2 0.3192 0.7307 0.000 0.888 0.112
#> GSM1124917 2 0.3267 0.7289 0.000 0.884 0.116
#> GSM1124918 2 0.4605 0.4910 0.000 0.796 0.204
#> GSM1124920 1 0.6204 0.2251 0.576 0.000 0.424
#> GSM1124922 2 0.1643 0.7370 0.000 0.956 0.044
#> GSM1124924 3 0.8824 0.1119 0.364 0.124 0.512
#> GSM1124926 2 0.0000 0.7354 0.000 1.000 0.000
#> GSM1124928 1 0.0000 0.8782 1.000 0.000 0.000
#> GSM1124930 3 0.3551 0.6171 0.000 0.132 0.868
#> GSM1124931 2 0.4291 0.5685 0.180 0.820 0.000
#> GSM1124935 2 0.3192 0.7307 0.000 0.888 0.112
#> GSM1124936 1 0.6008 0.3566 0.628 0.000 0.372
#> GSM1124938 3 0.5122 0.5890 0.012 0.200 0.788
#> GSM1124940 1 0.0000 0.8782 1.000 0.000 0.000
#> GSM1124941 2 0.0000 0.7354 0.000 1.000 0.000
#> GSM1124942 3 0.3619 0.6135 0.000 0.136 0.864
#> GSM1124943 3 0.3551 0.6171 0.000 0.132 0.868
#> GSM1124948 3 0.6753 0.4038 0.016 0.388 0.596
#> GSM1124949 1 0.0000 0.8782 1.000 0.000 0.000
#> GSM1124950 2 0.0000 0.7354 0.000 1.000 0.000
#> GSM1124954 1 0.6095 0.3092 0.608 0.000 0.392
#> GSM1124955 1 0.0000 0.8782 1.000 0.000 0.000
#> GSM1124956 2 0.0000 0.7354 0.000 1.000 0.000
#> GSM1124872 2 0.0000 0.7354 0.000 1.000 0.000
#> GSM1124873 2 0.0000 0.7354 0.000 1.000 0.000
#> GSM1124876 3 0.6286 0.0523 0.464 0.000 0.536
#> GSM1124877 1 0.0000 0.8782 1.000 0.000 0.000
#> GSM1124879 1 0.0000 0.8782 1.000 0.000 0.000
#> GSM1124883 2 0.5254 0.6665 0.000 0.736 0.264
#> GSM1124889 2 0.0000 0.7354 0.000 1.000 0.000
#> GSM1124892 1 0.0000 0.8782 1.000 0.000 0.000
#> GSM1124893 1 0.0000 0.8782 1.000 0.000 0.000
#> GSM1124909 2 0.5835 0.2949 0.340 0.660 0.000
#> GSM1124913 2 0.5254 0.6665 0.000 0.736 0.264
#> GSM1124916 2 0.2959 0.6481 0.100 0.900 0.000
#> GSM1124923 3 0.3482 0.6177 0.000 0.128 0.872
#> GSM1124925 1 0.0000 0.8782 1.000 0.000 0.000
#> GSM1124929 1 0.0000 0.8782 1.000 0.000 0.000
#> GSM1124934 1 0.3816 0.7386 0.852 0.000 0.148
#> GSM1124937 1 0.3038 0.8036 0.896 0.104 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1124891 3 0.3649 0.7506 0.204 0.000 0.796 0.000
#> GSM1124888 3 0.3649 0.7506 0.204 0.000 0.796 0.000
#> GSM1124890 3 0.0000 0.8411 0.000 0.000 1.000 0.000
#> GSM1124904 4 0.5035 0.7780 0.000 0.052 0.204 0.744
#> GSM1124927 2 0.1118 0.8605 0.036 0.964 0.000 0.000
#> GSM1124953 3 0.1118 0.8385 0.000 0.000 0.964 0.036
#> GSM1124869 1 0.0000 0.8861 1.000 0.000 0.000 0.000
#> GSM1124870 1 0.3873 0.6935 0.772 0.228 0.000 0.000
#> GSM1124882 1 0.0000 0.8861 1.000 0.000 0.000 0.000
#> GSM1124884 2 0.0188 0.8868 0.000 0.996 0.004 0.000
#> GSM1124898 2 0.7062 0.4860 0.000 0.572 0.204 0.224
#> GSM1124903 4 0.5035 0.7780 0.000 0.052 0.204 0.744
#> GSM1124905 1 0.0000 0.8861 1.000 0.000 0.000 0.000
#> GSM1124910 1 0.0000 0.8861 1.000 0.000 0.000 0.000
#> GSM1124919 3 0.0000 0.8411 0.000 0.000 1.000 0.000
#> GSM1124932 2 0.0000 0.8875 0.000 1.000 0.000 0.000
#> GSM1124933 3 0.3649 0.7506 0.204 0.000 0.796 0.000
#> GSM1124867 2 0.4890 0.6822 0.024 0.736 0.004 0.236
#> GSM1124868 4 0.0000 0.8558 0.000 0.000 0.000 1.000
#> GSM1124878 4 0.0817 0.8481 0.000 0.024 0.000 0.976
#> GSM1124895 4 0.0000 0.8558 0.000 0.000 0.000 1.000
#> GSM1124897 4 0.0000 0.8558 0.000 0.000 0.000 1.000
#> GSM1124902 4 0.0000 0.8558 0.000 0.000 0.000 1.000
#> GSM1124908 4 0.0000 0.8558 0.000 0.000 0.000 1.000
#> GSM1124921 4 0.0000 0.8558 0.000 0.000 0.000 1.000
#> GSM1124939 4 0.0000 0.8558 0.000 0.000 0.000 1.000
#> GSM1124944 4 0.0000 0.8558 0.000 0.000 0.000 1.000
#> GSM1124945 3 0.3649 0.7630 0.000 0.000 0.796 0.204
#> GSM1124946 4 0.0000 0.8558 0.000 0.000 0.000 1.000
#> GSM1124947 4 0.0000 0.8558 0.000 0.000 0.000 1.000
#> GSM1124951 3 0.3649 0.7630 0.000 0.000 0.796 0.204
#> GSM1124952 4 0.0000 0.8558 0.000 0.000 0.000 1.000
#> GSM1124957 3 0.3649 0.7630 0.000 0.000 0.796 0.204
#> GSM1124900 1 0.3764 0.7067 0.784 0.216 0.000 0.000
#> GSM1124914 4 0.5035 0.7780 0.000 0.052 0.204 0.744
#> GSM1124871 2 0.1118 0.8764 0.000 0.964 0.036 0.000
#> GSM1124874 2 0.0000 0.8875 0.000 1.000 0.000 0.000
#> GSM1124875 2 0.6871 0.5540 0.000 0.592 0.240 0.168
#> GSM1124880 1 0.3688 0.7151 0.792 0.208 0.000 0.000
#> GSM1124881 2 0.0000 0.8875 0.000 1.000 0.000 0.000
#> GSM1124885 4 0.5035 0.7780 0.000 0.052 0.204 0.744
#> GSM1124886 1 0.0000 0.8861 1.000 0.000 0.000 0.000
#> GSM1124887 4 0.4610 0.7683 0.000 0.020 0.236 0.744
#> GSM1124894 4 0.5150 0.3402 0.396 0.008 0.000 0.596
#> GSM1124896 1 0.0000 0.8861 1.000 0.000 0.000 0.000
#> GSM1124899 2 0.1388 0.8770 0.000 0.960 0.028 0.012
#> GSM1124901 2 0.6754 0.5691 0.000 0.612 0.204 0.184
#> GSM1124906 2 0.0000 0.8875 0.000 1.000 0.000 0.000
#> GSM1124907 4 0.5355 0.6207 0.000 0.020 0.360 0.620
#> GSM1124911 2 0.0000 0.8875 0.000 1.000 0.000 0.000
#> GSM1124912 1 0.0000 0.8861 1.000 0.000 0.000 0.000
#> GSM1124915 2 0.5111 0.7329 0.000 0.740 0.204 0.056
#> GSM1124917 2 0.4040 0.7404 0.000 0.752 0.248 0.000
#> GSM1124918 2 0.3688 0.7691 0.000 0.792 0.208 0.000
#> GSM1124920 1 0.4972 0.0812 0.544 0.000 0.456 0.000
#> GSM1124922 2 0.5665 0.7122 0.000 0.716 0.176 0.108
#> GSM1124924 3 0.4939 0.7043 0.040 0.220 0.740 0.000
#> GSM1124926 2 0.2011 0.8433 0.000 0.920 0.000 0.080
#> GSM1124928 1 0.0000 0.8861 1.000 0.000 0.000 0.000
#> GSM1124930 3 0.0000 0.8411 0.000 0.000 1.000 0.000
#> GSM1124931 2 0.0000 0.8875 0.000 1.000 0.000 0.000
#> GSM1124935 2 0.4617 0.7520 0.000 0.764 0.204 0.032
#> GSM1124936 1 0.4877 0.2370 0.592 0.000 0.408 0.000
#> GSM1124938 3 0.0188 0.8413 0.000 0.004 0.996 0.000
#> GSM1124940 1 0.0000 0.8861 1.000 0.000 0.000 0.000
#> GSM1124941 2 0.0000 0.8875 0.000 1.000 0.000 0.000
#> GSM1124942 3 0.0336 0.8357 0.000 0.000 0.992 0.008
#> GSM1124943 3 0.0000 0.8411 0.000 0.000 1.000 0.000
#> GSM1124948 3 0.3569 0.7468 0.000 0.196 0.804 0.000
#> GSM1124949 1 0.0000 0.8861 1.000 0.000 0.000 0.000
#> GSM1124950 2 0.0000 0.8875 0.000 1.000 0.000 0.000
#> GSM1124954 1 0.4985 0.0353 0.532 0.000 0.468 0.000
#> GSM1124955 1 0.0000 0.8861 1.000 0.000 0.000 0.000
#> GSM1124956 2 0.0000 0.8875 0.000 1.000 0.000 0.000
#> GSM1124872 2 0.0000 0.8875 0.000 1.000 0.000 0.000
#> GSM1124873 2 0.0000 0.8875 0.000 1.000 0.000 0.000
#> GSM1124876 3 0.3649 0.7506 0.204 0.000 0.796 0.000
#> GSM1124877 1 0.0000 0.8861 1.000 0.000 0.000 0.000
#> GSM1124879 1 0.0000 0.8861 1.000 0.000 0.000 0.000
#> GSM1124883 4 0.5035 0.7780 0.000 0.052 0.204 0.744
#> GSM1124889 2 0.0188 0.8868 0.000 0.996 0.004 0.000
#> GSM1124892 1 0.0000 0.8861 1.000 0.000 0.000 0.000
#> GSM1124893 1 0.0000 0.8861 1.000 0.000 0.000 0.000
#> GSM1124909 2 0.0000 0.8875 0.000 1.000 0.000 0.000
#> GSM1124913 4 0.5035 0.7780 0.000 0.052 0.204 0.744
#> GSM1124916 2 0.0000 0.8875 0.000 1.000 0.000 0.000
#> GSM1124923 3 0.0000 0.8411 0.000 0.000 1.000 0.000
#> GSM1124925 1 0.0000 0.8861 1.000 0.000 0.000 0.000
#> GSM1124929 1 0.0000 0.8861 1.000 0.000 0.000 0.000
#> GSM1124934 1 0.2216 0.8079 0.908 0.000 0.092 0.000
#> GSM1124937 1 0.3569 0.7251 0.804 0.196 0.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM1124891 3 0.2719 0.71710 0.144 0.000 0.852 0.000 0.004
#> GSM1124888 3 0.2719 0.71710 0.144 0.000 0.852 0.000 0.004
#> GSM1124890 3 0.2891 0.73447 0.000 0.000 0.824 0.000 0.176
#> GSM1124904 5 0.3774 0.72678 0.000 0.012 0.008 0.200 0.780
#> GSM1124927 2 0.1710 0.72099 0.016 0.940 0.040 0.000 0.004
#> GSM1124953 3 0.2900 0.75177 0.000 0.000 0.864 0.028 0.108
#> GSM1124869 1 0.0000 0.84962 1.000 0.000 0.000 0.000 0.000
#> GSM1124870 1 0.5168 0.27025 0.508 0.452 0.040 0.000 0.000
#> GSM1124882 1 0.0000 0.84962 1.000 0.000 0.000 0.000 0.000
#> GSM1124884 2 0.4806 0.73433 0.000 0.688 0.060 0.000 0.252
#> GSM1124898 5 0.2793 0.72245 0.000 0.088 0.000 0.036 0.876
#> GSM1124903 5 0.3630 0.72892 0.000 0.016 0.000 0.204 0.780
#> GSM1124905 1 0.0290 0.84693 0.992 0.008 0.000 0.000 0.000
#> GSM1124910 1 0.1205 0.82430 0.956 0.000 0.040 0.000 0.004
#> GSM1124919 3 0.3913 0.61807 0.000 0.000 0.676 0.000 0.324
#> GSM1124932 2 0.4064 0.77188 0.000 0.792 0.092 0.000 0.116
#> GSM1124933 3 0.2074 0.74001 0.104 0.000 0.896 0.000 0.000
#> GSM1124867 2 0.5127 0.02046 0.012 0.532 0.004 0.440 0.012
#> GSM1124868 4 0.3561 0.58618 0.000 0.000 0.000 0.740 0.260
#> GSM1124878 4 0.4546 0.03813 0.000 0.008 0.000 0.532 0.460
#> GSM1124895 4 0.0000 0.86024 0.000 0.000 0.000 1.000 0.000
#> GSM1124897 4 0.3796 0.51725 0.000 0.000 0.000 0.700 0.300
#> GSM1124902 4 0.0000 0.86024 0.000 0.000 0.000 1.000 0.000
#> GSM1124908 4 0.0290 0.85868 0.000 0.000 0.000 0.992 0.008
#> GSM1124921 4 0.0290 0.85868 0.000 0.000 0.000 0.992 0.008
#> GSM1124939 4 0.0000 0.86024 0.000 0.000 0.000 1.000 0.000
#> GSM1124944 4 0.0000 0.86024 0.000 0.000 0.000 1.000 0.000
#> GSM1124945 3 0.2890 0.70292 0.000 0.000 0.836 0.160 0.004
#> GSM1124946 4 0.0290 0.85868 0.000 0.000 0.000 0.992 0.008
#> GSM1124947 4 0.0000 0.86024 0.000 0.000 0.000 1.000 0.000
#> GSM1124951 3 0.2753 0.71741 0.000 0.000 0.856 0.136 0.008
#> GSM1124952 4 0.0000 0.86024 0.000 0.000 0.000 1.000 0.000
#> GSM1124957 3 0.2488 0.72120 0.000 0.000 0.872 0.124 0.004
#> GSM1124900 1 0.5118 0.36478 0.548 0.412 0.040 0.000 0.000
#> GSM1124914 5 0.3742 0.73842 0.000 0.020 0.004 0.188 0.788
#> GSM1124871 2 0.4524 0.63864 0.000 0.644 0.020 0.000 0.336
#> GSM1124874 2 0.4003 0.68249 0.000 0.704 0.008 0.000 0.288
#> GSM1124875 5 0.1806 0.70986 0.000 0.016 0.028 0.016 0.940
#> GSM1124880 1 0.5623 0.35347 0.520 0.416 0.056 0.000 0.008
#> GSM1124881 2 0.3123 0.77993 0.000 0.828 0.012 0.000 0.160
#> GSM1124885 5 0.3630 0.72892 0.000 0.016 0.000 0.204 0.780
#> GSM1124886 1 0.0324 0.84686 0.992 0.000 0.004 0.000 0.004
#> GSM1124887 5 0.4160 0.70812 0.000 0.008 0.036 0.184 0.772
#> GSM1124894 4 0.5962 0.52990 0.224 0.052 0.048 0.664 0.012
#> GSM1124896 1 0.0000 0.84962 1.000 0.000 0.000 0.000 0.000
#> GSM1124899 5 0.4979 -0.31858 0.000 0.480 0.028 0.000 0.492
#> GSM1124901 5 0.2940 0.73324 0.000 0.072 0.004 0.048 0.876
#> GSM1124906 2 0.4522 0.76480 0.000 0.736 0.068 0.000 0.196
#> GSM1124907 5 0.3412 0.68894 0.000 0.008 0.048 0.096 0.848
#> GSM1124911 2 0.3690 0.75826 0.000 0.764 0.012 0.000 0.224
#> GSM1124912 1 0.0000 0.84962 1.000 0.000 0.000 0.000 0.000
#> GSM1124915 5 0.3171 0.63170 0.000 0.176 0.000 0.008 0.816
#> GSM1124917 5 0.5304 0.39983 0.000 0.292 0.080 0.000 0.628
#> GSM1124918 2 0.5725 0.42122 0.000 0.488 0.084 0.000 0.428
#> GSM1124920 1 0.4448 0.00988 0.516 0.000 0.480 0.000 0.004
#> GSM1124922 5 0.5137 0.37958 0.004 0.296 0.020 0.024 0.656
#> GSM1124924 3 0.5678 0.35059 0.004 0.368 0.552 0.000 0.076
#> GSM1124926 2 0.5080 0.35381 0.000 0.524 0.016 0.012 0.448
#> GSM1124928 1 0.1412 0.83016 0.952 0.036 0.008 0.000 0.004
#> GSM1124930 3 0.4331 0.57859 0.000 0.004 0.596 0.000 0.400
#> GSM1124931 2 0.3248 0.73932 0.004 0.856 0.088 0.000 0.052
#> GSM1124935 5 0.3388 0.59206 0.000 0.200 0.000 0.008 0.792
#> GSM1124936 1 0.4403 0.15655 0.560 0.000 0.436 0.000 0.004
#> GSM1124938 3 0.3171 0.72737 0.000 0.008 0.816 0.000 0.176
#> GSM1124940 1 0.0000 0.84962 1.000 0.000 0.000 0.000 0.000
#> GSM1124941 2 0.4522 0.76480 0.000 0.736 0.068 0.000 0.196
#> GSM1124942 3 0.4714 0.48785 0.000 0.012 0.576 0.004 0.408
#> GSM1124943 3 0.3661 0.70186 0.000 0.000 0.724 0.000 0.276
#> GSM1124948 3 0.4761 0.65059 0.000 0.168 0.728 0.000 0.104
#> GSM1124949 1 0.0000 0.84962 1.000 0.000 0.000 0.000 0.000
#> GSM1124950 2 0.1831 0.72191 0.000 0.920 0.076 0.000 0.004
#> GSM1124954 3 0.4437 0.06135 0.464 0.000 0.532 0.000 0.004
#> GSM1124955 1 0.0000 0.84962 1.000 0.000 0.000 0.000 0.000
#> GSM1124956 2 0.3659 0.76023 0.000 0.768 0.012 0.000 0.220
#> GSM1124872 2 0.1197 0.72667 0.000 0.952 0.048 0.000 0.000
#> GSM1124873 2 0.2719 0.78084 0.000 0.852 0.004 0.000 0.144
#> GSM1124876 3 0.2561 0.71854 0.144 0.000 0.856 0.000 0.000
#> GSM1124877 1 0.0000 0.84962 1.000 0.000 0.000 0.000 0.000
#> GSM1124879 1 0.0162 0.84836 0.996 0.000 0.000 0.000 0.004
#> GSM1124883 5 0.3596 0.73203 0.000 0.016 0.000 0.200 0.784
#> GSM1124889 2 0.4114 0.72117 0.000 0.712 0.016 0.000 0.272
#> GSM1124892 1 0.0566 0.84338 0.984 0.000 0.012 0.000 0.004
#> GSM1124893 1 0.0000 0.84962 1.000 0.000 0.000 0.000 0.000
#> GSM1124909 2 0.1701 0.76292 0.000 0.936 0.016 0.000 0.048
#> GSM1124913 5 0.3752 0.73129 0.000 0.016 0.004 0.200 0.780
#> GSM1124916 2 0.1800 0.76404 0.000 0.932 0.020 0.000 0.048
#> GSM1124923 3 0.4264 0.56721 0.000 0.000 0.620 0.004 0.376
#> GSM1124925 1 0.0000 0.84962 1.000 0.000 0.000 0.000 0.000
#> GSM1124929 1 0.0000 0.84962 1.000 0.000 0.000 0.000 0.000
#> GSM1124934 1 0.4135 0.42816 0.656 0.000 0.340 0.000 0.004
#> GSM1124937 1 0.4467 0.64604 0.724 0.240 0.024 0.000 0.012
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1124891 3 0.1779 0.66217 0.064 0.000 0.920 0.000 0.000 0.016
#> GSM1124888 3 0.1625 0.66408 0.060 0.000 0.928 0.000 0.000 0.012
#> GSM1124890 3 0.4061 0.58945 0.000 0.000 0.748 0.000 0.164 0.088
#> GSM1124904 5 0.2741 0.68040 0.000 0.032 0.000 0.092 0.868 0.008
#> GSM1124927 6 0.3991 0.18319 0.004 0.472 0.000 0.000 0.000 0.524
#> GSM1124953 3 0.3317 0.63112 0.000 0.004 0.828 0.000 0.080 0.088
#> GSM1124869 1 0.0000 0.94772 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124870 6 0.5873 0.38972 0.352 0.204 0.000 0.000 0.000 0.444
#> GSM1124882 1 0.0000 0.94772 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124884 2 0.2858 0.66062 0.000 0.844 0.000 0.000 0.124 0.032
#> GSM1124898 5 0.2971 0.62492 0.000 0.144 0.000 0.020 0.832 0.004
#> GSM1124903 5 0.2629 0.68098 0.000 0.040 0.000 0.092 0.868 0.000
#> GSM1124905 1 0.1564 0.90574 0.936 0.000 0.024 0.000 0.000 0.040
#> GSM1124910 1 0.1812 0.87326 0.912 0.000 0.080 0.000 0.000 0.008
#> GSM1124919 3 0.5618 0.33673 0.000 0.004 0.528 0.000 0.320 0.148
#> GSM1124932 2 0.3088 0.51784 0.000 0.808 0.000 0.000 0.020 0.172
#> GSM1124933 3 0.0363 0.66187 0.012 0.000 0.988 0.000 0.000 0.000
#> GSM1124867 6 0.6048 0.20107 0.000 0.212 0.004 0.368 0.000 0.416
#> GSM1124868 4 0.3672 0.51060 0.000 0.008 0.000 0.688 0.304 0.000
#> GSM1124878 5 0.4508 0.22023 0.000 0.036 0.000 0.396 0.568 0.000
#> GSM1124895 4 0.0000 0.88165 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1124897 4 0.3795 0.39168 0.000 0.004 0.000 0.632 0.364 0.000
#> GSM1124902 4 0.0000 0.88165 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1124908 4 0.0363 0.87814 0.000 0.000 0.000 0.988 0.012 0.000
#> GSM1124921 4 0.0363 0.87886 0.000 0.000 0.000 0.988 0.012 0.000
#> GSM1124939 4 0.0000 0.88165 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1124944 4 0.0000 0.88165 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1124945 3 0.3196 0.61171 0.000 0.000 0.816 0.156 0.008 0.020
#> GSM1124946 4 0.0363 0.87886 0.000 0.000 0.000 0.988 0.012 0.000
#> GSM1124947 4 0.0000 0.88165 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1124951 3 0.3224 0.64206 0.000 0.000 0.848 0.084 0.032 0.036
#> GSM1124952 4 0.0146 0.87808 0.000 0.004 0.000 0.996 0.000 0.000
#> GSM1124957 3 0.0865 0.65650 0.000 0.000 0.964 0.036 0.000 0.000
#> GSM1124900 6 0.5746 0.35538 0.376 0.172 0.000 0.000 0.000 0.452
#> GSM1124914 5 0.3030 0.67645 0.000 0.056 0.000 0.092 0.848 0.004
#> GSM1124871 2 0.3974 0.56955 0.000 0.680 0.000 0.000 0.296 0.024
#> GSM1124874 2 0.4506 0.60941 0.000 0.704 0.000 0.000 0.176 0.120
#> GSM1124875 5 0.5032 0.40959 0.000 0.052 0.020 0.000 0.604 0.324
#> GSM1124880 6 0.5527 0.42484 0.240 0.152 0.012 0.000 0.000 0.596
#> GSM1124881 2 0.3551 0.57359 0.000 0.784 0.000 0.000 0.048 0.168
#> GSM1124885 5 0.2837 0.67795 0.000 0.056 0.000 0.088 0.856 0.000
#> GSM1124886 1 0.0363 0.94084 0.988 0.000 0.012 0.000 0.000 0.000
#> GSM1124887 5 0.3309 0.65455 0.000 0.004 0.028 0.080 0.848 0.040
#> GSM1124894 4 0.7002 0.42227 0.168 0.056 0.128 0.580 0.012 0.056
#> GSM1124896 1 0.0146 0.94556 0.996 0.000 0.004 0.000 0.000 0.000
#> GSM1124899 2 0.4770 0.49940 0.000 0.636 0.004 0.004 0.300 0.056
#> GSM1124901 5 0.3394 0.58119 0.000 0.188 0.000 0.012 0.788 0.012
#> GSM1124906 2 0.2527 0.64852 0.000 0.884 0.004 0.000 0.048 0.064
#> GSM1124907 5 0.4317 0.39621 0.000 0.004 0.028 0.000 0.640 0.328
#> GSM1124911 2 0.1588 0.66674 0.000 0.924 0.000 0.000 0.072 0.004
#> GSM1124912 1 0.0000 0.94772 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124915 5 0.3713 0.47327 0.000 0.284 0.000 0.008 0.704 0.004
#> GSM1124917 5 0.6858 0.20491 0.000 0.264 0.072 0.000 0.452 0.212
#> GSM1124918 6 0.6322 -0.05674 0.000 0.356 0.016 0.000 0.220 0.408
#> GSM1124920 3 0.4334 0.34172 0.408 0.000 0.568 0.000 0.000 0.024
#> GSM1124922 2 0.5347 0.16667 0.000 0.488 0.008 0.008 0.436 0.060
#> GSM1124924 6 0.4503 0.36510 0.000 0.084 0.152 0.000 0.024 0.740
#> GSM1124926 2 0.4306 0.51711 0.000 0.656 0.000 0.004 0.308 0.032
#> GSM1124928 1 0.2266 0.84693 0.880 0.000 0.012 0.000 0.000 0.108
#> GSM1124930 5 0.6051 -0.08173 0.000 0.000 0.260 0.000 0.396 0.344
#> GSM1124931 2 0.4234 -0.00119 0.004 0.576 0.000 0.000 0.012 0.408
#> GSM1124935 5 0.4004 0.42993 0.000 0.296 0.004 0.004 0.684 0.012
#> GSM1124936 3 0.4184 0.34893 0.408 0.000 0.576 0.000 0.000 0.016
#> GSM1124938 3 0.6356 0.30827 0.000 0.020 0.428 0.000 0.216 0.336
#> GSM1124940 1 0.0000 0.94772 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124941 2 0.2575 0.64509 0.000 0.880 0.004 0.000 0.044 0.072
#> GSM1124942 5 0.6444 -0.03818 0.000 0.020 0.236 0.000 0.380 0.364
#> GSM1124943 3 0.6108 0.21323 0.000 0.000 0.376 0.000 0.304 0.320
#> GSM1124948 6 0.6177 -0.02541 0.000 0.068 0.228 0.000 0.132 0.572
#> GSM1124949 1 0.0000 0.94772 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124950 6 0.3862 0.15337 0.000 0.476 0.000 0.000 0.000 0.524
#> GSM1124954 3 0.3592 0.55848 0.240 0.000 0.740 0.000 0.000 0.020
#> GSM1124955 1 0.0000 0.94772 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124956 2 0.1588 0.66674 0.000 0.924 0.000 0.000 0.072 0.004
#> GSM1124872 6 0.3866 0.15685 0.000 0.484 0.000 0.000 0.000 0.516
#> GSM1124873 2 0.2848 0.53270 0.000 0.828 0.004 0.000 0.008 0.160
#> GSM1124876 3 0.1524 0.66478 0.060 0.000 0.932 0.000 0.000 0.008
#> GSM1124877 1 0.0000 0.94772 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124879 1 0.0000 0.94772 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124883 5 0.2772 0.68058 0.000 0.040 0.000 0.092 0.864 0.004
#> GSM1124889 2 0.3062 0.66597 0.000 0.824 0.000 0.000 0.144 0.032
#> GSM1124892 1 0.1204 0.90588 0.944 0.000 0.056 0.000 0.000 0.000
#> GSM1124893 1 0.0000 0.94772 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124909 2 0.4062 0.05432 0.000 0.552 0.000 0.000 0.008 0.440
#> GSM1124913 5 0.2772 0.68102 0.000 0.040 0.000 0.092 0.864 0.004
#> GSM1124916 2 0.4032 0.09971 0.000 0.572 0.000 0.000 0.008 0.420
#> GSM1124923 3 0.5877 0.18553 0.000 0.000 0.428 0.000 0.372 0.200
#> GSM1124925 1 0.0000 0.94772 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124929 1 0.0000 0.94772 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124934 3 0.4428 0.32906 0.388 0.000 0.580 0.000 0.000 0.032
#> GSM1124937 1 0.5380 0.14052 0.540 0.096 0.008 0.000 0.000 0.356
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 tissue(p) k
#> SD:skmeans 85 2.64e-01 2
#> SD:skmeans 72 6.46e-02 3
#> SD:skmeans 86 2.15e-05 4
#> SD:skmeans 75 9.57e-07 5
#> SD:skmeans 58 6.40e-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["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 48983 rows and 91 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 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 0.689 0.816 0.907 0.3686 0.587 0.587
#> 3 3 0.723 0.795 0.876 0.4283 0.889 0.815
#> 4 4 0.756 0.883 0.923 0.2280 0.868 0.738
#> 5 5 0.762 0.742 0.894 0.1357 0.889 0.707
#> 6 6 0.721 0.776 0.865 0.0797 0.927 0.740
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
#> GSM1124891 1 0.0000 0.7167 1.000 0.000
#> GSM1124888 1 0.0000 0.7167 1.000 0.000
#> GSM1124890 2 0.9552 0.3204 0.376 0.624
#> GSM1124904 2 0.0000 0.9481 0.000 1.000
#> GSM1124927 2 0.0000 0.9481 0.000 1.000
#> GSM1124953 2 0.9710 0.2710 0.400 0.600
#> GSM1124869 1 0.9710 0.6640 0.600 0.400
#> GSM1124870 2 0.0672 0.9387 0.008 0.992
#> GSM1124882 1 0.9710 0.6640 0.600 0.400
#> GSM1124884 2 0.0000 0.9481 0.000 1.000
#> GSM1124898 2 0.0000 0.9481 0.000 1.000
#> GSM1124903 2 0.0000 0.9481 0.000 1.000
#> GSM1124905 2 0.9993 -0.4112 0.484 0.516
#> GSM1124910 1 0.9427 0.6742 0.640 0.360
#> GSM1124919 2 0.0000 0.9481 0.000 1.000
#> GSM1124932 2 0.0000 0.9481 0.000 1.000
#> GSM1124933 1 0.1843 0.7068 0.972 0.028
#> GSM1124867 2 0.0000 0.9481 0.000 1.000
#> GSM1124868 2 0.0000 0.9481 0.000 1.000
#> GSM1124878 2 0.0000 0.9481 0.000 1.000
#> GSM1124895 2 0.0000 0.9481 0.000 1.000
#> GSM1124897 2 0.0000 0.9481 0.000 1.000
#> GSM1124902 2 0.0000 0.9481 0.000 1.000
#> GSM1124908 2 0.0000 0.9481 0.000 1.000
#> GSM1124921 2 0.0000 0.9481 0.000 1.000
#> GSM1124939 2 0.0000 0.9481 0.000 1.000
#> GSM1124944 2 0.0000 0.9481 0.000 1.000
#> GSM1124945 2 0.9754 0.2531 0.408 0.592
#> GSM1124946 2 0.0000 0.9481 0.000 1.000
#> GSM1124947 2 0.0000 0.9481 0.000 1.000
#> GSM1124951 1 0.9998 -0.0136 0.508 0.492
#> GSM1124952 2 0.0000 0.9481 0.000 1.000
#> GSM1124957 1 0.0000 0.7167 1.000 0.000
#> GSM1124900 2 0.0000 0.9481 0.000 1.000
#> GSM1124914 2 0.0000 0.9481 0.000 1.000
#> GSM1124871 2 0.0000 0.9481 0.000 1.000
#> GSM1124874 2 0.0000 0.9481 0.000 1.000
#> GSM1124875 2 0.0000 0.9481 0.000 1.000
#> GSM1124880 2 0.0672 0.9387 0.008 0.992
#> GSM1124881 2 0.0000 0.9481 0.000 1.000
#> GSM1124885 2 0.0000 0.9481 0.000 1.000
#> GSM1124886 1 0.0000 0.7167 1.000 0.000
#> GSM1124887 2 0.0000 0.9481 0.000 1.000
#> GSM1124894 2 0.7139 0.6141 0.196 0.804
#> GSM1124896 1 0.9754 0.6495 0.592 0.408
#> GSM1124899 2 0.0000 0.9481 0.000 1.000
#> GSM1124901 2 0.0000 0.9481 0.000 1.000
#> GSM1124906 2 0.0000 0.9481 0.000 1.000
#> GSM1124907 2 0.0000 0.9481 0.000 1.000
#> GSM1124911 2 0.0000 0.9481 0.000 1.000
#> GSM1124912 1 0.9710 0.6640 0.600 0.400
#> GSM1124915 2 0.0000 0.9481 0.000 1.000
#> GSM1124917 2 0.0000 0.9481 0.000 1.000
#> GSM1124918 2 0.0000 0.9481 0.000 1.000
#> GSM1124920 1 0.0000 0.7167 1.000 0.000
#> GSM1124922 2 0.0000 0.9481 0.000 1.000
#> GSM1124924 2 0.0000 0.9481 0.000 1.000
#> GSM1124926 2 0.0000 0.9481 0.000 1.000
#> GSM1124928 1 0.9710 0.6640 0.600 0.400
#> GSM1124930 2 0.0000 0.9481 0.000 1.000
#> GSM1124931 2 0.0000 0.9481 0.000 1.000
#> GSM1124935 2 0.0000 0.9481 0.000 1.000
#> GSM1124936 1 0.0000 0.7167 1.000 0.000
#> GSM1124938 2 0.9710 0.2710 0.400 0.600
#> GSM1124940 1 0.9710 0.6640 0.600 0.400
#> GSM1124941 2 0.0000 0.9481 0.000 1.000
#> GSM1124942 2 0.0000 0.9481 0.000 1.000
#> GSM1124943 2 0.4562 0.8230 0.096 0.904
#> GSM1124948 2 0.0000 0.9481 0.000 1.000
#> GSM1124949 1 0.9710 0.6640 0.600 0.400
#> GSM1124950 2 0.0000 0.9481 0.000 1.000
#> GSM1124954 1 0.0000 0.7167 1.000 0.000
#> GSM1124955 1 0.9710 0.6640 0.600 0.400
#> GSM1124956 2 0.0000 0.9481 0.000 1.000
#> GSM1124872 2 0.0000 0.9481 0.000 1.000
#> GSM1124873 2 0.0000 0.9481 0.000 1.000
#> GSM1124876 1 0.0000 0.7167 1.000 0.000
#> GSM1124877 1 0.9710 0.6640 0.600 0.400
#> GSM1124879 1 0.9710 0.6640 0.600 0.400
#> GSM1124883 2 0.0000 0.9481 0.000 1.000
#> GSM1124889 2 0.0000 0.9481 0.000 1.000
#> GSM1124892 1 0.0000 0.7167 1.000 0.000
#> GSM1124893 1 0.9710 0.6640 0.600 0.400
#> GSM1124909 2 0.0000 0.9481 0.000 1.000
#> GSM1124913 2 0.0000 0.9481 0.000 1.000
#> GSM1124916 2 0.0000 0.9481 0.000 1.000
#> GSM1124923 2 0.0000 0.9481 0.000 1.000
#> GSM1124925 1 0.9710 0.6640 0.600 0.400
#> GSM1124929 1 0.9710 0.6640 0.600 0.400
#> GSM1124934 1 0.0000 0.7167 1.000 0.000
#> GSM1124937 2 0.0000 0.9481 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1124891 3 0.0000 0.568 0.000 0.000 1.000
#> GSM1124888 3 0.0000 0.568 0.000 0.000 1.000
#> GSM1124890 3 0.6204 0.398 0.000 0.424 0.576
#> GSM1124904 2 0.0000 0.904 0.000 1.000 0.000
#> GSM1124927 2 0.0000 0.904 0.000 1.000 0.000
#> GSM1124953 3 0.6111 0.455 0.000 0.396 0.604
#> GSM1124869 1 0.6111 0.999 0.604 0.000 0.396
#> GSM1124870 2 0.3826 0.771 0.124 0.868 0.008
#> GSM1124882 1 0.6111 0.999 0.604 0.000 0.396
#> GSM1124884 2 0.0000 0.904 0.000 1.000 0.000
#> GSM1124898 2 0.0000 0.904 0.000 1.000 0.000
#> GSM1124903 2 0.0000 0.904 0.000 1.000 0.000
#> GSM1124905 2 0.7015 0.249 0.024 0.584 0.392
#> GSM1124910 1 0.6111 0.999 0.604 0.000 0.396
#> GSM1124919 2 0.0000 0.904 0.000 1.000 0.000
#> GSM1124932 2 0.0000 0.904 0.000 1.000 0.000
#> GSM1124933 3 0.0592 0.571 0.000 0.012 0.988
#> GSM1124867 2 0.0000 0.904 0.000 1.000 0.000
#> GSM1124868 2 0.5882 0.597 0.348 0.652 0.000
#> GSM1124878 2 0.3340 0.819 0.120 0.880 0.000
#> GSM1124895 2 0.6111 0.544 0.396 0.604 0.000
#> GSM1124897 2 0.2878 0.837 0.096 0.904 0.000
#> GSM1124902 2 0.6111 0.544 0.396 0.604 0.000
#> GSM1124908 2 0.6111 0.544 0.396 0.604 0.000
#> GSM1124921 2 0.6111 0.544 0.396 0.604 0.000
#> GSM1124939 2 0.6111 0.544 0.396 0.604 0.000
#> GSM1124944 2 0.6111 0.544 0.396 0.604 0.000
#> GSM1124945 3 0.8528 0.521 0.156 0.240 0.604
#> GSM1124946 2 0.6111 0.544 0.396 0.604 0.000
#> GSM1124947 2 0.6111 0.544 0.396 0.604 0.000
#> GSM1124951 3 0.8595 0.519 0.180 0.216 0.604
#> GSM1124952 2 0.6111 0.544 0.396 0.604 0.000
#> GSM1124957 3 0.0747 0.566 0.016 0.000 0.984
#> GSM1124900 2 0.0000 0.904 0.000 1.000 0.000
#> GSM1124914 2 0.0000 0.904 0.000 1.000 0.000
#> GSM1124871 2 0.0000 0.904 0.000 1.000 0.000
#> GSM1124874 2 0.0000 0.904 0.000 1.000 0.000
#> GSM1124875 2 0.0000 0.904 0.000 1.000 0.000
#> GSM1124880 2 0.0747 0.892 0.000 0.984 0.016
#> GSM1124881 2 0.0000 0.904 0.000 1.000 0.000
#> GSM1124885 2 0.0000 0.904 0.000 1.000 0.000
#> GSM1124886 1 0.6111 0.999 0.604 0.000 0.396
#> GSM1124887 2 0.0000 0.904 0.000 1.000 0.000
#> GSM1124894 2 0.4605 0.678 0.000 0.796 0.204
#> GSM1124896 3 0.9921 -0.283 0.308 0.296 0.396
#> GSM1124899 2 0.0000 0.904 0.000 1.000 0.000
#> GSM1124901 2 0.0000 0.904 0.000 1.000 0.000
#> GSM1124906 2 0.0000 0.904 0.000 1.000 0.000
#> GSM1124907 2 0.0000 0.904 0.000 1.000 0.000
#> GSM1124911 2 0.0000 0.904 0.000 1.000 0.000
#> GSM1124912 1 0.6111 0.999 0.604 0.000 0.396
#> GSM1124915 2 0.0000 0.904 0.000 1.000 0.000
#> GSM1124917 2 0.0000 0.904 0.000 1.000 0.000
#> GSM1124918 2 0.0000 0.904 0.000 1.000 0.000
#> GSM1124920 3 0.0000 0.568 0.000 0.000 1.000
#> GSM1124922 2 0.0000 0.904 0.000 1.000 0.000
#> GSM1124924 2 0.0000 0.904 0.000 1.000 0.000
#> GSM1124926 2 0.0000 0.904 0.000 1.000 0.000
#> GSM1124928 1 0.6330 0.991 0.600 0.004 0.396
#> GSM1124930 2 0.0000 0.904 0.000 1.000 0.000
#> GSM1124931 2 0.0000 0.904 0.000 1.000 0.000
#> GSM1124935 2 0.0000 0.904 0.000 1.000 0.000
#> GSM1124936 3 0.0000 0.568 0.000 0.000 1.000
#> GSM1124938 3 0.6111 0.455 0.000 0.396 0.604
#> GSM1124940 1 0.6111 0.999 0.604 0.000 0.396
#> GSM1124941 2 0.0000 0.904 0.000 1.000 0.000
#> GSM1124942 2 0.0000 0.904 0.000 1.000 0.000
#> GSM1124943 2 0.3686 0.750 0.000 0.860 0.140
#> GSM1124948 2 0.0000 0.904 0.000 1.000 0.000
#> GSM1124949 1 0.6111 0.999 0.604 0.000 0.396
#> GSM1124950 2 0.0000 0.904 0.000 1.000 0.000
#> GSM1124954 3 0.0000 0.568 0.000 0.000 1.000
#> GSM1124955 1 0.6111 0.999 0.604 0.000 0.396
#> GSM1124956 2 0.0000 0.904 0.000 1.000 0.000
#> GSM1124872 2 0.0000 0.904 0.000 1.000 0.000
#> GSM1124873 2 0.0000 0.904 0.000 1.000 0.000
#> GSM1124876 3 0.0000 0.568 0.000 0.000 1.000
#> GSM1124877 1 0.6111 0.999 0.604 0.000 0.396
#> GSM1124879 1 0.6111 0.999 0.604 0.000 0.396
#> GSM1124883 2 0.0000 0.904 0.000 1.000 0.000
#> GSM1124889 2 0.0000 0.904 0.000 1.000 0.000
#> GSM1124892 1 0.6111 0.999 0.604 0.000 0.396
#> GSM1124893 1 0.6111 0.999 0.604 0.000 0.396
#> GSM1124909 2 0.0000 0.904 0.000 1.000 0.000
#> GSM1124913 2 0.0000 0.904 0.000 1.000 0.000
#> GSM1124916 2 0.0000 0.904 0.000 1.000 0.000
#> GSM1124923 2 0.1411 0.873 0.000 0.964 0.036
#> GSM1124925 1 0.6111 0.999 0.604 0.000 0.396
#> GSM1124929 1 0.6111 0.999 0.604 0.000 0.396
#> GSM1124934 3 0.0475 0.562 0.004 0.004 0.992
#> GSM1124937 2 0.0000 0.904 0.000 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1124891 3 0.3528 0.860 0.192 0.000 0.808 0.000
#> GSM1124888 3 0.3610 0.858 0.200 0.000 0.800 0.000
#> GSM1124890 3 0.1022 0.756 0.000 0.032 0.968 0.000
#> GSM1124904 2 0.3610 0.852 0.000 0.800 0.200 0.000
#> GSM1124927 2 0.0000 0.915 0.000 1.000 0.000 0.000
#> GSM1124953 3 0.0188 0.783 0.000 0.004 0.996 0.000
#> GSM1124869 1 0.0000 0.964 1.000 0.000 0.000 0.000
#> GSM1124870 2 0.2704 0.817 0.124 0.876 0.000 0.000
#> GSM1124882 1 0.0000 0.964 1.000 0.000 0.000 0.000
#> GSM1124884 2 0.0000 0.915 0.000 1.000 0.000 0.000
#> GSM1124898 2 0.3486 0.859 0.000 0.812 0.188 0.000
#> GSM1124903 2 0.3681 0.862 0.000 0.816 0.176 0.008
#> GSM1124905 2 0.4331 0.650 0.288 0.712 0.000 0.000
#> GSM1124910 1 0.0188 0.959 0.996 0.000 0.004 0.000
#> GSM1124919 2 0.3649 0.849 0.000 0.796 0.204 0.000
#> GSM1124932 2 0.0000 0.915 0.000 1.000 0.000 0.000
#> GSM1124933 3 0.3123 0.863 0.156 0.000 0.844 0.000
#> GSM1124867 2 0.0000 0.915 0.000 1.000 0.000 0.000
#> GSM1124868 4 0.2408 0.849 0.000 0.104 0.000 0.896
#> GSM1124878 2 0.5375 0.760 0.000 0.744 0.116 0.140
#> GSM1124895 4 0.0000 0.956 0.000 0.000 0.000 1.000
#> GSM1124897 2 0.4798 0.831 0.000 0.768 0.180 0.052
#> GSM1124902 4 0.0000 0.956 0.000 0.000 0.000 1.000
#> GSM1124908 4 0.3074 0.742 0.000 0.152 0.000 0.848
#> GSM1124921 4 0.0000 0.956 0.000 0.000 0.000 1.000
#> GSM1124939 4 0.0000 0.956 0.000 0.000 0.000 1.000
#> GSM1124944 4 0.0000 0.956 0.000 0.000 0.000 1.000
#> GSM1124945 3 0.1389 0.783 0.000 0.000 0.952 0.048
#> GSM1124946 4 0.0000 0.956 0.000 0.000 0.000 1.000
#> GSM1124947 4 0.0000 0.956 0.000 0.000 0.000 1.000
#> GSM1124951 3 0.0000 0.782 0.000 0.000 1.000 0.000
#> GSM1124952 4 0.0000 0.956 0.000 0.000 0.000 1.000
#> GSM1124957 3 0.3529 0.863 0.152 0.000 0.836 0.012
#> GSM1124900 2 0.0000 0.915 0.000 1.000 0.000 0.000
#> GSM1124914 2 0.3486 0.859 0.000 0.812 0.188 0.000
#> GSM1124871 2 0.0000 0.915 0.000 1.000 0.000 0.000
#> GSM1124874 2 0.0000 0.915 0.000 1.000 0.000 0.000
#> GSM1124875 2 0.1940 0.902 0.000 0.924 0.076 0.000
#> GSM1124880 2 0.0707 0.909 0.020 0.980 0.000 0.000
#> GSM1124881 2 0.0000 0.915 0.000 1.000 0.000 0.000
#> GSM1124885 2 0.3356 0.865 0.000 0.824 0.176 0.000
#> GSM1124886 1 0.0000 0.964 1.000 0.000 0.000 0.000
#> GSM1124887 2 0.3610 0.852 0.000 0.800 0.200 0.000
#> GSM1124894 2 0.3649 0.768 0.204 0.796 0.000 0.000
#> GSM1124896 1 0.4382 0.431 0.704 0.296 0.000 0.000
#> GSM1124899 2 0.0000 0.915 0.000 1.000 0.000 0.000
#> GSM1124901 2 0.1637 0.906 0.000 0.940 0.060 0.000
#> GSM1124906 2 0.0000 0.915 0.000 1.000 0.000 0.000
#> GSM1124907 2 0.3528 0.856 0.000 0.808 0.192 0.000
#> GSM1124911 2 0.0000 0.915 0.000 1.000 0.000 0.000
#> GSM1124912 1 0.0000 0.964 1.000 0.000 0.000 0.000
#> GSM1124915 2 0.0000 0.915 0.000 1.000 0.000 0.000
#> GSM1124917 2 0.0469 0.913 0.000 0.988 0.012 0.000
#> GSM1124918 2 0.0000 0.915 0.000 1.000 0.000 0.000
#> GSM1124920 3 0.3942 0.831 0.236 0.000 0.764 0.000
#> GSM1124922 2 0.1867 0.903 0.000 0.928 0.072 0.000
#> GSM1124924 2 0.1302 0.909 0.000 0.956 0.044 0.000
#> GSM1124926 2 0.0000 0.915 0.000 1.000 0.000 0.000
#> GSM1124928 1 0.0188 0.958 0.996 0.004 0.000 0.000
#> GSM1124930 2 0.3172 0.873 0.000 0.840 0.160 0.000
#> GSM1124931 2 0.0000 0.915 0.000 1.000 0.000 0.000
#> GSM1124935 2 0.1867 0.903 0.000 0.928 0.072 0.000
#> GSM1124936 3 0.3610 0.858 0.200 0.000 0.800 0.000
#> GSM1124938 3 0.1867 0.770 0.000 0.072 0.928 0.000
#> GSM1124940 1 0.0000 0.964 1.000 0.000 0.000 0.000
#> GSM1124941 2 0.0000 0.915 0.000 1.000 0.000 0.000
#> GSM1124942 2 0.3649 0.849 0.000 0.796 0.204 0.000
#> GSM1124943 2 0.4661 0.682 0.000 0.652 0.348 0.000
#> GSM1124948 2 0.1867 0.903 0.000 0.928 0.072 0.000
#> GSM1124949 1 0.0000 0.964 1.000 0.000 0.000 0.000
#> GSM1124950 2 0.0000 0.915 0.000 1.000 0.000 0.000
#> GSM1124954 3 0.3942 0.831 0.236 0.000 0.764 0.000
#> GSM1124955 1 0.0000 0.964 1.000 0.000 0.000 0.000
#> GSM1124956 2 0.0000 0.915 0.000 1.000 0.000 0.000
#> GSM1124872 2 0.0000 0.915 0.000 1.000 0.000 0.000
#> GSM1124873 2 0.0000 0.915 0.000 1.000 0.000 0.000
#> GSM1124876 3 0.3610 0.858 0.200 0.000 0.800 0.000
#> GSM1124877 1 0.0000 0.964 1.000 0.000 0.000 0.000
#> GSM1124879 1 0.0000 0.964 1.000 0.000 0.000 0.000
#> GSM1124883 2 0.3486 0.859 0.000 0.812 0.188 0.000
#> GSM1124889 2 0.0000 0.915 0.000 1.000 0.000 0.000
#> GSM1124892 1 0.0000 0.964 1.000 0.000 0.000 0.000
#> GSM1124893 1 0.0000 0.964 1.000 0.000 0.000 0.000
#> GSM1124909 2 0.0000 0.915 0.000 1.000 0.000 0.000
#> GSM1124913 2 0.3569 0.854 0.000 0.804 0.196 0.000
#> GSM1124916 2 0.0000 0.915 0.000 1.000 0.000 0.000
#> GSM1124923 2 0.4543 0.722 0.000 0.676 0.324 0.000
#> GSM1124925 1 0.0000 0.964 1.000 0.000 0.000 0.000
#> GSM1124929 1 0.0000 0.964 1.000 0.000 0.000 0.000
#> GSM1124934 3 0.4642 0.818 0.240 0.020 0.740 0.000
#> GSM1124937 2 0.0000 0.915 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
#> GSM1124891 3 0.0162 0.883 0.004 0.000 0.996 0.000 0.000
#> GSM1124888 3 0.0000 0.883 0.000 0.000 1.000 0.000 0.000
#> GSM1124890 5 0.3884 0.344 0.000 0.004 0.288 0.000 0.708
#> GSM1124904 5 0.4304 -0.149 0.000 0.484 0.000 0.000 0.516
#> GSM1124927 2 0.0000 0.853 0.000 1.000 0.000 0.000 0.000
#> GSM1124953 3 0.0162 0.881 0.000 0.000 0.996 0.000 0.004
#> GSM1124869 1 0.0000 0.959 1.000 0.000 0.000 0.000 0.000
#> GSM1124870 2 0.2329 0.719 0.124 0.876 0.000 0.000 0.000
#> GSM1124882 1 0.0000 0.959 1.000 0.000 0.000 0.000 0.000
#> GSM1124884 2 0.0000 0.853 0.000 1.000 0.000 0.000 0.000
#> GSM1124898 2 0.4278 0.238 0.000 0.548 0.000 0.000 0.452
#> GSM1124903 2 0.4446 0.162 0.000 0.520 0.000 0.004 0.476
#> GSM1124905 2 0.3949 0.440 0.332 0.668 0.000 0.000 0.000
#> GSM1124910 1 0.2439 0.837 0.876 0.000 0.004 0.000 0.120
#> GSM1124919 5 0.2930 0.594 0.000 0.164 0.004 0.000 0.832
#> GSM1124932 2 0.0000 0.853 0.000 1.000 0.000 0.000 0.000
#> GSM1124933 3 0.0000 0.883 0.000 0.000 1.000 0.000 0.000
#> GSM1124867 2 0.0000 0.853 0.000 1.000 0.000 0.000 0.000
#> GSM1124868 4 0.2280 0.808 0.000 0.120 0.000 0.880 0.000
#> GSM1124878 2 0.5418 0.287 0.000 0.568 0.000 0.068 0.364
#> GSM1124895 4 0.0000 0.943 0.000 0.000 0.000 1.000 0.000
#> GSM1124897 2 0.5605 0.193 0.000 0.520 0.000 0.076 0.404
#> GSM1124902 4 0.0000 0.943 0.000 0.000 0.000 1.000 0.000
#> GSM1124908 4 0.4083 0.678 0.000 0.132 0.000 0.788 0.080
#> GSM1124921 4 0.0404 0.937 0.000 0.000 0.000 0.988 0.012
#> GSM1124939 4 0.0000 0.943 0.000 0.000 0.000 1.000 0.000
#> GSM1124944 4 0.0000 0.943 0.000 0.000 0.000 1.000 0.000
#> GSM1124945 3 0.0000 0.883 0.000 0.000 1.000 0.000 0.000
#> GSM1124946 4 0.0963 0.919 0.000 0.000 0.000 0.964 0.036
#> GSM1124947 4 0.0000 0.943 0.000 0.000 0.000 1.000 0.000
#> GSM1124951 3 0.1478 0.839 0.000 0.000 0.936 0.000 0.064
#> GSM1124952 4 0.0000 0.943 0.000 0.000 0.000 1.000 0.000
#> GSM1124957 3 0.0000 0.883 0.000 0.000 1.000 0.000 0.000
#> GSM1124900 2 0.0000 0.853 0.000 1.000 0.000 0.000 0.000
#> GSM1124914 2 0.3661 0.573 0.000 0.724 0.000 0.000 0.276
#> GSM1124871 2 0.0000 0.853 0.000 1.000 0.000 0.000 0.000
#> GSM1124874 2 0.0000 0.853 0.000 1.000 0.000 0.000 0.000
#> GSM1124875 2 0.3074 0.671 0.000 0.804 0.000 0.000 0.196
#> GSM1124880 2 0.1549 0.819 0.016 0.944 0.040 0.000 0.000
#> GSM1124881 2 0.0000 0.853 0.000 1.000 0.000 0.000 0.000
#> GSM1124885 2 0.4273 0.244 0.000 0.552 0.000 0.000 0.448
#> GSM1124886 1 0.0000 0.959 1.000 0.000 0.000 0.000 0.000
#> GSM1124887 2 0.4287 0.217 0.000 0.540 0.000 0.000 0.460
#> GSM1124894 2 0.3955 0.679 0.084 0.800 0.116 0.000 0.000
#> GSM1124896 1 0.3928 0.432 0.700 0.296 0.004 0.000 0.000
#> GSM1124899 2 0.0000 0.853 0.000 1.000 0.000 0.000 0.000
#> GSM1124901 2 0.1197 0.824 0.000 0.952 0.000 0.000 0.048
#> GSM1124906 2 0.0000 0.853 0.000 1.000 0.000 0.000 0.000
#> GSM1124907 5 0.0000 0.585 0.000 0.000 0.000 0.000 1.000
#> GSM1124911 2 0.0000 0.853 0.000 1.000 0.000 0.000 0.000
#> GSM1124912 1 0.0000 0.959 1.000 0.000 0.000 0.000 0.000
#> GSM1124915 2 0.0000 0.853 0.000 1.000 0.000 0.000 0.000
#> GSM1124917 2 0.1270 0.821 0.000 0.948 0.000 0.000 0.052
#> GSM1124918 2 0.0162 0.851 0.000 0.996 0.000 0.000 0.004
#> GSM1124920 3 0.4879 0.716 0.228 0.000 0.696 0.000 0.076
#> GSM1124922 2 0.1341 0.818 0.000 0.944 0.000 0.000 0.056
#> GSM1124924 5 0.6369 0.391 0.000 0.236 0.244 0.000 0.520
#> GSM1124926 2 0.0000 0.853 0.000 1.000 0.000 0.000 0.000
#> GSM1124928 1 0.0162 0.954 0.996 0.004 0.000 0.000 0.000
#> GSM1124930 5 0.1341 0.616 0.000 0.056 0.000 0.000 0.944
#> GSM1124931 2 0.0000 0.853 0.000 1.000 0.000 0.000 0.000
#> GSM1124935 2 0.1341 0.818 0.000 0.944 0.000 0.000 0.056
#> GSM1124936 3 0.2773 0.804 0.164 0.000 0.836 0.000 0.000
#> GSM1124938 5 0.4211 0.237 0.000 0.004 0.360 0.000 0.636
#> GSM1124940 1 0.0000 0.959 1.000 0.000 0.000 0.000 0.000
#> GSM1124941 2 0.0000 0.853 0.000 1.000 0.000 0.000 0.000
#> GSM1124942 5 0.5008 0.539 0.000 0.152 0.140 0.000 0.708
#> GSM1124943 5 0.2077 0.625 0.000 0.084 0.008 0.000 0.908
#> GSM1124948 5 0.4030 0.457 0.000 0.352 0.000 0.000 0.648
#> GSM1124949 1 0.0000 0.959 1.000 0.000 0.000 0.000 0.000
#> GSM1124950 2 0.0000 0.853 0.000 1.000 0.000 0.000 0.000
#> GSM1124954 3 0.3336 0.753 0.228 0.000 0.772 0.000 0.000
#> GSM1124955 1 0.0000 0.959 1.000 0.000 0.000 0.000 0.000
#> GSM1124956 2 0.0000 0.853 0.000 1.000 0.000 0.000 0.000
#> GSM1124872 2 0.0290 0.849 0.000 0.992 0.008 0.000 0.000
#> GSM1124873 2 0.0000 0.853 0.000 1.000 0.000 0.000 0.000
#> GSM1124876 3 0.0000 0.883 0.000 0.000 1.000 0.000 0.000
#> GSM1124877 1 0.0000 0.959 1.000 0.000 0.000 0.000 0.000
#> GSM1124879 1 0.0000 0.959 1.000 0.000 0.000 0.000 0.000
#> GSM1124883 5 0.4306 -0.171 0.000 0.492 0.000 0.000 0.508
#> GSM1124889 2 0.0000 0.853 0.000 1.000 0.000 0.000 0.000
#> GSM1124892 1 0.0000 0.959 1.000 0.000 0.000 0.000 0.000
#> GSM1124893 1 0.0000 0.959 1.000 0.000 0.000 0.000 0.000
#> GSM1124909 2 0.0000 0.853 0.000 1.000 0.000 0.000 0.000
#> GSM1124913 2 0.4302 0.164 0.000 0.520 0.000 0.000 0.480
#> GSM1124916 2 0.0000 0.853 0.000 1.000 0.000 0.000 0.000
#> GSM1124923 5 0.1205 0.606 0.000 0.040 0.004 0.000 0.956
#> GSM1124925 1 0.0000 0.959 1.000 0.000 0.000 0.000 0.000
#> GSM1124929 1 0.0000 0.959 1.000 0.000 0.000 0.000 0.000
#> GSM1124934 3 0.4918 0.720 0.228 0.008 0.704 0.000 0.060
#> GSM1124937 2 0.0290 0.849 0.000 0.992 0.000 0.000 0.008
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1124891 3 0.0146 0.858 0.004 0.000 0.996 0.000 0.000 0.000
#> GSM1124888 3 0.0000 0.858 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1124890 6 0.5755 0.489 0.000 0.004 0.296 0.000 0.180 0.520
#> GSM1124904 5 0.2147 0.713 0.000 0.084 0.000 0.000 0.896 0.020
#> GSM1124927 2 0.2793 0.783 0.000 0.800 0.000 0.000 0.000 0.200
#> GSM1124953 3 0.0520 0.852 0.000 0.000 0.984 0.000 0.008 0.008
#> GSM1124869 1 0.0000 0.928 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124870 2 0.4449 0.691 0.124 0.712 0.000 0.000 0.000 0.164
#> GSM1124882 1 0.0632 0.911 0.976 0.000 0.000 0.000 0.000 0.024
#> GSM1124884 2 0.0790 0.846 0.000 0.968 0.000 0.000 0.032 0.000
#> GSM1124898 5 0.4945 0.573 0.000 0.304 0.000 0.000 0.604 0.092
#> GSM1124903 5 0.0692 0.639 0.000 0.004 0.000 0.000 0.976 0.020
#> GSM1124905 2 0.5272 0.479 0.276 0.584 0.000 0.000 0.000 0.140
#> GSM1124910 1 0.3774 0.426 0.592 0.000 0.000 0.000 0.000 0.408
#> GSM1124919 5 0.4895 0.389 0.000 0.068 0.004 0.000 0.600 0.328
#> GSM1124932 2 0.1863 0.823 0.000 0.896 0.000 0.000 0.104 0.000
#> GSM1124933 3 0.0000 0.858 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1124867 2 0.2135 0.817 0.000 0.872 0.000 0.000 0.000 0.128
#> GSM1124868 4 0.2191 0.802 0.000 0.120 0.000 0.876 0.000 0.004
#> GSM1124878 5 0.2494 0.708 0.000 0.120 0.000 0.016 0.864 0.000
#> GSM1124895 4 0.0000 0.928 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1124897 5 0.5394 0.571 0.000 0.308 0.000 0.024 0.588 0.080
#> GSM1124902 4 0.0000 0.928 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1124908 4 0.4159 0.655 0.000 0.116 0.000 0.744 0.140 0.000
#> GSM1124921 4 0.2260 0.815 0.000 0.000 0.000 0.860 0.140 0.000
#> GSM1124939 4 0.0000 0.928 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1124944 4 0.0000 0.928 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1124945 3 0.0000 0.858 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1124946 4 0.0405 0.921 0.000 0.000 0.000 0.988 0.004 0.008
#> GSM1124947 4 0.0000 0.928 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1124951 3 0.1349 0.818 0.000 0.000 0.940 0.000 0.056 0.004
#> GSM1124952 4 0.0000 0.928 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1124957 3 0.0000 0.858 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1124900 2 0.2668 0.799 0.000 0.828 0.000 0.000 0.004 0.168
#> GSM1124914 2 0.4971 0.332 0.000 0.604 0.000 0.000 0.300 0.096
#> GSM1124871 2 0.0146 0.845 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM1124874 2 0.2006 0.823 0.000 0.892 0.000 0.000 0.104 0.004
#> GSM1124875 2 0.3487 0.726 0.000 0.788 0.000 0.000 0.044 0.168
#> GSM1124880 2 0.3891 0.764 0.016 0.768 0.036 0.000 0.000 0.180
#> GSM1124881 2 0.0000 0.845 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1124885 5 0.3321 0.681 0.000 0.100 0.000 0.000 0.820 0.080
#> GSM1124886 1 0.0000 0.928 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124887 5 0.3390 0.622 0.000 0.296 0.000 0.000 0.704 0.000
#> GSM1124894 2 0.5399 0.682 0.072 0.688 0.092 0.000 0.004 0.144
#> GSM1124896 1 0.3878 0.469 0.688 0.296 0.008 0.000 0.000 0.008
#> GSM1124899 2 0.1367 0.844 0.000 0.944 0.000 0.000 0.044 0.012
#> GSM1124901 2 0.3068 0.802 0.000 0.840 0.000 0.000 0.088 0.072
#> GSM1124906 2 0.0790 0.846 0.000 0.968 0.000 0.000 0.032 0.000
#> GSM1124907 6 0.2883 0.749 0.000 0.000 0.000 0.000 0.212 0.788
#> GSM1124911 2 0.1863 0.823 0.000 0.896 0.000 0.000 0.104 0.000
#> GSM1124912 1 0.0000 0.928 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124915 2 0.2003 0.818 0.000 0.884 0.000 0.000 0.116 0.000
#> GSM1124917 2 0.3418 0.687 0.000 0.784 0.000 0.000 0.184 0.032
#> GSM1124918 2 0.0547 0.847 0.000 0.980 0.000 0.000 0.000 0.020
#> GSM1124920 3 0.5712 0.449 0.220 0.000 0.520 0.000 0.000 0.260
#> GSM1124922 2 0.2510 0.789 0.000 0.872 0.000 0.000 0.028 0.100
#> GSM1124924 6 0.2633 0.618 0.000 0.032 0.104 0.000 0.000 0.864
#> GSM1124926 2 0.1500 0.842 0.000 0.936 0.000 0.000 0.052 0.012
#> GSM1124928 1 0.2402 0.796 0.856 0.004 0.000 0.000 0.000 0.140
#> GSM1124930 6 0.2793 0.756 0.000 0.000 0.000 0.000 0.200 0.800
#> GSM1124931 2 0.2706 0.823 0.000 0.860 0.000 0.000 0.104 0.036
#> GSM1124935 2 0.3862 0.755 0.000 0.772 0.000 0.000 0.132 0.096
#> GSM1124936 3 0.2378 0.772 0.152 0.000 0.848 0.000 0.000 0.000
#> GSM1124938 6 0.3242 0.733 0.000 0.004 0.148 0.000 0.032 0.816
#> GSM1124940 1 0.0000 0.928 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124941 2 0.0713 0.847 0.000 0.972 0.000 0.000 0.028 0.000
#> GSM1124942 6 0.3387 0.772 0.000 0.000 0.040 0.000 0.164 0.796
#> GSM1124943 6 0.2980 0.764 0.000 0.000 0.008 0.000 0.192 0.800
#> GSM1124948 6 0.1151 0.709 0.000 0.012 0.000 0.000 0.032 0.956
#> GSM1124949 1 0.0000 0.928 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124950 2 0.2416 0.802 0.000 0.844 0.000 0.000 0.000 0.156
#> GSM1124954 3 0.2883 0.718 0.212 0.000 0.788 0.000 0.000 0.000
#> GSM1124955 1 0.0000 0.928 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124956 2 0.1863 0.823 0.000 0.896 0.000 0.000 0.104 0.000
#> GSM1124872 2 0.2933 0.781 0.000 0.796 0.004 0.000 0.000 0.200
#> GSM1124873 2 0.0000 0.845 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1124876 3 0.0000 0.858 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1124877 1 0.0000 0.928 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124879 1 0.0458 0.918 0.984 0.000 0.000 0.000 0.000 0.016
#> GSM1124883 5 0.2909 0.706 0.000 0.136 0.000 0.000 0.836 0.028
#> GSM1124889 2 0.0363 0.846 0.000 0.988 0.000 0.000 0.000 0.012
#> GSM1124892 1 0.0000 0.928 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124893 1 0.0000 0.928 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124909 2 0.1863 0.834 0.000 0.896 0.000 0.000 0.000 0.104
#> GSM1124913 5 0.2147 0.713 0.000 0.084 0.000 0.000 0.896 0.020
#> GSM1124916 2 0.1007 0.845 0.000 0.956 0.000 0.000 0.000 0.044
#> GSM1124923 5 0.3672 0.383 0.000 0.008 0.000 0.000 0.688 0.304
#> GSM1124925 1 0.0000 0.928 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124929 1 0.0000 0.928 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124934 3 0.5456 0.556 0.216 0.004 0.592 0.000 0.000 0.188
#> GSM1124937 2 0.2854 0.783 0.000 0.792 0.000 0.000 0.000 0.208
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 tissue(p) k
#> SD:pam 85 1.30e-01 2
#> SD:pam 86 1.01e-01 3
#> SD:pam 90 7.47e-09 4
#> SD:pam 76 3.91e-07 5
#> SD:pam 83 9.01e-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["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 48983 rows and 91 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 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 0.491 0.690 0.851 0.3897 0.693 0.693
#> 3 3 0.704 0.872 0.932 0.5699 0.670 0.532
#> 4 4 0.708 0.765 0.867 0.0714 0.932 0.836
#> 5 5 0.718 0.798 0.868 0.1496 0.841 0.592
#> 6 6 0.744 0.750 0.845 0.0426 0.931 0.733
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
#> GSM1124891 1 0.9732 0.536 0.596 0.404
#> GSM1124888 1 0.9833 0.499 0.576 0.424
#> GSM1124890 1 0.0000 0.779 1.000 0.000
#> GSM1124904 1 0.5178 0.679 0.884 0.116
#> GSM1124927 1 0.0938 0.776 0.988 0.012
#> GSM1124953 2 0.9866 -0.168 0.432 0.568
#> GSM1124869 1 0.9710 0.543 0.600 0.400
#> GSM1124870 1 0.9710 0.543 0.600 0.400
#> GSM1124882 1 0.9710 0.543 0.600 0.400
#> GSM1124884 1 0.0000 0.779 1.000 0.000
#> GSM1124898 1 0.0000 0.779 1.000 0.000
#> GSM1124903 1 0.6531 0.612 0.832 0.168
#> GSM1124905 1 0.9710 0.543 0.600 0.400
#> GSM1124910 1 0.9710 0.543 0.600 0.400
#> GSM1124919 1 0.0000 0.779 1.000 0.000
#> GSM1124932 1 0.0000 0.779 1.000 0.000
#> GSM1124933 1 0.9833 0.499 0.576 0.424
#> GSM1124867 1 0.9996 0.155 0.512 0.488
#> GSM1124868 2 0.0000 0.895 0.000 1.000
#> GSM1124878 2 0.9710 0.311 0.400 0.600
#> GSM1124895 2 0.0000 0.895 0.000 1.000
#> GSM1124897 2 0.4562 0.790 0.096 0.904
#> GSM1124902 2 0.0000 0.895 0.000 1.000
#> GSM1124908 2 0.0000 0.895 0.000 1.000
#> GSM1124921 2 0.0000 0.895 0.000 1.000
#> GSM1124939 2 0.0000 0.895 0.000 1.000
#> GSM1124944 2 0.0000 0.895 0.000 1.000
#> GSM1124945 2 0.0000 0.895 0.000 1.000
#> GSM1124946 2 0.0000 0.895 0.000 1.000
#> GSM1124947 2 0.0000 0.895 0.000 1.000
#> GSM1124951 2 0.0000 0.895 0.000 1.000
#> GSM1124952 2 0.0000 0.895 0.000 1.000
#> GSM1124957 2 0.0000 0.895 0.000 1.000
#> GSM1124900 1 0.9710 0.543 0.600 0.400
#> GSM1124914 1 0.0000 0.779 1.000 0.000
#> GSM1124871 1 0.0000 0.779 1.000 0.000
#> GSM1124874 1 0.0000 0.779 1.000 0.000
#> GSM1124875 1 0.0000 0.779 1.000 0.000
#> GSM1124880 1 0.0376 0.778 0.996 0.004
#> GSM1124881 1 0.0000 0.779 1.000 0.000
#> GSM1124885 1 0.4298 0.709 0.912 0.088
#> GSM1124886 1 0.9710 0.543 0.600 0.400
#> GSM1124887 1 0.5408 0.670 0.876 0.124
#> GSM1124894 2 0.8555 0.420 0.280 0.720
#> GSM1124896 1 0.9710 0.543 0.600 0.400
#> GSM1124899 1 0.0000 0.779 1.000 0.000
#> GSM1124901 1 0.0000 0.779 1.000 0.000
#> GSM1124906 1 0.0000 0.779 1.000 0.000
#> GSM1124907 1 0.0376 0.777 0.996 0.004
#> GSM1124911 1 0.0000 0.779 1.000 0.000
#> GSM1124912 1 0.9710 0.543 0.600 0.400
#> GSM1124915 1 0.0000 0.779 1.000 0.000
#> GSM1124917 1 0.0000 0.779 1.000 0.000
#> GSM1124918 1 0.0000 0.779 1.000 0.000
#> GSM1124920 1 0.9710 0.543 0.600 0.400
#> GSM1124922 1 0.1184 0.775 0.984 0.016
#> GSM1124924 1 0.0000 0.779 1.000 0.000
#> GSM1124926 1 0.6973 0.689 0.812 0.188
#> GSM1124928 1 0.9710 0.543 0.600 0.400
#> GSM1124930 1 0.0000 0.779 1.000 0.000
#> GSM1124931 1 0.0000 0.779 1.000 0.000
#> GSM1124935 1 0.0000 0.779 1.000 0.000
#> GSM1124936 1 0.9710 0.543 0.600 0.400
#> GSM1124938 1 0.0000 0.779 1.000 0.000
#> GSM1124940 1 0.9710 0.543 0.600 0.400
#> GSM1124941 1 0.0000 0.779 1.000 0.000
#> GSM1124942 1 0.0000 0.779 1.000 0.000
#> GSM1124943 1 0.0000 0.779 1.000 0.000
#> GSM1124948 1 0.0000 0.779 1.000 0.000
#> GSM1124949 1 0.9710 0.543 0.600 0.400
#> GSM1124950 1 0.0000 0.779 1.000 0.000
#> GSM1124954 1 0.9710 0.543 0.600 0.400
#> GSM1124955 1 0.9710 0.543 0.600 0.400
#> GSM1124956 1 0.0000 0.779 1.000 0.000
#> GSM1124872 1 0.0000 0.779 1.000 0.000
#> GSM1124873 1 0.0000 0.779 1.000 0.000
#> GSM1124876 1 0.9833 0.499 0.576 0.424
#> GSM1124877 1 0.9710 0.543 0.600 0.400
#> GSM1124879 1 0.9710 0.543 0.600 0.400
#> GSM1124883 1 0.0376 0.777 0.996 0.004
#> GSM1124889 1 0.0000 0.779 1.000 0.000
#> GSM1124892 1 0.9710 0.543 0.600 0.400
#> GSM1124893 1 0.9710 0.543 0.600 0.400
#> GSM1124909 1 0.0000 0.779 1.000 0.000
#> GSM1124913 1 0.3733 0.724 0.928 0.072
#> GSM1124916 1 0.0000 0.779 1.000 0.000
#> GSM1124923 1 0.4298 0.709 0.912 0.088
#> GSM1124925 1 0.9710 0.543 0.600 0.400
#> GSM1124929 1 0.9710 0.543 0.600 0.400
#> GSM1124934 1 0.9710 0.543 0.600 0.400
#> GSM1124937 1 0.5946 0.715 0.856 0.144
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1124891 1 0.4110 0.8405 0.844 0.004 0.152
#> GSM1124888 1 0.4110 0.8405 0.844 0.004 0.152
#> GSM1124890 2 0.3038 0.8805 0.000 0.896 0.104
#> GSM1124904 2 0.0424 0.9317 0.000 0.992 0.008
#> GSM1124927 2 0.4172 0.7857 0.156 0.840 0.004
#> GSM1124953 2 0.4121 0.8301 0.000 0.832 0.168
#> GSM1124869 1 0.0237 0.9469 0.996 0.004 0.000
#> GSM1124870 1 0.1453 0.9341 0.968 0.024 0.008
#> GSM1124882 1 0.0475 0.9466 0.992 0.004 0.004
#> GSM1124884 2 0.0000 0.9345 0.000 1.000 0.000
#> GSM1124898 2 0.0000 0.9345 0.000 1.000 0.000
#> GSM1124903 2 0.0747 0.9265 0.000 0.984 0.016
#> GSM1124905 1 0.1015 0.9433 0.980 0.012 0.008
#> GSM1124910 1 0.0661 0.9460 0.988 0.004 0.008
#> GSM1124919 2 0.3038 0.8805 0.000 0.896 0.104
#> GSM1124932 2 0.0000 0.9345 0.000 1.000 0.000
#> GSM1124933 1 0.4110 0.8405 0.844 0.004 0.152
#> GSM1124867 2 0.9457 -0.0836 0.204 0.484 0.312
#> GSM1124868 3 0.2959 0.9029 0.000 0.100 0.900
#> GSM1124878 3 0.6305 0.2543 0.000 0.484 0.516
#> GSM1124895 3 0.2959 0.9029 0.000 0.100 0.900
#> GSM1124897 3 0.6225 0.4031 0.000 0.432 0.568
#> GSM1124902 3 0.2959 0.9029 0.000 0.100 0.900
#> GSM1124908 3 0.2959 0.9029 0.000 0.100 0.900
#> GSM1124921 3 0.2959 0.9029 0.000 0.100 0.900
#> GSM1124939 3 0.2959 0.9029 0.000 0.100 0.900
#> GSM1124944 3 0.2959 0.9029 0.000 0.100 0.900
#> GSM1124945 3 0.0237 0.8290 0.004 0.000 0.996
#> GSM1124946 3 0.2959 0.9029 0.000 0.100 0.900
#> GSM1124947 3 0.3193 0.9019 0.004 0.100 0.896
#> GSM1124951 3 0.0000 0.8294 0.000 0.000 1.000
#> GSM1124952 3 0.3193 0.9019 0.004 0.100 0.896
#> GSM1124957 3 0.0237 0.8290 0.004 0.000 0.996
#> GSM1124900 1 0.2200 0.9017 0.940 0.056 0.004
#> GSM1124914 2 0.0000 0.9345 0.000 1.000 0.000
#> GSM1124871 2 0.0000 0.9345 0.000 1.000 0.000
#> GSM1124874 2 0.0000 0.9345 0.000 1.000 0.000
#> GSM1124875 2 0.0000 0.9345 0.000 1.000 0.000
#> GSM1124880 2 0.6267 0.2184 0.452 0.548 0.000
#> GSM1124881 2 0.0000 0.9345 0.000 1.000 0.000
#> GSM1124885 2 0.0424 0.9317 0.000 0.992 0.008
#> GSM1124886 1 0.0237 0.9469 0.996 0.004 0.000
#> GSM1124887 2 0.0424 0.9317 0.000 0.992 0.008
#> GSM1124894 3 0.6705 0.7752 0.144 0.108 0.748
#> GSM1124896 1 0.0475 0.9466 0.992 0.004 0.004
#> GSM1124899 2 0.0000 0.9345 0.000 1.000 0.000
#> GSM1124901 2 0.0000 0.9345 0.000 1.000 0.000
#> GSM1124906 2 0.0000 0.9345 0.000 1.000 0.000
#> GSM1124907 2 0.3038 0.8805 0.000 0.896 0.104
#> GSM1124911 2 0.0000 0.9345 0.000 1.000 0.000
#> GSM1124912 1 0.0237 0.9469 0.996 0.004 0.000
#> GSM1124915 2 0.0000 0.9345 0.000 1.000 0.000
#> GSM1124917 2 0.0000 0.9345 0.000 1.000 0.000
#> GSM1124918 2 0.2599 0.9034 0.016 0.932 0.052
#> GSM1124920 1 0.0661 0.9460 0.988 0.004 0.008
#> GSM1124922 2 0.0237 0.9327 0.000 0.996 0.004
#> GSM1124924 2 0.3573 0.8424 0.120 0.876 0.004
#> GSM1124926 2 0.0424 0.9317 0.000 0.992 0.008
#> GSM1124928 1 0.0983 0.9422 0.980 0.016 0.004
#> GSM1124930 2 0.3038 0.8805 0.000 0.896 0.104
#> GSM1124931 2 0.0237 0.9327 0.000 0.996 0.004
#> GSM1124935 2 0.0000 0.9345 0.000 1.000 0.000
#> GSM1124936 1 0.0661 0.9460 0.988 0.004 0.008
#> GSM1124938 2 0.3038 0.8805 0.000 0.896 0.104
#> GSM1124940 1 0.0237 0.9469 0.996 0.004 0.000
#> GSM1124941 2 0.0000 0.9345 0.000 1.000 0.000
#> GSM1124942 2 0.3038 0.8805 0.000 0.896 0.104
#> GSM1124943 2 0.3038 0.8805 0.000 0.896 0.104
#> GSM1124948 2 0.3590 0.8822 0.028 0.896 0.076
#> GSM1124949 1 0.0237 0.9469 0.996 0.004 0.000
#> GSM1124950 2 0.0000 0.9345 0.000 1.000 0.000
#> GSM1124954 1 0.0983 0.9423 0.980 0.004 0.016
#> GSM1124955 1 0.0237 0.9469 0.996 0.004 0.000
#> GSM1124956 2 0.0000 0.9345 0.000 1.000 0.000
#> GSM1124872 2 0.0000 0.9345 0.000 1.000 0.000
#> GSM1124873 2 0.0000 0.9345 0.000 1.000 0.000
#> GSM1124876 1 0.4110 0.8405 0.844 0.004 0.152
#> GSM1124877 1 0.0475 0.9466 0.992 0.004 0.004
#> GSM1124879 1 0.0661 0.9461 0.988 0.008 0.004
#> GSM1124883 2 0.0424 0.9317 0.000 0.992 0.008
#> GSM1124889 2 0.0000 0.9345 0.000 1.000 0.000
#> GSM1124892 1 0.0237 0.9469 0.996 0.004 0.000
#> GSM1124893 1 0.0237 0.9469 0.996 0.004 0.000
#> GSM1124909 2 0.2959 0.8614 0.100 0.900 0.000
#> GSM1124913 2 0.0424 0.9317 0.000 0.992 0.008
#> GSM1124916 2 0.0000 0.9345 0.000 1.000 0.000
#> GSM1124923 2 0.3116 0.8793 0.000 0.892 0.108
#> GSM1124925 1 0.0237 0.9469 0.996 0.004 0.000
#> GSM1124929 1 0.0237 0.9469 0.996 0.004 0.000
#> GSM1124934 1 0.1620 0.9327 0.964 0.024 0.012
#> GSM1124937 1 0.6495 0.0925 0.536 0.460 0.004
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1124891 3 0.5004 0.5848 0.392 0.004 0.604 0.000
#> GSM1124888 3 0.5004 0.5848 0.392 0.004 0.604 0.000
#> GSM1124890 2 0.2081 0.8533 0.000 0.916 0.084 0.000
#> GSM1124904 2 0.1211 0.8705 0.000 0.960 0.000 0.040
#> GSM1124927 2 0.4669 0.8241 0.100 0.796 0.104 0.000
#> GSM1124953 2 0.3117 0.8299 0.000 0.880 0.092 0.028
#> GSM1124869 1 0.0000 0.8876 1.000 0.000 0.000 0.000
#> GSM1124870 1 0.0804 0.8714 0.980 0.008 0.012 0.000
#> GSM1124882 1 0.0000 0.8876 1.000 0.000 0.000 0.000
#> GSM1124884 2 0.2814 0.8830 0.000 0.868 0.132 0.000
#> GSM1124898 2 0.0000 0.8811 0.000 1.000 0.000 0.000
#> GSM1124903 2 0.2334 0.8421 0.000 0.908 0.004 0.088
#> GSM1124905 1 0.0804 0.8714 0.980 0.008 0.012 0.000
#> GSM1124910 1 0.0000 0.8876 1.000 0.000 0.000 0.000
#> GSM1124919 2 0.2081 0.8533 0.000 0.916 0.084 0.000
#> GSM1124932 2 0.2868 0.8821 0.000 0.864 0.136 0.000
#> GSM1124933 3 0.5004 0.5848 0.392 0.004 0.604 0.000
#> GSM1124867 2 0.6681 0.4332 0.024 0.604 0.060 0.312
#> GSM1124868 4 0.1637 0.7238 0.000 0.000 0.060 0.940
#> GSM1124878 2 0.6336 0.1257 0.000 0.480 0.060 0.460
#> GSM1124895 4 0.3569 0.7865 0.000 0.000 0.196 0.804
#> GSM1124897 4 0.6315 -0.0328 0.000 0.432 0.060 0.508
#> GSM1124902 4 0.3569 0.7865 0.000 0.000 0.196 0.804
#> GSM1124908 4 0.1474 0.7270 0.000 0.000 0.052 0.948
#> GSM1124921 4 0.0000 0.7513 0.000 0.000 0.000 1.000
#> GSM1124939 4 0.3569 0.7865 0.000 0.000 0.196 0.804
#> GSM1124944 4 0.3569 0.7865 0.000 0.000 0.196 0.804
#> GSM1124945 3 0.4643 0.4239 0.000 0.000 0.656 0.344
#> GSM1124946 4 0.0000 0.7513 0.000 0.000 0.000 1.000
#> GSM1124947 4 0.3569 0.7865 0.000 0.000 0.196 0.804
#> GSM1124951 3 0.4624 0.4240 0.000 0.000 0.660 0.340
#> GSM1124952 4 0.3528 0.7864 0.000 0.000 0.192 0.808
#> GSM1124957 3 0.4454 0.3844 0.000 0.000 0.692 0.308
#> GSM1124900 1 0.1305 0.8402 0.960 0.036 0.004 0.000
#> GSM1124914 2 0.0188 0.8811 0.000 0.996 0.004 0.000
#> GSM1124871 2 0.1940 0.8869 0.000 0.924 0.076 0.000
#> GSM1124874 2 0.2814 0.8830 0.000 0.868 0.132 0.000
#> GSM1124875 2 0.0000 0.8811 0.000 1.000 0.000 0.000
#> GSM1124880 2 0.4855 0.5121 0.352 0.644 0.004 0.000
#> GSM1124881 2 0.2814 0.8830 0.000 0.868 0.132 0.000
#> GSM1124885 2 0.0779 0.8781 0.000 0.980 0.004 0.016
#> GSM1124886 1 0.0000 0.8876 1.000 0.000 0.000 0.000
#> GSM1124887 2 0.0336 0.8800 0.000 0.992 0.000 0.008
#> GSM1124894 4 0.6700 0.4513 0.148 0.096 0.060 0.696
#> GSM1124896 1 0.0188 0.8844 0.996 0.000 0.004 0.000
#> GSM1124899 2 0.2868 0.8821 0.000 0.864 0.136 0.000
#> GSM1124901 2 0.0000 0.8811 0.000 1.000 0.000 0.000
#> GSM1124906 2 0.2814 0.8830 0.000 0.868 0.132 0.000
#> GSM1124907 2 0.2081 0.8533 0.000 0.916 0.084 0.000
#> GSM1124911 2 0.2814 0.8830 0.000 0.868 0.132 0.000
#> GSM1124912 1 0.0000 0.8876 1.000 0.000 0.000 0.000
#> GSM1124915 2 0.0000 0.8811 0.000 1.000 0.000 0.000
#> GSM1124917 2 0.1022 0.8856 0.000 0.968 0.032 0.000
#> GSM1124918 2 0.0188 0.8820 0.000 0.996 0.004 0.000
#> GSM1124920 1 0.4933 -0.0847 0.568 0.000 0.432 0.000
#> GSM1124922 2 0.2868 0.8821 0.000 0.864 0.136 0.000
#> GSM1124924 2 0.4879 0.8396 0.092 0.780 0.128 0.000
#> GSM1124926 2 0.2868 0.8821 0.000 0.864 0.136 0.000
#> GSM1124928 1 0.2973 0.6488 0.856 0.144 0.000 0.000
#> GSM1124930 2 0.2081 0.8533 0.000 0.916 0.084 0.000
#> GSM1124931 2 0.2868 0.8821 0.000 0.864 0.136 0.000
#> GSM1124935 2 0.0000 0.8811 0.000 1.000 0.000 0.000
#> GSM1124936 1 0.4933 -0.0847 0.568 0.000 0.432 0.000
#> GSM1124938 2 0.2149 0.8548 0.000 0.912 0.088 0.000
#> GSM1124940 1 0.0000 0.8876 1.000 0.000 0.000 0.000
#> GSM1124941 2 0.2814 0.8830 0.000 0.868 0.132 0.000
#> GSM1124942 2 0.2081 0.8533 0.000 0.916 0.084 0.000
#> GSM1124943 2 0.2081 0.8533 0.000 0.916 0.084 0.000
#> GSM1124948 2 0.3764 0.8602 0.000 0.784 0.216 0.000
#> GSM1124949 1 0.0000 0.8876 1.000 0.000 0.000 0.000
#> GSM1124950 2 0.2814 0.8830 0.000 0.868 0.132 0.000
#> GSM1124954 1 0.4916 -0.0531 0.576 0.000 0.424 0.000
#> GSM1124955 1 0.0000 0.8876 1.000 0.000 0.000 0.000
#> GSM1124956 2 0.2814 0.8830 0.000 0.868 0.132 0.000
#> GSM1124872 2 0.2814 0.8830 0.000 0.868 0.132 0.000
#> GSM1124873 2 0.2814 0.8830 0.000 0.868 0.132 0.000
#> GSM1124876 3 0.5004 0.5848 0.392 0.004 0.604 0.000
#> GSM1124877 1 0.0000 0.8876 1.000 0.000 0.000 0.000
#> GSM1124879 1 0.0000 0.8876 1.000 0.000 0.000 0.000
#> GSM1124883 2 0.0188 0.8811 0.000 0.996 0.004 0.000
#> GSM1124889 2 0.2814 0.8830 0.000 0.868 0.132 0.000
#> GSM1124892 1 0.0000 0.8876 1.000 0.000 0.000 0.000
#> GSM1124893 1 0.0000 0.8876 1.000 0.000 0.000 0.000
#> GSM1124909 2 0.2814 0.8830 0.000 0.868 0.132 0.000
#> GSM1124913 2 0.2197 0.8478 0.000 0.916 0.004 0.080
#> GSM1124916 2 0.2814 0.8830 0.000 0.868 0.132 0.000
#> GSM1124923 2 0.2081 0.8533 0.000 0.916 0.084 0.000
#> GSM1124925 1 0.0000 0.8876 1.000 0.000 0.000 0.000
#> GSM1124929 1 0.0000 0.8876 1.000 0.000 0.000 0.000
#> GSM1124934 1 0.2714 0.7100 0.884 0.112 0.004 0.000
#> GSM1124937 2 0.6403 0.6598 0.232 0.640 0.128 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM1124891 3 0.2930 0.862 0.164 0.000 0.832 0.000 0.004
#> GSM1124888 3 0.2719 0.867 0.144 0.000 0.852 0.000 0.004
#> GSM1124890 5 0.1851 0.857 0.000 0.088 0.000 0.000 0.912
#> GSM1124904 2 0.4378 0.726 0.000 0.740 0.004 0.040 0.216
#> GSM1124927 2 0.3331 0.767 0.068 0.864 0.044 0.000 0.024
#> GSM1124953 5 0.2735 0.839 0.000 0.084 0.036 0.000 0.880
#> GSM1124869 1 0.0000 0.899 1.000 0.000 0.000 0.000 0.000
#> GSM1124870 1 0.1682 0.889 0.940 0.012 0.044 0.000 0.004
#> GSM1124882 1 0.0290 0.899 0.992 0.000 0.008 0.000 0.000
#> GSM1124884 2 0.0404 0.833 0.000 0.988 0.000 0.000 0.012
#> GSM1124898 2 0.2891 0.782 0.000 0.824 0.000 0.000 0.176
#> GSM1124903 2 0.5084 0.728 0.000 0.712 0.032 0.044 0.212
#> GSM1124905 1 0.2798 0.875 0.888 0.008 0.044 0.000 0.060
#> GSM1124910 1 0.1731 0.876 0.932 0.004 0.060 0.000 0.004
#> GSM1124919 5 0.1908 0.860 0.000 0.092 0.000 0.000 0.908
#> GSM1124932 2 0.0404 0.830 0.000 0.988 0.000 0.000 0.012
#> GSM1124933 3 0.2719 0.867 0.144 0.000 0.852 0.000 0.004
#> GSM1124867 2 0.7982 0.473 0.084 0.556 0.076 0.144 0.140
#> GSM1124868 4 0.1831 0.917 0.000 0.000 0.076 0.920 0.004
#> GSM1124878 2 0.6122 0.587 0.000 0.644 0.080 0.216 0.060
#> GSM1124895 4 0.0000 0.939 0.000 0.000 0.000 1.000 0.000
#> GSM1124897 2 0.5829 0.376 0.000 0.548 0.080 0.364 0.008
#> GSM1124902 4 0.0000 0.939 0.000 0.000 0.000 1.000 0.000
#> GSM1124908 4 0.1704 0.921 0.000 0.000 0.068 0.928 0.004
#> GSM1124921 4 0.1043 0.938 0.000 0.000 0.040 0.960 0.000
#> GSM1124939 4 0.0000 0.939 0.000 0.000 0.000 1.000 0.000
#> GSM1124944 4 0.0000 0.939 0.000 0.000 0.000 1.000 0.000
#> GSM1124945 3 0.2915 0.714 0.000 0.000 0.860 0.116 0.024
#> GSM1124946 4 0.1043 0.938 0.000 0.000 0.040 0.960 0.000
#> GSM1124947 4 0.0000 0.939 0.000 0.000 0.000 1.000 0.000
#> GSM1124951 3 0.2864 0.715 0.000 0.000 0.864 0.112 0.024
#> GSM1124952 4 0.1043 0.938 0.000 0.000 0.040 0.960 0.000
#> GSM1124957 3 0.2915 0.714 0.000 0.000 0.860 0.116 0.024
#> GSM1124900 1 0.3469 0.855 0.860 0.044 0.052 0.000 0.044
#> GSM1124914 2 0.3274 0.776 0.000 0.780 0.000 0.000 0.220
#> GSM1124871 2 0.1410 0.829 0.000 0.940 0.000 0.000 0.060
#> GSM1124874 2 0.0510 0.833 0.000 0.984 0.000 0.000 0.016
#> GSM1124875 2 0.3274 0.757 0.000 0.780 0.000 0.000 0.220
#> GSM1124880 5 0.7631 0.176 0.300 0.304 0.044 0.000 0.352
#> GSM1124881 2 0.0963 0.828 0.000 0.964 0.000 0.000 0.036
#> GSM1124885 2 0.4284 0.752 0.000 0.752 0.004 0.040 0.204
#> GSM1124886 1 0.0000 0.899 1.000 0.000 0.000 0.000 0.000
#> GSM1124887 2 0.4823 0.577 0.000 0.644 0.000 0.040 0.316
#> GSM1124894 4 0.6940 0.589 0.144 0.036 0.120 0.636 0.064
#> GSM1124896 1 0.3455 0.861 0.856 0.020 0.064 0.000 0.060
#> GSM1124899 2 0.0162 0.833 0.000 0.996 0.000 0.000 0.004
#> GSM1124901 2 0.2690 0.792 0.000 0.844 0.000 0.000 0.156
#> GSM1124906 2 0.0000 0.832 0.000 1.000 0.000 0.000 0.000
#> GSM1124907 5 0.1965 0.858 0.000 0.096 0.000 0.000 0.904
#> GSM1124911 2 0.0703 0.831 0.000 0.976 0.000 0.000 0.024
#> GSM1124912 1 0.0000 0.899 1.000 0.000 0.000 0.000 0.000
#> GSM1124915 2 0.2732 0.789 0.000 0.840 0.000 0.000 0.160
#> GSM1124917 2 0.3561 0.668 0.000 0.740 0.000 0.000 0.260
#> GSM1124918 5 0.3837 0.651 0.000 0.308 0.000 0.000 0.692
#> GSM1124920 3 0.3521 0.817 0.232 0.000 0.764 0.000 0.004
#> GSM1124922 2 0.0703 0.826 0.000 0.976 0.000 0.000 0.024
#> GSM1124924 5 0.5080 0.599 0.000 0.368 0.044 0.000 0.588
#> GSM1124926 2 0.0162 0.832 0.000 0.996 0.000 0.000 0.004
#> GSM1124928 1 0.4107 0.810 0.820 0.040 0.056 0.000 0.084
#> GSM1124930 5 0.1908 0.860 0.000 0.092 0.000 0.000 0.908
#> GSM1124931 2 0.1484 0.813 0.000 0.944 0.008 0.000 0.048
#> GSM1124935 2 0.2732 0.789 0.000 0.840 0.000 0.000 0.160
#> GSM1124936 3 0.3689 0.800 0.256 0.000 0.740 0.000 0.004
#> GSM1124938 5 0.2561 0.837 0.000 0.144 0.000 0.000 0.856
#> GSM1124940 1 0.1341 0.883 0.944 0.000 0.000 0.000 0.056
#> GSM1124941 2 0.1197 0.818 0.000 0.952 0.000 0.000 0.048
#> GSM1124942 5 0.1908 0.860 0.000 0.092 0.000 0.000 0.908
#> GSM1124943 5 0.1908 0.860 0.000 0.092 0.000 0.000 0.908
#> GSM1124948 5 0.3661 0.726 0.000 0.276 0.000 0.000 0.724
#> GSM1124949 1 0.0404 0.899 0.988 0.000 0.000 0.000 0.012
#> GSM1124950 2 0.0510 0.830 0.000 0.984 0.000 0.000 0.016
#> GSM1124954 3 0.3266 0.839 0.200 0.000 0.796 0.000 0.004
#> GSM1124955 1 0.0162 0.898 0.996 0.000 0.004 0.000 0.000
#> GSM1124956 2 0.0703 0.831 0.000 0.976 0.000 0.000 0.024
#> GSM1124872 2 0.0000 0.832 0.000 1.000 0.000 0.000 0.000
#> GSM1124873 2 0.0000 0.832 0.000 1.000 0.000 0.000 0.000
#> GSM1124876 3 0.2719 0.867 0.144 0.000 0.852 0.000 0.004
#> GSM1124877 1 0.0324 0.900 0.992 0.004 0.000 0.000 0.004
#> GSM1124879 1 0.3186 0.867 0.872 0.020 0.052 0.000 0.056
#> GSM1124883 2 0.3210 0.780 0.000 0.788 0.000 0.000 0.212
#> GSM1124889 2 0.0510 0.832 0.000 0.984 0.000 0.000 0.016
#> GSM1124892 1 0.0703 0.895 0.976 0.000 0.024 0.000 0.000
#> GSM1124893 1 0.0000 0.899 1.000 0.000 0.000 0.000 0.000
#> GSM1124909 2 0.3177 0.612 0.000 0.792 0.000 0.000 0.208
#> GSM1124913 2 0.4218 0.744 0.000 0.760 0.004 0.040 0.196
#> GSM1124916 2 0.3003 0.653 0.000 0.812 0.000 0.000 0.188
#> GSM1124923 5 0.1851 0.857 0.000 0.088 0.000 0.000 0.912
#> GSM1124925 1 0.0000 0.899 1.000 0.000 0.000 0.000 0.000
#> GSM1124929 1 0.1341 0.883 0.944 0.000 0.000 0.000 0.056
#> GSM1124934 1 0.5481 0.618 0.692 0.016 0.136 0.000 0.156
#> GSM1124937 1 0.7317 0.134 0.432 0.284 0.032 0.000 0.252
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1124891 3 0.1921 0.8036 0.052 0.000 0.916 0.000 0.000 0.032
#> GSM1124888 3 0.1075 0.8094 0.048 0.000 0.952 0.000 0.000 0.000
#> GSM1124890 5 0.1387 0.8189 0.000 0.068 0.000 0.000 0.932 0.000
#> GSM1124904 6 0.6151 0.7195 0.000 0.316 0.000 0.020 0.180 0.484
#> GSM1124927 2 0.3582 0.6345 0.124 0.816 0.016 0.000 0.004 0.040
#> GSM1124953 5 0.2568 0.7967 0.000 0.068 0.056 0.000 0.876 0.000
#> GSM1124869 1 0.0363 0.9081 0.988 0.000 0.000 0.000 0.000 0.012
#> GSM1124870 1 0.2570 0.8770 0.896 0.012 0.048 0.000 0.032 0.012
#> GSM1124882 1 0.0146 0.9095 0.996 0.000 0.004 0.000 0.000 0.000
#> GSM1124884 2 0.0146 0.8276 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM1124898 2 0.3456 0.6586 0.000 0.788 0.000 0.000 0.040 0.172
#> GSM1124903 6 0.6146 0.7575 0.000 0.252 0.064 0.020 0.072 0.592
#> GSM1124905 1 0.2620 0.8696 0.888 0.000 0.028 0.000 0.032 0.052
#> GSM1124910 1 0.2003 0.8786 0.912 0.000 0.044 0.000 0.000 0.044
#> GSM1124919 5 0.1387 0.8189 0.000 0.068 0.000 0.000 0.932 0.000
#> GSM1124932 2 0.0547 0.8172 0.000 0.980 0.000 0.000 0.000 0.020
#> GSM1124933 3 0.1075 0.8094 0.048 0.000 0.952 0.000 0.000 0.000
#> GSM1124867 2 0.6426 0.3772 0.060 0.660 0.136 0.048 0.044 0.052
#> GSM1124868 4 0.3297 0.7998 0.000 0.000 0.112 0.820 0.000 0.068
#> GSM1124878 6 0.6926 0.6650 0.000 0.212 0.112 0.140 0.012 0.524
#> GSM1124895 4 0.0000 0.9069 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1124897 6 0.7083 0.6105 0.000 0.192 0.112 0.180 0.012 0.504
#> GSM1124902 4 0.0000 0.9069 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1124908 4 0.2587 0.8334 0.000 0.000 0.108 0.868 0.004 0.020
#> GSM1124921 4 0.1088 0.8985 0.000 0.000 0.024 0.960 0.000 0.016
#> GSM1124939 4 0.0000 0.9069 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1124944 4 0.0146 0.9064 0.000 0.000 0.000 0.996 0.004 0.000
#> GSM1124945 3 0.4832 0.6793 0.000 0.000 0.684 0.060 0.028 0.228
#> GSM1124946 4 0.1088 0.8985 0.000 0.000 0.024 0.960 0.000 0.016
#> GSM1124947 4 0.0260 0.9055 0.000 0.000 0.000 0.992 0.008 0.000
#> GSM1124951 3 0.4832 0.6793 0.000 0.000 0.684 0.060 0.028 0.228
#> GSM1124952 4 0.0405 0.9063 0.000 0.000 0.004 0.988 0.008 0.000
#> GSM1124957 3 0.4832 0.6793 0.000 0.000 0.684 0.060 0.028 0.228
#> GSM1124900 1 0.2951 0.8592 0.880 0.040 0.024 0.000 0.020 0.036
#> GSM1124914 2 0.3938 0.5827 0.000 0.728 0.000 0.000 0.044 0.228
#> GSM1124871 2 0.1967 0.7766 0.000 0.904 0.000 0.000 0.012 0.084
#> GSM1124874 2 0.0146 0.8276 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM1124875 2 0.4402 0.5389 0.000 0.712 0.000 0.000 0.184 0.104
#> GSM1124880 2 0.6661 -0.0522 0.372 0.436 0.020 0.000 0.140 0.032
#> GSM1124881 2 0.0632 0.8142 0.000 0.976 0.000 0.000 0.000 0.024
#> GSM1124885 6 0.5365 0.7398 0.000 0.308 0.000 0.020 0.084 0.588
#> GSM1124886 1 0.0000 0.9099 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124887 5 0.4967 0.1611 0.000 0.420 0.000 0.020 0.528 0.032
#> GSM1124894 4 0.7573 0.3321 0.252 0.032 0.148 0.484 0.036 0.048
#> GSM1124896 1 0.1983 0.8779 0.908 0.000 0.072 0.000 0.000 0.020
#> GSM1124899 2 0.0000 0.8270 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1124901 2 0.3523 0.6471 0.000 0.780 0.000 0.000 0.040 0.180
#> GSM1124906 2 0.0260 0.8273 0.000 0.992 0.000 0.000 0.008 0.000
#> GSM1124907 5 0.2312 0.7950 0.000 0.112 0.000 0.000 0.876 0.012
#> GSM1124911 2 0.0260 0.8273 0.000 0.992 0.000 0.000 0.008 0.000
#> GSM1124912 1 0.0363 0.9081 0.988 0.000 0.000 0.000 0.000 0.012
#> GSM1124915 2 0.4396 0.1641 0.000 0.612 0.000 0.000 0.036 0.352
#> GSM1124917 2 0.4175 0.5916 0.000 0.740 0.000 0.000 0.156 0.104
#> GSM1124918 5 0.5051 0.4485 0.000 0.300 0.000 0.000 0.596 0.104
#> GSM1124920 3 0.3418 0.7501 0.184 0.000 0.784 0.000 0.000 0.032
#> GSM1124922 2 0.0632 0.8140 0.000 0.976 0.000 0.000 0.000 0.024
#> GSM1124924 5 0.5672 0.4959 0.100 0.280 0.012 0.000 0.592 0.016
#> GSM1124926 2 0.0713 0.8133 0.000 0.972 0.000 0.000 0.000 0.028
#> GSM1124928 1 0.2613 0.8710 0.896 0.016 0.044 0.000 0.028 0.016
#> GSM1124930 5 0.2088 0.8145 0.000 0.068 0.000 0.000 0.904 0.028
#> GSM1124931 2 0.0935 0.8084 0.000 0.964 0.004 0.000 0.000 0.032
#> GSM1124935 2 0.3163 0.6961 0.000 0.820 0.000 0.000 0.040 0.140
#> GSM1124936 3 0.3791 0.7237 0.236 0.000 0.732 0.000 0.000 0.032
#> GSM1124938 5 0.3123 0.7811 0.000 0.076 0.000 0.000 0.836 0.088
#> GSM1124940 1 0.0632 0.9063 0.976 0.000 0.000 0.000 0.000 0.024
#> GSM1124941 2 0.0520 0.8262 0.000 0.984 0.000 0.000 0.008 0.008
#> GSM1124942 5 0.1387 0.8189 0.000 0.068 0.000 0.000 0.932 0.000
#> GSM1124943 5 0.1387 0.8189 0.000 0.068 0.000 0.000 0.932 0.000
#> GSM1124948 5 0.3592 0.6910 0.000 0.240 0.000 0.000 0.740 0.020
#> GSM1124949 1 0.0363 0.9081 0.988 0.000 0.000 0.000 0.000 0.012
#> GSM1124950 2 0.0146 0.8273 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM1124954 3 0.3602 0.7218 0.208 0.000 0.760 0.000 0.000 0.032
#> GSM1124955 1 0.0000 0.9099 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124956 2 0.0260 0.8273 0.000 0.992 0.000 0.000 0.008 0.000
#> GSM1124872 2 0.0000 0.8270 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1124873 2 0.0000 0.8270 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1124876 3 0.1141 0.8094 0.052 0.000 0.948 0.000 0.000 0.000
#> GSM1124877 1 0.0000 0.9099 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124879 1 0.1624 0.8951 0.936 0.004 0.020 0.000 0.000 0.040
#> GSM1124883 6 0.4947 0.4357 0.000 0.456 0.000 0.000 0.064 0.480
#> GSM1124889 2 0.0260 0.8273 0.000 0.992 0.000 0.000 0.008 0.000
#> GSM1124892 1 0.0291 0.9094 0.992 0.000 0.004 0.000 0.000 0.004
#> GSM1124893 1 0.0363 0.9081 0.988 0.000 0.000 0.000 0.000 0.012
#> GSM1124909 2 0.1663 0.7502 0.000 0.912 0.000 0.000 0.088 0.000
#> GSM1124913 6 0.5984 0.7399 0.000 0.308 0.000 0.020 0.156 0.516
#> GSM1124916 2 0.0632 0.8197 0.000 0.976 0.000 0.000 0.024 0.000
#> GSM1124923 5 0.2492 0.8059 0.000 0.068 0.036 0.000 0.888 0.008
#> GSM1124925 1 0.0000 0.9099 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124929 1 0.0632 0.9063 0.976 0.000 0.000 0.000 0.000 0.024
#> GSM1124934 1 0.5639 0.5268 0.636 0.004 0.200 0.000 0.124 0.036
#> GSM1124937 1 0.6683 -0.0400 0.416 0.396 0.012 0.000 0.124 0.052
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 tissue(p) k
#> SD:mclust 84 5.32e-16 2
#> SD:mclust 86 2.14e-13 3
#> SD:mclust 81 8.62e-16 4
#> SD:mclust 87 1.01e-07 5
#> SD:mclust 82 9.95e-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["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 48983 rows and 91 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 0.803 0.878 0.948 0.4936 0.499 0.499
#> 3 3 0.637 0.783 0.902 0.2389 0.792 0.621
#> 4 4 0.727 0.763 0.887 0.1452 0.753 0.478
#> 5 5 0.848 0.833 0.911 0.1149 0.820 0.492
#> 6 6 0.805 0.725 0.865 0.0447 0.945 0.759
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
#> GSM1124891 1 0.0000 0.9065 1.000 0.000
#> GSM1124888 1 0.0000 0.9065 1.000 0.000
#> GSM1124890 1 0.7219 0.7435 0.800 0.200
#> GSM1124904 2 0.0000 0.9733 0.000 1.000
#> GSM1124927 1 0.9732 0.3336 0.596 0.404
#> GSM1124953 1 0.9732 0.4150 0.596 0.404
#> GSM1124869 1 0.0000 0.9065 1.000 0.000
#> GSM1124870 1 0.0000 0.9065 1.000 0.000
#> GSM1124882 1 0.0000 0.9065 1.000 0.000
#> GSM1124884 2 0.0000 0.9733 0.000 1.000
#> GSM1124898 2 0.0000 0.9733 0.000 1.000
#> GSM1124903 2 0.0000 0.9733 0.000 1.000
#> GSM1124905 1 0.0000 0.9065 1.000 0.000
#> GSM1124910 1 0.0000 0.9065 1.000 0.000
#> GSM1124919 2 0.0672 0.9668 0.008 0.992
#> GSM1124932 2 0.9580 0.3508 0.380 0.620
#> GSM1124933 1 0.0000 0.9065 1.000 0.000
#> GSM1124867 2 0.2778 0.9270 0.048 0.952
#> GSM1124868 2 0.0000 0.9733 0.000 1.000
#> GSM1124878 2 0.0000 0.9733 0.000 1.000
#> GSM1124895 2 0.0000 0.9733 0.000 1.000
#> GSM1124897 2 0.0000 0.9733 0.000 1.000
#> GSM1124902 2 0.0000 0.9733 0.000 1.000
#> GSM1124908 2 0.0000 0.9733 0.000 1.000
#> GSM1124921 2 0.0000 0.9733 0.000 1.000
#> GSM1124939 2 0.0000 0.9733 0.000 1.000
#> GSM1124944 2 0.0000 0.9733 0.000 1.000
#> GSM1124945 1 0.9323 0.5291 0.652 0.348
#> GSM1124946 2 0.0000 0.9733 0.000 1.000
#> GSM1124947 2 0.0000 0.9733 0.000 1.000
#> GSM1124951 1 0.9710 0.4239 0.600 0.400
#> GSM1124952 2 0.0000 0.9733 0.000 1.000
#> GSM1124957 1 0.7219 0.7435 0.800 0.200
#> GSM1124900 1 0.0000 0.9065 1.000 0.000
#> GSM1124914 2 0.0000 0.9733 0.000 1.000
#> GSM1124871 2 0.0000 0.9733 0.000 1.000
#> GSM1124874 2 0.0000 0.9733 0.000 1.000
#> GSM1124875 2 0.0000 0.9733 0.000 1.000
#> GSM1124880 1 0.0000 0.9065 1.000 0.000
#> GSM1124881 2 0.0000 0.9733 0.000 1.000
#> GSM1124885 2 0.0000 0.9733 0.000 1.000
#> GSM1124886 1 0.0000 0.9065 1.000 0.000
#> GSM1124887 2 0.0000 0.9733 0.000 1.000
#> GSM1124894 2 0.8016 0.6538 0.244 0.756
#> GSM1124896 1 0.0000 0.9065 1.000 0.000
#> GSM1124899 2 0.0000 0.9733 0.000 1.000
#> GSM1124901 2 0.0000 0.9733 0.000 1.000
#> GSM1124906 2 0.0000 0.9733 0.000 1.000
#> GSM1124907 2 0.0000 0.9733 0.000 1.000
#> GSM1124911 2 0.0000 0.9733 0.000 1.000
#> GSM1124912 1 0.0000 0.9065 1.000 0.000
#> GSM1124915 2 0.0000 0.9733 0.000 1.000
#> GSM1124917 2 0.0000 0.9733 0.000 1.000
#> GSM1124918 2 0.0000 0.9733 0.000 1.000
#> GSM1124920 1 0.0000 0.9065 1.000 0.000
#> GSM1124922 2 0.0000 0.9733 0.000 1.000
#> GSM1124924 1 0.0000 0.9065 1.000 0.000
#> GSM1124926 2 0.0000 0.9733 0.000 1.000
#> GSM1124928 1 0.0000 0.9065 1.000 0.000
#> GSM1124930 2 0.6973 0.7326 0.188 0.812
#> GSM1124931 1 0.9993 0.0847 0.516 0.484
#> GSM1124935 2 0.0000 0.9733 0.000 1.000
#> GSM1124936 1 0.0000 0.9065 1.000 0.000
#> GSM1124938 1 0.7219 0.7435 0.800 0.200
#> GSM1124940 1 0.0000 0.9065 1.000 0.000
#> GSM1124941 2 0.0000 0.9733 0.000 1.000
#> GSM1124942 2 0.0000 0.9733 0.000 1.000
#> GSM1124943 1 0.9710 0.4243 0.600 0.400
#> GSM1124948 1 0.2423 0.8808 0.960 0.040
#> GSM1124949 1 0.0000 0.9065 1.000 0.000
#> GSM1124950 2 0.3733 0.9037 0.072 0.928
#> GSM1124954 1 0.0000 0.9065 1.000 0.000
#> GSM1124955 1 0.0000 0.9065 1.000 0.000
#> GSM1124956 2 0.0000 0.9733 0.000 1.000
#> GSM1124872 2 0.5408 0.8401 0.124 0.876
#> GSM1124873 2 0.0000 0.9733 0.000 1.000
#> GSM1124876 1 0.0000 0.9065 1.000 0.000
#> GSM1124877 1 0.0000 0.9065 1.000 0.000
#> GSM1124879 1 0.0000 0.9065 1.000 0.000
#> GSM1124883 2 0.0000 0.9733 0.000 1.000
#> GSM1124889 2 0.0000 0.9733 0.000 1.000
#> GSM1124892 1 0.0000 0.9065 1.000 0.000
#> GSM1124893 1 0.0000 0.9065 1.000 0.000
#> GSM1124909 1 0.9933 0.2309 0.548 0.452
#> GSM1124913 2 0.0000 0.9733 0.000 1.000
#> GSM1124916 2 0.4022 0.8946 0.080 0.920
#> GSM1124923 2 0.0938 0.9629 0.012 0.988
#> GSM1124925 1 0.0000 0.9065 1.000 0.000
#> GSM1124929 1 0.0000 0.9065 1.000 0.000
#> GSM1124934 1 0.0000 0.9065 1.000 0.000
#> GSM1124937 1 0.0000 0.9065 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1124891 3 0.4555 0.737 0.200 0.000 0.800
#> GSM1124888 3 0.4555 0.737 0.200 0.000 0.800
#> GSM1124890 3 0.4555 0.764 0.000 0.200 0.800
#> GSM1124904 2 0.0000 0.902 0.000 1.000 0.000
#> GSM1124927 1 0.0000 0.867 1.000 0.000 0.000
#> GSM1124953 3 0.4504 0.766 0.000 0.196 0.804
#> GSM1124869 1 0.0000 0.867 1.000 0.000 0.000
#> GSM1124870 1 0.0000 0.867 1.000 0.000 0.000
#> GSM1124882 1 0.0000 0.867 1.000 0.000 0.000
#> GSM1124884 2 0.0000 0.902 0.000 1.000 0.000
#> GSM1124898 2 0.0000 0.902 0.000 1.000 0.000
#> GSM1124903 2 0.0000 0.902 0.000 1.000 0.000
#> GSM1124905 1 0.0000 0.867 1.000 0.000 0.000
#> GSM1124910 1 0.0000 0.867 1.000 0.000 0.000
#> GSM1124919 2 0.0000 0.902 0.000 1.000 0.000
#> GSM1124932 1 0.4121 0.708 0.832 0.168 0.000
#> GSM1124933 3 0.4555 0.737 0.200 0.000 0.800
#> GSM1124867 2 0.7091 0.449 0.320 0.640 0.040
#> GSM1124868 2 0.3267 0.836 0.000 0.884 0.116
#> GSM1124878 2 0.1289 0.888 0.000 0.968 0.032
#> GSM1124895 2 0.4555 0.770 0.000 0.800 0.200
#> GSM1124897 2 0.1163 0.890 0.000 0.972 0.028
#> GSM1124902 2 0.4555 0.770 0.000 0.800 0.200
#> GSM1124908 2 0.4555 0.770 0.000 0.800 0.200
#> GSM1124921 2 0.4555 0.770 0.000 0.800 0.200
#> GSM1124939 2 0.4555 0.770 0.000 0.800 0.200
#> GSM1124944 2 0.4654 0.763 0.000 0.792 0.208
#> GSM1124945 3 0.0000 0.768 0.000 0.000 1.000
#> GSM1124946 2 0.4555 0.770 0.000 0.800 0.200
#> GSM1124947 2 0.4605 0.767 0.000 0.796 0.204
#> GSM1124951 3 0.0000 0.768 0.000 0.000 1.000
#> GSM1124952 2 0.4605 0.767 0.000 0.796 0.204
#> GSM1124957 3 0.0000 0.768 0.000 0.000 1.000
#> GSM1124900 1 0.0000 0.867 1.000 0.000 0.000
#> GSM1124914 2 0.0000 0.902 0.000 1.000 0.000
#> GSM1124871 2 0.0000 0.902 0.000 1.000 0.000
#> GSM1124874 2 0.0000 0.902 0.000 1.000 0.000
#> GSM1124875 2 0.0000 0.902 0.000 1.000 0.000
#> GSM1124880 1 0.0000 0.867 1.000 0.000 0.000
#> GSM1124881 2 0.0237 0.900 0.004 0.996 0.000
#> GSM1124885 2 0.0000 0.902 0.000 1.000 0.000
#> GSM1124886 1 0.0000 0.867 1.000 0.000 0.000
#> GSM1124887 2 0.0000 0.902 0.000 1.000 0.000
#> GSM1124894 1 0.4733 0.688 0.800 0.004 0.196
#> GSM1124896 1 0.0000 0.867 1.000 0.000 0.000
#> GSM1124899 2 0.1964 0.858 0.056 0.944 0.000
#> GSM1124901 2 0.0000 0.902 0.000 1.000 0.000
#> GSM1124906 1 0.5905 0.463 0.648 0.352 0.000
#> GSM1124907 2 0.0000 0.902 0.000 1.000 0.000
#> GSM1124911 2 0.3412 0.784 0.124 0.876 0.000
#> GSM1124912 1 0.0000 0.867 1.000 0.000 0.000
#> GSM1124915 2 0.0000 0.902 0.000 1.000 0.000
#> GSM1124917 2 0.0000 0.902 0.000 1.000 0.000
#> GSM1124918 2 0.0000 0.902 0.000 1.000 0.000
#> GSM1124920 1 0.6008 0.256 0.628 0.000 0.372
#> GSM1124922 1 0.6299 0.183 0.524 0.476 0.000
#> GSM1124924 1 0.0424 0.861 0.992 0.000 0.008
#> GSM1124926 2 0.0424 0.897 0.008 0.992 0.000
#> GSM1124928 1 0.0000 0.867 1.000 0.000 0.000
#> GSM1124930 2 0.0000 0.902 0.000 1.000 0.000
#> GSM1124931 1 0.2066 0.819 0.940 0.060 0.000
#> GSM1124935 2 0.0000 0.902 0.000 1.000 0.000
#> GSM1124936 1 0.2261 0.807 0.932 0.000 0.068
#> GSM1124938 3 0.4605 0.762 0.000 0.204 0.796
#> GSM1124940 1 0.0000 0.867 1.000 0.000 0.000
#> GSM1124941 2 0.6111 0.262 0.396 0.604 0.000
#> GSM1124942 2 0.0000 0.902 0.000 1.000 0.000
#> GSM1124943 3 0.6095 0.493 0.000 0.392 0.608
#> GSM1124948 3 0.8516 0.559 0.112 0.328 0.560
#> GSM1124949 1 0.0000 0.867 1.000 0.000 0.000
#> GSM1124950 2 0.3686 0.763 0.140 0.860 0.000
#> GSM1124954 1 0.6225 0.056 0.568 0.000 0.432
#> GSM1124955 1 0.0000 0.867 1.000 0.000 0.000
#> GSM1124956 1 0.6308 0.115 0.508 0.492 0.000
#> GSM1124872 1 0.5835 0.482 0.660 0.340 0.000
#> GSM1124873 2 0.3619 0.769 0.136 0.864 0.000
#> GSM1124876 3 0.4555 0.737 0.200 0.000 0.800
#> GSM1124877 1 0.0000 0.867 1.000 0.000 0.000
#> GSM1124879 1 0.0000 0.867 1.000 0.000 0.000
#> GSM1124883 2 0.0000 0.902 0.000 1.000 0.000
#> GSM1124889 2 0.0000 0.902 0.000 1.000 0.000
#> GSM1124892 1 0.0000 0.867 1.000 0.000 0.000
#> GSM1124893 1 0.0000 0.867 1.000 0.000 0.000
#> GSM1124909 1 0.4750 0.652 0.784 0.216 0.000
#> GSM1124913 2 0.0000 0.902 0.000 1.000 0.000
#> GSM1124916 1 0.4842 0.643 0.776 0.224 0.000
#> GSM1124923 2 0.0892 0.893 0.000 0.980 0.020
#> GSM1124925 1 0.0000 0.867 1.000 0.000 0.000
#> GSM1124929 1 0.0000 0.867 1.000 0.000 0.000
#> GSM1124934 1 0.0000 0.867 1.000 0.000 0.000
#> GSM1124937 1 0.0000 0.867 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1124891 3 0.2345 0.757526 0.100 0.000 0.900 0.000
#> GSM1124888 3 0.2216 0.761662 0.092 0.000 0.908 0.000
#> GSM1124890 3 0.4072 0.594989 0.000 0.252 0.748 0.000
#> GSM1124904 2 0.5998 0.660686 0.000 0.668 0.092 0.240
#> GSM1124927 1 0.4522 0.543413 0.680 0.320 0.000 0.000
#> GSM1124953 3 0.4193 0.585972 0.000 0.268 0.732 0.000
#> GSM1124869 1 0.0000 0.897165 1.000 0.000 0.000 0.000
#> GSM1124870 1 0.2216 0.828783 0.908 0.092 0.000 0.000
#> GSM1124882 1 0.0000 0.897165 1.000 0.000 0.000 0.000
#> GSM1124884 2 0.0000 0.854756 0.000 1.000 0.000 0.000
#> GSM1124898 2 0.2401 0.847818 0.000 0.904 0.092 0.004
#> GSM1124903 2 0.6307 0.592478 0.000 0.620 0.092 0.288
#> GSM1124905 1 0.0000 0.897165 1.000 0.000 0.000 0.000
#> GSM1124910 1 0.0000 0.897165 1.000 0.000 0.000 0.000
#> GSM1124919 2 0.2466 0.846616 0.000 0.900 0.096 0.004
#> GSM1124932 2 0.4967 -0.014002 0.452 0.548 0.000 0.000
#> GSM1124933 3 0.2216 0.761662 0.092 0.000 0.908 0.000
#> GSM1124867 4 0.7016 0.314269 0.140 0.320 0.000 0.540
#> GSM1124868 4 0.0000 0.860451 0.000 0.000 0.000 1.000
#> GSM1124878 4 0.3591 0.660950 0.000 0.168 0.008 0.824
#> GSM1124895 4 0.0000 0.860451 0.000 0.000 0.000 1.000
#> GSM1124897 4 0.5160 0.556906 0.000 0.180 0.072 0.748
#> GSM1124902 4 0.0000 0.860451 0.000 0.000 0.000 1.000
#> GSM1124908 4 0.0188 0.857578 0.000 0.000 0.004 0.996
#> GSM1124921 4 0.0000 0.860451 0.000 0.000 0.000 1.000
#> GSM1124939 4 0.0000 0.860451 0.000 0.000 0.000 1.000
#> GSM1124944 4 0.0000 0.860451 0.000 0.000 0.000 1.000
#> GSM1124945 4 0.4776 0.275699 0.000 0.000 0.376 0.624
#> GSM1124946 4 0.0000 0.860451 0.000 0.000 0.000 1.000
#> GSM1124947 4 0.0000 0.860451 0.000 0.000 0.000 1.000
#> GSM1124951 3 0.4382 0.461939 0.000 0.000 0.704 0.296
#> GSM1124952 4 0.0000 0.860451 0.000 0.000 0.000 1.000
#> GSM1124957 3 0.2216 0.705025 0.000 0.000 0.908 0.092
#> GSM1124900 1 0.2216 0.828783 0.908 0.092 0.000 0.000
#> GSM1124914 2 0.5594 0.716540 0.000 0.716 0.092 0.192
#> GSM1124871 2 0.0000 0.854756 0.000 1.000 0.000 0.000
#> GSM1124874 2 0.0188 0.855018 0.000 0.996 0.000 0.004
#> GSM1124875 2 0.2216 0.848684 0.000 0.908 0.092 0.000
#> GSM1124880 1 0.3764 0.691451 0.784 0.216 0.000 0.000
#> GSM1124881 2 0.0000 0.854756 0.000 1.000 0.000 0.000
#> GSM1124885 2 0.5174 0.755139 0.000 0.756 0.092 0.152
#> GSM1124886 1 0.0000 0.897165 1.000 0.000 0.000 0.000
#> GSM1124887 2 0.5436 0.732740 0.000 0.732 0.092 0.176
#> GSM1124894 4 0.3024 0.695149 0.148 0.000 0.000 0.852
#> GSM1124896 1 0.0000 0.897165 1.000 0.000 0.000 0.000
#> GSM1124899 2 0.0469 0.856023 0.000 0.988 0.012 0.000
#> GSM1124901 2 0.2401 0.847818 0.000 0.904 0.092 0.004
#> GSM1124906 2 0.0000 0.854756 0.000 1.000 0.000 0.000
#> GSM1124907 2 0.4662 0.787683 0.000 0.796 0.092 0.112
#> GSM1124911 2 0.0000 0.854756 0.000 1.000 0.000 0.000
#> GSM1124912 1 0.0000 0.897165 1.000 0.000 0.000 0.000
#> GSM1124915 2 0.2197 0.851245 0.000 0.916 0.080 0.004
#> GSM1124917 2 0.2216 0.848684 0.000 0.908 0.092 0.000
#> GSM1124918 2 0.1118 0.855915 0.000 0.964 0.036 0.000
#> GSM1124920 1 0.4907 0.264161 0.580 0.000 0.420 0.000
#> GSM1124922 2 0.4068 0.796117 0.100 0.844 0.044 0.012
#> GSM1124924 1 0.7822 0.000862 0.380 0.364 0.256 0.000
#> GSM1124926 2 0.2596 0.827110 0.024 0.908 0.000 0.068
#> GSM1124928 1 0.0188 0.895142 0.996 0.004 0.000 0.000
#> GSM1124930 2 0.2216 0.848684 0.000 0.908 0.092 0.000
#> GSM1124931 2 0.4888 0.131696 0.412 0.588 0.000 0.000
#> GSM1124935 2 0.2216 0.848684 0.000 0.908 0.092 0.000
#> GSM1124936 1 0.1022 0.875793 0.968 0.000 0.032 0.000
#> GSM1124938 3 0.3528 0.655685 0.000 0.192 0.808 0.000
#> GSM1124940 1 0.0000 0.897165 1.000 0.000 0.000 0.000
#> GSM1124941 2 0.0000 0.854756 0.000 1.000 0.000 0.000
#> GSM1124942 2 0.2216 0.848684 0.000 0.908 0.092 0.000
#> GSM1124943 2 0.3873 0.746882 0.000 0.772 0.228 0.000
#> GSM1124948 2 0.1302 0.848825 0.000 0.956 0.044 0.000
#> GSM1124949 1 0.0000 0.897165 1.000 0.000 0.000 0.000
#> GSM1124950 2 0.0188 0.853362 0.000 0.996 0.000 0.004
#> GSM1124954 1 0.4855 0.312885 0.600 0.000 0.400 0.000
#> GSM1124955 1 0.0000 0.897165 1.000 0.000 0.000 0.000
#> GSM1124956 2 0.0000 0.854756 0.000 1.000 0.000 0.000
#> GSM1124872 2 0.0000 0.854756 0.000 1.000 0.000 0.000
#> GSM1124873 2 0.0000 0.854756 0.000 1.000 0.000 0.000
#> GSM1124876 3 0.2216 0.761662 0.092 0.000 0.908 0.000
#> GSM1124877 1 0.0000 0.897165 1.000 0.000 0.000 0.000
#> GSM1124879 1 0.0000 0.897165 1.000 0.000 0.000 0.000
#> GSM1124883 2 0.5907 0.676335 0.000 0.680 0.092 0.228
#> GSM1124889 2 0.0000 0.854756 0.000 1.000 0.000 0.000
#> GSM1124892 1 0.0000 0.897165 1.000 0.000 0.000 0.000
#> GSM1124893 1 0.0000 0.897165 1.000 0.000 0.000 0.000
#> GSM1124909 2 0.0000 0.854756 0.000 1.000 0.000 0.000
#> GSM1124913 2 0.5842 0.685864 0.000 0.688 0.092 0.220
#> GSM1124916 2 0.0000 0.854756 0.000 1.000 0.000 0.000
#> GSM1124923 2 0.5219 0.690862 0.000 0.712 0.244 0.044
#> GSM1124925 1 0.0000 0.897165 1.000 0.000 0.000 0.000
#> GSM1124929 1 0.0000 0.897165 1.000 0.000 0.000 0.000
#> GSM1124934 1 0.0524 0.891597 0.988 0.004 0.008 0.000
#> GSM1124937 1 0.2281 0.828711 0.904 0.096 0.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM1124891 3 0.4698 0.696 0.168 0.016 0.752 0.000 0.064
#> GSM1124888 3 0.1774 0.810 0.000 0.016 0.932 0.000 0.052
#> GSM1124890 3 0.3074 0.689 0.000 0.000 0.804 0.000 0.196
#> GSM1124904 5 0.1792 0.854 0.000 0.000 0.000 0.084 0.916
#> GSM1124927 2 0.0609 0.934 0.020 0.980 0.000 0.000 0.000
#> GSM1124953 3 0.4211 0.467 0.000 0.360 0.636 0.000 0.004
#> GSM1124869 1 0.0000 0.937 1.000 0.000 0.000 0.000 0.000
#> GSM1124870 2 0.4015 0.490 0.348 0.652 0.000 0.000 0.000
#> GSM1124882 1 0.0000 0.937 1.000 0.000 0.000 0.000 0.000
#> GSM1124884 2 0.0703 0.940 0.000 0.976 0.000 0.000 0.024
#> GSM1124898 5 0.1430 0.845 0.000 0.052 0.000 0.004 0.944
#> GSM1124903 5 0.1851 0.852 0.000 0.000 0.000 0.088 0.912
#> GSM1124905 1 0.0324 0.933 0.992 0.004 0.000 0.000 0.004
#> GSM1124910 1 0.0000 0.937 1.000 0.000 0.000 0.000 0.000
#> GSM1124919 5 0.2331 0.835 0.000 0.080 0.020 0.000 0.900
#> GSM1124932 2 0.0693 0.937 0.012 0.980 0.000 0.000 0.008
#> GSM1124933 3 0.0000 0.826 0.000 0.000 1.000 0.000 0.000
#> GSM1124867 2 0.1571 0.899 0.000 0.936 0.000 0.060 0.004
#> GSM1124868 4 0.1043 0.903 0.000 0.000 0.000 0.960 0.040
#> GSM1124878 5 0.3816 0.622 0.000 0.000 0.000 0.304 0.696
#> GSM1124895 4 0.0000 0.933 0.000 0.000 0.000 1.000 0.000
#> GSM1124897 5 0.2648 0.816 0.000 0.000 0.000 0.152 0.848
#> GSM1124902 4 0.0000 0.933 0.000 0.000 0.000 1.000 0.000
#> GSM1124908 5 0.4294 0.244 0.000 0.000 0.000 0.468 0.532
#> GSM1124921 4 0.0000 0.933 0.000 0.000 0.000 1.000 0.000
#> GSM1124939 4 0.0000 0.933 0.000 0.000 0.000 1.000 0.000
#> GSM1124944 4 0.0000 0.933 0.000 0.000 0.000 1.000 0.000
#> GSM1124945 4 0.3586 0.628 0.000 0.000 0.264 0.736 0.000
#> GSM1124946 4 0.2690 0.770 0.000 0.000 0.000 0.844 0.156
#> GSM1124947 4 0.0000 0.933 0.000 0.000 0.000 1.000 0.000
#> GSM1124951 3 0.0162 0.825 0.000 0.000 0.996 0.004 0.000
#> GSM1124952 4 0.0000 0.933 0.000 0.000 0.000 1.000 0.000
#> GSM1124957 3 0.0609 0.819 0.000 0.000 0.980 0.020 0.000
#> GSM1124900 2 0.2424 0.824 0.132 0.868 0.000 0.000 0.000
#> GSM1124914 5 0.1792 0.853 0.000 0.000 0.000 0.084 0.916
#> GSM1124871 2 0.0880 0.936 0.000 0.968 0.000 0.000 0.032
#> GSM1124874 2 0.0963 0.933 0.000 0.964 0.000 0.000 0.036
#> GSM1124875 5 0.1043 0.842 0.000 0.040 0.000 0.000 0.960
#> GSM1124880 2 0.0798 0.929 0.008 0.976 0.000 0.000 0.016
#> GSM1124881 2 0.0703 0.940 0.000 0.976 0.000 0.000 0.024
#> GSM1124885 5 0.1792 0.854 0.000 0.000 0.000 0.084 0.916
#> GSM1124886 1 0.0000 0.937 1.000 0.000 0.000 0.000 0.000
#> GSM1124887 5 0.1956 0.856 0.000 0.008 0.000 0.076 0.916
#> GSM1124894 4 0.1792 0.839 0.084 0.000 0.000 0.916 0.000
#> GSM1124896 1 0.0000 0.937 1.000 0.000 0.000 0.000 0.000
#> GSM1124899 5 0.1043 0.845 0.000 0.040 0.000 0.000 0.960
#> GSM1124901 5 0.1965 0.850 0.000 0.052 0.000 0.024 0.924
#> GSM1124906 2 0.0609 0.940 0.000 0.980 0.000 0.000 0.020
#> GSM1124907 5 0.1484 0.850 0.000 0.008 0.000 0.048 0.944
#> GSM1124911 2 0.0703 0.940 0.000 0.976 0.000 0.000 0.024
#> GSM1124912 1 0.0000 0.937 1.000 0.000 0.000 0.000 0.000
#> GSM1124915 5 0.3177 0.727 0.000 0.208 0.000 0.000 0.792
#> GSM1124917 2 0.3274 0.702 0.000 0.780 0.000 0.000 0.220
#> GSM1124918 2 0.1478 0.905 0.000 0.936 0.000 0.000 0.064
#> GSM1124920 1 0.5103 0.577 0.688 0.016 0.244 0.000 0.052
#> GSM1124922 5 0.2692 0.814 0.092 0.016 0.000 0.008 0.884
#> GSM1124924 2 0.1430 0.902 0.000 0.944 0.004 0.000 0.052
#> GSM1124926 5 0.4712 0.655 0.028 0.236 0.000 0.020 0.716
#> GSM1124928 1 0.1544 0.877 0.932 0.068 0.000 0.000 0.000
#> GSM1124930 5 0.0898 0.828 0.000 0.020 0.008 0.000 0.972
#> GSM1124931 2 0.0579 0.938 0.008 0.984 0.000 0.000 0.008
#> GSM1124935 5 0.2286 0.807 0.000 0.108 0.000 0.004 0.888
#> GSM1124936 1 0.0609 0.925 0.980 0.000 0.020 0.000 0.000
#> GSM1124938 3 0.4369 0.708 0.000 0.208 0.740 0.000 0.052
#> GSM1124940 1 0.0000 0.937 1.000 0.000 0.000 0.000 0.000
#> GSM1124941 2 0.0404 0.940 0.000 0.988 0.000 0.000 0.012
#> GSM1124942 5 0.5731 0.386 0.000 0.328 0.104 0.000 0.568
#> GSM1124943 5 0.4547 0.161 0.000 0.012 0.400 0.000 0.588
#> GSM1124948 2 0.1341 0.906 0.000 0.944 0.000 0.000 0.056
#> GSM1124949 1 0.0000 0.937 1.000 0.000 0.000 0.000 0.000
#> GSM1124950 2 0.0404 0.939 0.000 0.988 0.000 0.000 0.012
#> GSM1124954 1 0.6154 0.153 0.512 0.020 0.388 0.000 0.080
#> GSM1124955 1 0.0000 0.937 1.000 0.000 0.000 0.000 0.000
#> GSM1124956 2 0.0609 0.940 0.000 0.980 0.000 0.000 0.020
#> GSM1124872 2 0.0609 0.940 0.000 0.980 0.000 0.000 0.020
#> GSM1124873 2 0.0609 0.940 0.000 0.980 0.000 0.000 0.020
#> GSM1124876 3 0.0000 0.826 0.000 0.000 1.000 0.000 0.000
#> GSM1124877 1 0.0671 0.926 0.980 0.004 0.000 0.000 0.016
#> GSM1124879 1 0.0000 0.937 1.000 0.000 0.000 0.000 0.000
#> GSM1124883 5 0.1851 0.852 0.000 0.000 0.000 0.088 0.912
#> GSM1124889 2 0.0880 0.936 0.000 0.968 0.000 0.000 0.032
#> GSM1124892 1 0.0000 0.937 1.000 0.000 0.000 0.000 0.000
#> GSM1124893 1 0.0000 0.937 1.000 0.000 0.000 0.000 0.000
#> GSM1124909 2 0.1121 0.931 0.000 0.956 0.000 0.000 0.044
#> GSM1124913 5 0.1792 0.854 0.000 0.000 0.000 0.084 0.916
#> GSM1124916 2 0.1121 0.924 0.000 0.956 0.000 0.000 0.044
#> GSM1124923 5 0.2130 0.833 0.000 0.000 0.080 0.012 0.908
#> GSM1124925 1 0.0000 0.937 1.000 0.000 0.000 0.000 0.000
#> GSM1124929 1 0.0000 0.937 1.000 0.000 0.000 0.000 0.000
#> GSM1124934 1 0.4519 0.772 0.796 0.060 0.060 0.000 0.084
#> GSM1124937 1 0.3479 0.807 0.836 0.084 0.000 0.000 0.080
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1124891 6 0.5588 0.2364 0.132 0.000 0.316 0.000 0.008 0.544
#> GSM1124888 3 0.4246 0.0430 0.000 0.000 0.532 0.000 0.016 0.452
#> GSM1124890 3 0.3694 0.5548 0.000 0.000 0.784 0.000 0.140 0.076
#> GSM1124904 5 0.0547 0.8492 0.000 0.000 0.000 0.020 0.980 0.000
#> GSM1124927 2 0.0146 0.8504 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM1124953 3 0.4395 0.2288 0.000 0.404 0.568 0.000 0.000 0.028
#> GSM1124869 1 0.0000 0.9845 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124870 2 0.3448 0.4975 0.280 0.716 0.000 0.000 0.000 0.004
#> GSM1124882 1 0.0000 0.9845 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124884 2 0.1082 0.8455 0.000 0.956 0.000 0.000 0.004 0.040
#> GSM1124898 5 0.1674 0.8450 0.000 0.004 0.000 0.004 0.924 0.068
#> GSM1124903 5 0.0632 0.8486 0.000 0.000 0.000 0.024 0.976 0.000
#> GSM1124905 1 0.1297 0.9423 0.948 0.000 0.000 0.012 0.000 0.040
#> GSM1124910 1 0.1082 0.9435 0.956 0.000 0.000 0.000 0.004 0.040
#> GSM1124919 5 0.5548 0.3361 0.000 0.020 0.376 0.000 0.520 0.084
#> GSM1124932 2 0.1615 0.8355 0.000 0.928 0.000 0.004 0.004 0.064
#> GSM1124933 3 0.0000 0.7064 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1124867 2 0.1745 0.8267 0.000 0.924 0.000 0.056 0.000 0.020
#> GSM1124868 4 0.3172 0.7902 0.000 0.000 0.000 0.832 0.092 0.076
#> GSM1124878 5 0.1765 0.8189 0.000 0.000 0.000 0.096 0.904 0.000
#> GSM1124895 4 0.0146 0.9440 0.000 0.000 0.000 0.996 0.004 0.000
#> GSM1124897 5 0.2474 0.8405 0.000 0.004 0.000 0.032 0.884 0.080
#> GSM1124902 4 0.0146 0.9440 0.000 0.000 0.000 0.996 0.004 0.000
#> GSM1124908 5 0.3175 0.6543 0.000 0.000 0.000 0.256 0.744 0.000
#> GSM1124921 4 0.0260 0.9416 0.000 0.000 0.000 0.992 0.008 0.000
#> GSM1124939 4 0.0146 0.9440 0.000 0.000 0.000 0.996 0.004 0.000
#> GSM1124944 4 0.0146 0.9440 0.000 0.000 0.000 0.996 0.004 0.000
#> GSM1124945 3 0.3175 0.5521 0.000 0.000 0.744 0.256 0.000 0.000
#> GSM1124946 4 0.2730 0.7472 0.000 0.000 0.000 0.808 0.192 0.000
#> GSM1124947 4 0.0146 0.9440 0.000 0.000 0.000 0.996 0.004 0.000
#> GSM1124951 3 0.1141 0.6943 0.000 0.000 0.948 0.000 0.052 0.000
#> GSM1124952 4 0.0146 0.9440 0.000 0.000 0.000 0.996 0.004 0.000
#> GSM1124957 3 0.0547 0.7058 0.000 0.000 0.980 0.020 0.000 0.000
#> GSM1124900 2 0.1908 0.7812 0.096 0.900 0.000 0.000 0.000 0.004
#> GSM1124914 5 0.0692 0.8503 0.000 0.000 0.000 0.020 0.976 0.004
#> GSM1124871 2 0.0363 0.8502 0.000 0.988 0.000 0.000 0.000 0.012
#> GSM1124874 2 0.1610 0.8137 0.000 0.916 0.000 0.000 0.000 0.084
#> GSM1124875 5 0.1411 0.8456 0.000 0.004 0.000 0.000 0.936 0.060
#> GSM1124880 2 0.0725 0.8477 0.000 0.976 0.000 0.000 0.012 0.012
#> GSM1124881 2 0.1610 0.8137 0.000 0.916 0.000 0.000 0.000 0.084
#> GSM1124885 5 0.2237 0.8413 0.000 0.004 0.000 0.020 0.896 0.080
#> GSM1124886 1 0.0000 0.9845 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124887 5 0.0748 0.8505 0.000 0.004 0.000 0.016 0.976 0.004
#> GSM1124894 4 0.1065 0.9124 0.020 0.008 0.000 0.964 0.000 0.008
#> GSM1124896 1 0.0000 0.9845 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124899 5 0.2311 0.8343 0.000 0.016 0.000 0.000 0.880 0.104
#> GSM1124901 5 0.1900 0.8444 0.000 0.008 0.000 0.008 0.916 0.068
#> GSM1124906 2 0.1204 0.8388 0.000 0.944 0.000 0.000 0.000 0.056
#> GSM1124907 5 0.0363 0.8455 0.000 0.000 0.000 0.000 0.988 0.012
#> GSM1124911 2 0.2001 0.8199 0.000 0.900 0.000 0.004 0.004 0.092
#> GSM1124912 1 0.0000 0.9845 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124915 5 0.1663 0.8094 0.000 0.088 0.000 0.000 0.912 0.000
#> GSM1124917 2 0.5415 0.3103 0.000 0.564 0.000 0.004 0.304 0.128
#> GSM1124918 6 0.3695 0.3911 0.000 0.272 0.000 0.000 0.016 0.712
#> GSM1124920 6 0.6404 0.2063 0.280 0.000 0.292 0.000 0.016 0.412
#> GSM1124922 5 0.3943 0.7198 0.156 0.000 0.000 0.000 0.760 0.084
#> GSM1124924 2 0.4264 -0.0568 0.000 0.500 0.000 0.000 0.016 0.484
#> GSM1124926 5 0.5168 0.6596 0.064 0.156 0.000 0.000 0.696 0.084
#> GSM1124928 1 0.1444 0.9099 0.928 0.000 0.000 0.000 0.000 0.072
#> GSM1124930 5 0.3866 0.3005 0.000 0.000 0.000 0.000 0.516 0.484
#> GSM1124931 2 0.0458 0.8491 0.000 0.984 0.000 0.000 0.000 0.016
#> GSM1124935 5 0.3601 0.6347 0.000 0.000 0.000 0.004 0.684 0.312
#> GSM1124936 1 0.0363 0.9756 0.988 0.000 0.012 0.000 0.000 0.000
#> GSM1124938 6 0.5096 -0.1276 0.000 0.044 0.460 0.000 0.016 0.480
#> GSM1124940 1 0.0000 0.9845 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124941 2 0.1863 0.8067 0.000 0.896 0.000 0.000 0.000 0.104
#> GSM1124942 6 0.6804 0.0344 0.000 0.052 0.216 0.000 0.344 0.388
#> GSM1124943 5 0.5249 0.3299 0.000 0.000 0.104 0.000 0.528 0.368
#> GSM1124948 6 0.4303 0.2762 0.000 0.360 0.008 0.000 0.016 0.616
#> GSM1124949 1 0.0000 0.9845 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124950 2 0.0000 0.8502 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1124954 6 0.4092 0.4246 0.316 0.000 0.012 0.004 0.004 0.664
#> GSM1124955 1 0.0000 0.9845 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124956 2 0.2113 0.8277 0.000 0.896 0.000 0.004 0.008 0.092
#> GSM1124872 2 0.0146 0.8504 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM1124873 2 0.0146 0.8504 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM1124876 3 0.0000 0.7064 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1124877 1 0.1010 0.9507 0.960 0.000 0.000 0.000 0.004 0.036
#> GSM1124879 1 0.0000 0.9845 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124883 5 0.0547 0.8492 0.000 0.000 0.000 0.020 0.980 0.000
#> GSM1124889 2 0.0291 0.8512 0.000 0.992 0.000 0.000 0.004 0.004
#> GSM1124892 1 0.0000 0.9845 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124893 1 0.0000 0.9845 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124909 2 0.3989 0.1927 0.000 0.528 0.000 0.004 0.000 0.468
#> GSM1124913 5 0.0603 0.8497 0.000 0.004 0.000 0.016 0.980 0.000
#> GSM1124916 6 0.4211 -0.1022 0.000 0.456 0.000 0.004 0.008 0.532
#> GSM1124923 5 0.0937 0.8431 0.000 0.000 0.040 0.000 0.960 0.000
#> GSM1124925 1 0.0000 0.9845 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124929 1 0.0000 0.9845 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124934 6 0.2773 0.4497 0.164 0.000 0.000 0.004 0.004 0.828
#> GSM1124937 6 0.3819 0.3307 0.372 0.000 0.000 0.004 0.000 0.624
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 tissue(p) k
#> SD:NMF 84 1.17e-01 2
#> SD:NMF 82 1.14e-03 3
#> SD:NMF 83 1.47e-13 4
#> SD:NMF 85 2.68e-07 5
#> SD:NMF 72 4.02e-04 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 48983 rows and 91 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#> Subgroups are detected by 'hclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 4.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.447 0.788 0.889 0.261 0.820 0.820
#> 3 3 0.190 0.449 0.729 0.701 0.840 0.804
#> 4 4 0.282 0.632 0.749 0.318 0.730 0.603
#> 5 5 0.436 0.700 0.784 0.132 0.953 0.892
#> 6 6 0.526 0.553 0.717 0.110 0.996 0.991
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
#> GSM1124891 1 0.9286 0.717 0.656 0.344
#> GSM1124888 1 0.9129 0.744 0.672 0.328
#> GSM1124890 2 0.7602 0.703 0.220 0.780
#> GSM1124904 2 0.1414 0.868 0.020 0.980
#> GSM1124927 2 0.2423 0.871 0.040 0.960
#> GSM1124953 2 0.7815 0.684 0.232 0.768
#> GSM1124869 2 0.3733 0.860 0.072 0.928
#> GSM1124870 2 0.2423 0.871 0.040 0.960
#> GSM1124882 2 0.3733 0.860 0.072 0.928
#> GSM1124884 2 0.0938 0.876 0.012 0.988
#> GSM1124898 2 0.1414 0.868 0.020 0.980
#> GSM1124903 2 0.1414 0.868 0.020 0.980
#> GSM1124905 2 0.2948 0.873 0.052 0.948
#> GSM1124910 2 0.5842 0.805 0.140 0.860
#> GSM1124919 2 0.7602 0.703 0.220 0.780
#> GSM1124932 2 0.8955 0.415 0.312 0.688
#> GSM1124933 1 0.7602 0.793 0.780 0.220
#> GSM1124867 2 0.4815 0.846 0.104 0.896
#> GSM1124868 2 0.4298 0.826 0.088 0.912
#> GSM1124878 2 0.4298 0.826 0.088 0.912
#> GSM1124895 2 0.7674 0.666 0.224 0.776
#> GSM1124897 2 0.4298 0.826 0.088 0.912
#> GSM1124902 2 0.7674 0.666 0.224 0.776
#> GSM1124908 2 0.7674 0.666 0.224 0.776
#> GSM1124921 2 0.7674 0.666 0.224 0.776
#> GSM1124939 2 0.7674 0.666 0.224 0.776
#> GSM1124944 2 0.7674 0.666 0.224 0.776
#> GSM1124945 1 0.2423 0.750 0.960 0.040
#> GSM1124946 2 0.7674 0.666 0.224 0.776
#> GSM1124947 2 0.7674 0.666 0.224 0.776
#> GSM1124951 1 0.2423 0.750 0.960 0.040
#> GSM1124952 2 0.7674 0.666 0.224 0.776
#> GSM1124957 1 0.2423 0.750 0.960 0.040
#> GSM1124900 2 0.3274 0.867 0.060 0.940
#> GSM1124914 2 0.2948 0.874 0.052 0.948
#> GSM1124871 2 0.0938 0.875 0.012 0.988
#> GSM1124874 2 0.0938 0.875 0.012 0.988
#> GSM1124875 2 0.2043 0.874 0.032 0.968
#> GSM1124880 2 0.5059 0.833 0.112 0.888
#> GSM1124881 2 0.0672 0.875 0.008 0.992
#> GSM1124885 2 0.1414 0.868 0.020 0.980
#> GSM1124886 2 0.3879 0.858 0.076 0.924
#> GSM1124887 2 0.7602 0.703 0.220 0.780
#> GSM1124894 2 0.3584 0.871 0.068 0.932
#> GSM1124896 2 0.3733 0.860 0.072 0.928
#> GSM1124899 2 0.0376 0.873 0.004 0.996
#> GSM1124901 2 0.1414 0.868 0.020 0.980
#> GSM1124906 2 0.0938 0.876 0.012 0.988
#> GSM1124907 2 0.2043 0.874 0.032 0.968
#> GSM1124911 2 0.0376 0.875 0.004 0.996
#> GSM1124912 2 0.3733 0.860 0.072 0.928
#> GSM1124915 2 0.0938 0.876 0.012 0.988
#> GSM1124917 2 0.2043 0.874 0.032 0.968
#> GSM1124918 2 0.1843 0.874 0.028 0.972
#> GSM1124920 1 0.9129 0.744 0.672 0.328
#> GSM1124922 2 0.1633 0.874 0.024 0.976
#> GSM1124924 2 0.6531 0.778 0.168 0.832
#> GSM1124926 2 0.0376 0.873 0.004 0.996
#> GSM1124928 2 0.5059 0.833 0.112 0.888
#> GSM1124930 2 0.2043 0.874 0.032 0.968
#> GSM1124931 2 0.1843 0.874 0.028 0.972
#> GSM1124935 2 0.0672 0.875 0.008 0.992
#> GSM1124936 1 0.9754 0.578 0.592 0.408
#> GSM1124938 2 0.4815 0.835 0.104 0.896
#> GSM1124940 2 0.3733 0.860 0.072 0.928
#> GSM1124941 2 0.0938 0.876 0.012 0.988
#> GSM1124942 2 0.2043 0.874 0.032 0.968
#> GSM1124943 2 0.2043 0.874 0.032 0.968
#> GSM1124948 2 0.6343 0.788 0.160 0.840
#> GSM1124949 2 0.4022 0.856 0.080 0.920
#> GSM1124950 2 0.2236 0.872 0.036 0.964
#> GSM1124954 2 0.9491 0.284 0.368 0.632
#> GSM1124955 2 0.3733 0.860 0.072 0.928
#> GSM1124956 2 0.0376 0.875 0.004 0.996
#> GSM1124872 2 0.2236 0.872 0.036 0.964
#> GSM1124873 2 0.0672 0.875 0.008 0.992
#> GSM1124876 1 0.7602 0.793 0.780 0.220
#> GSM1124877 2 0.9491 0.284 0.368 0.632
#> GSM1124879 2 0.5842 0.807 0.140 0.860
#> GSM1124883 2 0.1414 0.868 0.020 0.980
#> GSM1124889 2 0.1184 0.876 0.016 0.984
#> GSM1124892 2 0.9686 0.166 0.396 0.604
#> GSM1124893 2 0.3733 0.860 0.072 0.928
#> GSM1124909 2 0.0672 0.875 0.008 0.992
#> GSM1124913 2 0.1414 0.868 0.020 0.980
#> GSM1124916 2 0.0672 0.875 0.008 0.992
#> GSM1124923 2 0.7602 0.703 0.220 0.780
#> GSM1124925 2 0.3733 0.860 0.072 0.928
#> GSM1124929 2 0.3879 0.858 0.076 0.924
#> GSM1124934 2 0.9491 0.284 0.368 0.632
#> GSM1124937 2 0.5946 0.802 0.144 0.856
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1124891 3 0.917 0.4557 0.312 0.172 0.516
#> GSM1124888 3 0.819 0.5501 0.372 0.080 0.548
#> GSM1124890 2 0.594 0.4915 0.036 0.760 0.204
#> GSM1124904 2 0.196 0.6440 0.056 0.944 0.000
#> GSM1124927 2 0.460 0.5359 0.204 0.796 0.000
#> GSM1124953 2 0.608 0.4744 0.036 0.748 0.216
#> GSM1124869 2 0.631 -0.4399 0.496 0.504 0.000
#> GSM1124870 2 0.460 0.5359 0.204 0.796 0.000
#> GSM1124882 2 0.631 -0.4399 0.496 0.504 0.000
#> GSM1124884 2 0.312 0.6393 0.108 0.892 0.000
#> GSM1124898 2 0.164 0.6537 0.044 0.956 0.000
#> GSM1124903 2 0.196 0.6440 0.056 0.944 0.000
#> GSM1124905 2 0.596 0.2624 0.324 0.672 0.004
#> GSM1124910 2 0.744 0.0618 0.348 0.604 0.048
#> GSM1124919 2 0.594 0.4915 0.036 0.760 0.204
#> GSM1124932 1 0.597 0.5568 0.636 0.364 0.000
#> GSM1124933 3 0.524 0.7040 0.132 0.048 0.820
#> GSM1124867 2 0.538 0.6230 0.112 0.820 0.068
#> GSM1124868 2 0.491 0.6032 0.088 0.844 0.068
#> GSM1124878 2 0.491 0.6032 0.088 0.844 0.068
#> GSM1124895 2 0.792 0.4412 0.180 0.664 0.156
#> GSM1124897 2 0.491 0.6032 0.088 0.844 0.068
#> GSM1124902 2 0.792 0.4412 0.180 0.664 0.156
#> GSM1124908 2 0.787 0.4454 0.176 0.668 0.156
#> GSM1124921 2 0.787 0.4454 0.176 0.668 0.156
#> GSM1124939 2 0.792 0.4412 0.180 0.664 0.156
#> GSM1124944 2 0.792 0.4412 0.180 0.664 0.156
#> GSM1124945 3 0.000 0.6816 0.000 0.000 1.000
#> GSM1124946 2 0.787 0.4454 0.176 0.668 0.156
#> GSM1124947 2 0.792 0.4412 0.180 0.664 0.156
#> GSM1124951 3 0.000 0.6816 0.000 0.000 1.000
#> GSM1124952 2 0.819 0.4347 0.204 0.640 0.156
#> GSM1124957 3 0.000 0.6816 0.000 0.000 1.000
#> GSM1124900 2 0.516 0.5143 0.216 0.776 0.008
#> GSM1124914 2 0.475 0.5373 0.216 0.784 0.000
#> GSM1124871 2 0.334 0.6436 0.120 0.880 0.000
#> GSM1124874 2 0.334 0.6327 0.120 0.880 0.000
#> GSM1124875 2 0.238 0.6580 0.056 0.936 0.008
#> GSM1124880 2 0.685 0.2742 0.300 0.664 0.036
#> GSM1124881 2 0.304 0.6435 0.104 0.896 0.000
#> GSM1124885 2 0.175 0.6468 0.048 0.952 0.000
#> GSM1124886 1 0.631 0.3782 0.500 0.500 0.000
#> GSM1124887 2 0.594 0.4915 0.036 0.760 0.204
#> GSM1124894 2 0.642 0.2943 0.304 0.676 0.020
#> GSM1124896 2 0.631 -0.4399 0.496 0.504 0.000
#> GSM1124899 2 0.175 0.6606 0.048 0.952 0.000
#> GSM1124901 2 0.141 0.6579 0.036 0.964 0.000
#> GSM1124906 2 0.280 0.6499 0.092 0.908 0.000
#> GSM1124907 2 0.206 0.6575 0.044 0.948 0.008
#> GSM1124911 2 0.207 0.6629 0.060 0.940 0.000
#> GSM1124912 2 0.631 -0.4399 0.496 0.504 0.000
#> GSM1124915 2 0.186 0.6635 0.052 0.948 0.000
#> GSM1124917 2 0.327 0.6623 0.080 0.904 0.016
#> GSM1124918 2 0.228 0.6615 0.052 0.940 0.008
#> GSM1124920 3 0.819 0.5501 0.372 0.080 0.548
#> GSM1124922 2 0.196 0.6629 0.056 0.944 0.000
#> GSM1124924 2 0.768 0.1402 0.328 0.608 0.064
#> GSM1124926 2 0.175 0.6606 0.048 0.952 0.000
#> GSM1124928 2 0.691 0.2319 0.308 0.656 0.036
#> GSM1124930 2 0.228 0.6575 0.052 0.940 0.008
#> GSM1124931 2 0.440 0.5614 0.188 0.812 0.000
#> GSM1124935 2 0.175 0.6631 0.048 0.952 0.000
#> GSM1124936 3 0.864 0.3705 0.440 0.100 0.460
#> GSM1124938 2 0.516 0.5620 0.140 0.820 0.040
#> GSM1124940 2 0.631 -0.4399 0.496 0.504 0.000
#> GSM1124941 2 0.280 0.6499 0.092 0.908 0.000
#> GSM1124942 2 0.228 0.6575 0.052 0.940 0.008
#> GSM1124943 2 0.228 0.6575 0.052 0.940 0.008
#> GSM1124948 2 0.719 0.2860 0.280 0.664 0.056
#> GSM1124949 1 0.631 0.3918 0.504 0.496 0.000
#> GSM1124950 2 0.440 0.5598 0.188 0.812 0.000
#> GSM1124954 1 0.460 0.5581 0.796 0.204 0.000
#> GSM1124955 2 0.631 -0.4399 0.496 0.504 0.000
#> GSM1124956 2 0.207 0.6629 0.060 0.940 0.000
#> GSM1124872 2 0.440 0.5598 0.188 0.812 0.000
#> GSM1124873 2 0.296 0.6452 0.100 0.900 0.000
#> GSM1124876 3 0.524 0.7040 0.132 0.048 0.820
#> GSM1124877 1 0.460 0.5581 0.796 0.204 0.000
#> GSM1124879 1 0.623 0.5415 0.564 0.436 0.000
#> GSM1124883 2 0.196 0.6440 0.056 0.944 0.000
#> GSM1124889 2 0.263 0.6573 0.084 0.916 0.000
#> GSM1124892 1 0.960 0.4098 0.476 0.268 0.256
#> GSM1124893 2 0.631 -0.4399 0.496 0.504 0.000
#> GSM1124909 2 0.355 0.6266 0.132 0.868 0.000
#> GSM1124913 2 0.196 0.6440 0.056 0.944 0.000
#> GSM1124916 2 0.355 0.6266 0.132 0.868 0.000
#> GSM1124923 2 0.594 0.4915 0.036 0.760 0.204
#> GSM1124925 2 0.631 -0.4399 0.496 0.504 0.000
#> GSM1124929 2 0.631 -0.4548 0.500 0.500 0.000
#> GSM1124934 1 0.460 0.5581 0.796 0.204 0.000
#> GSM1124937 1 0.628 0.5139 0.540 0.460 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1124891 3 0.813 0.446 0.268 0.124 0.540 0.068
#> GSM1124888 3 0.684 0.504 0.372 0.016 0.544 0.068
#> GSM1124890 2 0.462 0.476 0.020 0.760 0.216 0.004
#> GSM1124904 2 0.320 0.549 0.036 0.892 0.060 0.012
#> GSM1124927 2 0.541 0.563 0.296 0.668 0.036 0.000
#> GSM1124953 2 0.472 0.459 0.020 0.748 0.228 0.004
#> GSM1124869 1 0.172 0.836 0.936 0.064 0.000 0.000
#> GSM1124870 2 0.541 0.563 0.296 0.668 0.036 0.000
#> GSM1124882 1 0.172 0.836 0.936 0.064 0.000 0.000
#> GSM1124884 2 0.370 0.681 0.156 0.828 0.016 0.000
#> GSM1124898 2 0.294 0.608 0.044 0.900 0.052 0.004
#> GSM1124903 2 0.320 0.549 0.036 0.892 0.060 0.012
#> GSM1124905 2 0.651 0.284 0.452 0.492 0.040 0.016
#> GSM1124910 2 0.743 0.272 0.424 0.468 0.068 0.040
#> GSM1124919 2 0.462 0.476 0.020 0.760 0.216 0.004
#> GSM1124932 1 0.838 0.286 0.368 0.252 0.020 0.360
#> GSM1124933 3 0.358 0.690 0.180 0.004 0.816 0.000
#> GSM1124867 2 0.572 0.629 0.184 0.736 0.048 0.032
#> GSM1124868 2 0.469 0.432 0.016 0.804 0.044 0.136
#> GSM1124878 2 0.469 0.432 0.016 0.804 0.044 0.136
#> GSM1124895 4 0.482 0.994 0.000 0.388 0.000 0.612
#> GSM1124897 2 0.469 0.432 0.016 0.804 0.044 0.136
#> GSM1124902 4 0.482 0.994 0.000 0.388 0.000 0.612
#> GSM1124908 4 0.530 0.991 0.004 0.388 0.008 0.600
#> GSM1124921 4 0.530 0.991 0.004 0.388 0.008 0.600
#> GSM1124939 4 0.482 0.994 0.000 0.388 0.000 0.612
#> GSM1124944 4 0.482 0.994 0.000 0.388 0.000 0.612
#> GSM1124945 3 0.253 0.672 0.000 0.004 0.896 0.100
#> GSM1124946 4 0.530 0.991 0.004 0.388 0.008 0.600
#> GSM1124947 4 0.482 0.994 0.000 0.388 0.000 0.612
#> GSM1124951 3 0.253 0.672 0.000 0.004 0.896 0.100
#> GSM1124952 2 0.655 -0.234 0.092 0.568 0.000 0.340
#> GSM1124957 3 0.253 0.672 0.000 0.004 0.896 0.100
#> GSM1124900 2 0.545 0.552 0.304 0.660 0.036 0.000
#> GSM1124914 2 0.540 0.578 0.280 0.684 0.032 0.004
#> GSM1124871 2 0.396 0.684 0.152 0.820 0.028 0.000
#> GSM1124874 2 0.427 0.672 0.188 0.788 0.024 0.000
#> GSM1124875 2 0.297 0.676 0.096 0.884 0.020 0.000
#> GSM1124880 2 0.691 0.427 0.384 0.532 0.064 0.020
#> GSM1124881 2 0.386 0.686 0.152 0.824 0.024 0.000
#> GSM1124885 2 0.276 0.572 0.028 0.912 0.048 0.012
#> GSM1124886 1 0.190 0.835 0.932 0.064 0.000 0.004
#> GSM1124887 2 0.462 0.476 0.020 0.760 0.216 0.004
#> GSM1124894 2 0.685 0.328 0.420 0.504 0.056 0.020
#> GSM1124896 1 0.172 0.836 0.936 0.064 0.000 0.000
#> GSM1124899 2 0.233 0.685 0.088 0.908 0.004 0.000
#> GSM1124901 2 0.212 0.651 0.052 0.932 0.012 0.004
#> GSM1124906 2 0.381 0.687 0.156 0.824 0.020 0.000
#> GSM1124907 2 0.308 0.665 0.084 0.884 0.032 0.000
#> GSM1124911 2 0.286 0.687 0.096 0.888 0.016 0.000
#> GSM1124912 1 0.172 0.836 0.936 0.064 0.000 0.000
#> GSM1124915 2 0.326 0.678 0.096 0.876 0.024 0.004
#> GSM1124917 2 0.349 0.694 0.104 0.864 0.028 0.004
#> GSM1124918 2 0.299 0.686 0.104 0.880 0.016 0.000
#> GSM1124920 3 0.684 0.504 0.372 0.016 0.544 0.068
#> GSM1124922 2 0.220 0.677 0.080 0.916 0.004 0.000
#> GSM1124924 2 0.772 0.389 0.372 0.492 0.096 0.040
#> GSM1124926 2 0.233 0.685 0.088 0.908 0.004 0.000
#> GSM1124928 2 0.676 0.396 0.408 0.520 0.052 0.020
#> GSM1124930 2 0.284 0.667 0.088 0.892 0.020 0.000
#> GSM1124931 2 0.511 0.602 0.252 0.712 0.036 0.000
#> GSM1124935 2 0.308 0.683 0.096 0.880 0.024 0.000
#> GSM1124936 3 0.714 0.291 0.452 0.024 0.456 0.068
#> GSM1124938 2 0.540 0.599 0.140 0.772 0.052 0.036
#> GSM1124940 1 0.172 0.836 0.936 0.064 0.000 0.000
#> GSM1124941 2 0.381 0.687 0.156 0.824 0.020 0.000
#> GSM1124942 2 0.284 0.667 0.088 0.892 0.020 0.000
#> GSM1124943 2 0.284 0.667 0.088 0.892 0.020 0.000
#> GSM1124948 2 0.746 0.442 0.340 0.536 0.088 0.036
#> GSM1124949 1 0.205 0.833 0.928 0.064 0.000 0.008
#> GSM1124950 2 0.531 0.582 0.280 0.684 0.036 0.000
#> GSM1124954 1 0.588 0.506 0.572 0.040 0.000 0.388
#> GSM1124955 1 0.172 0.836 0.936 0.064 0.000 0.000
#> GSM1124956 2 0.286 0.687 0.096 0.888 0.016 0.000
#> GSM1124872 2 0.531 0.582 0.280 0.684 0.036 0.000
#> GSM1124873 2 0.374 0.686 0.160 0.824 0.016 0.000
#> GSM1124876 3 0.358 0.690 0.180 0.004 0.816 0.000
#> GSM1124877 1 0.581 0.508 0.576 0.036 0.000 0.388
#> GSM1124879 1 0.309 0.793 0.888 0.056 0.000 0.056
#> GSM1124883 2 0.320 0.549 0.036 0.892 0.060 0.012
#> GSM1124889 2 0.383 0.691 0.152 0.828 0.016 0.004
#> GSM1124892 1 0.651 0.328 0.656 0.032 0.252 0.060
#> GSM1124893 1 0.172 0.836 0.936 0.064 0.000 0.000
#> GSM1124909 2 0.435 0.669 0.196 0.780 0.024 0.000
#> GSM1124913 2 0.320 0.549 0.036 0.892 0.060 0.012
#> GSM1124916 2 0.435 0.669 0.196 0.780 0.024 0.000
#> GSM1124923 2 0.462 0.476 0.020 0.760 0.216 0.004
#> GSM1124925 1 0.172 0.836 0.936 0.064 0.000 0.000
#> GSM1124929 1 0.190 0.835 0.932 0.064 0.000 0.004
#> GSM1124934 1 0.588 0.506 0.572 0.040 0.000 0.388
#> GSM1124937 1 0.503 0.730 0.796 0.116 0.024 0.064
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM1124891 3 0.8221 0.378 0.152 0.116 0.516 0.048 0.168
#> GSM1124888 3 0.7108 0.530 0.288 0.020 0.544 0.048 0.100
#> GSM1124890 2 0.4580 0.661 0.008 0.740 0.200 0.000 0.052
#> GSM1124904 2 0.3379 0.724 0.016 0.828 0.000 0.008 0.148
#> GSM1124927 2 0.5490 0.671 0.200 0.652 0.000 0.000 0.148
#> GSM1124953 2 0.4674 0.648 0.008 0.728 0.212 0.000 0.052
#> GSM1124869 1 0.0955 0.782 0.968 0.028 0.000 0.000 0.004
#> GSM1124870 2 0.5490 0.671 0.200 0.652 0.000 0.000 0.148
#> GSM1124882 1 0.0794 0.782 0.972 0.028 0.000 0.000 0.000
#> GSM1124884 2 0.3888 0.772 0.136 0.800 0.000 0.000 0.064
#> GSM1124898 2 0.3340 0.751 0.032 0.840 0.000 0.004 0.124
#> GSM1124903 2 0.3379 0.724 0.016 0.828 0.000 0.008 0.148
#> GSM1124905 1 0.8189 0.106 0.396 0.300 0.004 0.136 0.164
#> GSM1124910 2 0.7686 0.308 0.324 0.448 0.044 0.020 0.164
#> GSM1124919 2 0.4580 0.661 0.008 0.740 0.200 0.000 0.052
#> GSM1124932 5 0.5190 0.443 0.096 0.236 0.000 0.000 0.668
#> GSM1124933 3 0.3048 0.648 0.176 0.000 0.820 0.000 0.004
#> GSM1124867 2 0.5520 0.744 0.172 0.712 0.064 0.004 0.048
#> GSM1124868 2 0.5058 0.715 0.008 0.768 0.064 0.104 0.056
#> GSM1124878 2 0.5058 0.715 0.008 0.768 0.064 0.104 0.056
#> GSM1124895 4 0.1197 0.986 0.000 0.048 0.000 0.952 0.000
#> GSM1124897 2 0.5058 0.715 0.008 0.768 0.064 0.104 0.056
#> GSM1124902 4 0.1197 0.986 0.000 0.048 0.000 0.952 0.000
#> GSM1124908 4 0.2053 0.977 0.000 0.048 0.024 0.924 0.004
#> GSM1124921 4 0.2308 0.967 0.000 0.048 0.036 0.912 0.004
#> GSM1124939 4 0.1197 0.986 0.000 0.048 0.000 0.952 0.000
#> GSM1124944 4 0.1197 0.986 0.000 0.048 0.000 0.952 0.000
#> GSM1124945 3 0.0162 0.623 0.000 0.000 0.996 0.004 0.000
#> GSM1124946 4 0.2053 0.977 0.000 0.048 0.024 0.924 0.004
#> GSM1124947 4 0.1197 0.986 0.000 0.048 0.000 0.952 0.000
#> GSM1124951 3 0.0162 0.623 0.000 0.000 0.996 0.004 0.000
#> GSM1124952 2 0.6473 0.448 0.092 0.532 0.000 0.340 0.036
#> GSM1124957 3 0.0162 0.623 0.000 0.000 0.996 0.004 0.000
#> GSM1124900 2 0.5575 0.659 0.224 0.648 0.004 0.000 0.124
#> GSM1124914 2 0.5818 0.692 0.220 0.636 0.004 0.004 0.136
#> GSM1124871 2 0.3857 0.781 0.084 0.808 0.000 0.000 0.108
#> GSM1124874 2 0.4017 0.766 0.148 0.788 0.000 0.000 0.064
#> GSM1124875 2 0.2952 0.780 0.088 0.872 0.004 0.000 0.036
#> GSM1124880 2 0.7069 0.473 0.272 0.512 0.032 0.004 0.180
#> GSM1124881 2 0.3507 0.781 0.120 0.828 0.000 0.000 0.052
#> GSM1124885 2 0.3059 0.740 0.016 0.856 0.000 0.008 0.120
#> GSM1124886 1 0.1195 0.777 0.960 0.028 0.000 0.000 0.012
#> GSM1124887 2 0.4580 0.661 0.008 0.740 0.200 0.000 0.052
#> GSM1124894 1 0.8617 0.072 0.360 0.308 0.020 0.140 0.172
#> GSM1124896 1 0.0955 0.781 0.968 0.028 0.000 0.000 0.004
#> GSM1124899 2 0.2208 0.787 0.072 0.908 0.000 0.000 0.020
#> GSM1124901 2 0.2214 0.778 0.028 0.916 0.000 0.004 0.052
#> GSM1124906 2 0.3413 0.782 0.124 0.832 0.000 0.000 0.044
#> GSM1124907 2 0.3142 0.774 0.076 0.864 0.004 0.000 0.056
#> GSM1124911 2 0.2654 0.785 0.048 0.888 0.000 0.000 0.064
#> GSM1124912 1 0.0794 0.782 0.972 0.028 0.000 0.000 0.000
#> GSM1124915 2 0.2888 0.783 0.056 0.880 0.000 0.004 0.060
#> GSM1124917 2 0.3383 0.796 0.072 0.856 0.012 0.000 0.060
#> GSM1124918 2 0.2608 0.787 0.088 0.888 0.004 0.000 0.020
#> GSM1124920 3 0.7108 0.530 0.288 0.020 0.544 0.048 0.100
#> GSM1124922 2 0.2726 0.787 0.052 0.884 0.000 0.000 0.064
#> GSM1124924 2 0.7882 0.417 0.224 0.476 0.060 0.020 0.220
#> GSM1124926 2 0.2208 0.787 0.072 0.908 0.000 0.000 0.020
#> GSM1124928 2 0.7041 0.411 0.312 0.496 0.032 0.004 0.156
#> GSM1124930 2 0.2989 0.774 0.080 0.872 0.004 0.000 0.044
#> GSM1124931 2 0.5159 0.703 0.124 0.688 0.000 0.000 0.188
#> GSM1124935 2 0.2927 0.783 0.060 0.872 0.000 0.000 0.068
#> GSM1124936 3 0.7151 0.371 0.392 0.016 0.456 0.048 0.088
#> GSM1124938 2 0.5331 0.705 0.116 0.752 0.036 0.020 0.076
#> GSM1124940 1 0.0955 0.782 0.968 0.028 0.000 0.000 0.004
#> GSM1124941 2 0.3413 0.782 0.124 0.832 0.000 0.000 0.044
#> GSM1124942 2 0.2989 0.774 0.080 0.872 0.004 0.000 0.044
#> GSM1124943 2 0.2989 0.774 0.080 0.872 0.004 0.000 0.044
#> GSM1124948 2 0.7536 0.494 0.204 0.528 0.052 0.020 0.196
#> GSM1124949 1 0.2067 0.739 0.924 0.028 0.000 0.004 0.044
#> GSM1124950 2 0.5367 0.686 0.184 0.668 0.000 0.000 0.148
#> GSM1124954 5 0.4400 0.784 0.308 0.020 0.000 0.000 0.672
#> GSM1124955 1 0.0955 0.781 0.968 0.028 0.000 0.000 0.004
#> GSM1124956 2 0.2654 0.785 0.048 0.888 0.000 0.000 0.064
#> GSM1124872 2 0.5367 0.686 0.184 0.668 0.000 0.000 0.148
#> GSM1124873 2 0.3507 0.781 0.120 0.828 0.000 0.000 0.052
#> GSM1124876 3 0.3048 0.648 0.176 0.000 0.820 0.000 0.004
#> GSM1124877 5 0.4251 0.772 0.316 0.012 0.000 0.000 0.672
#> GSM1124879 1 0.2492 0.723 0.908 0.024 0.000 0.048 0.020
#> GSM1124883 2 0.3379 0.724 0.016 0.828 0.000 0.008 0.148
#> GSM1124889 2 0.3567 0.786 0.112 0.832 0.000 0.004 0.052
#> GSM1124892 1 0.6542 0.109 0.608 0.016 0.252 0.040 0.084
#> GSM1124893 1 0.0955 0.782 0.968 0.028 0.000 0.000 0.004
#> GSM1124909 2 0.4127 0.766 0.136 0.784 0.000 0.000 0.080
#> GSM1124913 2 0.3336 0.727 0.016 0.832 0.000 0.008 0.144
#> GSM1124916 2 0.4127 0.766 0.136 0.784 0.000 0.000 0.080
#> GSM1124923 2 0.4580 0.661 0.008 0.740 0.200 0.000 0.052
#> GSM1124925 1 0.0955 0.781 0.968 0.028 0.000 0.000 0.004
#> GSM1124929 1 0.1907 0.741 0.928 0.028 0.000 0.000 0.044
#> GSM1124934 5 0.4400 0.784 0.308 0.020 0.000 0.000 0.672
#> GSM1124937 1 0.5297 0.523 0.744 0.092 0.004 0.048 0.112
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1124891 3 0.7432 0.4442 0.064 0.108 0.488 0.000 0.248 0.092
#> GSM1124888 3 0.7057 0.5745 0.196 0.020 0.520 0.000 0.164 0.100
#> GSM1124890 2 0.5507 0.3934 0.000 0.596 0.180 0.000 0.216 0.008
#> GSM1124904 2 0.4591 0.4188 0.000 0.552 0.000 0.000 0.408 0.040
#> GSM1124927 2 0.6013 0.4664 0.128 0.612 0.000 0.000 0.176 0.084
#> GSM1124953 2 0.5606 0.3709 0.000 0.580 0.192 0.000 0.220 0.008
#> GSM1124869 1 0.0405 0.8062 0.988 0.008 0.000 0.000 0.000 0.004
#> GSM1124870 2 0.6013 0.4664 0.128 0.612 0.000 0.000 0.176 0.084
#> GSM1124882 1 0.0405 0.8055 0.988 0.008 0.000 0.000 0.000 0.004
#> GSM1124884 2 0.4540 0.5798 0.084 0.744 0.000 0.000 0.140 0.032
#> GSM1124898 2 0.4435 0.5276 0.012 0.664 0.000 0.000 0.292 0.032
#> GSM1124903 2 0.4591 0.4188 0.000 0.552 0.000 0.000 0.408 0.040
#> GSM1124905 1 0.8437 -0.9345 0.312 0.128 0.000 0.108 0.304 0.148
#> GSM1124910 2 0.7834 -0.1107 0.224 0.340 0.028 0.000 0.300 0.108
#> GSM1124919 2 0.5507 0.3934 0.000 0.596 0.180 0.000 0.216 0.008
#> GSM1124932 6 0.4419 0.3002 0.020 0.244 0.000 0.000 0.036 0.700
#> GSM1124933 3 0.2981 0.6586 0.160 0.000 0.820 0.000 0.000 0.020
#> GSM1124867 2 0.5466 0.5670 0.108 0.700 0.068 0.000 0.108 0.016
#> GSM1124868 2 0.5945 0.4775 0.000 0.584 0.068 0.092 0.256 0.000
#> GSM1124878 2 0.5945 0.4775 0.000 0.584 0.068 0.092 0.256 0.000
#> GSM1124895 4 0.0000 0.9816 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1124897 2 0.5945 0.4775 0.000 0.584 0.068 0.092 0.256 0.000
#> GSM1124902 4 0.0000 0.9816 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1124908 4 0.0858 0.9699 0.000 0.000 0.028 0.968 0.004 0.000
#> GSM1124921 4 0.1082 0.9589 0.000 0.000 0.040 0.956 0.004 0.000
#> GSM1124939 4 0.0000 0.9816 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1124944 4 0.0000 0.9816 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1124945 3 0.0000 0.6370 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1124946 4 0.0858 0.9699 0.000 0.000 0.028 0.968 0.004 0.000
#> GSM1124947 4 0.0000 0.9816 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1124951 3 0.0000 0.6370 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1124952 2 0.6910 0.0636 0.060 0.460 0.000 0.340 0.112 0.028
#> GSM1124957 3 0.0000 0.6370 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1124900 2 0.6017 0.4584 0.144 0.612 0.000 0.000 0.164 0.080
#> GSM1124914 2 0.6124 0.4280 0.168 0.520 0.000 0.000 0.284 0.028
#> GSM1124871 2 0.4822 0.6132 0.060 0.704 0.000 0.000 0.196 0.040
#> GSM1124874 2 0.4250 0.6062 0.092 0.760 0.000 0.000 0.132 0.016
#> GSM1124875 2 0.3665 0.5911 0.052 0.800 0.000 0.000 0.136 0.012
#> GSM1124880 2 0.7492 0.0934 0.164 0.408 0.016 0.000 0.292 0.120
#> GSM1124881 2 0.3677 0.6279 0.064 0.804 0.000 0.000 0.120 0.012
#> GSM1124885 2 0.4170 0.5198 0.000 0.660 0.000 0.000 0.308 0.032
#> GSM1124886 1 0.0881 0.8002 0.972 0.008 0.000 0.000 0.008 0.012
#> GSM1124887 2 0.5507 0.3934 0.000 0.596 0.180 0.000 0.216 0.008
#> GSM1124894 5 0.8800 0.0000 0.276 0.132 0.012 0.112 0.308 0.160
#> GSM1124896 1 0.0405 0.8042 0.988 0.008 0.000 0.000 0.004 0.000
#> GSM1124899 2 0.3054 0.6240 0.036 0.828 0.000 0.000 0.136 0.000
#> GSM1124901 2 0.3459 0.5826 0.016 0.768 0.000 0.000 0.212 0.004
#> GSM1124906 2 0.3270 0.6241 0.072 0.836 0.000 0.000 0.084 0.008
#> GSM1124907 2 0.3900 0.5601 0.044 0.760 0.000 0.000 0.188 0.008
#> GSM1124911 2 0.3299 0.6303 0.028 0.844 0.000 0.000 0.080 0.048
#> GSM1124912 1 0.0260 0.8055 0.992 0.008 0.000 0.000 0.000 0.000
#> GSM1124915 2 0.3713 0.6208 0.032 0.812 0.000 0.000 0.108 0.048
#> GSM1124917 2 0.3416 0.6405 0.036 0.812 0.004 0.000 0.144 0.004
#> GSM1124918 2 0.3548 0.6000 0.052 0.816 0.000 0.000 0.116 0.016
#> GSM1124920 3 0.7057 0.5745 0.196 0.020 0.520 0.000 0.164 0.100
#> GSM1124922 2 0.2968 0.6333 0.028 0.840 0.000 0.000 0.128 0.004
#> GSM1124924 2 0.7726 -0.0268 0.112 0.360 0.040 0.000 0.348 0.140
#> GSM1124926 2 0.3054 0.6240 0.036 0.828 0.000 0.000 0.136 0.000
#> GSM1124928 2 0.7489 0.0320 0.212 0.400 0.016 0.000 0.276 0.096
#> GSM1124930 2 0.3934 0.5597 0.044 0.764 0.000 0.000 0.180 0.012
#> GSM1124931 2 0.5727 0.5138 0.064 0.640 0.000 0.000 0.160 0.136
#> GSM1124935 2 0.3791 0.6209 0.032 0.808 0.000 0.000 0.104 0.056
#> GSM1124936 3 0.7174 0.4452 0.308 0.012 0.436 0.000 0.148 0.096
#> GSM1124938 2 0.5138 0.4284 0.044 0.644 0.016 0.000 0.276 0.020
#> GSM1124940 1 0.0405 0.8062 0.988 0.008 0.000 0.000 0.000 0.004
#> GSM1124941 2 0.3270 0.6241 0.072 0.836 0.000 0.000 0.084 0.008
#> GSM1124942 2 0.3934 0.5597 0.044 0.764 0.000 0.000 0.180 0.012
#> GSM1124943 2 0.3934 0.5597 0.044 0.764 0.000 0.000 0.180 0.012
#> GSM1124948 2 0.7399 0.1031 0.100 0.416 0.032 0.000 0.332 0.120
#> GSM1124949 1 0.2136 0.7570 0.908 0.012 0.000 0.000 0.016 0.064
#> GSM1124950 2 0.5702 0.4930 0.112 0.648 0.000 0.000 0.160 0.080
#> GSM1124954 6 0.3171 0.7925 0.204 0.012 0.000 0.000 0.000 0.784
#> GSM1124955 1 0.0405 0.8042 0.988 0.008 0.000 0.000 0.004 0.000
#> GSM1124956 2 0.3299 0.6303 0.028 0.844 0.000 0.000 0.080 0.048
#> GSM1124872 2 0.5702 0.4930 0.112 0.648 0.000 0.000 0.160 0.080
#> GSM1124873 2 0.3548 0.6284 0.068 0.812 0.000 0.000 0.112 0.008
#> GSM1124876 3 0.2981 0.6586 0.160 0.000 0.820 0.000 0.000 0.020
#> GSM1124877 6 0.3133 0.7829 0.212 0.008 0.000 0.000 0.000 0.780
#> GSM1124879 1 0.2712 0.6923 0.864 0.012 0.000 0.000 0.108 0.016
#> GSM1124883 2 0.4591 0.4188 0.000 0.552 0.000 0.000 0.408 0.040
#> GSM1124889 2 0.3513 0.6355 0.072 0.816 0.000 0.000 0.104 0.008
#> GSM1124892 1 0.6735 0.0525 0.532 0.012 0.240 0.000 0.132 0.084
#> GSM1124893 1 0.0405 0.8062 0.988 0.008 0.000 0.000 0.000 0.004
#> GSM1124909 2 0.3809 0.6098 0.080 0.808 0.000 0.000 0.084 0.028
#> GSM1124913 2 0.4524 0.4281 0.000 0.560 0.000 0.000 0.404 0.036
#> GSM1124916 2 0.3809 0.6098 0.080 0.808 0.000 0.000 0.084 0.028
#> GSM1124923 2 0.5507 0.3934 0.000 0.596 0.180 0.000 0.216 0.008
#> GSM1124925 1 0.0405 0.8042 0.988 0.008 0.000 0.000 0.004 0.000
#> GSM1124929 1 0.1983 0.7616 0.916 0.012 0.000 0.000 0.012 0.060
#> GSM1124934 6 0.3171 0.7925 0.204 0.012 0.000 0.000 0.000 0.784
#> GSM1124937 1 0.5704 0.2648 0.624 0.104 0.000 0.000 0.216 0.056
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 tissue(p) k
#> CV:hclust 86 6.87e-02 2
#> CV:hclust 56 4.59e-02 3
#> CV:hclust 71 1.41e-07 4
#> CV:hclust 79 5.09e-05 5
#> CV:hclust 59 4.87e-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 48983 rows and 91 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 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 0.426 0.842 0.855 0.4283 0.546 0.546
#> 3 3 0.676 0.744 0.855 0.4046 0.839 0.713
#> 4 4 0.605 0.645 0.786 0.1701 0.879 0.718
#> 5 5 0.595 0.637 0.758 0.0907 0.821 0.505
#> 6 6 0.665 0.574 0.765 0.0553 0.929 0.709
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
#> GSM1124891 1 0.6343 0.914 0.840 0.160
#> GSM1124888 1 0.6343 0.914 0.840 0.160
#> GSM1124890 2 0.9087 0.481 0.324 0.676
#> GSM1124904 2 0.1184 0.862 0.016 0.984
#> GSM1124927 2 0.8327 0.548 0.264 0.736
#> GSM1124953 2 0.4690 0.836 0.100 0.900
#> GSM1124869 1 0.7883 0.951 0.764 0.236
#> GSM1124870 1 0.7883 0.951 0.764 0.236
#> GSM1124882 1 0.7883 0.951 0.764 0.236
#> GSM1124884 2 0.3733 0.856 0.072 0.928
#> GSM1124898 2 0.0376 0.866 0.004 0.996
#> GSM1124903 2 0.0000 0.865 0.000 1.000
#> GSM1124905 1 0.7883 0.951 0.764 0.236
#> GSM1124910 1 0.7219 0.941 0.800 0.200
#> GSM1124919 2 0.3584 0.850 0.068 0.932
#> GSM1124932 2 0.4431 0.853 0.092 0.908
#> GSM1124933 1 0.6343 0.914 0.840 0.160
#> GSM1124867 2 0.3584 0.858 0.068 0.932
#> GSM1124868 2 0.5842 0.787 0.140 0.860
#> GSM1124878 2 0.5408 0.799 0.124 0.876
#> GSM1124895 2 0.6343 0.780 0.160 0.840
#> GSM1124897 2 0.6247 0.782 0.156 0.844
#> GSM1124902 2 0.6343 0.780 0.160 0.840
#> GSM1124908 2 0.6343 0.780 0.160 0.840
#> GSM1124921 2 0.6343 0.780 0.160 0.840
#> GSM1124939 2 0.6343 0.780 0.160 0.840
#> GSM1124944 2 0.6343 0.780 0.160 0.840
#> GSM1124945 2 0.9909 0.443 0.444 0.556
#> GSM1124946 2 0.6343 0.780 0.160 0.840
#> GSM1124947 2 0.6343 0.780 0.160 0.840
#> GSM1124951 2 0.9909 0.443 0.444 0.556
#> GSM1124952 2 0.6343 0.780 0.160 0.840
#> GSM1124957 1 0.7376 0.547 0.792 0.208
#> GSM1124900 1 0.7883 0.951 0.764 0.236
#> GSM1124914 2 0.0376 0.866 0.004 0.996
#> GSM1124871 2 0.2043 0.865 0.032 0.968
#> GSM1124874 2 0.3879 0.854 0.076 0.924
#> GSM1124875 2 0.1414 0.866 0.020 0.980
#> GSM1124880 1 0.7883 0.951 0.764 0.236
#> GSM1124881 2 0.3879 0.854 0.076 0.924
#> GSM1124885 2 0.0376 0.866 0.004 0.996
#> GSM1124886 1 0.7139 0.939 0.804 0.196
#> GSM1124887 2 0.1414 0.861 0.020 0.980
#> GSM1124894 2 0.3584 0.858 0.068 0.932
#> GSM1124896 1 0.7883 0.951 0.764 0.236
#> GSM1124899 2 0.3879 0.854 0.076 0.924
#> GSM1124901 2 0.0938 0.866 0.012 0.988
#> GSM1124906 2 0.3879 0.854 0.076 0.924
#> GSM1124907 2 0.1414 0.861 0.020 0.980
#> GSM1124911 2 0.4431 0.853 0.092 0.908
#> GSM1124912 1 0.7883 0.951 0.764 0.236
#> GSM1124915 2 0.2043 0.866 0.032 0.968
#> GSM1124917 2 0.2043 0.866 0.032 0.968
#> GSM1124918 2 0.4431 0.853 0.092 0.908
#> GSM1124920 1 0.6247 0.916 0.844 0.156
#> GSM1124922 2 0.3879 0.854 0.076 0.924
#> GSM1124924 1 0.7139 0.940 0.804 0.196
#> GSM1124926 2 0.3879 0.854 0.076 0.924
#> GSM1124928 1 0.7883 0.951 0.764 0.236
#> GSM1124930 2 0.2948 0.863 0.052 0.948
#> GSM1124931 2 0.4431 0.853 0.092 0.908
#> GSM1124935 2 0.2423 0.866 0.040 0.960
#> GSM1124936 1 0.6247 0.916 0.844 0.156
#> GSM1124938 2 0.9686 0.343 0.396 0.604
#> GSM1124940 1 0.7883 0.951 0.764 0.236
#> GSM1124941 2 0.3879 0.854 0.076 0.924
#> GSM1124942 2 0.3114 0.861 0.056 0.944
#> GSM1124943 2 0.7883 0.674 0.236 0.764
#> GSM1124948 2 0.9491 0.393 0.368 0.632
#> GSM1124949 1 0.7883 0.951 0.764 0.236
#> GSM1124950 2 0.3879 0.854 0.076 0.924
#> GSM1124954 1 0.6801 0.932 0.820 0.180
#> GSM1124955 1 0.7883 0.951 0.764 0.236
#> GSM1124956 2 0.4431 0.853 0.092 0.908
#> GSM1124872 2 0.3879 0.854 0.076 0.924
#> GSM1124873 2 0.3879 0.854 0.076 0.924
#> GSM1124876 1 0.6343 0.914 0.840 0.160
#> GSM1124877 1 0.7602 0.941 0.780 0.220
#> GSM1124879 1 0.7883 0.951 0.764 0.236
#> GSM1124883 2 0.0000 0.865 0.000 1.000
#> GSM1124889 2 0.3879 0.854 0.076 0.924
#> GSM1124892 1 0.6712 0.926 0.824 0.176
#> GSM1124893 1 0.7883 0.951 0.764 0.236
#> GSM1124909 2 0.3879 0.854 0.076 0.924
#> GSM1124913 2 0.0000 0.865 0.000 1.000
#> GSM1124916 2 0.4431 0.853 0.092 0.908
#> GSM1124923 2 0.4022 0.832 0.080 0.920
#> GSM1124925 1 0.7883 0.951 0.764 0.236
#> GSM1124929 1 0.7883 0.951 0.764 0.236
#> GSM1124934 1 0.6801 0.932 0.820 0.180
#> GSM1124937 1 0.7883 0.951 0.764 0.236
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1124891 1 0.6490 0.6114 0.628 0.012 0.360
#> GSM1124888 1 0.6490 0.6114 0.628 0.012 0.360
#> GSM1124890 2 0.7517 0.3101 0.040 0.540 0.420
#> GSM1124904 2 0.1964 0.7700 0.000 0.944 0.056
#> GSM1124927 2 0.4475 0.7024 0.144 0.840 0.016
#> GSM1124953 2 0.6421 0.3560 0.004 0.572 0.424
#> GSM1124869 1 0.0424 0.9119 0.992 0.008 0.000
#> GSM1124870 1 0.3183 0.8491 0.908 0.076 0.016
#> GSM1124882 1 0.0424 0.9119 0.992 0.008 0.000
#> GSM1124884 2 0.0892 0.8056 0.020 0.980 0.000
#> GSM1124898 2 0.0000 0.8001 0.000 1.000 0.000
#> GSM1124903 2 0.1964 0.7700 0.000 0.944 0.056
#> GSM1124905 1 0.0661 0.9114 0.988 0.008 0.004
#> GSM1124910 1 0.0475 0.9108 0.992 0.004 0.004
#> GSM1124919 2 0.5956 0.4963 0.004 0.672 0.324
#> GSM1124932 2 0.3148 0.7950 0.048 0.916 0.036
#> GSM1124933 1 0.6566 0.5889 0.612 0.012 0.376
#> GSM1124867 2 0.1878 0.8083 0.044 0.952 0.004
#> GSM1124868 2 0.5431 0.2774 0.000 0.716 0.284
#> GSM1124878 2 0.2066 0.7662 0.000 0.940 0.060
#> GSM1124895 3 0.6111 0.8022 0.000 0.396 0.604
#> GSM1124897 2 0.2165 0.7632 0.000 0.936 0.064
#> GSM1124902 3 0.6111 0.8022 0.000 0.396 0.604
#> GSM1124908 3 0.6111 0.8022 0.000 0.396 0.604
#> GSM1124921 3 0.6111 0.8022 0.000 0.396 0.604
#> GSM1124939 3 0.6111 0.8022 0.000 0.396 0.604
#> GSM1124944 3 0.6111 0.8022 0.000 0.396 0.604
#> GSM1124945 3 0.3028 0.5047 0.048 0.032 0.920
#> GSM1124946 3 0.6111 0.8022 0.000 0.396 0.604
#> GSM1124947 3 0.6111 0.8022 0.000 0.396 0.604
#> GSM1124951 3 0.2918 0.5090 0.044 0.032 0.924
#> GSM1124952 3 0.6111 0.8022 0.000 0.396 0.604
#> GSM1124957 3 0.3120 0.4574 0.080 0.012 0.908
#> GSM1124900 1 0.3091 0.8537 0.912 0.072 0.016
#> GSM1124914 2 0.0592 0.7962 0.000 0.988 0.012
#> GSM1124871 2 0.0424 0.8038 0.008 0.992 0.000
#> GSM1124874 2 0.1643 0.8080 0.044 0.956 0.000
#> GSM1124875 2 0.1170 0.8056 0.008 0.976 0.016
#> GSM1124880 1 0.2096 0.8789 0.944 0.052 0.004
#> GSM1124881 2 0.1878 0.8083 0.044 0.952 0.004
#> GSM1124885 2 0.1860 0.7732 0.000 0.948 0.052
#> GSM1124886 1 0.0475 0.9108 0.992 0.004 0.004
#> GSM1124887 2 0.2066 0.7750 0.000 0.940 0.060
#> GSM1124894 2 0.6899 0.2001 0.364 0.612 0.024
#> GSM1124896 1 0.0661 0.9114 0.988 0.008 0.004
#> GSM1124899 2 0.1643 0.8080 0.044 0.956 0.000
#> GSM1124901 2 0.0000 0.8001 0.000 1.000 0.000
#> GSM1124906 2 0.1878 0.8083 0.044 0.952 0.004
#> GSM1124907 2 0.2772 0.7605 0.004 0.916 0.080
#> GSM1124911 2 0.3028 0.7955 0.048 0.920 0.032
#> GSM1124912 1 0.0424 0.9119 0.992 0.008 0.000
#> GSM1124915 2 0.1399 0.7983 0.004 0.968 0.028
#> GSM1124917 2 0.0829 0.8064 0.012 0.984 0.004
#> GSM1124918 2 0.3148 0.7962 0.036 0.916 0.048
#> GSM1124920 1 0.5254 0.7218 0.736 0.000 0.264
#> GSM1124922 2 0.1643 0.8080 0.044 0.956 0.000
#> GSM1124924 2 0.9998 -0.0321 0.336 0.340 0.324
#> GSM1124926 2 0.0892 0.8056 0.020 0.980 0.000
#> GSM1124928 1 0.0424 0.9119 0.992 0.008 0.000
#> GSM1124930 2 0.4931 0.6474 0.004 0.784 0.212
#> GSM1124931 2 0.3148 0.7950 0.048 0.916 0.036
#> GSM1124935 2 0.2031 0.8010 0.016 0.952 0.032
#> GSM1124936 1 0.5016 0.7431 0.760 0.000 0.240
#> GSM1124938 2 0.7931 0.2910 0.060 0.528 0.412
#> GSM1124940 1 0.0424 0.9119 0.992 0.008 0.000
#> GSM1124941 2 0.2063 0.8085 0.044 0.948 0.008
#> GSM1124942 2 0.4931 0.6474 0.004 0.784 0.212
#> GSM1124943 2 0.7213 0.3290 0.028 0.552 0.420
#> GSM1124948 2 0.7676 0.3635 0.056 0.584 0.360
#> GSM1124949 1 0.0424 0.9119 0.992 0.008 0.000
#> GSM1124950 2 0.1878 0.8083 0.044 0.952 0.004
#> GSM1124954 1 0.1525 0.8973 0.964 0.004 0.032
#> GSM1124955 1 0.0661 0.9114 0.988 0.008 0.004
#> GSM1124956 2 0.3028 0.7955 0.048 0.920 0.032
#> GSM1124872 2 0.2383 0.8053 0.044 0.940 0.016
#> GSM1124873 2 0.1878 0.8083 0.044 0.952 0.004
#> GSM1124876 1 0.6490 0.6114 0.628 0.012 0.360
#> GSM1124877 1 0.1525 0.8973 0.964 0.004 0.032
#> GSM1124879 1 0.0424 0.9119 0.992 0.008 0.000
#> GSM1124883 2 0.1860 0.7732 0.000 0.948 0.052
#> GSM1124889 2 0.1643 0.8080 0.044 0.956 0.000
#> GSM1124892 1 0.0475 0.9108 0.992 0.004 0.004
#> GSM1124893 1 0.0424 0.9119 0.992 0.008 0.000
#> GSM1124909 2 0.2383 0.8053 0.044 0.940 0.016
#> GSM1124913 2 0.1964 0.7700 0.000 0.944 0.056
#> GSM1124916 2 0.3148 0.7950 0.048 0.916 0.036
#> GSM1124923 2 0.6421 0.3565 0.004 0.572 0.424
#> GSM1124925 1 0.0661 0.9114 0.988 0.008 0.004
#> GSM1124929 1 0.0237 0.9110 0.996 0.004 0.000
#> GSM1124934 1 0.1525 0.8973 0.964 0.004 0.032
#> GSM1124937 1 0.0661 0.9114 0.988 0.008 0.004
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1124891 3 0.5070 0.3720 0.372 0.000 0.620 0.008
#> GSM1124888 3 0.5311 0.3533 0.392 0.004 0.596 0.008
#> GSM1124890 3 0.3479 0.6084 0.012 0.148 0.840 0.000
#> GSM1124904 2 0.7039 0.5306 0.000 0.568 0.256 0.176
#> GSM1124927 2 0.3778 0.6603 0.100 0.848 0.052 0.000
#> GSM1124953 3 0.3448 0.6037 0.000 0.168 0.828 0.004
#> GSM1124869 1 0.0188 0.8513 0.996 0.004 0.000 0.000
#> GSM1124870 1 0.6028 0.4072 0.584 0.364 0.052 0.000
#> GSM1124882 1 0.0188 0.8513 0.996 0.004 0.000 0.000
#> GSM1124884 2 0.0779 0.7363 0.004 0.980 0.000 0.016
#> GSM1124898 2 0.5025 0.6432 0.000 0.716 0.252 0.032
#> GSM1124903 2 0.7039 0.5306 0.000 0.568 0.256 0.176
#> GSM1124905 1 0.2174 0.8249 0.928 0.020 0.052 0.000
#> GSM1124910 1 0.0657 0.8436 0.984 0.000 0.012 0.004
#> GSM1124919 3 0.4978 0.2348 0.000 0.324 0.664 0.012
#> GSM1124932 2 0.4540 0.6543 0.008 0.816 0.104 0.072
#> GSM1124933 3 0.5404 0.4203 0.328 0.028 0.644 0.000
#> GSM1124867 2 0.2142 0.7204 0.016 0.928 0.056 0.000
#> GSM1124868 4 0.7154 -0.0805 0.000 0.428 0.132 0.440
#> GSM1124878 2 0.7138 0.5117 0.000 0.552 0.268 0.180
#> GSM1124895 4 0.2011 0.9161 0.000 0.080 0.000 0.920
#> GSM1124897 2 0.7117 0.5169 0.000 0.556 0.264 0.180
#> GSM1124902 4 0.2011 0.9161 0.000 0.080 0.000 0.920
#> GSM1124908 4 0.2593 0.9070 0.000 0.080 0.016 0.904
#> GSM1124921 4 0.2342 0.9129 0.000 0.080 0.008 0.912
#> GSM1124939 4 0.2011 0.9161 0.000 0.080 0.000 0.920
#> GSM1124944 4 0.2011 0.9161 0.000 0.080 0.000 0.920
#> GSM1124945 3 0.5443 0.3376 0.016 0.004 0.616 0.364
#> GSM1124946 4 0.2197 0.9145 0.000 0.080 0.004 0.916
#> GSM1124947 4 0.2011 0.9161 0.000 0.080 0.000 0.920
#> GSM1124951 3 0.4381 0.5083 0.012 0.008 0.780 0.200
#> GSM1124952 4 0.2011 0.9161 0.000 0.080 0.000 0.920
#> GSM1124957 3 0.5732 0.3377 0.028 0.004 0.604 0.364
#> GSM1124900 1 0.6028 0.4072 0.584 0.364 0.052 0.000
#> GSM1124914 2 0.5648 0.6258 0.000 0.684 0.252 0.064
#> GSM1124871 2 0.0657 0.7363 0.004 0.984 0.000 0.012
#> GSM1124874 2 0.1182 0.7320 0.016 0.968 0.016 0.000
#> GSM1124875 2 0.4571 0.6500 0.008 0.736 0.252 0.004
#> GSM1124880 1 0.6438 0.3687 0.560 0.376 0.056 0.008
#> GSM1124881 2 0.1059 0.7333 0.016 0.972 0.012 0.000
#> GSM1124885 2 0.6756 0.5662 0.000 0.600 0.252 0.148
#> GSM1124886 1 0.0188 0.8513 0.996 0.004 0.000 0.000
#> GSM1124887 2 0.6828 0.5555 0.000 0.588 0.264 0.148
#> GSM1124894 2 0.8566 0.2221 0.280 0.496 0.084 0.140
#> GSM1124896 1 0.0376 0.8499 0.992 0.004 0.004 0.000
#> GSM1124899 2 0.1182 0.7367 0.016 0.968 0.016 0.000
#> GSM1124901 2 0.5025 0.6432 0.000 0.716 0.252 0.032
#> GSM1124906 2 0.0779 0.7346 0.016 0.980 0.004 0.000
#> GSM1124907 2 0.5921 0.5966 0.004 0.652 0.288 0.056
#> GSM1124911 2 0.4020 0.6706 0.008 0.848 0.072 0.072
#> GSM1124912 1 0.0188 0.8513 0.996 0.004 0.000 0.000
#> GSM1124915 2 0.5907 0.6392 0.000 0.668 0.252 0.080
#> GSM1124917 2 0.3538 0.7014 0.004 0.832 0.160 0.004
#> GSM1124918 2 0.4149 0.6695 0.004 0.836 0.088 0.072
#> GSM1124920 1 0.5286 0.2209 0.604 0.004 0.384 0.008
#> GSM1124922 2 0.2222 0.7323 0.016 0.924 0.060 0.000
#> GSM1124924 2 0.6839 0.1186 0.088 0.552 0.352 0.008
#> GSM1124926 2 0.1139 0.7371 0.008 0.972 0.008 0.012
#> GSM1124928 1 0.2189 0.8252 0.932 0.020 0.044 0.004
#> GSM1124930 2 0.5482 0.4453 0.004 0.572 0.412 0.012
#> GSM1124931 2 0.4768 0.6452 0.008 0.800 0.120 0.072
#> GSM1124935 2 0.5850 0.6431 0.000 0.676 0.244 0.080
#> GSM1124936 1 0.4857 0.3934 0.668 0.000 0.324 0.008
#> GSM1124938 3 0.4767 0.5556 0.028 0.196 0.768 0.008
#> GSM1124940 1 0.0188 0.8513 0.996 0.004 0.000 0.000
#> GSM1124941 2 0.0895 0.7347 0.020 0.976 0.004 0.000
#> GSM1124942 2 0.5233 0.4594 0.004 0.580 0.412 0.004
#> GSM1124943 3 0.4514 0.5103 0.008 0.228 0.756 0.008
#> GSM1124948 2 0.6005 0.2187 0.036 0.600 0.356 0.008
#> GSM1124949 1 0.0188 0.8513 0.996 0.004 0.000 0.000
#> GSM1124950 2 0.2089 0.7207 0.020 0.932 0.048 0.000
#> GSM1124954 1 0.5361 0.7169 0.772 0.020 0.128 0.080
#> GSM1124955 1 0.0188 0.8513 0.996 0.004 0.000 0.000
#> GSM1124956 2 0.4020 0.6706 0.008 0.848 0.072 0.072
#> GSM1124872 2 0.1975 0.7209 0.016 0.936 0.048 0.000
#> GSM1124873 2 0.1297 0.7316 0.016 0.964 0.020 0.000
#> GSM1124876 3 0.4817 0.3650 0.388 0.000 0.612 0.000
#> GSM1124877 1 0.4148 0.7466 0.844 0.012 0.072 0.072
#> GSM1124879 1 0.0188 0.8513 0.996 0.004 0.000 0.000
#> GSM1124883 2 0.7005 0.5355 0.000 0.572 0.256 0.172
#> GSM1124889 2 0.0592 0.7349 0.016 0.984 0.000 0.000
#> GSM1124892 1 0.0376 0.8487 0.992 0.004 0.004 0.000
#> GSM1124893 1 0.0188 0.8513 0.996 0.004 0.000 0.000
#> GSM1124909 2 0.1888 0.7227 0.016 0.940 0.044 0.000
#> GSM1124913 2 0.7005 0.5363 0.000 0.572 0.256 0.172
#> GSM1124916 2 0.4656 0.6508 0.008 0.808 0.112 0.072
#> GSM1124923 3 0.3969 0.5584 0.000 0.180 0.804 0.016
#> GSM1124925 1 0.0188 0.8513 0.996 0.004 0.000 0.000
#> GSM1124929 1 0.0188 0.8513 0.996 0.004 0.000 0.000
#> GSM1124934 1 0.5361 0.7169 0.772 0.020 0.128 0.080
#> GSM1124937 1 0.3005 0.8044 0.900 0.048 0.044 0.008
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM1124891 3 0.3701 0.714 0.112 0.004 0.824 0.000 0.060
#> GSM1124888 3 0.3871 0.710 0.132 0.000 0.808 0.004 0.056
#> GSM1124890 3 0.4705 0.611 0.000 0.040 0.692 0.004 0.264
#> GSM1124904 5 0.6272 0.764 0.000 0.344 0.016 0.108 0.532
#> GSM1124927 2 0.3985 0.640 0.052 0.820 0.024 0.000 0.104
#> GSM1124953 3 0.5122 0.616 0.000 0.076 0.692 0.008 0.224
#> GSM1124869 1 0.0000 0.841 1.000 0.000 0.000 0.000 0.000
#> GSM1124870 2 0.6280 0.352 0.320 0.556 0.024 0.000 0.100
#> GSM1124882 1 0.0000 0.841 1.000 0.000 0.000 0.000 0.000
#> GSM1124884 2 0.1408 0.599 0.000 0.948 0.008 0.000 0.044
#> GSM1124898 5 0.4934 0.723 0.000 0.432 0.004 0.020 0.544
#> GSM1124903 5 0.6272 0.764 0.000 0.344 0.016 0.108 0.532
#> GSM1124905 1 0.6415 0.590 0.648 0.128 0.096 0.000 0.128
#> GSM1124910 1 0.3400 0.744 0.848 0.004 0.076 0.000 0.072
#> GSM1124919 5 0.6696 0.438 0.000 0.208 0.300 0.008 0.484
#> GSM1124932 2 0.5428 0.549 0.000 0.620 0.064 0.008 0.308
#> GSM1124933 3 0.2635 0.723 0.088 0.016 0.888 0.008 0.000
#> GSM1124867 2 0.2773 0.651 0.000 0.868 0.020 0.000 0.112
#> GSM1124868 5 0.7048 0.545 0.000 0.308 0.008 0.324 0.360
#> GSM1124878 5 0.6242 0.764 0.000 0.348 0.016 0.104 0.532
#> GSM1124895 4 0.0794 0.976 0.000 0.028 0.000 0.972 0.000
#> GSM1124897 5 0.6180 0.763 0.000 0.356 0.016 0.096 0.532
#> GSM1124902 4 0.0794 0.976 0.000 0.028 0.000 0.972 0.000
#> GSM1124908 4 0.2414 0.898 0.000 0.008 0.012 0.900 0.080
#> GSM1124921 4 0.1507 0.956 0.000 0.012 0.012 0.952 0.024
#> GSM1124939 4 0.0794 0.976 0.000 0.028 0.000 0.972 0.000
#> GSM1124944 4 0.0794 0.976 0.000 0.028 0.000 0.972 0.000
#> GSM1124945 3 0.3596 0.613 0.000 0.000 0.784 0.200 0.016
#> GSM1124946 4 0.1059 0.958 0.000 0.008 0.004 0.968 0.020
#> GSM1124947 4 0.0794 0.976 0.000 0.028 0.000 0.972 0.000
#> GSM1124951 3 0.3667 0.689 0.000 0.000 0.812 0.048 0.140
#> GSM1124952 4 0.0794 0.976 0.000 0.028 0.000 0.972 0.000
#> GSM1124957 3 0.3544 0.614 0.008 0.000 0.788 0.200 0.004
#> GSM1124900 2 0.6307 0.338 0.328 0.548 0.024 0.000 0.100
#> GSM1124914 5 0.5149 0.732 0.000 0.424 0.004 0.032 0.540
#> GSM1124871 2 0.1732 0.553 0.000 0.920 0.000 0.000 0.080
#> GSM1124874 2 0.1124 0.622 0.000 0.960 0.004 0.000 0.036
#> GSM1124875 5 0.5047 0.618 0.000 0.472 0.032 0.000 0.496
#> GSM1124880 2 0.7441 0.389 0.224 0.524 0.080 0.004 0.168
#> GSM1124881 2 0.0609 0.620 0.000 0.980 0.000 0.000 0.020
#> GSM1124885 5 0.5745 0.761 0.000 0.376 0.004 0.080 0.540
#> GSM1124886 1 0.0000 0.841 1.000 0.000 0.000 0.000 0.000
#> GSM1124887 5 0.5955 0.767 0.000 0.356 0.012 0.084 0.548
#> GSM1124894 2 0.7999 0.474 0.128 0.556 0.128 0.064 0.124
#> GSM1124896 1 0.0162 0.840 0.996 0.000 0.004 0.000 0.000
#> GSM1124899 2 0.3048 0.356 0.000 0.820 0.004 0.000 0.176
#> GSM1124901 5 0.4881 0.698 0.000 0.460 0.004 0.016 0.520
#> GSM1124906 2 0.0992 0.621 0.000 0.968 0.008 0.000 0.024
#> GSM1124907 5 0.5994 0.729 0.000 0.328 0.044 0.048 0.580
#> GSM1124911 2 0.4587 0.543 0.000 0.724 0.040 0.008 0.228
#> GSM1124912 1 0.0000 0.841 1.000 0.000 0.000 0.000 0.000
#> GSM1124915 5 0.5310 0.332 0.000 0.380 0.040 0.008 0.572
#> GSM1124917 2 0.3857 -0.151 0.000 0.688 0.000 0.000 0.312
#> GSM1124918 2 0.5379 0.498 0.000 0.612 0.056 0.008 0.324
#> GSM1124920 3 0.5508 0.334 0.384 0.000 0.552 0.004 0.060
#> GSM1124922 2 0.3700 0.127 0.000 0.752 0.008 0.000 0.240
#> GSM1124924 2 0.6663 0.428 0.016 0.556 0.212 0.004 0.212
#> GSM1124926 2 0.2690 0.411 0.000 0.844 0.000 0.000 0.156
#> GSM1124928 1 0.6633 0.550 0.620 0.152 0.080 0.000 0.148
#> GSM1124930 5 0.6137 0.607 0.000 0.336 0.116 0.008 0.540
#> GSM1124931 2 0.5587 0.547 0.000 0.600 0.072 0.008 0.320
#> GSM1124935 5 0.5507 0.293 0.000 0.384 0.052 0.008 0.556
#> GSM1124936 3 0.5283 0.204 0.444 0.000 0.508 0.000 0.048
#> GSM1124938 3 0.5571 0.558 0.000 0.072 0.604 0.008 0.316
#> GSM1124940 1 0.0000 0.841 1.000 0.000 0.000 0.000 0.000
#> GSM1124941 2 0.1106 0.620 0.000 0.964 0.012 0.000 0.024
#> GSM1124942 5 0.6670 0.573 0.000 0.344 0.184 0.008 0.464
#> GSM1124943 3 0.5844 0.475 0.000 0.084 0.556 0.008 0.352
#> GSM1124948 2 0.6209 0.438 0.000 0.572 0.212 0.004 0.212
#> GSM1124949 1 0.0000 0.841 1.000 0.000 0.000 0.000 0.000
#> GSM1124950 2 0.2864 0.650 0.000 0.864 0.024 0.000 0.112
#> GSM1124954 1 0.7814 0.320 0.396 0.064 0.180 0.008 0.352
#> GSM1124955 1 0.0162 0.840 0.996 0.000 0.004 0.000 0.000
#> GSM1124956 2 0.4587 0.543 0.000 0.724 0.040 0.008 0.228
#> GSM1124872 2 0.2761 0.650 0.000 0.872 0.024 0.000 0.104
#> GSM1124873 2 0.0290 0.627 0.000 0.992 0.000 0.000 0.008
#> GSM1124876 3 0.2719 0.711 0.144 0.000 0.852 0.004 0.000
#> GSM1124877 1 0.4735 0.604 0.720 0.000 0.052 0.008 0.220
#> GSM1124879 1 0.0162 0.840 0.996 0.000 0.004 0.000 0.000
#> GSM1124883 5 0.6272 0.764 0.000 0.344 0.016 0.108 0.532
#> GSM1124889 2 0.1043 0.604 0.000 0.960 0.000 0.000 0.040
#> GSM1124892 1 0.0000 0.841 1.000 0.000 0.000 0.000 0.000
#> GSM1124893 1 0.0000 0.841 1.000 0.000 0.000 0.000 0.000
#> GSM1124909 2 0.1952 0.652 0.000 0.912 0.004 0.000 0.084
#> GSM1124913 5 0.6272 0.764 0.000 0.344 0.016 0.108 0.532
#> GSM1124916 2 0.4332 0.612 0.000 0.732 0.024 0.008 0.236
#> GSM1124923 5 0.6446 0.203 0.000 0.120 0.372 0.016 0.492
#> GSM1124925 1 0.0162 0.840 0.996 0.000 0.004 0.000 0.000
#> GSM1124929 1 0.0000 0.841 1.000 0.000 0.000 0.000 0.000
#> GSM1124934 1 0.7814 0.320 0.396 0.064 0.180 0.008 0.352
#> GSM1124937 1 0.6839 0.511 0.588 0.196 0.052 0.004 0.160
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1124891 3 0.3184 0.68648 0.020 0.004 0.848 0.000 0.028 0.100
#> GSM1124888 3 0.3200 0.68436 0.024 0.004 0.844 0.000 0.020 0.108
#> GSM1124890 3 0.6138 0.48111 0.000 0.032 0.560 0.004 0.228 0.176
#> GSM1124904 5 0.3147 0.74776 0.000 0.028 0.008 0.056 0.864 0.044
#> GSM1124927 2 0.2420 0.48659 0.000 0.888 0.004 0.000 0.032 0.076
#> GSM1124953 3 0.5523 0.56720 0.000 0.040 0.660 0.004 0.160 0.136
#> GSM1124869 1 0.0000 0.94340 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124870 2 0.3790 0.40928 0.112 0.796 0.004 0.000 0.004 0.084
#> GSM1124882 1 0.0000 0.94340 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124884 2 0.4065 0.46921 0.000 0.724 0.000 0.000 0.220 0.056
#> GSM1124898 5 0.2356 0.74030 0.000 0.100 0.008 0.004 0.884 0.004
#> GSM1124903 5 0.3147 0.74776 0.000 0.028 0.008 0.056 0.864 0.044
#> GSM1124905 2 0.7177 0.06705 0.316 0.436 0.072 0.008 0.008 0.160
#> GSM1124910 1 0.5019 0.63435 0.724 0.060 0.068 0.000 0.008 0.140
#> GSM1124919 5 0.6241 0.41632 0.000 0.044 0.240 0.004 0.556 0.156
#> GSM1124932 6 0.4377 0.38971 0.000 0.436 0.000 0.000 0.024 0.540
#> GSM1124933 3 0.0810 0.69967 0.004 0.008 0.976 0.000 0.004 0.008
#> GSM1124867 2 0.1151 0.49916 0.000 0.956 0.000 0.000 0.012 0.032
#> GSM1124868 5 0.4744 0.57667 0.000 0.052 0.004 0.264 0.668 0.012
#> GSM1124878 5 0.3152 0.75983 0.000 0.060 0.008 0.056 0.860 0.016
#> GSM1124895 4 0.0520 0.95022 0.000 0.008 0.000 0.984 0.008 0.000
#> GSM1124897 5 0.3777 0.76121 0.000 0.068 0.016 0.056 0.828 0.032
#> GSM1124902 4 0.0520 0.95022 0.000 0.008 0.000 0.984 0.008 0.000
#> GSM1124908 4 0.4017 0.69425 0.000 0.004 0.004 0.732 0.228 0.032
#> GSM1124921 4 0.1621 0.92746 0.000 0.004 0.004 0.936 0.048 0.008
#> GSM1124939 4 0.0520 0.95022 0.000 0.008 0.000 0.984 0.008 0.000
#> GSM1124944 4 0.0520 0.95022 0.000 0.008 0.000 0.984 0.008 0.000
#> GSM1124945 3 0.2445 0.66518 0.000 0.000 0.872 0.108 0.000 0.020
#> GSM1124946 4 0.1969 0.91906 0.000 0.004 0.004 0.920 0.052 0.020
#> GSM1124947 4 0.0520 0.95022 0.000 0.008 0.000 0.984 0.008 0.000
#> GSM1124951 3 0.2476 0.69142 0.000 0.000 0.888 0.008 0.072 0.032
#> GSM1124952 4 0.0520 0.95022 0.000 0.008 0.000 0.984 0.008 0.000
#> GSM1124957 3 0.2053 0.66317 0.000 0.000 0.888 0.108 0.000 0.004
#> GSM1124900 2 0.3797 0.41369 0.112 0.800 0.008 0.000 0.004 0.076
#> GSM1124914 5 0.2243 0.74071 0.000 0.112 0.000 0.004 0.880 0.004
#> GSM1124871 2 0.3821 0.47624 0.000 0.740 0.000 0.000 0.220 0.040
#> GSM1124874 2 0.3189 0.50115 0.000 0.796 0.000 0.000 0.184 0.020
#> GSM1124875 5 0.5671 0.56096 0.000 0.180 0.020 0.004 0.616 0.180
#> GSM1124880 2 0.5369 0.33898 0.064 0.700 0.052 0.000 0.024 0.160
#> GSM1124881 2 0.3284 0.50159 0.000 0.800 0.000 0.000 0.168 0.032
#> GSM1124885 5 0.2691 0.75344 0.000 0.088 0.000 0.032 0.872 0.008
#> GSM1124886 1 0.0000 0.94340 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124887 5 0.2094 0.76310 0.000 0.032 0.004 0.028 0.920 0.016
#> GSM1124894 2 0.5918 0.33253 0.020 0.672 0.096 0.028 0.028 0.156
#> GSM1124896 1 0.0405 0.93740 0.988 0.000 0.004 0.000 0.000 0.008
#> GSM1124899 2 0.4696 0.33896 0.000 0.588 0.000 0.000 0.356 0.056
#> GSM1124901 5 0.3090 0.71066 0.000 0.140 0.000 0.004 0.828 0.028
#> GSM1124906 2 0.3916 0.48427 0.000 0.752 0.000 0.000 0.184 0.064
#> GSM1124907 5 0.4209 0.62528 0.000 0.020 0.016 0.012 0.736 0.216
#> GSM1124911 2 0.5296 -0.34102 0.000 0.456 0.000 0.000 0.100 0.444
#> GSM1124912 1 0.0000 0.94340 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124915 6 0.6016 0.36966 0.000 0.244 0.000 0.000 0.352 0.404
#> GSM1124917 2 0.4981 0.06175 0.000 0.488 0.000 0.004 0.452 0.056
#> GSM1124918 6 0.5280 0.39382 0.000 0.320 0.008 0.000 0.096 0.576
#> GSM1124920 3 0.5634 0.49960 0.248 0.004 0.604 0.000 0.020 0.124
#> GSM1124922 2 0.4641 0.28386 0.000 0.552 0.000 0.000 0.404 0.044
#> GSM1124924 2 0.5910 0.23686 0.000 0.588 0.064 0.004 0.076 0.268
#> GSM1124926 2 0.4524 0.36354 0.000 0.616 0.000 0.000 0.336 0.048
#> GSM1124928 2 0.7006 0.08358 0.324 0.440 0.072 0.000 0.012 0.152
#> GSM1124930 5 0.6124 0.50379 0.000 0.116 0.064 0.004 0.588 0.228
#> GSM1124931 6 0.4129 0.38163 0.000 0.424 0.000 0.000 0.012 0.564
#> GSM1124935 6 0.5845 0.44513 0.000 0.212 0.000 0.000 0.316 0.472
#> GSM1124936 3 0.5067 0.38028 0.340 0.004 0.584 0.000 0.004 0.068
#> GSM1124938 3 0.7073 0.39615 0.000 0.068 0.416 0.004 0.252 0.260
#> GSM1124940 1 0.0000 0.94340 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124941 2 0.3916 0.48427 0.000 0.752 0.000 0.000 0.184 0.064
#> GSM1124942 5 0.6221 0.51200 0.000 0.116 0.084 0.004 0.592 0.204
#> GSM1124943 3 0.7231 0.25180 0.000 0.080 0.364 0.004 0.320 0.232
#> GSM1124948 2 0.6275 0.17457 0.000 0.528 0.068 0.004 0.092 0.308
#> GSM1124949 1 0.0000 0.94340 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124950 2 0.1738 0.49344 0.000 0.928 0.004 0.000 0.016 0.052
#> GSM1124954 6 0.5608 0.39028 0.164 0.072 0.096 0.004 0.000 0.664
#> GSM1124955 1 0.0146 0.94196 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM1124956 2 0.5261 -0.34334 0.000 0.460 0.000 0.000 0.096 0.444
#> GSM1124872 2 0.1972 0.49394 0.000 0.916 0.004 0.000 0.024 0.056
#> GSM1124873 2 0.2909 0.50564 0.000 0.836 0.000 0.000 0.136 0.028
#> GSM1124876 3 0.1225 0.69789 0.036 0.000 0.952 0.000 0.000 0.012
#> GSM1124877 1 0.3955 0.24713 0.560 0.000 0.000 0.004 0.000 0.436
#> GSM1124879 1 0.0146 0.94196 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM1124883 5 0.3147 0.74776 0.000 0.028 0.008 0.056 0.864 0.044
#> GSM1124889 2 0.3794 0.48029 0.000 0.744 0.000 0.000 0.216 0.040
#> GSM1124892 1 0.0000 0.94340 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124893 1 0.0000 0.94340 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124909 2 0.0806 0.49910 0.000 0.972 0.000 0.000 0.020 0.008
#> GSM1124913 5 0.3147 0.74776 0.000 0.028 0.008 0.056 0.864 0.044
#> GSM1124916 2 0.3707 -0.00253 0.000 0.680 0.000 0.000 0.008 0.312
#> GSM1124923 5 0.5870 0.38447 0.000 0.016 0.248 0.004 0.568 0.164
#> GSM1124925 1 0.0146 0.94196 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM1124929 1 0.0000 0.94340 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124934 6 0.5596 0.39119 0.168 0.072 0.092 0.004 0.000 0.664
#> GSM1124937 2 0.6509 0.14609 0.264 0.508 0.020 0.000 0.020 0.188
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 tissue(p) k
#> CV:kmeans 86 8.31e-02 2
#> CV:kmeans 80 1.15e-09 3
#> CV:kmeans 73 3.96e-08 4
#> CV:kmeans 71 1.20e-05 5
#> CV:kmeans 50 1.44e-03 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 48983 rows and 91 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 0.787 0.880 0.952 0.4912 0.508 0.508
#> 3 3 0.590 0.661 0.843 0.3218 0.763 0.560
#> 4 4 0.759 0.810 0.902 0.1552 0.814 0.517
#> 5 5 0.709 0.751 0.837 0.0639 0.920 0.698
#> 6 6 0.728 0.644 0.794 0.0417 0.908 0.601
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
#> GSM1124891 1 0.0000 0.932 1.000 0.000
#> GSM1124888 1 0.0000 0.932 1.000 0.000
#> GSM1124890 1 0.9710 0.383 0.600 0.400
#> GSM1124904 2 0.0000 0.956 0.000 1.000
#> GSM1124927 1 0.9460 0.400 0.636 0.364
#> GSM1124953 2 0.0000 0.956 0.000 1.000
#> GSM1124869 1 0.0000 0.932 1.000 0.000
#> GSM1124870 1 0.0000 0.932 1.000 0.000
#> GSM1124882 1 0.0000 0.932 1.000 0.000
#> GSM1124884 2 0.0000 0.956 0.000 1.000
#> GSM1124898 2 0.0000 0.956 0.000 1.000
#> GSM1124903 2 0.0000 0.956 0.000 1.000
#> GSM1124905 1 0.0000 0.932 1.000 0.000
#> GSM1124910 1 0.0000 0.932 1.000 0.000
#> GSM1124919 2 0.0000 0.956 0.000 1.000
#> GSM1124932 2 0.9661 0.360 0.392 0.608
#> GSM1124933 1 0.0000 0.932 1.000 0.000
#> GSM1124867 2 0.0672 0.949 0.008 0.992
#> GSM1124868 2 0.0000 0.956 0.000 1.000
#> GSM1124878 2 0.0000 0.956 0.000 1.000
#> GSM1124895 2 0.0000 0.956 0.000 1.000
#> GSM1124897 2 0.0000 0.956 0.000 1.000
#> GSM1124902 2 0.0000 0.956 0.000 1.000
#> GSM1124908 2 0.0000 0.956 0.000 1.000
#> GSM1124921 2 0.0000 0.956 0.000 1.000
#> GSM1124939 2 0.0000 0.956 0.000 1.000
#> GSM1124944 2 0.0000 0.956 0.000 1.000
#> GSM1124945 1 0.9710 0.383 0.600 0.400
#> GSM1124946 2 0.0000 0.956 0.000 1.000
#> GSM1124947 2 0.0000 0.956 0.000 1.000
#> GSM1124951 1 0.9732 0.373 0.596 0.404
#> GSM1124952 2 0.0000 0.956 0.000 1.000
#> GSM1124957 1 0.7056 0.744 0.808 0.192
#> GSM1124900 1 0.0000 0.932 1.000 0.000
#> GSM1124914 2 0.0000 0.956 0.000 1.000
#> GSM1124871 2 0.0000 0.956 0.000 1.000
#> GSM1124874 2 0.2043 0.928 0.032 0.968
#> GSM1124875 2 0.0000 0.956 0.000 1.000
#> GSM1124880 1 0.0000 0.932 1.000 0.000
#> GSM1124881 2 0.0000 0.956 0.000 1.000
#> GSM1124885 2 0.0000 0.956 0.000 1.000
#> GSM1124886 1 0.0000 0.932 1.000 0.000
#> GSM1124887 2 0.0000 0.956 0.000 1.000
#> GSM1124894 1 0.9393 0.420 0.644 0.356
#> GSM1124896 1 0.0000 0.932 1.000 0.000
#> GSM1124899 2 0.0000 0.956 0.000 1.000
#> GSM1124901 2 0.0000 0.956 0.000 1.000
#> GSM1124906 2 0.0000 0.956 0.000 1.000
#> GSM1124907 2 0.0000 0.956 0.000 1.000
#> GSM1124911 2 0.0000 0.956 0.000 1.000
#> GSM1124912 1 0.0000 0.932 1.000 0.000
#> GSM1124915 2 0.0000 0.956 0.000 1.000
#> GSM1124917 2 0.0000 0.956 0.000 1.000
#> GSM1124918 2 0.0000 0.956 0.000 1.000
#> GSM1124920 1 0.0000 0.932 1.000 0.000
#> GSM1124922 2 0.0000 0.956 0.000 1.000
#> GSM1124924 1 0.0000 0.932 1.000 0.000
#> GSM1124926 2 0.0000 0.956 0.000 1.000
#> GSM1124928 1 0.0000 0.932 1.000 0.000
#> GSM1124930 2 0.0000 0.956 0.000 1.000
#> GSM1124931 2 0.9710 0.339 0.400 0.600
#> GSM1124935 2 0.0000 0.956 0.000 1.000
#> GSM1124936 1 0.0000 0.932 1.000 0.000
#> GSM1124938 1 0.6887 0.754 0.816 0.184
#> GSM1124940 1 0.0000 0.932 1.000 0.000
#> GSM1124941 2 0.0000 0.956 0.000 1.000
#> GSM1124942 2 0.0000 0.956 0.000 1.000
#> GSM1124943 2 0.9881 0.134 0.436 0.564
#> GSM1124948 1 0.0000 0.932 1.000 0.000
#> GSM1124949 1 0.0000 0.932 1.000 0.000
#> GSM1124950 2 0.7139 0.743 0.196 0.804
#> GSM1124954 1 0.0000 0.932 1.000 0.000
#> GSM1124955 1 0.0000 0.932 1.000 0.000
#> GSM1124956 2 0.0000 0.956 0.000 1.000
#> GSM1124872 2 0.7602 0.708 0.220 0.780
#> GSM1124873 2 0.0000 0.956 0.000 1.000
#> GSM1124876 1 0.0000 0.932 1.000 0.000
#> GSM1124877 1 0.0000 0.932 1.000 0.000
#> GSM1124879 1 0.0000 0.932 1.000 0.000
#> GSM1124883 2 0.0000 0.956 0.000 1.000
#> GSM1124889 2 0.0000 0.956 0.000 1.000
#> GSM1124892 1 0.0000 0.932 1.000 0.000
#> GSM1124893 1 0.0000 0.932 1.000 0.000
#> GSM1124909 2 0.6973 0.754 0.188 0.812
#> GSM1124913 2 0.0000 0.956 0.000 1.000
#> GSM1124916 2 0.6973 0.754 0.188 0.812
#> GSM1124923 2 0.0000 0.956 0.000 1.000
#> GSM1124925 1 0.0000 0.932 1.000 0.000
#> GSM1124929 1 0.0000 0.932 1.000 0.000
#> GSM1124934 1 0.0000 0.932 1.000 0.000
#> GSM1124937 1 0.0000 0.932 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1124891 1 0.5835 0.6359 0.660 0.000 0.340
#> GSM1124888 1 0.5835 0.6359 0.660 0.000 0.340
#> GSM1124890 3 0.5845 0.4107 0.004 0.308 0.688
#> GSM1124904 2 0.5216 0.4468 0.000 0.740 0.260
#> GSM1124927 1 0.5397 0.5911 0.720 0.280 0.000
#> GSM1124953 3 0.4504 0.4929 0.000 0.196 0.804
#> GSM1124869 1 0.0000 0.8954 1.000 0.000 0.000
#> GSM1124870 1 0.3619 0.7764 0.864 0.136 0.000
#> GSM1124882 1 0.0000 0.8954 1.000 0.000 0.000
#> GSM1124884 2 0.0000 0.8040 0.000 1.000 0.000
#> GSM1124898 2 0.1643 0.7701 0.000 0.956 0.044
#> GSM1124903 2 0.4931 0.5116 0.000 0.768 0.232
#> GSM1124905 1 0.0000 0.8954 1.000 0.000 0.000
#> GSM1124910 1 0.0000 0.8954 1.000 0.000 0.000
#> GSM1124919 3 0.5621 0.4121 0.000 0.308 0.692
#> GSM1124932 2 0.4504 0.5638 0.196 0.804 0.000
#> GSM1124933 1 0.5882 0.6260 0.652 0.000 0.348
#> GSM1124867 2 0.8754 -0.0859 0.116 0.508 0.376
#> GSM1124868 3 0.5948 0.5407 0.000 0.360 0.640
#> GSM1124878 3 0.6079 0.4917 0.000 0.388 0.612
#> GSM1124895 3 0.5835 0.5702 0.000 0.340 0.660
#> GSM1124897 3 0.5835 0.5702 0.000 0.340 0.660
#> GSM1124902 3 0.5835 0.5702 0.000 0.340 0.660
#> GSM1124908 3 0.5835 0.5702 0.000 0.340 0.660
#> GSM1124921 3 0.5835 0.5702 0.000 0.340 0.660
#> GSM1124939 3 0.5835 0.5702 0.000 0.340 0.660
#> GSM1124944 3 0.5835 0.5702 0.000 0.340 0.660
#> GSM1124945 3 0.0000 0.5332 0.000 0.000 1.000
#> GSM1124946 3 0.5835 0.5702 0.000 0.340 0.660
#> GSM1124947 3 0.5835 0.5702 0.000 0.340 0.660
#> GSM1124951 3 0.0000 0.5332 0.000 0.000 1.000
#> GSM1124952 3 0.5835 0.5702 0.000 0.340 0.660
#> GSM1124957 3 0.1031 0.5248 0.024 0.000 0.976
#> GSM1124900 1 0.0747 0.8846 0.984 0.016 0.000
#> GSM1124914 2 0.4750 0.5424 0.000 0.784 0.216
#> GSM1124871 2 0.0000 0.8040 0.000 1.000 0.000
#> GSM1124874 2 0.0000 0.8040 0.000 1.000 0.000
#> GSM1124875 2 0.4702 0.5883 0.000 0.788 0.212
#> GSM1124880 1 0.0000 0.8954 1.000 0.000 0.000
#> GSM1124881 2 0.0000 0.8040 0.000 1.000 0.000
#> GSM1124885 2 0.4750 0.5417 0.000 0.784 0.216
#> GSM1124886 1 0.0000 0.8954 1.000 0.000 0.000
#> GSM1124887 2 0.5591 0.3303 0.000 0.696 0.304
#> GSM1124894 1 0.8180 0.2248 0.532 0.076 0.392
#> GSM1124896 1 0.0000 0.8954 1.000 0.000 0.000
#> GSM1124899 2 0.0000 0.8040 0.000 1.000 0.000
#> GSM1124901 2 0.0592 0.7964 0.000 0.988 0.012
#> GSM1124906 2 0.0000 0.8040 0.000 1.000 0.000
#> GSM1124907 3 0.6095 0.2994 0.000 0.392 0.608
#> GSM1124911 2 0.0000 0.8040 0.000 1.000 0.000
#> GSM1124912 1 0.0000 0.8954 1.000 0.000 0.000
#> GSM1124915 2 0.0000 0.8040 0.000 1.000 0.000
#> GSM1124917 2 0.0000 0.8040 0.000 1.000 0.000
#> GSM1124918 2 0.5254 0.4147 0.000 0.736 0.264
#> GSM1124920 1 0.5810 0.6400 0.664 0.000 0.336
#> GSM1124922 2 0.0237 0.8019 0.000 0.996 0.004
#> GSM1124924 1 0.6381 0.6261 0.648 0.012 0.340
#> GSM1124926 2 0.0000 0.8040 0.000 1.000 0.000
#> GSM1124928 1 0.0000 0.8954 1.000 0.000 0.000
#> GSM1124930 3 0.6026 0.3130 0.000 0.376 0.624
#> GSM1124931 2 0.5138 0.4826 0.252 0.748 0.000
#> GSM1124935 2 0.0000 0.8040 0.000 1.000 0.000
#> GSM1124936 1 0.3116 0.8323 0.892 0.000 0.108
#> GSM1124938 3 0.8796 0.2156 0.120 0.372 0.508
#> GSM1124940 1 0.0000 0.8954 1.000 0.000 0.000
#> GSM1124941 2 0.0000 0.8040 0.000 1.000 0.000
#> GSM1124942 3 0.6008 0.3194 0.000 0.372 0.628
#> GSM1124943 3 0.5988 0.3264 0.000 0.368 0.632
#> GSM1124948 2 0.9328 -0.0568 0.168 0.460 0.372
#> GSM1124949 1 0.0000 0.8954 1.000 0.000 0.000
#> GSM1124950 2 0.0000 0.8040 0.000 1.000 0.000
#> GSM1124954 1 0.1031 0.8841 0.976 0.000 0.024
#> GSM1124955 1 0.0000 0.8954 1.000 0.000 0.000
#> GSM1124956 2 0.0000 0.8040 0.000 1.000 0.000
#> GSM1124872 2 0.0592 0.7945 0.012 0.988 0.000
#> GSM1124873 2 0.0000 0.8040 0.000 1.000 0.000
#> GSM1124876 1 0.5835 0.6359 0.660 0.000 0.340
#> GSM1124877 1 0.0000 0.8954 1.000 0.000 0.000
#> GSM1124879 1 0.0000 0.8954 1.000 0.000 0.000
#> GSM1124883 2 0.4931 0.5116 0.000 0.768 0.232
#> GSM1124889 2 0.0000 0.8040 0.000 1.000 0.000
#> GSM1124892 1 0.0000 0.8954 1.000 0.000 0.000
#> GSM1124893 1 0.0000 0.8954 1.000 0.000 0.000
#> GSM1124909 2 0.3941 0.6243 0.156 0.844 0.000
#> GSM1124913 2 0.4931 0.5116 0.000 0.768 0.232
#> GSM1124916 2 0.1860 0.7559 0.052 0.948 0.000
#> GSM1124923 3 0.5621 0.4121 0.000 0.308 0.692
#> GSM1124925 1 0.0000 0.8954 1.000 0.000 0.000
#> GSM1124929 1 0.0000 0.8954 1.000 0.000 0.000
#> GSM1124934 1 0.0000 0.8954 1.000 0.000 0.000
#> GSM1124937 1 0.0000 0.8954 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1124891 3 0.3801 0.736 0.220 0.000 0.780 0.000
#> GSM1124888 3 0.3801 0.736 0.220 0.000 0.780 0.000
#> GSM1124890 3 0.0000 0.825 0.000 0.000 1.000 0.000
#> GSM1124904 4 0.6576 0.666 0.000 0.152 0.220 0.628
#> GSM1124927 2 0.3024 0.736 0.148 0.852 0.000 0.000
#> GSM1124953 3 0.1356 0.821 0.000 0.032 0.960 0.008
#> GSM1124869 1 0.0000 0.953 1.000 0.000 0.000 0.000
#> GSM1124870 1 0.3764 0.721 0.784 0.216 0.000 0.000
#> GSM1124882 1 0.0000 0.953 1.000 0.000 0.000 0.000
#> GSM1124884 2 0.0000 0.881 0.000 1.000 0.000 0.000
#> GSM1124898 2 0.7439 0.288 0.000 0.508 0.220 0.272
#> GSM1124903 4 0.6576 0.666 0.000 0.152 0.220 0.628
#> GSM1124905 1 0.0000 0.953 1.000 0.000 0.000 0.000
#> GSM1124910 1 0.0000 0.953 1.000 0.000 0.000 0.000
#> GSM1124919 3 0.0000 0.825 0.000 0.000 1.000 0.000
#> GSM1124932 2 0.0000 0.881 0.000 1.000 0.000 0.000
#> GSM1124933 3 0.3801 0.736 0.220 0.000 0.780 0.000
#> GSM1124867 2 0.4877 0.377 0.000 0.592 0.000 0.408
#> GSM1124868 4 0.0000 0.836 0.000 0.000 0.000 1.000
#> GSM1124878 4 0.0592 0.830 0.000 0.016 0.000 0.984
#> GSM1124895 4 0.0000 0.836 0.000 0.000 0.000 1.000
#> GSM1124897 4 0.0000 0.836 0.000 0.000 0.000 1.000
#> GSM1124902 4 0.0000 0.836 0.000 0.000 0.000 1.000
#> GSM1124908 4 0.0000 0.836 0.000 0.000 0.000 1.000
#> GSM1124921 4 0.0000 0.836 0.000 0.000 0.000 1.000
#> GSM1124939 4 0.0000 0.836 0.000 0.000 0.000 1.000
#> GSM1124944 4 0.0000 0.836 0.000 0.000 0.000 1.000
#> GSM1124945 3 0.3801 0.725 0.000 0.000 0.780 0.220
#> GSM1124946 4 0.0000 0.836 0.000 0.000 0.000 1.000
#> GSM1124947 4 0.0000 0.836 0.000 0.000 0.000 1.000
#> GSM1124951 3 0.3764 0.728 0.000 0.000 0.784 0.216
#> GSM1124952 4 0.0000 0.836 0.000 0.000 0.000 1.000
#> GSM1124957 3 0.3801 0.725 0.000 0.000 0.780 0.220
#> GSM1124900 1 0.3528 0.751 0.808 0.192 0.000 0.000
#> GSM1124914 4 0.6616 0.661 0.000 0.156 0.220 0.624
#> GSM1124871 2 0.0469 0.877 0.000 0.988 0.012 0.000
#> GSM1124874 2 0.0000 0.881 0.000 1.000 0.000 0.000
#> GSM1124875 2 0.6371 0.567 0.000 0.608 0.300 0.092
#> GSM1124880 1 0.1940 0.883 0.924 0.076 0.000 0.000
#> GSM1124881 2 0.0000 0.881 0.000 1.000 0.000 0.000
#> GSM1124885 4 0.6576 0.666 0.000 0.152 0.220 0.628
#> GSM1124886 1 0.0000 0.953 1.000 0.000 0.000 0.000
#> GSM1124887 4 0.6180 0.628 0.000 0.080 0.296 0.624
#> GSM1124894 4 0.1716 0.779 0.064 0.000 0.000 0.936
#> GSM1124896 1 0.0000 0.953 1.000 0.000 0.000 0.000
#> GSM1124899 2 0.2489 0.847 0.000 0.912 0.068 0.020
#> GSM1124901 2 0.6695 0.543 0.000 0.616 0.220 0.164
#> GSM1124906 2 0.0000 0.881 0.000 1.000 0.000 0.000
#> GSM1124907 3 0.4103 0.459 0.000 0.000 0.744 0.256
#> GSM1124911 2 0.0000 0.881 0.000 1.000 0.000 0.000
#> GSM1124912 1 0.0000 0.953 1.000 0.000 0.000 0.000
#> GSM1124915 2 0.3610 0.764 0.000 0.800 0.200 0.000
#> GSM1124917 2 0.2868 0.816 0.000 0.864 0.136 0.000
#> GSM1124918 2 0.2921 0.812 0.000 0.860 0.140 0.000
#> GSM1124920 1 0.4761 0.327 0.628 0.000 0.372 0.000
#> GSM1124922 2 0.5889 0.661 0.000 0.696 0.188 0.116
#> GSM1124924 3 0.4323 0.727 0.020 0.204 0.776 0.000
#> GSM1124926 2 0.2401 0.823 0.000 0.904 0.004 0.092
#> GSM1124928 1 0.0000 0.953 1.000 0.000 0.000 0.000
#> GSM1124930 3 0.0000 0.825 0.000 0.000 1.000 0.000
#> GSM1124931 2 0.0000 0.881 0.000 1.000 0.000 0.000
#> GSM1124935 2 0.3610 0.764 0.000 0.800 0.200 0.000
#> GSM1124936 1 0.2281 0.858 0.904 0.000 0.096 0.000
#> GSM1124938 3 0.0000 0.825 0.000 0.000 1.000 0.000
#> GSM1124940 1 0.0000 0.953 1.000 0.000 0.000 0.000
#> GSM1124941 2 0.0000 0.881 0.000 1.000 0.000 0.000
#> GSM1124942 3 0.0000 0.825 0.000 0.000 1.000 0.000
#> GSM1124943 3 0.0000 0.825 0.000 0.000 1.000 0.000
#> GSM1124948 3 0.3610 0.735 0.000 0.200 0.800 0.000
#> GSM1124949 1 0.0000 0.953 1.000 0.000 0.000 0.000
#> GSM1124950 2 0.0000 0.881 0.000 1.000 0.000 0.000
#> GSM1124954 1 0.0592 0.940 0.984 0.000 0.016 0.000
#> GSM1124955 1 0.0000 0.953 1.000 0.000 0.000 0.000
#> GSM1124956 2 0.0000 0.881 0.000 1.000 0.000 0.000
#> GSM1124872 2 0.0000 0.881 0.000 1.000 0.000 0.000
#> GSM1124873 2 0.0000 0.881 0.000 1.000 0.000 0.000
#> GSM1124876 3 0.3801 0.736 0.220 0.000 0.780 0.000
#> GSM1124877 1 0.0000 0.953 1.000 0.000 0.000 0.000
#> GSM1124879 1 0.0000 0.953 1.000 0.000 0.000 0.000
#> GSM1124883 4 0.6576 0.666 0.000 0.152 0.220 0.628
#> GSM1124889 2 0.0000 0.881 0.000 1.000 0.000 0.000
#> GSM1124892 1 0.0000 0.953 1.000 0.000 0.000 0.000
#> GSM1124893 1 0.0000 0.953 1.000 0.000 0.000 0.000
#> GSM1124909 2 0.0000 0.881 0.000 1.000 0.000 0.000
#> GSM1124913 4 0.6576 0.666 0.000 0.152 0.220 0.628
#> GSM1124916 2 0.0000 0.881 0.000 1.000 0.000 0.000
#> GSM1124923 3 0.0000 0.825 0.000 0.000 1.000 0.000
#> GSM1124925 1 0.0000 0.953 1.000 0.000 0.000 0.000
#> GSM1124929 1 0.0000 0.953 1.000 0.000 0.000 0.000
#> GSM1124934 1 0.0000 0.953 1.000 0.000 0.000 0.000
#> GSM1124937 1 0.0000 0.953 1.000 0.000 0.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM1124891 3 0.2516 0.799 0.140 0.000 0.860 0.000 0.000
#> GSM1124888 3 0.2583 0.806 0.132 0.000 0.864 0.000 0.004
#> GSM1124890 3 0.2074 0.842 0.000 0.000 0.896 0.000 0.104
#> GSM1124904 5 0.3242 0.823 0.000 0.000 0.012 0.172 0.816
#> GSM1124927 2 0.4992 0.718 0.076 0.760 0.052 0.000 0.112
#> GSM1124953 3 0.1943 0.844 0.000 0.020 0.924 0.000 0.056
#> GSM1124869 1 0.0000 0.893 1.000 0.000 0.000 0.000 0.000
#> GSM1124870 1 0.5520 0.394 0.584 0.352 0.052 0.000 0.012
#> GSM1124882 1 0.0000 0.893 1.000 0.000 0.000 0.000 0.000
#> GSM1124884 2 0.4045 0.712 0.000 0.644 0.000 0.000 0.356
#> GSM1124898 5 0.2907 0.816 0.000 0.008 0.012 0.116 0.864
#> GSM1124903 5 0.3242 0.823 0.000 0.000 0.012 0.172 0.816
#> GSM1124905 1 0.0290 0.891 0.992 0.000 0.008 0.000 0.000
#> GSM1124910 1 0.0880 0.880 0.968 0.000 0.032 0.000 0.000
#> GSM1124919 3 0.3074 0.794 0.000 0.000 0.804 0.000 0.196
#> GSM1124932 2 0.1894 0.745 0.000 0.920 0.008 0.000 0.072
#> GSM1124933 3 0.1121 0.838 0.044 0.000 0.956 0.000 0.000
#> GSM1124867 4 0.5229 0.530 0.004 0.200 0.000 0.688 0.108
#> GSM1124868 4 0.2471 0.770 0.000 0.000 0.000 0.864 0.136
#> GSM1124878 4 0.4268 0.061 0.000 0.000 0.000 0.556 0.444
#> GSM1124895 4 0.0000 0.888 0.000 0.000 0.000 1.000 0.000
#> GSM1124897 4 0.3366 0.638 0.000 0.000 0.000 0.768 0.232
#> GSM1124902 4 0.0000 0.888 0.000 0.000 0.000 1.000 0.000
#> GSM1124908 4 0.0162 0.886 0.000 0.000 0.000 0.996 0.004
#> GSM1124921 4 0.0000 0.888 0.000 0.000 0.000 1.000 0.000
#> GSM1124939 4 0.0000 0.888 0.000 0.000 0.000 1.000 0.000
#> GSM1124944 4 0.0000 0.888 0.000 0.000 0.000 1.000 0.000
#> GSM1124945 3 0.3366 0.742 0.000 0.000 0.784 0.212 0.004
#> GSM1124946 4 0.0000 0.888 0.000 0.000 0.000 1.000 0.000
#> GSM1124947 4 0.0000 0.888 0.000 0.000 0.000 1.000 0.000
#> GSM1124951 3 0.3280 0.776 0.000 0.000 0.812 0.176 0.012
#> GSM1124952 4 0.0000 0.888 0.000 0.000 0.000 1.000 0.000
#> GSM1124957 3 0.3266 0.753 0.000 0.000 0.796 0.200 0.004
#> GSM1124900 1 0.5092 0.560 0.664 0.276 0.052 0.000 0.008
#> GSM1124914 5 0.3010 0.821 0.000 0.000 0.004 0.172 0.824
#> GSM1124871 2 0.4356 0.714 0.000 0.648 0.012 0.000 0.340
#> GSM1124874 2 0.4851 0.677 0.000 0.624 0.036 0.000 0.340
#> GSM1124875 5 0.3405 0.718 0.000 0.012 0.104 0.036 0.848
#> GSM1124880 1 0.5261 0.652 0.696 0.200 0.092 0.000 0.012
#> GSM1124881 2 0.4080 0.766 0.000 0.728 0.020 0.000 0.252
#> GSM1124885 5 0.3360 0.824 0.000 0.004 0.012 0.168 0.816
#> GSM1124886 1 0.0000 0.893 1.000 0.000 0.000 0.000 0.000
#> GSM1124887 5 0.3359 0.821 0.000 0.000 0.020 0.164 0.816
#> GSM1124894 4 0.1628 0.832 0.056 0.000 0.008 0.936 0.000
#> GSM1124896 1 0.0000 0.893 1.000 0.000 0.000 0.000 0.000
#> GSM1124899 5 0.3849 0.357 0.000 0.232 0.016 0.000 0.752
#> GSM1124901 5 0.2756 0.790 0.000 0.024 0.004 0.092 0.880
#> GSM1124906 2 0.3835 0.767 0.000 0.732 0.008 0.000 0.260
#> GSM1124907 5 0.4985 0.572 0.000 0.000 0.244 0.076 0.680
#> GSM1124911 2 0.2411 0.743 0.000 0.884 0.008 0.000 0.108
#> GSM1124912 1 0.0000 0.893 1.000 0.000 0.000 0.000 0.000
#> GSM1124915 2 0.4425 0.428 0.000 0.600 0.008 0.000 0.392
#> GSM1124917 2 0.4894 0.439 0.000 0.520 0.024 0.000 0.456
#> GSM1124918 2 0.4466 0.654 0.000 0.748 0.076 0.000 0.176
#> GSM1124920 1 0.4397 0.231 0.564 0.000 0.432 0.000 0.004
#> GSM1124922 5 0.3649 0.610 0.008 0.120 0.020 0.016 0.836
#> GSM1124924 3 0.4066 0.690 0.004 0.196 0.768 0.000 0.032
#> GSM1124926 5 0.4888 0.122 0.000 0.320 0.008 0.028 0.644
#> GSM1124928 1 0.1082 0.881 0.964 0.008 0.028 0.000 0.000
#> GSM1124930 3 0.3452 0.749 0.000 0.000 0.756 0.000 0.244
#> GSM1124931 2 0.1918 0.735 0.000 0.928 0.036 0.000 0.036
#> GSM1124935 2 0.4538 0.259 0.000 0.540 0.008 0.000 0.452
#> GSM1124936 1 0.3143 0.715 0.796 0.000 0.204 0.000 0.000
#> GSM1124938 3 0.2074 0.843 0.000 0.000 0.896 0.000 0.104
#> GSM1124940 1 0.0000 0.893 1.000 0.000 0.000 0.000 0.000
#> GSM1124941 2 0.3942 0.767 0.000 0.728 0.012 0.000 0.260
#> GSM1124942 3 0.2852 0.817 0.000 0.000 0.828 0.000 0.172
#> GSM1124943 3 0.2516 0.834 0.000 0.000 0.860 0.000 0.140
#> GSM1124948 3 0.2388 0.813 0.000 0.072 0.900 0.000 0.028
#> GSM1124949 1 0.0000 0.893 1.000 0.000 0.000 0.000 0.000
#> GSM1124950 2 0.4059 0.753 0.000 0.776 0.052 0.000 0.172
#> GSM1124954 1 0.5165 0.696 0.708 0.188 0.092 0.000 0.012
#> GSM1124955 1 0.0000 0.893 1.000 0.000 0.000 0.000 0.000
#> GSM1124956 2 0.2411 0.743 0.000 0.884 0.008 0.000 0.108
#> GSM1124872 2 0.3736 0.758 0.000 0.808 0.052 0.000 0.140
#> GSM1124873 2 0.3690 0.769 0.000 0.780 0.020 0.000 0.200
#> GSM1124876 3 0.2605 0.793 0.148 0.000 0.852 0.000 0.000
#> GSM1124877 1 0.3360 0.777 0.816 0.168 0.004 0.000 0.012
#> GSM1124879 1 0.0000 0.893 1.000 0.000 0.000 0.000 0.000
#> GSM1124883 5 0.3242 0.823 0.000 0.000 0.012 0.172 0.816
#> GSM1124889 2 0.4339 0.725 0.000 0.652 0.012 0.000 0.336
#> GSM1124892 1 0.0000 0.893 1.000 0.000 0.000 0.000 0.000
#> GSM1124893 1 0.0000 0.893 1.000 0.000 0.000 0.000 0.000
#> GSM1124909 2 0.3051 0.766 0.000 0.852 0.028 0.000 0.120
#> GSM1124913 5 0.3242 0.823 0.000 0.000 0.012 0.172 0.816
#> GSM1124916 2 0.1310 0.746 0.000 0.956 0.024 0.000 0.020
#> GSM1124923 3 0.3074 0.797 0.000 0.000 0.804 0.000 0.196
#> GSM1124925 1 0.0000 0.893 1.000 0.000 0.000 0.000 0.000
#> GSM1124929 1 0.0000 0.893 1.000 0.000 0.000 0.000 0.000
#> GSM1124934 1 0.4749 0.723 0.736 0.192 0.060 0.000 0.012
#> GSM1124937 1 0.0798 0.885 0.976 0.008 0.016 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1124891 3 0.2306 0.7447 0.092 0.004 0.888 0.000 0.000 0.016
#> GSM1124888 3 0.2569 0.7675 0.056 0.008 0.892 0.000 0.008 0.036
#> GSM1124890 3 0.2968 0.7746 0.000 0.004 0.840 0.000 0.128 0.028
#> GSM1124904 5 0.2146 0.7459 0.000 0.004 0.000 0.116 0.880 0.000
#> GSM1124927 2 0.1382 0.5501 0.036 0.948 0.008 0.000 0.000 0.008
#> GSM1124953 3 0.3031 0.7762 0.000 0.032 0.852 0.000 0.100 0.016
#> GSM1124869 1 0.0000 0.9007 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124870 2 0.4196 0.3401 0.340 0.640 0.008 0.000 0.008 0.004
#> GSM1124882 1 0.0000 0.9007 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124884 2 0.6127 0.3379 0.000 0.464 0.008 0.000 0.272 0.256
#> GSM1124898 5 0.2419 0.7347 0.000 0.016 0.000 0.060 0.896 0.028
#> GSM1124903 5 0.2146 0.7459 0.000 0.004 0.000 0.116 0.880 0.000
#> GSM1124905 1 0.1738 0.8633 0.928 0.052 0.004 0.000 0.000 0.016
#> GSM1124910 1 0.1806 0.8604 0.928 0.008 0.044 0.000 0.000 0.020
#> GSM1124919 3 0.4130 0.7009 0.000 0.008 0.716 0.000 0.240 0.036
#> GSM1124932 6 0.3698 0.6521 0.004 0.212 0.000 0.000 0.028 0.756
#> GSM1124933 3 0.1092 0.7749 0.020 0.020 0.960 0.000 0.000 0.000
#> GSM1124867 2 0.4220 -0.0258 0.000 0.520 0.000 0.468 0.004 0.008
#> GSM1124868 4 0.2454 0.7702 0.000 0.000 0.000 0.840 0.160 0.000
#> GSM1124878 5 0.3868 0.0411 0.000 0.000 0.000 0.496 0.504 0.000
#> GSM1124895 4 0.0000 0.9344 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1124897 4 0.3890 0.5187 0.000 0.004 0.008 0.692 0.292 0.004
#> GSM1124902 4 0.0000 0.9344 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1124908 4 0.0790 0.9175 0.000 0.000 0.000 0.968 0.032 0.000
#> GSM1124921 4 0.0146 0.9328 0.000 0.000 0.000 0.996 0.004 0.000
#> GSM1124939 4 0.0000 0.9344 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1124944 4 0.0000 0.9344 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1124945 3 0.2902 0.7047 0.000 0.000 0.800 0.196 0.004 0.000
#> GSM1124946 4 0.0363 0.9298 0.000 0.000 0.000 0.988 0.012 0.000
#> GSM1124947 4 0.0000 0.9344 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1124951 3 0.2664 0.7535 0.000 0.000 0.848 0.136 0.016 0.000
#> GSM1124952 4 0.0000 0.9344 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1124957 3 0.2631 0.7176 0.000 0.000 0.820 0.180 0.000 0.000
#> GSM1124900 2 0.4269 0.2164 0.404 0.580 0.008 0.000 0.004 0.004
#> GSM1124914 5 0.3701 0.7337 0.000 0.024 0.020 0.092 0.828 0.036
#> GSM1124871 2 0.5630 0.4264 0.000 0.552 0.004 0.000 0.268 0.176
#> GSM1124874 2 0.4276 0.5530 0.000 0.752 0.004 0.004 0.132 0.108
#> GSM1124875 5 0.4971 0.5487 0.000 0.028 0.072 0.004 0.692 0.204
#> GSM1124880 2 0.5217 0.3478 0.280 0.632 0.044 0.000 0.004 0.040
#> GSM1124881 2 0.4270 0.5498 0.000 0.748 0.008 0.000 0.100 0.144
#> GSM1124885 5 0.2313 0.7459 0.000 0.012 0.000 0.100 0.884 0.004
#> GSM1124886 1 0.0000 0.9007 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124887 5 0.2518 0.7378 0.000 0.004 0.008 0.096 0.880 0.012
#> GSM1124894 4 0.2333 0.8480 0.060 0.032 0.004 0.900 0.000 0.004
#> GSM1124896 1 0.0000 0.9007 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124899 5 0.6088 0.3533 0.000 0.248 0.028 0.004 0.556 0.164
#> GSM1124901 5 0.3126 0.7097 0.000 0.020 0.012 0.036 0.864 0.068
#> GSM1124906 2 0.5414 0.4489 0.000 0.596 0.012 0.000 0.120 0.272
#> GSM1124907 5 0.5431 0.4061 0.000 0.012 0.152 0.016 0.660 0.160
#> GSM1124911 6 0.3722 0.6495 0.000 0.196 0.004 0.000 0.036 0.764
#> GSM1124912 1 0.0000 0.9007 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124915 6 0.4485 0.5629 0.000 0.064 0.004 0.000 0.248 0.684
#> GSM1124917 5 0.6943 -0.1513 0.000 0.236 0.048 0.004 0.360 0.352
#> GSM1124918 6 0.2434 0.5818 0.000 0.056 0.016 0.000 0.032 0.896
#> GSM1124920 1 0.5091 0.0951 0.488 0.012 0.456 0.000 0.004 0.040
#> GSM1124922 5 0.5874 0.4985 0.004 0.200 0.028 0.012 0.628 0.128
#> GSM1124924 2 0.6611 -0.1713 0.004 0.440 0.348 0.000 0.044 0.164
#> GSM1124926 5 0.6177 0.3087 0.000 0.276 0.016 0.016 0.540 0.152
#> GSM1124928 1 0.3369 0.7942 0.832 0.108 0.032 0.000 0.000 0.028
#> GSM1124930 3 0.5831 0.6390 0.000 0.016 0.560 0.000 0.240 0.184
#> GSM1124931 6 0.3925 0.5733 0.000 0.332 0.004 0.000 0.008 0.656
#> GSM1124935 6 0.4011 0.6060 0.000 0.056 0.000 0.000 0.212 0.732
#> GSM1124936 1 0.3888 0.5453 0.672 0.000 0.312 0.000 0.000 0.016
#> GSM1124938 3 0.4882 0.7223 0.000 0.016 0.692 0.000 0.112 0.180
#> GSM1124940 1 0.0000 0.9007 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124941 2 0.5464 0.4473 0.000 0.584 0.012 0.000 0.120 0.284
#> GSM1124942 3 0.5453 0.6880 0.000 0.012 0.616 0.000 0.192 0.180
#> GSM1124943 3 0.5227 0.7142 0.000 0.012 0.648 0.000 0.164 0.176
#> GSM1124948 3 0.6146 0.5983 0.000 0.168 0.580 0.000 0.060 0.192
#> GSM1124949 1 0.0000 0.9007 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124950 2 0.1448 0.5608 0.000 0.948 0.012 0.000 0.016 0.024
#> GSM1124954 6 0.5157 0.2902 0.360 0.000 0.096 0.000 0.000 0.544
#> GSM1124955 1 0.0000 0.9007 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124956 6 0.3722 0.6495 0.000 0.196 0.004 0.000 0.036 0.764
#> GSM1124872 2 0.1483 0.5525 0.000 0.944 0.012 0.000 0.008 0.036
#> GSM1124873 2 0.4200 0.5479 0.000 0.760 0.012 0.000 0.092 0.136
#> GSM1124876 3 0.1714 0.7494 0.092 0.000 0.908 0.000 0.000 0.000
#> GSM1124877 1 0.3747 0.2471 0.604 0.000 0.000 0.000 0.000 0.396
#> GSM1124879 1 0.0000 0.9007 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124883 5 0.2146 0.7459 0.000 0.004 0.000 0.116 0.880 0.000
#> GSM1124889 2 0.5414 0.4959 0.000 0.628 0.016 0.000 0.200 0.156
#> GSM1124892 1 0.0146 0.8987 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM1124893 1 0.0000 0.9007 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124909 2 0.3367 0.5172 0.000 0.804 0.012 0.000 0.020 0.164
#> GSM1124913 5 0.2288 0.7456 0.000 0.004 0.000 0.116 0.876 0.004
#> GSM1124916 6 0.4318 0.5099 0.000 0.340 0.008 0.000 0.020 0.632
#> GSM1124923 3 0.4613 0.6889 0.000 0.008 0.676 0.000 0.252 0.064
#> GSM1124925 1 0.0000 0.9007 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124929 1 0.0000 0.9007 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124934 6 0.4972 0.2264 0.392 0.000 0.072 0.000 0.000 0.536
#> GSM1124937 1 0.3232 0.7924 0.840 0.112 0.008 0.000 0.008 0.032
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 tissue(p) k
#> CV:skmeans 83 9.38e-02 2
#> CV:skmeans 74 5.17e-12 3
#> CV:skmeans 87 7.43e-06 4
#> CV:skmeans 83 2.69e-07 5
#> CV:skmeans 71 2.04e-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["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 48983 rows and 91 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 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 0.669 0.873 0.917 0.3596 0.597 0.597
#> 3 3 0.733 0.800 0.883 0.4258 0.855 0.758
#> 4 4 0.723 0.837 0.910 0.2279 0.847 0.698
#> 5 5 0.708 0.821 0.863 0.1497 0.882 0.716
#> 6 6 0.745 0.732 0.862 0.0912 0.873 0.596
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
#> GSM1124891 1 0.2043 0.759 0.968 0.032
#> GSM1124888 1 0.1414 0.760 0.980 0.020
#> GSM1124890 2 0.4022 0.866 0.080 0.920
#> GSM1124904 2 0.0000 0.957 0.000 1.000
#> GSM1124927 2 0.0000 0.957 0.000 1.000
#> GSM1124953 2 0.8713 0.536 0.292 0.708
#> GSM1124869 1 0.8955 0.826 0.688 0.312
#> GSM1124870 2 0.0000 0.957 0.000 1.000
#> GSM1124882 1 0.9580 0.725 0.620 0.380
#> GSM1124884 2 0.0000 0.957 0.000 1.000
#> GSM1124898 2 0.0000 0.957 0.000 1.000
#> GSM1124903 2 0.0000 0.957 0.000 1.000
#> GSM1124905 2 0.9850 -0.214 0.428 0.572
#> GSM1124910 1 0.8955 0.826 0.688 0.312
#> GSM1124919 2 0.0000 0.957 0.000 1.000
#> GSM1124932 2 0.0000 0.957 0.000 1.000
#> GSM1124933 1 0.6973 0.682 0.812 0.188
#> GSM1124867 2 0.0000 0.957 0.000 1.000
#> GSM1124868 2 0.1414 0.942 0.020 0.980
#> GSM1124878 2 0.0000 0.957 0.000 1.000
#> GSM1124895 2 0.1414 0.942 0.020 0.980
#> GSM1124897 2 0.0000 0.957 0.000 1.000
#> GSM1124902 2 0.1414 0.942 0.020 0.980
#> GSM1124908 2 0.1414 0.942 0.020 0.980
#> GSM1124921 2 0.1414 0.942 0.020 0.980
#> GSM1124939 2 0.1414 0.942 0.020 0.980
#> GSM1124944 2 0.1414 0.942 0.020 0.980
#> GSM1124945 2 0.8909 0.521 0.308 0.692
#> GSM1124946 2 0.1414 0.942 0.020 0.980
#> GSM1124947 2 0.1414 0.942 0.020 0.980
#> GSM1124951 2 0.9427 0.412 0.360 0.640
#> GSM1124952 2 0.1414 0.942 0.020 0.980
#> GSM1124957 1 0.1184 0.749 0.984 0.016
#> GSM1124900 2 0.0000 0.957 0.000 1.000
#> GSM1124914 2 0.0000 0.957 0.000 1.000
#> GSM1124871 2 0.0000 0.957 0.000 1.000
#> GSM1124874 2 0.0000 0.957 0.000 1.000
#> GSM1124875 2 0.0000 0.957 0.000 1.000
#> GSM1124880 2 0.0376 0.954 0.004 0.996
#> GSM1124881 2 0.0000 0.957 0.000 1.000
#> GSM1124885 2 0.0000 0.957 0.000 1.000
#> GSM1124886 1 0.8813 0.825 0.700 0.300
#> GSM1124887 2 0.0000 0.957 0.000 1.000
#> GSM1124894 2 0.1414 0.937 0.020 0.980
#> GSM1124896 1 0.9686 0.695 0.604 0.396
#> GSM1124899 2 0.0000 0.957 0.000 1.000
#> GSM1124901 2 0.0000 0.957 0.000 1.000
#> GSM1124906 2 0.0000 0.957 0.000 1.000
#> GSM1124907 2 0.0000 0.957 0.000 1.000
#> GSM1124911 2 0.0000 0.957 0.000 1.000
#> GSM1124912 1 0.8955 0.826 0.688 0.312
#> GSM1124915 2 0.0000 0.957 0.000 1.000
#> GSM1124917 2 0.0000 0.957 0.000 1.000
#> GSM1124918 2 0.0000 0.957 0.000 1.000
#> GSM1124920 1 0.1414 0.760 0.980 0.020
#> GSM1124922 2 0.0000 0.957 0.000 1.000
#> GSM1124924 2 0.0000 0.957 0.000 1.000
#> GSM1124926 2 0.0000 0.957 0.000 1.000
#> GSM1124928 1 0.8955 0.826 0.688 0.312
#> GSM1124930 2 0.0000 0.957 0.000 1.000
#> GSM1124931 2 0.0000 0.957 0.000 1.000
#> GSM1124935 2 0.0000 0.957 0.000 1.000
#> GSM1124936 1 0.1414 0.760 0.980 0.020
#> GSM1124938 2 0.9427 0.407 0.360 0.640
#> GSM1124940 1 0.8955 0.826 0.688 0.312
#> GSM1124941 2 0.0000 0.957 0.000 1.000
#> GSM1124942 2 0.0000 0.957 0.000 1.000
#> GSM1124943 2 0.2603 0.912 0.044 0.956
#> GSM1124948 2 0.0000 0.957 0.000 1.000
#> GSM1124949 1 0.8955 0.826 0.688 0.312
#> GSM1124950 2 0.0000 0.957 0.000 1.000
#> GSM1124954 1 0.1414 0.760 0.980 0.020
#> GSM1124955 1 0.8955 0.826 0.688 0.312
#> GSM1124956 2 0.0000 0.957 0.000 1.000
#> GSM1124872 2 0.0000 0.957 0.000 1.000
#> GSM1124873 2 0.0000 0.957 0.000 1.000
#> GSM1124876 1 0.1414 0.760 0.980 0.020
#> GSM1124877 1 0.8955 0.826 0.688 0.312
#> GSM1124879 1 0.8955 0.826 0.688 0.312
#> GSM1124883 2 0.0000 0.957 0.000 1.000
#> GSM1124889 2 0.0000 0.957 0.000 1.000
#> GSM1124892 1 0.1414 0.760 0.980 0.020
#> GSM1124893 1 0.8955 0.826 0.688 0.312
#> GSM1124909 2 0.0000 0.957 0.000 1.000
#> GSM1124913 2 0.0000 0.957 0.000 1.000
#> GSM1124916 2 0.0000 0.957 0.000 1.000
#> GSM1124923 2 0.0000 0.957 0.000 1.000
#> GSM1124925 1 0.8955 0.826 0.688 0.312
#> GSM1124929 1 0.8955 0.826 0.688 0.312
#> GSM1124934 1 0.8555 0.822 0.720 0.280
#> GSM1124937 2 0.0000 0.957 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1124891 1 0.6896 0.6507 0.588 0.020 0.392
#> GSM1124888 1 0.6095 0.6669 0.608 0.000 0.392
#> GSM1124890 2 0.3038 0.7740 0.000 0.896 0.104
#> GSM1124904 2 0.0000 0.9311 0.000 1.000 0.000
#> GSM1124927 2 0.0000 0.9311 0.000 1.000 0.000
#> GSM1124953 2 0.6095 0.2562 0.000 0.608 0.392
#> GSM1124869 1 0.0000 0.8032 1.000 0.000 0.000
#> GSM1124870 2 0.0000 0.9311 0.000 1.000 0.000
#> GSM1124882 1 0.0424 0.7978 0.992 0.008 0.000
#> GSM1124884 2 0.0000 0.9311 0.000 1.000 0.000
#> GSM1124898 2 0.0000 0.9311 0.000 1.000 0.000
#> GSM1124903 2 0.0000 0.9311 0.000 1.000 0.000
#> GSM1124905 2 0.6252 -0.1424 0.444 0.556 0.000
#> GSM1124910 1 0.0424 0.7982 0.992 0.008 0.000
#> GSM1124919 2 0.0000 0.9311 0.000 1.000 0.000
#> GSM1124932 2 0.0000 0.9311 0.000 1.000 0.000
#> GSM1124933 1 0.9601 0.3862 0.408 0.200 0.392
#> GSM1124867 2 0.0000 0.9311 0.000 1.000 0.000
#> GSM1124868 3 0.6095 0.9155 0.000 0.392 0.608
#> GSM1124878 2 0.0000 0.9311 0.000 1.000 0.000
#> GSM1124895 3 0.6095 0.9155 0.000 0.392 0.608
#> GSM1124897 2 0.0000 0.9311 0.000 1.000 0.000
#> GSM1124902 3 0.6095 0.9155 0.000 0.392 0.608
#> GSM1124908 3 0.6095 0.9155 0.000 0.392 0.608
#> GSM1124921 3 0.6095 0.9155 0.000 0.392 0.608
#> GSM1124939 3 0.6095 0.9155 0.000 0.392 0.608
#> GSM1124944 3 0.6095 0.9155 0.000 0.392 0.608
#> GSM1124945 2 0.6267 0.1229 0.000 0.548 0.452
#> GSM1124946 3 0.6095 0.9155 0.000 0.392 0.608
#> GSM1124947 3 0.6095 0.9155 0.000 0.392 0.608
#> GSM1124951 2 0.6483 0.2413 0.008 0.600 0.392
#> GSM1124952 3 0.6095 0.9155 0.000 0.392 0.608
#> GSM1124957 3 0.5058 -0.2611 0.244 0.000 0.756
#> GSM1124900 2 0.0000 0.9311 0.000 1.000 0.000
#> GSM1124914 2 0.0000 0.9311 0.000 1.000 0.000
#> GSM1124871 2 0.0000 0.9311 0.000 1.000 0.000
#> GSM1124874 2 0.0000 0.9311 0.000 1.000 0.000
#> GSM1124875 2 0.0000 0.9311 0.000 1.000 0.000
#> GSM1124880 2 0.0000 0.9311 0.000 1.000 0.000
#> GSM1124881 2 0.0000 0.9311 0.000 1.000 0.000
#> GSM1124885 2 0.0000 0.9311 0.000 1.000 0.000
#> GSM1124886 1 0.0000 0.8032 1.000 0.000 0.000
#> GSM1124887 2 0.0000 0.9311 0.000 1.000 0.000
#> GSM1124894 2 0.1031 0.8954 0.024 0.976 0.000
#> GSM1124896 1 0.6215 0.0207 0.572 0.428 0.000
#> GSM1124899 2 0.0000 0.9311 0.000 1.000 0.000
#> GSM1124901 2 0.0000 0.9311 0.000 1.000 0.000
#> GSM1124906 2 0.0000 0.9311 0.000 1.000 0.000
#> GSM1124907 2 0.0000 0.9311 0.000 1.000 0.000
#> GSM1124911 2 0.0000 0.9311 0.000 1.000 0.000
#> GSM1124912 1 0.0000 0.8032 1.000 0.000 0.000
#> GSM1124915 2 0.0000 0.9311 0.000 1.000 0.000
#> GSM1124917 2 0.0000 0.9311 0.000 1.000 0.000
#> GSM1124918 2 0.0000 0.9311 0.000 1.000 0.000
#> GSM1124920 1 0.6095 0.6669 0.608 0.000 0.392
#> GSM1124922 2 0.0000 0.9311 0.000 1.000 0.000
#> GSM1124924 2 0.0000 0.9311 0.000 1.000 0.000
#> GSM1124926 2 0.0000 0.9311 0.000 1.000 0.000
#> GSM1124928 1 0.6095 0.1135 0.608 0.392 0.000
#> GSM1124930 2 0.0000 0.9311 0.000 1.000 0.000
#> GSM1124931 2 0.0000 0.9311 0.000 1.000 0.000
#> GSM1124935 2 0.0000 0.9311 0.000 1.000 0.000
#> GSM1124936 1 0.6095 0.6669 0.608 0.000 0.392
#> GSM1124938 2 0.6314 0.2490 0.004 0.604 0.392
#> GSM1124940 1 0.0000 0.8032 1.000 0.000 0.000
#> GSM1124941 2 0.0000 0.9311 0.000 1.000 0.000
#> GSM1124942 2 0.0000 0.9311 0.000 1.000 0.000
#> GSM1124943 2 0.1860 0.8577 0.000 0.948 0.052
#> GSM1124948 2 0.0000 0.9311 0.000 1.000 0.000
#> GSM1124949 1 0.0000 0.8032 1.000 0.000 0.000
#> GSM1124950 2 0.0000 0.9311 0.000 1.000 0.000
#> GSM1124954 1 0.6095 0.6669 0.608 0.000 0.392
#> GSM1124955 1 0.0000 0.8032 1.000 0.000 0.000
#> GSM1124956 2 0.0000 0.9311 0.000 1.000 0.000
#> GSM1124872 2 0.0000 0.9311 0.000 1.000 0.000
#> GSM1124873 2 0.0000 0.9311 0.000 1.000 0.000
#> GSM1124876 1 0.6095 0.6669 0.608 0.000 0.392
#> GSM1124877 1 0.0000 0.8032 1.000 0.000 0.000
#> GSM1124879 1 0.0000 0.8032 1.000 0.000 0.000
#> GSM1124883 2 0.0000 0.9311 0.000 1.000 0.000
#> GSM1124889 2 0.0000 0.9311 0.000 1.000 0.000
#> GSM1124892 1 0.0000 0.8032 1.000 0.000 0.000
#> GSM1124893 1 0.0000 0.8032 1.000 0.000 0.000
#> GSM1124909 2 0.0000 0.9311 0.000 1.000 0.000
#> GSM1124913 2 0.0000 0.9311 0.000 1.000 0.000
#> GSM1124916 2 0.0000 0.9311 0.000 1.000 0.000
#> GSM1124923 2 0.0000 0.9311 0.000 1.000 0.000
#> GSM1124925 1 0.0000 0.8032 1.000 0.000 0.000
#> GSM1124929 1 0.0000 0.8032 1.000 0.000 0.000
#> GSM1124934 1 0.7741 0.2663 0.608 0.324 0.068
#> GSM1124937 2 0.0000 0.9311 0.000 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1124891 3 0.3852 0.704 0.192 0.008 0.800 0.000
#> GSM1124888 3 0.3610 0.702 0.200 0.000 0.800 0.000
#> GSM1124890 3 0.4898 -0.103 0.000 0.416 0.584 0.000
#> GSM1124904 2 0.3610 0.831 0.000 0.800 0.200 0.000
#> GSM1124927 2 0.0000 0.882 0.000 1.000 0.000 0.000
#> GSM1124953 3 0.0000 0.739 0.000 0.000 1.000 0.000
#> GSM1124869 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> GSM1124870 2 0.0000 0.882 0.000 1.000 0.000 0.000
#> GSM1124882 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> GSM1124884 2 0.0000 0.882 0.000 1.000 0.000 0.000
#> GSM1124898 2 0.3528 0.835 0.000 0.808 0.192 0.000
#> GSM1124903 2 0.3610 0.831 0.000 0.800 0.200 0.000
#> GSM1124905 2 0.4830 0.484 0.392 0.608 0.000 0.000
#> GSM1124910 1 0.0592 0.969 0.984 0.016 0.000 0.000
#> GSM1124919 2 0.3610 0.831 0.000 0.800 0.200 0.000
#> GSM1124932 2 0.0000 0.882 0.000 1.000 0.000 0.000
#> GSM1124933 3 0.0000 0.739 0.000 0.000 1.000 0.000
#> GSM1124867 2 0.0000 0.882 0.000 1.000 0.000 0.000
#> GSM1124868 4 0.0000 0.985 0.000 0.000 0.000 1.000
#> GSM1124878 2 0.3569 0.833 0.000 0.804 0.196 0.000
#> GSM1124895 4 0.0000 0.985 0.000 0.000 0.000 1.000
#> GSM1124897 2 0.3569 0.833 0.000 0.804 0.196 0.000
#> GSM1124902 4 0.0000 0.985 0.000 0.000 0.000 1.000
#> GSM1124908 4 0.1940 0.857 0.000 0.076 0.000 0.924
#> GSM1124921 4 0.0000 0.985 0.000 0.000 0.000 1.000
#> GSM1124939 4 0.0000 0.985 0.000 0.000 0.000 1.000
#> GSM1124944 4 0.0000 0.985 0.000 0.000 0.000 1.000
#> GSM1124945 3 0.0000 0.739 0.000 0.000 1.000 0.000
#> GSM1124946 4 0.0000 0.985 0.000 0.000 0.000 1.000
#> GSM1124947 4 0.0000 0.985 0.000 0.000 0.000 1.000
#> GSM1124951 3 0.0000 0.739 0.000 0.000 1.000 0.000
#> GSM1124952 4 0.0000 0.985 0.000 0.000 0.000 1.000
#> GSM1124957 3 0.2647 0.676 0.000 0.000 0.880 0.120
#> GSM1124900 2 0.0469 0.880 0.012 0.988 0.000 0.000
#> GSM1124914 2 0.3400 0.839 0.000 0.820 0.180 0.000
#> GSM1124871 2 0.0000 0.882 0.000 1.000 0.000 0.000
#> GSM1124874 2 0.0000 0.882 0.000 1.000 0.000 0.000
#> GSM1124875 2 0.3610 0.831 0.000 0.800 0.200 0.000
#> GSM1124880 2 0.2469 0.837 0.108 0.892 0.000 0.000
#> GSM1124881 2 0.0000 0.882 0.000 1.000 0.000 0.000
#> GSM1124885 2 0.3444 0.838 0.000 0.816 0.184 0.000
#> GSM1124886 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> GSM1124887 2 0.3610 0.831 0.000 0.800 0.200 0.000
#> GSM1124894 2 0.2921 0.813 0.140 0.860 0.000 0.000
#> GSM1124896 2 0.4877 0.449 0.408 0.592 0.000 0.000
#> GSM1124899 2 0.0000 0.882 0.000 1.000 0.000 0.000
#> GSM1124901 2 0.3528 0.835 0.000 0.808 0.192 0.000
#> GSM1124906 2 0.0000 0.882 0.000 1.000 0.000 0.000
#> GSM1124907 2 0.3610 0.831 0.000 0.800 0.200 0.000
#> GSM1124911 2 0.0000 0.882 0.000 1.000 0.000 0.000
#> GSM1124912 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> GSM1124915 2 0.0000 0.882 0.000 1.000 0.000 0.000
#> GSM1124917 2 0.1557 0.875 0.000 0.944 0.056 0.000
#> GSM1124918 2 0.1867 0.870 0.000 0.928 0.072 0.000
#> GSM1124920 3 0.4866 0.381 0.404 0.000 0.596 0.000
#> GSM1124922 2 0.0000 0.882 0.000 1.000 0.000 0.000
#> GSM1124924 2 0.1792 0.859 0.068 0.932 0.000 0.000
#> GSM1124926 2 0.0000 0.882 0.000 1.000 0.000 0.000
#> GSM1124928 2 0.4866 0.458 0.404 0.596 0.000 0.000
#> GSM1124930 2 0.3610 0.831 0.000 0.800 0.200 0.000
#> GSM1124931 2 0.0000 0.882 0.000 1.000 0.000 0.000
#> GSM1124935 2 0.0000 0.882 0.000 1.000 0.000 0.000
#> GSM1124936 3 0.3610 0.702 0.200 0.000 0.800 0.000
#> GSM1124938 3 0.0592 0.737 0.000 0.016 0.984 0.000
#> GSM1124940 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> GSM1124941 2 0.0000 0.882 0.000 1.000 0.000 0.000
#> GSM1124942 2 0.3873 0.810 0.000 0.772 0.228 0.000
#> GSM1124943 2 0.4103 0.783 0.000 0.744 0.256 0.000
#> GSM1124948 2 0.2868 0.854 0.000 0.864 0.136 0.000
#> GSM1124949 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> GSM1124950 2 0.0000 0.882 0.000 1.000 0.000 0.000
#> GSM1124954 3 0.6528 0.475 0.300 0.104 0.596 0.000
#> GSM1124955 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> GSM1124956 2 0.0000 0.882 0.000 1.000 0.000 0.000
#> GSM1124872 2 0.0000 0.882 0.000 1.000 0.000 0.000
#> GSM1124873 2 0.0000 0.882 0.000 1.000 0.000 0.000
#> GSM1124876 3 0.3610 0.702 0.200 0.000 0.800 0.000
#> GSM1124877 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> GSM1124879 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> GSM1124883 2 0.3569 0.833 0.000 0.804 0.196 0.000
#> GSM1124889 2 0.0000 0.882 0.000 1.000 0.000 0.000
#> GSM1124892 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> GSM1124893 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> GSM1124909 2 0.0469 0.881 0.000 0.988 0.012 0.000
#> GSM1124913 2 0.3610 0.831 0.000 0.800 0.200 0.000
#> GSM1124916 2 0.0000 0.882 0.000 1.000 0.000 0.000
#> GSM1124923 2 0.4500 0.711 0.000 0.684 0.316 0.000
#> GSM1124925 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> GSM1124929 1 0.0000 0.998 1.000 0.000 0.000 0.000
#> GSM1124934 2 0.6660 0.354 0.288 0.592 0.120 0.000
#> GSM1124937 2 0.0707 0.877 0.020 0.980 0.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM1124891 3 0.2769 0.8570 0.092 0.032 0.876 0.000 0.000
#> GSM1124888 3 0.2280 0.8555 0.120 0.000 0.880 0.000 0.000
#> GSM1124890 2 0.6137 0.3773 0.000 0.476 0.392 0.000 0.132
#> GSM1124904 2 0.4801 0.7689 0.000 0.728 0.124 0.000 0.148
#> GSM1124927 2 0.1764 0.7867 0.000 0.928 0.008 0.000 0.064
#> GSM1124953 3 0.0162 0.8625 0.000 0.004 0.996 0.000 0.000
#> GSM1124869 1 0.0000 0.9490 1.000 0.000 0.000 0.000 0.000
#> GSM1124870 2 0.1908 0.7832 0.000 0.908 0.000 0.000 0.092
#> GSM1124882 1 0.0703 0.9269 0.976 0.024 0.000 0.000 0.000
#> GSM1124884 2 0.2929 0.7508 0.000 0.820 0.000 0.000 0.180
#> GSM1124898 2 0.4953 0.7626 0.000 0.712 0.124 0.000 0.164
#> GSM1124903 2 0.4840 0.7676 0.000 0.724 0.124 0.000 0.152
#> GSM1124905 2 0.4151 0.5892 0.344 0.652 0.004 0.000 0.000
#> GSM1124910 1 0.1830 0.8874 0.932 0.040 0.028 0.000 0.000
#> GSM1124919 2 0.4801 0.7689 0.000 0.728 0.124 0.000 0.148
#> GSM1124932 5 0.2605 0.9422 0.000 0.148 0.000 0.000 0.852
#> GSM1124933 3 0.0609 0.8654 0.000 0.020 0.980 0.000 0.000
#> GSM1124867 2 0.1792 0.7841 0.000 0.916 0.000 0.000 0.084
#> GSM1124868 4 0.0000 0.9827 0.000 0.000 0.000 1.000 0.000
#> GSM1124878 2 0.4617 0.7739 0.000 0.744 0.108 0.000 0.148
#> GSM1124895 4 0.0000 0.9827 0.000 0.000 0.000 1.000 0.000
#> GSM1124897 2 0.4665 0.7729 0.000 0.740 0.112 0.000 0.148
#> GSM1124902 4 0.0000 0.9827 0.000 0.000 0.000 1.000 0.000
#> GSM1124908 4 0.2450 0.8367 0.000 0.076 0.000 0.896 0.028
#> GSM1124921 4 0.0000 0.9827 0.000 0.000 0.000 1.000 0.000
#> GSM1124939 4 0.0000 0.9827 0.000 0.000 0.000 1.000 0.000
#> GSM1124944 4 0.0000 0.9827 0.000 0.000 0.000 1.000 0.000
#> GSM1124945 3 0.0000 0.8606 0.000 0.000 1.000 0.000 0.000
#> GSM1124946 4 0.0000 0.9827 0.000 0.000 0.000 1.000 0.000
#> GSM1124947 4 0.0000 0.9827 0.000 0.000 0.000 1.000 0.000
#> GSM1124951 3 0.1768 0.7937 0.000 0.072 0.924 0.000 0.004
#> GSM1124952 4 0.0000 0.9827 0.000 0.000 0.000 1.000 0.000
#> GSM1124957 3 0.1608 0.8444 0.000 0.000 0.928 0.072 0.000
#> GSM1124900 2 0.1857 0.7877 0.004 0.928 0.008 0.000 0.060
#> GSM1124914 2 0.4138 0.7808 0.000 0.780 0.072 0.000 0.148
#> GSM1124871 2 0.2074 0.7804 0.000 0.896 0.000 0.000 0.104
#> GSM1124874 2 0.1671 0.7839 0.000 0.924 0.000 0.000 0.076
#> GSM1124875 2 0.2612 0.7969 0.000 0.868 0.124 0.000 0.008
#> GSM1124880 2 0.1671 0.7838 0.000 0.924 0.076 0.000 0.000
#> GSM1124881 2 0.2230 0.7772 0.000 0.884 0.000 0.000 0.116
#> GSM1124885 2 0.4138 0.7804 0.000 0.780 0.072 0.000 0.148
#> GSM1124886 1 0.0000 0.9490 1.000 0.000 0.000 0.000 0.000
#> GSM1124887 2 0.4801 0.7689 0.000 0.728 0.124 0.000 0.148
#> GSM1124894 2 0.2208 0.7818 0.020 0.908 0.072 0.000 0.000
#> GSM1124896 2 0.4161 0.5171 0.392 0.608 0.000 0.000 0.000
#> GSM1124899 2 0.2424 0.7745 0.000 0.868 0.000 0.000 0.132
#> GSM1124901 2 0.3719 0.7929 0.000 0.816 0.116 0.000 0.068
#> GSM1124906 2 0.2891 0.7527 0.000 0.824 0.000 0.000 0.176
#> GSM1124907 2 0.4801 0.7689 0.000 0.728 0.124 0.000 0.148
#> GSM1124911 5 0.2605 0.9422 0.000 0.148 0.000 0.000 0.852
#> GSM1124912 1 0.0000 0.9490 1.000 0.000 0.000 0.000 0.000
#> GSM1124915 5 0.2605 0.9422 0.000 0.148 0.000 0.000 0.852
#> GSM1124917 2 0.3130 0.7921 0.000 0.856 0.048 0.000 0.096
#> GSM1124918 5 0.2605 0.9368 0.000 0.148 0.000 0.000 0.852
#> GSM1124920 3 0.3949 0.5454 0.332 0.000 0.668 0.000 0.000
#> GSM1124922 2 0.1908 0.7892 0.000 0.908 0.000 0.000 0.092
#> GSM1124924 2 0.1671 0.7838 0.000 0.924 0.076 0.000 0.000
#> GSM1124926 2 0.2424 0.7730 0.000 0.868 0.000 0.000 0.132
#> GSM1124928 2 0.4624 0.5793 0.340 0.636 0.024 0.000 0.000
#> GSM1124930 2 0.5093 0.7532 0.000 0.696 0.124 0.000 0.180
#> GSM1124931 5 0.2605 0.9422 0.000 0.148 0.000 0.000 0.852
#> GSM1124935 5 0.2605 0.9422 0.000 0.148 0.000 0.000 0.852
#> GSM1124936 3 0.2329 0.8515 0.124 0.000 0.876 0.000 0.000
#> GSM1124938 3 0.1484 0.8560 0.000 0.048 0.944 0.000 0.008
#> GSM1124940 1 0.0000 0.9490 1.000 0.000 0.000 0.000 0.000
#> GSM1124941 2 0.2891 0.7527 0.000 0.824 0.000 0.000 0.176
#> GSM1124942 2 0.4971 0.7695 0.000 0.712 0.144 0.000 0.144
#> GSM1124943 2 0.4879 0.7583 0.000 0.716 0.176 0.000 0.108
#> GSM1124948 2 0.2127 0.8023 0.000 0.892 0.108 0.000 0.000
#> GSM1124949 1 0.0000 0.9490 1.000 0.000 0.000 0.000 0.000
#> GSM1124950 2 0.1907 0.7883 0.000 0.928 0.028 0.000 0.044
#> GSM1124954 5 0.2690 0.7456 0.156 0.000 0.000 0.000 0.844
#> GSM1124955 1 0.0000 0.9490 1.000 0.000 0.000 0.000 0.000
#> GSM1124956 5 0.2605 0.9422 0.000 0.148 0.000 0.000 0.852
#> GSM1124872 2 0.1608 0.7843 0.000 0.928 0.072 0.000 0.000
#> GSM1124873 2 0.2605 0.7658 0.000 0.852 0.000 0.000 0.148
#> GSM1124876 3 0.2280 0.8555 0.120 0.000 0.880 0.000 0.000
#> GSM1124877 1 0.4307 0.0394 0.500 0.000 0.000 0.000 0.500
#> GSM1124879 1 0.0000 0.9490 1.000 0.000 0.000 0.000 0.000
#> GSM1124883 2 0.4757 0.7707 0.000 0.732 0.120 0.000 0.148
#> GSM1124889 2 0.1732 0.7846 0.000 0.920 0.000 0.000 0.080
#> GSM1124892 1 0.0000 0.9490 1.000 0.000 0.000 0.000 0.000
#> GSM1124893 1 0.0000 0.9490 1.000 0.000 0.000 0.000 0.000
#> GSM1124909 2 0.1410 0.7907 0.000 0.940 0.000 0.000 0.060
#> GSM1124913 2 0.4801 0.7689 0.000 0.728 0.124 0.000 0.148
#> GSM1124916 5 0.2773 0.9285 0.000 0.164 0.000 0.000 0.836
#> GSM1124923 2 0.5125 0.7507 0.000 0.696 0.156 0.000 0.148
#> GSM1124925 1 0.0000 0.9490 1.000 0.000 0.000 0.000 0.000
#> GSM1124929 1 0.0000 0.9490 1.000 0.000 0.000 0.000 0.000
#> GSM1124934 5 0.2605 0.7558 0.148 0.000 0.000 0.000 0.852
#> GSM1124937 2 0.1918 0.7879 0.036 0.928 0.000 0.000 0.036
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1124891 3 0.0000 0.8392 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1124888 3 0.3841 0.6001 0.000 0.000 0.616 0.000 0.380 0.004
#> GSM1124890 5 0.5434 0.5767 0.000 0.272 0.164 0.000 0.564 0.000
#> GSM1124904 5 0.3717 0.6442 0.000 0.384 0.000 0.000 0.616 0.000
#> GSM1124927 2 0.0000 0.8283 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1124953 3 0.2135 0.7916 0.000 0.000 0.872 0.000 0.128 0.000
#> GSM1124869 1 0.0000 0.9093 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124870 2 0.0713 0.8309 0.000 0.972 0.000 0.000 0.000 0.028
#> GSM1124882 1 0.2237 0.8233 0.896 0.068 0.036 0.000 0.000 0.000
#> GSM1124884 2 0.2454 0.7654 0.000 0.840 0.000 0.000 0.000 0.160
#> GSM1124898 5 0.3737 0.6382 0.000 0.392 0.000 0.000 0.608 0.000
#> GSM1124903 5 0.3717 0.6442 0.000 0.384 0.000 0.000 0.616 0.000
#> GSM1124905 2 0.2527 0.6708 0.168 0.832 0.000 0.000 0.000 0.000
#> GSM1124910 1 0.6178 0.2727 0.472 0.080 0.068 0.000 0.380 0.000
#> GSM1124919 5 0.3852 0.6444 0.000 0.384 0.004 0.000 0.612 0.000
#> GSM1124932 6 0.0260 0.9886 0.000 0.008 0.000 0.000 0.000 0.992
#> GSM1124933 3 0.0000 0.8392 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1124867 2 0.0713 0.8307 0.000 0.972 0.000 0.000 0.000 0.028
#> GSM1124868 4 0.0000 0.9605 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1124878 5 0.3765 0.6292 0.000 0.404 0.000 0.000 0.596 0.000
#> GSM1124895 4 0.0000 0.9605 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1124897 5 0.3789 0.6158 0.000 0.416 0.000 0.000 0.584 0.000
#> GSM1124902 4 0.0000 0.9605 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1124908 4 0.4114 0.6385 0.000 0.072 0.000 0.732 0.196 0.000
#> GSM1124921 4 0.0632 0.9404 0.000 0.000 0.000 0.976 0.024 0.000
#> GSM1124939 4 0.0000 0.9605 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1124944 4 0.0000 0.9605 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1124945 3 0.2135 0.7916 0.000 0.000 0.872 0.000 0.128 0.000
#> GSM1124946 4 0.0000 0.9605 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1124947 4 0.0000 0.9605 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1124951 3 0.2697 0.7342 0.000 0.000 0.812 0.000 0.188 0.000
#> GSM1124952 4 0.0000 0.9605 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1124957 3 0.0000 0.8392 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1124900 2 0.0000 0.8283 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1124914 2 0.3838 -0.3304 0.000 0.552 0.000 0.000 0.448 0.000
#> GSM1124871 2 0.1444 0.8186 0.000 0.928 0.000 0.000 0.000 0.072
#> GSM1124874 2 0.0146 0.8292 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM1124875 2 0.2814 0.6493 0.000 0.820 0.000 0.000 0.172 0.008
#> GSM1124880 2 0.1267 0.8054 0.000 0.940 0.060 0.000 0.000 0.000
#> GSM1124881 2 0.1444 0.8186 0.000 0.928 0.000 0.000 0.000 0.072
#> GSM1124885 5 0.3838 0.5629 0.000 0.448 0.000 0.000 0.552 0.000
#> GSM1124886 1 0.0000 0.9093 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124887 5 0.3717 0.6442 0.000 0.384 0.000 0.000 0.616 0.000
#> GSM1124894 2 0.0820 0.8197 0.016 0.972 0.012 0.000 0.000 0.000
#> GSM1124896 2 0.5028 0.4355 0.248 0.636 0.112 0.000 0.004 0.000
#> GSM1124899 2 0.1814 0.8096 0.000 0.900 0.000 0.000 0.000 0.100
#> GSM1124901 2 0.3837 0.5686 0.000 0.752 0.000 0.000 0.196 0.052
#> GSM1124906 2 0.2416 0.7687 0.000 0.844 0.000 0.000 0.000 0.156
#> GSM1124907 5 0.0260 0.3231 0.000 0.000 0.000 0.000 0.992 0.008
#> GSM1124911 6 0.0260 0.9886 0.000 0.008 0.000 0.000 0.000 0.992
#> GSM1124912 1 0.0000 0.9093 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124915 6 0.0260 0.9886 0.000 0.008 0.000 0.000 0.000 0.992
#> GSM1124917 2 0.2799 0.7778 0.000 0.860 0.000 0.000 0.064 0.076
#> GSM1124918 6 0.0363 0.9855 0.000 0.012 0.000 0.000 0.000 0.988
#> GSM1124920 3 0.5613 0.4445 0.148 0.000 0.468 0.000 0.384 0.000
#> GSM1124922 2 0.0891 0.8305 0.000 0.968 0.000 0.000 0.008 0.024
#> GSM1124924 2 0.4519 0.1823 0.000 0.584 0.024 0.000 0.384 0.008
#> GSM1124926 2 0.1910 0.8057 0.000 0.892 0.000 0.000 0.000 0.108
#> GSM1124928 2 0.2631 0.6535 0.180 0.820 0.000 0.000 0.000 0.000
#> GSM1124930 5 0.0806 0.3296 0.000 0.020 0.000 0.000 0.972 0.008
#> GSM1124931 6 0.0260 0.9886 0.000 0.008 0.000 0.000 0.000 0.992
#> GSM1124935 6 0.0260 0.9886 0.000 0.008 0.000 0.000 0.000 0.992
#> GSM1124936 3 0.0000 0.8392 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1124938 5 0.4982 -0.5255 0.000 0.048 0.456 0.000 0.488 0.008
#> GSM1124940 1 0.0000 0.9093 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124941 2 0.2416 0.7687 0.000 0.844 0.000 0.000 0.000 0.156
#> GSM1124942 5 0.3001 0.3175 0.000 0.128 0.024 0.000 0.840 0.008
#> GSM1124943 5 0.3533 0.2930 0.000 0.196 0.020 0.000 0.776 0.008
#> GSM1124948 5 0.4488 -0.0916 0.000 0.468 0.016 0.000 0.508 0.008
#> GSM1124949 1 0.0000 0.9093 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124950 2 0.0146 0.8297 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM1124954 6 0.0806 0.9734 0.000 0.008 0.020 0.000 0.000 0.972
#> GSM1124955 1 0.0000 0.9093 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124956 6 0.0260 0.9886 0.000 0.008 0.000 0.000 0.000 0.992
#> GSM1124872 2 0.0260 0.8251 0.000 0.992 0.008 0.000 0.000 0.000
#> GSM1124873 2 0.2300 0.7783 0.000 0.856 0.000 0.000 0.000 0.144
#> GSM1124876 3 0.0000 0.8392 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1124877 1 0.3869 0.0147 0.500 0.000 0.000 0.000 0.000 0.500
#> GSM1124879 1 0.0000 0.9093 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124883 5 0.3774 0.6170 0.000 0.408 0.000 0.000 0.592 0.000
#> GSM1124889 2 0.0458 0.8315 0.000 0.984 0.000 0.000 0.000 0.016
#> GSM1124892 1 0.0000 0.9093 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124893 1 0.0000 0.9093 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124909 2 0.0000 0.8283 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1124913 5 0.3717 0.6442 0.000 0.384 0.000 0.000 0.616 0.000
#> GSM1124916 6 0.0713 0.9700 0.000 0.028 0.000 0.000 0.000 0.972
#> GSM1124923 5 0.3945 0.6445 0.000 0.380 0.008 0.000 0.612 0.000
#> GSM1124925 1 0.0000 0.9093 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124929 1 0.0000 0.9093 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124934 6 0.1307 0.9546 0.032 0.008 0.008 0.000 0.000 0.952
#> GSM1124937 2 0.0000 0.8283 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 tissue(p) k
#> CV:pam 88 1.01e-01 2
#> CV:pam 81 1.24e-09 3
#> CV:pam 84 1.06e-08 4
#> CV:pam 89 2.60e-07 5
#> CV:pam 79 3.31e-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 48983 rows and 91 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 0.507 0.817 0.891 0.3641 0.693 0.693
#> 3 3 0.396 0.789 0.857 0.5987 0.636 0.486
#> 4 4 0.555 0.741 0.817 0.1124 0.904 0.764
#> 5 5 0.727 0.812 0.866 0.1371 0.901 0.724
#> 6 6 0.780 0.644 0.820 0.0449 0.979 0.920
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
#> GSM1124891 1 0.8144 0.777 0.748 0.252
#> GSM1124888 1 1.0000 0.274 0.504 0.496
#> GSM1124890 1 0.7453 0.808 0.788 0.212
#> GSM1124904 1 0.0376 0.856 0.996 0.004
#> GSM1124927 1 0.7950 0.801 0.760 0.240
#> GSM1124953 1 0.7219 0.814 0.800 0.200
#> GSM1124869 1 0.7950 0.801 0.760 0.240
#> GSM1124870 1 0.7950 0.801 0.760 0.240
#> GSM1124882 1 0.7950 0.801 0.760 0.240
#> GSM1124884 1 0.0376 0.857 0.996 0.004
#> GSM1124898 1 0.0000 0.857 1.000 0.000
#> GSM1124903 1 0.1843 0.851 0.972 0.028
#> GSM1124905 1 0.9996 0.346 0.512 0.488
#> GSM1124910 1 0.7528 0.806 0.784 0.216
#> GSM1124919 1 0.0000 0.857 1.000 0.000
#> GSM1124932 1 0.0000 0.857 1.000 0.000
#> GSM1124933 1 1.0000 0.274 0.504 0.496
#> GSM1124867 2 0.8016 0.565 0.244 0.756
#> GSM1124868 2 0.0000 0.929 0.000 1.000
#> GSM1124878 2 0.7602 0.711 0.220 0.780
#> GSM1124895 2 0.0000 0.929 0.000 1.000
#> GSM1124897 2 0.7528 0.714 0.216 0.784
#> GSM1124902 2 0.0000 0.929 0.000 1.000
#> GSM1124908 2 0.0000 0.929 0.000 1.000
#> GSM1124921 2 0.0000 0.929 0.000 1.000
#> GSM1124939 2 0.0000 0.929 0.000 1.000
#> GSM1124944 2 0.0000 0.929 0.000 1.000
#> GSM1124945 2 0.1633 0.918 0.024 0.976
#> GSM1124946 2 0.0000 0.929 0.000 1.000
#> GSM1124947 2 0.0000 0.929 0.000 1.000
#> GSM1124951 2 0.1633 0.918 0.024 0.976
#> GSM1124952 2 0.0000 0.929 0.000 1.000
#> GSM1124957 2 0.1633 0.918 0.024 0.976
#> GSM1124900 1 0.7950 0.801 0.760 0.240
#> GSM1124914 1 0.1184 0.855 0.984 0.016
#> GSM1124871 1 0.0376 0.857 0.996 0.004
#> GSM1124874 1 0.1414 0.854 0.980 0.020
#> GSM1124875 1 0.0000 0.857 1.000 0.000
#> GSM1124880 1 0.7528 0.806 0.784 0.216
#> GSM1124881 1 0.0938 0.856 0.988 0.012
#> GSM1124885 1 0.1184 0.855 0.984 0.016
#> GSM1124886 1 0.7950 0.801 0.760 0.240
#> GSM1124887 1 0.0000 0.857 1.000 0.000
#> GSM1124894 2 0.5737 0.776 0.136 0.864
#> GSM1124896 1 0.7950 0.801 0.760 0.240
#> GSM1124899 1 0.0938 0.856 0.988 0.012
#> GSM1124901 1 0.0000 0.857 1.000 0.000
#> GSM1124906 1 0.0938 0.856 0.988 0.012
#> GSM1124907 1 0.0000 0.857 1.000 0.000
#> GSM1124911 1 0.0000 0.857 1.000 0.000
#> GSM1124912 1 0.7950 0.801 0.760 0.240
#> GSM1124915 1 0.0000 0.857 1.000 0.000
#> GSM1124917 1 0.0000 0.857 1.000 0.000
#> GSM1124918 1 0.0000 0.857 1.000 0.000
#> GSM1124920 1 0.7528 0.806 0.784 0.216
#> GSM1124922 1 0.1633 0.852 0.976 0.024
#> GSM1124924 1 0.7376 0.810 0.792 0.208
#> GSM1124926 1 0.1633 0.852 0.976 0.024
#> GSM1124928 1 0.7883 0.802 0.764 0.236
#> GSM1124930 1 0.0000 0.857 1.000 0.000
#> GSM1124931 1 0.0000 0.857 1.000 0.000
#> GSM1124935 1 0.0000 0.857 1.000 0.000
#> GSM1124936 1 0.7528 0.806 0.784 0.216
#> GSM1124938 1 0.5519 0.834 0.872 0.128
#> GSM1124940 1 0.7950 0.801 0.760 0.240
#> GSM1124941 1 0.0000 0.857 1.000 0.000
#> GSM1124942 1 0.0000 0.857 1.000 0.000
#> GSM1124943 1 0.0000 0.857 1.000 0.000
#> GSM1124948 1 0.0000 0.857 1.000 0.000
#> GSM1124949 1 0.7950 0.801 0.760 0.240
#> GSM1124950 1 0.0000 0.857 1.000 0.000
#> GSM1124954 1 0.7528 0.806 0.784 0.216
#> GSM1124955 1 0.7950 0.801 0.760 0.240
#> GSM1124956 1 0.0000 0.857 1.000 0.000
#> GSM1124872 1 0.0376 0.857 0.996 0.004
#> GSM1124873 1 0.0672 0.857 0.992 0.008
#> GSM1124876 1 1.0000 0.274 0.504 0.496
#> GSM1124877 1 0.7950 0.801 0.760 0.240
#> GSM1124879 1 0.7950 0.801 0.760 0.240
#> GSM1124883 1 0.1184 0.855 0.984 0.016
#> GSM1124889 1 0.0000 0.857 1.000 0.000
#> GSM1124892 1 0.7950 0.801 0.760 0.240
#> GSM1124893 1 0.7950 0.801 0.760 0.240
#> GSM1124909 1 0.0000 0.857 1.000 0.000
#> GSM1124913 1 0.0672 0.857 0.992 0.008
#> GSM1124916 1 0.0000 0.857 1.000 0.000
#> GSM1124923 1 0.0000 0.857 1.000 0.000
#> GSM1124925 1 0.7950 0.801 0.760 0.240
#> GSM1124929 1 0.7950 0.801 0.760 0.240
#> GSM1124934 1 0.7528 0.806 0.784 0.216
#> GSM1124937 1 0.7950 0.801 0.760 0.240
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1124891 3 0.9623 -0.1266 0.336 0.216 0.448
#> GSM1124888 3 0.9243 -0.0790 0.368 0.160 0.472
#> GSM1124890 2 0.4473 0.8024 0.008 0.828 0.164
#> GSM1124904 2 0.3816 0.8199 0.000 0.852 0.148
#> GSM1124927 2 0.6577 -0.1476 0.420 0.572 0.008
#> GSM1124953 2 0.4233 0.8084 0.004 0.836 0.160
#> GSM1124869 1 0.4002 0.8878 0.840 0.160 0.000
#> GSM1124870 1 0.5461 0.8353 0.748 0.244 0.008
#> GSM1124882 1 0.4473 0.8890 0.828 0.164 0.008
#> GSM1124884 2 0.0892 0.9063 0.020 0.980 0.000
#> GSM1124898 2 0.0747 0.9074 0.016 0.984 0.000
#> GSM1124903 2 0.4473 0.8059 0.008 0.828 0.164
#> GSM1124905 1 0.6920 0.6600 0.732 0.104 0.164
#> GSM1124910 1 0.7724 0.7845 0.680 0.164 0.156
#> GSM1124919 2 0.4233 0.8084 0.004 0.836 0.160
#> GSM1124932 2 0.0892 0.9063 0.020 0.980 0.000
#> GSM1124933 3 0.9501 -0.0433 0.324 0.204 0.472
#> GSM1124867 3 0.6920 0.6641 0.164 0.104 0.732
#> GSM1124868 3 0.2636 0.7940 0.020 0.048 0.932
#> GSM1124878 3 0.6226 0.5778 0.028 0.252 0.720
#> GSM1124895 3 0.2066 0.8111 0.060 0.000 0.940
#> GSM1124897 3 0.3850 0.7641 0.028 0.088 0.884
#> GSM1124902 3 0.1860 0.8121 0.052 0.000 0.948
#> GSM1124908 3 0.0424 0.8119 0.008 0.000 0.992
#> GSM1124921 3 0.0000 0.8128 0.000 0.000 1.000
#> GSM1124939 3 0.2066 0.8111 0.060 0.000 0.940
#> GSM1124944 3 0.0747 0.8137 0.016 0.000 0.984
#> GSM1124945 3 0.3551 0.7953 0.132 0.000 0.868
#> GSM1124946 3 0.0000 0.8128 0.000 0.000 1.000
#> GSM1124947 3 0.1860 0.8127 0.052 0.000 0.948
#> GSM1124951 3 0.3412 0.7961 0.124 0.000 0.876
#> GSM1124952 3 0.0592 0.8125 0.012 0.000 0.988
#> GSM1124957 3 0.3551 0.7953 0.132 0.000 0.868
#> GSM1124900 1 0.4645 0.8858 0.816 0.176 0.008
#> GSM1124914 2 0.3459 0.8078 0.012 0.892 0.096
#> GSM1124871 2 0.0747 0.9074 0.016 0.984 0.000
#> GSM1124874 2 0.0892 0.9063 0.020 0.980 0.000
#> GSM1124875 2 0.0000 0.9012 0.000 1.000 0.000
#> GSM1124880 1 0.7880 0.7759 0.668 0.168 0.164
#> GSM1124881 2 0.0892 0.9063 0.020 0.980 0.000
#> GSM1124885 2 0.0661 0.8951 0.004 0.988 0.008
#> GSM1124886 1 0.4002 0.8878 0.840 0.160 0.000
#> GSM1124887 2 0.4110 0.8148 0.004 0.844 0.152
#> GSM1124894 3 0.1860 0.8048 0.052 0.000 0.948
#> GSM1124896 1 0.4531 0.8880 0.824 0.168 0.008
#> GSM1124899 2 0.0747 0.9074 0.016 0.984 0.000
#> GSM1124901 2 0.0747 0.9074 0.016 0.984 0.000
#> GSM1124906 2 0.0747 0.9074 0.016 0.984 0.000
#> GSM1124907 2 0.4413 0.8066 0.008 0.832 0.160
#> GSM1124911 2 0.0747 0.9074 0.016 0.984 0.000
#> GSM1124912 1 0.4002 0.8878 0.840 0.160 0.000
#> GSM1124915 2 0.0747 0.9074 0.016 0.984 0.000
#> GSM1124917 2 0.0747 0.9074 0.016 0.984 0.000
#> GSM1124918 2 0.1170 0.9038 0.016 0.976 0.008
#> GSM1124920 1 0.8199 0.7337 0.640 0.160 0.200
#> GSM1124922 2 0.0892 0.9063 0.020 0.980 0.000
#> GSM1124924 1 0.9342 0.4575 0.452 0.380 0.168
#> GSM1124926 2 0.1315 0.9022 0.020 0.972 0.008
#> GSM1124928 1 0.4473 0.8890 0.828 0.164 0.008
#> GSM1124930 2 0.4413 0.8066 0.008 0.832 0.160
#> GSM1124931 2 0.1753 0.8837 0.048 0.952 0.000
#> GSM1124935 2 0.0747 0.9074 0.016 0.984 0.000
#> GSM1124936 1 0.7878 0.7655 0.668 0.160 0.172
#> GSM1124938 2 0.4413 0.8066 0.008 0.832 0.160
#> GSM1124940 1 0.4062 0.8890 0.836 0.164 0.000
#> GSM1124941 2 0.1315 0.9013 0.020 0.972 0.008
#> GSM1124942 2 0.4413 0.8066 0.008 0.832 0.160
#> GSM1124943 2 0.4413 0.8066 0.008 0.832 0.160
#> GSM1124948 2 0.4723 0.8120 0.016 0.824 0.160
#> GSM1124949 1 0.4062 0.8890 0.836 0.164 0.000
#> GSM1124950 2 0.0892 0.9063 0.020 0.980 0.000
#> GSM1124954 1 0.8670 0.7114 0.592 0.240 0.168
#> GSM1124955 1 0.4062 0.8890 0.836 0.164 0.000
#> GSM1124956 2 0.0747 0.9074 0.016 0.984 0.000
#> GSM1124872 2 0.0892 0.9063 0.020 0.980 0.000
#> GSM1124873 2 0.0892 0.9063 0.020 0.980 0.000
#> GSM1124876 3 0.9243 -0.0790 0.368 0.160 0.472
#> GSM1124877 1 0.4473 0.8890 0.828 0.164 0.008
#> GSM1124879 1 0.4293 0.8893 0.832 0.164 0.004
#> GSM1124883 2 0.0237 0.8997 0.004 0.996 0.000
#> GSM1124889 2 0.0747 0.9074 0.016 0.984 0.000
#> GSM1124892 1 0.6848 0.8329 0.736 0.164 0.100
#> GSM1124893 1 0.4002 0.8878 0.840 0.160 0.000
#> GSM1124909 2 0.0747 0.9074 0.016 0.984 0.000
#> GSM1124913 2 0.0829 0.8985 0.004 0.984 0.012
#> GSM1124916 2 0.0747 0.9074 0.016 0.984 0.000
#> GSM1124923 2 0.4413 0.8066 0.008 0.832 0.160
#> GSM1124925 1 0.4062 0.8890 0.836 0.164 0.000
#> GSM1124929 1 0.4062 0.8890 0.836 0.164 0.000
#> GSM1124934 1 0.8495 0.7302 0.612 0.220 0.168
#> GSM1124937 1 0.5502 0.8326 0.744 0.248 0.008
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1124891 3 0.8622 0.7491 0.236 0.148 0.516 0.100
#> GSM1124888 3 0.8396 0.7611 0.212 0.140 0.548 0.100
#> GSM1124890 2 0.3925 0.7051 0.016 0.808 0.176 0.000
#> GSM1124904 2 0.3037 0.7876 0.036 0.888 0.076 0.000
#> GSM1124927 2 0.5178 0.4550 0.392 0.600 0.004 0.004
#> GSM1124953 2 0.3925 0.7051 0.016 0.808 0.176 0.000
#> GSM1124869 1 0.0592 0.8715 0.984 0.016 0.000 0.000
#> GSM1124870 1 0.2777 0.7818 0.888 0.104 0.004 0.004
#> GSM1124882 1 0.0592 0.8715 0.984 0.016 0.000 0.000
#> GSM1124884 2 0.3280 0.8316 0.124 0.860 0.016 0.000
#> GSM1124898 2 0.3542 0.8301 0.120 0.852 0.028 0.000
#> GSM1124903 2 0.3342 0.7948 0.032 0.880 0.080 0.008
#> GSM1124905 1 0.5274 0.4375 0.724 0.036 0.008 0.232
#> GSM1124910 1 0.1798 0.8309 0.944 0.016 0.040 0.000
#> GSM1124919 2 0.3925 0.7051 0.016 0.808 0.176 0.000
#> GSM1124932 2 0.3842 0.8286 0.128 0.836 0.036 0.000
#> GSM1124933 3 0.8408 0.7593 0.208 0.144 0.548 0.100
#> GSM1124867 2 0.8129 0.0580 0.024 0.424 0.176 0.376
#> GSM1124868 4 0.4439 0.7484 0.004 0.048 0.140 0.808
#> GSM1124878 2 0.7220 0.0777 0.000 0.440 0.140 0.420
#> GSM1124895 4 0.2760 0.8342 0.000 0.000 0.128 0.872
#> GSM1124897 2 0.7252 0.0642 0.000 0.436 0.144 0.420
#> GSM1124902 4 0.2760 0.8342 0.000 0.000 0.128 0.872
#> GSM1124908 4 0.2921 0.8009 0.000 0.000 0.140 0.860
#> GSM1124921 4 0.3764 0.8193 0.000 0.000 0.216 0.784
#> GSM1124939 4 0.2760 0.8342 0.000 0.000 0.128 0.872
#> GSM1124944 4 0.2469 0.8397 0.000 0.000 0.108 0.892
#> GSM1124945 3 0.2921 0.4396 0.000 0.000 0.860 0.140
#> GSM1124946 4 0.3907 0.8194 0.000 0.000 0.232 0.768
#> GSM1124947 4 0.2530 0.8280 0.000 0.000 0.112 0.888
#> GSM1124951 3 0.3311 0.4200 0.000 0.000 0.828 0.172
#> GSM1124952 4 0.1716 0.8266 0.000 0.000 0.064 0.936
#> GSM1124957 3 0.2921 0.4396 0.000 0.000 0.860 0.140
#> GSM1124900 1 0.1576 0.8450 0.948 0.048 0.000 0.004
#> GSM1124914 2 0.4071 0.8002 0.028 0.852 0.036 0.084
#> GSM1124871 2 0.3161 0.8326 0.124 0.864 0.012 0.000
#> GSM1124874 2 0.3842 0.8286 0.128 0.836 0.036 0.000
#> GSM1124875 2 0.3439 0.7919 0.048 0.868 0.084 0.000
#> GSM1124880 1 0.6042 0.1887 0.580 0.368 0.052 0.000
#> GSM1124881 2 0.4071 0.8301 0.104 0.844 0.036 0.016
#> GSM1124885 2 0.3272 0.8035 0.036 0.892 0.052 0.020
#> GSM1124886 1 0.0592 0.8715 0.984 0.016 0.000 0.000
#> GSM1124887 2 0.3161 0.7463 0.012 0.864 0.124 0.000
#> GSM1124894 4 0.5961 0.6686 0.108 0.004 0.188 0.700
#> GSM1124896 1 0.0592 0.8715 0.984 0.016 0.000 0.000
#> GSM1124899 2 0.3787 0.8286 0.124 0.840 0.036 0.000
#> GSM1124901 2 0.3653 0.8293 0.128 0.844 0.028 0.000
#> GSM1124906 2 0.3787 0.8286 0.124 0.840 0.036 0.000
#> GSM1124907 2 0.3597 0.7281 0.016 0.836 0.148 0.000
#> GSM1124911 2 0.3787 0.8286 0.124 0.840 0.036 0.000
#> GSM1124912 1 0.0592 0.8715 0.984 0.016 0.000 0.000
#> GSM1124915 2 0.3542 0.8304 0.120 0.852 0.028 0.000
#> GSM1124917 2 0.2799 0.8347 0.108 0.884 0.008 0.000
#> GSM1124918 2 0.3161 0.8318 0.124 0.864 0.012 0.000
#> GSM1124920 3 0.8262 0.7143 0.288 0.140 0.512 0.060
#> GSM1124922 2 0.3895 0.8303 0.132 0.832 0.036 0.000
#> GSM1124924 2 0.6603 0.4425 0.372 0.548 0.076 0.004
#> GSM1124926 2 0.3731 0.8302 0.120 0.844 0.036 0.000
#> GSM1124928 1 0.0921 0.8622 0.972 0.028 0.000 0.000
#> GSM1124930 2 0.3743 0.7190 0.016 0.824 0.160 0.000
#> GSM1124931 2 0.3787 0.8286 0.124 0.840 0.036 0.000
#> GSM1124935 2 0.3280 0.8311 0.124 0.860 0.016 0.000
#> GSM1124936 3 0.7452 0.5779 0.368 0.128 0.492 0.012
#> GSM1124938 2 0.4035 0.7015 0.020 0.804 0.176 0.000
#> GSM1124940 1 0.0592 0.8715 0.984 0.016 0.000 0.000
#> GSM1124941 2 0.3725 0.8313 0.120 0.848 0.028 0.004
#> GSM1124942 2 0.3790 0.7157 0.016 0.820 0.164 0.000
#> GSM1124943 2 0.3925 0.7051 0.016 0.808 0.176 0.000
#> GSM1124948 2 0.3143 0.7588 0.024 0.876 0.100 0.000
#> GSM1124949 1 0.0592 0.8715 0.984 0.016 0.000 0.000
#> GSM1124950 2 0.3731 0.8302 0.120 0.844 0.036 0.000
#> GSM1124954 1 0.6333 0.2184 0.576 0.364 0.052 0.008
#> GSM1124955 1 0.0592 0.8715 0.984 0.016 0.000 0.000
#> GSM1124956 2 0.3787 0.8286 0.124 0.840 0.036 0.000
#> GSM1124872 2 0.3842 0.8286 0.128 0.836 0.036 0.000
#> GSM1124873 2 0.3842 0.8286 0.128 0.836 0.036 0.000
#> GSM1124876 3 0.8396 0.7611 0.212 0.140 0.548 0.100
#> GSM1124877 1 0.0592 0.8715 0.984 0.016 0.000 0.000
#> GSM1124879 1 0.0592 0.8715 0.984 0.016 0.000 0.000
#> GSM1124883 2 0.3400 0.8038 0.044 0.880 0.068 0.008
#> GSM1124889 2 0.3787 0.8286 0.124 0.840 0.036 0.000
#> GSM1124892 1 0.0592 0.8715 0.984 0.016 0.000 0.000
#> GSM1124893 1 0.0592 0.8715 0.984 0.016 0.000 0.000
#> GSM1124909 2 0.3842 0.8286 0.128 0.836 0.036 0.000
#> GSM1124913 2 0.3641 0.7986 0.052 0.868 0.072 0.008
#> GSM1124916 2 0.3787 0.8286 0.124 0.840 0.036 0.000
#> GSM1124923 2 0.3925 0.7051 0.016 0.808 0.176 0.000
#> GSM1124925 1 0.0592 0.8715 0.984 0.016 0.000 0.000
#> GSM1124929 1 0.0592 0.8715 0.984 0.016 0.000 0.000
#> GSM1124934 1 0.5573 0.4428 0.676 0.272 0.052 0.000
#> GSM1124937 1 0.2654 0.7848 0.888 0.108 0.000 0.004
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM1124891 3 0.4662 0.816 0.208 0.008 0.736 0.004 0.044
#> GSM1124888 3 0.4575 0.824 0.184 0.000 0.744 0.004 0.068
#> GSM1124890 5 0.2605 0.914 0.000 0.148 0.000 0.000 0.852
#> GSM1124904 2 0.3016 0.786 0.000 0.848 0.020 0.000 0.132
#> GSM1124927 2 0.4791 0.230 0.460 0.524 0.004 0.000 0.012
#> GSM1124953 5 0.2648 0.917 0.000 0.152 0.000 0.000 0.848
#> GSM1124869 1 0.0807 0.920 0.976 0.012 0.000 0.000 0.012
#> GSM1124870 1 0.2654 0.888 0.888 0.064 0.048 0.000 0.000
#> GSM1124882 1 0.1341 0.904 0.944 0.000 0.056 0.000 0.000
#> GSM1124884 2 0.0566 0.854 0.000 0.984 0.004 0.000 0.012
#> GSM1124898 2 0.2367 0.834 0.004 0.904 0.020 0.000 0.072
#> GSM1124903 2 0.4838 0.790 0.076 0.768 0.020 0.008 0.128
#> GSM1124905 1 0.2968 0.868 0.896 0.024 0.028 0.028 0.024
#> GSM1124910 1 0.3442 0.813 0.836 0.000 0.104 0.000 0.060
#> GSM1124919 5 0.2648 0.917 0.000 0.152 0.000 0.000 0.848
#> GSM1124932 2 0.0955 0.857 0.028 0.968 0.000 0.000 0.004
#> GSM1124933 3 0.4815 0.819 0.180 0.008 0.740 0.004 0.068
#> GSM1124867 2 0.8407 -0.058 0.208 0.388 0.104 0.284 0.016
#> GSM1124868 4 0.5398 0.617 0.000 0.200 0.088 0.692 0.020
#> GSM1124878 2 0.5902 0.397 0.000 0.588 0.084 0.312 0.016
#> GSM1124895 4 0.0162 0.878 0.000 0.000 0.004 0.996 0.000
#> GSM1124897 2 0.5932 0.375 0.000 0.580 0.084 0.320 0.016
#> GSM1124902 4 0.0324 0.878 0.000 0.000 0.004 0.992 0.004
#> GSM1124908 4 0.2293 0.846 0.000 0.000 0.084 0.900 0.016
#> GSM1124921 4 0.1670 0.864 0.000 0.000 0.052 0.936 0.012
#> GSM1124939 4 0.0000 0.879 0.000 0.000 0.000 1.000 0.000
#> GSM1124944 4 0.0000 0.879 0.000 0.000 0.000 1.000 0.000
#> GSM1124945 3 0.3141 0.646 0.000 0.000 0.852 0.040 0.108
#> GSM1124946 4 0.1740 0.863 0.000 0.000 0.056 0.932 0.012
#> GSM1124947 4 0.0162 0.879 0.000 0.000 0.000 0.996 0.004
#> GSM1124951 3 0.3085 0.648 0.000 0.000 0.852 0.032 0.116
#> GSM1124952 4 0.0290 0.878 0.000 0.000 0.000 0.992 0.008
#> GSM1124957 3 0.3620 0.642 0.000 0.000 0.824 0.068 0.108
#> GSM1124900 1 0.1651 0.907 0.944 0.036 0.008 0.000 0.012
#> GSM1124914 2 0.4080 0.814 0.076 0.808 0.012 0.000 0.104
#> GSM1124871 2 0.1041 0.858 0.032 0.964 0.004 0.000 0.000
#> GSM1124874 2 0.1894 0.841 0.072 0.920 0.000 0.000 0.008
#> GSM1124875 2 0.3209 0.745 0.000 0.812 0.008 0.000 0.180
#> GSM1124880 1 0.3319 0.848 0.864 0.020 0.052 0.000 0.064
#> GSM1124881 2 0.1956 0.839 0.076 0.916 0.000 0.000 0.008
#> GSM1124885 2 0.4363 0.802 0.072 0.792 0.020 0.000 0.116
#> GSM1124886 1 0.1124 0.915 0.960 0.004 0.036 0.000 0.000
#> GSM1124887 2 0.3210 0.709 0.000 0.788 0.000 0.000 0.212
#> GSM1124894 4 0.7931 0.400 0.120 0.260 0.120 0.484 0.016
#> GSM1124896 1 0.0968 0.921 0.972 0.012 0.004 0.000 0.012
#> GSM1124899 2 0.0771 0.858 0.020 0.976 0.000 0.000 0.004
#> GSM1124901 2 0.1830 0.847 0.004 0.932 0.012 0.000 0.052
#> GSM1124906 2 0.0290 0.854 0.000 0.992 0.000 0.000 0.008
#> GSM1124907 5 0.2852 0.900 0.000 0.172 0.000 0.000 0.828
#> GSM1124911 2 0.0404 0.853 0.000 0.988 0.000 0.000 0.012
#> GSM1124912 1 0.0807 0.922 0.976 0.012 0.012 0.000 0.000
#> GSM1124915 2 0.1885 0.848 0.004 0.932 0.020 0.000 0.044
#> GSM1124917 2 0.0955 0.851 0.000 0.968 0.004 0.000 0.028
#> GSM1124918 2 0.1557 0.844 0.000 0.940 0.008 0.000 0.052
#> GSM1124920 3 0.4728 0.788 0.240 0.000 0.700 0.000 0.060
#> GSM1124922 2 0.2172 0.841 0.076 0.908 0.000 0.000 0.016
#> GSM1124924 5 0.7957 0.227 0.260 0.152 0.148 0.000 0.440
#> GSM1124926 2 0.1768 0.843 0.072 0.924 0.000 0.000 0.004
#> GSM1124928 1 0.2446 0.878 0.900 0.000 0.056 0.000 0.044
#> GSM1124930 5 0.2732 0.913 0.000 0.160 0.000 0.000 0.840
#> GSM1124931 2 0.1956 0.839 0.076 0.916 0.000 0.000 0.008
#> GSM1124935 2 0.0798 0.854 0.000 0.976 0.008 0.000 0.016
#> GSM1124936 3 0.4755 0.783 0.244 0.000 0.696 0.000 0.060
#> GSM1124938 5 0.2886 0.909 0.008 0.148 0.000 0.000 0.844
#> GSM1124940 1 0.0807 0.920 0.976 0.012 0.000 0.000 0.012
#> GSM1124941 2 0.0510 0.853 0.000 0.984 0.000 0.000 0.016
#> GSM1124942 5 0.2690 0.916 0.000 0.156 0.000 0.000 0.844
#> GSM1124943 5 0.2648 0.917 0.000 0.152 0.000 0.000 0.848
#> GSM1124948 5 0.4506 0.743 0.028 0.296 0.000 0.000 0.676
#> GSM1124949 1 0.0807 0.920 0.976 0.012 0.000 0.000 0.012
#> GSM1124950 2 0.0880 0.857 0.032 0.968 0.000 0.000 0.000
#> GSM1124954 1 0.2354 0.902 0.916 0.032 0.032 0.000 0.020
#> GSM1124955 1 0.1544 0.894 0.932 0.000 0.068 0.000 0.000
#> GSM1124956 2 0.0404 0.853 0.000 0.988 0.000 0.000 0.012
#> GSM1124872 2 0.1956 0.839 0.076 0.916 0.000 0.000 0.008
#> GSM1124873 2 0.1430 0.852 0.052 0.944 0.000 0.000 0.004
#> GSM1124876 3 0.4575 0.824 0.184 0.000 0.744 0.004 0.068
#> GSM1124877 1 0.1731 0.905 0.932 0.004 0.060 0.000 0.004
#> GSM1124879 1 0.0693 0.921 0.980 0.012 0.000 0.000 0.008
#> GSM1124883 2 0.4515 0.796 0.076 0.780 0.020 0.000 0.124
#> GSM1124889 2 0.0404 0.853 0.000 0.988 0.000 0.000 0.012
#> GSM1124892 1 0.1671 0.887 0.924 0.000 0.076 0.000 0.000
#> GSM1124893 1 0.0693 0.921 0.980 0.012 0.000 0.000 0.008
#> GSM1124909 2 0.0963 0.843 0.000 0.964 0.000 0.000 0.036
#> GSM1124913 2 0.4561 0.793 0.076 0.776 0.020 0.000 0.128
#> GSM1124916 2 0.0404 0.853 0.000 0.988 0.000 0.000 0.012
#> GSM1124923 5 0.2648 0.917 0.000 0.152 0.000 0.000 0.848
#> GSM1124925 1 0.1478 0.898 0.936 0.000 0.064 0.000 0.000
#> GSM1124929 1 0.0693 0.921 0.980 0.012 0.000 0.000 0.008
#> GSM1124934 1 0.3429 0.846 0.860 0.036 0.036 0.000 0.068
#> GSM1124937 1 0.1740 0.886 0.932 0.056 0.000 0.000 0.012
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1124891 3 0.3754 0.6002 0.212 0.000 0.756 0.000 0.016 0.016
#> GSM1124888 3 0.6216 0.6724 0.204 0.000 0.456 0.000 0.016 0.324
#> GSM1124890 5 0.0632 0.9656 0.000 0.024 0.000 0.000 0.976 0.000
#> GSM1124904 2 0.4863 0.4050 0.000 0.624 0.000 0.000 0.092 0.284
#> GSM1124927 2 0.5004 0.0257 0.400 0.548 0.028 0.000 0.004 0.020
#> GSM1124953 5 0.0632 0.9656 0.000 0.024 0.000 0.000 0.976 0.000
#> GSM1124869 1 0.0291 0.8386 0.992 0.004 0.000 0.000 0.004 0.000
#> GSM1124870 1 0.1251 0.8311 0.956 0.012 0.024 0.000 0.000 0.008
#> GSM1124882 1 0.0363 0.8367 0.988 0.000 0.012 0.000 0.000 0.000
#> GSM1124884 2 0.0146 0.7673 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM1124898 2 0.3798 0.6161 0.004 0.748 0.000 0.000 0.032 0.216
#> GSM1124903 2 0.4502 0.4627 0.004 0.660 0.000 0.004 0.040 0.292
#> GSM1124905 1 0.2775 0.7933 0.888 0.008 0.020 0.012 0.012 0.060
#> GSM1124910 1 0.4358 0.4254 0.624 0.000 0.348 0.000 0.012 0.016
#> GSM1124919 5 0.0632 0.9656 0.000 0.024 0.000 0.000 0.976 0.000
#> GSM1124932 2 0.0696 0.7660 0.004 0.980 0.004 0.000 0.004 0.008
#> GSM1124933 3 0.6312 0.6723 0.208 0.000 0.444 0.000 0.020 0.328
#> GSM1124867 2 0.8390 -0.6340 0.144 0.420 0.120 0.116 0.008 0.192
#> GSM1124868 4 0.6877 -0.0376 0.000 0.116 0.116 0.416 0.000 0.352
#> GSM1124878 2 0.7152 -0.6748 0.000 0.464 0.116 0.152 0.008 0.260
#> GSM1124895 4 0.0000 0.7881 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1124897 6 0.7216 0.0000 0.000 0.336 0.116 0.180 0.000 0.368
#> GSM1124902 4 0.0260 0.7869 0.000 0.000 0.000 0.992 0.000 0.008
#> GSM1124908 4 0.4992 0.6120 0.000 0.000 0.116 0.624 0.000 0.260
#> GSM1124921 4 0.4895 0.6240 0.000 0.000 0.108 0.636 0.000 0.256
#> GSM1124939 4 0.0000 0.7881 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1124944 4 0.0000 0.7881 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1124945 3 0.4720 0.4708 0.000 0.000 0.560 0.052 0.000 0.388
#> GSM1124946 4 0.4895 0.6240 0.000 0.000 0.108 0.636 0.000 0.256
#> GSM1124947 4 0.0547 0.7816 0.000 0.000 0.000 0.980 0.000 0.020
#> GSM1124951 3 0.4602 0.4772 0.000 0.000 0.572 0.044 0.000 0.384
#> GSM1124952 4 0.1152 0.7766 0.000 0.000 0.004 0.952 0.000 0.044
#> GSM1124957 3 0.4756 0.4743 0.000 0.000 0.564 0.056 0.000 0.380
#> GSM1124900 1 0.2774 0.7996 0.884 0.048 0.044 0.000 0.008 0.016
#> GSM1124914 2 0.2917 0.7075 0.004 0.852 0.000 0.000 0.040 0.104
#> GSM1124871 2 0.0935 0.7648 0.004 0.964 0.000 0.000 0.000 0.032
#> GSM1124874 2 0.0436 0.7654 0.004 0.988 0.004 0.000 0.000 0.004
#> GSM1124875 2 0.4134 0.4975 0.000 0.708 0.000 0.000 0.240 0.052
#> GSM1124880 1 0.4682 0.6682 0.760 0.096 0.092 0.000 0.036 0.016
#> GSM1124881 2 0.0665 0.7622 0.004 0.980 0.000 0.000 0.008 0.008
#> GSM1124885 2 0.4365 0.4714 0.004 0.664 0.000 0.000 0.040 0.292
#> GSM1124886 1 0.0000 0.8376 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124887 2 0.5350 -0.0424 0.000 0.476 0.000 0.000 0.416 0.108
#> GSM1124894 1 0.8906 -0.3967 0.300 0.140 0.128 0.216 0.008 0.208
#> GSM1124896 1 0.2290 0.8119 0.908 0.044 0.032 0.000 0.004 0.012
#> GSM1124899 2 0.0551 0.7683 0.004 0.984 0.000 0.000 0.004 0.008
#> GSM1124901 2 0.3450 0.6446 0.000 0.780 0.000 0.000 0.032 0.188
#> GSM1124906 2 0.0260 0.7670 0.000 0.992 0.000 0.000 0.008 0.000
#> GSM1124907 5 0.1168 0.9506 0.000 0.028 0.000 0.000 0.956 0.016
#> GSM1124911 2 0.0632 0.7627 0.000 0.976 0.000 0.000 0.024 0.000
#> GSM1124912 1 0.0000 0.8376 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124915 2 0.3669 0.6263 0.004 0.760 0.000 0.000 0.028 0.208
#> GSM1124917 2 0.2070 0.7443 0.000 0.908 0.000 0.000 0.044 0.048
#> GSM1124918 2 0.2201 0.7430 0.000 0.900 0.000 0.000 0.052 0.048
#> GSM1124920 3 0.3617 0.5590 0.244 0.000 0.736 0.000 0.020 0.000
#> GSM1124922 2 0.1408 0.7640 0.008 0.952 0.008 0.000 0.008 0.024
#> GSM1124924 3 0.6914 0.0208 0.360 0.068 0.400 0.000 0.168 0.004
#> GSM1124926 2 0.0436 0.7666 0.004 0.988 0.000 0.000 0.004 0.004
#> GSM1124928 1 0.2767 0.8016 0.888 0.028 0.048 0.000 0.020 0.016
#> GSM1124930 5 0.0632 0.9656 0.000 0.024 0.000 0.000 0.976 0.000
#> GSM1124931 2 0.1425 0.7515 0.012 0.952 0.020 0.000 0.008 0.008
#> GSM1124935 2 0.1633 0.7577 0.000 0.932 0.000 0.000 0.024 0.044
#> GSM1124936 3 0.3534 0.5362 0.276 0.000 0.716 0.000 0.008 0.000
#> GSM1124938 5 0.1074 0.9507 0.012 0.028 0.000 0.000 0.960 0.000
#> GSM1124940 1 0.0436 0.8380 0.988 0.004 0.000 0.000 0.004 0.004
#> GSM1124941 2 0.1341 0.7628 0.000 0.948 0.000 0.000 0.024 0.028
#> GSM1124942 5 0.0632 0.9656 0.000 0.024 0.000 0.000 0.976 0.000
#> GSM1124943 5 0.0632 0.9656 0.000 0.024 0.000 0.000 0.976 0.000
#> GSM1124948 5 0.3800 0.7182 0.028 0.176 0.000 0.000 0.776 0.020
#> GSM1124949 1 0.0291 0.8386 0.992 0.004 0.000 0.000 0.004 0.000
#> GSM1124950 2 0.0912 0.7640 0.004 0.972 0.012 0.000 0.004 0.008
#> GSM1124954 1 0.6117 0.2669 0.520 0.056 0.356 0.000 0.044 0.024
#> GSM1124955 1 0.0260 0.8356 0.992 0.000 0.008 0.000 0.000 0.000
#> GSM1124956 2 0.0632 0.7627 0.000 0.976 0.000 0.000 0.024 0.000
#> GSM1124872 2 0.0436 0.7654 0.004 0.988 0.004 0.000 0.000 0.004
#> GSM1124873 2 0.0291 0.7659 0.004 0.992 0.000 0.000 0.000 0.004
#> GSM1124876 3 0.6216 0.6724 0.204 0.000 0.456 0.000 0.016 0.324
#> GSM1124877 1 0.0862 0.8354 0.972 0.016 0.004 0.000 0.008 0.000
#> GSM1124879 1 0.0405 0.8385 0.988 0.004 0.008 0.000 0.000 0.000
#> GSM1124883 2 0.4427 0.4647 0.004 0.660 0.000 0.000 0.044 0.292
#> GSM1124889 2 0.0260 0.7670 0.000 0.992 0.000 0.000 0.008 0.000
#> GSM1124892 1 0.2697 0.6770 0.812 0.000 0.188 0.000 0.000 0.000
#> GSM1124893 1 0.0291 0.8386 0.992 0.004 0.000 0.000 0.004 0.000
#> GSM1124909 2 0.0692 0.7637 0.000 0.976 0.000 0.000 0.020 0.004
#> GSM1124913 2 0.4486 0.4617 0.004 0.656 0.000 0.000 0.048 0.292
#> GSM1124916 2 0.0405 0.7664 0.000 0.988 0.000 0.000 0.008 0.004
#> GSM1124923 5 0.0777 0.9635 0.000 0.024 0.000 0.000 0.972 0.004
#> GSM1124925 1 0.0000 0.8376 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124929 1 0.0291 0.8386 0.992 0.004 0.000 0.000 0.004 0.000
#> GSM1124934 1 0.6277 0.2431 0.500 0.068 0.360 0.000 0.056 0.016
#> GSM1124937 1 0.2374 0.8070 0.904 0.048 0.028 0.000 0.004 0.016
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 tissue(p) k
#> CV:mclust 87 2.44e-15 2
#> CV:mclust 85 6.01e-14 3
#> CV:mclust 79 7.15e-12 4
#> CV:mclust 85 5.74e-10 5
#> CV:mclust 71 6.17e-11 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 48983 rows and 91 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 6.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.670 0.852 0.937 0.4995 0.502 0.502
#> 3 3 0.588 0.724 0.867 0.2468 0.805 0.639
#> 4 4 0.695 0.823 0.906 0.1432 0.746 0.460
#> 5 5 0.765 0.828 0.903 0.0969 0.796 0.442
#> 6 6 0.869 0.871 0.921 0.0580 0.932 0.707
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 6
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM1124891 1 0.0000 0.942 1.000 0.000
#> GSM1124888 1 0.0000 0.942 1.000 0.000
#> GSM1124890 1 0.9393 0.433 0.644 0.356
#> GSM1124904 2 0.0000 0.917 0.000 1.000
#> GSM1124927 1 0.2778 0.901 0.952 0.048
#> GSM1124953 2 0.7674 0.693 0.224 0.776
#> GSM1124869 1 0.0000 0.942 1.000 0.000
#> GSM1124870 1 0.0000 0.942 1.000 0.000
#> GSM1124882 1 0.0000 0.942 1.000 0.000
#> GSM1124884 2 0.0000 0.917 0.000 1.000
#> GSM1124898 2 0.0000 0.917 0.000 1.000
#> GSM1124903 2 0.0000 0.917 0.000 1.000
#> GSM1124905 1 0.0000 0.942 1.000 0.000
#> GSM1124910 1 0.0000 0.942 1.000 0.000
#> GSM1124919 2 0.0000 0.917 0.000 1.000
#> GSM1124932 1 0.7674 0.669 0.776 0.224
#> GSM1124933 1 0.0000 0.942 1.000 0.000
#> GSM1124867 2 0.0938 0.911 0.012 0.988
#> GSM1124868 2 0.0000 0.917 0.000 1.000
#> GSM1124878 2 0.0000 0.917 0.000 1.000
#> GSM1124895 2 0.0000 0.917 0.000 1.000
#> GSM1124897 2 0.0000 0.917 0.000 1.000
#> GSM1124902 2 0.0000 0.917 0.000 1.000
#> GSM1124908 2 0.0000 0.917 0.000 1.000
#> GSM1124921 2 0.0000 0.917 0.000 1.000
#> GSM1124939 2 0.0000 0.917 0.000 1.000
#> GSM1124944 2 0.0000 0.917 0.000 1.000
#> GSM1124945 2 0.9608 0.371 0.384 0.616
#> GSM1124946 2 0.0000 0.917 0.000 1.000
#> GSM1124947 2 0.0000 0.917 0.000 1.000
#> GSM1124951 2 0.9044 0.522 0.320 0.680
#> GSM1124952 2 0.0000 0.917 0.000 1.000
#> GSM1124957 1 0.9552 0.384 0.624 0.376
#> GSM1124900 1 0.0000 0.942 1.000 0.000
#> GSM1124914 2 0.0000 0.917 0.000 1.000
#> GSM1124871 2 0.0000 0.917 0.000 1.000
#> GSM1124874 2 0.6973 0.780 0.188 0.812
#> GSM1124875 2 0.0000 0.917 0.000 1.000
#> GSM1124880 1 0.0000 0.942 1.000 0.000
#> GSM1124881 2 0.2948 0.891 0.052 0.948
#> GSM1124885 2 0.0000 0.917 0.000 1.000
#> GSM1124886 1 0.0000 0.942 1.000 0.000
#> GSM1124887 2 0.0000 0.917 0.000 1.000
#> GSM1124894 2 0.4939 0.845 0.108 0.892
#> GSM1124896 1 0.0000 0.942 1.000 0.000
#> GSM1124899 2 0.6623 0.796 0.172 0.828
#> GSM1124901 2 0.0000 0.917 0.000 1.000
#> GSM1124906 2 0.7376 0.756 0.208 0.792
#> GSM1124907 2 0.0000 0.917 0.000 1.000
#> GSM1124911 2 0.5059 0.850 0.112 0.888
#> GSM1124912 1 0.0000 0.942 1.000 0.000
#> GSM1124915 2 0.0000 0.917 0.000 1.000
#> GSM1124917 2 0.0938 0.911 0.012 0.988
#> GSM1124918 2 0.5946 0.828 0.144 0.856
#> GSM1124920 1 0.0000 0.942 1.000 0.000
#> GSM1124922 2 0.7528 0.747 0.216 0.784
#> GSM1124924 1 0.0000 0.942 1.000 0.000
#> GSM1124926 2 0.5842 0.827 0.140 0.860
#> GSM1124928 1 0.0000 0.942 1.000 0.000
#> GSM1124930 2 0.0000 0.917 0.000 1.000
#> GSM1124931 1 0.4431 0.854 0.908 0.092
#> GSM1124935 2 0.0000 0.917 0.000 1.000
#> GSM1124936 1 0.0000 0.942 1.000 0.000
#> GSM1124938 1 0.1184 0.929 0.984 0.016
#> GSM1124940 1 0.0000 0.942 1.000 0.000
#> GSM1124941 2 0.7219 0.766 0.200 0.800
#> GSM1124942 2 0.0000 0.917 0.000 1.000
#> GSM1124943 2 0.9881 0.223 0.436 0.564
#> GSM1124948 1 0.0000 0.942 1.000 0.000
#> GSM1124949 1 0.0000 0.942 1.000 0.000
#> GSM1124950 2 0.9686 0.396 0.396 0.604
#> GSM1124954 1 0.0000 0.942 1.000 0.000
#> GSM1124955 1 0.0000 0.942 1.000 0.000
#> GSM1124956 2 0.7139 0.771 0.196 0.804
#> GSM1124872 1 0.9896 0.142 0.560 0.440
#> GSM1124873 2 0.6973 0.781 0.188 0.812
#> GSM1124876 1 0.0000 0.942 1.000 0.000
#> GSM1124877 1 0.0000 0.942 1.000 0.000
#> GSM1124879 1 0.0000 0.942 1.000 0.000
#> GSM1124883 2 0.0000 0.917 0.000 1.000
#> GSM1124889 2 0.2423 0.897 0.040 0.960
#> GSM1124892 1 0.0000 0.942 1.000 0.000
#> GSM1124893 1 0.0000 0.942 1.000 0.000
#> GSM1124909 1 0.1843 0.919 0.972 0.028
#> GSM1124913 2 0.0000 0.917 0.000 1.000
#> GSM1124916 1 0.9850 0.181 0.572 0.428
#> GSM1124923 2 0.0000 0.917 0.000 1.000
#> GSM1124925 1 0.0000 0.942 1.000 0.000
#> GSM1124929 1 0.0000 0.942 1.000 0.000
#> GSM1124934 1 0.0000 0.942 1.000 0.000
#> GSM1124937 1 0.0000 0.942 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1124891 3 0.5733 0.6462 0.324 0.000 0.676
#> GSM1124888 3 0.5733 0.6462 0.324 0.000 0.676
#> GSM1124890 3 0.4750 0.6733 0.000 0.216 0.784
#> GSM1124904 2 0.0000 0.8328 0.000 1.000 0.000
#> GSM1124927 1 0.0000 0.8510 1.000 0.000 0.000
#> GSM1124953 3 0.4555 0.6828 0.000 0.200 0.800
#> GSM1124869 1 0.0000 0.8510 1.000 0.000 0.000
#> GSM1124870 1 0.0000 0.8510 1.000 0.000 0.000
#> GSM1124882 1 0.0000 0.8510 1.000 0.000 0.000
#> GSM1124884 2 0.0000 0.8328 0.000 1.000 0.000
#> GSM1124898 2 0.0000 0.8328 0.000 1.000 0.000
#> GSM1124903 2 0.0000 0.8328 0.000 1.000 0.000
#> GSM1124905 1 0.0000 0.8510 1.000 0.000 0.000
#> GSM1124910 1 0.0000 0.8510 1.000 0.000 0.000
#> GSM1124919 2 0.0237 0.8316 0.000 0.996 0.004
#> GSM1124932 1 0.5397 0.5810 0.720 0.280 0.000
#> GSM1124933 3 0.4654 0.7083 0.208 0.000 0.792
#> GSM1124867 2 0.7988 0.6203 0.144 0.656 0.200
#> GSM1124868 2 0.4654 0.7256 0.000 0.792 0.208
#> GSM1124878 2 0.4555 0.7304 0.000 0.800 0.200
#> GSM1124895 2 0.5760 0.6326 0.000 0.672 0.328
#> GSM1124897 2 0.4605 0.7287 0.000 0.796 0.204
#> GSM1124902 2 0.5760 0.6326 0.000 0.672 0.328
#> GSM1124908 2 0.5431 0.6715 0.000 0.716 0.284
#> GSM1124921 2 0.5785 0.6287 0.000 0.668 0.332
#> GSM1124939 2 0.5760 0.6326 0.000 0.672 0.328
#> GSM1124944 2 0.5859 0.6150 0.000 0.656 0.344
#> GSM1124945 3 0.0000 0.6961 0.000 0.000 1.000
#> GSM1124946 2 0.5810 0.6244 0.000 0.664 0.336
#> GSM1124947 2 0.5835 0.6199 0.000 0.660 0.340
#> GSM1124951 3 0.0000 0.6961 0.000 0.000 1.000
#> GSM1124952 2 0.5810 0.6244 0.000 0.664 0.336
#> GSM1124957 3 0.0000 0.6961 0.000 0.000 1.000
#> GSM1124900 1 0.0000 0.8510 1.000 0.000 0.000
#> GSM1124914 2 0.0000 0.8328 0.000 1.000 0.000
#> GSM1124871 2 0.0000 0.8328 0.000 1.000 0.000
#> GSM1124874 2 0.2356 0.7825 0.072 0.928 0.000
#> GSM1124875 2 0.0000 0.8328 0.000 1.000 0.000
#> GSM1124880 1 0.0000 0.8510 1.000 0.000 0.000
#> GSM1124881 2 0.0592 0.8264 0.012 0.988 0.000
#> GSM1124885 2 0.0000 0.8328 0.000 1.000 0.000
#> GSM1124886 1 0.0000 0.8510 1.000 0.000 0.000
#> GSM1124887 2 0.0000 0.8328 0.000 1.000 0.000
#> GSM1124894 1 0.8890 0.2327 0.532 0.140 0.328
#> GSM1124896 1 0.0000 0.8510 1.000 0.000 0.000
#> GSM1124899 2 0.2878 0.7609 0.096 0.904 0.000
#> GSM1124901 2 0.0000 0.8328 0.000 1.000 0.000
#> GSM1124906 1 0.6095 0.4376 0.608 0.392 0.000
#> GSM1124907 2 0.0000 0.8328 0.000 1.000 0.000
#> GSM1124911 1 0.6308 0.1804 0.508 0.492 0.000
#> GSM1124912 1 0.0000 0.8510 1.000 0.000 0.000
#> GSM1124915 2 0.0000 0.8328 0.000 1.000 0.000
#> GSM1124917 2 0.0000 0.8328 0.000 1.000 0.000
#> GSM1124918 2 0.3267 0.7405 0.116 0.884 0.000
#> GSM1124920 3 0.6180 0.5103 0.416 0.000 0.584
#> GSM1124922 2 0.5560 0.4717 0.300 0.700 0.000
#> GSM1124924 1 0.3412 0.7104 0.876 0.000 0.124
#> GSM1124926 2 0.0000 0.8328 0.000 1.000 0.000
#> GSM1124928 1 0.0000 0.8510 1.000 0.000 0.000
#> GSM1124930 2 0.0000 0.8328 0.000 1.000 0.000
#> GSM1124931 1 0.4887 0.6359 0.772 0.228 0.000
#> GSM1124935 2 0.0237 0.8310 0.004 0.996 0.000
#> GSM1124936 1 0.3192 0.7265 0.888 0.000 0.112
#> GSM1124938 3 0.7651 0.6918 0.108 0.220 0.672
#> GSM1124940 1 0.0000 0.8510 1.000 0.000 0.000
#> GSM1124941 2 0.6252 0.0253 0.444 0.556 0.000
#> GSM1124942 2 0.0592 0.8269 0.000 0.988 0.012
#> GSM1124943 3 0.5948 0.5040 0.000 0.360 0.640
#> GSM1124948 3 0.9106 0.5793 0.244 0.208 0.548
#> GSM1124949 1 0.0000 0.8510 1.000 0.000 0.000
#> GSM1124950 2 0.5785 0.3879 0.332 0.668 0.000
#> GSM1124954 1 0.0000 0.8510 1.000 0.000 0.000
#> GSM1124955 1 0.0000 0.8510 1.000 0.000 0.000
#> GSM1124956 1 0.5859 0.5067 0.656 0.344 0.000
#> GSM1124872 1 0.5785 0.5227 0.668 0.332 0.000
#> GSM1124873 2 0.3482 0.7254 0.128 0.872 0.000
#> GSM1124876 3 0.5497 0.6720 0.292 0.000 0.708
#> GSM1124877 1 0.0000 0.8510 1.000 0.000 0.000
#> GSM1124879 1 0.0000 0.8510 1.000 0.000 0.000
#> GSM1124883 2 0.0000 0.8328 0.000 1.000 0.000
#> GSM1124889 2 0.0000 0.8328 0.000 1.000 0.000
#> GSM1124892 1 0.0000 0.8510 1.000 0.000 0.000
#> GSM1124893 1 0.0000 0.8510 1.000 0.000 0.000
#> GSM1124909 1 0.5465 0.5725 0.712 0.288 0.000
#> GSM1124913 2 0.0000 0.8328 0.000 1.000 0.000
#> GSM1124916 1 0.5785 0.5224 0.668 0.332 0.000
#> GSM1124923 2 0.3941 0.7426 0.000 0.844 0.156
#> GSM1124925 1 0.0000 0.8510 1.000 0.000 0.000
#> GSM1124929 1 0.0000 0.8510 1.000 0.000 0.000
#> GSM1124934 1 0.0000 0.8510 1.000 0.000 0.000
#> GSM1124937 1 0.0000 0.8510 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1124891 3 0.2589 0.8597 0.116 0.000 0.884 0.000
#> GSM1124888 3 0.2589 0.8597 0.116 0.000 0.884 0.000
#> GSM1124890 3 0.0921 0.8227 0.000 0.028 0.972 0.000
#> GSM1124904 2 0.5850 0.7283 0.000 0.700 0.116 0.184
#> GSM1124927 1 0.4304 0.6252 0.716 0.284 0.000 0.000
#> GSM1124953 3 0.1211 0.8297 0.000 0.040 0.960 0.000
#> GSM1124869 1 0.0000 0.9509 1.000 0.000 0.000 0.000
#> GSM1124870 1 0.2589 0.8509 0.884 0.116 0.000 0.000
#> GSM1124882 1 0.0000 0.9509 1.000 0.000 0.000 0.000
#> GSM1124884 2 0.0000 0.8585 0.000 1.000 0.000 0.000
#> GSM1124898 2 0.2589 0.8378 0.000 0.884 0.116 0.000
#> GSM1124903 2 0.5889 0.7238 0.000 0.696 0.116 0.188
#> GSM1124905 1 0.0000 0.9509 1.000 0.000 0.000 0.000
#> GSM1124910 1 0.0000 0.9509 1.000 0.000 0.000 0.000
#> GSM1124919 2 0.3024 0.8270 0.000 0.852 0.148 0.000
#> GSM1124932 2 0.5000 -0.1515 0.496 0.504 0.000 0.000
#> GSM1124933 3 0.2589 0.8597 0.116 0.000 0.884 0.000
#> GSM1124867 4 0.4511 0.6815 0.040 0.176 0.000 0.784
#> GSM1124868 4 0.0000 0.9025 0.000 0.000 0.000 1.000
#> GSM1124878 4 0.3726 0.6583 0.000 0.212 0.000 0.788
#> GSM1124895 4 0.0000 0.9025 0.000 0.000 0.000 1.000
#> GSM1124897 4 0.3606 0.7477 0.000 0.132 0.024 0.844
#> GSM1124902 4 0.0000 0.9025 0.000 0.000 0.000 1.000
#> GSM1124908 4 0.0188 0.8998 0.000 0.000 0.004 0.996
#> GSM1124921 4 0.0000 0.9025 0.000 0.000 0.000 1.000
#> GSM1124939 4 0.0000 0.9025 0.000 0.000 0.000 1.000
#> GSM1124944 4 0.0000 0.9025 0.000 0.000 0.000 1.000
#> GSM1124945 4 0.4477 0.5009 0.000 0.000 0.312 0.688
#> GSM1124946 4 0.0000 0.9025 0.000 0.000 0.000 1.000
#> GSM1124947 4 0.0000 0.9025 0.000 0.000 0.000 1.000
#> GSM1124951 3 0.1389 0.8187 0.000 0.000 0.952 0.048
#> GSM1124952 4 0.0000 0.9025 0.000 0.000 0.000 1.000
#> GSM1124957 3 0.3444 0.7205 0.000 0.000 0.816 0.184
#> GSM1124900 1 0.2647 0.8473 0.880 0.120 0.000 0.000
#> GSM1124914 2 0.5375 0.7703 0.000 0.744 0.116 0.140
#> GSM1124871 2 0.0000 0.8585 0.000 1.000 0.000 0.000
#> GSM1124874 2 0.0000 0.8585 0.000 1.000 0.000 0.000
#> GSM1124875 2 0.2589 0.8378 0.000 0.884 0.116 0.000
#> GSM1124880 1 0.2589 0.8421 0.884 0.116 0.000 0.000
#> GSM1124881 2 0.0000 0.8585 0.000 1.000 0.000 0.000
#> GSM1124885 2 0.5277 0.7766 0.000 0.752 0.116 0.132
#> GSM1124886 1 0.0000 0.9509 1.000 0.000 0.000 0.000
#> GSM1124887 2 0.5423 0.7670 0.000 0.740 0.116 0.144
#> GSM1124894 4 0.2589 0.7813 0.116 0.000 0.000 0.884
#> GSM1124896 1 0.0000 0.9509 1.000 0.000 0.000 0.000
#> GSM1124899 2 0.1211 0.8561 0.000 0.960 0.040 0.000
#> GSM1124901 2 0.2589 0.8378 0.000 0.884 0.116 0.000
#> GSM1124906 2 0.0000 0.8585 0.000 1.000 0.000 0.000
#> GSM1124907 2 0.5277 0.7766 0.000 0.752 0.116 0.132
#> GSM1124911 2 0.0000 0.8585 0.000 1.000 0.000 0.000
#> GSM1124912 1 0.0000 0.9509 1.000 0.000 0.000 0.000
#> GSM1124915 2 0.0188 0.8585 0.000 0.996 0.004 0.000
#> GSM1124917 2 0.0921 0.8577 0.000 0.972 0.028 0.000
#> GSM1124918 2 0.0000 0.8585 0.000 1.000 0.000 0.000
#> GSM1124920 3 0.3400 0.8110 0.180 0.000 0.820 0.000
#> GSM1124922 2 0.4819 0.8119 0.048 0.808 0.116 0.028
#> GSM1124924 3 0.6084 0.6226 0.096 0.244 0.660 0.000
#> GSM1124926 2 0.2281 0.8280 0.000 0.904 0.000 0.096
#> GSM1124928 1 0.0000 0.9509 1.000 0.000 0.000 0.000
#> GSM1124930 2 0.2589 0.8378 0.000 0.884 0.116 0.000
#> GSM1124931 2 0.4941 0.0833 0.436 0.564 0.000 0.000
#> GSM1124935 2 0.0707 0.8585 0.000 0.980 0.020 0.000
#> GSM1124936 1 0.0592 0.9382 0.984 0.000 0.016 0.000
#> GSM1124938 3 0.0188 0.8328 0.000 0.004 0.996 0.000
#> GSM1124940 1 0.0000 0.9509 1.000 0.000 0.000 0.000
#> GSM1124941 2 0.0000 0.8585 0.000 1.000 0.000 0.000
#> GSM1124942 2 0.3400 0.8112 0.000 0.820 0.180 0.000
#> GSM1124943 2 0.4998 0.3259 0.000 0.512 0.488 0.000
#> GSM1124948 2 0.3172 0.7868 0.000 0.840 0.160 0.000
#> GSM1124949 1 0.0000 0.9509 1.000 0.000 0.000 0.000
#> GSM1124950 2 0.0000 0.8585 0.000 1.000 0.000 0.000
#> GSM1124954 1 0.4669 0.7778 0.796 0.100 0.104 0.000
#> GSM1124955 1 0.0000 0.9509 1.000 0.000 0.000 0.000
#> GSM1124956 2 0.0000 0.8585 0.000 1.000 0.000 0.000
#> GSM1124872 2 0.0000 0.8585 0.000 1.000 0.000 0.000
#> GSM1124873 2 0.0000 0.8585 0.000 1.000 0.000 0.000
#> GSM1124876 3 0.2589 0.8597 0.116 0.000 0.884 0.000
#> GSM1124877 1 0.0000 0.9509 1.000 0.000 0.000 0.000
#> GSM1124879 1 0.0000 0.9509 1.000 0.000 0.000 0.000
#> GSM1124883 2 0.5811 0.7326 0.000 0.704 0.116 0.180
#> GSM1124889 2 0.0000 0.8585 0.000 1.000 0.000 0.000
#> GSM1124892 1 0.0000 0.9509 1.000 0.000 0.000 0.000
#> GSM1124893 1 0.0000 0.9509 1.000 0.000 0.000 0.000
#> GSM1124909 2 0.0000 0.8585 0.000 1.000 0.000 0.000
#> GSM1124913 2 0.5731 0.7409 0.000 0.712 0.116 0.172
#> GSM1124916 2 0.0000 0.8585 0.000 1.000 0.000 0.000
#> GSM1124923 2 0.5036 0.7017 0.000 0.696 0.280 0.024
#> GSM1124925 1 0.0000 0.9509 1.000 0.000 0.000 0.000
#> GSM1124929 1 0.0000 0.9509 1.000 0.000 0.000 0.000
#> GSM1124934 1 0.2281 0.8748 0.904 0.096 0.000 0.000
#> GSM1124937 1 0.0000 0.9509 1.000 0.000 0.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM1124891 3 0.2536 0.830 0.128 0.000 0.868 0.000 0.004
#> GSM1124888 3 0.0671 0.916 0.016 0.000 0.980 0.000 0.004
#> GSM1124890 3 0.1478 0.890 0.000 0.000 0.936 0.000 0.064
#> GSM1124904 5 0.0794 0.871 0.000 0.000 0.000 0.028 0.972
#> GSM1124927 2 0.3011 0.795 0.140 0.844 0.000 0.000 0.016
#> GSM1124953 3 0.2522 0.831 0.000 0.108 0.880 0.000 0.012
#> GSM1124869 1 0.0000 0.958 1.000 0.000 0.000 0.000 0.000
#> GSM1124870 2 0.2929 0.773 0.180 0.820 0.000 0.000 0.000
#> GSM1124882 1 0.0000 0.958 1.000 0.000 0.000 0.000 0.000
#> GSM1124884 2 0.3081 0.839 0.000 0.832 0.012 0.000 0.156
#> GSM1124898 5 0.0162 0.875 0.000 0.004 0.000 0.000 0.996
#> GSM1124903 5 0.1043 0.863 0.000 0.000 0.000 0.040 0.960
#> GSM1124905 1 0.0000 0.958 1.000 0.000 0.000 0.000 0.000
#> GSM1124910 1 0.0324 0.953 0.992 0.004 0.000 0.000 0.004
#> GSM1124919 5 0.0162 0.875 0.000 0.004 0.000 0.000 0.996
#> GSM1124932 2 0.2351 0.771 0.088 0.896 0.016 0.000 0.000
#> GSM1124933 3 0.0510 0.917 0.016 0.000 0.984 0.000 0.000
#> GSM1124867 2 0.6384 0.340 0.084 0.516 0.000 0.368 0.032
#> GSM1124868 4 0.0000 0.977 0.000 0.000 0.000 1.000 0.000
#> GSM1124878 5 0.3983 0.536 0.000 0.000 0.000 0.340 0.660
#> GSM1124895 4 0.0000 0.977 0.000 0.000 0.000 1.000 0.000
#> GSM1124897 5 0.3684 0.637 0.000 0.000 0.000 0.280 0.720
#> GSM1124902 4 0.0000 0.977 0.000 0.000 0.000 1.000 0.000
#> GSM1124908 5 0.4219 0.354 0.000 0.000 0.000 0.416 0.584
#> GSM1124921 4 0.0000 0.977 0.000 0.000 0.000 1.000 0.000
#> GSM1124939 4 0.0000 0.977 0.000 0.000 0.000 1.000 0.000
#> GSM1124944 4 0.0000 0.977 0.000 0.000 0.000 1.000 0.000
#> GSM1124945 4 0.3074 0.735 0.000 0.000 0.196 0.804 0.000
#> GSM1124946 4 0.0162 0.973 0.000 0.000 0.000 0.996 0.004
#> GSM1124947 4 0.0000 0.977 0.000 0.000 0.000 1.000 0.000
#> GSM1124951 3 0.1701 0.895 0.000 0.000 0.936 0.048 0.016
#> GSM1124952 4 0.0000 0.977 0.000 0.000 0.000 1.000 0.000
#> GSM1124957 3 0.3074 0.741 0.000 0.000 0.804 0.196 0.000
#> GSM1124900 2 0.3774 0.640 0.296 0.704 0.000 0.000 0.000
#> GSM1124914 5 0.0404 0.876 0.000 0.000 0.000 0.012 0.988
#> GSM1124871 2 0.2813 0.832 0.000 0.832 0.000 0.000 0.168
#> GSM1124874 2 0.3109 0.809 0.000 0.800 0.000 0.000 0.200
#> GSM1124875 5 0.0000 0.875 0.000 0.000 0.000 0.000 1.000
#> GSM1124880 2 0.2848 0.784 0.156 0.840 0.000 0.000 0.004
#> GSM1124881 2 0.2813 0.832 0.000 0.832 0.000 0.000 0.168
#> GSM1124885 5 0.0162 0.876 0.000 0.000 0.000 0.004 0.996
#> GSM1124886 1 0.0000 0.958 1.000 0.000 0.000 0.000 0.000
#> GSM1124887 5 0.0290 0.876 0.000 0.000 0.000 0.008 0.992
#> GSM1124894 4 0.0162 0.973 0.004 0.000 0.000 0.996 0.000
#> GSM1124896 1 0.0162 0.956 0.996 0.000 0.004 0.000 0.000
#> GSM1124899 5 0.0955 0.861 0.000 0.028 0.004 0.000 0.968
#> GSM1124901 5 0.0162 0.875 0.000 0.004 0.000 0.000 0.996
#> GSM1124906 2 0.2690 0.838 0.000 0.844 0.000 0.000 0.156
#> GSM1124907 5 0.0162 0.876 0.000 0.000 0.000 0.004 0.996
#> GSM1124911 2 0.1012 0.809 0.000 0.968 0.012 0.000 0.020
#> GSM1124912 1 0.0000 0.958 1.000 0.000 0.000 0.000 0.000
#> GSM1124915 2 0.2909 0.753 0.000 0.848 0.012 0.000 0.140
#> GSM1124917 2 0.3336 0.799 0.000 0.772 0.000 0.000 0.228
#> GSM1124918 2 0.1597 0.804 0.000 0.940 0.012 0.000 0.048
#> GSM1124920 1 0.4397 0.181 0.564 0.000 0.432 0.000 0.004
#> GSM1124922 5 0.0609 0.867 0.000 0.020 0.000 0.000 0.980
#> GSM1124924 2 0.3190 0.781 0.008 0.840 0.140 0.000 0.012
#> GSM1124926 5 0.3616 0.628 0.000 0.224 0.004 0.004 0.768
#> GSM1124928 1 0.0880 0.926 0.968 0.032 0.000 0.000 0.000
#> GSM1124930 5 0.0510 0.871 0.000 0.000 0.016 0.000 0.984
#> GSM1124931 2 0.0404 0.808 0.000 0.988 0.012 0.000 0.000
#> GSM1124935 2 0.4065 0.592 0.000 0.720 0.016 0.000 0.264
#> GSM1124936 1 0.0162 0.956 0.996 0.000 0.004 0.000 0.000
#> GSM1124938 3 0.0609 0.913 0.000 0.000 0.980 0.000 0.020
#> GSM1124940 1 0.0000 0.958 1.000 0.000 0.000 0.000 0.000
#> GSM1124941 2 0.2732 0.837 0.000 0.840 0.000 0.000 0.160
#> GSM1124942 5 0.4937 0.260 0.000 0.028 0.428 0.000 0.544
#> GSM1124943 5 0.4235 0.235 0.000 0.000 0.424 0.000 0.576
#> GSM1124948 2 0.3593 0.827 0.000 0.824 0.060 0.000 0.116
#> GSM1124949 1 0.0000 0.958 1.000 0.000 0.000 0.000 0.000
#> GSM1124950 2 0.2690 0.838 0.000 0.844 0.000 0.000 0.156
#> GSM1124954 2 0.3757 0.656 0.208 0.772 0.020 0.000 0.000
#> GSM1124955 1 0.0162 0.956 0.996 0.000 0.004 0.000 0.000
#> GSM1124956 2 0.1117 0.808 0.000 0.964 0.016 0.000 0.020
#> GSM1124872 2 0.2690 0.838 0.000 0.844 0.000 0.000 0.156
#> GSM1124873 2 0.2690 0.838 0.000 0.844 0.000 0.000 0.156
#> GSM1124876 3 0.0510 0.917 0.016 0.000 0.984 0.000 0.000
#> GSM1124877 1 0.3759 0.698 0.764 0.220 0.016 0.000 0.000
#> GSM1124879 1 0.0000 0.958 1.000 0.000 0.000 0.000 0.000
#> GSM1124883 5 0.0794 0.871 0.000 0.000 0.000 0.028 0.972
#> GSM1124889 2 0.3305 0.786 0.000 0.776 0.000 0.000 0.224
#> GSM1124892 1 0.0000 0.958 1.000 0.000 0.000 0.000 0.000
#> GSM1124893 1 0.0000 0.958 1.000 0.000 0.000 0.000 0.000
#> GSM1124909 2 0.2690 0.838 0.000 0.844 0.000 0.000 0.156
#> GSM1124913 5 0.0794 0.871 0.000 0.000 0.000 0.028 0.972
#> GSM1124916 2 0.0404 0.809 0.000 0.988 0.012 0.000 0.000
#> GSM1124923 5 0.0880 0.864 0.000 0.000 0.032 0.000 0.968
#> GSM1124925 1 0.0162 0.956 0.996 0.000 0.004 0.000 0.000
#> GSM1124929 1 0.0000 0.958 1.000 0.000 0.000 0.000 0.000
#> GSM1124934 2 0.3596 0.668 0.200 0.784 0.016 0.000 0.000
#> GSM1124937 1 0.0162 0.956 0.996 0.000 0.000 0.000 0.004
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1124891 3 0.4263 0.718 0.144 0.012 0.768 0.000 0.012 0.064
#> GSM1124888 3 0.2803 0.778 0.000 0.016 0.856 0.000 0.012 0.116
#> GSM1124890 3 0.1610 0.773 0.000 0.000 0.916 0.000 0.084 0.000
#> GSM1124904 5 0.0363 0.913 0.000 0.000 0.000 0.012 0.988 0.000
#> GSM1124927 2 0.0717 0.932 0.008 0.976 0.000 0.000 0.000 0.016
#> GSM1124953 3 0.3706 0.350 0.000 0.380 0.620 0.000 0.000 0.000
#> GSM1124869 1 0.0000 0.959 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124870 2 0.1625 0.900 0.060 0.928 0.000 0.000 0.000 0.012
#> GSM1124882 1 0.0000 0.959 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124884 2 0.0914 0.932 0.000 0.968 0.000 0.000 0.016 0.016
#> GSM1124898 5 0.0405 0.912 0.000 0.008 0.000 0.004 0.988 0.000
#> GSM1124903 5 0.0363 0.913 0.000 0.000 0.000 0.012 0.988 0.000
#> GSM1124905 1 0.0146 0.956 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM1124910 1 0.1908 0.876 0.900 0.004 0.000 0.000 0.000 0.096
#> GSM1124919 5 0.2412 0.856 0.000 0.028 0.092 0.000 0.880 0.000
#> GSM1124932 6 0.2048 0.963 0.000 0.120 0.000 0.000 0.000 0.880
#> GSM1124933 3 0.0000 0.789 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1124867 2 0.2597 0.784 0.000 0.824 0.000 0.176 0.000 0.000
#> GSM1124868 4 0.0000 0.955 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1124878 5 0.3175 0.686 0.000 0.000 0.000 0.256 0.744 0.000
#> GSM1124895 4 0.0000 0.955 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1124897 5 0.2730 0.810 0.000 0.012 0.000 0.152 0.836 0.000
#> GSM1124902 4 0.0000 0.955 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1124908 5 0.2378 0.820 0.000 0.000 0.000 0.152 0.848 0.000
#> GSM1124921 4 0.0000 0.955 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1124939 4 0.0000 0.955 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1124944 4 0.0000 0.955 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1124945 4 0.3547 0.488 0.000 0.000 0.332 0.668 0.000 0.000
#> GSM1124946 4 0.1141 0.900 0.000 0.000 0.000 0.948 0.052 0.000
#> GSM1124947 4 0.0000 0.955 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1124951 3 0.0291 0.788 0.000 0.000 0.992 0.004 0.004 0.000
#> GSM1124952 4 0.0000 0.955 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1124957 3 0.2631 0.655 0.000 0.000 0.820 0.180 0.000 0.000
#> GSM1124900 2 0.1531 0.898 0.068 0.928 0.000 0.000 0.000 0.004
#> GSM1124914 5 0.0653 0.913 0.000 0.004 0.000 0.012 0.980 0.004
#> GSM1124871 2 0.0692 0.934 0.000 0.976 0.000 0.000 0.020 0.004
#> GSM1124874 2 0.0632 0.932 0.000 0.976 0.000 0.000 0.024 0.000
#> GSM1124875 5 0.1349 0.884 0.000 0.004 0.000 0.000 0.940 0.056
#> GSM1124880 2 0.1967 0.875 0.000 0.904 0.000 0.000 0.012 0.084
#> GSM1124881 2 0.0458 0.934 0.000 0.984 0.000 0.000 0.016 0.000
#> GSM1124885 5 0.0508 0.912 0.000 0.012 0.000 0.004 0.984 0.000
#> GSM1124886 1 0.0000 0.959 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124887 5 0.0405 0.913 0.000 0.004 0.000 0.008 0.988 0.000
#> GSM1124894 4 0.0508 0.941 0.012 0.000 0.000 0.984 0.000 0.004
#> GSM1124896 1 0.0000 0.959 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124899 5 0.1267 0.887 0.000 0.060 0.000 0.000 0.940 0.000
#> GSM1124901 5 0.0508 0.912 0.000 0.012 0.000 0.004 0.984 0.000
#> GSM1124906 2 0.0622 0.934 0.000 0.980 0.000 0.000 0.008 0.012
#> GSM1124907 5 0.1584 0.872 0.000 0.008 0.000 0.000 0.928 0.064
#> GSM1124911 6 0.2340 0.941 0.000 0.148 0.000 0.000 0.000 0.852
#> GSM1124912 1 0.0000 0.959 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124915 6 0.2266 0.960 0.000 0.108 0.000 0.000 0.012 0.880
#> GSM1124917 2 0.2632 0.779 0.000 0.832 0.000 0.000 0.164 0.004
#> GSM1124918 6 0.2003 0.963 0.000 0.116 0.000 0.000 0.000 0.884
#> GSM1124920 1 0.6000 0.218 0.544 0.020 0.308 0.000 0.012 0.116
#> GSM1124922 5 0.1080 0.904 0.004 0.032 0.000 0.000 0.960 0.004
#> GSM1124924 2 0.2402 0.838 0.000 0.868 0.000 0.000 0.012 0.120
#> GSM1124926 5 0.3647 0.461 0.000 0.360 0.000 0.000 0.640 0.000
#> GSM1124928 1 0.2136 0.884 0.904 0.048 0.000 0.000 0.000 0.048
#> GSM1124930 5 0.2536 0.815 0.000 0.020 0.000 0.000 0.864 0.116
#> GSM1124931 6 0.2048 0.963 0.000 0.120 0.000 0.000 0.000 0.880
#> GSM1124935 6 0.2509 0.939 0.000 0.088 0.000 0.000 0.036 0.876
#> GSM1124936 1 0.0000 0.959 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124938 3 0.2889 0.777 0.000 0.020 0.852 0.000 0.012 0.116
#> GSM1124940 1 0.0000 0.959 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124941 2 0.0632 0.927 0.000 0.976 0.000 0.000 0.000 0.024
#> GSM1124942 3 0.5400 0.702 0.000 0.084 0.684 0.000 0.116 0.116
#> GSM1124943 3 0.5873 0.279 0.000 0.020 0.452 0.000 0.412 0.116
#> GSM1124948 2 0.2656 0.831 0.000 0.860 0.008 0.000 0.012 0.120
#> GSM1124949 1 0.0000 0.959 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124950 2 0.0363 0.932 0.000 0.988 0.000 0.000 0.000 0.012
#> GSM1124954 6 0.2311 0.959 0.016 0.104 0.000 0.000 0.000 0.880
#> GSM1124955 1 0.0000 0.959 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124956 6 0.2048 0.963 0.000 0.120 0.000 0.000 0.000 0.880
#> GSM1124872 2 0.0622 0.934 0.000 0.980 0.000 0.000 0.008 0.012
#> GSM1124873 2 0.0520 0.934 0.000 0.984 0.000 0.000 0.008 0.008
#> GSM1124876 3 0.0000 0.789 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1124877 6 0.2257 0.826 0.116 0.008 0.000 0.000 0.000 0.876
#> GSM1124879 1 0.0000 0.959 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124883 5 0.0363 0.913 0.000 0.000 0.000 0.012 0.988 0.000
#> GSM1124889 2 0.0790 0.928 0.000 0.968 0.000 0.000 0.032 0.000
#> GSM1124892 1 0.0000 0.959 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124893 1 0.0000 0.959 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124909 2 0.0520 0.935 0.000 0.984 0.000 0.000 0.008 0.008
#> GSM1124913 5 0.0363 0.913 0.000 0.000 0.000 0.012 0.988 0.000
#> GSM1124916 6 0.2219 0.950 0.000 0.136 0.000 0.000 0.000 0.864
#> GSM1124923 5 0.0508 0.910 0.000 0.000 0.012 0.004 0.984 0.000
#> GSM1124925 1 0.0000 0.959 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124929 1 0.0000 0.959 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124934 6 0.2301 0.953 0.020 0.096 0.000 0.000 0.000 0.884
#> GSM1124937 1 0.1693 0.914 0.936 0.020 0.000 0.000 0.012 0.032
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 tissue(p) k
#> CV:NMF 84 7.76e-03 2
#> CV:NMF 85 2.09e-03 3
#> CV:NMF 88 2.63e-12 4
#> CV:NMF 86 7.94e-09 5
#> CV:NMF 86 8.13e-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", "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 48983 rows and 91 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'hclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.432 0.832 0.907 0.2483 0.785 0.785
#> 3 3 0.310 0.691 0.836 1.1641 0.660 0.579
#> 4 4 0.380 0.418 0.726 0.1967 0.958 0.915
#> 5 5 0.421 0.379 0.649 0.1011 0.868 0.713
#> 6 6 0.433 0.346 0.626 0.0452 0.904 0.725
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
#> GSM1124891 1 0.8813 0.667 0.700 0.300
#> GSM1124888 1 0.7299 0.722 0.796 0.204
#> GSM1124890 2 0.5842 0.814 0.140 0.860
#> GSM1124904 2 0.0000 0.904 0.000 1.000
#> GSM1124927 2 0.1843 0.903 0.028 0.972
#> GSM1124953 2 0.9129 0.430 0.328 0.672
#> GSM1124869 2 0.7219 0.783 0.200 0.800
#> GSM1124870 2 0.1843 0.903 0.028 0.972
#> GSM1124882 2 0.6438 0.819 0.164 0.836
#> GSM1124884 2 0.2236 0.900 0.036 0.964
#> GSM1124898 2 0.0000 0.904 0.000 1.000
#> GSM1124903 2 0.0000 0.904 0.000 1.000
#> GSM1124905 2 0.5178 0.849 0.116 0.884
#> GSM1124910 2 0.8144 0.707 0.252 0.748
#> GSM1124919 2 0.5737 0.819 0.136 0.864
#> GSM1124932 2 0.2043 0.901 0.032 0.968
#> GSM1124933 1 0.0000 0.727 1.000 0.000
#> GSM1124867 2 0.4161 0.881 0.084 0.916
#> GSM1124868 2 0.0000 0.904 0.000 1.000
#> GSM1124878 2 0.0000 0.904 0.000 1.000
#> GSM1124895 2 0.0000 0.904 0.000 1.000
#> GSM1124897 2 0.0000 0.904 0.000 1.000
#> GSM1124902 2 0.0376 0.905 0.004 0.996
#> GSM1124908 2 0.0376 0.905 0.004 0.996
#> GSM1124921 2 0.1843 0.901 0.028 0.972
#> GSM1124939 2 0.0376 0.905 0.004 0.996
#> GSM1124944 2 0.0376 0.905 0.004 0.996
#> GSM1124945 1 0.5408 0.726 0.876 0.124
#> GSM1124946 2 0.1843 0.901 0.028 0.972
#> GSM1124947 2 0.0938 0.906 0.012 0.988
#> GSM1124951 1 0.5629 0.725 0.868 0.132
#> GSM1124952 2 0.2236 0.900 0.036 0.964
#> GSM1124957 1 0.0000 0.727 1.000 0.000
#> GSM1124900 2 0.2236 0.901 0.036 0.964
#> GSM1124914 2 0.0000 0.904 0.000 1.000
#> GSM1124871 2 0.0000 0.904 0.000 1.000
#> GSM1124874 2 0.0672 0.905 0.008 0.992
#> GSM1124875 2 0.3584 0.886 0.068 0.932
#> GSM1124880 2 0.6623 0.816 0.172 0.828
#> GSM1124881 2 0.0672 0.905 0.008 0.992
#> GSM1124885 2 0.0000 0.904 0.000 1.000
#> GSM1124886 2 0.7602 0.757 0.220 0.780
#> GSM1124887 2 0.1843 0.901 0.028 0.972
#> GSM1124894 2 0.5178 0.849 0.116 0.884
#> GSM1124896 2 0.5059 0.851 0.112 0.888
#> GSM1124899 2 0.0376 0.905 0.004 0.996
#> GSM1124901 2 0.0000 0.904 0.000 1.000
#> GSM1124906 2 0.0938 0.905 0.012 0.988
#> GSM1124907 2 0.2603 0.895 0.044 0.956
#> GSM1124911 2 0.0000 0.904 0.000 1.000
#> GSM1124912 2 0.6247 0.825 0.156 0.844
#> GSM1124915 2 0.0000 0.904 0.000 1.000
#> GSM1124917 2 0.0672 0.905 0.008 0.992
#> GSM1124918 2 0.3584 0.886 0.068 0.932
#> GSM1124920 1 0.9248 0.620 0.660 0.340
#> GSM1124922 2 0.0376 0.905 0.004 0.996
#> GSM1124924 2 0.6973 0.795 0.188 0.812
#> GSM1124926 2 0.0376 0.905 0.004 0.996
#> GSM1124928 2 0.6973 0.800 0.188 0.812
#> GSM1124930 2 0.5178 0.848 0.116 0.884
#> GSM1124931 2 0.2778 0.895 0.048 0.952
#> GSM1124935 2 0.0000 0.904 0.000 1.000
#> GSM1124936 1 0.9998 0.135 0.508 0.492
#> GSM1124938 2 0.5294 0.848 0.120 0.880
#> GSM1124940 2 0.6247 0.825 0.156 0.844
#> GSM1124941 2 0.0938 0.905 0.012 0.988
#> GSM1124942 2 0.5294 0.848 0.120 0.880
#> GSM1124943 2 0.5294 0.846 0.120 0.880
#> GSM1124948 2 0.6247 0.815 0.156 0.844
#> GSM1124949 2 0.7528 0.763 0.216 0.784
#> GSM1124950 2 0.2043 0.901 0.032 0.968
#> GSM1124954 1 0.9209 0.626 0.664 0.336
#> GSM1124955 2 0.6247 0.825 0.156 0.844
#> GSM1124956 2 0.0000 0.904 0.000 1.000
#> GSM1124872 2 0.2043 0.901 0.032 0.968
#> GSM1124873 2 0.0000 0.904 0.000 1.000
#> GSM1124876 1 0.0000 0.727 1.000 0.000
#> GSM1124877 2 0.7299 0.774 0.204 0.796
#> GSM1124879 2 0.8144 0.707 0.252 0.748
#> GSM1124883 2 0.0000 0.904 0.000 1.000
#> GSM1124889 2 0.0000 0.904 0.000 1.000
#> GSM1124892 2 0.8443 0.668 0.272 0.728
#> GSM1124893 2 0.6247 0.825 0.156 0.844
#> GSM1124909 2 0.3879 0.883 0.076 0.924
#> GSM1124913 2 0.0000 0.904 0.000 1.000
#> GSM1124916 2 0.3879 0.883 0.076 0.924
#> GSM1124923 2 0.6973 0.747 0.188 0.812
#> GSM1124925 2 0.5059 0.851 0.112 0.888
#> GSM1124929 2 0.7528 0.763 0.216 0.784
#> GSM1124934 1 0.9608 0.527 0.616 0.384
#> GSM1124937 2 0.6438 0.813 0.164 0.836
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1124891 3 0.6180 0.2829 0.416 0.000 0.584
#> GSM1124888 3 0.5363 0.5664 0.276 0.000 0.724
#> GSM1124890 2 0.7535 0.6664 0.176 0.692 0.132
#> GSM1124904 2 0.0424 0.8234 0.008 0.992 0.000
#> GSM1124927 2 0.5291 0.7095 0.268 0.732 0.000
#> GSM1124953 2 0.7491 0.4886 0.056 0.620 0.324
#> GSM1124869 1 0.1647 0.7520 0.960 0.036 0.004
#> GSM1124870 2 0.5291 0.7095 0.268 0.732 0.000
#> GSM1124882 1 0.2261 0.7589 0.932 0.068 0.000
#> GSM1124884 2 0.3686 0.8074 0.140 0.860 0.000
#> GSM1124898 2 0.1643 0.8352 0.044 0.956 0.000
#> GSM1124903 2 0.0424 0.8234 0.008 0.992 0.000
#> GSM1124905 1 0.6155 0.5203 0.664 0.328 0.008
#> GSM1124910 1 0.3263 0.7363 0.912 0.048 0.040
#> GSM1124919 2 0.6383 0.7338 0.104 0.768 0.128
#> GSM1124932 2 0.3551 0.8124 0.132 0.868 0.000
#> GSM1124933 3 0.0000 0.7918 0.000 0.000 1.000
#> GSM1124867 2 0.6476 0.3908 0.448 0.548 0.004
#> GSM1124868 2 0.0424 0.8234 0.008 0.992 0.000
#> GSM1124878 2 0.0424 0.8234 0.008 0.992 0.000
#> GSM1124895 2 0.0747 0.8275 0.016 0.984 0.000
#> GSM1124897 2 0.0424 0.8254 0.008 0.992 0.000
#> GSM1124902 2 0.2448 0.8255 0.076 0.924 0.000
#> GSM1124908 2 0.2165 0.8321 0.064 0.936 0.000
#> GSM1124921 2 0.3276 0.8128 0.068 0.908 0.024
#> GSM1124939 2 0.2448 0.8255 0.076 0.924 0.000
#> GSM1124944 2 0.2448 0.8255 0.076 0.924 0.000
#> GSM1124945 3 0.3412 0.7333 0.000 0.124 0.876
#> GSM1124946 2 0.3276 0.8128 0.068 0.908 0.024
#> GSM1124947 2 0.2625 0.8265 0.084 0.916 0.000
#> GSM1124951 3 0.3551 0.7239 0.000 0.132 0.868
#> GSM1124952 2 0.3686 0.8074 0.140 0.860 0.000
#> GSM1124957 3 0.0000 0.7918 0.000 0.000 1.000
#> GSM1124900 2 0.5397 0.6933 0.280 0.720 0.000
#> GSM1124914 2 0.1289 0.8325 0.032 0.968 0.000
#> GSM1124871 2 0.2448 0.8334 0.076 0.924 0.000
#> GSM1124874 2 0.2261 0.8361 0.068 0.932 0.000
#> GSM1124875 2 0.6880 0.6086 0.304 0.660 0.036
#> GSM1124880 1 0.4861 0.6649 0.800 0.192 0.008
#> GSM1124881 2 0.3500 0.8281 0.116 0.880 0.004
#> GSM1124885 2 0.0424 0.8234 0.008 0.992 0.000
#> GSM1124886 1 0.1315 0.7418 0.972 0.020 0.008
#> GSM1124887 2 0.2564 0.8282 0.036 0.936 0.028
#> GSM1124894 1 0.6275 0.4730 0.644 0.348 0.008
#> GSM1124896 1 0.4605 0.6695 0.796 0.204 0.000
#> GSM1124899 2 0.2711 0.8355 0.088 0.912 0.000
#> GSM1124901 2 0.1163 0.8329 0.028 0.972 0.000
#> GSM1124906 2 0.3816 0.8052 0.148 0.852 0.000
#> GSM1124907 2 0.3921 0.8016 0.080 0.884 0.036
#> GSM1124911 2 0.2356 0.8317 0.072 0.928 0.000
#> GSM1124912 1 0.2448 0.7586 0.924 0.076 0.000
#> GSM1124915 2 0.1964 0.8339 0.056 0.944 0.000
#> GSM1124917 2 0.3349 0.8302 0.108 0.888 0.004
#> GSM1124918 2 0.6880 0.6086 0.304 0.660 0.036
#> GSM1124920 1 0.6295 -0.1125 0.528 0.000 0.472
#> GSM1124922 2 0.3879 0.8114 0.152 0.848 0.000
#> GSM1124924 1 0.5521 0.6613 0.788 0.180 0.032
#> GSM1124926 2 0.2711 0.8355 0.088 0.912 0.000
#> GSM1124928 1 0.4514 0.6988 0.832 0.156 0.012
#> GSM1124930 2 0.8243 0.2685 0.420 0.504 0.076
#> GSM1124931 2 0.4931 0.7465 0.232 0.768 0.000
#> GSM1124935 2 0.1753 0.8358 0.048 0.952 0.000
#> GSM1124936 1 0.5929 0.3471 0.676 0.004 0.320
#> GSM1124938 2 0.8260 0.2478 0.432 0.492 0.076
#> GSM1124940 1 0.2448 0.7586 0.924 0.076 0.000
#> GSM1124941 2 0.3816 0.8052 0.148 0.852 0.000
#> GSM1124942 2 0.8255 0.2622 0.428 0.496 0.076
#> GSM1124943 2 0.8310 0.2600 0.420 0.500 0.080
#> GSM1124948 1 0.6839 0.5209 0.684 0.272 0.044
#> GSM1124949 1 0.1129 0.7427 0.976 0.020 0.004
#> GSM1124950 2 0.5016 0.7358 0.240 0.760 0.000
#> GSM1124954 1 0.6309 -0.1606 0.504 0.000 0.496
#> GSM1124955 1 0.2448 0.7586 0.924 0.076 0.000
#> GSM1124956 2 0.2356 0.8317 0.072 0.928 0.000
#> GSM1124872 2 0.5016 0.7358 0.240 0.760 0.000
#> GSM1124873 2 0.3038 0.8291 0.104 0.896 0.000
#> GSM1124876 3 0.0000 0.7918 0.000 0.000 1.000
#> GSM1124877 1 0.2879 0.7564 0.924 0.052 0.024
#> GSM1124879 1 0.1751 0.7275 0.960 0.012 0.028
#> GSM1124883 2 0.0424 0.8234 0.008 0.992 0.000
#> GSM1124889 2 0.1964 0.8339 0.056 0.944 0.000
#> GSM1124892 1 0.2280 0.7029 0.940 0.008 0.052
#> GSM1124893 1 0.2448 0.7586 0.924 0.076 0.000
#> GSM1124909 2 0.6247 0.5503 0.376 0.620 0.004
#> GSM1124913 2 0.0424 0.8234 0.008 0.992 0.000
#> GSM1124916 2 0.6247 0.5503 0.376 0.620 0.004
#> GSM1124923 2 0.6835 0.6873 0.088 0.732 0.180
#> GSM1124925 1 0.4605 0.6695 0.796 0.204 0.000
#> GSM1124929 1 0.1129 0.7427 0.976 0.020 0.004
#> GSM1124934 1 0.6204 0.0723 0.576 0.000 0.424
#> GSM1124937 1 0.5060 0.6790 0.816 0.156 0.028
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1124891 3 0.3464 0.5939 0.124 0.004 0.856 0.016
#> GSM1124888 3 0.3601 0.6408 0.056 0.000 0.860 0.084
#> GSM1124890 2 0.7048 -0.1523 0.044 0.584 0.056 0.316
#> GSM1124904 2 0.3801 0.3493 0.000 0.780 0.000 0.220
#> GSM1124927 2 0.6694 0.4537 0.212 0.660 0.024 0.104
#> GSM1124953 4 0.6491 0.0000 0.000 0.396 0.076 0.528
#> GSM1124869 1 0.2631 0.6448 0.912 0.008 0.064 0.016
#> GSM1124870 2 0.6694 0.4537 0.212 0.660 0.024 0.104
#> GSM1124882 1 0.1139 0.6470 0.972 0.012 0.008 0.008
#> GSM1124884 2 0.4534 0.5619 0.132 0.800 0.000 0.068
#> GSM1124898 2 0.2021 0.5677 0.012 0.932 0.000 0.056
#> GSM1124903 2 0.3172 0.4510 0.000 0.840 0.000 0.160
#> GSM1124905 1 0.6893 0.4158 0.644 0.228 0.032 0.096
#> GSM1124910 1 0.7973 0.4111 0.520 0.040 0.300 0.140
#> GSM1124919 2 0.5815 -0.0583 0.028 0.636 0.012 0.324
#> GSM1124932 2 0.4428 0.5662 0.124 0.808 0.000 0.068
#> GSM1124933 3 0.4624 0.6200 0.000 0.000 0.660 0.340
#> GSM1124867 2 0.8048 0.1814 0.340 0.500 0.068 0.092
#> GSM1124868 2 0.2704 0.4942 0.000 0.876 0.000 0.124
#> GSM1124878 2 0.3942 0.3178 0.000 0.764 0.000 0.236
#> GSM1124895 2 0.2334 0.5300 0.004 0.908 0.000 0.088
#> GSM1124897 2 0.2345 0.5182 0.000 0.900 0.000 0.100
#> GSM1124902 2 0.3806 0.4455 0.020 0.824 0.000 0.156
#> GSM1124908 2 0.3217 0.4990 0.012 0.860 0.000 0.128
#> GSM1124921 2 0.5047 0.0685 0.016 0.668 0.000 0.316
#> GSM1124939 2 0.3757 0.4501 0.020 0.828 0.000 0.152
#> GSM1124944 2 0.3757 0.4501 0.020 0.828 0.000 0.152
#> GSM1124945 3 0.5827 0.5339 0.000 0.032 0.532 0.436
#> GSM1124946 2 0.5047 0.0685 0.016 0.668 0.000 0.316
#> GSM1124947 2 0.3891 0.4604 0.020 0.828 0.004 0.148
#> GSM1124951 3 0.5838 0.5280 0.000 0.032 0.524 0.444
#> GSM1124952 2 0.4534 0.5619 0.132 0.800 0.000 0.068
#> GSM1124957 3 0.4624 0.6200 0.000 0.000 0.660 0.340
#> GSM1124900 2 0.6789 0.4405 0.224 0.648 0.024 0.104
#> GSM1124914 2 0.2345 0.5256 0.000 0.900 0.000 0.100
#> GSM1124871 2 0.3400 0.5906 0.076 0.876 0.004 0.044
#> GSM1124874 2 0.3009 0.5908 0.052 0.892 0.000 0.056
#> GSM1124875 2 0.7949 0.1414 0.088 0.568 0.092 0.252
#> GSM1124880 1 0.9015 0.4235 0.464 0.176 0.252 0.108
#> GSM1124881 2 0.4007 0.5770 0.068 0.856 0.020 0.056
#> GSM1124885 2 0.2814 0.4867 0.000 0.868 0.000 0.132
#> GSM1124886 1 0.2987 0.6336 0.880 0.000 0.104 0.016
#> GSM1124887 2 0.4428 0.2363 0.000 0.720 0.004 0.276
#> GSM1124894 1 0.6849 0.4011 0.640 0.232 0.024 0.104
#> GSM1124896 1 0.4234 0.5467 0.816 0.132 0.000 0.052
#> GSM1124899 2 0.3088 0.5885 0.060 0.888 0.000 0.052
#> GSM1124901 2 0.1637 0.5572 0.000 0.940 0.000 0.060
#> GSM1124906 2 0.4686 0.5548 0.144 0.788 0.000 0.068
#> GSM1124907 2 0.5323 -0.0768 0.020 0.628 0.000 0.352
#> GSM1124911 2 0.3392 0.5850 0.072 0.872 0.000 0.056
#> GSM1124912 1 0.0844 0.6426 0.980 0.012 0.004 0.004
#> GSM1124915 2 0.3081 0.5876 0.064 0.888 0.000 0.048
#> GSM1124917 2 0.4227 0.5751 0.060 0.844 0.020 0.076
#> GSM1124918 2 0.7949 0.1414 0.088 0.568 0.092 0.252
#> GSM1124920 3 0.4188 0.4820 0.244 0.000 0.752 0.004
#> GSM1124922 2 0.4607 0.5721 0.100 0.816 0.012 0.072
#> GSM1124924 1 0.9520 0.3514 0.380 0.156 0.292 0.172
#> GSM1124926 2 0.3088 0.5885 0.060 0.888 0.000 0.052
#> GSM1124928 1 0.8709 0.4489 0.496 0.144 0.260 0.100
#> GSM1124930 2 0.8896 -0.2298 0.072 0.388 0.180 0.360
#> GSM1124931 2 0.6215 0.4947 0.188 0.700 0.020 0.092
#> GSM1124935 2 0.2060 0.5709 0.016 0.932 0.000 0.052
#> GSM1124936 3 0.5913 0.1800 0.352 0.000 0.600 0.048
#> GSM1124938 2 0.8898 -0.2168 0.072 0.384 0.180 0.364
#> GSM1124940 1 0.0657 0.6438 0.984 0.012 0.000 0.004
#> GSM1124941 2 0.4686 0.5548 0.144 0.788 0.000 0.068
#> GSM1124942 2 0.8875 -0.2143 0.072 0.388 0.176 0.364
#> GSM1124943 2 0.8915 -0.2360 0.072 0.388 0.184 0.356
#> GSM1124948 1 0.9921 0.2566 0.296 0.232 0.272 0.200
#> GSM1124949 1 0.2987 0.6348 0.880 0.000 0.104 0.016
#> GSM1124950 2 0.6280 0.4853 0.204 0.692 0.024 0.080
#> GSM1124954 3 0.4549 0.5248 0.188 0.000 0.776 0.036
#> GSM1124955 1 0.0804 0.6425 0.980 0.012 0.000 0.008
#> GSM1124956 2 0.3392 0.5850 0.072 0.872 0.000 0.056
#> GSM1124872 2 0.6280 0.4853 0.204 0.692 0.024 0.080
#> GSM1124873 2 0.4059 0.5863 0.092 0.844 0.008 0.056
#> GSM1124876 3 0.4624 0.6200 0.000 0.000 0.660 0.340
#> GSM1124877 1 0.3697 0.6338 0.868 0.012 0.068 0.052
#> GSM1124879 1 0.7128 0.4311 0.576 0.012 0.288 0.124
#> GSM1124883 2 0.3074 0.4595 0.000 0.848 0.000 0.152
#> GSM1124889 2 0.3081 0.5876 0.064 0.888 0.000 0.048
#> GSM1124892 1 0.4599 0.5284 0.736 0.000 0.248 0.016
#> GSM1124893 1 0.0657 0.6438 0.984 0.012 0.000 0.004
#> GSM1124909 2 0.7759 0.2972 0.240 0.588 0.076 0.096
#> GSM1124913 2 0.3975 0.3112 0.000 0.760 0.000 0.240
#> GSM1124916 2 0.7759 0.2972 0.240 0.588 0.076 0.096
#> GSM1124923 2 0.6631 -0.5996 0.016 0.508 0.048 0.428
#> GSM1124925 1 0.4234 0.5467 0.816 0.132 0.000 0.052
#> GSM1124929 1 0.2987 0.6348 0.880 0.000 0.104 0.016
#> GSM1124934 3 0.5156 0.4378 0.236 0.000 0.720 0.044
#> GSM1124937 1 0.9295 0.3622 0.408 0.152 0.300 0.140
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM1124891 3 0.275 0.6040 0.096 0.008 0.880 0.000 0.016
#> GSM1124888 3 0.284 0.6465 0.048 0.000 0.876 0.076 0.000
#> GSM1124890 4 0.708 0.4497 0.004 0.408 0.036 0.420 0.132
#> GSM1124904 5 0.465 0.7732 0.000 0.372 0.000 0.020 0.608
#> GSM1124927 2 0.559 0.3562 0.124 0.724 0.004 0.080 0.068
#> GSM1124953 4 0.658 0.2380 0.000 0.144 0.032 0.568 0.256
#> GSM1124869 1 0.170 0.6190 0.936 0.016 0.048 0.000 0.000
#> GSM1124870 2 0.559 0.3562 0.124 0.724 0.004 0.080 0.068
#> GSM1124882 1 0.173 0.6227 0.940 0.044 0.008 0.004 0.004
#> GSM1124884 2 0.255 0.5302 0.048 0.908 0.004 0.024 0.016
#> GSM1124898 2 0.430 0.3840 0.000 0.744 0.000 0.048 0.208
#> GSM1124903 5 0.489 0.6880 0.000 0.404 0.000 0.028 0.568
#> GSM1124905 1 0.852 0.3218 0.468 0.240 0.068 0.104 0.120
#> GSM1124910 1 0.784 0.3509 0.496 0.056 0.256 0.160 0.032
#> GSM1124919 2 0.656 -0.4158 0.004 0.424 0.000 0.400 0.172
#> GSM1124932 2 0.239 0.5335 0.044 0.916 0.004 0.020 0.016
#> GSM1124933 3 0.468 0.6612 0.000 0.000 0.652 0.316 0.032
#> GSM1124867 2 0.735 0.0654 0.296 0.528 0.032 0.088 0.056
#> GSM1124868 2 0.485 -0.3099 0.000 0.552 0.000 0.024 0.424
#> GSM1124878 5 0.466 0.7911 0.000 0.312 0.000 0.032 0.656
#> GSM1124895 2 0.458 0.1820 0.000 0.672 0.000 0.032 0.296
#> GSM1124897 2 0.499 -0.1698 0.000 0.580 0.000 0.036 0.384
#> GSM1124902 2 0.553 0.2941 0.004 0.656 0.000 0.128 0.212
#> GSM1124908 2 0.523 0.1526 0.000 0.636 0.000 0.076 0.288
#> GSM1124921 2 0.661 -0.2913 0.004 0.432 0.000 0.184 0.380
#> GSM1124939 2 0.556 0.2909 0.004 0.652 0.000 0.128 0.216
#> GSM1124944 2 0.545 0.3197 0.004 0.668 0.000 0.128 0.200
#> GSM1124945 3 0.599 0.6158 0.000 0.004 0.524 0.368 0.104
#> GSM1124946 2 0.661 -0.2913 0.004 0.432 0.000 0.184 0.380
#> GSM1124947 2 0.539 0.3412 0.004 0.676 0.000 0.128 0.192
#> GSM1124951 3 0.607 0.6118 0.000 0.004 0.516 0.368 0.112
#> GSM1124952 2 0.255 0.5302 0.048 0.908 0.004 0.024 0.016
#> GSM1124957 3 0.468 0.6612 0.000 0.000 0.652 0.316 0.032
#> GSM1124900 2 0.572 0.3409 0.136 0.712 0.004 0.080 0.068
#> GSM1124914 2 0.484 0.1433 0.000 0.652 0.000 0.044 0.304
#> GSM1124871 2 0.256 0.5183 0.020 0.884 0.000 0.000 0.096
#> GSM1124874 2 0.241 0.5142 0.004 0.896 0.000 0.012 0.088
#> GSM1124875 2 0.746 -0.3427 0.052 0.504 0.068 0.324 0.052
#> GSM1124880 1 0.893 0.2805 0.412 0.188 0.204 0.144 0.052
#> GSM1124881 2 0.412 0.5250 0.024 0.828 0.012 0.060 0.076
#> GSM1124885 2 0.490 -0.4697 0.000 0.504 0.000 0.024 0.472
#> GSM1124886 1 0.215 0.6072 0.912 0.004 0.076 0.004 0.004
#> GSM1124887 5 0.603 0.5329 0.000 0.388 0.000 0.120 0.492
#> GSM1124894 1 0.838 0.3188 0.476 0.240 0.060 0.084 0.140
#> GSM1124896 1 0.525 0.5097 0.740 0.128 0.000 0.064 0.068
#> GSM1124899 2 0.417 0.4675 0.024 0.788 0.000 0.028 0.160
#> GSM1124901 2 0.462 0.2730 0.000 0.692 0.000 0.044 0.264
#> GSM1124906 2 0.298 0.5341 0.068 0.884 0.004 0.016 0.028
#> GSM1124907 2 0.681 -0.2659 0.004 0.404 0.000 0.236 0.356
#> GSM1124911 2 0.120 0.5332 0.004 0.960 0.000 0.004 0.032
#> GSM1124912 1 0.168 0.6197 0.940 0.048 0.004 0.004 0.004
#> GSM1124915 2 0.172 0.5291 0.004 0.936 0.000 0.008 0.052
#> GSM1124917 2 0.451 0.5141 0.020 0.800 0.012 0.080 0.088
#> GSM1124918 2 0.746 -0.3427 0.052 0.504 0.068 0.324 0.052
#> GSM1124920 3 0.432 0.4586 0.260 0.000 0.716 0.012 0.012
#> GSM1124922 2 0.489 0.5371 0.060 0.776 0.004 0.060 0.100
#> GSM1124924 1 0.920 0.1831 0.328 0.184 0.244 0.200 0.044
#> GSM1124926 2 0.417 0.4675 0.024 0.788 0.000 0.028 0.160
#> GSM1124928 1 0.858 0.3709 0.460 0.152 0.212 0.128 0.048
#> GSM1124930 4 0.754 0.6831 0.028 0.324 0.148 0.468 0.032
#> GSM1124931 2 0.533 0.4198 0.092 0.760 0.020 0.072 0.056
#> GSM1124935 2 0.432 0.3915 0.004 0.748 0.000 0.040 0.208
#> GSM1124936 3 0.581 0.1624 0.360 0.000 0.560 0.064 0.016
#> GSM1124938 4 0.743 0.6766 0.028 0.336 0.148 0.464 0.024
#> GSM1124940 1 0.152 0.6207 0.944 0.048 0.000 0.004 0.004
#> GSM1124941 2 0.298 0.5341 0.068 0.884 0.004 0.016 0.028
#> GSM1124942 4 0.747 0.6783 0.028 0.336 0.144 0.464 0.028
#> GSM1124943 4 0.753 0.6842 0.028 0.320 0.148 0.472 0.032
#> GSM1124948 1 0.920 -0.0991 0.272 0.244 0.224 0.228 0.032
#> GSM1124949 1 0.221 0.6085 0.912 0.004 0.072 0.004 0.008
#> GSM1124950 2 0.488 0.4397 0.108 0.780 0.012 0.060 0.040
#> GSM1124954 3 0.501 0.5325 0.160 0.004 0.748 0.040 0.048
#> GSM1124955 1 0.159 0.6195 0.940 0.052 0.000 0.004 0.004
#> GSM1124956 2 0.120 0.5332 0.004 0.960 0.000 0.004 0.032
#> GSM1124872 2 0.488 0.4397 0.108 0.780 0.012 0.060 0.040
#> GSM1124873 2 0.235 0.5490 0.024 0.920 0.004 0.024 0.028
#> GSM1124876 3 0.468 0.6612 0.000 0.000 0.652 0.316 0.032
#> GSM1124877 1 0.373 0.6083 0.852 0.036 0.044 0.060 0.008
#> GSM1124879 1 0.733 0.3703 0.540 0.028 0.236 0.164 0.032
#> GSM1124883 2 0.483 -0.5120 0.000 0.496 0.000 0.020 0.484
#> GSM1124889 2 0.164 0.5307 0.004 0.940 0.000 0.008 0.048
#> GSM1124892 1 0.372 0.5090 0.776 0.000 0.208 0.004 0.012
#> GSM1124893 1 0.152 0.6207 0.944 0.048 0.000 0.004 0.004
#> GSM1124909 2 0.687 0.2059 0.216 0.616 0.052 0.080 0.036
#> GSM1124913 5 0.471 0.7618 0.000 0.280 0.000 0.044 0.676
#> GSM1124916 2 0.687 0.2059 0.216 0.616 0.052 0.080 0.036
#> GSM1124923 4 0.703 0.3133 0.004 0.268 0.012 0.464 0.252
#> GSM1124925 1 0.525 0.5097 0.740 0.128 0.000 0.064 0.068
#> GSM1124929 1 0.221 0.6085 0.912 0.004 0.072 0.004 0.008
#> GSM1124934 3 0.595 0.4632 0.188 0.012 0.684 0.060 0.056
#> GSM1124937 1 0.898 0.2496 0.376 0.172 0.236 0.176 0.040
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1124891 3 0.7410 0.4005 0.272 0.008 0.488 0.048 0.092 0.092
#> GSM1124888 3 0.6130 0.5776 0.176 0.000 0.640 0.044 0.084 0.056
#> GSM1124890 5 0.6222 0.5496 0.072 0.328 0.048 0.020 0.532 0.000
#> GSM1124904 4 0.4590 0.7264 0.000 0.268 0.000 0.668 0.056 0.008
#> GSM1124927 2 0.5030 0.4217 0.120 0.732 0.000 0.012 0.072 0.064
#> GSM1124953 5 0.5263 0.1721 0.000 0.056 0.216 0.036 0.676 0.016
#> GSM1124869 1 0.4403 -0.4642 0.520 0.012 0.000 0.000 0.008 0.460
#> GSM1124870 2 0.5030 0.4217 0.120 0.732 0.000 0.012 0.072 0.064
#> GSM1124882 6 0.4533 0.5552 0.468 0.024 0.000 0.000 0.004 0.504
#> GSM1124884 2 0.2233 0.5704 0.044 0.912 0.000 0.020 0.004 0.020
#> GSM1124898 2 0.4738 0.3886 0.020 0.692 0.000 0.220 0.068 0.000
#> GSM1124903 4 0.4563 0.7093 0.000 0.284 0.000 0.656 0.056 0.004
#> GSM1124905 6 0.5944 0.2485 0.080 0.240 0.000 0.016 0.052 0.612
#> GSM1124910 1 0.2550 0.3668 0.900 0.048 0.008 0.004 0.016 0.024
#> GSM1124919 5 0.5489 0.5035 0.016 0.328 0.044 0.028 0.584 0.000
#> GSM1124932 2 0.2076 0.5734 0.040 0.920 0.000 0.020 0.004 0.016
#> GSM1124933 3 0.0146 0.8109 0.000 0.000 0.996 0.000 0.004 0.000
#> GSM1124867 2 0.6085 0.1623 0.340 0.528 0.000 0.012 0.076 0.044
#> GSM1124868 2 0.5082 -0.3981 0.000 0.476 0.000 0.460 0.056 0.008
#> GSM1124878 4 0.4171 0.7009 0.000 0.208 0.000 0.732 0.052 0.008
#> GSM1124895 2 0.5246 0.1181 0.004 0.596 0.000 0.308 0.084 0.008
#> GSM1124897 2 0.5609 -0.2857 0.000 0.492 0.000 0.384 0.116 0.008
#> GSM1124902 2 0.5673 0.2938 0.004 0.584 0.000 0.172 0.232 0.008
#> GSM1124908 2 0.5801 0.1292 0.012 0.572 0.000 0.268 0.140 0.008
#> GSM1124921 2 0.6349 -0.2019 0.004 0.372 0.000 0.296 0.324 0.004
#> GSM1124939 2 0.5721 0.2846 0.004 0.576 0.000 0.176 0.236 0.008
#> GSM1124944 2 0.5609 0.3147 0.004 0.592 0.000 0.160 0.236 0.008
#> GSM1124945 3 0.3060 0.7727 0.000 0.000 0.836 0.020 0.132 0.012
#> GSM1124946 2 0.6349 -0.2019 0.004 0.372 0.000 0.296 0.324 0.004
#> GSM1124947 2 0.5549 0.3344 0.004 0.600 0.000 0.152 0.236 0.008
#> GSM1124951 3 0.3141 0.7691 0.000 0.000 0.828 0.020 0.140 0.012
#> GSM1124952 2 0.2233 0.5704 0.044 0.912 0.000 0.020 0.004 0.020
#> GSM1124957 3 0.0146 0.8109 0.000 0.000 0.996 0.000 0.004 0.000
#> GSM1124900 2 0.5147 0.4102 0.132 0.720 0.000 0.012 0.072 0.064
#> GSM1124914 2 0.5551 0.0954 0.012 0.580 0.000 0.304 0.096 0.008
#> GSM1124871 2 0.2909 0.5394 0.008 0.852 0.000 0.120 0.008 0.012
#> GSM1124874 2 0.2965 0.5439 0.012 0.856 0.000 0.108 0.016 0.008
#> GSM1124875 2 0.6111 -0.3206 0.216 0.464 0.000 0.004 0.312 0.004
#> GSM1124880 1 0.4706 0.3478 0.728 0.184 0.000 0.012 0.032 0.044
#> GSM1124881 2 0.3988 0.5581 0.056 0.808 0.000 0.068 0.064 0.004
#> GSM1124885 4 0.5000 0.4932 0.000 0.416 0.000 0.520 0.060 0.004
#> GSM1124886 1 0.4495 -0.3465 0.560 0.008 0.000 0.008 0.008 0.416
#> GSM1124887 4 0.6324 0.4248 0.004 0.284 0.000 0.436 0.268 0.008
#> GSM1124894 6 0.5819 0.2638 0.048 0.244 0.000 0.024 0.060 0.624
#> GSM1124896 6 0.4813 0.5528 0.248 0.104 0.000 0.000 0.000 0.648
#> GSM1124899 2 0.4263 0.4947 0.016 0.764 0.000 0.160 0.048 0.012
#> GSM1124901 2 0.5034 0.2560 0.016 0.632 0.000 0.280 0.072 0.000
#> GSM1124906 2 0.2638 0.5778 0.036 0.896 0.000 0.016 0.020 0.032
#> GSM1124907 5 0.6402 -0.0188 0.008 0.324 0.000 0.272 0.392 0.004
#> GSM1124911 2 0.1364 0.5681 0.000 0.944 0.000 0.048 0.004 0.004
#> GSM1124912 6 0.4593 0.5794 0.456 0.028 0.000 0.000 0.004 0.512
#> GSM1124915 2 0.1858 0.5621 0.000 0.912 0.000 0.076 0.012 0.000
#> GSM1124917 2 0.4646 0.5346 0.056 0.756 0.000 0.080 0.104 0.004
#> GSM1124918 2 0.6111 -0.3206 0.216 0.464 0.000 0.004 0.312 0.004
#> GSM1124920 1 0.6771 -0.2232 0.444 0.000 0.384 0.048 0.080 0.044
#> GSM1124922 2 0.4731 0.5624 0.040 0.772 0.000 0.052 0.072 0.064
#> GSM1124924 1 0.4134 0.3155 0.764 0.176 0.000 0.012 0.032 0.016
#> GSM1124926 2 0.4263 0.4947 0.016 0.764 0.000 0.160 0.048 0.012
#> GSM1124928 1 0.4200 0.3585 0.776 0.148 0.004 0.004 0.028 0.040
#> GSM1124930 5 0.6366 0.6154 0.316 0.280 0.000 0.012 0.392 0.000
#> GSM1124931 2 0.4583 0.4724 0.064 0.768 0.000 0.008 0.076 0.084
#> GSM1124935 2 0.4627 0.4077 0.024 0.704 0.000 0.216 0.056 0.000
#> GSM1124936 1 0.5744 0.1641 0.616 0.000 0.264 0.032 0.060 0.028
#> GSM1124938 5 0.6216 0.6067 0.320 0.292 0.000 0.004 0.384 0.000
#> GSM1124940 6 0.4463 0.5802 0.456 0.028 0.000 0.000 0.000 0.516
#> GSM1124941 2 0.2638 0.5778 0.036 0.896 0.000 0.016 0.020 0.032
#> GSM1124942 5 0.6211 0.6086 0.316 0.292 0.000 0.004 0.388 0.000
#> GSM1124943 5 0.6358 0.6176 0.316 0.276 0.000 0.012 0.396 0.000
#> GSM1124948 1 0.4727 0.0832 0.676 0.224 0.000 0.004 0.096 0.000
#> GSM1124949 1 0.4393 -0.3437 0.564 0.008 0.000 0.004 0.008 0.416
#> GSM1124950 2 0.4311 0.4913 0.104 0.788 0.000 0.016 0.048 0.044
#> GSM1124954 1 0.8911 -0.1759 0.292 0.004 0.176 0.156 0.152 0.220
#> GSM1124955 6 0.4523 0.5821 0.452 0.032 0.000 0.000 0.000 0.516
#> GSM1124956 2 0.1364 0.5681 0.000 0.944 0.000 0.048 0.004 0.004
#> GSM1124872 2 0.4311 0.4913 0.104 0.788 0.000 0.016 0.048 0.044
#> GSM1124873 2 0.2399 0.5880 0.024 0.908 0.000 0.024 0.032 0.012
#> GSM1124876 3 0.0146 0.8109 0.000 0.000 0.996 0.000 0.004 0.000
#> GSM1124877 1 0.4123 -0.4171 0.568 0.012 0.000 0.000 0.000 0.420
#> GSM1124879 1 0.1774 0.3419 0.936 0.024 0.000 0.004 0.016 0.020
#> GSM1124883 4 0.4627 0.5615 0.000 0.396 0.000 0.560 0.044 0.000
#> GSM1124889 2 0.1802 0.5640 0.000 0.916 0.000 0.072 0.012 0.000
#> GSM1124892 1 0.5465 -0.0777 0.628 0.008 0.040 0.012 0.032 0.280
#> GSM1124893 6 0.4463 0.5802 0.456 0.028 0.000 0.000 0.000 0.516
#> GSM1124909 2 0.5481 0.2930 0.280 0.616 0.000 0.008 0.060 0.036
#> GSM1124913 4 0.4277 0.6393 0.000 0.172 0.000 0.740 0.080 0.008
#> GSM1124916 2 0.5481 0.2930 0.280 0.616 0.000 0.008 0.060 0.036
#> GSM1124923 5 0.5340 0.4508 0.004 0.180 0.072 0.040 0.692 0.012
#> GSM1124925 6 0.4813 0.5528 0.248 0.104 0.000 0.000 0.000 0.648
#> GSM1124929 1 0.4393 -0.3437 0.564 0.008 0.000 0.004 0.008 0.416
#> GSM1124934 1 0.8729 -0.0831 0.336 0.008 0.104 0.164 0.152 0.236
#> GSM1124937 1 0.3494 0.3668 0.804 0.156 0.000 0.008 0.028 0.004
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n tissue(p) k
#> MAD:hclust 89 0.0814 2
#> MAD:hclust 79 0.0734 3
#> MAD:hclust 44 0.0352 4
#> MAD:hclust 45 0.1111 5
#> MAD:hclust 39 0.1118 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 48983 rows and 91 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 0.870 0.929 0.969 0.4806 0.512 0.512
#> 3 3 0.611 0.682 0.788 0.2909 0.882 0.775
#> 4 4 0.693 0.832 0.881 0.1729 0.754 0.471
#> 5 5 0.676 0.543 0.721 0.0747 0.899 0.661
#> 6 6 0.718 0.630 0.791 0.0478 0.904 0.619
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
#> GSM1124891 1 0.000 0.9408 1.000 0.000
#> GSM1124888 1 0.000 0.9408 1.000 0.000
#> GSM1124890 1 0.861 0.6572 0.716 0.284
#> GSM1124904 2 0.000 0.9848 0.000 1.000
#> GSM1124927 2 0.000 0.9848 0.000 1.000
#> GSM1124953 2 0.000 0.9848 0.000 1.000
#> GSM1124869 1 0.000 0.9408 1.000 0.000
#> GSM1124870 1 0.184 0.9234 0.972 0.028
#> GSM1124882 1 0.000 0.9408 1.000 0.000
#> GSM1124884 2 0.000 0.9848 0.000 1.000
#> GSM1124898 2 0.000 0.9848 0.000 1.000
#> GSM1124903 2 0.000 0.9848 0.000 1.000
#> GSM1124905 1 0.000 0.9408 1.000 0.000
#> GSM1124910 1 0.000 0.9408 1.000 0.000
#> GSM1124919 2 0.000 0.9848 0.000 1.000
#> GSM1124932 2 0.000 0.9848 0.000 1.000
#> GSM1124933 1 0.000 0.9408 1.000 0.000
#> GSM1124867 2 0.998 -0.0353 0.472 0.528
#> GSM1124868 2 0.000 0.9848 0.000 1.000
#> GSM1124878 2 0.000 0.9848 0.000 1.000
#> GSM1124895 2 0.000 0.9848 0.000 1.000
#> GSM1124897 2 0.000 0.9848 0.000 1.000
#> GSM1124902 2 0.000 0.9848 0.000 1.000
#> GSM1124908 2 0.000 0.9848 0.000 1.000
#> GSM1124921 2 0.000 0.9848 0.000 1.000
#> GSM1124939 2 0.000 0.9848 0.000 1.000
#> GSM1124944 2 0.000 0.9848 0.000 1.000
#> GSM1124945 1 0.855 0.6630 0.720 0.280
#> GSM1124946 2 0.000 0.9848 0.000 1.000
#> GSM1124947 2 0.000 0.9848 0.000 1.000
#> GSM1124951 1 0.866 0.6505 0.712 0.288
#> GSM1124952 2 0.000 0.9848 0.000 1.000
#> GSM1124957 1 0.000 0.9408 1.000 0.000
#> GSM1124900 1 0.184 0.9234 0.972 0.028
#> GSM1124914 2 0.000 0.9848 0.000 1.000
#> GSM1124871 2 0.000 0.9848 0.000 1.000
#> GSM1124874 2 0.000 0.9848 0.000 1.000
#> GSM1124875 2 0.000 0.9848 0.000 1.000
#> GSM1124880 1 0.000 0.9408 1.000 0.000
#> GSM1124881 2 0.000 0.9848 0.000 1.000
#> GSM1124885 2 0.000 0.9848 0.000 1.000
#> GSM1124886 1 0.000 0.9408 1.000 0.000
#> GSM1124887 2 0.000 0.9848 0.000 1.000
#> GSM1124894 2 0.788 0.6677 0.236 0.764
#> GSM1124896 1 0.000 0.9408 1.000 0.000
#> GSM1124899 2 0.000 0.9848 0.000 1.000
#> GSM1124901 2 0.000 0.9848 0.000 1.000
#> GSM1124906 2 0.000 0.9848 0.000 1.000
#> GSM1124907 2 0.000 0.9848 0.000 1.000
#> GSM1124911 2 0.000 0.9848 0.000 1.000
#> GSM1124912 1 0.000 0.9408 1.000 0.000
#> GSM1124915 2 0.000 0.9848 0.000 1.000
#> GSM1124917 2 0.000 0.9848 0.000 1.000
#> GSM1124918 2 0.000 0.9848 0.000 1.000
#> GSM1124920 1 0.000 0.9408 1.000 0.000
#> GSM1124922 2 0.000 0.9848 0.000 1.000
#> GSM1124924 1 0.000 0.9408 1.000 0.000
#> GSM1124926 2 0.000 0.9848 0.000 1.000
#> GSM1124928 1 0.000 0.9408 1.000 0.000
#> GSM1124930 2 0.000 0.9848 0.000 1.000
#> GSM1124931 2 0.184 0.9555 0.028 0.972
#> GSM1124935 2 0.000 0.9848 0.000 1.000
#> GSM1124936 1 0.000 0.9408 1.000 0.000
#> GSM1124938 1 0.861 0.6572 0.716 0.284
#> GSM1124940 1 0.000 0.9408 1.000 0.000
#> GSM1124941 2 0.000 0.9848 0.000 1.000
#> GSM1124942 2 0.000 0.9848 0.000 1.000
#> GSM1124943 1 0.958 0.4639 0.620 0.380
#> GSM1124948 1 0.861 0.6572 0.716 0.284
#> GSM1124949 1 0.000 0.9408 1.000 0.000
#> GSM1124950 2 0.000 0.9848 0.000 1.000
#> GSM1124954 1 0.000 0.9408 1.000 0.000
#> GSM1124955 1 0.000 0.9408 1.000 0.000
#> GSM1124956 2 0.000 0.9848 0.000 1.000
#> GSM1124872 2 0.000 0.9848 0.000 1.000
#> GSM1124873 2 0.000 0.9848 0.000 1.000
#> GSM1124876 1 0.000 0.9408 1.000 0.000
#> GSM1124877 1 0.000 0.9408 1.000 0.000
#> GSM1124879 1 0.000 0.9408 1.000 0.000
#> GSM1124883 2 0.000 0.9848 0.000 1.000
#> GSM1124889 2 0.000 0.9848 0.000 1.000
#> GSM1124892 1 0.000 0.9408 1.000 0.000
#> GSM1124893 1 0.000 0.9408 1.000 0.000
#> GSM1124909 2 0.000 0.9848 0.000 1.000
#> GSM1124913 2 0.000 0.9848 0.000 1.000
#> GSM1124916 2 0.000 0.9848 0.000 1.000
#> GSM1124923 2 0.000 0.9848 0.000 1.000
#> GSM1124925 1 0.184 0.9234 0.972 0.028
#> GSM1124929 1 0.000 0.9408 1.000 0.000
#> GSM1124934 1 0.000 0.9408 1.000 0.000
#> GSM1124937 1 0.625 0.8123 0.844 0.156
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1124891 3 0.5397 0.725 0.280 0.000 0.720
#> GSM1124888 3 0.5397 0.725 0.280 0.000 0.720
#> GSM1124890 3 0.5656 0.628 0.008 0.264 0.728
#> GSM1124904 2 0.0000 0.769 0.000 1.000 0.000
#> GSM1124927 2 0.9417 0.618 0.224 0.504 0.272
#> GSM1124953 2 0.2448 0.742 0.000 0.924 0.076
#> GSM1124869 1 0.3879 0.704 0.848 0.000 0.152
#> GSM1124870 1 0.5216 0.482 0.740 0.000 0.260
#> GSM1124882 1 0.3879 0.704 0.848 0.000 0.152
#> GSM1124884 2 0.8770 0.679 0.180 0.584 0.236
#> GSM1124898 2 0.0000 0.769 0.000 1.000 0.000
#> GSM1124903 2 0.0000 0.769 0.000 1.000 0.000
#> GSM1124905 1 0.0237 0.671 0.996 0.000 0.004
#> GSM1124910 1 0.4178 0.674 0.828 0.000 0.172
#> GSM1124919 2 0.1753 0.758 0.000 0.952 0.048
#> GSM1124932 2 0.9021 0.669 0.184 0.552 0.264
#> GSM1124933 3 0.5397 0.725 0.280 0.000 0.720
#> GSM1124867 1 0.9898 -0.259 0.404 0.308 0.288
#> GSM1124868 2 0.0000 0.769 0.000 1.000 0.000
#> GSM1124878 2 0.0000 0.769 0.000 1.000 0.000
#> GSM1124895 2 0.0000 0.769 0.000 1.000 0.000
#> GSM1124897 2 0.0000 0.769 0.000 1.000 0.000
#> GSM1124902 2 0.0000 0.769 0.000 1.000 0.000
#> GSM1124908 2 0.0592 0.765 0.000 0.988 0.012
#> GSM1124921 2 0.0592 0.765 0.000 0.988 0.012
#> GSM1124939 2 0.0000 0.769 0.000 1.000 0.000
#> GSM1124944 2 0.1529 0.761 0.000 0.960 0.040
#> GSM1124945 3 0.5406 0.654 0.012 0.224 0.764
#> GSM1124946 2 0.0592 0.765 0.000 0.988 0.012
#> GSM1124947 2 0.4994 0.749 0.052 0.836 0.112
#> GSM1124951 3 0.5285 0.641 0.004 0.244 0.752
#> GSM1124952 2 0.8981 0.672 0.180 0.556 0.264
#> GSM1124957 3 0.5397 0.725 0.280 0.000 0.720
#> GSM1124900 1 0.5216 0.482 0.740 0.000 0.260
#> GSM1124914 2 0.0000 0.769 0.000 1.000 0.000
#> GSM1124871 2 0.8444 0.693 0.152 0.612 0.236
#> GSM1124874 2 0.8683 0.684 0.172 0.592 0.236
#> GSM1124875 2 0.1643 0.760 0.000 0.956 0.044
#> GSM1124880 1 0.5291 0.476 0.732 0.000 0.268
#> GSM1124881 2 0.9083 0.671 0.180 0.540 0.280
#> GSM1124885 2 0.0000 0.769 0.000 1.000 0.000
#> GSM1124886 1 0.4235 0.667 0.824 0.000 0.176
#> GSM1124887 2 0.0592 0.765 0.000 0.988 0.012
#> GSM1124894 1 0.7757 0.343 0.664 0.224 0.112
#> GSM1124896 1 0.0000 0.673 1.000 0.000 0.000
#> GSM1124899 2 0.8883 0.680 0.176 0.568 0.256
#> GSM1124901 2 0.0000 0.769 0.000 1.000 0.000
#> GSM1124906 2 0.9007 0.671 0.180 0.552 0.268
#> GSM1124907 2 0.1163 0.763 0.000 0.972 0.028
#> GSM1124911 2 0.8770 0.679 0.180 0.584 0.236
#> GSM1124912 1 0.3879 0.704 0.848 0.000 0.152
#> GSM1124915 2 0.0000 0.769 0.000 1.000 0.000
#> GSM1124917 2 0.2261 0.764 0.000 0.932 0.068
#> GSM1124918 2 0.8872 0.682 0.156 0.556 0.288
#> GSM1124920 3 0.5678 0.691 0.316 0.000 0.684
#> GSM1124922 2 0.8953 0.676 0.180 0.560 0.260
#> GSM1124924 3 0.2261 0.523 0.068 0.000 0.932
#> GSM1124926 2 0.8683 0.684 0.172 0.592 0.236
#> GSM1124928 1 0.3412 0.702 0.876 0.000 0.124
#> GSM1124930 2 0.1860 0.756 0.000 0.948 0.052
#> GSM1124931 2 0.9112 0.663 0.188 0.540 0.272
#> GSM1124935 2 0.1860 0.766 0.052 0.948 0.000
#> GSM1124936 3 0.5859 0.651 0.344 0.000 0.656
#> GSM1124938 3 0.4861 0.647 0.008 0.192 0.800
#> GSM1124940 1 0.3879 0.704 0.848 0.000 0.152
#> GSM1124941 2 0.9007 0.671 0.180 0.552 0.268
#> GSM1124942 2 0.2165 0.750 0.000 0.936 0.064
#> GSM1124943 3 0.5397 0.612 0.000 0.280 0.720
#> GSM1124948 3 0.2176 0.523 0.020 0.032 0.948
#> GSM1124949 1 0.3879 0.704 0.848 0.000 0.152
#> GSM1124950 2 0.9058 0.668 0.180 0.544 0.276
#> GSM1124954 3 0.5465 0.718 0.288 0.000 0.712
#> GSM1124955 1 0.0000 0.673 1.000 0.000 0.000
#> GSM1124956 2 0.8770 0.679 0.180 0.584 0.236
#> GSM1124872 2 0.9058 0.668 0.180 0.544 0.276
#> GSM1124873 2 0.8981 0.675 0.180 0.556 0.264
#> GSM1124876 3 0.5397 0.725 0.280 0.000 0.720
#> GSM1124877 1 0.3879 0.704 0.848 0.000 0.152
#> GSM1124879 1 0.3879 0.704 0.848 0.000 0.152
#> GSM1124883 2 0.0000 0.769 0.000 1.000 0.000
#> GSM1124889 2 0.8727 0.682 0.176 0.588 0.236
#> GSM1124892 1 0.4291 0.660 0.820 0.000 0.180
#> GSM1124893 1 0.3879 0.704 0.848 0.000 0.152
#> GSM1124909 2 0.9168 0.662 0.184 0.528 0.288
#> GSM1124913 2 0.0000 0.769 0.000 1.000 0.000
#> GSM1124916 2 0.9168 0.662 0.184 0.528 0.288
#> GSM1124923 2 0.2165 0.748 0.000 0.936 0.064
#> GSM1124925 1 0.0000 0.673 1.000 0.000 0.000
#> GSM1124929 1 0.3879 0.704 0.848 0.000 0.152
#> GSM1124934 3 0.5650 0.695 0.312 0.000 0.688
#> GSM1124937 1 0.6969 0.503 0.596 0.024 0.380
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1124891 3 0.2466 0.873 0.096 0.004 0.900 0.000
#> GSM1124888 3 0.2466 0.873 0.096 0.004 0.900 0.000
#> GSM1124890 3 0.6041 0.674 0.008 0.076 0.680 0.236
#> GSM1124904 4 0.2334 0.903 0.000 0.088 0.004 0.908
#> GSM1124927 2 0.2505 0.855 0.004 0.920 0.036 0.040
#> GSM1124953 4 0.4072 0.770 0.000 0.052 0.120 0.828
#> GSM1124869 1 0.0000 0.976 1.000 0.000 0.000 0.000
#> GSM1124870 2 0.5577 0.538 0.328 0.636 0.036 0.000
#> GSM1124882 1 0.0000 0.976 1.000 0.000 0.000 0.000
#> GSM1124884 2 0.1792 0.850 0.000 0.932 0.000 0.068
#> GSM1124898 4 0.3196 0.880 0.000 0.136 0.008 0.856
#> GSM1124903 4 0.2714 0.899 0.000 0.112 0.004 0.884
#> GSM1124905 1 0.3674 0.821 0.848 0.116 0.036 0.000
#> GSM1124910 1 0.0657 0.966 0.984 0.004 0.012 0.000
#> GSM1124919 4 0.3009 0.832 0.000 0.056 0.052 0.892
#> GSM1124932 2 0.2499 0.856 0.004 0.920 0.032 0.044
#> GSM1124933 3 0.2021 0.871 0.056 0.000 0.932 0.012
#> GSM1124867 2 0.4098 0.815 0.044 0.856 0.040 0.060
#> GSM1124868 4 0.2859 0.898 0.000 0.112 0.008 0.880
#> GSM1124878 4 0.2859 0.898 0.000 0.112 0.008 0.880
#> GSM1124895 4 0.2988 0.898 0.000 0.112 0.012 0.876
#> GSM1124897 4 0.2799 0.900 0.000 0.108 0.008 0.884
#> GSM1124902 4 0.2730 0.903 0.000 0.088 0.016 0.896
#> GSM1124908 4 0.2125 0.902 0.000 0.076 0.004 0.920
#> GSM1124921 4 0.0927 0.881 0.000 0.016 0.008 0.976
#> GSM1124939 4 0.2676 0.903 0.000 0.092 0.012 0.896
#> GSM1124944 4 0.2060 0.861 0.000 0.052 0.016 0.932
#> GSM1124945 3 0.1722 0.853 0.008 0.000 0.944 0.048
#> GSM1124946 4 0.0927 0.881 0.000 0.016 0.008 0.976
#> GSM1124947 2 0.6061 0.284 0.000 0.552 0.048 0.400
#> GSM1124951 3 0.2342 0.847 0.008 0.000 0.912 0.080
#> GSM1124952 2 0.2759 0.853 0.000 0.904 0.044 0.052
#> GSM1124957 3 0.2255 0.873 0.068 0.000 0.920 0.012
#> GSM1124900 2 0.5742 0.460 0.368 0.596 0.036 0.000
#> GSM1124914 4 0.2081 0.903 0.000 0.084 0.000 0.916
#> GSM1124871 2 0.1940 0.846 0.000 0.924 0.000 0.076
#> GSM1124874 2 0.2473 0.847 0.000 0.908 0.012 0.080
#> GSM1124875 4 0.4375 0.759 0.000 0.180 0.032 0.788
#> GSM1124880 2 0.4857 0.721 0.176 0.772 0.048 0.004
#> GSM1124881 2 0.2053 0.839 0.000 0.924 0.004 0.072
#> GSM1124885 4 0.2859 0.898 0.000 0.112 0.008 0.880
#> GSM1124886 1 0.0188 0.974 0.996 0.000 0.004 0.000
#> GSM1124887 4 0.1209 0.887 0.000 0.032 0.004 0.964
#> GSM1124894 2 0.5657 0.663 0.220 0.716 0.048 0.016
#> GSM1124896 1 0.0469 0.968 0.988 0.012 0.000 0.000
#> GSM1124899 2 0.2473 0.849 0.000 0.908 0.012 0.080
#> GSM1124901 4 0.3105 0.887 0.000 0.140 0.004 0.856
#> GSM1124906 2 0.1398 0.856 0.004 0.956 0.000 0.040
#> GSM1124907 4 0.2363 0.852 0.000 0.056 0.024 0.920
#> GSM1124911 2 0.1792 0.850 0.000 0.932 0.000 0.068
#> GSM1124912 1 0.0000 0.976 1.000 0.000 0.000 0.000
#> GSM1124915 4 0.2921 0.888 0.000 0.140 0.000 0.860
#> GSM1124917 2 0.5573 0.323 0.000 0.604 0.028 0.368
#> GSM1124918 2 0.3787 0.787 0.000 0.840 0.036 0.124
#> GSM1124920 3 0.3539 0.826 0.176 0.004 0.820 0.000
#> GSM1124922 2 0.2587 0.852 0.004 0.908 0.012 0.076
#> GSM1124924 2 0.6441 0.610 0.084 0.668 0.228 0.020
#> GSM1124926 2 0.2216 0.839 0.000 0.908 0.000 0.092
#> GSM1124928 1 0.2596 0.888 0.908 0.068 0.024 0.000
#> GSM1124930 4 0.3301 0.824 0.000 0.076 0.048 0.876
#> GSM1124931 2 0.2505 0.854 0.004 0.920 0.036 0.040
#> GSM1124935 4 0.5220 0.316 0.000 0.424 0.008 0.568
#> GSM1124936 3 0.4088 0.768 0.232 0.004 0.764 0.000
#> GSM1124938 3 0.4807 0.781 0.008 0.088 0.800 0.104
#> GSM1124940 1 0.0000 0.976 1.000 0.000 0.000 0.000
#> GSM1124941 2 0.1305 0.856 0.004 0.960 0.000 0.036
#> GSM1124942 4 0.3542 0.824 0.000 0.076 0.060 0.864
#> GSM1124943 3 0.6158 0.653 0.008 0.076 0.664 0.252
#> GSM1124948 2 0.6396 0.568 0.008 0.652 0.244 0.096
#> GSM1124949 1 0.0000 0.976 1.000 0.000 0.000 0.000
#> GSM1124950 2 0.2007 0.851 0.004 0.940 0.036 0.020
#> GSM1124954 3 0.2737 0.870 0.104 0.008 0.888 0.000
#> GSM1124955 1 0.0469 0.968 0.988 0.012 0.000 0.000
#> GSM1124956 2 0.1792 0.850 0.000 0.932 0.000 0.068
#> GSM1124872 2 0.2007 0.851 0.004 0.940 0.036 0.020
#> GSM1124873 2 0.1118 0.856 0.000 0.964 0.000 0.036
#> GSM1124876 3 0.2741 0.872 0.096 0.000 0.892 0.012
#> GSM1124877 1 0.0000 0.976 1.000 0.000 0.000 0.000
#> GSM1124879 1 0.0000 0.976 1.000 0.000 0.000 0.000
#> GSM1124883 4 0.2466 0.903 0.000 0.096 0.004 0.900
#> GSM1124889 2 0.1792 0.850 0.000 0.932 0.000 0.068
#> GSM1124892 1 0.0524 0.969 0.988 0.004 0.008 0.000
#> GSM1124893 1 0.0000 0.976 1.000 0.000 0.000 0.000
#> GSM1124909 2 0.2365 0.840 0.004 0.920 0.012 0.064
#> GSM1124913 4 0.2714 0.899 0.000 0.112 0.004 0.884
#> GSM1124916 2 0.2365 0.840 0.004 0.920 0.012 0.064
#> GSM1124923 4 0.2844 0.829 0.000 0.052 0.048 0.900
#> GSM1124925 1 0.0469 0.968 0.988 0.012 0.000 0.000
#> GSM1124929 1 0.0000 0.976 1.000 0.000 0.000 0.000
#> GSM1124934 3 0.3450 0.840 0.156 0.008 0.836 0.000
#> GSM1124937 2 0.6069 0.451 0.352 0.600 0.040 0.008
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM1124891 3 0.1399 0.9004 0.020 0.000 0.952 0.028 0.000
#> GSM1124888 3 0.0771 0.9020 0.020 0.000 0.976 0.004 0.000
#> GSM1124890 5 0.5556 -0.0617 0.000 0.048 0.388 0.012 0.552
#> GSM1124904 5 0.4747 -0.8237 0.000 0.016 0.000 0.488 0.496
#> GSM1124927 2 0.3578 0.7530 0.000 0.784 0.008 0.204 0.004
#> GSM1124953 5 0.5270 0.2773 0.000 0.008 0.104 0.196 0.692
#> GSM1124869 1 0.0000 0.9386 1.000 0.000 0.000 0.000 0.000
#> GSM1124870 2 0.6138 0.6101 0.188 0.596 0.008 0.208 0.000
#> GSM1124882 1 0.0162 0.9383 0.996 0.000 0.000 0.004 0.000
#> GSM1124884 2 0.0880 0.7852 0.000 0.968 0.000 0.032 0.000
#> GSM1124898 5 0.6637 -0.1722 0.000 0.268 0.000 0.280 0.452
#> GSM1124903 4 0.4747 0.8088 0.000 0.016 0.000 0.496 0.488
#> GSM1124905 1 0.6575 0.4503 0.516 0.116 0.028 0.340 0.000
#> GSM1124910 1 0.1478 0.9045 0.936 0.000 0.000 0.064 0.000
#> GSM1124919 5 0.2943 0.2848 0.000 0.036 0.040 0.036 0.888
#> GSM1124932 2 0.2563 0.7781 0.000 0.872 0.008 0.120 0.000
#> GSM1124933 3 0.0833 0.8960 0.004 0.000 0.976 0.016 0.004
#> GSM1124867 2 0.5703 0.6998 0.008 0.676 0.008 0.160 0.148
#> GSM1124868 4 0.4731 0.8721 0.000 0.016 0.000 0.528 0.456
#> GSM1124878 4 0.4718 0.8474 0.000 0.016 0.000 0.540 0.444
#> GSM1124895 4 0.4723 0.8603 0.000 0.016 0.000 0.536 0.448
#> GSM1124897 4 0.4731 0.8721 0.000 0.016 0.000 0.528 0.456
#> GSM1124902 4 0.4718 0.8535 0.000 0.016 0.000 0.540 0.444
#> GSM1124908 5 0.4747 -0.8358 0.000 0.016 0.000 0.484 0.500
#> GSM1124921 5 0.4555 -0.7860 0.000 0.008 0.000 0.472 0.520
#> GSM1124939 4 0.4735 0.8547 0.000 0.016 0.000 0.524 0.460
#> GSM1124944 5 0.3999 -0.4218 0.000 0.000 0.000 0.344 0.656
#> GSM1124945 3 0.3060 0.8153 0.000 0.000 0.848 0.024 0.128
#> GSM1124946 5 0.4552 -0.7818 0.000 0.008 0.000 0.468 0.524
#> GSM1124947 2 0.6806 0.3339 0.000 0.432 0.004 0.312 0.252
#> GSM1124951 3 0.3445 0.7956 0.000 0.000 0.824 0.036 0.140
#> GSM1124952 2 0.3461 0.7503 0.000 0.772 0.004 0.224 0.000
#> GSM1124957 3 0.0833 0.8960 0.004 0.000 0.976 0.016 0.004
#> GSM1124900 2 0.6221 0.5898 0.204 0.584 0.008 0.204 0.000
#> GSM1124914 5 0.4827 -0.8342 0.000 0.020 0.000 0.476 0.504
#> GSM1124871 2 0.1408 0.7765 0.000 0.948 0.000 0.044 0.008
#> GSM1124874 2 0.2612 0.7703 0.000 0.868 0.000 0.124 0.008
#> GSM1124875 5 0.4025 0.3130 0.000 0.232 0.008 0.012 0.748
#> GSM1124880 2 0.6684 0.6608 0.084 0.608 0.008 0.228 0.072
#> GSM1124881 2 0.2629 0.7327 0.000 0.860 0.000 0.004 0.136
#> GSM1124885 4 0.4738 0.8708 0.000 0.016 0.000 0.520 0.464
#> GSM1124886 1 0.0794 0.9277 0.972 0.000 0.000 0.028 0.000
#> GSM1124887 5 0.4560 -0.8091 0.000 0.008 0.000 0.484 0.508
#> GSM1124894 2 0.6173 0.5689 0.060 0.528 0.036 0.376 0.000
#> GSM1124896 1 0.0609 0.9348 0.980 0.000 0.000 0.020 0.000
#> GSM1124899 2 0.1774 0.7728 0.000 0.932 0.000 0.016 0.052
#> GSM1124901 4 0.6099 0.6616 0.000 0.124 0.000 0.452 0.424
#> GSM1124906 2 0.0865 0.7865 0.000 0.972 0.000 0.024 0.004
#> GSM1124907 5 0.2017 0.1715 0.000 0.000 0.008 0.080 0.912
#> GSM1124911 2 0.1082 0.7808 0.000 0.964 0.000 0.028 0.008
#> GSM1124912 1 0.0000 0.9386 1.000 0.000 0.000 0.000 0.000
#> GSM1124915 4 0.5867 0.7413 0.000 0.100 0.000 0.496 0.404
#> GSM1124917 5 0.4559 -0.2042 0.000 0.480 0.000 0.008 0.512
#> GSM1124918 2 0.4967 0.3282 0.000 0.540 0.008 0.016 0.436
#> GSM1124920 3 0.3437 0.8507 0.120 0.000 0.832 0.048 0.000
#> GSM1124922 2 0.2304 0.7765 0.000 0.908 0.000 0.044 0.048
#> GSM1124924 2 0.7862 0.4342 0.008 0.420 0.060 0.224 0.288
#> GSM1124926 2 0.3002 0.7405 0.000 0.856 0.000 0.116 0.028
#> GSM1124928 1 0.4923 0.6543 0.700 0.088 0.000 0.212 0.000
#> GSM1124930 5 0.3340 0.3316 0.000 0.076 0.048 0.016 0.860
#> GSM1124931 2 0.3318 0.7609 0.000 0.800 0.008 0.192 0.000
#> GSM1124935 2 0.6410 0.0274 0.000 0.488 0.000 0.192 0.320
#> GSM1124936 3 0.3988 0.7744 0.196 0.000 0.768 0.036 0.000
#> GSM1124938 5 0.5886 -0.2190 0.000 0.048 0.448 0.024 0.480
#> GSM1124940 1 0.0162 0.9383 0.996 0.000 0.000 0.004 0.000
#> GSM1124941 2 0.0865 0.7865 0.000 0.972 0.000 0.024 0.004
#> GSM1124942 5 0.3003 0.3197 0.000 0.064 0.040 0.016 0.880
#> GSM1124943 5 0.5506 -0.0146 0.000 0.048 0.368 0.012 0.572
#> GSM1124948 5 0.7581 -0.1286 0.000 0.296 0.080 0.168 0.456
#> GSM1124949 1 0.0290 0.9369 0.992 0.000 0.000 0.008 0.000
#> GSM1124950 2 0.3124 0.7684 0.000 0.840 0.008 0.144 0.008
#> GSM1124954 3 0.2504 0.8897 0.040 0.000 0.896 0.064 0.000
#> GSM1124955 1 0.0609 0.9348 0.980 0.000 0.000 0.020 0.000
#> GSM1124956 2 0.0992 0.7818 0.000 0.968 0.000 0.024 0.008
#> GSM1124872 2 0.3210 0.7665 0.000 0.832 0.008 0.152 0.008
#> GSM1124873 2 0.0566 0.7836 0.000 0.984 0.000 0.004 0.012
#> GSM1124876 3 0.1117 0.9003 0.020 0.000 0.964 0.016 0.000
#> GSM1124877 1 0.0404 0.9369 0.988 0.000 0.000 0.012 0.000
#> GSM1124879 1 0.0510 0.9371 0.984 0.000 0.000 0.016 0.000
#> GSM1124883 5 0.4743 -0.8293 0.000 0.016 0.000 0.472 0.512
#> GSM1124889 2 0.0771 0.7819 0.000 0.976 0.000 0.020 0.004
#> GSM1124892 1 0.0794 0.9277 0.972 0.000 0.000 0.028 0.000
#> GSM1124893 1 0.0000 0.9386 1.000 0.000 0.000 0.000 0.000
#> GSM1124909 2 0.4019 0.7207 0.000 0.792 0.004 0.052 0.152
#> GSM1124913 4 0.4747 0.8088 0.000 0.016 0.000 0.496 0.488
#> GSM1124916 2 0.4019 0.7207 0.000 0.792 0.004 0.052 0.152
#> GSM1124923 5 0.3037 0.1918 0.000 0.000 0.040 0.100 0.860
#> GSM1124925 1 0.0609 0.9348 0.980 0.000 0.000 0.020 0.000
#> GSM1124929 1 0.0000 0.9386 1.000 0.000 0.000 0.000 0.000
#> GSM1124934 3 0.3476 0.8665 0.076 0.000 0.836 0.088 0.000
#> GSM1124937 2 0.8296 0.4570 0.140 0.464 0.020 0.164 0.212
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1124891 3 0.1382 0.8485 0.008 0.000 0.948 0.000 0.036 0.008
#> GSM1124888 3 0.1124 0.8496 0.008 0.000 0.956 0.000 0.036 0.000
#> GSM1124890 5 0.3667 0.7105 0.000 0.028 0.088 0.004 0.824 0.056
#> GSM1124904 4 0.1844 0.7902 0.000 0.004 0.000 0.924 0.048 0.024
#> GSM1124927 2 0.3807 0.2866 0.000 0.628 0.000 0.000 0.004 0.368
#> GSM1124953 5 0.5683 0.6030 0.000 0.004 0.044 0.124 0.640 0.188
#> GSM1124869 1 0.0000 0.9390 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124870 2 0.5449 -0.0401 0.108 0.500 0.000 0.000 0.004 0.388
#> GSM1124882 1 0.0146 0.9390 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM1124884 2 0.1396 0.6415 0.000 0.952 0.008 0.012 0.004 0.024
#> GSM1124898 4 0.6368 0.2094 0.000 0.352 0.000 0.436 0.184 0.028
#> GSM1124903 4 0.1777 0.7902 0.000 0.004 0.000 0.928 0.044 0.024
#> GSM1124905 6 0.5719 0.2401 0.328 0.080 0.016 0.000 0.016 0.560
#> GSM1124910 1 0.2196 0.8822 0.908 0.000 0.016 0.000 0.020 0.056
#> GSM1124919 5 0.3949 0.7154 0.000 0.012 0.000 0.136 0.780 0.072
#> GSM1124932 2 0.2655 0.5850 0.000 0.848 0.008 0.004 0.000 0.140
#> GSM1124933 3 0.1686 0.8421 0.004 0.000 0.932 0.004 0.008 0.052
#> GSM1124867 2 0.5745 -0.1636 0.000 0.460 0.004 0.000 0.148 0.388
#> GSM1124868 4 0.2913 0.7820 0.000 0.012 0.000 0.860 0.036 0.092
#> GSM1124878 4 0.1707 0.7924 0.000 0.004 0.000 0.928 0.012 0.056
#> GSM1124895 4 0.4122 0.7689 0.000 0.008 0.000 0.752 0.068 0.172
#> GSM1124897 4 0.3113 0.7841 0.000 0.008 0.000 0.844 0.048 0.100
#> GSM1124902 4 0.4068 0.7717 0.000 0.004 0.000 0.756 0.080 0.160
#> GSM1124908 4 0.4043 0.7687 0.000 0.000 0.000 0.756 0.128 0.116
#> GSM1124921 4 0.4267 0.7480 0.000 0.000 0.000 0.732 0.152 0.116
#> GSM1124939 4 0.4662 0.7559 0.000 0.008 0.000 0.700 0.100 0.192
#> GSM1124944 4 0.5885 0.3389 0.000 0.000 0.000 0.444 0.348 0.208
#> GSM1124945 3 0.5024 0.5645 0.000 0.000 0.640 0.004 0.240 0.116
#> GSM1124946 4 0.4459 0.7436 0.000 0.000 0.000 0.712 0.156 0.132
#> GSM1124947 6 0.7160 0.2529 0.000 0.200 0.004 0.148 0.172 0.476
#> GSM1124951 3 0.5207 0.5451 0.000 0.000 0.628 0.012 0.252 0.108
#> GSM1124952 2 0.4320 0.3730 0.000 0.668 0.008 0.008 0.016 0.300
#> GSM1124957 3 0.1686 0.8421 0.004 0.000 0.932 0.004 0.008 0.052
#> GSM1124900 2 0.5548 -0.0510 0.108 0.496 0.000 0.000 0.008 0.388
#> GSM1124914 4 0.4050 0.7835 0.000 0.016 0.000 0.780 0.100 0.104
#> GSM1124871 2 0.1232 0.6389 0.000 0.956 0.000 0.024 0.004 0.016
#> GSM1124874 2 0.3242 0.5967 0.000 0.844 0.000 0.040 0.024 0.092
#> GSM1124875 5 0.4243 0.6991 0.000 0.112 0.000 0.072 0.776 0.040
#> GSM1124880 6 0.6075 0.2152 0.036 0.388 0.000 0.000 0.112 0.464
#> GSM1124881 2 0.3453 0.5053 0.000 0.808 0.000 0.008 0.144 0.040
#> GSM1124885 4 0.2604 0.7822 0.000 0.004 0.000 0.872 0.028 0.096
#> GSM1124886 1 0.1003 0.9255 0.964 0.000 0.020 0.000 0.000 0.016
#> GSM1124887 4 0.2771 0.7753 0.000 0.000 0.000 0.852 0.116 0.032
#> GSM1124894 6 0.6172 0.1753 0.056 0.372 0.024 0.016 0.020 0.512
#> GSM1124896 1 0.0820 0.9345 0.972 0.000 0.000 0.000 0.012 0.016
#> GSM1124899 2 0.2818 0.5878 0.000 0.872 0.000 0.024 0.076 0.028
#> GSM1124901 4 0.4425 0.6808 0.000 0.136 0.000 0.744 0.104 0.016
#> GSM1124906 2 0.1268 0.6386 0.000 0.952 0.008 0.000 0.004 0.036
#> GSM1124907 5 0.3741 0.6605 0.000 0.004 0.000 0.208 0.756 0.032
#> GSM1124911 2 0.1377 0.6384 0.000 0.952 0.004 0.024 0.004 0.016
#> GSM1124912 1 0.0000 0.9390 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124915 4 0.3787 0.7465 0.000 0.100 0.000 0.808 0.028 0.064
#> GSM1124917 5 0.5323 0.3658 0.000 0.344 0.000 0.024 0.568 0.064
#> GSM1124918 5 0.4499 0.4562 0.000 0.288 0.000 0.000 0.652 0.060
#> GSM1124920 3 0.3149 0.8106 0.084 0.000 0.852 0.000 0.036 0.028
#> GSM1124922 2 0.3264 0.5816 0.000 0.844 0.000 0.020 0.080 0.056
#> GSM1124924 6 0.6378 0.3908 0.000 0.260 0.016 0.000 0.304 0.420
#> GSM1124926 2 0.3430 0.5552 0.000 0.836 0.000 0.076 0.028 0.060
#> GSM1124928 1 0.5429 -0.0693 0.492 0.060 0.008 0.000 0.012 0.428
#> GSM1124930 5 0.2456 0.7418 0.000 0.028 0.000 0.076 0.888 0.008
#> GSM1124931 2 0.3772 0.4094 0.000 0.692 0.008 0.004 0.000 0.296
#> GSM1124935 2 0.6274 0.1222 0.000 0.516 0.000 0.264 0.184 0.036
#> GSM1124936 3 0.3509 0.7796 0.128 0.000 0.816 0.000 0.032 0.024
#> GSM1124938 5 0.3273 0.6846 0.000 0.024 0.136 0.000 0.824 0.016
#> GSM1124940 1 0.0146 0.9390 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM1124941 2 0.1268 0.6386 0.000 0.952 0.008 0.000 0.004 0.036
#> GSM1124942 5 0.3114 0.7392 0.000 0.024 0.004 0.108 0.848 0.016
#> GSM1124943 5 0.2779 0.7151 0.000 0.024 0.088 0.004 0.872 0.012
#> GSM1124948 5 0.4355 0.5408 0.000 0.076 0.012 0.000 0.736 0.176
#> GSM1124949 1 0.0363 0.9357 0.988 0.000 0.000 0.000 0.000 0.012
#> GSM1124950 2 0.3844 0.3719 0.000 0.676 0.004 0.000 0.008 0.312
#> GSM1124954 3 0.2384 0.8389 0.008 0.000 0.896 0.000 0.056 0.040
#> GSM1124955 1 0.0820 0.9345 0.972 0.000 0.000 0.000 0.012 0.016
#> GSM1124956 2 0.1377 0.6384 0.000 0.952 0.004 0.024 0.004 0.016
#> GSM1124872 2 0.3878 0.3494 0.000 0.668 0.004 0.000 0.008 0.320
#> GSM1124873 2 0.0622 0.6405 0.000 0.980 0.000 0.000 0.008 0.012
#> GSM1124876 3 0.1484 0.8452 0.008 0.000 0.944 0.004 0.004 0.040
#> GSM1124877 1 0.0725 0.9355 0.976 0.000 0.000 0.000 0.012 0.012
#> GSM1124879 1 0.1196 0.9250 0.952 0.000 0.000 0.000 0.008 0.040
#> GSM1124883 4 0.1924 0.7952 0.000 0.004 0.000 0.920 0.048 0.028
#> GSM1124889 2 0.1237 0.6411 0.000 0.956 0.000 0.020 0.004 0.020
#> GSM1124892 1 0.1624 0.9068 0.936 0.000 0.040 0.000 0.004 0.020
#> GSM1124893 1 0.0146 0.9390 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM1124909 2 0.5388 0.2277 0.000 0.604 0.004 0.000 0.196 0.196
#> GSM1124913 4 0.1844 0.7888 0.000 0.004 0.000 0.924 0.048 0.024
#> GSM1124916 2 0.5388 0.2277 0.000 0.604 0.004 0.000 0.196 0.196
#> GSM1124923 5 0.4381 0.6275 0.000 0.000 0.000 0.236 0.692 0.072
#> GSM1124925 1 0.0820 0.9345 0.972 0.000 0.000 0.000 0.012 0.016
#> GSM1124929 1 0.0000 0.9390 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124934 3 0.4217 0.7685 0.036 0.000 0.772 0.000 0.060 0.132
#> GSM1124937 6 0.7144 0.3859 0.044 0.256 0.016 0.000 0.296 0.388
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 tissue(p) k
#> MAD:kmeans 89 0.38342 2
#> MAD:kmeans 86 0.11336 3
#> MAD:kmeans 86 0.03707 4
#> MAD:kmeans 64 0.06830 5
#> MAD:kmeans 69 0.00735 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 48983 rows and 91 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 0.869 0.935 0.970 0.4936 0.508 0.508
#> 3 3 0.564 0.511 0.778 0.3263 0.762 0.565
#> 4 4 0.853 0.856 0.931 0.1451 0.779 0.458
#> 5 5 0.698 0.612 0.791 0.0610 0.977 0.909
#> 6 6 0.683 0.537 0.753 0.0395 0.915 0.652
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
#> GSM1124891 1 0.000 0.968 1.000 0.000
#> GSM1124888 1 0.000 0.968 1.000 0.000
#> GSM1124890 1 0.706 0.790 0.808 0.192
#> GSM1124904 2 0.000 0.969 0.000 1.000
#> GSM1124927 2 0.722 0.760 0.200 0.800
#> GSM1124953 2 0.000 0.969 0.000 1.000
#> GSM1124869 1 0.000 0.968 1.000 0.000
#> GSM1124870 1 0.000 0.968 1.000 0.000
#> GSM1124882 1 0.000 0.968 1.000 0.000
#> GSM1124884 2 0.000 0.969 0.000 1.000
#> GSM1124898 2 0.000 0.969 0.000 1.000
#> GSM1124903 2 0.000 0.969 0.000 1.000
#> GSM1124905 1 0.000 0.968 1.000 0.000
#> GSM1124910 1 0.000 0.968 1.000 0.000
#> GSM1124919 2 0.000 0.969 0.000 1.000
#> GSM1124932 2 0.722 0.760 0.200 0.800
#> GSM1124933 1 0.000 0.968 1.000 0.000
#> GSM1124867 1 0.644 0.822 0.836 0.164
#> GSM1124868 2 0.000 0.969 0.000 1.000
#> GSM1124878 2 0.000 0.969 0.000 1.000
#> GSM1124895 2 0.000 0.969 0.000 1.000
#> GSM1124897 2 0.000 0.969 0.000 1.000
#> GSM1124902 2 0.000 0.969 0.000 1.000
#> GSM1124908 2 0.000 0.969 0.000 1.000
#> GSM1124921 2 0.000 0.969 0.000 1.000
#> GSM1124939 2 0.000 0.969 0.000 1.000
#> GSM1124944 2 0.000 0.969 0.000 1.000
#> GSM1124945 1 0.494 0.882 0.892 0.108
#> GSM1124946 2 0.000 0.969 0.000 1.000
#> GSM1124947 2 0.000 0.969 0.000 1.000
#> GSM1124951 1 0.722 0.779 0.800 0.200
#> GSM1124952 2 0.402 0.897 0.080 0.920
#> GSM1124957 1 0.000 0.968 1.000 0.000
#> GSM1124900 1 0.000 0.968 1.000 0.000
#> GSM1124914 2 0.000 0.969 0.000 1.000
#> GSM1124871 2 0.000 0.969 0.000 1.000
#> GSM1124874 2 0.000 0.969 0.000 1.000
#> GSM1124875 2 0.000 0.969 0.000 1.000
#> GSM1124880 1 0.000 0.968 1.000 0.000
#> GSM1124881 2 0.000 0.969 0.000 1.000
#> GSM1124885 2 0.000 0.969 0.000 1.000
#> GSM1124886 1 0.000 0.968 1.000 0.000
#> GSM1124887 2 0.000 0.969 0.000 1.000
#> GSM1124894 2 0.985 0.299 0.428 0.572
#> GSM1124896 1 0.000 0.968 1.000 0.000
#> GSM1124899 2 0.000 0.969 0.000 1.000
#> GSM1124901 2 0.000 0.969 0.000 1.000
#> GSM1124906 2 0.000 0.969 0.000 1.000
#> GSM1124907 2 0.000 0.969 0.000 1.000
#> GSM1124911 2 0.000 0.969 0.000 1.000
#> GSM1124912 1 0.000 0.968 1.000 0.000
#> GSM1124915 2 0.000 0.969 0.000 1.000
#> GSM1124917 2 0.000 0.969 0.000 1.000
#> GSM1124918 2 0.000 0.969 0.000 1.000
#> GSM1124920 1 0.000 0.968 1.000 0.000
#> GSM1124922 2 0.118 0.957 0.016 0.984
#> GSM1124924 1 0.000 0.968 1.000 0.000
#> GSM1124926 2 0.000 0.969 0.000 1.000
#> GSM1124928 1 0.000 0.968 1.000 0.000
#> GSM1124930 2 0.000 0.969 0.000 1.000
#> GSM1124931 2 0.722 0.760 0.200 0.800
#> GSM1124935 2 0.000 0.969 0.000 1.000
#> GSM1124936 1 0.000 0.968 1.000 0.000
#> GSM1124938 1 0.706 0.790 0.808 0.192
#> GSM1124940 1 0.000 0.968 1.000 0.000
#> GSM1124941 2 0.000 0.969 0.000 1.000
#> GSM1124942 2 0.000 0.969 0.000 1.000
#> GSM1124943 1 0.722 0.779 0.800 0.200
#> GSM1124948 1 0.402 0.906 0.920 0.080
#> GSM1124949 1 0.000 0.968 1.000 0.000
#> GSM1124950 2 0.118 0.957 0.016 0.984
#> GSM1124954 1 0.000 0.968 1.000 0.000
#> GSM1124955 1 0.000 0.968 1.000 0.000
#> GSM1124956 2 0.000 0.969 0.000 1.000
#> GSM1124872 2 0.163 0.950 0.024 0.976
#> GSM1124873 2 0.000 0.969 0.000 1.000
#> GSM1124876 1 0.000 0.968 1.000 0.000
#> GSM1124877 1 0.000 0.968 1.000 0.000
#> GSM1124879 1 0.000 0.968 1.000 0.000
#> GSM1124883 2 0.000 0.969 0.000 1.000
#> GSM1124889 2 0.000 0.969 0.000 1.000
#> GSM1124892 1 0.000 0.968 1.000 0.000
#> GSM1124893 1 0.000 0.968 1.000 0.000
#> GSM1124909 2 0.958 0.359 0.380 0.620
#> GSM1124913 2 0.000 0.969 0.000 1.000
#> GSM1124916 2 0.163 0.951 0.024 0.976
#> GSM1124923 2 0.000 0.969 0.000 1.000
#> GSM1124925 1 0.000 0.968 1.000 0.000
#> GSM1124929 1 0.000 0.968 1.000 0.000
#> GSM1124934 1 0.000 0.968 1.000 0.000
#> GSM1124937 1 0.000 0.968 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1124891 3 0.6180 0.1806 0.416 0.000 0.584
#> GSM1124888 3 0.6180 0.1806 0.416 0.000 0.584
#> GSM1124890 3 0.4504 0.5078 0.196 0.000 0.804
#> GSM1124904 2 0.6180 0.5719 0.000 0.584 0.416
#> GSM1124927 2 0.6267 -0.2066 0.452 0.548 0.000
#> GSM1124953 3 0.0000 0.5607 0.000 0.000 1.000
#> GSM1124869 1 0.0000 0.8274 1.000 0.000 0.000
#> GSM1124870 1 0.6154 0.4737 0.592 0.408 0.000
#> GSM1124882 1 0.0000 0.8274 1.000 0.000 0.000
#> GSM1124884 2 0.0000 0.6575 0.000 1.000 0.000
#> GSM1124898 2 0.6168 0.5771 0.000 0.588 0.412
#> GSM1124903 2 0.6168 0.5771 0.000 0.588 0.412
#> GSM1124905 1 0.0000 0.8274 1.000 0.000 0.000
#> GSM1124910 1 0.0424 0.8221 0.992 0.000 0.008
#> GSM1124919 3 0.0424 0.5562 0.000 0.008 0.992
#> GSM1124932 2 0.1411 0.6297 0.036 0.964 0.000
#> GSM1124933 3 0.6168 0.1887 0.412 0.000 0.588
#> GSM1124867 1 0.6950 0.4681 0.572 0.408 0.020
#> GSM1124868 2 0.6168 0.5771 0.000 0.588 0.412
#> GSM1124878 2 0.6168 0.5771 0.000 0.588 0.412
#> GSM1124895 2 0.6168 0.5771 0.000 0.588 0.412
#> GSM1124897 2 0.6168 0.5771 0.000 0.588 0.412
#> GSM1124902 2 0.6168 0.5771 0.000 0.588 0.412
#> GSM1124908 2 0.6180 0.5719 0.000 0.584 0.416
#> GSM1124921 3 0.6286 -0.3605 0.000 0.464 0.536
#> GSM1124939 2 0.6168 0.5771 0.000 0.588 0.412
#> GSM1124944 3 0.2711 0.4760 0.000 0.088 0.912
#> GSM1124945 3 0.4654 0.4974 0.208 0.000 0.792
#> GSM1124946 3 0.6280 -0.3520 0.000 0.460 0.540
#> GSM1124947 3 0.6483 -0.3391 0.004 0.452 0.544
#> GSM1124951 3 0.1753 0.5812 0.048 0.000 0.952
#> GSM1124952 2 0.0000 0.6575 0.000 1.000 0.000
#> GSM1124957 3 0.6168 0.1887 0.412 0.000 0.588
#> GSM1124900 1 0.6008 0.5160 0.628 0.372 0.000
#> GSM1124914 2 0.6168 0.5771 0.000 0.588 0.412
#> GSM1124871 2 0.0000 0.6575 0.000 1.000 0.000
#> GSM1124874 2 0.0000 0.6575 0.000 1.000 0.000
#> GSM1124875 3 0.6291 -0.3696 0.000 0.468 0.532
#> GSM1124880 1 0.4452 0.6833 0.808 0.192 0.000
#> GSM1124881 2 0.0000 0.6575 0.000 1.000 0.000
#> GSM1124885 2 0.6168 0.5771 0.000 0.588 0.412
#> GSM1124886 1 0.0000 0.8274 1.000 0.000 0.000
#> GSM1124887 2 0.6192 0.5658 0.000 0.580 0.420
#> GSM1124894 1 0.4974 0.6301 0.764 0.236 0.000
#> GSM1124896 1 0.0000 0.8274 1.000 0.000 0.000
#> GSM1124899 2 0.0892 0.6570 0.000 0.980 0.020
#> GSM1124901 2 0.6168 0.5771 0.000 0.588 0.412
#> GSM1124906 2 0.0000 0.6575 0.000 1.000 0.000
#> GSM1124907 3 0.6225 -0.2938 0.000 0.432 0.568
#> GSM1124911 2 0.0000 0.6575 0.000 1.000 0.000
#> GSM1124912 1 0.0000 0.8274 1.000 0.000 0.000
#> GSM1124915 2 0.6168 0.5771 0.000 0.588 0.412
#> GSM1124917 2 0.6180 0.5719 0.000 0.584 0.416
#> GSM1124918 2 0.6280 0.0148 0.000 0.540 0.460
#> GSM1124920 1 0.6215 0.1972 0.572 0.000 0.428
#> GSM1124922 2 0.4527 0.6273 0.052 0.860 0.088
#> GSM1124924 3 0.9550 0.0128 0.340 0.204 0.456
#> GSM1124926 2 0.0892 0.6570 0.000 0.980 0.020
#> GSM1124928 1 0.0000 0.8274 1.000 0.000 0.000
#> GSM1124930 3 0.0000 0.5607 0.000 0.000 1.000
#> GSM1124931 2 0.1163 0.6390 0.028 0.972 0.000
#> GSM1124935 2 0.6168 0.5771 0.000 0.588 0.412
#> GSM1124936 1 0.6168 0.2360 0.588 0.000 0.412
#> GSM1124938 3 0.4702 0.4932 0.212 0.000 0.788
#> GSM1124940 1 0.0000 0.8274 1.000 0.000 0.000
#> GSM1124941 2 0.0000 0.6575 0.000 1.000 0.000
#> GSM1124942 3 0.4121 0.3506 0.000 0.168 0.832
#> GSM1124943 3 0.2066 0.5844 0.060 0.000 0.940
#> GSM1124948 3 0.8767 0.2769 0.208 0.204 0.588
#> GSM1124949 1 0.0000 0.8274 1.000 0.000 0.000
#> GSM1124950 2 0.0000 0.6575 0.000 1.000 0.000
#> GSM1124954 1 0.6267 0.1347 0.548 0.000 0.452
#> GSM1124955 1 0.0000 0.8274 1.000 0.000 0.000
#> GSM1124956 2 0.0000 0.6575 0.000 1.000 0.000
#> GSM1124872 2 0.0237 0.6547 0.004 0.996 0.000
#> GSM1124873 2 0.0000 0.6575 0.000 1.000 0.000
#> GSM1124876 3 0.6180 0.1806 0.416 0.000 0.584
#> GSM1124877 1 0.0000 0.8274 1.000 0.000 0.000
#> GSM1124879 1 0.0000 0.8274 1.000 0.000 0.000
#> GSM1124883 2 0.6168 0.5771 0.000 0.588 0.412
#> GSM1124889 2 0.0000 0.6575 0.000 1.000 0.000
#> GSM1124892 1 0.0237 0.8248 0.996 0.000 0.004
#> GSM1124893 1 0.0000 0.8274 1.000 0.000 0.000
#> GSM1124909 2 0.6737 -0.0457 0.384 0.600 0.016
#> GSM1124913 2 0.6168 0.5771 0.000 0.588 0.412
#> GSM1124916 2 0.5698 0.3330 0.252 0.736 0.012
#> GSM1124923 3 0.0424 0.5562 0.000 0.008 0.992
#> GSM1124925 1 0.0000 0.8274 1.000 0.000 0.000
#> GSM1124929 1 0.0000 0.8274 1.000 0.000 0.000
#> GSM1124934 1 0.5591 0.4571 0.696 0.000 0.304
#> GSM1124937 1 0.5435 0.6714 0.784 0.192 0.024
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1124891 3 0.1637 0.86522 0.060 0.000 0.940 0.000
#> GSM1124888 3 0.1637 0.86522 0.060 0.000 0.940 0.000
#> GSM1124890 3 0.0469 0.87067 0.000 0.000 0.988 0.012
#> GSM1124904 4 0.0000 0.93755 0.000 0.000 0.000 1.000
#> GSM1124927 2 0.0336 0.91480 0.000 0.992 0.008 0.000
#> GSM1124953 3 0.4994 -0.00182 0.000 0.000 0.520 0.480
#> GSM1124869 1 0.0000 0.95953 1.000 0.000 0.000 0.000
#> GSM1124870 1 0.3196 0.82240 0.856 0.136 0.008 0.000
#> GSM1124882 1 0.0000 0.95953 1.000 0.000 0.000 0.000
#> GSM1124884 2 0.0000 0.91604 0.000 1.000 0.000 0.000
#> GSM1124898 4 0.1867 0.88462 0.000 0.072 0.000 0.928
#> GSM1124903 4 0.0000 0.93755 0.000 0.000 0.000 1.000
#> GSM1124905 1 0.0000 0.95953 1.000 0.000 0.000 0.000
#> GSM1124910 1 0.1389 0.92015 0.952 0.000 0.048 0.000
#> GSM1124919 4 0.3356 0.77891 0.000 0.000 0.176 0.824
#> GSM1124932 2 0.0336 0.91480 0.000 0.992 0.008 0.000
#> GSM1124933 3 0.0707 0.87252 0.020 0.000 0.980 0.000
#> GSM1124867 2 0.3071 0.84367 0.068 0.888 0.044 0.000
#> GSM1124868 4 0.0000 0.93755 0.000 0.000 0.000 1.000
#> GSM1124878 4 0.0000 0.93755 0.000 0.000 0.000 1.000
#> GSM1124895 4 0.0000 0.93755 0.000 0.000 0.000 1.000
#> GSM1124897 4 0.0000 0.93755 0.000 0.000 0.000 1.000
#> GSM1124902 4 0.0000 0.93755 0.000 0.000 0.000 1.000
#> GSM1124908 4 0.0000 0.93755 0.000 0.000 0.000 1.000
#> GSM1124921 4 0.0336 0.93468 0.000 0.000 0.008 0.992
#> GSM1124939 4 0.0000 0.93755 0.000 0.000 0.000 1.000
#> GSM1124944 4 0.0336 0.93484 0.000 0.000 0.008 0.992
#> GSM1124945 3 0.0469 0.87123 0.000 0.000 0.988 0.012
#> GSM1124946 4 0.0336 0.93468 0.000 0.000 0.008 0.992
#> GSM1124947 4 0.0927 0.92870 0.000 0.016 0.008 0.976
#> GSM1124951 3 0.0592 0.87055 0.000 0.000 0.984 0.016
#> GSM1124952 2 0.2926 0.85037 0.004 0.888 0.012 0.096
#> GSM1124957 3 0.0707 0.87252 0.020 0.000 0.980 0.000
#> GSM1124900 1 0.1722 0.91591 0.944 0.048 0.008 0.000
#> GSM1124914 4 0.0000 0.93755 0.000 0.000 0.000 1.000
#> GSM1124871 2 0.0817 0.90931 0.000 0.976 0.000 0.024
#> GSM1124874 2 0.2888 0.83785 0.000 0.872 0.004 0.124
#> GSM1124875 4 0.1489 0.91396 0.000 0.004 0.044 0.952
#> GSM1124880 1 0.2596 0.89191 0.908 0.068 0.024 0.000
#> GSM1124881 2 0.0336 0.91574 0.000 0.992 0.000 0.008
#> GSM1124885 4 0.0000 0.93755 0.000 0.000 0.000 1.000
#> GSM1124886 1 0.0000 0.95953 1.000 0.000 0.000 0.000
#> GSM1124887 4 0.0000 0.93755 0.000 0.000 0.000 1.000
#> GSM1124894 1 0.1209 0.93546 0.964 0.032 0.004 0.000
#> GSM1124896 1 0.0000 0.95953 1.000 0.000 0.000 0.000
#> GSM1124899 2 0.4155 0.69633 0.000 0.756 0.004 0.240
#> GSM1124901 4 0.0524 0.93247 0.000 0.008 0.004 0.988
#> GSM1124906 2 0.0000 0.91604 0.000 1.000 0.000 0.000
#> GSM1124907 4 0.1118 0.92078 0.000 0.000 0.036 0.964
#> GSM1124911 2 0.0469 0.91434 0.000 0.988 0.000 0.012
#> GSM1124912 1 0.0000 0.95953 1.000 0.000 0.000 0.000
#> GSM1124915 4 0.0592 0.92937 0.000 0.016 0.000 0.984
#> GSM1124917 4 0.5955 0.45888 0.000 0.328 0.056 0.616
#> GSM1124918 2 0.2999 0.81891 0.000 0.864 0.132 0.004
#> GSM1124920 3 0.3649 0.75964 0.204 0.000 0.796 0.000
#> GSM1124922 2 0.7940 0.10698 0.172 0.412 0.016 0.400
#> GSM1124924 3 0.2670 0.83787 0.024 0.072 0.904 0.000
#> GSM1124926 2 0.4585 0.54584 0.000 0.668 0.000 0.332
#> GSM1124928 1 0.0000 0.95953 1.000 0.000 0.000 0.000
#> GSM1124930 3 0.4643 0.45184 0.000 0.000 0.656 0.344
#> GSM1124931 2 0.0779 0.91103 0.004 0.980 0.016 0.000
#> GSM1124935 4 0.4920 0.37583 0.000 0.368 0.004 0.628
#> GSM1124936 3 0.4072 0.70212 0.252 0.000 0.748 0.000
#> GSM1124938 3 0.0336 0.87100 0.000 0.000 0.992 0.008
#> GSM1124940 1 0.0000 0.95953 1.000 0.000 0.000 0.000
#> GSM1124941 2 0.0000 0.91604 0.000 1.000 0.000 0.000
#> GSM1124942 4 0.4103 0.68009 0.000 0.000 0.256 0.744
#> GSM1124943 3 0.0921 0.86507 0.000 0.000 0.972 0.028
#> GSM1124948 3 0.0000 0.87004 0.000 0.000 1.000 0.000
#> GSM1124949 1 0.0000 0.95953 1.000 0.000 0.000 0.000
#> GSM1124950 2 0.0336 0.91480 0.000 0.992 0.008 0.000
#> GSM1124954 3 0.3311 0.79039 0.172 0.000 0.828 0.000
#> GSM1124955 1 0.0000 0.95953 1.000 0.000 0.000 0.000
#> GSM1124956 2 0.0188 0.91591 0.000 0.996 0.000 0.004
#> GSM1124872 2 0.0336 0.91480 0.000 0.992 0.008 0.000
#> GSM1124873 2 0.0000 0.91604 0.000 1.000 0.000 0.000
#> GSM1124876 3 0.1637 0.86522 0.060 0.000 0.940 0.000
#> GSM1124877 1 0.0000 0.95953 1.000 0.000 0.000 0.000
#> GSM1124879 1 0.0000 0.95953 1.000 0.000 0.000 0.000
#> GSM1124883 4 0.0000 0.93755 0.000 0.000 0.000 1.000
#> GSM1124889 2 0.0469 0.91434 0.000 0.988 0.000 0.012
#> GSM1124892 1 0.0469 0.95175 0.988 0.000 0.012 0.000
#> GSM1124893 1 0.0000 0.95953 1.000 0.000 0.000 0.000
#> GSM1124909 2 0.0188 0.91582 0.000 0.996 0.004 0.000
#> GSM1124913 4 0.0000 0.93755 0.000 0.000 0.000 1.000
#> GSM1124916 2 0.0188 0.91582 0.000 0.996 0.004 0.000
#> GSM1124923 4 0.3311 0.78960 0.000 0.000 0.172 0.828
#> GSM1124925 1 0.0000 0.95953 1.000 0.000 0.000 0.000
#> GSM1124929 1 0.0000 0.95953 1.000 0.000 0.000 0.000
#> GSM1124934 3 0.3942 0.72014 0.236 0.000 0.764 0.000
#> GSM1124937 1 0.6351 0.36528 0.588 0.080 0.332 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM1124891 3 0.1270 0.7281 0.052 0.000 0.948 0.000 0.000
#> GSM1124888 3 0.1197 0.7294 0.048 0.000 0.952 0.000 0.000
#> GSM1124890 3 0.3109 0.6355 0.000 0.000 0.800 0.200 0.000
#> GSM1124904 5 0.0510 0.7838 0.000 0.000 0.000 0.016 0.984
#> GSM1124927 2 0.3816 0.5950 0.000 0.696 0.000 0.304 0.000
#> GSM1124953 3 0.6746 -0.1510 0.000 0.000 0.392 0.344 0.264
#> GSM1124869 1 0.0000 0.8916 1.000 0.000 0.000 0.000 0.000
#> GSM1124870 1 0.5116 0.5608 0.668 0.084 0.000 0.248 0.000
#> GSM1124882 1 0.0000 0.8916 1.000 0.000 0.000 0.000 0.000
#> GSM1124884 2 0.1408 0.6807 0.000 0.948 0.000 0.044 0.008
#> GSM1124898 5 0.4637 0.5667 0.000 0.160 0.000 0.100 0.740
#> GSM1124903 5 0.0451 0.7846 0.000 0.008 0.000 0.004 0.988
#> GSM1124905 1 0.1195 0.8768 0.960 0.000 0.012 0.028 0.000
#> GSM1124910 1 0.3143 0.6988 0.796 0.000 0.204 0.000 0.000
#> GSM1124919 5 0.6268 0.1968 0.000 0.000 0.156 0.360 0.484
#> GSM1124932 2 0.2891 0.6492 0.000 0.824 0.000 0.176 0.000
#> GSM1124933 3 0.0162 0.7249 0.004 0.000 0.996 0.000 0.000
#> GSM1124867 2 0.6264 0.3143 0.052 0.472 0.044 0.432 0.000
#> GSM1124868 5 0.1444 0.7806 0.000 0.012 0.000 0.040 0.948
#> GSM1124878 5 0.0807 0.7845 0.000 0.012 0.000 0.012 0.976
#> GSM1124895 5 0.1830 0.7762 0.000 0.008 0.000 0.068 0.924
#> GSM1124897 5 0.1205 0.7845 0.000 0.004 0.000 0.040 0.956
#> GSM1124902 5 0.1732 0.7783 0.000 0.000 0.000 0.080 0.920
#> GSM1124908 5 0.1792 0.7795 0.000 0.000 0.000 0.084 0.916
#> GSM1124921 5 0.2377 0.7543 0.000 0.000 0.000 0.128 0.872
#> GSM1124939 5 0.1956 0.7759 0.000 0.008 0.000 0.076 0.916
#> GSM1124944 5 0.3430 0.7114 0.000 0.000 0.004 0.220 0.776
#> GSM1124945 3 0.2074 0.6973 0.000 0.000 0.896 0.104 0.000
#> GSM1124946 5 0.2389 0.7495 0.000 0.000 0.004 0.116 0.880
#> GSM1124947 5 0.4350 0.6261 0.000 0.028 0.000 0.268 0.704
#> GSM1124951 3 0.3300 0.6238 0.000 0.000 0.792 0.204 0.004
#> GSM1124952 2 0.5775 0.4832 0.000 0.608 0.000 0.244 0.148
#> GSM1124957 3 0.0566 0.7247 0.004 0.000 0.984 0.012 0.000
#> GSM1124900 1 0.4096 0.6826 0.760 0.040 0.000 0.200 0.000
#> GSM1124914 5 0.1331 0.7856 0.000 0.008 0.000 0.040 0.952
#> GSM1124871 2 0.3578 0.6082 0.000 0.820 0.000 0.048 0.132
#> GSM1124874 2 0.5766 0.4719 0.000 0.616 0.000 0.164 0.220
#> GSM1124875 5 0.5916 0.1735 0.000 0.020 0.056 0.440 0.484
#> GSM1124880 1 0.7204 0.1528 0.452 0.068 0.116 0.364 0.000
#> GSM1124881 2 0.4192 0.5754 0.000 0.736 0.000 0.232 0.032
#> GSM1124885 5 0.0992 0.7846 0.000 0.008 0.000 0.024 0.968
#> GSM1124886 1 0.0880 0.8749 0.968 0.000 0.032 0.000 0.000
#> GSM1124887 5 0.1792 0.7642 0.000 0.000 0.000 0.084 0.916
#> GSM1124894 1 0.7189 0.4279 0.584 0.176 0.044 0.168 0.028
#> GSM1124896 1 0.0000 0.8916 1.000 0.000 0.000 0.000 0.000
#> GSM1124899 2 0.5516 0.3984 0.000 0.640 0.000 0.128 0.232
#> GSM1124901 5 0.2653 0.7207 0.000 0.096 0.000 0.024 0.880
#> GSM1124906 2 0.1043 0.6789 0.000 0.960 0.000 0.040 0.000
#> GSM1124907 5 0.4789 0.3945 0.000 0.000 0.024 0.392 0.584
#> GSM1124911 2 0.1399 0.6721 0.000 0.952 0.000 0.028 0.020
#> GSM1124912 1 0.0000 0.8916 1.000 0.000 0.000 0.000 0.000
#> GSM1124915 5 0.2753 0.6932 0.000 0.136 0.000 0.008 0.856
#> GSM1124917 4 0.7403 0.1516 0.000 0.292 0.032 0.400 0.276
#> GSM1124918 4 0.5984 0.0706 0.000 0.376 0.068 0.536 0.020
#> GSM1124920 3 0.2966 0.6455 0.184 0.000 0.816 0.000 0.000
#> GSM1124922 2 0.8049 0.1126 0.156 0.404 0.000 0.144 0.296
#> GSM1124924 3 0.4948 0.3494 0.008 0.024 0.612 0.356 0.000
#> GSM1124926 2 0.5723 0.2216 0.000 0.520 0.000 0.088 0.392
#> GSM1124928 1 0.1216 0.8767 0.960 0.000 0.020 0.020 0.000
#> GSM1124930 4 0.6485 -0.0492 0.000 0.000 0.344 0.460 0.196
#> GSM1124931 2 0.4065 0.6042 0.000 0.720 0.016 0.264 0.000
#> GSM1124935 5 0.5912 0.1496 0.000 0.348 0.000 0.116 0.536
#> GSM1124936 3 0.3305 0.6049 0.224 0.000 0.776 0.000 0.000
#> GSM1124938 3 0.3039 0.6517 0.000 0.000 0.808 0.192 0.000
#> GSM1124940 1 0.0000 0.8916 1.000 0.000 0.000 0.000 0.000
#> GSM1124941 2 0.1043 0.6789 0.000 0.960 0.000 0.040 0.000
#> GSM1124942 5 0.6465 0.0421 0.000 0.004 0.156 0.416 0.424
#> GSM1124943 3 0.4206 0.5359 0.000 0.000 0.696 0.288 0.016
#> GSM1124948 3 0.4084 0.4952 0.000 0.004 0.668 0.328 0.000
#> GSM1124949 1 0.0000 0.8916 1.000 0.000 0.000 0.000 0.000
#> GSM1124950 2 0.3752 0.6036 0.000 0.708 0.000 0.292 0.000
#> GSM1124954 3 0.2424 0.6856 0.132 0.000 0.868 0.000 0.000
#> GSM1124955 1 0.0000 0.8916 1.000 0.000 0.000 0.000 0.000
#> GSM1124956 2 0.1300 0.6727 0.000 0.956 0.000 0.028 0.016
#> GSM1124872 2 0.3857 0.5914 0.000 0.688 0.000 0.312 0.000
#> GSM1124873 2 0.2130 0.6647 0.000 0.908 0.000 0.080 0.012
#> GSM1124876 3 0.1197 0.7289 0.048 0.000 0.952 0.000 0.000
#> GSM1124877 1 0.0000 0.8916 1.000 0.000 0.000 0.000 0.000
#> GSM1124879 1 0.0000 0.8916 1.000 0.000 0.000 0.000 0.000
#> GSM1124883 5 0.0451 0.7839 0.000 0.008 0.000 0.004 0.988
#> GSM1124889 2 0.1907 0.6742 0.000 0.928 0.000 0.044 0.028
#> GSM1124892 1 0.2471 0.7831 0.864 0.000 0.136 0.000 0.000
#> GSM1124893 1 0.0000 0.8916 1.000 0.000 0.000 0.000 0.000
#> GSM1124909 2 0.4030 0.4732 0.000 0.648 0.000 0.352 0.000
#> GSM1124913 5 0.0451 0.7846 0.000 0.008 0.000 0.004 0.988
#> GSM1124916 2 0.3999 0.4749 0.000 0.656 0.000 0.344 0.000
#> GSM1124923 5 0.6157 0.2131 0.000 0.000 0.140 0.364 0.496
#> GSM1124925 1 0.0000 0.8916 1.000 0.000 0.000 0.000 0.000
#> GSM1124929 1 0.0000 0.8916 1.000 0.000 0.000 0.000 0.000
#> GSM1124934 3 0.3093 0.6557 0.168 0.000 0.824 0.008 0.000
#> GSM1124937 4 0.7919 0.0152 0.308 0.072 0.276 0.344 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1124891 3 0.0935 0.77447 0.032 0.000 0.964 0.000 0.000 0.004
#> GSM1124888 3 0.0858 0.77589 0.028 0.000 0.968 0.000 0.004 0.000
#> GSM1124890 3 0.4141 0.46501 0.000 0.000 0.676 0.008 0.296 0.020
#> GSM1124904 4 0.1524 0.73304 0.000 0.000 0.000 0.932 0.060 0.008
#> GSM1124927 6 0.4755 -0.13771 0.000 0.460 0.000 0.000 0.048 0.492
#> GSM1124953 5 0.6151 0.39753 0.000 0.000 0.340 0.124 0.496 0.040
#> GSM1124869 1 0.0000 0.85883 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124870 1 0.5961 0.15498 0.476 0.080 0.000 0.000 0.048 0.396
#> GSM1124882 1 0.0000 0.85883 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124884 2 0.2240 0.56552 0.000 0.904 0.000 0.008 0.032 0.056
#> GSM1124898 4 0.5716 0.48413 0.000 0.180 0.000 0.640 0.072 0.108
#> GSM1124903 4 0.1082 0.73769 0.000 0.000 0.000 0.956 0.040 0.004
#> GSM1124905 1 0.3854 0.74514 0.808 0.000 0.044 0.000 0.056 0.092
#> GSM1124910 1 0.3626 0.54820 0.704 0.000 0.288 0.000 0.004 0.004
#> GSM1124919 5 0.6343 0.52524 0.000 0.008 0.108 0.296 0.532 0.056
#> GSM1124932 2 0.4170 0.42831 0.004 0.732 0.000 0.004 0.048 0.212
#> GSM1124933 3 0.0603 0.76744 0.004 0.000 0.980 0.000 0.016 0.000
#> GSM1124867 6 0.4522 0.43373 0.016 0.176 0.024 0.000 0.040 0.744
#> GSM1124868 4 0.2089 0.74042 0.000 0.020 0.000 0.916 0.044 0.020
#> GSM1124878 4 0.1480 0.74228 0.000 0.000 0.000 0.940 0.040 0.020
#> GSM1124895 4 0.3212 0.71799 0.000 0.012 0.000 0.840 0.100 0.048
#> GSM1124897 4 0.2592 0.73751 0.000 0.012 0.004 0.884 0.080 0.020
#> GSM1124902 4 0.3159 0.71603 0.000 0.004 0.000 0.836 0.108 0.052
#> GSM1124908 4 0.2494 0.72093 0.000 0.000 0.000 0.864 0.120 0.016
#> GSM1124921 4 0.3670 0.56612 0.000 0.000 0.000 0.736 0.240 0.024
#> GSM1124939 4 0.3419 0.71022 0.000 0.008 0.000 0.820 0.116 0.056
#> GSM1124944 4 0.5136 0.45314 0.000 0.004 0.004 0.584 0.332 0.076
#> GSM1124945 3 0.2358 0.70869 0.000 0.000 0.876 0.000 0.108 0.016
#> GSM1124946 4 0.3859 0.50154 0.000 0.000 0.000 0.692 0.288 0.020
#> GSM1124947 4 0.6702 0.33810 0.000 0.072 0.000 0.484 0.264 0.180
#> GSM1124951 3 0.3855 0.49241 0.000 0.000 0.704 0.004 0.276 0.016
#> GSM1124952 2 0.7026 0.25003 0.000 0.500 0.016 0.088 0.156 0.240
#> GSM1124957 3 0.0692 0.76667 0.004 0.000 0.976 0.000 0.020 0.000
#> GSM1124900 1 0.4972 0.38645 0.596 0.044 0.000 0.000 0.020 0.340
#> GSM1124914 4 0.1807 0.74030 0.000 0.000 0.000 0.920 0.060 0.020
#> GSM1124871 2 0.4862 0.48088 0.000 0.684 0.000 0.216 0.020 0.080
#> GSM1124874 2 0.6484 0.36973 0.000 0.492 0.000 0.256 0.044 0.208
#> GSM1124875 5 0.5590 0.53891 0.000 0.028 0.016 0.252 0.628 0.076
#> GSM1124880 6 0.5853 0.39700 0.212 0.032 0.100 0.000 0.024 0.632
#> GSM1124881 2 0.5466 0.27218 0.000 0.588 0.004 0.036 0.056 0.316
#> GSM1124885 4 0.1889 0.74251 0.000 0.004 0.000 0.920 0.056 0.020
#> GSM1124886 1 0.1141 0.83015 0.948 0.000 0.052 0.000 0.000 0.000
#> GSM1124887 4 0.2968 0.65944 0.000 0.000 0.000 0.816 0.168 0.016
#> GSM1124894 1 0.8717 -0.12029 0.316 0.296 0.056 0.032 0.128 0.172
#> GSM1124896 1 0.0000 0.85883 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124899 2 0.6025 0.37363 0.000 0.564 0.000 0.268 0.116 0.052
#> GSM1124901 4 0.3565 0.65847 0.000 0.112 0.000 0.816 0.056 0.016
#> GSM1124906 2 0.1995 0.56735 0.000 0.912 0.000 0.000 0.036 0.052
#> GSM1124907 5 0.3983 0.48113 0.000 0.000 0.008 0.348 0.640 0.004
#> GSM1124911 2 0.2332 0.58226 0.000 0.904 0.000 0.036 0.020 0.040
#> GSM1124912 1 0.0000 0.85883 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124915 4 0.4235 0.58744 0.000 0.212 0.000 0.728 0.048 0.012
#> GSM1124917 6 0.8024 -0.12082 0.000 0.176 0.024 0.200 0.276 0.324
#> GSM1124918 5 0.6339 0.02684 0.000 0.160 0.020 0.012 0.496 0.312
#> GSM1124920 3 0.2879 0.69888 0.176 0.000 0.816 0.000 0.004 0.004
#> GSM1124922 4 0.8494 -0.12458 0.168 0.284 0.004 0.312 0.164 0.068
#> GSM1124924 6 0.5694 -0.00761 0.004 0.012 0.424 0.000 0.096 0.464
#> GSM1124926 2 0.5821 0.16842 0.000 0.492 0.000 0.392 0.072 0.044
#> GSM1124928 1 0.3587 0.73728 0.804 0.000 0.056 0.000 0.008 0.132
#> GSM1124930 5 0.5100 0.56662 0.000 0.000 0.168 0.084 0.696 0.052
#> GSM1124931 2 0.5096 0.25900 0.000 0.596 0.004 0.004 0.076 0.320
#> GSM1124935 4 0.6652 0.02204 0.000 0.332 0.000 0.444 0.060 0.164
#> GSM1124936 3 0.2933 0.67896 0.200 0.000 0.796 0.000 0.000 0.004
#> GSM1124938 3 0.3547 0.46492 0.000 0.000 0.668 0.000 0.332 0.000
#> GSM1124940 1 0.0000 0.85883 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124941 2 0.1995 0.56735 0.000 0.912 0.000 0.000 0.036 0.052
#> GSM1124942 5 0.4322 0.64947 0.000 0.000 0.076 0.184 0.732 0.008
#> GSM1124943 5 0.4312 -0.13361 0.000 0.000 0.476 0.004 0.508 0.012
#> GSM1124948 3 0.5787 0.19655 0.000 0.000 0.480 0.000 0.324 0.196
#> GSM1124949 1 0.0000 0.85883 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124950 2 0.4264 -0.00648 0.000 0.496 0.000 0.000 0.016 0.488
#> GSM1124954 3 0.2009 0.75668 0.084 0.000 0.904 0.000 0.008 0.004
#> GSM1124955 1 0.0000 0.85883 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124956 2 0.2171 0.58160 0.000 0.912 0.000 0.032 0.016 0.040
#> GSM1124872 6 0.4157 -0.03070 0.000 0.444 0.000 0.000 0.012 0.544
#> GSM1124873 2 0.3418 0.50576 0.000 0.784 0.000 0.016 0.008 0.192
#> GSM1124876 3 0.0993 0.77491 0.024 0.000 0.964 0.000 0.012 0.000
#> GSM1124877 1 0.0000 0.85883 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124879 1 0.0000 0.85883 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124883 4 0.1003 0.74009 0.000 0.004 0.000 0.964 0.028 0.004
#> GSM1124889 2 0.3942 0.55273 0.000 0.784 0.000 0.084 0.012 0.120
#> GSM1124892 1 0.2562 0.72917 0.828 0.000 0.172 0.000 0.000 0.000
#> GSM1124893 1 0.0000 0.85883 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124909 6 0.4499 0.35741 0.000 0.288 0.000 0.000 0.060 0.652
#> GSM1124913 4 0.1265 0.73570 0.000 0.000 0.000 0.948 0.044 0.008
#> GSM1124916 6 0.4587 0.34496 0.000 0.296 0.000 0.000 0.064 0.640
#> GSM1124923 5 0.5324 0.58297 0.000 0.000 0.076 0.284 0.612 0.028
#> GSM1124925 1 0.0000 0.85883 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124929 1 0.0000 0.85883 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124934 3 0.3219 0.71611 0.132 0.000 0.828 0.000 0.012 0.028
#> GSM1124937 6 0.6724 0.44698 0.152 0.048 0.156 0.000 0.060 0.584
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 tissue(p) k
#> MAD:skmeans 89 0.4885 2
#> MAD:skmeans 65 0.0417 3
#> MAD:skmeans 85 0.0472 4
#> MAD:skmeans 68 0.0120 5
#> MAD:skmeans 57 0.1506 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 48983 rows and 91 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 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 0.655 0.770 0.909 0.3575 0.707 0.707
#> 3 3 0.760 0.888 0.934 0.4928 0.754 0.658
#> 4 4 0.763 0.771 0.910 0.3169 0.815 0.621
#> 5 5 0.833 0.849 0.921 0.0844 0.925 0.756
#> 6 6 0.792 0.735 0.850 0.0643 0.954 0.811
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
#> GSM1124891 1 0.0000 0.877 1.000 0.000
#> GSM1124888 1 0.0000 0.877 1.000 0.000
#> GSM1124890 2 0.3274 0.833 0.060 0.940
#> GSM1124904 2 0.0000 0.889 0.000 1.000
#> GSM1124927 2 0.0000 0.889 0.000 1.000
#> GSM1124953 1 0.9896 0.315 0.560 0.440
#> GSM1124869 2 0.9896 0.328 0.440 0.560
#> GSM1124870 2 0.0376 0.887 0.004 0.996
#> GSM1124882 2 0.9896 0.328 0.440 0.560
#> GSM1124884 2 0.0000 0.889 0.000 1.000
#> GSM1124898 2 0.0000 0.889 0.000 1.000
#> GSM1124903 2 0.0000 0.889 0.000 1.000
#> GSM1124905 2 0.8443 0.601 0.272 0.728
#> GSM1124910 1 0.0000 0.877 1.000 0.000
#> GSM1124919 2 0.0000 0.889 0.000 1.000
#> GSM1124932 2 0.0000 0.889 0.000 1.000
#> GSM1124933 1 0.0376 0.875 0.996 0.004
#> GSM1124867 2 0.0000 0.889 0.000 1.000
#> GSM1124868 2 0.0000 0.889 0.000 1.000
#> GSM1124878 2 0.0000 0.889 0.000 1.000
#> GSM1124895 2 0.0000 0.889 0.000 1.000
#> GSM1124897 2 0.0000 0.889 0.000 1.000
#> GSM1124902 2 0.0000 0.889 0.000 1.000
#> GSM1124908 2 0.0000 0.889 0.000 1.000
#> GSM1124921 2 0.0000 0.889 0.000 1.000
#> GSM1124939 2 0.0000 0.889 0.000 1.000
#> GSM1124944 2 0.0000 0.889 0.000 1.000
#> GSM1124945 1 0.9866 0.334 0.568 0.432
#> GSM1124946 2 0.0000 0.889 0.000 1.000
#> GSM1124947 2 0.0000 0.889 0.000 1.000
#> GSM1124951 1 0.8713 0.583 0.708 0.292
#> GSM1124952 2 0.0000 0.889 0.000 1.000
#> GSM1124957 1 0.0000 0.877 1.000 0.000
#> GSM1124900 2 0.0376 0.887 0.004 0.996
#> GSM1124914 2 0.0000 0.889 0.000 1.000
#> GSM1124871 2 0.0000 0.889 0.000 1.000
#> GSM1124874 2 0.0000 0.889 0.000 1.000
#> GSM1124875 2 0.0000 0.889 0.000 1.000
#> GSM1124880 2 0.0000 0.889 0.000 1.000
#> GSM1124881 2 0.0000 0.889 0.000 1.000
#> GSM1124885 2 0.0000 0.889 0.000 1.000
#> GSM1124886 1 0.0000 0.877 1.000 0.000
#> GSM1124887 2 0.0000 0.889 0.000 1.000
#> GSM1124894 2 0.5946 0.758 0.144 0.856
#> GSM1124896 2 0.9580 0.434 0.380 0.620
#> GSM1124899 2 0.0000 0.889 0.000 1.000
#> GSM1124901 2 0.0000 0.889 0.000 1.000
#> GSM1124906 2 0.0000 0.889 0.000 1.000
#> GSM1124907 2 0.0000 0.889 0.000 1.000
#> GSM1124911 2 0.0000 0.889 0.000 1.000
#> GSM1124912 2 0.9896 0.328 0.440 0.560
#> GSM1124915 2 0.0000 0.889 0.000 1.000
#> GSM1124917 2 0.0000 0.889 0.000 1.000
#> GSM1124918 2 0.0000 0.889 0.000 1.000
#> GSM1124920 1 0.0000 0.877 1.000 0.000
#> GSM1124922 2 0.0376 0.887 0.004 0.996
#> GSM1124924 2 0.5946 0.731 0.144 0.856
#> GSM1124926 2 0.0000 0.889 0.000 1.000
#> GSM1124928 2 0.9896 0.328 0.440 0.560
#> GSM1124930 2 0.0000 0.889 0.000 1.000
#> GSM1124931 2 0.0000 0.889 0.000 1.000
#> GSM1124935 2 0.0000 0.889 0.000 1.000
#> GSM1124936 1 0.0000 0.877 1.000 0.000
#> GSM1124938 1 0.9460 0.469 0.636 0.364
#> GSM1124940 2 0.9896 0.328 0.440 0.560
#> GSM1124941 2 0.0000 0.889 0.000 1.000
#> GSM1124942 2 0.0000 0.889 0.000 1.000
#> GSM1124943 2 0.9983 -0.123 0.476 0.524
#> GSM1124948 2 0.0000 0.889 0.000 1.000
#> GSM1124949 2 0.9896 0.328 0.440 0.560
#> GSM1124950 2 0.0000 0.889 0.000 1.000
#> GSM1124954 1 0.0000 0.877 1.000 0.000
#> GSM1124955 2 0.9896 0.328 0.440 0.560
#> GSM1124956 2 0.0000 0.889 0.000 1.000
#> GSM1124872 2 0.0000 0.889 0.000 1.000
#> GSM1124873 2 0.0000 0.889 0.000 1.000
#> GSM1124876 1 0.0000 0.877 1.000 0.000
#> GSM1124877 2 0.9896 0.328 0.440 0.560
#> GSM1124879 2 0.9896 0.328 0.440 0.560
#> GSM1124883 2 0.0000 0.889 0.000 1.000
#> GSM1124889 2 0.0000 0.889 0.000 1.000
#> GSM1124892 1 0.0000 0.877 1.000 0.000
#> GSM1124893 2 0.9896 0.328 0.440 0.560
#> GSM1124909 2 0.0000 0.889 0.000 1.000
#> GSM1124913 2 0.0000 0.889 0.000 1.000
#> GSM1124916 2 0.0000 0.889 0.000 1.000
#> GSM1124923 2 0.0000 0.889 0.000 1.000
#> GSM1124925 2 0.9896 0.328 0.440 0.560
#> GSM1124929 2 0.9963 0.269 0.464 0.536
#> GSM1124934 1 0.0000 0.877 1.000 0.000
#> GSM1124937 2 0.0000 0.889 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1124891 3 0.3116 0.9024 0.108 0.000 0.892
#> GSM1124888 3 0.3340 0.9011 0.120 0.000 0.880
#> GSM1124890 2 0.4399 0.7751 0.000 0.812 0.188
#> GSM1124904 2 0.2537 0.9225 0.000 0.920 0.080
#> GSM1124927 2 0.0000 0.9385 0.000 1.000 0.000
#> GSM1124953 3 0.2261 0.8294 0.000 0.068 0.932
#> GSM1124869 1 0.0000 0.9362 1.000 0.000 0.000
#> GSM1124870 1 0.3340 0.7685 0.880 0.120 0.000
#> GSM1124882 1 0.0000 0.9362 1.000 0.000 0.000
#> GSM1124884 2 0.0000 0.9385 0.000 1.000 0.000
#> GSM1124898 2 0.2796 0.9205 0.000 0.908 0.092
#> GSM1124903 2 0.3116 0.9127 0.000 0.892 0.108
#> GSM1124905 1 0.6299 0.0178 0.524 0.476 0.000
#> GSM1124910 1 0.0237 0.9323 0.996 0.000 0.004
#> GSM1124919 2 0.2796 0.9205 0.000 0.908 0.092
#> GSM1124932 2 0.0000 0.9385 0.000 1.000 0.000
#> GSM1124933 3 0.3116 0.9024 0.108 0.000 0.892
#> GSM1124867 2 0.0000 0.9385 0.000 1.000 0.000
#> GSM1124868 2 0.2959 0.9158 0.000 0.900 0.100
#> GSM1124878 2 0.3116 0.9127 0.000 0.892 0.108
#> GSM1124895 2 0.2959 0.9165 0.000 0.900 0.100
#> GSM1124897 2 0.3116 0.9127 0.000 0.892 0.108
#> GSM1124902 2 0.3116 0.9127 0.000 0.892 0.108
#> GSM1124908 2 0.2537 0.9248 0.000 0.920 0.080
#> GSM1124921 2 0.3340 0.9093 0.000 0.880 0.120
#> GSM1124939 2 0.1643 0.9327 0.000 0.956 0.044
#> GSM1124944 2 0.3340 0.9093 0.000 0.880 0.120
#> GSM1124945 3 0.2959 0.8372 0.000 0.100 0.900
#> GSM1124946 2 0.3340 0.9093 0.000 0.880 0.120
#> GSM1124947 2 0.1289 0.9339 0.000 0.968 0.032
#> GSM1124951 3 0.0000 0.8391 0.000 0.000 1.000
#> GSM1124952 2 0.0000 0.9385 0.000 1.000 0.000
#> GSM1124957 3 0.3116 0.9024 0.108 0.000 0.892
#> GSM1124900 2 0.4974 0.6722 0.236 0.764 0.000
#> GSM1124914 2 0.1860 0.9321 0.000 0.948 0.052
#> GSM1124871 2 0.0000 0.9385 0.000 1.000 0.000
#> GSM1124874 2 0.0592 0.9375 0.000 0.988 0.012
#> GSM1124875 2 0.1289 0.9348 0.000 0.968 0.032
#> GSM1124880 2 0.0475 0.9372 0.004 0.992 0.004
#> GSM1124881 2 0.0000 0.9385 0.000 1.000 0.000
#> GSM1124885 2 0.3340 0.9093 0.000 0.880 0.120
#> GSM1124886 1 0.0000 0.9362 1.000 0.000 0.000
#> GSM1124887 2 0.2537 0.9225 0.000 0.920 0.080
#> GSM1124894 2 0.3377 0.8830 0.092 0.896 0.012
#> GSM1124896 2 0.6937 0.3310 0.404 0.576 0.020
#> GSM1124899 2 0.0424 0.9374 0.000 0.992 0.008
#> GSM1124901 2 0.0592 0.9365 0.000 0.988 0.012
#> GSM1124906 2 0.0000 0.9385 0.000 1.000 0.000
#> GSM1124907 2 0.2878 0.9195 0.000 0.904 0.096
#> GSM1124911 2 0.0000 0.9385 0.000 1.000 0.000
#> GSM1124912 1 0.0000 0.9362 1.000 0.000 0.000
#> GSM1124915 2 0.0000 0.9385 0.000 1.000 0.000
#> GSM1124917 2 0.0424 0.9382 0.000 0.992 0.008
#> GSM1124918 2 0.0000 0.9385 0.000 1.000 0.000
#> GSM1124920 3 0.4121 0.8577 0.168 0.000 0.832
#> GSM1124922 2 0.0592 0.9365 0.000 0.988 0.012
#> GSM1124924 2 0.6577 0.1859 0.008 0.572 0.420
#> GSM1124926 2 0.0000 0.9385 0.000 1.000 0.000
#> GSM1124928 1 0.0000 0.9362 1.000 0.000 0.000
#> GSM1124930 2 0.3340 0.9093 0.000 0.880 0.120
#> GSM1124931 2 0.0237 0.9376 0.000 0.996 0.004
#> GSM1124935 2 0.0592 0.9365 0.000 0.988 0.012
#> GSM1124936 3 0.3340 0.9011 0.120 0.000 0.880
#> GSM1124938 3 0.3412 0.8177 0.000 0.124 0.876
#> GSM1124940 1 0.0000 0.9362 1.000 0.000 0.000
#> GSM1124941 2 0.0000 0.9385 0.000 1.000 0.000
#> GSM1124942 2 0.3038 0.9045 0.000 0.896 0.104
#> GSM1124943 3 0.5216 0.6390 0.000 0.260 0.740
#> GSM1124948 2 0.0592 0.9365 0.000 0.988 0.012
#> GSM1124949 1 0.0000 0.9362 1.000 0.000 0.000
#> GSM1124950 2 0.0000 0.9385 0.000 1.000 0.000
#> GSM1124954 3 0.3340 0.9011 0.120 0.000 0.880
#> GSM1124955 1 0.0000 0.9362 1.000 0.000 0.000
#> GSM1124956 2 0.0000 0.9385 0.000 1.000 0.000
#> GSM1124872 2 0.0000 0.9385 0.000 1.000 0.000
#> GSM1124873 2 0.0000 0.9385 0.000 1.000 0.000
#> GSM1124876 3 0.3340 0.9011 0.120 0.000 0.880
#> GSM1124877 1 0.0000 0.9362 1.000 0.000 0.000
#> GSM1124879 1 0.0000 0.9362 1.000 0.000 0.000
#> GSM1124883 2 0.3340 0.9093 0.000 0.880 0.120
#> GSM1124889 2 0.0000 0.9385 0.000 1.000 0.000
#> GSM1124892 1 0.0000 0.9362 1.000 0.000 0.000
#> GSM1124893 1 0.0000 0.9362 1.000 0.000 0.000
#> GSM1124909 2 0.0000 0.9385 0.000 1.000 0.000
#> GSM1124913 2 0.2537 0.9225 0.000 0.920 0.080
#> GSM1124916 2 0.0000 0.9385 0.000 1.000 0.000
#> GSM1124923 2 0.2796 0.9205 0.000 0.908 0.092
#> GSM1124925 1 0.0000 0.9362 1.000 0.000 0.000
#> GSM1124929 1 0.0000 0.9362 1.000 0.000 0.000
#> GSM1124934 3 0.3340 0.9011 0.120 0.000 0.880
#> GSM1124937 2 0.0000 0.9385 0.000 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1124891 3 0.0000 0.9331 0.000 0.000 1.000 0.000
#> GSM1124888 3 0.0000 0.9331 0.000 0.000 1.000 0.000
#> GSM1124890 2 0.3743 0.7152 0.000 0.824 0.160 0.016
#> GSM1124904 2 0.4855 0.3311 0.000 0.600 0.000 0.400
#> GSM1124927 2 0.0000 0.8641 0.000 1.000 0.000 0.000
#> GSM1124953 3 0.5328 0.6460 0.000 0.064 0.724 0.212
#> GSM1124869 1 0.0000 0.9601 1.000 0.000 0.000 0.000
#> GSM1124870 1 0.0188 0.9554 0.996 0.004 0.000 0.000
#> GSM1124882 1 0.0000 0.9601 1.000 0.000 0.000 0.000
#> GSM1124884 2 0.0000 0.8641 0.000 1.000 0.000 0.000
#> GSM1124898 2 0.4790 0.3713 0.000 0.620 0.000 0.380
#> GSM1124903 4 0.0000 0.7944 0.000 0.000 0.000 1.000
#> GSM1124905 1 0.4977 0.0768 0.540 0.460 0.000 0.000
#> GSM1124910 1 0.0188 0.9565 0.996 0.000 0.004 0.000
#> GSM1124919 2 0.4843 0.3395 0.000 0.604 0.000 0.396
#> GSM1124932 2 0.0000 0.8641 0.000 1.000 0.000 0.000
#> GSM1124933 3 0.0000 0.9331 0.000 0.000 1.000 0.000
#> GSM1124867 2 0.0000 0.8641 0.000 1.000 0.000 0.000
#> GSM1124868 4 0.0469 0.7908 0.000 0.012 0.000 0.988
#> GSM1124878 4 0.0000 0.7944 0.000 0.000 0.000 1.000
#> GSM1124895 4 0.0592 0.7904 0.000 0.016 0.000 0.984
#> GSM1124897 4 0.0000 0.7944 0.000 0.000 0.000 1.000
#> GSM1124902 4 0.0000 0.7944 0.000 0.000 0.000 1.000
#> GSM1124908 4 0.4925 0.2411 0.000 0.428 0.000 0.572
#> GSM1124921 4 0.0000 0.7944 0.000 0.000 0.000 1.000
#> GSM1124939 4 0.4843 0.4311 0.000 0.396 0.000 0.604
#> GSM1124944 4 0.0000 0.7944 0.000 0.000 0.000 1.000
#> GSM1124945 3 0.0000 0.9331 0.000 0.000 1.000 0.000
#> GSM1124946 4 0.0000 0.7944 0.000 0.000 0.000 1.000
#> GSM1124947 4 0.4843 0.4311 0.000 0.396 0.000 0.604
#> GSM1124951 3 0.0469 0.9262 0.000 0.000 0.988 0.012
#> GSM1124952 2 0.0000 0.8641 0.000 1.000 0.000 0.000
#> GSM1124957 3 0.0000 0.9331 0.000 0.000 1.000 0.000
#> GSM1124900 2 0.4661 0.4447 0.348 0.652 0.000 0.000
#> GSM1124914 4 0.4830 0.4332 0.000 0.392 0.000 0.608
#> GSM1124871 2 0.0000 0.8641 0.000 1.000 0.000 0.000
#> GSM1124874 4 0.4989 0.2656 0.000 0.472 0.000 0.528
#> GSM1124875 2 0.2921 0.7312 0.000 0.860 0.000 0.140
#> GSM1124880 2 0.0707 0.8536 0.000 0.980 0.020 0.000
#> GSM1124881 2 0.0000 0.8641 0.000 1.000 0.000 0.000
#> GSM1124885 4 0.0000 0.7944 0.000 0.000 0.000 1.000
#> GSM1124886 1 0.0000 0.9601 1.000 0.000 0.000 0.000
#> GSM1124887 2 0.4843 0.3395 0.000 0.604 0.000 0.396
#> GSM1124894 2 0.4150 0.7094 0.000 0.824 0.056 0.120
#> GSM1124896 2 0.6110 0.3126 0.368 0.576 0.056 0.000
#> GSM1124899 2 0.0000 0.8641 0.000 1.000 0.000 0.000
#> GSM1124901 2 0.0188 0.8619 0.000 0.996 0.000 0.004
#> GSM1124906 2 0.0000 0.8641 0.000 1.000 0.000 0.000
#> GSM1124907 2 0.4977 0.1787 0.000 0.540 0.000 0.460
#> GSM1124911 2 0.0000 0.8641 0.000 1.000 0.000 0.000
#> GSM1124912 1 0.0000 0.9601 1.000 0.000 0.000 0.000
#> GSM1124915 2 0.0000 0.8641 0.000 1.000 0.000 0.000
#> GSM1124917 2 0.0469 0.8572 0.000 0.988 0.000 0.012
#> GSM1124918 2 0.0000 0.8641 0.000 1.000 0.000 0.000
#> GSM1124920 3 0.2530 0.8321 0.112 0.000 0.888 0.000
#> GSM1124922 2 0.0000 0.8641 0.000 1.000 0.000 0.000
#> GSM1124924 2 0.4999 0.0283 0.000 0.508 0.492 0.000
#> GSM1124926 2 0.0000 0.8641 0.000 1.000 0.000 0.000
#> GSM1124928 1 0.0000 0.9601 1.000 0.000 0.000 0.000
#> GSM1124930 4 0.0000 0.7944 0.000 0.000 0.000 1.000
#> GSM1124931 2 0.0707 0.8527 0.000 0.980 0.020 0.000
#> GSM1124935 2 0.0000 0.8641 0.000 1.000 0.000 0.000
#> GSM1124936 3 0.0000 0.9331 0.000 0.000 1.000 0.000
#> GSM1124938 3 0.1867 0.8686 0.000 0.072 0.928 0.000
#> GSM1124940 1 0.0000 0.9601 1.000 0.000 0.000 0.000
#> GSM1124941 2 0.0000 0.8641 0.000 1.000 0.000 0.000
#> GSM1124942 2 0.5428 0.6416 0.000 0.740 0.120 0.140
#> GSM1124943 3 0.4482 0.6147 0.000 0.264 0.728 0.008
#> GSM1124948 2 0.0469 0.8580 0.000 0.988 0.012 0.000
#> GSM1124949 1 0.0000 0.9601 1.000 0.000 0.000 0.000
#> GSM1124950 2 0.0000 0.8641 0.000 1.000 0.000 0.000
#> GSM1124954 3 0.0000 0.9331 0.000 0.000 1.000 0.000
#> GSM1124955 1 0.0000 0.9601 1.000 0.000 0.000 0.000
#> GSM1124956 2 0.0000 0.8641 0.000 1.000 0.000 0.000
#> GSM1124872 2 0.0000 0.8641 0.000 1.000 0.000 0.000
#> GSM1124873 2 0.0000 0.8641 0.000 1.000 0.000 0.000
#> GSM1124876 3 0.0000 0.9331 0.000 0.000 1.000 0.000
#> GSM1124877 1 0.0000 0.9601 1.000 0.000 0.000 0.000
#> GSM1124879 1 0.0000 0.9601 1.000 0.000 0.000 0.000
#> GSM1124883 4 0.3649 0.6947 0.000 0.204 0.000 0.796
#> GSM1124889 2 0.0000 0.8641 0.000 1.000 0.000 0.000
#> GSM1124892 1 0.0000 0.9601 1.000 0.000 0.000 0.000
#> GSM1124893 1 0.0000 0.9601 1.000 0.000 0.000 0.000
#> GSM1124909 2 0.0000 0.8641 0.000 1.000 0.000 0.000
#> GSM1124913 4 0.3172 0.6899 0.000 0.160 0.000 0.840
#> GSM1124916 2 0.0000 0.8641 0.000 1.000 0.000 0.000
#> GSM1124923 2 0.5016 0.3341 0.000 0.600 0.004 0.396
#> GSM1124925 1 0.0000 0.9601 1.000 0.000 0.000 0.000
#> GSM1124929 1 0.0000 0.9601 1.000 0.000 0.000 0.000
#> GSM1124934 3 0.0000 0.9331 0.000 0.000 1.000 0.000
#> GSM1124937 2 0.0000 0.8641 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
#> GSM1124891 3 0.0000 0.9567 0.000 0.000 1.000 0.000 0.000
#> GSM1124888 3 0.0000 0.9567 0.000 0.000 1.000 0.000 0.000
#> GSM1124890 5 0.2304 0.8428 0.000 0.100 0.000 0.008 0.892
#> GSM1124904 2 0.4884 0.6523 0.000 0.720 0.000 0.128 0.152
#> GSM1124927 2 0.0510 0.9157 0.000 0.984 0.000 0.000 0.016
#> GSM1124953 3 0.4444 0.7523 0.000 0.056 0.800 0.088 0.056
#> GSM1124869 1 0.0000 0.9582 1.000 0.000 0.000 0.000 0.000
#> GSM1124870 1 0.0290 0.9496 0.992 0.008 0.000 0.000 0.000
#> GSM1124882 1 0.0000 0.9582 1.000 0.000 0.000 0.000 0.000
#> GSM1124884 2 0.0000 0.9183 0.000 1.000 0.000 0.000 0.000
#> GSM1124898 2 0.5423 0.5504 0.000 0.644 0.000 0.112 0.244
#> GSM1124903 4 0.2424 0.8013 0.000 0.000 0.000 0.868 0.132
#> GSM1124905 1 0.4283 0.0661 0.544 0.456 0.000 0.000 0.000
#> GSM1124910 1 0.0162 0.9541 0.996 0.000 0.004 0.000 0.000
#> GSM1124919 5 0.4219 0.7033 0.000 0.104 0.000 0.116 0.780
#> GSM1124932 2 0.0000 0.9183 0.000 1.000 0.000 0.000 0.000
#> GSM1124933 3 0.0000 0.9567 0.000 0.000 1.000 0.000 0.000
#> GSM1124867 2 0.0510 0.9157 0.000 0.984 0.000 0.000 0.016
#> GSM1124868 4 0.0404 0.8385 0.000 0.012 0.000 0.988 0.000
#> GSM1124878 4 0.2424 0.8013 0.000 0.000 0.000 0.868 0.132
#> GSM1124895 4 0.0404 0.8385 0.000 0.012 0.000 0.988 0.000
#> GSM1124897 4 0.0703 0.8369 0.000 0.000 0.000 0.976 0.024
#> GSM1124902 4 0.0000 0.8371 0.000 0.000 0.000 1.000 0.000
#> GSM1124908 4 0.5598 0.3334 0.000 0.376 0.000 0.544 0.080
#> GSM1124921 4 0.2471 0.8018 0.000 0.000 0.000 0.864 0.136
#> GSM1124939 4 0.2338 0.7863 0.000 0.112 0.000 0.884 0.004
#> GSM1124944 4 0.2020 0.7807 0.000 0.000 0.000 0.900 0.100
#> GSM1124945 3 0.0963 0.9317 0.000 0.000 0.964 0.000 0.036
#> GSM1124946 4 0.0703 0.8354 0.000 0.000 0.000 0.976 0.024
#> GSM1124947 4 0.2519 0.7867 0.000 0.100 0.000 0.884 0.016
#> GSM1124951 3 0.1892 0.8948 0.000 0.000 0.916 0.004 0.080
#> GSM1124952 2 0.0000 0.9183 0.000 1.000 0.000 0.000 0.000
#> GSM1124957 3 0.0000 0.9567 0.000 0.000 1.000 0.000 0.000
#> GSM1124900 2 0.3966 0.5206 0.336 0.664 0.000 0.000 0.000
#> GSM1124914 4 0.2690 0.7497 0.000 0.156 0.000 0.844 0.000
#> GSM1124871 2 0.0000 0.9183 0.000 1.000 0.000 0.000 0.000
#> GSM1124874 4 0.3452 0.6687 0.000 0.244 0.000 0.756 0.000
#> GSM1124875 2 0.5086 0.6445 0.000 0.700 0.000 0.144 0.156
#> GSM1124880 2 0.1934 0.8818 0.004 0.928 0.052 0.000 0.016
#> GSM1124881 2 0.0000 0.9183 0.000 1.000 0.000 0.000 0.000
#> GSM1124885 4 0.0000 0.8371 0.000 0.000 0.000 1.000 0.000
#> GSM1124886 1 0.0000 0.9582 1.000 0.000 0.000 0.000 0.000
#> GSM1124887 2 0.4711 0.6716 0.000 0.736 0.000 0.116 0.148
#> GSM1124894 2 0.4058 0.7283 0.000 0.784 0.064 0.152 0.000
#> GSM1124896 2 0.5882 0.3999 0.332 0.572 0.084 0.000 0.012
#> GSM1124899 2 0.0703 0.9091 0.000 0.976 0.000 0.000 0.024
#> GSM1124901 2 0.1121 0.8964 0.000 0.956 0.000 0.000 0.044
#> GSM1124906 2 0.0000 0.9183 0.000 1.000 0.000 0.000 0.000
#> GSM1124907 5 0.0510 0.8200 0.000 0.000 0.000 0.016 0.984
#> GSM1124911 2 0.0000 0.9183 0.000 1.000 0.000 0.000 0.000
#> GSM1124912 1 0.0000 0.9582 1.000 0.000 0.000 0.000 0.000
#> GSM1124915 2 0.0000 0.9183 0.000 1.000 0.000 0.000 0.000
#> GSM1124917 2 0.1341 0.8959 0.000 0.944 0.000 0.000 0.056
#> GSM1124918 2 0.0703 0.9142 0.000 0.976 0.000 0.000 0.024
#> GSM1124920 3 0.1671 0.8754 0.076 0.000 0.924 0.000 0.000
#> GSM1124922 2 0.1121 0.8964 0.000 0.956 0.000 0.000 0.044
#> GSM1124924 5 0.5137 0.6963 0.000 0.108 0.208 0.000 0.684
#> GSM1124926 2 0.0000 0.9183 0.000 1.000 0.000 0.000 0.000
#> GSM1124928 1 0.0000 0.9582 1.000 0.000 0.000 0.000 0.000
#> GSM1124930 5 0.2605 0.7887 0.000 0.000 0.000 0.148 0.852
#> GSM1124931 2 0.0609 0.9115 0.000 0.980 0.020 0.000 0.000
#> GSM1124935 2 0.1043 0.8994 0.000 0.960 0.000 0.000 0.040
#> GSM1124936 3 0.0000 0.9567 0.000 0.000 1.000 0.000 0.000
#> GSM1124938 5 0.3130 0.8388 0.000 0.048 0.096 0.000 0.856
#> GSM1124940 1 0.0000 0.9582 1.000 0.000 0.000 0.000 0.000
#> GSM1124941 2 0.0000 0.9183 0.000 1.000 0.000 0.000 0.000
#> GSM1124942 5 0.2735 0.8438 0.000 0.036 0.084 0.000 0.880
#> GSM1124943 5 0.2879 0.8524 0.000 0.080 0.032 0.008 0.880
#> GSM1124948 5 0.3075 0.8413 0.000 0.092 0.048 0.000 0.860
#> GSM1124949 1 0.0000 0.9582 1.000 0.000 0.000 0.000 0.000
#> GSM1124950 2 0.0162 0.9178 0.000 0.996 0.000 0.000 0.004
#> GSM1124954 3 0.0000 0.9567 0.000 0.000 1.000 0.000 0.000
#> GSM1124955 1 0.0000 0.9582 1.000 0.000 0.000 0.000 0.000
#> GSM1124956 2 0.0000 0.9183 0.000 1.000 0.000 0.000 0.000
#> GSM1124872 2 0.0510 0.9157 0.000 0.984 0.000 0.000 0.016
#> GSM1124873 2 0.0000 0.9183 0.000 1.000 0.000 0.000 0.000
#> GSM1124876 3 0.0000 0.9567 0.000 0.000 1.000 0.000 0.000
#> GSM1124877 1 0.0000 0.9582 1.000 0.000 0.000 0.000 0.000
#> GSM1124879 1 0.0000 0.9582 1.000 0.000 0.000 0.000 0.000
#> GSM1124883 4 0.3953 0.7743 0.000 0.060 0.000 0.792 0.148
#> GSM1124889 2 0.0000 0.9183 0.000 1.000 0.000 0.000 0.000
#> GSM1124892 1 0.0000 0.9582 1.000 0.000 0.000 0.000 0.000
#> GSM1124893 1 0.0000 0.9582 1.000 0.000 0.000 0.000 0.000
#> GSM1124909 2 0.0510 0.9157 0.000 0.984 0.000 0.000 0.016
#> GSM1124913 4 0.3995 0.7628 0.000 0.060 0.000 0.788 0.152
#> GSM1124916 2 0.0510 0.9157 0.000 0.984 0.000 0.000 0.016
#> GSM1124923 5 0.2389 0.7515 0.000 0.004 0.000 0.116 0.880
#> GSM1124925 1 0.0000 0.9582 1.000 0.000 0.000 0.000 0.000
#> GSM1124929 1 0.0000 0.9582 1.000 0.000 0.000 0.000 0.000
#> GSM1124934 3 0.0290 0.9529 0.000 0.000 0.992 0.000 0.008
#> GSM1124937 2 0.0609 0.9147 0.000 0.980 0.000 0.000 0.020
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1124891 3 0.0000 0.935 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1124888 3 0.0000 0.935 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1124890 5 0.4703 0.691 0.000 0.012 0.032 0.004 0.628 0.324
#> GSM1124904 6 0.3182 0.712 0.000 0.012 0.000 0.136 0.024 0.828
#> GSM1124927 2 0.3828 0.628 0.000 0.560 0.000 0.000 0.440 0.000
#> GSM1124953 3 0.3788 0.566 0.000 0.000 0.704 0.004 0.012 0.280
#> GSM1124869 1 0.0000 0.961 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124870 1 0.0858 0.939 0.968 0.004 0.000 0.000 0.028 0.000
#> GSM1124882 1 0.0000 0.961 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124884 2 0.0146 0.794 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM1124898 6 0.4222 0.403 0.000 0.252 0.000 0.004 0.044 0.700
#> GSM1124903 6 0.2854 0.691 0.000 0.000 0.000 0.208 0.000 0.792
#> GSM1124905 1 0.4683 0.325 0.616 0.320 0.000 0.000 0.064 0.000
#> GSM1124910 1 0.0777 0.942 0.972 0.000 0.004 0.000 0.024 0.000
#> GSM1124919 6 0.3163 0.354 0.000 0.004 0.000 0.000 0.232 0.764
#> GSM1124932 2 0.0260 0.793 0.000 0.992 0.000 0.000 0.000 0.008
#> GSM1124933 3 0.0000 0.935 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1124867 2 0.3804 0.638 0.000 0.576 0.000 0.000 0.424 0.000
#> GSM1124868 4 0.0146 0.788 0.000 0.004 0.000 0.996 0.000 0.000
#> GSM1124878 6 0.2883 0.687 0.000 0.000 0.000 0.212 0.000 0.788
#> GSM1124895 4 0.0146 0.788 0.000 0.000 0.000 0.996 0.000 0.004
#> GSM1124897 4 0.0937 0.764 0.000 0.000 0.000 0.960 0.000 0.040
#> GSM1124902 4 0.0146 0.788 0.000 0.000 0.000 0.996 0.000 0.004
#> GSM1124908 4 0.6545 -0.198 0.000 0.212 0.000 0.404 0.032 0.352
#> GSM1124921 6 0.3819 0.494 0.000 0.000 0.000 0.372 0.004 0.624
#> GSM1124939 4 0.0000 0.788 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1124944 4 0.0000 0.788 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1124945 3 0.0291 0.930 0.000 0.000 0.992 0.004 0.000 0.004
#> GSM1124946 4 0.3782 0.364 0.000 0.000 0.000 0.636 0.004 0.360
#> GSM1124947 4 0.0000 0.788 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1124951 3 0.1152 0.900 0.000 0.000 0.952 0.004 0.000 0.044
#> GSM1124952 2 0.2278 0.741 0.000 0.868 0.000 0.128 0.004 0.000
#> GSM1124957 3 0.0000 0.935 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1124900 2 0.3958 0.632 0.220 0.740 0.000 0.000 0.028 0.012
#> GSM1124914 4 0.1267 0.734 0.000 0.060 0.000 0.940 0.000 0.000
#> GSM1124871 2 0.0146 0.794 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM1124874 4 0.4161 0.276 0.000 0.448 0.000 0.540 0.000 0.012
#> GSM1124875 2 0.6381 0.411 0.000 0.524 0.000 0.284 0.080 0.112
#> GSM1124880 2 0.4682 0.620 0.000 0.556 0.048 0.000 0.396 0.000
#> GSM1124881 2 0.0458 0.795 0.000 0.984 0.000 0.000 0.016 0.000
#> GSM1124885 4 0.0146 0.788 0.000 0.000 0.000 0.996 0.000 0.004
#> GSM1124886 1 0.0000 0.961 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124887 6 0.3308 0.649 0.000 0.096 0.000 0.072 0.004 0.828
#> GSM1124894 2 0.5785 0.451 0.000 0.580 0.124 0.264 0.032 0.000
#> GSM1124896 2 0.6508 0.448 0.176 0.424 0.040 0.000 0.360 0.000
#> GSM1124899 2 0.0363 0.792 0.000 0.988 0.000 0.000 0.000 0.012
#> GSM1124901 2 0.0603 0.792 0.000 0.980 0.000 0.004 0.000 0.016
#> GSM1124906 2 0.0146 0.794 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM1124907 5 0.3961 0.578 0.000 0.000 0.000 0.004 0.556 0.440
#> GSM1124911 2 0.0363 0.792 0.000 0.988 0.000 0.000 0.000 0.012
#> GSM1124912 1 0.0000 0.961 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124915 2 0.2562 0.662 0.000 0.828 0.000 0.000 0.000 0.172
#> GSM1124917 2 0.4348 0.623 0.000 0.560 0.000 0.000 0.416 0.024
#> GSM1124918 2 0.4478 0.695 0.000 0.708 0.000 0.004 0.200 0.088
#> GSM1124920 3 0.2003 0.801 0.116 0.000 0.884 0.000 0.000 0.000
#> GSM1124922 2 0.0603 0.794 0.000 0.980 0.000 0.000 0.004 0.016
#> GSM1124924 5 0.5069 0.647 0.000 0.004 0.256 0.000 0.628 0.112
#> GSM1124926 2 0.0363 0.792 0.000 0.988 0.000 0.000 0.000 0.012
#> GSM1124928 1 0.0937 0.931 0.960 0.000 0.000 0.000 0.040 0.000
#> GSM1124930 5 0.5543 0.595 0.000 0.000 0.000 0.240 0.556 0.204
#> GSM1124931 2 0.1933 0.782 0.000 0.924 0.032 0.000 0.032 0.012
#> GSM1124935 2 0.0748 0.793 0.000 0.976 0.000 0.004 0.004 0.016
#> GSM1124936 3 0.0000 0.935 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1124938 5 0.5438 0.722 0.000 0.000 0.200 0.004 0.596 0.200
#> GSM1124940 1 0.0000 0.961 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124941 2 0.0000 0.794 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1124942 5 0.5341 0.732 0.000 0.000 0.132 0.004 0.588 0.276
#> GSM1124943 5 0.4209 0.740 0.000 0.000 0.044 0.008 0.716 0.232
#> GSM1124948 5 0.2737 0.671 0.000 0.004 0.000 0.004 0.832 0.160
#> GSM1124949 1 0.0000 0.961 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124950 2 0.3409 0.708 0.000 0.700 0.000 0.000 0.300 0.000
#> GSM1124954 3 0.0000 0.935 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1124955 1 0.0000 0.961 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124956 2 0.0363 0.792 0.000 0.988 0.000 0.000 0.000 0.012
#> GSM1124872 2 0.3833 0.625 0.000 0.556 0.000 0.000 0.444 0.000
#> GSM1124873 2 0.0260 0.795 0.000 0.992 0.000 0.000 0.008 0.000
#> GSM1124876 3 0.0000 0.935 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1124877 1 0.0000 0.961 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124879 1 0.0000 0.961 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124883 4 0.4495 0.276 0.000 0.028 0.000 0.580 0.004 0.388
#> GSM1124889 2 0.0146 0.794 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM1124892 1 0.0000 0.961 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124893 1 0.0000 0.961 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124909 2 0.3833 0.625 0.000 0.556 0.000 0.000 0.444 0.000
#> GSM1124913 6 0.2597 0.703 0.000 0.000 0.000 0.176 0.000 0.824
#> GSM1124916 2 0.3797 0.638 0.000 0.580 0.000 0.000 0.420 0.000
#> GSM1124923 6 0.2762 0.359 0.000 0.000 0.000 0.000 0.196 0.804
#> GSM1124925 1 0.0000 0.961 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124929 1 0.0000 0.961 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124934 3 0.1753 0.858 0.000 0.000 0.912 0.000 0.084 0.004
#> GSM1124937 2 0.3833 0.625 0.000 0.556 0.000 0.000 0.444 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 tissue(p) k
#> MAD:pam 74 0.368037 2
#> MAD:pam 88 0.076363 3
#> MAD:pam 76 0.000351 4
#> MAD:pam 88 0.000322 5
#> MAD:pam 79 0.000393 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 48983 rows and 91 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 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 0.952 0.976 0.4560 0.546 0.546
#> 3 3 0.619 0.725 0.841 0.2609 0.892 0.807
#> 4 4 0.541 0.462 0.746 0.2206 0.847 0.678
#> 5 5 0.562 0.510 0.734 0.0932 0.785 0.470
#> 6 6 0.818 0.834 0.901 0.0459 0.892 0.619
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
#> GSM1124891 1 0.1843 0.956 0.972 0.028
#> GSM1124888 1 0.1843 0.956 0.972 0.028
#> GSM1124890 2 0.4815 0.897 0.104 0.896
#> GSM1124904 2 0.0000 0.975 0.000 1.000
#> GSM1124927 2 0.5629 0.874 0.132 0.868
#> GSM1124953 2 0.4161 0.914 0.084 0.916
#> GSM1124869 1 0.0000 0.973 1.000 0.000
#> GSM1124870 1 0.0376 0.971 0.996 0.004
#> GSM1124882 1 0.0000 0.973 1.000 0.000
#> GSM1124884 2 0.0000 0.975 0.000 1.000
#> GSM1124898 2 0.0000 0.975 0.000 1.000
#> GSM1124903 2 0.0000 0.975 0.000 1.000
#> GSM1124905 1 0.0000 0.973 1.000 0.000
#> GSM1124910 1 0.0000 0.973 1.000 0.000
#> GSM1124919 2 0.0000 0.975 0.000 1.000
#> GSM1124932 2 0.5294 0.886 0.120 0.880
#> GSM1124933 1 0.1843 0.956 0.972 0.028
#> GSM1124867 2 0.0000 0.975 0.000 1.000
#> GSM1124868 2 0.0000 0.975 0.000 1.000
#> GSM1124878 2 0.0000 0.975 0.000 1.000
#> GSM1124895 2 0.0000 0.975 0.000 1.000
#> GSM1124897 2 0.0000 0.975 0.000 1.000
#> GSM1124902 2 0.0000 0.975 0.000 1.000
#> GSM1124908 2 0.0376 0.973 0.004 0.996
#> GSM1124921 2 0.0000 0.975 0.000 1.000
#> GSM1124939 2 0.0000 0.975 0.000 1.000
#> GSM1124944 2 0.0000 0.975 0.000 1.000
#> GSM1124945 1 0.9044 0.534 0.680 0.320
#> GSM1124946 2 0.0000 0.975 0.000 1.000
#> GSM1124947 2 0.5178 0.885 0.116 0.884
#> GSM1124951 1 0.8955 0.551 0.688 0.312
#> GSM1124952 2 0.6048 0.856 0.148 0.852
#> GSM1124957 1 0.1843 0.956 0.972 0.028
#> GSM1124900 1 0.0672 0.968 0.992 0.008
#> GSM1124914 2 0.0000 0.975 0.000 1.000
#> GSM1124871 2 0.0000 0.975 0.000 1.000
#> GSM1124874 2 0.1633 0.960 0.024 0.976
#> GSM1124875 2 0.0000 0.975 0.000 1.000
#> GSM1124880 2 0.4939 0.897 0.108 0.892
#> GSM1124881 2 0.0000 0.975 0.000 1.000
#> GSM1124885 2 0.0000 0.975 0.000 1.000
#> GSM1124886 1 0.0000 0.973 1.000 0.000
#> GSM1124887 2 0.0000 0.975 0.000 1.000
#> GSM1124894 1 0.0376 0.971 0.996 0.004
#> GSM1124896 1 0.0000 0.973 1.000 0.000
#> GSM1124899 2 0.0000 0.975 0.000 1.000
#> GSM1124901 2 0.0000 0.975 0.000 1.000
#> GSM1124906 2 0.0672 0.971 0.008 0.992
#> GSM1124907 2 0.0000 0.975 0.000 1.000
#> GSM1124911 2 0.1184 0.965 0.016 0.984
#> GSM1124912 1 0.0000 0.973 1.000 0.000
#> GSM1124915 2 0.0000 0.975 0.000 1.000
#> GSM1124917 2 0.0000 0.975 0.000 1.000
#> GSM1124918 2 0.0000 0.975 0.000 1.000
#> GSM1124920 1 0.0000 0.973 1.000 0.000
#> GSM1124922 2 0.6048 0.856 0.148 0.852
#> GSM1124924 2 0.0938 0.968 0.012 0.988
#> GSM1124926 2 0.5737 0.869 0.136 0.864
#> GSM1124928 1 0.0000 0.973 1.000 0.000
#> GSM1124930 2 0.0000 0.975 0.000 1.000
#> GSM1124931 2 0.6048 0.856 0.148 0.852
#> GSM1124935 2 0.0000 0.975 0.000 1.000
#> GSM1124936 1 0.0000 0.973 1.000 0.000
#> GSM1124938 2 0.4939 0.893 0.108 0.892
#> GSM1124940 1 0.0000 0.973 1.000 0.000
#> GSM1124941 2 0.0000 0.975 0.000 1.000
#> GSM1124942 2 0.0000 0.975 0.000 1.000
#> GSM1124943 2 0.0000 0.975 0.000 1.000
#> GSM1124948 2 0.0000 0.975 0.000 1.000
#> GSM1124949 1 0.0000 0.973 1.000 0.000
#> GSM1124950 2 0.0000 0.975 0.000 1.000
#> GSM1124954 1 0.0000 0.973 1.000 0.000
#> GSM1124955 1 0.0000 0.973 1.000 0.000
#> GSM1124956 2 0.0672 0.971 0.008 0.992
#> GSM1124872 2 0.0000 0.975 0.000 1.000
#> GSM1124873 2 0.0000 0.975 0.000 1.000
#> GSM1124876 1 0.1843 0.956 0.972 0.028
#> GSM1124877 1 0.0000 0.973 1.000 0.000
#> GSM1124879 1 0.0000 0.973 1.000 0.000
#> GSM1124883 2 0.0000 0.975 0.000 1.000
#> GSM1124889 2 0.0000 0.975 0.000 1.000
#> GSM1124892 1 0.0000 0.973 1.000 0.000
#> GSM1124893 1 0.0000 0.973 1.000 0.000
#> GSM1124909 2 0.0000 0.975 0.000 1.000
#> GSM1124913 2 0.0000 0.975 0.000 1.000
#> GSM1124916 2 0.0000 0.975 0.000 1.000
#> GSM1124923 2 0.0000 0.975 0.000 1.000
#> GSM1124925 1 0.0000 0.973 1.000 0.000
#> GSM1124929 1 0.0000 0.973 1.000 0.000
#> GSM1124934 1 0.0000 0.973 1.000 0.000
#> GSM1124937 2 0.0000 0.975 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1124891 3 0.6955 0.817 0.332 0.032 0.636
#> GSM1124888 3 0.6906 0.820 0.324 0.032 0.644
#> GSM1124890 2 0.2096 0.830 0.004 0.944 0.052
#> GSM1124904 2 0.5058 0.785 0.000 0.756 0.244
#> GSM1124927 1 0.8465 0.176 0.460 0.452 0.088
#> GSM1124953 2 0.4521 0.781 0.004 0.816 0.180
#> GSM1124869 1 0.0000 0.801 1.000 0.000 0.000
#> GSM1124870 1 0.4443 0.718 0.864 0.052 0.084
#> GSM1124882 1 0.0000 0.801 1.000 0.000 0.000
#> GSM1124884 2 0.0237 0.851 0.004 0.996 0.000
#> GSM1124898 2 0.2261 0.847 0.000 0.932 0.068
#> GSM1124903 2 0.5058 0.785 0.000 0.756 0.244
#> GSM1124905 1 0.3973 0.731 0.880 0.032 0.088
#> GSM1124910 1 0.2031 0.771 0.952 0.032 0.016
#> GSM1124919 2 0.1964 0.850 0.000 0.944 0.056
#> GSM1124932 2 0.8631 -0.156 0.432 0.468 0.100
#> GSM1124933 3 0.6906 0.820 0.324 0.032 0.644
#> GSM1124867 2 0.0237 0.851 0.004 0.996 0.000
#> GSM1124868 2 0.5058 0.785 0.000 0.756 0.244
#> GSM1124878 2 0.5058 0.785 0.000 0.756 0.244
#> GSM1124895 2 0.5058 0.785 0.000 0.756 0.244
#> GSM1124897 2 0.5058 0.785 0.000 0.756 0.244
#> GSM1124902 2 0.5058 0.785 0.000 0.756 0.244
#> GSM1124908 2 0.5785 0.723 0.000 0.668 0.332
#> GSM1124921 2 0.5760 0.728 0.000 0.672 0.328
#> GSM1124939 2 0.5016 0.787 0.000 0.760 0.240
#> GSM1124944 2 0.4750 0.799 0.000 0.784 0.216
#> GSM1124945 3 0.6541 0.586 0.056 0.212 0.732
#> GSM1124946 2 0.5058 0.785 0.000 0.756 0.244
#> GSM1124947 2 0.4390 0.782 0.012 0.840 0.148
#> GSM1124951 3 0.5970 0.615 0.060 0.160 0.780
#> GSM1124952 2 0.8789 -0.134 0.428 0.460 0.112
#> GSM1124957 3 0.6056 0.758 0.224 0.032 0.744
#> GSM1124900 1 0.4339 0.722 0.868 0.048 0.084
#> GSM1124914 2 0.4702 0.800 0.000 0.788 0.212
#> GSM1124871 2 0.0000 0.851 0.000 1.000 0.000
#> GSM1124874 2 0.1289 0.839 0.032 0.968 0.000
#> GSM1124875 2 0.0000 0.851 0.000 1.000 0.000
#> GSM1124880 1 0.5363 0.440 0.724 0.276 0.000
#> GSM1124881 2 0.0237 0.851 0.004 0.996 0.000
#> GSM1124885 2 0.5058 0.785 0.000 0.756 0.244
#> GSM1124886 1 0.0237 0.797 0.996 0.000 0.004
#> GSM1124887 2 0.5058 0.785 0.000 0.756 0.244
#> GSM1124894 1 0.5393 0.669 0.820 0.072 0.108
#> GSM1124896 1 0.2796 0.743 0.908 0.000 0.092
#> GSM1124899 2 0.0237 0.851 0.004 0.996 0.000
#> GSM1124901 2 0.2356 0.846 0.000 0.928 0.072
#> GSM1124906 2 0.2165 0.816 0.064 0.936 0.000
#> GSM1124907 2 0.2448 0.847 0.000 0.924 0.076
#> GSM1124911 2 0.0237 0.851 0.004 0.996 0.000
#> GSM1124912 1 0.0000 0.801 1.000 0.000 0.000
#> GSM1124915 2 0.2448 0.846 0.000 0.924 0.076
#> GSM1124917 2 0.0000 0.851 0.000 1.000 0.000
#> GSM1124918 2 0.0592 0.848 0.000 0.988 0.012
#> GSM1124920 3 0.7263 0.765 0.400 0.032 0.568
#> GSM1124922 2 0.8793 -0.173 0.436 0.452 0.112
#> GSM1124924 2 0.2939 0.819 0.072 0.916 0.012
#> GSM1124926 1 0.9300 0.122 0.428 0.412 0.160
#> GSM1124928 1 0.1525 0.775 0.964 0.032 0.004
#> GSM1124930 2 0.1163 0.851 0.000 0.972 0.028
#> GSM1124931 2 0.8688 -0.163 0.436 0.460 0.104
#> GSM1124935 2 0.1964 0.849 0.000 0.944 0.056
#> GSM1124936 3 0.7377 0.688 0.452 0.032 0.516
#> GSM1124938 2 0.4755 0.687 0.008 0.808 0.184
#> GSM1124940 1 0.0000 0.801 1.000 0.000 0.000
#> GSM1124941 2 0.0237 0.851 0.004 0.996 0.000
#> GSM1124942 2 0.0592 0.848 0.000 0.988 0.012
#> GSM1124943 2 0.1411 0.839 0.000 0.964 0.036
#> GSM1124948 2 0.0592 0.848 0.000 0.988 0.012
#> GSM1124949 1 0.0000 0.801 1.000 0.000 0.000
#> GSM1124950 2 0.0237 0.851 0.004 0.996 0.000
#> GSM1124954 3 0.7329 0.735 0.424 0.032 0.544
#> GSM1124955 1 0.0424 0.796 0.992 0.000 0.008
#> GSM1124956 2 0.0237 0.851 0.004 0.996 0.000
#> GSM1124872 2 0.0237 0.851 0.004 0.996 0.000
#> GSM1124873 2 0.0237 0.851 0.004 0.996 0.000
#> GSM1124876 3 0.6906 0.820 0.324 0.032 0.644
#> GSM1124877 1 0.0000 0.801 1.000 0.000 0.000
#> GSM1124879 1 0.0000 0.801 1.000 0.000 0.000
#> GSM1124883 2 0.4974 0.789 0.000 0.764 0.236
#> GSM1124889 2 0.0237 0.851 0.004 0.996 0.000
#> GSM1124892 1 0.0592 0.790 0.988 0.000 0.012
#> GSM1124893 1 0.0000 0.801 1.000 0.000 0.000
#> GSM1124909 2 0.0237 0.851 0.004 0.996 0.000
#> GSM1124913 2 0.5058 0.785 0.000 0.756 0.244
#> GSM1124916 2 0.0237 0.851 0.004 0.996 0.000
#> GSM1124923 2 0.3412 0.837 0.000 0.876 0.124
#> GSM1124925 1 0.0237 0.799 0.996 0.000 0.004
#> GSM1124929 1 0.0000 0.801 1.000 0.000 0.000
#> GSM1124934 1 0.7337 -0.541 0.540 0.032 0.428
#> GSM1124937 2 0.1753 0.835 0.048 0.952 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1124891 3 0.0000 0.67076 0.000 0.000 1.000 0.000
#> GSM1124888 3 0.0000 0.67076 0.000 0.000 1.000 0.000
#> GSM1124890 3 0.7581 -0.08913 0.000 0.380 0.424 0.196
#> GSM1124904 2 0.4477 0.11618 0.000 0.688 0.000 0.312
#> GSM1124927 4 0.6608 0.26256 0.168 0.204 0.000 0.628
#> GSM1124953 4 0.4967 0.50365 0.000 0.452 0.000 0.548
#> GSM1124869 1 0.0000 0.74511 1.000 0.000 0.000 0.000
#> GSM1124870 1 0.7211 0.52360 0.548 0.000 0.204 0.248
#> GSM1124882 1 0.0592 0.74412 0.984 0.000 0.016 0.000
#> GSM1124884 2 0.4713 0.57555 0.000 0.640 0.000 0.360
#> GSM1124898 2 0.3123 0.53919 0.000 0.844 0.000 0.156
#> GSM1124903 2 0.4406 0.14419 0.000 0.700 0.000 0.300
#> GSM1124905 1 0.7088 0.54465 0.568 0.000 0.204 0.228
#> GSM1124910 1 0.6033 0.61645 0.680 0.000 0.204 0.116
#> GSM1124919 2 0.1118 0.45523 0.000 0.964 0.000 0.036
#> GSM1124932 4 0.4920 0.40437 0.068 0.164 0.000 0.768
#> GSM1124933 3 0.0000 0.67076 0.000 0.000 1.000 0.000
#> GSM1124867 2 0.4713 0.57555 0.000 0.640 0.000 0.360
#> GSM1124868 2 0.4522 0.09677 0.000 0.680 0.000 0.320
#> GSM1124878 2 0.4356 0.15912 0.000 0.708 0.000 0.292
#> GSM1124895 2 0.4008 0.23298 0.000 0.756 0.000 0.244
#> GSM1124897 2 0.4356 0.15912 0.000 0.708 0.000 0.292
#> GSM1124902 2 0.4304 0.17336 0.000 0.716 0.000 0.284
#> GSM1124908 4 0.4746 0.50601 0.000 0.368 0.000 0.632
#> GSM1124921 4 0.4730 0.50111 0.000 0.364 0.000 0.636
#> GSM1124939 2 0.3400 0.31083 0.000 0.820 0.000 0.180
#> GSM1124944 4 0.4996 0.42324 0.000 0.484 0.000 0.516
#> GSM1124945 3 0.4874 0.45462 0.000 0.180 0.764 0.056
#> GSM1124946 4 0.4855 0.48229 0.000 0.400 0.000 0.600
#> GSM1124947 4 0.4643 0.51901 0.000 0.344 0.000 0.656
#> GSM1124951 3 0.3505 0.58917 0.000 0.088 0.864 0.048
#> GSM1124952 4 0.4008 0.49849 0.000 0.244 0.000 0.756
#> GSM1124957 3 0.0000 0.67076 0.000 0.000 1.000 0.000
#> GSM1124900 1 0.7234 0.52093 0.544 0.000 0.204 0.252
#> GSM1124914 2 0.3074 0.33720 0.000 0.848 0.000 0.152
#> GSM1124871 2 0.4679 0.57749 0.000 0.648 0.000 0.352
#> GSM1124874 2 0.4981 0.48598 0.000 0.536 0.000 0.464
#> GSM1124875 2 0.3311 0.53694 0.000 0.828 0.000 0.172
#> GSM1124880 1 0.9643 0.04416 0.348 0.152 0.200 0.300
#> GSM1124881 2 0.4697 0.57672 0.000 0.644 0.000 0.356
#> GSM1124885 2 0.4431 0.13543 0.000 0.696 0.000 0.304
#> GSM1124886 1 0.0336 0.74299 0.992 0.000 0.000 0.008
#> GSM1124887 4 0.4830 0.48904 0.000 0.392 0.000 0.608
#> GSM1124894 1 0.7608 0.39401 0.432 0.000 0.204 0.364
#> GSM1124896 1 0.7390 0.48844 0.512 0.000 0.204 0.284
#> GSM1124899 2 0.4866 0.54906 0.000 0.596 0.000 0.404
#> GSM1124901 2 0.1022 0.46966 0.000 0.968 0.000 0.032
#> GSM1124906 2 0.5070 0.56515 0.008 0.620 0.000 0.372
#> GSM1124907 2 0.0336 0.44989 0.000 0.992 0.000 0.008
#> GSM1124911 2 0.4843 0.55561 0.000 0.604 0.000 0.396
#> GSM1124912 1 0.0000 0.74511 1.000 0.000 0.000 0.000
#> GSM1124915 2 0.1474 0.48022 0.000 0.948 0.000 0.052
#> GSM1124917 2 0.4543 0.57965 0.000 0.676 0.000 0.324
#> GSM1124918 2 0.4543 0.57965 0.000 0.676 0.000 0.324
#> GSM1124920 3 0.4961 0.00333 0.448 0.000 0.552 0.000
#> GSM1124922 4 0.3219 0.45261 0.000 0.164 0.000 0.836
#> GSM1124924 2 0.7925 0.32945 0.008 0.456 0.236 0.300
#> GSM1124926 4 0.3074 0.45035 0.000 0.152 0.000 0.848
#> GSM1124928 1 0.4153 0.66043 0.784 0.004 0.204 0.008
#> GSM1124930 2 0.2737 0.50597 0.000 0.888 0.008 0.104
#> GSM1124931 4 0.3123 0.44547 0.000 0.156 0.000 0.844
#> GSM1124935 2 0.4431 0.57392 0.000 0.696 0.000 0.304
#> GSM1124936 3 0.5277 -0.05081 0.460 0.000 0.532 0.008
#> GSM1124938 2 0.6811 -0.02146 0.000 0.496 0.404 0.100
#> GSM1124940 1 0.0188 0.74535 0.996 0.000 0.004 0.000
#> GSM1124941 2 0.4713 0.57555 0.000 0.640 0.000 0.360
#> GSM1124942 2 0.1637 0.47969 0.000 0.940 0.000 0.060
#> GSM1124943 2 0.6400 0.22351 0.000 0.632 0.252 0.116
#> GSM1124948 2 0.4720 0.57857 0.000 0.672 0.004 0.324
#> GSM1124949 1 0.0000 0.74511 1.000 0.000 0.000 0.000
#> GSM1124950 2 0.4713 0.57555 0.000 0.640 0.000 0.360
#> GSM1124954 3 0.5263 -0.00907 0.448 0.000 0.544 0.008
#> GSM1124955 1 0.4534 0.67930 0.800 0.000 0.068 0.132
#> GSM1124956 2 0.4713 0.57555 0.000 0.640 0.000 0.360
#> GSM1124872 2 0.4713 0.57555 0.000 0.640 0.000 0.360
#> GSM1124873 2 0.4713 0.57555 0.000 0.640 0.000 0.360
#> GSM1124876 3 0.0000 0.67076 0.000 0.000 1.000 0.000
#> GSM1124877 1 0.0000 0.74511 1.000 0.000 0.000 0.000
#> GSM1124879 1 0.3157 0.69913 0.852 0.004 0.144 0.000
#> GSM1124883 2 0.3356 0.31455 0.000 0.824 0.000 0.176
#> GSM1124889 2 0.4713 0.57555 0.000 0.640 0.000 0.360
#> GSM1124892 1 0.0336 0.74299 0.992 0.000 0.000 0.008
#> GSM1124893 1 0.0000 0.74511 1.000 0.000 0.000 0.000
#> GSM1124909 2 0.4713 0.57555 0.000 0.640 0.000 0.360
#> GSM1124913 2 0.4406 0.14419 0.000 0.700 0.000 0.300
#> GSM1124916 2 0.4713 0.57555 0.000 0.640 0.000 0.360
#> GSM1124923 2 0.4454 -0.26052 0.000 0.692 0.000 0.308
#> GSM1124925 1 0.3569 0.66752 0.804 0.000 0.196 0.000
#> GSM1124929 1 0.0000 0.74511 1.000 0.000 0.000 0.000
#> GSM1124934 1 0.5163 0.12326 0.516 0.004 0.480 0.000
#> GSM1124937 2 0.5047 0.57388 0.004 0.636 0.004 0.356
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM1124891 3 0.0451 0.8056 0.004 0.000 0.988 0.000 0.008
#> GSM1124888 3 0.0000 0.8071 0.000 0.000 1.000 0.000 0.000
#> GSM1124890 5 0.7502 0.6827 0.000 0.212 0.088 0.196 0.504
#> GSM1124904 4 0.2074 0.7051 0.000 0.104 0.000 0.896 0.000
#> GSM1124927 2 0.6827 0.3209 0.056 0.464 0.000 0.088 0.392
#> GSM1124953 4 0.5762 0.2126 0.000 0.144 0.000 0.608 0.248
#> GSM1124869 1 0.0794 0.7910 0.972 0.000 0.000 0.000 0.028
#> GSM1124870 1 0.8137 0.5812 0.540 0.080 0.164 0.092 0.124
#> GSM1124882 1 0.1725 0.7955 0.936 0.000 0.020 0.000 0.044
#> GSM1124884 2 0.0000 0.5683 0.000 1.000 0.000 0.000 0.000
#> GSM1124898 2 0.2077 0.5150 0.000 0.908 0.000 0.084 0.008
#> GSM1124903 4 0.2329 0.7087 0.000 0.124 0.000 0.876 0.000
#> GSM1124905 1 0.6303 0.6789 0.652 0.000 0.164 0.092 0.092
#> GSM1124910 1 0.5309 0.7238 0.720 0.000 0.168 0.040 0.072
#> GSM1124919 4 0.6765 -0.3241 0.000 0.344 0.000 0.384 0.272
#> GSM1124932 2 0.6840 0.3114 0.048 0.456 0.000 0.100 0.396
#> GSM1124933 3 0.0000 0.8071 0.000 0.000 1.000 0.000 0.000
#> GSM1124867 2 0.4066 0.3535 0.004 0.672 0.000 0.000 0.324
#> GSM1124868 4 0.2677 0.7064 0.000 0.112 0.000 0.872 0.016
#> GSM1124878 4 0.2329 0.7087 0.000 0.124 0.000 0.876 0.000
#> GSM1124895 4 0.3752 0.5524 0.000 0.292 0.000 0.708 0.000
#> GSM1124897 4 0.2377 0.7069 0.000 0.128 0.000 0.872 0.000
#> GSM1124902 4 0.3143 0.6429 0.000 0.204 0.000 0.796 0.000
#> GSM1124908 4 0.3409 0.5828 0.000 0.112 0.000 0.836 0.052
#> GSM1124921 4 0.0880 0.6427 0.000 0.000 0.000 0.968 0.032
#> GSM1124939 4 0.4210 0.3789 0.000 0.412 0.000 0.588 0.000
#> GSM1124944 4 0.4583 0.4859 0.000 0.112 0.000 0.748 0.140
#> GSM1124945 3 0.7294 0.1469 0.000 0.148 0.552 0.120 0.180
#> GSM1124946 4 0.1197 0.6416 0.000 0.000 0.000 0.952 0.048
#> GSM1124947 4 0.6244 -0.0397 0.000 0.412 0.000 0.444 0.144
#> GSM1124951 3 0.4849 0.4603 0.000 0.136 0.724 0.000 0.140
#> GSM1124952 2 0.6080 0.3005 0.000 0.572 0.000 0.200 0.228
#> GSM1124957 3 0.0000 0.8071 0.000 0.000 1.000 0.000 0.000
#> GSM1124900 1 0.7510 0.6359 0.584 0.036 0.164 0.092 0.124
#> GSM1124914 2 0.4045 0.1813 0.000 0.644 0.000 0.356 0.000
#> GSM1124871 2 0.0000 0.5683 0.000 1.000 0.000 0.000 0.000
#> GSM1124874 2 0.3359 0.5202 0.020 0.816 0.000 0.000 0.164
#> GSM1124875 2 0.2149 0.5164 0.000 0.916 0.000 0.048 0.036
#> GSM1124880 2 0.8410 -0.1031 0.216 0.336 0.168 0.000 0.280
#> GSM1124881 2 0.0290 0.5704 0.000 0.992 0.000 0.000 0.008
#> GSM1124885 4 0.2329 0.7087 0.000 0.124 0.000 0.876 0.000
#> GSM1124886 1 0.1331 0.7805 0.952 0.000 0.008 0.000 0.040
#> GSM1124887 4 0.0963 0.6423 0.000 0.000 0.000 0.964 0.036
#> GSM1124894 1 0.9244 0.3220 0.356 0.092 0.164 0.116 0.272
#> GSM1124896 1 0.6742 0.6519 0.612 0.000 0.164 0.096 0.128
#> GSM1124899 2 0.2648 0.5371 0.000 0.848 0.000 0.000 0.152
#> GSM1124901 2 0.4108 0.1887 0.000 0.684 0.000 0.308 0.008
#> GSM1124906 2 0.4697 0.4828 0.036 0.660 0.000 0.000 0.304
#> GSM1124907 2 0.6749 -0.4898 0.000 0.388 0.000 0.348 0.264
#> GSM1124911 2 0.2377 0.5470 0.000 0.872 0.000 0.000 0.128
#> GSM1124912 1 0.1197 0.7884 0.952 0.000 0.000 0.000 0.048
#> GSM1124915 2 0.4517 -0.1730 0.000 0.556 0.000 0.436 0.008
#> GSM1124917 2 0.0703 0.5562 0.000 0.976 0.000 0.024 0.000
#> GSM1124918 2 0.2103 0.5348 0.000 0.920 0.004 0.020 0.056
#> GSM1124920 3 0.3810 0.6942 0.176 0.000 0.788 0.000 0.036
#> GSM1124922 2 0.6049 0.3044 0.000 0.576 0.000 0.192 0.232
#> GSM1124924 2 0.6346 -0.0835 0.004 0.516 0.160 0.000 0.320
#> GSM1124926 2 0.6202 0.2781 0.000 0.552 0.000 0.220 0.228
#> GSM1124928 1 0.4712 0.7301 0.732 0.000 0.168 0.000 0.100
#> GSM1124930 5 0.5868 0.7722 0.000 0.392 0.004 0.088 0.516
#> GSM1124931 2 0.7167 0.2918 0.000 0.444 0.032 0.192 0.332
#> GSM1124935 2 0.1408 0.5477 0.000 0.948 0.000 0.044 0.008
#> GSM1124936 3 0.4587 0.6386 0.204 0.000 0.728 0.000 0.068
#> GSM1124938 5 0.7087 0.8010 0.000 0.292 0.076 0.112 0.520
#> GSM1124940 1 0.1357 0.7899 0.948 0.000 0.004 0.000 0.048
#> GSM1124941 2 0.2516 0.5570 0.000 0.860 0.000 0.000 0.140
#> GSM1124942 2 0.6742 -0.5302 0.000 0.412 0.000 0.296 0.292
#> GSM1124943 5 0.5960 0.7885 0.000 0.396 0.004 0.096 0.504
#> GSM1124948 2 0.4705 -0.0926 0.000 0.504 0.008 0.004 0.484
#> GSM1124949 1 0.0703 0.7895 0.976 0.000 0.000 0.000 0.024
#> GSM1124950 2 0.2516 0.5570 0.000 0.860 0.000 0.000 0.140
#> GSM1124954 3 0.4421 0.6655 0.184 0.000 0.748 0.000 0.068
#> GSM1124955 1 0.3379 0.7856 0.860 0.000 0.076 0.024 0.040
#> GSM1124956 2 0.0510 0.5710 0.000 0.984 0.000 0.000 0.016
#> GSM1124872 2 0.2516 0.5570 0.000 0.860 0.000 0.000 0.140
#> GSM1124873 2 0.0162 0.5698 0.000 0.996 0.000 0.000 0.004
#> GSM1124876 3 0.0000 0.8071 0.000 0.000 1.000 0.000 0.000
#> GSM1124877 1 0.0880 0.7900 0.968 0.000 0.000 0.000 0.032
#> GSM1124879 1 0.3992 0.7629 0.796 0.000 0.124 0.000 0.080
#> GSM1124883 4 0.4161 0.3898 0.000 0.392 0.000 0.608 0.000
#> GSM1124889 2 0.0000 0.5683 0.000 1.000 0.000 0.000 0.000
#> GSM1124892 1 0.1444 0.7792 0.948 0.000 0.012 0.000 0.040
#> GSM1124893 1 0.0404 0.7925 0.988 0.000 0.000 0.000 0.012
#> GSM1124909 2 0.3177 0.5084 0.000 0.792 0.000 0.000 0.208
#> GSM1124913 4 0.2329 0.7087 0.000 0.124 0.000 0.876 0.000
#> GSM1124916 2 0.2561 0.5563 0.000 0.856 0.000 0.000 0.144
#> GSM1124923 4 0.5982 0.1955 0.000 0.132 0.004 0.580 0.284
#> GSM1124925 1 0.3944 0.7485 0.788 0.000 0.160 0.000 0.052
#> GSM1124929 1 0.1043 0.7830 0.960 0.000 0.000 0.000 0.040
#> GSM1124934 1 0.6744 0.1193 0.400 0.000 0.332 0.000 0.268
#> GSM1124937 2 0.4553 0.2233 0.008 0.604 0.004 0.000 0.384
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1124891 3 0.0713 0.877 0.000 0.000 0.972 0.000 0.000 0.028
#> GSM1124888 3 0.0000 0.880 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1124890 5 0.2725 0.836 0.000 0.020 0.060 0.004 0.884 0.032
#> GSM1124904 4 0.0000 0.927 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1124927 2 0.2547 0.852 0.000 0.868 0.000 0.016 0.004 0.112
#> GSM1124953 5 0.1285 0.877 0.000 0.004 0.000 0.052 0.944 0.000
#> GSM1124869 1 0.0603 0.921 0.980 0.000 0.000 0.000 0.016 0.004
#> GSM1124870 1 0.3815 0.838 0.808 0.024 0.040 0.000 0.008 0.120
#> GSM1124882 1 0.0291 0.923 0.992 0.000 0.000 0.000 0.004 0.004
#> GSM1124884 2 0.0291 0.882 0.000 0.992 0.000 0.004 0.000 0.004
#> GSM1124898 2 0.2768 0.765 0.000 0.832 0.000 0.156 0.012 0.000
#> GSM1124903 4 0.0000 0.927 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1124905 1 0.2675 0.886 0.876 0.000 0.040 0.000 0.008 0.076
#> GSM1124910 1 0.2925 0.879 0.860 0.000 0.052 0.000 0.008 0.080
#> GSM1124919 5 0.2651 0.866 0.000 0.028 0.000 0.112 0.860 0.000
#> GSM1124932 2 0.2611 0.857 0.000 0.876 0.004 0.016 0.008 0.096
#> GSM1124933 3 0.0000 0.880 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1124867 6 0.3619 0.628 0.000 0.316 0.000 0.000 0.004 0.680
#> GSM1124868 4 0.0000 0.927 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1124878 4 0.0000 0.927 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1124895 4 0.0146 0.926 0.000 0.004 0.000 0.996 0.000 0.000
#> GSM1124897 4 0.0000 0.927 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1124902 4 0.1141 0.897 0.000 0.052 0.000 0.948 0.000 0.000
#> GSM1124908 4 0.3107 0.788 0.000 0.116 0.000 0.832 0.052 0.000
#> GSM1124921 4 0.1075 0.908 0.000 0.000 0.000 0.952 0.048 0.000
#> GSM1124939 4 0.3634 0.415 0.000 0.356 0.000 0.644 0.000 0.000
#> GSM1124944 5 0.3445 0.728 0.000 0.012 0.000 0.244 0.744 0.000
#> GSM1124945 3 0.4180 0.513 0.000 0.012 0.632 0.008 0.348 0.000
#> GSM1124946 4 0.1007 0.910 0.000 0.000 0.000 0.956 0.044 0.000
#> GSM1124947 2 0.2443 0.835 0.000 0.880 0.000 0.096 0.020 0.004
#> GSM1124951 3 0.3940 0.558 0.000 0.000 0.652 0.008 0.336 0.004
#> GSM1124952 2 0.2059 0.871 0.000 0.924 0.024 0.024 0.020 0.008
#> GSM1124957 3 0.0405 0.877 0.000 0.000 0.988 0.000 0.008 0.004
#> GSM1124900 1 0.3763 0.834 0.800 0.012 0.040 0.000 0.008 0.140
#> GSM1124914 2 0.3945 0.430 0.000 0.612 0.000 0.380 0.008 0.000
#> GSM1124871 2 0.0551 0.881 0.000 0.984 0.000 0.008 0.004 0.004
#> GSM1124874 2 0.0951 0.880 0.000 0.968 0.000 0.020 0.008 0.004
#> GSM1124875 2 0.2629 0.825 0.000 0.868 0.000 0.040 0.092 0.000
#> GSM1124880 6 0.2081 0.737 0.000 0.036 0.036 0.000 0.012 0.916
#> GSM1124881 2 0.0508 0.882 0.000 0.984 0.000 0.000 0.004 0.012
#> GSM1124885 4 0.0000 0.927 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1124886 1 0.0000 0.922 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124887 4 0.1007 0.910 0.000 0.000 0.000 0.956 0.044 0.000
#> GSM1124894 1 0.5547 0.540 0.648 0.224 0.040 0.000 0.012 0.076
#> GSM1124896 1 0.2051 0.904 0.916 0.000 0.036 0.000 0.008 0.040
#> GSM1124899 2 0.0436 0.882 0.000 0.988 0.000 0.004 0.004 0.004
#> GSM1124901 2 0.3975 0.363 0.000 0.600 0.000 0.392 0.008 0.000
#> GSM1124906 2 0.1732 0.872 0.000 0.920 0.000 0.004 0.004 0.072
#> GSM1124907 5 0.2633 0.868 0.000 0.032 0.000 0.104 0.864 0.000
#> GSM1124911 2 0.0603 0.882 0.000 0.980 0.000 0.016 0.000 0.004
#> GSM1124912 1 0.0603 0.921 0.980 0.000 0.000 0.000 0.016 0.004
#> GSM1124915 4 0.2212 0.834 0.000 0.112 0.000 0.880 0.008 0.000
#> GSM1124917 2 0.0951 0.881 0.000 0.968 0.000 0.008 0.020 0.004
#> GSM1124918 2 0.4159 0.764 0.000 0.776 0.012 0.008 0.072 0.132
#> GSM1124920 3 0.1755 0.865 0.028 0.000 0.932 0.000 0.008 0.032
#> GSM1124922 2 0.1508 0.879 0.000 0.948 0.004 0.016 0.020 0.012
#> GSM1124924 6 0.2535 0.764 0.000 0.064 0.036 0.000 0.012 0.888
#> GSM1124926 2 0.1251 0.878 0.000 0.956 0.000 0.024 0.012 0.008
#> GSM1124928 1 0.3255 0.869 0.840 0.004 0.048 0.000 0.008 0.100
#> GSM1124930 5 0.2216 0.883 0.000 0.016 0.000 0.052 0.908 0.024
#> GSM1124931 2 0.3282 0.838 0.000 0.844 0.020 0.020 0.012 0.104
#> GSM1124935 2 0.2019 0.825 0.000 0.900 0.000 0.088 0.012 0.000
#> GSM1124936 3 0.2563 0.820 0.084 0.000 0.880 0.000 0.008 0.028
#> GSM1124938 5 0.2823 0.835 0.000 0.016 0.060 0.008 0.880 0.036
#> GSM1124940 1 0.0603 0.921 0.980 0.000 0.000 0.000 0.016 0.004
#> GSM1124941 2 0.1753 0.864 0.000 0.912 0.000 0.000 0.004 0.084
#> GSM1124942 5 0.1719 0.880 0.000 0.032 0.000 0.032 0.932 0.004
#> GSM1124943 5 0.2687 0.843 0.000 0.028 0.052 0.004 0.888 0.028
#> GSM1124948 6 0.3150 0.771 0.000 0.096 0.024 0.000 0.032 0.848
#> GSM1124949 1 0.0000 0.922 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124950 2 0.2302 0.846 0.000 0.872 0.000 0.000 0.008 0.120
#> GSM1124954 3 0.1542 0.864 0.004 0.000 0.936 0.000 0.008 0.052
#> GSM1124955 1 0.0870 0.923 0.972 0.000 0.012 0.000 0.012 0.004
#> GSM1124956 2 0.0291 0.882 0.000 0.992 0.000 0.004 0.000 0.004
#> GSM1124872 2 0.2257 0.848 0.000 0.876 0.000 0.000 0.008 0.116
#> GSM1124873 2 0.0508 0.882 0.000 0.984 0.000 0.000 0.004 0.012
#> GSM1124876 3 0.0000 0.880 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1124877 1 0.0146 0.923 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM1124879 1 0.1262 0.918 0.956 0.000 0.020 0.000 0.008 0.016
#> GSM1124883 4 0.0632 0.914 0.000 0.024 0.000 0.976 0.000 0.000
#> GSM1124889 2 0.0291 0.882 0.000 0.992 0.000 0.004 0.000 0.004
#> GSM1124892 1 0.1434 0.892 0.940 0.000 0.048 0.000 0.000 0.012
#> GSM1124893 1 0.0603 0.921 0.980 0.000 0.000 0.000 0.016 0.004
#> GSM1124909 6 0.3756 0.520 0.000 0.352 0.000 0.000 0.004 0.644
#> GSM1124913 4 0.0000 0.927 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1124916 2 0.2805 0.784 0.000 0.812 0.000 0.000 0.004 0.184
#> GSM1124923 5 0.2889 0.868 0.000 0.020 0.012 0.116 0.852 0.000
#> GSM1124925 1 0.1464 0.918 0.944 0.000 0.036 0.000 0.016 0.004
#> GSM1124929 1 0.0603 0.921 0.980 0.000 0.000 0.000 0.016 0.004
#> GSM1124934 6 0.4249 0.482 0.068 0.000 0.188 0.000 0.008 0.736
#> GSM1124937 6 0.2302 0.772 0.000 0.120 0.000 0.000 0.008 0.872
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 tissue(p) k
#> MAD:mclust 91 0.223736 2
#> MAD:mclust 83 0.053993 3
#> MAD:mclust 51 0.000122 4
#> MAD:mclust 63 0.000433 5
#> MAD:mclust 87 0.034844 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 48983 rows and 91 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 0.844 0.926 0.966 0.4948 0.505 0.505
#> 3 3 0.879 0.919 0.965 0.2352 0.766 0.583
#> 4 4 0.887 0.869 0.943 0.2028 0.790 0.509
#> 5 5 0.825 0.765 0.892 0.0606 0.917 0.705
#> 6 6 0.731 0.525 0.747 0.0482 0.931 0.715
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
#> GSM1124891 1 0.0000 0.957 1.000 0.000
#> GSM1124888 1 0.0000 0.957 1.000 0.000
#> GSM1124890 1 0.6801 0.797 0.820 0.180
#> GSM1124904 2 0.0000 0.968 0.000 1.000
#> GSM1124927 2 0.7815 0.707 0.232 0.768
#> GSM1124953 2 0.8443 0.607 0.272 0.728
#> GSM1124869 1 0.0000 0.957 1.000 0.000
#> GSM1124870 1 0.2236 0.933 0.964 0.036
#> GSM1124882 1 0.0000 0.957 1.000 0.000
#> GSM1124884 2 0.0000 0.968 0.000 1.000
#> GSM1124898 2 0.0000 0.968 0.000 1.000
#> GSM1124903 2 0.0000 0.968 0.000 1.000
#> GSM1124905 1 0.0000 0.957 1.000 0.000
#> GSM1124910 1 0.0000 0.957 1.000 0.000
#> GSM1124919 2 0.0000 0.968 0.000 1.000
#> GSM1124932 2 0.8661 0.614 0.288 0.712
#> GSM1124933 1 0.0000 0.957 1.000 0.000
#> GSM1124867 1 0.8207 0.680 0.744 0.256
#> GSM1124868 2 0.0000 0.968 0.000 1.000
#> GSM1124878 2 0.0000 0.968 0.000 1.000
#> GSM1124895 2 0.0000 0.968 0.000 1.000
#> GSM1124897 2 0.0000 0.968 0.000 1.000
#> GSM1124902 2 0.0000 0.968 0.000 1.000
#> GSM1124908 2 0.0000 0.968 0.000 1.000
#> GSM1124921 2 0.0000 0.968 0.000 1.000
#> GSM1124939 2 0.0000 0.968 0.000 1.000
#> GSM1124944 2 0.0000 0.968 0.000 1.000
#> GSM1124945 1 0.4562 0.884 0.904 0.096
#> GSM1124946 2 0.0000 0.968 0.000 1.000
#> GSM1124947 2 0.0000 0.968 0.000 1.000
#> GSM1124951 1 0.7219 0.772 0.800 0.200
#> GSM1124952 2 0.0376 0.965 0.004 0.996
#> GSM1124957 1 0.0000 0.957 1.000 0.000
#> GSM1124900 1 0.3879 0.900 0.924 0.076
#> GSM1124914 2 0.0000 0.968 0.000 1.000
#> GSM1124871 2 0.0000 0.968 0.000 1.000
#> GSM1124874 2 0.0000 0.968 0.000 1.000
#> GSM1124875 2 0.0000 0.968 0.000 1.000
#> GSM1124880 1 0.0000 0.957 1.000 0.000
#> GSM1124881 2 0.0000 0.968 0.000 1.000
#> GSM1124885 2 0.0000 0.968 0.000 1.000
#> GSM1124886 1 0.0000 0.957 1.000 0.000
#> GSM1124887 2 0.0000 0.968 0.000 1.000
#> GSM1124894 1 0.8499 0.622 0.724 0.276
#> GSM1124896 1 0.0000 0.957 1.000 0.000
#> GSM1124899 2 0.0000 0.968 0.000 1.000
#> GSM1124901 2 0.0000 0.968 0.000 1.000
#> GSM1124906 2 0.0000 0.968 0.000 1.000
#> GSM1124907 2 0.0000 0.968 0.000 1.000
#> GSM1124911 2 0.0000 0.968 0.000 1.000
#> GSM1124912 1 0.0000 0.957 1.000 0.000
#> GSM1124915 2 0.0000 0.968 0.000 1.000
#> GSM1124917 2 0.0000 0.968 0.000 1.000
#> GSM1124918 2 0.0000 0.968 0.000 1.000
#> GSM1124920 1 0.0000 0.957 1.000 0.000
#> GSM1124922 2 0.5519 0.845 0.128 0.872
#> GSM1124924 1 0.0000 0.957 1.000 0.000
#> GSM1124926 2 0.0000 0.968 0.000 1.000
#> GSM1124928 1 0.0000 0.957 1.000 0.000
#> GSM1124930 2 0.2043 0.940 0.032 0.968
#> GSM1124931 2 0.9358 0.480 0.352 0.648
#> GSM1124935 2 0.0000 0.968 0.000 1.000
#> GSM1124936 1 0.0000 0.957 1.000 0.000
#> GSM1124938 1 0.6973 0.787 0.812 0.188
#> GSM1124940 1 0.0000 0.957 1.000 0.000
#> GSM1124941 2 0.0000 0.968 0.000 1.000
#> GSM1124942 2 0.0000 0.968 0.000 1.000
#> GSM1124943 1 0.7299 0.767 0.796 0.204
#> GSM1124948 1 0.0000 0.957 1.000 0.000
#> GSM1124949 1 0.0000 0.957 1.000 0.000
#> GSM1124950 2 0.0376 0.965 0.004 0.996
#> GSM1124954 1 0.0000 0.957 1.000 0.000
#> GSM1124955 1 0.0000 0.957 1.000 0.000
#> GSM1124956 2 0.0000 0.968 0.000 1.000
#> GSM1124872 2 0.0376 0.965 0.004 0.996
#> GSM1124873 2 0.0000 0.968 0.000 1.000
#> GSM1124876 1 0.0000 0.957 1.000 0.000
#> GSM1124877 1 0.0000 0.957 1.000 0.000
#> GSM1124879 1 0.0000 0.957 1.000 0.000
#> GSM1124883 2 0.0000 0.968 0.000 1.000
#> GSM1124889 2 0.0000 0.968 0.000 1.000
#> GSM1124892 1 0.0000 0.957 1.000 0.000
#> GSM1124893 1 0.0000 0.957 1.000 0.000
#> GSM1124909 2 0.7528 0.733 0.216 0.784
#> GSM1124913 2 0.0000 0.968 0.000 1.000
#> GSM1124916 2 0.2423 0.934 0.040 0.960
#> GSM1124923 2 0.0000 0.968 0.000 1.000
#> GSM1124925 1 0.2778 0.924 0.952 0.048
#> GSM1124929 1 0.0000 0.957 1.000 0.000
#> GSM1124934 1 0.0000 0.957 1.000 0.000
#> GSM1124937 1 0.0000 0.957 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1124891 3 0.0000 0.942 0.000 0.000 1.000
#> GSM1124888 3 0.0000 0.942 0.000 0.000 1.000
#> GSM1124890 3 0.3551 0.856 0.000 0.132 0.868
#> GSM1124904 2 0.0000 0.977 0.000 1.000 0.000
#> GSM1124927 1 0.0000 0.935 1.000 0.000 0.000
#> GSM1124953 2 0.2625 0.894 0.000 0.916 0.084
#> GSM1124869 1 0.0237 0.935 0.996 0.000 0.004
#> GSM1124870 1 0.0000 0.935 1.000 0.000 0.000
#> GSM1124882 1 0.0237 0.935 0.996 0.000 0.004
#> GSM1124884 2 0.0237 0.975 0.004 0.996 0.000
#> GSM1124898 2 0.0000 0.977 0.000 1.000 0.000
#> GSM1124903 2 0.0000 0.977 0.000 1.000 0.000
#> GSM1124905 1 0.0000 0.935 1.000 0.000 0.000
#> GSM1124910 1 0.0237 0.935 0.996 0.000 0.004
#> GSM1124919 2 0.0000 0.977 0.000 1.000 0.000
#> GSM1124932 1 0.0000 0.935 1.000 0.000 0.000
#> GSM1124933 3 0.0000 0.942 0.000 0.000 1.000
#> GSM1124867 1 0.0592 0.926 0.988 0.012 0.000
#> GSM1124868 2 0.0000 0.977 0.000 1.000 0.000
#> GSM1124878 2 0.0000 0.977 0.000 1.000 0.000
#> GSM1124895 2 0.0000 0.977 0.000 1.000 0.000
#> GSM1124897 2 0.0000 0.977 0.000 1.000 0.000
#> GSM1124902 2 0.0000 0.977 0.000 1.000 0.000
#> GSM1124908 2 0.0000 0.977 0.000 1.000 0.000
#> GSM1124921 2 0.0000 0.977 0.000 1.000 0.000
#> GSM1124939 2 0.0000 0.977 0.000 1.000 0.000
#> GSM1124944 2 0.0000 0.977 0.000 1.000 0.000
#> GSM1124945 3 0.0000 0.942 0.000 0.000 1.000
#> GSM1124946 2 0.0000 0.977 0.000 1.000 0.000
#> GSM1124947 2 0.0000 0.977 0.000 1.000 0.000
#> GSM1124951 3 0.0237 0.941 0.000 0.004 0.996
#> GSM1124952 2 0.3619 0.824 0.136 0.864 0.000
#> GSM1124957 3 0.0000 0.942 0.000 0.000 1.000
#> GSM1124900 1 0.0000 0.935 1.000 0.000 0.000
#> GSM1124914 2 0.0000 0.977 0.000 1.000 0.000
#> GSM1124871 2 0.0237 0.975 0.004 0.996 0.000
#> GSM1124874 2 0.1031 0.958 0.024 0.976 0.000
#> GSM1124875 2 0.0000 0.977 0.000 1.000 0.000
#> GSM1124880 1 0.0000 0.935 1.000 0.000 0.000
#> GSM1124881 2 0.0424 0.972 0.008 0.992 0.000
#> GSM1124885 2 0.0000 0.977 0.000 1.000 0.000
#> GSM1124886 1 0.0237 0.935 0.996 0.000 0.004
#> GSM1124887 2 0.0000 0.977 0.000 1.000 0.000
#> GSM1124894 1 0.0000 0.935 1.000 0.000 0.000
#> GSM1124896 1 0.0237 0.935 0.996 0.000 0.004
#> GSM1124899 2 0.0747 0.966 0.016 0.984 0.000
#> GSM1124901 2 0.0000 0.977 0.000 1.000 0.000
#> GSM1124906 1 0.4504 0.728 0.804 0.196 0.000
#> GSM1124907 2 0.0000 0.977 0.000 1.000 0.000
#> GSM1124911 2 0.4555 0.734 0.200 0.800 0.000
#> GSM1124912 1 0.0237 0.935 0.996 0.000 0.004
#> GSM1124915 2 0.0000 0.977 0.000 1.000 0.000
#> GSM1124917 2 0.0000 0.977 0.000 1.000 0.000
#> GSM1124918 2 0.0000 0.977 0.000 1.000 0.000
#> GSM1124920 3 0.1643 0.918 0.044 0.000 0.956
#> GSM1124922 1 0.4887 0.684 0.772 0.228 0.000
#> GSM1124924 1 0.2878 0.856 0.904 0.000 0.096
#> GSM1124926 2 0.0747 0.966 0.016 0.984 0.000
#> GSM1124928 1 0.0237 0.935 0.996 0.000 0.004
#> GSM1124930 2 0.0000 0.977 0.000 1.000 0.000
#> GSM1124931 1 0.0000 0.935 1.000 0.000 0.000
#> GSM1124935 2 0.0000 0.977 0.000 1.000 0.000
#> GSM1124936 1 0.5882 0.447 0.652 0.000 0.348
#> GSM1124938 3 0.0424 0.941 0.000 0.008 0.992
#> GSM1124940 1 0.0237 0.935 0.996 0.000 0.004
#> GSM1124941 2 0.5465 0.584 0.288 0.712 0.000
#> GSM1124942 2 0.0000 0.977 0.000 1.000 0.000
#> GSM1124943 3 0.4399 0.788 0.000 0.188 0.812
#> GSM1124948 3 0.2796 0.891 0.000 0.092 0.908
#> GSM1124949 1 0.0237 0.935 0.996 0.000 0.004
#> GSM1124950 2 0.1031 0.958 0.024 0.976 0.000
#> GSM1124954 3 0.3941 0.809 0.156 0.000 0.844
#> GSM1124955 1 0.0000 0.935 1.000 0.000 0.000
#> GSM1124956 1 0.5706 0.547 0.680 0.320 0.000
#> GSM1124872 1 0.5363 0.616 0.724 0.276 0.000
#> GSM1124873 2 0.0424 0.972 0.008 0.992 0.000
#> GSM1124876 3 0.0000 0.942 0.000 0.000 1.000
#> GSM1124877 1 0.0237 0.935 0.996 0.000 0.004
#> GSM1124879 1 0.0237 0.935 0.996 0.000 0.004
#> GSM1124883 2 0.0000 0.977 0.000 1.000 0.000
#> GSM1124889 2 0.0237 0.975 0.004 0.996 0.000
#> GSM1124892 1 0.0237 0.935 0.996 0.000 0.004
#> GSM1124893 1 0.0237 0.935 0.996 0.000 0.004
#> GSM1124909 1 0.2796 0.851 0.908 0.092 0.000
#> GSM1124913 2 0.0000 0.977 0.000 1.000 0.000
#> GSM1124916 1 0.3686 0.799 0.860 0.140 0.000
#> GSM1124923 2 0.0000 0.977 0.000 1.000 0.000
#> GSM1124925 1 0.0000 0.935 1.000 0.000 0.000
#> GSM1124929 1 0.0237 0.935 0.996 0.000 0.004
#> GSM1124934 1 0.0237 0.935 0.996 0.000 0.004
#> GSM1124937 1 0.0000 0.935 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1124891 3 0.0336 0.89271 0.000 0.008 0.992 0.000
#> GSM1124888 3 0.0336 0.89271 0.000 0.008 0.992 0.000
#> GSM1124890 3 0.1716 0.85805 0.000 0.000 0.936 0.064
#> GSM1124904 4 0.0000 0.93742 0.000 0.000 0.000 1.000
#> GSM1124927 2 0.1302 0.90473 0.044 0.956 0.000 0.000
#> GSM1124953 4 0.7774 -0.07253 0.000 0.240 0.372 0.388
#> GSM1124869 1 0.0000 0.97419 1.000 0.000 0.000 0.000
#> GSM1124870 1 0.1716 0.91760 0.936 0.064 0.000 0.000
#> GSM1124882 1 0.0000 0.97419 1.000 0.000 0.000 0.000
#> GSM1124884 2 0.0817 0.92405 0.000 0.976 0.000 0.024
#> GSM1124898 4 0.1557 0.90027 0.000 0.056 0.000 0.944
#> GSM1124903 4 0.0000 0.93742 0.000 0.000 0.000 1.000
#> GSM1124905 1 0.0000 0.97419 1.000 0.000 0.000 0.000
#> GSM1124910 1 0.0000 0.97419 1.000 0.000 0.000 0.000
#> GSM1124919 4 0.0188 0.93589 0.000 0.004 0.000 0.996
#> GSM1124932 2 0.0592 0.92338 0.016 0.984 0.000 0.000
#> GSM1124933 3 0.0000 0.89300 0.000 0.000 1.000 0.000
#> GSM1124867 2 0.0336 0.92468 0.008 0.992 0.000 0.000
#> GSM1124868 4 0.0000 0.93742 0.000 0.000 0.000 1.000
#> GSM1124878 4 0.0000 0.93742 0.000 0.000 0.000 1.000
#> GSM1124895 4 0.0000 0.93742 0.000 0.000 0.000 1.000
#> GSM1124897 4 0.0000 0.93742 0.000 0.000 0.000 1.000
#> GSM1124902 4 0.0000 0.93742 0.000 0.000 0.000 1.000
#> GSM1124908 4 0.0000 0.93742 0.000 0.000 0.000 1.000
#> GSM1124921 4 0.0000 0.93742 0.000 0.000 0.000 1.000
#> GSM1124939 4 0.0000 0.93742 0.000 0.000 0.000 1.000
#> GSM1124944 4 0.0000 0.93742 0.000 0.000 0.000 1.000
#> GSM1124945 3 0.0000 0.89300 0.000 0.000 1.000 0.000
#> GSM1124946 4 0.0000 0.93742 0.000 0.000 0.000 1.000
#> GSM1124947 4 0.0592 0.92891 0.000 0.016 0.000 0.984
#> GSM1124951 3 0.0336 0.89143 0.000 0.000 0.992 0.008
#> GSM1124952 4 0.7456 0.23749 0.200 0.308 0.000 0.492
#> GSM1124957 3 0.0000 0.89300 0.000 0.000 1.000 0.000
#> GSM1124900 1 0.3074 0.80278 0.848 0.152 0.000 0.000
#> GSM1124914 4 0.0000 0.93742 0.000 0.000 0.000 1.000
#> GSM1124871 2 0.2973 0.80884 0.000 0.856 0.000 0.144
#> GSM1124874 2 0.4999 -0.00721 0.000 0.508 0.000 0.492
#> GSM1124875 4 0.0188 0.93589 0.000 0.004 0.000 0.996
#> GSM1124880 2 0.0336 0.92468 0.008 0.992 0.000 0.000
#> GSM1124881 2 0.1474 0.90619 0.000 0.948 0.000 0.052
#> GSM1124885 4 0.0000 0.93742 0.000 0.000 0.000 1.000
#> GSM1124886 1 0.0000 0.97419 1.000 0.000 0.000 0.000
#> GSM1124887 4 0.0000 0.93742 0.000 0.000 0.000 1.000
#> GSM1124894 1 0.0592 0.96260 0.984 0.016 0.000 0.000
#> GSM1124896 1 0.0000 0.97419 1.000 0.000 0.000 0.000
#> GSM1124899 4 0.1211 0.91305 0.000 0.040 0.000 0.960
#> GSM1124901 4 0.0188 0.93589 0.000 0.004 0.000 0.996
#> GSM1124906 2 0.0188 0.92585 0.000 0.996 0.000 0.004
#> GSM1124907 4 0.0000 0.93742 0.000 0.000 0.000 1.000
#> GSM1124911 2 0.1389 0.91064 0.000 0.952 0.000 0.048
#> GSM1124912 1 0.0000 0.97419 1.000 0.000 0.000 0.000
#> GSM1124915 4 0.2408 0.85515 0.000 0.104 0.000 0.896
#> GSM1124917 2 0.2704 0.83695 0.000 0.876 0.000 0.124
#> GSM1124918 2 0.0336 0.92571 0.000 0.992 0.000 0.008
#> GSM1124920 3 0.2480 0.84083 0.088 0.008 0.904 0.000
#> GSM1124922 1 0.2408 0.84962 0.896 0.000 0.000 0.104
#> GSM1124924 2 0.0000 0.92405 0.000 1.000 0.000 0.000
#> GSM1124926 4 0.1902 0.88333 0.064 0.004 0.000 0.932
#> GSM1124928 1 0.1792 0.90954 0.932 0.068 0.000 0.000
#> GSM1124930 4 0.1109 0.91788 0.000 0.004 0.028 0.968
#> GSM1124931 2 0.0592 0.92276 0.016 0.984 0.000 0.000
#> GSM1124935 4 0.4277 0.59134 0.000 0.280 0.000 0.720
#> GSM1124936 3 0.4999 0.09442 0.492 0.000 0.508 0.000
#> GSM1124938 3 0.0592 0.89108 0.000 0.016 0.984 0.000
#> GSM1124940 1 0.0000 0.97419 1.000 0.000 0.000 0.000
#> GSM1124941 2 0.0336 0.92657 0.000 0.992 0.000 0.008
#> GSM1124942 4 0.3550 0.83048 0.000 0.096 0.044 0.860
#> GSM1124943 3 0.3249 0.78770 0.000 0.008 0.852 0.140
#> GSM1124948 2 0.0707 0.91570 0.000 0.980 0.020 0.000
#> GSM1124949 1 0.0000 0.97419 1.000 0.000 0.000 0.000
#> GSM1124950 2 0.0336 0.92678 0.000 0.992 0.000 0.008
#> GSM1124954 3 0.1510 0.88153 0.028 0.016 0.956 0.000
#> GSM1124955 1 0.0000 0.97419 1.000 0.000 0.000 0.000
#> GSM1124956 2 0.1151 0.92368 0.008 0.968 0.000 0.024
#> GSM1124872 2 0.0336 0.92678 0.000 0.992 0.000 0.008
#> GSM1124873 2 0.0817 0.92405 0.000 0.976 0.000 0.024
#> GSM1124876 3 0.0000 0.89300 0.000 0.000 1.000 0.000
#> GSM1124877 1 0.0000 0.97419 1.000 0.000 0.000 0.000
#> GSM1124879 1 0.0188 0.97128 0.996 0.004 0.000 0.000
#> GSM1124883 4 0.0000 0.93742 0.000 0.000 0.000 1.000
#> GSM1124889 2 0.1211 0.91528 0.000 0.960 0.000 0.040
#> GSM1124892 1 0.0000 0.97419 1.000 0.000 0.000 0.000
#> GSM1124893 1 0.0000 0.97419 1.000 0.000 0.000 0.000
#> GSM1124909 2 0.0524 0.92548 0.008 0.988 0.000 0.004
#> GSM1124913 4 0.0000 0.93742 0.000 0.000 0.000 1.000
#> GSM1124916 2 0.0336 0.92346 0.008 0.992 0.000 0.000
#> GSM1124923 4 0.0188 0.93500 0.000 0.000 0.004 0.996
#> GSM1124925 1 0.0000 0.97419 1.000 0.000 0.000 0.000
#> GSM1124929 1 0.0000 0.97419 1.000 0.000 0.000 0.000
#> GSM1124934 3 0.7836 0.28173 0.304 0.288 0.408 0.000
#> GSM1124937 2 0.4382 0.54366 0.296 0.704 0.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM1124891 3 0.2813 0.798 0.000 0.000 0.832 0.168 0.000
#> GSM1124888 3 0.2127 0.825 0.000 0.000 0.892 0.108 0.000
#> GSM1124890 3 0.2674 0.742 0.000 0.000 0.856 0.004 0.140
#> GSM1124904 5 0.0000 0.945 0.000 0.000 0.000 0.000 1.000
#> GSM1124927 2 0.0290 0.754 0.000 0.992 0.000 0.008 0.000
#> GSM1124953 2 0.2471 0.635 0.000 0.864 0.136 0.000 0.000
#> GSM1124869 1 0.0000 0.919 1.000 0.000 0.000 0.000 0.000
#> GSM1124870 1 0.3612 0.674 0.732 0.268 0.000 0.000 0.000
#> GSM1124882 1 0.0000 0.919 1.000 0.000 0.000 0.000 0.000
#> GSM1124884 2 0.3816 0.613 0.000 0.696 0.000 0.304 0.000
#> GSM1124898 5 0.0880 0.931 0.000 0.000 0.000 0.032 0.968
#> GSM1124903 5 0.0000 0.945 0.000 0.000 0.000 0.000 1.000
#> GSM1124905 1 0.1012 0.905 0.968 0.020 0.000 0.012 0.000
#> GSM1124910 1 0.0290 0.916 0.992 0.000 0.000 0.008 0.000
#> GSM1124919 5 0.1549 0.918 0.000 0.016 0.040 0.000 0.944
#> GSM1124932 2 0.2929 0.735 0.000 0.820 0.000 0.180 0.000
#> GSM1124933 3 0.0000 0.843 0.000 0.000 1.000 0.000 0.000
#> GSM1124867 2 0.1478 0.768 0.000 0.936 0.000 0.064 0.000
#> GSM1124868 5 0.0794 0.936 0.000 0.028 0.000 0.000 0.972
#> GSM1124878 5 0.0404 0.943 0.000 0.012 0.000 0.000 0.988
#> GSM1124895 5 0.0510 0.942 0.000 0.016 0.000 0.000 0.984
#> GSM1124897 5 0.0880 0.935 0.000 0.032 0.000 0.000 0.968
#> GSM1124902 5 0.0290 0.944 0.000 0.008 0.000 0.000 0.992
#> GSM1124908 5 0.0000 0.945 0.000 0.000 0.000 0.000 1.000
#> GSM1124921 5 0.0000 0.945 0.000 0.000 0.000 0.000 1.000
#> GSM1124939 5 0.0404 0.943 0.000 0.012 0.000 0.000 0.988
#> GSM1124944 5 0.1544 0.906 0.000 0.068 0.000 0.000 0.932
#> GSM1124945 3 0.1197 0.824 0.000 0.048 0.952 0.000 0.000
#> GSM1124946 5 0.0000 0.945 0.000 0.000 0.000 0.000 1.000
#> GSM1124947 2 0.3693 0.574 0.008 0.824 0.044 0.000 0.124
#> GSM1124951 3 0.0000 0.843 0.000 0.000 1.000 0.000 0.000
#> GSM1124952 2 0.1116 0.727 0.028 0.964 0.004 0.000 0.004
#> GSM1124957 3 0.0000 0.843 0.000 0.000 1.000 0.000 0.000
#> GSM1124900 1 0.4045 0.480 0.644 0.356 0.000 0.000 0.000
#> GSM1124914 5 0.0000 0.945 0.000 0.000 0.000 0.000 1.000
#> GSM1124871 2 0.2439 0.760 0.000 0.876 0.000 0.120 0.004
#> GSM1124874 2 0.0451 0.748 0.000 0.988 0.000 0.004 0.008
#> GSM1124875 5 0.0162 0.944 0.000 0.000 0.000 0.004 0.996
#> GSM1124880 2 0.3003 0.732 0.000 0.812 0.000 0.188 0.000
#> GSM1124881 2 0.4101 0.491 0.000 0.628 0.000 0.372 0.000
#> GSM1124885 5 0.0290 0.944 0.000 0.008 0.000 0.000 0.992
#> GSM1124886 1 0.0000 0.919 1.000 0.000 0.000 0.000 0.000
#> GSM1124887 5 0.0000 0.945 0.000 0.000 0.000 0.000 1.000
#> GSM1124894 1 0.3983 0.577 0.660 0.340 0.000 0.000 0.000
#> GSM1124896 1 0.0794 0.905 0.972 0.000 0.000 0.028 0.000
#> GSM1124899 5 0.1851 0.883 0.000 0.000 0.000 0.088 0.912
#> GSM1124901 5 0.0162 0.945 0.000 0.000 0.000 0.004 0.996
#> GSM1124906 2 0.4273 0.290 0.000 0.552 0.000 0.448 0.000
#> GSM1124907 5 0.0290 0.943 0.000 0.000 0.000 0.008 0.992
#> GSM1124911 2 0.4551 0.480 0.000 0.616 0.000 0.368 0.016
#> GSM1124912 1 0.0000 0.919 1.000 0.000 0.000 0.000 0.000
#> GSM1124915 5 0.0703 0.934 0.000 0.024 0.000 0.000 0.976
#> GSM1124917 4 0.5341 0.504 0.000 0.212 0.000 0.664 0.124
#> GSM1124918 4 0.1270 0.719 0.000 0.052 0.000 0.948 0.000
#> GSM1124920 3 0.4021 0.753 0.036 0.000 0.764 0.200 0.000
#> GSM1124922 1 0.3323 0.781 0.844 0.000 0.000 0.056 0.100
#> GSM1124924 4 0.4291 -0.109 0.000 0.464 0.000 0.536 0.000
#> GSM1124926 5 0.4489 0.252 0.420 0.008 0.000 0.000 0.572
#> GSM1124928 1 0.0510 0.911 0.984 0.000 0.000 0.016 0.000
#> GSM1124930 5 0.1270 0.915 0.000 0.000 0.000 0.052 0.948
#> GSM1124931 2 0.0963 0.764 0.000 0.964 0.000 0.036 0.000
#> GSM1124935 5 0.4310 0.394 0.000 0.004 0.000 0.392 0.604
#> GSM1124936 1 0.4161 0.338 0.608 0.000 0.392 0.000 0.000
#> GSM1124938 3 0.2852 0.793 0.000 0.000 0.828 0.172 0.000
#> GSM1124940 1 0.0000 0.919 1.000 0.000 0.000 0.000 0.000
#> GSM1124941 4 0.3636 0.503 0.000 0.272 0.000 0.728 0.000
#> GSM1124942 5 0.3100 0.851 0.000 0.064 0.020 0.040 0.876
#> GSM1124943 3 0.6213 0.255 0.000 0.000 0.452 0.140 0.408
#> GSM1124948 4 0.2535 0.703 0.000 0.076 0.032 0.892 0.000
#> GSM1124949 1 0.0000 0.919 1.000 0.000 0.000 0.000 0.000
#> GSM1124950 2 0.1478 0.768 0.000 0.936 0.000 0.064 0.000
#> GSM1124954 4 0.3906 0.284 0.004 0.000 0.292 0.704 0.000
#> GSM1124955 1 0.0000 0.919 1.000 0.000 0.000 0.000 0.000
#> GSM1124956 4 0.4306 -0.191 0.000 0.492 0.000 0.508 0.000
#> GSM1124872 2 0.0963 0.765 0.000 0.964 0.000 0.036 0.000
#> GSM1124873 2 0.4161 0.448 0.000 0.608 0.000 0.392 0.000
#> GSM1124876 3 0.0000 0.843 0.000 0.000 1.000 0.000 0.000
#> GSM1124877 1 0.0290 0.916 0.992 0.000 0.000 0.008 0.000
#> GSM1124879 1 0.0162 0.917 0.996 0.000 0.000 0.004 0.000
#> GSM1124883 5 0.0000 0.945 0.000 0.000 0.000 0.000 1.000
#> GSM1124889 2 0.3366 0.695 0.000 0.768 0.000 0.232 0.000
#> GSM1124892 1 0.0000 0.919 1.000 0.000 0.000 0.000 0.000
#> GSM1124893 1 0.0000 0.919 1.000 0.000 0.000 0.000 0.000
#> GSM1124909 4 0.1851 0.702 0.000 0.088 0.000 0.912 0.000
#> GSM1124913 5 0.0000 0.945 0.000 0.000 0.000 0.000 1.000
#> GSM1124916 4 0.0880 0.718 0.000 0.032 0.000 0.968 0.000
#> GSM1124923 5 0.0000 0.945 0.000 0.000 0.000 0.000 1.000
#> GSM1124925 1 0.0000 0.919 1.000 0.000 0.000 0.000 0.000
#> GSM1124929 1 0.0000 0.919 1.000 0.000 0.000 0.000 0.000
#> GSM1124934 4 0.1461 0.696 0.028 0.004 0.016 0.952 0.000
#> GSM1124937 4 0.0955 0.703 0.028 0.004 0.000 0.968 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1124891 6 0.4902 -0.31015 0.000 0.000 0.460 0.060 0.000 0.480
#> GSM1124888 3 0.3847 0.35072 0.000 0.000 0.544 0.000 0.000 0.456
#> GSM1124890 3 0.2771 0.69570 0.000 0.000 0.868 0.068 0.060 0.004
#> GSM1124904 5 0.0000 0.75530 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1124927 2 0.1910 0.66180 0.000 0.892 0.000 0.108 0.000 0.000
#> GSM1124953 2 0.5847 0.22061 0.000 0.484 0.284 0.232 0.000 0.000
#> GSM1124869 1 0.0000 0.85997 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124870 1 0.3888 0.53717 0.672 0.312 0.000 0.016 0.000 0.000
#> GSM1124882 1 0.0000 0.85997 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124884 2 0.3222 0.67499 0.000 0.844 0.000 0.096 0.024 0.036
#> GSM1124898 5 0.4616 0.44092 0.000 0.000 0.000 0.316 0.624 0.060
#> GSM1124903 5 0.0000 0.75530 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1124905 1 0.5557 0.27532 0.512 0.004 0.000 0.356 0.000 0.128
#> GSM1124910 1 0.3431 0.64818 0.756 0.016 0.000 0.000 0.000 0.228
#> GSM1124919 5 0.5907 0.22446 0.000 0.004 0.320 0.196 0.480 0.000
#> GSM1124932 2 0.1865 0.69692 0.000 0.920 0.000 0.040 0.000 0.040
#> GSM1124933 3 0.0000 0.79650 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1124867 2 0.1713 0.69375 0.000 0.928 0.000 0.044 0.000 0.028
#> GSM1124868 5 0.3499 0.46457 0.000 0.000 0.000 0.320 0.680 0.000
#> GSM1124878 5 0.0146 0.75576 0.000 0.000 0.000 0.004 0.996 0.000
#> GSM1124895 4 0.3868 -0.02913 0.000 0.000 0.000 0.508 0.492 0.000
#> GSM1124897 5 0.3409 0.49908 0.000 0.000 0.000 0.300 0.700 0.000
#> GSM1124902 5 0.3817 0.02934 0.000 0.000 0.000 0.432 0.568 0.000
#> GSM1124908 5 0.0790 0.75088 0.000 0.000 0.000 0.032 0.968 0.000
#> GSM1124921 5 0.0603 0.75490 0.000 0.000 0.000 0.016 0.980 0.004
#> GSM1124939 4 0.3843 0.09644 0.000 0.000 0.000 0.548 0.452 0.000
#> GSM1124944 4 0.3965 0.23139 0.000 0.004 0.004 0.616 0.376 0.000
#> GSM1124945 3 0.0692 0.78598 0.000 0.020 0.976 0.004 0.000 0.000
#> GSM1124946 5 0.0000 0.75530 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1124947 4 0.4460 -0.00639 0.000 0.404 0.004 0.568 0.024 0.000
#> GSM1124951 3 0.0865 0.78019 0.000 0.000 0.964 0.000 0.036 0.000
#> GSM1124952 2 0.3830 0.29854 0.004 0.620 0.000 0.376 0.000 0.000
#> GSM1124957 3 0.0000 0.79650 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1124900 1 0.3371 0.57796 0.708 0.292 0.000 0.000 0.000 0.000
#> GSM1124914 5 0.1007 0.74675 0.000 0.000 0.000 0.044 0.956 0.000
#> GSM1124871 2 0.1492 0.70938 0.000 0.940 0.000 0.036 0.000 0.024
#> GSM1124874 2 0.3695 0.34768 0.000 0.624 0.000 0.376 0.000 0.000
#> GSM1124875 5 0.1716 0.72996 0.000 0.028 0.000 0.004 0.932 0.036
#> GSM1124880 2 0.1265 0.70615 0.000 0.948 0.000 0.008 0.000 0.044
#> GSM1124881 2 0.4913 0.47775 0.000 0.588 0.000 0.332 0.000 0.080
#> GSM1124885 5 0.3175 0.55691 0.000 0.000 0.000 0.256 0.744 0.000
#> GSM1124886 1 0.0000 0.85997 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124887 5 0.0000 0.75530 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1124894 4 0.6288 0.05049 0.324 0.204 0.000 0.452 0.000 0.020
#> GSM1124896 1 0.1257 0.83695 0.952 0.000 0.000 0.020 0.000 0.028
#> GSM1124899 5 0.5130 0.57781 0.000 0.080 0.000 0.124 0.708 0.088
#> GSM1124901 5 0.1701 0.73032 0.000 0.000 0.000 0.072 0.920 0.008
#> GSM1124906 2 0.3367 0.65408 0.000 0.816 0.000 0.080 0.000 0.104
#> GSM1124907 5 0.2219 0.66802 0.000 0.000 0.000 0.000 0.864 0.136
#> GSM1124911 2 0.4940 0.34491 0.000 0.532 0.000 0.400 0.000 0.068
#> GSM1124912 1 0.0000 0.85997 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124915 5 0.1320 0.74199 0.000 0.036 0.000 0.016 0.948 0.000
#> GSM1124917 4 0.6227 -0.34577 0.000 0.264 0.000 0.524 0.036 0.176
#> GSM1124918 6 0.4513 0.26517 0.000 0.312 0.000 0.044 0.004 0.640
#> GSM1124920 6 0.4473 -0.24810 0.036 0.000 0.380 0.000 0.000 0.584
#> GSM1124922 1 0.3952 0.70344 0.804 0.012 0.000 0.116 0.036 0.032
#> GSM1124924 6 0.4258 -0.02447 0.000 0.468 0.000 0.016 0.000 0.516
#> GSM1124926 1 0.5913 0.06017 0.496 0.004 0.000 0.220 0.280 0.000
#> GSM1124928 1 0.1714 0.81432 0.908 0.000 0.000 0.000 0.000 0.092
#> GSM1124930 5 0.5575 0.35968 0.000 0.000 0.000 0.168 0.528 0.304
#> GSM1124931 2 0.0632 0.70178 0.000 0.976 0.000 0.024 0.000 0.000
#> GSM1124935 5 0.5735 0.21704 0.000 0.000 0.000 0.296 0.504 0.200
#> GSM1124936 1 0.3857 0.16555 0.532 0.000 0.468 0.000 0.000 0.000
#> GSM1124938 3 0.3989 0.32912 0.000 0.004 0.528 0.000 0.000 0.468
#> GSM1124940 1 0.0000 0.85997 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124941 2 0.4897 0.41333 0.000 0.616 0.000 0.092 0.000 0.292
#> GSM1124942 5 0.6170 0.06772 0.000 0.176 0.012 0.004 0.460 0.348
#> GSM1124943 6 0.6602 -0.22384 0.000 0.000 0.368 0.040 0.196 0.396
#> GSM1124948 6 0.3916 0.29578 0.000 0.300 0.000 0.020 0.000 0.680
#> GSM1124949 1 0.0000 0.85997 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124950 2 0.0363 0.70339 0.000 0.988 0.000 0.012 0.000 0.000
#> GSM1124954 6 0.5392 0.38635 0.012 0.000 0.128 0.252 0.000 0.608
#> GSM1124955 1 0.0000 0.85997 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124956 2 0.5432 0.27195 0.000 0.500 0.000 0.376 0.000 0.124
#> GSM1124872 2 0.0632 0.70298 0.000 0.976 0.000 0.024 0.000 0.000
#> GSM1124873 2 0.4996 0.42706 0.000 0.604 0.000 0.296 0.000 0.100
#> GSM1124876 3 0.0146 0.79587 0.000 0.000 0.996 0.000 0.000 0.004
#> GSM1124877 1 0.0146 0.85837 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM1124879 1 0.0000 0.85997 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124883 5 0.0363 0.75570 0.000 0.000 0.000 0.012 0.988 0.000
#> GSM1124889 2 0.3395 0.65649 0.000 0.812 0.000 0.136 0.004 0.048
#> GSM1124892 1 0.0000 0.85997 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124893 1 0.0000 0.85997 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124909 6 0.5937 0.27264 0.000 0.220 0.000 0.352 0.000 0.428
#> GSM1124913 5 0.0000 0.75530 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1124916 6 0.5713 0.34596 0.000 0.172 0.000 0.352 0.000 0.476
#> GSM1124923 5 0.0713 0.74767 0.000 0.000 0.028 0.000 0.972 0.000
#> GSM1124925 1 0.0000 0.85997 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124929 1 0.0000 0.85997 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124934 6 0.3878 0.46038 0.004 0.004 0.000 0.348 0.000 0.644
#> GSM1124937 6 0.4365 0.45975 0.024 0.008 0.000 0.332 0.000 0.636
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 tissue(p) k
#> MAD:NMF 90 0.5720 2
#> MAD:NMF 90 0.0305 3
#> MAD:NMF 86 0.0166 4
#> MAD:NMF 79 0.0523 5
#> MAD:NMF 53 0.2935 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 48983 rows and 91 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'hclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 4.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.548 0.845 0.919 0.308 0.752 0.752
#> 3 3 0.585 0.780 0.876 0.187 0.993 0.990
#> 4 4 0.518 0.776 0.864 0.585 0.649 0.533
#> 5 5 0.578 0.600 0.809 0.148 0.936 0.845
#> 6 6 0.673 0.692 0.827 0.102 0.892 0.711
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
#> GSM1124891 1 0.0000 0.9354 1.000 0.000
#> GSM1124888 1 0.0376 0.9337 0.996 0.004
#> GSM1124890 2 0.8861 0.6759 0.304 0.696
#> GSM1124904 2 0.0000 0.9031 0.000 1.000
#> GSM1124927 2 0.0000 0.9031 0.000 1.000
#> GSM1124953 2 0.8909 0.6701 0.308 0.692
#> GSM1124869 2 0.7299 0.7947 0.204 0.796
#> GSM1124870 2 0.6973 0.8070 0.188 0.812
#> GSM1124882 2 0.7056 0.8042 0.192 0.808
#> GSM1124884 2 0.0000 0.9031 0.000 1.000
#> GSM1124898 2 0.0000 0.9031 0.000 1.000
#> GSM1124903 2 0.0000 0.9031 0.000 1.000
#> GSM1124905 2 0.6801 0.8125 0.180 0.820
#> GSM1124910 2 0.8267 0.7353 0.260 0.740
#> GSM1124919 2 0.8909 0.6701 0.308 0.692
#> GSM1124932 2 0.0376 0.9013 0.004 0.996
#> GSM1124933 1 0.0000 0.9354 1.000 0.000
#> GSM1124867 2 0.6048 0.8346 0.148 0.852
#> GSM1124868 2 0.0000 0.9031 0.000 1.000
#> GSM1124878 2 0.0000 0.9031 0.000 1.000
#> GSM1124895 2 0.0000 0.9031 0.000 1.000
#> GSM1124897 2 0.0000 0.9031 0.000 1.000
#> GSM1124902 2 0.0000 0.9031 0.000 1.000
#> GSM1124908 2 0.0000 0.9031 0.000 1.000
#> GSM1124921 2 0.0376 0.9018 0.004 0.996
#> GSM1124939 2 0.0000 0.9031 0.000 1.000
#> GSM1124944 2 0.0376 0.9018 0.004 0.996
#> GSM1124945 1 0.0000 0.9354 1.000 0.000
#> GSM1124946 2 0.0000 0.9031 0.000 1.000
#> GSM1124947 2 0.0376 0.9020 0.004 0.996
#> GSM1124951 1 0.0000 0.9354 1.000 0.000
#> GSM1124952 2 0.0000 0.9031 0.000 1.000
#> GSM1124957 1 0.0000 0.9354 1.000 0.000
#> GSM1124900 2 0.6973 0.8070 0.188 0.812
#> GSM1124914 2 0.0000 0.9031 0.000 1.000
#> GSM1124871 2 0.0000 0.9031 0.000 1.000
#> GSM1124874 2 0.0000 0.9031 0.000 1.000
#> GSM1124875 2 0.0000 0.9031 0.000 1.000
#> GSM1124880 2 0.7139 0.8010 0.196 0.804
#> GSM1124881 2 0.1843 0.8931 0.028 0.972
#> GSM1124885 2 0.0000 0.9031 0.000 1.000
#> GSM1124886 2 0.9815 0.4482 0.420 0.580
#> GSM1124887 2 0.0000 0.9031 0.000 1.000
#> GSM1124894 2 0.0000 0.9031 0.000 1.000
#> GSM1124896 2 0.0000 0.9031 0.000 1.000
#> GSM1124899 2 0.0000 0.9031 0.000 1.000
#> GSM1124901 2 0.0000 0.9031 0.000 1.000
#> GSM1124906 2 0.0000 0.9031 0.000 1.000
#> GSM1124907 2 0.0000 0.9031 0.000 1.000
#> GSM1124911 2 0.0000 0.9031 0.000 1.000
#> GSM1124912 2 0.6887 0.8097 0.184 0.816
#> GSM1124915 2 0.0000 0.9031 0.000 1.000
#> GSM1124917 2 0.0000 0.9031 0.000 1.000
#> GSM1124918 2 0.0000 0.9031 0.000 1.000
#> GSM1124920 1 0.3114 0.8923 0.944 0.056
#> GSM1124922 2 0.0000 0.9031 0.000 1.000
#> GSM1124924 2 0.8713 0.6944 0.292 0.708
#> GSM1124926 2 0.0000 0.9031 0.000 1.000
#> GSM1124928 2 0.7139 0.8010 0.196 0.804
#> GSM1124930 2 0.7883 0.7633 0.236 0.764
#> GSM1124931 2 0.0000 0.9031 0.000 1.000
#> GSM1124935 2 0.0376 0.9013 0.004 0.996
#> GSM1124936 1 0.0000 0.9354 1.000 0.000
#> GSM1124938 1 0.9866 0.0108 0.568 0.432
#> GSM1124940 2 0.6887 0.8097 0.184 0.816
#> GSM1124941 2 0.0000 0.9031 0.000 1.000
#> GSM1124942 2 0.0000 0.9031 0.000 1.000
#> GSM1124943 2 0.9522 0.5555 0.372 0.628
#> GSM1124948 2 0.8713 0.6944 0.292 0.708
#> GSM1124949 2 0.7299 0.7947 0.204 0.796
#> GSM1124950 2 0.0000 0.9031 0.000 1.000
#> GSM1124954 1 0.0000 0.9354 1.000 0.000
#> GSM1124955 2 0.6887 0.8097 0.184 0.816
#> GSM1124956 2 0.0000 0.9031 0.000 1.000
#> GSM1124872 2 0.0000 0.9031 0.000 1.000
#> GSM1124873 2 0.0000 0.9031 0.000 1.000
#> GSM1124876 1 0.0000 0.9354 1.000 0.000
#> GSM1124877 1 0.5842 0.7956 0.860 0.140
#> GSM1124879 2 0.7299 0.7942 0.204 0.796
#> GSM1124883 2 0.0000 0.9031 0.000 1.000
#> GSM1124889 2 0.0000 0.9031 0.000 1.000
#> GSM1124892 2 0.9815 0.4482 0.420 0.580
#> GSM1124893 2 0.6887 0.8097 0.184 0.816
#> GSM1124909 2 0.3733 0.8731 0.072 0.928
#> GSM1124913 2 0.0000 0.9031 0.000 1.000
#> GSM1124916 2 0.2043 0.8913 0.032 0.968
#> GSM1124923 2 0.8909 0.6701 0.308 0.692
#> GSM1124925 2 0.0000 0.9031 0.000 1.000
#> GSM1124929 2 0.7056 0.8042 0.192 0.808
#> GSM1124934 1 0.1184 0.9266 0.984 0.016
#> GSM1124937 2 0.8144 0.7459 0.252 0.748
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1124891 3 0.4974 0.8079 0.236 0.000 0.764
#> GSM1124888 3 0.4702 0.7874 0.212 0.000 0.788
#> GSM1124890 2 0.6373 0.5986 0.004 0.588 0.408
#> GSM1124904 2 0.0000 0.8629 0.000 1.000 0.000
#> GSM1124927 2 0.0000 0.8629 0.000 1.000 0.000
#> GSM1124953 2 0.6168 0.5967 0.000 0.588 0.412
#> GSM1124869 2 0.6172 0.7115 0.012 0.680 0.308
#> GSM1124870 2 0.6051 0.7242 0.012 0.696 0.292
#> GSM1124882 2 0.6082 0.7213 0.012 0.692 0.296
#> GSM1124884 2 0.0000 0.8629 0.000 1.000 0.000
#> GSM1124898 2 0.1015 0.8612 0.012 0.980 0.008
#> GSM1124903 2 0.0000 0.8629 0.000 1.000 0.000
#> GSM1124905 2 0.5986 0.7297 0.012 0.704 0.284
#> GSM1124910 2 0.6510 0.6509 0.012 0.624 0.364
#> GSM1124919 2 0.6168 0.5967 0.000 0.588 0.412
#> GSM1124932 2 0.0237 0.8615 0.004 0.996 0.000
#> GSM1124933 3 0.4974 0.8079 0.236 0.000 0.764
#> GSM1124867 2 0.5659 0.7550 0.012 0.740 0.248
#> GSM1124868 2 0.0000 0.8629 0.000 1.000 0.000
#> GSM1124878 2 0.0000 0.8629 0.000 1.000 0.000
#> GSM1124895 2 0.0000 0.8629 0.000 1.000 0.000
#> GSM1124897 2 0.0000 0.8629 0.000 1.000 0.000
#> GSM1124902 2 0.0000 0.8629 0.000 1.000 0.000
#> GSM1124908 2 0.1170 0.8609 0.016 0.976 0.008
#> GSM1124921 2 0.1337 0.8601 0.012 0.972 0.016
#> GSM1124939 2 0.0000 0.8629 0.000 1.000 0.000
#> GSM1124944 2 0.1491 0.8593 0.016 0.968 0.016
#> GSM1124945 3 0.4974 0.8079 0.236 0.000 0.764
#> GSM1124946 2 0.1170 0.8609 0.016 0.976 0.008
#> GSM1124947 2 0.1337 0.8604 0.016 0.972 0.012
#> GSM1124951 3 0.4974 0.8079 0.236 0.000 0.764
#> GSM1124952 2 0.0237 0.8629 0.000 0.996 0.004
#> GSM1124957 3 0.4974 0.8079 0.236 0.000 0.764
#> GSM1124900 2 0.6051 0.7242 0.012 0.696 0.292
#> GSM1124914 2 0.0000 0.8629 0.000 1.000 0.000
#> GSM1124871 2 0.0000 0.8629 0.000 1.000 0.000
#> GSM1124874 2 0.0000 0.8629 0.000 1.000 0.000
#> GSM1124875 2 0.1170 0.8609 0.016 0.976 0.008
#> GSM1124880 2 0.6113 0.7180 0.012 0.688 0.300
#> GSM1124881 2 0.2446 0.8485 0.012 0.936 0.052
#> GSM1124885 2 0.0000 0.8629 0.000 1.000 0.000
#> GSM1124886 2 0.8911 0.4740 0.176 0.564 0.260
#> GSM1124887 2 0.1170 0.8609 0.016 0.976 0.008
#> GSM1124894 2 0.0000 0.8629 0.000 1.000 0.000
#> GSM1124896 2 0.0237 0.8628 0.004 0.996 0.000
#> GSM1124899 2 0.0983 0.8612 0.016 0.980 0.004
#> GSM1124901 2 0.0000 0.8629 0.000 1.000 0.000
#> GSM1124906 2 0.0000 0.8629 0.000 1.000 0.000
#> GSM1124907 2 0.1170 0.8609 0.016 0.976 0.008
#> GSM1124911 2 0.0000 0.8629 0.000 1.000 0.000
#> GSM1124912 2 0.6019 0.7268 0.012 0.700 0.288
#> GSM1124915 2 0.0000 0.8629 0.000 1.000 0.000
#> GSM1124917 2 0.1170 0.8609 0.016 0.976 0.008
#> GSM1124918 2 0.0983 0.8612 0.016 0.980 0.004
#> GSM1124920 3 0.2796 0.6046 0.092 0.000 0.908
#> GSM1124922 2 0.0000 0.8629 0.000 1.000 0.000
#> GSM1124924 2 0.6783 0.6071 0.016 0.588 0.396
#> GSM1124926 2 0.0000 0.8629 0.000 1.000 0.000
#> GSM1124928 2 0.6113 0.7180 0.012 0.688 0.300
#> GSM1124930 2 0.6521 0.6753 0.016 0.644 0.340
#> GSM1124931 2 0.0000 0.8629 0.000 1.000 0.000
#> GSM1124935 2 0.0237 0.8615 0.004 0.996 0.000
#> GSM1124936 3 0.4974 0.8079 0.236 0.000 0.764
#> GSM1124938 3 0.7567 -0.0703 0.048 0.376 0.576
#> GSM1124940 2 0.6019 0.7268 0.012 0.700 0.288
#> GSM1124941 2 0.0000 0.8629 0.000 1.000 0.000
#> GSM1124942 2 0.1170 0.8609 0.016 0.976 0.008
#> GSM1124943 2 0.6513 0.4753 0.004 0.520 0.476
#> GSM1124948 2 0.6783 0.6071 0.016 0.588 0.396
#> GSM1124949 2 0.6172 0.7115 0.012 0.680 0.308
#> GSM1124950 2 0.0000 0.8629 0.000 1.000 0.000
#> GSM1124954 1 0.2165 0.7802 0.936 0.000 0.064
#> GSM1124955 2 0.6019 0.7268 0.012 0.700 0.288
#> GSM1124956 2 0.0000 0.8629 0.000 1.000 0.000
#> GSM1124872 2 0.0000 0.8629 0.000 1.000 0.000
#> GSM1124873 2 0.0000 0.8629 0.000 1.000 0.000
#> GSM1124876 3 0.4974 0.8079 0.236 0.000 0.764
#> GSM1124877 1 0.5633 0.6589 0.768 0.024 0.208
#> GSM1124879 2 0.6172 0.7110 0.012 0.680 0.308
#> GSM1124883 2 0.0000 0.8629 0.000 1.000 0.000
#> GSM1124889 2 0.0000 0.8629 0.000 1.000 0.000
#> GSM1124892 2 0.8911 0.4740 0.176 0.564 0.260
#> GSM1124893 2 0.6019 0.7268 0.012 0.700 0.288
#> GSM1124909 2 0.3845 0.8211 0.012 0.872 0.116
#> GSM1124913 2 0.0000 0.8629 0.000 1.000 0.000
#> GSM1124916 2 0.2939 0.8398 0.012 0.916 0.072
#> GSM1124923 2 0.6168 0.5967 0.000 0.588 0.412
#> GSM1124925 2 0.0237 0.8628 0.004 0.996 0.000
#> GSM1124929 2 0.6082 0.7213 0.012 0.692 0.296
#> GSM1124934 1 0.2261 0.7977 0.932 0.000 0.068
#> GSM1124937 2 0.6608 0.6584 0.016 0.628 0.356
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1124891 3 0.0000 0.905 0.000 0.000 1.000 0.000
#> GSM1124888 3 0.3962 0.722 0.152 0.000 0.820 0.028
#> GSM1124890 1 0.5449 0.712 0.756 0.084 0.012 0.148
#> GSM1124904 2 0.0000 0.881 0.000 1.000 0.000 0.000
#> GSM1124927 2 0.0469 0.881 0.012 0.988 0.000 0.000
#> GSM1124953 1 0.5100 0.708 0.768 0.076 0.004 0.152
#> GSM1124869 1 0.2704 0.782 0.876 0.124 0.000 0.000
#> GSM1124870 1 0.3873 0.772 0.772 0.228 0.000 0.000
#> GSM1124882 1 0.3400 0.789 0.820 0.180 0.000 0.000
#> GSM1124884 2 0.0469 0.881 0.012 0.988 0.000 0.000
#> GSM1124898 2 0.4008 0.690 0.244 0.756 0.000 0.000
#> GSM1124903 2 0.0000 0.881 0.000 1.000 0.000 0.000
#> GSM1124905 1 0.3873 0.774 0.772 0.228 0.000 0.000
#> GSM1124910 1 0.2660 0.757 0.908 0.072 0.008 0.012
#> GSM1124919 1 0.5100 0.708 0.768 0.076 0.004 0.152
#> GSM1124932 2 0.1576 0.850 0.048 0.948 0.000 0.004
#> GSM1124933 3 0.0000 0.905 0.000 0.000 1.000 0.000
#> GSM1124867 1 0.4193 0.683 0.732 0.268 0.000 0.000
#> GSM1124868 2 0.0000 0.881 0.000 1.000 0.000 0.000
#> GSM1124878 2 0.0000 0.881 0.000 1.000 0.000 0.000
#> GSM1124895 2 0.0000 0.881 0.000 1.000 0.000 0.000
#> GSM1124897 2 0.0000 0.881 0.000 1.000 0.000 0.000
#> GSM1124902 2 0.0592 0.880 0.016 0.984 0.000 0.000
#> GSM1124908 2 0.4193 0.657 0.268 0.732 0.000 0.000
#> GSM1124921 2 0.4222 0.651 0.272 0.728 0.000 0.000
#> GSM1124939 2 0.0592 0.880 0.016 0.984 0.000 0.000
#> GSM1124944 2 0.4222 0.654 0.272 0.728 0.000 0.000
#> GSM1124945 3 0.0000 0.905 0.000 0.000 1.000 0.000
#> GSM1124946 2 0.4134 0.670 0.260 0.740 0.000 0.000
#> GSM1124947 2 0.4981 0.119 0.464 0.536 0.000 0.000
#> GSM1124951 3 0.0000 0.905 0.000 0.000 1.000 0.000
#> GSM1124952 2 0.0707 0.881 0.020 0.980 0.000 0.000
#> GSM1124957 3 0.0000 0.905 0.000 0.000 1.000 0.000
#> GSM1124900 1 0.3764 0.778 0.784 0.216 0.000 0.000
#> GSM1124914 2 0.0817 0.877 0.024 0.976 0.000 0.000
#> GSM1124871 2 0.0188 0.882 0.004 0.996 0.000 0.000
#> GSM1124874 2 0.0469 0.881 0.012 0.988 0.000 0.000
#> GSM1124875 2 0.4040 0.684 0.248 0.752 0.000 0.000
#> GSM1124880 1 0.3569 0.788 0.804 0.196 0.000 0.000
#> GSM1124881 2 0.4431 0.595 0.304 0.696 0.000 0.000
#> GSM1124885 2 0.0000 0.881 0.000 1.000 0.000 0.000
#> GSM1124886 1 0.5990 0.620 0.740 0.048 0.144 0.068
#> GSM1124887 2 0.4040 0.687 0.248 0.752 0.000 0.000
#> GSM1124894 2 0.0921 0.877 0.028 0.972 0.000 0.000
#> GSM1124896 2 0.3356 0.761 0.176 0.824 0.000 0.000
#> GSM1124899 2 0.2149 0.847 0.088 0.912 0.000 0.000
#> GSM1124901 2 0.0000 0.881 0.000 1.000 0.000 0.000
#> GSM1124906 2 0.0469 0.881 0.012 0.988 0.000 0.000
#> GSM1124907 2 0.4331 0.625 0.288 0.712 0.000 0.000
#> GSM1124911 2 0.1389 0.851 0.048 0.952 0.000 0.000
#> GSM1124912 1 0.4072 0.757 0.748 0.252 0.000 0.000
#> GSM1124915 2 0.1389 0.851 0.048 0.952 0.000 0.000
#> GSM1124917 2 0.4040 0.687 0.248 0.752 0.000 0.000
#> GSM1124918 2 0.2216 0.850 0.092 0.908 0.000 0.000
#> GSM1124920 3 0.5951 0.582 0.152 0.000 0.696 0.152
#> GSM1124922 2 0.1557 0.865 0.056 0.944 0.000 0.000
#> GSM1124924 1 0.5241 0.721 0.780 0.064 0.024 0.132
#> GSM1124926 2 0.0469 0.881 0.012 0.988 0.000 0.000
#> GSM1124928 1 0.3569 0.788 0.804 0.196 0.000 0.000
#> GSM1124930 1 0.6194 0.734 0.712 0.136 0.020 0.132
#> GSM1124931 2 0.1389 0.851 0.048 0.952 0.000 0.000
#> GSM1124935 2 0.1576 0.850 0.048 0.948 0.000 0.004
#> GSM1124936 3 0.1211 0.879 0.040 0.000 0.960 0.000
#> GSM1124938 1 0.6821 0.330 0.592 0.000 0.256 0.152
#> GSM1124940 1 0.4072 0.757 0.748 0.252 0.000 0.000
#> GSM1124941 2 0.0469 0.881 0.012 0.988 0.000 0.000
#> GSM1124942 2 0.4331 0.625 0.288 0.712 0.000 0.000
#> GSM1124943 1 0.5967 0.642 0.740 0.048 0.064 0.148
#> GSM1124948 1 0.5241 0.721 0.780 0.064 0.024 0.132
#> GSM1124949 1 0.2704 0.782 0.876 0.124 0.000 0.000
#> GSM1124950 2 0.0592 0.881 0.016 0.984 0.000 0.000
#> GSM1124954 4 0.3074 0.796 0.000 0.000 0.152 0.848
#> GSM1124955 1 0.4072 0.757 0.748 0.252 0.000 0.000
#> GSM1124956 2 0.1389 0.851 0.048 0.952 0.000 0.000
#> GSM1124872 2 0.0469 0.881 0.012 0.988 0.000 0.000
#> GSM1124873 2 0.0469 0.881 0.012 0.988 0.000 0.000
#> GSM1124876 3 0.0000 0.905 0.000 0.000 1.000 0.000
#> GSM1124877 4 0.3610 0.691 0.200 0.000 0.000 0.800
#> GSM1124879 1 0.3400 0.791 0.820 0.180 0.000 0.000
#> GSM1124883 2 0.0000 0.881 0.000 1.000 0.000 0.000
#> GSM1124889 2 0.0469 0.881 0.012 0.988 0.000 0.000
#> GSM1124892 1 0.5990 0.620 0.740 0.048 0.144 0.068
#> GSM1124893 1 0.4072 0.757 0.748 0.252 0.000 0.000
#> GSM1124909 1 0.4972 0.256 0.544 0.456 0.000 0.000
#> GSM1124913 2 0.0000 0.881 0.000 1.000 0.000 0.000
#> GSM1124916 2 0.3569 0.754 0.196 0.804 0.000 0.000
#> GSM1124923 1 0.5100 0.708 0.768 0.076 0.004 0.152
#> GSM1124925 2 0.3356 0.761 0.176 0.824 0.000 0.000
#> GSM1124929 1 0.3444 0.788 0.816 0.184 0.000 0.000
#> GSM1124934 4 0.3161 0.810 0.012 0.000 0.124 0.864
#> GSM1124937 1 0.2821 0.760 0.900 0.076 0.020 0.004
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM1124891 3 0.0000 0.909 0.000 0.000 1.000 0.000 0.000
#> GSM1124888 3 0.4275 0.733 0.064 0.000 0.788 0.012 0.136
#> GSM1124890 5 0.5936 0.508 0.456 0.076 0.004 0.004 0.460
#> GSM1124904 2 0.0000 0.797 0.000 1.000 0.000 0.000 0.000
#> GSM1124927 2 0.0510 0.797 0.016 0.984 0.000 0.000 0.000
#> GSM1124953 5 0.5648 0.531 0.448 0.076 0.000 0.000 0.476
#> GSM1124869 1 0.0794 0.620 0.972 0.000 0.000 0.000 0.028
#> GSM1124870 1 0.1768 0.662 0.924 0.072 0.000 0.000 0.004
#> GSM1124882 1 0.1243 0.652 0.960 0.028 0.000 0.004 0.008
#> GSM1124884 2 0.0510 0.797 0.016 0.984 0.000 0.000 0.000
#> GSM1124898 2 0.4879 0.599 0.228 0.696 0.000 0.000 0.076
#> GSM1124903 2 0.0000 0.797 0.000 1.000 0.000 0.000 0.000
#> GSM1124905 1 0.1768 0.664 0.924 0.072 0.000 0.000 0.004
#> GSM1124910 1 0.3907 0.375 0.768 0.004 0.004 0.012 0.212
#> GSM1124919 5 0.5648 0.531 0.448 0.076 0.000 0.000 0.476
#> GSM1124932 2 0.4450 0.327 0.000 0.508 0.000 0.004 0.488
#> GSM1124933 3 0.0000 0.909 0.000 0.000 1.000 0.000 0.000
#> GSM1124867 1 0.5080 0.371 0.708 0.176 0.000 0.004 0.112
#> GSM1124868 2 0.0000 0.797 0.000 1.000 0.000 0.000 0.000
#> GSM1124878 2 0.0000 0.797 0.000 1.000 0.000 0.000 0.000
#> GSM1124895 2 0.0000 0.797 0.000 1.000 0.000 0.000 0.000
#> GSM1124897 2 0.0000 0.797 0.000 1.000 0.000 0.000 0.000
#> GSM1124902 2 0.0510 0.795 0.016 0.984 0.000 0.000 0.000
#> GSM1124908 2 0.5218 0.571 0.240 0.672 0.000 0.004 0.084
#> GSM1124921 2 0.5140 0.562 0.252 0.664 0.000 0.000 0.084
#> GSM1124939 2 0.0510 0.795 0.016 0.984 0.000 0.000 0.000
#> GSM1124944 2 0.5243 0.567 0.244 0.668 0.000 0.004 0.084
#> GSM1124945 3 0.0000 0.909 0.000 0.000 1.000 0.000 0.000
#> GSM1124946 2 0.5113 0.585 0.232 0.684 0.000 0.004 0.080
#> GSM1124947 1 0.5746 -0.110 0.472 0.452 0.000 0.004 0.072
#> GSM1124951 3 0.0000 0.909 0.000 0.000 1.000 0.000 0.000
#> GSM1124952 2 0.0703 0.797 0.024 0.976 0.000 0.000 0.000
#> GSM1124957 3 0.0000 0.909 0.000 0.000 1.000 0.000 0.000
#> GSM1124900 1 0.1571 0.665 0.936 0.060 0.000 0.000 0.004
#> GSM1124914 2 0.0794 0.792 0.028 0.972 0.000 0.000 0.000
#> GSM1124871 2 0.0162 0.797 0.004 0.996 0.000 0.000 0.000
#> GSM1124874 2 0.0510 0.797 0.016 0.984 0.000 0.000 0.000
#> GSM1124875 2 0.4933 0.614 0.200 0.712 0.000 0.004 0.084
#> GSM1124880 1 0.2437 0.662 0.904 0.060 0.000 0.004 0.032
#> GSM1124881 2 0.5323 0.500 0.296 0.624 0.000 0.000 0.080
#> GSM1124885 2 0.0000 0.797 0.000 1.000 0.000 0.000 0.000
#> GSM1124886 1 0.5564 0.261 0.676 0.000 0.016 0.196 0.112
#> GSM1124887 2 0.5032 0.595 0.228 0.692 0.000 0.004 0.076
#> GSM1124894 2 0.0880 0.793 0.032 0.968 0.000 0.000 0.000
#> GSM1124896 2 0.3969 0.570 0.304 0.692 0.000 0.000 0.004
#> GSM1124899 2 0.2784 0.746 0.108 0.872 0.000 0.004 0.016
#> GSM1124901 2 0.0000 0.797 0.000 1.000 0.000 0.000 0.000
#> GSM1124906 2 0.0510 0.797 0.016 0.984 0.000 0.000 0.000
#> GSM1124907 2 0.5439 0.544 0.244 0.652 0.000 0.004 0.100
#> GSM1124911 2 0.4305 0.331 0.000 0.512 0.000 0.000 0.488
#> GSM1124912 1 0.2124 0.652 0.900 0.096 0.000 0.000 0.004
#> GSM1124915 2 0.4305 0.331 0.000 0.512 0.000 0.000 0.488
#> GSM1124917 2 0.5032 0.595 0.228 0.692 0.000 0.004 0.076
#> GSM1124918 5 0.5738 -0.361 0.072 0.436 0.000 0.004 0.488
#> GSM1124920 3 0.5288 0.586 0.064 0.000 0.664 0.012 0.260
#> GSM1124922 2 0.2069 0.771 0.076 0.912 0.000 0.000 0.012
#> GSM1124924 1 0.5083 -0.314 0.556 0.004 0.016 0.008 0.416
#> GSM1124926 2 0.0510 0.797 0.016 0.984 0.000 0.000 0.000
#> GSM1124928 1 0.2437 0.662 0.904 0.060 0.000 0.004 0.032
#> GSM1124930 1 0.6402 -0.420 0.508 0.088 0.016 0.008 0.380
#> GSM1124931 2 0.4305 0.331 0.000 0.512 0.000 0.000 0.488
#> GSM1124935 2 0.4450 0.327 0.000 0.508 0.000 0.004 0.488
#> GSM1124936 3 0.1043 0.886 0.000 0.000 0.960 0.000 0.040
#> GSM1124938 5 0.6957 0.332 0.344 0.000 0.224 0.012 0.420
#> GSM1124940 1 0.2124 0.652 0.900 0.096 0.000 0.000 0.004
#> GSM1124941 2 0.0510 0.797 0.016 0.984 0.000 0.000 0.000
#> GSM1124942 2 0.5439 0.544 0.244 0.652 0.000 0.004 0.100
#> GSM1124943 5 0.6159 0.485 0.428 0.048 0.032 0.004 0.488
#> GSM1124948 1 0.5083 -0.314 0.556 0.004 0.016 0.008 0.416
#> GSM1124949 1 0.0794 0.620 0.972 0.000 0.000 0.000 0.028
#> GSM1124950 2 0.0609 0.797 0.020 0.980 0.000 0.000 0.000
#> GSM1124954 4 0.0794 0.845 0.000 0.000 0.028 0.972 0.000
#> GSM1124955 1 0.2124 0.652 0.900 0.096 0.000 0.000 0.004
#> GSM1124956 2 0.4305 0.331 0.000 0.512 0.000 0.000 0.488
#> GSM1124872 2 0.0510 0.797 0.016 0.984 0.000 0.000 0.000
#> GSM1124873 2 0.0510 0.797 0.016 0.984 0.000 0.000 0.000
#> GSM1124876 3 0.0000 0.909 0.000 0.000 1.000 0.000 0.000
#> GSM1124877 4 0.3003 0.729 0.188 0.000 0.000 0.812 0.000
#> GSM1124879 1 0.2459 0.656 0.904 0.052 0.000 0.004 0.040
#> GSM1124883 2 0.0000 0.797 0.000 1.000 0.000 0.000 0.000
#> GSM1124889 2 0.0510 0.797 0.016 0.984 0.000 0.000 0.000
#> GSM1124892 1 0.5564 0.261 0.676 0.000 0.016 0.196 0.112
#> GSM1124893 1 0.2124 0.652 0.900 0.096 0.000 0.000 0.004
#> GSM1124909 1 0.4283 0.240 0.644 0.348 0.000 0.000 0.008
#> GSM1124913 2 0.0000 0.797 0.000 1.000 0.000 0.000 0.000
#> GSM1124916 2 0.6455 0.351 0.236 0.500 0.000 0.000 0.264
#> GSM1124923 5 0.5648 0.531 0.448 0.076 0.000 0.000 0.476
#> GSM1124925 2 0.3969 0.570 0.304 0.692 0.000 0.000 0.004
#> GSM1124929 1 0.1168 0.654 0.960 0.032 0.000 0.000 0.008
#> GSM1124934 4 0.0000 0.848 0.000 0.000 0.000 1.000 0.000
#> GSM1124937 1 0.3292 0.483 0.836 0.000 0.016 0.008 0.140
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1124891 3 0.0000 0.901 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1124888 3 0.4659 0.685 0.000 0.000 0.704 0.008 0.108 0.180
#> GSM1124890 5 0.1780 0.845 0.028 0.028 0.000 0.000 0.932 0.012
#> GSM1124904 2 0.0000 0.733 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1124927 2 0.0865 0.730 0.036 0.964 0.000 0.000 0.000 0.000
#> GSM1124953 5 0.1401 0.849 0.004 0.028 0.000 0.000 0.948 0.020
#> GSM1124869 1 0.2212 0.782 0.880 0.000 0.000 0.000 0.112 0.008
#> GSM1124870 1 0.0603 0.789 0.980 0.004 0.000 0.000 0.016 0.000
#> GSM1124882 1 0.1923 0.794 0.916 0.000 0.000 0.004 0.064 0.016
#> GSM1124884 2 0.1995 0.711 0.036 0.912 0.000 0.000 0.000 0.052
#> GSM1124898 2 0.5573 0.528 0.068 0.628 0.000 0.000 0.236 0.068
#> GSM1124903 2 0.1141 0.719 0.000 0.948 0.000 0.000 0.000 0.052
#> GSM1124905 1 0.0993 0.793 0.964 0.012 0.000 0.000 0.024 0.000
#> GSM1124910 1 0.4841 0.372 0.544 0.004 0.000 0.008 0.412 0.032
#> GSM1124919 5 0.1401 0.849 0.004 0.028 0.000 0.000 0.948 0.020
#> GSM1124932 6 0.3266 0.867 0.000 0.272 0.000 0.000 0.000 0.728
#> GSM1124933 3 0.0000 0.901 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1124867 1 0.6133 0.268 0.492 0.164 0.000 0.004 0.324 0.016
#> GSM1124868 2 0.1141 0.719 0.000 0.948 0.000 0.000 0.000 0.052
#> GSM1124878 2 0.1141 0.719 0.000 0.948 0.000 0.000 0.000 0.052
#> GSM1124895 2 0.1141 0.719 0.000 0.948 0.000 0.000 0.000 0.052
#> GSM1124897 2 0.0000 0.733 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1124902 2 0.0458 0.733 0.000 0.984 0.000 0.000 0.016 0.000
#> GSM1124908 2 0.5749 0.493 0.068 0.592 0.000 0.000 0.272 0.068
#> GSM1124921 2 0.5871 0.487 0.080 0.584 0.000 0.000 0.268 0.068
#> GSM1124939 2 0.0458 0.733 0.000 0.984 0.000 0.000 0.016 0.000
#> GSM1124944 2 0.5826 0.492 0.076 0.588 0.000 0.000 0.268 0.068
#> GSM1124945 3 0.0000 0.901 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1124946 2 0.5500 0.533 0.064 0.636 0.000 0.000 0.232 0.068
#> GSM1124947 2 0.6941 0.116 0.212 0.396 0.000 0.000 0.324 0.068
#> GSM1124951 3 0.0000 0.901 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1124952 2 0.1572 0.727 0.036 0.936 0.000 0.000 0.000 0.028
#> GSM1124957 3 0.0000 0.901 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1124900 1 0.1003 0.794 0.964 0.004 0.000 0.000 0.028 0.004
#> GSM1124914 2 0.0777 0.731 0.004 0.972 0.000 0.000 0.024 0.000
#> GSM1124871 2 0.1285 0.719 0.004 0.944 0.000 0.000 0.000 0.052
#> GSM1124874 2 0.1765 0.716 0.024 0.924 0.000 0.000 0.000 0.052
#> GSM1124875 2 0.5391 0.528 0.048 0.632 0.000 0.000 0.252 0.068
#> GSM1124880 1 0.2884 0.774 0.848 0.008 0.000 0.004 0.128 0.012
#> GSM1124881 2 0.6245 0.446 0.128 0.548 0.000 0.000 0.260 0.064
#> GSM1124885 2 0.1141 0.719 0.000 0.948 0.000 0.000 0.000 0.052
#> GSM1124886 1 0.6159 0.525 0.584 0.000 0.000 0.192 0.156 0.068
#> GSM1124887 2 0.5594 0.525 0.068 0.624 0.000 0.000 0.240 0.068
#> GSM1124894 2 0.2563 0.686 0.072 0.876 0.000 0.000 0.000 0.052
#> GSM1124896 2 0.4807 0.289 0.392 0.556 0.000 0.000 0.004 0.048
#> GSM1124899 2 0.3950 0.660 0.076 0.804 0.000 0.000 0.052 0.068
#> GSM1124901 2 0.1141 0.719 0.000 0.948 0.000 0.000 0.000 0.052
#> GSM1124906 2 0.0865 0.730 0.036 0.964 0.000 0.000 0.000 0.000
#> GSM1124907 2 0.5845 0.465 0.068 0.568 0.000 0.000 0.296 0.068
#> GSM1124911 6 0.3288 0.870 0.000 0.276 0.000 0.000 0.000 0.724
#> GSM1124912 1 0.0520 0.783 0.984 0.008 0.000 0.000 0.008 0.000
#> GSM1124915 6 0.3288 0.870 0.000 0.276 0.000 0.000 0.000 0.724
#> GSM1124917 2 0.5594 0.525 0.068 0.624 0.000 0.000 0.240 0.068
#> GSM1124918 6 0.4514 0.650 0.040 0.264 0.000 0.000 0.016 0.680
#> GSM1124920 3 0.5593 0.530 0.000 0.000 0.580 0.008 0.232 0.180
#> GSM1124922 2 0.2277 0.707 0.076 0.892 0.000 0.000 0.032 0.000
#> GSM1124924 5 0.2734 0.764 0.148 0.000 0.000 0.004 0.840 0.008
#> GSM1124926 2 0.1995 0.711 0.036 0.912 0.000 0.000 0.000 0.052
#> GSM1124928 1 0.2884 0.774 0.848 0.008 0.000 0.004 0.128 0.012
#> GSM1124930 5 0.2594 0.790 0.068 0.040 0.000 0.004 0.884 0.004
#> GSM1124931 6 0.3288 0.870 0.000 0.276 0.000 0.000 0.000 0.724
#> GSM1124935 6 0.3266 0.867 0.000 0.272 0.000 0.000 0.000 0.728
#> GSM1124936 3 0.1196 0.876 0.000 0.000 0.952 0.000 0.008 0.040
#> GSM1124938 5 0.4947 0.595 0.004 0.000 0.140 0.008 0.688 0.160
#> GSM1124940 1 0.0520 0.783 0.984 0.008 0.000 0.000 0.008 0.000
#> GSM1124941 2 0.0865 0.730 0.036 0.964 0.000 0.000 0.000 0.000
#> GSM1124942 2 0.5845 0.465 0.068 0.568 0.000 0.000 0.296 0.068
#> GSM1124943 5 0.1958 0.796 0.004 0.000 0.000 0.000 0.896 0.100
#> GSM1124948 5 0.2734 0.764 0.148 0.000 0.000 0.004 0.840 0.008
#> GSM1124949 1 0.2212 0.782 0.880 0.000 0.000 0.000 0.112 0.008
#> GSM1124950 2 0.1225 0.731 0.036 0.952 0.000 0.000 0.000 0.012
#> GSM1124954 4 0.0909 0.849 0.000 0.000 0.020 0.968 0.000 0.012
#> GSM1124955 1 0.0405 0.780 0.988 0.008 0.000 0.000 0.004 0.000
#> GSM1124956 6 0.3288 0.870 0.000 0.276 0.000 0.000 0.000 0.724
#> GSM1124872 2 0.0865 0.730 0.036 0.964 0.000 0.000 0.000 0.000
#> GSM1124873 2 0.1225 0.730 0.036 0.952 0.000 0.000 0.000 0.012
#> GSM1124876 3 0.0000 0.901 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1124877 4 0.2838 0.734 0.188 0.000 0.000 0.808 0.000 0.004
#> GSM1124879 1 0.2896 0.773 0.840 0.004 0.000 0.004 0.140 0.012
#> GSM1124883 2 0.1141 0.719 0.000 0.948 0.000 0.000 0.000 0.052
#> GSM1124889 2 0.1765 0.716 0.024 0.924 0.000 0.000 0.000 0.052
#> GSM1124892 1 0.6159 0.525 0.584 0.000 0.000 0.192 0.156 0.068
#> GSM1124893 1 0.0520 0.783 0.984 0.008 0.000 0.000 0.008 0.000
#> GSM1124909 1 0.5405 0.393 0.624 0.264 0.000 0.000 0.060 0.052
#> GSM1124913 2 0.1141 0.719 0.000 0.948 0.000 0.000 0.000 0.052
#> GSM1124916 6 0.6209 0.379 0.212 0.348 0.000 0.000 0.012 0.428
#> GSM1124923 5 0.1401 0.849 0.004 0.028 0.000 0.000 0.948 0.020
#> GSM1124925 2 0.4807 0.289 0.392 0.556 0.000 0.000 0.004 0.048
#> GSM1124929 1 0.1327 0.792 0.936 0.000 0.000 0.000 0.064 0.000
#> GSM1124934 4 0.0146 0.851 0.000 0.000 0.000 0.996 0.000 0.004
#> GSM1124937 1 0.4459 0.268 0.516 0.000 0.000 0.004 0.460 0.020
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n tissue(p) k
#> ATC:hclust 88 0.1921 2
#> ATC:hclust 87 0.1564 3
#> ATC:hclust 88 0.1189 4
#> ATC:hclust 71 0.0328 5
#> ATC:hclust 77 0.1685 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 48983 rows and 91 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 0.651 0.816 0.859 0.3637 0.666 0.666
#> 3 3 0.559 0.769 0.868 0.6157 0.673 0.536
#> 4 4 0.603 0.425 0.648 0.1724 0.801 0.547
#> 5 5 0.677 0.730 0.834 0.0988 0.802 0.434
#> 6 6 0.748 0.692 0.803 0.0607 0.894 0.594
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
#> GSM1124891 1 0.402 0.929 0.920 0.080
#> GSM1124888 1 0.402 0.929 0.920 0.080
#> GSM1124890 1 0.584 0.884 0.860 0.140
#> GSM1124904 2 0.000 0.894 0.000 1.000
#> GSM1124927 2 0.000 0.894 0.000 1.000
#> GSM1124953 1 0.634 0.864 0.840 0.160
#> GSM1124869 2 0.861 0.610 0.284 0.716
#> GSM1124870 2 0.855 0.616 0.280 0.720
#> GSM1124882 2 0.861 0.610 0.284 0.716
#> GSM1124884 2 0.000 0.894 0.000 1.000
#> GSM1124898 2 0.000 0.894 0.000 1.000
#> GSM1124903 2 0.000 0.894 0.000 1.000
#> GSM1124905 2 0.855 0.616 0.280 0.720
#> GSM1124910 1 0.886 0.623 0.696 0.304
#> GSM1124919 2 0.311 0.854 0.056 0.944
#> GSM1124932 2 0.402 0.837 0.080 0.920
#> GSM1124933 1 0.402 0.929 0.920 0.080
#> GSM1124867 2 0.795 0.670 0.240 0.760
#> GSM1124868 2 0.000 0.894 0.000 1.000
#> GSM1124878 2 0.000 0.894 0.000 1.000
#> GSM1124895 2 0.000 0.894 0.000 1.000
#> GSM1124897 2 0.000 0.894 0.000 1.000
#> GSM1124902 2 0.000 0.894 0.000 1.000
#> GSM1124908 2 0.000 0.894 0.000 1.000
#> GSM1124921 2 0.000 0.894 0.000 1.000
#> GSM1124939 2 0.000 0.894 0.000 1.000
#> GSM1124944 2 0.000 0.894 0.000 1.000
#> GSM1124945 1 0.402 0.929 0.920 0.080
#> GSM1124946 2 0.000 0.894 0.000 1.000
#> GSM1124947 2 0.000 0.894 0.000 1.000
#> GSM1124951 1 0.402 0.929 0.920 0.080
#> GSM1124952 2 0.000 0.894 0.000 1.000
#> GSM1124957 1 0.402 0.929 0.920 0.080
#> GSM1124900 2 0.000 0.894 0.000 1.000
#> GSM1124914 2 0.000 0.894 0.000 1.000
#> GSM1124871 2 0.000 0.894 0.000 1.000
#> GSM1124874 2 0.000 0.894 0.000 1.000
#> GSM1124875 2 0.000 0.894 0.000 1.000
#> GSM1124880 2 0.855 0.616 0.280 0.720
#> GSM1124881 2 0.000 0.894 0.000 1.000
#> GSM1124885 2 0.000 0.894 0.000 1.000
#> GSM1124886 1 0.402 0.929 0.920 0.080
#> GSM1124887 2 0.000 0.894 0.000 1.000
#> GSM1124894 2 0.000 0.894 0.000 1.000
#> GSM1124896 2 0.000 0.894 0.000 1.000
#> GSM1124899 2 0.000 0.894 0.000 1.000
#> GSM1124901 2 0.000 0.894 0.000 1.000
#> GSM1124906 2 0.000 0.894 0.000 1.000
#> GSM1124907 2 0.000 0.894 0.000 1.000
#> GSM1124911 2 0.402 0.837 0.080 0.920
#> GSM1124912 2 0.855 0.616 0.280 0.720
#> GSM1124915 2 0.402 0.837 0.080 0.920
#> GSM1124917 2 0.000 0.894 0.000 1.000
#> GSM1124918 2 0.402 0.837 0.080 0.920
#> GSM1124920 1 0.402 0.929 0.920 0.080
#> GSM1124922 2 0.000 0.894 0.000 1.000
#> GSM1124924 2 0.988 0.233 0.436 0.564
#> GSM1124926 2 0.000 0.894 0.000 1.000
#> GSM1124928 2 0.861 0.610 0.284 0.716
#> GSM1124930 2 0.224 0.870 0.036 0.964
#> GSM1124931 2 0.402 0.837 0.080 0.920
#> GSM1124935 2 0.402 0.837 0.080 0.920
#> GSM1124936 1 0.402 0.929 0.920 0.080
#> GSM1124938 1 0.402 0.929 0.920 0.080
#> GSM1124940 2 0.855 0.616 0.280 0.720
#> GSM1124941 2 0.000 0.894 0.000 1.000
#> GSM1124942 2 0.000 0.894 0.000 1.000
#> GSM1124943 1 0.625 0.868 0.844 0.156
#> GSM1124948 2 0.988 0.233 0.436 0.564
#> GSM1124949 1 0.996 0.138 0.536 0.464
#> GSM1124950 2 0.000 0.894 0.000 1.000
#> GSM1124954 1 0.000 0.858 1.000 0.000
#> GSM1124955 2 0.855 0.616 0.280 0.720
#> GSM1124956 2 0.402 0.837 0.080 0.920
#> GSM1124872 2 0.000 0.894 0.000 1.000
#> GSM1124873 2 0.000 0.894 0.000 1.000
#> GSM1124876 1 0.402 0.929 0.920 0.080
#> GSM1124877 2 0.971 0.466 0.400 0.600
#> GSM1124879 2 0.861 0.610 0.284 0.716
#> GSM1124883 2 0.000 0.894 0.000 1.000
#> GSM1124889 2 0.000 0.894 0.000 1.000
#> GSM1124892 1 0.402 0.929 0.920 0.080
#> GSM1124893 2 0.861 0.610 0.284 0.716
#> GSM1124909 2 0.000 0.894 0.000 1.000
#> GSM1124913 2 0.000 0.894 0.000 1.000
#> GSM1124916 2 0.402 0.837 0.080 0.920
#> GSM1124923 2 0.563 0.773 0.132 0.868
#> GSM1124925 2 0.000 0.894 0.000 1.000
#> GSM1124929 2 0.886 0.597 0.304 0.696
#> GSM1124934 1 0.000 0.858 1.000 0.000
#> GSM1124937 2 0.921 0.511 0.336 0.664
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1124891 3 0.2711 0.9379 0.088 0.000 0.912
#> GSM1124888 3 0.2711 0.9379 0.088 0.000 0.912
#> GSM1124890 1 0.5775 0.5722 0.728 0.012 0.260
#> GSM1124904 2 0.0000 0.8742 0.000 1.000 0.000
#> GSM1124927 2 0.0000 0.8742 0.000 1.000 0.000
#> GSM1124953 3 0.9421 0.1076 0.388 0.176 0.436
#> GSM1124869 1 0.3610 0.8606 0.888 0.096 0.016
#> GSM1124870 1 0.3610 0.8606 0.888 0.096 0.016
#> GSM1124882 1 0.3610 0.8606 0.888 0.096 0.016
#> GSM1124884 2 0.0000 0.8742 0.000 1.000 0.000
#> GSM1124898 2 0.0000 0.8742 0.000 1.000 0.000
#> GSM1124903 2 0.0000 0.8742 0.000 1.000 0.000
#> GSM1124905 1 0.3610 0.8606 0.888 0.096 0.016
#> GSM1124910 1 0.3856 0.8441 0.888 0.072 0.040
#> GSM1124919 2 0.6189 0.4211 0.364 0.632 0.004
#> GSM1124932 2 0.7222 0.6319 0.220 0.696 0.084
#> GSM1124933 3 0.2711 0.9379 0.088 0.000 0.912
#> GSM1124867 1 0.3500 0.8428 0.880 0.116 0.004
#> GSM1124868 2 0.0000 0.8742 0.000 1.000 0.000
#> GSM1124878 2 0.0000 0.8742 0.000 1.000 0.000
#> GSM1124895 2 0.0000 0.8742 0.000 1.000 0.000
#> GSM1124897 2 0.0000 0.8742 0.000 1.000 0.000
#> GSM1124902 2 0.0000 0.8742 0.000 1.000 0.000
#> GSM1124908 2 0.1643 0.8518 0.044 0.956 0.000
#> GSM1124921 2 0.4654 0.7114 0.208 0.792 0.000
#> GSM1124939 2 0.0000 0.8742 0.000 1.000 0.000
#> GSM1124944 2 0.6104 0.4614 0.348 0.648 0.004
#> GSM1124945 3 0.2625 0.9359 0.084 0.000 0.916
#> GSM1124946 2 0.0747 0.8662 0.016 0.984 0.000
#> GSM1124947 2 0.5621 0.5533 0.308 0.692 0.000
#> GSM1124951 3 0.2625 0.9359 0.084 0.000 0.916
#> GSM1124952 2 0.0000 0.8742 0.000 1.000 0.000
#> GSM1124957 3 0.2711 0.9379 0.088 0.000 0.912
#> GSM1124900 1 0.4178 0.7926 0.828 0.172 0.000
#> GSM1124914 2 0.0000 0.8742 0.000 1.000 0.000
#> GSM1124871 2 0.0000 0.8742 0.000 1.000 0.000
#> GSM1124874 2 0.0000 0.8742 0.000 1.000 0.000
#> GSM1124875 2 0.4555 0.7208 0.200 0.800 0.000
#> GSM1124880 1 0.3112 0.8546 0.900 0.096 0.004
#> GSM1124881 2 0.5882 0.4674 0.348 0.652 0.000
#> GSM1124885 2 0.0000 0.8742 0.000 1.000 0.000
#> GSM1124886 1 0.4605 0.6404 0.796 0.000 0.204
#> GSM1124887 2 0.4504 0.7253 0.196 0.804 0.000
#> GSM1124894 2 0.0000 0.8742 0.000 1.000 0.000
#> GSM1124896 1 0.4974 0.7385 0.764 0.236 0.000
#> GSM1124899 2 0.0000 0.8742 0.000 1.000 0.000
#> GSM1124901 2 0.0000 0.8742 0.000 1.000 0.000
#> GSM1124906 2 0.0000 0.8742 0.000 1.000 0.000
#> GSM1124907 2 0.4883 0.7084 0.208 0.788 0.004
#> GSM1124911 2 0.6858 0.6712 0.188 0.728 0.084
#> GSM1124912 1 0.3610 0.8606 0.888 0.096 0.016
#> GSM1124915 2 0.5179 0.7520 0.088 0.832 0.080
#> GSM1124917 2 0.4346 0.7326 0.184 0.816 0.000
#> GSM1124918 2 0.8270 0.4479 0.376 0.540 0.084
#> GSM1124920 3 0.2711 0.9379 0.088 0.000 0.912
#> GSM1124922 2 0.0000 0.8742 0.000 1.000 0.000
#> GSM1124924 1 0.3539 0.8503 0.888 0.100 0.012
#> GSM1124926 2 0.0000 0.8742 0.000 1.000 0.000
#> GSM1124928 1 0.3610 0.8606 0.888 0.096 0.016
#> GSM1124930 1 0.6521 0.0225 0.500 0.496 0.004
#> GSM1124931 2 0.6705 0.6835 0.176 0.740 0.084
#> GSM1124935 2 0.6858 0.6712 0.188 0.728 0.084
#> GSM1124936 3 0.2711 0.9379 0.088 0.000 0.912
#> GSM1124938 3 0.3116 0.9192 0.108 0.000 0.892
#> GSM1124940 1 0.3610 0.8606 0.888 0.096 0.016
#> GSM1124941 2 0.0000 0.8742 0.000 1.000 0.000
#> GSM1124942 2 0.5024 0.6937 0.220 0.776 0.004
#> GSM1124943 1 0.6180 0.5640 0.716 0.024 0.260
#> GSM1124948 1 0.3539 0.8503 0.888 0.100 0.012
#> GSM1124949 1 0.3856 0.8441 0.888 0.072 0.040
#> GSM1124950 2 0.0000 0.8742 0.000 1.000 0.000
#> GSM1124954 3 0.2165 0.8476 0.064 0.000 0.936
#> GSM1124955 1 0.3610 0.8606 0.888 0.096 0.016
#> GSM1124956 2 0.6858 0.6712 0.188 0.728 0.084
#> GSM1124872 2 0.0000 0.8742 0.000 1.000 0.000
#> GSM1124873 2 0.0000 0.8742 0.000 1.000 0.000
#> GSM1124876 3 0.2711 0.9379 0.088 0.000 0.912
#> GSM1124877 1 0.2959 0.7222 0.900 0.000 0.100
#> GSM1124879 1 0.3610 0.8606 0.888 0.096 0.016
#> GSM1124883 2 0.0000 0.8742 0.000 1.000 0.000
#> GSM1124889 2 0.0000 0.8742 0.000 1.000 0.000
#> GSM1124892 1 0.6079 0.2454 0.612 0.000 0.388
#> GSM1124893 1 0.3610 0.8606 0.888 0.096 0.016
#> GSM1124909 1 0.4346 0.7795 0.816 0.184 0.000
#> GSM1124913 2 0.0000 0.8742 0.000 1.000 0.000
#> GSM1124916 1 0.4544 0.7125 0.860 0.056 0.084
#> GSM1124923 2 0.6189 0.4211 0.364 0.632 0.004
#> GSM1124925 2 0.5835 0.3957 0.340 0.660 0.000
#> GSM1124929 1 0.2297 0.8093 0.944 0.036 0.020
#> GSM1124934 1 0.6299 -0.0564 0.524 0.000 0.476
#> GSM1124937 1 0.3295 0.8586 0.896 0.096 0.008
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1124891 3 0.0921 0.9387 0.028 0.000 0.972 0.000
#> GSM1124888 3 0.0921 0.9387 0.028 0.000 0.972 0.000
#> GSM1124890 1 0.5902 0.5167 0.540 0.004 0.028 0.428
#> GSM1124904 4 0.4996 -0.1793 0.000 0.484 0.000 0.516
#> GSM1124927 4 0.5236 0.0104 0.008 0.432 0.000 0.560
#> GSM1124953 4 0.6224 -0.0444 0.264 0.004 0.084 0.648
#> GSM1124869 1 0.0469 0.8137 0.988 0.000 0.000 0.012
#> GSM1124870 1 0.1004 0.8131 0.972 0.004 0.000 0.024
#> GSM1124882 1 0.0469 0.8137 0.988 0.000 0.000 0.012
#> GSM1124884 2 0.4999 0.2279 0.000 0.508 0.000 0.492
#> GSM1124898 4 0.5372 -0.0212 0.012 0.444 0.000 0.544
#> GSM1124903 2 0.4998 0.2367 0.000 0.512 0.000 0.488
#> GSM1124905 1 0.1109 0.8119 0.968 0.004 0.000 0.028
#> GSM1124910 1 0.3494 0.7581 0.824 0.004 0.000 0.172
#> GSM1124919 4 0.3681 0.3454 0.176 0.008 0.000 0.816
#> GSM1124932 2 0.4059 0.2363 0.096 0.848 0.020 0.036
#> GSM1124933 3 0.0921 0.9387 0.028 0.000 0.972 0.000
#> GSM1124867 1 0.3355 0.7645 0.836 0.004 0.000 0.160
#> GSM1124868 2 0.4998 0.2367 0.000 0.512 0.000 0.488
#> GSM1124878 2 0.4998 0.2367 0.000 0.512 0.000 0.488
#> GSM1124895 2 0.4998 0.2367 0.000 0.512 0.000 0.488
#> GSM1124897 2 0.4998 0.2367 0.000 0.512 0.000 0.488
#> GSM1124902 2 0.4998 0.2367 0.000 0.512 0.000 0.488
#> GSM1124908 4 0.4842 0.3689 0.048 0.192 0.000 0.760
#> GSM1124921 4 0.4106 0.4301 0.084 0.084 0.000 0.832
#> GSM1124939 2 0.4998 0.2367 0.000 0.512 0.000 0.488
#> GSM1124944 4 0.3626 0.3774 0.184 0.004 0.000 0.812
#> GSM1124945 3 0.0921 0.9387 0.028 0.000 0.972 0.000
#> GSM1124946 4 0.4319 0.3501 0.012 0.228 0.000 0.760
#> GSM1124947 4 0.5031 0.4250 0.140 0.092 0.000 0.768
#> GSM1124951 3 0.0921 0.9387 0.028 0.000 0.972 0.000
#> GSM1124952 4 0.5517 0.0634 0.020 0.412 0.000 0.568
#> GSM1124957 3 0.0921 0.9387 0.028 0.000 0.972 0.000
#> GSM1124900 1 0.2401 0.7879 0.904 0.004 0.000 0.092
#> GSM1124914 4 0.4996 -0.1793 0.000 0.484 0.000 0.516
#> GSM1124871 2 0.4998 0.2367 0.000 0.512 0.000 0.488
#> GSM1124874 2 0.4998 0.2367 0.000 0.512 0.000 0.488
#> GSM1124875 4 0.4039 0.4303 0.080 0.084 0.000 0.836
#> GSM1124880 1 0.2999 0.7785 0.864 0.004 0.000 0.132
#> GSM1124881 4 0.5142 0.3980 0.192 0.064 0.000 0.744
#> GSM1124885 2 0.4998 0.2367 0.000 0.512 0.000 0.488
#> GSM1124886 1 0.3194 0.7580 0.888 0.004 0.052 0.056
#> GSM1124887 4 0.5185 0.4049 0.076 0.176 0.000 0.748
#> GSM1124894 4 0.4998 -0.2086 0.000 0.488 0.000 0.512
#> GSM1124896 1 0.3144 0.7746 0.884 0.044 0.000 0.072
#> GSM1124899 4 0.5329 0.0500 0.012 0.420 0.000 0.568
#> GSM1124901 2 0.4998 0.2367 0.000 0.512 0.000 0.488
#> GSM1124906 4 0.4972 -0.0769 0.000 0.456 0.000 0.544
#> GSM1124907 4 0.2376 0.4213 0.068 0.016 0.000 0.916
#> GSM1124911 2 0.3864 0.2404 0.084 0.860 0.020 0.036
#> GSM1124912 1 0.1004 0.8131 0.972 0.004 0.000 0.024
#> GSM1124915 2 0.1798 0.2360 0.000 0.944 0.016 0.040
#> GSM1124917 4 0.5384 0.3893 0.076 0.196 0.000 0.728
#> GSM1124918 2 0.7203 0.0666 0.160 0.612 0.020 0.208
#> GSM1124920 3 0.2764 0.8978 0.036 0.004 0.908 0.052
#> GSM1124922 4 0.5329 0.0500 0.012 0.420 0.000 0.568
#> GSM1124924 1 0.5147 0.5335 0.536 0.004 0.000 0.460
#> GSM1124926 2 0.4999 0.2279 0.000 0.508 0.000 0.492
#> GSM1124928 1 0.0469 0.8137 0.988 0.000 0.000 0.012
#> GSM1124930 4 0.4594 0.0857 0.280 0.008 0.000 0.712
#> GSM1124931 2 0.4516 0.2298 0.080 0.828 0.020 0.072
#> GSM1124935 2 0.4786 0.2222 0.084 0.812 0.020 0.084
#> GSM1124936 3 0.0921 0.9387 0.028 0.000 0.972 0.000
#> GSM1124938 3 0.5940 0.6354 0.052 0.004 0.640 0.304
#> GSM1124940 1 0.0817 0.8137 0.976 0.000 0.000 0.024
#> GSM1124941 4 0.5329 0.0500 0.012 0.420 0.000 0.568
#> GSM1124942 4 0.2450 0.4206 0.072 0.016 0.000 0.912
#> GSM1124943 1 0.6372 0.4764 0.496 0.004 0.052 0.448
#> GSM1124948 1 0.5151 0.5297 0.532 0.004 0.000 0.464
#> GSM1124949 1 0.1004 0.8094 0.972 0.004 0.000 0.024
#> GSM1124950 4 0.5329 0.0500 0.012 0.420 0.000 0.568
#> GSM1124954 3 0.4920 0.7290 0.028 0.228 0.740 0.004
#> GSM1124955 1 0.1510 0.8080 0.956 0.016 0.000 0.028
#> GSM1124956 2 0.3864 0.2404 0.084 0.860 0.020 0.036
#> GSM1124872 4 0.4948 -0.0207 0.000 0.440 0.000 0.560
#> GSM1124873 4 0.4961 -0.0445 0.000 0.448 0.000 0.552
#> GSM1124876 3 0.0921 0.9387 0.028 0.000 0.972 0.000
#> GSM1124877 1 0.5933 0.3127 0.516 0.452 0.028 0.004
#> GSM1124879 1 0.0469 0.8137 0.988 0.000 0.000 0.012
#> GSM1124883 2 0.4998 0.2367 0.000 0.512 0.000 0.488
#> GSM1124889 2 0.4999 0.2279 0.000 0.508 0.000 0.492
#> GSM1124892 1 0.4442 0.7029 0.812 0.004 0.128 0.056
#> GSM1124893 1 0.1004 0.8131 0.972 0.004 0.000 0.024
#> GSM1124909 1 0.3726 0.7430 0.788 0.000 0.000 0.212
#> GSM1124913 2 0.4998 0.2367 0.000 0.512 0.000 0.488
#> GSM1124916 2 0.7339 -0.3818 0.420 0.468 0.020 0.092
#> GSM1124923 4 0.3681 0.3454 0.176 0.008 0.000 0.816
#> GSM1124925 1 0.6824 0.2433 0.556 0.324 0.000 0.120
#> GSM1124929 1 0.0657 0.8132 0.984 0.004 0.000 0.012
#> GSM1124934 1 0.8546 0.1742 0.432 0.352 0.160 0.056
#> GSM1124937 1 0.2593 0.7873 0.892 0.004 0.000 0.104
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM1124891 3 0.0000 0.9376 0.000 0.000 1.000 0.000 0.000
#> GSM1124888 3 0.0000 0.9376 0.000 0.000 1.000 0.000 0.000
#> GSM1124890 5 0.4210 0.6074 0.184 0.000 0.016 0.028 0.772
#> GSM1124904 2 0.2378 0.7941 0.000 0.904 0.000 0.048 0.048
#> GSM1124927 2 0.1788 0.7592 0.004 0.932 0.000 0.008 0.056
#> GSM1124953 5 0.4315 0.6678 0.084 0.040 0.040 0.016 0.820
#> GSM1124869 1 0.0162 0.9124 0.996 0.000 0.000 0.000 0.004
#> GSM1124870 1 0.0566 0.9122 0.984 0.000 0.000 0.012 0.004
#> GSM1124882 1 0.0162 0.9124 0.996 0.000 0.000 0.000 0.004
#> GSM1124884 2 0.2280 0.8020 0.000 0.880 0.000 0.120 0.000
#> GSM1124898 2 0.3289 0.7517 0.004 0.852 0.000 0.048 0.096
#> GSM1124903 2 0.3681 0.8096 0.000 0.808 0.000 0.148 0.044
#> GSM1124905 1 0.0566 0.9122 0.984 0.000 0.000 0.012 0.004
#> GSM1124910 1 0.3370 0.7927 0.824 0.000 0.000 0.028 0.148
#> GSM1124919 5 0.2974 0.6952 0.052 0.080 0.000 0.000 0.868
#> GSM1124932 4 0.2079 0.7861 0.020 0.064 0.000 0.916 0.000
#> GSM1124933 3 0.0000 0.9376 0.000 0.000 1.000 0.000 0.000
#> GSM1124867 1 0.2949 0.8313 0.876 0.052 0.000 0.004 0.068
#> GSM1124868 2 0.3681 0.8096 0.000 0.808 0.000 0.148 0.044
#> GSM1124878 2 0.3681 0.8096 0.000 0.808 0.000 0.148 0.044
#> GSM1124895 2 0.3681 0.8096 0.000 0.808 0.000 0.148 0.044
#> GSM1124897 2 0.3681 0.8096 0.000 0.808 0.000 0.148 0.044
#> GSM1124902 2 0.3681 0.8096 0.000 0.808 0.000 0.148 0.044
#> GSM1124908 2 0.5014 0.0594 0.008 0.560 0.000 0.020 0.412
#> GSM1124921 5 0.5170 0.4087 0.012 0.400 0.000 0.024 0.564
#> GSM1124939 2 0.3681 0.8096 0.000 0.808 0.000 0.148 0.044
#> GSM1124944 5 0.4512 0.6663 0.040 0.204 0.000 0.012 0.744
#> GSM1124945 3 0.0162 0.9361 0.000 0.000 0.996 0.004 0.000
#> GSM1124946 2 0.4726 0.3472 0.004 0.644 0.000 0.024 0.328
#> GSM1124947 2 0.5253 -0.1066 0.024 0.564 0.000 0.016 0.396
#> GSM1124951 3 0.0162 0.9361 0.000 0.000 0.996 0.004 0.000
#> GSM1124952 2 0.2266 0.7454 0.008 0.912 0.000 0.016 0.064
#> GSM1124957 3 0.0000 0.9376 0.000 0.000 1.000 0.000 0.000
#> GSM1124900 1 0.1267 0.8985 0.960 0.004 0.000 0.012 0.024
#> GSM1124914 2 0.2726 0.7948 0.000 0.884 0.000 0.064 0.052
#> GSM1124871 2 0.3531 0.8091 0.000 0.816 0.000 0.148 0.036
#> GSM1124874 2 0.2377 0.8030 0.000 0.872 0.000 0.128 0.000
#> GSM1124875 5 0.5043 0.3904 0.012 0.420 0.000 0.016 0.552
#> GSM1124880 1 0.1644 0.8864 0.940 0.004 0.000 0.008 0.048
#> GSM1124881 2 0.5651 -0.2749 0.044 0.512 0.000 0.016 0.428
#> GSM1124885 2 0.3681 0.8096 0.000 0.808 0.000 0.148 0.044
#> GSM1124886 1 0.2777 0.8249 0.864 0.000 0.000 0.016 0.120
#> GSM1124887 5 0.5313 0.3194 0.012 0.444 0.000 0.028 0.516
#> GSM1124894 2 0.2813 0.7959 0.000 0.868 0.000 0.108 0.024
#> GSM1124896 1 0.1412 0.8952 0.952 0.008 0.000 0.036 0.004
#> GSM1124899 2 0.2238 0.7457 0.004 0.912 0.000 0.020 0.064
#> GSM1124901 2 0.3681 0.8096 0.000 0.808 0.000 0.148 0.044
#> GSM1124906 2 0.1408 0.7678 0.000 0.948 0.000 0.008 0.044
#> GSM1124907 5 0.4043 0.6620 0.012 0.220 0.000 0.012 0.756
#> GSM1124911 4 0.2144 0.7860 0.020 0.068 0.000 0.912 0.000
#> GSM1124912 1 0.0566 0.9122 0.984 0.000 0.000 0.012 0.004
#> GSM1124915 4 0.2439 0.7348 0.000 0.120 0.000 0.876 0.004
#> GSM1124917 5 0.5552 0.3275 0.012 0.420 0.000 0.044 0.524
#> GSM1124918 4 0.5333 0.6680 0.028 0.132 0.000 0.720 0.120
#> GSM1124920 3 0.3496 0.7831 0.016 0.000 0.840 0.028 0.116
#> GSM1124922 2 0.2238 0.7457 0.004 0.912 0.000 0.020 0.064
#> GSM1124924 5 0.3985 0.6040 0.196 0.004 0.000 0.028 0.772
#> GSM1124926 2 0.2280 0.8020 0.000 0.880 0.000 0.120 0.000
#> GSM1124928 1 0.0162 0.9124 0.996 0.000 0.000 0.000 0.004
#> GSM1124930 5 0.3512 0.6871 0.088 0.068 0.000 0.004 0.840
#> GSM1124931 4 0.3381 0.7829 0.016 0.160 0.000 0.820 0.004
#> GSM1124935 4 0.3236 0.7820 0.020 0.152 0.000 0.828 0.000
#> GSM1124936 3 0.0000 0.9376 0.000 0.000 1.000 0.000 0.000
#> GSM1124938 5 0.5052 0.2990 0.016 0.000 0.312 0.028 0.644
#> GSM1124940 1 0.0566 0.9122 0.984 0.000 0.000 0.012 0.004
#> GSM1124941 2 0.2141 0.7485 0.004 0.916 0.000 0.016 0.064
#> GSM1124942 5 0.4012 0.6640 0.012 0.216 0.000 0.012 0.760
#> GSM1124943 5 0.4252 0.6231 0.152 0.000 0.032 0.028 0.788
#> GSM1124948 5 0.3985 0.6040 0.196 0.004 0.000 0.028 0.772
#> GSM1124949 1 0.0290 0.9108 0.992 0.000 0.000 0.000 0.008
#> GSM1124950 2 0.2141 0.7485 0.004 0.916 0.000 0.016 0.064
#> GSM1124954 3 0.5537 0.4715 0.000 0.000 0.624 0.264 0.112
#> GSM1124955 1 0.0794 0.9045 0.972 0.000 0.000 0.028 0.000
#> GSM1124956 4 0.2144 0.7860 0.020 0.068 0.000 0.912 0.000
#> GSM1124872 2 0.1628 0.7615 0.000 0.936 0.000 0.008 0.056
#> GSM1124873 2 0.1168 0.7724 0.000 0.960 0.000 0.008 0.032
#> GSM1124876 3 0.0000 0.9376 0.000 0.000 1.000 0.000 0.000
#> GSM1124877 4 0.5426 0.5981 0.252 0.000 0.000 0.640 0.108
#> GSM1124879 1 0.0162 0.9124 0.996 0.000 0.000 0.000 0.004
#> GSM1124883 2 0.3681 0.8096 0.000 0.808 0.000 0.148 0.044
#> GSM1124889 2 0.2280 0.8020 0.000 0.880 0.000 0.120 0.000
#> GSM1124892 1 0.3813 0.7979 0.824 0.000 0.032 0.024 0.120
#> GSM1124893 1 0.0566 0.9122 0.984 0.000 0.000 0.012 0.004
#> GSM1124909 1 0.5148 0.6155 0.712 0.180 0.000 0.012 0.096
#> GSM1124913 2 0.3681 0.8096 0.000 0.808 0.000 0.148 0.044
#> GSM1124916 4 0.4704 0.7347 0.104 0.116 0.000 0.764 0.016
#> GSM1124923 5 0.2974 0.6952 0.052 0.080 0.000 0.000 0.868
#> GSM1124925 1 0.5733 0.4041 0.624 0.188 0.000 0.188 0.000
#> GSM1124929 1 0.0404 0.9118 0.988 0.000 0.000 0.012 0.000
#> GSM1124934 4 0.7107 0.4678 0.240 0.000 0.036 0.500 0.224
#> GSM1124937 1 0.0865 0.9035 0.972 0.000 0.000 0.004 0.024
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1124891 3 0.0000 0.910 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1124888 3 0.0260 0.906 0.000 0.000 0.992 0.008 0.000 0.000
#> GSM1124890 5 0.3168 0.794 0.028 0.012 0.000 0.096 0.852 0.012
#> GSM1124904 4 0.3765 0.861 0.000 0.404 0.000 0.596 0.000 0.000
#> GSM1124927 2 0.0717 0.603 0.000 0.976 0.000 0.016 0.000 0.008
#> GSM1124953 5 0.1779 0.809 0.012 0.016 0.004 0.016 0.940 0.012
#> GSM1124869 1 0.0146 0.905 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM1124870 1 0.0291 0.905 0.992 0.004 0.000 0.000 0.000 0.004
#> GSM1124882 1 0.0436 0.905 0.988 0.004 0.000 0.004 0.004 0.000
#> GSM1124884 2 0.3810 -0.438 0.000 0.572 0.000 0.428 0.000 0.000
#> GSM1124898 2 0.3421 0.267 0.000 0.736 0.000 0.256 0.000 0.008
#> GSM1124903 4 0.3592 0.976 0.000 0.344 0.000 0.656 0.000 0.000
#> GSM1124905 1 0.1237 0.905 0.956 0.004 0.000 0.020 0.000 0.020
#> GSM1124910 1 0.4024 0.722 0.752 0.000 0.000 0.064 0.180 0.004
#> GSM1124919 5 0.2187 0.804 0.008 0.036 0.000 0.028 0.916 0.012
#> GSM1124932 6 0.1926 0.809 0.000 0.020 0.000 0.068 0.000 0.912
#> GSM1124933 3 0.0000 0.910 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1124867 1 0.4334 0.551 0.676 0.288 0.000 0.012 0.020 0.004
#> GSM1124868 4 0.3592 0.976 0.000 0.344 0.000 0.656 0.000 0.000
#> GSM1124878 4 0.3592 0.976 0.000 0.344 0.000 0.656 0.000 0.000
#> GSM1124895 4 0.3592 0.976 0.000 0.344 0.000 0.656 0.000 0.000
#> GSM1124897 4 0.3592 0.976 0.000 0.344 0.000 0.656 0.000 0.000
#> GSM1124902 4 0.3592 0.976 0.000 0.344 0.000 0.656 0.000 0.000
#> GSM1124908 2 0.4261 0.448 0.000 0.692 0.000 0.056 0.252 0.000
#> GSM1124921 2 0.5562 0.310 0.000 0.532 0.000 0.168 0.300 0.000
#> GSM1124939 4 0.3592 0.976 0.000 0.344 0.000 0.656 0.000 0.000
#> GSM1124944 5 0.4410 0.322 0.000 0.412 0.000 0.028 0.560 0.000
#> GSM1124945 3 0.0000 0.910 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1124946 2 0.5450 0.379 0.000 0.560 0.000 0.276 0.164 0.000
#> GSM1124947 2 0.2653 0.580 0.000 0.844 0.000 0.012 0.144 0.000
#> GSM1124951 3 0.0000 0.910 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1124952 2 0.0405 0.609 0.000 0.988 0.000 0.004 0.000 0.008
#> GSM1124957 3 0.0000 0.910 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1124900 1 0.1237 0.905 0.956 0.004 0.000 0.020 0.000 0.020
#> GSM1124914 4 0.3923 0.818 0.000 0.416 0.000 0.580 0.000 0.004
#> GSM1124871 4 0.3607 0.969 0.000 0.348 0.000 0.652 0.000 0.000
#> GSM1124874 2 0.3833 -0.484 0.000 0.556 0.000 0.444 0.000 0.000
#> GSM1124875 2 0.3539 0.481 0.000 0.756 0.000 0.024 0.220 0.000
#> GSM1124880 1 0.2186 0.869 0.908 0.024 0.000 0.056 0.012 0.000
#> GSM1124881 2 0.3264 0.526 0.012 0.796 0.000 0.008 0.184 0.000
#> GSM1124885 4 0.3592 0.976 0.000 0.344 0.000 0.656 0.000 0.000
#> GSM1124886 1 0.3627 0.780 0.808 0.000 0.000 0.028 0.132 0.032
#> GSM1124887 2 0.5712 0.406 0.000 0.520 0.000 0.260 0.220 0.000
#> GSM1124894 2 0.3101 0.159 0.000 0.756 0.000 0.244 0.000 0.000
#> GSM1124896 1 0.1666 0.899 0.936 0.008 0.000 0.036 0.000 0.020
#> GSM1124899 2 0.0405 0.609 0.000 0.988 0.000 0.004 0.000 0.008
#> GSM1124901 4 0.3592 0.976 0.000 0.344 0.000 0.656 0.000 0.000
#> GSM1124906 2 0.1333 0.571 0.000 0.944 0.000 0.048 0.000 0.008
#> GSM1124907 5 0.3834 0.605 0.000 0.268 0.000 0.024 0.708 0.000
#> GSM1124911 6 0.2265 0.809 0.000 0.024 0.000 0.076 0.004 0.896
#> GSM1124912 1 0.1321 0.904 0.952 0.004 0.000 0.024 0.000 0.020
#> GSM1124915 6 0.3168 0.740 0.000 0.024 0.000 0.172 0.000 0.804
#> GSM1124917 2 0.5628 0.418 0.000 0.540 0.000 0.240 0.220 0.000
#> GSM1124918 6 0.3201 0.725 0.000 0.208 0.000 0.000 0.012 0.780
#> GSM1124920 3 0.4776 0.600 0.000 0.000 0.688 0.108 0.196 0.008
#> GSM1124922 2 0.0405 0.609 0.000 0.988 0.000 0.004 0.000 0.008
#> GSM1124924 5 0.2907 0.789 0.028 0.016 0.000 0.096 0.860 0.000
#> GSM1124926 2 0.3804 -0.428 0.000 0.576 0.000 0.424 0.000 0.000
#> GSM1124928 1 0.0291 0.905 0.992 0.004 0.000 0.000 0.004 0.000
#> GSM1124930 5 0.1426 0.809 0.016 0.028 0.000 0.008 0.948 0.000
#> GSM1124931 6 0.2122 0.812 0.000 0.076 0.000 0.024 0.000 0.900
#> GSM1124935 6 0.1812 0.807 0.000 0.080 0.000 0.008 0.000 0.912
#> GSM1124936 3 0.0000 0.910 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1124938 5 0.3508 0.716 0.000 0.000 0.080 0.104 0.812 0.004
#> GSM1124940 1 0.1321 0.904 0.952 0.004 0.000 0.024 0.000 0.020
#> GSM1124941 2 0.0520 0.608 0.000 0.984 0.000 0.008 0.000 0.008
#> GSM1124942 5 0.3834 0.605 0.000 0.268 0.000 0.024 0.708 0.000
#> GSM1124943 5 0.2894 0.787 0.020 0.012 0.000 0.104 0.860 0.004
#> GSM1124948 5 0.2815 0.790 0.028 0.012 0.000 0.096 0.864 0.000
#> GSM1124949 1 0.0146 0.903 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM1124950 2 0.0520 0.608 0.000 0.984 0.000 0.008 0.000 0.008
#> GSM1124954 3 0.6458 0.227 0.004 0.000 0.480 0.184 0.032 0.300
#> GSM1124955 1 0.1552 0.901 0.940 0.004 0.000 0.036 0.000 0.020
#> GSM1124956 6 0.2265 0.809 0.000 0.024 0.000 0.076 0.004 0.896
#> GSM1124872 2 0.0806 0.600 0.000 0.972 0.000 0.020 0.000 0.008
#> GSM1124873 2 0.1124 0.586 0.000 0.956 0.000 0.036 0.000 0.008
#> GSM1124876 3 0.0000 0.910 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1124877 6 0.5681 0.589 0.184 0.000 0.000 0.176 0.028 0.612
#> GSM1124879 1 0.0696 0.906 0.980 0.004 0.000 0.004 0.004 0.008
#> GSM1124883 4 0.3592 0.976 0.000 0.344 0.000 0.656 0.000 0.000
#> GSM1124889 2 0.3851 -0.530 0.000 0.540 0.000 0.460 0.000 0.000
#> GSM1124892 1 0.3740 0.774 0.800 0.000 0.000 0.032 0.136 0.032
#> GSM1124893 1 0.1321 0.904 0.952 0.004 0.000 0.024 0.000 0.020
#> GSM1124909 2 0.4238 0.233 0.340 0.636 0.000 0.016 0.008 0.000
#> GSM1124913 4 0.3592 0.976 0.000 0.344 0.000 0.656 0.000 0.000
#> GSM1124916 6 0.3037 0.750 0.016 0.176 0.000 0.000 0.000 0.808
#> GSM1124923 5 0.2187 0.804 0.008 0.036 0.000 0.028 0.916 0.012
#> GSM1124925 1 0.6025 0.543 0.620 0.112 0.000 0.124 0.000 0.144
#> GSM1124929 1 0.0551 0.906 0.984 0.004 0.000 0.008 0.000 0.004
#> GSM1124934 6 0.6898 0.483 0.128 0.000 0.004 0.196 0.156 0.516
#> GSM1124937 1 0.1801 0.876 0.924 0.000 0.000 0.056 0.016 0.004
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n tissue(p) k
#> ATC:kmeans 87 0.146 2
#> ATC:kmeans 81 0.174 3
#> ATC:kmeans 35 0.231 4
#> ATC:kmeans 79 0.301 5
#> ATC:kmeans 75 0.133 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 48983 rows and 91 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 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.932 0.927 0.972 0.4962 0.505 0.505
#> 3 3 0.807 0.780 0.917 0.2708 0.738 0.531
#> 4 4 0.807 0.836 0.917 0.1146 0.898 0.728
#> 5 5 0.798 0.798 0.905 0.0994 0.897 0.670
#> 6 6 0.781 0.694 0.832 0.0430 0.963 0.845
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
#> GSM1124891 1 0.000 0.967 1.000 0.000
#> GSM1124888 1 0.000 0.967 1.000 0.000
#> GSM1124890 1 0.000 0.967 1.000 0.000
#> GSM1124904 2 0.000 0.972 0.000 1.000
#> GSM1124927 2 0.000 0.972 0.000 1.000
#> GSM1124953 1 0.000 0.967 1.000 0.000
#> GSM1124869 1 0.000 0.967 1.000 0.000
#> GSM1124870 1 0.000 0.967 1.000 0.000
#> GSM1124882 1 0.000 0.967 1.000 0.000
#> GSM1124884 2 0.000 0.972 0.000 1.000
#> GSM1124898 2 0.000 0.972 0.000 1.000
#> GSM1124903 2 0.000 0.972 0.000 1.000
#> GSM1124905 1 0.242 0.931 0.960 0.040
#> GSM1124910 1 0.000 0.967 1.000 0.000
#> GSM1124919 2 0.971 0.333 0.400 0.600
#> GSM1124932 2 0.000 0.972 0.000 1.000
#> GSM1124933 1 0.000 0.967 1.000 0.000
#> GSM1124867 1 0.000 0.967 1.000 0.000
#> GSM1124868 2 0.000 0.972 0.000 1.000
#> GSM1124878 2 0.000 0.972 0.000 1.000
#> GSM1124895 2 0.000 0.972 0.000 1.000
#> GSM1124897 2 0.000 0.972 0.000 1.000
#> GSM1124902 2 0.000 0.972 0.000 1.000
#> GSM1124908 2 0.000 0.972 0.000 1.000
#> GSM1124921 2 0.000 0.972 0.000 1.000
#> GSM1124939 2 0.000 0.972 0.000 1.000
#> GSM1124944 2 0.541 0.837 0.124 0.876
#> GSM1124945 1 0.000 0.967 1.000 0.000
#> GSM1124946 2 0.000 0.972 0.000 1.000
#> GSM1124947 2 0.000 0.972 0.000 1.000
#> GSM1124951 1 0.000 0.967 1.000 0.000
#> GSM1124952 2 0.000 0.972 0.000 1.000
#> GSM1124957 1 0.000 0.967 1.000 0.000
#> GSM1124900 1 0.821 0.657 0.744 0.256
#> GSM1124914 2 0.000 0.972 0.000 1.000
#> GSM1124871 2 0.000 0.972 0.000 1.000
#> GSM1124874 2 0.000 0.972 0.000 1.000
#> GSM1124875 2 0.000 0.972 0.000 1.000
#> GSM1124880 1 0.000 0.967 1.000 0.000
#> GSM1124881 2 0.000 0.972 0.000 1.000
#> GSM1124885 2 0.000 0.972 0.000 1.000
#> GSM1124886 1 0.000 0.967 1.000 0.000
#> GSM1124887 2 0.000 0.972 0.000 1.000
#> GSM1124894 2 0.000 0.972 0.000 1.000
#> GSM1124896 1 0.983 0.276 0.576 0.424
#> GSM1124899 2 0.000 0.972 0.000 1.000
#> GSM1124901 2 0.000 0.972 0.000 1.000
#> GSM1124906 2 0.000 0.972 0.000 1.000
#> GSM1124907 2 0.000 0.972 0.000 1.000
#> GSM1124911 2 0.000 0.972 0.000 1.000
#> GSM1124912 1 0.000 0.967 1.000 0.000
#> GSM1124915 2 0.000 0.972 0.000 1.000
#> GSM1124917 2 0.000 0.972 0.000 1.000
#> GSM1124918 2 0.000 0.972 0.000 1.000
#> GSM1124920 1 0.000 0.967 1.000 0.000
#> GSM1124922 2 0.000 0.972 0.000 1.000
#> GSM1124924 1 0.000 0.967 1.000 0.000
#> GSM1124926 2 0.000 0.972 0.000 1.000
#> GSM1124928 1 0.000 0.967 1.000 0.000
#> GSM1124930 1 0.861 0.588 0.716 0.284
#> GSM1124931 2 0.000 0.972 0.000 1.000
#> GSM1124935 2 0.000 0.972 0.000 1.000
#> GSM1124936 1 0.000 0.967 1.000 0.000
#> GSM1124938 1 0.000 0.967 1.000 0.000
#> GSM1124940 1 0.000 0.967 1.000 0.000
#> GSM1124941 2 0.000 0.972 0.000 1.000
#> GSM1124942 2 0.000 0.972 0.000 1.000
#> GSM1124943 1 0.000 0.967 1.000 0.000
#> GSM1124948 1 0.000 0.967 1.000 0.000
#> GSM1124949 1 0.000 0.967 1.000 0.000
#> GSM1124950 2 0.000 0.972 0.000 1.000
#> GSM1124954 1 0.000 0.967 1.000 0.000
#> GSM1124955 1 0.714 0.750 0.804 0.196
#> GSM1124956 2 0.000 0.972 0.000 1.000
#> GSM1124872 2 0.000 0.972 0.000 1.000
#> GSM1124873 2 0.000 0.972 0.000 1.000
#> GSM1124876 1 0.000 0.967 1.000 0.000
#> GSM1124877 1 0.000 0.967 1.000 0.000
#> GSM1124879 1 0.000 0.967 1.000 0.000
#> GSM1124883 2 0.000 0.972 0.000 1.000
#> GSM1124889 2 0.000 0.972 0.000 1.000
#> GSM1124892 1 0.000 0.967 1.000 0.000
#> GSM1124893 1 0.000 0.967 1.000 0.000
#> GSM1124909 2 0.990 0.172 0.440 0.560
#> GSM1124913 2 0.000 0.972 0.000 1.000
#> GSM1124916 2 0.000 0.972 0.000 1.000
#> GSM1124923 2 0.971 0.333 0.400 0.600
#> GSM1124925 2 0.000 0.972 0.000 1.000
#> GSM1124929 1 0.000 0.967 1.000 0.000
#> GSM1124934 1 0.000 0.967 1.000 0.000
#> GSM1124937 1 0.000 0.967 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1124891 3 0.0000 0.9260 0.000 0.000 1.000
#> GSM1124888 3 0.0000 0.9260 0.000 0.000 1.000
#> GSM1124890 3 0.0000 0.9260 0.000 0.000 1.000
#> GSM1124904 2 0.6305 0.9625 0.484 0.516 0.000
#> GSM1124927 2 0.6305 0.9625 0.484 0.516 0.000
#> GSM1124953 3 0.0000 0.9260 0.000 0.000 1.000
#> GSM1124869 1 0.6680 0.7307 0.508 0.484 0.008
#> GSM1124870 1 0.6305 0.7318 0.516 0.484 0.000
#> GSM1124882 1 0.6823 0.7284 0.504 0.484 0.012
#> GSM1124884 2 0.6305 0.9625 0.484 0.516 0.000
#> GSM1124898 2 0.6305 0.9625 0.484 0.516 0.000
#> GSM1124903 2 0.6305 0.9625 0.484 0.516 0.000
#> GSM1124905 1 0.6680 0.7307 0.508 0.484 0.008
#> GSM1124910 3 0.2866 0.8792 0.076 0.008 0.916
#> GSM1124919 3 0.3192 0.8198 0.000 0.112 0.888
#> GSM1124932 1 0.0000 0.2271 1.000 0.000 0.000
#> GSM1124933 3 0.0000 0.9260 0.000 0.000 1.000
#> GSM1124867 3 0.5096 0.8128 0.080 0.084 0.836
#> GSM1124868 2 0.6305 0.9625 0.484 0.516 0.000
#> GSM1124878 2 0.6305 0.9625 0.484 0.516 0.000
#> GSM1124895 2 0.6305 0.9625 0.484 0.516 0.000
#> GSM1124897 2 0.6305 0.9625 0.484 0.516 0.000
#> GSM1124902 2 0.6305 0.9625 0.484 0.516 0.000
#> GSM1124908 2 0.6305 0.9625 0.484 0.516 0.000
#> GSM1124921 2 0.6305 0.9625 0.484 0.516 0.000
#> GSM1124939 2 0.6305 0.9625 0.484 0.516 0.000
#> GSM1124944 2 0.9304 0.6649 0.280 0.516 0.204
#> GSM1124945 3 0.0000 0.9260 0.000 0.000 1.000
#> GSM1124946 2 0.6305 0.9625 0.484 0.516 0.000
#> GSM1124947 2 0.6305 0.9625 0.484 0.516 0.000
#> GSM1124951 3 0.0000 0.9260 0.000 0.000 1.000
#> GSM1124952 2 0.6305 0.9625 0.484 0.516 0.000
#> GSM1124957 3 0.0000 0.9260 0.000 0.000 1.000
#> GSM1124900 1 0.6305 0.7318 0.516 0.484 0.000
#> GSM1124914 2 0.6305 0.9625 0.484 0.516 0.000
#> GSM1124871 2 0.6305 0.9625 0.484 0.516 0.000
#> GSM1124874 2 0.6305 0.9625 0.484 0.516 0.000
#> GSM1124875 2 0.6305 0.9625 0.484 0.516 0.000
#> GSM1124880 3 0.5823 0.7670 0.064 0.144 0.792
#> GSM1124881 2 0.6305 0.9625 0.484 0.516 0.000
#> GSM1124885 2 0.6305 0.9625 0.484 0.516 0.000
#> GSM1124886 3 0.7421 0.5842 0.240 0.084 0.676
#> GSM1124887 2 0.6305 0.9625 0.484 0.516 0.000
#> GSM1124894 2 0.6305 0.9625 0.484 0.516 0.000
#> GSM1124896 1 0.6305 0.7318 0.516 0.484 0.000
#> GSM1124899 2 0.6305 0.9625 0.484 0.516 0.000
#> GSM1124901 2 0.6305 0.9625 0.484 0.516 0.000
#> GSM1124906 2 0.6305 0.9625 0.484 0.516 0.000
#> GSM1124907 2 0.6305 0.9625 0.484 0.516 0.000
#> GSM1124911 1 0.3116 -0.0963 0.892 0.108 0.000
#> GSM1124912 1 0.6680 0.7307 0.508 0.484 0.008
#> GSM1124915 2 0.6308 0.9537 0.492 0.508 0.000
#> GSM1124917 2 0.6305 0.9625 0.484 0.516 0.000
#> GSM1124918 1 0.4931 -0.4651 0.768 0.232 0.000
#> GSM1124920 3 0.0000 0.9260 0.000 0.000 1.000
#> GSM1124922 2 0.6305 0.9625 0.484 0.516 0.000
#> GSM1124924 3 0.0000 0.9260 0.000 0.000 1.000
#> GSM1124926 2 0.6305 0.9625 0.484 0.516 0.000
#> GSM1124928 1 0.6823 0.7284 0.504 0.484 0.012
#> GSM1124930 3 0.0237 0.9230 0.000 0.004 0.996
#> GSM1124931 1 0.4931 -0.4651 0.768 0.232 0.000
#> GSM1124935 1 0.3116 -0.0963 0.892 0.108 0.000
#> GSM1124936 3 0.0000 0.9260 0.000 0.000 1.000
#> GSM1124938 3 0.0000 0.9260 0.000 0.000 1.000
#> GSM1124940 1 0.6680 0.7307 0.508 0.484 0.008
#> GSM1124941 2 0.6305 0.9625 0.484 0.516 0.000
#> GSM1124942 2 0.7883 0.8905 0.428 0.516 0.056
#> GSM1124943 3 0.0000 0.9260 0.000 0.000 1.000
#> GSM1124948 3 0.0000 0.9260 0.000 0.000 1.000
#> GSM1124949 2 0.9152 -0.7207 0.364 0.484 0.152
#> GSM1124950 2 0.6305 0.9625 0.484 0.516 0.000
#> GSM1124954 3 0.6672 0.1928 0.472 0.008 0.520
#> GSM1124955 1 0.6305 0.7318 0.516 0.484 0.000
#> GSM1124956 1 0.3116 -0.0963 0.892 0.108 0.000
#> GSM1124872 2 0.6305 0.9625 0.484 0.516 0.000
#> GSM1124873 2 0.6305 0.9625 0.484 0.516 0.000
#> GSM1124876 3 0.0000 0.9260 0.000 0.000 1.000
#> GSM1124877 1 0.6305 0.7318 0.516 0.484 0.000
#> GSM1124879 1 0.6823 0.7284 0.504 0.484 0.012
#> GSM1124883 2 0.6305 0.9625 0.484 0.516 0.000
#> GSM1124889 2 0.6305 0.9625 0.484 0.516 0.000
#> GSM1124892 3 0.2866 0.8792 0.076 0.008 0.916
#> GSM1124893 1 0.6680 0.7307 0.508 0.484 0.008
#> GSM1124909 1 0.6633 0.7097 0.548 0.444 0.008
#> GSM1124913 2 0.6305 0.9625 0.484 0.516 0.000
#> GSM1124916 1 0.5529 0.6509 0.704 0.296 0.000
#> GSM1124923 3 0.3192 0.8198 0.000 0.112 0.888
#> GSM1124925 1 0.5431 0.6441 0.716 0.284 0.000
#> GSM1124929 1 0.6305 0.7318 0.516 0.484 0.000
#> GSM1124934 1 0.7561 -0.0388 0.516 0.040 0.444
#> GSM1124937 3 0.2772 0.8786 0.004 0.080 0.916
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1124891 3 0.0000 0.9344 0.000 0.000 1.000 0.000
#> GSM1124888 3 0.0000 0.9344 0.000 0.000 1.000 0.000
#> GSM1124890 3 0.0000 0.9344 0.000 0.000 1.000 0.000
#> GSM1124904 2 0.0000 0.9337 0.000 1.000 0.000 0.000
#> GSM1124927 2 0.0188 0.9333 0.000 0.996 0.000 0.004
#> GSM1124953 3 0.0188 0.9316 0.000 0.000 0.996 0.004
#> GSM1124869 1 0.0000 0.8910 1.000 0.000 0.000 0.000
#> GSM1124870 1 0.0000 0.8910 1.000 0.000 0.000 0.000
#> GSM1124882 1 0.0000 0.8910 1.000 0.000 0.000 0.000
#> GSM1124884 2 0.0188 0.9333 0.000 0.996 0.000 0.004
#> GSM1124898 2 0.0188 0.9321 0.000 0.996 0.000 0.004
#> GSM1124903 2 0.0000 0.9337 0.000 1.000 0.000 0.000
#> GSM1124905 1 0.0000 0.8910 1.000 0.000 0.000 0.000
#> GSM1124910 3 0.4967 0.0835 0.452 0.000 0.548 0.000
#> GSM1124919 3 0.0779 0.9189 0.000 0.004 0.980 0.016
#> GSM1124932 4 0.4049 0.8270 0.008 0.212 0.000 0.780
#> GSM1124933 3 0.0000 0.9344 0.000 0.000 1.000 0.000
#> GSM1124867 1 0.4560 0.5837 0.700 0.000 0.296 0.004
#> GSM1124868 2 0.0000 0.9337 0.000 1.000 0.000 0.000
#> GSM1124878 2 0.0000 0.9337 0.000 1.000 0.000 0.000
#> GSM1124895 2 0.0000 0.9337 0.000 1.000 0.000 0.000
#> GSM1124897 2 0.0000 0.9337 0.000 1.000 0.000 0.000
#> GSM1124902 2 0.0000 0.9337 0.000 1.000 0.000 0.000
#> GSM1124908 2 0.0188 0.9320 0.000 0.996 0.000 0.004
#> GSM1124921 2 0.3764 0.7911 0.000 0.784 0.000 0.216
#> GSM1124939 2 0.0000 0.9337 0.000 1.000 0.000 0.000
#> GSM1124944 2 0.3764 0.7911 0.000 0.784 0.000 0.216
#> GSM1124945 3 0.0000 0.9344 0.000 0.000 1.000 0.000
#> GSM1124946 2 0.3764 0.7911 0.000 0.784 0.000 0.216
#> GSM1124947 2 0.3569 0.8075 0.000 0.804 0.000 0.196
#> GSM1124951 3 0.0000 0.9344 0.000 0.000 1.000 0.000
#> GSM1124952 2 0.0188 0.9333 0.000 0.996 0.000 0.004
#> GSM1124957 3 0.0000 0.9344 0.000 0.000 1.000 0.000
#> GSM1124900 1 0.0000 0.8910 1.000 0.000 0.000 0.000
#> GSM1124914 2 0.0000 0.9337 0.000 1.000 0.000 0.000
#> GSM1124871 2 0.0000 0.9337 0.000 1.000 0.000 0.000
#> GSM1124874 2 0.0188 0.9333 0.000 0.996 0.000 0.004
#> GSM1124875 2 0.3764 0.7911 0.000 0.784 0.000 0.216
#> GSM1124880 1 0.3688 0.7153 0.792 0.000 0.208 0.000
#> GSM1124881 2 0.3726 0.7935 0.000 0.788 0.000 0.212
#> GSM1124885 2 0.0000 0.9337 0.000 1.000 0.000 0.000
#> GSM1124886 1 0.4406 0.5799 0.700 0.000 0.300 0.000
#> GSM1124887 2 0.3764 0.7911 0.000 0.784 0.000 0.216
#> GSM1124894 2 0.0188 0.9333 0.000 0.996 0.000 0.004
#> GSM1124896 1 0.0000 0.8910 1.000 0.000 0.000 0.000
#> GSM1124899 2 0.0188 0.9333 0.000 0.996 0.000 0.004
#> GSM1124901 2 0.0000 0.9337 0.000 1.000 0.000 0.000
#> GSM1124906 2 0.0188 0.9333 0.000 0.996 0.000 0.004
#> GSM1124907 2 0.3764 0.7911 0.000 0.784 0.000 0.216
#> GSM1124911 4 0.4049 0.8270 0.008 0.212 0.000 0.780
#> GSM1124912 1 0.0000 0.8910 1.000 0.000 0.000 0.000
#> GSM1124915 4 0.3801 0.8210 0.000 0.220 0.000 0.780
#> GSM1124917 2 0.3764 0.7911 0.000 0.784 0.000 0.216
#> GSM1124918 4 0.0336 0.6601 0.008 0.000 0.000 0.992
#> GSM1124920 3 0.0000 0.9344 0.000 0.000 1.000 0.000
#> GSM1124922 2 0.0188 0.9333 0.000 0.996 0.000 0.004
#> GSM1124924 3 0.0000 0.9344 0.000 0.000 1.000 0.000
#> GSM1124926 2 0.0188 0.9333 0.000 0.996 0.000 0.004
#> GSM1124928 1 0.0188 0.8895 0.996 0.000 0.004 0.000
#> GSM1124930 3 0.0592 0.9223 0.000 0.000 0.984 0.016
#> GSM1124931 4 0.4049 0.8270 0.008 0.212 0.000 0.780
#> GSM1124935 4 0.4049 0.8270 0.008 0.212 0.000 0.780
#> GSM1124936 3 0.0000 0.9344 0.000 0.000 1.000 0.000
#> GSM1124938 3 0.0000 0.9344 0.000 0.000 1.000 0.000
#> GSM1124940 1 0.0000 0.8910 1.000 0.000 0.000 0.000
#> GSM1124941 2 0.0188 0.9333 0.000 0.996 0.000 0.004
#> GSM1124942 2 0.3764 0.7911 0.000 0.784 0.000 0.216
#> GSM1124943 3 0.0000 0.9344 0.000 0.000 1.000 0.000
#> GSM1124948 3 0.0000 0.9344 0.000 0.000 1.000 0.000
#> GSM1124949 1 0.0188 0.8895 0.996 0.000 0.004 0.000
#> GSM1124950 2 0.0188 0.9333 0.000 0.996 0.000 0.004
#> GSM1124954 4 0.5292 0.0773 0.008 0.000 0.480 0.512
#> GSM1124955 1 0.0000 0.8910 1.000 0.000 0.000 0.000
#> GSM1124956 4 0.4049 0.8270 0.008 0.212 0.000 0.780
#> GSM1124872 2 0.0188 0.9333 0.000 0.996 0.000 0.004
#> GSM1124873 2 0.0188 0.9333 0.000 0.996 0.000 0.004
#> GSM1124876 3 0.0000 0.9344 0.000 0.000 1.000 0.000
#> GSM1124877 4 0.3801 0.5961 0.220 0.000 0.000 0.780
#> GSM1124879 1 0.0188 0.8895 0.996 0.000 0.004 0.000
#> GSM1124883 2 0.0000 0.9337 0.000 1.000 0.000 0.000
#> GSM1124889 2 0.0188 0.9333 0.000 0.996 0.000 0.004
#> GSM1124892 3 0.4981 0.0388 0.464 0.000 0.536 0.000
#> GSM1124893 1 0.0000 0.8910 1.000 0.000 0.000 0.000
#> GSM1124909 1 0.2197 0.8076 0.916 0.080 0.000 0.004
#> GSM1124913 2 0.0000 0.9337 0.000 1.000 0.000 0.000
#> GSM1124916 4 0.4348 0.8183 0.024 0.196 0.000 0.780
#> GSM1124923 3 0.0779 0.9189 0.000 0.004 0.980 0.016
#> GSM1124925 1 0.6147 0.4623 0.672 0.200 0.000 0.128
#> GSM1124929 1 0.0000 0.8910 1.000 0.000 0.000 0.000
#> GSM1124934 4 0.5172 0.2837 0.008 0.000 0.404 0.588
#> GSM1124937 1 0.4967 0.2234 0.548 0.000 0.452 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM1124891 3 0.0000 0.9259 0.000 0.000 1.000 0.000 0.000
#> GSM1124888 3 0.0000 0.9259 0.000 0.000 1.000 0.000 0.000
#> GSM1124890 3 0.0000 0.9259 0.000 0.000 1.000 0.000 0.000
#> GSM1124904 2 0.2813 0.8776 0.000 0.832 0.000 0.000 0.168
#> GSM1124927 2 0.0000 0.8758 0.000 1.000 0.000 0.000 0.000
#> GSM1124953 3 0.0510 0.9185 0.000 0.000 0.984 0.000 0.016
#> GSM1124869 1 0.0451 0.8687 0.988 0.000 0.000 0.008 0.004
#> GSM1124870 1 0.0451 0.8687 0.988 0.000 0.000 0.008 0.004
#> GSM1124882 1 0.0451 0.8687 0.988 0.000 0.000 0.008 0.004
#> GSM1124884 2 0.0000 0.8758 0.000 1.000 0.000 0.000 0.000
#> GSM1124898 2 0.2813 0.8776 0.000 0.832 0.000 0.000 0.168
#> GSM1124903 2 0.2813 0.8776 0.000 0.832 0.000 0.000 0.168
#> GSM1124905 1 0.0000 0.8687 1.000 0.000 0.000 0.000 0.000
#> GSM1124910 3 0.3662 0.6330 0.252 0.000 0.744 0.004 0.000
#> GSM1124919 5 0.3242 0.6749 0.000 0.000 0.216 0.000 0.784
#> GSM1124932 4 0.0609 0.9034 0.000 0.020 0.000 0.980 0.000
#> GSM1124933 3 0.0000 0.9259 0.000 0.000 1.000 0.000 0.000
#> GSM1124867 1 0.5888 0.1541 0.496 0.064 0.428 0.008 0.004
#> GSM1124868 2 0.2813 0.8776 0.000 0.832 0.000 0.000 0.168
#> GSM1124878 2 0.2813 0.8776 0.000 0.832 0.000 0.000 0.168
#> GSM1124895 2 0.2813 0.8776 0.000 0.832 0.000 0.000 0.168
#> GSM1124897 2 0.2813 0.8776 0.000 0.832 0.000 0.000 0.168
#> GSM1124902 2 0.2813 0.8776 0.000 0.832 0.000 0.000 0.168
#> GSM1124908 5 0.4305 -0.2014 0.000 0.488 0.000 0.000 0.512
#> GSM1124921 5 0.0880 0.8229 0.000 0.032 0.000 0.000 0.968
#> GSM1124939 2 0.2813 0.8776 0.000 0.832 0.000 0.000 0.168
#> GSM1124944 5 0.0162 0.8151 0.000 0.000 0.004 0.000 0.996
#> GSM1124945 3 0.0000 0.9259 0.000 0.000 1.000 0.000 0.000
#> GSM1124946 5 0.1251 0.8203 0.000 0.036 0.000 0.008 0.956
#> GSM1124947 2 0.2233 0.7878 0.000 0.892 0.000 0.004 0.104
#> GSM1124951 3 0.0000 0.9259 0.000 0.000 1.000 0.000 0.000
#> GSM1124952 2 0.0000 0.8758 0.000 1.000 0.000 0.000 0.000
#> GSM1124957 3 0.0000 0.9259 0.000 0.000 1.000 0.000 0.000
#> GSM1124900 1 0.0000 0.8687 1.000 0.000 0.000 0.000 0.000
#> GSM1124914 2 0.2813 0.8776 0.000 0.832 0.000 0.000 0.168
#> GSM1124871 2 0.2690 0.8786 0.000 0.844 0.000 0.000 0.156
#> GSM1124874 2 0.0609 0.8790 0.000 0.980 0.000 0.000 0.020
#> GSM1124875 5 0.1697 0.8021 0.000 0.060 0.000 0.008 0.932
#> GSM1124880 1 0.4803 0.0202 0.496 0.000 0.488 0.012 0.004
#> GSM1124881 2 0.4150 0.2684 0.000 0.612 0.000 0.000 0.388
#> GSM1124885 2 0.2813 0.8776 0.000 0.832 0.000 0.000 0.168
#> GSM1124886 1 0.4664 0.2070 0.552 0.000 0.436 0.008 0.004
#> GSM1124887 5 0.0880 0.8229 0.000 0.032 0.000 0.000 0.968
#> GSM1124894 2 0.0000 0.8758 0.000 1.000 0.000 0.000 0.000
#> GSM1124896 1 0.0162 0.8673 0.996 0.000 0.000 0.004 0.000
#> GSM1124899 2 0.0000 0.8758 0.000 1.000 0.000 0.000 0.000
#> GSM1124901 2 0.2813 0.8776 0.000 0.832 0.000 0.000 0.168
#> GSM1124906 2 0.0000 0.8758 0.000 1.000 0.000 0.000 0.000
#> GSM1124907 5 0.0566 0.8164 0.000 0.004 0.000 0.012 0.984
#> GSM1124911 4 0.0609 0.9034 0.000 0.020 0.000 0.980 0.000
#> GSM1124912 1 0.0000 0.8687 1.000 0.000 0.000 0.000 0.000
#> GSM1124915 4 0.1341 0.8675 0.000 0.056 0.000 0.944 0.000
#> GSM1124917 5 0.0880 0.8229 0.000 0.032 0.000 0.000 0.968
#> GSM1124918 4 0.0404 0.8833 0.000 0.000 0.000 0.988 0.012
#> GSM1124920 3 0.0000 0.9259 0.000 0.000 1.000 0.000 0.000
#> GSM1124922 2 0.0000 0.8758 0.000 1.000 0.000 0.000 0.000
#> GSM1124924 3 0.0955 0.9133 0.000 0.000 0.968 0.004 0.028
#> GSM1124926 2 0.0162 0.8767 0.000 0.996 0.000 0.000 0.004
#> GSM1124928 1 0.0451 0.8687 0.988 0.000 0.000 0.008 0.004
#> GSM1124930 5 0.4182 0.4122 0.000 0.000 0.352 0.004 0.644
#> GSM1124931 4 0.0609 0.9034 0.000 0.020 0.000 0.980 0.000
#> GSM1124935 4 0.0609 0.9034 0.000 0.020 0.000 0.980 0.000
#> GSM1124936 3 0.0000 0.9259 0.000 0.000 1.000 0.000 0.000
#> GSM1124938 3 0.0865 0.9153 0.000 0.000 0.972 0.004 0.024
#> GSM1124940 1 0.0000 0.8687 1.000 0.000 0.000 0.000 0.000
#> GSM1124941 2 0.0000 0.8758 0.000 1.000 0.000 0.000 0.000
#> GSM1124942 5 0.0566 0.8145 0.000 0.000 0.004 0.012 0.984
#> GSM1124943 3 0.0955 0.9133 0.000 0.000 0.968 0.004 0.028
#> GSM1124948 3 0.0955 0.9133 0.000 0.000 0.968 0.004 0.028
#> GSM1124949 1 0.0451 0.8687 0.988 0.000 0.000 0.008 0.004
#> GSM1124950 2 0.0000 0.8758 0.000 1.000 0.000 0.000 0.000
#> GSM1124954 3 0.4350 0.2783 0.000 0.000 0.588 0.408 0.004
#> GSM1124955 1 0.0000 0.8687 1.000 0.000 0.000 0.000 0.000
#> GSM1124956 4 0.0609 0.9034 0.000 0.020 0.000 0.980 0.000
#> GSM1124872 2 0.0000 0.8758 0.000 1.000 0.000 0.000 0.000
#> GSM1124873 2 0.1197 0.8809 0.000 0.952 0.000 0.000 0.048
#> GSM1124876 3 0.0000 0.9259 0.000 0.000 1.000 0.000 0.000
#> GSM1124877 4 0.3160 0.7295 0.188 0.000 0.000 0.808 0.004
#> GSM1124879 1 0.0290 0.8689 0.992 0.000 0.000 0.008 0.000
#> GSM1124883 2 0.2813 0.8776 0.000 0.832 0.000 0.000 0.168
#> GSM1124889 2 0.0404 0.8781 0.000 0.988 0.000 0.000 0.012
#> GSM1124892 3 0.3402 0.7347 0.184 0.000 0.804 0.008 0.004
#> GSM1124893 1 0.0000 0.8687 1.000 0.000 0.000 0.000 0.000
#> GSM1124909 1 0.4468 0.6055 0.716 0.240 0.000 0.044 0.000
#> GSM1124913 2 0.2813 0.8776 0.000 0.832 0.000 0.000 0.168
#> GSM1124916 4 0.0609 0.9034 0.000 0.020 0.000 0.980 0.000
#> GSM1124923 5 0.3366 0.6771 0.000 0.000 0.212 0.004 0.784
#> GSM1124925 1 0.3835 0.6794 0.796 0.156 0.000 0.048 0.000
#> GSM1124929 1 0.0451 0.8687 0.988 0.000 0.000 0.008 0.004
#> GSM1124934 4 0.4415 0.0859 0.000 0.000 0.444 0.552 0.004
#> GSM1124937 3 0.3087 0.7751 0.152 0.000 0.836 0.008 0.004
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1124891 3 0.0000 0.7228 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1124888 3 0.0146 0.7220 0.000 0.000 0.996 0.000 0.004 0.000
#> GSM1124890 3 0.0260 0.7203 0.000 0.000 0.992 0.008 0.000 0.000
#> GSM1124904 2 0.0363 0.8051 0.000 0.988 0.000 0.000 0.012 0.000
#> GSM1124927 2 0.3747 0.6241 0.000 0.604 0.000 0.396 0.000 0.000
#> GSM1124953 3 0.2573 0.6430 0.000 0.000 0.864 0.024 0.112 0.000
#> GSM1124869 1 0.2100 0.8686 0.884 0.000 0.000 0.112 0.000 0.004
#> GSM1124870 1 0.1908 0.8793 0.900 0.000 0.000 0.096 0.000 0.004
#> GSM1124882 1 0.1958 0.8779 0.896 0.000 0.000 0.100 0.000 0.004
#> GSM1124884 2 0.3592 0.6647 0.000 0.656 0.000 0.344 0.000 0.000
#> GSM1124898 2 0.1141 0.7761 0.000 0.948 0.000 0.000 0.052 0.000
#> GSM1124903 2 0.0363 0.8051 0.000 0.988 0.000 0.000 0.012 0.000
#> GSM1124905 1 0.0935 0.8882 0.964 0.000 0.000 0.032 0.004 0.000
#> GSM1124910 3 0.5525 0.3868 0.168 0.000 0.628 0.180 0.024 0.000
#> GSM1124919 5 0.4368 0.6401 0.000 0.024 0.160 0.068 0.748 0.000
#> GSM1124932 6 0.0146 0.9598 0.000 0.004 0.000 0.000 0.000 0.996
#> GSM1124933 3 0.0000 0.7228 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1124867 4 0.5147 0.5097 0.152 0.000 0.124 0.692 0.028 0.004
#> GSM1124868 2 0.0363 0.8051 0.000 0.988 0.000 0.000 0.012 0.000
#> GSM1124878 2 0.0363 0.8051 0.000 0.988 0.000 0.000 0.012 0.000
#> GSM1124895 2 0.0363 0.8051 0.000 0.988 0.000 0.000 0.012 0.000
#> GSM1124897 2 0.0363 0.8051 0.000 0.988 0.000 0.000 0.012 0.000
#> GSM1124902 2 0.0363 0.8051 0.000 0.988 0.000 0.000 0.012 0.000
#> GSM1124908 2 0.3905 0.3928 0.000 0.668 0.000 0.016 0.316 0.000
#> GSM1124921 5 0.3247 0.7468 0.000 0.156 0.000 0.036 0.808 0.000
#> GSM1124939 2 0.0363 0.8051 0.000 0.988 0.000 0.000 0.012 0.000
#> GSM1124944 5 0.2106 0.7425 0.000 0.064 0.000 0.032 0.904 0.000
#> GSM1124945 3 0.0000 0.7228 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1124946 5 0.3373 0.7059 0.000 0.248 0.000 0.008 0.744 0.000
#> GSM1124947 2 0.5075 0.4417 0.000 0.468 0.000 0.456 0.076 0.000
#> GSM1124951 3 0.0000 0.7228 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1124952 2 0.3672 0.6492 0.000 0.632 0.000 0.368 0.000 0.000
#> GSM1124957 3 0.0000 0.7228 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1124900 1 0.0436 0.8925 0.988 0.000 0.000 0.004 0.004 0.004
#> GSM1124914 2 0.0363 0.8051 0.000 0.988 0.000 0.000 0.012 0.000
#> GSM1124871 2 0.0260 0.8050 0.000 0.992 0.000 0.000 0.008 0.000
#> GSM1124874 2 0.0865 0.8023 0.000 0.964 0.000 0.036 0.000 0.000
#> GSM1124875 5 0.3797 0.6585 0.000 0.292 0.000 0.016 0.692 0.000
#> GSM1124880 4 0.6658 0.2348 0.244 0.000 0.264 0.448 0.044 0.000
#> GSM1124881 2 0.6081 -0.0182 0.000 0.384 0.000 0.276 0.340 0.000
#> GSM1124885 2 0.0363 0.8051 0.000 0.988 0.000 0.000 0.012 0.000
#> GSM1124886 3 0.5657 -0.1468 0.412 0.000 0.452 0.132 0.000 0.004
#> GSM1124887 5 0.3071 0.7456 0.000 0.180 0.000 0.016 0.804 0.000
#> GSM1124894 2 0.3706 0.6385 0.000 0.620 0.000 0.380 0.000 0.000
#> GSM1124896 1 0.0291 0.8923 0.992 0.000 0.000 0.004 0.004 0.000
#> GSM1124899 2 0.2996 0.7333 0.000 0.772 0.000 0.228 0.000 0.000
#> GSM1124901 2 0.0363 0.8051 0.000 0.988 0.000 0.000 0.012 0.000
#> GSM1124906 2 0.3737 0.6282 0.000 0.608 0.000 0.392 0.000 0.000
#> GSM1124907 5 0.2724 0.7340 0.000 0.052 0.000 0.084 0.864 0.000
#> GSM1124911 6 0.0146 0.9598 0.000 0.004 0.000 0.000 0.000 0.996
#> GSM1124912 1 0.0000 0.8951 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124915 6 0.0937 0.9149 0.000 0.040 0.000 0.000 0.000 0.960
#> GSM1124917 5 0.3582 0.7006 0.000 0.252 0.000 0.016 0.732 0.000
#> GSM1124918 6 0.0363 0.9517 0.000 0.000 0.000 0.000 0.012 0.988
#> GSM1124920 3 0.2384 0.6738 0.000 0.000 0.884 0.084 0.032 0.000
#> GSM1124922 2 0.3727 0.6324 0.000 0.612 0.000 0.388 0.000 0.000
#> GSM1124924 3 0.5289 0.3979 0.000 0.000 0.560 0.316 0.124 0.000
#> GSM1124926 2 0.1863 0.7835 0.000 0.896 0.000 0.104 0.000 0.000
#> GSM1124928 1 0.2738 0.8198 0.820 0.000 0.000 0.176 0.000 0.004
#> GSM1124930 5 0.5356 0.3325 0.000 0.000 0.168 0.248 0.584 0.000
#> GSM1124931 6 0.0146 0.9598 0.000 0.004 0.000 0.000 0.000 0.996
#> GSM1124935 6 0.0146 0.9598 0.000 0.004 0.000 0.000 0.000 0.996
#> GSM1124936 3 0.0000 0.7228 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1124938 3 0.4061 0.5504 0.000 0.000 0.708 0.248 0.044 0.000
#> GSM1124940 1 0.0000 0.8951 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124941 2 0.3727 0.6319 0.000 0.612 0.000 0.388 0.000 0.000
#> GSM1124942 5 0.2971 0.7275 0.000 0.052 0.000 0.104 0.844 0.000
#> GSM1124943 3 0.5150 0.4570 0.000 0.000 0.608 0.256 0.136 0.000
#> GSM1124948 3 0.5341 0.3943 0.000 0.000 0.556 0.312 0.132 0.000
#> GSM1124949 1 0.2558 0.8375 0.840 0.000 0.000 0.156 0.000 0.004
#> GSM1124950 2 0.3727 0.6322 0.000 0.612 0.000 0.388 0.000 0.000
#> GSM1124954 3 0.5047 0.3016 0.000 0.000 0.564 0.088 0.000 0.348
#> GSM1124955 1 0.0146 0.8940 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM1124956 6 0.0146 0.9598 0.000 0.004 0.000 0.000 0.000 0.996
#> GSM1124872 2 0.3482 0.6827 0.000 0.684 0.000 0.316 0.000 0.000
#> GSM1124873 2 0.1007 0.8017 0.000 0.956 0.000 0.044 0.000 0.000
#> GSM1124876 3 0.0000 0.7228 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1124877 6 0.3587 0.7234 0.140 0.000 0.000 0.068 0.000 0.792
#> GSM1124879 1 0.2100 0.8653 0.884 0.000 0.000 0.112 0.004 0.000
#> GSM1124883 2 0.0363 0.8051 0.000 0.988 0.000 0.000 0.012 0.000
#> GSM1124889 2 0.1267 0.7976 0.000 0.940 0.000 0.060 0.000 0.000
#> GSM1124892 3 0.3818 0.5435 0.084 0.000 0.784 0.128 0.000 0.004
#> GSM1124893 1 0.0146 0.8940 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM1124909 4 0.5534 0.2681 0.356 0.072 0.000 0.548 0.008 0.016
#> GSM1124913 2 0.0363 0.8051 0.000 0.988 0.000 0.000 0.012 0.000
#> GSM1124916 6 0.0146 0.9559 0.004 0.000 0.000 0.000 0.000 0.996
#> GSM1124923 5 0.4368 0.6401 0.000 0.024 0.160 0.068 0.748 0.000
#> GSM1124925 1 0.3296 0.6718 0.836 0.088 0.000 0.004 0.004 0.068
#> GSM1124929 1 0.1908 0.8781 0.900 0.000 0.000 0.096 0.000 0.004
#> GSM1124934 3 0.5392 0.0650 0.000 0.000 0.448 0.112 0.000 0.440
#> GSM1124937 3 0.6017 0.1285 0.076 0.000 0.500 0.372 0.048 0.004
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n tissue(p) k
#> ATC:skmeans 87 0.4168 2
#> ATC:skmeans 82 0.0571 3
#> ATC:skmeans 85 0.2728 4
#> ATC:skmeans 83 0.4580 5
#> ATC:skmeans 77 0.3931 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 48983 rows and 91 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'pam' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.977 0.945 0.978 0.271 0.752 0.752
#> 3 3 0.939 0.932 0.974 1.119 0.629 0.523
#> 4 4 0.805 0.890 0.936 0.142 0.907 0.788
#> 5 5 0.798 0.864 0.926 0.118 0.906 0.737
#> 6 6 0.723 0.792 0.880 0.032 0.990 0.964
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
#> GSM1124891 1 0.0000 0.982 1.000 0.000
#> GSM1124888 1 0.0000 0.982 1.000 0.000
#> GSM1124890 2 0.9754 0.322 0.408 0.592
#> GSM1124904 2 0.0000 0.976 0.000 1.000
#> GSM1124927 2 0.0000 0.976 0.000 1.000
#> GSM1124953 2 0.9635 0.369 0.388 0.612
#> GSM1124869 2 0.0376 0.975 0.004 0.996
#> GSM1124870 2 0.0376 0.975 0.004 0.996
#> GSM1124882 2 0.0376 0.975 0.004 0.996
#> GSM1124884 2 0.0000 0.976 0.000 1.000
#> GSM1124898 2 0.0000 0.976 0.000 1.000
#> GSM1124903 2 0.0000 0.976 0.000 1.000
#> GSM1124905 2 0.0376 0.975 0.004 0.996
#> GSM1124910 2 0.2236 0.945 0.036 0.964
#> GSM1124919 2 0.0000 0.976 0.000 1.000
#> GSM1124932 2 0.0376 0.975 0.004 0.996
#> GSM1124933 1 0.0000 0.982 1.000 0.000
#> GSM1124867 2 0.0376 0.975 0.004 0.996
#> GSM1124868 2 0.0000 0.976 0.000 1.000
#> GSM1124878 2 0.0000 0.976 0.000 1.000
#> GSM1124895 2 0.0000 0.976 0.000 1.000
#> GSM1124897 2 0.0000 0.976 0.000 1.000
#> GSM1124902 2 0.0000 0.976 0.000 1.000
#> GSM1124908 2 0.0000 0.976 0.000 1.000
#> GSM1124921 2 0.0000 0.976 0.000 1.000
#> GSM1124939 2 0.0000 0.976 0.000 1.000
#> GSM1124944 2 0.0000 0.976 0.000 1.000
#> GSM1124945 1 0.0000 0.982 1.000 0.000
#> GSM1124946 2 0.0000 0.976 0.000 1.000
#> GSM1124947 2 0.0000 0.976 0.000 1.000
#> GSM1124951 1 0.0000 0.982 1.000 0.000
#> GSM1124952 2 0.0000 0.976 0.000 1.000
#> GSM1124957 1 0.0000 0.982 1.000 0.000
#> GSM1124900 2 0.0376 0.975 0.004 0.996
#> GSM1124914 2 0.0000 0.976 0.000 1.000
#> GSM1124871 2 0.0000 0.976 0.000 1.000
#> GSM1124874 2 0.0000 0.976 0.000 1.000
#> GSM1124875 2 0.0000 0.976 0.000 1.000
#> GSM1124880 2 0.0376 0.975 0.004 0.996
#> GSM1124881 2 0.0000 0.976 0.000 1.000
#> GSM1124885 2 0.0000 0.976 0.000 1.000
#> GSM1124886 2 0.9815 0.288 0.420 0.580
#> GSM1124887 2 0.0000 0.976 0.000 1.000
#> GSM1124894 2 0.0000 0.976 0.000 1.000
#> GSM1124896 2 0.0376 0.975 0.004 0.996
#> GSM1124899 2 0.0000 0.976 0.000 1.000
#> GSM1124901 2 0.0000 0.976 0.000 1.000
#> GSM1124906 2 0.0000 0.976 0.000 1.000
#> GSM1124907 2 0.0000 0.976 0.000 1.000
#> GSM1124911 2 0.0000 0.976 0.000 1.000
#> GSM1124912 2 0.0376 0.975 0.004 0.996
#> GSM1124915 2 0.0000 0.976 0.000 1.000
#> GSM1124917 2 0.0000 0.976 0.000 1.000
#> GSM1124918 2 0.0000 0.976 0.000 1.000
#> GSM1124920 1 0.0000 0.982 1.000 0.000
#> GSM1124922 2 0.0000 0.976 0.000 1.000
#> GSM1124924 2 0.0376 0.975 0.004 0.996
#> GSM1124926 2 0.0000 0.976 0.000 1.000
#> GSM1124928 2 0.0376 0.975 0.004 0.996
#> GSM1124930 2 0.0376 0.975 0.004 0.996
#> GSM1124931 2 0.0000 0.976 0.000 1.000
#> GSM1124935 2 0.0000 0.976 0.000 1.000
#> GSM1124936 1 0.0000 0.982 1.000 0.000
#> GSM1124938 1 0.5059 0.877 0.888 0.112
#> GSM1124940 2 0.0376 0.975 0.004 0.996
#> GSM1124941 2 0.0000 0.976 0.000 1.000
#> GSM1124942 2 0.0000 0.976 0.000 1.000
#> GSM1124943 2 0.9795 0.300 0.416 0.584
#> GSM1124948 2 0.0376 0.975 0.004 0.996
#> GSM1124949 2 0.2043 0.949 0.032 0.968
#> GSM1124950 2 0.0000 0.976 0.000 1.000
#> GSM1124954 1 0.0000 0.982 1.000 0.000
#> GSM1124955 2 0.0376 0.975 0.004 0.996
#> GSM1124956 2 0.0000 0.976 0.000 1.000
#> GSM1124872 2 0.0000 0.976 0.000 1.000
#> GSM1124873 2 0.0000 0.976 0.000 1.000
#> GSM1124876 1 0.0000 0.982 1.000 0.000
#> GSM1124877 2 0.0376 0.975 0.004 0.996
#> GSM1124879 2 0.0376 0.975 0.004 0.996
#> GSM1124883 2 0.0000 0.976 0.000 1.000
#> GSM1124889 2 0.0000 0.976 0.000 1.000
#> GSM1124892 1 0.4690 0.891 0.900 0.100
#> GSM1124893 2 0.0376 0.975 0.004 0.996
#> GSM1124909 2 0.0376 0.975 0.004 0.996
#> GSM1124913 2 0.0000 0.976 0.000 1.000
#> GSM1124916 2 0.0376 0.975 0.004 0.996
#> GSM1124923 2 0.0000 0.976 0.000 1.000
#> GSM1124925 2 0.0000 0.976 0.000 1.000
#> GSM1124929 2 0.0376 0.975 0.004 0.996
#> GSM1124934 1 0.0000 0.982 1.000 0.000
#> GSM1124937 2 0.0376 0.975 0.004 0.996
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1124891 3 0.0000 1.000 0.000 0.000 1.000
#> GSM1124888 3 0.0000 1.000 0.000 0.000 1.000
#> GSM1124890 1 0.0000 0.969 1.000 0.000 0.000
#> GSM1124904 2 0.0000 0.962 0.000 1.000 0.000
#> GSM1124927 2 0.0000 0.962 0.000 1.000 0.000
#> GSM1124953 2 0.6126 0.357 0.400 0.600 0.000
#> GSM1124869 1 0.0000 0.969 1.000 0.000 0.000
#> GSM1124870 1 0.0000 0.969 1.000 0.000 0.000
#> GSM1124882 1 0.0000 0.969 1.000 0.000 0.000
#> GSM1124884 2 0.0000 0.962 0.000 1.000 0.000
#> GSM1124898 2 0.0000 0.962 0.000 1.000 0.000
#> GSM1124903 2 0.0000 0.962 0.000 1.000 0.000
#> GSM1124905 1 0.0000 0.969 1.000 0.000 0.000
#> GSM1124910 1 0.0000 0.969 1.000 0.000 0.000
#> GSM1124919 2 0.4555 0.735 0.200 0.800 0.000
#> GSM1124932 2 0.6126 0.334 0.400 0.600 0.000
#> GSM1124933 3 0.0000 1.000 0.000 0.000 1.000
#> GSM1124867 1 0.0000 0.969 1.000 0.000 0.000
#> GSM1124868 2 0.0000 0.962 0.000 1.000 0.000
#> GSM1124878 2 0.0000 0.962 0.000 1.000 0.000
#> GSM1124895 2 0.0000 0.962 0.000 1.000 0.000
#> GSM1124897 2 0.0000 0.962 0.000 1.000 0.000
#> GSM1124902 2 0.0000 0.962 0.000 1.000 0.000
#> GSM1124908 2 0.0000 0.962 0.000 1.000 0.000
#> GSM1124921 2 0.0000 0.962 0.000 1.000 0.000
#> GSM1124939 2 0.0000 0.962 0.000 1.000 0.000
#> GSM1124944 2 0.2448 0.886 0.076 0.924 0.000
#> GSM1124945 3 0.0000 1.000 0.000 0.000 1.000
#> GSM1124946 2 0.0000 0.962 0.000 1.000 0.000
#> GSM1124947 2 0.0000 0.962 0.000 1.000 0.000
#> GSM1124951 3 0.0000 1.000 0.000 0.000 1.000
#> GSM1124952 2 0.0000 0.962 0.000 1.000 0.000
#> GSM1124957 3 0.0000 1.000 0.000 0.000 1.000
#> GSM1124900 1 0.0000 0.969 1.000 0.000 0.000
#> GSM1124914 2 0.0000 0.962 0.000 1.000 0.000
#> GSM1124871 2 0.0000 0.962 0.000 1.000 0.000
#> GSM1124874 2 0.0000 0.962 0.000 1.000 0.000
#> GSM1124875 2 0.0000 0.962 0.000 1.000 0.000
#> GSM1124880 1 0.0000 0.969 1.000 0.000 0.000
#> GSM1124881 2 0.0747 0.947 0.016 0.984 0.000
#> GSM1124885 2 0.0000 0.962 0.000 1.000 0.000
#> GSM1124886 1 0.0000 0.969 1.000 0.000 0.000
#> GSM1124887 2 0.0000 0.962 0.000 1.000 0.000
#> GSM1124894 2 0.0000 0.962 0.000 1.000 0.000
#> GSM1124896 1 0.4555 0.683 0.800 0.200 0.000
#> GSM1124899 2 0.0000 0.962 0.000 1.000 0.000
#> GSM1124901 2 0.0000 0.962 0.000 1.000 0.000
#> GSM1124906 2 0.0000 0.962 0.000 1.000 0.000
#> GSM1124907 2 0.0000 0.962 0.000 1.000 0.000
#> GSM1124911 2 0.0000 0.962 0.000 1.000 0.000
#> GSM1124912 1 0.0000 0.969 1.000 0.000 0.000
#> GSM1124915 2 0.0000 0.962 0.000 1.000 0.000
#> GSM1124917 2 0.0000 0.962 0.000 1.000 0.000
#> GSM1124918 2 0.0592 0.951 0.012 0.988 0.000
#> GSM1124920 1 0.5465 0.589 0.712 0.000 0.288
#> GSM1124922 2 0.0000 0.962 0.000 1.000 0.000
#> GSM1124924 1 0.0000 0.969 1.000 0.000 0.000
#> GSM1124926 2 0.0000 0.962 0.000 1.000 0.000
#> GSM1124928 1 0.0000 0.969 1.000 0.000 0.000
#> GSM1124930 1 0.4887 0.630 0.772 0.228 0.000
#> GSM1124931 2 0.0000 0.962 0.000 1.000 0.000
#> GSM1124935 2 0.0237 0.958 0.004 0.996 0.000
#> GSM1124936 3 0.0000 1.000 0.000 0.000 1.000
#> GSM1124938 1 0.0000 0.969 1.000 0.000 0.000
#> GSM1124940 1 0.0000 0.969 1.000 0.000 0.000
#> GSM1124941 2 0.0000 0.962 0.000 1.000 0.000
#> GSM1124942 2 0.0000 0.962 0.000 1.000 0.000
#> GSM1124943 1 0.0000 0.969 1.000 0.000 0.000
#> GSM1124948 1 0.0000 0.969 1.000 0.000 0.000
#> GSM1124949 1 0.0000 0.969 1.000 0.000 0.000
#> GSM1124950 2 0.0000 0.962 0.000 1.000 0.000
#> GSM1124954 3 0.0000 1.000 0.000 0.000 1.000
#> GSM1124955 1 0.0000 0.969 1.000 0.000 0.000
#> GSM1124956 2 0.0000 0.962 0.000 1.000 0.000
#> GSM1124872 2 0.0000 0.962 0.000 1.000 0.000
#> GSM1124873 2 0.0000 0.962 0.000 1.000 0.000
#> GSM1124876 3 0.0000 1.000 0.000 0.000 1.000
#> GSM1124877 1 0.0000 0.969 1.000 0.000 0.000
#> GSM1124879 1 0.0000 0.969 1.000 0.000 0.000
#> GSM1124883 2 0.0000 0.962 0.000 1.000 0.000
#> GSM1124889 2 0.0000 0.962 0.000 1.000 0.000
#> GSM1124892 1 0.0000 0.969 1.000 0.000 0.000
#> GSM1124893 1 0.0000 0.969 1.000 0.000 0.000
#> GSM1124909 1 0.0424 0.960 0.992 0.008 0.000
#> GSM1124913 2 0.0000 0.962 0.000 1.000 0.000
#> GSM1124916 1 0.0000 0.969 1.000 0.000 0.000
#> GSM1124923 2 0.4555 0.735 0.200 0.800 0.000
#> GSM1124925 2 0.5733 0.519 0.324 0.676 0.000
#> GSM1124929 1 0.0000 0.969 1.000 0.000 0.000
#> GSM1124934 1 0.0000 0.969 1.000 0.000 0.000
#> GSM1124937 1 0.0000 0.969 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1124891 3 0.0000 1.000 0.000 0.000 1.0 0.000
#> GSM1124888 3 0.0000 1.000 0.000 0.000 1.0 0.000
#> GSM1124890 1 0.3610 0.816 0.800 0.000 0.0 0.200
#> GSM1124904 2 0.0000 0.949 0.000 1.000 0.0 0.000
#> GSM1124927 2 0.0000 0.949 0.000 1.000 0.0 0.000
#> GSM1124953 2 0.6855 0.396 0.200 0.600 0.0 0.200
#> GSM1124869 1 0.0000 0.914 1.000 0.000 0.0 0.000
#> GSM1124870 1 0.0000 0.914 1.000 0.000 0.0 0.000
#> GSM1124882 1 0.0000 0.914 1.000 0.000 0.0 0.000
#> GSM1124884 2 0.0000 0.949 0.000 1.000 0.0 0.000
#> GSM1124898 2 0.0000 0.949 0.000 1.000 0.0 0.000
#> GSM1124903 2 0.0000 0.949 0.000 1.000 0.0 0.000
#> GSM1124905 1 0.0000 0.914 1.000 0.000 0.0 0.000
#> GSM1124910 1 0.3123 0.843 0.844 0.000 0.0 0.156
#> GSM1124919 2 0.3610 0.738 0.000 0.800 0.0 0.200
#> GSM1124932 4 0.3610 0.889 0.000 0.200 0.0 0.800
#> GSM1124933 3 0.0000 1.000 0.000 0.000 1.0 0.000
#> GSM1124867 1 0.0000 0.914 1.000 0.000 0.0 0.000
#> GSM1124868 2 0.0000 0.949 0.000 1.000 0.0 0.000
#> GSM1124878 2 0.0000 0.949 0.000 1.000 0.0 0.000
#> GSM1124895 2 0.0000 0.949 0.000 1.000 0.0 0.000
#> GSM1124897 2 0.0000 0.949 0.000 1.000 0.0 0.000
#> GSM1124902 2 0.0000 0.949 0.000 1.000 0.0 0.000
#> GSM1124908 2 0.0000 0.949 0.000 1.000 0.0 0.000
#> GSM1124921 2 0.1211 0.913 0.000 0.960 0.0 0.040
#> GSM1124939 2 0.0000 0.949 0.000 1.000 0.0 0.000
#> GSM1124944 2 0.3323 0.827 0.064 0.876 0.0 0.060
#> GSM1124945 3 0.0000 1.000 0.000 0.000 1.0 0.000
#> GSM1124946 2 0.0000 0.949 0.000 1.000 0.0 0.000
#> GSM1124947 2 0.0000 0.949 0.000 1.000 0.0 0.000
#> GSM1124951 3 0.0000 1.000 0.000 0.000 1.0 0.000
#> GSM1124952 2 0.0000 0.949 0.000 1.000 0.0 0.000
#> GSM1124957 3 0.0000 1.000 0.000 0.000 1.0 0.000
#> GSM1124900 1 0.0000 0.914 1.000 0.000 0.0 0.000
#> GSM1124914 2 0.0000 0.949 0.000 1.000 0.0 0.000
#> GSM1124871 2 0.0000 0.949 0.000 1.000 0.0 0.000
#> GSM1124874 2 0.0000 0.949 0.000 1.000 0.0 0.000
#> GSM1124875 2 0.0000 0.949 0.000 1.000 0.0 0.000
#> GSM1124880 1 0.0000 0.914 1.000 0.000 0.0 0.000
#> GSM1124881 2 0.0592 0.934 0.016 0.984 0.0 0.000
#> GSM1124885 2 0.0000 0.949 0.000 1.000 0.0 0.000
#> GSM1124886 1 0.0000 0.914 1.000 0.000 0.0 0.000
#> GSM1124887 2 0.0000 0.949 0.000 1.000 0.0 0.000
#> GSM1124894 2 0.0000 0.949 0.000 1.000 0.0 0.000
#> GSM1124896 1 0.3610 0.663 0.800 0.200 0.0 0.000
#> GSM1124899 2 0.0000 0.949 0.000 1.000 0.0 0.000
#> GSM1124901 2 0.0000 0.949 0.000 1.000 0.0 0.000
#> GSM1124906 2 0.0000 0.949 0.000 1.000 0.0 0.000
#> GSM1124907 2 0.3610 0.738 0.000 0.800 0.0 0.200
#> GSM1124911 4 0.3610 0.889 0.000 0.200 0.0 0.800
#> GSM1124912 1 0.0000 0.914 1.000 0.000 0.0 0.000
#> GSM1124915 4 0.3610 0.889 0.000 0.200 0.0 0.800
#> GSM1124917 2 0.0000 0.949 0.000 1.000 0.0 0.000
#> GSM1124918 4 0.4319 0.799 0.012 0.228 0.0 0.760
#> GSM1124920 1 0.5784 0.732 0.700 0.000 0.1 0.200
#> GSM1124922 2 0.0000 0.949 0.000 1.000 0.0 0.000
#> GSM1124924 1 0.3610 0.816 0.800 0.000 0.0 0.200
#> GSM1124926 2 0.0000 0.949 0.000 1.000 0.0 0.000
#> GSM1124928 1 0.0000 0.914 1.000 0.000 0.0 0.000
#> GSM1124930 1 0.7058 0.463 0.572 0.228 0.0 0.200
#> GSM1124931 4 0.3610 0.889 0.000 0.200 0.0 0.800
#> GSM1124935 4 0.3610 0.889 0.000 0.200 0.0 0.800
#> GSM1124936 3 0.0000 1.000 0.000 0.000 1.0 0.000
#> GSM1124938 1 0.3610 0.816 0.800 0.000 0.0 0.200
#> GSM1124940 1 0.0000 0.914 1.000 0.000 0.0 0.000
#> GSM1124941 2 0.0000 0.949 0.000 1.000 0.0 0.000
#> GSM1124942 2 0.3610 0.738 0.000 0.800 0.0 0.200
#> GSM1124943 1 0.3610 0.816 0.800 0.000 0.0 0.200
#> GSM1124948 1 0.3610 0.816 0.800 0.000 0.0 0.200
#> GSM1124949 1 0.0000 0.914 1.000 0.000 0.0 0.000
#> GSM1124950 2 0.0000 0.949 0.000 1.000 0.0 0.000
#> GSM1124954 3 0.0000 1.000 0.000 0.000 1.0 0.000
#> GSM1124955 1 0.0000 0.914 1.000 0.000 0.0 0.000
#> GSM1124956 4 0.3610 0.889 0.000 0.200 0.0 0.800
#> GSM1124872 2 0.0000 0.949 0.000 1.000 0.0 0.000
#> GSM1124873 2 0.0000 0.949 0.000 1.000 0.0 0.000
#> GSM1124876 3 0.0000 1.000 0.000 0.000 1.0 0.000
#> GSM1124877 4 0.3610 0.642 0.200 0.000 0.0 0.800
#> GSM1124879 1 0.0000 0.914 1.000 0.000 0.0 0.000
#> GSM1124883 2 0.0000 0.949 0.000 1.000 0.0 0.000
#> GSM1124889 2 0.0000 0.949 0.000 1.000 0.0 0.000
#> GSM1124892 1 0.1716 0.889 0.936 0.000 0.0 0.064
#> GSM1124893 1 0.0000 0.914 1.000 0.000 0.0 0.000
#> GSM1124909 1 0.0336 0.908 0.992 0.008 0.0 0.000
#> GSM1124913 2 0.0000 0.949 0.000 1.000 0.0 0.000
#> GSM1124916 4 0.3649 0.639 0.204 0.000 0.0 0.796
#> GSM1124923 2 0.3610 0.738 0.000 0.800 0.0 0.200
#> GSM1124925 2 0.4543 0.399 0.324 0.676 0.0 0.000
#> GSM1124929 1 0.0000 0.914 1.000 0.000 0.0 0.000
#> GSM1124934 1 0.2149 0.843 0.912 0.000 0.0 0.088
#> GSM1124937 1 0.0000 0.914 1.000 0.000 0.0 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM1124891 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000
#> GSM1124888 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000
#> GSM1124890 5 0.0290 0.8133 0.008 0.000 0 0.000 0.992
#> GSM1124904 2 0.0290 0.9101 0.000 0.992 0 0.000 0.008
#> GSM1124927 2 0.0000 0.9105 0.000 1.000 0 0.000 0.000
#> GSM1124953 5 0.0162 0.8110 0.000 0.000 0 0.004 0.996
#> GSM1124869 1 0.0000 0.9536 1.000 0.000 0 0.000 0.000
#> GSM1124870 1 0.0000 0.9536 1.000 0.000 0 0.000 0.000
#> GSM1124882 1 0.0000 0.9536 1.000 0.000 0 0.000 0.000
#> GSM1124884 2 0.1608 0.8926 0.000 0.928 0 0.072 0.000
#> GSM1124898 2 0.0404 0.9098 0.000 0.988 0 0.000 0.012
#> GSM1124903 2 0.3209 0.8293 0.000 0.812 0 0.180 0.008
#> GSM1124905 1 0.0000 0.9536 1.000 0.000 0 0.000 0.000
#> GSM1124910 1 0.3274 0.7124 0.780 0.000 0 0.000 0.220
#> GSM1124919 5 0.4410 0.0512 0.000 0.440 0 0.004 0.556
#> GSM1124932 4 0.2966 0.8375 0.000 0.184 0 0.816 0.000
#> GSM1124933 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000
#> GSM1124867 1 0.1124 0.9357 0.960 0.000 0 0.004 0.036
#> GSM1124868 2 0.3209 0.8293 0.000 0.812 0 0.180 0.008
#> GSM1124878 2 0.3209 0.8293 0.000 0.812 0 0.180 0.008
#> GSM1124895 2 0.3209 0.8293 0.000 0.812 0 0.180 0.008
#> GSM1124897 2 0.1168 0.9042 0.000 0.960 0 0.032 0.008
#> GSM1124902 2 0.1168 0.9042 0.000 0.960 0 0.032 0.008
#> GSM1124908 2 0.1205 0.8943 0.000 0.956 0 0.004 0.040
#> GSM1124921 2 0.2890 0.8020 0.000 0.836 0 0.004 0.160
#> GSM1124939 2 0.0162 0.9103 0.000 0.996 0 0.000 0.004
#> GSM1124944 2 0.4426 0.7213 0.052 0.748 0 0.004 0.196
#> GSM1124945 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000
#> GSM1124946 2 0.0671 0.9077 0.000 0.980 0 0.004 0.016
#> GSM1124947 2 0.0451 0.9077 0.000 0.988 0 0.004 0.008
#> GSM1124951 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000
#> GSM1124952 2 0.0000 0.9105 0.000 1.000 0 0.000 0.000
#> GSM1124957 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000
#> GSM1124900 1 0.0963 0.9242 0.964 0.036 0 0.000 0.000
#> GSM1124914 2 0.0162 0.9103 0.000 0.996 0 0.000 0.004
#> GSM1124871 2 0.1041 0.9041 0.000 0.964 0 0.032 0.004
#> GSM1124874 2 0.2471 0.8577 0.000 0.864 0 0.136 0.000
#> GSM1124875 2 0.0566 0.9075 0.000 0.984 0 0.004 0.012
#> GSM1124880 1 0.0963 0.9372 0.964 0.000 0 0.000 0.036
#> GSM1124881 2 0.1059 0.9018 0.020 0.968 0 0.004 0.008
#> GSM1124885 2 0.3132 0.8345 0.000 0.820 0 0.172 0.008
#> GSM1124886 1 0.0000 0.9536 1.000 0.000 0 0.000 0.000
#> GSM1124887 2 0.2763 0.8076 0.000 0.848 0 0.004 0.148
#> GSM1124894 2 0.0000 0.9105 0.000 1.000 0 0.000 0.000
#> GSM1124896 1 0.3274 0.6493 0.780 0.220 0 0.000 0.000
#> GSM1124899 2 0.0000 0.9105 0.000 1.000 0 0.000 0.000
#> GSM1124901 2 0.3086 0.8294 0.000 0.816 0 0.180 0.004
#> GSM1124906 2 0.0000 0.9105 0.000 1.000 0 0.000 0.000
#> GSM1124907 5 0.1768 0.7737 0.000 0.072 0 0.004 0.924
#> GSM1124911 4 0.1043 0.7836 0.000 0.040 0 0.960 0.000
#> GSM1124912 1 0.0000 0.9536 1.000 0.000 0 0.000 0.000
#> GSM1124915 4 0.0162 0.7476 0.000 0.004 0 0.996 0.000
#> GSM1124917 2 0.2719 0.8118 0.000 0.852 0 0.004 0.144
#> GSM1124918 4 0.4734 0.7576 0.020 0.228 0 0.720 0.032
#> GSM1124920 5 0.2605 0.7735 0.148 0.000 0 0.000 0.852
#> GSM1124922 2 0.0290 0.9084 0.008 0.992 0 0.000 0.000
#> GSM1124924 5 0.2605 0.7735 0.148 0.000 0 0.000 0.852
#> GSM1124926 2 0.1792 0.8863 0.000 0.916 0 0.084 0.000
#> GSM1124928 1 0.0000 0.9536 1.000 0.000 0 0.000 0.000
#> GSM1124930 5 0.0162 0.8117 0.000 0.004 0 0.000 0.996
#> GSM1124931 4 0.2966 0.8375 0.000 0.184 0 0.816 0.000
#> GSM1124935 4 0.2966 0.8375 0.000 0.184 0 0.816 0.000
#> GSM1124936 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000
#> GSM1124938 5 0.2605 0.7735 0.148 0.000 0 0.000 0.852
#> GSM1124940 1 0.0000 0.9536 1.000 0.000 0 0.000 0.000
#> GSM1124941 2 0.0000 0.9105 0.000 1.000 0 0.000 0.000
#> GSM1124942 5 0.3177 0.6340 0.000 0.208 0 0.000 0.792
#> GSM1124943 5 0.0290 0.8133 0.008 0.000 0 0.000 0.992
#> GSM1124948 5 0.2605 0.7735 0.148 0.000 0 0.000 0.852
#> GSM1124949 1 0.0000 0.9536 1.000 0.000 0 0.000 0.000
#> GSM1124950 2 0.0000 0.9105 0.000 1.000 0 0.000 0.000
#> GSM1124954 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000
#> GSM1124955 1 0.0000 0.9536 1.000 0.000 0 0.000 0.000
#> GSM1124956 4 0.2648 0.8354 0.000 0.152 0 0.848 0.000
#> GSM1124872 2 0.0000 0.9105 0.000 1.000 0 0.000 0.000
#> GSM1124873 2 0.0000 0.9105 0.000 1.000 0 0.000 0.000
#> GSM1124876 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000
#> GSM1124877 4 0.3109 0.7083 0.200 0.000 0 0.800 0.000
#> GSM1124879 1 0.0000 0.9536 1.000 0.000 0 0.000 0.000
#> GSM1124883 2 0.3209 0.8293 0.000 0.812 0 0.180 0.008
#> GSM1124889 2 0.0000 0.9105 0.000 1.000 0 0.000 0.000
#> GSM1124892 1 0.1544 0.9035 0.932 0.000 0 0.000 0.068
#> GSM1124893 1 0.0000 0.9536 1.000 0.000 0 0.000 0.000
#> GSM1124909 1 0.1197 0.9124 0.952 0.048 0 0.000 0.000
#> GSM1124913 2 0.3209 0.8293 0.000 0.812 0 0.180 0.008
#> GSM1124916 4 0.3650 0.7387 0.176 0.028 0 0.796 0.000
#> GSM1124923 5 0.0162 0.8110 0.000 0.000 0 0.004 0.996
#> GSM1124925 2 0.4770 0.4400 0.320 0.644 0 0.036 0.000
#> GSM1124929 1 0.0000 0.9536 1.000 0.000 0 0.000 0.000
#> GSM1124934 1 0.1965 0.8669 0.904 0.000 0 0.096 0.000
#> GSM1124937 1 0.0963 0.9372 0.964 0.000 0 0.000 0.036
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1124891 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1124888 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1124890 5 0.0291 0.735 0.004 0.000 0.000 0.004 0.992 0.000
#> GSM1124904 2 0.1700 0.863 0.000 0.916 0.000 0.080 0.004 0.000
#> GSM1124927 2 0.1462 0.851 0.008 0.936 0.000 0.056 0.000 0.000
#> GSM1124953 5 0.1934 0.716 0.000 0.000 0.000 0.044 0.916 0.040
#> GSM1124869 1 0.0363 0.869 0.988 0.000 0.000 0.012 0.000 0.000
#> GSM1124870 1 0.0000 0.872 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124882 1 0.0000 0.872 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124884 2 0.2378 0.849 0.000 0.848 0.000 0.152 0.000 0.000
#> GSM1124898 2 0.0520 0.864 0.000 0.984 0.000 0.008 0.008 0.000
#> GSM1124903 2 0.3221 0.823 0.000 0.772 0.000 0.220 0.004 0.004
#> GSM1124905 1 0.0000 0.872 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124910 1 0.3647 0.468 0.640 0.000 0.000 0.000 0.360 0.000
#> GSM1124919 5 0.5387 0.109 0.000 0.372 0.000 0.044 0.544 0.040
#> GSM1124932 6 0.1007 0.877 0.000 0.044 0.000 0.000 0.000 0.956
#> GSM1124933 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1124867 1 0.4663 0.659 0.748 0.012 0.000 0.052 0.148 0.040
#> GSM1124868 2 0.3221 0.823 0.000 0.772 0.000 0.220 0.004 0.004
#> GSM1124878 2 0.3221 0.823 0.000 0.772 0.000 0.220 0.004 0.004
#> GSM1124895 2 0.3221 0.823 0.000 0.772 0.000 0.220 0.004 0.004
#> GSM1124897 2 0.2442 0.850 0.000 0.852 0.000 0.144 0.004 0.000
#> GSM1124902 2 0.2234 0.856 0.000 0.872 0.000 0.124 0.004 0.000
#> GSM1124908 2 0.2833 0.830 0.008 0.864 0.000 0.040 0.088 0.000
#> GSM1124921 2 0.3835 0.785 0.012 0.820 0.000 0.040 0.088 0.040
#> GSM1124939 2 0.1444 0.863 0.000 0.928 0.000 0.072 0.000 0.000
#> GSM1124944 2 0.5284 0.669 0.024 0.680 0.000 0.044 0.212 0.040
#> GSM1124945 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1124946 2 0.3080 0.846 0.000 0.848 0.000 0.100 0.012 0.040
#> GSM1124947 2 0.3174 0.823 0.012 0.848 0.000 0.096 0.004 0.040
#> GSM1124951 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1124952 2 0.1462 0.851 0.008 0.936 0.000 0.056 0.000 0.000
#> GSM1124957 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1124900 1 0.2340 0.696 0.852 0.148 0.000 0.000 0.000 0.000
#> GSM1124914 2 0.1531 0.863 0.000 0.928 0.000 0.068 0.004 0.000
#> GSM1124871 2 0.2300 0.850 0.000 0.856 0.000 0.144 0.000 0.000
#> GSM1124874 2 0.2738 0.842 0.000 0.820 0.000 0.176 0.000 0.004
#> GSM1124875 2 0.2647 0.839 0.012 0.892 0.000 0.044 0.012 0.040
#> GSM1124880 1 0.2378 0.760 0.848 0.000 0.000 0.000 0.152 0.000
#> GSM1124881 2 0.3285 0.822 0.012 0.844 0.000 0.096 0.008 0.040
#> GSM1124885 2 0.3221 0.823 0.000 0.772 0.000 0.220 0.004 0.004
#> GSM1124886 1 0.0363 0.869 0.988 0.000 0.000 0.012 0.000 0.000
#> GSM1124887 2 0.4978 0.687 0.008 0.696 0.000 0.048 0.208 0.040
#> GSM1124894 2 0.1267 0.854 0.000 0.940 0.000 0.060 0.000 0.000
#> GSM1124896 1 0.3390 0.475 0.704 0.296 0.000 0.000 0.000 0.000
#> GSM1124899 2 0.1204 0.853 0.000 0.944 0.000 0.056 0.000 0.000
#> GSM1124901 2 0.3081 0.823 0.000 0.776 0.000 0.220 0.000 0.004
#> GSM1124906 2 0.1204 0.853 0.000 0.944 0.000 0.056 0.000 0.000
#> GSM1124907 5 0.3185 0.689 0.004 0.056 0.000 0.040 0.860 0.040
#> GSM1124911 6 0.1010 0.848 0.000 0.004 0.000 0.036 0.000 0.960
#> GSM1124912 1 0.0000 0.872 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124915 6 0.0937 0.844 0.000 0.000 0.000 0.040 0.000 0.960
#> GSM1124917 2 0.4890 0.697 0.008 0.704 0.000 0.044 0.204 0.040
#> GSM1124918 6 0.3994 0.557 0.012 0.196 0.000 0.040 0.000 0.752
#> GSM1124920 5 0.2854 0.645 0.208 0.000 0.000 0.000 0.792 0.000
#> GSM1124922 2 0.1745 0.847 0.020 0.924 0.000 0.056 0.000 0.000
#> GSM1124924 5 0.2823 0.648 0.204 0.000 0.000 0.000 0.796 0.000
#> GSM1124926 2 0.1765 0.860 0.000 0.904 0.000 0.096 0.000 0.000
#> GSM1124928 1 0.0000 0.872 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124930 5 0.0000 0.734 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1124931 6 0.1007 0.877 0.000 0.044 0.000 0.000 0.000 0.956
#> GSM1124935 6 0.1007 0.877 0.000 0.044 0.000 0.000 0.000 0.956
#> GSM1124936 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1124938 5 0.2854 0.645 0.208 0.000 0.000 0.000 0.792 0.000
#> GSM1124940 1 0.0000 0.872 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124941 2 0.1204 0.853 0.000 0.944 0.000 0.056 0.000 0.000
#> GSM1124942 5 0.3082 0.634 0.000 0.144 0.000 0.008 0.828 0.020
#> GSM1124943 5 0.0146 0.735 0.004 0.000 0.000 0.000 0.996 0.000
#> GSM1124948 5 0.2854 0.645 0.208 0.000 0.000 0.000 0.792 0.000
#> GSM1124949 1 0.0363 0.869 0.988 0.000 0.000 0.012 0.000 0.000
#> GSM1124950 2 0.1204 0.853 0.000 0.944 0.000 0.056 0.000 0.000
#> GSM1124954 4 0.3547 0.289 0.000 0.000 0.332 0.668 0.000 0.000
#> GSM1124955 1 0.0000 0.872 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124956 6 0.1007 0.877 0.000 0.044 0.000 0.000 0.000 0.956
#> GSM1124872 2 0.1204 0.853 0.000 0.944 0.000 0.056 0.000 0.000
#> GSM1124873 2 0.0865 0.865 0.000 0.964 0.000 0.036 0.000 0.000
#> GSM1124876 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1124877 4 0.4709 0.605 0.188 0.000 0.000 0.680 0.000 0.132
#> GSM1124879 1 0.0146 0.871 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM1124883 2 0.3221 0.823 0.000 0.772 0.000 0.220 0.004 0.004
#> GSM1124889 2 0.0260 0.864 0.000 0.992 0.000 0.008 0.000 0.000
#> GSM1124892 1 0.2019 0.802 0.900 0.000 0.000 0.012 0.088 0.000
#> GSM1124893 1 0.0363 0.869 0.988 0.000 0.000 0.012 0.000 0.000
#> GSM1124909 1 0.2491 0.674 0.836 0.164 0.000 0.000 0.000 0.000
#> GSM1124913 2 0.3221 0.823 0.000 0.772 0.000 0.220 0.004 0.004
#> GSM1124916 6 0.3563 0.675 0.132 0.072 0.000 0.000 0.000 0.796
#> GSM1124923 5 0.1934 0.716 0.000 0.000 0.000 0.044 0.916 0.040
#> GSM1124925 2 0.5458 0.314 0.364 0.544 0.000 0.032 0.000 0.060
#> GSM1124929 1 0.0363 0.869 0.988 0.000 0.000 0.012 0.000 0.000
#> GSM1124934 4 0.3707 0.594 0.312 0.000 0.000 0.680 0.000 0.008
#> GSM1124937 1 0.2340 0.763 0.852 0.000 0.000 0.000 0.148 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 tissue(p) k
#> ATC:pam 87 0.2443 2
#> ATC:pam 89 0.0507 3
#> ATC:pam 88 0.1602 4
#> ATC:pam 89 0.0292 5
#> ATC:pam 86 0.0194 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 48983 rows and 91 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 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 0.721 0.944 0.956 0.4007 0.561 0.561
#> 3 3 0.665 0.825 0.887 0.3581 0.827 0.711
#> 4 4 0.824 0.872 0.919 0.2548 0.743 0.507
#> 5 5 0.807 0.843 0.842 0.1451 0.865 0.589
#> 6 6 0.865 0.864 0.907 0.0537 0.921 0.647
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
#> GSM1124891 1 0.6247 0.886 0.844 0.156
#> GSM1124888 1 0.6247 0.886 0.844 0.156
#> GSM1124890 1 0.9323 0.645 0.652 0.348
#> GSM1124904 2 0.0000 0.993 0.000 1.000
#> GSM1124927 2 0.0000 0.993 0.000 1.000
#> GSM1124953 1 0.6247 0.886 0.844 0.156
#> GSM1124869 2 0.0672 0.992 0.008 0.992
#> GSM1124870 2 0.0938 0.989 0.012 0.988
#> GSM1124882 2 0.0672 0.992 0.008 0.992
#> GSM1124884 2 0.0000 0.993 0.000 1.000
#> GSM1124898 2 0.0000 0.993 0.000 1.000
#> GSM1124903 2 0.0000 0.993 0.000 1.000
#> GSM1124905 2 0.0672 0.992 0.008 0.992
#> GSM1124910 2 0.0672 0.992 0.008 0.992
#> GSM1124919 1 0.8016 0.803 0.756 0.244
#> GSM1124932 1 0.0000 0.867 1.000 0.000
#> GSM1124933 1 0.6247 0.886 0.844 0.156
#> GSM1124867 2 0.0672 0.992 0.008 0.992
#> GSM1124868 2 0.0000 0.993 0.000 1.000
#> GSM1124878 2 0.0000 0.993 0.000 1.000
#> GSM1124895 2 0.0000 0.993 0.000 1.000
#> GSM1124897 2 0.0000 0.993 0.000 1.000
#> GSM1124902 2 0.0000 0.993 0.000 1.000
#> GSM1124908 2 0.0000 0.993 0.000 1.000
#> GSM1124921 2 0.0672 0.988 0.008 0.992
#> GSM1124939 2 0.0000 0.993 0.000 1.000
#> GSM1124944 2 0.0000 0.993 0.000 1.000
#> GSM1124945 1 0.6247 0.886 0.844 0.156
#> GSM1124946 2 0.0376 0.993 0.004 0.996
#> GSM1124947 2 0.0000 0.993 0.000 1.000
#> GSM1124951 1 0.6247 0.886 0.844 0.156
#> GSM1124952 2 0.0000 0.993 0.000 1.000
#> GSM1124957 1 0.6247 0.886 0.844 0.156
#> GSM1124900 2 0.0672 0.992 0.008 0.992
#> GSM1124914 2 0.0000 0.993 0.000 1.000
#> GSM1124871 2 0.0000 0.993 0.000 1.000
#> GSM1124874 2 0.0000 0.993 0.000 1.000
#> GSM1124875 2 0.0000 0.993 0.000 1.000
#> GSM1124880 2 0.0672 0.992 0.008 0.992
#> GSM1124881 2 0.0000 0.993 0.000 1.000
#> GSM1124885 2 0.0000 0.993 0.000 1.000
#> GSM1124886 2 0.0672 0.992 0.008 0.992
#> GSM1124887 2 0.5519 0.833 0.128 0.872
#> GSM1124894 2 0.0000 0.993 0.000 1.000
#> GSM1124896 2 0.0672 0.992 0.008 0.992
#> GSM1124899 2 0.0000 0.993 0.000 1.000
#> GSM1124901 2 0.0000 0.993 0.000 1.000
#> GSM1124906 2 0.0000 0.993 0.000 1.000
#> GSM1124907 2 0.0672 0.992 0.008 0.992
#> GSM1124911 1 0.0000 0.867 1.000 0.000
#> GSM1124912 2 0.0672 0.992 0.008 0.992
#> GSM1124915 1 0.0000 0.867 1.000 0.000
#> GSM1124917 1 0.6438 0.883 0.836 0.164
#> GSM1124918 1 0.0938 0.865 0.988 0.012
#> GSM1124920 1 0.9635 0.571 0.612 0.388
#> GSM1124922 2 0.0000 0.993 0.000 1.000
#> GSM1124924 2 0.0672 0.992 0.008 0.992
#> GSM1124926 2 0.0000 0.993 0.000 1.000
#> GSM1124928 2 0.0672 0.992 0.008 0.992
#> GSM1124930 1 0.9000 0.707 0.684 0.316
#> GSM1124931 1 0.0000 0.867 1.000 0.000
#> GSM1124935 1 0.0000 0.867 1.000 0.000
#> GSM1124936 1 0.6247 0.886 0.844 0.156
#> GSM1124938 1 0.6247 0.886 0.844 0.156
#> GSM1124940 2 0.0672 0.992 0.008 0.992
#> GSM1124941 2 0.0000 0.993 0.000 1.000
#> GSM1124942 2 0.0672 0.992 0.008 0.992
#> GSM1124943 1 0.7815 0.820 0.768 0.232
#> GSM1124948 2 0.0672 0.992 0.008 0.992
#> GSM1124949 2 0.0672 0.992 0.008 0.992
#> GSM1124950 2 0.0000 0.993 0.000 1.000
#> GSM1124954 1 0.0000 0.867 1.000 0.000
#> GSM1124955 2 0.0672 0.992 0.008 0.992
#> GSM1124956 1 0.0000 0.867 1.000 0.000
#> GSM1124872 2 0.0000 0.993 0.000 1.000
#> GSM1124873 2 0.0000 0.993 0.000 1.000
#> GSM1124876 1 0.6247 0.886 0.844 0.156
#> GSM1124877 1 0.0000 0.867 1.000 0.000
#> GSM1124879 2 0.0672 0.992 0.008 0.992
#> GSM1124883 2 0.0000 0.993 0.000 1.000
#> GSM1124889 2 0.0000 0.993 0.000 1.000
#> GSM1124892 2 0.0672 0.992 0.008 0.992
#> GSM1124893 2 0.0672 0.992 0.008 0.992
#> GSM1124909 2 0.0672 0.992 0.008 0.992
#> GSM1124913 2 0.0000 0.993 0.000 1.000
#> GSM1124916 1 0.0000 0.867 1.000 0.000
#> GSM1124923 1 0.6247 0.886 0.844 0.156
#> GSM1124925 2 0.1184 0.986 0.016 0.984
#> GSM1124929 1 0.6973 0.786 0.812 0.188
#> GSM1124934 1 0.0000 0.867 1.000 0.000
#> GSM1124937 2 0.0672 0.992 0.008 0.992
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1124891 3 0.0000 0.728 0.000 0.000 1.000
#> GSM1124888 3 0.0000 0.728 0.000 0.000 1.000
#> GSM1124890 3 0.4172 0.713 0.004 0.156 0.840
#> GSM1124904 2 0.0000 0.907 0.000 1.000 0.000
#> GSM1124927 2 0.0000 0.907 0.000 1.000 0.000
#> GSM1124953 3 0.0000 0.728 0.000 0.000 1.000
#> GSM1124869 2 0.4605 0.834 0.204 0.796 0.000
#> GSM1124870 2 0.4605 0.834 0.204 0.796 0.000
#> GSM1124882 2 0.4605 0.834 0.204 0.796 0.000
#> GSM1124884 2 0.0000 0.907 0.000 1.000 0.000
#> GSM1124898 2 0.0000 0.907 0.000 1.000 0.000
#> GSM1124903 2 0.0000 0.907 0.000 1.000 0.000
#> GSM1124905 2 0.4605 0.834 0.204 0.796 0.000
#> GSM1124910 2 0.4784 0.833 0.200 0.796 0.004
#> GSM1124919 3 0.1411 0.731 0.000 0.036 0.964
#> GSM1124932 1 0.4605 0.994 0.796 0.000 0.204
#> GSM1124933 3 0.0000 0.728 0.000 0.000 1.000
#> GSM1124867 2 0.1163 0.899 0.028 0.972 0.000
#> GSM1124868 2 0.0000 0.907 0.000 1.000 0.000
#> GSM1124878 2 0.0000 0.907 0.000 1.000 0.000
#> GSM1124895 2 0.0000 0.907 0.000 1.000 0.000
#> GSM1124897 2 0.0000 0.907 0.000 1.000 0.000
#> GSM1124902 2 0.0000 0.907 0.000 1.000 0.000
#> GSM1124908 2 0.0592 0.898 0.000 0.988 0.012
#> GSM1124921 2 0.6299 -0.295 0.000 0.524 0.476
#> GSM1124939 2 0.0000 0.907 0.000 1.000 0.000
#> GSM1124944 3 0.6286 0.429 0.000 0.464 0.536
#> GSM1124945 3 0.0000 0.728 0.000 0.000 1.000
#> GSM1124946 2 0.0424 0.901 0.000 0.992 0.008
#> GSM1124947 2 0.0000 0.907 0.000 1.000 0.000
#> GSM1124951 3 0.0000 0.728 0.000 0.000 1.000
#> GSM1124952 2 0.0000 0.907 0.000 1.000 0.000
#> GSM1124957 3 0.0000 0.728 0.000 0.000 1.000
#> GSM1124900 2 0.4605 0.834 0.204 0.796 0.000
#> GSM1124914 2 0.0000 0.907 0.000 1.000 0.000
#> GSM1124871 2 0.0000 0.907 0.000 1.000 0.000
#> GSM1124874 2 0.0000 0.907 0.000 1.000 0.000
#> GSM1124875 2 0.0000 0.907 0.000 1.000 0.000
#> GSM1124880 2 0.3619 0.860 0.136 0.864 0.000
#> GSM1124881 2 0.0000 0.907 0.000 1.000 0.000
#> GSM1124885 2 0.0000 0.907 0.000 1.000 0.000
#> GSM1124886 2 0.4605 0.834 0.204 0.796 0.000
#> GSM1124887 3 0.6302 0.377 0.000 0.480 0.520
#> GSM1124894 2 0.0000 0.907 0.000 1.000 0.000
#> GSM1124896 2 0.4605 0.834 0.204 0.796 0.000
#> GSM1124899 2 0.0000 0.907 0.000 1.000 0.000
#> GSM1124901 2 0.0000 0.907 0.000 1.000 0.000
#> GSM1124906 2 0.0000 0.907 0.000 1.000 0.000
#> GSM1124907 3 0.6180 0.532 0.000 0.416 0.584
#> GSM1124911 1 0.4605 0.994 0.796 0.000 0.204
#> GSM1124912 2 0.4605 0.834 0.204 0.796 0.000
#> GSM1124915 1 0.4605 0.994 0.796 0.000 0.204
#> GSM1124917 3 0.6045 0.512 0.000 0.380 0.620
#> GSM1124918 1 0.5348 0.954 0.796 0.028 0.176
#> GSM1124920 3 0.4002 0.654 0.160 0.000 0.840
#> GSM1124922 2 0.0000 0.907 0.000 1.000 0.000
#> GSM1124924 3 0.4931 0.688 0.004 0.212 0.784
#> GSM1124926 2 0.0000 0.907 0.000 1.000 0.000
#> GSM1124928 2 0.4605 0.834 0.204 0.796 0.000
#> GSM1124930 3 0.5497 0.624 0.000 0.292 0.708
#> GSM1124931 1 0.4912 0.984 0.796 0.008 0.196
#> GSM1124935 1 0.4605 0.994 0.796 0.000 0.204
#> GSM1124936 3 0.0000 0.728 0.000 0.000 1.000
#> GSM1124938 3 0.1765 0.734 0.004 0.040 0.956
#> GSM1124940 2 0.4605 0.834 0.204 0.796 0.000
#> GSM1124941 2 0.0000 0.907 0.000 1.000 0.000
#> GSM1124942 3 0.6192 0.524 0.000 0.420 0.580
#> GSM1124943 3 0.3644 0.722 0.004 0.124 0.872
#> GSM1124948 3 0.4834 0.691 0.004 0.204 0.792
#> GSM1124949 2 0.4605 0.834 0.204 0.796 0.000
#> GSM1124950 2 0.0000 0.907 0.000 1.000 0.000
#> GSM1124954 1 0.4605 0.994 0.796 0.000 0.204
#> GSM1124955 2 0.4605 0.834 0.204 0.796 0.000
#> GSM1124956 1 0.4605 0.994 0.796 0.000 0.204
#> GSM1124872 2 0.0000 0.907 0.000 1.000 0.000
#> GSM1124873 2 0.0000 0.907 0.000 1.000 0.000
#> GSM1124876 3 0.0000 0.728 0.000 0.000 1.000
#> GSM1124877 1 0.4605 0.994 0.796 0.000 0.204
#> GSM1124879 2 0.4605 0.834 0.204 0.796 0.000
#> GSM1124883 2 0.0000 0.907 0.000 1.000 0.000
#> GSM1124889 2 0.0000 0.907 0.000 1.000 0.000
#> GSM1124892 2 0.5486 0.819 0.196 0.780 0.024
#> GSM1124893 2 0.4605 0.834 0.204 0.796 0.000
#> GSM1124909 2 0.0424 0.905 0.008 0.992 0.000
#> GSM1124913 2 0.0000 0.907 0.000 1.000 0.000
#> GSM1124916 1 0.4605 0.994 0.796 0.000 0.204
#> GSM1124923 3 0.0000 0.728 0.000 0.000 1.000
#> GSM1124925 2 0.4605 0.834 0.204 0.796 0.000
#> GSM1124929 2 0.5810 0.816 0.132 0.796 0.072
#> GSM1124934 1 0.4605 0.994 0.796 0.000 0.204
#> GSM1124937 2 0.4605 0.834 0.204 0.796 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1124891 3 0.2021 0.931 0.040 0.000 0.936 0.024
#> GSM1124888 3 0.1820 0.931 0.036 0.000 0.944 0.020
#> GSM1124890 3 0.1042 0.925 0.020 0.008 0.972 0.000
#> GSM1124904 2 0.0707 0.913 0.020 0.980 0.000 0.000
#> GSM1124927 2 0.0921 0.912 0.028 0.972 0.000 0.000
#> GSM1124953 3 0.1339 0.936 0.004 0.008 0.964 0.024
#> GSM1124869 1 0.1792 0.918 0.932 0.068 0.000 0.000
#> GSM1124870 1 0.1389 0.902 0.952 0.048 0.000 0.000
#> GSM1124882 1 0.1792 0.918 0.932 0.068 0.000 0.000
#> GSM1124884 2 0.0817 0.913 0.024 0.976 0.000 0.000
#> GSM1124898 2 0.1584 0.895 0.012 0.952 0.036 0.000
#> GSM1124903 2 0.0336 0.912 0.008 0.992 0.000 0.000
#> GSM1124905 1 0.1792 0.918 0.932 0.068 0.000 0.000
#> GSM1124910 1 0.5970 0.643 0.668 0.088 0.244 0.000
#> GSM1124919 3 0.1822 0.907 0.008 0.044 0.944 0.004
#> GSM1124932 4 0.0000 0.979 0.000 0.000 0.000 1.000
#> GSM1124933 3 0.2021 0.931 0.040 0.000 0.936 0.024
#> GSM1124867 2 0.6585 0.508 0.188 0.632 0.180 0.000
#> GSM1124868 2 0.0707 0.903 0.000 0.980 0.020 0.000
#> GSM1124878 2 0.0336 0.912 0.008 0.992 0.000 0.000
#> GSM1124895 2 0.0000 0.911 0.000 1.000 0.000 0.000
#> GSM1124897 2 0.0817 0.913 0.024 0.976 0.000 0.000
#> GSM1124902 2 0.0336 0.912 0.008 0.992 0.000 0.000
#> GSM1124908 2 0.1911 0.899 0.020 0.944 0.032 0.004
#> GSM1124921 2 0.3945 0.818 0.024 0.828 0.144 0.004
#> GSM1124939 2 0.0336 0.912 0.008 0.992 0.000 0.000
#> GSM1124944 2 0.4044 0.812 0.024 0.820 0.152 0.004
#> GSM1124945 3 0.1151 0.936 0.000 0.008 0.968 0.024
#> GSM1124946 2 0.3606 0.830 0.024 0.844 0.132 0.000
#> GSM1124947 2 0.4872 0.654 0.028 0.728 0.244 0.000
#> GSM1124951 3 0.1151 0.936 0.000 0.008 0.968 0.024
#> GSM1124952 2 0.1109 0.912 0.028 0.968 0.000 0.004
#> GSM1124957 3 0.2021 0.931 0.040 0.000 0.936 0.024
#> GSM1124900 1 0.1792 0.918 0.932 0.068 0.000 0.000
#> GSM1124914 2 0.0336 0.909 0.000 0.992 0.008 0.000
#> GSM1124871 2 0.0336 0.912 0.008 0.992 0.000 0.000
#> GSM1124874 2 0.0817 0.913 0.024 0.976 0.000 0.000
#> GSM1124875 2 0.1820 0.898 0.020 0.944 0.036 0.000
#> GSM1124880 2 0.6968 0.279 0.308 0.552 0.140 0.000
#> GSM1124881 2 0.0921 0.912 0.028 0.972 0.000 0.000
#> GSM1124885 2 0.0000 0.911 0.000 1.000 0.000 0.000
#> GSM1124886 1 0.1978 0.915 0.928 0.068 0.004 0.000
#> GSM1124887 2 0.4004 0.803 0.024 0.812 0.164 0.000
#> GSM1124894 2 0.2814 0.824 0.132 0.868 0.000 0.000
#> GSM1124896 1 0.1716 0.915 0.936 0.064 0.000 0.000
#> GSM1124899 2 0.0817 0.913 0.024 0.976 0.000 0.000
#> GSM1124901 2 0.0000 0.911 0.000 1.000 0.000 0.000
#> GSM1124906 2 0.0817 0.913 0.024 0.976 0.000 0.000
#> GSM1124907 2 0.4687 0.734 0.020 0.752 0.224 0.004
#> GSM1124911 4 0.0707 0.978 0.020 0.000 0.000 0.980
#> GSM1124912 1 0.1792 0.918 0.932 0.068 0.000 0.000
#> GSM1124915 4 0.0779 0.979 0.016 0.004 0.000 0.980
#> GSM1124917 2 0.6071 0.619 0.012 0.668 0.260 0.060
#> GSM1124918 4 0.2515 0.889 0.004 0.072 0.012 0.912
#> GSM1124920 3 0.1474 0.929 0.052 0.000 0.948 0.000
#> GSM1124922 2 0.0817 0.913 0.024 0.976 0.000 0.000
#> GSM1124924 3 0.3708 0.768 0.020 0.148 0.832 0.000
#> GSM1124926 2 0.0817 0.913 0.024 0.976 0.000 0.000
#> GSM1124928 1 0.1792 0.918 0.932 0.068 0.000 0.000
#> GSM1124930 2 0.5718 0.517 0.012 0.624 0.344 0.020
#> GSM1124931 4 0.0927 0.976 0.016 0.008 0.000 0.976
#> GSM1124935 4 0.0707 0.978 0.020 0.000 0.000 0.980
#> GSM1124936 3 0.2021 0.931 0.040 0.000 0.936 0.024
#> GSM1124938 3 0.0524 0.932 0.004 0.008 0.988 0.000
#> GSM1124940 1 0.1792 0.918 0.932 0.068 0.000 0.000
#> GSM1124941 2 0.0817 0.913 0.024 0.976 0.000 0.000
#> GSM1124942 2 0.4789 0.736 0.024 0.748 0.224 0.004
#> GSM1124943 3 0.0712 0.932 0.004 0.008 0.984 0.004
#> GSM1124948 3 0.3606 0.778 0.020 0.140 0.840 0.000
#> GSM1124949 1 0.1792 0.918 0.932 0.068 0.000 0.000
#> GSM1124950 2 0.0817 0.913 0.024 0.976 0.000 0.000
#> GSM1124954 4 0.0524 0.978 0.008 0.000 0.004 0.988
#> GSM1124955 1 0.1389 0.902 0.952 0.048 0.000 0.000
#> GSM1124956 4 0.0000 0.979 0.000 0.000 0.000 1.000
#> GSM1124872 2 0.0921 0.912 0.028 0.972 0.000 0.000
#> GSM1124873 2 0.0817 0.913 0.024 0.976 0.000 0.000
#> GSM1124876 3 0.2021 0.931 0.040 0.000 0.936 0.024
#> GSM1124877 4 0.0376 0.978 0.004 0.000 0.004 0.992
#> GSM1124879 1 0.1792 0.918 0.932 0.068 0.000 0.000
#> GSM1124883 2 0.0336 0.912 0.008 0.992 0.000 0.000
#> GSM1124889 2 0.0817 0.913 0.024 0.976 0.000 0.000
#> GSM1124892 1 0.5055 0.595 0.712 0.032 0.256 0.000
#> GSM1124893 1 0.1792 0.918 0.932 0.068 0.000 0.000
#> GSM1124909 2 0.1792 0.886 0.068 0.932 0.000 0.000
#> GSM1124913 2 0.0188 0.912 0.004 0.996 0.000 0.000
#> GSM1124916 4 0.0895 0.978 0.020 0.000 0.004 0.976
#> GSM1124923 3 0.0859 0.932 0.008 0.008 0.980 0.004
#> GSM1124925 1 0.1389 0.902 0.952 0.048 0.000 0.000
#> GSM1124929 1 0.4919 0.643 0.752 0.028 0.008 0.212
#> GSM1124934 4 0.0524 0.978 0.008 0.000 0.004 0.988
#> GSM1124937 1 0.6564 0.333 0.536 0.380 0.084 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM1124891 3 0.0000 0.8564 0.000 0.000 1.000 0.000 0.000
#> GSM1124888 3 0.2179 0.8806 0.000 0.112 0.888 0.000 0.000
#> GSM1124890 3 0.3741 0.8833 0.000 0.264 0.732 0.004 0.000
#> GSM1124904 5 0.0404 0.8079 0.000 0.012 0.000 0.000 0.988
#> GSM1124927 2 0.3814 0.9215 0.004 0.720 0.000 0.000 0.276
#> GSM1124953 3 0.2471 0.8827 0.000 0.136 0.864 0.000 0.000
#> GSM1124869 1 0.0703 0.9523 0.976 0.000 0.000 0.000 0.024
#> GSM1124870 1 0.0609 0.9500 0.980 0.000 0.000 0.000 0.020
#> GSM1124882 1 0.1121 0.9410 0.956 0.000 0.000 0.000 0.044
#> GSM1124884 2 0.3661 0.9228 0.000 0.724 0.000 0.000 0.276
#> GSM1124898 5 0.0703 0.8054 0.000 0.024 0.000 0.000 0.976
#> GSM1124903 5 0.0162 0.8077 0.000 0.004 0.000 0.000 0.996
#> GSM1124905 1 0.0703 0.9523 0.976 0.000 0.000 0.000 0.024
#> GSM1124910 1 0.5030 0.6288 0.688 0.236 0.004 0.000 0.072
#> GSM1124919 3 0.3766 0.8816 0.000 0.268 0.728 0.004 0.000
#> GSM1124932 4 0.0162 0.9744 0.000 0.000 0.004 0.996 0.000
#> GSM1124933 3 0.0000 0.8564 0.000 0.000 1.000 0.000 0.000
#> GSM1124867 2 0.5241 0.7872 0.148 0.696 0.004 0.000 0.152
#> GSM1124868 5 0.0290 0.8080 0.000 0.008 0.000 0.000 0.992
#> GSM1124878 5 0.0162 0.8077 0.000 0.004 0.000 0.000 0.996
#> GSM1124895 5 0.0000 0.8079 0.000 0.000 0.000 0.000 1.000
#> GSM1124897 5 0.0000 0.8079 0.000 0.000 0.000 0.000 1.000
#> GSM1124902 5 0.0880 0.7870 0.000 0.032 0.000 0.000 0.968
#> GSM1124908 5 0.1851 0.7388 0.000 0.088 0.000 0.000 0.912
#> GSM1124921 5 0.5856 0.5210 0.000 0.224 0.172 0.000 0.604
#> GSM1124939 5 0.0880 0.7867 0.000 0.032 0.000 0.000 0.968
#> GSM1124944 5 0.5604 0.5546 0.000 0.240 0.132 0.000 0.628
#> GSM1124945 3 0.1410 0.8746 0.000 0.060 0.940 0.000 0.000
#> GSM1124946 5 0.5740 0.5134 0.000 0.272 0.128 0.000 0.600
#> GSM1124947 2 0.3817 0.9065 0.004 0.740 0.004 0.000 0.252
#> GSM1124951 3 0.1410 0.8746 0.000 0.060 0.940 0.000 0.000
#> GSM1124952 2 0.3661 0.9228 0.000 0.724 0.000 0.000 0.276
#> GSM1124957 3 0.0000 0.8564 0.000 0.000 1.000 0.000 0.000
#> GSM1124900 1 0.1403 0.9390 0.952 0.024 0.000 0.000 0.024
#> GSM1124914 5 0.0609 0.7979 0.000 0.020 0.000 0.000 0.980
#> GSM1124871 5 0.0404 0.8038 0.000 0.012 0.000 0.000 0.988
#> GSM1124874 2 0.4250 0.9027 0.028 0.720 0.000 0.000 0.252
#> GSM1124875 5 0.1478 0.7891 0.000 0.064 0.000 0.000 0.936
#> GSM1124880 2 0.5354 0.7428 0.188 0.680 0.004 0.000 0.128
#> GSM1124881 2 0.3730 0.9138 0.000 0.712 0.000 0.000 0.288
#> GSM1124885 5 0.0000 0.8079 0.000 0.000 0.000 0.000 1.000
#> GSM1124886 1 0.0955 0.9506 0.968 0.000 0.004 0.000 0.028
#> GSM1124887 5 0.5839 0.5308 0.000 0.248 0.136 0.004 0.612
#> GSM1124894 2 0.5375 0.7529 0.200 0.664 0.000 0.000 0.136
#> GSM1124896 1 0.0703 0.9523 0.976 0.000 0.000 0.000 0.024
#> GSM1124899 2 0.3661 0.9228 0.000 0.724 0.000 0.000 0.276
#> GSM1124901 5 0.0162 0.8071 0.000 0.004 0.000 0.000 0.996
#> GSM1124906 2 0.3661 0.9228 0.000 0.724 0.000 0.000 0.276
#> GSM1124907 5 0.6721 0.0702 0.000 0.276 0.304 0.000 0.420
#> GSM1124911 4 0.0324 0.9740 0.004 0.000 0.004 0.992 0.000
#> GSM1124912 1 0.0703 0.9523 0.976 0.000 0.000 0.000 0.024
#> GSM1124915 4 0.0703 0.9676 0.024 0.000 0.000 0.976 0.000
#> GSM1124917 5 0.6392 0.3976 0.000 0.220 0.236 0.004 0.540
#> GSM1124918 4 0.3687 0.8493 0.004 0.036 0.052 0.852 0.056
#> GSM1124920 3 0.3123 0.8848 0.004 0.184 0.812 0.000 0.000
#> GSM1124922 2 0.3661 0.9228 0.000 0.724 0.000 0.000 0.276
#> GSM1124924 3 0.3741 0.8826 0.000 0.264 0.732 0.000 0.004
#> GSM1124926 2 0.3661 0.9228 0.000 0.724 0.000 0.000 0.276
#> GSM1124928 1 0.1197 0.9374 0.952 0.000 0.000 0.000 0.048
#> GSM1124930 3 0.3814 0.8770 0.000 0.276 0.720 0.000 0.004
#> GSM1124931 4 0.0880 0.9633 0.032 0.000 0.000 0.968 0.000
#> GSM1124935 4 0.0162 0.9744 0.000 0.000 0.004 0.996 0.000
#> GSM1124936 3 0.0000 0.8564 0.000 0.000 1.000 0.000 0.000
#> GSM1124938 3 0.3430 0.8905 0.000 0.220 0.776 0.004 0.000
#> GSM1124940 1 0.0703 0.9523 0.976 0.000 0.000 0.000 0.024
#> GSM1124941 2 0.3661 0.9228 0.000 0.724 0.000 0.000 0.276
#> GSM1124942 5 0.6210 0.4108 0.000 0.276 0.184 0.000 0.540
#> GSM1124943 3 0.3612 0.8831 0.000 0.268 0.732 0.000 0.000
#> GSM1124948 3 0.3612 0.8831 0.000 0.268 0.732 0.000 0.000
#> GSM1124949 1 0.0794 0.9513 0.972 0.000 0.000 0.000 0.028
#> GSM1124950 2 0.3661 0.9228 0.000 0.724 0.000 0.000 0.276
#> GSM1124954 4 0.0771 0.9702 0.020 0.000 0.004 0.976 0.000
#> GSM1124955 1 0.0609 0.9500 0.980 0.000 0.000 0.000 0.020
#> GSM1124956 4 0.0162 0.9744 0.000 0.000 0.004 0.996 0.000
#> GSM1124872 2 0.3752 0.9099 0.000 0.708 0.000 0.000 0.292
#> GSM1124873 2 0.3661 0.9228 0.000 0.724 0.000 0.000 0.276
#> GSM1124876 3 0.0000 0.8564 0.000 0.000 1.000 0.000 0.000
#> GSM1124877 4 0.0162 0.9744 0.000 0.000 0.004 0.996 0.000
#> GSM1124879 1 0.1197 0.9374 0.952 0.000 0.000 0.000 0.048
#> GSM1124883 5 0.0290 0.8054 0.000 0.008 0.000 0.000 0.992
#> GSM1124889 2 0.3661 0.9228 0.000 0.724 0.000 0.000 0.276
#> GSM1124892 1 0.1310 0.9390 0.956 0.000 0.020 0.000 0.024
#> GSM1124893 1 0.0703 0.9523 0.976 0.000 0.000 0.000 0.024
#> GSM1124909 2 0.4676 0.8722 0.072 0.720 0.000 0.000 0.208
#> GSM1124913 5 0.0162 0.8077 0.000 0.004 0.000 0.000 0.996
#> GSM1124916 4 0.0963 0.9628 0.036 0.000 0.000 0.964 0.000
#> GSM1124923 3 0.3766 0.8816 0.000 0.268 0.728 0.004 0.000
#> GSM1124925 1 0.0510 0.9463 0.984 0.000 0.000 0.000 0.016
#> GSM1124929 1 0.4268 0.5542 0.708 0.000 0.024 0.268 0.000
#> GSM1124934 4 0.0771 0.9702 0.020 0.000 0.004 0.976 0.000
#> GSM1124937 2 0.5590 0.5131 0.324 0.592 0.004 0.000 0.080
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1124891 3 0.0458 0.935 0.000 0.000 0.984 0.000 0.016 0.000
#> GSM1124888 3 0.2135 0.875 0.000 0.000 0.872 0.000 0.128 0.000
#> GSM1124890 5 0.2260 0.792 0.000 0.000 0.140 0.000 0.860 0.000
#> GSM1124904 4 0.2512 0.886 0.000 0.060 0.000 0.880 0.060 0.000
#> GSM1124927 2 0.1075 0.910 0.000 0.952 0.000 0.000 0.048 0.000
#> GSM1124953 5 0.3778 0.683 0.000 0.000 0.272 0.020 0.708 0.000
#> GSM1124869 1 0.0000 0.943 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124870 1 0.0000 0.943 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124882 1 0.0146 0.942 0.996 0.004 0.000 0.000 0.000 0.000
#> GSM1124884 2 0.0000 0.930 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1124898 4 0.5770 0.410 0.000 0.252 0.000 0.508 0.240 0.000
#> GSM1124903 4 0.0713 0.898 0.000 0.028 0.000 0.972 0.000 0.000
#> GSM1124905 1 0.0000 0.943 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124910 1 0.3623 0.752 0.764 0.008 0.020 0.000 0.208 0.000
#> GSM1124919 5 0.3309 0.788 0.000 0.004 0.172 0.024 0.800 0.000
#> GSM1124932 6 0.0000 0.975 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM1124933 3 0.0458 0.935 0.000 0.000 0.984 0.000 0.016 0.000
#> GSM1124867 2 0.5258 0.644 0.128 0.652 0.020 0.000 0.200 0.000
#> GSM1124868 4 0.0713 0.898 0.000 0.028 0.000 0.972 0.000 0.000
#> GSM1124878 4 0.0713 0.898 0.000 0.028 0.000 0.972 0.000 0.000
#> GSM1124895 4 0.1141 0.908 0.000 0.052 0.000 0.948 0.000 0.000
#> GSM1124897 4 0.1757 0.911 0.000 0.076 0.000 0.916 0.008 0.000
#> GSM1124902 4 0.2631 0.866 0.000 0.180 0.000 0.820 0.000 0.000
#> GSM1124908 5 0.5701 0.331 0.000 0.376 0.000 0.164 0.460 0.000
#> GSM1124921 5 0.3213 0.811 0.000 0.032 0.000 0.160 0.808 0.000
#> GSM1124939 4 0.2664 0.862 0.000 0.184 0.000 0.816 0.000 0.000
#> GSM1124944 5 0.3062 0.818 0.000 0.024 0.000 0.160 0.816 0.000
#> GSM1124945 3 0.0909 0.932 0.000 0.000 0.968 0.012 0.020 0.000
#> GSM1124946 5 0.3025 0.820 0.000 0.024 0.000 0.156 0.820 0.000
#> GSM1124947 2 0.2673 0.845 0.004 0.856 0.008 0.004 0.128 0.000
#> GSM1124951 3 0.0909 0.932 0.000 0.000 0.968 0.012 0.020 0.000
#> GSM1124952 2 0.0146 0.928 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM1124957 3 0.0458 0.935 0.000 0.000 0.984 0.000 0.016 0.000
#> GSM1124900 1 0.0632 0.925 0.976 0.024 0.000 0.000 0.000 0.000
#> GSM1124914 4 0.2597 0.868 0.000 0.176 0.000 0.824 0.000 0.000
#> GSM1124871 4 0.2340 0.885 0.000 0.148 0.000 0.852 0.000 0.000
#> GSM1124874 2 0.0146 0.927 0.004 0.996 0.000 0.000 0.000 0.000
#> GSM1124875 5 0.3939 0.748 0.000 0.068 0.000 0.180 0.752 0.000
#> GSM1124880 2 0.5964 0.173 0.380 0.468 0.020 0.000 0.132 0.000
#> GSM1124881 2 0.1049 0.903 0.000 0.960 0.000 0.032 0.008 0.000
#> GSM1124885 4 0.1387 0.911 0.000 0.068 0.000 0.932 0.000 0.000
#> GSM1124886 1 0.0146 0.941 0.996 0.000 0.004 0.000 0.000 0.000
#> GSM1124887 5 0.3062 0.818 0.000 0.024 0.000 0.160 0.816 0.000
#> GSM1124894 2 0.1713 0.901 0.028 0.928 0.000 0.000 0.044 0.000
#> GSM1124896 1 0.0000 0.943 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124899 2 0.0000 0.930 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1124901 4 0.1444 0.912 0.000 0.072 0.000 0.928 0.000 0.000
#> GSM1124906 2 0.0000 0.930 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1124907 5 0.3301 0.830 0.000 0.016 0.032 0.124 0.828 0.000
#> GSM1124911 6 0.0000 0.975 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM1124912 1 0.0000 0.943 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124915 6 0.0508 0.968 0.000 0.012 0.000 0.004 0.000 0.984
#> GSM1124917 5 0.3593 0.828 0.000 0.028 0.028 0.136 0.808 0.000
#> GSM1124918 6 0.3331 0.829 0.000 0.028 0.008 0.032 0.084 0.848
#> GSM1124920 3 0.2454 0.850 0.000 0.000 0.840 0.000 0.160 0.000
#> GSM1124922 2 0.0000 0.930 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1124924 5 0.2431 0.791 0.000 0.008 0.132 0.000 0.860 0.000
#> GSM1124926 2 0.0000 0.930 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1124928 1 0.0146 0.942 0.996 0.004 0.000 0.000 0.000 0.000
#> GSM1124930 5 0.2531 0.804 0.000 0.008 0.128 0.004 0.860 0.000
#> GSM1124931 6 0.0603 0.965 0.000 0.016 0.000 0.004 0.000 0.980
#> GSM1124935 6 0.0000 0.975 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM1124936 3 0.0458 0.935 0.000 0.000 0.984 0.000 0.016 0.000
#> GSM1124938 3 0.3371 0.649 0.000 0.000 0.708 0.000 0.292 0.000
#> GSM1124940 1 0.0000 0.943 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124941 2 0.0000 0.930 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1124942 5 0.2704 0.827 0.000 0.016 0.000 0.140 0.844 0.000
#> GSM1124943 5 0.2340 0.789 0.000 0.000 0.148 0.000 0.852 0.000
#> GSM1124948 5 0.2219 0.792 0.000 0.000 0.136 0.000 0.864 0.000
#> GSM1124949 1 0.0000 0.943 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124950 2 0.0000 0.930 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1124954 6 0.0146 0.975 0.000 0.000 0.000 0.004 0.000 0.996
#> GSM1124955 1 0.0000 0.943 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124956 6 0.0000 0.975 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM1124872 2 0.0000 0.930 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1124873 2 0.0000 0.930 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1124876 3 0.0458 0.935 0.000 0.000 0.984 0.000 0.016 0.000
#> GSM1124877 6 0.0146 0.975 0.000 0.000 0.000 0.004 0.000 0.996
#> GSM1124879 1 0.0146 0.942 0.996 0.004 0.000 0.000 0.000 0.000
#> GSM1124883 4 0.1714 0.909 0.000 0.092 0.000 0.908 0.000 0.000
#> GSM1124889 2 0.0000 0.930 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1124892 1 0.0291 0.940 0.992 0.004 0.004 0.000 0.000 0.000
#> GSM1124893 1 0.0000 0.943 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124909 2 0.1921 0.891 0.032 0.916 0.000 0.000 0.052 0.000
#> GSM1124913 4 0.0713 0.898 0.000 0.028 0.000 0.972 0.000 0.000
#> GSM1124916 6 0.0692 0.961 0.000 0.020 0.000 0.004 0.000 0.976
#> GSM1124923 5 0.3296 0.782 0.000 0.004 0.180 0.020 0.796 0.000
#> GSM1124925 1 0.0000 0.943 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124929 1 0.3774 0.298 0.592 0.000 0.000 0.000 0.000 0.408
#> GSM1124934 6 0.0146 0.975 0.000 0.000 0.000 0.004 0.000 0.996
#> GSM1124937 1 0.5072 0.611 0.676 0.184 0.020 0.000 0.120 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 tissue(p) k
#> ATC:mclust 91 0.3798 2
#> ATC:mclust 88 0.3062 3
#> ATC:mclust 89 0.0502 4
#> ATC:mclust 88 0.0326 5
#> ATC:mclust 87 0.1412 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 48983 rows and 91 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 0.720 0.878 0.943 0.4938 0.499 0.499
#> 3 3 0.682 0.799 0.910 0.2903 0.795 0.615
#> 4 4 0.805 0.833 0.918 0.1317 0.807 0.536
#> 5 5 0.751 0.780 0.882 0.0720 0.889 0.647
#> 6 6 0.761 0.728 0.865 0.0599 0.889 0.579
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
#> GSM1124891 1 0.0000 0.9714 1.000 0.000
#> GSM1124888 1 0.0000 0.9714 1.000 0.000
#> GSM1124890 1 0.0000 0.9714 1.000 0.000
#> GSM1124904 2 0.0000 0.9090 0.000 1.000
#> GSM1124927 2 0.2423 0.8942 0.040 0.960
#> GSM1124953 1 0.0000 0.9714 1.000 0.000
#> GSM1124869 1 0.0000 0.9714 1.000 0.000
#> GSM1124870 1 0.0000 0.9714 1.000 0.000
#> GSM1124882 1 0.0000 0.9714 1.000 0.000
#> GSM1124884 2 0.0000 0.9090 0.000 1.000
#> GSM1124898 2 0.0376 0.9079 0.004 0.996
#> GSM1124903 2 0.0000 0.9090 0.000 1.000
#> GSM1124905 1 0.0000 0.9714 1.000 0.000
#> GSM1124910 1 0.0000 0.9714 1.000 0.000
#> GSM1124919 2 1.0000 0.1597 0.500 0.500
#> GSM1124932 2 0.0000 0.9090 0.000 1.000
#> GSM1124933 1 0.0000 0.9714 1.000 0.000
#> GSM1124867 1 0.0000 0.9714 1.000 0.000
#> GSM1124868 2 0.0000 0.9090 0.000 1.000
#> GSM1124878 2 0.0000 0.9090 0.000 1.000
#> GSM1124895 2 0.0000 0.9090 0.000 1.000
#> GSM1124897 2 0.0000 0.9090 0.000 1.000
#> GSM1124902 2 0.0000 0.9090 0.000 1.000
#> GSM1124908 2 0.8016 0.7425 0.244 0.756
#> GSM1124921 2 0.7528 0.7772 0.216 0.784
#> GSM1124939 2 0.0000 0.9090 0.000 1.000
#> GSM1124944 2 0.9686 0.4649 0.396 0.604
#> GSM1124945 1 0.0000 0.9714 1.000 0.000
#> GSM1124946 2 0.0376 0.9079 0.004 0.996
#> GSM1124947 1 0.9963 -0.0581 0.536 0.464
#> GSM1124951 1 0.0000 0.9714 1.000 0.000
#> GSM1124952 2 0.7219 0.7937 0.200 0.800
#> GSM1124957 1 0.0000 0.9714 1.000 0.000
#> GSM1124900 1 0.0000 0.9714 1.000 0.000
#> GSM1124914 2 0.0000 0.9090 0.000 1.000
#> GSM1124871 2 0.0000 0.9090 0.000 1.000
#> GSM1124874 2 0.0000 0.9090 0.000 1.000
#> GSM1124875 2 0.7299 0.7898 0.204 0.796
#> GSM1124880 1 0.0000 0.9714 1.000 0.000
#> GSM1124881 2 0.8661 0.6778 0.288 0.712
#> GSM1124885 2 0.0000 0.9090 0.000 1.000
#> GSM1124886 1 0.0000 0.9714 1.000 0.000
#> GSM1124887 2 0.6531 0.8206 0.168 0.832
#> GSM1124894 2 0.0000 0.9090 0.000 1.000
#> GSM1124896 1 0.0938 0.9592 0.988 0.012
#> GSM1124899 2 0.3274 0.8854 0.060 0.940
#> GSM1124901 2 0.0000 0.9090 0.000 1.000
#> GSM1124906 2 0.0000 0.9090 0.000 1.000
#> GSM1124907 2 0.7299 0.7898 0.204 0.796
#> GSM1124911 2 0.0000 0.9090 0.000 1.000
#> GSM1124912 1 0.0000 0.9714 1.000 0.000
#> GSM1124915 2 0.0000 0.9090 0.000 1.000
#> GSM1124917 2 0.6887 0.8078 0.184 0.816
#> GSM1124918 2 0.7056 0.8011 0.192 0.808
#> GSM1124920 1 0.0000 0.9714 1.000 0.000
#> GSM1124922 2 0.4562 0.8673 0.096 0.904
#> GSM1124924 1 0.0000 0.9714 1.000 0.000
#> GSM1124926 2 0.0000 0.9090 0.000 1.000
#> GSM1124928 1 0.0000 0.9714 1.000 0.000
#> GSM1124930 1 0.9044 0.4420 0.680 0.320
#> GSM1124931 2 0.0000 0.9090 0.000 1.000
#> GSM1124935 2 0.0000 0.9090 0.000 1.000
#> GSM1124936 1 0.0000 0.9714 1.000 0.000
#> GSM1124938 1 0.0000 0.9714 1.000 0.000
#> GSM1124940 1 0.0000 0.9714 1.000 0.000
#> GSM1124941 2 0.6343 0.8265 0.160 0.840
#> GSM1124942 2 0.7674 0.7680 0.224 0.776
#> GSM1124943 1 0.0000 0.9714 1.000 0.000
#> GSM1124948 1 0.0000 0.9714 1.000 0.000
#> GSM1124949 1 0.0000 0.9714 1.000 0.000
#> GSM1124950 2 0.4690 0.8651 0.100 0.900
#> GSM1124954 1 0.0000 0.9714 1.000 0.000
#> GSM1124955 1 0.0000 0.9714 1.000 0.000
#> GSM1124956 2 0.0000 0.9090 0.000 1.000
#> GSM1124872 2 0.0000 0.9090 0.000 1.000
#> GSM1124873 2 0.0000 0.9090 0.000 1.000
#> GSM1124876 1 0.0000 0.9714 1.000 0.000
#> GSM1124877 1 0.0000 0.9714 1.000 0.000
#> GSM1124879 1 0.0000 0.9714 1.000 0.000
#> GSM1124883 2 0.0000 0.9090 0.000 1.000
#> GSM1124889 2 0.0000 0.9090 0.000 1.000
#> GSM1124892 1 0.0000 0.9714 1.000 0.000
#> GSM1124893 1 0.0000 0.9714 1.000 0.000
#> GSM1124909 1 0.6973 0.7217 0.812 0.188
#> GSM1124913 2 0.0000 0.9090 0.000 1.000
#> GSM1124916 2 0.7602 0.7727 0.220 0.780
#> GSM1124923 2 1.0000 0.1741 0.496 0.504
#> GSM1124925 2 0.0000 0.9090 0.000 1.000
#> GSM1124929 1 0.0000 0.9714 1.000 0.000
#> GSM1124934 1 0.0000 0.9714 1.000 0.000
#> GSM1124937 1 0.0000 0.9714 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1124891 3 0.0000 0.8861 0.000 0.000 1.000
#> GSM1124888 3 0.0000 0.8861 0.000 0.000 1.000
#> GSM1124890 3 0.0000 0.8861 0.000 0.000 1.000
#> GSM1124904 2 0.0000 0.8738 0.000 1.000 0.000
#> GSM1124927 2 0.1753 0.8659 0.000 0.952 0.048
#> GSM1124953 3 0.0000 0.8861 0.000 0.000 1.000
#> GSM1124869 3 0.5591 0.5330 0.304 0.000 0.696
#> GSM1124870 1 0.0424 0.9519 0.992 0.000 0.008
#> GSM1124882 3 0.0000 0.8861 0.000 0.000 1.000
#> GSM1124884 2 0.0000 0.8738 0.000 1.000 0.000
#> GSM1124898 2 0.2711 0.8533 0.000 0.912 0.088
#> GSM1124903 2 0.0000 0.8738 0.000 1.000 0.000
#> GSM1124905 3 0.6095 0.3191 0.392 0.000 0.608
#> GSM1124910 3 0.0000 0.8861 0.000 0.000 1.000
#> GSM1124919 3 0.6062 0.2672 0.000 0.384 0.616
#> GSM1124932 1 0.0000 0.9566 1.000 0.000 0.000
#> GSM1124933 3 0.0000 0.8861 0.000 0.000 1.000
#> GSM1124867 3 0.0000 0.8861 0.000 0.000 1.000
#> GSM1124868 2 0.0000 0.8738 0.000 1.000 0.000
#> GSM1124878 2 0.0000 0.8738 0.000 1.000 0.000
#> GSM1124895 2 0.0000 0.8738 0.000 1.000 0.000
#> GSM1124897 2 0.0000 0.8738 0.000 1.000 0.000
#> GSM1124902 2 0.0000 0.8738 0.000 1.000 0.000
#> GSM1124908 2 0.5810 0.5965 0.000 0.664 0.336
#> GSM1124921 2 0.5216 0.7194 0.000 0.740 0.260
#> GSM1124939 2 0.0000 0.8738 0.000 1.000 0.000
#> GSM1124944 2 0.6280 0.2841 0.000 0.540 0.460
#> GSM1124945 3 0.0000 0.8861 0.000 0.000 1.000
#> GSM1124946 2 0.3412 0.8369 0.000 0.876 0.124
#> GSM1124947 3 0.5968 0.3274 0.000 0.364 0.636
#> GSM1124951 3 0.0000 0.8861 0.000 0.000 1.000
#> GSM1124952 2 0.4654 0.7801 0.000 0.792 0.208
#> GSM1124957 3 0.0000 0.8861 0.000 0.000 1.000
#> GSM1124900 3 0.7074 0.0734 0.480 0.020 0.500
#> GSM1124914 2 0.0000 0.8738 0.000 1.000 0.000
#> GSM1124871 2 0.0000 0.8738 0.000 1.000 0.000
#> GSM1124874 2 0.2356 0.8425 0.072 0.928 0.000
#> GSM1124875 2 0.4796 0.7681 0.000 0.780 0.220
#> GSM1124880 3 0.0000 0.8861 0.000 0.000 1.000
#> GSM1124881 2 0.6095 0.4760 0.000 0.608 0.392
#> GSM1124885 2 0.0000 0.8738 0.000 1.000 0.000
#> GSM1124886 3 0.0000 0.8861 0.000 0.000 1.000
#> GSM1124887 2 0.4504 0.7909 0.000 0.804 0.196
#> GSM1124894 2 0.4555 0.7449 0.200 0.800 0.000
#> GSM1124896 1 0.0000 0.9566 1.000 0.000 0.000
#> GSM1124899 2 0.3752 0.8260 0.000 0.856 0.144
#> GSM1124901 2 0.0000 0.8738 0.000 1.000 0.000
#> GSM1124906 2 0.0424 0.8732 0.000 0.992 0.008
#> GSM1124907 2 0.4842 0.7637 0.000 0.776 0.224
#> GSM1124911 1 0.0000 0.9566 1.000 0.000 0.000
#> GSM1124912 1 0.2878 0.8746 0.904 0.000 0.096
#> GSM1124915 2 0.6308 -0.0639 0.492 0.508 0.000
#> GSM1124917 2 0.4504 0.7909 0.000 0.804 0.196
#> GSM1124918 1 0.4555 0.7062 0.800 0.000 0.200
#> GSM1124920 3 0.0000 0.8861 0.000 0.000 1.000
#> GSM1124922 2 0.2625 0.8547 0.000 0.916 0.084
#> GSM1124924 3 0.0000 0.8861 0.000 0.000 1.000
#> GSM1124926 2 0.0000 0.8738 0.000 1.000 0.000
#> GSM1124928 3 0.0237 0.8832 0.004 0.000 0.996
#> GSM1124930 3 0.4346 0.6998 0.000 0.184 0.816
#> GSM1124931 1 0.0424 0.9515 0.992 0.008 0.000
#> GSM1124935 1 0.0000 0.9566 1.000 0.000 0.000
#> GSM1124936 3 0.0000 0.8861 0.000 0.000 1.000
#> GSM1124938 3 0.0000 0.8861 0.000 0.000 1.000
#> GSM1124940 3 0.5678 0.5295 0.316 0.000 0.684
#> GSM1124941 2 0.4452 0.7942 0.000 0.808 0.192
#> GSM1124942 2 0.5529 0.6666 0.000 0.704 0.296
#> GSM1124943 3 0.0000 0.8861 0.000 0.000 1.000
#> GSM1124948 3 0.0000 0.8861 0.000 0.000 1.000
#> GSM1124949 3 0.1529 0.8539 0.040 0.000 0.960
#> GSM1124950 2 0.4346 0.8003 0.000 0.816 0.184
#> GSM1124954 1 0.2537 0.8845 0.920 0.000 0.080
#> GSM1124955 1 0.0000 0.9566 1.000 0.000 0.000
#> GSM1124956 1 0.0000 0.9566 1.000 0.000 0.000
#> GSM1124872 2 0.0237 0.8726 0.004 0.996 0.000
#> GSM1124873 2 0.0000 0.8738 0.000 1.000 0.000
#> GSM1124876 3 0.0000 0.8861 0.000 0.000 1.000
#> GSM1124877 1 0.0000 0.9566 1.000 0.000 0.000
#> GSM1124879 3 0.2261 0.8275 0.068 0.000 0.932
#> GSM1124883 2 0.0000 0.8738 0.000 1.000 0.000
#> GSM1124889 2 0.0000 0.8738 0.000 1.000 0.000
#> GSM1124892 3 0.0000 0.8861 0.000 0.000 1.000
#> GSM1124893 1 0.4452 0.7464 0.808 0.000 0.192
#> GSM1124909 3 0.4605 0.6700 0.000 0.204 0.796
#> GSM1124913 2 0.0000 0.8738 0.000 1.000 0.000
#> GSM1124916 1 0.0000 0.9566 1.000 0.000 0.000
#> GSM1124923 3 0.6180 0.1594 0.000 0.416 0.584
#> GSM1124925 1 0.0000 0.9566 1.000 0.000 0.000
#> GSM1124929 1 0.0000 0.9566 1.000 0.000 0.000
#> GSM1124934 1 0.0000 0.9566 1.000 0.000 0.000
#> GSM1124937 3 0.0000 0.8861 0.000 0.000 1.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1124891 3 0.0817 0.907 0.024 0.000 0.976 0.000
#> GSM1124888 3 0.0707 0.900 0.020 0.000 0.980 0.000
#> GSM1124890 3 0.0592 0.907 0.016 0.000 0.984 0.000
#> GSM1124904 2 0.0000 0.899 0.000 1.000 0.000 0.000
#> GSM1124927 1 0.0779 0.888 0.980 0.004 0.016 0.000
#> GSM1124953 3 0.0817 0.907 0.024 0.000 0.976 0.000
#> GSM1124869 1 0.0804 0.889 0.980 0.000 0.012 0.008
#> GSM1124870 1 0.0707 0.890 0.980 0.000 0.000 0.020
#> GSM1124882 1 0.0707 0.888 0.980 0.000 0.020 0.000
#> GSM1124884 2 0.0000 0.899 0.000 1.000 0.000 0.000
#> GSM1124898 2 0.0188 0.898 0.004 0.996 0.000 0.000
#> GSM1124903 2 0.0000 0.899 0.000 1.000 0.000 0.000
#> GSM1124905 1 0.1211 0.888 0.960 0.000 0.000 0.040
#> GSM1124910 1 0.4746 0.398 0.632 0.000 0.368 0.000
#> GSM1124919 3 0.4348 0.713 0.024 0.196 0.780 0.000
#> GSM1124932 4 0.0000 0.975 0.000 0.000 0.000 1.000
#> GSM1124933 3 0.0817 0.907 0.024 0.000 0.976 0.000
#> GSM1124867 1 0.1389 0.872 0.952 0.000 0.048 0.000
#> GSM1124868 2 0.0000 0.899 0.000 1.000 0.000 0.000
#> GSM1124878 2 0.0000 0.899 0.000 1.000 0.000 0.000
#> GSM1124895 2 0.0000 0.899 0.000 1.000 0.000 0.000
#> GSM1124897 2 0.0000 0.899 0.000 1.000 0.000 0.000
#> GSM1124902 2 0.0000 0.899 0.000 1.000 0.000 0.000
#> GSM1124908 2 0.4776 0.689 0.024 0.732 0.244 0.000
#> GSM1124921 2 0.5105 0.635 0.028 0.696 0.276 0.000
#> GSM1124939 2 0.0000 0.899 0.000 1.000 0.000 0.000
#> GSM1124944 2 0.5778 0.131 0.028 0.500 0.472 0.000
#> GSM1124945 3 0.0817 0.907 0.024 0.000 0.976 0.000
#> GSM1124946 2 0.3946 0.785 0.020 0.812 0.168 0.000
#> GSM1124947 3 0.7799 0.138 0.260 0.328 0.412 0.000
#> GSM1124951 3 0.0707 0.907 0.020 0.000 0.980 0.000
#> GSM1124952 1 0.6407 0.179 0.520 0.412 0.068 0.000
#> GSM1124957 3 0.0817 0.907 0.024 0.000 0.976 0.000
#> GSM1124900 1 0.1118 0.889 0.964 0.000 0.000 0.036
#> GSM1124914 2 0.0000 0.899 0.000 1.000 0.000 0.000
#> GSM1124871 2 0.0000 0.899 0.000 1.000 0.000 0.000
#> GSM1124874 2 0.0707 0.891 0.000 0.980 0.000 0.020
#> GSM1124875 2 0.4079 0.773 0.020 0.800 0.180 0.000
#> GSM1124880 1 0.3172 0.768 0.840 0.000 0.160 0.000
#> GSM1124881 2 0.5036 0.619 0.280 0.696 0.024 0.000
#> GSM1124885 2 0.0000 0.899 0.000 1.000 0.000 0.000
#> GSM1124886 1 0.0707 0.888 0.980 0.000 0.020 0.000
#> GSM1124887 2 0.3925 0.777 0.016 0.808 0.176 0.000
#> GSM1124894 1 0.1411 0.886 0.960 0.020 0.000 0.020
#> GSM1124896 1 0.1211 0.888 0.960 0.000 0.000 0.040
#> GSM1124899 2 0.1624 0.881 0.020 0.952 0.028 0.000
#> GSM1124901 2 0.0000 0.899 0.000 1.000 0.000 0.000
#> GSM1124906 1 0.4804 0.363 0.616 0.384 0.000 0.000
#> GSM1124907 2 0.5130 0.589 0.020 0.668 0.312 0.000
#> GSM1124911 4 0.0000 0.975 0.000 0.000 0.000 1.000
#> GSM1124912 1 0.1211 0.888 0.960 0.000 0.000 0.040
#> GSM1124915 4 0.1211 0.941 0.000 0.040 0.000 0.960
#> GSM1124917 2 0.3764 0.783 0.012 0.816 0.172 0.000
#> GSM1124918 4 0.2489 0.904 0.020 0.000 0.068 0.912
#> GSM1124920 3 0.0707 0.900 0.020 0.000 0.980 0.000
#> GSM1124922 1 0.3610 0.718 0.800 0.200 0.000 0.000
#> GSM1124924 3 0.0707 0.900 0.020 0.000 0.980 0.000
#> GSM1124926 2 0.0000 0.899 0.000 1.000 0.000 0.000
#> GSM1124928 1 0.0804 0.885 0.980 0.000 0.012 0.008
#> GSM1124930 3 0.2706 0.845 0.020 0.080 0.900 0.000
#> GSM1124931 4 0.0000 0.975 0.000 0.000 0.000 1.000
#> GSM1124935 4 0.0000 0.975 0.000 0.000 0.000 1.000
#> GSM1124936 3 0.0817 0.907 0.024 0.000 0.976 0.000
#> GSM1124938 3 0.0707 0.900 0.020 0.000 0.980 0.000
#> GSM1124940 1 0.0707 0.888 0.980 0.000 0.020 0.000
#> GSM1124941 2 0.2345 0.842 0.100 0.900 0.000 0.000
#> GSM1124942 2 0.5306 0.516 0.020 0.632 0.348 0.000
#> GSM1124943 3 0.0707 0.900 0.020 0.000 0.980 0.000
#> GSM1124948 3 0.0707 0.900 0.020 0.000 0.980 0.000
#> GSM1124949 1 0.0592 0.891 0.984 0.000 0.000 0.016
#> GSM1124950 2 0.2002 0.873 0.020 0.936 0.044 0.000
#> GSM1124954 4 0.2813 0.881 0.024 0.000 0.080 0.896
#> GSM1124955 1 0.1302 0.886 0.956 0.000 0.000 0.044
#> GSM1124956 4 0.0000 0.975 0.000 0.000 0.000 1.000
#> GSM1124872 2 0.1022 0.885 0.032 0.968 0.000 0.000
#> GSM1124873 2 0.0188 0.897 0.004 0.996 0.000 0.000
#> GSM1124876 3 0.0817 0.907 0.024 0.000 0.976 0.000
#> GSM1124877 4 0.0000 0.975 0.000 0.000 0.000 1.000
#> GSM1124879 1 0.0927 0.886 0.976 0.000 0.008 0.016
#> GSM1124883 2 0.0000 0.899 0.000 1.000 0.000 0.000
#> GSM1124889 2 0.0592 0.893 0.016 0.984 0.000 0.000
#> GSM1124892 1 0.1637 0.870 0.940 0.000 0.060 0.000
#> GSM1124893 1 0.1211 0.888 0.960 0.000 0.000 0.040
#> GSM1124909 1 0.2345 0.833 0.900 0.000 0.100 0.000
#> GSM1124913 2 0.0000 0.899 0.000 1.000 0.000 0.000
#> GSM1124916 4 0.0000 0.975 0.000 0.000 0.000 1.000
#> GSM1124923 3 0.4228 0.652 0.008 0.232 0.760 0.000
#> GSM1124925 1 0.1302 0.886 0.956 0.000 0.000 0.044
#> GSM1124929 1 0.1389 0.885 0.952 0.000 0.000 0.048
#> GSM1124934 4 0.0000 0.975 0.000 0.000 0.000 1.000
#> GSM1124937 1 0.3873 0.692 0.772 0.000 0.228 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM1124891 3 0.0290 0.910 0.000 0.000 0.992 0.000 0.008
#> GSM1124888 5 0.3508 0.705 0.000 0.000 0.252 0.000 0.748
#> GSM1124890 3 0.0404 0.906 0.000 0.000 0.988 0.000 0.012
#> GSM1124904 2 0.0510 0.824 0.000 0.984 0.000 0.000 0.016
#> GSM1124927 1 0.2970 0.808 0.828 0.004 0.000 0.000 0.168
#> GSM1124953 3 0.0000 0.914 0.000 0.000 1.000 0.000 0.000
#> GSM1124869 1 0.0000 0.909 1.000 0.000 0.000 0.000 0.000
#> GSM1124870 1 0.1908 0.871 0.908 0.000 0.000 0.000 0.092
#> GSM1124882 1 0.2424 0.842 0.868 0.000 0.000 0.000 0.132
#> GSM1124884 2 0.2773 0.747 0.000 0.836 0.000 0.000 0.164
#> GSM1124898 2 0.0162 0.825 0.000 0.996 0.000 0.000 0.004
#> GSM1124903 2 0.0000 0.826 0.000 1.000 0.000 0.000 0.000
#> GSM1124905 1 0.0162 0.909 0.996 0.000 0.000 0.000 0.004
#> GSM1124910 5 0.4080 0.531 0.252 0.000 0.020 0.000 0.728
#> GSM1124919 3 0.2074 0.820 0.000 0.104 0.896 0.000 0.000
#> GSM1124932 4 0.0000 0.954 0.000 0.000 0.000 1.000 0.000
#> GSM1124933 3 0.0000 0.914 0.000 0.000 1.000 0.000 0.000
#> GSM1124867 1 0.3586 0.721 0.736 0.000 0.000 0.000 0.264
#> GSM1124868 2 0.0290 0.824 0.000 0.992 0.000 0.000 0.008
#> GSM1124878 2 0.0162 0.826 0.000 0.996 0.000 0.000 0.004
#> GSM1124895 2 0.0290 0.824 0.000 0.992 0.000 0.000 0.008
#> GSM1124897 2 0.0162 0.826 0.000 0.996 0.000 0.000 0.004
#> GSM1124902 2 0.0162 0.825 0.000 0.996 0.000 0.000 0.004
#> GSM1124908 3 0.4003 0.587 0.000 0.288 0.704 0.000 0.008
#> GSM1124921 2 0.3962 0.733 0.000 0.800 0.088 0.000 0.112
#> GSM1124939 2 0.0000 0.826 0.000 1.000 0.000 0.000 0.000
#> GSM1124944 2 0.4183 0.517 0.000 0.668 0.324 0.000 0.008
#> GSM1124945 3 0.0000 0.914 0.000 0.000 1.000 0.000 0.000
#> GSM1124946 2 0.3424 0.655 0.000 0.760 0.000 0.000 0.240
#> GSM1124947 2 0.5905 0.360 0.088 0.488 0.004 0.000 0.420
#> GSM1124951 3 0.0000 0.914 0.000 0.000 1.000 0.000 0.000
#> GSM1124952 2 0.7361 0.365 0.112 0.492 0.296 0.000 0.100
#> GSM1124957 3 0.0000 0.914 0.000 0.000 1.000 0.000 0.000
#> GSM1124900 1 0.0290 0.908 0.992 0.000 0.000 0.000 0.008
#> GSM1124914 2 0.0162 0.825 0.000 0.996 0.000 0.000 0.004
#> GSM1124871 2 0.0880 0.820 0.000 0.968 0.000 0.000 0.032
#> GSM1124874 2 0.0693 0.821 0.012 0.980 0.000 0.000 0.008
#> GSM1124875 2 0.4268 0.398 0.000 0.556 0.000 0.000 0.444
#> GSM1124880 5 0.3210 0.587 0.212 0.000 0.000 0.000 0.788
#> GSM1124881 2 0.6135 0.523 0.236 0.580 0.004 0.000 0.180
#> GSM1124885 2 0.0162 0.826 0.000 0.996 0.000 0.000 0.004
#> GSM1124886 1 0.0000 0.909 1.000 0.000 0.000 0.000 0.000
#> GSM1124887 2 0.3861 0.585 0.000 0.712 0.284 0.000 0.004
#> GSM1124894 1 0.1908 0.871 0.908 0.000 0.000 0.000 0.092
#> GSM1124896 1 0.0162 0.909 0.996 0.000 0.000 0.000 0.004
#> GSM1124899 2 0.4425 0.444 0.004 0.544 0.000 0.000 0.452
#> GSM1124901 2 0.0162 0.825 0.000 0.996 0.000 0.000 0.004
#> GSM1124906 2 0.6443 0.187 0.376 0.444 0.000 0.000 0.180
#> GSM1124907 5 0.3274 0.660 0.000 0.220 0.000 0.000 0.780
#> GSM1124911 4 0.0162 0.952 0.000 0.000 0.000 0.996 0.004
#> GSM1124912 1 0.0162 0.909 0.996 0.000 0.000 0.000 0.004
#> GSM1124915 4 0.0000 0.954 0.000 0.000 0.000 1.000 0.000
#> GSM1124917 2 0.3231 0.702 0.000 0.800 0.196 0.000 0.004
#> GSM1124918 4 0.3003 0.725 0.000 0.000 0.000 0.812 0.188
#> GSM1124920 5 0.3039 0.760 0.000 0.000 0.192 0.000 0.808
#> GSM1124922 1 0.5984 0.466 0.588 0.208 0.000 0.000 0.204
#> GSM1124924 5 0.0613 0.737 0.004 0.004 0.008 0.000 0.984
#> GSM1124926 2 0.1571 0.809 0.004 0.936 0.000 0.000 0.060
#> GSM1124928 1 0.1121 0.897 0.956 0.000 0.000 0.000 0.044
#> GSM1124930 5 0.3278 0.769 0.000 0.020 0.156 0.000 0.824
#> GSM1124931 4 0.0000 0.954 0.000 0.000 0.000 1.000 0.000
#> GSM1124935 4 0.0000 0.954 0.000 0.000 0.000 1.000 0.000
#> GSM1124936 3 0.0000 0.914 0.000 0.000 1.000 0.000 0.000
#> GSM1124938 5 0.3109 0.756 0.000 0.000 0.200 0.000 0.800
#> GSM1124940 1 0.0324 0.908 0.992 0.000 0.004 0.000 0.004
#> GSM1124941 2 0.5369 0.622 0.124 0.660 0.000 0.000 0.216
#> GSM1124942 5 0.3455 0.670 0.000 0.208 0.008 0.000 0.784
#> GSM1124943 5 0.3074 0.758 0.000 0.000 0.196 0.000 0.804
#> GSM1124948 5 0.2127 0.769 0.000 0.000 0.108 0.000 0.892
#> GSM1124949 1 0.0162 0.909 0.996 0.000 0.000 0.000 0.004
#> GSM1124950 2 0.4341 0.528 0.004 0.592 0.000 0.000 0.404
#> GSM1124954 4 0.3074 0.729 0.000 0.000 0.196 0.804 0.000
#> GSM1124955 1 0.0162 0.909 0.996 0.000 0.000 0.000 0.004
#> GSM1124956 4 0.0000 0.954 0.000 0.000 0.000 1.000 0.000
#> GSM1124872 2 0.4162 0.708 0.056 0.768 0.000 0.000 0.176
#> GSM1124873 2 0.1282 0.815 0.004 0.952 0.000 0.000 0.044
#> GSM1124876 3 0.0162 0.912 0.000 0.000 0.996 0.000 0.004
#> GSM1124877 4 0.0000 0.954 0.000 0.000 0.000 1.000 0.000
#> GSM1124879 1 0.0510 0.905 0.984 0.000 0.000 0.000 0.016
#> GSM1124883 2 0.0162 0.825 0.000 0.996 0.000 0.000 0.004
#> GSM1124889 2 0.0451 0.825 0.008 0.988 0.000 0.000 0.004
#> GSM1124892 1 0.3061 0.800 0.844 0.000 0.136 0.000 0.020
#> GSM1124893 1 0.0162 0.909 0.996 0.000 0.000 0.000 0.004
#> GSM1124909 1 0.3857 0.642 0.688 0.000 0.000 0.000 0.312
#> GSM1124913 2 0.0162 0.826 0.000 0.996 0.000 0.000 0.004
#> GSM1124916 4 0.0000 0.954 0.000 0.000 0.000 1.000 0.000
#> GSM1124923 3 0.3177 0.706 0.000 0.208 0.792 0.000 0.000
#> GSM1124925 1 0.0290 0.908 0.992 0.000 0.000 0.000 0.008
#> GSM1124929 1 0.0579 0.907 0.984 0.000 0.000 0.008 0.008
#> GSM1124934 4 0.0000 0.954 0.000 0.000 0.000 1.000 0.000
#> GSM1124937 5 0.3999 0.497 0.344 0.000 0.000 0.000 0.656
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1124891 3 0.1010 0.8802 0.000 0.004 0.960 0.000 0.036 0.000
#> GSM1124888 5 0.0632 0.8431 0.000 0.000 0.024 0.000 0.976 0.000
#> GSM1124890 3 0.1075 0.8663 0.000 0.000 0.952 0.000 0.048 0.000
#> GSM1124904 4 0.2814 0.6856 0.000 0.172 0.008 0.820 0.000 0.000
#> GSM1124927 2 0.4907 0.5559 0.256 0.644 0.004 0.096 0.000 0.000
#> GSM1124953 3 0.0146 0.8970 0.000 0.004 0.996 0.000 0.000 0.000
#> GSM1124869 1 0.2340 0.8234 0.852 0.148 0.000 0.000 0.000 0.000
#> GSM1124870 2 0.3690 0.3515 0.288 0.700 0.012 0.000 0.000 0.000
#> GSM1124882 2 0.4178 0.2948 0.372 0.608 0.020 0.000 0.000 0.000
#> GSM1124884 2 0.3828 0.3389 0.000 0.560 0.000 0.440 0.000 0.000
#> GSM1124898 4 0.3050 0.6413 0.000 0.236 0.000 0.764 0.000 0.000
#> GSM1124903 4 0.0260 0.7939 0.000 0.008 0.000 0.992 0.000 0.000
#> GSM1124905 1 0.0000 0.9162 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124910 5 0.4144 0.4423 0.020 0.360 0.000 0.000 0.620 0.000
#> GSM1124919 3 0.0458 0.8894 0.000 0.000 0.984 0.016 0.000 0.000
#> GSM1124932 6 0.0000 0.9640 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM1124933 3 0.0000 0.8974 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1124867 2 0.1320 0.6611 0.016 0.948 0.000 0.000 0.036 0.000
#> GSM1124868 4 0.0260 0.7926 0.000 0.008 0.000 0.992 0.000 0.000
#> GSM1124878 4 0.1663 0.7651 0.000 0.088 0.000 0.912 0.000 0.000
#> GSM1124895 4 0.0260 0.7926 0.000 0.008 0.000 0.992 0.000 0.000
#> GSM1124897 4 0.2178 0.7350 0.000 0.132 0.000 0.868 0.000 0.000
#> GSM1124902 4 0.0790 0.7930 0.000 0.032 0.000 0.968 0.000 0.000
#> GSM1124908 4 0.4651 0.1283 0.004 0.032 0.448 0.516 0.000 0.000
#> GSM1124921 4 0.3101 0.6406 0.000 0.244 0.000 0.756 0.000 0.000
#> GSM1124939 4 0.1007 0.7909 0.000 0.044 0.000 0.956 0.000 0.000
#> GSM1124944 4 0.3766 0.6269 0.000 0.232 0.032 0.736 0.000 0.000
#> GSM1124945 3 0.0146 0.8970 0.000 0.004 0.996 0.000 0.000 0.000
#> GSM1124946 4 0.3053 0.6927 0.000 0.020 0.000 0.812 0.168 0.000
#> GSM1124947 2 0.2593 0.7010 0.000 0.844 0.000 0.148 0.008 0.000
#> GSM1124951 3 0.0000 0.8974 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1124952 2 0.5532 0.5110 0.004 0.580 0.204 0.212 0.000 0.000
#> GSM1124957 3 0.0363 0.8944 0.000 0.000 0.988 0.000 0.012 0.000
#> GSM1124900 1 0.0146 0.9146 0.996 0.004 0.000 0.000 0.000 0.000
#> GSM1124914 4 0.0632 0.7930 0.000 0.024 0.000 0.976 0.000 0.000
#> GSM1124871 4 0.2300 0.7182 0.000 0.144 0.000 0.856 0.000 0.000
#> GSM1124874 4 0.4047 0.3581 0.384 0.012 0.000 0.604 0.000 0.000
#> GSM1124875 4 0.5595 0.3620 0.000 0.192 0.000 0.540 0.268 0.000
#> GSM1124880 5 0.3455 0.7032 0.036 0.180 0.000 0.000 0.784 0.000
#> GSM1124881 2 0.0935 0.6902 0.000 0.964 0.000 0.032 0.004 0.000
#> GSM1124885 4 0.0363 0.7937 0.000 0.012 0.000 0.988 0.000 0.000
#> GSM1124886 1 0.3742 0.5322 0.648 0.348 0.004 0.000 0.000 0.000
#> GSM1124887 4 0.3420 0.6291 0.000 0.012 0.240 0.748 0.000 0.000
#> GSM1124894 2 0.4407 0.1369 0.480 0.496 0.000 0.024 0.000 0.000
#> GSM1124896 1 0.0000 0.9162 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1124899 2 0.5423 0.1379 0.000 0.444 0.000 0.440 0.116 0.000
#> GSM1124901 4 0.0260 0.7942 0.000 0.008 0.000 0.992 0.000 0.000
#> GSM1124906 2 0.1444 0.7152 0.000 0.928 0.000 0.072 0.000 0.000
#> GSM1124907 5 0.3390 0.5315 0.000 0.000 0.000 0.296 0.704 0.000
#> GSM1124911 6 0.0405 0.9599 0.008 0.004 0.000 0.000 0.000 0.988
#> GSM1124912 1 0.0146 0.9171 0.996 0.004 0.000 0.000 0.000 0.000
#> GSM1124915 6 0.0458 0.9554 0.000 0.000 0.000 0.016 0.000 0.984
#> GSM1124917 4 0.2571 0.7678 0.000 0.060 0.064 0.876 0.000 0.000
#> GSM1124918 6 0.1092 0.9422 0.000 0.020 0.000 0.000 0.020 0.960
#> GSM1124920 5 0.0146 0.8503 0.000 0.000 0.004 0.000 0.996 0.000
#> GSM1124922 2 0.3056 0.6900 0.008 0.804 0.000 0.184 0.004 0.000
#> GSM1124924 5 0.1141 0.8412 0.000 0.052 0.000 0.000 0.948 0.000
#> GSM1124926 4 0.3838 -0.0556 0.000 0.448 0.000 0.552 0.000 0.000
#> GSM1124928 1 0.3141 0.7531 0.788 0.200 0.000 0.000 0.012 0.000
#> GSM1124930 5 0.1219 0.8410 0.000 0.048 0.000 0.004 0.948 0.000
#> GSM1124931 6 0.0000 0.9640 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM1124935 6 0.0000 0.9640 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM1124936 3 0.0146 0.8970 0.004 0.000 0.996 0.000 0.000 0.000
#> GSM1124938 5 0.0146 0.8503 0.000 0.000 0.004 0.000 0.996 0.000
#> GSM1124940 1 0.0363 0.9146 0.988 0.012 0.000 0.000 0.000 0.000
#> GSM1124941 2 0.1806 0.7184 0.000 0.908 0.000 0.088 0.004 0.000
#> GSM1124942 5 0.1267 0.8218 0.000 0.000 0.000 0.060 0.940 0.000
#> GSM1124943 5 0.0146 0.8503 0.000 0.000 0.004 0.000 0.996 0.000
#> GSM1124948 5 0.0363 0.8501 0.000 0.012 0.000 0.000 0.988 0.000
#> GSM1124949 1 0.1610 0.8765 0.916 0.084 0.000 0.000 0.000 0.000
#> GSM1124950 2 0.4201 0.5687 0.000 0.664 0.000 0.300 0.036 0.000
#> GSM1124954 6 0.2608 0.8782 0.000 0.080 0.048 0.000 0.000 0.872
#> GSM1124955 1 0.0146 0.9171 0.996 0.004 0.000 0.000 0.000 0.000
#> GSM1124956 6 0.0000 0.9640 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM1124872 2 0.2631 0.6874 0.000 0.820 0.000 0.180 0.000 0.000
#> GSM1124873 4 0.3151 0.5702 0.000 0.252 0.000 0.748 0.000 0.000
#> GSM1124876 3 0.0146 0.8968 0.000 0.000 0.996 0.000 0.004 0.000
#> GSM1124877 6 0.0000 0.9640 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM1124879 1 0.0146 0.9171 0.996 0.004 0.000 0.000 0.000 0.000
#> GSM1124883 4 0.0260 0.7942 0.000 0.008 0.000 0.992 0.000 0.000
#> GSM1124889 4 0.1610 0.7685 0.000 0.084 0.000 0.916 0.000 0.000
#> GSM1124892 3 0.6042 0.0620 0.208 0.392 0.396 0.000 0.004 0.000
#> GSM1124893 1 0.0146 0.9171 0.996 0.004 0.000 0.000 0.000 0.000
#> GSM1124909 2 0.1594 0.6747 0.052 0.932 0.000 0.000 0.016 0.000
#> GSM1124913 4 0.0632 0.7919 0.000 0.024 0.000 0.976 0.000 0.000
#> GSM1124916 6 0.0260 0.9615 0.000 0.008 0.000 0.000 0.000 0.992
#> GSM1124923 3 0.3897 0.5138 0.000 0.000 0.696 0.280 0.024 0.000
#> GSM1124925 1 0.0260 0.9122 0.992 0.008 0.000 0.000 0.000 0.000
#> GSM1124929 1 0.3215 0.8155 0.828 0.072 0.000 0.000 0.000 0.100
#> GSM1124934 6 0.2219 0.8462 0.000 0.136 0.000 0.000 0.000 0.864
#> GSM1124937 5 0.5468 0.4859 0.244 0.188 0.000 0.000 0.568 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 tissue(p) k
#> ATC:NMF 86 0.4926 2
#> ATC:NMF 83 0.2908 3
#> ATC:NMF 86 0.2875 4
#> ATC:NMF 84 0.0568 5
#> ATC:NMF 79 0.0465 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