Date: 2019-12-25 21:58:04 CET, cola version: 1.3.2
Document is loading...
All available functions which can be applied to this res_list object:
res_list
#> A 'ConsensusPartitionList' object with 24 methods.
#> On a matrix with 38950 rows and 108 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] 38950 108
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 | ||
|---|---|---|---|---|---|---|
| CV:mclust | 2 | 1.000 | 0.970 | 0.973 | ** | |
| ATC:NMF | 2 | 1.000 | 0.968 | 0.987 | ** | |
| MAD:mclust | 4 | 0.946 | 0.886 | 0.938 | * | |
| ATC:skmeans | 5 | 0.926 | 0.839 | 0.935 | * | 2,3,4 |
| ATC:pam | 5 | 0.912 | 0.902 | 0.957 | * | |
| ATC:kmeans | 2 | 0.904 | 0.931 | 0.971 | * | |
| MAD:skmeans | 4 | 0.854 | 0.818 | 0.919 | ||
| CV:NMF | 2 | 0.850 | 0.916 | 0.963 | ||
| MAD:NMF | 2 | 0.842 | 0.895 | 0.957 | ||
| CV:kmeans | 2 | 0.835 | 0.902 | 0.960 | ||
| SD:skmeans | 4 | 0.814 | 0.829 | 0.911 | ||
| SD:NMF | 2 | 0.781 | 0.880 | 0.950 | ||
| CV:skmeans | 2 | 0.758 | 0.923 | 0.962 | ||
| SD:pam | 6 | 0.748 | 0.724 | 0.856 | ||
| SD:kmeans | 4 | 0.698 | 0.827 | 0.881 | ||
| MAD:kmeans | 2 | 0.695 | 0.876 | 0.936 | ||
| SD:mclust | 3 | 0.686 | 0.886 | 0.913 | ||
| MAD:pam | 6 | 0.678 | 0.667 | 0.819 | ||
| ATC:mclust | 2 | 0.620 | 0.729 | 0.889 | ||
| ATC:hclust | 4 | 0.585 | 0.587 | 0.804 | ||
| CV:hclust | 4 | 0.510 | 0.615 | 0.846 | ||
| CV:pam | 3 | 0.496 | 0.726 | 0.857 | ||
| MAD:hclust | 3 | 0.360 | 0.662 | 0.826 | ||
| SD:hclust | 3 | 0.331 | 0.691 | 0.833 |
**: 1-PAC > 0.95, *: 1-PAC > 0.9
Cumulative distribution function curves of consensus matrix for all methods.
collect_plots(res_list, fun = plot_ecdf)
Consensus heatmaps for all methods. (What is a consensus heatmap?)
collect_plots(res_list, k = 2, fun = consensus_heatmap, mc.cores = 4)
collect_plots(res_list, k = 3, fun = consensus_heatmap, mc.cores = 4)
collect_plots(res_list, k = 4, fun = consensus_heatmap, mc.cores = 4)
collect_plots(res_list, k = 5, fun = consensus_heatmap, mc.cores = 4)
collect_plots(res_list, k = 6, fun = consensus_heatmap, mc.cores = 4)
Membership heatmaps for all methods. (What is a membership heatmap?)
collect_plots(res_list, k = 2, fun = membership_heatmap, mc.cores = 4)
collect_plots(res_list, k = 3, fun = membership_heatmap, mc.cores = 4)
collect_plots(res_list, k = 4, fun = membership_heatmap, mc.cores = 4)
collect_plots(res_list, k = 5, fun = membership_heatmap, mc.cores = 4)
collect_plots(res_list, k = 6, fun = membership_heatmap, mc.cores = 4)
Signature heatmaps for all methods. (What is a signature heatmap?)
Note in following heatmaps, rows are scaled.
collect_plots(res_list, k = 2, fun = get_signatures, mc.cores = 4)
collect_plots(res_list, k = 3, fun = get_signatures, mc.cores = 4)
collect_plots(res_list, k = 4, fun = get_signatures, mc.cores = 4)
collect_plots(res_list, k = 5, fun = get_signatures, mc.cores = 4)
collect_plots(res_list, k = 6, fun = get_signatures, mc.cores = 4)
The statistics used for measuring the stability of consensus partitioning. (How are they defined?)
get_stats(res_list, k = 2)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 2 0.781 0.880 0.950 0.495 0.502 0.502
#> CV:NMF 2 0.850 0.916 0.963 0.489 0.509 0.509
#> MAD:NMF 2 0.842 0.895 0.957 0.495 0.504 0.504
#> ATC:NMF 2 1.000 0.968 0.987 0.474 0.529 0.529
#> SD:skmeans 2 0.608 0.820 0.923 0.502 0.500 0.500
#> CV:skmeans 2 0.758 0.923 0.962 0.503 0.497 0.497
#> MAD:skmeans 2 0.674 0.817 0.927 0.503 0.498 0.498
#> ATC:skmeans 2 1.000 0.997 0.999 0.504 0.496 0.496
#> SD:mclust 2 0.717 0.931 0.940 0.249 0.786 0.786
#> CV:mclust 2 1.000 0.970 0.973 0.460 0.529 0.529
#> MAD:mclust 2 0.278 0.625 0.798 0.407 0.525 0.525
#> ATC:mclust 2 0.620 0.729 0.889 0.439 0.595 0.595
#> SD:kmeans 2 0.588 0.770 0.898 0.445 0.565 0.565
#> CV:kmeans 2 0.835 0.902 0.960 0.476 0.516 0.516
#> MAD:kmeans 2 0.695 0.876 0.936 0.464 0.551 0.551
#> ATC:kmeans 2 0.904 0.931 0.971 0.492 0.504 0.504
#> SD:pam 2 0.278 0.428 0.691 0.466 0.540 0.540
#> CV:pam 2 0.356 0.697 0.842 0.498 0.496 0.496
#> MAD:pam 2 0.338 0.391 0.713 0.464 0.587 0.587
#> ATC:pam 2 0.389 0.546 0.802 0.455 0.621 0.621
#> SD:hclust 2 0.411 0.780 0.881 0.341 0.707 0.707
#> CV:hclust 2 0.319 0.603 0.813 0.278 0.673 0.673
#> MAD:hclust 2 0.206 0.216 0.600 0.409 0.506 0.506
#> ATC:hclust 2 0.600 0.776 0.907 0.423 0.587 0.587
get_stats(res_list, k = 3)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 3 0.640 0.774 0.898 0.255 0.747 0.555
#> CV:NMF 3 0.516 0.563 0.758 0.329 0.786 0.612
#> MAD:NMF 3 0.606 0.745 0.868 0.276 0.827 0.674
#> ATC:NMF 3 0.500 0.530 0.756 0.351 0.782 0.612
#> SD:skmeans 3 0.611 0.736 0.875 0.328 0.733 0.518
#> CV:skmeans 3 0.575 0.714 0.845 0.317 0.728 0.505
#> MAD:skmeans 3 0.479 0.539 0.745 0.327 0.721 0.505
#> ATC:skmeans 3 0.963 0.956 0.979 0.326 0.755 0.542
#> SD:mclust 3 0.686 0.886 0.913 0.928 0.740 0.670
#> CV:mclust 3 0.777 0.885 0.925 0.163 0.890 0.802
#> MAD:mclust 3 0.310 0.558 0.740 0.307 0.778 0.622
#> ATC:mclust 3 0.610 0.553 0.778 0.405 0.551 0.350
#> SD:kmeans 3 0.477 0.696 0.839 0.382 0.733 0.564
#> CV:kmeans 3 0.427 0.530 0.738 0.308 0.862 0.748
#> MAD:kmeans 3 0.427 0.590 0.770 0.345 0.796 0.649
#> ATC:kmeans 3 0.723 0.750 0.889 0.333 0.736 0.520
#> SD:pam 3 0.381 0.602 0.766 0.373 0.628 0.415
#> CV:pam 3 0.496 0.726 0.857 0.324 0.697 0.463
#> MAD:pam 3 0.331 0.485 0.763 0.331 0.468 0.291
#> ATC:pam 3 0.797 0.874 0.944 0.437 0.701 0.529
#> SD:hclust 3 0.331 0.691 0.833 0.534 0.788 0.701
#> CV:hclust 3 0.488 0.579 0.827 0.756 0.714 0.606
#> MAD:hclust 3 0.360 0.662 0.826 0.352 0.530 0.374
#> ATC:hclust 3 0.539 0.581 0.811 0.468 0.729 0.554
get_stats(res_list, k = 4)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 4 0.635 0.817 0.873 0.1886 0.792 0.508
#> CV:NMF 4 0.659 0.771 0.861 0.1368 0.738 0.414
#> MAD:NMF 4 0.683 0.813 0.886 0.1677 0.749 0.439
#> ATC:NMF 4 0.614 0.640 0.815 0.0965 0.750 0.470
#> SD:skmeans 4 0.814 0.829 0.911 0.1300 0.775 0.447
#> CV:skmeans 4 0.618 0.606 0.810 0.1335 0.808 0.500
#> MAD:skmeans 4 0.854 0.818 0.919 0.1285 0.796 0.490
#> ATC:skmeans 4 0.973 0.928 0.970 0.1148 0.885 0.672
#> SD:mclust 4 0.677 0.829 0.903 0.4877 0.710 0.469
#> CV:mclust 4 0.554 0.573 0.736 0.2639 0.777 0.539
#> MAD:mclust 4 0.946 0.886 0.938 0.3613 0.666 0.353
#> ATC:mclust 4 0.670 0.800 0.870 0.1075 0.785 0.515
#> SD:kmeans 4 0.698 0.827 0.881 0.1853 0.770 0.483
#> CV:kmeans 4 0.505 0.588 0.750 0.1491 0.762 0.497
#> MAD:kmeans 4 0.820 0.864 0.913 0.1692 0.772 0.499
#> ATC:kmeans 4 0.641 0.732 0.839 0.1169 0.843 0.582
#> SD:pam 4 0.584 0.747 0.828 0.1520 0.781 0.472
#> CV:pam 4 0.501 0.441 0.694 0.1074 0.845 0.581
#> MAD:pam 4 0.455 0.514 0.740 0.1661 0.727 0.396
#> ATC:pam 4 0.726 0.747 0.861 0.1088 0.916 0.768
#> SD:hclust 4 0.319 0.500 0.764 0.1283 0.890 0.797
#> CV:hclust 4 0.510 0.615 0.846 0.0985 0.920 0.844
#> MAD:hclust 4 0.343 0.517 0.752 0.1257 0.927 0.864
#> ATC:hclust 4 0.585 0.587 0.804 0.0926 0.909 0.769
get_stats(res_list, k = 5)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 5 0.674 0.665 0.820 0.0738 0.906 0.658
#> CV:NMF 5 0.661 0.649 0.812 0.0624 0.952 0.816
#> MAD:NMF 5 0.643 0.634 0.777 0.0729 0.895 0.625
#> ATC:NMF 5 0.647 0.679 0.832 0.0941 0.789 0.443
#> SD:skmeans 5 0.710 0.604 0.764 0.0645 0.939 0.763
#> CV:skmeans 5 0.600 0.539 0.728 0.0593 0.857 0.525
#> MAD:skmeans 5 0.740 0.629 0.769 0.0631 0.894 0.614
#> ATC:skmeans 5 0.926 0.839 0.935 0.0572 0.920 0.708
#> SD:mclust 5 0.641 0.768 0.857 0.0502 0.924 0.743
#> CV:mclust 5 0.564 0.603 0.749 0.1021 0.824 0.498
#> MAD:mclust 5 0.761 0.817 0.896 0.0471 0.921 0.729
#> ATC:mclust 5 0.756 0.766 0.881 0.1035 0.814 0.507
#> SD:kmeans 5 0.656 0.631 0.784 0.0752 0.964 0.871
#> CV:kmeans 5 0.583 0.628 0.765 0.0808 0.861 0.548
#> MAD:kmeans 5 0.692 0.651 0.789 0.0774 0.955 0.839
#> ATC:kmeans 5 0.772 0.701 0.821 0.0736 0.922 0.719
#> SD:pam 5 0.659 0.689 0.802 0.0674 0.920 0.704
#> CV:pam 5 0.646 0.697 0.836 0.0749 0.877 0.584
#> MAD:pam 5 0.581 0.458 0.704 0.0850 0.848 0.495
#> ATC:pam 5 0.912 0.902 0.957 0.0921 0.857 0.551
#> SD:hclust 5 0.352 0.506 0.703 0.0990 0.855 0.708
#> CV:hclust 5 0.553 0.594 0.819 0.0562 0.960 0.913
#> MAD:hclust 5 0.400 0.380 0.671 0.1143 0.852 0.696
#> ATC:hclust 5 0.575 0.537 0.758 0.0642 0.923 0.783
get_stats(res_list, k = 6)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 6 0.691 0.625 0.786 0.0439 0.904 0.592
#> CV:NMF 6 0.684 0.608 0.790 0.0427 0.921 0.672
#> MAD:NMF 6 0.669 0.632 0.778 0.0448 0.896 0.561
#> ATC:NMF 6 0.566 0.473 0.639 0.0460 0.891 0.590
#> SD:skmeans 6 0.710 0.549 0.748 0.0376 0.943 0.739
#> CV:skmeans 6 0.638 0.511 0.710 0.0396 0.920 0.660
#> MAD:skmeans 6 0.722 0.547 0.754 0.0391 0.888 0.531
#> ATC:skmeans 6 0.819 0.716 0.830 0.0461 0.920 0.655
#> SD:mclust 6 0.637 0.564 0.763 0.0645 0.909 0.657
#> CV:mclust 6 0.672 0.624 0.796 0.0644 0.892 0.616
#> MAD:mclust 6 0.785 0.693 0.839 0.0697 0.926 0.707
#> ATC:mclust 6 0.667 0.530 0.752 0.0490 0.900 0.641
#> SD:kmeans 6 0.643 0.453 0.678 0.0451 0.928 0.719
#> CV:kmeans 6 0.645 0.623 0.750 0.0411 0.957 0.809
#> MAD:kmeans 6 0.678 0.538 0.705 0.0476 0.892 0.586
#> ATC:kmeans 6 0.739 0.642 0.773 0.0410 0.952 0.785
#> SD:pam 6 0.748 0.724 0.856 0.0391 0.954 0.790
#> CV:pam 6 0.641 0.534 0.721 0.0375 0.945 0.753
#> MAD:pam 6 0.678 0.667 0.819 0.0422 0.921 0.658
#> ATC:pam 6 0.852 0.877 0.920 0.0297 0.972 0.865
#> SD:hclust 6 0.365 0.467 0.651 0.1020 0.876 0.676
#> CV:hclust 6 0.522 0.570 0.787 0.0687 0.956 0.901
#> MAD:hclust 6 0.431 0.424 0.649 0.0771 0.881 0.672
#> ATC:hclust 6 0.610 0.599 0.732 0.0457 0.870 0.598
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 disease.state(p) gender(p) k
#> SD:NMF 102 0.39176 0.753 2
#> CV:NMF 105 0.73098 1.000 2
#> MAD:NMF 102 0.48106 0.753 2
#> ATC:NMF 107 0.29010 1.000 2
#> SD:skmeans 101 0.28138 0.645 2
#> CV:skmeans 107 0.96390 0.747 2
#> MAD:skmeans 96 0.83703 1.000 2
#> ATC:skmeans 108 0.43138 0.957 2
#> SD:mclust 108 0.40381 0.706 2
#> CV:mclust 108 0.76287 1.000 2
#> MAD:mclust 92 0.84752 1.000 2
#> ATC:mclust 84 0.80360 0.942 2
#> SD:kmeans 95 0.85807 0.373 2
#> CV:kmeans 103 0.68325 1.000 2
#> MAD:kmeans 105 0.77262 0.330 2
#> ATC:kmeans 107 0.23517 0.813 2
#> SD:pam 76 0.00167 1.000 2
#> CV:pam 93 0.42806 1.000 2
#> MAD:pam 55 0.07190 0.922 2
#> ATC:pam 64 0.29230 0.625 2
#> SD:hclust 100 0.66823 1.000 2
#> CV:hclust 82 0.70571 0.177 2
#> MAD:hclust 34 NA NA 2
#> ATC:hclust 92 0.21993 0.879 2
test_to_known_factors(res_list, k = 3)
#> n disease.state(p) gender(p) k
#> SD:NMF 96 0.83267 0.383 3
#> CV:NMF 81 0.30690 0.857 3
#> MAD:NMF 95 0.94174 0.290 3
#> ATC:NMF 80 0.35795 0.930 3
#> SD:skmeans 91 0.76251 0.952 3
#> CV:skmeans 92 0.00555 0.913 3
#> MAD:skmeans 65 0.31240 0.732 3
#> ATC:skmeans 107 0.22553 0.923 3
#> SD:mclust 106 0.56865 0.136 3
#> CV:mclust 102 0.02039 0.783 3
#> MAD:mclust 75 0.00806 0.542 3
#> ATC:mclust 72 0.26354 0.731 3
#> SD:kmeans 92 0.88731 0.601 3
#> CV:kmeans 78 0.85960 0.985 3
#> MAD:kmeans 86 0.85051 0.697 3
#> ATC:kmeans 91 0.56321 0.964 3
#> SD:pam 87 0.14071 0.653 3
#> CV:pam 91 0.19282 0.528 3
#> MAD:pam 67 0.07715 0.745 3
#> ATC:pam 102 0.63089 0.243 3
#> SD:hclust 90 0.77854 0.555 3
#> CV:hclust 70 0.56416 0.689 3
#> MAD:hclust 90 0.79724 0.481 3
#> ATC:hclust 75 0.54906 0.762 3
test_to_known_factors(res_list, k = 4)
#> n disease.state(p) gender(p) k
#> SD:NMF 104 0.00425 0.7325 4
#> CV:NMF 100 0.00270 0.4353 4
#> MAD:NMF 102 0.00357 0.7012 4
#> ATC:NMF 89 0.31120 0.5760 4
#> SD:skmeans 97 0.00821 0.9911 4
#> CV:skmeans 79 0.01149 0.6986 4
#> MAD:skmeans 97 0.00542 0.9794 4
#> ATC:skmeans 103 0.25347 0.8363 4
#> SD:mclust 100 0.00400 0.5508 4
#> CV:mclust 57 0.00332 0.2233 4
#> MAD:mclust 105 0.00324 0.6986 4
#> ATC:mclust 104 0.37564 0.0799 4
#> SD:kmeans 101 0.01049 0.7969 4
#> CV:kmeans 83 0.01062 0.9758 4
#> MAD:kmeans 103 0.00304 0.5520 4
#> ATC:kmeans 94 0.68179 0.1699 4
#> SD:pam 99 0.02249 0.8648 4
#> CV:pam 60 0.01864 0.3328 4
#> MAD:pam 72 0.18347 0.6843 4
#> ATC:pam 93 0.99016 0.3352 4
#> SD:hclust 74 0.30061 0.8063 4
#> CV:hclust 67 0.90614 0.1465 4
#> MAD:hclust 72 0.84074 0.3911 4
#> ATC:hclust 75 0.57976 0.1738 4
test_to_known_factors(res_list, k = 5)
#> n disease.state(p) gender(p) k
#> SD:NMF 92 0.007545 0.359 5
#> CV:NMF 90 0.000921 0.016 5
#> MAD:NMF 88 0.017078 0.483 5
#> ATC:NMF 88 0.395552 0.440 5
#> SD:skmeans 77 0.000346 0.988 5
#> CV:skmeans 62 0.005429 0.186 5
#> MAD:skmeans 86 0.000385 0.997 5
#> ATC:skmeans 95 0.325055 0.242 5
#> SD:mclust 100 0.001271 0.411 5
#> CV:mclust 80 0.000833 0.406 5
#> MAD:mclust 103 0.003965 0.491 5
#> ATC:mclust 92 0.996923 0.252 5
#> SD:kmeans 88 0.001559 0.774 5
#> CV:kmeans 78 0.000305 0.472 5
#> MAD:kmeans 88 0.004028 0.746 5
#> ATC:kmeans 91 0.696724 0.091 5
#> SD:pam 99 0.077620 0.610 5
#> CV:pam 92 0.049094 0.410 5
#> MAD:pam 47 0.013712 0.855 5
#> ATC:pam 105 0.998042 0.323 5
#> SD:hclust 74 0.124396 0.454 5
#> CV:hclust 65 0.935880 0.162 5
#> MAD:hclust 34 0.541178 0.564 5
#> ATC:hclust 68 0.283954 0.208 5
test_to_known_factors(res_list, k = 6)
#> n disease.state(p) gender(p) k
#> SD:NMF 83 0.000926 0.0673 6
#> CV:NMF 86 0.023660 0.0282 6
#> MAD:NMF 88 0.003560 0.5312 6
#> ATC:NMF 66 0.057073 0.5576 6
#> SD:skmeans 68 0.001059 0.6986 6
#> CV:skmeans 70 0.000438 0.5016 6
#> MAD:skmeans 70 0.003139 0.9673 6
#> ATC:skmeans 89 0.147885 0.3914 6
#> SD:mclust 69 0.098346 0.6782 6
#> CV:mclust 77 0.001699 0.5770 6
#> MAD:mclust 88 0.028448 0.6494 6
#> ATC:mclust 80 0.958057 0.1733 6
#> SD:kmeans 56 0.080012 0.5215 6
#> CV:kmeans 83 0.000172 0.3758 6
#> MAD:kmeans 69 0.005757 0.1618 6
#> ATC:kmeans 84 0.240161 0.2112 6
#> SD:pam 99 0.056421 0.6115 6
#> CV:pam 70 0.003492 0.2609 6
#> MAD:pam 90 0.020161 0.4461 6
#> ATC:pam 106 0.985782 0.1162 6
#> SD:hclust 63 0.015805 0.2772 6
#> CV:hclust 63 0.770466 0.1118 6
#> MAD:hclust 53 0.084141 0.0821 6
#> ATC:hclust 83 0.213625 0.4094 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 38950 rows and 108 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'hclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.411 0.780 0.881 0.341 0.707 0.707
#> 3 3 0.331 0.691 0.833 0.534 0.788 0.701
#> 4 4 0.319 0.500 0.764 0.128 0.890 0.797
#> 5 5 0.352 0.506 0.703 0.099 0.855 0.708
#> 6 6 0.365 0.467 0.651 0.102 0.876 0.676
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 3
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM1068478 2 0.3879 0.863 0.076 0.924
#> GSM1068479 1 0.9933 0.471 0.548 0.452
#> GSM1068481 1 0.2423 0.789 0.960 0.040
#> GSM1068482 1 0.0000 0.796 1.000 0.000
#> GSM1068483 2 0.8443 0.705 0.272 0.728
#> GSM1068486 1 0.4690 0.771 0.900 0.100
#> GSM1068487 2 0.0000 0.874 0.000 1.000
#> GSM1068488 2 0.4022 0.855 0.080 0.920
#> GSM1068490 2 0.0000 0.874 0.000 1.000
#> GSM1068491 1 0.9896 0.498 0.560 0.440
#> GSM1068492 1 0.9896 0.498 0.560 0.440
#> GSM1068493 2 0.7219 0.787 0.200 0.800
#> GSM1068494 1 0.9896 0.114 0.560 0.440
#> GSM1068495 2 0.3879 0.867 0.076 0.924
#> GSM1068496 2 0.9933 0.183 0.452 0.548
#> GSM1068498 2 0.3879 0.863 0.076 0.924
#> GSM1068499 2 0.8608 0.689 0.284 0.716
#> GSM1068500 2 0.8443 0.705 0.272 0.728
#> GSM1068502 1 0.9896 0.498 0.560 0.440
#> GSM1068503 2 0.0000 0.874 0.000 1.000
#> GSM1068505 2 0.0376 0.874 0.004 0.996
#> GSM1068506 2 0.0000 0.874 0.000 1.000
#> GSM1068507 2 0.1184 0.875 0.016 0.984
#> GSM1068508 2 0.3114 0.871 0.056 0.944
#> GSM1068510 2 0.5408 0.840 0.124 0.876
#> GSM1068512 2 0.6148 0.810 0.152 0.848
#> GSM1068513 2 0.1184 0.875 0.016 0.984
#> GSM1068514 2 0.8016 0.603 0.244 0.756
#> GSM1068517 2 0.3879 0.863 0.076 0.924
#> GSM1068518 2 0.7602 0.734 0.220 0.780
#> GSM1068520 2 0.7883 0.741 0.236 0.764
#> GSM1068521 2 0.7815 0.745 0.232 0.768
#> GSM1068522 2 0.0000 0.874 0.000 1.000
#> GSM1068524 2 0.1184 0.873 0.016 0.984
#> GSM1068527 2 0.1184 0.873 0.016 0.984
#> GSM1068480 1 0.0376 0.796 0.996 0.004
#> GSM1068484 2 0.2236 0.870 0.036 0.964
#> GSM1068485 1 0.0000 0.796 1.000 0.000
#> GSM1068489 2 0.0376 0.874 0.004 0.996
#> GSM1068497 2 0.3879 0.863 0.076 0.924
#> GSM1068501 2 0.5408 0.840 0.124 0.876
#> GSM1068504 2 0.0000 0.874 0.000 1.000
#> GSM1068509 2 0.8955 0.616 0.312 0.688
#> GSM1068511 2 0.9358 0.433 0.352 0.648
#> GSM1068515 2 0.5059 0.848 0.112 0.888
#> GSM1068516 2 0.7528 0.745 0.216 0.784
#> GSM1068519 2 0.8661 0.681 0.288 0.712
#> GSM1068523 2 0.0000 0.874 0.000 1.000
#> GSM1068525 2 0.2236 0.870 0.036 0.964
#> GSM1068526 2 0.0672 0.873 0.008 0.992
#> GSM1068458 2 0.8016 0.733 0.244 0.756
#> GSM1068459 1 0.0000 0.796 1.000 0.000
#> GSM1068460 2 0.0000 0.874 0.000 1.000
#> GSM1068461 1 0.0000 0.796 1.000 0.000
#> GSM1068464 2 0.0000 0.874 0.000 1.000
#> GSM1068468 2 0.2423 0.873 0.040 0.960
#> GSM1068472 2 0.4161 0.862 0.084 0.916
#> GSM1068473 2 0.0000 0.874 0.000 1.000
#> GSM1068474 2 0.0000 0.874 0.000 1.000
#> GSM1068476 1 0.9775 0.535 0.588 0.412
#> GSM1068477 2 0.0000 0.874 0.000 1.000
#> GSM1068462 2 0.3733 0.868 0.072 0.928
#> GSM1068463 1 0.0000 0.796 1.000 0.000
#> GSM1068465 2 0.3114 0.871 0.056 0.944
#> GSM1068466 2 0.7883 0.742 0.236 0.764
#> GSM1068467 2 0.2423 0.873 0.040 0.960
#> GSM1068469 2 0.4161 0.860 0.084 0.916
#> GSM1068470 2 0.0000 0.874 0.000 1.000
#> GSM1068471 2 0.0000 0.874 0.000 1.000
#> GSM1068475 2 0.0000 0.874 0.000 1.000
#> GSM1068528 1 0.8608 0.561 0.716 0.284
#> GSM1068531 2 0.8267 0.712 0.260 0.740
#> GSM1068532 2 0.8661 0.678 0.288 0.712
#> GSM1068533 2 0.8016 0.733 0.244 0.756
#> GSM1068535 2 0.5519 0.837 0.128 0.872
#> GSM1068537 2 0.8555 0.688 0.280 0.720
#> GSM1068538 2 0.8661 0.678 0.288 0.712
#> GSM1068539 2 0.3879 0.867 0.076 0.924
#> GSM1068540 2 0.8267 0.712 0.260 0.740
#> GSM1068542 2 0.0000 0.874 0.000 1.000
#> GSM1068543 2 0.2423 0.868 0.040 0.960
#> GSM1068544 1 0.1184 0.793 0.984 0.016
#> GSM1068545 2 0.0000 0.874 0.000 1.000
#> GSM1068546 1 0.0000 0.796 1.000 0.000
#> GSM1068547 2 0.7883 0.741 0.236 0.764
#> GSM1068548 2 0.0000 0.874 0.000 1.000
#> GSM1068549 1 0.0000 0.796 1.000 0.000
#> GSM1068550 2 0.0672 0.873 0.008 0.992
#> GSM1068551 2 0.0000 0.874 0.000 1.000
#> GSM1068552 2 0.0000 0.874 0.000 1.000
#> GSM1068555 2 0.0000 0.874 0.000 1.000
#> GSM1068556 2 0.2423 0.868 0.040 0.960
#> GSM1068557 2 0.3879 0.868 0.076 0.924
#> GSM1068560 2 0.1184 0.873 0.016 0.984
#> GSM1068561 2 0.4161 0.866 0.084 0.916
#> GSM1068562 2 0.0672 0.873 0.008 0.992
#> GSM1068563 2 0.0000 0.874 0.000 1.000
#> GSM1068565 2 0.0000 0.874 0.000 1.000
#> GSM1068529 2 0.7883 0.698 0.236 0.764
#> GSM1068530 2 0.8443 0.698 0.272 0.728
#> GSM1068534 2 0.7883 0.698 0.236 0.764
#> GSM1068536 2 0.4815 0.857 0.104 0.896
#> GSM1068541 2 0.2778 0.873 0.048 0.952
#> GSM1068553 2 0.5408 0.840 0.124 0.876
#> GSM1068554 2 0.5408 0.840 0.124 0.876
#> GSM1068558 1 0.9000 0.629 0.684 0.316
#> GSM1068559 2 0.9580 0.236 0.380 0.620
#> GSM1068564 2 0.0376 0.874 0.004 0.996
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1068478 2 0.4002 0.759 0.160 0.840 0.000
#> GSM1068479 3 0.6483 0.330 0.004 0.452 0.544
#> GSM1068481 3 0.2810 0.675 0.036 0.036 0.928
#> GSM1068482 3 0.0747 0.688 0.016 0.000 0.984
#> GSM1068483 1 0.8168 0.668 0.612 0.280 0.108
#> GSM1068486 3 0.3752 0.651 0.020 0.096 0.884
#> GSM1068487 2 0.0000 0.826 0.000 1.000 0.000
#> GSM1068488 2 0.6424 0.729 0.180 0.752 0.068
#> GSM1068490 2 0.0000 0.826 0.000 1.000 0.000
#> GSM1068491 3 0.6460 0.364 0.004 0.440 0.556
#> GSM1068492 3 0.6460 0.364 0.004 0.440 0.556
#> GSM1068493 2 0.7179 0.637 0.184 0.712 0.104
#> GSM1068494 3 0.9242 0.115 0.240 0.228 0.532
#> GSM1068495 2 0.4196 0.799 0.112 0.864 0.024
#> GSM1068496 1 0.9901 0.135 0.392 0.272 0.336
#> GSM1068498 2 0.3816 0.767 0.148 0.852 0.000
#> GSM1068499 1 0.8109 0.657 0.628 0.256 0.116
#> GSM1068500 1 0.8168 0.668 0.612 0.280 0.108
#> GSM1068502 3 0.6460 0.364 0.004 0.440 0.556
#> GSM1068503 2 0.0000 0.826 0.000 1.000 0.000
#> GSM1068505 2 0.3573 0.811 0.120 0.876 0.004
#> GSM1068506 2 0.1964 0.827 0.056 0.944 0.000
#> GSM1068507 2 0.2173 0.828 0.048 0.944 0.008
#> GSM1068508 2 0.3619 0.798 0.136 0.864 0.000
#> GSM1068510 2 0.7775 0.472 0.304 0.620 0.076
#> GSM1068512 2 0.7345 0.664 0.192 0.700 0.108
#> GSM1068513 2 0.1950 0.828 0.040 0.952 0.008
#> GSM1068514 2 0.7634 0.552 0.100 0.668 0.232
#> GSM1068517 2 0.3816 0.767 0.148 0.852 0.000
#> GSM1068518 2 0.8657 0.471 0.244 0.592 0.164
#> GSM1068520 1 0.4750 0.768 0.784 0.216 0.000
#> GSM1068521 1 0.4702 0.769 0.788 0.212 0.000
#> GSM1068522 2 0.0592 0.827 0.012 0.988 0.000
#> GSM1068524 2 0.0983 0.827 0.004 0.980 0.016
#> GSM1068527 2 0.4805 0.775 0.176 0.812 0.012
#> GSM1068480 3 0.0747 0.688 0.016 0.000 0.984
#> GSM1068484 2 0.3028 0.827 0.048 0.920 0.032
#> GSM1068485 3 0.0424 0.689 0.008 0.000 0.992
#> GSM1068489 2 0.2400 0.826 0.064 0.932 0.004
#> GSM1068497 2 0.4002 0.759 0.160 0.840 0.000
#> GSM1068501 2 0.7826 0.459 0.312 0.612 0.076
#> GSM1068504 2 0.0000 0.826 0.000 1.000 0.000
#> GSM1068509 2 0.9455 0.151 0.304 0.488 0.208
#> GSM1068511 2 0.8588 0.285 0.112 0.544 0.344
#> GSM1068515 2 0.5982 0.495 0.328 0.668 0.004
#> GSM1068516 2 0.8408 0.514 0.244 0.612 0.144
#> GSM1068519 1 0.6001 0.752 0.784 0.144 0.072
#> GSM1068523 2 0.0000 0.826 0.000 1.000 0.000
#> GSM1068525 2 0.3028 0.827 0.048 0.920 0.032
#> GSM1068526 2 0.2774 0.825 0.072 0.920 0.008
#> GSM1068458 1 0.4002 0.775 0.840 0.160 0.000
#> GSM1068459 3 0.0892 0.688 0.020 0.000 0.980
#> GSM1068460 2 0.2165 0.829 0.064 0.936 0.000
#> GSM1068461 3 0.0424 0.690 0.008 0.000 0.992
#> GSM1068464 2 0.0000 0.826 0.000 1.000 0.000
#> GSM1068468 2 0.2550 0.823 0.056 0.932 0.012
#> GSM1068472 2 0.3755 0.789 0.120 0.872 0.008
#> GSM1068473 2 0.0000 0.826 0.000 1.000 0.000
#> GSM1068474 2 0.0000 0.826 0.000 1.000 0.000
#> GSM1068476 3 0.6168 0.423 0.000 0.412 0.588
#> GSM1068477 2 0.0592 0.827 0.012 0.988 0.000
#> GSM1068462 2 0.3695 0.799 0.108 0.880 0.012
#> GSM1068463 3 0.0892 0.688 0.020 0.000 0.980
#> GSM1068465 2 0.3686 0.795 0.140 0.860 0.000
#> GSM1068466 1 0.5291 0.718 0.732 0.268 0.000
#> GSM1068467 2 0.2446 0.823 0.052 0.936 0.012
#> GSM1068469 2 0.3686 0.775 0.140 0.860 0.000
#> GSM1068470 2 0.0000 0.826 0.000 1.000 0.000
#> GSM1068471 2 0.0000 0.826 0.000 1.000 0.000
#> GSM1068475 2 0.0000 0.826 0.000 1.000 0.000
#> GSM1068528 3 0.7905 0.170 0.376 0.064 0.560
#> GSM1068531 1 0.1289 0.740 0.968 0.032 0.000
#> GSM1068532 1 0.0848 0.714 0.984 0.008 0.008
#> GSM1068533 1 0.4002 0.775 0.840 0.160 0.000
#> GSM1068535 2 0.7948 0.458 0.320 0.600 0.080
#> GSM1068537 1 0.1170 0.724 0.976 0.016 0.008
#> GSM1068538 1 0.0848 0.714 0.984 0.008 0.008
#> GSM1068539 2 0.4196 0.799 0.112 0.864 0.024
#> GSM1068540 1 0.2066 0.757 0.940 0.060 0.000
#> GSM1068542 2 0.3116 0.816 0.108 0.892 0.000
#> GSM1068543 2 0.4563 0.802 0.112 0.852 0.036
#> GSM1068544 3 0.1529 0.679 0.040 0.000 0.960
#> GSM1068545 2 0.1860 0.827 0.052 0.948 0.000
#> GSM1068546 3 0.0424 0.690 0.008 0.000 0.992
#> GSM1068547 1 0.4605 0.773 0.796 0.204 0.000
#> GSM1068548 2 0.3267 0.813 0.116 0.884 0.000
#> GSM1068549 3 0.0424 0.690 0.008 0.000 0.992
#> GSM1068550 2 0.3129 0.821 0.088 0.904 0.008
#> GSM1068551 2 0.0000 0.826 0.000 1.000 0.000
#> GSM1068552 2 0.2066 0.827 0.060 0.940 0.000
#> GSM1068555 2 0.0000 0.826 0.000 1.000 0.000
#> GSM1068556 2 0.4708 0.797 0.120 0.844 0.036
#> GSM1068557 2 0.3989 0.803 0.124 0.864 0.012
#> GSM1068560 2 0.4805 0.775 0.176 0.812 0.012
#> GSM1068561 2 0.5000 0.784 0.124 0.832 0.044
#> GSM1068562 2 0.3129 0.821 0.088 0.904 0.008
#> GSM1068563 2 0.1860 0.827 0.052 0.948 0.000
#> GSM1068565 2 0.0000 0.826 0.000 1.000 0.000
#> GSM1068529 2 0.8484 0.524 0.196 0.616 0.188
#> GSM1068530 1 0.1031 0.733 0.976 0.024 0.000
#> GSM1068534 2 0.8484 0.524 0.196 0.616 0.188
#> GSM1068536 2 0.6601 0.614 0.296 0.676 0.028
#> GSM1068541 2 0.3267 0.812 0.116 0.884 0.000
#> GSM1068553 2 0.7826 0.459 0.312 0.612 0.076
#> GSM1068554 2 0.7826 0.459 0.312 0.612 0.076
#> GSM1068558 3 0.6075 0.503 0.008 0.316 0.676
#> GSM1068559 2 0.7980 0.305 0.072 0.572 0.356
#> GSM1068564 2 0.2400 0.826 0.064 0.932 0.004
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1068478 2 0.3711 0.6228 0.140 0.836 0.000 0.024
#> GSM1068479 2 0.7679 -0.3075 0.000 0.408 0.376 0.216
#> GSM1068481 3 0.2531 0.7432 0.020 0.032 0.924 0.024
#> GSM1068482 3 0.2334 0.7723 0.004 0.000 0.908 0.088
#> GSM1068483 1 0.7672 0.4992 0.596 0.236 0.096 0.072
#> GSM1068486 3 0.5589 0.6037 0.016 0.060 0.736 0.188
#> GSM1068487 2 0.0000 0.6966 0.000 1.000 0.000 0.000
#> GSM1068488 2 0.6973 0.3227 0.116 0.596 0.012 0.276
#> GSM1068490 2 0.0000 0.6966 0.000 1.000 0.000 0.000
#> GSM1068491 2 0.7699 -0.3165 0.000 0.400 0.380 0.220
#> GSM1068492 2 0.7714 -0.3172 0.000 0.400 0.376 0.224
#> GSM1068493 2 0.7312 0.3747 0.164 0.652 0.084 0.100
#> GSM1068494 4 0.6961 0.0179 0.132 0.012 0.244 0.612
#> GSM1068495 2 0.3974 0.6534 0.108 0.844 0.008 0.040
#> GSM1068496 1 0.9873 -0.2322 0.324 0.192 0.244 0.240
#> GSM1068498 2 0.3443 0.6322 0.136 0.848 0.000 0.016
#> GSM1068499 1 0.8143 0.4406 0.536 0.116 0.072 0.276
#> GSM1068500 1 0.7672 0.4992 0.596 0.236 0.096 0.072
#> GSM1068502 2 0.7714 -0.3172 0.000 0.400 0.376 0.224
#> GSM1068503 2 0.0000 0.6966 0.000 1.000 0.000 0.000
#> GSM1068505 2 0.4549 0.6365 0.100 0.804 0.000 0.096
#> GSM1068506 2 0.2965 0.6795 0.036 0.892 0.000 0.072
#> GSM1068507 2 0.2505 0.6930 0.040 0.920 0.004 0.036
#> GSM1068508 2 0.3907 0.6422 0.140 0.828 0.000 0.032
#> GSM1068510 2 0.8209 0.0954 0.212 0.516 0.040 0.232
#> GSM1068512 2 0.7418 0.1290 0.136 0.548 0.016 0.300
#> GSM1068513 2 0.2123 0.6936 0.032 0.936 0.004 0.028
#> GSM1068514 2 0.7747 -0.1713 0.044 0.508 0.096 0.352
#> GSM1068517 2 0.3443 0.6322 0.136 0.848 0.000 0.016
#> GSM1068518 2 0.8498 -0.3370 0.180 0.420 0.044 0.356
#> GSM1068520 1 0.4590 0.6807 0.792 0.148 0.000 0.060
#> GSM1068521 1 0.4636 0.6842 0.792 0.140 0.000 0.068
#> GSM1068522 2 0.0524 0.6975 0.008 0.988 0.000 0.004
#> GSM1068524 2 0.1489 0.6938 0.004 0.952 0.000 0.044
#> GSM1068527 2 0.6275 0.4161 0.104 0.640 0.000 0.256
#> GSM1068480 3 0.4500 0.6569 0.000 0.000 0.684 0.316
#> GSM1068484 2 0.4567 0.5172 0.008 0.716 0.000 0.276
#> GSM1068485 3 0.1302 0.7753 0.000 0.000 0.956 0.044
#> GSM1068489 2 0.3333 0.6719 0.040 0.872 0.000 0.088
#> GSM1068497 2 0.3711 0.6228 0.140 0.836 0.000 0.024
#> GSM1068501 2 0.8281 0.0783 0.220 0.504 0.040 0.236
#> GSM1068504 2 0.0000 0.6966 0.000 1.000 0.000 0.000
#> GSM1068509 4 0.9437 0.3655 0.232 0.300 0.108 0.360
#> GSM1068511 4 0.7849 0.4345 0.052 0.300 0.108 0.540
#> GSM1068515 2 0.5691 0.3522 0.304 0.648 0.000 0.048
#> GSM1068516 2 0.8359 -0.3042 0.168 0.432 0.040 0.360
#> GSM1068519 1 0.6064 0.5898 0.668 0.024 0.040 0.268
#> GSM1068523 2 0.0000 0.6966 0.000 1.000 0.000 0.000
#> GSM1068525 2 0.4567 0.5172 0.008 0.716 0.000 0.276
#> GSM1068526 2 0.3505 0.6696 0.048 0.864 0.000 0.088
#> GSM1068458 1 0.4100 0.6913 0.824 0.128 0.000 0.048
#> GSM1068459 3 0.0524 0.7765 0.004 0.000 0.988 0.008
#> GSM1068460 2 0.2908 0.6889 0.064 0.896 0.000 0.040
#> GSM1068461 3 0.3569 0.7443 0.000 0.000 0.804 0.196
#> GSM1068464 2 0.0000 0.6966 0.000 1.000 0.000 0.000
#> GSM1068468 2 0.2594 0.6871 0.044 0.916 0.004 0.036
#> GSM1068472 2 0.3389 0.6521 0.104 0.868 0.004 0.024
#> GSM1068473 2 0.0000 0.6966 0.000 1.000 0.000 0.000
#> GSM1068474 2 0.0000 0.6966 0.000 1.000 0.000 0.000
#> GSM1068476 3 0.7530 -0.2280 0.000 0.376 0.436 0.188
#> GSM1068477 2 0.0524 0.6975 0.008 0.988 0.000 0.004
#> GSM1068462 2 0.3366 0.6607 0.096 0.872 0.004 0.028
#> GSM1068463 3 0.0524 0.7765 0.004 0.000 0.988 0.008
#> GSM1068465 2 0.3863 0.6379 0.144 0.828 0.000 0.028
#> GSM1068466 1 0.5136 0.5893 0.728 0.224 0.000 0.048
#> GSM1068467 2 0.2310 0.6873 0.040 0.928 0.004 0.028
#> GSM1068469 2 0.3392 0.6401 0.124 0.856 0.000 0.020
#> GSM1068470 2 0.0000 0.6966 0.000 1.000 0.000 0.000
#> GSM1068471 2 0.0000 0.6966 0.000 1.000 0.000 0.000
#> GSM1068475 2 0.0000 0.6966 0.000 1.000 0.000 0.000
#> GSM1068528 3 0.7118 0.1789 0.352 0.052 0.552 0.044
#> GSM1068531 1 0.1004 0.7029 0.972 0.004 0.000 0.024
#> GSM1068532 1 0.1520 0.6961 0.956 0.000 0.020 0.024
#> GSM1068533 1 0.4100 0.6913 0.824 0.128 0.000 0.048
#> GSM1068535 2 0.8391 0.0532 0.240 0.484 0.040 0.236
#> GSM1068537 1 0.1174 0.6983 0.968 0.000 0.012 0.020
#> GSM1068538 1 0.1520 0.6961 0.956 0.000 0.020 0.024
#> GSM1068539 2 0.3884 0.6552 0.108 0.848 0.008 0.036
#> GSM1068540 1 0.1824 0.7010 0.936 0.004 0.000 0.060
#> GSM1068542 2 0.4513 0.6327 0.076 0.804 0.000 0.120
#> GSM1068543 2 0.5648 0.4901 0.064 0.684 0.000 0.252
#> GSM1068544 3 0.1174 0.7728 0.020 0.000 0.968 0.012
#> GSM1068545 2 0.2871 0.6806 0.032 0.896 0.000 0.072
#> GSM1068546 3 0.3649 0.7409 0.000 0.000 0.796 0.204
#> GSM1068547 1 0.4415 0.6887 0.804 0.140 0.000 0.056
#> GSM1068548 2 0.4646 0.6276 0.084 0.796 0.000 0.120
#> GSM1068549 3 0.3726 0.7390 0.000 0.000 0.788 0.212
#> GSM1068550 2 0.4055 0.6521 0.060 0.832 0.000 0.108
#> GSM1068551 2 0.0000 0.6966 0.000 1.000 0.000 0.000
#> GSM1068552 2 0.3056 0.6790 0.040 0.888 0.000 0.072
#> GSM1068555 2 0.0000 0.6966 0.000 1.000 0.000 0.000
#> GSM1068556 2 0.5687 0.4909 0.068 0.684 0.000 0.248
#> GSM1068557 2 0.4467 0.6431 0.104 0.816 0.004 0.076
#> GSM1068560 2 0.6275 0.4161 0.104 0.640 0.000 0.256
#> GSM1068561 2 0.5176 0.6064 0.108 0.780 0.012 0.100
#> GSM1068562 2 0.4055 0.6521 0.060 0.832 0.000 0.108
#> GSM1068563 2 0.2943 0.6790 0.032 0.892 0.000 0.076
#> GSM1068565 2 0.0000 0.6966 0.000 1.000 0.000 0.000
#> GSM1068529 4 0.8210 0.3277 0.120 0.404 0.052 0.424
#> GSM1068530 1 0.1191 0.7020 0.968 0.004 0.004 0.024
#> GSM1068534 4 0.8210 0.3277 0.120 0.404 0.052 0.424
#> GSM1068536 2 0.6761 0.3961 0.268 0.612 0.008 0.112
#> GSM1068541 2 0.3647 0.6754 0.108 0.852 0.000 0.040
#> GSM1068553 2 0.8278 0.0830 0.216 0.504 0.040 0.240
#> GSM1068554 2 0.8278 0.0830 0.216 0.504 0.040 0.240
#> GSM1068558 4 0.7220 -0.1874 0.000 0.144 0.384 0.472
#> GSM1068559 2 0.8445 -0.3410 0.036 0.420 0.196 0.348
#> GSM1068564 2 0.3266 0.6735 0.040 0.876 0.000 0.084
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM1068478 2 0.4121 0.58720 0.036 0.784 0.000 0.012 0.168
#> GSM1068479 4 0.8049 0.66904 0.000 0.320 0.244 0.344 0.092
#> GSM1068481 3 0.2459 0.72672 0.004 0.000 0.904 0.040 0.052
#> GSM1068482 3 0.2193 0.74926 0.000 0.000 0.912 0.060 0.028
#> GSM1068483 1 0.8277 0.43454 0.456 0.172 0.088 0.032 0.252
#> GSM1068486 3 0.5838 0.49634 0.004 0.008 0.588 0.320 0.080
#> GSM1068487 2 0.0000 0.70849 0.000 1.000 0.000 0.000 0.000
#> GSM1068488 2 0.6922 -0.13877 0.028 0.448 0.012 0.104 0.408
#> GSM1068490 2 0.0000 0.70849 0.000 1.000 0.000 0.000 0.000
#> GSM1068491 4 0.8037 0.67691 0.000 0.316 0.256 0.340 0.088
#> GSM1068492 4 0.8047 0.67819 0.000 0.316 0.244 0.348 0.092
#> GSM1068493 2 0.7328 0.24230 0.060 0.576 0.084 0.048 0.232
#> GSM1068494 5 0.7680 -0.12042 0.076 0.000 0.188 0.308 0.428
#> GSM1068495 2 0.3745 0.64013 0.036 0.820 0.000 0.012 0.132
#> GSM1068496 5 0.9762 0.00209 0.200 0.108 0.248 0.188 0.256
#> GSM1068498 2 0.3964 0.60034 0.032 0.796 0.000 0.012 0.160
#> GSM1068499 5 0.7552 -0.15077 0.304 0.064 0.036 0.088 0.508
#> GSM1068500 1 0.8277 0.43454 0.456 0.172 0.088 0.032 0.252
#> GSM1068502 4 0.8047 0.67819 0.000 0.316 0.244 0.348 0.092
#> GSM1068503 2 0.0000 0.70849 0.000 1.000 0.000 0.000 0.000
#> GSM1068505 2 0.5035 0.55305 0.044 0.712 0.000 0.028 0.216
#> GSM1068506 2 0.3234 0.65274 0.012 0.836 0.000 0.008 0.144
#> GSM1068507 2 0.2193 0.69581 0.000 0.900 0.000 0.008 0.092
#> GSM1068508 2 0.4224 0.63526 0.080 0.792 0.000 0.008 0.120
#> GSM1068510 5 0.5720 0.39178 0.004 0.388 0.008 0.056 0.544
#> GSM1068512 2 0.7804 -0.24009 0.076 0.420 0.012 0.140 0.352
#> GSM1068513 2 0.1952 0.69751 0.000 0.912 0.000 0.004 0.084
#> GSM1068514 2 0.8293 -0.34756 0.032 0.400 0.060 0.212 0.296
#> GSM1068517 2 0.3964 0.60034 0.032 0.796 0.000 0.012 0.160
#> GSM1068518 5 0.8456 0.31150 0.100 0.324 0.036 0.144 0.396
#> GSM1068520 1 0.5305 0.65902 0.708 0.116 0.000 0.016 0.160
#> GSM1068521 1 0.5259 0.65767 0.712 0.112 0.000 0.016 0.160
#> GSM1068522 2 0.1116 0.70638 0.004 0.964 0.000 0.004 0.028
#> GSM1068524 2 0.2011 0.69445 0.000 0.908 0.000 0.004 0.088
#> GSM1068527 2 0.6330 0.17182 0.052 0.532 0.000 0.056 0.360
#> GSM1068480 3 0.5856 0.49755 0.000 0.000 0.504 0.396 0.100
#> GSM1068484 2 0.5633 0.27147 0.004 0.580 0.000 0.080 0.336
#> GSM1068485 3 0.2753 0.73238 0.000 0.000 0.856 0.136 0.008
#> GSM1068489 2 0.3583 0.62148 0.012 0.792 0.000 0.004 0.192
#> GSM1068497 2 0.4121 0.58720 0.036 0.784 0.000 0.012 0.168
#> GSM1068501 5 0.5862 0.40160 0.008 0.376 0.008 0.060 0.548
#> GSM1068504 2 0.0162 0.70867 0.000 0.996 0.000 0.000 0.004
#> GSM1068509 5 0.9157 0.24893 0.128 0.200 0.100 0.160 0.412
#> GSM1068511 4 0.7858 -0.05573 0.020 0.148 0.060 0.388 0.384
#> GSM1068515 2 0.6370 0.24255 0.168 0.588 0.000 0.020 0.224
#> GSM1068516 5 0.8092 0.31319 0.072 0.352 0.028 0.144 0.404
#> GSM1068519 5 0.5800 -0.35855 0.396 0.000 0.008 0.072 0.524
#> GSM1068523 2 0.0290 0.70834 0.000 0.992 0.000 0.000 0.008
#> GSM1068525 2 0.5646 0.26181 0.004 0.576 0.000 0.080 0.340
#> GSM1068526 2 0.3786 0.60997 0.016 0.776 0.000 0.004 0.204
#> GSM1068458 1 0.5607 0.65849 0.664 0.080 0.000 0.024 0.232
#> GSM1068459 3 0.0290 0.75248 0.000 0.000 0.992 0.000 0.008
#> GSM1068460 2 0.3059 0.67792 0.028 0.860 0.000 0.004 0.108
#> GSM1068461 3 0.4575 0.67525 0.000 0.000 0.648 0.328 0.024
#> GSM1068464 2 0.0162 0.70870 0.000 0.996 0.000 0.000 0.004
#> GSM1068468 2 0.2337 0.69333 0.004 0.904 0.004 0.008 0.080
#> GSM1068472 2 0.3925 0.62294 0.016 0.804 0.004 0.020 0.156
#> GSM1068473 2 0.0162 0.70856 0.000 0.996 0.000 0.000 0.004
#> GSM1068474 2 0.0162 0.70867 0.000 0.996 0.000 0.000 0.004
#> GSM1068476 4 0.8008 0.63008 0.000 0.308 0.292 0.320 0.080
#> GSM1068477 2 0.1116 0.70638 0.004 0.964 0.000 0.004 0.028
#> GSM1068462 2 0.3376 0.65601 0.012 0.844 0.004 0.016 0.124
#> GSM1068463 3 0.0290 0.75248 0.000 0.000 0.992 0.000 0.008
#> GSM1068465 2 0.4233 0.62999 0.084 0.792 0.000 0.008 0.116
#> GSM1068466 1 0.6223 0.56266 0.612 0.180 0.000 0.020 0.188
#> GSM1068467 2 0.2150 0.69438 0.004 0.916 0.004 0.008 0.068
#> GSM1068469 2 0.3907 0.60222 0.016 0.788 0.000 0.016 0.180
#> GSM1068470 2 0.0162 0.70867 0.000 0.996 0.000 0.000 0.004
#> GSM1068471 2 0.0162 0.70870 0.000 0.996 0.000 0.000 0.004
#> GSM1068475 2 0.0162 0.70867 0.000 0.996 0.000 0.000 0.004
#> GSM1068528 3 0.6658 0.24705 0.272 0.000 0.556 0.036 0.136
#> GSM1068531 1 0.1740 0.69590 0.932 0.000 0.000 0.012 0.056
#> GSM1068532 1 0.2269 0.67523 0.920 0.000 0.020 0.032 0.028
#> GSM1068533 1 0.5607 0.65849 0.664 0.080 0.000 0.024 0.232
#> GSM1068535 5 0.6053 0.39143 0.024 0.356 0.008 0.052 0.560
#> GSM1068537 1 0.1596 0.68685 0.948 0.000 0.012 0.028 0.012
#> GSM1068538 1 0.2269 0.67523 0.920 0.000 0.020 0.032 0.028
#> GSM1068539 2 0.3699 0.64266 0.036 0.824 0.000 0.012 0.128
#> GSM1068540 1 0.2464 0.70029 0.904 0.004 0.000 0.048 0.044
#> GSM1068542 2 0.5185 0.52240 0.032 0.692 0.000 0.040 0.236
#> GSM1068543 2 0.6385 0.15380 0.024 0.520 0.004 0.084 0.368
#> GSM1068544 3 0.1087 0.74959 0.016 0.000 0.968 0.008 0.008
#> GSM1068545 2 0.3190 0.65528 0.012 0.840 0.000 0.008 0.140
#> GSM1068546 3 0.4763 0.66887 0.000 0.000 0.632 0.336 0.032
#> GSM1068547 1 0.5124 0.66669 0.720 0.112 0.000 0.012 0.156
#> GSM1068548 2 0.5259 0.51834 0.036 0.688 0.000 0.040 0.236
#> GSM1068549 3 0.4794 0.66538 0.000 0.000 0.624 0.344 0.032
#> GSM1068550 2 0.4096 0.57911 0.020 0.744 0.000 0.004 0.232
#> GSM1068551 2 0.0290 0.70834 0.000 0.992 0.000 0.000 0.008
#> GSM1068552 2 0.3421 0.64689 0.016 0.824 0.000 0.008 0.152
#> GSM1068555 2 0.0290 0.70834 0.000 0.992 0.000 0.000 0.008
#> GSM1068556 2 0.6347 0.15702 0.024 0.520 0.004 0.080 0.372
#> GSM1068557 2 0.4391 0.61616 0.024 0.772 0.004 0.024 0.176
#> GSM1068560 2 0.6330 0.17182 0.052 0.532 0.000 0.056 0.360
#> GSM1068561 2 0.4436 0.57072 0.040 0.744 0.000 0.008 0.208
#> GSM1068562 2 0.4096 0.57911 0.020 0.744 0.000 0.004 0.232
#> GSM1068563 2 0.3234 0.65273 0.012 0.836 0.000 0.008 0.144
#> GSM1068565 2 0.0162 0.70867 0.000 0.996 0.000 0.000 0.004
#> GSM1068529 5 0.8235 0.30003 0.056 0.316 0.040 0.176 0.412
#> GSM1068530 1 0.1490 0.68937 0.952 0.004 0.004 0.032 0.008
#> GSM1068534 5 0.8235 0.30003 0.056 0.316 0.040 0.176 0.412
#> GSM1068536 2 0.6318 0.29143 0.128 0.556 0.000 0.016 0.300
#> GSM1068541 2 0.3978 0.66139 0.052 0.796 0.000 0.004 0.148
#> GSM1068553 5 0.5806 0.40096 0.008 0.376 0.008 0.056 0.552
#> GSM1068554 5 0.5806 0.40096 0.008 0.376 0.008 0.056 0.552
#> GSM1068558 4 0.6441 -0.09314 0.000 0.016 0.208 0.572 0.204
#> GSM1068559 2 0.8610 -0.48926 0.008 0.344 0.160 0.224 0.264
#> GSM1068564 2 0.3583 0.62198 0.012 0.792 0.000 0.004 0.192
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1068478 2 0.3704 0.56746 0.016 0.744 0.000 0.000 0.232 0.008
#> GSM1068479 4 0.5924 0.55309 0.000 0.176 0.020 0.588 0.008 0.208
#> GSM1068481 3 0.5718 0.69002 0.000 0.000 0.528 0.176 0.292 0.004
#> GSM1068482 3 0.5689 0.71445 0.000 0.000 0.620 0.152 0.192 0.036
#> GSM1068483 1 0.7055 0.28874 0.404 0.128 0.000 0.012 0.368 0.088
#> GSM1068486 4 0.6058 -0.28325 0.000 0.004 0.244 0.528 0.212 0.012
#> GSM1068487 2 0.0458 0.69182 0.000 0.984 0.000 0.000 0.000 0.016
#> GSM1068488 6 0.5124 0.55933 0.000 0.228 0.000 0.036 0.072 0.664
#> GSM1068490 2 0.0458 0.69182 0.000 0.984 0.000 0.000 0.000 0.016
#> GSM1068491 4 0.5934 0.55901 0.000 0.176 0.032 0.596 0.004 0.192
#> GSM1068492 4 0.5586 0.56117 0.000 0.176 0.012 0.612 0.004 0.196
#> GSM1068493 2 0.6801 0.26114 0.048 0.536 0.004 0.036 0.264 0.112
#> GSM1068494 5 0.7788 0.16488 0.040 0.000 0.076 0.240 0.344 0.300
#> GSM1068495 2 0.4264 0.61872 0.020 0.764 0.000 0.000 0.116 0.100
#> GSM1068496 5 0.9115 0.36554 0.168 0.104 0.092 0.096 0.388 0.152
#> GSM1068498 2 0.3624 0.57938 0.016 0.756 0.000 0.000 0.220 0.008
#> GSM1068499 5 0.7182 0.04551 0.272 0.044 0.000 0.016 0.368 0.300
#> GSM1068500 1 0.7055 0.28874 0.404 0.128 0.000 0.012 0.368 0.088
#> GSM1068502 4 0.5586 0.56117 0.000 0.176 0.012 0.612 0.004 0.196
#> GSM1068503 2 0.0458 0.69182 0.000 0.984 0.000 0.000 0.000 0.016
#> GSM1068505 2 0.4929 0.15838 0.008 0.564 0.000 0.000 0.052 0.376
#> GSM1068506 2 0.3867 0.42197 0.000 0.688 0.000 0.004 0.012 0.296
#> GSM1068507 2 0.2843 0.65215 0.000 0.848 0.000 0.000 0.036 0.116
#> GSM1068508 2 0.4340 0.61543 0.040 0.764 0.000 0.000 0.132 0.064
#> GSM1068510 6 0.5136 0.44352 0.000 0.160 0.000 0.004 0.196 0.640
#> GSM1068512 6 0.6776 0.48719 0.032 0.292 0.000 0.056 0.108 0.512
#> GSM1068513 2 0.2633 0.65762 0.000 0.864 0.000 0.000 0.032 0.104
#> GSM1068514 6 0.7556 0.23121 0.008 0.236 0.000 0.200 0.152 0.404
#> GSM1068517 2 0.3624 0.57938 0.016 0.756 0.000 0.000 0.220 0.008
#> GSM1068518 6 0.7734 0.26039 0.048 0.216 0.000 0.084 0.228 0.424
#> GSM1068520 1 0.5755 0.56041 0.648 0.092 0.000 0.000 0.132 0.128
#> GSM1068521 1 0.5674 0.55650 0.656 0.088 0.000 0.000 0.124 0.132
#> GSM1068522 2 0.2489 0.63356 0.000 0.860 0.000 0.000 0.012 0.128
#> GSM1068524 2 0.3221 0.58962 0.000 0.792 0.000 0.000 0.020 0.188
#> GSM1068527 6 0.5729 0.49617 0.016 0.356 0.000 0.000 0.116 0.512
#> GSM1068480 4 0.6275 -0.18141 0.000 0.000 0.196 0.560 0.184 0.060
#> GSM1068484 6 0.5487 0.47451 0.000 0.384 0.000 0.016 0.084 0.516
#> GSM1068485 3 0.5837 0.64384 0.000 0.000 0.460 0.340 0.200 0.000
#> GSM1068489 2 0.4099 0.27215 0.000 0.612 0.000 0.000 0.016 0.372
#> GSM1068497 2 0.3704 0.56746 0.016 0.744 0.000 0.000 0.232 0.008
#> GSM1068501 6 0.5007 0.44600 0.000 0.144 0.000 0.004 0.196 0.656
#> GSM1068504 2 0.0508 0.69241 0.000 0.984 0.000 0.000 0.012 0.004
#> GSM1068509 5 0.8391 0.14182 0.088 0.164 0.008 0.100 0.360 0.280
#> GSM1068511 4 0.6655 -0.16463 0.000 0.076 0.000 0.400 0.128 0.396
#> GSM1068515 2 0.6470 0.28239 0.132 0.524 0.000 0.004 0.276 0.064
#> GSM1068516 6 0.7975 0.15423 0.040 0.288 0.004 0.080 0.268 0.320
#> GSM1068519 1 0.6104 -0.00422 0.364 0.000 0.000 0.000 0.348 0.288
#> GSM1068523 2 0.0692 0.69175 0.000 0.976 0.000 0.000 0.020 0.004
#> GSM1068525 6 0.5480 0.48137 0.000 0.380 0.000 0.016 0.084 0.520
#> GSM1068526 2 0.4093 0.20754 0.000 0.584 0.000 0.000 0.012 0.404
#> GSM1068458 1 0.5722 0.53807 0.596 0.044 0.000 0.004 0.276 0.080
#> GSM1068459 3 0.5069 0.74245 0.000 0.000 0.628 0.144 0.228 0.000
#> GSM1068460 2 0.3883 0.54061 0.004 0.752 0.000 0.000 0.044 0.200
#> GSM1068461 3 0.3271 0.56435 0.000 0.000 0.760 0.232 0.000 0.008
#> GSM1068464 2 0.0363 0.69242 0.000 0.988 0.000 0.000 0.000 0.012
#> GSM1068468 2 0.2983 0.67086 0.000 0.856 0.000 0.012 0.092 0.040
#> GSM1068472 2 0.3655 0.60537 0.004 0.776 0.000 0.012 0.192 0.016
#> GSM1068473 2 0.0632 0.69023 0.000 0.976 0.000 0.000 0.000 0.024
#> GSM1068474 2 0.0260 0.69329 0.000 0.992 0.000 0.000 0.000 0.008
#> GSM1068476 4 0.6853 0.54001 0.000 0.168 0.124 0.524 0.004 0.180
#> GSM1068477 2 0.2489 0.63356 0.000 0.860 0.000 0.000 0.012 0.128
#> GSM1068462 2 0.3657 0.62980 0.004 0.788 0.000 0.012 0.172 0.024
#> GSM1068463 3 0.5146 0.74004 0.000 0.000 0.616 0.148 0.236 0.000
#> GSM1068465 2 0.4330 0.61465 0.044 0.764 0.000 0.000 0.136 0.056
#> GSM1068466 1 0.6398 0.49590 0.552 0.120 0.000 0.000 0.232 0.096
#> GSM1068467 2 0.2815 0.67348 0.000 0.864 0.000 0.012 0.096 0.028
#> GSM1068469 2 0.3792 0.57883 0.004 0.744 0.000 0.004 0.228 0.020
#> GSM1068470 2 0.0508 0.69241 0.000 0.984 0.000 0.000 0.012 0.004
#> GSM1068471 2 0.0725 0.69309 0.000 0.976 0.000 0.000 0.012 0.012
#> GSM1068475 2 0.0508 0.69241 0.000 0.984 0.000 0.000 0.012 0.004
#> GSM1068528 5 0.7825 -0.17383 0.248 0.000 0.216 0.144 0.372 0.020
#> GSM1068531 1 0.2144 0.59597 0.908 0.000 0.000 0.004 0.048 0.040
#> GSM1068532 1 0.1524 0.57014 0.932 0.000 0.000 0.000 0.060 0.008
#> GSM1068533 1 0.5722 0.53807 0.596 0.044 0.000 0.004 0.276 0.080
#> GSM1068535 6 0.4659 0.46357 0.000 0.132 0.000 0.012 0.140 0.716
#> GSM1068537 1 0.0937 0.58488 0.960 0.000 0.000 0.000 0.040 0.000
#> GSM1068538 1 0.1524 0.57014 0.932 0.000 0.000 0.000 0.060 0.008
#> GSM1068539 2 0.4218 0.62233 0.020 0.768 0.000 0.000 0.116 0.096
#> GSM1068540 1 0.2420 0.59331 0.892 0.000 0.000 0.008 0.068 0.032
#> GSM1068542 2 0.4381 0.02265 0.004 0.524 0.000 0.000 0.016 0.456
#> GSM1068543 6 0.4245 0.56981 0.000 0.280 0.000 0.020 0.016 0.684
#> GSM1068544 3 0.5596 0.73527 0.020 0.000 0.604 0.148 0.228 0.000
#> GSM1068545 2 0.3848 0.42867 0.000 0.692 0.000 0.004 0.012 0.292
#> GSM1068546 3 0.2665 0.59058 0.000 0.000 0.868 0.104 0.016 0.012
#> GSM1068547 1 0.5637 0.56757 0.660 0.088 0.000 0.000 0.128 0.124
#> GSM1068548 2 0.4546 0.03502 0.008 0.528 0.000 0.000 0.020 0.444
#> GSM1068549 3 0.2666 0.58752 0.000 0.000 0.864 0.112 0.012 0.012
#> GSM1068550 2 0.4045 0.14515 0.000 0.564 0.000 0.000 0.008 0.428
#> GSM1068551 2 0.0603 0.69232 0.000 0.980 0.000 0.000 0.016 0.004
#> GSM1068552 2 0.3955 0.39111 0.000 0.668 0.000 0.004 0.012 0.316
#> GSM1068555 2 0.0692 0.69175 0.000 0.976 0.000 0.000 0.020 0.004
#> GSM1068556 6 0.4158 0.56838 0.000 0.280 0.000 0.020 0.012 0.688
#> GSM1068557 2 0.4817 0.57468 0.016 0.716 0.000 0.008 0.168 0.092
#> GSM1068560 6 0.5729 0.49617 0.016 0.356 0.000 0.000 0.116 0.512
#> GSM1068561 2 0.5251 0.49488 0.024 0.676 0.000 0.004 0.136 0.160
#> GSM1068562 2 0.4045 0.14515 0.000 0.564 0.000 0.000 0.008 0.428
#> GSM1068563 2 0.3867 0.42202 0.000 0.688 0.000 0.004 0.012 0.296
#> GSM1068565 2 0.0260 0.69329 0.000 0.992 0.000 0.000 0.000 0.008
#> GSM1068529 6 0.7760 0.29414 0.028 0.216 0.004 0.112 0.212 0.428
#> GSM1068530 1 0.0632 0.59071 0.976 0.000 0.000 0.000 0.024 0.000
#> GSM1068534 6 0.7760 0.29414 0.028 0.216 0.004 0.112 0.212 0.428
#> GSM1068536 2 0.6792 0.14867 0.080 0.480 0.000 0.000 0.192 0.248
#> GSM1068541 2 0.4548 0.61033 0.028 0.744 0.000 0.000 0.108 0.120
#> GSM1068553 6 0.4980 0.44770 0.000 0.144 0.000 0.004 0.192 0.660
#> GSM1068554 6 0.4980 0.44770 0.000 0.144 0.000 0.004 0.192 0.660
#> GSM1068558 4 0.4327 0.17798 0.000 0.000 0.016 0.748 0.080 0.156
#> GSM1068559 4 0.7269 0.02219 0.004 0.196 0.000 0.388 0.100 0.312
#> GSM1068564 2 0.4026 0.27138 0.000 0.612 0.000 0.000 0.012 0.376
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 disease.state(p) gender(p) k
#> SD:hclust 100 0.6682 1.000 2
#> SD:hclust 90 0.7785 0.555 3
#> SD:hclust 74 0.3006 0.806 4
#> SD:hclust 74 0.1244 0.454 5
#> SD:hclust 63 0.0158 0.277 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 38950 rows and 108 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 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 0.588 0.770 0.898 0.4447 0.565 0.565
#> 3 3 0.477 0.696 0.839 0.3817 0.733 0.564
#> 4 4 0.698 0.827 0.881 0.1853 0.770 0.483
#> 5 5 0.656 0.631 0.784 0.0752 0.964 0.871
#> 6 6 0.643 0.453 0.678 0.0451 0.928 0.719
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
#> GSM1068478 2 0.9954 -0.1264 0.460 0.540
#> GSM1068479 2 0.9129 0.5184 0.328 0.672
#> GSM1068481 1 0.0672 0.8400 0.992 0.008
#> GSM1068482 1 0.0000 0.8402 1.000 0.000
#> GSM1068483 1 0.8555 0.7124 0.720 0.280
#> GSM1068486 1 0.0672 0.8400 0.992 0.008
#> GSM1068487 2 0.0000 0.8941 0.000 1.000
#> GSM1068488 2 0.9087 0.5264 0.324 0.676
#> GSM1068490 2 0.0000 0.8941 0.000 1.000
#> GSM1068491 1 0.1843 0.8324 0.972 0.028
#> GSM1068492 2 0.9661 0.4068 0.392 0.608
#> GSM1068493 2 0.0672 0.8903 0.008 0.992
#> GSM1068494 1 0.0672 0.8403 0.992 0.008
#> GSM1068495 2 0.0672 0.8917 0.008 0.992
#> GSM1068496 1 0.0000 0.8402 1.000 0.000
#> GSM1068498 2 0.0672 0.8903 0.008 0.992
#> GSM1068499 1 0.2603 0.8346 0.956 0.044
#> GSM1068500 1 0.8144 0.7373 0.748 0.252
#> GSM1068502 2 0.9248 0.4995 0.340 0.660
#> GSM1068503 2 0.0000 0.8941 0.000 1.000
#> GSM1068505 2 0.0672 0.8917 0.008 0.992
#> GSM1068506 2 0.0376 0.8933 0.004 0.996
#> GSM1068507 2 0.0000 0.8941 0.000 1.000
#> GSM1068508 2 0.0672 0.8917 0.008 0.992
#> GSM1068510 2 0.0938 0.8873 0.012 0.988
#> GSM1068512 2 0.9491 0.4411 0.368 0.632
#> GSM1068513 2 0.0000 0.8941 0.000 1.000
#> GSM1068514 2 0.9970 0.2246 0.468 0.532
#> GSM1068517 2 0.0000 0.8941 0.000 1.000
#> GSM1068518 2 0.0672 0.8917 0.008 0.992
#> GSM1068520 1 0.9815 0.4582 0.580 0.420
#> GSM1068521 1 0.9170 0.6409 0.668 0.332
#> GSM1068522 2 0.0000 0.8941 0.000 1.000
#> GSM1068524 2 0.0000 0.8941 0.000 1.000
#> GSM1068527 2 0.0672 0.8917 0.008 0.992
#> GSM1068480 1 0.0672 0.8400 0.992 0.008
#> GSM1068484 2 0.0000 0.8941 0.000 1.000
#> GSM1068485 1 0.0672 0.8400 0.992 0.008
#> GSM1068489 2 0.0376 0.8933 0.004 0.996
#> GSM1068497 2 0.0672 0.8903 0.008 0.992
#> GSM1068501 2 0.0000 0.8941 0.000 1.000
#> GSM1068504 2 0.0000 0.8941 0.000 1.000
#> GSM1068509 1 0.8713 0.6992 0.708 0.292
#> GSM1068511 1 0.6973 0.7628 0.812 0.188
#> GSM1068515 2 0.9909 -0.0849 0.444 0.556
#> GSM1068516 2 0.0000 0.8941 0.000 1.000
#> GSM1068519 1 0.8499 0.7157 0.724 0.276
#> GSM1068523 2 0.0000 0.8941 0.000 1.000
#> GSM1068525 2 0.0000 0.8941 0.000 1.000
#> GSM1068526 2 0.0376 0.8933 0.004 0.996
#> GSM1068458 1 0.9170 0.6409 0.668 0.332
#> GSM1068459 1 0.0000 0.8402 1.000 0.000
#> GSM1068460 2 0.0672 0.8917 0.008 0.992
#> GSM1068461 1 0.0672 0.8400 0.992 0.008
#> GSM1068464 2 0.0000 0.8941 0.000 1.000
#> GSM1068468 2 0.0000 0.8941 0.000 1.000
#> GSM1068472 2 0.0000 0.8941 0.000 1.000
#> GSM1068473 2 0.0000 0.8941 0.000 1.000
#> GSM1068474 2 0.0000 0.8941 0.000 1.000
#> GSM1068476 1 0.7453 0.6446 0.788 0.212
#> GSM1068477 2 0.0000 0.8941 0.000 1.000
#> GSM1068462 2 0.0000 0.8941 0.000 1.000
#> GSM1068463 1 0.0376 0.8400 0.996 0.004
#> GSM1068465 2 0.1184 0.8869 0.016 0.984
#> GSM1068466 1 0.9286 0.6196 0.656 0.344
#> GSM1068467 2 0.0000 0.8941 0.000 1.000
#> GSM1068469 2 0.0672 0.8903 0.008 0.992
#> GSM1068470 2 0.0000 0.8941 0.000 1.000
#> GSM1068471 2 0.0000 0.8941 0.000 1.000
#> GSM1068475 2 0.0000 0.8941 0.000 1.000
#> GSM1068528 1 0.0000 0.8402 1.000 0.000
#> GSM1068531 1 0.8763 0.6947 0.704 0.296
#> GSM1068532 1 0.0000 0.8402 1.000 0.000
#> GSM1068533 1 0.7139 0.7726 0.804 0.196
#> GSM1068535 1 0.7883 0.7477 0.764 0.236
#> GSM1068537 1 0.3431 0.8286 0.936 0.064
#> GSM1068538 1 0.0000 0.8402 1.000 0.000
#> GSM1068539 2 0.0672 0.8917 0.008 0.992
#> GSM1068540 1 0.8763 0.6947 0.704 0.296
#> GSM1068542 2 0.0672 0.8917 0.008 0.992
#> GSM1068543 2 0.9580 0.4368 0.380 0.620
#> GSM1068544 1 0.0000 0.8402 1.000 0.000
#> GSM1068545 2 0.0376 0.8933 0.004 0.996
#> GSM1068546 1 0.0672 0.8400 0.992 0.008
#> GSM1068547 2 0.9963 -0.1413 0.464 0.536
#> GSM1068548 2 0.0672 0.8917 0.008 0.992
#> GSM1068549 1 0.0672 0.8400 0.992 0.008
#> GSM1068550 2 0.0672 0.8917 0.008 0.992
#> GSM1068551 2 0.0000 0.8941 0.000 1.000
#> GSM1068552 2 0.0376 0.8933 0.004 0.996
#> GSM1068555 2 0.0000 0.8941 0.000 1.000
#> GSM1068556 2 0.9427 0.4725 0.360 0.640
#> GSM1068557 2 0.0000 0.8941 0.000 1.000
#> GSM1068560 2 0.0672 0.8917 0.008 0.992
#> GSM1068561 2 0.0000 0.8941 0.000 1.000
#> GSM1068562 2 0.0376 0.8933 0.004 0.996
#> GSM1068563 2 0.2043 0.8739 0.032 0.968
#> GSM1068565 2 0.0000 0.8941 0.000 1.000
#> GSM1068529 2 0.9866 0.3203 0.432 0.568
#> GSM1068530 1 0.8763 0.6947 0.704 0.296
#> GSM1068534 2 0.8813 0.5613 0.300 0.700
#> GSM1068536 2 0.1184 0.8869 0.016 0.984
#> GSM1068541 2 0.0376 0.8933 0.004 0.996
#> GSM1068553 2 0.7219 0.6928 0.200 0.800
#> GSM1068554 2 0.0938 0.8873 0.012 0.988
#> GSM1068558 2 0.9850 0.3296 0.428 0.572
#> GSM1068559 2 0.9988 0.1891 0.480 0.520
#> GSM1068564 2 0.0376 0.8933 0.004 0.996
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1068478 1 0.4399 0.681 0.812 0.188 0.000
#> GSM1068479 3 0.5678 0.611 0.000 0.316 0.684
#> GSM1068481 3 0.3192 0.835 0.112 0.000 0.888
#> GSM1068482 3 0.3267 0.834 0.116 0.000 0.884
#> GSM1068483 1 0.1170 0.819 0.976 0.008 0.016
#> GSM1068486 3 0.3192 0.835 0.112 0.000 0.888
#> GSM1068487 2 0.0592 0.796 0.000 0.988 0.012
#> GSM1068488 2 0.9095 0.333 0.376 0.480 0.144
#> GSM1068490 2 0.0592 0.796 0.000 0.988 0.012
#> GSM1068491 3 0.3129 0.830 0.088 0.008 0.904
#> GSM1068492 3 0.5247 0.634 0.008 0.224 0.768
#> GSM1068493 2 0.1620 0.788 0.012 0.964 0.024
#> GSM1068494 1 0.3116 0.771 0.892 0.000 0.108
#> GSM1068495 2 0.5519 0.750 0.120 0.812 0.068
#> GSM1068496 1 0.2261 0.777 0.932 0.000 0.068
#> GSM1068498 2 0.5591 0.408 0.304 0.696 0.000
#> GSM1068499 1 0.1643 0.810 0.956 0.000 0.044
#> GSM1068500 1 0.1170 0.819 0.976 0.008 0.016
#> GSM1068502 3 0.5845 0.616 0.004 0.308 0.688
#> GSM1068503 2 0.0592 0.796 0.000 0.988 0.012
#> GSM1068505 2 0.7848 0.596 0.264 0.640 0.096
#> GSM1068506 2 0.7059 0.678 0.192 0.716 0.092
#> GSM1068507 2 0.7807 0.630 0.236 0.656 0.108
#> GSM1068508 2 0.0237 0.797 0.004 0.996 0.000
#> GSM1068510 2 0.4683 0.769 0.024 0.836 0.140
#> GSM1068512 1 0.8666 0.177 0.544 0.336 0.120
#> GSM1068513 2 0.0592 0.796 0.000 0.988 0.012
#> GSM1068514 3 0.5678 0.633 0.032 0.192 0.776
#> GSM1068517 2 0.4887 0.555 0.228 0.772 0.000
#> GSM1068518 2 0.8569 0.361 0.392 0.508 0.100
#> GSM1068520 1 0.0983 0.821 0.980 0.016 0.004
#> GSM1068521 1 0.0661 0.822 0.988 0.004 0.008
#> GSM1068522 2 0.0747 0.796 0.000 0.984 0.016
#> GSM1068524 2 0.1031 0.797 0.000 0.976 0.024
#> GSM1068527 1 0.8173 0.317 0.600 0.300 0.100
#> GSM1068480 3 0.3116 0.834 0.108 0.000 0.892
#> GSM1068484 2 0.4449 0.772 0.040 0.860 0.100
#> GSM1068485 3 0.3192 0.835 0.112 0.000 0.888
#> GSM1068489 2 0.7851 0.604 0.256 0.644 0.100
#> GSM1068497 2 0.4931 0.548 0.232 0.768 0.000
#> GSM1068501 2 0.5467 0.761 0.072 0.816 0.112
#> GSM1068504 2 0.0592 0.796 0.000 0.988 0.012
#> GSM1068509 1 0.1643 0.809 0.956 0.000 0.044
#> GSM1068511 1 0.8965 0.434 0.564 0.196 0.240
#> GSM1068515 1 0.6062 0.327 0.616 0.384 0.000
#> GSM1068516 2 0.6920 0.698 0.164 0.732 0.104
#> GSM1068519 1 0.0829 0.822 0.984 0.004 0.012
#> GSM1068523 2 0.0237 0.797 0.004 0.996 0.000
#> GSM1068525 2 0.3690 0.778 0.016 0.884 0.100
#> GSM1068526 2 0.8009 0.578 0.276 0.624 0.100
#> GSM1068458 1 0.0661 0.822 0.988 0.004 0.008
#> GSM1068459 3 0.3267 0.834 0.116 0.000 0.884
#> GSM1068460 1 0.4075 0.774 0.880 0.048 0.072
#> GSM1068461 3 0.3192 0.835 0.112 0.000 0.888
#> GSM1068464 2 0.0592 0.796 0.000 0.988 0.012
#> GSM1068468 2 0.1015 0.795 0.008 0.980 0.012
#> GSM1068472 2 0.1015 0.795 0.008 0.980 0.012
#> GSM1068473 2 0.0592 0.796 0.000 0.988 0.012
#> GSM1068474 2 0.0829 0.795 0.004 0.984 0.012
#> GSM1068476 3 0.2486 0.821 0.060 0.008 0.932
#> GSM1068477 2 0.0829 0.795 0.004 0.984 0.012
#> GSM1068462 2 0.1015 0.795 0.008 0.980 0.012
#> GSM1068463 3 0.3267 0.834 0.116 0.000 0.884
#> GSM1068465 1 0.5346 0.722 0.808 0.152 0.040
#> GSM1068466 1 0.0829 0.822 0.984 0.012 0.004
#> GSM1068467 2 0.1015 0.795 0.008 0.980 0.012
#> GSM1068469 2 0.5450 0.553 0.228 0.760 0.012
#> GSM1068470 2 0.0237 0.797 0.004 0.996 0.000
#> GSM1068471 2 0.0829 0.795 0.004 0.984 0.012
#> GSM1068475 2 0.0829 0.795 0.004 0.984 0.012
#> GSM1068528 1 0.5098 0.479 0.752 0.000 0.248
#> GSM1068531 1 0.0424 0.821 0.992 0.000 0.008
#> GSM1068532 1 0.0892 0.817 0.980 0.000 0.020
#> GSM1068533 1 0.0892 0.817 0.980 0.000 0.020
#> GSM1068535 1 0.7926 0.477 0.656 0.216 0.128
#> GSM1068537 1 0.0892 0.817 0.980 0.000 0.020
#> GSM1068538 1 0.0892 0.817 0.980 0.000 0.020
#> GSM1068539 2 0.4642 0.773 0.084 0.856 0.060
#> GSM1068540 1 0.0237 0.822 0.996 0.000 0.004
#> GSM1068542 2 0.8204 0.519 0.316 0.588 0.096
#> GSM1068543 2 0.8975 0.325 0.384 0.484 0.132
#> GSM1068544 3 0.3482 0.824 0.128 0.000 0.872
#> GSM1068545 2 0.2564 0.793 0.036 0.936 0.028
#> GSM1068546 3 0.3192 0.835 0.112 0.000 0.888
#> GSM1068547 1 0.2527 0.800 0.936 0.020 0.044
#> GSM1068548 2 0.8499 0.371 0.388 0.516 0.096
#> GSM1068549 3 0.3192 0.835 0.112 0.000 0.888
#> GSM1068550 2 0.8055 0.558 0.292 0.612 0.096
#> GSM1068551 2 0.0237 0.797 0.004 0.996 0.000
#> GSM1068552 2 0.5737 0.746 0.104 0.804 0.092
#> GSM1068555 2 0.0237 0.797 0.004 0.996 0.000
#> GSM1068556 2 0.8878 0.339 0.384 0.492 0.124
#> GSM1068557 2 0.1015 0.795 0.008 0.980 0.012
#> GSM1068560 2 0.8559 0.367 0.388 0.512 0.100
#> GSM1068561 2 0.2773 0.792 0.024 0.928 0.048
#> GSM1068562 2 0.8014 0.586 0.268 0.628 0.104
#> GSM1068563 2 0.8186 0.551 0.292 0.604 0.104
#> GSM1068565 2 0.0475 0.797 0.004 0.992 0.004
#> GSM1068529 3 0.7095 0.420 0.048 0.292 0.660
#> GSM1068530 1 0.0424 0.821 0.992 0.000 0.008
#> GSM1068534 1 0.9151 -0.181 0.436 0.420 0.144
#> GSM1068536 1 0.4121 0.769 0.876 0.040 0.084
#> GSM1068541 2 0.4874 0.748 0.144 0.828 0.028
#> GSM1068553 2 0.8936 0.324 0.388 0.484 0.128
#> GSM1068554 2 0.6349 0.739 0.092 0.768 0.140
#> GSM1068558 3 0.2492 0.755 0.016 0.048 0.936
#> GSM1068559 3 0.5678 0.633 0.032 0.192 0.776
#> GSM1068564 2 0.3445 0.783 0.016 0.896 0.088
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1068478 1 0.4239 0.821 0.812 0.152 0.004 0.032
#> GSM1068479 3 0.6438 0.161 0.000 0.436 0.496 0.068
#> GSM1068481 3 0.1406 0.899 0.024 0.000 0.960 0.016
#> GSM1068482 3 0.1297 0.900 0.020 0.000 0.964 0.016
#> GSM1068483 1 0.2261 0.907 0.932 0.036 0.008 0.024
#> GSM1068486 3 0.0657 0.902 0.012 0.000 0.984 0.004
#> GSM1068487 2 0.1824 0.904 0.000 0.936 0.004 0.060
#> GSM1068488 4 0.2131 0.863 0.008 0.040 0.016 0.936
#> GSM1068490 2 0.1637 0.905 0.000 0.940 0.000 0.060
#> GSM1068491 3 0.1229 0.895 0.004 0.008 0.968 0.020
#> GSM1068492 4 0.5564 0.245 0.000 0.020 0.436 0.544
#> GSM1068493 2 0.2352 0.873 0.016 0.928 0.012 0.044
#> GSM1068494 4 0.4354 0.799 0.108 0.032 0.028 0.832
#> GSM1068495 2 0.6227 0.316 0.052 0.572 0.004 0.372
#> GSM1068496 1 0.4327 0.850 0.836 0.028 0.036 0.100
#> GSM1068498 2 0.2360 0.856 0.052 0.924 0.004 0.020
#> GSM1068499 1 0.4484 0.836 0.816 0.032 0.020 0.132
#> GSM1068500 1 0.2269 0.908 0.932 0.032 0.008 0.028
#> GSM1068502 3 0.6451 0.260 0.000 0.404 0.524 0.072
#> GSM1068503 2 0.1824 0.904 0.000 0.936 0.004 0.060
#> GSM1068505 4 0.4583 0.845 0.112 0.076 0.004 0.808
#> GSM1068506 4 0.3598 0.846 0.028 0.124 0.000 0.848
#> GSM1068507 4 0.5690 0.756 0.084 0.196 0.004 0.716
#> GSM1068508 2 0.2335 0.898 0.020 0.920 0.000 0.060
#> GSM1068510 4 0.3224 0.850 0.000 0.120 0.016 0.864
#> GSM1068512 4 0.3400 0.861 0.044 0.068 0.008 0.880
#> GSM1068513 2 0.1824 0.904 0.000 0.936 0.004 0.060
#> GSM1068514 4 0.5108 0.543 0.000 0.020 0.308 0.672
#> GSM1068517 2 0.2189 0.864 0.044 0.932 0.004 0.020
#> GSM1068518 4 0.3439 0.850 0.048 0.084 0.000 0.868
#> GSM1068520 1 0.0895 0.913 0.976 0.020 0.000 0.004
#> GSM1068521 1 0.1406 0.913 0.960 0.016 0.000 0.024
#> GSM1068522 2 0.2164 0.900 0.004 0.924 0.004 0.068
#> GSM1068524 2 0.3196 0.853 0.000 0.856 0.008 0.136
#> GSM1068527 4 0.4150 0.845 0.120 0.056 0.000 0.824
#> GSM1068480 3 0.1635 0.887 0.008 0.000 0.948 0.044
#> GSM1068484 4 0.2048 0.868 0.008 0.064 0.000 0.928
#> GSM1068485 3 0.0895 0.902 0.020 0.000 0.976 0.004
#> GSM1068489 4 0.3127 0.869 0.032 0.068 0.008 0.892
#> GSM1068497 2 0.2189 0.864 0.044 0.932 0.004 0.020
#> GSM1068501 4 0.3695 0.853 0.028 0.108 0.008 0.856
#> GSM1068504 2 0.1637 0.905 0.000 0.940 0.000 0.060
#> GSM1068509 1 0.5022 0.755 0.736 0.044 0.000 0.220
#> GSM1068511 4 0.2895 0.841 0.016 0.032 0.044 0.908
#> GSM1068515 1 0.5522 0.633 0.668 0.288 0.000 0.044
#> GSM1068516 4 0.3027 0.859 0.020 0.088 0.004 0.888
#> GSM1068519 1 0.1847 0.906 0.940 0.004 0.004 0.052
#> GSM1068523 2 0.1661 0.904 0.000 0.944 0.004 0.052
#> GSM1068525 4 0.2010 0.867 0.004 0.060 0.004 0.932
#> GSM1068526 4 0.2919 0.869 0.044 0.060 0.000 0.896
#> GSM1068458 1 0.1174 0.912 0.968 0.020 0.000 0.012
#> GSM1068459 3 0.1510 0.898 0.028 0.000 0.956 0.016
#> GSM1068460 1 0.1624 0.909 0.952 0.028 0.000 0.020
#> GSM1068461 3 0.0804 0.901 0.012 0.000 0.980 0.008
#> GSM1068464 2 0.1557 0.905 0.000 0.944 0.000 0.056
#> GSM1068468 2 0.0657 0.894 0.004 0.984 0.000 0.012
#> GSM1068472 2 0.0657 0.896 0.004 0.984 0.000 0.012
#> GSM1068473 2 0.1824 0.904 0.000 0.936 0.004 0.060
#> GSM1068474 2 0.1557 0.905 0.000 0.944 0.000 0.056
#> GSM1068476 3 0.1339 0.893 0.004 0.008 0.964 0.024
#> GSM1068477 2 0.1389 0.906 0.000 0.952 0.000 0.048
#> GSM1068462 2 0.1593 0.877 0.004 0.956 0.016 0.024
#> GSM1068463 3 0.1510 0.898 0.028 0.000 0.956 0.016
#> GSM1068465 1 0.4155 0.848 0.828 0.100 0.000 0.072
#> GSM1068466 1 0.1042 0.912 0.972 0.020 0.000 0.008
#> GSM1068467 2 0.0524 0.894 0.004 0.988 0.000 0.008
#> GSM1068469 2 0.1798 0.867 0.040 0.944 0.000 0.016
#> GSM1068470 2 0.1902 0.905 0.000 0.932 0.004 0.064
#> GSM1068471 2 0.1557 0.905 0.000 0.944 0.000 0.056
#> GSM1068475 2 0.1474 0.905 0.000 0.948 0.000 0.052
#> GSM1068528 1 0.4114 0.777 0.812 0.008 0.164 0.016
#> GSM1068531 1 0.0336 0.911 0.992 0.000 0.000 0.008
#> GSM1068532 1 0.1624 0.899 0.952 0.000 0.020 0.028
#> GSM1068533 1 0.1042 0.904 0.972 0.000 0.008 0.020
#> GSM1068535 4 0.5379 0.687 0.264 0.016 0.020 0.700
#> GSM1068537 1 0.1174 0.901 0.968 0.000 0.012 0.020
#> GSM1068538 1 0.1174 0.901 0.968 0.000 0.012 0.020
#> GSM1068539 2 0.6227 0.316 0.052 0.572 0.004 0.372
#> GSM1068540 1 0.0895 0.912 0.976 0.004 0.000 0.020
#> GSM1068542 4 0.4254 0.851 0.104 0.064 0.004 0.828
#> GSM1068543 4 0.2307 0.869 0.016 0.048 0.008 0.928
#> GSM1068544 3 0.1706 0.893 0.036 0.000 0.948 0.016
#> GSM1068545 2 0.4795 0.632 0.012 0.696 0.000 0.292
#> GSM1068546 3 0.1388 0.897 0.012 0.000 0.960 0.028
#> GSM1068547 1 0.1182 0.913 0.968 0.016 0.000 0.016
#> GSM1068548 4 0.4663 0.831 0.148 0.064 0.000 0.788
#> GSM1068549 3 0.0804 0.900 0.008 0.000 0.980 0.012
#> GSM1068550 4 0.3320 0.866 0.056 0.068 0.000 0.876
#> GSM1068551 2 0.1902 0.905 0.000 0.932 0.004 0.064
#> GSM1068552 4 0.3970 0.845 0.036 0.124 0.004 0.836
#> GSM1068555 2 0.1661 0.903 0.000 0.944 0.004 0.052
#> GSM1068556 4 0.2421 0.868 0.020 0.048 0.008 0.924
#> GSM1068557 2 0.1543 0.889 0.008 0.956 0.004 0.032
#> GSM1068560 4 0.3168 0.867 0.060 0.056 0.000 0.884
#> GSM1068561 2 0.4128 0.760 0.020 0.808 0.004 0.168
#> GSM1068562 4 0.1722 0.868 0.008 0.048 0.000 0.944
#> GSM1068563 4 0.2542 0.866 0.012 0.084 0.000 0.904
#> GSM1068565 2 0.1557 0.905 0.000 0.944 0.000 0.056
#> GSM1068529 4 0.3325 0.828 0.008 0.044 0.064 0.884
#> GSM1068530 1 0.0336 0.909 0.992 0.000 0.000 0.008
#> GSM1068534 4 0.2804 0.852 0.016 0.060 0.016 0.908
#> GSM1068536 1 0.3836 0.856 0.852 0.052 0.004 0.092
#> GSM1068541 2 0.5102 0.706 0.064 0.748 0.000 0.188
#> GSM1068553 4 0.4061 0.853 0.092 0.044 0.016 0.848
#> GSM1068554 4 0.3820 0.857 0.028 0.100 0.016 0.856
#> GSM1068558 4 0.5138 0.371 0.000 0.008 0.392 0.600
#> GSM1068559 4 0.5527 0.445 0.000 0.028 0.356 0.616
#> GSM1068564 4 0.4747 0.710 0.016 0.244 0.004 0.736
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM1068478 1 0.5957 0.4136 0.508 0.068 0.000 0.016 0.408
#> GSM1068479 2 0.7651 -0.0264 0.000 0.420 0.348 0.112 0.120
#> GSM1068481 3 0.1197 0.9352 0.000 0.000 0.952 0.000 0.048
#> GSM1068482 3 0.2110 0.9231 0.016 0.000 0.912 0.000 0.072
#> GSM1068483 1 0.3742 0.7723 0.792 0.012 0.000 0.012 0.184
#> GSM1068486 3 0.0000 0.9362 0.000 0.000 1.000 0.000 0.000
#> GSM1068487 2 0.0324 0.6855 0.000 0.992 0.000 0.004 0.004
#> GSM1068488 4 0.2011 0.6945 0.000 0.004 0.000 0.908 0.088
#> GSM1068490 2 0.0324 0.6855 0.000 0.992 0.000 0.004 0.004
#> GSM1068491 3 0.1965 0.8987 0.000 0.000 0.904 0.000 0.096
#> GSM1068492 4 0.6377 0.3674 0.000 0.008 0.288 0.540 0.164
#> GSM1068493 2 0.4994 0.2249 0.020 0.604 0.000 0.012 0.364
#> GSM1068494 4 0.4686 0.5551 0.092 0.004 0.004 0.756 0.144
#> GSM1068495 5 0.6757 0.7126 0.020 0.184 0.000 0.280 0.516
#> GSM1068496 1 0.5476 0.6705 0.676 0.004 0.016 0.072 0.232
#> GSM1068498 2 0.4909 0.2318 0.032 0.588 0.000 0.000 0.380
#> GSM1068499 1 0.5960 0.6236 0.624 0.004 0.004 0.164 0.204
#> GSM1068500 1 0.4048 0.7594 0.764 0.012 0.000 0.016 0.208
#> GSM1068502 2 0.7964 -0.1238 0.000 0.372 0.352 0.132 0.144
#> GSM1068503 2 0.2304 0.6031 0.000 0.892 0.000 0.008 0.100
#> GSM1068505 4 0.5181 0.6245 0.032 0.028 0.000 0.668 0.272
#> GSM1068506 4 0.5190 0.5903 0.000 0.096 0.000 0.668 0.236
#> GSM1068507 4 0.6567 0.3971 0.008 0.240 0.000 0.524 0.228
#> GSM1068508 2 0.4207 0.4625 0.008 0.708 0.000 0.008 0.276
#> GSM1068510 4 0.5287 0.6137 0.000 0.092 0.000 0.648 0.260
#> GSM1068512 4 0.1628 0.6952 0.000 0.008 0.000 0.936 0.056
#> GSM1068513 2 0.2233 0.6105 0.000 0.892 0.000 0.004 0.104
#> GSM1068514 4 0.5831 0.4511 0.000 0.000 0.236 0.604 0.160
#> GSM1068517 2 0.4909 0.2318 0.032 0.588 0.000 0.000 0.380
#> GSM1068518 4 0.3234 0.6398 0.012 0.008 0.000 0.836 0.144
#> GSM1068520 1 0.2462 0.8012 0.880 0.000 0.000 0.008 0.112
#> GSM1068521 1 0.3002 0.7987 0.856 0.000 0.000 0.028 0.116
#> GSM1068522 2 0.4605 0.3336 0.000 0.732 0.000 0.076 0.192
#> GSM1068524 2 0.2914 0.6068 0.000 0.872 0.000 0.076 0.052
#> GSM1068527 4 0.3849 0.6722 0.052 0.004 0.000 0.808 0.136
#> GSM1068480 3 0.1981 0.8976 0.000 0.000 0.924 0.048 0.028
#> GSM1068484 4 0.1251 0.7043 0.000 0.008 0.000 0.956 0.036
#> GSM1068485 3 0.0609 0.9377 0.000 0.000 0.980 0.000 0.020
#> GSM1068489 4 0.3815 0.6783 0.004 0.012 0.000 0.764 0.220
#> GSM1068497 2 0.4886 0.2480 0.032 0.596 0.000 0.000 0.372
#> GSM1068501 4 0.5330 0.6120 0.004 0.072 0.000 0.636 0.288
#> GSM1068504 2 0.0162 0.6864 0.000 0.996 0.000 0.004 0.000
#> GSM1068509 1 0.6023 0.5749 0.600 0.004 0.000 0.208 0.188
#> GSM1068511 4 0.2462 0.6894 0.000 0.000 0.008 0.880 0.112
#> GSM1068515 1 0.6624 0.3950 0.516 0.164 0.000 0.016 0.304
#> GSM1068516 4 0.2358 0.6798 0.000 0.008 0.000 0.888 0.104
#> GSM1068519 1 0.3336 0.7892 0.844 0.000 0.000 0.060 0.096
#> GSM1068523 2 0.3231 0.5946 0.000 0.800 0.000 0.004 0.196
#> GSM1068525 4 0.1830 0.6950 0.000 0.008 0.000 0.924 0.068
#> GSM1068526 4 0.3821 0.6677 0.000 0.020 0.000 0.764 0.216
#> GSM1068458 1 0.2124 0.8031 0.900 0.000 0.000 0.004 0.096
#> GSM1068459 3 0.2110 0.9231 0.016 0.000 0.912 0.000 0.072
#> GSM1068460 1 0.3519 0.7585 0.776 0.000 0.000 0.008 0.216
#> GSM1068461 3 0.0404 0.9354 0.000 0.000 0.988 0.000 0.012
#> GSM1068464 2 0.0324 0.6859 0.000 0.992 0.000 0.004 0.004
#> GSM1068468 2 0.2439 0.6468 0.004 0.876 0.000 0.000 0.120
#> GSM1068472 2 0.2763 0.6210 0.004 0.848 0.000 0.000 0.148
#> GSM1068473 2 0.0324 0.6855 0.000 0.992 0.000 0.004 0.004
#> GSM1068474 2 0.0162 0.6864 0.000 0.996 0.000 0.004 0.000
#> GSM1068476 3 0.2020 0.8987 0.000 0.000 0.900 0.000 0.100
#> GSM1068477 2 0.1365 0.6861 0.004 0.952 0.000 0.004 0.040
#> GSM1068462 2 0.2763 0.6210 0.004 0.848 0.000 0.000 0.148
#> GSM1068463 3 0.2110 0.9231 0.016 0.000 0.912 0.000 0.072
#> GSM1068465 1 0.5328 0.6034 0.604 0.036 0.000 0.016 0.344
#> GSM1068466 1 0.2389 0.8009 0.880 0.000 0.000 0.004 0.116
#> GSM1068467 2 0.2439 0.6459 0.004 0.876 0.000 0.000 0.120
#> GSM1068469 2 0.3912 0.5013 0.020 0.752 0.000 0.000 0.228
#> GSM1068470 2 0.2763 0.6312 0.000 0.848 0.000 0.004 0.148
#> GSM1068471 2 0.0162 0.6864 0.000 0.996 0.000 0.004 0.000
#> GSM1068475 2 0.0955 0.6847 0.000 0.968 0.000 0.004 0.028
#> GSM1068528 1 0.5121 0.6642 0.708 0.004 0.152 0.000 0.136
#> GSM1068531 1 0.1082 0.7967 0.964 0.000 0.000 0.008 0.028
#> GSM1068532 1 0.1831 0.7747 0.920 0.000 0.000 0.004 0.076
#> GSM1068533 1 0.1041 0.7980 0.964 0.000 0.000 0.004 0.032
#> GSM1068535 4 0.6124 0.5337 0.200 0.000 0.000 0.564 0.236
#> GSM1068537 1 0.1205 0.7863 0.956 0.000 0.000 0.004 0.040
#> GSM1068538 1 0.1704 0.7764 0.928 0.000 0.000 0.004 0.068
#> GSM1068539 5 0.6797 0.7145 0.020 0.188 0.000 0.284 0.508
#> GSM1068540 1 0.1893 0.8042 0.928 0.000 0.000 0.024 0.048
#> GSM1068542 4 0.4364 0.6564 0.020 0.016 0.000 0.740 0.224
#> GSM1068543 4 0.0451 0.7075 0.000 0.004 0.000 0.988 0.008
#> GSM1068544 3 0.2694 0.9038 0.040 0.000 0.884 0.000 0.076
#> GSM1068545 2 0.6731 -0.3543 0.000 0.416 0.000 0.280 0.304
#> GSM1068546 3 0.1430 0.9231 0.000 0.000 0.944 0.004 0.052
#> GSM1068547 1 0.2304 0.8038 0.892 0.000 0.000 0.008 0.100
#> GSM1068548 4 0.5209 0.6353 0.076 0.016 0.000 0.700 0.208
#> GSM1068549 3 0.1121 0.9265 0.000 0.000 0.956 0.000 0.044
#> GSM1068550 4 0.4188 0.6576 0.008 0.020 0.000 0.744 0.228
#> GSM1068551 2 0.2583 0.6418 0.000 0.864 0.000 0.004 0.132
#> GSM1068552 4 0.5715 0.5191 0.000 0.152 0.000 0.620 0.228
#> GSM1068555 2 0.3196 0.5992 0.000 0.804 0.000 0.004 0.192
#> GSM1068556 4 0.0955 0.7094 0.000 0.004 0.000 0.968 0.028
#> GSM1068557 2 0.4029 0.4313 0.004 0.680 0.000 0.000 0.316
#> GSM1068560 4 0.3461 0.6704 0.016 0.004 0.000 0.812 0.168
#> GSM1068561 5 0.6504 0.3379 0.012 0.428 0.000 0.132 0.428
#> GSM1068562 4 0.1502 0.7072 0.000 0.004 0.000 0.940 0.056
#> GSM1068563 4 0.3791 0.6710 0.000 0.076 0.000 0.812 0.112
#> GSM1068565 2 0.1768 0.6736 0.000 0.924 0.000 0.004 0.072
#> GSM1068529 4 0.3880 0.6230 0.000 0.004 0.044 0.800 0.152
#> GSM1068530 1 0.0510 0.7937 0.984 0.000 0.000 0.000 0.016
#> GSM1068534 4 0.1864 0.6903 0.000 0.004 0.004 0.924 0.068
#> GSM1068536 1 0.5920 0.3356 0.464 0.004 0.000 0.088 0.444
#> GSM1068541 5 0.6914 0.5472 0.044 0.316 0.000 0.132 0.508
#> GSM1068553 4 0.4252 0.6577 0.020 0.000 0.000 0.700 0.280
#> GSM1068554 4 0.5637 0.5865 0.004 0.100 0.000 0.616 0.280
#> GSM1068558 4 0.6034 0.4179 0.000 0.000 0.256 0.572 0.172
#> GSM1068559 4 0.5899 0.4378 0.000 0.000 0.248 0.592 0.160
#> GSM1068564 4 0.6592 0.2001 0.000 0.300 0.000 0.460 0.240
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1068478 5 0.3950 0.1136 0.312 0.008 0.000 0.000 0.672 0.008
#> GSM1068479 2 0.8566 -0.0332 0.000 0.336 0.152 0.244 0.124 0.144
#> GSM1068481 3 0.0725 0.8699 0.000 0.000 0.976 0.012 0.012 0.000
#> GSM1068482 3 0.2095 0.8552 0.016 0.000 0.916 0.040 0.028 0.000
#> GSM1068483 1 0.4378 0.6183 0.672 0.008 0.000 0.012 0.292 0.016
#> GSM1068486 3 0.0692 0.8718 0.000 0.000 0.976 0.020 0.004 0.000
#> GSM1068487 2 0.0000 0.6642 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1068488 6 0.1787 0.4599 0.000 0.004 0.000 0.068 0.008 0.920
#> GSM1068490 2 0.0000 0.6642 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1068491 3 0.5192 0.7196 0.000 0.000 0.640 0.228 0.120 0.012
#> GSM1068492 6 0.7164 0.2124 0.000 0.008 0.140 0.280 0.120 0.452
#> GSM1068493 2 0.5808 -0.0743 0.008 0.460 0.000 0.028 0.436 0.068
#> GSM1068494 6 0.5537 0.3651 0.044 0.000 0.008 0.096 0.192 0.660
#> GSM1068495 5 0.5705 0.5195 0.012 0.092 0.000 0.040 0.636 0.220
#> GSM1068496 1 0.8076 0.3993 0.432 0.000 0.132 0.088 0.204 0.144
#> GSM1068498 5 0.3993 0.2294 0.008 0.400 0.000 0.000 0.592 0.000
#> GSM1068499 1 0.7674 0.4060 0.420 0.000 0.048 0.080 0.272 0.180
#> GSM1068500 1 0.4525 0.6001 0.656 0.012 0.000 0.012 0.304 0.016
#> GSM1068502 2 0.8702 -0.1141 0.000 0.288 0.160 0.260 0.120 0.172
#> GSM1068503 2 0.2668 0.5440 0.000 0.828 0.000 0.168 0.000 0.004
#> GSM1068505 4 0.5524 0.4192 0.032 0.008 0.000 0.492 0.040 0.428
#> GSM1068506 6 0.5556 -0.3246 0.000 0.048 0.000 0.408 0.044 0.500
#> GSM1068507 4 0.6473 0.5860 0.012 0.232 0.000 0.460 0.012 0.284
#> GSM1068508 2 0.4580 0.0930 0.004 0.528 0.000 0.028 0.440 0.000
#> GSM1068510 4 0.5968 0.5357 0.000 0.140 0.000 0.432 0.016 0.412
#> GSM1068512 6 0.1477 0.4854 0.000 0.004 0.000 0.008 0.048 0.940
#> GSM1068513 2 0.3043 0.5347 0.000 0.796 0.000 0.196 0.004 0.004
#> GSM1068514 6 0.6498 0.2564 0.000 0.000 0.096 0.272 0.112 0.520
#> GSM1068517 5 0.4010 0.2103 0.008 0.408 0.000 0.000 0.584 0.000
#> GSM1068518 6 0.4549 0.3943 0.008 0.004 0.000 0.056 0.236 0.696
#> GSM1068520 1 0.3473 0.6860 0.780 0.000 0.000 0.024 0.192 0.004
#> GSM1068521 1 0.4709 0.6731 0.696 0.000 0.000 0.060 0.220 0.024
#> GSM1068522 2 0.4676 0.0822 0.000 0.572 0.000 0.384 0.004 0.040
#> GSM1068524 2 0.3271 0.6027 0.000 0.844 0.000 0.020 0.076 0.060
#> GSM1068527 6 0.4927 0.3404 0.032 0.004 0.000 0.096 0.152 0.716
#> GSM1068480 3 0.4299 0.8110 0.000 0.000 0.776 0.100 0.072 0.052
#> GSM1068484 6 0.1672 0.4770 0.000 0.004 0.000 0.016 0.048 0.932
#> GSM1068485 3 0.0146 0.8719 0.000 0.000 0.996 0.004 0.000 0.000
#> GSM1068489 4 0.4224 0.4933 0.000 0.008 0.000 0.512 0.004 0.476
#> GSM1068497 5 0.4018 0.2006 0.008 0.412 0.000 0.000 0.580 0.000
#> GSM1068501 4 0.5770 0.6629 0.000 0.108 0.000 0.528 0.024 0.340
#> GSM1068504 2 0.0000 0.6642 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1068509 1 0.7024 0.3280 0.400 0.000 0.000 0.080 0.208 0.312
#> GSM1068511 6 0.3063 0.4577 0.000 0.000 0.016 0.076 0.052 0.856
#> GSM1068515 1 0.7091 0.1344 0.424 0.080 0.000 0.124 0.352 0.020
#> GSM1068516 6 0.3315 0.4635 0.004 0.004 0.000 0.056 0.104 0.832
#> GSM1068519 1 0.5044 0.6668 0.704 0.000 0.000 0.080 0.160 0.056
#> GSM1068523 2 0.4153 0.3609 0.000 0.636 0.000 0.024 0.340 0.000
#> GSM1068525 6 0.1313 0.4863 0.000 0.004 0.000 0.016 0.028 0.952
#> GSM1068526 6 0.4569 -0.2769 0.000 0.008 0.000 0.408 0.024 0.560
#> GSM1068458 1 0.2940 0.7005 0.848 0.000 0.000 0.036 0.112 0.004
#> GSM1068459 3 0.1787 0.8569 0.016 0.000 0.932 0.032 0.020 0.000
#> GSM1068460 1 0.4310 0.6106 0.684 0.000 0.000 0.044 0.268 0.004
#> GSM1068461 3 0.2197 0.8608 0.000 0.000 0.900 0.056 0.044 0.000
#> GSM1068464 2 0.0146 0.6640 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM1068468 2 0.3017 0.5989 0.000 0.816 0.000 0.020 0.164 0.000
#> GSM1068472 2 0.2859 0.6027 0.000 0.828 0.000 0.016 0.156 0.000
#> GSM1068473 2 0.0000 0.6642 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1068474 2 0.0000 0.6642 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1068476 3 0.5064 0.7275 0.000 0.000 0.652 0.216 0.124 0.008
#> GSM1068477 2 0.1714 0.6490 0.000 0.908 0.000 0.000 0.092 0.000
#> GSM1068462 2 0.3053 0.5923 0.000 0.812 0.000 0.020 0.168 0.000
#> GSM1068463 3 0.1710 0.8569 0.016 0.000 0.936 0.028 0.020 0.000
#> GSM1068465 5 0.4696 -0.3494 0.480 0.004 0.000 0.020 0.488 0.008
#> GSM1068466 1 0.3695 0.6881 0.776 0.000 0.000 0.044 0.176 0.004
#> GSM1068467 2 0.2932 0.5997 0.000 0.820 0.000 0.016 0.164 0.000
#> GSM1068469 2 0.3859 0.4316 0.000 0.692 0.000 0.020 0.288 0.000
#> GSM1068470 2 0.3509 0.5022 0.000 0.744 0.000 0.016 0.240 0.000
#> GSM1068471 2 0.0000 0.6642 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1068475 2 0.0865 0.6576 0.000 0.964 0.000 0.000 0.036 0.000
#> GSM1068528 1 0.6458 0.2533 0.456 0.000 0.364 0.076 0.104 0.000
#> GSM1068531 1 0.1268 0.7115 0.952 0.000 0.000 0.036 0.008 0.004
#> GSM1068532 1 0.3105 0.6766 0.848 0.000 0.008 0.080 0.064 0.000
#> GSM1068533 1 0.1484 0.7097 0.944 0.000 0.004 0.040 0.008 0.004
#> GSM1068535 4 0.6138 0.5044 0.176 0.000 0.000 0.484 0.020 0.320
#> GSM1068537 1 0.1401 0.7056 0.948 0.000 0.004 0.028 0.020 0.000
#> GSM1068538 1 0.1924 0.6968 0.920 0.000 0.004 0.048 0.028 0.000
#> GSM1068539 5 0.5698 0.5144 0.008 0.104 0.000 0.036 0.628 0.224
#> GSM1068540 1 0.3675 0.7011 0.804 0.000 0.000 0.052 0.128 0.016
#> GSM1068542 6 0.5513 -0.3252 0.032 0.008 0.000 0.416 0.040 0.504
#> GSM1068543 6 0.1464 0.4517 0.000 0.004 0.000 0.036 0.016 0.944
#> GSM1068544 3 0.2903 0.8178 0.036 0.000 0.872 0.056 0.036 0.000
#> GSM1068545 2 0.7721 -0.2153 0.000 0.280 0.000 0.252 0.232 0.236
#> GSM1068546 3 0.2164 0.8595 0.000 0.000 0.900 0.068 0.032 0.000
#> GSM1068547 1 0.3453 0.6929 0.788 0.000 0.000 0.028 0.180 0.004
#> GSM1068548 6 0.6083 -0.2977 0.052 0.012 0.000 0.376 0.060 0.500
#> GSM1068549 3 0.4117 0.7971 0.000 0.000 0.748 0.140 0.112 0.000
#> GSM1068550 6 0.5080 -0.2835 0.012 0.008 0.000 0.404 0.036 0.540
#> GSM1068551 2 0.3534 0.5040 0.000 0.740 0.000 0.016 0.244 0.000
#> GSM1068552 6 0.5805 -0.3550 0.000 0.080 0.000 0.404 0.036 0.480
#> GSM1068555 2 0.4124 0.3725 0.000 0.644 0.000 0.024 0.332 0.000
#> GSM1068556 6 0.2002 0.4233 0.000 0.004 0.000 0.076 0.012 0.908
#> GSM1068557 2 0.4282 0.2100 0.000 0.560 0.000 0.020 0.420 0.000
#> GSM1068560 6 0.4750 0.3410 0.004 0.004 0.000 0.120 0.172 0.700
#> GSM1068561 5 0.5592 0.4732 0.004 0.208 0.000 0.028 0.632 0.128
#> GSM1068562 6 0.2443 0.4017 0.000 0.004 0.000 0.096 0.020 0.880
#> GSM1068563 6 0.4629 0.0993 0.000 0.040 0.000 0.256 0.024 0.680
#> GSM1068565 2 0.2263 0.6322 0.000 0.884 0.000 0.016 0.100 0.000
#> GSM1068529 6 0.4251 0.4260 0.000 0.000 0.008 0.152 0.092 0.748
#> GSM1068530 1 0.0717 0.7096 0.976 0.000 0.000 0.016 0.008 0.000
#> GSM1068534 6 0.2003 0.4759 0.000 0.000 0.000 0.044 0.044 0.912
#> GSM1068536 5 0.6266 0.1210 0.292 0.000 0.000 0.084 0.532 0.092
#> GSM1068541 5 0.7101 0.4001 0.028 0.192 0.000 0.148 0.528 0.104
#> GSM1068553 4 0.4808 0.5838 0.016 0.004 0.000 0.548 0.020 0.412
#> GSM1068554 4 0.5759 0.6646 0.000 0.116 0.000 0.528 0.020 0.336
#> GSM1068558 6 0.6542 0.2735 0.000 0.000 0.120 0.256 0.100 0.524
#> GSM1068559 6 0.6543 0.2659 0.000 0.000 0.108 0.264 0.108 0.520
#> GSM1068564 4 0.6446 0.4437 0.000 0.248 0.000 0.440 0.024 0.288
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 disease.state(p) gender(p) k
#> SD:kmeans 95 0.85807 0.373 2
#> SD:kmeans 92 0.88731 0.601 3
#> SD:kmeans 101 0.01049 0.797 4
#> SD:kmeans 88 0.00156 0.774 5
#> SD:kmeans 56 0.08001 0.522 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 38950 rows and 108 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 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 0.608 0.820 0.923 0.5019 0.500 0.500
#> 3 3 0.611 0.736 0.875 0.3275 0.733 0.518
#> 4 4 0.814 0.829 0.911 0.1300 0.775 0.447
#> 5 5 0.710 0.604 0.764 0.0645 0.939 0.763
#> 6 6 0.710 0.549 0.748 0.0376 0.943 0.739
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
#> GSM1068478 1 0.9754 0.261 0.592 0.408
#> GSM1068479 2 0.9209 0.447 0.336 0.664
#> GSM1068481 1 0.0000 0.908 1.000 0.000
#> GSM1068482 1 0.0000 0.908 1.000 0.000
#> GSM1068483 1 0.0000 0.908 1.000 0.000
#> GSM1068486 1 0.0000 0.908 1.000 0.000
#> GSM1068487 2 0.0000 0.908 0.000 1.000
#> GSM1068488 1 0.8386 0.647 0.732 0.268
#> GSM1068490 2 0.0000 0.908 0.000 1.000
#> GSM1068491 1 0.0000 0.908 1.000 0.000
#> GSM1068492 2 0.9970 0.055 0.468 0.532
#> GSM1068493 1 0.3431 0.862 0.936 0.064
#> GSM1068494 1 0.0000 0.908 1.000 0.000
#> GSM1068495 2 0.0000 0.908 0.000 1.000
#> GSM1068496 1 0.0000 0.908 1.000 0.000
#> GSM1068498 2 0.8327 0.652 0.264 0.736
#> GSM1068499 1 0.0000 0.908 1.000 0.000
#> GSM1068500 1 0.0000 0.908 1.000 0.000
#> GSM1068502 2 0.9323 0.419 0.348 0.652
#> GSM1068503 2 0.0000 0.908 0.000 1.000
#> GSM1068505 2 0.0000 0.908 0.000 1.000
#> GSM1068506 2 0.0000 0.908 0.000 1.000
#> GSM1068507 2 0.0000 0.908 0.000 1.000
#> GSM1068508 2 0.0000 0.908 0.000 1.000
#> GSM1068510 2 0.0376 0.906 0.004 0.996
#> GSM1068512 1 0.7453 0.720 0.788 0.212
#> GSM1068513 2 0.0000 0.908 0.000 1.000
#> GSM1068514 1 0.8207 0.664 0.744 0.256
#> GSM1068517 2 0.7602 0.711 0.220 0.780
#> GSM1068518 1 0.4815 0.834 0.896 0.104
#> GSM1068520 1 0.8016 0.632 0.756 0.244
#> GSM1068521 1 0.0000 0.908 1.000 0.000
#> GSM1068522 2 0.0000 0.908 0.000 1.000
#> GSM1068524 2 0.0000 0.908 0.000 1.000
#> GSM1068527 2 0.7219 0.704 0.200 0.800
#> GSM1068480 1 0.0000 0.908 1.000 0.000
#> GSM1068484 2 0.0000 0.908 0.000 1.000
#> GSM1068485 1 0.0000 0.908 1.000 0.000
#> GSM1068489 2 0.0000 0.908 0.000 1.000
#> GSM1068497 2 0.8327 0.652 0.264 0.736
#> GSM1068501 2 0.0000 0.908 0.000 1.000
#> GSM1068504 2 0.0000 0.908 0.000 1.000
#> GSM1068509 1 0.0000 0.908 1.000 0.000
#> GSM1068511 1 0.0000 0.908 1.000 0.000
#> GSM1068515 1 0.9754 0.261 0.592 0.408
#> GSM1068516 2 0.9608 0.329 0.384 0.616
#> GSM1068519 1 0.0000 0.908 1.000 0.000
#> GSM1068523 2 0.0000 0.908 0.000 1.000
#> GSM1068525 2 0.0000 0.908 0.000 1.000
#> GSM1068526 2 0.0672 0.904 0.008 0.992
#> GSM1068458 1 0.0376 0.905 0.996 0.004
#> GSM1068459 1 0.0000 0.908 1.000 0.000
#> GSM1068460 2 0.7453 0.721 0.212 0.788
#> GSM1068461 1 0.0000 0.908 1.000 0.000
#> GSM1068464 2 0.0000 0.908 0.000 1.000
#> GSM1068468 2 0.0000 0.908 0.000 1.000
#> GSM1068472 2 0.6048 0.791 0.148 0.852
#> GSM1068473 2 0.0000 0.908 0.000 1.000
#> GSM1068474 2 0.0000 0.908 0.000 1.000
#> GSM1068476 1 0.5519 0.811 0.872 0.128
#> GSM1068477 2 0.0000 0.908 0.000 1.000
#> GSM1068462 2 0.8327 0.652 0.264 0.736
#> GSM1068463 1 0.0000 0.908 1.000 0.000
#> GSM1068465 2 0.8386 0.646 0.268 0.732
#> GSM1068466 1 0.7299 0.699 0.796 0.204
#> GSM1068467 2 0.0000 0.908 0.000 1.000
#> GSM1068469 2 0.8386 0.646 0.268 0.732
#> GSM1068470 2 0.0000 0.908 0.000 1.000
#> GSM1068471 2 0.0000 0.908 0.000 1.000
#> GSM1068475 2 0.0000 0.908 0.000 1.000
#> GSM1068528 1 0.0000 0.908 1.000 0.000
#> GSM1068531 1 0.0000 0.908 1.000 0.000
#> GSM1068532 1 0.0000 0.908 1.000 0.000
#> GSM1068533 1 0.0000 0.908 1.000 0.000
#> GSM1068535 1 0.0000 0.908 1.000 0.000
#> GSM1068537 1 0.0000 0.908 1.000 0.000
#> GSM1068538 1 0.0000 0.908 1.000 0.000
#> GSM1068539 2 0.0000 0.908 0.000 1.000
#> GSM1068540 1 0.0000 0.908 1.000 0.000
#> GSM1068542 2 0.0672 0.904 0.008 0.992
#> GSM1068543 1 0.8499 0.635 0.724 0.276
#> GSM1068544 1 0.0000 0.908 1.000 0.000
#> GSM1068545 2 0.0000 0.908 0.000 1.000
#> GSM1068546 1 0.0000 0.908 1.000 0.000
#> GSM1068547 1 0.9209 0.445 0.664 0.336
#> GSM1068548 2 0.1184 0.899 0.016 0.984
#> GSM1068549 1 0.0000 0.908 1.000 0.000
#> GSM1068550 2 0.0000 0.908 0.000 1.000
#> GSM1068551 2 0.0000 0.908 0.000 1.000
#> GSM1068552 2 0.0000 0.908 0.000 1.000
#> GSM1068555 2 0.0000 0.908 0.000 1.000
#> GSM1068556 1 0.8443 0.641 0.728 0.272
#> GSM1068557 2 0.0000 0.908 0.000 1.000
#> GSM1068560 2 0.1184 0.899 0.016 0.984
#> GSM1068561 2 0.6973 0.749 0.188 0.812
#> GSM1068562 2 0.3879 0.850 0.076 0.924
#> GSM1068563 2 0.8267 0.608 0.260 0.740
#> GSM1068565 2 0.0000 0.908 0.000 1.000
#> GSM1068529 1 0.0000 0.908 1.000 0.000
#> GSM1068530 1 0.0000 0.908 1.000 0.000
#> GSM1068534 1 0.0000 0.908 1.000 0.000
#> GSM1068536 2 0.8813 0.595 0.300 0.700
#> GSM1068541 2 0.0000 0.908 0.000 1.000
#> GSM1068553 1 0.8327 0.653 0.736 0.264
#> GSM1068554 2 0.0376 0.906 0.004 0.996
#> GSM1068558 1 0.8386 0.647 0.732 0.268
#> GSM1068559 1 0.2423 0.883 0.960 0.040
#> GSM1068564 2 0.0000 0.908 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1068478 1 0.3192 0.7766 0.888 0.112 0.000
#> GSM1068479 3 0.0747 0.8941 0.000 0.016 0.984
#> GSM1068481 3 0.0747 0.8997 0.016 0.000 0.984
#> GSM1068482 3 0.0747 0.8997 0.016 0.000 0.984
#> GSM1068483 1 0.3340 0.7722 0.880 0.000 0.120
#> GSM1068486 3 0.0747 0.8997 0.016 0.000 0.984
#> GSM1068487 2 0.0000 0.8406 0.000 1.000 0.000
#> GSM1068488 3 0.0983 0.8887 0.004 0.016 0.980
#> GSM1068490 2 0.0000 0.8406 0.000 1.000 0.000
#> GSM1068491 3 0.0747 0.8997 0.016 0.000 0.984
#> GSM1068492 3 0.0000 0.8972 0.000 0.000 1.000
#> GSM1068493 3 0.8301 0.4412 0.108 0.300 0.592
#> GSM1068494 3 0.5216 0.6554 0.260 0.000 0.740
#> GSM1068495 2 0.6299 0.0961 0.476 0.524 0.000
#> GSM1068496 1 0.6291 0.1741 0.532 0.000 0.468
#> GSM1068498 1 0.6079 0.4290 0.612 0.388 0.000
#> GSM1068499 1 0.6168 0.3349 0.588 0.000 0.412
#> GSM1068500 1 0.4555 0.6978 0.800 0.000 0.200
#> GSM1068502 3 0.0747 0.8941 0.000 0.016 0.984
#> GSM1068503 2 0.0000 0.8406 0.000 1.000 0.000
#> GSM1068505 2 0.5956 0.6689 0.264 0.720 0.016
#> GSM1068506 2 0.5219 0.7344 0.196 0.788 0.016
#> GSM1068507 2 0.7330 0.6635 0.216 0.692 0.092
#> GSM1068508 2 0.2448 0.8141 0.076 0.924 0.000
#> GSM1068510 3 0.6045 0.3864 0.000 0.380 0.620
#> GSM1068512 3 0.5315 0.7074 0.216 0.012 0.772
#> GSM1068513 2 0.0000 0.8406 0.000 1.000 0.000
#> GSM1068514 3 0.0000 0.8972 0.000 0.000 1.000
#> GSM1068517 1 0.6140 0.3946 0.596 0.404 0.000
#> GSM1068518 1 0.5024 0.6471 0.776 0.004 0.220
#> GSM1068520 1 0.0000 0.8359 1.000 0.000 0.000
#> GSM1068521 1 0.0000 0.8359 1.000 0.000 0.000
#> GSM1068522 2 0.0000 0.8406 0.000 1.000 0.000
#> GSM1068524 2 0.0000 0.8406 0.000 1.000 0.000
#> GSM1068527 1 0.2804 0.7847 0.924 0.060 0.016
#> GSM1068480 3 0.0747 0.8997 0.016 0.000 0.984
#> GSM1068484 2 0.0747 0.8370 0.000 0.984 0.016
#> GSM1068485 3 0.0747 0.8997 0.016 0.000 0.984
#> GSM1068489 2 0.5681 0.6984 0.236 0.748 0.016
#> GSM1068497 1 0.6095 0.4210 0.608 0.392 0.000
#> GSM1068501 2 0.2804 0.8172 0.060 0.924 0.016
#> GSM1068504 2 0.0000 0.8406 0.000 1.000 0.000
#> GSM1068509 1 0.4399 0.7093 0.812 0.000 0.188
#> GSM1068511 3 0.0237 0.8985 0.004 0.000 0.996
#> GSM1068515 1 0.4796 0.6771 0.780 0.220 0.000
#> GSM1068516 3 0.6107 0.7235 0.100 0.116 0.784
#> GSM1068519 1 0.0000 0.8359 1.000 0.000 0.000
#> GSM1068523 2 0.0000 0.8406 0.000 1.000 0.000
#> GSM1068525 2 0.0747 0.8370 0.000 0.984 0.016
#> GSM1068526 2 0.5633 0.7201 0.208 0.768 0.024
#> GSM1068458 1 0.0000 0.8359 1.000 0.000 0.000
#> GSM1068459 3 0.0747 0.8997 0.016 0.000 0.984
#> GSM1068460 1 0.0000 0.8359 1.000 0.000 0.000
#> GSM1068461 3 0.0747 0.8997 0.016 0.000 0.984
#> GSM1068464 2 0.0000 0.8406 0.000 1.000 0.000
#> GSM1068468 2 0.1636 0.8266 0.016 0.964 0.020
#> GSM1068472 2 0.1919 0.8226 0.024 0.956 0.020
#> GSM1068473 2 0.0000 0.8406 0.000 1.000 0.000
#> GSM1068474 2 0.0000 0.8406 0.000 1.000 0.000
#> GSM1068476 3 0.0829 0.8991 0.012 0.004 0.984
#> GSM1068477 2 0.0000 0.8406 0.000 1.000 0.000
#> GSM1068462 2 0.6777 0.3494 0.020 0.616 0.364
#> GSM1068463 3 0.0747 0.8997 0.016 0.000 0.984
#> GSM1068465 1 0.2625 0.7954 0.916 0.084 0.000
#> GSM1068466 1 0.0000 0.8359 1.000 0.000 0.000
#> GSM1068467 2 0.1482 0.8284 0.012 0.968 0.020
#> GSM1068469 1 0.7049 0.2516 0.528 0.452 0.020
#> GSM1068470 2 0.0000 0.8406 0.000 1.000 0.000
#> GSM1068471 2 0.0000 0.8406 0.000 1.000 0.000
#> GSM1068475 2 0.0000 0.8406 0.000 1.000 0.000
#> GSM1068528 1 0.5859 0.4857 0.656 0.000 0.344
#> GSM1068531 1 0.0000 0.8359 1.000 0.000 0.000
#> GSM1068532 1 0.1964 0.8082 0.944 0.000 0.056
#> GSM1068533 1 0.0000 0.8359 1.000 0.000 0.000
#> GSM1068535 3 0.6126 0.3861 0.400 0.000 0.600
#> GSM1068537 1 0.0000 0.8359 1.000 0.000 0.000
#> GSM1068538 1 0.0000 0.8359 1.000 0.000 0.000
#> GSM1068539 2 0.5650 0.5494 0.312 0.688 0.000
#> GSM1068540 1 0.0000 0.8359 1.000 0.000 0.000
#> GSM1068542 2 0.6396 0.5951 0.320 0.664 0.016
#> GSM1068543 3 0.3213 0.8467 0.060 0.028 0.912
#> GSM1068544 3 0.4002 0.7615 0.160 0.000 0.840
#> GSM1068545 2 0.1163 0.8339 0.028 0.972 0.000
#> GSM1068546 3 0.0747 0.8997 0.016 0.000 0.984
#> GSM1068547 1 0.0000 0.8359 1.000 0.000 0.000
#> GSM1068548 2 0.6783 0.4697 0.396 0.588 0.016
#> GSM1068549 3 0.0747 0.8997 0.016 0.000 0.984
#> GSM1068550 2 0.5956 0.6689 0.264 0.720 0.016
#> GSM1068551 2 0.0000 0.8406 0.000 1.000 0.000
#> GSM1068552 2 0.4539 0.7675 0.148 0.836 0.016
#> GSM1068555 2 0.0000 0.8406 0.000 1.000 0.000
#> GSM1068556 3 0.3967 0.8262 0.072 0.044 0.884
#> GSM1068557 2 0.1129 0.8313 0.004 0.976 0.020
#> GSM1068560 2 0.6912 0.3712 0.444 0.540 0.016
#> GSM1068561 2 0.7851 0.0324 0.412 0.532 0.056
#> GSM1068562 2 0.6880 0.6920 0.108 0.736 0.156
#> GSM1068563 2 0.6717 0.4614 0.020 0.628 0.352
#> GSM1068565 2 0.0000 0.8406 0.000 1.000 0.000
#> GSM1068529 3 0.0000 0.8972 0.000 0.000 1.000
#> GSM1068530 1 0.0000 0.8359 1.000 0.000 0.000
#> GSM1068534 3 0.0000 0.8972 0.000 0.000 1.000
#> GSM1068536 1 0.0237 0.8345 0.996 0.004 0.000
#> GSM1068541 2 0.5835 0.5162 0.340 0.660 0.000
#> GSM1068553 3 0.6771 0.5707 0.276 0.040 0.684
#> GSM1068554 2 0.8730 0.1309 0.108 0.472 0.420
#> GSM1068558 3 0.0000 0.8972 0.000 0.000 1.000
#> GSM1068559 3 0.0237 0.8984 0.004 0.000 0.996
#> GSM1068564 2 0.1905 0.8299 0.028 0.956 0.016
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1068478 1 0.1629 0.93075 0.952 0.024 0.000 0.024
#> GSM1068479 3 0.1557 0.88009 0.000 0.056 0.944 0.000
#> GSM1068481 3 0.0000 0.92158 0.000 0.000 1.000 0.000
#> GSM1068482 3 0.0000 0.92158 0.000 0.000 1.000 0.000
#> GSM1068483 1 0.1339 0.93554 0.964 0.008 0.024 0.004
#> GSM1068486 3 0.0000 0.92158 0.000 0.000 1.000 0.000
#> GSM1068487 2 0.0188 0.91495 0.000 0.996 0.000 0.004
#> GSM1068488 4 0.2593 0.83071 0.004 0.000 0.104 0.892
#> GSM1068490 2 0.0188 0.91495 0.000 0.996 0.000 0.004
#> GSM1068491 3 0.0000 0.92158 0.000 0.000 1.000 0.000
#> GSM1068492 3 0.0188 0.91965 0.000 0.000 0.996 0.004
#> GSM1068493 2 0.5004 0.34511 0.000 0.604 0.392 0.004
#> GSM1068494 3 0.7130 0.14544 0.396 0.000 0.472 0.132
#> GSM1068495 2 0.6639 0.45206 0.120 0.596 0.000 0.284
#> GSM1068496 3 0.5138 0.33854 0.392 0.000 0.600 0.008
#> GSM1068498 2 0.1867 0.87218 0.072 0.928 0.000 0.000
#> GSM1068499 3 0.5928 0.10359 0.456 0.000 0.508 0.036
#> GSM1068500 1 0.2281 0.87649 0.904 0.000 0.096 0.000
#> GSM1068502 3 0.1211 0.89424 0.000 0.040 0.960 0.000
#> GSM1068503 2 0.2281 0.83763 0.000 0.904 0.000 0.096
#> GSM1068505 4 0.2589 0.84977 0.116 0.000 0.000 0.884
#> GSM1068506 4 0.2131 0.86823 0.032 0.036 0.000 0.932
#> GSM1068507 4 0.6501 0.58228 0.116 0.268 0.000 0.616
#> GSM1068508 2 0.1929 0.88440 0.036 0.940 0.000 0.024
#> GSM1068510 4 0.5581 0.73219 0.000 0.140 0.132 0.728
#> GSM1068512 4 0.6364 0.64430 0.144 0.000 0.204 0.652
#> GSM1068513 2 0.0469 0.91212 0.000 0.988 0.000 0.012
#> GSM1068514 3 0.0000 0.92158 0.000 0.000 1.000 0.000
#> GSM1068517 2 0.1022 0.90100 0.032 0.968 0.000 0.000
#> GSM1068518 4 0.7379 0.18012 0.364 0.000 0.168 0.468
#> GSM1068520 1 0.0000 0.94506 1.000 0.000 0.000 0.000
#> GSM1068521 1 0.1118 0.93458 0.964 0.000 0.000 0.036
#> GSM1068522 2 0.5673 0.00761 0.024 0.528 0.000 0.448
#> GSM1068524 2 0.1118 0.90057 0.000 0.964 0.000 0.036
#> GSM1068527 4 0.3494 0.78573 0.172 0.004 0.000 0.824
#> GSM1068480 3 0.0000 0.92158 0.000 0.000 1.000 0.000
#> GSM1068484 4 0.1042 0.86837 0.008 0.020 0.000 0.972
#> GSM1068485 3 0.0000 0.92158 0.000 0.000 1.000 0.000
#> GSM1068489 4 0.1022 0.86947 0.032 0.000 0.000 0.968
#> GSM1068497 2 0.1118 0.89858 0.036 0.964 0.000 0.000
#> GSM1068501 4 0.2402 0.85611 0.012 0.076 0.000 0.912
#> GSM1068504 2 0.0188 0.91495 0.000 0.996 0.000 0.004
#> GSM1068509 1 0.2831 0.87287 0.876 0.000 0.004 0.120
#> GSM1068511 3 0.2675 0.84117 0.008 0.000 0.892 0.100
#> GSM1068515 1 0.4054 0.74476 0.796 0.188 0.000 0.016
#> GSM1068516 4 0.1443 0.86763 0.008 0.004 0.028 0.960
#> GSM1068519 1 0.1489 0.93099 0.952 0.000 0.004 0.044
#> GSM1068523 2 0.0336 0.91319 0.000 0.992 0.000 0.008
#> GSM1068525 4 0.1302 0.86516 0.000 0.044 0.000 0.956
#> GSM1068526 4 0.1118 0.86917 0.036 0.000 0.000 0.964
#> GSM1068458 1 0.0336 0.94301 0.992 0.000 0.000 0.008
#> GSM1068459 3 0.0000 0.92158 0.000 0.000 1.000 0.000
#> GSM1068460 1 0.0000 0.94506 1.000 0.000 0.000 0.000
#> GSM1068461 3 0.0000 0.92158 0.000 0.000 1.000 0.000
#> GSM1068464 2 0.0188 0.91495 0.000 0.996 0.000 0.004
#> GSM1068468 2 0.0000 0.91444 0.000 1.000 0.000 0.000
#> GSM1068472 2 0.0188 0.91495 0.000 0.996 0.000 0.004
#> GSM1068473 2 0.0188 0.91495 0.000 0.996 0.000 0.004
#> GSM1068474 2 0.0188 0.91495 0.000 0.996 0.000 0.004
#> GSM1068476 3 0.0000 0.92158 0.000 0.000 1.000 0.000
#> GSM1068477 2 0.0000 0.91444 0.000 1.000 0.000 0.000
#> GSM1068462 2 0.0336 0.91245 0.000 0.992 0.008 0.000
#> GSM1068463 3 0.0188 0.91970 0.004 0.000 0.996 0.000
#> GSM1068465 1 0.1733 0.92816 0.948 0.028 0.000 0.024
#> GSM1068466 1 0.0188 0.94413 0.996 0.000 0.000 0.004
#> GSM1068467 2 0.0000 0.91444 0.000 1.000 0.000 0.000
#> GSM1068469 2 0.0592 0.90911 0.016 0.984 0.000 0.000
#> GSM1068470 2 0.0336 0.91319 0.000 0.992 0.000 0.008
#> GSM1068471 2 0.0188 0.91495 0.000 0.996 0.000 0.004
#> GSM1068475 2 0.0188 0.91495 0.000 0.996 0.000 0.004
#> GSM1068528 1 0.4585 0.49686 0.668 0.000 0.332 0.000
#> GSM1068531 1 0.0000 0.94506 1.000 0.000 0.000 0.000
#> GSM1068532 1 0.1297 0.93804 0.964 0.000 0.016 0.020
#> GSM1068533 1 0.0336 0.94301 0.992 0.000 0.000 0.008
#> GSM1068535 4 0.4661 0.71731 0.256 0.000 0.016 0.728
#> GSM1068537 1 0.0188 0.94416 0.996 0.000 0.000 0.004
#> GSM1068538 1 0.0336 0.94301 0.992 0.000 0.000 0.008
#> GSM1068539 2 0.6371 0.46090 0.092 0.608 0.000 0.300
#> GSM1068540 1 0.1118 0.93458 0.964 0.000 0.000 0.036
#> GSM1068542 4 0.2408 0.85528 0.104 0.000 0.000 0.896
#> GSM1068543 4 0.1902 0.86022 0.004 0.000 0.064 0.932
#> GSM1068544 3 0.0469 0.91571 0.012 0.000 0.988 0.000
#> GSM1068545 4 0.4830 0.33077 0.000 0.392 0.000 0.608
#> GSM1068546 3 0.0000 0.92158 0.000 0.000 1.000 0.000
#> GSM1068547 1 0.0000 0.94506 1.000 0.000 0.000 0.000
#> GSM1068548 4 0.3208 0.83511 0.148 0.004 0.000 0.848
#> GSM1068549 3 0.0000 0.92158 0.000 0.000 1.000 0.000
#> GSM1068550 4 0.0817 0.87051 0.024 0.000 0.000 0.976
#> GSM1068551 2 0.0336 0.91319 0.000 0.992 0.000 0.008
#> GSM1068552 4 0.2036 0.86926 0.032 0.032 0.000 0.936
#> GSM1068555 2 0.0188 0.91424 0.000 0.996 0.000 0.004
#> GSM1068556 4 0.1661 0.86427 0.004 0.000 0.052 0.944
#> GSM1068557 2 0.0000 0.91444 0.000 1.000 0.000 0.000
#> GSM1068560 4 0.2714 0.83026 0.112 0.004 0.000 0.884
#> GSM1068561 2 0.2497 0.87808 0.016 0.924 0.020 0.040
#> GSM1068562 4 0.0188 0.86755 0.000 0.000 0.004 0.996
#> GSM1068563 4 0.2565 0.86052 0.000 0.032 0.056 0.912
#> GSM1068565 2 0.0188 0.91495 0.000 0.996 0.000 0.004
#> GSM1068529 3 0.1211 0.89672 0.000 0.000 0.960 0.040
#> GSM1068530 1 0.0000 0.94506 1.000 0.000 0.000 0.000
#> GSM1068534 3 0.2973 0.80249 0.000 0.000 0.856 0.144
#> GSM1068536 1 0.0657 0.94024 0.984 0.004 0.000 0.012
#> GSM1068541 2 0.7248 0.37943 0.284 0.532 0.000 0.184
#> GSM1068553 4 0.1824 0.86796 0.060 0.000 0.004 0.936
#> GSM1068554 4 0.3416 0.86165 0.036 0.040 0.036 0.888
#> GSM1068558 3 0.0188 0.91989 0.000 0.000 0.996 0.004
#> GSM1068559 3 0.0000 0.92158 0.000 0.000 1.000 0.000
#> GSM1068564 4 0.2670 0.85728 0.024 0.072 0.000 0.904
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM1068478 1 0.4298 0.5579 0.640 0.008 0.000 0.000 0.352
#> GSM1068479 3 0.3266 0.6893 0.000 0.200 0.796 0.000 0.004
#> GSM1068481 3 0.0404 0.8662 0.012 0.000 0.988 0.000 0.000
#> GSM1068482 3 0.0510 0.8655 0.016 0.000 0.984 0.000 0.000
#> GSM1068483 1 0.1168 0.8759 0.960 0.000 0.032 0.000 0.008
#> GSM1068486 3 0.0162 0.8668 0.004 0.000 0.996 0.000 0.000
#> GSM1068487 2 0.0451 0.7072 0.000 0.988 0.000 0.004 0.008
#> GSM1068488 4 0.6099 0.5241 0.008 0.000 0.100 0.504 0.388
#> GSM1068490 2 0.0451 0.7072 0.000 0.988 0.000 0.004 0.008
#> GSM1068491 3 0.0162 0.8668 0.000 0.000 0.996 0.000 0.004
#> GSM1068492 3 0.2438 0.8370 0.000 0.044 0.908 0.008 0.040
#> GSM1068493 2 0.6925 -0.0946 0.004 0.368 0.344 0.000 0.284
#> GSM1068494 5 0.7764 -0.0537 0.180 0.000 0.292 0.092 0.436
#> GSM1068495 5 0.3883 0.4828 0.032 0.052 0.000 0.084 0.832
#> GSM1068496 3 0.5506 0.1883 0.404 0.000 0.528 0.000 0.068
#> GSM1068498 5 0.4872 0.1950 0.024 0.436 0.000 0.000 0.540
#> GSM1068499 3 0.6718 0.0332 0.396 0.000 0.436 0.016 0.152
#> GSM1068500 1 0.1638 0.8567 0.932 0.000 0.064 0.000 0.004
#> GSM1068502 3 0.3343 0.7210 0.000 0.172 0.812 0.000 0.016
#> GSM1068503 2 0.3527 0.5260 0.000 0.792 0.000 0.192 0.016
#> GSM1068505 4 0.2954 0.7018 0.064 0.004 0.000 0.876 0.056
#> GSM1068506 4 0.1865 0.7077 0.008 0.032 0.000 0.936 0.024
#> GSM1068507 2 0.7067 -0.1032 0.060 0.444 0.004 0.400 0.092
#> GSM1068508 2 0.5246 -0.0192 0.020 0.524 0.000 0.016 0.440
#> GSM1068510 4 0.7829 0.3429 0.000 0.288 0.100 0.432 0.180
#> GSM1068512 4 0.6499 0.5774 0.056 0.000 0.100 0.596 0.248
#> GSM1068513 2 0.3301 0.5974 0.000 0.848 0.000 0.072 0.080
#> GSM1068514 3 0.1408 0.8549 0.000 0.000 0.948 0.008 0.044
#> GSM1068517 5 0.4538 0.1713 0.008 0.452 0.000 0.000 0.540
#> GSM1068518 5 0.4928 0.2126 0.072 0.000 0.012 0.192 0.724
#> GSM1068520 1 0.0671 0.8848 0.980 0.000 0.000 0.004 0.016
#> GSM1068521 1 0.1892 0.8645 0.916 0.000 0.000 0.004 0.080
#> GSM1068522 2 0.5063 0.3383 0.000 0.632 0.000 0.312 0.056
#> GSM1068524 2 0.4183 0.3411 0.000 0.668 0.000 0.008 0.324
#> GSM1068527 4 0.5393 0.5676 0.080 0.000 0.000 0.608 0.312
#> GSM1068480 3 0.1059 0.8617 0.004 0.000 0.968 0.008 0.020
#> GSM1068484 4 0.3966 0.5986 0.000 0.000 0.000 0.664 0.336
#> GSM1068485 3 0.0162 0.8668 0.004 0.000 0.996 0.000 0.000
#> GSM1068489 4 0.2116 0.7052 0.008 0.004 0.000 0.912 0.076
#> GSM1068497 5 0.4542 0.1621 0.008 0.456 0.000 0.000 0.536
#> GSM1068501 4 0.5850 0.4959 0.004 0.244 0.004 0.624 0.124
#> GSM1068504 2 0.0000 0.7107 0.000 1.000 0.000 0.000 0.000
#> GSM1068509 1 0.3958 0.7629 0.780 0.000 0.000 0.044 0.176
#> GSM1068511 3 0.5628 0.5648 0.016 0.000 0.660 0.224 0.100
#> GSM1068515 1 0.4983 0.7297 0.764 0.092 0.000 0.084 0.060
#> GSM1068516 5 0.3934 0.0885 0.000 0.000 0.008 0.276 0.716
#> GSM1068519 1 0.2997 0.8146 0.840 0.000 0.000 0.012 0.148
#> GSM1068523 5 0.4307 0.0660 0.000 0.496 0.000 0.000 0.504
#> GSM1068525 4 0.4586 0.4344 0.000 0.004 0.004 0.524 0.468
#> GSM1068526 4 0.0613 0.7149 0.008 0.004 0.000 0.984 0.004
#> GSM1068458 1 0.0566 0.8845 0.984 0.000 0.000 0.012 0.004
#> GSM1068459 3 0.0510 0.8655 0.016 0.000 0.984 0.000 0.000
#> GSM1068460 1 0.0898 0.8833 0.972 0.000 0.000 0.008 0.020
#> GSM1068461 3 0.0000 0.8668 0.000 0.000 1.000 0.000 0.000
#> GSM1068464 2 0.0162 0.7112 0.000 0.996 0.000 0.000 0.004
#> GSM1068468 2 0.1478 0.7022 0.000 0.936 0.000 0.000 0.064
#> GSM1068472 2 0.0703 0.7100 0.000 0.976 0.000 0.000 0.024
#> GSM1068473 2 0.0579 0.7052 0.000 0.984 0.000 0.008 0.008
#> GSM1068474 2 0.0000 0.7107 0.000 1.000 0.000 0.000 0.000
#> GSM1068476 3 0.0290 0.8663 0.000 0.000 0.992 0.000 0.008
#> GSM1068477 2 0.1965 0.6757 0.000 0.904 0.000 0.000 0.096
#> GSM1068462 2 0.1430 0.7039 0.000 0.944 0.004 0.000 0.052
#> GSM1068463 3 0.0510 0.8655 0.016 0.000 0.984 0.000 0.000
#> GSM1068465 1 0.3093 0.8010 0.824 0.008 0.000 0.000 0.168
#> GSM1068466 1 0.0693 0.8851 0.980 0.000 0.000 0.008 0.012
#> GSM1068467 2 0.1544 0.6996 0.000 0.932 0.000 0.000 0.068
#> GSM1068469 2 0.1792 0.6911 0.000 0.916 0.000 0.000 0.084
#> GSM1068470 2 0.4171 0.1618 0.000 0.604 0.000 0.000 0.396
#> GSM1068471 2 0.0162 0.7112 0.000 0.996 0.000 0.000 0.004
#> GSM1068475 2 0.1043 0.7067 0.000 0.960 0.000 0.000 0.040
#> GSM1068528 1 0.4341 0.2968 0.592 0.000 0.404 0.000 0.004
#> GSM1068531 1 0.0451 0.8849 0.988 0.000 0.000 0.008 0.004
#> GSM1068532 1 0.0609 0.8815 0.980 0.000 0.020 0.000 0.000
#> GSM1068533 1 0.0693 0.8837 0.980 0.000 0.008 0.012 0.000
#> GSM1068535 4 0.6239 0.4991 0.292 0.004 0.020 0.584 0.100
#> GSM1068537 1 0.0162 0.8848 0.996 0.000 0.004 0.000 0.000
#> GSM1068538 1 0.0566 0.8827 0.984 0.000 0.012 0.004 0.000
#> GSM1068539 5 0.4182 0.4970 0.028 0.076 0.000 0.084 0.812
#> GSM1068540 1 0.1704 0.8659 0.928 0.000 0.000 0.004 0.068
#> GSM1068542 4 0.1197 0.7133 0.048 0.000 0.000 0.952 0.000
#> GSM1068543 4 0.4840 0.6193 0.000 0.000 0.064 0.688 0.248
#> GSM1068544 3 0.1410 0.8417 0.060 0.000 0.940 0.000 0.000
#> GSM1068545 4 0.5607 0.3229 0.000 0.140 0.000 0.632 0.228
#> GSM1068546 3 0.0290 0.8667 0.000 0.000 0.992 0.000 0.008
#> GSM1068547 1 0.0693 0.8845 0.980 0.000 0.000 0.008 0.012
#> GSM1068548 4 0.3154 0.6818 0.148 0.004 0.000 0.836 0.012
#> GSM1068549 3 0.0162 0.8668 0.000 0.000 0.996 0.000 0.004
#> GSM1068550 4 0.1626 0.7174 0.016 0.000 0.000 0.940 0.044
#> GSM1068551 2 0.4030 0.2719 0.000 0.648 0.000 0.000 0.352
#> GSM1068552 4 0.1442 0.7099 0.012 0.032 0.000 0.952 0.004
#> GSM1068555 2 0.4307 -0.1544 0.000 0.504 0.000 0.000 0.496
#> GSM1068556 4 0.4328 0.6564 0.012 0.000 0.032 0.756 0.200
#> GSM1068557 2 0.4300 -0.0693 0.000 0.524 0.000 0.000 0.476
#> GSM1068560 4 0.5173 0.3970 0.040 0.000 0.000 0.500 0.460
#> GSM1068561 5 0.3990 0.3666 0.000 0.308 0.004 0.000 0.688
#> GSM1068562 4 0.3838 0.6292 0.000 0.000 0.004 0.716 0.280
#> GSM1068563 4 0.3005 0.7053 0.004 0.032 0.028 0.888 0.048
#> GSM1068565 2 0.2424 0.6426 0.000 0.868 0.000 0.000 0.132
#> GSM1068529 3 0.4177 0.7081 0.004 0.000 0.760 0.036 0.200
#> GSM1068530 1 0.0000 0.8852 1.000 0.000 0.000 0.000 0.000
#> GSM1068534 3 0.6282 0.3825 0.004 0.000 0.556 0.248 0.192
#> GSM1068536 1 0.4540 0.4913 0.640 0.000 0.000 0.020 0.340
#> GSM1068541 5 0.8239 0.3217 0.164 0.192 0.000 0.248 0.396
#> GSM1068553 4 0.3195 0.6939 0.032 0.004 0.004 0.860 0.100
#> GSM1068554 4 0.5832 0.5107 0.004 0.232 0.016 0.648 0.100
#> GSM1068558 3 0.2358 0.8168 0.000 0.000 0.888 0.008 0.104
#> GSM1068559 3 0.0510 0.8652 0.000 0.000 0.984 0.000 0.016
#> GSM1068564 4 0.3248 0.6739 0.004 0.104 0.000 0.852 0.040
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1068478 5 0.4533 -0.1668 0.468 0.004 0.000 0.024 0.504 0.000
#> GSM1068479 3 0.4628 0.6120 0.000 0.208 0.704 0.072 0.016 0.000
#> GSM1068481 3 0.0653 0.7970 0.004 0.000 0.980 0.012 0.004 0.000
#> GSM1068482 3 0.1007 0.7959 0.004 0.000 0.968 0.016 0.008 0.004
#> GSM1068483 1 0.1959 0.8531 0.924 0.000 0.020 0.032 0.024 0.000
#> GSM1068486 3 0.0000 0.7989 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1068487 2 0.0363 0.7549 0.000 0.988 0.000 0.012 0.000 0.000
#> GSM1068488 4 0.5900 0.0173 0.000 0.000 0.032 0.492 0.100 0.376
#> GSM1068490 2 0.0146 0.7569 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM1068491 3 0.1757 0.7903 0.000 0.000 0.916 0.076 0.008 0.000
#> GSM1068492 3 0.4820 0.7075 0.000 0.012 0.728 0.164 0.028 0.068
#> GSM1068493 5 0.7114 0.2445 0.008 0.232 0.308 0.048 0.400 0.004
#> GSM1068494 4 0.8596 -0.0246 0.076 0.000 0.176 0.256 0.252 0.240
#> GSM1068495 5 0.3621 0.5276 0.008 0.028 0.000 0.040 0.828 0.096
#> GSM1068496 3 0.6755 0.2245 0.332 0.000 0.492 0.076 0.052 0.048
#> GSM1068498 5 0.3648 0.5539 0.004 0.240 0.000 0.016 0.740 0.000
#> GSM1068499 3 0.7645 0.2313 0.268 0.000 0.448 0.132 0.088 0.064
#> GSM1068500 1 0.2541 0.8345 0.892 0.000 0.052 0.032 0.024 0.000
#> GSM1068502 3 0.4644 0.6604 0.000 0.160 0.728 0.092 0.016 0.004
#> GSM1068503 2 0.3030 0.6727 0.000 0.848 0.000 0.092 0.004 0.056
#> GSM1068505 6 0.4763 0.1799 0.016 0.008 0.000 0.420 0.012 0.544
#> GSM1068506 6 0.4049 0.5050 0.000 0.044 0.000 0.208 0.008 0.740
#> GSM1068507 4 0.5575 0.1582 0.016 0.432 0.000 0.464 0.000 0.088
#> GSM1068508 2 0.5462 -0.1077 0.004 0.476 0.000 0.008 0.432 0.080
#> GSM1068510 4 0.5127 0.4597 0.000 0.160 0.032 0.716 0.028 0.064
#> GSM1068512 6 0.5765 0.4147 0.068 0.000 0.036 0.156 0.064 0.676
#> GSM1068513 2 0.3428 0.4363 0.000 0.696 0.000 0.304 0.000 0.000
#> GSM1068514 3 0.4104 0.7265 0.000 0.000 0.760 0.172 0.020 0.048
#> GSM1068517 5 0.3509 0.5540 0.000 0.240 0.000 0.016 0.744 0.000
#> GSM1068518 6 0.6837 0.0873 0.032 0.000 0.008 0.236 0.356 0.368
#> GSM1068520 1 0.0547 0.8640 0.980 0.000 0.000 0.000 0.020 0.000
#> GSM1068521 1 0.1745 0.8518 0.924 0.000 0.000 0.020 0.056 0.000
#> GSM1068522 2 0.5015 0.2753 0.000 0.616 0.000 0.288 0.004 0.092
#> GSM1068524 2 0.5411 0.1667 0.000 0.548 0.000 0.092 0.348 0.012
#> GSM1068527 6 0.5491 0.4389 0.064 0.000 0.000 0.140 0.128 0.668
#> GSM1068480 3 0.1116 0.7991 0.000 0.000 0.960 0.028 0.004 0.008
#> GSM1068484 6 0.5191 0.3665 0.000 0.004 0.000 0.220 0.148 0.628
#> GSM1068485 3 0.0291 0.7990 0.000 0.000 0.992 0.004 0.004 0.000
#> GSM1068489 4 0.3890 0.1260 0.004 0.000 0.000 0.596 0.000 0.400
#> GSM1068497 5 0.3592 0.5522 0.000 0.240 0.000 0.020 0.740 0.000
#> GSM1068501 4 0.4143 0.4793 0.000 0.124 0.000 0.756 0.004 0.116
#> GSM1068504 2 0.0000 0.7572 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1068509 1 0.5390 0.6506 0.688 0.000 0.004 0.128 0.060 0.120
#> GSM1068511 3 0.6425 0.3378 0.020 0.000 0.512 0.132 0.028 0.308
#> GSM1068515 1 0.5748 0.6071 0.664 0.084 0.004 0.092 0.152 0.004
#> GSM1068516 5 0.5927 -0.0450 0.000 0.000 0.000 0.272 0.464 0.264
#> GSM1068519 1 0.4531 0.7147 0.744 0.000 0.000 0.148 0.072 0.036
#> GSM1068523 5 0.3684 0.4871 0.000 0.332 0.000 0.000 0.664 0.004
#> GSM1068525 6 0.5868 0.2900 0.000 0.016 0.000 0.200 0.228 0.556
#> GSM1068526 6 0.3412 0.5180 0.004 0.008 0.000 0.212 0.004 0.772
#> GSM1068458 1 0.0551 0.8653 0.984 0.000 0.004 0.004 0.008 0.000
#> GSM1068459 3 0.0912 0.7961 0.004 0.000 0.972 0.012 0.008 0.004
#> GSM1068460 1 0.1265 0.8567 0.948 0.000 0.000 0.000 0.044 0.008
#> GSM1068461 3 0.0972 0.7982 0.000 0.000 0.964 0.028 0.008 0.000
#> GSM1068464 2 0.0291 0.7573 0.000 0.992 0.000 0.004 0.004 0.000
#> GSM1068468 2 0.2930 0.7069 0.000 0.840 0.000 0.036 0.124 0.000
#> GSM1068472 2 0.2537 0.7207 0.000 0.872 0.000 0.032 0.096 0.000
#> GSM1068473 2 0.0547 0.7520 0.000 0.980 0.000 0.020 0.000 0.000
#> GSM1068474 2 0.0000 0.7572 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1068476 3 0.1812 0.7888 0.000 0.000 0.912 0.080 0.008 0.000
#> GSM1068477 2 0.2600 0.7128 0.000 0.860 0.000 0.008 0.124 0.008
#> GSM1068462 2 0.3473 0.6867 0.000 0.812 0.012 0.040 0.136 0.000
#> GSM1068463 3 0.0893 0.7959 0.004 0.000 0.972 0.016 0.004 0.004
#> GSM1068465 1 0.3732 0.6841 0.744 0.000 0.000 0.024 0.228 0.004
#> GSM1068466 1 0.0508 0.8650 0.984 0.000 0.000 0.004 0.012 0.000
#> GSM1068467 2 0.2706 0.7132 0.000 0.852 0.000 0.024 0.124 0.000
#> GSM1068469 2 0.3488 0.6520 0.000 0.780 0.000 0.036 0.184 0.000
#> GSM1068470 2 0.4524 -0.0583 0.000 0.520 0.000 0.004 0.452 0.024
#> GSM1068471 2 0.0260 0.7576 0.000 0.992 0.000 0.000 0.008 0.000
#> GSM1068475 2 0.0790 0.7517 0.000 0.968 0.000 0.000 0.032 0.000
#> GSM1068528 1 0.4936 0.1400 0.512 0.000 0.444 0.024 0.012 0.008
#> GSM1068531 1 0.0146 0.8657 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM1068532 1 0.1536 0.8561 0.940 0.000 0.000 0.040 0.016 0.004
#> GSM1068533 1 0.0665 0.8648 0.980 0.000 0.004 0.008 0.008 0.000
#> GSM1068535 4 0.5223 0.3739 0.220 0.000 0.004 0.624 0.000 0.152
#> GSM1068537 1 0.0603 0.8649 0.980 0.000 0.000 0.016 0.000 0.004
#> GSM1068538 1 0.0551 0.8647 0.984 0.000 0.004 0.008 0.000 0.004
#> GSM1068539 5 0.3713 0.5290 0.008 0.032 0.000 0.044 0.824 0.092
#> GSM1068540 1 0.2065 0.8469 0.912 0.000 0.000 0.032 0.052 0.004
#> GSM1068542 6 0.3576 0.5002 0.008 0.004 0.000 0.236 0.004 0.748
#> GSM1068543 6 0.3942 0.4686 0.000 0.000 0.008 0.120 0.092 0.780
#> GSM1068544 3 0.1760 0.7831 0.028 0.000 0.936 0.020 0.012 0.004
#> GSM1068545 6 0.6021 0.3875 0.000 0.108 0.000 0.108 0.168 0.616
#> GSM1068546 3 0.1471 0.7916 0.000 0.000 0.932 0.064 0.004 0.000
#> GSM1068547 1 0.0632 0.8638 0.976 0.000 0.000 0.000 0.024 0.000
#> GSM1068548 6 0.4690 0.4977 0.092 0.012 0.000 0.152 0.012 0.732
#> GSM1068549 3 0.1584 0.7925 0.000 0.000 0.928 0.064 0.008 0.000
#> GSM1068550 6 0.3599 0.5232 0.004 0.004 0.000 0.212 0.016 0.764
#> GSM1068551 2 0.4461 0.1051 0.000 0.564 0.000 0.000 0.404 0.032
#> GSM1068552 6 0.4142 0.5028 0.004 0.048 0.000 0.208 0.004 0.736
#> GSM1068555 5 0.3905 0.4493 0.000 0.356 0.000 0.004 0.636 0.004
#> GSM1068556 6 0.2277 0.5139 0.000 0.000 0.000 0.076 0.032 0.892
#> GSM1068557 5 0.4015 0.3373 0.000 0.372 0.000 0.012 0.616 0.000
#> GSM1068560 6 0.4965 0.4403 0.024 0.000 0.000 0.076 0.228 0.672
#> GSM1068561 5 0.3773 0.5838 0.000 0.140 0.004 0.028 0.800 0.028
#> GSM1068562 6 0.3073 0.5122 0.000 0.000 0.000 0.080 0.080 0.840
#> GSM1068563 6 0.3606 0.5332 0.000 0.048 0.008 0.132 0.004 0.808
#> GSM1068565 2 0.2859 0.6428 0.000 0.828 0.000 0.000 0.156 0.016
#> GSM1068529 3 0.6458 0.4747 0.000 0.000 0.560 0.148 0.104 0.188
#> GSM1068530 1 0.0405 0.8651 0.988 0.000 0.000 0.008 0.000 0.004
#> GSM1068534 3 0.6694 0.1106 0.004 0.000 0.404 0.140 0.060 0.392
#> GSM1068536 1 0.4798 0.2677 0.544 0.000 0.000 0.032 0.412 0.012
#> GSM1068541 5 0.7586 0.2430 0.140 0.072 0.000 0.068 0.452 0.268
#> GSM1068553 4 0.3653 0.3155 0.008 0.000 0.000 0.692 0.000 0.300
#> GSM1068554 4 0.4486 0.4556 0.000 0.112 0.000 0.704 0.000 0.184
#> GSM1068558 3 0.4758 0.6893 0.000 0.000 0.732 0.132 0.044 0.092
#> GSM1068559 3 0.1918 0.7882 0.000 0.000 0.904 0.088 0.008 0.000
#> GSM1068564 6 0.5771 0.2116 0.000 0.192 0.000 0.280 0.004 0.524
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 disease.state(p) gender(p) k
#> SD:skmeans 101 0.281381 0.645 2
#> SD:skmeans 91 0.762514 0.952 3
#> SD:skmeans 97 0.008215 0.991 4
#> SD:skmeans 77 0.000346 0.988 5
#> SD:skmeans 68 0.001059 0.699 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 38950 rows and 108 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 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 0.278 0.428 0.691 0.4656 0.540 0.540
#> 3 3 0.381 0.602 0.766 0.3732 0.628 0.415
#> 4 4 0.584 0.747 0.828 0.1520 0.781 0.472
#> 5 5 0.659 0.689 0.802 0.0674 0.920 0.704
#> 6 6 0.748 0.724 0.856 0.0391 0.954 0.790
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
#> GSM1068478 1 0.8016 0.2097 0.756 0.244
#> GSM1068479 1 0.9866 -0.3893 0.568 0.432
#> GSM1068481 1 0.0000 0.5789 1.000 0.000
#> GSM1068482 1 0.0000 0.5789 1.000 0.000
#> GSM1068483 1 0.3431 0.5468 0.936 0.064
#> GSM1068486 1 0.0000 0.5789 1.000 0.000
#> GSM1068487 2 0.9661 0.6664 0.392 0.608
#> GSM1068488 1 0.9661 0.5102 0.608 0.392
#> GSM1068490 2 0.9795 0.6466 0.416 0.584
#> GSM1068491 1 0.0000 0.5789 1.000 0.000
#> GSM1068492 1 0.5946 0.3608 0.856 0.144
#> GSM1068493 1 0.7950 0.1942 0.760 0.240
#> GSM1068494 1 0.1184 0.5827 0.984 0.016
#> GSM1068495 1 0.9209 -0.0695 0.664 0.336
#> GSM1068496 1 0.0938 0.5755 0.988 0.012
#> GSM1068498 2 0.9661 0.6664 0.392 0.608
#> GSM1068499 1 0.0000 0.5789 1.000 0.000
#> GSM1068500 1 0.3431 0.5468 0.936 0.064
#> GSM1068502 1 0.9491 -0.3253 0.632 0.368
#> GSM1068503 2 0.8713 0.3796 0.292 0.708
#> GSM1068505 2 0.6801 0.1632 0.180 0.820
#> GSM1068506 1 0.9944 0.4992 0.544 0.456
#> GSM1068507 2 0.9983 0.2017 0.476 0.524
#> GSM1068508 2 0.9661 0.6664 0.392 0.608
#> GSM1068510 2 0.7950 0.1143 0.240 0.760
#> GSM1068512 1 0.9661 0.5102 0.608 0.392
#> GSM1068513 2 0.8955 0.5889 0.312 0.688
#> GSM1068514 1 0.9815 0.4925 0.580 0.420
#> GSM1068517 2 0.9661 0.6664 0.392 0.608
#> GSM1068518 1 0.3114 0.5235 0.944 0.056
#> GSM1068520 1 0.3431 0.5468 0.936 0.064
#> GSM1068521 1 0.3431 0.5468 0.936 0.064
#> GSM1068522 2 0.0000 0.3689 0.000 1.000
#> GSM1068524 2 0.9580 0.6604 0.380 0.620
#> GSM1068527 1 0.9944 0.4992 0.544 0.456
#> GSM1068480 1 0.0000 0.5789 1.000 0.000
#> GSM1068484 2 0.9896 -0.3316 0.440 0.560
#> GSM1068485 1 0.0000 0.5789 1.000 0.000
#> GSM1068489 1 1.0000 0.4591 0.500 0.500
#> GSM1068497 2 0.9661 0.6664 0.392 0.608
#> GSM1068501 2 0.6148 0.2468 0.152 0.848
#> GSM1068504 2 0.9661 0.6664 0.392 0.608
#> GSM1068509 1 0.9286 0.5343 0.656 0.344
#> GSM1068511 1 0.9661 0.5102 0.608 0.392
#> GSM1068515 1 0.3431 0.5468 0.936 0.064
#> GSM1068516 1 0.9754 0.5101 0.592 0.408
#> GSM1068519 1 0.9661 0.5102 0.608 0.392
#> GSM1068523 2 0.9580 0.6604 0.380 0.620
#> GSM1068525 2 0.8909 -0.0353 0.308 0.692
#> GSM1068526 1 0.9933 0.5005 0.548 0.452
#> GSM1068458 1 0.3584 0.5491 0.932 0.068
#> GSM1068459 1 0.0000 0.5789 1.000 0.000
#> GSM1068460 1 0.9988 -0.1331 0.520 0.480
#> GSM1068461 1 0.0000 0.5789 1.000 0.000
#> GSM1068464 2 0.9661 0.6664 0.392 0.608
#> GSM1068468 2 0.9661 0.6664 0.392 0.608
#> GSM1068472 1 0.9866 -0.3893 0.568 0.432
#> GSM1068473 2 0.9661 0.6664 0.392 0.608
#> GSM1068474 2 0.9661 0.6664 0.392 0.608
#> GSM1068476 1 0.5629 0.4222 0.868 0.132
#> GSM1068477 2 0.9661 0.6664 0.392 0.608
#> GSM1068462 2 0.9970 0.5687 0.468 0.532
#> GSM1068463 1 0.0000 0.5789 1.000 0.000
#> GSM1068465 2 0.9983 -0.4327 0.476 0.524
#> GSM1068466 1 0.9087 0.5380 0.676 0.324
#> GSM1068467 2 0.9775 0.6469 0.412 0.588
#> GSM1068469 1 1.0000 -0.5301 0.500 0.500
#> GSM1068470 2 0.9661 0.6664 0.392 0.608
#> GSM1068471 2 0.9661 0.6664 0.392 0.608
#> GSM1068475 2 0.9661 0.6664 0.392 0.608
#> GSM1068528 1 0.3431 0.5468 0.936 0.064
#> GSM1068531 1 0.9944 0.4992 0.544 0.456
#> GSM1068532 1 0.8081 0.5504 0.752 0.248
#> GSM1068533 1 0.9922 0.5028 0.552 0.448
#> GSM1068535 1 0.9661 0.5102 0.608 0.392
#> GSM1068537 1 0.9661 0.5102 0.608 0.392
#> GSM1068538 1 0.9815 0.5084 0.580 0.420
#> GSM1068539 2 0.9732 0.6386 0.404 0.596
#> GSM1068540 1 0.3879 0.5530 0.924 0.076
#> GSM1068542 1 0.9710 0.5062 0.600 0.400
#> GSM1068543 1 0.9661 0.5102 0.608 0.392
#> GSM1068544 1 0.3431 0.5468 0.936 0.064
#> GSM1068545 2 0.7674 0.4368 0.224 0.776
#> GSM1068546 1 0.9661 0.5102 0.608 0.392
#> GSM1068547 1 0.8813 0.5455 0.700 0.300
#> GSM1068548 1 0.9661 0.5102 0.608 0.392
#> GSM1068549 1 0.1843 0.5842 0.972 0.028
#> GSM1068550 2 0.9608 -0.3134 0.384 0.616
#> GSM1068551 2 0.9661 0.6664 0.392 0.608
#> GSM1068552 2 0.9922 -0.4155 0.448 0.552
#> GSM1068555 2 0.9661 0.6664 0.392 0.608
#> GSM1068556 1 0.9661 0.5102 0.608 0.392
#> GSM1068557 1 0.9850 -0.3810 0.572 0.428
#> GSM1068560 1 0.5059 0.5668 0.888 0.112
#> GSM1068561 1 0.9552 -0.2599 0.624 0.376
#> GSM1068562 1 0.2778 0.5838 0.952 0.048
#> GSM1068563 1 0.9686 0.5103 0.604 0.396
#> GSM1068565 2 0.9661 0.6664 0.392 0.608
#> GSM1068529 1 0.3879 0.5530 0.924 0.076
#> GSM1068530 1 0.7453 0.5601 0.788 0.212
#> GSM1068534 1 0.9491 0.5172 0.632 0.368
#> GSM1068536 1 0.4690 0.5640 0.900 0.100
#> GSM1068541 1 0.8713 0.1235 0.708 0.292
#> GSM1068553 1 0.9661 0.5102 0.608 0.392
#> GSM1068554 2 0.7950 0.1143 0.240 0.760
#> GSM1068558 1 0.0938 0.5815 0.988 0.012
#> GSM1068559 1 0.0938 0.5815 0.988 0.012
#> GSM1068564 2 0.0376 0.3669 0.004 0.996
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1068478 1 0.6728 0.6805 0.748 0.124 0.128
#> GSM1068479 2 0.5200 0.6920 0.184 0.796 0.020
#> GSM1068481 3 0.7306 0.6264 0.236 0.080 0.684
#> GSM1068482 3 0.4702 0.6892 0.212 0.000 0.788
#> GSM1068483 1 0.7022 0.7701 0.684 0.056 0.260
#> GSM1068486 3 0.7306 0.6264 0.236 0.080 0.684
#> GSM1068487 2 0.0592 0.7577 0.012 0.988 0.000
#> GSM1068488 3 0.2165 0.6991 0.064 0.000 0.936
#> GSM1068490 2 0.0000 0.7573 0.000 1.000 0.000
#> GSM1068491 3 0.7531 0.6219 0.236 0.092 0.672
#> GSM1068492 3 0.4346 0.6487 0.000 0.184 0.816
#> GSM1068493 2 0.8842 0.4157 0.208 0.580 0.212
#> GSM1068494 3 0.4887 0.5298 0.228 0.000 0.772
#> GSM1068495 2 0.8255 0.1201 0.428 0.496 0.076
#> GSM1068496 3 0.5254 0.4486 0.264 0.000 0.736
#> GSM1068498 1 0.5465 0.4599 0.712 0.288 0.000
#> GSM1068499 3 0.2356 0.7137 0.072 0.000 0.928
#> GSM1068500 1 0.7022 0.7701 0.684 0.056 0.260
#> GSM1068502 3 0.8142 0.5290 0.112 0.268 0.620
#> GSM1068503 2 0.2711 0.7433 0.000 0.912 0.088
#> GSM1068505 2 0.9006 0.4550 0.188 0.556 0.256
#> GSM1068506 2 0.7786 0.5087 0.068 0.600 0.332
#> GSM1068507 2 0.4931 0.6543 0.000 0.768 0.232
#> GSM1068508 2 0.2711 0.7442 0.088 0.912 0.000
#> GSM1068510 2 0.7004 0.2231 0.020 0.552 0.428
#> GSM1068512 3 0.0237 0.7182 0.004 0.000 0.996
#> GSM1068513 2 0.0592 0.7580 0.000 0.988 0.012
#> GSM1068514 3 0.2878 0.7072 0.000 0.096 0.904
#> GSM1068517 2 0.6309 0.0972 0.496 0.504 0.000
#> GSM1068518 3 0.4095 0.7139 0.064 0.056 0.880
#> GSM1068520 1 0.5327 0.7835 0.728 0.000 0.272
#> GSM1068521 1 0.5480 0.7857 0.732 0.004 0.264
#> GSM1068522 2 0.4475 0.7207 0.064 0.864 0.072
#> GSM1068524 2 0.0892 0.7572 0.020 0.980 0.000
#> GSM1068527 3 0.6280 -0.2729 0.460 0.000 0.540
#> GSM1068480 3 0.2356 0.7137 0.072 0.000 0.928
#> GSM1068484 3 0.8033 -0.1729 0.064 0.424 0.512
#> GSM1068485 3 0.5216 0.6635 0.260 0.000 0.740
#> GSM1068489 2 0.7996 0.4516 0.068 0.552 0.380
#> GSM1068497 2 0.6215 0.2985 0.428 0.572 0.000
#> GSM1068501 3 0.7864 0.3149 0.072 0.332 0.596
#> GSM1068504 2 0.0892 0.7572 0.020 0.980 0.000
#> GSM1068509 3 0.5098 0.4482 0.248 0.000 0.752
#> GSM1068511 3 0.0237 0.7182 0.004 0.000 0.996
#> GSM1068515 2 0.9531 0.2010 0.208 0.468 0.324
#> GSM1068516 3 0.4605 0.5789 0.204 0.000 0.796
#> GSM1068519 3 0.3551 0.6877 0.132 0.000 0.868
#> GSM1068523 2 0.2165 0.7506 0.064 0.936 0.000
#> GSM1068525 2 0.5070 0.6628 0.004 0.772 0.224
#> GSM1068526 2 0.9294 0.3211 0.172 0.484 0.344
#> GSM1068458 1 0.5785 0.7653 0.696 0.004 0.300
#> GSM1068459 3 0.5216 0.6635 0.260 0.000 0.740
#> GSM1068460 1 0.6529 0.6955 0.760 0.124 0.116
#> GSM1068461 3 0.7306 0.6264 0.236 0.080 0.684
#> GSM1068464 2 0.0000 0.7573 0.000 1.000 0.000
#> GSM1068468 2 0.2280 0.7518 0.052 0.940 0.008
#> GSM1068472 2 0.4139 0.7179 0.016 0.860 0.124
#> GSM1068473 2 0.0000 0.7573 0.000 1.000 0.000
#> GSM1068474 2 0.0892 0.7572 0.020 0.980 0.000
#> GSM1068476 3 0.5958 0.6617 0.300 0.008 0.692
#> GSM1068477 2 0.2066 0.7511 0.060 0.940 0.000
#> GSM1068462 2 0.2400 0.7542 0.004 0.932 0.064
#> GSM1068463 3 0.5254 0.6641 0.264 0.000 0.736
#> GSM1068465 1 0.9472 0.5096 0.492 0.288 0.220
#> GSM1068466 1 0.5737 0.7542 0.732 0.012 0.256
#> GSM1068467 2 0.1015 0.7605 0.008 0.980 0.012
#> GSM1068469 2 0.3234 0.7474 0.020 0.908 0.072
#> GSM1068470 2 0.2165 0.7506 0.064 0.936 0.000
#> GSM1068471 2 0.0000 0.7573 0.000 1.000 0.000
#> GSM1068475 2 0.1964 0.7519 0.056 0.944 0.000
#> GSM1068528 1 0.4842 0.7048 0.776 0.000 0.224
#> GSM1068531 1 0.5678 0.7210 0.684 0.000 0.316
#> GSM1068532 3 0.1753 0.7195 0.048 0.000 0.952
#> GSM1068533 1 0.6026 0.7413 0.624 0.000 0.376
#> GSM1068535 3 0.2165 0.6991 0.064 0.000 0.936
#> GSM1068537 1 0.5968 0.7556 0.636 0.000 0.364
#> GSM1068538 3 0.6260 -0.3516 0.448 0.000 0.552
#> GSM1068539 1 0.6955 -0.1358 0.492 0.492 0.016
#> GSM1068540 1 0.5465 0.7803 0.712 0.000 0.288
#> GSM1068542 2 0.9147 0.3461 0.156 0.496 0.348
#> GSM1068543 3 0.2261 0.6976 0.068 0.000 0.932
#> GSM1068544 3 0.6305 0.3914 0.484 0.000 0.516
#> GSM1068545 2 0.2689 0.7597 0.032 0.932 0.036
#> GSM1068546 3 0.4654 0.6672 0.208 0.000 0.792
#> GSM1068547 1 0.5138 0.7795 0.748 0.000 0.252
#> GSM1068548 3 0.6154 -0.1159 0.408 0.000 0.592
#> GSM1068549 3 0.5058 0.6713 0.244 0.000 0.756
#> GSM1068550 2 0.7847 0.5021 0.068 0.588 0.344
#> GSM1068551 2 0.3116 0.7428 0.108 0.892 0.000
#> GSM1068552 2 0.7683 0.5187 0.064 0.608 0.328
#> GSM1068555 2 0.2165 0.7506 0.064 0.936 0.000
#> GSM1068556 3 0.1163 0.7135 0.028 0.000 0.972
#> GSM1068557 2 0.5276 0.7033 0.052 0.820 0.128
#> GSM1068560 1 0.5831 0.7535 0.708 0.008 0.284
#> GSM1068561 2 0.9212 0.0552 0.372 0.472 0.156
#> GSM1068562 3 0.2711 0.7196 0.088 0.000 0.912
#> GSM1068563 3 0.2165 0.6991 0.064 0.000 0.936
#> GSM1068565 2 0.1860 0.7528 0.052 0.948 0.000
#> GSM1068529 3 0.2261 0.7148 0.068 0.000 0.932
#> GSM1068530 1 0.5678 0.7684 0.684 0.000 0.316
#> GSM1068534 3 0.1289 0.7164 0.032 0.000 0.968
#> GSM1068536 1 0.5882 0.7607 0.652 0.000 0.348
#> GSM1068541 2 0.8336 0.5010 0.224 0.624 0.152
#> GSM1068553 3 0.2165 0.6991 0.064 0.000 0.936
#> GSM1068554 2 0.7536 0.5723 0.064 0.632 0.304
#> GSM1068558 3 0.2879 0.7170 0.052 0.024 0.924
#> GSM1068559 3 0.3237 0.7210 0.056 0.032 0.912
#> GSM1068564 2 0.5650 0.7025 0.084 0.808 0.108
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1068478 1 0.2011 0.720 0.920 0.080 0.000 0.000
#> GSM1068479 2 0.3649 0.748 0.000 0.796 0.204 0.000
#> GSM1068481 3 0.0336 0.769 0.000 0.008 0.992 0.000
#> GSM1068482 3 0.2266 0.791 0.004 0.000 0.912 0.084
#> GSM1068483 1 0.3970 0.745 0.840 0.084 0.000 0.076
#> GSM1068486 3 0.0524 0.770 0.000 0.008 0.988 0.004
#> GSM1068487 2 0.0921 0.853 0.028 0.972 0.000 0.000
#> GSM1068488 3 0.5203 0.795 0.048 0.000 0.720 0.232
#> GSM1068490 2 0.0000 0.851 0.000 1.000 0.000 0.000
#> GSM1068491 3 0.0000 0.773 0.000 0.000 1.000 0.000
#> GSM1068492 3 0.6255 0.787 0.064 0.056 0.720 0.160
#> GSM1068493 2 0.6393 0.346 0.332 0.604 0.020 0.044
#> GSM1068494 3 0.7341 0.552 0.252 0.000 0.528 0.220
#> GSM1068495 1 0.4857 0.456 0.668 0.324 0.000 0.008
#> GSM1068496 1 0.7179 0.275 0.544 0.000 0.276 0.180
#> GSM1068498 1 0.2530 0.710 0.896 0.100 0.000 0.004
#> GSM1068499 3 0.5397 0.799 0.068 0.000 0.720 0.212
#> GSM1068500 1 0.4039 0.744 0.836 0.084 0.000 0.080
#> GSM1068502 3 0.3937 0.697 0.012 0.188 0.800 0.000
#> GSM1068503 2 0.1488 0.840 0.032 0.956 0.000 0.012
#> GSM1068505 4 0.2924 0.797 0.016 0.100 0.000 0.884
#> GSM1068506 4 0.1743 0.849 0.056 0.004 0.000 0.940
#> GSM1068507 2 0.5246 0.718 0.060 0.796 0.084 0.060
#> GSM1068508 2 0.3710 0.774 0.192 0.804 0.000 0.004
#> GSM1068510 2 0.7028 0.436 0.000 0.568 0.172 0.260
#> GSM1068512 3 0.5431 0.795 0.064 0.000 0.712 0.224
#> GSM1068513 2 0.2376 0.822 0.000 0.916 0.016 0.068
#> GSM1068514 3 0.5507 0.800 0.064 0.004 0.720 0.212
#> GSM1068517 1 0.4741 0.454 0.668 0.328 0.000 0.004
#> GSM1068518 3 0.5507 0.800 0.064 0.004 0.720 0.212
#> GSM1068520 1 0.1211 0.758 0.960 0.000 0.000 0.040
#> GSM1068521 1 0.1305 0.759 0.960 0.004 0.000 0.036
#> GSM1068522 4 0.4018 0.670 0.004 0.224 0.000 0.772
#> GSM1068524 2 0.1398 0.851 0.040 0.956 0.000 0.004
#> GSM1068527 4 0.0817 0.850 0.024 0.000 0.000 0.976
#> GSM1068480 3 0.5328 0.800 0.064 0.000 0.724 0.212
#> GSM1068484 4 0.0937 0.851 0.000 0.012 0.012 0.976
#> GSM1068485 3 0.0000 0.773 0.000 0.000 1.000 0.000
#> GSM1068489 4 0.0188 0.852 0.000 0.000 0.004 0.996
#> GSM1068497 1 0.4781 0.437 0.660 0.336 0.000 0.004
#> GSM1068501 4 0.3662 0.752 0.012 0.148 0.004 0.836
#> GSM1068504 2 0.1398 0.851 0.040 0.956 0.000 0.004
#> GSM1068509 4 0.2334 0.840 0.088 0.000 0.004 0.908
#> GSM1068511 3 0.5785 0.742 0.064 0.000 0.664 0.272
#> GSM1068515 2 0.5685 0.269 0.024 0.516 0.000 0.460
#> GSM1068516 4 0.2943 0.831 0.076 0.000 0.032 0.892
#> GSM1068519 4 0.2412 0.819 0.084 0.000 0.008 0.908
#> GSM1068523 2 0.3105 0.817 0.140 0.856 0.000 0.004
#> GSM1068525 2 0.5629 0.665 0.064 0.756 0.032 0.148
#> GSM1068526 4 0.1902 0.846 0.064 0.004 0.000 0.932
#> GSM1068458 4 0.4509 0.591 0.288 0.004 0.000 0.708
#> GSM1068459 3 0.0000 0.773 0.000 0.000 1.000 0.000
#> GSM1068460 4 0.4655 0.673 0.208 0.032 0.000 0.760
#> GSM1068461 3 0.0000 0.773 0.000 0.000 1.000 0.000
#> GSM1068464 2 0.0000 0.851 0.000 1.000 0.000 0.000
#> GSM1068468 2 0.1975 0.850 0.048 0.936 0.016 0.000
#> GSM1068472 2 0.2300 0.814 0.064 0.920 0.000 0.016
#> GSM1068473 2 0.0000 0.851 0.000 1.000 0.000 0.000
#> GSM1068474 2 0.1211 0.851 0.040 0.960 0.000 0.000
#> GSM1068476 3 0.0000 0.773 0.000 0.000 1.000 0.000
#> GSM1068477 2 0.1978 0.845 0.068 0.928 0.000 0.004
#> GSM1068462 2 0.2057 0.835 0.032 0.940 0.020 0.008
#> GSM1068463 3 0.0336 0.773 0.000 0.000 0.992 0.008
#> GSM1068465 1 0.4578 0.714 0.788 0.160 0.000 0.052
#> GSM1068466 1 0.3160 0.733 0.872 0.020 0.000 0.108
#> GSM1068467 2 0.0376 0.851 0.004 0.992 0.000 0.004
#> GSM1068469 2 0.2048 0.835 0.064 0.928 0.000 0.008
#> GSM1068470 2 0.3105 0.817 0.140 0.856 0.000 0.004
#> GSM1068471 2 0.0376 0.852 0.004 0.992 0.000 0.004
#> GSM1068475 2 0.2197 0.841 0.080 0.916 0.000 0.004
#> GSM1068528 1 0.5493 0.696 0.744 0.004 0.144 0.108
#> GSM1068531 4 0.3266 0.769 0.168 0.000 0.000 0.832
#> GSM1068532 3 0.5511 0.796 0.084 0.000 0.720 0.196
#> GSM1068533 1 0.3907 0.619 0.768 0.000 0.000 0.232
#> GSM1068535 4 0.1059 0.850 0.016 0.000 0.012 0.972
#> GSM1068537 1 0.3052 0.716 0.860 0.000 0.004 0.136
#> GSM1068538 4 0.4697 0.525 0.356 0.000 0.000 0.644
#> GSM1068539 1 0.4655 0.482 0.684 0.312 0.000 0.004
#> GSM1068540 1 0.2345 0.744 0.900 0.000 0.000 0.100
#> GSM1068542 4 0.1792 0.846 0.068 0.000 0.000 0.932
#> GSM1068543 4 0.2048 0.844 0.064 0.000 0.008 0.928
#> GSM1068544 3 0.3837 0.532 0.224 0.000 0.776 0.000
#> GSM1068545 2 0.4235 0.809 0.092 0.824 0.000 0.084
#> GSM1068546 3 0.0188 0.772 0.000 0.000 0.996 0.004
#> GSM1068547 1 0.2973 0.735 0.856 0.000 0.000 0.144
#> GSM1068548 4 0.3105 0.821 0.140 0.004 0.000 0.856
#> GSM1068549 3 0.0000 0.773 0.000 0.000 1.000 0.000
#> GSM1068550 4 0.0817 0.849 0.024 0.000 0.000 0.976
#> GSM1068551 2 0.2944 0.823 0.128 0.868 0.000 0.004
#> GSM1068552 4 0.2466 0.843 0.056 0.028 0.000 0.916
#> GSM1068555 2 0.3105 0.817 0.140 0.856 0.000 0.004
#> GSM1068556 4 0.3547 0.799 0.064 0.000 0.072 0.864
#> GSM1068557 2 0.2888 0.802 0.004 0.872 0.000 0.124
#> GSM1068560 4 0.2216 0.845 0.092 0.000 0.000 0.908
#> GSM1068561 1 0.5284 0.456 0.616 0.368 0.000 0.016
#> GSM1068562 3 0.5431 0.795 0.064 0.000 0.712 0.224
#> GSM1068563 4 0.4114 0.750 0.060 0.000 0.112 0.828
#> GSM1068565 2 0.2654 0.833 0.108 0.888 0.000 0.004
#> GSM1068529 3 0.5431 0.795 0.064 0.000 0.712 0.224
#> GSM1068530 1 0.2760 0.724 0.872 0.000 0.000 0.128
#> GSM1068534 3 0.5590 0.776 0.064 0.000 0.692 0.244
#> GSM1068536 1 0.3801 0.643 0.780 0.000 0.000 0.220
#> GSM1068541 2 0.6224 0.633 0.144 0.668 0.000 0.188
#> GSM1068553 4 0.0336 0.852 0.000 0.000 0.008 0.992
#> GSM1068554 4 0.3157 0.763 0.000 0.144 0.004 0.852
#> GSM1068558 3 0.5576 0.796 0.064 0.004 0.712 0.220
#> GSM1068559 3 0.5363 0.798 0.064 0.000 0.720 0.216
#> GSM1068564 4 0.4139 0.723 0.040 0.144 0.000 0.816
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM1068478 5 0.2516 0.622 0.140 0.000 0.000 0.000 0.860
#> GSM1068479 2 0.4588 0.677 0.156 0.768 0.048 0.000 0.028
#> GSM1068481 3 0.5213 0.704 0.312 0.056 0.628 0.000 0.004
#> GSM1068482 3 0.3550 0.766 0.236 0.000 0.760 0.000 0.004
#> GSM1068483 1 0.6472 0.601 0.616 0.228 0.100 0.004 0.052
#> GSM1068486 3 0.5024 0.713 0.312 0.044 0.640 0.000 0.004
#> GSM1068487 2 0.1908 0.791 0.000 0.908 0.000 0.000 0.092
#> GSM1068488 3 0.1965 0.766 0.000 0.000 0.904 0.096 0.000
#> GSM1068490 2 0.0000 0.796 0.000 1.000 0.000 0.000 0.000
#> GSM1068491 3 0.3814 0.751 0.276 0.000 0.720 0.000 0.004
#> GSM1068492 3 0.2763 0.730 0.000 0.148 0.848 0.004 0.000
#> GSM1068493 2 0.2949 0.739 0.000 0.876 0.052 0.004 0.068
#> GSM1068494 3 0.2166 0.749 0.012 0.000 0.912 0.004 0.072
#> GSM1068495 5 0.0671 0.717 0.000 0.004 0.000 0.016 0.980
#> GSM1068496 3 0.1430 0.769 0.000 0.000 0.944 0.004 0.052
#> GSM1068498 5 0.1557 0.703 0.052 0.008 0.000 0.000 0.940
#> GSM1068499 3 0.0833 0.785 0.004 0.000 0.976 0.004 0.016
#> GSM1068500 1 0.6973 0.570 0.576 0.232 0.104 0.004 0.084
#> GSM1068502 3 0.3944 0.627 0.004 0.272 0.720 0.000 0.004
#> GSM1068503 2 0.0703 0.799 0.000 0.976 0.000 0.000 0.024
#> GSM1068505 4 0.0510 0.779 0.000 0.016 0.000 0.984 0.000
#> GSM1068506 4 0.3586 0.773 0.000 0.000 0.264 0.736 0.000
#> GSM1068507 2 0.1892 0.757 0.000 0.916 0.080 0.004 0.000
#> GSM1068508 2 0.3636 0.680 0.000 0.728 0.000 0.000 0.272
#> GSM1068510 2 0.6473 0.201 0.000 0.468 0.164 0.364 0.004
#> GSM1068512 3 0.0162 0.787 0.000 0.000 0.996 0.004 0.000
#> GSM1068513 2 0.0486 0.796 0.000 0.988 0.004 0.004 0.004
#> GSM1068514 3 0.0162 0.787 0.000 0.000 0.996 0.004 0.000
#> GSM1068517 5 0.0290 0.721 0.000 0.008 0.000 0.000 0.992
#> GSM1068518 3 0.0162 0.787 0.000 0.000 0.996 0.004 0.000
#> GSM1068520 1 0.5677 0.675 0.672 0.000 0.036 0.076 0.216
#> GSM1068521 1 0.5992 0.676 0.648 0.004 0.032 0.088 0.228
#> GSM1068522 4 0.2338 0.754 0.000 0.112 0.000 0.884 0.004
#> GSM1068524 2 0.3304 0.756 0.000 0.816 0.000 0.016 0.168
#> GSM1068527 4 0.0963 0.792 0.000 0.000 0.036 0.964 0.000
#> GSM1068480 3 0.0162 0.787 0.000 0.000 0.996 0.004 0.000
#> GSM1068484 4 0.2249 0.807 0.000 0.008 0.096 0.896 0.000
#> GSM1068485 3 0.4419 0.737 0.312 0.000 0.668 0.000 0.020
#> GSM1068489 4 0.2074 0.807 0.000 0.000 0.104 0.896 0.000
#> GSM1068497 5 0.1043 0.723 0.000 0.040 0.000 0.000 0.960
#> GSM1068501 4 0.2853 0.783 0.000 0.036 0.028 0.892 0.044
#> GSM1068504 2 0.2773 0.759 0.000 0.836 0.000 0.000 0.164
#> GSM1068509 4 0.4655 0.764 0.000 0.000 0.248 0.700 0.052
#> GSM1068511 3 0.1341 0.754 0.000 0.000 0.944 0.056 0.000
#> GSM1068515 5 0.7915 0.391 0.012 0.136 0.176 0.168 0.508
#> GSM1068516 4 0.4854 0.723 0.000 0.000 0.308 0.648 0.044
#> GSM1068519 4 0.1525 0.770 0.012 0.000 0.004 0.948 0.036
#> GSM1068523 2 0.4249 0.420 0.000 0.568 0.000 0.000 0.432
#> GSM1068525 2 0.4146 0.562 0.000 0.716 0.268 0.004 0.012
#> GSM1068526 4 0.3684 0.762 0.000 0.000 0.280 0.720 0.000
#> GSM1068458 1 0.5260 0.722 0.684 0.004 0.108 0.204 0.000
#> GSM1068459 3 0.4009 0.739 0.312 0.000 0.684 0.000 0.004
#> GSM1068460 4 0.1357 0.769 0.004 0.000 0.000 0.948 0.048
#> GSM1068461 3 0.4009 0.739 0.312 0.000 0.684 0.000 0.004
#> GSM1068464 2 0.0000 0.796 0.000 1.000 0.000 0.000 0.000
#> GSM1068468 2 0.1908 0.773 0.000 0.908 0.000 0.000 0.092
#> GSM1068472 2 0.0451 0.795 0.000 0.988 0.004 0.000 0.008
#> GSM1068473 2 0.0000 0.796 0.000 1.000 0.000 0.000 0.000
#> GSM1068474 2 0.2648 0.767 0.000 0.848 0.000 0.000 0.152
#> GSM1068476 3 0.4009 0.739 0.312 0.000 0.684 0.000 0.004
#> GSM1068477 2 0.4249 0.275 0.000 0.568 0.000 0.000 0.432
#> GSM1068462 2 0.0451 0.795 0.000 0.988 0.004 0.000 0.008
#> GSM1068463 3 0.4385 0.734 0.312 0.012 0.672 0.000 0.004
#> GSM1068465 1 0.6660 0.639 0.612 0.052 0.156 0.004 0.176
#> GSM1068466 1 0.5137 0.669 0.684 0.000 0.000 0.208 0.108
#> GSM1068467 2 0.0609 0.795 0.000 0.980 0.000 0.000 0.020
#> GSM1068469 5 0.4299 0.415 0.000 0.388 0.004 0.000 0.608
#> GSM1068470 5 0.2471 0.669 0.000 0.136 0.000 0.000 0.864
#> GSM1068471 2 0.1478 0.797 0.000 0.936 0.000 0.000 0.064
#> GSM1068475 2 0.3395 0.706 0.000 0.764 0.000 0.000 0.236
#> GSM1068528 1 0.4670 0.614 0.764 0.008 0.136 0.004 0.088
#> GSM1068531 4 0.2753 0.655 0.136 0.000 0.000 0.856 0.008
#> GSM1068532 3 0.2573 0.734 0.016 0.000 0.880 0.104 0.000
#> GSM1068533 1 0.5739 0.743 0.680 0.000 0.184 0.100 0.036
#> GSM1068535 4 0.0451 0.781 0.004 0.000 0.008 0.988 0.000
#> GSM1068537 1 0.5894 0.748 0.676 0.000 0.172 0.104 0.048
#> GSM1068538 1 0.5190 0.736 0.688 0.000 0.172 0.140 0.000
#> GSM1068539 5 0.0566 0.721 0.012 0.004 0.000 0.000 0.984
#> GSM1068540 1 0.6147 0.754 0.672 0.000 0.132 0.104 0.092
#> GSM1068542 4 0.3661 0.765 0.000 0.000 0.276 0.724 0.000
#> GSM1068543 4 0.3561 0.775 0.000 0.000 0.260 0.740 0.000
#> GSM1068544 1 0.5435 -0.285 0.576 0.000 0.352 0.000 0.072
#> GSM1068545 2 0.5342 0.654 0.000 0.676 0.024 0.056 0.244
#> GSM1068546 3 0.4584 0.733 0.312 0.000 0.660 0.000 0.028
#> GSM1068547 1 0.5913 0.731 0.684 0.000 0.060 0.148 0.108
#> GSM1068548 4 0.6147 0.305 0.256 0.000 0.188 0.556 0.000
#> GSM1068549 3 0.3861 0.749 0.284 0.000 0.712 0.000 0.004
#> GSM1068550 4 0.0510 0.786 0.000 0.000 0.016 0.984 0.000
#> GSM1068551 2 0.3774 0.664 0.000 0.704 0.000 0.000 0.296
#> GSM1068552 4 0.5122 0.756 0.000 0.008 0.224 0.692 0.076
#> GSM1068555 5 0.4262 -0.119 0.000 0.440 0.000 0.000 0.560
#> GSM1068556 4 0.4060 0.692 0.000 0.000 0.360 0.640 0.000
#> GSM1068557 2 0.1403 0.787 0.000 0.952 0.000 0.024 0.024
#> GSM1068560 4 0.4955 0.743 0.000 0.000 0.248 0.680 0.072
#> GSM1068561 2 0.3366 0.654 0.000 0.784 0.004 0.000 0.212
#> GSM1068562 3 0.1662 0.767 0.000 0.004 0.936 0.004 0.056
#> GSM1068563 4 0.4161 0.653 0.000 0.000 0.392 0.608 0.000
#> GSM1068565 2 0.3561 0.687 0.000 0.740 0.000 0.000 0.260
#> GSM1068529 3 0.1571 0.766 0.000 0.000 0.936 0.004 0.060
#> GSM1068530 1 0.5962 0.753 0.688 0.000 0.124 0.104 0.084
#> GSM1068534 3 0.0609 0.783 0.000 0.000 0.980 0.020 0.000
#> GSM1068536 5 0.6948 -0.239 0.300 0.000 0.272 0.008 0.420
#> GSM1068541 5 0.3882 0.689 0.004 0.080 0.056 0.024 0.836
#> GSM1068553 4 0.2074 0.807 0.000 0.000 0.104 0.896 0.000
#> GSM1068554 4 0.2592 0.796 0.000 0.052 0.056 0.892 0.000
#> GSM1068558 3 0.1205 0.776 0.000 0.000 0.956 0.004 0.040
#> GSM1068559 3 0.0771 0.784 0.000 0.000 0.976 0.004 0.020
#> GSM1068564 4 0.2554 0.759 0.000 0.036 0.000 0.892 0.072
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1068478 5 0.1910 0.6857 0.108 0.000 0.000 0.000 0.892 0.000
#> GSM1068479 2 0.3456 0.7328 0.000 0.800 0.156 0.000 0.004 0.040
#> GSM1068481 3 0.2932 0.8190 0.000 0.016 0.820 0.000 0.000 0.164
#> GSM1068482 3 0.3050 0.7582 0.000 0.000 0.764 0.000 0.000 0.236
#> GSM1068483 1 0.4439 0.6645 0.692 0.240 0.000 0.004 0.000 0.064
#> GSM1068486 3 0.2632 0.8218 0.000 0.004 0.832 0.000 0.000 0.164
#> GSM1068487 2 0.1556 0.8298 0.000 0.920 0.000 0.000 0.080 0.000
#> GSM1068488 6 0.1863 0.7894 0.000 0.000 0.000 0.104 0.000 0.896
#> GSM1068490 2 0.0000 0.8363 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1068491 6 0.3288 0.5126 0.000 0.000 0.276 0.000 0.000 0.724
#> GSM1068492 6 0.2491 0.7114 0.000 0.164 0.000 0.000 0.000 0.836
#> GSM1068493 2 0.1124 0.8208 0.000 0.956 0.000 0.000 0.008 0.036
#> GSM1068494 6 0.0767 0.8627 0.000 0.000 0.008 0.004 0.012 0.976
#> GSM1068495 5 0.0260 0.7678 0.008 0.000 0.000 0.000 0.992 0.000
#> GSM1068496 6 0.0146 0.8673 0.000 0.000 0.000 0.004 0.000 0.996
#> GSM1068498 5 0.0363 0.7659 0.012 0.000 0.000 0.000 0.988 0.000
#> GSM1068499 6 0.0146 0.8678 0.000 0.000 0.000 0.000 0.004 0.996
#> GSM1068500 1 0.4802 0.6441 0.668 0.252 0.000 0.004 0.008 0.068
#> GSM1068502 6 0.3288 0.5593 0.000 0.276 0.000 0.000 0.000 0.724
#> GSM1068503 2 0.0260 0.8374 0.000 0.992 0.000 0.000 0.008 0.000
#> GSM1068505 4 0.0146 0.7966 0.004 0.000 0.000 0.996 0.000 0.000
#> GSM1068506 4 0.3198 0.7315 0.000 0.000 0.000 0.740 0.000 0.260
#> GSM1068507 2 0.1327 0.8056 0.000 0.936 0.000 0.000 0.000 0.064
#> GSM1068508 2 0.3288 0.6950 0.000 0.724 0.000 0.000 0.276 0.000
#> GSM1068510 4 0.5696 -0.0566 0.000 0.372 0.000 0.464 0.000 0.164
#> GSM1068512 6 0.0146 0.8673 0.000 0.000 0.000 0.004 0.000 0.996
#> GSM1068513 2 0.0000 0.8363 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1068514 6 0.0000 0.8672 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM1068517 5 0.0000 0.7696 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1068518 6 0.0000 0.8672 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM1068520 1 0.3948 0.7270 0.748 0.000 0.000 0.000 0.188 0.064
#> GSM1068521 1 0.4120 0.7313 0.748 0.012 0.000 0.000 0.188 0.052
#> GSM1068522 4 0.0260 0.7957 0.000 0.008 0.000 0.992 0.000 0.000
#> GSM1068524 2 0.2868 0.7989 0.000 0.840 0.000 0.028 0.132 0.000
#> GSM1068527 4 0.1391 0.7932 0.016 0.000 0.000 0.944 0.000 0.040
#> GSM1068480 6 0.2730 0.6961 0.000 0.000 0.192 0.000 0.000 0.808
#> GSM1068484 4 0.0146 0.7969 0.000 0.000 0.000 0.996 0.000 0.004
#> GSM1068485 3 0.0000 0.8396 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1068489 4 0.0000 0.7974 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1068497 5 0.0146 0.7695 0.000 0.004 0.000 0.000 0.996 0.000
#> GSM1068501 4 0.0146 0.7969 0.000 0.004 0.000 0.996 0.000 0.000
#> GSM1068504 2 0.2178 0.8091 0.000 0.868 0.000 0.000 0.132 0.000
#> GSM1068509 4 0.3215 0.7411 0.000 0.000 0.000 0.756 0.004 0.240
#> GSM1068511 6 0.1204 0.8263 0.000 0.000 0.000 0.056 0.000 0.944
#> GSM1068515 5 0.6048 0.4411 0.000 0.084 0.000 0.248 0.580 0.088
#> GSM1068516 4 0.3672 0.6893 0.000 0.000 0.000 0.688 0.008 0.304
#> GSM1068519 4 0.0291 0.7964 0.004 0.000 0.000 0.992 0.004 0.000
#> GSM1068523 2 0.3851 0.3986 0.000 0.540 0.000 0.000 0.460 0.000
#> GSM1068525 2 0.3490 0.5935 0.000 0.724 0.000 0.000 0.008 0.268
#> GSM1068526 4 0.3288 0.7196 0.000 0.000 0.000 0.724 0.000 0.276
#> GSM1068458 1 0.4183 0.7385 0.752 0.004 0.000 0.116 0.000 0.128
#> GSM1068459 3 0.0865 0.8399 0.000 0.000 0.964 0.000 0.000 0.036
#> GSM1068460 4 0.0806 0.7903 0.020 0.000 0.000 0.972 0.008 0.000
#> GSM1068461 3 0.0000 0.8396 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1068464 2 0.0000 0.8363 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1068468 2 0.1411 0.8180 0.000 0.936 0.000 0.000 0.060 0.004
#> GSM1068472 2 0.0146 0.8359 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM1068473 2 0.0000 0.8363 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1068474 2 0.2092 0.8131 0.000 0.876 0.000 0.000 0.124 0.000
#> GSM1068476 3 0.3717 0.4732 0.000 0.000 0.616 0.000 0.000 0.384
#> GSM1068477 2 0.3817 0.3021 0.000 0.568 0.000 0.000 0.432 0.000
#> GSM1068462 2 0.0146 0.8359 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM1068463 3 0.0000 0.8396 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1068465 1 0.5668 0.6475 0.640 0.052 0.000 0.000 0.156 0.152
#> GSM1068466 1 0.3925 0.7069 0.744 0.000 0.000 0.200 0.056 0.000
#> GSM1068467 2 0.0260 0.8360 0.000 0.992 0.000 0.000 0.008 0.000
#> GSM1068469 5 0.3446 0.5227 0.000 0.308 0.000 0.000 0.692 0.000
#> GSM1068470 5 0.0790 0.7583 0.000 0.032 0.000 0.000 0.968 0.000
#> GSM1068471 2 0.1204 0.8352 0.000 0.944 0.000 0.000 0.056 0.000
#> GSM1068475 2 0.3198 0.7089 0.000 0.740 0.000 0.000 0.260 0.000
#> GSM1068528 3 0.2613 0.7389 0.000 0.000 0.848 0.000 0.012 0.140
#> GSM1068531 4 0.2632 0.6603 0.164 0.000 0.000 0.832 0.004 0.000
#> GSM1068532 6 0.3886 0.5949 0.264 0.000 0.028 0.000 0.000 0.708
#> GSM1068533 1 0.0146 0.8039 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM1068535 4 0.0146 0.7966 0.004 0.000 0.000 0.996 0.000 0.000
#> GSM1068537 1 0.0000 0.8046 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1068538 1 0.0000 0.8046 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1068539 5 0.0000 0.7696 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1068540 1 0.0000 0.8046 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1068542 4 0.3288 0.7196 0.000 0.000 0.000 0.724 0.000 0.276
#> GSM1068543 4 0.3198 0.7317 0.000 0.000 0.000 0.740 0.000 0.260
#> GSM1068544 3 0.0000 0.8396 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1068545 2 0.4784 0.6794 0.000 0.680 0.000 0.064 0.236 0.020
#> GSM1068546 3 0.2632 0.8214 0.000 0.000 0.832 0.000 0.004 0.164
#> GSM1068547 1 0.4640 0.7637 0.752 0.000 0.000 0.092 0.072 0.084
#> GSM1068548 4 0.5899 0.2401 0.360 0.000 0.000 0.432 0.000 0.208
#> GSM1068549 6 0.3371 0.4835 0.000 0.000 0.292 0.000 0.000 0.708
#> GSM1068550 4 0.0146 0.7966 0.004 0.000 0.000 0.996 0.000 0.000
#> GSM1068551 2 0.3428 0.6681 0.000 0.696 0.000 0.000 0.304 0.000
#> GSM1068552 4 0.4608 0.6977 0.000 0.000 0.000 0.680 0.100 0.220
#> GSM1068555 5 0.3765 -0.0637 0.000 0.404 0.000 0.000 0.596 0.000
#> GSM1068556 4 0.3634 0.6273 0.000 0.000 0.000 0.644 0.000 0.356
#> GSM1068557 2 0.0508 0.8338 0.000 0.984 0.000 0.012 0.004 0.000
#> GSM1068560 4 0.3767 0.7257 0.004 0.000 0.000 0.720 0.016 0.260
#> GSM1068561 2 0.2219 0.7629 0.000 0.864 0.000 0.000 0.136 0.000
#> GSM1068562 6 0.0405 0.8667 0.000 0.004 0.000 0.000 0.008 0.988
#> GSM1068563 4 0.3747 0.5666 0.000 0.000 0.000 0.604 0.000 0.396
#> GSM1068565 2 0.3371 0.6780 0.000 0.708 0.000 0.000 0.292 0.000
#> GSM1068529 6 0.0405 0.8667 0.000 0.000 0.000 0.004 0.008 0.988
#> GSM1068530 1 0.0000 0.8046 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1068534 6 0.0547 0.8602 0.000 0.000 0.000 0.020 0.000 0.980
#> GSM1068536 5 0.6337 -0.1480 0.352 0.000 0.000 0.012 0.384 0.252
#> GSM1068541 5 0.2638 0.7270 0.004 0.032 0.000 0.016 0.888 0.060
#> GSM1068553 4 0.0000 0.7974 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1068554 4 0.0146 0.7969 0.000 0.004 0.000 0.996 0.000 0.000
#> GSM1068558 6 0.0291 0.8672 0.000 0.000 0.000 0.004 0.004 0.992
#> GSM1068559 6 0.0146 0.8678 0.000 0.000 0.000 0.000 0.004 0.996
#> GSM1068564 4 0.0146 0.7969 0.000 0.004 0.000 0.996 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 disease.state(p) gender(p) k
#> SD:pam 76 0.00167 1.000 2
#> SD:pam 87 0.14071 0.653 3
#> SD:pam 99 0.02249 0.865 4
#> SD:pam 99 0.07762 0.610 5
#> SD:pam 99 0.05642 0.612 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 38950 rows and 108 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'mclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.717 0.931 0.940 0.2486 0.786 0.786
#> 3 3 0.686 0.886 0.913 0.9285 0.740 0.670
#> 4 4 0.677 0.829 0.903 0.4877 0.710 0.469
#> 5 5 0.641 0.768 0.857 0.0502 0.924 0.743
#> 6 6 0.637 0.564 0.763 0.0645 0.909 0.657
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 3
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM1068478 2 0.4562 0.937 0.096 0.904
#> GSM1068479 2 0.5294 0.927 0.120 0.880
#> GSM1068481 1 0.0000 0.972 1.000 0.000
#> GSM1068482 1 0.0000 0.972 1.000 0.000
#> GSM1068483 2 0.4939 0.934 0.108 0.892
#> GSM1068486 1 0.0000 0.972 1.000 0.000
#> GSM1068487 2 0.0000 0.933 0.000 1.000
#> GSM1068488 2 0.4690 0.936 0.100 0.900
#> GSM1068490 2 0.0000 0.933 0.000 1.000
#> GSM1068491 1 0.6801 0.767 0.820 0.180
#> GSM1068492 2 0.7299 0.847 0.204 0.796
#> GSM1068493 2 0.4431 0.938 0.092 0.908
#> GSM1068494 2 0.5737 0.919 0.136 0.864
#> GSM1068495 2 0.0000 0.933 0.000 1.000
#> GSM1068496 2 0.5737 0.919 0.136 0.864
#> GSM1068498 2 0.4562 0.937 0.096 0.904
#> GSM1068499 2 0.5737 0.919 0.136 0.864
#> GSM1068500 2 0.4939 0.934 0.108 0.892
#> GSM1068502 2 0.8608 0.727 0.284 0.716
#> GSM1068503 2 0.0000 0.933 0.000 1.000
#> GSM1068505 2 0.0000 0.933 0.000 1.000
#> GSM1068506 2 0.0000 0.933 0.000 1.000
#> GSM1068507 2 0.0000 0.933 0.000 1.000
#> GSM1068508 2 0.0000 0.933 0.000 1.000
#> GSM1068510 2 0.4431 0.938 0.092 0.908
#> GSM1068512 2 0.4939 0.933 0.108 0.892
#> GSM1068513 2 0.0000 0.933 0.000 1.000
#> GSM1068514 2 0.5294 0.927 0.120 0.880
#> GSM1068517 2 0.4431 0.938 0.092 0.908
#> GSM1068518 2 0.4022 0.939 0.080 0.920
#> GSM1068520 2 0.4562 0.937 0.096 0.904
#> GSM1068521 2 0.4690 0.936 0.100 0.900
#> GSM1068522 2 0.0000 0.933 0.000 1.000
#> GSM1068524 2 0.0000 0.933 0.000 1.000
#> GSM1068527 2 0.4690 0.936 0.100 0.900
#> GSM1068480 1 0.0000 0.972 1.000 0.000
#> GSM1068484 2 0.0000 0.933 0.000 1.000
#> GSM1068485 1 0.0000 0.972 1.000 0.000
#> GSM1068489 2 0.0000 0.933 0.000 1.000
#> GSM1068497 2 0.4431 0.938 0.092 0.908
#> GSM1068501 2 0.0376 0.934 0.004 0.996
#> GSM1068504 2 0.0000 0.933 0.000 1.000
#> GSM1068509 2 0.4690 0.936 0.100 0.900
#> GSM1068511 2 0.4562 0.937 0.096 0.904
#> GSM1068515 2 0.4562 0.937 0.096 0.904
#> GSM1068516 2 0.2778 0.938 0.048 0.952
#> GSM1068519 2 0.5737 0.919 0.136 0.864
#> GSM1068523 2 0.0000 0.933 0.000 1.000
#> GSM1068525 2 0.0000 0.933 0.000 1.000
#> GSM1068526 2 0.0000 0.933 0.000 1.000
#> GSM1068458 2 0.4690 0.936 0.100 0.900
#> GSM1068459 1 0.0000 0.972 1.000 0.000
#> GSM1068460 2 0.4431 0.938 0.092 0.908
#> GSM1068461 1 0.0000 0.972 1.000 0.000
#> GSM1068464 2 0.0000 0.933 0.000 1.000
#> GSM1068468 2 0.0000 0.933 0.000 1.000
#> GSM1068472 2 0.4298 0.938 0.088 0.912
#> GSM1068473 2 0.0000 0.933 0.000 1.000
#> GSM1068474 2 0.0000 0.933 0.000 1.000
#> GSM1068476 1 0.5408 0.850 0.876 0.124
#> GSM1068477 2 0.0000 0.933 0.000 1.000
#> GSM1068462 2 0.4431 0.938 0.092 0.908
#> GSM1068463 1 0.0000 0.972 1.000 0.000
#> GSM1068465 2 0.4431 0.938 0.092 0.908
#> GSM1068466 2 0.4562 0.937 0.096 0.904
#> GSM1068467 2 0.3431 0.939 0.064 0.936
#> GSM1068469 2 0.4431 0.938 0.092 0.908
#> GSM1068470 2 0.0000 0.933 0.000 1.000
#> GSM1068471 2 0.0000 0.933 0.000 1.000
#> GSM1068475 2 0.0000 0.933 0.000 1.000
#> GSM1068528 2 0.5737 0.919 0.136 0.864
#> GSM1068531 2 0.5737 0.919 0.136 0.864
#> GSM1068532 2 0.6531 0.893 0.168 0.832
#> GSM1068533 2 0.5737 0.919 0.136 0.864
#> GSM1068535 2 0.5294 0.927 0.120 0.880
#> GSM1068537 2 0.5737 0.919 0.136 0.864
#> GSM1068538 2 0.5737 0.919 0.136 0.864
#> GSM1068539 2 0.0000 0.933 0.000 1.000
#> GSM1068540 2 0.5737 0.919 0.136 0.864
#> GSM1068542 2 0.0000 0.933 0.000 1.000
#> GSM1068543 2 0.5294 0.927 0.120 0.880
#> GSM1068544 1 0.0000 0.972 1.000 0.000
#> GSM1068545 2 0.0000 0.933 0.000 1.000
#> GSM1068546 1 0.0000 0.972 1.000 0.000
#> GSM1068547 2 0.4815 0.935 0.104 0.896
#> GSM1068548 2 0.0000 0.933 0.000 1.000
#> GSM1068549 1 0.0000 0.972 1.000 0.000
#> GSM1068550 2 0.0000 0.933 0.000 1.000
#> GSM1068551 2 0.0000 0.933 0.000 1.000
#> GSM1068552 2 0.0000 0.933 0.000 1.000
#> GSM1068555 2 0.0000 0.933 0.000 1.000
#> GSM1068556 2 0.5294 0.927 0.120 0.880
#> GSM1068557 2 0.0000 0.933 0.000 1.000
#> GSM1068560 2 0.0000 0.933 0.000 1.000
#> GSM1068561 2 0.1633 0.936 0.024 0.976
#> GSM1068562 2 0.0000 0.933 0.000 1.000
#> GSM1068563 2 0.0000 0.933 0.000 1.000
#> GSM1068565 2 0.0000 0.933 0.000 1.000
#> GSM1068529 2 0.5294 0.927 0.120 0.880
#> GSM1068530 2 0.5737 0.919 0.136 0.864
#> GSM1068534 2 0.4431 0.938 0.092 0.908
#> GSM1068536 2 0.4431 0.938 0.092 0.908
#> GSM1068541 2 0.0376 0.934 0.004 0.996
#> GSM1068553 2 0.5059 0.931 0.112 0.888
#> GSM1068554 2 0.4431 0.938 0.092 0.908
#> GSM1068558 2 0.6247 0.901 0.156 0.844
#> GSM1068559 2 0.5294 0.927 0.120 0.880
#> GSM1068564 2 0.0000 0.933 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1068478 1 0.4702 0.7950 0.788 0.212 0.000
#> GSM1068479 2 0.2955 0.9140 0.080 0.912 0.008
#> GSM1068481 3 0.2860 0.9927 0.084 0.004 0.912
#> GSM1068482 3 0.2860 0.9927 0.084 0.004 0.912
#> GSM1068483 1 0.4399 0.8311 0.812 0.188 0.000
#> GSM1068486 3 0.2772 0.9910 0.080 0.004 0.916
#> GSM1068487 2 0.2356 0.9123 0.000 0.928 0.072
#> GSM1068488 2 0.2878 0.9099 0.096 0.904 0.000
#> GSM1068490 2 0.2625 0.9093 0.000 0.916 0.084
#> GSM1068491 3 0.3550 0.9619 0.080 0.024 0.896
#> GSM1068492 2 0.6119 0.7745 0.064 0.772 0.164
#> GSM1068493 2 0.2772 0.9137 0.080 0.916 0.004
#> GSM1068494 1 0.3851 0.8651 0.860 0.136 0.004
#> GSM1068495 2 0.2496 0.9192 0.068 0.928 0.004
#> GSM1068496 1 0.3619 0.8655 0.864 0.136 0.000
#> GSM1068498 2 0.3412 0.8913 0.124 0.876 0.000
#> GSM1068499 1 0.4121 0.8488 0.832 0.168 0.000
#> GSM1068500 1 0.4555 0.8162 0.800 0.200 0.000
#> GSM1068502 2 0.6875 0.7208 0.080 0.724 0.196
#> GSM1068503 2 0.0000 0.9174 0.000 1.000 0.000
#> GSM1068505 2 0.0424 0.9156 0.008 0.992 0.000
#> GSM1068506 2 0.0237 0.9160 0.004 0.996 0.000
#> GSM1068507 2 0.0424 0.9195 0.008 0.992 0.000
#> GSM1068508 2 0.2860 0.9137 0.084 0.912 0.004
#> GSM1068510 2 0.2165 0.9182 0.064 0.936 0.000
#> GSM1068512 2 0.2301 0.9061 0.060 0.936 0.004
#> GSM1068513 2 0.0000 0.9174 0.000 1.000 0.000
#> GSM1068514 2 0.3045 0.9161 0.064 0.916 0.020
#> GSM1068517 2 0.2711 0.9133 0.088 0.912 0.000
#> GSM1068518 2 0.3030 0.9106 0.092 0.904 0.004
#> GSM1068520 1 0.3941 0.8554 0.844 0.156 0.000
#> GSM1068521 1 0.3752 0.8633 0.856 0.144 0.000
#> GSM1068522 2 0.0237 0.9160 0.004 0.996 0.000
#> GSM1068524 2 0.0000 0.9174 0.000 1.000 0.000
#> GSM1068527 2 0.2165 0.8936 0.064 0.936 0.000
#> GSM1068480 3 0.2860 0.9927 0.084 0.004 0.912
#> GSM1068484 2 0.0424 0.9156 0.008 0.992 0.000
#> GSM1068485 3 0.2860 0.9927 0.084 0.004 0.912
#> GSM1068489 2 0.0237 0.9160 0.004 0.996 0.000
#> GSM1068497 2 0.2711 0.9133 0.088 0.912 0.000
#> GSM1068501 2 0.0237 0.9172 0.004 0.996 0.000
#> GSM1068504 2 0.2625 0.9093 0.000 0.916 0.084
#> GSM1068509 2 0.6521 -0.0376 0.496 0.500 0.004
#> GSM1068511 2 0.3425 0.9015 0.112 0.884 0.004
#> GSM1068515 2 0.4399 0.8131 0.188 0.812 0.000
#> GSM1068516 2 0.0475 0.9191 0.004 0.992 0.004
#> GSM1068519 1 0.3412 0.8656 0.876 0.124 0.000
#> GSM1068523 2 0.2860 0.9087 0.004 0.912 0.084
#> GSM1068525 2 0.0000 0.9174 0.000 1.000 0.000
#> GSM1068526 2 0.0237 0.9160 0.004 0.996 0.000
#> GSM1068458 1 0.3879 0.8596 0.848 0.152 0.000
#> GSM1068459 3 0.2860 0.9927 0.084 0.004 0.912
#> GSM1068460 2 0.3784 0.8921 0.132 0.864 0.004
#> GSM1068461 3 0.2860 0.9927 0.084 0.004 0.912
#> GSM1068464 2 0.2625 0.9093 0.000 0.916 0.084
#> GSM1068468 2 0.2772 0.9137 0.080 0.916 0.004
#> GSM1068472 2 0.2772 0.9137 0.080 0.916 0.004
#> GSM1068473 2 0.2625 0.9093 0.000 0.916 0.084
#> GSM1068474 2 0.2625 0.9093 0.000 0.916 0.084
#> GSM1068476 3 0.2955 0.9874 0.080 0.008 0.912
#> GSM1068477 2 0.2772 0.9137 0.080 0.916 0.004
#> GSM1068462 2 0.2772 0.9137 0.080 0.916 0.004
#> GSM1068463 3 0.2860 0.9927 0.084 0.004 0.912
#> GSM1068465 2 0.3983 0.8696 0.144 0.852 0.004
#> GSM1068466 1 0.4291 0.8361 0.820 0.180 0.000
#> GSM1068467 2 0.2772 0.9137 0.080 0.916 0.004
#> GSM1068469 2 0.2625 0.9131 0.084 0.916 0.000
#> GSM1068470 2 0.2860 0.9087 0.004 0.912 0.084
#> GSM1068471 2 0.2625 0.9093 0.000 0.916 0.084
#> GSM1068475 2 0.2860 0.9087 0.004 0.912 0.084
#> GSM1068528 1 0.3995 0.7686 0.868 0.016 0.116
#> GSM1068531 1 0.0237 0.8158 0.996 0.004 0.000
#> GSM1068532 1 0.0424 0.8178 0.992 0.008 0.000
#> GSM1068533 1 0.0424 0.8178 0.992 0.008 0.000
#> GSM1068535 2 0.6286 0.2448 0.464 0.536 0.000
#> GSM1068537 1 0.0424 0.8178 0.992 0.008 0.000
#> GSM1068538 1 0.0424 0.8178 0.992 0.008 0.000
#> GSM1068539 2 0.0829 0.9199 0.012 0.984 0.004
#> GSM1068540 1 0.0592 0.8231 0.988 0.012 0.000
#> GSM1068542 2 0.1643 0.9065 0.044 0.956 0.000
#> GSM1068543 2 0.2066 0.9024 0.060 0.940 0.000
#> GSM1068544 3 0.3193 0.9781 0.100 0.004 0.896
#> GSM1068545 2 0.0424 0.9185 0.008 0.992 0.000
#> GSM1068546 3 0.2772 0.9910 0.080 0.004 0.916
#> GSM1068547 1 0.2448 0.8547 0.924 0.076 0.000
#> GSM1068548 2 0.2356 0.8889 0.072 0.928 0.000
#> GSM1068549 3 0.2772 0.9910 0.080 0.004 0.916
#> GSM1068550 2 0.0747 0.9156 0.016 0.984 0.000
#> GSM1068551 2 0.2625 0.9093 0.000 0.916 0.084
#> GSM1068552 2 0.0237 0.9160 0.004 0.996 0.000
#> GSM1068555 2 0.2860 0.9087 0.004 0.912 0.084
#> GSM1068556 2 0.2261 0.8885 0.068 0.932 0.000
#> GSM1068557 2 0.2772 0.9137 0.080 0.916 0.004
#> GSM1068560 2 0.1289 0.9116 0.032 0.968 0.000
#> GSM1068561 2 0.2772 0.9137 0.080 0.916 0.004
#> GSM1068562 2 0.0424 0.9163 0.008 0.992 0.000
#> GSM1068563 2 0.0424 0.9163 0.008 0.992 0.000
#> GSM1068565 2 0.2860 0.9087 0.004 0.912 0.084
#> GSM1068529 2 0.2772 0.9137 0.080 0.916 0.004
#> GSM1068530 1 0.0237 0.8158 0.996 0.004 0.000
#> GSM1068534 2 0.2860 0.9131 0.084 0.912 0.004
#> GSM1068536 2 0.3573 0.8994 0.120 0.876 0.004
#> GSM1068541 2 0.2860 0.9137 0.084 0.912 0.004
#> GSM1068553 2 0.3340 0.8959 0.120 0.880 0.000
#> GSM1068554 2 0.1753 0.9213 0.048 0.952 0.000
#> GSM1068558 2 0.5884 0.7963 0.064 0.788 0.148
#> GSM1068559 2 0.2772 0.9137 0.080 0.916 0.004
#> GSM1068564 2 0.0424 0.9156 0.008 0.992 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1068478 1 0.3751 0.911 0.800 0.004 0.000 0.196
#> GSM1068479 2 0.2737 0.803 0.000 0.888 0.104 0.008
#> GSM1068481 3 0.0000 0.999 0.000 0.000 1.000 0.000
#> GSM1068482 3 0.0000 0.999 0.000 0.000 1.000 0.000
#> GSM1068483 1 0.3751 0.911 0.800 0.004 0.000 0.196
#> GSM1068486 3 0.0000 0.999 0.000 0.000 1.000 0.000
#> GSM1068487 2 0.0000 0.882 0.000 1.000 0.000 0.000
#> GSM1068488 4 0.0336 0.883 0.000 0.008 0.000 0.992
#> GSM1068490 2 0.0000 0.882 0.000 1.000 0.000 0.000
#> GSM1068491 3 0.0188 0.996 0.000 0.000 0.996 0.004
#> GSM1068492 4 0.4250 0.614 0.000 0.000 0.276 0.724
#> GSM1068493 2 0.3528 0.739 0.000 0.808 0.000 0.192
#> GSM1068494 4 0.4713 0.178 0.360 0.000 0.000 0.640
#> GSM1068495 2 0.4955 0.317 0.000 0.556 0.000 0.444
#> GSM1068496 1 0.3569 0.912 0.804 0.000 0.000 0.196
#> GSM1068498 2 0.2401 0.830 0.004 0.904 0.000 0.092
#> GSM1068499 1 0.3569 0.912 0.804 0.000 0.000 0.196
#> GSM1068500 1 0.3751 0.911 0.800 0.004 0.000 0.196
#> GSM1068502 2 0.5138 0.353 0.000 0.600 0.392 0.008
#> GSM1068503 2 0.1022 0.865 0.000 0.968 0.000 0.032
#> GSM1068505 4 0.2530 0.842 0.000 0.112 0.000 0.888
#> GSM1068506 4 0.2469 0.844 0.000 0.108 0.000 0.892
#> GSM1068507 4 0.3610 0.756 0.000 0.200 0.000 0.800
#> GSM1068508 2 0.3024 0.785 0.000 0.852 0.000 0.148
#> GSM1068510 4 0.3610 0.760 0.000 0.200 0.000 0.800
#> GSM1068512 4 0.0188 0.881 0.004 0.000 0.000 0.996
#> GSM1068513 2 0.4761 0.328 0.000 0.628 0.000 0.372
#> GSM1068514 4 0.1637 0.856 0.000 0.000 0.060 0.940
#> GSM1068517 2 0.0376 0.880 0.004 0.992 0.000 0.004
#> GSM1068518 4 0.0188 0.882 0.000 0.004 0.000 0.996
#> GSM1068520 1 0.3569 0.912 0.804 0.000 0.000 0.196
#> GSM1068521 1 0.3569 0.912 0.804 0.000 0.000 0.196
#> GSM1068522 4 0.4972 0.261 0.000 0.456 0.000 0.544
#> GSM1068524 2 0.3649 0.691 0.000 0.796 0.000 0.204
#> GSM1068527 4 0.0376 0.882 0.004 0.004 0.000 0.992
#> GSM1068480 3 0.0188 0.997 0.000 0.000 0.996 0.004
#> GSM1068484 4 0.0336 0.883 0.000 0.008 0.000 0.992
#> GSM1068485 3 0.0000 0.999 0.000 0.000 1.000 0.000
#> GSM1068489 4 0.0707 0.882 0.000 0.020 0.000 0.980
#> GSM1068497 2 0.0188 0.881 0.004 0.996 0.000 0.000
#> GSM1068501 4 0.2408 0.847 0.000 0.104 0.000 0.896
#> GSM1068504 2 0.0000 0.882 0.000 1.000 0.000 0.000
#> GSM1068509 1 0.4535 0.804 0.704 0.004 0.000 0.292
#> GSM1068511 4 0.0188 0.882 0.000 0.004 0.000 0.996
#> GSM1068515 2 0.7015 0.019 0.396 0.484 0.000 0.120
#> GSM1068516 4 0.0188 0.882 0.000 0.004 0.000 0.996
#> GSM1068519 1 0.3569 0.912 0.804 0.000 0.000 0.196
#> GSM1068523 2 0.0000 0.882 0.000 1.000 0.000 0.000
#> GSM1068525 4 0.1637 0.868 0.000 0.060 0.000 0.940
#> GSM1068526 4 0.0921 0.879 0.000 0.028 0.000 0.972
#> GSM1068458 1 0.3569 0.912 0.804 0.000 0.000 0.196
#> GSM1068459 3 0.0000 0.999 0.000 0.000 1.000 0.000
#> GSM1068460 1 0.3610 0.911 0.800 0.000 0.000 0.200
#> GSM1068461 3 0.0000 0.999 0.000 0.000 1.000 0.000
#> GSM1068464 2 0.0000 0.882 0.000 1.000 0.000 0.000
#> GSM1068468 2 0.0188 0.881 0.000 0.996 0.000 0.004
#> GSM1068472 2 0.0188 0.881 0.000 0.996 0.000 0.004
#> GSM1068473 2 0.0000 0.882 0.000 1.000 0.000 0.000
#> GSM1068474 2 0.0000 0.882 0.000 1.000 0.000 0.000
#> GSM1068476 3 0.0188 0.996 0.000 0.000 0.996 0.004
#> GSM1068477 2 0.0188 0.881 0.000 0.996 0.000 0.004
#> GSM1068462 2 0.0188 0.881 0.000 0.996 0.000 0.004
#> GSM1068463 3 0.0000 0.999 0.000 0.000 1.000 0.000
#> GSM1068465 1 0.6497 0.749 0.640 0.160 0.000 0.200
#> GSM1068466 1 0.3751 0.911 0.800 0.004 0.000 0.196
#> GSM1068467 2 0.0188 0.881 0.000 0.996 0.000 0.004
#> GSM1068469 2 0.0188 0.881 0.004 0.996 0.000 0.000
#> GSM1068470 2 0.0000 0.882 0.000 1.000 0.000 0.000
#> GSM1068471 2 0.0000 0.882 0.000 1.000 0.000 0.000
#> GSM1068475 2 0.0000 0.882 0.000 1.000 0.000 0.000
#> GSM1068528 1 0.4499 0.862 0.804 0.000 0.072 0.124
#> GSM1068531 1 0.0000 0.795 1.000 0.000 0.000 0.000
#> GSM1068532 1 0.0000 0.795 1.000 0.000 0.000 0.000
#> GSM1068533 1 0.0000 0.795 1.000 0.000 0.000 0.000
#> GSM1068535 4 0.3486 0.764 0.188 0.000 0.000 0.812
#> GSM1068537 1 0.0000 0.795 1.000 0.000 0.000 0.000
#> GSM1068538 1 0.0000 0.795 1.000 0.000 0.000 0.000
#> GSM1068539 4 0.4564 0.417 0.000 0.328 0.000 0.672
#> GSM1068540 1 0.3569 0.912 0.804 0.000 0.000 0.196
#> GSM1068542 4 0.0336 0.883 0.000 0.008 0.000 0.992
#> GSM1068543 4 0.0376 0.882 0.004 0.004 0.000 0.992
#> GSM1068544 3 0.0188 0.996 0.004 0.000 0.996 0.000
#> GSM1068545 2 0.3764 0.727 0.000 0.784 0.000 0.216
#> GSM1068546 3 0.0000 0.999 0.000 0.000 1.000 0.000
#> GSM1068547 1 0.3569 0.912 0.804 0.000 0.000 0.196
#> GSM1068548 4 0.0376 0.882 0.004 0.004 0.000 0.992
#> GSM1068549 3 0.0000 0.999 0.000 0.000 1.000 0.000
#> GSM1068550 4 0.0336 0.883 0.000 0.008 0.000 0.992
#> GSM1068551 2 0.0000 0.882 0.000 1.000 0.000 0.000
#> GSM1068552 4 0.3266 0.796 0.000 0.168 0.000 0.832
#> GSM1068555 2 0.0000 0.882 0.000 1.000 0.000 0.000
#> GSM1068556 4 0.0376 0.882 0.004 0.004 0.000 0.992
#> GSM1068557 2 0.2814 0.803 0.000 0.868 0.000 0.132
#> GSM1068560 4 0.0336 0.883 0.000 0.008 0.000 0.992
#> GSM1068561 2 0.4746 0.476 0.000 0.632 0.000 0.368
#> GSM1068562 4 0.0336 0.883 0.000 0.008 0.000 0.992
#> GSM1068563 4 0.0336 0.883 0.000 0.008 0.000 0.992
#> GSM1068565 2 0.0000 0.882 0.000 1.000 0.000 0.000
#> GSM1068529 4 0.0000 0.881 0.000 0.000 0.000 1.000
#> GSM1068530 1 0.0000 0.795 1.000 0.000 0.000 0.000
#> GSM1068534 4 0.0188 0.882 0.000 0.004 0.000 0.996
#> GSM1068536 1 0.3945 0.897 0.780 0.004 0.000 0.216
#> GSM1068541 2 0.3266 0.767 0.000 0.832 0.000 0.168
#> GSM1068553 4 0.0895 0.879 0.020 0.004 0.000 0.976
#> GSM1068554 4 0.3649 0.755 0.000 0.204 0.000 0.796
#> GSM1068558 4 0.3569 0.721 0.000 0.000 0.196 0.804
#> GSM1068559 4 0.0188 0.882 0.000 0.004 0.000 0.996
#> GSM1068564 4 0.3528 0.770 0.000 0.192 0.000 0.808
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM1068478 5 0.6144 0.263 0.280 0.000 0.000 0.172 0.548
#> GSM1068479 2 0.5505 0.628 0.000 0.712 0.056 0.072 0.160
#> GSM1068481 3 0.0162 0.982 0.004 0.000 0.996 0.000 0.000
#> GSM1068482 3 0.0162 0.982 0.004 0.000 0.996 0.000 0.000
#> GSM1068483 1 0.4883 0.772 0.708 0.000 0.000 0.200 0.092
#> GSM1068486 3 0.0000 0.979 0.000 0.000 1.000 0.000 0.000
#> GSM1068487 2 0.1671 0.775 0.000 0.924 0.000 0.076 0.000
#> GSM1068488 4 0.0807 0.869 0.000 0.012 0.000 0.976 0.012
#> GSM1068490 2 0.0000 0.804 0.000 1.000 0.000 0.000 0.000
#> GSM1068491 3 0.0510 0.972 0.000 0.000 0.984 0.000 0.016
#> GSM1068492 4 0.4872 0.686 0.000 0.000 0.120 0.720 0.160
#> GSM1068493 5 0.5530 0.646 0.000 0.096 0.004 0.268 0.632
#> GSM1068494 4 0.4697 0.144 0.360 0.000 0.008 0.620 0.012
#> GSM1068495 4 0.4709 0.605 0.000 0.068 0.004 0.728 0.200
#> GSM1068496 1 0.3550 0.819 0.796 0.000 0.000 0.184 0.020
#> GSM1068498 5 0.3972 0.697 0.012 0.188 0.000 0.020 0.780
#> GSM1068499 1 0.3511 0.819 0.800 0.000 0.004 0.184 0.012
#> GSM1068500 1 0.5169 0.749 0.688 0.000 0.000 0.184 0.128
#> GSM1068502 2 0.7021 0.432 0.000 0.564 0.204 0.072 0.160
#> GSM1068503 2 0.2377 0.740 0.000 0.872 0.000 0.128 0.000
#> GSM1068505 4 0.3397 0.846 0.004 0.080 0.000 0.848 0.068
#> GSM1068506 4 0.3229 0.827 0.000 0.128 0.000 0.840 0.032
#> GSM1068507 4 0.3691 0.775 0.000 0.164 0.004 0.804 0.028
#> GSM1068508 2 0.5192 0.544 0.000 0.696 0.004 0.184 0.116
#> GSM1068510 4 0.3143 0.748 0.000 0.204 0.000 0.796 0.000
#> GSM1068512 4 0.0566 0.868 0.000 0.000 0.004 0.984 0.012
#> GSM1068513 2 0.3983 0.426 0.000 0.660 0.000 0.340 0.000
#> GSM1068514 4 0.4010 0.754 0.000 0.000 0.056 0.784 0.160
#> GSM1068517 5 0.3596 0.671 0.000 0.212 0.000 0.012 0.776
#> GSM1068518 4 0.0727 0.864 0.004 0.000 0.004 0.980 0.012
#> GSM1068520 1 0.3995 0.814 0.788 0.000 0.000 0.152 0.060
#> GSM1068521 1 0.3419 0.822 0.804 0.000 0.000 0.180 0.016
#> GSM1068522 2 0.4527 0.310 0.000 0.596 0.000 0.392 0.012
#> GSM1068524 2 0.1908 0.767 0.000 0.908 0.000 0.092 0.000
#> GSM1068527 4 0.1356 0.869 0.004 0.012 0.000 0.956 0.028
#> GSM1068480 3 0.2536 0.872 0.004 0.000 0.868 0.000 0.128
#> GSM1068484 4 0.1668 0.873 0.000 0.032 0.000 0.940 0.028
#> GSM1068485 3 0.0162 0.982 0.004 0.000 0.996 0.000 0.000
#> GSM1068489 4 0.2228 0.868 0.000 0.040 0.000 0.912 0.048
#> GSM1068497 5 0.3628 0.672 0.012 0.216 0.000 0.000 0.772
#> GSM1068501 4 0.3002 0.834 0.000 0.116 0.000 0.856 0.028
#> GSM1068504 2 0.0000 0.804 0.000 1.000 0.000 0.000 0.000
#> GSM1068509 1 0.4507 0.632 0.644 0.000 0.004 0.340 0.012
#> GSM1068511 4 0.0162 0.866 0.000 0.000 0.004 0.996 0.000
#> GSM1068515 5 0.5302 0.712 0.072 0.112 0.000 0.076 0.740
#> GSM1068516 4 0.0613 0.866 0.000 0.004 0.004 0.984 0.008
#> GSM1068519 1 0.3550 0.820 0.796 0.000 0.000 0.184 0.020
#> GSM1068523 2 0.0404 0.802 0.000 0.988 0.000 0.000 0.012
#> GSM1068525 4 0.2074 0.849 0.000 0.104 0.000 0.896 0.000
#> GSM1068526 4 0.2300 0.867 0.000 0.040 0.000 0.908 0.052
#> GSM1068458 1 0.4031 0.816 0.796 0.008 0.000 0.148 0.048
#> GSM1068459 3 0.0162 0.982 0.004 0.000 0.996 0.000 0.000
#> GSM1068460 1 0.6365 0.511 0.540 0.000 0.004 0.260 0.196
#> GSM1068461 3 0.0162 0.982 0.004 0.000 0.996 0.000 0.000
#> GSM1068464 2 0.0290 0.803 0.000 0.992 0.000 0.008 0.000
#> GSM1068468 2 0.3516 0.704 0.000 0.812 0.004 0.020 0.164
#> GSM1068472 2 0.4089 0.604 0.000 0.736 0.004 0.016 0.244
#> GSM1068473 2 0.0000 0.804 0.000 1.000 0.000 0.000 0.000
#> GSM1068474 2 0.0000 0.804 0.000 1.000 0.000 0.000 0.000
#> GSM1068476 3 0.1485 0.937 0.000 0.000 0.948 0.020 0.032
#> GSM1068477 2 0.4100 0.698 0.000 0.784 0.004 0.052 0.160
#> GSM1068462 2 0.4571 0.494 0.000 0.664 0.004 0.020 0.312
#> GSM1068463 3 0.0162 0.982 0.004 0.000 0.996 0.000 0.000
#> GSM1068465 5 0.6361 0.380 0.240 0.008 0.004 0.172 0.576
#> GSM1068466 1 0.3888 0.816 0.796 0.000 0.000 0.148 0.056
#> GSM1068467 2 0.3435 0.713 0.000 0.820 0.004 0.020 0.156
#> GSM1068469 5 0.4096 0.622 0.012 0.260 0.000 0.004 0.724
#> GSM1068470 2 0.0404 0.802 0.000 0.988 0.000 0.000 0.012
#> GSM1068471 2 0.0000 0.804 0.000 1.000 0.000 0.000 0.000
#> GSM1068475 2 0.0290 0.803 0.000 0.992 0.000 0.000 0.008
#> GSM1068528 1 0.4022 0.775 0.804 0.000 0.100 0.092 0.004
#> GSM1068531 1 0.0290 0.747 0.992 0.000 0.000 0.000 0.008
#> GSM1068532 1 0.0000 0.744 1.000 0.000 0.000 0.000 0.000
#> GSM1068533 1 0.0000 0.744 1.000 0.000 0.000 0.000 0.000
#> GSM1068535 4 0.3039 0.767 0.192 0.000 0.000 0.808 0.000
#> GSM1068537 1 0.0000 0.744 1.000 0.000 0.000 0.000 0.000
#> GSM1068538 1 0.0000 0.744 1.000 0.000 0.000 0.000 0.000
#> GSM1068539 4 0.1978 0.859 0.000 0.024 0.004 0.928 0.044
#> GSM1068540 1 0.3419 0.822 0.804 0.000 0.000 0.180 0.016
#> GSM1068542 4 0.2037 0.865 0.004 0.012 0.000 0.920 0.064
#> GSM1068543 4 0.1074 0.871 0.004 0.012 0.000 0.968 0.016
#> GSM1068544 3 0.0290 0.979 0.008 0.000 0.992 0.000 0.000
#> GSM1068545 2 0.4779 0.418 0.000 0.628 0.000 0.340 0.032
#> GSM1068546 3 0.0162 0.982 0.004 0.000 0.996 0.000 0.000
#> GSM1068547 1 0.3691 0.819 0.804 0.000 0.000 0.156 0.040
#> GSM1068548 4 0.2037 0.865 0.004 0.012 0.000 0.920 0.064
#> GSM1068549 3 0.0000 0.979 0.000 0.000 1.000 0.000 0.000
#> GSM1068550 4 0.1970 0.865 0.004 0.012 0.000 0.924 0.060
#> GSM1068551 2 0.0000 0.804 0.000 1.000 0.000 0.000 0.000
#> GSM1068552 4 0.3432 0.821 0.000 0.132 0.000 0.828 0.040
#> GSM1068555 2 0.0000 0.804 0.000 1.000 0.000 0.000 0.000
#> GSM1068556 4 0.0968 0.868 0.004 0.012 0.000 0.972 0.012
#> GSM1068557 2 0.3340 0.710 0.000 0.824 0.004 0.156 0.016
#> GSM1068560 4 0.1356 0.869 0.004 0.012 0.000 0.956 0.028
#> GSM1068561 4 0.3548 0.763 0.000 0.188 0.004 0.796 0.012
#> GSM1068562 4 0.1117 0.872 0.000 0.020 0.000 0.964 0.016
#> GSM1068563 4 0.0807 0.870 0.000 0.012 0.000 0.976 0.012
#> GSM1068565 2 0.0290 0.803 0.000 0.992 0.000 0.000 0.008
#> GSM1068529 4 0.2798 0.801 0.000 0.000 0.008 0.852 0.140
#> GSM1068530 1 0.0290 0.747 0.992 0.000 0.000 0.000 0.008
#> GSM1068534 4 0.0162 0.866 0.000 0.000 0.004 0.996 0.000
#> GSM1068536 1 0.6094 0.517 0.552 0.000 0.004 0.312 0.132
#> GSM1068541 5 0.5384 0.652 0.000 0.104 0.004 0.228 0.664
#> GSM1068553 4 0.2047 0.869 0.012 0.020 0.000 0.928 0.040
#> GSM1068554 4 0.3333 0.746 0.000 0.208 0.000 0.788 0.004
#> GSM1068558 4 0.4317 0.739 0.000 0.000 0.076 0.764 0.160
#> GSM1068559 4 0.0671 0.867 0.000 0.000 0.016 0.980 0.004
#> GSM1068564 4 0.3977 0.745 0.000 0.204 0.000 0.764 0.032
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1068478 5 0.6278 -0.0943 0.404 0.004 0.000 0.024 0.420 0.148
#> GSM1068479 4 0.5632 0.0569 0.000 0.384 0.084 0.508 0.024 0.000
#> GSM1068481 3 0.0000 0.9306 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1068482 3 0.0000 0.9306 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1068483 1 0.4850 0.7741 0.740 0.016 0.000 0.040 0.064 0.140
#> GSM1068486 3 0.0000 0.9306 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1068487 2 0.0508 0.7831 0.000 0.984 0.000 0.012 0.000 0.004
#> GSM1068488 6 0.4821 0.3801 0.000 0.016 0.000 0.416 0.028 0.540
#> GSM1068490 2 0.0260 0.7850 0.000 0.992 0.000 0.000 0.000 0.008
#> GSM1068491 3 0.3244 0.6676 0.000 0.000 0.732 0.268 0.000 0.000
#> GSM1068492 4 0.4213 0.5249 0.000 0.000 0.100 0.772 0.024 0.104
#> GSM1068493 5 0.5250 0.5361 0.000 0.052 0.000 0.140 0.688 0.120
#> GSM1068494 1 0.6967 0.2741 0.456 0.016 0.000 0.200 0.048 0.280
#> GSM1068495 6 0.4734 0.4483 0.024 0.004 0.000 0.112 0.128 0.732
#> GSM1068496 1 0.4280 0.8039 0.796 0.016 0.000 0.064 0.052 0.072
#> GSM1068498 5 0.2250 0.6492 0.000 0.064 0.000 0.000 0.896 0.040
#> GSM1068499 1 0.4218 0.8000 0.796 0.016 0.000 0.060 0.036 0.092
#> GSM1068500 1 0.4850 0.7736 0.740 0.016 0.000 0.040 0.064 0.140
#> GSM1068502 4 0.5935 0.1411 0.000 0.340 0.128 0.508 0.024 0.000
#> GSM1068503 2 0.1982 0.7320 0.000 0.912 0.000 0.016 0.004 0.068
#> GSM1068505 6 0.0891 0.5956 0.000 0.008 0.000 0.000 0.024 0.968
#> GSM1068506 6 0.1753 0.5951 0.000 0.084 0.000 0.000 0.004 0.912
#> GSM1068507 6 0.6009 0.3364 0.008 0.104 0.000 0.360 0.024 0.504
#> GSM1068508 2 0.6076 0.3858 0.000 0.592 0.000 0.080 0.112 0.216
#> GSM1068510 6 0.5820 0.2501 0.000 0.144 0.000 0.392 0.008 0.456
#> GSM1068512 6 0.4821 0.3866 0.000 0.016 0.000 0.416 0.028 0.540
#> GSM1068513 2 0.3606 0.4960 0.000 0.728 0.000 0.016 0.000 0.256
#> GSM1068514 4 0.3293 0.4930 0.000 0.000 0.040 0.824 0.008 0.128
#> GSM1068517 5 0.2112 0.6401 0.000 0.088 0.000 0.000 0.896 0.016
#> GSM1068518 6 0.4361 0.5310 0.000 0.012 0.000 0.252 0.040 0.696
#> GSM1068520 1 0.3662 0.7954 0.780 0.000 0.000 0.004 0.044 0.172
#> GSM1068521 1 0.4147 0.8048 0.792 0.016 0.000 0.020 0.060 0.112
#> GSM1068522 2 0.3833 0.1911 0.000 0.556 0.000 0.000 0.000 0.444
#> GSM1068524 2 0.0964 0.7830 0.000 0.968 0.000 0.016 0.004 0.012
#> GSM1068527 6 0.1909 0.6015 0.000 0.004 0.000 0.024 0.052 0.920
#> GSM1068480 3 0.2790 0.8023 0.000 0.000 0.844 0.132 0.024 0.000
#> GSM1068484 6 0.2978 0.6019 0.000 0.012 0.000 0.072 0.056 0.860
#> GSM1068485 3 0.0000 0.9306 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1068489 6 0.1820 0.6061 0.000 0.056 0.000 0.008 0.012 0.924
#> GSM1068497 5 0.2070 0.6363 0.000 0.092 0.000 0.000 0.896 0.012
#> GSM1068501 6 0.4962 0.4865 0.000 0.060 0.000 0.280 0.020 0.640
#> GSM1068504 2 0.0260 0.7850 0.000 0.992 0.000 0.000 0.000 0.008
#> GSM1068509 1 0.5401 0.6853 0.680 0.016 0.000 0.060 0.052 0.192
#> GSM1068511 6 0.4886 0.3839 0.000 0.016 0.000 0.416 0.032 0.536
#> GSM1068515 5 0.5102 0.6451 0.068 0.084 0.000 0.020 0.736 0.092
#> GSM1068516 6 0.4724 0.4410 0.000 0.016 0.000 0.368 0.028 0.588
#> GSM1068519 1 0.4771 0.8112 0.744 0.004 0.000 0.072 0.060 0.120
#> GSM1068523 2 0.2121 0.7529 0.000 0.892 0.000 0.000 0.096 0.012
#> GSM1068525 6 0.5404 0.4385 0.000 0.076 0.000 0.324 0.024 0.576
#> GSM1068526 6 0.1826 0.6038 0.000 0.052 0.000 0.004 0.020 0.924
#> GSM1068458 1 0.3939 0.7973 0.796 0.040 0.000 0.004 0.032 0.128
#> GSM1068459 3 0.0000 0.9306 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1068460 6 0.6781 -0.1382 0.300 0.000 0.000 0.104 0.128 0.468
#> GSM1068461 3 0.0000 0.9306 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1068464 2 0.1194 0.7662 0.000 0.956 0.000 0.032 0.008 0.004
#> GSM1068468 2 0.4822 -0.1750 0.000 0.480 0.000 0.036 0.476 0.008
#> GSM1068472 5 0.4646 0.4036 0.000 0.356 0.000 0.036 0.600 0.008
#> GSM1068473 2 0.0146 0.7842 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM1068474 2 0.0146 0.7842 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM1068476 3 0.3727 0.4930 0.000 0.000 0.612 0.388 0.000 0.000
#> GSM1068477 2 0.4238 0.2887 0.000 0.636 0.000 0.008 0.340 0.016
#> GSM1068462 5 0.4585 0.4896 0.000 0.304 0.000 0.044 0.644 0.008
#> GSM1068463 3 0.0000 0.9306 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1068465 5 0.7006 0.3554 0.224 0.004 0.000 0.104 0.484 0.184
#> GSM1068466 1 0.4154 0.7928 0.780 0.044 0.000 0.004 0.036 0.136
#> GSM1068467 2 0.4821 -0.1626 0.000 0.484 0.000 0.036 0.472 0.008
#> GSM1068469 5 0.3353 0.6212 0.000 0.160 0.000 0.032 0.804 0.004
#> GSM1068470 2 0.2121 0.7529 0.000 0.892 0.000 0.000 0.096 0.012
#> GSM1068471 2 0.0291 0.7848 0.000 0.992 0.000 0.000 0.004 0.004
#> GSM1068475 2 0.1913 0.7609 0.000 0.908 0.000 0.000 0.080 0.012
#> GSM1068528 1 0.3809 0.7935 0.812 0.000 0.088 0.048 0.000 0.052
#> GSM1068531 1 0.2471 0.7712 0.888 0.000 0.000 0.056 0.004 0.052
#> GSM1068532 1 0.1204 0.7632 0.944 0.000 0.000 0.056 0.000 0.000
#> GSM1068533 1 0.0632 0.7727 0.976 0.000 0.000 0.024 0.000 0.000
#> GSM1068535 6 0.5861 0.1649 0.200 0.000 0.000 0.356 0.000 0.444
#> GSM1068537 1 0.1204 0.7632 0.944 0.000 0.000 0.056 0.000 0.000
#> GSM1068538 1 0.1204 0.7632 0.944 0.000 0.000 0.056 0.000 0.000
#> GSM1068539 6 0.3016 0.5563 0.000 0.008 0.000 0.092 0.048 0.852
#> GSM1068540 1 0.4572 0.8151 0.756 0.004 0.000 0.060 0.052 0.128
#> GSM1068542 6 0.0632 0.5965 0.000 0.000 0.000 0.000 0.024 0.976
#> GSM1068543 6 0.4747 0.3878 0.000 0.016 0.000 0.412 0.024 0.548
#> GSM1068544 3 0.0000 0.9306 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1068545 6 0.4935 0.1245 0.000 0.388 0.000 0.012 0.044 0.556
#> GSM1068546 3 0.0000 0.9306 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1068547 1 0.3691 0.8056 0.784 0.000 0.000 0.020 0.024 0.172
#> GSM1068548 6 0.0632 0.5965 0.000 0.000 0.000 0.000 0.024 0.976
#> GSM1068549 3 0.0000 0.9306 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1068550 6 0.0935 0.5981 0.000 0.004 0.000 0.000 0.032 0.964
#> GSM1068551 2 0.1049 0.7797 0.000 0.960 0.000 0.000 0.032 0.008
#> GSM1068552 6 0.2191 0.5796 0.000 0.120 0.000 0.000 0.004 0.876
#> GSM1068555 2 0.2020 0.7555 0.000 0.896 0.000 0.000 0.096 0.008
#> GSM1068556 6 0.4679 0.4455 0.000 0.016 0.000 0.352 0.028 0.604
#> GSM1068557 5 0.6027 0.2048 0.000 0.420 0.000 0.052 0.448 0.080
#> GSM1068560 6 0.1829 0.5992 0.000 0.004 0.000 0.012 0.064 0.920
#> GSM1068561 6 0.5626 0.4832 0.000 0.120 0.000 0.136 0.084 0.660
#> GSM1068562 6 0.3272 0.5885 0.000 0.016 0.000 0.144 0.020 0.820
#> GSM1068563 6 0.2375 0.6020 0.000 0.016 0.000 0.068 0.020 0.896
#> GSM1068565 2 0.1367 0.7727 0.000 0.944 0.000 0.000 0.044 0.012
#> GSM1068529 4 0.4726 -0.2234 0.000 0.008 0.004 0.540 0.024 0.424
#> GSM1068530 1 0.1989 0.7796 0.916 0.000 0.000 0.028 0.004 0.052
#> GSM1068534 6 0.4891 0.3786 0.000 0.016 0.000 0.420 0.032 0.532
#> GSM1068536 6 0.6839 -0.2392 0.348 0.000 0.000 0.104 0.124 0.424
#> GSM1068541 5 0.6726 0.3548 0.044 0.024 0.000 0.116 0.444 0.372
#> GSM1068553 6 0.5283 0.3823 0.064 0.000 0.000 0.356 0.020 0.560
#> GSM1068554 6 0.6080 0.2473 0.008 0.172 0.000 0.360 0.004 0.456
#> GSM1068558 4 0.4196 0.5224 0.000 0.000 0.084 0.772 0.024 0.120
#> GSM1068559 4 0.5272 -0.1892 0.000 0.012 0.028 0.532 0.024 0.404
#> GSM1068564 6 0.3587 0.4930 0.000 0.188 0.000 0.000 0.040 0.772
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 disease.state(p) gender(p) k
#> SD:mclust 108 0.40381 0.706 2
#> SD:mclust 106 0.56865 0.136 3
#> SD:mclust 100 0.00400 0.551 4
#> SD:mclust 100 0.00127 0.411 5
#> SD:mclust 69 0.09835 0.678 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 38950 rows and 108 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'NMF' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.781 0.880 0.950 0.4948 0.502 0.502
#> 3 3 0.640 0.774 0.898 0.2554 0.747 0.555
#> 4 4 0.635 0.817 0.873 0.1886 0.792 0.508
#> 5 5 0.674 0.665 0.820 0.0738 0.906 0.658
#> 6 6 0.691 0.625 0.786 0.0439 0.904 0.592
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
#> GSM1068478 1 0.9000 0.5737 0.684 0.316
#> GSM1068479 2 0.0000 0.9594 0.000 1.000
#> GSM1068481 1 0.0000 0.9279 1.000 0.000
#> GSM1068482 1 0.0000 0.9279 1.000 0.000
#> GSM1068483 1 0.0000 0.9279 1.000 0.000
#> GSM1068486 1 0.0000 0.9279 1.000 0.000
#> GSM1068487 2 0.0000 0.9594 0.000 1.000
#> GSM1068488 1 0.9993 0.0564 0.516 0.484
#> GSM1068490 2 0.0000 0.9594 0.000 1.000
#> GSM1068491 1 0.0000 0.9279 1.000 0.000
#> GSM1068492 2 0.9358 0.4594 0.352 0.648
#> GSM1068493 1 0.9044 0.5706 0.680 0.320
#> GSM1068494 1 0.0000 0.9279 1.000 0.000
#> GSM1068495 2 0.0000 0.9594 0.000 1.000
#> GSM1068496 1 0.0000 0.9279 1.000 0.000
#> GSM1068498 2 0.0000 0.9594 0.000 1.000
#> GSM1068499 1 0.0000 0.9279 1.000 0.000
#> GSM1068500 1 0.0000 0.9279 1.000 0.000
#> GSM1068502 2 0.0376 0.9565 0.004 0.996
#> GSM1068503 2 0.0000 0.9594 0.000 1.000
#> GSM1068505 2 0.0000 0.9594 0.000 1.000
#> GSM1068506 2 0.0000 0.9594 0.000 1.000
#> GSM1068507 2 0.5294 0.8510 0.120 0.880
#> GSM1068508 2 0.0000 0.9594 0.000 1.000
#> GSM1068510 2 0.0000 0.9594 0.000 1.000
#> GSM1068512 1 0.2778 0.8952 0.952 0.048
#> GSM1068513 2 0.0000 0.9594 0.000 1.000
#> GSM1068514 1 0.1843 0.9103 0.972 0.028
#> GSM1068517 2 0.0000 0.9594 0.000 1.000
#> GSM1068518 1 0.9460 0.4320 0.636 0.364
#> GSM1068520 1 0.0000 0.9279 1.000 0.000
#> GSM1068521 1 0.0000 0.9279 1.000 0.000
#> GSM1068522 2 0.0000 0.9594 0.000 1.000
#> GSM1068524 2 0.0000 0.9594 0.000 1.000
#> GSM1068527 2 0.9000 0.5436 0.316 0.684
#> GSM1068480 1 0.0000 0.9279 1.000 0.000
#> GSM1068484 2 0.0000 0.9594 0.000 1.000
#> GSM1068485 1 0.0000 0.9279 1.000 0.000
#> GSM1068489 2 0.0000 0.9594 0.000 1.000
#> GSM1068497 2 0.0000 0.9594 0.000 1.000
#> GSM1068501 2 0.0000 0.9594 0.000 1.000
#> GSM1068504 2 0.0000 0.9594 0.000 1.000
#> GSM1068509 1 0.0938 0.9214 0.988 0.012
#> GSM1068511 1 0.0000 0.9279 1.000 0.000
#> GSM1068515 1 0.8207 0.6723 0.744 0.256
#> GSM1068516 2 0.0000 0.9594 0.000 1.000
#> GSM1068519 1 0.0000 0.9279 1.000 0.000
#> GSM1068523 2 0.0000 0.9594 0.000 1.000
#> GSM1068525 2 0.0000 0.9594 0.000 1.000
#> GSM1068526 2 0.0000 0.9594 0.000 1.000
#> GSM1068458 1 0.0000 0.9279 1.000 0.000
#> GSM1068459 1 0.0000 0.9279 1.000 0.000
#> GSM1068460 2 0.8016 0.6796 0.244 0.756
#> GSM1068461 1 0.0000 0.9279 1.000 0.000
#> GSM1068464 2 0.0000 0.9594 0.000 1.000
#> GSM1068468 2 0.0000 0.9594 0.000 1.000
#> GSM1068472 2 0.0000 0.9594 0.000 1.000
#> GSM1068473 2 0.0000 0.9594 0.000 1.000
#> GSM1068474 2 0.0000 0.9594 0.000 1.000
#> GSM1068476 1 0.0000 0.9279 1.000 0.000
#> GSM1068477 2 0.0000 0.9594 0.000 1.000
#> GSM1068462 2 0.0000 0.9594 0.000 1.000
#> GSM1068463 1 0.0000 0.9279 1.000 0.000
#> GSM1068465 2 0.0000 0.9594 0.000 1.000
#> GSM1068466 1 0.7139 0.7486 0.804 0.196
#> GSM1068467 2 0.0000 0.9594 0.000 1.000
#> GSM1068469 2 0.0938 0.9499 0.012 0.988
#> GSM1068470 2 0.0000 0.9594 0.000 1.000
#> GSM1068471 2 0.0000 0.9594 0.000 1.000
#> GSM1068475 2 0.0000 0.9594 0.000 1.000
#> GSM1068528 1 0.0000 0.9279 1.000 0.000
#> GSM1068531 1 0.0000 0.9279 1.000 0.000
#> GSM1068532 1 0.0000 0.9279 1.000 0.000
#> GSM1068533 1 0.0000 0.9279 1.000 0.000
#> GSM1068535 1 0.0000 0.9279 1.000 0.000
#> GSM1068537 1 0.0000 0.9279 1.000 0.000
#> GSM1068538 1 0.0000 0.9279 1.000 0.000
#> GSM1068539 2 0.0000 0.9594 0.000 1.000
#> GSM1068540 1 0.0000 0.9279 1.000 0.000
#> GSM1068542 2 0.3584 0.9032 0.068 0.932
#> GSM1068543 1 0.9732 0.3232 0.596 0.404
#> GSM1068544 1 0.0000 0.9279 1.000 0.000
#> GSM1068545 2 0.0000 0.9594 0.000 1.000
#> GSM1068546 1 0.0000 0.9279 1.000 0.000
#> GSM1068547 1 0.0000 0.9279 1.000 0.000
#> GSM1068548 2 0.5737 0.8317 0.136 0.864
#> GSM1068549 1 0.0000 0.9279 1.000 0.000
#> GSM1068550 2 0.0000 0.9594 0.000 1.000
#> GSM1068551 2 0.0000 0.9594 0.000 1.000
#> GSM1068552 2 0.0000 0.9594 0.000 1.000
#> GSM1068555 2 0.0000 0.9594 0.000 1.000
#> GSM1068556 1 0.9954 0.1443 0.540 0.460
#> GSM1068557 2 0.0000 0.9594 0.000 1.000
#> GSM1068560 2 0.5294 0.8505 0.120 0.880
#> GSM1068561 2 0.0000 0.9594 0.000 1.000
#> GSM1068562 2 0.4431 0.8795 0.092 0.908
#> GSM1068563 2 0.7219 0.7470 0.200 0.800
#> GSM1068565 2 0.0000 0.9594 0.000 1.000
#> GSM1068529 1 0.4939 0.8464 0.892 0.108
#> GSM1068530 1 0.0000 0.9279 1.000 0.000
#> GSM1068534 1 0.2423 0.9028 0.960 0.040
#> GSM1068536 2 0.3274 0.9057 0.060 0.940
#> GSM1068541 2 0.0000 0.9594 0.000 1.000
#> GSM1068553 1 0.5178 0.8325 0.884 0.116
#> GSM1068554 2 0.0000 0.9594 0.000 1.000
#> GSM1068558 2 0.9977 0.0530 0.472 0.528
#> GSM1068559 1 0.1184 0.9188 0.984 0.016
#> GSM1068564 2 0.0000 0.9594 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1068478 1 0.4702 0.6837 0.788 0.212 0.000
#> GSM1068479 3 0.6235 0.2494 0.000 0.436 0.564
#> GSM1068481 3 0.1031 0.8387 0.024 0.000 0.976
#> GSM1068482 3 0.2448 0.8078 0.076 0.000 0.924
#> GSM1068483 1 0.0424 0.8679 0.992 0.000 0.008
#> GSM1068486 3 0.0747 0.8407 0.016 0.000 0.984
#> GSM1068487 2 0.0747 0.8951 0.000 0.984 0.016
#> GSM1068488 3 0.9717 0.1384 0.220 0.384 0.396
#> GSM1068490 2 0.0592 0.8956 0.000 0.988 0.012
#> GSM1068491 3 0.0237 0.8408 0.004 0.000 0.996
#> GSM1068492 3 0.4178 0.7339 0.000 0.172 0.828
#> GSM1068493 2 0.4540 0.7819 0.028 0.848 0.124
#> GSM1068494 1 0.3879 0.7548 0.848 0.000 0.152
#> GSM1068495 2 0.4452 0.7403 0.192 0.808 0.000
#> GSM1068496 1 0.0592 0.8671 0.988 0.000 0.012
#> GSM1068498 2 0.6267 0.1465 0.452 0.548 0.000
#> GSM1068499 1 0.3038 0.8056 0.896 0.000 0.104
#> GSM1068500 1 0.1289 0.8578 0.968 0.000 0.032
#> GSM1068502 3 0.5882 0.4716 0.000 0.348 0.652
#> GSM1068503 2 0.0747 0.8951 0.000 0.984 0.016
#> GSM1068505 2 0.2173 0.8780 0.048 0.944 0.008
#> GSM1068506 2 0.0237 0.8961 0.000 0.996 0.004
#> GSM1068507 2 0.3670 0.8335 0.092 0.888 0.020
#> GSM1068508 2 0.0592 0.8930 0.012 0.988 0.000
#> GSM1068510 2 0.4654 0.7021 0.000 0.792 0.208
#> GSM1068512 1 0.5858 0.6087 0.740 0.240 0.020
#> GSM1068513 2 0.0747 0.8951 0.000 0.984 0.016
#> GSM1068514 3 0.1031 0.8379 0.000 0.024 0.976
#> GSM1068517 2 0.3551 0.8066 0.132 0.868 0.000
#> GSM1068518 2 0.6647 0.2067 0.452 0.540 0.008
#> GSM1068520 1 0.0237 0.8670 0.996 0.004 0.000
#> GSM1068521 1 0.0475 0.8677 0.992 0.004 0.004
#> GSM1068522 2 0.0592 0.8956 0.000 0.988 0.012
#> GSM1068524 2 0.0747 0.8951 0.000 0.984 0.016
#> GSM1068527 1 0.3682 0.7809 0.876 0.116 0.008
#> GSM1068480 3 0.0747 0.8407 0.016 0.000 0.984
#> GSM1068484 2 0.0747 0.8951 0.000 0.984 0.016
#> GSM1068485 3 0.1163 0.8375 0.028 0.000 0.972
#> GSM1068489 2 0.0747 0.8951 0.000 0.984 0.016
#> GSM1068497 2 0.5882 0.4471 0.348 0.652 0.000
#> GSM1068501 2 0.0747 0.8951 0.000 0.984 0.016
#> GSM1068504 2 0.0592 0.8956 0.000 0.988 0.012
#> GSM1068509 1 0.2590 0.8281 0.924 0.072 0.004
#> GSM1068511 3 0.6026 0.3174 0.376 0.000 0.624
#> GSM1068515 1 0.7519 0.3163 0.568 0.388 0.044
#> GSM1068516 2 0.0424 0.8960 0.000 0.992 0.008
#> GSM1068519 1 0.0424 0.8679 0.992 0.000 0.008
#> GSM1068523 2 0.0237 0.8952 0.004 0.996 0.000
#> GSM1068525 2 0.0747 0.8951 0.000 0.984 0.016
#> GSM1068526 2 0.0747 0.8951 0.000 0.984 0.016
#> GSM1068458 1 0.0237 0.8681 0.996 0.000 0.004
#> GSM1068459 3 0.1411 0.8347 0.036 0.000 0.964
#> GSM1068460 1 0.0892 0.8614 0.980 0.020 0.000
#> GSM1068461 3 0.0747 0.8407 0.016 0.000 0.984
#> GSM1068464 2 0.0424 0.8960 0.000 0.992 0.008
#> GSM1068468 2 0.0661 0.8956 0.004 0.988 0.008
#> GSM1068472 2 0.0237 0.8952 0.004 0.996 0.000
#> GSM1068473 2 0.0592 0.8956 0.000 0.988 0.012
#> GSM1068474 2 0.0237 0.8959 0.000 0.996 0.004
#> GSM1068476 3 0.0747 0.8394 0.000 0.016 0.984
#> GSM1068477 2 0.0747 0.8913 0.016 0.984 0.000
#> GSM1068462 2 0.3349 0.8199 0.004 0.888 0.108
#> GSM1068463 3 0.2796 0.7923 0.092 0.000 0.908
#> GSM1068465 1 0.5678 0.5353 0.684 0.316 0.000
#> GSM1068466 1 0.3412 0.7779 0.876 0.124 0.000
#> GSM1068467 2 0.0475 0.8955 0.004 0.992 0.004
#> GSM1068469 2 0.1529 0.8792 0.040 0.960 0.000
#> GSM1068470 2 0.0424 0.8942 0.008 0.992 0.000
#> GSM1068471 2 0.0237 0.8959 0.000 0.996 0.004
#> GSM1068475 2 0.0237 0.8952 0.004 0.996 0.000
#> GSM1068528 1 0.0592 0.8674 0.988 0.000 0.012
#> GSM1068531 1 0.0237 0.8681 0.996 0.000 0.004
#> GSM1068532 1 0.0892 0.8640 0.980 0.000 0.020
#> GSM1068533 1 0.0424 0.8679 0.992 0.000 0.008
#> GSM1068535 1 0.5529 0.5481 0.704 0.000 0.296
#> GSM1068537 1 0.0592 0.8671 0.988 0.000 0.012
#> GSM1068538 1 0.0592 0.8671 0.988 0.000 0.012
#> GSM1068539 2 0.2959 0.8392 0.100 0.900 0.000
#> GSM1068540 1 0.0237 0.8681 0.996 0.000 0.004
#> GSM1068542 2 0.5420 0.6680 0.240 0.752 0.008
#> GSM1068543 2 0.9048 0.3424 0.288 0.540 0.172
#> GSM1068544 1 0.2165 0.8384 0.936 0.000 0.064
#> GSM1068545 2 0.0237 0.8952 0.004 0.996 0.000
#> GSM1068546 3 0.0747 0.8407 0.016 0.000 0.984
#> GSM1068547 1 0.0424 0.8658 0.992 0.008 0.000
#> GSM1068548 2 0.6577 0.3041 0.420 0.572 0.008
#> GSM1068549 3 0.0592 0.8408 0.012 0.000 0.988
#> GSM1068550 2 0.1950 0.8804 0.040 0.952 0.008
#> GSM1068551 2 0.0237 0.8952 0.004 0.996 0.000
#> GSM1068552 2 0.0829 0.8956 0.004 0.984 0.012
#> GSM1068555 2 0.0237 0.8952 0.004 0.996 0.000
#> GSM1068556 2 0.6848 0.3005 0.416 0.568 0.016
#> GSM1068557 2 0.0424 0.8942 0.008 0.992 0.000
#> GSM1068560 2 0.5797 0.6164 0.280 0.712 0.008
#> GSM1068561 2 0.0592 0.8930 0.012 0.988 0.000
#> GSM1068562 2 0.1905 0.8846 0.028 0.956 0.016
#> GSM1068563 2 0.5455 0.7069 0.028 0.788 0.184
#> GSM1068565 2 0.0237 0.8952 0.004 0.996 0.000
#> GSM1068529 3 0.3851 0.7698 0.004 0.136 0.860
#> GSM1068530 1 0.0237 0.8670 0.996 0.004 0.000
#> GSM1068534 3 0.7244 0.6224 0.092 0.208 0.700
#> GSM1068536 1 0.3816 0.7601 0.852 0.148 0.000
#> GSM1068541 2 0.3482 0.8106 0.128 0.872 0.000
#> GSM1068553 1 0.9649 -0.0643 0.404 0.208 0.388
#> GSM1068554 2 0.2356 0.8619 0.000 0.928 0.072
#> GSM1068558 3 0.1163 0.8373 0.000 0.028 0.972
#> GSM1068559 3 0.1031 0.8383 0.000 0.024 0.976
#> GSM1068564 2 0.0592 0.8956 0.000 0.988 0.012
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1068478 1 0.4982 0.797 0.772 0.136 0.000 0.092
#> GSM1068479 3 0.5298 0.390 0.000 0.372 0.612 0.016
#> GSM1068481 3 0.3008 0.859 0.044 0.020 0.904 0.032
#> GSM1068482 3 0.1637 0.866 0.060 0.000 0.940 0.000
#> GSM1068483 1 0.1576 0.907 0.948 0.048 0.000 0.004
#> GSM1068486 3 0.0895 0.882 0.004 0.000 0.976 0.020
#> GSM1068487 2 0.3266 0.791 0.000 0.832 0.000 0.168
#> GSM1068488 4 0.3105 0.795 0.140 0.000 0.004 0.856
#> GSM1068490 2 0.3219 0.813 0.000 0.836 0.000 0.164
#> GSM1068491 3 0.0000 0.882 0.000 0.000 1.000 0.000
#> GSM1068492 3 0.2984 0.830 0.000 0.028 0.888 0.084
#> GSM1068493 2 0.1305 0.882 0.036 0.960 0.004 0.000
#> GSM1068494 1 0.3402 0.862 0.832 0.000 0.004 0.164
#> GSM1068495 2 0.7093 0.172 0.396 0.476 0.000 0.128
#> GSM1068496 1 0.0937 0.917 0.976 0.012 0.000 0.012
#> GSM1068498 2 0.3691 0.819 0.076 0.856 0.000 0.068
#> GSM1068499 1 0.4101 0.868 0.848 0.024 0.036 0.092
#> GSM1068500 1 0.2170 0.905 0.936 0.036 0.016 0.012
#> GSM1068502 3 0.4630 0.632 0.000 0.252 0.732 0.016
#> GSM1068503 4 0.3907 0.767 0.000 0.232 0.000 0.768
#> GSM1068505 4 0.2401 0.827 0.004 0.092 0.000 0.904
#> GSM1068506 4 0.3311 0.809 0.000 0.172 0.000 0.828
#> GSM1068507 4 0.3570 0.824 0.048 0.092 0.000 0.860
#> GSM1068508 2 0.1637 0.891 0.000 0.940 0.000 0.060
#> GSM1068510 4 0.3306 0.816 0.000 0.156 0.004 0.840
#> GSM1068512 4 0.4277 0.695 0.280 0.000 0.000 0.720
#> GSM1068513 4 0.4713 0.532 0.000 0.360 0.000 0.640
#> GSM1068514 3 0.1792 0.857 0.000 0.000 0.932 0.068
#> GSM1068517 2 0.2844 0.852 0.052 0.900 0.000 0.048
#> GSM1068518 4 0.5473 0.515 0.324 0.032 0.000 0.644
#> GSM1068520 1 0.1059 0.913 0.972 0.016 0.000 0.012
#> GSM1068521 1 0.1807 0.909 0.940 0.008 0.000 0.052
#> GSM1068522 4 0.3528 0.787 0.000 0.192 0.000 0.808
#> GSM1068524 4 0.4585 0.659 0.000 0.332 0.000 0.668
#> GSM1068527 4 0.3172 0.766 0.160 0.000 0.000 0.840
#> GSM1068480 3 0.0921 0.880 0.000 0.000 0.972 0.028
#> GSM1068484 4 0.3024 0.807 0.000 0.148 0.000 0.852
#> GSM1068485 3 0.0804 0.882 0.012 0.008 0.980 0.000
#> GSM1068489 4 0.2469 0.815 0.000 0.108 0.000 0.892
#> GSM1068497 2 0.3009 0.846 0.056 0.892 0.000 0.052
#> GSM1068501 4 0.2814 0.811 0.000 0.132 0.000 0.868
#> GSM1068504 2 0.1557 0.892 0.000 0.944 0.000 0.056
#> GSM1068509 1 0.2928 0.885 0.880 0.012 0.000 0.108
#> GSM1068511 4 0.5035 0.736 0.204 0.000 0.052 0.744
#> GSM1068515 2 0.4397 0.784 0.120 0.820 0.008 0.052
#> GSM1068516 4 0.2831 0.813 0.004 0.120 0.000 0.876
#> GSM1068519 1 0.2921 0.882 0.860 0.000 0.000 0.140
#> GSM1068523 2 0.2589 0.869 0.000 0.884 0.000 0.116
#> GSM1068525 4 0.3219 0.801 0.000 0.164 0.000 0.836
#> GSM1068526 4 0.1792 0.831 0.000 0.068 0.000 0.932
#> GSM1068458 1 0.2796 0.874 0.892 0.016 0.000 0.092
#> GSM1068459 3 0.0817 0.881 0.024 0.000 0.976 0.000
#> GSM1068460 1 0.0592 0.915 0.984 0.000 0.000 0.016
#> GSM1068461 3 0.0188 0.883 0.000 0.000 0.996 0.004
#> GSM1068464 2 0.1474 0.895 0.000 0.948 0.000 0.052
#> GSM1068468 2 0.0657 0.896 0.004 0.984 0.000 0.012
#> GSM1068472 2 0.0937 0.893 0.012 0.976 0.000 0.012
#> GSM1068473 2 0.3486 0.779 0.000 0.812 0.000 0.188
#> GSM1068474 2 0.1716 0.891 0.000 0.936 0.000 0.064
#> GSM1068476 3 0.0000 0.882 0.000 0.000 1.000 0.000
#> GSM1068477 2 0.1118 0.895 0.000 0.964 0.000 0.036
#> GSM1068462 2 0.1739 0.880 0.016 0.952 0.024 0.008
#> GSM1068463 3 0.4464 0.680 0.224 0.004 0.760 0.012
#> GSM1068465 1 0.5798 0.698 0.696 0.208 0.000 0.096
#> GSM1068466 1 0.3464 0.872 0.868 0.076 0.000 0.056
#> GSM1068467 2 0.0672 0.894 0.008 0.984 0.000 0.008
#> GSM1068469 2 0.1545 0.880 0.040 0.952 0.000 0.008
#> GSM1068470 2 0.1389 0.898 0.000 0.952 0.000 0.048
#> GSM1068471 2 0.1022 0.898 0.000 0.968 0.000 0.032
#> GSM1068475 2 0.1211 0.897 0.000 0.960 0.000 0.040
#> GSM1068528 1 0.0937 0.914 0.976 0.012 0.000 0.012
#> GSM1068531 1 0.1389 0.909 0.952 0.000 0.000 0.048
#> GSM1068532 1 0.1118 0.910 0.964 0.000 0.000 0.036
#> GSM1068533 1 0.2469 0.873 0.892 0.000 0.000 0.108
#> GSM1068535 4 0.3764 0.714 0.216 0.000 0.000 0.784
#> GSM1068537 1 0.1302 0.911 0.956 0.000 0.000 0.044
#> GSM1068538 1 0.1474 0.910 0.948 0.000 0.000 0.052
#> GSM1068539 2 0.5188 0.705 0.044 0.716 0.000 0.240
#> GSM1068540 1 0.1557 0.913 0.944 0.000 0.000 0.056
#> GSM1068542 4 0.2216 0.805 0.092 0.000 0.000 0.908
#> GSM1068543 4 0.2412 0.809 0.084 0.000 0.008 0.908
#> GSM1068544 1 0.1042 0.913 0.972 0.008 0.020 0.000
#> GSM1068545 4 0.4941 0.427 0.000 0.436 0.000 0.564
#> GSM1068546 3 0.1489 0.876 0.004 0.000 0.952 0.044
#> GSM1068547 1 0.1022 0.913 0.968 0.000 0.000 0.032
#> GSM1068548 4 0.2469 0.801 0.108 0.000 0.000 0.892
#> GSM1068549 3 0.0000 0.882 0.000 0.000 1.000 0.000
#> GSM1068550 4 0.3597 0.827 0.016 0.148 0.000 0.836
#> GSM1068551 2 0.2216 0.871 0.000 0.908 0.000 0.092
#> GSM1068552 4 0.2973 0.816 0.000 0.144 0.000 0.856
#> GSM1068555 2 0.0921 0.898 0.000 0.972 0.000 0.028
#> GSM1068556 4 0.3569 0.777 0.196 0.000 0.000 0.804
#> GSM1068557 2 0.1042 0.896 0.008 0.972 0.000 0.020
#> GSM1068560 4 0.3108 0.801 0.112 0.016 0.000 0.872
#> GSM1068561 2 0.1389 0.897 0.000 0.952 0.000 0.048
#> GSM1068562 4 0.3484 0.822 0.008 0.144 0.004 0.844
#> GSM1068563 4 0.5435 0.748 0.016 0.056 0.180 0.748
#> GSM1068565 2 0.1940 0.883 0.000 0.924 0.000 0.076
#> GSM1068529 3 0.4692 0.726 0.000 0.032 0.756 0.212
#> GSM1068530 1 0.0469 0.916 0.988 0.000 0.000 0.012
#> GSM1068534 4 0.5460 0.727 0.028 0.068 0.136 0.768
#> GSM1068536 1 0.4318 0.852 0.816 0.068 0.000 0.116
#> GSM1068541 2 0.3796 0.836 0.056 0.848 0.000 0.096
#> GSM1068553 4 0.2647 0.792 0.120 0.000 0.000 0.880
#> GSM1068554 4 0.2859 0.824 0.000 0.112 0.008 0.880
#> GSM1068558 3 0.5024 0.391 0.000 0.008 0.632 0.360
#> GSM1068559 3 0.1174 0.879 0.000 0.012 0.968 0.020
#> GSM1068564 4 0.4072 0.770 0.000 0.252 0.000 0.748
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM1068478 1 0.3910 0.7004 0.772 0.196 0.000 0.000 0.032
#> GSM1068479 3 0.4639 0.4448 0.000 0.344 0.632 0.024 0.000
#> GSM1068481 3 0.4072 0.7738 0.152 0.008 0.792 0.048 0.000
#> GSM1068482 3 0.2329 0.8165 0.124 0.000 0.876 0.000 0.000
#> GSM1068483 1 0.0451 0.8447 0.988 0.008 0.004 0.000 0.000
#> GSM1068486 3 0.1124 0.8610 0.004 0.000 0.960 0.036 0.000
#> GSM1068487 2 0.5068 0.3476 0.000 0.592 0.000 0.364 0.044
#> GSM1068488 5 0.2878 0.6561 0.068 0.000 0.004 0.048 0.880
#> GSM1068490 2 0.4576 0.3811 0.000 0.608 0.000 0.376 0.016
#> GSM1068491 3 0.0000 0.8655 0.000 0.000 1.000 0.000 0.000
#> GSM1068492 3 0.2673 0.8233 0.000 0.024 0.900 0.048 0.028
#> GSM1068493 2 0.1571 0.8466 0.060 0.936 0.000 0.004 0.000
#> GSM1068494 5 0.4512 0.3976 0.300 0.000 0.020 0.004 0.676
#> GSM1068495 5 0.4841 0.5549 0.160 0.104 0.000 0.004 0.732
#> GSM1068496 1 0.0703 0.8433 0.976 0.000 0.000 0.000 0.024
#> GSM1068498 2 0.1851 0.8258 0.088 0.912 0.000 0.000 0.000
#> GSM1068499 1 0.4808 0.6628 0.696 0.012 0.020 0.008 0.264
#> GSM1068500 1 0.0162 0.8446 0.996 0.000 0.004 0.000 0.000
#> GSM1068502 3 0.3779 0.7397 0.000 0.124 0.816 0.056 0.004
#> GSM1068503 4 0.6396 0.4448 0.000 0.280 0.000 0.508 0.212
#> GSM1068505 4 0.4108 0.6136 0.000 0.008 0.000 0.684 0.308
#> GSM1068506 4 0.5309 0.6069 0.000 0.092 0.000 0.644 0.264
#> GSM1068507 4 0.1205 0.6507 0.004 0.040 0.000 0.956 0.000
#> GSM1068508 2 0.1915 0.8613 0.000 0.928 0.000 0.040 0.032
#> GSM1068510 4 0.5082 0.6241 0.000 0.052 0.012 0.680 0.256
#> GSM1068512 5 0.5086 0.5162 0.156 0.000 0.000 0.144 0.700
#> GSM1068513 4 0.3060 0.6361 0.000 0.128 0.000 0.848 0.024
#> GSM1068514 3 0.1012 0.8624 0.000 0.000 0.968 0.020 0.012
#> GSM1068517 2 0.1341 0.8460 0.056 0.944 0.000 0.000 0.000
#> GSM1068518 5 0.2377 0.6419 0.128 0.000 0.000 0.000 0.872
#> GSM1068520 1 0.0671 0.8455 0.980 0.004 0.000 0.016 0.000
#> GSM1068521 1 0.1197 0.8384 0.952 0.000 0.000 0.000 0.048
#> GSM1068522 4 0.1704 0.6495 0.000 0.068 0.000 0.928 0.004
#> GSM1068524 5 0.5739 0.2565 0.000 0.344 0.000 0.100 0.556
#> GSM1068527 5 0.1484 0.6668 0.048 0.000 0.000 0.008 0.944
#> GSM1068480 3 0.1043 0.8586 0.000 0.000 0.960 0.000 0.040
#> GSM1068484 5 0.2423 0.6213 0.000 0.024 0.000 0.080 0.896
#> GSM1068485 3 0.0324 0.8660 0.004 0.000 0.992 0.000 0.004
#> GSM1068489 4 0.4306 0.3459 0.000 0.000 0.000 0.508 0.492
#> GSM1068497 2 0.1478 0.8417 0.064 0.936 0.000 0.000 0.000
#> GSM1068501 4 0.4014 0.5781 0.000 0.016 0.000 0.728 0.256
#> GSM1068504 2 0.1549 0.8625 0.000 0.944 0.000 0.040 0.016
#> GSM1068509 1 0.4567 0.5481 0.628 0.012 0.000 0.004 0.356
#> GSM1068511 5 0.6796 0.3855 0.124 0.000 0.136 0.128 0.612
#> GSM1068515 2 0.5298 0.6823 0.140 0.736 0.016 0.016 0.092
#> GSM1068516 5 0.0671 0.6602 0.000 0.016 0.000 0.004 0.980
#> GSM1068519 1 0.5373 0.6458 0.620 0.000 0.000 0.084 0.296
#> GSM1068523 2 0.3906 0.5778 0.000 0.704 0.000 0.004 0.292
#> GSM1068525 5 0.0912 0.6600 0.000 0.012 0.000 0.016 0.972
#> GSM1068526 4 0.4961 0.4172 0.000 0.028 0.000 0.524 0.448
#> GSM1068458 1 0.3884 0.6807 0.708 0.000 0.004 0.288 0.000
#> GSM1068459 3 0.1410 0.8529 0.060 0.000 0.940 0.000 0.000
#> GSM1068460 1 0.2777 0.8238 0.864 0.000 0.000 0.120 0.016
#> GSM1068461 3 0.0162 0.8660 0.000 0.004 0.996 0.000 0.000
#> GSM1068464 2 0.2719 0.7989 0.000 0.852 0.000 0.144 0.004
#> GSM1068468 2 0.0794 0.8651 0.000 0.972 0.000 0.028 0.000
#> GSM1068472 2 0.0865 0.8650 0.000 0.972 0.004 0.024 0.000
#> GSM1068473 2 0.4420 0.2122 0.000 0.548 0.000 0.448 0.004
#> GSM1068474 2 0.2471 0.8067 0.000 0.864 0.000 0.136 0.000
#> GSM1068476 3 0.0671 0.8660 0.000 0.004 0.980 0.016 0.000
#> GSM1068477 2 0.1341 0.8604 0.000 0.944 0.000 0.056 0.000
#> GSM1068462 2 0.1931 0.8412 0.008 0.932 0.048 0.004 0.008
#> GSM1068463 3 0.4731 0.1722 0.456 0.000 0.528 0.016 0.000
#> GSM1068465 1 0.5927 0.6330 0.652 0.188 0.000 0.024 0.136
#> GSM1068466 1 0.4250 0.8034 0.804 0.012 0.004 0.092 0.088
#> GSM1068467 2 0.0451 0.8623 0.000 0.988 0.000 0.004 0.008
#> GSM1068469 2 0.1041 0.8538 0.032 0.964 0.004 0.000 0.000
#> GSM1068470 2 0.1041 0.8624 0.000 0.964 0.000 0.004 0.032
#> GSM1068471 2 0.1704 0.8548 0.000 0.928 0.000 0.068 0.004
#> GSM1068475 2 0.0865 0.8655 0.000 0.972 0.000 0.024 0.004
#> GSM1068528 1 0.0000 0.8446 1.000 0.000 0.000 0.000 0.000
#> GSM1068531 1 0.1792 0.8367 0.916 0.000 0.000 0.084 0.000
#> GSM1068532 1 0.1608 0.8398 0.928 0.000 0.000 0.072 0.000
#> GSM1068533 1 0.4350 0.5392 0.588 0.000 0.004 0.408 0.000
#> GSM1068535 4 0.2628 0.5823 0.088 0.000 0.000 0.884 0.028
#> GSM1068537 1 0.1608 0.8398 0.928 0.000 0.000 0.072 0.000
#> GSM1068538 1 0.3333 0.7642 0.788 0.000 0.004 0.208 0.000
#> GSM1068539 5 0.2511 0.6509 0.016 0.088 0.000 0.004 0.892
#> GSM1068540 1 0.1704 0.8318 0.928 0.000 0.000 0.004 0.068
#> GSM1068542 4 0.3980 0.6312 0.008 0.000 0.000 0.708 0.284
#> GSM1068543 5 0.2843 0.6077 0.008 0.000 0.000 0.144 0.848
#> GSM1068544 1 0.0404 0.8437 0.988 0.000 0.012 0.000 0.000
#> GSM1068545 5 0.6819 -0.2521 0.000 0.324 0.000 0.320 0.356
#> GSM1068546 3 0.3579 0.7125 0.004 0.000 0.756 0.240 0.000
#> GSM1068547 1 0.2110 0.8411 0.912 0.000 0.000 0.072 0.016
#> GSM1068548 4 0.4157 0.6298 0.020 0.000 0.000 0.716 0.264
#> GSM1068549 3 0.0324 0.8657 0.000 0.000 0.992 0.004 0.004
#> GSM1068550 5 0.5262 -0.1105 0.008 0.036 0.000 0.388 0.568
#> GSM1068551 2 0.2139 0.8493 0.000 0.916 0.000 0.032 0.052
#> GSM1068552 4 0.5353 0.5316 0.000 0.064 0.000 0.576 0.360
#> GSM1068555 2 0.1121 0.8580 0.000 0.956 0.000 0.000 0.044
#> GSM1068556 5 0.3988 0.5341 0.036 0.000 0.000 0.196 0.768
#> GSM1068557 2 0.0451 0.8634 0.000 0.988 0.000 0.008 0.004
#> GSM1068560 5 0.1357 0.6673 0.048 0.000 0.000 0.004 0.948
#> GSM1068561 5 0.5180 -0.0307 0.020 0.484 0.000 0.012 0.484
#> GSM1068562 5 0.2792 0.6439 0.004 0.040 0.000 0.072 0.884
#> GSM1068563 4 0.7849 0.3838 0.036 0.040 0.148 0.420 0.356
#> GSM1068565 2 0.2104 0.8527 0.000 0.916 0.000 0.060 0.024
#> GSM1068529 5 0.3439 0.5727 0.004 0.008 0.188 0.000 0.800
#> GSM1068530 1 0.0566 0.8463 0.984 0.000 0.000 0.012 0.004
#> GSM1068534 5 0.1891 0.6512 0.004 0.004 0.060 0.004 0.928
#> GSM1068536 1 0.5111 0.5046 0.592 0.016 0.000 0.020 0.372
#> GSM1068541 2 0.4202 0.7543 0.068 0.796 0.000 0.012 0.124
#> GSM1068553 4 0.1942 0.6443 0.012 0.000 0.000 0.920 0.068
#> GSM1068554 4 0.1377 0.6571 0.000 0.020 0.004 0.956 0.020
#> GSM1068558 5 0.3086 0.5898 0.000 0.000 0.180 0.004 0.816
#> GSM1068559 3 0.2074 0.8212 0.000 0.000 0.896 0.000 0.104
#> GSM1068564 5 0.5604 -0.4303 0.000 0.072 0.000 0.460 0.468
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1068478 1 0.5205 0.4136 0.580 0.336 0.000 0.004 0.008 0.072
#> GSM1068479 3 0.3087 0.7154 0.000 0.176 0.808 0.012 0.004 0.000
#> GSM1068481 3 0.5534 0.5111 0.300 0.020 0.604 0.032 0.044 0.000
#> GSM1068482 3 0.2773 0.8013 0.152 0.000 0.836 0.000 0.004 0.008
#> GSM1068483 1 0.0405 0.7764 0.988 0.008 0.000 0.004 0.000 0.000
#> GSM1068486 3 0.1452 0.8703 0.012 0.000 0.948 0.020 0.020 0.000
#> GSM1068487 2 0.4516 0.3137 0.000 0.564 0.000 0.400 0.036 0.000
#> GSM1068488 6 0.4457 0.1604 0.004 0.000 0.012 0.364 0.012 0.608
#> GSM1068490 4 0.4449 0.0499 0.000 0.440 0.000 0.532 0.028 0.000
#> GSM1068491 3 0.0000 0.8766 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1068492 3 0.2809 0.7904 0.000 0.004 0.848 0.128 0.000 0.020
#> GSM1068493 2 0.1294 0.8163 0.024 0.956 0.000 0.004 0.008 0.008
#> GSM1068494 6 0.1956 0.6561 0.080 0.000 0.000 0.004 0.008 0.908
#> GSM1068495 6 0.2827 0.6590 0.052 0.040 0.000 0.024 0.004 0.880
#> GSM1068496 1 0.1268 0.7717 0.952 0.000 0.000 0.008 0.004 0.036
#> GSM1068498 2 0.1636 0.8019 0.024 0.936 0.000 0.004 0.000 0.036
#> GSM1068499 6 0.7072 0.3256 0.208 0.020 0.064 0.000 0.224 0.484
#> GSM1068500 1 0.0767 0.7761 0.976 0.008 0.000 0.000 0.012 0.004
#> GSM1068502 3 0.2950 0.7607 0.000 0.024 0.828 0.148 0.000 0.000
#> GSM1068503 4 0.4142 0.6172 0.000 0.200 0.000 0.744 0.028 0.028
#> GSM1068505 4 0.4680 0.5431 0.000 0.000 0.000 0.680 0.200 0.120
#> GSM1068506 4 0.2645 0.6940 0.008 0.052 0.000 0.888 0.008 0.044
#> GSM1068507 5 0.3314 0.7385 0.000 0.004 0.000 0.256 0.740 0.000
#> GSM1068508 2 0.2346 0.7805 0.000 0.868 0.000 0.124 0.008 0.000
#> GSM1068510 5 0.4913 0.7131 0.000 0.000 0.040 0.180 0.704 0.076
#> GSM1068512 4 0.5920 0.3744 0.144 0.000 0.004 0.516 0.012 0.324
#> GSM1068513 5 0.4127 0.6894 0.000 0.036 0.000 0.284 0.680 0.000
#> GSM1068514 3 0.0862 0.8753 0.000 0.000 0.972 0.016 0.004 0.008
#> GSM1068517 2 0.1036 0.8124 0.008 0.964 0.000 0.004 0.000 0.024
#> GSM1068518 6 0.1924 0.6666 0.024 0.004 0.004 0.036 0.004 0.928
#> GSM1068520 1 0.2358 0.7667 0.908 0.016 0.000 0.012 0.044 0.020
#> GSM1068521 6 0.6257 -0.0901 0.412 0.028 0.000 0.008 0.120 0.432
#> GSM1068522 4 0.3312 0.5065 0.000 0.028 0.000 0.792 0.180 0.000
#> GSM1068524 2 0.6375 -0.0436 0.000 0.400 0.000 0.240 0.016 0.344
#> GSM1068527 6 0.1657 0.6563 0.000 0.000 0.000 0.056 0.016 0.928
#> GSM1068480 3 0.1010 0.8691 0.000 0.000 0.960 0.000 0.004 0.036
#> GSM1068484 6 0.3534 0.4704 0.000 0.000 0.000 0.244 0.016 0.740
#> GSM1068485 3 0.0146 0.8773 0.004 0.000 0.996 0.000 0.000 0.000
#> GSM1068489 4 0.5149 0.3288 0.000 0.000 0.000 0.476 0.440 0.084
#> GSM1068497 2 0.1854 0.8040 0.016 0.932 0.000 0.004 0.020 0.028
#> GSM1068501 5 0.2088 0.6605 0.000 0.000 0.000 0.028 0.904 0.068
#> GSM1068504 2 0.1686 0.8149 0.000 0.924 0.000 0.064 0.012 0.000
#> GSM1068509 1 0.5787 0.0752 0.444 0.000 0.000 0.000 0.180 0.376
#> GSM1068511 1 0.7398 0.1628 0.444 0.000 0.216 0.208 0.012 0.120
#> GSM1068515 2 0.5096 0.5836 0.064 0.688 0.000 0.012 0.208 0.028
#> GSM1068516 6 0.2006 0.6577 0.000 0.000 0.000 0.016 0.080 0.904
#> GSM1068519 5 0.3807 0.5008 0.052 0.000 0.000 0.000 0.756 0.192
#> GSM1068523 2 0.4175 0.6060 0.000 0.716 0.000 0.016 0.028 0.240
#> GSM1068525 6 0.2501 0.6297 0.000 0.000 0.004 0.108 0.016 0.872
#> GSM1068526 4 0.2738 0.6992 0.000 0.000 0.000 0.820 0.004 0.176
#> GSM1068458 1 0.4526 0.6416 0.708 0.004 0.000 0.100 0.188 0.000
#> GSM1068459 3 0.2527 0.7941 0.168 0.000 0.832 0.000 0.000 0.000
#> GSM1068460 1 0.5063 0.6536 0.704 0.004 0.000 0.072 0.172 0.048
#> GSM1068461 3 0.0622 0.8777 0.012 0.000 0.980 0.000 0.008 0.000
#> GSM1068464 2 0.3979 0.4510 0.000 0.628 0.000 0.360 0.012 0.000
#> GSM1068468 2 0.0858 0.8193 0.000 0.968 0.000 0.028 0.004 0.000
#> GSM1068472 2 0.0858 0.8183 0.000 0.968 0.000 0.028 0.004 0.000
#> GSM1068473 2 0.5643 0.3729 0.000 0.536 0.000 0.248 0.216 0.000
#> GSM1068474 2 0.2412 0.7955 0.000 0.880 0.000 0.092 0.028 0.000
#> GSM1068476 3 0.0632 0.8764 0.000 0.000 0.976 0.000 0.024 0.000
#> GSM1068477 2 0.1745 0.8169 0.000 0.924 0.000 0.056 0.020 0.000
#> GSM1068462 2 0.2695 0.7382 0.000 0.844 0.004 0.000 0.144 0.008
#> GSM1068463 1 0.4405 0.2586 0.604 0.000 0.368 0.008 0.020 0.000
#> GSM1068465 1 0.6681 0.4283 0.556 0.168 0.000 0.064 0.192 0.020
#> GSM1068466 5 0.5447 -0.1133 0.396 0.040 0.000 0.004 0.524 0.036
#> GSM1068467 2 0.0551 0.8174 0.000 0.984 0.000 0.004 0.008 0.004
#> GSM1068469 2 0.0924 0.8155 0.008 0.972 0.004 0.000 0.008 0.008
#> GSM1068470 2 0.0790 0.8187 0.000 0.968 0.000 0.032 0.000 0.000
#> GSM1068471 2 0.2019 0.8041 0.000 0.900 0.000 0.088 0.012 0.000
#> GSM1068475 2 0.1082 0.8171 0.000 0.956 0.000 0.040 0.004 0.000
#> GSM1068528 1 0.1026 0.7763 0.968 0.008 0.000 0.004 0.008 0.012
#> GSM1068531 1 0.3371 0.6784 0.780 0.000 0.000 0.004 0.200 0.016
#> GSM1068532 1 0.0622 0.7763 0.980 0.000 0.000 0.012 0.008 0.000
#> GSM1068533 1 0.4631 0.6093 0.692 0.000 0.000 0.168 0.140 0.000
#> GSM1068535 5 0.3813 0.7493 0.024 0.000 0.000 0.224 0.744 0.008
#> GSM1068537 1 0.0603 0.7755 0.980 0.000 0.000 0.016 0.004 0.000
#> GSM1068538 1 0.3055 0.7364 0.840 0.000 0.000 0.064 0.096 0.000
#> GSM1068539 6 0.2647 0.6624 0.024 0.032 0.000 0.032 0.016 0.896
#> GSM1068540 1 0.2114 0.7548 0.904 0.000 0.000 0.008 0.012 0.076
#> GSM1068542 4 0.3368 0.6924 0.004 0.000 0.000 0.820 0.060 0.116
#> GSM1068543 6 0.3134 0.5306 0.004 0.000 0.004 0.208 0.000 0.784
#> GSM1068544 1 0.1625 0.7553 0.928 0.000 0.060 0.000 0.000 0.012
#> GSM1068545 4 0.4809 0.6251 0.004 0.188 0.000 0.680 0.000 0.128
#> GSM1068546 5 0.4899 0.6243 0.004 0.000 0.216 0.104 0.672 0.004
#> GSM1068547 1 0.3103 0.7503 0.856 0.000 0.000 0.024 0.076 0.044
#> GSM1068548 4 0.2737 0.7032 0.024 0.000 0.000 0.868 0.012 0.096
#> GSM1068549 3 0.0363 0.8772 0.000 0.000 0.988 0.000 0.012 0.000
#> GSM1068550 4 0.3741 0.5814 0.000 0.000 0.000 0.672 0.008 0.320
#> GSM1068551 2 0.3969 0.4819 0.000 0.652 0.000 0.332 0.000 0.016
#> GSM1068552 4 0.2595 0.7062 0.000 0.004 0.000 0.836 0.000 0.160
#> GSM1068555 2 0.1296 0.8162 0.000 0.952 0.000 0.012 0.004 0.032
#> GSM1068556 4 0.4025 0.4048 0.008 0.000 0.000 0.576 0.000 0.416
#> GSM1068557 2 0.0893 0.8167 0.004 0.972 0.000 0.004 0.004 0.016
#> GSM1068560 6 0.1462 0.6562 0.000 0.000 0.000 0.056 0.008 0.936
#> GSM1068561 6 0.4784 0.1737 0.024 0.404 0.000 0.004 0.012 0.556
#> GSM1068562 6 0.3050 0.4956 0.000 0.000 0.000 0.236 0.000 0.764
#> GSM1068563 4 0.4721 0.6531 0.028 0.008 0.104 0.752 0.004 0.104
#> GSM1068565 2 0.2118 0.7926 0.000 0.888 0.000 0.104 0.008 0.000
#> GSM1068529 6 0.3828 0.5314 0.000 0.004 0.288 0.012 0.000 0.696
#> GSM1068530 1 0.0405 0.7759 0.988 0.004 0.000 0.008 0.000 0.000
#> GSM1068534 6 0.7709 0.2134 0.068 0.000 0.332 0.100 0.104 0.396
#> GSM1068536 6 0.5703 0.4363 0.128 0.024 0.000 0.004 0.240 0.604
#> GSM1068541 2 0.7255 0.2801 0.220 0.480 0.000 0.084 0.192 0.024
#> GSM1068553 5 0.3470 0.7466 0.000 0.000 0.000 0.248 0.740 0.012
#> GSM1068554 5 0.3265 0.7456 0.000 0.000 0.004 0.248 0.748 0.000
#> GSM1068558 6 0.5081 0.3809 0.000 0.000 0.356 0.068 0.008 0.568
#> GSM1068559 3 0.2436 0.8210 0.000 0.000 0.880 0.000 0.088 0.032
#> GSM1068564 4 0.4264 0.6904 0.000 0.012 0.000 0.752 0.148 0.088
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 disease.state(p) gender(p) k
#> SD:NMF 102 0.391757 0.7534 2
#> SD:NMF 96 0.832671 0.3825 3
#> SD:NMF 104 0.004254 0.7325 4
#> SD:NMF 92 0.007545 0.3586 5
#> SD:NMF 83 0.000926 0.0673 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 38950 rows and 108 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#> Subgroups are detected by 'hclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 4.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.319 0.603 0.813 0.2775 0.673 0.673
#> 3 3 0.488 0.579 0.827 0.7560 0.714 0.606
#> 4 4 0.510 0.615 0.846 0.0985 0.920 0.844
#> 5 5 0.553 0.594 0.819 0.0562 0.960 0.913
#> 6 6 0.522 0.570 0.787 0.0687 0.956 0.901
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
#> GSM1068478 2 0.8713 0.1968 0.292 0.708
#> GSM1068479 2 0.2043 0.7662 0.032 0.968
#> GSM1068481 1 0.9850 0.9169 0.572 0.428
#> GSM1068482 1 0.9996 0.7714 0.512 0.488
#> GSM1068483 1 0.9909 0.9081 0.556 0.444
#> GSM1068486 2 0.9323 -0.1166 0.348 0.652
#> GSM1068487 2 0.0000 0.7813 0.000 1.000
#> GSM1068488 2 0.2778 0.7501 0.048 0.952
#> GSM1068490 2 0.0000 0.7813 0.000 1.000
#> GSM1068491 2 0.2043 0.7662 0.032 0.968
#> GSM1068492 2 0.2043 0.7662 0.032 0.968
#> GSM1068493 2 0.8016 0.3836 0.244 0.756
#> GSM1068494 1 0.9850 0.9191 0.572 0.428
#> GSM1068495 2 0.8443 0.2803 0.272 0.728
#> GSM1068496 1 0.9866 0.9196 0.568 0.432
#> GSM1068498 2 0.8499 0.2608 0.276 0.724
#> GSM1068499 1 0.9833 0.9224 0.576 0.424
#> GSM1068500 1 0.9881 0.9181 0.564 0.436
#> GSM1068502 2 0.2043 0.7662 0.032 0.968
#> GSM1068503 2 0.0000 0.7813 0.000 1.000
#> GSM1068505 2 0.0000 0.7813 0.000 1.000
#> GSM1068506 2 0.0376 0.7801 0.004 0.996
#> GSM1068507 2 0.0000 0.7813 0.000 1.000
#> GSM1068508 2 0.0672 0.7792 0.008 0.992
#> GSM1068510 2 0.0000 0.7813 0.000 1.000
#> GSM1068512 2 0.1184 0.7767 0.016 0.984
#> GSM1068513 2 0.0000 0.7813 0.000 1.000
#> GSM1068514 2 0.2043 0.7662 0.032 0.968
#> GSM1068517 2 0.8499 0.2608 0.276 0.724
#> GSM1068518 2 0.7950 0.3955 0.240 0.760
#> GSM1068520 1 0.9996 0.8112 0.512 0.488
#> GSM1068521 2 0.9944 -0.6437 0.456 0.544
#> GSM1068522 2 0.0000 0.7813 0.000 1.000
#> GSM1068524 2 0.0000 0.7813 0.000 1.000
#> GSM1068527 2 0.0376 0.7801 0.004 0.996
#> GSM1068480 2 0.9988 -0.6816 0.480 0.520
#> GSM1068484 2 0.0000 0.7813 0.000 1.000
#> GSM1068485 2 0.9998 -0.7251 0.492 0.508
#> GSM1068489 2 0.0000 0.7813 0.000 1.000
#> GSM1068497 2 0.8608 0.2283 0.284 0.716
#> GSM1068501 2 0.0000 0.7813 0.000 1.000
#> GSM1068504 2 0.0000 0.7813 0.000 1.000
#> GSM1068509 2 0.7453 0.4782 0.212 0.788
#> GSM1068511 2 0.9608 0.2147 0.384 0.616
#> GSM1068515 2 0.9209 -0.0268 0.336 0.664
#> GSM1068516 2 0.8555 0.2467 0.280 0.720
#> GSM1068519 1 0.9815 0.9242 0.580 0.420
#> GSM1068523 2 0.0000 0.7813 0.000 1.000
#> GSM1068525 2 0.0000 0.7813 0.000 1.000
#> GSM1068526 2 0.0376 0.7801 0.004 0.996
#> GSM1068458 1 0.9775 0.9274 0.588 0.412
#> GSM1068459 1 0.9850 0.9161 0.572 0.428
#> GSM1068460 2 0.6438 0.5886 0.164 0.836
#> GSM1068461 2 0.9922 -0.5836 0.448 0.552
#> GSM1068464 2 0.0000 0.7813 0.000 1.000
#> GSM1068468 2 0.0376 0.7804 0.004 0.996
#> GSM1068472 2 0.5629 0.6468 0.132 0.868
#> GSM1068473 2 0.0000 0.7813 0.000 1.000
#> GSM1068474 2 0.0000 0.7813 0.000 1.000
#> GSM1068476 2 0.9815 -0.4818 0.420 0.580
#> GSM1068477 2 0.0000 0.7813 0.000 1.000
#> GSM1068462 2 0.6048 0.6182 0.148 0.852
#> GSM1068463 1 0.9732 0.9151 0.596 0.404
#> GSM1068465 2 0.8144 0.3417 0.252 0.748
#> GSM1068466 2 0.9998 -0.7644 0.492 0.508
#> GSM1068467 2 0.1843 0.7670 0.028 0.972
#> GSM1068469 2 0.7299 0.4977 0.204 0.796
#> GSM1068470 2 0.0000 0.7813 0.000 1.000
#> GSM1068471 2 0.0000 0.7813 0.000 1.000
#> GSM1068475 2 0.0000 0.7813 0.000 1.000
#> GSM1068528 1 0.9775 0.9247 0.588 0.412
#> GSM1068531 1 0.9686 0.9260 0.604 0.396
#> GSM1068532 1 0.9686 0.9260 0.604 0.396
#> GSM1068533 1 0.9710 0.9280 0.600 0.400
#> GSM1068535 2 0.2778 0.7508 0.048 0.952
#> GSM1068537 1 0.9686 0.9260 0.604 0.396
#> GSM1068538 1 0.9686 0.9260 0.604 0.396
#> GSM1068539 2 0.8608 0.2287 0.284 0.716
#> GSM1068540 1 0.9686 0.9260 0.604 0.396
#> GSM1068542 2 0.0376 0.7801 0.004 0.996
#> GSM1068543 2 0.0938 0.7782 0.012 0.988
#> GSM1068544 1 0.9850 0.9161 0.572 0.428
#> GSM1068545 2 0.0376 0.7801 0.004 0.996
#> GSM1068546 2 0.9977 -0.6619 0.472 0.528
#> GSM1068547 1 0.9993 0.8212 0.516 0.484
#> GSM1068548 2 0.0376 0.7801 0.004 0.996
#> GSM1068549 2 0.9922 -0.5836 0.448 0.552
#> GSM1068550 2 0.0376 0.7801 0.004 0.996
#> GSM1068551 2 0.0000 0.7813 0.000 1.000
#> GSM1068552 2 0.0376 0.7801 0.004 0.996
#> GSM1068555 2 0.0000 0.7813 0.000 1.000
#> GSM1068556 2 0.0672 0.7798 0.008 0.992
#> GSM1068557 2 0.4562 0.6953 0.096 0.904
#> GSM1068560 2 0.0376 0.7801 0.004 0.996
#> GSM1068561 2 0.8267 0.3258 0.260 0.740
#> GSM1068562 2 0.0376 0.7801 0.004 0.996
#> GSM1068563 2 0.0376 0.7801 0.004 0.996
#> GSM1068565 2 0.0000 0.7813 0.000 1.000
#> GSM1068529 2 0.5629 0.6440 0.132 0.868
#> GSM1068530 1 0.9686 0.9260 0.604 0.396
#> GSM1068534 2 0.5629 0.6440 0.132 0.868
#> GSM1068536 2 0.8555 0.2448 0.280 0.720
#> GSM1068541 2 0.6148 0.6080 0.152 0.848
#> GSM1068553 2 0.0938 0.7779 0.012 0.988
#> GSM1068554 2 0.0938 0.7779 0.012 0.988
#> GSM1068558 2 0.9635 0.2108 0.388 0.612
#> GSM1068559 2 0.0938 0.7785 0.012 0.988
#> GSM1068564 2 0.0000 0.7813 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1068478 1 0.6432 0.2862 0.568 0.428 0.004
#> GSM1068479 2 0.3406 0.7652 0.028 0.904 0.068
#> GSM1068481 1 0.6539 0.4347 0.684 0.028 0.288
#> GSM1068482 1 0.6204 0.2276 0.576 0.000 0.424
#> GSM1068483 1 0.6646 0.5345 0.740 0.076 0.184
#> GSM1068486 1 0.9680 0.1892 0.456 0.300 0.244
#> GSM1068487 2 0.0000 0.8542 0.000 1.000 0.000
#> GSM1068488 2 0.2229 0.8151 0.044 0.944 0.012
#> GSM1068490 2 0.0000 0.8542 0.000 1.000 0.000
#> GSM1068491 2 0.3406 0.7652 0.028 0.904 0.068
#> GSM1068492 2 0.3406 0.7652 0.028 0.904 0.068
#> GSM1068493 2 0.7744 -0.1396 0.448 0.504 0.048
#> GSM1068494 1 0.4887 0.5668 0.844 0.096 0.060
#> GSM1068495 2 0.6682 -0.1341 0.488 0.504 0.008
#> GSM1068496 1 0.6601 0.4222 0.676 0.028 0.296
#> GSM1068498 1 0.6483 0.2564 0.544 0.452 0.004
#> GSM1068499 1 0.2959 0.5753 0.900 0.100 0.000
#> GSM1068500 1 0.6348 0.5336 0.752 0.060 0.188
#> GSM1068502 2 0.3406 0.7652 0.028 0.904 0.068
#> GSM1068503 2 0.0000 0.8542 0.000 1.000 0.000
#> GSM1068505 2 0.0000 0.8542 0.000 1.000 0.000
#> GSM1068506 2 0.0237 0.8535 0.004 0.996 0.000
#> GSM1068507 2 0.0000 0.8542 0.000 1.000 0.000
#> GSM1068508 2 0.0424 0.8516 0.008 0.992 0.000
#> GSM1068510 2 0.0000 0.8542 0.000 1.000 0.000
#> GSM1068512 2 0.1129 0.8439 0.020 0.976 0.004
#> GSM1068513 2 0.0000 0.8542 0.000 1.000 0.000
#> GSM1068514 2 0.3310 0.7704 0.028 0.908 0.064
#> GSM1068517 1 0.6483 0.2564 0.544 0.452 0.004
#> GSM1068518 2 0.6633 0.0399 0.444 0.548 0.008
#> GSM1068520 1 0.4291 0.5577 0.840 0.152 0.008
#> GSM1068521 1 0.5406 0.4941 0.764 0.224 0.012
#> GSM1068522 2 0.0000 0.8542 0.000 1.000 0.000
#> GSM1068524 2 0.0000 0.8542 0.000 1.000 0.000
#> GSM1068527 2 0.0237 0.8535 0.004 0.996 0.000
#> GSM1068480 1 0.6799 0.1630 0.532 0.012 0.456
#> GSM1068484 2 0.0000 0.8542 0.000 1.000 0.000
#> GSM1068485 1 0.7130 0.2674 0.544 0.024 0.432
#> GSM1068489 2 0.0237 0.8529 0.004 0.996 0.000
#> GSM1068497 1 0.6451 0.2770 0.560 0.436 0.004
#> GSM1068501 2 0.0424 0.8510 0.008 0.992 0.000
#> GSM1068504 2 0.0000 0.8542 0.000 1.000 0.000
#> GSM1068509 2 0.6910 0.1790 0.396 0.584 0.020
#> GSM1068511 3 0.6513 0.1655 0.004 0.476 0.520
#> GSM1068515 1 0.7001 0.3053 0.588 0.388 0.024
#> GSM1068516 2 0.6683 -0.1429 0.492 0.500 0.008
#> GSM1068519 1 0.2878 0.5755 0.904 0.096 0.000
#> GSM1068523 2 0.0000 0.8542 0.000 1.000 0.000
#> GSM1068525 2 0.0000 0.8542 0.000 1.000 0.000
#> GSM1068526 2 0.0237 0.8535 0.004 0.996 0.000
#> GSM1068458 1 0.2590 0.5822 0.924 0.072 0.004
#> GSM1068459 1 0.6570 0.4118 0.668 0.024 0.308
#> GSM1068460 2 0.5690 0.4758 0.288 0.708 0.004
#> GSM1068461 3 0.7652 -0.1442 0.444 0.044 0.512
#> GSM1068464 2 0.0000 0.8542 0.000 1.000 0.000
#> GSM1068468 2 0.0892 0.8435 0.020 0.980 0.000
#> GSM1068472 2 0.6126 0.3339 0.352 0.644 0.004
#> GSM1068473 2 0.0000 0.8542 0.000 1.000 0.000
#> GSM1068474 2 0.0000 0.8542 0.000 1.000 0.000
#> GSM1068476 3 0.9487 0.2164 0.244 0.260 0.496
#> GSM1068477 2 0.0000 0.8542 0.000 1.000 0.000
#> GSM1068462 2 0.6095 0.2300 0.392 0.608 0.000
#> GSM1068463 1 0.5706 0.3456 0.680 0.000 0.320
#> GSM1068465 1 0.6994 0.2465 0.556 0.424 0.020
#> GSM1068466 1 0.4589 0.5430 0.820 0.172 0.008
#> GSM1068467 2 0.1643 0.8248 0.044 0.956 0.000
#> GSM1068469 2 0.6280 -0.0227 0.460 0.540 0.000
#> GSM1068470 2 0.0000 0.8542 0.000 1.000 0.000
#> GSM1068471 2 0.0000 0.8542 0.000 1.000 0.000
#> GSM1068475 2 0.0000 0.8542 0.000 1.000 0.000
#> GSM1068528 1 0.5803 0.4936 0.760 0.028 0.212
#> GSM1068531 1 0.1964 0.5782 0.944 0.056 0.000
#> GSM1068532 1 0.2313 0.5683 0.944 0.032 0.024
#> GSM1068533 1 0.2301 0.5797 0.936 0.060 0.004
#> GSM1068535 2 0.2229 0.8154 0.044 0.944 0.012
#> GSM1068537 1 0.2176 0.5691 0.948 0.032 0.020
#> GSM1068538 1 0.2313 0.5683 0.944 0.032 0.024
#> GSM1068539 2 0.6683 -0.1581 0.496 0.496 0.008
#> GSM1068540 1 0.2176 0.5691 0.948 0.032 0.020
#> GSM1068542 2 0.0237 0.8535 0.004 0.996 0.000
#> GSM1068543 2 0.1015 0.8451 0.012 0.980 0.008
#> GSM1068544 1 0.6570 0.4118 0.668 0.024 0.308
#> GSM1068545 2 0.0237 0.8535 0.004 0.996 0.000
#> GSM1068546 1 0.6299 0.1310 0.524 0.000 0.476
#> GSM1068547 1 0.4228 0.5601 0.844 0.148 0.008
#> GSM1068548 2 0.0237 0.8535 0.004 0.996 0.000
#> GSM1068549 3 0.7652 -0.1442 0.444 0.044 0.512
#> GSM1068550 2 0.0237 0.8535 0.004 0.996 0.000
#> GSM1068551 2 0.0000 0.8542 0.000 1.000 0.000
#> GSM1068552 2 0.0237 0.8535 0.004 0.996 0.000
#> GSM1068555 2 0.0000 0.8542 0.000 1.000 0.000
#> GSM1068556 2 0.0424 0.8523 0.008 0.992 0.000
#> GSM1068557 2 0.4555 0.6214 0.200 0.800 0.000
#> GSM1068560 2 0.0237 0.8535 0.004 0.996 0.000
#> GSM1068561 1 0.6682 0.1670 0.504 0.488 0.008
#> GSM1068562 2 0.0237 0.8535 0.004 0.996 0.000
#> GSM1068563 2 0.0237 0.8535 0.004 0.996 0.000
#> GSM1068565 2 0.0000 0.8542 0.000 1.000 0.000
#> GSM1068529 2 0.6051 0.4483 0.292 0.696 0.012
#> GSM1068530 1 0.2313 0.5683 0.944 0.032 0.024
#> GSM1068534 2 0.6047 0.4173 0.312 0.680 0.008
#> GSM1068536 1 0.6682 0.1605 0.504 0.488 0.008
#> GSM1068541 2 0.5690 0.4669 0.288 0.708 0.004
#> GSM1068553 2 0.1015 0.8457 0.008 0.980 0.012
#> GSM1068554 2 0.1015 0.8457 0.008 0.980 0.012
#> GSM1068558 3 0.6299 0.1649 0.000 0.476 0.524
#> GSM1068559 2 0.0747 0.8492 0.016 0.984 0.000
#> GSM1068564 2 0.0237 0.8529 0.004 0.996 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1068478 1 0.5016 0.47172 0.600 0.396 0.004 0.000
#> GSM1068479 2 0.3272 0.78069 0.004 0.860 0.128 0.008
#> GSM1068481 1 0.5660 -0.17409 0.576 0.004 0.400 0.020
#> GSM1068482 3 0.5256 0.50227 0.392 0.000 0.596 0.012
#> GSM1068483 1 0.5172 0.33542 0.736 0.036 0.220 0.008
#> GSM1068486 1 0.7872 0.24559 0.448 0.276 0.272 0.004
#> GSM1068487 2 0.0000 0.89554 0.000 1.000 0.000 0.000
#> GSM1068488 2 0.1767 0.85973 0.044 0.944 0.012 0.000
#> GSM1068490 2 0.0000 0.89554 0.000 1.000 0.000 0.000
#> GSM1068491 2 0.3272 0.78069 0.004 0.860 0.128 0.008
#> GSM1068492 2 0.3272 0.78069 0.004 0.860 0.128 0.008
#> GSM1068493 2 0.6449 -0.25194 0.452 0.480 0.068 0.000
#> GSM1068494 1 0.4374 0.44374 0.812 0.068 0.120 0.000
#> GSM1068495 1 0.5290 0.27304 0.516 0.476 0.008 0.000
#> GSM1068496 1 0.5223 -0.18461 0.584 0.004 0.408 0.004
#> GSM1068498 1 0.5080 0.42623 0.576 0.420 0.004 0.000
#> GSM1068499 1 0.1902 0.51676 0.932 0.064 0.004 0.000
#> GSM1068500 1 0.4862 0.30220 0.744 0.020 0.228 0.008
#> GSM1068502 2 0.3272 0.78069 0.004 0.860 0.128 0.008
#> GSM1068503 2 0.0000 0.89554 0.000 1.000 0.000 0.000
#> GSM1068505 2 0.0000 0.89554 0.000 1.000 0.000 0.000
#> GSM1068506 2 0.0188 0.89490 0.004 0.996 0.000 0.000
#> GSM1068507 2 0.0000 0.89554 0.000 1.000 0.000 0.000
#> GSM1068508 2 0.0336 0.89291 0.008 0.992 0.000 0.000
#> GSM1068510 2 0.0000 0.89554 0.000 1.000 0.000 0.000
#> GSM1068512 2 0.0895 0.88596 0.020 0.976 0.004 0.000
#> GSM1068513 2 0.0000 0.89554 0.000 1.000 0.000 0.000
#> GSM1068514 2 0.3160 0.78894 0.004 0.868 0.120 0.008
#> GSM1068517 1 0.5080 0.42623 0.576 0.420 0.004 0.000
#> GSM1068518 2 0.5281 -0.12661 0.464 0.528 0.008 0.000
#> GSM1068520 1 0.2976 0.54052 0.872 0.120 0.008 0.000
#> GSM1068521 1 0.3978 0.53450 0.796 0.192 0.012 0.000
#> GSM1068522 2 0.0000 0.89554 0.000 1.000 0.000 0.000
#> GSM1068524 2 0.0000 0.89554 0.000 1.000 0.000 0.000
#> GSM1068527 2 0.0188 0.89490 0.004 0.996 0.000 0.000
#> GSM1068480 3 0.5473 0.58198 0.324 0.000 0.644 0.032
#> GSM1068484 2 0.0000 0.89554 0.000 1.000 0.000 0.000
#> GSM1068485 3 0.5531 0.37477 0.436 0.004 0.548 0.012
#> GSM1068489 2 0.0188 0.89428 0.004 0.996 0.000 0.000
#> GSM1068497 1 0.5039 0.45782 0.592 0.404 0.004 0.000
#> GSM1068501 2 0.0336 0.89220 0.008 0.992 0.000 0.000
#> GSM1068504 2 0.0000 0.89554 0.000 1.000 0.000 0.000
#> GSM1068509 2 0.5708 0.00574 0.416 0.556 0.028 0.000
#> GSM1068511 4 0.0376 0.99281 0.004 0.004 0.000 0.992
#> GSM1068515 1 0.6617 0.45623 0.552 0.372 0.068 0.008
#> GSM1068516 1 0.5288 0.28453 0.520 0.472 0.008 0.000
#> GSM1068519 1 0.1637 0.51515 0.940 0.060 0.000 0.000
#> GSM1068523 2 0.0000 0.89554 0.000 1.000 0.000 0.000
#> GSM1068525 2 0.0000 0.89554 0.000 1.000 0.000 0.000
#> GSM1068526 2 0.0188 0.89490 0.004 0.996 0.000 0.000
#> GSM1068458 1 0.1305 0.50306 0.960 0.036 0.004 0.000
#> GSM1068459 1 0.5509 -0.23114 0.560 0.004 0.424 0.012
#> GSM1068460 2 0.4584 0.47373 0.300 0.696 0.004 0.000
#> GSM1068461 3 0.0188 0.43738 0.004 0.000 0.996 0.000
#> GSM1068464 2 0.0000 0.89554 0.000 1.000 0.000 0.000
#> GSM1068468 2 0.0707 0.88598 0.020 0.980 0.000 0.000
#> GSM1068472 2 0.5306 0.29037 0.348 0.632 0.020 0.000
#> GSM1068473 2 0.0000 0.89554 0.000 1.000 0.000 0.000
#> GSM1068474 2 0.0000 0.89554 0.000 1.000 0.000 0.000
#> GSM1068476 3 0.4126 0.25482 0.004 0.216 0.776 0.004
#> GSM1068477 2 0.0000 0.89554 0.000 1.000 0.000 0.000
#> GSM1068462 2 0.5387 0.10993 0.400 0.584 0.016 0.000
#> GSM1068463 3 0.5161 0.54039 0.300 0.000 0.676 0.024
#> GSM1068465 1 0.7362 0.42524 0.568 0.272 0.016 0.144
#> GSM1068466 1 0.3196 0.54129 0.856 0.136 0.008 0.000
#> GSM1068467 2 0.1302 0.86847 0.044 0.956 0.000 0.000
#> GSM1068469 2 0.5508 -0.19321 0.476 0.508 0.016 0.000
#> GSM1068470 2 0.0000 0.89554 0.000 1.000 0.000 0.000
#> GSM1068471 2 0.0000 0.89554 0.000 1.000 0.000 0.000
#> GSM1068475 2 0.0000 0.89554 0.000 1.000 0.000 0.000
#> GSM1068528 1 0.4661 0.18271 0.724 0.004 0.264 0.008
#> GSM1068531 1 0.0707 0.48792 0.980 0.020 0.000 0.000
#> GSM1068532 1 0.2156 0.44203 0.928 0.008 0.060 0.004
#> GSM1068533 1 0.1151 0.49139 0.968 0.024 0.008 0.000
#> GSM1068535 2 0.1767 0.86039 0.044 0.944 0.012 0.000
#> GSM1068537 1 0.1890 0.44598 0.936 0.008 0.056 0.000
#> GSM1068538 1 0.2156 0.44203 0.928 0.008 0.060 0.004
#> GSM1068539 1 0.5285 0.29707 0.524 0.468 0.008 0.000
#> GSM1068540 1 0.1890 0.44598 0.936 0.008 0.056 0.000
#> GSM1068542 2 0.0188 0.89490 0.004 0.996 0.000 0.000
#> GSM1068543 2 0.0804 0.88757 0.012 0.980 0.008 0.000
#> GSM1068544 1 0.5509 -0.23114 0.560 0.004 0.424 0.012
#> GSM1068545 2 0.0188 0.89490 0.004 0.996 0.000 0.000
#> GSM1068546 3 0.3831 0.60731 0.204 0.000 0.792 0.004
#> GSM1068547 1 0.2918 0.53959 0.876 0.116 0.008 0.000
#> GSM1068548 2 0.0188 0.89490 0.004 0.996 0.000 0.000
#> GSM1068549 3 0.0376 0.43524 0.004 0.000 0.992 0.004
#> GSM1068550 2 0.0188 0.89490 0.004 0.996 0.000 0.000
#> GSM1068551 2 0.0000 0.89554 0.000 1.000 0.000 0.000
#> GSM1068552 2 0.0188 0.89490 0.004 0.996 0.000 0.000
#> GSM1068555 2 0.0000 0.89554 0.000 1.000 0.000 0.000
#> GSM1068556 2 0.0336 0.89371 0.008 0.992 0.000 0.000
#> GSM1068557 2 0.3726 0.65082 0.212 0.788 0.000 0.000
#> GSM1068560 2 0.0188 0.89490 0.004 0.996 0.000 0.000
#> GSM1068561 1 0.5685 0.30830 0.516 0.460 0.024 0.000
#> GSM1068562 2 0.0188 0.89490 0.004 0.996 0.000 0.000
#> GSM1068563 2 0.0188 0.89490 0.004 0.996 0.000 0.000
#> GSM1068565 2 0.0000 0.89554 0.000 1.000 0.000 0.000
#> GSM1068529 2 0.5417 0.42646 0.284 0.676 0.040 0.000
#> GSM1068530 1 0.2156 0.44203 0.928 0.008 0.060 0.004
#> GSM1068534 2 0.5512 0.38354 0.300 0.660 0.040 0.000
#> GSM1068536 1 0.5281 0.31145 0.528 0.464 0.008 0.000
#> GSM1068541 2 0.4560 0.47366 0.296 0.700 0.004 0.000
#> GSM1068553 2 0.0859 0.88824 0.008 0.980 0.008 0.004
#> GSM1068554 2 0.0859 0.88824 0.008 0.980 0.008 0.004
#> GSM1068558 4 0.0188 0.99283 0.000 0.004 0.000 0.996
#> GSM1068559 2 0.0804 0.88982 0.012 0.980 0.008 0.000
#> GSM1068564 2 0.0188 0.89428 0.004 0.996 0.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM1068478 1 0.5048 0.4718 0.612 0.000 0.016 0.352 0.020
#> GSM1068479 4 0.2741 0.7736 0.000 0.004 0.132 0.860 0.004
#> GSM1068481 5 0.4866 0.7426 0.396 0.004 0.020 0.000 0.580
#> GSM1068482 3 0.4604 0.4912 0.012 0.000 0.560 0.000 0.428
#> GSM1068483 1 0.5423 -0.1424 0.632 0.004 0.036 0.020 0.308
#> GSM1068486 1 0.8282 0.1149 0.384 0.000 0.168 0.256 0.192
#> GSM1068487 4 0.0162 0.8790 0.000 0.000 0.000 0.996 0.004
#> GSM1068488 4 0.1787 0.8454 0.044 0.000 0.004 0.936 0.016
#> GSM1068490 4 0.0000 0.8789 0.000 0.000 0.000 1.000 0.000
#> GSM1068491 4 0.2741 0.7736 0.000 0.004 0.132 0.860 0.004
#> GSM1068492 4 0.2741 0.7736 0.000 0.004 0.132 0.860 0.004
#> GSM1068493 4 0.6236 -0.2766 0.436 0.000 0.024 0.464 0.076
#> GSM1068494 1 0.4986 0.1993 0.748 0.000 0.148 0.036 0.068
#> GSM1068495 1 0.4986 0.3428 0.532 0.000 0.012 0.444 0.012
#> GSM1068496 5 0.4862 0.7941 0.364 0.000 0.032 0.000 0.604
#> GSM1068498 1 0.5123 0.4641 0.588 0.000 0.016 0.376 0.020
#> GSM1068499 1 0.2149 0.3759 0.924 0.000 0.012 0.028 0.036
#> GSM1068500 1 0.4994 -0.1926 0.636 0.004 0.024 0.008 0.328
#> GSM1068502 4 0.2741 0.7736 0.000 0.004 0.132 0.860 0.004
#> GSM1068503 4 0.0162 0.8790 0.000 0.000 0.000 0.996 0.004
#> GSM1068505 4 0.0000 0.8789 0.000 0.000 0.000 1.000 0.000
#> GSM1068506 4 0.0162 0.8791 0.000 0.000 0.004 0.996 0.000
#> GSM1068507 4 0.0000 0.8789 0.000 0.000 0.000 1.000 0.000
#> GSM1068508 4 0.0290 0.8791 0.008 0.000 0.000 0.992 0.000
#> GSM1068510 4 0.0000 0.8789 0.000 0.000 0.000 1.000 0.000
#> GSM1068512 4 0.0960 0.8723 0.016 0.000 0.008 0.972 0.004
#> GSM1068513 4 0.0000 0.8789 0.000 0.000 0.000 1.000 0.000
#> GSM1068514 4 0.2646 0.7818 0.000 0.004 0.124 0.868 0.004
#> GSM1068517 1 0.5123 0.4641 0.588 0.000 0.016 0.376 0.020
#> GSM1068518 4 0.5098 -0.2212 0.480 0.000 0.012 0.492 0.016
#> GSM1068520 1 0.2588 0.4570 0.884 0.000 0.008 0.100 0.008
#> GSM1068521 1 0.3373 0.4729 0.816 0.000 0.008 0.168 0.008
#> GSM1068522 4 0.0000 0.8789 0.000 0.000 0.000 1.000 0.000
#> GSM1068524 4 0.0451 0.8785 0.000 0.000 0.008 0.988 0.004
#> GSM1068527 4 0.0451 0.8775 0.000 0.000 0.008 0.988 0.004
#> GSM1068480 3 0.4946 0.5313 0.012 0.024 0.636 0.000 0.328
#> GSM1068484 4 0.0324 0.8786 0.000 0.000 0.004 0.992 0.004
#> GSM1068485 5 0.6300 0.7052 0.336 0.000 0.168 0.000 0.496
#> GSM1068489 4 0.0162 0.8793 0.004 0.000 0.000 0.996 0.000
#> GSM1068497 1 0.5075 0.4696 0.604 0.000 0.016 0.360 0.020
#> GSM1068501 4 0.0324 0.8790 0.004 0.000 0.000 0.992 0.004
#> GSM1068504 4 0.0451 0.8780 0.000 0.000 0.004 0.988 0.008
#> GSM1068509 4 0.5283 -0.0403 0.420 0.000 0.012 0.540 0.028
#> GSM1068511 2 0.0162 0.9912 0.004 0.996 0.000 0.000 0.000
#> GSM1068515 4 0.8351 -0.3538 0.300 0.000 0.208 0.336 0.156
#> GSM1068516 1 0.5071 0.3542 0.532 0.000 0.012 0.440 0.016
#> GSM1068519 1 0.1978 0.3709 0.932 0.000 0.012 0.024 0.032
#> GSM1068523 4 0.0693 0.8755 0.000 0.000 0.012 0.980 0.008
#> GSM1068525 4 0.0324 0.8786 0.000 0.000 0.004 0.992 0.004
#> GSM1068526 4 0.0162 0.8791 0.000 0.000 0.004 0.996 0.000
#> GSM1068458 1 0.1200 0.3719 0.964 0.000 0.008 0.016 0.012
#> GSM1068459 5 0.4866 0.8091 0.344 0.000 0.036 0.000 0.620
#> GSM1068460 4 0.4502 0.4160 0.312 0.000 0.012 0.668 0.008
#> GSM1068461 3 0.3661 0.5749 0.000 0.000 0.724 0.000 0.276
#> GSM1068464 4 0.0290 0.8787 0.000 0.000 0.000 0.992 0.008
#> GSM1068468 4 0.0898 0.8703 0.020 0.000 0.000 0.972 0.008
#> GSM1068472 4 0.5523 0.2325 0.332 0.000 0.024 0.604 0.040
#> GSM1068473 4 0.0000 0.8789 0.000 0.000 0.000 1.000 0.000
#> GSM1068474 4 0.0451 0.8780 0.000 0.000 0.004 0.988 0.008
#> GSM1068476 3 0.6133 0.3221 0.000 0.000 0.564 0.216 0.220
#> GSM1068477 4 0.0162 0.8792 0.000 0.000 0.000 0.996 0.004
#> GSM1068462 4 0.5678 0.0462 0.380 0.000 0.028 0.556 0.036
#> GSM1068463 5 0.3495 -0.0508 0.032 0.000 0.152 0.000 0.816
#> GSM1068465 1 0.6843 0.3649 0.568 0.148 0.012 0.244 0.028
#> GSM1068466 1 0.2857 0.4626 0.868 0.000 0.008 0.112 0.012
#> GSM1068467 4 0.1569 0.8515 0.044 0.000 0.004 0.944 0.008
#> GSM1068469 4 0.5779 -0.2469 0.456 0.000 0.028 0.480 0.036
#> GSM1068470 4 0.0451 0.8780 0.000 0.000 0.004 0.988 0.008
#> GSM1068471 4 0.0162 0.8790 0.000 0.000 0.000 0.996 0.004
#> GSM1068475 4 0.0451 0.8780 0.000 0.000 0.004 0.988 0.008
#> GSM1068528 1 0.4546 -0.4719 0.532 0.000 0.008 0.000 0.460
#> GSM1068531 1 0.0693 0.3448 0.980 0.000 0.008 0.000 0.012
#> GSM1068532 1 0.3642 0.1068 0.760 0.000 0.008 0.000 0.232
#> GSM1068533 1 0.1059 0.3486 0.968 0.000 0.008 0.004 0.020
#> GSM1068535 4 0.1710 0.8486 0.040 0.000 0.004 0.940 0.016
#> GSM1068537 1 0.2971 0.2010 0.836 0.000 0.008 0.000 0.156
#> GSM1068538 1 0.3671 0.1013 0.756 0.000 0.008 0.000 0.236
#> GSM1068539 1 0.4976 0.3660 0.540 0.000 0.012 0.436 0.012
#> GSM1068540 1 0.2513 0.2411 0.876 0.000 0.008 0.000 0.116
#> GSM1068542 4 0.0162 0.8791 0.000 0.000 0.004 0.996 0.000
#> GSM1068543 4 0.0968 0.8708 0.012 0.000 0.012 0.972 0.004
#> GSM1068544 5 0.4866 0.8091 0.344 0.000 0.036 0.000 0.620
#> GSM1068545 4 0.0162 0.8791 0.000 0.000 0.004 0.996 0.000
#> GSM1068546 3 0.3318 0.5873 0.012 0.000 0.808 0.000 0.180
#> GSM1068547 1 0.2533 0.4545 0.888 0.000 0.008 0.096 0.008
#> GSM1068548 4 0.0162 0.8791 0.000 0.000 0.004 0.996 0.000
#> GSM1068549 3 0.3636 0.5741 0.000 0.000 0.728 0.000 0.272
#> GSM1068550 4 0.0162 0.8791 0.000 0.000 0.004 0.996 0.000
#> GSM1068551 4 0.0693 0.8755 0.000 0.000 0.012 0.980 0.008
#> GSM1068552 4 0.0162 0.8791 0.000 0.000 0.004 0.996 0.000
#> GSM1068555 4 0.0693 0.8755 0.000 0.000 0.012 0.980 0.008
#> GSM1068556 4 0.0324 0.8790 0.004 0.000 0.004 0.992 0.000
#> GSM1068557 4 0.3845 0.6070 0.224 0.000 0.012 0.760 0.004
#> GSM1068560 4 0.0451 0.8775 0.000 0.000 0.008 0.988 0.004
#> GSM1068561 1 0.5898 0.3523 0.496 0.000 0.032 0.432 0.040
#> GSM1068562 4 0.0290 0.8788 0.000 0.000 0.008 0.992 0.000
#> GSM1068563 4 0.0162 0.8791 0.000 0.000 0.004 0.996 0.000
#> GSM1068565 4 0.0162 0.8793 0.000 0.000 0.004 0.996 0.000
#> GSM1068529 4 0.5109 0.4023 0.284 0.000 0.044 0.660 0.012
#> GSM1068530 1 0.3671 0.1013 0.756 0.000 0.008 0.000 0.236
#> GSM1068534 4 0.5380 0.3603 0.288 0.000 0.048 0.644 0.020
#> GSM1068536 1 0.5061 0.3747 0.540 0.000 0.012 0.432 0.016
#> GSM1068541 4 0.4422 0.4328 0.300 0.000 0.004 0.680 0.016
#> GSM1068553 4 0.0727 0.8757 0.004 0.000 0.012 0.980 0.004
#> GSM1068554 4 0.0727 0.8757 0.004 0.000 0.012 0.980 0.004
#> GSM1068558 2 0.0000 0.9912 0.000 1.000 0.000 0.000 0.000
#> GSM1068559 4 0.0693 0.8767 0.008 0.000 0.012 0.980 0.000
#> GSM1068564 4 0.0162 0.8793 0.004 0.000 0.000 0.996 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1068478 1 0.5475 0.462333 0.600 0.220 0.008 0.000 0.172 0.000
#> GSM1068479 2 0.3996 0.717006 0.000 0.784 0.012 0.132 0.068 0.004
#> GSM1068481 3 0.4616 0.736082 0.384 0.000 0.576 0.004 0.036 0.000
#> GSM1068482 5 0.5362 0.000907 0.004 0.000 0.200 0.188 0.608 0.000
#> GSM1068483 1 0.5188 -0.196244 0.592 0.012 0.316 0.000 0.080 0.000
#> GSM1068486 1 0.8410 0.109509 0.356 0.244 0.164 0.092 0.144 0.000
#> GSM1068487 2 0.0713 0.871641 0.000 0.972 0.000 0.000 0.028 0.000
#> GSM1068488 2 0.1858 0.851655 0.052 0.924 0.012 0.000 0.012 0.000
#> GSM1068490 2 0.0146 0.873438 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM1068491 2 0.3996 0.717006 0.000 0.784 0.012 0.132 0.068 0.004
#> GSM1068492 2 0.3996 0.717006 0.000 0.784 0.012 0.132 0.068 0.004
#> GSM1068493 2 0.6320 -0.329371 0.412 0.428 0.084 0.000 0.076 0.000
#> GSM1068494 1 0.5174 0.193313 0.712 0.024 0.036 0.068 0.160 0.000
#> GSM1068495 1 0.5279 0.419084 0.544 0.356 0.004 0.000 0.096 0.000
#> GSM1068496 3 0.4383 0.785023 0.356 0.000 0.616 0.016 0.012 0.000
#> GSM1068498 1 0.5607 0.460041 0.576 0.240 0.008 0.000 0.176 0.000
#> GSM1068499 1 0.2784 0.339086 0.868 0.020 0.020 0.000 0.092 0.000
#> GSM1068500 1 0.4859 -0.239250 0.600 0.004 0.332 0.000 0.064 0.000
#> GSM1068502 2 0.3996 0.717006 0.000 0.784 0.012 0.132 0.068 0.004
#> GSM1068503 2 0.0547 0.872873 0.000 0.980 0.000 0.000 0.020 0.000
#> GSM1068505 2 0.0508 0.873905 0.004 0.984 0.000 0.000 0.012 0.000
#> GSM1068506 2 0.0603 0.874000 0.004 0.980 0.000 0.000 0.016 0.000
#> GSM1068507 2 0.0146 0.873438 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM1068508 2 0.1370 0.872785 0.012 0.948 0.004 0.000 0.036 0.000
#> GSM1068510 2 0.0405 0.874333 0.000 0.988 0.004 0.000 0.008 0.000
#> GSM1068512 2 0.1364 0.868319 0.020 0.952 0.012 0.000 0.016 0.000
#> GSM1068513 2 0.0260 0.874132 0.000 0.992 0.000 0.000 0.008 0.000
#> GSM1068514 2 0.3860 0.729116 0.000 0.796 0.012 0.124 0.064 0.004
#> GSM1068517 1 0.5607 0.460041 0.576 0.240 0.008 0.000 0.176 0.000
#> GSM1068518 1 0.5165 0.312179 0.492 0.436 0.008 0.000 0.064 0.000
#> GSM1068520 1 0.2401 0.421095 0.892 0.072 0.008 0.000 0.028 0.000
#> GSM1068521 1 0.3375 0.446286 0.824 0.112 0.008 0.000 0.056 0.000
#> GSM1068522 2 0.0146 0.873438 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM1068524 2 0.1908 0.846416 0.004 0.900 0.000 0.000 0.096 0.000
#> GSM1068527 2 0.0767 0.871200 0.008 0.976 0.004 0.000 0.012 0.000
#> GSM1068480 5 0.5769 -0.102956 0.004 0.000 0.108 0.296 0.568 0.024
#> GSM1068484 2 0.1167 0.873533 0.012 0.960 0.008 0.000 0.020 0.000
#> GSM1068485 3 0.6251 0.713678 0.336 0.000 0.492 0.124 0.048 0.000
#> GSM1068489 2 0.0622 0.874803 0.008 0.980 0.000 0.000 0.012 0.000
#> GSM1068497 1 0.5519 0.461542 0.592 0.228 0.008 0.000 0.172 0.000
#> GSM1068501 2 0.0665 0.873577 0.004 0.980 0.008 0.000 0.008 0.000
#> GSM1068504 2 0.1588 0.858538 0.004 0.924 0.000 0.000 0.072 0.000
#> GSM1068509 2 0.5844 -0.215419 0.416 0.460 0.028 0.000 0.096 0.000
#> GSM1068511 6 0.0291 0.989634 0.004 0.000 0.000 0.000 0.004 0.992
#> GSM1068515 5 0.7312 -0.125033 0.268 0.212 0.096 0.008 0.416 0.000
#> GSM1068516 1 0.5359 0.416670 0.536 0.364 0.008 0.000 0.092 0.000
#> GSM1068519 1 0.2611 0.333493 0.876 0.016 0.016 0.000 0.092 0.000
#> GSM1068523 2 0.2146 0.832979 0.004 0.880 0.000 0.000 0.116 0.000
#> GSM1068525 2 0.1251 0.873629 0.012 0.956 0.008 0.000 0.024 0.000
#> GSM1068526 2 0.0767 0.872761 0.004 0.976 0.008 0.000 0.012 0.000
#> GSM1068458 1 0.1757 0.328054 0.928 0.008 0.012 0.000 0.052 0.000
#> GSM1068459 3 0.4397 0.799943 0.336 0.000 0.632 0.020 0.012 0.000
#> GSM1068460 2 0.5067 0.312678 0.308 0.604 0.008 0.000 0.080 0.000
#> GSM1068461 4 0.1528 0.599886 0.000 0.000 0.048 0.936 0.016 0.000
#> GSM1068464 2 0.1219 0.866825 0.004 0.948 0.000 0.000 0.048 0.000
#> GSM1068468 2 0.1970 0.850667 0.028 0.912 0.000 0.000 0.060 0.000
#> GSM1068472 2 0.6148 0.090120 0.308 0.524 0.048 0.000 0.120 0.000
#> GSM1068473 2 0.0146 0.873438 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM1068474 2 0.1075 0.867767 0.000 0.952 0.000 0.000 0.048 0.000
#> GSM1068476 4 0.3969 0.301558 0.000 0.212 0.044 0.740 0.004 0.000
#> GSM1068477 2 0.0260 0.874074 0.000 0.992 0.000 0.000 0.008 0.000
#> GSM1068462 2 0.6317 -0.137552 0.356 0.468 0.048 0.000 0.128 0.000
#> GSM1068463 3 0.2809 0.187636 0.020 0.000 0.848 0.128 0.004 0.000
#> GSM1068465 1 0.6792 0.346421 0.552 0.212 0.036 0.000 0.056 0.144
#> GSM1068466 1 0.2728 0.430418 0.872 0.080 0.008 0.000 0.040 0.000
#> GSM1068467 2 0.2697 0.818881 0.044 0.864 0.000 0.000 0.092 0.000
#> GSM1068469 1 0.6508 0.349084 0.432 0.364 0.048 0.000 0.156 0.000
#> GSM1068470 2 0.1531 0.858471 0.004 0.928 0.000 0.000 0.068 0.000
#> GSM1068471 2 0.1285 0.865068 0.004 0.944 0.000 0.000 0.052 0.000
#> GSM1068475 2 0.1588 0.858538 0.004 0.924 0.000 0.000 0.072 0.000
#> GSM1068528 1 0.4649 -0.514100 0.492 0.000 0.468 0.000 0.040 0.000
#> GSM1068531 1 0.2121 0.293490 0.892 0.000 0.012 0.000 0.096 0.000
#> GSM1068532 1 0.4325 0.005239 0.692 0.000 0.244 0.000 0.064 0.000
#> GSM1068533 1 0.1983 0.294714 0.908 0.000 0.020 0.000 0.072 0.000
#> GSM1068535 2 0.1887 0.850301 0.048 0.924 0.016 0.000 0.012 0.000
#> GSM1068537 1 0.3894 0.112199 0.760 0.000 0.168 0.000 0.072 0.000
#> GSM1068538 1 0.4348 -0.001296 0.688 0.000 0.248 0.000 0.064 0.000
#> GSM1068539 1 0.5332 0.426059 0.548 0.352 0.008 0.000 0.092 0.000
#> GSM1068540 1 0.3678 0.155894 0.788 0.000 0.128 0.000 0.084 0.000
#> GSM1068542 2 0.0405 0.872092 0.004 0.988 0.000 0.000 0.008 0.000
#> GSM1068543 2 0.1452 0.866441 0.020 0.948 0.012 0.000 0.020 0.000
#> GSM1068544 3 0.4397 0.799943 0.336 0.000 0.632 0.020 0.012 0.000
#> GSM1068545 2 0.0777 0.874743 0.004 0.972 0.000 0.000 0.024 0.000
#> GSM1068546 4 0.4453 0.217224 0.000 0.000 0.044 0.624 0.332 0.000
#> GSM1068547 1 0.2344 0.417580 0.896 0.068 0.008 0.000 0.028 0.000
#> GSM1068548 2 0.0653 0.871974 0.004 0.980 0.004 0.000 0.012 0.000
#> GSM1068549 4 0.1007 0.601439 0.000 0.000 0.044 0.956 0.000 0.000
#> GSM1068550 2 0.0508 0.873044 0.004 0.984 0.000 0.000 0.012 0.000
#> GSM1068551 2 0.2100 0.835055 0.004 0.884 0.000 0.000 0.112 0.000
#> GSM1068552 2 0.0603 0.874000 0.004 0.980 0.000 0.000 0.016 0.000
#> GSM1068555 2 0.2146 0.832979 0.004 0.880 0.000 0.000 0.116 0.000
#> GSM1068556 2 0.0881 0.871797 0.012 0.972 0.008 0.000 0.008 0.000
#> GSM1068557 2 0.4307 0.555190 0.224 0.704 0.000 0.000 0.072 0.000
#> GSM1068560 2 0.0767 0.871200 0.008 0.976 0.004 0.000 0.012 0.000
#> GSM1068561 1 0.6185 0.379453 0.484 0.360 0.052 0.000 0.104 0.000
#> GSM1068562 2 0.0748 0.873142 0.004 0.976 0.004 0.000 0.016 0.000
#> GSM1068563 2 0.0837 0.875023 0.004 0.972 0.004 0.000 0.020 0.000
#> GSM1068565 2 0.0865 0.871525 0.000 0.964 0.000 0.000 0.036 0.000
#> GSM1068529 2 0.5805 0.278186 0.284 0.580 0.004 0.036 0.096 0.000
#> GSM1068530 1 0.4348 -0.001296 0.688 0.000 0.248 0.000 0.064 0.000
#> GSM1068534 2 0.6070 0.237467 0.288 0.568 0.020 0.032 0.092 0.000
#> GSM1068536 1 0.5401 0.430596 0.552 0.344 0.012 0.000 0.092 0.000
#> GSM1068541 2 0.5431 0.260713 0.304 0.576 0.012 0.000 0.108 0.000
#> GSM1068553 2 0.1140 0.871062 0.008 0.964 0.008 0.008 0.012 0.000
#> GSM1068554 2 0.1140 0.871062 0.008 0.964 0.008 0.008 0.012 0.000
#> GSM1068558 6 0.0000 0.989628 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM1068559 2 0.1604 0.871322 0.016 0.944 0.008 0.008 0.024 0.000
#> GSM1068564 2 0.0622 0.874803 0.008 0.980 0.000 0.000 0.012 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 disease.state(p) gender(p) k
#> CV:hclust 82 0.706 0.177 2
#> CV:hclust 70 0.564 0.689 3
#> CV:hclust 67 0.906 0.147 4
#> CV:hclust 65 0.936 0.162 5
#> CV:hclust 63 0.770 0.112 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 38950 rows and 108 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#> Subgroups are detected by 'kmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.835 0.902 0.960 0.4756 0.516 0.516
#> 3 3 0.427 0.530 0.738 0.3079 0.862 0.748
#> 4 4 0.505 0.588 0.750 0.1491 0.762 0.497
#> 5 5 0.583 0.628 0.765 0.0808 0.861 0.548
#> 6 6 0.645 0.623 0.750 0.0411 0.957 0.809
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
#> GSM1068478 1 0.0376 0.930 0.996 0.004
#> GSM1068479 2 0.0000 0.972 0.000 1.000
#> GSM1068481 1 0.0000 0.932 1.000 0.000
#> GSM1068482 1 0.0000 0.932 1.000 0.000
#> GSM1068483 1 0.0000 0.932 1.000 0.000
#> GSM1068486 1 0.0000 0.932 1.000 0.000
#> GSM1068487 2 0.0000 0.972 0.000 1.000
#> GSM1068488 2 0.0672 0.966 0.008 0.992
#> GSM1068490 2 0.0000 0.972 0.000 1.000
#> GSM1068491 2 0.0938 0.963 0.012 0.988
#> GSM1068492 2 0.0000 0.972 0.000 1.000
#> GSM1068493 1 0.7219 0.768 0.800 0.200
#> GSM1068494 1 0.0000 0.932 1.000 0.000
#> GSM1068495 2 0.8144 0.627 0.252 0.748
#> GSM1068496 1 0.0000 0.932 1.000 0.000
#> GSM1068498 1 0.7376 0.761 0.792 0.208
#> GSM1068499 1 0.0000 0.932 1.000 0.000
#> GSM1068500 1 0.0000 0.932 1.000 0.000
#> GSM1068502 2 0.0000 0.972 0.000 1.000
#> GSM1068503 2 0.0000 0.972 0.000 1.000
#> GSM1068505 2 0.0000 0.972 0.000 1.000
#> GSM1068506 2 0.0000 0.972 0.000 1.000
#> GSM1068507 2 0.0000 0.972 0.000 1.000
#> GSM1068508 2 0.0000 0.972 0.000 1.000
#> GSM1068510 2 0.0000 0.972 0.000 1.000
#> GSM1068512 2 0.0672 0.966 0.008 0.992
#> GSM1068513 2 0.0000 0.972 0.000 1.000
#> GSM1068514 2 0.0000 0.972 0.000 1.000
#> GSM1068517 2 1.0000 -0.103 0.496 0.504
#> GSM1068518 2 0.4939 0.858 0.108 0.892
#> GSM1068520 1 0.0000 0.932 1.000 0.000
#> GSM1068521 1 0.0376 0.930 0.996 0.004
#> GSM1068522 2 0.0000 0.972 0.000 1.000
#> GSM1068524 2 0.0000 0.972 0.000 1.000
#> GSM1068527 2 0.0000 0.972 0.000 1.000
#> GSM1068480 1 0.0000 0.932 1.000 0.000
#> GSM1068484 2 0.0000 0.972 0.000 1.000
#> GSM1068485 1 0.0000 0.932 1.000 0.000
#> GSM1068489 2 0.0000 0.972 0.000 1.000
#> GSM1068497 1 0.7453 0.756 0.788 0.212
#> GSM1068501 2 0.0000 0.972 0.000 1.000
#> GSM1068504 2 0.0000 0.972 0.000 1.000
#> GSM1068509 1 0.5408 0.842 0.876 0.124
#> GSM1068511 1 0.0000 0.932 1.000 0.000
#> GSM1068515 1 0.7219 0.770 0.800 0.200
#> GSM1068516 2 0.0376 0.969 0.004 0.996
#> GSM1068519 1 0.0376 0.930 0.996 0.004
#> GSM1068523 2 0.0000 0.972 0.000 1.000
#> GSM1068525 2 0.0000 0.972 0.000 1.000
#> GSM1068526 2 0.0000 0.972 0.000 1.000
#> GSM1068458 1 0.0000 0.932 1.000 0.000
#> GSM1068459 1 0.0000 0.932 1.000 0.000
#> GSM1068460 2 0.0000 0.972 0.000 1.000
#> GSM1068461 1 0.0000 0.932 1.000 0.000
#> GSM1068464 2 0.0000 0.972 0.000 1.000
#> GSM1068468 2 0.0000 0.972 0.000 1.000
#> GSM1068472 1 0.9983 0.175 0.524 0.476
#> GSM1068473 2 0.0000 0.972 0.000 1.000
#> GSM1068474 2 0.0000 0.972 0.000 1.000
#> GSM1068476 2 0.0938 0.963 0.012 0.988
#> GSM1068477 2 0.0000 0.972 0.000 1.000
#> GSM1068462 2 0.0000 0.972 0.000 1.000
#> GSM1068463 1 0.0000 0.932 1.000 0.000
#> GSM1068465 1 0.9460 0.492 0.636 0.364
#> GSM1068466 1 0.0376 0.930 0.996 0.004
#> GSM1068467 2 0.0000 0.972 0.000 1.000
#> GSM1068469 1 0.9580 0.455 0.620 0.380
#> GSM1068470 2 0.0000 0.972 0.000 1.000
#> GSM1068471 2 0.0000 0.972 0.000 1.000
#> GSM1068475 2 0.0000 0.972 0.000 1.000
#> GSM1068528 1 0.0000 0.932 1.000 0.000
#> GSM1068531 1 0.0000 0.932 1.000 0.000
#> GSM1068532 1 0.0000 0.932 1.000 0.000
#> GSM1068533 1 0.0000 0.932 1.000 0.000
#> GSM1068535 1 0.2423 0.905 0.960 0.040
#> GSM1068537 1 0.0000 0.932 1.000 0.000
#> GSM1068538 1 0.0000 0.932 1.000 0.000
#> GSM1068539 2 0.0000 0.972 0.000 1.000
#> GSM1068540 1 0.0000 0.932 1.000 0.000
#> GSM1068542 2 0.0000 0.972 0.000 1.000
#> GSM1068543 2 0.0672 0.966 0.008 0.992
#> GSM1068544 1 0.0000 0.932 1.000 0.000
#> GSM1068545 2 0.0000 0.972 0.000 1.000
#> GSM1068546 1 0.0000 0.932 1.000 0.000
#> GSM1068547 1 0.0376 0.930 0.996 0.004
#> GSM1068548 2 0.0000 0.972 0.000 1.000
#> GSM1068549 1 0.0000 0.932 1.000 0.000
#> GSM1068550 2 0.0000 0.972 0.000 1.000
#> GSM1068551 2 0.0000 0.972 0.000 1.000
#> GSM1068552 2 0.0000 0.972 0.000 1.000
#> GSM1068555 2 0.0000 0.972 0.000 1.000
#> GSM1068556 2 0.0672 0.966 0.008 0.992
#> GSM1068557 2 0.0000 0.972 0.000 1.000
#> GSM1068560 2 0.0000 0.972 0.000 1.000
#> GSM1068561 2 1.0000 -0.104 0.496 0.504
#> GSM1068562 2 0.0000 0.972 0.000 1.000
#> GSM1068563 2 0.0000 0.972 0.000 1.000
#> GSM1068565 2 0.0000 0.972 0.000 1.000
#> GSM1068529 2 0.6247 0.794 0.156 0.844
#> GSM1068530 1 0.0000 0.932 1.000 0.000
#> GSM1068534 1 0.8443 0.666 0.728 0.272
#> GSM1068536 1 0.7453 0.756 0.788 0.212
#> GSM1068541 2 0.0000 0.972 0.000 1.000
#> GSM1068553 2 0.0000 0.972 0.000 1.000
#> GSM1068554 2 0.0000 0.972 0.000 1.000
#> GSM1068558 2 0.3274 0.916 0.060 0.940
#> GSM1068559 2 0.0000 0.972 0.000 1.000
#> GSM1068564 2 0.0000 0.972 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1068478 1 0.1620 0.540 0.964 0.012 0.024
#> GSM1068479 2 0.6148 0.669 0.004 0.640 0.356
#> GSM1068481 1 0.6305 -0.670 0.516 0.000 0.484
#> GSM1068482 3 0.6308 0.658 0.492 0.000 0.508
#> GSM1068483 1 0.1411 0.512 0.964 0.000 0.036
#> GSM1068486 3 0.6798 0.721 0.400 0.016 0.584
#> GSM1068487 2 0.5621 0.758 0.000 0.692 0.308
#> GSM1068488 2 0.3539 0.731 0.012 0.888 0.100
#> GSM1068490 2 0.5591 0.759 0.000 0.696 0.304
#> GSM1068491 2 0.6357 0.626 0.012 0.652 0.336
#> GSM1068492 2 0.5201 0.708 0.004 0.760 0.236
#> GSM1068493 1 0.5344 0.520 0.824 0.084 0.092
#> GSM1068494 1 0.2176 0.535 0.948 0.032 0.020
#> GSM1068495 1 0.8971 0.347 0.520 0.336 0.144
#> GSM1068496 1 0.4121 0.312 0.832 0.000 0.168
#> GSM1068498 1 0.6304 0.477 0.752 0.056 0.192
#> GSM1068499 1 0.0892 0.526 0.980 0.000 0.020
#> GSM1068500 1 0.3192 0.409 0.888 0.000 0.112
#> GSM1068502 2 0.6189 0.702 0.004 0.632 0.364
#> GSM1068503 2 0.5560 0.762 0.000 0.700 0.300
#> GSM1068505 2 0.0983 0.779 0.004 0.980 0.016
#> GSM1068506 2 0.0475 0.779 0.004 0.992 0.004
#> GSM1068507 2 0.1031 0.779 0.000 0.976 0.024
#> GSM1068508 2 0.4452 0.777 0.000 0.808 0.192
#> GSM1068510 2 0.1964 0.777 0.000 0.944 0.056
#> GSM1068512 2 0.3618 0.728 0.012 0.884 0.104
#> GSM1068513 2 0.4931 0.774 0.000 0.768 0.232
#> GSM1068514 2 0.4413 0.691 0.008 0.832 0.160
#> GSM1068517 1 0.9148 0.311 0.504 0.160 0.336
#> GSM1068518 2 0.8339 -0.199 0.448 0.472 0.080
#> GSM1068520 1 0.0237 0.532 0.996 0.004 0.000
#> GSM1068521 1 0.1031 0.539 0.976 0.024 0.000
#> GSM1068522 2 0.5465 0.765 0.000 0.712 0.288
#> GSM1068524 2 0.5497 0.765 0.000 0.708 0.292
#> GSM1068527 2 0.0848 0.776 0.008 0.984 0.008
#> GSM1068480 3 0.6421 0.724 0.424 0.004 0.572
#> GSM1068484 2 0.0237 0.779 0.000 0.996 0.004
#> GSM1068485 3 0.6305 0.688 0.484 0.000 0.516
#> GSM1068489 2 0.0661 0.777 0.004 0.988 0.008
#> GSM1068497 1 0.6495 0.474 0.740 0.060 0.200
#> GSM1068501 2 0.1289 0.780 0.000 0.968 0.032
#> GSM1068504 2 0.5706 0.755 0.000 0.680 0.320
#> GSM1068509 1 0.5235 0.504 0.812 0.152 0.036
#> GSM1068511 1 0.6305 -0.642 0.516 0.000 0.484
#> GSM1068515 1 0.6573 0.500 0.756 0.140 0.104
#> GSM1068516 2 0.7890 -0.118 0.432 0.512 0.056
#> GSM1068519 1 0.1399 0.539 0.968 0.028 0.004
#> GSM1068523 2 0.5733 0.755 0.000 0.676 0.324
#> GSM1068525 2 0.1753 0.769 0.000 0.952 0.048
#> GSM1068526 2 0.0661 0.776 0.004 0.988 0.008
#> GSM1068458 1 0.0237 0.532 0.996 0.004 0.000
#> GSM1068459 1 0.6305 -0.670 0.516 0.000 0.484
#> GSM1068460 2 0.7835 -0.160 0.456 0.492 0.052
#> GSM1068461 3 0.6140 0.726 0.404 0.000 0.596
#> GSM1068464 2 0.5650 0.757 0.000 0.688 0.312
#> GSM1068468 2 0.7284 0.724 0.044 0.620 0.336
#> GSM1068472 1 0.9029 0.336 0.536 0.164 0.300
#> GSM1068473 2 0.5650 0.758 0.000 0.688 0.312
#> GSM1068474 2 0.5591 0.759 0.000 0.696 0.304
#> GSM1068476 2 0.5982 0.645 0.004 0.668 0.328
#> GSM1068477 2 0.5591 0.759 0.000 0.696 0.304
#> GSM1068462 2 0.7284 0.724 0.044 0.620 0.336
#> GSM1068463 1 0.6305 -0.670 0.516 0.000 0.484
#> GSM1068465 1 0.7915 0.435 0.644 0.248 0.108
#> GSM1068466 1 0.0475 0.534 0.992 0.004 0.004
#> GSM1068467 2 0.7727 0.706 0.064 0.600 0.336
#> GSM1068469 1 0.8771 0.355 0.556 0.140 0.304
#> GSM1068470 2 0.5706 0.755 0.000 0.680 0.320
#> GSM1068471 2 0.5706 0.755 0.000 0.680 0.320
#> GSM1068475 2 0.5706 0.755 0.000 0.680 0.320
#> GSM1068528 1 0.6126 -0.491 0.600 0.000 0.400
#> GSM1068531 1 0.1529 0.504 0.960 0.000 0.040
#> GSM1068532 1 0.3941 0.332 0.844 0.000 0.156
#> GSM1068533 1 0.2448 0.465 0.924 0.000 0.076
#> GSM1068535 1 0.7536 0.334 0.632 0.304 0.064
#> GSM1068537 1 0.3941 0.332 0.844 0.000 0.156
#> GSM1068538 1 0.3816 0.349 0.852 0.000 0.148
#> GSM1068539 2 0.9152 -0.125 0.424 0.432 0.144
#> GSM1068540 1 0.1525 0.514 0.964 0.004 0.032
#> GSM1068542 2 0.0983 0.775 0.004 0.980 0.016
#> GSM1068543 2 0.4099 0.700 0.008 0.852 0.140
#> GSM1068544 1 0.6305 -0.670 0.516 0.000 0.484
#> GSM1068545 2 0.4062 0.779 0.000 0.836 0.164
#> GSM1068546 3 0.6295 0.698 0.472 0.000 0.528
#> GSM1068547 1 0.1031 0.539 0.976 0.024 0.000
#> GSM1068548 2 0.1015 0.775 0.008 0.980 0.012
#> GSM1068549 3 0.7983 0.600 0.264 0.104 0.632
#> GSM1068550 2 0.0661 0.779 0.004 0.988 0.008
#> GSM1068551 2 0.5650 0.757 0.000 0.688 0.312
#> GSM1068552 2 0.3030 0.783 0.004 0.904 0.092
#> GSM1068555 2 0.5760 0.754 0.000 0.672 0.328
#> GSM1068556 2 0.3120 0.742 0.012 0.908 0.080
#> GSM1068557 2 0.6988 0.740 0.036 0.644 0.320
#> GSM1068560 2 0.0661 0.777 0.008 0.988 0.004
#> GSM1068561 1 0.8625 0.394 0.576 0.288 0.136
#> GSM1068562 2 0.0829 0.776 0.004 0.984 0.012
#> GSM1068563 2 0.0829 0.776 0.004 0.984 0.012
#> GSM1068565 2 0.5621 0.758 0.000 0.692 0.308
#> GSM1068529 1 0.9266 0.192 0.424 0.420 0.156
#> GSM1068530 1 0.1964 0.489 0.944 0.000 0.056
#> GSM1068534 1 0.7944 0.378 0.616 0.296 0.088
#> GSM1068536 1 0.6231 0.505 0.772 0.148 0.080
#> GSM1068541 1 0.9458 0.169 0.448 0.368 0.184
#> GSM1068553 2 0.1647 0.769 0.004 0.960 0.036
#> GSM1068554 2 0.1163 0.776 0.000 0.972 0.028
#> GSM1068558 3 0.6448 0.282 0.012 0.352 0.636
#> GSM1068559 2 0.4099 0.706 0.008 0.852 0.140
#> GSM1068564 2 0.3551 0.782 0.000 0.868 0.132
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1068478 1 0.1824 0.6517 0.936 0.004 0.000 0.060
#> GSM1068479 2 0.7281 0.3136 0.000 0.532 0.196 0.272
#> GSM1068481 3 0.3942 0.7552 0.236 0.000 0.764 0.000
#> GSM1068482 3 0.4285 0.7889 0.156 0.000 0.804 0.040
#> GSM1068483 1 0.3402 0.5156 0.832 0.000 0.164 0.004
#> GSM1068486 3 0.4608 0.7733 0.096 0.000 0.800 0.104
#> GSM1068487 2 0.0188 0.7970 0.000 0.996 0.000 0.004
#> GSM1068488 4 0.4719 0.7163 0.016 0.224 0.008 0.752
#> GSM1068490 2 0.0336 0.7943 0.000 0.992 0.000 0.008
#> GSM1068491 4 0.7834 0.1033 0.000 0.308 0.284 0.408
#> GSM1068492 4 0.7064 0.3651 0.000 0.280 0.164 0.556
#> GSM1068493 1 0.5601 0.6461 0.764 0.052 0.048 0.136
#> GSM1068494 1 0.3818 0.6491 0.844 0.000 0.048 0.108
#> GSM1068495 1 0.6751 0.5818 0.624 0.124 0.008 0.244
#> GSM1068496 1 0.4817 0.0575 0.612 0.000 0.388 0.000
#> GSM1068498 1 0.5782 0.5912 0.704 0.220 0.008 0.068
#> GSM1068499 1 0.3081 0.6445 0.888 0.000 0.048 0.064
#> GSM1068500 1 0.4428 0.3305 0.720 0.000 0.276 0.004
#> GSM1068502 2 0.6724 0.4282 0.000 0.612 0.164 0.224
#> GSM1068503 2 0.1474 0.7549 0.000 0.948 0.000 0.052
#> GSM1068505 4 0.4713 0.7452 0.000 0.360 0.000 0.640
#> GSM1068506 4 0.4697 0.7484 0.000 0.356 0.000 0.644
#> GSM1068507 4 0.4872 0.7470 0.000 0.356 0.004 0.640
#> GSM1068508 2 0.4193 0.3091 0.000 0.732 0.000 0.268
#> GSM1068510 4 0.5186 0.7377 0.000 0.344 0.016 0.640
#> GSM1068512 4 0.4339 0.7168 0.008 0.224 0.004 0.764
#> GSM1068513 2 0.3105 0.6373 0.000 0.856 0.004 0.140
#> GSM1068514 4 0.5470 0.5988 0.000 0.168 0.100 0.732
#> GSM1068517 1 0.6667 0.3902 0.532 0.392 0.008 0.068
#> GSM1068518 4 0.6217 0.1070 0.360 0.040 0.012 0.588
#> GSM1068520 1 0.0188 0.6384 0.996 0.000 0.000 0.004
#> GSM1068521 1 0.1637 0.6529 0.940 0.000 0.000 0.060
#> GSM1068522 2 0.2647 0.6620 0.000 0.880 0.000 0.120
#> GSM1068524 2 0.0469 0.7922 0.000 0.988 0.000 0.012
#> GSM1068527 4 0.4543 0.7548 0.000 0.324 0.000 0.676
#> GSM1068480 3 0.4804 0.7391 0.064 0.000 0.776 0.160
#> GSM1068484 4 0.4837 0.7538 0.000 0.348 0.004 0.648
#> GSM1068485 3 0.4105 0.7893 0.156 0.000 0.812 0.032
#> GSM1068489 4 0.4679 0.7507 0.000 0.352 0.000 0.648
#> GSM1068497 1 0.6083 0.5849 0.688 0.228 0.016 0.068
#> GSM1068501 4 0.5220 0.7408 0.000 0.352 0.016 0.632
#> GSM1068504 2 0.0000 0.7991 0.000 1.000 0.000 0.000
#> GSM1068509 1 0.4975 0.6346 0.752 0.008 0.032 0.208
#> GSM1068511 3 0.6351 0.6759 0.268 0.000 0.628 0.104
#> GSM1068515 1 0.6500 0.6305 0.696 0.064 0.056 0.184
#> GSM1068516 4 0.6215 0.0476 0.384 0.036 0.012 0.568
#> GSM1068519 1 0.1209 0.6489 0.964 0.000 0.004 0.032
#> GSM1068523 2 0.0000 0.7991 0.000 1.000 0.000 0.000
#> GSM1068525 4 0.4560 0.7475 0.000 0.296 0.004 0.700
#> GSM1068526 4 0.4624 0.7551 0.000 0.340 0.000 0.660
#> GSM1068458 1 0.0188 0.6384 0.996 0.000 0.000 0.004
#> GSM1068459 3 0.3907 0.7583 0.232 0.000 0.768 0.000
#> GSM1068460 1 0.5384 0.5550 0.648 0.028 0.000 0.324
#> GSM1068461 3 0.3734 0.7475 0.044 0.000 0.848 0.108
#> GSM1068464 2 0.0336 0.7951 0.000 0.992 0.000 0.008
#> GSM1068468 2 0.5352 0.5638 0.156 0.756 0.008 0.080
#> GSM1068472 1 0.7174 0.3234 0.484 0.420 0.024 0.072
#> GSM1068473 2 0.0336 0.7943 0.000 0.992 0.000 0.008
#> GSM1068474 2 0.0000 0.7991 0.000 1.000 0.000 0.000
#> GSM1068476 4 0.7823 0.1125 0.000 0.308 0.280 0.412
#> GSM1068477 2 0.0000 0.7991 0.000 1.000 0.000 0.000
#> GSM1068462 2 0.5397 0.5594 0.160 0.752 0.008 0.080
#> GSM1068463 3 0.3975 0.7535 0.240 0.000 0.760 0.000
#> GSM1068465 1 0.5751 0.6243 0.708 0.020 0.044 0.228
#> GSM1068466 1 0.0376 0.6365 0.992 0.000 0.004 0.004
#> GSM1068467 2 0.5694 0.5206 0.176 0.728 0.008 0.088
#> GSM1068469 1 0.7181 0.3062 0.476 0.428 0.024 0.072
#> GSM1068470 2 0.0000 0.7991 0.000 1.000 0.000 0.000
#> GSM1068471 2 0.0000 0.7991 0.000 1.000 0.000 0.000
#> GSM1068475 2 0.0000 0.7991 0.000 1.000 0.000 0.000
#> GSM1068528 3 0.4925 0.4809 0.428 0.000 0.572 0.000
#> GSM1068531 1 0.3052 0.5288 0.860 0.000 0.136 0.004
#> GSM1068532 1 0.4855 0.1339 0.644 0.000 0.352 0.004
#> GSM1068533 1 0.4313 0.3506 0.736 0.000 0.260 0.004
#> GSM1068535 4 0.6744 0.3849 0.276 0.024 0.076 0.624
#> GSM1068537 1 0.4837 0.1436 0.648 0.000 0.348 0.004
#> GSM1068538 1 0.4819 0.1586 0.652 0.000 0.344 0.004
#> GSM1068539 1 0.7367 0.4789 0.536 0.152 0.008 0.304
#> GSM1068540 1 0.3208 0.5134 0.848 0.000 0.148 0.004
#> GSM1068542 4 0.4713 0.7452 0.000 0.360 0.000 0.640
#> GSM1068543 4 0.3908 0.7074 0.000 0.212 0.004 0.784
#> GSM1068544 3 0.4134 0.7380 0.260 0.000 0.740 0.000
#> GSM1068545 2 0.4843 -0.2087 0.000 0.604 0.000 0.396
#> GSM1068546 3 0.4245 0.7896 0.116 0.000 0.820 0.064
#> GSM1068547 1 0.0469 0.6421 0.988 0.000 0.000 0.012
#> GSM1068548 4 0.4643 0.7543 0.000 0.344 0.000 0.656
#> GSM1068549 3 0.4327 0.6717 0.016 0.000 0.768 0.216
#> GSM1068550 4 0.4713 0.7452 0.000 0.360 0.000 0.640
#> GSM1068551 2 0.0000 0.7991 0.000 1.000 0.000 0.000
#> GSM1068552 4 0.4989 0.5555 0.000 0.472 0.000 0.528
#> GSM1068555 2 0.0188 0.7970 0.000 0.996 0.000 0.004
#> GSM1068556 4 0.4283 0.7317 0.000 0.256 0.004 0.740
#> GSM1068557 2 0.5941 0.5331 0.172 0.712 0.008 0.108
#> GSM1068560 4 0.4661 0.7533 0.000 0.348 0.000 0.652
#> GSM1068561 1 0.7426 0.5985 0.632 0.140 0.056 0.172
#> GSM1068562 4 0.4661 0.7533 0.000 0.348 0.000 0.652
#> GSM1068563 4 0.4624 0.7551 0.000 0.340 0.000 0.660
#> GSM1068565 2 0.0000 0.7991 0.000 1.000 0.000 0.000
#> GSM1068529 4 0.6684 0.0497 0.360 0.028 0.044 0.568
#> GSM1068530 1 0.3870 0.4391 0.788 0.000 0.208 0.004
#> GSM1068534 1 0.6734 0.3573 0.488 0.008 0.068 0.436
#> GSM1068536 1 0.4218 0.6468 0.796 0.012 0.008 0.184
#> GSM1068541 1 0.6549 0.5642 0.612 0.120 0.000 0.268
#> GSM1068553 4 0.5018 0.7527 0.000 0.332 0.012 0.656
#> GSM1068554 4 0.5220 0.7408 0.000 0.352 0.016 0.632
#> GSM1068558 3 0.4431 0.5999 0.000 0.000 0.696 0.304
#> GSM1068559 4 0.5185 0.6294 0.000 0.176 0.076 0.748
#> GSM1068564 2 0.4972 -0.4065 0.000 0.544 0.000 0.456
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM1068478 5 0.229 0.7229 0.108 0.004 0.000 0.000 0.888
#> GSM1068479 3 0.625 0.4329 0.000 0.284 0.572 0.128 0.016
#> GSM1068481 1 0.372 0.4405 0.784 0.004 0.196 0.000 0.016
#> GSM1068482 1 0.592 0.1544 0.580 0.032 0.332 0.000 0.056
#> GSM1068483 1 0.513 0.2859 0.536 0.024 0.008 0.000 0.432
#> GSM1068486 3 0.615 0.2441 0.356 0.032 0.552 0.004 0.056
#> GSM1068487 2 0.247 0.8586 0.000 0.864 0.000 0.136 0.000
#> GSM1068488 4 0.417 0.7108 0.000 0.008 0.148 0.788 0.056
#> GSM1068490 2 0.263 0.8574 0.000 0.860 0.004 0.136 0.000
#> GSM1068491 3 0.638 0.5020 0.012 0.148 0.616 0.208 0.016
#> GSM1068492 3 0.656 0.3324 0.000 0.120 0.548 0.300 0.032
#> GSM1068493 5 0.240 0.7641 0.028 0.016 0.012 0.024 0.920
#> GSM1068494 5 0.353 0.7460 0.064 0.016 0.032 0.024 0.864
#> GSM1068495 5 0.249 0.7651 0.000 0.036 0.000 0.068 0.896
#> GSM1068496 1 0.345 0.6279 0.812 0.000 0.024 0.000 0.164
#> GSM1068498 5 0.343 0.7196 0.040 0.132 0.000 0.000 0.828
#> GSM1068499 5 0.274 0.7413 0.084 0.004 0.016 0.008 0.888
#> GSM1068500 1 0.486 0.5060 0.636 0.008 0.024 0.000 0.332
#> GSM1068502 3 0.576 0.3149 0.000 0.364 0.560 0.060 0.016
#> GSM1068503 2 0.343 0.7882 0.000 0.776 0.004 0.220 0.000
#> GSM1068505 4 0.157 0.8329 0.000 0.044 0.004 0.944 0.008
#> GSM1068506 4 0.196 0.8365 0.000 0.048 0.004 0.928 0.020
#> GSM1068507 4 0.203 0.8303 0.000 0.056 0.008 0.924 0.012
#> GSM1068508 2 0.480 0.4348 0.000 0.580 0.000 0.396 0.024
#> GSM1068510 4 0.229 0.8138 0.000 0.048 0.024 0.916 0.012
#> GSM1068512 4 0.410 0.7132 0.000 0.008 0.148 0.792 0.052
#> GSM1068513 2 0.478 0.3756 0.000 0.532 0.012 0.452 0.004
#> GSM1068514 4 0.555 0.1284 0.000 0.016 0.456 0.492 0.036
#> GSM1068517 5 0.410 0.5743 0.004 0.300 0.000 0.004 0.692
#> GSM1068518 5 0.467 0.5984 0.000 0.000 0.056 0.240 0.704
#> GSM1068520 5 0.471 0.4827 0.292 0.040 0.000 0.000 0.668
#> GSM1068521 5 0.311 0.6989 0.132 0.024 0.000 0.000 0.844
#> GSM1068522 2 0.414 0.6719 0.000 0.684 0.004 0.308 0.004
#> GSM1068524 2 0.296 0.8491 0.000 0.840 0.004 0.152 0.004
#> GSM1068527 4 0.278 0.8250 0.000 0.032 0.032 0.896 0.040
#> GSM1068480 3 0.601 0.3567 0.252 0.064 0.632 0.000 0.052
#> GSM1068484 4 0.302 0.8258 0.000 0.040 0.032 0.884 0.044
#> GSM1068485 1 0.466 0.2029 0.624 0.004 0.356 0.000 0.016
#> GSM1068489 4 0.136 0.8353 0.000 0.028 0.004 0.956 0.012
#> GSM1068497 5 0.311 0.7337 0.028 0.112 0.004 0.000 0.856
#> GSM1068501 4 0.205 0.8223 0.000 0.040 0.020 0.928 0.012
#> GSM1068504 2 0.292 0.8569 0.000 0.852 0.000 0.132 0.016
#> GSM1068509 5 0.195 0.7637 0.024 0.000 0.008 0.036 0.932
#> GSM1068511 3 0.768 -0.0959 0.336 0.072 0.404 0.000 0.188
#> GSM1068515 5 0.332 0.7502 0.016 0.040 0.048 0.020 0.876
#> GSM1068516 5 0.419 0.6517 0.000 0.000 0.040 0.212 0.748
#> GSM1068519 5 0.477 0.5937 0.212 0.040 0.000 0.020 0.728
#> GSM1068523 2 0.306 0.8555 0.000 0.844 0.000 0.136 0.020
#> GSM1068525 4 0.335 0.8047 0.000 0.024 0.052 0.864 0.060
#> GSM1068526 4 0.205 0.8383 0.000 0.040 0.012 0.928 0.020
#> GSM1068458 5 0.476 0.4830 0.288 0.044 0.000 0.000 0.668
#> GSM1068459 1 0.346 0.4476 0.792 0.000 0.196 0.000 0.012
#> GSM1068460 5 0.334 0.7336 0.020 0.008 0.000 0.136 0.836
#> GSM1068461 3 0.501 0.3522 0.320 0.020 0.640 0.000 0.020
#> GSM1068464 2 0.254 0.8551 0.000 0.868 0.004 0.128 0.000
#> GSM1068468 2 0.340 0.6606 0.000 0.812 0.004 0.012 0.172
#> GSM1068472 5 0.445 0.5385 0.008 0.324 0.000 0.008 0.660
#> GSM1068473 2 0.263 0.8574 0.000 0.860 0.004 0.136 0.000
#> GSM1068474 2 0.247 0.8586 0.000 0.864 0.000 0.136 0.000
#> GSM1068476 3 0.635 0.5046 0.012 0.148 0.620 0.204 0.016
#> GSM1068477 2 0.247 0.8586 0.000 0.864 0.000 0.136 0.000
#> GSM1068462 2 0.399 0.5866 0.000 0.740 0.004 0.012 0.244
#> GSM1068463 1 0.346 0.4476 0.792 0.000 0.196 0.000 0.012
#> GSM1068465 5 0.364 0.7494 0.020 0.024 0.060 0.036 0.860
#> GSM1068466 5 0.471 0.4974 0.280 0.044 0.000 0.000 0.676
#> GSM1068467 2 0.416 0.5661 0.000 0.728 0.008 0.012 0.252
#> GSM1068469 5 0.448 0.4175 0.012 0.376 0.000 0.000 0.612
#> GSM1068470 2 0.306 0.8555 0.000 0.844 0.000 0.136 0.020
#> GSM1068471 2 0.292 0.8569 0.000 0.852 0.000 0.132 0.016
#> GSM1068475 2 0.292 0.8569 0.000 0.852 0.000 0.132 0.016
#> GSM1068528 1 0.191 0.5573 0.932 0.004 0.036 0.000 0.028
#> GSM1068531 1 0.517 0.4008 0.576 0.048 0.000 0.000 0.376
#> GSM1068532 1 0.392 0.6369 0.780 0.040 0.000 0.000 0.180
#> GSM1068533 1 0.427 0.6310 0.748 0.048 0.000 0.000 0.204
#> GSM1068535 4 0.514 0.6419 0.072 0.048 0.044 0.780 0.056
#> GSM1068537 1 0.392 0.6369 0.780 0.040 0.000 0.000 0.180
#> GSM1068538 1 0.410 0.6353 0.764 0.044 0.000 0.000 0.192
#> GSM1068539 5 0.356 0.7450 0.000 0.044 0.012 0.104 0.840
#> GSM1068540 1 0.506 0.3839 0.576 0.040 0.000 0.000 0.384
#> GSM1068542 4 0.188 0.8355 0.000 0.048 0.008 0.932 0.012
#> GSM1068543 4 0.392 0.7223 0.000 0.008 0.144 0.804 0.044
#> GSM1068544 1 0.282 0.4934 0.856 0.000 0.132 0.000 0.012
#> GSM1068545 4 0.472 0.2088 0.000 0.396 0.000 0.584 0.020
#> GSM1068546 1 0.630 -0.1045 0.472 0.028 0.440 0.012 0.048
#> GSM1068547 5 0.464 0.5037 0.280 0.040 0.000 0.000 0.680
#> GSM1068548 4 0.201 0.8375 0.000 0.044 0.008 0.928 0.020
#> GSM1068549 3 0.395 0.4607 0.176 0.012 0.792 0.012 0.008
#> GSM1068550 4 0.176 0.8348 0.000 0.048 0.004 0.936 0.012
#> GSM1068551 2 0.252 0.8580 0.000 0.860 0.000 0.140 0.000
#> GSM1068552 4 0.372 0.6304 0.000 0.228 0.000 0.760 0.012
#> GSM1068555 2 0.302 0.8544 0.000 0.848 0.000 0.132 0.020
#> GSM1068556 4 0.356 0.7623 0.000 0.016 0.108 0.840 0.036
#> GSM1068557 2 0.488 0.2494 0.000 0.572 0.004 0.020 0.404
#> GSM1068560 4 0.286 0.8280 0.000 0.040 0.028 0.892 0.040
#> GSM1068561 5 0.288 0.7617 0.008 0.044 0.024 0.028 0.896
#> GSM1068562 4 0.252 0.8324 0.000 0.040 0.028 0.908 0.024
#> GSM1068563 4 0.232 0.8376 0.000 0.044 0.016 0.916 0.024
#> GSM1068565 2 0.247 0.8586 0.000 0.864 0.000 0.136 0.000
#> GSM1068529 5 0.475 0.6373 0.000 0.004 0.080 0.184 0.732
#> GSM1068530 1 0.437 0.5996 0.724 0.040 0.000 0.000 0.236
#> GSM1068534 5 0.474 0.6728 0.008 0.012 0.068 0.148 0.764
#> GSM1068536 5 0.191 0.7616 0.036 0.004 0.000 0.028 0.932
#> GSM1068541 5 0.279 0.7614 0.000 0.056 0.000 0.064 0.880
#> GSM1068553 4 0.152 0.8293 0.000 0.020 0.016 0.952 0.012
#> GSM1068554 4 0.197 0.8233 0.000 0.036 0.020 0.932 0.012
#> GSM1068558 3 0.488 0.4512 0.124 0.068 0.768 0.004 0.036
#> GSM1068559 4 0.552 0.3715 0.000 0.016 0.364 0.576 0.044
#> GSM1068564 4 0.418 0.4147 0.000 0.324 0.000 0.668 0.008
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1068478 5 0.152 0.707 0.060 0.008 0.000 0.000 0.932 0.000
#> GSM1068479 4 0.534 0.702 0.000 0.156 0.060 0.680 0.000 0.104
#> GSM1068481 3 0.421 0.366 0.460 0.000 0.528 0.004 0.008 0.000
#> GSM1068482 3 0.431 0.567 0.260 0.000 0.692 0.040 0.008 0.000
#> GSM1068483 1 0.582 0.497 0.548 0.020 0.072 0.020 0.340 0.000
#> GSM1068486 3 0.437 0.602 0.064 0.004 0.740 0.180 0.012 0.000
#> GSM1068487 2 0.154 0.866 0.000 0.936 0.008 0.004 0.000 0.052
#> GSM1068488 6 0.366 0.670 0.000 0.008 0.004 0.184 0.024 0.780
#> GSM1068490 2 0.161 0.864 0.000 0.932 0.008 0.004 0.000 0.056
#> GSM1068491 4 0.557 0.721 0.000 0.088 0.100 0.676 0.004 0.132
#> GSM1068492 4 0.457 0.711 0.000 0.076 0.008 0.732 0.012 0.172
#> GSM1068493 5 0.262 0.732 0.004 0.028 0.032 0.024 0.900 0.012
#> GSM1068494 5 0.434 0.700 0.036 0.000 0.044 0.096 0.792 0.032
#> GSM1068495 5 0.269 0.737 0.000 0.040 0.004 0.012 0.884 0.060
#> GSM1068496 1 0.452 0.411 0.692 0.000 0.228 0.004 0.076 0.000
#> GSM1068498 5 0.310 0.712 0.020 0.104 0.016 0.008 0.852 0.000
#> GSM1068499 5 0.298 0.718 0.036 0.004 0.036 0.052 0.872 0.000
#> GSM1068500 1 0.582 0.577 0.588 0.012 0.128 0.016 0.256 0.000
#> GSM1068502 4 0.506 0.640 0.000 0.204 0.056 0.684 0.000 0.056
#> GSM1068503 2 0.273 0.783 0.000 0.840 0.008 0.004 0.000 0.148
#> GSM1068505 6 0.267 0.775 0.012 0.048 0.028 0.020 0.000 0.892
#> GSM1068506 6 0.182 0.789 0.000 0.056 0.000 0.012 0.008 0.924
#> GSM1068507 6 0.337 0.768 0.012 0.080 0.024 0.036 0.000 0.848
#> GSM1068508 2 0.392 0.399 0.000 0.620 0.000 0.008 0.000 0.372
#> GSM1068510 6 0.481 0.686 0.032 0.056 0.072 0.076 0.000 0.764
#> GSM1068512 6 0.345 0.686 0.000 0.008 0.004 0.160 0.024 0.804
#> GSM1068513 2 0.558 0.366 0.016 0.556 0.040 0.032 0.000 0.356
#> GSM1068514 4 0.422 0.547 0.000 0.012 0.000 0.660 0.016 0.312
#> GSM1068517 5 0.392 0.628 0.004 0.244 0.016 0.008 0.728 0.000
#> GSM1068518 5 0.488 0.603 0.000 0.004 0.008 0.116 0.692 0.180
#> GSM1068520 5 0.435 0.219 0.392 0.008 0.004 0.008 0.588 0.000
#> GSM1068521 5 0.286 0.658 0.136 0.004 0.000 0.012 0.844 0.004
#> GSM1068522 2 0.453 0.659 0.008 0.724 0.032 0.020 0.004 0.212
#> GSM1068524 2 0.164 0.867 0.000 0.932 0.004 0.012 0.000 0.052
#> GSM1068527 6 0.319 0.761 0.000 0.032 0.004 0.088 0.024 0.852
#> GSM1068480 3 0.335 0.542 0.008 0.000 0.792 0.184 0.016 0.000
#> GSM1068484 6 0.402 0.746 0.000 0.040 0.008 0.132 0.028 0.792
#> GSM1068485 3 0.497 0.537 0.296 0.000 0.624 0.068 0.012 0.000
#> GSM1068489 6 0.327 0.767 0.012 0.044 0.044 0.032 0.004 0.864
#> GSM1068497 5 0.286 0.717 0.016 0.092 0.016 0.008 0.868 0.000
#> GSM1068501 6 0.482 0.691 0.032 0.044 0.076 0.076 0.004 0.768
#> GSM1068504 2 0.169 0.867 0.000 0.932 0.004 0.008 0.004 0.052
#> GSM1068509 5 0.240 0.731 0.000 0.004 0.020 0.040 0.904 0.032
#> GSM1068511 3 0.729 0.359 0.160 0.028 0.524 0.156 0.128 0.004
#> GSM1068515 5 0.427 0.685 0.012 0.044 0.108 0.048 0.788 0.000
#> GSM1068516 5 0.429 0.682 0.000 0.008 0.016 0.088 0.772 0.116
#> GSM1068519 5 0.467 0.377 0.328 0.000 0.016 0.032 0.624 0.000
#> GSM1068523 2 0.188 0.864 0.000 0.924 0.004 0.016 0.004 0.052
#> GSM1068525 6 0.380 0.720 0.000 0.020 0.008 0.148 0.028 0.796
#> GSM1068526 6 0.187 0.792 0.000 0.048 0.000 0.020 0.008 0.924
#> GSM1068458 5 0.497 0.113 0.416 0.012 0.028 0.008 0.536 0.000
#> GSM1068459 3 0.409 0.368 0.464 0.000 0.528 0.000 0.008 0.000
#> GSM1068460 5 0.305 0.722 0.028 0.004 0.000 0.016 0.856 0.096
#> GSM1068461 3 0.424 0.378 0.028 0.000 0.628 0.344 0.000 0.000
#> GSM1068464 2 0.162 0.861 0.000 0.932 0.000 0.020 0.000 0.048
#> GSM1068468 2 0.324 0.695 0.000 0.832 0.012 0.024 0.128 0.004
#> GSM1068472 5 0.453 0.549 0.004 0.312 0.016 0.020 0.648 0.000
#> GSM1068473 2 0.161 0.864 0.000 0.932 0.008 0.004 0.000 0.056
#> GSM1068474 2 0.154 0.866 0.000 0.936 0.008 0.004 0.000 0.052
#> GSM1068476 4 0.551 0.715 0.000 0.096 0.104 0.684 0.004 0.112
#> GSM1068477 2 0.128 0.868 0.000 0.944 0.000 0.004 0.000 0.052
#> GSM1068462 2 0.432 0.454 0.000 0.680 0.016 0.024 0.280 0.000
#> GSM1068463 3 0.409 0.367 0.468 0.000 0.524 0.000 0.008 0.000
#> GSM1068465 5 0.448 0.692 0.024 0.032 0.088 0.040 0.796 0.020
#> GSM1068466 5 0.478 0.194 0.388 0.012 0.020 0.008 0.572 0.000
#> GSM1068467 2 0.470 0.357 0.000 0.632 0.016 0.028 0.320 0.004
#> GSM1068469 5 0.471 0.443 0.004 0.368 0.020 0.016 0.592 0.000
#> GSM1068470 2 0.179 0.865 0.000 0.928 0.004 0.012 0.004 0.052
#> GSM1068471 2 0.169 0.867 0.000 0.932 0.004 0.008 0.004 0.052
#> GSM1068475 2 0.143 0.867 0.000 0.940 0.000 0.004 0.004 0.052
#> GSM1068528 1 0.446 0.140 0.632 0.008 0.336 0.008 0.016 0.000
#> GSM1068531 1 0.420 0.628 0.704 0.008 0.020 0.008 0.260 0.000
#> GSM1068532 1 0.216 0.692 0.892 0.000 0.008 0.004 0.096 0.000
#> GSM1068533 1 0.304 0.688 0.848 0.008 0.020 0.008 0.116 0.000
#> GSM1068535 6 0.533 0.567 0.148 0.004 0.064 0.052 0.020 0.712
#> GSM1068537 1 0.202 0.692 0.896 0.000 0.008 0.000 0.096 0.000
#> GSM1068538 1 0.216 0.693 0.892 0.000 0.008 0.004 0.096 0.000
#> GSM1068539 5 0.410 0.713 0.000 0.040 0.004 0.068 0.796 0.092
#> GSM1068540 1 0.355 0.602 0.696 0.000 0.000 0.004 0.300 0.000
#> GSM1068542 6 0.135 0.789 0.004 0.056 0.000 0.000 0.000 0.940
#> GSM1068543 6 0.341 0.685 0.000 0.008 0.004 0.164 0.020 0.804
#> GSM1068544 1 0.418 -0.376 0.504 0.000 0.484 0.000 0.012 0.000
#> GSM1068545 6 0.417 0.231 0.000 0.424 0.000 0.008 0.004 0.564
#> GSM1068546 3 0.479 0.617 0.124 0.004 0.740 0.104 0.012 0.016
#> GSM1068547 5 0.450 0.209 0.400 0.008 0.004 0.008 0.576 0.004
#> GSM1068548 6 0.183 0.791 0.000 0.052 0.000 0.020 0.004 0.924
#> GSM1068549 4 0.434 0.229 0.016 0.000 0.344 0.628 0.000 0.012
#> GSM1068550 6 0.149 0.789 0.000 0.056 0.000 0.004 0.004 0.936
#> GSM1068551 2 0.164 0.866 0.000 0.932 0.004 0.012 0.000 0.052
#> GSM1068552 6 0.356 0.595 0.000 0.256 0.000 0.008 0.004 0.732
#> GSM1068555 2 0.186 0.859 0.000 0.928 0.004 0.016 0.008 0.044
#> GSM1068556 6 0.320 0.719 0.000 0.012 0.004 0.132 0.020 0.832
#> GSM1068557 5 0.521 0.221 0.000 0.432 0.008 0.032 0.508 0.020
#> GSM1068560 6 0.338 0.765 0.000 0.040 0.004 0.092 0.024 0.840
#> GSM1068561 5 0.351 0.736 0.000 0.048 0.020 0.036 0.848 0.048
#> GSM1068562 6 0.313 0.774 0.000 0.052 0.000 0.088 0.012 0.848
#> GSM1068563 6 0.265 0.786 0.000 0.048 0.000 0.052 0.016 0.884
#> GSM1068565 2 0.128 0.868 0.000 0.944 0.000 0.004 0.000 0.052
#> GSM1068529 5 0.497 0.659 0.000 0.008 0.036 0.132 0.724 0.100
#> GSM1068530 1 0.212 0.695 0.888 0.000 0.008 0.000 0.104 0.000
#> GSM1068534 5 0.563 0.644 0.004 0.008 0.104 0.096 0.688 0.100
#> GSM1068536 5 0.119 0.720 0.032 0.000 0.004 0.000 0.956 0.008
#> GSM1068541 5 0.281 0.737 0.004 0.040 0.020 0.000 0.880 0.056
#> GSM1068553 6 0.398 0.724 0.032 0.028 0.064 0.048 0.004 0.824
#> GSM1068554 6 0.466 0.697 0.032 0.044 0.076 0.064 0.004 0.780
#> GSM1068558 3 0.584 0.307 0.060 0.028 0.536 0.360 0.012 0.004
#> GSM1068559 4 0.464 0.314 0.000 0.012 0.004 0.564 0.016 0.404
#> GSM1068564 6 0.498 0.356 0.000 0.372 0.024 0.020 0.008 0.576
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 disease.state(p) gender(p) k
#> CV:kmeans 103 0.683245 1.000 2
#> CV:kmeans 78 0.859603 0.985 3
#> CV:kmeans 83 0.010616 0.976 4
#> CV:kmeans 78 0.000305 0.472 5
#> CV:kmeans 83 0.000172 0.376 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 38950 rows and 108 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.758 0.923 0.962 0.5029 0.497 0.497
#> 3 3 0.575 0.714 0.845 0.3170 0.728 0.505
#> 4 4 0.618 0.606 0.810 0.1335 0.808 0.500
#> 5 5 0.600 0.539 0.728 0.0593 0.857 0.525
#> 6 6 0.638 0.511 0.710 0.0396 0.920 0.660
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
#> GSM1068478 1 0.0000 0.953 1.000 0.000
#> GSM1068479 2 0.0000 0.964 0.000 1.000
#> GSM1068481 1 0.0000 0.953 1.000 0.000
#> GSM1068482 1 0.0000 0.953 1.000 0.000
#> GSM1068483 1 0.0000 0.953 1.000 0.000
#> GSM1068486 1 0.0000 0.953 1.000 0.000
#> GSM1068487 2 0.0000 0.964 0.000 1.000
#> GSM1068488 2 0.6973 0.787 0.188 0.812
#> GSM1068490 2 0.0000 0.964 0.000 1.000
#> GSM1068491 2 0.7056 0.782 0.192 0.808
#> GSM1068492 2 0.0000 0.964 0.000 1.000
#> GSM1068493 1 0.0000 0.953 1.000 0.000
#> GSM1068494 1 0.0000 0.953 1.000 0.000
#> GSM1068495 1 0.7056 0.800 0.808 0.192
#> GSM1068496 1 0.0000 0.953 1.000 0.000
#> GSM1068498 1 0.6623 0.820 0.828 0.172
#> GSM1068499 1 0.0000 0.953 1.000 0.000
#> GSM1068500 1 0.0000 0.953 1.000 0.000
#> GSM1068502 2 0.0000 0.964 0.000 1.000
#> GSM1068503 2 0.0000 0.964 0.000 1.000
#> GSM1068505 2 0.0000 0.964 0.000 1.000
#> GSM1068506 2 0.0000 0.964 0.000 1.000
#> GSM1068507 2 0.0000 0.964 0.000 1.000
#> GSM1068508 2 0.0000 0.964 0.000 1.000
#> GSM1068510 2 0.0000 0.964 0.000 1.000
#> GSM1068512 2 0.7219 0.772 0.200 0.800
#> GSM1068513 2 0.0000 0.964 0.000 1.000
#> GSM1068514 2 0.6148 0.827 0.152 0.848
#> GSM1068517 1 0.7219 0.792 0.800 0.200
#> GSM1068518 1 0.0000 0.953 1.000 0.000
#> GSM1068520 1 0.0000 0.953 1.000 0.000
#> GSM1068521 1 0.0000 0.953 1.000 0.000
#> GSM1068522 2 0.0000 0.964 0.000 1.000
#> GSM1068524 2 0.0000 0.964 0.000 1.000
#> GSM1068527 2 0.0000 0.964 0.000 1.000
#> GSM1068480 1 0.0000 0.953 1.000 0.000
#> GSM1068484 2 0.0000 0.964 0.000 1.000
#> GSM1068485 1 0.0000 0.953 1.000 0.000
#> GSM1068489 2 0.0000 0.964 0.000 1.000
#> GSM1068497 1 0.6623 0.820 0.828 0.172
#> GSM1068501 2 0.0000 0.964 0.000 1.000
#> GSM1068504 2 0.0000 0.964 0.000 1.000
#> GSM1068509 1 0.0000 0.953 1.000 0.000
#> GSM1068511 1 0.0000 0.953 1.000 0.000
#> GSM1068515 1 0.0376 0.951 0.996 0.004
#> GSM1068516 1 0.5294 0.870 0.880 0.120
#> GSM1068519 1 0.0000 0.953 1.000 0.000
#> GSM1068523 2 0.0000 0.964 0.000 1.000
#> GSM1068525 2 0.0000 0.964 0.000 1.000
#> GSM1068526 2 0.0000 0.964 0.000 1.000
#> GSM1068458 1 0.0000 0.953 1.000 0.000
#> GSM1068459 1 0.0000 0.953 1.000 0.000
#> GSM1068460 1 0.8016 0.732 0.756 0.244
#> GSM1068461 1 0.0000 0.953 1.000 0.000
#> GSM1068464 2 0.0000 0.964 0.000 1.000
#> GSM1068468 2 0.0000 0.964 0.000 1.000
#> GSM1068472 1 0.7219 0.792 0.800 0.200
#> GSM1068473 2 0.0000 0.964 0.000 1.000
#> GSM1068474 2 0.0000 0.964 0.000 1.000
#> GSM1068476 2 0.6623 0.805 0.172 0.828
#> GSM1068477 2 0.0000 0.964 0.000 1.000
#> GSM1068462 2 0.0000 0.964 0.000 1.000
#> GSM1068463 1 0.0000 0.953 1.000 0.000
#> GSM1068465 1 0.6712 0.816 0.824 0.176
#> GSM1068466 1 0.0000 0.953 1.000 0.000
#> GSM1068467 2 0.0000 0.964 0.000 1.000
#> GSM1068469 1 0.7219 0.792 0.800 0.200
#> GSM1068470 2 0.0000 0.964 0.000 1.000
#> GSM1068471 2 0.0000 0.964 0.000 1.000
#> GSM1068475 2 0.0000 0.964 0.000 1.000
#> GSM1068528 1 0.0000 0.953 1.000 0.000
#> GSM1068531 1 0.0000 0.953 1.000 0.000
#> GSM1068532 1 0.0000 0.953 1.000 0.000
#> GSM1068533 1 0.0000 0.953 1.000 0.000
#> GSM1068535 1 0.0000 0.953 1.000 0.000
#> GSM1068537 1 0.0000 0.953 1.000 0.000
#> GSM1068538 1 0.0000 0.953 1.000 0.000
#> GSM1068539 2 0.8327 0.610 0.264 0.736
#> GSM1068540 1 0.0000 0.953 1.000 0.000
#> GSM1068542 2 0.0000 0.964 0.000 1.000
#> GSM1068543 2 0.6531 0.810 0.168 0.832
#> GSM1068544 1 0.0000 0.953 1.000 0.000
#> GSM1068545 2 0.0000 0.964 0.000 1.000
#> GSM1068546 1 0.0000 0.953 1.000 0.000
#> GSM1068547 1 0.0000 0.953 1.000 0.000
#> GSM1068548 2 0.0000 0.964 0.000 1.000
#> GSM1068549 1 0.0000 0.953 1.000 0.000
#> GSM1068550 2 0.0000 0.964 0.000 1.000
#> GSM1068551 2 0.0000 0.964 0.000 1.000
#> GSM1068552 2 0.0000 0.964 0.000 1.000
#> GSM1068555 2 0.0000 0.964 0.000 1.000
#> GSM1068556 2 0.6623 0.805 0.172 0.828
#> GSM1068557 2 0.0000 0.964 0.000 1.000
#> GSM1068560 2 0.0000 0.964 0.000 1.000
#> GSM1068561 1 0.7219 0.792 0.800 0.200
#> GSM1068562 2 0.0000 0.964 0.000 1.000
#> GSM1068563 2 0.0000 0.964 0.000 1.000
#> GSM1068565 2 0.0000 0.964 0.000 1.000
#> GSM1068529 1 0.0000 0.953 1.000 0.000
#> GSM1068530 1 0.0000 0.953 1.000 0.000
#> GSM1068534 1 0.0000 0.953 1.000 0.000
#> GSM1068536 1 0.2948 0.919 0.948 0.052
#> GSM1068541 1 0.8861 0.635 0.696 0.304
#> GSM1068553 2 0.2236 0.935 0.036 0.964
#> GSM1068554 2 0.0000 0.964 0.000 1.000
#> GSM1068558 2 0.9460 0.489 0.364 0.636
#> GSM1068559 2 0.0000 0.964 0.000 1.000
#> GSM1068564 2 0.0000 0.964 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1068478 1 0.3879 0.8750 0.848 0.152 0.000
#> GSM1068479 3 0.6500 -0.0698 0.004 0.464 0.532
#> GSM1068481 1 0.0424 0.8960 0.992 0.000 0.008
#> GSM1068482 1 0.1163 0.8905 0.972 0.000 0.028
#> GSM1068483 1 0.1860 0.8982 0.948 0.052 0.000
#> GSM1068486 1 0.1163 0.8905 0.972 0.000 0.028
#> GSM1068487 2 0.4002 0.7621 0.000 0.840 0.160
#> GSM1068488 3 0.2261 0.7737 0.068 0.000 0.932
#> GSM1068490 2 0.4235 0.7509 0.000 0.824 0.176
#> GSM1068491 3 0.5901 0.6774 0.048 0.176 0.776
#> GSM1068492 3 0.3879 0.7268 0.000 0.152 0.848
#> GSM1068493 1 0.0475 0.8973 0.992 0.004 0.004
#> GSM1068494 1 0.2313 0.8968 0.944 0.032 0.024
#> GSM1068495 2 0.7424 0.0374 0.388 0.572 0.040
#> GSM1068496 1 0.0829 0.8989 0.984 0.012 0.004
#> GSM1068498 2 0.6204 -0.0445 0.424 0.576 0.000
#> GSM1068499 1 0.0237 0.8977 0.996 0.004 0.000
#> GSM1068500 1 0.1529 0.8990 0.960 0.040 0.000
#> GSM1068502 2 0.6291 0.2530 0.000 0.532 0.468
#> GSM1068503 2 0.5327 0.6480 0.000 0.728 0.272
#> GSM1068505 3 0.3482 0.7594 0.000 0.128 0.872
#> GSM1068506 3 0.3619 0.7590 0.000 0.136 0.864
#> GSM1068507 3 0.3038 0.7951 0.000 0.104 0.896
#> GSM1068508 2 0.5327 0.6654 0.000 0.728 0.272
#> GSM1068510 3 0.3038 0.7998 0.000 0.104 0.896
#> GSM1068512 3 0.1529 0.7961 0.040 0.000 0.960
#> GSM1068513 2 0.6154 0.4114 0.000 0.592 0.408
#> GSM1068514 3 0.2564 0.8060 0.028 0.036 0.936
#> GSM1068517 2 0.2261 0.6512 0.068 0.932 0.000
#> GSM1068518 3 0.7665 0.0320 0.456 0.044 0.500
#> GSM1068520 1 0.3879 0.8750 0.848 0.152 0.000
#> GSM1068521 1 0.3879 0.8750 0.848 0.152 0.000
#> GSM1068522 2 0.5988 0.4912 0.000 0.632 0.368
#> GSM1068524 2 0.4346 0.7485 0.000 0.816 0.184
#> GSM1068527 3 0.1163 0.8277 0.000 0.028 0.972
#> GSM1068480 1 0.1163 0.8905 0.972 0.000 0.028
#> GSM1068484 3 0.1289 0.8284 0.000 0.032 0.968
#> GSM1068485 1 0.1031 0.8921 0.976 0.000 0.024
#> GSM1068489 3 0.1289 0.8284 0.000 0.032 0.968
#> GSM1068497 2 0.6225 -0.0721 0.432 0.568 0.000
#> GSM1068501 3 0.3038 0.7922 0.000 0.104 0.896
#> GSM1068504 2 0.3879 0.7649 0.000 0.848 0.152
#> GSM1068509 1 0.1753 0.9005 0.952 0.048 0.000
#> GSM1068511 1 0.1031 0.8920 0.976 0.000 0.024
#> GSM1068515 1 0.2711 0.8895 0.912 0.088 0.000
#> GSM1068516 3 0.6632 0.5970 0.064 0.204 0.732
#> GSM1068519 1 0.3482 0.8851 0.872 0.128 0.000
#> GSM1068523 2 0.3879 0.7649 0.000 0.848 0.152
#> GSM1068525 3 0.1289 0.8284 0.000 0.032 0.968
#> GSM1068526 3 0.1289 0.8284 0.000 0.032 0.968
#> GSM1068458 1 0.3879 0.8750 0.848 0.152 0.000
#> GSM1068459 1 0.1163 0.8905 0.972 0.000 0.028
#> GSM1068460 2 0.9955 0.0119 0.316 0.380 0.304
#> GSM1068461 1 0.1163 0.8905 0.972 0.000 0.028
#> GSM1068464 2 0.4002 0.7621 0.000 0.840 0.160
#> GSM1068468 2 0.3116 0.7515 0.000 0.892 0.108
#> GSM1068472 2 0.5785 0.4173 0.332 0.668 0.000
#> GSM1068473 2 0.4062 0.7597 0.000 0.836 0.164
#> GSM1068474 2 0.4002 0.7621 0.000 0.840 0.160
#> GSM1068476 3 0.4897 0.7038 0.016 0.172 0.812
#> GSM1068477 2 0.3879 0.7649 0.000 0.848 0.152
#> GSM1068462 2 0.3607 0.7535 0.008 0.880 0.112
#> GSM1068463 1 0.0747 0.8943 0.984 0.000 0.016
#> GSM1068465 1 0.5536 0.6982 0.752 0.236 0.012
#> GSM1068466 1 0.3879 0.8750 0.848 0.152 0.000
#> GSM1068467 2 0.0829 0.6936 0.004 0.984 0.012
#> GSM1068469 2 0.5327 0.5112 0.272 0.728 0.000
#> GSM1068470 2 0.3879 0.7649 0.000 0.848 0.152
#> GSM1068471 2 0.4002 0.7621 0.000 0.840 0.160
#> GSM1068475 2 0.3879 0.7649 0.000 0.848 0.152
#> GSM1068528 1 0.0424 0.8987 0.992 0.008 0.000
#> GSM1068531 1 0.3340 0.8879 0.880 0.120 0.000
#> GSM1068532 1 0.2537 0.8957 0.920 0.080 0.000
#> GSM1068533 1 0.3340 0.8879 0.880 0.120 0.000
#> GSM1068535 3 0.8228 0.2632 0.364 0.084 0.552
#> GSM1068537 1 0.3192 0.8899 0.888 0.112 0.000
#> GSM1068538 1 0.3267 0.8889 0.884 0.116 0.000
#> GSM1068539 2 0.4744 0.6067 0.028 0.836 0.136
#> GSM1068540 1 0.3267 0.8891 0.884 0.116 0.000
#> GSM1068542 3 0.1411 0.8275 0.000 0.036 0.964
#> GSM1068543 3 0.0237 0.8171 0.004 0.000 0.996
#> GSM1068544 1 0.1129 0.8948 0.976 0.004 0.020
#> GSM1068545 2 0.6252 0.3617 0.000 0.556 0.444
#> GSM1068546 1 0.1163 0.8905 0.972 0.000 0.028
#> GSM1068547 1 0.3879 0.8750 0.848 0.152 0.000
#> GSM1068548 3 0.1289 0.8284 0.000 0.032 0.968
#> GSM1068549 1 0.5431 0.5762 0.716 0.000 0.284
#> GSM1068550 3 0.1964 0.8199 0.000 0.056 0.944
#> GSM1068551 2 0.3879 0.7649 0.000 0.848 0.152
#> GSM1068552 3 0.5138 0.5757 0.000 0.252 0.748
#> GSM1068555 2 0.3879 0.7649 0.000 0.848 0.152
#> GSM1068556 3 0.0661 0.8208 0.004 0.008 0.988
#> GSM1068557 2 0.3482 0.7588 0.000 0.872 0.128
#> GSM1068560 3 0.1289 0.8284 0.000 0.032 0.968
#> GSM1068561 1 0.6410 0.2146 0.576 0.420 0.004
#> GSM1068562 3 0.1289 0.8284 0.000 0.032 0.968
#> GSM1068563 3 0.3686 0.7546 0.000 0.140 0.860
#> GSM1068565 2 0.3879 0.7649 0.000 0.848 0.152
#> GSM1068529 1 0.6577 0.2348 0.572 0.008 0.420
#> GSM1068530 1 0.3340 0.8879 0.880 0.120 0.000
#> GSM1068534 1 0.3116 0.8332 0.892 0.000 0.108
#> GSM1068536 1 0.4293 0.8660 0.832 0.164 0.004
#> GSM1068541 2 0.5307 0.5962 0.056 0.820 0.124
#> GSM1068553 3 0.1163 0.8277 0.000 0.028 0.972
#> GSM1068554 3 0.1643 0.8256 0.000 0.044 0.956
#> GSM1068558 3 0.6229 0.4633 0.340 0.008 0.652
#> GSM1068559 3 0.2584 0.8033 0.008 0.064 0.928
#> GSM1068564 3 0.6204 0.0934 0.000 0.424 0.576
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1068478 1 0.0779 0.6502 0.980 0.016 0.004 0.000
#> GSM1068479 2 0.6516 0.4189 0.000 0.576 0.332 0.092
#> GSM1068481 3 0.4454 0.5381 0.308 0.000 0.692 0.000
#> GSM1068482 3 0.3266 0.6416 0.168 0.000 0.832 0.000
#> GSM1068483 1 0.4925 0.1321 0.572 0.000 0.428 0.000
#> GSM1068486 3 0.1118 0.6549 0.036 0.000 0.964 0.000
#> GSM1068487 2 0.1209 0.8726 0.000 0.964 0.004 0.032
#> GSM1068488 4 0.3662 0.7679 0.004 0.012 0.148 0.836
#> GSM1068490 2 0.1305 0.8709 0.000 0.960 0.004 0.036
#> GSM1068491 3 0.7483 -0.0614 0.000 0.360 0.456 0.184
#> GSM1068492 4 0.7922 0.0862 0.000 0.320 0.336 0.344
#> GSM1068493 3 0.5957 0.3843 0.364 0.048 0.588 0.000
#> GSM1068494 1 0.4992 -0.0651 0.524 0.000 0.476 0.000
#> GSM1068495 1 0.4339 0.5440 0.764 0.224 0.004 0.008
#> GSM1068496 1 0.5000 -0.1517 0.504 0.000 0.496 0.000
#> GSM1068498 1 0.4372 0.5139 0.728 0.268 0.004 0.000
#> GSM1068499 3 0.4898 0.3529 0.416 0.000 0.584 0.000
#> GSM1068500 1 0.4967 0.0413 0.548 0.000 0.452 0.000
#> GSM1068502 2 0.5898 0.4902 0.000 0.628 0.316 0.056
#> GSM1068503 2 0.3448 0.7562 0.000 0.828 0.004 0.168
#> GSM1068505 4 0.1211 0.8507 0.000 0.040 0.000 0.960
#> GSM1068506 4 0.1557 0.8458 0.000 0.056 0.000 0.944
#> GSM1068507 4 0.2342 0.8349 0.000 0.080 0.008 0.912
#> GSM1068508 2 0.4564 0.5006 0.000 0.672 0.000 0.328
#> GSM1068510 4 0.4776 0.7342 0.000 0.164 0.060 0.776
#> GSM1068512 4 0.2867 0.8003 0.000 0.012 0.104 0.884
#> GSM1068513 2 0.4343 0.6391 0.000 0.732 0.004 0.264
#> GSM1068514 4 0.6158 0.4394 0.000 0.056 0.384 0.560
#> GSM1068517 1 0.5229 0.2253 0.564 0.428 0.008 0.000
#> GSM1068518 1 0.7702 0.0327 0.416 0.000 0.360 0.224
#> GSM1068520 1 0.0376 0.6534 0.992 0.004 0.004 0.000
#> GSM1068521 1 0.0188 0.6525 0.996 0.004 0.000 0.000
#> GSM1068522 2 0.4877 0.2921 0.000 0.592 0.000 0.408
#> GSM1068524 2 0.2593 0.8272 0.000 0.892 0.004 0.104
#> GSM1068527 4 0.0376 0.8511 0.000 0.004 0.004 0.992
#> GSM1068480 3 0.1118 0.6547 0.036 0.000 0.964 0.000
#> GSM1068484 4 0.1488 0.8553 0.000 0.032 0.012 0.956
#> GSM1068485 3 0.2589 0.6583 0.116 0.000 0.884 0.000
#> GSM1068489 4 0.0921 0.8535 0.000 0.028 0.000 0.972
#> GSM1068497 1 0.5131 0.4952 0.692 0.280 0.028 0.000
#> GSM1068501 4 0.2124 0.8420 0.000 0.068 0.008 0.924
#> GSM1068504 2 0.1209 0.8726 0.000 0.964 0.004 0.032
#> GSM1068509 1 0.4277 0.4295 0.720 0.000 0.280 0.000
#> GSM1068511 3 0.4356 0.5426 0.292 0.000 0.708 0.000
#> GSM1068515 1 0.6336 -0.0579 0.480 0.060 0.460 0.000
#> GSM1068516 1 0.7708 0.2801 0.540 0.024 0.280 0.156
#> GSM1068519 1 0.1118 0.6531 0.964 0.000 0.036 0.000
#> GSM1068523 2 0.1118 0.8717 0.000 0.964 0.000 0.036
#> GSM1068525 4 0.3764 0.8117 0.000 0.072 0.076 0.852
#> GSM1068526 4 0.0469 0.8532 0.000 0.012 0.000 0.988
#> GSM1068458 1 0.1792 0.6486 0.932 0.000 0.068 0.000
#> GSM1068459 3 0.4072 0.5964 0.252 0.000 0.748 0.000
#> GSM1068460 1 0.4228 0.5082 0.760 0.008 0.000 0.232
#> GSM1068461 3 0.0921 0.6474 0.028 0.000 0.972 0.000
#> GSM1068464 2 0.0779 0.8685 0.000 0.980 0.004 0.016
#> GSM1068468 2 0.0804 0.8550 0.012 0.980 0.000 0.008
#> GSM1068472 2 0.6551 0.4161 0.136 0.624 0.240 0.000
#> GSM1068473 2 0.1209 0.8726 0.000 0.964 0.004 0.032
#> GSM1068474 2 0.1209 0.8726 0.000 0.964 0.004 0.032
#> GSM1068476 3 0.7626 -0.1478 0.000 0.384 0.412 0.204
#> GSM1068477 2 0.0921 0.8725 0.000 0.972 0.000 0.028
#> GSM1068462 2 0.0188 0.8586 0.004 0.996 0.000 0.000
#> GSM1068463 3 0.4454 0.5434 0.308 0.000 0.692 0.000
#> GSM1068465 1 0.6653 0.3547 0.592 0.048 0.332 0.028
#> GSM1068466 1 0.1302 0.6534 0.956 0.000 0.044 0.000
#> GSM1068467 2 0.1305 0.8362 0.036 0.960 0.000 0.004
#> GSM1068469 2 0.6534 0.4351 0.148 0.632 0.220 0.000
#> GSM1068470 2 0.1022 0.8727 0.000 0.968 0.000 0.032
#> GSM1068471 2 0.1109 0.8724 0.000 0.968 0.004 0.028
#> GSM1068475 2 0.1022 0.8727 0.000 0.968 0.000 0.032
#> GSM1068528 3 0.4888 0.3550 0.412 0.000 0.588 0.000
#> GSM1068531 1 0.2216 0.6397 0.908 0.000 0.092 0.000
#> GSM1068532 1 0.4304 0.4463 0.716 0.000 0.284 0.000
#> GSM1068533 1 0.3266 0.5902 0.832 0.000 0.168 0.000
#> GSM1068535 4 0.6381 0.4095 0.280 0.004 0.088 0.628
#> GSM1068537 1 0.3486 0.5727 0.812 0.000 0.188 0.000
#> GSM1068538 1 0.3444 0.5769 0.816 0.000 0.184 0.000
#> GSM1068539 1 0.5827 0.4250 0.632 0.316 0.000 0.052
#> GSM1068540 1 0.1637 0.6494 0.940 0.000 0.060 0.000
#> GSM1068542 4 0.0707 0.8538 0.000 0.020 0.000 0.980
#> GSM1068543 4 0.2737 0.8034 0.000 0.008 0.104 0.888
#> GSM1068544 3 0.4522 0.5245 0.320 0.000 0.680 0.000
#> GSM1068545 4 0.4661 0.4660 0.000 0.348 0.000 0.652
#> GSM1068546 3 0.2647 0.6587 0.120 0.000 0.880 0.000
#> GSM1068547 1 0.0188 0.6534 0.996 0.000 0.004 0.000
#> GSM1068548 4 0.0817 0.8541 0.000 0.024 0.000 0.976
#> GSM1068549 3 0.1884 0.6265 0.020 0.016 0.948 0.016
#> GSM1068550 4 0.0921 0.8535 0.000 0.028 0.000 0.972
#> GSM1068551 2 0.1022 0.8727 0.000 0.968 0.000 0.032
#> GSM1068552 4 0.3649 0.7111 0.000 0.204 0.000 0.796
#> GSM1068555 2 0.0817 0.8710 0.000 0.976 0.000 0.024
#> GSM1068556 4 0.0921 0.8425 0.000 0.000 0.028 0.972
#> GSM1068557 2 0.0844 0.8533 0.012 0.980 0.004 0.004
#> GSM1068560 4 0.0707 0.8542 0.000 0.020 0.000 0.980
#> GSM1068561 3 0.7795 0.0401 0.280 0.296 0.424 0.000
#> GSM1068562 4 0.0592 0.8535 0.000 0.016 0.000 0.984
#> GSM1068563 4 0.2408 0.8241 0.000 0.104 0.000 0.896
#> GSM1068565 2 0.1022 0.8727 0.000 0.968 0.000 0.032
#> GSM1068529 3 0.3614 0.5699 0.080 0.008 0.868 0.044
#> GSM1068530 1 0.2408 0.6338 0.896 0.000 0.104 0.000
#> GSM1068534 3 0.3245 0.6548 0.100 0.000 0.872 0.028
#> GSM1068536 1 0.2096 0.6426 0.940 0.016 0.016 0.028
#> GSM1068541 1 0.5664 0.5125 0.696 0.228 0.000 0.076
#> GSM1068553 4 0.0707 0.8538 0.000 0.020 0.000 0.980
#> GSM1068554 4 0.1545 0.8519 0.000 0.040 0.008 0.952
#> GSM1068558 3 0.1211 0.6159 0.000 0.000 0.960 0.040
#> GSM1068559 4 0.6759 0.4521 0.000 0.108 0.344 0.548
#> GSM1068564 4 0.4277 0.6014 0.000 0.280 0.000 0.720
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM1068478 5 0.3452 0.4958 0.244 0.000 0.000 0.000 0.756
#> GSM1068479 3 0.5059 0.4919 0.004 0.292 0.652 0.052 0.000
#> GSM1068481 1 0.3582 0.4981 0.768 0.000 0.224 0.000 0.008
#> GSM1068482 1 0.5057 0.3313 0.604 0.004 0.356 0.000 0.036
#> GSM1068483 1 0.4025 0.5131 0.796 0.004 0.060 0.000 0.140
#> GSM1068486 3 0.5130 0.0723 0.412 0.004 0.552 0.000 0.032
#> GSM1068487 2 0.0771 0.8504 0.000 0.976 0.000 0.020 0.004
#> GSM1068488 4 0.5228 0.4473 0.000 0.000 0.356 0.588 0.056
#> GSM1068490 2 0.0865 0.8489 0.000 0.972 0.000 0.024 0.004
#> GSM1068491 3 0.4527 0.6014 0.004 0.172 0.752 0.072 0.000
#> GSM1068492 3 0.5804 0.5015 0.000 0.148 0.656 0.180 0.016
#> GSM1068493 1 0.6366 0.3692 0.564 0.012 0.168 0.000 0.256
#> GSM1068494 1 0.6515 0.2424 0.464 0.000 0.208 0.000 0.328
#> GSM1068495 5 0.3302 0.6468 0.044 0.048 0.016 0.016 0.876
#> GSM1068496 1 0.3437 0.5570 0.832 0.000 0.120 0.000 0.048
#> GSM1068498 5 0.3433 0.6425 0.032 0.132 0.004 0.000 0.832
#> GSM1068499 1 0.5221 0.5078 0.696 0.004 0.172 0.000 0.128
#> GSM1068500 1 0.3648 0.5440 0.824 0.000 0.084 0.000 0.092
#> GSM1068502 3 0.4865 0.4400 0.000 0.324 0.640 0.032 0.004
#> GSM1068503 2 0.3456 0.6872 0.000 0.788 0.004 0.204 0.004
#> GSM1068505 4 0.1331 0.7974 0.000 0.040 0.000 0.952 0.008
#> GSM1068506 4 0.2349 0.7894 0.000 0.084 0.012 0.900 0.004
#> GSM1068507 4 0.3960 0.7400 0.000 0.148 0.044 0.800 0.008
#> GSM1068508 2 0.4774 0.3952 0.000 0.632 0.004 0.340 0.024
#> GSM1068510 4 0.6130 0.4901 0.000 0.268 0.144 0.580 0.008
#> GSM1068512 4 0.4975 0.5745 0.004 0.000 0.276 0.668 0.052
#> GSM1068513 2 0.4441 0.6057 0.000 0.716 0.024 0.252 0.008
#> GSM1068514 3 0.4495 0.4532 0.000 0.032 0.724 0.236 0.008
#> GSM1068517 5 0.3797 0.5831 0.008 0.232 0.004 0.000 0.756
#> GSM1068518 3 0.8442 0.0247 0.200 0.000 0.312 0.184 0.304
#> GSM1068520 5 0.4273 0.1767 0.448 0.000 0.000 0.000 0.552
#> GSM1068521 5 0.4161 0.2735 0.392 0.000 0.000 0.000 0.608
#> GSM1068522 2 0.4134 0.5462 0.000 0.704 0.004 0.284 0.008
#> GSM1068524 2 0.2664 0.8104 0.000 0.884 0.004 0.092 0.020
#> GSM1068527 4 0.2954 0.7715 0.000 0.004 0.064 0.876 0.056
#> GSM1068480 3 0.5158 0.1015 0.392 0.004 0.568 0.000 0.036
#> GSM1068484 4 0.4622 0.7404 0.000 0.036 0.120 0.780 0.064
#> GSM1068485 1 0.4604 0.2145 0.584 0.004 0.404 0.000 0.008
#> GSM1068489 4 0.1804 0.7949 0.000 0.024 0.012 0.940 0.024
#> GSM1068497 5 0.3602 0.6310 0.036 0.140 0.004 0.000 0.820
#> GSM1068501 4 0.4447 0.7095 0.000 0.172 0.032 0.768 0.028
#> GSM1068504 2 0.0798 0.8534 0.000 0.976 0.000 0.016 0.008
#> GSM1068509 1 0.6074 0.0751 0.452 0.004 0.104 0.000 0.440
#> GSM1068511 1 0.4900 0.4317 0.656 0.004 0.300 0.000 0.040
#> GSM1068515 5 0.7096 -0.0825 0.408 0.024 0.148 0.008 0.412
#> GSM1068516 5 0.5209 0.5170 0.028 0.004 0.128 0.100 0.740
#> GSM1068519 1 0.4434 0.0755 0.536 0.000 0.004 0.000 0.460
#> GSM1068523 2 0.2208 0.8369 0.000 0.908 0.000 0.020 0.072
#> GSM1068525 4 0.6237 0.5750 0.000 0.068 0.224 0.632 0.076
#> GSM1068526 4 0.1405 0.7995 0.000 0.016 0.020 0.956 0.008
#> GSM1068458 1 0.4067 0.3538 0.692 0.000 0.008 0.000 0.300
#> GSM1068459 1 0.3579 0.4864 0.756 0.000 0.240 0.000 0.004
#> GSM1068460 5 0.6246 0.3935 0.236 0.004 0.000 0.196 0.564
#> GSM1068461 3 0.4309 0.3509 0.308 0.000 0.676 0.000 0.016
#> GSM1068464 2 0.0486 0.8516 0.000 0.988 0.004 0.004 0.004
#> GSM1068468 2 0.2291 0.8215 0.000 0.908 0.036 0.000 0.056
#> GSM1068472 2 0.7559 0.1140 0.132 0.472 0.104 0.000 0.292
#> GSM1068473 2 0.0932 0.8492 0.000 0.972 0.004 0.020 0.004
#> GSM1068474 2 0.0671 0.8516 0.000 0.980 0.000 0.016 0.004
#> GSM1068476 3 0.4712 0.5911 0.004 0.180 0.736 0.080 0.000
#> GSM1068477 2 0.0671 0.8521 0.000 0.980 0.000 0.004 0.016
#> GSM1068462 2 0.2522 0.8014 0.000 0.880 0.012 0.000 0.108
#> GSM1068463 1 0.3521 0.4933 0.764 0.000 0.232 0.000 0.004
#> GSM1068465 1 0.6570 0.1876 0.540 0.016 0.088 0.020 0.336
#> GSM1068466 1 0.4517 0.0922 0.556 0.000 0.008 0.000 0.436
#> GSM1068467 2 0.4177 0.7132 0.004 0.776 0.052 0.000 0.168
#> GSM1068469 2 0.7034 0.1645 0.104 0.496 0.068 0.000 0.332
#> GSM1068470 2 0.1281 0.8505 0.000 0.956 0.000 0.012 0.032
#> GSM1068471 2 0.0771 0.8524 0.000 0.976 0.000 0.020 0.004
#> GSM1068475 2 0.0807 0.8534 0.000 0.976 0.000 0.012 0.012
#> GSM1068528 1 0.3412 0.5583 0.820 0.000 0.152 0.000 0.028
#> GSM1068531 1 0.3707 0.3850 0.716 0.000 0.000 0.000 0.284
#> GSM1068532 1 0.2891 0.4808 0.824 0.000 0.000 0.000 0.176
#> GSM1068533 1 0.3607 0.4251 0.752 0.000 0.004 0.000 0.244
#> GSM1068535 1 0.6371 0.1069 0.488 0.000 0.036 0.404 0.072
#> GSM1068537 1 0.3177 0.4570 0.792 0.000 0.000 0.000 0.208
#> GSM1068538 1 0.3336 0.4395 0.772 0.000 0.000 0.000 0.228
#> GSM1068539 5 0.4116 0.6399 0.020 0.112 0.016 0.032 0.820
#> GSM1068540 1 0.3983 0.3102 0.660 0.000 0.000 0.000 0.340
#> GSM1068542 4 0.0955 0.7977 0.000 0.028 0.000 0.968 0.004
#> GSM1068543 4 0.4394 0.6545 0.000 0.000 0.220 0.732 0.048
#> GSM1068544 1 0.3961 0.5239 0.760 0.000 0.212 0.000 0.028
#> GSM1068545 4 0.4402 0.4291 0.000 0.372 0.004 0.620 0.004
#> GSM1068546 1 0.5024 0.1348 0.528 0.000 0.440 0.000 0.032
#> GSM1068547 1 0.4305 -0.0793 0.512 0.000 0.000 0.000 0.488
#> GSM1068548 4 0.1612 0.8002 0.000 0.024 0.016 0.948 0.012
#> GSM1068549 3 0.3327 0.5198 0.160 0.004 0.824 0.004 0.008
#> GSM1068550 4 0.1280 0.7992 0.000 0.024 0.008 0.960 0.008
#> GSM1068551 2 0.0912 0.8538 0.000 0.972 0.000 0.016 0.012
#> GSM1068552 4 0.3123 0.7253 0.000 0.184 0.000 0.812 0.004
#> GSM1068555 2 0.1768 0.8378 0.000 0.924 0.000 0.004 0.072
#> GSM1068556 4 0.3359 0.7458 0.000 0.000 0.108 0.840 0.052
#> GSM1068557 2 0.3127 0.7860 0.000 0.848 0.020 0.004 0.128
#> GSM1068560 4 0.2842 0.7841 0.000 0.012 0.044 0.888 0.056
#> GSM1068561 5 0.6807 0.4069 0.176 0.116 0.104 0.000 0.604
#> GSM1068562 4 0.2597 0.7936 0.000 0.020 0.036 0.904 0.040
#> GSM1068563 4 0.3807 0.7644 0.000 0.116 0.056 0.820 0.008
#> GSM1068565 2 0.0693 0.8534 0.000 0.980 0.000 0.012 0.008
#> GSM1068529 3 0.5781 0.4918 0.120 0.012 0.692 0.020 0.156
#> GSM1068530 1 0.3612 0.4019 0.732 0.000 0.000 0.000 0.268
#> GSM1068534 1 0.7025 0.0730 0.440 0.004 0.404 0.048 0.104
#> GSM1068536 5 0.2984 0.5944 0.124 0.000 0.004 0.016 0.856
#> GSM1068541 5 0.5540 0.6131 0.084 0.120 0.004 0.064 0.728
#> GSM1068553 4 0.2006 0.7937 0.000 0.024 0.020 0.932 0.024
#> GSM1068554 4 0.3946 0.7505 0.000 0.124 0.032 0.816 0.028
#> GSM1068558 3 0.3641 0.4860 0.152 0.004 0.816 0.004 0.024
#> GSM1068559 3 0.5044 0.4000 0.000 0.044 0.672 0.272 0.012
#> GSM1068564 4 0.4592 0.5190 0.000 0.332 0.000 0.644 0.024
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1068478 5 0.4039 0.43590 0.352 0.000 0.016 0.000 0.632 0.000
#> GSM1068479 6 0.2851 0.72177 0.000 0.132 0.020 0.004 0.000 0.844
#> GSM1068481 3 0.4344 0.41922 0.424 0.000 0.556 0.000 0.004 0.016
#> GSM1068482 3 0.4135 0.58422 0.248 0.000 0.712 0.000 0.012 0.028
#> GSM1068483 1 0.5184 0.13393 0.608 0.000 0.292 0.000 0.088 0.012
#> GSM1068486 3 0.4710 0.57680 0.104 0.000 0.684 0.000 0.004 0.208
#> GSM1068487 2 0.1116 0.82289 0.000 0.960 0.004 0.028 0.000 0.008
#> GSM1068488 6 0.6788 -0.07643 0.012 0.000 0.132 0.348 0.060 0.448
#> GSM1068490 2 0.1528 0.81917 0.000 0.944 0.012 0.028 0.000 0.016
#> GSM1068491 6 0.2828 0.73128 0.000 0.040 0.080 0.012 0.000 0.868
#> GSM1068492 6 0.2722 0.73870 0.000 0.048 0.008 0.060 0.004 0.880
#> GSM1068493 3 0.6382 0.41318 0.244 0.012 0.516 0.000 0.208 0.020
#> GSM1068494 3 0.6977 0.01322 0.304 0.000 0.348 0.000 0.292 0.056
#> GSM1068495 5 0.3463 0.69720 0.088 0.032 0.020 0.004 0.844 0.012
#> GSM1068496 1 0.4018 -0.12637 0.580 0.000 0.412 0.000 0.008 0.000
#> GSM1068498 5 0.3955 0.68100 0.148 0.064 0.012 0.000 0.776 0.000
#> GSM1068499 3 0.5503 0.35845 0.372 0.000 0.520 0.000 0.096 0.012
#> GSM1068500 1 0.4684 -0.03468 0.576 0.000 0.372 0.000 0.052 0.000
#> GSM1068502 6 0.2933 0.66735 0.000 0.200 0.000 0.004 0.000 0.796
#> GSM1068503 2 0.3419 0.73656 0.000 0.820 0.020 0.136 0.004 0.020
#> GSM1068505 4 0.2231 0.70749 0.000 0.008 0.048 0.912 0.020 0.012
#> GSM1068506 4 0.2587 0.71566 0.000 0.048 0.028 0.896 0.016 0.012
#> GSM1068507 4 0.5841 0.56274 0.000 0.196 0.044 0.620 0.004 0.136
#> GSM1068508 2 0.5251 0.32381 0.000 0.592 0.020 0.336 0.036 0.016
#> GSM1068510 4 0.7716 0.25168 0.000 0.276 0.076 0.384 0.040 0.224
#> GSM1068512 4 0.6153 0.31518 0.008 0.000 0.116 0.500 0.028 0.348
#> GSM1068513 2 0.5360 0.53208 0.000 0.656 0.044 0.232 0.008 0.060
#> GSM1068514 6 0.2382 0.74297 0.000 0.020 0.024 0.048 0.004 0.904
#> GSM1068517 5 0.3963 0.66644 0.060 0.140 0.012 0.000 0.784 0.004
#> GSM1068518 5 0.8622 0.07281 0.112 0.000 0.260 0.136 0.300 0.192
#> GSM1068520 1 0.3565 0.33010 0.692 0.000 0.004 0.000 0.304 0.000
#> GSM1068521 1 0.4364 0.14775 0.608 0.000 0.024 0.004 0.364 0.000
#> GSM1068522 2 0.4852 0.49493 0.000 0.656 0.048 0.276 0.008 0.012
#> GSM1068524 2 0.3396 0.79545 0.000 0.852 0.012 0.056 0.040 0.040
#> GSM1068527 4 0.4971 0.63840 0.004 0.000 0.080 0.724 0.056 0.136
#> GSM1068480 3 0.4521 0.55807 0.068 0.000 0.716 0.000 0.016 0.200
#> GSM1068484 4 0.6165 0.63406 0.000 0.072 0.076 0.652 0.056 0.144
#> GSM1068485 3 0.5333 0.54998 0.300 0.000 0.564 0.000 0.000 0.136
#> GSM1068489 4 0.2787 0.70336 0.000 0.012 0.072 0.880 0.020 0.016
#> GSM1068497 5 0.4264 0.68396 0.092 0.072 0.040 0.000 0.788 0.008
#> GSM1068501 4 0.6494 0.54987 0.004 0.196 0.084 0.608 0.040 0.068
#> GSM1068504 2 0.1138 0.82511 0.000 0.960 0.000 0.024 0.012 0.004
#> GSM1068509 1 0.6605 0.01650 0.352 0.000 0.300 0.000 0.324 0.024
#> GSM1068511 3 0.4929 0.55423 0.260 0.000 0.664 0.004 0.040 0.032
#> GSM1068515 3 0.7523 0.01529 0.180 0.028 0.412 0.024 0.320 0.036
#> GSM1068516 5 0.5908 0.58104 0.056 0.004 0.084 0.044 0.680 0.132
#> GSM1068519 1 0.4490 0.42338 0.700 0.000 0.104 0.000 0.196 0.000
#> GSM1068523 2 0.3194 0.79200 0.000 0.852 0.012 0.032 0.092 0.012
#> GSM1068525 4 0.7583 0.30288 0.000 0.068 0.104 0.428 0.088 0.312
#> GSM1068526 4 0.2501 0.71329 0.000 0.004 0.040 0.896 0.012 0.048
#> GSM1068458 1 0.1719 0.59205 0.924 0.000 0.016 0.000 0.060 0.000
#> GSM1068459 3 0.4205 0.43469 0.420 0.000 0.564 0.000 0.000 0.016
#> GSM1068460 1 0.6623 0.02751 0.524 0.004 0.060 0.128 0.276 0.008
#> GSM1068461 3 0.5277 0.33865 0.088 0.000 0.512 0.000 0.004 0.396
#> GSM1068464 2 0.1232 0.82320 0.000 0.956 0.000 0.016 0.004 0.024
#> GSM1068468 2 0.3116 0.78138 0.004 0.864 0.012 0.008 0.040 0.072
#> GSM1068472 2 0.7494 -0.04581 0.044 0.388 0.244 0.000 0.280 0.044
#> GSM1068473 2 0.1511 0.81768 0.000 0.944 0.012 0.032 0.000 0.012
#> GSM1068474 2 0.1036 0.82339 0.000 0.964 0.008 0.024 0.000 0.004
#> GSM1068476 6 0.2619 0.74471 0.000 0.056 0.048 0.012 0.000 0.884
#> GSM1068477 2 0.1579 0.82502 0.000 0.944 0.004 0.024 0.020 0.008
#> GSM1068462 2 0.3482 0.75214 0.000 0.824 0.020 0.000 0.108 0.048
#> GSM1068463 3 0.4141 0.41541 0.432 0.000 0.556 0.000 0.000 0.012
#> GSM1068465 1 0.7237 -0.00252 0.340 0.004 0.336 0.020 0.268 0.032
#> GSM1068466 1 0.3136 0.50161 0.796 0.000 0.016 0.000 0.188 0.000
#> GSM1068467 2 0.4711 0.61266 0.000 0.704 0.008 0.004 0.192 0.092
#> GSM1068469 2 0.6795 -0.02793 0.020 0.412 0.192 0.000 0.352 0.024
#> GSM1068470 2 0.1836 0.81822 0.000 0.928 0.004 0.012 0.048 0.008
#> GSM1068471 2 0.1053 0.82431 0.000 0.964 0.004 0.020 0.012 0.000
#> GSM1068475 2 0.1059 0.82366 0.000 0.964 0.004 0.016 0.016 0.000
#> GSM1068528 1 0.3944 -0.16449 0.568 0.000 0.428 0.000 0.004 0.000
#> GSM1068531 1 0.1196 0.59232 0.952 0.000 0.008 0.000 0.040 0.000
#> GSM1068532 1 0.1501 0.54433 0.924 0.000 0.076 0.000 0.000 0.000
#> GSM1068533 1 0.1367 0.57206 0.944 0.000 0.044 0.000 0.012 0.000
#> GSM1068535 1 0.6667 0.22015 0.520 0.004 0.088 0.308 0.032 0.048
#> GSM1068537 1 0.1267 0.56071 0.940 0.000 0.060 0.000 0.000 0.000
#> GSM1068538 1 0.1075 0.56775 0.952 0.000 0.048 0.000 0.000 0.000
#> GSM1068539 5 0.4525 0.67494 0.056 0.060 0.048 0.012 0.796 0.028
#> GSM1068540 1 0.2066 0.58881 0.904 0.000 0.024 0.000 0.072 0.000
#> GSM1068542 4 0.1425 0.71569 0.000 0.008 0.020 0.952 0.008 0.012
#> GSM1068543 4 0.5345 0.34630 0.000 0.000 0.048 0.536 0.032 0.384
#> GSM1068544 1 0.4468 -0.36118 0.492 0.000 0.484 0.000 0.004 0.020
#> GSM1068545 4 0.4956 0.45630 0.000 0.320 0.020 0.620 0.032 0.008
#> GSM1068546 3 0.5598 0.59645 0.228 0.004 0.604 0.000 0.012 0.152
#> GSM1068547 1 0.3187 0.49135 0.796 0.000 0.012 0.004 0.188 0.000
#> GSM1068548 4 0.2554 0.71325 0.000 0.000 0.044 0.892 0.024 0.040
#> GSM1068549 6 0.3695 0.45142 0.016 0.000 0.272 0.000 0.000 0.712
#> GSM1068550 4 0.1755 0.71727 0.000 0.008 0.028 0.932 0.032 0.000
#> GSM1068551 2 0.1743 0.82150 0.000 0.936 0.004 0.024 0.028 0.008
#> GSM1068552 4 0.3958 0.65666 0.000 0.172 0.020 0.776 0.020 0.012
#> GSM1068555 2 0.2976 0.76675 0.000 0.844 0.008 0.004 0.128 0.016
#> GSM1068556 4 0.4920 0.60704 0.000 0.004 0.064 0.704 0.036 0.192
#> GSM1068557 2 0.4973 0.61274 0.000 0.684 0.020 0.004 0.212 0.080
#> GSM1068560 4 0.4591 0.68282 0.004 0.016 0.060 0.780 0.064 0.076
#> GSM1068561 5 0.5658 0.45955 0.012 0.064 0.272 0.004 0.616 0.032
#> GSM1068562 4 0.4412 0.69787 0.000 0.020 0.052 0.784 0.044 0.100
#> GSM1068563 4 0.5031 0.68076 0.000 0.084 0.052 0.744 0.028 0.092
#> GSM1068565 2 0.1003 0.82412 0.000 0.964 0.000 0.028 0.004 0.004
#> GSM1068529 6 0.6735 0.10059 0.012 0.004 0.364 0.020 0.196 0.404
#> GSM1068530 1 0.0914 0.58717 0.968 0.000 0.016 0.000 0.016 0.000
#> GSM1068534 3 0.5598 0.54395 0.072 0.004 0.712 0.040 0.076 0.096
#> GSM1068536 5 0.4263 0.55880 0.276 0.000 0.032 0.008 0.684 0.000
#> GSM1068541 5 0.6584 0.56564 0.192 0.052 0.040 0.116 0.596 0.004
#> GSM1068553 4 0.3683 0.68813 0.004 0.016 0.080 0.836 0.028 0.036
#> GSM1068554 4 0.5853 0.60703 0.004 0.124 0.076 0.684 0.032 0.080
#> GSM1068558 3 0.4965 0.03795 0.016 0.000 0.504 0.012 0.016 0.452
#> GSM1068559 6 0.3040 0.72246 0.000 0.024 0.024 0.088 0.004 0.860
#> GSM1068564 4 0.5086 0.33366 0.000 0.376 0.040 0.564 0.012 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 disease.state(p) gender(p) k
#> CV:skmeans 107 0.963896 0.747 2
#> CV:skmeans 92 0.005553 0.913 3
#> CV:skmeans 79 0.011492 0.699 4
#> CV:skmeans 62 0.005429 0.186 5
#> CV:skmeans 70 0.000438 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["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 38950 rows and 108 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#> Subgroups are detected by 'pam' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.356 0.697 0.842 0.4977 0.496 0.496
#> 3 3 0.496 0.726 0.857 0.3242 0.697 0.463
#> 4 4 0.501 0.441 0.694 0.1074 0.845 0.581
#> 5 5 0.646 0.697 0.836 0.0749 0.877 0.584
#> 6 6 0.641 0.534 0.721 0.0375 0.945 0.753
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 3
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM1068478 1 0.0000 0.8226 1.000 0.000
#> GSM1068479 2 0.0672 0.7607 0.008 0.992
#> GSM1068481 1 0.3274 0.8040 0.940 0.060
#> GSM1068482 1 0.8267 0.7119 0.740 0.260
#> GSM1068483 1 0.0000 0.8226 1.000 0.000
#> GSM1068486 1 0.9129 0.4335 0.672 0.328
#> GSM1068487 2 0.7219 0.7875 0.200 0.800
#> GSM1068488 2 0.6148 0.6264 0.152 0.848
#> GSM1068490 2 0.7219 0.7875 0.200 0.800
#> GSM1068491 2 0.0000 0.7576 0.000 1.000
#> GSM1068492 2 0.0000 0.7576 0.000 1.000
#> GSM1068493 1 0.0938 0.8212 0.988 0.012
#> GSM1068494 1 0.7528 0.7358 0.784 0.216
#> GSM1068495 1 0.0000 0.8226 1.000 0.000
#> GSM1068496 1 0.3274 0.8058 0.940 0.060
#> GSM1068498 1 0.0000 0.8226 1.000 0.000
#> GSM1068499 1 0.8267 0.7119 0.740 0.260
#> GSM1068500 1 0.0000 0.8226 1.000 0.000
#> GSM1068502 2 0.0000 0.7576 0.000 1.000
#> GSM1068503 2 0.7056 0.7891 0.192 0.808
#> GSM1068505 1 0.9552 0.1300 0.624 0.376
#> GSM1068506 2 0.8327 0.7663 0.264 0.736
#> GSM1068507 2 0.2603 0.7707 0.044 0.956
#> GSM1068508 2 0.8081 0.7740 0.248 0.752
#> GSM1068510 2 0.0000 0.7576 0.000 1.000
#> GSM1068512 2 0.6801 0.5854 0.180 0.820
#> GSM1068513 2 0.7139 0.7884 0.196 0.804
#> GSM1068514 2 0.0000 0.7576 0.000 1.000
#> GSM1068517 1 0.0376 0.8211 0.996 0.004
#> GSM1068518 2 0.8327 0.4324 0.264 0.736
#> GSM1068520 1 0.0000 0.8226 1.000 0.000
#> GSM1068521 1 0.0000 0.8226 1.000 0.000
#> GSM1068522 2 0.8327 0.7663 0.264 0.736
#> GSM1068524 2 0.7376 0.7875 0.208 0.792
#> GSM1068527 1 0.9795 0.4973 0.584 0.416
#> GSM1068480 1 0.8267 0.7119 0.740 0.260
#> GSM1068484 2 0.0000 0.7576 0.000 1.000
#> GSM1068485 1 0.8267 0.7119 0.740 0.260
#> GSM1068489 2 0.3431 0.7533 0.064 0.936
#> GSM1068497 1 0.0376 0.8211 0.996 0.004
#> GSM1068501 2 0.0000 0.7576 0.000 1.000
#> GSM1068504 2 0.6973 0.7893 0.188 0.812
#> GSM1068509 1 0.7219 0.7400 0.800 0.200
#> GSM1068511 1 0.8081 0.7213 0.752 0.248
#> GSM1068515 1 0.8386 0.4681 0.732 0.268
#> GSM1068516 1 0.8909 0.6816 0.692 0.308
#> GSM1068519 1 0.7453 0.7372 0.788 0.212
#> GSM1068523 2 0.8207 0.7701 0.256 0.744
#> GSM1068525 2 0.0000 0.7576 0.000 1.000
#> GSM1068526 2 0.0672 0.7586 0.008 0.992
#> GSM1068458 1 0.0000 0.8226 1.000 0.000
#> GSM1068459 1 0.8267 0.7119 0.740 0.260
#> GSM1068460 1 0.0672 0.8197 0.992 0.008
#> GSM1068461 2 0.9970 -0.3054 0.468 0.532
#> GSM1068464 2 0.7219 0.7875 0.200 0.800
#> GSM1068468 2 0.7376 0.7867 0.208 0.792
#> GSM1068472 1 0.2948 0.8052 0.948 0.052
#> GSM1068473 2 0.7299 0.7870 0.204 0.796
#> GSM1068474 2 0.8267 0.7674 0.260 0.740
#> GSM1068476 2 0.0376 0.7567 0.004 0.996
#> GSM1068477 2 0.8327 0.7663 0.264 0.736
#> GSM1068462 2 0.7219 0.7875 0.200 0.800
#> GSM1068463 1 0.4562 0.7988 0.904 0.096
#> GSM1068465 1 0.9815 -0.0355 0.580 0.420
#> GSM1068466 1 0.0000 0.8226 1.000 0.000
#> GSM1068467 2 0.8267 0.7684 0.260 0.740
#> GSM1068469 2 0.9881 0.4884 0.436 0.564
#> GSM1068470 2 0.8267 0.7674 0.260 0.740
#> GSM1068471 2 0.7219 0.7875 0.200 0.800
#> GSM1068475 2 0.8207 0.7697 0.256 0.744
#> GSM1068528 1 0.0376 0.8224 0.996 0.004
#> GSM1068531 1 0.0000 0.8226 1.000 0.000
#> GSM1068532 1 0.7299 0.7392 0.796 0.204
#> GSM1068533 1 0.0000 0.8226 1.000 0.000
#> GSM1068535 1 0.9000 0.6736 0.684 0.316
#> GSM1068537 1 0.4022 0.8030 0.920 0.080
#> GSM1068538 1 0.0000 0.8226 1.000 0.000
#> GSM1068539 1 0.0672 0.8229 0.992 0.008
#> GSM1068540 1 0.0938 0.8224 0.988 0.012
#> GSM1068542 2 0.8327 0.7669 0.264 0.736
#> GSM1068543 2 0.8813 0.2921 0.300 0.700
#> GSM1068544 1 0.7674 0.7319 0.776 0.224
#> GSM1068545 2 0.8267 0.7674 0.260 0.740
#> GSM1068546 1 0.8267 0.7119 0.740 0.260
#> GSM1068547 1 0.0000 0.8226 1.000 0.000
#> GSM1068548 1 0.8813 0.4531 0.700 0.300
#> GSM1068549 2 0.0376 0.7567 0.004 0.996
#> GSM1068550 2 0.8207 0.7712 0.256 0.744
#> GSM1068551 2 0.8267 0.7674 0.260 0.740
#> GSM1068552 2 0.8327 0.7663 0.264 0.736
#> GSM1068555 2 0.7299 0.7872 0.204 0.796
#> GSM1068556 2 0.9963 -0.2870 0.464 0.536
#> GSM1068557 2 0.9815 0.5576 0.420 0.580
#> GSM1068560 1 0.9522 0.4331 0.628 0.372
#> GSM1068561 1 0.0672 0.8198 0.992 0.008
#> GSM1068562 2 0.0376 0.7567 0.004 0.996
#> GSM1068563 2 0.0376 0.7567 0.004 0.996
#> GSM1068565 2 0.8267 0.7674 0.260 0.740
#> GSM1068529 1 0.9922 0.4867 0.552 0.448
#> GSM1068530 1 0.0000 0.8226 1.000 0.000
#> GSM1068534 1 0.6887 0.6940 0.816 0.184
#> GSM1068536 1 0.0000 0.8226 1.000 0.000
#> GSM1068541 1 0.3733 0.7727 0.928 0.072
#> GSM1068553 2 0.9970 -0.3036 0.468 0.532
#> GSM1068554 2 0.0000 0.7576 0.000 1.000
#> GSM1068558 2 0.7674 0.4797 0.224 0.776
#> GSM1068559 2 0.0000 0.7576 0.000 1.000
#> GSM1068564 2 0.6801 0.7821 0.180 0.820
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1068478 1 0.3038 0.899 0.896 0.104 0.000
#> GSM1068479 3 0.4504 0.644 0.000 0.196 0.804
#> GSM1068481 1 0.4128 0.886 0.856 0.132 0.012
#> GSM1068482 3 0.5785 0.571 0.332 0.000 0.668
#> GSM1068483 1 0.3038 0.899 0.896 0.104 0.000
#> GSM1068486 3 0.4960 0.708 0.040 0.128 0.832
#> GSM1068487 2 0.0000 0.822 0.000 1.000 0.000
#> GSM1068488 3 0.0237 0.791 0.000 0.004 0.996
#> GSM1068490 2 0.0000 0.822 0.000 1.000 0.000
#> GSM1068491 3 0.0237 0.791 0.000 0.004 0.996
#> GSM1068492 3 0.0237 0.791 0.000 0.004 0.996
#> GSM1068493 1 0.3965 0.887 0.860 0.132 0.008
#> GSM1068494 1 0.3038 0.848 0.896 0.000 0.104
#> GSM1068495 1 0.2261 0.900 0.932 0.068 0.000
#> GSM1068496 1 0.5728 0.681 0.772 0.032 0.196
#> GSM1068498 1 0.3192 0.897 0.888 0.112 0.000
#> GSM1068499 1 0.3619 0.824 0.864 0.000 0.136
#> GSM1068500 1 0.3038 0.899 0.896 0.104 0.000
#> GSM1068502 3 0.0237 0.791 0.000 0.004 0.996
#> GSM1068503 2 0.0000 0.822 0.000 1.000 0.000
#> GSM1068505 2 0.4842 0.671 0.224 0.776 0.000
#> GSM1068506 2 0.4058 0.788 0.044 0.880 0.076
#> GSM1068507 3 0.5785 0.453 0.000 0.332 0.668
#> GSM1068508 2 0.2229 0.809 0.012 0.944 0.044
#> GSM1068510 2 0.6280 0.211 0.000 0.540 0.460
#> GSM1068512 3 0.0237 0.791 0.000 0.004 0.996
#> GSM1068513 2 0.4062 0.732 0.000 0.836 0.164
#> GSM1068514 3 0.0237 0.791 0.000 0.004 0.996
#> GSM1068517 1 0.3192 0.897 0.888 0.112 0.000
#> GSM1068518 3 0.0237 0.791 0.000 0.004 0.996
#> GSM1068520 1 0.2959 0.900 0.900 0.100 0.000
#> GSM1068521 1 0.2261 0.900 0.932 0.068 0.000
#> GSM1068522 2 0.1643 0.814 0.044 0.956 0.000
#> GSM1068524 2 0.0424 0.822 0.000 0.992 0.008
#> GSM1068527 3 0.6435 0.708 0.168 0.076 0.756
#> GSM1068480 3 0.4842 0.671 0.224 0.000 0.776
#> GSM1068484 2 0.5988 0.422 0.000 0.632 0.368
#> GSM1068485 3 0.6154 0.262 0.408 0.000 0.592
#> GSM1068489 2 0.7238 0.473 0.044 0.628 0.328
#> GSM1068497 1 0.3192 0.897 0.888 0.112 0.000
#> GSM1068501 3 0.5760 0.463 0.000 0.328 0.672
#> GSM1068504 2 0.0424 0.822 0.000 0.992 0.008
#> GSM1068509 1 0.2448 0.864 0.924 0.000 0.076
#> GSM1068511 1 0.4172 0.804 0.840 0.004 0.156
#> GSM1068515 2 0.5905 0.370 0.352 0.648 0.000
#> GSM1068516 3 0.5692 0.616 0.268 0.008 0.724
#> GSM1068519 1 0.5138 0.658 0.748 0.000 0.252
#> GSM1068523 2 0.0892 0.821 0.020 0.980 0.000
#> GSM1068525 3 0.6309 -0.134 0.000 0.496 0.504
#> GSM1068526 2 0.5335 0.641 0.008 0.760 0.232
#> GSM1068458 1 0.2878 0.893 0.904 0.096 0.000
#> GSM1068459 3 0.6204 0.310 0.424 0.000 0.576
#> GSM1068460 1 0.3752 0.848 0.856 0.144 0.000
#> GSM1068461 3 0.0000 0.790 0.000 0.000 1.000
#> GSM1068464 2 0.2796 0.780 0.000 0.908 0.092
#> GSM1068468 2 0.5016 0.653 0.000 0.760 0.240
#> GSM1068472 1 0.4861 0.846 0.808 0.180 0.012
#> GSM1068473 2 0.0000 0.822 0.000 1.000 0.000
#> GSM1068474 2 0.0000 0.822 0.000 1.000 0.000
#> GSM1068476 3 0.0237 0.791 0.000 0.004 0.996
#> GSM1068477 2 0.1643 0.814 0.044 0.956 0.000
#> GSM1068462 2 0.4399 0.696 0.000 0.812 0.188
#> GSM1068463 1 0.4443 0.840 0.864 0.052 0.084
#> GSM1068465 2 0.7796 0.233 0.392 0.552 0.056
#> GSM1068466 1 0.2711 0.902 0.912 0.088 0.000
#> GSM1068467 2 0.5497 0.571 0.000 0.708 0.292
#> GSM1068469 2 0.5650 0.488 0.312 0.688 0.000
#> GSM1068470 2 0.0000 0.822 0.000 1.000 0.000
#> GSM1068471 2 0.0000 0.822 0.000 1.000 0.000
#> GSM1068475 2 0.0000 0.822 0.000 1.000 0.000
#> GSM1068528 1 0.2796 0.897 0.908 0.092 0.000
#> GSM1068531 1 0.0000 0.881 1.000 0.000 0.000
#> GSM1068532 1 0.2959 0.825 0.900 0.000 0.100
#> GSM1068533 1 0.0000 0.881 1.000 0.000 0.000
#> GSM1068535 3 0.1753 0.780 0.048 0.000 0.952
#> GSM1068537 1 0.0237 0.879 0.996 0.000 0.004
#> GSM1068538 1 0.0000 0.881 1.000 0.000 0.000
#> GSM1068539 1 0.2261 0.900 0.932 0.068 0.000
#> GSM1068540 1 0.0000 0.881 1.000 0.000 0.000
#> GSM1068542 2 0.6688 0.501 0.028 0.664 0.308
#> GSM1068543 3 0.0237 0.791 0.000 0.004 0.996
#> GSM1068544 1 0.3686 0.788 0.860 0.000 0.140
#> GSM1068545 2 0.1643 0.814 0.044 0.956 0.000
#> GSM1068546 3 0.5291 0.617 0.268 0.000 0.732
#> GSM1068547 1 0.2356 0.900 0.928 0.072 0.000
#> GSM1068548 3 0.9930 0.129 0.276 0.360 0.364
#> GSM1068549 3 0.0000 0.790 0.000 0.000 1.000
#> GSM1068550 2 0.3780 0.795 0.044 0.892 0.064
#> GSM1068551 2 0.1289 0.817 0.032 0.968 0.000
#> GSM1068552 2 0.1411 0.817 0.036 0.964 0.000
#> GSM1068555 2 0.0000 0.822 0.000 1.000 0.000
#> GSM1068556 3 0.3590 0.757 0.028 0.076 0.896
#> GSM1068557 2 0.6758 0.444 0.360 0.620 0.020
#> GSM1068560 2 0.8957 0.365 0.312 0.536 0.152
#> GSM1068561 1 0.3686 0.884 0.860 0.140 0.000
#> GSM1068562 3 0.3412 0.732 0.000 0.124 0.876
#> GSM1068563 3 0.2711 0.755 0.000 0.088 0.912
#> GSM1068565 2 0.0000 0.822 0.000 1.000 0.000
#> GSM1068529 3 0.0747 0.790 0.016 0.000 0.984
#> GSM1068530 1 0.0000 0.881 1.000 0.000 0.000
#> GSM1068534 3 0.8543 0.467 0.268 0.140 0.592
#> GSM1068536 1 0.2261 0.900 0.932 0.068 0.000
#> GSM1068541 1 0.6140 0.412 0.596 0.404 0.000
#> GSM1068553 3 0.9512 0.403 0.248 0.260 0.492
#> GSM1068554 2 0.5016 0.636 0.000 0.760 0.240
#> GSM1068558 3 0.5863 0.723 0.120 0.084 0.796
#> GSM1068559 3 0.0237 0.791 0.000 0.004 0.996
#> GSM1068564 2 0.2564 0.807 0.036 0.936 0.028
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1068478 1 0.2149 0.80957 0.912 0.088 0.000 0.000
#> GSM1068479 3 0.7717 0.28166 0.000 0.232 0.424 0.344
#> GSM1068481 1 0.6919 0.43258 0.500 0.112 0.388 0.000
#> GSM1068482 3 0.2480 0.30670 0.088 0.000 0.904 0.008
#> GSM1068483 1 0.2216 0.80872 0.908 0.092 0.000 0.000
#> GSM1068486 3 0.8063 0.41474 0.048 0.112 0.448 0.392
#> GSM1068487 2 0.0376 0.73626 0.004 0.992 0.000 0.004
#> GSM1068488 3 0.4961 0.53795 0.000 0.000 0.552 0.448
#> GSM1068490 2 0.0376 0.73626 0.004 0.992 0.000 0.004
#> GSM1068491 3 0.4961 0.53795 0.000 0.000 0.552 0.448
#> GSM1068492 3 0.4961 0.53795 0.000 0.000 0.552 0.448
#> GSM1068493 1 0.2988 0.80130 0.876 0.112 0.000 0.012
#> GSM1068494 1 0.3453 0.77073 0.868 0.000 0.080 0.052
#> GSM1068495 1 0.2926 0.80850 0.896 0.048 0.000 0.056
#> GSM1068496 1 0.4889 0.43342 0.636 0.004 0.360 0.000
#> GSM1068498 1 0.2216 0.81014 0.908 0.092 0.000 0.000
#> GSM1068499 1 0.3325 0.74632 0.864 0.000 0.112 0.024
#> GSM1068500 1 0.2796 0.80925 0.892 0.092 0.016 0.000
#> GSM1068502 3 0.4961 0.53795 0.000 0.000 0.552 0.448
#> GSM1068503 2 0.0895 0.73426 0.004 0.976 0.000 0.020
#> GSM1068505 4 0.7211 0.27486 0.248 0.204 0.000 0.548
#> GSM1068506 4 0.4985 -0.15547 0.000 0.468 0.000 0.532
#> GSM1068507 2 0.7921 -0.31720 0.000 0.348 0.320 0.332
#> GSM1068508 2 0.0937 0.73477 0.012 0.976 0.000 0.012
#> GSM1068510 2 0.7121 0.21624 0.000 0.544 0.292 0.164
#> GSM1068512 3 0.4961 0.53795 0.000 0.000 0.552 0.448
#> GSM1068513 2 0.2115 0.71895 0.004 0.936 0.024 0.036
#> GSM1068514 3 0.4961 0.53795 0.000 0.000 0.552 0.448
#> GSM1068517 1 0.2216 0.81014 0.908 0.092 0.000 0.000
#> GSM1068518 3 0.4961 0.53795 0.000 0.000 0.552 0.448
#> GSM1068520 1 0.2334 0.81005 0.908 0.088 0.000 0.004
#> GSM1068521 1 0.2926 0.80884 0.896 0.048 0.000 0.056
#> GSM1068522 2 0.4992 0.21248 0.000 0.524 0.000 0.476
#> GSM1068524 2 0.2665 0.70723 0.004 0.900 0.008 0.088
#> GSM1068527 4 0.6705 -0.05952 0.148 0.000 0.244 0.608
#> GSM1068480 3 0.1637 0.38357 0.000 0.000 0.940 0.060
#> GSM1068484 2 0.7345 0.18060 0.000 0.492 0.172 0.336
#> GSM1068485 3 0.5522 0.15108 0.288 0.000 0.668 0.044
#> GSM1068489 4 0.5040 0.00892 0.000 0.364 0.008 0.628
#> GSM1068497 1 0.2149 0.80957 0.912 0.088 0.000 0.000
#> GSM1068501 4 0.5670 0.25777 0.000 0.152 0.128 0.720
#> GSM1068504 2 0.1489 0.72273 0.000 0.952 0.004 0.044
#> GSM1068509 1 0.3806 0.75619 0.824 0.000 0.020 0.156
#> GSM1068511 3 0.6672 -0.25554 0.408 0.000 0.504 0.088
#> GSM1068515 4 0.7500 0.05338 0.400 0.156 0.004 0.440
#> GSM1068516 3 0.7215 0.23025 0.348 0.000 0.500 0.152
#> GSM1068519 1 0.5993 0.58945 0.692 0.000 0.148 0.160
#> GSM1068523 2 0.3725 0.63113 0.008 0.812 0.000 0.180
#> GSM1068525 2 0.6640 0.20802 0.000 0.552 0.352 0.096
#> GSM1068526 4 0.6611 -0.10890 0.000 0.456 0.080 0.464
#> GSM1068458 1 0.4610 0.78052 0.804 0.068 0.004 0.124
#> GSM1068459 3 0.4955 0.06187 0.344 0.000 0.648 0.008
#> GSM1068460 1 0.5414 0.46637 0.604 0.020 0.000 0.376
#> GSM1068461 3 0.4477 0.49494 0.000 0.000 0.688 0.312
#> GSM1068464 2 0.0524 0.73515 0.008 0.988 0.000 0.004
#> GSM1068468 2 0.4059 0.64487 0.008 0.844 0.092 0.056
#> GSM1068472 1 0.3627 0.78152 0.840 0.144 0.008 0.008
#> GSM1068473 2 0.0524 0.73567 0.008 0.988 0.000 0.004
#> GSM1068474 2 0.0188 0.73621 0.000 0.996 0.000 0.004
#> GSM1068476 3 0.4961 0.53795 0.000 0.000 0.552 0.448
#> GSM1068477 2 0.4837 0.41970 0.004 0.648 0.000 0.348
#> GSM1068462 2 0.3127 0.67542 0.008 0.892 0.032 0.068
#> GSM1068463 1 0.5669 0.28636 0.516 0.016 0.464 0.004
#> GSM1068465 1 0.8020 -0.13338 0.380 0.276 0.004 0.340
#> GSM1068466 1 0.2596 0.81262 0.908 0.068 0.000 0.024
#> GSM1068467 2 0.4184 0.64359 0.008 0.836 0.056 0.100
#> GSM1068469 2 0.3074 0.61085 0.152 0.848 0.000 0.000
#> GSM1068470 2 0.3142 0.67523 0.008 0.860 0.000 0.132
#> GSM1068471 2 0.0188 0.73570 0.004 0.996 0.000 0.000
#> GSM1068475 2 0.0376 0.73567 0.004 0.992 0.000 0.004
#> GSM1068528 1 0.3442 0.80443 0.880 0.068 0.040 0.012
#> GSM1068531 1 0.3249 0.77323 0.852 0.000 0.008 0.140
#> GSM1068532 3 0.6504 -0.28275 0.452 0.000 0.476 0.072
#> GSM1068533 1 0.3198 0.76645 0.880 0.000 0.040 0.080
#> GSM1068535 4 0.4713 -0.37763 0.000 0.000 0.360 0.640
#> GSM1068537 1 0.4673 0.53567 0.700 0.000 0.292 0.008
#> GSM1068538 1 0.4426 0.72765 0.812 0.000 0.096 0.092
#> GSM1068539 1 0.3850 0.79301 0.840 0.044 0.000 0.116
#> GSM1068540 1 0.0524 0.79187 0.988 0.000 0.008 0.004
#> GSM1068542 4 0.4605 0.16081 0.000 0.336 0.000 0.664
#> GSM1068543 4 0.4998 -0.51829 0.000 0.000 0.488 0.512
#> GSM1068544 3 0.5292 -0.25617 0.480 0.000 0.512 0.008
#> GSM1068545 2 0.4933 0.27044 0.000 0.568 0.000 0.432
#> GSM1068546 3 0.6523 0.26919 0.348 0.000 0.564 0.088
#> GSM1068547 1 0.4153 0.78347 0.820 0.048 0.000 0.132
#> GSM1068548 4 0.8684 0.23410 0.292 0.168 0.072 0.468
#> GSM1068549 3 0.4961 0.53795 0.000 0.000 0.552 0.448
#> GSM1068550 4 0.4955 -0.11807 0.000 0.444 0.000 0.556
#> GSM1068551 2 0.2281 0.69913 0.000 0.904 0.000 0.096
#> GSM1068552 2 0.4998 0.16106 0.000 0.512 0.000 0.488
#> GSM1068555 2 0.0336 0.73539 0.008 0.992 0.000 0.000
#> GSM1068556 4 0.4605 -0.28503 0.000 0.000 0.336 0.664
#> GSM1068557 2 0.6233 0.43168 0.216 0.660 0.000 0.124
#> GSM1068560 4 0.6907 0.17095 0.348 0.120 0.000 0.532
#> GSM1068561 1 0.3032 0.79847 0.868 0.124 0.000 0.008
#> GSM1068562 4 0.5047 -0.20952 0.000 0.016 0.316 0.668
#> GSM1068563 4 0.4624 -0.26347 0.000 0.000 0.340 0.660
#> GSM1068565 2 0.0336 0.73670 0.000 0.992 0.000 0.008
#> GSM1068529 3 0.5353 0.53215 0.012 0.000 0.556 0.432
#> GSM1068530 1 0.1545 0.78048 0.952 0.000 0.040 0.008
#> GSM1068534 3 0.8893 0.12418 0.356 0.116 0.412 0.116
#> GSM1068536 1 0.3890 0.78814 0.836 0.028 0.004 0.132
#> GSM1068541 1 0.7063 0.21989 0.508 0.112 0.004 0.376
#> GSM1068553 4 0.8420 0.26377 0.328 0.088 0.104 0.480
#> GSM1068554 4 0.6389 -0.06828 0.000 0.448 0.064 0.488
#> GSM1068558 3 0.8352 0.38602 0.128 0.092 0.540 0.240
#> GSM1068559 3 0.4961 0.53795 0.000 0.000 0.552 0.448
#> GSM1068564 2 0.4989 0.18304 0.000 0.528 0.000 0.472
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM1068478 1 0.0510 0.8487 0.984 0.016 0.000 0.000 0.000
#> GSM1068479 5 0.3690 0.6191 0.000 0.224 0.000 0.012 0.764
#> GSM1068481 3 0.4755 0.6332 0.244 0.060 0.696 0.000 0.000
#> GSM1068482 3 0.0794 0.8415 0.000 0.000 0.972 0.000 0.028
#> GSM1068483 1 0.0609 0.8481 0.980 0.020 0.000 0.000 0.000
#> GSM1068486 5 0.3080 0.7370 0.060 0.060 0.008 0.000 0.872
#> GSM1068487 2 0.1845 0.8376 0.016 0.928 0.000 0.056 0.000
#> GSM1068488 5 0.0000 0.8013 0.000 0.000 0.000 0.000 1.000
#> GSM1068490 2 0.2171 0.8360 0.024 0.912 0.000 0.064 0.000
#> GSM1068491 5 0.0000 0.8013 0.000 0.000 0.000 0.000 1.000
#> GSM1068492 5 0.0000 0.8013 0.000 0.000 0.000 0.000 1.000
#> GSM1068493 1 0.1697 0.8356 0.932 0.060 0.000 0.000 0.008
#> GSM1068494 1 0.2067 0.8425 0.920 0.000 0.000 0.032 0.048
#> GSM1068495 1 0.1331 0.8504 0.952 0.008 0.000 0.040 0.000
#> GSM1068496 1 0.4047 0.4903 0.676 0.004 0.320 0.000 0.000
#> GSM1068498 1 0.0703 0.8477 0.976 0.024 0.000 0.000 0.000
#> GSM1068499 1 0.1892 0.8248 0.916 0.000 0.000 0.004 0.080
#> GSM1068500 1 0.1117 0.8480 0.964 0.020 0.016 0.000 0.000
#> GSM1068502 5 0.0000 0.8013 0.000 0.000 0.000 0.000 1.000
#> GSM1068503 2 0.2236 0.8361 0.024 0.908 0.000 0.068 0.000
#> GSM1068505 4 0.0451 0.7350 0.000 0.008 0.004 0.988 0.000
#> GSM1068506 4 0.1365 0.7468 0.004 0.040 0.004 0.952 0.000
#> GSM1068507 5 0.5198 0.4530 0.004 0.284 0.000 0.064 0.648
#> GSM1068508 2 0.1568 0.8420 0.020 0.944 0.000 0.036 0.000
#> GSM1068510 2 0.5616 0.3240 0.000 0.552 0.000 0.084 0.364
#> GSM1068512 5 0.0000 0.8013 0.000 0.000 0.000 0.000 1.000
#> GSM1068513 2 0.2684 0.8315 0.024 0.900 0.000 0.032 0.044
#> GSM1068514 5 0.0000 0.8013 0.000 0.000 0.000 0.000 1.000
#> GSM1068517 1 0.0703 0.8477 0.976 0.024 0.000 0.000 0.000
#> GSM1068518 5 0.0000 0.8013 0.000 0.000 0.000 0.000 1.000
#> GSM1068520 1 0.0609 0.8481 0.980 0.020 0.000 0.000 0.000
#> GSM1068521 1 0.1408 0.8506 0.948 0.008 0.000 0.044 0.000
#> GSM1068522 4 0.2329 0.7265 0.000 0.124 0.000 0.876 0.000
#> GSM1068524 2 0.2522 0.7843 0.000 0.880 0.000 0.108 0.012
#> GSM1068527 4 0.5547 0.1404 0.060 0.000 0.004 0.532 0.404
#> GSM1068480 3 0.3816 0.5311 0.000 0.000 0.696 0.000 0.304
#> GSM1068484 4 0.6664 0.1575 0.000 0.360 0.000 0.408 0.232
#> GSM1068485 3 0.3231 0.7068 0.004 0.000 0.800 0.000 0.196
#> GSM1068489 4 0.2381 0.7379 0.000 0.052 0.004 0.908 0.036
#> GSM1068497 1 0.0000 0.8498 1.000 0.000 0.000 0.000 0.000
#> GSM1068501 4 0.5500 0.5737 0.000 0.124 0.000 0.640 0.236
#> GSM1068504 2 0.0880 0.8369 0.000 0.968 0.000 0.032 0.000
#> GSM1068509 1 0.3099 0.8117 0.848 0.000 0.008 0.132 0.012
#> GSM1068511 3 0.4679 0.7444 0.156 0.000 0.764 0.040 0.040
#> GSM1068515 4 0.5058 0.6317 0.216 0.068 0.012 0.704 0.000
#> GSM1068516 5 0.4840 0.5357 0.268 0.000 0.000 0.056 0.676
#> GSM1068519 1 0.5243 0.6277 0.680 0.000 0.000 0.132 0.188
#> GSM1068523 2 0.3656 0.6870 0.020 0.784 0.000 0.196 0.000
#> GSM1068525 2 0.4278 0.2146 0.000 0.548 0.000 0.000 0.452
#> GSM1068526 4 0.5265 0.5778 0.000 0.248 0.000 0.656 0.096
#> GSM1068458 1 0.4049 0.7928 0.788 0.040 0.008 0.164 0.000
#> GSM1068459 3 0.0451 0.8454 0.004 0.000 0.988 0.000 0.008
#> GSM1068460 1 0.4591 0.2275 0.516 0.004 0.004 0.476 0.000
#> GSM1068461 5 0.2516 0.7083 0.000 0.000 0.140 0.000 0.860
#> GSM1068464 2 0.0703 0.8380 0.024 0.976 0.000 0.000 0.000
#> GSM1068468 2 0.3915 0.7272 0.024 0.792 0.000 0.012 0.172
#> GSM1068472 1 0.2828 0.7992 0.872 0.104 0.004 0.000 0.020
#> GSM1068473 2 0.1965 0.8374 0.024 0.924 0.000 0.052 0.000
#> GSM1068474 2 0.1484 0.8387 0.008 0.944 0.000 0.048 0.000
#> GSM1068476 5 0.0000 0.8013 0.000 0.000 0.000 0.000 1.000
#> GSM1068477 4 0.3884 0.5752 0.000 0.288 0.004 0.708 0.000
#> GSM1068462 2 0.1978 0.8279 0.024 0.928 0.000 0.004 0.044
#> GSM1068463 3 0.0404 0.8458 0.012 0.000 0.988 0.000 0.000
#> GSM1068465 4 0.6180 0.5291 0.304 0.096 0.008 0.580 0.012
#> GSM1068466 1 0.1018 0.8516 0.968 0.016 0.000 0.016 0.000
#> GSM1068467 2 0.3396 0.7977 0.024 0.856 0.000 0.032 0.088
#> GSM1068469 2 0.2329 0.7857 0.124 0.876 0.000 0.000 0.000
#> GSM1068470 2 0.3513 0.6992 0.020 0.800 0.000 0.180 0.000
#> GSM1068471 2 0.0798 0.8405 0.016 0.976 0.000 0.008 0.000
#> GSM1068475 2 0.0451 0.8377 0.004 0.988 0.000 0.008 0.000
#> GSM1068528 1 0.3577 0.7798 0.808 0.032 0.160 0.000 0.000
#> GSM1068531 1 0.3039 0.8105 0.836 0.000 0.012 0.152 0.000
#> GSM1068532 3 0.0290 0.8448 0.008 0.000 0.992 0.000 0.000
#> GSM1068533 1 0.4467 0.7607 0.752 0.000 0.164 0.084 0.000
#> GSM1068535 5 0.3612 0.5868 0.000 0.000 0.000 0.268 0.732
#> GSM1068537 1 0.4161 0.4438 0.608 0.000 0.392 0.000 0.000
#> GSM1068538 1 0.5604 0.2421 0.468 0.000 0.460 0.072 0.000
#> GSM1068539 1 0.2583 0.8226 0.864 0.000 0.004 0.132 0.000
#> GSM1068540 1 0.1121 0.8449 0.956 0.000 0.044 0.000 0.000
#> GSM1068542 4 0.2069 0.7510 0.012 0.076 0.000 0.912 0.000
#> GSM1068543 5 0.2280 0.7425 0.000 0.000 0.000 0.120 0.880
#> GSM1068544 3 0.0404 0.8458 0.012 0.000 0.988 0.000 0.000
#> GSM1068545 4 0.3246 0.6973 0.008 0.184 0.000 0.808 0.000
#> GSM1068546 5 0.6780 0.1261 0.268 0.000 0.280 0.004 0.448
#> GSM1068547 1 0.3197 0.8107 0.832 0.012 0.004 0.152 0.000
#> GSM1068548 4 0.5352 0.7012 0.056 0.088 0.008 0.748 0.100
#> GSM1068549 5 0.0000 0.8013 0.000 0.000 0.000 0.000 1.000
#> GSM1068550 4 0.0566 0.7359 0.000 0.012 0.004 0.984 0.000
#> GSM1068551 2 0.3177 0.7031 0.000 0.792 0.000 0.208 0.000
#> GSM1068552 4 0.2248 0.7503 0.012 0.088 0.000 0.900 0.000
#> GSM1068555 2 0.0771 0.8341 0.020 0.976 0.000 0.004 0.000
#> GSM1068556 4 0.4294 0.1143 0.000 0.000 0.000 0.532 0.468
#> GSM1068557 2 0.5843 0.4837 0.204 0.624 0.004 0.168 0.000
#> GSM1068560 4 0.1992 0.7468 0.032 0.044 0.000 0.924 0.000
#> GSM1068561 1 0.1830 0.8341 0.924 0.068 0.000 0.008 0.000
#> GSM1068562 5 0.4738 -0.0731 0.000 0.016 0.000 0.464 0.520
#> GSM1068563 5 0.3796 0.4406 0.000 0.000 0.000 0.300 0.700
#> GSM1068565 2 0.2873 0.7909 0.016 0.856 0.000 0.128 0.000
#> GSM1068529 5 0.0693 0.7959 0.012 0.000 0.008 0.000 0.980
#> GSM1068530 1 0.2966 0.7741 0.816 0.000 0.184 0.000 0.000
#> GSM1068534 5 0.6527 0.4545 0.292 0.068 0.008 0.052 0.580
#> GSM1068536 1 0.2881 0.8292 0.860 0.004 0.012 0.124 0.000
#> GSM1068541 4 0.5696 0.2757 0.400 0.056 0.012 0.532 0.000
#> GSM1068553 4 0.3413 0.7100 0.000 0.044 0.000 0.832 0.124
#> GSM1068554 4 0.3459 0.7312 0.000 0.116 0.000 0.832 0.052
#> GSM1068558 5 0.4389 0.6855 0.120 0.092 0.008 0.000 0.780
#> GSM1068559 5 0.0000 0.8013 0.000 0.000 0.000 0.000 1.000
#> GSM1068564 4 0.3177 0.7037 0.000 0.208 0.000 0.792 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1068478 5 0.4494 0.64323 0.424 0.032 0.000 0.000 0.544 0.000
#> GSM1068479 6 0.3788 0.56626 0.000 0.232 0.000 0.020 0.008 0.740
#> GSM1068481 3 0.5033 0.58573 0.072 0.060 0.704 0.000 0.164 0.000
#> GSM1068482 3 0.1672 0.83834 0.000 0.000 0.932 0.004 0.048 0.016
#> GSM1068483 5 0.4615 0.63884 0.424 0.040 0.000 0.000 0.536 0.000
#> GSM1068486 6 0.3862 0.67925 0.048 0.060 0.000 0.004 0.072 0.816
#> GSM1068487 2 0.3168 0.75892 0.000 0.804 0.000 0.172 0.024 0.000
#> GSM1068488 6 0.0000 0.76242 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM1068490 2 0.3737 0.74746 0.008 0.772 0.000 0.184 0.036 0.000
#> GSM1068491 6 0.0000 0.76242 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM1068492 6 0.0000 0.76242 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM1068493 5 0.5040 0.57408 0.380 0.060 0.000 0.000 0.552 0.008
#> GSM1068494 1 0.4468 0.08086 0.660 0.000 0.000 0.004 0.288 0.048
#> GSM1068495 1 0.3707 0.04934 0.680 0.000 0.000 0.008 0.312 0.000
#> GSM1068496 5 0.5814 0.42919 0.248 0.004 0.224 0.000 0.524 0.000
#> GSM1068498 5 0.3774 0.63505 0.408 0.000 0.000 0.000 0.592 0.000
#> GSM1068499 1 0.4740 0.06571 0.632 0.000 0.000 0.004 0.300 0.064
#> GSM1068500 5 0.4995 0.64294 0.408 0.040 0.016 0.000 0.536 0.000
#> GSM1068502 6 0.0000 0.76242 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM1068503 2 0.3364 0.74928 0.000 0.780 0.000 0.196 0.024 0.000
#> GSM1068505 4 0.3668 0.63102 0.328 0.004 0.000 0.668 0.000 0.000
#> GSM1068506 4 0.2114 0.73943 0.076 0.012 0.000 0.904 0.008 0.000
#> GSM1068507 6 0.5977 0.33682 0.000 0.216 0.000 0.192 0.028 0.564
#> GSM1068508 2 0.1966 0.80251 0.028 0.924 0.000 0.024 0.024 0.000
#> GSM1068510 2 0.5156 0.43655 0.000 0.580 0.000 0.112 0.000 0.308
#> GSM1068512 6 0.0508 0.75873 0.000 0.000 0.000 0.004 0.012 0.984
#> GSM1068513 2 0.1616 0.79386 0.000 0.932 0.000 0.048 0.000 0.020
#> GSM1068514 6 0.0000 0.76242 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM1068517 5 0.3782 0.62655 0.412 0.000 0.000 0.000 0.588 0.000
#> GSM1068518 6 0.0000 0.76242 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM1068520 5 0.4615 0.63884 0.424 0.040 0.000 0.000 0.536 0.000
#> GSM1068521 5 0.4096 0.55039 0.484 0.000 0.000 0.008 0.508 0.000
#> GSM1068522 4 0.4882 0.69631 0.236 0.104 0.000 0.656 0.004 0.000
#> GSM1068524 2 0.2581 0.74771 0.000 0.856 0.000 0.128 0.016 0.000
#> GSM1068527 1 0.5983 -0.37914 0.412 0.000 0.000 0.356 0.000 0.232
#> GSM1068480 3 0.4244 0.55075 0.004 0.000 0.680 0.000 0.036 0.280
#> GSM1068484 4 0.5746 0.16094 0.000 0.376 0.000 0.452 0.000 0.172
#> GSM1068485 3 0.2913 0.71879 0.004 0.000 0.812 0.004 0.000 0.180
#> GSM1068489 4 0.3665 0.71379 0.172 0.032 0.000 0.784 0.000 0.012
#> GSM1068497 5 0.4032 0.64125 0.420 0.008 0.000 0.000 0.572 0.000
#> GSM1068501 4 0.4882 0.58805 0.000 0.152 0.000 0.660 0.000 0.188
#> GSM1068504 2 0.1257 0.79939 0.000 0.952 0.000 0.028 0.020 0.000
#> GSM1068509 1 0.5699 -0.09306 0.476 0.000 0.000 0.104 0.404 0.016
#> GSM1068511 5 0.6173 -0.57985 0.052 0.000 0.440 0.032 0.440 0.036
#> GSM1068515 4 0.5823 0.49547 0.260 0.060 0.000 0.592 0.088 0.000
#> GSM1068516 6 0.4705 0.14944 0.472 0.000 0.000 0.044 0.000 0.484
#> GSM1068519 1 0.6536 0.30214 0.548 0.000 0.000 0.132 0.204 0.116
#> GSM1068523 2 0.3610 0.70343 0.004 0.792 0.000 0.152 0.052 0.000
#> GSM1068525 2 0.3774 0.34457 0.000 0.592 0.000 0.000 0.000 0.408
#> GSM1068526 4 0.3555 0.60128 0.000 0.176 0.000 0.780 0.000 0.044
#> GSM1068458 1 0.3428 0.38363 0.808 0.016 0.000 0.024 0.152 0.000
#> GSM1068459 3 0.0000 0.85653 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1068460 1 0.2664 0.39071 0.816 0.000 0.000 0.184 0.000 0.000
#> GSM1068461 6 0.2402 0.67323 0.000 0.000 0.140 0.000 0.004 0.856
#> GSM1068464 2 0.0777 0.79951 0.004 0.972 0.000 0.000 0.024 0.000
#> GSM1068468 2 0.4866 0.66221 0.044 0.716 0.000 0.020 0.028 0.192
#> GSM1068472 1 0.5265 -0.04807 0.520 0.088 0.000 0.000 0.388 0.004
#> GSM1068473 2 0.3569 0.75586 0.008 0.792 0.000 0.164 0.036 0.000
#> GSM1068474 2 0.3284 0.76067 0.000 0.800 0.000 0.168 0.032 0.000
#> GSM1068476 6 0.0146 0.76186 0.000 0.000 0.000 0.000 0.004 0.996
#> GSM1068477 4 0.6302 0.55947 0.348 0.172 0.000 0.452 0.028 0.000
#> GSM1068462 2 0.0363 0.80007 0.000 0.988 0.000 0.000 0.012 0.000
#> GSM1068463 3 0.0000 0.85653 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1068465 4 0.6955 0.44609 0.100 0.132 0.000 0.528 0.220 0.020
#> GSM1068466 1 0.4613 -0.44367 0.528 0.024 0.000 0.008 0.440 0.000
#> GSM1068467 2 0.2550 0.78532 0.036 0.892 0.000 0.000 0.024 0.048
#> GSM1068469 2 0.2237 0.77595 0.068 0.896 0.000 0.000 0.036 0.000
#> GSM1068470 2 0.3530 0.69469 0.000 0.792 0.000 0.152 0.056 0.000
#> GSM1068471 2 0.0363 0.80010 0.000 0.988 0.000 0.000 0.012 0.000
#> GSM1068475 2 0.1074 0.79940 0.000 0.960 0.000 0.012 0.028 0.000
#> GSM1068528 1 0.6396 -0.22336 0.436 0.036 0.164 0.000 0.364 0.000
#> GSM1068531 1 0.0405 0.44294 0.988 0.000 0.000 0.008 0.004 0.000
#> GSM1068532 3 0.0000 0.85653 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1068533 1 0.4526 0.26407 0.704 0.000 0.164 0.000 0.132 0.000
#> GSM1068535 6 0.5201 0.41583 0.184 0.000 0.000 0.200 0.000 0.616
#> GSM1068537 5 0.5902 0.30432 0.212 0.000 0.348 0.000 0.440 0.000
#> GSM1068538 1 0.3619 0.33224 0.680 0.000 0.316 0.004 0.000 0.000
#> GSM1068539 1 0.1524 0.43448 0.932 0.000 0.000 0.008 0.060 0.000
#> GSM1068540 5 0.4666 0.63075 0.420 0.000 0.044 0.000 0.536 0.000
#> GSM1068542 4 0.1959 0.72676 0.024 0.020 0.000 0.924 0.032 0.000
#> GSM1068543 6 0.2623 0.69676 0.016 0.000 0.000 0.132 0.000 0.852
#> GSM1068544 3 0.0000 0.85653 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1068545 4 0.3827 0.66529 0.040 0.124 0.000 0.800 0.036 0.000
#> GSM1068546 6 0.6046 0.10922 0.396 0.000 0.168 0.000 0.012 0.424
#> GSM1068547 1 0.0520 0.44338 0.984 0.000 0.000 0.008 0.008 0.000
#> GSM1068548 4 0.4905 0.65910 0.176 0.020 0.000 0.716 0.072 0.016
#> GSM1068549 6 0.0146 0.76186 0.000 0.000 0.000 0.000 0.004 0.996
#> GSM1068550 4 0.2871 0.70191 0.192 0.004 0.000 0.804 0.000 0.000
#> GSM1068551 2 0.4907 0.61060 0.020 0.636 0.000 0.292 0.052 0.000
#> GSM1068552 4 0.1682 0.71982 0.000 0.052 0.000 0.928 0.020 0.000
#> GSM1068555 2 0.1285 0.79372 0.000 0.944 0.000 0.004 0.052 0.000
#> GSM1068556 4 0.4988 0.13202 0.068 0.000 0.000 0.484 0.000 0.448
#> GSM1068557 2 0.4895 0.40915 0.412 0.540 0.000 0.024 0.024 0.000
#> GSM1068560 4 0.3918 0.68839 0.248 0.004 0.000 0.724 0.020 0.004
#> GSM1068561 1 0.4932 0.05174 0.616 0.060 0.000 0.012 0.312 0.000
#> GSM1068562 6 0.3854 0.00729 0.000 0.000 0.000 0.464 0.000 0.536
#> GSM1068563 6 0.3288 0.48426 0.000 0.000 0.000 0.276 0.000 0.724
#> GSM1068565 2 0.3586 0.72157 0.000 0.756 0.000 0.216 0.028 0.000
#> GSM1068529 6 0.1785 0.74136 0.016 0.000 0.000 0.008 0.048 0.928
#> GSM1068530 5 0.5823 0.45093 0.372 0.000 0.188 0.000 0.440 0.000
#> GSM1068534 6 0.6702 0.08805 0.408 0.052 0.000 0.040 0.072 0.428
#> GSM1068536 1 0.2613 0.40652 0.848 0.000 0.000 0.012 0.140 0.000
#> GSM1068541 1 0.6561 0.15824 0.432 0.048 0.000 0.352 0.168 0.000
#> GSM1068553 4 0.1524 0.72227 0.008 0.000 0.000 0.932 0.000 0.060
#> GSM1068554 4 0.0993 0.72900 0.000 0.024 0.000 0.964 0.000 0.012
#> GSM1068558 6 0.5252 0.47893 0.032 0.036 0.000 0.004 0.360 0.568
#> GSM1068559 6 0.0000 0.76242 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM1068564 4 0.2871 0.68189 0.004 0.192 0.000 0.804 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 disease.state(p) gender(p) k
#> CV:pam 93 0.42806 1.000 2
#> CV:pam 91 0.19282 0.528 3
#> CV:pam 60 0.01864 0.333 4
#> CV:pam 92 0.04909 0.410 5
#> CV:pam 70 0.00349 0.261 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 38950 rows and 108 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 0.970 0.973 0.4600 0.529 0.529
#> 3 3 0.777 0.885 0.925 0.1633 0.890 0.802
#> 4 4 0.554 0.573 0.736 0.2639 0.777 0.539
#> 5 5 0.564 0.603 0.749 0.1021 0.824 0.498
#> 6 6 0.672 0.624 0.796 0.0644 0.892 0.616
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
#> GSM1068478 1 0.1843 0.985 0.972 0.028
#> GSM1068479 1 0.0672 0.975 0.992 0.008
#> GSM1068481 1 0.0000 0.973 1.000 0.000
#> GSM1068482 1 0.0000 0.973 1.000 0.000
#> GSM1068483 1 0.1843 0.985 0.972 0.028
#> GSM1068486 1 0.0000 0.973 1.000 0.000
#> GSM1068487 2 0.0000 0.971 0.000 1.000
#> GSM1068488 1 0.2236 0.985 0.964 0.036
#> GSM1068490 2 0.0000 0.971 0.000 1.000
#> GSM1068491 1 0.0672 0.975 0.992 0.008
#> GSM1068492 1 0.2948 0.943 0.948 0.052
#> GSM1068493 1 0.2236 0.985 0.964 0.036
#> GSM1068494 1 0.1843 0.985 0.972 0.028
#> GSM1068495 1 0.2236 0.985 0.964 0.036
#> GSM1068496 1 0.1633 0.985 0.976 0.024
#> GSM1068498 1 0.2043 0.985 0.968 0.032
#> GSM1068499 1 0.1633 0.985 0.976 0.024
#> GSM1068500 1 0.1843 0.985 0.972 0.028
#> GSM1068502 1 0.0672 0.975 0.992 0.008
#> GSM1068503 2 0.0938 0.976 0.012 0.988
#> GSM1068505 2 0.0938 0.976 0.012 0.988
#> GSM1068506 2 0.0938 0.976 0.012 0.988
#> GSM1068507 2 0.1414 0.972 0.020 0.980
#> GSM1068508 2 0.1184 0.974 0.016 0.984
#> GSM1068510 2 0.0938 0.976 0.012 0.988
#> GSM1068512 1 0.2236 0.985 0.964 0.036
#> GSM1068513 2 0.0938 0.976 0.012 0.988
#> GSM1068514 1 0.0672 0.975 0.992 0.008
#> GSM1068517 1 0.2236 0.985 0.964 0.036
#> GSM1068518 1 0.2236 0.985 0.964 0.036
#> GSM1068520 1 0.1843 0.985 0.972 0.028
#> GSM1068521 1 0.1843 0.985 0.972 0.028
#> GSM1068522 2 0.0938 0.976 0.012 0.988
#> GSM1068524 2 0.0938 0.976 0.012 0.988
#> GSM1068527 2 0.0938 0.976 0.012 0.988
#> GSM1068480 1 0.0000 0.973 1.000 0.000
#> GSM1068484 2 0.0938 0.976 0.012 0.988
#> GSM1068485 1 0.0000 0.973 1.000 0.000
#> GSM1068489 2 0.0938 0.976 0.012 0.988
#> GSM1068497 1 0.2043 0.985 0.968 0.032
#> GSM1068501 2 0.0938 0.976 0.012 0.988
#> GSM1068504 2 0.0000 0.971 0.000 1.000
#> GSM1068509 1 0.2043 0.985 0.968 0.032
#> GSM1068511 1 0.1633 0.985 0.976 0.024
#> GSM1068515 1 0.2236 0.985 0.964 0.036
#> GSM1068516 1 0.2236 0.985 0.964 0.036
#> GSM1068519 1 0.1843 0.985 0.972 0.028
#> GSM1068523 2 0.0000 0.971 0.000 1.000
#> GSM1068525 2 0.1843 0.966 0.028 0.972
#> GSM1068526 2 0.0938 0.976 0.012 0.988
#> GSM1068458 1 0.1843 0.985 0.972 0.028
#> GSM1068459 1 0.0000 0.973 1.000 0.000
#> GSM1068460 1 0.2236 0.985 0.964 0.036
#> GSM1068461 1 0.0000 0.973 1.000 0.000
#> GSM1068464 2 0.8713 0.590 0.292 0.708
#> GSM1068468 1 0.2236 0.985 0.964 0.036
#> GSM1068472 1 0.2236 0.985 0.964 0.036
#> GSM1068473 2 0.0000 0.971 0.000 1.000
#> GSM1068474 2 0.0000 0.971 0.000 1.000
#> GSM1068476 1 0.0672 0.975 0.992 0.008
#> GSM1068477 1 0.2236 0.985 0.964 0.036
#> GSM1068462 1 0.2236 0.985 0.964 0.036
#> GSM1068463 1 0.0000 0.973 1.000 0.000
#> GSM1068465 1 0.2236 0.985 0.964 0.036
#> GSM1068466 1 0.1843 0.985 0.972 0.028
#> GSM1068467 1 0.2236 0.985 0.964 0.036
#> GSM1068469 1 0.2236 0.985 0.964 0.036
#> GSM1068470 2 0.0000 0.971 0.000 1.000
#> GSM1068471 2 0.5059 0.877 0.112 0.888
#> GSM1068475 2 0.0000 0.971 0.000 1.000
#> GSM1068528 1 0.0376 0.975 0.996 0.004
#> GSM1068531 1 0.1843 0.985 0.972 0.028
#> GSM1068532 1 0.1843 0.985 0.972 0.028
#> GSM1068533 1 0.1843 0.985 0.972 0.028
#> GSM1068535 1 0.2236 0.985 0.964 0.036
#> GSM1068537 1 0.1843 0.985 0.972 0.028
#> GSM1068538 1 0.1843 0.985 0.972 0.028
#> GSM1068539 1 0.2236 0.985 0.964 0.036
#> GSM1068540 1 0.1843 0.985 0.972 0.028
#> GSM1068542 2 0.0938 0.976 0.012 0.988
#> GSM1068543 2 0.3114 0.942 0.056 0.944
#> GSM1068544 1 0.0000 0.973 1.000 0.000
#> GSM1068545 2 0.0938 0.976 0.012 0.988
#> GSM1068546 1 0.0000 0.973 1.000 0.000
#> GSM1068547 1 0.1843 0.985 0.972 0.028
#> GSM1068548 2 0.1843 0.966 0.028 0.972
#> GSM1068549 1 0.0000 0.973 1.000 0.000
#> GSM1068550 2 0.0938 0.976 0.012 0.988
#> GSM1068551 2 0.0000 0.971 0.000 1.000
#> GSM1068552 2 0.0938 0.976 0.012 0.988
#> GSM1068555 2 0.6887 0.780 0.184 0.816
#> GSM1068556 2 0.4815 0.894 0.104 0.896
#> GSM1068557 1 0.2236 0.985 0.964 0.036
#> GSM1068560 2 0.0938 0.976 0.012 0.988
#> GSM1068561 1 0.2236 0.985 0.964 0.036
#> GSM1068562 2 0.0938 0.976 0.012 0.988
#> GSM1068563 1 0.6887 0.806 0.816 0.184
#> GSM1068565 2 0.0000 0.971 0.000 1.000
#> GSM1068529 1 0.2043 0.985 0.968 0.032
#> GSM1068530 1 0.1843 0.985 0.972 0.028
#> GSM1068534 1 0.2236 0.985 0.964 0.036
#> GSM1068536 1 0.2236 0.985 0.964 0.036
#> GSM1068541 1 0.2236 0.985 0.964 0.036
#> GSM1068553 2 0.1414 0.972 0.020 0.980
#> GSM1068554 2 0.0938 0.976 0.012 0.988
#> GSM1068558 1 0.0672 0.975 0.992 0.008
#> GSM1068559 1 0.1633 0.983 0.976 0.024
#> GSM1068564 2 0.0938 0.976 0.012 0.988
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1068478 3 0.0747 0.945 0.016 0.000 0.984
#> GSM1068479 3 0.3610 0.914 0.096 0.016 0.888
#> GSM1068481 3 0.3192 0.914 0.112 0.000 0.888
#> GSM1068482 3 0.2959 0.915 0.100 0.000 0.900
#> GSM1068483 3 0.0747 0.945 0.016 0.000 0.984
#> GSM1068486 3 0.2959 0.915 0.100 0.000 0.900
#> GSM1068487 1 0.3966 0.936 0.876 0.100 0.024
#> GSM1068488 3 0.1774 0.939 0.016 0.024 0.960
#> GSM1068490 1 0.4335 0.926 0.864 0.100 0.036
#> GSM1068491 3 0.3610 0.914 0.096 0.016 0.888
#> GSM1068492 3 0.4174 0.904 0.092 0.036 0.872
#> GSM1068493 3 0.0237 0.945 0.004 0.000 0.996
#> GSM1068494 3 0.0829 0.944 0.012 0.004 0.984
#> GSM1068495 3 0.0829 0.944 0.012 0.004 0.984
#> GSM1068496 3 0.2625 0.926 0.084 0.000 0.916
#> GSM1068498 3 0.0747 0.945 0.016 0.000 0.984
#> GSM1068499 3 0.1411 0.940 0.036 0.000 0.964
#> GSM1068500 3 0.1031 0.944 0.024 0.000 0.976
#> GSM1068502 3 0.3610 0.914 0.096 0.016 0.888
#> GSM1068503 2 0.5728 0.641 0.196 0.772 0.032
#> GSM1068505 2 0.0000 0.892 0.000 1.000 0.000
#> GSM1068506 2 0.0000 0.892 0.000 1.000 0.000
#> GSM1068507 2 0.0983 0.887 0.004 0.980 0.016
#> GSM1068508 2 0.2955 0.817 0.008 0.912 0.080
#> GSM1068510 2 0.5815 0.476 0.004 0.692 0.304
#> GSM1068512 3 0.2845 0.909 0.012 0.068 0.920
#> GSM1068513 2 0.2056 0.870 0.024 0.952 0.024
#> GSM1068514 3 0.3415 0.924 0.080 0.020 0.900
#> GSM1068517 3 0.0829 0.945 0.012 0.004 0.984
#> GSM1068518 3 0.1015 0.944 0.012 0.008 0.980
#> GSM1068520 3 0.1031 0.944 0.024 0.000 0.976
#> GSM1068521 3 0.1267 0.944 0.024 0.004 0.972
#> GSM1068522 2 0.0237 0.892 0.004 0.996 0.000
#> GSM1068524 1 0.8743 0.171 0.452 0.440 0.108
#> GSM1068527 2 0.0000 0.892 0.000 1.000 0.000
#> GSM1068480 3 0.2959 0.915 0.100 0.000 0.900
#> GSM1068484 2 0.0983 0.887 0.004 0.980 0.016
#> GSM1068485 3 0.3192 0.914 0.112 0.000 0.888
#> GSM1068489 2 0.0000 0.892 0.000 1.000 0.000
#> GSM1068497 3 0.0747 0.945 0.016 0.000 0.984
#> GSM1068501 2 0.0000 0.892 0.000 1.000 0.000
#> GSM1068504 1 0.3966 0.936 0.876 0.100 0.024
#> GSM1068509 3 0.0592 0.944 0.012 0.000 0.988
#> GSM1068511 3 0.0983 0.945 0.016 0.004 0.980
#> GSM1068515 3 0.0237 0.945 0.000 0.004 0.996
#> GSM1068516 3 0.0983 0.943 0.016 0.004 0.980
#> GSM1068519 3 0.0983 0.944 0.016 0.004 0.980
#> GSM1068523 1 0.3966 0.936 0.876 0.100 0.024
#> GSM1068525 2 0.5988 0.476 0.008 0.688 0.304
#> GSM1068526 2 0.0983 0.887 0.004 0.980 0.016
#> GSM1068458 3 0.1267 0.944 0.024 0.004 0.972
#> GSM1068459 3 0.3192 0.914 0.112 0.000 0.888
#> GSM1068460 3 0.1337 0.942 0.012 0.016 0.972
#> GSM1068461 3 0.2959 0.915 0.100 0.000 0.900
#> GSM1068464 1 0.4786 0.849 0.844 0.044 0.112
#> GSM1068468 3 0.0475 0.944 0.004 0.004 0.992
#> GSM1068472 3 0.0237 0.945 0.000 0.004 0.996
#> GSM1068473 1 0.3966 0.936 0.876 0.100 0.024
#> GSM1068474 1 0.3966 0.936 0.876 0.100 0.024
#> GSM1068476 3 0.3610 0.914 0.096 0.016 0.888
#> GSM1068477 3 0.6228 0.345 0.372 0.004 0.624
#> GSM1068462 3 0.0661 0.944 0.008 0.004 0.988
#> GSM1068463 3 0.3192 0.914 0.112 0.000 0.888
#> GSM1068465 3 0.1015 0.944 0.012 0.008 0.980
#> GSM1068466 3 0.1031 0.944 0.024 0.000 0.976
#> GSM1068467 3 0.0475 0.944 0.004 0.004 0.992
#> GSM1068469 3 0.0475 0.945 0.004 0.004 0.992
#> GSM1068470 1 0.3966 0.936 0.876 0.100 0.024
#> GSM1068471 1 0.4179 0.893 0.876 0.052 0.072
#> GSM1068475 1 0.3966 0.936 0.876 0.100 0.024
#> GSM1068528 3 0.2711 0.925 0.088 0.000 0.912
#> GSM1068531 3 0.1267 0.944 0.024 0.004 0.972
#> GSM1068532 3 0.1267 0.944 0.024 0.004 0.972
#> GSM1068533 3 0.1267 0.944 0.024 0.004 0.972
#> GSM1068535 3 0.1015 0.944 0.012 0.008 0.980
#> GSM1068537 3 0.1267 0.944 0.024 0.004 0.972
#> GSM1068538 3 0.1267 0.944 0.024 0.004 0.972
#> GSM1068539 3 0.2031 0.932 0.016 0.032 0.952
#> GSM1068540 3 0.1267 0.944 0.024 0.004 0.972
#> GSM1068542 2 0.0000 0.892 0.000 1.000 0.000
#> GSM1068543 3 0.6421 0.250 0.004 0.424 0.572
#> GSM1068544 3 0.3192 0.914 0.112 0.000 0.888
#> GSM1068545 2 0.0237 0.892 0.004 0.996 0.000
#> GSM1068546 3 0.2959 0.915 0.100 0.000 0.900
#> GSM1068547 3 0.1267 0.944 0.024 0.004 0.972
#> GSM1068548 2 0.0000 0.892 0.000 1.000 0.000
#> GSM1068549 3 0.2959 0.915 0.100 0.000 0.900
#> GSM1068550 2 0.0000 0.892 0.000 1.000 0.000
#> GSM1068551 1 0.3966 0.936 0.876 0.100 0.024
#> GSM1068552 2 0.0237 0.892 0.004 0.996 0.000
#> GSM1068555 1 0.4121 0.882 0.876 0.040 0.084
#> GSM1068556 2 0.6345 0.325 0.004 0.596 0.400
#> GSM1068557 3 0.0475 0.944 0.004 0.004 0.992
#> GSM1068560 2 0.0000 0.892 0.000 1.000 0.000
#> GSM1068561 3 0.0237 0.945 0.000 0.004 0.996
#> GSM1068562 2 0.0983 0.887 0.004 0.980 0.016
#> GSM1068563 2 0.2866 0.825 0.008 0.916 0.076
#> GSM1068565 1 0.3966 0.936 0.876 0.100 0.024
#> GSM1068529 3 0.1765 0.942 0.040 0.004 0.956
#> GSM1068530 3 0.1267 0.944 0.024 0.004 0.972
#> GSM1068534 3 0.1015 0.944 0.012 0.008 0.980
#> GSM1068536 3 0.0829 0.944 0.012 0.004 0.984
#> GSM1068541 3 0.1015 0.944 0.012 0.008 0.980
#> GSM1068553 3 0.5058 0.705 0.000 0.244 0.756
#> GSM1068554 2 0.1765 0.867 0.004 0.956 0.040
#> GSM1068558 3 0.3610 0.914 0.096 0.016 0.888
#> GSM1068559 3 0.3530 0.922 0.068 0.032 0.900
#> GSM1068564 2 0.0237 0.892 0.004 0.996 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1068478 1 0.4647 0.556 0.704 0.008 0.288 0.000
#> GSM1068479 3 0.3056 0.353 0.000 0.040 0.888 0.072
#> GSM1068481 1 0.5256 0.449 0.596 0.012 0.392 0.000
#> GSM1068482 1 0.5511 0.293 0.500 0.016 0.484 0.000
#> GSM1068483 1 0.4356 0.562 0.708 0.000 0.292 0.000
#> GSM1068486 3 0.5395 0.299 0.184 0.084 0.732 0.000
#> GSM1068487 2 0.2081 0.957 0.000 0.916 0.000 0.084
#> GSM1068488 3 0.6726 0.333 0.124 0.000 0.584 0.292
#> GSM1068490 2 0.2266 0.956 0.004 0.912 0.000 0.084
#> GSM1068491 3 0.2943 0.349 0.000 0.076 0.892 0.032
#> GSM1068492 3 0.3659 0.338 0.000 0.024 0.840 0.136
#> GSM1068493 3 0.5960 0.378 0.420 0.016 0.548 0.016
#> GSM1068494 1 0.5256 0.336 0.596 0.012 0.392 0.000
#> GSM1068495 3 0.6066 0.388 0.424 0.016 0.540 0.020
#> GSM1068496 1 0.4406 0.561 0.700 0.000 0.300 0.000
#> GSM1068498 1 0.5596 0.471 0.632 0.036 0.332 0.000
#> GSM1068499 1 0.4522 0.559 0.680 0.000 0.320 0.000
#> GSM1068500 1 0.4164 0.582 0.736 0.000 0.264 0.000
#> GSM1068502 3 0.3398 0.354 0.000 0.060 0.872 0.068
#> GSM1068503 4 0.4590 0.774 0.000 0.148 0.060 0.792
#> GSM1068505 4 0.0000 0.920 0.000 0.000 0.000 1.000
#> GSM1068506 4 0.0000 0.920 0.000 0.000 0.000 1.000
#> GSM1068507 4 0.1305 0.909 0.036 0.000 0.004 0.960
#> GSM1068508 4 0.2497 0.888 0.020 0.016 0.040 0.924
#> GSM1068510 4 0.3399 0.846 0.000 0.040 0.092 0.868
#> GSM1068512 3 0.7109 0.292 0.144 0.000 0.520 0.336
#> GSM1068513 4 0.1722 0.908 0.000 0.048 0.008 0.944
#> GSM1068514 3 0.5255 0.382 0.064 0.036 0.788 0.112
#> GSM1068517 1 0.6176 0.335 0.572 0.060 0.368 0.000
#> GSM1068518 3 0.6326 0.425 0.376 0.000 0.556 0.068
#> GSM1068520 1 0.3907 0.587 0.768 0.000 0.232 0.000
#> GSM1068521 1 0.4277 0.562 0.720 0.000 0.280 0.000
#> GSM1068522 4 0.1209 0.915 0.000 0.032 0.004 0.964
#> GSM1068524 2 0.7156 0.217 0.000 0.476 0.136 0.388
#> GSM1068527 4 0.0000 0.920 0.000 0.000 0.000 1.000
#> GSM1068480 3 0.6425 -0.239 0.424 0.068 0.508 0.000
#> GSM1068484 4 0.1305 0.914 0.000 0.036 0.004 0.960
#> GSM1068485 3 0.5781 -0.316 0.484 0.028 0.488 0.000
#> GSM1068489 4 0.0000 0.920 0.000 0.000 0.000 1.000
#> GSM1068497 1 0.5677 0.466 0.628 0.040 0.332 0.000
#> GSM1068501 4 0.0921 0.917 0.000 0.028 0.000 0.972
#> GSM1068504 2 0.2197 0.958 0.000 0.916 0.004 0.080
#> GSM1068509 3 0.5570 0.363 0.440 0.000 0.540 0.020
#> GSM1068511 3 0.5746 0.015 0.444 0.020 0.532 0.004
#> GSM1068515 3 0.6185 0.396 0.404 0.032 0.552 0.012
#> GSM1068516 3 0.6404 0.424 0.388 0.004 0.548 0.060
#> GSM1068519 1 0.4957 0.470 0.684 0.000 0.300 0.016
#> GSM1068523 2 0.2197 0.958 0.000 0.916 0.004 0.080
#> GSM1068525 4 0.5874 0.664 0.024 0.060 0.196 0.720
#> GSM1068526 4 0.1305 0.914 0.000 0.036 0.004 0.960
#> GSM1068458 1 0.4222 0.550 0.728 0.000 0.272 0.000
#> GSM1068459 1 0.5407 0.297 0.504 0.012 0.484 0.000
#> GSM1068460 3 0.6262 0.413 0.400 0.000 0.540 0.060
#> GSM1068461 3 0.6400 -0.217 0.408 0.068 0.524 0.000
#> GSM1068464 2 0.2528 0.950 0.008 0.908 0.004 0.080
#> GSM1068468 3 0.6409 0.407 0.364 0.076 0.560 0.000
#> GSM1068472 3 0.6286 0.396 0.384 0.064 0.552 0.000
#> GSM1068473 2 0.2081 0.957 0.000 0.916 0.000 0.084
#> GSM1068474 2 0.2081 0.957 0.000 0.916 0.000 0.084
#> GSM1068476 3 0.2965 0.349 0.000 0.072 0.892 0.036
#> GSM1068477 3 0.8068 0.245 0.236 0.312 0.440 0.012
#> GSM1068462 3 0.6454 0.411 0.344 0.084 0.572 0.000
#> GSM1068463 1 0.5402 0.318 0.516 0.012 0.472 0.000
#> GSM1068465 3 0.6066 0.388 0.424 0.016 0.540 0.020
#> GSM1068466 1 0.3764 0.586 0.784 0.000 0.216 0.000
#> GSM1068467 3 0.6386 0.399 0.376 0.072 0.552 0.000
#> GSM1068469 1 0.6121 0.388 0.588 0.060 0.352 0.000
#> GSM1068470 2 0.2197 0.958 0.000 0.916 0.004 0.080
#> GSM1068471 2 0.2197 0.958 0.000 0.916 0.004 0.080
#> GSM1068475 2 0.2197 0.958 0.000 0.916 0.004 0.080
#> GSM1068528 1 0.4500 0.548 0.684 0.000 0.316 0.000
#> GSM1068531 1 0.0469 0.538 0.988 0.000 0.012 0.000
#> GSM1068532 1 0.0707 0.542 0.980 0.000 0.020 0.000
#> GSM1068533 1 0.0469 0.538 0.988 0.000 0.012 0.000
#> GSM1068535 3 0.6337 0.413 0.380 0.000 0.552 0.068
#> GSM1068537 1 0.0469 0.538 0.988 0.000 0.012 0.000
#> GSM1068538 1 0.0469 0.538 0.988 0.000 0.012 0.000
#> GSM1068539 3 0.6625 0.427 0.388 0.016 0.544 0.052
#> GSM1068540 1 0.0469 0.538 0.988 0.000 0.012 0.000
#> GSM1068542 4 0.0000 0.920 0.000 0.000 0.000 1.000
#> GSM1068543 4 0.4094 0.794 0.056 0.000 0.116 0.828
#> GSM1068544 1 0.5231 0.461 0.604 0.012 0.384 0.000
#> GSM1068545 4 0.0188 0.921 0.000 0.000 0.004 0.996
#> GSM1068546 3 0.6537 -0.241 0.424 0.076 0.500 0.000
#> GSM1068547 1 0.5186 0.360 0.640 0.000 0.344 0.016
#> GSM1068548 4 0.0895 0.916 0.020 0.000 0.004 0.976
#> GSM1068549 3 0.4297 0.329 0.096 0.084 0.820 0.000
#> GSM1068550 4 0.0000 0.920 0.000 0.000 0.000 1.000
#> GSM1068551 2 0.2081 0.957 0.000 0.916 0.000 0.084
#> GSM1068552 4 0.0188 0.921 0.000 0.000 0.004 0.996
#> GSM1068555 2 0.2706 0.944 0.000 0.900 0.020 0.080
#> GSM1068556 4 0.3090 0.856 0.056 0.000 0.056 0.888
#> GSM1068557 3 0.6554 0.416 0.376 0.056 0.556 0.012
#> GSM1068560 4 0.0000 0.920 0.000 0.000 0.000 1.000
#> GSM1068561 3 0.6525 0.421 0.388 0.024 0.552 0.036
#> GSM1068562 4 0.1471 0.918 0.012 0.024 0.004 0.960
#> GSM1068563 4 0.3547 0.812 0.072 0.000 0.064 0.864
#> GSM1068565 2 0.2081 0.957 0.000 0.916 0.000 0.084
#> GSM1068529 3 0.6505 0.428 0.360 0.012 0.572 0.056
#> GSM1068530 1 0.0469 0.538 0.988 0.000 0.012 0.000
#> GSM1068534 3 0.6212 0.418 0.380 0.000 0.560 0.060
#> GSM1068536 3 0.5888 0.389 0.424 0.000 0.540 0.036
#> GSM1068541 3 0.6120 0.383 0.416 0.040 0.540 0.004
#> GSM1068553 4 0.4459 0.697 0.032 0.000 0.188 0.780
#> GSM1068554 4 0.1584 0.914 0.000 0.036 0.012 0.952
#> GSM1068558 3 0.2515 0.337 0.004 0.072 0.912 0.012
#> GSM1068559 3 0.6205 0.375 0.096 0.016 0.696 0.192
#> GSM1068564 4 0.0188 0.921 0.000 0.000 0.004 0.996
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM1068478 5 0.6414 -0.00103 0.400 0.016 0.112 0.000 0.472
#> GSM1068479 5 0.6822 0.03430 0.028 0.040 0.384 0.052 0.496
#> GSM1068481 3 0.3081 0.76210 0.056 0.004 0.868 0.000 0.072
#> GSM1068482 3 0.2842 0.76858 0.044 0.012 0.888 0.000 0.056
#> GSM1068483 1 0.6868 0.31122 0.428 0.008 0.228 0.000 0.336
#> GSM1068486 3 0.5326 0.24270 0.012 0.028 0.496 0.000 0.464
#> GSM1068487 2 0.1410 0.95937 0.000 0.940 0.000 0.060 0.000
#> GSM1068488 4 0.5456 0.11185 0.000 0.000 0.060 0.484 0.456
#> GSM1068490 2 0.1478 0.95609 0.000 0.936 0.000 0.064 0.000
#> GSM1068491 5 0.6660 0.02085 0.028 0.044 0.396 0.036 0.496
#> GSM1068492 5 0.6918 0.04147 0.028 0.036 0.384 0.064 0.488
#> GSM1068493 5 0.4428 0.62822 0.072 0.044 0.016 0.052 0.816
#> GSM1068494 5 0.6685 0.15700 0.280 0.000 0.284 0.000 0.436
#> GSM1068495 5 0.5154 0.62748 0.068 0.084 0.012 0.068 0.768
#> GSM1068496 1 0.6037 -0.00764 0.456 0.004 0.440 0.000 0.100
#> GSM1068498 5 0.5888 0.53017 0.072 0.128 0.108 0.000 0.692
#> GSM1068499 5 0.6469 0.31898 0.252 0.004 0.220 0.000 0.524
#> GSM1068500 1 0.6798 0.33965 0.436 0.004 0.252 0.000 0.308
#> GSM1068502 5 0.6822 0.03430 0.028 0.040 0.384 0.052 0.496
#> GSM1068503 4 0.5951 0.61727 0.000 0.208 0.020 0.640 0.132
#> GSM1068505 4 0.0000 0.86463 0.000 0.000 0.000 1.000 0.000
#> GSM1068506 4 0.0000 0.86463 0.000 0.000 0.000 1.000 0.000
#> GSM1068507 4 0.2338 0.84794 0.000 0.004 0.000 0.884 0.112
#> GSM1068508 4 0.3646 0.80901 0.000 0.036 0.004 0.816 0.144
#> GSM1068510 4 0.3674 0.81050 0.004 0.008 0.020 0.816 0.152
#> GSM1068512 4 0.4735 0.20162 0.000 0.000 0.016 0.524 0.460
#> GSM1068513 4 0.2919 0.84786 0.000 0.024 0.004 0.868 0.104
#> GSM1068514 5 0.7513 0.14339 0.016 0.028 0.376 0.180 0.400
#> GSM1068517 5 0.5421 0.56021 0.056 0.140 0.080 0.000 0.724
#> GSM1068518 5 0.3387 0.57835 0.004 0.000 0.004 0.196 0.796
#> GSM1068520 5 0.5527 0.00608 0.472 0.012 0.040 0.000 0.476
#> GSM1068521 5 0.5187 0.04709 0.460 0.004 0.032 0.000 0.504
#> GSM1068522 4 0.1168 0.86724 0.000 0.008 0.000 0.960 0.032
#> GSM1068524 2 0.6506 0.39717 0.004 0.568 0.020 0.272 0.136
#> GSM1068527 4 0.0000 0.86463 0.000 0.000 0.000 1.000 0.000
#> GSM1068480 3 0.2331 0.76504 0.024 0.004 0.908 0.000 0.064
#> GSM1068484 4 0.0798 0.86704 0.000 0.008 0.000 0.976 0.016
#> GSM1068485 3 0.2820 0.76712 0.056 0.004 0.884 0.000 0.056
#> GSM1068489 4 0.0000 0.86463 0.000 0.000 0.000 1.000 0.000
#> GSM1068497 5 0.5836 0.53178 0.060 0.140 0.108 0.000 0.692
#> GSM1068501 4 0.0290 0.86315 0.000 0.008 0.000 0.992 0.000
#> GSM1068504 2 0.1410 0.95937 0.000 0.940 0.000 0.060 0.000
#> GSM1068509 5 0.4004 0.58002 0.164 0.000 0.012 0.032 0.792
#> GSM1068511 3 0.6744 0.33162 0.164 0.020 0.500 0.000 0.316
#> GSM1068515 5 0.4199 0.62793 0.036 0.100 0.012 0.032 0.820
#> GSM1068516 5 0.3132 0.59230 0.008 0.000 0.000 0.172 0.820
#> GSM1068519 5 0.5330 0.04059 0.480 0.012 0.028 0.000 0.480
#> GSM1068523 2 0.1410 0.95937 0.000 0.940 0.000 0.060 0.000
#> GSM1068525 4 0.4944 0.67677 0.004 0.020 0.032 0.708 0.236
#> GSM1068526 4 0.0290 0.86315 0.000 0.008 0.000 0.992 0.000
#> GSM1068458 5 0.5166 0.10291 0.436 0.004 0.032 0.000 0.528
#> GSM1068459 3 0.2954 0.76509 0.064 0.004 0.876 0.000 0.056
#> GSM1068460 5 0.4993 0.59830 0.060 0.012 0.012 0.176 0.740
#> GSM1068461 3 0.3031 0.74100 0.020 0.004 0.856 0.000 0.120
#> GSM1068464 2 0.1809 0.94743 0.000 0.928 0.000 0.060 0.012
#> GSM1068468 5 0.3538 0.61782 0.000 0.128 0.012 0.028 0.832
#> GSM1068472 5 0.3919 0.61975 0.008 0.128 0.016 0.028 0.820
#> GSM1068473 2 0.1410 0.95937 0.000 0.940 0.000 0.060 0.000
#> GSM1068474 2 0.1410 0.95937 0.000 0.940 0.000 0.060 0.000
#> GSM1068476 5 0.6660 0.02085 0.028 0.044 0.396 0.036 0.496
#> GSM1068477 5 0.5782 0.51467 0.020 0.284 0.020 0.040 0.636
#> GSM1068462 5 0.3992 0.61413 0.000 0.128 0.032 0.028 0.812
#> GSM1068463 3 0.2954 0.76509 0.064 0.004 0.876 0.000 0.056
#> GSM1068465 5 0.5213 0.62582 0.072 0.084 0.012 0.068 0.764
#> GSM1068466 5 0.5787 0.00105 0.456 0.016 0.052 0.000 0.476
#> GSM1068467 5 0.3695 0.61936 0.004 0.128 0.012 0.028 0.828
#> GSM1068469 5 0.5255 0.55959 0.044 0.128 0.092 0.000 0.736
#> GSM1068470 2 0.1410 0.95937 0.000 0.940 0.000 0.060 0.000
#> GSM1068471 2 0.1410 0.95937 0.000 0.940 0.000 0.060 0.000
#> GSM1068475 2 0.1410 0.95937 0.000 0.940 0.000 0.060 0.000
#> GSM1068528 3 0.5905 -0.01291 0.420 0.004 0.488 0.000 0.088
#> GSM1068531 1 0.1041 0.78238 0.964 0.004 0.000 0.000 0.032
#> GSM1068532 1 0.1041 0.78195 0.964 0.000 0.004 0.000 0.032
#> GSM1068533 1 0.1121 0.78069 0.956 0.000 0.000 0.000 0.044
#> GSM1068535 5 0.7221 0.32623 0.048 0.004 0.260 0.172 0.516
#> GSM1068537 1 0.1041 0.78195 0.964 0.000 0.004 0.000 0.032
#> GSM1068538 1 0.1041 0.78195 0.964 0.000 0.004 0.000 0.032
#> GSM1068539 5 0.4352 0.61563 0.036 0.040 0.000 0.132 0.792
#> GSM1068540 1 0.1281 0.77913 0.956 0.012 0.000 0.000 0.032
#> GSM1068542 4 0.0000 0.86463 0.000 0.000 0.000 1.000 0.000
#> GSM1068543 4 0.3608 0.80353 0.000 0.000 0.040 0.812 0.148
#> GSM1068544 3 0.3743 0.72109 0.096 0.004 0.824 0.000 0.076
#> GSM1068545 4 0.2230 0.84500 0.000 0.000 0.000 0.884 0.116
#> GSM1068546 3 0.3146 0.76263 0.040 0.028 0.876 0.000 0.056
#> GSM1068547 5 0.5175 0.08954 0.464 0.012 0.020 0.000 0.504
#> GSM1068548 4 0.0162 0.86485 0.000 0.000 0.000 0.996 0.004
#> GSM1068549 3 0.5083 0.32976 0.004 0.028 0.540 0.000 0.428
#> GSM1068550 4 0.0000 0.86463 0.000 0.000 0.000 1.000 0.000
#> GSM1068551 2 0.1410 0.95937 0.000 0.940 0.000 0.060 0.000
#> GSM1068552 4 0.0000 0.86463 0.000 0.000 0.000 1.000 0.000
#> GSM1068555 2 0.2278 0.92400 0.000 0.908 0.000 0.060 0.032
#> GSM1068556 4 0.2886 0.82298 0.000 0.000 0.008 0.844 0.148
#> GSM1068557 5 0.3926 0.61988 0.000 0.112 0.020 0.048 0.820
#> GSM1068560 4 0.0000 0.86463 0.000 0.000 0.000 1.000 0.000
#> GSM1068561 5 0.3670 0.62562 0.004 0.088 0.004 0.068 0.836
#> GSM1068562 4 0.0992 0.86731 0.000 0.008 0.000 0.968 0.024
#> GSM1068563 4 0.1544 0.85798 0.000 0.000 0.000 0.932 0.068
#> GSM1068565 2 0.1410 0.95937 0.000 0.940 0.000 0.060 0.000
#> GSM1068529 5 0.5316 0.50714 0.000 0.004 0.152 0.156 0.688
#> GSM1068530 1 0.1492 0.77961 0.948 0.004 0.008 0.000 0.040
#> GSM1068534 5 0.5258 0.54792 0.012 0.000 0.124 0.156 0.708
#> GSM1068536 5 0.4787 0.58613 0.148 0.012 0.012 0.064 0.764
#> GSM1068541 5 0.5107 0.62176 0.064 0.116 0.012 0.044 0.764
#> GSM1068553 4 0.3274 0.73623 0.000 0.000 0.000 0.780 0.220
#> GSM1068554 4 0.2563 0.84305 0.000 0.008 0.000 0.872 0.120
#> GSM1068558 5 0.5631 -0.10553 0.008 0.044 0.456 0.004 0.488
#> GSM1068559 5 0.7374 0.21233 0.008 0.016 0.280 0.292 0.404
#> GSM1068564 4 0.0510 0.86779 0.000 0.000 0.000 0.984 0.016
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1068478 5 0.5091 0.2001 0.364 0.000 0.076 0.000 0.556 0.004
#> GSM1068479 6 0.4212 0.8147 0.000 0.024 0.056 0.016 0.116 0.788
#> GSM1068481 3 0.0146 0.8901 0.004 0.000 0.996 0.000 0.000 0.000
#> GSM1068482 3 0.1151 0.8822 0.000 0.000 0.956 0.000 0.012 0.032
#> GSM1068483 1 0.5992 0.2724 0.420 0.000 0.240 0.000 0.340 0.000
#> GSM1068486 3 0.6213 0.0139 0.008 0.000 0.432 0.000 0.296 0.264
#> GSM1068487 2 0.0508 0.8855 0.000 0.984 0.000 0.012 0.000 0.004
#> GSM1068488 4 0.6148 0.2546 0.000 0.012 0.008 0.512 0.288 0.180
#> GSM1068490 2 0.0363 0.8865 0.000 0.988 0.000 0.012 0.000 0.000
#> GSM1068491 6 0.4122 0.8132 0.000 0.024 0.056 0.012 0.116 0.792
#> GSM1068492 6 0.4376 0.8118 0.000 0.024 0.056 0.024 0.116 0.780
#> GSM1068493 5 0.1887 0.6509 0.028 0.004 0.024 0.008 0.932 0.004
#> GSM1068494 1 0.6122 0.1089 0.484 0.000 0.104 0.024 0.376 0.012
#> GSM1068495 5 0.1180 0.6536 0.012 0.012 0.000 0.016 0.960 0.000
#> GSM1068496 1 0.4578 0.4999 0.624 0.000 0.320 0.000 0.056 0.000
#> GSM1068498 5 0.5388 0.4814 0.088 0.016 0.044 0.000 0.692 0.160
#> GSM1068499 5 0.5418 0.1161 0.368 0.000 0.124 0.000 0.508 0.000
#> GSM1068500 1 0.5993 0.3656 0.440 0.000 0.272 0.000 0.288 0.000
#> GSM1068502 6 0.4212 0.8147 0.000 0.024 0.056 0.016 0.116 0.788
#> GSM1068503 2 0.6570 0.0224 0.000 0.468 0.012 0.360 0.064 0.096
#> GSM1068505 4 0.0146 0.8361 0.000 0.000 0.000 0.996 0.004 0.000
#> GSM1068506 4 0.0260 0.8355 0.000 0.000 0.000 0.992 0.008 0.000
#> GSM1068507 4 0.3045 0.7820 0.000 0.000 0.000 0.840 0.060 0.100
#> GSM1068508 4 0.5274 0.5822 0.000 0.220 0.000 0.660 0.060 0.060
#> GSM1068510 4 0.3449 0.7616 0.000 0.000 0.000 0.808 0.076 0.116
#> GSM1068512 4 0.5971 0.2886 0.004 0.004 0.012 0.528 0.316 0.136
#> GSM1068513 4 0.4543 0.7232 0.000 0.112 0.000 0.756 0.056 0.076
#> GSM1068514 6 0.5816 0.7209 0.000 0.024 0.056 0.132 0.120 0.668
#> GSM1068517 5 0.4958 0.5431 0.024 0.048 0.040 0.000 0.728 0.160
#> GSM1068518 5 0.5590 0.2452 0.000 0.000 0.028 0.320 0.564 0.088
#> GSM1068520 5 0.4528 0.0838 0.428 0.000 0.020 0.000 0.544 0.008
#> GSM1068521 5 0.4046 0.2474 0.368 0.000 0.004 0.000 0.620 0.008
#> GSM1068522 4 0.1780 0.8304 0.000 0.028 0.000 0.932 0.028 0.012
#> GSM1068524 2 0.4752 0.6030 0.000 0.756 0.012 0.068 0.060 0.104
#> GSM1068527 4 0.0000 0.8357 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1068480 3 0.1500 0.8762 0.000 0.000 0.936 0.000 0.012 0.052
#> GSM1068484 4 0.1074 0.8372 0.000 0.000 0.000 0.960 0.028 0.012
#> GSM1068485 3 0.0146 0.8901 0.004 0.000 0.996 0.000 0.000 0.000
#> GSM1068489 4 0.0146 0.8361 0.000 0.000 0.000 0.996 0.004 0.000
#> GSM1068497 5 0.5388 0.4814 0.088 0.016 0.044 0.000 0.692 0.160
#> GSM1068501 4 0.0260 0.8355 0.000 0.000 0.000 0.992 0.008 0.000
#> GSM1068504 2 0.0508 0.8855 0.000 0.984 0.000 0.012 0.000 0.004
#> GSM1068509 5 0.2507 0.6329 0.072 0.000 0.004 0.004 0.888 0.032
#> GSM1068511 5 0.6540 0.0963 0.156 0.000 0.372 0.004 0.428 0.040
#> GSM1068515 5 0.1621 0.6547 0.012 0.008 0.020 0.016 0.944 0.000
#> GSM1068516 5 0.4959 0.4160 0.000 0.000 0.020 0.224 0.672 0.084
#> GSM1068519 1 0.4294 0.2627 0.592 0.000 0.008 0.012 0.388 0.000
#> GSM1068523 2 0.0291 0.8884 0.000 0.992 0.000 0.004 0.000 0.004
#> GSM1068525 4 0.6201 0.4864 0.000 0.072 0.016 0.600 0.088 0.224
#> GSM1068526 4 0.0146 0.8371 0.000 0.000 0.000 0.996 0.004 0.000
#> GSM1068458 5 0.4009 0.2698 0.356 0.000 0.004 0.000 0.632 0.008
#> GSM1068459 3 0.0291 0.8901 0.004 0.000 0.992 0.000 0.000 0.004
#> GSM1068460 5 0.3073 0.5277 0.008 0.000 0.000 0.204 0.788 0.000
#> GSM1068461 3 0.2531 0.8139 0.000 0.000 0.856 0.000 0.012 0.132
#> GSM1068464 2 0.0405 0.8867 0.000 0.988 0.000 0.004 0.000 0.008
#> GSM1068468 5 0.5006 0.4093 0.000 0.292 0.032 0.000 0.632 0.044
#> GSM1068472 5 0.1977 0.6502 0.000 0.040 0.032 0.000 0.920 0.008
#> GSM1068473 2 0.0508 0.8855 0.000 0.984 0.000 0.012 0.000 0.004
#> GSM1068474 2 0.0146 0.8881 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM1068476 6 0.4122 0.8132 0.000 0.024 0.056 0.012 0.116 0.792
#> GSM1068477 2 0.5260 0.1036 0.000 0.516 0.000 0.008 0.400 0.076
#> GSM1068462 5 0.5977 0.1949 0.000 0.360 0.032 0.000 0.496 0.112
#> GSM1068463 3 0.0146 0.8901 0.004 0.000 0.996 0.000 0.000 0.000
#> GSM1068465 5 0.1508 0.6545 0.016 0.012 0.000 0.020 0.948 0.004
#> GSM1068466 5 0.4751 0.0223 0.448 0.000 0.032 0.000 0.512 0.008
#> GSM1068467 5 0.4327 0.5024 0.000 0.240 0.032 0.000 0.708 0.020
#> GSM1068469 5 0.6202 0.5154 0.024 0.144 0.092 0.000 0.636 0.104
#> GSM1068470 2 0.0146 0.8863 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM1068471 2 0.0000 0.8867 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1068475 2 0.0146 0.8863 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM1068528 1 0.4581 0.3018 0.516 0.000 0.448 0.000 0.036 0.000
#> GSM1068531 1 0.0146 0.7087 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM1068532 1 0.0363 0.7054 0.988 0.000 0.012 0.000 0.000 0.000
#> GSM1068533 1 0.0146 0.7087 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM1068535 4 0.6138 0.1930 0.016 0.000 0.020 0.480 0.380 0.104
#> GSM1068537 1 0.0363 0.7054 0.988 0.000 0.012 0.000 0.000 0.000
#> GSM1068538 1 0.0363 0.7054 0.988 0.000 0.012 0.000 0.000 0.000
#> GSM1068539 5 0.2218 0.6245 0.000 0.012 0.000 0.104 0.884 0.000
#> GSM1068540 1 0.0146 0.7087 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM1068542 4 0.0000 0.8357 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1068543 4 0.3630 0.7646 0.000 0.000 0.020 0.816 0.064 0.100
#> GSM1068544 3 0.0146 0.8901 0.004 0.000 0.996 0.000 0.000 0.000
#> GSM1068545 4 0.3510 0.7723 0.000 0.096 0.000 0.828 0.044 0.032
#> GSM1068546 3 0.1930 0.8683 0.012 0.000 0.924 0.000 0.028 0.036
#> GSM1068547 1 0.3828 0.1776 0.560 0.000 0.000 0.000 0.440 0.000
#> GSM1068548 4 0.0000 0.8357 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1068549 6 0.6259 0.0557 0.008 0.000 0.332 0.000 0.260 0.400
#> GSM1068550 4 0.0146 0.8354 0.000 0.000 0.000 0.996 0.004 0.000
#> GSM1068551 2 0.0146 0.8881 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM1068552 4 0.0405 0.8371 0.000 0.000 0.000 0.988 0.004 0.008
#> GSM1068555 2 0.0458 0.8835 0.000 0.984 0.000 0.000 0.000 0.016
#> GSM1068556 4 0.3297 0.7772 0.000 0.000 0.008 0.832 0.060 0.100
#> GSM1068557 5 0.3911 0.6011 0.000 0.032 0.032 0.024 0.816 0.096
#> GSM1068560 4 0.0000 0.8357 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1068561 5 0.3388 0.6220 0.004 0.012 0.024 0.032 0.852 0.076
#> GSM1068562 4 0.0993 0.8369 0.000 0.000 0.000 0.964 0.024 0.012
#> GSM1068563 4 0.1511 0.8331 0.000 0.004 0.000 0.940 0.044 0.012
#> GSM1068565 2 0.0291 0.8884 0.000 0.992 0.000 0.004 0.000 0.004
#> GSM1068529 5 0.5364 0.4774 0.000 0.008 0.048 0.128 0.692 0.124
#> GSM1068530 1 0.0260 0.7089 0.992 0.000 0.000 0.000 0.008 0.000
#> GSM1068534 5 0.5783 0.3030 0.000 0.000 0.032 0.256 0.584 0.128
#> GSM1068536 5 0.1812 0.6261 0.080 0.000 0.000 0.008 0.912 0.000
#> GSM1068541 5 0.1180 0.6536 0.012 0.012 0.000 0.016 0.960 0.000
#> GSM1068553 4 0.3372 0.7615 0.000 0.000 0.000 0.816 0.084 0.100
#> GSM1068554 4 0.3045 0.7840 0.000 0.000 0.000 0.840 0.060 0.100
#> GSM1068558 6 0.4304 0.7701 0.000 0.024 0.092 0.004 0.108 0.772
#> GSM1068559 6 0.6857 0.1890 0.000 0.024 0.052 0.376 0.120 0.428
#> GSM1068564 4 0.1218 0.8372 0.000 0.004 0.000 0.956 0.028 0.012
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) gender(p) k
#> CV:mclust 108 0.762869 1.000 2
#> CV:mclust 102 0.020389 0.783 3
#> CV:mclust 57 0.003318 0.223 4
#> CV:mclust 80 0.000833 0.406 5
#> CV:mclust 77 0.001699 0.577 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 38950 rows and 108 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#> Subgroups are detected by 'NMF' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.850 0.916 0.963 0.4891 0.509 0.509
#> 3 3 0.516 0.563 0.758 0.3286 0.786 0.612
#> 4 4 0.659 0.771 0.861 0.1368 0.738 0.414
#> 5 5 0.661 0.649 0.812 0.0624 0.952 0.816
#> 6 6 0.684 0.608 0.790 0.0427 0.921 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
#> GSM1068478 1 0.0000 0.950 1.000 0.000
#> GSM1068479 2 0.0000 0.968 0.000 1.000
#> GSM1068481 1 0.0000 0.950 1.000 0.000
#> GSM1068482 1 0.0000 0.950 1.000 0.000
#> GSM1068483 1 0.0000 0.950 1.000 0.000
#> GSM1068486 1 0.0000 0.950 1.000 0.000
#> GSM1068487 2 0.0000 0.968 0.000 1.000
#> GSM1068488 2 0.4815 0.871 0.104 0.896
#> GSM1068490 2 0.0000 0.968 0.000 1.000
#> GSM1068491 2 0.4562 0.880 0.096 0.904
#> GSM1068492 2 0.0000 0.968 0.000 1.000
#> GSM1068493 1 0.0000 0.950 1.000 0.000
#> GSM1068494 1 0.0000 0.950 1.000 0.000
#> GSM1068495 1 0.9209 0.534 0.664 0.336
#> GSM1068496 1 0.0000 0.950 1.000 0.000
#> GSM1068498 1 0.7056 0.772 0.808 0.192
#> GSM1068499 1 0.0000 0.950 1.000 0.000
#> GSM1068500 1 0.0000 0.950 1.000 0.000
#> GSM1068502 2 0.0000 0.968 0.000 1.000
#> GSM1068503 2 0.0000 0.968 0.000 1.000
#> GSM1068505 2 0.0000 0.968 0.000 1.000
#> GSM1068506 2 0.0000 0.968 0.000 1.000
#> GSM1068507 2 0.0000 0.968 0.000 1.000
#> GSM1068508 2 0.0000 0.968 0.000 1.000
#> GSM1068510 2 0.0000 0.968 0.000 1.000
#> GSM1068512 2 0.7674 0.713 0.224 0.776
#> GSM1068513 2 0.0000 0.968 0.000 1.000
#> GSM1068514 2 0.0000 0.968 0.000 1.000
#> GSM1068517 2 0.3879 0.899 0.076 0.924
#> GSM1068518 1 0.6048 0.817 0.852 0.148
#> GSM1068520 1 0.0000 0.950 1.000 0.000
#> GSM1068521 1 0.0000 0.950 1.000 0.000
#> GSM1068522 2 0.0000 0.968 0.000 1.000
#> GSM1068524 2 0.0000 0.968 0.000 1.000
#> GSM1068527 2 0.0000 0.968 0.000 1.000
#> GSM1068480 1 0.0000 0.950 1.000 0.000
#> GSM1068484 2 0.0000 0.968 0.000 1.000
#> GSM1068485 1 0.0000 0.950 1.000 0.000
#> GSM1068489 2 0.0000 0.968 0.000 1.000
#> GSM1068497 1 0.7056 0.772 0.808 0.192
#> GSM1068501 2 0.0000 0.968 0.000 1.000
#> GSM1068504 2 0.0000 0.968 0.000 1.000
#> GSM1068509 1 0.0000 0.950 1.000 0.000
#> GSM1068511 1 0.0000 0.950 1.000 0.000
#> GSM1068515 1 0.6887 0.784 0.816 0.184
#> GSM1068516 2 0.0938 0.959 0.012 0.988
#> GSM1068519 1 0.0000 0.950 1.000 0.000
#> GSM1068523 2 0.0000 0.968 0.000 1.000
#> GSM1068525 2 0.0000 0.968 0.000 1.000
#> GSM1068526 2 0.0000 0.968 0.000 1.000
#> GSM1068458 1 0.0000 0.950 1.000 0.000
#> GSM1068459 1 0.0000 0.950 1.000 0.000
#> GSM1068460 2 0.9209 0.510 0.336 0.664
#> GSM1068461 1 0.0000 0.950 1.000 0.000
#> GSM1068464 2 0.0000 0.968 0.000 1.000
#> GSM1068468 2 0.0000 0.968 0.000 1.000
#> GSM1068472 1 0.9608 0.426 0.616 0.384
#> GSM1068473 2 0.0000 0.968 0.000 1.000
#> GSM1068474 2 0.0000 0.968 0.000 1.000
#> GSM1068476 2 0.0000 0.968 0.000 1.000
#> GSM1068477 2 0.0000 0.968 0.000 1.000
#> GSM1068462 2 0.0000 0.968 0.000 1.000
#> GSM1068463 1 0.0000 0.950 1.000 0.000
#> GSM1068465 1 0.6148 0.819 0.848 0.152
#> GSM1068466 1 0.0000 0.950 1.000 0.000
#> GSM1068467 2 0.0000 0.968 0.000 1.000
#> GSM1068469 2 0.9775 0.251 0.412 0.588
#> GSM1068470 2 0.0000 0.968 0.000 1.000
#> GSM1068471 2 0.0000 0.968 0.000 1.000
#> GSM1068475 2 0.0000 0.968 0.000 1.000
#> GSM1068528 1 0.0000 0.950 1.000 0.000
#> GSM1068531 1 0.0000 0.950 1.000 0.000
#> GSM1068532 1 0.0000 0.950 1.000 0.000
#> GSM1068533 1 0.0000 0.950 1.000 0.000
#> GSM1068535 1 0.0000 0.950 1.000 0.000
#> GSM1068537 1 0.0000 0.950 1.000 0.000
#> GSM1068538 1 0.0000 0.950 1.000 0.000
#> GSM1068539 2 0.0000 0.968 0.000 1.000
#> GSM1068540 1 0.0000 0.950 1.000 0.000
#> GSM1068542 2 0.0000 0.968 0.000 1.000
#> GSM1068543 2 0.0000 0.968 0.000 1.000
#> GSM1068544 1 0.0000 0.950 1.000 0.000
#> GSM1068545 2 0.0000 0.968 0.000 1.000
#> GSM1068546 1 0.0000 0.950 1.000 0.000
#> GSM1068547 1 0.0000 0.950 1.000 0.000
#> GSM1068548 2 0.0000 0.968 0.000 1.000
#> GSM1068549 1 0.0000 0.950 1.000 0.000
#> GSM1068550 2 0.0000 0.968 0.000 1.000
#> GSM1068551 2 0.0000 0.968 0.000 1.000
#> GSM1068552 2 0.0000 0.968 0.000 1.000
#> GSM1068555 2 0.0000 0.968 0.000 1.000
#> GSM1068556 2 0.6623 0.787 0.172 0.828
#> GSM1068557 2 0.0000 0.968 0.000 1.000
#> GSM1068560 2 0.0000 0.968 0.000 1.000
#> GSM1068561 2 0.8763 0.559 0.296 0.704
#> GSM1068562 2 0.0000 0.968 0.000 1.000
#> GSM1068563 2 0.0672 0.962 0.008 0.992
#> GSM1068565 2 0.0000 0.968 0.000 1.000
#> GSM1068529 1 0.9909 0.257 0.556 0.444
#> GSM1068530 1 0.0000 0.950 1.000 0.000
#> GSM1068534 1 0.2778 0.916 0.952 0.048
#> GSM1068536 1 0.2236 0.925 0.964 0.036
#> GSM1068541 2 0.0376 0.965 0.004 0.996
#> GSM1068553 2 0.0000 0.968 0.000 1.000
#> GSM1068554 2 0.0000 0.968 0.000 1.000
#> GSM1068558 2 0.4298 0.885 0.088 0.912
#> GSM1068559 2 0.0000 0.968 0.000 1.000
#> GSM1068564 2 0.0000 0.968 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1068478 1 0.5760 0.7839 0.672 0.000 0.328
#> GSM1068479 2 0.6442 -0.0205 0.004 0.564 0.432
#> GSM1068481 1 0.6062 0.7746 0.616 0.000 0.384
#> GSM1068482 3 0.3551 0.3447 0.132 0.000 0.868
#> GSM1068483 1 0.5926 0.7901 0.644 0.000 0.356
#> GSM1068486 3 0.3325 0.5751 0.020 0.076 0.904
#> GSM1068487 2 0.4452 0.6911 0.192 0.808 0.000
#> GSM1068488 3 0.6416 0.4806 0.008 0.376 0.616
#> GSM1068490 2 0.3482 0.6980 0.128 0.872 0.000
#> GSM1068491 3 0.6476 0.3137 0.004 0.448 0.548
#> GSM1068492 2 0.6140 0.0908 0.000 0.596 0.404
#> GSM1068493 1 0.6154 0.7531 0.592 0.000 0.408
#> GSM1068494 3 0.5905 -0.3394 0.352 0.000 0.648
#> GSM1068495 1 0.4505 0.5834 0.860 0.048 0.092
#> GSM1068496 1 0.5948 0.7899 0.640 0.000 0.360
#> GSM1068498 1 0.1163 0.5225 0.972 0.028 0.000
#> GSM1068499 1 0.6111 0.7724 0.604 0.000 0.396
#> GSM1068500 1 0.5926 0.7901 0.644 0.000 0.356
#> GSM1068502 2 0.5070 0.5118 0.004 0.772 0.224
#> GSM1068503 2 0.0475 0.6944 0.004 0.992 0.004
#> GSM1068505 2 0.1163 0.6877 0.000 0.972 0.028
#> GSM1068506 2 0.1832 0.6881 0.008 0.956 0.036
#> GSM1068507 2 0.4062 0.5843 0.000 0.836 0.164
#> GSM1068508 2 0.4346 0.6934 0.184 0.816 0.000
#> GSM1068510 2 0.6045 0.1405 0.000 0.620 0.380
#> GSM1068512 3 0.7013 0.4974 0.028 0.364 0.608
#> GSM1068513 2 0.1182 0.6951 0.012 0.976 0.012
#> GSM1068514 3 0.6008 0.4689 0.000 0.372 0.628
#> GSM1068517 1 0.4654 0.3156 0.792 0.208 0.000
#> GSM1068518 3 0.8261 0.3921 0.080 0.396 0.524
#> GSM1068520 1 0.5882 0.7897 0.652 0.000 0.348
#> GSM1068521 1 0.5905 0.7901 0.648 0.000 0.352
#> GSM1068522 2 0.1643 0.6981 0.044 0.956 0.000
#> GSM1068524 2 0.1636 0.6948 0.016 0.964 0.020
#> GSM1068527 2 0.4291 0.5644 0.000 0.820 0.180
#> GSM1068480 3 0.3941 0.2778 0.156 0.000 0.844
#> GSM1068484 2 0.2448 0.6689 0.000 0.924 0.076
#> GSM1068485 1 0.6154 0.7654 0.592 0.000 0.408
#> GSM1068489 2 0.2625 0.6635 0.000 0.916 0.084
#> GSM1068497 1 0.0892 0.5294 0.980 0.020 0.000
#> GSM1068501 2 0.2550 0.6820 0.012 0.932 0.056
#> GSM1068504 2 0.5760 0.6284 0.328 0.672 0.000
#> GSM1068509 1 0.5859 0.7776 0.656 0.000 0.344
#> GSM1068511 3 0.4172 0.3019 0.156 0.004 0.840
#> GSM1068515 1 0.4281 0.4709 0.872 0.072 0.056
#> GSM1068516 2 0.7107 0.6367 0.196 0.712 0.092
#> GSM1068519 1 0.5948 0.7899 0.640 0.000 0.360
#> GSM1068523 2 0.5905 0.6127 0.352 0.648 0.000
#> GSM1068525 2 0.5122 0.5485 0.012 0.788 0.200
#> GSM1068526 2 0.4121 0.5861 0.000 0.832 0.168
#> GSM1068458 1 0.5882 0.7897 0.652 0.000 0.348
#> GSM1068459 1 0.6286 0.6871 0.536 0.000 0.464
#> GSM1068460 1 0.7972 0.4485 0.644 0.240 0.116
#> GSM1068461 3 0.4683 0.3634 0.140 0.024 0.836
#> GSM1068464 2 0.4452 0.6901 0.192 0.808 0.000
#> GSM1068468 2 0.5706 0.6351 0.320 0.680 0.000
#> GSM1068472 1 0.4712 0.4872 0.848 0.108 0.044
#> GSM1068473 2 0.5016 0.6762 0.240 0.760 0.000
#> GSM1068474 2 0.5497 0.6512 0.292 0.708 0.000
#> GSM1068476 2 0.6305 -0.1768 0.000 0.516 0.484
#> GSM1068477 2 0.5016 0.6745 0.240 0.760 0.000
#> GSM1068462 2 0.6975 0.6004 0.356 0.616 0.028
#> GSM1068463 1 0.6126 0.7621 0.600 0.000 0.400
#> GSM1068465 1 0.4235 0.6616 0.824 0.000 0.176
#> GSM1068466 1 0.5529 0.7685 0.704 0.000 0.296
#> GSM1068467 2 0.6984 0.5324 0.420 0.560 0.020
#> GSM1068469 1 0.4861 0.3244 0.800 0.192 0.008
#> GSM1068470 2 0.5926 0.6093 0.356 0.644 0.000
#> GSM1068471 2 0.5882 0.6157 0.348 0.652 0.000
#> GSM1068475 2 0.5905 0.6127 0.352 0.648 0.000
#> GSM1068528 1 0.5948 0.7899 0.640 0.000 0.360
#> GSM1068531 1 0.5948 0.7899 0.640 0.000 0.360
#> GSM1068532 1 0.5968 0.7882 0.636 0.000 0.364
#> GSM1068533 1 0.5948 0.7899 0.640 0.000 0.360
#> GSM1068535 3 0.7605 0.5919 0.124 0.192 0.684
#> GSM1068537 1 0.5948 0.7899 0.640 0.000 0.360
#> GSM1068538 1 0.5948 0.7899 0.640 0.000 0.360
#> GSM1068539 2 0.5529 0.6467 0.296 0.704 0.000
#> GSM1068540 1 0.5948 0.7899 0.640 0.000 0.360
#> GSM1068542 2 0.2878 0.6499 0.000 0.904 0.096
#> GSM1068543 2 0.6291 -0.1537 0.000 0.532 0.468
#> GSM1068544 1 0.5968 0.7882 0.636 0.000 0.364
#> GSM1068545 2 0.3816 0.6962 0.148 0.852 0.000
#> GSM1068546 3 0.3412 0.3594 0.124 0.000 0.876
#> GSM1068547 1 0.5882 0.7897 0.652 0.000 0.348
#> GSM1068548 2 0.3192 0.6369 0.000 0.888 0.112
#> GSM1068549 3 0.5431 0.5591 0.000 0.284 0.716
#> GSM1068550 2 0.1163 0.6877 0.000 0.972 0.028
#> GSM1068551 2 0.4605 0.6867 0.204 0.796 0.000
#> GSM1068552 2 0.1529 0.6833 0.000 0.960 0.040
#> GSM1068555 2 0.5835 0.6216 0.340 0.660 0.000
#> GSM1068556 3 0.7049 0.3300 0.020 0.452 0.528
#> GSM1068557 2 0.4654 0.6856 0.208 0.792 0.000
#> GSM1068560 2 0.1860 0.6778 0.000 0.948 0.052
#> GSM1068561 1 0.9908 -0.1869 0.372 0.268 0.360
#> GSM1068562 2 0.3116 0.6403 0.000 0.892 0.108
#> GSM1068563 2 0.2173 0.6844 0.008 0.944 0.048
#> GSM1068565 2 0.5465 0.6535 0.288 0.712 0.000
#> GSM1068529 3 0.7756 0.5232 0.128 0.200 0.672
#> GSM1068530 1 0.5926 0.7901 0.644 0.000 0.356
#> GSM1068534 3 0.3112 0.5666 0.028 0.056 0.916
#> GSM1068536 1 0.4504 0.7027 0.804 0.000 0.196
#> GSM1068541 1 0.5835 -0.0849 0.660 0.340 0.000
#> GSM1068553 2 0.6291 -0.1422 0.000 0.532 0.468
#> GSM1068554 2 0.4750 0.5225 0.000 0.784 0.216
#> GSM1068558 3 0.6126 0.4923 0.004 0.352 0.644
#> GSM1068559 2 0.6286 -0.1125 0.000 0.536 0.464
#> GSM1068564 2 0.4974 0.6733 0.236 0.764 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1068478 1 0.0927 0.891 0.976 0.008 0.016 0.000
#> GSM1068479 3 0.2816 0.804 0.000 0.036 0.900 0.064
#> GSM1068481 1 0.4770 0.610 0.700 0.012 0.288 0.000
#> GSM1068482 3 0.3662 0.728 0.148 0.012 0.836 0.004
#> GSM1068483 1 0.1406 0.888 0.960 0.016 0.024 0.000
#> GSM1068486 3 0.1575 0.811 0.004 0.012 0.956 0.028
#> GSM1068487 2 0.3907 0.737 0.000 0.768 0.000 0.232
#> GSM1068488 4 0.2281 0.823 0.000 0.000 0.096 0.904
#> GSM1068490 2 0.4164 0.692 0.000 0.736 0.000 0.264
#> GSM1068491 3 0.2021 0.811 0.000 0.012 0.932 0.056
#> GSM1068492 3 0.6186 0.415 0.000 0.064 0.584 0.352
#> GSM1068493 1 0.5339 0.606 0.688 0.040 0.272 0.000
#> GSM1068494 1 0.3215 0.826 0.876 0.000 0.032 0.092
#> GSM1068495 1 0.2670 0.847 0.904 0.072 0.000 0.024
#> GSM1068496 1 0.0895 0.891 0.976 0.004 0.020 0.000
#> GSM1068498 2 0.4304 0.549 0.284 0.716 0.000 0.000
#> GSM1068499 1 0.4283 0.815 0.820 0.048 0.128 0.004
#> GSM1068500 1 0.1388 0.888 0.960 0.012 0.028 0.000
#> GSM1068502 3 0.6820 0.260 0.000 0.364 0.528 0.108
#> GSM1068503 4 0.3942 0.715 0.000 0.236 0.000 0.764
#> GSM1068505 4 0.1510 0.874 0.016 0.028 0.000 0.956
#> GSM1068506 4 0.1890 0.873 0.008 0.056 0.000 0.936
#> GSM1068507 4 0.1118 0.877 0.000 0.036 0.000 0.964
#> GSM1068508 4 0.4222 0.661 0.000 0.272 0.000 0.728
#> GSM1068510 4 0.1256 0.870 0.000 0.008 0.028 0.964
#> GSM1068512 4 0.1576 0.850 0.004 0.000 0.048 0.948
#> GSM1068513 4 0.3311 0.794 0.000 0.172 0.000 0.828
#> GSM1068514 3 0.2831 0.794 0.000 0.004 0.876 0.120
#> GSM1068517 2 0.1824 0.790 0.060 0.936 0.000 0.004
#> GSM1068518 4 0.6025 0.433 0.332 0.012 0.036 0.620
#> GSM1068520 1 0.0188 0.892 0.996 0.004 0.000 0.000
#> GSM1068521 1 0.0000 0.892 1.000 0.000 0.000 0.000
#> GSM1068522 4 0.3311 0.795 0.000 0.172 0.000 0.828
#> GSM1068524 4 0.4382 0.609 0.000 0.296 0.000 0.704
#> GSM1068527 4 0.0672 0.872 0.008 0.000 0.008 0.984
#> GSM1068480 3 0.1396 0.797 0.004 0.032 0.960 0.004
#> GSM1068484 4 0.2174 0.872 0.000 0.052 0.020 0.928
#> GSM1068485 3 0.5036 0.523 0.280 0.024 0.696 0.000
#> GSM1068489 4 0.1837 0.862 0.000 0.028 0.028 0.944
#> GSM1068497 2 0.4470 0.667 0.172 0.792 0.032 0.004
#> GSM1068501 4 0.1936 0.870 0.000 0.028 0.032 0.940
#> GSM1068504 2 0.1576 0.821 0.000 0.948 0.004 0.048
#> GSM1068509 1 0.4090 0.816 0.844 0.096 0.048 0.012
#> GSM1068511 3 0.5813 0.427 0.320 0.016 0.640 0.024
#> GSM1068515 2 0.4687 0.701 0.084 0.812 0.092 0.012
#> GSM1068516 4 0.6908 0.470 0.072 0.284 0.032 0.612
#> GSM1068519 1 0.2807 0.859 0.912 0.016 0.032 0.040
#> GSM1068523 2 0.2313 0.810 0.000 0.924 0.032 0.044
#> GSM1068525 4 0.4663 0.780 0.000 0.148 0.064 0.788
#> GSM1068526 4 0.0927 0.871 0.000 0.008 0.016 0.976
#> GSM1068458 1 0.0188 0.891 0.996 0.000 0.000 0.004
#> GSM1068459 1 0.4866 0.366 0.596 0.000 0.404 0.000
#> GSM1068460 1 0.4454 0.527 0.692 0.000 0.000 0.308
#> GSM1068461 3 0.1593 0.811 0.016 0.004 0.956 0.024
#> GSM1068464 2 0.3749 0.801 0.000 0.840 0.032 0.128
#> GSM1068468 2 0.1557 0.821 0.000 0.944 0.000 0.056
#> GSM1068472 2 0.2870 0.804 0.044 0.908 0.012 0.036
#> GSM1068473 2 0.4277 0.669 0.000 0.720 0.000 0.280
#> GSM1068474 2 0.3266 0.792 0.000 0.832 0.000 0.168
#> GSM1068476 3 0.3099 0.795 0.000 0.020 0.876 0.104
#> GSM1068477 2 0.3837 0.746 0.000 0.776 0.000 0.224
#> GSM1068462 2 0.1584 0.798 0.000 0.952 0.036 0.012
#> GSM1068463 1 0.4122 0.698 0.760 0.004 0.236 0.000
#> GSM1068465 1 0.5797 0.693 0.716 0.200 0.072 0.012
#> GSM1068466 1 0.1798 0.881 0.944 0.040 0.016 0.000
#> GSM1068467 2 0.1617 0.804 0.008 0.956 0.024 0.012
#> GSM1068469 2 0.1488 0.793 0.032 0.956 0.012 0.000
#> GSM1068470 2 0.1474 0.821 0.000 0.948 0.000 0.052
#> GSM1068471 2 0.2131 0.808 0.000 0.932 0.032 0.036
#> GSM1068475 2 0.1389 0.821 0.000 0.952 0.000 0.048
#> GSM1068528 1 0.1004 0.890 0.972 0.004 0.024 0.000
#> GSM1068531 1 0.0469 0.890 0.988 0.000 0.000 0.012
#> GSM1068532 1 0.0804 0.891 0.980 0.000 0.008 0.012
#> GSM1068533 1 0.0188 0.891 0.996 0.000 0.000 0.004
#> GSM1068535 4 0.4599 0.592 0.248 0.000 0.016 0.736
#> GSM1068537 1 0.0779 0.892 0.980 0.000 0.016 0.004
#> GSM1068538 1 0.0592 0.888 0.984 0.000 0.000 0.016
#> GSM1068539 2 0.7091 0.590 0.224 0.568 0.000 0.208
#> GSM1068540 1 0.0524 0.892 0.988 0.000 0.008 0.004
#> GSM1068542 4 0.0937 0.876 0.012 0.012 0.000 0.976
#> GSM1068543 4 0.1211 0.856 0.000 0.000 0.040 0.960
#> GSM1068544 1 0.1004 0.891 0.972 0.004 0.024 0.000
#> GSM1068545 4 0.3649 0.762 0.000 0.204 0.000 0.796
#> GSM1068546 3 0.2222 0.796 0.060 0.000 0.924 0.016
#> GSM1068547 1 0.0707 0.887 0.980 0.000 0.000 0.020
#> GSM1068548 4 0.0657 0.876 0.004 0.012 0.000 0.984
#> GSM1068549 3 0.1489 0.813 0.000 0.004 0.952 0.044
#> GSM1068550 4 0.1706 0.874 0.016 0.036 0.000 0.948
#> GSM1068551 2 0.3873 0.742 0.000 0.772 0.000 0.228
#> GSM1068552 4 0.1302 0.876 0.000 0.044 0.000 0.956
#> GSM1068555 2 0.1637 0.821 0.000 0.940 0.000 0.060
#> GSM1068556 4 0.1118 0.858 0.000 0.000 0.036 0.964
#> GSM1068557 2 0.3636 0.786 0.000 0.820 0.008 0.172
#> GSM1068560 4 0.0895 0.877 0.004 0.020 0.000 0.976
#> GSM1068561 2 0.8445 0.334 0.088 0.512 0.276 0.124
#> GSM1068562 4 0.1209 0.877 0.000 0.032 0.004 0.964
#> GSM1068563 4 0.2266 0.857 0.004 0.084 0.000 0.912
#> GSM1068565 2 0.3649 0.765 0.000 0.796 0.000 0.204
#> GSM1068529 3 0.5484 0.687 0.000 0.164 0.732 0.104
#> GSM1068530 1 0.0000 0.892 1.000 0.000 0.000 0.000
#> GSM1068534 3 0.6097 0.711 0.088 0.104 0.744 0.064
#> GSM1068536 1 0.3517 0.829 0.868 0.088 0.040 0.004
#> GSM1068541 2 0.4828 0.707 0.160 0.788 0.032 0.020
#> GSM1068553 4 0.0817 0.865 0.000 0.000 0.024 0.976
#> GSM1068554 4 0.1151 0.873 0.000 0.008 0.024 0.968
#> GSM1068558 3 0.2179 0.815 0.000 0.012 0.924 0.064
#> GSM1068559 3 0.5630 0.443 0.000 0.032 0.608 0.360
#> GSM1068564 4 0.4008 0.804 0.000 0.148 0.032 0.820
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM1068478 1 0.1195 0.8258 0.960 0.012 0.000 0.000 0.028
#> GSM1068479 3 0.1399 0.7652 0.000 0.028 0.952 0.020 0.000
#> GSM1068481 1 0.3117 0.7752 0.860 0.004 0.100 0.000 0.036
#> GSM1068482 3 0.6349 0.3038 0.244 0.000 0.524 0.000 0.232
#> GSM1068483 1 0.1894 0.8090 0.920 0.008 0.000 0.000 0.072
#> GSM1068486 3 0.2674 0.7020 0.004 0.000 0.856 0.000 0.140
#> GSM1068487 2 0.1410 0.8157 0.000 0.940 0.000 0.060 0.000
#> GSM1068488 4 0.4937 0.6341 0.000 0.000 0.064 0.672 0.264
#> GSM1068490 2 0.2179 0.7841 0.000 0.888 0.000 0.112 0.000
#> GSM1068491 3 0.1300 0.7661 0.000 0.016 0.956 0.028 0.000
#> GSM1068492 3 0.5055 0.5900 0.000 0.056 0.744 0.152 0.048
#> GSM1068493 1 0.4878 0.6409 0.724 0.076 0.008 0.000 0.192
#> GSM1068494 1 0.5213 0.5004 0.652 0.000 0.004 0.068 0.276
#> GSM1068495 1 0.5500 0.5085 0.648 0.212 0.000 0.000 0.140
#> GSM1068496 1 0.0510 0.8283 0.984 0.000 0.000 0.000 0.016
#> GSM1068498 2 0.4612 0.5577 0.232 0.712 0.000 0.000 0.056
#> GSM1068499 1 0.4594 0.4888 0.624 0.008 0.008 0.000 0.360
#> GSM1068500 1 0.1205 0.8214 0.956 0.000 0.004 0.000 0.040
#> GSM1068502 3 0.4305 0.5895 0.000 0.200 0.748 0.052 0.000
#> GSM1068503 4 0.4575 0.4816 0.000 0.328 0.000 0.648 0.024
#> GSM1068505 4 0.2116 0.7597 0.000 0.008 0.004 0.912 0.076
#> GSM1068506 4 0.2597 0.7475 0.004 0.040 0.000 0.896 0.060
#> GSM1068507 4 0.2592 0.7380 0.000 0.056 0.000 0.892 0.052
#> GSM1068508 4 0.5828 0.3831 0.000 0.380 0.000 0.520 0.100
#> GSM1068510 4 0.2796 0.7481 0.000 0.008 0.008 0.868 0.116
#> GSM1068512 4 0.3544 0.7240 0.004 0.004 0.004 0.792 0.196
#> GSM1068513 4 0.3970 0.5928 0.000 0.236 0.000 0.744 0.020
#> GSM1068514 3 0.1041 0.7660 0.000 0.004 0.964 0.032 0.000
#> GSM1068517 2 0.2426 0.7873 0.036 0.900 0.000 0.000 0.064
#> GSM1068518 4 0.6950 -0.0586 0.320 0.004 0.000 0.360 0.316
#> GSM1068520 1 0.1093 0.8295 0.968 0.004 0.004 0.004 0.020
#> GSM1068521 1 0.1652 0.8274 0.944 0.004 0.004 0.008 0.040
#> GSM1068522 4 0.3527 0.6604 0.000 0.172 0.000 0.804 0.024
#> GSM1068524 2 0.6132 0.0525 0.000 0.508 0.000 0.352 0.140
#> GSM1068527 4 0.3861 0.6511 0.000 0.000 0.004 0.712 0.284
#> GSM1068480 3 0.5447 0.1863 0.060 0.000 0.500 0.000 0.440
#> GSM1068484 4 0.4451 0.6116 0.000 0.016 0.000 0.644 0.340
#> GSM1068485 3 0.3123 0.6313 0.184 0.000 0.812 0.000 0.004
#> GSM1068489 4 0.2629 0.7291 0.000 0.000 0.004 0.860 0.136
#> GSM1068497 2 0.4884 0.5956 0.152 0.720 0.000 0.000 0.128
#> GSM1068501 4 0.3534 0.5541 0.000 0.000 0.000 0.744 0.256
#> GSM1068504 2 0.1310 0.8247 0.000 0.956 0.000 0.020 0.024
#> GSM1068509 1 0.4659 0.1674 0.496 0.000 0.000 0.012 0.492
#> GSM1068511 5 0.5546 0.4503 0.188 0.000 0.108 0.020 0.684
#> GSM1068515 5 0.5278 0.4512 0.156 0.148 0.000 0.004 0.692
#> GSM1068516 5 0.5965 -0.1017 0.072 0.008 0.004 0.408 0.508
#> GSM1068519 1 0.5486 0.5290 0.624 0.000 0.004 0.084 0.288
#> GSM1068523 2 0.3779 0.6509 0.000 0.752 0.000 0.012 0.236
#> GSM1068525 5 0.4718 0.0976 0.000 0.016 0.008 0.340 0.636
#> GSM1068526 4 0.2017 0.7619 0.000 0.008 0.000 0.912 0.080
#> GSM1068458 1 0.2536 0.8035 0.900 0.000 0.004 0.052 0.044
#> GSM1068459 1 0.3462 0.7086 0.792 0.000 0.196 0.000 0.012
#> GSM1068460 1 0.5418 0.4092 0.608 0.000 0.004 0.320 0.068
#> GSM1068461 3 0.0162 0.7596 0.004 0.000 0.996 0.000 0.000
#> GSM1068464 2 0.2068 0.7992 0.000 0.904 0.000 0.092 0.004
#> GSM1068468 2 0.0671 0.8235 0.000 0.980 0.000 0.016 0.004
#> GSM1068472 2 0.1018 0.8189 0.016 0.968 0.000 0.000 0.016
#> GSM1068473 2 0.3690 0.6509 0.000 0.764 0.000 0.224 0.012
#> GSM1068474 2 0.1571 0.8152 0.000 0.936 0.000 0.060 0.004
#> GSM1068476 3 0.1978 0.7579 0.000 0.024 0.928 0.044 0.004
#> GSM1068477 2 0.2011 0.8009 0.000 0.908 0.000 0.088 0.004
#> GSM1068462 2 0.3003 0.7095 0.000 0.812 0.000 0.000 0.188
#> GSM1068463 1 0.3628 0.6866 0.772 0.000 0.216 0.000 0.012
#> GSM1068465 5 0.3675 0.5001 0.216 0.008 0.000 0.004 0.772
#> GSM1068466 1 0.0771 0.8305 0.976 0.004 0.000 0.000 0.020
#> GSM1068467 2 0.1043 0.8194 0.000 0.960 0.000 0.000 0.040
#> GSM1068469 2 0.2464 0.7805 0.016 0.888 0.000 0.000 0.096
#> GSM1068470 2 0.0955 0.8207 0.000 0.968 0.000 0.004 0.028
#> GSM1068471 2 0.2964 0.7643 0.000 0.856 0.000 0.024 0.120
#> GSM1068475 2 0.0579 0.8237 0.000 0.984 0.000 0.008 0.008
#> GSM1068528 1 0.0510 0.8281 0.984 0.000 0.000 0.000 0.016
#> GSM1068531 1 0.1828 0.8202 0.936 0.000 0.004 0.032 0.028
#> GSM1068532 1 0.1106 0.8288 0.964 0.000 0.000 0.012 0.024
#> GSM1068533 1 0.2075 0.8149 0.924 0.000 0.004 0.040 0.032
#> GSM1068535 4 0.3701 0.6506 0.112 0.000 0.004 0.824 0.060
#> GSM1068537 1 0.0579 0.8304 0.984 0.000 0.000 0.008 0.008
#> GSM1068538 1 0.2459 0.8058 0.904 0.000 0.004 0.052 0.040
#> GSM1068539 2 0.6322 0.4969 0.140 0.640 0.004 0.040 0.176
#> GSM1068540 1 0.0794 0.8304 0.972 0.000 0.000 0.000 0.028
#> GSM1068542 4 0.1518 0.7401 0.004 0.000 0.004 0.944 0.048
#> GSM1068543 4 0.3554 0.7037 0.004 0.000 0.004 0.776 0.216
#> GSM1068544 1 0.0451 0.8298 0.988 0.000 0.008 0.000 0.004
#> GSM1068545 4 0.5032 0.6260 0.000 0.220 0.000 0.688 0.092
#> GSM1068546 3 0.3704 0.6954 0.088 0.000 0.820 0.000 0.092
#> GSM1068547 1 0.2464 0.8106 0.904 0.000 0.004 0.048 0.044
#> GSM1068548 4 0.1544 0.7629 0.000 0.000 0.000 0.932 0.068
#> GSM1068549 3 0.0324 0.7606 0.004 0.000 0.992 0.004 0.000
#> GSM1068550 4 0.2899 0.7490 0.004 0.004 0.004 0.856 0.132
#> GSM1068551 2 0.0963 0.8218 0.000 0.964 0.000 0.036 0.000
#> GSM1068552 4 0.2054 0.7543 0.000 0.028 0.000 0.920 0.052
#> GSM1068555 2 0.1444 0.8194 0.000 0.948 0.000 0.012 0.040
#> GSM1068556 4 0.3586 0.6821 0.000 0.000 0.000 0.736 0.264
#> GSM1068557 2 0.0798 0.8231 0.000 0.976 0.000 0.008 0.016
#> GSM1068560 4 0.3928 0.6501 0.000 0.000 0.004 0.700 0.296
#> GSM1068561 5 0.6416 0.1324 0.076 0.396 0.004 0.028 0.496
#> GSM1068562 4 0.3783 0.6966 0.000 0.008 0.000 0.740 0.252
#> GSM1068563 4 0.3427 0.7517 0.004 0.056 0.000 0.844 0.096
#> GSM1068565 2 0.1197 0.8187 0.000 0.952 0.000 0.048 0.000
#> GSM1068529 5 0.4435 0.5159 0.008 0.000 0.092 0.124 0.776
#> GSM1068530 1 0.0162 0.8291 0.996 0.000 0.000 0.000 0.004
#> GSM1068534 5 0.4299 0.5176 0.036 0.000 0.104 0.056 0.804
#> GSM1068536 1 0.4063 0.6078 0.708 0.012 0.000 0.000 0.280
#> GSM1068541 2 0.6739 -0.0813 0.260 0.392 0.000 0.000 0.348
#> GSM1068553 4 0.1851 0.7519 0.000 0.000 0.000 0.912 0.088
#> GSM1068554 4 0.2890 0.7088 0.000 0.004 0.000 0.836 0.160
#> GSM1068558 5 0.5128 0.1717 0.000 0.000 0.344 0.052 0.604
#> GSM1068559 3 0.5869 0.4612 0.000 0.020 0.656 0.160 0.164
#> GSM1068564 4 0.4277 0.7027 0.000 0.076 0.000 0.768 0.156
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1068478 1 0.1628 0.8153 0.940 0.008 0.000 0.004 0.036 0.012
#> GSM1068479 3 0.0767 0.7549 0.000 0.008 0.976 0.012 0.000 0.004
#> GSM1068481 1 0.3951 0.7249 0.796 0.004 0.104 0.004 0.084 0.008
#> GSM1068482 3 0.6203 -0.0517 0.232 0.000 0.404 0.000 0.356 0.008
#> GSM1068483 1 0.3020 0.7286 0.812 0.004 0.000 0.004 0.176 0.004
#> GSM1068486 3 0.4472 0.5248 0.008 0.000 0.700 0.064 0.228 0.000
#> GSM1068487 2 0.1588 0.8206 0.000 0.924 0.000 0.072 0.000 0.004
#> GSM1068488 6 0.1911 0.6676 0.004 0.000 0.020 0.036 0.012 0.928
#> GSM1068490 2 0.3163 0.7236 0.000 0.780 0.000 0.212 0.004 0.004
#> GSM1068491 3 0.0976 0.7552 0.000 0.008 0.968 0.016 0.000 0.008
#> GSM1068492 3 0.5140 0.4495 0.000 0.024 0.628 0.068 0.000 0.280
#> GSM1068493 1 0.5468 0.5366 0.648 0.136 0.004 0.004 0.192 0.016
#> GSM1068494 6 0.4741 0.4007 0.244 0.000 0.012 0.016 0.040 0.688
#> GSM1068495 6 0.6726 0.0573 0.344 0.200 0.000 0.004 0.040 0.412
#> GSM1068496 1 0.0653 0.8195 0.980 0.000 0.000 0.004 0.012 0.004
#> GSM1068498 2 0.4130 0.5622 0.200 0.744 0.000 0.004 0.044 0.008
#> GSM1068499 1 0.5277 0.3954 0.556 0.012 0.004 0.016 0.380 0.032
#> GSM1068500 1 0.2431 0.7642 0.860 0.000 0.000 0.008 0.132 0.000
#> GSM1068502 3 0.4006 0.5981 0.000 0.136 0.772 0.084 0.000 0.008
#> GSM1068503 4 0.5160 0.4007 0.000 0.324 0.000 0.592 0.016 0.068
#> GSM1068505 4 0.3934 0.5580 0.000 0.000 0.000 0.616 0.008 0.376
#> GSM1068506 4 0.4749 0.3939 0.004 0.020 0.000 0.536 0.012 0.428
#> GSM1068507 4 0.2345 0.6768 0.000 0.028 0.004 0.904 0.012 0.052
#> GSM1068508 6 0.6208 0.1312 0.000 0.320 0.000 0.204 0.016 0.460
#> GSM1068510 4 0.4946 0.6047 0.000 0.000 0.008 0.656 0.100 0.236
#> GSM1068512 6 0.3701 0.5441 0.012 0.000 0.008 0.184 0.016 0.780
#> GSM1068513 4 0.3159 0.6553 0.000 0.108 0.000 0.836 0.004 0.052
#> GSM1068514 3 0.0837 0.7527 0.000 0.004 0.972 0.004 0.000 0.020
#> GSM1068517 2 0.1950 0.7841 0.020 0.924 0.000 0.004 0.044 0.008
#> GSM1068518 6 0.1562 0.6604 0.032 0.000 0.000 0.004 0.024 0.940
#> GSM1068520 1 0.0551 0.8212 0.984 0.000 0.000 0.008 0.004 0.004
#> GSM1068521 1 0.1887 0.8122 0.924 0.000 0.000 0.016 0.012 0.048
#> GSM1068522 4 0.2591 0.6747 0.000 0.064 0.000 0.880 0.004 0.052
#> GSM1068524 2 0.5447 0.0398 0.000 0.496 0.000 0.052 0.032 0.420
#> GSM1068527 6 0.1010 0.6668 0.000 0.000 0.000 0.036 0.004 0.960
#> GSM1068480 5 0.4612 0.1780 0.012 0.000 0.384 0.008 0.584 0.012
#> GSM1068484 6 0.2066 0.6573 0.000 0.000 0.000 0.072 0.024 0.904
#> GSM1068485 3 0.2002 0.7012 0.076 0.000 0.908 0.000 0.004 0.012
#> GSM1068489 4 0.5343 0.5896 0.000 0.000 0.000 0.580 0.156 0.264
#> GSM1068497 2 0.3961 0.6662 0.096 0.792 0.000 0.004 0.096 0.012
#> GSM1068501 4 0.4707 0.5615 0.000 0.000 0.000 0.660 0.244 0.096
#> GSM1068504 2 0.1268 0.8233 0.000 0.952 0.000 0.036 0.008 0.004
#> GSM1068509 1 0.5539 0.2583 0.504 0.000 0.000 0.020 0.396 0.080
#> GSM1068511 5 0.4902 0.5552 0.184 0.000 0.028 0.016 0.716 0.056
#> GSM1068515 5 0.2255 0.6161 0.044 0.024 0.000 0.024 0.908 0.000
#> GSM1068516 6 0.4247 0.5856 0.040 0.020 0.000 0.028 0.128 0.784
#> GSM1068519 1 0.6331 0.4151 0.560 0.000 0.000 0.104 0.232 0.104
#> GSM1068523 2 0.4403 0.5930 0.000 0.708 0.000 0.000 0.196 0.096
#> GSM1068525 6 0.2126 0.6618 0.000 0.004 0.000 0.020 0.072 0.904
#> GSM1068526 4 0.4457 0.4302 0.000 0.008 0.000 0.544 0.016 0.432
#> GSM1068458 1 0.2673 0.7777 0.856 0.008 0.000 0.128 0.004 0.004
#> GSM1068459 1 0.4047 0.6384 0.732 0.000 0.232 0.012 0.016 0.008
#> GSM1068460 1 0.4770 0.5579 0.672 0.000 0.000 0.100 0.004 0.224
#> GSM1068461 3 0.0508 0.7496 0.004 0.000 0.984 0.012 0.000 0.000
#> GSM1068464 2 0.2704 0.7752 0.000 0.844 0.000 0.140 0.000 0.016
#> GSM1068468 2 0.1501 0.8175 0.000 0.924 0.000 0.076 0.000 0.000
#> GSM1068472 2 0.2032 0.8106 0.036 0.920 0.000 0.024 0.020 0.000
#> GSM1068473 2 0.4413 0.0911 0.000 0.496 0.000 0.484 0.012 0.008
#> GSM1068474 2 0.1958 0.8080 0.000 0.896 0.000 0.100 0.000 0.004
#> GSM1068476 3 0.0891 0.7529 0.000 0.008 0.968 0.024 0.000 0.000
#> GSM1068477 2 0.2006 0.8047 0.000 0.892 0.000 0.104 0.000 0.004
#> GSM1068462 2 0.3454 0.6668 0.000 0.768 0.000 0.024 0.208 0.000
#> GSM1068463 1 0.4076 0.5952 0.704 0.000 0.268 0.012 0.008 0.008
#> GSM1068465 5 0.2898 0.6311 0.068 0.000 0.004 0.016 0.872 0.040
#> GSM1068466 1 0.1371 0.8192 0.948 0.004 0.000 0.004 0.040 0.004
#> GSM1068467 2 0.0692 0.8147 0.000 0.976 0.000 0.004 0.020 0.000
#> GSM1068469 2 0.3142 0.7149 0.016 0.820 0.000 0.004 0.156 0.004
#> GSM1068470 2 0.0622 0.8140 0.000 0.980 0.000 0.000 0.012 0.008
#> GSM1068471 2 0.4364 0.6906 0.000 0.732 0.000 0.112 0.152 0.004
#> GSM1068475 2 0.0777 0.8206 0.000 0.972 0.000 0.024 0.000 0.004
#> GSM1068528 1 0.0717 0.8200 0.976 0.000 0.000 0.008 0.016 0.000
#> GSM1068531 1 0.1219 0.8188 0.948 0.000 0.000 0.048 0.004 0.000
#> GSM1068532 1 0.0777 0.8211 0.972 0.000 0.000 0.024 0.000 0.004
#> GSM1068533 1 0.1901 0.8091 0.912 0.000 0.000 0.076 0.004 0.008
#> GSM1068535 4 0.4067 0.6121 0.108 0.000 0.000 0.784 0.024 0.084
#> GSM1068537 1 0.0806 0.8184 0.972 0.000 0.000 0.020 0.000 0.008
#> GSM1068538 1 0.2110 0.8010 0.900 0.000 0.000 0.084 0.004 0.012
#> GSM1068539 6 0.5165 0.4965 0.112 0.160 0.000 0.004 0.032 0.692
#> GSM1068540 1 0.1410 0.8182 0.944 0.000 0.000 0.004 0.008 0.044
#> GSM1068542 4 0.3691 0.6538 0.008 0.000 0.000 0.724 0.008 0.260
#> GSM1068543 6 0.1644 0.6587 0.004 0.000 0.000 0.076 0.000 0.920
#> GSM1068544 1 0.1003 0.8217 0.964 0.000 0.028 0.004 0.000 0.004
#> GSM1068545 6 0.6490 -0.1826 0.000 0.212 0.000 0.356 0.028 0.404
#> GSM1068546 3 0.7231 0.3216 0.092 0.000 0.500 0.208 0.172 0.028
#> GSM1068547 1 0.1672 0.8158 0.932 0.000 0.000 0.048 0.004 0.016
#> GSM1068548 4 0.4364 0.5369 0.000 0.004 0.000 0.608 0.024 0.364
#> GSM1068549 3 0.0000 0.7503 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1068550 6 0.3703 0.3026 0.004 0.004 0.000 0.304 0.000 0.688
#> GSM1068551 2 0.2036 0.8182 0.000 0.912 0.000 0.064 0.008 0.016
#> GSM1068552 4 0.3970 0.6500 0.000 0.016 0.000 0.712 0.012 0.260
#> GSM1068555 2 0.1341 0.8068 0.000 0.948 0.000 0.000 0.024 0.028
#> GSM1068556 6 0.1863 0.6355 0.000 0.000 0.000 0.104 0.000 0.896
#> GSM1068557 2 0.0767 0.8170 0.000 0.976 0.000 0.004 0.012 0.008
#> GSM1068560 6 0.0790 0.6673 0.000 0.000 0.000 0.032 0.000 0.968
#> GSM1068561 5 0.6830 0.1962 0.076 0.360 0.000 0.008 0.432 0.124
#> GSM1068562 6 0.1958 0.6425 0.000 0.000 0.000 0.100 0.004 0.896
#> GSM1068563 6 0.5245 -0.2373 0.008 0.044 0.000 0.460 0.012 0.476
#> GSM1068565 2 0.1285 0.8213 0.000 0.944 0.000 0.052 0.000 0.004
#> GSM1068529 6 0.5252 0.5321 0.020 0.012 0.120 0.016 0.116 0.716
#> GSM1068530 1 0.1059 0.8215 0.964 0.000 0.000 0.016 0.004 0.016
#> GSM1068534 5 0.1917 0.6222 0.016 0.000 0.016 0.004 0.928 0.036
#> GSM1068536 1 0.4718 0.5622 0.664 0.020 0.000 0.008 0.280 0.028
#> GSM1068541 5 0.6757 0.1700 0.332 0.204 0.000 0.020 0.424 0.020
#> GSM1068553 4 0.3412 0.6431 0.000 0.000 0.000 0.808 0.064 0.128
#> GSM1068554 4 0.3448 0.6518 0.000 0.000 0.004 0.816 0.108 0.072
#> GSM1068558 5 0.4892 0.4838 0.000 0.000 0.176 0.020 0.696 0.108
#> GSM1068559 3 0.5065 0.5625 0.000 0.004 0.708 0.036 0.136 0.116
#> GSM1068564 4 0.6111 0.5835 0.000 0.044 0.000 0.568 0.192 0.196
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 disease.state(p) gender(p) k
#> CV:NMF 105 0.730975 1.0000 2
#> CV:NMF 81 0.306899 0.8571 3
#> CV:NMF 100 0.002697 0.4353 4
#> CV:NMF 90 0.000921 0.0160 5
#> CV:NMF 86 0.023660 0.0282 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 38950 rows and 108 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'hclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.206 0.216 0.600 0.4088 0.506 0.506
#> 3 3 0.360 0.662 0.826 0.3522 0.530 0.374
#> 4 4 0.343 0.517 0.752 0.1257 0.927 0.864
#> 5 5 0.400 0.380 0.671 0.1143 0.852 0.696
#> 6 6 0.431 0.424 0.649 0.0771 0.881 0.672
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 3
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM1068478 1 0.9087 -0.165787 0.676 0.324
#> GSM1068479 2 0.4562 0.126534 0.096 0.904
#> GSM1068481 1 0.9977 0.305208 0.528 0.472
#> GSM1068482 1 0.9977 0.305208 0.528 0.472
#> GSM1068483 1 0.4939 0.359130 0.892 0.108
#> GSM1068486 2 0.9988 -0.318921 0.480 0.520
#> GSM1068487 2 0.9977 0.677530 0.472 0.528
#> GSM1068488 2 0.9993 0.536035 0.484 0.516
#> GSM1068490 2 0.9977 0.677530 0.472 0.528
#> GSM1068491 2 0.3431 0.138415 0.064 0.936
#> GSM1068492 2 0.3274 0.143353 0.060 0.940
#> GSM1068493 1 0.9248 0.035970 0.660 0.340
#> GSM1068494 1 0.9661 0.314500 0.608 0.392
#> GSM1068495 1 0.9970 -0.565130 0.532 0.468
#> GSM1068496 1 0.9460 0.273800 0.636 0.364
#> GSM1068498 1 0.9087 -0.165787 0.676 0.324
#> GSM1068499 1 0.6148 0.355284 0.848 0.152
#> GSM1068500 1 0.4939 0.359130 0.892 0.108
#> GSM1068502 2 0.3274 0.143353 0.060 0.940
#> GSM1068503 2 0.9977 0.677530 0.472 0.528
#> GSM1068505 1 0.9963 -0.591008 0.536 0.464
#> GSM1068506 2 0.9983 0.674121 0.476 0.524
#> GSM1068507 2 0.9996 0.592143 0.488 0.512
#> GSM1068508 1 0.9209 -0.204906 0.664 0.336
#> GSM1068510 1 0.9996 -0.464581 0.512 0.488
#> GSM1068512 1 0.9732 -0.170090 0.596 0.404
#> GSM1068513 2 1.0000 0.605157 0.496 0.504
#> GSM1068514 2 0.9963 0.001669 0.464 0.536
#> GSM1068517 1 0.9087 -0.165787 0.676 0.324
#> GSM1068518 1 0.9815 0.182725 0.580 0.420
#> GSM1068520 1 0.3431 0.315619 0.936 0.064
#> GSM1068521 1 0.3114 0.323219 0.944 0.056
#> GSM1068522 2 0.9977 0.677530 0.472 0.528
#> GSM1068524 2 0.9993 0.644793 0.484 0.516
#> GSM1068527 1 0.9209 -0.236091 0.664 0.336
#> GSM1068480 1 0.9970 0.305964 0.532 0.468
#> GSM1068484 2 0.9944 0.561136 0.456 0.544
#> GSM1068485 1 0.9977 0.305208 0.528 0.472
#> GSM1068489 2 0.9988 0.669616 0.480 0.520
#> GSM1068497 1 0.9087 -0.165787 0.676 0.324
#> GSM1068501 1 0.9988 -0.468805 0.520 0.480
#> GSM1068504 2 0.9977 0.677530 0.472 0.528
#> GSM1068509 1 0.9608 0.258788 0.616 0.384
#> GSM1068511 2 0.9988 -0.111226 0.480 0.520
#> GSM1068515 1 0.8555 -0.000141 0.720 0.280
#> GSM1068516 2 0.9933 0.109554 0.452 0.548
#> GSM1068519 1 0.3274 0.363989 0.940 0.060
#> GSM1068523 2 0.9977 0.677530 0.472 0.528
#> GSM1068525 2 0.9922 0.572487 0.448 0.552
#> GSM1068526 2 0.9977 0.670840 0.472 0.528
#> GSM1068458 1 0.0376 0.364829 0.996 0.004
#> GSM1068459 1 0.9977 0.305208 0.528 0.472
#> GSM1068460 1 0.9970 -0.598228 0.532 0.468
#> GSM1068461 1 0.9977 0.305208 0.528 0.472
#> GSM1068464 2 0.9977 0.677530 0.472 0.528
#> GSM1068468 1 0.9977 -0.533416 0.528 0.472
#> GSM1068472 1 0.9661 -0.293668 0.608 0.392
#> GSM1068473 2 0.9977 0.677530 0.472 0.528
#> GSM1068474 2 0.9977 0.677530 0.472 0.528
#> GSM1068476 2 0.6712 -0.020957 0.176 0.824
#> GSM1068477 2 0.9977 0.677530 0.472 0.528
#> GSM1068462 1 0.9427 -0.189890 0.640 0.360
#> GSM1068463 1 0.9977 0.305208 0.528 0.472
#> GSM1068465 1 0.9209 -0.204906 0.664 0.336
#> GSM1068466 1 0.2043 0.349223 0.968 0.032
#> GSM1068467 1 0.9977 -0.533416 0.528 0.472
#> GSM1068469 1 0.9393 -0.194165 0.644 0.356
#> GSM1068470 2 0.9977 0.677530 0.472 0.528
#> GSM1068471 2 0.9977 0.677530 0.472 0.528
#> GSM1068475 2 0.9977 0.677530 0.472 0.528
#> GSM1068528 1 0.9732 0.315436 0.596 0.404
#> GSM1068531 1 0.0672 0.365365 0.992 0.008
#> GSM1068532 1 0.0672 0.364641 0.992 0.008
#> GSM1068533 1 0.0376 0.364829 0.996 0.004
#> GSM1068535 1 0.9993 -0.346079 0.516 0.484
#> GSM1068537 1 0.0000 0.363132 1.000 0.000
#> GSM1068538 1 0.0938 0.365805 0.988 0.012
#> GSM1068539 1 0.9970 -0.565130 0.532 0.468
#> GSM1068540 1 0.0000 0.363132 1.000 0.000
#> GSM1068542 2 1.0000 0.642171 0.500 0.500
#> GSM1068543 2 0.9996 0.565538 0.488 0.512
#> GSM1068544 1 0.9963 0.306437 0.536 0.464
#> GSM1068545 2 0.9983 0.674121 0.476 0.524
#> GSM1068546 1 0.9977 0.305208 0.528 0.472
#> GSM1068547 1 0.3431 0.315619 0.936 0.064
#> GSM1068548 1 0.9996 -0.639018 0.512 0.488
#> GSM1068549 1 0.9977 0.305208 0.528 0.472
#> GSM1068550 2 0.9988 0.665376 0.480 0.520
#> GSM1068551 2 0.9977 0.677530 0.472 0.528
#> GSM1068552 2 0.9977 0.677530 0.472 0.528
#> GSM1068555 2 0.9977 0.677530 0.472 0.528
#> GSM1068556 2 0.9996 0.565538 0.488 0.512
#> GSM1068557 1 0.9954 -0.500423 0.540 0.460
#> GSM1068560 1 0.9209 -0.236091 0.664 0.336
#> GSM1068561 1 0.9833 -0.443074 0.576 0.424
#> GSM1068562 2 0.9993 0.661196 0.484 0.516
#> GSM1068563 2 0.9983 0.674121 0.476 0.524
#> GSM1068565 2 0.9977 0.677530 0.472 0.528
#> GSM1068529 1 0.9944 0.171378 0.544 0.456
#> GSM1068530 1 0.0000 0.363132 1.000 0.000
#> GSM1068534 1 0.9944 0.171378 0.544 0.456
#> GSM1068536 1 0.9286 -0.267104 0.656 0.344
#> GSM1068541 1 0.9983 -0.606731 0.524 0.476
#> GSM1068553 2 1.0000 0.392987 0.496 0.504
#> GSM1068554 1 0.9993 -0.463972 0.516 0.484
#> GSM1068558 2 0.9580 -0.110692 0.380 0.620
#> GSM1068559 2 0.8555 0.158750 0.280 0.720
#> GSM1068564 2 0.9977 0.677530 0.472 0.528
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1068478 2 0.5785 0.5327 0.332 0.668 0.000
#> GSM1068479 2 0.6451 0.2369 0.004 0.560 0.436
#> GSM1068481 3 0.0592 0.8460 0.012 0.000 0.988
#> GSM1068482 3 0.0747 0.8448 0.016 0.000 0.984
#> GSM1068483 1 0.7441 0.6714 0.700 0.136 0.164
#> GSM1068486 3 0.2339 0.8042 0.012 0.048 0.940
#> GSM1068487 2 0.0000 0.7908 0.000 1.000 0.000
#> GSM1068488 2 0.4413 0.7677 0.036 0.860 0.104
#> GSM1068490 2 0.0000 0.7908 0.000 1.000 0.000
#> GSM1068491 2 0.6432 0.2629 0.004 0.568 0.428
#> GSM1068492 2 0.6421 0.2744 0.004 0.572 0.424
#> GSM1068493 2 0.8825 0.4065 0.296 0.556 0.148
#> GSM1068494 3 0.6247 0.6080 0.212 0.044 0.744
#> GSM1068495 2 0.4413 0.7631 0.104 0.860 0.036
#> GSM1068496 1 0.9840 0.2116 0.408 0.256 0.336
#> GSM1068498 2 0.5785 0.5327 0.332 0.668 0.000
#> GSM1068499 1 0.7076 0.5949 0.684 0.060 0.256
#> GSM1068500 1 0.7441 0.6714 0.700 0.136 0.164
#> GSM1068502 2 0.6421 0.2744 0.004 0.572 0.424
#> GSM1068503 2 0.0000 0.7908 0.000 1.000 0.000
#> GSM1068505 2 0.3482 0.7565 0.128 0.872 0.000
#> GSM1068506 2 0.0892 0.7915 0.020 0.980 0.000
#> GSM1068507 2 0.3472 0.7859 0.056 0.904 0.040
#> GSM1068508 2 0.5733 0.5621 0.324 0.676 0.000
#> GSM1068510 2 0.5408 0.7365 0.052 0.812 0.136
#> GSM1068512 2 0.8275 0.4943 0.296 0.596 0.108
#> GSM1068513 2 0.3009 0.7872 0.052 0.920 0.028
#> GSM1068514 2 0.9028 0.4246 0.168 0.540 0.292
#> GSM1068517 2 0.5785 0.5327 0.332 0.668 0.000
#> GSM1068518 2 0.9912 0.0578 0.300 0.400 0.300
#> GSM1068520 1 0.3752 0.7639 0.856 0.144 0.000
#> GSM1068521 1 0.3619 0.7698 0.864 0.136 0.000
#> GSM1068522 2 0.0237 0.7906 0.004 0.996 0.000
#> GSM1068524 2 0.1525 0.7906 0.004 0.964 0.032
#> GSM1068527 2 0.7337 0.5534 0.300 0.644 0.056
#> GSM1068480 3 0.1031 0.8404 0.024 0.000 0.976
#> GSM1068484 2 0.3769 0.7705 0.016 0.880 0.104
#> GSM1068485 3 0.0592 0.8460 0.012 0.000 0.988
#> GSM1068489 2 0.1031 0.7909 0.024 0.976 0.000
#> GSM1068497 2 0.5785 0.5327 0.332 0.668 0.000
#> GSM1068501 2 0.5403 0.7382 0.060 0.816 0.124
#> GSM1068504 2 0.0000 0.7908 0.000 1.000 0.000
#> GSM1068509 1 0.9987 0.1509 0.348 0.308 0.344
#> GSM1068511 2 0.7828 0.1913 0.052 0.500 0.448
#> GSM1068515 2 0.6941 0.2149 0.464 0.520 0.016
#> GSM1068516 2 0.8743 0.4962 0.156 0.576 0.268
#> GSM1068519 1 0.4390 0.7210 0.840 0.012 0.148
#> GSM1068523 2 0.0000 0.7908 0.000 1.000 0.000
#> GSM1068525 2 0.3610 0.7732 0.016 0.888 0.096
#> GSM1068526 2 0.1129 0.7920 0.020 0.976 0.004
#> GSM1068458 1 0.0983 0.8047 0.980 0.016 0.004
#> GSM1068459 3 0.0592 0.8460 0.012 0.000 0.988
#> GSM1068460 2 0.3482 0.7541 0.128 0.872 0.000
#> GSM1068461 3 0.0237 0.8438 0.004 0.000 0.996
#> GSM1068464 2 0.0000 0.7908 0.000 1.000 0.000
#> GSM1068468 2 0.3921 0.7604 0.112 0.872 0.016
#> GSM1068472 2 0.6217 0.6123 0.264 0.712 0.024
#> GSM1068473 2 0.0000 0.7908 0.000 1.000 0.000
#> GSM1068474 2 0.0000 0.7908 0.000 1.000 0.000
#> GSM1068476 3 0.6489 0.0739 0.004 0.456 0.540
#> GSM1068477 2 0.0237 0.7906 0.004 0.996 0.000
#> GSM1068462 2 0.6501 0.5393 0.316 0.664 0.020
#> GSM1068463 3 0.0592 0.8460 0.012 0.000 0.988
#> GSM1068465 2 0.5733 0.5621 0.324 0.676 0.000
#> GSM1068466 1 0.3193 0.7892 0.896 0.100 0.004
#> GSM1068467 2 0.3921 0.7604 0.112 0.872 0.016
#> GSM1068469 2 0.6369 0.5428 0.316 0.668 0.016
#> GSM1068470 2 0.0000 0.7908 0.000 1.000 0.000
#> GSM1068471 2 0.0000 0.7908 0.000 1.000 0.000
#> GSM1068475 2 0.0000 0.7908 0.000 1.000 0.000
#> GSM1068528 3 0.4700 0.6821 0.180 0.008 0.812
#> GSM1068531 1 0.0661 0.7941 0.988 0.008 0.004
#> GSM1068532 1 0.1170 0.8040 0.976 0.016 0.008
#> GSM1068533 1 0.0983 0.8047 0.980 0.016 0.004
#> GSM1068535 2 0.7615 0.6322 0.148 0.688 0.164
#> GSM1068537 1 0.0747 0.8040 0.984 0.016 0.000
#> GSM1068538 1 0.1337 0.8040 0.972 0.016 0.012
#> GSM1068539 2 0.4413 0.7631 0.104 0.860 0.036
#> GSM1068540 1 0.1129 0.8047 0.976 0.020 0.004
#> GSM1068542 2 0.1964 0.7886 0.056 0.944 0.000
#> GSM1068543 2 0.4339 0.7725 0.048 0.868 0.084
#> GSM1068544 3 0.0892 0.8422 0.020 0.000 0.980
#> GSM1068545 2 0.0892 0.7915 0.020 0.980 0.000
#> GSM1068546 3 0.0424 0.8395 0.008 0.000 0.992
#> GSM1068547 1 0.3752 0.7639 0.856 0.144 0.000
#> GSM1068548 2 0.2448 0.7834 0.076 0.924 0.000
#> GSM1068549 3 0.0237 0.8438 0.004 0.000 0.996
#> GSM1068550 2 0.1399 0.7922 0.028 0.968 0.004
#> GSM1068551 2 0.0000 0.7908 0.000 1.000 0.000
#> GSM1068552 2 0.0592 0.7904 0.012 0.988 0.000
#> GSM1068555 2 0.0000 0.7908 0.000 1.000 0.000
#> GSM1068556 2 0.4339 0.7725 0.048 0.868 0.084
#> GSM1068557 2 0.4915 0.7448 0.132 0.832 0.036
#> GSM1068560 2 0.7337 0.5534 0.300 0.644 0.056
#> GSM1068561 2 0.5884 0.7290 0.148 0.788 0.064
#> GSM1068562 2 0.1525 0.7918 0.032 0.964 0.004
#> GSM1068563 2 0.0892 0.7915 0.020 0.980 0.000
#> GSM1068565 2 0.0000 0.7908 0.000 1.000 0.000
#> GSM1068529 2 0.9873 0.0931 0.268 0.404 0.328
#> GSM1068530 1 0.0747 0.8040 0.984 0.016 0.000
#> GSM1068534 2 0.9873 0.0931 0.268 0.404 0.328
#> GSM1068536 2 0.7208 0.5700 0.308 0.644 0.048
#> GSM1068541 2 0.2625 0.7792 0.084 0.916 0.000
#> GSM1068553 2 0.6181 0.7083 0.072 0.772 0.156
#> GSM1068554 2 0.5471 0.7354 0.060 0.812 0.128
#> GSM1068558 3 0.6676 -0.1097 0.008 0.476 0.516
#> GSM1068559 2 0.8456 0.4107 0.108 0.564 0.328
#> GSM1068564 2 0.0592 0.7904 0.012 0.988 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1068478 2 0.5897 0.30929 0.284 0.656 0.004 0.056
#> GSM1068479 2 0.7384 -0.10738 0.000 0.476 0.172 0.352
#> GSM1068481 3 0.0000 0.86807 0.000 0.000 1.000 0.000
#> GSM1068482 3 0.2704 0.84570 0.000 0.000 0.876 0.124
#> GSM1068483 1 0.6982 0.56043 0.664 0.124 0.168 0.044
#> GSM1068486 3 0.3367 0.80235 0.000 0.028 0.864 0.108
#> GSM1068487 2 0.0469 0.65427 0.000 0.988 0.000 0.012
#> GSM1068488 2 0.5232 0.49365 0.012 0.644 0.004 0.340
#> GSM1068490 2 0.0469 0.65343 0.000 0.988 0.000 0.012
#> GSM1068491 2 0.7385 -0.09102 0.000 0.484 0.176 0.340
#> GSM1068492 2 0.7314 -0.08214 0.000 0.488 0.164 0.348
#> GSM1068493 2 0.8573 -0.00221 0.252 0.516 0.116 0.116
#> GSM1068494 4 0.7566 -0.29245 0.172 0.004 0.360 0.464
#> GSM1068495 2 0.4419 0.58184 0.088 0.820 0.004 0.088
#> GSM1068496 1 0.9554 -0.43162 0.360 0.196 0.140 0.304
#> GSM1068498 2 0.5897 0.30929 0.284 0.656 0.004 0.056
#> GSM1068499 1 0.7255 0.50442 0.636 0.040 0.152 0.172
#> GSM1068500 1 0.6982 0.56043 0.664 0.124 0.168 0.044
#> GSM1068502 2 0.7314 -0.08214 0.000 0.488 0.164 0.348
#> GSM1068503 2 0.0469 0.65427 0.000 0.988 0.000 0.012
#> GSM1068505 2 0.4444 0.62498 0.120 0.808 0.000 0.072
#> GSM1068506 2 0.3495 0.64082 0.016 0.844 0.000 0.140
#> GSM1068507 2 0.4440 0.64242 0.044 0.832 0.028 0.096
#> GSM1068508 2 0.5921 0.33519 0.288 0.652 0.004 0.056
#> GSM1068510 2 0.6641 0.46564 0.032 0.620 0.052 0.296
#> GSM1068512 2 0.8044 0.01731 0.272 0.508 0.028 0.192
#> GSM1068513 2 0.3843 0.64711 0.040 0.860 0.016 0.084
#> GSM1068514 2 0.8723 -0.32962 0.136 0.432 0.084 0.348
#> GSM1068517 2 0.5897 0.30929 0.284 0.656 0.004 0.056
#> GSM1068518 4 0.9398 0.58024 0.256 0.304 0.096 0.344
#> GSM1068520 1 0.3447 0.71104 0.852 0.128 0.000 0.020
#> GSM1068521 1 0.3280 0.71848 0.860 0.124 0.000 0.016
#> GSM1068522 2 0.2125 0.65454 0.004 0.920 0.000 0.076
#> GSM1068524 2 0.1940 0.64792 0.000 0.924 0.000 0.076
#> GSM1068527 2 0.7122 0.30023 0.272 0.568 0.004 0.156
#> GSM1068480 3 0.4730 0.64117 0.000 0.000 0.636 0.364
#> GSM1068484 2 0.4699 0.51304 0.000 0.676 0.004 0.320
#> GSM1068485 3 0.0336 0.86704 0.000 0.000 0.992 0.008
#> GSM1068489 2 0.3900 0.63169 0.020 0.816 0.000 0.164
#> GSM1068497 2 0.5897 0.30929 0.284 0.656 0.004 0.056
#> GSM1068501 2 0.6739 0.47249 0.040 0.624 0.052 0.284
#> GSM1068504 2 0.0469 0.65338 0.000 0.988 0.000 0.012
#> GSM1068509 4 0.9685 0.47250 0.304 0.236 0.140 0.320
#> GSM1068511 4 0.8338 0.49707 0.032 0.276 0.224 0.468
#> GSM1068515 2 0.6848 0.04412 0.420 0.504 0.020 0.056
#> GSM1068516 2 0.8453 -0.15844 0.124 0.492 0.080 0.304
#> GSM1068519 1 0.4570 0.69689 0.804 0.004 0.060 0.132
#> GSM1068523 2 0.0469 0.65054 0.000 0.988 0.000 0.012
#> GSM1068525 2 0.4608 0.52903 0.000 0.692 0.004 0.304
#> GSM1068526 2 0.3992 0.62507 0.008 0.800 0.004 0.188
#> GSM1068458 1 0.1229 0.80005 0.968 0.008 0.004 0.020
#> GSM1068459 3 0.0000 0.86807 0.000 0.000 1.000 0.000
#> GSM1068460 2 0.4458 0.62234 0.116 0.808 0.000 0.076
#> GSM1068461 3 0.4331 0.77378 0.000 0.000 0.712 0.288
#> GSM1068464 2 0.0336 0.65258 0.000 0.992 0.000 0.008
#> GSM1068468 2 0.3902 0.59754 0.092 0.856 0.020 0.032
#> GSM1068472 2 0.5897 0.40025 0.232 0.700 0.028 0.040
#> GSM1068473 2 0.0592 0.65477 0.000 0.984 0.000 0.016
#> GSM1068474 2 0.0469 0.65427 0.000 0.988 0.000 0.012
#> GSM1068476 2 0.7818 -0.23033 0.000 0.408 0.324 0.268
#> GSM1068477 2 0.2125 0.65454 0.004 0.920 0.000 0.076
#> GSM1068462 2 0.6321 0.31647 0.272 0.652 0.024 0.052
#> GSM1068463 3 0.0000 0.86807 0.000 0.000 1.000 0.000
#> GSM1068465 2 0.5921 0.33519 0.288 0.652 0.004 0.056
#> GSM1068466 1 0.3382 0.75771 0.876 0.080 0.004 0.040
#> GSM1068467 2 0.3806 0.59706 0.092 0.860 0.020 0.028
#> GSM1068469 2 0.6223 0.31993 0.272 0.656 0.020 0.052
#> GSM1068470 2 0.0336 0.65140 0.000 0.992 0.000 0.008
#> GSM1068471 2 0.0336 0.65258 0.000 0.992 0.000 0.008
#> GSM1068475 2 0.0469 0.65162 0.000 0.988 0.000 0.012
#> GSM1068528 3 0.3870 0.72056 0.164 0.008 0.820 0.008
#> GSM1068531 1 0.0336 0.78993 0.992 0.000 0.000 0.008
#> GSM1068532 1 0.0992 0.79959 0.976 0.008 0.004 0.012
#> GSM1068533 1 0.1229 0.80005 0.968 0.008 0.004 0.020
#> GSM1068535 2 0.8110 0.31221 0.124 0.536 0.064 0.276
#> GSM1068537 1 0.0672 0.79926 0.984 0.008 0.000 0.008
#> GSM1068538 1 0.1007 0.79981 0.976 0.008 0.008 0.008
#> GSM1068539 2 0.4419 0.58184 0.088 0.820 0.004 0.088
#> GSM1068540 1 0.1124 0.79976 0.972 0.012 0.004 0.012
#> GSM1068542 2 0.4552 0.62370 0.044 0.784 0.000 0.172
#> GSM1068543 2 0.5427 0.49492 0.020 0.640 0.004 0.336
#> GSM1068544 3 0.0524 0.86555 0.008 0.000 0.988 0.004
#> GSM1068545 2 0.3495 0.64082 0.016 0.844 0.000 0.140
#> GSM1068546 3 0.3810 0.81994 0.008 0.000 0.804 0.188
#> GSM1068547 1 0.3447 0.71104 0.852 0.128 0.000 0.020
#> GSM1068548 2 0.4820 0.61641 0.060 0.772 0.000 0.168
#> GSM1068549 3 0.4331 0.77378 0.000 0.000 0.712 0.288
#> GSM1068550 2 0.4201 0.62045 0.012 0.788 0.004 0.196
#> GSM1068551 2 0.0469 0.65054 0.000 0.988 0.000 0.012
#> GSM1068552 2 0.3450 0.63466 0.008 0.836 0.000 0.156
#> GSM1068555 2 0.0469 0.65054 0.000 0.988 0.000 0.012
#> GSM1068556 2 0.5427 0.49492 0.020 0.640 0.004 0.336
#> GSM1068557 2 0.4800 0.57389 0.108 0.812 0.032 0.048
#> GSM1068560 2 0.7122 0.30023 0.272 0.568 0.004 0.156
#> GSM1068561 2 0.5871 0.51752 0.120 0.728 0.012 0.140
#> GSM1068562 2 0.4317 0.61890 0.016 0.784 0.004 0.196
#> GSM1068563 2 0.3495 0.64082 0.016 0.844 0.000 0.140
#> GSM1068565 2 0.0336 0.65449 0.000 0.992 0.000 0.008
#> GSM1068529 4 0.9483 0.61571 0.224 0.304 0.116 0.356
#> GSM1068530 1 0.0672 0.79926 0.984 0.008 0.000 0.008
#> GSM1068534 4 0.9483 0.61571 0.224 0.304 0.116 0.356
#> GSM1068536 2 0.7122 0.31495 0.272 0.568 0.004 0.156
#> GSM1068541 2 0.4401 0.64330 0.076 0.812 0.000 0.112
#> GSM1068553 2 0.7287 0.40080 0.052 0.576 0.064 0.308
#> GSM1068554 2 0.6832 0.46274 0.040 0.616 0.056 0.288
#> GSM1068558 4 0.7644 0.47517 0.000 0.272 0.260 0.468
#> GSM1068559 2 0.8946 -0.16034 0.100 0.468 0.176 0.256
#> GSM1068564 2 0.3498 0.63314 0.008 0.832 0.000 0.160
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM1068478 2 0.6251 0.32224 0.084 0.600 0.000 0.272 0.044
#> GSM1068479 5 0.7688 0.16019 0.000 0.352 0.096 0.144 0.408
#> GSM1068481 3 0.0609 0.83827 0.000 0.000 0.980 0.000 0.020
#> GSM1068482 3 0.2424 0.81918 0.000 0.000 0.868 0.000 0.132
#> GSM1068483 1 0.8174 0.52457 0.528 0.092 0.132 0.180 0.068
#> GSM1068486 3 0.3467 0.77994 0.000 0.004 0.832 0.036 0.128
#> GSM1068487 2 0.0404 0.49081 0.000 0.988 0.000 0.012 0.000
#> GSM1068488 4 0.6409 0.63696 0.008 0.420 0.000 0.440 0.132
#> GSM1068490 2 0.0404 0.49447 0.000 0.988 0.000 0.012 0.000
#> GSM1068491 5 0.7677 0.16995 0.000 0.352 0.092 0.148 0.408
#> GSM1068492 5 0.7522 0.17125 0.000 0.356 0.076 0.148 0.420
#> GSM1068493 2 0.8512 0.09414 0.092 0.468 0.088 0.248 0.104
#> GSM1068494 5 0.7608 -0.18517 0.160 0.000 0.280 0.092 0.468
#> GSM1068495 2 0.4636 0.44021 0.036 0.768 0.000 0.152 0.044
#> GSM1068496 5 0.9479 0.34136 0.224 0.132 0.092 0.232 0.320
#> GSM1068498 2 0.6251 0.32224 0.084 0.600 0.000 0.272 0.044
#> GSM1068499 1 0.7712 0.43872 0.528 0.032 0.060 0.224 0.156
#> GSM1068500 1 0.8174 0.52457 0.528 0.092 0.132 0.180 0.068
#> GSM1068502 5 0.7522 0.17125 0.000 0.356 0.076 0.148 0.420
#> GSM1068503 2 0.0404 0.49081 0.000 0.988 0.000 0.012 0.000
#> GSM1068505 2 0.5109 0.17817 0.096 0.708 0.000 0.188 0.008
#> GSM1068506 2 0.4156 0.07354 0.004 0.700 0.000 0.288 0.008
#> GSM1068507 2 0.3787 0.36976 0.008 0.784 0.004 0.196 0.008
#> GSM1068508 2 0.6417 0.34809 0.140 0.596 0.000 0.232 0.032
#> GSM1068510 4 0.5095 0.72381 0.000 0.400 0.000 0.560 0.040
#> GSM1068512 2 0.8225 -0.20944 0.176 0.372 0.000 0.296 0.156
#> GSM1068513 2 0.3086 0.38675 0.004 0.816 0.000 0.180 0.000
#> GSM1068514 4 0.8452 -0.27112 0.048 0.296 0.040 0.312 0.304
#> GSM1068517 2 0.6251 0.32224 0.084 0.600 0.000 0.272 0.044
#> GSM1068518 5 0.9081 0.40482 0.124 0.220 0.048 0.296 0.312
#> GSM1068520 1 0.4021 0.73021 0.808 0.108 0.000 0.076 0.008
#> GSM1068521 1 0.3911 0.73588 0.816 0.104 0.000 0.072 0.008
#> GSM1068522 2 0.2648 0.35154 0.000 0.848 0.000 0.152 0.000
#> GSM1068524 2 0.2871 0.46314 0.000 0.872 0.000 0.088 0.040
#> GSM1068527 2 0.7684 -0.26251 0.220 0.428 0.000 0.284 0.068
#> GSM1068480 3 0.4974 0.49890 0.000 0.000 0.508 0.028 0.464
#> GSM1068484 2 0.6215 -0.62361 0.000 0.448 0.000 0.412 0.140
#> GSM1068485 3 0.1197 0.83046 0.000 0.000 0.952 0.000 0.048
#> GSM1068489 2 0.4066 -0.01251 0.004 0.672 0.000 0.324 0.000
#> GSM1068497 2 0.6251 0.32224 0.084 0.600 0.000 0.272 0.044
#> GSM1068501 4 0.5033 0.72035 0.004 0.400 0.000 0.568 0.028
#> GSM1068504 2 0.0162 0.49429 0.000 0.996 0.000 0.004 0.000
#> GSM1068509 5 0.9400 0.43260 0.172 0.160 0.080 0.256 0.332
#> GSM1068511 5 0.8432 0.32620 0.032 0.080 0.192 0.300 0.396
#> GSM1068515 2 0.7497 0.15435 0.220 0.468 0.004 0.260 0.048
#> GSM1068516 2 0.8095 -0.21019 0.052 0.420 0.028 0.204 0.296
#> GSM1068519 1 0.4605 0.66727 0.732 0.000 0.000 0.192 0.076
#> GSM1068523 2 0.1251 0.49936 0.000 0.956 0.000 0.036 0.008
#> GSM1068525 2 0.6233 -0.60111 0.000 0.460 0.000 0.396 0.144
#> GSM1068526 2 0.4268 -0.08786 0.000 0.648 0.000 0.344 0.008
#> GSM1068458 1 0.3169 0.77016 0.840 0.000 0.004 0.140 0.016
#> GSM1068459 3 0.0000 0.83708 0.000 0.000 1.000 0.000 0.000
#> GSM1068460 2 0.5264 0.14147 0.100 0.700 0.000 0.188 0.012
#> GSM1068461 3 0.4570 0.71909 0.000 0.000 0.632 0.020 0.348
#> GSM1068464 2 0.0162 0.49549 0.000 0.996 0.000 0.004 0.000
#> GSM1068468 2 0.4098 0.47211 0.020 0.808 0.004 0.132 0.036
#> GSM1068472 2 0.6238 0.39102 0.084 0.652 0.012 0.208 0.044
#> GSM1068473 2 0.0510 0.48941 0.000 0.984 0.000 0.016 0.000
#> GSM1068474 2 0.0404 0.49081 0.000 0.988 0.000 0.012 0.000
#> GSM1068476 2 0.8234 -0.39763 0.000 0.316 0.284 0.112 0.288
#> GSM1068477 2 0.2648 0.35154 0.000 0.848 0.000 0.152 0.000
#> GSM1068462 2 0.6541 0.32648 0.084 0.600 0.004 0.252 0.060
#> GSM1068463 3 0.0609 0.83827 0.000 0.000 0.980 0.000 0.020
#> GSM1068465 2 0.6417 0.34809 0.140 0.596 0.000 0.232 0.032
#> GSM1068466 1 0.4896 0.72395 0.752 0.060 0.004 0.160 0.024
#> GSM1068467 2 0.3911 0.47325 0.020 0.824 0.004 0.116 0.036
#> GSM1068469 2 0.6423 0.33320 0.084 0.608 0.004 0.252 0.052
#> GSM1068470 2 0.0794 0.49863 0.000 0.972 0.000 0.028 0.000
#> GSM1068471 2 0.0162 0.49549 0.000 0.996 0.000 0.004 0.000
#> GSM1068475 2 0.0609 0.49762 0.000 0.980 0.000 0.020 0.000
#> GSM1068528 3 0.3799 0.72204 0.144 0.000 0.812 0.012 0.032
#> GSM1068531 1 0.0992 0.77935 0.968 0.000 0.000 0.024 0.008
#> GSM1068532 1 0.0579 0.79309 0.984 0.000 0.000 0.008 0.008
#> GSM1068533 1 0.3169 0.77016 0.840 0.000 0.004 0.140 0.016
#> GSM1068535 4 0.6672 0.63968 0.084 0.324 0.004 0.540 0.048
#> GSM1068537 1 0.0290 0.79352 0.992 0.000 0.000 0.000 0.008
#> GSM1068538 1 0.0613 0.79364 0.984 0.000 0.004 0.008 0.004
#> GSM1068539 2 0.4636 0.44021 0.036 0.768 0.000 0.152 0.044
#> GSM1068540 1 0.0727 0.79403 0.980 0.004 0.004 0.000 0.012
#> GSM1068542 2 0.5020 -0.15083 0.020 0.620 0.000 0.344 0.016
#> GSM1068543 4 0.6429 0.65248 0.016 0.420 0.000 0.452 0.112
#> GSM1068544 3 0.0579 0.83677 0.008 0.000 0.984 0.000 0.008
#> GSM1068545 2 0.4156 0.07354 0.004 0.700 0.000 0.288 0.008
#> GSM1068546 3 0.4555 0.76724 0.000 0.000 0.732 0.068 0.200
#> GSM1068547 1 0.4021 0.73021 0.808 0.108 0.000 0.076 0.008
#> GSM1068548 2 0.5371 -0.17351 0.036 0.612 0.000 0.332 0.020
#> GSM1068549 3 0.4575 0.72051 0.000 0.000 0.648 0.024 0.328
#> GSM1068550 2 0.4586 -0.10293 0.004 0.644 0.000 0.336 0.016
#> GSM1068551 2 0.1124 0.49929 0.000 0.960 0.000 0.036 0.004
#> GSM1068552 2 0.3876 0.00304 0.000 0.684 0.000 0.316 0.000
#> GSM1068555 2 0.1251 0.49936 0.000 0.956 0.000 0.036 0.008
#> GSM1068556 4 0.6429 0.65248 0.016 0.420 0.000 0.452 0.112
#> GSM1068557 2 0.4865 0.45132 0.020 0.756 0.008 0.160 0.056
#> GSM1068560 2 0.7684 -0.26251 0.220 0.428 0.000 0.284 0.068
#> GSM1068561 2 0.5898 0.37160 0.048 0.676 0.008 0.204 0.064
#> GSM1068562 2 0.4749 -0.14069 0.008 0.628 0.000 0.348 0.016
#> GSM1068563 2 0.4156 0.07354 0.004 0.700 0.000 0.288 0.008
#> GSM1068565 2 0.0609 0.49422 0.000 0.980 0.000 0.020 0.000
#> GSM1068529 5 0.8982 0.42855 0.092 0.216 0.060 0.292 0.340
#> GSM1068530 1 0.0290 0.79352 0.992 0.000 0.000 0.000 0.008
#> GSM1068534 5 0.8982 0.42855 0.092 0.216 0.060 0.292 0.340
#> GSM1068536 2 0.7283 0.09323 0.192 0.508 0.000 0.240 0.060
#> GSM1068541 2 0.4786 0.29261 0.020 0.696 0.000 0.260 0.024
#> GSM1068553 4 0.5309 0.71134 0.008 0.352 0.004 0.600 0.036
#> GSM1068554 4 0.5088 0.72536 0.004 0.392 0.000 0.572 0.032
#> GSM1068558 5 0.7744 0.30035 0.000 0.092 0.212 0.236 0.460
#> GSM1068559 2 0.9029 -0.38141 0.052 0.356 0.116 0.220 0.256
#> GSM1068564 2 0.3895 -0.00496 0.000 0.680 0.000 0.320 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1068478 2 0.4701 0.08895 0.036 0.524 0.000 0.004 0.436 0.000
#> GSM1068479 4 0.7550 0.62219 0.000 0.200 0.040 0.452 0.084 0.224
#> GSM1068481 3 0.0790 0.79343 0.000 0.000 0.968 0.032 0.000 0.000
#> GSM1068482 3 0.3198 0.75937 0.000 0.000 0.844 0.100 0.032 0.024
#> GSM1068483 1 0.7296 0.42771 0.472 0.072 0.120 0.032 0.296 0.008
#> GSM1068486 3 0.3792 0.69488 0.000 0.000 0.780 0.160 0.008 0.052
#> GSM1068487 2 0.0692 0.59661 0.000 0.976 0.000 0.000 0.004 0.020
#> GSM1068488 6 0.6071 0.55184 0.000 0.224 0.000 0.056 0.140 0.580
#> GSM1068490 2 0.0717 0.59880 0.000 0.976 0.000 0.000 0.008 0.016
#> GSM1068491 4 0.7958 0.63259 0.000 0.200 0.052 0.404 0.112 0.232
#> GSM1068492 4 0.7759 0.62957 0.000 0.200 0.032 0.416 0.120 0.232
#> GSM1068493 5 0.7035 0.15444 0.052 0.400 0.084 0.024 0.420 0.020
#> GSM1068494 5 0.8438 -0.19073 0.116 0.000 0.240 0.260 0.300 0.084
#> GSM1068495 2 0.4460 0.44424 0.020 0.728 0.000 0.004 0.200 0.048
#> GSM1068496 5 0.7864 0.46143 0.160 0.076 0.088 0.076 0.536 0.064
#> GSM1068498 2 0.4701 0.08895 0.036 0.524 0.000 0.004 0.436 0.000
#> GSM1068499 1 0.7395 0.34737 0.468 0.004 0.056 0.060 0.288 0.124
#> GSM1068500 1 0.7296 0.42771 0.472 0.072 0.120 0.032 0.296 0.008
#> GSM1068502 4 0.7759 0.62957 0.000 0.200 0.032 0.416 0.120 0.232
#> GSM1068503 2 0.0692 0.59661 0.000 0.976 0.000 0.000 0.004 0.020
#> GSM1068505 2 0.5798 0.28372 0.068 0.612 0.000 0.012 0.052 0.256
#> GSM1068506 2 0.4364 0.21217 0.004 0.608 0.000 0.000 0.024 0.364
#> GSM1068507 2 0.4178 0.48105 0.000 0.728 0.000 0.004 0.060 0.208
#> GSM1068508 2 0.6128 0.08946 0.088 0.516 0.000 0.004 0.340 0.052
#> GSM1068510 6 0.4563 0.53596 0.000 0.232 0.000 0.040 0.028 0.700
#> GSM1068512 5 0.7751 0.10935 0.112 0.240 0.000 0.020 0.352 0.276
#> GSM1068513 2 0.3683 0.49477 0.000 0.764 0.000 0.000 0.044 0.192
#> GSM1068514 5 0.8354 0.14683 0.028 0.176 0.040 0.108 0.376 0.272
#> GSM1068517 2 0.4701 0.08895 0.036 0.524 0.000 0.004 0.436 0.000
#> GSM1068518 5 0.7862 0.49808 0.072 0.124 0.044 0.076 0.528 0.156
#> GSM1068520 1 0.4641 0.68758 0.764 0.068 0.000 0.020 0.112 0.036
#> GSM1068521 1 0.4469 0.69194 0.776 0.068 0.000 0.016 0.104 0.036
#> GSM1068522 2 0.3109 0.43857 0.000 0.772 0.000 0.000 0.004 0.224
#> GSM1068524 2 0.3515 0.55908 0.000 0.828 0.000 0.024 0.084 0.064
#> GSM1068527 6 0.8090 0.24499 0.152 0.288 0.000 0.036 0.196 0.328
#> GSM1068480 3 0.6578 0.44467 0.000 0.000 0.460 0.260 0.240 0.040
#> GSM1068484 6 0.6219 0.52535 0.000 0.260 0.000 0.052 0.144 0.544
#> GSM1068485 3 0.1418 0.78324 0.000 0.000 0.944 0.032 0.024 0.000
#> GSM1068489 2 0.4024 0.17666 0.004 0.592 0.000 0.000 0.004 0.400
#> GSM1068497 2 0.4701 0.08895 0.036 0.524 0.000 0.004 0.436 0.000
#> GSM1068501 6 0.4133 0.54106 0.000 0.232 0.000 0.020 0.024 0.724
#> GSM1068504 2 0.0508 0.59860 0.000 0.984 0.000 0.000 0.004 0.012
#> GSM1068509 5 0.8025 0.49525 0.120 0.080 0.076 0.092 0.532 0.100
#> GSM1068511 6 0.7620 -0.13715 0.012 0.008 0.156 0.116 0.332 0.376
#> GSM1068515 5 0.6427 0.08366 0.144 0.388 0.000 0.024 0.432 0.012
#> GSM1068516 5 0.7675 0.27985 0.028 0.364 0.024 0.076 0.388 0.120
#> GSM1068519 1 0.5600 0.59089 0.648 0.000 0.000 0.052 0.160 0.140
#> GSM1068523 2 0.1471 0.58888 0.000 0.932 0.000 0.004 0.064 0.000
#> GSM1068525 6 0.6295 0.50978 0.000 0.272 0.000 0.052 0.148 0.528
#> GSM1068526 2 0.4238 0.04873 0.000 0.540 0.000 0.000 0.016 0.444
#> GSM1068458 1 0.3409 0.71266 0.788 0.000 0.004 0.024 0.184 0.000
#> GSM1068459 3 0.0000 0.79339 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1068460 2 0.5884 0.23956 0.072 0.600 0.000 0.012 0.052 0.264
#> GSM1068461 4 0.4083 -0.54796 0.000 0.000 0.460 0.532 0.008 0.000
#> GSM1068464 2 0.0717 0.59822 0.000 0.976 0.000 0.000 0.008 0.016
#> GSM1068468 2 0.3599 0.49183 0.004 0.764 0.000 0.004 0.212 0.016
#> GSM1068472 2 0.5028 0.19494 0.036 0.584 0.008 0.004 0.360 0.008
#> GSM1068473 2 0.0777 0.59576 0.000 0.972 0.000 0.000 0.004 0.024
#> GSM1068474 2 0.0692 0.59661 0.000 0.976 0.000 0.000 0.004 0.020
#> GSM1068476 4 0.7973 0.51941 0.000 0.188 0.224 0.364 0.024 0.200
#> GSM1068477 2 0.3109 0.43857 0.000 0.772 0.000 0.000 0.004 0.224
#> GSM1068462 2 0.4928 0.08102 0.036 0.520 0.000 0.008 0.432 0.004
#> GSM1068463 3 0.0790 0.79343 0.000 0.000 0.968 0.032 0.000 0.000
#> GSM1068465 2 0.6128 0.08946 0.088 0.516 0.000 0.004 0.340 0.052
#> GSM1068466 1 0.4944 0.65340 0.692 0.048 0.004 0.024 0.224 0.008
#> GSM1068467 2 0.3354 0.49387 0.004 0.780 0.000 0.004 0.204 0.008
#> GSM1068469 2 0.4914 0.09922 0.036 0.532 0.000 0.008 0.420 0.004
#> GSM1068470 2 0.0865 0.59691 0.000 0.964 0.000 0.000 0.036 0.000
#> GSM1068471 2 0.0717 0.59822 0.000 0.976 0.000 0.000 0.008 0.016
#> GSM1068475 2 0.0777 0.59979 0.000 0.972 0.000 0.000 0.024 0.004
#> GSM1068528 3 0.3661 0.68428 0.136 0.000 0.804 0.024 0.036 0.000
#> GSM1068531 1 0.1777 0.74023 0.932 0.000 0.000 0.032 0.024 0.012
#> GSM1068532 1 0.1369 0.75217 0.952 0.000 0.000 0.016 0.016 0.016
#> GSM1068533 1 0.3409 0.71266 0.788 0.000 0.004 0.024 0.184 0.000
#> GSM1068535 6 0.5341 0.47288 0.060 0.168 0.000 0.032 0.040 0.700
#> GSM1068537 1 0.1078 0.75403 0.964 0.000 0.000 0.016 0.012 0.008
#> GSM1068538 1 0.1007 0.75397 0.968 0.000 0.004 0.016 0.004 0.008
#> GSM1068539 2 0.4460 0.44424 0.020 0.728 0.000 0.004 0.200 0.048
#> GSM1068540 1 0.1519 0.75576 0.948 0.004 0.004 0.008 0.028 0.008
#> GSM1068542 2 0.5059 0.00873 0.012 0.512 0.000 0.008 0.032 0.436
#> GSM1068543 6 0.5575 0.56634 0.000 0.224 0.000 0.036 0.116 0.624
#> GSM1068544 3 0.0520 0.79360 0.008 0.000 0.984 0.000 0.008 0.000
#> GSM1068545 2 0.4364 0.21217 0.004 0.608 0.000 0.000 0.024 0.364
#> GSM1068546 3 0.5149 0.58294 0.000 0.000 0.580 0.348 0.036 0.036
#> GSM1068547 1 0.4641 0.68758 0.764 0.068 0.000 0.020 0.112 0.036
#> GSM1068548 2 0.5280 -0.02127 0.012 0.504 0.000 0.008 0.048 0.428
#> GSM1068549 3 0.4227 0.45749 0.000 0.000 0.500 0.488 0.008 0.004
#> GSM1068550 2 0.4636 0.02520 0.000 0.532 0.000 0.004 0.032 0.432
#> GSM1068551 2 0.1411 0.58996 0.000 0.936 0.000 0.004 0.060 0.000
#> GSM1068552 2 0.3890 0.16619 0.000 0.596 0.000 0.000 0.004 0.400
#> GSM1068555 2 0.1327 0.58868 0.000 0.936 0.000 0.000 0.064 0.000
#> GSM1068556 6 0.5575 0.56634 0.000 0.224 0.000 0.036 0.116 0.624
#> GSM1068557 2 0.4383 0.43290 0.004 0.700 0.004 0.012 0.256 0.024
#> GSM1068560 6 0.8090 0.24499 0.152 0.288 0.000 0.036 0.196 0.328
#> GSM1068561 2 0.5845 0.33339 0.024 0.632 0.008 0.020 0.228 0.088
#> GSM1068562 2 0.4709 -0.01524 0.000 0.516 0.000 0.004 0.036 0.444
#> GSM1068563 2 0.4364 0.21217 0.004 0.608 0.000 0.000 0.024 0.364
#> GSM1068565 2 0.0914 0.60005 0.000 0.968 0.000 0.000 0.016 0.016
#> GSM1068529 5 0.7851 0.50449 0.052 0.116 0.056 0.092 0.532 0.152
#> GSM1068530 1 0.0976 0.75443 0.968 0.000 0.000 0.008 0.016 0.008
#> GSM1068534 5 0.7851 0.50449 0.052 0.116 0.056 0.092 0.532 0.152
#> GSM1068536 2 0.7555 0.05094 0.144 0.448 0.000 0.024 0.232 0.152
#> GSM1068541 2 0.5524 0.37141 0.016 0.600 0.000 0.000 0.136 0.248
#> GSM1068553 6 0.4082 0.51981 0.000 0.192 0.000 0.028 0.028 0.752
#> GSM1068554 6 0.4159 0.54169 0.000 0.224 0.000 0.024 0.024 0.728
#> GSM1068558 6 0.7880 -0.26081 0.000 0.012 0.164 0.264 0.256 0.304
#> GSM1068559 4 0.8975 0.33788 0.020 0.196 0.076 0.260 0.188 0.260
#> GSM1068564 2 0.3899 0.16045 0.000 0.592 0.000 0.000 0.004 0.404
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 disease.state(p) gender(p) k
#> MAD:hclust 34 NA NA 2
#> MAD:hclust 90 0.7972 0.4806 3
#> MAD:hclust 72 0.8407 0.3911 4
#> MAD:hclust 34 0.5412 0.5637 5
#> MAD:hclust 53 0.0841 0.0821 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 38950 rows and 108 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'kmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.695 0.876 0.936 0.4636 0.551 0.551
#> 3 3 0.427 0.590 0.770 0.3451 0.796 0.649
#> 4 4 0.820 0.864 0.913 0.1692 0.772 0.499
#> 5 5 0.692 0.651 0.789 0.0774 0.955 0.839
#> 6 6 0.678 0.538 0.705 0.0476 0.892 0.586
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
#> GSM1068478 1 0.6973 0.784 0.812 0.188
#> GSM1068479 2 0.3114 0.894 0.056 0.944
#> GSM1068481 1 0.0376 0.947 0.996 0.004
#> GSM1068482 1 0.0376 0.947 0.996 0.004
#> GSM1068483 1 0.0000 0.948 1.000 0.000
#> GSM1068486 1 0.0376 0.947 0.996 0.004
#> GSM1068487 2 0.0000 0.921 0.000 1.000
#> GSM1068488 2 0.5294 0.859 0.120 0.880
#> GSM1068490 2 0.0000 0.921 0.000 1.000
#> GSM1068491 1 0.7528 0.700 0.784 0.216
#> GSM1068492 2 0.3431 0.892 0.064 0.936
#> GSM1068493 2 0.7602 0.714 0.220 0.780
#> GSM1068494 1 0.0000 0.948 1.000 0.000
#> GSM1068495 2 0.0376 0.922 0.004 0.996
#> GSM1068496 1 0.0000 0.948 1.000 0.000
#> GSM1068498 2 0.7528 0.725 0.216 0.784
#> GSM1068499 1 0.0000 0.948 1.000 0.000
#> GSM1068500 1 0.0000 0.948 1.000 0.000
#> GSM1068502 2 0.3114 0.894 0.056 0.944
#> GSM1068503 2 0.0000 0.921 0.000 1.000
#> GSM1068505 2 0.0376 0.922 0.004 0.996
#> GSM1068506 2 0.0376 0.922 0.004 0.996
#> GSM1068507 2 0.2423 0.908 0.040 0.960
#> GSM1068508 2 0.0376 0.922 0.004 0.996
#> GSM1068510 2 0.0000 0.921 0.000 1.000
#> GSM1068512 2 0.8909 0.642 0.308 0.692
#> GSM1068513 2 0.0000 0.921 0.000 1.000
#> GSM1068514 2 0.7453 0.776 0.212 0.788
#> GSM1068517 2 0.1633 0.914 0.024 0.976
#> GSM1068518 2 0.4298 0.882 0.088 0.912
#> GSM1068520 1 0.3114 0.927 0.944 0.056
#> GSM1068521 1 0.3114 0.927 0.944 0.056
#> GSM1068522 2 0.0376 0.922 0.004 0.996
#> GSM1068524 2 0.0000 0.921 0.000 1.000
#> GSM1068527 2 0.6438 0.819 0.164 0.836
#> GSM1068480 1 0.0376 0.947 0.996 0.004
#> GSM1068484 2 0.0376 0.922 0.004 0.996
#> GSM1068485 1 0.0376 0.947 0.996 0.004
#> GSM1068489 2 0.0938 0.920 0.012 0.988
#> GSM1068497 2 0.7528 0.725 0.216 0.784
#> GSM1068501 2 0.0376 0.922 0.004 0.996
#> GSM1068504 2 0.0000 0.921 0.000 1.000
#> GSM1068509 1 0.1184 0.944 0.984 0.016
#> GSM1068511 1 0.9710 0.203 0.600 0.400
#> GSM1068515 1 0.7674 0.733 0.776 0.224
#> GSM1068516 2 0.1414 0.918 0.020 0.980
#> GSM1068519 1 0.3114 0.927 0.944 0.056
#> GSM1068523 2 0.0376 0.922 0.004 0.996
#> GSM1068525 2 0.0000 0.921 0.000 1.000
#> GSM1068526 2 0.2236 0.911 0.036 0.964
#> GSM1068458 1 0.1843 0.940 0.972 0.028
#> GSM1068459 1 0.0376 0.947 0.996 0.004
#> GSM1068460 2 0.6438 0.825 0.164 0.836
#> GSM1068461 1 0.0376 0.947 0.996 0.004
#> GSM1068464 2 0.0000 0.921 0.000 1.000
#> GSM1068468 2 0.0000 0.921 0.000 1.000
#> GSM1068472 2 0.0000 0.921 0.000 1.000
#> GSM1068473 2 0.0000 0.921 0.000 1.000
#> GSM1068474 2 0.0000 0.921 0.000 1.000
#> GSM1068476 2 0.9661 0.460 0.392 0.608
#> GSM1068477 2 0.0376 0.922 0.004 0.996
#> GSM1068462 2 0.0000 0.921 0.000 1.000
#> GSM1068463 1 0.0376 0.947 0.996 0.004
#> GSM1068465 2 0.7219 0.747 0.200 0.800
#> GSM1068466 1 0.3114 0.927 0.944 0.056
#> GSM1068467 2 0.0000 0.921 0.000 1.000
#> GSM1068469 2 0.7883 0.689 0.236 0.764
#> GSM1068470 2 0.0376 0.922 0.004 0.996
#> GSM1068471 2 0.0000 0.921 0.000 1.000
#> GSM1068475 2 0.0000 0.921 0.000 1.000
#> GSM1068528 1 0.0000 0.948 1.000 0.000
#> GSM1068531 1 0.3114 0.927 0.944 0.056
#> GSM1068532 1 0.0000 0.948 1.000 0.000
#> GSM1068533 1 0.0000 0.948 1.000 0.000
#> GSM1068535 1 0.3431 0.922 0.936 0.064
#> GSM1068537 1 0.0000 0.948 1.000 0.000
#> GSM1068538 1 0.0000 0.948 1.000 0.000
#> GSM1068539 2 0.0376 0.922 0.004 0.996
#> GSM1068540 1 0.3114 0.927 0.944 0.056
#> GSM1068542 2 0.2236 0.911 0.036 0.964
#> GSM1068543 2 0.7528 0.775 0.216 0.784
#> GSM1068544 1 0.0000 0.948 1.000 0.000
#> GSM1068545 2 0.0376 0.922 0.004 0.996
#> GSM1068546 1 0.0376 0.947 0.996 0.004
#> GSM1068547 1 0.3114 0.927 0.944 0.056
#> GSM1068548 2 0.4431 0.879 0.092 0.908
#> GSM1068549 1 0.0376 0.947 0.996 0.004
#> GSM1068550 2 0.0376 0.922 0.004 0.996
#> GSM1068551 2 0.0376 0.922 0.004 0.996
#> GSM1068552 2 0.0376 0.922 0.004 0.996
#> GSM1068555 2 0.0000 0.921 0.000 1.000
#> GSM1068556 2 0.7528 0.775 0.216 0.784
#> GSM1068557 2 0.0000 0.921 0.000 1.000
#> GSM1068560 2 0.4431 0.879 0.092 0.908
#> GSM1068561 2 0.0376 0.922 0.004 0.996
#> GSM1068562 2 0.2778 0.906 0.048 0.952
#> GSM1068563 2 0.1843 0.915 0.028 0.972
#> GSM1068565 2 0.0376 0.922 0.004 0.996
#> GSM1068529 2 0.7602 0.767 0.220 0.780
#> GSM1068530 1 0.3114 0.927 0.944 0.056
#> GSM1068534 2 0.9881 0.382 0.436 0.564
#> GSM1068536 2 0.8955 0.614 0.312 0.688
#> GSM1068541 2 0.0376 0.922 0.004 0.996
#> GSM1068553 2 0.6343 0.823 0.160 0.840
#> GSM1068554 2 0.0376 0.921 0.004 0.996
#> GSM1068558 2 0.6801 0.810 0.180 0.820
#> GSM1068559 2 0.9286 0.569 0.344 0.656
#> GSM1068564 2 0.0376 0.922 0.004 0.996
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1068478 1 0.1878 0.80025 0.952 0.044 0.004
#> GSM1068479 2 0.5835 0.27940 0.000 0.660 0.340
#> GSM1068481 3 0.5098 0.64183 0.248 0.000 0.752
#> GSM1068482 3 0.5138 0.64078 0.252 0.000 0.748
#> GSM1068483 1 0.1964 0.78976 0.944 0.000 0.056
#> GSM1068486 3 0.5058 0.64307 0.244 0.000 0.756
#> GSM1068487 2 0.0237 0.70435 0.000 0.996 0.004
#> GSM1068488 2 0.9745 0.40238 0.232 0.420 0.348
#> GSM1068490 2 0.0237 0.70435 0.000 0.996 0.004
#> GSM1068491 3 0.5406 0.63224 0.200 0.020 0.780
#> GSM1068492 3 0.6192 -0.13846 0.000 0.420 0.580
#> GSM1068493 2 0.6452 0.51846 0.088 0.760 0.152
#> GSM1068494 1 0.4796 0.63637 0.780 0.000 0.220
#> GSM1068495 2 0.8213 0.61211 0.228 0.632 0.140
#> GSM1068496 1 0.3941 0.69674 0.844 0.000 0.156
#> GSM1068498 2 0.6786 0.00272 0.448 0.540 0.012
#> GSM1068499 1 0.4555 0.63191 0.800 0.000 0.200
#> GSM1068500 1 0.2066 0.78703 0.940 0.000 0.060
#> GSM1068502 2 0.6008 0.25180 0.000 0.628 0.372
#> GSM1068503 2 0.1031 0.70549 0.000 0.976 0.024
#> GSM1068505 2 0.8926 0.58153 0.192 0.568 0.240
#> GSM1068506 2 0.8063 0.63299 0.132 0.644 0.224
#> GSM1068507 2 0.7558 0.65752 0.124 0.688 0.188
#> GSM1068508 2 0.0661 0.70531 0.008 0.988 0.004
#> GSM1068510 2 0.6699 0.65942 0.044 0.700 0.256
#> GSM1068512 2 0.9971 0.30091 0.352 0.352 0.296
#> GSM1068513 2 0.0747 0.70617 0.000 0.984 0.016
#> GSM1068514 3 0.7267 0.14963 0.064 0.268 0.668
#> GSM1068517 2 0.6600 0.19187 0.384 0.604 0.012
#> GSM1068518 2 0.9829 0.38881 0.352 0.400 0.248
#> GSM1068520 1 0.0000 0.81899 1.000 0.000 0.000
#> GSM1068521 1 0.0747 0.81525 0.984 0.000 0.016
#> GSM1068522 2 0.1860 0.70486 0.000 0.948 0.052
#> GSM1068524 2 0.1529 0.70780 0.000 0.960 0.040
#> GSM1068527 1 0.9412 0.04211 0.508 0.244 0.248
#> GSM1068480 3 0.5098 0.64294 0.248 0.000 0.752
#> GSM1068484 2 0.6482 0.66326 0.040 0.716 0.244
#> GSM1068485 3 0.5138 0.64078 0.252 0.000 0.748
#> GSM1068489 2 0.8886 0.58466 0.188 0.572 0.240
#> GSM1068497 2 0.6632 0.17052 0.392 0.596 0.012
#> GSM1068501 2 0.7062 0.65532 0.068 0.696 0.236
#> GSM1068504 2 0.0237 0.70435 0.000 0.996 0.004
#> GSM1068509 1 0.1163 0.81436 0.972 0.000 0.028
#> GSM1068511 3 0.9659 -0.04376 0.340 0.220 0.440
#> GSM1068515 1 0.4121 0.65534 0.832 0.168 0.000
#> GSM1068516 2 0.9191 0.57262 0.208 0.536 0.256
#> GSM1068519 1 0.0892 0.81602 0.980 0.000 0.020
#> GSM1068523 2 0.0661 0.70544 0.004 0.988 0.008
#> GSM1068525 2 0.6187 0.66643 0.028 0.724 0.248
#> GSM1068526 2 0.8511 0.60941 0.152 0.604 0.244
#> GSM1068458 1 0.0747 0.81457 0.984 0.000 0.016
#> GSM1068459 3 0.5138 0.64078 0.252 0.000 0.748
#> GSM1068460 1 0.6034 0.64011 0.780 0.068 0.152
#> GSM1068461 3 0.5098 0.64183 0.248 0.000 0.752
#> GSM1068464 2 0.0237 0.70435 0.000 0.996 0.004
#> GSM1068468 2 0.1015 0.70236 0.012 0.980 0.008
#> GSM1068472 2 0.1015 0.70236 0.012 0.980 0.008
#> GSM1068473 2 0.0237 0.70435 0.000 0.996 0.004
#> GSM1068474 2 0.0237 0.70435 0.000 0.996 0.004
#> GSM1068476 3 0.3850 0.58447 0.088 0.028 0.884
#> GSM1068477 2 0.0237 0.70482 0.004 0.996 0.000
#> GSM1068462 2 0.2116 0.68278 0.012 0.948 0.040
#> GSM1068463 3 0.5138 0.64078 0.252 0.000 0.748
#> GSM1068465 1 0.6621 0.61029 0.752 0.148 0.100
#> GSM1068466 1 0.0000 0.81899 1.000 0.000 0.000
#> GSM1068467 2 0.1015 0.70236 0.012 0.980 0.008
#> GSM1068469 2 0.6467 0.18760 0.388 0.604 0.008
#> GSM1068470 2 0.0237 0.70592 0.000 0.996 0.004
#> GSM1068471 2 0.0237 0.70435 0.000 0.996 0.004
#> GSM1068475 2 0.0000 0.70511 0.000 1.000 0.000
#> GSM1068528 1 0.5363 0.47444 0.724 0.000 0.276
#> GSM1068531 1 0.0237 0.81831 0.996 0.000 0.004
#> GSM1068532 1 0.1643 0.80231 0.956 0.000 0.044
#> GSM1068533 1 0.1529 0.80349 0.960 0.000 0.040
#> GSM1068535 1 0.9455 0.12396 0.488 0.208 0.304
#> GSM1068537 1 0.1529 0.80349 0.960 0.000 0.040
#> GSM1068538 1 0.1529 0.80349 0.960 0.000 0.040
#> GSM1068539 2 0.7702 0.64783 0.180 0.680 0.140
#> GSM1068540 1 0.0892 0.81602 0.980 0.000 0.020
#> GSM1068542 2 0.9411 0.52142 0.252 0.508 0.240
#> GSM1068543 2 0.9744 0.44615 0.256 0.444 0.300
#> GSM1068544 3 0.5216 0.63129 0.260 0.000 0.740
#> GSM1068545 2 0.4636 0.69824 0.036 0.848 0.116
#> GSM1068546 3 0.5098 0.64294 0.248 0.000 0.752
#> GSM1068547 1 0.1860 0.78990 0.948 0.000 0.052
#> GSM1068548 2 0.9484 0.50750 0.264 0.496 0.240
#> GSM1068549 3 0.5098 0.64294 0.248 0.000 0.752
#> GSM1068550 2 0.9040 0.57091 0.204 0.556 0.240
#> GSM1068551 2 0.0237 0.70592 0.000 0.996 0.004
#> GSM1068552 2 0.6481 0.66770 0.048 0.728 0.224
#> GSM1068555 2 0.0661 0.70544 0.004 0.988 0.008
#> GSM1068556 2 0.9698 0.45948 0.256 0.456 0.288
#> GSM1068557 2 0.1015 0.70253 0.012 0.980 0.008
#> GSM1068560 2 0.9702 0.47280 0.300 0.452 0.248
#> GSM1068561 2 0.5481 0.68008 0.108 0.816 0.076
#> GSM1068562 2 0.8607 0.60422 0.152 0.592 0.256
#> GSM1068563 2 0.8392 0.61560 0.148 0.616 0.236
#> GSM1068565 2 0.0000 0.70511 0.000 1.000 0.000
#> GSM1068529 3 0.7975 0.21333 0.140 0.204 0.656
#> GSM1068530 1 0.0000 0.81899 1.000 0.000 0.000
#> GSM1068534 3 0.9840 -0.15348 0.336 0.256 0.408
#> GSM1068536 1 0.5932 0.63895 0.780 0.056 0.164
#> GSM1068541 2 0.7944 0.52765 0.296 0.616 0.088
#> GSM1068553 2 0.9510 0.50458 0.264 0.492 0.244
#> GSM1068554 2 0.7053 0.65496 0.064 0.692 0.244
#> GSM1068558 3 0.5413 0.38387 0.036 0.164 0.800
#> GSM1068559 3 0.6850 0.40783 0.072 0.208 0.720
#> GSM1068564 2 0.6168 0.67017 0.036 0.740 0.224
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1068478 1 0.2515 0.894 0.912 0.072 0.012 0.004
#> GSM1068479 2 0.3962 0.825 0.000 0.832 0.124 0.044
#> GSM1068481 3 0.0707 0.990 0.020 0.000 0.980 0.000
#> GSM1068482 3 0.1042 0.989 0.020 0.000 0.972 0.008
#> GSM1068483 1 0.1256 0.925 0.964 0.028 0.008 0.000
#> GSM1068486 3 0.0707 0.990 0.020 0.000 0.980 0.000
#> GSM1068487 2 0.2053 0.899 0.000 0.924 0.004 0.072
#> GSM1068488 4 0.1114 0.902 0.016 0.004 0.008 0.972
#> GSM1068490 2 0.2053 0.899 0.000 0.924 0.004 0.072
#> GSM1068491 3 0.0779 0.978 0.004 0.016 0.980 0.000
#> GSM1068492 4 0.4095 0.808 0.004 0.028 0.148 0.820
#> GSM1068493 2 0.2310 0.869 0.016 0.932 0.032 0.020
#> GSM1068494 1 0.7092 0.264 0.540 0.040 0.052 0.368
#> GSM1068495 4 0.5887 0.381 0.020 0.392 0.012 0.576
#> GSM1068496 1 0.2684 0.902 0.912 0.060 0.016 0.012
#> GSM1068498 2 0.2433 0.853 0.060 0.920 0.012 0.008
#> GSM1068499 1 0.3637 0.866 0.864 0.052 0.080 0.004
#> GSM1068500 1 0.1256 0.925 0.964 0.028 0.008 0.000
#> GSM1068502 2 0.4322 0.797 0.000 0.804 0.152 0.044
#> GSM1068503 2 0.2125 0.898 0.000 0.920 0.004 0.076
#> GSM1068505 4 0.1151 0.902 0.008 0.024 0.000 0.968
#> GSM1068506 4 0.2408 0.856 0.000 0.104 0.000 0.896
#> GSM1068507 4 0.4607 0.589 0.004 0.276 0.004 0.716
#> GSM1068508 2 0.1909 0.899 0.008 0.940 0.004 0.048
#> GSM1068510 4 0.0937 0.901 0.000 0.012 0.012 0.976
#> GSM1068512 4 0.1929 0.899 0.036 0.024 0.000 0.940
#> GSM1068513 2 0.2266 0.898 0.000 0.912 0.004 0.084
#> GSM1068514 4 0.3380 0.825 0.004 0.008 0.136 0.852
#> GSM1068517 2 0.2231 0.864 0.044 0.932 0.012 0.012
#> GSM1068518 4 0.2748 0.877 0.020 0.072 0.004 0.904
#> GSM1068520 1 0.0376 0.932 0.992 0.000 0.004 0.004
#> GSM1068521 1 0.0188 0.931 0.996 0.000 0.000 0.004
#> GSM1068522 2 0.3751 0.795 0.000 0.800 0.004 0.196
#> GSM1068524 2 0.4795 0.654 0.000 0.696 0.012 0.292
#> GSM1068527 4 0.4079 0.781 0.180 0.020 0.000 0.800
#> GSM1068480 3 0.1004 0.987 0.024 0.000 0.972 0.004
#> GSM1068484 4 0.1042 0.902 0.008 0.020 0.000 0.972
#> GSM1068485 3 0.0707 0.990 0.020 0.000 0.980 0.000
#> GSM1068489 4 0.0895 0.902 0.004 0.020 0.000 0.976
#> GSM1068497 2 0.2186 0.862 0.048 0.932 0.012 0.008
#> GSM1068501 4 0.0927 0.900 0.000 0.016 0.008 0.976
#> GSM1068504 2 0.1867 0.900 0.000 0.928 0.000 0.072
#> GSM1068509 1 0.2589 0.899 0.912 0.044 0.000 0.044
#> GSM1068511 4 0.1958 0.895 0.028 0.008 0.020 0.944
#> GSM1068515 1 0.2988 0.864 0.876 0.112 0.000 0.012
#> GSM1068516 4 0.2853 0.875 0.016 0.076 0.008 0.900
#> GSM1068519 1 0.0336 0.931 0.992 0.000 0.000 0.008
#> GSM1068523 2 0.1575 0.891 0.004 0.956 0.012 0.028
#> GSM1068525 4 0.1229 0.902 0.008 0.020 0.004 0.968
#> GSM1068526 4 0.1004 0.901 0.004 0.024 0.000 0.972
#> GSM1068458 1 0.0524 0.931 0.988 0.000 0.008 0.004
#> GSM1068459 3 0.1042 0.989 0.020 0.000 0.972 0.008
#> GSM1068460 1 0.1151 0.923 0.968 0.000 0.008 0.024
#> GSM1068461 3 0.0895 0.989 0.020 0.004 0.976 0.000
#> GSM1068464 2 0.2053 0.899 0.000 0.924 0.004 0.072
#> GSM1068468 2 0.1004 0.893 0.000 0.972 0.004 0.024
#> GSM1068472 2 0.1004 0.894 0.000 0.972 0.004 0.024
#> GSM1068473 2 0.2053 0.899 0.000 0.924 0.004 0.072
#> GSM1068474 2 0.1867 0.900 0.000 0.928 0.000 0.072
#> GSM1068476 3 0.0779 0.975 0.000 0.016 0.980 0.004
#> GSM1068477 2 0.1557 0.901 0.000 0.944 0.000 0.056
#> GSM1068462 2 0.0672 0.886 0.000 0.984 0.008 0.008
#> GSM1068463 3 0.0895 0.990 0.020 0.000 0.976 0.004
#> GSM1068465 1 0.2040 0.911 0.936 0.048 0.004 0.012
#> GSM1068466 1 0.0712 0.931 0.984 0.004 0.008 0.004
#> GSM1068467 2 0.0524 0.887 0.000 0.988 0.004 0.008
#> GSM1068469 2 0.1396 0.873 0.032 0.960 0.004 0.004
#> GSM1068470 2 0.2238 0.900 0.004 0.920 0.004 0.072
#> GSM1068471 2 0.1867 0.900 0.000 0.928 0.000 0.072
#> GSM1068475 2 0.1867 0.900 0.000 0.928 0.000 0.072
#> GSM1068528 1 0.4540 0.678 0.740 0.008 0.248 0.004
#> GSM1068531 1 0.0524 0.931 0.988 0.000 0.004 0.008
#> GSM1068532 1 0.0672 0.930 0.984 0.000 0.008 0.008
#> GSM1068533 1 0.0672 0.930 0.984 0.000 0.008 0.008
#> GSM1068535 4 0.1902 0.877 0.064 0.000 0.004 0.932
#> GSM1068537 1 0.0524 0.930 0.988 0.000 0.008 0.004
#> GSM1068538 1 0.0672 0.930 0.984 0.000 0.008 0.008
#> GSM1068539 4 0.5751 0.417 0.016 0.380 0.012 0.592
#> GSM1068540 1 0.0188 0.931 0.996 0.000 0.000 0.004
#> GSM1068542 4 0.1406 0.902 0.016 0.024 0.000 0.960
#> GSM1068543 4 0.1284 0.902 0.024 0.012 0.000 0.964
#> GSM1068544 3 0.1256 0.983 0.028 0.000 0.964 0.008
#> GSM1068545 2 0.5313 0.249 0.004 0.536 0.004 0.456
#> GSM1068546 3 0.1297 0.982 0.016 0.000 0.964 0.020
#> GSM1068547 1 0.0592 0.929 0.984 0.000 0.000 0.016
#> GSM1068548 4 0.1520 0.902 0.024 0.020 0.000 0.956
#> GSM1068549 3 0.0779 0.988 0.016 0.004 0.980 0.000
#> GSM1068550 4 0.1151 0.902 0.008 0.024 0.000 0.968
#> GSM1068551 2 0.2311 0.899 0.004 0.916 0.004 0.076
#> GSM1068552 4 0.2345 0.856 0.000 0.100 0.000 0.900
#> GSM1068555 2 0.1471 0.889 0.004 0.960 0.012 0.024
#> GSM1068556 4 0.1284 0.902 0.024 0.012 0.000 0.964
#> GSM1068557 2 0.1247 0.885 0.004 0.968 0.012 0.016
#> GSM1068560 4 0.2546 0.894 0.028 0.044 0.008 0.920
#> GSM1068561 2 0.5257 0.514 0.012 0.680 0.012 0.296
#> GSM1068562 4 0.1174 0.903 0.012 0.020 0.000 0.968
#> GSM1068563 4 0.2530 0.858 0.004 0.100 0.000 0.896
#> GSM1068565 2 0.1978 0.900 0.000 0.928 0.004 0.068
#> GSM1068529 4 0.3626 0.857 0.012 0.056 0.060 0.872
#> GSM1068530 1 0.0376 0.932 0.992 0.000 0.004 0.004
#> GSM1068534 4 0.2870 0.875 0.020 0.052 0.020 0.908
#> GSM1068536 1 0.3614 0.869 0.872 0.048 0.012 0.068
#> GSM1068541 2 0.5579 0.462 0.028 0.640 0.004 0.328
#> GSM1068553 4 0.1082 0.899 0.020 0.004 0.004 0.972
#> GSM1068554 4 0.0927 0.900 0.000 0.016 0.008 0.976
#> GSM1068558 4 0.3863 0.791 0.004 0.008 0.176 0.812
#> GSM1068559 4 0.5131 0.639 0.000 0.028 0.280 0.692
#> GSM1068564 4 0.2647 0.836 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
#> GSM1068478 1 0.4403 0.45107 0.560 0.004 0.000 0.000 0.436
#> GSM1068479 2 0.3875 0.65065 0.000 0.816 0.048 0.012 0.124
#> GSM1068481 3 0.0290 0.95996 0.008 0.000 0.992 0.000 0.000
#> GSM1068482 3 0.0693 0.95806 0.008 0.000 0.980 0.000 0.012
#> GSM1068483 1 0.2445 0.81194 0.884 0.004 0.004 0.000 0.108
#> GSM1068486 3 0.0451 0.96002 0.004 0.000 0.988 0.000 0.008
#> GSM1068487 2 0.0510 0.76044 0.000 0.984 0.000 0.016 0.000
#> GSM1068488 4 0.3635 0.67988 0.000 0.000 0.004 0.748 0.248
#> GSM1068490 2 0.0510 0.76044 0.000 0.984 0.000 0.016 0.000
#> GSM1068491 3 0.2929 0.88307 0.000 0.012 0.856 0.004 0.128
#> GSM1068492 4 0.5837 0.55410 0.000 0.016 0.068 0.564 0.352
#> GSM1068493 5 0.5083 -0.23748 0.008 0.476 0.020 0.000 0.496
#> GSM1068494 5 0.7332 0.09750 0.248 0.000 0.036 0.272 0.444
#> GSM1068495 5 0.5183 0.53326 0.016 0.072 0.000 0.212 0.700
#> GSM1068496 1 0.5554 0.50617 0.568 0.000 0.068 0.004 0.360
#> GSM1068498 2 0.5176 0.17414 0.040 0.492 0.000 0.000 0.468
#> GSM1068499 1 0.6335 0.46146 0.528 0.000 0.144 0.008 0.320
#> GSM1068500 1 0.2445 0.81194 0.884 0.004 0.004 0.000 0.108
#> GSM1068502 2 0.4422 0.60505 0.000 0.776 0.068 0.012 0.144
#> GSM1068503 2 0.2561 0.64861 0.000 0.856 0.000 0.144 0.000
#> GSM1068505 4 0.1983 0.67816 0.008 0.008 0.000 0.924 0.060
#> GSM1068506 4 0.2616 0.63400 0.000 0.100 0.000 0.880 0.020
#> GSM1068507 4 0.5630 0.30318 0.004 0.288 0.000 0.612 0.096
#> GSM1068508 2 0.4499 0.56342 0.008 0.684 0.000 0.016 0.292
#> GSM1068510 4 0.4288 0.66294 0.000 0.032 0.004 0.740 0.224
#> GSM1068512 4 0.3741 0.66524 0.000 0.004 0.000 0.732 0.264
#> GSM1068513 2 0.2914 0.70538 0.000 0.872 0.000 0.052 0.076
#> GSM1068514 4 0.5406 0.58304 0.000 0.008 0.052 0.592 0.348
#> GSM1068517 2 0.5176 0.17414 0.040 0.492 0.000 0.000 0.468
#> GSM1068518 4 0.4899 0.34348 0.012 0.008 0.000 0.524 0.456
#> GSM1068520 1 0.0865 0.83319 0.972 0.004 0.000 0.000 0.024
#> GSM1068521 1 0.1908 0.82066 0.908 0.000 0.000 0.000 0.092
#> GSM1068522 2 0.4927 0.32588 0.000 0.652 0.000 0.296 0.052
#> GSM1068524 2 0.5144 0.45884 0.000 0.692 0.000 0.132 0.176
#> GSM1068527 4 0.5383 0.56945 0.084 0.004 0.000 0.644 0.268
#> GSM1068480 3 0.0865 0.95555 0.000 0.000 0.972 0.004 0.024
#> GSM1068484 4 0.3196 0.69084 0.000 0.004 0.000 0.804 0.192
#> GSM1068485 3 0.0324 0.95996 0.004 0.000 0.992 0.000 0.004
#> GSM1068489 4 0.1557 0.68670 0.000 0.008 0.000 0.940 0.052
#> GSM1068497 2 0.5176 0.17414 0.040 0.492 0.000 0.000 0.468
#> GSM1068501 4 0.3420 0.65079 0.000 0.036 0.004 0.836 0.124
#> GSM1068504 2 0.0510 0.76044 0.000 0.984 0.000 0.016 0.000
#> GSM1068509 1 0.4794 0.55902 0.624 0.000 0.000 0.032 0.344
#> GSM1068511 4 0.3883 0.68352 0.000 0.004 0.008 0.744 0.244
#> GSM1068515 1 0.4584 0.68999 0.732 0.032 0.000 0.016 0.220
#> GSM1068516 4 0.4585 0.48834 0.004 0.008 0.000 0.592 0.396
#> GSM1068519 1 0.2011 0.81656 0.908 0.000 0.004 0.000 0.088
#> GSM1068523 2 0.3857 0.54085 0.000 0.688 0.000 0.000 0.312
#> GSM1068525 4 0.3550 0.67241 0.000 0.004 0.000 0.760 0.236
#> GSM1068526 4 0.0798 0.69225 0.000 0.008 0.000 0.976 0.016
#> GSM1068458 1 0.0932 0.83254 0.972 0.004 0.000 0.004 0.020
#> GSM1068459 3 0.0693 0.95806 0.008 0.000 0.980 0.000 0.012
#> GSM1068460 1 0.1662 0.83155 0.936 0.004 0.000 0.004 0.056
#> GSM1068461 3 0.1041 0.95313 0.004 0.000 0.964 0.000 0.032
#> GSM1068464 2 0.0404 0.75994 0.000 0.988 0.000 0.012 0.000
#> GSM1068468 2 0.2604 0.73648 0.004 0.880 0.004 0.004 0.108
#> GSM1068472 2 0.2445 0.73397 0.000 0.884 0.004 0.004 0.108
#> GSM1068473 2 0.0510 0.76044 0.000 0.984 0.000 0.016 0.000
#> GSM1068474 2 0.0510 0.76044 0.000 0.984 0.000 0.016 0.000
#> GSM1068476 3 0.2881 0.88633 0.000 0.012 0.860 0.004 0.124
#> GSM1068477 2 0.1300 0.76048 0.000 0.956 0.000 0.016 0.028
#> GSM1068462 2 0.2945 0.71956 0.004 0.852 0.004 0.004 0.136
#> GSM1068463 3 0.0579 0.95867 0.008 0.000 0.984 0.000 0.008
#> GSM1068465 1 0.3607 0.72596 0.752 0.004 0.000 0.000 0.244
#> GSM1068466 1 0.0865 0.83319 0.972 0.004 0.000 0.000 0.024
#> GSM1068467 2 0.2497 0.73670 0.000 0.880 0.004 0.004 0.112
#> GSM1068469 2 0.3652 0.65735 0.012 0.784 0.004 0.000 0.200
#> GSM1068470 2 0.3055 0.71036 0.000 0.840 0.000 0.016 0.144
#> GSM1068471 2 0.0510 0.76044 0.000 0.984 0.000 0.016 0.000
#> GSM1068475 2 0.0510 0.76044 0.000 0.984 0.000 0.016 0.000
#> GSM1068528 1 0.5389 0.22131 0.508 0.000 0.436 0.000 0.056
#> GSM1068531 1 0.0162 0.83177 0.996 0.000 0.000 0.004 0.000
#> GSM1068532 1 0.0960 0.82799 0.972 0.000 0.008 0.004 0.016
#> GSM1068533 1 0.1016 0.82794 0.972 0.004 0.008 0.004 0.012
#> GSM1068535 4 0.3845 0.63529 0.060 0.000 0.004 0.812 0.124
#> GSM1068537 1 0.0740 0.82788 0.980 0.000 0.008 0.004 0.008
#> GSM1068538 1 0.0854 0.82791 0.976 0.000 0.008 0.004 0.012
#> GSM1068539 5 0.5269 0.51926 0.016 0.072 0.000 0.224 0.688
#> GSM1068540 1 0.1544 0.82366 0.932 0.000 0.000 0.000 0.068
#> GSM1068542 4 0.1087 0.69084 0.008 0.008 0.000 0.968 0.016
#> GSM1068543 4 0.3048 0.69547 0.000 0.004 0.000 0.820 0.176
#> GSM1068544 3 0.0693 0.95669 0.012 0.000 0.980 0.000 0.008
#> GSM1068545 4 0.5845 0.00992 0.000 0.352 0.000 0.540 0.108
#> GSM1068546 3 0.1365 0.94252 0.004 0.000 0.952 0.004 0.040
#> GSM1068547 1 0.0833 0.83357 0.976 0.004 0.000 0.004 0.016
#> GSM1068548 4 0.1200 0.69010 0.008 0.012 0.000 0.964 0.016
#> GSM1068549 3 0.1544 0.93391 0.000 0.000 0.932 0.000 0.068
#> GSM1068550 4 0.0898 0.69289 0.000 0.008 0.000 0.972 0.020
#> GSM1068551 2 0.2777 0.72557 0.000 0.864 0.000 0.016 0.120
#> GSM1068552 4 0.3304 0.55597 0.000 0.168 0.000 0.816 0.016
#> GSM1068555 2 0.3857 0.54085 0.000 0.688 0.000 0.000 0.312
#> GSM1068556 4 0.2763 0.70172 0.000 0.004 0.000 0.848 0.148
#> GSM1068557 2 0.4594 0.22106 0.004 0.508 0.004 0.000 0.484
#> GSM1068560 4 0.4478 0.53792 0.008 0.004 0.000 0.628 0.360
#> GSM1068561 5 0.5751 0.46513 0.008 0.240 0.000 0.120 0.632
#> GSM1068562 4 0.3196 0.69458 0.000 0.004 0.000 0.804 0.192
#> GSM1068563 4 0.3058 0.64668 0.000 0.096 0.000 0.860 0.044
#> GSM1068565 2 0.1701 0.75565 0.000 0.936 0.000 0.016 0.048
#> GSM1068529 4 0.4774 0.44283 0.000 0.004 0.012 0.540 0.444
#> GSM1068530 1 0.0324 0.83235 0.992 0.000 0.000 0.004 0.004
#> GSM1068534 4 0.3817 0.67131 0.000 0.004 0.004 0.740 0.252
#> GSM1068536 1 0.5508 0.38420 0.552 0.004 0.000 0.060 0.384
#> GSM1068541 5 0.7725 0.27707 0.056 0.256 0.000 0.324 0.364
#> GSM1068553 4 0.2770 0.66390 0.008 0.000 0.004 0.864 0.124
#> GSM1068554 4 0.3372 0.65016 0.000 0.036 0.004 0.840 0.120
#> GSM1068558 4 0.5304 0.57702 0.000 0.004 0.052 0.592 0.352
#> GSM1068559 4 0.6122 0.50212 0.000 0.004 0.124 0.528 0.344
#> GSM1068564 4 0.4548 0.44332 0.000 0.232 0.000 0.716 0.052
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1068478 5 0.4185 0.1635 0.332 0.000 0.000 0.020 0.644 0.004
#> GSM1068479 2 0.6030 0.4523 0.000 0.616 0.016 0.220 0.088 0.060
#> GSM1068481 3 0.0508 0.8939 0.000 0.000 0.984 0.012 0.004 0.000
#> GSM1068482 3 0.0458 0.8937 0.000 0.000 0.984 0.016 0.000 0.000
#> GSM1068483 1 0.4421 0.6334 0.684 0.000 0.004 0.056 0.256 0.000
#> GSM1068486 3 0.0260 0.8948 0.000 0.000 0.992 0.008 0.000 0.000
#> GSM1068487 2 0.0146 0.7351 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM1068488 6 0.1524 0.5387 0.000 0.000 0.000 0.060 0.008 0.932
#> GSM1068490 2 0.0000 0.7352 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1068491 3 0.5154 0.6998 0.000 0.008 0.672 0.208 0.096 0.016
#> GSM1068492 6 0.5961 0.4048 0.000 0.044 0.016 0.240 0.092 0.608
#> GSM1068493 5 0.6322 0.3953 0.000 0.284 0.012 0.048 0.544 0.112
#> GSM1068494 6 0.6219 0.3499 0.080 0.000 0.028 0.072 0.204 0.616
#> GSM1068495 5 0.4867 0.4466 0.008 0.028 0.000 0.024 0.644 0.296
#> GSM1068496 5 0.8381 0.2109 0.176 0.000 0.124 0.088 0.328 0.284
#> GSM1068498 5 0.4040 0.4081 0.032 0.280 0.000 0.000 0.688 0.000
#> GSM1068499 5 0.8596 0.1784 0.212 0.000 0.164 0.088 0.304 0.232
#> GSM1068500 1 0.4421 0.6334 0.684 0.000 0.004 0.056 0.256 0.000
#> GSM1068502 2 0.6335 0.4182 0.000 0.592 0.016 0.216 0.092 0.084
#> GSM1068503 2 0.2544 0.6511 0.000 0.852 0.000 0.140 0.004 0.004
#> GSM1068505 4 0.4379 0.6375 0.004 0.020 0.000 0.576 0.000 0.400
#> GSM1068506 4 0.5556 0.5770 0.000 0.056 0.000 0.488 0.036 0.420
#> GSM1068507 4 0.6532 0.4305 0.000 0.260 0.000 0.472 0.040 0.228
#> GSM1068508 2 0.4644 0.3590 0.004 0.584 0.000 0.024 0.380 0.008
#> GSM1068510 6 0.5724 -0.3965 0.000 0.052 0.000 0.424 0.052 0.472
#> GSM1068512 6 0.1950 0.5393 0.000 0.000 0.000 0.064 0.024 0.912
#> GSM1068513 2 0.3834 0.5922 0.000 0.768 0.000 0.184 0.036 0.012
#> GSM1068514 6 0.5084 0.4297 0.000 0.004 0.012 0.260 0.080 0.644
#> GSM1068517 5 0.3816 0.3885 0.016 0.296 0.000 0.000 0.688 0.000
#> GSM1068518 6 0.3789 0.5069 0.004 0.000 0.000 0.040 0.196 0.760
#> GSM1068520 1 0.1594 0.8350 0.932 0.000 0.000 0.016 0.052 0.000
#> GSM1068521 1 0.3332 0.7659 0.808 0.000 0.000 0.048 0.144 0.000
#> GSM1068522 2 0.4600 -0.0900 0.000 0.500 0.000 0.468 0.004 0.028
#> GSM1068524 2 0.4621 0.5336 0.000 0.724 0.000 0.016 0.112 0.148
#> GSM1068527 6 0.5432 0.4247 0.064 0.000 0.000 0.156 0.108 0.672
#> GSM1068480 3 0.2422 0.8697 0.000 0.000 0.896 0.052 0.040 0.012
#> GSM1068484 6 0.2261 0.4650 0.000 0.004 0.000 0.104 0.008 0.884
#> GSM1068485 3 0.0260 0.8945 0.000 0.000 0.992 0.008 0.000 0.000
#> GSM1068489 4 0.4010 0.6287 0.000 0.008 0.000 0.584 0.000 0.408
#> GSM1068497 5 0.3867 0.3869 0.012 0.296 0.000 0.004 0.688 0.000
#> GSM1068501 4 0.5633 0.5970 0.000 0.060 0.000 0.552 0.048 0.340
#> GSM1068504 2 0.0146 0.7351 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM1068509 6 0.7353 -0.1764 0.292 0.000 0.004 0.088 0.276 0.340
#> GSM1068511 6 0.2320 0.5372 0.000 0.000 0.004 0.080 0.024 0.892
#> GSM1068515 1 0.5133 0.3081 0.524 0.020 0.000 0.044 0.412 0.000
#> GSM1068516 6 0.2730 0.5348 0.000 0.000 0.000 0.012 0.152 0.836
#> GSM1068519 1 0.3521 0.7535 0.812 0.000 0.000 0.060 0.120 0.008
#> GSM1068523 2 0.4625 0.3781 0.000 0.604 0.000 0.020 0.356 0.020
#> GSM1068525 6 0.1257 0.5482 0.000 0.000 0.000 0.020 0.028 0.952
#> GSM1068526 6 0.4701 -0.5769 0.000 0.008 0.000 0.480 0.028 0.484
#> GSM1068458 1 0.1594 0.8366 0.932 0.000 0.000 0.016 0.052 0.000
#> GSM1068459 3 0.0405 0.8945 0.000 0.000 0.988 0.008 0.004 0.000
#> GSM1068460 1 0.2019 0.8282 0.900 0.000 0.000 0.012 0.088 0.000
#> GSM1068461 3 0.1003 0.8920 0.000 0.000 0.964 0.016 0.020 0.000
#> GSM1068464 2 0.0520 0.7342 0.000 0.984 0.000 0.008 0.008 0.000
#> GSM1068468 2 0.4077 0.5875 0.000 0.724 0.000 0.044 0.228 0.004
#> GSM1068472 2 0.3741 0.5991 0.000 0.756 0.000 0.032 0.208 0.004
#> GSM1068473 2 0.0146 0.7351 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM1068474 2 0.0000 0.7352 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1068476 3 0.5111 0.7035 0.000 0.008 0.672 0.208 0.100 0.012
#> GSM1068477 2 0.1858 0.7217 0.000 0.912 0.000 0.012 0.076 0.000
#> GSM1068462 2 0.4163 0.5744 0.000 0.716 0.000 0.048 0.232 0.004
#> GSM1068463 3 0.0508 0.8939 0.000 0.000 0.984 0.012 0.004 0.000
#> GSM1068465 1 0.4614 0.3923 0.548 0.000 0.000 0.032 0.416 0.004
#> GSM1068466 1 0.1719 0.8341 0.924 0.000 0.000 0.016 0.060 0.000
#> GSM1068467 2 0.3997 0.5963 0.000 0.736 0.000 0.044 0.216 0.004
#> GSM1068469 2 0.4493 0.3782 0.000 0.612 0.000 0.044 0.344 0.000
#> GSM1068470 2 0.3401 0.6217 0.000 0.776 0.000 0.016 0.204 0.004
#> GSM1068471 2 0.0000 0.7352 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1068475 2 0.0508 0.7343 0.000 0.984 0.000 0.004 0.012 0.000
#> GSM1068528 3 0.5422 0.4463 0.276 0.000 0.624 0.048 0.044 0.008
#> GSM1068531 1 0.0520 0.8382 0.984 0.000 0.000 0.008 0.008 0.000
#> GSM1068532 1 0.2221 0.8113 0.908 0.000 0.004 0.044 0.040 0.004
#> GSM1068533 1 0.0837 0.8389 0.972 0.000 0.004 0.020 0.004 0.000
#> GSM1068535 4 0.5633 0.5665 0.048 0.000 0.000 0.536 0.056 0.360
#> GSM1068537 1 0.1138 0.8329 0.960 0.000 0.004 0.012 0.024 0.000
#> GSM1068538 1 0.0767 0.8369 0.976 0.000 0.004 0.012 0.008 0.000
#> GSM1068539 5 0.4933 0.4244 0.008 0.028 0.000 0.024 0.628 0.312
#> GSM1068540 1 0.2595 0.7933 0.872 0.000 0.000 0.044 0.084 0.000
#> GSM1068542 4 0.4828 0.5505 0.004 0.008 0.000 0.492 0.028 0.468
#> GSM1068543 6 0.2432 0.4617 0.000 0.000 0.000 0.100 0.024 0.876
#> GSM1068544 3 0.0692 0.8873 0.004 0.000 0.976 0.020 0.000 0.000
#> GSM1068545 4 0.7367 0.3773 0.000 0.276 0.000 0.384 0.140 0.200
#> GSM1068546 3 0.1572 0.8824 0.000 0.000 0.936 0.036 0.028 0.000
#> GSM1068547 1 0.1007 0.8406 0.956 0.000 0.000 0.000 0.044 0.000
#> GSM1068548 4 0.5152 0.5521 0.008 0.016 0.000 0.488 0.032 0.456
#> GSM1068549 3 0.3063 0.8351 0.000 0.000 0.840 0.092 0.068 0.000
#> GSM1068550 6 0.4701 -0.5769 0.000 0.008 0.000 0.480 0.028 0.484
#> GSM1068551 2 0.3393 0.6312 0.000 0.784 0.000 0.020 0.192 0.004
#> GSM1068552 4 0.6206 0.5826 0.000 0.148 0.000 0.480 0.032 0.340
#> GSM1068555 2 0.4710 0.3661 0.000 0.596 0.000 0.020 0.360 0.024
#> GSM1068556 6 0.3394 0.2852 0.000 0.000 0.000 0.200 0.024 0.776
#> GSM1068557 5 0.4960 0.2949 0.000 0.336 0.000 0.020 0.600 0.044
#> GSM1068560 6 0.4832 0.4425 0.012 0.000 0.000 0.132 0.160 0.696
#> GSM1068561 5 0.5631 0.5152 0.004 0.104 0.000 0.028 0.608 0.256
#> GSM1068562 6 0.2831 0.4107 0.000 0.000 0.000 0.136 0.024 0.840
#> GSM1068563 6 0.5490 -0.4649 0.000 0.052 0.000 0.404 0.036 0.508
#> GSM1068565 2 0.1779 0.7199 0.000 0.920 0.000 0.016 0.064 0.000
#> GSM1068529 6 0.3862 0.5282 0.000 0.000 0.000 0.096 0.132 0.772
#> GSM1068530 1 0.0291 0.8399 0.992 0.000 0.000 0.004 0.004 0.000
#> GSM1068534 6 0.1408 0.5518 0.000 0.000 0.000 0.036 0.020 0.944
#> GSM1068536 5 0.5636 -0.0322 0.428 0.000 0.000 0.012 0.456 0.104
#> GSM1068541 5 0.6860 0.3684 0.044 0.108 0.000 0.232 0.548 0.068
#> GSM1068553 4 0.4939 0.5993 0.004 0.004 0.000 0.552 0.048 0.392
#> GSM1068554 4 0.5622 0.5950 0.000 0.060 0.000 0.556 0.048 0.336
#> GSM1068558 6 0.4288 0.5075 0.000 0.000 0.020 0.148 0.076 0.756
#> GSM1068559 6 0.5602 0.4277 0.000 0.000 0.080 0.216 0.068 0.636
#> GSM1068564 4 0.6313 0.5411 0.000 0.236 0.000 0.496 0.028 0.240
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 disease.state(p) gender(p) k
#> MAD:kmeans 105 0.77262 0.330 2
#> MAD:kmeans 86 0.85051 0.697 3
#> MAD:kmeans 103 0.00304 0.552 4
#> MAD:kmeans 88 0.00403 0.746 5
#> MAD:kmeans 69 0.00576 0.162 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 38950 rows and 108 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 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 0.674 0.817 0.927 0.5035 0.498 0.498
#> 3 3 0.479 0.539 0.745 0.3271 0.721 0.505
#> 4 4 0.854 0.818 0.919 0.1285 0.796 0.490
#> 5 5 0.740 0.629 0.769 0.0631 0.894 0.614
#> 6 6 0.722 0.547 0.754 0.0391 0.888 0.531
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
#> GSM1068478 1 0.6247 0.7796 0.844 0.156
#> GSM1068479 2 0.0000 0.9085 0.000 1.000
#> GSM1068481 1 0.0000 0.9214 1.000 0.000
#> GSM1068482 1 0.0000 0.9214 1.000 0.000
#> GSM1068483 1 0.0000 0.9214 1.000 0.000
#> GSM1068486 1 0.0000 0.9214 1.000 0.000
#> GSM1068487 2 0.0000 0.9085 0.000 1.000
#> GSM1068488 2 0.8081 0.6700 0.248 0.752
#> GSM1068490 2 0.0000 0.9085 0.000 1.000
#> GSM1068491 1 0.0376 0.9187 0.996 0.004
#> GSM1068492 2 0.1184 0.8997 0.016 0.984
#> GSM1068493 1 0.7453 0.7106 0.788 0.212
#> GSM1068494 1 0.0000 0.9214 1.000 0.000
#> GSM1068495 2 0.0376 0.9061 0.004 0.996
#> GSM1068496 1 0.0000 0.9214 1.000 0.000
#> GSM1068498 1 0.9710 0.3562 0.600 0.400
#> GSM1068499 1 0.0000 0.9214 1.000 0.000
#> GSM1068500 1 0.0000 0.9214 1.000 0.000
#> GSM1068502 2 0.0000 0.9085 0.000 1.000
#> GSM1068503 2 0.0000 0.9085 0.000 1.000
#> GSM1068505 2 0.0000 0.9085 0.000 1.000
#> GSM1068506 2 0.0000 0.9085 0.000 1.000
#> GSM1068507 2 0.5294 0.8190 0.120 0.880
#> GSM1068508 2 0.0000 0.9085 0.000 1.000
#> GSM1068510 2 0.0000 0.9085 0.000 1.000
#> GSM1068512 1 0.7950 0.6275 0.760 0.240
#> GSM1068513 2 0.0000 0.9085 0.000 1.000
#> GSM1068514 2 0.9710 0.3816 0.400 0.600
#> GSM1068517 2 0.9944 0.0943 0.456 0.544
#> GSM1068518 1 0.3879 0.8581 0.924 0.076
#> GSM1068520 1 0.0000 0.9214 1.000 0.000
#> GSM1068521 1 0.0000 0.9214 1.000 0.000
#> GSM1068522 2 0.0000 0.9085 0.000 1.000
#> GSM1068524 2 0.0000 0.9085 0.000 1.000
#> GSM1068527 1 0.9775 0.2111 0.588 0.412
#> GSM1068480 1 0.0000 0.9214 1.000 0.000
#> GSM1068484 2 0.0000 0.9085 0.000 1.000
#> GSM1068485 1 0.0000 0.9214 1.000 0.000
#> GSM1068489 2 0.0000 0.9085 0.000 1.000
#> GSM1068497 1 0.9710 0.3562 0.600 0.400
#> GSM1068501 2 0.0000 0.9085 0.000 1.000
#> GSM1068504 2 0.0000 0.9085 0.000 1.000
#> GSM1068509 1 0.0000 0.9214 1.000 0.000
#> GSM1068511 1 0.0000 0.9214 1.000 0.000
#> GSM1068515 1 0.6973 0.7414 0.812 0.188
#> GSM1068516 2 0.2948 0.8786 0.052 0.948
#> GSM1068519 1 0.0000 0.9214 1.000 0.000
#> GSM1068523 2 0.0000 0.9085 0.000 1.000
#> GSM1068525 2 0.0000 0.9085 0.000 1.000
#> GSM1068526 2 0.4022 0.8550 0.080 0.920
#> GSM1068458 1 0.0000 0.9214 1.000 0.000
#> GSM1068459 1 0.0000 0.9214 1.000 0.000
#> GSM1068460 1 0.0938 0.9143 0.988 0.012
#> GSM1068461 1 0.0000 0.9214 1.000 0.000
#> GSM1068464 2 0.0000 0.9085 0.000 1.000
#> GSM1068468 2 0.0938 0.9010 0.012 0.988
#> GSM1068472 2 0.8499 0.5671 0.276 0.724
#> GSM1068473 2 0.0000 0.9085 0.000 1.000
#> GSM1068474 2 0.0000 0.9085 0.000 1.000
#> GSM1068476 1 0.9998 -0.0816 0.508 0.492
#> GSM1068477 2 0.0000 0.9085 0.000 1.000
#> GSM1068462 2 0.9963 0.0654 0.464 0.536
#> GSM1068463 1 0.0000 0.9214 1.000 0.000
#> GSM1068465 1 0.7674 0.6940 0.776 0.224
#> GSM1068466 1 0.0000 0.9214 1.000 0.000
#> GSM1068467 2 0.0000 0.9085 0.000 1.000
#> GSM1068469 1 0.9710 0.3562 0.600 0.400
#> GSM1068470 2 0.0000 0.9085 0.000 1.000
#> GSM1068471 2 0.0000 0.9085 0.000 1.000
#> GSM1068475 2 0.0000 0.9085 0.000 1.000
#> GSM1068528 1 0.0000 0.9214 1.000 0.000
#> GSM1068531 1 0.0000 0.9214 1.000 0.000
#> GSM1068532 1 0.0000 0.9214 1.000 0.000
#> GSM1068533 1 0.0000 0.9214 1.000 0.000
#> GSM1068535 1 0.0000 0.9214 1.000 0.000
#> GSM1068537 1 0.0000 0.9214 1.000 0.000
#> GSM1068538 1 0.0000 0.9214 1.000 0.000
#> GSM1068539 2 0.0000 0.9085 0.000 1.000
#> GSM1068540 1 0.0000 0.9214 1.000 0.000
#> GSM1068542 2 0.3879 0.8584 0.076 0.924
#> GSM1068543 2 0.9686 0.3914 0.396 0.604
#> GSM1068544 1 0.0000 0.9214 1.000 0.000
#> GSM1068545 2 0.0000 0.9085 0.000 1.000
#> GSM1068546 1 0.0000 0.9214 1.000 0.000
#> GSM1068547 1 0.0000 0.9214 1.000 0.000
#> GSM1068548 2 0.7219 0.7333 0.200 0.800
#> GSM1068549 1 0.0000 0.9214 1.000 0.000
#> GSM1068550 2 0.0000 0.9085 0.000 1.000
#> GSM1068551 2 0.0000 0.9085 0.000 1.000
#> GSM1068552 2 0.0000 0.9085 0.000 1.000
#> GSM1068555 2 0.0000 0.9085 0.000 1.000
#> GSM1068556 2 0.9710 0.3816 0.400 0.600
#> GSM1068557 2 0.0000 0.9085 0.000 1.000
#> GSM1068560 2 0.7219 0.7333 0.200 0.800
#> GSM1068561 2 0.9833 0.1995 0.424 0.576
#> GSM1068562 2 0.4562 0.8425 0.096 0.904
#> GSM1068563 2 0.2043 0.8906 0.032 0.968
#> GSM1068565 2 0.0000 0.9085 0.000 1.000
#> GSM1068529 1 0.0000 0.9214 1.000 0.000
#> GSM1068530 1 0.0000 0.9214 1.000 0.000
#> GSM1068534 1 0.0000 0.9214 1.000 0.000
#> GSM1068536 1 0.1184 0.9102 0.984 0.016
#> GSM1068541 2 0.0000 0.9085 0.000 1.000
#> GSM1068553 2 0.9661 0.4009 0.392 0.608
#> GSM1068554 2 0.0000 0.9085 0.000 1.000
#> GSM1068558 2 0.7602 0.7085 0.220 0.780
#> GSM1068559 1 0.4690 0.8301 0.900 0.100
#> GSM1068564 2 0.0000 0.9085 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1068478 1 0.2537 0.7821 0.920 0.080 0.000
#> GSM1068479 3 0.6215 0.2515 0.000 0.428 0.572
#> GSM1068481 3 0.5948 0.4623 0.360 0.000 0.640
#> GSM1068482 3 0.5948 0.4623 0.360 0.000 0.640
#> GSM1068483 1 0.2711 0.7848 0.912 0.000 0.088
#> GSM1068486 3 0.5948 0.4623 0.360 0.000 0.640
#> GSM1068487 2 0.0000 0.7022 0.000 1.000 0.000
#> GSM1068488 3 0.3253 0.5737 0.036 0.052 0.912
#> GSM1068490 2 0.0000 0.7022 0.000 1.000 0.000
#> GSM1068491 3 0.6427 0.4662 0.348 0.012 0.640
#> GSM1068492 3 0.0892 0.6095 0.000 0.020 0.980
#> GSM1068493 3 0.7188 0.1038 0.024 0.488 0.488
#> GSM1068494 1 0.5859 0.3215 0.656 0.000 0.344
#> GSM1068495 2 0.7661 0.1317 0.452 0.504 0.044
#> GSM1068496 1 0.5785 0.4051 0.668 0.000 0.332
#> GSM1068498 2 0.6302 0.0712 0.480 0.520 0.000
#> GSM1068499 1 0.5560 0.4758 0.700 0.000 0.300
#> GSM1068500 1 0.3116 0.7658 0.892 0.000 0.108
#> GSM1068502 3 0.6235 0.2386 0.000 0.436 0.564
#> GSM1068503 2 0.0237 0.7017 0.000 0.996 0.004
#> GSM1068505 2 0.9224 0.3863 0.160 0.480 0.360
#> GSM1068506 2 0.6954 0.5013 0.028 0.620 0.352
#> GSM1068507 2 0.6880 0.5918 0.108 0.736 0.156
#> GSM1068508 2 0.0592 0.7000 0.012 0.988 0.000
#> GSM1068510 3 0.5988 0.1895 0.008 0.304 0.688
#> GSM1068512 3 0.2749 0.6070 0.064 0.012 0.924
#> GSM1068513 2 0.0000 0.7022 0.000 1.000 0.000
#> GSM1068514 3 0.0424 0.6083 0.008 0.000 0.992
#> GSM1068517 2 0.6302 0.0712 0.480 0.520 0.000
#> GSM1068518 3 0.6215 0.0736 0.428 0.000 0.572
#> GSM1068520 1 0.0000 0.8434 1.000 0.000 0.000
#> GSM1068521 1 0.0000 0.8434 1.000 0.000 0.000
#> GSM1068522 2 0.2066 0.6866 0.000 0.940 0.060
#> GSM1068524 2 0.1031 0.6978 0.000 0.976 0.024
#> GSM1068527 1 0.8143 0.1716 0.560 0.080 0.360
#> GSM1068480 3 0.5948 0.4623 0.360 0.000 0.640
#> GSM1068484 2 0.5968 0.5083 0.000 0.636 0.364
#> GSM1068485 3 0.5948 0.4623 0.360 0.000 0.640
#> GSM1068489 2 0.9076 0.3911 0.144 0.488 0.368
#> GSM1068497 2 0.6302 0.0712 0.480 0.520 0.000
#> GSM1068501 2 0.8212 0.4530 0.084 0.556 0.360
#> GSM1068504 2 0.0000 0.7022 0.000 1.000 0.000
#> GSM1068509 1 0.2165 0.8027 0.936 0.000 0.064
#> GSM1068511 3 0.1643 0.6114 0.044 0.000 0.956
#> GSM1068515 1 0.3267 0.7421 0.884 0.116 0.000
#> GSM1068516 3 0.6955 0.4520 0.172 0.100 0.728
#> GSM1068519 1 0.0424 0.8391 0.992 0.000 0.008
#> GSM1068523 2 0.0000 0.7022 0.000 1.000 0.000
#> GSM1068525 2 0.5988 0.5047 0.000 0.632 0.368
#> GSM1068526 2 0.8902 0.3692 0.124 0.480 0.396
#> GSM1068458 1 0.0000 0.8434 1.000 0.000 0.000
#> GSM1068459 3 0.5948 0.4623 0.360 0.000 0.640
#> GSM1068460 1 0.0592 0.8364 0.988 0.000 0.012
#> GSM1068461 3 0.5948 0.4623 0.360 0.000 0.640
#> GSM1068464 2 0.0000 0.7022 0.000 1.000 0.000
#> GSM1068468 2 0.4469 0.6320 0.076 0.864 0.060
#> GSM1068472 2 0.4556 0.6295 0.080 0.860 0.060
#> GSM1068473 2 0.0000 0.7022 0.000 1.000 0.000
#> GSM1068474 2 0.0000 0.7022 0.000 1.000 0.000
#> GSM1068476 3 0.5919 0.5206 0.276 0.012 0.712
#> GSM1068477 2 0.0000 0.7022 0.000 1.000 0.000
#> GSM1068462 2 0.5688 0.5465 0.044 0.788 0.168
#> GSM1068463 3 0.5948 0.4623 0.360 0.000 0.640
#> GSM1068465 1 0.5058 0.5473 0.756 0.244 0.000
#> GSM1068466 1 0.0000 0.8434 1.000 0.000 0.000
#> GSM1068467 2 0.4288 0.6369 0.068 0.872 0.060
#> GSM1068469 2 0.7784 0.2168 0.388 0.556 0.056
#> GSM1068470 2 0.0000 0.7022 0.000 1.000 0.000
#> GSM1068471 2 0.0000 0.7022 0.000 1.000 0.000
#> GSM1068475 2 0.0000 0.7022 0.000 1.000 0.000
#> GSM1068528 1 0.4399 0.6736 0.812 0.000 0.188
#> GSM1068531 1 0.0000 0.8434 1.000 0.000 0.000
#> GSM1068532 1 0.0424 0.8403 0.992 0.000 0.008
#> GSM1068533 1 0.0000 0.8434 1.000 0.000 0.000
#> GSM1068535 3 0.5450 0.4579 0.228 0.012 0.760
#> GSM1068537 1 0.0000 0.8434 1.000 0.000 0.000
#> GSM1068538 1 0.0000 0.8434 1.000 0.000 0.000
#> GSM1068539 2 0.7864 0.3989 0.332 0.596 0.072
#> GSM1068540 1 0.0000 0.8434 1.000 0.000 0.000
#> GSM1068542 2 0.9224 0.3863 0.160 0.480 0.360
#> GSM1068543 3 0.4945 0.5090 0.056 0.104 0.840
#> GSM1068544 1 0.6299 -0.0695 0.524 0.000 0.476
#> GSM1068545 2 0.4291 0.6308 0.000 0.820 0.180
#> GSM1068546 3 0.5948 0.4623 0.360 0.000 0.640
#> GSM1068547 1 0.0424 0.8391 0.992 0.000 0.008
#> GSM1068548 2 0.9224 0.3863 0.160 0.480 0.360
#> GSM1068549 3 0.5948 0.4623 0.360 0.000 0.640
#> GSM1068550 2 0.9224 0.3863 0.160 0.480 0.360
#> GSM1068551 2 0.0000 0.7022 0.000 1.000 0.000
#> GSM1068552 2 0.6448 0.5100 0.012 0.636 0.352
#> GSM1068555 2 0.0000 0.7022 0.000 1.000 0.000
#> GSM1068556 3 0.5835 0.4282 0.052 0.164 0.784
#> GSM1068557 2 0.3038 0.6359 0.000 0.896 0.104
#> GSM1068560 2 0.9883 0.2789 0.260 0.380 0.360
#> GSM1068561 2 0.9198 0.2198 0.280 0.528 0.192
#> GSM1068562 2 0.7995 0.3171 0.060 0.480 0.460
#> GSM1068563 3 0.7075 -0.3279 0.020 0.488 0.492
#> GSM1068565 2 0.0000 0.7022 0.000 1.000 0.000
#> GSM1068529 3 0.1289 0.6115 0.032 0.000 0.968
#> GSM1068530 1 0.0000 0.8434 1.000 0.000 0.000
#> GSM1068534 3 0.1643 0.6110 0.044 0.000 0.956
#> GSM1068536 1 0.1031 0.8262 0.976 0.000 0.024
#> GSM1068541 2 0.6337 0.5248 0.264 0.708 0.028
#> GSM1068553 3 0.7888 0.2807 0.140 0.196 0.664
#> GSM1068554 2 0.8039 0.3804 0.064 0.508 0.428
#> GSM1068558 3 0.0000 0.6063 0.000 0.000 1.000
#> GSM1068559 3 0.5291 0.5273 0.268 0.000 0.732
#> GSM1068564 2 0.5905 0.5170 0.000 0.648 0.352
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1068478 1 0.0524 0.9103 0.988 0.008 0.004 0.000
#> GSM1068479 3 0.4454 0.5454 0.000 0.308 0.692 0.000
#> GSM1068481 3 0.0188 0.9101 0.004 0.000 0.996 0.000
#> GSM1068482 3 0.0376 0.9097 0.004 0.000 0.992 0.004
#> GSM1068483 1 0.0336 0.9139 0.992 0.000 0.008 0.000
#> GSM1068486 3 0.0188 0.9101 0.004 0.000 0.996 0.000
#> GSM1068487 2 0.0336 0.9228 0.000 0.992 0.000 0.008
#> GSM1068488 4 0.2011 0.8494 0.000 0.000 0.080 0.920
#> GSM1068490 2 0.0336 0.9228 0.000 0.992 0.000 0.008
#> GSM1068491 3 0.0188 0.9101 0.004 0.000 0.996 0.000
#> GSM1068492 3 0.2319 0.8729 0.000 0.036 0.924 0.040
#> GSM1068493 2 0.4977 0.1685 0.000 0.540 0.460 0.000
#> GSM1068494 1 0.5792 0.2441 0.552 0.000 0.416 0.032
#> GSM1068495 2 0.7221 0.4600 0.236 0.568 0.004 0.192
#> GSM1068496 3 0.4916 0.2072 0.424 0.000 0.576 0.000
#> GSM1068498 2 0.3208 0.8054 0.148 0.848 0.004 0.000
#> GSM1068499 1 0.5155 0.1176 0.528 0.000 0.468 0.004
#> GSM1068500 1 0.1118 0.8932 0.964 0.000 0.036 0.000
#> GSM1068502 3 0.4072 0.6475 0.000 0.252 0.748 0.000
#> GSM1068503 2 0.3764 0.6949 0.000 0.784 0.000 0.216
#> GSM1068505 4 0.0188 0.9034 0.000 0.004 0.000 0.996
#> GSM1068506 4 0.1211 0.8903 0.000 0.040 0.000 0.960
#> GSM1068507 4 0.6279 0.1496 0.020 0.440 0.024 0.516
#> GSM1068508 2 0.0804 0.9202 0.008 0.980 0.000 0.012
#> GSM1068510 4 0.1637 0.8696 0.000 0.000 0.060 0.940
#> GSM1068512 3 0.5987 0.1626 0.040 0.000 0.520 0.440
#> GSM1068513 2 0.0592 0.9202 0.000 0.984 0.000 0.016
#> GSM1068514 3 0.1211 0.8953 0.000 0.000 0.960 0.040
#> GSM1068517 2 0.2197 0.8732 0.080 0.916 0.004 0.000
#> GSM1068518 4 0.7997 0.0471 0.328 0.004 0.272 0.396
#> GSM1068520 1 0.0000 0.9162 1.000 0.000 0.000 0.000
#> GSM1068521 1 0.0000 0.9162 1.000 0.000 0.000 0.000
#> GSM1068522 4 0.4585 0.5173 0.000 0.332 0.000 0.668
#> GSM1068524 2 0.2401 0.8661 0.000 0.904 0.004 0.092
#> GSM1068527 4 0.4713 0.4378 0.360 0.000 0.000 0.640
#> GSM1068480 3 0.0376 0.9097 0.004 0.000 0.992 0.004
#> GSM1068484 4 0.0188 0.9034 0.000 0.004 0.000 0.996
#> GSM1068485 3 0.0188 0.9101 0.004 0.000 0.996 0.000
#> GSM1068489 4 0.0188 0.9034 0.000 0.004 0.000 0.996
#> GSM1068497 2 0.2125 0.8765 0.076 0.920 0.004 0.000
#> GSM1068501 4 0.0000 0.9025 0.000 0.000 0.000 1.000
#> GSM1068504 2 0.0336 0.9228 0.000 0.992 0.000 0.008
#> GSM1068509 1 0.0000 0.9162 1.000 0.000 0.000 0.000
#> GSM1068511 3 0.3300 0.7974 0.008 0.000 0.848 0.144
#> GSM1068515 1 0.1970 0.8635 0.932 0.060 0.008 0.000
#> GSM1068516 4 0.1452 0.8846 0.000 0.008 0.036 0.956
#> GSM1068519 1 0.0000 0.9162 1.000 0.000 0.000 0.000
#> GSM1068523 2 0.0376 0.9215 0.000 0.992 0.004 0.004
#> GSM1068525 4 0.0336 0.9031 0.000 0.008 0.000 0.992
#> GSM1068526 4 0.0188 0.9034 0.000 0.004 0.000 0.996
#> GSM1068458 1 0.0336 0.9139 0.992 0.000 0.008 0.000
#> GSM1068459 3 0.0376 0.9097 0.004 0.000 0.992 0.004
#> GSM1068460 1 0.0000 0.9162 1.000 0.000 0.000 0.000
#> GSM1068461 3 0.0188 0.9101 0.004 0.000 0.996 0.000
#> GSM1068464 2 0.0336 0.9228 0.000 0.992 0.000 0.008
#> GSM1068468 2 0.0188 0.9212 0.000 0.996 0.004 0.000
#> GSM1068472 2 0.0188 0.9212 0.000 0.996 0.004 0.000
#> GSM1068473 2 0.0336 0.9228 0.000 0.992 0.000 0.008
#> GSM1068474 2 0.0336 0.9228 0.000 0.992 0.000 0.008
#> GSM1068476 3 0.0188 0.9101 0.004 0.000 0.996 0.000
#> GSM1068477 2 0.0000 0.9218 0.000 1.000 0.000 0.000
#> GSM1068462 2 0.0469 0.9186 0.000 0.988 0.012 0.000
#> GSM1068463 3 0.0188 0.9101 0.004 0.000 0.996 0.000
#> GSM1068465 1 0.0000 0.9162 1.000 0.000 0.000 0.000
#> GSM1068466 1 0.0000 0.9162 1.000 0.000 0.000 0.000
#> GSM1068467 2 0.0188 0.9212 0.000 0.996 0.004 0.000
#> GSM1068469 2 0.1356 0.9059 0.032 0.960 0.008 0.000
#> GSM1068470 2 0.0336 0.9225 0.000 0.992 0.000 0.008
#> GSM1068471 2 0.0336 0.9228 0.000 0.992 0.000 0.008
#> GSM1068475 2 0.0336 0.9228 0.000 0.992 0.000 0.008
#> GSM1068528 1 0.4543 0.5202 0.676 0.000 0.324 0.000
#> GSM1068531 1 0.0000 0.9162 1.000 0.000 0.000 0.000
#> GSM1068532 1 0.0188 0.9154 0.996 0.000 0.004 0.000
#> GSM1068533 1 0.0336 0.9139 0.992 0.000 0.008 0.000
#> GSM1068535 4 0.1042 0.8936 0.020 0.000 0.008 0.972
#> GSM1068537 1 0.0188 0.9154 0.996 0.000 0.004 0.000
#> GSM1068538 1 0.0336 0.9139 0.992 0.000 0.008 0.000
#> GSM1068539 2 0.6691 0.3999 0.096 0.580 0.004 0.320
#> GSM1068540 1 0.0000 0.9162 1.000 0.000 0.000 0.000
#> GSM1068542 4 0.0188 0.9034 0.000 0.004 0.000 0.996
#> GSM1068543 4 0.0188 0.9014 0.000 0.000 0.004 0.996
#> GSM1068544 3 0.2081 0.8459 0.084 0.000 0.916 0.000
#> GSM1068545 4 0.3528 0.7429 0.000 0.192 0.000 0.808
#> GSM1068546 3 0.0376 0.9097 0.004 0.000 0.992 0.004
#> GSM1068547 1 0.0000 0.9162 1.000 0.000 0.000 0.000
#> GSM1068548 4 0.0524 0.9027 0.004 0.008 0.000 0.988
#> GSM1068549 3 0.0188 0.9101 0.004 0.000 0.996 0.000
#> GSM1068550 4 0.0188 0.9034 0.000 0.004 0.000 0.996
#> GSM1068551 2 0.0469 0.9217 0.000 0.988 0.000 0.012
#> GSM1068552 4 0.1118 0.8923 0.000 0.036 0.000 0.964
#> GSM1068555 2 0.0376 0.9215 0.000 0.992 0.004 0.004
#> GSM1068556 4 0.0188 0.9014 0.000 0.000 0.004 0.996
#> GSM1068557 2 0.0469 0.9197 0.000 0.988 0.012 0.000
#> GSM1068560 4 0.2586 0.8408 0.092 0.004 0.004 0.900
#> GSM1068561 2 0.4413 0.7779 0.008 0.812 0.140 0.040
#> GSM1068562 4 0.0188 0.9034 0.000 0.004 0.000 0.996
#> GSM1068563 4 0.1474 0.8834 0.000 0.052 0.000 0.948
#> GSM1068565 2 0.0336 0.9228 0.000 0.992 0.000 0.008
#> GSM1068529 3 0.0592 0.9057 0.000 0.000 0.984 0.016
#> GSM1068530 1 0.0000 0.9162 1.000 0.000 0.000 0.000
#> GSM1068534 3 0.1022 0.9000 0.000 0.000 0.968 0.032
#> GSM1068536 1 0.0376 0.9124 0.992 0.004 0.004 0.000
#> GSM1068541 1 0.7449 0.1890 0.480 0.332 0.000 0.188
#> GSM1068553 4 0.0000 0.9025 0.000 0.000 0.000 1.000
#> GSM1068554 4 0.0188 0.9024 0.000 0.000 0.004 0.996
#> GSM1068558 3 0.0817 0.9027 0.000 0.000 0.976 0.024
#> GSM1068559 3 0.0000 0.9090 0.000 0.000 1.000 0.000
#> GSM1068564 4 0.1557 0.8809 0.000 0.056 0.000 0.944
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM1068478 1 0.4419 5.65e-01 0.668 0.020 0.000 0.000 0.312
#> GSM1068479 3 0.5948 5.83e-02 0.000 0.408 0.484 0.000 0.108
#> GSM1068481 3 0.0000 8.22e-01 0.000 0.000 1.000 0.000 0.000
#> GSM1068482 3 0.0162 8.21e-01 0.000 0.004 0.996 0.000 0.000
#> GSM1068483 1 0.0510 9.12e-01 0.984 0.000 0.016 0.000 0.000
#> GSM1068486 3 0.0290 8.22e-01 0.000 0.008 0.992 0.000 0.000
#> GSM1068487 2 0.4182 7.41e-01 0.000 0.600 0.000 0.000 0.400
#> GSM1068488 4 0.4986 6.72e-01 0.000 0.336 0.036 0.624 0.004
#> GSM1068490 2 0.4182 7.41e-01 0.000 0.600 0.000 0.000 0.400
#> GSM1068491 3 0.0609 8.20e-01 0.000 0.020 0.980 0.000 0.000
#> GSM1068492 3 0.4668 6.56e-01 0.000 0.276 0.688 0.028 0.008
#> GSM1068493 3 0.5604 3.42e-02 0.000 0.072 0.468 0.000 0.460
#> GSM1068494 2 0.8690 -4.01e-01 0.168 0.340 0.316 0.020 0.156
#> GSM1068495 5 0.5162 4.99e-01 0.028 0.256 0.000 0.036 0.680
#> GSM1068496 3 0.5543 5.01e-01 0.276 0.076 0.636 0.000 0.012
#> GSM1068498 5 0.1357 5.30e-01 0.048 0.004 0.000 0.000 0.948
#> GSM1068499 3 0.5264 5.24e-01 0.264 0.068 0.660 0.000 0.008
#> GSM1068500 1 0.1197 8.93e-01 0.952 0.000 0.048 0.000 0.000
#> GSM1068502 3 0.5601 6.92e-02 0.000 0.448 0.480 0.000 0.072
#> GSM1068503 2 0.6060 4.77e-01 0.000 0.576 0.000 0.208 0.216
#> GSM1068505 4 0.0290 7.99e-01 0.000 0.008 0.000 0.992 0.000
#> GSM1068506 4 0.1942 7.76e-01 0.000 0.068 0.000 0.920 0.012
#> GSM1068507 2 0.6287 2.12e-01 0.008 0.508 0.016 0.392 0.076
#> GSM1068508 5 0.4422 -8.40e-02 0.012 0.320 0.000 0.004 0.664
#> GSM1068510 4 0.4979 6.95e-01 0.000 0.228 0.028 0.708 0.036
#> GSM1068512 4 0.6590 4.78e-01 0.020 0.180 0.248 0.552 0.000
#> GSM1068513 2 0.5128 6.71e-01 0.000 0.604 0.000 0.052 0.344
#> GSM1068514 3 0.3134 7.75e-01 0.000 0.120 0.848 0.032 0.000
#> GSM1068517 5 0.0290 5.29e-01 0.008 0.000 0.000 0.000 0.992
#> GSM1068518 5 0.7844 1.82e-01 0.044 0.356 0.032 0.148 0.420
#> GSM1068520 1 0.0000 9.16e-01 1.000 0.000 0.000 0.000 0.000
#> GSM1068521 1 0.0963 9.02e-01 0.964 0.036 0.000 0.000 0.000
#> GSM1068522 2 0.5100 1.11e-01 0.000 0.516 0.000 0.448 0.036
#> GSM1068524 5 0.4585 2.17e-01 0.000 0.352 0.000 0.020 0.628
#> GSM1068527 4 0.6023 5.79e-01 0.176 0.248 0.000 0.576 0.000
#> GSM1068480 3 0.0609 8.19e-01 0.000 0.020 0.980 0.000 0.000
#> GSM1068484 4 0.3957 7.09e-01 0.000 0.280 0.000 0.712 0.008
#> GSM1068485 3 0.0000 8.22e-01 0.000 0.000 1.000 0.000 0.000
#> GSM1068489 4 0.0794 7.97e-01 0.000 0.028 0.000 0.972 0.000
#> GSM1068497 5 0.0671 5.30e-01 0.016 0.000 0.004 0.000 0.980
#> GSM1068501 4 0.2471 7.54e-01 0.000 0.136 0.000 0.864 0.000
#> GSM1068504 2 0.4182 7.41e-01 0.000 0.600 0.000 0.000 0.400
#> GSM1068509 1 0.3197 7.98e-01 0.832 0.152 0.012 0.000 0.004
#> GSM1068511 3 0.5876 4.80e-01 0.004 0.140 0.608 0.248 0.000
#> GSM1068515 1 0.1996 8.85e-01 0.932 0.008 0.016 0.004 0.040
#> GSM1068516 5 0.6489 1.39e-01 0.000 0.360 0.000 0.192 0.448
#> GSM1068519 1 0.1608 8.79e-01 0.928 0.072 0.000 0.000 0.000
#> GSM1068523 5 0.0963 5.12e-01 0.000 0.036 0.000 0.000 0.964
#> GSM1068525 4 0.5659 5.93e-01 0.000 0.320 0.000 0.580 0.100
#> GSM1068526 4 0.0162 8.00e-01 0.000 0.000 0.000 0.996 0.004
#> GSM1068458 1 0.0404 9.14e-01 0.988 0.000 0.012 0.000 0.000
#> GSM1068459 3 0.0162 8.21e-01 0.000 0.004 0.996 0.000 0.000
#> GSM1068460 1 0.0000 9.16e-01 1.000 0.000 0.000 0.000 0.000
#> GSM1068461 3 0.0404 8.21e-01 0.000 0.012 0.988 0.000 0.000
#> GSM1068464 2 0.4182 7.41e-01 0.000 0.600 0.000 0.000 0.400
#> GSM1068468 2 0.4287 6.88e-01 0.000 0.540 0.000 0.000 0.460
#> GSM1068472 2 0.4249 7.21e-01 0.000 0.568 0.000 0.000 0.432
#> GSM1068473 2 0.4321 7.39e-01 0.000 0.600 0.000 0.004 0.396
#> GSM1068474 2 0.4182 7.41e-01 0.000 0.600 0.000 0.000 0.400
#> GSM1068476 3 0.0609 8.20e-01 0.000 0.020 0.980 0.000 0.000
#> GSM1068477 2 0.4262 7.07e-01 0.000 0.560 0.000 0.000 0.440
#> GSM1068462 2 0.4434 6.80e-01 0.000 0.536 0.004 0.000 0.460
#> GSM1068463 3 0.0000 8.22e-01 0.000 0.000 1.000 0.000 0.000
#> GSM1068465 1 0.1041 9.01e-01 0.964 0.004 0.000 0.000 0.032
#> GSM1068466 1 0.0000 9.16e-01 1.000 0.000 0.000 0.000 0.000
#> GSM1068467 2 0.4294 6.78e-01 0.000 0.532 0.000 0.000 0.468
#> GSM1068469 5 0.4816 -6.39e-01 0.008 0.488 0.008 0.000 0.496
#> GSM1068470 5 0.3752 -3.23e-02 0.000 0.292 0.000 0.000 0.708
#> GSM1068471 2 0.4182 7.41e-01 0.000 0.600 0.000 0.000 0.400
#> GSM1068475 2 0.4182 7.41e-01 0.000 0.600 0.000 0.000 0.400
#> GSM1068528 1 0.4452 2.69e-05 0.500 0.000 0.496 0.000 0.004
#> GSM1068531 1 0.0000 9.16e-01 1.000 0.000 0.000 0.000 0.000
#> GSM1068532 1 0.0000 9.16e-01 1.000 0.000 0.000 0.000 0.000
#> GSM1068533 1 0.0404 9.14e-01 0.988 0.000 0.012 0.000 0.000
#> GSM1068535 4 0.2381 7.82e-01 0.052 0.036 0.004 0.908 0.000
#> GSM1068537 1 0.0000 9.16e-01 1.000 0.000 0.000 0.000 0.000
#> GSM1068538 1 0.0404 9.14e-01 0.988 0.000 0.012 0.000 0.000
#> GSM1068539 5 0.5252 4.77e-01 0.020 0.276 0.000 0.044 0.660
#> GSM1068540 1 0.0510 9.11e-01 0.984 0.016 0.000 0.000 0.000
#> GSM1068542 4 0.0000 8.00e-01 0.000 0.000 0.000 1.000 0.000
#> GSM1068543 4 0.3689 7.23e-01 0.000 0.256 0.000 0.740 0.004
#> GSM1068544 3 0.1121 8.04e-01 0.044 0.000 0.956 0.000 0.000
#> GSM1068545 4 0.5538 4.63e-01 0.000 0.144 0.000 0.644 0.212
#> GSM1068546 3 0.0290 8.22e-01 0.000 0.008 0.992 0.000 0.000
#> GSM1068547 1 0.0000 9.16e-01 1.000 0.000 0.000 0.000 0.000
#> GSM1068548 4 0.0579 8.00e-01 0.008 0.008 0.000 0.984 0.000
#> GSM1068549 3 0.0609 8.20e-01 0.000 0.020 0.980 0.000 0.000
#> GSM1068550 4 0.0510 8.01e-01 0.000 0.016 0.000 0.984 0.000
#> GSM1068551 5 0.3932 -1.58e-01 0.000 0.328 0.000 0.000 0.672
#> GSM1068552 4 0.1877 7.78e-01 0.000 0.064 0.000 0.924 0.012
#> GSM1068555 5 0.1043 5.07e-01 0.000 0.040 0.000 0.000 0.960
#> GSM1068556 4 0.3039 7.53e-01 0.000 0.192 0.000 0.808 0.000
#> GSM1068557 5 0.0404 5.22e-01 0.000 0.012 0.000 0.000 0.988
#> GSM1068560 4 0.7337 3.13e-01 0.032 0.312 0.000 0.416 0.240
#> GSM1068561 5 0.3826 5.24e-01 0.004 0.180 0.020 0.004 0.792
#> GSM1068562 4 0.4040 7.09e-01 0.000 0.276 0.000 0.712 0.012
#> GSM1068563 4 0.2864 7.72e-01 0.000 0.136 0.000 0.852 0.012
#> GSM1068565 2 0.4291 6.70e-01 0.000 0.536 0.000 0.000 0.464
#> GSM1068529 3 0.5193 6.17e-01 0.000 0.272 0.660 0.008 0.060
#> GSM1068530 1 0.0000 9.16e-01 1.000 0.000 0.000 0.000 0.000
#> GSM1068534 3 0.4618 6.74e-01 0.000 0.208 0.724 0.068 0.000
#> GSM1068536 1 0.4401 5.26e-01 0.656 0.016 0.000 0.000 0.328
#> GSM1068541 5 0.7541 9.72e-02 0.368 0.056 0.000 0.192 0.384
#> GSM1068553 4 0.0963 7.96e-01 0.000 0.036 0.000 0.964 0.000
#> GSM1068554 4 0.2377 7.56e-01 0.000 0.128 0.000 0.872 0.000
#> GSM1068558 3 0.4456 6.78e-01 0.000 0.248 0.716 0.004 0.032
#> GSM1068559 3 0.0794 8.21e-01 0.000 0.028 0.972 0.000 0.000
#> GSM1068564 4 0.2624 7.47e-01 0.000 0.116 0.000 0.872 0.012
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1068478 5 0.3950 0.0150 0.432 0.000 0.000 0.000 0.564 0.004
#> GSM1068479 2 0.5656 0.4356 0.000 0.596 0.260 0.004 0.020 0.120
#> GSM1068481 3 0.0405 0.8034 0.000 0.000 0.988 0.000 0.004 0.008
#> GSM1068482 3 0.0405 0.8028 0.000 0.000 0.988 0.000 0.004 0.008
#> GSM1068483 1 0.2202 0.8779 0.908 0.000 0.028 0.000 0.052 0.012
#> GSM1068486 3 0.0508 0.8040 0.000 0.000 0.984 0.000 0.004 0.012
#> GSM1068487 2 0.0405 0.7376 0.000 0.988 0.000 0.000 0.008 0.004
#> GSM1068488 6 0.3630 0.3130 0.000 0.000 0.020 0.196 0.012 0.772
#> GSM1068490 2 0.0291 0.7381 0.000 0.992 0.000 0.000 0.004 0.004
#> GSM1068491 3 0.2253 0.7658 0.000 0.004 0.896 0.004 0.012 0.084
#> GSM1068492 6 0.6504 0.1353 0.000 0.128 0.368 0.032 0.016 0.456
#> GSM1068493 5 0.6165 0.1986 0.000 0.116 0.392 0.000 0.452 0.040
#> GSM1068494 6 0.6919 0.3558 0.096 0.000 0.184 0.008 0.200 0.512
#> GSM1068495 5 0.3699 0.5569 0.012 0.000 0.000 0.032 0.780 0.176
#> GSM1068496 3 0.6042 0.4278 0.196 0.000 0.600 0.000 0.072 0.132
#> GSM1068498 5 0.2581 0.7115 0.020 0.120 0.000 0.000 0.860 0.000
#> GSM1068499 3 0.5384 0.4811 0.212 0.000 0.652 0.000 0.044 0.092
#> GSM1068500 1 0.3561 0.7911 0.812 0.000 0.120 0.000 0.056 0.012
#> GSM1068502 2 0.5889 0.3769 0.000 0.556 0.272 0.004 0.016 0.152
#> GSM1068503 2 0.2294 0.7006 0.000 0.896 0.000 0.076 0.020 0.008
#> GSM1068505 4 0.2949 0.5936 0.000 0.008 0.000 0.848 0.028 0.116
#> GSM1068506 4 0.2240 0.6071 0.000 0.044 0.000 0.908 0.016 0.032
#> GSM1068507 2 0.6694 0.3148 0.016 0.532 0.000 0.204 0.052 0.196
#> GSM1068508 2 0.5302 -0.0549 0.000 0.472 0.000 0.068 0.448 0.012
#> GSM1068510 6 0.6392 -0.2433 0.000 0.088 0.008 0.380 0.060 0.464
#> GSM1068512 6 0.6013 0.2103 0.020 0.000 0.088 0.396 0.016 0.480
#> GSM1068513 2 0.4650 0.5680 0.000 0.720 0.000 0.040 0.052 0.188
#> GSM1068514 6 0.4772 -0.0334 0.000 0.004 0.452 0.012 0.020 0.512
#> GSM1068517 5 0.2431 0.7106 0.008 0.132 0.000 0.000 0.860 0.000
#> GSM1068518 6 0.5777 0.3708 0.016 0.000 0.020 0.096 0.276 0.592
#> GSM1068520 1 0.0291 0.9117 0.992 0.000 0.000 0.000 0.004 0.004
#> GSM1068521 1 0.1320 0.8986 0.948 0.000 0.000 0.000 0.036 0.016
#> GSM1068522 2 0.5614 0.2127 0.000 0.548 0.000 0.344 0.036 0.072
#> GSM1068524 2 0.5645 0.1796 0.000 0.552 0.000 0.008 0.288 0.152
#> GSM1068527 4 0.6644 -0.0240 0.180 0.000 0.000 0.436 0.052 0.332
#> GSM1068480 3 0.0692 0.8033 0.000 0.000 0.976 0.000 0.004 0.020
#> GSM1068484 6 0.4262 0.1252 0.000 0.012 0.000 0.424 0.004 0.560
#> GSM1068485 3 0.0146 0.8041 0.000 0.000 0.996 0.000 0.000 0.004
#> GSM1068489 4 0.3377 0.5573 0.000 0.000 0.000 0.784 0.028 0.188
#> GSM1068497 5 0.2362 0.7081 0.004 0.136 0.000 0.000 0.860 0.000
#> GSM1068501 4 0.5960 0.3977 0.000 0.096 0.000 0.552 0.052 0.300
#> GSM1068504 2 0.0000 0.7387 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1068509 1 0.4297 0.7284 0.752 0.000 0.032 0.000 0.048 0.168
#> GSM1068511 3 0.6317 -0.2789 0.000 0.000 0.392 0.244 0.012 0.352
#> GSM1068515 1 0.3494 0.8106 0.828 0.012 0.016 0.012 0.124 0.008
#> GSM1068516 6 0.5439 0.1428 0.004 0.000 0.000 0.104 0.408 0.484
#> GSM1068519 1 0.2721 0.8482 0.868 0.000 0.004 0.000 0.040 0.088
#> GSM1068523 5 0.4029 0.5946 0.000 0.292 0.000 0.000 0.680 0.028
#> GSM1068525 6 0.4989 0.3033 0.000 0.008 0.000 0.312 0.072 0.608
#> GSM1068526 4 0.1196 0.6115 0.000 0.008 0.000 0.952 0.000 0.040
#> GSM1068458 1 0.0146 0.9122 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM1068459 3 0.0260 0.8039 0.000 0.000 0.992 0.000 0.000 0.008
#> GSM1068460 1 0.0508 0.9102 0.984 0.000 0.000 0.000 0.012 0.004
#> GSM1068461 3 0.1082 0.7958 0.000 0.000 0.956 0.000 0.004 0.040
#> GSM1068464 2 0.0717 0.7374 0.000 0.976 0.000 0.000 0.008 0.016
#> GSM1068468 2 0.3351 0.6782 0.000 0.800 0.000 0.000 0.160 0.040
#> GSM1068472 2 0.2956 0.6932 0.000 0.840 0.000 0.000 0.120 0.040
#> GSM1068473 2 0.0405 0.7376 0.000 0.988 0.000 0.000 0.008 0.004
#> GSM1068474 2 0.0000 0.7387 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1068476 3 0.2255 0.7647 0.000 0.000 0.892 0.004 0.016 0.088
#> GSM1068477 2 0.2006 0.7104 0.000 0.892 0.000 0.004 0.104 0.000
#> GSM1068462 2 0.4013 0.6558 0.000 0.768 0.016 0.000 0.164 0.052
#> GSM1068463 3 0.0260 0.8039 0.000 0.000 0.992 0.000 0.000 0.008
#> GSM1068465 1 0.2907 0.8101 0.828 0.000 0.000 0.000 0.152 0.020
#> GSM1068466 1 0.0146 0.9122 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM1068467 2 0.3054 0.6882 0.000 0.828 0.000 0.000 0.136 0.036
#> GSM1068469 2 0.4184 0.5976 0.000 0.720 0.008 0.000 0.228 0.044
#> GSM1068470 2 0.3934 0.2694 0.000 0.616 0.000 0.008 0.376 0.000
#> GSM1068471 2 0.0000 0.7387 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1068475 2 0.0458 0.7366 0.000 0.984 0.000 0.000 0.016 0.000
#> GSM1068528 3 0.3954 0.4986 0.292 0.000 0.688 0.000 0.012 0.008
#> GSM1068531 1 0.0146 0.9123 0.996 0.000 0.004 0.000 0.000 0.000
#> GSM1068532 1 0.1053 0.9049 0.964 0.000 0.012 0.000 0.004 0.020
#> GSM1068533 1 0.0146 0.9123 0.996 0.000 0.004 0.000 0.000 0.000
#> GSM1068535 4 0.5853 0.4144 0.068 0.000 0.008 0.572 0.048 0.304
#> GSM1068537 1 0.0291 0.9120 0.992 0.000 0.004 0.000 0.000 0.004
#> GSM1068538 1 0.0291 0.9120 0.992 0.000 0.004 0.000 0.000 0.004
#> GSM1068539 5 0.3788 0.5575 0.012 0.004 0.000 0.024 0.772 0.188
#> GSM1068540 1 0.1498 0.8925 0.940 0.000 0.000 0.000 0.028 0.032
#> GSM1068542 4 0.0837 0.6155 0.000 0.004 0.000 0.972 0.004 0.020
#> GSM1068543 6 0.4096 0.0376 0.000 0.000 0.000 0.484 0.008 0.508
#> GSM1068544 3 0.1226 0.7835 0.040 0.000 0.952 0.000 0.004 0.004
#> GSM1068545 4 0.4265 0.5089 0.000 0.120 0.000 0.756 0.112 0.012
#> GSM1068546 3 0.1088 0.7956 0.000 0.000 0.960 0.000 0.016 0.024
#> GSM1068547 1 0.0146 0.9122 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM1068548 4 0.2038 0.6097 0.028 0.020 0.000 0.920 0.000 0.032
#> GSM1068549 3 0.1901 0.7766 0.000 0.000 0.912 0.004 0.008 0.076
#> GSM1068550 4 0.1542 0.6111 0.000 0.004 0.000 0.936 0.008 0.052
#> GSM1068551 2 0.3883 0.3673 0.000 0.656 0.000 0.012 0.332 0.000
#> GSM1068552 4 0.2207 0.6032 0.000 0.076 0.000 0.900 0.008 0.016
#> GSM1068555 5 0.4045 0.5603 0.000 0.312 0.000 0.000 0.664 0.024
#> GSM1068556 4 0.3841 0.1469 0.000 0.000 0.000 0.616 0.004 0.380
#> GSM1068557 5 0.3720 0.6233 0.000 0.236 0.000 0.000 0.736 0.028
#> GSM1068560 4 0.6301 -0.1668 0.020 0.000 0.000 0.400 0.192 0.388
#> GSM1068561 5 0.4047 0.6511 0.000 0.084 0.004 0.000 0.760 0.152
#> GSM1068562 4 0.4199 0.0668 0.000 0.000 0.000 0.568 0.016 0.416
#> GSM1068563 4 0.3809 0.5328 0.000 0.064 0.000 0.796 0.016 0.124
#> GSM1068565 2 0.2006 0.7004 0.000 0.892 0.000 0.004 0.104 0.000
#> GSM1068529 6 0.5318 0.2546 0.000 0.000 0.384 0.008 0.084 0.524
#> GSM1068530 1 0.0000 0.9122 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1068534 3 0.5750 -0.1912 0.000 0.000 0.456 0.092 0.024 0.428
#> GSM1068536 1 0.4697 0.1890 0.548 0.000 0.000 0.000 0.404 0.048
#> GSM1068541 4 0.7408 -0.0473 0.184 0.080 0.000 0.360 0.356 0.020
#> GSM1068553 4 0.4313 0.4819 0.000 0.000 0.000 0.668 0.048 0.284
#> GSM1068554 4 0.5851 0.4125 0.000 0.088 0.000 0.568 0.052 0.292
#> GSM1068558 6 0.4922 0.2237 0.000 0.000 0.400 0.016 0.036 0.548
#> GSM1068559 3 0.2162 0.7677 0.000 0.000 0.896 0.004 0.012 0.088
#> GSM1068564 4 0.3201 0.5715 0.000 0.148 0.000 0.820 0.008 0.024
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) gender(p) k
#> MAD:skmeans 96 0.837025 1.000 2
#> MAD:skmeans 65 0.312401 0.732 3
#> MAD:skmeans 97 0.005424 0.979 4
#> MAD:skmeans 86 0.000385 0.997 5
#> MAD:skmeans 70 0.003139 0.967 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 38950 rows and 108 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 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 0.338 0.391 0.713 0.4639 0.587 0.587
#> 3 3 0.331 0.485 0.763 0.3312 0.468 0.291
#> 4 4 0.455 0.514 0.740 0.1661 0.727 0.396
#> 5 5 0.581 0.458 0.704 0.0850 0.848 0.495
#> 6 6 0.678 0.667 0.819 0.0422 0.921 0.658
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
#> GSM1068478 1 0.0000 0.53992 1.000 0.000
#> GSM1068479 1 0.9993 0.45080 0.516 0.484
#> GSM1068481 1 0.0000 0.53992 1.000 0.000
#> GSM1068482 1 0.0000 0.53992 1.000 0.000
#> GSM1068483 1 0.0000 0.53992 1.000 0.000
#> GSM1068486 1 0.0000 0.53992 1.000 0.000
#> GSM1068487 1 0.9993 0.45080 0.516 0.484
#> GSM1068488 1 1.0000 -0.62871 0.504 0.496
#> GSM1068490 1 0.9993 0.45080 0.516 0.484
#> GSM1068491 1 0.0000 0.53992 1.000 0.000
#> GSM1068492 1 0.6887 0.49709 0.816 0.184
#> GSM1068493 1 0.2778 0.53667 0.952 0.048
#> GSM1068494 1 0.0672 0.53346 0.992 0.008
#> GSM1068495 1 0.3274 0.53252 0.940 0.060
#> GSM1068496 1 0.0000 0.53992 1.000 0.000
#> GSM1068498 1 0.9993 0.45080 0.516 0.484
#> GSM1068499 1 0.0000 0.53992 1.000 0.000
#> GSM1068500 1 0.0000 0.53992 1.000 0.000
#> GSM1068502 1 0.9993 0.45080 0.516 0.484
#> GSM1068503 2 0.6247 0.14703 0.156 0.844
#> GSM1068505 2 0.7139 0.50291 0.196 0.804
#> GSM1068506 2 0.9993 0.63457 0.484 0.516
#> GSM1068507 1 0.9977 0.27302 0.528 0.472
#> GSM1068508 2 1.0000 -0.45779 0.496 0.504
#> GSM1068510 2 0.1414 0.35615 0.020 0.980
#> GSM1068512 1 1.0000 -0.63309 0.500 0.500
#> GSM1068513 2 0.9129 -0.21971 0.328 0.672
#> GSM1068514 2 0.9977 0.61391 0.472 0.528
#> GSM1068517 1 0.9993 0.45080 0.516 0.484
#> GSM1068518 1 0.2043 0.53614 0.968 0.032
#> GSM1068520 1 0.1414 0.52204 0.980 0.020
#> GSM1068521 1 0.0376 0.53713 0.996 0.004
#> GSM1068522 2 0.0000 0.37095 0.000 1.000
#> GSM1068524 1 0.9996 0.44883 0.512 0.488
#> GSM1068527 2 0.9993 0.63457 0.484 0.516
#> GSM1068480 1 0.0000 0.53992 1.000 0.000
#> GSM1068484 2 0.7219 0.50512 0.200 0.800
#> GSM1068485 1 0.0000 0.53992 1.000 0.000
#> GSM1068489 2 0.9954 0.63063 0.460 0.540
#> GSM1068497 1 0.9993 0.45080 0.516 0.484
#> GSM1068501 2 0.0000 0.37095 0.000 1.000
#> GSM1068504 1 0.9993 0.45080 0.516 0.484
#> GSM1068509 1 0.7950 0.00786 0.760 0.240
#> GSM1068511 2 1.0000 0.61683 0.500 0.500
#> GSM1068515 1 0.0000 0.53992 1.000 0.000
#> GSM1068516 1 0.9909 -0.49373 0.556 0.444
#> GSM1068519 2 0.9993 0.63457 0.484 0.516
#> GSM1068523 1 0.9996 0.44883 0.512 0.488
#> GSM1068525 2 0.7950 0.48250 0.240 0.760
#> GSM1068526 2 0.9993 0.63457 0.484 0.516
#> GSM1068458 1 0.1633 0.51440 0.976 0.024
#> GSM1068459 1 0.0000 0.53992 1.000 0.000
#> GSM1068460 1 0.9608 -0.34995 0.616 0.384
#> GSM1068461 1 0.0000 0.53992 1.000 0.000
#> GSM1068464 1 0.9993 0.45080 0.516 0.484
#> GSM1068468 1 0.9993 0.45080 0.516 0.484
#> GSM1068472 1 0.9993 0.45080 0.516 0.484
#> GSM1068473 1 0.9996 0.44751 0.512 0.488
#> GSM1068474 1 0.9993 0.45080 0.516 0.484
#> GSM1068476 1 0.4939 0.52000 0.892 0.108
#> GSM1068477 1 0.9993 0.45080 0.516 0.484
#> GSM1068462 1 0.9993 0.45080 0.516 0.484
#> GSM1068463 1 0.0000 0.53992 1.000 0.000
#> GSM1068465 1 0.9896 -0.53994 0.560 0.440
#> GSM1068466 1 0.5408 0.33727 0.876 0.124
#> GSM1068467 1 0.9993 0.45080 0.516 0.484
#> GSM1068469 1 0.9993 0.45080 0.516 0.484
#> GSM1068470 1 0.9993 0.45080 0.516 0.484
#> GSM1068471 1 0.9993 0.45080 0.516 0.484
#> GSM1068475 1 0.9993 0.45080 0.516 0.484
#> GSM1068528 1 0.0000 0.53992 1.000 0.000
#> GSM1068531 1 1.0000 -0.62871 0.504 0.496
#> GSM1068532 2 1.0000 0.61567 0.500 0.500
#> GSM1068533 1 0.9977 -0.59573 0.528 0.472
#> GSM1068535 2 0.9993 0.63457 0.484 0.516
#> GSM1068537 2 0.9993 0.63457 0.484 0.516
#> GSM1068538 2 0.9993 0.63457 0.484 0.516
#> GSM1068539 1 0.8608 0.46787 0.716 0.284
#> GSM1068540 1 0.0376 0.53713 0.996 0.004
#> GSM1068542 2 0.9970 0.63290 0.468 0.532
#> GSM1068543 2 0.9993 0.63457 0.484 0.516
#> GSM1068544 1 0.0000 0.53992 1.000 0.000
#> GSM1068545 2 0.8861 0.35899 0.304 0.696
#> GSM1068546 1 1.0000 -0.62871 0.504 0.496
#> GSM1068547 1 0.5737 0.32645 0.864 0.136
#> GSM1068548 2 0.9993 0.63457 0.484 0.516
#> GSM1068549 1 0.0672 0.53357 0.992 0.008
#> GSM1068550 2 0.9922 0.62662 0.448 0.552
#> GSM1068551 1 0.9996 0.44883 0.512 0.488
#> GSM1068552 2 0.9248 0.57680 0.340 0.660
#> GSM1068555 1 0.9993 0.45080 0.516 0.484
#> GSM1068556 2 0.9993 0.63457 0.484 0.516
#> GSM1068557 1 0.9909 0.45297 0.556 0.444
#> GSM1068560 1 0.1633 0.51893 0.976 0.024
#> GSM1068561 1 0.3584 0.53259 0.932 0.068
#> GSM1068562 1 0.2423 0.52459 0.960 0.040
#> GSM1068563 2 0.9993 0.63457 0.484 0.516
#> GSM1068565 1 0.9993 0.45080 0.516 0.484
#> GSM1068529 1 0.0376 0.53713 0.996 0.004
#> GSM1068530 1 0.8081 -0.00680 0.752 0.248
#> GSM1068534 1 1.0000 -0.62871 0.504 0.496
#> GSM1068536 1 0.0376 0.53713 0.996 0.004
#> GSM1068541 1 0.5178 0.42443 0.884 0.116
#> GSM1068553 2 0.9993 0.63457 0.484 0.516
#> GSM1068554 2 0.0000 0.37095 0.000 1.000
#> GSM1068558 1 0.0376 0.53713 0.996 0.004
#> GSM1068559 1 0.1633 0.53749 0.976 0.024
#> GSM1068564 2 0.0000 0.37095 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1068478 1 0.2165 0.53869 0.936 0.000 0.064
#> GSM1068479 2 0.5138 0.47756 0.252 0.748 0.000
#> GSM1068481 3 0.5737 0.58580 0.256 0.012 0.732
#> GSM1068482 3 0.5216 0.59131 0.260 0.000 0.740
#> GSM1068483 1 0.6359 0.09343 0.628 0.008 0.364
#> GSM1068486 3 0.5737 0.58580 0.256 0.012 0.732
#> GSM1068487 2 0.0000 0.75661 0.000 1.000 0.000
#> GSM1068488 3 0.2866 0.60471 0.076 0.008 0.916
#> GSM1068490 2 0.0000 0.75661 0.000 1.000 0.000
#> GSM1068491 3 0.5404 0.59080 0.256 0.004 0.740
#> GSM1068492 2 0.6291 0.07526 0.000 0.532 0.468
#> GSM1068493 3 0.9423 0.24786 0.304 0.204 0.492
#> GSM1068494 3 0.5529 0.56673 0.296 0.000 0.704
#> GSM1068495 1 0.8220 0.45668 0.636 0.212 0.152
#> GSM1068496 3 0.5291 0.58708 0.268 0.000 0.732
#> GSM1068498 1 0.2261 0.54902 0.932 0.068 0.000
#> GSM1068499 3 0.5291 0.58767 0.268 0.000 0.732
#> GSM1068500 3 0.6286 0.33384 0.464 0.000 0.536
#> GSM1068502 2 0.4121 0.61431 0.000 0.832 0.168
#> GSM1068503 2 0.0000 0.75661 0.000 1.000 0.000
#> GSM1068505 3 0.8581 -0.21694 0.096 0.448 0.456
#> GSM1068506 3 0.6174 0.48700 0.064 0.168 0.768
#> GSM1068507 2 0.4002 0.63369 0.000 0.840 0.160
#> GSM1068508 2 0.3941 0.67723 0.156 0.844 0.000
#> GSM1068510 2 0.3337 0.72456 0.060 0.908 0.032
#> GSM1068512 3 0.0000 0.61882 0.000 0.000 1.000
#> GSM1068513 2 0.0000 0.75661 0.000 1.000 0.000
#> GSM1068514 3 0.5560 0.42672 0.000 0.300 0.700
#> GSM1068517 1 0.5016 0.43390 0.760 0.240 0.000
#> GSM1068518 3 0.5618 0.58896 0.260 0.008 0.732
#> GSM1068520 1 0.2625 0.53692 0.916 0.000 0.084
#> GSM1068521 1 0.2165 0.53869 0.936 0.000 0.064
#> GSM1068522 2 0.7015 0.55116 0.064 0.696 0.240
#> GSM1068524 2 0.0000 0.75661 0.000 1.000 0.000
#> GSM1068527 3 0.2959 0.59456 0.100 0.000 0.900
#> GSM1068480 3 0.5216 0.59131 0.260 0.000 0.740
#> GSM1068484 2 0.8068 0.22556 0.064 0.480 0.456
#> GSM1068485 3 0.5216 0.59131 0.260 0.000 0.740
#> GSM1068489 3 0.5285 0.54236 0.064 0.112 0.824
#> GSM1068497 1 0.5988 0.24353 0.632 0.368 0.000
#> GSM1068501 2 0.8025 0.35135 0.064 0.516 0.420
#> GSM1068504 2 0.0000 0.75661 0.000 1.000 0.000
#> GSM1068509 3 0.4887 0.61077 0.228 0.000 0.772
#> GSM1068511 3 0.0747 0.62260 0.016 0.000 0.984
#> GSM1068515 3 0.8674 0.41682 0.296 0.136 0.568
#> GSM1068516 3 0.6283 0.59133 0.176 0.064 0.760
#> GSM1068519 3 0.5098 0.44074 0.248 0.000 0.752
#> GSM1068523 2 0.5138 0.58641 0.252 0.748 0.000
#> GSM1068525 2 0.4605 0.57167 0.000 0.796 0.204
#> GSM1068526 3 0.6349 0.49810 0.092 0.140 0.768
#> GSM1068458 1 0.5968 -0.02066 0.636 0.000 0.364
#> GSM1068459 3 0.5216 0.59131 0.260 0.000 0.740
#> GSM1068460 1 0.5465 0.28619 0.712 0.000 0.288
#> GSM1068461 3 0.5737 0.58580 0.256 0.012 0.732
#> GSM1068464 2 0.0000 0.75661 0.000 1.000 0.000
#> GSM1068468 1 0.6274 0.04235 0.544 0.456 0.000
#> GSM1068472 2 0.4452 0.58527 0.192 0.808 0.000
#> GSM1068473 2 0.0000 0.75661 0.000 1.000 0.000
#> GSM1068474 2 0.0000 0.75661 0.000 1.000 0.000
#> GSM1068476 3 0.8803 0.41306 0.320 0.136 0.544
#> GSM1068477 1 0.6260 0.06234 0.552 0.448 0.000
#> GSM1068462 2 0.2066 0.73050 0.060 0.940 0.000
#> GSM1068463 3 0.5216 0.59131 0.260 0.000 0.740
#> GSM1068465 3 0.5913 0.51663 0.144 0.068 0.788
#> GSM1068466 1 0.1753 0.52440 0.952 0.000 0.048
#> GSM1068467 2 0.3192 0.69096 0.112 0.888 0.000
#> GSM1068469 1 0.6295 -0.02654 0.528 0.472 0.000
#> GSM1068470 2 0.5621 0.51332 0.308 0.692 0.000
#> GSM1068471 2 0.0000 0.75661 0.000 1.000 0.000
#> GSM1068475 2 0.0000 0.75661 0.000 1.000 0.000
#> GSM1068528 3 0.6215 0.40417 0.428 0.000 0.572
#> GSM1068531 3 0.6267 0.18473 0.452 0.000 0.548
#> GSM1068532 3 0.0747 0.62325 0.016 0.000 0.984
#> GSM1068533 3 0.6215 0.20228 0.428 0.000 0.572
#> GSM1068535 3 0.2165 0.59991 0.064 0.000 0.936
#> GSM1068537 3 0.5835 0.27124 0.340 0.000 0.660
#> GSM1068538 3 0.5785 0.28465 0.332 0.000 0.668
#> GSM1068539 1 0.5859 0.27241 0.656 0.344 0.000
#> GSM1068540 1 0.6008 0.07485 0.628 0.000 0.372
#> GSM1068542 3 0.6229 0.48258 0.064 0.172 0.764
#> GSM1068543 3 0.2165 0.59991 0.064 0.000 0.936
#> GSM1068544 1 0.6267 -0.16662 0.548 0.000 0.452
#> GSM1068545 2 0.5778 0.57478 0.032 0.768 0.200
#> GSM1068546 3 0.1411 0.62181 0.036 0.000 0.964
#> GSM1068547 1 0.5058 0.32535 0.756 0.000 0.244
#> GSM1068548 3 0.1964 0.61087 0.056 0.000 0.944
#> GSM1068549 3 0.5216 0.59131 0.260 0.000 0.740
#> GSM1068550 3 0.6119 0.49276 0.064 0.164 0.772
#> GSM1068551 2 0.5650 0.41494 0.312 0.688 0.000
#> GSM1068552 3 0.7748 0.21462 0.064 0.340 0.596
#> GSM1068555 2 0.5621 0.51332 0.308 0.692 0.000
#> GSM1068556 3 0.0237 0.61847 0.004 0.000 0.996
#> GSM1068557 2 0.6490 0.41599 0.256 0.708 0.036
#> GSM1068560 3 0.7767 0.44111 0.412 0.052 0.536
#> GSM1068561 1 0.9625 -0.00961 0.408 0.204 0.388
#> GSM1068562 3 0.5397 0.59894 0.280 0.000 0.720
#> GSM1068563 3 0.2165 0.59991 0.064 0.000 0.936
#> GSM1068565 2 0.0237 0.75542 0.004 0.996 0.000
#> GSM1068529 3 0.5216 0.59131 0.260 0.000 0.740
#> GSM1068530 1 0.6235 0.06266 0.564 0.000 0.436
#> GSM1068534 3 0.0983 0.62260 0.016 0.004 0.980
#> GSM1068536 3 0.6252 0.37184 0.444 0.000 0.556
#> GSM1068541 3 0.8765 0.41295 0.252 0.168 0.580
#> GSM1068553 3 0.2165 0.59991 0.064 0.000 0.936
#> GSM1068554 2 0.7267 0.52425 0.064 0.668 0.268
#> GSM1068558 3 0.7830 0.51086 0.136 0.196 0.668
#> GSM1068559 3 0.5728 0.59211 0.272 0.008 0.720
#> GSM1068564 2 0.7091 0.54426 0.064 0.688 0.248
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1068478 1 0.2704 0.47485 0.876 0.000 0.124 0.000
#> GSM1068479 2 0.3400 0.66578 0.000 0.820 0.180 0.000
#> GSM1068481 3 0.0707 0.62603 0.000 0.020 0.980 0.000
#> GSM1068482 3 0.3649 0.67858 0.000 0.000 0.796 0.204
#> GSM1068483 1 0.7112 0.42107 0.624 0.020 0.192 0.164
#> GSM1068486 3 0.0707 0.62603 0.000 0.020 0.980 0.000
#> GSM1068487 2 0.0000 0.75229 0.000 1.000 0.000 0.000
#> GSM1068488 4 0.4761 0.07850 0.000 0.000 0.372 0.628
#> GSM1068490 2 0.0000 0.75229 0.000 1.000 0.000 0.000
#> GSM1068491 3 0.0336 0.63305 0.000 0.008 0.992 0.000
#> GSM1068492 3 0.7375 0.39880 0.000 0.336 0.488 0.176
#> GSM1068493 2 0.8728 -0.03416 0.064 0.400 0.368 0.168
#> GSM1068494 3 0.5334 0.62053 0.036 0.000 0.680 0.284
#> GSM1068495 1 0.8153 -0.18484 0.448 0.340 0.188 0.024
#> GSM1068496 3 0.5109 0.65175 0.052 0.000 0.736 0.212
#> GSM1068498 1 0.0000 0.52660 1.000 0.000 0.000 0.000
#> GSM1068499 3 0.4175 0.67401 0.012 0.000 0.776 0.212
#> GSM1068500 1 0.7830 -0.01476 0.408 0.008 0.396 0.188
#> GSM1068502 2 0.4761 0.25607 0.000 0.628 0.372 0.000
#> GSM1068503 2 0.0000 0.75229 0.000 1.000 0.000 0.000
#> GSM1068505 4 0.2011 0.71584 0.000 0.080 0.000 0.920
#> GSM1068506 4 0.1118 0.74297 0.000 0.000 0.036 0.964
#> GSM1068507 2 0.3616 0.65676 0.000 0.852 0.036 0.112
#> GSM1068508 2 0.3123 0.69991 0.156 0.844 0.000 0.000
#> GSM1068510 2 0.5256 0.27495 0.000 0.596 0.012 0.392
#> GSM1068512 3 0.4866 0.47217 0.000 0.000 0.596 0.404
#> GSM1068513 2 0.0000 0.75229 0.000 1.000 0.000 0.000
#> GSM1068514 3 0.7007 0.53353 0.000 0.208 0.580 0.212
#> GSM1068517 1 0.7159 -0.00930 0.548 0.272 0.180 0.000
#> GSM1068518 3 0.4175 0.67512 0.000 0.012 0.776 0.212
#> GSM1068520 1 0.0000 0.52660 1.000 0.000 0.000 0.000
#> GSM1068521 1 0.0000 0.52660 1.000 0.000 0.000 0.000
#> GSM1068522 4 0.3764 0.59447 0.000 0.216 0.000 0.784
#> GSM1068524 2 0.0707 0.74626 0.000 0.980 0.000 0.020
#> GSM1068527 4 0.0000 0.74901 0.000 0.000 0.000 1.000
#> GSM1068480 3 0.3726 0.67732 0.000 0.000 0.788 0.212
#> GSM1068484 4 0.2081 0.71441 0.000 0.084 0.000 0.916
#> GSM1068485 3 0.0000 0.63680 0.000 0.000 1.000 0.000
#> GSM1068489 4 0.0000 0.74901 0.000 0.000 0.000 1.000
#> GSM1068497 1 0.7475 -0.24247 0.448 0.372 0.180 0.000
#> GSM1068501 4 0.3726 0.59709 0.000 0.212 0.000 0.788
#> GSM1068504 2 0.0000 0.75229 0.000 1.000 0.000 0.000
#> GSM1068509 4 0.4741 0.32741 0.004 0.000 0.328 0.668
#> GSM1068511 3 0.4898 0.45137 0.000 0.000 0.584 0.416
#> GSM1068515 4 0.7544 -0.08019 0.000 0.200 0.340 0.460
#> GSM1068516 4 0.3356 0.63013 0.000 0.000 0.176 0.824
#> GSM1068519 4 0.1211 0.73495 0.040 0.000 0.000 0.960
#> GSM1068523 2 0.4103 0.63281 0.256 0.744 0.000 0.000
#> GSM1068525 2 0.4586 0.59502 0.000 0.796 0.068 0.136
#> GSM1068526 4 0.2011 0.71948 0.000 0.000 0.080 0.920
#> GSM1068458 1 0.6975 0.38359 0.560 0.000 0.148 0.292
#> GSM1068459 3 0.0000 0.63680 0.000 0.000 1.000 0.000
#> GSM1068460 4 0.4312 0.64152 0.132 0.000 0.056 0.812
#> GSM1068461 3 0.0707 0.62603 0.000 0.020 0.980 0.000
#> GSM1068464 2 0.0000 0.75229 0.000 1.000 0.000 0.000
#> GSM1068468 2 0.7369 0.40133 0.324 0.496 0.180 0.000
#> GSM1068472 2 0.3172 0.68294 0.000 0.840 0.160 0.000
#> GSM1068473 2 0.0000 0.75229 0.000 1.000 0.000 0.000
#> GSM1068474 2 0.0000 0.75229 0.000 1.000 0.000 0.000
#> GSM1068476 3 0.3266 0.50742 0.000 0.000 0.832 0.168
#> GSM1068477 2 0.7449 0.35522 0.356 0.464 0.180 0.000
#> GSM1068462 2 0.1557 0.74173 0.000 0.944 0.056 0.000
#> GSM1068463 3 0.0000 0.63680 0.000 0.000 1.000 0.000
#> GSM1068465 1 0.7120 0.32793 0.564 0.000 0.224 0.212
#> GSM1068466 1 0.1471 0.52862 0.960 0.004 0.012 0.024
#> GSM1068467 2 0.2334 0.72853 0.004 0.908 0.088 0.000
#> GSM1068469 2 0.7421 0.36119 0.356 0.468 0.176 0.000
#> GSM1068470 2 0.4776 0.51554 0.376 0.624 0.000 0.000
#> GSM1068471 2 0.0000 0.75229 0.000 1.000 0.000 0.000
#> GSM1068475 2 0.0469 0.74999 0.012 0.988 0.000 0.000
#> GSM1068528 3 0.7362 0.00756 0.372 0.000 0.464 0.164
#> GSM1068531 1 0.4967 0.21201 0.548 0.000 0.000 0.452
#> GSM1068532 3 0.6883 0.51237 0.192 0.000 0.596 0.212
#> GSM1068533 1 0.6476 0.41355 0.616 0.000 0.272 0.112
#> GSM1068535 4 0.0188 0.74869 0.000 0.000 0.004 0.996
#> GSM1068537 1 0.6850 0.38184 0.600 0.000 0.212 0.188
#> GSM1068538 1 0.7196 0.34923 0.552 0.000 0.212 0.236
#> GSM1068539 1 0.8262 -0.19953 0.448 0.340 0.180 0.032
#> GSM1068540 1 0.6634 0.40832 0.624 0.000 0.212 0.164
#> GSM1068542 4 0.1792 0.72788 0.000 0.000 0.068 0.932
#> GSM1068543 4 0.1792 0.72788 0.000 0.000 0.068 0.932
#> GSM1068544 3 0.4661 0.06801 0.348 0.000 0.652 0.000
#> GSM1068545 2 0.4699 0.51344 0.004 0.676 0.000 0.320
#> GSM1068546 3 0.4697 0.24871 0.000 0.000 0.644 0.356
#> GSM1068547 1 0.4514 0.51632 0.796 0.000 0.056 0.148
#> GSM1068548 4 0.3945 0.53205 0.004 0.000 0.216 0.780
#> GSM1068549 3 0.0000 0.63680 0.000 0.000 1.000 0.000
#> GSM1068550 4 0.0000 0.74901 0.000 0.000 0.000 1.000
#> GSM1068551 2 0.4949 0.65196 0.060 0.760 0.180 0.000
#> GSM1068552 4 0.1913 0.74699 0.000 0.020 0.040 0.940
#> GSM1068555 2 0.4761 0.52007 0.372 0.628 0.000 0.000
#> GSM1068556 4 0.4103 0.46777 0.000 0.000 0.256 0.744
#> GSM1068557 2 0.5417 0.62637 0.000 0.732 0.180 0.088
#> GSM1068560 4 0.4277 0.44896 0.000 0.000 0.280 0.720
#> GSM1068561 2 0.9178 0.08194 0.140 0.392 0.340 0.128
#> GSM1068562 3 0.4382 0.63488 0.000 0.000 0.704 0.296
#> GSM1068563 4 0.2149 0.71829 0.000 0.000 0.088 0.912
#> GSM1068565 2 0.0707 0.74776 0.020 0.980 0.000 0.000
#> GSM1068529 3 0.3837 0.67467 0.000 0.000 0.776 0.224
#> GSM1068530 1 0.5906 0.47423 0.700 0.000 0.148 0.152
#> GSM1068534 3 0.4877 0.46570 0.000 0.000 0.592 0.408
#> GSM1068536 3 0.7629 -0.02707 0.392 0.000 0.404 0.204
#> GSM1068541 4 0.8985 -0.18169 0.060 0.240 0.336 0.364
#> GSM1068553 4 0.0000 0.74901 0.000 0.000 0.000 1.000
#> GSM1068554 4 0.3726 0.59709 0.000 0.212 0.000 0.788
#> GSM1068558 3 0.7007 0.52201 0.000 0.208 0.580 0.212
#> GSM1068559 3 0.4053 0.67481 0.000 0.004 0.768 0.228
#> GSM1068564 4 0.3764 0.59447 0.000 0.216 0.000 0.784
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM1068478 1 0.3452 0.24505 0.756 0.000 0.244 0.000 0.000
#> GSM1068479 2 0.4182 0.40520 0.000 0.644 0.352 0.000 0.004
#> GSM1068481 3 0.4307 0.28778 0.000 0.000 0.504 0.000 0.496
#> GSM1068482 5 0.0794 0.31695 0.000 0.000 0.028 0.000 0.972
#> GSM1068483 1 0.4604 0.46855 0.584 0.008 0.004 0.000 0.404
#> GSM1068486 5 0.4307 -0.34717 0.000 0.000 0.496 0.000 0.504
#> GSM1068487 2 0.0000 0.78202 0.000 1.000 0.000 0.000 0.000
#> GSM1068488 4 0.3123 0.69887 0.000 0.000 0.012 0.828 0.160
#> GSM1068490 2 0.0162 0.78103 0.000 0.996 0.004 0.000 0.000
#> GSM1068491 3 0.2966 0.24164 0.000 0.000 0.816 0.000 0.184
#> GSM1068492 2 0.4632 0.17002 0.000 0.540 0.012 0.000 0.448
#> GSM1068493 5 0.7181 0.30836 0.044 0.152 0.384 0.000 0.420
#> GSM1068494 5 0.5143 0.49422 0.000 0.000 0.368 0.048 0.584
#> GSM1068495 3 0.6190 0.26845 0.412 0.044 0.496 0.000 0.048
#> GSM1068496 5 0.4060 0.49999 0.000 0.000 0.360 0.000 0.640
#> GSM1068498 1 0.2389 0.42200 0.880 0.004 0.116 0.000 0.000
#> GSM1068499 5 0.4171 0.48912 0.000 0.000 0.396 0.000 0.604
#> GSM1068500 5 0.6108 0.43875 0.136 0.000 0.356 0.000 0.508
#> GSM1068502 2 0.2997 0.66694 0.000 0.840 0.012 0.000 0.148
#> GSM1068503 2 0.0000 0.78202 0.000 1.000 0.000 0.000 0.000
#> GSM1068505 4 0.0290 0.83118 0.000 0.000 0.000 0.992 0.008
#> GSM1068506 4 0.2605 0.75687 0.000 0.000 0.000 0.852 0.148
#> GSM1068507 2 0.2848 0.67439 0.000 0.840 0.004 0.000 0.156
#> GSM1068508 2 0.2690 0.71032 0.156 0.844 0.000 0.000 0.000
#> GSM1068510 4 0.4455 0.17940 0.000 0.404 0.000 0.588 0.008
#> GSM1068512 5 0.3999 0.31859 0.000 0.000 0.000 0.344 0.656
#> GSM1068513 2 0.0162 0.78103 0.000 0.996 0.000 0.000 0.004
#> GSM1068514 5 0.4444 0.27819 0.000 0.364 0.012 0.000 0.624
#> GSM1068517 3 0.5596 0.25291 0.444 0.052 0.496 0.000 0.008
#> GSM1068518 5 0.4196 0.49676 0.000 0.004 0.356 0.000 0.640
#> GSM1068520 1 0.0000 0.51726 1.000 0.000 0.000 0.000 0.000
#> GSM1068521 1 0.0290 0.52161 0.992 0.000 0.000 0.008 0.000
#> GSM1068522 4 0.0609 0.82607 0.000 0.020 0.000 0.980 0.000
#> GSM1068524 2 0.0404 0.77842 0.000 0.988 0.000 0.012 0.000
#> GSM1068527 4 0.0703 0.82694 0.000 0.000 0.000 0.976 0.024
#> GSM1068480 5 0.0290 0.33744 0.000 0.000 0.008 0.000 0.992
#> GSM1068484 4 0.0579 0.83054 0.000 0.008 0.000 0.984 0.008
#> GSM1068485 3 0.4278 0.29942 0.000 0.000 0.548 0.000 0.452
#> GSM1068489 4 0.0290 0.83118 0.000 0.000 0.000 0.992 0.008
#> GSM1068497 3 0.5795 0.27914 0.412 0.092 0.496 0.000 0.000
#> GSM1068501 4 0.0290 0.82901 0.000 0.008 0.000 0.992 0.000
#> GSM1068504 2 0.0000 0.78202 0.000 1.000 0.000 0.000 0.000
#> GSM1068509 5 0.6699 0.37995 0.000 0.000 0.268 0.304 0.428
#> GSM1068511 5 0.3999 0.31859 0.000 0.000 0.000 0.344 0.656
#> GSM1068515 3 0.6644 -0.30067 0.000 0.008 0.460 0.176 0.356
#> GSM1068516 4 0.5304 0.45494 0.000 0.000 0.088 0.640 0.272
#> GSM1068519 4 0.0290 0.82800 0.000 0.000 0.000 0.992 0.008
#> GSM1068523 2 0.3612 0.61617 0.268 0.732 0.000 0.000 0.000
#> GSM1068525 2 0.3177 0.61739 0.000 0.792 0.000 0.000 0.208
#> GSM1068526 4 0.3336 0.66473 0.000 0.000 0.000 0.772 0.228
#> GSM1068458 1 0.5396 0.50208 0.588 0.000 0.000 0.072 0.340
#> GSM1068459 3 0.4307 0.28429 0.000 0.000 0.500 0.000 0.500
#> GSM1068460 4 0.2605 0.69637 0.000 0.000 0.148 0.852 0.000
#> GSM1068461 3 0.4304 0.29147 0.000 0.000 0.516 0.000 0.484
#> GSM1068464 2 0.0000 0.78202 0.000 1.000 0.000 0.000 0.000
#> GSM1068468 3 0.6465 0.27146 0.376 0.184 0.440 0.000 0.000
#> GSM1068472 2 0.3424 0.58791 0.000 0.760 0.240 0.000 0.000
#> GSM1068473 2 0.0162 0.78103 0.000 0.996 0.004 0.000 0.000
#> GSM1068474 2 0.0000 0.78202 0.000 1.000 0.000 0.000 0.000
#> GSM1068476 3 0.2674 0.24065 0.000 0.000 0.856 0.004 0.140
#> GSM1068477 3 0.6110 0.28268 0.396 0.128 0.476 0.000 0.000
#> GSM1068462 2 0.1732 0.74746 0.000 0.920 0.080 0.000 0.000
#> GSM1068463 5 0.4306 -0.34534 0.000 0.000 0.492 0.000 0.508
#> GSM1068465 5 0.4450 -0.31137 0.488 0.000 0.004 0.000 0.508
#> GSM1068466 1 0.0865 0.52843 0.972 0.000 0.000 0.024 0.004
#> GSM1068467 2 0.2971 0.68588 0.008 0.836 0.156 0.000 0.000
#> GSM1068469 3 0.6519 0.23025 0.400 0.192 0.408 0.000 0.000
#> GSM1068470 2 0.5915 0.29723 0.412 0.484 0.104 0.000 0.000
#> GSM1068471 2 0.0000 0.78202 0.000 1.000 0.000 0.000 0.000
#> GSM1068475 2 0.0000 0.78202 0.000 1.000 0.000 0.000 0.000
#> GSM1068528 3 0.5740 -0.27906 0.112 0.000 0.580 0.000 0.308
#> GSM1068531 1 0.4268 0.21163 0.556 0.000 0.000 0.444 0.000
#> GSM1068532 5 0.4639 0.00138 0.344 0.000 0.012 0.008 0.636
#> GSM1068533 1 0.6179 0.50256 0.588 0.000 0.128 0.016 0.268
#> GSM1068535 4 0.0510 0.83001 0.000 0.000 0.000 0.984 0.016
#> GSM1068537 1 0.4455 0.47828 0.588 0.000 0.000 0.008 0.404
#> GSM1068538 1 0.4455 0.47828 0.588 0.000 0.000 0.008 0.404
#> GSM1068539 3 0.6128 0.27801 0.412 0.076 0.496 0.008 0.008
#> GSM1068540 1 0.4455 0.47828 0.588 0.000 0.000 0.008 0.404
#> GSM1068542 4 0.3109 0.70346 0.000 0.000 0.000 0.800 0.200
#> GSM1068543 4 0.3109 0.70346 0.000 0.000 0.000 0.800 0.200
#> GSM1068544 3 0.5989 0.27682 0.128 0.000 0.536 0.000 0.336
#> GSM1068545 2 0.4747 0.42999 0.000 0.620 0.000 0.352 0.028
#> GSM1068546 3 0.5454 0.28028 0.000 0.000 0.488 0.060 0.452
#> GSM1068547 1 0.3565 0.58453 0.800 0.000 0.000 0.024 0.176
#> GSM1068548 4 0.4307 -0.02437 0.000 0.000 0.000 0.504 0.496
#> GSM1068549 3 0.4307 0.28670 0.000 0.000 0.504 0.000 0.496
#> GSM1068550 4 0.0290 0.83118 0.000 0.000 0.000 0.992 0.008
#> GSM1068551 2 0.5320 0.32136 0.060 0.572 0.368 0.000 0.000
#> GSM1068552 4 0.2439 0.77886 0.000 0.004 0.000 0.876 0.120
#> GSM1068555 2 0.5519 0.34886 0.412 0.520 0.068 0.000 0.000
#> GSM1068556 5 0.4304 -0.00230 0.000 0.000 0.000 0.484 0.516
#> GSM1068557 2 0.6653 0.13794 0.000 0.420 0.384 0.192 0.004
#> GSM1068560 3 0.6792 -0.29601 0.000 0.000 0.372 0.340 0.288
#> GSM1068561 3 0.7809 -0.24354 0.084 0.188 0.384 0.000 0.344
#> GSM1068562 5 0.5518 0.47456 0.000 0.000 0.384 0.072 0.544
#> GSM1068563 4 0.3177 0.70206 0.000 0.000 0.000 0.792 0.208
#> GSM1068565 2 0.0162 0.78113 0.004 0.996 0.000 0.000 0.000
#> GSM1068529 5 0.4238 0.49773 0.000 0.000 0.368 0.004 0.628
#> GSM1068530 1 0.4201 0.54165 0.664 0.000 0.000 0.008 0.328
#> GSM1068534 5 0.3999 0.31859 0.000 0.000 0.000 0.344 0.656
#> GSM1068536 5 0.5329 0.44194 0.052 0.000 0.432 0.000 0.516
#> GSM1068541 5 0.8224 0.37126 0.040 0.112 0.312 0.096 0.440
#> GSM1068553 4 0.0290 0.83118 0.000 0.000 0.000 0.992 0.008
#> GSM1068554 4 0.0290 0.82901 0.000 0.008 0.000 0.992 0.000
#> GSM1068558 5 0.5702 0.44526 0.000 0.192 0.180 0.000 0.628
#> GSM1068559 5 0.4460 0.48951 0.000 0.004 0.392 0.004 0.600
#> GSM1068564 4 0.0510 0.82670 0.000 0.016 0.000 0.984 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1068478 5 0.1141 0.756 0.052 0.000 0.000 0.000 0.948 0.000
#> GSM1068479 2 0.3240 0.692 0.000 0.812 0.000 0.000 0.148 0.040
#> GSM1068481 3 0.1610 0.769 0.000 0.000 0.916 0.000 0.000 0.084
#> GSM1068482 6 0.3765 0.326 0.000 0.000 0.404 0.000 0.000 0.596
#> GSM1068483 1 0.3245 0.652 0.764 0.008 0.000 0.000 0.000 0.228
#> GSM1068486 3 0.1814 0.761 0.000 0.000 0.900 0.000 0.000 0.100
#> GSM1068487 2 0.0000 0.845 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1068488 4 0.5096 0.323 0.000 0.000 0.100 0.576 0.000 0.324
#> GSM1068490 2 0.0000 0.845 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1068491 3 0.3634 0.554 0.000 0.000 0.644 0.000 0.000 0.356
#> GSM1068492 6 0.4929 0.330 0.000 0.280 0.100 0.000 0.000 0.620
#> GSM1068493 6 0.5302 0.514 0.000 0.208 0.000 0.000 0.192 0.600
#> GSM1068494 6 0.2944 0.669 0.008 0.000 0.000 0.012 0.148 0.832
#> GSM1068495 5 0.0865 0.783 0.000 0.000 0.000 0.000 0.964 0.036
#> GSM1068496 6 0.1910 0.671 0.000 0.000 0.000 0.000 0.108 0.892
#> GSM1068498 5 0.2340 0.623 0.148 0.000 0.000 0.000 0.852 0.000
#> GSM1068499 6 0.3717 0.660 0.000 0.000 0.072 0.000 0.148 0.780
#> GSM1068500 6 0.4728 0.618 0.176 0.000 0.000 0.000 0.144 0.680
#> GSM1068502 2 0.5083 0.362 0.000 0.580 0.100 0.000 0.000 0.320
#> GSM1068503 2 0.0000 0.845 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1068505 4 0.0000 0.835 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1068506 4 0.2340 0.760 0.000 0.000 0.000 0.852 0.000 0.148
#> GSM1068507 2 0.2416 0.714 0.000 0.844 0.000 0.000 0.000 0.156
#> GSM1068508 2 0.2416 0.724 0.000 0.844 0.000 0.000 0.156 0.000
#> GSM1068510 4 0.4100 0.238 0.000 0.388 0.008 0.600 0.000 0.004
#> GSM1068512 6 0.0865 0.636 0.000 0.000 0.000 0.036 0.000 0.964
#> GSM1068513 2 0.0000 0.845 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1068514 6 0.3103 0.569 0.000 0.064 0.100 0.000 0.000 0.836
#> GSM1068517 5 0.0000 0.791 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1068518 6 0.1958 0.596 0.000 0.004 0.100 0.000 0.000 0.896
#> GSM1068520 1 0.2941 0.730 0.780 0.000 0.000 0.000 0.220 0.000
#> GSM1068521 1 0.2697 0.766 0.812 0.000 0.000 0.000 0.188 0.000
#> GSM1068522 4 0.0363 0.832 0.000 0.012 0.000 0.988 0.000 0.000
#> GSM1068524 2 0.0363 0.840 0.000 0.988 0.000 0.012 0.000 0.000
#> GSM1068527 4 0.1700 0.815 0.048 0.000 0.000 0.928 0.000 0.024
#> GSM1068480 6 0.3288 0.509 0.000 0.000 0.276 0.000 0.000 0.724
#> GSM1068484 4 0.0146 0.834 0.000 0.004 0.000 0.996 0.000 0.000
#> GSM1068485 3 0.0000 0.767 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1068489 4 0.0000 0.835 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1068497 5 0.0000 0.791 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1068501 4 0.0000 0.835 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1068504 2 0.0000 0.845 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1068509 6 0.5188 0.502 0.000 0.000 0.000 0.288 0.124 0.588
#> GSM1068511 6 0.1007 0.637 0.000 0.000 0.000 0.044 0.000 0.956
#> GSM1068515 6 0.5587 0.472 0.000 0.000 0.000 0.168 0.308 0.524
#> GSM1068516 4 0.4597 0.494 0.000 0.000 0.000 0.652 0.072 0.276
#> GSM1068519 4 0.0363 0.832 0.012 0.000 0.000 0.988 0.000 0.000
#> GSM1068523 2 0.3244 0.553 0.000 0.732 0.000 0.000 0.268 0.000
#> GSM1068525 2 0.2823 0.654 0.000 0.796 0.000 0.000 0.000 0.204
#> GSM1068526 4 0.2996 0.674 0.000 0.000 0.000 0.772 0.000 0.228
#> GSM1068458 1 0.3098 0.748 0.812 0.000 0.000 0.024 0.000 0.164
#> GSM1068459 3 0.1863 0.743 0.000 0.000 0.896 0.000 0.000 0.104
#> GSM1068460 4 0.1511 0.801 0.004 0.000 0.000 0.940 0.044 0.012
#> GSM1068461 3 0.0000 0.767 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1068464 2 0.0000 0.845 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1068468 5 0.4315 0.440 0.000 0.328 0.000 0.000 0.636 0.036
#> GSM1068472 2 0.2930 0.725 0.000 0.840 0.000 0.000 0.124 0.036
#> GSM1068473 2 0.0000 0.845 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1068474 2 0.0000 0.845 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1068476 3 0.4941 0.508 0.000 0.000 0.648 0.000 0.140 0.212
#> GSM1068477 5 0.3247 0.716 0.000 0.156 0.000 0.000 0.808 0.036
#> GSM1068462 2 0.1333 0.819 0.000 0.944 0.000 0.000 0.048 0.008
#> GSM1068463 3 0.1765 0.764 0.000 0.000 0.904 0.000 0.000 0.096
#> GSM1068465 6 0.3634 0.402 0.356 0.000 0.000 0.000 0.000 0.644
#> GSM1068466 1 0.2805 0.769 0.812 0.000 0.000 0.004 0.184 0.000
#> GSM1068467 2 0.1983 0.797 0.000 0.908 0.000 0.000 0.072 0.020
#> GSM1068469 5 0.0458 0.792 0.000 0.016 0.000 0.000 0.984 0.000
#> GSM1068470 5 0.3023 0.657 0.000 0.232 0.000 0.000 0.768 0.000
#> GSM1068471 2 0.0000 0.845 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1068475 2 0.0000 0.845 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1068528 3 0.5636 -0.166 0.000 0.000 0.428 0.000 0.148 0.424
#> GSM1068531 1 0.2730 0.732 0.808 0.000 0.000 0.192 0.000 0.000
#> GSM1068532 6 0.4892 0.313 0.272 0.000 0.100 0.000 0.000 0.628
#> GSM1068533 1 0.0363 0.840 0.988 0.000 0.012 0.000 0.000 0.000
#> GSM1068535 4 0.0146 0.834 0.000 0.000 0.004 0.996 0.000 0.000
#> GSM1068537 1 0.0146 0.844 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM1068538 1 0.0000 0.843 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1068539 5 0.1010 0.782 0.004 0.000 0.000 0.000 0.960 0.036
#> GSM1068540 1 0.0146 0.844 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM1068542 4 0.2793 0.709 0.000 0.000 0.000 0.800 0.000 0.200
#> GSM1068543 4 0.2793 0.709 0.000 0.000 0.000 0.800 0.000 0.200
#> GSM1068544 3 0.2070 0.717 0.100 0.000 0.892 0.000 0.000 0.008
#> GSM1068545 2 0.4144 0.437 0.000 0.620 0.000 0.360 0.000 0.020
#> GSM1068546 3 0.2006 0.765 0.000 0.000 0.904 0.016 0.000 0.080
#> GSM1068547 1 0.3384 0.782 0.820 0.000 0.000 0.016 0.032 0.132
#> GSM1068548 6 0.3955 0.219 0.004 0.000 0.000 0.436 0.000 0.560
#> GSM1068549 3 0.3499 0.583 0.000 0.000 0.680 0.000 0.000 0.320
#> GSM1068550 4 0.0000 0.835 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1068551 2 0.3671 0.647 0.000 0.756 0.000 0.000 0.208 0.036
#> GSM1068552 4 0.2003 0.784 0.000 0.000 0.000 0.884 0.000 0.116
#> GSM1068555 5 0.3838 0.253 0.000 0.448 0.000 0.000 0.552 0.000
#> GSM1068556 6 0.3706 0.353 0.000 0.000 0.000 0.380 0.000 0.620
#> GSM1068557 2 0.5708 0.507 0.000 0.616 0.000 0.200 0.148 0.036
#> GSM1068560 4 0.5891 0.250 0.020 0.000 0.000 0.536 0.148 0.296
#> GSM1068561 6 0.5781 0.373 0.000 0.264 0.000 0.000 0.232 0.504
#> GSM1068562 6 0.5748 0.625 0.000 0.004 0.084 0.108 0.148 0.656
#> GSM1068563 4 0.2964 0.707 0.000 0.000 0.004 0.792 0.000 0.204
#> GSM1068565 2 0.0146 0.843 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM1068529 6 0.2340 0.666 0.000 0.000 0.000 0.000 0.148 0.852
#> GSM1068530 1 0.0000 0.843 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1068534 6 0.0865 0.636 0.000 0.000 0.000 0.036 0.000 0.964
#> GSM1068536 6 0.5029 0.488 0.076 0.000 0.000 0.000 0.400 0.524
#> GSM1068541 6 0.6441 0.510 0.036 0.048 0.000 0.072 0.312 0.532
#> GSM1068553 4 0.0000 0.835 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1068554 4 0.0000 0.835 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1068558 6 0.3978 0.637 0.000 0.160 0.000 0.000 0.084 0.756
#> GSM1068559 6 0.4492 0.648 0.000 0.000 0.100 0.016 0.148 0.736
#> GSM1068564 4 0.0146 0.834 0.000 0.004 0.000 0.996 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 disease.state(p) gender(p) k
#> MAD:pam 55 0.0719 0.922 2
#> MAD:pam 67 0.0772 0.745 3
#> MAD:pam 72 0.1835 0.684 4
#> MAD:pam 47 0.0137 0.855 5
#> MAD:pam 90 0.0202 0.446 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 38950 rows and 108 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'mclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 4.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.278 0.625 0.798 0.4073 0.525 0.525
#> 3 3 0.310 0.558 0.740 0.3072 0.778 0.622
#> 4 4 0.946 0.886 0.938 0.3613 0.666 0.353
#> 5 5 0.761 0.817 0.896 0.0471 0.921 0.729
#> 6 6 0.785 0.693 0.839 0.0697 0.926 0.707
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
#> GSM1068478 1 0.0938 0.8144 0.988 0.012
#> GSM1068479 1 0.6438 0.7224 0.836 0.164
#> GSM1068481 1 0.0938 0.8070 0.988 0.012
#> GSM1068482 1 0.0938 0.8070 0.988 0.012
#> GSM1068483 1 0.0000 0.8146 1.000 0.000
#> GSM1068486 1 0.0938 0.8070 0.988 0.012
#> GSM1068487 2 0.9795 0.5391 0.416 0.584
#> GSM1068488 2 0.9988 -0.0231 0.480 0.520
#> GSM1068490 2 0.9522 0.5751 0.372 0.628
#> GSM1068491 1 0.4690 0.7936 0.900 0.100
#> GSM1068492 1 0.7674 0.6328 0.776 0.224
#> GSM1068493 1 0.5178 0.7725 0.884 0.116
#> GSM1068494 1 0.2603 0.8086 0.956 0.044
#> GSM1068495 1 0.9922 -0.1758 0.552 0.448
#> GSM1068496 1 0.0000 0.8146 1.000 0.000
#> GSM1068498 1 0.2603 0.8086 0.956 0.044
#> GSM1068499 1 0.0000 0.8146 1.000 0.000
#> GSM1068500 1 0.0000 0.8146 1.000 0.000
#> GSM1068502 1 0.7056 0.6837 0.808 0.192
#> GSM1068503 2 0.9795 0.5425 0.416 0.584
#> GSM1068505 2 0.3879 0.6420 0.076 0.924
#> GSM1068506 2 0.5842 0.6471 0.140 0.860
#> GSM1068507 1 0.9608 0.1330 0.616 0.384
#> GSM1068508 2 0.9881 0.5139 0.436 0.564
#> GSM1068510 2 0.9954 0.0481 0.460 0.540
#> GSM1068512 1 0.8327 0.6138 0.736 0.264
#> GSM1068513 2 0.9850 0.5250 0.428 0.572
#> GSM1068514 1 0.8555 0.5839 0.720 0.280
#> GSM1068517 1 0.4562 0.7866 0.904 0.096
#> GSM1068518 1 0.7674 0.6708 0.776 0.224
#> GSM1068520 1 0.0000 0.8146 1.000 0.000
#> GSM1068521 1 0.0000 0.8146 1.000 0.000
#> GSM1068522 2 0.5946 0.6466 0.144 0.856
#> GSM1068524 2 0.9815 0.5348 0.420 0.580
#> GSM1068527 1 0.9775 0.2797 0.588 0.412
#> GSM1068480 1 0.0938 0.8070 0.988 0.012
#> GSM1068484 2 0.3879 0.6420 0.076 0.924
#> GSM1068485 1 0.0938 0.8070 0.988 0.012
#> GSM1068489 2 0.7219 0.5634 0.200 0.800
#> GSM1068497 1 0.3584 0.8005 0.932 0.068
#> GSM1068501 2 0.4815 0.6351 0.104 0.896
#> GSM1068504 2 0.9522 0.5751 0.372 0.628
#> GSM1068509 1 0.4562 0.7861 0.904 0.096
#> GSM1068511 1 0.9170 0.4555 0.668 0.332
#> GSM1068515 1 0.2043 0.8114 0.968 0.032
#> GSM1068516 1 0.9209 0.4636 0.664 0.336
#> GSM1068519 1 0.0000 0.8146 1.000 0.000
#> GSM1068523 2 0.9522 0.5751 0.372 0.628
#> GSM1068525 2 0.3879 0.6420 0.076 0.924
#> GSM1068526 2 0.3879 0.6420 0.076 0.924
#> GSM1068458 1 0.0000 0.8146 1.000 0.000
#> GSM1068459 1 0.0938 0.8070 0.988 0.012
#> GSM1068460 1 0.5408 0.7663 0.876 0.124
#> GSM1068461 1 0.0938 0.8070 0.988 0.012
#> GSM1068464 2 0.9608 0.5670 0.384 0.616
#> GSM1068468 1 0.9286 0.3009 0.656 0.344
#> GSM1068472 1 0.6343 0.7275 0.840 0.160
#> GSM1068473 2 0.9522 0.5751 0.372 0.628
#> GSM1068474 2 0.9522 0.5751 0.372 0.628
#> GSM1068476 1 0.5408 0.7802 0.876 0.124
#> GSM1068477 2 0.9881 0.5139 0.436 0.564
#> GSM1068462 1 0.5294 0.7694 0.880 0.120
#> GSM1068463 1 0.0938 0.8070 0.988 0.012
#> GSM1068465 1 0.5178 0.7725 0.884 0.116
#> GSM1068466 1 0.0000 0.8146 1.000 0.000
#> GSM1068467 1 0.6712 0.7078 0.824 0.176
#> GSM1068469 1 0.3431 0.8021 0.936 0.064
#> GSM1068470 2 0.9522 0.5751 0.372 0.628
#> GSM1068471 2 0.9522 0.5751 0.372 0.628
#> GSM1068475 2 0.9522 0.5751 0.372 0.628
#> GSM1068528 1 0.0000 0.8146 1.000 0.000
#> GSM1068531 1 0.0000 0.8146 1.000 0.000
#> GSM1068532 1 0.0000 0.8146 1.000 0.000
#> GSM1068533 1 0.0000 0.8146 1.000 0.000
#> GSM1068535 1 0.9963 0.1163 0.536 0.464
#> GSM1068537 1 0.0000 0.8146 1.000 0.000
#> GSM1068538 1 0.0000 0.8146 1.000 0.000
#> GSM1068539 2 0.9881 0.5139 0.436 0.564
#> GSM1068540 1 0.0000 0.8146 1.000 0.000
#> GSM1068542 2 0.4161 0.6409 0.084 0.916
#> GSM1068543 2 0.9944 0.0510 0.456 0.544
#> GSM1068544 1 0.0938 0.8070 0.988 0.012
#> GSM1068545 2 0.9754 0.5469 0.408 0.592
#> GSM1068546 1 0.0938 0.8070 0.988 0.012
#> GSM1068547 1 0.1184 0.8141 0.984 0.016
#> GSM1068548 2 0.4939 0.6334 0.108 0.892
#> GSM1068549 1 0.0938 0.8070 0.988 0.012
#> GSM1068550 2 0.4022 0.6416 0.080 0.920
#> GSM1068551 2 0.9522 0.5751 0.372 0.628
#> GSM1068552 2 0.3879 0.6420 0.076 0.924
#> GSM1068555 2 0.9522 0.5751 0.372 0.628
#> GSM1068556 2 0.9963 0.0269 0.464 0.536
#> GSM1068557 1 0.9286 0.2978 0.656 0.344
#> GSM1068560 2 0.6712 0.6202 0.176 0.824
#> GSM1068561 1 0.8608 0.4883 0.716 0.284
#> GSM1068562 2 0.3879 0.6420 0.076 0.924
#> GSM1068563 2 0.3879 0.6420 0.076 0.924
#> GSM1068565 2 0.9522 0.5751 0.372 0.628
#> GSM1068529 1 0.6343 0.7419 0.840 0.160
#> GSM1068530 1 0.0000 0.8146 1.000 0.000
#> GSM1068534 1 0.5737 0.7605 0.864 0.136
#> GSM1068536 1 0.5408 0.7663 0.876 0.124
#> GSM1068541 1 0.9795 -0.0248 0.584 0.416
#> GSM1068553 1 0.9983 0.1009 0.524 0.476
#> GSM1068554 2 0.9661 0.2237 0.392 0.608
#> GSM1068558 1 0.8909 0.5178 0.692 0.308
#> GSM1068559 1 0.5737 0.7548 0.864 0.136
#> GSM1068564 2 0.3879 0.6420 0.076 0.924
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1068478 1 0.0237 0.6679 0.996 0.004 0.000
#> GSM1068479 1 0.6994 0.4022 0.556 0.424 0.020
#> GSM1068481 1 0.6244 0.5676 0.560 0.000 0.440
#> GSM1068482 1 0.6244 0.5676 0.560 0.000 0.440
#> GSM1068483 1 0.0000 0.6677 1.000 0.000 0.000
#> GSM1068486 1 0.6244 0.5676 0.560 0.000 0.440
#> GSM1068487 2 0.6952 -0.8596 0.016 0.504 0.480
#> GSM1068488 2 0.5591 0.5300 0.304 0.696 0.000
#> GSM1068490 3 0.6625 0.9948 0.008 0.440 0.552
#> GSM1068491 1 0.7770 0.5715 0.560 0.056 0.384
#> GSM1068492 2 0.7074 -0.2239 0.480 0.500 0.020
#> GSM1068493 1 0.6126 0.4517 0.600 0.400 0.000
#> GSM1068494 1 0.1774 0.6655 0.960 0.016 0.024
#> GSM1068495 1 0.5968 0.4093 0.636 0.364 0.000
#> GSM1068496 1 0.0000 0.6677 1.000 0.000 0.000
#> GSM1068498 1 0.5254 0.5826 0.736 0.264 0.000
#> GSM1068499 1 0.0424 0.6675 0.992 0.008 0.000
#> GSM1068500 1 0.0000 0.6677 1.000 0.000 0.000
#> GSM1068502 1 0.7013 0.3861 0.548 0.432 0.020
#> GSM1068503 2 0.1999 0.5409 0.036 0.952 0.012
#> GSM1068505 2 0.2878 0.6304 0.096 0.904 0.000
#> GSM1068506 2 0.1964 0.6105 0.056 0.944 0.000
#> GSM1068507 2 0.6180 0.3005 0.416 0.584 0.000
#> GSM1068508 1 0.6280 0.3365 0.540 0.460 0.000
#> GSM1068510 2 0.5541 0.4885 0.252 0.740 0.008
#> GSM1068512 2 0.6295 0.1750 0.472 0.528 0.000
#> GSM1068513 2 0.6719 0.3192 0.204 0.728 0.068
#> GSM1068514 1 0.7067 0.0395 0.512 0.468 0.020
#> GSM1068517 1 0.6026 0.4819 0.624 0.376 0.000
#> GSM1068518 1 0.6267 0.1308 0.548 0.452 0.000
#> GSM1068520 1 0.0424 0.6653 0.992 0.000 0.008
#> GSM1068521 1 0.0424 0.6653 0.992 0.000 0.008
#> GSM1068522 2 0.0000 0.5256 0.000 1.000 0.000
#> GSM1068524 2 0.4768 0.4561 0.100 0.848 0.052
#> GSM1068527 2 0.5363 0.5675 0.276 0.724 0.000
#> GSM1068480 1 0.6244 0.5676 0.560 0.000 0.440
#> GSM1068484 2 0.1289 0.5814 0.032 0.968 0.000
#> GSM1068485 1 0.6244 0.5676 0.560 0.000 0.440
#> GSM1068489 2 0.2711 0.6310 0.088 0.912 0.000
#> GSM1068497 1 0.6026 0.4819 0.624 0.376 0.000
#> GSM1068501 2 0.2796 0.6363 0.092 0.908 0.000
#> GSM1068504 3 0.6625 0.9948 0.008 0.440 0.552
#> GSM1068509 1 0.1031 0.6656 0.976 0.024 0.000
#> GSM1068511 2 0.6308 0.0767 0.492 0.508 0.000
#> GSM1068515 1 0.4605 0.5930 0.796 0.204 0.000
#> GSM1068516 2 0.4452 0.6169 0.192 0.808 0.000
#> GSM1068519 1 0.0237 0.6666 0.996 0.000 0.004
#> GSM1068523 3 0.6625 0.9948 0.008 0.440 0.552
#> GSM1068525 2 0.1643 0.5561 0.044 0.956 0.000
#> GSM1068526 2 0.2711 0.6310 0.088 0.912 0.000
#> GSM1068458 1 0.0424 0.6653 0.992 0.000 0.008
#> GSM1068459 1 0.6244 0.5676 0.560 0.000 0.440
#> GSM1068460 1 0.3715 0.6323 0.868 0.128 0.004
#> GSM1068461 1 0.6244 0.5676 0.560 0.000 0.440
#> GSM1068464 3 0.7360 0.9414 0.032 0.440 0.528
#> GSM1068468 1 0.6244 0.3862 0.560 0.440 0.000
#> GSM1068472 1 0.6244 0.3862 0.560 0.440 0.000
#> GSM1068473 3 0.6625 0.9948 0.008 0.440 0.552
#> GSM1068474 3 0.6625 0.9948 0.008 0.440 0.552
#> GSM1068476 1 0.8264 0.5675 0.556 0.088 0.356
#> GSM1068477 1 0.6244 0.3862 0.560 0.440 0.000
#> GSM1068462 1 0.6192 0.4214 0.580 0.420 0.000
#> GSM1068463 1 0.6244 0.5676 0.560 0.000 0.440
#> GSM1068465 1 0.4033 0.6299 0.856 0.136 0.008
#> GSM1068466 1 0.0424 0.6653 0.992 0.000 0.008
#> GSM1068467 1 0.6244 0.3862 0.560 0.440 0.000
#> GSM1068469 1 0.6079 0.4683 0.612 0.388 0.000
#> GSM1068470 3 0.6625 0.9948 0.008 0.440 0.552
#> GSM1068471 3 0.6625 0.9948 0.008 0.440 0.552
#> GSM1068475 3 0.6625 0.9948 0.008 0.440 0.552
#> GSM1068528 1 0.0000 0.6677 1.000 0.000 0.000
#> GSM1068531 1 0.0424 0.6653 0.992 0.000 0.008
#> GSM1068532 1 0.0237 0.6666 0.996 0.000 0.004
#> GSM1068533 1 0.0424 0.6653 0.992 0.000 0.008
#> GSM1068535 2 0.6309 0.1289 0.496 0.504 0.000
#> GSM1068537 1 0.0424 0.6653 0.992 0.000 0.008
#> GSM1068538 1 0.0424 0.6653 0.992 0.000 0.008
#> GSM1068539 2 0.6235 0.2603 0.436 0.564 0.000
#> GSM1068540 1 0.0424 0.6653 0.992 0.000 0.008
#> GSM1068542 2 0.2796 0.6305 0.092 0.908 0.000
#> GSM1068543 2 0.3752 0.6395 0.144 0.856 0.000
#> GSM1068544 1 0.6168 0.5771 0.588 0.000 0.412
#> GSM1068545 2 0.3038 0.5449 0.104 0.896 0.000
#> GSM1068546 1 0.6244 0.5676 0.560 0.000 0.440
#> GSM1068547 1 0.0424 0.6653 0.992 0.000 0.008
#> GSM1068548 2 0.3038 0.6251 0.104 0.896 0.000
#> GSM1068549 1 0.6244 0.5676 0.560 0.000 0.440
#> GSM1068550 2 0.2878 0.6304 0.096 0.904 0.000
#> GSM1068551 3 0.6625 0.9948 0.008 0.440 0.552
#> GSM1068552 2 0.0592 0.5491 0.012 0.988 0.000
#> GSM1068555 3 0.6625 0.9948 0.008 0.440 0.552
#> GSM1068556 2 0.3482 0.6396 0.128 0.872 0.000
#> GSM1068557 1 0.6244 0.3862 0.560 0.440 0.000
#> GSM1068560 2 0.3116 0.6230 0.108 0.892 0.000
#> GSM1068561 1 0.6215 0.3857 0.572 0.428 0.000
#> GSM1068562 2 0.2796 0.6334 0.092 0.908 0.000
#> GSM1068563 2 0.2625 0.6300 0.084 0.916 0.000
#> GSM1068565 3 0.6625 0.9948 0.008 0.440 0.552
#> GSM1068529 1 0.6140 0.3168 0.596 0.404 0.000
#> GSM1068530 1 0.0424 0.6653 0.992 0.000 0.008
#> GSM1068534 1 0.6026 0.3639 0.624 0.376 0.000
#> GSM1068536 1 0.3192 0.6397 0.888 0.112 0.000
#> GSM1068541 1 0.5905 0.4451 0.648 0.352 0.000
#> GSM1068553 2 0.5948 0.4836 0.360 0.640 0.000
#> GSM1068554 2 0.3116 0.6307 0.108 0.892 0.000
#> GSM1068558 2 0.7049 0.1170 0.452 0.528 0.020
#> GSM1068559 1 0.5926 0.4090 0.644 0.356 0.000
#> GSM1068564 2 0.0000 0.5256 0.000 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1068478 1 0.0000 0.954 1.000 0.000 0.000 0.000
#> GSM1068479 2 0.0188 0.865 0.000 0.996 0.000 0.004
#> GSM1068481 3 0.0000 0.982 0.000 0.000 1.000 0.000
#> GSM1068482 3 0.0000 0.982 0.000 0.000 1.000 0.000
#> GSM1068483 1 0.0000 0.954 1.000 0.000 0.000 0.000
#> GSM1068486 3 0.0000 0.982 0.000 0.000 1.000 0.000
#> GSM1068487 2 0.2281 0.884 0.000 0.904 0.000 0.096
#> GSM1068488 4 0.0188 0.941 0.000 0.004 0.000 0.996
#> GSM1068490 2 0.2216 0.885 0.000 0.908 0.000 0.092
#> GSM1068491 3 0.2401 0.899 0.000 0.092 0.904 0.004
#> GSM1068492 4 0.1716 0.921 0.000 0.064 0.000 0.936
#> GSM1068493 2 0.0188 0.865 0.000 0.996 0.000 0.004
#> GSM1068494 1 0.4772 0.780 0.808 0.068 0.016 0.108
#> GSM1068495 4 0.5607 0.101 0.020 0.488 0.000 0.492
#> GSM1068496 1 0.0000 0.954 1.000 0.000 0.000 0.000
#> GSM1068498 2 0.2401 0.843 0.092 0.904 0.000 0.004
#> GSM1068499 1 0.1661 0.915 0.944 0.000 0.052 0.004
#> GSM1068500 1 0.0000 0.954 1.000 0.000 0.000 0.000
#> GSM1068502 2 0.0707 0.871 0.000 0.980 0.000 0.020
#> GSM1068503 2 0.4543 0.652 0.000 0.676 0.000 0.324
#> GSM1068505 4 0.0000 0.942 0.000 0.000 0.000 1.000
#> GSM1068506 4 0.0000 0.942 0.000 0.000 0.000 1.000
#> GSM1068507 4 0.2216 0.907 0.000 0.092 0.000 0.908
#> GSM1068508 2 0.2281 0.884 0.000 0.904 0.000 0.096
#> GSM1068510 4 0.0000 0.942 0.000 0.000 0.000 1.000
#> GSM1068512 4 0.2216 0.907 0.000 0.092 0.000 0.908
#> GSM1068513 2 0.4996 0.276 0.000 0.516 0.000 0.484
#> GSM1068514 4 0.2216 0.907 0.000 0.092 0.000 0.908
#> GSM1068517 2 0.2053 0.853 0.072 0.924 0.000 0.004
#> GSM1068518 4 0.2216 0.907 0.000 0.092 0.000 0.908
#> GSM1068520 1 0.0000 0.954 1.000 0.000 0.000 0.000
#> GSM1068521 1 0.0000 0.954 1.000 0.000 0.000 0.000
#> GSM1068522 4 0.1389 0.904 0.000 0.048 0.000 0.952
#> GSM1068524 2 0.4730 0.578 0.000 0.636 0.000 0.364
#> GSM1068527 4 0.1489 0.913 0.044 0.004 0.000 0.952
#> GSM1068480 3 0.0000 0.982 0.000 0.000 1.000 0.000
#> GSM1068484 4 0.0000 0.942 0.000 0.000 0.000 1.000
#> GSM1068485 3 0.0000 0.982 0.000 0.000 1.000 0.000
#> GSM1068489 4 0.0000 0.942 0.000 0.000 0.000 1.000
#> GSM1068497 2 0.2401 0.843 0.092 0.904 0.000 0.004
#> GSM1068501 4 0.0000 0.942 0.000 0.000 0.000 1.000
#> GSM1068504 2 0.2281 0.884 0.000 0.904 0.000 0.096
#> GSM1068509 1 0.2676 0.877 0.896 0.092 0.000 0.012
#> GSM1068511 4 0.2216 0.907 0.000 0.092 0.000 0.908
#> GSM1068515 1 0.4509 0.559 0.708 0.288 0.000 0.004
#> GSM1068516 4 0.2216 0.907 0.000 0.092 0.000 0.908
#> GSM1068519 1 0.0000 0.954 1.000 0.000 0.000 0.000
#> GSM1068523 2 0.2216 0.885 0.000 0.908 0.000 0.092
#> GSM1068525 4 0.0000 0.942 0.000 0.000 0.000 1.000
#> GSM1068526 4 0.0000 0.942 0.000 0.000 0.000 1.000
#> GSM1068458 1 0.0000 0.954 1.000 0.000 0.000 0.000
#> GSM1068459 3 0.0000 0.982 0.000 0.000 1.000 0.000
#> GSM1068460 1 0.2401 0.883 0.904 0.092 0.000 0.004
#> GSM1068461 3 0.0000 0.982 0.000 0.000 1.000 0.000
#> GSM1068464 2 0.2216 0.885 0.000 0.908 0.000 0.092
#> GSM1068468 2 0.0188 0.865 0.000 0.996 0.000 0.004
#> GSM1068472 2 0.0188 0.865 0.000 0.996 0.000 0.004
#> GSM1068473 2 0.2216 0.885 0.000 0.908 0.000 0.092
#> GSM1068474 2 0.2216 0.885 0.000 0.908 0.000 0.092
#> GSM1068476 3 0.2676 0.893 0.000 0.092 0.896 0.012
#> GSM1068477 2 0.0188 0.865 0.000 0.996 0.000 0.004
#> GSM1068462 2 0.0188 0.865 0.000 0.996 0.000 0.004
#> GSM1068463 3 0.0000 0.982 0.000 0.000 1.000 0.000
#> GSM1068465 1 0.2401 0.883 0.904 0.092 0.000 0.004
#> GSM1068466 1 0.0000 0.954 1.000 0.000 0.000 0.000
#> GSM1068467 2 0.0188 0.865 0.000 0.996 0.000 0.004
#> GSM1068469 2 0.2401 0.843 0.092 0.904 0.000 0.004
#> GSM1068470 2 0.2216 0.885 0.000 0.908 0.000 0.092
#> GSM1068471 2 0.2216 0.885 0.000 0.908 0.000 0.092
#> GSM1068475 2 0.2216 0.885 0.000 0.908 0.000 0.092
#> GSM1068528 1 0.0188 0.952 0.996 0.000 0.004 0.000
#> GSM1068531 1 0.0000 0.954 1.000 0.000 0.000 0.000
#> GSM1068532 1 0.0000 0.954 1.000 0.000 0.000 0.000
#> GSM1068533 1 0.0000 0.954 1.000 0.000 0.000 0.000
#> GSM1068535 4 0.2216 0.907 0.000 0.092 0.000 0.908
#> GSM1068537 1 0.0000 0.954 1.000 0.000 0.000 0.000
#> GSM1068538 1 0.0000 0.954 1.000 0.000 0.000 0.000
#> GSM1068539 4 0.3400 0.835 0.000 0.180 0.000 0.820
#> GSM1068540 1 0.0000 0.954 1.000 0.000 0.000 0.000
#> GSM1068542 4 0.0000 0.942 0.000 0.000 0.000 1.000
#> GSM1068543 4 0.0000 0.942 0.000 0.000 0.000 1.000
#> GSM1068544 3 0.0188 0.979 0.004 0.000 0.996 0.000
#> GSM1068545 2 0.4994 0.252 0.000 0.520 0.000 0.480
#> GSM1068546 3 0.0000 0.982 0.000 0.000 1.000 0.000
#> GSM1068547 1 0.0000 0.954 1.000 0.000 0.000 0.000
#> GSM1068548 4 0.0000 0.942 0.000 0.000 0.000 1.000
#> GSM1068549 3 0.0000 0.982 0.000 0.000 1.000 0.000
#> GSM1068550 4 0.0000 0.942 0.000 0.000 0.000 1.000
#> GSM1068551 2 0.2216 0.885 0.000 0.908 0.000 0.092
#> GSM1068552 4 0.0000 0.942 0.000 0.000 0.000 1.000
#> GSM1068555 2 0.2216 0.885 0.000 0.908 0.000 0.092
#> GSM1068556 4 0.0000 0.942 0.000 0.000 0.000 1.000
#> GSM1068557 2 0.0188 0.865 0.000 0.996 0.000 0.004
#> GSM1068560 4 0.0000 0.942 0.000 0.000 0.000 1.000
#> GSM1068561 2 0.4277 0.569 0.000 0.720 0.000 0.280
#> GSM1068562 4 0.0000 0.942 0.000 0.000 0.000 1.000
#> GSM1068563 4 0.0000 0.942 0.000 0.000 0.000 1.000
#> GSM1068565 2 0.2216 0.885 0.000 0.908 0.000 0.092
#> GSM1068529 4 0.2216 0.907 0.000 0.092 0.000 0.908
#> GSM1068530 1 0.0000 0.954 1.000 0.000 0.000 0.000
#> GSM1068534 4 0.2216 0.907 0.000 0.092 0.000 0.908
#> GSM1068536 1 0.2401 0.883 0.904 0.092 0.000 0.004
#> GSM1068541 2 0.0469 0.863 0.000 0.988 0.000 0.012
#> GSM1068553 4 0.0000 0.942 0.000 0.000 0.000 1.000
#> GSM1068554 4 0.0000 0.942 0.000 0.000 0.000 1.000
#> GSM1068558 4 0.1867 0.917 0.000 0.072 0.000 0.928
#> GSM1068559 4 0.2216 0.907 0.000 0.092 0.000 0.908
#> GSM1068564 4 0.0000 0.942 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
#> GSM1068478 1 0.3242 0.7385 0.784 0.000 0.000 0.000 0.216
#> GSM1068479 2 0.3710 0.7180 0.000 0.808 0.000 0.144 0.048
#> GSM1068481 3 0.0000 0.9514 0.000 0.000 1.000 0.000 0.000
#> GSM1068482 3 0.0000 0.9514 0.000 0.000 1.000 0.000 0.000
#> GSM1068483 1 0.3039 0.7665 0.808 0.000 0.000 0.000 0.192
#> GSM1068486 3 0.0000 0.9514 0.000 0.000 1.000 0.000 0.000
#> GSM1068487 2 0.2648 0.8101 0.000 0.848 0.000 0.152 0.000
#> GSM1068488 4 0.1310 0.8926 0.000 0.024 0.000 0.956 0.020
#> GSM1068490 2 0.1043 0.9077 0.000 0.960 0.000 0.040 0.000
#> GSM1068491 3 0.4149 0.6939 0.000 0.004 0.792 0.124 0.080
#> GSM1068492 4 0.2446 0.8700 0.000 0.056 0.000 0.900 0.044
#> GSM1068493 5 0.3016 0.7140 0.000 0.020 0.000 0.132 0.848
#> GSM1068494 1 0.4027 0.7118 0.800 0.000 0.012 0.144 0.044
#> GSM1068495 4 0.4752 0.5180 0.000 0.036 0.000 0.648 0.316
#> GSM1068496 1 0.0865 0.8927 0.972 0.000 0.000 0.004 0.024
#> GSM1068498 5 0.2627 0.7507 0.044 0.044 0.000 0.012 0.900
#> GSM1068499 1 0.2011 0.8459 0.908 0.000 0.088 0.004 0.000
#> GSM1068500 1 0.2852 0.7863 0.828 0.000 0.000 0.000 0.172
#> GSM1068502 2 0.3681 0.7196 0.000 0.808 0.000 0.148 0.044
#> GSM1068503 2 0.2852 0.7898 0.000 0.828 0.000 0.172 0.000
#> GSM1068505 4 0.1216 0.8960 0.000 0.020 0.000 0.960 0.020
#> GSM1068506 4 0.1211 0.8959 0.000 0.024 0.000 0.960 0.016
#> GSM1068507 4 0.1341 0.8867 0.000 0.000 0.000 0.944 0.056
#> GSM1068508 5 0.6392 0.1564 0.000 0.400 0.000 0.168 0.432
#> GSM1068510 4 0.1741 0.8856 0.000 0.024 0.000 0.936 0.040
#> GSM1068512 4 0.1410 0.8858 0.000 0.000 0.000 0.940 0.060
#> GSM1068513 4 0.4552 0.0112 0.000 0.468 0.000 0.524 0.008
#> GSM1068514 4 0.2077 0.8769 0.000 0.040 0.000 0.920 0.040
#> GSM1068517 5 0.1682 0.7552 0.004 0.044 0.000 0.012 0.940
#> GSM1068518 4 0.1544 0.8833 0.000 0.000 0.000 0.932 0.068
#> GSM1068520 1 0.0000 0.9015 1.000 0.000 0.000 0.000 0.000
#> GSM1068521 1 0.0000 0.9015 1.000 0.000 0.000 0.000 0.000
#> GSM1068522 4 0.3928 0.5401 0.000 0.296 0.000 0.700 0.004
#> GSM1068524 2 0.3086 0.7801 0.000 0.816 0.000 0.180 0.004
#> GSM1068527 4 0.2153 0.8773 0.040 0.000 0.000 0.916 0.044
#> GSM1068480 3 0.0000 0.9514 0.000 0.000 1.000 0.000 0.000
#> GSM1068484 4 0.0992 0.8972 0.000 0.024 0.000 0.968 0.008
#> GSM1068485 3 0.0000 0.9514 0.000 0.000 1.000 0.000 0.000
#> GSM1068489 4 0.1018 0.8975 0.000 0.016 0.000 0.968 0.016
#> GSM1068497 5 0.2627 0.7507 0.044 0.044 0.000 0.012 0.900
#> GSM1068501 4 0.0510 0.8993 0.000 0.016 0.000 0.984 0.000
#> GSM1068504 2 0.2179 0.8533 0.000 0.888 0.000 0.112 0.000
#> GSM1068509 1 0.2992 0.8128 0.868 0.000 0.000 0.064 0.068
#> GSM1068511 4 0.1478 0.8847 0.000 0.000 0.000 0.936 0.064
#> GSM1068515 5 0.3599 0.6686 0.160 0.008 0.000 0.020 0.812
#> GSM1068516 4 0.1478 0.8847 0.000 0.000 0.000 0.936 0.064
#> GSM1068519 1 0.0000 0.9015 1.000 0.000 0.000 0.000 0.000
#> GSM1068523 2 0.1043 0.9077 0.000 0.960 0.000 0.040 0.000
#> GSM1068525 4 0.0992 0.8990 0.000 0.024 0.000 0.968 0.008
#> GSM1068526 4 0.1117 0.8965 0.000 0.020 0.000 0.964 0.016
#> GSM1068458 1 0.0000 0.9015 1.000 0.000 0.000 0.000 0.000
#> GSM1068459 3 0.0000 0.9514 0.000 0.000 1.000 0.000 0.000
#> GSM1068460 1 0.3980 0.7060 0.796 0.000 0.000 0.128 0.076
#> GSM1068461 3 0.0000 0.9514 0.000 0.000 1.000 0.000 0.000
#> GSM1068464 2 0.1043 0.9077 0.000 0.960 0.000 0.040 0.000
#> GSM1068468 5 0.4428 0.6906 0.000 0.268 0.000 0.032 0.700
#> GSM1068472 5 0.4141 0.7075 0.000 0.248 0.000 0.024 0.728
#> GSM1068473 2 0.1043 0.9077 0.000 0.960 0.000 0.040 0.000
#> GSM1068474 2 0.1043 0.9077 0.000 0.960 0.000 0.040 0.000
#> GSM1068476 3 0.4670 0.6780 0.000 0.040 0.768 0.148 0.044
#> GSM1068477 5 0.5843 0.5196 0.000 0.304 0.000 0.124 0.572
#> GSM1068462 5 0.3942 0.7165 0.000 0.232 0.000 0.020 0.748
#> GSM1068463 3 0.0000 0.9514 0.000 0.000 1.000 0.000 0.000
#> GSM1068465 1 0.4747 0.1706 0.496 0.000 0.000 0.016 0.488
#> GSM1068466 1 0.0880 0.8905 0.968 0.000 0.000 0.000 0.032
#> GSM1068467 5 0.4229 0.6825 0.000 0.276 0.000 0.020 0.704
#> GSM1068469 5 0.3556 0.7542 0.044 0.104 0.000 0.012 0.840
#> GSM1068470 2 0.1043 0.9077 0.000 0.960 0.000 0.040 0.000
#> GSM1068471 2 0.1043 0.9077 0.000 0.960 0.000 0.040 0.000
#> GSM1068475 2 0.1043 0.9077 0.000 0.960 0.000 0.040 0.000
#> GSM1068528 1 0.0162 0.9003 0.996 0.000 0.004 0.000 0.000
#> GSM1068531 1 0.0000 0.9015 1.000 0.000 0.000 0.000 0.000
#> GSM1068532 1 0.0000 0.9015 1.000 0.000 0.000 0.000 0.000
#> GSM1068533 1 0.0000 0.9015 1.000 0.000 0.000 0.000 0.000
#> GSM1068535 4 0.1270 0.8876 0.000 0.000 0.000 0.948 0.052
#> GSM1068537 1 0.0000 0.9015 1.000 0.000 0.000 0.000 0.000
#> GSM1068538 1 0.0000 0.9015 1.000 0.000 0.000 0.000 0.000
#> GSM1068539 4 0.2628 0.8623 0.000 0.028 0.000 0.884 0.088
#> GSM1068540 1 0.0000 0.9015 1.000 0.000 0.000 0.000 0.000
#> GSM1068542 4 0.1216 0.8960 0.000 0.020 0.000 0.960 0.020
#> GSM1068543 4 0.0162 0.8996 0.000 0.000 0.000 0.996 0.004
#> GSM1068544 3 0.0162 0.9476 0.004 0.000 0.996 0.000 0.000
#> GSM1068545 4 0.4390 0.1712 0.000 0.428 0.000 0.568 0.004
#> GSM1068546 3 0.0000 0.9514 0.000 0.000 1.000 0.000 0.000
#> GSM1068547 1 0.0324 0.8986 0.992 0.000 0.000 0.004 0.004
#> GSM1068548 4 0.1216 0.8960 0.000 0.020 0.000 0.960 0.020
#> GSM1068549 3 0.0000 0.9514 0.000 0.000 1.000 0.000 0.000
#> GSM1068550 4 0.1216 0.8960 0.000 0.020 0.000 0.960 0.020
#> GSM1068551 2 0.1043 0.9077 0.000 0.960 0.000 0.040 0.000
#> GSM1068552 4 0.1168 0.8944 0.000 0.032 0.000 0.960 0.008
#> GSM1068555 2 0.1043 0.9077 0.000 0.960 0.000 0.040 0.000
#> GSM1068556 4 0.0162 0.8998 0.000 0.004 0.000 0.996 0.000
#> GSM1068557 5 0.5504 0.6642 0.000 0.224 0.000 0.132 0.644
#> GSM1068560 4 0.1216 0.8960 0.000 0.020 0.000 0.960 0.020
#> GSM1068561 4 0.4726 0.3649 0.000 0.020 0.000 0.580 0.400
#> GSM1068562 4 0.0798 0.8989 0.000 0.016 0.000 0.976 0.008
#> GSM1068563 4 0.1117 0.8965 0.000 0.020 0.000 0.964 0.016
#> GSM1068565 2 0.1043 0.9077 0.000 0.960 0.000 0.040 0.000
#> GSM1068529 4 0.1965 0.8745 0.000 0.000 0.000 0.904 0.096
#> GSM1068530 1 0.0000 0.9015 1.000 0.000 0.000 0.000 0.000
#> GSM1068534 4 0.1671 0.8818 0.000 0.000 0.000 0.924 0.076
#> GSM1068536 1 0.3946 0.7190 0.800 0.000 0.000 0.120 0.080
#> GSM1068541 5 0.3578 0.7220 0.000 0.048 0.000 0.132 0.820
#> GSM1068553 4 0.0162 0.8996 0.000 0.000 0.000 0.996 0.004
#> GSM1068554 4 0.0162 0.8996 0.000 0.000 0.000 0.996 0.004
#> GSM1068558 4 0.2153 0.8750 0.000 0.040 0.000 0.916 0.044
#> GSM1068559 4 0.1965 0.8745 0.000 0.000 0.000 0.904 0.096
#> GSM1068564 4 0.1168 0.8944 0.000 0.032 0.000 0.960 0.008
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1068478 1 0.2941 0.7488 0.780 0.000 0.000 0.000 0.220 0.000
#> GSM1068479 4 0.4193 0.4986 0.000 0.272 0.000 0.684 0.000 0.044
#> GSM1068481 3 0.0000 0.9812 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1068482 3 0.0000 0.9812 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1068483 1 0.3247 0.7954 0.808 0.000 0.000 0.036 0.156 0.000
#> GSM1068486 3 0.0790 0.9509 0.000 0.000 0.968 0.032 0.000 0.000
#> GSM1068487 2 0.0260 0.8905 0.000 0.992 0.000 0.008 0.000 0.000
#> GSM1068488 6 0.3728 0.5521 0.000 0.000 0.000 0.344 0.004 0.652
#> GSM1068490 2 0.0000 0.8955 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1068491 4 0.3944 0.1755 0.000 0.000 0.428 0.568 0.000 0.004
#> GSM1068492 4 0.3283 0.5699 0.000 0.036 0.000 0.804 0.000 0.160
#> GSM1068493 5 0.4302 0.5691 0.000 0.036 0.000 0.292 0.668 0.004
#> GSM1068494 1 0.2577 0.8686 0.896 0.000 0.048 0.024 0.008 0.024
#> GSM1068495 6 0.5738 -0.1457 0.000 0.004 0.000 0.144 0.424 0.428
#> GSM1068496 1 0.2500 0.8852 0.896 0.000 0.032 0.036 0.036 0.000
#> GSM1068498 5 0.0146 0.7851 0.000 0.004 0.000 0.000 0.996 0.000
#> GSM1068499 1 0.2519 0.8747 0.884 0.000 0.068 0.044 0.004 0.000
#> GSM1068500 1 0.3247 0.7954 0.808 0.000 0.000 0.036 0.156 0.000
#> GSM1068502 4 0.3584 0.4463 0.000 0.308 0.000 0.688 0.000 0.004
#> GSM1068503 2 0.0520 0.8853 0.000 0.984 0.000 0.008 0.000 0.008
#> GSM1068505 6 0.0363 0.6505 0.000 0.000 0.000 0.012 0.000 0.988
#> GSM1068506 6 0.0146 0.6566 0.000 0.000 0.000 0.004 0.000 0.996
#> GSM1068507 6 0.3944 0.5287 0.000 0.000 0.000 0.428 0.004 0.568
#> GSM1068508 2 0.6851 -0.0536 0.000 0.428 0.000 0.132 0.340 0.100
#> GSM1068510 6 0.4371 0.4683 0.000 0.028 0.000 0.392 0.000 0.580
#> GSM1068512 6 0.3944 0.5287 0.000 0.000 0.000 0.428 0.004 0.568
#> GSM1068513 6 0.4449 0.0370 0.000 0.440 0.000 0.028 0.000 0.532
#> GSM1068514 4 0.3428 0.3855 0.000 0.000 0.000 0.696 0.000 0.304
#> GSM1068517 5 0.0146 0.7851 0.000 0.004 0.000 0.000 0.996 0.000
#> GSM1068518 6 0.3601 0.5741 0.000 0.000 0.000 0.312 0.004 0.684
#> GSM1068520 1 0.0790 0.9043 0.968 0.000 0.000 0.032 0.000 0.000
#> GSM1068521 1 0.0146 0.9081 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM1068522 2 0.3634 0.3559 0.000 0.644 0.000 0.000 0.000 0.356
#> GSM1068524 2 0.0405 0.8865 0.000 0.988 0.000 0.008 0.000 0.004
#> GSM1068527 6 0.2588 0.6217 0.024 0.000 0.000 0.092 0.008 0.876
#> GSM1068480 3 0.0000 0.9812 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1068484 6 0.2376 0.6499 0.000 0.068 0.000 0.044 0.000 0.888
#> GSM1068485 3 0.0000 0.9812 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1068489 6 0.0458 0.6594 0.000 0.000 0.000 0.016 0.000 0.984
#> GSM1068497 5 0.0146 0.7851 0.000 0.004 0.000 0.000 0.996 0.000
#> GSM1068501 6 0.1082 0.6660 0.000 0.000 0.000 0.040 0.004 0.956
#> GSM1068504 2 0.0146 0.8934 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM1068509 1 0.2839 0.8549 0.876 0.000 0.000 0.032 0.052 0.040
#> GSM1068511 6 0.3944 0.5287 0.000 0.000 0.000 0.428 0.004 0.568
#> GSM1068515 5 0.1321 0.7825 0.020 0.000 0.000 0.024 0.952 0.004
#> GSM1068516 6 0.3841 0.5585 0.000 0.000 0.000 0.380 0.004 0.616
#> GSM1068519 1 0.0725 0.9072 0.976 0.000 0.000 0.012 0.012 0.000
#> GSM1068523 2 0.0146 0.8938 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM1068525 6 0.5076 0.4886 0.000 0.248 0.000 0.132 0.000 0.620
#> GSM1068526 6 0.0937 0.6612 0.000 0.000 0.000 0.040 0.000 0.960
#> GSM1068458 1 0.0865 0.9034 0.964 0.000 0.000 0.036 0.000 0.000
#> GSM1068459 3 0.0000 0.9812 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1068460 1 0.5536 0.4631 0.608 0.000 0.000 0.072 0.272 0.048
#> GSM1068461 3 0.0000 0.9812 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1068464 2 0.0000 0.8955 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1068468 5 0.4166 0.7569 0.000 0.160 0.000 0.088 0.748 0.004
#> GSM1068472 5 0.4131 0.7595 0.000 0.156 0.000 0.088 0.752 0.004
#> GSM1068473 2 0.0000 0.8955 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1068474 2 0.0000 0.8955 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1068476 4 0.4305 0.4548 0.000 0.000 0.260 0.684 0.000 0.056
#> GSM1068477 5 0.3955 0.7589 0.000 0.132 0.000 0.092 0.772 0.004
#> GSM1068462 5 0.3563 0.7887 0.000 0.100 0.000 0.088 0.808 0.004
#> GSM1068463 3 0.0000 0.9812 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1068465 5 0.4577 0.4390 0.272 0.000 0.000 0.072 0.656 0.000
#> GSM1068466 1 0.1320 0.9009 0.948 0.000 0.000 0.036 0.016 0.000
#> GSM1068467 5 0.3806 0.7739 0.000 0.136 0.000 0.088 0.776 0.000
#> GSM1068469 5 0.1398 0.7897 0.000 0.052 0.000 0.008 0.940 0.000
#> GSM1068470 2 0.0000 0.8955 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1068471 2 0.0000 0.8955 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1068475 2 0.0000 0.8955 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1068528 1 0.1010 0.9033 0.960 0.000 0.004 0.036 0.000 0.000
#> GSM1068531 1 0.0622 0.9073 0.980 0.000 0.000 0.012 0.008 0.000
#> GSM1068532 1 0.0363 0.9074 0.988 0.000 0.000 0.012 0.000 0.000
#> GSM1068533 1 0.0000 0.9077 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1068535 6 0.3915 0.5351 0.000 0.000 0.000 0.412 0.004 0.584
#> GSM1068537 1 0.0363 0.9074 0.988 0.000 0.000 0.012 0.000 0.000
#> GSM1068538 1 0.0363 0.9074 0.988 0.000 0.000 0.012 0.000 0.000
#> GSM1068539 6 0.3164 0.5797 0.000 0.004 0.000 0.140 0.032 0.824
#> GSM1068540 1 0.0622 0.9073 0.980 0.000 0.000 0.012 0.008 0.000
#> GSM1068542 6 0.0260 0.6523 0.000 0.000 0.000 0.008 0.000 0.992
#> GSM1068543 6 0.3728 0.5521 0.000 0.000 0.000 0.344 0.004 0.652
#> GSM1068544 3 0.1556 0.8964 0.080 0.000 0.920 0.000 0.000 0.000
#> GSM1068545 2 0.4402 0.2513 0.000 0.564 0.000 0.020 0.004 0.412
#> GSM1068546 3 0.0000 0.9812 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1068547 1 0.0632 0.9057 0.976 0.000 0.000 0.000 0.024 0.000
#> GSM1068548 6 0.0291 0.6535 0.000 0.000 0.000 0.004 0.004 0.992
#> GSM1068549 3 0.0713 0.9557 0.000 0.000 0.972 0.028 0.000 0.000
#> GSM1068550 6 0.0363 0.6505 0.000 0.000 0.000 0.012 0.000 0.988
#> GSM1068551 2 0.0000 0.8955 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1068552 6 0.3690 0.4433 0.000 0.308 0.000 0.008 0.000 0.684
#> GSM1068555 2 0.0146 0.8938 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM1068556 6 0.3109 0.6228 0.000 0.000 0.000 0.224 0.004 0.772
#> GSM1068557 5 0.3366 0.7910 0.000 0.080 0.000 0.092 0.824 0.004
#> GSM1068560 6 0.1285 0.6485 0.000 0.000 0.000 0.052 0.004 0.944
#> GSM1068561 5 0.6197 -0.0583 0.000 0.008 0.000 0.368 0.396 0.228
#> GSM1068562 6 0.2048 0.6495 0.000 0.000 0.000 0.120 0.000 0.880
#> GSM1068563 6 0.1204 0.6613 0.000 0.000 0.000 0.056 0.000 0.944
#> GSM1068565 2 0.0146 0.8938 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM1068529 6 0.3862 0.4495 0.000 0.000 0.000 0.476 0.000 0.524
#> GSM1068530 1 0.0260 0.9079 0.992 0.000 0.000 0.000 0.008 0.000
#> GSM1068534 6 0.3944 0.5287 0.000 0.000 0.000 0.428 0.004 0.568
#> GSM1068536 1 0.5788 0.4284 0.584 0.000 0.000 0.088 0.276 0.052
#> GSM1068541 5 0.4866 0.6851 0.008 0.012 0.000 0.140 0.712 0.128
#> GSM1068553 6 0.3728 0.5521 0.000 0.000 0.000 0.344 0.004 0.652
#> GSM1068554 6 0.4373 0.5394 0.000 0.028 0.000 0.344 0.004 0.624
#> GSM1068558 4 0.3558 0.5232 0.000 0.028 0.000 0.760 0.000 0.212
#> GSM1068559 4 0.3747 -0.1937 0.000 0.000 0.000 0.604 0.000 0.396
#> GSM1068564 6 0.3684 0.4035 0.000 0.332 0.000 0.004 0.000 0.664
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 disease.state(p) gender(p) k
#> MAD:mclust 92 0.84752 1.000 2
#> MAD:mclust 75 0.00806 0.542 3
#> MAD:mclust 105 0.00324 0.699 4
#> MAD:mclust 103 0.00397 0.491 5
#> MAD:mclust 88 0.02845 0.649 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 38950 rows and 108 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'NMF' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.842 0.895 0.957 0.4946 0.504 0.504
#> 3 3 0.606 0.745 0.868 0.2760 0.827 0.674
#> 4 4 0.683 0.813 0.886 0.1677 0.749 0.439
#> 5 5 0.643 0.634 0.777 0.0729 0.895 0.625
#> 6 6 0.669 0.632 0.778 0.0448 0.896 0.561
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
#> GSM1068478 1 0.2948 0.9077 0.948 0.052
#> GSM1068479 2 0.0000 0.9569 0.000 1.000
#> GSM1068481 1 0.0000 0.9468 1.000 0.000
#> GSM1068482 1 0.0000 0.9468 1.000 0.000
#> GSM1068483 1 0.0000 0.9468 1.000 0.000
#> GSM1068486 1 0.0000 0.9468 1.000 0.000
#> GSM1068487 2 0.0000 0.9569 0.000 1.000
#> GSM1068488 2 0.5842 0.8315 0.140 0.860
#> GSM1068490 2 0.0000 0.9569 0.000 1.000
#> GSM1068491 1 0.0000 0.9468 1.000 0.000
#> GSM1068492 2 0.0000 0.9569 0.000 1.000
#> GSM1068493 1 0.8327 0.6447 0.736 0.264
#> GSM1068494 1 0.0000 0.9468 1.000 0.000
#> GSM1068495 2 0.0000 0.9569 0.000 1.000
#> GSM1068496 1 0.0000 0.9468 1.000 0.000
#> GSM1068498 2 0.5519 0.8409 0.128 0.872
#> GSM1068499 1 0.0000 0.9468 1.000 0.000
#> GSM1068500 1 0.0000 0.9468 1.000 0.000
#> GSM1068502 2 0.0000 0.9569 0.000 1.000
#> GSM1068503 2 0.0000 0.9569 0.000 1.000
#> GSM1068505 2 0.0000 0.9569 0.000 1.000
#> GSM1068506 2 0.0000 0.9569 0.000 1.000
#> GSM1068507 2 0.4562 0.8797 0.096 0.904
#> GSM1068508 2 0.0000 0.9569 0.000 1.000
#> GSM1068510 2 0.0000 0.9569 0.000 1.000
#> GSM1068512 1 0.3274 0.9003 0.940 0.060
#> GSM1068513 2 0.0000 0.9569 0.000 1.000
#> GSM1068514 1 0.8955 0.5405 0.688 0.312
#> GSM1068517 2 0.0000 0.9569 0.000 1.000
#> GSM1068518 1 1.0000 -0.0130 0.504 0.496
#> GSM1068520 1 0.0000 0.9468 1.000 0.000
#> GSM1068521 1 0.0000 0.9468 1.000 0.000
#> GSM1068522 2 0.0000 0.9569 0.000 1.000
#> GSM1068524 2 0.0000 0.9569 0.000 1.000
#> GSM1068527 1 0.9993 0.0438 0.516 0.484
#> GSM1068480 1 0.0000 0.9468 1.000 0.000
#> GSM1068484 2 0.0000 0.9569 0.000 1.000
#> GSM1068485 1 0.0000 0.9468 1.000 0.000
#> GSM1068489 2 0.0000 0.9569 0.000 1.000
#> GSM1068497 2 0.2603 0.9246 0.044 0.956
#> GSM1068501 2 0.0000 0.9569 0.000 1.000
#> GSM1068504 2 0.0000 0.9569 0.000 1.000
#> GSM1068509 1 0.0000 0.9468 1.000 0.000
#> GSM1068511 1 0.0000 0.9468 1.000 0.000
#> GSM1068515 1 0.4562 0.8641 0.904 0.096
#> GSM1068516 2 0.3274 0.9140 0.060 0.940
#> GSM1068519 1 0.0000 0.9468 1.000 0.000
#> GSM1068523 2 0.0000 0.9569 0.000 1.000
#> GSM1068525 2 0.0000 0.9569 0.000 1.000
#> GSM1068526 2 0.0376 0.9546 0.004 0.996
#> GSM1068458 1 0.0000 0.9468 1.000 0.000
#> GSM1068459 1 0.0000 0.9468 1.000 0.000
#> GSM1068460 1 0.9427 0.4267 0.640 0.360
#> GSM1068461 1 0.0000 0.9468 1.000 0.000
#> GSM1068464 2 0.0000 0.9569 0.000 1.000
#> GSM1068468 2 0.0000 0.9569 0.000 1.000
#> GSM1068472 2 0.0000 0.9569 0.000 1.000
#> GSM1068473 2 0.0000 0.9569 0.000 1.000
#> GSM1068474 2 0.0000 0.9569 0.000 1.000
#> GSM1068476 1 0.1414 0.9335 0.980 0.020
#> GSM1068477 2 0.0000 0.9569 0.000 1.000
#> GSM1068462 2 0.0000 0.9569 0.000 1.000
#> GSM1068463 1 0.0000 0.9468 1.000 0.000
#> GSM1068465 2 0.7453 0.7284 0.212 0.788
#> GSM1068466 1 0.0000 0.9468 1.000 0.000
#> GSM1068467 2 0.0000 0.9569 0.000 1.000
#> GSM1068469 2 0.5946 0.8213 0.144 0.856
#> GSM1068470 2 0.0000 0.9569 0.000 1.000
#> GSM1068471 2 0.0000 0.9569 0.000 1.000
#> GSM1068475 2 0.0000 0.9569 0.000 1.000
#> GSM1068528 1 0.0000 0.9468 1.000 0.000
#> GSM1068531 1 0.0000 0.9468 1.000 0.000
#> GSM1068532 1 0.0000 0.9468 1.000 0.000
#> GSM1068533 1 0.0000 0.9468 1.000 0.000
#> GSM1068535 1 0.0000 0.9468 1.000 0.000
#> GSM1068537 1 0.0000 0.9468 1.000 0.000
#> GSM1068538 1 0.0000 0.9468 1.000 0.000
#> GSM1068539 2 0.0000 0.9569 0.000 1.000
#> GSM1068540 1 0.0000 0.9468 1.000 0.000
#> GSM1068542 2 0.0672 0.9521 0.008 0.992
#> GSM1068543 2 0.9635 0.3733 0.388 0.612
#> GSM1068544 1 0.0000 0.9468 1.000 0.000
#> GSM1068545 2 0.0000 0.9569 0.000 1.000
#> GSM1068546 1 0.0000 0.9468 1.000 0.000
#> GSM1068547 1 0.0000 0.9468 1.000 0.000
#> GSM1068548 2 0.4161 0.8909 0.084 0.916
#> GSM1068549 1 0.0000 0.9468 1.000 0.000
#> GSM1068550 2 0.0000 0.9569 0.000 1.000
#> GSM1068551 2 0.0000 0.9569 0.000 1.000
#> GSM1068552 2 0.0000 0.9569 0.000 1.000
#> GSM1068555 2 0.0000 0.9569 0.000 1.000
#> GSM1068556 2 0.9393 0.4558 0.356 0.644
#> GSM1068557 2 0.0000 0.9569 0.000 1.000
#> GSM1068560 2 0.7139 0.7563 0.196 0.804
#> GSM1068561 2 0.0376 0.9546 0.004 0.996
#> GSM1068562 2 0.1414 0.9439 0.020 0.980
#> GSM1068563 2 0.0376 0.9546 0.004 0.996
#> GSM1068565 2 0.0000 0.9569 0.000 1.000
#> GSM1068529 1 0.2423 0.9195 0.960 0.040
#> GSM1068530 1 0.0000 0.9468 1.000 0.000
#> GSM1068534 1 0.0000 0.9468 1.000 0.000
#> GSM1068536 1 0.4562 0.8677 0.904 0.096
#> GSM1068541 2 0.0000 0.9569 0.000 1.000
#> GSM1068553 2 0.9732 0.3291 0.404 0.596
#> GSM1068554 2 0.0000 0.9569 0.000 1.000
#> GSM1068558 2 0.5059 0.8633 0.112 0.888
#> GSM1068559 1 0.0672 0.9418 0.992 0.008
#> GSM1068564 2 0.0000 0.9569 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1068478 1 0.1711 0.8439 0.960 0.008 0.032
#> GSM1068479 2 0.5926 0.4614 0.000 0.644 0.356
#> GSM1068481 3 0.2625 0.8238 0.084 0.000 0.916
#> GSM1068482 3 0.2537 0.8277 0.080 0.000 0.920
#> GSM1068483 1 0.2066 0.8380 0.940 0.000 0.060
#> GSM1068486 3 0.1860 0.8341 0.052 0.000 0.948
#> GSM1068487 2 0.0892 0.8531 0.000 0.980 0.020
#> GSM1068488 3 0.8671 0.2034 0.104 0.416 0.480
#> GSM1068490 2 0.1031 0.8536 0.000 0.976 0.024
#> GSM1068491 3 0.1647 0.8338 0.036 0.004 0.960
#> GSM1068492 3 0.6126 0.2749 0.000 0.400 0.600
#> GSM1068493 3 0.8065 0.0566 0.064 0.452 0.484
#> GSM1068494 1 0.5431 0.5705 0.716 0.000 0.284
#> GSM1068495 2 0.7175 0.4118 0.376 0.592 0.032
#> GSM1068496 1 0.1643 0.8583 0.956 0.000 0.044
#> GSM1068498 1 0.6762 0.5130 0.676 0.288 0.036
#> GSM1068499 1 0.4178 0.7517 0.828 0.000 0.172
#> GSM1068500 1 0.4504 0.6896 0.804 0.000 0.196
#> GSM1068502 2 0.6140 0.3305 0.000 0.596 0.404
#> GSM1068503 2 0.1031 0.8517 0.000 0.976 0.024
#> GSM1068505 2 0.3213 0.8176 0.092 0.900 0.008
#> GSM1068506 2 0.0661 0.8555 0.008 0.988 0.004
#> GSM1068507 2 0.4351 0.7545 0.004 0.828 0.168
#> GSM1068508 2 0.2689 0.8437 0.036 0.932 0.032
#> GSM1068510 2 0.5621 0.5137 0.000 0.692 0.308
#> GSM1068512 1 0.8102 0.2622 0.556 0.368 0.076
#> GSM1068513 2 0.1163 0.8520 0.000 0.972 0.028
#> GSM1068514 3 0.2590 0.8120 0.004 0.072 0.924
#> GSM1068517 2 0.7209 0.4214 0.360 0.604 0.036
#> GSM1068518 2 0.5956 0.6502 0.264 0.720 0.016
#> GSM1068520 1 0.0592 0.8577 0.988 0.012 0.000
#> GSM1068521 1 0.0000 0.8608 1.000 0.000 0.000
#> GSM1068522 2 0.0237 0.8545 0.000 0.996 0.004
#> GSM1068524 2 0.1031 0.8523 0.000 0.976 0.024
#> GSM1068527 1 0.5597 0.6453 0.764 0.216 0.020
#> GSM1068480 3 0.2066 0.8326 0.060 0.000 0.940
#> GSM1068484 2 0.1031 0.8510 0.000 0.976 0.024
#> GSM1068485 3 0.3340 0.7978 0.120 0.000 0.880
#> GSM1068489 2 0.1267 0.8513 0.004 0.972 0.024
#> GSM1068497 2 0.7672 0.0840 0.468 0.488 0.044
#> GSM1068501 2 0.1031 0.8510 0.000 0.976 0.024
#> GSM1068504 2 0.1411 0.8545 0.000 0.964 0.036
#> GSM1068509 1 0.1289 0.8610 0.968 0.000 0.032
#> GSM1068511 3 0.5526 0.7150 0.172 0.036 0.792
#> GSM1068515 1 0.7742 0.4764 0.632 0.080 0.288
#> GSM1068516 2 0.2297 0.8462 0.036 0.944 0.020
#> GSM1068519 1 0.1411 0.8600 0.964 0.000 0.036
#> GSM1068523 2 0.2297 0.8485 0.020 0.944 0.036
#> GSM1068525 2 0.1031 0.8510 0.000 0.976 0.024
#> GSM1068526 2 0.0892 0.8517 0.000 0.980 0.020
#> GSM1068458 1 0.0424 0.8610 0.992 0.000 0.008
#> GSM1068459 3 0.2796 0.8222 0.092 0.000 0.908
#> GSM1068460 1 0.1585 0.8468 0.964 0.028 0.008
#> GSM1068461 3 0.2066 0.8326 0.060 0.000 0.940
#> GSM1068464 2 0.1878 0.8533 0.004 0.952 0.044
#> GSM1068468 2 0.2903 0.8449 0.028 0.924 0.048
#> GSM1068472 2 0.3112 0.8438 0.028 0.916 0.056
#> GSM1068473 2 0.0592 0.8548 0.000 0.988 0.012
#> GSM1068474 2 0.1878 0.8522 0.004 0.952 0.044
#> GSM1068476 3 0.2804 0.8193 0.016 0.060 0.924
#> GSM1068477 2 0.2926 0.8411 0.040 0.924 0.036
#> GSM1068462 2 0.6026 0.6791 0.024 0.732 0.244
#> GSM1068463 3 0.3816 0.7700 0.148 0.000 0.852
#> GSM1068465 1 0.4931 0.7222 0.828 0.140 0.032
#> GSM1068466 1 0.0000 0.8608 1.000 0.000 0.000
#> GSM1068467 2 0.2903 0.8449 0.028 0.924 0.048
#> GSM1068469 2 0.9001 0.4394 0.280 0.548 0.172
#> GSM1068470 2 0.2564 0.8453 0.028 0.936 0.036
#> GSM1068471 2 0.1878 0.8522 0.004 0.952 0.044
#> GSM1068475 2 0.2152 0.8496 0.016 0.948 0.036
#> GSM1068528 1 0.1964 0.8529 0.944 0.000 0.056
#> GSM1068531 1 0.1289 0.8610 0.968 0.000 0.032
#> GSM1068532 1 0.1643 0.8571 0.956 0.000 0.044
#> GSM1068533 1 0.1529 0.8598 0.960 0.000 0.040
#> GSM1068535 1 0.7192 0.2703 0.560 0.028 0.412
#> GSM1068537 1 0.1289 0.8610 0.968 0.000 0.032
#> GSM1068538 1 0.1289 0.8610 0.968 0.000 0.032
#> GSM1068539 2 0.4295 0.8156 0.104 0.864 0.032
#> GSM1068540 1 0.0892 0.8619 0.980 0.000 0.020
#> GSM1068542 2 0.5366 0.7099 0.208 0.776 0.016
#> GSM1068543 2 0.7844 0.5845 0.220 0.660 0.120
#> GSM1068544 1 0.3619 0.7933 0.864 0.000 0.136
#> GSM1068545 2 0.1905 0.8518 0.028 0.956 0.016
#> GSM1068546 3 0.2261 0.8316 0.068 0.000 0.932
#> GSM1068547 1 0.0592 0.8577 0.988 0.012 0.000
#> GSM1068548 2 0.5728 0.6381 0.272 0.720 0.008
#> GSM1068549 3 0.1860 0.8342 0.052 0.000 0.948
#> GSM1068550 2 0.2902 0.8325 0.064 0.920 0.016
#> GSM1068551 2 0.2152 0.8496 0.016 0.948 0.036
#> GSM1068552 2 0.0747 0.8526 0.000 0.984 0.016
#> GSM1068555 2 0.1832 0.8515 0.008 0.956 0.036
#> GSM1068556 2 0.6341 0.6491 0.252 0.716 0.032
#> GSM1068557 2 0.2773 0.8466 0.024 0.928 0.048
#> GSM1068560 2 0.6062 0.6256 0.276 0.708 0.016
#> GSM1068561 2 0.2448 0.8474 0.000 0.924 0.076
#> GSM1068562 2 0.1453 0.8505 0.008 0.968 0.024
#> GSM1068563 2 0.0661 0.8550 0.004 0.988 0.008
#> GSM1068565 2 0.2297 0.8485 0.020 0.944 0.036
#> GSM1068529 3 0.4209 0.7847 0.020 0.120 0.860
#> GSM1068530 1 0.0000 0.8608 1.000 0.000 0.000
#> GSM1068534 3 0.5167 0.7401 0.172 0.024 0.804
#> GSM1068536 1 0.2187 0.8375 0.948 0.028 0.024
#> GSM1068541 2 0.5574 0.7344 0.184 0.784 0.032
#> GSM1068553 2 0.9736 -0.0476 0.228 0.416 0.356
#> GSM1068554 2 0.2878 0.8190 0.000 0.904 0.096
#> GSM1068558 3 0.3038 0.7964 0.000 0.104 0.896
#> GSM1068559 3 0.2846 0.8215 0.020 0.056 0.924
#> GSM1068564 2 0.0424 0.8539 0.000 0.992 0.008
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1068478 1 0.2647 0.88499 0.880 0.120 0.000 0.000
#> GSM1068479 3 0.6079 0.00562 0.000 0.464 0.492 0.044
#> GSM1068481 3 0.2186 0.85417 0.008 0.048 0.932 0.012
#> GSM1068482 3 0.0188 0.87859 0.000 0.000 0.996 0.004
#> GSM1068483 1 0.3099 0.88732 0.876 0.104 0.020 0.000
#> GSM1068486 3 0.0469 0.87807 0.000 0.000 0.988 0.012
#> GSM1068487 2 0.4776 0.48349 0.000 0.624 0.000 0.376
#> GSM1068488 4 0.2413 0.85732 0.036 0.004 0.036 0.924
#> GSM1068490 2 0.3801 0.77880 0.000 0.780 0.000 0.220
#> GSM1068491 3 0.0188 0.87877 0.000 0.004 0.996 0.000
#> GSM1068492 3 0.5859 0.49078 0.000 0.064 0.652 0.284
#> GSM1068493 2 0.3674 0.75068 0.044 0.852 0.104 0.000
#> GSM1068494 1 0.3216 0.86630 0.880 0.000 0.044 0.076
#> GSM1068495 1 0.6383 0.41251 0.612 0.292 0.000 0.096
#> GSM1068496 1 0.1661 0.91741 0.944 0.052 0.004 0.000
#> GSM1068498 2 0.2281 0.78987 0.096 0.904 0.000 0.000
#> GSM1068499 1 0.3899 0.85251 0.840 0.052 0.108 0.000
#> GSM1068500 1 0.3556 0.88185 0.864 0.096 0.036 0.004
#> GSM1068502 3 0.6477 0.10591 0.000 0.420 0.508 0.072
#> GSM1068503 4 0.2973 0.82483 0.000 0.144 0.000 0.856
#> GSM1068505 4 0.1297 0.86592 0.020 0.016 0.000 0.964
#> GSM1068506 4 0.2149 0.86335 0.000 0.088 0.000 0.912
#> GSM1068507 4 0.1824 0.86959 0.000 0.060 0.004 0.936
#> GSM1068508 2 0.2773 0.87094 0.004 0.880 0.000 0.116
#> GSM1068510 4 0.2623 0.87008 0.000 0.064 0.028 0.908
#> GSM1068512 4 0.4632 0.59400 0.308 0.000 0.004 0.688
#> GSM1068513 4 0.4072 0.67442 0.000 0.252 0.000 0.748
#> GSM1068514 3 0.3626 0.72574 0.000 0.004 0.812 0.184
#> GSM1068517 2 0.1637 0.81724 0.060 0.940 0.000 0.000
#> GSM1068518 4 0.4149 0.80494 0.152 0.036 0.000 0.812
#> GSM1068520 1 0.1637 0.91481 0.940 0.060 0.000 0.000
#> GSM1068521 1 0.1118 0.91962 0.964 0.036 0.000 0.000
#> GSM1068522 4 0.2647 0.84239 0.000 0.120 0.000 0.880
#> GSM1068524 4 0.3266 0.79861 0.000 0.168 0.000 0.832
#> GSM1068527 4 0.4477 0.59049 0.312 0.000 0.000 0.688
#> GSM1068480 3 0.0188 0.87859 0.000 0.000 0.996 0.004
#> GSM1068484 4 0.2011 0.86587 0.000 0.080 0.000 0.920
#> GSM1068485 3 0.1510 0.86009 0.016 0.028 0.956 0.000
#> GSM1068489 4 0.1256 0.87207 0.008 0.028 0.000 0.964
#> GSM1068497 2 0.1637 0.81724 0.060 0.940 0.000 0.000
#> GSM1068501 4 0.1637 0.87204 0.000 0.060 0.000 0.940
#> GSM1068504 2 0.2921 0.85649 0.000 0.860 0.000 0.140
#> GSM1068509 1 0.1118 0.91839 0.964 0.000 0.000 0.036
#> GSM1068511 4 0.4483 0.76180 0.088 0.000 0.104 0.808
#> GSM1068515 2 0.4178 0.71784 0.140 0.824 0.016 0.020
#> GSM1068516 4 0.2549 0.86932 0.024 0.056 0.004 0.916
#> GSM1068519 1 0.1576 0.90979 0.948 0.000 0.004 0.048
#> GSM1068523 2 0.2647 0.86924 0.000 0.880 0.000 0.120
#> GSM1068525 4 0.2216 0.86133 0.000 0.092 0.000 0.908
#> GSM1068526 4 0.1474 0.87161 0.000 0.052 0.000 0.948
#> GSM1068458 1 0.2282 0.91432 0.924 0.052 0.000 0.024
#> GSM1068459 3 0.0469 0.87597 0.012 0.000 0.988 0.000
#> GSM1068460 1 0.0921 0.91941 0.972 0.000 0.000 0.028
#> GSM1068461 3 0.0000 0.87883 0.000 0.000 1.000 0.000
#> GSM1068464 2 0.2345 0.87301 0.000 0.900 0.000 0.100
#> GSM1068468 2 0.0967 0.85420 0.004 0.976 0.004 0.016
#> GSM1068472 2 0.1191 0.83856 0.024 0.968 0.004 0.004
#> GSM1068473 2 0.4331 0.67784 0.000 0.712 0.000 0.288
#> GSM1068474 2 0.2345 0.87301 0.000 0.900 0.000 0.100
#> GSM1068476 3 0.0376 0.87925 0.000 0.004 0.992 0.004
#> GSM1068477 2 0.2053 0.87094 0.004 0.924 0.000 0.072
#> GSM1068462 2 0.1411 0.82986 0.020 0.960 0.020 0.000
#> GSM1068463 3 0.0937 0.87499 0.012 0.000 0.976 0.012
#> GSM1068465 1 0.2976 0.87801 0.872 0.120 0.000 0.008
#> GSM1068466 1 0.2048 0.91305 0.928 0.064 0.000 0.008
#> GSM1068467 2 0.0712 0.84687 0.008 0.984 0.004 0.004
#> GSM1068469 2 0.1722 0.82051 0.048 0.944 0.008 0.000
#> GSM1068470 2 0.2408 0.87300 0.000 0.896 0.000 0.104
#> GSM1068471 2 0.2345 0.87301 0.000 0.900 0.000 0.100
#> GSM1068475 2 0.2281 0.87312 0.000 0.904 0.000 0.096
#> GSM1068528 1 0.2521 0.90902 0.912 0.064 0.024 0.000
#> GSM1068531 1 0.1118 0.91687 0.964 0.000 0.000 0.036
#> GSM1068532 1 0.1489 0.91224 0.952 0.000 0.004 0.044
#> GSM1068533 1 0.1584 0.92058 0.952 0.012 0.000 0.036
#> GSM1068535 4 0.3937 0.72297 0.188 0.000 0.012 0.800
#> GSM1068537 1 0.1302 0.91668 0.956 0.000 0.000 0.044
#> GSM1068538 1 0.1716 0.91166 0.936 0.000 0.000 0.064
#> GSM1068539 2 0.6495 0.59960 0.108 0.608 0.000 0.284
#> GSM1068540 1 0.0921 0.91941 0.972 0.000 0.000 0.028
#> GSM1068542 4 0.1256 0.86199 0.028 0.008 0.000 0.964
#> GSM1068543 4 0.1211 0.85997 0.040 0.000 0.000 0.960
#> GSM1068544 1 0.2644 0.90240 0.908 0.032 0.060 0.000
#> GSM1068545 4 0.4134 0.65759 0.000 0.260 0.000 0.740
#> GSM1068546 3 0.0817 0.87445 0.000 0.000 0.976 0.024
#> GSM1068547 1 0.1118 0.91687 0.964 0.000 0.000 0.036
#> GSM1068548 4 0.1716 0.84735 0.064 0.000 0.000 0.936
#> GSM1068549 3 0.0188 0.87914 0.000 0.000 0.996 0.004
#> GSM1068550 4 0.1520 0.87081 0.024 0.020 0.000 0.956
#> GSM1068551 2 0.2921 0.85693 0.000 0.860 0.000 0.140
#> GSM1068552 4 0.1940 0.86623 0.000 0.076 0.000 0.924
#> GSM1068555 2 0.2281 0.87379 0.000 0.904 0.000 0.096
#> GSM1068556 4 0.2149 0.83359 0.088 0.000 0.000 0.912
#> GSM1068557 2 0.1109 0.85940 0.000 0.968 0.004 0.028
#> GSM1068560 4 0.1807 0.86524 0.052 0.008 0.000 0.940
#> GSM1068561 2 0.4225 0.81188 0.000 0.792 0.024 0.184
#> GSM1068562 4 0.2021 0.87188 0.012 0.056 0.000 0.932
#> GSM1068563 4 0.2654 0.85159 0.000 0.108 0.004 0.888
#> GSM1068565 2 0.2647 0.86748 0.000 0.880 0.000 0.120
#> GSM1068529 3 0.4034 0.71286 0.004 0.008 0.796 0.192
#> GSM1068530 1 0.0336 0.92228 0.992 0.008 0.000 0.000
#> GSM1068534 4 0.5358 0.61091 0.048 0.000 0.252 0.700
#> GSM1068536 1 0.1151 0.92217 0.968 0.008 0.000 0.024
#> GSM1068541 2 0.3972 0.84867 0.080 0.840 0.000 0.080
#> GSM1068553 4 0.2081 0.82137 0.084 0.000 0.000 0.916
#> GSM1068554 4 0.1302 0.87273 0.000 0.044 0.000 0.956
#> GSM1068558 4 0.4998 0.08594 0.000 0.000 0.488 0.512
#> GSM1068559 3 0.0188 0.87914 0.000 0.000 0.996 0.004
#> GSM1068564 4 0.2345 0.85707 0.000 0.100 0.000 0.900
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM1068478 1 0.4100 0.7439 0.764 0.192 0.000 0.000 0.044
#> GSM1068479 2 0.4886 0.1738 0.000 0.528 0.448 0.024 0.000
#> GSM1068481 3 0.3907 0.8382 0.068 0.032 0.832 0.068 0.000
#> GSM1068482 3 0.0510 0.8905 0.000 0.000 0.984 0.000 0.016
#> GSM1068483 1 0.1768 0.8485 0.924 0.072 0.000 0.004 0.000
#> GSM1068486 3 0.2632 0.8641 0.040 0.000 0.888 0.072 0.000
#> GSM1068487 2 0.5557 -0.0451 0.000 0.472 0.000 0.460 0.068
#> GSM1068488 5 0.2286 0.5701 0.000 0.000 0.004 0.108 0.888
#> GSM1068490 2 0.4727 0.1753 0.000 0.532 0.000 0.452 0.016
#> GSM1068491 3 0.0613 0.8915 0.000 0.008 0.984 0.004 0.004
#> GSM1068492 3 0.5078 0.6679 0.000 0.040 0.740 0.156 0.064
#> GSM1068493 2 0.1788 0.7951 0.056 0.932 0.004 0.000 0.008
#> GSM1068494 5 0.4415 0.1161 0.388 0.000 0.008 0.000 0.604
#> GSM1068495 5 0.5274 0.1275 0.372 0.056 0.000 0.000 0.572
#> GSM1068496 1 0.2144 0.8499 0.912 0.020 0.000 0.000 0.068
#> GSM1068498 2 0.2798 0.7348 0.140 0.852 0.000 0.000 0.008
#> GSM1068499 1 0.4888 0.6285 0.676 0.004 0.048 0.000 0.272
#> GSM1068500 1 0.1638 0.8504 0.932 0.064 0.000 0.004 0.000
#> GSM1068502 3 0.6255 0.3681 0.000 0.248 0.572 0.172 0.008
#> GSM1068503 4 0.6399 0.5155 0.000 0.196 0.000 0.496 0.308
#> GSM1068505 4 0.4807 0.4045 0.008 0.008 0.000 0.520 0.464
#> GSM1068506 4 0.3795 0.6174 0.000 0.028 0.000 0.780 0.192
#> GSM1068507 4 0.1996 0.5288 0.036 0.032 0.004 0.928 0.000
#> GSM1068508 2 0.2740 0.8077 0.000 0.876 0.000 0.096 0.028
#> GSM1068510 5 0.4701 -0.0651 0.000 0.016 0.004 0.368 0.612
#> GSM1068512 5 0.5264 0.4255 0.128 0.000 0.000 0.196 0.676
#> GSM1068513 4 0.4577 0.5664 0.000 0.176 0.000 0.740 0.084
#> GSM1068514 3 0.2074 0.8638 0.000 0.000 0.920 0.044 0.036
#> GSM1068517 2 0.2193 0.7734 0.092 0.900 0.000 0.000 0.008
#> GSM1068518 5 0.2237 0.6056 0.084 0.008 0.004 0.000 0.904
#> GSM1068520 1 0.0992 0.8600 0.968 0.024 0.000 0.008 0.000
#> GSM1068521 1 0.1571 0.8545 0.936 0.004 0.000 0.000 0.060
#> GSM1068522 4 0.1942 0.5730 0.000 0.068 0.000 0.920 0.012
#> GSM1068524 5 0.5339 0.3124 0.000 0.176 0.000 0.152 0.672
#> GSM1068527 5 0.2597 0.5967 0.092 0.000 0.000 0.024 0.884
#> GSM1068480 3 0.1341 0.8703 0.000 0.000 0.944 0.000 0.056
#> GSM1068484 5 0.2966 0.5270 0.000 0.016 0.000 0.136 0.848
#> GSM1068485 3 0.0613 0.8921 0.008 0.004 0.984 0.000 0.004
#> GSM1068489 4 0.4510 0.4656 0.000 0.008 0.000 0.560 0.432
#> GSM1068497 2 0.2358 0.7658 0.104 0.888 0.000 0.000 0.008
#> GSM1068501 4 0.4206 0.5951 0.000 0.016 0.000 0.696 0.288
#> GSM1068504 2 0.2932 0.7953 0.000 0.864 0.000 0.104 0.032
#> GSM1068509 1 0.3756 0.7039 0.744 0.000 0.000 0.008 0.248
#> GSM1068511 5 0.7467 -0.0669 0.064 0.000 0.176 0.304 0.456
#> GSM1068515 2 0.3999 0.6375 0.240 0.740 0.000 0.020 0.000
#> GSM1068516 5 0.0566 0.6189 0.012 0.004 0.000 0.000 0.984
#> GSM1068519 1 0.4289 0.7826 0.760 0.000 0.000 0.064 0.176
#> GSM1068523 2 0.3696 0.6842 0.000 0.772 0.000 0.016 0.212
#> GSM1068525 5 0.1597 0.6036 0.000 0.012 0.000 0.048 0.940
#> GSM1068526 5 0.4826 -0.3857 0.000 0.020 0.000 0.472 0.508
#> GSM1068458 1 0.4181 0.6883 0.676 0.004 0.004 0.316 0.000
#> GSM1068459 3 0.0798 0.8929 0.016 0.000 0.976 0.000 0.008
#> GSM1068460 1 0.3370 0.8329 0.824 0.000 0.000 0.148 0.028
#> GSM1068461 3 0.1579 0.8845 0.032 0.000 0.944 0.024 0.000
#> GSM1068464 2 0.3942 0.6517 0.000 0.728 0.000 0.260 0.012
#> GSM1068468 2 0.1357 0.8179 0.004 0.948 0.000 0.048 0.000
#> GSM1068472 2 0.1365 0.8181 0.004 0.952 0.004 0.040 0.000
#> GSM1068473 4 0.4803 0.0278 0.000 0.444 0.000 0.536 0.020
#> GSM1068474 2 0.3690 0.7045 0.000 0.764 0.000 0.224 0.012
#> GSM1068476 3 0.0290 0.8933 0.000 0.000 0.992 0.008 0.000
#> GSM1068477 2 0.1965 0.8120 0.000 0.904 0.000 0.096 0.000
#> GSM1068462 2 0.1393 0.8123 0.008 0.956 0.024 0.012 0.000
#> GSM1068463 3 0.4531 0.7646 0.144 0.004 0.760 0.092 0.000
#> GSM1068465 1 0.5402 0.7812 0.720 0.124 0.000 0.120 0.036
#> GSM1068466 1 0.2409 0.8549 0.900 0.032 0.000 0.068 0.000
#> GSM1068467 2 0.0451 0.8132 0.008 0.988 0.000 0.004 0.000
#> GSM1068469 2 0.1153 0.8085 0.024 0.964 0.008 0.004 0.000
#> GSM1068470 2 0.2153 0.8111 0.000 0.916 0.000 0.040 0.044
#> GSM1068471 2 0.3280 0.7557 0.000 0.812 0.000 0.176 0.012
#> GSM1068475 2 0.2573 0.8040 0.000 0.880 0.000 0.104 0.016
#> GSM1068528 1 0.0955 0.8585 0.968 0.028 0.000 0.000 0.004
#> GSM1068531 1 0.1697 0.8579 0.932 0.000 0.000 0.060 0.008
#> GSM1068532 1 0.2017 0.8531 0.912 0.000 0.000 0.080 0.008
#> GSM1068533 1 0.4166 0.6527 0.648 0.000 0.004 0.348 0.000
#> GSM1068535 4 0.4803 0.3588 0.184 0.000 0.000 0.720 0.096
#> GSM1068537 1 0.1908 0.8502 0.908 0.000 0.000 0.092 0.000
#> GSM1068538 1 0.3816 0.7106 0.696 0.000 0.000 0.304 0.000
#> GSM1068539 5 0.3359 0.5771 0.108 0.052 0.000 0.000 0.840
#> GSM1068540 1 0.1764 0.8535 0.928 0.000 0.000 0.008 0.064
#> GSM1068542 4 0.4530 0.5306 0.008 0.004 0.000 0.612 0.376
#> GSM1068543 5 0.1430 0.6057 0.004 0.000 0.000 0.052 0.944
#> GSM1068544 1 0.1168 0.8581 0.960 0.000 0.032 0.000 0.008
#> GSM1068545 4 0.6233 0.4706 0.000 0.144 0.000 0.460 0.396
#> GSM1068546 3 0.3795 0.8187 0.044 0.000 0.808 0.144 0.004
#> GSM1068547 1 0.2708 0.8541 0.884 0.000 0.000 0.072 0.044
#> GSM1068548 4 0.3562 0.5963 0.016 0.000 0.000 0.788 0.196
#> GSM1068549 3 0.0162 0.8929 0.000 0.000 0.996 0.004 0.000
#> GSM1068550 5 0.4510 -0.2460 0.000 0.008 0.000 0.432 0.560
#> GSM1068551 2 0.3297 0.7845 0.000 0.848 0.000 0.084 0.068
#> GSM1068552 4 0.5396 0.4598 0.000 0.056 0.000 0.500 0.444
#> GSM1068555 2 0.2172 0.8026 0.000 0.908 0.000 0.016 0.076
#> GSM1068556 5 0.3838 0.2761 0.004 0.000 0.000 0.280 0.716
#> GSM1068557 2 0.0798 0.8122 0.008 0.976 0.000 0.000 0.016
#> GSM1068560 5 0.0162 0.6181 0.004 0.000 0.000 0.000 0.996
#> GSM1068561 5 0.5076 0.2525 0.028 0.372 0.000 0.008 0.592
#> GSM1068562 5 0.2358 0.5673 0.000 0.008 0.000 0.104 0.888
#> GSM1068563 4 0.4972 0.5643 0.000 0.044 0.000 0.620 0.336
#> GSM1068565 2 0.2873 0.7946 0.000 0.860 0.000 0.120 0.020
#> GSM1068529 5 0.3742 0.5419 0.012 0.012 0.184 0.000 0.792
#> GSM1068530 1 0.1485 0.8609 0.948 0.000 0.000 0.020 0.032
#> GSM1068534 5 0.3516 0.5741 0.020 0.000 0.152 0.008 0.820
#> GSM1068536 1 0.3632 0.7737 0.800 0.020 0.000 0.004 0.176
#> GSM1068541 2 0.3882 0.7802 0.100 0.824 0.000 0.060 0.016
#> GSM1068553 4 0.4116 0.5105 0.028 0.000 0.004 0.756 0.212
#> GSM1068554 4 0.2699 0.5880 0.000 0.008 0.012 0.880 0.100
#> GSM1068558 5 0.3597 0.5530 0.000 0.008 0.180 0.012 0.800
#> GSM1068559 3 0.0162 0.8922 0.000 0.000 0.996 0.000 0.004
#> GSM1068564 4 0.5747 0.5028 0.000 0.088 0.000 0.504 0.408
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1068478 1 0.4631 0.4590 0.596 0.352 0.000 0.000 0.000 0.052
#> GSM1068479 3 0.4803 0.4089 0.000 0.316 0.616 0.064 0.004 0.000
#> GSM1068481 3 0.5367 0.6928 0.108 0.060 0.712 0.012 0.104 0.004
#> GSM1068482 3 0.1637 0.8068 0.056 0.000 0.932 0.004 0.004 0.004
#> GSM1068483 1 0.1398 0.7639 0.940 0.052 0.000 0.008 0.000 0.000
#> GSM1068486 3 0.2739 0.7728 0.012 0.004 0.864 0.004 0.112 0.004
#> GSM1068487 4 0.5279 -0.0109 0.000 0.416 0.000 0.500 0.076 0.008
#> GSM1068488 6 0.4543 0.0901 0.000 0.000 0.016 0.384 0.016 0.584
#> GSM1068490 2 0.5234 0.3489 0.000 0.532 0.000 0.384 0.076 0.008
#> GSM1068491 3 0.0000 0.8068 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1068492 3 0.4165 0.5283 0.000 0.004 0.676 0.292 0.000 0.028
#> GSM1068493 2 0.2095 0.8051 0.040 0.916 0.028 0.000 0.000 0.016
#> GSM1068494 6 0.3166 0.6367 0.156 0.000 0.004 0.000 0.024 0.816
#> GSM1068495 6 0.4347 0.6118 0.176 0.064 0.000 0.008 0.008 0.744
#> GSM1068496 1 0.1225 0.7622 0.956 0.004 0.004 0.004 0.000 0.032
#> GSM1068498 2 0.2333 0.7846 0.060 0.900 0.000 0.004 0.004 0.032
#> GSM1068499 6 0.5443 0.3778 0.308 0.036 0.032 0.000 0.020 0.604
#> GSM1068500 1 0.1637 0.7628 0.932 0.056 0.000 0.004 0.004 0.004
#> GSM1068502 3 0.4522 0.5439 0.000 0.076 0.672 0.252 0.000 0.000
#> GSM1068503 4 0.4143 0.6749 0.000 0.064 0.000 0.788 0.052 0.096
#> GSM1068505 4 0.5574 0.4245 0.000 0.000 0.000 0.512 0.332 0.156
#> GSM1068506 4 0.0748 0.6566 0.000 0.004 0.000 0.976 0.004 0.016
#> GSM1068507 5 0.1699 0.7936 0.008 0.012 0.000 0.040 0.936 0.004
#> GSM1068508 2 0.3479 0.7790 0.004 0.796 0.000 0.172 0.008 0.020
#> GSM1068510 5 0.4430 0.7090 0.000 0.000 0.016 0.132 0.744 0.108
#> GSM1068512 4 0.6354 0.2641 0.252 0.000 0.008 0.412 0.004 0.324
#> GSM1068513 5 0.3472 0.6956 0.000 0.092 0.000 0.100 0.808 0.000
#> GSM1068514 3 0.1807 0.7950 0.000 0.000 0.920 0.060 0.000 0.020
#> GSM1068517 2 0.1788 0.8038 0.028 0.928 0.000 0.004 0.000 0.040
#> GSM1068518 6 0.1625 0.7086 0.060 0.000 0.000 0.012 0.000 0.928
#> GSM1068520 1 0.2973 0.7598 0.860 0.040 0.000 0.000 0.084 0.016
#> GSM1068521 1 0.5936 0.3229 0.504 0.032 0.000 0.000 0.108 0.356
#> GSM1068522 4 0.3301 0.5562 0.000 0.024 0.000 0.788 0.188 0.000
#> GSM1068524 6 0.5946 0.2865 0.000 0.180 0.000 0.212 0.032 0.576
#> GSM1068527 6 0.1857 0.7038 0.028 0.000 0.000 0.004 0.044 0.924
#> GSM1068480 3 0.0937 0.8037 0.000 0.000 0.960 0.000 0.000 0.040
#> GSM1068484 6 0.3073 0.5652 0.000 0.000 0.000 0.204 0.008 0.788
#> GSM1068485 3 0.0363 0.8091 0.012 0.000 0.988 0.000 0.000 0.000
#> GSM1068489 4 0.5464 0.5260 0.000 0.000 0.000 0.564 0.260 0.176
#> GSM1068497 2 0.1708 0.8067 0.024 0.932 0.000 0.004 0.000 0.040
#> GSM1068501 5 0.3435 0.7530 0.000 0.000 0.000 0.060 0.804 0.136
#> GSM1068504 2 0.3732 0.7598 0.000 0.776 0.000 0.180 0.032 0.012
#> GSM1068509 1 0.2902 0.6731 0.800 0.000 0.000 0.004 0.000 0.196
#> GSM1068511 1 0.7743 -0.1019 0.352 0.000 0.252 0.244 0.012 0.140
#> GSM1068515 2 0.2721 0.7601 0.088 0.868 0.000 0.000 0.040 0.004
#> GSM1068516 6 0.2299 0.7001 0.012 0.008 0.000 0.064 0.012 0.904
#> GSM1068519 5 0.5157 0.3245 0.096 0.000 0.000 0.000 0.544 0.360
#> GSM1068523 2 0.4146 0.5474 0.000 0.676 0.000 0.036 0.000 0.288
#> GSM1068525 6 0.2482 0.6420 0.000 0.004 0.000 0.148 0.000 0.848
#> GSM1068526 4 0.3929 0.6180 0.000 0.000 0.000 0.700 0.028 0.272
#> GSM1068458 1 0.5742 0.5666 0.580 0.008 0.000 0.156 0.248 0.008
#> GSM1068459 3 0.1788 0.7996 0.076 0.000 0.916 0.004 0.000 0.004
#> GSM1068460 1 0.5999 0.5665 0.580 0.004 0.000 0.064 0.268 0.084
#> GSM1068461 3 0.1788 0.8038 0.012 0.004 0.928 0.004 0.052 0.000
#> GSM1068464 4 0.4569 -0.1384 0.000 0.456 0.000 0.516 0.016 0.012
#> GSM1068468 2 0.1674 0.8260 0.004 0.924 0.000 0.068 0.004 0.000
#> GSM1068472 2 0.1686 0.8267 0.004 0.932 0.004 0.052 0.008 0.000
#> GSM1068473 2 0.6043 0.3620 0.000 0.488 0.000 0.240 0.264 0.008
#> GSM1068474 2 0.3210 0.7754 0.000 0.804 0.000 0.168 0.028 0.000
#> GSM1068476 3 0.1471 0.7997 0.000 0.004 0.932 0.000 0.064 0.000
#> GSM1068477 2 0.1657 0.8281 0.000 0.928 0.000 0.056 0.016 0.000
#> GSM1068462 2 0.0520 0.8248 0.000 0.984 0.000 0.008 0.008 0.000
#> GSM1068463 3 0.5193 0.5363 0.284 0.000 0.616 0.008 0.088 0.004
#> GSM1068465 1 0.5615 0.5237 0.604 0.220 0.000 0.160 0.004 0.012
#> GSM1068466 1 0.4483 0.7087 0.728 0.068 0.000 0.004 0.188 0.012
#> GSM1068467 2 0.0405 0.8247 0.000 0.988 0.000 0.008 0.004 0.000
#> GSM1068469 2 0.0551 0.8227 0.004 0.984 0.000 0.000 0.008 0.004
#> GSM1068470 2 0.2436 0.8205 0.000 0.880 0.000 0.088 0.000 0.032
#> GSM1068471 2 0.3404 0.7310 0.000 0.760 0.000 0.224 0.016 0.000
#> GSM1068475 2 0.2278 0.8085 0.000 0.868 0.000 0.128 0.004 0.000
#> GSM1068528 1 0.1950 0.7629 0.924 0.044 0.004 0.000 0.008 0.020
#> GSM1068531 1 0.3641 0.6924 0.748 0.000 0.000 0.000 0.224 0.028
#> GSM1068532 1 0.1787 0.7626 0.932 0.000 0.000 0.016 0.020 0.032
#> GSM1068533 1 0.4579 0.6716 0.696 0.000 0.000 0.092 0.208 0.004
#> GSM1068535 5 0.1350 0.7987 0.020 0.000 0.000 0.020 0.952 0.008
#> GSM1068537 1 0.1806 0.7613 0.928 0.000 0.000 0.020 0.044 0.008
#> GSM1068538 1 0.4584 0.6762 0.700 0.000 0.000 0.100 0.196 0.004
#> GSM1068539 6 0.3964 0.6701 0.092 0.064 0.000 0.024 0.012 0.808
#> GSM1068540 1 0.1753 0.7548 0.912 0.000 0.000 0.000 0.004 0.084
#> GSM1068542 4 0.4389 0.6633 0.000 0.000 0.000 0.712 0.100 0.188
#> GSM1068543 6 0.2402 0.6578 0.000 0.000 0.000 0.120 0.012 0.868
#> GSM1068544 1 0.3385 0.6889 0.816 0.008 0.148 0.000 0.016 0.012
#> GSM1068545 4 0.2955 0.6764 0.000 0.008 0.000 0.816 0.004 0.172
#> GSM1068546 5 0.3777 0.6198 0.020 0.000 0.216 0.000 0.752 0.012
#> GSM1068547 1 0.4107 0.7320 0.776 0.000 0.000 0.020 0.124 0.080
#> GSM1068548 4 0.3205 0.6424 0.040 0.000 0.000 0.852 0.036 0.072
#> GSM1068549 3 0.0547 0.8080 0.000 0.000 0.980 0.000 0.020 0.000
#> GSM1068550 4 0.3742 0.5161 0.000 0.000 0.000 0.648 0.004 0.348
#> GSM1068551 2 0.5176 0.2894 0.000 0.532 0.000 0.384 0.004 0.080
#> GSM1068552 4 0.3071 0.6746 0.000 0.000 0.000 0.804 0.016 0.180
#> GSM1068555 2 0.2558 0.7928 0.000 0.868 0.000 0.028 0.000 0.104
#> GSM1068556 4 0.4053 0.4983 0.004 0.000 0.004 0.628 0.004 0.360
#> GSM1068557 2 0.1152 0.8137 0.000 0.952 0.000 0.004 0.000 0.044
#> GSM1068560 6 0.0984 0.7045 0.008 0.000 0.000 0.012 0.012 0.968
#> GSM1068561 6 0.5227 0.4411 0.048 0.316 0.000 0.004 0.028 0.604
#> GSM1068562 6 0.2402 0.6655 0.000 0.000 0.000 0.120 0.012 0.868
#> GSM1068563 4 0.1082 0.6661 0.000 0.004 0.000 0.956 0.000 0.040
#> GSM1068565 2 0.2613 0.8001 0.000 0.848 0.000 0.140 0.000 0.012
#> GSM1068529 6 0.3918 0.5830 0.004 0.004 0.248 0.020 0.000 0.724
#> GSM1068530 1 0.0984 0.7643 0.968 0.000 0.000 0.012 0.008 0.012
#> GSM1068534 3 0.6621 0.3299 0.128 0.000 0.512 0.100 0.000 0.260
#> GSM1068536 6 0.5814 0.4038 0.268 0.060 0.000 0.004 0.072 0.596
#> GSM1068541 4 0.6326 -0.0272 0.328 0.196 0.000 0.452 0.000 0.024
#> GSM1068553 5 0.1434 0.8038 0.000 0.000 0.000 0.048 0.940 0.012
#> GSM1068554 5 0.1812 0.7959 0.000 0.000 0.000 0.080 0.912 0.008
#> GSM1068558 6 0.4002 0.5492 0.000 0.000 0.260 0.036 0.000 0.704
#> GSM1068559 3 0.1528 0.8061 0.000 0.000 0.936 0.000 0.048 0.016
#> GSM1068564 4 0.3461 0.6820 0.000 0.008 0.000 0.804 0.036 0.152
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 disease.state(p) gender(p) k
#> MAD:NMF 102 0.48106 0.753 2
#> MAD:NMF 95 0.94174 0.290 3
#> MAD:NMF 102 0.00357 0.701 4
#> MAD:NMF 88 0.01708 0.483 5
#> MAD:NMF 88 0.00356 0.531 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 38950 rows and 108 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 0.600 0.776 0.907 0.4225 0.587 0.587
#> 3 3 0.539 0.581 0.811 0.4685 0.729 0.554
#> 4 4 0.585 0.587 0.804 0.0926 0.909 0.769
#> 5 5 0.575 0.537 0.758 0.0642 0.923 0.783
#> 6 6 0.610 0.599 0.732 0.0457 0.870 0.598
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
#> GSM1068478 2 0.6148 0.7717 0.152 0.848
#> GSM1068479 2 0.9323 0.4590 0.348 0.652
#> GSM1068481 2 0.2603 0.8706 0.044 0.956
#> GSM1068482 1 0.6712 0.7589 0.824 0.176
#> GSM1068483 1 0.0376 0.8667 0.996 0.004
#> GSM1068486 2 0.0672 0.8974 0.008 0.992
#> GSM1068487 2 0.0000 0.8993 0.000 1.000
#> GSM1068488 2 0.0376 0.8984 0.004 0.996
#> GSM1068490 2 0.0000 0.8993 0.000 1.000
#> GSM1068491 1 0.9635 0.3671 0.612 0.388
#> GSM1068492 2 0.0000 0.8993 0.000 1.000
#> GSM1068493 2 0.0672 0.8974 0.008 0.992
#> GSM1068494 2 0.7883 0.6683 0.236 0.764
#> GSM1068495 2 0.9552 0.4025 0.376 0.624
#> GSM1068496 2 0.2603 0.8706 0.044 0.956
#> GSM1068498 1 0.0672 0.8693 0.992 0.008
#> GSM1068499 1 0.0938 0.8697 0.988 0.012
#> GSM1068500 1 0.9922 0.2599 0.552 0.448
#> GSM1068502 2 0.0000 0.8993 0.000 1.000
#> GSM1068503 2 0.0000 0.8993 0.000 1.000
#> GSM1068505 2 0.0376 0.8984 0.004 0.996
#> GSM1068506 2 0.0000 0.8993 0.000 1.000
#> GSM1068507 2 0.0672 0.8969 0.008 0.992
#> GSM1068508 2 0.9393 0.4406 0.356 0.644
#> GSM1068510 2 0.0376 0.8984 0.004 0.996
#> GSM1068512 2 0.0000 0.8993 0.000 1.000
#> GSM1068513 2 0.0376 0.8984 0.004 0.996
#> GSM1068514 2 0.0000 0.8993 0.000 1.000
#> GSM1068517 2 0.9977 0.1149 0.472 0.528
#> GSM1068518 2 0.9988 0.0836 0.480 0.520
#> GSM1068520 1 0.6801 0.7613 0.820 0.180
#> GSM1068521 1 0.0672 0.8693 0.992 0.008
#> GSM1068522 2 0.0000 0.8993 0.000 1.000
#> GSM1068524 2 0.0376 0.8984 0.004 0.996
#> GSM1068527 2 0.0672 0.8969 0.008 0.992
#> GSM1068480 2 0.7883 0.6683 0.236 0.764
#> GSM1068484 2 0.0000 0.8993 0.000 1.000
#> GSM1068485 1 0.0938 0.8697 0.988 0.012
#> GSM1068489 2 0.0376 0.8984 0.004 0.996
#> GSM1068497 2 0.8144 0.6441 0.252 0.748
#> GSM1068501 2 0.0000 0.8993 0.000 1.000
#> GSM1068504 2 0.0000 0.8993 0.000 1.000
#> GSM1068509 1 0.0376 0.8667 0.996 0.004
#> GSM1068511 2 0.0000 0.8993 0.000 1.000
#> GSM1068515 1 0.0672 0.8693 0.992 0.008
#> GSM1068516 2 0.9552 0.4025 0.376 0.624
#> GSM1068519 1 0.0376 0.8667 0.996 0.004
#> GSM1068523 2 0.0376 0.8984 0.004 0.996
#> GSM1068525 2 0.0376 0.8984 0.004 0.996
#> GSM1068526 2 0.0000 0.8993 0.000 1.000
#> GSM1068458 1 0.0672 0.8693 0.992 0.008
#> GSM1068459 2 0.0672 0.8940 0.008 0.992
#> GSM1068460 2 0.9954 0.1463 0.460 0.540
#> GSM1068461 1 0.0672 0.8693 0.992 0.008
#> GSM1068464 2 0.0000 0.8993 0.000 1.000
#> GSM1068468 1 0.3879 0.8484 0.924 0.076
#> GSM1068472 1 0.3274 0.8560 0.940 0.060
#> GSM1068473 2 0.0000 0.8993 0.000 1.000
#> GSM1068474 2 0.0000 0.8993 0.000 1.000
#> GSM1068476 2 0.7815 0.6672 0.232 0.768
#> GSM1068477 2 0.9866 0.2389 0.432 0.568
#> GSM1068462 2 0.9944 0.1611 0.456 0.544
#> GSM1068463 1 0.9866 0.2643 0.568 0.432
#> GSM1068465 1 0.4022 0.8470 0.920 0.080
#> GSM1068466 1 0.6801 0.7613 0.820 0.180
#> GSM1068467 1 1.0000 -0.0309 0.500 0.500
#> GSM1068469 1 0.2043 0.8651 0.968 0.032
#> GSM1068470 2 0.0000 0.8993 0.000 1.000
#> GSM1068471 2 0.0000 0.8993 0.000 1.000
#> GSM1068475 2 0.0000 0.8993 0.000 1.000
#> GSM1068528 1 0.0938 0.8697 0.988 0.012
#> GSM1068531 2 0.5737 0.7753 0.136 0.864
#> GSM1068532 1 0.0376 0.8667 0.996 0.004
#> GSM1068533 1 0.8081 0.6789 0.752 0.248
#> GSM1068535 2 0.0000 0.8993 0.000 1.000
#> GSM1068537 1 0.5629 0.8076 0.868 0.132
#> GSM1068538 1 0.0376 0.8667 0.996 0.004
#> GSM1068539 2 0.9552 0.4025 0.376 0.624
#> GSM1068540 1 0.9922 0.2599 0.552 0.448
#> GSM1068542 2 0.0000 0.8993 0.000 1.000
#> GSM1068543 2 0.0376 0.8984 0.004 0.996
#> GSM1068544 1 0.0938 0.8697 0.988 0.012
#> GSM1068545 2 0.0000 0.8993 0.000 1.000
#> GSM1068546 2 0.0672 0.8974 0.008 0.992
#> GSM1068547 1 0.0376 0.8680 0.996 0.004
#> GSM1068548 2 0.0000 0.8993 0.000 1.000
#> GSM1068549 2 0.0672 0.8974 0.008 0.992
#> GSM1068550 2 0.0000 0.8993 0.000 1.000
#> GSM1068551 2 0.0000 0.8993 0.000 1.000
#> GSM1068552 2 0.0000 0.8993 0.000 1.000
#> GSM1068555 2 0.0376 0.8984 0.004 0.996
#> GSM1068556 2 0.0000 0.8993 0.000 1.000
#> GSM1068557 2 0.8443 0.6049 0.272 0.728
#> GSM1068560 2 0.0376 0.8984 0.004 0.996
#> GSM1068561 2 0.0376 0.8984 0.004 0.996
#> GSM1068562 2 0.0376 0.8984 0.004 0.996
#> GSM1068563 2 0.0000 0.8993 0.000 1.000
#> GSM1068565 2 0.0000 0.8993 0.000 1.000
#> GSM1068529 2 0.0376 0.8984 0.004 0.996
#> GSM1068530 1 0.0376 0.8667 0.996 0.004
#> GSM1068534 2 0.0376 0.8984 0.004 0.996
#> GSM1068536 2 0.8144 0.6441 0.252 0.748
#> GSM1068541 1 0.3274 0.8560 0.940 0.060
#> GSM1068553 2 0.0000 0.8993 0.000 1.000
#> GSM1068554 2 0.0000 0.8993 0.000 1.000
#> GSM1068558 2 0.0376 0.8984 0.004 0.996
#> GSM1068559 2 0.9686 0.3453 0.396 0.604
#> GSM1068564 2 0.0000 0.8993 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1068478 3 0.5435 0.5951 0.144 0.048 0.808
#> GSM1068479 2 0.9241 -0.0445 0.352 0.484 0.164
#> GSM1068481 3 0.3856 0.6085 0.040 0.072 0.888
#> GSM1068482 1 0.4178 0.6747 0.828 0.000 0.172
#> GSM1068483 1 0.0475 0.7886 0.992 0.004 0.004
#> GSM1068486 3 0.5138 0.5125 0.000 0.252 0.748
#> GSM1068487 2 0.0000 0.8076 0.000 1.000 0.000
#> GSM1068488 2 0.6154 0.3591 0.000 0.592 0.408
#> GSM1068490 2 0.0000 0.8076 0.000 1.000 0.000
#> GSM1068491 1 0.8233 0.4094 0.616 0.264 0.120
#> GSM1068492 2 0.0000 0.8076 0.000 1.000 0.000
#> GSM1068493 3 0.5138 0.5125 0.000 0.252 0.748
#> GSM1068494 3 0.6806 0.5722 0.228 0.060 0.712
#> GSM1068495 3 0.7756 0.3563 0.380 0.056 0.564
#> GSM1068496 3 0.3856 0.6085 0.040 0.072 0.888
#> GSM1068498 1 0.0237 0.7888 0.996 0.000 0.004
#> GSM1068499 1 0.0424 0.7888 0.992 0.000 0.008
#> GSM1068500 1 0.8131 0.3464 0.548 0.076 0.376
#> GSM1068502 2 0.0000 0.8076 0.000 1.000 0.000
#> GSM1068503 2 0.0000 0.8076 0.000 1.000 0.000
#> GSM1068505 2 0.2711 0.7725 0.000 0.912 0.088
#> GSM1068506 2 0.0000 0.8076 0.000 1.000 0.000
#> GSM1068507 2 0.2301 0.7831 0.004 0.936 0.060
#> GSM1068508 2 0.9338 -0.0723 0.360 0.468 0.172
#> GSM1068510 2 0.6154 0.3591 0.000 0.592 0.408
#> GSM1068512 2 0.0000 0.8076 0.000 1.000 0.000
#> GSM1068513 2 0.2711 0.7725 0.000 0.912 0.088
#> GSM1068514 2 0.0000 0.8076 0.000 1.000 0.000
#> GSM1068517 3 0.6950 0.1284 0.476 0.016 0.508
#> GSM1068518 3 0.7295 0.1015 0.484 0.028 0.488
#> GSM1068520 1 0.5375 0.6955 0.816 0.056 0.128
#> GSM1068521 1 0.0237 0.7888 0.996 0.000 0.004
#> GSM1068522 2 0.0000 0.8076 0.000 1.000 0.000
#> GSM1068524 2 0.6154 0.3591 0.000 0.592 0.408
#> GSM1068527 2 0.2301 0.7831 0.004 0.936 0.060
#> GSM1068480 3 0.6806 0.5722 0.228 0.060 0.712
#> GSM1068484 2 0.0000 0.8076 0.000 1.000 0.000
#> GSM1068485 1 0.0424 0.7888 0.992 0.000 0.008
#> GSM1068489 2 0.2711 0.7725 0.000 0.912 0.088
#> GSM1068497 3 0.6965 0.5601 0.244 0.060 0.696
#> GSM1068501 2 0.0000 0.8076 0.000 1.000 0.000
#> GSM1068504 2 0.2959 0.7597 0.000 0.900 0.100
#> GSM1068509 1 0.0475 0.7886 0.992 0.004 0.004
#> GSM1068511 2 0.2356 0.7763 0.000 0.928 0.072
#> GSM1068515 1 0.0237 0.7888 0.996 0.000 0.004
#> GSM1068516 3 0.7756 0.3563 0.380 0.056 0.564
#> GSM1068519 1 0.0475 0.7886 0.992 0.004 0.004
#> GSM1068523 2 0.6168 0.3571 0.000 0.588 0.412
#> GSM1068525 2 0.6154 0.3591 0.000 0.592 0.408
#> GSM1068526 2 0.1529 0.7957 0.000 0.960 0.040
#> GSM1068458 1 0.0237 0.7888 0.996 0.000 0.004
#> GSM1068459 3 0.2772 0.6101 0.004 0.080 0.916
#> GSM1068460 1 0.9489 0.1524 0.464 0.340 0.196
#> GSM1068461 1 0.0237 0.7888 0.996 0.000 0.004
#> GSM1068464 2 0.0000 0.8076 0.000 1.000 0.000
#> GSM1068468 1 0.2982 0.7664 0.920 0.056 0.024
#> GSM1068472 1 0.2492 0.7729 0.936 0.048 0.016
#> GSM1068473 2 0.0000 0.8076 0.000 1.000 0.000
#> GSM1068474 2 0.0000 0.8076 0.000 1.000 0.000
#> GSM1068476 2 0.9543 0.0119 0.236 0.484 0.280
#> GSM1068477 1 0.9550 0.0942 0.436 0.368 0.196
#> GSM1068462 1 0.9500 0.1453 0.460 0.344 0.196
#> GSM1068463 1 0.7610 0.0840 0.564 0.048 0.388
#> GSM1068465 1 0.3045 0.7644 0.916 0.064 0.020
#> GSM1068466 1 0.5375 0.6955 0.816 0.056 0.128
#> GSM1068467 1 0.9245 0.2222 0.504 0.320 0.176
#> GSM1068469 1 0.1620 0.7845 0.964 0.024 0.012
#> GSM1068470 2 0.2959 0.7597 0.000 0.900 0.100
#> GSM1068471 2 0.0000 0.8076 0.000 1.000 0.000
#> GSM1068475 2 0.0000 0.8076 0.000 1.000 0.000
#> GSM1068528 1 0.0424 0.7888 0.992 0.000 0.008
#> GSM1068531 3 0.5981 0.5138 0.132 0.080 0.788
#> GSM1068532 1 0.0475 0.7886 0.992 0.004 0.004
#> GSM1068533 1 0.6438 0.6277 0.748 0.064 0.188
#> GSM1068535 3 0.6244 0.1781 0.000 0.440 0.560
#> GSM1068537 1 0.4469 0.7234 0.864 0.060 0.076
#> GSM1068538 1 0.0475 0.7886 0.992 0.004 0.004
#> GSM1068539 3 0.7756 0.3563 0.380 0.056 0.564
#> GSM1068540 1 0.8131 0.3464 0.548 0.076 0.376
#> GSM1068542 2 0.0000 0.8076 0.000 1.000 0.000
#> GSM1068543 2 0.6154 0.3591 0.000 0.592 0.408
#> GSM1068544 1 0.0424 0.7888 0.992 0.000 0.008
#> GSM1068545 2 0.0000 0.8076 0.000 1.000 0.000
#> GSM1068546 3 0.2537 0.6128 0.000 0.080 0.920
#> GSM1068547 1 0.0000 0.7888 1.000 0.000 0.000
#> GSM1068548 2 0.0000 0.8076 0.000 1.000 0.000
#> GSM1068549 3 0.2537 0.6128 0.000 0.080 0.920
#> GSM1068550 2 0.0424 0.8050 0.000 0.992 0.008
#> GSM1068551 2 0.2878 0.7621 0.000 0.904 0.096
#> GSM1068552 2 0.0000 0.8076 0.000 1.000 0.000
#> GSM1068555 2 0.6168 0.3571 0.000 0.588 0.412
#> GSM1068556 2 0.2356 0.7763 0.000 0.928 0.072
#> GSM1068557 2 0.9792 -0.1113 0.276 0.436 0.288
#> GSM1068560 2 0.6168 0.3571 0.000 0.588 0.412
#> GSM1068561 3 0.6244 0.0593 0.000 0.440 0.560
#> GSM1068562 2 0.6168 0.3571 0.000 0.588 0.412
#> GSM1068563 2 0.0000 0.8076 0.000 1.000 0.000
#> GSM1068565 2 0.3192 0.7536 0.000 0.888 0.112
#> GSM1068529 3 0.6308 -0.0425 0.000 0.492 0.508
#> GSM1068530 1 0.0475 0.7886 0.992 0.004 0.004
#> GSM1068534 3 0.6308 -0.0425 0.000 0.492 0.508
#> GSM1068536 3 0.6965 0.5601 0.244 0.060 0.696
#> GSM1068541 1 0.2492 0.7729 0.936 0.048 0.016
#> GSM1068553 3 0.6244 0.1781 0.000 0.440 0.560
#> GSM1068554 2 0.0000 0.8076 0.000 1.000 0.000
#> GSM1068558 2 0.6154 0.3591 0.000 0.592 0.408
#> GSM1068559 1 0.9786 -0.0126 0.400 0.364 0.236
#> GSM1068564 2 0.0000 0.8076 0.000 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1068478 2 0.5667 0.4109 0.060 0.696 0.240 0.004
#> GSM1068479 4 0.7598 -0.1640 0.240 0.284 0.000 0.476
#> GSM1068481 3 0.2224 0.7251 0.032 0.040 0.928 0.000
#> GSM1068482 1 0.4220 0.6856 0.748 0.248 0.004 0.000
#> GSM1068483 1 0.0804 0.8090 0.980 0.012 0.008 0.000
#> GSM1068486 2 0.6360 0.3623 0.000 0.656 0.164 0.180
#> GSM1068487 4 0.0000 0.7778 0.000 0.000 0.000 1.000
#> GSM1068488 4 0.4985 0.3222 0.000 0.468 0.000 0.532
#> GSM1068490 4 0.0000 0.7778 0.000 0.000 0.000 1.000
#> GSM1068491 1 0.7084 0.2444 0.560 0.176 0.000 0.264
#> GSM1068492 4 0.0000 0.7778 0.000 0.000 0.000 1.000
#> GSM1068493 2 0.6360 0.3623 0.000 0.656 0.164 0.180
#> GSM1068494 2 0.1388 0.5596 0.012 0.960 0.028 0.000
#> GSM1068495 2 0.2921 0.5883 0.140 0.860 0.000 0.000
#> GSM1068496 3 0.2224 0.7251 0.032 0.040 0.928 0.000
#> GSM1068498 1 0.2973 0.7864 0.856 0.144 0.000 0.000
#> GSM1068499 1 0.2149 0.8136 0.912 0.088 0.000 0.000
#> GSM1068500 1 0.7607 0.3518 0.464 0.180 0.352 0.004
#> GSM1068502 4 0.0000 0.7778 0.000 0.000 0.000 1.000
#> GSM1068503 4 0.0000 0.7778 0.000 0.000 0.000 1.000
#> GSM1068505 4 0.2473 0.7467 0.000 0.080 0.012 0.908
#> GSM1068506 4 0.0000 0.7778 0.000 0.000 0.000 1.000
#> GSM1068507 4 0.2021 0.7562 0.000 0.056 0.012 0.932
#> GSM1068508 4 0.7659 -0.1991 0.244 0.296 0.000 0.460
#> GSM1068510 4 0.4985 0.3222 0.000 0.468 0.000 0.532
#> GSM1068512 4 0.0000 0.7778 0.000 0.000 0.000 1.000
#> GSM1068513 4 0.2473 0.7467 0.000 0.080 0.012 0.908
#> GSM1068514 4 0.0000 0.7778 0.000 0.000 0.000 1.000
#> GSM1068517 2 0.3942 0.4981 0.236 0.764 0.000 0.000
#> GSM1068518 2 0.4485 0.4873 0.248 0.740 0.000 0.012
#> GSM1068520 1 0.4948 0.7354 0.776 0.124 0.100 0.000
#> GSM1068521 1 0.1022 0.8177 0.968 0.032 0.000 0.000
#> GSM1068522 4 0.0000 0.7778 0.000 0.000 0.000 1.000
#> GSM1068524 4 0.4985 0.3222 0.000 0.468 0.000 0.532
#> GSM1068527 4 0.2021 0.7562 0.000 0.056 0.012 0.932
#> GSM1068480 2 0.1388 0.5596 0.012 0.960 0.028 0.000
#> GSM1068484 4 0.0000 0.7778 0.000 0.000 0.000 1.000
#> GSM1068485 1 0.2149 0.8136 0.912 0.088 0.000 0.000
#> GSM1068489 4 0.2473 0.7467 0.000 0.080 0.012 0.908
#> GSM1068497 2 0.0937 0.5665 0.012 0.976 0.012 0.000
#> GSM1068501 4 0.0000 0.7778 0.000 0.000 0.000 1.000
#> GSM1068504 4 0.2704 0.7187 0.000 0.124 0.000 0.876
#> GSM1068509 1 0.0804 0.8090 0.980 0.012 0.008 0.000
#> GSM1068511 4 0.2345 0.7265 0.000 0.000 0.100 0.900
#> GSM1068515 1 0.1716 0.8153 0.936 0.064 0.000 0.000
#> GSM1068516 2 0.2921 0.5883 0.140 0.860 0.000 0.000
#> GSM1068519 1 0.0804 0.8155 0.980 0.012 0.008 0.000
#> GSM1068523 4 0.4989 0.3140 0.000 0.472 0.000 0.528
#> GSM1068525 4 0.4985 0.3222 0.000 0.468 0.000 0.532
#> GSM1068526 4 0.1302 0.7653 0.000 0.044 0.000 0.956
#> GSM1068458 1 0.1637 0.8162 0.940 0.060 0.000 0.000
#> GSM1068459 3 0.0188 0.7272 0.004 0.000 0.996 0.000
#> GSM1068460 2 0.7918 0.2163 0.316 0.352 0.000 0.332
#> GSM1068461 1 0.2973 0.7864 0.856 0.144 0.000 0.000
#> GSM1068464 4 0.0000 0.7778 0.000 0.000 0.000 1.000
#> GSM1068468 1 0.3493 0.7899 0.876 0.064 0.008 0.052
#> GSM1068472 1 0.3170 0.7987 0.892 0.056 0.008 0.044
#> GSM1068473 4 0.0000 0.7778 0.000 0.000 0.000 1.000
#> GSM1068474 4 0.0000 0.7778 0.000 0.000 0.000 1.000
#> GSM1068476 4 0.7184 -0.1292 0.136 0.416 0.000 0.448
#> GSM1068477 4 0.7896 -0.3834 0.292 0.348 0.000 0.360
#> GSM1068462 2 0.7916 0.2222 0.312 0.352 0.000 0.336
#> GSM1068463 1 0.6187 0.2287 0.516 0.052 0.432 0.000
#> GSM1068465 1 0.3508 0.7848 0.872 0.060 0.004 0.064
#> GSM1068466 1 0.4948 0.7354 0.776 0.124 0.100 0.000
#> GSM1068467 1 0.7901 -0.1821 0.372 0.316 0.000 0.312
#> GSM1068469 1 0.2421 0.8136 0.924 0.048 0.008 0.020
#> GSM1068470 4 0.2704 0.7187 0.000 0.124 0.000 0.876
#> GSM1068471 4 0.0000 0.7778 0.000 0.000 0.000 1.000
#> GSM1068475 4 0.0000 0.7778 0.000 0.000 0.000 1.000
#> GSM1068528 1 0.2149 0.8136 0.912 0.088 0.000 0.000
#> GSM1068531 3 0.4153 0.6517 0.132 0.048 0.820 0.000
#> GSM1068532 1 0.0804 0.8090 0.980 0.012 0.008 0.000
#> GSM1068533 1 0.6557 0.6265 0.648 0.196 0.152 0.004
#> GSM1068535 3 0.5112 0.3436 0.000 0.008 0.608 0.384
#> GSM1068537 1 0.3324 0.7421 0.852 0.012 0.136 0.000
#> GSM1068538 1 0.0804 0.8090 0.980 0.012 0.008 0.000
#> GSM1068539 2 0.2921 0.5883 0.140 0.860 0.000 0.000
#> GSM1068540 1 0.7607 0.3518 0.464 0.180 0.352 0.004
#> GSM1068542 4 0.0000 0.7778 0.000 0.000 0.000 1.000
#> GSM1068543 4 0.4985 0.3222 0.000 0.468 0.000 0.532
#> GSM1068544 1 0.2149 0.8136 0.912 0.088 0.000 0.000
#> GSM1068545 4 0.0000 0.7778 0.000 0.000 0.000 1.000
#> GSM1068546 3 0.2530 0.7144 0.000 0.112 0.888 0.000
#> GSM1068547 1 0.1661 0.8197 0.944 0.052 0.004 0.000
#> GSM1068548 4 0.0000 0.7778 0.000 0.000 0.000 1.000
#> GSM1068549 3 0.2589 0.7121 0.000 0.116 0.884 0.000
#> GSM1068550 4 0.0336 0.7758 0.000 0.008 0.000 0.992
#> GSM1068551 4 0.2647 0.7212 0.000 0.120 0.000 0.880
#> GSM1068552 4 0.0000 0.7778 0.000 0.000 0.000 1.000
#> GSM1068555 4 0.4989 0.3140 0.000 0.472 0.000 0.528
#> GSM1068556 4 0.2345 0.7265 0.000 0.000 0.100 0.900
#> GSM1068557 2 0.7399 0.1742 0.164 0.420 0.000 0.416
#> GSM1068560 4 0.4989 0.3140 0.000 0.472 0.000 0.528
#> GSM1068561 2 0.7028 0.0140 0.000 0.496 0.124 0.380
#> GSM1068562 4 0.4989 0.3140 0.000 0.472 0.000 0.528
#> GSM1068563 4 0.0000 0.7778 0.000 0.000 0.000 1.000
#> GSM1068565 4 0.2973 0.7063 0.000 0.144 0.000 0.856
#> GSM1068529 4 0.7706 0.0717 0.000 0.228 0.348 0.424
#> GSM1068530 1 0.0804 0.8090 0.980 0.012 0.008 0.000
#> GSM1068534 4 0.7706 0.0717 0.000 0.228 0.348 0.424
#> GSM1068536 2 0.0937 0.5665 0.012 0.976 0.012 0.000
#> GSM1068541 1 0.3170 0.7987 0.892 0.056 0.008 0.044
#> GSM1068553 3 0.5112 0.3436 0.000 0.008 0.608 0.384
#> GSM1068554 4 0.0000 0.7778 0.000 0.000 0.000 1.000
#> GSM1068558 4 0.4985 0.3222 0.000 0.468 0.000 0.532
#> GSM1068559 2 0.7818 0.3197 0.256 0.388 0.000 0.356
#> GSM1068564 4 0.0000 0.7778 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
#> GSM1068478 5 0.5365 0.4984 0.020 0.060 0.228 0.004 0.688
#> GSM1068479 4 0.7197 -0.0979 0.024 0.296 0.000 0.432 0.248
#> GSM1068481 3 0.1978 0.6647 0.012 0.024 0.932 0.000 0.032
#> GSM1068482 2 0.5289 0.4802 0.128 0.688 0.004 0.000 0.180
#> GSM1068483 1 0.1544 0.7377 0.932 0.068 0.000 0.000 0.000
#> GSM1068486 5 0.5481 0.4859 0.000 0.016 0.136 0.156 0.692
#> GSM1068487 4 0.0162 0.7672 0.000 0.000 0.000 0.996 0.004
#> GSM1068488 4 0.4656 0.2764 0.000 0.012 0.000 0.508 0.480
#> GSM1068490 4 0.0162 0.7674 0.000 0.004 0.000 0.996 0.000
#> GSM1068491 2 0.8214 0.2203 0.296 0.356 0.000 0.224 0.124
#> GSM1068492 4 0.0324 0.7677 0.000 0.004 0.000 0.992 0.004
#> GSM1068493 5 0.5481 0.4859 0.000 0.016 0.136 0.156 0.692
#> GSM1068494 5 0.0404 0.6815 0.000 0.012 0.000 0.000 0.988
#> GSM1068495 5 0.2930 0.6325 0.004 0.164 0.000 0.000 0.832
#> GSM1068496 3 0.1978 0.6647 0.012 0.024 0.932 0.000 0.032
#> GSM1068498 2 0.3697 0.5265 0.100 0.820 0.000 0.000 0.080
#> GSM1068499 2 0.4132 0.4618 0.260 0.720 0.000 0.000 0.020
#> GSM1068500 1 0.7487 0.3225 0.476 0.064 0.296 0.004 0.160
#> GSM1068502 4 0.1121 0.7583 0.000 0.044 0.000 0.956 0.000
#> GSM1068503 4 0.0162 0.7672 0.000 0.000 0.000 0.996 0.004
#> GSM1068505 4 0.2464 0.7392 0.000 0.004 0.012 0.892 0.092
#> GSM1068506 4 0.0510 0.7655 0.000 0.016 0.000 0.984 0.000
#> GSM1068507 4 0.2208 0.7506 0.000 0.012 0.012 0.916 0.060
#> GSM1068508 4 0.7284 -0.0736 0.032 0.256 0.000 0.444 0.268
#> GSM1068510 4 0.4656 0.2764 0.000 0.012 0.000 0.508 0.480
#> GSM1068512 4 0.0510 0.7655 0.000 0.016 0.000 0.984 0.000
#> GSM1068513 4 0.2464 0.7392 0.000 0.004 0.012 0.892 0.092
#> GSM1068514 4 0.0162 0.7672 0.000 0.000 0.000 0.996 0.004
#> GSM1068517 5 0.4014 0.5249 0.016 0.256 0.000 0.000 0.728
#> GSM1068518 5 0.4206 0.5006 0.020 0.272 0.000 0.000 0.708
#> GSM1068520 1 0.4690 0.6703 0.780 0.080 0.040 0.000 0.100
#> GSM1068521 2 0.3796 0.4106 0.300 0.700 0.000 0.000 0.000
#> GSM1068522 4 0.0510 0.7655 0.000 0.016 0.000 0.984 0.000
#> GSM1068524 4 0.4656 0.2764 0.000 0.012 0.000 0.508 0.480
#> GSM1068527 4 0.2208 0.7506 0.000 0.012 0.012 0.916 0.060
#> GSM1068480 5 0.0404 0.6815 0.000 0.012 0.000 0.000 0.988
#> GSM1068484 4 0.1121 0.7583 0.000 0.044 0.000 0.956 0.000
#> GSM1068485 2 0.4132 0.4618 0.260 0.720 0.000 0.000 0.020
#> GSM1068489 4 0.2464 0.7392 0.000 0.004 0.012 0.892 0.092
#> GSM1068497 5 0.0794 0.6851 0.000 0.028 0.000 0.000 0.972
#> GSM1068501 4 0.0794 0.7637 0.000 0.028 0.000 0.972 0.000
#> GSM1068504 4 0.2471 0.7134 0.000 0.000 0.000 0.864 0.136
#> GSM1068509 1 0.1544 0.7377 0.932 0.068 0.000 0.000 0.000
#> GSM1068511 4 0.2519 0.7185 0.000 0.016 0.100 0.884 0.000
#> GSM1068515 2 0.2929 0.4975 0.180 0.820 0.000 0.000 0.000
#> GSM1068516 5 0.2930 0.6325 0.004 0.164 0.000 0.000 0.832
#> GSM1068519 1 0.2471 0.7033 0.864 0.136 0.000 0.000 0.000
#> GSM1068523 4 0.4449 0.2806 0.000 0.004 0.000 0.512 0.484
#> GSM1068525 4 0.4656 0.2764 0.000 0.012 0.000 0.508 0.480
#> GSM1068526 4 0.1197 0.7604 0.000 0.000 0.000 0.952 0.048
#> GSM1068458 2 0.3143 0.4839 0.204 0.796 0.000 0.000 0.000
#> GSM1068459 3 0.0000 0.6666 0.000 0.000 1.000 0.000 0.000
#> GSM1068460 2 0.7652 0.1233 0.048 0.372 0.000 0.292 0.288
#> GSM1068461 2 0.3697 0.5265 0.100 0.820 0.000 0.000 0.080
#> GSM1068464 4 0.1121 0.7583 0.000 0.044 0.000 0.956 0.000
#> GSM1068468 1 0.4713 0.6694 0.724 0.224 0.000 0.028 0.024
#> GSM1068472 1 0.4263 0.6910 0.760 0.200 0.000 0.024 0.016
#> GSM1068473 4 0.0794 0.7637 0.000 0.028 0.000 0.972 0.000
#> GSM1068474 4 0.1121 0.7583 0.000 0.044 0.000 0.956 0.000
#> GSM1068476 4 0.6661 -0.0360 0.012 0.156 0.000 0.432 0.400
#> GSM1068477 2 0.7525 0.0818 0.036 0.356 0.000 0.312 0.296
#> GSM1068462 2 0.7658 0.1196 0.048 0.368 0.000 0.296 0.288
#> GSM1068463 3 0.6902 -0.0783 0.372 0.172 0.436 0.000 0.020
#> GSM1068465 1 0.4836 0.6673 0.724 0.212 0.000 0.044 0.020
#> GSM1068466 1 0.4690 0.6703 0.780 0.080 0.040 0.000 0.100
#> GSM1068467 2 0.8132 0.1775 0.104 0.360 0.000 0.276 0.260
#> GSM1068469 1 0.3809 0.7027 0.804 0.160 0.000 0.020 0.016
#> GSM1068470 4 0.2471 0.7134 0.000 0.000 0.000 0.864 0.136
#> GSM1068471 4 0.1121 0.7583 0.000 0.044 0.000 0.956 0.000
#> GSM1068475 4 0.1121 0.7583 0.000 0.044 0.000 0.956 0.000
#> GSM1068528 2 0.4132 0.4618 0.260 0.720 0.000 0.000 0.020
#> GSM1068531 3 0.4814 0.5575 0.188 0.016 0.736 0.000 0.060
#> GSM1068532 1 0.1544 0.7377 0.932 0.068 0.000 0.000 0.000
#> GSM1068533 1 0.6521 0.5339 0.640 0.092 0.092 0.004 0.172
#> GSM1068535 3 0.4817 0.3427 0.000 0.016 0.608 0.368 0.008
#> GSM1068537 1 0.2233 0.7018 0.904 0.016 0.080 0.000 0.000
#> GSM1068538 1 0.1544 0.7377 0.932 0.068 0.000 0.000 0.000
#> GSM1068539 5 0.2930 0.6325 0.004 0.164 0.000 0.000 0.832
#> GSM1068540 1 0.7487 0.3225 0.476 0.064 0.296 0.004 0.160
#> GSM1068542 4 0.1121 0.7583 0.000 0.044 0.000 0.956 0.000
#> GSM1068543 4 0.4656 0.2764 0.000 0.012 0.000 0.508 0.480
#> GSM1068544 2 0.4132 0.4618 0.260 0.720 0.000 0.000 0.020
#> GSM1068545 4 0.1121 0.7583 0.000 0.044 0.000 0.956 0.000
#> GSM1068546 3 0.4112 0.6471 0.048 0.016 0.800 0.000 0.136
#> GSM1068547 2 0.4225 0.2139 0.364 0.632 0.000 0.000 0.004
#> GSM1068548 4 0.0609 0.7652 0.000 0.020 0.000 0.980 0.000
#> GSM1068549 3 0.4084 0.6453 0.044 0.016 0.800 0.000 0.140
#> GSM1068550 4 0.0404 0.7678 0.000 0.000 0.000 0.988 0.012
#> GSM1068551 4 0.2424 0.7158 0.000 0.000 0.000 0.868 0.132
#> GSM1068552 4 0.0000 0.7672 0.000 0.000 0.000 1.000 0.000
#> GSM1068555 4 0.4449 0.2806 0.000 0.004 0.000 0.512 0.484
#> GSM1068556 4 0.2519 0.7185 0.000 0.016 0.100 0.884 0.000
#> GSM1068557 4 0.7031 -0.1294 0.024 0.180 0.000 0.400 0.396
#> GSM1068560 4 0.4304 0.2875 0.000 0.000 0.000 0.516 0.484
#> GSM1068561 5 0.6302 0.0771 0.000 0.016 0.108 0.356 0.520
#> GSM1068562 4 0.4304 0.2875 0.000 0.000 0.000 0.516 0.484
#> GSM1068563 4 0.0609 0.7652 0.000 0.020 0.000 0.980 0.000
#> GSM1068565 4 0.2690 0.7017 0.000 0.000 0.000 0.844 0.156
#> GSM1068529 4 0.7233 0.0703 0.004 0.016 0.340 0.400 0.240
#> GSM1068530 1 0.1544 0.7377 0.932 0.068 0.000 0.000 0.000
#> GSM1068534 4 0.7233 0.0703 0.004 0.016 0.340 0.400 0.240
#> GSM1068536 5 0.0794 0.6851 0.000 0.028 0.000 0.000 0.972
#> GSM1068541 1 0.4263 0.6910 0.760 0.200 0.000 0.024 0.016
#> GSM1068553 3 0.4817 0.3427 0.000 0.016 0.608 0.368 0.008
#> GSM1068554 4 0.0794 0.7637 0.000 0.028 0.000 0.972 0.000
#> GSM1068558 4 0.4656 0.2764 0.000 0.012 0.000 0.508 0.480
#> GSM1068559 5 0.7540 -0.1086 0.036 0.320 0.000 0.308 0.336
#> GSM1068564 4 0.0000 0.7672 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
#> GSM1068478 6 0.5881 0.2694 0.000 0.000 0.216 0.276 0.004 0.504
#> GSM1068479 2 0.5646 -0.7157 0.000 0.440 0.000 0.436 0.008 0.116
#> GSM1068481 3 0.2013 0.6344 0.000 0.000 0.908 0.076 0.008 0.008
#> GSM1068482 5 0.5509 0.4861 0.040 0.000 0.000 0.336 0.564 0.060
#> GSM1068483 1 0.0000 0.7265 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1068486 6 0.5377 0.4831 0.000 0.080 0.136 0.100 0.000 0.684
#> GSM1068487 2 0.0458 0.8424 0.000 0.984 0.000 0.000 0.000 0.016
#> GSM1068488 6 0.3725 0.5511 0.000 0.316 0.000 0.008 0.000 0.676
#> GSM1068490 2 0.0508 0.8436 0.000 0.984 0.000 0.004 0.000 0.012
#> GSM1068491 4 0.7555 0.5141 0.256 0.228 0.000 0.412 0.036 0.068
#> GSM1068492 2 0.0603 0.8433 0.000 0.980 0.000 0.004 0.000 0.016
#> GSM1068493 6 0.5377 0.4831 0.000 0.080 0.136 0.100 0.000 0.684
#> GSM1068494 6 0.3052 0.4478 0.000 0.000 0.004 0.216 0.000 0.780
#> GSM1068495 6 0.3795 0.3167 0.000 0.000 0.000 0.364 0.004 0.632
#> GSM1068496 3 0.2013 0.6344 0.000 0.000 0.908 0.076 0.008 0.008
#> GSM1068498 5 0.4261 0.5543 0.000 0.000 0.000 0.252 0.692 0.056
#> GSM1068499 5 0.5409 0.5557 0.188 0.000 0.000 0.232 0.580 0.000
#> GSM1068500 1 0.6944 0.3306 0.392 0.000 0.280 0.284 0.012 0.032
#> GSM1068502 2 0.0790 0.8334 0.000 0.968 0.000 0.032 0.000 0.000
#> GSM1068503 2 0.0458 0.8424 0.000 0.984 0.000 0.000 0.000 0.016
#> GSM1068505 2 0.2714 0.7496 0.000 0.848 0.012 0.004 0.000 0.136
#> GSM1068506 2 0.0508 0.8398 0.000 0.984 0.000 0.012 0.004 0.000
#> GSM1068507 2 0.2525 0.7795 0.000 0.876 0.012 0.012 0.000 0.100
#> GSM1068508 4 0.5891 0.6780 0.000 0.412 0.000 0.412 0.004 0.172
#> GSM1068510 6 0.3725 0.5511 0.000 0.316 0.000 0.008 0.000 0.676
#> GSM1068512 2 0.0508 0.8398 0.000 0.984 0.000 0.012 0.004 0.000
#> GSM1068513 2 0.2714 0.7496 0.000 0.848 0.012 0.004 0.000 0.136
#> GSM1068514 2 0.0458 0.8424 0.000 0.984 0.000 0.000 0.000 0.016
#> GSM1068517 6 0.5174 0.2360 0.000 0.000 0.000 0.368 0.096 0.536
#> GSM1068518 6 0.5235 0.2098 0.000 0.000 0.000 0.380 0.100 0.520
#> GSM1068520 1 0.4866 0.6713 0.684 0.000 0.028 0.244 0.024 0.020
#> GSM1068521 5 0.5689 0.4554 0.288 0.000 0.000 0.196 0.516 0.000
#> GSM1068522 2 0.0508 0.8398 0.000 0.984 0.000 0.012 0.004 0.000
#> GSM1068524 6 0.3725 0.5511 0.000 0.316 0.000 0.008 0.000 0.676
#> GSM1068527 2 0.2525 0.7795 0.000 0.876 0.012 0.012 0.000 0.100
#> GSM1068480 6 0.3052 0.4478 0.000 0.000 0.004 0.216 0.000 0.780
#> GSM1068484 2 0.0790 0.8334 0.000 0.968 0.000 0.032 0.000 0.000
#> GSM1068485 5 0.5409 0.5557 0.188 0.000 0.000 0.232 0.580 0.000
#> GSM1068489 2 0.2714 0.7496 0.000 0.848 0.012 0.004 0.000 0.136
#> GSM1068497 6 0.3136 0.4437 0.000 0.000 0.000 0.228 0.004 0.768
#> GSM1068501 2 0.0603 0.8405 0.000 0.980 0.000 0.016 0.004 0.000
#> GSM1068504 2 0.2664 0.6977 0.000 0.816 0.000 0.000 0.000 0.184
#> GSM1068509 1 0.0000 0.7265 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1068511 2 0.2456 0.7568 0.000 0.880 0.100 0.012 0.004 0.004
#> GSM1068515 5 0.4570 0.5842 0.092 0.000 0.000 0.228 0.680 0.000
#> GSM1068516 6 0.3795 0.3167 0.000 0.000 0.000 0.364 0.004 0.632
#> GSM1068519 1 0.1531 0.6906 0.928 0.000 0.000 0.004 0.068 0.000
#> GSM1068523 6 0.3699 0.5344 0.000 0.336 0.000 0.004 0.000 0.660
#> GSM1068525 6 0.3725 0.5511 0.000 0.316 0.000 0.008 0.000 0.676
#> GSM1068526 2 0.1501 0.8124 0.000 0.924 0.000 0.000 0.000 0.076
#> GSM1068458 5 0.5002 0.5724 0.136 0.000 0.000 0.228 0.636 0.000
#> GSM1068459 3 0.0820 0.6347 0.000 0.000 0.972 0.016 0.012 0.000
#> GSM1068460 4 0.6257 0.8463 0.004 0.296 0.000 0.524 0.040 0.136
#> GSM1068461 5 0.4261 0.5543 0.000 0.000 0.000 0.252 0.692 0.056
#> GSM1068464 2 0.0790 0.8334 0.000 0.968 0.000 0.032 0.000 0.000
#> GSM1068468 1 0.4326 0.6759 0.724 0.032 0.000 0.216 0.028 0.000
#> GSM1068472 1 0.3755 0.6973 0.768 0.028 0.000 0.192 0.012 0.000
#> GSM1068473 2 0.0603 0.8405 0.000 0.980 0.000 0.016 0.004 0.000
#> GSM1068474 2 0.0790 0.8334 0.000 0.968 0.000 0.032 0.000 0.000
#> GSM1068476 2 0.6075 -0.4411 0.000 0.396 0.000 0.280 0.000 0.324
#> GSM1068477 4 0.6141 0.8465 0.000 0.312 0.000 0.508 0.032 0.148
#> GSM1068462 4 0.6270 0.8481 0.004 0.300 0.000 0.520 0.040 0.136
#> GSM1068463 3 0.6505 0.0218 0.392 0.000 0.416 0.040 0.148 0.004
#> GSM1068465 1 0.4474 0.6724 0.724 0.048 0.000 0.200 0.028 0.000
#> GSM1068466 1 0.4866 0.6713 0.684 0.000 0.028 0.244 0.024 0.020
#> GSM1068467 4 0.6978 0.8102 0.060 0.280 0.000 0.500 0.036 0.124
#> GSM1068469 1 0.3294 0.7153 0.812 0.020 0.000 0.156 0.012 0.000
#> GSM1068470 2 0.2664 0.6977 0.000 0.816 0.000 0.000 0.000 0.184
#> GSM1068471 2 0.0790 0.8334 0.000 0.968 0.000 0.032 0.000 0.000
#> GSM1068475 2 0.0790 0.8334 0.000 0.968 0.000 0.032 0.000 0.000
#> GSM1068528 5 0.5409 0.5557 0.188 0.000 0.000 0.232 0.580 0.000
#> GSM1068531 3 0.5195 0.5626 0.128 0.000 0.716 0.096 0.016 0.044
#> GSM1068532 1 0.0146 0.7265 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM1068533 1 0.6094 0.5537 0.544 0.000 0.076 0.324 0.024 0.032
#> GSM1068535 3 0.4809 0.3283 0.000 0.348 0.604 0.020 0.004 0.024
#> GSM1068537 1 0.3109 0.6887 0.848 0.000 0.068 0.076 0.008 0.000
#> GSM1068538 1 0.0146 0.7265 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM1068539 6 0.3795 0.3167 0.000 0.000 0.000 0.364 0.004 0.632
#> GSM1068540 1 0.6944 0.3306 0.392 0.000 0.280 0.284 0.012 0.032
#> GSM1068542 2 0.0790 0.8334 0.000 0.968 0.000 0.032 0.000 0.000
#> GSM1068543 6 0.3725 0.5511 0.000 0.316 0.000 0.008 0.000 0.676
#> GSM1068544 5 0.5409 0.5557 0.188 0.000 0.000 0.232 0.580 0.000
#> GSM1068545 2 0.0790 0.8334 0.000 0.968 0.000 0.032 0.000 0.000
#> GSM1068546 3 0.4104 0.6232 0.000 0.000 0.784 0.092 0.028 0.096
#> GSM1068547 5 0.5617 0.3836 0.280 0.000 0.000 0.188 0.532 0.000
#> GSM1068548 2 0.0603 0.8393 0.000 0.980 0.000 0.016 0.004 0.000
#> GSM1068549 3 0.4102 0.6225 0.000 0.000 0.784 0.088 0.028 0.100
#> GSM1068550 2 0.0790 0.8374 0.000 0.968 0.000 0.000 0.000 0.032
#> GSM1068551 2 0.2631 0.7026 0.000 0.820 0.000 0.000 0.000 0.180
#> GSM1068552 2 0.0363 0.8431 0.000 0.988 0.000 0.000 0.000 0.012
#> GSM1068555 6 0.3699 0.5344 0.000 0.336 0.000 0.004 0.000 0.660
#> GSM1068556 2 0.2456 0.7568 0.000 0.880 0.100 0.012 0.004 0.004
#> GSM1068557 2 0.6119 -0.5371 0.000 0.364 0.000 0.324 0.000 0.312
#> GSM1068560 6 0.3742 0.5224 0.000 0.348 0.000 0.004 0.000 0.648
#> GSM1068561 6 0.4582 0.5302 0.000 0.184 0.108 0.004 0.000 0.704
#> GSM1068562 6 0.3742 0.5224 0.000 0.348 0.000 0.004 0.000 0.648
#> GSM1068563 2 0.0603 0.8393 0.000 0.980 0.000 0.016 0.004 0.000
#> GSM1068565 2 0.3023 0.6555 0.000 0.784 0.000 0.004 0.000 0.212
#> GSM1068529 6 0.6305 0.1944 0.000 0.280 0.340 0.008 0.000 0.372
#> GSM1068530 1 0.0146 0.7265 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM1068534 6 0.6305 0.1944 0.000 0.280 0.340 0.008 0.000 0.372
#> GSM1068536 6 0.3136 0.4437 0.000 0.000 0.000 0.228 0.004 0.768
#> GSM1068541 1 0.3755 0.6973 0.768 0.028 0.000 0.192 0.012 0.000
#> GSM1068553 3 0.4809 0.3283 0.000 0.348 0.604 0.020 0.004 0.024
#> GSM1068554 2 0.0603 0.8405 0.000 0.980 0.000 0.016 0.004 0.000
#> GSM1068558 6 0.3725 0.5511 0.000 0.316 0.000 0.008 0.000 0.676
#> GSM1068559 4 0.6370 0.8087 0.000 0.308 0.000 0.472 0.032 0.188
#> GSM1068564 2 0.0363 0.8431 0.000 0.988 0.000 0.000 0.000 0.012
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) gender(p) k
#> ATC:hclust 92 0.220 0.879 2
#> ATC:hclust 75 0.549 0.762 3
#> ATC:hclust 75 0.580 0.174 4
#> ATC:hclust 68 0.284 0.208 5
#> ATC:hclust 83 0.214 0.409 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 38950 rows and 108 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 0.904 0.931 0.971 0.4922 0.504 0.504
#> 3 3 0.723 0.750 0.889 0.3332 0.736 0.520
#> 4 4 0.641 0.732 0.839 0.1169 0.843 0.582
#> 5 5 0.772 0.701 0.821 0.0736 0.922 0.719
#> 6 6 0.739 0.642 0.773 0.0410 0.952 0.785
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
#> GSM1068478 1 0.000 0.956 1.000 0.000
#> GSM1068479 2 0.000 0.979 0.000 1.000
#> GSM1068481 1 0.917 0.537 0.668 0.332
#> GSM1068482 1 0.000 0.956 1.000 0.000
#> GSM1068483 1 0.000 0.956 1.000 0.000
#> GSM1068486 2 0.184 0.951 0.028 0.972
#> GSM1068487 2 0.000 0.979 0.000 1.000
#> GSM1068488 2 0.000 0.979 0.000 1.000
#> GSM1068490 2 0.000 0.979 0.000 1.000
#> GSM1068491 1 0.000 0.956 1.000 0.000
#> GSM1068492 2 0.000 0.979 0.000 1.000
#> GSM1068493 2 0.963 0.332 0.388 0.612
#> GSM1068494 1 0.886 0.590 0.696 0.304
#> GSM1068495 2 0.662 0.776 0.172 0.828
#> GSM1068496 1 0.000 0.956 1.000 0.000
#> GSM1068498 1 0.000 0.956 1.000 0.000
#> GSM1068499 1 0.000 0.956 1.000 0.000
#> GSM1068500 1 0.000 0.956 1.000 0.000
#> GSM1068502 2 0.000 0.979 0.000 1.000
#> GSM1068503 2 0.000 0.979 0.000 1.000
#> GSM1068505 2 0.000 0.979 0.000 1.000
#> GSM1068506 2 0.000 0.979 0.000 1.000
#> GSM1068507 2 0.000 0.979 0.000 1.000
#> GSM1068508 2 0.000 0.979 0.000 1.000
#> GSM1068510 2 0.000 0.979 0.000 1.000
#> GSM1068512 2 0.000 0.979 0.000 1.000
#> GSM1068513 2 0.000 0.979 0.000 1.000
#> GSM1068514 2 0.000 0.979 0.000 1.000
#> GSM1068517 1 0.000 0.956 1.000 0.000
#> GSM1068518 1 0.000 0.956 1.000 0.000
#> GSM1068520 1 0.000 0.956 1.000 0.000
#> GSM1068521 1 0.000 0.956 1.000 0.000
#> GSM1068522 2 0.000 0.979 0.000 1.000
#> GSM1068524 2 0.000 0.979 0.000 1.000
#> GSM1068527 2 0.000 0.979 0.000 1.000
#> GSM1068480 1 0.000 0.956 1.000 0.000
#> GSM1068484 2 0.000 0.979 0.000 1.000
#> GSM1068485 1 0.000 0.956 1.000 0.000
#> GSM1068489 2 0.000 0.979 0.000 1.000
#> GSM1068497 1 0.000 0.956 1.000 0.000
#> GSM1068501 2 0.000 0.979 0.000 1.000
#> GSM1068504 2 0.000 0.979 0.000 1.000
#> GSM1068509 1 0.000 0.956 1.000 0.000
#> GSM1068511 2 0.000 0.979 0.000 1.000
#> GSM1068515 1 0.000 0.956 1.000 0.000
#> GSM1068516 1 0.925 0.520 0.660 0.340
#> GSM1068519 1 0.000 0.956 1.000 0.000
#> GSM1068523 2 0.000 0.979 0.000 1.000
#> GSM1068525 2 0.000 0.979 0.000 1.000
#> GSM1068526 2 0.000 0.979 0.000 1.000
#> GSM1068458 1 0.000 0.956 1.000 0.000
#> GSM1068459 1 0.000 0.956 1.000 0.000
#> GSM1068460 1 0.625 0.806 0.844 0.156
#> GSM1068461 1 0.000 0.956 1.000 0.000
#> GSM1068464 2 0.000 0.979 0.000 1.000
#> GSM1068468 1 0.000 0.956 1.000 0.000
#> GSM1068472 1 0.000 0.956 1.000 0.000
#> GSM1068473 2 0.000 0.979 0.000 1.000
#> GSM1068474 2 0.000 0.979 0.000 1.000
#> GSM1068476 2 0.000 0.979 0.000 1.000
#> GSM1068477 2 0.000 0.979 0.000 1.000
#> GSM1068462 1 0.574 0.829 0.864 0.136
#> GSM1068463 1 0.000 0.956 1.000 0.000
#> GSM1068465 1 0.000 0.956 1.000 0.000
#> GSM1068466 1 0.000 0.956 1.000 0.000
#> GSM1068467 1 0.000 0.956 1.000 0.000
#> GSM1068469 1 0.000 0.956 1.000 0.000
#> GSM1068470 2 0.000 0.979 0.000 1.000
#> GSM1068471 2 0.000 0.979 0.000 1.000
#> GSM1068475 2 0.000 0.979 0.000 1.000
#> GSM1068528 1 0.000 0.956 1.000 0.000
#> GSM1068531 1 0.000 0.956 1.000 0.000
#> GSM1068532 1 0.000 0.956 1.000 0.000
#> GSM1068533 1 0.000 0.956 1.000 0.000
#> GSM1068535 2 0.000 0.979 0.000 1.000
#> GSM1068537 1 0.000 0.956 1.000 0.000
#> GSM1068538 1 0.000 0.956 1.000 0.000
#> GSM1068539 2 0.871 0.569 0.292 0.708
#> GSM1068540 1 0.000 0.956 1.000 0.000
#> GSM1068542 2 0.000 0.979 0.000 1.000
#> GSM1068543 2 0.000 0.979 0.000 1.000
#> GSM1068544 1 0.000 0.956 1.000 0.000
#> GSM1068545 2 0.000 0.979 0.000 1.000
#> GSM1068546 1 0.917 0.537 0.668 0.332
#> GSM1068547 1 0.000 0.956 1.000 0.000
#> GSM1068548 2 0.000 0.979 0.000 1.000
#> GSM1068549 1 0.000 0.956 1.000 0.000
#> GSM1068550 2 0.000 0.979 0.000 1.000
#> GSM1068551 2 0.000 0.979 0.000 1.000
#> GSM1068552 2 0.000 0.979 0.000 1.000
#> GSM1068555 2 0.000 0.979 0.000 1.000
#> GSM1068556 2 0.000 0.979 0.000 1.000
#> GSM1068557 2 0.000 0.979 0.000 1.000
#> GSM1068560 2 0.000 0.979 0.000 1.000
#> GSM1068561 2 0.000 0.979 0.000 1.000
#> GSM1068562 2 0.000 0.979 0.000 1.000
#> GSM1068563 2 0.000 0.979 0.000 1.000
#> GSM1068565 2 0.000 0.979 0.000 1.000
#> GSM1068529 2 0.000 0.979 0.000 1.000
#> GSM1068530 1 0.000 0.956 1.000 0.000
#> GSM1068534 2 0.000 0.979 0.000 1.000
#> GSM1068536 1 0.925 0.520 0.660 0.340
#> GSM1068541 1 0.000 0.956 1.000 0.000
#> GSM1068553 2 0.000 0.979 0.000 1.000
#> GSM1068554 2 0.000 0.979 0.000 1.000
#> GSM1068558 2 0.000 0.979 0.000 1.000
#> GSM1068559 2 0.895 0.526 0.312 0.688
#> GSM1068564 2 0.000 0.979 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1068478 3 0.2774 0.6896 0.072 0.008 0.920
#> GSM1068479 2 0.0424 0.9202 0.000 0.992 0.008
#> GSM1068481 3 0.1585 0.7331 0.028 0.008 0.964
#> GSM1068482 3 0.6295 -0.2542 0.472 0.000 0.528
#> GSM1068483 1 0.0000 0.9100 1.000 0.000 0.000
#> GSM1068486 3 0.1163 0.7462 0.000 0.028 0.972
#> GSM1068487 2 0.0424 0.9202 0.000 0.992 0.008
#> GSM1068488 3 0.6260 0.2895 0.000 0.448 0.552
#> GSM1068490 2 0.0424 0.9202 0.000 0.992 0.008
#> GSM1068491 1 0.1411 0.9080 0.964 0.000 0.036
#> GSM1068492 2 0.0424 0.9202 0.000 0.992 0.008
#> GSM1068493 3 0.0892 0.7445 0.000 0.020 0.980
#> GSM1068494 3 0.0237 0.7432 0.000 0.004 0.996
#> GSM1068495 3 0.0424 0.7451 0.000 0.008 0.992
#> GSM1068496 1 0.6398 0.5513 0.620 0.008 0.372
#> GSM1068498 1 0.2165 0.8854 0.936 0.000 0.064
#> GSM1068499 1 0.1031 0.9103 0.976 0.000 0.024
#> GSM1068500 1 0.6513 0.5015 0.592 0.008 0.400
#> GSM1068502 2 0.0000 0.9196 0.000 1.000 0.000
#> GSM1068503 2 0.0424 0.9202 0.000 0.992 0.008
#> GSM1068505 2 0.0000 0.9196 0.000 1.000 0.000
#> GSM1068506 2 0.0237 0.9177 0.000 0.996 0.004
#> GSM1068507 2 0.5529 0.5200 0.000 0.704 0.296
#> GSM1068508 2 0.6140 0.2149 0.000 0.596 0.404
#> GSM1068510 3 0.6026 0.4590 0.000 0.376 0.624
#> GSM1068512 2 0.0237 0.9177 0.000 0.996 0.004
#> GSM1068513 2 0.5591 0.4941 0.000 0.696 0.304
#> GSM1068514 2 0.0237 0.9201 0.000 0.996 0.004
#> GSM1068517 3 0.5905 0.3150 0.352 0.000 0.648
#> GSM1068518 3 0.3752 0.6623 0.144 0.000 0.856
#> GSM1068520 1 0.0000 0.9100 1.000 0.000 0.000
#> GSM1068521 1 0.1031 0.9103 0.976 0.000 0.024
#> GSM1068522 2 0.0000 0.9196 0.000 1.000 0.000
#> GSM1068524 3 0.6308 0.1760 0.000 0.492 0.508
#> GSM1068527 2 0.0237 0.9177 0.000 0.996 0.004
#> GSM1068480 3 0.0237 0.7409 0.004 0.000 0.996
#> GSM1068484 2 0.0424 0.9202 0.000 0.992 0.008
#> GSM1068485 1 0.0747 0.9111 0.984 0.000 0.016
#> GSM1068489 2 0.0000 0.9196 0.000 1.000 0.000
#> GSM1068497 3 0.0237 0.7409 0.004 0.000 0.996
#> GSM1068501 2 0.0000 0.9196 0.000 1.000 0.000
#> GSM1068504 2 0.6008 0.3107 0.000 0.628 0.372
#> GSM1068509 1 0.0000 0.9100 1.000 0.000 0.000
#> GSM1068511 2 0.0237 0.9177 0.000 0.996 0.004
#> GSM1068515 1 0.1031 0.9103 0.976 0.000 0.024
#> GSM1068516 3 0.0424 0.7451 0.000 0.008 0.992
#> GSM1068519 1 0.0000 0.9100 1.000 0.000 0.000
#> GSM1068523 3 0.6062 0.4484 0.000 0.384 0.616
#> GSM1068525 2 0.5948 0.3479 0.000 0.640 0.360
#> GSM1068526 2 0.0424 0.9202 0.000 0.992 0.008
#> GSM1068458 1 0.0892 0.9107 0.980 0.000 0.020
#> GSM1068459 1 0.6498 0.5090 0.596 0.008 0.396
#> GSM1068460 3 0.4744 0.6722 0.136 0.028 0.836
#> GSM1068461 1 0.1031 0.9103 0.976 0.000 0.024
#> GSM1068464 2 0.0424 0.9202 0.000 0.992 0.008
#> GSM1068468 1 0.1411 0.9080 0.964 0.000 0.036
#> GSM1068472 1 0.1964 0.8979 0.944 0.000 0.056
#> GSM1068473 2 0.0000 0.9196 0.000 1.000 0.000
#> GSM1068474 2 0.0424 0.9202 0.000 0.992 0.008
#> GSM1068476 3 0.6062 0.4484 0.000 0.384 0.616
#> GSM1068477 3 0.5529 0.5735 0.000 0.296 0.704
#> GSM1068462 3 0.5047 0.6691 0.140 0.036 0.824
#> GSM1068463 1 0.1860 0.8850 0.948 0.000 0.052
#> GSM1068465 1 0.0592 0.9090 0.988 0.000 0.012
#> GSM1068466 1 0.0000 0.9100 1.000 0.000 0.000
#> GSM1068467 1 0.4555 0.7712 0.800 0.000 0.200
#> GSM1068469 1 0.0892 0.9107 0.980 0.000 0.020
#> GSM1068470 2 0.5497 0.5191 0.000 0.708 0.292
#> GSM1068471 2 0.0424 0.9202 0.000 0.992 0.008
#> GSM1068475 2 0.0424 0.9202 0.000 0.992 0.008
#> GSM1068528 1 0.0892 0.9111 0.980 0.000 0.020
#> GSM1068531 1 0.6359 0.5642 0.628 0.008 0.364
#> GSM1068532 1 0.0000 0.9100 1.000 0.000 0.000
#> GSM1068533 1 0.4702 0.7474 0.788 0.000 0.212
#> GSM1068535 2 0.3619 0.7650 0.000 0.864 0.136
#> GSM1068537 1 0.0424 0.9083 0.992 0.000 0.008
#> GSM1068538 1 0.0000 0.9100 1.000 0.000 0.000
#> GSM1068539 3 0.0592 0.7464 0.000 0.012 0.988
#> GSM1068540 1 0.6359 0.5642 0.628 0.008 0.364
#> GSM1068542 2 0.0000 0.9196 0.000 1.000 0.000
#> GSM1068543 3 0.6299 0.2275 0.000 0.476 0.524
#> GSM1068544 1 0.1031 0.9103 0.976 0.000 0.024
#> GSM1068545 2 0.0424 0.9202 0.000 0.992 0.008
#> GSM1068546 3 0.1315 0.7357 0.020 0.008 0.972
#> GSM1068547 1 0.0424 0.9108 0.992 0.000 0.008
#> GSM1068548 2 0.0237 0.9177 0.000 0.996 0.004
#> GSM1068549 3 0.5948 0.0856 0.360 0.000 0.640
#> GSM1068550 2 0.0424 0.9202 0.000 0.992 0.008
#> GSM1068551 2 0.0424 0.9202 0.000 0.992 0.008
#> GSM1068552 2 0.0424 0.9202 0.000 0.992 0.008
#> GSM1068555 3 0.6126 0.4178 0.000 0.400 0.600
#> GSM1068556 2 0.0237 0.9177 0.000 0.996 0.004
#> GSM1068557 3 0.2796 0.7390 0.000 0.092 0.908
#> GSM1068560 3 0.6126 0.4178 0.000 0.400 0.600
#> GSM1068561 3 0.1289 0.7494 0.000 0.032 0.968
#> GSM1068562 3 0.6305 0.2023 0.000 0.484 0.516
#> GSM1068563 2 0.0237 0.9177 0.000 0.996 0.004
#> GSM1068565 2 0.4702 0.6695 0.000 0.788 0.212
#> GSM1068529 3 0.1289 0.7494 0.000 0.032 0.968
#> GSM1068530 1 0.0000 0.9100 1.000 0.000 0.000
#> GSM1068534 2 0.1289 0.8942 0.000 0.968 0.032
#> GSM1068536 3 0.0237 0.7432 0.000 0.004 0.996
#> GSM1068541 1 0.1163 0.9103 0.972 0.000 0.028
#> GSM1068553 2 0.0237 0.9177 0.000 0.996 0.004
#> GSM1068554 2 0.0000 0.9196 0.000 1.000 0.000
#> GSM1068558 3 0.6026 0.4590 0.000 0.376 0.624
#> GSM1068559 3 0.1031 0.7488 0.000 0.024 0.976
#> GSM1068564 2 0.0424 0.9202 0.000 0.992 0.008
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1068478 3 0.2466 0.6957 0.000 0.096 0.900 0.004
#> GSM1068479 4 0.3751 0.7332 0.000 0.196 0.004 0.800
#> GSM1068481 3 0.2684 0.7176 0.012 0.060 0.912 0.016
#> GSM1068482 3 0.6356 0.4170 0.308 0.088 0.604 0.000
#> GSM1068483 1 0.4248 0.7562 0.768 0.012 0.220 0.000
#> GSM1068486 3 0.4769 0.4368 0.000 0.308 0.684 0.008
#> GSM1068487 4 0.1389 0.9233 0.000 0.048 0.000 0.952
#> GSM1068488 2 0.3610 0.7406 0.000 0.800 0.000 0.200
#> GSM1068490 4 0.0592 0.9395 0.000 0.016 0.000 0.984
#> GSM1068491 1 0.5425 0.7474 0.752 0.060 0.172 0.016
#> GSM1068492 4 0.0592 0.9395 0.000 0.016 0.000 0.984
#> GSM1068493 3 0.4897 0.3966 0.000 0.332 0.660 0.008
#> GSM1068494 2 0.4830 0.3657 0.000 0.608 0.392 0.000
#> GSM1068495 2 0.3486 0.6233 0.000 0.812 0.188 0.000
#> GSM1068496 3 0.2778 0.7137 0.080 0.004 0.900 0.016
#> GSM1068498 1 0.4483 0.6768 0.808 0.104 0.088 0.000
#> GSM1068499 1 0.3323 0.7582 0.876 0.064 0.060 0.000
#> GSM1068500 3 0.3046 0.7120 0.096 0.004 0.884 0.016
#> GSM1068502 4 0.0188 0.9395 0.000 0.000 0.004 0.996
#> GSM1068503 4 0.0592 0.9395 0.000 0.016 0.000 0.984
#> GSM1068505 4 0.1677 0.9228 0.000 0.040 0.012 0.948
#> GSM1068506 4 0.0707 0.9361 0.000 0.000 0.020 0.980
#> GSM1068507 2 0.4053 0.7155 0.000 0.768 0.004 0.228
#> GSM1068508 2 0.3945 0.7261 0.000 0.780 0.004 0.216
#> GSM1068510 2 0.2973 0.7596 0.000 0.856 0.000 0.144
#> GSM1068512 4 0.1913 0.9208 0.000 0.040 0.020 0.940
#> GSM1068513 2 0.4188 0.7053 0.000 0.752 0.004 0.244
#> GSM1068514 4 0.0937 0.9403 0.000 0.012 0.012 0.976
#> GSM1068517 1 0.6613 0.4205 0.628 0.200 0.172 0.000
#> GSM1068518 2 0.6969 0.3577 0.224 0.584 0.192 0.000
#> GSM1068520 1 0.3870 0.7596 0.788 0.004 0.208 0.000
#> GSM1068521 1 0.1388 0.7843 0.960 0.012 0.028 0.000
#> GSM1068522 4 0.0469 0.9385 0.000 0.000 0.012 0.988
#> GSM1068524 2 0.3444 0.7482 0.000 0.816 0.000 0.184
#> GSM1068527 4 0.1624 0.9277 0.000 0.028 0.020 0.952
#> GSM1068480 2 0.5229 0.2692 0.008 0.564 0.428 0.000
#> GSM1068484 4 0.0592 0.9395 0.000 0.016 0.000 0.984
#> GSM1068485 1 0.2174 0.7874 0.928 0.020 0.052 0.000
#> GSM1068489 4 0.1767 0.9200 0.000 0.044 0.012 0.944
#> GSM1068497 2 0.5085 0.3547 0.008 0.616 0.376 0.000
#> GSM1068501 4 0.0469 0.9385 0.000 0.000 0.012 0.988
#> GSM1068504 2 0.3726 0.7363 0.000 0.788 0.000 0.212
#> GSM1068509 1 0.4248 0.7562 0.768 0.012 0.220 0.000
#> GSM1068511 4 0.0707 0.9361 0.000 0.000 0.020 0.980
#> GSM1068515 1 0.1837 0.7828 0.944 0.028 0.028 0.000
#> GSM1068516 2 0.3688 0.6079 0.000 0.792 0.208 0.000
#> GSM1068519 1 0.3529 0.7853 0.836 0.012 0.152 0.000
#> GSM1068523 2 0.2281 0.7554 0.000 0.904 0.000 0.096
#> GSM1068525 2 0.4040 0.7056 0.000 0.752 0.000 0.248
#> GSM1068526 4 0.2345 0.8773 0.000 0.100 0.000 0.900
#> GSM1068458 1 0.0188 0.7953 0.996 0.004 0.000 0.000
#> GSM1068459 3 0.2778 0.7137 0.080 0.004 0.900 0.016
#> GSM1068460 2 0.6966 0.5092 0.112 0.652 0.200 0.036
#> GSM1068461 1 0.3464 0.7355 0.868 0.076 0.056 0.000
#> GSM1068464 4 0.0779 0.9386 0.000 0.016 0.004 0.980
#> GSM1068468 1 0.5955 0.7346 0.732 0.060 0.168 0.040
#> GSM1068472 1 0.6240 0.7167 0.712 0.060 0.180 0.048
#> GSM1068473 4 0.0000 0.9397 0.000 0.000 0.000 1.000
#> GSM1068474 4 0.0779 0.9386 0.000 0.016 0.004 0.980
#> GSM1068476 2 0.2345 0.7564 0.000 0.900 0.000 0.100
#> GSM1068477 2 0.4389 0.7324 0.000 0.812 0.072 0.116
#> GSM1068462 2 0.7031 0.5026 0.120 0.648 0.196 0.036
#> GSM1068463 3 0.5290 0.0979 0.404 0.012 0.584 0.000
#> GSM1068465 1 0.5629 0.7364 0.724 0.052 0.208 0.016
#> GSM1068466 1 0.3945 0.7546 0.780 0.004 0.216 0.000
#> GSM1068467 1 0.6891 0.5145 0.648 0.172 0.160 0.020
#> GSM1068469 1 0.3307 0.7948 0.868 0.028 0.104 0.000
#> GSM1068470 2 0.4103 0.7012 0.000 0.744 0.000 0.256
#> GSM1068471 4 0.0592 0.9395 0.000 0.016 0.000 0.984
#> GSM1068475 4 0.0779 0.9386 0.000 0.016 0.004 0.980
#> GSM1068528 1 0.2845 0.7794 0.896 0.028 0.076 0.000
#> GSM1068531 3 0.2989 0.7077 0.100 0.004 0.884 0.012
#> GSM1068532 1 0.3718 0.7783 0.820 0.012 0.168 0.000
#> GSM1068533 3 0.3831 0.6224 0.204 0.004 0.792 0.000
#> GSM1068535 4 0.6101 0.3235 0.000 0.052 0.388 0.560
#> GSM1068537 3 0.5366 -0.0601 0.440 0.012 0.548 0.000
#> GSM1068538 1 0.3718 0.7783 0.820 0.012 0.168 0.000
#> GSM1068539 2 0.3751 0.6138 0.004 0.800 0.196 0.000
#> GSM1068540 3 0.2989 0.7077 0.100 0.004 0.884 0.012
#> GSM1068542 4 0.0188 0.9397 0.000 0.000 0.004 0.996
#> GSM1068543 2 0.3311 0.7531 0.000 0.828 0.000 0.172
#> GSM1068544 1 0.3820 0.7563 0.848 0.064 0.088 0.000
#> GSM1068545 4 0.0779 0.9386 0.000 0.016 0.004 0.980
#> GSM1068546 3 0.4567 0.4923 0.000 0.276 0.716 0.008
#> GSM1068547 1 0.2011 0.8041 0.920 0.000 0.080 0.000
#> GSM1068548 4 0.0707 0.9361 0.000 0.000 0.020 0.980
#> GSM1068549 3 0.4507 0.6394 0.044 0.168 0.788 0.000
#> GSM1068550 4 0.1388 0.9370 0.000 0.028 0.012 0.960
#> GSM1068551 4 0.4741 0.4623 0.000 0.328 0.004 0.668
#> GSM1068552 4 0.0592 0.9395 0.000 0.016 0.000 0.984
#> GSM1068555 2 0.2921 0.7601 0.000 0.860 0.000 0.140
#> GSM1068556 4 0.0707 0.9361 0.000 0.000 0.020 0.980
#> GSM1068557 2 0.3687 0.7286 0.000 0.856 0.080 0.064
#> GSM1068560 2 0.2647 0.7590 0.000 0.880 0.000 0.120
#> GSM1068561 2 0.2859 0.6987 0.000 0.880 0.112 0.008
#> GSM1068562 2 0.3311 0.7534 0.000 0.828 0.000 0.172
#> GSM1068563 4 0.0707 0.9361 0.000 0.000 0.020 0.980
#> GSM1068565 2 0.4454 0.6327 0.000 0.692 0.000 0.308
#> GSM1068529 2 0.2987 0.7023 0.000 0.880 0.104 0.016
#> GSM1068530 1 0.3718 0.7783 0.820 0.012 0.168 0.000
#> GSM1068534 4 0.4452 0.7795 0.000 0.048 0.156 0.796
#> GSM1068536 2 0.4817 0.3669 0.000 0.612 0.388 0.000
#> GSM1068541 1 0.5251 0.7620 0.768 0.052 0.160 0.020
#> GSM1068553 4 0.1474 0.9106 0.000 0.000 0.052 0.948
#> GSM1068554 4 0.0469 0.9385 0.000 0.000 0.012 0.988
#> GSM1068558 2 0.2973 0.7596 0.000 0.856 0.000 0.144
#> GSM1068559 2 0.3577 0.6546 0.000 0.832 0.156 0.012
#> GSM1068564 4 0.0592 0.9395 0.000 0.016 0.000 0.984
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM1068478 3 0.1106 0.7476 0.000 0.012 0.964 0.000 0.024
#> GSM1068479 4 0.4769 0.6169 0.000 0.056 0.000 0.688 0.256
#> GSM1068481 3 0.0566 0.7570 0.000 0.004 0.984 0.000 0.012
#> GSM1068482 5 0.6740 0.2702 0.252 0.012 0.232 0.000 0.504
#> GSM1068483 1 0.1408 0.7358 0.948 0.000 0.044 0.000 0.008
#> GSM1068486 3 0.3019 0.6680 0.000 0.088 0.864 0.000 0.048
#> GSM1068487 4 0.2953 0.8146 0.000 0.144 0.000 0.844 0.012
#> GSM1068488 2 0.1243 0.8352 0.000 0.960 0.004 0.028 0.008
#> GSM1068490 4 0.1168 0.9250 0.000 0.008 0.000 0.960 0.032
#> GSM1068491 1 0.5760 0.4972 0.508 0.008 0.040 0.012 0.432
#> GSM1068492 4 0.0693 0.9289 0.000 0.008 0.000 0.980 0.012
#> GSM1068493 3 0.5167 0.3574 0.000 0.088 0.664 0.000 0.248
#> GSM1068494 5 0.6553 0.5198 0.000 0.364 0.204 0.000 0.432
#> GSM1068495 5 0.5594 0.4358 0.000 0.436 0.072 0.000 0.492
#> GSM1068496 3 0.1571 0.7716 0.060 0.000 0.936 0.000 0.004
#> GSM1068498 5 0.3876 -0.0232 0.316 0.000 0.000 0.000 0.684
#> GSM1068499 1 0.3741 0.6283 0.732 0.000 0.004 0.000 0.264
#> GSM1068500 3 0.1430 0.7717 0.052 0.000 0.944 0.000 0.004
#> GSM1068502 4 0.1638 0.9114 0.000 0.000 0.004 0.932 0.064
#> GSM1068503 4 0.0798 0.9294 0.000 0.008 0.000 0.976 0.016
#> GSM1068505 4 0.1780 0.9203 0.000 0.024 0.008 0.940 0.028
#> GSM1068506 4 0.1281 0.9241 0.000 0.000 0.012 0.956 0.032
#> GSM1068507 2 0.3916 0.7189 0.000 0.804 0.000 0.092 0.104
#> GSM1068508 2 0.3805 0.6755 0.000 0.784 0.000 0.032 0.184
#> GSM1068510 2 0.0854 0.8340 0.000 0.976 0.004 0.012 0.008
#> GSM1068512 4 0.1787 0.9205 0.000 0.016 0.012 0.940 0.032
#> GSM1068513 2 0.1992 0.8217 0.000 0.924 0.000 0.044 0.032
#> GSM1068514 4 0.1153 0.9282 0.000 0.008 0.004 0.964 0.024
#> GSM1068517 5 0.3325 0.4515 0.104 0.032 0.012 0.000 0.852
#> GSM1068518 5 0.2770 0.5456 0.020 0.076 0.016 0.000 0.888
#> GSM1068520 1 0.4149 0.7214 0.784 0.000 0.088 0.000 0.128
#> GSM1068521 1 0.3684 0.6901 0.720 0.000 0.000 0.000 0.280
#> GSM1068522 4 0.0451 0.9302 0.000 0.000 0.008 0.988 0.004
#> GSM1068524 2 0.1243 0.8352 0.000 0.960 0.004 0.028 0.008
#> GSM1068527 4 0.1787 0.9200 0.000 0.016 0.012 0.940 0.032
#> GSM1068480 5 0.6526 0.5486 0.000 0.260 0.256 0.000 0.484
#> GSM1068484 4 0.1168 0.9250 0.000 0.008 0.000 0.960 0.032
#> GSM1068485 1 0.3086 0.6877 0.816 0.000 0.004 0.000 0.180
#> GSM1068489 4 0.2352 0.9033 0.000 0.048 0.008 0.912 0.032
#> GSM1068497 5 0.6246 0.6006 0.000 0.272 0.192 0.000 0.536
#> GSM1068501 4 0.0898 0.9296 0.000 0.000 0.008 0.972 0.020
#> GSM1068504 2 0.1626 0.8290 0.000 0.940 0.000 0.044 0.016
#> GSM1068509 1 0.1701 0.7354 0.936 0.000 0.048 0.000 0.016
#> GSM1068511 4 0.1281 0.9241 0.000 0.000 0.012 0.956 0.032
#> GSM1068515 1 0.4015 0.6676 0.652 0.000 0.000 0.000 0.348
#> GSM1068516 5 0.5579 0.4613 0.000 0.420 0.072 0.000 0.508
#> GSM1068519 1 0.0162 0.7427 0.996 0.000 0.004 0.000 0.000
#> GSM1068523 2 0.0510 0.8288 0.000 0.984 0.000 0.000 0.016
#> GSM1068525 2 0.1701 0.8264 0.000 0.936 0.000 0.048 0.016
#> GSM1068526 4 0.4599 0.3501 0.000 0.384 0.000 0.600 0.016
#> GSM1068458 1 0.2813 0.7405 0.832 0.000 0.000 0.000 0.168
#> GSM1068459 3 0.1628 0.7721 0.056 0.000 0.936 0.000 0.008
#> GSM1068460 5 0.4427 0.5709 0.004 0.212 0.020 0.016 0.748
#> GSM1068461 1 0.4287 0.5275 0.540 0.000 0.000 0.000 0.460
#> GSM1068464 4 0.1638 0.9104 0.000 0.004 0.000 0.932 0.064
#> GSM1068468 1 0.6082 0.5009 0.500 0.008 0.036 0.032 0.424
#> GSM1068472 1 0.6581 0.4844 0.480 0.008 0.036 0.068 0.408
#> GSM1068473 4 0.0451 0.9304 0.000 0.000 0.004 0.988 0.008
#> GSM1068474 4 0.1704 0.9081 0.000 0.004 0.000 0.928 0.068
#> GSM1068476 2 0.1331 0.8237 0.000 0.952 0.000 0.008 0.040
#> GSM1068477 2 0.5084 -0.1626 0.000 0.488 0.008 0.020 0.484
#> GSM1068462 5 0.4488 0.5705 0.004 0.208 0.020 0.020 0.748
#> GSM1068463 3 0.4150 0.4225 0.388 0.000 0.612 0.000 0.000
#> GSM1068465 1 0.6224 0.5242 0.524 0.008 0.044 0.036 0.388
#> GSM1068466 1 0.4406 0.7079 0.764 0.000 0.108 0.000 0.128
#> GSM1068467 5 0.2537 0.4553 0.056 0.024 0.016 0.000 0.904
#> GSM1068469 1 0.4302 0.7060 0.720 0.000 0.032 0.000 0.248
#> GSM1068470 2 0.1845 0.8210 0.000 0.928 0.000 0.056 0.016
#> GSM1068471 4 0.1041 0.9256 0.000 0.004 0.000 0.964 0.032
#> GSM1068475 4 0.1764 0.9102 0.000 0.008 0.000 0.928 0.064
#> GSM1068528 1 0.3231 0.6776 0.800 0.000 0.004 0.000 0.196
#> GSM1068531 3 0.1704 0.7710 0.068 0.000 0.928 0.000 0.004
#> GSM1068532 1 0.0510 0.7420 0.984 0.000 0.016 0.000 0.000
#> GSM1068533 3 0.3688 0.6998 0.124 0.000 0.816 0.000 0.060
#> GSM1068535 3 0.5296 0.3340 0.000 0.016 0.596 0.356 0.032
#> GSM1068537 3 0.4557 0.3604 0.404 0.000 0.584 0.000 0.012
#> GSM1068538 1 0.0510 0.7420 0.984 0.000 0.016 0.000 0.000
#> GSM1068539 5 0.5579 0.4613 0.000 0.420 0.072 0.000 0.508
#> GSM1068540 3 0.1704 0.7710 0.068 0.000 0.928 0.000 0.004
#> GSM1068542 4 0.0771 0.9300 0.000 0.000 0.004 0.976 0.020
#> GSM1068543 2 0.0833 0.8362 0.000 0.976 0.004 0.016 0.004
#> GSM1068544 1 0.3662 0.6288 0.744 0.000 0.004 0.000 0.252
#> GSM1068545 4 0.1704 0.9081 0.000 0.004 0.000 0.928 0.068
#> GSM1068546 3 0.1915 0.7263 0.000 0.032 0.928 0.000 0.040
#> GSM1068547 1 0.2377 0.7465 0.872 0.000 0.000 0.000 0.128
#> GSM1068548 4 0.0798 0.9300 0.000 0.000 0.008 0.976 0.016
#> GSM1068549 3 0.4640 0.1215 0.000 0.016 0.584 0.000 0.400
#> GSM1068550 4 0.1471 0.9243 0.000 0.020 0.004 0.952 0.024
#> GSM1068551 2 0.4840 0.5055 0.000 0.688 0.000 0.248 0.064
#> GSM1068552 4 0.0693 0.9298 0.000 0.008 0.000 0.980 0.012
#> GSM1068555 2 0.0727 0.8349 0.000 0.980 0.004 0.012 0.004
#> GSM1068556 4 0.0798 0.9292 0.000 0.000 0.008 0.976 0.016
#> GSM1068557 2 0.3849 0.5177 0.000 0.752 0.016 0.000 0.232
#> GSM1068560 2 0.0451 0.8332 0.000 0.988 0.000 0.004 0.008
#> GSM1068561 2 0.2036 0.7768 0.000 0.920 0.056 0.000 0.024
#> GSM1068562 2 0.0898 0.8371 0.000 0.972 0.000 0.020 0.008
#> GSM1068563 4 0.0798 0.9300 0.000 0.000 0.008 0.976 0.016
#> GSM1068565 2 0.2236 0.8073 0.000 0.908 0.000 0.068 0.024
#> GSM1068529 2 0.2438 0.7799 0.000 0.900 0.060 0.000 0.040
#> GSM1068530 1 0.0510 0.7420 0.984 0.000 0.016 0.000 0.000
#> GSM1068534 4 0.4929 0.5911 0.000 0.016 0.260 0.688 0.036
#> GSM1068536 5 0.6706 0.5301 0.000 0.328 0.256 0.000 0.416
#> GSM1068541 1 0.6182 0.5281 0.516 0.008 0.036 0.040 0.400
#> GSM1068553 4 0.1469 0.9216 0.000 0.000 0.016 0.948 0.036
#> GSM1068554 4 0.0798 0.9291 0.000 0.000 0.008 0.976 0.016
#> GSM1068558 2 0.0854 0.8340 0.000 0.976 0.004 0.012 0.008
#> GSM1068559 2 0.5153 -0.2172 0.000 0.524 0.040 0.000 0.436
#> GSM1068564 4 0.0579 0.9292 0.000 0.008 0.000 0.984 0.008
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1068478 3 0.2420 0.69148 0.000 0.004 0.864 0.000 0.128 0.004
#> GSM1068479 4 0.5414 0.21322 0.000 0.388 0.000 0.528 0.036 0.048
#> GSM1068481 3 0.1524 0.72184 0.000 0.008 0.932 0.000 0.060 0.000
#> GSM1068482 5 0.5251 0.45418 0.108 0.124 0.072 0.000 0.696 0.000
#> GSM1068483 1 0.1564 0.66719 0.936 0.024 0.040 0.000 0.000 0.000
#> GSM1068486 3 0.4175 0.54298 0.000 0.016 0.716 0.000 0.240 0.028
#> GSM1068487 4 0.4063 0.61627 0.000 0.028 0.000 0.712 0.008 0.252
#> GSM1068488 6 0.1370 0.81406 0.000 0.012 0.000 0.004 0.036 0.948
#> GSM1068490 4 0.1957 0.84189 0.000 0.072 0.000 0.912 0.008 0.008
#> GSM1068491 2 0.5610 0.58438 0.292 0.600 0.016 0.008 0.076 0.008
#> GSM1068492 4 0.1230 0.85465 0.000 0.028 0.000 0.956 0.008 0.008
#> GSM1068493 3 0.4734 0.32521 0.000 0.016 0.604 0.000 0.348 0.032
#> GSM1068494 5 0.4094 0.67245 0.000 0.000 0.088 0.000 0.744 0.168
#> GSM1068495 5 0.3586 0.64369 0.000 0.028 0.000 0.000 0.756 0.216
#> GSM1068496 3 0.1194 0.73854 0.032 0.004 0.956 0.000 0.008 0.000
#> GSM1068498 5 0.5449 0.17033 0.188 0.240 0.000 0.000 0.572 0.000
#> GSM1068499 1 0.5051 0.60439 0.648 0.140 0.004 0.000 0.208 0.000
#> GSM1068500 3 0.1861 0.73692 0.020 0.036 0.928 0.000 0.016 0.000
#> GSM1068502 4 0.2445 0.82563 0.000 0.120 0.000 0.868 0.008 0.004
#> GSM1068503 4 0.1149 0.85565 0.000 0.024 0.000 0.960 0.008 0.008
#> GSM1068505 4 0.4307 0.79363 0.000 0.160 0.028 0.764 0.036 0.012
#> GSM1068506 4 0.3883 0.80681 0.000 0.144 0.028 0.788 0.040 0.000
#> GSM1068507 6 0.6037 0.44754 0.000 0.292 0.012 0.080 0.048 0.568
#> GSM1068508 6 0.5305 0.36716 0.000 0.344 0.000 0.052 0.032 0.572
#> GSM1068510 6 0.1155 0.81575 0.000 0.004 0.000 0.004 0.036 0.956
#> GSM1068512 4 0.4210 0.79584 0.000 0.160 0.028 0.768 0.036 0.008
#> GSM1068513 6 0.2290 0.79384 0.000 0.060 0.004 0.008 0.024 0.904
#> GSM1068514 4 0.3235 0.82652 0.000 0.124 0.024 0.832 0.020 0.000
#> GSM1068517 5 0.3373 0.48350 0.032 0.140 0.000 0.000 0.816 0.012
#> GSM1068518 5 0.4181 0.34105 0.028 0.256 0.000 0.000 0.704 0.012
#> GSM1068520 1 0.4841 0.47457 0.660 0.236 0.100 0.000 0.004 0.000
#> GSM1068521 1 0.4923 0.62319 0.656 0.176 0.000 0.000 0.168 0.000
#> GSM1068522 4 0.1230 0.85913 0.000 0.028 0.008 0.956 0.008 0.000
#> GSM1068524 6 0.1155 0.81575 0.000 0.004 0.000 0.004 0.036 0.956
#> GSM1068527 4 0.4498 0.78647 0.000 0.172 0.028 0.748 0.036 0.016
#> GSM1068480 5 0.3961 0.66735 0.000 0.000 0.112 0.000 0.764 0.124
#> GSM1068484 4 0.1781 0.84592 0.000 0.060 0.000 0.924 0.008 0.008
#> GSM1068485 1 0.4379 0.64076 0.732 0.124 0.004 0.000 0.140 0.000
#> GSM1068489 4 0.4480 0.78873 0.000 0.160 0.028 0.756 0.036 0.020
#> GSM1068497 5 0.4006 0.67453 0.000 0.008 0.084 0.000 0.772 0.136
#> GSM1068501 4 0.1657 0.85677 0.000 0.056 0.016 0.928 0.000 0.000
#> GSM1068504 6 0.1167 0.81140 0.000 0.020 0.000 0.012 0.008 0.960
#> GSM1068509 1 0.2119 0.64907 0.904 0.060 0.036 0.000 0.000 0.000
#> GSM1068511 4 0.3853 0.80839 0.000 0.148 0.028 0.788 0.036 0.000
#> GSM1068515 1 0.5520 0.53732 0.532 0.312 0.000 0.000 0.156 0.000
#> GSM1068516 5 0.3529 0.64773 0.000 0.028 0.000 0.000 0.764 0.208
#> GSM1068519 1 0.0632 0.68328 0.976 0.000 0.024 0.000 0.000 0.000
#> GSM1068523 6 0.0458 0.81757 0.000 0.000 0.000 0.000 0.016 0.984
#> GSM1068525 6 0.1382 0.81573 0.000 0.008 0.000 0.008 0.036 0.948
#> GSM1068526 6 0.5076 0.05633 0.000 0.056 0.000 0.444 0.008 0.492
#> GSM1068458 1 0.3230 0.57414 0.776 0.212 0.000 0.000 0.012 0.000
#> GSM1068459 3 0.1218 0.73771 0.028 0.012 0.956 0.000 0.004 0.000
#> GSM1068460 2 0.5128 0.46099 0.000 0.604 0.004 0.004 0.304 0.084
#> GSM1068461 1 0.5888 0.48945 0.476 0.268 0.000 0.000 0.256 0.000
#> GSM1068464 4 0.2566 0.81850 0.000 0.112 0.000 0.868 0.012 0.008
#> GSM1068468 2 0.5725 0.58628 0.292 0.600 0.016 0.032 0.056 0.004
#> GSM1068472 2 0.6075 0.57305 0.236 0.608 0.016 0.092 0.044 0.004
#> GSM1068473 4 0.0937 0.85741 0.000 0.040 0.000 0.960 0.000 0.000
#> GSM1068474 4 0.2512 0.81657 0.000 0.116 0.000 0.868 0.008 0.008
#> GSM1068476 6 0.2006 0.79371 0.000 0.080 0.000 0.000 0.016 0.904
#> GSM1068477 2 0.6520 0.09734 0.000 0.396 0.000 0.024 0.248 0.332
#> GSM1068462 2 0.5128 0.46099 0.000 0.604 0.004 0.004 0.304 0.084
#> GSM1068463 3 0.4481 0.31266 0.400 0.020 0.572 0.000 0.008 0.000
#> GSM1068465 2 0.5688 0.53242 0.320 0.584 0.024 0.028 0.040 0.004
#> GSM1068466 1 0.5430 0.38492 0.592 0.236 0.168 0.000 0.004 0.000
#> GSM1068467 2 0.4567 0.44400 0.008 0.612 0.000 0.004 0.352 0.024
#> GSM1068469 1 0.4703 -0.00457 0.532 0.432 0.016 0.000 0.020 0.000
#> GSM1068470 6 0.1350 0.80796 0.000 0.020 0.000 0.020 0.008 0.952
#> GSM1068471 4 0.1882 0.84611 0.000 0.060 0.000 0.920 0.012 0.008
#> GSM1068475 4 0.2512 0.81657 0.000 0.116 0.000 0.868 0.008 0.008
#> GSM1068528 1 0.4651 0.62993 0.700 0.124 0.004 0.000 0.172 0.000
#> GSM1068531 3 0.1832 0.73754 0.032 0.032 0.928 0.000 0.008 0.000
#> GSM1068532 1 0.0790 0.68161 0.968 0.000 0.032 0.000 0.000 0.000
#> GSM1068533 3 0.4763 0.51140 0.092 0.220 0.680 0.000 0.008 0.000
#> GSM1068535 3 0.6378 0.18649 0.000 0.172 0.488 0.300 0.040 0.000
#> GSM1068537 3 0.4900 0.24448 0.416 0.052 0.528 0.000 0.004 0.000
#> GSM1068538 1 0.0713 0.68284 0.972 0.000 0.028 0.000 0.000 0.000
#> GSM1068539 5 0.3529 0.64773 0.000 0.028 0.000 0.000 0.764 0.208
#> GSM1068540 3 0.1832 0.73588 0.032 0.032 0.928 0.000 0.008 0.000
#> GSM1068542 4 0.0891 0.85868 0.000 0.024 0.008 0.968 0.000 0.000
#> GSM1068543 6 0.1080 0.81691 0.000 0.004 0.000 0.004 0.032 0.960
#> GSM1068544 1 0.4882 0.61116 0.668 0.124 0.004 0.000 0.204 0.000
#> GSM1068545 4 0.2466 0.81901 0.000 0.112 0.000 0.872 0.008 0.008
#> GSM1068546 3 0.3309 0.64405 0.000 0.024 0.800 0.000 0.172 0.004
#> GSM1068547 1 0.3502 0.58254 0.780 0.192 0.020 0.000 0.008 0.000
#> GSM1068548 4 0.1382 0.85823 0.000 0.036 0.008 0.948 0.008 0.000
#> GSM1068549 5 0.4499 0.08252 0.000 0.032 0.428 0.000 0.540 0.000
#> GSM1068550 4 0.3359 0.82176 0.000 0.136 0.024 0.820 0.020 0.000
#> GSM1068551 6 0.4589 0.58776 0.000 0.096 0.000 0.188 0.008 0.708
#> GSM1068552 4 0.1065 0.85639 0.000 0.020 0.000 0.964 0.008 0.008
#> GSM1068555 6 0.1155 0.81575 0.000 0.004 0.000 0.004 0.036 0.956
#> GSM1068556 4 0.1757 0.85611 0.000 0.052 0.008 0.928 0.012 0.000
#> GSM1068557 6 0.5579 0.20996 0.000 0.204 0.000 0.000 0.248 0.548
#> GSM1068560 6 0.0891 0.81550 0.000 0.024 0.000 0.000 0.008 0.968
#> GSM1068561 6 0.3203 0.69406 0.000 0.024 0.004 0.000 0.160 0.812
#> GSM1068562 6 0.0951 0.81714 0.000 0.020 0.000 0.004 0.008 0.968
#> GSM1068563 4 0.1382 0.85823 0.000 0.036 0.008 0.948 0.008 0.000
#> GSM1068565 6 0.2240 0.79177 0.000 0.056 0.000 0.032 0.008 0.904
#> GSM1068529 6 0.4426 0.66486 0.000 0.060 0.044 0.000 0.140 0.756
#> GSM1068530 1 0.0713 0.68284 0.972 0.000 0.028 0.000 0.000 0.000
#> GSM1068534 4 0.6126 0.48659 0.000 0.176 0.240 0.548 0.036 0.000
#> GSM1068536 5 0.4737 0.66025 0.000 0.016 0.120 0.000 0.712 0.152
#> GSM1068541 2 0.5724 0.51524 0.340 0.564 0.016 0.032 0.044 0.004
#> GSM1068553 4 0.4066 0.79890 0.000 0.156 0.040 0.772 0.032 0.000
#> GSM1068554 4 0.1657 0.85677 0.000 0.056 0.016 0.928 0.000 0.000
#> GSM1068558 6 0.1155 0.81575 0.000 0.004 0.000 0.004 0.036 0.956
#> GSM1068559 5 0.5895 0.22793 0.000 0.208 0.000 0.000 0.436 0.356
#> GSM1068564 4 0.1230 0.85465 0.000 0.028 0.000 0.956 0.008 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 disease.state(p) gender(p) k
#> ATC:kmeans 107 0.235 0.813 2
#> ATC:kmeans 91 0.563 0.964 3
#> ATC:kmeans 94 0.682 0.170 4
#> ATC:kmeans 91 0.697 0.091 5
#> ATC:kmeans 84 0.240 0.211 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 38950 rows and 108 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 5.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.997 0.999 0.5044 0.496 0.496
#> 3 3 0.963 0.956 0.979 0.3261 0.755 0.542
#> 4 4 0.973 0.928 0.970 0.1148 0.885 0.672
#> 5 5 0.926 0.839 0.935 0.0572 0.920 0.708
#> 6 6 0.819 0.716 0.830 0.0461 0.920 0.655
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 5
#> attr(,"optional")
#> [1] 2 3 4
There is also optional best \(k\) = 2 3 4 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
#> GSM1068478 1 0.0000 0.997 1.000 0.000
#> GSM1068479 2 0.0000 1.000 0.000 1.000
#> GSM1068481 1 0.0000 0.997 1.000 0.000
#> GSM1068482 1 0.0000 0.997 1.000 0.000
#> GSM1068483 1 0.0000 0.997 1.000 0.000
#> GSM1068486 1 0.0672 0.989 0.992 0.008
#> GSM1068487 2 0.0000 1.000 0.000 1.000
#> GSM1068488 2 0.0000 1.000 0.000 1.000
#> GSM1068490 2 0.0000 1.000 0.000 1.000
#> GSM1068491 1 0.0000 0.997 1.000 0.000
#> GSM1068492 2 0.0000 1.000 0.000 1.000
#> GSM1068493 1 0.0000 0.997 1.000 0.000
#> GSM1068494 1 0.0000 0.997 1.000 0.000
#> GSM1068495 1 0.0000 0.997 1.000 0.000
#> GSM1068496 1 0.0000 0.997 1.000 0.000
#> GSM1068498 1 0.0000 0.997 1.000 0.000
#> GSM1068499 1 0.0000 0.997 1.000 0.000
#> GSM1068500 1 0.0000 0.997 1.000 0.000
#> GSM1068502 2 0.0000 1.000 0.000 1.000
#> GSM1068503 2 0.0000 1.000 0.000 1.000
#> GSM1068505 2 0.0000 1.000 0.000 1.000
#> GSM1068506 2 0.0000 1.000 0.000 1.000
#> GSM1068507 2 0.0000 1.000 0.000 1.000
#> GSM1068508 2 0.0000 1.000 0.000 1.000
#> GSM1068510 2 0.0000 1.000 0.000 1.000
#> GSM1068512 2 0.0000 1.000 0.000 1.000
#> GSM1068513 2 0.0000 1.000 0.000 1.000
#> GSM1068514 2 0.0000 1.000 0.000 1.000
#> GSM1068517 1 0.0000 0.997 1.000 0.000
#> GSM1068518 1 0.0000 0.997 1.000 0.000
#> GSM1068520 1 0.0000 0.997 1.000 0.000
#> GSM1068521 1 0.0000 0.997 1.000 0.000
#> GSM1068522 2 0.0000 1.000 0.000 1.000
#> GSM1068524 2 0.0000 1.000 0.000 1.000
#> GSM1068527 2 0.0000 1.000 0.000 1.000
#> GSM1068480 1 0.0000 0.997 1.000 0.000
#> GSM1068484 2 0.0000 1.000 0.000 1.000
#> GSM1068485 1 0.0000 0.997 1.000 0.000
#> GSM1068489 2 0.0000 1.000 0.000 1.000
#> GSM1068497 1 0.0000 0.997 1.000 0.000
#> GSM1068501 2 0.0000 1.000 0.000 1.000
#> GSM1068504 2 0.0000 1.000 0.000 1.000
#> GSM1068509 1 0.0000 0.997 1.000 0.000
#> GSM1068511 2 0.0000 1.000 0.000 1.000
#> GSM1068515 1 0.0000 0.997 1.000 0.000
#> GSM1068516 1 0.0000 0.997 1.000 0.000
#> GSM1068519 1 0.0000 0.997 1.000 0.000
#> GSM1068523 2 0.0000 1.000 0.000 1.000
#> GSM1068525 2 0.0000 1.000 0.000 1.000
#> GSM1068526 2 0.0000 1.000 0.000 1.000
#> GSM1068458 1 0.0000 0.997 1.000 0.000
#> GSM1068459 1 0.0000 0.997 1.000 0.000
#> GSM1068460 1 0.0000 0.997 1.000 0.000
#> GSM1068461 1 0.0000 0.997 1.000 0.000
#> GSM1068464 2 0.0000 1.000 0.000 1.000
#> GSM1068468 1 0.0000 0.997 1.000 0.000
#> GSM1068472 1 0.0000 0.997 1.000 0.000
#> GSM1068473 2 0.0000 1.000 0.000 1.000
#> GSM1068474 2 0.0000 1.000 0.000 1.000
#> GSM1068476 2 0.0000 1.000 0.000 1.000
#> GSM1068477 2 0.0000 1.000 0.000 1.000
#> GSM1068462 1 0.0000 0.997 1.000 0.000
#> GSM1068463 1 0.0000 0.997 1.000 0.000
#> GSM1068465 1 0.0000 0.997 1.000 0.000
#> GSM1068466 1 0.0000 0.997 1.000 0.000
#> GSM1068467 1 0.0000 0.997 1.000 0.000
#> GSM1068469 1 0.0000 0.997 1.000 0.000
#> GSM1068470 2 0.0000 1.000 0.000 1.000
#> GSM1068471 2 0.0000 1.000 0.000 1.000
#> GSM1068475 2 0.0000 1.000 0.000 1.000
#> GSM1068528 1 0.0000 0.997 1.000 0.000
#> GSM1068531 1 0.0000 0.997 1.000 0.000
#> GSM1068532 1 0.0000 0.997 1.000 0.000
#> GSM1068533 1 0.0000 0.997 1.000 0.000
#> GSM1068535 2 0.0000 1.000 0.000 1.000
#> GSM1068537 1 0.0000 0.997 1.000 0.000
#> GSM1068538 1 0.0000 0.997 1.000 0.000
#> GSM1068539 1 0.0000 0.997 1.000 0.000
#> GSM1068540 1 0.0000 0.997 1.000 0.000
#> GSM1068542 2 0.0000 1.000 0.000 1.000
#> GSM1068543 2 0.0000 1.000 0.000 1.000
#> GSM1068544 1 0.0000 0.997 1.000 0.000
#> GSM1068545 2 0.0000 1.000 0.000 1.000
#> GSM1068546 1 0.0000 0.997 1.000 0.000
#> GSM1068547 1 0.0000 0.997 1.000 0.000
#> GSM1068548 2 0.0000 1.000 0.000 1.000
#> GSM1068549 1 0.0000 0.997 1.000 0.000
#> GSM1068550 2 0.0000 1.000 0.000 1.000
#> GSM1068551 2 0.0000 1.000 0.000 1.000
#> GSM1068552 2 0.0000 1.000 0.000 1.000
#> GSM1068555 2 0.0000 1.000 0.000 1.000
#> GSM1068556 2 0.0000 1.000 0.000 1.000
#> GSM1068557 2 0.0000 1.000 0.000 1.000
#> GSM1068560 2 0.0000 1.000 0.000 1.000
#> GSM1068561 2 0.0000 1.000 0.000 1.000
#> GSM1068562 2 0.0000 1.000 0.000 1.000
#> GSM1068563 2 0.0000 1.000 0.000 1.000
#> GSM1068565 2 0.0000 1.000 0.000 1.000
#> GSM1068529 2 0.0000 1.000 0.000 1.000
#> GSM1068530 1 0.0000 0.997 1.000 0.000
#> GSM1068534 2 0.0000 1.000 0.000 1.000
#> GSM1068536 1 0.0000 0.997 1.000 0.000
#> GSM1068541 1 0.0000 0.997 1.000 0.000
#> GSM1068553 2 0.0000 1.000 0.000 1.000
#> GSM1068554 2 0.0000 1.000 0.000 1.000
#> GSM1068558 2 0.0000 1.000 0.000 1.000
#> GSM1068559 1 0.5842 0.837 0.860 0.140
#> GSM1068564 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
#> GSM1068478 1 0.296 0.888 0.900 0.000 0.100
#> GSM1068479 2 0.000 1.000 0.000 1.000 0.000
#> GSM1068481 1 0.489 0.717 0.772 0.000 0.228
#> GSM1068482 1 0.254 0.908 0.920 0.000 0.080
#> GSM1068483 1 0.000 0.978 1.000 0.000 0.000
#> GSM1068486 3 0.000 0.952 0.000 0.000 1.000
#> GSM1068487 2 0.000 1.000 0.000 1.000 0.000
#> GSM1068488 3 0.000 0.952 0.000 0.000 1.000
#> GSM1068490 2 0.000 1.000 0.000 1.000 0.000
#> GSM1068491 1 0.000 0.978 1.000 0.000 0.000
#> GSM1068492 2 0.000 1.000 0.000 1.000 0.000
#> GSM1068493 3 0.263 0.886 0.084 0.000 0.916
#> GSM1068494 3 0.000 0.952 0.000 0.000 1.000
#> GSM1068495 3 0.000 0.952 0.000 0.000 1.000
#> GSM1068496 1 0.000 0.978 1.000 0.000 0.000
#> GSM1068498 1 0.000 0.978 1.000 0.000 0.000
#> GSM1068499 1 0.000 0.978 1.000 0.000 0.000
#> GSM1068500 1 0.000 0.978 1.000 0.000 0.000
#> GSM1068502 2 0.000 1.000 0.000 1.000 0.000
#> GSM1068503 2 0.000 1.000 0.000 1.000 0.000
#> GSM1068505 2 0.000 1.000 0.000 1.000 0.000
#> GSM1068506 2 0.000 1.000 0.000 1.000 0.000
#> GSM1068507 3 0.559 0.595 0.000 0.304 0.696
#> GSM1068508 3 0.280 0.885 0.000 0.092 0.908
#> GSM1068510 3 0.000 0.952 0.000 0.000 1.000
#> GSM1068512 2 0.000 1.000 0.000 1.000 0.000
#> GSM1068513 3 0.288 0.883 0.000 0.096 0.904
#> GSM1068514 2 0.000 1.000 0.000 1.000 0.000
#> GSM1068517 1 0.271 0.900 0.912 0.000 0.088
#> GSM1068518 1 0.590 0.471 0.648 0.000 0.352
#> GSM1068520 1 0.000 0.978 1.000 0.000 0.000
#> GSM1068521 1 0.000 0.978 1.000 0.000 0.000
#> GSM1068522 2 0.000 1.000 0.000 1.000 0.000
#> GSM1068524 3 0.000 0.952 0.000 0.000 1.000
#> GSM1068527 2 0.000 1.000 0.000 1.000 0.000
#> GSM1068480 3 0.341 0.845 0.124 0.000 0.876
#> GSM1068484 2 0.000 1.000 0.000 1.000 0.000
#> GSM1068485 1 0.000 0.978 1.000 0.000 0.000
#> GSM1068489 2 0.000 1.000 0.000 1.000 0.000
#> GSM1068497 3 0.406 0.795 0.164 0.000 0.836
#> GSM1068501 2 0.000 1.000 0.000 1.000 0.000
#> GSM1068504 3 0.000 0.952 0.000 0.000 1.000
#> GSM1068509 1 0.000 0.978 1.000 0.000 0.000
#> GSM1068511 2 0.000 1.000 0.000 1.000 0.000
#> GSM1068515 1 0.000 0.978 1.000 0.000 0.000
#> GSM1068516 3 0.000 0.952 0.000 0.000 1.000
#> GSM1068519 1 0.000 0.978 1.000 0.000 0.000
#> GSM1068523 3 0.000 0.952 0.000 0.000 1.000
#> GSM1068525 3 0.382 0.835 0.000 0.148 0.852
#> GSM1068526 2 0.000 1.000 0.000 1.000 0.000
#> GSM1068458 1 0.000 0.978 1.000 0.000 0.000
#> GSM1068459 1 0.000 0.978 1.000 0.000 0.000
#> GSM1068460 1 0.000 0.978 1.000 0.000 0.000
#> GSM1068461 1 0.000 0.978 1.000 0.000 0.000
#> GSM1068464 2 0.000 1.000 0.000 1.000 0.000
#> GSM1068468 1 0.000 0.978 1.000 0.000 0.000
#> GSM1068472 1 0.000 0.978 1.000 0.000 0.000
#> GSM1068473 2 0.000 1.000 0.000 1.000 0.000
#> GSM1068474 2 0.000 1.000 0.000 1.000 0.000
#> GSM1068476 3 0.000 0.952 0.000 0.000 1.000
#> GSM1068477 3 0.000 0.952 0.000 0.000 1.000
#> GSM1068462 1 0.000 0.978 1.000 0.000 0.000
#> GSM1068463 1 0.000 0.978 1.000 0.000 0.000
#> GSM1068465 1 0.000 0.978 1.000 0.000 0.000
#> GSM1068466 1 0.000 0.978 1.000 0.000 0.000
#> GSM1068467 1 0.000 0.978 1.000 0.000 0.000
#> GSM1068469 1 0.000 0.978 1.000 0.000 0.000
#> GSM1068470 3 0.475 0.756 0.000 0.216 0.784
#> GSM1068471 2 0.000 1.000 0.000 1.000 0.000
#> GSM1068475 2 0.000 1.000 0.000 1.000 0.000
#> GSM1068528 1 0.000 0.978 1.000 0.000 0.000
#> GSM1068531 1 0.000 0.978 1.000 0.000 0.000
#> GSM1068532 1 0.000 0.978 1.000 0.000 0.000
#> GSM1068533 1 0.000 0.978 1.000 0.000 0.000
#> GSM1068535 2 0.000 1.000 0.000 1.000 0.000
#> GSM1068537 1 0.000 0.978 1.000 0.000 0.000
#> GSM1068538 1 0.000 0.978 1.000 0.000 0.000
#> GSM1068539 3 0.000 0.952 0.000 0.000 1.000
#> GSM1068540 1 0.000 0.978 1.000 0.000 0.000
#> GSM1068542 2 0.000 1.000 0.000 1.000 0.000
#> GSM1068543 3 0.000 0.952 0.000 0.000 1.000
#> GSM1068544 1 0.000 0.978 1.000 0.000 0.000
#> GSM1068545 2 0.000 1.000 0.000 1.000 0.000
#> GSM1068546 3 0.000 0.952 0.000 0.000 1.000
#> GSM1068547 1 0.000 0.978 1.000 0.000 0.000
#> GSM1068548 2 0.000 1.000 0.000 1.000 0.000
#> GSM1068549 1 0.000 0.978 1.000 0.000 0.000
#> GSM1068550 2 0.000 1.000 0.000 1.000 0.000
#> GSM1068551 2 0.000 1.000 0.000 1.000 0.000
#> GSM1068552 2 0.000 1.000 0.000 1.000 0.000
#> GSM1068555 3 0.000 0.952 0.000 0.000 1.000
#> GSM1068556 2 0.000 1.000 0.000 1.000 0.000
#> GSM1068557 3 0.000 0.952 0.000 0.000 1.000
#> GSM1068560 3 0.000 0.952 0.000 0.000 1.000
#> GSM1068561 3 0.000 0.952 0.000 0.000 1.000
#> GSM1068562 3 0.000 0.952 0.000 0.000 1.000
#> GSM1068563 2 0.000 1.000 0.000 1.000 0.000
#> GSM1068565 3 0.480 0.748 0.000 0.220 0.780
#> GSM1068529 3 0.000 0.952 0.000 0.000 1.000
#> GSM1068530 1 0.000 0.978 1.000 0.000 0.000
#> GSM1068534 2 0.000 1.000 0.000 1.000 0.000
#> GSM1068536 3 0.000 0.952 0.000 0.000 1.000
#> GSM1068541 1 0.000 0.978 1.000 0.000 0.000
#> GSM1068553 2 0.000 1.000 0.000 1.000 0.000
#> GSM1068554 2 0.000 1.000 0.000 1.000 0.000
#> GSM1068558 3 0.000 0.952 0.000 0.000 1.000
#> GSM1068559 3 0.000 0.952 0.000 0.000 1.000
#> GSM1068564 2 0.000 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
#> GSM1068478 3 0.0000 0.907 0.000 0.000 1.000 0.000
#> GSM1068479 4 0.0804 0.968 0.008 0.012 0.000 0.980
#> GSM1068481 3 0.0188 0.907 0.004 0.000 0.996 0.000
#> GSM1068482 3 0.0000 0.907 0.000 0.000 1.000 0.000
#> GSM1068483 1 0.0336 0.966 0.992 0.000 0.008 0.000
#> GSM1068486 3 0.0469 0.904 0.000 0.012 0.988 0.000
#> GSM1068487 4 0.0000 0.987 0.000 0.000 0.000 1.000
#> GSM1068488 2 0.0000 0.983 0.000 1.000 0.000 0.000
#> GSM1068490 4 0.0000 0.987 0.000 0.000 0.000 1.000
#> GSM1068491 1 0.0000 0.967 1.000 0.000 0.000 0.000
#> GSM1068492 4 0.0000 0.987 0.000 0.000 0.000 1.000
#> GSM1068493 3 0.0000 0.907 0.000 0.000 1.000 0.000
#> GSM1068494 3 0.4697 0.452 0.000 0.356 0.644 0.000
#> GSM1068495 2 0.0469 0.976 0.000 0.988 0.012 0.000
#> GSM1068496 3 0.0469 0.906 0.012 0.000 0.988 0.000
#> GSM1068498 1 0.0336 0.964 0.992 0.000 0.008 0.000
#> GSM1068499 1 0.0469 0.966 0.988 0.000 0.012 0.000
#> GSM1068500 3 0.0469 0.906 0.012 0.000 0.988 0.000
#> GSM1068502 4 0.0000 0.987 0.000 0.000 0.000 1.000
#> GSM1068503 4 0.0000 0.987 0.000 0.000 0.000 1.000
#> GSM1068505 4 0.0000 0.987 0.000 0.000 0.000 1.000
#> GSM1068506 4 0.0000 0.987 0.000 0.000 0.000 1.000
#> GSM1068507 2 0.0000 0.983 0.000 1.000 0.000 0.000
#> GSM1068508 2 0.0000 0.983 0.000 1.000 0.000 0.000
#> GSM1068510 2 0.0000 0.983 0.000 1.000 0.000 0.000
#> GSM1068512 4 0.0000 0.987 0.000 0.000 0.000 1.000
#> GSM1068513 2 0.0000 0.983 0.000 1.000 0.000 0.000
#> GSM1068514 4 0.0000 0.987 0.000 0.000 0.000 1.000
#> GSM1068517 1 0.0469 0.961 0.988 0.000 0.012 0.000
#> GSM1068518 1 0.0469 0.961 0.988 0.000 0.012 0.000
#> GSM1068520 1 0.0336 0.966 0.992 0.000 0.008 0.000
#> GSM1068521 1 0.0188 0.966 0.996 0.000 0.004 0.000
#> GSM1068522 4 0.0000 0.987 0.000 0.000 0.000 1.000
#> GSM1068524 2 0.0000 0.983 0.000 1.000 0.000 0.000
#> GSM1068527 4 0.0000 0.987 0.000 0.000 0.000 1.000
#> GSM1068480 3 0.1557 0.877 0.000 0.056 0.944 0.000
#> GSM1068484 4 0.0000 0.987 0.000 0.000 0.000 1.000
#> GSM1068485 1 0.0336 0.966 0.992 0.000 0.008 0.000
#> GSM1068489 4 0.0000 0.987 0.000 0.000 0.000 1.000
#> GSM1068497 3 0.4382 0.574 0.000 0.296 0.704 0.000
#> GSM1068501 4 0.0000 0.987 0.000 0.000 0.000 1.000
#> GSM1068504 2 0.0000 0.983 0.000 1.000 0.000 0.000
#> GSM1068509 1 0.0336 0.966 0.992 0.000 0.008 0.000
#> GSM1068511 4 0.0000 0.987 0.000 0.000 0.000 1.000
#> GSM1068515 1 0.0188 0.966 0.996 0.000 0.004 0.000
#> GSM1068516 2 0.0469 0.976 0.000 0.988 0.012 0.000
#> GSM1068519 1 0.0336 0.966 0.992 0.000 0.008 0.000
#> GSM1068523 2 0.0000 0.983 0.000 1.000 0.000 0.000
#> GSM1068525 2 0.0000 0.983 0.000 1.000 0.000 0.000
#> GSM1068526 4 0.0188 0.983 0.000 0.004 0.000 0.996
#> GSM1068458 1 0.0000 0.967 1.000 0.000 0.000 0.000
#> GSM1068459 3 0.0336 0.906 0.008 0.000 0.992 0.000
#> GSM1068460 1 0.0188 0.966 0.996 0.000 0.004 0.000
#> GSM1068461 1 0.0188 0.966 0.996 0.000 0.004 0.000
#> GSM1068464 4 0.0000 0.987 0.000 0.000 0.000 1.000
#> GSM1068468 1 0.0000 0.967 1.000 0.000 0.000 0.000
#> GSM1068472 1 0.0000 0.967 1.000 0.000 0.000 0.000
#> GSM1068473 4 0.0000 0.987 0.000 0.000 0.000 1.000
#> GSM1068474 4 0.0000 0.987 0.000 0.000 0.000 1.000
#> GSM1068476 2 0.0000 0.983 0.000 1.000 0.000 0.000
#> GSM1068477 2 0.0336 0.977 0.008 0.992 0.000 0.000
#> GSM1068462 1 0.0188 0.966 0.996 0.000 0.004 0.000
#> GSM1068463 3 0.3801 0.672 0.220 0.000 0.780 0.000
#> GSM1068465 1 0.0000 0.967 1.000 0.000 0.000 0.000
#> GSM1068466 1 0.0336 0.966 0.992 0.000 0.008 0.000
#> GSM1068467 1 0.0188 0.966 0.996 0.000 0.004 0.000
#> GSM1068469 1 0.0000 0.967 1.000 0.000 0.000 0.000
#> GSM1068470 2 0.0000 0.983 0.000 1.000 0.000 0.000
#> GSM1068471 4 0.0000 0.987 0.000 0.000 0.000 1.000
#> GSM1068475 4 0.0000 0.987 0.000 0.000 0.000 1.000
#> GSM1068528 1 0.0469 0.966 0.988 0.000 0.012 0.000
#> GSM1068531 3 0.0469 0.906 0.012 0.000 0.988 0.000
#> GSM1068532 1 0.0336 0.966 0.992 0.000 0.008 0.000
#> GSM1068533 1 0.4989 0.142 0.528 0.000 0.472 0.000
#> GSM1068535 3 0.4877 0.282 0.000 0.000 0.592 0.408
#> GSM1068537 1 0.4643 0.480 0.656 0.000 0.344 0.000
#> GSM1068538 1 0.0336 0.966 0.992 0.000 0.008 0.000
#> GSM1068539 2 0.0469 0.976 0.000 0.988 0.012 0.000
#> GSM1068540 3 0.0469 0.906 0.012 0.000 0.988 0.000
#> GSM1068542 4 0.0000 0.987 0.000 0.000 0.000 1.000
#> GSM1068543 2 0.0000 0.983 0.000 1.000 0.000 0.000
#> GSM1068544 1 0.0469 0.966 0.988 0.000 0.012 0.000
#> GSM1068545 4 0.0000 0.987 0.000 0.000 0.000 1.000
#> GSM1068546 3 0.0469 0.904 0.000 0.012 0.988 0.000
#> GSM1068547 1 0.0000 0.967 1.000 0.000 0.000 0.000
#> GSM1068548 4 0.0000 0.987 0.000 0.000 0.000 1.000
#> GSM1068549 3 0.0000 0.907 0.000 0.000 1.000 0.000
#> GSM1068550 4 0.0000 0.987 0.000 0.000 0.000 1.000
#> GSM1068551 2 0.3172 0.779 0.000 0.840 0.000 0.160
#> GSM1068552 4 0.0000 0.987 0.000 0.000 0.000 1.000
#> GSM1068555 2 0.0000 0.983 0.000 1.000 0.000 0.000
#> GSM1068556 4 0.0000 0.987 0.000 0.000 0.000 1.000
#> GSM1068557 2 0.0000 0.983 0.000 1.000 0.000 0.000
#> GSM1068560 2 0.0000 0.983 0.000 1.000 0.000 0.000
#> GSM1068561 2 0.0188 0.981 0.000 0.996 0.004 0.000
#> GSM1068562 2 0.0000 0.983 0.000 1.000 0.000 0.000
#> GSM1068563 4 0.0000 0.987 0.000 0.000 0.000 1.000
#> GSM1068565 2 0.0000 0.983 0.000 1.000 0.000 0.000
#> GSM1068529 2 0.3074 0.804 0.000 0.848 0.152 0.000
#> GSM1068530 1 0.0336 0.966 0.992 0.000 0.008 0.000
#> GSM1068534 4 0.4804 0.347 0.000 0.000 0.384 0.616
#> GSM1068536 3 0.1557 0.877 0.000 0.056 0.944 0.000
#> GSM1068541 1 0.0000 0.967 1.000 0.000 0.000 0.000
#> GSM1068553 4 0.0000 0.987 0.000 0.000 0.000 1.000
#> GSM1068554 4 0.0000 0.987 0.000 0.000 0.000 1.000
#> GSM1068558 2 0.0000 0.983 0.000 1.000 0.000 0.000
#> GSM1068559 2 0.0188 0.981 0.000 0.996 0.004 0.000
#> GSM1068564 4 0.0000 0.987 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
#> GSM1068478 3 0.0290 0.89656 0.000 0.000 0.992 0.000 0.008
#> GSM1068479 4 0.1173 0.96796 0.004 0.020 0.000 0.964 0.012
#> GSM1068481 3 0.0162 0.89778 0.000 0.000 0.996 0.000 0.004
#> GSM1068482 5 0.1082 0.83335 0.008 0.000 0.028 0.000 0.964
#> GSM1068483 1 0.0290 0.86912 0.992 0.000 0.008 0.000 0.000
#> GSM1068486 3 0.0404 0.89529 0.000 0.000 0.988 0.000 0.012
#> GSM1068487 4 0.0290 0.99143 0.000 0.000 0.000 0.992 0.008
#> GSM1068488 2 0.0000 0.95280 0.000 1.000 0.000 0.000 0.000
#> GSM1068490 4 0.0290 0.99143 0.000 0.000 0.000 0.992 0.008
#> GSM1068491 1 0.0162 0.86726 0.996 0.000 0.000 0.000 0.004
#> GSM1068492 4 0.0290 0.99143 0.000 0.000 0.000 0.992 0.008
#> GSM1068493 3 0.0880 0.88103 0.000 0.000 0.968 0.000 0.032
#> GSM1068494 5 0.0693 0.84008 0.000 0.008 0.012 0.000 0.980
#> GSM1068495 5 0.0609 0.83964 0.000 0.020 0.000 0.000 0.980
#> GSM1068496 3 0.0000 0.89839 0.000 0.000 1.000 0.000 0.000
#> GSM1068498 5 0.0880 0.83512 0.032 0.000 0.000 0.000 0.968
#> GSM1068499 1 0.4305 0.00639 0.512 0.000 0.000 0.000 0.488
#> GSM1068500 3 0.0000 0.89839 0.000 0.000 1.000 0.000 0.000
#> GSM1068502 4 0.0290 0.99143 0.000 0.000 0.000 0.992 0.008
#> GSM1068503 4 0.0290 0.99143 0.000 0.000 0.000 0.992 0.008
#> GSM1068505 4 0.0579 0.98611 0.000 0.008 0.000 0.984 0.008
#> GSM1068506 4 0.0290 0.98987 0.000 0.000 0.000 0.992 0.008
#> GSM1068507 2 0.0451 0.94733 0.000 0.988 0.000 0.008 0.004
#> GSM1068508 2 0.0404 0.94837 0.000 0.988 0.000 0.000 0.012
#> GSM1068510 2 0.0000 0.95280 0.000 1.000 0.000 0.000 0.000
#> GSM1068512 4 0.0579 0.98611 0.000 0.008 0.000 0.984 0.008
#> GSM1068513 2 0.0000 0.95280 0.000 1.000 0.000 0.000 0.000
#> GSM1068514 4 0.0290 0.98987 0.000 0.000 0.000 0.992 0.008
#> GSM1068517 5 0.0794 0.83712 0.028 0.000 0.000 0.000 0.972
#> GSM1068518 5 0.0794 0.83712 0.028 0.000 0.000 0.000 0.972
#> GSM1068520 1 0.0290 0.86912 0.992 0.000 0.008 0.000 0.000
#> GSM1068521 1 0.0162 0.86698 0.996 0.000 0.000 0.000 0.004
#> GSM1068522 4 0.0000 0.99153 0.000 0.000 0.000 1.000 0.000
#> GSM1068524 2 0.0000 0.95280 0.000 1.000 0.000 0.000 0.000
#> GSM1068527 4 0.0579 0.98611 0.000 0.008 0.000 0.984 0.008
#> GSM1068480 5 0.0693 0.84008 0.000 0.008 0.012 0.000 0.980
#> GSM1068484 4 0.0290 0.99143 0.000 0.000 0.000 0.992 0.008
#> GSM1068485 1 0.0162 0.86903 0.996 0.000 0.004 0.000 0.000
#> GSM1068489 4 0.0579 0.98611 0.000 0.008 0.000 0.984 0.008
#> GSM1068497 5 0.0693 0.84008 0.000 0.008 0.012 0.000 0.980
#> GSM1068501 4 0.0290 0.98987 0.000 0.000 0.000 0.992 0.008
#> GSM1068504 2 0.0290 0.94971 0.000 0.992 0.000 0.000 0.008
#> GSM1068509 1 0.0290 0.86912 0.992 0.000 0.008 0.000 0.000
#> GSM1068511 4 0.0290 0.98987 0.000 0.000 0.000 0.992 0.008
#> GSM1068515 1 0.0162 0.86698 0.996 0.000 0.000 0.000 0.004
#> GSM1068516 5 0.0609 0.83964 0.000 0.020 0.000 0.000 0.980
#> GSM1068519 1 0.0290 0.86912 0.992 0.000 0.008 0.000 0.000
#> GSM1068523 2 0.0000 0.95280 0.000 1.000 0.000 0.000 0.000
#> GSM1068525 2 0.0000 0.95280 0.000 1.000 0.000 0.000 0.000
#> GSM1068526 2 0.4455 0.30561 0.000 0.588 0.000 0.404 0.008
#> GSM1068458 1 0.0000 0.86851 1.000 0.000 0.000 0.000 0.000
#> GSM1068459 3 0.0000 0.89839 0.000 0.000 1.000 0.000 0.000
#> GSM1068460 1 0.4278 0.16332 0.548 0.000 0.000 0.000 0.452
#> GSM1068461 5 0.4300 0.02926 0.476 0.000 0.000 0.000 0.524
#> GSM1068464 4 0.0290 0.99143 0.000 0.000 0.000 0.992 0.008
#> GSM1068468 1 0.0000 0.86851 1.000 0.000 0.000 0.000 0.000
#> GSM1068472 1 0.0000 0.86851 1.000 0.000 0.000 0.000 0.000
#> GSM1068473 4 0.0000 0.99153 0.000 0.000 0.000 1.000 0.000
#> GSM1068474 4 0.0290 0.99143 0.000 0.000 0.000 0.992 0.008
#> GSM1068476 2 0.0162 0.95137 0.000 0.996 0.000 0.000 0.004
#> GSM1068477 2 0.0880 0.93520 0.000 0.968 0.000 0.000 0.032
#> GSM1068462 1 0.3913 0.47605 0.676 0.000 0.000 0.000 0.324
#> GSM1068463 3 0.3837 0.49520 0.308 0.000 0.692 0.000 0.000
#> GSM1068465 1 0.0290 0.86912 0.992 0.000 0.008 0.000 0.000
#> GSM1068466 1 0.0290 0.86912 0.992 0.000 0.008 0.000 0.000
#> GSM1068467 1 0.4045 0.40627 0.644 0.000 0.000 0.000 0.356
#> GSM1068469 1 0.0000 0.86851 1.000 0.000 0.000 0.000 0.000
#> GSM1068470 2 0.0290 0.94971 0.000 0.992 0.000 0.000 0.008
#> GSM1068471 4 0.0290 0.99143 0.000 0.000 0.000 0.992 0.008
#> GSM1068475 4 0.0290 0.99143 0.000 0.000 0.000 0.992 0.008
#> GSM1068528 1 0.3715 0.57368 0.736 0.000 0.004 0.000 0.260
#> GSM1068531 3 0.0000 0.89839 0.000 0.000 1.000 0.000 0.000
#> GSM1068532 1 0.0290 0.86912 0.992 0.000 0.008 0.000 0.000
#> GSM1068533 1 0.4219 0.28379 0.584 0.000 0.416 0.000 0.000
#> GSM1068535 3 0.3487 0.68458 0.000 0.000 0.780 0.212 0.008
#> GSM1068537 1 0.4302 0.06705 0.520 0.000 0.480 0.000 0.000
#> GSM1068538 1 0.0290 0.86912 0.992 0.000 0.008 0.000 0.000
#> GSM1068539 5 0.0609 0.83964 0.000 0.020 0.000 0.000 0.980
#> GSM1068540 3 0.0000 0.89839 0.000 0.000 1.000 0.000 0.000
#> GSM1068542 4 0.0000 0.99153 0.000 0.000 0.000 1.000 0.000
#> GSM1068543 2 0.0000 0.95280 0.000 1.000 0.000 0.000 0.000
#> GSM1068544 5 0.4403 0.14961 0.436 0.000 0.004 0.000 0.560
#> GSM1068545 4 0.0290 0.99143 0.000 0.000 0.000 0.992 0.008
#> GSM1068546 3 0.0404 0.89529 0.000 0.000 0.988 0.000 0.012
#> GSM1068547 1 0.0162 0.86903 0.996 0.000 0.004 0.000 0.000
#> GSM1068548 4 0.0000 0.99153 0.000 0.000 0.000 1.000 0.000
#> GSM1068549 5 0.4101 0.39351 0.000 0.000 0.372 0.000 0.628
#> GSM1068550 4 0.0290 0.98987 0.000 0.000 0.000 0.992 0.008
#> GSM1068551 2 0.0579 0.94391 0.000 0.984 0.000 0.008 0.008
#> GSM1068552 4 0.0290 0.99143 0.000 0.000 0.000 0.992 0.008
#> GSM1068555 2 0.0000 0.95280 0.000 1.000 0.000 0.000 0.000
#> GSM1068556 4 0.0162 0.99092 0.000 0.000 0.000 0.996 0.004
#> GSM1068557 2 0.0000 0.95280 0.000 1.000 0.000 0.000 0.000
#> GSM1068560 2 0.0000 0.95280 0.000 1.000 0.000 0.000 0.000
#> GSM1068561 2 0.0404 0.94467 0.000 0.988 0.000 0.000 0.012
#> GSM1068562 2 0.0000 0.95280 0.000 1.000 0.000 0.000 0.000
#> GSM1068563 4 0.0000 0.99153 0.000 0.000 0.000 1.000 0.000
#> GSM1068565 2 0.0290 0.94971 0.000 0.992 0.000 0.000 0.008
#> GSM1068529 2 0.4313 0.40450 0.000 0.636 0.356 0.000 0.008
#> GSM1068530 1 0.0290 0.86912 0.992 0.000 0.008 0.000 0.000
#> GSM1068534 3 0.4147 0.54737 0.000 0.000 0.676 0.316 0.008
#> GSM1068536 5 0.2358 0.76785 0.000 0.008 0.104 0.000 0.888
#> GSM1068541 1 0.0000 0.86851 1.000 0.000 0.000 0.000 0.000
#> GSM1068553 4 0.0451 0.98822 0.000 0.000 0.004 0.988 0.008
#> GSM1068554 4 0.0290 0.98987 0.000 0.000 0.000 0.992 0.008
#> GSM1068558 2 0.0000 0.95280 0.000 1.000 0.000 0.000 0.000
#> GSM1068559 5 0.4114 0.34653 0.000 0.376 0.000 0.000 0.624
#> GSM1068564 4 0.0290 0.99143 0.000 0.000 0.000 0.992 0.008
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1068478 3 0.0632 0.852 0.000 0.000 0.976 0.000 0.024 0.000
#> GSM1068479 2 0.2553 0.570 0.000 0.848 0.000 0.144 0.000 0.008
#> GSM1068481 3 0.0146 0.857 0.000 0.000 0.996 0.000 0.004 0.000
#> GSM1068482 5 0.1635 0.893 0.020 0.020 0.020 0.000 0.940 0.000
#> GSM1068483 1 0.0405 0.833 0.988 0.004 0.008 0.000 0.000 0.000
#> GSM1068486 3 0.1563 0.835 0.000 0.012 0.932 0.000 0.056 0.000
#> GSM1068487 2 0.4566 0.810 0.000 0.652 0.000 0.280 0.000 0.068
#> GSM1068488 6 0.1918 0.861 0.000 0.008 0.000 0.088 0.000 0.904
#> GSM1068490 2 0.3607 0.937 0.000 0.652 0.000 0.348 0.000 0.000
#> GSM1068491 1 0.2664 0.789 0.816 0.184 0.000 0.000 0.000 0.000
#> GSM1068492 2 0.3672 0.921 0.000 0.632 0.000 0.368 0.000 0.000
#> GSM1068493 3 0.1951 0.822 0.000 0.016 0.908 0.000 0.076 0.000
#> GSM1068494 5 0.0603 0.910 0.000 0.004 0.000 0.000 0.980 0.016
#> GSM1068495 5 0.0547 0.908 0.000 0.000 0.000 0.000 0.980 0.020
#> GSM1068496 3 0.0260 0.856 0.000 0.008 0.992 0.000 0.000 0.000
#> GSM1068498 5 0.2039 0.877 0.020 0.076 0.000 0.000 0.904 0.000
#> GSM1068499 1 0.4566 0.441 0.596 0.036 0.004 0.000 0.364 0.000
#> GSM1068500 3 0.0000 0.858 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1068502 2 0.3634 0.924 0.000 0.644 0.000 0.356 0.000 0.000
#> GSM1068503 2 0.3607 0.926 0.000 0.652 0.000 0.348 0.000 0.000
#> GSM1068505 4 0.0508 0.645 0.000 0.004 0.000 0.984 0.000 0.012
#> GSM1068506 4 0.0000 0.646 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1068507 6 0.3382 0.832 0.000 0.112 0.000 0.064 0.004 0.820
#> GSM1068508 6 0.2520 0.848 0.000 0.152 0.000 0.000 0.004 0.844
#> GSM1068510 6 0.0000 0.913 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM1068512 4 0.0653 0.645 0.000 0.004 0.004 0.980 0.000 0.012
#> GSM1068513 6 0.0146 0.912 0.000 0.000 0.000 0.004 0.000 0.996
#> GSM1068514 4 0.0363 0.642 0.000 0.012 0.000 0.988 0.000 0.000
#> GSM1068517 5 0.1398 0.896 0.008 0.052 0.000 0.000 0.940 0.000
#> GSM1068518 5 0.1701 0.887 0.008 0.072 0.000 0.000 0.920 0.000
#> GSM1068520 1 0.1049 0.835 0.960 0.032 0.008 0.000 0.000 0.000
#> GSM1068521 1 0.1682 0.828 0.928 0.052 0.000 0.000 0.020 0.000
#> GSM1068522 4 0.3864 -0.507 0.000 0.480 0.000 0.520 0.000 0.000
#> GSM1068524 6 0.0000 0.913 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM1068527 4 0.0405 0.647 0.000 0.000 0.004 0.988 0.000 0.008
#> GSM1068480 5 0.0291 0.912 0.000 0.004 0.004 0.000 0.992 0.000
#> GSM1068484 2 0.3607 0.937 0.000 0.652 0.000 0.348 0.000 0.000
#> GSM1068485 1 0.1049 0.832 0.960 0.032 0.008 0.000 0.000 0.000
#> GSM1068489 4 0.0748 0.642 0.000 0.004 0.004 0.976 0.000 0.016
#> GSM1068497 5 0.0291 0.912 0.000 0.004 0.004 0.000 0.992 0.000
#> GSM1068501 4 0.3482 0.179 0.000 0.316 0.000 0.684 0.000 0.000
#> GSM1068504 6 0.0260 0.912 0.000 0.008 0.000 0.000 0.000 0.992
#> GSM1068509 1 0.0520 0.833 0.984 0.008 0.008 0.000 0.000 0.000
#> GSM1068511 4 0.0508 0.645 0.000 0.012 0.004 0.984 0.000 0.000
#> GSM1068515 1 0.2859 0.806 0.828 0.156 0.000 0.000 0.016 0.000
#> GSM1068516 5 0.0291 0.914 0.000 0.004 0.000 0.000 0.992 0.004
#> GSM1068519 1 0.0405 0.833 0.988 0.004 0.008 0.000 0.000 0.000
#> GSM1068523 6 0.0363 0.911 0.000 0.012 0.000 0.000 0.000 0.988
#> GSM1068525 6 0.0146 0.912 0.000 0.000 0.000 0.004 0.000 0.996
#> GSM1068526 6 0.5399 0.424 0.000 0.208 0.000 0.208 0.000 0.584
#> GSM1068458 1 0.2003 0.824 0.884 0.116 0.000 0.000 0.000 0.000
#> GSM1068459 3 0.0000 0.858 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1068460 1 0.6085 0.266 0.392 0.320 0.000 0.000 0.288 0.000
#> GSM1068461 1 0.5195 0.401 0.540 0.100 0.000 0.000 0.360 0.000
#> GSM1068464 2 0.3607 0.937 0.000 0.652 0.000 0.348 0.000 0.000
#> GSM1068468 1 0.1814 0.824 0.900 0.100 0.000 0.000 0.000 0.000
#> GSM1068472 1 0.1444 0.830 0.928 0.072 0.000 0.000 0.000 0.000
#> GSM1068473 2 0.3659 0.906 0.000 0.636 0.000 0.364 0.000 0.000
#> GSM1068474 2 0.3607 0.937 0.000 0.652 0.000 0.348 0.000 0.000
#> GSM1068476 6 0.2053 0.867 0.000 0.108 0.000 0.000 0.004 0.888
#> GSM1068477 6 0.3630 0.769 0.000 0.212 0.000 0.000 0.032 0.756
#> GSM1068462 1 0.5775 0.463 0.480 0.328 0.000 0.000 0.192 0.000
#> GSM1068463 3 0.3690 0.568 0.308 0.008 0.684 0.000 0.000 0.000
#> GSM1068465 1 0.1908 0.825 0.900 0.096 0.004 0.000 0.000 0.000
#> GSM1068466 1 0.1151 0.834 0.956 0.032 0.012 0.000 0.000 0.000
#> GSM1068467 1 0.5705 0.478 0.516 0.204 0.000 0.000 0.280 0.000
#> GSM1068469 1 0.1141 0.837 0.948 0.052 0.000 0.000 0.000 0.000
#> GSM1068470 6 0.0260 0.912 0.000 0.008 0.000 0.000 0.000 0.992
#> GSM1068471 2 0.3607 0.937 0.000 0.652 0.000 0.348 0.000 0.000
#> GSM1068475 2 0.3607 0.937 0.000 0.652 0.000 0.348 0.000 0.000
#> GSM1068528 1 0.3769 0.697 0.768 0.036 0.008 0.000 0.188 0.000
#> GSM1068531 3 0.0000 0.858 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1068532 1 0.0405 0.833 0.988 0.004 0.008 0.000 0.000 0.000
#> GSM1068533 3 0.3933 0.639 0.248 0.036 0.716 0.000 0.000 0.000
#> GSM1068535 4 0.3820 0.333 0.000 0.008 0.284 0.700 0.000 0.008
#> GSM1068537 3 0.3937 0.366 0.424 0.004 0.572 0.000 0.000 0.000
#> GSM1068538 1 0.0405 0.833 0.988 0.004 0.008 0.000 0.000 0.000
#> GSM1068539 5 0.0291 0.914 0.000 0.004 0.000 0.000 0.992 0.004
#> GSM1068540 3 0.0000 0.858 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1068542 4 0.3847 -0.360 0.000 0.456 0.000 0.544 0.000 0.000
#> GSM1068543 6 0.0000 0.913 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM1068544 1 0.4814 0.222 0.504 0.036 0.008 0.000 0.452 0.000
#> GSM1068545 2 0.3607 0.937 0.000 0.652 0.000 0.348 0.000 0.000
#> GSM1068546 3 0.0909 0.850 0.000 0.012 0.968 0.000 0.020 0.000
#> GSM1068547 1 0.0146 0.835 0.996 0.000 0.004 0.000 0.000 0.000
#> GSM1068548 4 0.3838 -0.344 0.000 0.448 0.000 0.552 0.000 0.000
#> GSM1068549 3 0.4836 0.227 0.040 0.008 0.536 0.000 0.416 0.000
#> GSM1068550 4 0.0146 0.646 0.000 0.004 0.000 0.996 0.000 0.000
#> GSM1068551 6 0.3555 0.646 0.000 0.280 0.000 0.008 0.000 0.712
#> GSM1068552 2 0.3620 0.922 0.000 0.648 0.000 0.352 0.000 0.000
#> GSM1068555 6 0.0000 0.913 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM1068556 4 0.3747 -0.148 0.000 0.396 0.000 0.604 0.000 0.000
#> GSM1068557 6 0.1700 0.883 0.000 0.080 0.000 0.000 0.004 0.916
#> GSM1068560 6 0.0260 0.912 0.000 0.008 0.000 0.000 0.000 0.992
#> GSM1068561 6 0.1462 0.882 0.000 0.008 0.000 0.000 0.056 0.936
#> GSM1068562 6 0.0000 0.913 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM1068563 4 0.3828 -0.349 0.000 0.440 0.000 0.560 0.000 0.000
#> GSM1068565 6 0.0363 0.911 0.000 0.012 0.000 0.000 0.000 0.988
#> GSM1068529 6 0.4347 0.716 0.000 0.008 0.152 0.072 0.012 0.756
#> GSM1068530 1 0.0405 0.833 0.988 0.004 0.008 0.000 0.000 0.000
#> GSM1068534 4 0.3736 0.368 0.000 0.008 0.268 0.716 0.000 0.008
#> GSM1068536 5 0.1524 0.867 0.000 0.008 0.060 0.000 0.932 0.000
#> GSM1068541 1 0.1663 0.827 0.912 0.088 0.000 0.000 0.000 0.000
#> GSM1068553 4 0.0820 0.643 0.000 0.016 0.012 0.972 0.000 0.000
#> GSM1068554 4 0.3482 0.179 0.000 0.316 0.000 0.684 0.000 0.000
#> GSM1068558 6 0.0000 0.913 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM1068559 5 0.5659 0.254 0.000 0.168 0.000 0.000 0.496 0.336
#> GSM1068564 2 0.3647 0.929 0.000 0.640 0.000 0.360 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 disease.state(p) gender(p) k
#> ATC:skmeans 108 0.431 0.957 2
#> ATC:skmeans 107 0.226 0.923 3
#> ATC:skmeans 103 0.253 0.836 4
#> ATC:skmeans 95 0.325 0.242 5
#> ATC:skmeans 89 0.148 0.391 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 38950 rows and 108 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 5.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.389 0.546 0.802 0.4549 0.621 0.621
#> 3 3 0.797 0.874 0.944 0.4372 0.701 0.529
#> 4 4 0.726 0.747 0.861 0.1088 0.916 0.768
#> 5 5 0.912 0.902 0.957 0.0921 0.857 0.551
#> 6 6 0.852 0.877 0.920 0.0297 0.972 0.865
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 5
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM1068478 2 0.722 0.4321 0.200 0.800
#> GSM1068479 2 0.000 0.6829 0.000 1.000
#> GSM1068481 2 0.000 0.6829 0.000 1.000
#> GSM1068482 2 0.963 -0.0569 0.388 0.612
#> GSM1068483 1 0.000 0.7032 1.000 0.000
#> GSM1068486 2 0.000 0.6829 0.000 1.000
#> GSM1068487 2 0.981 0.4965 0.420 0.580
#> GSM1068488 2 0.000 0.6829 0.000 1.000
#> GSM1068490 2 0.343 0.6581 0.064 0.936
#> GSM1068491 1 0.722 0.4116 0.800 0.200
#> GSM1068492 2 0.981 0.4965 0.420 0.580
#> GSM1068493 2 0.000 0.6829 0.000 1.000
#> GSM1068494 2 0.706 0.4463 0.192 0.808
#> GSM1068495 2 0.000 0.6829 0.000 1.000
#> GSM1068496 2 0.000 0.6829 0.000 1.000
#> GSM1068498 1 0.981 0.5074 0.580 0.420
#> GSM1068499 1 0.981 0.5074 0.580 0.420
#> GSM1068500 2 0.775 0.3770 0.228 0.772
#> GSM1068502 2 0.981 0.4965 0.420 0.580
#> GSM1068503 2 0.981 0.4965 0.420 0.580
#> GSM1068505 2 0.981 0.4965 0.420 0.580
#> GSM1068506 2 0.981 0.4965 0.420 0.580
#> GSM1068507 2 0.000 0.6829 0.000 1.000
#> GSM1068508 2 0.000 0.6829 0.000 1.000
#> GSM1068510 2 0.000 0.6829 0.000 1.000
#> GSM1068512 2 0.000 0.6829 0.000 1.000
#> GSM1068513 2 0.000 0.6829 0.000 1.000
#> GSM1068514 2 0.981 0.4965 0.420 0.580
#> GSM1068517 2 0.971 -0.0962 0.400 0.600
#> GSM1068518 2 0.943 0.0329 0.360 0.640
#> GSM1068520 1 0.981 0.5074 0.580 0.420
#> GSM1068521 1 0.552 0.6690 0.872 0.128
#> GSM1068522 2 0.981 0.4965 0.420 0.580
#> GSM1068524 2 0.000 0.6829 0.000 1.000
#> GSM1068527 2 0.981 0.4965 0.420 0.580
#> GSM1068480 2 0.730 0.4248 0.204 0.796
#> GSM1068484 2 0.981 0.4965 0.420 0.580
#> GSM1068485 1 0.981 0.5074 0.580 0.420
#> GSM1068489 2 0.980 0.4978 0.416 0.584
#> GSM1068497 2 0.958 -0.0308 0.380 0.620
#> GSM1068501 2 0.981 0.4965 0.420 0.580
#> GSM1068504 2 0.000 0.6829 0.000 1.000
#> GSM1068509 1 0.000 0.7032 1.000 0.000
#> GSM1068511 2 0.981 0.4965 0.420 0.580
#> GSM1068515 1 0.456 0.6828 0.904 0.096
#> GSM1068516 2 0.000 0.6829 0.000 1.000
#> GSM1068519 1 0.000 0.7032 1.000 0.000
#> GSM1068523 2 0.000 0.6829 0.000 1.000
#> GSM1068525 2 0.000 0.6829 0.000 1.000
#> GSM1068526 2 0.000 0.6829 0.000 1.000
#> GSM1068458 1 0.000 0.7032 1.000 0.000
#> GSM1068459 2 0.000 0.6829 0.000 1.000
#> GSM1068460 2 0.662 0.4796 0.172 0.828
#> GSM1068461 1 0.981 0.5074 0.580 0.420
#> GSM1068464 2 0.981 0.4965 0.420 0.580
#> GSM1068468 1 0.000 0.7032 1.000 0.000
#> GSM1068472 1 0.697 0.4268 0.812 0.188
#> GSM1068473 2 0.981 0.4965 0.420 0.580
#> GSM1068474 2 0.981 0.4965 0.420 0.580
#> GSM1068476 2 0.000 0.6829 0.000 1.000
#> GSM1068477 2 0.000 0.6829 0.000 1.000
#> GSM1068462 2 0.494 0.5707 0.108 0.892
#> GSM1068463 1 0.402 0.6885 0.920 0.080
#> GSM1068465 1 0.000 0.7032 1.000 0.000
#> GSM1068466 1 0.981 0.5074 0.580 0.420
#> GSM1068467 2 0.958 -0.0308 0.380 0.620
#> GSM1068469 1 0.981 0.5074 0.580 0.420
#> GSM1068470 2 0.000 0.6829 0.000 1.000
#> GSM1068471 2 0.981 0.4965 0.420 0.580
#> GSM1068475 2 0.981 0.4965 0.420 0.580
#> GSM1068528 1 0.981 0.5074 0.580 0.420
#> GSM1068531 1 0.981 0.5074 0.580 0.420
#> GSM1068532 1 0.000 0.7032 1.000 0.000
#> GSM1068533 2 0.992 -0.2415 0.448 0.552
#> GSM1068535 2 0.981 0.4965 0.420 0.580
#> GSM1068537 1 0.000 0.7032 1.000 0.000
#> GSM1068538 1 0.000 0.7032 1.000 0.000
#> GSM1068539 2 0.000 0.6829 0.000 1.000
#> GSM1068540 2 0.963 -0.0570 0.388 0.612
#> GSM1068542 2 0.981 0.4965 0.420 0.580
#> GSM1068543 2 0.000 0.6829 0.000 1.000
#> GSM1068544 1 0.981 0.5074 0.580 0.420
#> GSM1068545 2 0.981 0.4965 0.420 0.580
#> GSM1068546 2 0.000 0.6829 0.000 1.000
#> GSM1068547 1 0.000 0.7032 1.000 0.000
#> GSM1068548 2 0.981 0.4965 0.420 0.580
#> GSM1068549 2 0.958 -0.0308 0.380 0.620
#> GSM1068550 2 0.981 0.4965 0.420 0.580
#> GSM1068551 2 0.000 0.6829 0.000 1.000
#> GSM1068552 2 0.981 0.4965 0.420 0.580
#> GSM1068555 2 0.000 0.6829 0.000 1.000
#> GSM1068556 2 0.981 0.4965 0.420 0.580
#> GSM1068557 2 0.000 0.6829 0.000 1.000
#> GSM1068560 2 0.000 0.6829 0.000 1.000
#> GSM1068561 2 0.000 0.6829 0.000 1.000
#> GSM1068562 2 0.000 0.6829 0.000 1.000
#> GSM1068563 2 0.981 0.4965 0.420 0.580
#> GSM1068565 2 0.000 0.6829 0.000 1.000
#> GSM1068529 2 0.000 0.6829 0.000 1.000
#> GSM1068530 1 0.000 0.7032 1.000 0.000
#> GSM1068534 2 0.000 0.6829 0.000 1.000
#> GSM1068536 2 0.000 0.6829 0.000 1.000
#> GSM1068541 1 0.000 0.7032 1.000 0.000
#> GSM1068553 2 0.981 0.4965 0.420 0.580
#> GSM1068554 2 0.981 0.4965 0.420 0.580
#> GSM1068558 2 0.000 0.6829 0.000 1.000
#> GSM1068559 2 0.000 0.6829 0.000 1.000
#> GSM1068564 2 0.981 0.4965 0.420 0.580
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1068478 3 0.0000 0.9139 0.000 0.000 1.000
#> GSM1068479 3 0.0000 0.9139 0.000 0.000 1.000
#> GSM1068481 3 0.0000 0.9139 0.000 0.000 1.000
#> GSM1068482 3 0.5678 0.5744 0.316 0.000 0.684
#> GSM1068483 1 0.0000 0.9900 1.000 0.000 0.000
#> GSM1068486 3 0.0000 0.9139 0.000 0.000 1.000
#> GSM1068487 2 0.0000 0.9343 0.000 1.000 0.000
#> GSM1068488 3 0.4452 0.7570 0.000 0.192 0.808
#> GSM1068490 2 0.4654 0.7269 0.000 0.792 0.208
#> GSM1068491 3 0.4887 0.6690 0.000 0.228 0.772
#> GSM1068492 2 0.0000 0.9343 0.000 1.000 0.000
#> GSM1068493 3 0.0000 0.9139 0.000 0.000 1.000
#> GSM1068494 3 0.0000 0.9139 0.000 0.000 1.000
#> GSM1068495 3 0.0000 0.9139 0.000 0.000 1.000
#> GSM1068496 3 0.0000 0.9139 0.000 0.000 1.000
#> GSM1068498 1 0.0000 0.9900 1.000 0.000 0.000
#> GSM1068499 1 0.0000 0.9900 1.000 0.000 0.000
#> GSM1068500 3 0.0000 0.9139 0.000 0.000 1.000
#> GSM1068502 2 0.0000 0.9343 0.000 1.000 0.000
#> GSM1068503 2 0.0000 0.9343 0.000 1.000 0.000
#> GSM1068505 2 0.0000 0.9343 0.000 1.000 0.000
#> GSM1068506 2 0.0000 0.9343 0.000 1.000 0.000
#> GSM1068507 3 0.0000 0.9139 0.000 0.000 1.000
#> GSM1068508 3 0.0000 0.9139 0.000 0.000 1.000
#> GSM1068510 3 0.0592 0.9067 0.000 0.012 0.988
#> GSM1068512 3 0.6079 0.4002 0.000 0.388 0.612
#> GSM1068513 3 0.4235 0.7743 0.000 0.176 0.824
#> GSM1068514 2 0.0000 0.9343 0.000 1.000 0.000
#> GSM1068517 3 0.6111 0.4131 0.396 0.000 0.604
#> GSM1068518 3 0.0000 0.9139 0.000 0.000 1.000
#> GSM1068520 1 0.0000 0.9900 1.000 0.000 0.000
#> GSM1068521 1 0.0000 0.9900 1.000 0.000 0.000
#> GSM1068522 2 0.0000 0.9343 0.000 1.000 0.000
#> GSM1068524 3 0.4291 0.7703 0.000 0.180 0.820
#> GSM1068527 3 0.6140 0.4007 0.000 0.404 0.596
#> GSM1068480 3 0.0000 0.9139 0.000 0.000 1.000
#> GSM1068484 2 0.0000 0.9343 0.000 1.000 0.000
#> GSM1068485 1 0.0000 0.9900 1.000 0.000 0.000
#> GSM1068489 2 0.0000 0.9343 0.000 1.000 0.000
#> GSM1068497 3 0.0000 0.9139 0.000 0.000 1.000
#> GSM1068501 2 0.0000 0.9343 0.000 1.000 0.000
#> GSM1068504 3 0.5905 0.4669 0.000 0.352 0.648
#> GSM1068509 1 0.0000 0.9900 1.000 0.000 0.000
#> GSM1068511 2 0.0000 0.9343 0.000 1.000 0.000
#> GSM1068515 1 0.0000 0.9900 1.000 0.000 0.000
#> GSM1068516 3 0.0000 0.9139 0.000 0.000 1.000
#> GSM1068519 1 0.0000 0.9900 1.000 0.000 0.000
#> GSM1068523 3 0.0000 0.9139 0.000 0.000 1.000
#> GSM1068525 2 0.4974 0.6878 0.000 0.764 0.236
#> GSM1068526 2 0.4887 0.6998 0.000 0.772 0.228
#> GSM1068458 1 0.0000 0.9900 1.000 0.000 0.000
#> GSM1068459 3 0.0000 0.9139 0.000 0.000 1.000
#> GSM1068460 3 0.0000 0.9139 0.000 0.000 1.000
#> GSM1068461 1 0.0000 0.9900 1.000 0.000 0.000
#> GSM1068464 2 0.0000 0.9343 0.000 1.000 0.000
#> GSM1068468 1 0.0424 0.9827 0.992 0.008 0.000
#> GSM1068472 2 0.9522 -0.0482 0.400 0.412 0.188
#> GSM1068473 2 0.0000 0.9343 0.000 1.000 0.000
#> GSM1068474 2 0.0000 0.9343 0.000 1.000 0.000
#> GSM1068476 3 0.0000 0.9139 0.000 0.000 1.000
#> GSM1068477 3 0.0000 0.9139 0.000 0.000 1.000
#> GSM1068462 3 0.0000 0.9139 0.000 0.000 1.000
#> GSM1068463 1 0.0000 0.9900 1.000 0.000 0.000
#> GSM1068465 1 0.4887 0.7101 0.772 0.228 0.000
#> GSM1068466 1 0.0000 0.9900 1.000 0.000 0.000
#> GSM1068467 3 0.0000 0.9139 0.000 0.000 1.000
#> GSM1068469 1 0.0000 0.9900 1.000 0.000 0.000
#> GSM1068470 2 0.4887 0.6998 0.000 0.772 0.228
#> GSM1068471 2 0.0000 0.9343 0.000 1.000 0.000
#> GSM1068475 2 0.0000 0.9343 0.000 1.000 0.000
#> GSM1068528 1 0.0000 0.9900 1.000 0.000 0.000
#> GSM1068531 1 0.0000 0.9900 1.000 0.000 0.000
#> GSM1068532 1 0.0000 0.9900 1.000 0.000 0.000
#> GSM1068533 3 0.6215 0.3322 0.428 0.000 0.572
#> GSM1068535 2 0.0592 0.9251 0.000 0.988 0.012
#> GSM1068537 1 0.0000 0.9900 1.000 0.000 0.000
#> GSM1068538 1 0.0000 0.9900 1.000 0.000 0.000
#> GSM1068539 3 0.0000 0.9139 0.000 0.000 1.000
#> GSM1068540 3 0.5138 0.6760 0.252 0.000 0.748
#> GSM1068542 2 0.0000 0.9343 0.000 1.000 0.000
#> GSM1068543 3 0.3551 0.8163 0.000 0.132 0.868
#> GSM1068544 1 0.0000 0.9900 1.000 0.000 0.000
#> GSM1068545 2 0.0000 0.9343 0.000 1.000 0.000
#> GSM1068546 3 0.0000 0.9139 0.000 0.000 1.000
#> GSM1068547 1 0.0000 0.9900 1.000 0.000 0.000
#> GSM1068548 2 0.0000 0.9343 0.000 1.000 0.000
#> GSM1068549 3 0.0000 0.9139 0.000 0.000 1.000
#> GSM1068550 2 0.0000 0.9343 0.000 1.000 0.000
#> GSM1068551 2 0.4887 0.6998 0.000 0.772 0.228
#> GSM1068552 2 0.0000 0.9343 0.000 1.000 0.000
#> GSM1068555 3 0.0000 0.9139 0.000 0.000 1.000
#> GSM1068556 2 0.0000 0.9343 0.000 1.000 0.000
#> GSM1068557 3 0.0000 0.9139 0.000 0.000 1.000
#> GSM1068560 3 0.0000 0.9139 0.000 0.000 1.000
#> GSM1068561 3 0.0000 0.9139 0.000 0.000 1.000
#> GSM1068562 3 0.4235 0.7743 0.000 0.176 0.824
#> GSM1068563 2 0.0000 0.9343 0.000 1.000 0.000
#> GSM1068565 3 0.4452 0.7569 0.000 0.192 0.808
#> GSM1068529 3 0.0000 0.9139 0.000 0.000 1.000
#> GSM1068530 1 0.0000 0.9900 1.000 0.000 0.000
#> GSM1068534 2 0.4931 0.6939 0.000 0.768 0.232
#> GSM1068536 3 0.0000 0.9139 0.000 0.000 1.000
#> GSM1068541 1 0.0000 0.9900 1.000 0.000 0.000
#> GSM1068553 2 0.0000 0.9343 0.000 1.000 0.000
#> GSM1068554 2 0.0000 0.9343 0.000 1.000 0.000
#> GSM1068558 3 0.0000 0.9139 0.000 0.000 1.000
#> GSM1068559 3 0.0000 0.9139 0.000 0.000 1.000
#> GSM1068564 2 0.0000 0.9343 0.000 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1068478 3 0.4331 0.855 0.000 0.288 0.712 0.000
#> GSM1068479 2 0.1174 0.771 0.000 0.968 0.020 0.012
#> GSM1068481 3 0.4331 0.855 0.000 0.288 0.712 0.000
#> GSM1068482 2 0.5767 0.314 0.280 0.660 0.060 0.000
#> GSM1068483 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> GSM1068486 2 0.0000 0.772 0.000 1.000 0.000 0.000
#> GSM1068487 4 0.4331 0.702 0.000 0.000 0.288 0.712
#> GSM1068488 2 0.7265 0.482 0.000 0.528 0.288 0.184
#> GSM1068490 4 0.6495 0.599 0.000 0.108 0.284 0.608
#> GSM1068491 2 0.4866 0.340 0.000 0.596 0.000 0.404
#> GSM1068492 4 0.0000 0.835 0.000 0.000 0.000 1.000
#> GSM1068493 2 0.0000 0.772 0.000 1.000 0.000 0.000
#> GSM1068494 2 0.0000 0.772 0.000 1.000 0.000 0.000
#> GSM1068495 2 0.0000 0.772 0.000 1.000 0.000 0.000
#> GSM1068496 3 0.5055 0.836 0.000 0.256 0.712 0.032
#> GSM1068498 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> GSM1068499 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> GSM1068500 3 0.4331 0.855 0.000 0.288 0.712 0.000
#> GSM1068502 4 0.0000 0.835 0.000 0.000 0.000 1.000
#> GSM1068503 4 0.0188 0.834 0.000 0.000 0.004 0.996
#> GSM1068505 4 0.0188 0.834 0.000 0.000 0.004 0.996
#> GSM1068506 4 0.0000 0.835 0.000 0.000 0.000 1.000
#> GSM1068507 2 0.1022 0.761 0.000 0.968 0.000 0.032
#> GSM1068508 2 0.1022 0.771 0.000 0.968 0.032 0.000
#> GSM1068510 2 0.4770 0.631 0.000 0.700 0.288 0.012
#> GSM1068512 2 0.4933 0.198 0.000 0.568 0.000 0.432
#> GSM1068513 2 0.7203 0.496 0.000 0.536 0.288 0.176
#> GSM1068514 4 0.0000 0.835 0.000 0.000 0.000 1.000
#> GSM1068517 2 0.4843 0.207 0.396 0.604 0.000 0.000
#> GSM1068518 2 0.1174 0.770 0.012 0.968 0.020 0.000
#> GSM1068520 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> GSM1068521 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> GSM1068522 4 0.0000 0.835 0.000 0.000 0.000 1.000
#> GSM1068524 2 0.7235 0.489 0.000 0.532 0.288 0.180
#> GSM1068527 4 0.4907 0.103 0.000 0.420 0.000 0.580
#> GSM1068480 2 0.0000 0.772 0.000 1.000 0.000 0.000
#> GSM1068484 4 0.0000 0.835 0.000 0.000 0.000 1.000
#> GSM1068485 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> GSM1068489 4 0.4304 0.704 0.000 0.000 0.284 0.716
#> GSM1068497 2 0.0000 0.772 0.000 1.000 0.000 0.000
#> GSM1068501 4 0.0000 0.835 0.000 0.000 0.000 1.000
#> GSM1068504 2 0.7458 0.422 0.000 0.500 0.288 0.212
#> GSM1068509 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> GSM1068511 4 0.0000 0.835 0.000 0.000 0.000 1.000
#> GSM1068515 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> GSM1068516 2 0.0000 0.772 0.000 1.000 0.000 0.000
#> GSM1068519 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> GSM1068523 2 0.4331 0.636 0.000 0.712 0.288 0.000
#> GSM1068525 4 0.6876 0.546 0.000 0.140 0.288 0.572
#> GSM1068526 4 0.6660 0.578 0.000 0.120 0.288 0.592
#> GSM1068458 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> GSM1068459 3 0.4331 0.855 0.000 0.288 0.712 0.000
#> GSM1068460 2 0.0000 0.772 0.000 1.000 0.000 0.000
#> GSM1068461 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> GSM1068464 4 0.4304 0.704 0.000 0.000 0.284 0.716
#> GSM1068468 1 0.1211 0.923 0.960 0.000 0.000 0.040
#> GSM1068472 4 0.6850 0.372 0.212 0.188 0.000 0.600
#> GSM1068473 4 0.0000 0.835 0.000 0.000 0.000 1.000
#> GSM1068474 4 0.0000 0.835 0.000 0.000 0.000 1.000
#> GSM1068476 2 0.1022 0.771 0.000 0.968 0.032 0.000
#> GSM1068477 2 0.1151 0.772 0.000 0.968 0.024 0.008
#> GSM1068462 2 0.0921 0.772 0.000 0.972 0.028 0.000
#> GSM1068463 3 0.4356 0.577 0.292 0.000 0.708 0.000
#> GSM1068465 1 0.4866 0.368 0.596 0.000 0.000 0.404
#> GSM1068466 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> GSM1068467 2 0.1022 0.759 0.032 0.968 0.000 0.000
#> GSM1068469 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> GSM1068470 4 0.6660 0.578 0.000 0.120 0.288 0.592
#> GSM1068471 4 0.1389 0.818 0.000 0.000 0.048 0.952
#> GSM1068475 4 0.4304 0.704 0.000 0.000 0.284 0.716
#> GSM1068528 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> GSM1068531 3 0.5055 0.625 0.256 0.032 0.712 0.000
#> GSM1068532 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> GSM1068533 3 0.5219 0.842 0.044 0.244 0.712 0.000
#> GSM1068535 4 0.5698 0.340 0.000 0.036 0.356 0.608
#> GSM1068537 3 0.4331 0.582 0.288 0.000 0.712 0.000
#> GSM1068538 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> GSM1068539 2 0.0000 0.772 0.000 1.000 0.000 0.000
#> GSM1068540 3 0.4483 0.855 0.004 0.284 0.712 0.000
#> GSM1068542 4 0.0000 0.835 0.000 0.000 0.000 1.000
#> GSM1068543 2 0.6793 0.554 0.000 0.580 0.288 0.132
#> GSM1068544 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> GSM1068545 4 0.0000 0.835 0.000 0.000 0.000 1.000
#> GSM1068546 3 0.4331 0.855 0.000 0.288 0.712 0.000
#> GSM1068547 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> GSM1068548 4 0.0000 0.835 0.000 0.000 0.000 1.000
#> GSM1068549 2 0.0000 0.772 0.000 1.000 0.000 0.000
#> GSM1068550 4 0.0000 0.835 0.000 0.000 0.000 1.000
#> GSM1068551 4 0.6660 0.578 0.000 0.120 0.288 0.592
#> GSM1068552 4 0.0000 0.835 0.000 0.000 0.000 1.000
#> GSM1068555 2 0.4331 0.636 0.000 0.712 0.288 0.000
#> GSM1068556 4 0.0000 0.835 0.000 0.000 0.000 1.000
#> GSM1068557 2 0.0000 0.772 0.000 1.000 0.000 0.000
#> GSM1068560 2 0.1022 0.771 0.000 0.968 0.032 0.000
#> GSM1068561 2 0.0000 0.772 0.000 1.000 0.000 0.000
#> GSM1068562 2 0.7203 0.496 0.000 0.536 0.288 0.176
#> GSM1068563 4 0.0000 0.835 0.000 0.000 0.000 1.000
#> GSM1068565 2 0.7325 0.468 0.000 0.520 0.288 0.192
#> GSM1068529 2 0.0000 0.772 0.000 1.000 0.000 0.000
#> GSM1068530 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> GSM1068534 4 0.4866 0.346 0.000 0.404 0.000 0.596
#> GSM1068536 2 0.0000 0.772 0.000 1.000 0.000 0.000
#> GSM1068541 1 0.0000 0.972 1.000 0.000 0.000 0.000
#> GSM1068553 4 0.0000 0.835 0.000 0.000 0.000 1.000
#> GSM1068554 4 0.0000 0.835 0.000 0.000 0.000 1.000
#> GSM1068558 2 0.4331 0.636 0.000 0.712 0.288 0.000
#> GSM1068559 2 0.0000 0.772 0.000 1.000 0.000 0.000
#> GSM1068564 4 0.4304 0.704 0.000 0.000 0.284 0.716
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM1068478 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000
#> GSM1068479 5 0.0510 0.935 0.000 0.000 0.000 0.016 0.984
#> GSM1068481 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000
#> GSM1068482 5 0.4988 0.549 0.284 0.000 0.060 0.000 0.656
#> GSM1068483 1 0.0404 0.961 0.988 0.000 0.012 0.000 0.000
#> GSM1068486 5 0.0000 0.948 0.000 0.000 0.000 0.000 1.000
#> GSM1068487 2 0.0000 0.928 0.000 1.000 0.000 0.000 0.000
#> GSM1068488 2 0.0000 0.928 0.000 1.000 0.000 0.000 0.000
#> GSM1068490 2 0.3177 0.788 0.000 0.792 0.000 0.208 0.000
#> GSM1068491 4 0.3210 0.712 0.000 0.000 0.000 0.788 0.212
#> GSM1068492 4 0.0162 0.928 0.000 0.004 0.000 0.996 0.000
#> GSM1068493 5 0.0000 0.948 0.000 0.000 0.000 0.000 1.000
#> GSM1068494 5 0.0000 0.948 0.000 0.000 0.000 0.000 1.000
#> GSM1068495 5 0.0000 0.948 0.000 0.000 0.000 0.000 1.000
#> GSM1068496 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000
#> GSM1068498 1 0.0000 0.968 1.000 0.000 0.000 0.000 0.000
#> GSM1068499 1 0.0000 0.968 1.000 0.000 0.000 0.000 0.000
#> GSM1068500 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000
#> GSM1068502 4 0.0000 0.928 0.000 0.000 0.000 1.000 0.000
#> GSM1068503 4 0.3039 0.731 0.000 0.192 0.000 0.808 0.000
#> GSM1068505 4 0.2732 0.779 0.000 0.160 0.000 0.840 0.000
#> GSM1068506 4 0.0162 0.928 0.000 0.004 0.000 0.996 0.000
#> GSM1068507 5 0.0000 0.948 0.000 0.000 0.000 0.000 1.000
#> GSM1068508 5 0.0000 0.948 0.000 0.000 0.000 0.000 1.000
#> GSM1068510 2 0.0162 0.928 0.000 0.996 0.000 0.000 0.004
#> GSM1068512 5 0.3766 0.628 0.000 0.004 0.000 0.268 0.728
#> GSM1068513 2 0.0404 0.926 0.000 0.988 0.000 0.000 0.012
#> GSM1068514 4 0.0162 0.928 0.000 0.004 0.000 0.996 0.000
#> GSM1068517 5 0.4171 0.382 0.396 0.000 0.000 0.000 0.604
#> GSM1068518 5 0.0000 0.948 0.000 0.000 0.000 0.000 1.000
#> GSM1068520 1 0.0404 0.961 0.988 0.000 0.012 0.000 0.000
#> GSM1068521 1 0.0000 0.968 1.000 0.000 0.000 0.000 0.000
#> GSM1068522 4 0.0000 0.928 0.000 0.000 0.000 1.000 0.000
#> GSM1068524 2 0.0000 0.928 0.000 1.000 0.000 0.000 0.000
#> GSM1068527 4 0.3300 0.720 0.000 0.004 0.000 0.792 0.204
#> GSM1068480 5 0.0000 0.948 0.000 0.000 0.000 0.000 1.000
#> GSM1068484 4 0.0000 0.928 0.000 0.000 0.000 1.000 0.000
#> GSM1068485 1 0.0000 0.968 1.000 0.000 0.000 0.000 0.000
#> GSM1068489 2 0.3177 0.784 0.000 0.792 0.000 0.208 0.000
#> GSM1068497 5 0.0000 0.948 0.000 0.000 0.000 0.000 1.000
#> GSM1068501 4 0.0162 0.928 0.000 0.004 0.000 0.996 0.000
#> GSM1068504 2 0.0162 0.928 0.000 0.996 0.000 0.000 0.004
#> GSM1068509 1 0.0000 0.968 1.000 0.000 0.000 0.000 0.000
#> GSM1068511 4 0.0162 0.928 0.000 0.004 0.000 0.996 0.000
#> GSM1068515 1 0.0000 0.968 1.000 0.000 0.000 0.000 0.000
#> GSM1068516 5 0.0000 0.948 0.000 0.000 0.000 0.000 1.000
#> GSM1068519 1 0.0000 0.968 1.000 0.000 0.000 0.000 0.000
#> GSM1068523 2 0.0703 0.919 0.000 0.976 0.000 0.000 0.024
#> GSM1068525 2 0.0000 0.928 0.000 1.000 0.000 0.000 0.000
#> GSM1068526 2 0.0703 0.921 0.000 0.976 0.000 0.024 0.000
#> GSM1068458 1 0.0000 0.968 1.000 0.000 0.000 0.000 0.000
#> GSM1068459 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000
#> GSM1068460 5 0.0000 0.948 0.000 0.000 0.000 0.000 1.000
#> GSM1068461 1 0.0000 0.968 1.000 0.000 0.000 0.000 0.000
#> GSM1068464 2 0.3366 0.759 0.000 0.768 0.000 0.232 0.000
#> GSM1068468 1 0.4192 0.306 0.596 0.000 0.000 0.404 0.000
#> GSM1068472 4 0.1484 0.888 0.008 0.000 0.000 0.944 0.048
#> GSM1068473 4 0.0000 0.928 0.000 0.000 0.000 1.000 0.000
#> GSM1068474 4 0.0000 0.928 0.000 0.000 0.000 1.000 0.000
#> GSM1068476 5 0.0000 0.948 0.000 0.000 0.000 0.000 1.000
#> GSM1068477 5 0.0000 0.948 0.000 0.000 0.000 0.000 1.000
#> GSM1068462 5 0.0000 0.948 0.000 0.000 0.000 0.000 1.000
#> GSM1068463 3 0.0162 0.996 0.004 0.000 0.996 0.000 0.000
#> GSM1068465 4 0.3177 0.698 0.208 0.000 0.000 0.792 0.000
#> GSM1068466 1 0.0404 0.961 0.988 0.000 0.012 0.000 0.000
#> GSM1068467 5 0.0000 0.948 0.000 0.000 0.000 0.000 1.000
#> GSM1068469 1 0.0404 0.958 0.988 0.000 0.000 0.000 0.012
#> GSM1068470 2 0.0000 0.928 0.000 1.000 0.000 0.000 0.000
#> GSM1068471 4 0.2074 0.842 0.000 0.104 0.000 0.896 0.000
#> GSM1068475 2 0.3177 0.788 0.000 0.792 0.000 0.208 0.000
#> GSM1068528 1 0.0000 0.968 1.000 0.000 0.000 0.000 0.000
#> GSM1068531 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000
#> GSM1068532 1 0.0000 0.968 1.000 0.000 0.000 0.000 0.000
#> GSM1068533 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000
#> GSM1068535 4 0.4410 0.225 0.000 0.004 0.440 0.556 0.000
#> GSM1068537 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000
#> GSM1068538 1 0.0000 0.968 1.000 0.000 0.000 0.000 0.000
#> GSM1068539 5 0.0000 0.948 0.000 0.000 0.000 0.000 1.000
#> GSM1068540 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000
#> GSM1068542 4 0.0000 0.928 0.000 0.000 0.000 1.000 0.000
#> GSM1068543 2 0.0162 0.928 0.000 0.996 0.000 0.000 0.004
#> GSM1068544 1 0.0000 0.968 1.000 0.000 0.000 0.000 0.000
#> GSM1068545 4 0.0000 0.928 0.000 0.000 0.000 1.000 0.000
#> GSM1068546 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000
#> GSM1068547 1 0.0000 0.968 1.000 0.000 0.000 0.000 0.000
#> GSM1068548 4 0.0000 0.928 0.000 0.000 0.000 1.000 0.000
#> GSM1068549 5 0.0162 0.945 0.000 0.000 0.004 0.000 0.996
#> GSM1068550 4 0.0609 0.919 0.000 0.020 0.000 0.980 0.000
#> GSM1068551 2 0.0703 0.923 0.000 0.976 0.000 0.024 0.000
#> GSM1068552 4 0.0290 0.926 0.000 0.008 0.000 0.992 0.000
#> GSM1068555 2 0.0162 0.928 0.000 0.996 0.000 0.000 0.004
#> GSM1068556 4 0.0162 0.928 0.000 0.004 0.000 0.996 0.000
#> GSM1068557 5 0.0000 0.948 0.000 0.000 0.000 0.000 1.000
#> GSM1068560 5 0.0000 0.948 0.000 0.000 0.000 0.000 1.000
#> GSM1068561 5 0.0000 0.948 0.000 0.000 0.000 0.000 1.000
#> GSM1068562 2 0.0609 0.922 0.000 0.980 0.000 0.000 0.020
#> GSM1068563 4 0.0000 0.928 0.000 0.000 0.000 1.000 0.000
#> GSM1068565 2 0.0798 0.924 0.000 0.976 0.000 0.008 0.016
#> GSM1068529 5 0.0000 0.948 0.000 0.000 0.000 0.000 1.000
#> GSM1068530 1 0.0000 0.968 1.000 0.000 0.000 0.000 0.000
#> GSM1068534 5 0.3366 0.707 0.000 0.004 0.000 0.212 0.784
#> GSM1068536 5 0.0000 0.948 0.000 0.000 0.000 0.000 1.000
#> GSM1068541 1 0.1851 0.874 0.912 0.000 0.000 0.088 0.000
#> GSM1068553 4 0.0162 0.928 0.000 0.004 0.000 0.996 0.000
#> GSM1068554 4 0.0162 0.928 0.000 0.004 0.000 0.996 0.000
#> GSM1068558 2 0.0162 0.928 0.000 0.996 0.000 0.000 0.004
#> GSM1068559 5 0.0000 0.948 0.000 0.000 0.000 0.000 1.000
#> GSM1068564 2 0.3143 0.788 0.000 0.796 0.000 0.204 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1068478 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1068479 6 0.0632 0.948 0.000 0.000 0.000 0.024 0.000 0.976
#> GSM1068481 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1068482 5 0.2701 0.834 0.028 0.000 0.004 0.000 0.864 0.104
#> GSM1068483 1 0.0260 0.922 0.992 0.000 0.008 0.000 0.000 0.000
#> GSM1068486 6 0.0000 0.970 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM1068487 2 0.2135 0.844 0.000 0.872 0.000 0.000 0.128 0.000
#> GSM1068488 2 0.0632 0.861 0.000 0.976 0.000 0.000 0.024 0.000
#> GSM1068490 2 0.4687 0.718 0.000 0.684 0.000 0.180 0.136 0.000
#> GSM1068491 4 0.2933 0.695 0.004 0.000 0.000 0.796 0.000 0.200
#> GSM1068492 4 0.1471 0.879 0.000 0.004 0.000 0.932 0.064 0.000
#> GSM1068493 6 0.0000 0.970 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM1068494 6 0.0000 0.970 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM1068495 6 0.0000 0.970 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM1068496 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1068498 5 0.2219 0.942 0.136 0.000 0.000 0.000 0.864 0.000
#> GSM1068499 5 0.2219 0.942 0.136 0.000 0.000 0.000 0.864 0.000
#> GSM1068500 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1068502 4 0.0000 0.884 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1068503 4 0.4595 0.668 0.000 0.168 0.000 0.696 0.136 0.000
#> GSM1068505 4 0.4281 0.727 0.000 0.132 0.000 0.732 0.136 0.000
#> GSM1068506 4 0.1753 0.875 0.000 0.004 0.000 0.912 0.084 0.000
#> GSM1068507 6 0.0000 0.970 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM1068508 6 0.0000 0.970 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM1068510 2 0.0146 0.863 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM1068512 6 0.3833 0.633 0.000 0.004 0.000 0.232 0.028 0.736
#> GSM1068513 2 0.1863 0.825 0.000 0.896 0.000 0.000 0.000 0.104
#> GSM1068514 4 0.1753 0.875 0.000 0.004 0.000 0.912 0.084 0.000
#> GSM1068517 5 0.2679 0.847 0.040 0.000 0.000 0.000 0.864 0.096
#> GSM1068518 6 0.0000 0.970 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM1068520 1 0.0260 0.922 0.992 0.000 0.008 0.000 0.000 0.000
#> GSM1068521 5 0.2941 0.851 0.220 0.000 0.000 0.000 0.780 0.000
#> GSM1068522 4 0.2219 0.856 0.000 0.000 0.000 0.864 0.136 0.000
#> GSM1068524 2 0.0000 0.863 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1068527 4 0.2738 0.716 0.000 0.004 0.000 0.820 0.000 0.176
#> GSM1068480 6 0.0000 0.970 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM1068484 4 0.2219 0.856 0.000 0.000 0.000 0.864 0.136 0.000
#> GSM1068485 5 0.2219 0.942 0.136 0.000 0.000 0.000 0.864 0.000
#> GSM1068489 2 0.4687 0.713 0.000 0.684 0.000 0.180 0.136 0.000
#> GSM1068497 6 0.0000 0.970 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM1068501 4 0.0146 0.884 0.000 0.004 0.000 0.996 0.000 0.000
#> GSM1068504 2 0.0146 0.863 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM1068509 1 0.0000 0.924 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM1068511 4 0.0692 0.883 0.000 0.004 0.000 0.976 0.020 0.000
#> GSM1068515 1 0.0146 0.925 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM1068516 6 0.0000 0.970 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM1068519 1 0.2178 0.771 0.868 0.000 0.000 0.000 0.132 0.000
#> GSM1068523 2 0.2003 0.818 0.000 0.884 0.000 0.000 0.000 0.116
#> GSM1068525 2 0.0000 0.863 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1068526 2 0.2623 0.838 0.000 0.852 0.000 0.016 0.132 0.000
#> GSM1068458 1 0.0146 0.925 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM1068459 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1068460 6 0.0000 0.970 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM1068461 5 0.2219 0.942 0.136 0.000 0.000 0.000 0.864 0.000
#> GSM1068464 2 0.4756 0.691 0.000 0.664 0.000 0.224 0.112 0.000
#> GSM1068468 1 0.3804 0.280 0.576 0.000 0.000 0.424 0.000 0.000
#> GSM1068472 4 0.1434 0.848 0.012 0.000 0.000 0.940 0.000 0.048
#> GSM1068473 4 0.0000 0.884 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1068474 4 0.0146 0.885 0.000 0.000 0.000 0.996 0.004 0.000
#> GSM1068476 6 0.0000 0.970 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM1068477 6 0.0000 0.970 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM1068462 6 0.0000 0.970 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM1068463 3 0.0146 0.995 0.004 0.000 0.996 0.000 0.000 0.000
#> GSM1068465 4 0.2631 0.707 0.180 0.000 0.000 0.820 0.000 0.000
#> GSM1068466 1 0.0260 0.922 0.992 0.000 0.008 0.000 0.000 0.000
#> GSM1068467 6 0.0146 0.967 0.004 0.000 0.000 0.000 0.000 0.996
#> GSM1068469 1 0.0146 0.922 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM1068470 2 0.0000 0.863 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM1068471 4 0.3992 0.764 0.000 0.104 0.000 0.760 0.136 0.000
#> GSM1068475 2 0.4456 0.736 0.000 0.708 0.000 0.180 0.112 0.000
#> GSM1068528 5 0.2219 0.942 0.136 0.000 0.000 0.000 0.864 0.000
#> GSM1068531 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1068532 1 0.0146 0.925 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM1068533 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1068535 4 0.4508 0.193 0.000 0.004 0.436 0.536 0.024 0.000
#> GSM1068537 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1068538 1 0.0146 0.925 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM1068539 6 0.0000 0.970 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM1068540 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1068542 4 0.0000 0.884 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1068543 2 0.0146 0.863 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM1068544 5 0.2219 0.942 0.136 0.000 0.000 0.000 0.864 0.000
#> GSM1068545 4 0.0000 0.884 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1068546 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1068547 1 0.0146 0.925 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM1068548 4 0.0000 0.884 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1068549 6 0.0146 0.967 0.000 0.000 0.004 0.000 0.000 0.996
#> GSM1068550 4 0.2572 0.852 0.000 0.012 0.000 0.852 0.136 0.000
#> GSM1068551 2 0.2358 0.847 0.000 0.876 0.000 0.016 0.108 0.000
#> GSM1068552 4 0.2473 0.854 0.000 0.008 0.000 0.856 0.136 0.000
#> GSM1068555 2 0.0146 0.863 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM1068556 4 0.2362 0.856 0.000 0.004 0.000 0.860 0.136 0.000
#> GSM1068557 6 0.0000 0.970 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM1068560 6 0.0000 0.970 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM1068561 6 0.0000 0.970 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM1068562 2 0.2048 0.815 0.000 0.880 0.000 0.000 0.000 0.120
#> GSM1068563 4 0.0146 0.885 0.000 0.000 0.000 0.996 0.004 0.000
#> GSM1068565 2 0.2257 0.818 0.000 0.876 0.000 0.008 0.000 0.116
#> GSM1068529 6 0.0000 0.970 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM1068530 1 0.0146 0.925 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM1068534 6 0.4853 0.553 0.000 0.004 0.000 0.184 0.136 0.676
#> GSM1068536 6 0.0000 0.970 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM1068541 1 0.1814 0.819 0.900 0.000 0.000 0.100 0.000 0.000
#> GSM1068553 4 0.0146 0.884 0.000 0.004 0.000 0.996 0.000 0.000
#> GSM1068554 4 0.0146 0.884 0.000 0.004 0.000 0.996 0.000 0.000
#> GSM1068558 2 0.0146 0.863 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM1068559 6 0.0000 0.970 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM1068564 2 0.4657 0.718 0.000 0.688 0.000 0.176 0.136 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 disease.state(p) gender(p) k
#> ATC:pam 64 0.292 0.625 2
#> ATC:pam 102 0.631 0.243 3
#> ATC:pam 93 0.990 0.335 4
#> ATC:pam 105 0.998 0.323 5
#> ATC:pam 106 0.986 0.116 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 38950 rows and 108 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'mclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.620 0.729 0.889 0.439 0.595 0.595
#> 3 3 0.610 0.553 0.778 0.405 0.551 0.350
#> 4 4 0.670 0.800 0.870 0.107 0.785 0.515
#> 5 5 0.756 0.766 0.881 0.104 0.814 0.507
#> 6 6 0.667 0.530 0.752 0.049 0.900 0.641
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
#> GSM1068478 2 0.997 0.3112 0.468 0.532
#> GSM1068479 2 0.000 0.8375 0.000 1.000
#> GSM1068481 2 0.997 0.3112 0.468 0.532
#> GSM1068482 1 0.000 0.9360 1.000 0.000
#> GSM1068483 1 0.000 0.9360 1.000 0.000
#> GSM1068486 2 0.997 0.3112 0.468 0.532
#> GSM1068487 2 0.000 0.8375 0.000 1.000
#> GSM1068488 2 0.000 0.8375 0.000 1.000
#> GSM1068490 2 0.000 0.8375 0.000 1.000
#> GSM1068491 1 0.981 0.0496 0.580 0.420
#> GSM1068492 2 0.000 0.8375 0.000 1.000
#> GSM1068493 2 0.909 0.5437 0.324 0.676
#> GSM1068494 2 0.997 0.3112 0.468 0.532
#> GSM1068495 2 0.997 0.3112 0.468 0.532
#> GSM1068496 2 0.998 0.2888 0.476 0.524
#> GSM1068498 1 0.000 0.9360 1.000 0.000
#> GSM1068499 1 0.000 0.9360 1.000 0.000
#> GSM1068500 2 0.997 0.3112 0.468 0.532
#> GSM1068502 2 0.000 0.8375 0.000 1.000
#> GSM1068503 2 0.000 0.8375 0.000 1.000
#> GSM1068505 2 0.000 0.8375 0.000 1.000
#> GSM1068506 2 0.000 0.8375 0.000 1.000
#> GSM1068507 2 0.000 0.8375 0.000 1.000
#> GSM1068508 2 0.000 0.8375 0.000 1.000
#> GSM1068510 2 0.000 0.8375 0.000 1.000
#> GSM1068512 2 0.000 0.8375 0.000 1.000
#> GSM1068513 2 0.000 0.8375 0.000 1.000
#> GSM1068514 2 0.000 0.8375 0.000 1.000
#> GSM1068517 1 0.000 0.9360 1.000 0.000
#> GSM1068518 2 0.997 0.3112 0.468 0.532
#> GSM1068520 1 0.000 0.9360 1.000 0.000
#> GSM1068521 1 0.000 0.9360 1.000 0.000
#> GSM1068522 2 0.000 0.8375 0.000 1.000
#> GSM1068524 2 0.000 0.8375 0.000 1.000
#> GSM1068527 2 0.000 0.8375 0.000 1.000
#> GSM1068480 2 0.997 0.3112 0.468 0.532
#> GSM1068484 2 0.000 0.8375 0.000 1.000
#> GSM1068485 1 0.000 0.9360 1.000 0.000
#> GSM1068489 2 0.000 0.8375 0.000 1.000
#> GSM1068497 2 0.997 0.3112 0.468 0.532
#> GSM1068501 2 0.000 0.8375 0.000 1.000
#> GSM1068504 2 0.000 0.8375 0.000 1.000
#> GSM1068509 1 0.000 0.9360 1.000 0.000
#> GSM1068511 2 0.000 0.8375 0.000 1.000
#> GSM1068515 1 0.000 0.9360 1.000 0.000
#> GSM1068516 2 0.997 0.3112 0.468 0.532
#> GSM1068519 1 0.000 0.9360 1.000 0.000
#> GSM1068523 2 0.000 0.8375 0.000 1.000
#> GSM1068525 2 0.000 0.8375 0.000 1.000
#> GSM1068526 2 0.000 0.8375 0.000 1.000
#> GSM1068458 1 0.000 0.9360 1.000 0.000
#> GSM1068459 2 0.998 0.2887 0.476 0.524
#> GSM1068460 2 0.997 0.3112 0.468 0.532
#> GSM1068461 1 0.000 0.9360 1.000 0.000
#> GSM1068464 2 0.000 0.8375 0.000 1.000
#> GSM1068468 2 0.997 0.3112 0.468 0.532
#> GSM1068472 2 0.997 0.3112 0.468 0.532
#> GSM1068473 2 0.000 0.8375 0.000 1.000
#> GSM1068474 2 0.000 0.8375 0.000 1.000
#> GSM1068476 2 0.000 0.8375 0.000 1.000
#> GSM1068477 2 0.595 0.7389 0.144 0.856
#> GSM1068462 2 0.996 0.3186 0.464 0.536
#> GSM1068463 1 0.000 0.9360 1.000 0.000
#> GSM1068465 1 0.980 0.0652 0.584 0.416
#> GSM1068466 1 0.000 0.9360 1.000 0.000
#> GSM1068467 2 0.997 0.3112 0.468 0.532
#> GSM1068469 1 0.000 0.9360 1.000 0.000
#> GSM1068470 2 0.000 0.8375 0.000 1.000
#> GSM1068471 2 0.000 0.8375 0.000 1.000
#> GSM1068475 2 0.000 0.8375 0.000 1.000
#> GSM1068528 1 0.000 0.9360 1.000 0.000
#> GSM1068531 1 0.000 0.9360 1.000 0.000
#> GSM1068532 1 0.000 0.9360 1.000 0.000
#> GSM1068533 1 0.000 0.9360 1.000 0.000
#> GSM1068535 2 0.634 0.7253 0.160 0.840
#> GSM1068537 1 0.000 0.9360 1.000 0.000
#> GSM1068538 1 0.000 0.9360 1.000 0.000
#> GSM1068539 2 0.997 0.3112 0.468 0.532
#> GSM1068540 1 0.494 0.8044 0.892 0.108
#> GSM1068542 2 0.000 0.8375 0.000 1.000
#> GSM1068543 2 0.000 0.8375 0.000 1.000
#> GSM1068544 1 0.000 0.9360 1.000 0.000
#> GSM1068545 2 0.000 0.8375 0.000 1.000
#> GSM1068546 2 0.997 0.3112 0.468 0.532
#> GSM1068547 1 0.000 0.9360 1.000 0.000
#> GSM1068548 2 0.000 0.8375 0.000 1.000
#> GSM1068549 1 1.000 -0.2100 0.512 0.488
#> GSM1068550 2 0.000 0.8375 0.000 1.000
#> GSM1068551 2 0.000 0.8375 0.000 1.000
#> GSM1068552 2 0.000 0.8375 0.000 1.000
#> GSM1068555 2 0.000 0.8375 0.000 1.000
#> GSM1068556 2 0.000 0.8375 0.000 1.000
#> GSM1068557 2 0.595 0.7389 0.144 0.856
#> GSM1068560 2 0.000 0.8375 0.000 1.000
#> GSM1068561 2 0.373 0.7926 0.072 0.928
#> GSM1068562 2 0.000 0.8375 0.000 1.000
#> GSM1068563 2 0.000 0.8375 0.000 1.000
#> GSM1068565 2 0.000 0.8375 0.000 1.000
#> GSM1068529 2 0.000 0.8375 0.000 1.000
#> GSM1068530 1 0.000 0.9360 1.000 0.000
#> GSM1068534 2 0.000 0.8375 0.000 1.000
#> GSM1068536 2 0.997 0.3112 0.468 0.532
#> GSM1068541 1 0.000 0.9360 1.000 0.000
#> GSM1068553 2 0.000 0.8375 0.000 1.000
#> GSM1068554 2 0.000 0.8375 0.000 1.000
#> GSM1068558 2 0.000 0.8375 0.000 1.000
#> GSM1068559 2 0.958 0.4621 0.380 0.620
#> GSM1068564 2 0.000 0.8375 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1068478 1 0.0000 0.83532 1.000 0.000 0.000
#> GSM1068479 2 0.0424 0.89445 0.000 0.992 0.008
#> GSM1068481 1 0.0424 0.83341 0.992 0.000 0.008
#> GSM1068482 1 0.6280 0.22425 0.540 0.000 0.460
#> GSM1068483 3 0.6252 -0.03039 0.444 0.000 0.556
#> GSM1068486 1 0.0424 0.83341 0.992 0.000 0.008
#> GSM1068487 2 0.0892 0.88710 0.000 0.980 0.020
#> GSM1068488 2 0.8795 0.10293 0.112 0.444 0.444
#> GSM1068490 2 0.0000 0.89834 0.000 1.000 0.000
#> GSM1068491 1 0.3619 0.78805 0.864 0.000 0.136
#> GSM1068492 2 0.0000 0.89834 0.000 1.000 0.000
#> GSM1068493 1 0.1453 0.83362 0.968 0.024 0.008
#> GSM1068494 1 0.1453 0.83362 0.968 0.024 0.008
#> GSM1068495 1 0.1774 0.82625 0.960 0.024 0.016
#> GSM1068496 1 0.1860 0.82767 0.948 0.000 0.052
#> GSM1068498 3 0.6252 -0.03039 0.444 0.000 0.556
#> GSM1068499 3 0.6252 -0.03039 0.444 0.000 0.556
#> GSM1068500 1 0.0892 0.83532 0.980 0.000 0.020
#> GSM1068502 2 0.0000 0.89834 0.000 1.000 0.000
#> GSM1068503 2 0.0000 0.89834 0.000 1.000 0.000
#> GSM1068505 2 0.0237 0.89609 0.004 0.996 0.000
#> GSM1068506 2 0.0000 0.89834 0.000 1.000 0.000
#> GSM1068507 2 0.2339 0.85855 0.012 0.940 0.048
#> GSM1068508 2 0.2903 0.84403 0.028 0.924 0.048
#> GSM1068510 3 0.8795 -0.15722 0.112 0.444 0.444
#> GSM1068512 2 0.0000 0.89834 0.000 1.000 0.000
#> GSM1068513 3 0.8795 -0.15722 0.112 0.444 0.444
#> GSM1068514 2 0.0000 0.89834 0.000 1.000 0.000
#> GSM1068517 1 0.4931 0.66494 0.768 0.000 0.232
#> GSM1068518 1 0.1031 0.83562 0.976 0.024 0.000
#> GSM1068520 1 0.6260 0.29695 0.552 0.000 0.448
#> GSM1068521 3 0.6252 -0.03039 0.444 0.000 0.556
#> GSM1068522 2 0.0000 0.89834 0.000 1.000 0.000
#> GSM1068524 3 0.8795 -0.15722 0.112 0.444 0.444
#> GSM1068527 2 0.0000 0.89834 0.000 1.000 0.000
#> GSM1068480 1 0.1453 0.83362 0.968 0.024 0.008
#> GSM1068484 2 0.0000 0.89834 0.000 1.000 0.000
#> GSM1068485 3 0.6252 -0.03039 0.444 0.000 0.556
#> GSM1068489 2 0.4324 0.76489 0.112 0.860 0.028
#> GSM1068497 1 0.1453 0.83362 0.968 0.024 0.008
#> GSM1068501 2 0.0000 0.89834 0.000 1.000 0.000
#> GSM1068504 2 0.8795 0.10293 0.112 0.444 0.444
#> GSM1068509 1 0.4842 0.70418 0.776 0.000 0.224
#> GSM1068511 2 0.0237 0.89602 0.004 0.996 0.000
#> GSM1068515 3 0.6252 -0.03039 0.444 0.000 0.556
#> GSM1068516 1 0.1453 0.83362 0.968 0.024 0.008
#> GSM1068519 3 0.6252 -0.03039 0.444 0.000 0.556
#> GSM1068523 3 0.8795 -0.15722 0.112 0.444 0.444
#> GSM1068525 3 0.8795 -0.15722 0.112 0.444 0.444
#> GSM1068526 2 0.0424 0.89445 0.000 0.992 0.008
#> GSM1068458 3 0.6252 -0.03039 0.444 0.000 0.556
#> GSM1068459 1 0.0892 0.83532 0.980 0.000 0.020
#> GSM1068460 1 0.1453 0.83362 0.968 0.024 0.008
#> GSM1068461 3 0.6252 -0.03039 0.444 0.000 0.556
#> GSM1068464 2 0.0000 0.89834 0.000 1.000 0.000
#> GSM1068468 1 0.3619 0.78805 0.864 0.000 0.136
#> GSM1068472 1 0.4196 0.80063 0.864 0.024 0.112
#> GSM1068473 2 0.0000 0.89834 0.000 1.000 0.000
#> GSM1068474 2 0.0000 0.89834 0.000 1.000 0.000
#> GSM1068476 3 0.8795 -0.15722 0.112 0.444 0.444
#> GSM1068477 1 0.1751 0.82681 0.960 0.028 0.012
#> GSM1068462 1 0.1453 0.83362 0.968 0.024 0.008
#> GSM1068463 1 0.5560 0.57827 0.700 0.000 0.300
#> GSM1068465 1 0.3619 0.78805 0.864 0.000 0.136
#> GSM1068466 1 0.5706 0.56706 0.680 0.000 0.320
#> GSM1068467 1 0.3120 0.82283 0.908 0.012 0.080
#> GSM1068469 1 0.3619 0.78805 0.864 0.000 0.136
#> GSM1068470 3 0.8795 -0.15722 0.112 0.444 0.444
#> GSM1068471 2 0.0000 0.89834 0.000 1.000 0.000
#> GSM1068475 2 0.0000 0.89834 0.000 1.000 0.000
#> GSM1068528 3 0.6252 -0.03039 0.444 0.000 0.556
#> GSM1068531 1 0.2165 0.82326 0.936 0.000 0.064
#> GSM1068532 3 0.6252 -0.03039 0.444 0.000 0.556
#> GSM1068533 1 0.3619 0.78805 0.864 0.000 0.136
#> GSM1068535 1 0.6984 -0.00743 0.560 0.420 0.020
#> GSM1068537 1 0.5529 0.58459 0.704 0.000 0.296
#> GSM1068538 3 0.6252 -0.03039 0.444 0.000 0.556
#> GSM1068539 1 0.1453 0.83362 0.968 0.024 0.008
#> GSM1068540 1 0.2261 0.82150 0.932 0.000 0.068
#> GSM1068542 2 0.0000 0.89834 0.000 1.000 0.000
#> GSM1068543 2 0.8795 0.10293 0.112 0.444 0.444
#> GSM1068544 3 0.6252 -0.03039 0.444 0.000 0.556
#> GSM1068545 2 0.0000 0.89834 0.000 1.000 0.000
#> GSM1068546 1 0.0424 0.83341 0.992 0.000 0.008
#> GSM1068547 3 0.6252 -0.03039 0.444 0.000 0.556
#> GSM1068548 2 0.0000 0.89834 0.000 1.000 0.000
#> GSM1068549 1 0.0000 0.83532 1.000 0.000 0.000
#> GSM1068550 2 0.0000 0.89834 0.000 1.000 0.000
#> GSM1068551 2 0.1529 0.87343 0.000 0.960 0.040
#> GSM1068552 2 0.0000 0.89834 0.000 1.000 0.000
#> GSM1068555 3 0.8795 -0.15722 0.112 0.444 0.444
#> GSM1068556 2 0.0000 0.89834 0.000 1.000 0.000
#> GSM1068557 1 0.3780 0.74269 0.892 0.064 0.044
#> GSM1068560 3 0.8795 -0.15722 0.112 0.444 0.444
#> GSM1068561 3 0.8938 -0.14559 0.124 0.432 0.444
#> GSM1068562 3 0.8795 -0.15722 0.112 0.444 0.444
#> GSM1068563 2 0.0000 0.89834 0.000 1.000 0.000
#> GSM1068565 2 0.7601 0.31455 0.044 0.540 0.416
#> GSM1068529 2 0.9759 0.08192 0.284 0.444 0.272
#> GSM1068530 3 0.6252 -0.03039 0.444 0.000 0.556
#> GSM1068534 2 0.4063 0.77108 0.112 0.868 0.020
#> GSM1068536 1 0.1453 0.83362 0.968 0.024 0.008
#> GSM1068541 1 0.3619 0.78805 0.864 0.000 0.136
#> GSM1068553 2 0.5831 0.48316 0.284 0.708 0.008
#> GSM1068554 2 0.0000 0.89834 0.000 1.000 0.000
#> GSM1068558 3 0.8795 -0.15722 0.112 0.444 0.444
#> GSM1068559 1 0.1453 0.83362 0.968 0.024 0.008
#> GSM1068564 2 0.0000 0.89834 0.000 1.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1068478 3 0.2973 0.8650 0.000 0.144 0.856 0.000
#> GSM1068479 4 0.0336 0.8951 0.008 0.000 0.000 0.992
#> GSM1068481 3 0.2921 0.8680 0.000 0.140 0.860 0.000
#> GSM1068482 1 0.3249 0.7517 0.852 0.140 0.008 0.000
#> GSM1068483 1 0.0336 0.9376 0.992 0.008 0.000 0.000
#> GSM1068486 3 0.4624 0.6134 0.000 0.340 0.660 0.000
#> GSM1068487 4 0.1635 0.8809 0.008 0.000 0.044 0.948
#> GSM1068488 4 0.6286 0.7068 0.000 0.200 0.140 0.660
#> GSM1068490 4 0.0336 0.8951 0.008 0.000 0.000 0.992
#> GSM1068491 2 0.3764 0.6730 0.216 0.784 0.000 0.000
#> GSM1068492 4 0.0336 0.8951 0.008 0.000 0.000 0.992
#> GSM1068493 2 0.0000 0.7870 0.000 1.000 0.000 0.000
#> GSM1068494 2 0.2868 0.6704 0.000 0.864 0.136 0.000
#> GSM1068495 2 0.0000 0.7870 0.000 1.000 0.000 0.000
#> GSM1068496 3 0.2921 0.8680 0.000 0.140 0.860 0.000
#> GSM1068498 1 0.1474 0.8889 0.948 0.052 0.000 0.000
#> GSM1068499 1 0.0336 0.9376 0.992 0.008 0.000 0.000
#> GSM1068500 3 0.2921 0.8680 0.000 0.140 0.860 0.000
#> GSM1068502 4 0.0336 0.8927 0.000 0.008 0.000 0.992
#> GSM1068503 4 0.0336 0.8951 0.008 0.000 0.000 0.992
#> GSM1068505 4 0.0000 0.8953 0.000 0.000 0.000 1.000
#> GSM1068506 4 0.0000 0.8953 0.000 0.000 0.000 1.000
#> GSM1068507 4 0.0000 0.8953 0.000 0.000 0.000 1.000
#> GSM1068508 4 0.0524 0.8943 0.008 0.004 0.000 0.988
#> GSM1068510 4 0.6286 0.7068 0.000 0.200 0.140 0.660
#> GSM1068512 4 0.0000 0.8953 0.000 0.000 0.000 1.000
#> GSM1068513 4 0.2921 0.8402 0.000 0.000 0.140 0.860
#> GSM1068514 4 0.0000 0.8953 0.000 0.000 0.000 1.000
#> GSM1068517 2 0.4999 0.0741 0.492 0.508 0.000 0.000
#> GSM1068518 2 0.0000 0.7870 0.000 1.000 0.000 0.000
#> GSM1068520 1 0.0336 0.9376 0.992 0.008 0.000 0.000
#> GSM1068521 1 0.0336 0.9376 0.992 0.008 0.000 0.000
#> GSM1068522 4 0.0000 0.8953 0.000 0.000 0.000 1.000
#> GSM1068524 4 0.6286 0.7068 0.000 0.200 0.140 0.660
#> GSM1068527 4 0.0000 0.8953 0.000 0.000 0.000 1.000
#> GSM1068480 2 0.1940 0.7364 0.000 0.924 0.076 0.000
#> GSM1068484 4 0.0336 0.8951 0.008 0.000 0.000 0.992
#> GSM1068485 1 0.0336 0.9376 0.992 0.008 0.000 0.000
#> GSM1068489 4 0.0000 0.8953 0.000 0.000 0.000 1.000
#> GSM1068497 2 0.0000 0.7870 0.000 1.000 0.000 0.000
#> GSM1068501 4 0.0000 0.8953 0.000 0.000 0.000 1.000
#> GSM1068504 4 0.6179 0.7173 0.000 0.188 0.140 0.672
#> GSM1068509 1 0.4925 0.1682 0.572 0.428 0.000 0.000
#> GSM1068511 4 0.0000 0.8953 0.000 0.000 0.000 1.000
#> GSM1068515 1 0.0336 0.9376 0.992 0.008 0.000 0.000
#> GSM1068516 2 0.0000 0.7870 0.000 1.000 0.000 0.000
#> GSM1068519 1 0.0336 0.9376 0.992 0.008 0.000 0.000
#> GSM1068523 4 0.6275 0.7057 0.000 0.204 0.136 0.660
#> GSM1068525 4 0.5815 0.7459 0.000 0.152 0.140 0.708
#> GSM1068526 4 0.0336 0.8951 0.008 0.000 0.000 0.992
#> GSM1068458 1 0.0336 0.9376 0.992 0.008 0.000 0.000
#> GSM1068459 3 0.2921 0.8680 0.000 0.140 0.860 0.000
#> GSM1068460 2 0.2408 0.7489 0.000 0.896 0.000 0.104
#> GSM1068461 1 0.0336 0.9376 0.992 0.008 0.000 0.000
#> GSM1068464 4 0.0336 0.8951 0.008 0.000 0.000 0.992
#> GSM1068468 2 0.4843 0.7065 0.112 0.784 0.000 0.104
#> GSM1068472 2 0.3610 0.6454 0.000 0.800 0.000 0.200
#> GSM1068473 4 0.0000 0.8953 0.000 0.000 0.000 1.000
#> GSM1068474 4 0.0336 0.8951 0.008 0.000 0.000 0.992
#> GSM1068476 4 0.5705 0.6940 0.000 0.260 0.064 0.676
#> GSM1068477 2 0.2921 0.7203 0.000 0.860 0.000 0.140
#> GSM1068462 2 0.3074 0.7073 0.000 0.848 0.000 0.152
#> GSM1068463 3 0.5159 0.3987 0.364 0.012 0.624 0.000
#> GSM1068465 2 0.4948 0.6974 0.100 0.776 0.000 0.124
#> GSM1068466 1 0.5144 0.6266 0.732 0.216 0.052 0.000
#> GSM1068467 2 0.3610 0.6829 0.200 0.800 0.000 0.000
#> GSM1068469 2 0.4605 0.5062 0.336 0.664 0.000 0.000
#> GSM1068470 4 0.3249 0.8366 0.000 0.008 0.140 0.852
#> GSM1068471 4 0.0336 0.8951 0.008 0.000 0.000 0.992
#> GSM1068475 4 0.0336 0.8951 0.008 0.000 0.000 0.992
#> GSM1068528 1 0.0336 0.9376 0.992 0.008 0.000 0.000
#> GSM1068531 3 0.2921 0.8680 0.000 0.140 0.860 0.000
#> GSM1068532 1 0.0336 0.9376 0.992 0.008 0.000 0.000
#> GSM1068533 3 0.7526 0.3133 0.200 0.332 0.468 0.000
#> GSM1068535 4 0.5815 0.6697 0.000 0.140 0.152 0.708
#> GSM1068537 3 0.3351 0.7269 0.148 0.008 0.844 0.000
#> GSM1068538 1 0.0336 0.9376 0.992 0.008 0.000 0.000
#> GSM1068539 2 0.0000 0.7870 0.000 1.000 0.000 0.000
#> GSM1068540 3 0.2921 0.8680 0.000 0.140 0.860 0.000
#> GSM1068542 4 0.0000 0.8953 0.000 0.000 0.000 1.000
#> GSM1068543 4 0.6286 0.7068 0.000 0.200 0.140 0.660
#> GSM1068544 1 0.0336 0.9376 0.992 0.008 0.000 0.000
#> GSM1068545 4 0.0336 0.8951 0.008 0.000 0.000 0.992
#> GSM1068546 3 0.2921 0.8680 0.000 0.140 0.860 0.000
#> GSM1068547 1 0.0336 0.9376 0.992 0.008 0.000 0.000
#> GSM1068548 4 0.0000 0.8953 0.000 0.000 0.000 1.000
#> GSM1068549 2 0.3873 0.5591 0.000 0.772 0.228 0.000
#> GSM1068550 4 0.0000 0.8953 0.000 0.000 0.000 1.000
#> GSM1068551 4 0.0336 0.8951 0.008 0.000 0.000 0.992
#> GSM1068552 4 0.0336 0.8951 0.008 0.000 0.000 0.992
#> GSM1068555 4 0.6286 0.7068 0.000 0.200 0.140 0.660
#> GSM1068556 4 0.0000 0.8953 0.000 0.000 0.000 1.000
#> GSM1068557 2 0.1716 0.7350 0.000 0.936 0.000 0.064
#> GSM1068560 4 0.6286 0.7068 0.000 0.200 0.140 0.660
#> GSM1068561 4 0.4973 0.6169 0.000 0.348 0.008 0.644
#> GSM1068562 4 0.6286 0.7068 0.000 0.200 0.140 0.660
#> GSM1068563 4 0.0000 0.8953 0.000 0.000 0.000 1.000
#> GSM1068565 4 0.2737 0.8556 0.008 0.000 0.104 0.888
#> GSM1068529 4 0.4624 0.6338 0.000 0.340 0.000 0.660
#> GSM1068530 1 0.0336 0.9376 0.992 0.008 0.000 0.000
#> GSM1068534 4 0.1940 0.8522 0.000 0.076 0.000 0.924
#> GSM1068536 2 0.0000 0.7870 0.000 1.000 0.000 0.000
#> GSM1068541 2 0.4134 0.6342 0.260 0.740 0.000 0.000
#> GSM1068553 4 0.0817 0.8851 0.000 0.024 0.000 0.976
#> GSM1068554 4 0.0000 0.8953 0.000 0.000 0.000 1.000
#> GSM1068558 4 0.6286 0.7068 0.000 0.200 0.140 0.660
#> GSM1068559 2 0.0000 0.7870 0.000 1.000 0.000 0.000
#> GSM1068564 4 0.0336 0.8951 0.008 0.000 0.000 0.992
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM1068478 3 0.1124 0.8451 0.000 0.004 0.960 0.000 0.036
#> GSM1068479 4 0.0854 0.9024 0.000 0.008 0.012 0.976 0.004
#> GSM1068481 3 0.0703 0.8546 0.000 0.000 0.976 0.000 0.024
#> GSM1068482 5 0.4464 0.2636 0.408 0.000 0.008 0.000 0.584
#> GSM1068483 1 0.0000 0.8735 1.000 0.000 0.000 0.000 0.000
#> GSM1068486 3 0.2054 0.8045 0.000 0.052 0.920 0.000 0.028
#> GSM1068487 4 0.2411 0.8169 0.000 0.108 0.008 0.884 0.000
#> GSM1068488 2 0.1357 0.8508 0.000 0.948 0.000 0.048 0.004
#> GSM1068490 4 0.0671 0.9025 0.000 0.004 0.016 0.980 0.000
#> GSM1068491 1 0.6073 0.1833 0.496 0.004 0.000 0.108 0.392
#> GSM1068492 4 0.0671 0.9032 0.000 0.004 0.016 0.980 0.000
#> GSM1068493 5 0.3997 0.7117 0.000 0.076 0.004 0.116 0.804
#> GSM1068494 5 0.2859 0.7780 0.000 0.056 0.068 0.000 0.876
#> GSM1068495 5 0.1608 0.7948 0.000 0.072 0.000 0.000 0.928
#> GSM1068496 3 0.0703 0.8546 0.000 0.000 0.976 0.000 0.024
#> GSM1068498 5 0.4249 0.2116 0.432 0.000 0.000 0.000 0.568
#> GSM1068499 1 0.0510 0.8703 0.984 0.000 0.000 0.000 0.016
#> GSM1068500 3 0.0703 0.8546 0.000 0.000 0.976 0.000 0.024
#> GSM1068502 4 0.0162 0.9030 0.000 0.000 0.000 0.996 0.004
#> GSM1068503 4 0.0671 0.9025 0.000 0.004 0.016 0.980 0.000
#> GSM1068505 4 0.0510 0.9008 0.000 0.016 0.000 0.984 0.000
#> GSM1068506 4 0.0451 0.9020 0.000 0.004 0.000 0.988 0.008
#> GSM1068507 4 0.2408 0.8393 0.000 0.092 0.000 0.892 0.016
#> GSM1068508 4 0.1267 0.8950 0.000 0.024 0.004 0.960 0.012
#> GSM1068510 2 0.1502 0.8591 0.000 0.940 0.000 0.056 0.004
#> GSM1068512 4 0.1124 0.8922 0.000 0.036 0.000 0.960 0.004
#> GSM1068513 2 0.2852 0.8621 0.000 0.828 0.000 0.172 0.000
#> GSM1068514 4 0.0162 0.9031 0.000 0.004 0.000 0.996 0.000
#> GSM1068517 5 0.3152 0.7237 0.136 0.024 0.000 0.000 0.840
#> GSM1068518 5 0.1197 0.7954 0.000 0.048 0.000 0.000 0.952
#> GSM1068520 1 0.0000 0.8735 1.000 0.000 0.000 0.000 0.000
#> GSM1068521 1 0.0404 0.8717 0.988 0.000 0.000 0.000 0.012
#> GSM1068522 4 0.0290 0.9029 0.000 0.000 0.000 0.992 0.008
#> GSM1068524 2 0.2439 0.8783 0.000 0.876 0.000 0.120 0.004
#> GSM1068527 4 0.1608 0.8617 0.000 0.072 0.000 0.928 0.000
#> GSM1068480 5 0.2592 0.7874 0.000 0.056 0.052 0.000 0.892
#> GSM1068484 4 0.0510 0.9033 0.000 0.000 0.016 0.984 0.000
#> GSM1068485 1 0.0000 0.8735 1.000 0.000 0.000 0.000 0.000
#> GSM1068489 4 0.1205 0.8881 0.000 0.040 0.000 0.956 0.004
#> GSM1068497 5 0.2291 0.7937 0.000 0.056 0.036 0.000 0.908
#> GSM1068501 4 0.1041 0.8906 0.000 0.032 0.000 0.964 0.004
#> GSM1068504 2 0.3231 0.8446 0.000 0.800 0.000 0.196 0.004
#> GSM1068509 1 0.2890 0.7587 0.836 0.004 0.000 0.000 0.160
#> GSM1068511 4 0.0992 0.8944 0.000 0.024 0.000 0.968 0.008
#> GSM1068515 1 0.0404 0.8717 0.988 0.000 0.000 0.000 0.012
#> GSM1068516 5 0.1341 0.7971 0.000 0.056 0.000 0.000 0.944
#> GSM1068519 1 0.0000 0.8735 1.000 0.000 0.000 0.000 0.000
#> GSM1068523 2 0.2629 0.8860 0.000 0.860 0.000 0.136 0.004
#> GSM1068525 2 0.2629 0.8778 0.000 0.860 0.000 0.136 0.004
#> GSM1068526 4 0.1117 0.8979 0.000 0.020 0.016 0.964 0.000
#> GSM1068458 1 0.0000 0.8735 1.000 0.000 0.000 0.000 0.000
#> GSM1068459 3 0.0703 0.8546 0.000 0.000 0.976 0.000 0.024
#> GSM1068460 5 0.4003 0.5311 0.000 0.008 0.000 0.288 0.704
#> GSM1068461 1 0.1965 0.8107 0.904 0.000 0.000 0.000 0.096
#> GSM1068464 4 0.0510 0.9033 0.000 0.000 0.016 0.984 0.000
#> GSM1068468 4 0.6110 0.0939 0.112 0.004 0.000 0.492 0.392
#> GSM1068472 4 0.4449 0.3401 0.004 0.004 0.000 0.604 0.388
#> GSM1068473 4 0.0162 0.9030 0.000 0.000 0.000 0.996 0.004
#> GSM1068474 4 0.0510 0.9033 0.000 0.000 0.016 0.984 0.000
#> GSM1068476 2 0.3809 0.7938 0.000 0.736 0.000 0.256 0.008
#> GSM1068477 4 0.4367 0.2801 0.000 0.004 0.000 0.580 0.416
#> GSM1068462 5 0.4283 0.4054 0.000 0.008 0.000 0.348 0.644
#> GSM1068463 3 0.4262 0.2148 0.440 0.000 0.560 0.000 0.000
#> GSM1068465 4 0.6477 -0.0110 0.160 0.004 0.000 0.448 0.388
#> GSM1068466 1 0.2505 0.8045 0.888 0.000 0.020 0.000 0.092
#> GSM1068467 5 0.1357 0.7699 0.048 0.004 0.000 0.000 0.948
#> GSM1068469 1 0.4166 0.4750 0.648 0.004 0.000 0.000 0.348
#> GSM1068470 2 0.3305 0.8254 0.000 0.776 0.000 0.224 0.000
#> GSM1068471 4 0.0510 0.9033 0.000 0.000 0.016 0.984 0.000
#> GSM1068475 4 0.0671 0.9025 0.000 0.004 0.016 0.980 0.000
#> GSM1068528 1 0.0404 0.8717 0.988 0.000 0.000 0.000 0.012
#> GSM1068531 3 0.0703 0.8546 0.000 0.000 0.976 0.000 0.024
#> GSM1068532 1 0.0000 0.8735 1.000 0.000 0.000 0.000 0.000
#> GSM1068533 1 0.5637 0.4160 0.604 0.000 0.284 0.000 0.112
#> GSM1068535 3 0.6357 0.4033 0.000 0.172 0.600 0.204 0.024
#> GSM1068537 3 0.4291 0.0984 0.464 0.000 0.536 0.000 0.000
#> GSM1068538 1 0.0290 0.8719 0.992 0.000 0.008 0.000 0.000
#> GSM1068539 5 0.1341 0.7971 0.000 0.056 0.000 0.000 0.944
#> GSM1068540 3 0.0703 0.8546 0.000 0.000 0.976 0.000 0.024
#> GSM1068542 4 0.0162 0.9030 0.000 0.000 0.000 0.996 0.004
#> GSM1068543 2 0.1704 0.8678 0.000 0.928 0.000 0.068 0.004
#> GSM1068544 1 0.0703 0.8664 0.976 0.000 0.000 0.000 0.024
#> GSM1068545 4 0.0510 0.9033 0.000 0.000 0.016 0.984 0.000
#> GSM1068546 3 0.0703 0.8546 0.000 0.000 0.976 0.000 0.024
#> GSM1068547 1 0.0000 0.8735 1.000 0.000 0.000 0.000 0.000
#> GSM1068548 4 0.0290 0.9029 0.000 0.000 0.000 0.992 0.008
#> GSM1068549 5 0.2871 0.7666 0.000 0.040 0.088 0.000 0.872
#> GSM1068550 4 0.0162 0.9031 0.000 0.004 0.000 0.996 0.000
#> GSM1068551 4 0.1018 0.8982 0.000 0.016 0.016 0.968 0.000
#> GSM1068552 4 0.0510 0.9033 0.000 0.000 0.016 0.984 0.000
#> GSM1068555 2 0.1892 0.8736 0.000 0.916 0.000 0.080 0.004
#> GSM1068556 4 0.0290 0.9029 0.000 0.000 0.000 0.992 0.008
#> GSM1068557 5 0.4689 0.2837 0.000 0.424 0.000 0.016 0.560
#> GSM1068560 2 0.3521 0.8170 0.000 0.764 0.000 0.232 0.004
#> GSM1068561 2 0.3724 0.8318 0.000 0.788 0.000 0.184 0.028
#> GSM1068562 2 0.2848 0.8815 0.000 0.840 0.000 0.156 0.004
#> GSM1068563 4 0.0290 0.9029 0.000 0.000 0.000 0.992 0.008
#> GSM1068565 4 0.4380 0.1877 0.000 0.376 0.008 0.616 0.000
#> GSM1068529 2 0.4054 0.8003 0.000 0.748 0.000 0.224 0.028
#> GSM1068530 1 0.0290 0.8719 0.992 0.000 0.008 0.000 0.000
#> GSM1068534 4 0.2763 0.7922 0.000 0.148 0.000 0.848 0.004
#> GSM1068536 5 0.2782 0.7879 0.000 0.072 0.048 0.000 0.880
#> GSM1068541 1 0.4644 0.3962 0.604 0.004 0.000 0.012 0.380
#> GSM1068553 4 0.2672 0.8208 0.000 0.116 0.004 0.872 0.008
#> GSM1068554 4 0.0324 0.9025 0.000 0.004 0.000 0.992 0.004
#> GSM1068558 2 0.1430 0.8554 0.000 0.944 0.000 0.052 0.004
#> GSM1068559 5 0.2020 0.7843 0.000 0.100 0.000 0.000 0.900
#> GSM1068564 4 0.0510 0.9033 0.000 0.000 0.016 0.984 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1068478 3 0.3819 0.45620 0.000 0.000 0.672 0.012 0.316 0.000
#> GSM1068479 2 0.2647 0.60236 0.000 0.868 0.000 0.088 0.000 0.044
#> GSM1068481 3 0.0790 0.77145 0.000 0.000 0.968 0.032 0.000 0.000
#> GSM1068482 5 0.6827 -0.05326 0.324 0.000 0.168 0.076 0.432 0.000
#> GSM1068483 1 0.3288 0.80657 0.724 0.000 0.000 0.276 0.000 0.000
#> GSM1068486 3 0.4180 0.66055 0.000 0.000 0.732 0.216 0.024 0.028
#> GSM1068487 2 0.4532 -0.22826 0.000 0.500 0.000 0.032 0.000 0.468
#> GSM1068488 6 0.2744 0.82156 0.000 0.144 0.000 0.016 0.000 0.840
#> GSM1068490 2 0.1714 0.63019 0.000 0.908 0.000 0.092 0.000 0.000
#> GSM1068491 5 0.6339 0.53456 0.212 0.152 0.000 0.076 0.560 0.000
#> GSM1068492 2 0.1141 0.64337 0.000 0.948 0.000 0.052 0.000 0.000
#> GSM1068493 5 0.1789 0.71741 0.000 0.000 0.032 0.000 0.924 0.044
#> GSM1068494 5 0.1821 0.71857 0.000 0.000 0.008 0.024 0.928 0.040
#> GSM1068495 5 0.0937 0.72481 0.000 0.000 0.000 0.000 0.960 0.040
#> GSM1068496 3 0.0547 0.77579 0.000 0.000 0.980 0.020 0.000 0.000
#> GSM1068498 1 0.3972 0.44440 0.680 0.000 0.000 0.004 0.300 0.016
#> GSM1068499 1 0.0937 0.77973 0.960 0.000 0.000 0.000 0.040 0.000
#> GSM1068500 3 0.0000 0.77570 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1068502 2 0.1075 0.63832 0.000 0.952 0.000 0.048 0.000 0.000
#> GSM1068503 2 0.1556 0.63543 0.000 0.920 0.000 0.080 0.000 0.000
#> GSM1068505 2 0.5119 -0.28314 0.000 0.624 0.000 0.220 0.000 0.156
#> GSM1068506 2 0.5196 -0.41656 0.000 0.604 0.000 0.252 0.000 0.144
#> GSM1068507 2 0.1765 0.63568 0.000 0.924 0.000 0.052 0.000 0.024
#> GSM1068508 2 0.2826 0.59404 0.000 0.856 0.000 0.092 0.000 0.052
#> GSM1068510 6 0.2527 0.83971 0.000 0.168 0.000 0.000 0.000 0.832
#> GSM1068512 2 0.5314 -0.47589 0.000 0.584 0.000 0.264 0.000 0.152
#> GSM1068513 6 0.3190 0.82306 0.000 0.220 0.000 0.008 0.000 0.772
#> GSM1068514 2 0.1471 0.61593 0.000 0.932 0.000 0.064 0.000 0.004
#> GSM1068517 5 0.3515 0.55308 0.192 0.000 0.000 0.012 0.780 0.016
#> GSM1068518 5 0.0603 0.71610 0.004 0.000 0.000 0.016 0.980 0.000
#> GSM1068520 1 0.3330 0.80248 0.716 0.000 0.000 0.284 0.000 0.000
#> GSM1068521 1 0.1391 0.77284 0.944 0.000 0.000 0.000 0.040 0.016
#> GSM1068522 2 0.4757 -0.13153 0.000 0.676 0.000 0.180 0.000 0.144
#> GSM1068524 6 0.2730 0.84030 0.000 0.192 0.000 0.000 0.000 0.808
#> GSM1068527 2 0.2988 0.49234 0.000 0.828 0.000 0.144 0.000 0.028
#> GSM1068480 5 0.1821 0.71857 0.000 0.000 0.008 0.024 0.928 0.040
#> GSM1068484 2 0.1501 0.63557 0.000 0.924 0.000 0.076 0.000 0.000
#> GSM1068485 1 0.1957 0.79973 0.888 0.000 0.000 0.112 0.000 0.000
#> GSM1068489 6 0.5906 -0.51830 0.000 0.300 0.000 0.236 0.000 0.464
#> GSM1068497 5 0.1821 0.71857 0.000 0.000 0.008 0.024 0.928 0.040
#> GSM1068501 2 0.1657 0.61019 0.000 0.928 0.000 0.056 0.000 0.016
#> GSM1068504 6 0.3012 0.83803 0.000 0.196 0.000 0.008 0.000 0.796
#> GSM1068509 1 0.4373 0.73925 0.624 0.000 0.004 0.344 0.028 0.000
#> GSM1068511 2 0.5304 -0.50217 0.000 0.580 0.000 0.276 0.000 0.144
#> GSM1068515 1 0.1391 0.77284 0.944 0.000 0.000 0.000 0.040 0.016
#> GSM1068516 5 0.0937 0.72481 0.000 0.000 0.000 0.000 0.960 0.040
#> GSM1068519 1 0.3288 0.80657 0.724 0.000 0.000 0.276 0.000 0.000
#> GSM1068523 6 0.3659 0.62983 0.000 0.364 0.000 0.000 0.000 0.636
#> GSM1068525 6 0.2762 0.83986 0.000 0.196 0.000 0.000 0.000 0.804
#> GSM1068526 2 0.2499 0.61095 0.000 0.880 0.000 0.072 0.000 0.048
#> GSM1068458 1 0.0964 0.78476 0.968 0.000 0.000 0.004 0.012 0.016
#> GSM1068459 3 0.0260 0.77500 0.000 0.000 0.992 0.008 0.000 0.000
#> GSM1068460 5 0.3983 0.63311 0.000 0.208 0.000 0.056 0.736 0.000
#> GSM1068461 1 0.1838 0.75282 0.916 0.000 0.000 0.000 0.068 0.016
#> GSM1068464 2 0.1007 0.64170 0.000 0.956 0.000 0.044 0.000 0.000
#> GSM1068468 5 0.5983 0.49370 0.076 0.296 0.000 0.072 0.556 0.000
#> GSM1068472 5 0.5127 0.42890 0.000 0.348 0.000 0.096 0.556 0.000
#> GSM1068473 2 0.1075 0.61956 0.000 0.952 0.000 0.048 0.000 0.000
#> GSM1068474 2 0.1387 0.63761 0.000 0.932 0.000 0.068 0.000 0.000
#> GSM1068476 2 0.3996 -0.28892 0.000 0.512 0.000 0.004 0.000 0.484
#> GSM1068477 5 0.5044 0.46087 0.000 0.320 0.000 0.096 0.584 0.000
#> GSM1068462 5 0.4577 0.55814 0.000 0.272 0.000 0.072 0.656 0.000
#> GSM1068463 3 0.5682 0.32556 0.208 0.000 0.524 0.268 0.000 0.000
#> GSM1068465 5 0.6467 0.55122 0.160 0.188 0.000 0.096 0.556 0.000
#> GSM1068466 1 0.3774 0.76684 0.664 0.000 0.008 0.328 0.000 0.000
#> GSM1068467 5 0.2046 0.69998 0.060 0.000 0.000 0.032 0.908 0.000
#> GSM1068469 5 0.5219 0.36681 0.340 0.000 0.000 0.108 0.552 0.000
#> GSM1068470 6 0.3287 0.82736 0.000 0.220 0.000 0.012 0.000 0.768
#> GSM1068471 2 0.0458 0.63571 0.000 0.984 0.000 0.016 0.000 0.000
#> GSM1068475 2 0.1663 0.63089 0.000 0.912 0.000 0.088 0.000 0.000
#> GSM1068528 1 0.1151 0.78451 0.956 0.000 0.000 0.012 0.032 0.000
#> GSM1068531 3 0.1007 0.77326 0.000 0.000 0.956 0.044 0.000 0.000
#> GSM1068532 1 0.3288 0.80657 0.724 0.000 0.000 0.276 0.000 0.000
#> GSM1068533 3 0.7411 0.03129 0.320 0.000 0.348 0.172 0.160 0.000
#> GSM1068535 3 0.7124 0.11547 0.000 0.112 0.408 0.308 0.000 0.172
#> GSM1068537 3 0.4980 0.51125 0.184 0.000 0.648 0.168 0.000 0.000
#> GSM1068538 1 0.3288 0.80657 0.724 0.000 0.000 0.276 0.000 0.000
#> GSM1068539 5 0.0937 0.72481 0.000 0.000 0.000 0.000 0.960 0.040
#> GSM1068540 3 0.1141 0.77215 0.000 0.000 0.948 0.052 0.000 0.000
#> GSM1068542 2 0.0632 0.63473 0.000 0.976 0.000 0.024 0.000 0.000
#> GSM1068543 6 0.2527 0.83971 0.000 0.168 0.000 0.000 0.000 0.832
#> GSM1068544 1 0.1082 0.77884 0.956 0.000 0.000 0.004 0.040 0.000
#> GSM1068545 2 0.1387 0.63761 0.000 0.932 0.000 0.068 0.000 0.000
#> GSM1068546 3 0.1151 0.76986 0.000 0.000 0.956 0.032 0.012 0.000
#> GSM1068547 1 0.3309 0.80473 0.720 0.000 0.000 0.280 0.000 0.000
#> GSM1068548 2 0.2726 0.50130 0.000 0.856 0.000 0.112 0.000 0.032
#> GSM1068549 5 0.1765 0.70150 0.000 0.000 0.052 0.024 0.924 0.000
#> GSM1068550 2 0.1910 0.58674 0.000 0.892 0.000 0.108 0.000 0.000
#> GSM1068551 2 0.2112 0.63020 0.000 0.896 0.000 0.088 0.000 0.016
#> GSM1068552 2 0.1007 0.64093 0.000 0.956 0.000 0.044 0.000 0.000
#> GSM1068555 6 0.2562 0.84058 0.000 0.172 0.000 0.000 0.000 0.828
#> GSM1068556 2 0.4569 -0.03096 0.000 0.700 0.000 0.156 0.000 0.144
#> GSM1068557 5 0.5745 0.35947 0.000 0.124 0.000 0.020 0.548 0.308
#> GSM1068560 2 0.3869 -0.31429 0.000 0.500 0.000 0.000 0.000 0.500
#> GSM1068561 6 0.6113 0.63340 0.000 0.240 0.000 0.184 0.032 0.544
#> GSM1068562 6 0.3244 0.77793 0.000 0.268 0.000 0.000 0.000 0.732
#> GSM1068563 2 0.4500 0.00628 0.000 0.708 0.000 0.148 0.000 0.144
#> GSM1068565 2 0.4389 -0.23678 0.000 0.528 0.000 0.024 0.000 0.448
#> GSM1068529 6 0.5939 0.47788 0.000 0.356 0.000 0.144 0.016 0.484
#> GSM1068530 1 0.3288 0.80657 0.724 0.000 0.000 0.276 0.000 0.000
#> GSM1068534 4 0.5765 0.00000 0.000 0.412 0.000 0.416 0.000 0.172
#> GSM1068536 5 0.1265 0.72414 0.000 0.000 0.000 0.008 0.948 0.044
#> GSM1068541 5 0.6100 0.46393 0.280 0.080 0.000 0.084 0.556 0.000
#> GSM1068553 2 0.5842 -0.83857 0.000 0.472 0.004 0.352 0.000 0.172
#> GSM1068554 2 0.2178 0.52569 0.000 0.868 0.000 0.132 0.000 0.000
#> GSM1068558 6 0.2527 0.83971 0.000 0.168 0.000 0.000 0.000 0.832
#> GSM1068559 5 0.1471 0.71995 0.000 0.000 0.000 0.004 0.932 0.064
#> GSM1068564 2 0.1075 0.62179 0.000 0.952 0.000 0.048 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 disease.state(p) gender(p) k
#> ATC:mclust 84 0.804 0.9424 2
#> ATC:mclust 72 0.264 0.7307 3
#> ATC:mclust 104 0.376 0.0799 4
#> ATC:mclust 92 0.997 0.2515 5
#> ATC:mclust 80 0.958 0.1733 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 38950 rows and 108 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 0.968 0.987 0.4743 0.529 0.529
#> 3 3 0.500 0.530 0.756 0.3508 0.782 0.612
#> 4 4 0.614 0.640 0.815 0.0965 0.750 0.470
#> 5 5 0.647 0.679 0.832 0.0941 0.789 0.443
#> 6 6 0.566 0.473 0.639 0.0460 0.891 0.590
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
#> GSM1068478 1 0.0000 0.991 1.000 0.000
#> GSM1068479 2 0.0000 0.984 0.000 1.000
#> GSM1068481 2 0.9087 0.525 0.324 0.676
#> GSM1068482 1 0.0000 0.991 1.000 0.000
#> GSM1068483 1 0.0000 0.991 1.000 0.000
#> GSM1068486 2 0.0000 0.984 0.000 1.000
#> GSM1068487 2 0.0000 0.984 0.000 1.000
#> GSM1068488 2 0.0000 0.984 0.000 1.000
#> GSM1068490 2 0.0000 0.984 0.000 1.000
#> GSM1068491 1 0.0000 0.991 1.000 0.000
#> GSM1068492 2 0.0000 0.984 0.000 1.000
#> GSM1068493 2 0.0000 0.984 0.000 1.000
#> GSM1068494 2 0.6148 0.817 0.152 0.848
#> GSM1068495 2 0.0000 0.984 0.000 1.000
#> GSM1068496 1 0.0000 0.991 1.000 0.000
#> GSM1068498 1 0.0000 0.991 1.000 0.000
#> GSM1068499 1 0.0000 0.991 1.000 0.000
#> GSM1068500 1 0.0000 0.991 1.000 0.000
#> GSM1068502 2 0.0000 0.984 0.000 1.000
#> GSM1068503 2 0.0000 0.984 0.000 1.000
#> GSM1068505 2 0.0000 0.984 0.000 1.000
#> GSM1068506 2 0.0000 0.984 0.000 1.000
#> GSM1068507 2 0.0000 0.984 0.000 1.000
#> GSM1068508 2 0.0000 0.984 0.000 1.000
#> GSM1068510 2 0.0000 0.984 0.000 1.000
#> GSM1068512 2 0.0000 0.984 0.000 1.000
#> GSM1068513 2 0.0000 0.984 0.000 1.000
#> GSM1068514 2 0.0000 0.984 0.000 1.000
#> GSM1068517 1 0.0000 0.991 1.000 0.000
#> GSM1068518 1 0.1633 0.970 0.976 0.024
#> GSM1068520 1 0.0000 0.991 1.000 0.000
#> GSM1068521 1 0.0000 0.991 1.000 0.000
#> GSM1068522 2 0.0000 0.984 0.000 1.000
#> GSM1068524 2 0.0000 0.984 0.000 1.000
#> GSM1068527 2 0.0000 0.984 0.000 1.000
#> GSM1068480 1 0.6801 0.779 0.820 0.180
#> GSM1068484 2 0.0000 0.984 0.000 1.000
#> GSM1068485 1 0.0000 0.991 1.000 0.000
#> GSM1068489 2 0.0000 0.984 0.000 1.000
#> GSM1068497 1 0.3274 0.934 0.940 0.060
#> GSM1068501 2 0.0000 0.984 0.000 1.000
#> GSM1068504 2 0.0000 0.984 0.000 1.000
#> GSM1068509 1 0.0000 0.991 1.000 0.000
#> GSM1068511 2 0.0000 0.984 0.000 1.000
#> GSM1068515 1 0.0000 0.991 1.000 0.000
#> GSM1068516 2 0.0000 0.984 0.000 1.000
#> GSM1068519 1 0.0000 0.991 1.000 0.000
#> GSM1068523 2 0.0000 0.984 0.000 1.000
#> GSM1068525 2 0.0000 0.984 0.000 1.000
#> GSM1068526 2 0.0000 0.984 0.000 1.000
#> GSM1068458 1 0.0000 0.991 1.000 0.000
#> GSM1068459 1 0.0000 0.991 1.000 0.000
#> GSM1068460 2 0.9970 0.119 0.468 0.532
#> GSM1068461 1 0.0000 0.991 1.000 0.000
#> GSM1068464 2 0.0000 0.984 0.000 1.000
#> GSM1068468 1 0.0376 0.987 0.996 0.004
#> GSM1068472 1 0.4161 0.908 0.916 0.084
#> GSM1068473 2 0.0000 0.984 0.000 1.000
#> GSM1068474 2 0.0000 0.984 0.000 1.000
#> GSM1068476 2 0.0000 0.984 0.000 1.000
#> GSM1068477 2 0.0000 0.984 0.000 1.000
#> GSM1068462 2 0.4161 0.899 0.084 0.916
#> GSM1068463 1 0.0000 0.991 1.000 0.000
#> GSM1068465 1 0.0000 0.991 1.000 0.000
#> GSM1068466 1 0.0000 0.991 1.000 0.000
#> GSM1068467 1 0.0000 0.991 1.000 0.000
#> GSM1068469 1 0.0000 0.991 1.000 0.000
#> GSM1068470 2 0.0000 0.984 0.000 1.000
#> GSM1068471 2 0.0000 0.984 0.000 1.000
#> GSM1068475 2 0.0000 0.984 0.000 1.000
#> GSM1068528 1 0.0000 0.991 1.000 0.000
#> GSM1068531 1 0.0000 0.991 1.000 0.000
#> GSM1068532 1 0.0000 0.991 1.000 0.000
#> GSM1068533 1 0.0000 0.991 1.000 0.000
#> GSM1068535 2 0.0000 0.984 0.000 1.000
#> GSM1068537 1 0.0000 0.991 1.000 0.000
#> GSM1068538 1 0.0000 0.991 1.000 0.000
#> GSM1068539 2 0.0000 0.984 0.000 1.000
#> GSM1068540 1 0.0000 0.991 1.000 0.000
#> GSM1068542 2 0.0000 0.984 0.000 1.000
#> GSM1068543 2 0.0000 0.984 0.000 1.000
#> GSM1068544 1 0.0000 0.991 1.000 0.000
#> GSM1068545 2 0.0000 0.984 0.000 1.000
#> GSM1068546 2 0.2236 0.950 0.036 0.964
#> GSM1068547 1 0.0000 0.991 1.000 0.000
#> GSM1068548 2 0.0000 0.984 0.000 1.000
#> GSM1068549 1 0.0000 0.991 1.000 0.000
#> GSM1068550 2 0.0000 0.984 0.000 1.000
#> GSM1068551 2 0.0000 0.984 0.000 1.000
#> GSM1068552 2 0.0000 0.984 0.000 1.000
#> GSM1068555 2 0.0000 0.984 0.000 1.000
#> GSM1068556 2 0.0000 0.984 0.000 1.000
#> GSM1068557 2 0.0000 0.984 0.000 1.000
#> GSM1068560 2 0.0000 0.984 0.000 1.000
#> GSM1068561 2 0.0000 0.984 0.000 1.000
#> GSM1068562 2 0.0000 0.984 0.000 1.000
#> GSM1068563 2 0.0000 0.984 0.000 1.000
#> GSM1068565 2 0.0000 0.984 0.000 1.000
#> GSM1068529 2 0.0000 0.984 0.000 1.000
#> GSM1068530 1 0.0000 0.991 1.000 0.000
#> GSM1068534 2 0.0000 0.984 0.000 1.000
#> GSM1068536 2 0.0000 0.984 0.000 1.000
#> GSM1068541 1 0.0000 0.991 1.000 0.000
#> GSM1068553 2 0.0000 0.984 0.000 1.000
#> GSM1068554 2 0.0000 0.984 0.000 1.000
#> GSM1068558 2 0.0000 0.984 0.000 1.000
#> GSM1068559 2 0.0000 0.984 0.000 1.000
#> GSM1068564 2 0.0000 0.984 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1068478 1 0.5497 0.6116 0.708 0.292 0.000
#> GSM1068479 2 0.6079 0.5418 0.000 0.612 0.388
#> GSM1068481 1 0.6809 0.3146 0.524 0.464 0.012
#> GSM1068482 1 0.3116 0.7419 0.892 0.108 0.000
#> GSM1068483 3 0.6252 -0.0678 0.444 0.000 0.556
#> GSM1068486 2 0.4121 0.5075 0.168 0.832 0.000
#> GSM1068487 2 0.5216 0.6105 0.000 0.740 0.260
#> GSM1068488 2 0.0424 0.6445 0.008 0.992 0.000
#> GSM1068490 2 0.6111 0.5333 0.000 0.604 0.396
#> GSM1068491 3 0.5138 0.3758 0.252 0.000 0.748
#> GSM1068492 2 0.6154 0.5188 0.000 0.592 0.408
#> GSM1068493 2 0.3879 0.5233 0.152 0.848 0.000
#> GSM1068494 1 0.6274 0.3929 0.544 0.456 0.000
#> GSM1068495 2 0.6026 0.0604 0.376 0.624 0.000
#> GSM1068496 1 0.5785 0.5436 0.696 0.004 0.300
#> GSM1068498 1 0.0424 0.7861 0.992 0.000 0.008
#> GSM1068499 1 0.0424 0.7861 0.992 0.000 0.008
#> GSM1068500 1 0.5406 0.6659 0.780 0.020 0.200
#> GSM1068502 3 0.1163 0.6409 0.000 0.028 0.972
#> GSM1068503 2 0.6062 0.5451 0.000 0.616 0.384
#> GSM1068505 2 0.6079 0.5416 0.000 0.612 0.388
#> GSM1068506 3 0.4555 0.4785 0.000 0.200 0.800
#> GSM1068507 2 0.6045 0.5486 0.000 0.620 0.380
#> GSM1068508 2 0.4555 0.6291 0.000 0.800 0.200
#> GSM1068510 2 0.0000 0.6483 0.000 1.000 0.000
#> GSM1068512 2 0.6140 0.5239 0.000 0.596 0.404
#> GSM1068513 2 0.2165 0.6518 0.000 0.936 0.064
#> GSM1068514 2 0.6095 0.5377 0.000 0.608 0.392
#> GSM1068517 1 0.3686 0.7220 0.860 0.140 0.000
#> GSM1068518 1 0.5831 0.6161 0.708 0.284 0.008
#> GSM1068520 1 0.1529 0.7821 0.960 0.000 0.040
#> GSM1068521 1 0.0592 0.7863 0.988 0.000 0.012
#> GSM1068522 3 0.5591 0.2701 0.000 0.304 0.696
#> GSM1068524 2 0.0237 0.6498 0.000 0.996 0.004
#> GSM1068527 2 0.6252 0.4596 0.000 0.556 0.444
#> GSM1068480 1 0.6140 0.4890 0.596 0.404 0.000
#> GSM1068484 2 0.6180 0.5067 0.000 0.584 0.416
#> GSM1068485 1 0.1411 0.7831 0.964 0.000 0.036
#> GSM1068489 2 0.6026 0.5512 0.000 0.624 0.376
#> GSM1068497 1 0.6095 0.5071 0.608 0.392 0.000
#> GSM1068501 2 0.6309 0.3379 0.000 0.500 0.500
#> GSM1068504 2 0.0747 0.6516 0.000 0.984 0.016
#> GSM1068509 3 0.6008 0.1199 0.372 0.000 0.628
#> GSM1068511 3 0.3412 0.5765 0.000 0.124 0.876
#> GSM1068515 1 0.0892 0.7857 0.980 0.000 0.020
#> GSM1068516 2 0.6180 -0.0700 0.416 0.584 0.000
#> GSM1068519 1 0.3267 0.7432 0.884 0.000 0.116
#> GSM1068523 2 0.0475 0.6485 0.004 0.992 0.004
#> GSM1068525 2 0.0237 0.6498 0.000 0.996 0.004
#> GSM1068526 2 0.5560 0.5940 0.000 0.700 0.300
#> GSM1068458 1 0.1529 0.7826 0.960 0.000 0.040
#> GSM1068459 1 0.7056 0.3446 0.572 0.024 0.404
#> GSM1068460 2 0.6600 0.1265 0.384 0.604 0.012
#> GSM1068461 1 0.0592 0.7863 0.988 0.000 0.012
#> GSM1068464 3 0.6308 -0.3621 0.000 0.492 0.508
#> GSM1068468 3 0.1163 0.6452 0.028 0.000 0.972
#> GSM1068472 3 0.0237 0.6474 0.004 0.000 0.996
#> GSM1068473 3 0.5835 0.1589 0.000 0.340 0.660
#> GSM1068474 2 0.6308 0.3576 0.000 0.508 0.492
#> GSM1068476 2 0.0592 0.6513 0.000 0.988 0.012
#> GSM1068477 2 0.2625 0.6493 0.000 0.916 0.084
#> GSM1068462 2 0.5263 0.6259 0.060 0.824 0.116
#> GSM1068463 1 0.1964 0.7762 0.944 0.000 0.056
#> GSM1068465 3 0.0424 0.6473 0.008 0.000 0.992
#> GSM1068466 1 0.4399 0.6806 0.812 0.000 0.188
#> GSM1068467 1 0.0592 0.7863 0.988 0.000 0.012
#> GSM1068469 1 0.5497 0.5675 0.708 0.000 0.292
#> GSM1068470 2 0.1289 0.6522 0.000 0.968 0.032
#> GSM1068471 2 0.6280 0.4300 0.000 0.540 0.460
#> GSM1068475 2 0.6140 0.5240 0.000 0.596 0.404
#> GSM1068528 1 0.0424 0.7861 0.992 0.000 0.008
#> GSM1068531 1 0.1711 0.7844 0.960 0.008 0.032
#> GSM1068532 1 0.5678 0.5197 0.684 0.000 0.316
#> GSM1068533 1 0.1267 0.7853 0.972 0.004 0.024
#> GSM1068535 2 0.6008 0.5717 0.004 0.664 0.332
#> GSM1068537 3 0.6225 -0.0351 0.432 0.000 0.568
#> GSM1068538 3 0.6309 -0.1941 0.496 0.000 0.504
#> GSM1068539 2 0.6062 0.0366 0.384 0.616 0.000
#> GSM1068540 1 0.1711 0.7844 0.960 0.008 0.032
#> GSM1068542 3 0.3482 0.5721 0.000 0.128 0.872
#> GSM1068543 2 0.0237 0.6498 0.000 0.996 0.004
#> GSM1068544 1 0.0424 0.7861 0.992 0.000 0.008
#> GSM1068545 2 0.6291 0.4132 0.000 0.532 0.468
#> GSM1068546 1 0.6295 0.3587 0.528 0.472 0.000
#> GSM1068547 1 0.3551 0.7319 0.868 0.000 0.132
#> GSM1068548 3 0.0747 0.6443 0.000 0.016 0.984
#> GSM1068549 1 0.2537 0.7575 0.920 0.080 0.000
#> GSM1068550 2 0.6095 0.5377 0.000 0.608 0.392
#> GSM1068551 2 0.5363 0.6045 0.000 0.724 0.276
#> GSM1068552 2 0.6126 0.5288 0.000 0.600 0.400
#> GSM1068555 2 0.0000 0.6483 0.000 1.000 0.000
#> GSM1068556 3 0.5529 0.2928 0.000 0.296 0.704
#> GSM1068557 2 0.0592 0.6422 0.012 0.988 0.000
#> GSM1068560 2 0.0237 0.6498 0.000 0.996 0.004
#> GSM1068561 2 0.1163 0.6317 0.028 0.972 0.000
#> GSM1068562 2 0.0592 0.6513 0.000 0.988 0.012
#> GSM1068563 3 0.1289 0.6389 0.000 0.032 0.968
#> GSM1068565 2 0.4750 0.6248 0.000 0.784 0.216
#> GSM1068529 2 0.0424 0.6445 0.008 0.992 0.000
#> GSM1068530 1 0.6307 0.1693 0.512 0.000 0.488
#> GSM1068534 2 0.5948 0.5611 0.000 0.640 0.360
#> GSM1068536 2 0.6204 -0.1006 0.424 0.576 0.000
#> GSM1068541 3 0.2959 0.5927 0.100 0.000 0.900
#> GSM1068553 3 0.5497 0.3013 0.000 0.292 0.708
#> GSM1068554 2 0.6280 0.4300 0.000 0.540 0.460
#> GSM1068558 2 0.0424 0.6445 0.008 0.992 0.000
#> GSM1068559 2 0.1860 0.6141 0.052 0.948 0.000
#> GSM1068564 2 0.6168 0.5128 0.000 0.588 0.412
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1068478 3 0.0188 0.8007 0.004 0.000 0.996 0.000
#> GSM1068479 4 0.1706 0.8361 0.036 0.016 0.000 0.948
#> GSM1068481 3 0.0000 0.8013 0.000 0.000 1.000 0.000
#> GSM1068482 1 0.5272 0.5355 0.680 0.032 0.288 0.000
#> GSM1068483 2 0.5972 0.5223 0.304 0.632 0.064 0.000
#> GSM1068486 3 0.1305 0.7908 0.000 0.004 0.960 0.036
#> GSM1068487 4 0.0469 0.8424 0.000 0.012 0.000 0.988
#> GSM1068488 4 0.5157 0.5964 0.000 0.028 0.284 0.688
#> GSM1068490 4 0.0000 0.8427 0.000 0.000 0.000 1.000
#> GSM1068491 2 0.4699 0.5602 0.320 0.676 0.000 0.004
#> GSM1068492 4 0.0592 0.8409 0.000 0.016 0.000 0.984
#> GSM1068493 3 0.1398 0.7887 0.000 0.004 0.956 0.040
#> GSM1068494 1 0.7276 0.3541 0.524 0.148 0.324 0.004
#> GSM1068495 1 0.6821 0.4894 0.592 0.256 0.000 0.152
#> GSM1068496 3 0.3402 0.6780 0.164 0.004 0.832 0.000
#> GSM1068498 1 0.3610 0.6187 0.800 0.200 0.000 0.000
#> GSM1068499 1 0.1489 0.6102 0.952 0.004 0.044 0.000
#> GSM1068500 3 0.0000 0.8013 0.000 0.000 1.000 0.000
#> GSM1068502 2 0.4916 0.3863 0.000 0.576 0.000 0.424
#> GSM1068503 4 0.0336 0.8421 0.000 0.008 0.000 0.992
#> GSM1068505 4 0.0657 0.8414 0.000 0.012 0.004 0.984
#> GSM1068506 4 0.4103 0.5827 0.000 0.256 0.000 0.744
#> GSM1068507 4 0.0000 0.8427 0.000 0.000 0.000 1.000
#> GSM1068508 4 0.1970 0.8312 0.008 0.060 0.000 0.932
#> GSM1068510 4 0.5383 0.7102 0.000 0.128 0.128 0.744
#> GSM1068512 4 0.2483 0.8163 0.000 0.032 0.052 0.916
#> GSM1068513 4 0.1733 0.8375 0.000 0.028 0.024 0.948
#> GSM1068514 4 0.0657 0.8414 0.000 0.012 0.004 0.984
#> GSM1068517 1 0.4008 0.6112 0.756 0.244 0.000 0.000
#> GSM1068518 1 0.4767 0.5984 0.724 0.256 0.000 0.020
#> GSM1068520 1 0.6019 0.4005 0.688 0.136 0.176 0.000
#> GSM1068521 1 0.1406 0.6062 0.960 0.024 0.016 0.000
#> GSM1068522 4 0.2469 0.7898 0.000 0.108 0.000 0.892
#> GSM1068524 4 0.4252 0.7085 0.000 0.252 0.004 0.744
#> GSM1068527 4 0.2334 0.8036 0.000 0.088 0.004 0.908
#> GSM1068480 1 0.6194 0.5275 0.644 0.096 0.260 0.000
#> GSM1068484 4 0.0592 0.8409 0.000 0.016 0.000 0.984
#> GSM1068485 1 0.3550 0.5713 0.860 0.044 0.096 0.000
#> GSM1068489 4 0.0804 0.8420 0.000 0.012 0.008 0.980
#> GSM1068497 1 0.4864 0.6087 0.724 0.256 0.012 0.008
#> GSM1068501 4 0.1792 0.8189 0.000 0.068 0.000 0.932
#> GSM1068504 4 0.3400 0.7679 0.000 0.180 0.000 0.820
#> GSM1068509 2 0.4813 0.6100 0.268 0.716 0.012 0.004
#> GSM1068511 4 0.4699 0.4306 0.000 0.320 0.004 0.676
#> GSM1068515 1 0.0817 0.6078 0.976 0.024 0.000 0.000
#> GSM1068516 1 0.6054 0.5530 0.656 0.256 0.000 0.088
#> GSM1068519 1 0.5668 0.2457 0.652 0.300 0.048 0.000
#> GSM1068523 4 0.6698 0.5399 0.140 0.256 0.000 0.604
#> GSM1068525 4 0.3196 0.7929 0.000 0.136 0.008 0.856
#> GSM1068526 4 0.0000 0.8427 0.000 0.000 0.000 1.000
#> GSM1068458 1 0.2814 0.5437 0.868 0.132 0.000 0.000
#> GSM1068459 3 0.0000 0.8013 0.000 0.000 1.000 0.000
#> GSM1068460 1 0.5520 0.5797 0.696 0.244 0.000 0.060
#> GSM1068461 1 0.1305 0.6209 0.960 0.036 0.004 0.000
#> GSM1068464 4 0.1716 0.8253 0.000 0.064 0.000 0.936
#> GSM1068468 2 0.5902 0.6725 0.140 0.700 0.000 0.160
#> GSM1068472 2 0.4711 0.6517 0.024 0.740 0.000 0.236
#> GSM1068473 4 0.2281 0.8034 0.000 0.096 0.000 0.904
#> GSM1068474 4 0.1716 0.8240 0.000 0.064 0.000 0.936
#> GSM1068476 4 0.4155 0.7188 0.004 0.240 0.000 0.756
#> GSM1068477 4 0.5598 0.6714 0.076 0.220 0.000 0.704
#> GSM1068462 1 0.7700 0.2675 0.448 0.248 0.000 0.304
#> GSM1068463 3 0.5090 0.4416 0.324 0.016 0.660 0.000
#> GSM1068465 2 0.5250 0.6719 0.176 0.744 0.000 0.080
#> GSM1068466 1 0.7101 0.1618 0.504 0.136 0.360 0.000
#> GSM1068467 1 0.3982 0.6144 0.776 0.220 0.000 0.004
#> GSM1068469 1 0.3978 0.4691 0.796 0.192 0.012 0.000
#> GSM1068470 4 0.1792 0.8290 0.000 0.068 0.000 0.932
#> GSM1068471 4 0.1118 0.8342 0.000 0.036 0.000 0.964
#> GSM1068475 4 0.0336 0.8435 0.008 0.000 0.000 0.992
#> GSM1068528 1 0.3325 0.5763 0.864 0.024 0.112 0.000
#> GSM1068531 3 0.0469 0.7992 0.012 0.000 0.988 0.000
#> GSM1068532 1 0.6862 -0.1618 0.488 0.408 0.104 0.000
#> GSM1068533 3 0.3982 0.6182 0.220 0.004 0.776 0.000
#> GSM1068535 3 0.2081 0.7554 0.000 0.000 0.916 0.084
#> GSM1068537 3 0.6112 0.4749 0.248 0.096 0.656 0.000
#> GSM1068538 2 0.4961 0.3362 0.448 0.552 0.000 0.000
#> GSM1068539 1 0.6656 0.5057 0.608 0.256 0.000 0.136
#> GSM1068540 3 0.0895 0.7953 0.020 0.004 0.976 0.000
#> GSM1068542 4 0.4250 0.5556 0.000 0.276 0.000 0.724
#> GSM1068543 4 0.5109 0.6902 0.000 0.060 0.196 0.744
#> GSM1068544 1 0.2480 0.5956 0.904 0.008 0.088 0.000
#> GSM1068545 4 0.1557 0.8272 0.000 0.056 0.000 0.944
#> GSM1068546 3 0.0188 0.8014 0.000 0.000 0.996 0.004
#> GSM1068547 1 0.4908 0.2948 0.692 0.292 0.016 0.000
#> GSM1068548 2 0.4543 0.6047 0.000 0.676 0.000 0.324
#> GSM1068549 3 0.1022 0.7918 0.032 0.000 0.968 0.000
#> GSM1068550 4 0.0657 0.8414 0.000 0.012 0.004 0.984
#> GSM1068551 4 0.0817 0.8411 0.000 0.024 0.000 0.976
#> GSM1068552 4 0.0469 0.8414 0.000 0.012 0.000 0.988
#> GSM1068555 4 0.4422 0.7001 0.008 0.256 0.000 0.736
#> GSM1068556 4 0.2469 0.7886 0.000 0.108 0.000 0.892
#> GSM1068557 4 0.4546 0.6962 0.012 0.256 0.000 0.732
#> GSM1068560 4 0.3610 0.7535 0.000 0.200 0.000 0.800
#> GSM1068561 3 0.5760 0.0512 0.000 0.028 0.524 0.448
#> GSM1068562 4 0.1867 0.8275 0.000 0.072 0.000 0.928
#> GSM1068563 2 0.4500 0.6127 0.000 0.684 0.000 0.316
#> GSM1068565 4 0.1022 0.8395 0.000 0.032 0.000 0.968
#> GSM1068529 3 0.5452 0.1513 0.000 0.016 0.556 0.428
#> GSM1068530 1 0.5606 -0.2563 0.500 0.480 0.020 0.000
#> GSM1068534 3 0.4103 0.5637 0.000 0.000 0.744 0.256
#> GSM1068536 3 0.3129 0.7655 0.040 0.028 0.900 0.032
#> GSM1068541 2 0.5170 0.6531 0.228 0.724 0.000 0.048
#> GSM1068553 4 0.6690 0.2101 0.000 0.100 0.352 0.548
#> GSM1068554 4 0.1637 0.8230 0.000 0.060 0.000 0.940
#> GSM1068558 4 0.5466 0.7043 0.008 0.200 0.060 0.732
#> GSM1068559 4 0.7611 0.2480 0.268 0.256 0.000 0.476
#> GSM1068564 4 0.0592 0.8409 0.000 0.016 0.000 0.984
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM1068478 3 0.1205 0.8468 0.040 0.000 0.956 0.000 0.004
#> GSM1068479 4 0.4249 0.3172 0.000 0.432 0.000 0.568 0.000
#> GSM1068481 3 0.1357 0.8432 0.048 0.000 0.948 0.000 0.004
#> GSM1068482 1 0.1408 0.7857 0.948 0.000 0.044 0.000 0.008
#> GSM1068483 1 0.4169 0.6994 0.784 0.000 0.116 0.000 0.100
#> GSM1068486 3 0.1082 0.8504 0.028 0.000 0.964 0.000 0.008
#> GSM1068487 4 0.0771 0.8499 0.000 0.020 0.000 0.976 0.004
#> GSM1068488 4 0.2465 0.8293 0.004 0.028 0.044 0.912 0.012
#> GSM1068490 4 0.1043 0.8476 0.000 0.040 0.000 0.960 0.000
#> GSM1068491 5 0.1281 0.6860 0.032 0.012 0.000 0.000 0.956
#> GSM1068492 4 0.0162 0.8496 0.000 0.004 0.000 0.996 0.000
#> GSM1068493 1 0.6025 0.4046 0.612 0.000 0.180 0.200 0.008
#> GSM1068494 2 0.4644 0.4511 0.024 0.708 0.252 0.000 0.016
#> GSM1068495 2 0.2942 0.7067 0.128 0.856 0.000 0.008 0.008
#> GSM1068496 1 0.4644 0.3824 0.604 0.000 0.380 0.012 0.004
#> GSM1068498 2 0.4350 0.4080 0.408 0.588 0.000 0.000 0.004
#> GSM1068499 1 0.0703 0.7898 0.976 0.024 0.000 0.000 0.000
#> GSM1068500 3 0.1095 0.8529 0.012 0.008 0.968 0.000 0.012
#> GSM1068502 4 0.3039 0.7244 0.000 0.000 0.000 0.808 0.192
#> GSM1068503 4 0.0404 0.8506 0.000 0.012 0.000 0.988 0.000
#> GSM1068505 4 0.0000 0.8486 0.000 0.000 0.000 1.000 0.000
#> GSM1068506 5 0.4268 0.4370 0.000 0.000 0.008 0.344 0.648
#> GSM1068507 2 0.6077 -0.0359 0.000 0.512 0.392 0.016 0.080
#> GSM1068508 2 0.2367 0.7084 0.000 0.904 0.004 0.072 0.020
#> GSM1068510 4 0.5903 0.4656 0.000 0.312 0.092 0.584 0.012
#> GSM1068512 3 0.3617 0.7609 0.000 0.028 0.840 0.104 0.028
#> GSM1068513 4 0.6286 0.4646 0.000 0.112 0.272 0.588 0.028
#> GSM1068514 4 0.0162 0.8496 0.000 0.004 0.000 0.996 0.000
#> GSM1068517 2 0.4151 0.5180 0.344 0.652 0.000 0.000 0.004
#> GSM1068518 2 0.4434 0.2796 0.460 0.536 0.000 0.000 0.004
#> GSM1068520 5 0.4478 0.5519 0.240 0.016 0.020 0.000 0.724
#> GSM1068521 1 0.1893 0.7741 0.928 0.048 0.000 0.000 0.024
#> GSM1068522 4 0.0880 0.8422 0.000 0.000 0.000 0.968 0.032
#> GSM1068524 4 0.3242 0.7758 0.000 0.172 0.000 0.816 0.012
#> GSM1068527 3 0.5097 0.7284 0.000 0.068 0.752 0.060 0.120
#> GSM1068480 3 0.6972 -0.1504 0.256 0.348 0.388 0.000 0.008
#> GSM1068484 4 0.0404 0.8503 0.000 0.012 0.000 0.988 0.000
#> GSM1068485 1 0.0451 0.7946 0.988 0.008 0.004 0.000 0.000
#> GSM1068489 4 0.3634 0.7195 0.000 0.008 0.184 0.796 0.012
#> GSM1068497 2 0.4549 0.2797 0.464 0.528 0.000 0.000 0.008
#> GSM1068501 4 0.0162 0.8477 0.000 0.000 0.000 0.996 0.004
#> GSM1068504 2 0.4242 0.1334 0.000 0.572 0.000 0.428 0.000
#> GSM1068509 1 0.3267 0.7564 0.864 0.000 0.044 0.016 0.076
#> GSM1068511 4 0.1026 0.8394 0.004 0.000 0.004 0.968 0.024
#> GSM1068515 1 0.4453 0.5448 0.724 0.228 0.000 0.000 0.048
#> GSM1068516 2 0.3521 0.6882 0.172 0.808 0.000 0.012 0.008
#> GSM1068519 1 0.2074 0.7691 0.896 0.000 0.000 0.000 0.104
#> GSM1068523 2 0.2575 0.7076 0.012 0.884 0.000 0.100 0.004
#> GSM1068525 4 0.1704 0.8382 0.000 0.068 0.000 0.928 0.004
#> GSM1068526 4 0.1410 0.8425 0.000 0.060 0.000 0.940 0.000
#> GSM1068458 5 0.4824 0.3364 0.376 0.028 0.000 0.000 0.596
#> GSM1068459 3 0.1892 0.8224 0.080 0.000 0.916 0.000 0.004
#> GSM1068460 2 0.2630 0.6666 0.016 0.892 0.012 0.000 0.080
#> GSM1068461 1 0.3861 0.4465 0.712 0.284 0.000 0.000 0.004
#> GSM1068464 4 0.3783 0.6667 0.000 0.008 0.000 0.740 0.252
#> GSM1068468 5 0.1329 0.6907 0.032 0.004 0.000 0.008 0.956
#> GSM1068472 5 0.6066 0.3394 0.124 0.000 0.000 0.388 0.488
#> GSM1068473 4 0.0162 0.8477 0.000 0.000 0.000 0.996 0.004
#> GSM1068474 4 0.4058 0.6825 0.000 0.024 0.000 0.740 0.236
#> GSM1068476 2 0.2100 0.6970 0.000 0.924 0.048 0.012 0.016
#> GSM1068477 2 0.3419 0.6575 0.016 0.804 0.000 0.180 0.000
#> GSM1068462 2 0.3164 0.7021 0.076 0.868 0.000 0.012 0.044
#> GSM1068463 1 0.3048 0.7005 0.820 0.000 0.176 0.000 0.004
#> GSM1068465 5 0.1059 0.6892 0.020 0.004 0.000 0.008 0.968
#> GSM1068466 3 0.5397 0.1594 0.032 0.012 0.488 0.000 0.468
#> GSM1068467 2 0.3491 0.6518 0.228 0.768 0.000 0.000 0.004
#> GSM1068469 1 0.1469 0.7851 0.948 0.016 0.000 0.000 0.036
#> GSM1068470 4 0.3242 0.7374 0.000 0.216 0.000 0.784 0.000
#> GSM1068471 4 0.0771 0.8487 0.000 0.004 0.000 0.976 0.020
#> GSM1068475 4 0.1341 0.8436 0.000 0.056 0.000 0.944 0.000
#> GSM1068528 1 0.0510 0.7946 0.984 0.000 0.016 0.000 0.000
#> GSM1068531 3 0.1365 0.8522 0.040 0.004 0.952 0.000 0.004
#> GSM1068532 1 0.3201 0.7575 0.852 0.000 0.052 0.000 0.096
#> GSM1068533 3 0.2597 0.8407 0.020 0.040 0.904 0.000 0.036
#> GSM1068535 3 0.0566 0.8532 0.012 0.000 0.984 0.004 0.000
#> GSM1068537 3 0.4255 0.7183 0.128 0.000 0.776 0.000 0.096
#> GSM1068538 5 0.3837 0.4647 0.308 0.000 0.000 0.000 0.692
#> GSM1068539 2 0.4979 0.6530 0.212 0.708 0.000 0.072 0.008
#> GSM1068540 3 0.1444 0.8506 0.040 0.000 0.948 0.000 0.012
#> GSM1068542 4 0.2852 0.7529 0.000 0.000 0.000 0.828 0.172
#> GSM1068543 4 0.4845 0.7373 0.000 0.124 0.108 0.752 0.016
#> GSM1068544 1 0.0671 0.7919 0.980 0.016 0.004 0.000 0.000
#> GSM1068545 4 0.3099 0.7971 0.000 0.028 0.000 0.848 0.124
#> GSM1068546 3 0.0613 0.8534 0.004 0.008 0.984 0.000 0.004
#> GSM1068547 1 0.3741 0.5783 0.732 0.004 0.000 0.000 0.264
#> GSM1068548 4 0.2230 0.7940 0.000 0.000 0.000 0.884 0.116
#> GSM1068549 3 0.1885 0.8469 0.020 0.044 0.932 0.000 0.004
#> GSM1068550 4 0.0290 0.8501 0.000 0.008 0.000 0.992 0.000
#> GSM1068551 4 0.3876 0.5911 0.000 0.316 0.000 0.684 0.000
#> GSM1068552 4 0.0162 0.8496 0.000 0.004 0.000 0.996 0.000
#> GSM1068555 2 0.3129 0.6702 0.000 0.832 0.004 0.156 0.008
#> GSM1068556 4 0.0290 0.8464 0.000 0.000 0.000 0.992 0.008
#> GSM1068557 2 0.2316 0.7085 0.000 0.916 0.036 0.036 0.012
#> GSM1068560 2 0.3113 0.6986 0.000 0.868 0.044 0.080 0.008
#> GSM1068561 3 0.3093 0.7830 0.000 0.168 0.824 0.000 0.008
#> GSM1068562 4 0.4806 0.3604 0.000 0.408 0.016 0.572 0.004
#> GSM1068563 5 0.3816 0.5371 0.000 0.000 0.000 0.304 0.696
#> GSM1068565 2 0.4370 0.4056 0.000 0.656 0.008 0.332 0.004
#> GSM1068529 3 0.3106 0.7990 0.000 0.140 0.840 0.000 0.020
#> GSM1068530 1 0.2179 0.7639 0.888 0.000 0.000 0.000 0.112
#> GSM1068534 3 0.1012 0.8502 0.000 0.000 0.968 0.020 0.012
#> GSM1068536 3 0.2612 0.8114 0.000 0.124 0.868 0.000 0.008
#> GSM1068541 5 0.2208 0.6858 0.072 0.000 0.000 0.020 0.908
#> GSM1068553 4 0.2802 0.7538 0.016 0.000 0.100 0.876 0.008
#> GSM1068554 4 0.0162 0.8477 0.000 0.000 0.000 0.996 0.004
#> GSM1068558 4 0.4394 0.6661 0.000 0.256 0.016 0.716 0.012
#> GSM1068559 2 0.1300 0.6998 0.000 0.956 0.028 0.000 0.016
#> GSM1068564 4 0.0290 0.8501 0.000 0.008 0.000 0.992 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1068478 3 0.3799 0.6485 0.112 0.000 0.804 0.008 0.068 0.008
#> GSM1068479 2 0.7369 -0.0465 0.000 0.396 0.000 0.152 0.192 0.260
#> GSM1068481 3 0.2532 0.6789 0.032 0.000 0.884 0.008 0.076 0.000
#> GSM1068482 1 0.4988 0.5435 0.676 0.000 0.096 0.012 0.212 0.004
#> GSM1068483 1 0.5077 0.5958 0.712 0.000 0.124 0.072 0.092 0.000
#> GSM1068486 3 0.2503 0.6884 0.024 0.000 0.896 0.008 0.060 0.012
#> GSM1068487 2 0.1829 0.7379 0.000 0.920 0.000 0.012 0.004 0.064
#> GSM1068488 2 0.4710 0.6770 0.008 0.756 0.036 0.012 0.048 0.140
#> GSM1068490 2 0.2393 0.7424 0.000 0.884 0.000 0.020 0.004 0.092
#> GSM1068491 4 0.4668 0.5358 0.040 0.044 0.000 0.764 0.032 0.120
#> GSM1068492 2 0.2039 0.7497 0.000 0.908 0.000 0.016 0.004 0.072
#> GSM1068493 3 0.7687 0.0484 0.268 0.152 0.400 0.012 0.164 0.004
#> GSM1068494 6 0.5665 0.1505 0.000 0.000 0.172 0.004 0.284 0.540
#> GSM1068495 6 0.5079 -0.0496 0.048 0.008 0.000 0.004 0.416 0.524
#> GSM1068496 3 0.6130 0.3416 0.288 0.020 0.560 0.012 0.112 0.008
#> GSM1068498 5 0.4455 0.6619 0.160 0.000 0.000 0.000 0.712 0.128
#> GSM1068499 1 0.2823 0.5997 0.796 0.000 0.000 0.000 0.204 0.000
#> GSM1068500 3 0.2293 0.6925 0.004 0.000 0.896 0.016 0.004 0.080
#> GSM1068502 2 0.3317 0.6662 0.000 0.804 0.000 0.168 0.012 0.016
#> GSM1068503 2 0.1367 0.7447 0.000 0.944 0.000 0.000 0.012 0.044
#> GSM1068505 2 0.2146 0.7388 0.000 0.880 0.000 0.004 0.000 0.116
#> GSM1068506 4 0.5945 0.3620 0.008 0.328 0.044 0.560 0.012 0.048
#> GSM1068507 6 0.6284 0.1827 0.000 0.044 0.116 0.184 0.040 0.616
#> GSM1068508 6 0.6804 0.1930 0.000 0.088 0.000 0.172 0.260 0.480
#> GSM1068510 6 0.5745 0.1922 0.000 0.356 0.004 0.012 0.112 0.516
#> GSM1068512 3 0.6701 0.3781 0.000 0.136 0.584 0.120 0.024 0.136
#> GSM1068513 6 0.6515 0.0427 0.000 0.392 0.148 0.028 0.012 0.420
#> GSM1068514 2 0.1820 0.7529 0.000 0.924 0.000 0.012 0.008 0.056
#> GSM1068517 5 0.5080 0.6447 0.236 0.000 0.000 0.000 0.624 0.140
#> GSM1068518 5 0.4955 0.6533 0.204 0.000 0.000 0.004 0.660 0.132
#> GSM1068520 4 0.7585 0.0545 0.344 0.000 0.180 0.364 0.076 0.036
#> GSM1068521 1 0.4040 0.5097 0.688 0.000 0.000 0.032 0.280 0.000
#> GSM1068522 2 0.2002 0.7417 0.000 0.908 0.000 0.076 0.004 0.012
#> GSM1068524 2 0.4303 0.5015 0.000 0.640 0.000 0.012 0.016 0.332
#> GSM1068527 4 0.7538 0.2231 0.004 0.088 0.220 0.432 0.020 0.236
#> GSM1068480 5 0.6840 0.4316 0.136 0.000 0.268 0.004 0.492 0.100
#> GSM1068484 2 0.0972 0.7517 0.000 0.964 0.000 0.008 0.000 0.028
#> GSM1068485 1 0.2613 0.6599 0.848 0.000 0.012 0.000 0.140 0.000
#> GSM1068489 2 0.5116 0.5722 0.000 0.684 0.088 0.040 0.000 0.188
#> GSM1068497 5 0.5387 0.5101 0.332 0.000 0.004 0.004 0.560 0.100
#> GSM1068501 2 0.6130 0.6066 0.104 0.684 0.020 0.056 0.056 0.080
#> GSM1068504 2 0.5214 0.0982 0.000 0.480 0.000 0.008 0.068 0.444
#> GSM1068509 1 0.4194 0.6384 0.796 0.008 0.072 0.028 0.092 0.004
#> GSM1068511 2 0.3541 0.6943 0.016 0.848 0.020 0.040 0.068 0.008
#> GSM1068515 5 0.5282 0.5901 0.252 0.000 0.000 0.044 0.640 0.064
#> GSM1068516 6 0.6764 0.0220 0.196 0.048 0.000 0.004 0.312 0.440
#> GSM1068519 1 0.2461 0.6747 0.888 0.000 0.004 0.064 0.044 0.000
#> GSM1068523 6 0.4875 0.2308 0.000 0.048 0.000 0.008 0.376 0.568
#> GSM1068525 2 0.2946 0.7101 0.000 0.824 0.000 0.012 0.004 0.160
#> GSM1068526 2 0.2882 0.7011 0.000 0.812 0.000 0.008 0.000 0.180
#> GSM1068458 4 0.6004 0.0577 0.300 0.000 0.000 0.468 0.228 0.004
#> GSM1068459 3 0.3716 0.6584 0.060 0.004 0.812 0.008 0.112 0.004
#> GSM1068460 6 0.6215 0.0423 0.004 0.000 0.016 0.228 0.244 0.508
#> GSM1068461 5 0.4513 0.4284 0.368 0.000 0.000 0.004 0.596 0.032
#> GSM1068464 2 0.5132 0.5545 0.000 0.624 0.000 0.284 0.020 0.072
#> GSM1068468 4 0.3151 0.5757 0.016 0.052 0.000 0.856 0.072 0.004
#> GSM1068472 4 0.7574 0.3754 0.184 0.340 0.000 0.340 0.124 0.012
#> GSM1068473 2 0.0881 0.7433 0.000 0.972 0.000 0.012 0.008 0.008
#> GSM1068474 2 0.5037 0.5662 0.000 0.636 0.000 0.284 0.036 0.044
#> GSM1068476 6 0.3763 0.3754 0.000 0.012 0.016 0.052 0.104 0.816
#> GSM1068477 6 0.6525 0.4046 0.000 0.212 0.000 0.076 0.180 0.532
#> GSM1068462 5 0.6377 0.1182 0.004 0.012 0.000 0.236 0.432 0.316
#> GSM1068463 1 0.3958 0.6259 0.784 0.000 0.108 0.012 0.096 0.000
#> GSM1068465 4 0.1578 0.5476 0.048 0.012 0.000 0.936 0.000 0.004
#> GSM1068466 3 0.7584 0.0411 0.168 0.000 0.376 0.340 0.040 0.076
#> GSM1068467 5 0.5180 0.5740 0.076 0.000 0.000 0.048 0.676 0.200
#> GSM1068469 5 0.6472 -0.1344 0.388 0.008 0.136 0.036 0.432 0.000
#> GSM1068470 2 0.4266 0.5984 0.000 0.700 0.000 0.008 0.040 0.252
#> GSM1068471 2 0.3463 0.7189 0.000 0.816 0.000 0.120 0.008 0.056
#> GSM1068475 2 0.3659 0.7294 0.000 0.824 0.000 0.064 0.044 0.068
#> GSM1068528 1 0.3737 0.6167 0.772 0.000 0.036 0.008 0.184 0.000
#> GSM1068531 3 0.4990 0.6632 0.084 0.000 0.740 0.012 0.080 0.084
#> GSM1068532 1 0.4024 0.5990 0.772 0.000 0.016 0.152 0.060 0.000
#> GSM1068533 3 0.5033 0.6176 0.036 0.000 0.708 0.064 0.012 0.180
#> GSM1068535 3 0.4409 0.6761 0.004 0.040 0.784 0.008 0.088 0.076
#> GSM1068537 3 0.5320 0.5708 0.176 0.000 0.676 0.060 0.088 0.000
#> GSM1068538 1 0.4534 0.0686 0.492 0.000 0.000 0.476 0.032 0.000
#> GSM1068539 5 0.5835 0.5535 0.128 0.040 0.000 0.004 0.612 0.216
#> GSM1068540 3 0.1007 0.6974 0.004 0.000 0.968 0.008 0.004 0.016
#> GSM1068542 2 0.6493 0.2526 0.096 0.504 0.000 0.332 0.024 0.044
#> GSM1068543 6 0.4808 -0.1778 0.000 0.480 0.008 0.016 0.012 0.484
#> GSM1068544 1 0.3012 0.6004 0.796 0.000 0.008 0.000 0.196 0.000
#> GSM1068545 2 0.4388 0.6805 0.000 0.732 0.000 0.168 0.008 0.092
#> GSM1068546 3 0.3703 0.6684 0.000 0.000 0.800 0.012 0.060 0.128
#> GSM1068547 1 0.4136 0.5970 0.732 0.000 0.000 0.192 0.076 0.000
#> GSM1068548 2 0.3855 0.5621 0.012 0.728 0.000 0.248 0.008 0.004
#> GSM1068549 3 0.7332 0.4443 0.132 0.000 0.464 0.020 0.128 0.256
#> GSM1068550 2 0.2544 0.7229 0.000 0.852 0.000 0.004 0.004 0.140
#> GSM1068551 2 0.5478 0.4696 0.000 0.616 0.000 0.060 0.056 0.268
#> GSM1068552 2 0.1701 0.7380 0.000 0.920 0.000 0.000 0.008 0.072
#> GSM1068555 6 0.4755 0.4268 0.000 0.244 0.000 0.004 0.088 0.664
#> GSM1068556 2 0.1659 0.7378 0.004 0.940 0.000 0.020 0.028 0.008
#> GSM1068557 6 0.5975 0.2589 0.000 0.032 0.028 0.076 0.272 0.592
#> GSM1068560 6 0.4169 0.4753 0.000 0.140 0.004 0.000 0.104 0.752
#> GSM1068561 3 0.4254 0.5409 0.000 0.000 0.624 0.004 0.020 0.352
#> GSM1068562 6 0.4527 -0.0826 0.000 0.456 0.000 0.004 0.024 0.516
#> GSM1068563 4 0.4407 0.4341 0.008 0.332 0.004 0.640 0.008 0.008
#> GSM1068565 6 0.5696 0.0820 0.000 0.408 0.000 0.064 0.040 0.488
#> GSM1068529 6 0.6057 -0.2740 0.000 0.044 0.428 0.004 0.080 0.444
#> GSM1068530 1 0.1682 0.6850 0.928 0.000 0.000 0.052 0.020 0.000
#> GSM1068534 3 0.3030 0.6807 0.000 0.052 0.868 0.008 0.016 0.056
#> GSM1068536 3 0.4917 0.5916 0.004 0.000 0.660 0.012 0.068 0.256
#> GSM1068541 4 0.5425 0.4946 0.132 0.080 0.000 0.680 0.108 0.000
#> GSM1068553 2 0.7110 0.4529 0.116 0.608 0.084 0.036 0.096 0.060
#> GSM1068554 2 0.3821 0.6987 0.056 0.828 0.004 0.008 0.044 0.060
#> GSM1068558 6 0.5627 0.0523 0.000 0.412 0.000 0.012 0.104 0.472
#> GSM1068559 6 0.3208 0.3491 0.000 0.004 0.016 0.016 0.132 0.832
#> GSM1068564 2 0.0935 0.7504 0.000 0.964 0.000 0.004 0.000 0.032
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) gender(p) k
#> ATC:NMF 107 0.2901 1.000 2
#> ATC:NMF 80 0.3579 0.930 3
#> ATC:NMF 89 0.3112 0.576 4
#> ATC:NMF 88 0.3956 0.440 5
#> ATC:NMF 66 0.0571 0.558 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