Date: 2019-12-25 20:40:24 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 21168 rows and 107 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] 21168 107
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 | ||
|---|---|---|---|---|---|---|
| SD:hclust | 2 | 1.000 | 0.969 | 0.987 | ** | |
| SD:kmeans | 2 | 1.000 | 0.997 | 0.999 | ** | |
| SD:skmeans | 2 | 1.000 | 0.991 | 0.996 | ** | |
| SD:pam | 2 | 1.000 | 0.994 | 0.997 | ** | |
| SD:mclust | 2 | 1.000 | 0.995 | 0.998 | ** | |
| SD:NMF | 2 | 1.000 | 0.971 | 0.988 | ** | |
| CV:hclust | 2 | 1.000 | 0.971 | 0.988 | ** | |
| CV:kmeans | 2 | 1.000 | 0.995 | 0.998 | ** | |
| CV:skmeans | 2 | 1.000 | 0.991 | 0.996 | ** | |
| CV:mclust | 2 | 1.000 | 0.996 | 0.998 | ** | |
| CV:NMF | 2 | 1.000 | 0.972 | 0.988 | ** | |
| MAD:hclust | 2 | 1.000 | 0.976 | 0.988 | ** | |
| MAD:kmeans | 2 | 1.000 | 0.995 | 0.998 | ** | |
| MAD:skmeans | 2 | 1.000 | 0.987 | 0.994 | ** | |
| MAD:pam | 2 | 1.000 | 0.995 | 0.998 | ** | |
| MAD:mclust | 2 | 1.000 | 1.000 | 1.000 | ** | |
| MAD:NMF | 2 | 1.000 | 0.975 | 0.989 | ** | |
| ATC:kmeans | 2 | 1.000 | 0.975 | 0.990 | ** | |
| ATC:NMF | 2 | 1.000 | 0.989 | 0.996 | ** | |
| ATC:mclust | 6 | 0.988 | 0.945 | 0.970 | ** | |
| ATC:skmeans | 4 | 0.981 | 0.922 | 0.939 | ** | 2,3 |
| ATC:pam | 6 | 0.971 | 0.909 | 0.953 | ** | 2 |
| CV:pam | 3 | 0.919 | 0.932 | 0.967 | * | 2 |
| ATC:hclust | 4 | 0.832 | 0.872 | 0.924 |
**: 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 1.000 0.971 0.988 0.504 0.497 0.497
#> CV:NMF 2 1.000 0.972 0.988 0.504 0.497 0.497
#> MAD:NMF 2 1.000 0.975 0.989 0.503 0.497 0.497
#> ATC:NMF 2 1.000 0.989 0.996 0.504 0.496 0.496
#> SD:skmeans 2 1.000 0.991 0.996 0.503 0.497 0.497
#> CV:skmeans 2 1.000 0.991 0.996 0.503 0.497 0.497
#> MAD:skmeans 2 1.000 0.987 0.994 0.502 0.499 0.499
#> ATC:skmeans 2 1.000 0.987 0.995 0.504 0.496 0.496
#> SD:mclust 2 1.000 0.995 0.998 0.496 0.505 0.505
#> CV:mclust 2 1.000 0.996 0.998 0.496 0.505 0.505
#> MAD:mclust 2 1.000 1.000 1.000 0.495 0.505 0.505
#> ATC:mclust 2 0.340 0.372 0.641 0.394 0.556 0.556
#> SD:kmeans 2 1.000 0.997 0.999 0.498 0.503 0.503
#> CV:kmeans 2 1.000 0.995 0.998 0.498 0.503 0.503
#> MAD:kmeans 2 1.000 0.995 0.998 0.498 0.503 0.503
#> ATC:kmeans 2 1.000 0.975 0.990 0.503 0.497 0.497
#> SD:pam 2 1.000 0.994 0.997 0.498 0.503 0.503
#> CV:pam 2 1.000 0.991 0.996 0.499 0.503 0.503
#> MAD:pam 2 1.000 0.995 0.998 0.498 0.503 0.503
#> ATC:pam 2 1.000 0.987 0.994 0.502 0.499 0.499
#> SD:hclust 2 1.000 0.969 0.987 0.498 0.501 0.501
#> CV:hclust 2 1.000 0.971 0.988 0.498 0.505 0.505
#> MAD:hclust 2 1.000 0.976 0.988 0.500 0.501 0.501
#> ATC:hclust 2 0.239 0.629 0.808 0.383 0.730 0.730
get_stats(res_list, k = 3)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 3 0.587 0.615 0.795 0.233 0.938 0.876
#> CV:NMF 3 0.601 0.632 0.811 0.239 0.935 0.871
#> MAD:NMF 3 0.618 0.632 0.804 0.229 0.962 0.924
#> ATC:NMF 3 0.887 0.898 0.955 0.318 0.759 0.549
#> SD:skmeans 3 0.844 0.909 0.944 0.261 0.857 0.716
#> CV:skmeans 3 0.819 0.842 0.889 0.270 0.862 0.725
#> MAD:skmeans 3 0.809 0.776 0.865 0.252 0.846 0.698
#> ATC:skmeans 3 0.990 0.960 0.978 0.299 0.810 0.631
#> SD:mclust 3 0.715 0.850 0.900 0.296 0.849 0.701
#> CV:mclust 3 0.781 0.847 0.918 0.300 0.846 0.695
#> MAD:mclust 3 0.755 0.828 0.896 0.267 0.859 0.721
#> ATC:mclust 3 0.698 0.873 0.864 0.637 0.591 0.371
#> SD:kmeans 3 0.692 0.873 0.863 0.286 0.835 0.675
#> CV:kmeans 3 0.646 0.694 0.759 0.282 0.828 0.662
#> MAD:kmeans 3 0.706 0.878 0.854 0.283 0.833 0.671
#> ATC:kmeans 3 0.716 0.743 0.812 0.266 0.816 0.646
#> SD:pam 3 0.900 0.898 0.957 0.280 0.855 0.714
#> CV:pam 3 0.919 0.932 0.967 0.272 0.860 0.723
#> MAD:pam 3 0.691 0.875 0.901 0.272 0.860 0.723
#> ATC:pam 3 0.698 0.876 0.930 0.239 0.886 0.773
#> SD:hclust 3 0.728 0.687 0.866 0.266 0.856 0.713
#> CV:hclust 3 0.655 0.757 0.843 0.265 0.849 0.701
#> MAD:hclust 3 0.620 0.618 0.794 0.232 0.928 0.856
#> ATC:hclust 3 0.637 0.768 0.882 0.610 0.621 0.501
get_stats(res_list, k = 4)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 4 0.653 0.603 0.793 0.1420 0.809 0.586
#> CV:NMF 4 0.670 0.703 0.835 0.1397 0.822 0.608
#> MAD:NMF 4 0.626 0.735 0.843 0.1343 0.813 0.609
#> ATC:NMF 4 0.586 0.563 0.765 0.1144 0.794 0.489
#> SD:skmeans 4 0.699 0.752 0.779 0.1159 0.892 0.709
#> CV:skmeans 4 0.684 0.779 0.798 0.1217 0.882 0.688
#> MAD:skmeans 4 0.648 0.554 0.738 0.1266 0.878 0.688
#> ATC:skmeans 4 0.981 0.922 0.939 0.1473 0.870 0.639
#> SD:mclust 4 0.602 0.363 0.695 0.0794 0.886 0.717
#> CV:mclust 4 0.577 0.545 0.769 0.0889 0.974 0.927
#> MAD:mclust 4 0.650 0.571 0.773 0.1174 0.930 0.817
#> ATC:mclust 4 0.702 0.758 0.806 0.0764 0.763 0.466
#> SD:kmeans 4 0.649 0.515 0.710 0.1263 0.972 0.921
#> CV:kmeans 4 0.622 0.544 0.742 0.1312 0.856 0.629
#> MAD:kmeans 4 0.613 0.579 0.785 0.1298 0.920 0.776
#> ATC:kmeans 4 0.811 0.850 0.911 0.1491 0.888 0.696
#> SD:pam 4 0.761 0.820 0.898 0.1379 0.893 0.712
#> CV:pam 4 0.783 0.859 0.917 0.1450 0.891 0.711
#> MAD:pam 4 0.717 0.795 0.891 0.1589 0.882 0.690
#> ATC:pam 4 0.882 0.885 0.952 0.1439 0.863 0.667
#> SD:hclust 4 0.586 0.599 0.746 0.1015 0.891 0.726
#> CV:hclust 4 0.613 0.640 0.731 0.1289 0.898 0.729
#> MAD:hclust 4 0.569 0.520 0.753 0.1384 0.822 0.603
#> ATC:hclust 4 0.832 0.872 0.924 0.1296 0.905 0.767
get_stats(res_list, k = 5)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 5 0.716 0.690 0.833 0.0496 0.904 0.714
#> CV:NMF 5 0.735 0.719 0.850 0.0502 0.921 0.753
#> MAD:NMF 5 0.675 0.647 0.763 0.0665 0.838 0.562
#> ATC:NMF 5 0.715 0.690 0.841 0.0469 0.888 0.632
#> SD:skmeans 5 0.669 0.555 0.739 0.0785 0.868 0.572
#> CV:skmeans 5 0.679 0.736 0.811 0.0817 0.868 0.563
#> MAD:skmeans 5 0.666 0.588 0.760 0.0765 0.867 0.586
#> ATC:skmeans 5 0.843 0.837 0.906 0.0581 0.899 0.632
#> SD:mclust 5 0.714 0.801 0.867 0.0829 0.813 0.515
#> CV:mclust 5 0.801 0.808 0.885 0.0898 0.888 0.674
#> MAD:mclust 5 0.624 0.454 0.747 0.0859 0.919 0.762
#> ATC:mclust 5 0.740 0.634 0.768 0.0757 0.877 0.640
#> SD:kmeans 5 0.642 0.560 0.709 0.0736 0.823 0.496
#> CV:kmeans 5 0.623 0.664 0.725 0.0741 0.840 0.504
#> MAD:kmeans 5 0.652 0.584 0.745 0.0719 0.894 0.652
#> ATC:kmeans 5 0.746 0.613 0.757 0.0753 0.958 0.847
#> SD:pam 5 0.767 0.729 0.871 0.0409 0.975 0.909
#> CV:pam 5 0.770 0.785 0.887 0.0371 0.977 0.919
#> MAD:pam 5 0.726 0.740 0.850 0.0403 0.970 0.892
#> ATC:pam 5 0.826 0.863 0.914 0.0618 0.910 0.723
#> SD:hclust 5 0.610 0.593 0.704 0.0654 0.969 0.908
#> CV:hclust 5 0.614 0.394 0.636 0.0622 0.825 0.501
#> MAD:hclust 5 0.591 0.527 0.720 0.0770 0.889 0.638
#> ATC:hclust 5 0.800 0.789 0.859 0.1063 0.913 0.722
get_stats(res_list, k = 6)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 6 0.746 0.675 0.826 0.0400 0.959 0.858
#> CV:NMF 6 0.740 0.681 0.829 0.0385 0.968 0.886
#> MAD:NMF 6 0.705 0.629 0.801 0.0463 0.929 0.748
#> ATC:NMF 6 0.643 0.446 0.683 0.0413 0.891 0.587
#> SD:skmeans 6 0.660 0.597 0.756 0.0512 0.922 0.674
#> CV:skmeans 6 0.687 0.663 0.782 0.0410 0.926 0.678
#> MAD:skmeans 6 0.651 0.641 0.771 0.0491 0.941 0.741
#> ATC:skmeans 6 0.818 0.772 0.883 0.0285 0.968 0.846
#> SD:mclust 6 0.715 0.715 0.800 0.0521 0.958 0.829
#> CV:mclust 6 0.703 0.683 0.793 0.0335 0.975 0.895
#> MAD:mclust 6 0.661 0.478 0.734 0.0437 0.877 0.580
#> ATC:mclust 6 0.988 0.945 0.970 0.1121 0.831 0.424
#> SD:kmeans 6 0.645 0.526 0.729 0.0425 0.917 0.672
#> CV:kmeans 6 0.640 0.552 0.716 0.0444 0.943 0.744
#> MAD:kmeans 6 0.641 0.489 0.701 0.0498 0.934 0.727
#> ATC:kmeans 6 0.771 0.755 0.796 0.0475 0.889 0.583
#> SD:pam 6 0.674 0.523 0.736 0.0561 0.953 0.819
#> CV:pam 6 0.685 0.486 0.724 0.0563 0.934 0.755
#> MAD:pam 6 0.701 0.581 0.760 0.0445 0.952 0.813
#> ATC:pam 6 0.971 0.909 0.953 0.0763 0.920 0.696
#> SD:hclust 6 0.632 0.473 0.687 0.0514 0.919 0.755
#> CV:hclust 6 0.673 0.503 0.699 0.0424 0.885 0.559
#> MAD:hclust 6 0.635 0.577 0.721 0.0427 0.925 0.692
#> ATC:hclust 6 0.785 0.748 0.837 0.0413 0.985 0.935
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) individual(p) disease.state(p) gender(p) other(p) k
#> SD:NMF 106 3.87e-23 0.515 0.690 0.697 1.000 2
#> CV:NMF 106 3.87e-23 0.515 0.690 0.697 1.000 2
#> MAD:NMF 107 2.35e-23 0.555 0.665 0.611 0.958 2
#> ATC:NMF 106 1.55e-21 0.413 0.571 0.751 0.997 2
#> SD:skmeans 107 2.35e-23 0.555 0.665 0.611 0.958 2
#> CV:skmeans 107 2.35e-23 0.555 0.665 0.611 0.958 2
#> MAD:skmeans 107 3.34e-24 0.644 0.777 0.441 1.000 2
#> ATC:skmeans 106 2.52e-22 0.413 0.571 0.595 0.997 2
#> SD:mclust 107 1.00e-21 0.640 0.454 0.733 0.961 2
#> CV:mclust 107 1.00e-21 0.640 0.454 0.733 0.961 2
#> MAD:mclust 107 1.00e-21 0.640 0.454 0.733 0.961 2
#> ATC:mclust 0 NA NA NA NA NA 2
#> SD:kmeans 107 1.59e-22 0.777 0.577 0.628 0.872 2
#> CV:kmeans 107 1.59e-22 0.777 0.577 0.628 0.872 2
#> MAD:kmeans 107 1.59e-22 0.777 0.577 0.628 0.872 2
#> ATC:kmeans 106 1.78e-21 0.453 0.489 0.697 0.730 2
#> SD:pam 107 1.59e-22 0.777 0.577 0.628 0.872 2
#> CV:pam 107 1.59e-22 0.777 0.577 0.628 0.872 2
#> MAD:pam 107 1.59e-22 0.777 0.577 0.628 0.872 2
#> ATC:pam 106 3.92e-23 0.588 0.614 0.401 0.814 2
#> SD:hclust 105 6.63e-23 0.681 0.534 0.639 1.000 2
#> CV:hclust 105 6.63e-23 0.681 0.534 0.639 1.000 2
#> MAD:hclust 106 5.55e-24 0.696 0.705 0.487 1.000 2
#> ATC:hclust 97 2.04e-04 0.335 0.382 0.351 0.902 2
test_to_known_factors(res_list, k = 3)
#> n tissue(p) individual(p) disease.state(p) gender(p) other(p) k
#> SD:NMF 84 4.25e-18 0.24703 0.6084 0.75527 0.753 3
#> CV:NMF 87 8.02e-20 0.18268 0.6543 0.93957 1.000 3
#> MAD:NMF 87 8.02e-20 0.24627 0.7842 0.71288 1.000 3
#> ATC:NMF 103 1.53e-17 0.27787 0.4130 0.51953 0.119 3
#> SD:skmeans 105 1.05e-22 0.00281 0.6365 0.00518 0.930 3
#> CV:skmeans 104 1.74e-22 0.00150 0.6831 0.00907 0.947 3
#> MAD:skmeans 100 1.93e-22 0.00147 0.4031 0.01113 0.653 3
#> ATC:skmeans 106 2.97e-21 0.12316 0.3880 0.85428 0.416 3
#> SD:mclust 100 3.17e-20 0.02926 0.4981 0.05616 0.846 3
#> CV:mclust 99 1.14e-20 0.00566 0.5108 0.03398 0.779 3
#> MAD:mclust 103 6.81e-21 0.01678 0.5750 0.37052 0.962 3
#> ATC:mclust 104 4.76e-20 0.02886 0.9024 0.62535 0.934 3
#> SD:kmeans 105 6.36e-22 0.00199 0.5418 0.10573 0.892 3
#> CV:kmeans 98 2.05e-20 0.00176 0.5573 0.11778 0.628 3
#> MAD:kmeans 105 6.36e-22 0.00199 0.5418 0.10573 0.892 3
#> ATC:kmeans 95 1.67e-20 0.24226 0.7880 0.74800 0.912 3
#> SD:pam 99 1.41e-20 0.00669 0.1502 0.34863 0.175 3
#> CV:pam 106 2.95e-21 0.01026 0.0912 0.55101 0.237 3
#> MAD:pam 104 1.20e-21 0.03217 0.3190 0.44097 0.191 3
#> ATC:pam 101 5.30e-21 0.10018 0.7065 0.16016 0.524 3
#> SD:hclust 80 3.07e-17 0.17418 0.2800 0.36470 0.229 3
#> CV:hclust 96 9.84e-21 0.44144 0.6636 0.53269 1.000 3
#> MAD:hclust 88 7.78e-20 0.63507 0.3720 0.36788 0.239 3
#> ATC:hclust 89 4.72e-20 0.52829 0.6632 0.28636 0.875 3
test_to_known_factors(res_list, k = 4)
#> n tissue(p) individual(p) disease.state(p) gender(p) other(p) k
#> SD:NMF 77 1.90e-17 0.050079 0.405 0.35719 0.1803 4
#> CV:NMF 86 4.26e-18 0.031503 0.471 0.18106 0.1032 4
#> MAD:NMF 95 1.10e-19 0.656859 0.596 0.45585 0.8984 4
#> ATC:NMF 77 1.91e-12 0.041585 0.760 0.86172 0.4877 4
#> SD:skmeans 98 4.18e-21 0.006039 0.814 0.01322 0.9776 4
#> CV:skmeans 100 1.55e-21 0.018320 0.790 0.03269 0.9747 4
#> MAD:skmeans 84 4.25e-18 0.005616 0.763 0.00814 0.9944 4
#> ATC:skmeans 102 9.68e-19 0.228909 0.837 0.97481 0.9254 4
#> SD:mclust 64 1.62e-13 0.135147 0.486 0.02788 1.0000 4
#> CV:mclust 81 1.13e-15 0.006318 0.683 0.24848 0.7787 4
#> MAD:mclust 91 2.36e-17 0.020502 0.603 0.34462 0.7308 4
#> ATC:mclust 103 1.07e-19 0.055709 0.855 0.58302 0.7853 4
#> SD:kmeans 58 3.26e-01 0.000295 0.397 0.10403 1.0000 4
#> CV:kmeans 88 2.46e-17 0.010846 0.852 0.22383 0.9812 4
#> MAD:kmeans 83 3.11e-16 0.002148 0.925 0.16486 0.9220 4
#> ATC:kmeans 98 2.86e-20 0.179829 0.766 0.33008 0.9842 4
#> SD:pam 99 1.75e-20 0.002891 0.437 0.19003 0.5229 4
#> CV:pam 103 2.47e-21 0.011583 0.393 0.29101 0.6122 4
#> MAD:pam 104 9.35e-21 0.011851 0.557 0.22580 0.4115 4
#> ATC:pam 105 2.38e-19 0.137330 0.820 0.66636 0.8179 4
#> SD:hclust 74 4.48e-15 0.235779 0.157 0.09348 0.0368 4
#> CV:hclust 93 4.97e-20 0.035926 0.655 0.06588 0.7549 4
#> MAD:hclust 77 1.35e-16 0.601905 0.483 0.53356 0.3495 4
#> ATC:hclust 101 9.47e-22 0.245262 0.798 0.35947 0.8840 4
test_to_known_factors(res_list, k = 5)
#> n tissue(p) individual(p) disease.state(p) gender(p) other(p) k
#> SD:NMF 89 3.59e-19 0.01234 0.2022 0.217422 0.0452 5
#> CV:NMF 92 4.95e-19 0.04683 0.1887 0.210627 0.0426 5
#> MAD:NMF 84 2.47e-17 0.00724 0.6168 0.023880 0.5171 5
#> ATC:NMF 96 1.01e-13 0.02181 0.5691 0.621644 0.4389 5
#> SD:skmeans 71 2.61e-15 0.00587 0.6660 0.001926 0.9276 5
#> CV:skmeans 95 1.14e-19 0.00388 0.8053 0.011618 0.9630 5
#> MAD:skmeans 80 1.74e-16 0.01080 0.6031 0.000863 0.5815 5
#> ATC:skmeans 103 4.58e-19 0.29641 0.7362 0.438062 0.5036 5
#> SD:mclust 101 1.01e-18 0.02609 0.6612 0.402826 0.4632 5
#> CV:mclust 99 2.89e-18 0.00570 0.6342 0.247974 0.4468 5
#> MAD:mclust 50 5.13e-09 0.45487 0.8123 0.580092 0.8671 5
#> ATC:mclust 84 1.59e-16 0.00674 0.9627 0.994172 0.9575 5
#> SD:kmeans 71 4.63e-13 0.02933 0.3506 0.215170 0.2947 5
#> CV:kmeans 89 7.24e-17 0.06638 0.3153 0.093737 0.3110 5
#> MAD:kmeans 80 6.50e-15 0.00493 0.5000 0.104329 0.5658 5
#> ATC:kmeans 81 1.07e-16 0.23307 0.6389 0.213487 0.9708 5
#> SD:pam 95 7.61e-19 0.01689 0.6627 0.199015 0.4411 5
#> CV:pam 98 1.77e-19 0.11249 0.8318 0.202478 0.5107 5
#> MAD:pam 101 2.46e-19 0.06629 0.7365 0.204403 0.3037 5
#> ATC:pam 104 2.85e-19 0.10669 0.5816 0.478786 0.6017 5
#> SD:hclust 74 4.28e-15 0.26381 0.0754 0.066574 0.0200 5
#> CV:hclust 50 3.61e-10 0.02833 0.8340 0.035004 0.6843 5
#> MAD:hclust 63 6.79e-13 0.02725 0.7794 0.898083 0.5140 5
#> ATC:hclust 99 1.61e-20 0.07531 0.7759 0.089036 0.7177 5
test_to_known_factors(res_list, k = 6)
#> n tissue(p) individual(p) disease.state(p) gender(p) other(p) k
#> SD:NMF 84 4.25e-18 0.01192 0.267 0.1488 0.0634 6
#> CV:NMF 84 4.25e-18 0.01583 0.135 0.0943 0.0222 6
#> MAD:NMF 77 1.35e-16 0.00210 0.407 0.0513 0.1415 6
#> ATC:NMF 55 5.42e-10 0.03289 0.609 0.1322 0.5642 6
#> SD:skmeans 80 8.39e-16 0.08953 0.294 0.1093 0.3797 6
#> CV:skmeans 88 1.77e-17 0.00815 0.708 0.0490 0.9425 6
#> MAD:skmeans 90 6.72e-18 0.01369 0.697 0.1038 0.8189 6
#> ATC:skmeans 94 2.87e-17 0.13218 0.525 0.2666 0.5972 6
#> SD:mclust 95 2.62e-17 0.00567 0.706 0.4174 0.5024 6
#> CV:mclust 92 4.88e-17 0.03193 0.913 0.2926 0.5595 6
#> MAD:mclust 69 3.14e-12 0.26325 0.491 0.4200 0.6933 6
#> ATC:mclust 106 1.18e-19 0.01011 0.851 0.8348 0.6523 6
#> SD:kmeans 75 8.90e-14 0.00304 0.252 0.0854 0.2004 6
#> CV:kmeans 78 6.98e-14 0.02658 0.352 0.2971 0.2461 6
#> MAD:kmeans 72 3.80e-13 0.00516 0.298 0.0750 0.2621 6
#> ATC:kmeans 100 1.54e-18 0.14248 0.604 0.1540 0.6607 6
#> SD:pam 78 3.07e-15 0.00155 0.835 0.3500 0.6915 6
#> CV:pam 59 3.31e-11 0.00860 0.925 0.3955 0.6820 6
#> MAD:pam 79 7.62e-14 0.09848 0.652 0.2252 0.3449 6
#> ATC:pam 103 3.20e-18 0.18254 0.318 0.4733 0.8667 6
#> SD:hclust 52 3.58e-11 0.08475 0.639 0.0237 0.3586 6
#> CV:hclust 70 6.25e-13 0.07229 0.768 0.0666 0.7165 6
#> MAD:hclust 74 1.50e-14 0.03700 0.705 0.1793 0.6864 6
#> ATC:hclust 97 4.28e-20 0.09839 0.768 0.0864 0.7717 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 21168 rows and 107 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'hclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.969 0.987 0.4979 0.501 0.501
#> 3 3 0.728 0.687 0.866 0.2665 0.856 0.713
#> 4 4 0.586 0.599 0.746 0.1015 0.891 0.726
#> 5 5 0.610 0.593 0.704 0.0654 0.969 0.908
#> 6 6 0.632 0.473 0.687 0.0514 0.919 0.755
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
#> GSM254629 1 0.0672 0.989 0.992 0.008
#> GSM254648 1 0.1184 0.983 0.984 0.016
#> GSM254694 1 0.0938 0.987 0.988 0.012
#> GSM254701 1 0.0672 0.989 0.992 0.008
#> GSM254728 1 0.0672 0.989 0.992 0.008
#> GSM254726 1 0.1414 0.980 0.980 0.020
#> GSM254639 1 0.0672 0.989 0.992 0.008
#> GSM254652 1 0.0672 0.989 0.992 0.008
#> GSM254700 1 0.0000 0.991 1.000 0.000
#> GSM254625 1 0.0938 0.987 0.988 0.012
#> GSM254636 1 0.0000 0.991 1.000 0.000
#> GSM254659 1 0.0672 0.989 0.992 0.008
#> GSM254680 1 0.0000 0.991 1.000 0.000
#> GSM254686 1 0.0672 0.989 0.992 0.008
#> GSM254718 1 0.0938 0.987 0.988 0.012
#> GSM254674 1 0.0000 0.991 1.000 0.000
#> GSM254668 1 0.0000 0.991 1.000 0.000
#> GSM254697 1 0.0000 0.991 1.000 0.000
#> GSM254704 1 0.0000 0.991 1.000 0.000
#> GSM254707 1 0.0000 0.991 1.000 0.000
#> GSM254714 1 0.0000 0.991 1.000 0.000
#> GSM254722 1 0.0000 0.991 1.000 0.000
#> GSM254627 1 0.0000 0.991 1.000 0.000
#> GSM254630 1 0.0672 0.989 0.992 0.008
#> GSM254633 1 0.0000 0.991 1.000 0.000
#> GSM254670 1 0.0672 0.989 0.992 0.008
#> GSM254716 1 0.0672 0.989 0.992 0.008
#> GSM254720 1 0.0376 0.990 0.996 0.004
#> GSM254729 1 0.0938 0.987 0.988 0.012
#> GSM254654 1 0.0938 0.987 0.988 0.012
#> GSM254656 1 0.6623 0.796 0.828 0.172
#> GSM254631 1 0.0000 0.991 1.000 0.000
#> GSM254657 1 0.0672 0.989 0.992 0.008
#> GSM254664 1 0.0000 0.991 1.000 0.000
#> GSM254672 1 0.0000 0.991 1.000 0.000
#> GSM254692 1 0.0000 0.991 1.000 0.000
#> GSM254645 1 0.0672 0.989 0.992 0.008
#> GSM254666 1 0.0672 0.989 0.992 0.008
#> GSM254675 1 0.0000 0.991 1.000 0.000
#> GSM254678 1 0.0000 0.991 1.000 0.000
#> GSM254688 1 0.0000 0.991 1.000 0.000
#> GSM254690 1 0.0000 0.991 1.000 0.000
#> GSM254696 1 0.0000 0.991 1.000 0.000
#> GSM254705 1 0.0000 0.991 1.000 0.000
#> GSM254642 1 0.0000 0.991 1.000 0.000
#> GSM254661 1 0.0672 0.989 0.992 0.008
#> GSM254698 1 0.0000 0.991 1.000 0.000
#> GSM254641 1 0.0000 0.991 1.000 0.000
#> GSM254647 1 0.0000 0.991 1.000 0.000
#> GSM254663 1 0.0000 0.991 1.000 0.000
#> GSM254682 1 0.0000 0.991 1.000 0.000
#> GSM254709 1 0.0376 0.990 0.996 0.004
#> GSM254721 1 0.0000 0.991 1.000 0.000
#> GSM254724 1 0.0000 0.991 1.000 0.000
#> GSM254650 1 0.0000 0.991 1.000 0.000
#> GSM254687 1 0.0000 0.991 1.000 0.000
#> GSM254637 1 0.0000 0.991 1.000 0.000
#> GSM254684 1 0.0000 0.991 1.000 0.000
#> GSM254649 2 0.0000 0.980 0.000 1.000
#> GSM254660 2 0.0000 0.980 0.000 1.000
#> GSM254693 2 0.0000 0.980 0.000 1.000
#> GSM254695 2 0.2423 0.942 0.040 0.960
#> GSM254702 2 0.0000 0.980 0.000 1.000
#> GSM254643 2 0.0000 0.980 0.000 1.000
#> GSM254727 2 0.0000 0.980 0.000 1.000
#> GSM254640 2 0.0000 0.980 0.000 1.000
#> GSM254626 2 0.0000 0.980 0.000 1.000
#> GSM254635 2 0.0000 0.980 0.000 1.000
#> GSM254653 2 0.0000 0.980 0.000 1.000
#> GSM254658 2 0.0000 0.980 0.000 1.000
#> GSM254681 2 0.0000 0.980 0.000 1.000
#> GSM254719 2 0.0000 0.980 0.000 1.000
#> GSM254673 2 0.0000 0.980 0.000 1.000
#> GSM254655 2 0.0000 0.980 0.000 1.000
#> GSM254669 2 0.0000 0.980 0.000 1.000
#> GSM254699 2 0.0000 0.980 0.000 1.000
#> GSM254703 2 0.0000 0.980 0.000 1.000
#> GSM254708 2 0.0000 0.980 0.000 1.000
#> GSM254715 2 0.0000 0.980 0.000 1.000
#> GSM254628 2 0.0000 0.980 0.000 1.000
#> GSM254634 2 0.0000 0.980 0.000 1.000
#> GSM254646 2 0.0000 0.980 0.000 1.000
#> GSM254671 2 0.0000 0.980 0.000 1.000
#> GSM254711 2 0.0000 0.980 0.000 1.000
#> GSM254717 2 0.0000 0.980 0.000 1.000
#> GSM254723 1 0.5737 0.846 0.864 0.136
#> GSM254730 2 0.0000 0.980 0.000 1.000
#> GSM254731 2 0.0000 0.980 0.000 1.000
#> GSM254632 2 0.9815 0.276 0.420 0.580
#> GSM254662 2 0.0000 0.980 0.000 1.000
#> GSM254677 2 0.0000 0.980 0.000 1.000
#> GSM254665 2 0.0000 0.980 0.000 1.000
#> GSM254691 2 0.0000 0.980 0.000 1.000
#> GSM254644 2 0.0000 0.980 0.000 1.000
#> GSM254667 2 0.1414 0.962 0.020 0.980
#> GSM254676 2 0.0000 0.980 0.000 1.000
#> GSM254679 2 0.0000 0.980 0.000 1.000
#> GSM254689 2 0.0000 0.980 0.000 1.000
#> GSM254706 2 0.0000 0.980 0.000 1.000
#> GSM254712 2 0.0000 0.980 0.000 1.000
#> GSM254713 2 0.0000 0.980 0.000 1.000
#> GSM254683 2 0.0000 0.980 0.000 1.000
#> GSM254710 2 0.9815 0.276 0.420 0.580
#> GSM254725 2 0.0000 0.980 0.000 1.000
#> GSM254651 2 0.0000 0.980 0.000 1.000
#> GSM254638 2 0.0000 0.980 0.000 1.000
#> GSM254685 2 0.0000 0.980 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM254629 3 0.1289 0.701331 0.032 0.000 0.968
#> GSM254648 3 0.1711 0.701786 0.032 0.008 0.960
#> GSM254694 3 0.2200 0.698980 0.056 0.004 0.940
#> GSM254701 3 0.1289 0.701331 0.032 0.000 0.968
#> GSM254728 3 0.2625 0.700065 0.084 0.000 0.916
#> GSM254726 3 0.2339 0.700373 0.048 0.012 0.940
#> GSM254639 3 0.4887 0.565119 0.228 0.000 0.772
#> GSM254652 3 0.1753 0.705201 0.048 0.000 0.952
#> GSM254700 1 0.0747 0.618480 0.984 0.000 0.016
#> GSM254625 3 0.1267 0.695144 0.024 0.004 0.972
#> GSM254636 1 0.6305 0.161769 0.516 0.000 0.484
#> GSM254659 3 0.2796 0.686711 0.092 0.000 0.908
#> GSM254680 1 0.6308 0.172571 0.508 0.000 0.492
#> GSM254686 3 0.0424 0.692359 0.008 0.000 0.992
#> GSM254718 3 0.3030 0.700997 0.092 0.004 0.904
#> GSM254674 3 0.5968 0.315408 0.364 0.000 0.636
#> GSM254668 3 0.5733 0.361944 0.324 0.000 0.676
#> GSM254697 1 0.0747 0.620601 0.984 0.000 0.016
#> GSM254704 1 0.0747 0.618480 0.984 0.000 0.016
#> GSM254707 3 0.4750 0.563001 0.216 0.000 0.784
#> GSM254714 1 0.5621 0.499809 0.692 0.000 0.308
#> GSM254722 1 0.4702 0.598676 0.788 0.000 0.212
#> GSM254627 1 0.0747 0.620601 0.984 0.000 0.016
#> GSM254630 3 0.2165 0.699778 0.064 0.000 0.936
#> GSM254633 3 0.6291 -0.078099 0.468 0.000 0.532
#> GSM254670 3 0.4887 0.565119 0.228 0.000 0.772
#> GSM254716 3 0.0424 0.692359 0.008 0.000 0.992
#> GSM254720 3 0.6267 0.058942 0.452 0.000 0.548
#> GSM254729 3 0.2301 0.697718 0.060 0.004 0.936
#> GSM254654 3 0.1525 0.701795 0.032 0.004 0.964
#> GSM254656 3 0.8026 0.390417 0.180 0.164 0.656
#> GSM254631 1 0.6308 0.172571 0.508 0.000 0.492
#> GSM254657 3 0.3551 0.673583 0.132 0.000 0.868
#> GSM254664 1 0.6308 0.172571 0.508 0.000 0.492
#> GSM254672 1 0.3752 0.616430 0.856 0.000 0.144
#> GSM254692 1 0.5058 0.488647 0.756 0.000 0.244
#> GSM254645 3 0.4750 0.582234 0.216 0.000 0.784
#> GSM254666 3 0.2165 0.695841 0.064 0.000 0.936
#> GSM254675 1 0.5926 0.440278 0.644 0.000 0.356
#> GSM254678 1 0.6008 0.434739 0.628 0.000 0.372
#> GSM254688 3 0.5138 0.519241 0.252 0.000 0.748
#> GSM254690 1 0.6252 0.291481 0.556 0.000 0.444
#> GSM254696 3 0.6307 -0.113550 0.488 0.000 0.512
#> GSM254705 3 0.6299 -0.014690 0.476 0.000 0.524
#> GSM254642 1 0.0747 0.620601 0.984 0.000 0.016
#> GSM254661 3 0.1643 0.703354 0.044 0.000 0.956
#> GSM254698 1 0.4702 0.598676 0.788 0.000 0.212
#> GSM254641 3 0.5591 0.438972 0.304 0.000 0.696
#> GSM254647 1 0.6126 0.357385 0.600 0.000 0.400
#> GSM254663 3 0.5650 0.424471 0.312 0.000 0.688
#> GSM254682 3 0.5591 0.430718 0.304 0.000 0.696
#> GSM254709 3 0.6192 0.196784 0.420 0.000 0.580
#> GSM254721 1 0.0892 0.620772 0.980 0.000 0.020
#> GSM254724 1 0.0747 0.618480 0.984 0.000 0.016
#> GSM254650 3 0.6180 0.176102 0.416 0.000 0.584
#> GSM254687 3 0.6295 0.000619 0.472 0.000 0.528
#> GSM254637 1 0.6308 0.172571 0.508 0.000 0.492
#> GSM254684 1 0.6307 0.134112 0.512 0.000 0.488
#> GSM254649 2 0.0000 0.976963 0.000 1.000 0.000
#> GSM254660 2 0.0000 0.976963 0.000 1.000 0.000
#> GSM254693 2 0.0000 0.976963 0.000 1.000 0.000
#> GSM254695 2 0.2173 0.931461 0.008 0.944 0.048
#> GSM254702 2 0.0000 0.976963 0.000 1.000 0.000
#> GSM254643 2 0.0000 0.976963 0.000 1.000 0.000
#> GSM254727 2 0.0000 0.976963 0.000 1.000 0.000
#> GSM254640 2 0.0424 0.974745 0.008 0.992 0.000
#> GSM254626 2 0.0000 0.976963 0.000 1.000 0.000
#> GSM254635 2 0.0424 0.974745 0.008 0.992 0.000
#> GSM254653 2 0.0000 0.976963 0.000 1.000 0.000
#> GSM254658 2 0.0000 0.976963 0.000 1.000 0.000
#> GSM254681 2 0.0000 0.976963 0.000 1.000 0.000
#> GSM254719 2 0.0000 0.976963 0.000 1.000 0.000
#> GSM254673 2 0.0000 0.976963 0.000 1.000 0.000
#> GSM254655 2 0.0000 0.976963 0.000 1.000 0.000
#> GSM254669 2 0.0000 0.976963 0.000 1.000 0.000
#> GSM254699 2 0.0000 0.976963 0.000 1.000 0.000
#> GSM254703 2 0.0424 0.974745 0.008 0.992 0.000
#> GSM254708 2 0.0000 0.976963 0.000 1.000 0.000
#> GSM254715 2 0.0424 0.974745 0.008 0.992 0.000
#> GSM254628 2 0.0000 0.976963 0.000 1.000 0.000
#> GSM254634 2 0.0424 0.974745 0.008 0.992 0.000
#> GSM254646 2 0.0000 0.976963 0.000 1.000 0.000
#> GSM254671 2 0.0424 0.974745 0.008 0.992 0.000
#> GSM254711 2 0.0424 0.974745 0.008 0.992 0.000
#> GSM254717 2 0.0000 0.976963 0.000 1.000 0.000
#> GSM254723 3 0.3896 0.554131 0.008 0.128 0.864
#> GSM254730 2 0.0000 0.976963 0.000 1.000 0.000
#> GSM254731 2 0.0000 0.976963 0.000 1.000 0.000
#> GSM254632 2 0.6215 0.275774 0.000 0.572 0.428
#> GSM254662 2 0.0000 0.976963 0.000 1.000 0.000
#> GSM254677 2 0.0424 0.974745 0.008 0.992 0.000
#> GSM254665 2 0.0000 0.976963 0.000 1.000 0.000
#> GSM254691 2 0.0000 0.976963 0.000 1.000 0.000
#> GSM254644 2 0.0424 0.974745 0.008 0.992 0.000
#> GSM254667 2 0.1163 0.952908 0.000 0.972 0.028
#> GSM254676 2 0.0000 0.976963 0.000 1.000 0.000
#> GSM254679 2 0.0424 0.974745 0.008 0.992 0.000
#> GSM254689 2 0.0000 0.976963 0.000 1.000 0.000
#> GSM254706 2 0.0000 0.976963 0.000 1.000 0.000
#> GSM254712 2 0.0424 0.974745 0.008 0.992 0.000
#> GSM254713 2 0.0424 0.974745 0.008 0.992 0.000
#> GSM254683 2 0.0000 0.976963 0.000 1.000 0.000
#> GSM254710 2 0.6215 0.275774 0.000 0.572 0.428
#> GSM254725 2 0.0424 0.974745 0.008 0.992 0.000
#> GSM254651 2 0.0000 0.976963 0.000 1.000 0.000
#> GSM254638 2 0.0424 0.974745 0.008 0.992 0.000
#> GSM254685 2 0.0000 0.976963 0.000 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM254629 3 0.1724 0.7191 0.020 0.000 0.948 0.032
#> GSM254648 3 0.2057 0.7177 0.020 0.008 0.940 0.032
#> GSM254694 3 0.2597 0.7256 0.040 0.004 0.916 0.040
#> GSM254701 3 0.1724 0.7191 0.020 0.000 0.948 0.032
#> GSM254728 3 0.3367 0.7168 0.108 0.000 0.864 0.028
#> GSM254726 3 0.3493 0.7323 0.052 0.008 0.876 0.064
#> GSM254639 3 0.5088 0.5283 0.288 0.000 0.688 0.024
#> GSM254652 3 0.3793 0.7002 0.112 0.000 0.844 0.044
#> GSM254700 1 0.5508 -0.6962 0.508 0.000 0.016 0.476
#> GSM254625 3 0.6571 0.4050 0.264 0.000 0.612 0.124
#> GSM254636 1 0.5200 0.4844 0.700 0.000 0.264 0.036
#> GSM254659 3 0.4467 0.6736 0.172 0.000 0.788 0.040
#> GSM254680 1 0.4391 0.5240 0.740 0.000 0.252 0.008
#> GSM254686 3 0.5689 0.5713 0.184 0.000 0.712 0.104
#> GSM254718 3 0.3439 0.7252 0.084 0.000 0.868 0.048
#> GSM254674 1 0.6123 0.3443 0.572 0.000 0.372 0.056
#> GSM254668 1 0.6079 0.3629 0.568 0.000 0.380 0.052
#> GSM254697 1 0.5038 -0.4164 0.652 0.000 0.012 0.336
#> GSM254704 4 0.5435 0.7861 0.420 0.000 0.016 0.564
#> GSM254707 1 0.6843 0.1659 0.460 0.000 0.440 0.100
#> GSM254714 4 0.7732 0.1828 0.268 0.000 0.288 0.444
#> GSM254722 1 0.5267 0.0236 0.740 0.000 0.076 0.184
#> GSM254627 1 0.5038 -0.4164 0.652 0.000 0.012 0.336
#> GSM254630 3 0.5180 0.5864 0.196 0.000 0.740 0.064
#> GSM254633 1 0.4908 0.4982 0.692 0.000 0.292 0.016
#> GSM254670 3 0.4988 0.5280 0.288 0.000 0.692 0.020
#> GSM254716 3 0.5766 0.5601 0.192 0.000 0.704 0.104
#> GSM254720 3 0.7176 0.0328 0.252 0.000 0.552 0.196
#> GSM254729 3 0.3198 0.7284 0.080 0.000 0.880 0.040
#> GSM254654 3 0.1911 0.7187 0.020 0.004 0.944 0.032
#> GSM254656 3 0.8182 0.3612 0.232 0.156 0.548 0.064
#> GSM254631 1 0.4422 0.5238 0.736 0.000 0.256 0.008
#> GSM254657 3 0.3647 0.6892 0.152 0.000 0.832 0.016
#> GSM254664 1 0.4422 0.5238 0.736 0.000 0.256 0.008
#> GSM254672 1 0.6546 -0.4734 0.524 0.000 0.080 0.396
#> GSM254692 1 0.7497 -0.2519 0.424 0.000 0.180 0.396
#> GSM254645 3 0.4903 0.5713 0.248 0.000 0.724 0.028
#> GSM254666 3 0.6054 0.4552 0.256 0.000 0.656 0.088
#> GSM254675 1 0.6714 0.1972 0.616 0.000 0.176 0.208
#> GSM254678 1 0.5728 0.3890 0.708 0.000 0.188 0.104
#> GSM254688 1 0.6857 0.2442 0.492 0.000 0.404 0.104
#> GSM254690 1 0.4137 0.5087 0.780 0.000 0.208 0.012
#> GSM254696 1 0.5577 0.4089 0.636 0.000 0.328 0.036
#> GSM254705 1 0.7278 0.4540 0.528 0.000 0.284 0.188
#> GSM254642 1 0.5038 -0.4164 0.652 0.000 0.012 0.336
#> GSM254661 3 0.3587 0.6986 0.104 0.000 0.856 0.040
#> GSM254698 1 0.5267 0.0236 0.740 0.000 0.076 0.184
#> GSM254641 1 0.7210 0.2544 0.456 0.000 0.404 0.140
#> GSM254647 1 0.6170 0.4563 0.672 0.000 0.192 0.136
#> GSM254663 1 0.7202 0.2726 0.464 0.000 0.396 0.140
#> GSM254682 1 0.6738 0.3386 0.544 0.000 0.352 0.104
#> GSM254709 1 0.7352 0.3283 0.496 0.000 0.328 0.176
#> GSM254721 4 0.5716 0.7788 0.420 0.000 0.028 0.552
#> GSM254724 4 0.5435 0.7861 0.420 0.000 0.016 0.564
#> GSM254650 1 0.7285 0.4154 0.516 0.000 0.308 0.176
#> GSM254687 1 0.7295 0.4522 0.524 0.000 0.288 0.188
#> GSM254637 1 0.4422 0.5238 0.736 0.000 0.256 0.008
#> GSM254684 1 0.6258 0.3259 0.600 0.000 0.324 0.076
#> GSM254649 2 0.0336 0.8892 0.000 0.992 0.000 0.008
#> GSM254660 2 0.2345 0.8857 0.000 0.900 0.000 0.100
#> GSM254693 2 0.0336 0.8892 0.000 0.992 0.000 0.008
#> GSM254695 2 0.4964 0.8164 0.000 0.716 0.028 0.256
#> GSM254702 2 0.2281 0.8863 0.000 0.904 0.000 0.096
#> GSM254643 2 0.3074 0.8752 0.000 0.848 0.000 0.152
#> GSM254727 2 0.0336 0.8911 0.000 0.992 0.000 0.008
#> GSM254640 2 0.3873 0.8519 0.000 0.772 0.000 0.228
#> GSM254626 2 0.0336 0.8892 0.000 0.992 0.000 0.008
#> GSM254635 2 0.3942 0.8481 0.000 0.764 0.000 0.236
#> GSM254653 2 0.0188 0.8915 0.000 0.996 0.000 0.004
#> GSM254658 2 0.0336 0.8892 0.000 0.992 0.000 0.008
#> GSM254681 2 0.0336 0.8892 0.000 0.992 0.000 0.008
#> GSM254719 2 0.0188 0.8915 0.000 0.996 0.000 0.004
#> GSM254673 2 0.0000 0.8911 0.000 1.000 0.000 0.000
#> GSM254655 2 0.2216 0.8867 0.000 0.908 0.000 0.092
#> GSM254669 2 0.0000 0.8911 0.000 1.000 0.000 0.000
#> GSM254699 2 0.2281 0.8863 0.000 0.904 0.000 0.096
#> GSM254703 2 0.3907 0.8504 0.000 0.768 0.000 0.232
#> GSM254708 2 0.0000 0.8911 0.000 1.000 0.000 0.000
#> GSM254715 2 0.3942 0.8481 0.000 0.764 0.000 0.236
#> GSM254628 2 0.0336 0.8892 0.000 0.992 0.000 0.008
#> GSM254634 2 0.3907 0.8502 0.000 0.768 0.000 0.232
#> GSM254646 2 0.0336 0.8892 0.000 0.992 0.000 0.008
#> GSM254671 2 0.3907 0.8502 0.000 0.768 0.000 0.232
#> GSM254711 2 0.3907 0.8502 0.000 0.768 0.000 0.232
#> GSM254717 2 0.0336 0.8911 0.000 0.992 0.000 0.008
#> GSM254723 3 0.5154 0.6008 0.024 0.120 0.788 0.068
#> GSM254730 2 0.2345 0.8857 0.000 0.900 0.000 0.100
#> GSM254731 2 0.2281 0.8863 0.000 0.904 0.000 0.096
#> GSM254632 2 0.7231 0.2524 0.020 0.560 0.316 0.104
#> GSM254662 2 0.0000 0.8911 0.000 1.000 0.000 0.000
#> GSM254677 2 0.3975 0.8460 0.000 0.760 0.000 0.240
#> GSM254665 2 0.0592 0.8919 0.000 0.984 0.000 0.016
#> GSM254691 2 0.0707 0.8915 0.000 0.980 0.000 0.020
#> GSM254644 2 0.3873 0.8519 0.000 0.772 0.000 0.228
#> GSM254667 2 0.1388 0.8731 0.000 0.960 0.012 0.028
#> GSM254676 2 0.0707 0.8915 0.000 0.980 0.000 0.020
#> GSM254679 2 0.3907 0.8502 0.000 0.768 0.000 0.232
#> GSM254689 2 0.0336 0.8892 0.000 0.992 0.000 0.008
#> GSM254706 2 0.0336 0.8892 0.000 0.992 0.000 0.008
#> GSM254712 2 0.3942 0.8481 0.000 0.764 0.000 0.236
#> GSM254713 2 0.3942 0.8481 0.000 0.764 0.000 0.236
#> GSM254683 2 0.0336 0.8892 0.000 0.992 0.000 0.008
#> GSM254710 2 0.7231 0.2524 0.020 0.560 0.316 0.104
#> GSM254725 2 0.3907 0.8502 0.000 0.768 0.000 0.232
#> GSM254651 2 0.0336 0.8892 0.000 0.992 0.000 0.008
#> GSM254638 2 0.3942 0.8481 0.000 0.764 0.000 0.236
#> GSM254685 2 0.3356 0.8688 0.000 0.824 0.000 0.176
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM254629 3 0.2850 0.6442 0.000 0.000 0.872 NA 0.036
#> GSM254648 3 0.2871 0.6456 0.000 0.004 0.876 NA 0.032
#> GSM254694 3 0.2927 0.6520 0.000 0.000 0.868 NA 0.040
#> GSM254701 3 0.2850 0.6442 0.000 0.000 0.872 NA 0.036
#> GSM254728 3 0.3389 0.6372 0.000 0.000 0.836 NA 0.116
#> GSM254726 3 0.3392 0.6592 0.000 0.004 0.848 NA 0.064
#> GSM254639 3 0.5360 0.4465 0.012 0.000 0.636 NA 0.296
#> GSM254652 3 0.3921 0.6001 0.000 0.000 0.784 NA 0.172
#> GSM254700 1 0.4016 0.6632 0.796 0.000 0.000 NA 0.112
#> GSM254625 3 0.6088 0.1220 0.000 0.000 0.492 NA 0.380
#> GSM254636 5 0.5727 0.5240 0.080 0.000 0.188 NA 0.684
#> GSM254659 3 0.5022 0.5770 0.028 0.000 0.736 NA 0.168
#> GSM254680 5 0.5377 0.5806 0.108 0.000 0.168 NA 0.704
#> GSM254686 3 0.5816 0.3647 0.000 0.000 0.588 NA 0.280
#> GSM254718 3 0.3558 0.6519 0.000 0.000 0.828 NA 0.064
#> GSM254674 5 0.6177 0.4720 0.056 0.000 0.284 NA 0.600
#> GSM254668 5 0.5162 0.4880 0.020 0.000 0.300 NA 0.648
#> GSM254697 1 0.6085 0.5759 0.556 0.000 0.000 NA 0.280
#> GSM254704 1 0.0898 0.6626 0.972 0.000 0.000 NA 0.020
#> GSM254707 5 0.5671 0.3678 0.000 0.000 0.336 NA 0.568
#> GSM254714 1 0.5840 0.2981 0.624 0.000 0.252 NA 0.112
#> GSM254722 5 0.6631 -0.1363 0.240 0.000 0.020 NA 0.548
#> GSM254627 1 0.6085 0.5759 0.556 0.000 0.000 NA 0.280
#> GSM254630 3 0.5140 0.4206 0.004 0.000 0.652 NA 0.284
#> GSM254633 5 0.5503 0.5637 0.088 0.000 0.192 NA 0.692
#> GSM254670 3 0.5379 0.4458 0.012 0.000 0.632 NA 0.300
#> GSM254716 3 0.5851 0.3486 0.000 0.000 0.580 NA 0.288
#> GSM254720 3 0.7314 0.1147 0.272 0.000 0.488 NA 0.184
#> GSM254729 3 0.3338 0.6550 0.004 0.000 0.852 NA 0.076
#> GSM254654 3 0.2769 0.6452 0.000 0.000 0.876 NA 0.032
#> GSM254656 3 0.7983 0.3187 0.012 0.116 0.476 NA 0.248
#> GSM254631 5 0.5321 0.5797 0.108 0.000 0.172 NA 0.704
#> GSM254657 3 0.3914 0.5986 0.000 0.000 0.788 NA 0.164
#> GSM254664 5 0.5321 0.5797 0.108 0.000 0.172 NA 0.704
#> GSM254672 1 0.6354 0.4969 0.580 0.000 0.040 NA 0.288
#> GSM254692 1 0.7666 0.2817 0.484 0.000 0.160 NA 0.244
#> GSM254645 3 0.4977 0.4985 0.016 0.000 0.680 NA 0.268
#> GSM254666 3 0.5874 0.1834 0.000 0.000 0.528 NA 0.364
#> GSM254675 5 0.7296 0.0137 0.348 0.000 0.100 NA 0.460
#> GSM254678 5 0.6126 0.3630 0.152 0.000 0.124 NA 0.664
#> GSM254688 5 0.5745 0.4113 0.004 0.000 0.304 NA 0.592
#> GSM254690 5 0.5209 0.5486 0.124 0.000 0.124 NA 0.728
#> GSM254696 5 0.6284 0.4254 0.080 0.000 0.252 NA 0.612
#> GSM254705 5 0.7321 0.4833 0.156 0.000 0.220 NA 0.532
#> GSM254642 1 0.6085 0.5759 0.556 0.000 0.000 NA 0.280
#> GSM254661 3 0.3616 0.5989 0.000 0.000 0.804 NA 0.164
#> GSM254698 5 0.6631 -0.1363 0.240 0.000 0.020 NA 0.548
#> GSM254641 5 0.7088 0.3895 0.092 0.000 0.312 NA 0.508
#> GSM254647 5 0.6779 0.4811 0.196 0.000 0.132 NA 0.596
#> GSM254663 5 0.7161 0.3978 0.096 0.000 0.308 NA 0.504
#> GSM254682 5 0.5622 0.4690 0.004 0.000 0.260 NA 0.628
#> GSM254709 5 0.7733 0.3715 0.200 0.000 0.272 NA 0.444
#> GSM254721 1 0.1393 0.6605 0.956 0.000 0.012 NA 0.024
#> GSM254724 1 0.0609 0.6650 0.980 0.000 0.000 NA 0.020
#> GSM254650 5 0.7231 0.4801 0.116 0.000 0.244 NA 0.536
#> GSM254687 5 0.7342 0.4870 0.156 0.000 0.224 NA 0.528
#> GSM254637 5 0.5321 0.5797 0.108 0.000 0.172 NA 0.704
#> GSM254684 5 0.7074 0.2706 0.088 0.000 0.264 NA 0.540
#> GSM254649 2 0.0162 0.8060 0.000 0.996 0.000 NA 0.000
#> GSM254660 2 0.2929 0.8010 0.000 0.820 0.000 NA 0.000
#> GSM254693 2 0.0162 0.8060 0.000 0.996 0.000 NA 0.000
#> GSM254695 2 0.4648 0.6624 0.000 0.524 0.012 NA 0.000
#> GSM254702 2 0.2773 0.8034 0.000 0.836 0.000 NA 0.000
#> GSM254643 2 0.3508 0.7836 0.000 0.748 0.000 NA 0.000
#> GSM254727 2 0.0290 0.8084 0.000 0.992 0.000 NA 0.000
#> GSM254640 2 0.4088 0.7442 0.000 0.632 0.000 NA 0.000
#> GSM254626 2 0.0162 0.8060 0.000 0.996 0.000 NA 0.000
#> GSM254635 2 0.4138 0.7355 0.000 0.616 0.000 NA 0.000
#> GSM254653 2 0.0404 0.8088 0.000 0.988 0.000 NA 0.000
#> GSM254658 2 0.1544 0.7824 0.000 0.932 0.000 NA 0.000
#> GSM254681 2 0.1544 0.7824 0.000 0.932 0.000 NA 0.000
#> GSM254719 2 0.0510 0.8094 0.000 0.984 0.000 NA 0.000
#> GSM254673 2 0.0162 0.8077 0.000 0.996 0.000 NA 0.000
#> GSM254655 2 0.2561 0.8058 0.000 0.856 0.000 NA 0.000
#> GSM254669 2 0.0162 0.8077 0.000 0.996 0.000 NA 0.000
#> GSM254699 2 0.2773 0.8034 0.000 0.836 0.000 NA 0.000
#> GSM254703 2 0.4138 0.7376 0.000 0.616 0.000 NA 0.000
#> GSM254708 2 0.1043 0.8018 0.000 0.960 0.000 NA 0.000
#> GSM254715 2 0.4138 0.7355 0.000 0.616 0.000 NA 0.000
#> GSM254628 2 0.0162 0.8060 0.000 0.996 0.000 NA 0.000
#> GSM254634 2 0.4138 0.7372 0.000 0.616 0.000 NA 0.000
#> GSM254646 2 0.1544 0.7824 0.000 0.932 0.000 NA 0.000
#> GSM254671 2 0.4088 0.7437 0.000 0.632 0.000 NA 0.000
#> GSM254711 2 0.4138 0.7372 0.000 0.616 0.000 NA 0.000
#> GSM254717 2 0.1043 0.8114 0.000 0.960 0.000 NA 0.000
#> GSM254723 3 0.5461 0.5538 0.000 0.092 0.716 NA 0.044
#> GSM254730 2 0.2929 0.8010 0.000 0.820 0.000 NA 0.000
#> GSM254731 2 0.2773 0.8034 0.000 0.836 0.000 NA 0.000
#> GSM254632 2 0.7455 0.1205 0.000 0.476 0.240 NA 0.060
#> GSM254662 2 0.0162 0.8077 0.000 0.996 0.000 NA 0.000
#> GSM254677 2 0.4171 0.7283 0.000 0.604 0.000 NA 0.000
#> GSM254665 2 0.1197 0.8107 0.000 0.952 0.000 NA 0.000
#> GSM254691 2 0.1732 0.7882 0.000 0.920 0.000 NA 0.000
#> GSM254644 2 0.4088 0.7442 0.000 0.632 0.000 NA 0.000
#> GSM254667 2 0.2488 0.7505 0.000 0.872 0.004 NA 0.000
#> GSM254676 2 0.1732 0.7882 0.000 0.920 0.000 NA 0.000
#> GSM254679 2 0.4088 0.7437 0.000 0.632 0.000 NA 0.000
#> GSM254689 2 0.1544 0.7824 0.000 0.932 0.000 NA 0.000
#> GSM254706 2 0.1608 0.7811 0.000 0.928 0.000 NA 0.000
#> GSM254712 2 0.4138 0.7355 0.000 0.616 0.000 NA 0.000
#> GSM254713 2 0.4138 0.7355 0.000 0.616 0.000 NA 0.000
#> GSM254683 2 0.1608 0.7811 0.000 0.928 0.000 NA 0.000
#> GSM254710 2 0.7455 0.1205 0.000 0.476 0.240 NA 0.060
#> GSM254725 2 0.4138 0.7372 0.000 0.616 0.000 NA 0.000
#> GSM254651 2 0.1608 0.7811 0.000 0.928 0.000 NA 0.000
#> GSM254638 2 0.4138 0.7355 0.000 0.616 0.000 NA 0.000
#> GSM254685 2 0.3661 0.7764 0.000 0.724 0.000 NA 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM254629 3 0.2866 0.6981 0.000 0.000 0.868 0.060 0.012 0.060
#> GSM254648 3 0.2762 0.7011 0.000 0.004 0.880 0.048 0.012 0.056
#> GSM254694 3 0.1850 0.7148 0.000 0.000 0.924 0.052 0.008 0.016
#> GSM254701 3 0.2866 0.6981 0.000 0.000 0.868 0.060 0.012 0.060
#> GSM254728 3 0.4511 0.6771 0.000 0.000 0.752 0.060 0.136 0.052
#> GSM254726 3 0.3083 0.7233 0.000 0.004 0.864 0.060 0.048 0.024
#> GSM254639 3 0.5557 0.5538 0.000 0.000 0.600 0.032 0.096 0.272
#> GSM254652 3 0.5028 0.4959 0.000 0.000 0.628 0.040 0.296 0.036
#> GSM254700 1 0.4120 0.2784 0.724 0.000 0.000 0.008 0.040 0.228
#> GSM254625 5 0.4569 0.4529 0.000 0.000 0.200 0.096 0.700 0.004
#> GSM254636 5 0.6667 0.3366 0.044 0.000 0.148 0.016 0.496 0.296
#> GSM254659 3 0.5327 0.6207 0.008 0.000 0.700 0.084 0.140 0.068
#> GSM254680 5 0.6906 0.4667 0.076 0.000 0.108 0.036 0.532 0.248
#> GSM254686 5 0.5338 0.2878 0.000 0.000 0.284 0.116 0.592 0.008
#> GSM254718 3 0.3291 0.7171 0.000 0.000 0.848 0.056 0.060 0.036
#> GSM254674 5 0.5981 0.4908 0.028 0.000 0.168 0.012 0.604 0.188
#> GSM254668 5 0.3908 0.5739 0.008 0.000 0.052 0.028 0.808 0.104
#> GSM254697 6 0.4619 0.3120 0.388 0.000 0.000 0.012 0.024 0.576
#> GSM254704 1 0.0291 0.4857 0.992 0.000 0.000 0.004 0.000 0.004
#> GSM254707 5 0.1801 0.5748 0.000 0.000 0.056 0.016 0.924 0.004
#> GSM254714 1 0.4998 0.2942 0.644 0.000 0.272 0.004 0.012 0.068
#> GSM254722 6 0.3576 0.3784 0.076 0.000 0.024 0.000 0.076 0.824
#> GSM254627 6 0.4619 0.3120 0.388 0.000 0.000 0.012 0.024 0.576
#> GSM254630 5 0.5554 0.1927 0.008 0.000 0.352 0.068 0.552 0.020
#> GSM254633 5 0.6706 0.4570 0.052 0.000 0.132 0.032 0.548 0.236
#> GSM254670 3 0.5598 0.5521 0.000 0.000 0.596 0.032 0.100 0.272
#> GSM254716 5 0.5304 0.3046 0.000 0.000 0.276 0.116 0.600 0.008
#> GSM254720 3 0.7578 0.1479 0.264 0.000 0.456 0.060 0.084 0.136
#> GSM254729 3 0.2911 0.7233 0.004 0.000 0.876 0.052 0.036 0.032
#> GSM254654 3 0.2683 0.7008 0.000 0.000 0.880 0.052 0.012 0.056
#> GSM254656 3 0.7115 0.3664 0.000 0.036 0.436 0.252 0.028 0.248
#> GSM254631 5 0.6941 0.4666 0.076 0.000 0.112 0.036 0.528 0.248
#> GSM254657 3 0.4567 0.6789 0.000 0.000 0.744 0.032 0.096 0.128
#> GSM254664 5 0.6941 0.4666 0.076 0.000 0.112 0.036 0.528 0.248
#> GSM254672 1 0.5925 0.1321 0.488 0.000 0.060 0.004 0.052 0.396
#> GSM254692 1 0.6578 0.0622 0.376 0.000 0.004 0.016 0.316 0.288
#> GSM254645 3 0.5607 0.5991 0.004 0.000 0.636 0.048 0.088 0.224
#> GSM254666 5 0.4836 0.4134 0.004 0.000 0.232 0.080 0.676 0.008
#> GSM254675 1 0.7880 -0.0432 0.296 0.000 0.104 0.028 0.288 0.284
#> GSM254678 5 0.7015 0.0905 0.092 0.000 0.116 0.012 0.392 0.388
#> GSM254688 5 0.1794 0.5694 0.000 0.000 0.028 0.016 0.932 0.024
#> GSM254690 5 0.6419 0.4312 0.084 0.000 0.048 0.028 0.536 0.304
#> GSM254696 5 0.7030 0.2270 0.040 0.000 0.220 0.016 0.400 0.324
#> GSM254705 5 0.5088 0.4485 0.136 0.000 0.012 0.016 0.700 0.136
#> GSM254642 6 0.4619 0.3120 0.388 0.000 0.000 0.012 0.024 0.576
#> GSM254661 3 0.4347 0.5299 0.000 0.000 0.672 0.028 0.288 0.012
#> GSM254698 6 0.3576 0.3784 0.076 0.000 0.024 0.000 0.076 0.824
#> GSM254641 5 0.4973 0.5347 0.092 0.000 0.060 0.036 0.752 0.060
#> GSM254647 5 0.6563 0.3833 0.160 0.000 0.032 0.024 0.532 0.252
#> GSM254663 5 0.5026 0.5317 0.092 0.000 0.056 0.036 0.748 0.068
#> GSM254682 5 0.2308 0.5525 0.000 0.000 0.012 0.016 0.896 0.076
#> GSM254709 5 0.6550 0.3799 0.164 0.000 0.072 0.040 0.604 0.120
#> GSM254721 1 0.0551 0.4870 0.984 0.000 0.008 0.004 0.004 0.000
#> GSM254724 1 0.0632 0.4824 0.976 0.000 0.000 0.000 0.000 0.024
#> GSM254650 5 0.4213 0.4883 0.084 0.000 0.004 0.016 0.772 0.124
#> GSM254687 5 0.5051 0.4528 0.136 0.000 0.012 0.016 0.704 0.132
#> GSM254637 5 0.6941 0.4666 0.076 0.000 0.112 0.036 0.528 0.248
#> GSM254684 6 0.7114 -0.1000 0.020 0.000 0.224 0.044 0.292 0.420
#> GSM254649 2 0.0713 0.6073 0.000 0.972 0.000 0.028 0.000 0.000
#> GSM254660 2 0.2823 0.6005 0.000 0.796 0.000 0.204 0.000 0.000
#> GSM254693 2 0.0713 0.6073 0.000 0.972 0.000 0.028 0.000 0.000
#> GSM254695 4 0.3747 -0.3521 0.000 0.396 0.000 0.604 0.000 0.000
#> GSM254702 2 0.2697 0.6072 0.000 0.812 0.000 0.188 0.000 0.000
#> GSM254643 2 0.3309 0.5538 0.000 0.720 0.000 0.280 0.000 0.000
#> GSM254727 2 0.0713 0.6204 0.000 0.972 0.000 0.028 0.000 0.000
#> GSM254640 2 0.3797 0.4618 0.000 0.580 0.000 0.420 0.000 0.000
#> GSM254626 2 0.0363 0.6152 0.000 0.988 0.000 0.012 0.000 0.000
#> GSM254635 2 0.3851 0.4229 0.000 0.540 0.000 0.460 0.000 0.000
#> GSM254653 2 0.0713 0.6218 0.000 0.972 0.000 0.028 0.000 0.000
#> GSM254658 2 0.2260 0.5100 0.000 0.860 0.000 0.140 0.000 0.000
#> GSM254681 2 0.2260 0.5100 0.000 0.860 0.000 0.140 0.000 0.000
#> GSM254719 2 0.0632 0.6238 0.000 0.976 0.000 0.024 0.000 0.000
#> GSM254673 2 0.0363 0.6185 0.000 0.988 0.000 0.012 0.000 0.000
#> GSM254655 2 0.2378 0.6153 0.000 0.848 0.000 0.152 0.000 0.000
#> GSM254669 2 0.0363 0.6185 0.000 0.988 0.000 0.012 0.000 0.000
#> GSM254699 2 0.2697 0.6072 0.000 0.812 0.000 0.188 0.000 0.000
#> GSM254703 2 0.3833 0.4433 0.000 0.556 0.000 0.444 0.000 0.000
#> GSM254708 2 0.2048 0.5560 0.000 0.880 0.000 0.120 0.000 0.000
#> GSM254715 2 0.3843 0.4300 0.000 0.548 0.000 0.452 0.000 0.000
#> GSM254628 2 0.0713 0.6115 0.000 0.972 0.000 0.028 0.000 0.000
#> GSM254634 2 0.3854 0.4185 0.000 0.536 0.000 0.464 0.000 0.000
#> GSM254646 2 0.2260 0.5100 0.000 0.860 0.000 0.140 0.000 0.000
#> GSM254671 2 0.3789 0.4616 0.000 0.584 0.000 0.416 0.000 0.000
#> GSM254711 2 0.3843 0.4337 0.000 0.548 0.000 0.452 0.000 0.000
#> GSM254717 2 0.1714 0.6174 0.000 0.908 0.000 0.092 0.000 0.000
#> GSM254723 3 0.5723 0.5792 0.000 0.080 0.676 0.152 0.068 0.024
#> GSM254730 2 0.2854 0.6005 0.000 0.792 0.000 0.208 0.000 0.000
#> GSM254731 2 0.2697 0.6072 0.000 0.812 0.000 0.188 0.000 0.000
#> GSM254632 4 0.6990 0.4828 0.000 0.348 0.060 0.380 0.208 0.004
#> GSM254662 2 0.0363 0.6185 0.000 0.988 0.000 0.012 0.000 0.000
#> GSM254677 2 0.3857 0.4077 0.000 0.532 0.000 0.468 0.000 0.000
#> GSM254665 2 0.1501 0.6211 0.000 0.924 0.000 0.076 0.000 0.000
#> GSM254691 2 0.2378 0.5257 0.000 0.848 0.000 0.152 0.000 0.000
#> GSM254644 2 0.3817 0.4545 0.000 0.568 0.000 0.432 0.000 0.000
#> GSM254667 2 0.3265 0.2960 0.000 0.748 0.004 0.248 0.000 0.000
#> GSM254676 2 0.2378 0.5257 0.000 0.848 0.000 0.152 0.000 0.000
#> GSM254679 2 0.3804 0.4572 0.000 0.576 0.000 0.424 0.000 0.000
#> GSM254689 2 0.2260 0.5100 0.000 0.860 0.000 0.140 0.000 0.000
#> GSM254706 2 0.2300 0.5063 0.000 0.856 0.000 0.144 0.000 0.000
#> GSM254712 2 0.3843 0.4300 0.000 0.548 0.000 0.452 0.000 0.000
#> GSM254713 2 0.3843 0.4300 0.000 0.548 0.000 0.452 0.000 0.000
#> GSM254683 2 0.2300 0.5136 0.000 0.856 0.000 0.144 0.000 0.000
#> GSM254710 4 0.6990 0.4828 0.000 0.348 0.060 0.380 0.208 0.004
#> GSM254725 2 0.3854 0.4185 0.000 0.536 0.000 0.464 0.000 0.000
#> GSM254651 2 0.2300 0.5063 0.000 0.856 0.000 0.144 0.000 0.000
#> GSM254638 2 0.3851 0.4229 0.000 0.540 0.000 0.460 0.000 0.000
#> GSM254685 2 0.3446 0.5360 0.000 0.692 0.000 0.308 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) individual(p) disease.state(p) gender(p) other(p) k
#> SD:hclust 105 6.63e-23 0.6809 0.5344 0.6385 1.0000 2
#> SD:hclust 80 3.07e-17 0.1742 0.2800 0.3647 0.2287 3
#> SD:hclust 74 4.48e-15 0.2358 0.1567 0.0935 0.0368 4
#> SD:hclust 74 4.28e-15 0.2638 0.0754 0.0666 0.0200 5
#> SD:hclust 52 3.58e-11 0.0847 0.6394 0.0237 0.3586 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 21168 rows and 107 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 1.000 0.997 0.999 0.4981 0.503 0.503
#> 3 3 0.692 0.873 0.863 0.2861 0.835 0.675
#> 4 4 0.649 0.515 0.710 0.1263 0.972 0.921
#> 5 5 0.642 0.560 0.709 0.0736 0.823 0.496
#> 6 6 0.645 0.526 0.729 0.0425 0.917 0.672
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
#> GSM254629 1 0.000 0.998 1.000 0.000
#> GSM254648 1 0.000 0.998 1.000 0.000
#> GSM254694 1 0.000 0.998 1.000 0.000
#> GSM254701 1 0.000 0.998 1.000 0.000
#> GSM254728 1 0.000 0.998 1.000 0.000
#> GSM254726 1 0.000 0.998 1.000 0.000
#> GSM254639 1 0.000 0.998 1.000 0.000
#> GSM254652 1 0.000 0.998 1.000 0.000
#> GSM254700 1 0.000 0.998 1.000 0.000
#> GSM254625 1 0.000 0.998 1.000 0.000
#> GSM254636 1 0.000 0.998 1.000 0.000
#> GSM254659 1 0.000 0.998 1.000 0.000
#> GSM254680 1 0.000 0.998 1.000 0.000
#> GSM254686 1 0.000 0.998 1.000 0.000
#> GSM254718 1 0.000 0.998 1.000 0.000
#> GSM254674 1 0.000 0.998 1.000 0.000
#> GSM254668 1 0.000 0.998 1.000 0.000
#> GSM254697 1 0.000 0.998 1.000 0.000
#> GSM254704 1 0.000 0.998 1.000 0.000
#> GSM254707 1 0.000 0.998 1.000 0.000
#> GSM254714 1 0.000 0.998 1.000 0.000
#> GSM254722 1 0.000 0.998 1.000 0.000
#> GSM254627 1 0.000 0.998 1.000 0.000
#> GSM254630 1 0.000 0.998 1.000 0.000
#> GSM254633 1 0.000 0.998 1.000 0.000
#> GSM254670 1 0.000 0.998 1.000 0.000
#> GSM254716 1 0.000 0.998 1.000 0.000
#> GSM254720 1 0.000 0.998 1.000 0.000
#> GSM254729 1 0.000 0.998 1.000 0.000
#> GSM254654 1 0.000 0.998 1.000 0.000
#> GSM254656 1 0.000 0.998 1.000 0.000
#> GSM254631 1 0.000 0.998 1.000 0.000
#> GSM254657 1 0.000 0.998 1.000 0.000
#> GSM254664 1 0.000 0.998 1.000 0.000
#> GSM254672 1 0.000 0.998 1.000 0.000
#> GSM254692 1 0.000 0.998 1.000 0.000
#> GSM254645 1 0.000 0.998 1.000 0.000
#> GSM254666 1 0.000 0.998 1.000 0.000
#> GSM254675 1 0.000 0.998 1.000 0.000
#> GSM254678 1 0.000 0.998 1.000 0.000
#> GSM254688 1 0.000 0.998 1.000 0.000
#> GSM254690 1 0.000 0.998 1.000 0.000
#> GSM254696 1 0.000 0.998 1.000 0.000
#> GSM254705 1 0.000 0.998 1.000 0.000
#> GSM254642 1 0.000 0.998 1.000 0.000
#> GSM254661 1 0.000 0.998 1.000 0.000
#> GSM254698 1 0.000 0.998 1.000 0.000
#> GSM254641 1 0.000 0.998 1.000 0.000
#> GSM254647 1 0.000 0.998 1.000 0.000
#> GSM254663 1 0.000 0.998 1.000 0.000
#> GSM254682 1 0.000 0.998 1.000 0.000
#> GSM254709 1 0.000 0.998 1.000 0.000
#> GSM254721 1 0.000 0.998 1.000 0.000
#> GSM254724 1 0.000 0.998 1.000 0.000
#> GSM254650 1 0.000 0.998 1.000 0.000
#> GSM254687 1 0.000 0.998 1.000 0.000
#> GSM254637 1 0.000 0.998 1.000 0.000
#> GSM254684 1 0.000 0.998 1.000 0.000
#> GSM254649 2 0.000 1.000 0.000 1.000
#> GSM254660 2 0.000 1.000 0.000 1.000
#> GSM254693 2 0.000 1.000 0.000 1.000
#> GSM254695 2 0.000 1.000 0.000 1.000
#> GSM254702 2 0.000 1.000 0.000 1.000
#> GSM254643 2 0.000 1.000 0.000 1.000
#> GSM254727 2 0.000 1.000 0.000 1.000
#> GSM254640 2 0.000 1.000 0.000 1.000
#> GSM254626 2 0.000 1.000 0.000 1.000
#> GSM254635 2 0.000 1.000 0.000 1.000
#> GSM254653 2 0.000 1.000 0.000 1.000
#> GSM254658 2 0.000 1.000 0.000 1.000
#> GSM254681 2 0.000 1.000 0.000 1.000
#> GSM254719 2 0.000 1.000 0.000 1.000
#> GSM254673 2 0.000 1.000 0.000 1.000
#> GSM254655 2 0.000 1.000 0.000 1.000
#> GSM254669 2 0.000 1.000 0.000 1.000
#> GSM254699 2 0.000 1.000 0.000 1.000
#> GSM254703 2 0.000 1.000 0.000 1.000
#> GSM254708 2 0.000 1.000 0.000 1.000
#> GSM254715 2 0.000 1.000 0.000 1.000
#> GSM254628 2 0.000 1.000 0.000 1.000
#> GSM254634 2 0.000 1.000 0.000 1.000
#> GSM254646 2 0.000 1.000 0.000 1.000
#> GSM254671 2 0.000 1.000 0.000 1.000
#> GSM254711 2 0.000 1.000 0.000 1.000
#> GSM254717 2 0.000 1.000 0.000 1.000
#> GSM254723 1 0.574 0.843 0.864 0.136
#> GSM254730 2 0.000 1.000 0.000 1.000
#> GSM254731 2 0.000 1.000 0.000 1.000
#> GSM254632 1 0.000 0.998 1.000 0.000
#> GSM254662 2 0.000 1.000 0.000 1.000
#> GSM254677 2 0.000 1.000 0.000 1.000
#> GSM254665 2 0.000 1.000 0.000 1.000
#> GSM254691 2 0.000 1.000 0.000 1.000
#> GSM254644 2 0.000 1.000 0.000 1.000
#> GSM254667 2 0.000 1.000 0.000 1.000
#> GSM254676 2 0.000 1.000 0.000 1.000
#> GSM254679 2 0.000 1.000 0.000 1.000
#> GSM254689 2 0.000 1.000 0.000 1.000
#> GSM254706 2 0.000 1.000 0.000 1.000
#> GSM254712 2 0.000 1.000 0.000 1.000
#> GSM254713 2 0.000 1.000 0.000 1.000
#> GSM254683 2 0.000 1.000 0.000 1.000
#> GSM254710 2 0.000 1.000 0.000 1.000
#> GSM254725 2 0.000 1.000 0.000 1.000
#> GSM254651 2 0.000 1.000 0.000 1.000
#> GSM254638 2 0.000 1.000 0.000 1.000
#> GSM254685 2 0.000 1.000 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM254629 3 0.5016 0.900 0.240 0.000 0.760
#> GSM254648 3 0.4452 0.876 0.192 0.000 0.808
#> GSM254694 3 0.5216 0.904 0.260 0.000 0.740
#> GSM254701 3 0.5216 0.904 0.260 0.000 0.740
#> GSM254728 3 0.5216 0.904 0.260 0.000 0.740
#> GSM254726 3 0.4452 0.876 0.192 0.000 0.808
#> GSM254639 3 0.5254 0.903 0.264 0.000 0.736
#> GSM254652 3 0.5016 0.900 0.240 0.000 0.760
#> GSM254700 1 0.0000 0.877 1.000 0.000 0.000
#> GSM254625 3 0.5431 0.828 0.284 0.000 0.716
#> GSM254636 1 0.4062 0.825 0.836 0.000 0.164
#> GSM254659 3 0.5216 0.904 0.260 0.000 0.740
#> GSM254680 1 0.3816 0.840 0.852 0.000 0.148
#> GSM254686 3 0.5529 0.843 0.296 0.000 0.704
#> GSM254718 3 0.5216 0.904 0.260 0.000 0.740
#> GSM254674 1 0.3752 0.839 0.856 0.000 0.144
#> GSM254668 1 0.4346 0.833 0.816 0.000 0.184
#> GSM254697 1 0.0000 0.877 1.000 0.000 0.000
#> GSM254704 1 0.1031 0.875 0.976 0.000 0.024
#> GSM254707 1 0.4346 0.833 0.816 0.000 0.184
#> GSM254714 3 0.5291 0.899 0.268 0.000 0.732
#> GSM254722 1 0.0000 0.877 1.000 0.000 0.000
#> GSM254627 1 0.0000 0.877 1.000 0.000 0.000
#> GSM254630 1 0.5948 0.345 0.640 0.000 0.360
#> GSM254633 1 0.4178 0.816 0.828 0.000 0.172
#> GSM254670 3 0.5254 0.903 0.264 0.000 0.736
#> GSM254716 3 0.5431 0.828 0.284 0.000 0.716
#> GSM254720 1 0.3941 0.772 0.844 0.000 0.156
#> GSM254729 3 0.5254 0.903 0.264 0.000 0.736
#> GSM254654 3 0.4750 0.881 0.216 0.000 0.784
#> GSM254656 3 0.3192 0.754 0.112 0.000 0.888
#> GSM254631 1 0.3941 0.832 0.844 0.000 0.156
#> GSM254657 3 0.5254 0.903 0.264 0.000 0.736
#> GSM254664 1 0.3551 0.847 0.868 0.000 0.132
#> GSM254672 1 0.1411 0.872 0.964 0.000 0.036
#> GSM254692 1 0.1643 0.860 0.956 0.000 0.044
#> GSM254645 3 0.5254 0.903 0.264 0.000 0.736
#> GSM254666 3 0.5098 0.869 0.248 0.000 0.752
#> GSM254675 1 0.1411 0.883 0.964 0.000 0.036
#> GSM254678 1 0.1964 0.872 0.944 0.000 0.056
#> GSM254688 1 0.2959 0.870 0.900 0.000 0.100
#> GSM254690 1 0.0747 0.882 0.984 0.000 0.016
#> GSM254696 1 0.4062 0.825 0.836 0.000 0.164
#> GSM254705 1 0.2261 0.870 0.932 0.000 0.068
#> GSM254642 1 0.0892 0.868 0.980 0.000 0.020
#> GSM254661 3 0.5016 0.900 0.240 0.000 0.760
#> GSM254698 1 0.1411 0.872 0.964 0.000 0.036
#> GSM254641 1 0.4346 0.820 0.816 0.000 0.184
#> GSM254647 1 0.0000 0.877 1.000 0.000 0.000
#> GSM254663 1 0.1753 0.862 0.952 0.000 0.048
#> GSM254682 1 0.2537 0.871 0.920 0.000 0.080
#> GSM254709 1 0.5431 0.605 0.716 0.000 0.284
#> GSM254721 1 0.0000 0.877 1.000 0.000 0.000
#> GSM254724 1 0.0000 0.877 1.000 0.000 0.000
#> GSM254650 1 0.2959 0.870 0.900 0.000 0.100
#> GSM254687 1 0.2959 0.870 0.900 0.000 0.100
#> GSM254637 1 0.4178 0.816 0.828 0.000 0.172
#> GSM254684 1 0.3816 0.840 0.852 0.000 0.148
#> GSM254649 2 0.0237 0.929 0.000 0.996 0.004
#> GSM254660 2 0.2066 0.923 0.000 0.940 0.060
#> GSM254693 2 0.0424 0.929 0.000 0.992 0.008
#> GSM254695 2 0.4702 0.884 0.000 0.788 0.212
#> GSM254702 2 0.4121 0.900 0.000 0.832 0.168
#> GSM254643 2 0.0237 0.930 0.000 0.996 0.004
#> GSM254727 2 0.0237 0.929 0.000 0.996 0.004
#> GSM254640 2 0.4062 0.900 0.000 0.836 0.164
#> GSM254626 2 0.0237 0.930 0.000 0.996 0.004
#> GSM254635 2 0.4702 0.884 0.000 0.788 0.212
#> GSM254653 2 0.0237 0.929 0.000 0.996 0.004
#> GSM254658 2 0.0237 0.929 0.000 0.996 0.004
#> GSM254681 2 0.0424 0.928 0.000 0.992 0.008
#> GSM254719 2 0.0237 0.930 0.000 0.996 0.004
#> GSM254673 2 0.0237 0.930 0.000 0.996 0.004
#> GSM254655 2 0.0592 0.930 0.000 0.988 0.012
#> GSM254669 2 0.0237 0.930 0.000 0.996 0.004
#> GSM254699 2 0.0237 0.930 0.000 0.996 0.004
#> GSM254703 2 0.4654 0.885 0.000 0.792 0.208
#> GSM254708 2 0.0747 0.928 0.000 0.984 0.016
#> GSM254715 2 0.4605 0.887 0.000 0.796 0.204
#> GSM254628 2 0.0237 0.929 0.000 0.996 0.004
#> GSM254634 2 0.4654 0.885 0.000 0.792 0.208
#> GSM254646 2 0.0237 0.929 0.000 0.996 0.004
#> GSM254671 2 0.4121 0.900 0.000 0.832 0.168
#> GSM254711 2 0.4654 0.885 0.000 0.792 0.208
#> GSM254717 2 0.0237 0.929 0.000 0.996 0.004
#> GSM254723 3 0.3359 0.734 0.084 0.016 0.900
#> GSM254730 2 0.1753 0.925 0.000 0.952 0.048
#> GSM254731 2 0.4121 0.900 0.000 0.832 0.168
#> GSM254632 3 0.4399 0.874 0.188 0.000 0.812
#> GSM254662 2 0.0237 0.930 0.000 0.996 0.004
#> GSM254677 2 0.4654 0.885 0.000 0.792 0.208
#> GSM254665 2 0.0424 0.930 0.000 0.992 0.008
#> GSM254691 2 0.0237 0.930 0.000 0.996 0.004
#> GSM254644 2 0.4062 0.900 0.000 0.836 0.164
#> GSM254667 2 0.0747 0.928 0.000 0.984 0.016
#> GSM254676 2 0.0237 0.930 0.000 0.996 0.004
#> GSM254679 2 0.4654 0.885 0.000 0.792 0.208
#> GSM254689 2 0.0424 0.928 0.000 0.992 0.008
#> GSM254706 2 0.0424 0.928 0.000 0.992 0.008
#> GSM254712 2 0.4702 0.884 0.000 0.788 0.212
#> GSM254713 2 0.4702 0.884 0.000 0.788 0.212
#> GSM254683 2 0.0424 0.928 0.000 0.992 0.008
#> GSM254710 3 0.6045 0.345 0.000 0.380 0.620
#> GSM254725 2 0.4654 0.885 0.000 0.792 0.208
#> GSM254651 2 0.0424 0.928 0.000 0.992 0.008
#> GSM254638 2 0.4702 0.884 0.000 0.788 0.212
#> GSM254685 2 0.4605 0.887 0.000 0.796 0.204
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM254629 3 0.2222 0.8878 0.060 0.000 0.924 0.016
#> GSM254648 3 0.1820 0.8771 0.020 0.000 0.944 0.036
#> GSM254694 3 0.2227 0.8808 0.036 0.000 0.928 0.036
#> GSM254701 3 0.2489 0.8876 0.068 0.000 0.912 0.020
#> GSM254728 3 0.1902 0.8891 0.064 0.000 0.932 0.004
#> GSM254726 3 0.1510 0.8776 0.016 0.000 0.956 0.028
#> GSM254639 3 0.3286 0.8786 0.080 0.000 0.876 0.044
#> GSM254652 3 0.2021 0.8878 0.056 0.000 0.932 0.012
#> GSM254700 1 0.4500 0.7220 0.684 0.000 0.000 0.316
#> GSM254625 3 0.6037 0.5509 0.304 0.000 0.628 0.068
#> GSM254636 1 0.5062 0.6868 0.752 0.000 0.184 0.064
#> GSM254659 3 0.1978 0.8887 0.068 0.000 0.928 0.004
#> GSM254680 1 0.3547 0.7239 0.840 0.000 0.144 0.016
#> GSM254686 3 0.5649 0.5889 0.284 0.000 0.664 0.052
#> GSM254718 3 0.2255 0.8893 0.068 0.000 0.920 0.012
#> GSM254674 1 0.4731 0.7040 0.780 0.000 0.160 0.060
#> GSM254668 1 0.4638 0.7036 0.788 0.000 0.152 0.060
#> GSM254697 1 0.4454 0.7260 0.692 0.000 0.000 0.308
#> GSM254704 1 0.5173 0.7152 0.660 0.000 0.020 0.320
#> GSM254707 1 0.4829 0.7005 0.776 0.000 0.156 0.068
#> GSM254714 3 0.3966 0.8456 0.088 0.000 0.840 0.072
#> GSM254722 1 0.4522 0.7237 0.680 0.000 0.000 0.320
#> GSM254627 1 0.4454 0.7260 0.692 0.000 0.000 0.308
#> GSM254630 1 0.6394 0.4164 0.596 0.000 0.316 0.088
#> GSM254633 1 0.4364 0.6821 0.764 0.000 0.220 0.016
#> GSM254670 3 0.3634 0.8712 0.096 0.000 0.856 0.048
#> GSM254716 3 0.6016 0.5583 0.300 0.000 0.632 0.068
#> GSM254720 1 0.7707 0.4678 0.452 0.000 0.276 0.272
#> GSM254729 3 0.2500 0.8828 0.044 0.000 0.916 0.040
#> GSM254654 3 0.2032 0.8779 0.028 0.000 0.936 0.036
#> GSM254656 3 0.3256 0.8434 0.020 0.020 0.888 0.072
#> GSM254631 1 0.4095 0.7007 0.792 0.000 0.192 0.016
#> GSM254657 3 0.3505 0.8732 0.088 0.000 0.864 0.048
#> GSM254664 1 0.3695 0.7248 0.828 0.000 0.156 0.016
#> GSM254672 1 0.5970 0.7012 0.600 0.000 0.052 0.348
#> GSM254692 1 0.4914 0.7272 0.676 0.000 0.012 0.312
#> GSM254645 3 0.3286 0.8786 0.080 0.000 0.876 0.044
#> GSM254666 3 0.5627 0.6665 0.240 0.000 0.692 0.068
#> GSM254675 1 0.4356 0.7655 0.804 0.000 0.048 0.148
#> GSM254678 1 0.5265 0.7395 0.748 0.000 0.092 0.160
#> GSM254688 1 0.3679 0.7440 0.856 0.000 0.060 0.084
#> GSM254690 1 0.2844 0.7651 0.900 0.000 0.052 0.048
#> GSM254696 1 0.5307 0.6846 0.736 0.000 0.188 0.076
#> GSM254705 1 0.3160 0.7519 0.872 0.000 0.020 0.108
#> GSM254642 1 0.4454 0.7260 0.692 0.000 0.000 0.308
#> GSM254661 3 0.2021 0.8878 0.056 0.000 0.932 0.012
#> GSM254698 1 0.5712 0.7132 0.644 0.000 0.048 0.308
#> GSM254641 1 0.4881 0.6872 0.756 0.000 0.196 0.048
#> GSM254647 1 0.4356 0.7323 0.708 0.000 0.000 0.292
#> GSM254663 1 0.3554 0.7584 0.844 0.000 0.020 0.136
#> GSM254682 1 0.3652 0.7464 0.856 0.000 0.052 0.092
#> GSM254709 1 0.5669 0.6352 0.708 0.000 0.200 0.092
#> GSM254721 1 0.4522 0.7215 0.680 0.000 0.000 0.320
#> GSM254724 1 0.4500 0.7218 0.684 0.000 0.000 0.316
#> GSM254650 1 0.3439 0.7487 0.868 0.000 0.048 0.084
#> GSM254687 1 0.3679 0.7456 0.856 0.000 0.060 0.084
#> GSM254637 1 0.4501 0.6837 0.764 0.000 0.212 0.024
#> GSM254684 1 0.5291 0.6907 0.740 0.000 0.180 0.080
#> GSM254649 2 0.4955 0.0316 0.000 0.556 0.000 0.444
#> GSM254660 2 0.2589 0.4492 0.000 0.884 0.000 0.116
#> GSM254693 2 0.4898 0.0860 0.000 0.584 0.000 0.416
#> GSM254695 2 0.3907 0.3811 0.000 0.836 0.044 0.120
#> GSM254702 2 0.1716 0.4693 0.000 0.936 0.000 0.064
#> GSM254643 2 0.4877 0.1045 0.000 0.592 0.000 0.408
#> GSM254727 2 0.4925 0.0638 0.000 0.572 0.000 0.428
#> GSM254640 2 0.2473 0.4713 0.000 0.908 0.012 0.080
#> GSM254626 2 0.4877 0.1045 0.000 0.592 0.000 0.408
#> GSM254635 2 0.2222 0.4455 0.000 0.924 0.016 0.060
#> GSM254653 2 0.4925 0.0638 0.000 0.572 0.000 0.428
#> GSM254658 2 0.4955 0.0316 0.000 0.556 0.000 0.444
#> GSM254681 2 0.4955 0.0316 0.000 0.556 0.000 0.444
#> GSM254719 2 0.4866 0.1086 0.000 0.596 0.000 0.404
#> GSM254673 2 0.4877 0.1045 0.000 0.592 0.000 0.408
#> GSM254655 2 0.4564 0.2134 0.000 0.672 0.000 0.328
#> GSM254669 2 0.4877 0.1045 0.000 0.592 0.000 0.408
#> GSM254699 2 0.4679 0.1780 0.000 0.648 0.000 0.352
#> GSM254703 2 0.2909 0.4345 0.000 0.888 0.020 0.092
#> GSM254708 2 0.4989 -0.1972 0.000 0.528 0.000 0.472
#> GSM254715 2 0.0592 0.4678 0.000 0.984 0.016 0.000
#> GSM254628 2 0.4955 0.0316 0.000 0.556 0.000 0.444
#> GSM254634 2 0.2741 0.4346 0.000 0.892 0.012 0.096
#> GSM254646 2 0.4941 0.0508 0.000 0.564 0.000 0.436
#> GSM254671 2 0.1302 0.4726 0.000 0.956 0.000 0.044
#> GSM254711 2 0.2741 0.4346 0.000 0.892 0.012 0.096
#> GSM254717 2 0.4925 0.0638 0.000 0.572 0.000 0.428
#> GSM254723 3 0.2099 0.8569 0.004 0.020 0.936 0.040
#> GSM254730 2 0.3024 0.4402 0.000 0.852 0.000 0.148
#> GSM254731 2 0.1716 0.4693 0.000 0.936 0.000 0.064
#> GSM254632 3 0.2376 0.8668 0.016 0.000 0.916 0.068
#> GSM254662 2 0.4877 0.1045 0.000 0.592 0.000 0.408
#> GSM254677 2 0.3367 0.4207 0.000 0.864 0.028 0.108
#> GSM254665 2 0.4761 0.0619 0.000 0.628 0.000 0.372
#> GSM254691 2 0.4985 -0.1952 0.000 0.532 0.000 0.468
#> GSM254644 2 0.2542 0.4696 0.000 0.904 0.012 0.084
#> GSM254667 2 0.4994 -0.2061 0.000 0.520 0.000 0.480
#> GSM254676 2 0.4985 -0.1952 0.000 0.532 0.000 0.468
#> GSM254679 2 0.2741 0.4346 0.000 0.892 0.012 0.096
#> GSM254689 2 0.4941 0.0508 0.000 0.564 0.000 0.436
#> GSM254706 4 0.4992 0.0988 0.000 0.476 0.000 0.524
#> GSM254712 2 0.0817 0.4661 0.000 0.976 0.024 0.000
#> GSM254713 2 0.0817 0.4661 0.000 0.976 0.024 0.000
#> GSM254683 4 0.4994 0.0977 0.000 0.480 0.000 0.520
#> GSM254710 4 0.6708 -0.1171 0.040 0.028 0.396 0.536
#> GSM254725 2 0.2805 0.4316 0.000 0.888 0.012 0.100
#> GSM254651 4 0.4994 0.0977 0.000 0.480 0.000 0.520
#> GSM254638 2 0.2915 0.4249 0.000 0.892 0.028 0.080
#> GSM254685 2 0.0592 0.4678 0.000 0.984 0.016 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM254629 3 0.0740 0.9179 0.004 0.008 0.980 0.000 0.008
#> GSM254648 3 0.0854 0.9171 0.008 0.012 0.976 0.000 0.004
#> GSM254694 3 0.0854 0.9171 0.008 0.012 0.976 0.000 0.004
#> GSM254701 3 0.0740 0.9179 0.004 0.008 0.980 0.000 0.008
#> GSM254728 3 0.1588 0.9112 0.008 0.016 0.948 0.000 0.028
#> GSM254726 3 0.0854 0.9173 0.012 0.008 0.976 0.000 0.004
#> GSM254639 3 0.3761 0.8615 0.064 0.068 0.840 0.000 0.028
#> GSM254652 3 0.1393 0.9131 0.008 0.012 0.956 0.000 0.024
#> GSM254700 1 0.4403 0.7982 0.608 0.008 0.000 0.000 0.384
#> GSM254625 5 0.5198 0.2315 0.016 0.020 0.408 0.000 0.556
#> GSM254636 5 0.7031 0.3570 0.160 0.144 0.112 0.000 0.584
#> GSM254659 3 0.0867 0.9179 0.008 0.008 0.976 0.000 0.008
#> GSM254680 5 0.4261 0.5390 0.032 0.088 0.072 0.000 0.808
#> GSM254686 5 0.5292 0.1851 0.008 0.032 0.452 0.000 0.508
#> GSM254718 3 0.0613 0.9182 0.004 0.004 0.984 0.000 0.008
#> GSM254674 5 0.3928 0.5605 0.008 0.092 0.084 0.000 0.816
#> GSM254668 5 0.2694 0.5614 0.004 0.032 0.076 0.000 0.888
#> GSM254697 1 0.5044 0.7992 0.556 0.036 0.000 0.000 0.408
#> GSM254704 1 0.4998 0.7691 0.632 0.008 0.032 0.000 0.328
#> GSM254707 5 0.1956 0.5638 0.000 0.008 0.076 0.000 0.916
#> GSM254714 3 0.3291 0.8378 0.100 0.016 0.856 0.000 0.028
#> GSM254722 1 0.5195 0.7910 0.564 0.048 0.000 0.000 0.388
#> GSM254627 1 0.5044 0.7992 0.556 0.036 0.000 0.000 0.408
#> GSM254630 5 0.4703 0.4444 0.040 0.020 0.204 0.000 0.736
#> GSM254633 5 0.6696 0.4147 0.128 0.104 0.148 0.000 0.620
#> GSM254670 3 0.5106 0.7758 0.064 0.132 0.748 0.000 0.056
#> GSM254716 5 0.5205 0.2213 0.016 0.020 0.412 0.000 0.552
#> GSM254720 1 0.6883 0.4540 0.488 0.016 0.252 0.000 0.244
#> GSM254729 3 0.1377 0.9120 0.020 0.020 0.956 0.000 0.004
#> GSM254654 3 0.0854 0.9171 0.008 0.012 0.976 0.000 0.004
#> GSM254656 3 0.4255 0.8513 0.096 0.076 0.808 0.008 0.012
#> GSM254631 5 0.6571 0.4046 0.140 0.096 0.132 0.000 0.632
#> GSM254657 3 0.4406 0.8364 0.068 0.072 0.804 0.000 0.056
#> GSM254664 5 0.5781 0.4508 0.116 0.092 0.088 0.000 0.704
#> GSM254672 1 0.4880 0.7394 0.660 0.004 0.040 0.000 0.296
#> GSM254692 5 0.4380 -0.4188 0.376 0.008 0.000 0.000 0.616
#> GSM254645 3 0.3736 0.8624 0.064 0.072 0.840 0.000 0.024
#> GSM254666 5 0.5069 0.1166 0.008 0.020 0.452 0.000 0.520
#> GSM254675 5 0.4954 -0.1359 0.352 0.020 0.012 0.000 0.616
#> GSM254678 5 0.7156 0.1128 0.276 0.144 0.064 0.000 0.516
#> GSM254688 5 0.1087 0.5266 0.016 0.008 0.008 0.000 0.968
#> GSM254690 5 0.4545 0.3448 0.132 0.116 0.000 0.000 0.752
#> GSM254696 5 0.7131 0.3551 0.156 0.160 0.112 0.000 0.572
#> GSM254705 5 0.2141 0.4906 0.064 0.016 0.004 0.000 0.916
#> GSM254642 1 0.5044 0.7992 0.556 0.036 0.000 0.000 0.408
#> GSM254661 3 0.1186 0.9160 0.008 0.008 0.964 0.000 0.020
#> GSM254698 1 0.7049 0.5886 0.476 0.152 0.040 0.000 0.332
#> GSM254641 5 0.3558 0.5616 0.004 0.036 0.136 0.000 0.824
#> GSM254647 1 0.5232 0.7428 0.500 0.044 0.000 0.000 0.456
#> GSM254663 5 0.2077 0.4431 0.084 0.008 0.000 0.000 0.908
#> GSM254682 5 0.1179 0.5224 0.016 0.016 0.004 0.000 0.964
#> GSM254709 5 0.4118 0.4683 0.032 0.008 0.188 0.000 0.772
#> GSM254721 1 0.4403 0.7982 0.608 0.008 0.000 0.000 0.384
#> GSM254724 1 0.4403 0.7982 0.608 0.008 0.000 0.000 0.384
#> GSM254650 5 0.1644 0.4991 0.048 0.004 0.008 0.000 0.940
#> GSM254687 5 0.1695 0.5044 0.044 0.008 0.008 0.000 0.940
#> GSM254637 5 0.6684 0.4098 0.140 0.096 0.144 0.000 0.620
#> GSM254684 5 0.7090 0.3511 0.156 0.160 0.108 0.000 0.576
#> GSM254649 2 0.4701 0.7719 0.060 0.704 0.000 0.236 0.000
#> GSM254660 4 0.4982 0.2039 0.032 0.412 0.000 0.556 0.000
#> GSM254693 2 0.3551 0.7841 0.008 0.772 0.000 0.220 0.000
#> GSM254695 4 0.2608 0.5443 0.088 0.020 0.004 0.888 0.000
#> GSM254702 4 0.4898 0.3079 0.032 0.376 0.000 0.592 0.000
#> GSM254643 2 0.4806 0.7560 0.060 0.688 0.000 0.252 0.000
#> GSM254727 2 0.3728 0.7814 0.008 0.748 0.000 0.244 0.000
#> GSM254640 4 0.4360 0.4340 0.020 0.300 0.000 0.680 0.000
#> GSM254626 2 0.4451 0.7670 0.040 0.712 0.000 0.248 0.000
#> GSM254635 4 0.2790 0.5841 0.068 0.052 0.000 0.880 0.000
#> GSM254653 2 0.3756 0.7807 0.008 0.744 0.000 0.248 0.000
#> GSM254658 2 0.4762 0.7703 0.064 0.700 0.000 0.236 0.000
#> GSM254681 2 0.5237 0.7433 0.100 0.664 0.000 0.236 0.000
#> GSM254719 2 0.4276 0.7610 0.028 0.716 0.000 0.256 0.000
#> GSM254673 2 0.4378 0.7653 0.036 0.716 0.000 0.248 0.000
#> GSM254655 2 0.4546 0.6849 0.028 0.668 0.000 0.304 0.000
#> GSM254669 2 0.4352 0.7680 0.036 0.720 0.000 0.244 0.000
#> GSM254699 2 0.4442 0.7234 0.028 0.688 0.000 0.284 0.000
#> GSM254703 4 0.1281 0.5903 0.032 0.012 0.000 0.956 0.000
#> GSM254708 4 0.6257 -0.2684 0.148 0.392 0.000 0.460 0.000
#> GSM254715 4 0.4179 0.5510 0.072 0.152 0.000 0.776 0.000
#> GSM254628 2 0.4701 0.7722 0.060 0.704 0.000 0.236 0.000
#> GSM254634 4 0.0703 0.5861 0.024 0.000 0.000 0.976 0.000
#> GSM254646 2 0.4906 0.7656 0.076 0.692 0.000 0.232 0.000
#> GSM254671 4 0.4836 0.3409 0.032 0.356 0.000 0.612 0.000
#> GSM254711 4 0.1018 0.5891 0.016 0.016 0.000 0.968 0.000
#> GSM254717 2 0.3728 0.7820 0.008 0.748 0.000 0.244 0.000
#> GSM254723 3 0.1729 0.9091 0.032 0.012 0.944 0.004 0.008
#> GSM254730 4 0.4410 0.1405 0.004 0.440 0.000 0.556 0.000
#> GSM254731 4 0.4824 0.3081 0.028 0.376 0.000 0.596 0.000
#> GSM254632 3 0.4513 0.7547 0.096 0.012 0.784 0.104 0.004
#> GSM254662 2 0.4404 0.7633 0.036 0.712 0.000 0.252 0.000
#> GSM254677 4 0.1282 0.5886 0.044 0.004 0.000 0.952 0.000
#> GSM254665 2 0.5864 0.6149 0.120 0.560 0.000 0.320 0.000
#> GSM254691 4 0.5996 -0.2453 0.116 0.388 0.000 0.496 0.000
#> GSM254644 4 0.4503 0.4208 0.024 0.312 0.000 0.664 0.000
#> GSM254667 4 0.6405 -0.2456 0.176 0.364 0.000 0.460 0.000
#> GSM254676 4 0.6007 -0.2585 0.116 0.396 0.000 0.488 0.000
#> GSM254679 4 0.0798 0.5875 0.016 0.008 0.000 0.976 0.000
#> GSM254689 2 0.5258 0.7458 0.104 0.664 0.000 0.232 0.000
#> GSM254706 4 0.6395 -0.3374 0.168 0.408 0.000 0.424 0.000
#> GSM254712 4 0.4212 0.5523 0.080 0.144 0.000 0.776 0.000
#> GSM254713 4 0.4197 0.5516 0.076 0.148 0.000 0.776 0.000
#> GSM254683 2 0.6325 0.3175 0.156 0.428 0.000 0.416 0.000
#> GSM254710 2 0.9439 0.0693 0.184 0.364 0.120 0.208 0.124
#> GSM254725 4 0.0609 0.5871 0.020 0.000 0.000 0.980 0.000
#> GSM254651 2 0.6312 0.3548 0.156 0.452 0.000 0.392 0.000
#> GSM254638 4 0.1943 0.5864 0.056 0.020 0.000 0.924 0.000
#> GSM254685 4 0.4197 0.5516 0.076 0.148 0.000 0.776 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM254629 3 0.2034 0.84467 0.008 0.000 0.924 0.024 0.012 0.032
#> GSM254648 3 0.2189 0.83993 0.008 0.000 0.912 0.032 0.004 0.044
#> GSM254694 3 0.1899 0.84273 0.008 0.000 0.928 0.028 0.004 0.032
#> GSM254701 3 0.1785 0.84434 0.008 0.000 0.936 0.016 0.012 0.028
#> GSM254728 3 0.1732 0.84653 0.000 0.000 0.920 0.004 0.004 0.072
#> GSM254726 3 0.1078 0.84717 0.008 0.000 0.964 0.012 0.000 0.016
#> GSM254639 3 0.4056 0.77493 0.020 0.000 0.748 0.016 0.008 0.208
#> GSM254652 3 0.2174 0.84570 0.000 0.000 0.896 0.008 0.008 0.088
#> GSM254700 1 0.2454 0.77662 0.840 0.000 0.000 0.000 0.160 0.000
#> GSM254625 5 0.4947 0.46851 0.004 0.000 0.272 0.012 0.648 0.064
#> GSM254636 5 0.6296 0.42524 0.104 0.000 0.048 0.008 0.512 0.328
#> GSM254659 3 0.1442 0.85088 0.000 0.000 0.944 0.004 0.012 0.040
#> GSM254680 5 0.4922 0.59062 0.036 0.000 0.044 0.016 0.712 0.192
#> GSM254686 5 0.4880 0.40151 0.000 0.000 0.344 0.012 0.596 0.048
#> GSM254718 3 0.0551 0.85156 0.004 0.000 0.984 0.000 0.008 0.004
#> GSM254674 5 0.3963 0.61135 0.000 0.000 0.040 0.012 0.756 0.192
#> GSM254668 5 0.2384 0.63289 0.000 0.000 0.040 0.016 0.900 0.044
#> GSM254697 1 0.5753 0.75896 0.628 0.000 0.000 0.072 0.204 0.096
#> GSM254704 1 0.2624 0.77501 0.844 0.000 0.004 0.000 0.148 0.004
#> GSM254707 5 0.1737 0.63351 0.000 0.000 0.040 0.008 0.932 0.020
#> GSM254714 3 0.3914 0.69842 0.228 0.000 0.740 0.004 0.012 0.016
#> GSM254722 1 0.5984 0.74741 0.608 0.000 0.000 0.076 0.196 0.120
#> GSM254627 1 0.5753 0.75896 0.628 0.000 0.000 0.072 0.204 0.096
#> GSM254630 5 0.4989 0.52187 0.016 0.000 0.168 0.020 0.712 0.084
#> GSM254633 5 0.6410 0.51313 0.104 0.000 0.084 0.016 0.588 0.208
#> GSM254670 3 0.5483 0.59131 0.020 0.000 0.576 0.016 0.052 0.336
#> GSM254716 5 0.5092 0.42836 0.004 0.000 0.292 0.008 0.620 0.076
#> GSM254720 1 0.5409 0.55876 0.644 0.000 0.216 0.008 0.116 0.016
#> GSM254729 3 0.1909 0.85003 0.004 0.000 0.920 0.024 0.000 0.052
#> GSM254654 3 0.1973 0.84172 0.008 0.000 0.924 0.028 0.004 0.036
#> GSM254656 3 0.5269 0.71200 0.032 0.000 0.640 0.064 0.004 0.260
#> GSM254631 5 0.6320 0.51463 0.104 0.000 0.076 0.016 0.596 0.208
#> GSM254657 3 0.4945 0.73701 0.020 0.000 0.692 0.016 0.052 0.220
#> GSM254664 5 0.5956 0.53475 0.100 0.000 0.056 0.016 0.632 0.196
#> GSM254672 1 0.3224 0.76060 0.824 0.000 0.004 0.000 0.132 0.040
#> GSM254692 5 0.4864 -0.33609 0.448 0.000 0.000 0.024 0.508 0.020
#> GSM254645 3 0.4028 0.77737 0.020 0.000 0.752 0.016 0.008 0.204
#> GSM254666 5 0.5076 0.41865 0.000 0.000 0.288 0.008 0.616 0.088
#> GSM254675 1 0.4456 0.21636 0.520 0.000 0.020 0.000 0.456 0.004
#> GSM254678 5 0.6557 0.13723 0.304 0.000 0.016 0.004 0.392 0.284
#> GSM254688 5 0.1116 0.61565 0.008 0.000 0.000 0.004 0.960 0.028
#> GSM254690 5 0.5297 0.51185 0.100 0.000 0.008 0.016 0.652 0.224
#> GSM254696 5 0.6379 0.40125 0.096 0.000 0.064 0.004 0.480 0.356
#> GSM254705 5 0.2471 0.59046 0.044 0.000 0.000 0.020 0.896 0.040
#> GSM254642 1 0.5801 0.75799 0.624 0.000 0.000 0.076 0.204 0.096
#> GSM254661 3 0.2110 0.84515 0.000 0.000 0.900 0.012 0.004 0.084
#> GSM254698 1 0.6827 0.50929 0.404 0.000 0.004 0.056 0.176 0.360
#> GSM254641 5 0.3780 0.63307 0.004 0.000 0.096 0.020 0.812 0.068
#> GSM254647 1 0.5952 0.64869 0.544 0.000 0.000 0.036 0.300 0.120
#> GSM254663 5 0.2510 0.56095 0.060 0.000 0.000 0.024 0.892 0.024
#> GSM254682 5 0.1268 0.61590 0.008 0.000 0.000 0.004 0.952 0.036
#> GSM254709 5 0.4024 0.54355 0.012 0.000 0.192 0.012 0.760 0.024
#> GSM254721 1 0.2595 0.77622 0.836 0.000 0.000 0.004 0.160 0.000
#> GSM254724 1 0.2454 0.77662 0.840 0.000 0.000 0.000 0.160 0.000
#> GSM254650 5 0.1526 0.60330 0.036 0.000 0.004 0.008 0.944 0.008
#> GSM254687 5 0.1893 0.60279 0.036 0.000 0.004 0.008 0.928 0.024
#> GSM254637 5 0.6485 0.50893 0.112 0.000 0.084 0.016 0.580 0.208
#> GSM254684 5 0.6375 0.39894 0.100 0.000 0.060 0.004 0.476 0.360
#> GSM254649 2 0.3626 0.49334 0.020 0.800 0.000 0.032 0.000 0.148
#> GSM254660 2 0.3827 0.22777 0.008 0.680 0.000 0.308 0.000 0.004
#> GSM254693 2 0.1151 0.57865 0.012 0.956 0.000 0.000 0.000 0.032
#> GSM254695 4 0.5112 0.58641 0.012 0.136 0.008 0.684 0.000 0.160
#> GSM254702 2 0.4224 0.08877 0.008 0.640 0.000 0.336 0.000 0.016
#> GSM254643 2 0.2116 0.57590 0.024 0.916 0.000 0.024 0.000 0.036
#> GSM254727 2 0.1788 0.57412 0.004 0.928 0.000 0.040 0.000 0.028
#> GSM254640 2 0.6002 -0.37595 0.060 0.436 0.000 0.436 0.000 0.068
#> GSM254626 2 0.1346 0.58758 0.016 0.952 0.000 0.024 0.000 0.008
#> GSM254635 4 0.4046 0.78463 0.020 0.208 0.000 0.744 0.000 0.028
#> GSM254653 2 0.1624 0.57742 0.004 0.936 0.000 0.040 0.000 0.020
#> GSM254658 2 0.3626 0.49334 0.020 0.800 0.000 0.032 0.000 0.148
#> GSM254681 2 0.4146 0.42369 0.020 0.736 0.000 0.032 0.000 0.212
#> GSM254719 2 0.1080 0.58750 0.004 0.960 0.000 0.032 0.000 0.004
#> GSM254673 2 0.0777 0.58797 0.000 0.972 0.000 0.024 0.000 0.004
#> GSM254655 2 0.2261 0.57034 0.008 0.884 0.000 0.104 0.000 0.004
#> GSM254669 2 0.0891 0.58764 0.000 0.968 0.000 0.024 0.000 0.008
#> GSM254699 2 0.1787 0.58123 0.008 0.920 0.000 0.068 0.000 0.004
#> GSM254703 4 0.4745 0.78569 0.052 0.184 0.000 0.716 0.000 0.048
#> GSM254708 2 0.5956 -0.17625 0.004 0.488 0.000 0.272 0.000 0.236
#> GSM254715 4 0.6195 0.68485 0.084 0.308 0.000 0.528 0.000 0.080
#> GSM254628 2 0.3663 0.49115 0.020 0.796 0.000 0.032 0.000 0.152
#> GSM254634 4 0.3203 0.77785 0.004 0.160 0.000 0.812 0.000 0.024
#> GSM254646 2 0.3865 0.46202 0.020 0.768 0.000 0.028 0.000 0.184
#> GSM254671 2 0.4015 -0.03285 0.004 0.596 0.000 0.396 0.000 0.004
#> GSM254711 4 0.2980 0.77845 0.000 0.180 0.000 0.808 0.000 0.012
#> GSM254717 2 0.1829 0.57572 0.008 0.928 0.000 0.036 0.000 0.028
#> GSM254723 3 0.2677 0.81763 0.024 0.000 0.884 0.036 0.000 0.056
#> GSM254730 2 0.4159 0.30543 0.008 0.672 0.000 0.300 0.000 0.020
#> GSM254731 2 0.4074 0.08906 0.008 0.640 0.000 0.344 0.000 0.008
#> GSM254632 3 0.4838 0.60255 0.020 0.000 0.696 0.092 0.000 0.192
#> GSM254662 2 0.1049 0.58815 0.000 0.960 0.000 0.032 0.000 0.008
#> GSM254677 4 0.4949 0.76079 0.072 0.136 0.000 0.720 0.000 0.072
#> GSM254665 2 0.5107 0.41162 0.032 0.688 0.000 0.160 0.000 0.120
#> GSM254691 2 0.5799 -0.01788 0.004 0.500 0.000 0.320 0.000 0.176
#> GSM254644 2 0.6043 -0.34151 0.064 0.448 0.000 0.420 0.000 0.068
#> GSM254667 2 0.6121 -0.36680 0.004 0.420 0.000 0.244 0.000 0.332
#> GSM254676 2 0.5778 -0.00643 0.004 0.508 0.000 0.312 0.000 0.176
#> GSM254679 4 0.3053 0.77973 0.004 0.172 0.000 0.812 0.000 0.012
#> GSM254689 2 0.4146 0.43141 0.020 0.736 0.000 0.032 0.000 0.212
#> GSM254706 2 0.5828 -0.32134 0.004 0.480 0.000 0.172 0.000 0.344
#> GSM254712 4 0.6195 0.68485 0.084 0.308 0.000 0.528 0.000 0.080
#> GSM254713 4 0.6195 0.68485 0.084 0.308 0.000 0.528 0.000 0.080
#> GSM254683 2 0.5562 -0.15493 0.000 0.532 0.000 0.168 0.000 0.300
#> GSM254710 6 0.8197 0.00000 0.016 0.284 0.076 0.176 0.064 0.384
#> GSM254725 4 0.3139 0.77489 0.000 0.152 0.000 0.816 0.000 0.032
#> GSM254651 2 0.5684 -0.11978 0.008 0.536 0.000 0.148 0.000 0.308
#> GSM254638 4 0.4217 0.78930 0.024 0.184 0.000 0.748 0.000 0.044
#> GSM254685 4 0.6236 0.68412 0.088 0.308 0.000 0.524 0.000 0.080
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) individual(p) disease.state(p) gender(p) other(p) k
#> SD:kmeans 107 1.59e-22 0.776968 0.577 0.6277 0.872 2
#> SD:kmeans 105 6.36e-22 0.001993 0.542 0.1057 0.892 3
#> SD:kmeans 58 3.26e-01 0.000295 0.397 0.1040 1.000 4
#> SD:kmeans 71 4.63e-13 0.029332 0.351 0.2152 0.295 5
#> SD:kmeans 75 8.90e-14 0.003035 0.252 0.0854 0.200 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 21168 rows and 107 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 1.000 0.991 0.996 0.5027 0.497 0.497
#> 3 3 0.844 0.909 0.944 0.2612 0.857 0.716
#> 4 4 0.699 0.752 0.779 0.1159 0.892 0.709
#> 5 5 0.669 0.555 0.739 0.0785 0.868 0.572
#> 6 6 0.660 0.597 0.756 0.0512 0.922 0.674
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
#> GSM254629 1 0.000 0.998 1.000 0.000
#> GSM254648 2 0.518 0.868 0.116 0.884
#> GSM254694 1 0.000 0.998 1.000 0.000
#> GSM254701 1 0.000 0.998 1.000 0.000
#> GSM254728 1 0.000 0.998 1.000 0.000
#> GSM254726 1 0.416 0.908 0.916 0.084
#> GSM254639 1 0.000 0.998 1.000 0.000
#> GSM254652 1 0.000 0.998 1.000 0.000
#> GSM254700 1 0.000 0.998 1.000 0.000
#> GSM254625 1 0.000 0.998 1.000 0.000
#> GSM254636 1 0.000 0.998 1.000 0.000
#> GSM254659 1 0.000 0.998 1.000 0.000
#> GSM254680 1 0.000 0.998 1.000 0.000
#> GSM254686 1 0.000 0.998 1.000 0.000
#> GSM254718 1 0.000 0.998 1.000 0.000
#> GSM254674 1 0.000 0.998 1.000 0.000
#> GSM254668 1 0.000 0.998 1.000 0.000
#> GSM254697 1 0.000 0.998 1.000 0.000
#> GSM254704 1 0.000 0.998 1.000 0.000
#> GSM254707 1 0.000 0.998 1.000 0.000
#> GSM254714 1 0.000 0.998 1.000 0.000
#> GSM254722 1 0.000 0.998 1.000 0.000
#> GSM254627 1 0.000 0.998 1.000 0.000
#> GSM254630 1 0.000 0.998 1.000 0.000
#> GSM254633 1 0.000 0.998 1.000 0.000
#> GSM254670 1 0.000 0.998 1.000 0.000
#> GSM254716 1 0.000 0.998 1.000 0.000
#> GSM254720 1 0.000 0.998 1.000 0.000
#> GSM254729 1 0.000 0.998 1.000 0.000
#> GSM254654 1 0.000 0.998 1.000 0.000
#> GSM254656 1 0.184 0.970 0.972 0.028
#> GSM254631 1 0.000 0.998 1.000 0.000
#> GSM254657 1 0.000 0.998 1.000 0.000
#> GSM254664 1 0.000 0.998 1.000 0.000
#> GSM254672 1 0.000 0.998 1.000 0.000
#> GSM254692 1 0.000 0.998 1.000 0.000
#> GSM254645 1 0.000 0.998 1.000 0.000
#> GSM254666 1 0.000 0.998 1.000 0.000
#> GSM254675 1 0.000 0.998 1.000 0.000
#> GSM254678 1 0.000 0.998 1.000 0.000
#> GSM254688 1 0.000 0.998 1.000 0.000
#> GSM254690 1 0.000 0.998 1.000 0.000
#> GSM254696 1 0.000 0.998 1.000 0.000
#> GSM254705 1 0.000 0.998 1.000 0.000
#> GSM254642 1 0.000 0.998 1.000 0.000
#> GSM254661 1 0.000 0.998 1.000 0.000
#> GSM254698 1 0.000 0.998 1.000 0.000
#> GSM254641 1 0.000 0.998 1.000 0.000
#> GSM254647 1 0.000 0.998 1.000 0.000
#> GSM254663 1 0.000 0.998 1.000 0.000
#> GSM254682 1 0.000 0.998 1.000 0.000
#> GSM254709 1 0.000 0.998 1.000 0.000
#> GSM254721 1 0.000 0.998 1.000 0.000
#> GSM254724 1 0.000 0.998 1.000 0.000
#> GSM254650 1 0.000 0.998 1.000 0.000
#> GSM254687 1 0.000 0.998 1.000 0.000
#> GSM254637 1 0.000 0.998 1.000 0.000
#> GSM254684 1 0.000 0.998 1.000 0.000
#> GSM254649 2 0.000 0.993 0.000 1.000
#> GSM254660 2 0.000 0.993 0.000 1.000
#> GSM254693 2 0.000 0.993 0.000 1.000
#> GSM254695 2 0.000 0.993 0.000 1.000
#> GSM254702 2 0.000 0.993 0.000 1.000
#> GSM254643 2 0.000 0.993 0.000 1.000
#> GSM254727 2 0.000 0.993 0.000 1.000
#> GSM254640 2 0.000 0.993 0.000 1.000
#> GSM254626 2 0.000 0.993 0.000 1.000
#> GSM254635 2 0.000 0.993 0.000 1.000
#> GSM254653 2 0.000 0.993 0.000 1.000
#> GSM254658 2 0.000 0.993 0.000 1.000
#> GSM254681 2 0.000 0.993 0.000 1.000
#> GSM254719 2 0.000 0.993 0.000 1.000
#> GSM254673 2 0.000 0.993 0.000 1.000
#> GSM254655 2 0.000 0.993 0.000 1.000
#> GSM254669 2 0.000 0.993 0.000 1.000
#> GSM254699 2 0.000 0.993 0.000 1.000
#> GSM254703 2 0.000 0.993 0.000 1.000
#> GSM254708 2 0.000 0.993 0.000 1.000
#> GSM254715 2 0.000 0.993 0.000 1.000
#> GSM254628 2 0.000 0.993 0.000 1.000
#> GSM254634 2 0.000 0.993 0.000 1.000
#> GSM254646 2 0.000 0.993 0.000 1.000
#> GSM254671 2 0.000 0.993 0.000 1.000
#> GSM254711 2 0.000 0.993 0.000 1.000
#> GSM254717 2 0.000 0.993 0.000 1.000
#> GSM254723 2 0.722 0.751 0.200 0.800
#> GSM254730 2 0.000 0.993 0.000 1.000
#> GSM254731 2 0.000 0.993 0.000 1.000
#> GSM254632 2 0.000 0.993 0.000 1.000
#> GSM254662 2 0.000 0.993 0.000 1.000
#> GSM254677 2 0.000 0.993 0.000 1.000
#> GSM254665 2 0.000 0.993 0.000 1.000
#> GSM254691 2 0.000 0.993 0.000 1.000
#> GSM254644 2 0.000 0.993 0.000 1.000
#> GSM254667 2 0.000 0.993 0.000 1.000
#> GSM254676 2 0.000 0.993 0.000 1.000
#> GSM254679 2 0.000 0.993 0.000 1.000
#> GSM254689 2 0.000 0.993 0.000 1.000
#> GSM254706 2 0.000 0.993 0.000 1.000
#> GSM254712 2 0.000 0.993 0.000 1.000
#> GSM254713 2 0.000 0.993 0.000 1.000
#> GSM254683 2 0.000 0.993 0.000 1.000
#> GSM254710 2 0.000 0.993 0.000 1.000
#> GSM254725 2 0.000 0.993 0.000 1.000
#> GSM254651 2 0.000 0.993 0.000 1.000
#> GSM254638 2 0.000 0.993 0.000 1.000
#> GSM254685 2 0.000 0.993 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM254629 3 0.0424 0.845 0.008 0.000 0.992
#> GSM254648 3 0.0424 0.842 0.000 0.008 0.992
#> GSM254694 3 0.3038 0.877 0.104 0.000 0.896
#> GSM254701 3 0.3038 0.877 0.104 0.000 0.896
#> GSM254728 3 0.3267 0.877 0.116 0.000 0.884
#> GSM254726 3 0.0000 0.842 0.000 0.000 1.000
#> GSM254639 3 0.3340 0.876 0.120 0.000 0.880
#> GSM254652 3 0.0892 0.847 0.020 0.000 0.980
#> GSM254700 1 0.0000 0.919 1.000 0.000 0.000
#> GSM254625 1 0.5560 0.663 0.700 0.000 0.300
#> GSM254636 1 0.0424 0.916 0.992 0.000 0.008
#> GSM254659 3 0.3192 0.878 0.112 0.000 0.888
#> GSM254680 1 0.0592 0.916 0.988 0.000 0.012
#> GSM254686 1 0.6062 0.503 0.616 0.000 0.384
#> GSM254718 3 0.3038 0.877 0.104 0.000 0.896
#> GSM254674 1 0.0592 0.916 0.988 0.000 0.012
#> GSM254668 1 0.3192 0.883 0.888 0.000 0.112
#> GSM254697 1 0.0000 0.919 1.000 0.000 0.000
#> GSM254704 1 0.0237 0.918 0.996 0.000 0.004
#> GSM254707 1 0.3192 0.883 0.888 0.000 0.112
#> GSM254714 3 0.6291 0.388 0.468 0.000 0.532
#> GSM254722 1 0.0000 0.919 1.000 0.000 0.000
#> GSM254627 1 0.0000 0.919 1.000 0.000 0.000
#> GSM254630 1 0.2878 0.889 0.904 0.000 0.096
#> GSM254633 1 0.1031 0.910 0.976 0.000 0.024
#> GSM254670 3 0.5291 0.763 0.268 0.000 0.732
#> GSM254716 1 0.6045 0.511 0.620 0.000 0.380
#> GSM254720 1 0.3551 0.775 0.868 0.000 0.132
#> GSM254729 3 0.3267 0.877 0.116 0.000 0.884
#> GSM254654 3 0.3038 0.877 0.104 0.000 0.896
#> GSM254656 3 0.4796 0.811 0.220 0.000 0.780
#> GSM254631 1 0.0000 0.919 1.000 0.000 0.000
#> GSM254657 3 0.5138 0.783 0.252 0.000 0.748
#> GSM254664 1 0.0424 0.917 0.992 0.000 0.008
#> GSM254672 1 0.0237 0.918 0.996 0.000 0.004
#> GSM254692 1 0.2878 0.889 0.904 0.000 0.096
#> GSM254645 3 0.6302 0.360 0.480 0.000 0.520
#> GSM254666 1 0.5497 0.672 0.708 0.000 0.292
#> GSM254675 1 0.0000 0.919 1.000 0.000 0.000
#> GSM254678 1 0.0237 0.918 0.996 0.000 0.004
#> GSM254688 1 0.2878 0.889 0.904 0.000 0.096
#> GSM254690 1 0.0000 0.919 1.000 0.000 0.000
#> GSM254696 1 0.0424 0.916 0.992 0.000 0.008
#> GSM254705 1 0.2878 0.889 0.904 0.000 0.096
#> GSM254642 1 0.0000 0.919 1.000 0.000 0.000
#> GSM254661 3 0.0592 0.846 0.012 0.000 0.988
#> GSM254698 1 0.0237 0.918 0.996 0.000 0.004
#> GSM254641 1 0.3116 0.884 0.892 0.000 0.108
#> GSM254647 1 0.0000 0.919 1.000 0.000 0.000
#> GSM254663 1 0.2878 0.889 0.904 0.000 0.096
#> GSM254682 1 0.2878 0.889 0.904 0.000 0.096
#> GSM254709 1 0.3038 0.886 0.896 0.000 0.104
#> GSM254721 1 0.0000 0.919 1.000 0.000 0.000
#> GSM254724 1 0.0000 0.919 1.000 0.000 0.000
#> GSM254650 1 0.2878 0.889 0.904 0.000 0.096
#> GSM254687 1 0.2878 0.889 0.904 0.000 0.096
#> GSM254637 1 0.0237 0.918 0.996 0.000 0.004
#> GSM254684 1 0.0237 0.918 0.996 0.000 0.004
#> GSM254649 2 0.0000 0.992 0.000 1.000 0.000
#> GSM254660 2 0.0000 0.992 0.000 1.000 0.000
#> GSM254693 2 0.0000 0.992 0.000 1.000 0.000
#> GSM254695 2 0.0424 0.990 0.000 0.992 0.008
#> GSM254702 2 0.0424 0.990 0.000 0.992 0.008
#> GSM254643 2 0.0000 0.992 0.000 1.000 0.000
#> GSM254727 2 0.0000 0.992 0.000 1.000 0.000
#> GSM254640 2 0.0424 0.990 0.000 0.992 0.008
#> GSM254626 2 0.0000 0.992 0.000 1.000 0.000
#> GSM254635 2 0.0424 0.990 0.000 0.992 0.008
#> GSM254653 2 0.0000 0.992 0.000 1.000 0.000
#> GSM254658 2 0.0000 0.992 0.000 1.000 0.000
#> GSM254681 2 0.0000 0.992 0.000 1.000 0.000
#> GSM254719 2 0.0000 0.992 0.000 1.000 0.000
#> GSM254673 2 0.0000 0.992 0.000 1.000 0.000
#> GSM254655 2 0.0000 0.992 0.000 1.000 0.000
#> GSM254669 2 0.0000 0.992 0.000 1.000 0.000
#> GSM254699 2 0.0000 0.992 0.000 1.000 0.000
#> GSM254703 2 0.0424 0.990 0.000 0.992 0.008
#> GSM254708 2 0.0000 0.992 0.000 1.000 0.000
#> GSM254715 2 0.0424 0.990 0.000 0.992 0.008
#> GSM254628 2 0.0000 0.992 0.000 1.000 0.000
#> GSM254634 2 0.0424 0.990 0.000 0.992 0.008
#> GSM254646 2 0.0000 0.992 0.000 1.000 0.000
#> GSM254671 2 0.0424 0.990 0.000 0.992 0.008
#> GSM254711 2 0.0424 0.990 0.000 0.992 0.008
#> GSM254717 2 0.0000 0.992 0.000 1.000 0.000
#> GSM254723 3 0.4551 0.767 0.020 0.140 0.840
#> GSM254730 2 0.0000 0.992 0.000 1.000 0.000
#> GSM254731 2 0.0424 0.990 0.000 0.992 0.008
#> GSM254632 2 0.4128 0.838 0.012 0.856 0.132
#> GSM254662 2 0.0000 0.992 0.000 1.000 0.000
#> GSM254677 2 0.0424 0.990 0.000 0.992 0.008
#> GSM254665 2 0.0000 0.992 0.000 1.000 0.000
#> GSM254691 2 0.0000 0.992 0.000 1.000 0.000
#> GSM254644 2 0.0424 0.990 0.000 0.992 0.008
#> GSM254667 2 0.0000 0.992 0.000 1.000 0.000
#> GSM254676 2 0.0000 0.992 0.000 1.000 0.000
#> GSM254679 2 0.0424 0.990 0.000 0.992 0.008
#> GSM254689 2 0.0000 0.992 0.000 1.000 0.000
#> GSM254706 2 0.0000 0.992 0.000 1.000 0.000
#> GSM254712 2 0.0424 0.990 0.000 0.992 0.008
#> GSM254713 2 0.0424 0.990 0.000 0.992 0.008
#> GSM254683 2 0.0000 0.992 0.000 1.000 0.000
#> GSM254710 2 0.2878 0.891 0.000 0.904 0.096
#> GSM254725 2 0.0424 0.990 0.000 0.992 0.008
#> GSM254651 2 0.0000 0.992 0.000 1.000 0.000
#> GSM254638 2 0.0424 0.990 0.000 0.992 0.008
#> GSM254685 2 0.0424 0.990 0.000 0.992 0.008
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM254629 3 0.4283 0.798 0.004 0.256 0.740 0.000
#> GSM254648 3 0.4164 0.797 0.000 0.264 0.736 0.000
#> GSM254694 3 0.4767 0.802 0.020 0.256 0.724 0.000
#> GSM254701 3 0.4767 0.802 0.020 0.256 0.724 0.000
#> GSM254728 3 0.1724 0.810 0.032 0.020 0.948 0.000
#> GSM254726 3 0.4193 0.801 0.000 0.268 0.732 0.000
#> GSM254639 3 0.1151 0.811 0.024 0.008 0.968 0.000
#> GSM254652 3 0.0779 0.805 0.004 0.016 0.980 0.000
#> GSM254700 1 0.0000 0.852 1.000 0.000 0.000 0.000
#> GSM254625 1 0.7285 0.570 0.520 0.180 0.300 0.000
#> GSM254636 1 0.4050 0.813 0.808 0.024 0.168 0.000
#> GSM254659 3 0.1833 0.815 0.024 0.032 0.944 0.000
#> GSM254680 1 0.3616 0.840 0.852 0.036 0.112 0.000
#> GSM254686 1 0.6973 0.615 0.556 0.144 0.300 0.000
#> GSM254718 3 0.3659 0.817 0.024 0.136 0.840 0.000
#> GSM254674 1 0.3842 0.835 0.836 0.036 0.128 0.000
#> GSM254668 1 0.5483 0.806 0.736 0.128 0.136 0.000
#> GSM254697 1 0.0188 0.852 0.996 0.004 0.000 0.000
#> GSM254704 1 0.0000 0.852 1.000 0.000 0.000 0.000
#> GSM254707 1 0.5719 0.795 0.716 0.132 0.152 0.000
#> GSM254714 3 0.5161 0.304 0.476 0.004 0.520 0.000
#> GSM254722 1 0.0779 0.853 0.980 0.004 0.016 0.000
#> GSM254627 1 0.0188 0.852 0.996 0.004 0.000 0.000
#> GSM254630 1 0.4840 0.804 0.784 0.100 0.116 0.000
#> GSM254633 1 0.3907 0.829 0.828 0.032 0.140 0.000
#> GSM254670 3 0.4957 0.624 0.204 0.048 0.748 0.000
#> GSM254716 1 0.7359 0.543 0.504 0.184 0.312 0.000
#> GSM254720 1 0.3583 0.665 0.816 0.004 0.180 0.000
#> GSM254729 3 0.1305 0.811 0.036 0.004 0.960 0.000
#> GSM254654 3 0.4767 0.802 0.020 0.256 0.724 0.000
#> GSM254656 3 0.7605 0.424 0.032 0.104 0.516 0.348
#> GSM254631 1 0.2385 0.857 0.920 0.028 0.052 0.000
#> GSM254657 3 0.3667 0.753 0.088 0.056 0.856 0.000
#> GSM254664 1 0.2385 0.857 0.920 0.028 0.052 0.000
#> GSM254672 1 0.0707 0.848 0.980 0.000 0.020 0.000
#> GSM254692 1 0.2973 0.837 0.884 0.096 0.020 0.000
#> GSM254645 3 0.5080 0.393 0.420 0.004 0.576 0.000
#> GSM254666 1 0.7039 0.606 0.540 0.144 0.316 0.000
#> GSM254675 1 0.0000 0.852 1.000 0.000 0.000 0.000
#> GSM254678 1 0.1767 0.850 0.944 0.012 0.044 0.000
#> GSM254688 1 0.5676 0.801 0.720 0.136 0.144 0.000
#> GSM254690 1 0.2943 0.853 0.892 0.032 0.076 0.000
#> GSM254696 1 0.5021 0.746 0.724 0.036 0.240 0.000
#> GSM254705 1 0.3616 0.838 0.852 0.112 0.036 0.000
#> GSM254642 1 0.0188 0.852 0.996 0.004 0.000 0.000
#> GSM254661 3 0.1118 0.815 0.000 0.036 0.964 0.000
#> GSM254698 1 0.1890 0.843 0.936 0.008 0.056 0.000
#> GSM254641 1 0.3833 0.853 0.848 0.072 0.080 0.000
#> GSM254647 1 0.0000 0.852 1.000 0.000 0.000 0.000
#> GSM254663 1 0.2973 0.841 0.884 0.096 0.020 0.000
#> GSM254682 1 0.5897 0.787 0.700 0.136 0.164 0.000
#> GSM254709 1 0.3205 0.836 0.872 0.104 0.024 0.000
#> GSM254721 1 0.0000 0.852 1.000 0.000 0.000 0.000
#> GSM254724 1 0.0000 0.852 1.000 0.000 0.000 0.000
#> GSM254650 1 0.3278 0.837 0.864 0.116 0.020 0.000
#> GSM254687 1 0.3278 0.837 0.864 0.116 0.020 0.000
#> GSM254637 1 0.2300 0.857 0.924 0.028 0.048 0.000
#> GSM254684 1 0.4406 0.793 0.780 0.028 0.192 0.000
#> GSM254649 2 0.4998 0.933 0.000 0.512 0.000 0.488
#> GSM254660 4 0.4304 -0.111 0.000 0.284 0.000 0.716
#> GSM254693 2 0.4999 0.932 0.000 0.508 0.000 0.492
#> GSM254695 4 0.1557 0.731 0.000 0.056 0.000 0.944
#> GSM254702 4 0.1302 0.760 0.000 0.044 0.000 0.956
#> GSM254643 2 0.5000 0.930 0.000 0.504 0.000 0.496
#> GSM254727 2 0.4998 0.933 0.000 0.512 0.000 0.488
#> GSM254640 4 0.1716 0.738 0.000 0.064 0.000 0.936
#> GSM254626 2 0.5000 0.930 0.000 0.504 0.000 0.496
#> GSM254635 4 0.0000 0.792 0.000 0.000 0.000 1.000
#> GSM254653 2 0.4999 0.931 0.000 0.508 0.000 0.492
#> GSM254658 2 0.4998 0.933 0.000 0.512 0.000 0.488
#> GSM254681 2 0.4998 0.933 0.000 0.512 0.000 0.488
#> GSM254719 2 0.5000 0.930 0.000 0.504 0.000 0.496
#> GSM254673 2 0.5000 0.930 0.000 0.504 0.000 0.496
#> GSM254655 4 0.4843 -0.635 0.000 0.396 0.000 0.604
#> GSM254669 2 0.4999 0.932 0.000 0.508 0.000 0.492
#> GSM254699 4 0.4843 -0.635 0.000 0.396 0.000 0.604
#> GSM254703 4 0.0000 0.792 0.000 0.000 0.000 1.000
#> GSM254708 2 0.4998 0.933 0.000 0.512 0.000 0.488
#> GSM254715 4 0.0000 0.792 0.000 0.000 0.000 1.000
#> GSM254628 2 0.4998 0.933 0.000 0.512 0.000 0.488
#> GSM254634 4 0.0188 0.791 0.000 0.004 0.000 0.996
#> GSM254646 2 0.4999 0.932 0.000 0.508 0.000 0.492
#> GSM254671 4 0.1557 0.745 0.000 0.056 0.000 0.944
#> GSM254711 4 0.0336 0.791 0.000 0.008 0.000 0.992
#> GSM254717 2 0.4998 0.933 0.000 0.512 0.000 0.488
#> GSM254723 4 0.8057 -0.202 0.020 0.216 0.280 0.484
#> GSM254730 4 0.4356 -0.138 0.000 0.292 0.000 0.708
#> GSM254731 4 0.1557 0.745 0.000 0.056 0.000 0.944
#> GSM254632 2 0.5398 0.243 0.004 0.732 0.064 0.200
#> GSM254662 2 0.5000 0.930 0.000 0.504 0.000 0.496
#> GSM254677 4 0.0817 0.772 0.000 0.024 0.000 0.976
#> GSM254665 2 0.5000 0.930 0.000 0.504 0.000 0.496
#> GSM254691 2 0.4999 0.931 0.000 0.508 0.000 0.492
#> GSM254644 4 0.1389 0.760 0.000 0.048 0.000 0.952
#> GSM254667 2 0.4972 0.881 0.000 0.544 0.000 0.456
#> GSM254676 2 0.4999 0.931 0.000 0.508 0.000 0.492
#> GSM254679 4 0.0336 0.791 0.000 0.008 0.000 0.992
#> GSM254689 2 0.4999 0.932 0.000 0.508 0.000 0.492
#> GSM254706 2 0.4972 0.881 0.000 0.544 0.000 0.456
#> GSM254712 4 0.0000 0.792 0.000 0.000 0.000 1.000
#> GSM254713 4 0.0000 0.792 0.000 0.000 0.000 1.000
#> GSM254683 2 0.4998 0.933 0.000 0.512 0.000 0.488
#> GSM254710 2 0.4933 0.540 0.000 0.688 0.016 0.296
#> GSM254725 4 0.0336 0.789 0.000 0.008 0.000 0.992
#> GSM254651 2 0.4998 0.933 0.000 0.512 0.000 0.488
#> GSM254638 4 0.0000 0.792 0.000 0.000 0.000 1.000
#> GSM254685 4 0.0336 0.790 0.000 0.008 0.000 0.992
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM254629 3 0.0486 0.8081 0.004 0.000 0.988 0.004 0.004
#> GSM254648 3 0.0162 0.8085 0.004 0.000 0.996 0.000 0.000
#> GSM254694 3 0.0162 0.8085 0.004 0.000 0.996 0.000 0.000
#> GSM254701 3 0.0324 0.8087 0.004 0.000 0.992 0.000 0.004
#> GSM254728 3 0.5390 0.5109 0.004 0.000 0.536 0.048 0.412
#> GSM254726 3 0.1216 0.8013 0.000 0.000 0.960 0.020 0.020
#> GSM254639 5 0.6021 -0.4465 0.020 0.000 0.444 0.064 0.472
#> GSM254652 3 0.5125 0.5256 0.000 0.000 0.544 0.040 0.416
#> GSM254700 1 0.0000 0.6364 1.000 0.000 0.000 0.000 0.000
#> GSM254625 5 0.4109 0.4285 0.192 0.000 0.004 0.036 0.768
#> GSM254636 1 0.5107 0.1977 0.520 0.000 0.004 0.028 0.448
#> GSM254659 3 0.4920 0.6027 0.008 0.000 0.620 0.024 0.348
#> GSM254680 1 0.4470 0.2350 0.596 0.000 0.004 0.004 0.396
#> GSM254686 5 0.4116 0.4039 0.248 0.000 0.016 0.004 0.732
#> GSM254718 3 0.3914 0.7477 0.012 0.000 0.780 0.016 0.192
#> GSM254674 1 0.4758 0.1502 0.552 0.000 0.004 0.012 0.432
#> GSM254668 5 0.4318 0.2637 0.348 0.000 0.004 0.004 0.644
#> GSM254697 1 0.0000 0.6364 1.000 0.000 0.000 0.000 0.000
#> GSM254704 1 0.1341 0.6199 0.944 0.000 0.000 0.000 0.056
#> GSM254707 5 0.4335 0.3081 0.324 0.000 0.004 0.008 0.664
#> GSM254714 1 0.4597 0.3320 0.672 0.000 0.300 0.004 0.024
#> GSM254722 1 0.2170 0.6048 0.904 0.000 0.004 0.004 0.088
#> GSM254627 1 0.0000 0.6364 1.000 0.000 0.000 0.000 0.000
#> GSM254630 5 0.4748 -0.0386 0.492 0.000 0.000 0.016 0.492
#> GSM254633 1 0.4299 0.3810 0.672 0.000 0.004 0.008 0.316
#> GSM254670 5 0.6549 0.1700 0.272 0.000 0.084 0.064 0.580
#> GSM254716 5 0.4270 0.4303 0.188 0.000 0.008 0.040 0.764
#> GSM254720 1 0.2291 0.6020 0.908 0.000 0.036 0.000 0.056
#> GSM254729 5 0.6212 -0.4024 0.048 0.000 0.440 0.044 0.468
#> GSM254654 3 0.0162 0.8085 0.004 0.000 0.996 0.000 0.000
#> GSM254656 4 0.5875 -0.1459 0.036 0.000 0.048 0.584 0.332
#> GSM254631 1 0.3585 0.5158 0.772 0.000 0.004 0.004 0.220
#> GSM254657 5 0.6621 -0.1850 0.080 0.000 0.300 0.064 0.556
#> GSM254664 1 0.3317 0.5272 0.804 0.000 0.004 0.004 0.188
#> GSM254672 1 0.2848 0.5452 0.840 0.000 0.004 0.000 0.156
#> GSM254692 1 0.4074 0.2496 0.636 0.000 0.000 0.000 0.364
#> GSM254645 1 0.6205 0.2541 0.584 0.000 0.060 0.052 0.304
#> GSM254666 5 0.3724 0.4275 0.204 0.000 0.000 0.020 0.776
#> GSM254675 1 0.0510 0.6335 0.984 0.000 0.000 0.000 0.016
#> GSM254678 1 0.3783 0.5167 0.768 0.000 0.004 0.012 0.216
#> GSM254688 5 0.4288 0.3080 0.324 0.000 0.000 0.012 0.664
#> GSM254690 1 0.3579 0.4759 0.756 0.000 0.000 0.004 0.240
#> GSM254696 5 0.5428 -0.0219 0.400 0.000 0.004 0.052 0.544
#> GSM254705 1 0.4481 0.1703 0.576 0.000 0.000 0.008 0.416
#> GSM254642 1 0.0000 0.6364 1.000 0.000 0.000 0.000 0.000
#> GSM254661 3 0.4193 0.7320 0.000 0.000 0.748 0.040 0.212
#> GSM254698 1 0.4496 0.4558 0.724 0.000 0.004 0.040 0.232
#> GSM254641 1 0.4580 0.0971 0.532 0.000 0.004 0.004 0.460
#> GSM254647 1 0.0404 0.6345 0.988 0.000 0.000 0.000 0.012
#> GSM254663 1 0.4030 0.2803 0.648 0.000 0.000 0.000 0.352
#> GSM254682 5 0.4213 0.3382 0.308 0.000 0.000 0.012 0.680
#> GSM254709 1 0.4331 0.2004 0.596 0.000 0.004 0.000 0.400
#> GSM254721 1 0.0000 0.6364 1.000 0.000 0.000 0.000 0.000
#> GSM254724 1 0.0000 0.6364 1.000 0.000 0.000 0.000 0.000
#> GSM254650 1 0.4390 0.1590 0.568 0.000 0.000 0.004 0.428
#> GSM254687 1 0.4390 0.1563 0.568 0.000 0.000 0.004 0.428
#> GSM254637 1 0.3422 0.5337 0.792 0.000 0.004 0.004 0.200
#> GSM254684 5 0.5377 -0.1384 0.456 0.000 0.004 0.044 0.496
#> GSM254649 2 0.0000 0.8701 0.000 1.000 0.000 0.000 0.000
#> GSM254660 2 0.3857 0.1998 0.000 0.688 0.000 0.312 0.000
#> GSM254693 2 0.0703 0.8699 0.000 0.976 0.000 0.024 0.000
#> GSM254695 4 0.2377 0.5862 0.000 0.128 0.000 0.872 0.000
#> GSM254702 4 0.4235 0.7616 0.000 0.424 0.000 0.576 0.000
#> GSM254643 2 0.1197 0.8599 0.000 0.952 0.000 0.048 0.000
#> GSM254727 2 0.0162 0.8710 0.000 0.996 0.000 0.004 0.000
#> GSM254640 4 0.4287 0.7129 0.000 0.460 0.000 0.540 0.000
#> GSM254626 2 0.0963 0.8648 0.000 0.964 0.000 0.036 0.000
#> GSM254635 4 0.4045 0.8209 0.000 0.356 0.000 0.644 0.000
#> GSM254653 2 0.0703 0.8685 0.000 0.976 0.000 0.024 0.000
#> GSM254658 2 0.0000 0.8701 0.000 1.000 0.000 0.000 0.000
#> GSM254681 2 0.0290 0.8676 0.000 0.992 0.000 0.000 0.008
#> GSM254719 2 0.0963 0.8648 0.000 0.964 0.000 0.036 0.000
#> GSM254673 2 0.0880 0.8673 0.000 0.968 0.000 0.032 0.000
#> GSM254655 2 0.3274 0.5322 0.000 0.780 0.000 0.220 0.000
#> GSM254669 2 0.0880 0.8673 0.000 0.968 0.000 0.032 0.000
#> GSM254699 2 0.3039 0.6019 0.000 0.808 0.000 0.192 0.000
#> GSM254703 4 0.4074 0.8188 0.000 0.364 0.000 0.636 0.000
#> GSM254708 2 0.1124 0.8501 0.000 0.960 0.000 0.036 0.004
#> GSM254715 4 0.4045 0.8209 0.000 0.356 0.000 0.644 0.000
#> GSM254628 2 0.0000 0.8701 0.000 1.000 0.000 0.000 0.000
#> GSM254634 4 0.4060 0.8145 0.000 0.360 0.000 0.640 0.000
#> GSM254646 2 0.0290 0.8702 0.000 0.992 0.000 0.008 0.000
#> GSM254671 4 0.4256 0.7443 0.000 0.436 0.000 0.564 0.000
#> GSM254711 4 0.4114 0.8159 0.000 0.376 0.000 0.624 0.000
#> GSM254717 2 0.0290 0.8714 0.000 0.992 0.000 0.008 0.000
#> GSM254723 4 0.5777 0.0293 0.012 0.000 0.204 0.648 0.136
#> GSM254730 2 0.3661 0.3240 0.000 0.724 0.000 0.276 0.000
#> GSM254731 4 0.4273 0.7227 0.000 0.448 0.000 0.552 0.000
#> GSM254632 5 0.7276 0.1156 0.004 0.244 0.020 0.308 0.424
#> GSM254662 2 0.0880 0.8673 0.000 0.968 0.000 0.032 0.000
#> GSM254677 4 0.3534 0.7297 0.000 0.256 0.000 0.744 0.000
#> GSM254665 2 0.1282 0.8632 0.000 0.952 0.000 0.044 0.004
#> GSM254691 2 0.1041 0.8684 0.000 0.964 0.000 0.032 0.004
#> GSM254644 4 0.4262 0.7498 0.000 0.440 0.000 0.560 0.000
#> GSM254667 2 0.3013 0.6884 0.000 0.832 0.000 0.160 0.008
#> GSM254676 2 0.0955 0.8688 0.000 0.968 0.000 0.028 0.004
#> GSM254679 4 0.4088 0.8170 0.000 0.368 0.000 0.632 0.000
#> GSM254689 2 0.0579 0.8691 0.000 0.984 0.000 0.008 0.008
#> GSM254706 2 0.2660 0.7323 0.000 0.864 0.000 0.128 0.008
#> GSM254712 4 0.4045 0.8209 0.000 0.356 0.000 0.644 0.000
#> GSM254713 4 0.4045 0.8209 0.000 0.356 0.000 0.644 0.000
#> GSM254683 2 0.1082 0.8479 0.000 0.964 0.000 0.028 0.008
#> GSM254710 2 0.6586 0.1650 0.000 0.464 0.000 0.292 0.244
#> GSM254725 4 0.3876 0.7846 0.000 0.316 0.000 0.684 0.000
#> GSM254651 2 0.1697 0.8155 0.000 0.932 0.000 0.060 0.008
#> GSM254638 4 0.4045 0.8209 0.000 0.356 0.000 0.644 0.000
#> GSM254685 4 0.4074 0.8178 0.000 0.364 0.000 0.636 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM254629 3 0.0458 0.7888 0.000 0.000 0.984 0.000 0.016 0.000
#> GSM254648 3 0.0000 0.7910 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM254694 3 0.0000 0.7910 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM254701 3 0.0363 0.7895 0.000 0.000 0.988 0.000 0.012 0.000
#> GSM254728 6 0.5871 0.2360 0.016 0.000 0.336 0.000 0.140 0.508
#> GSM254726 3 0.2257 0.7589 0.000 0.000 0.904 0.008 0.040 0.048
#> GSM254639 6 0.4551 0.5529 0.024 0.000 0.152 0.000 0.088 0.736
#> GSM254652 6 0.6143 0.2651 0.004 0.000 0.268 0.000 0.304 0.424
#> GSM254700 1 0.0146 0.7155 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM254625 5 0.1624 0.5957 0.020 0.000 0.000 0.004 0.936 0.040
#> GSM254636 6 0.6196 0.2870 0.276 0.000 0.000 0.004 0.328 0.392
#> GSM254659 3 0.6545 -0.0825 0.020 0.000 0.428 0.004 0.264 0.284
#> GSM254680 5 0.4816 0.4090 0.264 0.000 0.000 0.004 0.648 0.084
#> GSM254686 5 0.3442 0.6060 0.076 0.000 0.016 0.004 0.836 0.068
#> GSM254718 3 0.5320 0.4960 0.032 0.000 0.652 0.000 0.104 0.212
#> GSM254674 5 0.4657 0.4799 0.220 0.000 0.000 0.004 0.684 0.092
#> GSM254668 5 0.2527 0.6374 0.108 0.000 0.000 0.000 0.868 0.024
#> GSM254697 1 0.1053 0.7156 0.964 0.000 0.000 0.004 0.020 0.012
#> GSM254704 1 0.0458 0.7153 0.984 0.000 0.000 0.000 0.000 0.016
#> GSM254707 5 0.1918 0.6446 0.088 0.000 0.000 0.000 0.904 0.008
#> GSM254714 1 0.2917 0.6258 0.852 0.000 0.104 0.000 0.004 0.040
#> GSM254722 1 0.2527 0.6878 0.880 0.000 0.000 0.004 0.032 0.084
#> GSM254627 1 0.1232 0.7143 0.956 0.000 0.000 0.004 0.024 0.016
#> GSM254630 5 0.5379 0.3717 0.420 0.000 0.000 0.004 0.480 0.096
#> GSM254633 1 0.5812 0.1327 0.444 0.000 0.004 0.004 0.412 0.136
#> GSM254670 6 0.4317 0.6358 0.084 0.000 0.024 0.000 0.132 0.760
#> GSM254716 5 0.2723 0.5495 0.020 0.000 0.000 0.004 0.856 0.120
#> GSM254720 1 0.1382 0.7152 0.948 0.000 0.008 0.000 0.008 0.036
#> GSM254729 6 0.5582 0.5474 0.024 0.000 0.172 0.000 0.184 0.620
#> GSM254654 3 0.0000 0.7910 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM254656 6 0.3374 0.4417 0.004 0.000 0.016 0.140 0.020 0.820
#> GSM254631 1 0.5258 0.2899 0.540 0.000 0.000 0.004 0.364 0.092
#> GSM254657 6 0.4420 0.6058 0.040 0.000 0.076 0.000 0.124 0.760
#> GSM254664 1 0.5061 0.3225 0.568 0.000 0.000 0.004 0.352 0.076
#> GSM254672 1 0.1501 0.6956 0.924 0.000 0.000 0.000 0.000 0.076
#> GSM254692 1 0.3966 -0.2837 0.552 0.000 0.000 0.000 0.444 0.004
#> GSM254645 6 0.4535 0.3999 0.332 0.000 0.004 0.004 0.032 0.628
#> GSM254666 5 0.3830 0.5019 0.044 0.000 0.000 0.000 0.744 0.212
#> GSM254675 1 0.0551 0.7149 0.984 0.000 0.000 0.004 0.008 0.004
#> GSM254678 1 0.4086 0.5396 0.728 0.000 0.000 0.004 0.048 0.220
#> GSM254688 5 0.2653 0.6516 0.100 0.000 0.000 0.004 0.868 0.028
#> GSM254690 1 0.5659 0.1727 0.472 0.000 0.000 0.008 0.400 0.120
#> GSM254696 6 0.5553 0.5697 0.172 0.000 0.000 0.004 0.256 0.568
#> GSM254705 5 0.4513 0.4029 0.440 0.000 0.000 0.004 0.532 0.024
#> GSM254642 1 0.1296 0.7120 0.952 0.000 0.000 0.004 0.032 0.012
#> GSM254661 3 0.4972 0.3143 0.000 0.000 0.568 0.000 0.080 0.352
#> GSM254698 1 0.4860 0.0459 0.516 0.000 0.000 0.008 0.040 0.436
#> GSM254641 5 0.4249 0.4478 0.328 0.000 0.000 0.000 0.640 0.032
#> GSM254647 1 0.1942 0.6902 0.916 0.000 0.000 0.008 0.064 0.012
#> GSM254663 5 0.4208 0.3953 0.452 0.000 0.000 0.004 0.536 0.008
#> GSM254682 5 0.3078 0.6472 0.108 0.000 0.000 0.000 0.836 0.056
#> GSM254709 5 0.4191 0.5066 0.388 0.000 0.012 0.000 0.596 0.004
#> GSM254721 1 0.0260 0.7154 0.992 0.000 0.000 0.000 0.000 0.008
#> GSM254724 1 0.0260 0.7154 0.992 0.000 0.000 0.000 0.000 0.008
#> GSM254650 5 0.3795 0.5338 0.364 0.000 0.000 0.004 0.632 0.000
#> GSM254687 5 0.3830 0.5212 0.376 0.000 0.000 0.004 0.620 0.000
#> GSM254637 1 0.5162 0.3456 0.576 0.000 0.000 0.004 0.328 0.092
#> GSM254684 6 0.5804 0.5226 0.220 0.000 0.000 0.008 0.228 0.544
#> GSM254649 2 0.0547 0.8103 0.000 0.980 0.000 0.000 0.000 0.020
#> GSM254660 2 0.3838 -0.1942 0.000 0.552 0.000 0.448 0.000 0.000
#> GSM254693 2 0.1204 0.8070 0.000 0.944 0.000 0.056 0.000 0.000
#> GSM254695 4 0.3307 0.5505 0.000 0.064 0.000 0.832 0.008 0.096
#> GSM254702 4 0.3717 0.6807 0.000 0.384 0.000 0.616 0.000 0.000
#> GSM254643 2 0.1765 0.7905 0.000 0.904 0.000 0.096 0.000 0.000
#> GSM254727 2 0.0458 0.8110 0.000 0.984 0.000 0.016 0.000 0.000
#> GSM254640 4 0.3923 0.6796 0.000 0.416 0.000 0.580 0.000 0.004
#> GSM254626 2 0.1714 0.7930 0.000 0.908 0.000 0.092 0.000 0.000
#> GSM254635 4 0.3081 0.8281 0.000 0.220 0.000 0.776 0.000 0.004
#> GSM254653 2 0.1007 0.8047 0.000 0.956 0.000 0.044 0.000 0.000
#> GSM254658 2 0.0547 0.8103 0.000 0.980 0.000 0.000 0.000 0.020
#> GSM254681 2 0.1461 0.7988 0.000 0.940 0.000 0.000 0.016 0.044
#> GSM254719 2 0.1814 0.7883 0.000 0.900 0.000 0.100 0.000 0.000
#> GSM254673 2 0.1714 0.7930 0.000 0.908 0.000 0.092 0.000 0.000
#> GSM254655 2 0.3309 0.4754 0.000 0.720 0.000 0.280 0.000 0.000
#> GSM254669 2 0.1556 0.7984 0.000 0.920 0.000 0.080 0.000 0.000
#> GSM254699 2 0.3076 0.5741 0.000 0.760 0.000 0.240 0.000 0.000
#> GSM254703 4 0.3398 0.8263 0.000 0.252 0.000 0.740 0.000 0.008
#> GSM254708 2 0.2349 0.7850 0.000 0.892 0.000 0.080 0.008 0.020
#> GSM254715 4 0.3245 0.8282 0.000 0.228 0.000 0.764 0.000 0.008
#> GSM254628 2 0.0603 0.8116 0.000 0.980 0.000 0.004 0.000 0.016
#> GSM254634 4 0.2562 0.8080 0.000 0.172 0.000 0.828 0.000 0.000
#> GSM254646 2 0.1528 0.8097 0.000 0.944 0.000 0.016 0.012 0.028
#> GSM254671 4 0.3756 0.6518 0.000 0.400 0.000 0.600 0.000 0.000
#> GSM254711 4 0.3198 0.8195 0.000 0.260 0.000 0.740 0.000 0.000
#> GSM254717 2 0.0603 0.8110 0.000 0.980 0.000 0.016 0.000 0.004
#> GSM254723 4 0.7526 -0.0464 0.036 0.004 0.204 0.452 0.068 0.236
#> GSM254730 2 0.3499 0.2359 0.000 0.680 0.000 0.320 0.000 0.000
#> GSM254731 4 0.3747 0.6603 0.000 0.396 0.000 0.604 0.000 0.000
#> GSM254632 5 0.7476 0.1537 0.000 0.148 0.012 0.184 0.440 0.216
#> GSM254662 2 0.1765 0.7912 0.000 0.904 0.000 0.096 0.000 0.000
#> GSM254677 4 0.3500 0.7903 0.000 0.204 0.000 0.768 0.000 0.028
#> GSM254665 2 0.2455 0.7913 0.000 0.872 0.000 0.112 0.004 0.012
#> GSM254691 2 0.2877 0.7869 0.000 0.848 0.000 0.124 0.008 0.020
#> GSM254644 4 0.3899 0.7028 0.000 0.404 0.000 0.592 0.000 0.004
#> GSM254667 2 0.4415 0.6368 0.000 0.740 0.000 0.172 0.024 0.064
#> GSM254676 2 0.2505 0.7930 0.000 0.880 0.000 0.092 0.008 0.020
#> GSM254679 4 0.2941 0.8104 0.000 0.220 0.000 0.780 0.000 0.000
#> GSM254689 2 0.2001 0.8025 0.000 0.920 0.000 0.020 0.016 0.044
#> GSM254706 2 0.4258 0.6528 0.000 0.756 0.000 0.160 0.024 0.060
#> GSM254712 4 0.3245 0.8282 0.000 0.228 0.000 0.764 0.000 0.008
#> GSM254713 4 0.3245 0.8282 0.000 0.228 0.000 0.764 0.000 0.008
#> GSM254683 2 0.3203 0.7412 0.000 0.848 0.000 0.080 0.020 0.052
#> GSM254710 2 0.7448 0.1469 0.000 0.388 0.000 0.184 0.244 0.184
#> GSM254725 4 0.2491 0.7908 0.000 0.164 0.000 0.836 0.000 0.000
#> GSM254651 2 0.3900 0.6892 0.000 0.792 0.000 0.128 0.024 0.056
#> GSM254638 4 0.2838 0.8222 0.000 0.188 0.000 0.808 0.000 0.004
#> GSM254685 4 0.3271 0.8268 0.000 0.232 0.000 0.760 0.000 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) individual(p) disease.state(p) gender(p) other(p) k
#> SD:skmeans 107 2.35e-23 0.55450 0.665 0.61079 0.958 2
#> SD:skmeans 105 1.05e-22 0.00281 0.637 0.00518 0.930 3
#> SD:skmeans 98 4.18e-21 0.00604 0.814 0.01322 0.978 4
#> SD:skmeans 71 2.61e-15 0.00587 0.666 0.00193 0.928 5
#> SD:skmeans 80 8.39e-16 0.08953 0.294 0.10927 0.380 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 21168 rows and 107 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'pam' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.994 0.997 0.4984 0.503 0.503
#> 3 3 0.900 0.898 0.957 0.2802 0.855 0.714
#> 4 4 0.761 0.820 0.898 0.1379 0.893 0.712
#> 5 5 0.767 0.729 0.871 0.0409 0.975 0.909
#> 6 6 0.674 0.523 0.736 0.0561 0.953 0.819
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 2
Following 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
#> GSM254629 1 0.0000 0.995 1.000 0.000
#> GSM254648 1 0.4161 0.907 0.916 0.084
#> GSM254694 1 0.0000 0.995 1.000 0.000
#> GSM254701 1 0.0000 0.995 1.000 0.000
#> GSM254728 1 0.0000 0.995 1.000 0.000
#> GSM254726 1 0.0000 0.995 1.000 0.000
#> GSM254639 1 0.0000 0.995 1.000 0.000
#> GSM254652 1 0.0000 0.995 1.000 0.000
#> GSM254700 1 0.0000 0.995 1.000 0.000
#> GSM254625 1 0.0000 0.995 1.000 0.000
#> GSM254636 1 0.0000 0.995 1.000 0.000
#> GSM254659 1 0.0000 0.995 1.000 0.000
#> GSM254680 1 0.0000 0.995 1.000 0.000
#> GSM254686 1 0.0000 0.995 1.000 0.000
#> GSM254718 1 0.0000 0.995 1.000 0.000
#> GSM254674 1 0.0000 0.995 1.000 0.000
#> GSM254668 1 0.0000 0.995 1.000 0.000
#> GSM254697 1 0.0000 0.995 1.000 0.000
#> GSM254704 1 0.0000 0.995 1.000 0.000
#> GSM254707 1 0.0000 0.995 1.000 0.000
#> GSM254714 1 0.0000 0.995 1.000 0.000
#> GSM254722 1 0.0000 0.995 1.000 0.000
#> GSM254627 1 0.0000 0.995 1.000 0.000
#> GSM254630 1 0.0000 0.995 1.000 0.000
#> GSM254633 1 0.0000 0.995 1.000 0.000
#> GSM254670 1 0.0000 0.995 1.000 0.000
#> GSM254716 1 0.0000 0.995 1.000 0.000
#> GSM254720 1 0.0000 0.995 1.000 0.000
#> GSM254729 1 0.0000 0.995 1.000 0.000
#> GSM254654 1 0.0000 0.995 1.000 0.000
#> GSM254656 1 0.0000 0.995 1.000 0.000
#> GSM254631 1 0.0000 0.995 1.000 0.000
#> GSM254657 1 0.0000 0.995 1.000 0.000
#> GSM254664 1 0.0000 0.995 1.000 0.000
#> GSM254672 1 0.0000 0.995 1.000 0.000
#> GSM254692 1 0.0000 0.995 1.000 0.000
#> GSM254645 1 0.0000 0.995 1.000 0.000
#> GSM254666 1 0.0000 0.995 1.000 0.000
#> GSM254675 1 0.0000 0.995 1.000 0.000
#> GSM254678 1 0.0000 0.995 1.000 0.000
#> GSM254688 1 0.0000 0.995 1.000 0.000
#> GSM254690 1 0.0000 0.995 1.000 0.000
#> GSM254696 1 0.0000 0.995 1.000 0.000
#> GSM254705 1 0.0000 0.995 1.000 0.000
#> GSM254642 1 0.0000 0.995 1.000 0.000
#> GSM254661 1 0.0000 0.995 1.000 0.000
#> GSM254698 1 0.0000 0.995 1.000 0.000
#> GSM254641 1 0.0000 0.995 1.000 0.000
#> GSM254647 1 0.0000 0.995 1.000 0.000
#> GSM254663 1 0.0000 0.995 1.000 0.000
#> GSM254682 1 0.0000 0.995 1.000 0.000
#> GSM254709 1 0.0000 0.995 1.000 0.000
#> GSM254721 1 0.0000 0.995 1.000 0.000
#> GSM254724 1 0.0000 0.995 1.000 0.000
#> GSM254650 1 0.0000 0.995 1.000 0.000
#> GSM254687 1 0.0000 0.995 1.000 0.000
#> GSM254637 1 0.0000 0.995 1.000 0.000
#> GSM254684 1 0.0000 0.995 1.000 0.000
#> GSM254649 2 0.0000 1.000 0.000 1.000
#> GSM254660 2 0.0000 1.000 0.000 1.000
#> GSM254693 2 0.0000 1.000 0.000 1.000
#> GSM254695 2 0.0000 1.000 0.000 1.000
#> GSM254702 2 0.0000 1.000 0.000 1.000
#> GSM254643 2 0.0000 1.000 0.000 1.000
#> GSM254727 2 0.0000 1.000 0.000 1.000
#> GSM254640 2 0.0000 1.000 0.000 1.000
#> GSM254626 2 0.0000 1.000 0.000 1.000
#> GSM254635 2 0.0000 1.000 0.000 1.000
#> GSM254653 2 0.0000 1.000 0.000 1.000
#> GSM254658 2 0.0000 1.000 0.000 1.000
#> GSM254681 2 0.0000 1.000 0.000 1.000
#> GSM254719 2 0.0000 1.000 0.000 1.000
#> GSM254673 2 0.0000 1.000 0.000 1.000
#> GSM254655 2 0.0000 1.000 0.000 1.000
#> GSM254669 2 0.0000 1.000 0.000 1.000
#> GSM254699 2 0.0000 1.000 0.000 1.000
#> GSM254703 2 0.0000 1.000 0.000 1.000
#> GSM254708 2 0.0000 1.000 0.000 1.000
#> GSM254715 2 0.0000 1.000 0.000 1.000
#> GSM254628 2 0.0000 1.000 0.000 1.000
#> GSM254634 2 0.0000 1.000 0.000 1.000
#> GSM254646 2 0.0000 1.000 0.000 1.000
#> GSM254671 2 0.0000 1.000 0.000 1.000
#> GSM254711 2 0.0000 1.000 0.000 1.000
#> GSM254717 2 0.0000 1.000 0.000 1.000
#> GSM254723 1 0.0672 0.987 0.992 0.008
#> GSM254730 2 0.0000 1.000 0.000 1.000
#> GSM254731 2 0.0000 1.000 0.000 1.000
#> GSM254632 1 0.7602 0.721 0.780 0.220
#> GSM254662 2 0.0000 1.000 0.000 1.000
#> GSM254677 2 0.0000 1.000 0.000 1.000
#> GSM254665 2 0.0000 1.000 0.000 1.000
#> GSM254691 2 0.0000 1.000 0.000 1.000
#> GSM254644 2 0.0000 1.000 0.000 1.000
#> GSM254667 2 0.0000 1.000 0.000 1.000
#> GSM254676 2 0.0000 1.000 0.000 1.000
#> GSM254679 2 0.0000 1.000 0.000 1.000
#> GSM254689 2 0.0000 1.000 0.000 1.000
#> GSM254706 2 0.0000 1.000 0.000 1.000
#> GSM254712 2 0.0000 1.000 0.000 1.000
#> GSM254713 2 0.0000 1.000 0.000 1.000
#> GSM254683 2 0.0000 1.000 0.000 1.000
#> GSM254710 2 0.0000 1.000 0.000 1.000
#> GSM254725 2 0.0000 1.000 0.000 1.000
#> GSM254651 2 0.0000 1.000 0.000 1.000
#> GSM254638 2 0.0000 1.000 0.000 1.000
#> GSM254685 2 0.0000 1.000 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM254629 3 0.0000 0.915 0.000 0.000 1.000
#> GSM254648 3 0.3038 0.807 0.000 0.104 0.896
#> GSM254694 3 0.0000 0.915 0.000 0.000 1.000
#> GSM254701 3 0.0000 0.915 0.000 0.000 1.000
#> GSM254728 3 0.0000 0.915 0.000 0.000 1.000
#> GSM254726 3 0.0000 0.915 0.000 0.000 1.000
#> GSM254639 3 0.0000 0.915 0.000 0.000 1.000
#> GSM254652 3 0.0000 0.915 0.000 0.000 1.000
#> GSM254700 1 0.0000 0.901 1.000 0.000 0.000
#> GSM254625 1 0.5859 0.478 0.656 0.000 0.344
#> GSM254636 3 0.0000 0.915 0.000 0.000 1.000
#> GSM254659 3 0.0000 0.915 0.000 0.000 1.000
#> GSM254680 3 0.0000 0.915 0.000 0.000 1.000
#> GSM254686 3 0.0000 0.915 0.000 0.000 1.000
#> GSM254718 3 0.0000 0.915 0.000 0.000 1.000
#> GSM254674 3 0.0000 0.915 0.000 0.000 1.000
#> GSM254668 1 0.6062 0.390 0.616 0.000 0.384
#> GSM254697 3 0.5016 0.699 0.240 0.000 0.760
#> GSM254704 1 0.6192 0.158 0.580 0.000 0.420
#> GSM254707 1 0.0000 0.901 1.000 0.000 0.000
#> GSM254714 3 0.6235 0.301 0.436 0.000 0.564
#> GSM254722 3 0.5988 0.473 0.368 0.000 0.632
#> GSM254627 3 0.4555 0.751 0.200 0.000 0.800
#> GSM254630 1 0.0000 0.901 1.000 0.000 0.000
#> GSM254633 3 0.0000 0.915 0.000 0.000 1.000
#> GSM254670 3 0.0000 0.915 0.000 0.000 1.000
#> GSM254716 3 0.0747 0.906 0.016 0.000 0.984
#> GSM254720 3 0.0000 0.915 0.000 0.000 1.000
#> GSM254729 3 0.0000 0.915 0.000 0.000 1.000
#> GSM254654 3 0.0000 0.915 0.000 0.000 1.000
#> GSM254656 3 0.0000 0.915 0.000 0.000 1.000
#> GSM254631 3 0.0000 0.915 0.000 0.000 1.000
#> GSM254657 3 0.1031 0.904 0.024 0.000 0.976
#> GSM254664 3 0.0000 0.915 0.000 0.000 1.000
#> GSM254672 3 0.4504 0.755 0.196 0.000 0.804
#> GSM254692 1 0.0000 0.901 1.000 0.000 0.000
#> GSM254645 3 0.2356 0.872 0.072 0.000 0.928
#> GSM254666 3 0.6079 0.429 0.388 0.000 0.612
#> GSM254675 3 0.3412 0.828 0.124 0.000 0.876
#> GSM254678 3 0.6126 0.398 0.400 0.000 0.600
#> GSM254688 1 0.0000 0.901 1.000 0.000 0.000
#> GSM254690 3 0.1860 0.886 0.052 0.000 0.948
#> GSM254696 3 0.0000 0.915 0.000 0.000 1.000
#> GSM254705 1 0.0000 0.901 1.000 0.000 0.000
#> GSM254642 1 0.0000 0.901 1.000 0.000 0.000
#> GSM254661 3 0.0000 0.915 0.000 0.000 1.000
#> GSM254698 3 0.1643 0.891 0.044 0.000 0.956
#> GSM254641 3 0.0000 0.915 0.000 0.000 1.000
#> GSM254647 1 0.2165 0.848 0.936 0.000 0.064
#> GSM254663 1 0.0000 0.901 1.000 0.000 0.000
#> GSM254682 1 0.0000 0.901 1.000 0.000 0.000
#> GSM254709 1 0.0000 0.901 1.000 0.000 0.000
#> GSM254721 1 0.0000 0.901 1.000 0.000 0.000
#> GSM254724 1 0.0000 0.901 1.000 0.000 0.000
#> GSM254650 1 0.0000 0.901 1.000 0.000 0.000
#> GSM254687 1 0.0000 0.901 1.000 0.000 0.000
#> GSM254637 3 0.0000 0.915 0.000 0.000 1.000
#> GSM254684 3 0.4555 0.751 0.200 0.000 0.800
#> GSM254649 2 0.0000 1.000 0.000 1.000 0.000
#> GSM254660 2 0.0000 1.000 0.000 1.000 0.000
#> GSM254693 2 0.0000 1.000 0.000 1.000 0.000
#> GSM254695 2 0.0000 1.000 0.000 1.000 0.000
#> GSM254702 2 0.0000 1.000 0.000 1.000 0.000
#> GSM254643 2 0.0000 1.000 0.000 1.000 0.000
#> GSM254727 2 0.0000 1.000 0.000 1.000 0.000
#> GSM254640 2 0.0000 1.000 0.000 1.000 0.000
#> GSM254626 2 0.0000 1.000 0.000 1.000 0.000
#> GSM254635 2 0.0000 1.000 0.000 1.000 0.000
#> GSM254653 2 0.0000 1.000 0.000 1.000 0.000
#> GSM254658 2 0.0000 1.000 0.000 1.000 0.000
#> GSM254681 2 0.0000 1.000 0.000 1.000 0.000
#> GSM254719 2 0.0000 1.000 0.000 1.000 0.000
#> GSM254673 2 0.0000 1.000 0.000 1.000 0.000
#> GSM254655 2 0.0000 1.000 0.000 1.000 0.000
#> GSM254669 2 0.0000 1.000 0.000 1.000 0.000
#> GSM254699 2 0.0000 1.000 0.000 1.000 0.000
#> GSM254703 2 0.0000 1.000 0.000 1.000 0.000
#> GSM254708 2 0.0000 1.000 0.000 1.000 0.000
#> GSM254715 2 0.0000 1.000 0.000 1.000 0.000
#> GSM254628 2 0.0000 1.000 0.000 1.000 0.000
#> GSM254634 2 0.0000 1.000 0.000 1.000 0.000
#> GSM254646 2 0.0000 1.000 0.000 1.000 0.000
#> GSM254671 2 0.0000 1.000 0.000 1.000 0.000
#> GSM254711 2 0.0000 1.000 0.000 1.000 0.000
#> GSM254717 2 0.0000 1.000 0.000 1.000 0.000
#> GSM254723 3 0.0424 0.909 0.000 0.008 0.992
#> GSM254730 2 0.0000 1.000 0.000 1.000 0.000
#> GSM254731 2 0.0000 1.000 0.000 1.000 0.000
#> GSM254632 3 0.4452 0.680 0.000 0.192 0.808
#> GSM254662 2 0.0000 1.000 0.000 1.000 0.000
#> GSM254677 2 0.0000 1.000 0.000 1.000 0.000
#> GSM254665 2 0.0000 1.000 0.000 1.000 0.000
#> GSM254691 2 0.0000 1.000 0.000 1.000 0.000
#> GSM254644 2 0.0000 1.000 0.000 1.000 0.000
#> GSM254667 2 0.0000 1.000 0.000 1.000 0.000
#> GSM254676 2 0.0000 1.000 0.000 1.000 0.000
#> GSM254679 2 0.0000 1.000 0.000 1.000 0.000
#> GSM254689 2 0.0000 1.000 0.000 1.000 0.000
#> GSM254706 2 0.0000 1.000 0.000 1.000 0.000
#> GSM254712 2 0.0000 1.000 0.000 1.000 0.000
#> GSM254713 2 0.0000 1.000 0.000 1.000 0.000
#> GSM254683 2 0.0000 1.000 0.000 1.000 0.000
#> GSM254710 1 0.5859 0.456 0.656 0.344 0.000
#> GSM254725 2 0.0000 1.000 0.000 1.000 0.000
#> GSM254651 2 0.0000 1.000 0.000 1.000 0.000
#> GSM254638 2 0.0000 1.000 0.000 1.000 0.000
#> GSM254685 2 0.0000 1.000 0.000 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM254629 3 0.0000 0.908 0.000 0.000 1.000 0.000
#> GSM254648 3 0.2843 0.818 0.000 0.088 0.892 0.020
#> GSM254694 3 0.0000 0.908 0.000 0.000 1.000 0.000
#> GSM254701 3 0.0000 0.908 0.000 0.000 1.000 0.000
#> GSM254728 3 0.0000 0.908 0.000 0.000 1.000 0.000
#> GSM254726 3 0.0000 0.908 0.000 0.000 1.000 0.000
#> GSM254639 3 0.0000 0.908 0.000 0.000 1.000 0.000
#> GSM254652 3 0.0000 0.908 0.000 0.000 1.000 0.000
#> GSM254700 1 0.0000 0.905 1.000 0.000 0.000 0.000
#> GSM254625 1 0.4643 0.496 0.656 0.000 0.344 0.000
#> GSM254636 3 0.0000 0.908 0.000 0.000 1.000 0.000
#> GSM254659 3 0.0000 0.908 0.000 0.000 1.000 0.000
#> GSM254680 3 0.0000 0.908 0.000 0.000 1.000 0.000
#> GSM254686 3 0.0000 0.908 0.000 0.000 1.000 0.000
#> GSM254718 3 0.0000 0.908 0.000 0.000 1.000 0.000
#> GSM254674 3 0.0000 0.908 0.000 0.000 1.000 0.000
#> GSM254668 1 0.4804 0.409 0.616 0.000 0.384 0.000
#> GSM254697 3 0.5096 0.729 0.156 0.000 0.760 0.084
#> GSM254704 1 0.4907 0.157 0.580 0.000 0.420 0.000
#> GSM254707 1 0.0000 0.905 1.000 0.000 0.000 0.000
#> GSM254714 3 0.4941 0.304 0.436 0.000 0.564 0.000
#> GSM254722 3 0.6172 0.522 0.284 0.000 0.632 0.084
#> GSM254627 3 0.4591 0.774 0.116 0.000 0.800 0.084
#> GSM254630 1 0.0000 0.905 1.000 0.000 0.000 0.000
#> GSM254633 3 0.0000 0.908 0.000 0.000 1.000 0.000
#> GSM254670 3 0.0000 0.908 0.000 0.000 1.000 0.000
#> GSM254716 3 0.0592 0.900 0.016 0.000 0.984 0.000
#> GSM254720 3 0.0000 0.908 0.000 0.000 1.000 0.000
#> GSM254729 3 0.0000 0.908 0.000 0.000 1.000 0.000
#> GSM254654 3 0.0000 0.908 0.000 0.000 1.000 0.000
#> GSM254656 4 0.4713 0.434 0.000 0.000 0.360 0.640
#> GSM254631 3 0.0000 0.908 0.000 0.000 1.000 0.000
#> GSM254657 3 0.0817 0.898 0.024 0.000 0.976 0.000
#> GSM254664 3 0.0000 0.908 0.000 0.000 1.000 0.000
#> GSM254672 3 0.3569 0.754 0.196 0.000 0.804 0.000
#> GSM254692 1 0.0000 0.905 1.000 0.000 0.000 0.000
#> GSM254645 3 0.1867 0.868 0.072 0.000 0.928 0.000
#> GSM254666 3 0.4817 0.423 0.388 0.000 0.612 0.000
#> GSM254675 3 0.2704 0.827 0.124 0.000 0.876 0.000
#> GSM254678 3 0.4855 0.399 0.400 0.000 0.600 0.000
#> GSM254688 1 0.0000 0.905 1.000 0.000 0.000 0.000
#> GSM254690 3 0.1474 0.880 0.052 0.000 0.948 0.000
#> GSM254696 3 0.0000 0.908 0.000 0.000 1.000 0.000
#> GSM254705 1 0.0000 0.905 1.000 0.000 0.000 0.000
#> GSM254642 1 0.2081 0.850 0.916 0.000 0.000 0.084
#> GSM254661 3 0.0000 0.908 0.000 0.000 1.000 0.000
#> GSM254698 3 0.2984 0.849 0.028 0.000 0.888 0.084
#> GSM254641 3 0.0000 0.908 0.000 0.000 1.000 0.000
#> GSM254647 1 0.1716 0.852 0.936 0.000 0.064 0.000
#> GSM254663 1 0.0000 0.905 1.000 0.000 0.000 0.000
#> GSM254682 1 0.0000 0.905 1.000 0.000 0.000 0.000
#> GSM254709 1 0.0000 0.905 1.000 0.000 0.000 0.000
#> GSM254721 1 0.0000 0.905 1.000 0.000 0.000 0.000
#> GSM254724 1 0.0000 0.905 1.000 0.000 0.000 0.000
#> GSM254650 1 0.0000 0.905 1.000 0.000 0.000 0.000
#> GSM254687 1 0.0000 0.905 1.000 0.000 0.000 0.000
#> GSM254637 3 0.0000 0.908 0.000 0.000 1.000 0.000
#> GSM254684 3 0.3610 0.750 0.200 0.000 0.800 0.000
#> GSM254649 2 0.0000 0.890 0.000 1.000 0.000 0.000
#> GSM254660 2 0.1022 0.886 0.000 0.968 0.000 0.032
#> GSM254693 2 0.0000 0.890 0.000 1.000 0.000 0.000
#> GSM254695 4 0.2081 0.844 0.000 0.084 0.000 0.916
#> GSM254702 2 0.4008 0.525 0.000 0.756 0.000 0.244
#> GSM254643 2 0.0817 0.886 0.000 0.976 0.000 0.024
#> GSM254727 2 0.0000 0.890 0.000 1.000 0.000 0.000
#> GSM254640 2 0.1940 0.864 0.000 0.924 0.000 0.076
#> GSM254626 2 0.0188 0.890 0.000 0.996 0.000 0.004
#> GSM254635 4 0.3610 0.833 0.000 0.200 0.000 0.800
#> GSM254653 2 0.0000 0.890 0.000 1.000 0.000 0.000
#> GSM254658 2 0.0000 0.890 0.000 1.000 0.000 0.000
#> GSM254681 2 0.2589 0.841 0.000 0.884 0.000 0.116
#> GSM254719 2 0.0817 0.886 0.000 0.976 0.000 0.024
#> GSM254673 2 0.0817 0.886 0.000 0.976 0.000 0.024
#> GSM254655 2 0.0817 0.886 0.000 0.976 0.000 0.024
#> GSM254669 2 0.0000 0.890 0.000 1.000 0.000 0.000
#> GSM254699 2 0.0817 0.886 0.000 0.976 0.000 0.024
#> GSM254703 4 0.2345 0.848 0.000 0.100 0.000 0.900
#> GSM254708 2 0.3528 0.797 0.000 0.808 0.000 0.192
#> GSM254715 4 0.4776 0.693 0.000 0.376 0.000 0.624
#> GSM254628 2 0.0000 0.890 0.000 1.000 0.000 0.000
#> GSM254634 4 0.2216 0.847 0.000 0.092 0.000 0.908
#> GSM254646 2 0.0000 0.890 0.000 1.000 0.000 0.000
#> GSM254671 4 0.4250 0.805 0.000 0.276 0.000 0.724
#> GSM254711 4 0.3311 0.846 0.000 0.172 0.000 0.828
#> GSM254717 2 0.0817 0.886 0.000 0.976 0.000 0.024
#> GSM254723 3 0.0336 0.903 0.000 0.008 0.992 0.000
#> GSM254730 2 0.2281 0.861 0.000 0.904 0.000 0.096
#> GSM254731 4 0.4804 0.680 0.000 0.384 0.000 0.616
#> GSM254632 3 0.7341 0.305 0.000 0.220 0.528 0.252
#> GSM254662 2 0.0817 0.886 0.000 0.976 0.000 0.024
#> GSM254677 4 0.3486 0.843 0.000 0.188 0.000 0.812
#> GSM254665 2 0.3610 0.793 0.000 0.800 0.000 0.200
#> GSM254691 2 0.4454 0.676 0.000 0.692 0.000 0.308
#> GSM254644 4 0.4331 0.797 0.000 0.288 0.000 0.712
#> GSM254667 4 0.2281 0.837 0.000 0.096 0.000 0.904
#> GSM254676 4 0.2081 0.844 0.000 0.084 0.000 0.916
#> GSM254679 4 0.2081 0.844 0.000 0.084 0.000 0.916
#> GSM254689 2 0.3528 0.797 0.000 0.808 0.000 0.192
#> GSM254706 2 0.3873 0.764 0.000 0.772 0.000 0.228
#> GSM254712 4 0.4356 0.794 0.000 0.292 0.000 0.708
#> GSM254713 4 0.4250 0.804 0.000 0.276 0.000 0.724
#> GSM254683 2 0.3610 0.790 0.000 0.800 0.000 0.200
#> GSM254710 2 0.5180 0.738 0.064 0.740 0.000 0.196
#> GSM254725 4 0.2081 0.844 0.000 0.084 0.000 0.916
#> GSM254651 2 0.3486 0.800 0.000 0.812 0.000 0.188
#> GSM254638 4 0.2081 0.844 0.000 0.084 0.000 0.916
#> GSM254685 4 0.3801 0.829 0.000 0.220 0.000 0.780
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM254629 3 0.0000 0.8780 0.000 0.000 1.000 0.000 0.000
#> GSM254648 3 0.2952 0.7583 0.004 0.088 0.872 0.036 0.000
#> GSM254694 3 0.0000 0.8780 0.000 0.000 1.000 0.000 0.000
#> GSM254701 3 0.0000 0.8780 0.000 0.000 1.000 0.000 0.000
#> GSM254728 3 0.0000 0.8780 0.000 0.000 1.000 0.000 0.000
#> GSM254726 3 0.0000 0.8780 0.000 0.000 1.000 0.000 0.000
#> GSM254639 3 0.0000 0.8780 0.000 0.000 1.000 0.000 0.000
#> GSM254652 3 0.0000 0.8780 0.000 0.000 1.000 0.000 0.000
#> GSM254700 1 0.4268 0.5106 0.556 0.000 0.000 0.000 0.444
#> GSM254625 5 0.3999 0.3048 0.000 0.000 0.344 0.000 0.656
#> GSM254636 3 0.0162 0.8771 0.004 0.000 0.996 0.000 0.000
#> GSM254659 3 0.0000 0.8780 0.000 0.000 1.000 0.000 0.000
#> GSM254680 3 0.0290 0.8762 0.008 0.000 0.992 0.000 0.000
#> GSM254686 3 0.0000 0.8780 0.000 0.000 1.000 0.000 0.000
#> GSM254718 3 0.0000 0.8780 0.000 0.000 1.000 0.000 0.000
#> GSM254674 3 0.0162 0.8771 0.004 0.000 0.996 0.000 0.000
#> GSM254668 5 0.4392 0.2507 0.008 0.000 0.380 0.000 0.612
#> GSM254697 1 0.0609 0.3702 0.980 0.000 0.020 0.000 0.000
#> GSM254704 1 0.4696 0.5115 0.556 0.000 0.016 0.000 0.428
#> GSM254707 5 0.0162 0.8040 0.004 0.000 0.000 0.000 0.996
#> GSM254714 3 0.4403 0.2186 0.004 0.000 0.560 0.000 0.436
#> GSM254722 3 0.4504 0.3020 0.428 0.000 0.564 0.000 0.008
#> GSM254627 1 0.4304 -0.2571 0.516 0.000 0.484 0.000 0.000
#> GSM254630 5 0.0000 0.8071 0.000 0.000 0.000 0.000 1.000
#> GSM254633 3 0.0000 0.8780 0.000 0.000 1.000 0.000 0.000
#> GSM254670 3 0.0000 0.8780 0.000 0.000 1.000 0.000 0.000
#> GSM254716 3 0.0510 0.8705 0.000 0.000 0.984 0.000 0.016
#> GSM254720 3 0.3730 0.5285 0.288 0.000 0.712 0.000 0.000
#> GSM254729 3 0.0000 0.8780 0.000 0.000 1.000 0.000 0.000
#> GSM254654 3 0.0000 0.8780 0.000 0.000 1.000 0.000 0.000
#> GSM254656 4 0.4211 0.3762 0.004 0.000 0.360 0.636 0.000
#> GSM254631 3 0.0290 0.8762 0.008 0.000 0.992 0.000 0.000
#> GSM254657 3 0.0703 0.8661 0.000 0.000 0.976 0.000 0.024
#> GSM254664 3 0.0290 0.8762 0.008 0.000 0.992 0.000 0.000
#> GSM254672 3 0.3231 0.6889 0.004 0.000 0.800 0.000 0.196
#> GSM254692 5 0.0000 0.8071 0.000 0.000 0.000 0.000 1.000
#> GSM254645 3 0.1608 0.8295 0.000 0.000 0.928 0.000 0.072
#> GSM254666 3 0.4150 0.3793 0.000 0.000 0.612 0.000 0.388
#> GSM254675 3 0.2329 0.7778 0.000 0.000 0.876 0.000 0.124
#> GSM254678 3 0.4331 0.3011 0.004 0.000 0.596 0.000 0.400
#> GSM254688 5 0.0000 0.8071 0.000 0.000 0.000 0.000 1.000
#> GSM254690 3 0.1557 0.8444 0.008 0.000 0.940 0.000 0.052
#> GSM254696 3 0.0000 0.8780 0.000 0.000 1.000 0.000 0.000
#> GSM254705 5 0.0000 0.8071 0.000 0.000 0.000 0.000 1.000
#> GSM254642 5 0.4242 0.2312 0.428 0.000 0.000 0.000 0.572
#> GSM254661 3 0.0000 0.8780 0.000 0.000 1.000 0.000 0.000
#> GSM254698 3 0.4242 0.3228 0.428 0.000 0.572 0.000 0.000
#> GSM254641 3 0.0162 0.8771 0.004 0.000 0.996 0.000 0.000
#> GSM254647 5 0.1764 0.6948 0.008 0.000 0.064 0.000 0.928
#> GSM254663 5 0.0162 0.8040 0.004 0.000 0.000 0.000 0.996
#> GSM254682 5 0.0000 0.8071 0.000 0.000 0.000 0.000 1.000
#> GSM254709 5 0.0000 0.8071 0.000 0.000 0.000 0.000 1.000
#> GSM254721 1 0.4278 0.5039 0.548 0.000 0.000 0.000 0.452
#> GSM254724 1 0.4273 0.5090 0.552 0.000 0.000 0.000 0.448
#> GSM254650 5 0.0000 0.8071 0.000 0.000 0.000 0.000 1.000
#> GSM254687 5 0.0000 0.8071 0.000 0.000 0.000 0.000 1.000
#> GSM254637 3 0.0290 0.8762 0.008 0.000 0.992 0.000 0.000
#> GSM254684 3 0.3266 0.6838 0.004 0.000 0.796 0.000 0.200
#> GSM254649 2 0.0000 0.8558 0.000 1.000 0.000 0.000 0.000
#> GSM254660 2 0.1597 0.8448 0.012 0.940 0.000 0.048 0.000
#> GSM254693 2 0.0000 0.8558 0.000 1.000 0.000 0.000 0.000
#> GSM254695 4 0.0000 0.7823 0.000 0.000 0.000 1.000 0.000
#> GSM254702 2 0.3835 0.5113 0.012 0.744 0.000 0.244 0.000
#> GSM254643 2 0.0865 0.8504 0.004 0.972 0.000 0.024 0.000
#> GSM254727 2 0.0162 0.8554 0.004 0.996 0.000 0.000 0.000
#> GSM254640 2 0.2771 0.8068 0.012 0.860 0.000 0.128 0.000
#> GSM254626 2 0.0162 0.8556 0.000 0.996 0.000 0.004 0.000
#> GSM254635 4 0.3462 0.7617 0.012 0.196 0.000 0.792 0.000
#> GSM254653 2 0.0000 0.8558 0.000 1.000 0.000 0.000 0.000
#> GSM254658 2 0.0000 0.8558 0.000 1.000 0.000 0.000 0.000
#> GSM254681 2 0.2424 0.8037 0.000 0.868 0.000 0.132 0.000
#> GSM254719 2 0.1195 0.8456 0.012 0.960 0.000 0.028 0.000
#> GSM254673 2 0.0703 0.8517 0.000 0.976 0.000 0.024 0.000
#> GSM254655 2 0.1195 0.8456 0.012 0.960 0.000 0.028 0.000
#> GSM254669 2 0.0000 0.8558 0.000 1.000 0.000 0.000 0.000
#> GSM254699 2 0.1195 0.8456 0.012 0.960 0.000 0.028 0.000
#> GSM254703 4 0.0671 0.7881 0.004 0.016 0.000 0.980 0.000
#> GSM254708 2 0.3814 0.7176 0.004 0.720 0.000 0.276 0.000
#> GSM254715 4 0.4482 0.6095 0.012 0.376 0.000 0.612 0.000
#> GSM254628 2 0.0000 0.8558 0.000 1.000 0.000 0.000 0.000
#> GSM254634 4 0.0671 0.7883 0.004 0.016 0.000 0.980 0.000
#> GSM254646 2 0.0000 0.8558 0.000 1.000 0.000 0.000 0.000
#> GSM254671 4 0.4040 0.7293 0.012 0.276 0.000 0.712 0.000
#> GSM254711 4 0.2522 0.7944 0.012 0.108 0.000 0.880 0.000
#> GSM254717 2 0.0703 0.8517 0.000 0.976 0.000 0.024 0.000
#> GSM254723 3 0.0740 0.8666 0.008 0.008 0.980 0.004 0.000
#> GSM254730 2 0.3039 0.8004 0.012 0.836 0.000 0.152 0.000
#> GSM254731 4 0.4505 0.5959 0.012 0.384 0.000 0.604 0.000
#> GSM254632 3 0.6741 0.0565 0.004 0.220 0.432 0.344 0.000
#> GSM254662 2 0.0703 0.8517 0.000 0.976 0.000 0.024 0.000
#> GSM254677 4 0.2522 0.7936 0.012 0.108 0.000 0.880 0.000
#> GSM254665 2 0.3906 0.7058 0.004 0.704 0.000 0.292 0.000
#> GSM254691 2 0.4341 0.5692 0.004 0.592 0.000 0.404 0.000
#> GSM254644 4 0.4086 0.7237 0.012 0.284 0.000 0.704 0.000
#> GSM254667 4 0.0566 0.7735 0.004 0.012 0.000 0.984 0.000
#> GSM254676 4 0.0162 0.7805 0.004 0.000 0.000 0.996 0.000
#> GSM254679 4 0.0000 0.7823 0.000 0.000 0.000 1.000 0.000
#> GSM254689 2 0.3561 0.7291 0.000 0.740 0.000 0.260 0.000
#> GSM254706 2 0.3857 0.6811 0.000 0.688 0.000 0.312 0.000
#> GSM254712 4 0.4130 0.7165 0.012 0.292 0.000 0.696 0.000
#> GSM254713 4 0.4040 0.7289 0.012 0.276 0.000 0.712 0.000
#> GSM254683 2 0.3928 0.6985 0.004 0.700 0.000 0.296 0.000
#> GSM254710 2 0.3684 0.7130 0.000 0.720 0.000 0.280 0.000
#> GSM254725 4 0.0290 0.7839 0.008 0.000 0.000 0.992 0.000
#> GSM254651 2 0.3636 0.7203 0.000 0.728 0.000 0.272 0.000
#> GSM254638 4 0.0162 0.7805 0.004 0.000 0.000 0.996 0.000
#> GSM254685 4 0.2848 0.7854 0.004 0.156 0.000 0.840 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM254629 3 0.2454 0.7203 0.000 0.000 0.840 0.000 0.000 0.160
#> GSM254648 3 0.3565 0.5988 0.000 0.004 0.692 0.000 0.000 0.304
#> GSM254694 3 0.0000 0.7552 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM254701 3 0.0146 0.7553 0.000 0.000 0.996 0.000 0.000 0.004
#> GSM254728 3 0.0000 0.7552 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM254726 3 0.2527 0.7165 0.000 0.000 0.832 0.000 0.000 0.168
#> GSM254639 3 0.1556 0.7471 0.000 0.000 0.920 0.000 0.000 0.080
#> GSM254652 3 0.2340 0.7257 0.000 0.000 0.852 0.000 0.000 0.148
#> GSM254700 1 0.6027 0.4745 0.400 0.000 0.000 0.000 0.352 0.248
#> GSM254625 5 0.3592 0.3051 0.000 0.000 0.344 0.000 0.656 0.000
#> GSM254636 3 0.3464 0.6225 0.312 0.000 0.688 0.000 0.000 0.000
#> GSM254659 3 0.0000 0.7552 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM254680 3 0.3690 0.6190 0.308 0.000 0.684 0.000 0.000 0.008
#> GSM254686 3 0.0000 0.7552 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM254718 3 0.0000 0.7552 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM254674 3 0.2178 0.7282 0.132 0.000 0.868 0.000 0.000 0.000
#> GSM254668 5 0.6912 0.1612 0.308 0.000 0.200 0.000 0.420 0.072
#> GSM254697 1 0.4977 0.3739 0.636 0.000 0.000 0.236 0.000 0.128
#> GSM254704 1 0.6152 0.4751 0.396 0.000 0.004 0.000 0.352 0.248
#> GSM254707 5 0.3446 0.4366 0.308 0.000 0.000 0.000 0.692 0.000
#> GSM254714 5 0.5971 -0.0333 0.000 0.000 0.344 0.000 0.424 0.232
#> GSM254722 3 0.5969 -0.0116 0.332 0.000 0.432 0.236 0.000 0.000
#> GSM254627 1 0.5875 0.0993 0.476 0.000 0.288 0.236 0.000 0.000
#> GSM254630 5 0.0000 0.6538 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM254633 3 0.2527 0.7162 0.168 0.000 0.832 0.000 0.000 0.000
#> GSM254670 3 0.2632 0.7182 0.004 0.000 0.832 0.000 0.000 0.164
#> GSM254716 3 0.0458 0.7554 0.000 0.000 0.984 0.000 0.016 0.000
#> GSM254720 3 0.4475 0.4928 0.200 0.000 0.700 0.000 0.000 0.100
#> GSM254729 3 0.0000 0.7552 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM254654 3 0.2527 0.7165 0.000 0.000 0.832 0.000 0.000 0.168
#> GSM254656 4 0.5819 -0.0799 0.000 0.000 0.396 0.420 0.000 0.184
#> GSM254631 3 0.4736 0.5651 0.308 0.000 0.620 0.000 0.000 0.072
#> GSM254657 3 0.3274 0.7137 0.004 0.000 0.804 0.000 0.024 0.168
#> GSM254664 3 0.4736 0.5651 0.308 0.000 0.620 0.000 0.000 0.072
#> GSM254672 3 0.4253 0.6007 0.004 0.000 0.728 0.000 0.196 0.072
#> GSM254692 5 0.0000 0.6538 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM254645 3 0.3860 0.6985 0.000 0.000 0.764 0.000 0.072 0.164
#> GSM254666 3 0.5434 0.5008 0.000 0.000 0.564 0.000 0.272 0.164
#> GSM254675 3 0.2092 0.7151 0.000 0.000 0.876 0.000 0.124 0.000
#> GSM254678 3 0.5025 0.1784 0.004 0.000 0.532 0.000 0.400 0.064
#> GSM254688 5 0.0000 0.6538 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM254690 3 0.5387 0.5269 0.312 0.000 0.588 0.000 0.028 0.072
#> GSM254696 3 0.2664 0.7097 0.184 0.000 0.816 0.000 0.000 0.000
#> GSM254705 5 0.0000 0.6538 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM254642 5 0.5886 0.0617 0.292 0.000 0.000 0.236 0.472 0.000
#> GSM254661 3 0.2527 0.7165 0.000 0.000 0.832 0.000 0.000 0.168
#> GSM254698 3 0.5988 0.0222 0.348 0.000 0.416 0.236 0.000 0.000
#> GSM254641 3 0.4594 0.6823 0.092 0.000 0.676 0.000 0.000 0.232
#> GSM254647 5 0.4679 0.4308 0.136 0.000 0.056 0.000 0.740 0.068
#> GSM254663 5 0.2491 0.5553 0.164 0.000 0.000 0.000 0.836 0.000
#> GSM254682 5 0.0000 0.6538 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM254709 5 0.0000 0.6538 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM254721 5 0.5781 -0.4814 0.396 0.000 0.000 0.000 0.428 0.176
#> GSM254724 1 0.6029 0.4727 0.396 0.000 0.000 0.000 0.356 0.248
#> GSM254650 5 0.0000 0.6538 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM254687 5 0.0000 0.6538 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM254637 3 0.4736 0.5651 0.308 0.000 0.620 0.000 0.000 0.072
#> GSM254684 3 0.4617 0.6072 0.252 0.000 0.664 0.000 0.084 0.000
#> GSM254649 2 0.0000 0.7463 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM254660 2 0.4167 0.5144 0.000 0.632 0.000 0.344 0.000 0.024
#> GSM254693 2 0.0000 0.7463 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM254695 4 0.3847 -0.4390 0.000 0.000 0.000 0.544 0.000 0.456
#> GSM254702 4 0.3989 0.0123 0.000 0.468 0.000 0.528 0.000 0.004
#> GSM254643 2 0.2362 0.7069 0.000 0.860 0.000 0.136 0.000 0.004
#> GSM254727 2 0.2730 0.6501 0.000 0.808 0.000 0.192 0.000 0.000
#> GSM254640 2 0.4190 0.6678 0.000 0.740 0.000 0.112 0.000 0.148
#> GSM254626 2 0.0260 0.7463 0.000 0.992 0.000 0.008 0.000 0.000
#> GSM254635 4 0.4937 0.5381 0.000 0.196 0.000 0.652 0.000 0.152
#> GSM254653 2 0.2772 0.6613 0.000 0.816 0.000 0.180 0.000 0.004
#> GSM254658 2 0.0000 0.7463 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM254681 2 0.1663 0.7196 0.000 0.912 0.000 0.000 0.000 0.088
#> GSM254719 2 0.3215 0.6372 0.000 0.756 0.000 0.240 0.000 0.004
#> GSM254673 2 0.2234 0.7126 0.000 0.872 0.000 0.124 0.000 0.004
#> GSM254655 2 0.3728 0.5205 0.000 0.652 0.000 0.344 0.000 0.004
#> GSM254669 2 0.0508 0.7457 0.000 0.984 0.000 0.012 0.000 0.004
#> GSM254699 2 0.3728 0.5205 0.000 0.652 0.000 0.344 0.000 0.004
#> GSM254703 6 0.4136 0.5703 0.000 0.012 0.000 0.428 0.000 0.560
#> GSM254708 2 0.3446 0.5649 0.000 0.692 0.000 0.000 0.000 0.308
#> GSM254715 4 0.3566 0.6005 0.000 0.236 0.000 0.744 0.000 0.020
#> GSM254628 2 0.0000 0.7463 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM254634 6 0.4039 0.5873 0.000 0.008 0.000 0.424 0.000 0.568
#> GSM254646 2 0.0000 0.7463 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM254671 4 0.4764 0.5788 0.000 0.232 0.000 0.660 0.000 0.108
#> GSM254711 4 0.3852 0.4177 0.000 0.064 0.000 0.760 0.000 0.176
#> GSM254717 2 0.2003 0.7172 0.000 0.884 0.000 0.116 0.000 0.000
#> GSM254723 3 0.1477 0.7414 0.000 0.008 0.940 0.048 0.000 0.004
#> GSM254730 2 0.4729 0.6475 0.000 0.676 0.000 0.196 0.000 0.128
#> GSM254731 4 0.3215 0.5892 0.000 0.240 0.000 0.756 0.000 0.004
#> GSM254632 6 0.4749 0.1421 0.000 0.028 0.292 0.032 0.000 0.648
#> GSM254662 2 0.2191 0.7143 0.000 0.876 0.000 0.120 0.000 0.004
#> GSM254677 4 0.4495 0.3042 0.000 0.064 0.000 0.660 0.000 0.276
#> GSM254665 2 0.4179 0.3085 0.000 0.516 0.000 0.012 0.000 0.472
#> GSM254691 6 0.4116 -0.2181 0.000 0.416 0.000 0.012 0.000 0.572
#> GSM254644 4 0.3163 0.5947 0.000 0.232 0.000 0.764 0.000 0.004
#> GSM254667 6 0.3797 0.6024 0.000 0.000 0.000 0.420 0.000 0.580
#> GSM254676 6 0.3797 0.6024 0.000 0.000 0.000 0.420 0.000 0.580
#> GSM254679 6 0.3838 0.5537 0.000 0.000 0.000 0.448 0.000 0.552
#> GSM254689 2 0.2912 0.6287 0.000 0.784 0.000 0.000 0.000 0.216
#> GSM254706 2 0.3765 0.4225 0.000 0.596 0.000 0.000 0.000 0.404
#> GSM254712 4 0.4223 0.6006 0.000 0.236 0.000 0.704 0.000 0.060
#> GSM254713 4 0.4085 0.6033 0.000 0.232 0.000 0.716 0.000 0.052
#> GSM254683 2 0.3862 0.3036 0.000 0.524 0.000 0.000 0.000 0.476
#> GSM254710 2 0.3737 0.4399 0.000 0.608 0.000 0.000 0.000 0.392
#> GSM254725 4 0.3847 -0.3558 0.000 0.000 0.000 0.544 0.000 0.456
#> GSM254651 2 0.3309 0.5766 0.000 0.720 0.000 0.000 0.000 0.280
#> GSM254638 6 0.3797 0.6024 0.000 0.000 0.000 0.420 0.000 0.580
#> GSM254685 4 0.5037 -0.1529 0.000 0.080 0.000 0.540 0.000 0.380
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) individual(p) disease.state(p) gender(p) other(p) k
#> SD:pam 107 1.59e-22 0.77697 0.577 0.628 0.872 2
#> SD:pam 99 1.41e-20 0.00669 0.150 0.349 0.175 3
#> SD:pam 99 1.75e-20 0.00289 0.437 0.190 0.523 4
#> SD:pam 95 7.61e-19 0.01689 0.663 0.199 0.441 5
#> SD:pam 78 3.07e-15 0.00155 0.835 0.350 0.692 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 21168 rows and 107 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 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.995 0.998 0.4956 0.505 0.505
#> 3 3 0.715 0.850 0.900 0.2959 0.849 0.701
#> 4 4 0.602 0.363 0.695 0.0794 0.886 0.717
#> 5 5 0.714 0.801 0.867 0.0829 0.813 0.515
#> 6 6 0.715 0.715 0.800 0.0521 0.958 0.829
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
#> GSM254629 1 0.000 0.997 1.000 0.000
#> GSM254648 1 0.000 0.997 1.000 0.000
#> GSM254694 1 0.000 0.997 1.000 0.000
#> GSM254701 1 0.000 0.997 1.000 0.000
#> GSM254728 1 0.000 0.997 1.000 0.000
#> GSM254726 1 0.000 0.997 1.000 0.000
#> GSM254639 1 0.000 0.997 1.000 0.000
#> GSM254652 1 0.000 0.997 1.000 0.000
#> GSM254700 1 0.000 0.997 1.000 0.000
#> GSM254625 1 0.000 0.997 1.000 0.000
#> GSM254636 1 0.000 0.997 1.000 0.000
#> GSM254659 1 0.000 0.997 1.000 0.000
#> GSM254680 1 0.000 0.997 1.000 0.000
#> GSM254686 1 0.000 0.997 1.000 0.000
#> GSM254718 1 0.000 0.997 1.000 0.000
#> GSM254674 1 0.000 0.997 1.000 0.000
#> GSM254668 1 0.000 0.997 1.000 0.000
#> GSM254697 1 0.000 0.997 1.000 0.000
#> GSM254704 1 0.000 0.997 1.000 0.000
#> GSM254707 1 0.000 0.997 1.000 0.000
#> GSM254714 1 0.000 0.997 1.000 0.000
#> GSM254722 1 0.000 0.997 1.000 0.000
#> GSM254627 1 0.000 0.997 1.000 0.000
#> GSM254630 1 0.000 0.997 1.000 0.000
#> GSM254633 1 0.000 0.997 1.000 0.000
#> GSM254670 1 0.000 0.997 1.000 0.000
#> GSM254716 1 0.000 0.997 1.000 0.000
#> GSM254720 1 0.000 0.997 1.000 0.000
#> GSM254729 1 0.000 0.997 1.000 0.000
#> GSM254654 1 0.000 0.997 1.000 0.000
#> GSM254656 1 0.000 0.997 1.000 0.000
#> GSM254631 1 0.000 0.997 1.000 0.000
#> GSM254657 1 0.000 0.997 1.000 0.000
#> GSM254664 1 0.000 0.997 1.000 0.000
#> GSM254672 1 0.000 0.997 1.000 0.000
#> GSM254692 1 0.000 0.997 1.000 0.000
#> GSM254645 1 0.000 0.997 1.000 0.000
#> GSM254666 1 0.000 0.997 1.000 0.000
#> GSM254675 1 0.000 0.997 1.000 0.000
#> GSM254678 1 0.000 0.997 1.000 0.000
#> GSM254688 1 0.000 0.997 1.000 0.000
#> GSM254690 1 0.000 0.997 1.000 0.000
#> GSM254696 1 0.000 0.997 1.000 0.000
#> GSM254705 1 0.000 0.997 1.000 0.000
#> GSM254642 1 0.000 0.997 1.000 0.000
#> GSM254661 1 0.000 0.997 1.000 0.000
#> GSM254698 1 0.000 0.997 1.000 0.000
#> GSM254641 1 0.000 0.997 1.000 0.000
#> GSM254647 1 0.000 0.997 1.000 0.000
#> GSM254663 1 0.000 0.997 1.000 0.000
#> GSM254682 1 0.000 0.997 1.000 0.000
#> GSM254709 1 0.000 0.997 1.000 0.000
#> GSM254721 1 0.000 0.997 1.000 0.000
#> GSM254724 1 0.000 0.997 1.000 0.000
#> GSM254650 1 0.000 0.997 1.000 0.000
#> GSM254687 1 0.000 0.997 1.000 0.000
#> GSM254637 1 0.000 0.997 1.000 0.000
#> GSM254684 1 0.000 0.997 1.000 0.000
#> GSM254649 2 0.000 0.998 0.000 1.000
#> GSM254660 2 0.000 0.998 0.000 1.000
#> GSM254693 2 0.000 0.998 0.000 1.000
#> GSM254695 2 0.000 0.998 0.000 1.000
#> GSM254702 2 0.000 0.998 0.000 1.000
#> GSM254643 2 0.000 0.998 0.000 1.000
#> GSM254727 2 0.000 0.998 0.000 1.000
#> GSM254640 2 0.000 0.998 0.000 1.000
#> GSM254626 2 0.000 0.998 0.000 1.000
#> GSM254635 2 0.000 0.998 0.000 1.000
#> GSM254653 2 0.000 0.998 0.000 1.000
#> GSM254658 2 0.000 0.998 0.000 1.000
#> GSM254681 2 0.000 0.998 0.000 1.000
#> GSM254719 2 0.000 0.998 0.000 1.000
#> GSM254673 2 0.000 0.998 0.000 1.000
#> GSM254655 2 0.000 0.998 0.000 1.000
#> GSM254669 2 0.000 0.998 0.000 1.000
#> GSM254699 2 0.000 0.998 0.000 1.000
#> GSM254703 2 0.000 0.998 0.000 1.000
#> GSM254708 2 0.000 0.998 0.000 1.000
#> GSM254715 2 0.000 0.998 0.000 1.000
#> GSM254628 2 0.000 0.998 0.000 1.000
#> GSM254634 2 0.000 0.998 0.000 1.000
#> GSM254646 2 0.000 0.998 0.000 1.000
#> GSM254671 2 0.000 0.998 0.000 1.000
#> GSM254711 2 0.000 0.998 0.000 1.000
#> GSM254717 2 0.000 0.998 0.000 1.000
#> GSM254723 1 0.000 0.997 1.000 0.000
#> GSM254730 2 0.000 0.998 0.000 1.000
#> GSM254731 2 0.000 0.998 0.000 1.000
#> GSM254632 1 0.000 0.997 1.000 0.000
#> GSM254662 2 0.000 0.998 0.000 1.000
#> GSM254677 2 0.000 0.998 0.000 1.000
#> GSM254665 2 0.000 0.998 0.000 1.000
#> GSM254691 2 0.000 0.998 0.000 1.000
#> GSM254644 2 0.000 0.998 0.000 1.000
#> GSM254667 2 0.358 0.927 0.068 0.932
#> GSM254676 2 0.000 0.998 0.000 1.000
#> GSM254679 2 0.000 0.998 0.000 1.000
#> GSM254689 2 0.000 0.998 0.000 1.000
#> GSM254706 2 0.000 0.998 0.000 1.000
#> GSM254712 2 0.000 0.998 0.000 1.000
#> GSM254713 2 0.000 0.998 0.000 1.000
#> GSM254683 2 0.000 0.998 0.000 1.000
#> GSM254710 1 0.697 0.768 0.812 0.188
#> GSM254725 2 0.000 0.998 0.000 1.000
#> GSM254651 2 0.000 0.998 0.000 1.000
#> GSM254638 2 0.000 0.998 0.000 1.000
#> GSM254685 2 0.000 0.998 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM254629 3 0.3038 0.860 0.104 0.000 0.896
#> GSM254648 3 0.3192 0.863 0.112 0.000 0.888
#> GSM254694 3 0.3192 0.863 0.112 0.000 0.888
#> GSM254701 3 0.3038 0.860 0.104 0.000 0.896
#> GSM254728 3 0.4452 0.848 0.192 0.000 0.808
#> GSM254726 3 0.3192 0.863 0.112 0.000 0.888
#> GSM254639 3 0.4702 0.840 0.212 0.000 0.788
#> GSM254652 3 0.4654 0.844 0.208 0.000 0.792
#> GSM254700 1 0.1289 0.842 0.968 0.000 0.032
#> GSM254625 3 0.6126 0.450 0.400 0.000 0.600
#> GSM254636 1 0.5621 0.617 0.692 0.000 0.308
#> GSM254659 3 0.4504 0.850 0.196 0.000 0.804
#> GSM254680 1 0.3686 0.820 0.860 0.000 0.140
#> GSM254686 1 0.3752 0.818 0.856 0.000 0.144
#> GSM254718 3 0.4121 0.856 0.168 0.000 0.832
#> GSM254674 1 0.3752 0.818 0.856 0.000 0.144
#> GSM254668 1 0.3879 0.818 0.848 0.000 0.152
#> GSM254697 1 0.1289 0.842 0.968 0.000 0.032
#> GSM254704 1 0.1643 0.838 0.956 0.000 0.044
#> GSM254707 1 0.3752 0.821 0.856 0.000 0.144
#> GSM254714 1 0.5835 0.452 0.660 0.000 0.340
#> GSM254722 1 0.0424 0.850 0.992 0.000 0.008
#> GSM254627 1 0.1163 0.844 0.972 0.000 0.028
#> GSM254630 1 0.0892 0.851 0.980 0.000 0.020
#> GSM254633 1 0.4235 0.798 0.824 0.000 0.176
#> GSM254670 3 0.4750 0.844 0.216 0.000 0.784
#> GSM254716 3 0.6215 0.388 0.428 0.000 0.572
#> GSM254720 1 0.4235 0.714 0.824 0.000 0.176
#> GSM254729 3 0.3192 0.863 0.112 0.000 0.888
#> GSM254654 3 0.3192 0.863 0.112 0.000 0.888
#> GSM254656 3 0.4702 0.812 0.212 0.000 0.788
#> GSM254631 1 0.3192 0.831 0.888 0.000 0.112
#> GSM254657 3 0.4931 0.839 0.232 0.000 0.768
#> GSM254664 1 0.3267 0.828 0.884 0.000 0.116
#> GSM254672 1 0.0424 0.849 0.992 0.000 0.008
#> GSM254692 1 0.0892 0.845 0.980 0.000 0.020
#> GSM254645 3 0.5291 0.798 0.268 0.000 0.732
#> GSM254666 1 0.6302 -0.145 0.520 0.000 0.480
#> GSM254675 1 0.1411 0.852 0.964 0.000 0.036
#> GSM254678 1 0.0592 0.850 0.988 0.000 0.012
#> GSM254688 1 0.3551 0.829 0.868 0.000 0.132
#> GSM254690 1 0.3482 0.828 0.872 0.000 0.128
#> GSM254696 1 0.6204 0.282 0.576 0.000 0.424
#> GSM254705 1 0.0424 0.848 0.992 0.000 0.008
#> GSM254642 1 0.1163 0.842 0.972 0.000 0.028
#> GSM254661 3 0.4452 0.852 0.192 0.000 0.808
#> GSM254698 1 0.4452 0.664 0.808 0.000 0.192
#> GSM254641 1 0.3412 0.824 0.876 0.000 0.124
#> GSM254647 1 0.0000 0.850 1.000 0.000 0.000
#> GSM254663 1 0.0892 0.852 0.980 0.000 0.020
#> GSM254682 1 0.1529 0.851 0.960 0.000 0.040
#> GSM254709 1 0.3192 0.832 0.888 0.000 0.112
#> GSM254721 1 0.1643 0.838 0.956 0.000 0.044
#> GSM254724 1 0.1643 0.838 0.956 0.000 0.044
#> GSM254650 1 0.0592 0.850 0.988 0.000 0.012
#> GSM254687 1 0.0892 0.852 0.980 0.000 0.020
#> GSM254637 1 0.6126 0.343 0.600 0.000 0.400
#> GSM254684 1 0.5591 0.404 0.696 0.000 0.304
#> GSM254649 2 0.0592 0.973 0.000 0.988 0.012
#> GSM254660 2 0.0747 0.973 0.000 0.984 0.016
#> GSM254693 2 0.0592 0.973 0.000 0.988 0.012
#> GSM254695 2 0.4346 0.843 0.000 0.816 0.184
#> GSM254702 2 0.0747 0.973 0.000 0.984 0.016
#> GSM254643 2 0.0592 0.973 0.000 0.988 0.012
#> GSM254727 2 0.0000 0.974 0.000 1.000 0.000
#> GSM254640 2 0.0592 0.973 0.000 0.988 0.012
#> GSM254626 2 0.0592 0.973 0.000 0.988 0.012
#> GSM254635 2 0.2448 0.942 0.000 0.924 0.076
#> GSM254653 2 0.0592 0.973 0.000 0.988 0.012
#> GSM254658 2 0.0592 0.973 0.000 0.988 0.012
#> GSM254681 2 0.0000 0.974 0.000 1.000 0.000
#> GSM254719 2 0.0592 0.973 0.000 0.988 0.012
#> GSM254673 2 0.0424 0.974 0.000 0.992 0.008
#> GSM254655 2 0.0592 0.973 0.000 0.988 0.012
#> GSM254669 2 0.0000 0.974 0.000 1.000 0.000
#> GSM254699 2 0.0592 0.973 0.000 0.988 0.012
#> GSM254703 2 0.3116 0.919 0.000 0.892 0.108
#> GSM254708 2 0.0424 0.972 0.000 0.992 0.008
#> GSM254715 2 0.2261 0.952 0.000 0.932 0.068
#> GSM254628 2 0.0592 0.973 0.000 0.988 0.012
#> GSM254634 2 0.1289 0.965 0.000 0.968 0.032
#> GSM254646 2 0.0000 0.974 0.000 1.000 0.000
#> GSM254671 2 0.0237 0.973 0.000 0.996 0.004
#> GSM254711 2 0.1964 0.952 0.000 0.944 0.056
#> GSM254717 2 0.0000 0.974 0.000 1.000 0.000
#> GSM254723 3 0.4702 0.812 0.212 0.000 0.788
#> GSM254730 2 0.0592 0.973 0.000 0.988 0.012
#> GSM254731 2 0.0747 0.973 0.000 0.984 0.016
#> GSM254632 3 0.4654 0.816 0.208 0.000 0.792
#> GSM254662 2 0.0000 0.974 0.000 1.000 0.000
#> GSM254677 2 0.3116 0.920 0.000 0.892 0.108
#> GSM254665 2 0.0000 0.974 0.000 1.000 0.000
#> GSM254691 2 0.0000 0.974 0.000 1.000 0.000
#> GSM254644 2 0.0592 0.973 0.000 0.988 0.012
#> GSM254667 2 0.3551 0.871 0.000 0.868 0.132
#> GSM254676 2 0.0000 0.974 0.000 1.000 0.000
#> GSM254679 2 0.0424 0.973 0.000 0.992 0.008
#> GSM254689 2 0.0000 0.974 0.000 1.000 0.000
#> GSM254706 2 0.0424 0.972 0.000 0.992 0.008
#> GSM254712 2 0.3116 0.919 0.000 0.892 0.108
#> GSM254713 2 0.2261 0.952 0.000 0.932 0.068
#> GSM254683 2 0.0000 0.974 0.000 1.000 0.000
#> GSM254710 3 0.8433 0.595 0.176 0.204 0.620
#> GSM254725 2 0.2165 0.949 0.000 0.936 0.064
#> GSM254651 2 0.0000 0.974 0.000 1.000 0.000
#> GSM254638 2 0.3879 0.880 0.000 0.848 0.152
#> GSM254685 2 0.2261 0.952 0.000 0.932 0.068
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM254629 3 0.0927 0.5638 0.016 0.000 0.976 0.008
#> GSM254648 3 0.2480 0.5527 0.008 0.000 0.904 0.088
#> GSM254694 3 0.2412 0.5548 0.008 0.000 0.908 0.084
#> GSM254701 3 0.2174 0.5568 0.020 0.000 0.928 0.052
#> GSM254728 3 0.2489 0.5474 0.068 0.000 0.912 0.020
#> GSM254726 3 0.3142 0.5502 0.008 0.000 0.860 0.132
#> GSM254639 3 0.2699 0.5542 0.068 0.000 0.904 0.028
#> GSM254652 3 0.1902 0.5544 0.064 0.000 0.932 0.004
#> GSM254700 1 0.0188 0.3080 0.996 0.000 0.000 0.004
#> GSM254625 3 0.4741 0.4721 0.028 0.000 0.744 0.228
#> GSM254636 3 0.7299 -0.4522 0.296 0.000 0.520 0.184
#> GSM254659 3 0.3219 0.5065 0.112 0.000 0.868 0.020
#> GSM254680 1 0.7421 -0.3544 0.432 0.000 0.400 0.168
#> GSM254686 1 0.7398 -0.3648 0.424 0.000 0.412 0.164
#> GSM254718 3 0.1888 0.5633 0.044 0.000 0.940 0.016
#> GSM254674 1 0.7412 -0.3480 0.444 0.000 0.388 0.168
#> GSM254668 3 0.7398 -0.6587 0.412 0.000 0.424 0.164
#> GSM254697 1 0.0188 0.3080 0.996 0.000 0.000 0.004
#> GSM254704 1 0.0336 0.3079 0.992 0.000 0.008 0.000
#> GSM254707 3 0.7811 -0.6626 0.336 0.000 0.404 0.260
#> GSM254714 3 0.7740 -0.4635 0.320 0.000 0.432 0.248
#> GSM254722 1 0.7459 -0.4167 0.508 0.000 0.248 0.244
#> GSM254627 1 0.1792 0.2883 0.932 0.000 0.068 0.000
#> GSM254630 1 0.7782 -0.6193 0.424 0.000 0.264 0.312
#> GSM254633 3 0.7272 -0.5673 0.344 0.000 0.496 0.160
#> GSM254670 3 0.2973 0.5463 0.020 0.000 0.884 0.096
#> GSM254716 3 0.5964 0.3791 0.096 0.000 0.676 0.228
#> GSM254720 3 0.7793 -0.5517 0.356 0.000 0.396 0.248
#> GSM254729 3 0.2408 0.5577 0.000 0.000 0.896 0.104
#> GSM254654 3 0.2412 0.5548 0.008 0.000 0.908 0.084
#> GSM254656 3 0.4889 0.5201 0.032 0.028 0.792 0.148
#> GSM254631 3 0.7761 -0.6080 0.340 0.000 0.416 0.244
#> GSM254657 3 0.3335 0.5349 0.020 0.000 0.860 0.120
#> GSM254664 1 0.7369 -0.2653 0.496 0.000 0.324 0.180
#> GSM254672 1 0.6523 -0.0759 0.636 0.000 0.208 0.156
#> GSM254692 1 0.2861 0.2667 0.888 0.000 0.096 0.016
#> GSM254645 3 0.4307 0.5150 0.048 0.000 0.808 0.144
#> GSM254666 3 0.6916 -0.2478 0.236 0.000 0.588 0.176
#> GSM254675 1 0.7500 -0.3586 0.500 0.000 0.252 0.248
#> GSM254678 3 0.7861 -0.7209 0.360 0.000 0.368 0.272
#> GSM254688 1 0.7780 -0.5538 0.428 0.000 0.300 0.272
#> GSM254690 1 0.7640 -0.4413 0.464 0.000 0.296 0.240
#> GSM254696 3 0.7402 -0.3734 0.264 0.000 0.516 0.220
#> GSM254705 1 0.7763 -0.5969 0.432 0.000 0.264 0.304
#> GSM254642 1 0.0000 0.3075 1.000 0.000 0.000 0.000
#> GSM254661 3 0.2483 0.5592 0.052 0.000 0.916 0.032
#> GSM254698 3 0.7756 -0.6257 0.348 0.000 0.412 0.240
#> GSM254641 1 0.7383 -0.3402 0.448 0.000 0.388 0.164
#> GSM254647 1 0.6449 -0.0581 0.644 0.000 0.204 0.152
#> GSM254663 1 0.7436 -0.3160 0.512 0.000 0.236 0.252
#> GSM254682 4 0.7871 0.0000 0.332 0.000 0.284 0.384
#> GSM254709 1 0.7489 -0.2977 0.492 0.000 0.296 0.212
#> GSM254721 1 0.0188 0.3080 0.996 0.000 0.000 0.004
#> GSM254724 1 0.0188 0.3080 0.996 0.000 0.000 0.004
#> GSM254650 1 0.7606 -0.4780 0.468 0.000 0.228 0.304
#> GSM254687 1 0.7593 -0.4605 0.472 0.000 0.228 0.300
#> GSM254637 3 0.7337 -0.3175 0.272 0.000 0.524 0.204
#> GSM254684 3 0.7529 -0.4031 0.224 0.000 0.488 0.288
#> GSM254649 2 0.4431 0.8552 0.000 0.696 0.000 0.304
#> GSM254660 2 0.2921 0.8448 0.000 0.860 0.000 0.140
#> GSM254693 2 0.4431 0.8552 0.000 0.696 0.000 0.304
#> GSM254695 2 0.4996 0.6946 0.000 0.752 0.056 0.192
#> GSM254702 2 0.2081 0.8165 0.000 0.916 0.000 0.084
#> GSM254643 2 0.4382 0.8582 0.000 0.704 0.000 0.296
#> GSM254727 2 0.4164 0.8591 0.000 0.736 0.000 0.264
#> GSM254640 2 0.4040 0.8576 0.000 0.752 0.000 0.248
#> GSM254626 2 0.4431 0.8552 0.000 0.696 0.000 0.304
#> GSM254635 2 0.2999 0.7723 0.000 0.864 0.004 0.132
#> GSM254653 2 0.4431 0.8552 0.000 0.696 0.000 0.304
#> GSM254658 2 0.4431 0.8552 0.000 0.696 0.000 0.304
#> GSM254681 2 0.4072 0.8591 0.000 0.748 0.000 0.252
#> GSM254719 2 0.4431 0.8552 0.000 0.696 0.000 0.304
#> GSM254673 2 0.4277 0.8580 0.000 0.720 0.000 0.280
#> GSM254655 2 0.4431 0.8552 0.000 0.696 0.000 0.304
#> GSM254669 2 0.4250 0.8584 0.000 0.724 0.000 0.276
#> GSM254699 2 0.4431 0.8552 0.000 0.696 0.000 0.304
#> GSM254703 2 0.2546 0.7870 0.000 0.900 0.008 0.092
#> GSM254708 2 0.4331 0.8275 0.000 0.712 0.000 0.288
#> GSM254715 2 0.2647 0.7983 0.000 0.880 0.000 0.120
#> GSM254628 2 0.4431 0.8552 0.000 0.696 0.000 0.304
#> GSM254634 2 0.3024 0.7663 0.000 0.852 0.000 0.148
#> GSM254646 2 0.4040 0.8594 0.000 0.752 0.000 0.248
#> GSM254671 2 0.1474 0.8145 0.000 0.948 0.000 0.052
#> GSM254711 2 0.2011 0.7954 0.000 0.920 0.000 0.080
#> GSM254717 2 0.4103 0.8589 0.000 0.744 0.000 0.256
#> GSM254723 3 0.5075 0.5157 0.032 0.028 0.776 0.164
#> GSM254730 2 0.4250 0.8583 0.000 0.724 0.000 0.276
#> GSM254731 2 0.1792 0.8215 0.000 0.932 0.000 0.068
#> GSM254632 3 0.5438 0.4955 0.024 0.028 0.728 0.220
#> GSM254662 2 0.4103 0.8589 0.000 0.744 0.000 0.256
#> GSM254677 2 0.3300 0.7622 0.000 0.848 0.008 0.144
#> GSM254665 2 0.4220 0.8599 0.000 0.748 0.004 0.248
#> GSM254691 2 0.4356 0.8529 0.000 0.708 0.000 0.292
#> GSM254644 2 0.2704 0.8383 0.000 0.876 0.000 0.124
#> GSM254667 2 0.6161 0.6399 0.008 0.592 0.044 0.356
#> GSM254676 2 0.4382 0.8537 0.000 0.704 0.000 0.296
#> GSM254679 2 0.1940 0.8007 0.000 0.924 0.000 0.076
#> GSM254689 2 0.4040 0.8592 0.000 0.752 0.000 0.248
#> GSM254706 2 0.4624 0.8400 0.000 0.660 0.000 0.340
#> GSM254712 2 0.2676 0.7854 0.000 0.896 0.012 0.092
#> GSM254713 2 0.2647 0.7983 0.000 0.880 0.000 0.120
#> GSM254683 2 0.4585 0.8437 0.000 0.668 0.000 0.332
#> GSM254710 3 0.6786 0.3786 0.020 0.064 0.572 0.344
#> GSM254725 2 0.2921 0.7704 0.000 0.860 0.000 0.140
#> GSM254651 2 0.4331 0.8540 0.000 0.712 0.000 0.288
#> GSM254638 2 0.5040 0.7019 0.008 0.764 0.048 0.180
#> GSM254685 2 0.2408 0.8040 0.000 0.896 0.000 0.104
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM254629 3 0.1894 0.881 0.000 0.000 0.920 0.008 0.072
#> GSM254648 3 0.3463 0.869 0.044 0.000 0.860 0.040 0.056
#> GSM254694 3 0.3221 0.873 0.044 0.000 0.872 0.028 0.056
#> GSM254701 3 0.4235 0.537 0.000 0.000 0.656 0.008 0.336
#> GSM254728 3 0.3003 0.818 0.000 0.000 0.812 0.000 0.188
#> GSM254726 3 0.3530 0.871 0.044 0.000 0.856 0.040 0.060
#> GSM254639 3 0.2127 0.882 0.000 0.000 0.892 0.000 0.108
#> GSM254652 3 0.1908 0.879 0.000 0.000 0.908 0.000 0.092
#> GSM254700 1 0.1410 0.998 0.940 0.000 0.000 0.000 0.060
#> GSM254625 3 0.1894 0.882 0.000 0.000 0.920 0.008 0.072
#> GSM254636 5 0.1831 0.910 0.000 0.000 0.076 0.004 0.920
#> GSM254659 3 0.4114 0.465 0.000 0.000 0.624 0.000 0.376
#> GSM254680 5 0.2445 0.898 0.004 0.000 0.108 0.004 0.884
#> GSM254686 5 0.2536 0.882 0.000 0.000 0.128 0.004 0.868
#> GSM254718 3 0.3305 0.776 0.000 0.000 0.776 0.000 0.224
#> GSM254674 5 0.2338 0.894 0.000 0.000 0.112 0.004 0.884
#> GSM254668 5 0.2497 0.895 0.004 0.000 0.112 0.004 0.880
#> GSM254697 1 0.1341 0.995 0.944 0.000 0.000 0.000 0.056
#> GSM254704 1 0.1410 0.998 0.940 0.000 0.000 0.000 0.060
#> GSM254707 5 0.2389 0.892 0.000 0.000 0.116 0.004 0.880
#> GSM254714 5 0.2179 0.873 0.004 0.000 0.100 0.000 0.896
#> GSM254722 5 0.2463 0.880 0.100 0.000 0.004 0.008 0.888
#> GSM254627 1 0.1410 0.998 0.940 0.000 0.000 0.000 0.060
#> GSM254630 5 0.0854 0.912 0.004 0.000 0.012 0.008 0.976
#> GSM254633 5 0.2497 0.895 0.004 0.000 0.112 0.004 0.880
#> GSM254670 3 0.2193 0.881 0.000 0.000 0.900 0.008 0.092
#> GSM254716 3 0.2462 0.869 0.000 0.000 0.880 0.008 0.112
#> GSM254720 5 0.0404 0.913 0.012 0.000 0.000 0.000 0.988
#> GSM254729 3 0.1914 0.880 0.000 0.000 0.924 0.016 0.060
#> GSM254654 3 0.3305 0.872 0.044 0.000 0.868 0.032 0.056
#> GSM254656 3 0.4654 0.843 0.044 0.004 0.788 0.056 0.108
#> GSM254631 5 0.0798 0.916 0.008 0.000 0.016 0.000 0.976
#> GSM254657 3 0.2723 0.872 0.000 0.000 0.864 0.012 0.124
#> GSM254664 5 0.2228 0.904 0.004 0.000 0.092 0.004 0.900
#> GSM254672 5 0.3074 0.777 0.196 0.000 0.000 0.000 0.804
#> GSM254692 1 0.1478 0.994 0.936 0.000 0.000 0.000 0.064
#> GSM254645 3 0.3282 0.835 0.000 0.000 0.804 0.008 0.188
#> GSM254666 5 0.4046 0.651 0.000 0.000 0.296 0.008 0.696
#> GSM254675 5 0.0404 0.913 0.012 0.000 0.000 0.000 0.988
#> GSM254678 5 0.0794 0.911 0.028 0.000 0.000 0.000 0.972
#> GSM254688 5 0.0798 0.915 0.016 0.000 0.008 0.000 0.976
#> GSM254690 5 0.0451 0.914 0.008 0.000 0.004 0.000 0.988
#> GSM254696 5 0.2770 0.862 0.008 0.000 0.124 0.004 0.864
#> GSM254705 5 0.0854 0.912 0.012 0.000 0.004 0.008 0.976
#> GSM254642 1 0.1341 0.995 0.944 0.000 0.000 0.000 0.056
#> GSM254661 3 0.1792 0.880 0.000 0.000 0.916 0.000 0.084
#> GSM254698 5 0.2408 0.884 0.096 0.000 0.004 0.008 0.892
#> GSM254641 5 0.2179 0.901 0.000 0.000 0.100 0.004 0.896
#> GSM254647 5 0.3143 0.763 0.204 0.000 0.000 0.000 0.796
#> GSM254663 5 0.0404 0.913 0.012 0.000 0.000 0.000 0.988
#> GSM254682 5 0.0867 0.912 0.008 0.000 0.008 0.008 0.976
#> GSM254709 5 0.1731 0.913 0.004 0.000 0.060 0.004 0.932
#> GSM254721 1 0.1410 0.998 0.940 0.000 0.000 0.000 0.060
#> GSM254724 1 0.1410 0.998 0.940 0.000 0.000 0.000 0.060
#> GSM254650 5 0.0740 0.911 0.008 0.000 0.004 0.008 0.980
#> GSM254687 5 0.0740 0.911 0.008 0.000 0.004 0.008 0.980
#> GSM254637 5 0.1704 0.912 0.004 0.000 0.068 0.000 0.928
#> GSM254684 5 0.2116 0.880 0.004 0.000 0.076 0.008 0.912
#> GSM254649 2 0.0162 0.827 0.004 0.996 0.000 0.000 0.000
#> GSM254660 2 0.2299 0.804 0.004 0.912 0.052 0.032 0.000
#> GSM254693 2 0.0000 0.827 0.000 1.000 0.000 0.000 0.000
#> GSM254695 4 0.1478 0.724 0.000 0.064 0.000 0.936 0.000
#> GSM254702 2 0.0609 0.822 0.000 0.980 0.000 0.020 0.000
#> GSM254643 2 0.3248 0.779 0.004 0.856 0.052 0.088 0.000
#> GSM254727 2 0.0000 0.827 0.000 1.000 0.000 0.000 0.000
#> GSM254640 2 0.0162 0.827 0.000 0.996 0.000 0.004 0.000
#> GSM254626 2 0.0000 0.827 0.000 1.000 0.000 0.000 0.000
#> GSM254635 4 0.2338 0.754 0.004 0.112 0.000 0.884 0.000
#> GSM254653 2 0.0000 0.827 0.000 1.000 0.000 0.000 0.000
#> GSM254658 2 0.0510 0.828 0.000 0.984 0.000 0.016 0.000
#> GSM254681 2 0.2806 0.760 0.004 0.844 0.000 0.152 0.000
#> GSM254719 2 0.0000 0.827 0.000 1.000 0.000 0.000 0.000
#> GSM254673 2 0.0000 0.827 0.000 1.000 0.000 0.000 0.000
#> GSM254655 2 0.0162 0.827 0.000 0.996 0.000 0.004 0.000
#> GSM254669 2 0.0162 0.829 0.000 0.996 0.000 0.004 0.000
#> GSM254699 2 0.0162 0.827 0.000 0.996 0.000 0.004 0.000
#> GSM254703 4 0.4314 0.644 0.004 0.280 0.016 0.700 0.000
#> GSM254708 4 0.3949 0.649 0.000 0.332 0.000 0.668 0.000
#> GSM254715 2 0.4644 0.618 0.004 0.720 0.052 0.224 0.000
#> GSM254628 2 0.0510 0.828 0.000 0.984 0.000 0.016 0.000
#> GSM254634 4 0.3661 0.714 0.000 0.276 0.000 0.724 0.000
#> GSM254646 2 0.2536 0.777 0.004 0.868 0.000 0.128 0.000
#> GSM254671 2 0.2773 0.759 0.000 0.836 0.000 0.164 0.000
#> GSM254711 2 0.4779 0.227 0.004 0.584 0.016 0.396 0.000
#> GSM254717 2 0.3039 0.727 0.000 0.808 0.000 0.192 0.000
#> GSM254723 3 0.4654 0.843 0.044 0.004 0.788 0.056 0.108
#> GSM254730 2 0.0290 0.829 0.000 0.992 0.000 0.008 0.000
#> GSM254731 2 0.0404 0.825 0.000 0.988 0.000 0.012 0.000
#> GSM254632 3 0.4346 0.852 0.044 0.004 0.812 0.060 0.080
#> GSM254662 2 0.0000 0.827 0.000 1.000 0.000 0.000 0.000
#> GSM254677 4 0.1478 0.724 0.000 0.064 0.000 0.936 0.000
#> GSM254665 2 0.4615 0.633 0.004 0.724 0.052 0.220 0.000
#> GSM254691 2 0.4182 0.258 0.000 0.600 0.000 0.400 0.000
#> GSM254644 2 0.0963 0.823 0.000 0.964 0.000 0.036 0.000
#> GSM254667 4 0.3730 0.709 0.000 0.288 0.000 0.712 0.000
#> GSM254676 2 0.3928 0.541 0.004 0.700 0.000 0.296 0.000
#> GSM254679 2 0.4242 0.143 0.000 0.572 0.000 0.428 0.000
#> GSM254689 2 0.2843 0.763 0.008 0.848 0.000 0.144 0.000
#> GSM254706 4 0.4443 0.254 0.004 0.472 0.000 0.524 0.000
#> GSM254712 4 0.5018 0.606 0.004 0.284 0.052 0.660 0.000
#> GSM254713 2 0.4673 0.611 0.004 0.716 0.052 0.228 0.000
#> GSM254683 2 0.4264 0.326 0.004 0.620 0.000 0.376 0.000
#> GSM254710 3 0.5702 0.760 0.044 0.020 0.720 0.148 0.068
#> GSM254725 4 0.3424 0.739 0.000 0.240 0.000 0.760 0.000
#> GSM254651 2 0.3906 0.563 0.004 0.704 0.000 0.292 0.000
#> GSM254638 4 0.1365 0.691 0.004 0.040 0.004 0.952 0.000
#> GSM254685 2 0.3465 0.772 0.004 0.840 0.052 0.104 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM254629 3 0.1958 0.7392 0.000 0.000 0.896 0.000 0.004 0.100
#> GSM254648 6 0.3528 0.8019 0.004 0.000 0.296 0.000 0.000 0.700
#> GSM254694 6 0.3975 0.5460 0.004 0.000 0.452 0.000 0.000 0.544
#> GSM254701 3 0.1984 0.6876 0.000 0.000 0.912 0.000 0.056 0.032
#> GSM254728 3 0.1007 0.6990 0.000 0.000 0.956 0.000 0.044 0.000
#> GSM254726 6 0.3409 0.8005 0.000 0.000 0.300 0.000 0.000 0.700
#> GSM254639 3 0.3025 0.7457 0.000 0.000 0.820 0.000 0.024 0.156
#> GSM254652 3 0.2980 0.7367 0.000 0.000 0.808 0.000 0.012 0.180
#> GSM254700 1 0.0146 0.9913 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM254625 3 0.3312 0.7269 0.000 0.000 0.792 0.000 0.028 0.180
#> GSM254636 5 0.3956 0.7666 0.000 0.000 0.292 0.000 0.684 0.024
#> GSM254659 3 0.2176 0.6661 0.000 0.000 0.896 0.000 0.080 0.024
#> GSM254680 5 0.2948 0.8139 0.000 0.000 0.188 0.000 0.804 0.008
#> GSM254686 5 0.3967 0.7125 0.000 0.000 0.356 0.000 0.632 0.012
#> GSM254718 3 0.1196 0.7085 0.000 0.000 0.952 0.000 0.040 0.008
#> GSM254674 5 0.3898 0.7383 0.000 0.000 0.336 0.000 0.652 0.012
#> GSM254668 5 0.2814 0.8153 0.000 0.000 0.172 0.000 0.820 0.008
#> GSM254697 1 0.0146 0.9913 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM254704 1 0.0260 0.9890 0.992 0.000 0.000 0.000 0.008 0.000
#> GSM254707 5 0.3835 0.7445 0.000 0.000 0.320 0.000 0.668 0.012
#> GSM254714 5 0.3141 0.7105 0.012 0.000 0.200 0.000 0.788 0.000
#> GSM254722 5 0.2398 0.8204 0.088 0.000 0.016 0.004 0.888 0.004
#> GSM254627 1 0.0458 0.9804 0.984 0.000 0.000 0.000 0.016 0.000
#> GSM254630 5 0.0146 0.8379 0.000 0.000 0.004 0.000 0.996 0.000
#> GSM254633 5 0.3629 0.7820 0.000 0.000 0.276 0.000 0.712 0.012
#> GSM254670 3 0.3248 0.7411 0.000 0.000 0.804 0.000 0.032 0.164
#> GSM254716 3 0.3939 0.7331 0.000 0.000 0.752 0.000 0.068 0.180
#> GSM254720 5 0.1096 0.8396 0.008 0.000 0.020 0.004 0.964 0.004
#> GSM254729 3 0.3711 0.5849 0.000 0.000 0.720 0.000 0.020 0.260
#> GSM254654 6 0.3907 0.6576 0.004 0.000 0.408 0.000 0.000 0.588
#> GSM254656 6 0.4887 0.7972 0.000 0.000 0.236 0.020 0.072 0.672
#> GSM254631 5 0.1765 0.8455 0.000 0.000 0.096 0.000 0.904 0.000
#> GSM254657 3 0.3978 0.6900 0.000 0.000 0.756 0.000 0.084 0.160
#> GSM254664 5 0.2653 0.8273 0.012 0.000 0.144 0.000 0.844 0.000
#> GSM254672 5 0.2994 0.7456 0.208 0.000 0.004 0.000 0.788 0.000
#> GSM254692 1 0.0865 0.9648 0.964 0.000 0.000 0.000 0.036 0.000
#> GSM254645 3 0.2613 0.6132 0.000 0.000 0.848 0.000 0.140 0.012
#> GSM254666 5 0.4097 0.4756 0.000 0.000 0.492 0.000 0.500 0.008
#> GSM254675 5 0.0837 0.8389 0.000 0.000 0.020 0.004 0.972 0.004
#> GSM254678 5 0.1261 0.8417 0.008 0.000 0.028 0.004 0.956 0.004
#> GSM254688 5 0.1116 0.8422 0.000 0.000 0.028 0.004 0.960 0.008
#> GSM254690 5 0.0865 0.8429 0.000 0.000 0.036 0.000 0.964 0.000
#> GSM254696 5 0.3734 0.7759 0.000 0.000 0.264 0.000 0.716 0.020
#> GSM254705 5 0.0653 0.8337 0.004 0.000 0.000 0.004 0.980 0.012
#> GSM254642 1 0.0146 0.9913 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM254661 3 0.3078 0.7253 0.000 0.000 0.796 0.000 0.012 0.192
#> GSM254698 5 0.4680 0.7824 0.084 0.000 0.176 0.004 0.720 0.016
#> GSM254641 5 0.2814 0.8196 0.000 0.000 0.172 0.000 0.820 0.008
#> GSM254647 5 0.3198 0.6606 0.260 0.000 0.000 0.000 0.740 0.000
#> GSM254663 5 0.0551 0.8355 0.000 0.000 0.004 0.004 0.984 0.008
#> GSM254682 5 0.2884 0.8060 0.000 0.000 0.164 0.004 0.824 0.008
#> GSM254709 5 0.2775 0.8300 0.000 0.000 0.104 0.000 0.856 0.040
#> GSM254721 1 0.0146 0.9913 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM254724 1 0.0146 0.9913 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM254650 5 0.1296 0.8254 0.000 0.000 0.004 0.004 0.948 0.044
#> GSM254687 5 0.1296 0.8254 0.000 0.000 0.004 0.004 0.948 0.044
#> GSM254637 5 0.2762 0.8279 0.000 0.000 0.196 0.000 0.804 0.000
#> GSM254684 5 0.3401 0.7857 0.000 0.000 0.204 0.004 0.776 0.016
#> GSM254649 2 0.0806 0.8002 0.000 0.972 0.000 0.020 0.000 0.008
#> GSM254660 2 0.2766 0.7285 0.000 0.852 0.004 0.020 0.000 0.124
#> GSM254693 2 0.0146 0.7996 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM254695 4 0.0717 0.6502 0.000 0.008 0.000 0.976 0.000 0.016
#> GSM254702 2 0.1219 0.7850 0.000 0.948 0.000 0.048 0.000 0.004
#> GSM254643 2 0.2973 0.7212 0.000 0.836 0.004 0.024 0.000 0.136
#> GSM254727 2 0.0777 0.7990 0.000 0.972 0.000 0.024 0.000 0.004
#> GSM254640 2 0.0458 0.7992 0.000 0.984 0.000 0.016 0.000 0.000
#> GSM254626 2 0.0458 0.7985 0.000 0.984 0.000 0.000 0.000 0.016
#> GSM254635 4 0.4012 0.7239 0.000 0.176 0.000 0.748 0.000 0.076
#> GSM254653 2 0.0146 0.7996 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM254658 2 0.0291 0.8010 0.000 0.992 0.000 0.004 0.000 0.004
#> GSM254681 2 0.4426 0.3767 0.000 0.652 0.000 0.296 0.000 0.052
#> GSM254719 2 0.0146 0.7996 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM254673 2 0.0000 0.8001 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM254655 2 0.0291 0.8002 0.000 0.992 0.000 0.004 0.000 0.004
#> GSM254669 2 0.0520 0.8008 0.000 0.984 0.000 0.008 0.000 0.008
#> GSM254699 2 0.0146 0.8006 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM254703 4 0.4048 0.6602 0.000 0.132 0.004 0.764 0.000 0.100
#> GSM254708 4 0.3740 0.7091 0.000 0.228 0.000 0.740 0.000 0.032
#> GSM254715 2 0.4520 0.5769 0.000 0.716 0.004 0.156 0.000 0.124
#> GSM254628 2 0.0914 0.8006 0.000 0.968 0.000 0.016 0.000 0.016
#> GSM254634 4 0.2631 0.7367 0.000 0.180 0.000 0.820 0.000 0.000
#> GSM254646 2 0.4033 0.5154 0.000 0.724 0.000 0.224 0.000 0.052
#> GSM254671 2 0.2003 0.7556 0.000 0.884 0.000 0.116 0.000 0.000
#> GSM254711 4 0.5354 0.1735 0.000 0.444 0.004 0.460 0.000 0.092
#> GSM254717 2 0.3508 0.4535 0.000 0.704 0.000 0.292 0.000 0.004
#> GSM254723 6 0.4859 0.8002 0.000 0.000 0.240 0.020 0.068 0.672
#> GSM254730 2 0.0777 0.8004 0.000 0.972 0.000 0.024 0.000 0.004
#> GSM254731 2 0.0937 0.7901 0.000 0.960 0.000 0.040 0.000 0.000
#> GSM254632 6 0.4574 0.8136 0.000 0.000 0.260 0.020 0.040 0.680
#> GSM254662 2 0.0692 0.7995 0.000 0.976 0.000 0.020 0.000 0.004
#> GSM254677 4 0.0363 0.6656 0.000 0.012 0.000 0.988 0.000 0.000
#> GSM254665 2 0.5432 0.0448 0.000 0.536 0.004 0.344 0.000 0.116
#> GSM254691 4 0.4269 0.4451 0.000 0.412 0.000 0.568 0.000 0.020
#> GSM254644 2 0.1500 0.7926 0.000 0.936 0.000 0.052 0.000 0.012
#> GSM254667 4 0.3558 0.7300 0.000 0.184 0.004 0.780 0.000 0.032
#> GSM254676 2 0.4574 -0.1438 0.000 0.524 0.000 0.440 0.000 0.036
#> GSM254679 2 0.3851 0.0048 0.000 0.540 0.000 0.460 0.000 0.000
#> GSM254689 2 0.4423 0.4029 0.000 0.668 0.000 0.272 0.000 0.060
#> GSM254706 4 0.4152 0.6341 0.000 0.304 0.000 0.664 0.000 0.032
#> GSM254712 4 0.4843 0.5616 0.000 0.216 0.004 0.668 0.000 0.112
#> GSM254713 2 0.4934 0.4944 0.000 0.660 0.004 0.212 0.000 0.124
#> GSM254683 4 0.4499 0.3903 0.000 0.428 0.000 0.540 0.000 0.032
#> GSM254710 6 0.5295 0.6641 0.000 0.000 0.148 0.152 0.032 0.668
#> GSM254725 4 0.2597 0.7374 0.000 0.176 0.000 0.824 0.000 0.000
#> GSM254651 2 0.4218 0.0210 0.000 0.556 0.000 0.428 0.000 0.016
#> GSM254638 4 0.2169 0.6484 0.000 0.012 0.008 0.900 0.000 0.080
#> GSM254685 2 0.3777 0.6812 0.000 0.788 0.004 0.084 0.000 0.124
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) individual(p) disease.state(p) gender(p) other(p) k
#> SD:mclust 107 1.00e-21 0.64037 0.454 0.7329 0.961 2
#> SD:mclust 100 3.17e-20 0.02926 0.498 0.0562 0.846 3
#> SD:mclust 64 1.62e-13 0.13515 0.486 0.0279 1.000 4
#> SD:mclust 101 1.01e-18 0.02609 0.661 0.4028 0.463 5
#> SD:mclust 95 2.62e-17 0.00567 0.706 0.4174 0.502 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 21168 rows and 107 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 1.000 0.971 0.988 0.5037 0.497 0.497
#> 3 3 0.587 0.615 0.795 0.2334 0.938 0.876
#> 4 4 0.653 0.603 0.793 0.1420 0.809 0.586
#> 5 5 0.716 0.690 0.833 0.0496 0.904 0.714
#> 6 6 0.746 0.675 0.826 0.0400 0.959 0.858
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
#> GSM254629 1 0.0000 0.981 1.000 0.000
#> GSM254648 2 0.0938 0.984 0.012 0.988
#> GSM254694 1 0.6801 0.782 0.820 0.180
#> GSM254701 1 0.0000 0.981 1.000 0.000
#> GSM254728 1 0.0000 0.981 1.000 0.000
#> GSM254726 1 0.7745 0.707 0.772 0.228
#> GSM254639 1 0.0000 0.981 1.000 0.000
#> GSM254652 1 0.0000 0.981 1.000 0.000
#> GSM254700 1 0.0000 0.981 1.000 0.000
#> GSM254625 1 0.0000 0.981 1.000 0.000
#> GSM254636 1 0.0000 0.981 1.000 0.000
#> GSM254659 1 0.0000 0.981 1.000 0.000
#> GSM254680 1 0.0000 0.981 1.000 0.000
#> GSM254686 1 0.0000 0.981 1.000 0.000
#> GSM254718 1 0.0000 0.981 1.000 0.000
#> GSM254674 1 0.0000 0.981 1.000 0.000
#> GSM254668 1 0.0000 0.981 1.000 0.000
#> GSM254697 1 0.0000 0.981 1.000 0.000
#> GSM254704 1 0.0000 0.981 1.000 0.000
#> GSM254707 1 0.0000 0.981 1.000 0.000
#> GSM254714 1 0.0000 0.981 1.000 0.000
#> GSM254722 1 0.0000 0.981 1.000 0.000
#> GSM254627 1 0.0000 0.981 1.000 0.000
#> GSM254630 1 0.0000 0.981 1.000 0.000
#> GSM254633 1 0.0000 0.981 1.000 0.000
#> GSM254670 1 0.0000 0.981 1.000 0.000
#> GSM254716 1 0.0000 0.981 1.000 0.000
#> GSM254720 1 0.0000 0.981 1.000 0.000
#> GSM254729 1 0.0000 0.981 1.000 0.000
#> GSM254654 1 0.7139 0.760 0.804 0.196
#> GSM254656 1 0.9963 0.142 0.536 0.464
#> GSM254631 1 0.0000 0.981 1.000 0.000
#> GSM254657 1 0.0000 0.981 1.000 0.000
#> GSM254664 1 0.0000 0.981 1.000 0.000
#> GSM254672 1 0.0000 0.981 1.000 0.000
#> GSM254692 1 0.0000 0.981 1.000 0.000
#> GSM254645 1 0.0000 0.981 1.000 0.000
#> GSM254666 1 0.0000 0.981 1.000 0.000
#> GSM254675 1 0.0000 0.981 1.000 0.000
#> GSM254678 1 0.0000 0.981 1.000 0.000
#> GSM254688 1 0.0000 0.981 1.000 0.000
#> GSM254690 1 0.0000 0.981 1.000 0.000
#> GSM254696 1 0.0000 0.981 1.000 0.000
#> GSM254705 1 0.0000 0.981 1.000 0.000
#> GSM254642 1 0.0000 0.981 1.000 0.000
#> GSM254661 1 0.0000 0.981 1.000 0.000
#> GSM254698 1 0.0000 0.981 1.000 0.000
#> GSM254641 1 0.0000 0.981 1.000 0.000
#> GSM254647 1 0.0000 0.981 1.000 0.000
#> GSM254663 1 0.0000 0.981 1.000 0.000
#> GSM254682 1 0.0000 0.981 1.000 0.000
#> GSM254709 1 0.0000 0.981 1.000 0.000
#> GSM254721 1 0.0000 0.981 1.000 0.000
#> GSM254724 1 0.0000 0.981 1.000 0.000
#> GSM254650 1 0.0000 0.981 1.000 0.000
#> GSM254687 1 0.0000 0.981 1.000 0.000
#> GSM254637 1 0.0000 0.981 1.000 0.000
#> GSM254684 1 0.0000 0.981 1.000 0.000
#> GSM254649 2 0.0000 0.995 0.000 1.000
#> GSM254660 2 0.0000 0.995 0.000 1.000
#> GSM254693 2 0.0000 0.995 0.000 1.000
#> GSM254695 2 0.0000 0.995 0.000 1.000
#> GSM254702 2 0.0000 0.995 0.000 1.000
#> GSM254643 2 0.0000 0.995 0.000 1.000
#> GSM254727 2 0.0000 0.995 0.000 1.000
#> GSM254640 2 0.0000 0.995 0.000 1.000
#> GSM254626 2 0.0000 0.995 0.000 1.000
#> GSM254635 2 0.0000 0.995 0.000 1.000
#> GSM254653 2 0.0000 0.995 0.000 1.000
#> GSM254658 2 0.0000 0.995 0.000 1.000
#> GSM254681 2 0.0000 0.995 0.000 1.000
#> GSM254719 2 0.0000 0.995 0.000 1.000
#> GSM254673 2 0.0000 0.995 0.000 1.000
#> GSM254655 2 0.0000 0.995 0.000 1.000
#> GSM254669 2 0.0000 0.995 0.000 1.000
#> GSM254699 2 0.0000 0.995 0.000 1.000
#> GSM254703 2 0.0000 0.995 0.000 1.000
#> GSM254708 2 0.0000 0.995 0.000 1.000
#> GSM254715 2 0.0000 0.995 0.000 1.000
#> GSM254628 2 0.0000 0.995 0.000 1.000
#> GSM254634 2 0.0000 0.995 0.000 1.000
#> GSM254646 2 0.0000 0.995 0.000 1.000
#> GSM254671 2 0.0000 0.995 0.000 1.000
#> GSM254711 2 0.0000 0.995 0.000 1.000
#> GSM254717 2 0.0000 0.995 0.000 1.000
#> GSM254723 2 0.4690 0.888 0.100 0.900
#> GSM254730 2 0.0000 0.995 0.000 1.000
#> GSM254731 2 0.0000 0.995 0.000 1.000
#> GSM254632 2 0.5059 0.872 0.112 0.888
#> GSM254662 2 0.0000 0.995 0.000 1.000
#> GSM254677 2 0.0000 0.995 0.000 1.000
#> GSM254665 2 0.0000 0.995 0.000 1.000
#> GSM254691 2 0.0000 0.995 0.000 1.000
#> GSM254644 2 0.0000 0.995 0.000 1.000
#> GSM254667 2 0.0000 0.995 0.000 1.000
#> GSM254676 2 0.0000 0.995 0.000 1.000
#> GSM254679 2 0.0000 0.995 0.000 1.000
#> GSM254689 2 0.0000 0.995 0.000 1.000
#> GSM254706 2 0.0000 0.995 0.000 1.000
#> GSM254712 2 0.0000 0.995 0.000 1.000
#> GSM254713 2 0.0000 0.995 0.000 1.000
#> GSM254683 2 0.0000 0.995 0.000 1.000
#> GSM254710 2 0.0000 0.995 0.000 1.000
#> GSM254725 2 0.0000 0.995 0.000 1.000
#> GSM254651 2 0.0000 0.995 0.000 1.000
#> GSM254638 2 0.0000 0.995 0.000 1.000
#> GSM254685 2 0.0000 0.995 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM254629 1 0.5760 0.4304 0.672 0.000 0.328
#> GSM254648 2 0.6865 0.5965 0.020 0.596 0.384
#> GSM254694 1 0.9379 -0.2899 0.504 0.208 0.288
#> GSM254701 1 0.4555 0.5647 0.800 0.000 0.200
#> GSM254728 1 0.6168 0.1098 0.588 0.000 0.412
#> GSM254726 3 0.9130 0.4462 0.356 0.152 0.492
#> GSM254639 3 0.6192 0.5200 0.420 0.000 0.580
#> GSM254652 1 0.6062 0.1913 0.616 0.000 0.384
#> GSM254700 1 0.0000 0.6648 1.000 0.000 0.000
#> GSM254625 1 0.6308 0.0861 0.508 0.000 0.492
#> GSM254636 1 0.6008 0.2207 0.628 0.000 0.372
#> GSM254659 1 0.5882 0.2662 0.652 0.000 0.348
#> GSM254680 1 0.2959 0.6527 0.900 0.000 0.100
#> GSM254686 1 0.3879 0.6232 0.848 0.000 0.152
#> GSM254718 1 0.6192 -0.1242 0.580 0.000 0.420
#> GSM254674 1 0.4062 0.6106 0.836 0.000 0.164
#> GSM254668 1 0.2959 0.6549 0.900 0.000 0.100
#> GSM254697 1 0.1031 0.6641 0.976 0.000 0.024
#> GSM254704 1 0.0237 0.6651 0.996 0.000 0.004
#> GSM254707 1 0.4796 0.5894 0.780 0.000 0.220
#> GSM254714 1 0.1031 0.6634 0.976 0.000 0.024
#> GSM254722 1 0.3879 0.6161 0.848 0.000 0.152
#> GSM254627 1 0.1031 0.6641 0.976 0.000 0.024
#> GSM254630 1 0.4178 0.6087 0.828 0.000 0.172
#> GSM254633 1 0.4346 0.5894 0.816 0.000 0.184
#> GSM254670 1 0.6308 -0.2916 0.508 0.000 0.492
#> GSM254716 3 0.6062 0.0494 0.384 0.000 0.616
#> GSM254720 1 0.0424 0.6651 0.992 0.000 0.008
#> GSM254729 3 0.6451 0.5060 0.436 0.004 0.560
#> GSM254654 1 0.9284 -0.2797 0.512 0.192 0.296
#> GSM254656 3 0.7847 0.2561 0.068 0.344 0.588
#> GSM254631 1 0.2066 0.6669 0.940 0.000 0.060
#> GSM254657 3 0.6215 0.5147 0.428 0.000 0.572
#> GSM254664 1 0.1289 0.6687 0.968 0.000 0.032
#> GSM254672 1 0.3412 0.5584 0.876 0.000 0.124
#> GSM254692 1 0.2448 0.6094 0.924 0.000 0.076
#> GSM254645 3 0.6274 0.4661 0.456 0.000 0.544
#> GSM254666 1 0.5810 0.3413 0.664 0.000 0.336
#> GSM254675 1 0.0237 0.6651 0.996 0.000 0.004
#> GSM254678 1 0.1411 0.6651 0.964 0.000 0.036
#> GSM254688 1 0.4504 0.5943 0.804 0.000 0.196
#> GSM254690 1 0.4002 0.6208 0.840 0.000 0.160
#> GSM254696 1 0.6180 0.0903 0.584 0.000 0.416
#> GSM254705 1 0.1964 0.6567 0.944 0.000 0.056
#> GSM254642 1 0.1289 0.6624 0.968 0.000 0.032
#> GSM254661 1 0.6095 0.1733 0.608 0.000 0.392
#> GSM254698 1 0.6079 0.1576 0.612 0.000 0.388
#> GSM254641 1 0.3116 0.6486 0.892 0.000 0.108
#> GSM254647 1 0.0000 0.6648 1.000 0.000 0.000
#> GSM254663 1 0.0592 0.6613 0.988 0.000 0.012
#> GSM254682 1 0.4842 0.5685 0.776 0.000 0.224
#> GSM254709 1 0.5882 0.2148 0.652 0.000 0.348
#> GSM254721 1 0.0000 0.6648 1.000 0.000 0.000
#> GSM254724 1 0.0000 0.6648 1.000 0.000 0.000
#> GSM254650 1 0.5327 0.3241 0.728 0.000 0.272
#> GSM254687 1 0.4399 0.4572 0.812 0.000 0.188
#> GSM254637 1 0.1643 0.6565 0.956 0.000 0.044
#> GSM254684 1 0.6095 0.1729 0.608 0.000 0.392
#> GSM254649 2 0.4346 0.8013 0.000 0.816 0.184
#> GSM254660 2 0.0592 0.8710 0.000 0.988 0.012
#> GSM254693 2 0.2066 0.8615 0.000 0.940 0.060
#> GSM254695 2 0.4452 0.7857 0.000 0.808 0.192
#> GSM254702 2 0.2066 0.8595 0.000 0.940 0.060
#> GSM254643 2 0.2261 0.8590 0.000 0.932 0.068
#> GSM254727 2 0.0592 0.8708 0.000 0.988 0.012
#> GSM254640 2 0.0424 0.8713 0.000 0.992 0.008
#> GSM254626 2 0.2066 0.8618 0.000 0.940 0.060
#> GSM254635 2 0.4605 0.7755 0.000 0.796 0.204
#> GSM254653 2 0.0747 0.8704 0.000 0.984 0.016
#> GSM254658 2 0.4702 0.7836 0.000 0.788 0.212
#> GSM254681 2 0.6126 0.6056 0.000 0.600 0.400
#> GSM254719 2 0.0237 0.8713 0.000 0.996 0.004
#> GSM254673 2 0.1643 0.8654 0.000 0.956 0.044
#> GSM254655 2 0.0424 0.8713 0.000 0.992 0.008
#> GSM254669 2 0.2165 0.8603 0.000 0.936 0.064
#> GSM254699 2 0.0424 0.8713 0.000 0.992 0.008
#> GSM254703 2 0.3340 0.8351 0.000 0.880 0.120
#> GSM254708 2 0.0000 0.8715 0.000 1.000 0.000
#> GSM254715 2 0.3551 0.8287 0.000 0.868 0.132
#> GSM254628 2 0.4062 0.8140 0.000 0.836 0.164
#> GSM254634 2 0.3482 0.8309 0.000 0.872 0.128
#> GSM254646 2 0.5948 0.6500 0.000 0.640 0.360
#> GSM254671 2 0.2537 0.8525 0.000 0.920 0.080
#> GSM254711 2 0.3340 0.8351 0.000 0.880 0.120
#> GSM254717 2 0.0592 0.8714 0.000 0.988 0.012
#> GSM254723 2 0.4912 0.7761 0.008 0.796 0.196
#> GSM254730 2 0.0747 0.8706 0.000 0.984 0.016
#> GSM254731 2 0.0747 0.8706 0.000 0.984 0.016
#> GSM254632 2 0.6398 0.6455 0.192 0.748 0.060
#> GSM254662 2 0.0424 0.8711 0.000 0.992 0.008
#> GSM254677 2 0.4974 0.7440 0.000 0.764 0.236
#> GSM254665 2 0.1643 0.8654 0.000 0.956 0.044
#> GSM254691 2 0.0000 0.8715 0.000 1.000 0.000
#> GSM254644 2 0.0747 0.8706 0.000 0.984 0.016
#> GSM254667 2 0.2165 0.8606 0.000 0.936 0.064
#> GSM254676 2 0.0000 0.8715 0.000 1.000 0.000
#> GSM254679 2 0.3752 0.8215 0.000 0.856 0.144
#> GSM254689 2 0.5835 0.6705 0.000 0.660 0.340
#> GSM254706 2 0.4346 0.8023 0.000 0.816 0.184
#> GSM254712 2 0.3551 0.8287 0.000 0.868 0.132
#> GSM254713 2 0.3686 0.8240 0.000 0.860 0.140
#> GSM254683 2 0.5621 0.7019 0.000 0.692 0.308
#> GSM254710 2 0.6680 0.4588 0.008 0.508 0.484
#> GSM254725 2 0.4796 0.7595 0.000 0.780 0.220
#> GSM254651 2 0.4931 0.7687 0.000 0.768 0.232
#> GSM254638 2 0.4796 0.7595 0.000 0.780 0.220
#> GSM254685 2 0.0747 0.8706 0.000 0.984 0.016
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM254629 4 0.6653 -0.0405 0.196 0.000 0.180 0.624
#> GSM254648 4 0.4050 0.3819 0.000 0.168 0.024 0.808
#> GSM254694 1 0.9234 0.0600 0.388 0.132 0.144 0.336
#> GSM254701 1 0.7785 -0.0460 0.428 0.000 0.284 0.288
#> GSM254728 3 0.5256 0.6715 0.040 0.000 0.700 0.260
#> GSM254726 3 0.6336 0.5729 0.004 0.076 0.616 0.304
#> GSM254639 3 0.3969 0.6712 0.016 0.000 0.804 0.180
#> GSM254652 3 0.4934 0.6730 0.028 0.000 0.720 0.252
#> GSM254700 1 0.0188 0.7703 0.996 0.000 0.000 0.004
#> GSM254625 4 0.5013 0.1761 0.056 0.004 0.176 0.764
#> GSM254636 3 0.4375 0.6778 0.032 0.000 0.788 0.180
#> GSM254659 3 0.5565 0.6669 0.056 0.000 0.684 0.260
#> GSM254680 3 0.7761 0.3707 0.340 0.000 0.416 0.244
#> GSM254686 3 0.7841 0.4326 0.284 0.000 0.404 0.312
#> GSM254718 3 0.7917 0.2572 0.344 0.000 0.344 0.312
#> GSM254674 3 0.7059 0.5997 0.184 0.000 0.568 0.248
#> GSM254668 1 0.7609 0.0562 0.464 0.000 0.224 0.312
#> GSM254697 1 0.0000 0.7701 1.000 0.000 0.000 0.000
#> GSM254704 1 0.0336 0.7699 0.992 0.000 0.000 0.008
#> GSM254707 4 0.6229 0.0172 0.132 0.000 0.204 0.664
#> GSM254714 1 0.2563 0.7447 0.908 0.000 0.020 0.072
#> GSM254722 1 0.3893 0.6662 0.796 0.000 0.196 0.008
#> GSM254627 1 0.0000 0.7701 1.000 0.000 0.000 0.000
#> GSM254630 1 0.3895 0.6729 0.804 0.000 0.184 0.012
#> GSM254633 3 0.7436 0.5473 0.236 0.000 0.512 0.252
#> GSM254670 3 0.0895 0.6259 0.020 0.000 0.976 0.004
#> GSM254716 4 0.4807 0.0733 0.024 0.000 0.248 0.728
#> GSM254720 1 0.0707 0.7679 0.980 0.000 0.000 0.020
#> GSM254729 3 0.4748 0.6692 0.016 0.000 0.716 0.268
#> GSM254654 4 0.9587 -0.0407 0.304 0.228 0.128 0.340
#> GSM254656 3 0.2131 0.5936 0.016 0.008 0.936 0.040
#> GSM254631 1 0.6819 0.3594 0.604 0.000 0.188 0.208
#> GSM254657 3 0.1406 0.6262 0.016 0.000 0.960 0.024
#> GSM254664 1 0.5106 0.5538 0.720 0.000 0.040 0.240
#> GSM254672 1 0.0592 0.7690 0.984 0.000 0.000 0.016
#> GSM254692 1 0.0921 0.7589 0.972 0.000 0.000 0.028
#> GSM254645 1 0.6737 0.2287 0.488 0.000 0.420 0.092
#> GSM254666 3 0.6684 0.5728 0.104 0.000 0.560 0.336
#> GSM254675 1 0.1302 0.7632 0.956 0.000 0.000 0.044
#> GSM254678 1 0.2011 0.7452 0.920 0.000 0.080 0.000
#> GSM254688 3 0.7808 0.1328 0.272 0.000 0.416 0.312
#> GSM254690 1 0.5296 0.1256 0.500 0.000 0.492 0.008
#> GSM254696 3 0.0707 0.6236 0.020 0.000 0.980 0.000
#> GSM254705 1 0.1305 0.7583 0.960 0.000 0.004 0.036
#> GSM254642 1 0.0000 0.7701 1.000 0.000 0.000 0.000
#> GSM254661 3 0.4868 0.6720 0.024 0.000 0.720 0.256
#> GSM254698 3 0.2799 0.5647 0.108 0.000 0.884 0.008
#> GSM254641 1 0.7220 0.2340 0.544 0.000 0.196 0.260
#> GSM254647 1 0.0336 0.7705 0.992 0.000 0.008 0.000
#> GSM254663 1 0.0657 0.7692 0.984 0.000 0.004 0.012
#> GSM254682 3 0.6637 0.3238 0.144 0.000 0.616 0.240
#> GSM254709 4 0.5250 -0.2296 0.440 0.000 0.008 0.552
#> GSM254721 1 0.0188 0.7703 0.996 0.000 0.000 0.004
#> GSM254724 1 0.0188 0.7703 0.996 0.000 0.000 0.004
#> GSM254650 1 0.4522 0.4435 0.680 0.000 0.000 0.320
#> GSM254687 1 0.4624 0.4193 0.660 0.000 0.000 0.340
#> GSM254637 1 0.3978 0.6602 0.796 0.000 0.012 0.192
#> GSM254684 3 0.1151 0.6187 0.024 0.000 0.968 0.008
#> GSM254649 2 0.4072 0.6652 0.000 0.748 0.000 0.252
#> GSM254660 2 0.0188 0.8637 0.000 0.996 0.000 0.004
#> GSM254693 2 0.2647 0.8176 0.000 0.880 0.000 0.120
#> GSM254695 2 0.2125 0.8434 0.000 0.920 0.004 0.076
#> GSM254702 2 0.0921 0.8596 0.000 0.972 0.000 0.028
#> GSM254643 2 0.1940 0.8470 0.000 0.924 0.000 0.076
#> GSM254727 2 0.1940 0.8487 0.000 0.924 0.000 0.076
#> GSM254640 2 0.0000 0.8639 0.000 1.000 0.000 0.000
#> GSM254626 2 0.2149 0.8402 0.000 0.912 0.000 0.088
#> GSM254635 2 0.1940 0.8405 0.000 0.924 0.000 0.076
#> GSM254653 2 0.1118 0.8602 0.000 0.964 0.000 0.036
#> GSM254658 2 0.3975 0.6863 0.000 0.760 0.000 0.240
#> GSM254681 4 0.4843 0.1558 0.000 0.396 0.000 0.604
#> GSM254719 2 0.0592 0.8631 0.000 0.984 0.000 0.016
#> GSM254673 2 0.2011 0.8448 0.000 0.920 0.000 0.080
#> GSM254655 2 0.0188 0.8639 0.000 0.996 0.000 0.004
#> GSM254669 2 0.2760 0.8108 0.000 0.872 0.000 0.128
#> GSM254699 2 0.0188 0.8639 0.000 0.996 0.000 0.004
#> GSM254703 2 0.2222 0.8412 0.000 0.924 0.016 0.060
#> GSM254708 2 0.2081 0.8428 0.000 0.916 0.000 0.084
#> GSM254715 2 0.1970 0.8453 0.000 0.932 0.008 0.060
#> GSM254628 2 0.3444 0.7569 0.000 0.816 0.000 0.184
#> GSM254634 2 0.1389 0.8535 0.000 0.952 0.000 0.048
#> GSM254646 2 0.4998 0.1078 0.000 0.512 0.000 0.488
#> GSM254671 2 0.0707 0.8619 0.000 0.980 0.000 0.020
#> GSM254711 2 0.1474 0.8519 0.000 0.948 0.000 0.052
#> GSM254717 2 0.1940 0.8499 0.000 0.924 0.000 0.076
#> GSM254723 2 0.3224 0.7955 0.000 0.864 0.016 0.120
#> GSM254730 2 0.0000 0.8639 0.000 1.000 0.000 0.000
#> GSM254731 2 0.0592 0.8624 0.000 0.984 0.000 0.016
#> GSM254632 4 0.7280 0.2146 0.004 0.384 0.132 0.480
#> GSM254662 2 0.1557 0.8542 0.000 0.944 0.000 0.056
#> GSM254677 2 0.3099 0.8086 0.000 0.876 0.020 0.104
#> GSM254665 2 0.1867 0.8491 0.000 0.928 0.000 0.072
#> GSM254691 2 0.1022 0.8611 0.000 0.968 0.000 0.032
#> GSM254644 2 0.0592 0.8638 0.000 0.984 0.000 0.016
#> GSM254667 2 0.4790 0.4315 0.000 0.620 0.000 0.380
#> GSM254676 2 0.1211 0.8591 0.000 0.960 0.000 0.040
#> GSM254679 2 0.1637 0.8489 0.000 0.940 0.000 0.060
#> GSM254689 4 0.4994 -0.0969 0.000 0.480 0.000 0.520
#> GSM254706 2 0.4933 0.2972 0.000 0.568 0.000 0.432
#> GSM254712 2 0.2522 0.8295 0.000 0.908 0.016 0.076
#> GSM254713 2 0.2300 0.8385 0.000 0.920 0.016 0.064
#> GSM254683 2 0.4996 0.1118 0.000 0.516 0.000 0.484
#> GSM254710 4 0.5213 0.2959 0.000 0.328 0.020 0.652
#> GSM254725 2 0.1902 0.8451 0.000 0.932 0.004 0.064
#> GSM254651 2 0.4776 0.4413 0.000 0.624 0.000 0.376
#> GSM254638 2 0.2861 0.8122 0.000 0.888 0.016 0.096
#> GSM254685 2 0.1388 0.8580 0.000 0.960 0.012 0.028
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM254629 3 0.3280 0.6825 0.004 0.000 0.808 0.004 0.184
#> GSM254648 3 0.3422 0.6703 0.000 0.004 0.792 0.004 0.200
#> GSM254694 3 0.3461 0.6736 0.008 0.000 0.848 0.076 0.068
#> GSM254701 3 0.2353 0.7173 0.004 0.000 0.908 0.060 0.028
#> GSM254728 3 0.2079 0.7527 0.000 0.000 0.916 0.064 0.020
#> GSM254726 3 0.1579 0.7424 0.000 0.000 0.944 0.032 0.024
#> GSM254639 4 0.4961 0.3473 0.000 0.000 0.448 0.524 0.028
#> GSM254652 3 0.2464 0.7465 0.000 0.000 0.888 0.096 0.016
#> GSM254700 1 0.0000 0.8577 1.000 0.000 0.000 0.000 0.000
#> GSM254625 3 0.4706 0.0304 0.000 0.004 0.496 0.008 0.492
#> GSM254636 4 0.4508 0.5974 0.000 0.000 0.332 0.648 0.020
#> GSM254659 3 0.1430 0.7560 0.000 0.000 0.944 0.052 0.004
#> GSM254680 3 0.4352 0.7023 0.036 0.000 0.792 0.132 0.040
#> GSM254686 3 0.2488 0.7370 0.000 0.000 0.872 0.004 0.124
#> GSM254718 3 0.2654 0.7049 0.000 0.000 0.884 0.084 0.032
#> GSM254674 3 0.3940 0.6195 0.000 0.000 0.756 0.220 0.024
#> GSM254668 3 0.3848 0.6785 0.012 0.000 0.780 0.012 0.196
#> GSM254697 1 0.0566 0.8575 0.984 0.000 0.000 0.004 0.012
#> GSM254704 1 0.0000 0.8577 1.000 0.000 0.000 0.000 0.000
#> GSM254707 3 0.4359 0.3123 0.000 0.000 0.584 0.004 0.412
#> GSM254714 1 0.7222 0.1164 0.436 0.000 0.380 0.116 0.068
#> GSM254722 1 0.3427 0.7191 0.796 0.000 0.000 0.192 0.012
#> GSM254627 1 0.0566 0.8575 0.984 0.000 0.000 0.004 0.012
#> GSM254630 1 0.3601 0.7557 0.820 0.000 0.000 0.128 0.052
#> GSM254633 3 0.2800 0.7482 0.016 0.000 0.888 0.072 0.024
#> GSM254670 4 0.2516 0.7981 0.000 0.000 0.140 0.860 0.000
#> GSM254716 5 0.4392 0.0900 0.000 0.000 0.380 0.008 0.612
#> GSM254720 1 0.0579 0.8543 0.984 0.000 0.008 0.000 0.008
#> GSM254729 3 0.2674 0.7289 0.000 0.000 0.868 0.120 0.012
#> GSM254654 3 0.3876 0.6301 0.004 0.000 0.812 0.116 0.068
#> GSM254656 4 0.2722 0.7888 0.000 0.004 0.120 0.868 0.008
#> GSM254631 3 0.5153 0.5823 0.208 0.000 0.708 0.060 0.024
#> GSM254657 4 0.3809 0.7293 0.000 0.000 0.256 0.736 0.008
#> GSM254664 3 0.4527 0.5400 0.272 0.000 0.696 0.004 0.028
#> GSM254672 1 0.0162 0.8571 0.996 0.000 0.004 0.000 0.000
#> GSM254692 1 0.0510 0.8575 0.984 0.000 0.000 0.000 0.016
#> GSM254645 1 0.7774 0.0363 0.376 0.000 0.140 0.376 0.108
#> GSM254666 3 0.3882 0.6600 0.000 0.000 0.756 0.020 0.224
#> GSM254675 1 0.1544 0.8178 0.932 0.000 0.068 0.000 0.000
#> GSM254678 1 0.1836 0.8252 0.932 0.000 0.036 0.032 0.000
#> GSM254688 5 0.8330 -0.1967 0.144 0.000 0.220 0.312 0.324
#> GSM254690 1 0.6265 0.0948 0.488 0.000 0.088 0.404 0.020
#> GSM254696 4 0.2864 0.7986 0.000 0.000 0.136 0.852 0.012
#> GSM254705 1 0.0162 0.8578 0.996 0.000 0.000 0.004 0.000
#> GSM254642 1 0.0566 0.8575 0.984 0.000 0.000 0.004 0.012
#> GSM254661 3 0.2139 0.7553 0.000 0.000 0.916 0.032 0.052
#> GSM254698 4 0.3035 0.6825 0.112 0.000 0.032 0.856 0.000
#> GSM254641 3 0.2630 0.7493 0.012 0.000 0.892 0.016 0.080
#> GSM254647 1 0.0566 0.8575 0.984 0.000 0.000 0.004 0.012
#> GSM254663 1 0.1278 0.8499 0.960 0.000 0.020 0.004 0.016
#> GSM254682 4 0.6050 0.6687 0.124 0.000 0.132 0.676 0.068
#> GSM254709 5 0.5223 -0.1743 0.044 0.000 0.444 0.000 0.512
#> GSM254721 1 0.0000 0.8577 1.000 0.000 0.000 0.000 0.000
#> GSM254724 1 0.0000 0.8577 1.000 0.000 0.000 0.000 0.000
#> GSM254650 1 0.3039 0.7596 0.836 0.000 0.012 0.000 0.152
#> GSM254687 1 0.4547 0.3818 0.588 0.000 0.012 0.000 0.400
#> GSM254637 3 0.4965 0.3113 0.404 0.000 0.568 0.004 0.024
#> GSM254684 4 0.2818 0.7983 0.004 0.000 0.128 0.860 0.008
#> GSM254649 2 0.2377 0.8066 0.000 0.872 0.000 0.000 0.128
#> GSM254660 2 0.0404 0.8698 0.000 0.988 0.000 0.000 0.012
#> GSM254693 2 0.1341 0.8579 0.000 0.944 0.000 0.000 0.056
#> GSM254695 2 0.2408 0.8423 0.000 0.892 0.000 0.016 0.092
#> GSM254702 2 0.0963 0.8645 0.000 0.964 0.000 0.000 0.036
#> GSM254643 2 0.0510 0.8702 0.000 0.984 0.000 0.000 0.016
#> GSM254727 2 0.0963 0.8676 0.000 0.964 0.000 0.000 0.036
#> GSM254640 2 0.0671 0.8704 0.000 0.980 0.000 0.004 0.016
#> GSM254626 2 0.0963 0.8658 0.000 0.964 0.000 0.000 0.036
#> GSM254635 2 0.2408 0.8310 0.000 0.892 0.004 0.008 0.096
#> GSM254653 2 0.0794 0.8683 0.000 0.972 0.000 0.000 0.028
#> GSM254658 2 0.2179 0.8204 0.000 0.888 0.000 0.000 0.112
#> GSM254681 5 0.3999 0.3229 0.000 0.344 0.000 0.000 0.656
#> GSM254719 2 0.0404 0.8705 0.000 0.988 0.000 0.000 0.012
#> GSM254673 2 0.0963 0.8658 0.000 0.964 0.000 0.000 0.036
#> GSM254655 2 0.0162 0.8708 0.000 0.996 0.000 0.000 0.004
#> GSM254669 2 0.1410 0.8549 0.000 0.940 0.000 0.000 0.060
#> GSM254699 2 0.0162 0.8708 0.000 0.996 0.000 0.000 0.004
#> GSM254703 2 0.4377 0.7512 0.000 0.800 0.036 0.100 0.064
#> GSM254708 2 0.0963 0.8662 0.000 0.964 0.000 0.000 0.036
#> GSM254715 2 0.1981 0.8477 0.000 0.920 0.000 0.016 0.064
#> GSM254628 2 0.1671 0.8465 0.000 0.924 0.000 0.000 0.076
#> GSM254634 2 0.1282 0.8611 0.000 0.952 0.000 0.004 0.044
#> GSM254646 2 0.4060 0.4673 0.000 0.640 0.000 0.000 0.360
#> GSM254671 2 0.0880 0.8656 0.000 0.968 0.000 0.000 0.032
#> GSM254711 2 0.1430 0.8605 0.000 0.944 0.000 0.004 0.052
#> GSM254717 2 0.0963 0.8676 0.000 0.964 0.000 0.000 0.036
#> GSM254723 2 0.7757 0.2781 0.012 0.504 0.116 0.120 0.248
#> GSM254730 2 0.0404 0.8703 0.000 0.988 0.000 0.000 0.012
#> GSM254731 2 0.0963 0.8663 0.000 0.964 0.000 0.000 0.036
#> GSM254632 5 0.7508 0.3375 0.000 0.252 0.144 0.104 0.500
#> GSM254662 2 0.0703 0.8688 0.000 0.976 0.000 0.000 0.024
#> GSM254677 2 0.5801 0.6337 0.000 0.692 0.052 0.116 0.140
#> GSM254665 2 0.0510 0.8702 0.000 0.984 0.000 0.000 0.016
#> GSM254691 2 0.0404 0.8705 0.000 0.988 0.000 0.000 0.012
#> GSM254644 2 0.1082 0.8683 0.000 0.964 0.000 0.008 0.028
#> GSM254667 2 0.4497 0.6631 0.000 0.732 0.000 0.060 0.208
#> GSM254676 2 0.0609 0.8700 0.000 0.980 0.000 0.000 0.020
#> GSM254679 2 0.1357 0.8594 0.000 0.948 0.000 0.004 0.048
#> GSM254689 2 0.4297 0.1713 0.000 0.528 0.000 0.000 0.472
#> GSM254706 2 0.4088 0.4864 0.000 0.632 0.000 0.000 0.368
#> GSM254712 2 0.5067 0.6966 0.000 0.752 0.044 0.116 0.088
#> GSM254713 2 0.4308 0.7554 0.000 0.804 0.032 0.096 0.068
#> GSM254683 2 0.3837 0.5618 0.000 0.692 0.000 0.000 0.308
#> GSM254710 5 0.2970 0.4300 0.000 0.168 0.000 0.004 0.828
#> GSM254725 2 0.1697 0.8536 0.000 0.932 0.000 0.008 0.060
#> GSM254651 2 0.3895 0.5799 0.000 0.680 0.000 0.000 0.320
#> GSM254638 2 0.5430 0.6647 0.000 0.728 0.060 0.092 0.120
#> GSM254685 2 0.2304 0.8425 0.000 0.908 0.000 0.044 0.048
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM254629 3 0.2911 0.7719 0.000 0.000 0.832 0.144 0.024 0.000
#> GSM254648 3 0.3102 0.7660 0.000 0.000 0.816 0.156 0.028 0.000
#> GSM254694 3 0.2553 0.7762 0.000 0.000 0.848 0.144 0.008 0.000
#> GSM254701 3 0.2402 0.7768 0.000 0.000 0.856 0.140 0.004 0.000
#> GSM254728 3 0.3642 0.7151 0.000 0.000 0.760 0.204 0.036 0.000
#> GSM254726 3 0.3236 0.7512 0.000 0.000 0.796 0.180 0.024 0.000
#> GSM254639 6 0.4810 0.5940 0.000 0.000 0.140 0.160 0.008 0.692
#> GSM254652 3 0.1972 0.7932 0.000 0.000 0.916 0.056 0.004 0.024
#> GSM254700 1 0.1364 0.8454 0.944 0.000 0.004 0.048 0.004 0.000
#> GSM254625 3 0.4653 -0.0685 0.000 0.000 0.492 0.020 0.476 0.012
#> GSM254636 6 0.3903 0.4811 0.000 0.000 0.304 0.004 0.012 0.680
#> GSM254659 3 0.1528 0.7888 0.000 0.000 0.936 0.048 0.016 0.000
#> GSM254680 3 0.2332 0.7730 0.020 0.000 0.908 0.004 0.032 0.036
#> GSM254686 3 0.2506 0.7556 0.000 0.000 0.880 0.052 0.068 0.000
#> GSM254718 3 0.4449 0.5781 0.000 0.000 0.664 0.284 0.048 0.004
#> GSM254674 3 0.2169 0.7668 0.008 0.000 0.900 0.000 0.012 0.080
#> GSM254668 3 0.2822 0.7575 0.012 0.000 0.868 0.016 0.096 0.008
#> GSM254697 1 0.1426 0.8476 0.948 0.000 0.000 0.016 0.008 0.028
#> GSM254704 1 0.1429 0.8441 0.940 0.000 0.004 0.052 0.004 0.000
#> GSM254707 3 0.4570 0.2855 0.000 0.000 0.596 0.024 0.368 0.012
#> GSM254714 4 0.4644 0.2247 0.268 0.000 0.068 0.660 0.004 0.000
#> GSM254722 1 0.3268 0.7672 0.808 0.000 0.000 0.020 0.008 0.164
#> GSM254627 1 0.1503 0.8461 0.944 0.000 0.000 0.016 0.008 0.032
#> GSM254630 1 0.3920 0.7876 0.804 0.000 0.000 0.052 0.092 0.052
#> GSM254633 3 0.0767 0.7867 0.000 0.000 0.976 0.008 0.004 0.012
#> GSM254670 6 0.1225 0.7508 0.000 0.000 0.012 0.036 0.000 0.952
#> GSM254716 5 0.5422 0.4780 0.000 0.000 0.240 0.112 0.624 0.024
#> GSM254720 1 0.2239 0.8354 0.900 0.000 0.020 0.072 0.008 0.000
#> GSM254729 3 0.3130 0.7314 0.000 0.000 0.824 0.028 0.004 0.144
#> GSM254654 3 0.2772 0.7628 0.000 0.000 0.816 0.180 0.004 0.000
#> GSM254656 6 0.1285 0.7464 0.000 0.000 0.004 0.052 0.000 0.944
#> GSM254631 3 0.3571 0.7079 0.116 0.000 0.816 0.000 0.020 0.048
#> GSM254657 6 0.5874 0.3916 0.000 0.000 0.072 0.392 0.048 0.488
#> GSM254664 3 0.2076 0.7737 0.060 0.000 0.912 0.000 0.016 0.012
#> GSM254672 1 0.1732 0.8412 0.920 0.000 0.004 0.072 0.004 0.000
#> GSM254692 1 0.1405 0.8501 0.948 0.000 0.000 0.024 0.024 0.004
#> GSM254645 4 0.5110 -0.1593 0.072 0.000 0.000 0.528 0.004 0.396
#> GSM254666 5 0.6143 0.1782 0.000 0.000 0.396 0.120 0.448 0.036
#> GSM254675 1 0.3399 0.7878 0.832 0.000 0.064 0.088 0.016 0.000
#> GSM254678 1 0.4163 0.7078 0.748 0.000 0.008 0.052 0.004 0.188
#> GSM254688 5 0.7320 0.1674 0.072 0.000 0.168 0.028 0.436 0.296
#> GSM254690 1 0.6081 0.0198 0.448 0.000 0.136 0.008 0.012 0.396
#> GSM254696 6 0.1872 0.7434 0.008 0.000 0.064 0.004 0.004 0.920
#> GSM254705 1 0.1498 0.8524 0.940 0.000 0.000 0.032 0.000 0.028
#> GSM254642 1 0.1503 0.8465 0.944 0.000 0.000 0.016 0.008 0.032
#> GSM254661 3 0.3736 0.7565 0.000 0.000 0.784 0.168 0.024 0.024
#> GSM254698 6 0.1723 0.7291 0.048 0.000 0.004 0.012 0.004 0.932
#> GSM254641 3 0.2403 0.7889 0.012 0.000 0.904 0.044 0.032 0.008
#> GSM254647 1 0.1149 0.8489 0.960 0.000 0.000 0.008 0.008 0.024
#> GSM254663 1 0.2680 0.8279 0.892 0.000 0.048 0.016 0.016 0.028
#> GSM254682 6 0.5888 0.5402 0.064 0.000 0.088 0.044 0.128 0.676
#> GSM254709 3 0.5141 0.2619 0.032 0.000 0.536 0.032 0.400 0.000
#> GSM254721 1 0.1285 0.8442 0.944 0.000 0.000 0.052 0.004 0.000
#> GSM254724 1 0.1364 0.8454 0.944 0.000 0.004 0.048 0.004 0.000
#> GSM254650 1 0.3636 0.6956 0.764 0.000 0.012 0.000 0.208 0.016
#> GSM254687 1 0.4208 0.2512 0.536 0.000 0.000 0.004 0.452 0.008
#> GSM254637 3 0.3334 0.7244 0.120 0.000 0.832 0.024 0.020 0.004
#> GSM254684 6 0.1036 0.7480 0.024 0.000 0.008 0.004 0.000 0.964
#> GSM254649 2 0.1007 0.8560 0.000 0.956 0.000 0.000 0.044 0.000
#> GSM254660 2 0.0260 0.8674 0.000 0.992 0.000 0.008 0.000 0.000
#> GSM254693 2 0.0363 0.8665 0.000 0.988 0.000 0.000 0.012 0.000
#> GSM254695 2 0.4328 0.6478 0.000 0.724 0.000 0.164 0.112 0.000
#> GSM254702 2 0.1141 0.8562 0.000 0.948 0.000 0.052 0.000 0.000
#> GSM254643 2 0.0405 0.8679 0.000 0.988 0.000 0.004 0.008 0.000
#> GSM254727 2 0.0820 0.8635 0.000 0.972 0.000 0.012 0.016 0.000
#> GSM254640 2 0.2300 0.7837 0.000 0.856 0.000 0.144 0.000 0.000
#> GSM254626 2 0.0508 0.8671 0.000 0.984 0.000 0.004 0.012 0.000
#> GSM254635 2 0.2219 0.7999 0.000 0.864 0.000 0.136 0.000 0.000
#> GSM254653 2 0.0146 0.8671 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM254658 2 0.1219 0.8516 0.000 0.948 0.000 0.004 0.048 0.000
#> GSM254681 5 0.3565 0.2063 0.000 0.304 0.000 0.004 0.692 0.000
#> GSM254719 2 0.0146 0.8675 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM254673 2 0.0405 0.8679 0.000 0.988 0.000 0.004 0.008 0.000
#> GSM254655 2 0.0146 0.8675 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM254669 2 0.0146 0.8671 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM254699 2 0.0291 0.8681 0.000 0.992 0.000 0.004 0.004 0.000
#> GSM254703 2 0.3747 0.3564 0.000 0.604 0.000 0.396 0.000 0.000
#> GSM254708 2 0.0146 0.8671 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM254715 2 0.3607 0.4776 0.000 0.652 0.000 0.348 0.000 0.000
#> GSM254628 2 0.1152 0.8589 0.000 0.952 0.000 0.004 0.044 0.000
#> GSM254634 2 0.0865 0.8612 0.000 0.964 0.000 0.036 0.000 0.000
#> GSM254646 2 0.2730 0.7321 0.000 0.808 0.000 0.000 0.192 0.000
#> GSM254671 2 0.0508 0.8667 0.000 0.984 0.000 0.012 0.004 0.000
#> GSM254711 2 0.1141 0.8547 0.000 0.948 0.000 0.052 0.000 0.000
#> GSM254717 2 0.1003 0.8649 0.000 0.964 0.000 0.016 0.020 0.000
#> GSM254723 4 0.4477 0.2439 0.012 0.032 0.056 0.780 0.112 0.008
#> GSM254730 2 0.0000 0.8674 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM254731 2 0.0363 0.8670 0.000 0.988 0.000 0.012 0.000 0.000
#> GSM254632 5 0.7187 0.3328 0.004 0.092 0.096 0.052 0.536 0.220
#> GSM254662 2 0.0291 0.8677 0.000 0.992 0.000 0.004 0.004 0.000
#> GSM254677 4 0.4640 0.3639 0.000 0.240 0.000 0.676 0.080 0.004
#> GSM254665 2 0.0717 0.8673 0.000 0.976 0.000 0.016 0.008 0.000
#> GSM254691 2 0.0363 0.8673 0.000 0.988 0.000 0.012 0.000 0.000
#> GSM254644 2 0.2823 0.7227 0.000 0.796 0.000 0.204 0.000 0.000
#> GSM254667 2 0.4901 0.5912 0.000 0.704 0.000 0.024 0.148 0.124
#> GSM254676 2 0.0260 0.8680 0.000 0.992 0.000 0.008 0.000 0.000
#> GSM254679 2 0.1141 0.8554 0.000 0.948 0.000 0.052 0.000 0.000
#> GSM254689 2 0.3868 0.1060 0.000 0.504 0.000 0.000 0.496 0.000
#> GSM254706 2 0.4482 0.4089 0.000 0.600 0.000 0.040 0.360 0.000
#> GSM254712 4 0.3851 -0.0161 0.000 0.460 0.000 0.540 0.000 0.000
#> GSM254713 2 0.3804 0.2675 0.000 0.576 0.000 0.424 0.000 0.000
#> GSM254683 2 0.2416 0.7710 0.000 0.844 0.000 0.000 0.156 0.000
#> GSM254710 5 0.1946 0.4284 0.000 0.072 0.000 0.012 0.912 0.004
#> GSM254725 2 0.1897 0.8310 0.000 0.908 0.000 0.084 0.004 0.004
#> GSM254651 2 0.3848 0.6311 0.000 0.736 0.000 0.040 0.224 0.000
#> GSM254638 2 0.3329 0.6979 0.000 0.768 0.004 0.220 0.008 0.000
#> GSM254685 2 0.3101 0.6697 0.000 0.756 0.000 0.244 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) individual(p) disease.state(p) gender(p) other(p) k
#> SD:NMF 106 3.87e-23 0.5154 0.690 0.697 1.0000 2
#> SD:NMF 84 4.25e-18 0.2470 0.608 0.755 0.7526 3
#> SD:NMF 77 1.90e-17 0.0501 0.405 0.357 0.1803 4
#> SD:NMF 89 3.59e-19 0.0123 0.202 0.217 0.0452 5
#> SD:NMF 84 4.25e-18 0.0119 0.267 0.149 0.0634 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 21168 rows and 107 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#> Subgroups are detected by 'hclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.971 0.988 0.4976 0.505 0.505
#> 3 3 0.655 0.757 0.843 0.2652 0.849 0.701
#> 4 4 0.613 0.640 0.731 0.1289 0.898 0.729
#> 5 5 0.614 0.394 0.636 0.0622 0.825 0.501
#> 6 6 0.673 0.503 0.699 0.0424 0.885 0.559
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
#> GSM254629 1 0.0672 0.975 0.992 0.008
#> GSM254648 1 0.1414 0.966 0.980 0.020
#> GSM254694 1 0.1184 0.969 0.984 0.016
#> GSM254701 1 0.0672 0.975 0.992 0.008
#> GSM254728 1 0.0000 0.979 1.000 0.000
#> GSM254726 1 0.1633 0.963 0.976 0.024
#> GSM254639 1 0.0000 0.979 1.000 0.000
#> GSM254652 1 0.0000 0.979 1.000 0.000
#> GSM254700 1 0.0000 0.979 1.000 0.000
#> GSM254625 1 0.0672 0.975 0.992 0.008
#> GSM254636 1 0.0000 0.979 1.000 0.000
#> GSM254659 1 0.0672 0.975 0.992 0.008
#> GSM254680 1 0.0000 0.979 1.000 0.000
#> GSM254686 1 0.0672 0.975 0.992 0.008
#> GSM254718 1 0.0000 0.979 1.000 0.000
#> GSM254674 1 0.0000 0.979 1.000 0.000
#> GSM254668 1 0.0000 0.979 1.000 0.000
#> GSM254697 1 0.0000 0.979 1.000 0.000
#> GSM254704 1 0.0000 0.979 1.000 0.000
#> GSM254707 1 0.0000 0.979 1.000 0.000
#> GSM254714 1 0.0000 0.979 1.000 0.000
#> GSM254722 1 0.0000 0.979 1.000 0.000
#> GSM254627 1 0.0000 0.979 1.000 0.000
#> GSM254630 1 0.0000 0.979 1.000 0.000
#> GSM254633 1 0.0000 0.979 1.000 0.000
#> GSM254670 1 0.0000 0.979 1.000 0.000
#> GSM254716 1 0.0672 0.975 0.992 0.008
#> GSM254720 1 0.0000 0.979 1.000 0.000
#> GSM254729 1 0.0938 0.973 0.988 0.012
#> GSM254654 1 0.1414 0.966 0.980 0.020
#> GSM254656 1 0.4161 0.902 0.916 0.084
#> GSM254631 1 0.0000 0.979 1.000 0.000
#> GSM254657 1 0.0376 0.977 0.996 0.004
#> GSM254664 1 0.0000 0.979 1.000 0.000
#> GSM254672 1 0.0000 0.979 1.000 0.000
#> GSM254692 1 0.0000 0.979 1.000 0.000
#> GSM254645 1 0.0376 0.977 0.996 0.004
#> GSM254666 1 0.0000 0.979 1.000 0.000
#> GSM254675 1 0.0000 0.979 1.000 0.000
#> GSM254678 1 0.0000 0.979 1.000 0.000
#> GSM254688 1 0.0000 0.979 1.000 0.000
#> GSM254690 1 0.0000 0.979 1.000 0.000
#> GSM254696 1 0.0000 0.979 1.000 0.000
#> GSM254705 1 0.0000 0.979 1.000 0.000
#> GSM254642 1 0.0000 0.979 1.000 0.000
#> GSM254661 1 0.0376 0.977 0.996 0.004
#> GSM254698 1 0.0000 0.979 1.000 0.000
#> GSM254641 1 0.0000 0.979 1.000 0.000
#> GSM254647 1 0.0000 0.979 1.000 0.000
#> GSM254663 1 0.0000 0.979 1.000 0.000
#> GSM254682 1 0.0000 0.979 1.000 0.000
#> GSM254709 1 0.0000 0.979 1.000 0.000
#> GSM254721 1 0.0000 0.979 1.000 0.000
#> GSM254724 1 0.0000 0.979 1.000 0.000
#> GSM254650 1 0.0000 0.979 1.000 0.000
#> GSM254687 1 0.0000 0.979 1.000 0.000
#> GSM254637 1 0.0000 0.979 1.000 0.000
#> GSM254684 1 0.0000 0.979 1.000 0.000
#> GSM254649 2 0.0000 1.000 0.000 1.000
#> GSM254660 2 0.0000 1.000 0.000 1.000
#> GSM254693 2 0.0000 1.000 0.000 1.000
#> GSM254695 2 0.0938 0.988 0.012 0.988
#> GSM254702 2 0.0000 1.000 0.000 1.000
#> GSM254643 2 0.0000 1.000 0.000 1.000
#> GSM254727 2 0.0000 1.000 0.000 1.000
#> GSM254640 2 0.0000 1.000 0.000 1.000
#> GSM254626 2 0.0000 1.000 0.000 1.000
#> GSM254635 2 0.0000 1.000 0.000 1.000
#> GSM254653 2 0.0000 1.000 0.000 1.000
#> GSM254658 2 0.0000 1.000 0.000 1.000
#> GSM254681 2 0.0000 1.000 0.000 1.000
#> GSM254719 2 0.0000 1.000 0.000 1.000
#> GSM254673 2 0.0000 1.000 0.000 1.000
#> GSM254655 2 0.0000 1.000 0.000 1.000
#> GSM254669 2 0.0000 1.000 0.000 1.000
#> GSM254699 2 0.0000 1.000 0.000 1.000
#> GSM254703 2 0.0000 1.000 0.000 1.000
#> GSM254708 2 0.0000 1.000 0.000 1.000
#> GSM254715 2 0.0000 1.000 0.000 1.000
#> GSM254628 2 0.0000 1.000 0.000 1.000
#> GSM254634 2 0.0000 1.000 0.000 1.000
#> GSM254646 2 0.0000 1.000 0.000 1.000
#> GSM254671 2 0.0000 1.000 0.000 1.000
#> GSM254711 2 0.0000 1.000 0.000 1.000
#> GSM254717 2 0.0000 1.000 0.000 1.000
#> GSM254723 1 0.4690 0.885 0.900 0.100
#> GSM254730 2 0.0000 1.000 0.000 1.000
#> GSM254731 2 0.0000 1.000 0.000 1.000
#> GSM254632 1 0.9922 0.219 0.552 0.448
#> GSM254662 2 0.0000 1.000 0.000 1.000
#> GSM254677 2 0.0000 1.000 0.000 1.000
#> GSM254665 2 0.0000 1.000 0.000 1.000
#> GSM254691 2 0.0000 1.000 0.000 1.000
#> GSM254644 2 0.0000 1.000 0.000 1.000
#> GSM254667 2 0.0672 0.992 0.008 0.992
#> GSM254676 2 0.0000 1.000 0.000 1.000
#> GSM254679 2 0.0000 1.000 0.000 1.000
#> GSM254689 2 0.0000 1.000 0.000 1.000
#> GSM254706 2 0.0000 1.000 0.000 1.000
#> GSM254712 2 0.0000 1.000 0.000 1.000
#> GSM254713 2 0.0000 1.000 0.000 1.000
#> GSM254683 2 0.0000 1.000 0.000 1.000
#> GSM254710 1 0.9922 0.219 0.552 0.448
#> GSM254725 2 0.0000 1.000 0.000 1.000
#> GSM254651 2 0.0000 1.000 0.000 1.000
#> GSM254638 2 0.0000 1.000 0.000 1.000
#> GSM254685 2 0.0000 1.000 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM254629 3 0.2165 0.710 0.064 0.000 0.936
#> GSM254648 3 0.2550 0.710 0.056 0.012 0.932
#> GSM254694 3 0.2584 0.711 0.064 0.008 0.928
#> GSM254701 3 0.2165 0.710 0.064 0.000 0.936
#> GSM254728 3 0.4121 0.680 0.168 0.000 0.832
#> GSM254726 3 0.2703 0.709 0.056 0.016 0.928
#> GSM254639 3 0.5138 0.477 0.252 0.000 0.748
#> GSM254652 3 0.3192 0.696 0.112 0.000 0.888
#> GSM254700 1 0.3816 0.755 0.852 0.000 0.148
#> GSM254625 3 0.2400 0.707 0.064 0.004 0.932
#> GSM254636 1 0.6079 0.680 0.612 0.000 0.388
#> GSM254659 3 0.3816 0.687 0.148 0.000 0.852
#> GSM254680 1 0.6204 0.634 0.576 0.000 0.424
#> GSM254686 3 0.1964 0.704 0.056 0.000 0.944
#> GSM254718 3 0.2711 0.711 0.088 0.000 0.912
#> GSM254674 3 0.6308 -0.390 0.492 0.000 0.508
#> GSM254668 3 0.4887 0.567 0.228 0.000 0.772
#> GSM254697 1 0.4002 0.760 0.840 0.000 0.160
#> GSM254704 1 0.3816 0.755 0.852 0.000 0.148
#> GSM254707 3 0.4121 0.648 0.168 0.000 0.832
#> GSM254714 1 0.5465 0.677 0.712 0.000 0.288
#> GSM254722 1 0.5098 0.768 0.752 0.000 0.248
#> GSM254627 1 0.4002 0.760 0.840 0.000 0.160
#> GSM254630 3 0.2711 0.715 0.088 0.000 0.912
#> GSM254633 1 0.6126 0.665 0.600 0.000 0.400
#> GSM254670 3 0.5138 0.477 0.252 0.000 0.748
#> GSM254716 3 0.0237 0.694 0.004 0.000 0.996
#> GSM254720 3 0.6192 0.170 0.420 0.000 0.580
#> GSM254729 3 0.3295 0.714 0.096 0.008 0.896
#> GSM254654 3 0.2550 0.710 0.056 0.012 0.932
#> GSM254656 3 0.6407 0.563 0.160 0.080 0.760
#> GSM254631 1 0.6180 0.644 0.584 0.000 0.416
#> GSM254657 3 0.4409 0.629 0.172 0.004 0.824
#> GSM254664 1 0.6180 0.644 0.584 0.000 0.416
#> GSM254672 1 0.4178 0.763 0.828 0.000 0.172
#> GSM254692 3 0.6062 0.244 0.384 0.000 0.616
#> GSM254645 3 0.4733 0.623 0.196 0.004 0.800
#> GSM254666 3 0.2878 0.711 0.096 0.000 0.904
#> GSM254675 3 0.5733 0.416 0.324 0.000 0.676
#> GSM254678 1 0.5650 0.738 0.688 0.000 0.312
#> GSM254688 3 0.4399 0.628 0.188 0.000 0.812
#> GSM254690 1 0.5905 0.720 0.648 0.000 0.352
#> GSM254696 1 0.6267 0.540 0.548 0.000 0.452
#> GSM254705 3 0.5497 0.481 0.292 0.000 0.708
#> GSM254642 1 0.4002 0.760 0.840 0.000 0.160
#> GSM254661 3 0.2590 0.713 0.072 0.004 0.924
#> GSM254698 1 0.5098 0.768 0.752 0.000 0.248
#> GSM254641 3 0.4178 0.651 0.172 0.000 0.828
#> GSM254647 1 0.5905 0.705 0.648 0.000 0.352
#> GSM254663 3 0.4931 0.579 0.232 0.000 0.768
#> GSM254682 3 0.4121 0.651 0.168 0.000 0.832
#> GSM254709 3 0.4346 0.645 0.184 0.000 0.816
#> GSM254721 1 0.3879 0.757 0.848 0.000 0.152
#> GSM254724 1 0.3816 0.755 0.852 0.000 0.148
#> GSM254650 3 0.4974 0.575 0.236 0.000 0.764
#> GSM254687 3 0.5431 0.496 0.284 0.000 0.716
#> GSM254637 1 0.6192 0.638 0.580 0.000 0.420
#> GSM254684 1 0.6305 0.459 0.516 0.000 0.484
#> GSM254649 2 0.1289 0.953 0.032 0.968 0.000
#> GSM254660 2 0.0747 0.952 0.016 0.984 0.000
#> GSM254693 2 0.1289 0.953 0.032 0.968 0.000
#> GSM254695 2 0.4063 0.918 0.112 0.868 0.020
#> GSM254702 2 0.0747 0.952 0.016 0.984 0.000
#> GSM254643 2 0.1289 0.954 0.032 0.968 0.000
#> GSM254727 2 0.0000 0.953 0.000 1.000 0.000
#> GSM254640 2 0.3192 0.929 0.112 0.888 0.000
#> GSM254626 2 0.1289 0.953 0.032 0.968 0.000
#> GSM254635 2 0.3192 0.929 0.112 0.888 0.000
#> GSM254653 2 0.0000 0.953 0.000 1.000 0.000
#> GSM254658 2 0.1289 0.953 0.032 0.968 0.000
#> GSM254681 2 0.1289 0.953 0.032 0.968 0.000
#> GSM254719 2 0.1031 0.954 0.024 0.976 0.000
#> GSM254673 2 0.1163 0.953 0.028 0.972 0.000
#> GSM254655 2 0.0747 0.952 0.016 0.984 0.000
#> GSM254669 2 0.1163 0.953 0.028 0.972 0.000
#> GSM254699 2 0.0747 0.952 0.016 0.984 0.000
#> GSM254703 2 0.3192 0.929 0.112 0.888 0.000
#> GSM254708 2 0.1289 0.953 0.032 0.968 0.000
#> GSM254715 2 0.3192 0.929 0.112 0.888 0.000
#> GSM254628 2 0.1289 0.953 0.032 0.968 0.000
#> GSM254634 2 0.3192 0.929 0.112 0.888 0.000
#> GSM254646 2 0.1289 0.953 0.032 0.968 0.000
#> GSM254671 2 0.3116 0.930 0.108 0.892 0.000
#> GSM254711 2 0.3192 0.929 0.112 0.888 0.000
#> GSM254717 2 0.0592 0.954 0.012 0.988 0.000
#> GSM254723 3 0.4709 0.635 0.056 0.092 0.852
#> GSM254730 2 0.0747 0.952 0.016 0.984 0.000
#> GSM254731 2 0.0747 0.952 0.016 0.984 0.000
#> GSM254632 3 0.6869 0.208 0.016 0.424 0.560
#> GSM254662 2 0.1163 0.953 0.028 0.972 0.000
#> GSM254677 2 0.3425 0.927 0.112 0.884 0.004
#> GSM254665 2 0.1289 0.953 0.032 0.968 0.000
#> GSM254691 2 0.1289 0.953 0.032 0.968 0.000
#> GSM254644 2 0.3192 0.929 0.112 0.888 0.000
#> GSM254667 2 0.1877 0.948 0.032 0.956 0.012
#> GSM254676 2 0.1289 0.953 0.032 0.968 0.000
#> GSM254679 2 0.3116 0.930 0.108 0.892 0.000
#> GSM254689 2 0.1289 0.953 0.032 0.968 0.000
#> GSM254706 2 0.1289 0.953 0.032 0.968 0.000
#> GSM254712 2 0.3192 0.929 0.112 0.888 0.000
#> GSM254713 2 0.3192 0.929 0.112 0.888 0.000
#> GSM254683 2 0.1289 0.953 0.032 0.968 0.000
#> GSM254710 3 0.6869 0.208 0.016 0.424 0.560
#> GSM254725 2 0.3425 0.927 0.112 0.884 0.004
#> GSM254651 2 0.1289 0.953 0.032 0.968 0.000
#> GSM254638 2 0.3192 0.929 0.112 0.888 0.000
#> GSM254685 2 0.3116 0.930 0.108 0.892 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM254629 3 0.6638 0.693 0.420 0.000 0.496 0.084
#> GSM254648 3 0.6768 0.686 0.416 0.008 0.504 0.072
#> GSM254694 3 0.7006 0.699 0.400 0.008 0.500 0.092
#> GSM254701 3 0.6638 0.693 0.420 0.000 0.496 0.084
#> GSM254728 1 0.7393 -0.607 0.436 0.000 0.400 0.164
#> GSM254726 3 0.6931 0.684 0.412 0.012 0.500 0.076
#> GSM254639 3 0.7313 0.299 0.156 0.000 0.464 0.380
#> GSM254652 3 0.7119 0.655 0.428 0.000 0.444 0.128
#> GSM254700 4 0.1398 0.722 0.040 0.000 0.004 0.956
#> GSM254625 1 0.2466 0.576 0.900 0.004 0.096 0.000
#> GSM254636 4 0.5773 0.693 0.320 0.000 0.048 0.632
#> GSM254659 1 0.7304 -0.466 0.492 0.000 0.344 0.164
#> GSM254680 4 0.5911 0.659 0.372 0.000 0.044 0.584
#> GSM254686 1 0.5227 0.262 0.704 0.000 0.256 0.040
#> GSM254718 3 0.6881 0.696 0.428 0.000 0.468 0.104
#> GSM254674 4 0.6638 0.541 0.420 0.000 0.084 0.496
#> GSM254668 1 0.3037 0.617 0.880 0.000 0.020 0.100
#> GSM254697 4 0.2859 0.733 0.112 0.000 0.008 0.880
#> GSM254704 4 0.1004 0.695 0.004 0.000 0.024 0.972
#> GSM254707 1 0.1610 0.645 0.952 0.000 0.016 0.032
#> GSM254714 4 0.5109 0.468 0.212 0.000 0.052 0.736
#> GSM254722 4 0.4387 0.746 0.144 0.000 0.052 0.804
#> GSM254627 4 0.2859 0.733 0.112 0.000 0.008 0.880
#> GSM254630 1 0.5565 0.149 0.684 0.000 0.260 0.056
#> GSM254633 4 0.5730 0.681 0.344 0.000 0.040 0.616
#> GSM254670 3 0.7313 0.299 0.156 0.000 0.464 0.380
#> GSM254716 1 0.4072 0.333 0.748 0.000 0.252 0.000
#> GSM254720 4 0.7631 -0.262 0.320 0.000 0.224 0.456
#> GSM254729 3 0.7171 0.681 0.424 0.008 0.464 0.104
#> GSM254654 3 0.6768 0.686 0.416 0.008 0.504 0.072
#> GSM254656 3 0.8953 0.519 0.284 0.072 0.428 0.216
#> GSM254631 4 0.5835 0.661 0.372 0.000 0.040 0.588
#> GSM254657 3 0.7576 0.601 0.324 0.000 0.464 0.212
#> GSM254664 4 0.5835 0.661 0.372 0.000 0.040 0.588
#> GSM254672 4 0.2021 0.728 0.056 0.000 0.012 0.932
#> GSM254692 1 0.4252 0.546 0.744 0.000 0.004 0.252
#> GSM254645 3 0.7668 0.574 0.348 0.000 0.432 0.220
#> GSM254666 1 0.4139 0.472 0.816 0.000 0.144 0.040
#> GSM254675 1 0.6263 0.114 0.576 0.000 0.068 0.356
#> GSM254678 4 0.4864 0.734 0.172 0.000 0.060 0.768
#> GSM254688 1 0.1635 0.646 0.948 0.000 0.008 0.044
#> GSM254690 4 0.5322 0.708 0.312 0.000 0.028 0.660
#> GSM254696 4 0.6790 0.627 0.228 0.000 0.168 0.604
#> GSM254705 1 0.4050 0.604 0.808 0.000 0.024 0.168
#> GSM254642 4 0.2859 0.733 0.112 0.000 0.008 0.880
#> GSM254661 3 0.6661 0.664 0.456 0.000 0.460 0.084
#> GSM254698 4 0.4387 0.746 0.144 0.000 0.052 0.804
#> GSM254641 1 0.2919 0.626 0.896 0.000 0.044 0.060
#> GSM254647 4 0.5233 0.677 0.332 0.000 0.020 0.648
#> GSM254663 1 0.2799 0.644 0.884 0.000 0.008 0.108
#> GSM254682 1 0.1520 0.643 0.956 0.000 0.024 0.020
#> GSM254709 1 0.2565 0.644 0.912 0.000 0.032 0.056
#> GSM254721 4 0.1151 0.697 0.008 0.000 0.024 0.968
#> GSM254724 4 0.1004 0.695 0.004 0.000 0.024 0.972
#> GSM254650 1 0.2610 0.643 0.900 0.000 0.012 0.088
#> GSM254687 1 0.3806 0.613 0.824 0.000 0.020 0.156
#> GSM254637 4 0.5848 0.659 0.376 0.000 0.040 0.584
#> GSM254684 4 0.7122 0.555 0.248 0.000 0.192 0.560
#> GSM254649 2 0.0469 0.825 0.000 0.988 0.012 0.000
#> GSM254660 2 0.3123 0.830 0.000 0.844 0.156 0.000
#> GSM254693 2 0.0469 0.825 0.000 0.988 0.012 0.000
#> GSM254695 2 0.4916 0.746 0.000 0.576 0.424 0.000
#> GSM254702 2 0.3356 0.828 0.000 0.824 0.176 0.000
#> GSM254643 2 0.1867 0.836 0.000 0.928 0.072 0.000
#> GSM254727 2 0.1474 0.836 0.000 0.948 0.052 0.000
#> GSM254640 2 0.4790 0.770 0.000 0.620 0.380 0.000
#> GSM254626 2 0.0469 0.825 0.000 0.988 0.012 0.000
#> GSM254635 2 0.4866 0.759 0.000 0.596 0.404 0.000
#> GSM254653 2 0.1474 0.836 0.000 0.948 0.052 0.000
#> GSM254658 2 0.0707 0.823 0.000 0.980 0.020 0.000
#> GSM254681 2 0.0707 0.823 0.000 0.980 0.020 0.000
#> GSM254719 2 0.0707 0.832 0.000 0.980 0.020 0.000
#> GSM254673 2 0.0336 0.830 0.000 0.992 0.008 0.000
#> GSM254655 2 0.3356 0.828 0.000 0.824 0.176 0.000
#> GSM254669 2 0.0336 0.828 0.000 0.992 0.008 0.000
#> GSM254699 2 0.3356 0.828 0.000 0.824 0.176 0.000
#> GSM254703 2 0.4790 0.770 0.000 0.620 0.380 0.000
#> GSM254708 2 0.0707 0.823 0.000 0.980 0.020 0.000
#> GSM254715 2 0.4866 0.759 0.000 0.596 0.404 0.000
#> GSM254628 2 0.0592 0.827 0.000 0.984 0.016 0.000
#> GSM254634 2 0.4866 0.759 0.000 0.596 0.404 0.000
#> GSM254646 2 0.0707 0.823 0.000 0.980 0.020 0.000
#> GSM254671 2 0.4790 0.770 0.000 0.620 0.380 0.000
#> GSM254711 2 0.4866 0.759 0.000 0.596 0.404 0.000
#> GSM254717 2 0.2530 0.836 0.000 0.888 0.112 0.000
#> GSM254723 1 0.8108 -0.444 0.448 0.084 0.396 0.072
#> GSM254730 2 0.3400 0.828 0.000 0.820 0.180 0.000
#> GSM254731 2 0.3356 0.828 0.000 0.824 0.176 0.000
#> GSM254632 2 0.7617 -0.137 0.372 0.424 0.204 0.000
#> GSM254662 2 0.0336 0.830 0.000 0.992 0.008 0.000
#> GSM254677 2 0.4877 0.757 0.000 0.592 0.408 0.000
#> GSM254665 2 0.1474 0.836 0.000 0.948 0.052 0.000
#> GSM254691 2 0.1211 0.835 0.000 0.960 0.040 0.000
#> GSM254644 2 0.4830 0.765 0.000 0.608 0.392 0.000
#> GSM254667 2 0.1022 0.817 0.000 0.968 0.032 0.000
#> GSM254676 2 0.1211 0.835 0.000 0.960 0.040 0.000
#> GSM254679 2 0.4790 0.770 0.000 0.620 0.380 0.000
#> GSM254689 2 0.0707 0.823 0.000 0.980 0.020 0.000
#> GSM254706 2 0.0707 0.823 0.000 0.980 0.020 0.000
#> GSM254712 2 0.4866 0.759 0.000 0.596 0.404 0.000
#> GSM254713 2 0.4866 0.759 0.000 0.596 0.404 0.000
#> GSM254683 2 0.0707 0.823 0.000 0.980 0.020 0.000
#> GSM254710 2 0.7617 -0.137 0.372 0.424 0.204 0.000
#> GSM254725 2 0.4877 0.757 0.000 0.592 0.408 0.000
#> GSM254651 2 0.0707 0.823 0.000 0.980 0.020 0.000
#> GSM254638 2 0.4866 0.759 0.000 0.596 0.404 0.000
#> GSM254685 2 0.4406 0.795 0.000 0.700 0.300 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM254629 3 0.0992 0.5277 0.008 0.024 0.968 0.000 0.000
#> GSM254648 3 0.1012 0.5255 0.000 0.020 0.968 0.012 0.000
#> GSM254694 3 0.1405 0.5273 0.016 0.020 0.956 0.008 0.000
#> GSM254701 3 0.0992 0.5277 0.008 0.024 0.968 0.000 0.000
#> GSM254728 3 0.4313 0.4878 0.100 0.036 0.804 0.000 0.060
#> GSM254726 3 0.1729 0.5253 0.004 0.032 0.944 0.012 0.008
#> GSM254639 3 0.6195 0.2789 0.204 0.008 0.588 0.000 0.200
#> GSM254652 3 0.3062 0.5138 0.080 0.004 0.868 0.000 0.048
#> GSM254700 1 0.4930 0.4804 0.580 0.032 0.000 0.000 0.388
#> GSM254625 3 0.7642 -0.3374 0.224 0.060 0.416 0.000 0.300
#> GSM254636 1 0.3702 0.4772 0.820 0.000 0.084 0.000 0.096
#> GSM254659 3 0.4452 0.4347 0.156 0.032 0.776 0.000 0.036
#> GSM254680 1 0.2517 0.4828 0.884 0.004 0.104 0.000 0.008
#> GSM254686 3 0.6318 0.1220 0.120 0.040 0.616 0.000 0.224
#> GSM254718 3 0.1492 0.5309 0.040 0.008 0.948 0.000 0.004
#> GSM254674 1 0.5716 0.2971 0.652 0.008 0.168 0.000 0.172
#> GSM254668 1 0.6795 -0.6355 0.400 0.004 0.368 0.000 0.228
#> GSM254697 1 0.6250 0.4734 0.540 0.204 0.000 0.000 0.256
#> GSM254704 1 0.5329 0.4540 0.516 0.052 0.000 0.000 0.432
#> GSM254707 3 0.7276 -0.4765 0.320 0.020 0.364 0.000 0.296
#> GSM254714 5 0.7695 -0.2687 0.292 0.052 0.284 0.000 0.372
#> GSM254722 1 0.7078 0.4504 0.524 0.164 0.052 0.000 0.260
#> GSM254627 1 0.6250 0.4734 0.540 0.204 0.000 0.000 0.256
#> GSM254630 3 0.6310 0.2095 0.140 0.036 0.620 0.000 0.204
#> GSM254633 1 0.3178 0.4910 0.860 0.004 0.088 0.000 0.048
#> GSM254670 3 0.6195 0.2789 0.204 0.008 0.588 0.000 0.200
#> GSM254716 3 0.6700 0.0280 0.104 0.060 0.568 0.000 0.268
#> GSM254720 3 0.6964 0.0217 0.224 0.036 0.528 0.000 0.212
#> GSM254729 3 0.2297 0.5270 0.060 0.020 0.912 0.008 0.000
#> GSM254654 3 0.1012 0.5255 0.000 0.020 0.968 0.012 0.000
#> GSM254656 3 0.6586 0.3919 0.104 0.056 0.680 0.064 0.096
#> GSM254631 1 0.2642 0.4814 0.880 0.008 0.104 0.000 0.008
#> GSM254657 3 0.4645 0.4485 0.124 0.012 0.772 0.004 0.088
#> GSM254664 1 0.2642 0.4814 0.880 0.008 0.104 0.000 0.008
#> GSM254672 1 0.5365 0.4928 0.572 0.032 0.016 0.000 0.380
#> GSM254692 5 0.7844 0.5465 0.308 0.064 0.280 0.000 0.348
#> GSM254645 3 0.5154 0.4459 0.140 0.032 0.744 0.004 0.080
#> GSM254666 3 0.6812 -0.0740 0.196 0.024 0.524 0.000 0.256
#> GSM254675 1 0.6707 -0.2758 0.492 0.008 0.232 0.000 0.268
#> GSM254678 1 0.6073 0.4564 0.532 0.008 0.104 0.000 0.356
#> GSM254688 3 0.7287 -0.5037 0.332 0.020 0.348 0.000 0.300
#> GSM254690 1 0.3736 0.5107 0.840 0.072 0.064 0.000 0.024
#> GSM254696 1 0.6773 0.3804 0.588 0.064 0.208 0.000 0.140
#> GSM254705 5 0.7767 0.5888 0.256 0.060 0.320 0.000 0.364
#> GSM254642 1 0.6250 0.4734 0.540 0.204 0.000 0.000 0.256
#> GSM254661 3 0.2120 0.5252 0.048 0.004 0.924 0.004 0.020
#> GSM254698 1 0.7078 0.4504 0.524 0.164 0.052 0.000 0.260
#> GSM254641 3 0.6842 -0.3843 0.300 0.004 0.424 0.000 0.272
#> GSM254647 1 0.4760 0.4582 0.772 0.052 0.052 0.000 0.124
#> GSM254663 3 0.7364 -0.5590 0.328 0.024 0.332 0.000 0.316
#> GSM254682 3 0.7417 -0.4975 0.308 0.028 0.352 0.000 0.312
#> GSM254709 3 0.7021 -0.4889 0.284 0.008 0.376 0.000 0.332
#> GSM254721 1 0.5334 0.4528 0.512 0.052 0.000 0.000 0.436
#> GSM254724 1 0.5329 0.4540 0.516 0.052 0.000 0.000 0.432
#> GSM254650 5 0.7479 0.5151 0.300 0.032 0.328 0.000 0.340
#> GSM254687 5 0.7555 0.5886 0.272 0.040 0.324 0.000 0.364
#> GSM254637 1 0.2629 0.4807 0.880 0.004 0.104 0.000 0.012
#> GSM254684 1 0.7276 0.2601 0.460 0.036 0.264 0.000 0.240
#> GSM254649 2 0.4150 0.8183 0.000 0.612 0.000 0.388 0.000
#> GSM254660 4 0.3932 0.2579 0.000 0.328 0.000 0.672 0.000
#> GSM254693 2 0.4150 0.8183 0.000 0.612 0.000 0.388 0.000
#> GSM254695 4 0.1281 0.6540 0.000 0.032 0.012 0.956 0.000
#> GSM254702 4 0.3876 0.2953 0.000 0.316 0.000 0.684 0.000
#> GSM254643 4 0.4161 -0.0659 0.000 0.392 0.000 0.608 0.000
#> GSM254727 2 0.4278 0.6925 0.000 0.548 0.000 0.452 0.000
#> GSM254640 4 0.1608 0.6649 0.000 0.072 0.000 0.928 0.000
#> GSM254626 2 0.4150 0.8183 0.000 0.612 0.000 0.388 0.000
#> GSM254635 4 0.0162 0.6862 0.000 0.004 0.000 0.996 0.000
#> GSM254653 2 0.4278 0.6925 0.000 0.548 0.000 0.452 0.000
#> GSM254658 2 0.4126 0.8209 0.000 0.620 0.000 0.380 0.000
#> GSM254681 2 0.4126 0.8209 0.000 0.620 0.000 0.380 0.000
#> GSM254719 2 0.4227 0.7727 0.000 0.580 0.000 0.420 0.000
#> GSM254673 2 0.4201 0.7945 0.000 0.592 0.000 0.408 0.000
#> GSM254655 4 0.4045 0.1371 0.000 0.356 0.000 0.644 0.000
#> GSM254669 2 0.4182 0.8058 0.000 0.600 0.000 0.400 0.000
#> GSM254699 4 0.3857 0.3079 0.000 0.312 0.000 0.688 0.000
#> GSM254703 4 0.0794 0.6868 0.000 0.028 0.000 0.972 0.000
#> GSM254708 2 0.4114 0.8160 0.000 0.624 0.000 0.376 0.000
#> GSM254715 4 0.0000 0.6879 0.000 0.000 0.000 1.000 0.000
#> GSM254628 2 0.4171 0.8109 0.000 0.604 0.000 0.396 0.000
#> GSM254634 4 0.0162 0.6862 0.000 0.004 0.000 0.996 0.000
#> GSM254646 2 0.4126 0.8209 0.000 0.620 0.000 0.380 0.000
#> GSM254671 4 0.0880 0.6863 0.000 0.032 0.000 0.968 0.000
#> GSM254711 4 0.0162 0.6883 0.000 0.004 0.000 0.996 0.000
#> GSM254717 4 0.4210 -0.1800 0.000 0.412 0.000 0.588 0.000
#> GSM254723 3 0.4739 0.4165 0.008 0.080 0.792 0.056 0.064
#> GSM254730 4 0.3796 0.3378 0.000 0.300 0.000 0.700 0.000
#> GSM254731 4 0.3857 0.3079 0.000 0.312 0.000 0.688 0.000
#> GSM254632 2 0.8559 -0.0960 0.012 0.336 0.324 0.168 0.160
#> GSM254662 2 0.4201 0.7945 0.000 0.592 0.000 0.408 0.000
#> GSM254677 4 0.0671 0.6733 0.000 0.016 0.004 0.980 0.000
#> GSM254665 4 0.4300 -0.4614 0.000 0.476 0.000 0.524 0.000
#> GSM254691 4 0.4307 -0.5285 0.000 0.496 0.000 0.504 0.000
#> GSM254644 4 0.1197 0.6786 0.000 0.048 0.000 0.952 0.000
#> GSM254667 2 0.4225 0.7961 0.000 0.632 0.004 0.364 0.000
#> GSM254676 4 0.4307 -0.5285 0.000 0.496 0.000 0.504 0.000
#> GSM254679 4 0.0880 0.6863 0.000 0.032 0.000 0.968 0.000
#> GSM254689 2 0.4126 0.8209 0.000 0.620 0.000 0.380 0.000
#> GSM254706 2 0.4126 0.8209 0.000 0.620 0.000 0.380 0.000
#> GSM254712 4 0.0000 0.6879 0.000 0.000 0.000 1.000 0.000
#> GSM254713 4 0.0000 0.6879 0.000 0.000 0.000 1.000 0.000
#> GSM254683 2 0.4126 0.8209 0.000 0.620 0.000 0.380 0.000
#> GSM254710 2 0.8559 -0.0960 0.012 0.336 0.324 0.168 0.160
#> GSM254725 4 0.0671 0.6733 0.000 0.016 0.004 0.980 0.000
#> GSM254651 2 0.4126 0.8209 0.000 0.620 0.000 0.380 0.000
#> GSM254638 4 0.0162 0.6862 0.000 0.004 0.000 0.996 0.000
#> GSM254685 4 0.2561 0.5954 0.000 0.144 0.000 0.856 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM254629 3 0.0862 0.7875 0.000 0.016 0.972 0.000 0.004 0.008
#> GSM254648 3 0.0748 0.7846 0.000 0.016 0.976 0.004 0.004 0.000
#> GSM254694 3 0.1148 0.7871 0.000 0.016 0.960 0.004 0.000 0.020
#> GSM254701 3 0.0862 0.7875 0.000 0.016 0.972 0.000 0.004 0.008
#> GSM254728 3 0.4795 0.7194 0.004 0.032 0.732 0.000 0.104 0.128
#> GSM254726 3 0.1476 0.7864 0.000 0.028 0.948 0.004 0.012 0.008
#> GSM254639 3 0.5264 0.3685 0.004 0.016 0.500 0.000 0.048 0.432
#> GSM254652 3 0.3613 0.7570 0.000 0.016 0.816 0.000 0.092 0.076
#> GSM254700 1 0.3164 0.4718 0.832 0.004 0.000 0.000 0.044 0.120
#> GSM254625 5 0.2532 0.5931 0.000 0.052 0.060 0.000 0.884 0.004
#> GSM254636 6 0.6845 0.5051 0.220 0.012 0.036 0.000 0.292 0.440
#> GSM254659 3 0.5587 0.6505 0.044 0.056 0.700 0.000 0.128 0.072
#> GSM254680 5 0.7331 -0.4105 0.320 0.016 0.056 0.000 0.332 0.276
#> GSM254686 5 0.5928 0.4669 0.024 0.112 0.216 0.000 0.620 0.028
#> GSM254718 3 0.2115 0.7910 0.000 0.020 0.916 0.000 0.032 0.032
#> GSM254674 5 0.6999 -0.4590 0.148 0.004 0.088 0.000 0.384 0.376
#> GSM254668 5 0.3556 0.5739 0.048 0.008 0.044 0.000 0.840 0.060
#> GSM254697 1 0.5406 0.4339 0.496 0.044 0.000 0.000 0.036 0.424
#> GSM254704 1 0.0405 0.5507 0.988 0.004 0.000 0.000 0.000 0.008
#> GSM254707 5 0.1760 0.6080 0.020 0.004 0.028 0.000 0.936 0.012
#> GSM254714 1 0.5421 0.1712 0.604 0.004 0.264 0.000 0.008 0.120
#> GSM254722 6 0.3601 0.4384 0.084 0.016 0.004 0.000 0.072 0.824
#> GSM254627 1 0.5406 0.4339 0.496 0.044 0.000 0.000 0.036 0.424
#> GSM254630 5 0.5702 0.3182 0.008 0.044 0.320 0.000 0.572 0.056
#> GSM254633 6 0.7051 0.4320 0.276 0.016 0.032 0.000 0.316 0.360
#> GSM254670 3 0.5264 0.3685 0.004 0.016 0.500 0.000 0.048 0.432
#> GSM254716 5 0.4876 0.4856 0.000 0.124 0.164 0.000 0.696 0.016
#> GSM254720 3 0.5606 0.2425 0.392 0.024 0.524 0.000 0.024 0.036
#> GSM254729 3 0.2582 0.7858 0.004 0.016 0.896 0.004 0.028 0.052
#> GSM254654 3 0.0748 0.7846 0.000 0.016 0.976 0.004 0.004 0.000
#> GSM254656 3 0.5898 0.6131 0.000 0.128 0.616 0.016 0.028 0.212
#> GSM254631 5 0.7393 -0.4003 0.324 0.020 0.056 0.000 0.328 0.272
#> GSM254657 3 0.4606 0.6861 0.000 0.020 0.716 0.004 0.056 0.204
#> GSM254664 5 0.7393 -0.4003 0.324 0.020 0.056 0.000 0.328 0.272
#> GSM254672 1 0.4507 0.2504 0.668 0.004 0.004 0.000 0.044 0.280
#> GSM254692 5 0.4551 0.5202 0.088 0.024 0.000 0.000 0.736 0.152
#> GSM254645 3 0.5042 0.6689 0.004 0.040 0.692 0.000 0.064 0.200
#> GSM254666 5 0.4535 0.4970 0.004 0.032 0.216 0.000 0.716 0.032
#> GSM254675 5 0.6551 0.2400 0.264 0.008 0.088 0.000 0.536 0.104
#> GSM254678 6 0.6771 0.2478 0.392 0.008 0.068 0.000 0.124 0.408
#> GSM254688 5 0.1690 0.6077 0.020 0.004 0.020 0.000 0.940 0.016
#> GSM254690 6 0.6658 0.3589 0.304 0.008 0.020 0.000 0.256 0.412
#> GSM254696 6 0.6782 0.5183 0.132 0.012 0.140 0.000 0.160 0.556
#> GSM254705 5 0.4351 0.5587 0.084 0.036 0.012 0.000 0.784 0.084
#> GSM254642 1 0.5406 0.4339 0.496 0.044 0.000 0.000 0.036 0.424
#> GSM254661 3 0.2566 0.7812 0.000 0.020 0.888 0.000 0.064 0.028
#> GSM254698 6 0.3601 0.4384 0.084 0.016 0.004 0.000 0.072 0.824
#> GSM254641 5 0.3432 0.6006 0.032 0.008 0.092 0.000 0.840 0.028
#> GSM254647 1 0.6617 -0.3698 0.360 0.008 0.012 0.000 0.312 0.308
#> GSM254663 5 0.3120 0.5997 0.040 0.008 0.016 0.000 0.860 0.076
#> GSM254682 5 0.1350 0.6093 0.000 0.008 0.020 0.000 0.952 0.020
#> GSM254709 5 0.3085 0.6081 0.032 0.008 0.056 0.000 0.868 0.036
#> GSM254721 1 0.0508 0.5520 0.984 0.004 0.000 0.000 0.000 0.012
#> GSM254724 1 0.0777 0.5462 0.972 0.004 0.000 0.000 0.000 0.024
#> GSM254650 5 0.2607 0.5981 0.052 0.012 0.008 0.000 0.892 0.036
#> GSM254687 5 0.4001 0.5704 0.080 0.028 0.012 0.000 0.808 0.072
#> GSM254637 5 0.7323 -0.4016 0.324 0.016 0.056 0.000 0.336 0.268
#> GSM254684 6 0.6256 0.4529 0.048 0.016 0.188 0.000 0.152 0.596
#> GSM254649 2 0.3620 0.8141 0.000 0.648 0.000 0.352 0.000 0.000
#> GSM254660 4 0.3620 0.2001 0.000 0.352 0.000 0.648 0.000 0.000
#> GSM254693 2 0.3620 0.8141 0.000 0.648 0.000 0.352 0.000 0.000
#> GSM254695 4 0.1957 0.6302 0.000 0.112 0.000 0.888 0.000 0.000
#> GSM254702 4 0.3620 0.2018 0.000 0.352 0.000 0.648 0.000 0.000
#> GSM254643 4 0.3756 -0.0907 0.000 0.400 0.000 0.600 0.000 0.000
#> GSM254727 2 0.3789 0.7225 0.000 0.584 0.000 0.416 0.000 0.000
#> GSM254640 4 0.1814 0.6697 0.000 0.100 0.000 0.900 0.000 0.000
#> GSM254626 2 0.3620 0.8141 0.000 0.648 0.000 0.352 0.000 0.000
#> GSM254635 4 0.0260 0.7066 0.000 0.008 0.000 0.992 0.000 0.000
#> GSM254653 2 0.3789 0.7225 0.000 0.584 0.000 0.416 0.000 0.000
#> GSM254658 2 0.3563 0.8136 0.000 0.664 0.000 0.336 0.000 0.000
#> GSM254681 2 0.3563 0.8136 0.000 0.664 0.000 0.336 0.000 0.000
#> GSM254719 2 0.3717 0.7826 0.000 0.616 0.000 0.384 0.000 0.000
#> GSM254673 2 0.3684 0.7984 0.000 0.628 0.000 0.372 0.000 0.000
#> GSM254655 4 0.3737 0.0061 0.000 0.392 0.000 0.608 0.000 0.000
#> GSM254669 2 0.3659 0.8062 0.000 0.636 0.000 0.364 0.000 0.000
#> GSM254699 4 0.3607 0.2176 0.000 0.348 0.000 0.652 0.000 0.000
#> GSM254703 4 0.0790 0.7078 0.000 0.032 0.000 0.968 0.000 0.000
#> GSM254708 2 0.3547 0.8095 0.000 0.668 0.000 0.332 0.000 0.000
#> GSM254715 4 0.0000 0.7093 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM254628 2 0.3647 0.8101 0.000 0.640 0.000 0.360 0.000 0.000
#> GSM254634 4 0.0458 0.7062 0.000 0.016 0.000 0.984 0.000 0.000
#> GSM254646 2 0.3563 0.8136 0.000 0.664 0.000 0.336 0.000 0.000
#> GSM254671 4 0.1141 0.7042 0.000 0.052 0.000 0.948 0.000 0.000
#> GSM254711 4 0.0363 0.7098 0.000 0.012 0.000 0.988 0.000 0.000
#> GSM254717 4 0.3833 -0.2878 0.000 0.444 0.000 0.556 0.000 0.000
#> GSM254723 3 0.5061 0.6345 0.000 0.164 0.716 0.032 0.068 0.020
#> GSM254730 4 0.3531 0.2784 0.000 0.328 0.000 0.672 0.000 0.000
#> GSM254731 4 0.3607 0.2176 0.000 0.348 0.000 0.652 0.000 0.000
#> GSM254632 2 0.6077 -0.1778 0.000 0.540 0.064 0.056 0.328 0.012
#> GSM254662 2 0.3684 0.7984 0.000 0.628 0.000 0.372 0.000 0.000
#> GSM254677 4 0.1204 0.6657 0.000 0.056 0.000 0.944 0.000 0.000
#> GSM254665 4 0.3866 -0.4794 0.000 0.484 0.000 0.516 0.000 0.000
#> GSM254691 2 0.3860 0.5695 0.000 0.528 0.000 0.472 0.000 0.000
#> GSM254644 4 0.1444 0.6898 0.000 0.072 0.000 0.928 0.000 0.000
#> GSM254667 2 0.3409 0.7621 0.000 0.700 0.000 0.300 0.000 0.000
#> GSM254676 2 0.3860 0.5695 0.000 0.528 0.000 0.472 0.000 0.000
#> GSM254679 4 0.1204 0.7047 0.000 0.056 0.000 0.944 0.000 0.000
#> GSM254689 2 0.3563 0.8136 0.000 0.664 0.000 0.336 0.000 0.000
#> GSM254706 2 0.3563 0.8140 0.000 0.664 0.000 0.336 0.000 0.000
#> GSM254712 4 0.0000 0.7093 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM254713 4 0.0000 0.7093 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM254683 2 0.3578 0.8150 0.000 0.660 0.000 0.340 0.000 0.000
#> GSM254710 2 0.6077 -0.1778 0.000 0.540 0.064 0.056 0.328 0.012
#> GSM254725 4 0.1267 0.6699 0.000 0.060 0.000 0.940 0.000 0.000
#> GSM254651 2 0.3563 0.8140 0.000 0.664 0.000 0.336 0.000 0.000
#> GSM254638 4 0.0260 0.7066 0.000 0.008 0.000 0.992 0.000 0.000
#> GSM254685 4 0.2378 0.6081 0.000 0.152 0.000 0.848 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) individual(p) disease.state(p) gender(p) other(p) k
#> CV:hclust 105 6.63e-23 0.6809 0.534 0.6385 1.000 2
#> CV:hclust 96 9.84e-21 0.4414 0.664 0.5327 1.000 3
#> CV:hclust 93 4.97e-20 0.0359 0.655 0.0659 0.755 4
#> CV:hclust 50 3.61e-10 0.0283 0.834 0.0350 0.684 5
#> CV:hclust 70 6.25e-13 0.0723 0.768 0.0666 0.717 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 21168 rows and 107 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#> Subgroups are detected by 'kmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.995 0.998 0.4983 0.503 0.503
#> 3 3 0.646 0.694 0.759 0.2820 0.828 0.662
#> 4 4 0.622 0.544 0.742 0.1312 0.856 0.629
#> 5 5 0.623 0.664 0.725 0.0741 0.840 0.504
#> 6 6 0.640 0.552 0.716 0.0444 0.943 0.744
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
#> GSM254629 1 0.0000 0.996 1.000 0.000
#> GSM254648 1 0.0000 0.996 1.000 0.000
#> GSM254694 1 0.0000 0.996 1.000 0.000
#> GSM254701 1 0.0000 0.996 1.000 0.000
#> GSM254728 1 0.0000 0.996 1.000 0.000
#> GSM254726 1 0.0000 0.996 1.000 0.000
#> GSM254639 1 0.0000 0.996 1.000 0.000
#> GSM254652 1 0.0000 0.996 1.000 0.000
#> GSM254700 1 0.0000 0.996 1.000 0.000
#> GSM254625 1 0.0000 0.996 1.000 0.000
#> GSM254636 1 0.0000 0.996 1.000 0.000
#> GSM254659 1 0.0000 0.996 1.000 0.000
#> GSM254680 1 0.0000 0.996 1.000 0.000
#> GSM254686 1 0.0000 0.996 1.000 0.000
#> GSM254718 1 0.0000 0.996 1.000 0.000
#> GSM254674 1 0.0000 0.996 1.000 0.000
#> GSM254668 1 0.0000 0.996 1.000 0.000
#> GSM254697 1 0.0000 0.996 1.000 0.000
#> GSM254704 1 0.0000 0.996 1.000 0.000
#> GSM254707 1 0.0000 0.996 1.000 0.000
#> GSM254714 1 0.0000 0.996 1.000 0.000
#> GSM254722 1 0.0000 0.996 1.000 0.000
#> GSM254627 1 0.0000 0.996 1.000 0.000
#> GSM254630 1 0.0000 0.996 1.000 0.000
#> GSM254633 1 0.0000 0.996 1.000 0.000
#> GSM254670 1 0.0000 0.996 1.000 0.000
#> GSM254716 1 0.0000 0.996 1.000 0.000
#> GSM254720 1 0.0000 0.996 1.000 0.000
#> GSM254729 1 0.0000 0.996 1.000 0.000
#> GSM254654 1 0.0000 0.996 1.000 0.000
#> GSM254656 1 0.0000 0.996 1.000 0.000
#> GSM254631 1 0.0000 0.996 1.000 0.000
#> GSM254657 1 0.0000 0.996 1.000 0.000
#> GSM254664 1 0.0000 0.996 1.000 0.000
#> GSM254672 1 0.0000 0.996 1.000 0.000
#> GSM254692 1 0.0000 0.996 1.000 0.000
#> GSM254645 1 0.0000 0.996 1.000 0.000
#> GSM254666 1 0.0000 0.996 1.000 0.000
#> GSM254675 1 0.0000 0.996 1.000 0.000
#> GSM254678 1 0.0000 0.996 1.000 0.000
#> GSM254688 1 0.0000 0.996 1.000 0.000
#> GSM254690 1 0.0000 0.996 1.000 0.000
#> GSM254696 1 0.0000 0.996 1.000 0.000
#> GSM254705 1 0.0000 0.996 1.000 0.000
#> GSM254642 1 0.0000 0.996 1.000 0.000
#> GSM254661 1 0.0000 0.996 1.000 0.000
#> GSM254698 1 0.0000 0.996 1.000 0.000
#> GSM254641 1 0.0000 0.996 1.000 0.000
#> GSM254647 1 0.0000 0.996 1.000 0.000
#> GSM254663 1 0.0000 0.996 1.000 0.000
#> GSM254682 1 0.0000 0.996 1.000 0.000
#> GSM254709 1 0.0000 0.996 1.000 0.000
#> GSM254721 1 0.0000 0.996 1.000 0.000
#> GSM254724 1 0.0000 0.996 1.000 0.000
#> GSM254650 1 0.0000 0.996 1.000 0.000
#> GSM254687 1 0.0000 0.996 1.000 0.000
#> GSM254637 1 0.0000 0.996 1.000 0.000
#> GSM254684 1 0.0000 0.996 1.000 0.000
#> GSM254649 2 0.0000 1.000 0.000 1.000
#> GSM254660 2 0.0000 1.000 0.000 1.000
#> GSM254693 2 0.0000 1.000 0.000 1.000
#> GSM254695 2 0.0000 1.000 0.000 1.000
#> GSM254702 2 0.0000 1.000 0.000 1.000
#> GSM254643 2 0.0000 1.000 0.000 1.000
#> GSM254727 2 0.0000 1.000 0.000 1.000
#> GSM254640 2 0.0000 1.000 0.000 1.000
#> GSM254626 2 0.0000 1.000 0.000 1.000
#> GSM254635 2 0.0000 1.000 0.000 1.000
#> GSM254653 2 0.0000 1.000 0.000 1.000
#> GSM254658 2 0.0000 1.000 0.000 1.000
#> GSM254681 2 0.0000 1.000 0.000 1.000
#> GSM254719 2 0.0000 1.000 0.000 1.000
#> GSM254673 2 0.0000 1.000 0.000 1.000
#> GSM254655 2 0.0000 1.000 0.000 1.000
#> GSM254669 2 0.0000 1.000 0.000 1.000
#> GSM254699 2 0.0000 1.000 0.000 1.000
#> GSM254703 2 0.0000 1.000 0.000 1.000
#> GSM254708 2 0.0000 1.000 0.000 1.000
#> GSM254715 2 0.0000 1.000 0.000 1.000
#> GSM254628 2 0.0000 1.000 0.000 1.000
#> GSM254634 2 0.0000 1.000 0.000 1.000
#> GSM254646 2 0.0000 1.000 0.000 1.000
#> GSM254671 2 0.0000 1.000 0.000 1.000
#> GSM254711 2 0.0000 1.000 0.000 1.000
#> GSM254717 2 0.0000 1.000 0.000 1.000
#> GSM254723 1 0.7883 0.691 0.764 0.236
#> GSM254730 2 0.0000 1.000 0.000 1.000
#> GSM254731 2 0.0000 1.000 0.000 1.000
#> GSM254632 1 0.0376 0.992 0.996 0.004
#> GSM254662 2 0.0000 1.000 0.000 1.000
#> GSM254677 2 0.0000 1.000 0.000 1.000
#> GSM254665 2 0.0000 1.000 0.000 1.000
#> GSM254691 2 0.0000 1.000 0.000 1.000
#> GSM254644 2 0.0000 1.000 0.000 1.000
#> GSM254667 2 0.0000 1.000 0.000 1.000
#> GSM254676 2 0.0000 1.000 0.000 1.000
#> GSM254679 2 0.0000 1.000 0.000 1.000
#> GSM254689 2 0.0000 1.000 0.000 1.000
#> GSM254706 2 0.0000 1.000 0.000 1.000
#> GSM254712 2 0.0000 1.000 0.000 1.000
#> GSM254713 2 0.0000 1.000 0.000 1.000
#> GSM254683 2 0.0000 1.000 0.000 1.000
#> GSM254710 2 0.0000 1.000 0.000 1.000
#> GSM254725 2 0.0000 1.000 0.000 1.000
#> GSM254651 2 0.0000 1.000 0.000 1.000
#> GSM254638 2 0.0000 1.000 0.000 1.000
#> GSM254685 2 0.0000 1.000 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM254629 3 0.1964 0.671 0.056 0.000 0.944
#> GSM254648 3 0.2878 0.661 0.096 0.000 0.904
#> GSM254694 3 0.1411 0.673 0.036 0.000 0.964
#> GSM254701 3 0.0237 0.680 0.004 0.000 0.996
#> GSM254728 3 0.0237 0.680 0.004 0.000 0.996
#> GSM254726 3 0.2878 0.661 0.096 0.000 0.904
#> GSM254639 3 0.0424 0.678 0.008 0.000 0.992
#> GSM254652 3 0.1964 0.671 0.056 0.000 0.944
#> GSM254700 1 0.5810 0.841 0.664 0.000 0.336
#> GSM254625 3 0.4654 0.533 0.208 0.000 0.792
#> GSM254636 3 0.6280 -0.562 0.460 0.000 0.540
#> GSM254659 3 0.0237 0.680 0.004 0.000 0.996
#> GSM254680 1 0.6305 0.662 0.516 0.000 0.484
#> GSM254686 3 0.3941 0.584 0.156 0.000 0.844
#> GSM254718 3 0.0237 0.680 0.004 0.000 0.996
#> GSM254674 1 0.6260 0.664 0.552 0.000 0.448
#> GSM254668 1 0.6140 0.674 0.596 0.000 0.404
#> GSM254697 1 0.5810 0.841 0.664 0.000 0.336
#> GSM254704 1 0.6095 0.798 0.608 0.000 0.392
#> GSM254707 1 0.6140 0.674 0.596 0.000 0.404
#> GSM254714 3 0.0424 0.679 0.008 0.000 0.992
#> GSM254722 1 0.5810 0.841 0.664 0.000 0.336
#> GSM254627 1 0.5810 0.841 0.664 0.000 0.336
#> GSM254630 3 0.6307 -0.265 0.488 0.000 0.512
#> GSM254633 3 0.6274 -0.554 0.456 0.000 0.544
#> GSM254670 3 0.0592 0.676 0.012 0.000 0.988
#> GSM254716 3 0.4654 0.533 0.208 0.000 0.792
#> GSM254720 3 0.6215 -0.466 0.428 0.000 0.572
#> GSM254729 3 0.1289 0.677 0.032 0.000 0.968
#> GSM254654 3 0.1643 0.669 0.044 0.000 0.956
#> GSM254656 3 0.4964 0.560 0.048 0.116 0.836
#> GSM254631 3 0.6295 -0.590 0.472 0.000 0.528
#> GSM254657 3 0.0237 0.680 0.004 0.000 0.996
#> GSM254664 1 0.6302 0.668 0.520 0.000 0.480
#> GSM254672 1 0.6140 0.789 0.596 0.000 0.404
#> GSM254692 1 0.5178 0.808 0.744 0.000 0.256
#> GSM254645 3 0.0592 0.676 0.012 0.000 0.988
#> GSM254666 3 0.2878 0.648 0.096 0.000 0.904
#> GSM254675 1 0.6111 0.803 0.604 0.000 0.396
#> GSM254678 1 0.6154 0.785 0.592 0.000 0.408
#> GSM254688 1 0.5254 0.809 0.736 0.000 0.264
#> GSM254690 1 0.5835 0.840 0.660 0.000 0.340
#> GSM254696 3 0.6286 -0.571 0.464 0.000 0.536
#> GSM254705 1 0.5178 0.808 0.744 0.000 0.256
#> GSM254642 1 0.5560 0.827 0.700 0.000 0.300
#> GSM254661 3 0.1964 0.671 0.056 0.000 0.944
#> GSM254698 1 0.6111 0.796 0.604 0.000 0.396
#> GSM254641 1 0.6299 0.576 0.524 0.000 0.476
#> GSM254647 1 0.5810 0.841 0.664 0.000 0.336
#> GSM254663 1 0.5178 0.808 0.744 0.000 0.256
#> GSM254682 1 0.5254 0.809 0.736 0.000 0.264
#> GSM254709 1 0.6225 0.509 0.568 0.000 0.432
#> GSM254721 1 0.5810 0.841 0.664 0.000 0.336
#> GSM254724 1 0.5810 0.841 0.664 0.000 0.336
#> GSM254650 1 0.5254 0.809 0.736 0.000 0.264
#> GSM254687 1 0.5254 0.809 0.736 0.000 0.264
#> GSM254637 3 0.6280 -0.564 0.460 0.000 0.540
#> GSM254684 3 0.6309 -0.645 0.496 0.000 0.504
#> GSM254649 2 0.4605 0.919 0.204 0.796 0.000
#> GSM254660 2 0.2711 0.909 0.088 0.912 0.000
#> GSM254693 2 0.4605 0.919 0.204 0.796 0.000
#> GSM254695 2 0.2096 0.872 0.052 0.944 0.004
#> GSM254702 2 0.0000 0.891 0.000 1.000 0.000
#> GSM254643 2 0.4504 0.920 0.196 0.804 0.000
#> GSM254727 2 0.4605 0.919 0.204 0.796 0.000
#> GSM254640 2 0.0000 0.891 0.000 1.000 0.000
#> GSM254626 2 0.4504 0.920 0.196 0.804 0.000
#> GSM254635 2 0.1525 0.878 0.032 0.964 0.004
#> GSM254653 2 0.4605 0.919 0.204 0.796 0.000
#> GSM254658 2 0.4605 0.919 0.204 0.796 0.000
#> GSM254681 2 0.4605 0.919 0.204 0.796 0.000
#> GSM254719 2 0.4504 0.920 0.196 0.804 0.000
#> GSM254673 2 0.4504 0.920 0.196 0.804 0.000
#> GSM254655 2 0.3038 0.912 0.104 0.896 0.000
#> GSM254669 2 0.4504 0.920 0.196 0.804 0.000
#> GSM254699 2 0.4062 0.919 0.164 0.836 0.000
#> GSM254703 2 0.0983 0.885 0.016 0.980 0.004
#> GSM254708 2 0.4750 0.915 0.216 0.784 0.000
#> GSM254715 2 0.0983 0.885 0.016 0.980 0.004
#> GSM254628 2 0.4605 0.919 0.204 0.796 0.000
#> GSM254634 2 0.1129 0.883 0.020 0.976 0.004
#> GSM254646 2 0.4605 0.919 0.204 0.796 0.000
#> GSM254671 2 0.0475 0.889 0.004 0.992 0.004
#> GSM254711 2 0.0983 0.885 0.016 0.980 0.004
#> GSM254717 2 0.4605 0.919 0.204 0.796 0.000
#> GSM254723 3 0.5852 0.510 0.044 0.180 0.776
#> GSM254730 2 0.3412 0.915 0.124 0.876 0.000
#> GSM254731 2 0.0000 0.891 0.000 1.000 0.000
#> GSM254632 3 0.3192 0.652 0.112 0.000 0.888
#> GSM254662 2 0.4504 0.920 0.196 0.804 0.000
#> GSM254677 2 0.1878 0.872 0.044 0.952 0.004
#> GSM254665 2 0.4504 0.920 0.196 0.804 0.000
#> GSM254691 2 0.4452 0.920 0.192 0.808 0.000
#> GSM254644 2 0.0000 0.891 0.000 1.000 0.000
#> GSM254667 2 0.4931 0.909 0.232 0.768 0.000
#> GSM254676 2 0.4452 0.920 0.192 0.808 0.000
#> GSM254679 2 0.0983 0.885 0.016 0.980 0.004
#> GSM254689 2 0.4605 0.919 0.204 0.796 0.000
#> GSM254706 2 0.4605 0.919 0.204 0.796 0.000
#> GSM254712 2 0.0983 0.885 0.016 0.980 0.004
#> GSM254713 2 0.0983 0.885 0.016 0.980 0.004
#> GSM254683 2 0.4605 0.919 0.204 0.796 0.000
#> GSM254710 3 0.9986 -0.198 0.320 0.320 0.360
#> GSM254725 2 0.1878 0.872 0.044 0.952 0.004
#> GSM254651 2 0.4605 0.919 0.204 0.796 0.000
#> GSM254638 2 0.1878 0.872 0.044 0.952 0.004
#> GSM254685 2 0.0424 0.889 0.008 0.992 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM254629 3 0.2048 0.8531 0.064 0.000 0.928 0.008
#> GSM254648 3 0.3245 0.8363 0.056 0.000 0.880 0.064
#> GSM254694 3 0.2313 0.8525 0.044 0.000 0.924 0.032
#> GSM254701 3 0.1854 0.8539 0.048 0.000 0.940 0.012
#> GSM254728 3 0.1389 0.8543 0.048 0.000 0.952 0.000
#> GSM254726 3 0.2926 0.8416 0.056 0.000 0.896 0.048
#> GSM254639 3 0.3245 0.8330 0.056 0.000 0.880 0.064
#> GSM254652 3 0.1716 0.8528 0.064 0.000 0.936 0.000
#> GSM254700 1 0.5344 0.7177 0.668 0.000 0.032 0.300
#> GSM254625 1 0.5296 -0.2432 0.500 0.000 0.492 0.008
#> GSM254636 1 0.7256 0.6027 0.540 0.000 0.256 0.204
#> GSM254659 3 0.1389 0.8543 0.048 0.000 0.952 0.000
#> GSM254680 1 0.5705 0.7006 0.712 0.000 0.180 0.108
#> GSM254686 3 0.4866 0.3839 0.404 0.000 0.596 0.000
#> GSM254718 3 0.1389 0.8543 0.048 0.000 0.952 0.000
#> GSM254674 1 0.3991 0.6613 0.808 0.000 0.172 0.020
#> GSM254668 1 0.3052 0.6575 0.860 0.000 0.136 0.004
#> GSM254697 1 0.5321 0.7189 0.672 0.000 0.032 0.296
#> GSM254704 1 0.6937 0.6580 0.532 0.000 0.124 0.344
#> GSM254707 1 0.3052 0.6575 0.860 0.000 0.136 0.004
#> GSM254714 3 0.3037 0.8343 0.076 0.000 0.888 0.036
#> GSM254722 1 0.5495 0.7092 0.624 0.000 0.028 0.348
#> GSM254627 1 0.5321 0.7189 0.672 0.000 0.032 0.296
#> GSM254630 1 0.4008 0.4677 0.756 0.000 0.244 0.000
#> GSM254633 1 0.7172 0.5895 0.532 0.000 0.304 0.164
#> GSM254670 3 0.3617 0.8238 0.076 0.000 0.860 0.064
#> GSM254716 3 0.5168 0.2103 0.492 0.000 0.504 0.004
#> GSM254720 3 0.7853 -0.3338 0.364 0.000 0.368 0.268
#> GSM254729 3 0.2586 0.8520 0.048 0.000 0.912 0.040
#> GSM254654 3 0.2675 0.8482 0.044 0.000 0.908 0.048
#> GSM254656 3 0.3498 0.7762 0.008 0.000 0.832 0.160
#> GSM254631 1 0.7079 0.6199 0.556 0.000 0.276 0.168
#> GSM254657 3 0.3542 0.8269 0.076 0.000 0.864 0.060
#> GSM254664 1 0.6330 0.6958 0.656 0.000 0.200 0.144
#> GSM254672 1 0.7120 0.6416 0.496 0.000 0.136 0.368
#> GSM254692 1 0.2345 0.7011 0.900 0.000 0.000 0.100
#> GSM254645 3 0.3245 0.8330 0.056 0.000 0.880 0.064
#> GSM254666 3 0.4697 0.5440 0.356 0.000 0.644 0.000
#> GSM254675 1 0.6274 0.7178 0.664 0.000 0.152 0.184
#> GSM254678 1 0.6993 0.6595 0.556 0.000 0.148 0.296
#> GSM254688 1 0.1388 0.7029 0.960 0.000 0.028 0.012
#> GSM254690 1 0.5332 0.7402 0.736 0.000 0.080 0.184
#> GSM254696 1 0.7336 0.6034 0.528 0.000 0.256 0.216
#> GSM254705 1 0.0000 0.7079 1.000 0.000 0.000 0.000
#> GSM254642 1 0.5038 0.7193 0.684 0.000 0.020 0.296
#> GSM254661 3 0.1716 0.8528 0.064 0.000 0.936 0.000
#> GSM254698 1 0.7091 0.6471 0.508 0.000 0.136 0.356
#> GSM254641 1 0.4574 0.6165 0.756 0.000 0.220 0.024
#> GSM254647 1 0.5207 0.7207 0.680 0.000 0.028 0.292
#> GSM254663 1 0.1302 0.7070 0.956 0.000 0.000 0.044
#> GSM254682 1 0.1411 0.7043 0.960 0.000 0.020 0.020
#> GSM254709 1 0.3479 0.6147 0.840 0.000 0.148 0.012
#> GSM254721 1 0.5367 0.7171 0.664 0.000 0.032 0.304
#> GSM254724 1 0.5367 0.7171 0.664 0.000 0.032 0.304
#> GSM254650 1 0.0672 0.7071 0.984 0.000 0.008 0.008
#> GSM254687 1 0.0657 0.7065 0.984 0.000 0.012 0.004
#> GSM254637 1 0.7189 0.5927 0.532 0.000 0.300 0.168
#> GSM254684 1 0.7293 0.6126 0.536 0.000 0.248 0.216
#> GSM254649 2 0.0336 0.6460 0.000 0.992 0.008 0.000
#> GSM254660 2 0.5174 -0.3772 0.000 0.620 0.012 0.368
#> GSM254693 2 0.0188 0.6466 0.000 0.996 0.000 0.004
#> GSM254695 4 0.5310 0.9233 0.000 0.412 0.012 0.576
#> GSM254702 2 0.5452 -0.5887 0.000 0.556 0.016 0.428
#> GSM254643 2 0.0657 0.6461 0.000 0.984 0.004 0.012
#> GSM254727 2 0.0937 0.6432 0.000 0.976 0.012 0.012
#> GSM254640 2 0.5257 -0.6477 0.000 0.548 0.008 0.444
#> GSM254626 2 0.0469 0.6462 0.000 0.988 0.000 0.012
#> GSM254635 4 0.5543 0.9326 0.000 0.424 0.020 0.556
#> GSM254653 2 0.0937 0.6432 0.000 0.976 0.012 0.012
#> GSM254658 2 0.0336 0.6460 0.000 0.992 0.008 0.000
#> GSM254681 2 0.0657 0.6437 0.000 0.984 0.012 0.004
#> GSM254719 2 0.1284 0.6382 0.000 0.964 0.012 0.024
#> GSM254673 2 0.0937 0.6439 0.000 0.976 0.012 0.012
#> GSM254655 2 0.4635 0.0836 0.000 0.720 0.012 0.268
#> GSM254669 2 0.0937 0.6439 0.000 0.976 0.012 0.012
#> GSM254699 2 0.3895 0.3677 0.000 0.804 0.012 0.184
#> GSM254703 4 0.5388 0.8959 0.000 0.456 0.012 0.532
#> GSM254708 2 0.2859 0.5560 0.000 0.880 0.008 0.112
#> GSM254715 2 0.5404 -0.7228 0.000 0.512 0.012 0.476
#> GSM254628 2 0.0336 0.6460 0.000 0.992 0.008 0.000
#> GSM254634 4 0.5345 0.9322 0.000 0.428 0.012 0.560
#> GSM254646 2 0.0336 0.6460 0.000 0.992 0.008 0.000
#> GSM254671 2 0.5493 -0.6645 0.000 0.528 0.016 0.456
#> GSM254711 4 0.5388 0.9012 0.000 0.456 0.012 0.532
#> GSM254717 2 0.0524 0.6464 0.000 0.988 0.004 0.008
#> GSM254723 3 0.2530 0.7879 0.000 0.000 0.888 0.112
#> GSM254730 2 0.4516 0.1387 0.000 0.736 0.012 0.252
#> GSM254731 2 0.5452 -0.5887 0.000 0.556 0.016 0.428
#> GSM254632 3 0.4301 0.7951 0.064 0.000 0.816 0.120
#> GSM254662 2 0.1174 0.6404 0.000 0.968 0.012 0.020
#> GSM254677 4 0.5408 0.9245 0.000 0.408 0.016 0.576
#> GSM254665 2 0.1388 0.6365 0.000 0.960 0.012 0.028
#> GSM254691 2 0.2799 0.5729 0.000 0.884 0.008 0.108
#> GSM254644 2 0.5126 -0.6340 0.000 0.552 0.004 0.444
#> GSM254667 2 0.3324 0.5254 0.000 0.852 0.012 0.136
#> GSM254676 2 0.2799 0.5729 0.000 0.884 0.008 0.108
#> GSM254679 4 0.5388 0.9012 0.000 0.456 0.012 0.532
#> GSM254689 2 0.0657 0.6437 0.000 0.984 0.012 0.004
#> GSM254706 2 0.2542 0.5812 0.000 0.904 0.012 0.084
#> GSM254712 2 0.5508 -0.7437 0.000 0.508 0.016 0.476
#> GSM254713 2 0.5506 -0.7316 0.000 0.512 0.016 0.472
#> GSM254683 2 0.2101 0.6044 0.000 0.928 0.012 0.060
#> GSM254710 2 0.8543 0.1558 0.184 0.544 0.148 0.124
#> GSM254725 4 0.5300 0.9267 0.000 0.408 0.012 0.580
#> GSM254651 2 0.2021 0.6062 0.000 0.932 0.012 0.056
#> GSM254638 4 0.5592 0.9216 0.000 0.404 0.024 0.572
#> GSM254685 2 0.5404 -0.7228 0.000 0.512 0.012 0.476
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM254629 3 0.1059 0.9143 0.004 0.008 0.968 0.000 0.020
#> GSM254648 3 0.1413 0.9089 0.000 0.012 0.956 0.020 0.012
#> GSM254694 3 0.1200 0.9144 0.012 0.008 0.964 0.016 0.000
#> GSM254701 3 0.1095 0.9157 0.012 0.008 0.968 0.000 0.012
#> GSM254728 3 0.1369 0.9149 0.008 0.008 0.956 0.000 0.028
#> GSM254726 3 0.1200 0.9089 0.000 0.008 0.964 0.016 0.012
#> GSM254639 3 0.3673 0.8702 0.060 0.040 0.848 0.000 0.052
#> GSM254652 3 0.1251 0.9135 0.000 0.008 0.956 0.000 0.036
#> GSM254700 1 0.1988 0.5933 0.928 0.016 0.008 0.000 0.048
#> GSM254625 5 0.5584 0.5731 0.072 0.016 0.276 0.000 0.636
#> GSM254636 1 0.7718 0.4136 0.472 0.100 0.192 0.000 0.236
#> GSM254659 3 0.1153 0.9158 0.008 0.004 0.964 0.000 0.024
#> GSM254680 5 0.7107 0.3204 0.364 0.072 0.100 0.000 0.464
#> GSM254686 5 0.6045 0.3622 0.060 0.024 0.440 0.000 0.476
#> GSM254718 3 0.0968 0.9160 0.012 0.004 0.972 0.000 0.012
#> GSM254674 5 0.6944 0.5601 0.260 0.072 0.116 0.000 0.552
#> GSM254668 5 0.5599 0.6748 0.244 0.020 0.080 0.000 0.656
#> GSM254697 1 0.3496 0.5884 0.844 0.056 0.008 0.000 0.092
#> GSM254704 1 0.1943 0.6105 0.924 0.020 0.056 0.000 0.000
#> GSM254707 5 0.5013 0.6844 0.240 0.000 0.080 0.000 0.680
#> GSM254714 3 0.2625 0.8552 0.108 0.016 0.876 0.000 0.000
#> GSM254722 1 0.3266 0.6050 0.860 0.076 0.008 0.000 0.056
#> GSM254627 1 0.3496 0.5884 0.844 0.056 0.008 0.000 0.092
#> GSM254630 5 0.5974 0.6491 0.188 0.016 0.160 0.000 0.636
#> GSM254633 1 0.7697 0.3508 0.448 0.076 0.244 0.000 0.232
#> GSM254670 3 0.4984 0.7924 0.060 0.088 0.764 0.000 0.088
#> GSM254716 5 0.5663 0.5593 0.072 0.016 0.292 0.000 0.620
#> GSM254720 1 0.4335 0.4648 0.708 0.020 0.268 0.000 0.004
#> GSM254729 3 0.1617 0.9162 0.020 0.012 0.948 0.000 0.020
#> GSM254654 3 0.1413 0.9125 0.012 0.012 0.956 0.020 0.000
#> GSM254656 3 0.4691 0.8406 0.048 0.044 0.808 0.052 0.048
#> GSM254631 1 0.7663 0.3532 0.456 0.076 0.224 0.000 0.244
#> GSM254657 3 0.4308 0.8408 0.056 0.048 0.808 0.000 0.088
#> GSM254664 1 0.7552 0.2202 0.464 0.076 0.176 0.000 0.284
#> GSM254672 1 0.2674 0.6095 0.896 0.032 0.060 0.000 0.012
#> GSM254692 5 0.5033 0.4159 0.448 0.024 0.004 0.000 0.524
#> GSM254645 3 0.3673 0.8702 0.060 0.040 0.848 0.000 0.052
#> GSM254666 5 0.4994 0.3896 0.012 0.016 0.396 0.000 0.576
#> GSM254675 1 0.6223 0.4769 0.636 0.044 0.120 0.000 0.200
#> GSM254678 1 0.6071 0.5690 0.680 0.120 0.096 0.000 0.104
#> GSM254688 5 0.4421 0.6747 0.268 0.004 0.024 0.000 0.704
#> GSM254690 1 0.6516 0.2669 0.528 0.096 0.036 0.000 0.340
#> GSM254696 1 0.7867 0.4174 0.452 0.116 0.184 0.000 0.248
#> GSM254705 5 0.4422 0.6449 0.320 0.012 0.004 0.000 0.664
#> GSM254642 1 0.3323 0.5797 0.844 0.056 0.000 0.000 0.100
#> GSM254661 3 0.1444 0.9136 0.000 0.012 0.948 0.000 0.040
#> GSM254698 1 0.5251 0.6026 0.736 0.140 0.056 0.000 0.068
#> GSM254641 5 0.6656 0.6115 0.228 0.032 0.172 0.000 0.568
#> GSM254647 1 0.3187 0.5892 0.860 0.036 0.008 0.000 0.096
#> GSM254663 5 0.4804 0.5718 0.364 0.016 0.008 0.000 0.612
#> GSM254682 5 0.4363 0.6692 0.268 0.008 0.016 0.000 0.708
#> GSM254709 5 0.5831 0.6686 0.212 0.004 0.160 0.000 0.624
#> GSM254721 1 0.1988 0.5933 0.928 0.016 0.008 0.000 0.048
#> GSM254724 1 0.1988 0.5933 0.928 0.016 0.008 0.000 0.048
#> GSM254650 5 0.4484 0.6678 0.308 0.000 0.024 0.000 0.668
#> GSM254687 5 0.4465 0.6696 0.304 0.000 0.024 0.000 0.672
#> GSM254637 1 0.7691 0.3520 0.456 0.080 0.240 0.000 0.224
#> GSM254684 1 0.7869 0.4146 0.452 0.120 0.176 0.000 0.252
#> GSM254649 2 0.4524 0.7967 0.000 0.736 0.004 0.208 0.052
#> GSM254660 4 0.4382 0.5241 0.000 0.288 0.000 0.688 0.024
#> GSM254693 2 0.3430 0.7930 0.000 0.776 0.000 0.220 0.004
#> GSM254695 4 0.2958 0.7102 0.000 0.024 0.020 0.880 0.076
#> GSM254702 4 0.3882 0.6471 0.000 0.224 0.000 0.756 0.020
#> GSM254643 2 0.4541 0.7545 0.000 0.680 0.000 0.288 0.032
#> GSM254727 2 0.4029 0.7797 0.000 0.744 0.000 0.232 0.024
#> GSM254640 4 0.4506 0.6887 0.004 0.244 0.000 0.716 0.036
#> GSM254626 2 0.4206 0.7581 0.000 0.696 0.000 0.288 0.016
#> GSM254635 4 0.1652 0.7536 0.004 0.008 0.004 0.944 0.040
#> GSM254653 2 0.3912 0.7787 0.000 0.752 0.000 0.228 0.020
#> GSM254658 2 0.4524 0.7967 0.000 0.736 0.004 0.208 0.052
#> GSM254681 2 0.5200 0.7917 0.004 0.696 0.004 0.208 0.088
#> GSM254719 2 0.4400 0.7347 0.000 0.672 0.000 0.308 0.020
#> GSM254673 2 0.4318 0.7498 0.000 0.688 0.000 0.292 0.020
#> GSM254655 4 0.4707 0.2253 0.000 0.392 0.000 0.588 0.020
#> GSM254669 2 0.4297 0.7529 0.000 0.692 0.000 0.288 0.020
#> GSM254699 4 0.4821 -0.1092 0.000 0.464 0.000 0.516 0.020
#> GSM254703 4 0.2940 0.7476 0.004 0.048 0.000 0.876 0.072
#> GSM254708 2 0.6318 0.6820 0.004 0.548 0.008 0.312 0.128
#> GSM254715 4 0.3073 0.7553 0.004 0.076 0.000 0.868 0.052
#> GSM254628 2 0.4457 0.7969 0.000 0.740 0.004 0.208 0.048
#> GSM254634 4 0.2178 0.7460 0.000 0.024 0.008 0.920 0.048
#> GSM254646 2 0.4984 0.7948 0.004 0.712 0.004 0.208 0.072
#> GSM254671 4 0.3556 0.6998 0.000 0.168 0.004 0.808 0.020
#> GSM254711 4 0.2308 0.7516 0.000 0.048 0.004 0.912 0.036
#> GSM254717 2 0.3727 0.7906 0.000 0.768 0.000 0.216 0.016
#> GSM254723 3 0.1883 0.8856 0.000 0.012 0.932 0.048 0.008
#> GSM254730 4 0.4904 0.0876 0.000 0.472 0.000 0.504 0.024
#> GSM254731 4 0.3882 0.6471 0.000 0.224 0.000 0.756 0.020
#> GSM254632 3 0.4431 0.7693 0.004 0.020 0.796 0.076 0.104
#> GSM254662 2 0.4400 0.7347 0.000 0.672 0.000 0.308 0.020
#> GSM254677 4 0.2945 0.7280 0.004 0.012 0.020 0.880 0.084
#> GSM254665 2 0.5337 0.7140 0.004 0.596 0.000 0.344 0.056
#> GSM254691 2 0.5909 0.6543 0.004 0.544 0.000 0.352 0.100
#> GSM254644 4 0.4482 0.6815 0.004 0.252 0.000 0.712 0.032
#> GSM254667 2 0.6885 0.6432 0.004 0.488 0.016 0.316 0.176
#> GSM254676 2 0.5898 0.6585 0.004 0.548 0.000 0.348 0.100
#> GSM254679 4 0.2308 0.7504 0.000 0.048 0.004 0.912 0.036
#> GSM254689 2 0.5200 0.7917 0.004 0.696 0.004 0.208 0.088
#> GSM254706 2 0.6333 0.7172 0.004 0.564 0.008 0.280 0.144
#> GSM254712 4 0.3012 0.7559 0.004 0.072 0.000 0.872 0.052
#> GSM254713 4 0.3012 0.7559 0.004 0.072 0.000 0.872 0.052
#> GSM254683 2 0.6129 0.7315 0.004 0.584 0.004 0.268 0.140
#> GSM254710 2 0.7221 0.4161 0.004 0.512 0.076 0.108 0.300
#> GSM254725 4 0.2198 0.7307 0.000 0.012 0.020 0.920 0.048
#> GSM254651 2 0.5960 0.7390 0.004 0.600 0.004 0.272 0.120
#> GSM254638 4 0.2393 0.7372 0.004 0.000 0.016 0.900 0.080
#> GSM254685 4 0.3202 0.7542 0.004 0.080 0.000 0.860 0.056
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM254629 3 0.1007 0.8619 0.008 0.004 0.968 0.004 0.016 0.000
#> GSM254648 3 0.1408 0.8603 0.008 0.008 0.952 0.000 0.008 0.024
#> GSM254694 3 0.1266 0.8622 0.008 0.004 0.960 0.004 0.008 0.016
#> GSM254701 3 0.0862 0.8621 0.008 0.000 0.972 0.004 0.016 0.000
#> GSM254728 3 0.1829 0.8616 0.000 0.008 0.928 0.000 0.028 0.036
#> GSM254726 3 0.2093 0.8547 0.000 0.020 0.920 0.004 0.020 0.036
#> GSM254639 3 0.4806 0.7546 0.048 0.012 0.732 0.008 0.024 0.176
#> GSM254652 3 0.1921 0.8611 0.000 0.012 0.928 0.004 0.032 0.024
#> GSM254700 1 0.1663 0.6335 0.912 0.000 0.000 0.000 0.088 0.000
#> GSM254625 5 0.4506 0.4618 0.000 0.036 0.212 0.000 0.716 0.036
#> GSM254636 6 0.6955 0.9172 0.328 0.000 0.056 0.000 0.264 0.352
#> GSM254659 3 0.0964 0.8638 0.000 0.000 0.968 0.004 0.016 0.012
#> GSM254680 5 0.6888 -0.1647 0.212 0.016 0.052 0.004 0.516 0.200
#> GSM254686 5 0.5722 0.3174 0.000 0.032 0.384 0.004 0.512 0.068
#> GSM254718 3 0.0551 0.8662 0.000 0.000 0.984 0.004 0.008 0.004
#> GSM254674 5 0.6034 0.2363 0.108 0.016 0.048 0.004 0.632 0.192
#> GSM254668 5 0.4702 0.4863 0.092 0.024 0.040 0.004 0.772 0.068
#> GSM254697 1 0.4900 0.6124 0.724 0.052 0.000 0.000 0.112 0.112
#> GSM254704 1 0.1340 0.6144 0.948 0.000 0.008 0.000 0.040 0.004
#> GSM254707 5 0.2991 0.5459 0.084 0.008 0.044 0.000 0.860 0.004
#> GSM254714 3 0.3263 0.7564 0.176 0.004 0.800 0.000 0.020 0.000
#> GSM254722 1 0.5015 0.5449 0.700 0.052 0.000 0.000 0.072 0.176
#> GSM254627 1 0.4900 0.6124 0.724 0.052 0.000 0.000 0.112 0.112
#> GSM254630 5 0.4783 0.5104 0.068 0.008 0.140 0.000 0.740 0.044
#> GSM254633 5 0.7672 -0.6022 0.320 0.016 0.092 0.004 0.344 0.224
#> GSM254670 3 0.5752 0.6289 0.048 0.012 0.628 0.008 0.052 0.252
#> GSM254716 5 0.4780 0.4440 0.000 0.032 0.236 0.000 0.684 0.048
#> GSM254720 1 0.3312 0.4395 0.792 0.000 0.180 0.000 0.028 0.000
#> GSM254729 3 0.2239 0.8532 0.012 0.012 0.908 0.008 0.000 0.060
#> GSM254654 3 0.1266 0.8622 0.008 0.004 0.960 0.004 0.008 0.016
#> GSM254656 3 0.6192 0.6934 0.048 0.016 0.624 0.040 0.044 0.228
#> GSM254631 5 0.7665 -0.5977 0.328 0.016 0.092 0.004 0.340 0.220
#> GSM254657 3 0.5140 0.7445 0.048 0.012 0.720 0.008 0.052 0.160
#> GSM254664 5 0.7561 -0.5337 0.312 0.016 0.080 0.004 0.368 0.220
#> GSM254672 1 0.2186 0.5713 0.908 0.000 0.012 0.000 0.024 0.056
#> GSM254692 5 0.4428 0.3612 0.312 0.008 0.000 0.000 0.648 0.032
#> GSM254645 3 0.4848 0.7569 0.048 0.012 0.732 0.008 0.028 0.172
#> GSM254666 5 0.5099 0.4102 0.012 0.028 0.268 0.000 0.652 0.040
#> GSM254675 1 0.5995 -0.0775 0.592 0.016 0.044 0.004 0.276 0.068
#> GSM254678 1 0.5756 -0.4301 0.548 0.000 0.016 0.000 0.140 0.296
#> GSM254688 5 0.2313 0.5471 0.100 0.000 0.004 0.000 0.884 0.012
#> GSM254690 5 0.6870 -0.5583 0.320 0.024 0.008 0.004 0.388 0.256
#> GSM254696 6 0.7060 0.9495 0.312 0.004 0.056 0.000 0.260 0.368
#> GSM254705 5 0.3013 0.5393 0.140 0.004 0.000 0.000 0.832 0.024
#> GSM254642 1 0.4941 0.6107 0.720 0.052 0.000 0.000 0.116 0.112
#> GSM254661 3 0.1794 0.8620 0.000 0.016 0.932 0.000 0.024 0.028
#> GSM254698 1 0.5692 0.2099 0.556 0.036 0.012 0.000 0.052 0.344
#> GSM254641 5 0.6149 0.4269 0.088 0.028 0.124 0.004 0.656 0.100
#> GSM254647 1 0.4550 0.5889 0.740 0.032 0.000 0.000 0.152 0.076
#> GSM254663 5 0.3878 0.4804 0.212 0.008 0.000 0.000 0.748 0.032
#> GSM254682 5 0.2492 0.5456 0.100 0.000 0.004 0.000 0.876 0.020
#> GSM254709 5 0.4117 0.5410 0.100 0.008 0.128 0.000 0.764 0.000
#> GSM254721 1 0.1753 0.6340 0.912 0.000 0.000 0.000 0.084 0.004
#> GSM254724 1 0.1610 0.6334 0.916 0.000 0.000 0.000 0.084 0.000
#> GSM254650 5 0.2584 0.5473 0.144 0.004 0.004 0.000 0.848 0.000
#> GSM254687 5 0.2716 0.5488 0.132 0.004 0.004 0.000 0.852 0.008
#> GSM254637 1 0.7655 -0.6299 0.340 0.016 0.092 0.004 0.332 0.216
#> GSM254684 6 0.7113 0.9394 0.316 0.008 0.052 0.000 0.260 0.364
#> GSM254649 2 0.2048 0.7065 0.000 0.880 0.000 0.120 0.000 0.000
#> GSM254660 4 0.5801 0.2640 0.000 0.260 0.000 0.500 0.000 0.240
#> GSM254693 2 0.3953 0.7086 0.000 0.764 0.000 0.132 0.000 0.104
#> GSM254695 4 0.3968 0.6422 0.000 0.020 0.012 0.792 0.036 0.140
#> GSM254702 4 0.5579 0.4394 0.000 0.204 0.000 0.548 0.000 0.248
#> GSM254643 2 0.4942 0.6788 0.000 0.652 0.000 0.156 0.000 0.192
#> GSM254727 2 0.4410 0.6927 0.000 0.716 0.000 0.120 0.000 0.164
#> GSM254640 4 0.5527 0.6234 0.008 0.252 0.000 0.628 0.032 0.080
#> GSM254626 2 0.4828 0.6816 0.000 0.668 0.000 0.156 0.000 0.176
#> GSM254635 4 0.1594 0.7545 0.000 0.016 0.000 0.932 0.000 0.052
#> GSM254653 2 0.4474 0.6898 0.000 0.708 0.000 0.120 0.000 0.172
#> GSM254658 2 0.2048 0.7065 0.000 0.880 0.000 0.120 0.000 0.000
#> GSM254681 2 0.3272 0.6946 0.004 0.824 0.000 0.124 0.000 0.048
#> GSM254719 2 0.5156 0.6534 0.000 0.616 0.000 0.152 0.000 0.232
#> GSM254673 2 0.5088 0.6621 0.000 0.628 0.000 0.152 0.000 0.220
#> GSM254655 2 0.6012 0.2017 0.000 0.396 0.000 0.364 0.000 0.240
#> GSM254669 2 0.5040 0.6652 0.000 0.636 0.000 0.152 0.000 0.212
#> GSM254699 2 0.5930 0.3719 0.000 0.456 0.000 0.304 0.000 0.240
#> GSM254703 4 0.2443 0.7461 0.008 0.040 0.000 0.904 0.024 0.024
#> GSM254708 2 0.6262 0.5024 0.004 0.456 0.000 0.344 0.016 0.180
#> GSM254715 4 0.4747 0.7306 0.008 0.076 0.000 0.744 0.040 0.132
#> GSM254628 2 0.2092 0.7056 0.000 0.876 0.000 0.124 0.000 0.000
#> GSM254634 4 0.1485 0.7403 0.000 0.028 0.000 0.944 0.004 0.024
#> GSM254646 2 0.2804 0.7017 0.004 0.852 0.000 0.120 0.000 0.024
#> GSM254671 4 0.4932 0.5806 0.000 0.128 0.000 0.644 0.000 0.228
#> GSM254711 4 0.1549 0.7458 0.000 0.044 0.000 0.936 0.000 0.020
#> GSM254717 2 0.4124 0.7032 0.000 0.748 0.000 0.120 0.000 0.132
#> GSM254723 3 0.3556 0.8188 0.000 0.028 0.836 0.020 0.024 0.092
#> GSM254730 2 0.5724 0.3375 0.000 0.492 0.000 0.324 0.000 0.184
#> GSM254731 4 0.5561 0.4393 0.000 0.204 0.000 0.552 0.000 0.244
#> GSM254632 3 0.6463 0.6018 0.000 0.044 0.600 0.088 0.064 0.204
#> GSM254662 2 0.5156 0.6534 0.000 0.616 0.000 0.152 0.000 0.232
#> GSM254677 4 0.2912 0.7269 0.008 0.012 0.008 0.880 0.032 0.060
#> GSM254665 2 0.5624 0.6304 0.004 0.580 0.000 0.232 0.004 0.180
#> GSM254691 2 0.6001 0.4817 0.004 0.460 0.000 0.384 0.012 0.140
#> GSM254644 4 0.5757 0.5992 0.008 0.264 0.000 0.604 0.040 0.084
#> GSM254667 2 0.6461 0.4743 0.004 0.484 0.000 0.304 0.036 0.172
#> GSM254676 2 0.5974 0.4842 0.004 0.464 0.000 0.384 0.012 0.136
#> GSM254679 4 0.1480 0.7442 0.000 0.040 0.000 0.940 0.000 0.020
#> GSM254689 2 0.3272 0.6946 0.004 0.824 0.000 0.124 0.000 0.048
#> GSM254706 2 0.5503 0.5687 0.000 0.604 0.000 0.224 0.012 0.160
#> GSM254712 4 0.4456 0.7395 0.008 0.076 0.000 0.772 0.040 0.104
#> GSM254713 4 0.4456 0.7395 0.008 0.076 0.000 0.772 0.040 0.104
#> GSM254683 2 0.5317 0.5985 0.004 0.640 0.000 0.220 0.012 0.124
#> GSM254710 2 0.7123 0.3461 0.004 0.516 0.016 0.108 0.140 0.216
#> GSM254725 4 0.1565 0.7399 0.000 0.028 0.004 0.940 0.000 0.028
#> GSM254651 2 0.5037 0.6115 0.000 0.668 0.000 0.192 0.012 0.128
#> GSM254638 4 0.1584 0.7455 0.004 0.012 0.004 0.944 0.004 0.032
#> GSM254685 4 0.4747 0.7306 0.008 0.076 0.000 0.744 0.040 0.132
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) individual(p) disease.state(p) gender(p) other(p) k
#> CV:kmeans 107 1.59e-22 0.77697 0.577 0.6277 0.872 2
#> CV:kmeans 98 2.05e-20 0.00176 0.557 0.1178 0.628 3
#> CV:kmeans 88 2.46e-17 0.01085 0.852 0.2238 0.981 4
#> CV:kmeans 89 7.24e-17 0.06638 0.315 0.0937 0.311 5
#> CV:kmeans 78 6.98e-14 0.02658 0.352 0.2971 0.246 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 21168 rows and 107 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 1.000 0.991 0.996 0.5026 0.497 0.497
#> 3 3 0.819 0.842 0.889 0.2700 0.862 0.725
#> 4 4 0.684 0.779 0.798 0.1217 0.882 0.688
#> 5 5 0.679 0.736 0.811 0.0817 0.868 0.563
#> 6 6 0.687 0.663 0.782 0.0410 0.926 0.678
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
#> GSM254629 1 0.0000 0.999 1.000 0.000
#> GSM254648 2 0.6887 0.777 0.184 0.816
#> GSM254694 1 0.0000 0.999 1.000 0.000
#> GSM254701 1 0.0000 0.999 1.000 0.000
#> GSM254728 1 0.0000 0.999 1.000 0.000
#> GSM254726 1 0.2778 0.949 0.952 0.048
#> GSM254639 1 0.0000 0.999 1.000 0.000
#> GSM254652 1 0.0000 0.999 1.000 0.000
#> GSM254700 1 0.0000 0.999 1.000 0.000
#> GSM254625 1 0.0000 0.999 1.000 0.000
#> GSM254636 1 0.0000 0.999 1.000 0.000
#> GSM254659 1 0.0000 0.999 1.000 0.000
#> GSM254680 1 0.0000 0.999 1.000 0.000
#> GSM254686 1 0.0000 0.999 1.000 0.000
#> GSM254718 1 0.0000 0.999 1.000 0.000
#> GSM254674 1 0.0000 0.999 1.000 0.000
#> GSM254668 1 0.0000 0.999 1.000 0.000
#> GSM254697 1 0.0000 0.999 1.000 0.000
#> GSM254704 1 0.0000 0.999 1.000 0.000
#> GSM254707 1 0.0000 0.999 1.000 0.000
#> GSM254714 1 0.0000 0.999 1.000 0.000
#> GSM254722 1 0.0000 0.999 1.000 0.000
#> GSM254627 1 0.0000 0.999 1.000 0.000
#> GSM254630 1 0.0000 0.999 1.000 0.000
#> GSM254633 1 0.0000 0.999 1.000 0.000
#> GSM254670 1 0.0000 0.999 1.000 0.000
#> GSM254716 1 0.0000 0.999 1.000 0.000
#> GSM254720 1 0.0000 0.999 1.000 0.000
#> GSM254729 1 0.0000 0.999 1.000 0.000
#> GSM254654 1 0.0000 0.999 1.000 0.000
#> GSM254656 1 0.0672 0.991 0.992 0.008
#> GSM254631 1 0.0000 0.999 1.000 0.000
#> GSM254657 1 0.0000 0.999 1.000 0.000
#> GSM254664 1 0.0000 0.999 1.000 0.000
#> GSM254672 1 0.0000 0.999 1.000 0.000
#> GSM254692 1 0.0000 0.999 1.000 0.000
#> GSM254645 1 0.0000 0.999 1.000 0.000
#> GSM254666 1 0.0000 0.999 1.000 0.000
#> GSM254675 1 0.0000 0.999 1.000 0.000
#> GSM254678 1 0.0000 0.999 1.000 0.000
#> GSM254688 1 0.0000 0.999 1.000 0.000
#> GSM254690 1 0.0000 0.999 1.000 0.000
#> GSM254696 1 0.0000 0.999 1.000 0.000
#> GSM254705 1 0.0000 0.999 1.000 0.000
#> GSM254642 1 0.0000 0.999 1.000 0.000
#> GSM254661 1 0.0000 0.999 1.000 0.000
#> GSM254698 1 0.0000 0.999 1.000 0.000
#> GSM254641 1 0.0000 0.999 1.000 0.000
#> GSM254647 1 0.0000 0.999 1.000 0.000
#> GSM254663 1 0.0000 0.999 1.000 0.000
#> GSM254682 1 0.0000 0.999 1.000 0.000
#> GSM254709 1 0.0000 0.999 1.000 0.000
#> GSM254721 1 0.0000 0.999 1.000 0.000
#> GSM254724 1 0.0000 0.999 1.000 0.000
#> GSM254650 1 0.0000 0.999 1.000 0.000
#> GSM254687 1 0.0000 0.999 1.000 0.000
#> GSM254637 1 0.0000 0.999 1.000 0.000
#> GSM254684 1 0.0000 0.999 1.000 0.000
#> GSM254649 2 0.0000 0.992 0.000 1.000
#> GSM254660 2 0.0000 0.992 0.000 1.000
#> GSM254693 2 0.0000 0.992 0.000 1.000
#> GSM254695 2 0.0000 0.992 0.000 1.000
#> GSM254702 2 0.0000 0.992 0.000 1.000
#> GSM254643 2 0.0000 0.992 0.000 1.000
#> GSM254727 2 0.0000 0.992 0.000 1.000
#> GSM254640 2 0.0000 0.992 0.000 1.000
#> GSM254626 2 0.0000 0.992 0.000 1.000
#> GSM254635 2 0.0000 0.992 0.000 1.000
#> GSM254653 2 0.0000 0.992 0.000 1.000
#> GSM254658 2 0.0000 0.992 0.000 1.000
#> GSM254681 2 0.0000 0.992 0.000 1.000
#> GSM254719 2 0.0000 0.992 0.000 1.000
#> GSM254673 2 0.0000 0.992 0.000 1.000
#> GSM254655 2 0.0000 0.992 0.000 1.000
#> GSM254669 2 0.0000 0.992 0.000 1.000
#> GSM254699 2 0.0000 0.992 0.000 1.000
#> GSM254703 2 0.0000 0.992 0.000 1.000
#> GSM254708 2 0.0000 0.992 0.000 1.000
#> GSM254715 2 0.0000 0.992 0.000 1.000
#> GSM254628 2 0.0000 0.992 0.000 1.000
#> GSM254634 2 0.0000 0.992 0.000 1.000
#> GSM254646 2 0.0000 0.992 0.000 1.000
#> GSM254671 2 0.0000 0.992 0.000 1.000
#> GSM254711 2 0.0000 0.992 0.000 1.000
#> GSM254717 2 0.0000 0.992 0.000 1.000
#> GSM254723 2 0.7219 0.752 0.200 0.800
#> GSM254730 2 0.0000 0.992 0.000 1.000
#> GSM254731 2 0.0000 0.992 0.000 1.000
#> GSM254632 2 0.0000 0.992 0.000 1.000
#> GSM254662 2 0.0000 0.992 0.000 1.000
#> GSM254677 2 0.0000 0.992 0.000 1.000
#> GSM254665 2 0.0000 0.992 0.000 1.000
#> GSM254691 2 0.0000 0.992 0.000 1.000
#> GSM254644 2 0.0000 0.992 0.000 1.000
#> GSM254667 2 0.0000 0.992 0.000 1.000
#> GSM254676 2 0.0000 0.992 0.000 1.000
#> GSM254679 2 0.0000 0.992 0.000 1.000
#> GSM254689 2 0.0000 0.992 0.000 1.000
#> GSM254706 2 0.0000 0.992 0.000 1.000
#> GSM254712 2 0.0000 0.992 0.000 1.000
#> GSM254713 2 0.0000 0.992 0.000 1.000
#> GSM254683 2 0.0000 0.992 0.000 1.000
#> GSM254710 2 0.0000 0.992 0.000 1.000
#> GSM254725 2 0.0000 0.992 0.000 1.000
#> GSM254651 2 0.0000 0.992 0.000 1.000
#> GSM254638 2 0.0000 0.992 0.000 1.000
#> GSM254685 2 0.0000 0.992 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM254629 3 0.0747 0.710 0.016 0.000 0.984
#> GSM254648 3 0.0848 0.709 0.008 0.008 0.984
#> GSM254694 3 0.5254 0.832 0.264 0.000 0.736
#> GSM254701 3 0.5254 0.832 0.264 0.000 0.736
#> GSM254728 3 0.5254 0.831 0.264 0.000 0.736
#> GSM254726 3 0.0000 0.705 0.000 0.000 1.000
#> GSM254639 3 0.5216 0.833 0.260 0.000 0.740
#> GSM254652 3 0.1031 0.719 0.024 0.000 0.976
#> GSM254700 1 0.0000 0.814 1.000 0.000 0.000
#> GSM254625 1 0.6045 0.624 0.620 0.000 0.380
#> GSM254636 1 0.0424 0.812 0.992 0.000 0.008
#> GSM254659 3 0.5216 0.833 0.260 0.000 0.740
#> GSM254680 1 0.0237 0.814 0.996 0.000 0.004
#> GSM254686 1 0.6299 0.463 0.524 0.000 0.476
#> GSM254718 3 0.5254 0.832 0.264 0.000 0.736
#> GSM254674 1 0.3482 0.785 0.872 0.000 0.128
#> GSM254668 1 0.5138 0.745 0.748 0.000 0.252
#> GSM254697 1 0.0000 0.814 1.000 0.000 0.000
#> GSM254704 1 0.0237 0.812 0.996 0.000 0.004
#> GSM254707 1 0.5216 0.742 0.740 0.000 0.260
#> GSM254714 3 0.6302 0.505 0.480 0.000 0.520
#> GSM254722 1 0.0237 0.814 0.996 0.000 0.004
#> GSM254627 1 0.0000 0.814 1.000 0.000 0.000
#> GSM254630 1 0.5178 0.744 0.744 0.000 0.256
#> GSM254633 1 0.0237 0.812 0.996 0.000 0.004
#> GSM254670 3 0.5835 0.759 0.340 0.000 0.660
#> GSM254716 1 0.6299 0.469 0.524 0.000 0.476
#> GSM254720 1 0.4291 0.555 0.820 0.000 0.180
#> GSM254729 3 0.5216 0.833 0.260 0.000 0.740
#> GSM254654 3 0.5178 0.830 0.256 0.000 0.744
#> GSM254656 3 0.5363 0.822 0.276 0.000 0.724
#> GSM254631 1 0.0000 0.814 1.000 0.000 0.000
#> GSM254657 3 0.5760 0.774 0.328 0.000 0.672
#> GSM254664 1 0.0000 0.814 1.000 0.000 0.000
#> GSM254672 1 0.0237 0.812 0.996 0.000 0.004
#> GSM254692 1 0.5138 0.745 0.748 0.000 0.252
#> GSM254645 1 0.6309 -0.478 0.504 0.000 0.496
#> GSM254666 1 0.6111 0.601 0.604 0.000 0.396
#> GSM254675 1 0.0000 0.814 1.000 0.000 0.000
#> GSM254678 1 0.0424 0.812 0.992 0.000 0.008
#> GSM254688 1 0.5178 0.744 0.744 0.000 0.256
#> GSM254690 1 0.0237 0.814 0.996 0.000 0.004
#> GSM254696 1 0.0424 0.812 0.992 0.000 0.008
#> GSM254705 1 0.5178 0.744 0.744 0.000 0.256
#> GSM254642 1 0.0000 0.814 1.000 0.000 0.000
#> GSM254661 3 0.0592 0.710 0.012 0.000 0.988
#> GSM254698 1 0.0424 0.812 0.992 0.000 0.008
#> GSM254641 1 0.5098 0.746 0.752 0.000 0.248
#> GSM254647 1 0.0000 0.814 1.000 0.000 0.000
#> GSM254663 1 0.5138 0.745 0.748 0.000 0.252
#> GSM254682 1 0.5178 0.744 0.744 0.000 0.256
#> GSM254709 1 0.5138 0.745 0.748 0.000 0.252
#> GSM254721 1 0.0000 0.814 1.000 0.000 0.000
#> GSM254724 1 0.0000 0.814 1.000 0.000 0.000
#> GSM254650 1 0.5138 0.745 0.748 0.000 0.252
#> GSM254687 1 0.5178 0.744 0.744 0.000 0.256
#> GSM254637 1 0.0000 0.814 1.000 0.000 0.000
#> GSM254684 1 0.0424 0.812 0.992 0.000 0.008
#> GSM254649 2 0.0000 0.985 0.000 1.000 0.000
#> GSM254660 2 0.0000 0.985 0.000 1.000 0.000
#> GSM254693 2 0.0000 0.985 0.000 1.000 0.000
#> GSM254695 2 0.0424 0.983 0.000 0.992 0.008
#> GSM254702 2 0.0424 0.983 0.000 0.992 0.008
#> GSM254643 2 0.0000 0.985 0.000 1.000 0.000
#> GSM254727 2 0.0000 0.985 0.000 1.000 0.000
#> GSM254640 2 0.0424 0.983 0.000 0.992 0.008
#> GSM254626 2 0.0000 0.985 0.000 1.000 0.000
#> GSM254635 2 0.0424 0.983 0.000 0.992 0.008
#> GSM254653 2 0.0000 0.985 0.000 1.000 0.000
#> GSM254658 2 0.0000 0.985 0.000 1.000 0.000
#> GSM254681 2 0.0000 0.985 0.000 1.000 0.000
#> GSM254719 2 0.0000 0.985 0.000 1.000 0.000
#> GSM254673 2 0.0000 0.985 0.000 1.000 0.000
#> GSM254655 2 0.0000 0.985 0.000 1.000 0.000
#> GSM254669 2 0.0000 0.985 0.000 1.000 0.000
#> GSM254699 2 0.0000 0.985 0.000 1.000 0.000
#> GSM254703 2 0.0424 0.983 0.000 0.992 0.008
#> GSM254708 2 0.0000 0.985 0.000 1.000 0.000
#> GSM254715 2 0.0424 0.983 0.000 0.992 0.008
#> GSM254628 2 0.0000 0.985 0.000 1.000 0.000
#> GSM254634 2 0.0424 0.983 0.000 0.992 0.008
#> GSM254646 2 0.0000 0.985 0.000 1.000 0.000
#> GSM254671 2 0.0424 0.983 0.000 0.992 0.008
#> GSM254711 2 0.0424 0.983 0.000 0.992 0.008
#> GSM254717 2 0.0000 0.985 0.000 1.000 0.000
#> GSM254723 3 0.5138 0.603 0.000 0.252 0.748
#> GSM254730 2 0.0000 0.985 0.000 1.000 0.000
#> GSM254731 2 0.0424 0.983 0.000 0.992 0.008
#> GSM254632 2 0.6143 0.631 0.024 0.720 0.256
#> GSM254662 2 0.0000 0.985 0.000 1.000 0.000
#> GSM254677 2 0.0424 0.983 0.000 0.992 0.008
#> GSM254665 2 0.0000 0.985 0.000 1.000 0.000
#> GSM254691 2 0.0000 0.985 0.000 1.000 0.000
#> GSM254644 2 0.0424 0.983 0.000 0.992 0.008
#> GSM254667 2 0.0000 0.985 0.000 1.000 0.000
#> GSM254676 2 0.0000 0.985 0.000 1.000 0.000
#> GSM254679 2 0.0424 0.983 0.000 0.992 0.008
#> GSM254689 2 0.0000 0.985 0.000 1.000 0.000
#> GSM254706 2 0.0000 0.985 0.000 1.000 0.000
#> GSM254712 2 0.0424 0.983 0.000 0.992 0.008
#> GSM254713 2 0.0424 0.983 0.000 0.992 0.008
#> GSM254683 2 0.0000 0.985 0.000 1.000 0.000
#> GSM254710 2 0.5365 0.667 0.004 0.744 0.252
#> GSM254725 2 0.0424 0.983 0.000 0.992 0.008
#> GSM254651 2 0.0000 0.985 0.000 1.000 0.000
#> GSM254638 2 0.0424 0.983 0.000 0.992 0.008
#> GSM254685 2 0.0424 0.983 0.000 0.992 0.008
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM254629 3 0.3402 0.8003 0.004 0.164 0.832 0.000
#> GSM254648 3 0.3444 0.7981 0.000 0.184 0.816 0.000
#> GSM254694 3 0.5527 0.8308 0.104 0.168 0.728 0.000
#> GSM254701 3 0.5527 0.8308 0.104 0.168 0.728 0.000
#> GSM254728 3 0.2987 0.8374 0.104 0.016 0.880 0.000
#> GSM254726 3 0.3494 0.8009 0.000 0.172 0.824 0.004
#> GSM254639 3 0.2867 0.8383 0.104 0.012 0.884 0.000
#> GSM254652 3 0.0336 0.8091 0.008 0.000 0.992 0.000
#> GSM254700 1 0.0000 0.8277 1.000 0.000 0.000 0.000
#> GSM254625 1 0.7281 0.6165 0.532 0.196 0.272 0.000
#> GSM254636 1 0.2342 0.8068 0.912 0.008 0.080 0.000
#> GSM254659 3 0.2867 0.8415 0.104 0.012 0.884 0.000
#> GSM254680 1 0.1975 0.8268 0.936 0.016 0.048 0.000
#> GSM254686 1 0.7145 0.5576 0.508 0.144 0.348 0.000
#> GSM254718 3 0.4669 0.8416 0.104 0.100 0.796 0.000
#> GSM254674 1 0.3307 0.8191 0.868 0.028 0.104 0.000
#> GSM254668 1 0.6037 0.7566 0.688 0.156 0.156 0.000
#> GSM254697 1 0.0000 0.8277 1.000 0.000 0.000 0.000
#> GSM254704 1 0.0657 0.8215 0.984 0.004 0.012 0.000
#> GSM254707 1 0.6162 0.7512 0.676 0.156 0.168 0.000
#> GSM254714 3 0.5300 0.5493 0.408 0.012 0.580 0.000
#> GSM254722 1 0.0804 0.8264 0.980 0.012 0.008 0.000
#> GSM254627 1 0.0000 0.8277 1.000 0.000 0.000 0.000
#> GSM254630 1 0.6570 0.7135 0.632 0.164 0.204 0.000
#> GSM254633 1 0.2053 0.8155 0.924 0.004 0.072 0.000
#> GSM254670 3 0.4993 0.7146 0.260 0.028 0.712 0.000
#> GSM254716 1 0.7453 0.5618 0.496 0.204 0.300 0.000
#> GSM254720 1 0.4088 0.4929 0.764 0.004 0.232 0.000
#> GSM254729 3 0.3392 0.8389 0.124 0.020 0.856 0.000
#> GSM254654 3 0.5527 0.8308 0.104 0.168 0.728 0.000
#> GSM254656 3 0.7346 0.5601 0.068 0.060 0.592 0.280
#> GSM254631 1 0.0657 0.8289 0.984 0.004 0.012 0.000
#> GSM254657 3 0.4149 0.8013 0.168 0.028 0.804 0.000
#> GSM254664 1 0.0524 0.8288 0.988 0.004 0.008 0.000
#> GSM254672 1 0.0779 0.8201 0.980 0.004 0.016 0.000
#> GSM254692 1 0.5351 0.7675 0.744 0.152 0.104 0.000
#> GSM254645 3 0.5269 0.5819 0.364 0.016 0.620 0.000
#> GSM254666 1 0.7203 0.6202 0.536 0.176 0.288 0.000
#> GSM254675 1 0.0336 0.8290 0.992 0.008 0.000 0.000
#> GSM254678 1 0.1356 0.8211 0.960 0.008 0.032 0.000
#> GSM254688 1 0.6080 0.7554 0.684 0.160 0.156 0.000
#> GSM254690 1 0.1938 0.8203 0.936 0.012 0.052 0.000
#> GSM254696 1 0.3443 0.7612 0.848 0.016 0.136 0.000
#> GSM254705 1 0.5462 0.7677 0.736 0.152 0.112 0.000
#> GSM254642 1 0.0188 0.8287 0.996 0.000 0.004 0.000
#> GSM254661 3 0.0707 0.8031 0.000 0.020 0.980 0.000
#> GSM254698 1 0.1488 0.8170 0.956 0.012 0.032 0.000
#> GSM254641 1 0.4565 0.7985 0.796 0.064 0.140 0.000
#> GSM254647 1 0.0000 0.8277 1.000 0.000 0.000 0.000
#> GSM254663 1 0.5257 0.7712 0.752 0.144 0.104 0.000
#> GSM254682 1 0.6080 0.7554 0.684 0.160 0.156 0.000
#> GSM254709 1 0.5351 0.7675 0.744 0.152 0.104 0.000
#> GSM254721 1 0.0000 0.8277 1.000 0.000 0.000 0.000
#> GSM254724 1 0.0000 0.8277 1.000 0.000 0.000 0.000
#> GSM254650 1 0.5351 0.7675 0.744 0.152 0.104 0.000
#> GSM254687 1 0.5452 0.7669 0.736 0.156 0.108 0.000
#> GSM254637 1 0.0524 0.8288 0.988 0.004 0.008 0.000
#> GSM254684 1 0.2861 0.7924 0.888 0.016 0.096 0.000
#> GSM254649 2 0.4830 0.9438 0.000 0.608 0.000 0.392
#> GSM254660 4 0.4250 0.2071 0.000 0.276 0.000 0.724
#> GSM254693 2 0.4843 0.9428 0.000 0.604 0.000 0.396
#> GSM254695 4 0.0817 0.8339 0.000 0.024 0.000 0.976
#> GSM254702 4 0.0336 0.8502 0.000 0.008 0.000 0.992
#> GSM254643 2 0.4866 0.9375 0.000 0.596 0.000 0.404
#> GSM254727 2 0.4830 0.9438 0.000 0.608 0.000 0.392
#> GSM254640 4 0.0592 0.8414 0.000 0.016 0.000 0.984
#> GSM254626 2 0.4866 0.9375 0.000 0.596 0.000 0.404
#> GSM254635 4 0.0000 0.8544 0.000 0.000 0.000 1.000
#> GSM254653 2 0.4843 0.9428 0.000 0.604 0.000 0.396
#> GSM254658 2 0.4830 0.9438 0.000 0.608 0.000 0.392
#> GSM254681 2 0.4830 0.9438 0.000 0.608 0.000 0.392
#> GSM254719 2 0.4866 0.9375 0.000 0.596 0.000 0.404
#> GSM254673 2 0.4866 0.9375 0.000 0.596 0.000 0.404
#> GSM254655 4 0.4477 0.0338 0.000 0.312 0.000 0.688
#> GSM254669 2 0.4855 0.9408 0.000 0.600 0.000 0.400
#> GSM254699 4 0.4761 -0.2793 0.000 0.372 0.000 0.628
#> GSM254703 4 0.0000 0.8544 0.000 0.000 0.000 1.000
#> GSM254708 2 0.4830 0.9438 0.000 0.608 0.000 0.392
#> GSM254715 4 0.0000 0.8544 0.000 0.000 0.000 1.000
#> GSM254628 2 0.4830 0.9438 0.000 0.608 0.000 0.392
#> GSM254634 4 0.0000 0.8544 0.000 0.000 0.000 1.000
#> GSM254646 2 0.4830 0.9438 0.000 0.608 0.000 0.392
#> GSM254671 4 0.0336 0.8502 0.000 0.008 0.000 0.992
#> GSM254711 4 0.0000 0.8544 0.000 0.000 0.000 1.000
#> GSM254717 2 0.4830 0.9438 0.000 0.608 0.000 0.392
#> GSM254723 4 0.7203 -0.0702 0.004 0.136 0.336 0.524
#> GSM254730 4 0.4624 -0.1197 0.000 0.340 0.000 0.660
#> GSM254731 4 0.0336 0.8502 0.000 0.008 0.000 0.992
#> GSM254632 2 0.5633 0.3947 0.008 0.740 0.108 0.144
#> GSM254662 2 0.4866 0.9375 0.000 0.596 0.000 0.404
#> GSM254677 4 0.0469 0.8438 0.000 0.012 0.000 0.988
#> GSM254665 2 0.4866 0.9375 0.000 0.596 0.000 0.404
#> GSM254691 2 0.4855 0.9408 0.000 0.600 0.000 0.400
#> GSM254644 4 0.0336 0.8502 0.000 0.008 0.000 0.992
#> GSM254667 2 0.4790 0.9296 0.000 0.620 0.000 0.380
#> GSM254676 2 0.4855 0.9408 0.000 0.600 0.000 0.400
#> GSM254679 4 0.0000 0.8544 0.000 0.000 0.000 1.000
#> GSM254689 2 0.4830 0.9438 0.000 0.608 0.000 0.392
#> GSM254706 2 0.4790 0.9296 0.000 0.620 0.000 0.380
#> GSM254712 4 0.0000 0.8544 0.000 0.000 0.000 1.000
#> GSM254713 4 0.0000 0.8544 0.000 0.000 0.000 1.000
#> GSM254683 2 0.4830 0.9438 0.000 0.608 0.000 0.392
#> GSM254710 2 0.5421 0.5172 0.000 0.724 0.076 0.200
#> GSM254725 4 0.0336 0.8477 0.000 0.008 0.000 0.992
#> GSM254651 2 0.4830 0.9438 0.000 0.608 0.000 0.392
#> GSM254638 4 0.0000 0.8544 0.000 0.000 0.000 1.000
#> GSM254685 4 0.0000 0.8544 0.000 0.000 0.000 1.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM254629 3 0.0486 0.7989 0.004 0.000 0.988 0.004 0.004
#> GSM254648 3 0.0324 0.7967 0.000 0.004 0.992 0.004 0.000
#> GSM254694 3 0.0324 0.7979 0.004 0.000 0.992 0.004 0.000
#> GSM254701 3 0.0324 0.7979 0.004 0.000 0.992 0.004 0.000
#> GSM254728 3 0.5804 0.7669 0.056 0.000 0.648 0.048 0.248
#> GSM254726 3 0.0898 0.7967 0.000 0.000 0.972 0.008 0.020
#> GSM254639 3 0.7330 0.6809 0.180 0.000 0.520 0.076 0.224
#> GSM254652 3 0.5535 0.7666 0.024 0.000 0.656 0.064 0.256
#> GSM254700 1 0.2497 0.7605 0.880 0.000 0.004 0.004 0.112
#> GSM254625 5 0.1331 0.7489 0.040 0.000 0.000 0.008 0.952
#> GSM254636 1 0.3569 0.7028 0.816 0.000 0.004 0.028 0.152
#> GSM254659 3 0.4993 0.7842 0.048 0.000 0.708 0.020 0.224
#> GSM254680 1 0.4507 0.4143 0.580 0.000 0.004 0.004 0.412
#> GSM254686 5 0.2701 0.7253 0.048 0.000 0.044 0.012 0.896
#> GSM254718 3 0.3830 0.8041 0.040 0.000 0.820 0.016 0.124
#> GSM254674 1 0.4704 0.2125 0.508 0.000 0.004 0.008 0.480
#> GSM254668 5 0.2732 0.7909 0.160 0.000 0.000 0.000 0.840
#> GSM254697 1 0.2513 0.7586 0.876 0.000 0.008 0.000 0.116
#> GSM254704 1 0.1412 0.7689 0.952 0.000 0.008 0.004 0.036
#> GSM254707 5 0.2648 0.7933 0.152 0.000 0.000 0.000 0.848
#> GSM254714 1 0.4822 0.1990 0.564 0.000 0.416 0.004 0.016
#> GSM254722 1 0.0992 0.7657 0.968 0.000 0.000 0.008 0.024
#> GSM254627 1 0.2230 0.7601 0.884 0.000 0.000 0.000 0.116
#> GSM254630 5 0.3487 0.7790 0.212 0.000 0.000 0.008 0.780
#> GSM254633 1 0.3554 0.7226 0.776 0.000 0.004 0.004 0.216
#> GSM254670 1 0.7008 0.2722 0.556 0.000 0.128 0.076 0.240
#> GSM254716 5 0.1329 0.7359 0.032 0.000 0.004 0.008 0.956
#> GSM254720 1 0.1630 0.7627 0.944 0.000 0.036 0.004 0.016
#> GSM254729 3 0.7240 0.6498 0.212 0.000 0.524 0.064 0.200
#> GSM254654 3 0.0324 0.7979 0.004 0.000 0.992 0.004 0.000
#> GSM254656 4 0.7387 -0.0960 0.184 0.000 0.100 0.532 0.184
#> GSM254631 1 0.3243 0.7522 0.812 0.000 0.004 0.004 0.180
#> GSM254657 3 0.7847 0.5042 0.280 0.000 0.400 0.076 0.244
#> GSM254664 1 0.3706 0.7147 0.756 0.000 0.004 0.004 0.236
#> GSM254672 1 0.0451 0.7606 0.988 0.000 0.008 0.004 0.000
#> GSM254692 5 0.3837 0.7467 0.308 0.000 0.000 0.000 0.692
#> GSM254645 1 0.5791 0.5136 0.700 0.000 0.112 0.068 0.120
#> GSM254666 5 0.1739 0.7197 0.032 0.000 0.004 0.024 0.940
#> GSM254675 1 0.2976 0.7510 0.852 0.000 0.012 0.004 0.132
#> GSM254678 1 0.0912 0.7588 0.972 0.000 0.000 0.016 0.012
#> GSM254688 5 0.2690 0.7940 0.156 0.000 0.000 0.000 0.844
#> GSM254690 1 0.3689 0.7135 0.740 0.000 0.000 0.004 0.256
#> GSM254696 1 0.4468 0.6243 0.728 0.000 0.004 0.040 0.228
#> GSM254705 5 0.3774 0.7582 0.296 0.000 0.000 0.000 0.704
#> GSM254642 1 0.2605 0.7363 0.852 0.000 0.000 0.000 0.148
#> GSM254661 3 0.4709 0.7856 0.004 0.000 0.716 0.056 0.224
#> GSM254698 1 0.2157 0.7361 0.920 0.000 0.004 0.040 0.036
#> GSM254641 5 0.4333 0.4988 0.352 0.000 0.004 0.004 0.640
#> GSM254647 1 0.2377 0.7540 0.872 0.000 0.000 0.000 0.128
#> GSM254663 5 0.3774 0.7568 0.296 0.000 0.000 0.000 0.704
#> GSM254682 5 0.2690 0.7961 0.156 0.000 0.000 0.000 0.844
#> GSM254709 5 0.3707 0.7672 0.284 0.000 0.000 0.000 0.716
#> GSM254721 1 0.2672 0.7576 0.872 0.000 0.008 0.004 0.116
#> GSM254724 1 0.2621 0.7592 0.876 0.000 0.008 0.004 0.112
#> GSM254650 5 0.3661 0.7723 0.276 0.000 0.000 0.000 0.724
#> GSM254687 5 0.3612 0.7759 0.268 0.000 0.000 0.000 0.732
#> GSM254637 1 0.3205 0.7543 0.816 0.000 0.004 0.004 0.176
#> GSM254684 1 0.4077 0.6721 0.780 0.000 0.004 0.044 0.172
#> GSM254649 2 0.0162 0.8790 0.000 0.996 0.000 0.000 0.004
#> GSM254660 2 0.4060 0.2632 0.000 0.640 0.000 0.360 0.000
#> GSM254693 2 0.0609 0.8781 0.000 0.980 0.000 0.020 0.000
#> GSM254695 4 0.2230 0.7567 0.000 0.116 0.000 0.884 0.000
#> GSM254702 4 0.3684 0.8510 0.000 0.280 0.000 0.720 0.000
#> GSM254643 2 0.1341 0.8644 0.000 0.944 0.000 0.056 0.000
#> GSM254727 2 0.0000 0.8790 0.000 1.000 0.000 0.000 0.000
#> GSM254640 4 0.3913 0.8073 0.000 0.324 0.000 0.676 0.000
#> GSM254626 2 0.1043 0.8734 0.000 0.960 0.000 0.040 0.000
#> GSM254635 4 0.3395 0.8809 0.000 0.236 0.000 0.764 0.000
#> GSM254653 2 0.0510 0.8781 0.000 0.984 0.000 0.016 0.000
#> GSM254658 2 0.0162 0.8790 0.000 0.996 0.000 0.000 0.004
#> GSM254681 2 0.0324 0.8774 0.000 0.992 0.000 0.004 0.004
#> GSM254719 2 0.1270 0.8670 0.000 0.948 0.000 0.052 0.000
#> GSM254673 2 0.1043 0.8734 0.000 0.960 0.000 0.040 0.000
#> GSM254655 2 0.3932 0.3727 0.000 0.672 0.000 0.328 0.000
#> GSM254669 2 0.0963 0.8745 0.000 0.964 0.000 0.036 0.000
#> GSM254699 2 0.3480 0.5754 0.000 0.752 0.000 0.248 0.000
#> GSM254703 4 0.3452 0.8804 0.000 0.244 0.000 0.756 0.000
#> GSM254708 2 0.0703 0.8736 0.000 0.976 0.000 0.024 0.000
#> GSM254715 4 0.3395 0.8809 0.000 0.236 0.000 0.764 0.000
#> GSM254628 2 0.0162 0.8790 0.000 0.996 0.000 0.000 0.004
#> GSM254634 4 0.3274 0.8754 0.000 0.220 0.000 0.780 0.000
#> GSM254646 2 0.0162 0.8790 0.000 0.996 0.000 0.000 0.004
#> GSM254671 4 0.3661 0.8549 0.000 0.276 0.000 0.724 0.000
#> GSM254711 4 0.3452 0.8790 0.000 0.244 0.000 0.756 0.000
#> GSM254717 2 0.0162 0.8785 0.000 0.996 0.000 0.004 0.000
#> GSM254723 4 0.6147 0.0873 0.000 0.004 0.328 0.536 0.132
#> GSM254730 2 0.3730 0.4538 0.000 0.712 0.000 0.288 0.000
#> GSM254731 4 0.3684 0.8510 0.000 0.280 0.000 0.720 0.000
#> GSM254632 5 0.6385 0.3701 0.000 0.260 0.012 0.168 0.560
#> GSM254662 2 0.1043 0.8734 0.000 0.960 0.000 0.040 0.000
#> GSM254677 4 0.3109 0.8497 0.000 0.200 0.000 0.800 0.000
#> GSM254665 2 0.1478 0.8584 0.000 0.936 0.000 0.064 0.000
#> GSM254691 2 0.1341 0.8653 0.000 0.944 0.000 0.056 0.000
#> GSM254644 4 0.3774 0.8460 0.000 0.296 0.000 0.704 0.000
#> GSM254667 2 0.2753 0.7609 0.000 0.856 0.000 0.136 0.008
#> GSM254676 2 0.1270 0.8680 0.000 0.948 0.000 0.052 0.000
#> GSM254679 4 0.3424 0.8780 0.000 0.240 0.000 0.760 0.000
#> GSM254689 2 0.0162 0.8790 0.000 0.996 0.000 0.000 0.004
#> GSM254706 2 0.2563 0.7786 0.000 0.872 0.000 0.120 0.008
#> GSM254712 4 0.3395 0.8809 0.000 0.236 0.000 0.764 0.000
#> GSM254713 4 0.3395 0.8809 0.000 0.236 0.000 0.764 0.000
#> GSM254683 2 0.1251 0.8567 0.000 0.956 0.000 0.036 0.008
#> GSM254710 2 0.6186 0.2186 0.000 0.512 0.000 0.152 0.336
#> GSM254725 4 0.2891 0.8392 0.000 0.176 0.000 0.824 0.000
#> GSM254651 2 0.1894 0.8265 0.000 0.920 0.000 0.072 0.008
#> GSM254638 4 0.3366 0.8808 0.000 0.232 0.000 0.768 0.000
#> GSM254685 4 0.3424 0.8797 0.000 0.240 0.000 0.760 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM254629 3 0.0862 0.8125 0.004 0.000 0.972 0.000 0.016 0.008
#> GSM254648 3 0.0291 0.8135 0.004 0.000 0.992 0.000 0.000 0.004
#> GSM254694 3 0.0665 0.8137 0.008 0.000 0.980 0.000 0.004 0.008
#> GSM254701 3 0.0551 0.8144 0.004 0.000 0.984 0.000 0.008 0.004
#> GSM254728 6 0.5699 -0.0232 0.008 0.012 0.400 0.000 0.088 0.492
#> GSM254726 3 0.2515 0.7765 0.000 0.016 0.892 0.000 0.040 0.052
#> GSM254639 6 0.4232 0.5749 0.044 0.000 0.132 0.000 0.052 0.772
#> GSM254652 6 0.6215 -0.0633 0.000 0.016 0.392 0.000 0.188 0.404
#> GSM254700 1 0.0547 0.7830 0.980 0.000 0.000 0.000 0.020 0.000
#> GSM254625 5 0.1801 0.6993 0.012 0.012 0.004 0.000 0.932 0.040
#> GSM254636 6 0.6346 0.2567 0.336 0.016 0.004 0.000 0.200 0.444
#> GSM254659 3 0.6100 0.3597 0.016 0.016 0.556 0.000 0.148 0.264
#> GSM254680 5 0.5468 0.2066 0.368 0.016 0.008 0.000 0.544 0.064
#> GSM254686 5 0.4432 0.6744 0.072 0.016 0.036 0.000 0.780 0.096
#> GSM254718 3 0.5065 0.5701 0.036 0.004 0.668 0.000 0.052 0.240
#> GSM254674 5 0.5158 0.4984 0.256 0.016 0.008 0.000 0.648 0.072
#> GSM254668 5 0.2757 0.7306 0.084 0.012 0.008 0.000 0.876 0.020
#> GSM254697 1 0.1418 0.7820 0.944 0.000 0.000 0.000 0.032 0.024
#> GSM254704 1 0.0363 0.7751 0.988 0.000 0.000 0.000 0.000 0.012
#> GSM254707 5 0.1982 0.7345 0.068 0.000 0.004 0.000 0.912 0.016
#> GSM254714 1 0.3485 0.5820 0.784 0.000 0.184 0.000 0.004 0.028
#> GSM254722 1 0.2581 0.7107 0.856 0.000 0.000 0.000 0.016 0.128
#> GSM254627 1 0.1408 0.7823 0.944 0.000 0.000 0.000 0.036 0.020
#> GSM254630 5 0.4855 0.6687 0.256 0.000 0.000 0.000 0.640 0.104
#> GSM254633 1 0.5870 0.4497 0.576 0.016 0.016 0.000 0.280 0.112
#> GSM254670 6 0.3878 0.6359 0.108 0.000 0.028 0.000 0.064 0.800
#> GSM254716 5 0.2983 0.6585 0.012 0.012 0.004 0.000 0.844 0.128
#> GSM254720 1 0.0935 0.7679 0.964 0.000 0.004 0.000 0.000 0.032
#> GSM254729 6 0.5459 0.5255 0.032 0.004 0.200 0.000 0.112 0.652
#> GSM254654 3 0.0291 0.8135 0.004 0.000 0.992 0.000 0.000 0.004
#> GSM254656 6 0.3863 0.4835 0.020 0.028 0.000 0.144 0.012 0.796
#> GSM254631 1 0.5072 0.5696 0.652 0.012 0.008 0.000 0.256 0.072
#> GSM254657 6 0.4327 0.6080 0.076 0.000 0.088 0.000 0.060 0.776
#> GSM254664 1 0.4992 0.5699 0.656 0.012 0.008 0.000 0.260 0.064
#> GSM254672 1 0.1556 0.7364 0.920 0.000 0.000 0.000 0.000 0.080
#> GSM254692 5 0.3992 0.6091 0.364 0.000 0.000 0.000 0.624 0.012
#> GSM254645 6 0.4551 0.5509 0.268 0.000 0.020 0.000 0.036 0.676
#> GSM254666 5 0.3627 0.6158 0.020 0.000 0.004 0.000 0.752 0.224
#> GSM254675 1 0.1049 0.7844 0.960 0.000 0.000 0.000 0.032 0.008
#> GSM254678 1 0.3778 0.4846 0.708 0.000 0.000 0.000 0.020 0.272
#> GSM254688 5 0.2432 0.7485 0.100 0.000 0.000 0.000 0.876 0.024
#> GSM254690 1 0.5445 0.5071 0.584 0.012 0.000 0.000 0.288 0.116
#> GSM254696 6 0.5536 0.5185 0.248 0.012 0.000 0.000 0.148 0.592
#> GSM254705 5 0.3990 0.6882 0.284 0.000 0.000 0.000 0.688 0.028
#> GSM254642 1 0.1845 0.7714 0.920 0.000 0.000 0.000 0.052 0.028
#> GSM254661 3 0.4736 0.3707 0.000 0.000 0.588 0.000 0.060 0.352
#> GSM254698 1 0.4177 -0.0158 0.520 0.000 0.000 0.000 0.012 0.468
#> GSM254641 5 0.4743 0.5783 0.260 0.012 0.012 0.000 0.676 0.040
#> GSM254647 1 0.1983 0.7649 0.908 0.000 0.000 0.000 0.072 0.020
#> GSM254663 5 0.3871 0.6750 0.308 0.000 0.000 0.000 0.676 0.016
#> GSM254682 5 0.2784 0.7533 0.124 0.000 0.000 0.000 0.848 0.028
#> GSM254709 5 0.3692 0.7239 0.244 0.000 0.008 0.000 0.736 0.012
#> GSM254721 1 0.0891 0.7807 0.968 0.000 0.000 0.000 0.024 0.008
#> GSM254724 1 0.0692 0.7821 0.976 0.000 0.000 0.000 0.020 0.004
#> GSM254650 5 0.3240 0.7274 0.244 0.000 0.000 0.000 0.752 0.004
#> GSM254687 5 0.3189 0.7312 0.236 0.000 0.000 0.000 0.760 0.004
#> GSM254637 1 0.4838 0.6028 0.688 0.012 0.008 0.000 0.224 0.068
#> GSM254684 6 0.5303 0.4365 0.312 0.004 0.000 0.000 0.112 0.572
#> GSM254649 2 0.2738 0.8846 0.000 0.820 0.000 0.176 0.000 0.004
#> GSM254660 4 0.4568 0.2318 0.000 0.344 0.004 0.612 0.000 0.040
#> GSM254693 2 0.2805 0.8850 0.000 0.812 0.004 0.184 0.000 0.000
#> GSM254695 4 0.3816 0.6509 0.000 0.160 0.000 0.784 0.024 0.032
#> GSM254702 4 0.2670 0.7765 0.000 0.084 0.004 0.872 0.000 0.040
#> GSM254643 2 0.3627 0.8685 0.000 0.752 0.004 0.224 0.000 0.020
#> GSM254727 2 0.3630 0.8781 0.000 0.780 0.004 0.176 0.000 0.040
#> GSM254640 4 0.2257 0.7797 0.000 0.116 0.000 0.876 0.000 0.008
#> GSM254626 2 0.3488 0.8727 0.000 0.764 0.004 0.216 0.000 0.016
#> GSM254635 4 0.0000 0.8319 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM254653 2 0.3695 0.8757 0.000 0.772 0.004 0.184 0.000 0.040
#> GSM254658 2 0.2738 0.8846 0.000 0.820 0.000 0.176 0.000 0.004
#> GSM254681 2 0.3343 0.8791 0.004 0.796 0.000 0.176 0.000 0.024
#> GSM254719 2 0.3960 0.8579 0.000 0.736 0.004 0.220 0.000 0.040
#> GSM254673 2 0.3933 0.8612 0.000 0.740 0.004 0.216 0.000 0.040
#> GSM254655 4 0.4746 -0.0971 0.000 0.424 0.004 0.532 0.000 0.040
#> GSM254669 2 0.3867 0.8636 0.000 0.744 0.004 0.216 0.000 0.036
#> GSM254699 2 0.4751 0.4247 0.000 0.528 0.004 0.428 0.000 0.040
#> GSM254703 4 0.0547 0.8315 0.000 0.020 0.000 0.980 0.000 0.000
#> GSM254708 2 0.2668 0.8812 0.000 0.828 0.000 0.168 0.000 0.004
#> GSM254715 4 0.0363 0.8319 0.000 0.012 0.000 0.988 0.000 0.000
#> GSM254628 2 0.2738 0.8846 0.000 0.820 0.000 0.176 0.000 0.004
#> GSM254634 4 0.0458 0.8288 0.000 0.016 0.000 0.984 0.000 0.000
#> GSM254646 2 0.3087 0.8825 0.004 0.808 0.000 0.176 0.000 0.012
#> GSM254671 4 0.2563 0.7829 0.000 0.076 0.004 0.880 0.000 0.040
#> GSM254711 4 0.0363 0.8313 0.000 0.012 0.000 0.988 0.000 0.000
#> GSM254717 2 0.3630 0.8781 0.000 0.780 0.004 0.176 0.000 0.040
#> GSM254723 4 0.7940 -0.2448 0.012 0.036 0.308 0.348 0.068 0.228
#> GSM254730 4 0.4780 -0.2508 0.000 0.476 0.004 0.480 0.000 0.040
#> GSM254731 4 0.2670 0.7765 0.000 0.084 0.004 0.872 0.000 0.040
#> GSM254632 5 0.6046 0.3560 0.004 0.272 0.020 0.008 0.564 0.132
#> GSM254662 2 0.3933 0.8612 0.000 0.740 0.004 0.216 0.000 0.040
#> GSM254677 4 0.1075 0.8105 0.000 0.048 0.000 0.952 0.000 0.000
#> GSM254665 2 0.3384 0.8741 0.004 0.760 0.000 0.228 0.000 0.008
#> GSM254691 2 0.3301 0.8703 0.004 0.772 0.000 0.216 0.000 0.008
#> GSM254644 4 0.2313 0.7900 0.000 0.100 0.004 0.884 0.000 0.012
#> GSM254667 2 0.3087 0.7118 0.004 0.864 0.000 0.052 0.024 0.056
#> GSM254676 2 0.3273 0.8737 0.004 0.776 0.000 0.212 0.000 0.008
#> GSM254679 4 0.0363 0.8310 0.000 0.012 0.000 0.988 0.000 0.000
#> GSM254689 2 0.3343 0.8791 0.004 0.796 0.000 0.176 0.000 0.024
#> GSM254706 2 0.3001 0.7391 0.004 0.868 0.000 0.060 0.020 0.048
#> GSM254712 4 0.0146 0.8322 0.000 0.004 0.000 0.996 0.000 0.000
#> GSM254713 4 0.0146 0.8322 0.000 0.004 0.000 0.996 0.000 0.000
#> GSM254683 2 0.3493 0.8535 0.004 0.808 0.000 0.148 0.008 0.032
#> GSM254710 2 0.5497 0.1549 0.004 0.584 0.004 0.004 0.292 0.112
#> GSM254725 4 0.1387 0.7939 0.000 0.068 0.000 0.932 0.000 0.000
#> GSM254651 2 0.3380 0.7885 0.004 0.836 0.000 0.100 0.016 0.044
#> GSM254638 4 0.0000 0.8319 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM254685 4 0.0508 0.8312 0.000 0.012 0.000 0.984 0.000 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) individual(p) disease.state(p) gender(p) other(p) k
#> CV:skmeans 107 2.35e-23 0.55450 0.665 0.61079 0.958 2
#> CV:skmeans 104 1.74e-22 0.00150 0.683 0.00907 0.947 3
#> CV:skmeans 100 1.55e-21 0.01832 0.790 0.03269 0.975 4
#> CV:skmeans 95 1.14e-19 0.00388 0.805 0.01162 0.963 5
#> CV:skmeans 88 1.77e-17 0.00815 0.708 0.04898 0.942 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 21168 rows and 107 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#> Subgroups are detected by 'pam' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.991 0.996 0.4986 0.503 0.503
#> 3 3 0.919 0.932 0.967 0.2717 0.860 0.723
#> 4 4 0.783 0.859 0.917 0.1450 0.891 0.711
#> 5 5 0.770 0.785 0.887 0.0371 0.977 0.919
#> 6 6 0.685 0.486 0.724 0.0563 0.934 0.755
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
#> GSM254629 1 0.000 0.992 1.000 0.000
#> GSM254648 1 0.808 0.676 0.752 0.248
#> GSM254694 1 0.000 0.992 1.000 0.000
#> GSM254701 1 0.000 0.992 1.000 0.000
#> GSM254728 1 0.000 0.992 1.000 0.000
#> GSM254726 1 0.000 0.992 1.000 0.000
#> GSM254639 1 0.000 0.992 1.000 0.000
#> GSM254652 1 0.000 0.992 1.000 0.000
#> GSM254700 1 0.000 0.992 1.000 0.000
#> GSM254625 1 0.000 0.992 1.000 0.000
#> GSM254636 1 0.000 0.992 1.000 0.000
#> GSM254659 1 0.000 0.992 1.000 0.000
#> GSM254680 1 0.000 0.992 1.000 0.000
#> GSM254686 1 0.000 0.992 1.000 0.000
#> GSM254718 1 0.000 0.992 1.000 0.000
#> GSM254674 1 0.000 0.992 1.000 0.000
#> GSM254668 1 0.000 0.992 1.000 0.000
#> GSM254697 1 0.000 0.992 1.000 0.000
#> GSM254704 1 0.000 0.992 1.000 0.000
#> GSM254707 1 0.000 0.992 1.000 0.000
#> GSM254714 1 0.000 0.992 1.000 0.000
#> GSM254722 1 0.000 0.992 1.000 0.000
#> GSM254627 1 0.000 0.992 1.000 0.000
#> GSM254630 1 0.000 0.992 1.000 0.000
#> GSM254633 1 0.000 0.992 1.000 0.000
#> GSM254670 1 0.000 0.992 1.000 0.000
#> GSM254716 1 0.000 0.992 1.000 0.000
#> GSM254720 1 0.000 0.992 1.000 0.000
#> GSM254729 1 0.000 0.992 1.000 0.000
#> GSM254654 1 0.000 0.992 1.000 0.000
#> GSM254656 1 0.000 0.992 1.000 0.000
#> GSM254631 1 0.000 0.992 1.000 0.000
#> GSM254657 1 0.000 0.992 1.000 0.000
#> GSM254664 1 0.000 0.992 1.000 0.000
#> GSM254672 1 0.000 0.992 1.000 0.000
#> GSM254692 1 0.000 0.992 1.000 0.000
#> GSM254645 1 0.000 0.992 1.000 0.000
#> GSM254666 1 0.000 0.992 1.000 0.000
#> GSM254675 1 0.000 0.992 1.000 0.000
#> GSM254678 1 0.000 0.992 1.000 0.000
#> GSM254688 1 0.000 0.992 1.000 0.000
#> GSM254690 1 0.000 0.992 1.000 0.000
#> GSM254696 1 0.000 0.992 1.000 0.000
#> GSM254705 1 0.000 0.992 1.000 0.000
#> GSM254642 1 0.000 0.992 1.000 0.000
#> GSM254661 1 0.000 0.992 1.000 0.000
#> GSM254698 1 0.000 0.992 1.000 0.000
#> GSM254641 1 0.000 0.992 1.000 0.000
#> GSM254647 1 0.000 0.992 1.000 0.000
#> GSM254663 1 0.000 0.992 1.000 0.000
#> GSM254682 1 0.000 0.992 1.000 0.000
#> GSM254709 1 0.000 0.992 1.000 0.000
#> GSM254721 1 0.000 0.992 1.000 0.000
#> GSM254724 1 0.000 0.992 1.000 0.000
#> GSM254650 1 0.000 0.992 1.000 0.000
#> GSM254687 1 0.000 0.992 1.000 0.000
#> GSM254637 1 0.000 0.992 1.000 0.000
#> GSM254684 1 0.000 0.992 1.000 0.000
#> GSM254649 2 0.000 1.000 0.000 1.000
#> GSM254660 2 0.000 1.000 0.000 1.000
#> GSM254693 2 0.000 1.000 0.000 1.000
#> GSM254695 2 0.000 1.000 0.000 1.000
#> GSM254702 2 0.000 1.000 0.000 1.000
#> GSM254643 2 0.000 1.000 0.000 1.000
#> GSM254727 2 0.000 1.000 0.000 1.000
#> GSM254640 2 0.000 1.000 0.000 1.000
#> GSM254626 2 0.000 1.000 0.000 1.000
#> GSM254635 2 0.000 1.000 0.000 1.000
#> GSM254653 2 0.000 1.000 0.000 1.000
#> GSM254658 2 0.000 1.000 0.000 1.000
#> GSM254681 2 0.000 1.000 0.000 1.000
#> GSM254719 2 0.000 1.000 0.000 1.000
#> GSM254673 2 0.000 1.000 0.000 1.000
#> GSM254655 2 0.000 1.000 0.000 1.000
#> GSM254669 2 0.000 1.000 0.000 1.000
#> GSM254699 2 0.000 1.000 0.000 1.000
#> GSM254703 2 0.000 1.000 0.000 1.000
#> GSM254708 2 0.000 1.000 0.000 1.000
#> GSM254715 2 0.000 1.000 0.000 1.000
#> GSM254628 2 0.000 1.000 0.000 1.000
#> GSM254634 2 0.000 1.000 0.000 1.000
#> GSM254646 2 0.000 1.000 0.000 1.000
#> GSM254671 2 0.000 1.000 0.000 1.000
#> GSM254711 2 0.000 1.000 0.000 1.000
#> GSM254717 2 0.000 1.000 0.000 1.000
#> GSM254723 1 0.000 0.992 1.000 0.000
#> GSM254730 2 0.000 1.000 0.000 1.000
#> GSM254731 2 0.000 1.000 0.000 1.000
#> GSM254632 1 0.714 0.759 0.804 0.196
#> GSM254662 2 0.000 1.000 0.000 1.000
#> GSM254677 2 0.000 1.000 0.000 1.000
#> GSM254665 2 0.000 1.000 0.000 1.000
#> GSM254691 2 0.000 1.000 0.000 1.000
#> GSM254644 2 0.000 1.000 0.000 1.000
#> GSM254667 2 0.000 1.000 0.000 1.000
#> GSM254676 2 0.000 1.000 0.000 1.000
#> GSM254679 2 0.000 1.000 0.000 1.000
#> GSM254689 2 0.000 1.000 0.000 1.000
#> GSM254706 2 0.000 1.000 0.000 1.000
#> GSM254712 2 0.000 1.000 0.000 1.000
#> GSM254713 2 0.000 1.000 0.000 1.000
#> GSM254683 2 0.000 1.000 0.000 1.000
#> GSM254710 2 0.000 1.000 0.000 1.000
#> GSM254725 2 0.000 1.000 0.000 1.000
#> GSM254651 2 0.000 1.000 0.000 1.000
#> GSM254638 2 0.000 1.000 0.000 1.000
#> GSM254685 2 0.000 1.000 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM254629 3 0.0000 0.935 0.000 0.000 1.000
#> GSM254648 3 0.5098 0.621 0.000 0.248 0.752
#> GSM254694 3 0.0000 0.935 0.000 0.000 1.000
#> GSM254701 3 0.0000 0.935 0.000 0.000 1.000
#> GSM254728 3 0.0000 0.935 0.000 0.000 1.000
#> GSM254726 3 0.0000 0.935 0.000 0.000 1.000
#> GSM254639 3 0.0000 0.935 0.000 0.000 1.000
#> GSM254652 3 0.0000 0.935 0.000 0.000 1.000
#> GSM254700 1 0.0000 0.936 1.000 0.000 0.000
#> GSM254625 1 0.2448 0.887 0.924 0.000 0.076
#> GSM254636 3 0.1031 0.929 0.024 0.000 0.976
#> GSM254659 3 0.0000 0.935 0.000 0.000 1.000
#> GSM254680 3 0.1031 0.929 0.024 0.000 0.976
#> GSM254686 3 0.0000 0.935 0.000 0.000 1.000
#> GSM254718 3 0.0000 0.935 0.000 0.000 1.000
#> GSM254674 3 0.0747 0.932 0.016 0.000 0.984
#> GSM254668 1 0.6026 0.400 0.624 0.000 0.376
#> GSM254697 3 0.3340 0.861 0.120 0.000 0.880
#> GSM254704 3 0.5650 0.597 0.312 0.000 0.688
#> GSM254707 1 0.0000 0.936 1.000 0.000 0.000
#> GSM254714 3 0.4399 0.782 0.188 0.000 0.812
#> GSM254722 3 0.4750 0.760 0.216 0.000 0.784
#> GSM254627 3 0.4555 0.780 0.200 0.000 0.800
#> GSM254630 1 0.3038 0.863 0.896 0.000 0.104
#> GSM254633 3 0.0892 0.931 0.020 0.000 0.980
#> GSM254670 3 0.0000 0.935 0.000 0.000 1.000
#> GSM254716 3 0.4974 0.672 0.236 0.000 0.764
#> GSM254720 3 0.0000 0.935 0.000 0.000 1.000
#> GSM254729 3 0.0000 0.935 0.000 0.000 1.000
#> GSM254654 3 0.0000 0.935 0.000 0.000 1.000
#> GSM254656 3 0.0000 0.935 0.000 0.000 1.000
#> GSM254631 3 0.1031 0.929 0.024 0.000 0.976
#> GSM254657 3 0.0000 0.935 0.000 0.000 1.000
#> GSM254664 3 0.1031 0.929 0.024 0.000 0.976
#> GSM254672 3 0.1860 0.912 0.052 0.000 0.948
#> GSM254692 1 0.0000 0.936 1.000 0.000 0.000
#> GSM254645 3 0.0237 0.934 0.004 0.000 0.996
#> GSM254666 3 0.1031 0.927 0.024 0.000 0.976
#> GSM254675 3 0.0592 0.932 0.012 0.000 0.988
#> GSM254678 3 0.5291 0.666 0.268 0.000 0.732
#> GSM254688 1 0.0000 0.936 1.000 0.000 0.000
#> GSM254690 3 0.1289 0.926 0.032 0.000 0.968
#> GSM254696 3 0.0424 0.934 0.008 0.000 0.992
#> GSM254705 1 0.0237 0.933 0.996 0.000 0.004
#> GSM254642 1 0.0000 0.936 1.000 0.000 0.000
#> GSM254661 3 0.0000 0.935 0.000 0.000 1.000
#> GSM254698 3 0.0892 0.931 0.020 0.000 0.980
#> GSM254641 3 0.0592 0.933 0.012 0.000 0.988
#> GSM254647 1 0.4399 0.732 0.812 0.000 0.188
#> GSM254663 1 0.0000 0.936 1.000 0.000 0.000
#> GSM254682 1 0.0000 0.936 1.000 0.000 0.000
#> GSM254709 1 0.0000 0.936 1.000 0.000 0.000
#> GSM254721 1 0.0000 0.936 1.000 0.000 0.000
#> GSM254724 1 0.0000 0.936 1.000 0.000 0.000
#> GSM254650 1 0.0000 0.936 1.000 0.000 0.000
#> GSM254687 1 0.0000 0.936 1.000 0.000 0.000
#> GSM254637 3 0.1031 0.929 0.024 0.000 0.976
#> GSM254684 3 0.4235 0.807 0.176 0.000 0.824
#> GSM254649 2 0.0000 1.000 0.000 1.000 0.000
#> GSM254660 2 0.0000 1.000 0.000 1.000 0.000
#> GSM254693 2 0.0000 1.000 0.000 1.000 0.000
#> GSM254695 2 0.0000 1.000 0.000 1.000 0.000
#> GSM254702 2 0.0000 1.000 0.000 1.000 0.000
#> GSM254643 2 0.0000 1.000 0.000 1.000 0.000
#> GSM254727 2 0.0000 1.000 0.000 1.000 0.000
#> GSM254640 2 0.0000 1.000 0.000 1.000 0.000
#> GSM254626 2 0.0000 1.000 0.000 1.000 0.000
#> GSM254635 2 0.0000 1.000 0.000 1.000 0.000
#> GSM254653 2 0.0000 1.000 0.000 1.000 0.000
#> GSM254658 2 0.0000 1.000 0.000 1.000 0.000
#> GSM254681 2 0.0000 1.000 0.000 1.000 0.000
#> GSM254719 2 0.0000 1.000 0.000 1.000 0.000
#> GSM254673 2 0.0000 1.000 0.000 1.000 0.000
#> GSM254655 2 0.0000 1.000 0.000 1.000 0.000
#> GSM254669 2 0.0000 1.000 0.000 1.000 0.000
#> GSM254699 2 0.0000 1.000 0.000 1.000 0.000
#> GSM254703 2 0.0000 1.000 0.000 1.000 0.000
#> GSM254708 2 0.0000 1.000 0.000 1.000 0.000
#> GSM254715 2 0.0000 1.000 0.000 1.000 0.000
#> GSM254628 2 0.0000 1.000 0.000 1.000 0.000
#> GSM254634 2 0.0000 1.000 0.000 1.000 0.000
#> GSM254646 2 0.0000 1.000 0.000 1.000 0.000
#> GSM254671 2 0.0000 1.000 0.000 1.000 0.000
#> GSM254711 2 0.0000 1.000 0.000 1.000 0.000
#> GSM254717 2 0.0000 1.000 0.000 1.000 0.000
#> GSM254723 3 0.0000 0.935 0.000 0.000 1.000
#> GSM254730 2 0.0000 1.000 0.000 1.000 0.000
#> GSM254731 2 0.0000 1.000 0.000 1.000 0.000
#> GSM254632 3 0.4504 0.702 0.000 0.196 0.804
#> GSM254662 2 0.0000 1.000 0.000 1.000 0.000
#> GSM254677 2 0.0000 1.000 0.000 1.000 0.000
#> GSM254665 2 0.0000 1.000 0.000 1.000 0.000
#> GSM254691 2 0.0000 1.000 0.000 1.000 0.000
#> GSM254644 2 0.0000 1.000 0.000 1.000 0.000
#> GSM254667 2 0.0000 1.000 0.000 1.000 0.000
#> GSM254676 2 0.0000 1.000 0.000 1.000 0.000
#> GSM254679 2 0.0000 1.000 0.000 1.000 0.000
#> GSM254689 2 0.0000 1.000 0.000 1.000 0.000
#> GSM254706 2 0.0000 1.000 0.000 1.000 0.000
#> GSM254712 2 0.0000 1.000 0.000 1.000 0.000
#> GSM254713 2 0.0000 1.000 0.000 1.000 0.000
#> GSM254683 2 0.0000 1.000 0.000 1.000 0.000
#> GSM254710 1 0.5178 0.637 0.744 0.256 0.000
#> GSM254725 2 0.0000 1.000 0.000 1.000 0.000
#> GSM254651 2 0.0000 1.000 0.000 1.000 0.000
#> GSM254638 2 0.0000 1.000 0.000 1.000 0.000
#> GSM254685 2 0.0000 1.000 0.000 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM254629 3 0.0000 0.927 0.000 0.000 1.000 0.000
#> GSM254648 3 0.4415 0.745 0.000 0.140 0.804 0.056
#> GSM254694 3 0.0000 0.927 0.000 0.000 1.000 0.000
#> GSM254701 3 0.0000 0.927 0.000 0.000 1.000 0.000
#> GSM254728 3 0.0000 0.927 0.000 0.000 1.000 0.000
#> GSM254726 3 0.0000 0.927 0.000 0.000 1.000 0.000
#> GSM254639 3 0.0000 0.927 0.000 0.000 1.000 0.000
#> GSM254652 3 0.0000 0.927 0.000 0.000 1.000 0.000
#> GSM254700 1 0.0000 0.940 1.000 0.000 0.000 0.000
#> GSM254625 1 0.2011 0.883 0.920 0.000 0.080 0.000
#> GSM254636 3 0.0921 0.922 0.028 0.000 0.972 0.000
#> GSM254659 3 0.0000 0.927 0.000 0.000 1.000 0.000
#> GSM254680 3 0.0921 0.922 0.028 0.000 0.972 0.000
#> GSM254686 3 0.0000 0.927 0.000 0.000 1.000 0.000
#> GSM254718 3 0.0000 0.927 0.000 0.000 1.000 0.000
#> GSM254674 3 0.0707 0.924 0.020 0.000 0.980 0.000
#> GSM254668 1 0.4761 0.411 0.628 0.000 0.372 0.000
#> GSM254697 3 0.3367 0.853 0.108 0.000 0.864 0.028
#> GSM254704 3 0.4477 0.597 0.312 0.000 0.688 0.000
#> GSM254707 1 0.0000 0.940 1.000 0.000 0.000 0.000
#> GSM254714 3 0.3486 0.779 0.188 0.000 0.812 0.000
#> GSM254722 3 0.4406 0.766 0.192 0.000 0.780 0.028
#> GSM254627 3 0.4238 0.785 0.176 0.000 0.796 0.028
#> GSM254630 1 0.2469 0.859 0.892 0.000 0.108 0.000
#> GSM254633 3 0.0817 0.923 0.024 0.000 0.976 0.000
#> GSM254670 3 0.0000 0.927 0.000 0.000 1.000 0.000
#> GSM254716 3 0.3942 0.668 0.236 0.000 0.764 0.000
#> GSM254720 3 0.0000 0.927 0.000 0.000 1.000 0.000
#> GSM254729 3 0.0000 0.927 0.000 0.000 1.000 0.000
#> GSM254654 3 0.0000 0.927 0.000 0.000 1.000 0.000
#> GSM254656 4 0.4790 0.391 0.000 0.000 0.380 0.620
#> GSM254631 3 0.0921 0.922 0.028 0.000 0.972 0.000
#> GSM254657 3 0.0000 0.927 0.000 0.000 1.000 0.000
#> GSM254664 3 0.0921 0.922 0.028 0.000 0.972 0.000
#> GSM254672 3 0.1474 0.908 0.052 0.000 0.948 0.000
#> GSM254692 1 0.0000 0.940 1.000 0.000 0.000 0.000
#> GSM254645 3 0.0188 0.927 0.004 0.000 0.996 0.000
#> GSM254666 3 0.0817 0.920 0.024 0.000 0.976 0.000
#> GSM254675 3 0.0469 0.925 0.012 0.000 0.988 0.000
#> GSM254678 3 0.4164 0.670 0.264 0.000 0.736 0.000
#> GSM254688 1 0.0000 0.940 1.000 0.000 0.000 0.000
#> GSM254690 3 0.1118 0.919 0.036 0.000 0.964 0.000
#> GSM254696 3 0.0469 0.926 0.012 0.000 0.988 0.000
#> GSM254705 1 0.0188 0.937 0.996 0.000 0.004 0.000
#> GSM254642 1 0.0921 0.925 0.972 0.000 0.000 0.028
#> GSM254661 3 0.0000 0.927 0.000 0.000 1.000 0.000
#> GSM254698 3 0.1733 0.913 0.024 0.000 0.948 0.028
#> GSM254641 3 0.0592 0.925 0.016 0.000 0.984 0.000
#> GSM254647 1 0.3486 0.741 0.812 0.000 0.188 0.000
#> GSM254663 1 0.0000 0.940 1.000 0.000 0.000 0.000
#> GSM254682 1 0.0000 0.940 1.000 0.000 0.000 0.000
#> GSM254709 1 0.0000 0.940 1.000 0.000 0.000 0.000
#> GSM254721 1 0.0000 0.940 1.000 0.000 0.000 0.000
#> GSM254724 1 0.0000 0.940 1.000 0.000 0.000 0.000
#> GSM254650 1 0.0000 0.940 1.000 0.000 0.000 0.000
#> GSM254687 1 0.0000 0.940 1.000 0.000 0.000 0.000
#> GSM254637 3 0.0921 0.922 0.028 0.000 0.972 0.000
#> GSM254684 3 0.3400 0.803 0.180 0.000 0.820 0.000
#> GSM254649 2 0.0000 0.897 0.000 1.000 0.000 0.000
#> GSM254660 2 0.2814 0.857 0.000 0.868 0.000 0.132
#> GSM254693 2 0.0000 0.897 0.000 1.000 0.000 0.000
#> GSM254695 4 0.1022 0.865 0.000 0.032 0.000 0.968
#> GSM254702 4 0.4989 0.300 0.000 0.472 0.000 0.528
#> GSM254643 2 0.2011 0.882 0.000 0.920 0.000 0.080
#> GSM254727 2 0.0921 0.896 0.000 0.972 0.000 0.028
#> GSM254640 2 0.2530 0.886 0.000 0.888 0.000 0.112
#> GSM254626 2 0.0000 0.897 0.000 1.000 0.000 0.000
#> GSM254635 4 0.3569 0.834 0.000 0.196 0.000 0.804
#> GSM254653 2 0.1211 0.895 0.000 0.960 0.000 0.040
#> GSM254658 2 0.0000 0.897 0.000 1.000 0.000 0.000
#> GSM254681 2 0.2345 0.866 0.000 0.900 0.000 0.100
#> GSM254719 2 0.2149 0.877 0.000 0.912 0.000 0.088
#> GSM254673 2 0.1474 0.892 0.000 0.948 0.000 0.052
#> GSM254655 2 0.2408 0.866 0.000 0.896 0.000 0.104
#> GSM254669 2 0.0000 0.897 0.000 1.000 0.000 0.000
#> GSM254699 2 0.2408 0.866 0.000 0.896 0.000 0.104
#> GSM254703 4 0.0921 0.863 0.000 0.028 0.000 0.972
#> GSM254708 2 0.3123 0.854 0.000 0.844 0.000 0.156
#> GSM254715 4 0.3764 0.818 0.000 0.216 0.000 0.784
#> GSM254628 2 0.0000 0.897 0.000 1.000 0.000 0.000
#> GSM254634 4 0.1022 0.865 0.000 0.032 0.000 0.968
#> GSM254646 2 0.0000 0.897 0.000 1.000 0.000 0.000
#> GSM254671 4 0.3074 0.862 0.000 0.152 0.000 0.848
#> GSM254711 4 0.2469 0.873 0.000 0.108 0.000 0.892
#> GSM254717 2 0.1867 0.895 0.000 0.928 0.000 0.072
#> GSM254723 3 0.0000 0.927 0.000 0.000 1.000 0.000
#> GSM254730 2 0.2814 0.871 0.000 0.868 0.000 0.132
#> GSM254731 4 0.3726 0.821 0.000 0.212 0.000 0.788
#> GSM254632 3 0.7345 0.135 0.000 0.336 0.492 0.172
#> GSM254662 2 0.1474 0.892 0.000 0.948 0.000 0.052
#> GSM254677 4 0.2345 0.873 0.000 0.100 0.000 0.900
#> GSM254665 2 0.3610 0.831 0.000 0.800 0.000 0.200
#> GSM254691 2 0.4304 0.769 0.000 0.716 0.000 0.284
#> GSM254644 4 0.3024 0.864 0.000 0.148 0.000 0.852
#> GSM254667 4 0.1716 0.845 0.000 0.064 0.000 0.936
#> GSM254676 4 0.1474 0.851 0.000 0.052 0.000 0.948
#> GSM254679 4 0.0921 0.863 0.000 0.028 0.000 0.972
#> GSM254689 2 0.2868 0.852 0.000 0.864 0.000 0.136
#> GSM254706 2 0.3356 0.832 0.000 0.824 0.000 0.176
#> GSM254712 4 0.3219 0.856 0.000 0.164 0.000 0.836
#> GSM254713 4 0.3172 0.858 0.000 0.160 0.000 0.840
#> GSM254683 2 0.3356 0.832 0.000 0.824 0.000 0.176
#> GSM254710 2 0.3356 0.832 0.000 0.824 0.000 0.176
#> GSM254725 4 0.1022 0.865 0.000 0.032 0.000 0.968
#> GSM254651 2 0.3356 0.832 0.000 0.824 0.000 0.176
#> GSM254638 4 0.0921 0.863 0.000 0.028 0.000 0.972
#> GSM254685 4 0.2647 0.871 0.000 0.120 0.000 0.880
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM254629 3 0.0000 0.901 0.000 0.000 1.000 0.000 0.000
#> GSM254648 3 0.4319 0.683 0.012 0.140 0.784 0.064 0.000
#> GSM254694 3 0.0000 0.901 0.000 0.000 1.000 0.000 0.000
#> GSM254701 3 0.0000 0.901 0.000 0.000 1.000 0.000 0.000
#> GSM254728 3 0.0000 0.901 0.000 0.000 1.000 0.000 0.000
#> GSM254726 3 0.0000 0.901 0.000 0.000 1.000 0.000 0.000
#> GSM254639 3 0.0000 0.901 0.000 0.000 1.000 0.000 0.000
#> GSM254652 3 0.0000 0.901 0.000 0.000 1.000 0.000 0.000
#> GSM254700 1 0.4161 0.768 0.608 0.000 0.000 0.000 0.392
#> GSM254625 5 0.1478 0.751 0.000 0.000 0.064 0.000 0.936
#> GSM254636 3 0.0566 0.898 0.004 0.000 0.984 0.000 0.012
#> GSM254659 3 0.0000 0.901 0.000 0.000 1.000 0.000 0.000
#> GSM254680 3 0.0566 0.898 0.004 0.000 0.984 0.000 0.012
#> GSM254686 3 0.0000 0.901 0.000 0.000 1.000 0.000 0.000
#> GSM254718 3 0.0000 0.901 0.000 0.000 1.000 0.000 0.000
#> GSM254674 3 0.0324 0.900 0.004 0.000 0.992 0.000 0.004
#> GSM254668 5 0.4288 0.240 0.004 0.000 0.384 0.000 0.612
#> GSM254697 1 0.0912 0.520 0.972 0.000 0.016 0.000 0.012
#> GSM254704 1 0.5213 0.703 0.640 0.000 0.076 0.000 0.284
#> GSM254707 5 0.0162 0.810 0.004 0.000 0.000 0.000 0.996
#> GSM254714 3 0.3074 0.721 0.000 0.000 0.804 0.000 0.196
#> GSM254722 3 0.4588 0.474 0.380 0.000 0.604 0.000 0.016
#> GSM254627 3 0.4622 0.366 0.440 0.000 0.548 0.000 0.012
#> GSM254630 5 0.2020 0.699 0.000 0.000 0.100 0.000 0.900
#> GSM254633 3 0.0451 0.899 0.004 0.000 0.988 0.000 0.008
#> GSM254670 3 0.0000 0.901 0.000 0.000 1.000 0.000 0.000
#> GSM254716 3 0.3395 0.650 0.000 0.000 0.764 0.000 0.236
#> GSM254720 3 0.4201 0.314 0.408 0.000 0.592 0.000 0.000
#> GSM254729 3 0.0000 0.901 0.000 0.000 1.000 0.000 0.000
#> GSM254654 3 0.0000 0.901 0.000 0.000 1.000 0.000 0.000
#> GSM254656 4 0.4565 0.289 0.012 0.000 0.408 0.580 0.000
#> GSM254631 3 0.0566 0.898 0.004 0.000 0.984 0.000 0.012
#> GSM254657 3 0.0000 0.901 0.000 0.000 1.000 0.000 0.000
#> GSM254664 3 0.0566 0.898 0.004 0.000 0.984 0.000 0.012
#> GSM254672 3 0.1741 0.867 0.024 0.000 0.936 0.000 0.040
#> GSM254692 5 0.0162 0.810 0.004 0.000 0.000 0.000 0.996
#> GSM254645 3 0.0162 0.901 0.000 0.000 0.996 0.000 0.004
#> GSM254666 3 0.0703 0.891 0.000 0.000 0.976 0.000 0.024
#> GSM254675 3 0.0404 0.898 0.000 0.000 0.988 0.000 0.012
#> GSM254678 3 0.3661 0.598 0.000 0.000 0.724 0.000 0.276
#> GSM254688 5 0.0000 0.811 0.000 0.000 0.000 0.000 1.000
#> GSM254690 3 0.0771 0.895 0.004 0.000 0.976 0.000 0.020
#> GSM254696 3 0.0162 0.901 0.004 0.000 0.996 0.000 0.000
#> GSM254705 5 0.0000 0.811 0.000 0.000 0.000 0.000 1.000
#> GSM254642 5 0.4150 0.309 0.388 0.000 0.000 0.000 0.612
#> GSM254661 3 0.0000 0.901 0.000 0.000 1.000 0.000 0.000
#> GSM254698 3 0.4341 0.515 0.364 0.000 0.628 0.000 0.008
#> GSM254641 3 0.0324 0.900 0.004 0.000 0.992 0.000 0.004
#> GSM254647 5 0.3919 0.446 0.036 0.000 0.188 0.000 0.776
#> GSM254663 5 0.0880 0.791 0.032 0.000 0.000 0.000 0.968
#> GSM254682 5 0.0000 0.811 0.000 0.000 0.000 0.000 1.000
#> GSM254709 5 0.0000 0.811 0.000 0.000 0.000 0.000 1.000
#> GSM254721 1 0.4182 0.766 0.600 0.000 0.000 0.000 0.400
#> GSM254724 1 0.4182 0.766 0.600 0.000 0.000 0.000 0.400
#> GSM254650 5 0.0000 0.811 0.000 0.000 0.000 0.000 1.000
#> GSM254687 5 0.0000 0.811 0.000 0.000 0.000 0.000 1.000
#> GSM254637 3 0.0566 0.898 0.004 0.000 0.984 0.000 0.012
#> GSM254684 3 0.2930 0.765 0.004 0.000 0.832 0.000 0.164
#> GSM254649 2 0.0000 0.871 0.000 1.000 0.000 0.000 0.000
#> GSM254660 2 0.3010 0.811 0.004 0.824 0.000 0.172 0.000
#> GSM254693 2 0.0000 0.871 0.000 1.000 0.000 0.000 0.000
#> GSM254695 4 0.0162 0.824 0.000 0.004 0.000 0.996 0.000
#> GSM254702 4 0.4430 0.291 0.004 0.456 0.000 0.540 0.000
#> GSM254643 2 0.2074 0.849 0.000 0.896 0.000 0.104 0.000
#> GSM254727 2 0.1041 0.869 0.004 0.964 0.000 0.032 0.000
#> GSM254640 2 0.2763 0.847 0.004 0.848 0.000 0.148 0.000
#> GSM254626 2 0.0000 0.871 0.000 1.000 0.000 0.000 0.000
#> GSM254635 4 0.3231 0.796 0.004 0.196 0.000 0.800 0.000
#> GSM254653 2 0.1638 0.864 0.004 0.932 0.000 0.064 0.000
#> GSM254658 2 0.0000 0.871 0.000 1.000 0.000 0.000 0.000
#> GSM254681 2 0.2280 0.831 0.000 0.880 0.000 0.120 0.000
#> GSM254719 2 0.2488 0.835 0.004 0.872 0.000 0.124 0.000
#> GSM254673 2 0.1270 0.867 0.000 0.948 0.000 0.052 0.000
#> GSM254655 2 0.2674 0.823 0.004 0.856 0.000 0.140 0.000
#> GSM254669 2 0.0000 0.871 0.000 1.000 0.000 0.000 0.000
#> GSM254699 2 0.2674 0.823 0.004 0.856 0.000 0.140 0.000
#> GSM254703 4 0.0404 0.817 0.012 0.000 0.000 0.988 0.000
#> GSM254708 2 0.3496 0.813 0.012 0.788 0.000 0.200 0.000
#> GSM254715 4 0.3398 0.782 0.004 0.216 0.000 0.780 0.000
#> GSM254628 2 0.0000 0.871 0.000 1.000 0.000 0.000 0.000
#> GSM254634 4 0.0566 0.820 0.012 0.004 0.000 0.984 0.000
#> GSM254646 2 0.0000 0.871 0.000 1.000 0.000 0.000 0.000
#> GSM254671 4 0.2719 0.830 0.004 0.144 0.000 0.852 0.000
#> GSM254711 4 0.2124 0.841 0.004 0.096 0.000 0.900 0.000
#> GSM254717 2 0.1608 0.870 0.000 0.928 0.000 0.072 0.000
#> GSM254723 3 0.0290 0.898 0.000 0.000 0.992 0.008 0.000
#> GSM254730 2 0.2970 0.828 0.004 0.828 0.000 0.168 0.000
#> GSM254731 4 0.3333 0.788 0.004 0.208 0.000 0.788 0.000
#> GSM254632 3 0.6814 0.100 0.012 0.316 0.468 0.204 0.000
#> GSM254662 2 0.1478 0.864 0.000 0.936 0.000 0.064 0.000
#> GSM254677 4 0.1952 0.841 0.004 0.084 0.000 0.912 0.000
#> GSM254665 2 0.3890 0.779 0.012 0.736 0.000 0.252 0.000
#> GSM254691 2 0.4356 0.702 0.012 0.648 0.000 0.340 0.000
#> GSM254644 4 0.2674 0.831 0.004 0.140 0.000 0.856 0.000
#> GSM254667 4 0.1444 0.795 0.012 0.040 0.000 0.948 0.000
#> GSM254676 4 0.1106 0.805 0.012 0.024 0.000 0.964 0.000
#> GSM254679 4 0.0000 0.822 0.000 0.000 0.000 1.000 0.000
#> GSM254689 2 0.2690 0.817 0.000 0.844 0.000 0.156 0.000
#> GSM254706 2 0.3177 0.791 0.000 0.792 0.000 0.208 0.000
#> GSM254712 4 0.2848 0.823 0.004 0.156 0.000 0.840 0.000
#> GSM254713 4 0.2806 0.826 0.004 0.152 0.000 0.844 0.000
#> GSM254683 2 0.3563 0.785 0.012 0.780 0.000 0.208 0.000
#> GSM254710 2 0.3177 0.791 0.000 0.792 0.000 0.208 0.000
#> GSM254725 4 0.0162 0.824 0.000 0.004 0.000 0.996 0.000
#> GSM254651 2 0.3143 0.793 0.000 0.796 0.000 0.204 0.000
#> GSM254638 4 0.0404 0.817 0.012 0.000 0.000 0.988 0.000
#> GSM254685 4 0.2574 0.840 0.012 0.112 0.000 0.876 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM254629 3 0.1204 0.73284 0.000 0.000 0.944 0.000 0.000 0.056
#> GSM254648 3 0.5626 0.37912 0.012 0.088 0.668 0.172 0.000 0.060
#> GSM254694 3 0.0000 0.74221 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM254701 3 0.0146 0.74240 0.000 0.000 0.996 0.000 0.000 0.004
#> GSM254728 3 0.0146 0.74240 0.000 0.000 0.996 0.000 0.000 0.004
#> GSM254726 3 0.1411 0.72994 0.000 0.000 0.936 0.004 0.000 0.060
#> GSM254639 3 0.0260 0.74262 0.000 0.000 0.992 0.000 0.000 0.008
#> GSM254652 3 0.1007 0.73713 0.000 0.000 0.956 0.000 0.000 0.044
#> GSM254700 1 0.5951 0.03939 0.456 0.000 0.000 0.000 0.276 0.268
#> GSM254625 5 0.1327 0.77261 0.000 0.000 0.064 0.000 0.936 0.000
#> GSM254636 3 0.5527 0.37296 0.152 0.000 0.596 0.000 0.012 0.240
#> GSM254659 3 0.0000 0.74221 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM254680 3 0.5271 0.43641 0.152 0.000 0.640 0.000 0.012 0.196
#> GSM254686 3 0.0000 0.74221 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM254718 3 0.0000 0.74221 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM254674 3 0.4788 0.49350 0.132 0.000 0.684 0.000 0.004 0.180
#> GSM254668 5 0.7354 -0.01880 0.152 0.000 0.248 0.000 0.404 0.196
#> GSM254697 6 0.3653 0.00181 0.300 0.000 0.000 0.000 0.008 0.692
#> GSM254704 1 0.6900 0.03924 0.392 0.000 0.056 0.000 0.248 0.304
#> GSM254707 5 0.4937 0.54041 0.152 0.000 0.000 0.000 0.652 0.196
#> GSM254714 3 0.4148 0.56024 0.012 0.000 0.748 0.000 0.184 0.056
#> GSM254722 6 0.4089 0.40267 0.000 0.000 0.468 0.000 0.008 0.524
#> GSM254627 6 0.3733 0.55014 0.004 0.000 0.288 0.000 0.008 0.700
#> GSM254630 5 0.1814 0.73980 0.000 0.000 0.100 0.000 0.900 0.000
#> GSM254633 3 0.2593 0.66448 0.148 0.000 0.844 0.000 0.008 0.000
#> GSM254670 3 0.1814 0.72039 0.000 0.000 0.900 0.000 0.000 0.100
#> GSM254716 3 0.3050 0.51942 0.000 0.000 0.764 0.000 0.236 0.000
#> GSM254720 3 0.5365 0.10878 0.256 0.000 0.580 0.000 0.000 0.164
#> GSM254729 3 0.0000 0.74221 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM254654 3 0.1267 0.73154 0.000 0.000 0.940 0.000 0.000 0.060
#> GSM254656 4 0.3782 0.04624 0.000 0.000 0.412 0.588 0.000 0.000
#> GSM254631 3 0.5271 0.43641 0.152 0.000 0.640 0.000 0.012 0.196
#> GSM254657 3 0.1387 0.73167 0.000 0.000 0.932 0.000 0.000 0.068
#> GSM254664 3 0.5271 0.43641 0.152 0.000 0.640 0.000 0.012 0.196
#> GSM254672 3 0.3444 0.66101 0.076 0.000 0.836 0.000 0.032 0.056
#> GSM254692 5 0.0146 0.80383 0.004 0.000 0.000 0.000 0.996 0.000
#> GSM254645 3 0.1349 0.73365 0.000 0.000 0.940 0.000 0.004 0.056
#> GSM254666 3 0.1829 0.72959 0.000 0.000 0.920 0.000 0.024 0.056
#> GSM254675 3 0.0363 0.74110 0.000 0.000 0.988 0.000 0.012 0.000
#> GSM254678 3 0.4214 0.33263 0.000 0.000 0.680 0.000 0.276 0.044
#> GSM254688 5 0.0000 0.80380 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM254690 3 0.5527 0.37296 0.152 0.000 0.596 0.000 0.012 0.240
#> GSM254696 3 0.3229 0.64929 0.140 0.000 0.816 0.000 0.000 0.044
#> GSM254705 5 0.0000 0.80380 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM254642 5 0.3828 0.38037 0.000 0.000 0.000 0.000 0.560 0.440
#> GSM254661 3 0.1267 0.73154 0.000 0.000 0.940 0.000 0.000 0.060
#> GSM254698 6 0.3950 0.41087 0.000 0.000 0.432 0.000 0.004 0.564
#> GSM254641 3 0.4293 0.62176 0.096 0.000 0.736 0.000 0.004 0.164
#> GSM254647 5 0.4767 0.46069 0.000 0.000 0.076 0.000 0.620 0.304
#> GSM254663 5 0.2942 0.72661 0.132 0.000 0.000 0.000 0.836 0.032
#> GSM254682 5 0.0000 0.80380 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM254709 5 0.0363 0.80242 0.012 0.000 0.000 0.000 0.988 0.000
#> GSM254721 1 0.6063 0.02018 0.388 0.000 0.000 0.000 0.348 0.264
#> GSM254724 1 0.6060 0.02560 0.392 0.000 0.000 0.000 0.344 0.264
#> GSM254650 5 0.0000 0.80380 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM254687 5 0.0000 0.80380 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM254637 3 0.5323 0.42787 0.152 0.000 0.632 0.000 0.012 0.204
#> GSM254684 3 0.6021 0.31817 0.152 0.000 0.568 0.000 0.040 0.240
#> GSM254649 2 0.0000 0.73667 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM254660 2 0.4526 0.43882 0.456 0.512 0.000 0.032 0.000 0.000
#> GSM254693 2 0.0000 0.73667 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM254695 4 0.2854 0.46569 0.208 0.000 0.000 0.792 0.000 0.000
#> GSM254702 1 0.5951 -0.09118 0.456 0.268 0.000 0.276 0.000 0.000
#> GSM254643 2 0.2320 0.71679 0.132 0.864 0.000 0.004 0.000 0.000
#> GSM254727 2 0.3601 0.57135 0.312 0.684 0.000 0.004 0.000 0.000
#> GSM254640 2 0.5044 0.53169 0.320 0.584 0.000 0.096 0.000 0.000
#> GSM254626 2 0.0146 0.73699 0.004 0.996 0.000 0.000 0.000 0.000
#> GSM254635 4 0.5702 0.14987 0.324 0.180 0.000 0.496 0.000 0.000
#> GSM254653 2 0.3023 0.66314 0.212 0.784 0.000 0.004 0.000 0.000
#> GSM254658 2 0.0000 0.73667 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM254681 2 0.1957 0.66186 0.000 0.888 0.000 0.112 0.000 0.000
#> GSM254719 2 0.3636 0.61260 0.320 0.676 0.000 0.004 0.000 0.000
#> GSM254673 2 0.2092 0.71914 0.124 0.876 0.000 0.000 0.000 0.000
#> GSM254655 2 0.3979 0.47215 0.456 0.540 0.000 0.004 0.000 0.000
#> GSM254669 2 0.0260 0.73710 0.008 0.992 0.000 0.000 0.000 0.000
#> GSM254699 2 0.3979 0.47215 0.456 0.540 0.000 0.004 0.000 0.000
#> GSM254703 4 0.0458 0.50003 0.016 0.000 0.000 0.984 0.000 0.000
#> GSM254708 2 0.3509 0.59184 0.016 0.744 0.000 0.240 0.000 0.000
#> GSM254715 1 0.5814 -0.12061 0.448 0.188 0.000 0.364 0.000 0.000
#> GSM254628 2 0.0000 0.73667 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM254634 4 0.0508 0.50091 0.012 0.004 0.000 0.984 0.000 0.000
#> GSM254646 2 0.0000 0.73667 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM254671 4 0.5390 0.21791 0.328 0.132 0.000 0.540 0.000 0.000
#> GSM254711 4 0.5227 0.14306 0.452 0.092 0.000 0.456 0.000 0.000
#> GSM254717 2 0.2581 0.72064 0.120 0.860 0.000 0.020 0.000 0.000
#> GSM254723 3 0.2006 0.64366 0.104 0.000 0.892 0.000 0.000 0.004
#> GSM254730 2 0.4002 0.65465 0.260 0.704 0.000 0.036 0.000 0.000
#> GSM254731 1 0.5804 -0.11383 0.456 0.188 0.000 0.356 0.000 0.000
#> GSM254632 4 0.6491 0.08172 0.000 0.180 0.244 0.516 0.000 0.060
#> GSM254662 2 0.2300 0.71455 0.144 0.856 0.000 0.000 0.000 0.000
#> GSM254677 4 0.4855 0.29262 0.328 0.076 0.000 0.596 0.000 0.000
#> GSM254665 4 0.4401 -0.22633 0.024 0.464 0.000 0.512 0.000 0.000
#> GSM254691 4 0.4105 -0.01076 0.020 0.348 0.000 0.632 0.000 0.000
#> GSM254644 1 0.5508 -0.22598 0.440 0.128 0.000 0.432 0.000 0.000
#> GSM254667 4 0.0000 0.49838 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM254676 4 0.0458 0.50003 0.016 0.000 0.000 0.984 0.000 0.000
#> GSM254679 4 0.2135 0.48232 0.128 0.000 0.000 0.872 0.000 0.000
#> GSM254689 2 0.2300 0.63858 0.000 0.856 0.000 0.144 0.000 0.000
#> GSM254706 2 0.3867 0.20730 0.000 0.512 0.000 0.488 0.000 0.000
#> GSM254712 4 0.5612 0.07301 0.428 0.144 0.000 0.428 0.000 0.000
#> GSM254713 4 0.5581 0.09847 0.408 0.140 0.000 0.452 0.000 0.000
#> GSM254683 4 0.3864 -0.22394 0.000 0.480 0.000 0.520 0.000 0.000
#> GSM254710 2 0.3833 0.28726 0.000 0.556 0.000 0.444 0.000 0.000
#> GSM254725 4 0.3446 0.38056 0.308 0.000 0.000 0.692 0.000 0.000
#> GSM254651 2 0.3684 0.39289 0.000 0.628 0.000 0.372 0.000 0.000
#> GSM254638 4 0.0000 0.49838 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM254685 4 0.4023 0.41827 0.144 0.100 0.000 0.756 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) individual(p) disease.state(p) gender(p) other(p) k
#> CV:pam 107 1.59e-22 0.7770 0.5773 0.628 0.872 2
#> CV:pam 106 2.95e-21 0.0103 0.0912 0.551 0.237 3
#> CV:pam 103 2.47e-21 0.0116 0.3929 0.291 0.612 4
#> CV:pam 98 1.77e-19 0.1125 0.8318 0.202 0.511 5
#> CV:pam 59 3.31e-11 0.0086 0.9253 0.395 0.682 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 21168 rows and 107 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 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.996 0.998 0.4956 0.505 0.505
#> 3 3 0.781 0.847 0.918 0.3002 0.846 0.695
#> 4 4 0.577 0.545 0.769 0.0889 0.974 0.927
#> 5 5 0.801 0.808 0.885 0.0898 0.888 0.674
#> 6 6 0.703 0.683 0.793 0.0335 0.975 0.895
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
#> GSM254629 1 0.0376 0.994 0.996 0.004
#> GSM254648 1 0.1184 0.985 0.984 0.016
#> GSM254694 1 0.1184 0.985 0.984 0.016
#> GSM254701 1 0.0000 0.996 1.000 0.000
#> GSM254728 1 0.0000 0.996 1.000 0.000
#> GSM254726 1 0.1184 0.985 0.984 0.016
#> GSM254639 1 0.0000 0.996 1.000 0.000
#> GSM254652 1 0.0000 0.996 1.000 0.000
#> GSM254700 1 0.0000 0.996 1.000 0.000
#> GSM254625 1 0.0376 0.994 0.996 0.004
#> GSM254636 1 0.0000 0.996 1.000 0.000
#> GSM254659 1 0.0000 0.996 1.000 0.000
#> GSM254680 1 0.0000 0.996 1.000 0.000
#> GSM254686 1 0.0000 0.996 1.000 0.000
#> GSM254718 1 0.0000 0.996 1.000 0.000
#> GSM254674 1 0.0000 0.996 1.000 0.000
#> GSM254668 1 0.0000 0.996 1.000 0.000
#> GSM254697 1 0.0000 0.996 1.000 0.000
#> GSM254704 1 0.0000 0.996 1.000 0.000
#> GSM254707 1 0.0000 0.996 1.000 0.000
#> GSM254714 1 0.0000 0.996 1.000 0.000
#> GSM254722 1 0.0000 0.996 1.000 0.000
#> GSM254627 1 0.0000 0.996 1.000 0.000
#> GSM254630 1 0.0000 0.996 1.000 0.000
#> GSM254633 1 0.0000 0.996 1.000 0.000
#> GSM254670 1 0.0000 0.996 1.000 0.000
#> GSM254716 1 0.0000 0.996 1.000 0.000
#> GSM254720 1 0.0000 0.996 1.000 0.000
#> GSM254729 1 0.1184 0.985 0.984 0.016
#> GSM254654 1 0.1184 0.985 0.984 0.016
#> GSM254656 1 0.1184 0.985 0.984 0.016
#> GSM254631 1 0.0000 0.996 1.000 0.000
#> GSM254657 1 0.0000 0.996 1.000 0.000
#> GSM254664 1 0.0000 0.996 1.000 0.000
#> GSM254672 1 0.0000 0.996 1.000 0.000
#> GSM254692 1 0.0000 0.996 1.000 0.000
#> GSM254645 1 0.0000 0.996 1.000 0.000
#> GSM254666 1 0.0000 0.996 1.000 0.000
#> GSM254675 1 0.0000 0.996 1.000 0.000
#> GSM254678 1 0.0000 0.996 1.000 0.000
#> GSM254688 1 0.0000 0.996 1.000 0.000
#> GSM254690 1 0.0000 0.996 1.000 0.000
#> GSM254696 1 0.0000 0.996 1.000 0.000
#> GSM254705 1 0.0000 0.996 1.000 0.000
#> GSM254642 1 0.0000 0.996 1.000 0.000
#> GSM254661 1 0.0000 0.996 1.000 0.000
#> GSM254698 1 0.0000 0.996 1.000 0.000
#> GSM254641 1 0.0000 0.996 1.000 0.000
#> GSM254647 1 0.0000 0.996 1.000 0.000
#> GSM254663 1 0.0000 0.996 1.000 0.000
#> GSM254682 1 0.0000 0.996 1.000 0.000
#> GSM254709 1 0.0000 0.996 1.000 0.000
#> GSM254721 1 0.0000 0.996 1.000 0.000
#> GSM254724 1 0.0000 0.996 1.000 0.000
#> GSM254650 1 0.0000 0.996 1.000 0.000
#> GSM254687 1 0.0000 0.996 1.000 0.000
#> GSM254637 1 0.0000 0.996 1.000 0.000
#> GSM254684 1 0.0000 0.996 1.000 0.000
#> GSM254649 2 0.0000 1.000 0.000 1.000
#> GSM254660 2 0.0000 1.000 0.000 1.000
#> GSM254693 2 0.0000 1.000 0.000 1.000
#> GSM254695 2 0.0000 1.000 0.000 1.000
#> GSM254702 2 0.0000 1.000 0.000 1.000
#> GSM254643 2 0.0000 1.000 0.000 1.000
#> GSM254727 2 0.0000 1.000 0.000 1.000
#> GSM254640 2 0.0000 1.000 0.000 1.000
#> GSM254626 2 0.0000 1.000 0.000 1.000
#> GSM254635 2 0.0000 1.000 0.000 1.000
#> GSM254653 2 0.0000 1.000 0.000 1.000
#> GSM254658 2 0.0000 1.000 0.000 1.000
#> GSM254681 2 0.0000 1.000 0.000 1.000
#> GSM254719 2 0.0000 1.000 0.000 1.000
#> GSM254673 2 0.0000 1.000 0.000 1.000
#> GSM254655 2 0.0000 1.000 0.000 1.000
#> GSM254669 2 0.0000 1.000 0.000 1.000
#> GSM254699 2 0.0000 1.000 0.000 1.000
#> GSM254703 2 0.0000 1.000 0.000 1.000
#> GSM254708 2 0.0000 1.000 0.000 1.000
#> GSM254715 2 0.0000 1.000 0.000 1.000
#> GSM254628 2 0.0000 1.000 0.000 1.000
#> GSM254634 2 0.0000 1.000 0.000 1.000
#> GSM254646 2 0.0000 1.000 0.000 1.000
#> GSM254671 2 0.0000 1.000 0.000 1.000
#> GSM254711 2 0.0000 1.000 0.000 1.000
#> GSM254717 2 0.0000 1.000 0.000 1.000
#> GSM254723 1 0.1184 0.985 0.984 0.016
#> GSM254730 2 0.0000 1.000 0.000 1.000
#> GSM254731 2 0.0000 1.000 0.000 1.000
#> GSM254632 1 0.1184 0.985 0.984 0.016
#> GSM254662 2 0.0000 1.000 0.000 1.000
#> GSM254677 2 0.0000 1.000 0.000 1.000
#> GSM254665 2 0.0000 1.000 0.000 1.000
#> GSM254691 2 0.0000 1.000 0.000 1.000
#> GSM254644 2 0.0000 1.000 0.000 1.000
#> GSM254667 2 0.0938 0.988 0.012 0.988
#> GSM254676 2 0.0000 1.000 0.000 1.000
#> GSM254679 2 0.0000 1.000 0.000 1.000
#> GSM254689 2 0.0000 1.000 0.000 1.000
#> GSM254706 2 0.0000 1.000 0.000 1.000
#> GSM254712 2 0.0000 1.000 0.000 1.000
#> GSM254713 2 0.0000 1.000 0.000 1.000
#> GSM254683 2 0.0000 1.000 0.000 1.000
#> GSM254710 1 0.4298 0.908 0.912 0.088
#> GSM254725 2 0.0000 1.000 0.000 1.000
#> GSM254651 2 0.0000 1.000 0.000 1.000
#> GSM254638 2 0.0000 1.000 0.000 1.000
#> GSM254685 2 0.0000 1.000 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM254629 3 0.3267 0.8405 0.116 0.000 0.884
#> GSM254648 3 0.1289 0.8100 0.032 0.000 0.968
#> GSM254694 3 0.1964 0.8284 0.056 0.000 0.944
#> GSM254701 3 0.4121 0.8377 0.168 0.000 0.832
#> GSM254728 3 0.4555 0.8241 0.200 0.000 0.800
#> GSM254726 3 0.1860 0.8252 0.052 0.000 0.948
#> GSM254639 3 0.4654 0.8173 0.208 0.000 0.792
#> GSM254652 3 0.4555 0.8241 0.200 0.000 0.800
#> GSM254700 1 0.0000 0.9019 1.000 0.000 0.000
#> GSM254625 3 0.6280 0.3088 0.460 0.000 0.540
#> GSM254636 1 0.5733 0.4364 0.676 0.000 0.324
#> GSM254659 3 0.4605 0.8214 0.204 0.000 0.796
#> GSM254680 1 0.0000 0.9019 1.000 0.000 0.000
#> GSM254686 1 0.2796 0.8246 0.908 0.000 0.092
#> GSM254718 3 0.4346 0.8311 0.184 0.000 0.816
#> GSM254674 1 0.0000 0.9019 1.000 0.000 0.000
#> GSM254668 1 0.0237 0.9017 0.996 0.000 0.004
#> GSM254697 1 0.0000 0.9019 1.000 0.000 0.000
#> GSM254704 1 0.0592 0.8983 0.988 0.000 0.012
#> GSM254707 1 0.0424 0.9006 0.992 0.000 0.008
#> GSM254714 1 0.6295 -0.0639 0.528 0.000 0.472
#> GSM254722 1 0.0592 0.8996 0.988 0.000 0.012
#> GSM254627 1 0.0000 0.9019 1.000 0.000 0.000
#> GSM254630 1 0.0747 0.8981 0.984 0.000 0.016
#> GSM254633 1 0.4555 0.6840 0.800 0.000 0.200
#> GSM254670 3 0.4504 0.8253 0.196 0.000 0.804
#> GSM254716 3 0.6305 0.2511 0.484 0.000 0.516
#> GSM254720 1 0.3267 0.8131 0.884 0.000 0.116
#> GSM254729 3 0.2796 0.8383 0.092 0.000 0.908
#> GSM254654 3 0.2066 0.8301 0.060 0.000 0.940
#> GSM254656 3 0.2165 0.8305 0.064 0.000 0.936
#> GSM254631 1 0.0747 0.8954 0.984 0.000 0.016
#> GSM254657 3 0.4504 0.8246 0.196 0.000 0.804
#> GSM254664 1 0.0237 0.9016 0.996 0.000 0.004
#> GSM254672 1 0.0237 0.9016 0.996 0.000 0.004
#> GSM254692 1 0.0237 0.9017 0.996 0.000 0.004
#> GSM254645 3 0.4062 0.8391 0.164 0.000 0.836
#> GSM254666 1 0.6204 -0.0205 0.576 0.000 0.424
#> GSM254675 1 0.0000 0.9019 1.000 0.000 0.000
#> GSM254678 1 0.1860 0.8699 0.948 0.000 0.052
#> GSM254688 1 0.0424 0.9006 0.992 0.000 0.008
#> GSM254690 1 0.2448 0.8456 0.924 0.000 0.076
#> GSM254696 1 0.6204 0.1369 0.576 0.000 0.424
#> GSM254705 1 0.0424 0.9006 0.992 0.000 0.008
#> GSM254642 1 0.0000 0.9019 1.000 0.000 0.000
#> GSM254661 3 0.3941 0.8407 0.156 0.000 0.844
#> GSM254698 1 0.5138 0.5962 0.748 0.000 0.252
#> GSM254641 1 0.0000 0.9019 1.000 0.000 0.000
#> GSM254647 1 0.0000 0.9019 1.000 0.000 0.000
#> GSM254663 1 0.0000 0.9019 1.000 0.000 0.000
#> GSM254682 1 0.0424 0.9006 0.992 0.000 0.008
#> GSM254709 1 0.0424 0.9006 0.992 0.000 0.008
#> GSM254721 1 0.0592 0.8983 0.988 0.000 0.012
#> GSM254724 1 0.0592 0.8983 0.988 0.000 0.012
#> GSM254650 1 0.0424 0.9006 0.992 0.000 0.008
#> GSM254687 1 0.0424 0.9006 0.992 0.000 0.008
#> GSM254637 1 0.5291 0.5668 0.732 0.000 0.268
#> GSM254684 3 0.6244 0.3606 0.440 0.000 0.560
#> GSM254649 2 0.0000 0.9672 0.000 1.000 0.000
#> GSM254660 2 0.0000 0.9672 0.000 1.000 0.000
#> GSM254693 2 0.0000 0.9672 0.000 1.000 0.000
#> GSM254695 2 0.4555 0.8373 0.000 0.800 0.200
#> GSM254702 2 0.0000 0.9672 0.000 1.000 0.000
#> GSM254643 2 0.0000 0.9672 0.000 1.000 0.000
#> GSM254727 2 0.0000 0.9672 0.000 1.000 0.000
#> GSM254640 2 0.0000 0.9672 0.000 1.000 0.000
#> GSM254626 2 0.0000 0.9672 0.000 1.000 0.000
#> GSM254635 2 0.4121 0.8706 0.000 0.832 0.168
#> GSM254653 2 0.0000 0.9672 0.000 1.000 0.000
#> GSM254658 2 0.0000 0.9672 0.000 1.000 0.000
#> GSM254681 2 0.0237 0.9669 0.000 0.996 0.004
#> GSM254719 2 0.0000 0.9672 0.000 1.000 0.000
#> GSM254673 2 0.0000 0.9672 0.000 1.000 0.000
#> GSM254655 2 0.0000 0.9672 0.000 1.000 0.000
#> GSM254669 2 0.0000 0.9672 0.000 1.000 0.000
#> GSM254699 2 0.0000 0.9672 0.000 1.000 0.000
#> GSM254703 2 0.1289 0.9610 0.000 0.968 0.032
#> GSM254708 2 0.3267 0.9088 0.000 0.884 0.116
#> GSM254715 2 0.0237 0.9665 0.000 0.996 0.004
#> GSM254628 2 0.0000 0.9672 0.000 1.000 0.000
#> GSM254634 2 0.2878 0.9227 0.000 0.904 0.096
#> GSM254646 2 0.0237 0.9669 0.000 0.996 0.004
#> GSM254671 2 0.1031 0.9629 0.000 0.976 0.024
#> GSM254711 2 0.1163 0.9621 0.000 0.972 0.028
#> GSM254717 2 0.1031 0.9629 0.000 0.976 0.024
#> GSM254723 3 0.1753 0.8225 0.048 0.000 0.952
#> GSM254730 2 0.0000 0.9672 0.000 1.000 0.000
#> GSM254731 2 0.0000 0.9672 0.000 1.000 0.000
#> GSM254632 3 0.1289 0.8100 0.032 0.000 0.968
#> GSM254662 2 0.0237 0.9669 0.000 0.996 0.004
#> GSM254677 2 0.4121 0.8706 0.000 0.832 0.168
#> GSM254665 2 0.1031 0.9629 0.000 0.976 0.024
#> GSM254691 2 0.1411 0.9586 0.000 0.964 0.036
#> GSM254644 2 0.0000 0.9672 0.000 1.000 0.000
#> GSM254667 2 0.4504 0.8397 0.000 0.804 0.196
#> GSM254676 2 0.1031 0.9629 0.000 0.976 0.024
#> GSM254679 2 0.1031 0.9629 0.000 0.976 0.024
#> GSM254689 2 0.0237 0.9669 0.000 0.996 0.004
#> GSM254706 2 0.3686 0.8917 0.000 0.860 0.140
#> GSM254712 2 0.1411 0.9595 0.000 0.964 0.036
#> GSM254713 2 0.0237 0.9665 0.000 0.996 0.004
#> GSM254683 2 0.1031 0.9629 0.000 0.976 0.024
#> GSM254710 3 0.6881 0.3891 0.032 0.320 0.648
#> GSM254725 2 0.4178 0.8669 0.000 0.828 0.172
#> GSM254651 2 0.1031 0.9629 0.000 0.976 0.024
#> GSM254638 2 0.4235 0.8633 0.000 0.824 0.176
#> GSM254685 2 0.0237 0.9665 0.000 0.996 0.004
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM254629 3 0.4353 0.8353 0.232 0.000 0.756 0.012
#> GSM254648 3 0.4083 0.8012 0.100 0.000 0.832 0.068
#> GSM254694 3 0.3958 0.8148 0.112 0.000 0.836 0.052
#> GSM254701 3 0.4744 0.7889 0.284 0.000 0.704 0.012
#> GSM254728 3 0.4422 0.8203 0.256 0.000 0.736 0.008
#> GSM254726 3 0.4843 0.7879 0.104 0.000 0.784 0.112
#> GSM254639 3 0.4748 0.8256 0.268 0.000 0.716 0.016
#> GSM254652 3 0.4838 0.8260 0.252 0.000 0.724 0.024
#> GSM254700 1 0.6508 0.5961 0.600 0.000 0.104 0.296
#> GSM254625 3 0.7241 0.7247 0.264 0.000 0.540 0.196
#> GSM254636 1 0.3937 0.6179 0.800 0.000 0.188 0.012
#> GSM254659 3 0.4356 0.7792 0.292 0.000 0.708 0.000
#> GSM254680 1 0.1356 0.7454 0.960 0.000 0.032 0.008
#> GSM254686 1 0.4546 0.5142 0.732 0.000 0.256 0.012
#> GSM254718 3 0.4295 0.8289 0.240 0.000 0.752 0.008
#> GSM254674 1 0.2255 0.7331 0.920 0.000 0.068 0.012
#> GSM254668 1 0.1356 0.7452 0.960 0.000 0.032 0.008
#> GSM254697 1 0.6528 0.5946 0.596 0.000 0.104 0.300
#> GSM254704 1 0.6488 0.5971 0.604 0.000 0.104 0.292
#> GSM254707 1 0.3453 0.7176 0.868 0.000 0.052 0.080
#> GSM254714 1 0.6273 0.5658 0.636 0.000 0.264 0.100
#> GSM254722 1 0.1610 0.7539 0.952 0.000 0.016 0.032
#> GSM254627 1 0.6350 0.6037 0.612 0.000 0.092 0.296
#> GSM254630 1 0.3768 0.6376 0.808 0.000 0.184 0.008
#> GSM254633 1 0.2888 0.6904 0.872 0.000 0.124 0.004
#> GSM254670 3 0.4482 0.8223 0.264 0.000 0.728 0.008
#> GSM254716 3 0.7489 0.5534 0.364 0.000 0.452 0.184
#> GSM254720 1 0.5798 0.6308 0.696 0.000 0.208 0.096
#> GSM254729 3 0.3088 0.8263 0.128 0.000 0.864 0.008
#> GSM254654 3 0.4015 0.8164 0.116 0.000 0.832 0.052
#> GSM254656 3 0.3818 0.8132 0.108 0.000 0.844 0.048
#> GSM254631 1 0.3306 0.6593 0.840 0.000 0.156 0.004
#> GSM254657 3 0.4122 0.8314 0.236 0.000 0.760 0.004
#> GSM254664 1 0.2342 0.7483 0.912 0.000 0.008 0.080
#> GSM254672 1 0.3052 0.7329 0.860 0.000 0.004 0.136
#> GSM254692 1 0.5522 0.6570 0.668 0.000 0.044 0.288
#> GSM254645 3 0.4295 0.8289 0.240 0.000 0.752 0.008
#> GSM254666 1 0.6064 -0.1533 0.512 0.000 0.444 0.044
#> GSM254675 1 0.1576 0.7532 0.948 0.000 0.004 0.048
#> GSM254678 1 0.2987 0.7051 0.880 0.000 0.104 0.016
#> GSM254688 1 0.1488 0.7446 0.956 0.000 0.032 0.012
#> GSM254690 1 0.0657 0.7477 0.984 0.000 0.012 0.004
#> GSM254696 1 0.4770 0.4297 0.700 0.000 0.288 0.012
#> GSM254705 1 0.1936 0.7494 0.940 0.000 0.028 0.032
#> GSM254642 1 0.6466 0.5998 0.608 0.000 0.104 0.288
#> GSM254661 3 0.5851 0.8230 0.236 0.000 0.680 0.084
#> GSM254698 1 0.3791 0.6044 0.796 0.000 0.200 0.004
#> GSM254641 1 0.1716 0.7381 0.936 0.000 0.064 0.000
#> GSM254647 1 0.4008 0.6891 0.756 0.000 0.000 0.244
#> GSM254663 1 0.3910 0.7312 0.820 0.000 0.024 0.156
#> GSM254682 1 0.3383 0.7203 0.872 0.000 0.052 0.076
#> GSM254709 1 0.3143 0.7446 0.876 0.000 0.024 0.100
#> GSM254721 1 0.6508 0.5961 0.600 0.000 0.104 0.296
#> GSM254724 1 0.6508 0.5961 0.600 0.000 0.104 0.296
#> GSM254650 1 0.3205 0.7454 0.872 0.000 0.024 0.104
#> GSM254687 1 0.2949 0.7483 0.888 0.000 0.024 0.088
#> GSM254637 1 0.5055 0.5289 0.712 0.000 0.256 0.032
#> GSM254684 1 0.5057 0.2918 0.648 0.000 0.340 0.012
#> GSM254649 2 0.0592 0.6252 0.000 0.984 0.000 0.016
#> GSM254660 2 0.3486 0.4452 0.000 0.812 0.000 0.188
#> GSM254693 2 0.0817 0.6211 0.000 0.976 0.000 0.024
#> GSM254695 4 0.6337 0.5102 0.000 0.468 0.060 0.472
#> GSM254702 2 0.4250 0.2449 0.000 0.724 0.000 0.276
#> GSM254643 2 0.0707 0.6276 0.000 0.980 0.000 0.020
#> GSM254727 2 0.1118 0.6252 0.000 0.964 0.000 0.036
#> GSM254640 2 0.3123 0.4981 0.000 0.844 0.000 0.156
#> GSM254626 2 0.0592 0.6242 0.000 0.984 0.000 0.016
#> GSM254635 2 0.5800 -0.3316 0.000 0.548 0.032 0.420
#> GSM254653 2 0.0469 0.6251 0.000 0.988 0.000 0.012
#> GSM254658 2 0.0592 0.6252 0.000 0.984 0.000 0.016
#> GSM254681 2 0.1557 0.6167 0.000 0.944 0.000 0.056
#> GSM254719 2 0.0592 0.6259 0.000 0.984 0.000 0.016
#> GSM254673 2 0.0336 0.6258 0.000 0.992 0.000 0.008
#> GSM254655 2 0.0817 0.6270 0.000 0.976 0.000 0.024
#> GSM254669 2 0.0469 0.6268 0.000 0.988 0.000 0.012
#> GSM254699 2 0.0817 0.6270 0.000 0.976 0.000 0.024
#> GSM254703 2 0.5183 -0.1629 0.000 0.584 0.008 0.408
#> GSM254708 2 0.5254 0.0921 0.000 0.672 0.028 0.300
#> GSM254715 2 0.4356 0.2150 0.000 0.708 0.000 0.292
#> GSM254628 2 0.0592 0.6252 0.000 0.984 0.000 0.016
#> GSM254634 2 0.5337 -0.2598 0.000 0.564 0.012 0.424
#> GSM254646 2 0.1211 0.6232 0.000 0.960 0.000 0.040
#> GSM254671 2 0.4790 -0.0380 0.000 0.620 0.000 0.380
#> GSM254711 2 0.4866 -0.1119 0.000 0.596 0.000 0.404
#> GSM254717 2 0.2408 0.5819 0.000 0.896 0.000 0.104
#> GSM254723 3 0.4155 0.7980 0.100 0.000 0.828 0.072
#> GSM254730 2 0.0592 0.6267 0.000 0.984 0.000 0.016
#> GSM254731 2 0.4304 0.2450 0.000 0.716 0.000 0.284
#> GSM254632 3 0.5102 0.7709 0.100 0.000 0.764 0.136
#> GSM254662 2 0.1022 0.6283 0.000 0.968 0.000 0.032
#> GSM254677 2 0.5821 -0.3923 0.000 0.536 0.032 0.432
#> GSM254665 2 0.2345 0.5880 0.000 0.900 0.000 0.100
#> GSM254691 2 0.3583 0.4858 0.000 0.816 0.004 0.180
#> GSM254644 2 0.4040 0.3353 0.000 0.752 0.000 0.248
#> GSM254667 4 0.6264 0.6168 0.000 0.376 0.064 0.560
#> GSM254676 2 0.2973 0.5324 0.000 0.856 0.000 0.144
#> GSM254679 2 0.4843 -0.0873 0.000 0.604 0.000 0.396
#> GSM254689 2 0.1557 0.6167 0.000 0.944 0.000 0.056
#> GSM254706 2 0.4238 0.4153 0.000 0.796 0.028 0.176
#> GSM254712 2 0.5050 -0.1443 0.000 0.588 0.004 0.408
#> GSM254713 2 0.4356 0.2150 0.000 0.708 0.000 0.292
#> GSM254683 2 0.3266 0.4995 0.000 0.832 0.000 0.168
#> GSM254710 3 0.8700 0.4395 0.104 0.136 0.496 0.264
#> GSM254725 2 0.5792 -0.3156 0.000 0.552 0.032 0.416
#> GSM254651 2 0.2868 0.5452 0.000 0.864 0.000 0.136
#> GSM254638 2 0.5815 -0.3716 0.000 0.540 0.032 0.428
#> GSM254685 2 0.4331 0.2203 0.000 0.712 0.000 0.288
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM254629 3 0.0404 0.9233 0.000 0.000 0.988 0.000 0.012
#> GSM254648 3 0.0162 0.9215 0.000 0.000 0.996 0.004 0.000
#> GSM254694 3 0.0162 0.9215 0.000 0.000 0.996 0.004 0.000
#> GSM254701 3 0.1671 0.8770 0.000 0.000 0.924 0.000 0.076
#> GSM254728 3 0.3242 0.7191 0.000 0.000 0.784 0.000 0.216
#> GSM254726 3 0.0510 0.9179 0.000 0.000 0.984 0.016 0.000
#> GSM254639 3 0.0807 0.9231 0.000 0.000 0.976 0.012 0.012
#> GSM254652 3 0.0609 0.9213 0.000 0.000 0.980 0.000 0.020
#> GSM254700 1 0.0703 0.9857 0.976 0.000 0.000 0.000 0.024
#> GSM254625 3 0.2264 0.8906 0.024 0.000 0.912 0.004 0.060
#> GSM254636 5 0.1106 0.9267 0.012 0.000 0.024 0.000 0.964
#> GSM254659 3 0.3837 0.5584 0.000 0.000 0.692 0.000 0.308
#> GSM254680 5 0.0566 0.9316 0.012 0.000 0.004 0.000 0.984
#> GSM254686 5 0.3074 0.7697 0.000 0.000 0.196 0.000 0.804
#> GSM254718 3 0.0609 0.9212 0.000 0.000 0.980 0.000 0.020
#> GSM254674 5 0.0671 0.9315 0.004 0.000 0.016 0.000 0.980
#> GSM254668 5 0.0000 0.9296 0.000 0.000 0.000 0.000 1.000
#> GSM254697 1 0.0794 0.9827 0.972 0.000 0.000 0.000 0.028
#> GSM254704 1 0.0703 0.9857 0.976 0.000 0.000 0.000 0.024
#> GSM254707 5 0.0000 0.9296 0.000 0.000 0.000 0.000 1.000
#> GSM254714 5 0.4527 0.6065 0.036 0.000 0.272 0.000 0.692
#> GSM254722 5 0.1059 0.9314 0.020 0.000 0.008 0.004 0.968
#> GSM254627 1 0.0703 0.9857 0.976 0.000 0.000 0.000 0.024
#> GSM254630 5 0.2648 0.8072 0.000 0.000 0.152 0.000 0.848
#> GSM254633 5 0.0693 0.9315 0.012 0.000 0.008 0.000 0.980
#> GSM254670 3 0.0807 0.9231 0.000 0.000 0.976 0.012 0.012
#> GSM254716 3 0.4000 0.7083 0.024 0.000 0.748 0.000 0.228
#> GSM254720 5 0.0898 0.9309 0.020 0.000 0.008 0.000 0.972
#> GSM254729 3 0.0290 0.9232 0.000 0.000 0.992 0.000 0.008
#> GSM254654 3 0.0162 0.9215 0.000 0.000 0.996 0.004 0.000
#> GSM254656 3 0.1430 0.9028 0.000 0.000 0.944 0.052 0.004
#> GSM254631 5 0.0693 0.9315 0.012 0.000 0.008 0.000 0.980
#> GSM254657 3 0.0404 0.9233 0.000 0.000 0.988 0.000 0.012
#> GSM254664 5 0.0865 0.9285 0.024 0.000 0.004 0.000 0.972
#> GSM254672 5 0.1671 0.9018 0.076 0.000 0.000 0.000 0.924
#> GSM254692 1 0.2127 0.9111 0.892 0.000 0.000 0.000 0.108
#> GSM254645 3 0.0404 0.9233 0.000 0.000 0.988 0.000 0.012
#> GSM254666 5 0.4101 0.4398 0.000 0.000 0.372 0.000 0.628
#> GSM254675 5 0.0794 0.9281 0.028 0.000 0.000 0.000 0.972
#> GSM254678 5 0.0960 0.9319 0.016 0.000 0.008 0.004 0.972
#> GSM254688 5 0.0324 0.9293 0.004 0.000 0.000 0.004 0.992
#> GSM254690 5 0.0693 0.9315 0.012 0.000 0.008 0.000 0.980
#> GSM254696 5 0.1197 0.9146 0.000 0.000 0.048 0.000 0.952
#> GSM254705 5 0.0451 0.9288 0.008 0.000 0.000 0.004 0.988
#> GSM254642 1 0.0794 0.9835 0.972 0.000 0.000 0.000 0.028
#> GSM254661 3 0.0807 0.9231 0.000 0.000 0.976 0.012 0.012
#> GSM254698 5 0.1018 0.9311 0.016 0.000 0.016 0.000 0.968
#> GSM254641 5 0.0807 0.9313 0.012 0.000 0.012 0.000 0.976
#> GSM254647 5 0.3796 0.6157 0.300 0.000 0.000 0.000 0.700
#> GSM254663 5 0.1478 0.8985 0.064 0.000 0.000 0.000 0.936
#> GSM254682 5 0.0324 0.9293 0.004 0.000 0.000 0.004 0.992
#> GSM254709 5 0.0290 0.9291 0.008 0.000 0.000 0.000 0.992
#> GSM254721 1 0.0703 0.9857 0.976 0.000 0.000 0.000 0.024
#> GSM254724 1 0.0703 0.9857 0.976 0.000 0.000 0.000 0.024
#> GSM254650 5 0.0671 0.9275 0.016 0.000 0.000 0.004 0.980
#> GSM254687 5 0.0671 0.9275 0.016 0.000 0.000 0.004 0.980
#> GSM254637 5 0.1485 0.9219 0.020 0.000 0.032 0.000 0.948
#> GSM254684 5 0.2124 0.8778 0.004 0.000 0.096 0.000 0.900
#> GSM254649 2 0.0609 0.8058 0.000 0.980 0.000 0.020 0.000
#> GSM254660 2 0.2329 0.7755 0.000 0.876 0.000 0.124 0.000
#> GSM254693 2 0.0290 0.8050 0.000 0.992 0.000 0.008 0.000
#> GSM254695 4 0.1331 0.7913 0.000 0.040 0.008 0.952 0.000
#> GSM254702 2 0.1732 0.7624 0.000 0.920 0.000 0.080 0.000
#> GSM254643 2 0.2280 0.7746 0.000 0.880 0.000 0.120 0.000
#> GSM254727 2 0.0404 0.8032 0.000 0.988 0.000 0.012 0.000
#> GSM254640 2 0.0290 0.8052 0.000 0.992 0.000 0.008 0.000
#> GSM254626 2 0.0162 0.8062 0.000 0.996 0.000 0.004 0.000
#> GSM254635 4 0.2707 0.8345 0.000 0.132 0.008 0.860 0.000
#> GSM254653 2 0.0162 0.8062 0.000 0.996 0.000 0.004 0.000
#> GSM254658 2 0.1544 0.8001 0.000 0.932 0.000 0.068 0.000
#> GSM254681 2 0.3074 0.7280 0.000 0.804 0.000 0.196 0.000
#> GSM254719 2 0.0000 0.8065 0.000 1.000 0.000 0.000 0.000
#> GSM254673 2 0.0162 0.8079 0.000 0.996 0.000 0.004 0.000
#> GSM254655 2 0.0000 0.8065 0.000 1.000 0.000 0.000 0.000
#> GSM254669 2 0.0162 0.8077 0.000 0.996 0.000 0.004 0.000
#> GSM254699 2 0.0000 0.8065 0.000 1.000 0.000 0.000 0.000
#> GSM254703 4 0.3366 0.7766 0.000 0.232 0.000 0.768 0.000
#> GSM254708 4 0.3700 0.7455 0.000 0.240 0.008 0.752 0.000
#> GSM254715 2 0.4060 0.4485 0.000 0.640 0.000 0.360 0.000
#> GSM254628 2 0.1792 0.7953 0.000 0.916 0.000 0.084 0.000
#> GSM254634 4 0.2233 0.8344 0.000 0.104 0.004 0.892 0.000
#> GSM254646 2 0.2891 0.7446 0.000 0.824 0.000 0.176 0.000
#> GSM254671 2 0.4242 0.2718 0.000 0.572 0.000 0.428 0.000
#> GSM254711 4 0.3707 0.7124 0.000 0.284 0.000 0.716 0.000
#> GSM254717 2 0.3039 0.7324 0.000 0.808 0.000 0.192 0.000
#> GSM254723 3 0.0162 0.9215 0.000 0.000 0.996 0.004 0.000
#> GSM254730 2 0.0000 0.8065 0.000 1.000 0.000 0.000 0.000
#> GSM254731 2 0.1732 0.7624 0.000 0.920 0.000 0.080 0.000
#> GSM254632 3 0.0162 0.9215 0.000 0.000 0.996 0.004 0.000
#> GSM254662 2 0.0290 0.8080 0.000 0.992 0.000 0.008 0.000
#> GSM254677 4 0.1830 0.8171 0.000 0.068 0.008 0.924 0.000
#> GSM254665 2 0.3561 0.6354 0.000 0.740 0.000 0.260 0.000
#> GSM254691 2 0.4273 0.0974 0.000 0.552 0.000 0.448 0.000
#> GSM254644 2 0.1043 0.7938 0.000 0.960 0.000 0.040 0.000
#> GSM254667 4 0.3053 0.7760 0.000 0.164 0.008 0.828 0.000
#> GSM254676 2 0.4249 0.1651 0.000 0.568 0.000 0.432 0.000
#> GSM254679 4 0.3636 0.7352 0.000 0.272 0.000 0.728 0.000
#> GSM254689 2 0.3074 0.7280 0.000 0.804 0.000 0.196 0.000
#> GSM254706 4 0.4367 0.5062 0.000 0.372 0.008 0.620 0.000
#> GSM254712 4 0.3366 0.7766 0.000 0.232 0.000 0.768 0.000
#> GSM254713 2 0.4219 0.2856 0.000 0.584 0.000 0.416 0.000
#> GSM254683 2 0.4227 0.2455 0.000 0.580 0.000 0.420 0.000
#> GSM254710 3 0.4275 0.6667 0.024 0.008 0.740 0.228 0.000
#> GSM254725 4 0.2411 0.8355 0.000 0.108 0.008 0.884 0.000
#> GSM254651 2 0.3949 0.4995 0.000 0.668 0.000 0.332 0.000
#> GSM254638 4 0.2017 0.8247 0.000 0.080 0.008 0.912 0.000
#> GSM254685 2 0.3366 0.6852 0.000 0.768 0.000 0.232 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM254629 3 0.2020 0.68288 0.000 0.000 0.896 0.000 0.008 0.096
#> GSM254648 6 0.3607 0.91804 0.000 0.000 0.348 0.000 0.000 0.652
#> GSM254694 3 0.3797 -0.29501 0.000 0.000 0.580 0.000 0.000 0.420
#> GSM254701 3 0.1950 0.67320 0.000 0.000 0.912 0.000 0.064 0.024
#> GSM254728 3 0.0547 0.70677 0.000 0.000 0.980 0.000 0.020 0.000
#> GSM254726 6 0.3620 0.91519 0.000 0.000 0.352 0.000 0.000 0.648
#> GSM254639 3 0.1672 0.71658 0.000 0.000 0.932 0.004 0.016 0.048
#> GSM254652 3 0.1616 0.71765 0.000 0.000 0.932 0.000 0.020 0.048
#> GSM254700 1 0.0363 0.96516 0.988 0.000 0.000 0.000 0.012 0.000
#> GSM254625 3 0.4297 0.47739 0.000 0.000 0.724 0.000 0.176 0.100
#> GSM254636 5 0.3720 0.79846 0.000 0.000 0.236 0.000 0.736 0.028
#> GSM254659 3 0.1204 0.67396 0.000 0.000 0.944 0.000 0.056 0.000
#> GSM254680 5 0.2505 0.82173 0.008 0.000 0.092 0.000 0.880 0.020
#> GSM254686 5 0.4015 0.62297 0.000 0.000 0.372 0.000 0.616 0.012
#> GSM254718 3 0.1257 0.70935 0.000 0.000 0.952 0.000 0.020 0.028
#> GSM254674 5 0.3248 0.80944 0.004 0.000 0.224 0.000 0.768 0.004
#> GSM254668 5 0.1370 0.78132 0.000 0.000 0.012 0.004 0.948 0.036
#> GSM254697 1 0.0363 0.96516 0.988 0.000 0.000 0.000 0.012 0.000
#> GSM254704 1 0.0363 0.96158 0.988 0.000 0.000 0.000 0.012 0.000
#> GSM254707 5 0.2113 0.78261 0.000 0.000 0.060 0.004 0.908 0.028
#> GSM254714 5 0.4203 0.76559 0.056 0.000 0.220 0.000 0.720 0.004
#> GSM254722 5 0.3568 0.82581 0.008 0.000 0.172 0.000 0.788 0.032
#> GSM254627 1 0.0363 0.96516 0.988 0.000 0.000 0.000 0.012 0.000
#> GSM254630 5 0.3187 0.79233 0.004 0.000 0.188 0.000 0.796 0.012
#> GSM254633 5 0.3454 0.80847 0.004 0.000 0.224 0.000 0.760 0.012
#> GSM254670 3 0.1760 0.71719 0.000 0.000 0.928 0.004 0.020 0.048
#> GSM254716 3 0.4297 0.47739 0.000 0.000 0.724 0.000 0.176 0.100
#> GSM254720 5 0.3053 0.82645 0.024 0.000 0.144 0.000 0.828 0.004
#> GSM254729 3 0.3052 0.51495 0.000 0.000 0.780 0.000 0.004 0.216
#> GSM254654 3 0.3867 -0.53646 0.000 0.000 0.512 0.000 0.000 0.488
#> GSM254656 6 0.3984 0.90288 0.000 0.000 0.336 0.016 0.000 0.648
#> GSM254631 5 0.2841 0.82611 0.012 0.000 0.164 0.000 0.824 0.000
#> GSM254657 3 0.1812 0.69835 0.000 0.000 0.912 0.000 0.008 0.080
#> GSM254664 5 0.3564 0.82475 0.040 0.000 0.136 0.000 0.808 0.016
#> GSM254672 5 0.3875 0.78512 0.144 0.000 0.068 0.000 0.780 0.008
#> GSM254692 1 0.3189 0.78143 0.796 0.000 0.000 0.000 0.184 0.020
#> GSM254645 3 0.1124 0.71152 0.000 0.000 0.956 0.000 0.008 0.036
#> GSM254666 3 0.3819 0.00365 0.000 0.000 0.624 0.000 0.372 0.004
#> GSM254675 5 0.3580 0.82559 0.036 0.000 0.136 0.000 0.808 0.020
#> GSM254678 5 0.3268 0.82671 0.008 0.000 0.164 0.000 0.808 0.020
#> GSM254688 5 0.2596 0.75277 0.004 0.000 0.016 0.004 0.872 0.104
#> GSM254690 5 0.3018 0.82469 0.004 0.000 0.168 0.000 0.816 0.012
#> GSM254696 5 0.3617 0.79568 0.000 0.000 0.244 0.000 0.736 0.020
#> GSM254705 5 0.3074 0.67853 0.004 0.000 0.000 0.004 0.792 0.200
#> GSM254642 1 0.0458 0.96249 0.984 0.000 0.000 0.000 0.016 0.000
#> GSM254661 3 0.2191 0.65976 0.000 0.000 0.876 0.000 0.004 0.120
#> GSM254698 5 0.3888 0.80563 0.008 0.000 0.224 0.000 0.740 0.028
#> GSM254641 5 0.2737 0.82663 0.004 0.000 0.160 0.000 0.832 0.004
#> GSM254647 5 0.4394 0.16394 0.484 0.000 0.004 0.000 0.496 0.016
#> GSM254663 5 0.3907 0.66464 0.088 0.000 0.000 0.004 0.776 0.132
#> GSM254682 5 0.3386 0.75204 0.004 0.000 0.064 0.004 0.828 0.100
#> GSM254709 5 0.2320 0.72802 0.004 0.000 0.000 0.000 0.864 0.132
#> GSM254721 1 0.0146 0.96089 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM254724 1 0.0146 0.96089 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM254650 5 0.3248 0.65711 0.004 0.000 0.000 0.004 0.768 0.224
#> GSM254687 5 0.3248 0.65711 0.004 0.000 0.000 0.004 0.768 0.224
#> GSM254637 5 0.3122 0.82665 0.020 0.000 0.160 0.000 0.816 0.004
#> GSM254684 5 0.3859 0.75863 0.000 0.000 0.288 0.000 0.692 0.020
#> GSM254649 2 0.1116 0.77802 0.000 0.960 0.004 0.008 0.000 0.028
#> GSM254660 2 0.2778 0.69872 0.000 0.824 0.000 0.168 0.000 0.008
#> GSM254693 2 0.0363 0.78008 0.000 0.988 0.000 0.000 0.000 0.012
#> GSM254695 4 0.1391 0.75876 0.000 0.040 0.000 0.944 0.000 0.016
#> GSM254702 2 0.2823 0.64232 0.000 0.796 0.000 0.204 0.000 0.000
#> GSM254643 2 0.1434 0.77452 0.000 0.940 0.000 0.048 0.000 0.012
#> GSM254727 2 0.0603 0.77987 0.000 0.980 0.004 0.000 0.000 0.016
#> GSM254640 2 0.0632 0.77752 0.000 0.976 0.000 0.024 0.000 0.000
#> GSM254626 2 0.0260 0.78007 0.000 0.992 0.000 0.000 0.000 0.008
#> GSM254635 4 0.2513 0.80692 0.000 0.140 0.000 0.852 0.000 0.008
#> GSM254653 2 0.0000 0.78042 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM254658 2 0.1313 0.77705 0.000 0.952 0.004 0.016 0.000 0.028
#> GSM254681 2 0.4023 0.64164 0.004 0.756 0.004 0.184 0.000 0.052
#> GSM254719 2 0.0260 0.78109 0.000 0.992 0.000 0.008 0.000 0.000
#> GSM254673 2 0.0291 0.78120 0.000 0.992 0.000 0.004 0.000 0.004
#> GSM254655 2 0.0363 0.78055 0.000 0.988 0.000 0.012 0.000 0.000
#> GSM254669 2 0.0260 0.78183 0.000 0.992 0.000 0.008 0.000 0.000
#> GSM254699 2 0.0458 0.77999 0.000 0.984 0.000 0.016 0.000 0.000
#> GSM254703 4 0.2178 0.80142 0.000 0.132 0.000 0.868 0.000 0.000
#> GSM254708 4 0.4010 0.43397 0.000 0.408 0.000 0.584 0.000 0.008
#> GSM254715 2 0.3774 0.46190 0.000 0.664 0.000 0.328 0.000 0.008
#> GSM254628 2 0.1401 0.77613 0.000 0.948 0.004 0.020 0.000 0.028
#> GSM254634 4 0.2520 0.80175 0.000 0.152 0.000 0.844 0.000 0.004
#> GSM254646 2 0.3761 0.66949 0.004 0.784 0.004 0.160 0.000 0.048
#> GSM254671 2 0.3634 0.45414 0.000 0.644 0.000 0.356 0.000 0.000
#> GSM254711 4 0.3445 0.68315 0.000 0.260 0.000 0.732 0.000 0.008
#> GSM254717 2 0.3549 0.65096 0.004 0.784 0.004 0.184 0.000 0.024
#> GSM254723 6 0.3620 0.91519 0.000 0.000 0.352 0.000 0.000 0.648
#> GSM254730 2 0.0405 0.78089 0.000 0.988 0.000 0.008 0.000 0.004
#> GSM254731 2 0.2762 0.64751 0.000 0.804 0.000 0.196 0.000 0.000
#> GSM254632 6 0.3607 0.91804 0.000 0.000 0.348 0.000 0.000 0.652
#> GSM254662 2 0.0146 0.78064 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM254677 4 0.1701 0.78890 0.000 0.072 0.000 0.920 0.000 0.008
#> GSM254665 2 0.3789 0.39704 0.000 0.660 0.000 0.332 0.000 0.008
#> GSM254691 2 0.3979 -0.06230 0.000 0.540 0.000 0.456 0.000 0.004
#> GSM254644 2 0.2100 0.72373 0.000 0.884 0.000 0.112 0.000 0.004
#> GSM254667 4 0.4121 0.65128 0.000 0.208 0.004 0.732 0.000 0.056
#> GSM254676 2 0.3823 0.02675 0.000 0.564 0.000 0.436 0.000 0.000
#> GSM254679 4 0.3288 0.66274 0.000 0.276 0.000 0.724 0.000 0.000
#> GSM254689 2 0.3962 0.64055 0.004 0.760 0.004 0.184 0.000 0.048
#> GSM254706 4 0.4338 0.16205 0.000 0.488 0.000 0.492 0.000 0.020
#> GSM254712 4 0.2234 0.80084 0.000 0.124 0.000 0.872 0.000 0.004
#> GSM254713 2 0.4039 0.26393 0.000 0.568 0.000 0.424 0.000 0.008
#> GSM254683 2 0.4918 0.02159 0.004 0.536 0.004 0.412 0.000 0.044
#> GSM254710 6 0.5201 0.69854 0.000 0.012 0.196 0.128 0.004 0.660
#> GSM254725 4 0.2613 0.80712 0.000 0.140 0.000 0.848 0.000 0.012
#> GSM254651 2 0.4117 0.53171 0.004 0.708 0.004 0.256 0.000 0.028
#> GSM254638 4 0.1802 0.78861 0.000 0.072 0.000 0.916 0.000 0.012
#> GSM254685 2 0.3575 0.57672 0.000 0.708 0.000 0.284 0.000 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) individual(p) disease.state(p) gender(p) other(p) k
#> CV:mclust 107 1.00e-21 0.64037 0.454 0.733 0.961 2
#> CV:mclust 99 1.14e-20 0.00566 0.511 0.034 0.779 3
#> CV:mclust 81 1.13e-15 0.00632 0.683 0.248 0.779 4
#> CV:mclust 99 2.89e-18 0.00570 0.634 0.248 0.447 5
#> CV:mclust 92 4.88e-17 0.03193 0.913 0.293 0.559 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 21168 rows and 107 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#> Subgroups are detected by 'NMF' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.972 0.988 0.5036 0.497 0.497
#> 3 3 0.601 0.632 0.811 0.2388 0.935 0.871
#> 4 4 0.670 0.703 0.835 0.1397 0.822 0.608
#> 5 5 0.735 0.719 0.850 0.0502 0.921 0.753
#> 6 6 0.740 0.681 0.829 0.0385 0.968 0.886
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
#> GSM254629 1 0.000 0.982 1.000 0.000
#> GSM254648 2 0.141 0.976 0.020 0.980
#> GSM254694 1 0.689 0.776 0.816 0.184
#> GSM254701 1 0.000 0.982 1.000 0.000
#> GSM254728 1 0.000 0.982 1.000 0.000
#> GSM254726 1 0.595 0.828 0.856 0.144
#> GSM254639 1 0.000 0.982 1.000 0.000
#> GSM254652 1 0.000 0.982 1.000 0.000
#> GSM254700 1 0.000 0.982 1.000 0.000
#> GSM254625 1 0.000 0.982 1.000 0.000
#> GSM254636 1 0.000 0.982 1.000 0.000
#> GSM254659 1 0.000 0.982 1.000 0.000
#> GSM254680 1 0.000 0.982 1.000 0.000
#> GSM254686 1 0.000 0.982 1.000 0.000
#> GSM254718 1 0.000 0.982 1.000 0.000
#> GSM254674 1 0.000 0.982 1.000 0.000
#> GSM254668 1 0.000 0.982 1.000 0.000
#> GSM254697 1 0.000 0.982 1.000 0.000
#> GSM254704 1 0.000 0.982 1.000 0.000
#> GSM254707 1 0.000 0.982 1.000 0.000
#> GSM254714 1 0.000 0.982 1.000 0.000
#> GSM254722 1 0.000 0.982 1.000 0.000
#> GSM254627 1 0.000 0.982 1.000 0.000
#> GSM254630 1 0.000 0.982 1.000 0.000
#> GSM254633 1 0.000 0.982 1.000 0.000
#> GSM254670 1 0.000 0.982 1.000 0.000
#> GSM254716 1 0.000 0.982 1.000 0.000
#> GSM254720 1 0.000 0.982 1.000 0.000
#> GSM254729 1 0.000 0.982 1.000 0.000
#> GSM254654 1 0.714 0.759 0.804 0.196
#> GSM254656 1 0.996 0.136 0.536 0.464
#> GSM254631 1 0.000 0.982 1.000 0.000
#> GSM254657 1 0.000 0.982 1.000 0.000
#> GSM254664 1 0.000 0.982 1.000 0.000
#> GSM254672 1 0.000 0.982 1.000 0.000
#> GSM254692 1 0.000 0.982 1.000 0.000
#> GSM254645 1 0.000 0.982 1.000 0.000
#> GSM254666 1 0.000 0.982 1.000 0.000
#> GSM254675 1 0.000 0.982 1.000 0.000
#> GSM254678 1 0.000 0.982 1.000 0.000
#> GSM254688 1 0.000 0.982 1.000 0.000
#> GSM254690 1 0.000 0.982 1.000 0.000
#> GSM254696 1 0.000 0.982 1.000 0.000
#> GSM254705 1 0.000 0.982 1.000 0.000
#> GSM254642 1 0.000 0.982 1.000 0.000
#> GSM254661 1 0.000 0.982 1.000 0.000
#> GSM254698 1 0.000 0.982 1.000 0.000
#> GSM254641 1 0.000 0.982 1.000 0.000
#> GSM254647 1 0.000 0.982 1.000 0.000
#> GSM254663 1 0.000 0.982 1.000 0.000
#> GSM254682 1 0.000 0.982 1.000 0.000
#> GSM254709 1 0.000 0.982 1.000 0.000
#> GSM254721 1 0.000 0.982 1.000 0.000
#> GSM254724 1 0.000 0.982 1.000 0.000
#> GSM254650 1 0.000 0.982 1.000 0.000
#> GSM254687 1 0.000 0.982 1.000 0.000
#> GSM254637 1 0.000 0.982 1.000 0.000
#> GSM254684 1 0.000 0.982 1.000 0.000
#> GSM254649 2 0.000 0.995 0.000 1.000
#> GSM254660 2 0.000 0.995 0.000 1.000
#> GSM254693 2 0.000 0.995 0.000 1.000
#> GSM254695 2 0.000 0.995 0.000 1.000
#> GSM254702 2 0.000 0.995 0.000 1.000
#> GSM254643 2 0.000 0.995 0.000 1.000
#> GSM254727 2 0.000 0.995 0.000 1.000
#> GSM254640 2 0.000 0.995 0.000 1.000
#> GSM254626 2 0.000 0.995 0.000 1.000
#> GSM254635 2 0.000 0.995 0.000 1.000
#> GSM254653 2 0.000 0.995 0.000 1.000
#> GSM254658 2 0.000 0.995 0.000 1.000
#> GSM254681 2 0.000 0.995 0.000 1.000
#> GSM254719 2 0.000 0.995 0.000 1.000
#> GSM254673 2 0.000 0.995 0.000 1.000
#> GSM254655 2 0.000 0.995 0.000 1.000
#> GSM254669 2 0.000 0.995 0.000 1.000
#> GSM254699 2 0.000 0.995 0.000 1.000
#> GSM254703 2 0.000 0.995 0.000 1.000
#> GSM254708 2 0.000 0.995 0.000 1.000
#> GSM254715 2 0.000 0.995 0.000 1.000
#> GSM254628 2 0.000 0.995 0.000 1.000
#> GSM254634 2 0.000 0.995 0.000 1.000
#> GSM254646 2 0.000 0.995 0.000 1.000
#> GSM254671 2 0.000 0.995 0.000 1.000
#> GSM254711 2 0.000 0.995 0.000 1.000
#> GSM254717 2 0.000 0.995 0.000 1.000
#> GSM254723 2 0.358 0.925 0.068 0.932
#> GSM254730 2 0.000 0.995 0.000 1.000
#> GSM254731 2 0.000 0.995 0.000 1.000
#> GSM254632 2 0.634 0.806 0.160 0.840
#> GSM254662 2 0.000 0.995 0.000 1.000
#> GSM254677 2 0.000 0.995 0.000 1.000
#> GSM254665 2 0.000 0.995 0.000 1.000
#> GSM254691 2 0.000 0.995 0.000 1.000
#> GSM254644 2 0.000 0.995 0.000 1.000
#> GSM254667 2 0.000 0.995 0.000 1.000
#> GSM254676 2 0.000 0.995 0.000 1.000
#> GSM254679 2 0.000 0.995 0.000 1.000
#> GSM254689 2 0.000 0.995 0.000 1.000
#> GSM254706 2 0.000 0.995 0.000 1.000
#> GSM254712 2 0.000 0.995 0.000 1.000
#> GSM254713 2 0.000 0.995 0.000 1.000
#> GSM254683 2 0.000 0.995 0.000 1.000
#> GSM254710 2 0.000 0.995 0.000 1.000
#> GSM254725 2 0.000 0.995 0.000 1.000
#> GSM254651 2 0.000 0.995 0.000 1.000
#> GSM254638 2 0.000 0.995 0.000 1.000
#> GSM254685 2 0.000 0.995 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM254629 3 0.6252 -0.1807 0.444 0.000 0.556
#> GSM254648 3 0.6521 -0.2524 0.004 0.496 0.500
#> GSM254694 1 0.9072 0.2113 0.532 0.168 0.300
#> GSM254701 1 0.5529 0.6443 0.704 0.000 0.296
#> GSM254728 1 0.5968 0.5915 0.636 0.000 0.364
#> GSM254726 3 0.8887 -0.2963 0.424 0.120 0.456
#> GSM254639 1 0.6302 0.4349 0.520 0.000 0.480
#> GSM254652 1 0.5785 0.6132 0.668 0.000 0.332
#> GSM254700 1 0.0424 0.7003 0.992 0.000 0.008
#> GSM254625 3 0.6026 0.0677 0.376 0.000 0.624
#> GSM254636 1 0.5733 0.6233 0.676 0.000 0.324
#> GSM254659 1 0.5905 0.6014 0.648 0.000 0.352
#> GSM254680 1 0.4121 0.6946 0.832 0.000 0.168
#> GSM254686 1 0.5016 0.6714 0.760 0.000 0.240
#> GSM254718 1 0.6168 0.5361 0.588 0.000 0.412
#> GSM254674 1 0.4062 0.6942 0.836 0.000 0.164
#> GSM254668 1 0.4504 0.6870 0.804 0.000 0.196
#> GSM254697 1 0.0747 0.6989 0.984 0.000 0.016
#> GSM254704 1 0.0892 0.7041 0.980 0.000 0.020
#> GSM254707 1 0.6274 0.2702 0.544 0.000 0.456
#> GSM254714 1 0.1643 0.7030 0.956 0.000 0.044
#> GSM254722 1 0.0747 0.7060 0.984 0.000 0.016
#> GSM254627 1 0.0747 0.6989 0.984 0.000 0.016
#> GSM254630 1 0.4178 0.6928 0.828 0.000 0.172
#> GSM254633 1 0.5254 0.6602 0.736 0.000 0.264
#> GSM254670 1 0.6180 0.5351 0.584 0.000 0.416
#> GSM254716 3 0.5098 0.1826 0.248 0.000 0.752
#> GSM254720 1 0.1031 0.7039 0.976 0.000 0.024
#> GSM254729 1 0.6516 0.4264 0.516 0.004 0.480
#> GSM254654 1 0.9424 0.0817 0.504 0.228 0.268
#> GSM254656 3 0.9268 0.2084 0.168 0.348 0.484
#> GSM254631 1 0.0747 0.7078 0.984 0.000 0.016
#> GSM254657 1 0.6291 0.4554 0.532 0.000 0.468
#> GSM254664 1 0.0747 0.7015 0.984 0.000 0.016
#> GSM254672 1 0.2165 0.6893 0.936 0.000 0.064
#> GSM254692 1 0.4399 0.5009 0.812 0.000 0.188
#> GSM254645 1 0.6140 0.5204 0.596 0.000 0.404
#> GSM254666 1 0.5905 0.6123 0.648 0.000 0.352
#> GSM254675 1 0.0237 0.7031 0.996 0.000 0.004
#> GSM254678 1 0.1031 0.7040 0.976 0.000 0.024
#> GSM254688 1 0.4346 0.6822 0.816 0.000 0.184
#> GSM254690 1 0.3038 0.7084 0.896 0.000 0.104
#> GSM254696 1 0.5859 0.6086 0.656 0.000 0.344
#> GSM254705 1 0.2165 0.6688 0.936 0.000 0.064
#> GSM254642 1 0.2165 0.6693 0.936 0.000 0.064
#> GSM254661 1 0.5835 0.6101 0.660 0.000 0.340
#> GSM254698 1 0.5327 0.6499 0.728 0.000 0.272
#> GSM254641 1 0.4452 0.6880 0.808 0.000 0.192
#> GSM254647 1 0.0424 0.7003 0.992 0.000 0.008
#> GSM254663 1 0.2356 0.6585 0.928 0.000 0.072
#> GSM254682 1 0.4504 0.6802 0.804 0.000 0.196
#> GSM254709 1 0.6291 -0.0768 0.532 0.000 0.468
#> GSM254721 1 0.0424 0.7003 0.992 0.000 0.008
#> GSM254724 1 0.0424 0.7003 0.992 0.000 0.008
#> GSM254650 1 0.6225 -0.0213 0.568 0.000 0.432
#> GSM254687 1 0.5926 0.1475 0.644 0.000 0.356
#> GSM254637 1 0.1289 0.7052 0.968 0.000 0.032
#> GSM254684 1 0.5706 0.6259 0.680 0.000 0.320
#> GSM254649 2 0.4654 0.7233 0.000 0.792 0.208
#> GSM254660 2 0.0000 0.8668 0.000 1.000 0.000
#> GSM254693 2 0.1860 0.8564 0.000 0.948 0.052
#> GSM254695 2 0.3551 0.8085 0.000 0.868 0.132
#> GSM254702 2 0.2165 0.8505 0.000 0.936 0.064
#> GSM254643 2 0.1860 0.8564 0.000 0.948 0.052
#> GSM254727 2 0.0892 0.8657 0.000 0.980 0.020
#> GSM254640 2 0.0237 0.8665 0.000 0.996 0.004
#> GSM254626 2 0.2066 0.8526 0.000 0.940 0.060
#> GSM254635 2 0.3686 0.8015 0.000 0.860 0.140
#> GSM254653 2 0.0892 0.8657 0.000 0.980 0.020
#> GSM254658 2 0.4235 0.7595 0.000 0.824 0.176
#> GSM254681 2 0.6291 0.2330 0.000 0.532 0.468
#> GSM254719 2 0.0592 0.8667 0.000 0.988 0.012
#> GSM254673 2 0.1529 0.8605 0.000 0.960 0.040
#> GSM254655 2 0.0000 0.8668 0.000 1.000 0.000
#> GSM254669 2 0.1860 0.8564 0.000 0.948 0.052
#> GSM254699 2 0.0000 0.8668 0.000 1.000 0.000
#> GSM254703 2 0.2711 0.8398 0.000 0.912 0.088
#> GSM254708 2 0.0892 0.8657 0.000 0.980 0.020
#> GSM254715 2 0.2796 0.8378 0.000 0.908 0.092
#> GSM254628 2 0.3686 0.7944 0.000 0.860 0.140
#> GSM254634 2 0.2796 0.8378 0.000 0.908 0.092
#> GSM254646 2 0.6180 0.3646 0.000 0.584 0.416
#> GSM254671 2 0.2448 0.8455 0.000 0.924 0.076
#> GSM254711 2 0.2796 0.8378 0.000 0.908 0.092
#> GSM254717 2 0.1031 0.8650 0.000 0.976 0.024
#> GSM254723 2 0.5201 0.6665 0.004 0.760 0.236
#> GSM254730 2 0.0237 0.8665 0.000 0.996 0.004
#> GSM254731 2 0.1163 0.8622 0.000 0.972 0.028
#> GSM254632 2 0.7097 0.5215 0.052 0.668 0.280
#> GSM254662 2 0.0747 0.8663 0.000 0.984 0.016
#> GSM254677 2 0.4002 0.7811 0.000 0.840 0.160
#> GSM254665 2 0.2066 0.8526 0.000 0.940 0.060
#> GSM254691 2 0.0747 0.8666 0.000 0.984 0.016
#> GSM254644 2 0.0747 0.8650 0.000 0.984 0.016
#> GSM254667 2 0.1860 0.8566 0.000 0.948 0.052
#> GSM254676 2 0.0592 0.8667 0.000 0.988 0.012
#> GSM254679 2 0.3116 0.8275 0.000 0.892 0.108
#> GSM254689 2 0.6244 0.3081 0.000 0.560 0.440
#> GSM254706 2 0.3879 0.7831 0.000 0.848 0.152
#> GSM254712 2 0.2959 0.8330 0.000 0.900 0.100
#> GSM254713 2 0.2959 0.8330 0.000 0.900 0.100
#> GSM254683 2 0.6008 0.4592 0.000 0.628 0.372
#> GSM254710 3 0.6654 -0.1552 0.008 0.456 0.536
#> GSM254725 2 0.3816 0.7937 0.000 0.852 0.148
#> GSM254651 2 0.4504 0.7375 0.000 0.804 0.196
#> GSM254638 2 0.3816 0.7937 0.000 0.852 0.148
#> GSM254685 2 0.0747 0.8650 0.000 0.984 0.016
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM254629 4 0.5920 0.1049 0.052 0.000 0.336 0.612
#> GSM254648 4 0.4265 0.5316 0.016 0.076 0.068 0.840
#> GSM254694 3 0.9284 0.4109 0.236 0.116 0.428 0.220
#> GSM254701 3 0.6944 0.6147 0.216 0.000 0.588 0.196
#> GSM254728 3 0.4289 0.7110 0.032 0.000 0.796 0.172
#> GSM254726 3 0.4845 0.6835 0.012 0.020 0.756 0.212
#> GSM254639 3 0.2742 0.6898 0.024 0.000 0.900 0.076
#> GSM254652 3 0.4323 0.7060 0.028 0.000 0.788 0.184
#> GSM254700 1 0.0376 0.8843 0.992 0.000 0.004 0.004
#> GSM254625 4 0.4520 0.4698 0.036 0.008 0.156 0.800
#> GSM254636 3 0.3749 0.7070 0.032 0.000 0.840 0.128
#> GSM254659 3 0.4590 0.7078 0.036 0.000 0.772 0.192
#> GSM254680 3 0.6723 0.6546 0.188 0.000 0.616 0.196
#> GSM254686 3 0.6317 0.6652 0.116 0.000 0.644 0.240
#> GSM254718 3 0.6754 0.6419 0.204 0.000 0.612 0.184
#> GSM254674 3 0.5672 0.7028 0.100 0.000 0.712 0.188
#> GSM254668 3 0.7830 0.3948 0.268 0.000 0.400 0.332
#> GSM254697 1 0.0376 0.8843 0.992 0.000 0.004 0.004
#> GSM254704 1 0.0524 0.8794 0.988 0.000 0.008 0.004
#> GSM254707 4 0.5011 0.4318 0.076 0.000 0.160 0.764
#> GSM254714 1 0.2586 0.8428 0.912 0.000 0.040 0.048
#> GSM254722 1 0.3088 0.8071 0.864 0.000 0.128 0.008
#> GSM254627 1 0.0376 0.8843 0.992 0.000 0.004 0.004
#> GSM254630 1 0.4012 0.7439 0.800 0.000 0.184 0.016
#> GSM254633 3 0.5744 0.7031 0.108 0.000 0.708 0.184
#> GSM254670 3 0.1004 0.6579 0.024 0.000 0.972 0.004
#> GSM254716 4 0.4485 0.3977 0.012 0.000 0.248 0.740
#> GSM254720 1 0.0336 0.8822 0.992 0.000 0.000 0.008
#> GSM254729 3 0.4149 0.7103 0.028 0.000 0.804 0.168
#> GSM254654 3 0.9634 0.2156 0.148 0.244 0.372 0.236
#> GSM254656 3 0.3017 0.6061 0.024 0.028 0.904 0.044
#> GSM254631 1 0.5716 0.5003 0.700 0.000 0.212 0.088
#> GSM254657 3 0.1151 0.6625 0.024 0.000 0.968 0.008
#> GSM254664 1 0.5267 0.5972 0.740 0.000 0.076 0.184
#> GSM254672 1 0.0524 0.8833 0.988 0.000 0.004 0.008
#> GSM254692 1 0.1118 0.8694 0.964 0.000 0.000 0.036
#> GSM254645 3 0.6394 0.2709 0.384 0.004 0.552 0.060
#> GSM254666 3 0.4888 0.6718 0.036 0.000 0.740 0.224
#> GSM254675 1 0.0469 0.8820 0.988 0.000 0.000 0.012
#> GSM254678 1 0.2011 0.8456 0.920 0.000 0.080 0.000
#> GSM254688 4 0.7531 0.2848 0.208 0.000 0.316 0.476
#> GSM254690 3 0.5290 0.0884 0.476 0.000 0.516 0.008
#> GSM254696 3 0.1452 0.6585 0.036 0.000 0.956 0.008
#> GSM254705 1 0.0895 0.8806 0.976 0.000 0.004 0.020
#> GSM254642 1 0.0524 0.8837 0.988 0.000 0.004 0.008
#> GSM254661 3 0.4365 0.7049 0.028 0.000 0.784 0.188
#> GSM254698 3 0.4535 0.4238 0.292 0.000 0.704 0.004
#> GSM254641 3 0.7668 0.4626 0.348 0.000 0.432 0.220
#> GSM254647 1 0.0524 0.8837 0.988 0.000 0.004 0.008
#> GSM254663 1 0.1489 0.8718 0.952 0.000 0.004 0.044
#> GSM254682 4 0.7540 0.2651 0.192 0.000 0.364 0.444
#> GSM254709 4 0.4933 0.3332 0.296 0.000 0.016 0.688
#> GSM254721 1 0.0000 0.8837 1.000 0.000 0.000 0.000
#> GSM254724 1 0.0188 0.8840 0.996 0.000 0.004 0.000
#> GSM254650 1 0.4679 0.4315 0.648 0.000 0.000 0.352
#> GSM254687 1 0.4981 0.1533 0.536 0.000 0.000 0.464
#> GSM254637 1 0.3279 0.7950 0.872 0.000 0.032 0.096
#> GSM254684 3 0.2198 0.6367 0.072 0.000 0.920 0.008
#> GSM254649 2 0.3688 0.7368 0.000 0.792 0.000 0.208
#> GSM254660 2 0.0188 0.8945 0.000 0.996 0.000 0.004
#> GSM254693 2 0.1792 0.8770 0.000 0.932 0.000 0.068
#> GSM254695 2 0.2174 0.8785 0.000 0.928 0.020 0.052
#> GSM254702 2 0.1211 0.8891 0.000 0.960 0.000 0.040
#> GSM254643 2 0.1474 0.8863 0.000 0.948 0.000 0.052
#> GSM254727 2 0.1211 0.8890 0.000 0.960 0.000 0.040
#> GSM254640 2 0.0336 0.8943 0.000 0.992 0.000 0.008
#> GSM254626 2 0.1716 0.8791 0.000 0.936 0.000 0.064
#> GSM254635 2 0.2335 0.8740 0.000 0.920 0.020 0.060
#> GSM254653 2 0.0921 0.8919 0.000 0.972 0.000 0.028
#> GSM254658 2 0.2760 0.8298 0.000 0.872 0.000 0.128
#> GSM254681 4 0.4477 0.4381 0.000 0.312 0.000 0.688
#> GSM254719 2 0.0592 0.8936 0.000 0.984 0.000 0.016
#> GSM254673 2 0.1557 0.8830 0.000 0.944 0.000 0.056
#> GSM254655 2 0.0188 0.8945 0.000 0.996 0.000 0.004
#> GSM254669 2 0.1867 0.8746 0.000 0.928 0.000 0.072
#> GSM254699 2 0.0188 0.8945 0.000 0.996 0.000 0.004
#> GSM254703 2 0.2363 0.8766 0.000 0.920 0.024 0.056
#> GSM254708 2 0.1557 0.8833 0.000 0.944 0.000 0.056
#> GSM254715 2 0.2335 0.8756 0.000 0.920 0.020 0.060
#> GSM254628 2 0.2408 0.8530 0.000 0.896 0.000 0.104
#> GSM254634 2 0.1743 0.8828 0.000 0.940 0.004 0.056
#> GSM254646 2 0.4996 0.0854 0.000 0.516 0.000 0.484
#> GSM254671 2 0.1489 0.8873 0.000 0.952 0.004 0.044
#> GSM254711 2 0.1743 0.8829 0.000 0.940 0.004 0.056
#> GSM254717 2 0.1118 0.8901 0.000 0.964 0.000 0.036
#> GSM254723 2 0.4337 0.7822 0.004 0.824 0.072 0.100
#> GSM254730 2 0.0000 0.8945 0.000 1.000 0.000 0.000
#> GSM254731 2 0.1022 0.8909 0.000 0.968 0.000 0.032
#> GSM254632 4 0.5605 0.4437 0.012 0.320 0.020 0.648
#> GSM254662 2 0.1118 0.8901 0.000 0.964 0.000 0.036
#> GSM254677 2 0.3104 0.8572 0.004 0.892 0.044 0.060
#> GSM254665 2 0.1637 0.8827 0.000 0.940 0.000 0.060
#> GSM254691 2 0.1022 0.8933 0.000 0.968 0.000 0.032
#> GSM254644 2 0.0921 0.8917 0.000 0.972 0.000 0.028
#> GSM254667 2 0.4072 0.6755 0.000 0.748 0.000 0.252
#> GSM254676 2 0.0707 0.8931 0.000 0.980 0.000 0.020
#> GSM254679 2 0.1824 0.8811 0.000 0.936 0.004 0.060
#> GSM254689 4 0.4941 0.1344 0.000 0.436 0.000 0.564
#> GSM254706 2 0.4500 0.5571 0.000 0.684 0.000 0.316
#> GSM254712 2 0.2546 0.8715 0.000 0.912 0.028 0.060
#> GSM254713 2 0.2443 0.8736 0.000 0.916 0.024 0.060
#> GSM254683 2 0.4996 0.0619 0.000 0.516 0.000 0.484
#> GSM254710 4 0.4122 0.5433 0.000 0.236 0.004 0.760
#> GSM254725 2 0.2466 0.8717 0.000 0.916 0.028 0.056
#> GSM254651 2 0.4103 0.6714 0.000 0.744 0.000 0.256
#> GSM254638 2 0.2996 0.8572 0.000 0.892 0.044 0.064
#> GSM254685 2 0.1284 0.8919 0.000 0.964 0.012 0.024
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM254629 3 0.2929 0.7446 0.004 0.000 0.856 0.012 0.128
#> GSM254648 3 0.3201 0.7358 0.000 0.008 0.844 0.016 0.132
#> GSM254694 3 0.2965 0.7461 0.008 0.004 0.884 0.052 0.052
#> GSM254701 3 0.1569 0.7801 0.008 0.000 0.944 0.044 0.004
#> GSM254728 3 0.1410 0.7847 0.000 0.000 0.940 0.060 0.000
#> GSM254726 3 0.1153 0.7846 0.000 0.008 0.964 0.024 0.004
#> GSM254639 4 0.4114 0.5103 0.000 0.000 0.376 0.624 0.000
#> GSM254652 3 0.2012 0.7865 0.000 0.000 0.920 0.060 0.020
#> GSM254700 1 0.0609 0.9084 0.980 0.000 0.000 0.000 0.020
#> GSM254625 5 0.4482 0.2776 0.000 0.000 0.376 0.012 0.612
#> GSM254636 4 0.4401 0.6133 0.004 0.000 0.296 0.684 0.016
#> GSM254659 3 0.1443 0.7897 0.004 0.000 0.948 0.044 0.004
#> GSM254680 3 0.3178 0.7736 0.036 0.000 0.872 0.068 0.024
#> GSM254686 3 0.2429 0.7862 0.008 0.000 0.904 0.020 0.068
#> GSM254718 3 0.1808 0.7772 0.008 0.000 0.936 0.044 0.012
#> GSM254674 3 0.3004 0.7592 0.008 0.000 0.864 0.108 0.020
#> GSM254668 3 0.3044 0.7414 0.008 0.000 0.840 0.004 0.148
#> GSM254697 1 0.0162 0.9075 0.996 0.000 0.000 0.004 0.000
#> GSM254704 1 0.0609 0.9084 0.980 0.000 0.000 0.000 0.020
#> GSM254707 3 0.4452 -0.0309 0.000 0.000 0.500 0.004 0.496
#> GSM254714 3 0.7091 0.1974 0.360 0.000 0.468 0.104 0.068
#> GSM254722 1 0.1965 0.8414 0.904 0.000 0.000 0.096 0.000
#> GSM254627 1 0.0000 0.9080 1.000 0.000 0.000 0.000 0.000
#> GSM254630 1 0.1894 0.8616 0.920 0.000 0.000 0.072 0.008
#> GSM254633 3 0.2131 0.7873 0.008 0.000 0.920 0.056 0.016
#> GSM254670 4 0.2127 0.7500 0.000 0.000 0.108 0.892 0.000
#> GSM254716 5 0.4046 0.4066 0.000 0.000 0.296 0.008 0.696
#> GSM254720 1 0.0771 0.9076 0.976 0.000 0.004 0.000 0.020
#> GSM254729 3 0.2416 0.7616 0.000 0.000 0.888 0.100 0.012
#> GSM254654 3 0.3276 0.7185 0.004 0.004 0.856 0.100 0.036
#> GSM254656 4 0.2074 0.7495 0.000 0.000 0.104 0.896 0.000
#> GSM254631 3 0.5552 0.2754 0.432 0.000 0.516 0.028 0.024
#> GSM254657 4 0.3884 0.6620 0.000 0.000 0.288 0.708 0.004
#> GSM254664 3 0.4001 0.6545 0.208 0.000 0.764 0.004 0.024
#> GSM254672 1 0.0609 0.9084 0.980 0.000 0.000 0.000 0.020
#> GSM254692 1 0.0000 0.9080 1.000 0.000 0.000 0.000 0.000
#> GSM254645 4 0.7165 0.2580 0.328 0.000 0.104 0.488 0.080
#> GSM254666 3 0.3861 0.7276 0.000 0.000 0.804 0.068 0.128
#> GSM254675 1 0.1800 0.8731 0.932 0.000 0.048 0.000 0.020
#> GSM254678 1 0.2002 0.8824 0.932 0.000 0.020 0.028 0.020
#> GSM254688 5 0.7964 0.0985 0.256 0.000 0.140 0.164 0.440
#> GSM254690 1 0.5336 0.3881 0.632 0.000 0.052 0.304 0.012
#> GSM254696 4 0.2389 0.7505 0.004 0.000 0.116 0.880 0.000
#> GSM254705 1 0.0404 0.9089 0.988 0.000 0.000 0.000 0.012
#> GSM254642 1 0.0162 0.9075 0.996 0.000 0.000 0.004 0.000
#> GSM254661 3 0.1701 0.7898 0.000 0.000 0.936 0.048 0.016
#> GSM254698 4 0.2583 0.6592 0.132 0.000 0.004 0.864 0.000
#> GSM254641 3 0.2474 0.7826 0.008 0.000 0.896 0.012 0.084
#> GSM254647 1 0.0000 0.9080 1.000 0.000 0.000 0.000 0.000
#> GSM254663 1 0.0912 0.8972 0.972 0.000 0.012 0.000 0.016
#> GSM254682 4 0.7189 0.4766 0.252 0.000 0.100 0.536 0.112
#> GSM254709 5 0.5714 0.0656 0.072 0.000 0.412 0.004 0.512
#> GSM254721 1 0.0609 0.9084 0.980 0.000 0.000 0.000 0.020
#> GSM254724 1 0.0609 0.9084 0.980 0.000 0.000 0.000 0.020
#> GSM254650 1 0.3700 0.6583 0.752 0.000 0.008 0.000 0.240
#> GSM254687 1 0.4882 0.1861 0.532 0.000 0.024 0.000 0.444
#> GSM254637 3 0.5305 0.2762 0.436 0.000 0.524 0.012 0.028
#> GSM254684 4 0.2249 0.7480 0.008 0.000 0.096 0.896 0.000
#> GSM254649 2 0.3395 0.7036 0.000 0.764 0.000 0.000 0.236
#> GSM254660 2 0.0000 0.8764 0.000 1.000 0.000 0.000 0.000
#> GSM254693 2 0.1671 0.8604 0.000 0.924 0.000 0.000 0.076
#> GSM254695 2 0.2011 0.8652 0.000 0.928 0.008 0.020 0.044
#> GSM254702 2 0.0771 0.8722 0.000 0.976 0.004 0.000 0.020
#> GSM254643 2 0.1043 0.8767 0.000 0.960 0.000 0.000 0.040
#> GSM254727 2 0.1557 0.8719 0.000 0.940 0.008 0.000 0.052
#> GSM254640 2 0.0740 0.8781 0.000 0.980 0.004 0.008 0.008
#> GSM254626 2 0.1270 0.8726 0.000 0.948 0.000 0.000 0.052
#> GSM254635 2 0.1768 0.8490 0.000 0.924 0.004 0.000 0.072
#> GSM254653 2 0.0880 0.8766 0.000 0.968 0.000 0.000 0.032
#> GSM254658 2 0.2648 0.8039 0.000 0.848 0.000 0.000 0.152
#> GSM254681 5 0.3143 0.5351 0.000 0.204 0.000 0.000 0.796
#> GSM254719 2 0.0703 0.8769 0.000 0.976 0.000 0.000 0.024
#> GSM254673 2 0.1121 0.8738 0.000 0.956 0.000 0.000 0.044
#> GSM254655 2 0.0162 0.8769 0.000 0.996 0.000 0.000 0.004
#> GSM254669 2 0.1908 0.8498 0.000 0.908 0.000 0.000 0.092
#> GSM254699 2 0.0162 0.8769 0.000 0.996 0.000 0.000 0.004
#> GSM254703 2 0.3856 0.7834 0.000 0.832 0.024 0.080 0.064
#> GSM254708 2 0.1270 0.8713 0.000 0.948 0.000 0.000 0.052
#> GSM254715 2 0.2576 0.8375 0.000 0.900 0.008 0.036 0.056
#> GSM254628 2 0.2377 0.8283 0.000 0.872 0.000 0.000 0.128
#> GSM254634 2 0.0609 0.8724 0.000 0.980 0.000 0.000 0.020
#> GSM254646 2 0.4304 0.1240 0.000 0.516 0.000 0.000 0.484
#> GSM254671 2 0.0162 0.8759 0.000 0.996 0.000 0.000 0.004
#> GSM254711 2 0.1282 0.8636 0.000 0.952 0.004 0.000 0.044
#> GSM254717 2 0.1628 0.8709 0.000 0.936 0.008 0.000 0.056
#> GSM254723 2 0.5930 0.6084 0.000 0.692 0.100 0.092 0.116
#> GSM254730 2 0.0404 0.8776 0.000 0.988 0.000 0.000 0.012
#> GSM254731 2 0.0451 0.8753 0.000 0.988 0.004 0.000 0.008
#> GSM254632 5 0.4175 0.5314 0.000 0.200 0.020 0.016 0.764
#> GSM254662 2 0.0963 0.8752 0.000 0.964 0.000 0.000 0.036
#> GSM254677 2 0.4453 0.7525 0.000 0.796 0.036 0.092 0.076
#> GSM254665 2 0.1197 0.8756 0.000 0.952 0.000 0.000 0.048
#> GSM254691 2 0.0963 0.8752 0.000 0.964 0.000 0.000 0.036
#> GSM254644 2 0.0579 0.8778 0.000 0.984 0.008 0.000 0.008
#> GSM254667 2 0.4636 0.5568 0.000 0.664 0.004 0.024 0.308
#> GSM254676 2 0.1121 0.8737 0.000 0.956 0.000 0.000 0.044
#> GSM254679 2 0.1041 0.8683 0.000 0.964 0.004 0.000 0.032
#> GSM254689 5 0.4114 0.2657 0.000 0.376 0.000 0.000 0.624
#> GSM254706 2 0.4333 0.5045 0.000 0.640 0.004 0.004 0.352
#> GSM254712 2 0.4462 0.7471 0.000 0.796 0.040 0.096 0.068
#> GSM254713 2 0.3860 0.7832 0.000 0.832 0.024 0.076 0.068
#> GSM254683 2 0.4294 0.1762 0.000 0.532 0.000 0.000 0.468
#> GSM254710 5 0.2548 0.5318 0.000 0.116 0.004 0.004 0.876
#> GSM254725 2 0.1638 0.8540 0.000 0.932 0.000 0.004 0.064
#> GSM254651 2 0.4066 0.5710 0.000 0.672 0.004 0.000 0.324
#> GSM254638 2 0.4098 0.7658 0.000 0.816 0.024 0.080 0.080
#> GSM254685 2 0.1978 0.8549 0.000 0.928 0.004 0.044 0.024
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM254629 3 0.2473 0.7995 0.000 0.000 0.856 0.136 0.008 0.000
#> GSM254648 3 0.2988 0.7945 0.000 0.000 0.828 0.144 0.028 0.000
#> GSM254694 3 0.2519 0.8064 0.000 0.004 0.864 0.124 0.004 0.004
#> GSM254701 3 0.2288 0.8054 0.000 0.000 0.876 0.116 0.004 0.004
#> GSM254728 3 0.3104 0.7715 0.000 0.000 0.800 0.184 0.000 0.016
#> GSM254726 3 0.2442 0.7977 0.000 0.000 0.852 0.144 0.000 0.004
#> GSM254639 6 0.4575 0.5413 0.000 0.000 0.180 0.124 0.000 0.696
#> GSM254652 3 0.2085 0.8200 0.000 0.000 0.912 0.056 0.008 0.024
#> GSM254700 1 0.1908 0.8533 0.900 0.000 0.000 0.096 0.004 0.000
#> GSM254625 3 0.4314 0.0632 0.000 0.000 0.500 0.004 0.484 0.012
#> GSM254636 6 0.4543 0.4743 0.012 0.000 0.332 0.008 0.016 0.632
#> GSM254659 3 0.1141 0.8181 0.000 0.000 0.948 0.052 0.000 0.000
#> GSM254680 3 0.1693 0.8010 0.000 0.000 0.932 0.004 0.020 0.044
#> GSM254686 3 0.1970 0.8137 0.000 0.000 0.912 0.060 0.028 0.000
#> GSM254718 3 0.3543 0.6914 0.000 0.000 0.720 0.272 0.004 0.004
#> GSM254674 3 0.1863 0.7999 0.004 0.000 0.920 0.000 0.016 0.060
#> GSM254668 3 0.1924 0.7988 0.000 0.000 0.920 0.004 0.048 0.028
#> GSM254697 1 0.0405 0.8543 0.988 0.000 0.000 0.004 0.000 0.008
#> GSM254704 1 0.2053 0.8502 0.888 0.000 0.000 0.108 0.004 0.000
#> GSM254707 3 0.4418 0.3571 0.000 0.000 0.604 0.012 0.368 0.016
#> GSM254714 4 0.4893 0.3327 0.200 0.000 0.128 0.668 0.004 0.000
#> GSM254722 1 0.2146 0.7872 0.880 0.000 0.000 0.004 0.000 0.116
#> GSM254627 1 0.0405 0.8543 0.988 0.000 0.000 0.004 0.000 0.008
#> GSM254630 1 0.2556 0.8139 0.888 0.000 0.000 0.012 0.052 0.048
#> GSM254633 3 0.1074 0.8092 0.000 0.000 0.960 0.000 0.012 0.028
#> GSM254670 6 0.0508 0.7287 0.000 0.000 0.004 0.012 0.000 0.984
#> GSM254716 5 0.4260 0.4056 0.000 0.000 0.248 0.048 0.700 0.004
#> GSM254720 1 0.2587 0.8437 0.864 0.000 0.008 0.120 0.004 0.004
#> GSM254729 3 0.2011 0.8114 0.000 0.000 0.912 0.020 0.004 0.064
#> GSM254654 3 0.2632 0.7890 0.000 0.000 0.832 0.164 0.004 0.000
#> GSM254656 6 0.1152 0.7194 0.000 0.000 0.004 0.044 0.000 0.952
#> GSM254631 3 0.4580 0.6303 0.180 0.000 0.732 0.004 0.028 0.056
#> GSM254657 6 0.5209 0.3418 0.000 0.000 0.088 0.360 0.004 0.548
#> GSM254664 3 0.2302 0.7880 0.060 0.000 0.900 0.008 0.032 0.000
#> GSM254672 1 0.2234 0.8463 0.872 0.000 0.000 0.124 0.004 0.000
#> GSM254692 1 0.0891 0.8600 0.968 0.000 0.000 0.024 0.008 0.000
#> GSM254645 4 0.5608 -0.0733 0.072 0.000 0.020 0.488 0.004 0.416
#> GSM254666 3 0.6495 0.0738 0.000 0.000 0.448 0.076 0.368 0.108
#> GSM254675 1 0.3482 0.7978 0.812 0.000 0.068 0.116 0.004 0.000
#> GSM254678 1 0.4647 0.7038 0.708 0.000 0.004 0.104 0.004 0.180
#> GSM254688 5 0.6968 0.1694 0.148 0.000 0.124 0.004 0.496 0.228
#> GSM254690 1 0.5851 0.0861 0.512 0.000 0.116 0.004 0.016 0.352
#> GSM254696 6 0.2067 0.7253 0.016 0.000 0.064 0.004 0.004 0.912
#> GSM254705 1 0.1471 0.8595 0.932 0.000 0.000 0.064 0.000 0.004
#> GSM254642 1 0.0405 0.8543 0.988 0.000 0.000 0.004 0.000 0.008
#> GSM254661 3 0.3542 0.7897 0.000 0.000 0.800 0.156 0.028 0.016
#> GSM254698 6 0.1814 0.6850 0.100 0.000 0.000 0.000 0.000 0.900
#> GSM254641 3 0.2008 0.8118 0.004 0.000 0.920 0.040 0.032 0.004
#> GSM254647 1 0.0551 0.8544 0.984 0.000 0.008 0.004 0.000 0.004
#> GSM254663 1 0.1452 0.8422 0.948 0.000 0.008 0.004 0.032 0.008
#> GSM254682 6 0.5868 0.5440 0.156 0.000 0.052 0.012 0.136 0.644
#> GSM254709 5 0.5319 -0.0185 0.048 0.000 0.408 0.028 0.516 0.000
#> GSM254721 1 0.2006 0.8509 0.892 0.000 0.000 0.104 0.004 0.000
#> GSM254724 1 0.2006 0.8509 0.892 0.000 0.000 0.104 0.004 0.000
#> GSM254650 1 0.4275 0.4244 0.652 0.000 0.020 0.004 0.320 0.004
#> GSM254687 5 0.4253 -0.0340 0.472 0.000 0.004 0.004 0.516 0.004
#> GSM254637 3 0.3537 0.6806 0.164 0.000 0.796 0.016 0.024 0.000
#> GSM254684 6 0.0508 0.7299 0.012 0.000 0.004 0.000 0.000 0.984
#> GSM254649 2 0.1663 0.8377 0.000 0.912 0.000 0.000 0.088 0.000
#> GSM254660 2 0.0146 0.8636 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM254693 2 0.0777 0.8623 0.000 0.972 0.000 0.004 0.024 0.000
#> GSM254695 2 0.3601 0.7155 0.000 0.792 0.000 0.160 0.040 0.008
#> GSM254702 2 0.0000 0.8631 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM254643 2 0.1297 0.8584 0.000 0.948 0.000 0.012 0.040 0.000
#> GSM254727 2 0.0806 0.8617 0.000 0.972 0.000 0.020 0.008 0.000
#> GSM254640 2 0.2854 0.7204 0.000 0.792 0.000 0.208 0.000 0.000
#> GSM254626 2 0.1124 0.8606 0.000 0.956 0.000 0.008 0.036 0.000
#> GSM254635 2 0.1349 0.8435 0.000 0.940 0.000 0.056 0.004 0.000
#> GSM254653 2 0.0260 0.8635 0.000 0.992 0.000 0.000 0.008 0.000
#> GSM254658 2 0.1462 0.8510 0.000 0.936 0.000 0.008 0.056 0.000
#> GSM254681 5 0.2613 0.4269 0.000 0.140 0.000 0.012 0.848 0.000
#> GSM254719 2 0.0000 0.8631 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM254673 2 0.0458 0.8635 0.000 0.984 0.000 0.000 0.016 0.000
#> GSM254655 2 0.0000 0.8631 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM254669 2 0.0458 0.8639 0.000 0.984 0.000 0.000 0.016 0.000
#> GSM254699 2 0.0000 0.8631 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM254703 2 0.3765 0.3881 0.000 0.596 0.000 0.404 0.000 0.000
#> GSM254708 2 0.0622 0.8636 0.000 0.980 0.000 0.008 0.012 0.000
#> GSM254715 2 0.3464 0.5714 0.000 0.688 0.000 0.312 0.000 0.000
#> GSM254628 2 0.1714 0.8389 0.000 0.908 0.000 0.000 0.092 0.000
#> GSM254634 2 0.0713 0.8623 0.000 0.972 0.000 0.028 0.000 0.000
#> GSM254646 2 0.3547 0.6126 0.000 0.696 0.000 0.004 0.300 0.000
#> GSM254671 2 0.0000 0.8631 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM254711 2 0.0363 0.8627 0.000 0.988 0.000 0.012 0.000 0.000
#> GSM254717 2 0.1245 0.8620 0.000 0.952 0.000 0.016 0.032 0.000
#> GSM254723 4 0.5226 0.3971 0.000 0.112 0.120 0.712 0.040 0.016
#> GSM254730 2 0.0000 0.8631 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM254731 2 0.0146 0.8630 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM254632 5 0.4197 0.4682 0.000 0.060 0.036 0.052 0.808 0.044
#> GSM254662 2 0.0260 0.8635 0.000 0.992 0.000 0.000 0.008 0.000
#> GSM254677 4 0.4058 0.2821 0.000 0.320 0.000 0.660 0.016 0.004
#> GSM254665 2 0.1921 0.8478 0.000 0.916 0.000 0.032 0.052 0.000
#> GSM254691 2 0.1176 0.8621 0.000 0.956 0.000 0.020 0.024 0.000
#> GSM254644 2 0.2854 0.7227 0.000 0.792 0.000 0.208 0.000 0.000
#> GSM254667 2 0.5809 0.5061 0.000 0.612 0.000 0.072 0.228 0.088
#> GSM254676 2 0.0717 0.8637 0.000 0.976 0.000 0.008 0.016 0.000
#> GSM254679 2 0.0547 0.8622 0.000 0.980 0.000 0.020 0.000 0.000
#> GSM254689 5 0.3872 0.1325 0.000 0.392 0.000 0.004 0.604 0.000
#> GSM254706 2 0.5035 0.3788 0.000 0.556 0.000 0.084 0.360 0.000
#> GSM254712 2 0.3843 0.2618 0.000 0.548 0.000 0.452 0.000 0.000
#> GSM254713 2 0.3737 0.4160 0.000 0.608 0.000 0.392 0.000 0.000
#> GSM254683 2 0.3645 0.6878 0.000 0.740 0.000 0.024 0.236 0.000
#> GSM254710 5 0.1168 0.4698 0.000 0.016 0.000 0.028 0.956 0.000
#> GSM254725 2 0.1010 0.8555 0.000 0.960 0.000 0.036 0.000 0.004
#> GSM254651 2 0.4441 0.6161 0.000 0.700 0.000 0.092 0.208 0.000
#> GSM254638 2 0.3302 0.6991 0.000 0.760 0.004 0.232 0.004 0.000
#> GSM254685 2 0.3499 0.5692 0.000 0.680 0.000 0.320 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) individual(p) disease.state(p) gender(p) other(p) k
#> CV:NMF 106 3.87e-23 0.5154 0.690 0.6973 1.0000 2
#> CV:NMF 87 8.02e-20 0.1827 0.654 0.9396 1.0000 3
#> CV:NMF 86 4.26e-18 0.0315 0.471 0.1811 0.1032 4
#> CV:NMF 92 4.95e-19 0.0468 0.189 0.2106 0.0426 5
#> CV:NMF 84 4.25e-18 0.0158 0.135 0.0943 0.0222 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 21168 rows and 107 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'hclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.976 0.988 0.5000 0.501 0.501
#> 3 3 0.620 0.618 0.794 0.2318 0.928 0.856
#> 4 4 0.569 0.520 0.753 0.1384 0.822 0.603
#> 5 5 0.591 0.527 0.720 0.0770 0.889 0.638
#> 6 6 0.635 0.577 0.721 0.0427 0.925 0.692
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
#> GSM254629 1 0.1184 0.974 0.984 0.016
#> GSM254648 1 0.3733 0.927 0.928 0.072
#> GSM254694 1 0.3584 0.931 0.932 0.068
#> GSM254701 1 0.1184 0.974 0.984 0.016
#> GSM254728 1 0.0000 0.984 1.000 0.000
#> GSM254726 1 0.4815 0.892 0.896 0.104
#> GSM254639 1 0.0000 0.984 1.000 0.000
#> GSM254652 1 0.0000 0.984 1.000 0.000
#> GSM254700 1 0.0000 0.984 1.000 0.000
#> GSM254625 1 0.1414 0.971 0.980 0.020
#> GSM254636 1 0.0000 0.984 1.000 0.000
#> GSM254659 1 0.0000 0.984 1.000 0.000
#> GSM254680 1 0.0000 0.984 1.000 0.000
#> GSM254686 1 0.0000 0.984 1.000 0.000
#> GSM254718 1 0.0938 0.977 0.988 0.012
#> GSM254674 1 0.0000 0.984 1.000 0.000
#> GSM254668 1 0.0000 0.984 1.000 0.000
#> GSM254697 1 0.0000 0.984 1.000 0.000
#> GSM254704 1 0.0000 0.984 1.000 0.000
#> GSM254707 1 0.0000 0.984 1.000 0.000
#> GSM254714 1 0.0000 0.984 1.000 0.000
#> GSM254722 1 0.0000 0.984 1.000 0.000
#> GSM254627 1 0.0000 0.984 1.000 0.000
#> GSM254630 1 0.0938 0.977 0.988 0.012
#> GSM254633 1 0.0000 0.984 1.000 0.000
#> GSM254670 1 0.0000 0.984 1.000 0.000
#> GSM254716 1 0.1414 0.971 0.980 0.020
#> GSM254720 1 0.0000 0.984 1.000 0.000
#> GSM254729 1 0.3584 0.931 0.932 0.068
#> GSM254654 1 0.3584 0.931 0.932 0.068
#> GSM254656 1 0.3584 0.931 0.932 0.068
#> GSM254631 1 0.0000 0.984 1.000 0.000
#> GSM254657 1 0.0000 0.984 1.000 0.000
#> GSM254664 1 0.0000 0.984 1.000 0.000
#> GSM254672 1 0.0000 0.984 1.000 0.000
#> GSM254692 1 0.0000 0.984 1.000 0.000
#> GSM254645 1 0.1414 0.971 0.980 0.020
#> GSM254666 1 0.0000 0.984 1.000 0.000
#> GSM254675 1 0.0000 0.984 1.000 0.000
#> GSM254678 1 0.0000 0.984 1.000 0.000
#> GSM254688 1 0.0000 0.984 1.000 0.000
#> GSM254690 1 0.0000 0.984 1.000 0.000
#> GSM254696 1 0.0000 0.984 1.000 0.000
#> GSM254705 1 0.0000 0.984 1.000 0.000
#> GSM254642 1 0.0000 0.984 1.000 0.000
#> GSM254661 1 0.0000 0.984 1.000 0.000
#> GSM254698 1 0.0000 0.984 1.000 0.000
#> GSM254641 1 0.0000 0.984 1.000 0.000
#> GSM254647 1 0.0000 0.984 1.000 0.000
#> GSM254663 1 0.0000 0.984 1.000 0.000
#> GSM254682 1 0.0000 0.984 1.000 0.000
#> GSM254709 1 0.0000 0.984 1.000 0.000
#> GSM254721 1 0.0000 0.984 1.000 0.000
#> GSM254724 1 0.0000 0.984 1.000 0.000
#> GSM254650 1 0.0000 0.984 1.000 0.000
#> GSM254687 1 0.0000 0.984 1.000 0.000
#> GSM254637 1 0.0000 0.984 1.000 0.000
#> GSM254684 1 0.0000 0.984 1.000 0.000
#> GSM254649 2 0.0000 0.993 0.000 1.000
#> GSM254660 2 0.0000 0.993 0.000 1.000
#> GSM254693 2 0.0000 0.993 0.000 1.000
#> GSM254695 2 0.1843 0.967 0.028 0.972
#> GSM254702 2 0.0000 0.993 0.000 1.000
#> GSM254643 2 0.0000 0.993 0.000 1.000
#> GSM254727 2 0.0000 0.993 0.000 1.000
#> GSM254640 2 0.0000 0.993 0.000 1.000
#> GSM254626 2 0.0000 0.993 0.000 1.000
#> GSM254635 2 0.0000 0.993 0.000 1.000
#> GSM254653 2 0.0000 0.993 0.000 1.000
#> GSM254658 2 0.0000 0.993 0.000 1.000
#> GSM254681 2 0.0000 0.993 0.000 1.000
#> GSM254719 2 0.0000 0.993 0.000 1.000
#> GSM254673 2 0.0000 0.993 0.000 1.000
#> GSM254655 2 0.0000 0.993 0.000 1.000
#> GSM254669 2 0.0000 0.993 0.000 1.000
#> GSM254699 2 0.0000 0.993 0.000 1.000
#> GSM254703 2 0.0000 0.993 0.000 1.000
#> GSM254708 2 0.0376 0.990 0.004 0.996
#> GSM254715 2 0.0000 0.993 0.000 1.000
#> GSM254628 2 0.0000 0.993 0.000 1.000
#> GSM254634 2 0.0000 0.993 0.000 1.000
#> GSM254646 2 0.0000 0.993 0.000 1.000
#> GSM254671 2 0.0000 0.993 0.000 1.000
#> GSM254711 2 0.0000 0.993 0.000 1.000
#> GSM254717 2 0.0000 0.993 0.000 1.000
#> GSM254723 1 0.9522 0.425 0.628 0.372
#> GSM254730 2 0.0000 0.993 0.000 1.000
#> GSM254731 2 0.0000 0.993 0.000 1.000
#> GSM254632 2 0.5842 0.837 0.140 0.860
#> GSM254662 2 0.0000 0.993 0.000 1.000
#> GSM254677 2 0.0000 0.993 0.000 1.000
#> GSM254665 2 0.0000 0.993 0.000 1.000
#> GSM254691 2 0.0000 0.993 0.000 1.000
#> GSM254644 2 0.0000 0.993 0.000 1.000
#> GSM254667 2 0.0376 0.990 0.004 0.996
#> GSM254676 2 0.0000 0.993 0.000 1.000
#> GSM254679 2 0.0000 0.993 0.000 1.000
#> GSM254689 2 0.0000 0.993 0.000 1.000
#> GSM254706 2 0.0000 0.993 0.000 1.000
#> GSM254712 2 0.0000 0.993 0.000 1.000
#> GSM254713 2 0.0000 0.993 0.000 1.000
#> GSM254683 2 0.0376 0.990 0.004 0.996
#> GSM254710 2 0.5737 0.842 0.136 0.864
#> GSM254725 2 0.0000 0.993 0.000 1.000
#> GSM254651 2 0.0000 0.993 0.000 1.000
#> GSM254638 2 0.0000 0.993 0.000 1.000
#> GSM254685 2 0.0000 0.993 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM254629 3 0.2878 0.63334 0.096 0.000 0.904
#> GSM254648 3 0.4045 0.58280 0.104 0.024 0.872
#> GSM254694 3 0.3832 0.58669 0.100 0.020 0.880
#> GSM254701 3 0.2878 0.63334 0.096 0.000 0.904
#> GSM254728 3 0.1163 0.63821 0.028 0.000 0.972
#> GSM254726 3 0.4945 0.54875 0.104 0.056 0.840
#> GSM254639 3 0.2066 0.63540 0.060 0.000 0.940
#> GSM254652 3 0.1411 0.63717 0.036 0.000 0.964
#> GSM254700 1 0.6267 0.97376 0.548 0.000 0.452
#> GSM254625 3 0.1753 0.63522 0.048 0.000 0.952
#> GSM254636 3 0.5733 -0.03977 0.324 0.000 0.676
#> GSM254659 3 0.3116 0.63214 0.108 0.000 0.892
#> GSM254680 3 0.6062 -0.33209 0.384 0.000 0.616
#> GSM254686 3 0.3116 0.63445 0.108 0.000 0.892
#> GSM254718 3 0.2165 0.62786 0.064 0.000 0.936
#> GSM254674 3 0.4504 0.47063 0.196 0.000 0.804
#> GSM254668 3 0.5733 0.00354 0.324 0.000 0.676
#> GSM254697 1 0.6260 0.98002 0.552 0.000 0.448
#> GSM254704 1 0.6252 0.97512 0.556 0.000 0.444
#> GSM254707 3 0.2448 0.63032 0.076 0.000 0.924
#> GSM254714 3 0.6299 -0.75120 0.476 0.000 0.524
#> GSM254722 3 0.6045 -0.31894 0.380 0.000 0.620
#> GSM254627 1 0.6260 0.98002 0.552 0.000 0.448
#> GSM254630 3 0.2537 0.64131 0.080 0.000 0.920
#> GSM254633 3 0.5859 -0.19116 0.344 0.000 0.656
#> GSM254670 3 0.2878 0.61323 0.096 0.000 0.904
#> GSM254716 3 0.1964 0.63102 0.056 0.000 0.944
#> GSM254720 3 0.5650 0.12043 0.312 0.000 0.688
#> GSM254729 3 0.4063 0.58845 0.112 0.020 0.868
#> GSM254654 3 0.3832 0.58794 0.100 0.020 0.880
#> GSM254656 3 0.4209 0.58931 0.128 0.016 0.856
#> GSM254631 3 0.6126 -0.40386 0.400 0.000 0.600
#> GSM254657 3 0.1860 0.63789 0.052 0.000 0.948
#> GSM254664 3 0.6140 -0.42441 0.404 0.000 0.596
#> GSM254672 1 0.6305 0.89166 0.516 0.000 0.484
#> GSM254692 3 0.5760 0.04636 0.328 0.000 0.672
#> GSM254645 3 0.2959 0.62783 0.100 0.000 0.900
#> GSM254666 3 0.1964 0.63987 0.056 0.000 0.944
#> GSM254675 3 0.5363 0.26169 0.276 0.000 0.724
#> GSM254678 3 0.5706 0.02913 0.320 0.000 0.680
#> GSM254688 3 0.2625 0.62125 0.084 0.000 0.916
#> GSM254690 3 0.6140 -0.45614 0.404 0.000 0.596
#> GSM254696 3 0.4654 0.43066 0.208 0.000 0.792
#> GSM254705 3 0.2711 0.63157 0.088 0.000 0.912
#> GSM254642 1 0.6260 0.98002 0.552 0.000 0.448
#> GSM254661 3 0.1289 0.63778 0.032 0.000 0.968
#> GSM254698 3 0.6045 -0.31894 0.380 0.000 0.620
#> GSM254641 3 0.3879 0.55518 0.152 0.000 0.848
#> GSM254647 3 0.6154 -0.46840 0.408 0.000 0.592
#> GSM254663 3 0.3941 0.54886 0.156 0.000 0.844
#> GSM254682 3 0.2261 0.63144 0.068 0.000 0.932
#> GSM254709 3 0.4452 0.51463 0.192 0.000 0.808
#> GSM254721 1 0.6252 0.97512 0.556 0.000 0.444
#> GSM254724 1 0.6260 0.98002 0.552 0.000 0.448
#> GSM254650 3 0.3816 0.57061 0.148 0.000 0.852
#> GSM254687 3 0.2711 0.62210 0.088 0.000 0.912
#> GSM254637 3 0.6111 -0.38646 0.396 0.000 0.604
#> GSM254684 3 0.4002 0.55072 0.160 0.000 0.840
#> GSM254649 2 0.0000 0.89597 0.000 1.000 0.000
#> GSM254660 2 0.1643 0.89615 0.044 0.956 0.000
#> GSM254693 2 0.0000 0.89597 0.000 1.000 0.000
#> GSM254695 2 0.6587 0.78201 0.352 0.632 0.016
#> GSM254702 2 0.1643 0.89615 0.044 0.956 0.000
#> GSM254643 2 0.2959 0.88460 0.100 0.900 0.000
#> GSM254727 2 0.0892 0.89708 0.020 0.980 0.000
#> GSM254640 2 0.5560 0.82303 0.300 0.700 0.000
#> GSM254626 2 0.0000 0.89597 0.000 1.000 0.000
#> GSM254635 2 0.5621 0.81934 0.308 0.692 0.000
#> GSM254653 2 0.0892 0.89708 0.020 0.980 0.000
#> GSM254658 2 0.1163 0.89006 0.028 0.972 0.000
#> GSM254681 2 0.1163 0.89006 0.028 0.972 0.000
#> GSM254719 2 0.0892 0.89708 0.020 0.980 0.000
#> GSM254673 2 0.0000 0.89597 0.000 1.000 0.000
#> GSM254655 2 0.1643 0.89615 0.044 0.956 0.000
#> GSM254669 2 0.0000 0.89597 0.000 1.000 0.000
#> GSM254699 2 0.1643 0.89615 0.044 0.956 0.000
#> GSM254703 2 0.5560 0.82303 0.300 0.700 0.000
#> GSM254708 2 0.1411 0.89260 0.036 0.964 0.000
#> GSM254715 2 0.5621 0.81934 0.308 0.692 0.000
#> GSM254628 2 0.0000 0.89597 0.000 1.000 0.000
#> GSM254634 2 0.5621 0.81934 0.308 0.692 0.000
#> GSM254646 2 0.1163 0.89006 0.028 0.972 0.000
#> GSM254671 2 0.5497 0.82624 0.292 0.708 0.000
#> GSM254711 2 0.5621 0.81934 0.308 0.692 0.000
#> GSM254717 2 0.0892 0.89708 0.020 0.980 0.000
#> GSM254723 3 0.8286 0.15716 0.104 0.308 0.588
#> GSM254730 2 0.1643 0.89615 0.044 0.956 0.000
#> GSM254731 2 0.1643 0.89615 0.044 0.956 0.000
#> GSM254632 2 0.6184 0.73976 0.112 0.780 0.108
#> GSM254662 2 0.0000 0.89597 0.000 1.000 0.000
#> GSM254677 2 0.5733 0.81149 0.324 0.676 0.000
#> GSM254665 2 0.0424 0.89469 0.008 0.992 0.000
#> GSM254691 2 0.1031 0.89121 0.024 0.976 0.000
#> GSM254644 2 0.5621 0.81934 0.308 0.692 0.000
#> GSM254667 2 0.1643 0.89194 0.044 0.956 0.000
#> GSM254676 2 0.1031 0.89121 0.024 0.976 0.000
#> GSM254679 2 0.5497 0.82624 0.292 0.708 0.000
#> GSM254689 2 0.1163 0.89006 0.028 0.972 0.000
#> GSM254706 2 0.1163 0.89006 0.028 0.972 0.000
#> GSM254712 2 0.5621 0.81934 0.308 0.692 0.000
#> GSM254713 2 0.5621 0.81934 0.308 0.692 0.000
#> GSM254683 2 0.1289 0.88856 0.032 0.968 0.000
#> GSM254710 2 0.6111 0.74403 0.112 0.784 0.104
#> GSM254725 2 0.5621 0.81934 0.308 0.692 0.000
#> GSM254651 2 0.1163 0.89006 0.028 0.972 0.000
#> GSM254638 2 0.5621 0.81934 0.308 0.692 0.000
#> GSM254685 2 0.4555 0.85756 0.200 0.800 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM254629 3 0.6403 0.5935 0.128 0.000 0.640 0.232
#> GSM254648 3 0.6169 0.5922 0.068 0.004 0.632 0.296
#> GSM254694 3 0.6016 0.5926 0.068 0.000 0.632 0.300
#> GSM254701 3 0.6403 0.5935 0.128 0.000 0.640 0.232
#> GSM254728 3 0.4010 0.6650 0.064 0.000 0.836 0.100
#> GSM254726 3 0.6715 0.5803 0.064 0.028 0.620 0.288
#> GSM254639 3 0.5874 0.5375 0.192 0.000 0.696 0.112
#> GSM254652 3 0.3612 0.6713 0.044 0.000 0.856 0.100
#> GSM254700 1 0.2329 0.6184 0.916 0.000 0.072 0.012
#> GSM254625 3 0.2142 0.6615 0.016 0.000 0.928 0.056
#> GSM254636 1 0.5938 0.2425 0.488 0.000 0.476 0.036
#> GSM254659 3 0.4985 0.6429 0.152 0.000 0.768 0.080
#> GSM254680 1 0.5500 0.3602 0.520 0.000 0.464 0.016
#> GSM254686 3 0.3634 0.6472 0.096 0.000 0.856 0.048
#> GSM254718 3 0.5288 0.6404 0.068 0.000 0.732 0.200
#> GSM254674 3 0.5793 0.2435 0.324 0.000 0.628 0.048
#> GSM254668 3 0.5284 0.0553 0.368 0.000 0.616 0.016
#> GSM254697 1 0.2081 0.6214 0.916 0.000 0.084 0.000
#> GSM254704 1 0.1411 0.5896 0.960 0.000 0.020 0.020
#> GSM254707 3 0.2412 0.6290 0.084 0.000 0.908 0.008
#> GSM254714 1 0.4764 0.5814 0.748 0.000 0.220 0.032
#> GSM254722 1 0.5511 0.4569 0.636 0.000 0.332 0.032
#> GSM254627 1 0.2081 0.6214 0.916 0.000 0.084 0.000
#> GSM254630 3 0.3088 0.6711 0.060 0.000 0.888 0.052
#> GSM254633 3 0.5512 -0.3413 0.488 0.000 0.496 0.016
#> GSM254670 3 0.5631 0.4839 0.224 0.000 0.700 0.076
#> GSM254716 3 0.1824 0.6633 0.004 0.000 0.936 0.060
#> GSM254720 1 0.6627 0.1919 0.504 0.000 0.412 0.084
#> GSM254729 3 0.5590 0.6276 0.064 0.000 0.692 0.244
#> GSM254654 3 0.6079 0.5878 0.072 0.000 0.628 0.300
#> GSM254656 3 0.6157 0.5889 0.108 0.000 0.660 0.232
#> GSM254631 1 0.5466 0.4137 0.548 0.000 0.436 0.016
#> GSM254657 3 0.4957 0.6268 0.112 0.000 0.776 0.112
#> GSM254664 1 0.5452 0.4271 0.556 0.000 0.428 0.016
#> GSM254672 1 0.3377 0.6052 0.848 0.000 0.140 0.012
#> GSM254692 1 0.5155 0.2783 0.528 0.000 0.468 0.004
#> GSM254645 3 0.5569 0.6355 0.104 0.000 0.724 0.172
#> GSM254666 3 0.1706 0.6578 0.036 0.000 0.948 0.016
#> GSM254675 1 0.4933 0.3016 0.568 0.000 0.432 0.000
#> GSM254678 1 0.5937 0.2339 0.492 0.000 0.472 0.036
#> GSM254688 3 0.2741 0.6172 0.096 0.000 0.892 0.012
#> GSM254690 1 0.5060 0.4699 0.584 0.000 0.412 0.004
#> GSM254696 3 0.6079 0.1067 0.380 0.000 0.568 0.052
#> GSM254705 3 0.3404 0.6285 0.104 0.000 0.864 0.032
#> GSM254642 1 0.2081 0.6214 0.916 0.000 0.084 0.000
#> GSM254661 3 0.3367 0.6737 0.028 0.000 0.864 0.108
#> GSM254698 1 0.5530 0.4496 0.632 0.000 0.336 0.032
#> GSM254641 3 0.3710 0.5323 0.192 0.000 0.804 0.004
#> GSM254647 1 0.5050 0.4726 0.588 0.000 0.408 0.004
#> GSM254663 3 0.3751 0.5262 0.196 0.000 0.800 0.004
#> GSM254682 3 0.2402 0.6286 0.076 0.000 0.912 0.012
#> GSM254709 3 0.4964 0.1062 0.380 0.000 0.616 0.004
#> GSM254721 1 0.2174 0.6080 0.928 0.000 0.052 0.020
#> GSM254724 1 0.2179 0.6160 0.924 0.000 0.064 0.012
#> GSM254650 3 0.3764 0.5520 0.172 0.000 0.816 0.012
#> GSM254687 3 0.3108 0.6160 0.112 0.000 0.872 0.016
#> GSM254637 1 0.5472 0.4071 0.544 0.000 0.440 0.016
#> GSM254684 3 0.5966 0.3455 0.316 0.000 0.624 0.060
#> GSM254649 2 0.0188 0.7419 0.000 0.996 0.000 0.004
#> GSM254660 2 0.2704 0.6128 0.000 0.876 0.000 0.124
#> GSM254693 2 0.0188 0.7419 0.000 0.996 0.000 0.004
#> GSM254695 4 0.4830 0.7633 0.000 0.392 0.000 0.608
#> GSM254702 2 0.2011 0.6812 0.000 0.920 0.000 0.080
#> GSM254643 2 0.3726 0.3445 0.000 0.788 0.000 0.212
#> GSM254727 2 0.1022 0.7329 0.000 0.968 0.000 0.032
#> GSM254640 2 0.4972 -0.8143 0.000 0.544 0.000 0.456
#> GSM254626 2 0.0000 0.7415 0.000 1.000 0.000 0.000
#> GSM254635 4 0.4992 0.9250 0.000 0.476 0.000 0.524
#> GSM254653 2 0.1022 0.7280 0.000 0.968 0.000 0.032
#> GSM254658 2 0.1474 0.7288 0.000 0.948 0.000 0.052
#> GSM254681 2 0.1474 0.7288 0.000 0.948 0.000 0.052
#> GSM254719 2 0.1022 0.7280 0.000 0.968 0.000 0.032
#> GSM254673 2 0.0188 0.7408 0.000 0.996 0.000 0.004
#> GSM254655 2 0.2011 0.6812 0.000 0.920 0.000 0.080
#> GSM254669 2 0.0188 0.7408 0.000 0.996 0.000 0.004
#> GSM254699 2 0.2011 0.6812 0.000 0.920 0.000 0.080
#> GSM254703 2 0.4994 -0.8683 0.000 0.520 0.000 0.480
#> GSM254708 2 0.2408 0.7123 0.000 0.896 0.000 0.104
#> GSM254715 4 0.4996 0.9225 0.000 0.484 0.000 0.516
#> GSM254628 2 0.0188 0.7419 0.000 0.996 0.000 0.004
#> GSM254634 4 0.5000 0.9053 0.000 0.496 0.000 0.504
#> GSM254646 2 0.1474 0.7288 0.000 0.948 0.000 0.052
#> GSM254671 2 0.4981 -0.8330 0.000 0.536 0.000 0.464
#> GSM254711 4 0.5000 0.9032 0.000 0.496 0.000 0.504
#> GSM254717 2 0.1389 0.7271 0.000 0.952 0.000 0.048
#> GSM254723 3 0.8642 0.2370 0.040 0.232 0.408 0.320
#> GSM254730 2 0.2647 0.6201 0.000 0.880 0.000 0.120
#> GSM254731 2 0.2011 0.6812 0.000 0.920 0.000 0.080
#> GSM254632 2 0.5387 0.3830 0.000 0.696 0.048 0.256
#> GSM254662 2 0.0188 0.7408 0.000 0.996 0.000 0.004
#> GSM254677 4 0.5060 0.8345 0.004 0.412 0.000 0.584
#> GSM254665 2 0.2011 0.7008 0.000 0.920 0.000 0.080
#> GSM254691 2 0.1716 0.7289 0.000 0.936 0.000 0.064
#> GSM254644 2 0.4996 -0.8836 0.000 0.516 0.000 0.484
#> GSM254667 2 0.2469 0.7055 0.000 0.892 0.000 0.108
#> GSM254676 2 0.1716 0.7289 0.000 0.936 0.000 0.064
#> GSM254679 2 0.4985 -0.8374 0.000 0.532 0.000 0.468
#> GSM254689 2 0.1474 0.7288 0.000 0.948 0.000 0.052
#> GSM254706 2 0.1474 0.7288 0.000 0.948 0.000 0.052
#> GSM254712 4 0.4994 0.9249 0.000 0.480 0.000 0.520
#> GSM254713 4 0.4996 0.9225 0.000 0.484 0.000 0.516
#> GSM254683 2 0.1940 0.7191 0.000 0.924 0.000 0.076
#> GSM254710 2 0.5309 0.3889 0.000 0.700 0.044 0.256
#> GSM254725 4 0.4999 0.9088 0.000 0.492 0.000 0.508
#> GSM254651 2 0.1474 0.7288 0.000 0.948 0.000 0.052
#> GSM254638 4 0.4992 0.9250 0.000 0.476 0.000 0.524
#> GSM254685 2 0.4477 -0.1884 0.000 0.688 0.000 0.312
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM254629 3 0.5774 0.44883 0.044 0.000 0.532 0.024 0.400
#> GSM254648 3 0.5733 0.47823 0.004 0.004 0.532 0.064 0.396
#> GSM254694 3 0.5280 0.50983 0.004 0.000 0.584 0.048 0.364
#> GSM254701 3 0.5774 0.44883 0.044 0.000 0.532 0.024 0.400
#> GSM254728 5 0.4242 -0.31457 0.000 0.000 0.428 0.000 0.572
#> GSM254726 3 0.6233 0.50506 0.000 0.032 0.532 0.072 0.364
#> GSM254639 3 0.4687 0.36101 0.028 0.000 0.636 0.000 0.336
#> GSM254652 5 0.3913 -0.01327 0.000 0.000 0.324 0.000 0.676
#> GSM254700 1 0.1901 0.60515 0.928 0.000 0.004 0.012 0.056
#> GSM254625 5 0.2208 0.45336 0.000 0.000 0.072 0.020 0.908
#> GSM254636 5 0.6888 -0.21446 0.348 0.000 0.264 0.004 0.384
#> GSM254659 5 0.5619 -0.05588 0.080 0.000 0.332 0.004 0.584
#> GSM254680 5 0.5171 -0.17622 0.456 0.000 0.040 0.000 0.504
#> GSM254686 5 0.4368 0.41939 0.080 0.000 0.144 0.004 0.772
#> GSM254718 3 0.4637 0.44337 0.000 0.000 0.536 0.012 0.452
#> GSM254674 5 0.6393 0.16050 0.228 0.000 0.228 0.004 0.540
#> GSM254668 5 0.4484 0.29191 0.308 0.000 0.024 0.000 0.668
#> GSM254697 1 0.3277 0.59885 0.856 0.000 0.072 0.004 0.068
#> GSM254704 1 0.1106 0.59311 0.964 0.000 0.024 0.012 0.000
#> GSM254707 5 0.1082 0.53211 0.028 0.000 0.008 0.000 0.964
#> GSM254714 1 0.5029 0.50878 0.728 0.000 0.104 0.012 0.156
#> GSM254722 1 0.6707 0.37923 0.480 0.000 0.304 0.008 0.208
#> GSM254627 1 0.3277 0.59885 0.856 0.000 0.072 0.004 0.068
#> GSM254630 5 0.3852 0.38477 0.028 0.000 0.168 0.008 0.796
#> GSM254633 5 0.5425 -0.12705 0.420 0.000 0.060 0.000 0.520
#> GSM254670 3 0.5501 0.27160 0.064 0.000 0.572 0.004 0.360
#> GSM254716 5 0.2722 0.41424 0.000 0.000 0.108 0.020 0.872
#> GSM254720 1 0.7079 0.04434 0.452 0.000 0.248 0.020 0.280
#> GSM254729 3 0.5501 0.45993 0.000 0.000 0.492 0.064 0.444
#> GSM254654 3 0.5473 0.47675 0.008 0.000 0.548 0.048 0.396
#> GSM254656 3 0.5491 0.42550 0.000 0.000 0.600 0.088 0.312
#> GSM254631 1 0.5177 0.17870 0.488 0.000 0.040 0.000 0.472
#> GSM254657 3 0.4249 0.37371 0.000 0.000 0.568 0.000 0.432
#> GSM254664 1 0.5112 0.19092 0.496 0.000 0.036 0.000 0.468
#> GSM254672 1 0.4124 0.57414 0.796 0.000 0.140 0.012 0.052
#> GSM254692 1 0.5551 0.19729 0.488 0.000 0.068 0.000 0.444
#> GSM254645 3 0.5445 0.41032 0.016 0.000 0.564 0.036 0.384
#> GSM254666 5 0.2448 0.47340 0.020 0.000 0.088 0.000 0.892
#> GSM254675 1 0.5938 0.29019 0.512 0.000 0.112 0.000 0.376
#> GSM254678 1 0.6920 0.18685 0.368 0.000 0.280 0.004 0.348
#> GSM254688 5 0.1082 0.53412 0.028 0.000 0.008 0.000 0.964
#> GSM254690 1 0.5473 0.30308 0.520 0.000 0.064 0.000 0.416
#> GSM254696 5 0.6830 -0.05517 0.240 0.000 0.360 0.004 0.396
#> GSM254705 5 0.2654 0.52807 0.064 0.000 0.048 0.000 0.888
#> GSM254642 1 0.3277 0.59885 0.856 0.000 0.072 0.004 0.068
#> GSM254661 5 0.4127 0.00371 0.000 0.000 0.312 0.008 0.680
#> GSM254698 1 0.6729 0.37156 0.472 0.000 0.312 0.008 0.208
#> GSM254641 5 0.3051 0.52871 0.120 0.000 0.028 0.000 0.852
#> GSM254647 1 0.5414 0.31024 0.528 0.000 0.060 0.000 0.412
#> GSM254663 5 0.3099 0.52696 0.124 0.000 0.028 0.000 0.848
#> GSM254682 5 0.0579 0.52314 0.008 0.000 0.008 0.000 0.984
#> GSM254709 5 0.5584 0.14006 0.312 0.000 0.096 0.000 0.592
#> GSM254721 1 0.1393 0.59930 0.956 0.000 0.008 0.012 0.024
#> GSM254724 1 0.1605 0.60313 0.944 0.000 0.004 0.012 0.040
#> GSM254650 5 0.2377 0.53233 0.128 0.000 0.000 0.000 0.872
#> GSM254687 5 0.2236 0.53777 0.068 0.000 0.024 0.000 0.908
#> GSM254637 1 0.5177 0.17401 0.488 0.000 0.040 0.000 0.472
#> GSM254684 3 0.6113 0.14453 0.116 0.000 0.508 0.004 0.372
#> GSM254649 2 0.0290 0.81373 0.000 0.992 0.000 0.008 0.000
#> GSM254660 2 0.3177 0.57848 0.000 0.792 0.000 0.208 0.000
#> GSM254693 2 0.0290 0.81373 0.000 0.992 0.000 0.008 0.000
#> GSM254695 4 0.3882 0.73275 0.000 0.224 0.020 0.756 0.000
#> GSM254702 2 0.2329 0.72604 0.000 0.876 0.000 0.124 0.000
#> GSM254643 2 0.3508 0.40918 0.000 0.748 0.000 0.252 0.000
#> GSM254727 2 0.1270 0.80074 0.000 0.948 0.000 0.052 0.000
#> GSM254640 4 0.4278 0.79068 0.000 0.452 0.000 0.548 0.000
#> GSM254626 2 0.0404 0.81319 0.000 0.988 0.000 0.012 0.000
#> GSM254635 4 0.3949 0.89864 0.000 0.332 0.000 0.668 0.000
#> GSM254653 2 0.1478 0.78885 0.000 0.936 0.000 0.064 0.000
#> GSM254658 2 0.1671 0.79397 0.000 0.924 0.000 0.076 0.000
#> GSM254681 2 0.1671 0.79397 0.000 0.924 0.000 0.076 0.000
#> GSM254719 2 0.1478 0.78885 0.000 0.936 0.000 0.064 0.000
#> GSM254673 2 0.0510 0.81233 0.000 0.984 0.000 0.016 0.000
#> GSM254655 2 0.2329 0.72604 0.000 0.876 0.000 0.124 0.000
#> GSM254669 2 0.0510 0.81233 0.000 0.984 0.000 0.016 0.000
#> GSM254699 2 0.2329 0.72604 0.000 0.876 0.000 0.124 0.000
#> GSM254703 4 0.4182 0.87762 0.000 0.400 0.000 0.600 0.000
#> GSM254708 2 0.2890 0.75790 0.000 0.836 0.000 0.160 0.004
#> GSM254715 4 0.4060 0.89661 0.000 0.360 0.000 0.640 0.000
#> GSM254628 2 0.0510 0.81367 0.000 0.984 0.000 0.016 0.000
#> GSM254634 4 0.4045 0.90123 0.000 0.356 0.000 0.644 0.000
#> GSM254646 2 0.1671 0.79397 0.000 0.924 0.000 0.076 0.000
#> GSM254671 4 0.4227 0.85172 0.000 0.420 0.000 0.580 0.000
#> GSM254711 4 0.4045 0.90061 0.000 0.356 0.000 0.644 0.000
#> GSM254717 2 0.1608 0.79378 0.000 0.928 0.000 0.072 0.000
#> GSM254723 3 0.8474 0.28505 0.000 0.200 0.316 0.196 0.288
#> GSM254730 2 0.3177 0.59072 0.000 0.792 0.000 0.208 0.000
#> GSM254731 2 0.2329 0.72604 0.000 0.876 0.000 0.124 0.000
#> GSM254632 2 0.5724 0.43758 0.000 0.616 0.056 0.300 0.028
#> GSM254662 2 0.0510 0.81233 0.000 0.984 0.000 0.016 0.000
#> GSM254677 4 0.3521 0.78625 0.000 0.232 0.004 0.764 0.000
#> GSM254665 2 0.2280 0.75194 0.000 0.880 0.000 0.120 0.000
#> GSM254691 2 0.2329 0.78249 0.000 0.876 0.000 0.124 0.000
#> GSM254644 4 0.4182 0.87365 0.000 0.400 0.000 0.600 0.000
#> GSM254667 2 0.3010 0.74563 0.000 0.824 0.000 0.172 0.004
#> GSM254676 2 0.2329 0.78249 0.000 0.876 0.000 0.124 0.000
#> GSM254679 4 0.4210 0.85602 0.000 0.412 0.000 0.588 0.000
#> GSM254689 2 0.1671 0.79397 0.000 0.924 0.000 0.076 0.000
#> GSM254706 2 0.1792 0.79409 0.000 0.916 0.000 0.084 0.000
#> GSM254712 4 0.3999 0.90100 0.000 0.344 0.000 0.656 0.000
#> GSM254713 4 0.4060 0.89661 0.000 0.360 0.000 0.640 0.000
#> GSM254683 2 0.2536 0.77434 0.000 0.868 0.000 0.128 0.004
#> GSM254710 2 0.5681 0.43795 0.000 0.616 0.052 0.304 0.028
#> GSM254725 4 0.4030 0.90100 0.000 0.352 0.000 0.648 0.000
#> GSM254651 2 0.1792 0.79409 0.000 0.916 0.000 0.084 0.000
#> GSM254638 4 0.3949 0.89864 0.000 0.332 0.000 0.668 0.000
#> GSM254685 2 0.4030 -0.08159 0.000 0.648 0.000 0.352 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM254629 3 0.3620 0.6219 0.008 0.000 0.836 0.048 0.060 0.048
#> GSM254648 3 0.3065 0.6399 0.008 0.004 0.872 0.028 0.060 0.028
#> GSM254694 3 0.2898 0.6460 0.000 0.000 0.868 0.020 0.072 0.040
#> GSM254701 3 0.3620 0.6219 0.008 0.000 0.836 0.048 0.060 0.048
#> GSM254728 3 0.4934 0.4976 0.000 0.000 0.632 0.000 0.256 0.112
#> GSM254726 3 0.4265 0.6375 0.000 0.032 0.800 0.036 0.080 0.052
#> GSM254639 6 0.5696 0.0934 0.000 0.000 0.372 0.000 0.164 0.464
#> GSM254652 5 0.4932 0.0383 0.000 0.000 0.372 0.000 0.556 0.072
#> GSM254700 1 0.2748 0.6986 0.848 0.000 0.000 0.000 0.024 0.128
#> GSM254625 5 0.2602 0.5512 0.000 0.000 0.052 0.020 0.888 0.040
#> GSM254636 6 0.6167 0.4639 0.160 0.000 0.040 0.000 0.260 0.540
#> GSM254659 3 0.6049 0.4108 0.064 0.000 0.552 0.004 0.304 0.076
#> GSM254680 5 0.6656 0.2647 0.336 0.000 0.076 0.000 0.452 0.136
#> GSM254686 5 0.4451 0.5301 0.064 0.000 0.136 0.004 0.760 0.036
#> GSM254718 3 0.4012 0.6092 0.000 0.000 0.752 0.000 0.164 0.084
#> GSM254674 5 0.6491 -0.3151 0.128 0.000 0.060 0.000 0.424 0.388
#> GSM254668 5 0.5037 0.4964 0.196 0.000 0.016 0.000 0.672 0.116
#> GSM254697 1 0.5227 0.6320 0.612 0.000 0.044 0.000 0.044 0.300
#> GSM254704 1 0.1082 0.6968 0.956 0.000 0.000 0.000 0.004 0.040
#> GSM254707 5 0.0891 0.5851 0.000 0.000 0.008 0.000 0.968 0.024
#> GSM254714 1 0.4431 0.4922 0.740 0.000 0.176 0.000 0.036 0.048
#> GSM254722 6 0.4012 0.4236 0.164 0.000 0.000 0.000 0.084 0.752
#> GSM254627 1 0.5227 0.6320 0.612 0.000 0.044 0.000 0.044 0.300
#> GSM254630 5 0.4378 0.4743 0.012 0.000 0.196 0.004 0.732 0.056
#> GSM254633 5 0.6678 0.2506 0.304 0.000 0.056 0.000 0.452 0.188
#> GSM254670 6 0.5559 0.3168 0.000 0.000 0.284 0.000 0.176 0.540
#> GSM254716 5 0.3243 0.5277 0.000 0.000 0.088 0.020 0.844 0.048
#> GSM254720 3 0.6591 0.0878 0.412 0.000 0.432 0.044 0.036 0.076
#> GSM254729 3 0.4733 0.6029 0.000 0.000 0.728 0.032 0.104 0.136
#> GSM254654 3 0.2572 0.6393 0.008 0.000 0.892 0.020 0.064 0.016
#> GSM254656 3 0.6340 0.0992 0.000 0.000 0.448 0.072 0.092 0.388
#> GSM254631 5 0.6840 0.2193 0.352 0.000 0.072 0.004 0.424 0.148
#> GSM254657 3 0.5965 0.0681 0.000 0.000 0.448 0.004 0.196 0.352
#> GSM254664 5 0.6824 0.2083 0.360 0.000 0.072 0.004 0.420 0.144
#> GSM254672 1 0.4041 0.5338 0.684 0.000 0.008 0.000 0.016 0.292
#> GSM254692 5 0.6071 -0.0874 0.416 0.000 0.044 0.000 0.444 0.096
#> GSM254645 3 0.6173 0.1917 0.008 0.000 0.484 0.024 0.124 0.360
#> GSM254666 5 0.2476 0.5558 0.008 0.000 0.072 0.000 0.888 0.032
#> GSM254675 1 0.6197 0.1519 0.464 0.000 0.028 0.000 0.356 0.152
#> GSM254678 6 0.6056 0.5160 0.184 0.000 0.036 0.000 0.216 0.564
#> GSM254688 5 0.0790 0.5838 0.000 0.000 0.000 0.000 0.968 0.032
#> GSM254690 5 0.6266 0.1094 0.340 0.000 0.008 0.000 0.392 0.260
#> GSM254696 6 0.6350 0.5944 0.108 0.000 0.100 0.000 0.236 0.556
#> GSM254705 5 0.2786 0.5887 0.036 0.000 0.036 0.004 0.884 0.040
#> GSM254642 1 0.5227 0.6320 0.612 0.000 0.044 0.000 0.044 0.300
#> GSM254661 5 0.4970 0.1873 0.000 0.000 0.320 0.008 0.604 0.068
#> GSM254698 6 0.3871 0.4446 0.148 0.000 0.000 0.000 0.084 0.768
#> GSM254641 5 0.2619 0.5891 0.048 0.000 0.012 0.000 0.884 0.056
#> GSM254647 5 0.6227 0.1172 0.356 0.000 0.008 0.000 0.396 0.240
#> GSM254663 5 0.2683 0.5883 0.052 0.000 0.012 0.000 0.880 0.056
#> GSM254682 5 0.0865 0.5776 0.000 0.000 0.000 0.000 0.964 0.036
#> GSM254709 5 0.5804 0.2874 0.264 0.000 0.064 0.000 0.592 0.080
#> GSM254721 1 0.0820 0.7019 0.972 0.000 0.000 0.000 0.016 0.012
#> GSM254724 1 0.1391 0.7027 0.944 0.000 0.000 0.000 0.016 0.040
#> GSM254650 5 0.2361 0.5915 0.088 0.000 0.000 0.000 0.884 0.028
#> GSM254687 5 0.2332 0.5924 0.032 0.000 0.020 0.004 0.908 0.036
#> GSM254637 5 0.6821 0.2206 0.356 0.000 0.072 0.004 0.424 0.144
#> GSM254684 6 0.5759 0.4754 0.012 0.000 0.232 0.000 0.192 0.564
#> GSM254649 2 0.0260 0.8121 0.000 0.992 0.000 0.008 0.000 0.000
#> GSM254660 2 0.2854 0.5802 0.000 0.792 0.000 0.208 0.000 0.000
#> GSM254693 2 0.0260 0.8121 0.000 0.992 0.000 0.008 0.000 0.000
#> GSM254695 4 0.3726 0.7372 0.000 0.216 0.028 0.752 0.000 0.004
#> GSM254702 2 0.2092 0.7257 0.000 0.876 0.000 0.124 0.000 0.000
#> GSM254643 2 0.3151 0.4177 0.000 0.748 0.000 0.252 0.000 0.000
#> GSM254727 2 0.1141 0.7992 0.000 0.948 0.000 0.052 0.000 0.000
#> GSM254640 4 0.3838 0.7851 0.000 0.448 0.000 0.552 0.000 0.000
#> GSM254626 2 0.0363 0.8116 0.000 0.988 0.000 0.012 0.000 0.000
#> GSM254635 4 0.3515 0.8990 0.000 0.324 0.000 0.676 0.000 0.000
#> GSM254653 2 0.1327 0.7873 0.000 0.936 0.000 0.064 0.000 0.000
#> GSM254658 2 0.1610 0.7918 0.000 0.916 0.000 0.084 0.000 0.000
#> GSM254681 2 0.1610 0.7918 0.000 0.916 0.000 0.084 0.000 0.000
#> GSM254719 2 0.1327 0.7873 0.000 0.936 0.000 0.064 0.000 0.000
#> GSM254673 2 0.0458 0.8107 0.000 0.984 0.000 0.016 0.000 0.000
#> GSM254655 2 0.2092 0.7257 0.000 0.876 0.000 0.124 0.000 0.000
#> GSM254669 2 0.0458 0.8107 0.000 0.984 0.000 0.016 0.000 0.000
#> GSM254699 2 0.2092 0.7257 0.000 0.876 0.000 0.124 0.000 0.000
#> GSM254703 4 0.3737 0.8777 0.000 0.392 0.000 0.608 0.000 0.000
#> GSM254708 2 0.2876 0.7607 0.000 0.836 0.004 0.148 0.004 0.008
#> GSM254715 4 0.3620 0.8984 0.000 0.352 0.000 0.648 0.000 0.000
#> GSM254628 2 0.0632 0.8119 0.000 0.976 0.000 0.024 0.000 0.000
#> GSM254634 4 0.3607 0.9020 0.000 0.348 0.000 0.652 0.000 0.000
#> GSM254646 2 0.1610 0.7918 0.000 0.916 0.000 0.084 0.000 0.000
#> GSM254671 4 0.3789 0.8482 0.000 0.416 0.000 0.584 0.000 0.000
#> GSM254711 4 0.3607 0.9014 0.000 0.348 0.000 0.652 0.000 0.000
#> GSM254717 2 0.1501 0.7902 0.000 0.924 0.000 0.076 0.000 0.000
#> GSM254723 3 0.7167 0.3604 0.000 0.196 0.524 0.164 0.052 0.064
#> GSM254730 2 0.2854 0.5922 0.000 0.792 0.000 0.208 0.000 0.000
#> GSM254731 2 0.2092 0.7257 0.000 0.876 0.000 0.124 0.000 0.000
#> GSM254632 2 0.5807 0.4473 0.000 0.612 0.056 0.264 0.024 0.044
#> GSM254662 2 0.0458 0.8107 0.000 0.984 0.000 0.016 0.000 0.000
#> GSM254677 4 0.3487 0.7883 0.000 0.224 0.020 0.756 0.000 0.000
#> GSM254665 2 0.2048 0.7524 0.000 0.880 0.000 0.120 0.000 0.000
#> GSM254691 2 0.2302 0.7823 0.000 0.872 0.000 0.120 0.000 0.008
#> GSM254644 4 0.3737 0.8749 0.000 0.392 0.000 0.608 0.000 0.000
#> GSM254667 2 0.3061 0.7438 0.000 0.816 0.004 0.168 0.004 0.008
#> GSM254676 2 0.2302 0.7823 0.000 0.872 0.000 0.120 0.000 0.008
#> GSM254679 4 0.3774 0.8527 0.000 0.408 0.000 0.592 0.000 0.000
#> GSM254689 2 0.1610 0.7918 0.000 0.916 0.000 0.084 0.000 0.000
#> GSM254706 2 0.1714 0.7919 0.000 0.908 0.000 0.092 0.000 0.000
#> GSM254712 4 0.3563 0.9014 0.000 0.336 0.000 0.664 0.000 0.000
#> GSM254713 4 0.3620 0.8984 0.000 0.352 0.000 0.648 0.000 0.000
#> GSM254683 2 0.2531 0.7716 0.000 0.860 0.000 0.128 0.004 0.008
#> GSM254710 2 0.5771 0.4475 0.000 0.612 0.052 0.268 0.024 0.044
#> GSM254725 4 0.3592 0.9017 0.000 0.344 0.000 0.656 0.000 0.000
#> GSM254651 2 0.1714 0.7919 0.000 0.908 0.000 0.092 0.000 0.000
#> GSM254638 4 0.3515 0.8990 0.000 0.324 0.000 0.676 0.000 0.000
#> GSM254685 2 0.3620 -0.0603 0.000 0.648 0.000 0.352 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) individual(p) disease.state(p) gender(p) other(p) k
#> MAD:hclust 106 5.55e-24 0.6963 0.705 0.487 1.000 2
#> MAD:hclust 88 7.78e-20 0.6351 0.372 0.368 0.239 3
#> MAD:hclust 77 1.35e-16 0.6019 0.483 0.534 0.349 4
#> MAD:hclust 63 6.79e-13 0.0273 0.779 0.898 0.514 5
#> MAD:hclust 74 1.50e-14 0.0370 0.705 0.179 0.686 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 21168 rows and 107 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'kmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.995 0.998 0.4983 0.503 0.503
#> 3 3 0.706 0.878 0.854 0.2834 0.833 0.671
#> 4 4 0.613 0.579 0.785 0.1298 0.920 0.776
#> 5 5 0.652 0.584 0.745 0.0719 0.894 0.652
#> 6 6 0.641 0.489 0.701 0.0498 0.934 0.727
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
#> GSM254629 1 0.000 0.996 1.000 0.000
#> GSM254648 1 0.000 0.996 1.000 0.000
#> GSM254694 1 0.000 0.996 1.000 0.000
#> GSM254701 1 0.000 0.996 1.000 0.000
#> GSM254728 1 0.000 0.996 1.000 0.000
#> GSM254726 1 0.000 0.996 1.000 0.000
#> GSM254639 1 0.000 0.996 1.000 0.000
#> GSM254652 1 0.000 0.996 1.000 0.000
#> GSM254700 1 0.000 0.996 1.000 0.000
#> GSM254625 1 0.000 0.996 1.000 0.000
#> GSM254636 1 0.000 0.996 1.000 0.000
#> GSM254659 1 0.000 0.996 1.000 0.000
#> GSM254680 1 0.000 0.996 1.000 0.000
#> GSM254686 1 0.000 0.996 1.000 0.000
#> GSM254718 1 0.000 0.996 1.000 0.000
#> GSM254674 1 0.000 0.996 1.000 0.000
#> GSM254668 1 0.000 0.996 1.000 0.000
#> GSM254697 1 0.000 0.996 1.000 0.000
#> GSM254704 1 0.000 0.996 1.000 0.000
#> GSM254707 1 0.000 0.996 1.000 0.000
#> GSM254714 1 0.000 0.996 1.000 0.000
#> GSM254722 1 0.000 0.996 1.000 0.000
#> GSM254627 1 0.000 0.996 1.000 0.000
#> GSM254630 1 0.000 0.996 1.000 0.000
#> GSM254633 1 0.000 0.996 1.000 0.000
#> GSM254670 1 0.000 0.996 1.000 0.000
#> GSM254716 1 0.000 0.996 1.000 0.000
#> GSM254720 1 0.000 0.996 1.000 0.000
#> GSM254729 1 0.000 0.996 1.000 0.000
#> GSM254654 1 0.000 0.996 1.000 0.000
#> GSM254656 1 0.000 0.996 1.000 0.000
#> GSM254631 1 0.000 0.996 1.000 0.000
#> GSM254657 1 0.000 0.996 1.000 0.000
#> GSM254664 1 0.000 0.996 1.000 0.000
#> GSM254672 1 0.000 0.996 1.000 0.000
#> GSM254692 1 0.000 0.996 1.000 0.000
#> GSM254645 1 0.000 0.996 1.000 0.000
#> GSM254666 1 0.000 0.996 1.000 0.000
#> GSM254675 1 0.000 0.996 1.000 0.000
#> GSM254678 1 0.000 0.996 1.000 0.000
#> GSM254688 1 0.000 0.996 1.000 0.000
#> GSM254690 1 0.000 0.996 1.000 0.000
#> GSM254696 1 0.000 0.996 1.000 0.000
#> GSM254705 1 0.000 0.996 1.000 0.000
#> GSM254642 1 0.000 0.996 1.000 0.000
#> GSM254661 1 0.000 0.996 1.000 0.000
#> GSM254698 1 0.000 0.996 1.000 0.000
#> GSM254641 1 0.000 0.996 1.000 0.000
#> GSM254647 1 0.000 0.996 1.000 0.000
#> GSM254663 1 0.000 0.996 1.000 0.000
#> GSM254682 1 0.000 0.996 1.000 0.000
#> GSM254709 1 0.000 0.996 1.000 0.000
#> GSM254721 1 0.000 0.996 1.000 0.000
#> GSM254724 1 0.000 0.996 1.000 0.000
#> GSM254650 1 0.000 0.996 1.000 0.000
#> GSM254687 1 0.000 0.996 1.000 0.000
#> GSM254637 1 0.000 0.996 1.000 0.000
#> GSM254684 1 0.000 0.996 1.000 0.000
#> GSM254649 2 0.000 1.000 0.000 1.000
#> GSM254660 2 0.000 1.000 0.000 1.000
#> GSM254693 2 0.000 1.000 0.000 1.000
#> GSM254695 2 0.000 1.000 0.000 1.000
#> GSM254702 2 0.000 1.000 0.000 1.000
#> GSM254643 2 0.000 1.000 0.000 1.000
#> GSM254727 2 0.000 1.000 0.000 1.000
#> GSM254640 2 0.000 1.000 0.000 1.000
#> GSM254626 2 0.000 1.000 0.000 1.000
#> GSM254635 2 0.000 1.000 0.000 1.000
#> GSM254653 2 0.000 1.000 0.000 1.000
#> GSM254658 2 0.000 1.000 0.000 1.000
#> GSM254681 2 0.000 1.000 0.000 1.000
#> GSM254719 2 0.000 1.000 0.000 1.000
#> GSM254673 2 0.000 1.000 0.000 1.000
#> GSM254655 2 0.000 1.000 0.000 1.000
#> GSM254669 2 0.000 1.000 0.000 1.000
#> GSM254699 2 0.000 1.000 0.000 1.000
#> GSM254703 2 0.000 1.000 0.000 1.000
#> GSM254708 2 0.000 1.000 0.000 1.000
#> GSM254715 2 0.000 1.000 0.000 1.000
#> GSM254628 2 0.000 1.000 0.000 1.000
#> GSM254634 2 0.000 1.000 0.000 1.000
#> GSM254646 2 0.000 1.000 0.000 1.000
#> GSM254671 2 0.000 1.000 0.000 1.000
#> GSM254711 2 0.000 1.000 0.000 1.000
#> GSM254717 2 0.000 1.000 0.000 1.000
#> GSM254723 1 0.802 0.677 0.756 0.244
#> GSM254730 2 0.000 1.000 0.000 1.000
#> GSM254731 2 0.000 1.000 0.000 1.000
#> GSM254632 1 0.000 0.996 1.000 0.000
#> GSM254662 2 0.000 1.000 0.000 1.000
#> GSM254677 2 0.000 1.000 0.000 1.000
#> GSM254665 2 0.000 1.000 0.000 1.000
#> GSM254691 2 0.000 1.000 0.000 1.000
#> GSM254644 2 0.000 1.000 0.000 1.000
#> GSM254667 2 0.000 1.000 0.000 1.000
#> GSM254676 2 0.000 1.000 0.000 1.000
#> GSM254679 2 0.000 1.000 0.000 1.000
#> GSM254689 2 0.000 1.000 0.000 1.000
#> GSM254706 2 0.000 1.000 0.000 1.000
#> GSM254712 2 0.000 1.000 0.000 1.000
#> GSM254713 2 0.000 1.000 0.000 1.000
#> GSM254683 2 0.000 1.000 0.000 1.000
#> GSM254710 2 0.000 1.000 0.000 1.000
#> GSM254725 2 0.000 1.000 0.000 1.000
#> GSM254651 2 0.000 1.000 0.000 1.000
#> GSM254638 2 0.000 1.000 0.000 1.000
#> GSM254685 2 0.000 1.000 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM254629 3 0.0592 0.8696 0.012 0.000 0.988
#> GSM254648 3 0.2261 0.8345 0.068 0.000 0.932
#> GSM254694 3 0.1031 0.8739 0.024 0.000 0.976
#> GSM254701 3 0.1411 0.8728 0.036 0.000 0.964
#> GSM254728 3 0.1643 0.8703 0.044 0.000 0.956
#> GSM254726 3 0.2356 0.8312 0.072 0.000 0.928
#> GSM254639 3 0.2066 0.8621 0.060 0.000 0.940
#> GSM254652 3 0.0892 0.8680 0.020 0.000 0.980
#> GSM254700 1 0.5291 0.9314 0.732 0.000 0.268
#> GSM254625 3 0.2796 0.8064 0.092 0.000 0.908
#> GSM254636 1 0.5785 0.9106 0.668 0.000 0.332
#> GSM254659 3 0.1411 0.8728 0.036 0.000 0.964
#> GSM254680 1 0.5397 0.9328 0.720 0.000 0.280
#> GSM254686 3 0.3192 0.7815 0.112 0.000 0.888
#> GSM254718 3 0.1163 0.8742 0.028 0.000 0.972
#> GSM254674 1 0.5529 0.9305 0.704 0.000 0.296
#> GSM254668 1 0.5650 0.9175 0.688 0.000 0.312
#> GSM254697 1 0.5291 0.9314 0.732 0.000 0.268
#> GSM254704 1 0.5760 0.9064 0.672 0.000 0.328
#> GSM254707 1 0.5706 0.9129 0.680 0.000 0.320
#> GSM254714 3 0.1753 0.8683 0.048 0.000 0.952
#> GSM254722 1 0.5291 0.9314 0.732 0.000 0.268
#> GSM254627 1 0.5291 0.9314 0.732 0.000 0.268
#> GSM254630 3 0.5905 0.0537 0.352 0.000 0.648
#> GSM254633 1 0.5785 0.9106 0.668 0.000 0.332
#> GSM254670 3 0.2066 0.8621 0.060 0.000 0.940
#> GSM254716 3 0.2959 0.8008 0.100 0.000 0.900
#> GSM254720 1 0.6267 0.6982 0.548 0.000 0.452
#> GSM254729 3 0.1289 0.8721 0.032 0.000 0.968
#> GSM254654 3 0.2356 0.8517 0.072 0.000 0.928
#> GSM254656 3 0.3816 0.7889 0.148 0.000 0.852
#> GSM254631 1 0.5785 0.9106 0.668 0.000 0.332
#> GSM254657 3 0.1860 0.8674 0.052 0.000 0.948
#> GSM254664 1 0.5397 0.9328 0.720 0.000 0.280
#> GSM254672 1 0.5760 0.9039 0.672 0.000 0.328
#> GSM254692 1 0.5560 0.9158 0.700 0.000 0.300
#> GSM254645 3 0.1753 0.8678 0.048 0.000 0.952
#> GSM254666 3 0.3192 0.7922 0.112 0.000 0.888
#> GSM254675 1 0.5327 0.9324 0.728 0.000 0.272
#> GSM254678 1 0.5785 0.9106 0.668 0.000 0.332
#> GSM254688 1 0.5650 0.9175 0.688 0.000 0.312
#> GSM254690 1 0.5397 0.9328 0.720 0.000 0.280
#> GSM254696 1 0.5835 0.9027 0.660 0.000 0.340
#> GSM254705 1 0.5678 0.9140 0.684 0.000 0.316
#> GSM254642 1 0.5216 0.9290 0.740 0.000 0.260
#> GSM254661 3 0.0000 0.8691 0.000 0.000 1.000
#> GSM254698 1 0.5706 0.9103 0.680 0.000 0.320
#> GSM254641 1 0.5706 0.9213 0.680 0.000 0.320
#> GSM254647 1 0.5291 0.9314 0.732 0.000 0.268
#> GSM254663 1 0.5621 0.9174 0.692 0.000 0.308
#> GSM254682 1 0.5650 0.9175 0.688 0.000 0.312
#> GSM254709 1 0.6307 0.6031 0.512 0.000 0.488
#> GSM254721 1 0.5363 0.9308 0.724 0.000 0.276
#> GSM254724 1 0.5363 0.9308 0.724 0.000 0.276
#> GSM254650 1 0.5650 0.9175 0.688 0.000 0.312
#> GSM254687 1 0.5706 0.9129 0.680 0.000 0.320
#> GSM254637 1 0.5835 0.9057 0.660 0.000 0.340
#> GSM254684 1 0.5785 0.9106 0.668 0.000 0.332
#> GSM254649 2 0.0592 0.9175 0.012 0.988 0.000
#> GSM254660 2 0.3038 0.9116 0.104 0.896 0.000
#> GSM254693 2 0.0424 0.9180 0.008 0.992 0.000
#> GSM254695 2 0.5698 0.8670 0.252 0.736 0.012
#> GSM254702 2 0.4002 0.9002 0.160 0.840 0.000
#> GSM254643 2 0.0424 0.9186 0.008 0.992 0.000
#> GSM254727 2 0.0592 0.9175 0.012 0.988 0.000
#> GSM254640 2 0.3941 0.9012 0.156 0.844 0.000
#> GSM254626 2 0.0000 0.9190 0.000 1.000 0.000
#> GSM254635 2 0.5098 0.8698 0.248 0.752 0.000
#> GSM254653 2 0.0000 0.9190 0.000 1.000 0.000
#> GSM254658 2 0.0592 0.9175 0.012 0.988 0.000
#> GSM254681 2 0.0592 0.9175 0.012 0.988 0.000
#> GSM254719 2 0.0000 0.9190 0.000 1.000 0.000
#> GSM254673 2 0.0000 0.9190 0.000 1.000 0.000
#> GSM254655 2 0.2878 0.9128 0.096 0.904 0.000
#> GSM254669 2 0.0000 0.9190 0.000 1.000 0.000
#> GSM254699 2 0.2537 0.9148 0.080 0.920 0.000
#> GSM254703 2 0.5016 0.8734 0.240 0.760 0.000
#> GSM254708 2 0.1529 0.9131 0.040 0.960 0.000
#> GSM254715 2 0.4796 0.8801 0.220 0.780 0.000
#> GSM254628 2 0.0592 0.9175 0.012 0.988 0.000
#> GSM254634 2 0.5016 0.8734 0.240 0.760 0.000
#> GSM254646 2 0.0592 0.9175 0.012 0.988 0.000
#> GSM254671 2 0.4062 0.8991 0.164 0.836 0.000
#> GSM254711 2 0.5016 0.8734 0.240 0.760 0.000
#> GSM254717 2 0.0592 0.9175 0.012 0.988 0.000
#> GSM254723 3 0.4953 0.7046 0.176 0.016 0.808
#> GSM254730 2 0.2878 0.9128 0.096 0.904 0.000
#> GSM254731 2 0.4002 0.9002 0.160 0.840 0.000
#> GSM254632 3 0.2537 0.8276 0.080 0.000 0.920
#> GSM254662 2 0.0000 0.9190 0.000 1.000 0.000
#> GSM254677 2 0.5016 0.8734 0.240 0.760 0.000
#> GSM254665 2 0.0424 0.9186 0.008 0.992 0.000
#> GSM254691 2 0.1031 0.9166 0.024 0.976 0.000
#> GSM254644 2 0.4002 0.9002 0.160 0.840 0.000
#> GSM254667 2 0.1529 0.9131 0.040 0.960 0.000
#> GSM254676 2 0.0424 0.9180 0.008 0.992 0.000
#> GSM254679 2 0.5016 0.8734 0.240 0.760 0.000
#> GSM254689 2 0.0592 0.9175 0.012 0.988 0.000
#> GSM254706 2 0.1163 0.9152 0.028 0.972 0.000
#> GSM254712 2 0.4931 0.8759 0.232 0.768 0.000
#> GSM254713 2 0.4931 0.8759 0.232 0.768 0.000
#> GSM254683 2 0.0592 0.9175 0.012 0.988 0.000
#> GSM254710 3 0.7181 0.4840 0.048 0.304 0.648
#> GSM254725 2 0.5016 0.8734 0.240 0.760 0.000
#> GSM254651 2 0.0592 0.9175 0.012 0.988 0.000
#> GSM254638 2 0.5098 0.8698 0.248 0.752 0.000
#> GSM254685 2 0.4654 0.8852 0.208 0.792 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM254629 3 0.1938 0.8611 0.052 0.000 0.936 0.012
#> GSM254648 3 0.3037 0.8456 0.036 0.000 0.888 0.076
#> GSM254694 3 0.3168 0.8543 0.060 0.000 0.884 0.056
#> GSM254701 3 0.2124 0.8613 0.068 0.000 0.924 0.008
#> GSM254728 3 0.2742 0.8558 0.076 0.000 0.900 0.024
#> GSM254726 3 0.3176 0.8435 0.036 0.000 0.880 0.084
#> GSM254639 3 0.3732 0.8407 0.092 0.000 0.852 0.056
#> GSM254652 3 0.2443 0.8544 0.060 0.000 0.916 0.024
#> GSM254700 1 0.3161 0.7645 0.864 0.000 0.012 0.124
#> GSM254625 3 0.7168 0.4202 0.256 0.000 0.552 0.192
#> GSM254636 1 0.4206 0.7589 0.816 0.000 0.136 0.048
#> GSM254659 3 0.1824 0.8629 0.060 0.000 0.936 0.004
#> GSM254680 1 0.2775 0.7898 0.896 0.000 0.084 0.020
#> GSM254686 3 0.6970 0.4319 0.256 0.000 0.576 0.168
#> GSM254718 3 0.1902 0.8628 0.064 0.000 0.932 0.004
#> GSM254674 1 0.5551 0.7365 0.728 0.000 0.112 0.160
#> GSM254668 1 0.5678 0.7254 0.716 0.000 0.112 0.172
#> GSM254697 1 0.2714 0.7703 0.884 0.000 0.004 0.112
#> GSM254704 1 0.4387 0.7404 0.804 0.000 0.052 0.144
#> GSM254707 1 0.6001 0.7083 0.688 0.000 0.128 0.184
#> GSM254714 3 0.2984 0.8522 0.084 0.000 0.888 0.028
#> GSM254722 1 0.2773 0.7733 0.880 0.000 0.004 0.116
#> GSM254627 1 0.2714 0.7703 0.884 0.000 0.004 0.112
#> GSM254630 1 0.7551 0.2439 0.448 0.000 0.356 0.196
#> GSM254633 1 0.3907 0.7632 0.828 0.000 0.140 0.032
#> GSM254670 3 0.4261 0.8223 0.112 0.000 0.820 0.068
#> GSM254716 3 0.7190 0.4074 0.260 0.000 0.548 0.192
#> GSM254720 1 0.6819 0.3585 0.564 0.000 0.312 0.124
#> GSM254729 3 0.3474 0.8543 0.068 0.000 0.868 0.064
#> GSM254654 3 0.3168 0.8543 0.060 0.000 0.884 0.056
#> GSM254656 3 0.4100 0.8239 0.036 0.000 0.816 0.148
#> GSM254631 1 0.3674 0.7748 0.848 0.000 0.116 0.036
#> GSM254657 3 0.3900 0.8418 0.084 0.000 0.844 0.072
#> GSM254664 1 0.2329 0.7945 0.916 0.000 0.072 0.012
#> GSM254672 1 0.4706 0.7278 0.788 0.000 0.072 0.140
#> GSM254692 1 0.4868 0.7404 0.720 0.000 0.024 0.256
#> GSM254645 3 0.3333 0.8478 0.088 0.000 0.872 0.040
#> GSM254666 3 0.7201 0.3947 0.268 0.000 0.544 0.188
#> GSM254675 1 0.3301 0.7965 0.876 0.000 0.076 0.048
#> GSM254678 1 0.3674 0.7649 0.852 0.000 0.104 0.044
#> GSM254688 1 0.5902 0.7152 0.696 0.000 0.120 0.184
#> GSM254690 1 0.1635 0.7957 0.948 0.000 0.044 0.008
#> GSM254696 1 0.4829 0.7455 0.776 0.000 0.156 0.068
#> GSM254705 1 0.5803 0.7242 0.700 0.000 0.104 0.196
#> GSM254642 1 0.2530 0.7706 0.888 0.000 0.000 0.112
#> GSM254661 3 0.1661 0.8603 0.052 0.000 0.944 0.004
#> GSM254698 1 0.4301 0.7431 0.816 0.000 0.064 0.120
#> GSM254641 1 0.5209 0.7494 0.756 0.000 0.104 0.140
#> GSM254647 1 0.2197 0.7791 0.916 0.000 0.004 0.080
#> GSM254663 1 0.4898 0.7549 0.772 0.000 0.072 0.156
#> GSM254682 1 0.5902 0.7152 0.696 0.000 0.120 0.184
#> GSM254709 1 0.6993 0.5676 0.572 0.000 0.260 0.168
#> GSM254721 1 0.3280 0.7631 0.860 0.000 0.016 0.124
#> GSM254724 1 0.3280 0.7631 0.860 0.000 0.016 0.124
#> GSM254650 1 0.5850 0.7216 0.700 0.000 0.116 0.184
#> GSM254687 1 0.6027 0.7127 0.684 0.000 0.124 0.192
#> GSM254637 1 0.4224 0.7592 0.812 0.000 0.144 0.044
#> GSM254684 1 0.4944 0.7416 0.768 0.000 0.160 0.072
#> GSM254649 2 0.0336 0.6183 0.000 0.992 0.008 0.000
#> GSM254660 2 0.5339 -0.3626 0.000 0.600 0.016 0.384
#> GSM254693 2 0.0336 0.6194 0.000 0.992 0.000 0.008
#> GSM254695 4 0.5040 0.8381 0.000 0.364 0.008 0.628
#> GSM254702 2 0.5364 -0.3907 0.000 0.592 0.016 0.392
#> GSM254643 2 0.1356 0.6175 0.000 0.960 0.008 0.032
#> GSM254727 2 0.0927 0.6187 0.000 0.976 0.016 0.008
#> GSM254640 2 0.5183 -0.4761 0.000 0.584 0.008 0.408
#> GSM254626 2 0.1356 0.6175 0.000 0.960 0.008 0.032
#> GSM254635 4 0.5097 0.8995 0.000 0.428 0.004 0.568
#> GSM254653 2 0.1610 0.6146 0.000 0.952 0.016 0.032
#> GSM254658 2 0.0336 0.6183 0.000 0.992 0.008 0.000
#> GSM254681 2 0.0469 0.6172 0.000 0.988 0.012 0.000
#> GSM254719 2 0.1798 0.6111 0.000 0.944 0.016 0.040
#> GSM254673 2 0.1452 0.6163 0.000 0.956 0.008 0.036
#> GSM254655 2 0.4599 0.1901 0.000 0.736 0.016 0.248
#> GSM254669 2 0.1256 0.6187 0.000 0.964 0.008 0.028
#> GSM254699 2 0.3969 0.3891 0.000 0.804 0.016 0.180
#> GSM254703 4 0.5236 0.9107 0.000 0.432 0.008 0.560
#> GSM254708 2 0.3355 0.4758 0.000 0.836 0.004 0.160
#> GSM254715 2 0.5399 -0.6231 0.000 0.520 0.012 0.468
#> GSM254628 2 0.0336 0.6183 0.000 0.992 0.008 0.000
#> GSM254634 4 0.5203 0.9223 0.000 0.416 0.008 0.576
#> GSM254646 2 0.0672 0.6186 0.000 0.984 0.008 0.008
#> GSM254671 2 0.5466 -0.5314 0.000 0.548 0.016 0.436
#> GSM254711 4 0.5236 0.9123 0.000 0.432 0.008 0.560
#> GSM254717 2 0.0657 0.6200 0.000 0.984 0.012 0.004
#> GSM254723 3 0.3249 0.8020 0.008 0.000 0.852 0.140
#> GSM254730 2 0.5047 -0.1540 0.000 0.668 0.016 0.316
#> GSM254731 2 0.5364 -0.3907 0.000 0.592 0.016 0.392
#> GSM254632 3 0.3372 0.8410 0.036 0.000 0.868 0.096
#> GSM254662 2 0.1798 0.6111 0.000 0.944 0.016 0.040
#> GSM254677 4 0.5125 0.8951 0.000 0.388 0.008 0.604
#> GSM254665 2 0.2342 0.5850 0.000 0.912 0.008 0.080
#> GSM254691 2 0.3355 0.4930 0.000 0.836 0.004 0.160
#> GSM254644 2 0.5161 -0.4506 0.000 0.592 0.008 0.400
#> GSM254667 2 0.3764 0.4528 0.000 0.816 0.012 0.172
#> GSM254676 2 0.3306 0.4990 0.000 0.840 0.004 0.156
#> GSM254679 4 0.5236 0.9123 0.000 0.432 0.008 0.560
#> GSM254689 2 0.0469 0.6172 0.000 0.988 0.012 0.000
#> GSM254706 2 0.2988 0.5199 0.000 0.876 0.012 0.112
#> GSM254712 2 0.5399 -0.6231 0.000 0.520 0.012 0.468
#> GSM254713 2 0.5399 -0.6231 0.000 0.520 0.012 0.468
#> GSM254683 2 0.2610 0.5465 0.000 0.900 0.012 0.088
#> GSM254710 2 0.8152 -0.0866 0.008 0.384 0.300 0.308
#> GSM254725 4 0.5193 0.9219 0.000 0.412 0.008 0.580
#> GSM254651 2 0.2610 0.5465 0.000 0.900 0.012 0.088
#> GSM254638 4 0.4950 0.8894 0.000 0.376 0.004 0.620
#> GSM254685 2 0.5399 -0.6231 0.000 0.520 0.012 0.468
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM254629 3 0.1914 0.9062 0.004 0.000 0.932 0.032 0.032
#> GSM254648 3 0.1630 0.9019 0.000 0.004 0.944 0.036 0.016
#> GSM254694 3 0.1461 0.9056 0.016 0.000 0.952 0.028 0.004
#> GSM254701 3 0.1904 0.9071 0.016 0.000 0.936 0.028 0.020
#> GSM254728 3 0.2492 0.8990 0.020 0.000 0.900 0.008 0.072
#> GSM254726 3 0.1630 0.9012 0.004 0.000 0.944 0.036 0.016
#> GSM254639 3 0.3921 0.8724 0.072 0.000 0.828 0.024 0.076
#> GSM254652 3 0.2407 0.8936 0.004 0.000 0.896 0.012 0.088
#> GSM254700 1 0.2116 0.6401 0.912 0.000 0.004 0.008 0.076
#> GSM254625 5 0.5539 0.5339 0.048 0.000 0.304 0.024 0.624
#> GSM254636 1 0.6786 0.4904 0.564 0.000 0.056 0.124 0.256
#> GSM254659 3 0.1701 0.9096 0.016 0.000 0.936 0.000 0.048
#> GSM254680 5 0.6180 -0.1489 0.432 0.000 0.008 0.104 0.456
#> GSM254686 5 0.5653 0.5302 0.048 0.000 0.332 0.024 0.596
#> GSM254718 3 0.1211 0.9112 0.016 0.000 0.960 0.000 0.024
#> GSM254674 5 0.5815 0.4691 0.272 0.000 0.012 0.100 0.616
#> GSM254668 5 0.4671 0.6512 0.232 0.000 0.016 0.032 0.720
#> GSM254697 1 0.2830 0.6396 0.876 0.000 0.000 0.044 0.080
#> GSM254704 1 0.1799 0.6398 0.940 0.000 0.028 0.012 0.020
#> GSM254707 5 0.4104 0.6788 0.220 0.000 0.032 0.000 0.748
#> GSM254714 3 0.2844 0.8825 0.064 0.000 0.888 0.032 0.016
#> GSM254722 1 0.2450 0.6450 0.900 0.000 0.000 0.052 0.048
#> GSM254627 1 0.2830 0.6396 0.876 0.000 0.000 0.044 0.080
#> GSM254630 5 0.5574 0.6037 0.132 0.000 0.212 0.004 0.652
#> GSM254633 1 0.6832 0.4873 0.552 0.000 0.064 0.108 0.276
#> GSM254670 3 0.5424 0.7796 0.072 0.000 0.732 0.096 0.100
#> GSM254716 5 0.5618 0.5280 0.048 0.000 0.304 0.028 0.620
#> GSM254720 1 0.4236 0.4271 0.728 0.000 0.248 0.016 0.008
#> GSM254729 3 0.2513 0.9032 0.060 0.000 0.904 0.016 0.020
#> GSM254654 3 0.1461 0.9056 0.016 0.000 0.952 0.028 0.004
#> GSM254656 3 0.4734 0.8448 0.052 0.000 0.780 0.096 0.072
#> GSM254631 1 0.6547 0.5205 0.588 0.000 0.052 0.108 0.252
#> GSM254657 3 0.4148 0.8601 0.072 0.000 0.812 0.024 0.092
#> GSM254664 1 0.6105 0.4451 0.552 0.000 0.012 0.104 0.332
#> GSM254672 1 0.1772 0.6340 0.940 0.000 0.032 0.020 0.008
#> GSM254692 1 0.4268 -0.1222 0.556 0.000 0.000 0.000 0.444
#> GSM254645 3 0.3736 0.8798 0.072 0.000 0.840 0.024 0.064
#> GSM254666 5 0.5205 0.5405 0.048 0.000 0.312 0.008 0.632
#> GSM254675 1 0.4935 0.4921 0.668 0.000 0.016 0.028 0.288
#> GSM254678 1 0.6474 0.5447 0.612 0.000 0.052 0.124 0.212
#> GSM254688 5 0.4004 0.6727 0.232 0.000 0.016 0.004 0.748
#> GSM254690 1 0.6086 0.4635 0.556 0.000 0.008 0.116 0.320
#> GSM254696 1 0.7123 0.3934 0.504 0.000 0.064 0.128 0.304
#> GSM254705 5 0.4169 0.6641 0.256 0.000 0.016 0.004 0.724
#> GSM254642 1 0.2946 0.6347 0.868 0.000 0.000 0.044 0.088
#> GSM254661 3 0.1970 0.9056 0.004 0.000 0.924 0.012 0.060
#> GSM254698 1 0.4044 0.6301 0.804 0.000 0.028 0.140 0.028
#> GSM254641 5 0.5324 0.5309 0.304 0.000 0.020 0.040 0.636
#> GSM254647 1 0.4926 0.6188 0.712 0.000 0.000 0.112 0.176
#> GSM254663 5 0.4742 0.5454 0.324 0.000 0.008 0.020 0.648
#> GSM254682 5 0.4004 0.6727 0.232 0.000 0.016 0.004 0.748
#> GSM254709 5 0.6222 0.5971 0.192 0.000 0.176 0.020 0.612
#> GSM254721 1 0.2414 0.6383 0.900 0.000 0.008 0.012 0.080
#> GSM254724 1 0.2414 0.6383 0.900 0.000 0.008 0.012 0.080
#> GSM254650 5 0.4132 0.6673 0.260 0.000 0.020 0.000 0.720
#> GSM254687 5 0.4141 0.6739 0.248 0.000 0.024 0.000 0.728
#> GSM254637 1 0.6731 0.5253 0.580 0.000 0.068 0.108 0.244
#> GSM254684 1 0.7101 0.3769 0.504 0.000 0.064 0.124 0.308
#> GSM254649 2 0.1764 0.6527 0.000 0.928 0.008 0.000 0.064
#> GSM254660 2 0.5096 -0.0744 0.016 0.616 0.004 0.348 0.016
#> GSM254693 2 0.0609 0.6556 0.000 0.980 0.000 0.000 0.020
#> GSM254695 4 0.5530 0.7123 0.004 0.204 0.020 0.688 0.084
#> GSM254702 2 0.5185 -0.2055 0.016 0.588 0.004 0.376 0.016
#> GSM254643 2 0.1018 0.6517 0.000 0.968 0.000 0.016 0.016
#> GSM254727 2 0.1917 0.6465 0.016 0.936 0.004 0.008 0.036
#> GSM254640 2 0.5576 -0.4947 0.004 0.496 0.004 0.448 0.048
#> GSM254626 2 0.0510 0.6525 0.000 0.984 0.000 0.016 0.000
#> GSM254635 4 0.4318 0.8134 0.000 0.292 0.000 0.688 0.020
#> GSM254653 2 0.1834 0.6454 0.016 0.940 0.004 0.008 0.032
#> GSM254658 2 0.1830 0.6520 0.000 0.924 0.008 0.000 0.068
#> GSM254681 2 0.2304 0.6428 0.000 0.892 0.008 0.000 0.100
#> GSM254719 2 0.1784 0.6381 0.016 0.944 0.004 0.020 0.016
#> GSM254673 2 0.1256 0.6471 0.012 0.964 0.004 0.012 0.008
#> GSM254655 2 0.4303 0.3601 0.016 0.756 0.004 0.208 0.016
#> GSM254669 2 0.1256 0.6471 0.012 0.964 0.004 0.012 0.008
#> GSM254699 2 0.3988 0.4390 0.016 0.792 0.004 0.172 0.016
#> GSM254703 4 0.4632 0.8139 0.004 0.264 0.004 0.700 0.028
#> GSM254708 2 0.5295 0.4239 0.000 0.664 0.000 0.224 0.112
#> GSM254715 4 0.5536 0.6720 0.004 0.412 0.004 0.532 0.048
#> GSM254628 2 0.1830 0.6520 0.000 0.924 0.008 0.000 0.068
#> GSM254634 4 0.4347 0.8200 0.004 0.256 0.000 0.716 0.024
#> GSM254646 2 0.2077 0.6481 0.000 0.908 0.008 0.000 0.084
#> GSM254671 2 0.5298 -0.3799 0.016 0.532 0.004 0.432 0.016
#> GSM254711 4 0.4268 0.8199 0.004 0.272 0.000 0.708 0.016
#> GSM254717 2 0.1041 0.6563 0.000 0.964 0.000 0.004 0.032
#> GSM254723 3 0.2616 0.8674 0.000 0.000 0.888 0.076 0.036
#> GSM254730 2 0.5100 0.1580 0.016 0.672 0.004 0.276 0.032
#> GSM254731 2 0.5185 -0.2055 0.016 0.588 0.004 0.376 0.016
#> GSM254632 3 0.3274 0.8525 0.000 0.004 0.856 0.064 0.076
#> GSM254662 2 0.1784 0.6381 0.016 0.944 0.004 0.020 0.016
#> GSM254677 4 0.4974 0.7952 0.004 0.232 0.008 0.704 0.052
#> GSM254665 2 0.3593 0.5703 0.000 0.824 0.000 0.116 0.060
#> GSM254691 2 0.5064 0.4094 0.000 0.672 0.000 0.248 0.080
#> GSM254644 2 0.5558 -0.4293 0.004 0.516 0.004 0.428 0.048
#> GSM254667 2 0.5848 0.4034 0.000 0.628 0.008 0.224 0.140
#> GSM254676 2 0.5039 0.4157 0.000 0.676 0.000 0.244 0.080
#> GSM254679 4 0.4268 0.8199 0.004 0.272 0.000 0.708 0.016
#> GSM254689 2 0.2304 0.6428 0.000 0.892 0.008 0.000 0.100
#> GSM254706 2 0.5481 0.4606 0.000 0.676 0.008 0.184 0.132
#> GSM254712 4 0.5574 0.6860 0.004 0.400 0.004 0.540 0.052
#> GSM254713 4 0.5574 0.6860 0.004 0.400 0.004 0.540 0.052
#> GSM254683 2 0.5145 0.4942 0.000 0.712 0.008 0.160 0.120
#> GSM254710 2 0.8150 0.1285 0.000 0.348 0.120 0.204 0.328
#> GSM254725 4 0.4347 0.8200 0.004 0.256 0.000 0.716 0.024
#> GSM254651 2 0.5163 0.5049 0.000 0.712 0.008 0.144 0.136
#> GSM254638 4 0.4506 0.8204 0.000 0.244 0.004 0.716 0.036
#> GSM254685 4 0.5582 0.6806 0.004 0.404 0.004 0.536 0.052
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM254629 3 0.1989 0.82703 0.024 0.000 0.928 0.016 0.020 0.012
#> GSM254648 3 0.2187 0.81913 0.012 0.000 0.916 0.028 0.008 0.036
#> GSM254694 3 0.2250 0.82441 0.020 0.000 0.916 0.016 0.020 0.028
#> GSM254701 3 0.1895 0.82658 0.024 0.000 0.932 0.012 0.020 0.012
#> GSM254728 3 0.2821 0.81973 0.004 0.000 0.860 0.000 0.096 0.040
#> GSM254726 3 0.2145 0.81665 0.004 0.000 0.912 0.020 0.008 0.056
#> GSM254639 3 0.5482 0.72090 0.052 0.000 0.652 0.000 0.100 0.196
#> GSM254652 3 0.3059 0.81695 0.004 0.000 0.848 0.004 0.104 0.040
#> GSM254700 1 0.2558 0.71108 0.840 0.000 0.000 0.004 0.156 0.000
#> GSM254625 5 0.3761 0.54295 0.004 0.000 0.160 0.004 0.784 0.048
#> GSM254636 5 0.7093 -0.14811 0.300 0.000 0.040 0.012 0.352 0.296
#> GSM254659 3 0.2002 0.83335 0.008 0.000 0.916 0.000 0.056 0.020
#> GSM254680 5 0.5645 0.35785 0.176 0.000 0.012 0.028 0.648 0.136
#> GSM254686 5 0.4314 0.54042 0.012 0.000 0.208 0.024 0.736 0.020
#> GSM254718 3 0.1078 0.83401 0.008 0.000 0.964 0.000 0.016 0.012
#> GSM254674 5 0.3687 0.54683 0.020 0.000 0.008 0.028 0.808 0.136
#> GSM254668 5 0.2637 0.59363 0.036 0.000 0.004 0.028 0.892 0.040
#> GSM254697 1 0.4733 0.70307 0.712 0.000 0.000 0.024 0.180 0.084
#> GSM254704 1 0.2261 0.71012 0.884 0.000 0.008 0.004 0.104 0.000
#> GSM254707 5 0.0603 0.61732 0.000 0.000 0.016 0.000 0.980 0.004
#> GSM254714 3 0.3649 0.77098 0.140 0.000 0.808 0.012 0.028 0.012
#> GSM254722 1 0.4766 0.69200 0.716 0.000 0.000 0.024 0.156 0.104
#> GSM254627 1 0.4733 0.70307 0.712 0.000 0.000 0.024 0.180 0.084
#> GSM254630 5 0.3525 0.56565 0.016 0.000 0.132 0.004 0.816 0.032
#> GSM254633 5 0.7109 -0.14423 0.368 0.000 0.048 0.028 0.396 0.160
#> GSM254670 3 0.6173 0.58989 0.052 0.000 0.540 0.000 0.128 0.280
#> GSM254716 5 0.3703 0.54007 0.004 0.000 0.176 0.004 0.780 0.036
#> GSM254720 1 0.4042 0.57754 0.760 0.000 0.172 0.004 0.060 0.004
#> GSM254729 3 0.3364 0.80403 0.036 0.000 0.820 0.000 0.012 0.132
#> GSM254654 3 0.2250 0.82441 0.020 0.000 0.916 0.016 0.020 0.028
#> GSM254656 3 0.5627 0.69897 0.040 0.000 0.620 0.028 0.040 0.272
#> GSM254631 5 0.6909 -0.16682 0.388 0.000 0.032 0.028 0.392 0.160
#> GSM254657 3 0.5404 0.72988 0.044 0.000 0.660 0.000 0.112 0.184
#> GSM254664 5 0.6426 -0.10610 0.372 0.000 0.012 0.028 0.452 0.136
#> GSM254672 1 0.4022 0.68138 0.788 0.000 0.024 0.000 0.104 0.084
#> GSM254692 5 0.4180 0.20536 0.348 0.000 0.000 0.008 0.632 0.012
#> GSM254645 3 0.5238 0.73983 0.052 0.000 0.680 0.000 0.088 0.180
#> GSM254666 5 0.3277 0.54088 0.000 0.000 0.188 0.004 0.792 0.016
#> GSM254675 1 0.5079 0.39206 0.572 0.000 0.016 0.020 0.372 0.020
#> GSM254678 1 0.6915 0.14529 0.352 0.000 0.032 0.008 0.324 0.284
#> GSM254688 5 0.0291 0.61684 0.000 0.000 0.004 0.000 0.992 0.004
#> GSM254690 5 0.6411 -0.09268 0.336 0.000 0.004 0.028 0.460 0.172
#> GSM254696 5 0.7017 -0.02091 0.228 0.000 0.060 0.004 0.400 0.308
#> GSM254705 5 0.1750 0.61056 0.016 0.000 0.012 0.000 0.932 0.040
#> GSM254642 1 0.4930 0.69434 0.696 0.000 0.000 0.028 0.184 0.092
#> GSM254661 3 0.2465 0.83040 0.004 0.000 0.892 0.004 0.064 0.036
#> GSM254698 1 0.6239 0.50294 0.492 0.000 0.020 0.008 0.152 0.328
#> GSM254641 5 0.4260 0.52855 0.124 0.000 0.016 0.032 0.784 0.044
#> GSM254647 1 0.5803 0.56101 0.568 0.000 0.000 0.028 0.276 0.128
#> GSM254663 5 0.3058 0.55780 0.104 0.000 0.000 0.012 0.848 0.036
#> GSM254682 5 0.0291 0.61684 0.000 0.000 0.004 0.000 0.992 0.004
#> GSM254709 5 0.4128 0.55920 0.060 0.000 0.128 0.008 0.784 0.020
#> GSM254721 1 0.2734 0.70873 0.840 0.000 0.000 0.004 0.148 0.008
#> GSM254724 1 0.2442 0.71129 0.852 0.000 0.000 0.004 0.144 0.000
#> GSM254650 5 0.1511 0.60653 0.044 0.000 0.004 0.000 0.940 0.012
#> GSM254687 5 0.1262 0.61227 0.020 0.000 0.008 0.000 0.956 0.016
#> GSM254637 1 0.7064 0.12859 0.408 0.000 0.040 0.032 0.360 0.160
#> GSM254684 5 0.6890 -0.00793 0.224 0.000 0.060 0.000 0.400 0.316
#> GSM254649 2 0.3219 0.49531 0.016 0.808 0.000 0.008 0.000 0.168
#> GSM254660 2 0.4076 0.11112 0.000 0.620 0.000 0.364 0.000 0.016
#> GSM254693 2 0.1889 0.56434 0.020 0.920 0.000 0.004 0.000 0.056
#> GSM254695 4 0.5018 0.58002 0.008 0.084 0.020 0.692 0.000 0.196
#> GSM254702 2 0.4219 -0.02489 0.000 0.592 0.000 0.388 0.000 0.020
#> GSM254643 2 0.2964 0.56385 0.036 0.868 0.000 0.036 0.000 0.060
#> GSM254727 2 0.1320 0.57143 0.000 0.948 0.000 0.016 0.000 0.036
#> GSM254640 4 0.5896 0.49639 0.036 0.360 0.000 0.508 0.000 0.096
#> GSM254626 2 0.2259 0.57746 0.020 0.908 0.000 0.032 0.000 0.040
#> GSM254635 4 0.3844 0.78263 0.012 0.136 0.016 0.800 0.000 0.036
#> GSM254653 2 0.1498 0.57850 0.000 0.940 0.000 0.028 0.000 0.032
#> GSM254658 2 0.3254 0.49279 0.016 0.804 0.000 0.008 0.000 0.172
#> GSM254681 2 0.3875 0.38728 0.016 0.716 0.000 0.008 0.000 0.260
#> GSM254719 2 0.1124 0.57980 0.000 0.956 0.000 0.036 0.000 0.008
#> GSM254673 2 0.0790 0.58099 0.000 0.968 0.000 0.032 0.000 0.000
#> GSM254655 2 0.3141 0.47141 0.000 0.788 0.000 0.200 0.000 0.012
#> GSM254669 2 0.0790 0.58099 0.000 0.968 0.000 0.032 0.000 0.000
#> GSM254699 2 0.2653 0.53813 0.000 0.844 0.000 0.144 0.000 0.012
#> GSM254703 4 0.3873 0.77951 0.020 0.104 0.004 0.804 0.000 0.068
#> GSM254708 2 0.6047 -0.21034 0.004 0.496 0.004 0.252 0.000 0.244
#> GSM254715 4 0.5525 0.73739 0.052 0.216 0.000 0.640 0.000 0.092
#> GSM254628 2 0.3321 0.48927 0.016 0.796 0.000 0.008 0.000 0.180
#> GSM254634 4 0.3186 0.76206 0.008 0.092 0.016 0.852 0.000 0.032
#> GSM254646 2 0.3401 0.46410 0.016 0.776 0.000 0.004 0.000 0.204
#> GSM254671 2 0.4315 -0.20667 0.000 0.524 0.008 0.460 0.000 0.008
#> GSM254711 4 0.3377 0.76778 0.008 0.108 0.016 0.836 0.000 0.032
#> GSM254717 2 0.1657 0.56667 0.000 0.928 0.000 0.016 0.000 0.056
#> GSM254723 3 0.2957 0.76748 0.004 0.000 0.844 0.032 0.000 0.120
#> GSM254730 2 0.4316 0.24279 0.000 0.648 0.000 0.312 0.000 0.040
#> GSM254731 2 0.4150 -0.02415 0.000 0.592 0.000 0.392 0.000 0.016
#> GSM254632 3 0.4096 0.70073 0.004 0.000 0.748 0.036 0.012 0.200
#> GSM254662 2 0.1124 0.57980 0.000 0.956 0.000 0.036 0.000 0.008
#> GSM254677 4 0.4405 0.75080 0.040 0.092 0.000 0.764 0.000 0.104
#> GSM254665 2 0.5694 0.34122 0.040 0.636 0.004 0.188 0.000 0.132
#> GSM254691 2 0.6030 -0.09346 0.004 0.492 0.004 0.288 0.000 0.212
#> GSM254644 4 0.5813 0.48580 0.032 0.368 0.000 0.508 0.000 0.092
#> GSM254667 2 0.6196 -0.40982 0.004 0.424 0.004 0.224 0.000 0.344
#> GSM254676 2 0.5999 -0.08594 0.004 0.500 0.004 0.284 0.000 0.208
#> GSM254679 4 0.3377 0.76778 0.008 0.108 0.016 0.836 0.000 0.032
#> GSM254689 2 0.3875 0.39478 0.016 0.716 0.000 0.008 0.000 0.260
#> GSM254706 2 0.5667 -0.36919 0.000 0.472 0.000 0.160 0.000 0.368
#> GSM254712 4 0.5543 0.73889 0.052 0.212 0.000 0.640 0.000 0.096
#> GSM254713 4 0.5543 0.73889 0.052 0.212 0.000 0.640 0.000 0.096
#> GSM254683 2 0.5509 -0.22056 0.000 0.524 0.000 0.148 0.000 0.328
#> GSM254710 6 0.7634 0.00000 0.004 0.284 0.036 0.136 0.104 0.436
#> GSM254725 4 0.3327 0.75799 0.008 0.092 0.016 0.844 0.000 0.040
#> GSM254651 2 0.5377 -0.17643 0.000 0.528 0.000 0.124 0.000 0.348
#> GSM254638 4 0.3493 0.77477 0.012 0.092 0.016 0.836 0.000 0.044
#> GSM254685 4 0.5640 0.73647 0.056 0.212 0.000 0.632 0.000 0.100
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) individual(p) disease.state(p) gender(p) other(p) k
#> MAD:kmeans 107 1.59e-22 0.77697 0.577 0.628 0.872 2
#> MAD:kmeans 105 6.36e-22 0.00199 0.542 0.106 0.892 3
#> MAD:kmeans 83 3.11e-16 0.00215 0.925 0.165 0.922 4
#> MAD:kmeans 80 6.50e-15 0.00493 0.500 0.104 0.566 5
#> MAD:kmeans 72 3.80e-13 0.00516 0.298 0.075 0.262 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 21168 rows and 107 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.987 0.994 0.5021 0.499 0.499
#> 3 3 0.809 0.776 0.865 0.2523 0.846 0.698
#> 4 4 0.648 0.554 0.738 0.1266 0.878 0.688
#> 5 5 0.666 0.588 0.760 0.0765 0.867 0.586
#> 6 6 0.651 0.641 0.771 0.0491 0.941 0.741
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
#> GSM254629 1 0.000 0.991 1.000 0.000
#> GSM254648 1 0.900 0.543 0.684 0.316
#> GSM254694 1 0.000 0.991 1.000 0.000
#> GSM254701 1 0.000 0.991 1.000 0.000
#> GSM254728 1 0.000 0.991 1.000 0.000
#> GSM254726 1 0.680 0.780 0.820 0.180
#> GSM254639 1 0.000 0.991 1.000 0.000
#> GSM254652 1 0.000 0.991 1.000 0.000
#> GSM254700 1 0.000 0.991 1.000 0.000
#> GSM254625 1 0.000 0.991 1.000 0.000
#> GSM254636 1 0.000 0.991 1.000 0.000
#> GSM254659 1 0.000 0.991 1.000 0.000
#> GSM254680 1 0.000 0.991 1.000 0.000
#> GSM254686 1 0.000 0.991 1.000 0.000
#> GSM254718 1 0.000 0.991 1.000 0.000
#> GSM254674 1 0.000 0.991 1.000 0.000
#> GSM254668 1 0.000 0.991 1.000 0.000
#> GSM254697 1 0.000 0.991 1.000 0.000
#> GSM254704 1 0.000 0.991 1.000 0.000
#> GSM254707 1 0.000 0.991 1.000 0.000
#> GSM254714 1 0.000 0.991 1.000 0.000
#> GSM254722 1 0.000 0.991 1.000 0.000
#> GSM254627 1 0.000 0.991 1.000 0.000
#> GSM254630 1 0.000 0.991 1.000 0.000
#> GSM254633 1 0.000 0.991 1.000 0.000
#> GSM254670 1 0.000 0.991 1.000 0.000
#> GSM254716 1 0.000 0.991 1.000 0.000
#> GSM254720 1 0.000 0.991 1.000 0.000
#> GSM254729 1 0.000 0.991 1.000 0.000
#> GSM254654 1 0.000 0.991 1.000 0.000
#> GSM254656 1 0.000 0.991 1.000 0.000
#> GSM254631 1 0.000 0.991 1.000 0.000
#> GSM254657 1 0.000 0.991 1.000 0.000
#> GSM254664 1 0.000 0.991 1.000 0.000
#> GSM254672 1 0.000 0.991 1.000 0.000
#> GSM254692 1 0.000 0.991 1.000 0.000
#> GSM254645 1 0.000 0.991 1.000 0.000
#> GSM254666 1 0.000 0.991 1.000 0.000
#> GSM254675 1 0.000 0.991 1.000 0.000
#> GSM254678 1 0.000 0.991 1.000 0.000
#> GSM254688 1 0.000 0.991 1.000 0.000
#> GSM254690 1 0.000 0.991 1.000 0.000
#> GSM254696 1 0.000 0.991 1.000 0.000
#> GSM254705 1 0.000 0.991 1.000 0.000
#> GSM254642 1 0.000 0.991 1.000 0.000
#> GSM254661 1 0.000 0.991 1.000 0.000
#> GSM254698 1 0.000 0.991 1.000 0.000
#> GSM254641 1 0.000 0.991 1.000 0.000
#> GSM254647 1 0.000 0.991 1.000 0.000
#> GSM254663 1 0.000 0.991 1.000 0.000
#> GSM254682 1 0.000 0.991 1.000 0.000
#> GSM254709 1 0.000 0.991 1.000 0.000
#> GSM254721 1 0.000 0.991 1.000 0.000
#> GSM254724 1 0.000 0.991 1.000 0.000
#> GSM254650 1 0.000 0.991 1.000 0.000
#> GSM254687 1 0.000 0.991 1.000 0.000
#> GSM254637 1 0.000 0.991 1.000 0.000
#> GSM254684 1 0.000 0.991 1.000 0.000
#> GSM254649 2 0.000 0.998 0.000 1.000
#> GSM254660 2 0.000 0.998 0.000 1.000
#> GSM254693 2 0.000 0.998 0.000 1.000
#> GSM254695 2 0.000 0.998 0.000 1.000
#> GSM254702 2 0.000 0.998 0.000 1.000
#> GSM254643 2 0.000 0.998 0.000 1.000
#> GSM254727 2 0.000 0.998 0.000 1.000
#> GSM254640 2 0.000 0.998 0.000 1.000
#> GSM254626 2 0.000 0.998 0.000 1.000
#> GSM254635 2 0.000 0.998 0.000 1.000
#> GSM254653 2 0.000 0.998 0.000 1.000
#> GSM254658 2 0.000 0.998 0.000 1.000
#> GSM254681 2 0.000 0.998 0.000 1.000
#> GSM254719 2 0.000 0.998 0.000 1.000
#> GSM254673 2 0.000 0.998 0.000 1.000
#> GSM254655 2 0.000 0.998 0.000 1.000
#> GSM254669 2 0.000 0.998 0.000 1.000
#> GSM254699 2 0.000 0.998 0.000 1.000
#> GSM254703 2 0.000 0.998 0.000 1.000
#> GSM254708 2 0.000 0.998 0.000 1.000
#> GSM254715 2 0.000 0.998 0.000 1.000
#> GSM254628 2 0.000 0.998 0.000 1.000
#> GSM254634 2 0.000 0.998 0.000 1.000
#> GSM254646 2 0.000 0.998 0.000 1.000
#> GSM254671 2 0.000 0.998 0.000 1.000
#> GSM254711 2 0.000 0.998 0.000 1.000
#> GSM254717 2 0.000 0.998 0.000 1.000
#> GSM254723 2 0.242 0.958 0.040 0.960
#> GSM254730 2 0.000 0.998 0.000 1.000
#> GSM254731 2 0.000 0.998 0.000 1.000
#> GSM254632 2 0.358 0.927 0.068 0.932
#> GSM254662 2 0.000 0.998 0.000 1.000
#> GSM254677 2 0.000 0.998 0.000 1.000
#> GSM254665 2 0.000 0.998 0.000 1.000
#> GSM254691 2 0.000 0.998 0.000 1.000
#> GSM254644 2 0.000 0.998 0.000 1.000
#> GSM254667 2 0.000 0.998 0.000 1.000
#> GSM254676 2 0.000 0.998 0.000 1.000
#> GSM254679 2 0.000 0.998 0.000 1.000
#> GSM254689 2 0.000 0.998 0.000 1.000
#> GSM254706 2 0.000 0.998 0.000 1.000
#> GSM254712 2 0.000 0.998 0.000 1.000
#> GSM254713 2 0.000 0.998 0.000 1.000
#> GSM254683 2 0.000 0.998 0.000 1.000
#> GSM254710 2 0.000 0.998 0.000 1.000
#> GSM254725 2 0.000 0.998 0.000 1.000
#> GSM254651 2 0.000 0.998 0.000 1.000
#> GSM254638 2 0.000 0.998 0.000 1.000
#> GSM254685 2 0.000 0.998 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM254629 3 0.5988 0.512 0.368 0.000 0.632
#> GSM254648 3 0.6677 0.510 0.324 0.024 0.652
#> GSM254694 3 0.1411 0.651 0.036 0.000 0.964
#> GSM254701 3 0.1411 0.651 0.036 0.000 0.964
#> GSM254728 3 0.4235 0.633 0.176 0.000 0.824
#> GSM254726 3 0.5591 0.515 0.304 0.000 0.696
#> GSM254639 3 0.4235 0.633 0.176 0.000 0.824
#> GSM254652 3 0.6305 0.453 0.484 0.000 0.516
#> GSM254700 1 0.5591 0.725 0.696 0.000 0.304
#> GSM254625 1 0.1529 0.621 0.960 0.000 0.040
#> GSM254636 1 0.5733 0.705 0.676 0.000 0.324
#> GSM254659 3 0.4291 0.631 0.180 0.000 0.820
#> GSM254680 1 0.5560 0.726 0.700 0.000 0.300
#> GSM254686 1 0.1031 0.632 0.976 0.000 0.024
#> GSM254718 3 0.1031 0.654 0.024 0.000 0.976
#> GSM254674 1 0.5497 0.726 0.708 0.000 0.292
#> GSM254668 1 0.0000 0.646 1.000 0.000 0.000
#> GSM254697 1 0.5560 0.726 0.700 0.000 0.300
#> GSM254704 1 0.5785 0.706 0.668 0.000 0.332
#> GSM254707 1 0.1031 0.636 0.976 0.000 0.024
#> GSM254714 3 0.5216 0.510 0.260 0.000 0.740
#> GSM254722 1 0.5560 0.724 0.700 0.000 0.300
#> GSM254627 1 0.5560 0.726 0.700 0.000 0.300
#> GSM254630 1 0.1031 0.636 0.976 0.000 0.024
#> GSM254633 1 0.5760 0.705 0.672 0.000 0.328
#> GSM254670 3 0.5926 0.227 0.356 0.000 0.644
#> GSM254716 1 0.1753 0.611 0.952 0.000 0.048
#> GSM254720 1 0.6260 0.476 0.552 0.000 0.448
#> GSM254729 3 0.4178 0.633 0.172 0.000 0.828
#> GSM254654 3 0.1411 0.651 0.036 0.000 0.964
#> GSM254656 3 0.4654 0.593 0.208 0.000 0.792
#> GSM254631 1 0.5591 0.724 0.696 0.000 0.304
#> GSM254657 3 0.4605 0.603 0.204 0.000 0.796
#> GSM254664 1 0.5591 0.725 0.696 0.000 0.304
#> GSM254672 1 0.5760 0.705 0.672 0.000 0.328
#> GSM254692 1 0.0237 0.646 0.996 0.000 0.004
#> GSM254645 3 0.5859 0.287 0.344 0.000 0.656
#> GSM254666 1 0.1289 0.629 0.968 0.000 0.032
#> GSM254675 1 0.5591 0.724 0.696 0.000 0.304
#> GSM254678 1 0.5733 0.705 0.676 0.000 0.324
#> GSM254688 1 0.1031 0.636 0.976 0.000 0.024
#> GSM254690 1 0.5529 0.726 0.704 0.000 0.296
#> GSM254696 1 0.5882 0.691 0.652 0.000 0.348
#> GSM254705 1 0.1031 0.636 0.976 0.000 0.024
#> GSM254642 1 0.5560 0.726 0.700 0.000 0.300
#> GSM254661 3 0.6235 0.492 0.436 0.000 0.564
#> GSM254698 1 0.5733 0.705 0.676 0.000 0.324
#> GSM254641 1 0.0424 0.648 0.992 0.000 0.008
#> GSM254647 1 0.5560 0.726 0.700 0.000 0.300
#> GSM254663 1 0.0237 0.646 0.996 0.000 0.004
#> GSM254682 1 0.1031 0.636 0.976 0.000 0.024
#> GSM254709 1 0.0747 0.639 0.984 0.000 0.016
#> GSM254721 1 0.5650 0.720 0.688 0.000 0.312
#> GSM254724 1 0.5650 0.720 0.688 0.000 0.312
#> GSM254650 1 0.0237 0.646 0.996 0.000 0.004
#> GSM254687 1 0.0892 0.639 0.980 0.000 0.020
#> GSM254637 1 0.5706 0.715 0.680 0.000 0.320
#> GSM254684 1 0.5882 0.691 0.652 0.000 0.348
#> GSM254649 2 0.0000 0.987 0.000 1.000 0.000
#> GSM254660 2 0.0592 0.987 0.000 0.988 0.012
#> GSM254693 2 0.0000 0.987 0.000 1.000 0.000
#> GSM254695 2 0.0592 0.987 0.000 0.988 0.012
#> GSM254702 2 0.0592 0.987 0.000 0.988 0.012
#> GSM254643 2 0.0000 0.987 0.000 1.000 0.000
#> GSM254727 2 0.0000 0.987 0.000 1.000 0.000
#> GSM254640 2 0.0592 0.987 0.000 0.988 0.012
#> GSM254626 2 0.0000 0.987 0.000 1.000 0.000
#> GSM254635 2 0.0592 0.987 0.000 0.988 0.012
#> GSM254653 2 0.0000 0.987 0.000 1.000 0.000
#> GSM254658 2 0.0000 0.987 0.000 1.000 0.000
#> GSM254681 2 0.0000 0.987 0.000 1.000 0.000
#> GSM254719 2 0.0000 0.987 0.000 1.000 0.000
#> GSM254673 2 0.0000 0.987 0.000 1.000 0.000
#> GSM254655 2 0.0592 0.987 0.000 0.988 0.012
#> GSM254669 2 0.0000 0.987 0.000 1.000 0.000
#> GSM254699 2 0.0592 0.987 0.000 0.988 0.012
#> GSM254703 2 0.0592 0.987 0.000 0.988 0.012
#> GSM254708 2 0.0000 0.987 0.000 1.000 0.000
#> GSM254715 2 0.0592 0.987 0.000 0.988 0.012
#> GSM254628 2 0.0000 0.987 0.000 1.000 0.000
#> GSM254634 2 0.0592 0.987 0.000 0.988 0.012
#> GSM254646 2 0.0000 0.987 0.000 1.000 0.000
#> GSM254671 2 0.0592 0.987 0.000 0.988 0.012
#> GSM254711 2 0.0592 0.987 0.000 0.988 0.012
#> GSM254717 2 0.0000 0.987 0.000 1.000 0.000
#> GSM254723 3 0.6584 0.199 0.012 0.380 0.608
#> GSM254730 2 0.0592 0.987 0.000 0.988 0.012
#> GSM254731 2 0.0592 0.987 0.000 0.988 0.012
#> GSM254632 1 0.9305 -0.131 0.504 0.308 0.188
#> GSM254662 2 0.0000 0.987 0.000 1.000 0.000
#> GSM254677 2 0.0592 0.987 0.000 0.988 0.012
#> GSM254665 2 0.0000 0.987 0.000 1.000 0.000
#> GSM254691 2 0.0000 0.987 0.000 1.000 0.000
#> GSM254644 2 0.0592 0.987 0.000 0.988 0.012
#> GSM254667 2 0.0000 0.987 0.000 1.000 0.000
#> GSM254676 2 0.0000 0.987 0.000 1.000 0.000
#> GSM254679 2 0.0592 0.987 0.000 0.988 0.012
#> GSM254689 2 0.0000 0.987 0.000 1.000 0.000
#> GSM254706 2 0.0000 0.987 0.000 1.000 0.000
#> GSM254712 2 0.0592 0.987 0.000 0.988 0.012
#> GSM254713 2 0.0592 0.987 0.000 0.988 0.012
#> GSM254683 2 0.0000 0.987 0.000 1.000 0.000
#> GSM254710 2 0.5621 0.561 0.308 0.692 0.000
#> GSM254725 2 0.0592 0.987 0.000 0.988 0.012
#> GSM254651 2 0.0000 0.987 0.000 1.000 0.000
#> GSM254638 2 0.0592 0.987 0.000 0.988 0.012
#> GSM254685 2 0.0592 0.987 0.000 0.988 0.012
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM254629 3 0.4415 0.6570 0.056 0.000 0.804 0.140
#> GSM254648 3 0.4508 0.6580 0.012 0.032 0.804 0.152
#> GSM254694 3 0.6482 0.7230 0.208 0.000 0.640 0.152
#> GSM254701 3 0.6472 0.7221 0.212 0.000 0.640 0.148
#> GSM254728 3 0.4567 0.6922 0.276 0.000 0.716 0.008
#> GSM254726 3 0.3837 0.6653 0.000 0.000 0.776 0.224
#> GSM254639 3 0.4539 0.6985 0.272 0.000 0.720 0.008
#> GSM254652 3 0.2521 0.6401 0.064 0.000 0.912 0.024
#> GSM254700 1 0.0376 0.6980 0.992 0.000 0.004 0.004
#> GSM254625 1 0.7894 0.3116 0.364 0.000 0.344 0.292
#> GSM254636 1 0.2198 0.6658 0.920 0.000 0.072 0.008
#> GSM254659 3 0.4372 0.7131 0.268 0.000 0.728 0.004
#> GSM254680 1 0.1297 0.6983 0.964 0.000 0.020 0.016
#> GSM254686 1 0.6883 0.5461 0.584 0.000 0.260 0.156
#> GSM254718 3 0.5939 0.7306 0.248 0.000 0.668 0.084
#> GSM254674 1 0.2376 0.6862 0.916 0.000 0.068 0.016
#> GSM254668 1 0.6702 0.5632 0.616 0.000 0.216 0.168
#> GSM254697 1 0.0188 0.6987 0.996 0.000 0.000 0.004
#> GSM254704 1 0.1305 0.6858 0.960 0.000 0.036 0.004
#> GSM254707 1 0.7373 0.4870 0.500 0.000 0.316 0.184
#> GSM254714 1 0.5383 -0.2953 0.536 0.000 0.452 0.012
#> GSM254722 1 0.1284 0.6943 0.964 0.000 0.024 0.012
#> GSM254627 1 0.0188 0.6987 0.996 0.000 0.000 0.004
#> GSM254630 1 0.7344 0.4980 0.512 0.000 0.300 0.188
#> GSM254633 1 0.1302 0.6838 0.956 0.000 0.044 0.000
#> GSM254670 1 0.5378 -0.1718 0.540 0.000 0.448 0.012
#> GSM254716 1 0.7896 0.3043 0.360 0.000 0.348 0.292
#> GSM254720 1 0.4098 0.4320 0.784 0.000 0.204 0.012
#> GSM254729 3 0.5055 0.5796 0.368 0.000 0.624 0.008
#> GSM254654 3 0.6482 0.7230 0.208 0.000 0.640 0.152
#> GSM254656 3 0.7226 0.5026 0.144 0.000 0.468 0.388
#> GSM254631 1 0.0188 0.6982 0.996 0.000 0.004 0.000
#> GSM254657 3 0.5420 0.5811 0.352 0.000 0.624 0.024
#> GSM254664 1 0.0188 0.6986 0.996 0.000 0.004 0.000
#> GSM254672 1 0.1488 0.6794 0.956 0.000 0.032 0.012
#> GSM254692 1 0.6542 0.5693 0.636 0.000 0.196 0.168
#> GSM254645 1 0.5244 -0.0368 0.600 0.000 0.388 0.012
#> GSM254666 1 0.7460 0.4490 0.468 0.000 0.348 0.184
#> GSM254675 1 0.0336 0.6997 0.992 0.000 0.000 0.008
#> GSM254678 1 0.1545 0.6806 0.952 0.000 0.040 0.008
#> GSM254688 1 0.7373 0.4877 0.500 0.000 0.316 0.184
#> GSM254690 1 0.0336 0.6990 0.992 0.000 0.008 0.000
#> GSM254696 1 0.3978 0.5664 0.796 0.000 0.192 0.012
#> GSM254705 1 0.7221 0.5208 0.540 0.000 0.272 0.188
#> GSM254642 1 0.0376 0.6997 0.992 0.000 0.004 0.004
#> GSM254661 3 0.1452 0.6599 0.036 0.000 0.956 0.008
#> GSM254698 1 0.2124 0.6667 0.924 0.000 0.068 0.008
#> GSM254641 1 0.4406 0.6289 0.780 0.000 0.192 0.028
#> GSM254647 1 0.0000 0.6989 1.000 0.000 0.000 0.000
#> GSM254663 1 0.5911 0.5962 0.692 0.000 0.196 0.112
#> GSM254682 1 0.7398 0.4792 0.492 0.000 0.324 0.184
#> GSM254709 1 0.6709 0.5602 0.616 0.000 0.212 0.172
#> GSM254721 1 0.0779 0.6939 0.980 0.000 0.016 0.004
#> GSM254724 1 0.0779 0.6939 0.980 0.000 0.016 0.004
#> GSM254650 1 0.6756 0.5582 0.612 0.000 0.200 0.188
#> GSM254687 1 0.6936 0.5505 0.588 0.000 0.224 0.188
#> GSM254637 1 0.0817 0.6923 0.976 0.000 0.024 0.000
#> GSM254684 1 0.3852 0.5810 0.808 0.000 0.180 0.012
#> GSM254649 2 0.0000 0.7688 0.000 1.000 0.000 0.000
#> GSM254660 2 0.4222 0.1743 0.000 0.728 0.000 0.272
#> GSM254693 2 0.0188 0.7687 0.000 0.996 0.000 0.004
#> GSM254695 4 0.4981 0.7113 0.000 0.464 0.000 0.536
#> GSM254702 2 0.4998 -0.7229 0.000 0.512 0.000 0.488
#> GSM254643 2 0.0592 0.7659 0.000 0.984 0.000 0.016
#> GSM254727 2 0.0000 0.7688 0.000 1.000 0.000 0.000
#> GSM254640 2 0.4996 -0.7132 0.000 0.516 0.000 0.484
#> GSM254626 2 0.0592 0.7659 0.000 0.984 0.000 0.016
#> GSM254635 4 0.4998 0.7662 0.000 0.488 0.000 0.512
#> GSM254653 2 0.0592 0.7659 0.000 0.984 0.000 0.016
#> GSM254658 2 0.0000 0.7688 0.000 1.000 0.000 0.000
#> GSM254681 2 0.0000 0.7688 0.000 1.000 0.000 0.000
#> GSM254719 2 0.0592 0.7659 0.000 0.984 0.000 0.016
#> GSM254673 2 0.0469 0.7675 0.000 0.988 0.000 0.012
#> GSM254655 2 0.3801 0.3656 0.000 0.780 0.000 0.220
#> GSM254669 2 0.0469 0.7675 0.000 0.988 0.000 0.012
#> GSM254699 2 0.3837 0.3532 0.000 0.776 0.000 0.224
#> GSM254703 4 0.4998 0.7662 0.000 0.488 0.000 0.512
#> GSM254708 2 0.0000 0.7688 0.000 1.000 0.000 0.000
#> GSM254715 4 0.4998 0.7662 0.000 0.488 0.000 0.512
#> GSM254628 2 0.0000 0.7688 0.000 1.000 0.000 0.000
#> GSM254634 4 0.4998 0.7662 0.000 0.488 0.000 0.512
#> GSM254646 2 0.0000 0.7688 0.000 1.000 0.000 0.000
#> GSM254671 2 0.4998 -0.7229 0.000 0.512 0.000 0.488
#> GSM254711 4 0.4998 0.7662 0.000 0.488 0.000 0.512
#> GSM254717 2 0.0000 0.7688 0.000 1.000 0.000 0.000
#> GSM254723 4 0.4663 -0.0656 0.000 0.012 0.272 0.716
#> GSM254730 2 0.4103 0.2397 0.000 0.744 0.000 0.256
#> GSM254731 2 0.4998 -0.7229 0.000 0.512 0.000 0.488
#> GSM254632 4 0.8170 -0.2777 0.008 0.308 0.312 0.372
#> GSM254662 2 0.0469 0.7675 0.000 0.988 0.000 0.012
#> GSM254677 4 0.4994 0.7563 0.000 0.480 0.000 0.520
#> GSM254665 2 0.0592 0.7659 0.000 0.984 0.000 0.016
#> GSM254691 2 0.0592 0.7659 0.000 0.984 0.000 0.016
#> GSM254644 2 0.4996 -0.7132 0.000 0.516 0.000 0.484
#> GSM254667 2 0.0188 0.7650 0.000 0.996 0.000 0.004
#> GSM254676 2 0.0592 0.7659 0.000 0.984 0.000 0.016
#> GSM254679 4 0.4998 0.7662 0.000 0.488 0.000 0.512
#> GSM254689 2 0.0000 0.7688 0.000 1.000 0.000 0.000
#> GSM254706 2 0.0188 0.7650 0.000 0.996 0.000 0.004
#> GSM254712 4 0.4998 0.7662 0.000 0.488 0.000 0.512
#> GSM254713 4 0.4998 0.7662 0.000 0.488 0.000 0.512
#> GSM254683 2 0.0000 0.7688 0.000 1.000 0.000 0.000
#> GSM254710 2 0.7344 0.0812 0.000 0.504 0.180 0.316
#> GSM254725 4 0.4998 0.7662 0.000 0.488 0.000 0.512
#> GSM254651 2 0.0000 0.7688 0.000 1.000 0.000 0.000
#> GSM254638 4 0.4998 0.7662 0.000 0.488 0.000 0.512
#> GSM254685 4 0.4998 0.7662 0.000 0.488 0.000 0.512
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM254629 3 0.1310 0.7746 0.020 0.000 0.956 0.000 0.024
#> GSM254648 3 0.1200 0.7736 0.008 0.016 0.964 0.000 0.012
#> GSM254694 3 0.1041 0.7819 0.032 0.000 0.964 0.000 0.004
#> GSM254701 3 0.0794 0.7815 0.028 0.000 0.972 0.000 0.000
#> GSM254728 3 0.6526 0.5674 0.072 0.000 0.552 0.060 0.316
#> GSM254726 3 0.2790 0.7535 0.000 0.000 0.880 0.068 0.052
#> GSM254639 5 0.7269 -0.4376 0.100 0.000 0.408 0.084 0.408
#> GSM254652 3 0.6308 0.5537 0.044 0.000 0.536 0.064 0.356
#> GSM254700 1 0.0451 0.7032 0.988 0.000 0.004 0.000 0.008
#> GSM254625 5 0.3577 0.5339 0.160 0.000 0.000 0.032 0.808
#> GSM254636 1 0.5400 0.5439 0.660 0.000 0.024 0.052 0.264
#> GSM254659 3 0.6511 0.5896 0.108 0.000 0.568 0.040 0.284
#> GSM254680 1 0.3914 0.4696 0.760 0.000 0.004 0.016 0.220
#> GSM254686 5 0.4759 0.4325 0.388 0.000 0.016 0.004 0.592
#> GSM254718 3 0.4803 0.7304 0.084 0.000 0.756 0.020 0.140
#> GSM254674 1 0.4555 0.2908 0.636 0.000 0.000 0.020 0.344
#> GSM254668 5 0.4545 0.3800 0.432 0.000 0.004 0.004 0.560
#> GSM254697 1 0.0290 0.7059 0.992 0.000 0.000 0.000 0.008
#> GSM254704 1 0.1894 0.6959 0.920 0.000 0.008 0.000 0.072
#> GSM254707 5 0.3730 0.5389 0.288 0.000 0.000 0.000 0.712
#> GSM254714 1 0.4624 0.3613 0.636 0.000 0.340 0.000 0.024
#> GSM254722 1 0.3087 0.6584 0.836 0.000 0.008 0.004 0.152
#> GSM254627 1 0.0290 0.7059 0.992 0.000 0.000 0.000 0.008
#> GSM254630 5 0.3837 0.5322 0.308 0.000 0.000 0.000 0.692
#> GSM254633 1 0.2835 0.6884 0.868 0.000 0.004 0.016 0.112
#> GSM254670 5 0.7263 -0.1918 0.388 0.000 0.100 0.084 0.428
#> GSM254716 5 0.3577 0.5339 0.160 0.000 0.000 0.032 0.808
#> GSM254720 1 0.2946 0.6702 0.868 0.000 0.044 0.000 0.088
#> GSM254729 5 0.7740 -0.3086 0.176 0.000 0.332 0.084 0.408
#> GSM254654 3 0.0794 0.7815 0.028 0.000 0.972 0.000 0.000
#> GSM254656 5 0.7859 -0.1410 0.168 0.000 0.100 0.348 0.384
#> GSM254631 1 0.1862 0.7077 0.932 0.000 0.004 0.016 0.048
#> GSM254657 5 0.7676 -0.2903 0.172 0.000 0.308 0.084 0.436
#> GSM254664 1 0.1651 0.6979 0.944 0.000 0.012 0.008 0.036
#> GSM254672 1 0.3129 0.6436 0.832 0.000 0.008 0.004 0.156
#> GSM254692 1 0.4304 -0.3210 0.516 0.000 0.000 0.000 0.484
#> GSM254645 1 0.7022 0.2855 0.496 0.000 0.088 0.080 0.336
#> GSM254666 5 0.3521 0.5438 0.232 0.000 0.004 0.000 0.764
#> GSM254675 1 0.0609 0.7042 0.980 0.000 0.000 0.000 0.020
#> GSM254678 1 0.4501 0.6062 0.740 0.000 0.012 0.036 0.212
#> GSM254688 5 0.3707 0.5395 0.284 0.000 0.000 0.000 0.716
#> GSM254690 1 0.2448 0.6793 0.892 0.000 0.000 0.020 0.088
#> GSM254696 1 0.6356 0.2860 0.484 0.000 0.032 0.076 0.408
#> GSM254705 5 0.4015 0.4947 0.348 0.000 0.000 0.000 0.652
#> GSM254642 1 0.0880 0.6931 0.968 0.000 0.000 0.000 0.032
#> GSM254661 3 0.5197 0.6635 0.012 0.000 0.660 0.052 0.276
#> GSM254698 1 0.5254 0.5494 0.684 0.000 0.024 0.052 0.240
#> GSM254641 1 0.4029 0.1951 0.680 0.000 0.000 0.004 0.316
#> GSM254647 1 0.0992 0.7046 0.968 0.000 0.000 0.008 0.024
#> GSM254663 1 0.4410 -0.2083 0.556 0.000 0.000 0.004 0.440
#> GSM254682 5 0.3636 0.5424 0.272 0.000 0.000 0.000 0.728
#> GSM254709 5 0.4561 0.2865 0.488 0.000 0.008 0.000 0.504
#> GSM254721 1 0.0579 0.7022 0.984 0.000 0.008 0.000 0.008
#> GSM254724 1 0.0579 0.7022 0.984 0.000 0.008 0.000 0.008
#> GSM254650 5 0.4437 0.3399 0.464 0.000 0.000 0.004 0.532
#> GSM254687 5 0.4171 0.4455 0.396 0.000 0.000 0.000 0.604
#> GSM254637 1 0.2139 0.7070 0.920 0.000 0.012 0.012 0.056
#> GSM254684 1 0.6141 0.3113 0.500 0.000 0.028 0.064 0.408
#> GSM254649 2 0.0000 0.8439 0.000 1.000 0.000 0.000 0.000
#> GSM254660 2 0.4630 -0.3285 0.000 0.572 0.004 0.416 0.008
#> GSM254693 2 0.0703 0.8450 0.000 0.976 0.000 0.024 0.000
#> GSM254695 4 0.2561 0.6570 0.000 0.144 0.000 0.856 0.000
#> GSM254702 4 0.4595 0.8111 0.000 0.400 0.004 0.588 0.008
#> GSM254643 2 0.1197 0.8396 0.000 0.952 0.000 0.048 0.000
#> GSM254727 2 0.0854 0.8444 0.000 0.976 0.004 0.012 0.008
#> GSM254640 4 0.4219 0.7973 0.000 0.416 0.000 0.584 0.000
#> GSM254626 2 0.1121 0.8395 0.000 0.956 0.000 0.044 0.000
#> GSM254635 4 0.3966 0.8734 0.000 0.336 0.000 0.664 0.000
#> GSM254653 2 0.1569 0.8362 0.000 0.944 0.004 0.044 0.008
#> GSM254658 2 0.0000 0.8439 0.000 1.000 0.000 0.000 0.000
#> GSM254681 2 0.0162 0.8422 0.000 0.996 0.000 0.004 0.000
#> GSM254719 2 0.1644 0.8328 0.000 0.940 0.004 0.048 0.008
#> GSM254673 2 0.1569 0.8359 0.000 0.944 0.004 0.044 0.008
#> GSM254655 2 0.4199 0.2507 0.000 0.692 0.004 0.296 0.008
#> GSM254669 2 0.1492 0.8386 0.000 0.948 0.004 0.040 0.008
#> GSM254699 2 0.4220 0.2283 0.000 0.688 0.004 0.300 0.008
#> GSM254703 4 0.3983 0.8724 0.000 0.340 0.000 0.660 0.000
#> GSM254708 2 0.1121 0.8335 0.000 0.956 0.000 0.044 0.000
#> GSM254715 4 0.3966 0.8734 0.000 0.336 0.000 0.664 0.000
#> GSM254628 2 0.0000 0.8439 0.000 1.000 0.000 0.000 0.000
#> GSM254634 4 0.3816 0.8601 0.000 0.304 0.000 0.696 0.000
#> GSM254646 2 0.0000 0.8439 0.000 1.000 0.000 0.000 0.000
#> GSM254671 4 0.4621 0.7925 0.000 0.412 0.004 0.576 0.008
#> GSM254711 4 0.3949 0.8726 0.000 0.332 0.000 0.668 0.000
#> GSM254717 2 0.0960 0.8438 0.000 0.972 0.004 0.016 0.008
#> GSM254723 4 0.5265 0.0575 0.004 0.004 0.232 0.680 0.080
#> GSM254730 2 0.4530 -0.1480 0.000 0.612 0.004 0.376 0.008
#> GSM254731 4 0.4621 0.7924 0.000 0.412 0.004 0.576 0.008
#> GSM254632 5 0.6284 0.2359 0.004 0.180 0.004 0.236 0.576
#> GSM254662 2 0.1569 0.8359 0.000 0.944 0.004 0.044 0.008
#> GSM254677 4 0.3366 0.7784 0.000 0.232 0.000 0.768 0.000
#> GSM254665 2 0.1341 0.8331 0.000 0.944 0.000 0.056 0.000
#> GSM254691 2 0.1671 0.8308 0.000 0.924 0.000 0.076 0.000
#> GSM254644 4 0.4201 0.8094 0.000 0.408 0.000 0.592 0.000
#> GSM254667 2 0.1544 0.7933 0.000 0.932 0.000 0.068 0.000
#> GSM254676 2 0.1544 0.8346 0.000 0.932 0.000 0.068 0.000
#> GSM254679 4 0.3876 0.8669 0.000 0.316 0.000 0.684 0.000
#> GSM254689 2 0.0162 0.8422 0.000 0.996 0.000 0.004 0.000
#> GSM254706 2 0.1478 0.7978 0.000 0.936 0.000 0.064 0.000
#> GSM254712 4 0.3966 0.8734 0.000 0.336 0.000 0.664 0.000
#> GSM254713 4 0.3966 0.8734 0.000 0.336 0.000 0.664 0.000
#> GSM254683 2 0.0963 0.8243 0.000 0.964 0.000 0.036 0.000
#> GSM254710 2 0.6661 -0.0448 0.000 0.412 0.000 0.232 0.356
#> GSM254725 4 0.3837 0.8614 0.000 0.308 0.000 0.692 0.000
#> GSM254651 2 0.1121 0.8176 0.000 0.956 0.000 0.044 0.000
#> GSM254638 4 0.3876 0.8681 0.000 0.316 0.000 0.684 0.000
#> GSM254685 4 0.4030 0.8648 0.000 0.352 0.000 0.648 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM254629 3 0.1078 0.75930 0.012 0.000 0.964 0.000 0.016 0.008
#> GSM254648 3 0.0520 0.75827 0.000 0.000 0.984 0.000 0.008 0.008
#> GSM254694 3 0.0717 0.76194 0.016 0.000 0.976 0.000 0.000 0.008
#> GSM254701 3 0.0767 0.76230 0.012 0.000 0.976 0.000 0.004 0.008
#> GSM254728 6 0.6225 0.34699 0.032 0.000 0.296 0.004 0.148 0.520
#> GSM254726 3 0.4177 0.67271 0.000 0.000 0.772 0.036 0.052 0.140
#> GSM254639 6 0.5073 0.63182 0.044 0.000 0.132 0.004 0.108 0.712
#> GSM254652 6 0.7029 0.19892 0.044 0.000 0.276 0.008 0.296 0.376
#> GSM254700 1 0.0363 0.80009 0.988 0.000 0.000 0.000 0.012 0.000
#> GSM254625 5 0.1906 0.70468 0.036 0.000 0.000 0.008 0.924 0.032
#> GSM254636 1 0.5677 -0.17052 0.440 0.000 0.004 0.004 0.116 0.436
#> GSM254659 3 0.7233 -0.12245 0.088 0.000 0.396 0.008 0.184 0.324
#> GSM254680 1 0.5062 0.20635 0.560 0.000 0.008 0.008 0.380 0.044
#> GSM254686 5 0.4427 0.73474 0.228 0.000 0.020 0.008 0.716 0.028
#> GSM254718 3 0.6181 0.42628 0.060 0.000 0.584 0.008 0.112 0.236
#> GSM254674 5 0.5740 0.08621 0.424 0.000 0.008 0.008 0.460 0.100
#> GSM254668 5 0.3674 0.74857 0.220 0.000 0.004 0.008 0.756 0.012
#> GSM254697 1 0.1088 0.80457 0.960 0.000 0.000 0.000 0.024 0.016
#> GSM254704 1 0.1167 0.79615 0.960 0.000 0.012 0.000 0.008 0.020
#> GSM254707 5 0.2257 0.76997 0.116 0.000 0.000 0.000 0.876 0.008
#> GSM254714 1 0.3875 0.62383 0.776 0.000 0.172 0.004 0.012 0.036
#> GSM254722 1 0.2901 0.73186 0.840 0.000 0.000 0.000 0.032 0.128
#> GSM254627 1 0.1168 0.80416 0.956 0.000 0.000 0.000 0.028 0.016
#> GSM254630 5 0.3746 0.75832 0.192 0.000 0.000 0.000 0.760 0.048
#> GSM254633 1 0.4819 0.66292 0.712 0.000 0.012 0.008 0.168 0.100
#> GSM254670 6 0.4914 0.70225 0.124 0.000 0.016 0.004 0.152 0.704
#> GSM254716 5 0.2147 0.69213 0.032 0.000 0.000 0.012 0.912 0.044
#> GSM254720 1 0.1708 0.78384 0.932 0.000 0.024 0.000 0.004 0.040
#> GSM254729 6 0.5670 0.66431 0.076 0.000 0.124 0.004 0.132 0.664
#> GSM254654 3 0.0520 0.76167 0.008 0.000 0.984 0.000 0.000 0.008
#> GSM254656 6 0.3630 0.54773 0.020 0.000 0.000 0.100 0.064 0.816
#> GSM254631 1 0.3393 0.77825 0.836 0.000 0.008 0.008 0.092 0.056
#> GSM254657 6 0.4925 0.64923 0.040 0.000 0.076 0.004 0.164 0.716
#> GSM254664 1 0.3069 0.77397 0.856 0.000 0.012 0.008 0.096 0.028
#> GSM254672 1 0.2389 0.72604 0.864 0.000 0.000 0.000 0.008 0.128
#> GSM254692 5 0.3789 0.58859 0.416 0.000 0.000 0.000 0.584 0.000
#> GSM254645 6 0.4973 0.65722 0.248 0.000 0.012 0.004 0.076 0.660
#> GSM254666 5 0.3123 0.73299 0.088 0.000 0.000 0.000 0.836 0.076
#> GSM254675 1 0.1371 0.79880 0.948 0.000 0.004 0.004 0.040 0.004
#> GSM254678 1 0.4294 0.53902 0.692 0.000 0.000 0.000 0.060 0.248
#> GSM254688 5 0.2558 0.76527 0.104 0.000 0.000 0.000 0.868 0.028
#> GSM254690 1 0.4106 0.73282 0.768 0.000 0.004 0.008 0.148 0.072
#> GSM254696 6 0.5113 0.68389 0.204 0.000 0.000 0.000 0.168 0.628
#> GSM254705 5 0.3771 0.76893 0.180 0.000 0.000 0.000 0.764 0.056
#> GSM254642 1 0.1528 0.79821 0.936 0.000 0.000 0.000 0.048 0.016
#> GSM254661 3 0.5732 0.30997 0.004 0.000 0.544 0.004 0.160 0.288
#> GSM254698 6 0.4833 0.24403 0.428 0.000 0.000 0.000 0.056 0.516
#> GSM254641 1 0.4877 -0.01793 0.544 0.000 0.008 0.008 0.412 0.028
#> GSM254647 1 0.1984 0.80025 0.912 0.000 0.000 0.000 0.056 0.032
#> GSM254663 5 0.4444 0.53717 0.396 0.000 0.000 0.004 0.576 0.024
#> GSM254682 5 0.2558 0.76527 0.104 0.000 0.000 0.000 0.868 0.028
#> GSM254709 5 0.3942 0.64519 0.368 0.000 0.004 0.000 0.624 0.004
#> GSM254721 1 0.0870 0.79523 0.972 0.000 0.012 0.000 0.012 0.004
#> GSM254724 1 0.0767 0.79680 0.976 0.000 0.012 0.000 0.008 0.004
#> GSM254650 5 0.3244 0.73934 0.268 0.000 0.000 0.000 0.732 0.000
#> GSM254687 5 0.2883 0.77337 0.212 0.000 0.000 0.000 0.788 0.000
#> GSM254637 1 0.3170 0.78573 0.856 0.000 0.012 0.008 0.072 0.052
#> GSM254684 6 0.5409 0.65449 0.232 0.000 0.000 0.004 0.168 0.596
#> GSM254649 2 0.0692 0.79122 0.000 0.976 0.000 0.000 0.004 0.020
#> GSM254660 2 0.4389 -0.34543 0.000 0.512 0.000 0.468 0.004 0.016
#> GSM254693 2 0.0790 0.79078 0.000 0.968 0.000 0.032 0.000 0.000
#> GSM254695 4 0.3172 0.59015 0.000 0.040 0.000 0.844 0.016 0.100
#> GSM254702 4 0.4223 0.70491 0.000 0.368 0.000 0.612 0.004 0.016
#> GSM254643 2 0.1700 0.77503 0.000 0.916 0.000 0.080 0.000 0.004
#> GSM254727 2 0.1148 0.78916 0.000 0.960 0.000 0.016 0.004 0.020
#> GSM254640 4 0.3737 0.69269 0.000 0.392 0.000 0.608 0.000 0.000
#> GSM254626 2 0.1588 0.77601 0.000 0.924 0.000 0.072 0.000 0.004
#> GSM254635 4 0.3163 0.83041 0.000 0.232 0.000 0.764 0.000 0.004
#> GSM254653 2 0.1738 0.77772 0.000 0.928 0.000 0.052 0.004 0.016
#> GSM254658 2 0.0858 0.78995 0.000 0.968 0.000 0.000 0.004 0.028
#> GSM254681 2 0.1461 0.78387 0.000 0.940 0.000 0.000 0.016 0.044
#> GSM254719 2 0.2039 0.76919 0.000 0.908 0.000 0.072 0.004 0.016
#> GSM254673 2 0.2039 0.76919 0.000 0.908 0.000 0.072 0.004 0.016
#> GSM254655 2 0.4223 0.07262 0.000 0.612 0.000 0.368 0.004 0.016
#> GSM254669 2 0.1738 0.78047 0.000 0.928 0.000 0.052 0.004 0.016
#> GSM254699 2 0.4090 0.22637 0.000 0.652 0.000 0.328 0.004 0.016
#> GSM254703 4 0.3265 0.82857 0.000 0.248 0.000 0.748 0.000 0.004
#> GSM254708 2 0.2757 0.75192 0.000 0.864 0.000 0.104 0.016 0.016
#> GSM254715 4 0.3189 0.82953 0.000 0.236 0.000 0.760 0.000 0.004
#> GSM254628 2 0.0603 0.79146 0.000 0.980 0.000 0.000 0.004 0.016
#> GSM254634 4 0.2416 0.80358 0.000 0.156 0.000 0.844 0.000 0.000
#> GSM254646 2 0.1074 0.78881 0.000 0.960 0.000 0.000 0.012 0.028
#> GSM254671 4 0.4223 0.70408 0.000 0.368 0.000 0.612 0.004 0.016
#> GSM254711 4 0.2969 0.82526 0.000 0.224 0.000 0.776 0.000 0.000
#> GSM254717 2 0.1262 0.78985 0.000 0.956 0.000 0.016 0.008 0.020
#> GSM254723 4 0.7237 -0.08807 0.000 0.004 0.176 0.440 0.124 0.256
#> GSM254730 2 0.4317 -0.15780 0.000 0.572 0.000 0.408 0.004 0.016
#> GSM254731 4 0.4223 0.70491 0.000 0.368 0.000 0.612 0.004 0.016
#> GSM254632 5 0.6115 0.32864 0.000 0.032 0.024 0.120 0.600 0.224
#> GSM254662 2 0.2039 0.76919 0.000 0.908 0.000 0.072 0.004 0.016
#> GSM254677 4 0.3490 0.78715 0.000 0.176 0.000 0.784 0.000 0.040
#> GSM254665 2 0.2734 0.76612 0.000 0.864 0.000 0.104 0.008 0.024
#> GSM254691 2 0.3375 0.74722 0.000 0.808 0.000 0.156 0.012 0.024
#> GSM254644 4 0.3647 0.74421 0.000 0.360 0.000 0.640 0.000 0.000
#> GSM254667 2 0.4010 0.67643 0.000 0.776 0.000 0.152 0.024 0.048
#> GSM254676 2 0.3300 0.74975 0.000 0.816 0.000 0.148 0.012 0.024
#> GSM254679 4 0.2562 0.80015 0.000 0.172 0.000 0.828 0.000 0.000
#> GSM254689 2 0.1461 0.78387 0.000 0.940 0.000 0.000 0.016 0.044
#> GSM254706 2 0.3935 0.68315 0.000 0.784 0.000 0.144 0.024 0.048
#> GSM254712 4 0.3163 0.83041 0.000 0.232 0.000 0.764 0.000 0.004
#> GSM254713 4 0.3163 0.83041 0.000 0.232 0.000 0.764 0.000 0.004
#> GSM254683 2 0.3239 0.72765 0.000 0.840 0.000 0.100 0.016 0.044
#> GSM254710 2 0.7577 0.00227 0.000 0.348 0.008 0.160 0.324 0.160
#> GSM254725 4 0.2300 0.79620 0.000 0.144 0.000 0.856 0.000 0.000
#> GSM254651 2 0.3550 0.70965 0.000 0.816 0.000 0.120 0.020 0.044
#> GSM254638 4 0.2527 0.81530 0.000 0.168 0.000 0.832 0.000 0.000
#> GSM254685 4 0.3175 0.82137 0.000 0.256 0.000 0.744 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) individual(p) disease.state(p) gender(p) other(p) k
#> MAD:skmeans 107 3.34e-24 0.64356 0.777 0.440630 1.000 2
#> MAD:skmeans 100 1.93e-22 0.00147 0.403 0.011132 0.653 3
#> MAD:skmeans 84 4.25e-18 0.00562 0.763 0.008144 0.994 4
#> MAD:skmeans 80 1.74e-16 0.01080 0.603 0.000863 0.581 5
#> MAD:skmeans 90 6.72e-18 0.01369 0.697 0.103826 0.819 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 21168 rows and 107 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'pam' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.995 0.998 0.4981 0.503 0.503
#> 3 3 0.691 0.875 0.901 0.2716 0.860 0.723
#> 4 4 0.717 0.795 0.891 0.1589 0.882 0.690
#> 5 5 0.726 0.740 0.850 0.0403 0.970 0.892
#> 6 6 0.701 0.581 0.760 0.0445 0.952 0.813
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
#> GSM254629 1 0.0000 0.997 1.000 0.000
#> GSM254648 1 0.5519 0.855 0.872 0.128
#> GSM254694 1 0.0000 0.997 1.000 0.000
#> GSM254701 1 0.0000 0.997 1.000 0.000
#> GSM254728 1 0.0000 0.997 1.000 0.000
#> GSM254726 1 0.0000 0.997 1.000 0.000
#> GSM254639 1 0.0000 0.997 1.000 0.000
#> GSM254652 1 0.0000 0.997 1.000 0.000
#> GSM254700 1 0.0000 0.997 1.000 0.000
#> GSM254625 1 0.0000 0.997 1.000 0.000
#> GSM254636 1 0.0000 0.997 1.000 0.000
#> GSM254659 1 0.0000 0.997 1.000 0.000
#> GSM254680 1 0.0000 0.997 1.000 0.000
#> GSM254686 1 0.0000 0.997 1.000 0.000
#> GSM254718 1 0.0000 0.997 1.000 0.000
#> GSM254674 1 0.0000 0.997 1.000 0.000
#> GSM254668 1 0.0000 0.997 1.000 0.000
#> GSM254697 1 0.0000 0.997 1.000 0.000
#> GSM254704 1 0.0000 0.997 1.000 0.000
#> GSM254707 1 0.0000 0.997 1.000 0.000
#> GSM254714 1 0.0000 0.997 1.000 0.000
#> GSM254722 1 0.0000 0.997 1.000 0.000
#> GSM254627 1 0.0000 0.997 1.000 0.000
#> GSM254630 1 0.0000 0.997 1.000 0.000
#> GSM254633 1 0.0000 0.997 1.000 0.000
#> GSM254670 1 0.0000 0.997 1.000 0.000
#> GSM254716 1 0.0000 0.997 1.000 0.000
#> GSM254720 1 0.0000 0.997 1.000 0.000
#> GSM254729 1 0.0000 0.997 1.000 0.000
#> GSM254654 1 0.0000 0.997 1.000 0.000
#> GSM254656 1 0.0000 0.997 1.000 0.000
#> GSM254631 1 0.0000 0.997 1.000 0.000
#> GSM254657 1 0.0000 0.997 1.000 0.000
#> GSM254664 1 0.0000 0.997 1.000 0.000
#> GSM254672 1 0.0000 0.997 1.000 0.000
#> GSM254692 1 0.0000 0.997 1.000 0.000
#> GSM254645 1 0.0000 0.997 1.000 0.000
#> GSM254666 1 0.0000 0.997 1.000 0.000
#> GSM254675 1 0.0000 0.997 1.000 0.000
#> GSM254678 1 0.0000 0.997 1.000 0.000
#> GSM254688 1 0.0000 0.997 1.000 0.000
#> GSM254690 1 0.0000 0.997 1.000 0.000
#> GSM254696 1 0.0000 0.997 1.000 0.000
#> GSM254705 1 0.0000 0.997 1.000 0.000
#> GSM254642 1 0.0000 0.997 1.000 0.000
#> GSM254661 1 0.0000 0.997 1.000 0.000
#> GSM254698 1 0.0000 0.997 1.000 0.000
#> GSM254641 1 0.0000 0.997 1.000 0.000
#> GSM254647 1 0.0000 0.997 1.000 0.000
#> GSM254663 1 0.0000 0.997 1.000 0.000
#> GSM254682 1 0.0000 0.997 1.000 0.000
#> GSM254709 1 0.0000 0.997 1.000 0.000
#> GSM254721 1 0.0000 0.997 1.000 0.000
#> GSM254724 1 0.0000 0.997 1.000 0.000
#> GSM254650 1 0.0000 0.997 1.000 0.000
#> GSM254687 1 0.0000 0.997 1.000 0.000
#> GSM254637 1 0.0000 0.997 1.000 0.000
#> GSM254684 1 0.0000 0.997 1.000 0.000
#> GSM254649 2 0.0000 0.999 0.000 1.000
#> GSM254660 2 0.0000 0.999 0.000 1.000
#> GSM254693 2 0.0000 0.999 0.000 1.000
#> GSM254695 2 0.0000 0.999 0.000 1.000
#> GSM254702 2 0.0000 0.999 0.000 1.000
#> GSM254643 2 0.0000 0.999 0.000 1.000
#> GSM254727 2 0.0000 0.999 0.000 1.000
#> GSM254640 2 0.0000 0.999 0.000 1.000
#> GSM254626 2 0.0000 0.999 0.000 1.000
#> GSM254635 2 0.0000 0.999 0.000 1.000
#> GSM254653 2 0.0000 0.999 0.000 1.000
#> GSM254658 2 0.0000 0.999 0.000 1.000
#> GSM254681 2 0.0000 0.999 0.000 1.000
#> GSM254719 2 0.0000 0.999 0.000 1.000
#> GSM254673 2 0.0000 0.999 0.000 1.000
#> GSM254655 2 0.0000 0.999 0.000 1.000
#> GSM254669 2 0.0000 0.999 0.000 1.000
#> GSM254699 2 0.0000 0.999 0.000 1.000
#> GSM254703 2 0.0000 0.999 0.000 1.000
#> GSM254708 2 0.0000 0.999 0.000 1.000
#> GSM254715 2 0.0000 0.999 0.000 1.000
#> GSM254628 2 0.0000 0.999 0.000 1.000
#> GSM254634 2 0.0000 0.999 0.000 1.000
#> GSM254646 2 0.0000 0.999 0.000 1.000
#> GSM254671 2 0.0000 0.999 0.000 1.000
#> GSM254711 2 0.0000 0.999 0.000 1.000
#> GSM254717 2 0.0000 0.999 0.000 1.000
#> GSM254723 1 0.0672 0.989 0.992 0.008
#> GSM254730 2 0.0000 0.999 0.000 1.000
#> GSM254731 2 0.0000 0.999 0.000 1.000
#> GSM254632 1 0.3431 0.932 0.936 0.064
#> GSM254662 2 0.0000 0.999 0.000 1.000
#> GSM254677 2 0.0672 0.991 0.008 0.992
#> GSM254665 2 0.0000 0.999 0.000 1.000
#> GSM254691 2 0.0000 0.999 0.000 1.000
#> GSM254644 2 0.0000 0.999 0.000 1.000
#> GSM254667 2 0.0000 0.999 0.000 1.000
#> GSM254676 2 0.0000 0.999 0.000 1.000
#> GSM254679 2 0.0000 0.999 0.000 1.000
#> GSM254689 2 0.0000 0.999 0.000 1.000
#> GSM254706 2 0.0000 0.999 0.000 1.000
#> GSM254712 2 0.0000 0.999 0.000 1.000
#> GSM254713 2 0.0000 0.999 0.000 1.000
#> GSM254683 2 0.0000 0.999 0.000 1.000
#> GSM254710 2 0.2423 0.958 0.040 0.960
#> GSM254725 2 0.0000 0.999 0.000 1.000
#> GSM254651 2 0.0000 0.999 0.000 1.000
#> GSM254638 2 0.0000 0.999 0.000 1.000
#> GSM254685 2 0.0000 0.999 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM254629 3 0.0000 0.8907 0.000 0.000 1.000
#> GSM254648 3 0.3116 0.7697 0.000 0.108 0.892
#> GSM254694 3 0.0000 0.8907 0.000 0.000 1.000
#> GSM254701 3 0.0000 0.8907 0.000 0.000 1.000
#> GSM254728 3 0.0000 0.8907 0.000 0.000 1.000
#> GSM254726 3 0.0000 0.8907 0.000 0.000 1.000
#> GSM254639 3 0.0000 0.8907 0.000 0.000 1.000
#> GSM254652 3 0.0000 0.8907 0.000 0.000 1.000
#> GSM254700 1 0.4002 0.9255 0.840 0.000 0.160
#> GSM254625 1 0.5291 0.8428 0.732 0.000 0.268
#> GSM254636 3 0.3116 0.8612 0.108 0.000 0.892
#> GSM254659 3 0.0000 0.8907 0.000 0.000 1.000
#> GSM254680 3 0.3116 0.8612 0.108 0.000 0.892
#> GSM254686 3 0.0000 0.8907 0.000 0.000 1.000
#> GSM254718 3 0.0000 0.8907 0.000 0.000 1.000
#> GSM254674 3 0.3116 0.8624 0.108 0.000 0.892
#> GSM254668 1 0.4235 0.9127 0.824 0.000 0.176
#> GSM254697 3 0.4504 0.7908 0.196 0.000 0.804
#> GSM254704 3 0.4842 0.7534 0.224 0.000 0.776
#> GSM254707 1 0.4002 0.9255 0.840 0.000 0.160
#> GSM254714 3 0.3482 0.8020 0.128 0.000 0.872
#> GSM254722 3 0.4504 0.7782 0.196 0.000 0.804
#> GSM254627 3 0.4062 0.8235 0.164 0.000 0.836
#> GSM254630 1 0.5291 0.8428 0.732 0.000 0.268
#> GSM254633 3 0.2796 0.8686 0.092 0.000 0.908
#> GSM254670 3 0.0000 0.8907 0.000 0.000 1.000
#> GSM254716 3 0.6062 0.0536 0.384 0.000 0.616
#> GSM254720 3 0.0000 0.8907 0.000 0.000 1.000
#> GSM254729 3 0.0000 0.8907 0.000 0.000 1.000
#> GSM254654 3 0.0000 0.8907 0.000 0.000 1.000
#> GSM254656 3 0.0000 0.8907 0.000 0.000 1.000
#> GSM254631 3 0.3116 0.8612 0.108 0.000 0.892
#> GSM254657 3 0.0237 0.8900 0.004 0.000 0.996
#> GSM254664 3 0.3116 0.8612 0.108 0.000 0.892
#> GSM254672 3 0.3038 0.8633 0.104 0.000 0.896
#> GSM254692 1 0.4002 0.9255 0.840 0.000 0.160
#> GSM254645 3 0.0237 0.8900 0.004 0.000 0.996
#> GSM254666 1 0.5291 0.8428 0.732 0.000 0.268
#> GSM254675 3 0.0000 0.8907 0.000 0.000 1.000
#> GSM254678 3 0.4452 0.7911 0.192 0.000 0.808
#> GSM254688 1 0.4002 0.9255 0.840 0.000 0.160
#> GSM254690 3 0.4235 0.8137 0.176 0.000 0.824
#> GSM254696 3 0.3116 0.8612 0.108 0.000 0.892
#> GSM254705 1 0.5291 0.8428 0.732 0.000 0.268
#> GSM254642 1 0.4002 0.9255 0.840 0.000 0.160
#> GSM254661 3 0.0000 0.8907 0.000 0.000 1.000
#> GSM254698 3 0.0237 0.8906 0.004 0.000 0.996
#> GSM254641 3 0.2711 0.8727 0.088 0.000 0.912
#> GSM254647 3 0.6274 0.1245 0.456 0.000 0.544
#> GSM254663 1 0.4002 0.9255 0.840 0.000 0.160
#> GSM254682 1 0.4002 0.9255 0.840 0.000 0.160
#> GSM254709 1 0.4235 0.9173 0.824 0.000 0.176
#> GSM254721 1 0.4002 0.9255 0.840 0.000 0.160
#> GSM254724 1 0.4002 0.9255 0.840 0.000 0.160
#> GSM254650 1 0.4002 0.9255 0.840 0.000 0.160
#> GSM254687 1 0.4062 0.9238 0.836 0.000 0.164
#> GSM254637 3 0.3551 0.8480 0.132 0.000 0.868
#> GSM254684 3 0.4504 0.7909 0.196 0.000 0.804
#> GSM254649 2 0.3267 0.9320 0.116 0.884 0.000
#> GSM254660 2 0.2711 0.9318 0.088 0.912 0.000
#> GSM254693 2 0.3412 0.9317 0.124 0.876 0.000
#> GSM254695 2 0.1643 0.9220 0.044 0.956 0.000
#> GSM254702 2 0.3267 0.9320 0.116 0.884 0.000
#> GSM254643 2 0.3267 0.9320 0.116 0.884 0.000
#> GSM254727 2 0.3267 0.9320 0.116 0.884 0.000
#> GSM254640 2 0.1289 0.9241 0.032 0.968 0.000
#> GSM254626 2 0.3267 0.9320 0.116 0.884 0.000
#> GSM254635 2 0.1289 0.9287 0.032 0.968 0.000
#> GSM254653 2 0.3267 0.9320 0.116 0.884 0.000
#> GSM254658 2 0.3267 0.9320 0.116 0.884 0.000
#> GSM254681 2 0.1411 0.9237 0.036 0.964 0.000
#> GSM254719 2 0.3267 0.9320 0.116 0.884 0.000
#> GSM254673 2 0.3267 0.9320 0.116 0.884 0.000
#> GSM254655 2 0.3267 0.9320 0.116 0.884 0.000
#> GSM254669 2 0.3267 0.9320 0.116 0.884 0.000
#> GSM254699 2 0.3267 0.9320 0.116 0.884 0.000
#> GSM254703 2 0.1643 0.9220 0.044 0.956 0.000
#> GSM254708 2 0.1529 0.9226 0.040 0.960 0.000
#> GSM254715 2 0.3267 0.9320 0.116 0.884 0.000
#> GSM254628 2 0.3267 0.9320 0.116 0.884 0.000
#> GSM254634 2 0.1643 0.9220 0.044 0.956 0.000
#> GSM254646 2 0.3267 0.9320 0.116 0.884 0.000
#> GSM254671 2 0.3340 0.9316 0.120 0.880 0.000
#> GSM254711 2 0.1411 0.9236 0.036 0.964 0.000
#> GSM254717 2 0.3267 0.9320 0.116 0.884 0.000
#> GSM254723 3 0.0592 0.8831 0.000 0.012 0.988
#> GSM254730 2 0.1289 0.9241 0.032 0.968 0.000
#> GSM254731 2 0.3267 0.9320 0.116 0.884 0.000
#> GSM254632 3 0.4615 0.7066 0.020 0.144 0.836
#> GSM254662 2 0.3267 0.9320 0.116 0.884 0.000
#> GSM254677 2 0.1832 0.9203 0.036 0.956 0.008
#> GSM254665 2 0.1529 0.9226 0.040 0.960 0.000
#> GSM254691 2 0.1643 0.9220 0.044 0.956 0.000
#> GSM254644 2 0.3340 0.9316 0.120 0.880 0.000
#> GSM254667 2 0.1643 0.9220 0.044 0.956 0.000
#> GSM254676 2 0.1643 0.9220 0.044 0.956 0.000
#> GSM254679 2 0.1643 0.9220 0.044 0.956 0.000
#> GSM254689 2 0.1529 0.9226 0.040 0.960 0.000
#> GSM254706 2 0.1529 0.9226 0.040 0.960 0.000
#> GSM254712 2 0.3340 0.9316 0.120 0.880 0.000
#> GSM254713 2 0.3340 0.9316 0.120 0.880 0.000
#> GSM254683 2 0.1643 0.9220 0.044 0.956 0.000
#> GSM254710 1 0.6865 0.3164 0.596 0.384 0.020
#> GSM254725 2 0.1643 0.9220 0.044 0.956 0.000
#> GSM254651 2 0.1529 0.9226 0.040 0.960 0.000
#> GSM254638 2 0.1643 0.9220 0.044 0.956 0.000
#> GSM254685 2 0.1643 0.9220 0.044 0.956 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM254629 3 0.0000 0.894 0.000 0.000 1.000 0.000
#> GSM254648 3 0.2868 0.776 0.000 0.136 0.864 0.000
#> GSM254694 3 0.0000 0.894 0.000 0.000 1.000 0.000
#> GSM254701 3 0.0000 0.894 0.000 0.000 1.000 0.000
#> GSM254728 3 0.0000 0.894 0.000 0.000 1.000 0.000
#> GSM254726 3 0.0000 0.894 0.000 0.000 1.000 0.000
#> GSM254639 3 0.0000 0.894 0.000 0.000 1.000 0.000
#> GSM254652 3 0.0000 0.894 0.000 0.000 1.000 0.000
#> GSM254700 1 0.0000 0.920 1.000 0.000 0.000 0.000
#> GSM254625 1 0.2814 0.830 0.868 0.000 0.132 0.000
#> GSM254636 3 0.2814 0.863 0.132 0.000 0.868 0.000
#> GSM254659 3 0.0000 0.894 0.000 0.000 1.000 0.000
#> GSM254680 3 0.2814 0.863 0.132 0.000 0.868 0.000
#> GSM254686 3 0.0000 0.894 0.000 0.000 1.000 0.000
#> GSM254718 3 0.0000 0.894 0.000 0.000 1.000 0.000
#> GSM254674 3 0.2760 0.866 0.128 0.000 0.872 0.000
#> GSM254668 1 0.1792 0.868 0.932 0.000 0.068 0.000
#> GSM254697 3 0.3569 0.822 0.196 0.000 0.804 0.000
#> GSM254704 3 0.4382 0.703 0.296 0.000 0.704 0.000
#> GSM254707 1 0.0000 0.920 1.000 0.000 0.000 0.000
#> GSM254714 3 0.3649 0.729 0.204 0.000 0.796 0.000
#> GSM254722 3 0.4431 0.662 0.304 0.000 0.696 0.000
#> GSM254627 3 0.3486 0.829 0.188 0.000 0.812 0.000
#> GSM254630 1 0.2814 0.830 0.868 0.000 0.132 0.000
#> GSM254633 3 0.2589 0.870 0.116 0.000 0.884 0.000
#> GSM254670 3 0.0000 0.894 0.000 0.000 1.000 0.000
#> GSM254716 3 0.4804 0.267 0.384 0.000 0.616 0.000
#> GSM254720 3 0.0000 0.894 0.000 0.000 1.000 0.000
#> GSM254729 3 0.0000 0.894 0.000 0.000 1.000 0.000
#> GSM254654 3 0.0000 0.894 0.000 0.000 1.000 0.000
#> GSM254656 4 0.4981 0.202 0.000 0.000 0.464 0.536
#> GSM254631 3 0.2814 0.863 0.132 0.000 0.868 0.000
#> GSM254657 3 0.0188 0.894 0.004 0.000 0.996 0.000
#> GSM254664 3 0.2814 0.863 0.132 0.000 0.868 0.000
#> GSM254672 3 0.2647 0.869 0.120 0.000 0.880 0.000
#> GSM254692 1 0.0000 0.920 1.000 0.000 0.000 0.000
#> GSM254645 3 0.0188 0.894 0.004 0.000 0.996 0.000
#> GSM254666 1 0.2973 0.823 0.856 0.000 0.144 0.000
#> GSM254675 3 0.0000 0.894 0.000 0.000 1.000 0.000
#> GSM254678 3 0.4040 0.760 0.248 0.000 0.752 0.000
#> GSM254688 1 0.0000 0.920 1.000 0.000 0.000 0.000
#> GSM254690 3 0.3528 0.827 0.192 0.000 0.808 0.000
#> GSM254696 3 0.2814 0.863 0.132 0.000 0.868 0.000
#> GSM254705 1 0.2814 0.830 0.868 0.000 0.132 0.000
#> GSM254642 1 0.0000 0.920 1.000 0.000 0.000 0.000
#> GSM254661 3 0.0000 0.894 0.000 0.000 1.000 0.000
#> GSM254698 3 0.0188 0.894 0.004 0.000 0.996 0.000
#> GSM254641 3 0.2469 0.875 0.108 0.000 0.892 0.000
#> GSM254647 1 0.4804 0.195 0.616 0.000 0.384 0.000
#> GSM254663 1 0.0000 0.920 1.000 0.000 0.000 0.000
#> GSM254682 1 0.0000 0.920 1.000 0.000 0.000 0.000
#> GSM254709 1 0.0707 0.911 0.980 0.000 0.020 0.000
#> GSM254721 1 0.0000 0.920 1.000 0.000 0.000 0.000
#> GSM254724 1 0.0000 0.920 1.000 0.000 0.000 0.000
#> GSM254650 1 0.0000 0.920 1.000 0.000 0.000 0.000
#> GSM254687 1 0.0188 0.919 0.996 0.000 0.004 0.000
#> GSM254637 3 0.3074 0.853 0.152 0.000 0.848 0.000
#> GSM254684 3 0.3610 0.819 0.200 0.000 0.800 0.000
#> GSM254649 2 0.0000 0.829 0.000 1.000 0.000 0.000
#> GSM254660 2 0.1940 0.805 0.000 0.924 0.000 0.076
#> GSM254693 2 0.0336 0.827 0.000 0.992 0.000 0.008
#> GSM254695 4 0.0000 0.793 0.000 0.000 0.000 1.000
#> GSM254702 2 0.3801 0.526 0.000 0.780 0.000 0.220
#> GSM254643 2 0.0000 0.829 0.000 1.000 0.000 0.000
#> GSM254727 2 0.0000 0.829 0.000 1.000 0.000 0.000
#> GSM254640 2 0.4040 0.720 0.000 0.752 0.000 0.248
#> GSM254626 2 0.0000 0.829 0.000 1.000 0.000 0.000
#> GSM254635 4 0.3688 0.718 0.000 0.208 0.000 0.792
#> GSM254653 2 0.0000 0.829 0.000 1.000 0.000 0.000
#> GSM254658 2 0.0000 0.829 0.000 1.000 0.000 0.000
#> GSM254681 2 0.3486 0.757 0.000 0.812 0.000 0.188
#> GSM254719 2 0.0000 0.829 0.000 1.000 0.000 0.000
#> GSM254673 2 0.0000 0.829 0.000 1.000 0.000 0.000
#> GSM254655 2 0.0469 0.822 0.000 0.988 0.000 0.012
#> GSM254669 2 0.0000 0.829 0.000 1.000 0.000 0.000
#> GSM254699 2 0.0000 0.829 0.000 1.000 0.000 0.000
#> GSM254703 4 0.0188 0.793 0.000 0.004 0.000 0.996
#> GSM254708 2 0.4564 0.665 0.000 0.672 0.000 0.328
#> GSM254715 4 0.4817 0.580 0.000 0.388 0.000 0.612
#> GSM254628 2 0.0000 0.829 0.000 1.000 0.000 0.000
#> GSM254634 4 0.0000 0.793 0.000 0.000 0.000 1.000
#> GSM254646 2 0.0000 0.829 0.000 1.000 0.000 0.000
#> GSM254671 4 0.4500 0.653 0.000 0.316 0.000 0.684
#> GSM254711 4 0.1022 0.791 0.000 0.032 0.000 0.968
#> GSM254717 2 0.0000 0.829 0.000 1.000 0.000 0.000
#> GSM254723 3 0.0336 0.891 0.000 0.008 0.992 0.000
#> GSM254730 2 0.4164 0.711 0.000 0.736 0.000 0.264
#> GSM254731 4 0.4804 0.586 0.000 0.384 0.000 0.616
#> GSM254632 3 0.4583 0.735 0.004 0.076 0.808 0.112
#> GSM254662 2 0.0000 0.829 0.000 1.000 0.000 0.000
#> GSM254677 4 0.1211 0.790 0.000 0.040 0.000 0.960
#> GSM254665 2 0.4500 0.673 0.000 0.684 0.000 0.316
#> GSM254691 2 0.4855 0.581 0.000 0.600 0.000 0.400
#> GSM254644 4 0.4713 0.618 0.000 0.360 0.000 0.640
#> GSM254667 4 0.0000 0.793 0.000 0.000 0.000 1.000
#> GSM254676 4 0.0000 0.793 0.000 0.000 0.000 1.000
#> GSM254679 4 0.0000 0.793 0.000 0.000 0.000 1.000
#> GSM254689 2 0.4304 0.698 0.000 0.716 0.000 0.284
#> GSM254706 2 0.4713 0.631 0.000 0.640 0.000 0.360
#> GSM254712 4 0.4713 0.615 0.000 0.360 0.000 0.640
#> GSM254713 4 0.4605 0.638 0.000 0.336 0.000 0.664
#> GSM254683 2 0.4746 0.622 0.000 0.632 0.000 0.368
#> GSM254710 2 0.6412 0.562 0.080 0.572 0.000 0.348
#> GSM254725 4 0.0000 0.793 0.000 0.000 0.000 1.000
#> GSM254651 2 0.4713 0.631 0.000 0.640 0.000 0.360
#> GSM254638 4 0.0000 0.793 0.000 0.000 0.000 1.000
#> GSM254685 4 0.1637 0.779 0.000 0.060 0.000 0.940
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM254629 3 0.0000 0.8722 0.000 0.000 1.000 0.000 0.000
#> GSM254648 3 0.2471 0.7301 0.000 0.136 0.864 0.000 0.000
#> GSM254694 3 0.0000 0.8722 0.000 0.000 1.000 0.000 0.000
#> GSM254701 3 0.0000 0.8722 0.000 0.000 1.000 0.000 0.000
#> GSM254728 3 0.0000 0.8722 0.000 0.000 1.000 0.000 0.000
#> GSM254726 3 0.0000 0.8722 0.000 0.000 1.000 0.000 0.000
#> GSM254639 3 0.0000 0.8722 0.000 0.000 1.000 0.000 0.000
#> GSM254652 3 0.0000 0.8722 0.000 0.000 1.000 0.000 0.000
#> GSM254700 1 0.3730 0.6764 0.712 0.000 0.000 0.000 0.288
#> GSM254625 5 0.1341 0.8244 0.000 0.000 0.056 0.000 0.944
#> GSM254636 3 0.2922 0.8385 0.072 0.000 0.872 0.000 0.056
#> GSM254659 3 0.0000 0.8722 0.000 0.000 1.000 0.000 0.000
#> GSM254680 3 0.2922 0.8385 0.072 0.000 0.872 0.000 0.056
#> GSM254686 3 0.0000 0.8722 0.000 0.000 1.000 0.000 0.000
#> GSM254718 3 0.0000 0.8722 0.000 0.000 1.000 0.000 0.000
#> GSM254674 3 0.2859 0.8416 0.068 0.000 0.876 0.000 0.056
#> GSM254668 5 0.3119 0.7119 0.072 0.000 0.068 0.000 0.860
#> GSM254697 1 0.2179 0.6379 0.888 0.000 0.112 0.000 0.000
#> GSM254704 1 0.5263 0.6986 0.660 0.000 0.100 0.000 0.240
#> GSM254707 5 0.1544 0.8005 0.068 0.000 0.000 0.000 0.932
#> GSM254714 3 0.3684 0.5853 0.000 0.000 0.720 0.000 0.280
#> GSM254722 3 0.5640 0.5171 0.176 0.000 0.636 0.000 0.188
#> GSM254627 3 0.4425 0.3238 0.452 0.000 0.544 0.000 0.004
#> GSM254630 5 0.1341 0.8244 0.000 0.000 0.056 0.000 0.944
#> GSM254633 3 0.2719 0.8445 0.068 0.000 0.884 0.000 0.048
#> GSM254670 3 0.0000 0.8722 0.000 0.000 1.000 0.000 0.000
#> GSM254716 3 0.4138 0.2671 0.000 0.000 0.616 0.000 0.384
#> GSM254720 1 0.4114 0.4788 0.624 0.000 0.376 0.000 0.000
#> GSM254729 3 0.0000 0.8722 0.000 0.000 1.000 0.000 0.000
#> GSM254654 3 0.0000 0.8722 0.000 0.000 1.000 0.000 0.000
#> GSM254656 4 0.4302 0.1365 0.000 0.000 0.480 0.520 0.000
#> GSM254631 3 0.2922 0.8385 0.072 0.000 0.872 0.000 0.056
#> GSM254657 3 0.0162 0.8713 0.000 0.000 0.996 0.000 0.004
#> GSM254664 3 0.2922 0.8385 0.072 0.000 0.872 0.000 0.056
#> GSM254672 3 0.2795 0.8424 0.064 0.000 0.880 0.000 0.056
#> GSM254692 5 0.0000 0.8499 0.000 0.000 0.000 0.000 1.000
#> GSM254645 3 0.0162 0.8713 0.000 0.000 0.996 0.000 0.004
#> GSM254666 5 0.1544 0.8166 0.000 0.000 0.068 0.000 0.932
#> GSM254675 3 0.0000 0.8722 0.000 0.000 1.000 0.000 0.000
#> GSM254678 3 0.3612 0.6564 0.000 0.000 0.732 0.000 0.268
#> GSM254688 5 0.0000 0.8499 0.000 0.000 0.000 0.000 1.000
#> GSM254690 3 0.3631 0.8046 0.072 0.000 0.824 0.000 0.104
#> GSM254696 3 0.2922 0.8385 0.072 0.000 0.872 0.000 0.056
#> GSM254705 5 0.1341 0.8244 0.000 0.000 0.056 0.000 0.944
#> GSM254642 5 0.3003 0.6749 0.188 0.000 0.000 0.000 0.812
#> GSM254661 3 0.0000 0.8722 0.000 0.000 1.000 0.000 0.000
#> GSM254698 3 0.2891 0.7633 0.176 0.000 0.824 0.000 0.000
#> GSM254641 3 0.2588 0.8498 0.048 0.000 0.892 0.000 0.060
#> GSM254647 5 0.5467 0.0493 0.068 0.000 0.384 0.000 0.548
#> GSM254663 5 0.1608 0.7962 0.072 0.000 0.000 0.000 0.928
#> GSM254682 5 0.0000 0.8499 0.000 0.000 0.000 0.000 1.000
#> GSM254709 5 0.0290 0.8501 0.000 0.000 0.008 0.000 0.992
#> GSM254721 1 0.4060 0.6364 0.640 0.000 0.000 0.000 0.360
#> GSM254724 1 0.3966 0.6637 0.664 0.000 0.000 0.000 0.336
#> GSM254650 5 0.0000 0.8499 0.000 0.000 0.000 0.000 1.000
#> GSM254687 5 0.0162 0.8505 0.000 0.000 0.004 0.000 0.996
#> GSM254637 3 0.3119 0.8318 0.072 0.000 0.860 0.000 0.068
#> GSM254684 3 0.3921 0.7777 0.072 0.000 0.800 0.000 0.128
#> GSM254649 2 0.0000 0.8134 0.000 1.000 0.000 0.000 0.000
#> GSM254660 2 0.3670 0.7625 0.112 0.820 0.000 0.068 0.000
#> GSM254693 2 0.0000 0.8134 0.000 1.000 0.000 0.000 0.000
#> GSM254695 4 0.0404 0.7470 0.012 0.000 0.000 0.988 0.000
#> GSM254702 2 0.5268 0.4188 0.112 0.668 0.000 0.220 0.000
#> GSM254643 2 0.0963 0.8059 0.036 0.964 0.000 0.000 0.000
#> GSM254727 2 0.1341 0.7993 0.056 0.944 0.000 0.000 0.000
#> GSM254640 2 0.4270 0.7286 0.048 0.748 0.000 0.204 0.000
#> GSM254626 2 0.0000 0.8134 0.000 1.000 0.000 0.000 0.000
#> GSM254635 4 0.4994 0.6884 0.112 0.184 0.000 0.704 0.000
#> GSM254653 2 0.1341 0.7993 0.056 0.944 0.000 0.000 0.000
#> GSM254658 2 0.0000 0.8134 0.000 1.000 0.000 0.000 0.000
#> GSM254681 2 0.2516 0.7711 0.000 0.860 0.000 0.140 0.000
#> GSM254719 2 0.2179 0.7728 0.112 0.888 0.000 0.000 0.000
#> GSM254673 2 0.0510 0.8111 0.016 0.984 0.000 0.000 0.000
#> GSM254655 2 0.2677 0.7630 0.112 0.872 0.000 0.016 0.000
#> GSM254669 2 0.0000 0.8134 0.000 1.000 0.000 0.000 0.000
#> GSM254699 2 0.2179 0.7728 0.112 0.888 0.000 0.000 0.000
#> GSM254703 4 0.0162 0.7458 0.000 0.004 0.000 0.996 0.000
#> GSM254708 2 0.3966 0.6505 0.000 0.664 0.000 0.336 0.000
#> GSM254715 4 0.5740 0.6041 0.112 0.308 0.000 0.580 0.000
#> GSM254628 2 0.0000 0.8134 0.000 1.000 0.000 0.000 0.000
#> GSM254634 4 0.0000 0.7443 0.000 0.000 0.000 1.000 0.000
#> GSM254646 2 0.0000 0.8134 0.000 1.000 0.000 0.000 0.000
#> GSM254671 4 0.5537 0.6409 0.112 0.264 0.000 0.624 0.000
#> GSM254711 4 0.2813 0.7409 0.108 0.024 0.000 0.868 0.000
#> GSM254717 2 0.0000 0.8134 0.000 1.000 0.000 0.000 0.000
#> GSM254723 3 0.0290 0.8680 0.000 0.008 0.992 0.000 0.000
#> GSM254730 2 0.5060 0.7022 0.092 0.684 0.000 0.224 0.000
#> GSM254731 4 0.5740 0.6041 0.112 0.308 0.000 0.580 0.000
#> GSM254632 3 0.4656 0.5818 0.000 0.076 0.740 0.180 0.004
#> GSM254662 2 0.0404 0.8121 0.012 0.988 0.000 0.000 0.000
#> GSM254677 4 0.3035 0.7390 0.112 0.032 0.000 0.856 0.000
#> GSM254665 2 0.4114 0.6191 0.000 0.624 0.000 0.376 0.000
#> GSM254691 2 0.4256 0.5461 0.000 0.564 0.000 0.436 0.000
#> GSM254644 4 0.5740 0.6049 0.112 0.308 0.000 0.580 0.000
#> GSM254667 4 0.0000 0.7443 0.000 0.000 0.000 1.000 0.000
#> GSM254676 4 0.0000 0.7443 0.000 0.000 0.000 1.000 0.000
#> GSM254679 4 0.0000 0.7443 0.000 0.000 0.000 1.000 0.000
#> GSM254689 2 0.3366 0.7155 0.000 0.768 0.000 0.232 0.000
#> GSM254706 2 0.4074 0.6188 0.000 0.636 0.000 0.364 0.000
#> GSM254712 4 0.5740 0.6041 0.112 0.308 0.000 0.580 0.000
#> GSM254713 4 0.5637 0.6270 0.112 0.284 0.000 0.604 0.000
#> GSM254683 2 0.4227 0.5660 0.000 0.580 0.000 0.420 0.000
#> GSM254710 2 0.5168 0.5830 0.000 0.592 0.000 0.356 0.052
#> GSM254725 4 0.0609 0.7478 0.020 0.000 0.000 0.980 0.000
#> GSM254651 2 0.3913 0.6482 0.000 0.676 0.000 0.324 0.000
#> GSM254638 4 0.0000 0.7443 0.000 0.000 0.000 1.000 0.000
#> GSM254685 4 0.1410 0.7420 0.000 0.060 0.000 0.940 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM254629 3 0.1714 0.7823 0.000 0.000 0.908 0.000 0.000 0.092
#> GSM254648 3 0.3099 0.7509 0.000 0.044 0.848 0.012 0.000 0.096
#> GSM254694 3 0.0000 0.8040 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM254701 3 0.0146 0.8037 0.000 0.000 0.996 0.000 0.000 0.004
#> GSM254728 3 0.0146 0.8037 0.000 0.000 0.996 0.000 0.000 0.004
#> GSM254726 3 0.1908 0.7797 0.000 0.000 0.900 0.004 0.000 0.096
#> GSM254639 3 0.0000 0.8040 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM254652 3 0.1075 0.7967 0.000 0.000 0.952 0.000 0.000 0.048
#> GSM254700 1 0.3455 0.7615 0.800 0.000 0.000 0.000 0.056 0.144
#> GSM254625 5 0.0713 0.8243 0.000 0.000 0.028 0.000 0.972 0.000
#> GSM254636 3 0.3833 0.7316 0.004 0.000 0.736 0.000 0.028 0.232
#> GSM254659 3 0.0000 0.8040 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM254680 3 0.3419 0.7560 0.004 0.000 0.792 0.000 0.028 0.176
#> GSM254686 3 0.0000 0.8040 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM254718 3 0.0000 0.8040 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM254674 3 0.3316 0.7625 0.004 0.000 0.804 0.000 0.028 0.164
#> GSM254668 5 0.3936 0.6514 0.004 0.000 0.060 0.000 0.760 0.176
#> GSM254697 1 0.2378 0.6726 0.848 0.000 0.000 0.000 0.000 0.152
#> GSM254704 1 0.3557 0.8104 0.800 0.000 0.008 0.000 0.148 0.044
#> GSM254707 5 0.2482 0.7396 0.004 0.000 0.000 0.000 0.848 0.148
#> GSM254714 3 0.4960 0.5228 0.000 0.000 0.600 0.000 0.308 0.092
#> GSM254722 3 0.6323 0.4316 0.200 0.000 0.536 0.000 0.048 0.216
#> GSM254627 3 0.5791 0.1411 0.380 0.000 0.440 0.000 0.000 0.180
#> GSM254630 5 0.0713 0.8243 0.000 0.000 0.028 0.000 0.972 0.000
#> GSM254633 3 0.2890 0.7762 0.004 0.000 0.844 0.000 0.024 0.128
#> GSM254670 3 0.2300 0.7804 0.000 0.000 0.856 0.000 0.000 0.144
#> GSM254716 3 0.3717 0.3366 0.000 0.000 0.616 0.000 0.384 0.000
#> GSM254720 1 0.2969 0.6683 0.776 0.000 0.224 0.000 0.000 0.000
#> GSM254729 3 0.0000 0.8040 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM254654 3 0.1765 0.7810 0.000 0.000 0.904 0.000 0.000 0.096
#> GSM254656 3 0.3864 0.1372 0.000 0.000 0.520 0.480 0.000 0.000
#> GSM254631 3 0.3419 0.7560 0.004 0.000 0.792 0.000 0.028 0.176
#> GSM254657 3 0.2482 0.7774 0.000 0.000 0.848 0.000 0.004 0.148
#> GSM254664 3 0.3419 0.7560 0.004 0.000 0.792 0.000 0.028 0.176
#> GSM254672 3 0.3419 0.7635 0.004 0.000 0.792 0.000 0.028 0.176
#> GSM254692 5 0.0146 0.8342 0.000 0.000 0.000 0.000 0.996 0.004
#> GSM254645 3 0.1970 0.7832 0.000 0.000 0.900 0.000 0.008 0.092
#> GSM254666 5 0.2629 0.7395 0.000 0.000 0.040 0.000 0.868 0.092
#> GSM254675 3 0.0000 0.8040 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM254678 3 0.4360 0.6263 0.000 0.000 0.680 0.000 0.260 0.060
#> GSM254688 5 0.0000 0.8350 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM254690 3 0.4039 0.7241 0.004 0.000 0.724 0.000 0.040 0.232
#> GSM254696 3 0.3640 0.7484 0.004 0.000 0.764 0.000 0.028 0.204
#> GSM254705 5 0.0713 0.8243 0.000 0.000 0.028 0.000 0.972 0.000
#> GSM254642 5 0.5015 0.4889 0.208 0.000 0.000 0.000 0.640 0.152
#> GSM254661 3 0.1765 0.7810 0.000 0.000 0.904 0.000 0.000 0.096
#> GSM254698 3 0.5327 0.5305 0.196 0.000 0.596 0.000 0.000 0.208
#> GSM254641 3 0.3727 0.7752 0.004 0.000 0.768 0.000 0.040 0.188
#> GSM254647 5 0.6039 0.1227 0.004 0.000 0.324 0.000 0.448 0.224
#> GSM254663 5 0.2558 0.7322 0.004 0.000 0.000 0.000 0.840 0.156
#> GSM254682 5 0.0000 0.8350 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM254709 5 0.0260 0.8343 0.000 0.000 0.008 0.000 0.992 0.000
#> GSM254721 1 0.2823 0.7745 0.796 0.000 0.000 0.000 0.204 0.000
#> GSM254724 1 0.3354 0.8041 0.796 0.000 0.000 0.000 0.168 0.036
#> GSM254650 5 0.0000 0.8350 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM254687 5 0.0146 0.8350 0.000 0.000 0.004 0.000 0.996 0.000
#> GSM254637 3 0.3419 0.7560 0.004 0.000 0.792 0.000 0.028 0.176
#> GSM254684 3 0.4163 0.7186 0.004 0.000 0.716 0.000 0.048 0.232
#> GSM254649 2 0.0000 0.5649 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM254660 6 0.4996 0.6602 0.000 0.408 0.000 0.072 0.000 0.520
#> GSM254693 2 0.0000 0.5649 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM254695 4 0.3659 0.4129 0.000 0.000 0.000 0.636 0.000 0.364
#> GSM254702 6 0.5319 0.7384 0.000 0.368 0.000 0.112 0.000 0.520
#> GSM254643 2 0.2854 0.2583 0.000 0.792 0.000 0.000 0.000 0.208
#> GSM254727 2 0.3266 0.0313 0.000 0.728 0.000 0.000 0.000 0.272
#> GSM254640 2 0.4577 0.4881 0.000 0.656 0.000 0.272 0.000 0.072
#> GSM254626 2 0.0547 0.5538 0.000 0.980 0.000 0.000 0.000 0.020
#> GSM254635 4 0.5066 0.3522 0.000 0.176 0.000 0.636 0.000 0.188
#> GSM254653 2 0.3390 -0.0451 0.000 0.704 0.000 0.000 0.000 0.296
#> GSM254658 2 0.0000 0.5649 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM254681 2 0.2092 0.5810 0.000 0.876 0.000 0.124 0.000 0.000
#> GSM254719 6 0.3868 0.6355 0.000 0.492 0.000 0.000 0.000 0.508
#> GSM254673 2 0.3607 -0.2371 0.000 0.652 0.000 0.000 0.000 0.348
#> GSM254655 6 0.4328 0.6853 0.000 0.460 0.000 0.020 0.000 0.520
#> GSM254669 2 0.0547 0.5520 0.000 0.980 0.000 0.000 0.000 0.020
#> GSM254699 6 0.3867 0.6450 0.000 0.488 0.000 0.000 0.000 0.512
#> GSM254703 4 0.0603 0.6652 0.000 0.004 0.000 0.980 0.000 0.016
#> GSM254708 2 0.3765 0.4191 0.000 0.596 0.000 0.404 0.000 0.000
#> GSM254715 6 0.6065 0.4738 0.000 0.280 0.000 0.316 0.000 0.404
#> GSM254628 2 0.0146 0.5632 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM254634 4 0.0363 0.6659 0.000 0.000 0.000 0.988 0.000 0.012
#> GSM254646 2 0.0000 0.5649 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM254671 4 0.5543 0.1489 0.000 0.240 0.000 0.556 0.000 0.204
#> GSM254711 4 0.4338 0.1627 0.000 0.020 0.000 0.492 0.000 0.488
#> GSM254717 2 0.3428 -0.1071 0.000 0.696 0.000 0.000 0.000 0.304
#> GSM254723 3 0.1700 0.7728 0.000 0.004 0.916 0.000 0.000 0.080
#> GSM254730 2 0.5605 0.3230 0.000 0.544 0.000 0.212 0.000 0.244
#> GSM254731 6 0.5684 0.6653 0.000 0.280 0.000 0.200 0.000 0.520
#> GSM254632 3 0.5352 0.5151 0.000 0.024 0.644 0.232 0.004 0.096
#> GSM254662 2 0.3634 -0.2705 0.000 0.644 0.000 0.000 0.000 0.356
#> GSM254677 4 0.3409 0.5684 0.000 0.028 0.000 0.780 0.000 0.192
#> GSM254665 2 0.3817 0.3926 0.000 0.568 0.000 0.432 0.000 0.000
#> GSM254691 4 0.3756 0.0127 0.000 0.352 0.000 0.644 0.000 0.004
#> GSM254644 6 0.5705 0.6621 0.000 0.280 0.000 0.204 0.000 0.516
#> GSM254667 4 0.0363 0.6638 0.000 0.012 0.000 0.988 0.000 0.000
#> GSM254676 4 0.0363 0.6638 0.000 0.012 0.000 0.988 0.000 0.000
#> GSM254679 4 0.0632 0.6666 0.000 0.000 0.000 0.976 0.000 0.024
#> GSM254689 2 0.2854 0.5676 0.000 0.792 0.000 0.208 0.000 0.000
#> GSM254706 2 0.3706 0.4516 0.000 0.620 0.000 0.380 0.000 0.000
#> GSM254712 4 0.6004 -0.2476 0.000 0.280 0.000 0.436 0.000 0.284
#> GSM254713 4 0.5983 -0.2232 0.000 0.256 0.000 0.440 0.000 0.304
#> GSM254683 2 0.3860 0.3190 0.000 0.528 0.000 0.472 0.000 0.000
#> GSM254710 2 0.4453 0.4374 0.000 0.592 0.000 0.372 0.036 0.000
#> GSM254725 4 0.1714 0.6506 0.000 0.000 0.000 0.908 0.000 0.092
#> GSM254651 2 0.3482 0.5113 0.000 0.684 0.000 0.316 0.000 0.000
#> GSM254638 4 0.0000 0.6645 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM254685 4 0.4476 0.3600 0.000 0.052 0.000 0.640 0.000 0.308
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) individual(p) disease.state(p) gender(p) other(p) k
#> MAD:pam 107 1.59e-22 0.7770 0.577 0.628 0.872 2
#> MAD:pam 104 1.20e-21 0.0322 0.319 0.441 0.191 3
#> MAD:pam 104 9.35e-21 0.0119 0.557 0.226 0.411 4
#> MAD:pam 101 2.46e-19 0.0663 0.737 0.204 0.304 5
#> MAD:pam 79 7.62e-14 0.0985 0.652 0.225 0.345 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 21168 rows and 107 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 1.000 1.000 1.000 0.4953 0.505 0.505
#> 3 3 0.755 0.828 0.896 0.2675 0.859 0.721
#> 4 4 0.650 0.571 0.773 0.1174 0.930 0.817
#> 5 5 0.624 0.454 0.747 0.0859 0.919 0.762
#> 6 6 0.661 0.478 0.734 0.0437 0.877 0.580
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
#> GSM254629 1 0.0000 1.000 1.000 0.000
#> GSM254648 1 0.0000 1.000 1.000 0.000
#> GSM254694 1 0.0000 1.000 1.000 0.000
#> GSM254701 1 0.0000 1.000 1.000 0.000
#> GSM254728 1 0.0000 1.000 1.000 0.000
#> GSM254726 1 0.0000 1.000 1.000 0.000
#> GSM254639 1 0.0000 1.000 1.000 0.000
#> GSM254652 1 0.0000 1.000 1.000 0.000
#> GSM254700 1 0.0000 1.000 1.000 0.000
#> GSM254625 1 0.0000 1.000 1.000 0.000
#> GSM254636 1 0.0000 1.000 1.000 0.000
#> GSM254659 1 0.0000 1.000 1.000 0.000
#> GSM254680 1 0.0000 1.000 1.000 0.000
#> GSM254686 1 0.0000 1.000 1.000 0.000
#> GSM254718 1 0.0000 1.000 1.000 0.000
#> GSM254674 1 0.0000 1.000 1.000 0.000
#> GSM254668 1 0.0000 1.000 1.000 0.000
#> GSM254697 1 0.0000 1.000 1.000 0.000
#> GSM254704 1 0.0000 1.000 1.000 0.000
#> GSM254707 1 0.0000 1.000 1.000 0.000
#> GSM254714 1 0.0000 1.000 1.000 0.000
#> GSM254722 1 0.0000 1.000 1.000 0.000
#> GSM254627 1 0.0000 1.000 1.000 0.000
#> GSM254630 1 0.0000 1.000 1.000 0.000
#> GSM254633 1 0.0000 1.000 1.000 0.000
#> GSM254670 1 0.0000 1.000 1.000 0.000
#> GSM254716 1 0.0000 1.000 1.000 0.000
#> GSM254720 1 0.0000 1.000 1.000 0.000
#> GSM254729 1 0.0000 1.000 1.000 0.000
#> GSM254654 1 0.0000 1.000 1.000 0.000
#> GSM254656 1 0.0000 1.000 1.000 0.000
#> GSM254631 1 0.0000 1.000 1.000 0.000
#> GSM254657 1 0.0000 1.000 1.000 0.000
#> GSM254664 1 0.0000 1.000 1.000 0.000
#> GSM254672 1 0.0000 1.000 1.000 0.000
#> GSM254692 1 0.0000 1.000 1.000 0.000
#> GSM254645 1 0.0000 1.000 1.000 0.000
#> GSM254666 1 0.0000 1.000 1.000 0.000
#> GSM254675 1 0.0000 1.000 1.000 0.000
#> GSM254678 1 0.0000 1.000 1.000 0.000
#> GSM254688 1 0.0000 1.000 1.000 0.000
#> GSM254690 1 0.0000 1.000 1.000 0.000
#> GSM254696 1 0.0000 1.000 1.000 0.000
#> GSM254705 1 0.0000 1.000 1.000 0.000
#> GSM254642 1 0.0000 1.000 1.000 0.000
#> GSM254661 1 0.0000 1.000 1.000 0.000
#> GSM254698 1 0.0000 1.000 1.000 0.000
#> GSM254641 1 0.0000 1.000 1.000 0.000
#> GSM254647 1 0.0000 1.000 1.000 0.000
#> GSM254663 1 0.0000 1.000 1.000 0.000
#> GSM254682 1 0.0000 1.000 1.000 0.000
#> GSM254709 1 0.0000 1.000 1.000 0.000
#> GSM254721 1 0.0000 1.000 1.000 0.000
#> GSM254724 1 0.0000 1.000 1.000 0.000
#> GSM254650 1 0.0000 1.000 1.000 0.000
#> GSM254687 1 0.0000 1.000 1.000 0.000
#> GSM254637 1 0.0000 1.000 1.000 0.000
#> GSM254684 1 0.0000 1.000 1.000 0.000
#> GSM254649 2 0.0000 1.000 0.000 1.000
#> GSM254660 2 0.0000 1.000 0.000 1.000
#> GSM254693 2 0.0000 1.000 0.000 1.000
#> GSM254695 2 0.0000 1.000 0.000 1.000
#> GSM254702 2 0.0000 1.000 0.000 1.000
#> GSM254643 2 0.0000 1.000 0.000 1.000
#> GSM254727 2 0.0000 1.000 0.000 1.000
#> GSM254640 2 0.0000 1.000 0.000 1.000
#> GSM254626 2 0.0000 1.000 0.000 1.000
#> GSM254635 2 0.0000 1.000 0.000 1.000
#> GSM254653 2 0.0000 1.000 0.000 1.000
#> GSM254658 2 0.0000 1.000 0.000 1.000
#> GSM254681 2 0.0000 1.000 0.000 1.000
#> GSM254719 2 0.0000 1.000 0.000 1.000
#> GSM254673 2 0.0000 1.000 0.000 1.000
#> GSM254655 2 0.0000 1.000 0.000 1.000
#> GSM254669 2 0.0000 1.000 0.000 1.000
#> GSM254699 2 0.0000 1.000 0.000 1.000
#> GSM254703 2 0.0000 1.000 0.000 1.000
#> GSM254708 2 0.0000 1.000 0.000 1.000
#> GSM254715 2 0.0000 1.000 0.000 1.000
#> GSM254628 2 0.0000 1.000 0.000 1.000
#> GSM254634 2 0.0000 1.000 0.000 1.000
#> GSM254646 2 0.0000 1.000 0.000 1.000
#> GSM254671 2 0.0000 1.000 0.000 1.000
#> GSM254711 2 0.0000 1.000 0.000 1.000
#> GSM254717 2 0.0000 1.000 0.000 1.000
#> GSM254723 1 0.0672 0.992 0.992 0.008
#> GSM254730 2 0.0000 1.000 0.000 1.000
#> GSM254731 2 0.0000 1.000 0.000 1.000
#> GSM254632 1 0.0000 1.000 1.000 0.000
#> GSM254662 2 0.0000 1.000 0.000 1.000
#> GSM254677 2 0.0000 1.000 0.000 1.000
#> GSM254665 2 0.0000 1.000 0.000 1.000
#> GSM254691 2 0.0000 1.000 0.000 1.000
#> GSM254644 2 0.0000 1.000 0.000 1.000
#> GSM254667 2 0.0672 0.992 0.008 0.992
#> GSM254676 2 0.0000 1.000 0.000 1.000
#> GSM254679 2 0.0000 1.000 0.000 1.000
#> GSM254689 2 0.0000 1.000 0.000 1.000
#> GSM254706 2 0.0000 1.000 0.000 1.000
#> GSM254712 2 0.0000 1.000 0.000 1.000
#> GSM254713 2 0.0000 1.000 0.000 1.000
#> GSM254683 2 0.0000 1.000 0.000 1.000
#> GSM254710 1 0.0672 0.992 0.992 0.008
#> GSM254725 2 0.0000 1.000 0.000 1.000
#> GSM254651 2 0.0000 1.000 0.000 1.000
#> GSM254638 2 0.0000 1.000 0.000 1.000
#> GSM254685 2 0.0000 1.000 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM254629 3 0.6168 0.6441 0.412 0.000 0.588
#> GSM254648 3 0.2959 0.7449 0.100 0.000 0.900
#> GSM254694 3 0.5058 0.7967 0.244 0.000 0.756
#> GSM254701 3 0.6192 0.6274 0.420 0.000 0.580
#> GSM254728 1 0.6260 -0.3839 0.552 0.000 0.448
#> GSM254726 3 0.2959 0.7449 0.100 0.000 0.900
#> GSM254639 3 0.6280 0.6137 0.460 0.000 0.540
#> GSM254652 1 0.6274 -0.4197 0.544 0.000 0.456
#> GSM254700 1 0.1643 0.8766 0.956 0.000 0.044
#> GSM254625 3 0.6180 0.6772 0.416 0.000 0.584
#> GSM254636 1 0.0747 0.8973 0.984 0.000 0.016
#> GSM254659 1 0.6062 -0.0376 0.616 0.000 0.384
#> GSM254680 1 0.0592 0.8973 0.988 0.000 0.012
#> GSM254686 1 0.0592 0.8973 0.988 0.000 0.012
#> GSM254718 3 0.5760 0.7639 0.328 0.000 0.672
#> GSM254674 1 0.0592 0.8973 0.988 0.000 0.012
#> GSM254668 1 0.0592 0.8973 0.988 0.000 0.012
#> GSM254697 1 0.1289 0.8843 0.968 0.000 0.032
#> GSM254704 1 0.3941 0.7418 0.844 0.000 0.156
#> GSM254707 1 0.0592 0.8973 0.988 0.000 0.012
#> GSM254714 1 0.6111 0.0323 0.604 0.000 0.396
#> GSM254722 1 0.0747 0.8973 0.984 0.000 0.016
#> GSM254627 1 0.1289 0.8843 0.968 0.000 0.032
#> GSM254630 1 0.0592 0.8973 0.988 0.000 0.012
#> GSM254633 1 0.0747 0.8973 0.984 0.000 0.016
#> GSM254670 3 0.6286 0.6006 0.464 0.000 0.536
#> GSM254716 3 0.6235 0.6514 0.436 0.000 0.564
#> GSM254720 1 0.4235 0.7066 0.824 0.000 0.176
#> GSM254729 3 0.5291 0.7960 0.268 0.000 0.732
#> GSM254654 3 0.5138 0.7973 0.252 0.000 0.748
#> GSM254656 3 0.3879 0.7690 0.152 0.000 0.848
#> GSM254631 1 0.1031 0.8937 0.976 0.000 0.024
#> GSM254657 3 0.5621 0.7861 0.308 0.000 0.692
#> GSM254664 1 0.1643 0.8741 0.956 0.000 0.044
#> GSM254672 1 0.2878 0.8131 0.904 0.000 0.096
#> GSM254692 1 0.0592 0.8881 0.988 0.000 0.012
#> GSM254645 3 0.5650 0.7779 0.312 0.000 0.688
#> GSM254666 1 0.0892 0.8934 0.980 0.000 0.020
#> GSM254675 1 0.0424 0.8907 0.992 0.000 0.008
#> GSM254678 1 0.0747 0.8973 0.984 0.000 0.016
#> GSM254688 1 0.0592 0.8973 0.988 0.000 0.012
#> GSM254690 1 0.0747 0.8973 0.984 0.000 0.016
#> GSM254696 1 0.0892 0.8961 0.980 0.000 0.020
#> GSM254705 1 0.0592 0.8973 0.988 0.000 0.012
#> GSM254642 1 0.1163 0.8849 0.972 0.000 0.028
#> GSM254661 3 0.5465 0.7911 0.288 0.000 0.712
#> GSM254698 1 0.0892 0.8961 0.980 0.000 0.020
#> GSM254641 1 0.0237 0.8963 0.996 0.000 0.004
#> GSM254647 1 0.0237 0.8963 0.996 0.000 0.004
#> GSM254663 1 0.0000 0.8950 1.000 0.000 0.000
#> GSM254682 1 0.0592 0.8973 0.988 0.000 0.012
#> GSM254709 1 0.0424 0.8907 0.992 0.000 0.008
#> GSM254721 1 0.3941 0.7418 0.844 0.000 0.156
#> GSM254724 1 0.2448 0.8466 0.924 0.000 0.076
#> GSM254650 1 0.0237 0.8931 0.996 0.000 0.004
#> GSM254687 1 0.0000 0.8950 1.000 0.000 0.000
#> GSM254637 1 0.4235 0.7073 0.824 0.000 0.176
#> GSM254684 1 0.0892 0.8961 0.980 0.000 0.020
#> GSM254649 2 0.0000 0.9483 0.000 1.000 0.000
#> GSM254660 2 0.2356 0.9361 0.000 0.928 0.072
#> GSM254693 2 0.0000 0.9483 0.000 1.000 0.000
#> GSM254695 2 0.5138 0.8235 0.000 0.748 0.252
#> GSM254702 2 0.2356 0.9361 0.000 0.928 0.072
#> GSM254643 2 0.0000 0.9483 0.000 1.000 0.000
#> GSM254727 2 0.0000 0.9483 0.000 1.000 0.000
#> GSM254640 2 0.0747 0.9468 0.000 0.984 0.016
#> GSM254626 2 0.0000 0.9483 0.000 1.000 0.000
#> GSM254635 2 0.4931 0.8407 0.000 0.768 0.232
#> GSM254653 2 0.0000 0.9483 0.000 1.000 0.000
#> GSM254658 2 0.0000 0.9483 0.000 1.000 0.000
#> GSM254681 2 0.0000 0.9483 0.000 1.000 0.000
#> GSM254719 2 0.0000 0.9483 0.000 1.000 0.000
#> GSM254673 2 0.0000 0.9483 0.000 1.000 0.000
#> GSM254655 2 0.0000 0.9483 0.000 1.000 0.000
#> GSM254669 2 0.0000 0.9483 0.000 1.000 0.000
#> GSM254699 2 0.0000 0.9483 0.000 1.000 0.000
#> GSM254703 2 0.2356 0.9361 0.000 0.928 0.072
#> GSM254708 2 0.3941 0.8599 0.000 0.844 0.156
#> GSM254715 2 0.2356 0.9361 0.000 0.928 0.072
#> GSM254628 2 0.0000 0.9483 0.000 1.000 0.000
#> GSM254634 2 0.3267 0.9194 0.000 0.884 0.116
#> GSM254646 2 0.0000 0.9483 0.000 1.000 0.000
#> GSM254671 2 0.2356 0.9361 0.000 0.928 0.072
#> GSM254711 2 0.2356 0.9361 0.000 0.928 0.072
#> GSM254717 2 0.0000 0.9483 0.000 1.000 0.000
#> GSM254723 3 0.2796 0.7388 0.092 0.000 0.908
#> GSM254730 2 0.1289 0.9444 0.000 0.968 0.032
#> GSM254731 2 0.2356 0.9361 0.000 0.928 0.072
#> GSM254632 3 0.2711 0.7352 0.088 0.000 0.912
#> GSM254662 2 0.0000 0.9483 0.000 1.000 0.000
#> GSM254677 2 0.4887 0.8441 0.000 0.772 0.228
#> GSM254665 2 0.0000 0.9483 0.000 1.000 0.000
#> GSM254691 2 0.0592 0.9459 0.000 0.988 0.012
#> GSM254644 2 0.2165 0.9380 0.000 0.936 0.064
#> GSM254667 2 0.5216 0.7506 0.000 0.740 0.260
#> GSM254676 2 0.0237 0.9477 0.000 0.996 0.004
#> GSM254679 2 0.2448 0.9353 0.000 0.924 0.076
#> GSM254689 2 0.0000 0.9483 0.000 1.000 0.000
#> GSM254706 2 0.4178 0.8469 0.000 0.828 0.172
#> GSM254712 2 0.2356 0.9361 0.000 0.928 0.072
#> GSM254713 2 0.2356 0.9361 0.000 0.928 0.072
#> GSM254683 2 0.0237 0.9477 0.000 0.996 0.004
#> GSM254710 3 0.4902 0.6936 0.092 0.064 0.844
#> GSM254725 2 0.5058 0.8305 0.000 0.756 0.244
#> GSM254651 2 0.0237 0.9477 0.000 0.996 0.004
#> GSM254638 2 0.5138 0.8235 0.000 0.748 0.252
#> GSM254685 2 0.2356 0.9361 0.000 0.928 0.072
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM254629 3 0.7080 0.58345 0.196 0.000 0.568 0.236
#> GSM254648 3 0.2198 0.71795 0.072 0.000 0.920 0.008
#> GSM254694 3 0.2011 0.71932 0.080 0.000 0.920 0.000
#> GSM254701 3 0.7093 0.49647 0.272 0.000 0.556 0.172
#> GSM254728 3 0.6454 0.63998 0.380 0.000 0.544 0.076
#> GSM254726 3 0.4852 0.68666 0.072 0.000 0.776 0.152
#> GSM254639 3 0.6831 0.67436 0.352 0.000 0.536 0.112
#> GSM254652 3 0.7541 0.63897 0.388 0.000 0.424 0.188
#> GSM254700 1 0.7121 0.66112 0.544 0.000 0.164 0.292
#> GSM254625 3 0.7598 0.67399 0.324 0.000 0.460 0.216
#> GSM254636 1 0.2473 0.68760 0.908 0.000 0.080 0.012
#> GSM254659 1 0.5817 0.26054 0.676 0.000 0.248 0.076
#> GSM254680 1 0.3486 0.75904 0.812 0.000 0.000 0.188
#> GSM254686 1 0.0469 0.72338 0.988 0.000 0.000 0.012
#> GSM254718 3 0.6396 0.65208 0.360 0.000 0.564 0.076
#> GSM254674 1 0.0469 0.70967 0.988 0.000 0.000 0.012
#> GSM254668 1 0.2530 0.75721 0.888 0.000 0.000 0.112
#> GSM254697 1 0.7155 0.65862 0.540 0.000 0.168 0.292
#> GSM254704 1 0.6488 0.68246 0.604 0.000 0.104 0.292
#> GSM254707 1 0.0188 0.71458 0.996 0.000 0.000 0.004
#> GSM254714 3 0.7782 -0.11974 0.360 0.000 0.396 0.244
#> GSM254722 1 0.4352 0.67673 0.816 0.000 0.080 0.104
#> GSM254627 1 0.6770 0.68630 0.580 0.000 0.128 0.292
#> GSM254630 1 0.1940 0.65522 0.924 0.000 0.000 0.076
#> GSM254633 1 0.4549 0.75784 0.776 0.000 0.036 0.188
#> GSM254670 3 0.6409 0.65830 0.364 0.000 0.560 0.076
#> GSM254716 1 0.7544 -0.56558 0.460 0.000 0.340 0.200
#> GSM254720 1 0.6545 0.68651 0.632 0.000 0.152 0.216
#> GSM254729 3 0.3335 0.72870 0.128 0.000 0.856 0.016
#> GSM254654 3 0.2011 0.71932 0.080 0.000 0.920 0.000
#> GSM254656 3 0.3144 0.71735 0.072 0.000 0.884 0.044
#> GSM254631 1 0.5500 0.74243 0.708 0.000 0.068 0.224
#> GSM254657 3 0.6508 0.67776 0.344 0.000 0.568 0.088
#> GSM254664 1 0.5078 0.73862 0.700 0.000 0.028 0.272
#> GSM254672 1 0.4399 0.75494 0.760 0.000 0.016 0.224
#> GSM254692 1 0.4483 0.73246 0.712 0.000 0.004 0.284
#> GSM254645 3 0.6369 0.66980 0.352 0.000 0.572 0.076
#> GSM254666 1 0.2011 0.65204 0.920 0.000 0.000 0.080
#> GSM254675 1 0.4808 0.75234 0.736 0.000 0.028 0.236
#> GSM254678 1 0.2255 0.70725 0.920 0.000 0.068 0.012
#> GSM254688 1 0.0469 0.70967 0.988 0.000 0.000 0.012
#> GSM254690 1 0.2466 0.74431 0.916 0.000 0.028 0.056
#> GSM254696 1 0.4872 0.52509 0.776 0.000 0.148 0.076
#> GSM254705 1 0.1940 0.68003 0.924 0.000 0.000 0.076
#> GSM254642 1 0.6488 0.67882 0.604 0.000 0.104 0.292
#> GSM254661 3 0.7634 0.67012 0.340 0.000 0.444 0.216
#> GSM254698 1 0.3764 0.62773 0.852 0.000 0.072 0.076
#> GSM254641 1 0.3311 0.76018 0.828 0.000 0.000 0.172
#> GSM254647 1 0.3975 0.74884 0.760 0.000 0.000 0.240
#> GSM254663 1 0.3907 0.75105 0.768 0.000 0.000 0.232
#> GSM254682 1 0.1022 0.69621 0.968 0.000 0.000 0.032
#> GSM254709 1 0.4250 0.73641 0.724 0.000 0.000 0.276
#> GSM254721 1 0.6896 0.67336 0.568 0.000 0.140 0.292
#> GSM254724 1 0.6896 0.67336 0.568 0.000 0.140 0.292
#> GSM254650 1 0.2647 0.75760 0.880 0.000 0.000 0.120
#> GSM254687 1 0.2589 0.75698 0.884 0.000 0.000 0.116
#> GSM254637 1 0.6724 0.67339 0.612 0.000 0.164 0.224
#> GSM254684 1 0.4401 0.56201 0.812 0.000 0.112 0.076
#> GSM254649 2 0.0000 0.75254 0.000 1.000 0.000 0.000
#> GSM254660 2 0.1867 0.68668 0.000 0.928 0.000 0.072
#> GSM254693 2 0.0000 0.75254 0.000 1.000 0.000 0.000
#> GSM254695 4 0.6013 0.63829 0.000 0.312 0.064 0.624
#> GSM254702 2 0.3649 0.41467 0.000 0.796 0.000 0.204
#> GSM254643 2 0.0000 0.75254 0.000 1.000 0.000 0.000
#> GSM254727 2 0.0000 0.75254 0.000 1.000 0.000 0.000
#> GSM254640 2 0.0188 0.75126 0.000 0.996 0.000 0.004
#> GSM254626 2 0.0000 0.75254 0.000 1.000 0.000 0.000
#> GSM254635 2 0.4998 -0.75428 0.000 0.512 0.000 0.488
#> GSM254653 2 0.0000 0.75254 0.000 1.000 0.000 0.000
#> GSM254658 2 0.0000 0.75254 0.000 1.000 0.000 0.000
#> GSM254681 2 0.0336 0.75015 0.000 0.992 0.000 0.008
#> GSM254719 2 0.0000 0.75254 0.000 1.000 0.000 0.000
#> GSM254673 2 0.0000 0.75254 0.000 1.000 0.000 0.000
#> GSM254655 2 0.0000 0.75254 0.000 1.000 0.000 0.000
#> GSM254669 2 0.0000 0.75254 0.000 1.000 0.000 0.000
#> GSM254699 2 0.0000 0.75254 0.000 1.000 0.000 0.000
#> GSM254703 2 0.4830 -0.38126 0.000 0.608 0.000 0.392
#> GSM254708 2 0.3356 0.52879 0.000 0.824 0.000 0.176
#> GSM254715 2 0.4790 -0.34231 0.000 0.620 0.000 0.380
#> GSM254628 2 0.0000 0.75254 0.000 1.000 0.000 0.000
#> GSM254634 4 0.5000 0.73025 0.000 0.496 0.000 0.504
#> GSM254646 2 0.0336 0.75015 0.000 0.992 0.000 0.008
#> GSM254671 2 0.4761 -0.30463 0.000 0.628 0.000 0.372
#> GSM254711 2 0.4817 -0.36897 0.000 0.612 0.000 0.388
#> GSM254717 2 0.0000 0.75254 0.000 1.000 0.000 0.000
#> GSM254723 3 0.3693 0.70832 0.072 0.000 0.856 0.072
#> GSM254730 2 0.0592 0.74401 0.000 0.984 0.000 0.016
#> GSM254731 2 0.2973 0.56391 0.000 0.856 0.000 0.144
#> GSM254632 3 0.4944 0.68378 0.072 0.000 0.768 0.160
#> GSM254662 2 0.0000 0.75254 0.000 1.000 0.000 0.000
#> GSM254677 4 0.4989 0.79443 0.000 0.472 0.000 0.528
#> GSM254665 2 0.0469 0.74816 0.000 0.988 0.000 0.012
#> GSM254691 2 0.2011 0.68491 0.000 0.920 0.000 0.080
#> GSM254644 2 0.1637 0.70158 0.000 0.940 0.000 0.060
#> GSM254667 2 0.5057 -0.00966 0.000 0.648 0.012 0.340
#> GSM254676 2 0.1637 0.70817 0.000 0.940 0.000 0.060
#> GSM254679 2 0.4830 -0.38126 0.000 0.608 0.000 0.392
#> GSM254689 2 0.0336 0.75015 0.000 0.992 0.000 0.008
#> GSM254706 2 0.2469 0.64472 0.000 0.892 0.000 0.108
#> GSM254712 2 0.4830 -0.38126 0.000 0.608 0.000 0.392
#> GSM254713 2 0.4804 -0.35541 0.000 0.616 0.000 0.384
#> GSM254683 2 0.1940 0.69162 0.000 0.924 0.000 0.076
#> GSM254710 3 0.5033 0.67988 0.072 0.000 0.760 0.168
#> GSM254725 2 0.4999 -0.76328 0.000 0.508 0.000 0.492
#> GSM254651 2 0.0707 0.74336 0.000 0.980 0.000 0.020
#> GSM254638 4 0.4967 0.80193 0.000 0.452 0.000 0.548
#> GSM254685 2 0.3569 0.43929 0.000 0.804 0.000 0.196
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM254629 3 0.5579 0.3847 0.300 0.000 0.600 0.000 0.100
#> GSM254648 3 0.1408 0.6510 0.000 0.000 0.948 0.008 0.044
#> GSM254694 3 0.1251 0.6529 0.000 0.000 0.956 0.008 0.036
#> GSM254701 3 0.5775 0.1386 0.440 0.000 0.472 0.000 0.088
#> GSM254728 3 0.5941 0.4804 0.188 0.000 0.612 0.004 0.196
#> GSM254726 3 0.2104 0.6336 0.000 0.000 0.916 0.060 0.024
#> GSM254639 3 0.5077 0.5986 0.088 0.000 0.696 0.004 0.212
#> GSM254652 3 0.5505 0.5652 0.128 0.000 0.660 0.004 0.208
#> GSM254700 1 0.4262 0.2696 0.560 0.000 0.000 0.000 0.440
#> GSM254625 5 0.7020 0.2584 0.132 0.000 0.364 0.044 0.460
#> GSM254636 1 0.5435 0.4394 0.672 0.000 0.188 0.004 0.136
#> GSM254659 1 0.6553 0.1319 0.472 0.000 0.340 0.004 0.184
#> GSM254680 1 0.1478 0.4648 0.936 0.000 0.000 0.000 0.064
#> GSM254686 1 0.2648 0.4420 0.848 0.000 0.000 0.000 0.152
#> GSM254718 3 0.5707 0.4801 0.216 0.000 0.624 0.000 0.160
#> GSM254674 1 0.4499 0.4711 0.764 0.000 0.096 0.004 0.136
#> GSM254668 1 0.3966 0.1982 0.664 0.000 0.000 0.000 0.336
#> GSM254697 1 0.4300 0.2563 0.524 0.000 0.000 0.000 0.476
#> GSM254704 1 0.4522 0.2646 0.552 0.000 0.008 0.000 0.440
#> GSM254707 1 0.4171 0.1323 0.604 0.000 0.000 0.000 0.396
#> GSM254714 1 0.6172 0.1490 0.544 0.000 0.280 0.000 0.176
#> GSM254722 1 0.5135 0.4489 0.704 0.000 0.120 0.004 0.172
#> GSM254627 1 0.4300 0.2563 0.524 0.000 0.000 0.000 0.476
#> GSM254630 1 0.4291 0.0129 0.536 0.000 0.000 0.000 0.464
#> GSM254633 1 0.2462 0.5096 0.880 0.000 0.112 0.000 0.008
#> GSM254670 3 0.5393 0.5729 0.120 0.000 0.672 0.004 0.204
#> GSM254716 5 0.6990 0.3875 0.248 0.000 0.236 0.024 0.492
#> GSM254720 1 0.4094 0.4910 0.788 0.000 0.084 0.000 0.128
#> GSM254729 3 0.0865 0.6619 0.004 0.000 0.972 0.000 0.024
#> GSM254654 3 0.1569 0.6524 0.004 0.000 0.944 0.008 0.044
#> GSM254656 3 0.1018 0.6566 0.000 0.000 0.968 0.016 0.016
#> GSM254631 1 0.2773 0.5087 0.868 0.000 0.112 0.000 0.020
#> GSM254657 3 0.4702 0.6184 0.072 0.000 0.732 0.004 0.192
#> GSM254664 1 0.3109 0.4555 0.800 0.000 0.000 0.000 0.200
#> GSM254672 1 0.2969 0.4983 0.852 0.000 0.020 0.000 0.128
#> GSM254692 5 0.4291 -0.1090 0.464 0.000 0.000 0.000 0.536
#> GSM254645 3 0.4641 0.6251 0.080 0.000 0.744 0.004 0.172
#> GSM254666 1 0.4130 0.3018 0.696 0.000 0.012 0.000 0.292
#> GSM254675 1 0.2074 0.4979 0.896 0.000 0.000 0.000 0.104
#> GSM254678 1 0.4779 0.4750 0.740 0.000 0.144 0.004 0.112
#> GSM254688 1 0.4150 0.1433 0.612 0.000 0.000 0.000 0.388
#> GSM254690 1 0.4375 0.4930 0.776 0.000 0.116 0.004 0.104
#> GSM254696 1 0.6417 0.2775 0.528 0.000 0.272 0.004 0.196
#> GSM254705 1 0.4437 0.0305 0.532 0.000 0.000 0.004 0.464
#> GSM254642 1 0.4300 0.2563 0.524 0.000 0.000 0.000 0.476
#> GSM254661 3 0.5410 0.6104 0.080 0.000 0.692 0.024 0.204
#> GSM254698 1 0.6130 0.3517 0.584 0.000 0.216 0.004 0.196
#> GSM254641 1 0.0671 0.4883 0.980 0.000 0.004 0.000 0.016
#> GSM254647 1 0.2471 0.4905 0.864 0.000 0.000 0.000 0.136
#> GSM254663 1 0.3480 0.2594 0.752 0.000 0.000 0.000 0.248
#> GSM254682 1 0.4375 0.0856 0.576 0.000 0.004 0.000 0.420
#> GSM254709 1 0.3752 0.2173 0.708 0.000 0.000 0.000 0.292
#> GSM254721 1 0.4262 0.2696 0.560 0.000 0.000 0.000 0.440
#> GSM254724 1 0.4262 0.2696 0.560 0.000 0.000 0.000 0.440
#> GSM254650 1 0.4126 0.1447 0.620 0.000 0.000 0.000 0.380
#> GSM254687 1 0.4138 0.1431 0.616 0.000 0.000 0.000 0.384
#> GSM254637 1 0.4159 0.4803 0.776 0.000 0.156 0.000 0.068
#> GSM254684 1 0.6442 0.2680 0.524 0.000 0.272 0.004 0.200
#> GSM254649 2 0.0162 0.7737 0.000 0.996 0.000 0.000 0.004
#> GSM254660 2 0.4238 0.0437 0.000 0.628 0.000 0.368 0.004
#> GSM254693 2 0.0000 0.7739 0.000 1.000 0.000 0.000 0.000
#> GSM254695 4 0.1168 0.5937 0.000 0.032 0.008 0.960 0.000
#> GSM254702 2 0.4114 -0.0078 0.000 0.624 0.000 0.376 0.000
#> GSM254643 2 0.1282 0.7628 0.000 0.952 0.000 0.044 0.004
#> GSM254727 2 0.0162 0.7737 0.000 0.996 0.000 0.000 0.004
#> GSM254640 2 0.1270 0.7381 0.000 0.948 0.000 0.052 0.000
#> GSM254626 2 0.0000 0.7739 0.000 1.000 0.000 0.000 0.000
#> GSM254635 4 0.2561 0.6755 0.000 0.144 0.000 0.856 0.000
#> GSM254653 2 0.0162 0.7737 0.000 0.996 0.000 0.000 0.004
#> GSM254658 2 0.0324 0.7721 0.000 0.992 0.000 0.004 0.004
#> GSM254681 2 0.2124 0.7348 0.000 0.900 0.000 0.096 0.004
#> GSM254719 2 0.0000 0.7739 0.000 1.000 0.000 0.000 0.000
#> GSM254673 2 0.0000 0.7739 0.000 1.000 0.000 0.000 0.000
#> GSM254655 2 0.0000 0.7739 0.000 1.000 0.000 0.000 0.000
#> GSM254669 2 0.0000 0.7739 0.000 1.000 0.000 0.000 0.000
#> GSM254699 2 0.0000 0.7739 0.000 1.000 0.000 0.000 0.000
#> GSM254703 4 0.4045 0.5565 0.000 0.356 0.000 0.644 0.000
#> GSM254708 2 0.4235 0.1321 0.000 0.576 0.000 0.424 0.000
#> GSM254715 2 0.4403 -0.1722 0.000 0.560 0.000 0.436 0.004
#> GSM254628 2 0.0324 0.7721 0.000 0.992 0.000 0.004 0.004
#> GSM254634 4 0.3003 0.6697 0.000 0.188 0.000 0.812 0.000
#> GSM254646 2 0.1952 0.7427 0.000 0.912 0.000 0.084 0.004
#> GSM254671 2 0.4294 -0.2490 0.000 0.532 0.000 0.468 0.000
#> GSM254711 4 0.4283 0.3860 0.000 0.456 0.000 0.544 0.000
#> GSM254717 2 0.0865 0.7693 0.000 0.972 0.000 0.024 0.004
#> GSM254723 3 0.1914 0.6381 0.000 0.000 0.924 0.060 0.016
#> GSM254730 2 0.0404 0.7700 0.000 0.988 0.000 0.012 0.000
#> GSM254731 2 0.4126 -0.0196 0.000 0.620 0.000 0.380 0.000
#> GSM254632 3 0.2171 0.6307 0.000 0.000 0.912 0.064 0.024
#> GSM254662 2 0.0162 0.7742 0.000 0.996 0.000 0.004 0.000
#> GSM254677 4 0.2127 0.6598 0.000 0.108 0.000 0.892 0.000
#> GSM254665 2 0.2536 0.7062 0.000 0.868 0.000 0.128 0.004
#> GSM254691 2 0.3074 0.6220 0.000 0.804 0.000 0.196 0.000
#> GSM254644 2 0.4047 0.1889 0.000 0.676 0.000 0.320 0.004
#> GSM254667 4 0.4306 -0.0335 0.000 0.492 0.000 0.508 0.000
#> GSM254676 2 0.2648 0.6772 0.000 0.848 0.000 0.152 0.000
#> GSM254679 4 0.4242 0.4395 0.000 0.428 0.000 0.572 0.000
#> GSM254689 2 0.2124 0.7348 0.000 0.900 0.000 0.096 0.004
#> GSM254706 2 0.3916 0.5119 0.000 0.732 0.000 0.256 0.012
#> GSM254712 4 0.4060 0.5512 0.000 0.360 0.000 0.640 0.000
#> GSM254713 4 0.4452 0.2959 0.000 0.496 0.000 0.500 0.004
#> GSM254683 2 0.2953 0.6801 0.000 0.844 0.000 0.144 0.012
#> GSM254710 3 0.6115 0.3196 0.000 0.156 0.668 0.104 0.072
#> GSM254725 4 0.3143 0.6685 0.000 0.204 0.000 0.796 0.000
#> GSM254651 2 0.2677 0.7098 0.000 0.872 0.000 0.112 0.016
#> GSM254638 4 0.1544 0.6461 0.000 0.068 0.000 0.932 0.000
#> GSM254685 2 0.4367 -0.1090 0.000 0.580 0.000 0.416 0.004
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM254629 3 0.5516 0.2416 0.232 0.000 0.640 0.004 0.048 0.076
#> GSM254648 6 0.4039 0.9048 0.008 0.000 0.424 0.000 0.000 0.568
#> GSM254694 3 0.4118 -0.5932 0.008 0.000 0.592 0.004 0.000 0.396
#> GSM254701 3 0.6027 0.2904 0.260 0.000 0.560 0.004 0.148 0.028
#> GSM254728 3 0.3481 0.5931 0.000 0.000 0.804 0.000 0.124 0.072
#> GSM254726 6 0.3797 0.9131 0.000 0.000 0.420 0.000 0.000 0.580
#> GSM254639 3 0.3843 0.5795 0.004 0.000 0.784 0.000 0.104 0.108
#> GSM254652 3 0.2302 0.5937 0.000 0.000 0.872 0.000 0.120 0.008
#> GSM254700 1 0.1588 0.5948 0.924 0.000 0.004 0.000 0.072 0.000
#> GSM254625 5 0.4482 -0.1228 0.000 0.000 0.416 0.000 0.552 0.032
#> GSM254636 5 0.6997 0.2462 0.148 0.000 0.328 0.000 0.416 0.108
#> GSM254659 3 0.5136 0.5026 0.020 0.000 0.656 0.000 0.224 0.100
#> GSM254680 5 0.4326 0.1908 0.300 0.000 0.044 0.000 0.656 0.000
#> GSM254686 5 0.2794 0.5251 0.080 0.000 0.060 0.000 0.860 0.000
#> GSM254718 3 0.3830 0.5581 0.040 0.000 0.800 0.004 0.132 0.024
#> GSM254674 5 0.4740 0.4174 0.108 0.000 0.228 0.000 0.664 0.000
#> GSM254668 5 0.1219 0.5381 0.048 0.000 0.004 0.000 0.948 0.000
#> GSM254697 1 0.2318 0.5913 0.892 0.000 0.044 0.000 0.064 0.000
#> GSM254704 1 0.1982 0.5918 0.912 0.000 0.016 0.004 0.068 0.000
#> GSM254707 5 0.0260 0.5655 0.000 0.000 0.008 0.000 0.992 0.000
#> GSM254714 1 0.6201 0.2355 0.396 0.000 0.292 0.004 0.308 0.000
#> GSM254722 5 0.6056 0.1022 0.296 0.000 0.292 0.000 0.412 0.000
#> GSM254627 1 0.2318 0.5913 0.892 0.000 0.044 0.000 0.064 0.000
#> GSM254630 5 0.0935 0.5617 0.004 0.000 0.032 0.000 0.964 0.000
#> GSM254633 5 0.5871 -0.2565 0.408 0.000 0.168 0.000 0.420 0.004
#> GSM254670 3 0.3930 0.5799 0.004 0.000 0.776 0.000 0.116 0.104
#> GSM254716 5 0.4219 -0.0755 0.000 0.000 0.388 0.000 0.592 0.020
#> GSM254720 1 0.5319 0.3502 0.504 0.000 0.108 0.000 0.388 0.000
#> GSM254729 3 0.2980 0.1916 0.000 0.000 0.808 0.000 0.012 0.180
#> GSM254654 3 0.4144 -0.6230 0.008 0.000 0.580 0.004 0.000 0.408
#> GSM254656 6 0.4204 0.8660 0.008 0.000 0.448 0.000 0.004 0.540
#> GSM254631 1 0.5353 0.2905 0.472 0.000 0.108 0.000 0.420 0.000
#> GSM254657 3 0.2191 0.5896 0.000 0.000 0.876 0.000 0.120 0.004
#> GSM254664 1 0.4343 0.4226 0.592 0.000 0.028 0.000 0.380 0.000
#> GSM254672 1 0.5414 0.3008 0.468 0.000 0.116 0.000 0.416 0.000
#> GSM254692 1 0.3810 0.1787 0.572 0.000 0.000 0.000 0.428 0.000
#> GSM254645 3 0.3023 0.5711 0.000 0.000 0.836 0.000 0.120 0.044
#> GSM254666 5 0.1844 0.5645 0.024 0.000 0.048 0.000 0.924 0.004
#> GSM254675 1 0.4639 0.3190 0.512 0.000 0.040 0.000 0.448 0.000
#> GSM254678 5 0.6141 0.1622 0.244 0.000 0.292 0.000 0.456 0.008
#> GSM254688 5 0.0146 0.5641 0.000 0.000 0.004 0.000 0.996 0.000
#> GSM254690 5 0.6019 0.0800 0.300 0.000 0.232 0.000 0.464 0.004
#> GSM254696 3 0.6911 -0.2658 0.128 0.000 0.396 0.000 0.368 0.108
#> GSM254705 5 0.1327 0.5569 0.000 0.000 0.064 0.000 0.936 0.000
#> GSM254642 1 0.2318 0.5913 0.892 0.000 0.044 0.000 0.064 0.000
#> GSM254661 3 0.3285 0.5507 0.000 0.000 0.820 0.000 0.116 0.064
#> GSM254698 5 0.6981 0.2551 0.140 0.000 0.364 0.000 0.388 0.108
#> GSM254641 5 0.5007 -0.1884 0.416 0.000 0.072 0.000 0.512 0.000
#> GSM254647 1 0.4925 0.3427 0.512 0.000 0.064 0.000 0.424 0.000
#> GSM254663 5 0.3470 0.3557 0.200 0.000 0.028 0.000 0.772 0.000
#> GSM254682 5 0.0363 0.5642 0.000 0.000 0.012 0.000 0.988 0.000
#> GSM254709 5 0.3309 0.3758 0.192 0.000 0.016 0.004 0.788 0.000
#> GSM254721 1 0.2011 0.5892 0.912 0.000 0.020 0.004 0.064 0.000
#> GSM254724 1 0.2011 0.5892 0.912 0.000 0.020 0.004 0.064 0.000
#> GSM254650 5 0.0547 0.5558 0.020 0.000 0.000 0.000 0.980 0.000
#> GSM254687 5 0.0291 0.5644 0.004 0.000 0.004 0.000 0.992 0.000
#> GSM254637 1 0.5542 0.3315 0.492 0.000 0.120 0.004 0.384 0.000
#> GSM254684 5 0.6809 0.1507 0.112 0.000 0.388 0.000 0.392 0.108
#> GSM254649 2 0.0972 0.7815 0.008 0.964 0.000 0.000 0.000 0.028
#> GSM254660 2 0.4141 0.2432 0.008 0.676 0.000 0.296 0.000 0.020
#> GSM254693 2 0.0000 0.7833 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM254695 4 0.1858 0.5850 0.004 0.000 0.000 0.904 0.000 0.092
#> GSM254702 2 0.3955 -0.2359 0.000 0.560 0.000 0.436 0.000 0.004
#> GSM254643 2 0.1434 0.7749 0.008 0.948 0.000 0.024 0.000 0.020
#> GSM254727 2 0.1053 0.7817 0.012 0.964 0.000 0.004 0.000 0.020
#> GSM254640 2 0.1173 0.7788 0.008 0.960 0.000 0.016 0.000 0.016
#> GSM254626 2 0.0291 0.7836 0.000 0.992 0.000 0.004 0.000 0.004
#> GSM254635 4 0.1610 0.6999 0.000 0.084 0.000 0.916 0.000 0.000
#> GSM254653 2 0.0603 0.7829 0.004 0.980 0.000 0.000 0.000 0.016
#> GSM254658 2 0.1036 0.7812 0.008 0.964 0.000 0.004 0.000 0.024
#> GSM254681 2 0.4434 0.5867 0.008 0.684 0.000 0.048 0.000 0.260
#> GSM254719 2 0.0291 0.7827 0.000 0.992 0.000 0.004 0.000 0.004
#> GSM254673 2 0.0146 0.7830 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM254655 2 0.0291 0.7827 0.000 0.992 0.000 0.004 0.000 0.004
#> GSM254669 2 0.0291 0.7832 0.004 0.992 0.000 0.004 0.000 0.000
#> GSM254699 2 0.0291 0.7827 0.000 0.992 0.000 0.004 0.000 0.004
#> GSM254703 4 0.2631 0.6910 0.000 0.180 0.000 0.820 0.000 0.000
#> GSM254708 2 0.4384 0.1748 0.004 0.520 0.000 0.460 0.000 0.016
#> GSM254715 4 0.4387 0.5440 0.004 0.404 0.000 0.572 0.000 0.020
#> GSM254628 2 0.1036 0.7812 0.008 0.964 0.000 0.004 0.000 0.024
#> GSM254634 4 0.1700 0.6975 0.004 0.080 0.000 0.916 0.000 0.000
#> GSM254646 2 0.4178 0.6007 0.008 0.700 0.000 0.032 0.000 0.260
#> GSM254671 4 0.3966 0.4782 0.004 0.444 0.000 0.552 0.000 0.000
#> GSM254711 4 0.3969 0.6106 0.004 0.344 0.000 0.644 0.000 0.008
#> GSM254717 2 0.1592 0.7746 0.008 0.940 0.000 0.032 0.000 0.020
#> GSM254723 6 0.3797 0.9131 0.000 0.000 0.420 0.000 0.000 0.580
#> GSM254730 2 0.0653 0.7803 0.004 0.980 0.000 0.012 0.000 0.004
#> GSM254731 2 0.3950 -0.2245 0.000 0.564 0.000 0.432 0.000 0.004
#> GSM254632 6 0.3797 0.9131 0.000 0.000 0.420 0.000 0.000 0.580
#> GSM254662 2 0.0405 0.7835 0.004 0.988 0.000 0.008 0.000 0.000
#> GSM254677 4 0.1584 0.6933 0.000 0.064 0.000 0.928 0.000 0.008
#> GSM254665 2 0.3212 0.6443 0.004 0.800 0.000 0.180 0.000 0.016
#> GSM254691 2 0.3969 0.4294 0.004 0.644 0.000 0.344 0.000 0.008
#> GSM254644 2 0.3313 0.6017 0.008 0.808 0.000 0.160 0.000 0.024
#> GSM254667 4 0.5656 0.0471 0.000 0.380 0.004 0.480 0.000 0.136
#> GSM254676 2 0.3672 0.5329 0.004 0.712 0.000 0.276 0.000 0.008
#> GSM254679 4 0.3756 0.6046 0.004 0.352 0.000 0.644 0.000 0.000
#> GSM254689 2 0.4373 0.5901 0.008 0.688 0.000 0.044 0.000 0.260
#> GSM254706 2 0.4732 0.3522 0.004 0.588 0.000 0.360 0.000 0.048
#> GSM254712 4 0.3673 0.6681 0.004 0.244 0.000 0.736 0.000 0.016
#> GSM254713 4 0.4446 0.5647 0.008 0.384 0.000 0.588 0.000 0.020
#> GSM254683 2 0.5287 0.3999 0.004 0.588 0.000 0.288 0.000 0.120
#> GSM254710 6 0.5034 0.6787 0.008 0.044 0.280 0.004 0.016 0.648
#> GSM254725 4 0.1644 0.6955 0.000 0.076 0.000 0.920 0.000 0.004
#> GSM254651 2 0.2288 0.7518 0.004 0.896 0.000 0.072 0.000 0.028
#> GSM254638 4 0.0603 0.6553 0.000 0.016 0.000 0.980 0.000 0.004
#> GSM254685 4 0.4516 0.5064 0.008 0.420 0.000 0.552 0.000 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) individual(p) disease.state(p) gender(p) other(p) k
#> MAD:mclust 107 1.00e-21 0.6404 0.454 0.733 0.961 2
#> MAD:mclust 103 6.81e-21 0.0168 0.575 0.371 0.962 3
#> MAD:mclust 91 2.36e-17 0.0205 0.603 0.345 0.731 4
#> MAD:mclust 50 5.13e-09 0.4549 0.812 0.580 0.867 5
#> MAD:mclust 69 3.14e-12 0.2633 0.491 0.420 0.693 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 21168 rows and 107 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 1.000 0.975 0.989 0.5034 0.497 0.497
#> 3 3 0.618 0.632 0.804 0.2293 0.962 0.924
#> 4 4 0.626 0.735 0.843 0.1343 0.813 0.609
#> 5 5 0.675 0.647 0.763 0.0665 0.838 0.562
#> 6 6 0.705 0.629 0.801 0.0463 0.929 0.748
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
#> GSM254629 1 0.0000 0.984 1.000 0.000
#> GSM254648 2 0.0938 0.982 0.012 0.988
#> GSM254694 1 0.7299 0.748 0.796 0.204
#> GSM254701 1 0.0000 0.984 1.000 0.000
#> GSM254728 1 0.0000 0.984 1.000 0.000
#> GSM254726 1 0.7056 0.766 0.808 0.192
#> GSM254639 1 0.0000 0.984 1.000 0.000
#> GSM254652 1 0.0000 0.984 1.000 0.000
#> GSM254700 1 0.0000 0.984 1.000 0.000
#> GSM254625 1 0.0000 0.984 1.000 0.000
#> GSM254636 1 0.0000 0.984 1.000 0.000
#> GSM254659 1 0.0000 0.984 1.000 0.000
#> GSM254680 1 0.0000 0.984 1.000 0.000
#> GSM254686 1 0.0000 0.984 1.000 0.000
#> GSM254718 1 0.0000 0.984 1.000 0.000
#> GSM254674 1 0.0000 0.984 1.000 0.000
#> GSM254668 1 0.0000 0.984 1.000 0.000
#> GSM254697 1 0.0000 0.984 1.000 0.000
#> GSM254704 1 0.0000 0.984 1.000 0.000
#> GSM254707 1 0.0000 0.984 1.000 0.000
#> GSM254714 1 0.0000 0.984 1.000 0.000
#> GSM254722 1 0.0000 0.984 1.000 0.000
#> GSM254627 1 0.0000 0.984 1.000 0.000
#> GSM254630 1 0.0000 0.984 1.000 0.000
#> GSM254633 1 0.0000 0.984 1.000 0.000
#> GSM254670 1 0.0000 0.984 1.000 0.000
#> GSM254716 1 0.0000 0.984 1.000 0.000
#> GSM254720 1 0.0000 0.984 1.000 0.000
#> GSM254729 1 0.0000 0.984 1.000 0.000
#> GSM254654 1 0.6148 0.821 0.848 0.152
#> GSM254656 1 0.9000 0.545 0.684 0.316
#> GSM254631 1 0.0000 0.984 1.000 0.000
#> GSM254657 1 0.0000 0.984 1.000 0.000
#> GSM254664 1 0.0000 0.984 1.000 0.000
#> GSM254672 1 0.0000 0.984 1.000 0.000
#> GSM254692 1 0.0000 0.984 1.000 0.000
#> GSM254645 1 0.0000 0.984 1.000 0.000
#> GSM254666 1 0.0000 0.984 1.000 0.000
#> GSM254675 1 0.0000 0.984 1.000 0.000
#> GSM254678 1 0.0000 0.984 1.000 0.000
#> GSM254688 1 0.0000 0.984 1.000 0.000
#> GSM254690 1 0.0000 0.984 1.000 0.000
#> GSM254696 1 0.0000 0.984 1.000 0.000
#> GSM254705 1 0.0000 0.984 1.000 0.000
#> GSM254642 1 0.0000 0.984 1.000 0.000
#> GSM254661 1 0.0000 0.984 1.000 0.000
#> GSM254698 1 0.0000 0.984 1.000 0.000
#> GSM254641 1 0.0000 0.984 1.000 0.000
#> GSM254647 1 0.0000 0.984 1.000 0.000
#> GSM254663 1 0.0000 0.984 1.000 0.000
#> GSM254682 1 0.0000 0.984 1.000 0.000
#> GSM254709 1 0.0000 0.984 1.000 0.000
#> GSM254721 1 0.0000 0.984 1.000 0.000
#> GSM254724 1 0.0000 0.984 1.000 0.000
#> GSM254650 1 0.0000 0.984 1.000 0.000
#> GSM254687 1 0.0000 0.984 1.000 0.000
#> GSM254637 1 0.0000 0.984 1.000 0.000
#> GSM254684 1 0.0000 0.984 1.000 0.000
#> GSM254649 2 0.0000 0.993 0.000 1.000
#> GSM254660 2 0.0000 0.993 0.000 1.000
#> GSM254693 2 0.0000 0.993 0.000 1.000
#> GSM254695 2 0.0000 0.993 0.000 1.000
#> GSM254702 2 0.0000 0.993 0.000 1.000
#> GSM254643 2 0.0000 0.993 0.000 1.000
#> GSM254727 2 0.0000 0.993 0.000 1.000
#> GSM254640 2 0.0000 0.993 0.000 1.000
#> GSM254626 2 0.0000 0.993 0.000 1.000
#> GSM254635 2 0.0000 0.993 0.000 1.000
#> GSM254653 2 0.0000 0.993 0.000 1.000
#> GSM254658 2 0.0000 0.993 0.000 1.000
#> GSM254681 2 0.0000 0.993 0.000 1.000
#> GSM254719 2 0.0000 0.993 0.000 1.000
#> GSM254673 2 0.0000 0.993 0.000 1.000
#> GSM254655 2 0.0000 0.993 0.000 1.000
#> GSM254669 2 0.0000 0.993 0.000 1.000
#> GSM254699 2 0.0000 0.993 0.000 1.000
#> GSM254703 2 0.0000 0.993 0.000 1.000
#> GSM254708 2 0.0000 0.993 0.000 1.000
#> GSM254715 2 0.0000 0.993 0.000 1.000
#> GSM254628 2 0.0000 0.993 0.000 1.000
#> GSM254634 2 0.0000 0.993 0.000 1.000
#> GSM254646 2 0.0000 0.993 0.000 1.000
#> GSM254671 2 0.0000 0.993 0.000 1.000
#> GSM254711 2 0.0000 0.993 0.000 1.000
#> GSM254717 2 0.0000 0.993 0.000 1.000
#> GSM254723 2 0.0376 0.990 0.004 0.996
#> GSM254730 2 0.0000 0.993 0.000 1.000
#> GSM254731 2 0.0000 0.993 0.000 1.000
#> GSM254632 2 0.8555 0.599 0.280 0.720
#> GSM254662 2 0.0000 0.993 0.000 1.000
#> GSM254677 2 0.0000 0.993 0.000 1.000
#> GSM254665 2 0.0000 0.993 0.000 1.000
#> GSM254691 2 0.0000 0.993 0.000 1.000
#> GSM254644 2 0.0000 0.993 0.000 1.000
#> GSM254667 2 0.0000 0.993 0.000 1.000
#> GSM254676 2 0.0000 0.993 0.000 1.000
#> GSM254679 2 0.0000 0.993 0.000 1.000
#> GSM254689 2 0.0000 0.993 0.000 1.000
#> GSM254706 2 0.0000 0.993 0.000 1.000
#> GSM254712 2 0.0000 0.993 0.000 1.000
#> GSM254713 2 0.0000 0.993 0.000 1.000
#> GSM254683 2 0.0000 0.993 0.000 1.000
#> GSM254710 2 0.1414 0.974 0.020 0.980
#> GSM254725 2 0.0000 0.993 0.000 1.000
#> GSM254651 2 0.0000 0.993 0.000 1.000
#> GSM254638 2 0.0000 0.993 0.000 1.000
#> GSM254685 2 0.0000 0.993 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM254629 1 0.4750 0.5557 0.784 0.000 0.216
#> GSM254648 2 0.6973 0.3232 0.020 0.564 0.416
#> GSM254694 1 0.8141 0.2297 0.624 0.116 0.260
#> GSM254701 1 0.1289 0.7694 0.968 0.000 0.032
#> GSM254728 1 0.5291 0.6447 0.732 0.000 0.268
#> GSM254726 1 0.9891 -0.2531 0.404 0.316 0.280
#> GSM254639 3 0.6302 -0.3609 0.480 0.000 0.520
#> GSM254652 1 0.5327 0.6250 0.728 0.000 0.272
#> GSM254700 1 0.0237 0.7725 0.996 0.000 0.004
#> GSM254625 1 0.5882 0.4506 0.652 0.000 0.348
#> GSM254636 1 0.5397 0.6279 0.720 0.000 0.280
#> GSM254659 1 0.2448 0.7650 0.924 0.000 0.076
#> GSM254680 1 0.0000 0.7730 1.000 0.000 0.000
#> GSM254686 1 0.0424 0.7717 0.992 0.000 0.008
#> GSM254718 1 0.4796 0.6112 0.780 0.000 0.220
#> GSM254674 1 0.1289 0.7743 0.968 0.000 0.032
#> GSM254668 1 0.0424 0.7717 0.992 0.000 0.008
#> GSM254697 1 0.1031 0.7746 0.976 0.000 0.024
#> GSM254704 1 0.0747 0.7729 0.984 0.000 0.016
#> GSM254707 1 0.3116 0.7448 0.892 0.000 0.108
#> GSM254714 1 0.0747 0.7729 0.984 0.000 0.016
#> GSM254722 1 0.4504 0.6968 0.804 0.000 0.196
#> GSM254627 1 0.0424 0.7734 0.992 0.000 0.008
#> GSM254630 1 0.4002 0.7236 0.840 0.000 0.160
#> GSM254633 1 0.0892 0.7733 0.980 0.000 0.020
#> GSM254670 1 0.6062 0.4853 0.616 0.000 0.384
#> GSM254716 3 0.6095 -0.1795 0.392 0.000 0.608
#> GSM254720 1 0.0892 0.7723 0.980 0.000 0.020
#> GSM254729 1 0.6483 0.2684 0.544 0.004 0.452
#> GSM254654 1 0.7295 0.3519 0.676 0.072 0.252
#> GSM254656 3 0.8295 0.0299 0.088 0.364 0.548
#> GSM254631 1 0.0592 0.7732 0.988 0.000 0.012
#> GSM254657 1 0.6302 0.2574 0.520 0.000 0.480
#> GSM254664 1 0.0000 0.7730 1.000 0.000 0.000
#> GSM254672 1 0.2796 0.7349 0.908 0.000 0.092
#> GSM254692 1 0.2448 0.7307 0.924 0.000 0.076
#> GSM254645 1 0.6235 0.3552 0.564 0.000 0.436
#> GSM254666 1 0.4796 0.6814 0.780 0.000 0.220
#> GSM254675 1 0.0592 0.7732 0.988 0.000 0.012
#> GSM254678 1 0.3192 0.7531 0.888 0.000 0.112
#> GSM254688 1 0.4452 0.7071 0.808 0.000 0.192
#> GSM254690 1 0.3267 0.7433 0.884 0.000 0.116
#> GSM254696 1 0.5785 0.5670 0.668 0.000 0.332
#> GSM254705 1 0.4654 0.7001 0.792 0.000 0.208
#> GSM254642 1 0.1031 0.7715 0.976 0.000 0.024
#> GSM254661 1 0.5327 0.6250 0.728 0.000 0.272
#> GSM254698 1 0.5621 0.5963 0.692 0.000 0.308
#> GSM254641 1 0.0000 0.7730 1.000 0.000 0.000
#> GSM254647 1 0.0747 0.7746 0.984 0.000 0.016
#> GSM254663 1 0.0747 0.7716 0.984 0.000 0.016
#> GSM254682 1 0.5178 0.6478 0.744 0.000 0.256
#> GSM254709 1 0.6079 0.2284 0.612 0.000 0.388
#> GSM254721 1 0.0237 0.7725 0.996 0.000 0.004
#> GSM254724 1 0.0237 0.7725 0.996 0.000 0.004
#> GSM254650 1 0.5810 0.3382 0.664 0.000 0.336
#> GSM254687 1 0.5016 0.5195 0.760 0.000 0.240
#> GSM254637 1 0.1031 0.7716 0.976 0.000 0.024
#> GSM254684 1 0.5560 0.6048 0.700 0.000 0.300
#> GSM254649 2 0.4887 0.6412 0.000 0.772 0.228
#> GSM254660 2 0.1289 0.8028 0.000 0.968 0.032
#> GSM254693 2 0.1753 0.7883 0.000 0.952 0.048
#> GSM254695 2 0.5138 0.6769 0.000 0.748 0.252
#> GSM254702 2 0.3482 0.7703 0.000 0.872 0.128
#> GSM254643 2 0.1860 0.7864 0.000 0.948 0.052
#> GSM254727 2 0.0237 0.8031 0.000 0.996 0.004
#> GSM254640 2 0.0747 0.8042 0.000 0.984 0.016
#> GSM254626 2 0.1411 0.7940 0.000 0.964 0.036
#> GSM254635 2 0.5098 0.6814 0.000 0.752 0.248
#> GSM254653 2 0.0237 0.8031 0.000 0.996 0.004
#> GSM254658 2 0.5216 0.6061 0.000 0.740 0.260
#> GSM254681 2 0.6225 0.3376 0.000 0.568 0.432
#> GSM254719 2 0.0237 0.8031 0.000 0.996 0.004
#> GSM254673 2 0.0592 0.8014 0.000 0.988 0.012
#> GSM254655 2 0.0892 0.8041 0.000 0.980 0.020
#> GSM254669 2 0.1289 0.7954 0.000 0.968 0.032
#> GSM254699 2 0.1411 0.8022 0.000 0.964 0.036
#> GSM254703 2 0.4121 0.7500 0.000 0.832 0.168
#> GSM254708 2 0.0237 0.8031 0.000 0.996 0.004
#> GSM254715 2 0.4504 0.7306 0.000 0.804 0.196
#> GSM254628 2 0.4974 0.6329 0.000 0.764 0.236
#> GSM254634 2 0.4399 0.7368 0.000 0.812 0.188
#> GSM254646 2 0.6154 0.3834 0.000 0.592 0.408
#> GSM254671 2 0.3941 0.7565 0.000 0.844 0.156
#> GSM254711 2 0.4121 0.7499 0.000 0.832 0.168
#> GSM254717 2 0.0424 0.8025 0.000 0.992 0.008
#> GSM254723 2 0.5138 0.6845 0.000 0.748 0.252
#> GSM254730 2 0.1411 0.8022 0.000 0.964 0.036
#> GSM254731 2 0.2356 0.7926 0.000 0.928 0.072
#> GSM254632 2 0.7820 0.1358 0.324 0.604 0.072
#> GSM254662 2 0.0237 0.8031 0.000 0.996 0.004
#> GSM254677 2 0.5291 0.6633 0.000 0.732 0.268
#> GSM254665 2 0.1289 0.7956 0.000 0.968 0.032
#> GSM254691 2 0.0424 0.8044 0.000 0.992 0.008
#> GSM254644 2 0.1031 0.8038 0.000 0.976 0.024
#> GSM254667 2 0.2165 0.7812 0.000 0.936 0.064
#> GSM254676 2 0.0000 0.8035 0.000 1.000 0.000
#> GSM254679 2 0.4504 0.7309 0.000 0.804 0.196
#> GSM254689 2 0.6192 0.3603 0.000 0.580 0.420
#> GSM254706 2 0.5327 0.5946 0.000 0.728 0.272
#> GSM254712 2 0.4291 0.7426 0.000 0.820 0.180
#> GSM254713 2 0.4555 0.7273 0.000 0.800 0.200
#> GSM254683 2 0.5905 0.4777 0.000 0.648 0.352
#> GSM254710 3 0.6823 -0.3520 0.012 0.484 0.504
#> GSM254725 2 0.5178 0.6723 0.000 0.744 0.256
#> GSM254651 2 0.5497 0.5704 0.000 0.708 0.292
#> GSM254638 2 0.5178 0.6723 0.000 0.744 0.256
#> GSM254685 2 0.1529 0.8015 0.000 0.960 0.040
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM254629 1 0.3718 0.792 0.820 0.000 0.012 0.168
#> GSM254648 4 0.6495 0.582 0.108 0.284 0.000 0.608
#> GSM254694 1 0.6092 0.666 0.724 0.136 0.024 0.116
#> GSM254701 1 0.3215 0.813 0.876 0.000 0.032 0.092
#> GSM254728 3 0.5941 0.588 0.276 0.000 0.652 0.072
#> GSM254726 2 0.7789 0.318 0.120 0.620 0.144 0.116
#> GSM254639 3 0.3156 0.878 0.048 0.000 0.884 0.068
#> GSM254652 3 0.4535 0.852 0.112 0.000 0.804 0.084
#> GSM254700 1 0.0188 0.836 0.996 0.000 0.004 0.000
#> GSM254625 4 0.5454 0.365 0.172 0.000 0.096 0.732
#> GSM254636 3 0.2271 0.887 0.076 0.000 0.916 0.008
#> GSM254659 1 0.5850 0.620 0.676 0.000 0.244 0.080
#> GSM254680 1 0.3548 0.814 0.864 0.000 0.068 0.068
#> GSM254686 1 0.2915 0.824 0.892 0.000 0.028 0.080
#> GSM254718 1 0.4449 0.793 0.824 0.012 0.060 0.104
#> GSM254674 1 0.4436 0.714 0.764 0.000 0.216 0.020
#> GSM254668 1 0.2660 0.831 0.908 0.000 0.036 0.056
#> GSM254697 1 0.1406 0.834 0.960 0.000 0.024 0.016
#> GSM254704 1 0.0524 0.835 0.988 0.000 0.008 0.004
#> GSM254707 4 0.6520 -0.080 0.384 0.000 0.080 0.536
#> GSM254714 1 0.2578 0.816 0.912 0.000 0.052 0.036
#> GSM254722 1 0.4767 0.657 0.724 0.000 0.256 0.020
#> GSM254627 1 0.1406 0.834 0.960 0.000 0.024 0.016
#> GSM254630 1 0.4728 0.776 0.792 0.000 0.104 0.104
#> GSM254633 1 0.3247 0.818 0.880 0.000 0.060 0.060
#> GSM254670 3 0.1576 0.887 0.048 0.000 0.948 0.004
#> GSM254716 4 0.5118 0.335 0.072 0.000 0.176 0.752
#> GSM254720 1 0.1004 0.836 0.972 0.000 0.004 0.024
#> GSM254729 3 0.4707 0.841 0.116 0.004 0.800 0.080
#> GSM254654 1 0.6178 0.693 0.736 0.092 0.056 0.116
#> GSM254656 3 0.2010 0.869 0.040 0.012 0.940 0.008
#> GSM254631 1 0.1209 0.837 0.964 0.000 0.032 0.004
#> GSM254657 3 0.2319 0.880 0.036 0.000 0.924 0.040
#> GSM254664 1 0.0000 0.836 1.000 0.000 0.000 0.000
#> GSM254672 1 0.1209 0.837 0.964 0.000 0.004 0.032
#> GSM254692 1 0.2081 0.822 0.916 0.000 0.000 0.084
#> GSM254645 1 0.5819 0.655 0.696 0.016 0.240 0.048
#> GSM254666 1 0.7515 0.100 0.448 0.000 0.364 0.188
#> GSM254675 1 0.1022 0.835 0.968 0.000 0.000 0.032
#> GSM254678 1 0.3529 0.777 0.836 0.000 0.152 0.012
#> GSM254688 1 0.7525 0.281 0.492 0.000 0.276 0.232
#> GSM254690 1 0.4095 0.739 0.792 0.000 0.192 0.016
#> GSM254696 3 0.1807 0.887 0.052 0.000 0.940 0.008
#> GSM254705 1 0.7142 0.370 0.524 0.000 0.152 0.324
#> GSM254642 1 0.1520 0.835 0.956 0.000 0.024 0.020
#> GSM254661 3 0.4424 0.860 0.100 0.000 0.812 0.088
#> GSM254698 3 0.2101 0.887 0.060 0.000 0.928 0.012
#> GSM254641 1 0.1902 0.828 0.932 0.000 0.004 0.064
#> GSM254647 1 0.2142 0.827 0.928 0.000 0.056 0.016
#> GSM254663 1 0.1624 0.837 0.952 0.000 0.020 0.028
#> GSM254682 3 0.5994 0.700 0.152 0.000 0.692 0.156
#> GSM254709 1 0.2921 0.808 0.860 0.000 0.000 0.140
#> GSM254721 1 0.0336 0.835 0.992 0.000 0.008 0.000
#> GSM254724 1 0.0000 0.836 1.000 0.000 0.000 0.000
#> GSM254650 1 0.5151 0.270 0.532 0.000 0.004 0.464
#> GSM254687 1 0.5203 0.403 0.576 0.000 0.008 0.416
#> GSM254637 1 0.0188 0.836 0.996 0.000 0.000 0.004
#> GSM254684 3 0.1661 0.888 0.052 0.000 0.944 0.004
#> GSM254649 2 0.4907 0.115 0.000 0.580 0.000 0.420
#> GSM254660 2 0.0707 0.858 0.000 0.980 0.000 0.020
#> GSM254693 2 0.3569 0.730 0.000 0.804 0.000 0.196
#> GSM254695 2 0.1716 0.835 0.000 0.936 0.000 0.064
#> GSM254702 2 0.0336 0.853 0.000 0.992 0.000 0.008
#> GSM254643 2 0.2345 0.843 0.000 0.900 0.000 0.100
#> GSM254727 2 0.2281 0.843 0.000 0.904 0.000 0.096
#> GSM254640 2 0.1743 0.858 0.000 0.940 0.004 0.056
#> GSM254626 2 0.2760 0.814 0.000 0.872 0.000 0.128
#> GSM254635 2 0.1305 0.841 0.000 0.960 0.004 0.036
#> GSM254653 2 0.2081 0.849 0.000 0.916 0.000 0.084
#> GSM254658 2 0.4866 0.200 0.000 0.596 0.000 0.404
#> GSM254681 4 0.4040 0.702 0.000 0.248 0.000 0.752
#> GSM254719 2 0.1792 0.853 0.000 0.932 0.000 0.068
#> GSM254673 2 0.2469 0.832 0.000 0.892 0.000 0.108
#> GSM254655 2 0.1474 0.857 0.000 0.948 0.000 0.052
#> GSM254669 2 0.3610 0.721 0.000 0.800 0.000 0.200
#> GSM254699 2 0.1389 0.858 0.000 0.952 0.000 0.048
#> GSM254703 2 0.2124 0.825 0.000 0.932 0.040 0.028
#> GSM254708 2 0.3074 0.790 0.000 0.848 0.000 0.152
#> GSM254715 2 0.1936 0.830 0.000 0.940 0.032 0.028
#> GSM254628 2 0.4761 0.336 0.000 0.628 0.000 0.372
#> GSM254634 2 0.0336 0.853 0.000 0.992 0.000 0.008
#> GSM254646 4 0.4624 0.633 0.000 0.340 0.000 0.660
#> GSM254671 2 0.0000 0.855 0.000 1.000 0.000 0.000
#> GSM254711 2 0.0707 0.849 0.000 0.980 0.000 0.020
#> GSM254717 2 0.2530 0.835 0.000 0.888 0.000 0.112
#> GSM254723 2 0.4959 0.612 0.000 0.752 0.052 0.196
#> GSM254730 2 0.1302 0.858 0.000 0.956 0.000 0.044
#> GSM254731 2 0.0336 0.856 0.000 0.992 0.000 0.008
#> GSM254632 4 0.7446 0.601 0.024 0.208 0.176 0.592
#> GSM254662 2 0.2081 0.846 0.000 0.916 0.000 0.084
#> GSM254677 2 0.4282 0.716 0.000 0.816 0.060 0.124
#> GSM254665 2 0.2345 0.847 0.000 0.900 0.000 0.100
#> GSM254691 2 0.2149 0.845 0.000 0.912 0.000 0.088
#> GSM254644 2 0.1389 0.859 0.000 0.952 0.000 0.048
#> GSM254667 4 0.4855 0.616 0.000 0.352 0.004 0.644
#> GSM254676 2 0.2081 0.846 0.000 0.916 0.000 0.084
#> GSM254679 2 0.0592 0.850 0.000 0.984 0.000 0.016
#> GSM254689 4 0.4304 0.688 0.000 0.284 0.000 0.716
#> GSM254706 4 0.4164 0.693 0.000 0.264 0.000 0.736
#> GSM254712 2 0.2300 0.819 0.000 0.924 0.048 0.028
#> GSM254713 2 0.2124 0.825 0.000 0.932 0.040 0.028
#> GSM254683 4 0.4661 0.633 0.000 0.348 0.000 0.652
#> GSM254710 4 0.3047 0.661 0.000 0.116 0.012 0.872
#> GSM254725 2 0.1356 0.841 0.000 0.960 0.008 0.032
#> GSM254651 4 0.4564 0.629 0.000 0.328 0.000 0.672
#> GSM254638 2 0.2483 0.811 0.000 0.916 0.032 0.052
#> GSM254685 2 0.1488 0.845 0.000 0.956 0.032 0.012
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM254629 3 0.5959 0.43842 0.088 0.004 0.612 0.016 0.280
#> GSM254648 3 0.5799 0.32928 0.020 0.044 0.588 0.008 0.340
#> GSM254694 3 0.6870 0.52932 0.180 0.016 0.596 0.172 0.036
#> GSM254701 3 0.5933 0.55006 0.108 0.000 0.628 0.244 0.020
#> GSM254728 3 0.1914 0.54017 0.032 0.000 0.932 0.032 0.004
#> GSM254726 3 0.4290 0.57354 0.008 0.016 0.768 0.192 0.016
#> GSM254639 3 0.3779 0.09236 0.000 0.000 0.752 0.236 0.012
#> GSM254652 3 0.1913 0.51350 0.008 0.000 0.932 0.044 0.016
#> GSM254700 1 0.0000 0.86103 1.000 0.000 0.000 0.000 0.000
#> GSM254625 5 0.4575 0.41677 0.024 0.004 0.268 0.004 0.700
#> GSM254636 4 0.4736 0.73582 0.020 0.000 0.404 0.576 0.000
#> GSM254659 3 0.2050 0.57691 0.064 0.000 0.920 0.008 0.008
#> GSM254680 3 0.5039 0.38681 0.360 0.000 0.604 0.028 0.008
#> GSM254686 3 0.5354 0.50109 0.128 0.000 0.680 0.004 0.188
#> GSM254718 3 0.5524 0.53779 0.060 0.000 0.640 0.280 0.020
#> GSM254674 3 0.6259 0.10518 0.248 0.000 0.540 0.212 0.000
#> GSM254668 1 0.6296 -0.22873 0.440 0.000 0.408 0.000 0.152
#> GSM254697 1 0.0510 0.86092 0.984 0.000 0.000 0.016 0.000
#> GSM254704 1 0.0162 0.86184 0.996 0.000 0.004 0.000 0.000
#> GSM254707 5 0.5540 0.13287 0.060 0.000 0.400 0.004 0.536
#> GSM254714 3 0.7131 0.30080 0.340 0.000 0.352 0.296 0.012
#> GSM254722 1 0.3958 0.69274 0.780 0.000 0.044 0.176 0.000
#> GSM254627 1 0.0162 0.86171 0.996 0.000 0.000 0.004 0.000
#> GSM254630 1 0.5313 0.66376 0.716 0.000 0.048 0.056 0.180
#> GSM254633 3 0.3266 0.56292 0.200 0.000 0.796 0.004 0.000
#> GSM254670 4 0.4362 0.78936 0.004 0.000 0.360 0.632 0.004
#> GSM254716 5 0.3861 0.41850 0.000 0.000 0.264 0.008 0.728
#> GSM254720 1 0.0290 0.86173 0.992 0.000 0.008 0.000 0.000
#> GSM254729 3 0.2731 0.43044 0.004 0.000 0.876 0.104 0.016
#> GSM254654 3 0.6019 0.51433 0.072 0.004 0.600 0.300 0.024
#> GSM254656 4 0.4135 0.78435 0.004 0.000 0.340 0.656 0.000
#> GSM254631 1 0.2377 0.79588 0.872 0.000 0.128 0.000 0.000
#> GSM254657 3 0.5045 -0.10615 0.008 0.000 0.620 0.340 0.032
#> GSM254664 1 0.2471 0.76756 0.864 0.000 0.136 0.000 0.000
#> GSM254672 1 0.0566 0.86153 0.984 0.000 0.004 0.012 0.000
#> GSM254692 1 0.1310 0.85403 0.956 0.000 0.020 0.000 0.024
#> GSM254645 4 0.6546 0.00902 0.336 0.004 0.124 0.520 0.016
#> GSM254666 3 0.5100 0.26106 0.024 0.000 0.592 0.012 0.372
#> GSM254675 1 0.3010 0.71598 0.824 0.000 0.172 0.000 0.004
#> GSM254678 1 0.4059 0.68322 0.776 0.000 0.172 0.052 0.000
#> GSM254688 5 0.8361 0.17808 0.244 0.000 0.196 0.188 0.372
#> GSM254690 1 0.3003 0.79810 0.864 0.000 0.044 0.092 0.000
#> GSM254696 4 0.4478 0.79106 0.008 0.000 0.360 0.628 0.004
#> GSM254705 1 0.3339 0.79889 0.860 0.000 0.032 0.084 0.024
#> GSM254642 1 0.0703 0.85900 0.976 0.000 0.000 0.024 0.000
#> GSM254661 3 0.3209 0.56401 0.000 0.004 0.848 0.028 0.120
#> GSM254698 4 0.5620 0.73924 0.092 0.000 0.296 0.608 0.004
#> GSM254641 3 0.5131 0.35063 0.420 0.000 0.540 0.000 0.040
#> GSM254647 1 0.0451 0.86205 0.988 0.000 0.008 0.004 0.000
#> GSM254663 1 0.0693 0.86171 0.980 0.000 0.008 0.000 0.012
#> GSM254682 4 0.7246 0.64739 0.100 0.000 0.324 0.484 0.092
#> GSM254709 5 0.6396 0.21270 0.280 0.000 0.212 0.000 0.508
#> GSM254721 1 0.0290 0.86179 0.992 0.000 0.008 0.000 0.000
#> GSM254724 1 0.0162 0.86184 0.996 0.000 0.004 0.000 0.000
#> GSM254650 1 0.3928 0.52891 0.700 0.000 0.004 0.000 0.296
#> GSM254687 5 0.4779 0.37075 0.340 0.000 0.032 0.000 0.628
#> GSM254637 1 0.2574 0.78954 0.876 0.000 0.112 0.012 0.000
#> GSM254684 4 0.4552 0.79214 0.012 0.000 0.352 0.632 0.004
#> GSM254649 2 0.2179 0.82185 0.000 0.888 0.000 0.000 0.112
#> GSM254660 2 0.0794 0.86228 0.000 0.972 0.000 0.000 0.028
#> GSM254693 2 0.1270 0.85625 0.000 0.948 0.000 0.000 0.052
#> GSM254695 2 0.2795 0.83329 0.000 0.880 0.000 0.056 0.064
#> GSM254702 2 0.1399 0.85839 0.000 0.952 0.000 0.020 0.028
#> GSM254643 2 0.0963 0.86264 0.000 0.964 0.000 0.000 0.036
#> GSM254727 2 0.0955 0.86379 0.000 0.968 0.000 0.004 0.028
#> GSM254640 2 0.1331 0.86350 0.000 0.952 0.000 0.040 0.008
#> GSM254626 2 0.1282 0.86143 0.000 0.952 0.000 0.004 0.044
#> GSM254635 2 0.2054 0.84627 0.000 0.920 0.000 0.028 0.052
#> GSM254653 2 0.0609 0.86335 0.000 0.980 0.000 0.000 0.020
#> GSM254658 2 0.2074 0.82791 0.000 0.896 0.000 0.000 0.104
#> GSM254681 5 0.3586 0.45794 0.000 0.264 0.000 0.000 0.736
#> GSM254719 2 0.0566 0.86477 0.000 0.984 0.000 0.004 0.012
#> GSM254673 2 0.0865 0.86381 0.000 0.972 0.000 0.004 0.024
#> GSM254655 2 0.0162 0.86444 0.000 0.996 0.000 0.000 0.004
#> GSM254669 2 0.1121 0.85871 0.000 0.956 0.000 0.000 0.044
#> GSM254699 2 0.0451 0.86528 0.000 0.988 0.000 0.008 0.004
#> GSM254703 2 0.4597 0.66004 0.000 0.696 0.000 0.260 0.044
#> GSM254708 2 0.0963 0.86119 0.000 0.964 0.000 0.000 0.036
#> GSM254715 2 0.3639 0.77936 0.000 0.812 0.000 0.144 0.044
#> GSM254628 2 0.1792 0.84162 0.000 0.916 0.000 0.000 0.084
#> GSM254634 2 0.1211 0.86100 0.000 0.960 0.000 0.016 0.024
#> GSM254646 2 0.3983 0.52560 0.000 0.660 0.000 0.000 0.340
#> GSM254671 2 0.1082 0.86073 0.000 0.964 0.000 0.008 0.028
#> GSM254711 2 0.1568 0.85547 0.000 0.944 0.000 0.020 0.036
#> GSM254717 2 0.1626 0.86208 0.000 0.940 0.000 0.016 0.044
#> GSM254723 2 0.8380 -0.00318 0.004 0.396 0.164 0.232 0.204
#> GSM254730 2 0.0451 0.86405 0.000 0.988 0.000 0.004 0.008
#> GSM254731 2 0.1403 0.85948 0.000 0.952 0.000 0.024 0.024
#> GSM254632 5 0.7179 0.34577 0.004 0.112 0.256 0.084 0.544
#> GSM254662 2 0.0703 0.86293 0.000 0.976 0.000 0.000 0.024
#> GSM254677 2 0.5797 0.47522 0.000 0.560 0.008 0.352 0.080
#> GSM254665 2 0.1282 0.86205 0.000 0.952 0.000 0.004 0.044
#> GSM254691 2 0.0955 0.86373 0.000 0.968 0.000 0.004 0.028
#> GSM254644 2 0.1408 0.86249 0.000 0.948 0.000 0.044 0.008
#> GSM254667 2 0.4810 0.64106 0.000 0.712 0.000 0.084 0.204
#> GSM254676 2 0.0865 0.86458 0.000 0.972 0.000 0.004 0.024
#> GSM254679 2 0.1386 0.85784 0.000 0.952 0.000 0.016 0.032
#> GSM254689 5 0.4182 0.17199 0.000 0.400 0.000 0.000 0.600
#> GSM254706 2 0.4339 0.54188 0.000 0.652 0.000 0.012 0.336
#> GSM254712 2 0.5267 0.53539 0.000 0.604 0.008 0.344 0.044
#> GSM254713 2 0.4756 0.62715 0.000 0.668 0.000 0.288 0.044
#> GSM254683 2 0.3837 0.58029 0.000 0.692 0.000 0.000 0.308
#> GSM254710 5 0.1965 0.50086 0.000 0.096 0.000 0.000 0.904
#> GSM254725 2 0.2054 0.84715 0.000 0.920 0.000 0.028 0.052
#> GSM254651 2 0.4063 0.63885 0.000 0.708 0.000 0.012 0.280
#> GSM254638 2 0.5223 0.63207 0.000 0.680 0.012 0.240 0.068
#> GSM254685 2 0.3449 0.77042 0.000 0.812 0.000 0.164 0.024
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM254629 3 0.1511 0.7842 0.000 0.000 0.940 0.044 0.012 0.004
#> GSM254648 3 0.1906 0.7776 0.000 0.008 0.924 0.032 0.036 0.000
#> GSM254694 3 0.1411 0.7888 0.000 0.000 0.936 0.060 0.004 0.000
#> GSM254701 3 0.0935 0.7893 0.000 0.000 0.964 0.032 0.004 0.000
#> GSM254728 3 0.4330 0.7066 0.000 0.000 0.756 0.156 0.040 0.048
#> GSM254726 3 0.2728 0.7732 0.000 0.000 0.872 0.080 0.040 0.008
#> GSM254639 6 0.4835 0.3792 0.000 0.000 0.360 0.048 0.008 0.584
#> GSM254652 3 0.1951 0.7873 0.000 0.000 0.916 0.004 0.020 0.060
#> GSM254700 1 0.1176 0.8474 0.956 0.000 0.024 0.020 0.000 0.000
#> GSM254625 5 0.4823 0.4782 0.004 0.000 0.212 0.068 0.696 0.020
#> GSM254636 6 0.3560 0.7542 0.068 0.000 0.076 0.012 0.012 0.832
#> GSM254659 3 0.1749 0.7873 0.000 0.000 0.932 0.036 0.024 0.008
#> GSM254680 3 0.5056 0.6605 0.172 0.000 0.716 0.028 0.032 0.052
#> GSM254686 3 0.3579 0.7265 0.000 0.000 0.804 0.120 0.072 0.004
#> GSM254718 3 0.3886 0.7016 0.000 0.000 0.772 0.164 0.056 0.008
#> GSM254674 3 0.5709 0.4655 0.172 0.000 0.568 0.000 0.012 0.248
#> GSM254668 3 0.5504 0.6097 0.172 0.000 0.676 0.032 0.100 0.020
#> GSM254697 1 0.0551 0.8490 0.984 0.000 0.004 0.004 0.000 0.008
#> GSM254704 1 0.1341 0.8469 0.948 0.000 0.028 0.024 0.000 0.000
#> GSM254707 5 0.5608 0.1698 0.004 0.000 0.388 0.100 0.500 0.008
#> GSM254714 4 0.6043 0.0361 0.260 0.000 0.272 0.464 0.004 0.000
#> GSM254722 1 0.2402 0.7813 0.856 0.000 0.000 0.004 0.000 0.140
#> GSM254627 1 0.0551 0.8497 0.984 0.000 0.004 0.004 0.000 0.008
#> GSM254630 1 0.4751 0.6783 0.736 0.000 0.020 0.056 0.164 0.024
#> GSM254633 3 0.2101 0.7802 0.072 0.000 0.908 0.008 0.008 0.004
#> GSM254670 6 0.0405 0.7929 0.000 0.000 0.008 0.004 0.000 0.988
#> GSM254716 5 0.5243 0.4649 0.000 0.000 0.144 0.128 0.684 0.044
#> GSM254720 1 0.2106 0.8340 0.904 0.000 0.064 0.032 0.000 0.000
#> GSM254729 3 0.3852 0.5880 0.000 0.000 0.720 0.016 0.008 0.256
#> GSM254654 3 0.1267 0.7833 0.000 0.000 0.940 0.060 0.000 0.000
#> GSM254656 6 0.1818 0.7785 0.004 0.000 0.004 0.068 0.004 0.920
#> GSM254631 1 0.4934 0.4285 0.624 0.000 0.316 0.012 0.012 0.036
#> GSM254657 6 0.6136 0.4942 0.000 0.000 0.128 0.236 0.064 0.572
#> GSM254664 3 0.4397 0.1550 0.452 0.000 0.528 0.008 0.012 0.000
#> GSM254672 1 0.1408 0.8492 0.944 0.000 0.020 0.036 0.000 0.000
#> GSM254692 1 0.2959 0.8040 0.868 0.000 0.028 0.056 0.048 0.000
#> GSM254645 4 0.6364 -0.0864 0.192 0.000 0.028 0.456 0.000 0.324
#> GSM254666 5 0.6654 0.2519 0.000 0.000 0.328 0.144 0.456 0.072
#> GSM254675 1 0.4636 0.6806 0.732 0.000 0.160 0.072 0.036 0.000
#> GSM254678 1 0.3743 0.6750 0.756 0.000 0.012 0.012 0.004 0.216
#> GSM254688 5 0.7113 0.1555 0.144 0.000 0.052 0.044 0.488 0.272
#> GSM254690 1 0.2615 0.7835 0.852 0.000 0.004 0.000 0.008 0.136
#> GSM254696 6 0.1344 0.7976 0.012 0.000 0.008 0.012 0.012 0.956
#> GSM254705 1 0.1285 0.8393 0.944 0.000 0.000 0.004 0.000 0.052
#> GSM254642 1 0.0551 0.8490 0.984 0.000 0.004 0.004 0.000 0.008
#> GSM254661 3 0.2563 0.7717 0.000 0.000 0.892 0.028 0.044 0.036
#> GSM254698 6 0.1663 0.7617 0.088 0.000 0.000 0.000 0.000 0.912
#> GSM254641 3 0.3068 0.7371 0.124 0.000 0.840 0.020 0.016 0.000
#> GSM254647 1 0.0622 0.8489 0.980 0.000 0.000 0.000 0.012 0.008
#> GSM254663 1 0.0767 0.8509 0.976 0.000 0.008 0.004 0.012 0.000
#> GSM254682 6 0.5343 0.6629 0.088 0.000 0.032 0.056 0.104 0.720
#> GSM254709 5 0.5758 -0.0301 0.132 0.000 0.428 0.008 0.432 0.000
#> GSM254721 1 0.1418 0.8462 0.944 0.000 0.032 0.024 0.000 0.000
#> GSM254724 1 0.1257 0.8472 0.952 0.000 0.028 0.020 0.000 0.000
#> GSM254650 1 0.3534 0.5885 0.716 0.000 0.008 0.000 0.276 0.000
#> GSM254687 5 0.3758 0.3837 0.284 0.000 0.016 0.000 0.700 0.000
#> GSM254637 1 0.4662 0.1993 0.548 0.000 0.420 0.016 0.012 0.004
#> GSM254684 6 0.0622 0.7936 0.012 0.000 0.000 0.000 0.008 0.980
#> GSM254649 2 0.1387 0.8193 0.000 0.932 0.000 0.000 0.068 0.000
#> GSM254660 2 0.0891 0.8376 0.000 0.968 0.000 0.024 0.008 0.000
#> GSM254693 2 0.0692 0.8368 0.000 0.976 0.000 0.004 0.020 0.000
#> GSM254695 2 0.4999 0.4722 0.000 0.652 0.000 0.244 0.092 0.012
#> GSM254702 2 0.0935 0.8325 0.000 0.964 0.000 0.032 0.004 0.000
#> GSM254643 2 0.0622 0.8384 0.000 0.980 0.000 0.008 0.012 0.000
#> GSM254727 2 0.1492 0.8308 0.000 0.940 0.000 0.024 0.036 0.000
#> GSM254640 2 0.3265 0.5822 0.000 0.748 0.000 0.248 0.004 0.000
#> GSM254626 2 0.0820 0.8388 0.000 0.972 0.000 0.012 0.016 0.000
#> GSM254635 2 0.1643 0.8139 0.000 0.924 0.000 0.068 0.008 0.000
#> GSM254653 2 0.0622 0.8387 0.000 0.980 0.000 0.012 0.008 0.000
#> GSM254658 2 0.1802 0.8099 0.000 0.916 0.000 0.012 0.072 0.000
#> GSM254681 5 0.2964 0.3689 0.000 0.204 0.000 0.004 0.792 0.000
#> GSM254719 2 0.0363 0.8383 0.000 0.988 0.000 0.012 0.000 0.000
#> GSM254673 2 0.0603 0.8394 0.000 0.980 0.000 0.016 0.004 0.000
#> GSM254655 2 0.0363 0.8374 0.000 0.988 0.000 0.012 0.000 0.000
#> GSM254669 2 0.0891 0.8384 0.000 0.968 0.000 0.024 0.008 0.000
#> GSM254699 2 0.0790 0.8384 0.000 0.968 0.000 0.032 0.000 0.000
#> GSM254703 2 0.3838 0.0249 0.000 0.552 0.000 0.448 0.000 0.000
#> GSM254708 2 0.0972 0.8340 0.000 0.964 0.000 0.008 0.028 0.000
#> GSM254715 2 0.3823 0.0588 0.000 0.564 0.000 0.436 0.000 0.000
#> GSM254628 2 0.2340 0.7538 0.000 0.852 0.000 0.000 0.148 0.000
#> GSM254634 2 0.1268 0.8364 0.000 0.952 0.000 0.036 0.008 0.004
#> GSM254646 2 0.3426 0.5859 0.000 0.720 0.000 0.004 0.276 0.000
#> GSM254671 2 0.0972 0.8382 0.000 0.964 0.000 0.028 0.008 0.000
#> GSM254711 2 0.1367 0.8334 0.000 0.944 0.000 0.044 0.012 0.000
#> GSM254717 2 0.2263 0.8062 0.000 0.896 0.000 0.048 0.056 0.000
#> GSM254723 4 0.5797 0.1676 0.000 0.096 0.104 0.676 0.104 0.020
#> GSM254730 2 0.0717 0.8375 0.000 0.976 0.000 0.016 0.008 0.000
#> GSM254731 2 0.0891 0.8343 0.000 0.968 0.000 0.024 0.008 0.000
#> GSM254632 5 0.6844 0.1366 0.000 0.024 0.056 0.136 0.500 0.284
#> GSM254662 2 0.0692 0.8388 0.000 0.976 0.000 0.020 0.004 0.000
#> GSM254677 4 0.3853 0.4709 0.000 0.304 0.000 0.680 0.016 0.000
#> GSM254665 2 0.1418 0.8334 0.000 0.944 0.000 0.024 0.032 0.000
#> GSM254691 2 0.1003 0.8381 0.000 0.964 0.000 0.020 0.016 0.000
#> GSM254644 2 0.4371 0.1739 0.000 0.580 0.000 0.396 0.020 0.004
#> GSM254667 2 0.6921 0.0827 0.000 0.464 0.000 0.096 0.260 0.180
#> GSM254676 2 0.0914 0.8380 0.000 0.968 0.000 0.016 0.016 0.000
#> GSM254679 2 0.1049 0.8338 0.000 0.960 0.000 0.032 0.008 0.000
#> GSM254689 5 0.3695 0.1506 0.000 0.376 0.000 0.000 0.624 0.000
#> GSM254706 5 0.5089 0.0750 0.000 0.384 0.000 0.072 0.540 0.004
#> GSM254712 4 0.3765 0.3316 0.000 0.404 0.000 0.596 0.000 0.000
#> GSM254713 4 0.3864 0.1139 0.000 0.480 0.000 0.520 0.000 0.000
#> GSM254683 2 0.3934 0.5784 0.000 0.708 0.000 0.032 0.260 0.000
#> GSM254710 5 0.2197 0.4504 0.000 0.044 0.000 0.056 0.900 0.000
#> GSM254725 2 0.2267 0.8060 0.000 0.904 0.004 0.064 0.008 0.020
#> GSM254651 2 0.4682 0.5314 0.000 0.680 0.000 0.092 0.224 0.004
#> GSM254638 2 0.4025 0.5797 0.000 0.720 0.020 0.248 0.008 0.004
#> GSM254685 2 0.3592 0.3678 0.000 0.656 0.000 0.344 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) individual(p) disease.state(p) gender(p) other(p) k
#> MAD:NMF 107 2.35e-23 0.55450 0.665 0.6108 0.958 2
#> MAD:NMF 87 8.02e-20 0.24627 0.784 0.7129 1.000 3
#> MAD:NMF 95 1.10e-19 0.65686 0.596 0.4558 0.898 4
#> MAD:NMF 84 2.47e-17 0.00724 0.617 0.0239 0.517 5
#> MAD:NMF 77 1.35e-16 0.00210 0.407 0.0513 0.142 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 21168 rows and 107 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 0.239 0.629 0.808 0.3828 0.730 0.730
#> 3 3 0.637 0.768 0.882 0.6104 0.621 0.501
#> 4 4 0.832 0.872 0.924 0.1296 0.905 0.767
#> 5 5 0.800 0.789 0.859 0.1063 0.913 0.722
#> 6 6 0.785 0.748 0.837 0.0413 0.985 0.935
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
#> GSM254629 1 0.753 0.65466 0.784 0.216
#> GSM254648 1 0.753 0.65466 0.784 0.216
#> GSM254694 1 0.753 0.65466 0.784 0.216
#> GSM254701 1 0.753 0.65466 0.784 0.216
#> GSM254728 1 0.730 0.66167 0.796 0.204
#> GSM254726 1 0.827 0.65917 0.740 0.260
#> GSM254639 2 1.000 -0.03623 0.496 0.504
#> GSM254652 1 0.753 0.65466 0.784 0.216
#> GSM254700 1 0.662 0.67814 0.828 0.172
#> GSM254625 1 0.000 0.69198 1.000 0.000
#> GSM254636 1 0.871 0.56850 0.708 0.292
#> GSM254659 1 0.753 0.65466 0.784 0.216
#> GSM254680 1 0.662 0.67814 0.828 0.172
#> GSM254686 1 0.662 0.67814 0.828 0.172
#> GSM254718 1 0.861 0.58326 0.716 0.284
#> GSM254674 1 0.662 0.67814 0.828 0.172
#> GSM254668 1 0.000 0.69198 1.000 0.000
#> GSM254697 1 0.861 0.57973 0.716 0.284
#> GSM254704 1 0.952 0.41373 0.628 0.372
#> GSM254707 1 0.000 0.69198 1.000 0.000
#> GSM254714 1 0.866 0.57756 0.712 0.288
#> GSM254722 1 0.861 0.57973 0.716 0.284
#> GSM254627 1 0.871 0.56850 0.708 0.292
#> GSM254630 1 0.730 0.66167 0.796 0.204
#> GSM254633 1 0.921 0.49305 0.664 0.336
#> GSM254670 1 1.000 0.00238 0.504 0.496
#> GSM254716 1 0.000 0.69198 1.000 0.000
#> GSM254720 1 0.753 0.65466 0.784 0.216
#> GSM254729 1 0.871 0.57171 0.708 0.292
#> GSM254654 1 0.871 0.57171 0.708 0.292
#> GSM254656 2 0.996 0.09584 0.464 0.536
#> GSM254631 1 0.921 0.49305 0.664 0.336
#> GSM254657 2 0.996 0.09584 0.464 0.536
#> GSM254664 1 0.680 0.67497 0.820 0.180
#> GSM254672 1 0.952 0.41373 0.628 0.372
#> GSM254692 1 0.000 0.69198 1.000 0.000
#> GSM254645 1 0.991 0.19958 0.556 0.444
#> GSM254666 1 0.738 0.65972 0.792 0.208
#> GSM254675 1 0.680 0.67497 0.820 0.180
#> GSM254678 1 0.909 0.51586 0.676 0.324
#> GSM254688 1 0.000 0.69198 1.000 0.000
#> GSM254690 1 0.662 0.67814 0.828 0.172
#> GSM254696 1 0.871 0.56850 0.708 0.292
#> GSM254705 1 0.000 0.69198 1.000 0.000
#> GSM254642 1 0.662 0.67814 0.828 0.172
#> GSM254661 1 0.753 0.65466 0.784 0.216
#> GSM254698 1 0.871 0.56850 0.708 0.292
#> GSM254641 1 0.730 0.66167 0.796 0.204
#> GSM254647 1 0.662 0.67814 0.828 0.172
#> GSM254663 1 0.000 0.69198 1.000 0.000
#> GSM254682 1 0.000 0.69198 1.000 0.000
#> GSM254709 1 0.000 0.69198 1.000 0.000
#> GSM254721 1 0.662 0.67814 0.828 0.172
#> GSM254724 1 0.662 0.67814 0.828 0.172
#> GSM254650 1 0.000 0.69198 1.000 0.000
#> GSM254687 1 0.000 0.69198 1.000 0.000
#> GSM254637 1 0.689 0.67338 0.816 0.184
#> GSM254684 1 0.971 0.34508 0.600 0.400
#> GSM254649 1 0.753 0.63086 0.784 0.216
#> GSM254660 1 0.802 0.60896 0.756 0.244
#> GSM254693 1 0.753 0.63086 0.784 0.216
#> GSM254695 1 0.971 0.50346 0.600 0.400
#> GSM254702 1 0.802 0.60896 0.756 0.244
#> GSM254643 1 0.775 0.62047 0.772 0.228
#> GSM254727 1 0.753 0.63086 0.784 0.216
#> GSM254640 2 0.552 0.71846 0.128 0.872
#> GSM254626 1 0.753 0.63086 0.784 0.216
#> GSM254635 2 0.000 0.85737 0.000 1.000
#> GSM254653 1 0.753 0.63086 0.784 0.216
#> GSM254658 1 0.775 0.62047 0.772 0.228
#> GSM254681 1 0.714 0.64106 0.804 0.196
#> GSM254719 1 0.802 0.60896 0.756 0.244
#> GSM254673 1 0.753 0.63086 0.784 0.216
#> GSM254655 1 0.802 0.60896 0.756 0.244
#> GSM254669 1 0.753 0.63086 0.784 0.216
#> GSM254699 1 0.802 0.60896 0.756 0.244
#> GSM254703 1 0.839 0.60242 0.732 0.268
#> GSM254708 1 0.767 0.62442 0.776 0.224
#> GSM254715 2 0.000 0.85737 0.000 1.000
#> GSM254628 1 0.753 0.63086 0.784 0.216
#> GSM254634 2 0.000 0.85737 0.000 1.000
#> GSM254646 1 0.753 0.63086 0.784 0.216
#> GSM254671 2 0.000 0.85737 0.000 1.000
#> GSM254711 2 0.000 0.85737 0.000 1.000
#> GSM254717 1 0.767 0.62442 0.776 0.224
#> GSM254723 1 0.827 0.65917 0.740 0.260
#> GSM254730 1 0.802 0.60896 0.756 0.244
#> GSM254731 1 0.802 0.60896 0.756 0.244
#> GSM254632 1 0.839 0.65725 0.732 0.268
#> GSM254662 1 0.753 0.63086 0.784 0.216
#> GSM254677 2 0.000 0.85737 0.000 1.000
#> GSM254665 1 0.767 0.62442 0.776 0.224
#> GSM254691 1 0.767 0.62442 0.776 0.224
#> GSM254644 2 0.494 0.73522 0.108 0.892
#> GSM254667 1 0.615 0.66558 0.848 0.152
#> GSM254676 1 0.767 0.62442 0.776 0.224
#> GSM254679 2 0.000 0.85737 0.000 1.000
#> GSM254689 1 0.714 0.64106 0.804 0.196
#> GSM254706 1 0.767 0.62442 0.776 0.224
#> GSM254712 2 0.000 0.85737 0.000 1.000
#> GSM254713 2 0.000 0.85737 0.000 1.000
#> GSM254683 1 0.714 0.64106 0.804 0.196
#> GSM254710 1 0.714 0.64106 0.804 0.196
#> GSM254725 2 0.000 0.85737 0.000 1.000
#> GSM254651 1 0.767 0.62442 0.776 0.224
#> GSM254638 2 0.000 0.85737 0.000 1.000
#> GSM254685 2 0.000 0.85737 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM254629 1 0.1643 0.8169 0.956 0.000 0.044
#> GSM254648 1 0.1643 0.8169 0.956 0.000 0.044
#> GSM254694 1 0.1643 0.8169 0.956 0.000 0.044
#> GSM254701 1 0.1643 0.8169 0.956 0.000 0.044
#> GSM254728 1 0.1289 0.8169 0.968 0.000 0.032
#> GSM254726 2 0.7581 0.1757 0.408 0.548 0.044
#> GSM254639 1 0.5810 0.5608 0.664 0.000 0.336
#> GSM254652 1 0.1643 0.8169 0.956 0.000 0.044
#> GSM254700 1 0.0000 0.8126 1.000 0.000 0.000
#> GSM254625 1 0.6717 0.4479 0.628 0.352 0.020
#> GSM254636 1 0.3340 0.7917 0.880 0.000 0.120
#> GSM254659 1 0.1643 0.8169 0.956 0.000 0.044
#> GSM254680 1 0.0000 0.8126 1.000 0.000 0.000
#> GSM254686 1 0.0000 0.8126 1.000 0.000 0.000
#> GSM254718 1 0.3192 0.7957 0.888 0.000 0.112
#> GSM254674 1 0.0000 0.8126 1.000 0.000 0.000
#> GSM254668 1 0.6717 0.4479 0.628 0.352 0.020
#> GSM254697 1 0.3192 0.7954 0.888 0.000 0.112
#> GSM254704 1 0.4555 0.7325 0.800 0.000 0.200
#> GSM254707 1 0.6717 0.4479 0.628 0.352 0.020
#> GSM254714 1 0.3267 0.7937 0.884 0.000 0.116
#> GSM254722 1 0.3192 0.7954 0.888 0.000 0.112
#> GSM254627 1 0.3340 0.7917 0.880 0.000 0.120
#> GSM254630 1 0.1289 0.8169 0.968 0.000 0.032
#> GSM254633 1 0.4062 0.7639 0.836 0.000 0.164
#> GSM254670 1 0.5760 0.5743 0.672 0.000 0.328
#> GSM254716 1 0.6717 0.4479 0.628 0.352 0.020
#> GSM254720 1 0.1643 0.8169 0.956 0.000 0.044
#> GSM254729 1 0.3340 0.7914 0.880 0.000 0.120
#> GSM254654 1 0.3340 0.7914 0.880 0.000 0.120
#> GSM254656 1 0.6215 0.4048 0.572 0.000 0.428
#> GSM254631 1 0.4062 0.7639 0.836 0.000 0.164
#> GSM254657 1 0.6215 0.4048 0.572 0.000 0.428
#> GSM254664 1 0.0424 0.8144 0.992 0.000 0.008
#> GSM254672 1 0.4555 0.7325 0.800 0.000 0.200
#> GSM254692 1 0.6717 0.4479 0.628 0.352 0.020
#> GSM254645 1 0.5363 0.6442 0.724 0.000 0.276
#> GSM254666 1 0.1411 0.8171 0.964 0.000 0.036
#> GSM254675 1 0.0424 0.8144 0.992 0.000 0.008
#> GSM254678 1 0.3879 0.7725 0.848 0.000 0.152
#> GSM254688 1 0.6717 0.4479 0.628 0.352 0.020
#> GSM254690 1 0.0000 0.8126 1.000 0.000 0.000
#> GSM254696 1 0.3340 0.7917 0.880 0.000 0.120
#> GSM254705 1 0.6717 0.4479 0.628 0.352 0.020
#> GSM254642 1 0.0000 0.8126 1.000 0.000 0.000
#> GSM254661 1 0.1643 0.8169 0.956 0.000 0.044
#> GSM254698 1 0.3340 0.7917 0.880 0.000 0.120
#> GSM254641 1 0.1289 0.8169 0.968 0.000 0.032
#> GSM254647 1 0.0000 0.8126 1.000 0.000 0.000
#> GSM254663 1 0.6717 0.4479 0.628 0.352 0.020
#> GSM254682 1 0.6717 0.4479 0.628 0.352 0.020
#> GSM254709 1 0.6717 0.4479 0.628 0.352 0.020
#> GSM254721 1 0.0000 0.8126 1.000 0.000 0.000
#> GSM254724 1 0.0000 0.8126 1.000 0.000 0.000
#> GSM254650 1 0.6717 0.4479 0.628 0.352 0.020
#> GSM254687 1 0.6717 0.4479 0.628 0.352 0.020
#> GSM254637 1 0.0592 0.8150 0.988 0.000 0.012
#> GSM254684 1 0.4887 0.7067 0.772 0.000 0.228
#> GSM254649 2 0.0000 0.9008 0.000 1.000 0.000
#> GSM254660 2 0.2356 0.8708 0.000 0.928 0.072
#> GSM254693 2 0.0000 0.9008 0.000 1.000 0.000
#> GSM254695 2 0.9468 0.2216 0.300 0.488 0.212
#> GSM254702 2 0.2537 0.8651 0.000 0.920 0.080
#> GSM254643 2 0.0592 0.8993 0.000 0.988 0.012
#> GSM254727 2 0.0000 0.9008 0.000 1.000 0.000
#> GSM254640 3 0.4605 0.7704 0.000 0.204 0.796
#> GSM254626 2 0.0000 0.9008 0.000 1.000 0.000
#> GSM254635 3 0.0892 0.9757 0.000 0.020 0.980
#> GSM254653 2 0.0000 0.9008 0.000 1.000 0.000
#> GSM254658 2 0.0592 0.8993 0.000 0.988 0.012
#> GSM254681 2 0.0892 0.8898 0.000 0.980 0.020
#> GSM254719 2 0.2356 0.8708 0.000 0.928 0.072
#> GSM254673 2 0.0000 0.9008 0.000 1.000 0.000
#> GSM254655 2 0.2356 0.8708 0.000 0.928 0.072
#> GSM254669 2 0.0000 0.9008 0.000 1.000 0.000
#> GSM254699 2 0.2356 0.8708 0.000 0.928 0.072
#> GSM254703 2 0.4399 0.7484 0.000 0.812 0.188
#> GSM254708 2 0.0424 0.9007 0.000 0.992 0.008
#> GSM254715 3 0.0892 0.9757 0.000 0.020 0.980
#> GSM254628 2 0.0000 0.9008 0.000 1.000 0.000
#> GSM254634 3 0.0892 0.9757 0.000 0.020 0.980
#> GSM254646 2 0.0000 0.9008 0.000 1.000 0.000
#> GSM254671 3 0.0892 0.9757 0.000 0.020 0.980
#> GSM254711 3 0.0892 0.9757 0.000 0.020 0.980
#> GSM254717 2 0.0424 0.9007 0.000 0.992 0.008
#> GSM254723 2 0.7581 0.1757 0.408 0.548 0.044
#> GSM254730 2 0.2356 0.8708 0.000 0.928 0.072
#> GSM254731 2 0.2537 0.8651 0.000 0.920 0.080
#> GSM254632 2 0.7665 0.0177 0.456 0.500 0.044
#> GSM254662 2 0.0000 0.9008 0.000 1.000 0.000
#> GSM254677 3 0.0892 0.9757 0.000 0.020 0.980
#> GSM254665 2 0.0424 0.9007 0.000 0.992 0.008
#> GSM254691 2 0.0424 0.9007 0.000 0.992 0.008
#> GSM254644 3 0.3482 0.8714 0.000 0.128 0.872
#> GSM254667 2 0.3826 0.7859 0.124 0.868 0.008
#> GSM254676 2 0.0424 0.9007 0.000 0.992 0.008
#> GSM254679 3 0.0892 0.9757 0.000 0.020 0.980
#> GSM254689 2 0.0892 0.8898 0.000 0.980 0.020
#> GSM254706 2 0.0424 0.9007 0.000 0.992 0.008
#> GSM254712 3 0.0892 0.9757 0.000 0.020 0.980
#> GSM254713 3 0.0892 0.9757 0.000 0.020 0.980
#> GSM254683 2 0.0892 0.8898 0.000 0.980 0.020
#> GSM254710 2 0.0892 0.8898 0.000 0.980 0.020
#> GSM254725 3 0.0892 0.9757 0.000 0.020 0.980
#> GSM254651 2 0.0424 0.9007 0.000 0.992 0.008
#> GSM254638 3 0.0892 0.9757 0.000 0.020 0.980
#> GSM254685 3 0.0892 0.9757 0.000 0.020 0.980
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM254629 3 0.0804 0.9099 0.012 0.000 0.980 0.008
#> GSM254648 3 0.0804 0.9099 0.012 0.000 0.980 0.008
#> GSM254694 3 0.0804 0.9099 0.012 0.000 0.980 0.008
#> GSM254701 3 0.0804 0.9099 0.012 0.000 0.980 0.008
#> GSM254728 3 0.0592 0.9086 0.016 0.000 0.984 0.000
#> GSM254726 2 0.5257 0.2335 0.000 0.548 0.444 0.008
#> GSM254639 3 0.5499 0.7252 0.072 0.000 0.712 0.216
#> GSM254652 3 0.0804 0.9099 0.012 0.000 0.980 0.008
#> GSM254700 3 0.1637 0.8959 0.060 0.000 0.940 0.000
#> GSM254625 1 0.1867 1.0000 0.928 0.000 0.072 0.000
#> GSM254636 3 0.1867 0.8911 0.072 0.000 0.928 0.000
#> GSM254659 3 0.0804 0.9099 0.012 0.000 0.980 0.008
#> GSM254680 3 0.1637 0.8959 0.060 0.000 0.940 0.000
#> GSM254686 3 0.1557 0.8977 0.056 0.000 0.944 0.000
#> GSM254718 3 0.1716 0.8957 0.000 0.000 0.936 0.064
#> GSM254674 3 0.1716 0.8934 0.064 0.000 0.936 0.000
#> GSM254668 1 0.1867 1.0000 0.928 0.000 0.072 0.000
#> GSM254697 3 0.1867 0.8939 0.072 0.000 0.928 0.000
#> GSM254704 3 0.3833 0.8576 0.072 0.000 0.848 0.080
#> GSM254707 1 0.1867 1.0000 0.928 0.000 0.072 0.000
#> GSM254714 3 0.1792 0.8940 0.000 0.000 0.932 0.068
#> GSM254722 3 0.1867 0.8939 0.072 0.000 0.928 0.000
#> GSM254627 3 0.1867 0.8911 0.072 0.000 0.928 0.000
#> GSM254630 3 0.0817 0.9079 0.024 0.000 0.976 0.000
#> GSM254633 3 0.3144 0.8779 0.072 0.000 0.884 0.044
#> GSM254670 3 0.5429 0.7348 0.072 0.000 0.720 0.208
#> GSM254716 1 0.1867 1.0000 0.928 0.000 0.072 0.000
#> GSM254720 3 0.0804 0.9099 0.012 0.000 0.980 0.008
#> GSM254729 3 0.1867 0.8919 0.000 0.000 0.928 0.072
#> GSM254654 3 0.1867 0.8919 0.000 0.000 0.928 0.072
#> GSM254656 3 0.4898 0.4395 0.000 0.000 0.584 0.416
#> GSM254631 3 0.3144 0.8779 0.072 0.000 0.884 0.044
#> GSM254657 3 0.4898 0.4395 0.000 0.000 0.584 0.416
#> GSM254664 3 0.1211 0.9039 0.040 0.000 0.960 0.000
#> GSM254672 3 0.3833 0.8576 0.072 0.000 0.848 0.080
#> GSM254692 1 0.1867 1.0000 0.928 0.000 0.072 0.000
#> GSM254645 3 0.4679 0.7804 0.044 0.000 0.772 0.184
#> GSM254666 3 0.0779 0.9093 0.016 0.000 0.980 0.004
#> GSM254675 3 0.1211 0.9039 0.040 0.000 0.960 0.000
#> GSM254678 3 0.2892 0.8833 0.068 0.000 0.896 0.036
#> GSM254688 1 0.1867 1.0000 0.928 0.000 0.072 0.000
#> GSM254690 3 0.1637 0.8959 0.060 0.000 0.940 0.000
#> GSM254696 3 0.1867 0.8911 0.072 0.000 0.928 0.000
#> GSM254705 1 0.1867 1.0000 0.928 0.000 0.072 0.000
#> GSM254642 3 0.1637 0.8959 0.060 0.000 0.940 0.000
#> GSM254661 3 0.0804 0.9099 0.012 0.000 0.980 0.008
#> GSM254698 3 0.1867 0.8911 0.072 0.000 0.928 0.000
#> GSM254641 3 0.0817 0.9079 0.024 0.000 0.976 0.000
#> GSM254647 3 0.1637 0.8959 0.060 0.000 0.940 0.000
#> GSM254663 1 0.1867 1.0000 0.928 0.000 0.072 0.000
#> GSM254682 1 0.1867 1.0000 0.928 0.000 0.072 0.000
#> GSM254709 1 0.1867 1.0000 0.928 0.000 0.072 0.000
#> GSM254721 3 0.1637 0.8959 0.060 0.000 0.940 0.000
#> GSM254724 3 0.1637 0.8959 0.060 0.000 0.940 0.000
#> GSM254650 1 0.1867 1.0000 0.928 0.000 0.072 0.000
#> GSM254687 1 0.1867 1.0000 0.928 0.000 0.072 0.000
#> GSM254637 3 0.1398 0.9048 0.040 0.000 0.956 0.004
#> GSM254684 3 0.4274 0.8403 0.072 0.000 0.820 0.108
#> GSM254649 2 0.0000 0.9015 0.000 1.000 0.000 0.000
#> GSM254660 2 0.1867 0.8726 0.000 0.928 0.000 0.072
#> GSM254693 2 0.0000 0.9015 0.000 1.000 0.000 0.000
#> GSM254695 2 0.7375 0.2768 0.000 0.488 0.336 0.176
#> GSM254702 2 0.2011 0.8667 0.000 0.920 0.000 0.080
#> GSM254643 2 0.0469 0.9008 0.000 0.988 0.000 0.012
#> GSM254727 2 0.0000 0.9015 0.000 1.000 0.000 0.000
#> GSM254640 4 0.3649 0.7321 0.000 0.204 0.000 0.796
#> GSM254626 2 0.0000 0.9015 0.000 1.000 0.000 0.000
#> GSM254635 4 0.0000 0.9663 0.000 0.000 0.000 1.000
#> GSM254653 2 0.0000 0.9015 0.000 1.000 0.000 0.000
#> GSM254658 2 0.0469 0.9008 0.000 0.988 0.000 0.012
#> GSM254681 2 0.0707 0.8932 0.020 0.980 0.000 0.000
#> GSM254719 2 0.1867 0.8726 0.000 0.928 0.000 0.072
#> GSM254673 2 0.0000 0.9015 0.000 1.000 0.000 0.000
#> GSM254655 2 0.1867 0.8726 0.000 0.928 0.000 0.072
#> GSM254669 2 0.0000 0.9015 0.000 1.000 0.000 0.000
#> GSM254699 2 0.1867 0.8726 0.000 0.928 0.000 0.072
#> GSM254703 2 0.3486 0.7492 0.000 0.812 0.000 0.188
#> GSM254708 2 0.0336 0.9018 0.000 0.992 0.000 0.008
#> GSM254715 4 0.0000 0.9663 0.000 0.000 0.000 1.000
#> GSM254628 2 0.0000 0.9015 0.000 1.000 0.000 0.000
#> GSM254634 4 0.0000 0.9663 0.000 0.000 0.000 1.000
#> GSM254646 2 0.0000 0.9015 0.000 1.000 0.000 0.000
#> GSM254671 4 0.0000 0.9663 0.000 0.000 0.000 1.000
#> GSM254711 4 0.0000 0.9663 0.000 0.000 0.000 1.000
#> GSM254717 2 0.0336 0.9018 0.000 0.992 0.000 0.008
#> GSM254723 2 0.5257 0.2335 0.000 0.548 0.444 0.008
#> GSM254730 2 0.1867 0.8726 0.000 0.928 0.000 0.072
#> GSM254731 2 0.2011 0.8667 0.000 0.920 0.000 0.080
#> GSM254632 2 0.5296 0.0898 0.000 0.500 0.492 0.008
#> GSM254662 2 0.0000 0.9015 0.000 1.000 0.000 0.000
#> GSM254677 4 0.0000 0.9663 0.000 0.000 0.000 1.000
#> GSM254665 2 0.0336 0.9018 0.000 0.992 0.000 0.008
#> GSM254691 2 0.0336 0.9018 0.000 0.992 0.000 0.008
#> GSM254644 4 0.2760 0.8291 0.000 0.128 0.000 0.872
#> GSM254667 2 0.3032 0.7874 0.000 0.868 0.124 0.008
#> GSM254676 2 0.0336 0.9018 0.000 0.992 0.000 0.008
#> GSM254679 4 0.0000 0.9663 0.000 0.000 0.000 1.000
#> GSM254689 2 0.0707 0.8932 0.020 0.980 0.000 0.000
#> GSM254706 2 0.0336 0.9018 0.000 0.992 0.000 0.008
#> GSM254712 4 0.0000 0.9663 0.000 0.000 0.000 1.000
#> GSM254713 4 0.0000 0.9663 0.000 0.000 0.000 1.000
#> GSM254683 2 0.0707 0.8932 0.020 0.980 0.000 0.000
#> GSM254710 2 0.0817 0.8909 0.024 0.976 0.000 0.000
#> GSM254725 4 0.0000 0.9663 0.000 0.000 0.000 1.000
#> GSM254651 2 0.0336 0.9018 0.000 0.992 0.000 0.008
#> GSM254638 4 0.0000 0.9663 0.000 0.000 0.000 1.000
#> GSM254685 4 0.0000 0.9663 0.000 0.000 0.000 1.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM254629 3 0.3508 0.724 0.252 0.000 0.748 0.000 0.000
#> GSM254648 3 0.3508 0.724 0.252 0.000 0.748 0.000 0.000
#> GSM254694 3 0.3774 0.691 0.296 0.000 0.704 0.000 0.000
#> GSM254701 3 0.3816 0.680 0.304 0.000 0.696 0.000 0.000
#> GSM254728 3 0.4029 0.659 0.316 0.000 0.680 0.000 0.004
#> GSM254726 2 0.5378 0.270 0.060 0.548 0.392 0.000 0.000
#> GSM254639 3 0.4289 0.517 0.176 0.000 0.760 0.064 0.000
#> GSM254652 3 0.3534 0.722 0.256 0.000 0.744 0.000 0.000
#> GSM254700 1 0.3794 0.750 0.800 0.000 0.152 0.000 0.048
#> GSM254625 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> GSM254636 1 0.0794 0.740 0.972 0.000 0.028 0.000 0.000
#> GSM254659 3 0.3774 0.691 0.296 0.000 0.704 0.000 0.000
#> GSM254680 1 0.4394 0.722 0.732 0.000 0.220 0.000 0.048
#> GSM254686 1 0.4325 0.722 0.736 0.000 0.220 0.000 0.044
#> GSM254718 3 0.2848 0.726 0.156 0.000 0.840 0.004 0.000
#> GSM254674 1 0.4490 0.717 0.724 0.000 0.224 0.000 0.052
#> GSM254668 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> GSM254697 1 0.0404 0.749 0.988 0.000 0.012 0.000 0.000
#> GSM254704 1 0.3480 0.581 0.752 0.000 0.248 0.000 0.000
#> GSM254707 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> GSM254714 3 0.2806 0.724 0.152 0.000 0.844 0.004 0.000
#> GSM254722 1 0.0609 0.748 0.980 0.000 0.020 0.000 0.000
#> GSM254627 1 0.0404 0.745 0.988 0.000 0.012 0.000 0.000
#> GSM254630 1 0.4505 0.420 0.604 0.000 0.384 0.000 0.012
#> GSM254633 1 0.2732 0.672 0.840 0.000 0.160 0.000 0.000
#> GSM254670 3 0.4429 0.518 0.192 0.000 0.744 0.064 0.000
#> GSM254716 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> GSM254720 3 0.4138 0.500 0.384 0.000 0.616 0.000 0.000
#> GSM254729 3 0.2719 0.723 0.144 0.000 0.852 0.004 0.000
#> GSM254654 3 0.2719 0.723 0.144 0.000 0.852 0.004 0.000
#> GSM254656 3 0.4348 0.361 0.016 0.000 0.668 0.316 0.000
#> GSM254631 1 0.2732 0.672 0.840 0.000 0.160 0.000 0.000
#> GSM254657 3 0.4348 0.361 0.016 0.000 0.668 0.316 0.000
#> GSM254664 1 0.3910 0.737 0.772 0.000 0.196 0.000 0.032
#> GSM254672 1 0.3480 0.581 0.752 0.000 0.248 0.000 0.000
#> GSM254692 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> GSM254645 3 0.3657 0.622 0.116 0.000 0.820 0.064 0.000
#> GSM254666 3 0.3990 0.667 0.308 0.000 0.688 0.000 0.004
#> GSM254675 1 0.3977 0.733 0.764 0.000 0.204 0.000 0.032
#> GSM254678 1 0.2561 0.718 0.856 0.000 0.144 0.000 0.000
#> GSM254688 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> GSM254690 1 0.4394 0.722 0.732 0.000 0.220 0.000 0.048
#> GSM254696 1 0.0510 0.745 0.984 0.000 0.016 0.000 0.000
#> GSM254705 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> GSM254642 1 0.3794 0.750 0.800 0.000 0.152 0.000 0.048
#> GSM254661 3 0.3508 0.724 0.252 0.000 0.748 0.000 0.000
#> GSM254698 1 0.0510 0.743 0.984 0.000 0.016 0.000 0.000
#> GSM254641 1 0.4505 0.420 0.604 0.000 0.384 0.000 0.012
#> GSM254647 1 0.3794 0.750 0.800 0.000 0.152 0.000 0.048
#> GSM254663 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> GSM254682 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> GSM254709 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> GSM254721 1 0.3794 0.750 0.800 0.000 0.152 0.000 0.048
#> GSM254724 1 0.3794 0.750 0.800 0.000 0.152 0.000 0.048
#> GSM254650 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> GSM254687 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> GSM254637 1 0.4073 0.726 0.752 0.000 0.216 0.000 0.032
#> GSM254684 1 0.3969 0.519 0.692 0.000 0.304 0.004 0.000
#> GSM254649 2 0.0880 0.901 0.000 0.968 0.032 0.000 0.000
#> GSM254660 2 0.1608 0.877 0.000 0.928 0.000 0.072 0.000
#> GSM254693 2 0.0880 0.901 0.000 0.968 0.032 0.000 0.000
#> GSM254695 2 0.6859 0.258 0.024 0.488 0.320 0.168 0.000
#> GSM254702 2 0.1732 0.872 0.000 0.920 0.000 0.080 0.000
#> GSM254643 2 0.0404 0.901 0.000 0.988 0.000 0.012 0.000
#> GSM254727 2 0.0880 0.901 0.000 0.968 0.032 0.000 0.000
#> GSM254640 4 0.3143 0.739 0.000 0.204 0.000 0.796 0.000
#> GSM254626 2 0.0880 0.901 0.000 0.968 0.032 0.000 0.000
#> GSM254635 4 0.0162 0.966 0.000 0.000 0.004 0.996 0.000
#> GSM254653 2 0.0880 0.901 0.000 0.968 0.032 0.000 0.000
#> GSM254658 2 0.0404 0.901 0.000 0.988 0.000 0.012 0.000
#> GSM254681 2 0.1648 0.890 0.000 0.940 0.040 0.000 0.020
#> GSM254719 2 0.1608 0.877 0.000 0.928 0.000 0.072 0.000
#> GSM254673 2 0.0880 0.901 0.000 0.968 0.032 0.000 0.000
#> GSM254655 2 0.1608 0.877 0.000 0.928 0.000 0.072 0.000
#> GSM254669 2 0.0880 0.901 0.000 0.968 0.032 0.000 0.000
#> GSM254699 2 0.1608 0.877 0.000 0.928 0.000 0.072 0.000
#> GSM254703 2 0.3003 0.759 0.000 0.812 0.000 0.188 0.000
#> GSM254708 2 0.0290 0.902 0.000 0.992 0.000 0.008 0.000
#> GSM254715 4 0.0162 0.966 0.000 0.000 0.004 0.996 0.000
#> GSM254628 2 0.0703 0.901 0.000 0.976 0.024 0.000 0.000
#> GSM254634 4 0.0162 0.966 0.000 0.000 0.004 0.996 0.000
#> GSM254646 2 0.0880 0.901 0.000 0.968 0.032 0.000 0.000
#> GSM254671 4 0.0000 0.966 0.000 0.000 0.000 1.000 0.000
#> GSM254711 4 0.0000 0.966 0.000 0.000 0.000 1.000 0.000
#> GSM254717 2 0.0290 0.902 0.000 0.992 0.000 0.008 0.000
#> GSM254723 2 0.5378 0.270 0.060 0.548 0.392 0.000 0.000
#> GSM254730 2 0.1608 0.877 0.000 0.928 0.000 0.072 0.000
#> GSM254731 2 0.1732 0.872 0.000 0.920 0.000 0.080 0.000
#> GSM254632 2 0.5542 0.117 0.068 0.500 0.432 0.000 0.000
#> GSM254662 2 0.0880 0.901 0.000 0.968 0.032 0.000 0.000
#> GSM254677 4 0.0162 0.966 0.000 0.000 0.004 0.996 0.000
#> GSM254665 2 0.0290 0.902 0.000 0.992 0.000 0.008 0.000
#> GSM254691 2 0.0290 0.902 0.000 0.992 0.000 0.008 0.000
#> GSM254644 4 0.2377 0.837 0.000 0.128 0.000 0.872 0.000
#> GSM254667 2 0.2612 0.824 0.000 0.868 0.124 0.008 0.000
#> GSM254676 2 0.0290 0.902 0.000 0.992 0.000 0.008 0.000
#> GSM254679 4 0.0000 0.966 0.000 0.000 0.000 1.000 0.000
#> GSM254689 2 0.1648 0.890 0.000 0.940 0.040 0.000 0.020
#> GSM254706 2 0.0290 0.902 0.000 0.992 0.000 0.008 0.000
#> GSM254712 4 0.0162 0.966 0.000 0.000 0.004 0.996 0.000
#> GSM254713 4 0.0162 0.966 0.000 0.000 0.004 0.996 0.000
#> GSM254683 2 0.1648 0.890 0.000 0.940 0.040 0.000 0.020
#> GSM254710 2 0.1741 0.888 0.000 0.936 0.040 0.000 0.024
#> GSM254725 4 0.0000 0.966 0.000 0.000 0.000 1.000 0.000
#> GSM254651 2 0.0290 0.902 0.000 0.992 0.000 0.008 0.000
#> GSM254638 4 0.0162 0.966 0.000 0.000 0.004 0.996 0.000
#> GSM254685 4 0.0000 0.966 0.000 0.000 0.000 1.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM254629 3 0.1204 0.722 0.056 0.000 0.944 0.000 0.000 NA
#> GSM254648 3 0.1204 0.722 0.056 0.000 0.944 0.000 0.000 NA
#> GSM254694 3 0.1814 0.708 0.100 0.000 0.900 0.000 0.000 NA
#> GSM254701 3 0.1910 0.702 0.108 0.000 0.892 0.000 0.000 NA
#> GSM254728 3 0.2178 0.686 0.132 0.000 0.868 0.000 0.000 NA
#> GSM254726 2 0.4103 0.294 0.004 0.544 0.448 0.000 0.000 NA
#> GSM254639 3 0.4801 0.472 0.016 0.000 0.484 0.024 0.000 NA
#> GSM254652 3 0.1267 0.721 0.060 0.000 0.940 0.000 0.000 NA
#> GSM254700 1 0.3073 0.757 0.816 0.000 0.164 0.000 0.016 NA
#> GSM254625 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000 NA
#> GSM254636 1 0.1682 0.761 0.928 0.000 0.020 0.000 0.000 NA
#> GSM254659 3 0.1814 0.708 0.100 0.000 0.900 0.000 0.000 NA
#> GSM254680 1 0.3990 0.659 0.676 0.000 0.304 0.000 0.016 NA
#> GSM254686 1 0.3903 0.659 0.680 0.000 0.304 0.000 0.012 NA
#> GSM254718 3 0.2402 0.706 0.012 0.000 0.868 0.000 0.000 NA
#> GSM254674 1 0.4106 0.649 0.664 0.000 0.312 0.000 0.020 NA
#> GSM254668 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000 NA
#> GSM254697 1 0.1334 0.768 0.948 0.000 0.020 0.000 0.000 NA
#> GSM254704 1 0.4254 0.640 0.712 0.000 0.072 0.000 0.000 NA
#> GSM254707 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000 NA
#> GSM254714 3 0.2624 0.705 0.020 0.000 0.856 0.000 0.000 NA
#> GSM254722 1 0.1644 0.767 0.932 0.000 0.028 0.000 0.000 NA
#> GSM254627 1 0.1124 0.765 0.956 0.000 0.008 0.000 0.000 NA
#> GSM254630 3 0.4051 -0.109 0.432 0.000 0.560 0.000 0.008 NA
#> GSM254633 1 0.3470 0.711 0.796 0.000 0.052 0.000 0.000 NA
#> GSM254670 3 0.4874 0.472 0.020 0.000 0.484 0.024 0.000 NA
#> GSM254716 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000 NA
#> GSM254720 3 0.2871 0.599 0.192 0.000 0.804 0.000 0.000 NA
#> GSM254729 3 0.2489 0.703 0.012 0.000 0.860 0.000 0.000 NA
#> GSM254654 3 0.2489 0.703 0.012 0.000 0.860 0.000 0.000 NA
#> GSM254656 3 0.6082 0.317 0.008 0.000 0.476 0.276 0.000 NA
#> GSM254631 1 0.3470 0.711 0.796 0.000 0.052 0.000 0.000 NA
#> GSM254657 3 0.6082 0.317 0.008 0.000 0.476 0.276 0.000 NA
#> GSM254664 1 0.3404 0.718 0.744 0.000 0.248 0.000 0.004 NA
#> GSM254672 1 0.4254 0.640 0.712 0.000 0.072 0.000 0.000 NA
#> GSM254692 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000 NA
#> GSM254645 3 0.4499 0.571 0.012 0.000 0.620 0.024 0.000 NA
#> GSM254666 3 0.2178 0.686 0.132 0.000 0.868 0.000 0.000 NA
#> GSM254675 1 0.3560 0.711 0.732 0.000 0.256 0.000 0.004 NA
#> GSM254678 1 0.3341 0.744 0.816 0.000 0.068 0.000 0.000 NA
#> GSM254688 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000 NA
#> GSM254690 1 0.3990 0.659 0.676 0.000 0.304 0.000 0.016 NA
#> GSM254696 1 0.1398 0.764 0.940 0.000 0.008 0.000 0.000 NA
#> GSM254705 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000 NA
#> GSM254642 1 0.3073 0.757 0.816 0.000 0.164 0.000 0.016 NA
#> GSM254661 3 0.1204 0.722 0.056 0.000 0.944 0.000 0.000 NA
#> GSM254698 1 0.1461 0.764 0.940 0.000 0.016 0.000 0.000 NA
#> GSM254641 3 0.4051 -0.109 0.432 0.000 0.560 0.000 0.008 NA
#> GSM254647 1 0.3073 0.757 0.816 0.000 0.164 0.000 0.016 NA
#> GSM254663 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000 NA
#> GSM254682 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000 NA
#> GSM254709 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000 NA
#> GSM254721 1 0.3073 0.757 0.816 0.000 0.164 0.000 0.016 NA
#> GSM254724 1 0.3073 0.757 0.816 0.000 0.164 0.000 0.016 NA
#> GSM254650 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000 NA
#> GSM254687 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000 NA
#> GSM254637 1 0.3650 0.699 0.716 0.000 0.272 0.000 0.004 NA
#> GSM254684 1 0.5241 0.516 0.532 0.000 0.104 0.000 0.000 NA
#> GSM254649 2 0.2697 0.805 0.000 0.812 0.000 0.000 0.000 NA
#> GSM254660 2 0.1500 0.817 0.000 0.936 0.000 0.012 0.000 NA
#> GSM254693 2 0.2562 0.811 0.000 0.828 0.000 0.000 0.000 NA
#> GSM254695 2 0.6018 0.276 0.000 0.488 0.344 0.148 0.000 NA
#> GSM254702 2 0.1682 0.814 0.000 0.928 0.000 0.020 0.000 NA
#> GSM254643 2 0.0146 0.832 0.000 0.996 0.000 0.000 0.000 NA
#> GSM254727 2 0.2454 0.814 0.000 0.840 0.000 0.000 0.000 NA
#> GSM254640 4 0.3952 0.725 0.000 0.212 0.000 0.736 0.000 NA
#> GSM254626 2 0.2697 0.805 0.000 0.812 0.000 0.000 0.000 NA
#> GSM254635 4 0.0260 0.946 0.000 0.000 0.000 0.992 0.000 NA
#> GSM254653 2 0.2454 0.814 0.000 0.840 0.000 0.000 0.000 NA
#> GSM254658 2 0.0146 0.832 0.000 0.996 0.000 0.000 0.000 NA
#> GSM254681 2 0.3843 0.625 0.000 0.548 0.000 0.000 0.000 NA
#> GSM254719 2 0.1500 0.817 0.000 0.936 0.000 0.012 0.000 NA
#> GSM254673 2 0.2454 0.814 0.000 0.840 0.000 0.000 0.000 NA
#> GSM254655 2 0.1500 0.817 0.000 0.936 0.000 0.012 0.000 NA
#> GSM254669 2 0.2562 0.811 0.000 0.828 0.000 0.000 0.000 NA
#> GSM254699 2 0.1500 0.817 0.000 0.936 0.000 0.012 0.000 NA
#> GSM254703 2 0.3190 0.730 0.000 0.820 0.000 0.136 0.000 NA
#> GSM254708 2 0.0000 0.832 0.000 1.000 0.000 0.000 0.000 NA
#> GSM254715 4 0.0713 0.947 0.000 0.000 0.000 0.972 0.000 NA
#> GSM254628 2 0.2378 0.816 0.000 0.848 0.000 0.000 0.000 NA
#> GSM254634 4 0.0363 0.945 0.000 0.000 0.000 0.988 0.000 NA
#> GSM254646 2 0.2697 0.805 0.000 0.812 0.000 0.000 0.000 NA
#> GSM254671 4 0.1152 0.939 0.000 0.004 0.000 0.952 0.000 NA
#> GSM254711 4 0.1152 0.939 0.000 0.004 0.000 0.952 0.000 NA
#> GSM254717 2 0.0000 0.832 0.000 1.000 0.000 0.000 0.000 NA
#> GSM254723 2 0.4103 0.294 0.004 0.544 0.448 0.000 0.000 NA
#> GSM254730 2 0.1500 0.817 0.000 0.936 0.000 0.012 0.000 NA
#> GSM254731 2 0.1682 0.814 0.000 0.928 0.000 0.020 0.000 NA
#> GSM254632 2 0.4129 0.147 0.004 0.496 0.496 0.000 0.000 NA
#> GSM254662 2 0.2454 0.814 0.000 0.840 0.000 0.000 0.000 NA
#> GSM254677 4 0.0363 0.945 0.000 0.000 0.000 0.988 0.000 NA
#> GSM254665 2 0.0000 0.832 0.000 1.000 0.000 0.000 0.000 NA
#> GSM254691 2 0.0000 0.832 0.000 1.000 0.000 0.000 0.000 NA
#> GSM254644 4 0.3190 0.821 0.000 0.136 0.000 0.820 0.000 NA
#> GSM254667 2 0.2234 0.773 0.000 0.872 0.124 0.000 0.000 NA
#> GSM254676 2 0.0000 0.832 0.000 1.000 0.000 0.000 0.000 NA
#> GSM254679 4 0.0146 0.947 0.000 0.000 0.000 0.996 0.000 NA
#> GSM254689 2 0.3843 0.625 0.000 0.548 0.000 0.000 0.000 NA
#> GSM254706 2 0.0000 0.832 0.000 1.000 0.000 0.000 0.000 NA
#> GSM254712 4 0.0363 0.945 0.000 0.000 0.000 0.988 0.000 NA
#> GSM254713 4 0.0713 0.947 0.000 0.000 0.000 0.972 0.000 NA
#> GSM254683 2 0.3847 0.622 0.000 0.544 0.000 0.000 0.000 NA
#> GSM254710 2 0.3979 0.619 0.000 0.540 0.000 0.000 0.004 NA
#> GSM254725 4 0.0603 0.946 0.000 0.004 0.000 0.980 0.000 NA
#> GSM254651 2 0.0000 0.832 0.000 1.000 0.000 0.000 0.000 NA
#> GSM254638 4 0.0363 0.945 0.000 0.000 0.000 0.988 0.000 NA
#> GSM254685 4 0.0865 0.943 0.000 0.000 0.000 0.964 0.000 NA
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n tissue(p) individual(p) disease.state(p) gender(p) other(p) k
#> ATC:hclust 97 2.04e-04 0.3347 0.382 0.3506 0.902 2
#> ATC:hclust 89 4.72e-20 0.5283 0.663 0.2864 0.875 3
#> ATC:hclust 101 9.47e-22 0.2453 0.798 0.3595 0.884 4
#> ATC:hclust 99 1.61e-20 0.0753 0.776 0.0890 0.718 5
#> ATC:hclust 97 4.28e-20 0.0984 0.768 0.0864 0.772 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 21168 rows and 107 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'kmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.975 0.990 0.5028 0.497 0.497
#> 3 3 0.716 0.743 0.812 0.2663 0.816 0.646
#> 4 4 0.811 0.850 0.911 0.1491 0.888 0.696
#> 5 5 0.746 0.613 0.757 0.0753 0.958 0.847
#> 6 6 0.771 0.755 0.796 0.0475 0.889 0.583
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
#> GSM254629 1 0.0000 0.990 1.000 0.000
#> GSM254648 2 0.9000 0.536 0.316 0.684
#> GSM254694 1 0.0000 0.990 1.000 0.000
#> GSM254701 1 0.0000 0.990 1.000 0.000
#> GSM254728 1 0.0000 0.990 1.000 0.000
#> GSM254726 2 0.7674 0.708 0.224 0.776
#> GSM254639 1 0.0000 0.990 1.000 0.000
#> GSM254652 1 0.0000 0.990 1.000 0.000
#> GSM254700 1 0.0000 0.990 1.000 0.000
#> GSM254625 1 0.0000 0.990 1.000 0.000
#> GSM254636 1 0.0000 0.990 1.000 0.000
#> GSM254659 1 0.0000 0.990 1.000 0.000
#> GSM254680 1 0.0000 0.990 1.000 0.000
#> GSM254686 1 0.0000 0.990 1.000 0.000
#> GSM254718 1 0.0000 0.990 1.000 0.000
#> GSM254674 1 0.0000 0.990 1.000 0.000
#> GSM254668 1 0.0000 0.990 1.000 0.000
#> GSM254697 1 0.0000 0.990 1.000 0.000
#> GSM254704 1 0.0000 0.990 1.000 0.000
#> GSM254707 1 0.0000 0.990 1.000 0.000
#> GSM254714 1 0.0000 0.990 1.000 0.000
#> GSM254722 1 0.0000 0.990 1.000 0.000
#> GSM254627 1 0.0000 0.990 1.000 0.000
#> GSM254630 1 0.0000 0.990 1.000 0.000
#> GSM254633 1 0.0000 0.990 1.000 0.000
#> GSM254670 1 0.0000 0.990 1.000 0.000
#> GSM254716 1 0.0000 0.990 1.000 0.000
#> GSM254720 1 0.0000 0.990 1.000 0.000
#> GSM254729 1 0.0000 0.990 1.000 0.000
#> GSM254654 1 0.0000 0.990 1.000 0.000
#> GSM254656 1 0.9286 0.468 0.656 0.344
#> GSM254631 1 0.0000 0.990 1.000 0.000
#> GSM254657 1 0.0000 0.990 1.000 0.000
#> GSM254664 1 0.0000 0.990 1.000 0.000
#> GSM254672 1 0.0000 0.990 1.000 0.000
#> GSM254692 1 0.0000 0.990 1.000 0.000
#> GSM254645 1 0.0000 0.990 1.000 0.000
#> GSM254666 1 0.0000 0.990 1.000 0.000
#> GSM254675 1 0.0000 0.990 1.000 0.000
#> GSM254678 1 0.0000 0.990 1.000 0.000
#> GSM254688 1 0.0000 0.990 1.000 0.000
#> GSM254690 1 0.0000 0.990 1.000 0.000
#> GSM254696 1 0.0000 0.990 1.000 0.000
#> GSM254705 1 0.0000 0.990 1.000 0.000
#> GSM254642 1 0.0000 0.990 1.000 0.000
#> GSM254661 1 0.0000 0.990 1.000 0.000
#> GSM254698 1 0.0000 0.990 1.000 0.000
#> GSM254641 1 0.0000 0.990 1.000 0.000
#> GSM254647 1 0.0000 0.990 1.000 0.000
#> GSM254663 1 0.0000 0.990 1.000 0.000
#> GSM254682 1 0.0000 0.990 1.000 0.000
#> GSM254709 1 0.0000 0.990 1.000 0.000
#> GSM254721 1 0.0000 0.990 1.000 0.000
#> GSM254724 1 0.0000 0.990 1.000 0.000
#> GSM254650 1 0.0000 0.990 1.000 0.000
#> GSM254687 1 0.0000 0.990 1.000 0.000
#> GSM254637 1 0.0000 0.990 1.000 0.000
#> GSM254684 1 0.0000 0.990 1.000 0.000
#> GSM254649 2 0.0000 0.989 0.000 1.000
#> GSM254660 2 0.0000 0.989 0.000 1.000
#> GSM254693 2 0.0000 0.989 0.000 1.000
#> GSM254695 2 0.0000 0.989 0.000 1.000
#> GSM254702 2 0.0000 0.989 0.000 1.000
#> GSM254643 2 0.0000 0.989 0.000 1.000
#> GSM254727 2 0.0000 0.989 0.000 1.000
#> GSM254640 2 0.0000 0.989 0.000 1.000
#> GSM254626 2 0.0000 0.989 0.000 1.000
#> GSM254635 2 0.0000 0.989 0.000 1.000
#> GSM254653 2 0.0000 0.989 0.000 1.000
#> GSM254658 2 0.0000 0.989 0.000 1.000
#> GSM254681 2 0.0000 0.989 0.000 1.000
#> GSM254719 2 0.0000 0.989 0.000 1.000
#> GSM254673 2 0.0000 0.989 0.000 1.000
#> GSM254655 2 0.0000 0.989 0.000 1.000
#> GSM254669 2 0.0000 0.989 0.000 1.000
#> GSM254699 2 0.0000 0.989 0.000 1.000
#> GSM254703 2 0.0000 0.989 0.000 1.000
#> GSM254708 2 0.0000 0.989 0.000 1.000
#> GSM254715 2 0.0000 0.989 0.000 1.000
#> GSM254628 2 0.0000 0.989 0.000 1.000
#> GSM254634 2 0.0000 0.989 0.000 1.000
#> GSM254646 2 0.0000 0.989 0.000 1.000
#> GSM254671 2 0.0000 0.989 0.000 1.000
#> GSM254711 2 0.0000 0.989 0.000 1.000
#> GSM254717 2 0.0000 0.989 0.000 1.000
#> GSM254723 2 0.0000 0.989 0.000 1.000
#> GSM254730 2 0.0000 0.989 0.000 1.000
#> GSM254731 2 0.0000 0.989 0.000 1.000
#> GSM254632 1 0.7528 0.720 0.784 0.216
#> GSM254662 2 0.0000 0.989 0.000 1.000
#> GSM254677 2 0.0000 0.989 0.000 1.000
#> GSM254665 2 0.0000 0.989 0.000 1.000
#> GSM254691 2 0.0000 0.989 0.000 1.000
#> GSM254644 2 0.0000 0.989 0.000 1.000
#> GSM254667 2 0.0000 0.989 0.000 1.000
#> GSM254676 2 0.0000 0.989 0.000 1.000
#> GSM254679 2 0.0000 0.989 0.000 1.000
#> GSM254689 2 0.0000 0.989 0.000 1.000
#> GSM254706 2 0.0000 0.989 0.000 1.000
#> GSM254712 2 0.0000 0.989 0.000 1.000
#> GSM254713 2 0.0000 0.989 0.000 1.000
#> GSM254683 2 0.0000 0.989 0.000 1.000
#> GSM254710 2 0.0376 0.985 0.004 0.996
#> GSM254725 2 0.0000 0.989 0.000 1.000
#> GSM254651 2 0.0000 0.989 0.000 1.000
#> GSM254638 2 0.0000 0.989 0.000 1.000
#> GSM254685 2 0.0000 0.989 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM254629 3 0.0237 0.819 0.004 0.000 0.996
#> GSM254648 3 0.7065 0.258 0.032 0.352 0.616
#> GSM254694 3 0.0000 0.821 0.000 0.000 1.000
#> GSM254701 3 0.0000 0.821 0.000 0.000 1.000
#> GSM254728 3 0.4796 0.362 0.220 0.000 0.780
#> GSM254726 2 0.4452 0.692 0.000 0.808 0.192
#> GSM254639 3 0.5785 0.479 0.332 0.000 0.668
#> GSM254652 3 0.0237 0.819 0.004 0.000 0.996
#> GSM254700 3 0.6079 -0.361 0.388 0.000 0.612
#> GSM254625 1 0.6079 0.866 0.612 0.000 0.388
#> GSM254636 3 0.0237 0.821 0.004 0.000 0.996
#> GSM254659 3 0.0000 0.821 0.000 0.000 1.000
#> GSM254680 1 0.6252 0.807 0.556 0.000 0.444
#> GSM254686 1 0.6204 0.839 0.576 0.000 0.424
#> GSM254718 3 0.0237 0.821 0.004 0.000 0.996
#> GSM254674 1 0.6095 0.864 0.608 0.000 0.392
#> GSM254668 1 0.6079 0.866 0.612 0.000 0.388
#> GSM254697 3 0.0237 0.819 0.004 0.000 0.996
#> GSM254704 3 0.0237 0.821 0.004 0.000 0.996
#> GSM254707 1 0.6079 0.866 0.612 0.000 0.388
#> GSM254714 3 0.0237 0.821 0.004 0.000 0.996
#> GSM254722 3 0.0237 0.819 0.004 0.000 0.996
#> GSM254627 3 0.0237 0.819 0.004 0.000 0.996
#> GSM254630 1 0.6192 0.843 0.580 0.000 0.420
#> GSM254633 3 0.0237 0.821 0.004 0.000 0.996
#> GSM254670 3 0.2796 0.742 0.092 0.000 0.908
#> GSM254716 1 0.6079 0.866 0.612 0.000 0.388
#> GSM254720 3 0.0000 0.821 0.000 0.000 1.000
#> GSM254729 3 0.2796 0.742 0.092 0.000 0.908
#> GSM254654 3 0.5785 0.479 0.332 0.000 0.668
#> GSM254656 3 0.6079 0.421 0.388 0.000 0.612
#> GSM254631 3 0.0237 0.821 0.004 0.000 0.996
#> GSM254657 3 0.6079 0.421 0.388 0.000 0.612
#> GSM254664 3 0.0237 0.819 0.004 0.000 0.996
#> GSM254672 3 0.0892 0.809 0.020 0.000 0.980
#> GSM254692 1 0.6079 0.866 0.612 0.000 0.388
#> GSM254645 3 0.2796 0.742 0.092 0.000 0.908
#> GSM254666 3 0.0237 0.819 0.004 0.000 0.996
#> GSM254675 3 0.0237 0.819 0.004 0.000 0.996
#> GSM254678 3 0.0237 0.821 0.004 0.000 0.996
#> GSM254688 1 0.6079 0.866 0.612 0.000 0.388
#> GSM254690 1 0.6225 0.828 0.568 0.000 0.432
#> GSM254696 3 0.0000 0.821 0.000 0.000 1.000
#> GSM254705 1 0.6079 0.866 0.612 0.000 0.388
#> GSM254642 1 0.6204 0.839 0.576 0.000 0.424
#> GSM254661 3 0.0000 0.821 0.000 0.000 1.000
#> GSM254698 3 0.0237 0.821 0.004 0.000 0.996
#> GSM254641 3 0.6180 -0.459 0.416 0.000 0.584
#> GSM254647 1 0.6204 0.839 0.576 0.000 0.424
#> GSM254663 1 0.6079 0.866 0.612 0.000 0.388
#> GSM254682 1 0.6079 0.866 0.612 0.000 0.388
#> GSM254709 1 0.6079 0.866 0.612 0.000 0.388
#> GSM254721 1 0.6225 0.828 0.568 0.000 0.432
#> GSM254724 3 0.6079 -0.361 0.388 0.000 0.612
#> GSM254650 1 0.6079 0.866 0.612 0.000 0.388
#> GSM254687 1 0.6079 0.866 0.612 0.000 0.388
#> GSM254637 3 0.0000 0.821 0.000 0.000 1.000
#> GSM254684 3 0.0237 0.821 0.004 0.000 0.996
#> GSM254649 2 0.0000 0.866 0.000 1.000 0.000
#> GSM254660 2 0.0000 0.866 0.000 1.000 0.000
#> GSM254693 2 0.0000 0.866 0.000 1.000 0.000
#> GSM254695 2 0.6282 0.718 0.384 0.612 0.004
#> GSM254702 2 0.0424 0.865 0.008 0.992 0.000
#> GSM254643 2 0.0000 0.866 0.000 1.000 0.000
#> GSM254727 2 0.0000 0.866 0.000 1.000 0.000
#> GSM254640 2 0.6062 0.720 0.384 0.616 0.000
#> GSM254626 2 0.0000 0.866 0.000 1.000 0.000
#> GSM254635 2 0.6298 0.716 0.388 0.608 0.004
#> GSM254653 2 0.0000 0.866 0.000 1.000 0.000
#> GSM254658 2 0.0000 0.866 0.000 1.000 0.000
#> GSM254681 2 0.1411 0.845 0.036 0.964 0.000
#> GSM254719 2 0.0000 0.866 0.000 1.000 0.000
#> GSM254673 2 0.0000 0.866 0.000 1.000 0.000
#> GSM254655 2 0.0000 0.866 0.000 1.000 0.000
#> GSM254669 2 0.0000 0.866 0.000 1.000 0.000
#> GSM254699 2 0.0000 0.866 0.000 1.000 0.000
#> GSM254703 2 0.6008 0.726 0.372 0.628 0.000
#> GSM254708 2 0.0000 0.866 0.000 1.000 0.000
#> GSM254715 2 0.6298 0.716 0.388 0.608 0.004
#> GSM254628 2 0.0000 0.866 0.000 1.000 0.000
#> GSM254634 2 0.6298 0.716 0.388 0.608 0.004
#> GSM254646 2 0.0000 0.866 0.000 1.000 0.000
#> GSM254671 2 0.6282 0.718 0.384 0.612 0.004
#> GSM254711 2 0.6282 0.718 0.384 0.612 0.004
#> GSM254717 2 0.0000 0.866 0.000 1.000 0.000
#> GSM254723 2 0.0747 0.863 0.016 0.984 0.000
#> GSM254730 2 0.0000 0.866 0.000 1.000 0.000
#> GSM254731 2 0.0424 0.865 0.008 0.992 0.000
#> GSM254632 2 0.6359 0.393 0.008 0.628 0.364
#> GSM254662 2 0.0000 0.866 0.000 1.000 0.000
#> GSM254677 1 0.9793 -0.442 0.388 0.376 0.236
#> GSM254665 2 0.0000 0.866 0.000 1.000 0.000
#> GSM254691 2 0.0000 0.866 0.000 1.000 0.000
#> GSM254644 2 0.6062 0.720 0.384 0.616 0.000
#> GSM254667 2 0.0747 0.863 0.016 0.984 0.000
#> GSM254676 2 0.0000 0.866 0.000 1.000 0.000
#> GSM254679 2 0.6298 0.716 0.388 0.608 0.004
#> GSM254689 2 0.1411 0.845 0.036 0.964 0.000
#> GSM254706 2 0.0000 0.866 0.000 1.000 0.000
#> GSM254712 2 0.6298 0.716 0.388 0.608 0.004
#> GSM254713 2 0.6298 0.716 0.388 0.608 0.004
#> GSM254683 2 0.1411 0.845 0.036 0.964 0.000
#> GSM254710 1 0.6095 0.267 0.608 0.392 0.000
#> GSM254725 2 0.6298 0.716 0.388 0.608 0.004
#> GSM254651 2 0.0000 0.866 0.000 1.000 0.000
#> GSM254638 2 0.6298 0.716 0.388 0.608 0.004
#> GSM254685 2 0.6282 0.718 0.384 0.612 0.004
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM254629 3 0.1792 0.9002 0.000 0.000 0.932 0.068
#> GSM254648 3 0.2797 0.8854 0.000 0.032 0.900 0.068
#> GSM254694 3 0.1792 0.9002 0.000 0.000 0.932 0.068
#> GSM254701 3 0.1792 0.9002 0.000 0.000 0.932 0.068
#> GSM254728 3 0.2635 0.8886 0.020 0.000 0.904 0.076
#> GSM254726 2 0.4144 0.7605 0.000 0.828 0.104 0.068
#> GSM254639 3 0.1792 0.8979 0.000 0.000 0.932 0.068
#> GSM254652 3 0.1792 0.9002 0.000 0.000 0.932 0.068
#> GSM254700 3 0.5964 0.0419 0.424 0.000 0.536 0.040
#> GSM254625 1 0.0469 0.9037 0.988 0.000 0.012 0.000
#> GSM254636 3 0.1211 0.9014 0.000 0.000 0.960 0.040
#> GSM254659 3 0.1792 0.9002 0.000 0.000 0.932 0.068
#> GSM254680 1 0.5915 0.3808 0.560 0.000 0.400 0.040
#> GSM254686 1 0.3587 0.8555 0.856 0.000 0.104 0.040
#> GSM254718 3 0.1637 0.9011 0.000 0.000 0.940 0.060
#> GSM254674 1 0.2124 0.8871 0.932 0.000 0.040 0.028
#> GSM254668 1 0.0469 0.9037 0.988 0.000 0.012 0.000
#> GSM254697 3 0.1211 0.8943 0.000 0.000 0.960 0.040
#> GSM254704 3 0.0921 0.9036 0.000 0.000 0.972 0.028
#> GSM254707 1 0.0469 0.9037 0.988 0.000 0.012 0.000
#> GSM254714 3 0.1867 0.9014 0.000 0.000 0.928 0.072
#> GSM254722 3 0.0817 0.8982 0.000 0.000 0.976 0.024
#> GSM254627 3 0.0817 0.8982 0.000 0.000 0.976 0.024
#> GSM254630 1 0.3372 0.8616 0.868 0.000 0.096 0.036
#> GSM254633 3 0.0921 0.9036 0.000 0.000 0.972 0.028
#> GSM254670 3 0.1637 0.9011 0.000 0.000 0.940 0.060
#> GSM254716 1 0.0469 0.9037 0.988 0.000 0.012 0.000
#> GSM254720 3 0.1302 0.8982 0.000 0.000 0.956 0.044
#> GSM254729 3 0.1637 0.9011 0.000 0.000 0.940 0.060
#> GSM254654 3 0.2081 0.8984 0.000 0.000 0.916 0.084
#> GSM254656 4 0.4382 0.4727 0.000 0.000 0.296 0.704
#> GSM254631 3 0.0921 0.9036 0.000 0.000 0.972 0.028
#> GSM254657 3 0.4843 0.4009 0.000 0.000 0.604 0.396
#> GSM254664 3 0.1211 0.8943 0.000 0.000 0.960 0.040
#> GSM254672 3 0.0921 0.9036 0.000 0.000 0.972 0.028
#> GSM254692 1 0.0469 0.9037 0.988 0.000 0.012 0.000
#> GSM254645 3 0.1637 0.9011 0.000 0.000 0.940 0.060
#> GSM254666 3 0.1637 0.9012 0.000 0.000 0.940 0.060
#> GSM254675 3 0.1302 0.8940 0.000 0.000 0.956 0.044
#> GSM254678 3 0.0921 0.9036 0.000 0.000 0.972 0.028
#> GSM254688 1 0.0469 0.9037 0.988 0.000 0.012 0.000
#> GSM254690 1 0.5578 0.5800 0.648 0.000 0.312 0.040
#> GSM254696 3 0.0188 0.9048 0.000 0.000 0.996 0.004
#> GSM254705 1 0.0469 0.9037 0.988 0.000 0.012 0.000
#> GSM254642 1 0.3198 0.8701 0.880 0.000 0.080 0.040
#> GSM254661 3 0.1792 0.9002 0.000 0.000 0.932 0.068
#> GSM254698 3 0.1389 0.9000 0.000 0.000 0.952 0.048
#> GSM254641 3 0.6170 0.0424 0.420 0.000 0.528 0.052
#> GSM254647 1 0.3821 0.8448 0.840 0.000 0.120 0.040
#> GSM254663 1 0.0469 0.9037 0.988 0.000 0.012 0.000
#> GSM254682 1 0.0469 0.9037 0.988 0.000 0.012 0.000
#> GSM254709 1 0.0469 0.9037 0.988 0.000 0.012 0.000
#> GSM254721 1 0.5658 0.5557 0.632 0.000 0.328 0.040
#> GSM254724 3 0.5964 0.0419 0.424 0.000 0.536 0.040
#> GSM254650 1 0.0469 0.9037 0.988 0.000 0.012 0.000
#> GSM254687 1 0.0469 0.9037 0.988 0.000 0.012 0.000
#> GSM254637 3 0.0336 0.9048 0.000 0.000 0.992 0.008
#> GSM254684 3 0.0921 0.9036 0.000 0.000 0.972 0.028
#> GSM254649 2 0.0469 0.9342 0.012 0.988 0.000 0.000
#> GSM254660 2 0.0000 0.9342 0.000 1.000 0.000 0.000
#> GSM254693 2 0.0469 0.9342 0.012 0.988 0.000 0.000
#> GSM254695 4 0.2704 0.9571 0.000 0.124 0.000 0.876
#> GSM254702 2 0.4866 0.1873 0.000 0.596 0.000 0.404
#> GSM254643 2 0.0000 0.9342 0.000 1.000 0.000 0.000
#> GSM254727 2 0.0469 0.9342 0.012 0.988 0.000 0.000
#> GSM254640 4 0.2814 0.9583 0.000 0.132 0.000 0.868
#> GSM254626 2 0.0469 0.9342 0.012 0.988 0.000 0.000
#> GSM254635 4 0.2647 0.9648 0.000 0.120 0.000 0.880
#> GSM254653 2 0.0469 0.9342 0.012 0.988 0.000 0.000
#> GSM254658 2 0.0469 0.9342 0.012 0.988 0.000 0.000
#> GSM254681 2 0.0707 0.9298 0.020 0.980 0.000 0.000
#> GSM254719 2 0.0336 0.9344 0.008 0.992 0.000 0.000
#> GSM254673 2 0.0469 0.9342 0.012 0.988 0.000 0.000
#> GSM254655 2 0.0000 0.9342 0.000 1.000 0.000 0.000
#> GSM254669 2 0.0469 0.9342 0.012 0.988 0.000 0.000
#> GSM254699 2 0.0000 0.9342 0.000 1.000 0.000 0.000
#> GSM254703 4 0.2814 0.9583 0.000 0.132 0.000 0.868
#> GSM254708 2 0.0000 0.9342 0.000 1.000 0.000 0.000
#> GSM254715 4 0.2647 0.9648 0.000 0.120 0.000 0.880
#> GSM254628 2 0.0469 0.9342 0.012 0.988 0.000 0.000
#> GSM254634 4 0.2589 0.9623 0.000 0.116 0.000 0.884
#> GSM254646 2 0.0469 0.9342 0.012 0.988 0.000 0.000
#> GSM254671 4 0.2704 0.9634 0.000 0.124 0.000 0.876
#> GSM254711 4 0.2647 0.9648 0.000 0.120 0.000 0.880
#> GSM254717 2 0.0000 0.9342 0.000 1.000 0.000 0.000
#> GSM254723 2 0.1474 0.8888 0.000 0.948 0.000 0.052
#> GSM254730 2 0.0000 0.9342 0.000 1.000 0.000 0.000
#> GSM254731 2 0.4866 0.1873 0.000 0.596 0.000 0.404
#> GSM254632 2 0.4534 0.7263 0.000 0.800 0.132 0.068
#> GSM254662 2 0.0469 0.9342 0.012 0.988 0.000 0.000
#> GSM254677 4 0.2466 0.8975 0.000 0.056 0.028 0.916
#> GSM254665 2 0.0000 0.9342 0.000 1.000 0.000 0.000
#> GSM254691 2 0.0000 0.9342 0.000 1.000 0.000 0.000
#> GSM254644 4 0.2704 0.9634 0.000 0.124 0.000 0.876
#> GSM254667 2 0.0000 0.9342 0.000 1.000 0.000 0.000
#> GSM254676 2 0.0000 0.9342 0.000 1.000 0.000 0.000
#> GSM254679 4 0.2647 0.9648 0.000 0.120 0.000 0.880
#> GSM254689 2 0.0707 0.9298 0.020 0.980 0.000 0.000
#> GSM254706 2 0.0000 0.9342 0.000 1.000 0.000 0.000
#> GSM254712 4 0.2647 0.9648 0.000 0.120 0.000 0.880
#> GSM254713 4 0.2647 0.9648 0.000 0.120 0.000 0.880
#> GSM254683 2 0.0707 0.9298 0.020 0.980 0.000 0.000
#> GSM254710 2 0.4977 0.2398 0.460 0.540 0.000 0.000
#> GSM254725 4 0.2647 0.9648 0.000 0.120 0.000 0.880
#> GSM254651 2 0.0000 0.9342 0.000 1.000 0.000 0.000
#> GSM254638 4 0.2530 0.9593 0.000 0.112 0.000 0.888
#> GSM254685 4 0.2647 0.9648 0.000 0.120 0.000 0.880
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM254629 3 0.4756 0.5340 0.288 0.000 0.668 0.044 0.000
#> GSM254648 3 0.7352 0.2777 0.312 0.200 0.444 0.044 0.000
#> GSM254694 3 0.4735 0.5363 0.284 0.000 0.672 0.044 0.000
#> GSM254701 3 0.4735 0.5363 0.284 0.000 0.672 0.044 0.000
#> GSM254728 3 0.5713 0.3980 0.372 0.000 0.560 0.044 0.024
#> GSM254726 2 0.7479 0.1713 0.300 0.420 0.236 0.044 0.000
#> GSM254639 3 0.0000 0.5963 0.000 0.000 1.000 0.000 0.000
#> GSM254652 3 0.4735 0.5363 0.284 0.000 0.672 0.044 0.000
#> GSM254700 1 0.6493 0.7235 0.492 0.000 0.260 0.000 0.248
#> GSM254625 5 0.0290 0.7493 0.008 0.000 0.000 0.000 0.992
#> GSM254636 3 0.3366 0.5142 0.232 0.000 0.768 0.000 0.000
#> GSM254659 3 0.4713 0.5383 0.280 0.000 0.676 0.044 0.000
#> GSM254680 1 0.5747 0.6613 0.504 0.000 0.088 0.000 0.408
#> GSM254686 5 0.4481 0.2525 0.312 0.000 0.016 0.004 0.668
#> GSM254718 3 0.2389 0.5887 0.116 0.000 0.880 0.004 0.000
#> GSM254674 5 0.3838 0.3640 0.280 0.000 0.000 0.004 0.716
#> GSM254668 5 0.0000 0.7548 0.000 0.000 0.000 0.000 1.000
#> GSM254697 3 0.4302 0.1810 0.480 0.000 0.520 0.000 0.000
#> GSM254704 3 0.3242 0.5310 0.216 0.000 0.784 0.000 0.000
#> GSM254707 5 0.0000 0.7548 0.000 0.000 0.000 0.000 1.000
#> GSM254714 3 0.4201 0.5631 0.204 0.000 0.752 0.044 0.000
#> GSM254722 3 0.4242 0.2611 0.428 0.000 0.572 0.000 0.000
#> GSM254627 3 0.4249 0.2544 0.432 0.000 0.568 0.000 0.000
#> GSM254630 5 0.4422 0.2898 0.300 0.000 0.016 0.004 0.680
#> GSM254633 3 0.3242 0.5310 0.216 0.000 0.784 0.000 0.000
#> GSM254670 3 0.0000 0.5963 0.000 0.000 1.000 0.000 0.000
#> GSM254716 5 0.0290 0.7493 0.008 0.000 0.000 0.000 0.992
#> GSM254720 3 0.4760 0.4538 0.416 0.000 0.564 0.020 0.000
#> GSM254729 3 0.2439 0.5879 0.120 0.000 0.876 0.004 0.000
#> GSM254654 3 0.3691 0.5724 0.156 0.000 0.804 0.040 0.000
#> GSM254656 3 0.4971 -0.0369 0.028 0.000 0.512 0.460 0.000
#> GSM254631 3 0.3242 0.5310 0.216 0.000 0.784 0.000 0.000
#> GSM254657 3 0.4355 0.4450 0.044 0.000 0.732 0.224 0.000
#> GSM254664 3 0.4305 0.1673 0.488 0.000 0.512 0.000 0.000
#> GSM254672 3 0.3242 0.5310 0.216 0.000 0.784 0.000 0.000
#> GSM254692 5 0.0000 0.7548 0.000 0.000 0.000 0.000 1.000
#> GSM254645 3 0.0000 0.5963 0.000 0.000 1.000 0.000 0.000
#> GSM254666 3 0.4622 0.5458 0.264 0.000 0.692 0.044 0.000
#> GSM254675 3 0.4307 0.1511 0.500 0.000 0.500 0.000 0.000
#> GSM254678 3 0.3242 0.5310 0.216 0.000 0.784 0.000 0.000
#> GSM254688 5 0.0000 0.7548 0.000 0.000 0.000 0.000 1.000
#> GSM254690 5 0.4968 -0.4489 0.456 0.000 0.028 0.000 0.516
#> GSM254696 3 0.3730 0.4919 0.288 0.000 0.712 0.000 0.000
#> GSM254705 5 0.0000 0.7548 0.000 0.000 0.000 0.000 1.000
#> GSM254642 5 0.4538 -0.3540 0.452 0.000 0.008 0.000 0.540
#> GSM254661 3 0.4713 0.5383 0.280 0.000 0.676 0.044 0.000
#> GSM254698 3 0.3452 0.5039 0.244 0.000 0.756 0.000 0.000
#> GSM254641 1 0.6171 0.6990 0.552 0.000 0.148 0.004 0.296
#> GSM254647 5 0.4644 -0.3929 0.460 0.000 0.012 0.000 0.528
#> GSM254663 5 0.0000 0.7548 0.000 0.000 0.000 0.000 1.000
#> GSM254682 5 0.0000 0.7548 0.000 0.000 0.000 0.000 1.000
#> GSM254709 5 0.0000 0.7548 0.000 0.000 0.000 0.000 1.000
#> GSM254721 1 0.5047 0.4725 0.496 0.000 0.032 0.000 0.472
#> GSM254724 1 0.6493 0.7235 0.492 0.000 0.260 0.000 0.248
#> GSM254650 5 0.0000 0.7548 0.000 0.000 0.000 0.000 1.000
#> GSM254687 5 0.0000 0.7548 0.000 0.000 0.000 0.000 1.000
#> GSM254637 3 0.3752 0.4959 0.292 0.000 0.708 0.000 0.000
#> GSM254684 3 0.3242 0.5310 0.216 0.000 0.784 0.000 0.000
#> GSM254649 2 0.3534 0.7477 0.256 0.744 0.000 0.000 0.000
#> GSM254660 2 0.0963 0.8166 0.036 0.964 0.000 0.000 0.000
#> GSM254693 2 0.3003 0.7792 0.188 0.812 0.000 0.000 0.000
#> GSM254695 4 0.6439 0.4921 0.084 0.300 0.048 0.568 0.000
#> GSM254702 2 0.4921 0.2629 0.036 0.604 0.000 0.360 0.000
#> GSM254643 2 0.0000 0.8214 0.000 1.000 0.000 0.000 0.000
#> GSM254727 2 0.1965 0.8179 0.096 0.904 0.000 0.000 0.000
#> GSM254640 4 0.1270 0.9409 0.000 0.052 0.000 0.948 0.000
#> GSM254626 2 0.3534 0.7477 0.256 0.744 0.000 0.000 0.000
#> GSM254635 4 0.1282 0.9445 0.004 0.044 0.000 0.952 0.000
#> GSM254653 2 0.1965 0.8179 0.096 0.904 0.000 0.000 0.000
#> GSM254658 2 0.0880 0.8219 0.032 0.968 0.000 0.000 0.000
#> GSM254681 2 0.3910 0.7403 0.272 0.720 0.000 0.000 0.008
#> GSM254719 2 0.1197 0.8188 0.048 0.952 0.000 0.000 0.000
#> GSM254673 2 0.3336 0.7787 0.228 0.772 0.000 0.000 0.000
#> GSM254655 2 0.0963 0.8166 0.036 0.964 0.000 0.000 0.000
#> GSM254669 2 0.3003 0.7792 0.188 0.812 0.000 0.000 0.000
#> GSM254699 2 0.0963 0.8166 0.036 0.964 0.000 0.000 0.000
#> GSM254703 4 0.4526 0.6107 0.028 0.300 0.000 0.672 0.000
#> GSM254708 2 0.1121 0.8134 0.044 0.956 0.000 0.000 0.000
#> GSM254715 4 0.1121 0.9455 0.000 0.044 0.000 0.956 0.000
#> GSM254628 2 0.3480 0.7518 0.248 0.752 0.000 0.000 0.000
#> GSM254634 4 0.1522 0.9418 0.012 0.044 0.000 0.944 0.000
#> GSM254646 2 0.3534 0.7477 0.256 0.744 0.000 0.000 0.000
#> GSM254671 4 0.1121 0.9455 0.000 0.044 0.000 0.956 0.000
#> GSM254711 4 0.1121 0.9455 0.000 0.044 0.000 0.956 0.000
#> GSM254717 2 0.0000 0.8214 0.000 1.000 0.000 0.000 0.000
#> GSM254723 2 0.7304 0.2831 0.248 0.476 0.232 0.044 0.000
#> GSM254730 2 0.0963 0.8166 0.036 0.964 0.000 0.000 0.000
#> GSM254731 2 0.4921 0.2629 0.036 0.604 0.000 0.360 0.000
#> GSM254632 2 0.7397 0.1760 0.264 0.452 0.240 0.044 0.000
#> GSM254662 2 0.1478 0.8198 0.064 0.936 0.000 0.000 0.000
#> GSM254677 4 0.1651 0.8989 0.012 0.008 0.036 0.944 0.000
#> GSM254665 2 0.0703 0.8187 0.024 0.976 0.000 0.000 0.000
#> GSM254691 2 0.1043 0.8143 0.040 0.960 0.000 0.000 0.000
#> GSM254644 4 0.1121 0.9455 0.000 0.044 0.000 0.956 0.000
#> GSM254667 2 0.2260 0.7890 0.064 0.908 0.028 0.000 0.000
#> GSM254676 2 0.1121 0.8142 0.044 0.956 0.000 0.000 0.000
#> GSM254679 4 0.1121 0.9455 0.000 0.044 0.000 0.956 0.000
#> GSM254689 2 0.3910 0.7403 0.272 0.720 0.000 0.000 0.008
#> GSM254706 2 0.0609 0.8200 0.020 0.980 0.000 0.000 0.000
#> GSM254712 4 0.1522 0.9418 0.012 0.044 0.000 0.944 0.000
#> GSM254713 4 0.1121 0.9455 0.000 0.044 0.000 0.956 0.000
#> GSM254683 2 0.3910 0.7403 0.272 0.720 0.000 0.000 0.008
#> GSM254710 5 0.6006 0.2768 0.300 0.144 0.000 0.000 0.556
#> GSM254725 4 0.1121 0.9455 0.000 0.044 0.000 0.956 0.000
#> GSM254651 2 0.0000 0.8214 0.000 1.000 0.000 0.000 0.000
#> GSM254638 4 0.1522 0.9418 0.012 0.044 0.000 0.944 0.000
#> GSM254685 4 0.1121 0.9455 0.000 0.044 0.000 0.956 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM254629 3 0.3991 0.790 0.088 0.000 0.756 0.000 0.000 0.156
#> GSM254648 3 0.4269 0.724 0.016 0.108 0.760 0.000 0.000 0.116
#> GSM254694 3 0.4431 0.784 0.096 0.000 0.704 0.000 0.000 0.200
#> GSM254701 3 0.4431 0.784 0.096 0.000 0.704 0.000 0.000 0.200
#> GSM254728 3 0.4624 0.689 0.208 0.000 0.700 0.004 0.004 0.084
#> GSM254726 3 0.3485 0.651 0.020 0.204 0.772 0.000 0.000 0.004
#> GSM254639 6 0.2573 0.739 0.008 0.000 0.132 0.004 0.000 0.856
#> GSM254652 3 0.4431 0.784 0.096 0.000 0.704 0.000 0.000 0.200
#> GSM254700 1 0.4313 0.749 0.668 0.000 0.000 0.000 0.048 0.284
#> GSM254625 5 0.0458 0.941 0.016 0.000 0.000 0.000 0.984 0.000
#> GSM254636 6 0.0363 0.806 0.012 0.000 0.000 0.000 0.000 0.988
#> GSM254659 3 0.4486 0.778 0.096 0.000 0.696 0.000 0.000 0.208
#> GSM254680 1 0.4809 0.777 0.668 0.000 0.000 0.000 0.192 0.140
#> GSM254686 1 0.5029 0.646 0.596 0.000 0.068 0.004 0.328 0.004
#> GSM254718 6 0.3330 0.506 0.000 0.000 0.284 0.000 0.000 0.716
#> GSM254674 1 0.4794 0.631 0.596 0.000 0.056 0.004 0.344 0.000
#> GSM254668 5 0.0000 0.953 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM254697 1 0.3563 0.708 0.664 0.000 0.000 0.000 0.000 0.336
#> GSM254704 6 0.0146 0.808 0.004 0.000 0.000 0.000 0.000 0.996
#> GSM254707 5 0.0000 0.953 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM254714 3 0.3390 0.693 0.000 0.000 0.704 0.000 0.000 0.296
#> GSM254722 1 0.3647 0.681 0.640 0.000 0.000 0.000 0.000 0.360
#> GSM254627 1 0.3620 0.691 0.648 0.000 0.000 0.000 0.000 0.352
#> GSM254630 1 0.4908 0.640 0.596 0.000 0.068 0.004 0.332 0.000
#> GSM254633 6 0.0260 0.808 0.008 0.000 0.000 0.000 0.000 0.992
#> GSM254670 6 0.2135 0.740 0.000 0.000 0.128 0.000 0.000 0.872
#> GSM254716 5 0.0458 0.941 0.016 0.000 0.000 0.000 0.984 0.000
#> GSM254720 6 0.6064 -0.182 0.220 0.000 0.352 0.004 0.000 0.424
#> GSM254729 6 0.3351 0.498 0.000 0.000 0.288 0.000 0.000 0.712
#> GSM254654 3 0.3647 0.579 0.000 0.000 0.640 0.000 0.000 0.360
#> GSM254656 6 0.5195 0.505 0.008 0.000 0.136 0.220 0.000 0.636
#> GSM254631 6 0.0260 0.808 0.008 0.000 0.000 0.000 0.000 0.992
#> GSM254657 6 0.4914 0.566 0.008 0.000 0.160 0.152 0.000 0.680
#> GSM254664 1 0.3563 0.708 0.664 0.000 0.000 0.000 0.000 0.336
#> GSM254672 6 0.0000 0.808 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM254692 5 0.0000 0.953 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM254645 6 0.2219 0.734 0.000 0.000 0.136 0.000 0.000 0.864
#> GSM254666 3 0.4549 0.759 0.088 0.000 0.680 0.000 0.000 0.232
#> GSM254675 1 0.3547 0.710 0.668 0.000 0.000 0.000 0.000 0.332
#> GSM254678 6 0.0260 0.808 0.008 0.000 0.000 0.000 0.000 0.992
#> GSM254688 5 0.0000 0.953 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM254690 1 0.4592 0.743 0.664 0.000 0.000 0.004 0.268 0.064
#> GSM254696 6 0.1814 0.728 0.100 0.000 0.000 0.000 0.000 0.900
#> GSM254705 5 0.0000 0.953 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM254642 1 0.4183 0.715 0.668 0.000 0.000 0.000 0.296 0.036
#> GSM254661 3 0.3912 0.788 0.076 0.000 0.760 0.000 0.000 0.164
#> GSM254698 6 0.0937 0.789 0.040 0.000 0.000 0.000 0.000 0.960
#> GSM254641 1 0.6272 0.730 0.604 0.000 0.124 0.004 0.140 0.128
#> GSM254647 1 0.4352 0.732 0.668 0.000 0.000 0.000 0.280 0.052
#> GSM254663 5 0.0146 0.950 0.004 0.000 0.000 0.000 0.996 0.000
#> GSM254682 5 0.0000 0.953 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM254709 5 0.0000 0.953 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM254721 1 0.4681 0.764 0.668 0.000 0.000 0.000 0.232 0.100
#> GSM254724 1 0.4313 0.749 0.668 0.000 0.000 0.000 0.048 0.284
#> GSM254650 5 0.0000 0.953 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM254687 5 0.0000 0.953 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM254637 6 0.1204 0.773 0.056 0.000 0.000 0.000 0.000 0.944
#> GSM254684 6 0.0000 0.808 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM254649 2 0.5504 0.657 0.252 0.560 0.188 0.000 0.000 0.000
#> GSM254660 2 0.1092 0.812 0.020 0.960 0.020 0.000 0.000 0.000
#> GSM254693 2 0.3735 0.779 0.092 0.784 0.124 0.000 0.000 0.000
#> GSM254695 4 0.6653 0.219 0.052 0.356 0.176 0.416 0.000 0.000
#> GSM254702 2 0.4531 0.418 0.036 0.668 0.016 0.280 0.000 0.000
#> GSM254643 2 0.0622 0.818 0.008 0.980 0.012 0.000 0.000 0.000
#> GSM254727 2 0.2221 0.815 0.032 0.896 0.072 0.000 0.000 0.000
#> GSM254640 4 0.1867 0.882 0.036 0.036 0.004 0.924 0.000 0.000
#> GSM254626 2 0.5504 0.657 0.252 0.560 0.188 0.000 0.000 0.000
#> GSM254635 4 0.0291 0.910 0.000 0.004 0.004 0.992 0.000 0.000
#> GSM254653 2 0.2277 0.814 0.032 0.892 0.076 0.000 0.000 0.000
#> GSM254658 2 0.1461 0.820 0.016 0.940 0.044 0.000 0.000 0.000
#> GSM254681 2 0.5587 0.646 0.272 0.540 0.188 0.000 0.000 0.000
#> GSM254719 2 0.1657 0.819 0.016 0.928 0.056 0.000 0.000 0.000
#> GSM254673 2 0.3707 0.781 0.080 0.784 0.136 0.000 0.000 0.000
#> GSM254655 2 0.1003 0.813 0.020 0.964 0.016 0.000 0.000 0.000
#> GSM254669 2 0.3637 0.780 0.084 0.792 0.124 0.000 0.000 0.000
#> GSM254699 2 0.1003 0.813 0.020 0.964 0.016 0.000 0.000 0.000
#> GSM254703 4 0.5830 0.264 0.048 0.392 0.068 0.492 0.000 0.000
#> GSM254708 2 0.1895 0.795 0.016 0.912 0.072 0.000 0.000 0.000
#> GSM254715 4 0.0291 0.910 0.000 0.004 0.004 0.992 0.000 0.000
#> GSM254628 2 0.4887 0.716 0.156 0.660 0.184 0.000 0.000 0.000
#> GSM254634 4 0.0665 0.906 0.008 0.004 0.008 0.980 0.000 0.000
#> GSM254646 2 0.5504 0.657 0.252 0.560 0.188 0.000 0.000 0.000
#> GSM254671 4 0.1268 0.899 0.036 0.008 0.004 0.952 0.000 0.000
#> GSM254711 4 0.1268 0.899 0.036 0.008 0.004 0.952 0.000 0.000
#> GSM254717 2 0.0508 0.817 0.004 0.984 0.012 0.000 0.000 0.000
#> GSM254723 3 0.3403 0.647 0.020 0.212 0.768 0.000 0.000 0.000
#> GSM254730 2 0.1003 0.813 0.020 0.964 0.016 0.000 0.000 0.000
#> GSM254731 2 0.4531 0.418 0.036 0.668 0.016 0.280 0.000 0.000
#> GSM254632 3 0.3599 0.649 0.020 0.220 0.756 0.000 0.000 0.004
#> GSM254662 2 0.1563 0.819 0.012 0.932 0.056 0.000 0.000 0.000
#> GSM254677 4 0.0862 0.899 0.008 0.000 0.016 0.972 0.000 0.004
#> GSM254665 2 0.1584 0.802 0.008 0.928 0.064 0.000 0.000 0.000
#> GSM254691 2 0.1895 0.795 0.016 0.912 0.072 0.000 0.000 0.000
#> GSM254644 4 0.1080 0.903 0.032 0.004 0.004 0.960 0.000 0.000
#> GSM254667 2 0.3319 0.700 0.036 0.800 0.164 0.000 0.000 0.000
#> GSM254676 2 0.2221 0.788 0.032 0.896 0.072 0.000 0.000 0.000
#> GSM254679 4 0.0146 0.910 0.000 0.004 0.000 0.996 0.000 0.000
#> GSM254689 2 0.5587 0.646 0.272 0.540 0.188 0.000 0.000 0.000
#> GSM254706 2 0.1151 0.816 0.012 0.956 0.032 0.000 0.000 0.000
#> GSM254712 4 0.0767 0.907 0.008 0.004 0.012 0.976 0.000 0.000
#> GSM254713 4 0.0291 0.910 0.000 0.004 0.004 0.992 0.000 0.000
#> GSM254683 2 0.5587 0.646 0.272 0.540 0.188 0.000 0.000 0.000
#> GSM254710 5 0.6041 0.467 0.272 0.036 0.144 0.000 0.548 0.000
#> GSM254725 4 0.0508 0.909 0.012 0.004 0.000 0.984 0.000 0.000
#> GSM254651 2 0.0508 0.818 0.004 0.984 0.012 0.000 0.000 0.000
#> GSM254638 4 0.0405 0.909 0.000 0.004 0.008 0.988 0.000 0.000
#> GSM254685 4 0.0291 0.910 0.000 0.004 0.004 0.992 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) individual(p) disease.state(p) gender(p) other(p) k
#> ATC:kmeans 106 1.78e-21 0.453 0.489 0.697 0.730 2
#> ATC:kmeans 95 1.67e-20 0.242 0.788 0.748 0.912 3
#> ATC:kmeans 98 2.86e-20 0.180 0.766 0.330 0.984 4
#> ATC:kmeans 81 1.07e-16 0.233 0.639 0.213 0.971 5
#> ATC:kmeans 100 1.54e-18 0.142 0.604 0.154 0.661 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 21168 rows and 107 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 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 1.000 0.987 0.995 0.5043 0.496 0.496
#> 3 3 0.990 0.960 0.978 0.2985 0.810 0.631
#> 4 4 0.981 0.922 0.939 0.1473 0.870 0.639
#> 5 5 0.843 0.837 0.906 0.0581 0.899 0.632
#> 6 6 0.818 0.772 0.883 0.0285 0.968 0.846
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 4
#> attr(,"optional")
#> [1] 2 3
There is also optional best \(k\) = 2 3 that is worth to check.
Following 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
#> GSM254629 1 0.000 0.993 1.000 0.000
#> GSM254648 2 0.000 0.997 0.000 1.000
#> GSM254694 1 0.000 0.993 1.000 0.000
#> GSM254701 1 0.000 0.993 1.000 0.000
#> GSM254728 1 0.000 0.993 1.000 0.000
#> GSM254726 2 0.000 0.997 0.000 1.000
#> GSM254639 1 0.000 0.993 1.000 0.000
#> GSM254652 1 0.000 0.993 1.000 0.000
#> GSM254700 1 0.000 0.993 1.000 0.000
#> GSM254625 1 0.000 0.993 1.000 0.000
#> GSM254636 1 0.000 0.993 1.000 0.000
#> GSM254659 1 0.000 0.993 1.000 0.000
#> GSM254680 1 0.000 0.993 1.000 0.000
#> GSM254686 1 0.000 0.993 1.000 0.000
#> GSM254718 1 0.000 0.993 1.000 0.000
#> GSM254674 1 0.000 0.993 1.000 0.000
#> GSM254668 1 0.000 0.993 1.000 0.000
#> GSM254697 1 0.000 0.993 1.000 0.000
#> GSM254704 1 0.000 0.993 1.000 0.000
#> GSM254707 1 0.000 0.993 1.000 0.000
#> GSM254714 1 0.000 0.993 1.000 0.000
#> GSM254722 1 0.000 0.993 1.000 0.000
#> GSM254627 1 0.000 0.993 1.000 0.000
#> GSM254630 1 0.000 0.993 1.000 0.000
#> GSM254633 1 0.000 0.993 1.000 0.000
#> GSM254670 1 0.000 0.993 1.000 0.000
#> GSM254716 1 0.000 0.993 1.000 0.000
#> GSM254720 1 0.000 0.993 1.000 0.000
#> GSM254729 1 0.000 0.993 1.000 0.000
#> GSM254654 1 0.000 0.993 1.000 0.000
#> GSM254656 1 0.973 0.319 0.596 0.404
#> GSM254631 1 0.000 0.993 1.000 0.000
#> GSM254657 1 0.000 0.993 1.000 0.000
#> GSM254664 1 0.000 0.993 1.000 0.000
#> GSM254672 1 0.000 0.993 1.000 0.000
#> GSM254692 1 0.000 0.993 1.000 0.000
#> GSM254645 1 0.000 0.993 1.000 0.000
#> GSM254666 1 0.000 0.993 1.000 0.000
#> GSM254675 1 0.000 0.993 1.000 0.000
#> GSM254678 1 0.000 0.993 1.000 0.000
#> GSM254688 1 0.000 0.993 1.000 0.000
#> GSM254690 1 0.000 0.993 1.000 0.000
#> GSM254696 1 0.000 0.993 1.000 0.000
#> GSM254705 1 0.000 0.993 1.000 0.000
#> GSM254642 1 0.000 0.993 1.000 0.000
#> GSM254661 1 0.000 0.993 1.000 0.000
#> GSM254698 1 0.000 0.993 1.000 0.000
#> GSM254641 1 0.000 0.993 1.000 0.000
#> GSM254647 1 0.000 0.993 1.000 0.000
#> GSM254663 1 0.000 0.993 1.000 0.000
#> GSM254682 1 0.000 0.993 1.000 0.000
#> GSM254709 1 0.000 0.993 1.000 0.000
#> GSM254721 1 0.000 0.993 1.000 0.000
#> GSM254724 1 0.000 0.993 1.000 0.000
#> GSM254650 1 0.000 0.993 1.000 0.000
#> GSM254687 1 0.000 0.993 1.000 0.000
#> GSM254637 1 0.000 0.993 1.000 0.000
#> GSM254684 1 0.000 0.993 1.000 0.000
#> GSM254649 2 0.000 0.997 0.000 1.000
#> GSM254660 2 0.000 0.997 0.000 1.000
#> GSM254693 2 0.000 0.997 0.000 1.000
#> GSM254695 2 0.000 0.997 0.000 1.000
#> GSM254702 2 0.000 0.997 0.000 1.000
#> GSM254643 2 0.000 0.997 0.000 1.000
#> GSM254727 2 0.000 0.997 0.000 1.000
#> GSM254640 2 0.000 0.997 0.000 1.000
#> GSM254626 2 0.000 0.997 0.000 1.000
#> GSM254635 2 0.000 0.997 0.000 1.000
#> GSM254653 2 0.000 0.997 0.000 1.000
#> GSM254658 2 0.000 0.997 0.000 1.000
#> GSM254681 2 0.000 0.997 0.000 1.000
#> GSM254719 2 0.000 0.997 0.000 1.000
#> GSM254673 2 0.000 0.997 0.000 1.000
#> GSM254655 2 0.000 0.997 0.000 1.000
#> GSM254669 2 0.000 0.997 0.000 1.000
#> GSM254699 2 0.000 0.997 0.000 1.000
#> GSM254703 2 0.000 0.997 0.000 1.000
#> GSM254708 2 0.000 0.997 0.000 1.000
#> GSM254715 2 0.000 0.997 0.000 1.000
#> GSM254628 2 0.000 0.997 0.000 1.000
#> GSM254634 2 0.000 0.997 0.000 1.000
#> GSM254646 2 0.000 0.997 0.000 1.000
#> GSM254671 2 0.000 0.997 0.000 1.000
#> GSM254711 2 0.000 0.997 0.000 1.000
#> GSM254717 2 0.000 0.997 0.000 1.000
#> GSM254723 2 0.000 0.997 0.000 1.000
#> GSM254730 2 0.000 0.997 0.000 1.000
#> GSM254731 2 0.000 0.997 0.000 1.000
#> GSM254632 2 0.595 0.830 0.144 0.856
#> GSM254662 2 0.000 0.997 0.000 1.000
#> GSM254677 2 0.000 0.997 0.000 1.000
#> GSM254665 2 0.000 0.997 0.000 1.000
#> GSM254691 2 0.000 0.997 0.000 1.000
#> GSM254644 2 0.000 0.997 0.000 1.000
#> GSM254667 2 0.000 0.997 0.000 1.000
#> GSM254676 2 0.000 0.997 0.000 1.000
#> GSM254679 2 0.000 0.997 0.000 1.000
#> GSM254689 2 0.000 0.997 0.000 1.000
#> GSM254706 2 0.000 0.997 0.000 1.000
#> GSM254712 2 0.000 0.997 0.000 1.000
#> GSM254713 2 0.000 0.997 0.000 1.000
#> GSM254683 2 0.000 0.997 0.000 1.000
#> GSM254710 2 0.000 0.997 0.000 1.000
#> GSM254725 2 0.000 0.997 0.000 1.000
#> GSM254651 2 0.000 0.997 0.000 1.000
#> GSM254638 2 0.000 0.997 0.000 1.000
#> GSM254685 2 0.000 0.997 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM254629 1 0.3816 0.825 0.852 0.000 0.148
#> GSM254648 2 0.5016 0.705 0.000 0.760 0.240
#> GSM254694 3 0.1031 0.977 0.024 0.000 0.976
#> GSM254701 3 0.1031 0.977 0.024 0.000 0.976
#> GSM254728 1 0.0000 0.966 1.000 0.000 0.000
#> GSM254726 1 0.3116 0.864 0.892 0.108 0.000
#> GSM254639 3 0.0000 0.965 0.000 0.000 1.000
#> GSM254652 1 0.3267 0.862 0.884 0.000 0.116
#> GSM254700 1 0.4931 0.706 0.768 0.000 0.232
#> GSM254625 1 0.0000 0.966 1.000 0.000 0.000
#> GSM254636 3 0.1031 0.977 0.024 0.000 0.976
#> GSM254659 3 0.1031 0.977 0.024 0.000 0.976
#> GSM254680 1 0.0000 0.966 1.000 0.000 0.000
#> GSM254686 1 0.0000 0.966 1.000 0.000 0.000
#> GSM254718 3 0.0424 0.970 0.008 0.000 0.992
#> GSM254674 1 0.0000 0.966 1.000 0.000 0.000
#> GSM254668 1 0.0000 0.966 1.000 0.000 0.000
#> GSM254697 3 0.1031 0.977 0.024 0.000 0.976
#> GSM254704 3 0.1031 0.977 0.024 0.000 0.976
#> GSM254707 1 0.0000 0.966 1.000 0.000 0.000
#> GSM254714 3 0.1031 0.977 0.024 0.000 0.976
#> GSM254722 3 0.1031 0.977 0.024 0.000 0.976
#> GSM254627 3 0.1031 0.977 0.024 0.000 0.976
#> GSM254630 1 0.0000 0.966 1.000 0.000 0.000
#> GSM254633 3 0.1031 0.977 0.024 0.000 0.976
#> GSM254670 3 0.0237 0.968 0.004 0.000 0.996
#> GSM254716 1 0.0000 0.966 1.000 0.000 0.000
#> GSM254720 3 0.1031 0.977 0.024 0.000 0.976
#> GSM254729 3 0.0237 0.968 0.004 0.000 0.996
#> GSM254654 3 0.0000 0.965 0.000 0.000 1.000
#> GSM254656 3 0.0000 0.965 0.000 0.000 1.000
#> GSM254631 3 0.1031 0.977 0.024 0.000 0.976
#> GSM254657 3 0.0000 0.965 0.000 0.000 1.000
#> GSM254664 3 0.1031 0.977 0.024 0.000 0.976
#> GSM254672 3 0.0424 0.970 0.008 0.000 0.992
#> GSM254692 1 0.0000 0.966 1.000 0.000 0.000
#> GSM254645 3 0.0237 0.968 0.004 0.000 0.996
#> GSM254666 3 0.1031 0.977 0.024 0.000 0.976
#> GSM254675 3 0.1031 0.977 0.024 0.000 0.976
#> GSM254678 3 0.1031 0.977 0.024 0.000 0.976
#> GSM254688 1 0.0000 0.966 1.000 0.000 0.000
#> GSM254690 1 0.0000 0.966 1.000 0.000 0.000
#> GSM254696 3 0.1031 0.977 0.024 0.000 0.976
#> GSM254705 1 0.0000 0.966 1.000 0.000 0.000
#> GSM254642 1 0.0000 0.966 1.000 0.000 0.000
#> GSM254661 3 0.1753 0.956 0.048 0.000 0.952
#> GSM254698 3 0.1031 0.977 0.024 0.000 0.976
#> GSM254641 1 0.0000 0.966 1.000 0.000 0.000
#> GSM254647 1 0.0000 0.966 1.000 0.000 0.000
#> GSM254663 1 0.0000 0.966 1.000 0.000 0.000
#> GSM254682 1 0.0000 0.966 1.000 0.000 0.000
#> GSM254709 1 0.0000 0.966 1.000 0.000 0.000
#> GSM254721 1 0.0000 0.966 1.000 0.000 0.000
#> GSM254724 1 0.4931 0.706 0.768 0.000 0.232
#> GSM254650 1 0.0000 0.966 1.000 0.000 0.000
#> GSM254687 1 0.0000 0.966 1.000 0.000 0.000
#> GSM254637 3 0.1031 0.977 0.024 0.000 0.976
#> GSM254684 3 0.1031 0.977 0.024 0.000 0.976
#> GSM254649 2 0.0000 0.988 0.000 1.000 0.000
#> GSM254660 2 0.0000 0.988 0.000 1.000 0.000
#> GSM254693 2 0.0000 0.988 0.000 1.000 0.000
#> GSM254695 2 0.1031 0.980 0.000 0.976 0.024
#> GSM254702 2 0.0000 0.988 0.000 1.000 0.000
#> GSM254643 2 0.0000 0.988 0.000 1.000 0.000
#> GSM254727 2 0.0000 0.988 0.000 1.000 0.000
#> GSM254640 2 0.1031 0.980 0.000 0.976 0.024
#> GSM254626 2 0.0000 0.988 0.000 1.000 0.000
#> GSM254635 2 0.1031 0.980 0.000 0.976 0.024
#> GSM254653 2 0.0000 0.988 0.000 1.000 0.000
#> GSM254658 2 0.0000 0.988 0.000 1.000 0.000
#> GSM254681 2 0.0000 0.988 0.000 1.000 0.000
#> GSM254719 2 0.0000 0.988 0.000 1.000 0.000
#> GSM254673 2 0.0000 0.988 0.000 1.000 0.000
#> GSM254655 2 0.0000 0.988 0.000 1.000 0.000
#> GSM254669 2 0.0000 0.988 0.000 1.000 0.000
#> GSM254699 2 0.0000 0.988 0.000 1.000 0.000
#> GSM254703 2 0.1031 0.980 0.000 0.976 0.024
#> GSM254708 2 0.0000 0.988 0.000 1.000 0.000
#> GSM254715 2 0.1031 0.980 0.000 0.976 0.024
#> GSM254628 2 0.0000 0.988 0.000 1.000 0.000
#> GSM254634 2 0.1031 0.980 0.000 0.976 0.024
#> GSM254646 2 0.0000 0.988 0.000 1.000 0.000
#> GSM254671 2 0.1031 0.980 0.000 0.976 0.024
#> GSM254711 2 0.1031 0.980 0.000 0.976 0.024
#> GSM254717 2 0.0000 0.988 0.000 1.000 0.000
#> GSM254723 2 0.0000 0.988 0.000 1.000 0.000
#> GSM254730 2 0.0000 0.988 0.000 1.000 0.000
#> GSM254731 2 0.0000 0.988 0.000 1.000 0.000
#> GSM254632 1 0.1031 0.946 0.976 0.024 0.000
#> GSM254662 2 0.0000 0.988 0.000 1.000 0.000
#> GSM254677 3 0.5926 0.416 0.000 0.356 0.644
#> GSM254665 2 0.0000 0.988 0.000 1.000 0.000
#> GSM254691 2 0.0000 0.988 0.000 1.000 0.000
#> GSM254644 2 0.1031 0.980 0.000 0.976 0.024
#> GSM254667 2 0.0000 0.988 0.000 1.000 0.000
#> GSM254676 2 0.0000 0.988 0.000 1.000 0.000
#> GSM254679 2 0.1031 0.980 0.000 0.976 0.024
#> GSM254689 2 0.0000 0.988 0.000 1.000 0.000
#> GSM254706 2 0.0000 0.988 0.000 1.000 0.000
#> GSM254712 2 0.1031 0.980 0.000 0.976 0.024
#> GSM254713 2 0.1031 0.980 0.000 0.976 0.024
#> GSM254683 2 0.0000 0.988 0.000 1.000 0.000
#> GSM254710 1 0.1031 0.946 0.976 0.024 0.000
#> GSM254725 2 0.1031 0.980 0.000 0.976 0.024
#> GSM254651 2 0.0000 0.988 0.000 1.000 0.000
#> GSM254638 2 0.1031 0.980 0.000 0.976 0.024
#> GSM254685 2 0.1031 0.980 0.000 0.976 0.024
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM254629 1 0.1637 0.896 0.940 0.000 0.060 0.000
#> GSM254648 4 0.0000 0.943 0.000 0.000 0.000 1.000
#> GSM254694 3 0.0000 0.987 0.000 0.000 1.000 0.000
#> GSM254701 3 0.0000 0.987 0.000 0.000 1.000 0.000
#> GSM254728 1 0.0000 0.946 1.000 0.000 0.000 0.000
#> GSM254726 2 0.0707 0.960 0.020 0.980 0.000 0.000
#> GSM254639 3 0.0000 0.987 0.000 0.000 1.000 0.000
#> GSM254652 1 0.1474 0.903 0.948 0.000 0.052 0.000
#> GSM254700 1 0.4907 0.302 0.580 0.000 0.420 0.000
#> GSM254625 1 0.0000 0.946 1.000 0.000 0.000 0.000
#> GSM254636 3 0.0000 0.987 0.000 0.000 1.000 0.000
#> GSM254659 3 0.0000 0.987 0.000 0.000 1.000 0.000
#> GSM254680 1 0.0000 0.946 1.000 0.000 0.000 0.000
#> GSM254686 1 0.0000 0.946 1.000 0.000 0.000 0.000
#> GSM254718 3 0.0000 0.987 0.000 0.000 1.000 0.000
#> GSM254674 1 0.0000 0.946 1.000 0.000 0.000 0.000
#> GSM254668 1 0.0000 0.946 1.000 0.000 0.000 0.000
#> GSM254697 3 0.0000 0.987 0.000 0.000 1.000 0.000
#> GSM254704 3 0.0000 0.987 0.000 0.000 1.000 0.000
#> GSM254707 1 0.0000 0.946 1.000 0.000 0.000 0.000
#> GSM254714 3 0.0000 0.987 0.000 0.000 1.000 0.000
#> GSM254722 3 0.0000 0.987 0.000 0.000 1.000 0.000
#> GSM254627 3 0.0000 0.987 0.000 0.000 1.000 0.000
#> GSM254630 1 0.0000 0.946 1.000 0.000 0.000 0.000
#> GSM254633 3 0.0000 0.987 0.000 0.000 1.000 0.000
#> GSM254670 3 0.0000 0.987 0.000 0.000 1.000 0.000
#> GSM254716 1 0.0000 0.946 1.000 0.000 0.000 0.000
#> GSM254720 3 0.0000 0.987 0.000 0.000 1.000 0.000
#> GSM254729 3 0.0000 0.987 0.000 0.000 1.000 0.000
#> GSM254654 3 0.0469 0.977 0.000 0.000 0.988 0.012
#> GSM254656 4 0.0188 0.940 0.000 0.000 0.004 0.996
#> GSM254631 3 0.0000 0.987 0.000 0.000 1.000 0.000
#> GSM254657 3 0.4164 0.640 0.000 0.000 0.736 0.264
#> GSM254664 3 0.0000 0.987 0.000 0.000 1.000 0.000
#> GSM254672 3 0.0000 0.987 0.000 0.000 1.000 0.000
#> GSM254692 1 0.0000 0.946 1.000 0.000 0.000 0.000
#> GSM254645 3 0.0000 0.987 0.000 0.000 1.000 0.000
#> GSM254666 3 0.0000 0.987 0.000 0.000 1.000 0.000
#> GSM254675 3 0.0000 0.987 0.000 0.000 1.000 0.000
#> GSM254678 3 0.0000 0.987 0.000 0.000 1.000 0.000
#> GSM254688 1 0.0000 0.946 1.000 0.000 0.000 0.000
#> GSM254690 1 0.0000 0.946 1.000 0.000 0.000 0.000
#> GSM254696 3 0.0000 0.987 0.000 0.000 1.000 0.000
#> GSM254705 1 0.0000 0.946 1.000 0.000 0.000 0.000
#> GSM254642 1 0.0000 0.946 1.000 0.000 0.000 0.000
#> GSM254661 3 0.1867 0.911 0.072 0.000 0.928 0.000
#> GSM254698 3 0.0000 0.987 0.000 0.000 1.000 0.000
#> GSM254641 1 0.0188 0.943 0.996 0.000 0.004 0.000
#> GSM254647 1 0.0000 0.946 1.000 0.000 0.000 0.000
#> GSM254663 1 0.0000 0.946 1.000 0.000 0.000 0.000
#> GSM254682 1 0.0000 0.946 1.000 0.000 0.000 0.000
#> GSM254709 1 0.0000 0.946 1.000 0.000 0.000 0.000
#> GSM254721 1 0.0000 0.946 1.000 0.000 0.000 0.000
#> GSM254724 1 0.4907 0.302 0.580 0.000 0.420 0.000
#> GSM254650 1 0.0000 0.946 1.000 0.000 0.000 0.000
#> GSM254687 1 0.0000 0.946 1.000 0.000 0.000 0.000
#> GSM254637 3 0.0000 0.987 0.000 0.000 1.000 0.000
#> GSM254684 3 0.0000 0.987 0.000 0.000 1.000 0.000
#> GSM254649 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> GSM254660 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> GSM254693 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> GSM254695 4 0.0000 0.943 0.000 0.000 0.000 1.000
#> GSM254702 4 0.4989 0.157 0.000 0.472 0.000 0.528
#> GSM254643 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> GSM254727 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> GSM254640 4 0.0000 0.943 0.000 0.000 0.000 1.000
#> GSM254626 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> GSM254635 4 0.0000 0.943 0.000 0.000 0.000 1.000
#> GSM254653 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> GSM254658 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> GSM254681 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> GSM254719 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> GSM254673 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> GSM254655 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> GSM254669 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> GSM254699 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> GSM254703 4 0.0000 0.943 0.000 0.000 0.000 1.000
#> GSM254708 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> GSM254715 4 0.0000 0.943 0.000 0.000 0.000 1.000
#> GSM254628 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> GSM254634 4 0.0000 0.943 0.000 0.000 0.000 1.000
#> GSM254646 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> GSM254671 4 0.0000 0.943 0.000 0.000 0.000 1.000
#> GSM254711 4 0.0000 0.943 0.000 0.000 0.000 1.000
#> GSM254717 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> GSM254723 2 0.2081 0.889 0.000 0.916 0.000 0.084
#> GSM254730 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> GSM254731 4 0.4989 0.157 0.000 0.472 0.000 0.528
#> GSM254632 1 0.4406 0.542 0.700 0.300 0.000 0.000
#> GSM254662 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> GSM254677 4 0.0000 0.943 0.000 0.000 0.000 1.000
#> GSM254665 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> GSM254691 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> GSM254644 4 0.0000 0.943 0.000 0.000 0.000 1.000
#> GSM254667 4 0.2530 0.843 0.000 0.112 0.000 0.888
#> GSM254676 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> GSM254679 4 0.0000 0.943 0.000 0.000 0.000 1.000
#> GSM254689 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> GSM254706 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> GSM254712 4 0.0000 0.943 0.000 0.000 0.000 1.000
#> GSM254713 4 0.0000 0.943 0.000 0.000 0.000 1.000
#> GSM254683 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> GSM254710 2 0.4898 0.268 0.416 0.584 0.000 0.000
#> GSM254725 4 0.0000 0.943 0.000 0.000 0.000 1.000
#> GSM254651 2 0.0000 0.980 0.000 1.000 0.000 0.000
#> GSM254638 4 0.0000 0.943 0.000 0.000 0.000 1.000
#> GSM254685 4 0.0000 0.943 0.000 0.000 0.000 1.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM254629 1 0.4787 0.5120 0.712 0.000 0.080 0.000 0.208
#> GSM254648 4 0.5718 0.6300 0.140 0.020 0.168 0.672 0.000
#> GSM254694 1 0.3730 0.6412 0.712 0.000 0.288 0.000 0.000
#> GSM254701 1 0.2179 0.7641 0.888 0.000 0.112 0.000 0.000
#> GSM254728 1 0.2230 0.7714 0.884 0.000 0.000 0.000 0.116
#> GSM254726 2 0.6162 0.0441 0.132 0.440 0.000 0.000 0.428
#> GSM254639 3 0.0000 0.8605 0.000 0.000 1.000 0.000 0.000
#> GSM254652 1 0.1671 0.7771 0.924 0.000 0.000 0.000 0.076
#> GSM254700 1 0.3485 0.7929 0.828 0.000 0.124 0.000 0.048
#> GSM254625 5 0.0000 0.9406 0.000 0.000 0.000 0.000 1.000
#> GSM254636 3 0.2230 0.8014 0.116 0.000 0.884 0.000 0.000
#> GSM254659 1 0.3983 0.6183 0.660 0.000 0.340 0.000 0.000
#> GSM254680 1 0.2852 0.7749 0.828 0.000 0.000 0.000 0.172
#> GSM254686 1 0.3143 0.7445 0.796 0.000 0.000 0.000 0.204
#> GSM254718 3 0.0162 0.8605 0.004 0.000 0.996 0.000 0.000
#> GSM254674 5 0.2852 0.8190 0.172 0.000 0.000 0.000 0.828
#> GSM254668 5 0.1043 0.9527 0.040 0.000 0.000 0.000 0.960
#> GSM254697 1 0.3242 0.7553 0.784 0.000 0.216 0.000 0.000
#> GSM254704 3 0.0794 0.8577 0.028 0.000 0.972 0.000 0.000
#> GSM254707 5 0.0880 0.9544 0.032 0.000 0.000 0.000 0.968
#> GSM254714 3 0.0794 0.8494 0.028 0.000 0.972 0.000 0.000
#> GSM254722 1 0.3661 0.7049 0.724 0.000 0.276 0.000 0.000
#> GSM254627 1 0.3395 0.7415 0.764 0.000 0.236 0.000 0.000
#> GSM254630 5 0.3039 0.7877 0.192 0.000 0.000 0.000 0.808
#> GSM254633 3 0.1341 0.8479 0.056 0.000 0.944 0.000 0.000
#> GSM254670 3 0.0000 0.8605 0.000 0.000 1.000 0.000 0.000
#> GSM254716 5 0.0000 0.9406 0.000 0.000 0.000 0.000 1.000
#> GSM254720 1 0.3895 0.6487 0.680 0.000 0.320 0.000 0.000
#> GSM254729 3 0.0000 0.8605 0.000 0.000 1.000 0.000 0.000
#> GSM254654 3 0.1331 0.8397 0.040 0.000 0.952 0.008 0.000
#> GSM254656 3 0.3707 0.5303 0.000 0.000 0.716 0.284 0.000
#> GSM254631 3 0.1851 0.8275 0.088 0.000 0.912 0.000 0.000
#> GSM254657 3 0.1830 0.8242 0.028 0.000 0.932 0.040 0.000
#> GSM254664 1 0.3274 0.7529 0.780 0.000 0.220 0.000 0.000
#> GSM254672 3 0.0290 0.8604 0.008 0.000 0.992 0.000 0.000
#> GSM254692 5 0.1043 0.9527 0.040 0.000 0.000 0.000 0.960
#> GSM254645 3 0.0000 0.8605 0.000 0.000 1.000 0.000 0.000
#> GSM254666 3 0.3895 0.5621 0.320 0.000 0.680 0.000 0.000
#> GSM254675 1 0.3242 0.7553 0.784 0.000 0.216 0.000 0.000
#> GSM254678 3 0.1851 0.8275 0.088 0.000 0.912 0.000 0.000
#> GSM254688 5 0.1043 0.9527 0.040 0.000 0.000 0.000 0.960
#> GSM254690 1 0.2929 0.7693 0.820 0.000 0.000 0.000 0.180
#> GSM254696 1 0.4192 0.4892 0.596 0.000 0.404 0.000 0.000
#> GSM254705 5 0.0880 0.9544 0.032 0.000 0.000 0.000 0.968
#> GSM254642 1 0.3039 0.7586 0.808 0.000 0.000 0.000 0.192
#> GSM254661 3 0.4232 0.5871 0.312 0.000 0.676 0.000 0.012
#> GSM254698 3 0.4300 -0.1829 0.476 0.000 0.524 0.000 0.000
#> GSM254641 1 0.2966 0.7666 0.816 0.000 0.000 0.000 0.184
#> GSM254647 1 0.2966 0.7661 0.816 0.000 0.000 0.000 0.184
#> GSM254663 5 0.1121 0.9505 0.044 0.000 0.000 0.000 0.956
#> GSM254682 5 0.0880 0.9544 0.032 0.000 0.000 0.000 0.968
#> GSM254709 5 0.0880 0.9544 0.032 0.000 0.000 0.000 0.968
#> GSM254721 1 0.2852 0.7749 0.828 0.000 0.000 0.000 0.172
#> GSM254724 1 0.3507 0.7937 0.828 0.000 0.120 0.000 0.052
#> GSM254650 5 0.0703 0.9526 0.024 0.000 0.000 0.000 0.976
#> GSM254687 5 0.0703 0.9526 0.024 0.000 0.000 0.000 0.976
#> GSM254637 3 0.3242 0.6718 0.216 0.000 0.784 0.000 0.000
#> GSM254684 3 0.1121 0.8530 0.044 0.000 0.956 0.000 0.000
#> GSM254649 2 0.0000 0.9632 0.000 1.000 0.000 0.000 0.000
#> GSM254660 2 0.0880 0.9556 0.032 0.968 0.000 0.000 0.000
#> GSM254693 2 0.0000 0.9632 0.000 1.000 0.000 0.000 0.000
#> GSM254695 4 0.0609 0.9173 0.020 0.000 0.000 0.980 0.000
#> GSM254702 4 0.4073 0.7031 0.032 0.216 0.000 0.752 0.000
#> GSM254643 2 0.0000 0.9632 0.000 1.000 0.000 0.000 0.000
#> GSM254727 2 0.0880 0.9556 0.032 0.968 0.000 0.000 0.000
#> GSM254640 4 0.0000 0.9298 0.000 0.000 0.000 1.000 0.000
#> GSM254626 2 0.0000 0.9632 0.000 1.000 0.000 0.000 0.000
#> GSM254635 4 0.0000 0.9298 0.000 0.000 0.000 1.000 0.000
#> GSM254653 2 0.0880 0.9556 0.032 0.968 0.000 0.000 0.000
#> GSM254658 2 0.0000 0.9632 0.000 1.000 0.000 0.000 0.000
#> GSM254681 2 0.0703 0.9514 0.000 0.976 0.000 0.000 0.024
#> GSM254719 2 0.0880 0.9556 0.032 0.968 0.000 0.000 0.000
#> GSM254673 2 0.0880 0.9556 0.032 0.968 0.000 0.000 0.000
#> GSM254655 2 0.0880 0.9556 0.032 0.968 0.000 0.000 0.000
#> GSM254669 2 0.0000 0.9632 0.000 1.000 0.000 0.000 0.000
#> GSM254699 2 0.0880 0.9556 0.032 0.968 0.000 0.000 0.000
#> GSM254703 4 0.0000 0.9298 0.000 0.000 0.000 1.000 0.000
#> GSM254708 2 0.0000 0.9632 0.000 1.000 0.000 0.000 0.000
#> GSM254715 4 0.0000 0.9298 0.000 0.000 0.000 1.000 0.000
#> GSM254628 2 0.0000 0.9632 0.000 1.000 0.000 0.000 0.000
#> GSM254634 4 0.0000 0.9298 0.000 0.000 0.000 1.000 0.000
#> GSM254646 2 0.0000 0.9632 0.000 1.000 0.000 0.000 0.000
#> GSM254671 4 0.0000 0.9298 0.000 0.000 0.000 1.000 0.000
#> GSM254711 4 0.0000 0.9298 0.000 0.000 0.000 1.000 0.000
#> GSM254717 2 0.0000 0.9632 0.000 1.000 0.000 0.000 0.000
#> GSM254723 2 0.3736 0.7849 0.052 0.808 0.000 0.140 0.000
#> GSM254730 2 0.0880 0.9556 0.032 0.968 0.000 0.000 0.000
#> GSM254731 4 0.4010 0.7144 0.032 0.208 0.000 0.760 0.000
#> GSM254632 5 0.1484 0.8952 0.008 0.048 0.000 0.000 0.944
#> GSM254662 2 0.0880 0.9556 0.032 0.968 0.000 0.000 0.000
#> GSM254677 4 0.0000 0.9298 0.000 0.000 0.000 1.000 0.000
#> GSM254665 2 0.0000 0.9632 0.000 1.000 0.000 0.000 0.000
#> GSM254691 2 0.0000 0.9632 0.000 1.000 0.000 0.000 0.000
#> GSM254644 4 0.0000 0.9298 0.000 0.000 0.000 1.000 0.000
#> GSM254667 4 0.4219 0.3390 0.000 0.416 0.000 0.584 0.000
#> GSM254676 2 0.0000 0.9632 0.000 1.000 0.000 0.000 0.000
#> GSM254679 4 0.0000 0.9298 0.000 0.000 0.000 1.000 0.000
#> GSM254689 2 0.0703 0.9514 0.000 0.976 0.000 0.000 0.024
#> GSM254706 2 0.0000 0.9632 0.000 1.000 0.000 0.000 0.000
#> GSM254712 4 0.0000 0.9298 0.000 0.000 0.000 1.000 0.000
#> GSM254713 4 0.0000 0.9298 0.000 0.000 0.000 1.000 0.000
#> GSM254683 2 0.0703 0.9514 0.000 0.976 0.000 0.000 0.024
#> GSM254710 5 0.1544 0.8780 0.000 0.068 0.000 0.000 0.932
#> GSM254725 4 0.0000 0.9298 0.000 0.000 0.000 1.000 0.000
#> GSM254651 2 0.0000 0.9632 0.000 1.000 0.000 0.000 0.000
#> GSM254638 4 0.0000 0.9298 0.000 0.000 0.000 1.000 0.000
#> GSM254685 4 0.0000 0.9298 0.000 0.000 0.000 1.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM254629 6 0.3827 0.6110 0.076 0.000 0.024 0.000 0.096 0.804
#> GSM254648 6 0.3569 0.6078 0.000 0.056 0.036 0.080 0.000 0.828
#> GSM254694 1 0.6017 0.1261 0.428 0.000 0.304 0.000 0.000 0.268
#> GSM254701 6 0.4804 0.0882 0.456 0.000 0.052 0.000 0.000 0.492
#> GSM254728 1 0.4766 0.1978 0.612 0.000 0.000 0.000 0.072 0.316
#> GSM254726 6 0.5731 0.4314 0.064 0.120 0.000 0.000 0.180 0.636
#> GSM254639 3 0.0363 0.8134 0.000 0.000 0.988 0.000 0.000 0.012
#> GSM254652 6 0.4650 0.0856 0.472 0.000 0.000 0.000 0.040 0.488
#> GSM254700 1 0.1524 0.7548 0.932 0.000 0.060 0.000 0.008 0.000
#> GSM254625 5 0.0405 0.9039 0.004 0.000 0.000 0.000 0.988 0.008
#> GSM254636 3 0.2416 0.7621 0.156 0.000 0.844 0.000 0.000 0.000
#> GSM254659 1 0.3634 0.4914 0.644 0.000 0.356 0.000 0.000 0.000
#> GSM254680 1 0.1556 0.7338 0.920 0.000 0.000 0.000 0.080 0.000
#> GSM254686 1 0.2996 0.5408 0.772 0.000 0.000 0.000 0.228 0.000
#> GSM254718 3 0.0405 0.8157 0.008 0.000 0.988 0.000 0.000 0.004
#> GSM254674 5 0.2912 0.7310 0.216 0.000 0.000 0.000 0.784 0.000
#> GSM254668 5 0.0865 0.9343 0.036 0.000 0.000 0.000 0.964 0.000
#> GSM254697 1 0.1957 0.7483 0.888 0.000 0.112 0.000 0.000 0.000
#> GSM254704 3 0.1267 0.8117 0.060 0.000 0.940 0.000 0.000 0.000
#> GSM254707 5 0.0865 0.9343 0.036 0.000 0.000 0.000 0.964 0.000
#> GSM254714 3 0.1075 0.7928 0.000 0.000 0.952 0.000 0.000 0.048
#> GSM254722 1 0.2491 0.7229 0.836 0.000 0.164 0.000 0.000 0.000
#> GSM254627 1 0.2260 0.7390 0.860 0.000 0.140 0.000 0.000 0.000
#> GSM254630 5 0.3101 0.6868 0.244 0.000 0.000 0.000 0.756 0.000
#> GSM254633 3 0.2092 0.7852 0.124 0.000 0.876 0.000 0.000 0.000
#> GSM254670 3 0.0260 0.8144 0.000 0.000 0.992 0.000 0.000 0.008
#> GSM254716 5 0.0260 0.9071 0.000 0.000 0.000 0.000 0.992 0.008
#> GSM254720 1 0.3244 0.6242 0.732 0.000 0.268 0.000 0.000 0.000
#> GSM254729 3 0.0260 0.8144 0.000 0.000 0.992 0.000 0.000 0.008
#> GSM254654 3 0.2070 0.7418 0.000 0.000 0.892 0.008 0.000 0.100
#> GSM254656 3 0.3136 0.5935 0.000 0.000 0.796 0.188 0.000 0.016
#> GSM254631 3 0.2340 0.7688 0.148 0.000 0.852 0.000 0.000 0.000
#> GSM254657 3 0.2058 0.7565 0.000 0.000 0.908 0.036 0.000 0.056
#> GSM254664 1 0.2219 0.7410 0.864 0.000 0.136 0.000 0.000 0.000
#> GSM254672 3 0.0363 0.8157 0.012 0.000 0.988 0.000 0.000 0.000
#> GSM254692 5 0.0865 0.9343 0.036 0.000 0.000 0.000 0.964 0.000
#> GSM254645 3 0.0363 0.8134 0.000 0.000 0.988 0.000 0.000 0.012
#> GSM254666 3 0.5990 0.1092 0.368 0.000 0.400 0.000 0.000 0.232
#> GSM254675 1 0.1910 0.7489 0.892 0.000 0.108 0.000 0.000 0.000
#> GSM254678 3 0.2178 0.7805 0.132 0.000 0.868 0.000 0.000 0.000
#> GSM254688 5 0.0865 0.9343 0.036 0.000 0.000 0.000 0.964 0.000
#> GSM254690 1 0.1663 0.7290 0.912 0.000 0.000 0.000 0.088 0.000
#> GSM254696 1 0.3706 0.4372 0.620 0.000 0.380 0.000 0.000 0.000
#> GSM254705 5 0.0865 0.9343 0.036 0.000 0.000 0.000 0.964 0.000
#> GSM254642 1 0.1910 0.7118 0.892 0.000 0.000 0.000 0.108 0.000
#> GSM254661 6 0.3570 0.4713 0.016 0.000 0.228 0.000 0.004 0.752
#> GSM254698 3 0.3866 -0.0521 0.484 0.000 0.516 0.000 0.000 0.000
#> GSM254641 1 0.2537 0.7042 0.872 0.000 0.000 0.000 0.096 0.032
#> GSM254647 1 0.1663 0.7290 0.912 0.000 0.000 0.000 0.088 0.000
#> GSM254663 5 0.1007 0.9289 0.044 0.000 0.000 0.000 0.956 0.000
#> GSM254682 5 0.0865 0.9343 0.036 0.000 0.000 0.000 0.964 0.000
#> GSM254709 5 0.0865 0.9343 0.036 0.000 0.000 0.000 0.964 0.000
#> GSM254721 1 0.1556 0.7338 0.920 0.000 0.000 0.000 0.080 0.000
#> GSM254724 1 0.1524 0.7548 0.932 0.000 0.060 0.000 0.008 0.000
#> GSM254650 5 0.0865 0.9343 0.036 0.000 0.000 0.000 0.964 0.000
#> GSM254687 5 0.0865 0.9343 0.036 0.000 0.000 0.000 0.964 0.000
#> GSM254637 3 0.3634 0.4485 0.356 0.000 0.644 0.000 0.000 0.000
#> GSM254684 3 0.1204 0.8127 0.056 0.000 0.944 0.000 0.000 0.000
#> GSM254649 2 0.0632 0.8991 0.000 0.976 0.000 0.000 0.000 0.024
#> GSM254660 2 0.2875 0.8582 0.052 0.852 0.000 0.000 0.000 0.096
#> GSM254693 2 0.0000 0.9049 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM254695 4 0.1261 0.9128 0.024 0.000 0.000 0.952 0.000 0.024
#> GSM254702 4 0.4963 0.6181 0.052 0.136 0.000 0.716 0.000 0.096
#> GSM254643 2 0.0000 0.9049 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM254727 2 0.2923 0.8578 0.052 0.848 0.000 0.000 0.000 0.100
#> GSM254640 4 0.0000 0.9563 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM254626 2 0.0632 0.8991 0.000 0.976 0.000 0.000 0.000 0.024
#> GSM254635 4 0.0000 0.9563 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM254653 2 0.2923 0.8578 0.052 0.848 0.000 0.000 0.000 0.100
#> GSM254658 2 0.0000 0.9049 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM254681 2 0.1464 0.8813 0.004 0.944 0.000 0.000 0.016 0.036
#> GSM254719 2 0.2923 0.8578 0.052 0.848 0.000 0.000 0.000 0.100
#> GSM254673 2 0.2875 0.8598 0.052 0.852 0.000 0.000 0.000 0.096
#> GSM254655 2 0.2923 0.8578 0.052 0.848 0.000 0.000 0.000 0.100
#> GSM254669 2 0.0508 0.9035 0.004 0.984 0.000 0.000 0.000 0.012
#> GSM254699 2 0.2923 0.8578 0.052 0.848 0.000 0.000 0.000 0.100
#> GSM254703 4 0.0000 0.9563 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM254708 2 0.0000 0.9049 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM254715 4 0.0000 0.9563 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM254628 2 0.0146 0.9040 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM254634 4 0.0000 0.9563 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM254646 2 0.0632 0.8991 0.000 0.976 0.000 0.000 0.000 0.024
#> GSM254671 4 0.0000 0.9563 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM254711 4 0.0000 0.9563 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM254717 2 0.0000 0.9049 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM254723 2 0.6358 0.4340 0.064 0.540 0.000 0.152 0.000 0.244
#> GSM254730 2 0.2875 0.8582 0.052 0.852 0.000 0.000 0.000 0.096
#> GSM254731 4 0.4925 0.6247 0.052 0.132 0.000 0.720 0.000 0.096
#> GSM254632 5 0.2401 0.8107 0.004 0.060 0.000 0.000 0.892 0.044
#> GSM254662 2 0.2923 0.8578 0.052 0.848 0.000 0.000 0.000 0.100
#> GSM254677 4 0.0000 0.9563 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM254665 2 0.0000 0.9049 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM254691 2 0.0000 0.9049 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM254644 4 0.0000 0.9563 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM254667 2 0.3695 0.3555 0.000 0.624 0.000 0.376 0.000 0.000
#> GSM254676 2 0.0000 0.9049 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM254679 4 0.0000 0.9563 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM254689 2 0.1624 0.8764 0.004 0.936 0.000 0.000 0.020 0.040
#> GSM254706 2 0.0363 0.9018 0.000 0.988 0.000 0.000 0.000 0.012
#> GSM254712 4 0.0000 0.9563 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM254713 4 0.0000 0.9563 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM254683 2 0.1624 0.8764 0.004 0.936 0.000 0.000 0.020 0.040
#> GSM254710 5 0.2333 0.8134 0.004 0.060 0.000 0.000 0.896 0.040
#> GSM254725 4 0.0000 0.9563 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM254651 2 0.0000 0.9049 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM254638 4 0.0000 0.9563 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM254685 4 0.0000 0.9563 0.000 0.000 0.000 1.000 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) individual(p) disease.state(p) gender(p) other(p) k
#> ATC:skmeans 106 2.52e-22 0.413 0.571 0.595 0.997 2
#> ATC:skmeans 106 2.97e-21 0.123 0.388 0.854 0.416 3
#> ATC:skmeans 102 9.68e-19 0.229 0.837 0.975 0.925 4
#> ATC:skmeans 103 4.58e-19 0.296 0.736 0.438 0.504 5
#> ATC:skmeans 94 2.87e-17 0.132 0.525 0.267 0.597 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 21168 rows and 107 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'pam' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 6.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.987 0.994 0.5018 0.499 0.499
#> 3 3 0.698 0.876 0.930 0.2387 0.886 0.773
#> 4 4 0.882 0.885 0.952 0.1439 0.863 0.667
#> 5 5 0.826 0.863 0.914 0.0618 0.910 0.723
#> 6 6 0.971 0.909 0.953 0.0763 0.920 0.696
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 6
#> attr(,"optional")
#> [1] 2
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
#> GSM254629 1 0.0000 0.993 1.000 0.000
#> GSM254648 1 0.1414 0.974 0.980 0.020
#> GSM254694 1 0.0000 0.993 1.000 0.000
#> GSM254701 1 0.0000 0.993 1.000 0.000
#> GSM254728 1 0.0000 0.993 1.000 0.000
#> GSM254726 2 0.7376 0.734 0.208 0.792
#> GSM254639 1 0.0000 0.993 1.000 0.000
#> GSM254652 1 0.0000 0.993 1.000 0.000
#> GSM254700 1 0.0000 0.993 1.000 0.000
#> GSM254625 1 0.0000 0.993 1.000 0.000
#> GSM254636 1 0.0000 0.993 1.000 0.000
#> GSM254659 1 0.0000 0.993 1.000 0.000
#> GSM254680 1 0.0000 0.993 1.000 0.000
#> GSM254686 1 0.0000 0.993 1.000 0.000
#> GSM254718 1 0.0000 0.993 1.000 0.000
#> GSM254674 1 0.0000 0.993 1.000 0.000
#> GSM254668 1 0.0000 0.993 1.000 0.000
#> GSM254697 1 0.0000 0.993 1.000 0.000
#> GSM254704 1 0.0000 0.993 1.000 0.000
#> GSM254707 1 0.0000 0.993 1.000 0.000
#> GSM254714 1 0.0000 0.993 1.000 0.000
#> GSM254722 1 0.0000 0.993 1.000 0.000
#> GSM254627 1 0.0000 0.993 1.000 0.000
#> GSM254630 1 0.0000 0.993 1.000 0.000
#> GSM254633 1 0.0000 0.993 1.000 0.000
#> GSM254670 1 0.0000 0.993 1.000 0.000
#> GSM254716 1 0.0000 0.993 1.000 0.000
#> GSM254720 1 0.0000 0.993 1.000 0.000
#> GSM254729 1 0.0000 0.993 1.000 0.000
#> GSM254654 1 0.0000 0.993 1.000 0.000
#> GSM254656 1 0.1414 0.974 0.980 0.020
#> GSM254631 1 0.0000 0.993 1.000 0.000
#> GSM254657 1 0.0000 0.993 1.000 0.000
#> GSM254664 1 0.0000 0.993 1.000 0.000
#> GSM254672 1 0.0000 0.993 1.000 0.000
#> GSM254692 1 0.0000 0.993 1.000 0.000
#> GSM254645 1 0.0000 0.993 1.000 0.000
#> GSM254666 1 0.0000 0.993 1.000 0.000
#> GSM254675 1 0.0000 0.993 1.000 0.000
#> GSM254678 1 0.0000 0.993 1.000 0.000
#> GSM254688 1 0.0000 0.993 1.000 0.000
#> GSM254690 1 0.0000 0.993 1.000 0.000
#> GSM254696 1 0.0000 0.993 1.000 0.000
#> GSM254705 1 0.0000 0.993 1.000 0.000
#> GSM254642 1 0.0000 0.993 1.000 0.000
#> GSM254661 1 0.0000 0.993 1.000 0.000
#> GSM254698 1 0.0000 0.993 1.000 0.000
#> GSM254641 1 0.0000 0.993 1.000 0.000
#> GSM254647 1 0.0000 0.993 1.000 0.000
#> GSM254663 1 0.0000 0.993 1.000 0.000
#> GSM254682 1 0.0000 0.993 1.000 0.000
#> GSM254709 1 0.0000 0.993 1.000 0.000
#> GSM254721 1 0.0000 0.993 1.000 0.000
#> GSM254724 1 0.0000 0.993 1.000 0.000
#> GSM254650 1 0.0000 0.993 1.000 0.000
#> GSM254687 1 0.0000 0.993 1.000 0.000
#> GSM254637 1 0.0000 0.993 1.000 0.000
#> GSM254684 1 0.0000 0.993 1.000 0.000
#> GSM254649 2 0.0000 0.995 0.000 1.000
#> GSM254660 2 0.0000 0.995 0.000 1.000
#> GSM254693 2 0.0000 0.995 0.000 1.000
#> GSM254695 2 0.0000 0.995 0.000 1.000
#> GSM254702 2 0.0000 0.995 0.000 1.000
#> GSM254643 2 0.0000 0.995 0.000 1.000
#> GSM254727 2 0.0000 0.995 0.000 1.000
#> GSM254640 2 0.0000 0.995 0.000 1.000
#> GSM254626 2 0.0000 0.995 0.000 1.000
#> GSM254635 2 0.0000 0.995 0.000 1.000
#> GSM254653 2 0.0000 0.995 0.000 1.000
#> GSM254658 2 0.0000 0.995 0.000 1.000
#> GSM254681 2 0.0000 0.995 0.000 1.000
#> GSM254719 2 0.0000 0.995 0.000 1.000
#> GSM254673 2 0.0000 0.995 0.000 1.000
#> GSM254655 2 0.0000 0.995 0.000 1.000
#> GSM254669 2 0.0000 0.995 0.000 1.000
#> GSM254699 2 0.0000 0.995 0.000 1.000
#> GSM254703 2 0.0000 0.995 0.000 1.000
#> GSM254708 2 0.0000 0.995 0.000 1.000
#> GSM254715 2 0.0000 0.995 0.000 1.000
#> GSM254628 2 0.0000 0.995 0.000 1.000
#> GSM254634 2 0.0000 0.995 0.000 1.000
#> GSM254646 2 0.0000 0.995 0.000 1.000
#> GSM254671 2 0.0000 0.995 0.000 1.000
#> GSM254711 2 0.0000 0.995 0.000 1.000
#> GSM254717 2 0.0000 0.995 0.000 1.000
#> GSM254723 2 0.0938 0.984 0.012 0.988
#> GSM254730 2 0.0000 0.995 0.000 1.000
#> GSM254731 2 0.0000 0.995 0.000 1.000
#> GSM254632 1 0.9248 0.481 0.660 0.340
#> GSM254662 2 0.0000 0.995 0.000 1.000
#> GSM254677 2 0.0000 0.995 0.000 1.000
#> GSM254665 2 0.0000 0.995 0.000 1.000
#> GSM254691 2 0.0000 0.995 0.000 1.000
#> GSM254644 2 0.0000 0.995 0.000 1.000
#> GSM254667 2 0.0000 0.995 0.000 1.000
#> GSM254676 2 0.0000 0.995 0.000 1.000
#> GSM254679 2 0.0000 0.995 0.000 1.000
#> GSM254689 2 0.0000 0.995 0.000 1.000
#> GSM254706 2 0.0000 0.995 0.000 1.000
#> GSM254712 2 0.0000 0.995 0.000 1.000
#> GSM254713 2 0.0000 0.995 0.000 1.000
#> GSM254683 2 0.0000 0.995 0.000 1.000
#> GSM254710 2 0.0000 0.995 0.000 1.000
#> GSM254725 2 0.0000 0.995 0.000 1.000
#> GSM254651 2 0.0000 0.995 0.000 1.000
#> GSM254638 2 0.0000 0.995 0.000 1.000
#> GSM254685 2 0.0000 0.995 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM254629 3 0.3116 0.8006 0.000 0.108 0.892
#> GSM254648 3 0.3192 0.7961 0.000 0.112 0.888
#> GSM254694 3 0.0000 0.8920 0.000 0.000 1.000
#> GSM254701 3 0.0000 0.8920 0.000 0.000 1.000
#> GSM254728 3 0.0000 0.8920 0.000 0.000 1.000
#> GSM254726 2 0.4887 0.6794 0.000 0.772 0.228
#> GSM254639 3 0.3192 0.8191 0.112 0.000 0.888
#> GSM254652 3 0.0000 0.8920 0.000 0.000 1.000
#> GSM254700 3 0.4555 0.7037 0.200 0.000 0.800
#> GSM254625 1 0.3192 0.9487 0.888 0.000 0.112
#> GSM254636 3 0.0000 0.8920 0.000 0.000 1.000
#> GSM254659 3 0.0000 0.8920 0.000 0.000 1.000
#> GSM254680 3 0.4555 0.7037 0.200 0.000 0.800
#> GSM254686 3 0.0000 0.8920 0.000 0.000 1.000
#> GSM254718 3 0.0000 0.8920 0.000 0.000 1.000
#> GSM254674 3 0.5859 0.4297 0.344 0.000 0.656
#> GSM254668 1 0.3192 0.9487 0.888 0.000 0.112
#> GSM254697 3 0.0000 0.8920 0.000 0.000 1.000
#> GSM254704 3 0.0000 0.8920 0.000 0.000 1.000
#> GSM254707 1 0.3192 0.9487 0.888 0.000 0.112
#> GSM254714 3 0.0000 0.8920 0.000 0.000 1.000
#> GSM254722 3 0.0000 0.8920 0.000 0.000 1.000
#> GSM254627 3 0.0000 0.8920 0.000 0.000 1.000
#> GSM254630 3 0.4654 0.6924 0.208 0.000 0.792
#> GSM254633 3 0.0000 0.8920 0.000 0.000 1.000
#> GSM254670 3 0.3192 0.8191 0.112 0.000 0.888
#> GSM254716 1 0.3192 0.9487 0.888 0.000 0.112
#> GSM254720 3 0.0000 0.8920 0.000 0.000 1.000
#> GSM254729 3 0.1860 0.8632 0.052 0.000 0.948
#> GSM254654 3 0.3192 0.8191 0.112 0.000 0.888
#> GSM254656 3 0.3192 0.8191 0.112 0.000 0.888
#> GSM254631 3 0.0000 0.8920 0.000 0.000 1.000
#> GSM254657 3 0.3192 0.8191 0.112 0.000 0.888
#> GSM254664 3 0.0000 0.8920 0.000 0.000 1.000
#> GSM254672 3 0.0000 0.8920 0.000 0.000 1.000
#> GSM254692 1 0.3192 0.9487 0.888 0.000 0.112
#> GSM254645 3 0.2625 0.8411 0.084 0.000 0.916
#> GSM254666 3 0.0000 0.8920 0.000 0.000 1.000
#> GSM254675 3 0.0000 0.8920 0.000 0.000 1.000
#> GSM254678 3 0.0000 0.8920 0.000 0.000 1.000
#> GSM254688 1 0.3192 0.9487 0.888 0.000 0.112
#> GSM254690 3 0.5810 0.4501 0.336 0.000 0.664
#> GSM254696 3 0.0000 0.8920 0.000 0.000 1.000
#> GSM254705 1 0.3192 0.9487 0.888 0.000 0.112
#> GSM254642 1 0.6309 0.0883 0.504 0.000 0.496
#> GSM254661 3 0.3116 0.8006 0.000 0.108 0.892
#> GSM254698 3 0.0000 0.8920 0.000 0.000 1.000
#> GSM254641 3 0.4121 0.7444 0.168 0.000 0.832
#> GSM254647 3 0.5810 0.4501 0.336 0.000 0.664
#> GSM254663 1 0.3192 0.9487 0.888 0.000 0.112
#> GSM254682 1 0.3192 0.9487 0.888 0.000 0.112
#> GSM254709 1 0.3192 0.9487 0.888 0.000 0.112
#> GSM254721 3 0.5810 0.4501 0.336 0.000 0.664
#> GSM254724 3 0.4346 0.7249 0.184 0.000 0.816
#> GSM254650 1 0.3192 0.9487 0.888 0.000 0.112
#> GSM254687 1 0.3192 0.9487 0.888 0.000 0.112
#> GSM254637 3 0.0000 0.8920 0.000 0.000 1.000
#> GSM254684 3 0.0000 0.8920 0.000 0.000 1.000
#> GSM254649 2 0.0000 0.9604 0.000 1.000 0.000
#> GSM254660 2 0.0000 0.9604 0.000 1.000 0.000
#> GSM254693 2 0.0000 0.9604 0.000 1.000 0.000
#> GSM254695 2 0.0237 0.9591 0.004 0.996 0.000
#> GSM254702 2 0.0000 0.9604 0.000 1.000 0.000
#> GSM254643 2 0.0000 0.9604 0.000 1.000 0.000
#> GSM254727 2 0.0000 0.9604 0.000 1.000 0.000
#> GSM254640 2 0.3192 0.9133 0.112 0.888 0.000
#> GSM254626 2 0.0000 0.9604 0.000 1.000 0.000
#> GSM254635 2 0.3192 0.9133 0.112 0.888 0.000
#> GSM254653 2 0.0000 0.9604 0.000 1.000 0.000
#> GSM254658 2 0.0000 0.9604 0.000 1.000 0.000
#> GSM254681 2 0.0000 0.9604 0.000 1.000 0.000
#> GSM254719 2 0.0000 0.9604 0.000 1.000 0.000
#> GSM254673 2 0.0000 0.9604 0.000 1.000 0.000
#> GSM254655 2 0.0000 0.9604 0.000 1.000 0.000
#> GSM254669 2 0.0000 0.9604 0.000 1.000 0.000
#> GSM254699 2 0.0000 0.9604 0.000 1.000 0.000
#> GSM254703 2 0.0237 0.9591 0.004 0.996 0.000
#> GSM254708 2 0.0000 0.9604 0.000 1.000 0.000
#> GSM254715 2 0.3192 0.9133 0.112 0.888 0.000
#> GSM254628 2 0.0000 0.9604 0.000 1.000 0.000
#> GSM254634 2 0.3192 0.9133 0.112 0.888 0.000
#> GSM254646 2 0.0000 0.9604 0.000 1.000 0.000
#> GSM254671 2 0.3192 0.9133 0.112 0.888 0.000
#> GSM254711 2 0.3192 0.9133 0.112 0.888 0.000
#> GSM254717 2 0.0000 0.9604 0.000 1.000 0.000
#> GSM254723 2 0.0747 0.9487 0.000 0.984 0.016
#> GSM254730 2 0.0000 0.9604 0.000 1.000 0.000
#> GSM254731 2 0.0000 0.9604 0.000 1.000 0.000
#> GSM254632 3 0.5835 0.4527 0.000 0.340 0.660
#> GSM254662 2 0.0000 0.9604 0.000 1.000 0.000
#> GSM254677 2 0.3192 0.9133 0.112 0.888 0.000
#> GSM254665 2 0.0000 0.9604 0.000 1.000 0.000
#> GSM254691 2 0.0000 0.9604 0.000 1.000 0.000
#> GSM254644 2 0.3192 0.9133 0.112 0.888 0.000
#> GSM254667 2 0.0000 0.9604 0.000 1.000 0.000
#> GSM254676 2 0.0000 0.9604 0.000 1.000 0.000
#> GSM254679 2 0.3192 0.9133 0.112 0.888 0.000
#> GSM254689 2 0.0000 0.9604 0.000 1.000 0.000
#> GSM254706 2 0.0000 0.9604 0.000 1.000 0.000
#> GSM254712 2 0.3192 0.9133 0.112 0.888 0.000
#> GSM254713 2 0.3192 0.9133 0.112 0.888 0.000
#> GSM254683 2 0.0000 0.9604 0.000 1.000 0.000
#> GSM254710 1 0.3192 0.8046 0.888 0.112 0.000
#> GSM254725 2 0.3192 0.9133 0.112 0.888 0.000
#> GSM254651 2 0.0000 0.9604 0.000 1.000 0.000
#> GSM254638 2 0.3192 0.9133 0.112 0.888 0.000
#> GSM254685 2 0.3192 0.9133 0.112 0.888 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM254629 3 0.000 0.925 0.000 0.000 1.000 0.000
#> GSM254648 3 0.000 0.925 0.000 0.000 1.000 0.000
#> GSM254694 3 0.000 0.925 0.000 0.000 1.000 0.000
#> GSM254701 3 0.000 0.925 0.000 0.000 1.000 0.000
#> GSM254728 3 0.000 0.925 0.000 0.000 1.000 0.000
#> GSM254726 3 0.380 0.680 0.000 0.220 0.780 0.000
#> GSM254639 3 0.156 0.881 0.000 0.000 0.944 0.056
#> GSM254652 3 0.000 0.925 0.000 0.000 1.000 0.000
#> GSM254700 3 0.361 0.756 0.200 0.000 0.800 0.000
#> GSM254625 1 0.000 0.920 1.000 0.000 0.000 0.000
#> GSM254636 3 0.000 0.925 0.000 0.000 1.000 0.000
#> GSM254659 3 0.000 0.925 0.000 0.000 1.000 0.000
#> GSM254680 3 0.361 0.756 0.200 0.000 0.800 0.000
#> GSM254686 3 0.000 0.925 0.000 0.000 1.000 0.000
#> GSM254718 3 0.000 0.925 0.000 0.000 1.000 0.000
#> GSM254674 3 0.468 0.522 0.352 0.000 0.648 0.000
#> GSM254668 1 0.000 0.920 1.000 0.000 0.000 0.000
#> GSM254697 3 0.000 0.925 0.000 0.000 1.000 0.000
#> GSM254704 3 0.000 0.925 0.000 0.000 1.000 0.000
#> GSM254707 1 0.000 0.920 1.000 0.000 0.000 0.000
#> GSM254714 3 0.000 0.925 0.000 0.000 1.000 0.000
#> GSM254722 3 0.000 0.925 0.000 0.000 1.000 0.000
#> GSM254627 3 0.000 0.925 0.000 0.000 1.000 0.000
#> GSM254630 3 0.373 0.741 0.212 0.000 0.788 0.000
#> GSM254633 3 0.000 0.925 0.000 0.000 1.000 0.000
#> GSM254670 3 0.000 0.925 0.000 0.000 1.000 0.000
#> GSM254716 1 0.000 0.920 1.000 0.000 0.000 0.000
#> GSM254720 3 0.000 0.925 0.000 0.000 1.000 0.000
#> GSM254729 3 0.000 0.925 0.000 0.000 1.000 0.000
#> GSM254654 3 0.000 0.925 0.000 0.000 1.000 0.000
#> GSM254656 4 0.000 0.917 0.000 0.000 0.000 1.000
#> GSM254631 3 0.000 0.925 0.000 0.000 1.000 0.000
#> GSM254657 4 0.460 0.497 0.000 0.000 0.336 0.664
#> GSM254664 3 0.000 0.925 0.000 0.000 1.000 0.000
#> GSM254672 3 0.000 0.925 0.000 0.000 1.000 0.000
#> GSM254692 1 0.000 0.920 1.000 0.000 0.000 0.000
#> GSM254645 3 0.000 0.925 0.000 0.000 1.000 0.000
#> GSM254666 3 0.000 0.925 0.000 0.000 1.000 0.000
#> GSM254675 3 0.000 0.925 0.000 0.000 1.000 0.000
#> GSM254678 3 0.000 0.925 0.000 0.000 1.000 0.000
#> GSM254688 1 0.000 0.920 1.000 0.000 0.000 0.000
#> GSM254690 3 0.464 0.538 0.344 0.000 0.656 0.000
#> GSM254696 3 0.000 0.925 0.000 0.000 1.000 0.000
#> GSM254705 1 0.000 0.920 1.000 0.000 0.000 0.000
#> GSM254642 1 0.500 -0.166 0.504 0.000 0.496 0.000
#> GSM254661 3 0.000 0.925 0.000 0.000 1.000 0.000
#> GSM254698 3 0.000 0.925 0.000 0.000 1.000 0.000
#> GSM254641 3 0.327 0.790 0.168 0.000 0.832 0.000
#> GSM254647 3 0.464 0.538 0.344 0.000 0.656 0.000
#> GSM254663 1 0.000 0.920 1.000 0.000 0.000 0.000
#> GSM254682 1 0.000 0.920 1.000 0.000 0.000 0.000
#> GSM254709 1 0.000 0.920 1.000 0.000 0.000 0.000
#> GSM254721 3 0.464 0.538 0.344 0.000 0.656 0.000
#> GSM254724 3 0.344 0.773 0.184 0.000 0.816 0.000
#> GSM254650 1 0.000 0.920 1.000 0.000 0.000 0.000
#> GSM254687 1 0.000 0.920 1.000 0.000 0.000 0.000
#> GSM254637 3 0.000 0.925 0.000 0.000 1.000 0.000
#> GSM254684 3 0.000 0.925 0.000 0.000 1.000 0.000
#> GSM254649 2 0.000 0.987 0.000 1.000 0.000 0.000
#> GSM254660 2 0.000 0.987 0.000 1.000 0.000 0.000
#> GSM254693 2 0.000 0.987 0.000 1.000 0.000 0.000
#> GSM254695 4 0.387 0.718 0.000 0.228 0.000 0.772
#> GSM254702 2 0.331 0.782 0.000 0.828 0.000 0.172
#> GSM254643 2 0.000 0.987 0.000 1.000 0.000 0.000
#> GSM254727 2 0.000 0.987 0.000 1.000 0.000 0.000
#> GSM254640 4 0.387 0.718 0.000 0.228 0.000 0.772
#> GSM254626 2 0.000 0.987 0.000 1.000 0.000 0.000
#> GSM254635 4 0.000 0.917 0.000 0.000 0.000 1.000
#> GSM254653 2 0.000 0.987 0.000 1.000 0.000 0.000
#> GSM254658 2 0.000 0.987 0.000 1.000 0.000 0.000
#> GSM254681 2 0.000 0.987 0.000 1.000 0.000 0.000
#> GSM254719 2 0.000 0.987 0.000 1.000 0.000 0.000
#> GSM254673 2 0.000 0.987 0.000 1.000 0.000 0.000
#> GSM254655 2 0.000 0.987 0.000 1.000 0.000 0.000
#> GSM254669 2 0.000 0.987 0.000 1.000 0.000 0.000
#> GSM254699 2 0.000 0.987 0.000 1.000 0.000 0.000
#> GSM254703 4 0.401 0.695 0.000 0.244 0.000 0.756
#> GSM254708 2 0.000 0.987 0.000 1.000 0.000 0.000
#> GSM254715 4 0.000 0.917 0.000 0.000 0.000 1.000
#> GSM254628 2 0.000 0.987 0.000 1.000 0.000 0.000
#> GSM254634 4 0.000 0.917 0.000 0.000 0.000 1.000
#> GSM254646 2 0.000 0.987 0.000 1.000 0.000 0.000
#> GSM254671 4 0.000 0.917 0.000 0.000 0.000 1.000
#> GSM254711 4 0.139 0.882 0.000 0.048 0.000 0.952
#> GSM254717 2 0.000 0.987 0.000 1.000 0.000 0.000
#> GSM254723 3 0.387 0.668 0.000 0.228 0.772 0.000
#> GSM254730 2 0.000 0.987 0.000 1.000 0.000 0.000
#> GSM254731 2 0.208 0.904 0.000 0.916 0.000 0.084
#> GSM254632 3 0.215 0.842 0.000 0.088 0.912 0.000
#> GSM254662 2 0.000 0.987 0.000 1.000 0.000 0.000
#> GSM254677 4 0.000 0.917 0.000 0.000 0.000 1.000
#> GSM254665 2 0.000 0.987 0.000 1.000 0.000 0.000
#> GSM254691 2 0.000 0.987 0.000 1.000 0.000 0.000
#> GSM254644 4 0.000 0.917 0.000 0.000 0.000 1.000
#> GSM254667 2 0.228 0.888 0.000 0.904 0.000 0.096
#> GSM254676 2 0.000 0.987 0.000 1.000 0.000 0.000
#> GSM254679 4 0.000 0.917 0.000 0.000 0.000 1.000
#> GSM254689 2 0.000 0.987 0.000 1.000 0.000 0.000
#> GSM254706 2 0.000 0.987 0.000 1.000 0.000 0.000
#> GSM254712 4 0.000 0.917 0.000 0.000 0.000 1.000
#> GSM254713 4 0.000 0.917 0.000 0.000 0.000 1.000
#> GSM254683 2 0.000 0.987 0.000 1.000 0.000 0.000
#> GSM254710 1 0.410 0.592 0.744 0.256 0.000 0.000
#> GSM254725 4 0.000 0.917 0.000 0.000 0.000 1.000
#> GSM254651 2 0.000 0.987 0.000 1.000 0.000 0.000
#> GSM254638 4 0.000 0.917 0.000 0.000 0.000 1.000
#> GSM254685 4 0.000 0.917 0.000 0.000 0.000 1.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM254629 3 0.0000 0.88966 0.000 0.000 1.000 0.000 0.000
#> GSM254648 3 0.0162 0.88754 0.000 0.004 0.996 0.000 0.000
#> GSM254694 3 0.0000 0.88966 0.000 0.000 1.000 0.000 0.000
#> GSM254701 3 0.0000 0.88966 0.000 0.000 1.000 0.000 0.000
#> GSM254728 3 0.0000 0.88966 0.000 0.000 1.000 0.000 0.000
#> GSM254726 2 0.3336 0.64481 0.000 0.772 0.228 0.000 0.000
#> GSM254639 3 0.1341 0.85231 0.000 0.000 0.944 0.056 0.000
#> GSM254652 3 0.0000 0.88966 0.000 0.000 1.000 0.000 0.000
#> GSM254700 3 0.3266 0.82198 0.200 0.000 0.796 0.000 0.004
#> GSM254625 5 0.0000 0.98416 0.000 0.000 0.000 0.000 1.000
#> GSM254636 3 0.0162 0.88899 0.004 0.000 0.996 0.000 0.000
#> GSM254659 3 0.0000 0.88966 0.000 0.000 1.000 0.000 0.000
#> GSM254680 3 0.3109 0.82441 0.200 0.000 0.800 0.000 0.000
#> GSM254686 3 0.3109 0.82441 0.200 0.000 0.800 0.000 0.000
#> GSM254718 3 0.0000 0.88966 0.000 0.000 1.000 0.000 0.000
#> GSM254674 3 0.6146 0.54618 0.200 0.000 0.560 0.000 0.240
#> GSM254668 5 0.0000 0.98416 0.000 0.000 0.000 0.000 1.000
#> GSM254697 3 0.3109 0.82441 0.200 0.000 0.800 0.000 0.000
#> GSM254704 3 0.0000 0.88966 0.000 0.000 1.000 0.000 0.000
#> GSM254707 5 0.0000 0.98416 0.000 0.000 0.000 0.000 1.000
#> GSM254714 3 0.0000 0.88966 0.000 0.000 1.000 0.000 0.000
#> GSM254722 3 0.3109 0.82441 0.200 0.000 0.800 0.000 0.000
#> GSM254627 3 0.3109 0.82441 0.200 0.000 0.800 0.000 0.000
#> GSM254630 3 0.3863 0.80522 0.200 0.000 0.772 0.000 0.028
#> GSM254633 3 0.0000 0.88966 0.000 0.000 1.000 0.000 0.000
#> GSM254670 3 0.0000 0.88966 0.000 0.000 1.000 0.000 0.000
#> GSM254716 5 0.0000 0.98416 0.000 0.000 0.000 0.000 1.000
#> GSM254720 3 0.0000 0.88966 0.000 0.000 1.000 0.000 0.000
#> GSM254729 3 0.0000 0.88966 0.000 0.000 1.000 0.000 0.000
#> GSM254654 3 0.0000 0.88966 0.000 0.000 1.000 0.000 0.000
#> GSM254656 4 0.0000 0.95547 0.000 0.000 0.000 1.000 0.000
#> GSM254631 3 0.0000 0.88966 0.000 0.000 1.000 0.000 0.000
#> GSM254657 4 0.3966 0.50096 0.000 0.000 0.336 0.664 0.000
#> GSM254664 3 0.3074 0.82627 0.196 0.000 0.804 0.000 0.000
#> GSM254672 3 0.0000 0.88966 0.000 0.000 1.000 0.000 0.000
#> GSM254692 5 0.0000 0.98416 0.000 0.000 0.000 0.000 1.000
#> GSM254645 3 0.0000 0.88966 0.000 0.000 1.000 0.000 0.000
#> GSM254666 3 0.0000 0.88966 0.000 0.000 1.000 0.000 0.000
#> GSM254675 3 0.0404 0.88722 0.012 0.000 0.988 0.000 0.000
#> GSM254678 3 0.0000 0.88966 0.000 0.000 1.000 0.000 0.000
#> GSM254688 5 0.2377 0.84986 0.128 0.000 0.000 0.000 0.872
#> GSM254690 3 0.5904 0.60979 0.200 0.000 0.600 0.000 0.200
#> GSM254696 3 0.0000 0.88966 0.000 0.000 1.000 0.000 0.000
#> GSM254705 5 0.0000 0.98416 0.000 0.000 0.000 0.000 1.000
#> GSM254642 3 0.6491 0.33689 0.200 0.000 0.464 0.000 0.336
#> GSM254661 3 0.0000 0.88966 0.000 0.000 1.000 0.000 0.000
#> GSM254698 3 0.2732 0.84105 0.160 0.000 0.840 0.000 0.000
#> GSM254641 3 0.3109 0.82441 0.200 0.000 0.800 0.000 0.000
#> GSM254647 3 0.6146 0.54618 0.200 0.000 0.560 0.000 0.240
#> GSM254663 5 0.0703 0.96398 0.024 0.000 0.000 0.000 0.976
#> GSM254682 5 0.0000 0.98416 0.000 0.000 0.000 0.000 1.000
#> GSM254709 5 0.0000 0.98416 0.000 0.000 0.000 0.000 1.000
#> GSM254721 3 0.6080 0.56630 0.200 0.000 0.572 0.000 0.228
#> GSM254724 3 0.3109 0.82441 0.200 0.000 0.800 0.000 0.000
#> GSM254650 5 0.0000 0.98416 0.000 0.000 0.000 0.000 1.000
#> GSM254687 5 0.0000 0.98416 0.000 0.000 0.000 0.000 1.000
#> GSM254637 3 0.2074 0.86094 0.104 0.000 0.896 0.000 0.000
#> GSM254684 3 0.0000 0.88966 0.000 0.000 1.000 0.000 0.000
#> GSM254649 1 0.3109 0.96811 0.800 0.200 0.000 0.000 0.000
#> GSM254660 2 0.0000 0.90777 0.000 1.000 0.000 0.000 0.000
#> GSM254693 1 0.3143 0.96396 0.796 0.204 0.000 0.000 0.000
#> GSM254695 2 0.3109 0.73085 0.000 0.800 0.000 0.200 0.000
#> GSM254702 2 0.0000 0.90777 0.000 1.000 0.000 0.000 0.000
#> GSM254643 2 0.0000 0.90777 0.000 1.000 0.000 0.000 0.000
#> GSM254727 2 0.0162 0.90677 0.004 0.996 0.000 0.000 0.000
#> GSM254640 2 0.3143 0.72745 0.000 0.796 0.000 0.204 0.000
#> GSM254626 1 0.3109 0.96811 0.800 0.200 0.000 0.000 0.000
#> GSM254635 4 0.0000 0.95547 0.000 0.000 0.000 1.000 0.000
#> GSM254653 2 0.0162 0.90677 0.004 0.996 0.000 0.000 0.000
#> GSM254658 2 0.0162 0.90597 0.004 0.996 0.000 0.000 0.000
#> GSM254681 1 0.3109 0.96811 0.800 0.200 0.000 0.000 0.000
#> GSM254719 2 0.0162 0.90677 0.004 0.996 0.000 0.000 0.000
#> GSM254673 1 0.3109 0.96811 0.800 0.200 0.000 0.000 0.000
#> GSM254655 2 0.0162 0.90677 0.004 0.996 0.000 0.000 0.000
#> GSM254669 2 0.4192 -0.00241 0.404 0.596 0.000 0.000 0.000
#> GSM254699 2 0.0162 0.90677 0.004 0.996 0.000 0.000 0.000
#> GSM254703 2 0.3109 0.73085 0.000 0.800 0.000 0.200 0.000
#> GSM254708 2 0.0000 0.90777 0.000 1.000 0.000 0.000 0.000
#> GSM254715 4 0.0000 0.95547 0.000 0.000 0.000 1.000 0.000
#> GSM254628 1 0.3109 0.96811 0.800 0.200 0.000 0.000 0.000
#> GSM254634 4 0.0000 0.95547 0.000 0.000 0.000 1.000 0.000
#> GSM254646 1 0.3109 0.96811 0.800 0.200 0.000 0.000 0.000
#> GSM254671 4 0.0000 0.95547 0.000 0.000 0.000 1.000 0.000
#> GSM254711 4 0.2377 0.80158 0.000 0.128 0.000 0.872 0.000
#> GSM254717 2 0.0000 0.90777 0.000 1.000 0.000 0.000 0.000
#> GSM254723 2 0.3109 0.68277 0.000 0.800 0.200 0.000 0.000
#> GSM254730 2 0.0000 0.90777 0.000 1.000 0.000 0.000 0.000
#> GSM254731 2 0.0703 0.89290 0.000 0.976 0.000 0.024 0.000
#> GSM254632 3 0.3913 0.44409 0.000 0.324 0.676 0.000 0.000
#> GSM254662 2 0.0162 0.90677 0.004 0.996 0.000 0.000 0.000
#> GSM254677 4 0.0000 0.95547 0.000 0.000 0.000 1.000 0.000
#> GSM254665 2 0.0000 0.90777 0.000 1.000 0.000 0.000 0.000
#> GSM254691 2 0.0000 0.90777 0.000 1.000 0.000 0.000 0.000
#> GSM254644 4 0.0000 0.95547 0.000 0.000 0.000 1.000 0.000
#> GSM254667 2 0.1965 0.83274 0.000 0.904 0.000 0.096 0.000
#> GSM254676 2 0.0000 0.90777 0.000 1.000 0.000 0.000 0.000
#> GSM254679 4 0.0000 0.95547 0.000 0.000 0.000 1.000 0.000
#> GSM254689 1 0.3109 0.96811 0.800 0.200 0.000 0.000 0.000
#> GSM254706 2 0.1121 0.86797 0.044 0.956 0.000 0.000 0.000
#> GSM254712 4 0.0000 0.95547 0.000 0.000 0.000 1.000 0.000
#> GSM254713 4 0.0000 0.95547 0.000 0.000 0.000 1.000 0.000
#> GSM254683 1 0.3109 0.96811 0.800 0.200 0.000 0.000 0.000
#> GSM254710 1 0.3143 0.65726 0.796 0.000 0.000 0.000 0.204
#> GSM254725 4 0.0000 0.95547 0.000 0.000 0.000 1.000 0.000
#> GSM254651 2 0.0000 0.90777 0.000 1.000 0.000 0.000 0.000
#> GSM254638 4 0.0000 0.95547 0.000 0.000 0.000 1.000 0.000
#> GSM254685 4 0.0000 0.95547 0.000 0.000 0.000 1.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM254629 3 0.0000 0.9388 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM254648 3 0.1387 0.8711 0.000 0.068 0.932 0.000 0.000 0.000
#> GSM254694 3 0.0000 0.9388 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM254701 3 0.0000 0.9388 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM254728 3 0.0000 0.9388 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM254726 2 0.2996 0.6499 0.000 0.772 0.228 0.000 0.000 0.000
#> GSM254639 3 0.1204 0.8906 0.000 0.000 0.944 0.056 0.000 0.000
#> GSM254652 3 0.0000 0.9388 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM254700 1 0.1327 0.9883 0.936 0.000 0.064 0.000 0.000 0.000
#> GSM254625 5 0.0000 0.9836 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM254636 3 0.0146 0.9359 0.004 0.000 0.996 0.000 0.000 0.000
#> GSM254659 3 0.0000 0.9388 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM254680 1 0.1327 0.9883 0.936 0.000 0.064 0.000 0.000 0.000
#> GSM254686 1 0.1327 0.9883 0.936 0.000 0.064 0.000 0.000 0.000
#> GSM254718 3 0.0000 0.9388 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM254674 1 0.1327 0.9883 0.936 0.000 0.064 0.000 0.000 0.000
#> GSM254668 5 0.0000 0.9836 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM254697 1 0.1327 0.9883 0.936 0.000 0.064 0.000 0.000 0.000
#> GSM254704 3 0.0000 0.9388 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM254707 5 0.0000 0.9836 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM254714 3 0.0000 0.9388 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM254722 3 0.3774 0.2623 0.408 0.000 0.592 0.000 0.000 0.000
#> GSM254627 1 0.1327 0.9883 0.936 0.000 0.064 0.000 0.000 0.000
#> GSM254630 1 0.1387 0.9840 0.932 0.000 0.068 0.000 0.000 0.000
#> GSM254633 3 0.0000 0.9388 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM254670 3 0.0000 0.9388 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM254716 5 0.0000 0.9836 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM254720 3 0.0000 0.9388 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM254729 3 0.0000 0.9388 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM254654 3 0.0000 0.9388 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM254656 4 0.0000 0.9591 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM254631 3 0.0000 0.9388 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM254657 4 0.3563 0.4849 0.000 0.000 0.336 0.664 0.000 0.000
#> GSM254664 3 0.3756 0.2876 0.400 0.000 0.600 0.000 0.000 0.000
#> GSM254672 3 0.0000 0.9388 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM254692 5 0.0000 0.9836 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM254645 3 0.0000 0.9388 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM254666 3 0.0000 0.9388 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM254675 3 0.0363 0.9293 0.012 0.000 0.988 0.000 0.000 0.000
#> GSM254678 3 0.0000 0.9388 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM254688 5 0.2135 0.8428 0.128 0.000 0.000 0.000 0.872 0.000
#> GSM254690 1 0.1327 0.9883 0.936 0.000 0.064 0.000 0.000 0.000
#> GSM254696 3 0.0000 0.9388 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM254705 5 0.0000 0.9836 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM254642 1 0.1327 0.9883 0.936 0.000 0.064 0.000 0.000 0.000
#> GSM254661 3 0.0000 0.9388 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM254698 3 0.3351 0.5569 0.288 0.000 0.712 0.000 0.000 0.000
#> GSM254641 1 0.1327 0.9883 0.936 0.000 0.064 0.000 0.000 0.000
#> GSM254647 1 0.1327 0.9883 0.936 0.000 0.064 0.000 0.000 0.000
#> GSM254663 5 0.0632 0.9626 0.024 0.000 0.000 0.000 0.976 0.000
#> GSM254682 5 0.0000 0.9836 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM254709 5 0.0000 0.9836 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM254721 1 0.1327 0.9883 0.936 0.000 0.064 0.000 0.000 0.000
#> GSM254724 1 0.1327 0.9883 0.936 0.000 0.064 0.000 0.000 0.000
#> GSM254650 5 0.0000 0.9836 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM254687 5 0.0000 0.9836 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM254637 1 0.2562 0.8469 0.828 0.000 0.172 0.000 0.000 0.000
#> GSM254684 3 0.0000 0.9388 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM254649 6 0.0000 0.9051 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM254660 2 0.0000 0.9560 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM254693 6 0.0260 0.9003 0.000 0.008 0.000 0.000 0.000 0.992
#> GSM254695 2 0.0000 0.9560 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM254702 2 0.1327 0.9480 0.064 0.936 0.000 0.000 0.000 0.000
#> GSM254643 2 0.0000 0.9560 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM254727 2 0.1471 0.9469 0.064 0.932 0.000 0.000 0.000 0.004
#> GSM254640 2 0.0146 0.9548 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM254626 6 0.0000 0.9051 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM254635 4 0.0000 0.9591 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM254653 2 0.1471 0.9469 0.064 0.932 0.000 0.000 0.000 0.004
#> GSM254658 2 0.1387 0.9261 0.000 0.932 0.000 0.000 0.000 0.068
#> GSM254681 6 0.0000 0.9051 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM254719 2 0.1471 0.9469 0.064 0.932 0.000 0.000 0.000 0.004
#> GSM254673 6 0.1327 0.8629 0.064 0.000 0.000 0.000 0.000 0.936
#> GSM254655 2 0.1471 0.9469 0.064 0.932 0.000 0.000 0.000 0.004
#> GSM254669 6 0.4903 -0.0296 0.060 0.464 0.000 0.000 0.000 0.476
#> GSM254699 2 0.1471 0.9469 0.064 0.932 0.000 0.000 0.000 0.004
#> GSM254703 2 0.0000 0.9560 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM254708 2 0.0000 0.9560 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM254715 4 0.0000 0.9591 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM254628 6 0.0000 0.9051 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM254634 4 0.0000 0.9591 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM254646 6 0.0000 0.9051 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM254671 4 0.0000 0.9591 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM254711 4 0.1714 0.8494 0.000 0.092 0.000 0.908 0.000 0.000
#> GSM254717 2 0.0000 0.9560 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM254723 2 0.1719 0.9414 0.060 0.924 0.016 0.000 0.000 0.000
#> GSM254730 2 0.1327 0.9480 0.064 0.936 0.000 0.000 0.000 0.000
#> GSM254731 2 0.1327 0.9480 0.064 0.936 0.000 0.000 0.000 0.000
#> GSM254632 3 0.3515 0.5201 0.000 0.324 0.676 0.000 0.000 0.000
#> GSM254662 2 0.1471 0.9469 0.064 0.932 0.000 0.000 0.000 0.004
#> GSM254677 4 0.0000 0.9591 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM254665 2 0.0000 0.9560 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM254691 2 0.0000 0.9560 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM254644 4 0.0000 0.9591 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM254667 2 0.0000 0.9560 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM254676 2 0.0000 0.9560 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM254679 4 0.0000 0.9591 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM254689 6 0.0000 0.9051 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM254706 2 0.0865 0.9343 0.000 0.964 0.000 0.000 0.000 0.036
#> GSM254712 4 0.0000 0.9591 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM254713 4 0.0000 0.9591 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM254683 6 0.0000 0.9051 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM254710 6 0.3023 0.6541 0.000 0.000 0.000 0.000 0.232 0.768
#> GSM254725 4 0.0000 0.9591 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM254651 2 0.0000 0.9560 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM254638 4 0.0000 0.9591 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM254685 4 0.0000 0.9591 0.000 0.000 0.000 1.000 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) individual(p) disease.state(p) gender(p) other(p) k
#> ATC:pam 106 3.92e-23 0.588 0.614 0.401 0.814 2
#> ATC:pam 101 5.30e-21 0.100 0.706 0.160 0.524 3
#> ATC:pam 105 2.38e-19 0.137 0.820 0.666 0.818 4
#> ATC:pam 104 2.85e-19 0.107 0.582 0.479 0.602 5
#> ATC:pam 103 3.20e-18 0.183 0.318 0.473 0.867 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 21168 rows and 107 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 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 0.340 0.372 0.641 0.3935 0.556 0.556
#> 3 3 0.698 0.873 0.864 0.6366 0.591 0.371
#> 4 4 0.702 0.758 0.806 0.0764 0.763 0.466
#> 5 5 0.740 0.634 0.768 0.0757 0.877 0.640
#> 6 6 0.988 0.945 0.970 0.1121 0.831 0.424
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
#> GSM254629 2 0.9833 0.3693 0.424 0.576
#> GSM254648 2 0.9833 0.3693 0.424 0.576
#> GSM254694 1 0.9248 0.4625 0.660 0.340
#> GSM254701 1 0.9248 0.4625 0.660 0.340
#> GSM254728 1 0.9248 0.4625 0.660 0.340
#> GSM254726 1 0.9248 0.4625 0.660 0.340
#> GSM254639 1 0.9323 0.4639 0.652 0.348
#> GSM254652 2 0.9833 0.3693 0.424 0.576
#> GSM254700 1 0.9323 0.4639 0.652 0.348
#> GSM254625 1 0.9248 0.4542 0.660 0.340
#> GSM254636 1 0.9323 0.4639 0.652 0.348
#> GSM254659 1 0.9323 0.4639 0.652 0.348
#> GSM254680 2 0.9833 0.3693 0.424 0.576
#> GSM254686 1 0.9323 0.4639 0.652 0.348
#> GSM254718 1 0.9323 0.4639 0.652 0.348
#> GSM254674 1 0.9323 0.4639 0.652 0.348
#> GSM254668 2 0.9881 0.3603 0.436 0.564
#> GSM254697 1 0.9358 0.4535 0.648 0.352
#> GSM254704 2 0.9833 0.3693 0.424 0.576
#> GSM254707 2 0.9881 0.3603 0.436 0.564
#> GSM254714 2 0.9833 0.3693 0.424 0.576
#> GSM254722 1 0.9323 0.4639 0.652 0.348
#> GSM254627 2 0.9833 0.3693 0.424 0.576
#> GSM254630 2 0.9833 0.3693 0.424 0.576
#> GSM254633 2 0.9833 0.3693 0.424 0.576
#> GSM254670 1 0.9323 0.4639 0.652 0.348
#> GSM254716 1 0.9983 -0.0783 0.524 0.476
#> GSM254720 1 0.9323 0.4639 0.652 0.348
#> GSM254729 2 0.9850 0.3597 0.428 0.572
#> GSM254654 2 0.9833 0.3693 0.424 0.576
#> GSM254656 2 0.9833 0.3693 0.424 0.576
#> GSM254631 2 0.9833 0.3693 0.424 0.576
#> GSM254657 2 0.9833 0.3693 0.424 0.576
#> GSM254664 2 0.9833 0.3693 0.424 0.576
#> GSM254672 2 0.9833 0.3693 0.424 0.576
#> GSM254692 2 0.9881 0.3603 0.436 0.564
#> GSM254645 2 0.9833 0.3693 0.424 0.576
#> GSM254666 2 0.9833 0.3693 0.424 0.576
#> GSM254675 2 0.9833 0.3693 0.424 0.576
#> GSM254678 2 0.9833 0.3693 0.424 0.576
#> GSM254688 2 0.9833 0.3693 0.424 0.576
#> GSM254690 1 0.9427 0.4298 0.640 0.360
#> GSM254696 1 0.9323 0.4639 0.652 0.348
#> GSM254705 2 0.9881 0.3603 0.436 0.564
#> GSM254642 2 0.9833 0.3693 0.424 0.576
#> GSM254661 2 0.9833 0.3693 0.424 0.576
#> GSM254698 1 0.9323 0.4639 0.652 0.348
#> GSM254641 2 0.9833 0.3693 0.424 0.576
#> GSM254647 1 0.9323 0.4639 0.652 0.348
#> GSM254663 2 0.9850 0.3658 0.428 0.572
#> GSM254682 2 0.9881 0.3603 0.436 0.564
#> GSM254709 2 0.9881 0.3603 0.436 0.564
#> GSM254721 1 0.9323 0.4639 0.652 0.348
#> GSM254724 1 0.9323 0.4639 0.652 0.348
#> GSM254650 2 0.9881 0.3603 0.436 0.564
#> GSM254687 2 0.9881 0.3603 0.436 0.564
#> GSM254637 2 0.9833 0.3693 0.424 0.576
#> GSM254684 1 0.9323 0.4639 0.652 0.348
#> GSM254649 2 0.0672 0.4770 0.008 0.992
#> GSM254660 2 0.9963 -0.1501 0.464 0.536
#> GSM254693 2 0.0000 0.4819 0.000 1.000
#> GSM254695 2 0.8016 0.0916 0.244 0.756
#> GSM254702 1 0.9881 0.1915 0.564 0.436
#> GSM254643 2 0.0000 0.4819 0.000 1.000
#> GSM254727 1 0.9881 0.1915 0.564 0.436
#> GSM254640 2 0.0000 0.4819 0.000 1.000
#> GSM254626 2 0.0672 0.4770 0.008 0.992
#> GSM254635 2 0.3114 0.4075 0.056 0.944
#> GSM254653 1 0.9881 0.1915 0.564 0.436
#> GSM254658 2 0.0000 0.4819 0.000 1.000
#> GSM254681 2 0.0000 0.4819 0.000 1.000
#> GSM254719 1 0.9881 0.1915 0.564 0.436
#> GSM254673 2 1.0000 -0.1807 0.500 0.500
#> GSM254655 1 0.9881 0.1915 0.564 0.436
#> GSM254669 2 0.9522 -0.0628 0.372 0.628
#> GSM254699 1 0.9881 0.1915 0.564 0.436
#> GSM254703 2 0.0000 0.4819 0.000 1.000
#> GSM254708 2 0.0000 0.4819 0.000 1.000
#> GSM254715 2 0.0000 0.4819 0.000 1.000
#> GSM254628 2 0.0000 0.4819 0.000 1.000
#> GSM254634 2 0.0000 0.4819 0.000 1.000
#> GSM254646 2 0.0672 0.4770 0.008 0.992
#> GSM254671 1 0.9896 0.1894 0.560 0.440
#> GSM254711 2 0.9661 -0.0848 0.392 0.608
#> GSM254717 2 0.0000 0.4819 0.000 1.000
#> GSM254723 1 0.9393 0.4408 0.644 0.356
#> GSM254730 2 0.9963 -0.1501 0.464 0.536
#> GSM254731 1 0.9881 0.1915 0.564 0.436
#> GSM254632 2 0.9833 0.3693 0.424 0.576
#> GSM254662 1 0.9881 0.1915 0.564 0.436
#> GSM254677 2 0.0000 0.4819 0.000 1.000
#> GSM254665 2 0.0000 0.4819 0.000 1.000
#> GSM254691 2 0.0000 0.4819 0.000 1.000
#> GSM254644 2 0.0000 0.4819 0.000 1.000
#> GSM254667 2 0.6148 0.4215 0.152 0.848
#> GSM254676 2 0.0000 0.4819 0.000 1.000
#> GSM254679 2 0.0000 0.4819 0.000 1.000
#> GSM254689 2 0.0672 0.4770 0.008 0.992
#> GSM254706 2 0.0000 0.4819 0.000 1.000
#> GSM254712 2 0.0000 0.4819 0.000 1.000
#> GSM254713 2 0.0000 0.4819 0.000 1.000
#> GSM254683 2 0.0672 0.4770 0.008 0.992
#> GSM254710 2 0.9815 0.3661 0.420 0.580
#> GSM254725 1 0.9998 0.1534 0.508 0.492
#> GSM254651 2 0.0000 0.4819 0.000 1.000
#> GSM254638 2 0.0000 0.4819 0.000 1.000
#> GSM254685 2 0.0000 0.4819 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM254629 1 0.1585 0.869 0.964 0.008 0.028
#> GSM254648 1 0.1751 0.868 0.960 0.012 0.028
#> GSM254694 3 0.4002 0.922 0.160 0.000 0.840
#> GSM254701 3 0.4062 0.924 0.164 0.000 0.836
#> GSM254728 3 0.4062 0.924 0.164 0.000 0.836
#> GSM254726 3 0.4475 0.906 0.144 0.016 0.840
#> GSM254639 3 0.4121 0.926 0.168 0.000 0.832
#> GSM254652 1 0.6008 0.486 0.664 0.004 0.332
#> GSM254700 3 0.4399 0.931 0.188 0.000 0.812
#> GSM254625 1 0.1411 0.871 0.964 0.000 0.036
#> GSM254636 3 0.4399 0.931 0.188 0.000 0.812
#> GSM254659 3 0.4121 0.926 0.168 0.000 0.832
#> GSM254680 3 0.6045 0.648 0.380 0.000 0.620
#> GSM254686 3 0.4399 0.931 0.188 0.000 0.812
#> GSM254718 3 0.4121 0.926 0.168 0.000 0.832
#> GSM254674 3 0.4399 0.931 0.188 0.000 0.812
#> GSM254668 1 0.0747 0.876 0.984 0.000 0.016
#> GSM254697 3 0.4399 0.931 0.188 0.000 0.812
#> GSM254704 1 0.5202 0.674 0.772 0.008 0.220
#> GSM254707 1 0.0747 0.876 0.984 0.000 0.016
#> GSM254714 1 0.5502 0.658 0.744 0.008 0.248
#> GSM254722 3 0.4399 0.931 0.188 0.000 0.812
#> GSM254627 1 0.3607 0.804 0.880 0.008 0.112
#> GSM254630 1 0.0747 0.877 0.984 0.000 0.016
#> GSM254633 1 0.5335 0.654 0.760 0.008 0.232
#> GSM254670 3 0.4121 0.926 0.168 0.000 0.832
#> GSM254716 1 0.1163 0.874 0.972 0.000 0.028
#> GSM254720 3 0.4346 0.930 0.184 0.000 0.816
#> GSM254729 3 0.4861 0.907 0.192 0.008 0.800
#> GSM254654 1 0.5202 0.702 0.772 0.008 0.220
#> GSM254656 1 0.4968 0.744 0.800 0.012 0.188
#> GSM254631 1 0.1015 0.873 0.980 0.008 0.012
#> GSM254657 1 0.1711 0.869 0.960 0.008 0.032
#> GSM254664 3 0.6180 0.734 0.332 0.008 0.660
#> GSM254672 1 0.5420 0.639 0.752 0.008 0.240
#> GSM254692 1 0.0747 0.876 0.984 0.000 0.016
#> GSM254645 3 0.6641 0.381 0.448 0.008 0.544
#> GSM254666 1 0.0848 0.874 0.984 0.008 0.008
#> GSM254675 3 0.6314 0.614 0.392 0.004 0.604
#> GSM254678 1 0.5928 0.529 0.696 0.008 0.296
#> GSM254688 1 0.0424 0.876 0.992 0.000 0.008
#> GSM254690 3 0.4654 0.916 0.208 0.000 0.792
#> GSM254696 3 0.4399 0.931 0.188 0.000 0.812
#> GSM254705 1 0.0747 0.876 0.984 0.000 0.016
#> GSM254642 1 0.0424 0.876 0.992 0.000 0.008
#> GSM254661 1 0.1585 0.869 0.964 0.008 0.028
#> GSM254698 3 0.4346 0.930 0.184 0.000 0.816
#> GSM254641 1 0.0848 0.874 0.984 0.008 0.008
#> GSM254647 3 0.4399 0.931 0.188 0.000 0.812
#> GSM254663 1 0.0592 0.876 0.988 0.000 0.012
#> GSM254682 1 0.0747 0.876 0.984 0.000 0.016
#> GSM254709 1 0.0592 0.876 0.988 0.000 0.012
#> GSM254721 3 0.4399 0.931 0.188 0.000 0.812
#> GSM254724 3 0.4399 0.931 0.188 0.000 0.812
#> GSM254650 1 0.0747 0.876 0.984 0.000 0.016
#> GSM254687 1 0.0747 0.876 0.984 0.000 0.016
#> GSM254637 1 0.5502 0.638 0.744 0.008 0.248
#> GSM254684 3 0.4399 0.931 0.188 0.000 0.812
#> GSM254649 2 0.0424 0.952 0.008 0.992 0.000
#> GSM254660 2 0.4002 0.904 0.000 0.840 0.160
#> GSM254693 2 0.0424 0.952 0.008 0.992 0.000
#> GSM254695 3 0.6243 0.773 0.100 0.124 0.776
#> GSM254702 2 0.4178 0.899 0.000 0.828 0.172
#> GSM254643 2 0.0000 0.952 0.000 1.000 0.000
#> GSM254727 2 0.4178 0.899 0.000 0.828 0.172
#> GSM254640 2 0.0000 0.952 0.000 1.000 0.000
#> GSM254626 2 0.0424 0.952 0.008 0.992 0.000
#> GSM254635 2 0.0237 0.952 0.000 0.996 0.004
#> GSM254653 2 0.4178 0.899 0.000 0.828 0.172
#> GSM254658 2 0.0424 0.952 0.008 0.992 0.000
#> GSM254681 2 0.0424 0.952 0.008 0.992 0.000
#> GSM254719 2 0.4178 0.899 0.000 0.828 0.172
#> GSM254673 2 0.4178 0.899 0.000 0.828 0.172
#> GSM254655 2 0.4178 0.899 0.000 0.828 0.172
#> GSM254669 2 0.4062 0.902 0.000 0.836 0.164
#> GSM254699 2 0.4178 0.899 0.000 0.828 0.172
#> GSM254703 2 0.0000 0.952 0.000 1.000 0.000
#> GSM254708 2 0.0424 0.952 0.008 0.992 0.000
#> GSM254715 2 0.0000 0.952 0.000 1.000 0.000
#> GSM254628 2 0.0424 0.952 0.008 0.992 0.000
#> GSM254634 2 0.0000 0.952 0.000 1.000 0.000
#> GSM254646 2 0.0424 0.952 0.008 0.992 0.000
#> GSM254671 2 0.3192 0.922 0.000 0.888 0.112
#> GSM254711 2 0.2066 0.938 0.000 0.940 0.060
#> GSM254717 2 0.0424 0.952 0.008 0.992 0.000
#> GSM254723 3 0.4966 0.847 0.100 0.060 0.840
#> GSM254730 2 0.4178 0.899 0.000 0.828 0.172
#> GSM254731 2 0.4178 0.899 0.000 0.828 0.172
#> GSM254632 1 0.1163 0.870 0.972 0.000 0.028
#> GSM254662 2 0.4178 0.899 0.000 0.828 0.172
#> GSM254677 2 0.0000 0.952 0.000 1.000 0.000
#> GSM254665 2 0.0000 0.952 0.000 1.000 0.000
#> GSM254691 2 0.0000 0.952 0.000 1.000 0.000
#> GSM254644 2 0.0000 0.952 0.000 1.000 0.000
#> GSM254667 1 0.6033 0.455 0.660 0.336 0.004
#> GSM254676 2 0.0000 0.952 0.000 1.000 0.000
#> GSM254679 2 0.0000 0.952 0.000 1.000 0.000
#> GSM254689 2 0.0424 0.952 0.008 0.992 0.000
#> GSM254706 2 0.0892 0.946 0.020 0.980 0.000
#> GSM254712 2 0.0000 0.952 0.000 1.000 0.000
#> GSM254713 2 0.0000 0.952 0.000 1.000 0.000
#> GSM254683 2 0.3752 0.832 0.144 0.856 0.000
#> GSM254710 1 0.2434 0.853 0.940 0.024 0.036
#> GSM254725 2 0.2711 0.930 0.000 0.912 0.088
#> GSM254651 2 0.0424 0.952 0.008 0.992 0.000
#> GSM254638 2 0.0000 0.952 0.000 1.000 0.000
#> GSM254685 2 0.0000 0.952 0.000 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM254629 3 0.2928 0.659 0.108 0.000 0.880 0.012
#> GSM254648 3 0.4060 0.571 0.024 0.132 0.832 0.012
#> GSM254694 3 0.4608 0.764 0.004 0.000 0.692 0.304
#> GSM254701 3 0.4608 0.764 0.004 0.000 0.692 0.304
#> GSM254728 3 0.4608 0.764 0.004 0.000 0.692 0.304
#> GSM254726 3 0.4837 0.752 0.004 0.000 0.648 0.348
#> GSM254639 3 0.4431 0.765 0.000 0.000 0.696 0.304
#> GSM254652 3 0.6570 0.748 0.116 0.000 0.604 0.280
#> GSM254700 3 0.4933 0.769 0.016 0.000 0.688 0.296
#> GSM254625 1 0.4606 0.658 0.724 0.000 0.264 0.012
#> GSM254636 3 0.4933 0.769 0.016 0.000 0.688 0.296
#> GSM254659 3 0.4431 0.765 0.000 0.000 0.696 0.304
#> GSM254680 3 0.6773 0.748 0.132 0.000 0.584 0.284
#> GSM254686 3 0.4933 0.769 0.016 0.000 0.688 0.296
#> GSM254718 3 0.4431 0.765 0.000 0.000 0.696 0.304
#> GSM254674 3 0.4933 0.769 0.016 0.000 0.688 0.296
#> GSM254668 1 0.0817 0.863 0.976 0.000 0.024 0.000
#> GSM254697 3 0.4933 0.769 0.016 0.000 0.688 0.296
#> GSM254704 3 0.2976 0.663 0.120 0.000 0.872 0.008
#> GSM254707 1 0.0592 0.864 0.984 0.000 0.016 0.000
#> GSM254714 3 0.2867 0.663 0.104 0.000 0.884 0.012
#> GSM254722 3 0.4933 0.769 0.016 0.000 0.688 0.296
#> GSM254627 3 0.2976 0.663 0.120 0.000 0.872 0.008
#> GSM254630 3 0.3105 0.658 0.140 0.000 0.856 0.004
#> GSM254633 3 0.2976 0.663 0.120 0.000 0.872 0.008
#> GSM254670 3 0.4431 0.765 0.000 0.000 0.696 0.304
#> GSM254716 1 0.4420 0.670 0.748 0.000 0.240 0.012
#> GSM254720 3 0.4820 0.768 0.012 0.000 0.692 0.296
#> GSM254729 3 0.5972 0.764 0.064 0.000 0.632 0.304
#> GSM254654 3 0.3465 0.656 0.072 0.028 0.880 0.020
#> GSM254656 3 0.4576 0.555 0.036 0.132 0.812 0.020
#> GSM254631 3 0.2888 0.661 0.124 0.000 0.872 0.004
#> GSM254657 3 0.3761 0.633 0.044 0.068 0.868 0.020
#> GSM254664 3 0.6773 0.748 0.132 0.000 0.584 0.284
#> GSM254672 3 0.2976 0.663 0.120 0.000 0.872 0.008
#> GSM254692 1 0.0707 0.864 0.980 0.000 0.020 0.000
#> GSM254645 3 0.3205 0.675 0.104 0.000 0.872 0.024
#> GSM254666 3 0.3105 0.660 0.120 0.000 0.868 0.012
#> GSM254675 3 0.6729 0.749 0.128 0.000 0.588 0.284
#> GSM254678 3 0.3105 0.674 0.120 0.000 0.868 0.012
#> GSM254688 1 0.4730 0.582 0.636 0.000 0.364 0.000
#> GSM254690 3 0.5791 0.764 0.060 0.000 0.656 0.284
#> GSM254696 3 0.4933 0.769 0.016 0.000 0.688 0.296
#> GSM254705 1 0.0592 0.864 0.984 0.000 0.016 0.000
#> GSM254642 3 0.2973 0.655 0.144 0.000 0.856 0.000
#> GSM254661 3 0.2928 0.659 0.108 0.000 0.880 0.012
#> GSM254698 3 0.4933 0.769 0.016 0.000 0.688 0.296
#> GSM254641 3 0.2760 0.661 0.128 0.000 0.872 0.000
#> GSM254647 3 0.4933 0.769 0.016 0.000 0.688 0.296
#> GSM254663 1 0.3764 0.756 0.784 0.000 0.216 0.000
#> GSM254682 1 0.0592 0.864 0.984 0.000 0.016 0.000
#> GSM254709 1 0.0592 0.864 0.984 0.000 0.016 0.000
#> GSM254721 3 0.4933 0.769 0.016 0.000 0.688 0.296
#> GSM254724 3 0.4933 0.769 0.016 0.000 0.688 0.296
#> GSM254650 1 0.0592 0.864 0.984 0.000 0.016 0.000
#> GSM254687 1 0.0592 0.864 0.984 0.000 0.016 0.000
#> GSM254637 3 0.2647 0.667 0.120 0.000 0.880 0.000
#> GSM254684 3 0.4933 0.769 0.016 0.000 0.688 0.296
#> GSM254649 2 0.0000 0.911 0.000 1.000 0.000 0.000
#> GSM254660 4 0.4585 0.879 0.000 0.332 0.000 0.668
#> GSM254693 2 0.0000 0.911 0.000 1.000 0.000 0.000
#> GSM254695 4 0.7253 -0.579 0.000 0.144 0.428 0.428
#> GSM254702 4 0.4585 0.879 0.000 0.332 0.000 0.668
#> GSM254643 2 0.0000 0.911 0.000 1.000 0.000 0.000
#> GSM254727 4 0.4585 0.879 0.000 0.332 0.000 0.668
#> GSM254640 2 0.0469 0.908 0.012 0.988 0.000 0.000
#> GSM254626 2 0.0000 0.911 0.000 1.000 0.000 0.000
#> GSM254635 2 0.1388 0.893 0.012 0.960 0.000 0.028
#> GSM254653 4 0.4585 0.879 0.000 0.332 0.000 0.668
#> GSM254658 2 0.0000 0.911 0.000 1.000 0.000 0.000
#> GSM254681 2 0.0000 0.911 0.000 1.000 0.000 0.000
#> GSM254719 4 0.4585 0.879 0.000 0.332 0.000 0.668
#> GSM254673 4 0.4585 0.879 0.000 0.332 0.000 0.668
#> GSM254655 4 0.4585 0.879 0.000 0.332 0.000 0.668
#> GSM254669 4 0.4624 0.871 0.000 0.340 0.000 0.660
#> GSM254699 4 0.4585 0.879 0.000 0.332 0.000 0.668
#> GSM254703 2 0.0336 0.909 0.008 0.992 0.000 0.000
#> GSM254708 2 0.0000 0.911 0.000 1.000 0.000 0.000
#> GSM254715 2 0.1174 0.899 0.012 0.968 0.000 0.020
#> GSM254628 2 0.0000 0.911 0.000 1.000 0.000 0.000
#> GSM254634 2 0.0937 0.905 0.012 0.976 0.000 0.012
#> GSM254646 2 0.0000 0.911 0.000 1.000 0.000 0.000
#> GSM254671 4 0.4948 0.713 0.000 0.440 0.000 0.560
#> GSM254711 2 0.4985 -0.530 0.000 0.532 0.000 0.468
#> GSM254717 2 0.0000 0.911 0.000 1.000 0.000 0.000
#> GSM254723 3 0.6967 0.629 0.004 0.108 0.532 0.356
#> GSM254730 4 0.4585 0.879 0.000 0.332 0.000 0.668
#> GSM254731 4 0.4585 0.879 0.000 0.332 0.000 0.668
#> GSM254632 3 0.3134 0.659 0.100 0.008 0.880 0.012
#> GSM254662 4 0.4585 0.879 0.000 0.332 0.000 0.668
#> GSM254677 2 0.0937 0.905 0.012 0.976 0.000 0.012
#> GSM254665 2 0.0000 0.911 0.000 1.000 0.000 0.000
#> GSM254691 2 0.0000 0.911 0.000 1.000 0.000 0.000
#> GSM254644 2 0.0937 0.905 0.012 0.976 0.000 0.012
#> GSM254667 2 0.5537 0.306 0.016 0.588 0.392 0.004
#> GSM254676 2 0.0000 0.911 0.000 1.000 0.000 0.000
#> GSM254679 2 0.1059 0.903 0.012 0.972 0.000 0.016
#> GSM254689 2 0.3764 0.617 0.216 0.784 0.000 0.000
#> GSM254706 2 0.0000 0.911 0.000 1.000 0.000 0.000
#> GSM254712 2 0.0937 0.905 0.012 0.976 0.000 0.012
#> GSM254713 2 0.0937 0.905 0.012 0.976 0.000 0.012
#> GSM254683 2 0.4795 0.498 0.292 0.696 0.000 0.012
#> GSM254710 1 0.6486 0.597 0.644 0.088 0.256 0.012
#> GSM254725 4 0.4992 0.638 0.000 0.476 0.000 0.524
#> GSM254651 2 0.0000 0.911 0.000 1.000 0.000 0.000
#> GSM254638 2 0.1471 0.891 0.012 0.960 0.004 0.024
#> GSM254685 2 0.0937 0.905 0.012 0.976 0.000 0.012
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM254629 3 0.0000 0.663 0.000 0.000 1.000 0.000 0.000
#> GSM254648 3 0.1851 0.589 0.000 0.000 0.912 0.088 0.000
#> GSM254694 3 0.3534 0.415 0.256 0.000 0.744 0.000 0.000
#> GSM254701 3 0.3534 0.415 0.256 0.000 0.744 0.000 0.000
#> GSM254728 3 0.3534 0.415 0.256 0.000 0.744 0.000 0.000
#> GSM254726 3 0.6060 0.399 0.136 0.116 0.676 0.072 0.000
#> GSM254639 3 0.3508 0.418 0.252 0.000 0.748 0.000 0.000
#> GSM254652 3 0.2790 0.629 0.068 0.000 0.880 0.000 0.052
#> GSM254700 1 0.4735 0.752 0.524 0.000 0.460 0.000 0.016
#> GSM254625 3 0.4113 0.494 0.028 0.000 0.740 0.000 0.232
#> GSM254636 1 0.4735 0.752 0.524 0.000 0.460 0.000 0.016
#> GSM254659 3 0.3662 0.414 0.252 0.000 0.744 0.000 0.004
#> GSM254680 3 0.6233 -0.448 0.396 0.000 0.460 0.000 0.144
#> GSM254686 1 0.4735 0.752 0.524 0.000 0.460 0.000 0.016
#> GSM254718 3 0.3534 0.415 0.256 0.000 0.744 0.000 0.000
#> GSM254674 1 0.4735 0.752 0.524 0.000 0.460 0.000 0.016
#> GSM254668 5 0.0324 0.955 0.004 0.000 0.004 0.000 0.992
#> GSM254697 1 0.4735 0.752 0.524 0.000 0.460 0.000 0.016
#> GSM254704 3 0.2236 0.640 0.068 0.000 0.908 0.000 0.024
#> GSM254707 5 0.0000 0.958 0.000 0.000 0.000 0.000 1.000
#> GSM254714 3 0.0000 0.663 0.000 0.000 1.000 0.000 0.000
#> GSM254722 1 0.4735 0.752 0.524 0.000 0.460 0.000 0.016
#> GSM254627 3 0.2236 0.640 0.068 0.000 0.908 0.000 0.024
#> GSM254630 3 0.2790 0.629 0.068 0.000 0.880 0.000 0.052
#> GSM254633 3 0.2236 0.640 0.068 0.000 0.908 0.000 0.024
#> GSM254670 3 0.3534 0.415 0.256 0.000 0.744 0.000 0.000
#> GSM254716 3 0.4141 0.491 0.028 0.000 0.736 0.000 0.236
#> GSM254720 3 0.3861 0.384 0.264 0.000 0.728 0.000 0.008
#> GSM254729 3 0.1908 0.626 0.092 0.000 0.908 0.000 0.000
#> GSM254654 3 0.0000 0.663 0.000 0.000 1.000 0.000 0.000
#> GSM254656 3 0.0510 0.660 0.000 0.000 0.984 0.016 0.000
#> GSM254631 3 0.2236 0.640 0.068 0.000 0.908 0.000 0.024
#> GSM254657 3 0.0000 0.663 0.000 0.000 1.000 0.000 0.000
#> GSM254664 3 0.5598 -0.442 0.400 0.000 0.524 0.000 0.076
#> GSM254672 3 0.2144 0.642 0.068 0.000 0.912 0.000 0.020
#> GSM254692 5 0.1544 0.917 0.068 0.000 0.000 0.000 0.932
#> GSM254645 3 0.0000 0.663 0.000 0.000 1.000 0.000 0.000
#> GSM254666 3 0.2079 0.644 0.064 0.000 0.916 0.000 0.020
#> GSM254675 3 0.5309 0.281 0.240 0.000 0.656 0.000 0.104
#> GSM254678 3 0.2300 0.640 0.072 0.000 0.904 0.000 0.024
#> GSM254688 5 0.2927 0.881 0.068 0.000 0.060 0.000 0.872
#> GSM254690 1 0.5042 0.716 0.508 0.000 0.460 0.000 0.032
#> GSM254696 1 0.4747 0.658 0.496 0.000 0.488 0.000 0.016
#> GSM254705 5 0.0162 0.957 0.004 0.000 0.000 0.000 0.996
#> GSM254642 3 0.5447 0.190 0.072 0.000 0.572 0.000 0.356
#> GSM254661 3 0.0000 0.663 0.000 0.000 1.000 0.000 0.000
#> GSM254698 1 0.4735 0.752 0.524 0.000 0.460 0.000 0.016
#> GSM254641 3 0.2325 0.639 0.068 0.000 0.904 0.000 0.028
#> GSM254647 1 0.4735 0.752 0.524 0.000 0.460 0.000 0.016
#> GSM254663 5 0.2859 0.884 0.068 0.000 0.056 0.000 0.876
#> GSM254682 5 0.0000 0.958 0.000 0.000 0.000 0.000 1.000
#> GSM254709 5 0.0000 0.958 0.000 0.000 0.000 0.000 1.000
#> GSM254721 1 0.4735 0.752 0.524 0.000 0.460 0.000 0.016
#> GSM254724 1 0.4735 0.752 0.524 0.000 0.460 0.000 0.016
#> GSM254650 5 0.0000 0.958 0.000 0.000 0.000 0.000 1.000
#> GSM254687 5 0.0000 0.958 0.000 0.000 0.000 0.000 1.000
#> GSM254637 3 0.2236 0.640 0.068 0.000 0.908 0.000 0.024
#> GSM254684 3 0.4696 -0.395 0.428 0.000 0.556 0.000 0.016
#> GSM254649 4 0.5296 0.738 0.468 0.048 0.000 0.484 0.000
#> GSM254660 2 0.0290 0.880 0.000 0.992 0.000 0.008 0.000
#> GSM254693 4 0.5296 0.738 0.468 0.048 0.000 0.484 0.000
#> GSM254695 4 0.7801 -0.122 0.112 0.252 0.176 0.460 0.000
#> GSM254702 2 0.0000 0.884 0.000 1.000 0.000 0.000 0.000
#> GSM254643 4 0.5296 0.738 0.468 0.048 0.000 0.484 0.000
#> GSM254727 2 0.0000 0.884 0.000 1.000 0.000 0.000 0.000
#> GSM254640 4 0.1357 0.658 0.004 0.048 0.000 0.948 0.000
#> GSM254626 4 0.5296 0.738 0.468 0.048 0.000 0.484 0.000
#> GSM254635 4 0.1197 0.656 0.000 0.048 0.000 0.952 0.000
#> GSM254653 2 0.0000 0.884 0.000 1.000 0.000 0.000 0.000
#> GSM254658 4 0.5296 0.738 0.468 0.048 0.000 0.484 0.000
#> GSM254681 4 0.4294 0.741 0.468 0.000 0.000 0.532 0.000
#> GSM254719 2 0.0000 0.884 0.000 1.000 0.000 0.000 0.000
#> GSM254673 2 0.0000 0.884 0.000 1.000 0.000 0.000 0.000
#> GSM254655 2 0.0000 0.884 0.000 1.000 0.000 0.000 0.000
#> GSM254669 2 0.0404 0.877 0.000 0.988 0.000 0.012 0.000
#> GSM254699 2 0.0000 0.884 0.000 1.000 0.000 0.000 0.000
#> GSM254703 4 0.2230 0.698 0.116 0.000 0.000 0.884 0.000
#> GSM254708 4 0.4126 0.744 0.380 0.000 0.000 0.620 0.000
#> GSM254715 4 0.1197 0.656 0.000 0.048 0.000 0.952 0.000
#> GSM254628 4 0.5296 0.738 0.468 0.048 0.000 0.484 0.000
#> GSM254634 4 0.0000 0.667 0.000 0.000 0.000 1.000 0.000
#> GSM254646 4 0.4294 0.741 0.468 0.000 0.000 0.532 0.000
#> GSM254671 2 0.4268 0.427 0.000 0.556 0.000 0.444 0.000
#> GSM254711 2 0.4297 0.374 0.000 0.528 0.000 0.472 0.000
#> GSM254717 4 0.4440 0.741 0.468 0.004 0.000 0.528 0.000
#> GSM254723 3 0.6321 0.376 0.136 0.116 0.656 0.092 0.000
#> GSM254730 2 0.0000 0.884 0.000 1.000 0.000 0.000 0.000
#> GSM254731 2 0.0000 0.884 0.000 1.000 0.000 0.000 0.000
#> GSM254632 3 0.0290 0.662 0.000 0.000 0.992 0.008 0.000
#> GSM254662 2 0.0000 0.884 0.000 1.000 0.000 0.000 0.000
#> GSM254677 4 0.0000 0.667 0.000 0.000 0.000 1.000 0.000
#> GSM254665 4 0.4294 0.741 0.468 0.000 0.000 0.532 0.000
#> GSM254691 4 0.4294 0.741 0.468 0.000 0.000 0.532 0.000
#> GSM254644 4 0.1197 0.656 0.000 0.048 0.000 0.952 0.000
#> GSM254667 4 0.4017 0.679 0.148 0.000 0.064 0.788 0.000
#> GSM254676 4 0.4138 0.744 0.384 0.000 0.000 0.616 0.000
#> GSM254679 4 0.1197 0.656 0.000 0.048 0.000 0.952 0.000
#> GSM254689 4 0.4294 0.741 0.468 0.000 0.000 0.532 0.000
#> GSM254706 4 0.4294 0.741 0.468 0.000 0.000 0.532 0.000
#> GSM254712 4 0.1043 0.659 0.000 0.040 0.000 0.960 0.000
#> GSM254713 4 0.1197 0.656 0.000 0.048 0.000 0.952 0.000
#> GSM254683 1 0.5693 -0.768 0.468 0.080 0.000 0.452 0.000
#> GSM254710 3 0.5458 0.420 0.016 0.000 0.672 0.084 0.228
#> GSM254725 2 0.4291 0.389 0.000 0.536 0.000 0.464 0.000
#> GSM254651 4 0.4294 0.741 0.468 0.000 0.000 0.532 0.000
#> GSM254638 4 0.0000 0.667 0.000 0.000 0.000 1.000 0.000
#> GSM254685 4 0.1197 0.656 0.000 0.048 0.000 0.952 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM254629 3 0.0363 0.950 0.012 0.000 0.988 0.000 0.000 0.000
#> GSM254648 3 0.0363 0.950 0.012 0.000 0.988 0.000 0.000 0.000
#> GSM254694 1 0.1075 0.940 0.952 0.000 0.048 0.000 0.000 0.000
#> GSM254701 1 0.1075 0.940 0.952 0.000 0.048 0.000 0.000 0.000
#> GSM254728 1 0.1075 0.940 0.952 0.000 0.048 0.000 0.000 0.000
#> GSM254726 6 0.2263 0.893 0.056 0.000 0.048 0.000 0.000 0.896
#> GSM254639 1 0.1075 0.940 0.952 0.000 0.048 0.000 0.000 0.000
#> GSM254652 3 0.1444 0.917 0.072 0.000 0.928 0.000 0.000 0.000
#> GSM254700 1 0.0000 0.959 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM254625 5 0.1434 0.931 0.012 0.000 0.048 0.000 0.940 0.000
#> GSM254636 1 0.0000 0.959 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM254659 1 0.1075 0.940 0.952 0.000 0.048 0.000 0.000 0.000
#> GSM254680 1 0.1957 0.862 0.888 0.000 0.000 0.000 0.112 0.000
#> GSM254686 1 0.0000 0.959 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM254718 1 0.1075 0.940 0.952 0.000 0.048 0.000 0.000 0.000
#> GSM254674 1 0.0000 0.959 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM254668 5 0.0000 0.981 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM254697 1 0.0000 0.959 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM254704 3 0.1267 0.943 0.060 0.000 0.940 0.000 0.000 0.000
#> GSM254707 5 0.0000 0.981 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM254714 3 0.0363 0.950 0.012 0.000 0.988 0.000 0.000 0.000
#> GSM254722 1 0.0000 0.959 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM254627 3 0.1267 0.943 0.060 0.000 0.940 0.000 0.000 0.000
#> GSM254630 3 0.3356 0.823 0.052 0.000 0.808 0.000 0.140 0.000
#> GSM254633 3 0.1267 0.943 0.060 0.000 0.940 0.000 0.000 0.000
#> GSM254670 1 0.1075 0.940 0.952 0.000 0.048 0.000 0.000 0.000
#> GSM254716 5 0.1434 0.931 0.012 0.000 0.048 0.000 0.940 0.000
#> GSM254720 1 0.0000 0.959 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM254729 3 0.2378 0.828 0.152 0.000 0.848 0.000 0.000 0.000
#> GSM254654 3 0.0363 0.950 0.012 0.000 0.988 0.000 0.000 0.000
#> GSM254656 3 0.0363 0.950 0.012 0.000 0.988 0.000 0.000 0.000
#> GSM254631 3 0.1267 0.943 0.060 0.000 0.940 0.000 0.000 0.000
#> GSM254657 3 0.0363 0.950 0.012 0.000 0.988 0.000 0.000 0.000
#> GSM254664 1 0.1714 0.882 0.908 0.000 0.092 0.000 0.000 0.000
#> GSM254672 3 0.1267 0.943 0.060 0.000 0.940 0.000 0.000 0.000
#> GSM254692 5 0.0000 0.981 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM254645 3 0.0363 0.950 0.012 0.000 0.988 0.000 0.000 0.000
#> GSM254666 3 0.0363 0.950 0.012 0.000 0.988 0.000 0.000 0.000
#> GSM254675 1 0.3244 0.604 0.732 0.000 0.268 0.000 0.000 0.000
#> GSM254678 3 0.2300 0.877 0.144 0.000 0.856 0.000 0.000 0.000
#> GSM254688 5 0.0000 0.981 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM254690 1 0.0000 0.959 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM254696 1 0.0000 0.959 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM254705 5 0.0000 0.981 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM254642 5 0.1267 0.924 0.060 0.000 0.000 0.000 0.940 0.000
#> GSM254661 3 0.0363 0.950 0.012 0.000 0.988 0.000 0.000 0.000
#> GSM254698 1 0.0000 0.959 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM254641 3 0.1267 0.943 0.060 0.000 0.940 0.000 0.000 0.000
#> GSM254647 1 0.0000 0.959 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM254663 5 0.0000 0.981 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM254682 5 0.0000 0.981 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM254709 5 0.0000 0.981 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM254721 1 0.0000 0.959 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM254724 1 0.0000 0.959 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM254650 5 0.0000 0.981 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM254687 5 0.0000 0.981 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM254637 3 0.1267 0.943 0.060 0.000 0.940 0.000 0.000 0.000
#> GSM254684 1 0.0000 0.959 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM254649 2 0.0146 0.964 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM254660 6 0.0000 0.975 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM254693 2 0.0000 0.964 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM254695 6 0.1461 0.942 0.000 0.000 0.044 0.016 0.000 0.940
#> GSM254702 6 0.0146 0.974 0.000 0.000 0.000 0.004 0.000 0.996
#> GSM254643 2 0.0260 0.961 0.000 0.992 0.000 0.008 0.000 0.000
#> GSM254727 6 0.0000 0.975 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM254640 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM254626 2 0.0146 0.964 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM254635 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM254653 6 0.0000 0.975 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM254658 2 0.0000 0.964 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM254681 2 0.0146 0.964 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM254719 6 0.0000 0.975 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM254673 6 0.0000 0.975 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM254655 6 0.0000 0.975 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM254669 6 0.0000 0.975 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM254699 6 0.0000 0.975 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM254703 2 0.1387 0.908 0.000 0.932 0.000 0.068 0.000 0.000
#> GSM254708 2 0.0260 0.963 0.000 0.992 0.008 0.000 0.000 0.000
#> GSM254715 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM254628 2 0.0146 0.964 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM254634 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM254646 2 0.0146 0.964 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM254671 6 0.1267 0.943 0.000 0.000 0.000 0.060 0.000 0.940
#> GSM254711 6 0.1267 0.943 0.000 0.000 0.000 0.060 0.000 0.940
#> GSM254717 2 0.0260 0.963 0.000 0.992 0.008 0.000 0.000 0.000
#> GSM254723 6 0.1434 0.932 0.012 0.000 0.048 0.000 0.000 0.940
#> GSM254730 6 0.0000 0.975 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM254731 6 0.0146 0.974 0.000 0.000 0.000 0.004 0.000 0.996
#> GSM254632 3 0.0820 0.943 0.012 0.016 0.972 0.000 0.000 0.000
#> GSM254662 6 0.0000 0.975 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM254677 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM254665 2 0.0260 0.963 0.000 0.992 0.008 0.000 0.000 0.000
#> GSM254691 2 0.0260 0.963 0.000 0.992 0.008 0.000 0.000 0.000
#> GSM254644 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM254667 2 0.1434 0.915 0.000 0.940 0.048 0.012 0.000 0.000
#> GSM254676 2 0.0260 0.963 0.000 0.992 0.008 0.000 0.000 0.000
#> GSM254679 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM254689 2 0.0146 0.964 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM254706 2 0.0260 0.963 0.000 0.992 0.008 0.000 0.000 0.000
#> GSM254712 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM254713 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM254683 2 0.0146 0.964 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM254710 2 0.5050 0.127 0.012 0.512 0.048 0.000 0.428 0.000
#> GSM254725 6 0.1267 0.943 0.000 0.000 0.000 0.060 0.000 0.940
#> GSM254651 2 0.0260 0.963 0.000 0.992 0.008 0.000 0.000 0.000
#> GSM254638 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM254685 4 0.0000 1.000 0.000 0.000 0.000 1.000 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) individual(p) disease.state(p) gender(p) other(p) k
#> ATC:mclust 0 NA NA NA NA NA 2
#> ATC:mclust 104 4.76e-20 0.02886 0.902 0.625 0.934 3
#> ATC:mclust 103 1.07e-19 0.05571 0.855 0.583 0.785 4
#> ATC:mclust 84 1.59e-16 0.00674 0.963 0.994 0.958 5
#> ATC:mclust 106 1.18e-19 0.01011 0.851 0.835 0.652 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 21168 rows and 107 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'NMF' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.989 0.996 0.5045 0.496 0.496
#> 3 3 0.887 0.898 0.955 0.3176 0.759 0.549
#> 4 4 0.586 0.563 0.765 0.1144 0.794 0.489
#> 5 5 0.715 0.690 0.841 0.0469 0.888 0.632
#> 6 6 0.643 0.446 0.683 0.0413 0.891 0.587
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 2
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM254629 1 0.0000 1.000 1.000 0.000
#> GSM254648 2 0.0000 0.991 0.000 1.000
#> GSM254694 1 0.0000 1.000 1.000 0.000
#> GSM254701 1 0.0000 1.000 1.000 0.000
#> GSM254728 1 0.0000 1.000 1.000 0.000
#> GSM254726 2 0.1843 0.965 0.028 0.972
#> GSM254639 1 0.0000 1.000 1.000 0.000
#> GSM254652 1 0.0000 1.000 1.000 0.000
#> GSM254700 1 0.0000 1.000 1.000 0.000
#> GSM254625 1 0.0000 1.000 1.000 0.000
#> GSM254636 1 0.0000 1.000 1.000 0.000
#> GSM254659 1 0.0000 1.000 1.000 0.000
#> GSM254680 1 0.0000 1.000 1.000 0.000
#> GSM254686 1 0.0000 1.000 1.000 0.000
#> GSM254718 1 0.0000 1.000 1.000 0.000
#> GSM254674 1 0.0000 1.000 1.000 0.000
#> GSM254668 1 0.0000 1.000 1.000 0.000
#> GSM254697 1 0.0000 1.000 1.000 0.000
#> GSM254704 1 0.0000 1.000 1.000 0.000
#> GSM254707 1 0.0000 1.000 1.000 0.000
#> GSM254714 1 0.0000 1.000 1.000 0.000
#> GSM254722 1 0.0000 1.000 1.000 0.000
#> GSM254627 1 0.0000 1.000 1.000 0.000
#> GSM254630 1 0.0000 1.000 1.000 0.000
#> GSM254633 1 0.0000 1.000 1.000 0.000
#> GSM254670 1 0.0000 1.000 1.000 0.000
#> GSM254716 1 0.0000 1.000 1.000 0.000
#> GSM254720 1 0.0000 1.000 1.000 0.000
#> GSM254729 1 0.0000 1.000 1.000 0.000
#> GSM254654 1 0.0000 1.000 1.000 0.000
#> GSM254656 2 0.0376 0.988 0.004 0.996
#> GSM254631 1 0.0000 1.000 1.000 0.000
#> GSM254657 1 0.1414 0.979 0.980 0.020
#> GSM254664 1 0.0000 1.000 1.000 0.000
#> GSM254672 1 0.0000 1.000 1.000 0.000
#> GSM254692 1 0.0000 1.000 1.000 0.000
#> GSM254645 1 0.0000 1.000 1.000 0.000
#> GSM254666 1 0.0000 1.000 1.000 0.000
#> GSM254675 1 0.0000 1.000 1.000 0.000
#> GSM254678 1 0.0000 1.000 1.000 0.000
#> GSM254688 1 0.0000 1.000 1.000 0.000
#> GSM254690 1 0.0000 1.000 1.000 0.000
#> GSM254696 1 0.0000 1.000 1.000 0.000
#> GSM254705 1 0.0000 1.000 1.000 0.000
#> GSM254642 1 0.0000 1.000 1.000 0.000
#> GSM254661 1 0.0000 1.000 1.000 0.000
#> GSM254698 1 0.0000 1.000 1.000 0.000
#> GSM254641 1 0.0000 1.000 1.000 0.000
#> GSM254647 1 0.0000 1.000 1.000 0.000
#> GSM254663 1 0.0000 1.000 1.000 0.000
#> GSM254682 1 0.0000 1.000 1.000 0.000
#> GSM254709 1 0.0000 1.000 1.000 0.000
#> GSM254721 1 0.0000 1.000 1.000 0.000
#> GSM254724 1 0.0000 1.000 1.000 0.000
#> GSM254650 1 0.0000 1.000 1.000 0.000
#> GSM254687 1 0.0000 1.000 1.000 0.000
#> GSM254637 1 0.0000 1.000 1.000 0.000
#> GSM254684 1 0.0000 1.000 1.000 0.000
#> GSM254649 2 0.0000 0.991 0.000 1.000
#> GSM254660 2 0.0000 0.991 0.000 1.000
#> GSM254693 2 0.0000 0.991 0.000 1.000
#> GSM254695 2 0.0000 0.991 0.000 1.000
#> GSM254702 2 0.0000 0.991 0.000 1.000
#> GSM254643 2 0.0000 0.991 0.000 1.000
#> GSM254727 2 0.0000 0.991 0.000 1.000
#> GSM254640 2 0.0000 0.991 0.000 1.000
#> GSM254626 2 0.0000 0.991 0.000 1.000
#> GSM254635 2 0.0000 0.991 0.000 1.000
#> GSM254653 2 0.0000 0.991 0.000 1.000
#> GSM254658 2 0.0000 0.991 0.000 1.000
#> GSM254681 2 0.0000 0.991 0.000 1.000
#> GSM254719 2 0.0000 0.991 0.000 1.000
#> GSM254673 2 0.0000 0.991 0.000 1.000
#> GSM254655 2 0.0000 0.991 0.000 1.000
#> GSM254669 2 0.0000 0.991 0.000 1.000
#> GSM254699 2 0.0000 0.991 0.000 1.000
#> GSM254703 2 0.0000 0.991 0.000 1.000
#> GSM254708 2 0.0000 0.991 0.000 1.000
#> GSM254715 2 0.0000 0.991 0.000 1.000
#> GSM254628 2 0.0000 0.991 0.000 1.000
#> GSM254634 2 0.0000 0.991 0.000 1.000
#> GSM254646 2 0.0000 0.991 0.000 1.000
#> GSM254671 2 0.0000 0.991 0.000 1.000
#> GSM254711 2 0.0000 0.991 0.000 1.000
#> GSM254717 2 0.0000 0.991 0.000 1.000
#> GSM254723 2 0.0000 0.991 0.000 1.000
#> GSM254730 2 0.0000 0.991 0.000 1.000
#> GSM254731 2 0.0000 0.991 0.000 1.000
#> GSM254632 2 0.9427 0.442 0.360 0.640
#> GSM254662 2 0.0000 0.991 0.000 1.000
#> GSM254677 2 0.0000 0.991 0.000 1.000
#> GSM254665 2 0.0000 0.991 0.000 1.000
#> GSM254691 2 0.0000 0.991 0.000 1.000
#> GSM254644 2 0.0000 0.991 0.000 1.000
#> GSM254667 2 0.0000 0.991 0.000 1.000
#> GSM254676 2 0.0000 0.991 0.000 1.000
#> GSM254679 2 0.0000 0.991 0.000 1.000
#> GSM254689 2 0.0000 0.991 0.000 1.000
#> GSM254706 2 0.0000 0.991 0.000 1.000
#> GSM254712 2 0.0000 0.991 0.000 1.000
#> GSM254713 2 0.0000 0.991 0.000 1.000
#> GSM254683 2 0.0000 0.991 0.000 1.000
#> GSM254710 2 0.3274 0.932 0.060 0.940
#> GSM254725 2 0.0000 0.991 0.000 1.000
#> GSM254651 2 0.0000 0.991 0.000 1.000
#> GSM254638 2 0.0000 0.991 0.000 1.000
#> GSM254685 2 0.0000 0.991 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM254629 1 0.0237 0.977 0.996 0.000 0.004
#> GSM254648 3 0.5591 0.575 0.000 0.304 0.696
#> GSM254694 3 0.0237 0.903 0.004 0.000 0.996
#> GSM254701 1 0.6126 0.270 0.600 0.000 0.400
#> GSM254728 1 0.0000 0.978 1.000 0.000 0.000
#> GSM254726 2 0.0424 0.961 0.008 0.992 0.000
#> GSM254639 3 0.0000 0.903 0.000 0.000 1.000
#> GSM254652 1 0.0237 0.977 0.996 0.000 0.004
#> GSM254700 1 0.0237 0.977 0.996 0.000 0.004
#> GSM254625 1 0.0000 0.978 1.000 0.000 0.000
#> GSM254636 3 0.1163 0.894 0.028 0.000 0.972
#> GSM254659 3 0.5835 0.503 0.340 0.000 0.660
#> GSM254680 1 0.0237 0.977 0.996 0.000 0.004
#> GSM254686 1 0.0000 0.978 1.000 0.000 0.000
#> GSM254718 3 0.0000 0.903 0.000 0.000 1.000
#> GSM254674 1 0.0000 0.978 1.000 0.000 0.000
#> GSM254668 1 0.0000 0.978 1.000 0.000 0.000
#> GSM254697 1 0.0592 0.972 0.988 0.000 0.012
#> GSM254704 3 0.0000 0.903 0.000 0.000 1.000
#> GSM254707 1 0.0000 0.978 1.000 0.000 0.000
#> GSM254714 3 0.0237 0.902 0.004 0.000 0.996
#> GSM254722 1 0.3816 0.813 0.852 0.000 0.148
#> GSM254627 1 0.1163 0.958 0.972 0.000 0.028
#> GSM254630 1 0.0000 0.978 1.000 0.000 0.000
#> GSM254633 3 0.0592 0.900 0.012 0.000 0.988
#> GSM254670 3 0.0000 0.903 0.000 0.000 1.000
#> GSM254716 1 0.0000 0.978 1.000 0.000 0.000
#> GSM254720 3 0.4062 0.776 0.164 0.000 0.836
#> GSM254729 3 0.0000 0.903 0.000 0.000 1.000
#> GSM254654 3 0.0000 0.903 0.000 0.000 1.000
#> GSM254656 3 0.0000 0.903 0.000 0.000 1.000
#> GSM254631 3 0.1411 0.890 0.036 0.000 0.964
#> GSM254657 3 0.0000 0.903 0.000 0.000 1.000
#> GSM254664 1 0.1031 0.962 0.976 0.000 0.024
#> GSM254672 3 0.0000 0.903 0.000 0.000 1.000
#> GSM254692 1 0.0000 0.978 1.000 0.000 0.000
#> GSM254645 3 0.0000 0.903 0.000 0.000 1.000
#> GSM254666 1 0.0592 0.972 0.988 0.000 0.012
#> GSM254675 1 0.0424 0.975 0.992 0.000 0.008
#> GSM254678 3 0.1163 0.894 0.028 0.000 0.972
#> GSM254688 1 0.0000 0.978 1.000 0.000 0.000
#> GSM254690 1 0.0237 0.977 0.996 0.000 0.004
#> GSM254696 3 0.2448 0.862 0.076 0.000 0.924
#> GSM254705 1 0.0000 0.978 1.000 0.000 0.000
#> GSM254642 1 0.0000 0.978 1.000 0.000 0.000
#> GSM254661 3 0.6260 0.240 0.448 0.000 0.552
#> GSM254698 3 0.1163 0.894 0.028 0.000 0.972
#> GSM254641 1 0.0237 0.977 0.996 0.000 0.004
#> GSM254647 1 0.0000 0.978 1.000 0.000 0.000
#> GSM254663 1 0.0000 0.978 1.000 0.000 0.000
#> GSM254682 1 0.0000 0.978 1.000 0.000 0.000
#> GSM254709 1 0.0000 0.978 1.000 0.000 0.000
#> GSM254721 1 0.0237 0.977 0.996 0.000 0.004
#> GSM254724 1 0.0237 0.977 0.996 0.000 0.004
#> GSM254650 1 0.0000 0.978 1.000 0.000 0.000
#> GSM254687 1 0.0000 0.978 1.000 0.000 0.000
#> GSM254637 3 0.5560 0.593 0.300 0.000 0.700
#> GSM254684 3 0.0000 0.903 0.000 0.000 1.000
#> GSM254649 2 0.0000 0.966 0.000 1.000 0.000
#> GSM254660 2 0.0000 0.966 0.000 1.000 0.000
#> GSM254693 2 0.0000 0.966 0.000 1.000 0.000
#> GSM254695 2 0.5431 0.589 0.000 0.716 0.284
#> GSM254702 2 0.0000 0.966 0.000 1.000 0.000
#> GSM254643 2 0.0000 0.966 0.000 1.000 0.000
#> GSM254727 2 0.0000 0.966 0.000 1.000 0.000
#> GSM254640 2 0.0424 0.961 0.000 0.992 0.008
#> GSM254626 2 0.0000 0.966 0.000 1.000 0.000
#> GSM254635 3 0.3482 0.819 0.000 0.128 0.872
#> GSM254653 2 0.0000 0.966 0.000 1.000 0.000
#> GSM254658 2 0.0000 0.966 0.000 1.000 0.000
#> GSM254681 2 0.0237 0.963 0.004 0.996 0.000
#> GSM254719 2 0.0000 0.966 0.000 1.000 0.000
#> GSM254673 2 0.0000 0.966 0.000 1.000 0.000
#> GSM254655 2 0.0000 0.966 0.000 1.000 0.000
#> GSM254669 2 0.0000 0.966 0.000 1.000 0.000
#> GSM254699 2 0.0000 0.966 0.000 1.000 0.000
#> GSM254703 2 0.0424 0.961 0.000 0.992 0.008
#> GSM254708 2 0.0000 0.966 0.000 1.000 0.000
#> GSM254715 2 0.6192 0.242 0.000 0.580 0.420
#> GSM254628 2 0.0000 0.966 0.000 1.000 0.000
#> GSM254634 3 0.0747 0.898 0.000 0.016 0.984
#> GSM254646 2 0.0000 0.966 0.000 1.000 0.000
#> GSM254671 2 0.2878 0.877 0.000 0.904 0.096
#> GSM254711 2 0.4887 0.693 0.000 0.772 0.228
#> GSM254717 2 0.0000 0.966 0.000 1.000 0.000
#> GSM254723 2 0.0000 0.966 0.000 1.000 0.000
#> GSM254730 2 0.0000 0.966 0.000 1.000 0.000
#> GSM254731 2 0.0000 0.966 0.000 1.000 0.000
#> GSM254632 2 0.3192 0.856 0.112 0.888 0.000
#> GSM254662 2 0.0000 0.966 0.000 1.000 0.000
#> GSM254677 3 0.0237 0.902 0.000 0.004 0.996
#> GSM254665 2 0.0000 0.966 0.000 1.000 0.000
#> GSM254691 2 0.0000 0.966 0.000 1.000 0.000
#> GSM254644 2 0.1031 0.949 0.000 0.976 0.024
#> GSM254667 2 0.0000 0.966 0.000 1.000 0.000
#> GSM254676 2 0.0000 0.966 0.000 1.000 0.000
#> GSM254679 3 0.4796 0.709 0.000 0.220 0.780
#> GSM254689 2 0.0237 0.963 0.004 0.996 0.000
#> GSM254706 2 0.0000 0.966 0.000 1.000 0.000
#> GSM254712 3 0.1964 0.877 0.000 0.056 0.944
#> GSM254713 3 0.6244 0.225 0.000 0.440 0.560
#> GSM254683 2 0.0237 0.963 0.004 0.996 0.000
#> GSM254710 2 0.1860 0.921 0.052 0.948 0.000
#> GSM254725 3 0.3941 0.790 0.000 0.156 0.844
#> GSM254651 2 0.0000 0.966 0.000 1.000 0.000
#> GSM254638 3 0.1031 0.895 0.000 0.024 0.976
#> GSM254685 2 0.1529 0.935 0.000 0.960 0.040
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM254629 4 0.3306 0.5230 0.004 0.000 0.156 0.840
#> GSM254648 4 0.5163 0.0231 0.004 0.000 0.480 0.516
#> GSM254694 1 0.1489 0.6444 0.952 0.044 0.004 0.000
#> GSM254701 1 0.3684 0.5717 0.844 0.004 0.132 0.020
#> GSM254728 1 0.0524 0.6840 0.988 0.004 0.000 0.008
#> GSM254726 2 0.5184 0.5960 0.212 0.732 0.000 0.056
#> GSM254639 3 0.4193 0.6786 0.268 0.000 0.732 0.000
#> GSM254652 4 0.5050 0.3722 0.152 0.000 0.084 0.764
#> GSM254700 1 0.3123 0.7455 0.844 0.000 0.000 0.156
#> GSM254625 4 0.0707 0.5437 0.020 0.000 0.000 0.980
#> GSM254636 1 0.5025 0.3516 0.716 0.000 0.252 0.032
#> GSM254659 1 0.6121 0.6407 0.680 0.000 0.164 0.156
#> GSM254680 1 0.4193 0.7195 0.732 0.000 0.000 0.268
#> GSM254686 1 0.4250 0.7167 0.724 0.000 0.000 0.276
#> GSM254718 3 0.4978 0.5762 0.384 0.004 0.612 0.000
#> GSM254674 1 0.4331 0.7072 0.712 0.000 0.000 0.288
#> GSM254668 1 0.4981 0.4912 0.536 0.000 0.000 0.464
#> GSM254697 1 0.2530 0.7354 0.888 0.000 0.000 0.112
#> GSM254704 3 0.4406 0.6465 0.300 0.000 0.700 0.000
#> GSM254707 4 0.3610 0.3592 0.200 0.000 0.000 0.800
#> GSM254714 3 0.0188 0.7488 0.004 0.000 0.996 0.000
#> GSM254722 1 0.1637 0.7153 0.940 0.000 0.000 0.060
#> GSM254627 1 0.3494 0.7467 0.824 0.000 0.004 0.172
#> GSM254630 1 0.5000 0.4100 0.500 0.000 0.000 0.500
#> GSM254633 3 0.4406 0.6468 0.300 0.000 0.700 0.000
#> GSM254670 3 0.5028 0.5692 0.400 0.004 0.596 0.000
#> GSM254716 4 0.1211 0.5355 0.040 0.000 0.000 0.960
#> GSM254720 1 0.0376 0.6851 0.992 0.000 0.004 0.004
#> GSM254729 3 0.3528 0.7348 0.192 0.000 0.808 0.000
#> GSM254654 3 0.0000 0.7484 0.000 0.000 1.000 0.000
#> GSM254656 3 0.0188 0.7488 0.004 0.000 0.996 0.000
#> GSM254631 3 0.3448 0.7277 0.168 0.000 0.828 0.004
#> GSM254657 3 0.0188 0.7488 0.004 0.000 0.996 0.000
#> GSM254664 1 0.3528 0.7448 0.808 0.000 0.000 0.192
#> GSM254672 3 0.3975 0.6982 0.240 0.000 0.760 0.000
#> GSM254692 1 0.4977 0.4977 0.540 0.000 0.000 0.460
#> GSM254645 3 0.2469 0.7476 0.108 0.000 0.892 0.000
#> GSM254666 3 0.5310 0.2566 0.012 0.000 0.576 0.412
#> GSM254675 1 0.4072 0.7267 0.748 0.000 0.000 0.252
#> GSM254678 3 0.5075 0.5733 0.344 0.000 0.644 0.012
#> GSM254688 4 0.4916 -0.2838 0.424 0.000 0.000 0.576
#> GSM254690 1 0.4164 0.7221 0.736 0.000 0.000 0.264
#> GSM254696 1 0.0336 0.6885 0.992 0.000 0.000 0.008
#> GSM254705 1 0.4977 0.4977 0.540 0.000 0.000 0.460
#> GSM254642 1 0.4477 0.6830 0.688 0.000 0.000 0.312
#> GSM254661 4 0.5050 0.1686 0.004 0.000 0.408 0.588
#> GSM254698 1 0.0592 0.6798 0.984 0.000 0.016 0.000
#> GSM254641 1 0.5039 0.5706 0.592 0.000 0.004 0.404
#> GSM254647 1 0.3610 0.7437 0.800 0.000 0.000 0.200
#> GSM254663 1 0.4977 0.4977 0.540 0.000 0.000 0.460
#> GSM254682 4 0.4967 -0.3649 0.452 0.000 0.000 0.548
#> GSM254709 4 0.3610 0.3771 0.200 0.000 0.000 0.800
#> GSM254721 1 0.3311 0.7473 0.828 0.000 0.000 0.172
#> GSM254724 1 0.3172 0.7459 0.840 0.000 0.000 0.160
#> GSM254650 4 0.3074 0.4491 0.152 0.000 0.000 0.848
#> GSM254687 4 0.3219 0.4240 0.164 0.000 0.000 0.836
#> GSM254637 1 0.6545 0.5978 0.632 0.000 0.216 0.152
#> GSM254684 1 0.4877 -0.2421 0.592 0.000 0.408 0.000
#> GSM254649 2 0.4222 0.6451 0.000 0.728 0.000 0.272
#> GSM254660 2 0.0000 0.7746 0.000 1.000 0.000 0.000
#> GSM254693 2 0.3266 0.7302 0.000 0.832 0.000 0.168
#> GSM254695 2 0.3870 0.6477 0.208 0.788 0.004 0.000
#> GSM254702 2 0.0657 0.7710 0.012 0.984 0.004 0.000
#> GSM254643 2 0.3356 0.7255 0.000 0.824 0.000 0.176
#> GSM254727 2 0.0000 0.7746 0.000 1.000 0.000 0.000
#> GSM254640 2 0.4553 0.6787 0.000 0.780 0.180 0.040
#> GSM254626 2 0.3873 0.6868 0.000 0.772 0.000 0.228
#> GSM254635 3 0.5543 0.1055 0.020 0.424 0.556 0.000
#> GSM254653 2 0.0188 0.7746 0.000 0.996 0.000 0.004
#> GSM254658 2 0.3172 0.7348 0.000 0.840 0.000 0.160
#> GSM254681 4 0.4977 -0.1820 0.000 0.460 0.000 0.540
#> GSM254719 2 0.0000 0.7746 0.000 1.000 0.000 0.000
#> GSM254673 2 0.0921 0.7733 0.000 0.972 0.000 0.028
#> GSM254655 2 0.0000 0.7746 0.000 1.000 0.000 0.000
#> GSM254669 2 0.1302 0.7717 0.000 0.956 0.000 0.044
#> GSM254699 2 0.0188 0.7739 0.004 0.996 0.000 0.000
#> GSM254703 2 0.2124 0.7700 0.000 0.932 0.040 0.028
#> GSM254708 2 0.2760 0.7500 0.000 0.872 0.000 0.128
#> GSM254715 2 0.5126 0.2078 0.004 0.552 0.444 0.000
#> GSM254628 2 0.4356 0.6215 0.000 0.708 0.000 0.292
#> GSM254634 3 0.0524 0.7467 0.004 0.008 0.988 0.000
#> GSM254646 2 0.4925 0.3950 0.000 0.572 0.000 0.428
#> GSM254671 2 0.2799 0.7247 0.108 0.884 0.008 0.000
#> GSM254711 2 0.3820 0.7112 0.064 0.848 0.088 0.000
#> GSM254717 2 0.2647 0.7531 0.000 0.880 0.000 0.120
#> GSM254723 2 0.3945 0.6395 0.216 0.780 0.004 0.000
#> GSM254730 2 0.0000 0.7746 0.000 1.000 0.000 0.000
#> GSM254731 2 0.1661 0.7560 0.052 0.944 0.004 0.000
#> GSM254632 4 0.2216 0.5552 0.000 0.092 0.000 0.908
#> GSM254662 2 0.0000 0.7746 0.000 1.000 0.000 0.000
#> GSM254677 3 0.0336 0.7487 0.008 0.000 0.992 0.000
#> GSM254665 2 0.4730 0.5158 0.000 0.636 0.000 0.364
#> GSM254691 2 0.3975 0.6765 0.000 0.760 0.000 0.240
#> GSM254644 2 0.2401 0.7387 0.004 0.904 0.092 0.000
#> GSM254667 4 0.7849 -0.0507 0.000 0.284 0.316 0.400
#> GSM254676 2 0.2345 0.7596 0.000 0.900 0.000 0.100
#> GSM254679 2 0.5606 0.0782 0.020 0.500 0.480 0.000
#> GSM254689 4 0.4977 -0.1820 0.000 0.460 0.000 0.540
#> GSM254706 2 0.4866 0.4432 0.000 0.596 0.000 0.404
#> GSM254712 3 0.1489 0.7270 0.004 0.044 0.952 0.000
#> GSM254713 2 0.5137 0.1881 0.004 0.544 0.452 0.000
#> GSM254683 2 0.5000 0.2349 0.000 0.504 0.000 0.496
#> GSM254710 4 0.3266 0.4772 0.000 0.168 0.000 0.832
#> GSM254725 2 0.7098 0.2776 0.152 0.536 0.312 0.000
#> GSM254651 2 0.4304 0.6314 0.000 0.716 0.000 0.284
#> GSM254638 3 0.1209 0.7356 0.004 0.032 0.964 0.000
#> GSM254685 3 0.4872 0.2462 0.000 0.356 0.640 0.004
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM254629 5 0.1195 0.7475 0.000 0.000 0.012 0.028 0.960
#> GSM254648 5 0.3690 0.5624 0.000 0.000 0.012 0.224 0.764
#> GSM254694 3 0.0566 0.7873 0.012 0.000 0.984 0.004 0.000
#> GSM254701 3 0.1648 0.7872 0.020 0.000 0.940 0.000 0.040
#> GSM254728 3 0.1845 0.7883 0.056 0.000 0.928 0.000 0.016
#> GSM254726 3 0.2554 0.7505 0.000 0.036 0.892 0.000 0.072
#> GSM254639 3 0.3916 0.7403 0.056 0.000 0.804 0.136 0.004
#> GSM254652 5 0.1901 0.7325 0.012 0.000 0.056 0.004 0.928
#> GSM254700 1 0.0880 0.7858 0.968 0.000 0.032 0.000 0.000
#> GSM254625 5 0.0955 0.7508 0.028 0.000 0.004 0.000 0.968
#> GSM254636 1 0.3777 0.6038 0.784 0.000 0.192 0.020 0.004
#> GSM254659 1 0.6022 0.2672 0.572 0.000 0.336 0.044 0.048
#> GSM254680 1 0.0162 0.7958 0.996 0.000 0.000 0.000 0.004
#> GSM254686 1 0.6211 0.5320 0.548 0.000 0.204 0.000 0.248
#> GSM254718 3 0.2694 0.7889 0.068 0.000 0.892 0.032 0.008
#> GSM254674 1 0.3462 0.7368 0.792 0.000 0.012 0.000 0.196
#> GSM254668 1 0.3534 0.6851 0.744 0.000 0.000 0.000 0.256
#> GSM254697 1 0.0794 0.7864 0.972 0.000 0.028 0.000 0.000
#> GSM254704 4 0.4892 0.5769 0.276 0.000 0.040 0.676 0.008
#> GSM254707 5 0.2488 0.6958 0.124 0.000 0.004 0.000 0.872
#> GSM254714 4 0.2673 0.7327 0.008 0.000 0.072 0.892 0.028
#> GSM254722 1 0.1270 0.7747 0.948 0.000 0.052 0.000 0.000
#> GSM254627 1 0.0854 0.7897 0.976 0.000 0.012 0.004 0.008
#> GSM254630 1 0.3231 0.7340 0.800 0.000 0.000 0.004 0.196
#> GSM254633 4 0.5417 0.4496 0.372 0.000 0.048 0.572 0.008
#> GSM254670 3 0.3446 0.7695 0.108 0.000 0.840 0.048 0.004
#> GSM254716 5 0.1281 0.7495 0.032 0.000 0.012 0.000 0.956
#> GSM254720 1 0.4291 -0.0925 0.536 0.000 0.464 0.000 0.000
#> GSM254729 4 0.4694 0.5090 0.032 0.004 0.288 0.676 0.000
#> GSM254654 4 0.1522 0.7371 0.000 0.000 0.044 0.944 0.012
#> GSM254656 4 0.1043 0.7383 0.000 0.000 0.040 0.960 0.000
#> GSM254631 4 0.4438 0.6303 0.228 0.000 0.032 0.732 0.008
#> GSM254657 4 0.1082 0.7369 0.000 0.000 0.028 0.964 0.008
#> GSM254664 1 0.1124 0.7827 0.960 0.000 0.036 0.004 0.000
#> GSM254672 4 0.4674 0.6193 0.212 0.000 0.052 0.728 0.008
#> GSM254692 1 0.3177 0.7218 0.792 0.000 0.000 0.000 0.208
#> GSM254645 4 0.3635 0.7060 0.112 0.000 0.056 0.828 0.004
#> GSM254666 4 0.4597 0.5502 0.020 0.000 0.020 0.716 0.244
#> GSM254675 1 0.1117 0.7968 0.964 0.000 0.020 0.000 0.016
#> GSM254678 1 0.5364 0.0841 0.572 0.000 0.044 0.376 0.008
#> GSM254688 1 0.3816 0.6215 0.696 0.000 0.000 0.000 0.304
#> GSM254690 1 0.0290 0.7947 0.992 0.000 0.008 0.000 0.000
#> GSM254696 3 0.3579 0.7010 0.240 0.000 0.756 0.004 0.000
#> GSM254705 1 0.3210 0.7188 0.788 0.000 0.000 0.000 0.212
#> GSM254642 1 0.0671 0.7959 0.980 0.000 0.004 0.000 0.016
#> GSM254661 5 0.2124 0.7209 0.000 0.000 0.004 0.096 0.900
#> GSM254698 3 0.4251 0.5195 0.372 0.000 0.624 0.004 0.000
#> GSM254641 1 0.3289 0.7513 0.816 0.000 0.008 0.004 0.172
#> GSM254647 1 0.0404 0.7936 0.988 0.000 0.012 0.000 0.000
#> GSM254663 1 0.2966 0.7391 0.816 0.000 0.000 0.000 0.184
#> GSM254682 1 0.3796 0.6275 0.700 0.000 0.000 0.000 0.300
#> GSM254709 5 0.4305 -0.1985 0.488 0.000 0.000 0.000 0.512
#> GSM254721 1 0.0671 0.7946 0.980 0.000 0.016 0.000 0.004
#> GSM254724 1 0.0609 0.7912 0.980 0.000 0.020 0.000 0.000
#> GSM254650 1 0.3837 0.6174 0.692 0.000 0.000 0.000 0.308
#> GSM254687 5 0.3395 0.5468 0.236 0.000 0.000 0.000 0.764
#> GSM254637 1 0.1525 0.7889 0.948 0.000 0.004 0.036 0.012
#> GSM254684 3 0.4944 0.5502 0.344 0.000 0.620 0.032 0.004
#> GSM254649 2 0.1270 0.8619 0.000 0.948 0.000 0.000 0.052
#> GSM254660 2 0.0609 0.8755 0.000 0.980 0.020 0.000 0.000
#> GSM254693 2 0.0703 0.8726 0.000 0.976 0.000 0.000 0.024
#> GSM254695 2 0.4798 0.2353 0.000 0.512 0.472 0.012 0.004
#> GSM254702 2 0.2452 0.8473 0.000 0.896 0.084 0.016 0.004
#> GSM254643 2 0.1117 0.8742 0.000 0.964 0.000 0.016 0.020
#> GSM254727 2 0.1484 0.8723 0.000 0.944 0.048 0.000 0.008
#> GSM254640 2 0.2439 0.8271 0.000 0.876 0.004 0.120 0.000
#> GSM254626 2 0.0880 0.8701 0.000 0.968 0.000 0.000 0.032
#> GSM254635 2 0.6301 0.2448 0.000 0.492 0.140 0.364 0.004
#> GSM254653 2 0.0566 0.8766 0.000 0.984 0.012 0.000 0.004
#> GSM254658 2 0.0510 0.8747 0.000 0.984 0.000 0.000 0.016
#> GSM254681 5 0.3863 0.5913 0.000 0.248 0.012 0.000 0.740
#> GSM254719 2 0.0290 0.8766 0.000 0.992 0.008 0.000 0.000
#> GSM254673 2 0.0510 0.8749 0.000 0.984 0.000 0.000 0.016
#> GSM254655 2 0.0880 0.8738 0.000 0.968 0.032 0.000 0.000
#> GSM254669 2 0.0404 0.8751 0.000 0.988 0.000 0.000 0.012
#> GSM254699 2 0.1768 0.8598 0.000 0.924 0.072 0.000 0.004
#> GSM254703 2 0.2228 0.8520 0.000 0.908 0.012 0.076 0.004
#> GSM254708 2 0.0794 0.8714 0.000 0.972 0.000 0.000 0.028
#> GSM254715 2 0.4557 0.7031 0.000 0.736 0.056 0.204 0.004
#> GSM254628 2 0.1270 0.8614 0.000 0.948 0.000 0.000 0.052
#> GSM254634 4 0.1568 0.7384 0.000 0.020 0.036 0.944 0.000
#> GSM254646 2 0.4126 0.3384 0.000 0.620 0.000 0.000 0.380
#> GSM254671 2 0.4602 0.5544 0.000 0.640 0.340 0.016 0.004
#> GSM254711 2 0.3722 0.7946 0.000 0.812 0.144 0.040 0.004
#> GSM254717 2 0.0451 0.8761 0.000 0.988 0.000 0.004 0.008
#> GSM254723 3 0.1630 0.7695 0.004 0.036 0.944 0.000 0.016
#> GSM254730 2 0.1116 0.8731 0.000 0.964 0.028 0.004 0.004
#> GSM254731 2 0.2783 0.8310 0.000 0.868 0.116 0.012 0.004
#> GSM254632 5 0.2710 0.7426 0.032 0.056 0.000 0.016 0.896
#> GSM254662 2 0.0404 0.8764 0.000 0.988 0.012 0.000 0.000
#> GSM254677 4 0.2011 0.7273 0.000 0.004 0.088 0.908 0.000
#> GSM254665 2 0.3078 0.7950 0.000 0.848 0.004 0.016 0.132
#> GSM254691 2 0.1485 0.8698 0.000 0.948 0.000 0.020 0.032
#> GSM254644 2 0.2983 0.8312 0.000 0.868 0.032 0.096 0.004
#> GSM254667 4 0.5094 0.0597 0.000 0.468 0.012 0.504 0.016
#> GSM254676 2 0.0404 0.8762 0.000 0.988 0.000 0.012 0.000
#> GSM254679 2 0.5057 0.6404 0.000 0.684 0.072 0.240 0.004
#> GSM254689 5 0.3814 0.5739 0.000 0.276 0.004 0.000 0.720
#> GSM254706 2 0.2408 0.8303 0.000 0.892 0.004 0.008 0.096
#> GSM254712 4 0.1430 0.7259 0.000 0.052 0.004 0.944 0.000
#> GSM254713 2 0.4550 0.6286 0.000 0.692 0.028 0.276 0.004
#> GSM254683 5 0.4242 0.2627 0.000 0.428 0.000 0.000 0.572
#> GSM254710 5 0.1357 0.7481 0.004 0.048 0.000 0.000 0.948
#> GSM254725 3 0.4588 0.5148 0.000 0.208 0.732 0.056 0.004
#> GSM254651 2 0.1124 0.8697 0.000 0.960 0.000 0.004 0.036
#> GSM254638 4 0.2017 0.7095 0.000 0.080 0.008 0.912 0.000
#> GSM254685 4 0.4251 0.2620 0.000 0.372 0.004 0.624 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM254629 5 0.6861 0.2215 0.000 0.000 0.196 0.312 0.424 0.068
#> GSM254648 3 0.7404 0.0531 0.000 0.032 0.424 0.268 0.216 0.060
#> GSM254694 6 0.3877 0.5801 0.012 0.000 0.004 0.236 0.012 0.736
#> GSM254701 6 0.4719 0.5472 0.008 0.000 0.004 0.256 0.060 0.672
#> GSM254728 6 0.4959 0.4844 0.008 0.000 0.000 0.184 0.136 0.672
#> GSM254726 6 0.5984 0.2942 0.000 0.004 0.000 0.276 0.240 0.480
#> GSM254639 6 0.4077 0.4939 0.008 0.000 0.180 0.060 0.000 0.752
#> GSM254652 5 0.6398 0.2713 0.004 0.000 0.048 0.228 0.540 0.180
#> GSM254700 1 0.0748 0.7181 0.976 0.000 0.004 0.004 0.000 0.016
#> GSM254625 5 0.1637 0.5295 0.004 0.004 0.000 0.056 0.932 0.004
#> GSM254636 1 0.6924 0.0700 0.472 0.000 0.168 0.064 0.012 0.284
#> GSM254659 6 0.8100 0.3788 0.104 0.000 0.184 0.216 0.084 0.412
#> GSM254680 1 0.3269 0.7041 0.848 0.000 0.020 0.004 0.044 0.084
#> GSM254686 5 0.6852 0.2853 0.100 0.000 0.000 0.208 0.492 0.200
#> GSM254718 6 0.4575 0.5797 0.016 0.000 0.044 0.208 0.012 0.720
#> GSM254674 1 0.4735 0.2708 0.568 0.000 0.000 0.044 0.384 0.004
#> GSM254668 5 0.5014 0.0283 0.428 0.000 0.000 0.052 0.512 0.008
#> GSM254697 1 0.1434 0.7129 0.948 0.000 0.012 0.012 0.000 0.028
#> GSM254704 3 0.5572 0.1896 0.388 0.000 0.504 0.016 0.000 0.092
#> GSM254707 5 0.2328 0.5427 0.052 0.000 0.000 0.056 0.892 0.000
#> GSM254714 3 0.6521 0.2634 0.000 0.000 0.404 0.360 0.204 0.032
#> GSM254722 1 0.2568 0.6825 0.876 0.000 0.012 0.016 0.000 0.096
#> GSM254627 1 0.3557 0.6451 0.824 0.000 0.100 0.044 0.000 0.032
#> GSM254630 1 0.3702 0.5386 0.720 0.000 0.004 0.012 0.264 0.000
#> GSM254633 3 0.6485 0.1737 0.232 0.000 0.520 0.060 0.000 0.188
#> GSM254670 6 0.4792 0.4893 0.064 0.000 0.148 0.060 0.000 0.728
#> GSM254716 5 0.2512 0.5180 0.008 0.000 0.000 0.116 0.868 0.008
#> GSM254720 1 0.5654 -0.1086 0.492 0.000 0.024 0.084 0.000 0.400
#> GSM254729 3 0.6359 0.4033 0.028 0.000 0.484 0.252 0.000 0.236
#> GSM254654 3 0.5167 0.3631 0.000 0.000 0.564 0.360 0.016 0.060
#> GSM254656 3 0.4405 0.5379 0.000 0.000 0.688 0.240 0.000 0.072
#> GSM254631 3 0.4830 0.3619 0.196 0.000 0.704 0.044 0.000 0.056
#> GSM254657 3 0.2266 0.5345 0.000 0.000 0.880 0.108 0.000 0.012
#> GSM254664 1 0.5277 0.5015 0.676 0.000 0.124 0.012 0.016 0.172
#> GSM254672 3 0.4971 0.3587 0.260 0.000 0.656 0.032 0.000 0.052
#> GSM254692 1 0.3398 0.5415 0.740 0.000 0.000 0.008 0.252 0.000
#> GSM254645 3 0.5675 0.5007 0.136 0.000 0.644 0.160 0.000 0.060
#> GSM254666 5 0.5124 0.1240 0.008 0.000 0.444 0.060 0.488 0.000
#> GSM254675 1 0.3279 0.7045 0.860 0.000 0.016 0.048 0.048 0.028
#> GSM254678 3 0.6278 0.0365 0.396 0.000 0.408 0.024 0.000 0.172
#> GSM254688 5 0.3890 0.1474 0.400 0.000 0.000 0.004 0.596 0.000
#> GSM254690 1 0.4082 0.6821 0.780 0.000 0.012 0.004 0.120 0.084
#> GSM254696 6 0.4728 0.4902 0.180 0.000 0.052 0.040 0.004 0.724
#> GSM254705 1 0.3398 0.5436 0.740 0.000 0.000 0.008 0.252 0.000
#> GSM254642 1 0.1299 0.7161 0.952 0.000 0.004 0.004 0.036 0.004
#> GSM254661 5 0.6163 0.2436 0.000 0.000 0.312 0.184 0.484 0.020
#> GSM254698 6 0.5920 0.2916 0.356 0.000 0.068 0.060 0.000 0.516
#> GSM254641 1 0.6923 0.2709 0.468 0.000 0.176 0.060 0.284 0.012
#> GSM254647 1 0.0725 0.7192 0.976 0.000 0.000 0.000 0.012 0.012
#> GSM254663 1 0.3245 0.5735 0.764 0.000 0.000 0.008 0.228 0.000
#> GSM254682 5 0.4089 -0.0415 0.468 0.000 0.000 0.008 0.524 0.000
#> GSM254709 5 0.4844 0.3256 0.312 0.000 0.000 0.080 0.608 0.000
#> GSM254721 1 0.0972 0.7126 0.964 0.000 0.000 0.008 0.028 0.000
#> GSM254724 1 0.0405 0.7176 0.988 0.000 0.000 0.004 0.000 0.008
#> GSM254650 1 0.4039 0.3892 0.632 0.000 0.000 0.016 0.352 0.000
#> GSM254687 5 0.3780 0.4229 0.248 0.000 0.000 0.020 0.728 0.004
#> GSM254637 1 0.3962 0.6713 0.796 0.000 0.112 0.004 0.068 0.020
#> GSM254684 6 0.6451 0.3150 0.252 0.000 0.168 0.060 0.000 0.520
#> GSM254649 2 0.0622 0.7268 0.000 0.980 0.000 0.008 0.012 0.000
#> GSM254660 2 0.2454 0.5887 0.000 0.840 0.000 0.160 0.000 0.000
#> GSM254693 2 0.0000 0.7330 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM254695 4 0.6092 0.5076 0.000 0.348 0.000 0.372 0.000 0.280
#> GSM254702 2 0.3690 0.2292 0.000 0.684 0.000 0.308 0.000 0.008
#> GSM254643 2 0.0520 0.7292 0.000 0.984 0.008 0.008 0.000 0.000
#> GSM254727 2 0.2558 0.5968 0.000 0.840 0.000 0.156 0.000 0.004
#> GSM254640 2 0.5868 -0.5763 0.000 0.448 0.204 0.348 0.000 0.000
#> GSM254626 2 0.0000 0.7330 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM254635 4 0.5692 0.6286 0.000 0.260 0.216 0.524 0.000 0.000
#> GSM254653 2 0.1444 0.6908 0.000 0.928 0.000 0.072 0.000 0.000
#> GSM254658 2 0.0000 0.7330 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM254681 5 0.5189 -0.0601 0.000 0.444 0.000 0.088 0.468 0.000
#> GSM254719 2 0.1267 0.7006 0.000 0.940 0.000 0.060 0.000 0.000
#> GSM254673 2 0.0000 0.7330 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM254655 2 0.2219 0.6250 0.000 0.864 0.000 0.136 0.000 0.000
#> GSM254669 2 0.0000 0.7330 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM254699 2 0.3509 0.4148 0.000 0.744 0.000 0.240 0.000 0.016
#> GSM254703 2 0.4764 -0.3382 0.000 0.560 0.056 0.384 0.000 0.000
#> GSM254708 2 0.0000 0.7330 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM254715 4 0.5541 0.6341 0.000 0.392 0.136 0.472 0.000 0.000
#> GSM254628 2 0.1092 0.7166 0.000 0.960 0.000 0.020 0.020 0.000
#> GSM254634 3 0.4800 0.4807 0.000 0.028 0.636 0.304 0.000 0.032
#> GSM254646 2 0.3952 0.4764 0.000 0.736 0.000 0.052 0.212 0.000
#> GSM254671 4 0.5608 0.5840 0.000 0.380 0.000 0.472 0.000 0.148
#> GSM254711 4 0.4535 0.5080 0.000 0.472 0.004 0.500 0.000 0.024
#> GSM254717 2 0.0146 0.7329 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM254723 6 0.4119 0.3670 0.000 0.004 0.000 0.336 0.016 0.644
#> GSM254730 2 0.3126 0.4171 0.000 0.752 0.000 0.248 0.000 0.000
#> GSM254731 2 0.4079 0.2317 0.000 0.680 0.000 0.288 0.000 0.032
#> GSM254632 5 0.3396 0.5176 0.040 0.056 0.012 0.040 0.852 0.000
#> GSM254662 2 0.0260 0.7314 0.000 0.992 0.000 0.008 0.000 0.000
#> GSM254677 3 0.4241 0.4489 0.000 0.000 0.608 0.368 0.000 0.024
#> GSM254665 2 0.1606 0.7023 0.000 0.932 0.008 0.056 0.004 0.000
#> GSM254691 2 0.0405 0.7307 0.000 0.988 0.008 0.004 0.000 0.000
#> GSM254644 2 0.4903 -0.5661 0.000 0.476 0.060 0.464 0.000 0.000
#> GSM254667 2 0.4161 0.3715 0.000 0.716 0.240 0.036 0.004 0.004
#> GSM254676 2 0.0260 0.7317 0.000 0.992 0.000 0.008 0.000 0.000
#> GSM254679 4 0.5933 0.6822 0.000 0.316 0.184 0.492 0.000 0.008
#> GSM254689 2 0.4886 0.1402 0.000 0.508 0.000 0.060 0.432 0.000
#> GSM254706 2 0.2122 0.6683 0.000 0.900 0.000 0.024 0.076 0.000
#> GSM254712 3 0.3974 0.4376 0.000 0.024 0.680 0.296 0.000 0.000
#> GSM254713 4 0.5818 0.6653 0.000 0.340 0.196 0.464 0.000 0.000
#> GSM254683 2 0.4575 0.3021 0.000 0.600 0.000 0.048 0.352 0.000
#> GSM254710 5 0.4284 0.3268 0.000 0.256 0.000 0.056 0.688 0.000
#> GSM254725 4 0.6038 0.3687 0.000 0.116 0.040 0.524 0.000 0.320
#> GSM254651 2 0.1257 0.7111 0.000 0.952 0.000 0.020 0.028 0.000
#> GSM254638 3 0.4408 0.3835 0.000 0.044 0.636 0.320 0.000 0.000
#> GSM254685 3 0.5539 0.0374 0.000 0.180 0.548 0.272 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) individual(p) disease.state(p) gender(p) other(p) k
#> ATC:NMF 106 1.55e-21 0.4126 0.571 0.751 0.997 2
#> ATC:NMF 103 1.53e-17 0.2779 0.413 0.520 0.119 3
#> ATC:NMF 77 1.91e-12 0.0416 0.760 0.862 0.488 4
#> ATC:NMF 96 1.01e-13 0.0218 0.569 0.622 0.439 5
#> ATC:NMF 55 5.42e-10 0.0329 0.609 0.132 0.564 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