Date: 2019-12-25 21:24:38 CET, cola version: 1.3.2
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All available functions which can be applied to this res_list object:
res_list
#> A 'ConsensusPartitionList' object with 24 methods.
#> On a matrix with 51941 rows and 130 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] 51941 130
The density distribution for each sample is visualized as in one column in the following heatmap. The clustering is based on the distance which is the Kolmogorov-Smirnov statistic between two distributions.
library(ComplexHeatmap)
densityHeatmap(mat, top_annotation = HeatmapAnnotation(df = get_anno(res_list),
col = get_anno_col(res_list)), ylab = "value", cluster_columns = TRUE, show_column_names = FALSE,
mc.cores = 4)
Folowing table shows the best k (number of partitions) for each combination
of top-value methods and partition methods. Clicking on the method name in
the table goes to the section for a single combination of methods.
The cola vignette explains the definition of the metrics used for determining the best number of partitions.
suggest_best_k(res_list)
| The best k | 1-PAC | Mean silhouette | Concordance | Optional k | ||
|---|---|---|---|---|---|---|
| ATC:hclust | 2 | 1.000 | 0.996 | 0.997 | ** | |
| ATC:kmeans | 3 | 1.000 | 0.969 | 0.989 | ** | 2 |
| ATC:mclust | 3 | 1.000 | 0.991 | 0.982 | ** | 2 |
| ATC:NMF | 2 | 1.000 | 0.965 | 0.986 | ** | |
| ATC:pam | 6 | 0.957 | 0.927 | 0.960 | ** | 2,5 |
| ATC:skmeans | 6 | 0.925 | 0.878 | 0.946 | * | 2,3,4,5 |
| SD:NMF | 3 | 0.903 | 0.918 | 0.965 | * | |
| CV:NMF | 3 | 0.846 | 0.893 | 0.955 | ||
| MAD:NMF | 3 | 0.841 | 0.871 | 0.947 | ||
| CV:skmeans | 4 | 0.840 | 0.799 | 0.909 | ||
| MAD:mclust | 2 | 0.806 | 0.881 | 0.930 | ||
| MAD:pam | 2 | 0.802 | 0.918 | 0.959 | ||
| SD:kmeans | 3 | 0.730 | 0.849 | 0.915 | ||
| SD:skmeans | 3 | 0.705 | 0.826 | 0.907 | ||
| MAD:skmeans | 2 | 0.639 | 0.825 | 0.929 | ||
| MAD:kmeans | 2 | 0.635 | 0.812 | 0.920 | ||
| CV:kmeans | 3 | 0.634 | 0.777 | 0.898 | ||
| CV:hclust | 4 | 0.599 | 0.804 | 0.900 | ||
| CV:mclust | 2 | 0.573 | 0.884 | 0.928 | ||
| MAD:hclust | 2 | 0.533 | 0.776 | 0.886 | ||
| CV:pam | 3 | 0.527 | 0.820 | 0.902 | ||
| SD:pam | 3 | 0.499 | 0.798 | 0.863 | ||
| SD:mclust | 2 | 0.472 | 0.825 | 0.874 | ||
| SD:hclust | 2 | 0.417 | 0.723 | 0.859 |
**: 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 0.387 0.525 0.821 0.467 0.565 0.565
#> CV:NMF 2 0.703 0.885 0.947 0.439 0.571 0.571
#> MAD:NMF 2 0.516 0.881 0.893 0.485 0.508 0.508
#> ATC:NMF 2 1.000 0.965 0.986 0.413 0.590 0.590
#> SD:skmeans 2 0.521 0.791 0.892 0.496 0.504 0.504
#> CV:skmeans 2 0.511 0.852 0.903 0.502 0.498 0.498
#> MAD:skmeans 2 0.639 0.825 0.929 0.494 0.502 0.502
#> ATC:skmeans 2 1.000 0.959 0.984 0.483 0.516 0.516
#> SD:mclust 2 0.472 0.825 0.874 0.490 0.500 0.500
#> CV:mclust 2 0.573 0.884 0.928 0.489 0.497 0.497
#> MAD:mclust 2 0.806 0.881 0.930 0.494 0.497 0.497
#> ATC:mclust 2 1.000 1.000 1.000 0.466 0.535 0.535
#> SD:kmeans 2 0.479 0.730 0.849 0.447 0.544 0.544
#> CV:kmeans 2 0.297 0.583 0.786 0.433 0.565 0.565
#> MAD:kmeans 2 0.635 0.812 0.920 0.468 0.527 0.527
#> ATC:kmeans 2 1.000 0.994 0.998 0.440 0.559 0.559
#> SD:pam 2 0.378 0.587 0.841 0.432 0.577 0.577
#> CV:pam 2 0.588 0.897 0.939 0.276 0.771 0.771
#> MAD:pam 2 0.802 0.918 0.959 0.444 0.554 0.554
#> ATC:pam 2 1.000 0.969 0.988 0.453 0.549 0.549
#> SD:hclust 2 0.417 0.723 0.859 0.384 0.603 0.603
#> CV:hclust 2 0.409 0.660 0.826 0.334 0.706 0.706
#> MAD:hclust 2 0.533 0.776 0.886 0.389 0.706 0.706
#> ATC:hclust 2 1.000 0.996 0.997 0.434 0.565 0.565
get_stats(res_list, k = 3)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 3 0.903 0.918 0.965 0.314 0.654 0.468
#> CV:NMF 3 0.846 0.893 0.955 0.443 0.693 0.508
#> MAD:NMF 3 0.841 0.871 0.947 0.266 0.611 0.396
#> ATC:NMF 3 0.776 0.860 0.930 0.584 0.723 0.537
#> SD:skmeans 3 0.705 0.826 0.907 0.312 0.720 0.502
#> CV:skmeans 3 0.674 0.799 0.901 0.323 0.696 0.463
#> MAD:skmeans 3 0.657 0.769 0.889 0.320 0.717 0.494
#> ATC:skmeans 3 0.979 0.953 0.980 0.263 0.851 0.717
#> SD:mclust 3 0.398 0.183 0.618 0.280 0.713 0.511
#> CV:mclust 3 0.454 0.489 0.755 0.254 0.814 0.653
#> MAD:mclust 3 0.637 0.785 0.889 0.264 0.814 0.644
#> ATC:mclust 3 1.000 0.991 0.982 0.407 0.805 0.636
#> SD:kmeans 3 0.730 0.849 0.915 0.338 0.749 0.576
#> CV:kmeans 3 0.634 0.777 0.898 0.388 0.598 0.412
#> MAD:kmeans 3 0.620 0.785 0.859 0.324 0.792 0.629
#> ATC:kmeans 3 1.000 0.969 0.989 0.526 0.759 0.574
#> SD:pam 3 0.499 0.798 0.863 0.283 0.797 0.676
#> CV:pam 3 0.527 0.820 0.902 0.996 0.685 0.592
#> MAD:pam 3 0.517 0.716 0.816 0.345 0.805 0.669
#> ATC:pam 3 0.787 0.933 0.949 0.400 0.790 0.626
#> SD:hclust 3 0.566 0.702 0.865 0.383 0.845 0.745
#> CV:hclust 3 0.480 0.739 0.838 0.433 0.695 0.584
#> MAD:hclust 3 0.625 0.749 0.872 0.383 0.766 0.673
#> ATC:hclust 3 0.819 0.920 0.945 0.506 0.764 0.582
get_stats(res_list, k = 4)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 4 0.810 0.812 0.914 0.1129 0.881 0.709
#> CV:NMF 4 0.859 0.869 0.932 0.1180 0.874 0.682
#> MAD:NMF 4 0.589 0.482 0.703 0.1355 0.809 0.554
#> ATC:NMF 4 0.697 0.742 0.861 0.1008 0.883 0.678
#> SD:skmeans 4 0.571 0.625 0.690 0.1310 0.871 0.662
#> CV:skmeans 4 0.840 0.799 0.909 0.1126 0.866 0.631
#> MAD:skmeans 4 0.673 0.738 0.822 0.1310 0.802 0.498
#> ATC:skmeans 4 0.977 0.944 0.949 0.1452 0.898 0.742
#> SD:mclust 4 0.591 0.562 0.759 0.1123 0.737 0.485
#> CV:mclust 4 0.481 0.443 0.703 0.1230 0.749 0.483
#> MAD:mclust 4 0.681 0.816 0.884 0.1358 0.710 0.382
#> ATC:mclust 4 0.893 0.860 0.940 0.0964 0.953 0.861
#> SD:kmeans 4 0.572 0.660 0.791 0.1489 0.862 0.680
#> CV:kmeans 4 0.623 0.731 0.829 0.1340 0.853 0.674
#> MAD:kmeans 4 0.590 0.627 0.766 0.1512 0.808 0.554
#> ATC:kmeans 4 0.711 0.631 0.743 0.0922 0.959 0.880
#> SD:pam 4 0.591 0.570 0.785 0.2448 0.812 0.609
#> CV:pam 4 0.578 0.700 0.833 0.3139 0.737 0.470
#> MAD:pam 4 0.657 0.814 0.883 0.2146 0.795 0.542
#> ATC:pam 4 0.782 0.784 0.837 0.1035 0.940 0.838
#> SD:hclust 4 0.595 0.722 0.847 0.1128 0.936 0.863
#> CV:hclust 4 0.599 0.804 0.900 0.2271 0.938 0.866
#> MAD:hclust 4 0.522 0.685 0.778 0.2249 0.855 0.710
#> ATC:hclust 4 0.782 0.844 0.888 0.0927 0.940 0.819
get_stats(res_list, k = 5)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 5 0.864 0.859 0.937 0.0741 0.889 0.676
#> CV:NMF 5 0.798 0.841 0.913 0.0794 0.908 0.703
#> MAD:NMF 5 0.627 0.630 0.806 0.0608 0.798 0.445
#> ATC:NMF 5 0.622 0.616 0.794 0.0661 0.842 0.528
#> SD:skmeans 5 0.674 0.537 0.757 0.0764 0.858 0.554
#> CV:skmeans 5 0.759 0.595 0.798 0.0758 0.874 0.569
#> MAD:skmeans 5 0.847 0.808 0.905 0.0712 0.822 0.453
#> ATC:skmeans 5 0.933 0.833 0.915 0.0391 0.972 0.908
#> SD:mclust 5 0.604 0.639 0.791 0.0583 0.788 0.480
#> CV:mclust 5 0.647 0.715 0.799 0.1119 0.767 0.409
#> MAD:mclust 5 0.756 0.867 0.915 0.0587 0.904 0.694
#> ATC:mclust 5 0.790 0.704 0.830 0.0813 0.887 0.628
#> SD:kmeans 5 0.641 0.669 0.802 0.0891 0.862 0.612
#> CV:kmeans 5 0.658 0.730 0.833 0.0943 0.856 0.612
#> MAD:kmeans 5 0.672 0.767 0.826 0.0816 0.816 0.458
#> ATC:kmeans 5 0.673 0.564 0.697 0.0621 0.828 0.498
#> SD:pam 5 0.598 0.565 0.764 0.1219 0.827 0.503
#> CV:pam 5 0.618 0.680 0.829 0.0569 0.898 0.662
#> MAD:pam 5 0.630 0.578 0.789 0.0803 0.807 0.420
#> ATC:pam 5 0.946 0.918 0.965 0.1054 0.834 0.534
#> SD:hclust 5 0.597 0.684 0.823 0.0973 0.993 0.984
#> CV:hclust 5 0.653 0.792 0.891 0.0421 0.979 0.948
#> MAD:hclust 5 0.514 0.578 0.730 0.0912 0.943 0.845
#> ATC:hclust 5 0.764 0.826 0.839 0.0591 0.953 0.831
get_stats(res_list, k = 6)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 6 0.676 0.646 0.817 0.0713 0.901 0.656
#> CV:NMF 6 0.740 0.630 0.806 0.0465 0.927 0.719
#> MAD:NMF 6 0.622 0.646 0.798 0.0633 0.904 0.668
#> ATC:NMF 6 0.713 0.629 0.811 0.0336 0.875 0.573
#> SD:skmeans 6 0.753 0.728 0.819 0.0471 0.903 0.579
#> CV:skmeans 6 0.833 0.765 0.875 0.0472 0.914 0.615
#> MAD:skmeans 6 0.755 0.730 0.821 0.0450 0.930 0.692
#> ATC:skmeans 6 0.925 0.878 0.946 0.0339 0.939 0.793
#> SD:mclust 6 0.622 0.579 0.681 0.0557 0.950 0.804
#> CV:mclust 6 0.677 0.676 0.771 0.0423 0.958 0.825
#> MAD:mclust 6 0.710 0.572 0.785 0.0635 0.956 0.827
#> ATC:mclust 6 0.771 0.682 0.813 0.0285 0.931 0.714
#> SD:kmeans 6 0.628 0.614 0.757 0.0565 0.957 0.831
#> CV:kmeans 6 0.698 0.650 0.808 0.0545 0.942 0.777
#> MAD:kmeans 6 0.701 0.678 0.804 0.0475 0.995 0.979
#> ATC:kmeans 6 0.700 0.628 0.746 0.0470 0.912 0.613
#> SD:pam 6 0.618 0.521 0.733 0.0462 0.883 0.550
#> CV:pam 6 0.626 0.654 0.805 0.0464 0.836 0.434
#> MAD:pam 6 0.787 0.728 0.866 0.0576 0.892 0.554
#> ATC:pam 6 0.957 0.927 0.960 0.0306 0.951 0.794
#> SD:hclust 6 0.611 0.626 0.793 0.0530 0.981 0.953
#> CV:hclust 6 0.691 0.780 0.883 0.0331 0.997 0.992
#> MAD:hclust 6 0.551 0.505 0.705 0.0404 0.937 0.806
#> ATC:hclust 6 0.785 0.804 0.888 0.0435 0.973 0.883
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) development.stage(p) other(p) k
#> SD:NMF 79 3.48e-10 0.279731 1.42e-12 2
#> CV:NMF 126 2.72e-06 0.064229 1.43e-13 2
#> MAD:NMF 129 3.41e-01 0.000926 8.61e-11 2
#> ATC:NMF 128 1.43e-01 0.154258 7.09e-06 2
#> SD:skmeans 127 3.41e-13 0.065334 2.99e-15 2
#> CV:skmeans 129 1.00e+00 0.069684 3.89e-08 2
#> MAD:skmeans 115 4.21e-11 0.016017 3.61e-16 2
#> ATC:skmeans 127 4.26e-01 0.751408 4.09e-05 2
#> SD:mclust 126 5.12e-09 0.010269 2.99e-13 2
#> CV:mclust 127 1.18e-09 0.026084 4.35e-13 2
#> MAD:mclust 125 1.49e-10 0.005323 3.46e-14 2
#> ATC:mclust 130 4.13e-01 0.669155 5.46e-04 2
#> SD:kmeans 121 6.34e-20 0.057005 1.56e-19 2
#> CV:kmeans 100 1.14e-02 0.049720 2.51e-10 2
#> MAD:kmeans 117 1.04e-12 0.038842 2.23e-15 2
#> ATC:kmeans 130 3.47e-01 0.253184 3.72e-05 2
#> SD:pam 89 1.74e-06 0.006283 8.03e-12 2
#> CV:pam 128 3.39e-11 0.564705 1.70e-10 2
#> MAD:pam 129 1.26e-08 0.082266 1.18e-11 2
#> ATC:pam 128 4.49e-01 0.231163 8.41e-06 2
#> SD:hclust 121 1.66e-03 0.000155 2.89e-09 2
#> CV:hclust 109 5.94e-24 0.612640 1.80e-23 2
#> MAD:hclust 115 2.78e-08 0.012649 5.48e-09 2
#> ATC:hclust 130 2.91e-01 0.306307 2.96e-05 2
test_to_known_factors(res_list, k = 3)
#> n disease.state(p) development.stage(p) other(p) k
#> SD:NMF 125 1.86e-11 0.132176 1.14e-17 3
#> CV:NMF 126 1.35e-10 0.055205 1.29e-18 3
#> MAD:NMF 122 1.69e-09 0.019900 5.07e-19 3
#> ATC:NMF 123 1.97e-01 0.010693 9.97e-12 3
#> SD:skmeans 121 5.26e-08 0.001015 1.66e-20 3
#> CV:skmeans 120 1.60e-06 0.001539 2.48e-25 3
#> MAD:skmeans 117 7.71e-08 0.001568 3.99e-21 3
#> ATC:skmeans 128 7.08e-02 0.567559 5.22e-07 3
#> SD:mclust 37 2.00e-03 0.226969 3.72e-05 3
#> CV:mclust 75 1.18e-13 0.353051 1.88e-13 3
#> MAD:mclust 119 1.26e-09 0.000849 5.58e-21 3
#> ATC:mclust 130 7.01e-02 0.478838 4.39e-04 3
#> SD:kmeans 128 1.29e-11 0.020698 3.03e-17 3
#> CV:kmeans 116 9.45e-15 0.006292 1.05e-21 3
#> MAD:kmeans 124 7.50e-11 0.005313 9.50e-19 3
#> ATC:kmeans 127 2.91e-01 0.096114 2.51e-09 3
#> SD:pam 125 1.45e-17 0.040989 3.43e-20 3
#> CV:pam 125 2.06e-12 0.070806 2.22e-17 3
#> MAD:pam 120 4.28e-14 0.018014 1.66e-16 3
#> ATC:pam 130 9.46e-02 0.200180 8.30e-09 3
#> SD:hclust 104 1.93e-17 0.259845 2.13e-18 3
#> CV:hclust 116 7.81e-18 0.136327 3.96e-23 3
#> MAD:hclust 113 6.82e-18 0.009458 4.92e-17 3
#> ATC:hclust 127 3.84e-01 0.163242 1.43e-07 3
test_to_known_factors(res_list, k = 4)
#> n disease.state(p) development.stage(p) other(p) k
#> SD:NMF 120 5.28e-15 0.04755 4.25e-24 4
#> CV:NMF 124 1.37e-12 0.09094 1.93e-24 4
#> MAD:NMF 83 6.36e-06 0.03222 7.97e-15 4
#> ATC:NMF 116 1.03e-01 0.15361 9.33e-08 4
#> SD:skmeans 111 5.32e-16 0.01194 1.90e-27 4
#> CV:skmeans 115 1.02e-12 0.00788 1.88e-31 4
#> MAD:skmeans 121 1.36e-10 0.00278 8.27e-19 4
#> ATC:skmeans 127 3.47e-01 0.56568 4.42e-07 4
#> SD:mclust 94 1.30e-10 0.03012 8.18e-13 4
#> CV:mclust 59 1.54e-13 0.31969 5.75e-14 4
#> MAD:mclust 122 5.31e-18 0.02498 7.80e-22 4
#> ATC:mclust 119 1.04e-01 0.58945 4.51e-04 4
#> SD:kmeans 107 4.67e-22 0.01640 2.32e-27 4
#> CV:kmeans 117 3.71e-24 0.05082 1.36e-30 4
#> MAD:kmeans 108 2.59e-12 0.01221 1.63e-22 4
#> ATC:kmeans 108 5.02e-01 0.10440 4.87e-08 4
#> SD:pam 94 1.04e-12 0.06078 4.85e-20 4
#> CV:pam 120 1.78e-11 0.26070 4.32e-18 4
#> MAD:pam 123 7.26e-15 0.03136 3.28e-19 4
#> ATC:pam 125 3.75e-07 0.22077 1.46e-14 4
#> SD:hclust 105 1.37e-20 0.00279 7.97e-28 4
#> CV:hclust 122 9.49e-18 0.01365 7.46e-23 4
#> MAD:hclust 109 1.66e-15 0.00333 2.11e-24 4
#> ATC:hclust 124 5.33e-05 0.17637 8.86e-11 4
test_to_known_factors(res_list, k = 5)
#> n disease.state(p) development.stage(p) other(p) k
#> SD:NMF 125 2.14e-15 0.075929 7.19e-30 5
#> CV:NMF 126 9.99e-14 0.023929 2.80e-31 5
#> MAD:NMF 101 6.56e-16 0.090942 3.61e-24 5
#> ATC:NMF 97 8.90e-03 0.001634 3.21e-08 5
#> SD:skmeans 73 6.25e-11 0.182692 5.50e-20 5
#> CV:skmeans 76 2.96e-09 0.251659 2.39e-21 5
#> MAD:skmeans 116 4.91e-16 0.015261 6.22e-38 5
#> ATC:skmeans 119 2.50e-01 0.346888 7.31e-08 5
#> SD:mclust 107 3.06e-18 0.188425 3.37e-25 5
#> CV:mclust 116 4.41e-23 0.158030 1.08e-35 5
#> MAD:mclust 127 7.18e-17 0.006301 5.49e-24 5
#> ATC:mclust 108 1.25e-02 0.746610 7.32e-07 5
#> SD:kmeans 110 7.28e-23 0.030458 1.50e-47 5
#> CV:kmeans 115 6.24e-24 0.020245 7.94e-50 5
#> MAD:kmeans 124 8.25e-20 0.000705 1.28e-44 5
#> ATC:kmeans 93 1.27e-04 0.053099 8.65e-10 5
#> SD:pam 90 1.08e-17 0.083973 3.95e-24 5
#> CV:pam 115 8.31e-23 0.154265 6.90e-28 5
#> MAD:pam 84 3.69e-12 0.027925 6.25e-31 5
#> ATC:pam 123 5.31e-07 0.317248 1.49e-10 5
#> SD:hclust 104 1.10e-16 0.025770 7.66e-21 5
#> CV:hclust 118 1.58e-16 0.003419 2.26e-21 5
#> MAD:hclust 97 3.10e-13 0.008175 5.49e-26 5
#> ATC:hclust 123 6.29e-06 0.236282 5.12e-11 5
test_to_known_factors(res_list, k = 6)
#> n disease.state(p) development.stage(p) other(p) k
#> SD:NMF 98 2.48e-17 0.075278 1.70e-30 6
#> CV:NMF 96 7.27e-17 0.008806 3.95e-32 6
#> MAD:NMF 106 6.33e-14 0.032664 9.08e-27 6
#> ATC:NMF 97 1.40e-01 0.021478 1.45e-06 6
#> SD:skmeans 121 4.54e-19 0.044259 4.89e-38 6
#> CV:skmeans 119 5.06e-18 0.008536 2.26e-36 6
#> MAD:skmeans 115 1.37e-15 0.006448 1.26e-34 6
#> ATC:skmeans 124 4.14e-01 0.093402 4.48e-07 6
#> SD:mclust 95 1.14e-19 0.129743 1.34e-26 6
#> CV:mclust 108 5.71e-19 0.232906 3.44e-32 6
#> MAD:mclust 89 3.31e-13 0.002433 6.29e-22 6
#> ATC:mclust 103 1.55e-04 0.365739 1.23e-05 6
#> SD:kmeans 104 1.38e-21 0.012045 3.86e-48 6
#> CV:kmeans 99 1.61e-20 0.221613 1.80e-40 6
#> MAD:kmeans 116 4.39e-18 0.000986 1.03e-40 6
#> ATC:kmeans 107 1.22e-04 0.278954 8.77e-10 6
#> SD:pam 76 3.96e-14 0.314121 2.13e-20 6
#> CV:pam 114 9.55e-21 0.112786 5.82e-37 6
#> MAD:pam 110 7.05e-16 0.002839 1.28e-35 6
#> ATC:pam 128 6.67e-07 0.236237 2.25e-12 6
#> SD:hclust 98 3.23e-19 0.000678 4.96e-26 6
#> CV:hclust 111 4.77e-18 0.000109 1.58e-22 6
#> MAD:hclust 86 1.44e-12 0.029089 1.46e-28 6
#> ATC:hclust 123 1.85e-05 0.222804 5.80e-11 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 51941 rows and 130 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'hclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.417 0.723 0.859 0.3841 0.603 0.603
#> 3 3 0.566 0.702 0.865 0.3833 0.845 0.745
#> 4 4 0.595 0.722 0.847 0.1128 0.936 0.863
#> 5 5 0.597 0.684 0.823 0.0973 0.993 0.984
#> 6 6 0.611 0.626 0.793 0.0530 0.981 0.953
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
#> GSM648605 2 0.9970 0.5447 0.468 0.532
#> GSM648618 1 0.9460 0.0936 0.636 0.364
#> GSM648620 2 0.9970 0.5447 0.468 0.532
#> GSM648646 2 0.8443 0.7131 0.272 0.728
#> GSM648649 1 0.2236 0.8462 0.964 0.036
#> GSM648675 1 0.9460 0.0936 0.636 0.364
#> GSM648682 2 0.9909 0.5776 0.444 0.556
#> GSM648698 2 0.9970 0.5447 0.468 0.532
#> GSM648708 2 0.9970 0.5447 0.468 0.532
#> GSM648628 1 0.3584 0.8304 0.932 0.068
#> GSM648595 1 0.1843 0.8504 0.972 0.028
#> GSM648635 1 0.2236 0.8462 0.964 0.036
#> GSM648645 1 0.1843 0.8512 0.972 0.028
#> GSM648647 2 0.9970 0.5447 0.468 0.532
#> GSM648667 1 0.9522 0.0372 0.628 0.372
#> GSM648695 2 0.9983 0.5258 0.476 0.524
#> GSM648704 2 0.5178 0.7618 0.116 0.884
#> GSM648706 2 0.6973 0.7516 0.188 0.812
#> GSM648593 1 0.3114 0.8300 0.944 0.056
#> GSM648594 1 0.2603 0.8419 0.956 0.044
#> GSM648600 1 0.1843 0.8504 0.972 0.028
#> GSM648621 1 0.0672 0.8586 0.992 0.008
#> GSM648622 1 0.0376 0.8583 0.996 0.004
#> GSM648623 1 0.0376 0.8583 0.996 0.004
#> GSM648636 1 0.2778 0.8373 0.952 0.048
#> GSM648655 1 0.3114 0.8300 0.944 0.056
#> GSM648661 1 0.0672 0.8579 0.992 0.008
#> GSM648664 1 0.0672 0.8579 0.992 0.008
#> GSM648683 1 0.0672 0.8589 0.992 0.008
#> GSM648685 1 0.0672 0.8579 0.992 0.008
#> GSM648702 1 0.2778 0.8373 0.952 0.048
#> GSM648597 1 0.2603 0.8419 0.956 0.044
#> GSM648603 1 0.0376 0.8583 0.996 0.004
#> GSM648606 1 0.3584 0.8304 0.932 0.068
#> GSM648613 1 0.3584 0.8304 0.932 0.068
#> GSM648619 1 0.3584 0.8304 0.932 0.068
#> GSM648654 1 0.0672 0.8579 0.992 0.008
#> GSM648663 1 0.3584 0.8304 0.932 0.068
#> GSM648670 1 0.9522 0.0579 0.628 0.372
#> GSM648707 2 0.9775 0.4456 0.412 0.588
#> GSM648615 2 0.9963 0.5511 0.464 0.536
#> GSM648643 2 0.9866 0.5926 0.432 0.568
#> GSM648650 1 0.2603 0.8417 0.956 0.044
#> GSM648656 2 0.9491 0.6483 0.368 0.632
#> GSM648715 1 0.9522 0.0372 0.628 0.372
#> GSM648598 1 0.0376 0.8583 0.996 0.004
#> GSM648601 1 0.0376 0.8583 0.996 0.004
#> GSM648602 1 0.0376 0.8583 0.996 0.004
#> GSM648604 1 0.0672 0.8579 0.992 0.008
#> GSM648614 1 0.3584 0.8304 0.932 0.068
#> GSM648624 1 0.0376 0.8583 0.996 0.004
#> GSM648625 1 0.3584 0.8208 0.932 0.068
#> GSM648629 1 0.0672 0.8579 0.992 0.008
#> GSM648634 1 0.0672 0.8578 0.992 0.008
#> GSM648648 1 0.2236 0.8462 0.964 0.036
#> GSM648651 1 0.0376 0.8583 0.996 0.004
#> GSM648657 1 0.1843 0.8512 0.972 0.028
#> GSM648660 1 0.0376 0.8583 0.996 0.004
#> GSM648697 1 0.0938 0.8570 0.988 0.012
#> GSM648710 1 0.0672 0.8579 0.992 0.008
#> GSM648591 1 0.9522 0.0719 0.628 0.372
#> GSM648592 1 0.2603 0.8419 0.956 0.044
#> GSM648607 1 0.3584 0.8304 0.932 0.068
#> GSM648611 1 0.3584 0.8304 0.932 0.068
#> GSM648612 1 0.3584 0.8304 0.932 0.068
#> GSM648616 2 0.9491 0.5452 0.368 0.632
#> GSM648617 1 0.1843 0.8504 0.972 0.028
#> GSM648626 1 0.0376 0.8583 0.996 0.004
#> GSM648711 1 0.3584 0.8304 0.932 0.068
#> GSM648712 1 0.3584 0.8304 0.932 0.068
#> GSM648713 1 0.3584 0.8304 0.932 0.068
#> GSM648714 1 0.3584 0.8304 0.932 0.068
#> GSM648716 1 0.3584 0.8304 0.932 0.068
#> GSM648717 1 0.3584 0.8304 0.932 0.068
#> GSM648590 1 0.3431 0.8220 0.936 0.064
#> GSM648596 1 0.9522 0.0372 0.628 0.372
#> GSM648642 2 0.9970 0.5447 0.468 0.532
#> GSM648696 1 0.1843 0.8504 0.972 0.028
#> GSM648705 1 0.2236 0.8462 0.964 0.036
#> GSM648718 2 0.9963 0.5511 0.464 0.536
#> GSM648599 1 0.0672 0.8586 0.992 0.008
#> GSM648608 1 0.0672 0.8579 0.992 0.008
#> GSM648609 1 0.0672 0.8579 0.992 0.008
#> GSM648610 1 0.0672 0.8586 0.992 0.008
#> GSM648633 1 0.1843 0.8504 0.972 0.028
#> GSM648644 2 0.5178 0.7618 0.116 0.884
#> GSM648652 1 0.2236 0.8462 0.964 0.036
#> GSM648653 1 0.0376 0.8583 0.996 0.004
#> GSM648658 1 0.3114 0.8300 0.944 0.056
#> GSM648659 1 0.5737 0.7348 0.864 0.136
#> GSM648662 1 0.0672 0.8579 0.992 0.008
#> GSM648665 1 0.0672 0.8579 0.992 0.008
#> GSM648666 1 0.0938 0.8570 0.988 0.012
#> GSM648680 1 0.2236 0.8462 0.964 0.036
#> GSM648684 1 0.0672 0.8589 0.992 0.008
#> GSM648709 2 0.9983 0.5258 0.476 0.524
#> GSM648719 1 0.0376 0.8583 0.996 0.004
#> GSM648627 1 0.3584 0.8304 0.932 0.068
#> GSM648637 2 0.7219 0.7291 0.200 0.800
#> GSM648638 2 0.7219 0.7291 0.200 0.800
#> GSM648641 1 0.7602 0.6889 0.780 0.220
#> GSM648672 2 0.3584 0.7488 0.068 0.932
#> GSM648674 2 0.7056 0.7383 0.192 0.808
#> GSM648703 2 0.4298 0.7598 0.088 0.912
#> GSM648631 1 0.8608 0.6061 0.716 0.284
#> GSM648669 2 0.3879 0.7516 0.076 0.924
#> GSM648671 2 0.3879 0.7516 0.076 0.924
#> GSM648678 2 0.3584 0.7488 0.068 0.932
#> GSM648679 2 0.4815 0.7551 0.104 0.896
#> GSM648681 1 0.8763 0.3711 0.704 0.296
#> GSM648686 1 0.8608 0.6061 0.716 0.284
#> GSM648689 1 0.7883 0.6684 0.764 0.236
#> GSM648690 1 0.8608 0.6061 0.716 0.284
#> GSM648691 1 0.8608 0.6061 0.716 0.284
#> GSM648693 1 0.8608 0.6061 0.716 0.284
#> GSM648700 2 0.4298 0.7598 0.088 0.912
#> GSM648630 1 0.8608 0.6061 0.716 0.284
#> GSM648632 1 0.8608 0.6061 0.716 0.284
#> GSM648639 1 0.8909 0.5696 0.692 0.308
#> GSM648640 1 0.8909 0.5696 0.692 0.308
#> GSM648668 2 0.3584 0.7488 0.068 0.932
#> GSM648676 2 0.4298 0.7598 0.088 0.912
#> GSM648692 1 0.8608 0.6061 0.716 0.284
#> GSM648694 1 0.8608 0.6061 0.716 0.284
#> GSM648699 2 0.4298 0.7598 0.088 0.912
#> GSM648701 2 0.4298 0.7598 0.088 0.912
#> GSM648673 2 0.3879 0.7516 0.076 0.924
#> GSM648677 2 0.3733 0.7514 0.072 0.928
#> GSM648687 1 0.8608 0.6061 0.716 0.284
#> GSM648688 1 0.8608 0.6061 0.716 0.284
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM648605 2 0.6180 0.4975 0.416 0.584 0.000
#> GSM648618 1 0.7582 0.0797 0.572 0.380 0.048
#> GSM648620 2 0.6180 0.4975 0.416 0.584 0.000
#> GSM648646 2 0.4796 0.6462 0.220 0.780 0.000
#> GSM648649 1 0.1289 0.8681 0.968 0.032 0.000
#> GSM648675 1 0.7582 0.0797 0.572 0.380 0.048
#> GSM648682 2 0.6095 0.5327 0.392 0.608 0.000
#> GSM648698 2 0.6180 0.4975 0.416 0.584 0.000
#> GSM648708 2 0.6180 0.4975 0.416 0.584 0.000
#> GSM648628 1 0.4291 0.7509 0.820 0.000 0.180
#> GSM648595 1 0.1031 0.8706 0.976 0.024 0.000
#> GSM648635 1 0.1289 0.8681 0.968 0.032 0.000
#> GSM648645 1 0.1170 0.8725 0.976 0.016 0.008
#> GSM648647 2 0.6180 0.4975 0.416 0.584 0.000
#> GSM648667 1 0.6111 0.1633 0.604 0.396 0.000
#> GSM648695 2 0.6204 0.4776 0.424 0.576 0.000
#> GSM648704 2 0.2165 0.6546 0.064 0.936 0.000
#> GSM648706 2 0.3619 0.6548 0.136 0.864 0.000
#> GSM648593 1 0.1860 0.8567 0.948 0.052 0.000
#> GSM648594 1 0.1711 0.8662 0.960 0.032 0.008
#> GSM648600 1 0.1031 0.8706 0.976 0.024 0.000
#> GSM648621 1 0.0237 0.8762 0.996 0.000 0.004
#> GSM648622 1 0.0000 0.8758 1.000 0.000 0.000
#> GSM648623 1 0.0000 0.8758 1.000 0.000 0.000
#> GSM648636 1 0.1643 0.8620 0.956 0.044 0.000
#> GSM648655 1 0.1860 0.8567 0.948 0.052 0.000
#> GSM648661 1 0.0592 0.8739 0.988 0.000 0.012
#> GSM648664 1 0.0592 0.8739 0.988 0.000 0.012
#> GSM648683 1 0.0661 0.8760 0.988 0.004 0.008
#> GSM648685 1 0.0592 0.8739 0.988 0.000 0.012
#> GSM648702 1 0.1643 0.8620 0.956 0.044 0.000
#> GSM648597 1 0.1711 0.8662 0.960 0.032 0.008
#> GSM648603 1 0.0237 0.8760 0.996 0.000 0.004
#> GSM648606 1 0.6062 0.4535 0.616 0.000 0.384
#> GSM648613 1 0.6062 0.4535 0.616 0.000 0.384
#> GSM648619 1 0.2878 0.8261 0.904 0.000 0.096
#> GSM648654 1 0.0592 0.8739 0.988 0.000 0.012
#> GSM648663 1 0.5882 0.5239 0.652 0.000 0.348
#> GSM648670 1 0.7546 0.0166 0.560 0.396 0.044
#> GSM648707 3 0.8437 0.0658 0.092 0.388 0.520
#> GSM648615 2 0.6168 0.5044 0.412 0.588 0.000
#> GSM648643 2 0.6045 0.5472 0.380 0.620 0.000
#> GSM648650 1 0.1529 0.8655 0.960 0.040 0.000
#> GSM648656 2 0.5678 0.6136 0.316 0.684 0.000
#> GSM648715 1 0.6111 0.1633 0.604 0.396 0.000
#> GSM648598 1 0.0000 0.8758 1.000 0.000 0.000
#> GSM648601 1 0.0000 0.8758 1.000 0.000 0.000
#> GSM648602 1 0.0000 0.8758 1.000 0.000 0.000
#> GSM648604 1 0.0592 0.8739 0.988 0.000 0.012
#> GSM648614 1 0.6062 0.4535 0.616 0.000 0.384
#> GSM648624 1 0.0000 0.8758 1.000 0.000 0.000
#> GSM648625 1 0.2448 0.8379 0.924 0.076 0.000
#> GSM648629 1 0.0592 0.8739 0.988 0.000 0.012
#> GSM648634 1 0.0237 0.8757 0.996 0.004 0.000
#> GSM648648 1 0.1289 0.8681 0.968 0.032 0.000
#> GSM648651 1 0.0000 0.8758 1.000 0.000 0.000
#> GSM648657 1 0.1170 0.8725 0.976 0.016 0.008
#> GSM648660 1 0.0000 0.8758 1.000 0.000 0.000
#> GSM648697 1 0.0424 0.8757 0.992 0.008 0.000
#> GSM648710 1 0.0592 0.8739 0.988 0.000 0.012
#> GSM648591 1 0.7919 0.0292 0.556 0.380 0.064
#> GSM648592 1 0.1711 0.8662 0.960 0.032 0.008
#> GSM648607 1 0.2878 0.8261 0.904 0.000 0.096
#> GSM648611 1 0.4291 0.7509 0.820 0.000 0.180
#> GSM648612 1 0.2878 0.8261 0.904 0.000 0.096
#> GSM648616 3 0.7453 0.0287 0.036 0.436 0.528
#> GSM648617 1 0.1031 0.8706 0.976 0.024 0.000
#> GSM648626 1 0.0237 0.8760 0.996 0.000 0.004
#> GSM648711 1 0.2878 0.8261 0.904 0.000 0.096
#> GSM648712 1 0.2878 0.8261 0.904 0.000 0.096
#> GSM648713 1 0.2878 0.8261 0.904 0.000 0.096
#> GSM648714 1 0.6062 0.4535 0.616 0.000 0.384
#> GSM648716 1 0.2878 0.8261 0.904 0.000 0.096
#> GSM648717 1 0.6062 0.4535 0.616 0.000 0.384
#> GSM648590 1 0.2165 0.8477 0.936 0.064 0.000
#> GSM648596 1 0.6111 0.1633 0.604 0.396 0.000
#> GSM648642 2 0.6180 0.4975 0.416 0.584 0.000
#> GSM648696 1 0.1031 0.8706 0.976 0.024 0.000
#> GSM648705 1 0.1289 0.8681 0.968 0.032 0.000
#> GSM648718 2 0.6168 0.5044 0.412 0.588 0.000
#> GSM648599 1 0.0237 0.8762 0.996 0.000 0.004
#> GSM648608 1 0.0592 0.8739 0.988 0.000 0.012
#> GSM648609 1 0.0592 0.8739 0.988 0.000 0.012
#> GSM648610 1 0.0237 0.8762 0.996 0.000 0.004
#> GSM648633 1 0.1031 0.8706 0.976 0.024 0.000
#> GSM648644 2 0.2165 0.6546 0.064 0.936 0.000
#> GSM648652 1 0.1289 0.8681 0.968 0.032 0.000
#> GSM648653 1 0.0000 0.8758 1.000 0.000 0.000
#> GSM648658 1 0.1860 0.8567 0.948 0.052 0.000
#> GSM648659 1 0.3619 0.7685 0.864 0.136 0.000
#> GSM648662 1 0.0592 0.8739 0.988 0.000 0.012
#> GSM648665 1 0.0592 0.8739 0.988 0.000 0.012
#> GSM648666 1 0.0424 0.8757 0.992 0.008 0.000
#> GSM648680 1 0.1289 0.8681 0.968 0.032 0.000
#> GSM648684 1 0.0661 0.8760 0.988 0.004 0.008
#> GSM648709 2 0.6204 0.4776 0.424 0.576 0.000
#> GSM648719 1 0.0000 0.8758 1.000 0.000 0.000
#> GSM648627 1 0.4291 0.7509 0.820 0.000 0.180
#> GSM648637 2 0.6566 0.2700 0.012 0.612 0.376
#> GSM648638 2 0.6566 0.2700 0.012 0.612 0.376
#> GSM648641 3 0.4842 0.6551 0.224 0.000 0.776
#> GSM648672 2 0.1453 0.6190 0.008 0.968 0.024
#> GSM648674 2 0.7013 0.3546 0.036 0.640 0.324
#> GSM648703 2 0.1411 0.6477 0.036 0.964 0.000
#> GSM648631 3 0.1289 0.8654 0.032 0.000 0.968
#> GSM648669 2 0.2682 0.5805 0.004 0.920 0.076
#> GSM648671 2 0.2682 0.5805 0.004 0.920 0.076
#> GSM648678 2 0.0424 0.6248 0.008 0.992 0.000
#> GSM648679 2 0.3459 0.5739 0.012 0.892 0.096
#> GSM648681 1 0.6570 0.3923 0.668 0.308 0.024
#> GSM648686 3 0.1289 0.8654 0.032 0.000 0.968
#> GSM648689 3 0.4555 0.6944 0.200 0.000 0.800
#> GSM648690 3 0.1289 0.8654 0.032 0.000 0.968
#> GSM648691 3 0.1289 0.8654 0.032 0.000 0.968
#> GSM648693 3 0.1289 0.8654 0.032 0.000 0.968
#> GSM648700 2 0.1411 0.6477 0.036 0.964 0.000
#> GSM648630 3 0.1289 0.8654 0.032 0.000 0.968
#> GSM648632 3 0.1289 0.8654 0.032 0.000 0.968
#> GSM648639 3 0.1781 0.8466 0.020 0.020 0.960
#> GSM648640 3 0.1781 0.8466 0.020 0.020 0.960
#> GSM648668 2 0.1453 0.6190 0.008 0.968 0.024
#> GSM648676 2 0.1411 0.6477 0.036 0.964 0.000
#> GSM648692 3 0.1289 0.8654 0.032 0.000 0.968
#> GSM648694 3 0.1289 0.8654 0.032 0.000 0.968
#> GSM648699 2 0.1411 0.6477 0.036 0.964 0.000
#> GSM648701 2 0.1411 0.6477 0.036 0.964 0.000
#> GSM648673 2 0.2682 0.5805 0.004 0.920 0.076
#> GSM648677 2 0.1182 0.6264 0.012 0.976 0.012
#> GSM648687 3 0.4062 0.7599 0.164 0.000 0.836
#> GSM648688 3 0.4062 0.7599 0.164 0.000 0.836
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM648605 2 0.4277 0.669 0.280 0.720 0.000 0.000
#> GSM648618 1 0.5276 0.348 0.560 0.004 0.004 0.432
#> GSM648620 2 0.4277 0.669 0.280 0.720 0.000 0.000
#> GSM648646 2 0.2546 0.578 0.092 0.900 0.000 0.008
#> GSM648649 1 0.1118 0.873 0.964 0.036 0.000 0.000
#> GSM648675 1 0.5276 0.348 0.560 0.004 0.004 0.432
#> GSM648682 2 0.4103 0.666 0.256 0.744 0.000 0.000
#> GSM648698 2 0.4277 0.669 0.280 0.720 0.000 0.000
#> GSM648708 2 0.4277 0.669 0.280 0.720 0.000 0.000
#> GSM648628 1 0.3768 0.748 0.808 0.000 0.184 0.008
#> GSM648595 1 0.0921 0.876 0.972 0.028 0.000 0.000
#> GSM648635 1 0.1118 0.873 0.964 0.036 0.000 0.000
#> GSM648645 1 0.1059 0.878 0.972 0.016 0.000 0.012
#> GSM648647 2 0.4304 0.666 0.284 0.716 0.000 0.000
#> GSM648667 2 0.4989 0.309 0.472 0.528 0.000 0.000
#> GSM648695 2 0.4356 0.660 0.292 0.708 0.000 0.000
#> GSM648704 2 0.1867 0.482 0.000 0.928 0.000 0.072
#> GSM648706 2 0.0672 0.508 0.008 0.984 0.000 0.008
#> GSM648593 1 0.1637 0.859 0.940 0.060 0.000 0.000
#> GSM648594 1 0.1820 0.868 0.944 0.020 0.000 0.036
#> GSM648600 1 0.0921 0.876 0.972 0.028 0.000 0.000
#> GSM648621 1 0.0376 0.881 0.992 0.000 0.004 0.004
#> GSM648622 1 0.0188 0.881 0.996 0.000 0.000 0.004
#> GSM648623 1 0.0188 0.881 0.996 0.000 0.000 0.004
#> GSM648636 1 0.1389 0.867 0.952 0.048 0.000 0.000
#> GSM648655 1 0.1716 0.857 0.936 0.064 0.000 0.000
#> GSM648661 1 0.0657 0.881 0.984 0.004 0.012 0.000
#> GSM648664 1 0.0469 0.881 0.988 0.000 0.012 0.000
#> GSM648683 1 0.0672 0.882 0.984 0.008 0.008 0.000
#> GSM648685 1 0.0469 0.881 0.988 0.000 0.012 0.000
#> GSM648702 1 0.1389 0.867 0.952 0.048 0.000 0.000
#> GSM648597 1 0.1820 0.868 0.944 0.020 0.000 0.036
#> GSM648603 1 0.0336 0.881 0.992 0.000 0.000 0.008
#> GSM648606 1 0.5873 0.342 0.548 0.036 0.416 0.000
#> GSM648613 1 0.5873 0.342 0.548 0.036 0.416 0.000
#> GSM648619 1 0.2611 0.831 0.896 0.000 0.096 0.008
#> GSM648654 1 0.2255 0.843 0.920 0.068 0.012 0.000
#> GSM648663 1 0.5085 0.478 0.616 0.008 0.376 0.000
#> GSM648670 1 0.5421 0.315 0.548 0.008 0.004 0.440
#> GSM648707 4 0.5540 0.533 0.068 0.004 0.208 0.720
#> GSM648615 2 0.4250 0.669 0.276 0.724 0.000 0.000
#> GSM648643 2 0.4252 0.663 0.252 0.744 0.000 0.004
#> GSM648650 1 0.2345 0.827 0.900 0.100 0.000 0.000
#> GSM648656 2 0.3400 0.634 0.180 0.820 0.000 0.000
#> GSM648715 2 0.4989 0.309 0.472 0.528 0.000 0.000
#> GSM648598 1 0.0188 0.881 0.996 0.000 0.000 0.004
#> GSM648601 1 0.0188 0.881 0.996 0.000 0.000 0.004
#> GSM648602 1 0.0188 0.881 0.996 0.000 0.000 0.004
#> GSM648604 1 0.0657 0.880 0.984 0.000 0.012 0.004
#> GSM648614 1 0.5873 0.342 0.548 0.036 0.416 0.000
#> GSM648624 1 0.0188 0.881 0.996 0.000 0.000 0.004
#> GSM648625 1 0.2266 0.841 0.912 0.084 0.000 0.004
#> GSM648629 1 0.0657 0.880 0.984 0.000 0.012 0.004
#> GSM648634 1 0.0336 0.881 0.992 0.008 0.000 0.000
#> GSM648648 1 0.1118 0.873 0.964 0.036 0.000 0.000
#> GSM648651 1 0.0188 0.881 0.996 0.000 0.000 0.004
#> GSM648657 1 0.1059 0.878 0.972 0.016 0.000 0.012
#> GSM648660 1 0.0188 0.881 0.996 0.000 0.000 0.004
#> GSM648697 1 0.0469 0.881 0.988 0.012 0.000 0.000
#> GSM648710 1 0.0657 0.880 0.984 0.000 0.012 0.004
#> GSM648591 1 0.5372 0.307 0.544 0.000 0.012 0.444
#> GSM648592 1 0.1820 0.868 0.944 0.020 0.000 0.036
#> GSM648607 1 0.2611 0.831 0.896 0.000 0.096 0.008
#> GSM648611 1 0.3768 0.748 0.808 0.000 0.184 0.008
#> GSM648612 1 0.2611 0.831 0.896 0.000 0.096 0.008
#> GSM648616 4 0.4773 0.584 0.016 0.012 0.216 0.756
#> GSM648617 1 0.0921 0.876 0.972 0.028 0.000 0.000
#> GSM648626 1 0.0336 0.881 0.992 0.000 0.000 0.008
#> GSM648711 1 0.2611 0.831 0.896 0.000 0.096 0.008
#> GSM648712 1 0.2611 0.831 0.896 0.000 0.096 0.008
#> GSM648713 1 0.2611 0.831 0.896 0.000 0.096 0.008
#> GSM648714 1 0.5873 0.342 0.548 0.036 0.416 0.000
#> GSM648716 1 0.2611 0.831 0.896 0.000 0.096 0.008
#> GSM648717 1 0.5792 0.349 0.552 0.032 0.416 0.000
#> GSM648590 1 0.1867 0.851 0.928 0.072 0.000 0.000
#> GSM648596 2 0.5161 0.296 0.476 0.520 0.000 0.004
#> GSM648642 2 0.4277 0.669 0.280 0.720 0.000 0.000
#> GSM648696 1 0.0921 0.876 0.972 0.028 0.000 0.000
#> GSM648705 1 0.1118 0.873 0.964 0.036 0.000 0.000
#> GSM648718 2 0.4250 0.669 0.276 0.724 0.000 0.000
#> GSM648599 1 0.0376 0.881 0.992 0.000 0.004 0.004
#> GSM648608 1 0.0657 0.881 0.984 0.004 0.012 0.000
#> GSM648609 1 0.0657 0.880 0.984 0.000 0.012 0.004
#> GSM648610 1 0.0376 0.881 0.992 0.000 0.004 0.004
#> GSM648633 1 0.1004 0.878 0.972 0.024 0.000 0.004
#> GSM648644 2 0.1867 0.482 0.000 0.928 0.000 0.072
#> GSM648652 1 0.1118 0.873 0.964 0.036 0.000 0.000
#> GSM648653 1 0.0188 0.881 0.996 0.000 0.000 0.004
#> GSM648658 1 0.1557 0.862 0.944 0.056 0.000 0.000
#> GSM648659 1 0.3907 0.633 0.768 0.232 0.000 0.000
#> GSM648662 1 0.0657 0.881 0.984 0.004 0.012 0.000
#> GSM648665 1 0.0657 0.881 0.984 0.004 0.012 0.000
#> GSM648666 1 0.0336 0.881 0.992 0.008 0.000 0.000
#> GSM648680 1 0.1118 0.873 0.964 0.036 0.000 0.000
#> GSM648684 1 0.0672 0.882 0.984 0.008 0.008 0.000
#> GSM648709 2 0.4356 0.660 0.292 0.708 0.000 0.000
#> GSM648719 1 0.0188 0.881 0.996 0.000 0.000 0.004
#> GSM648627 1 0.3768 0.748 0.808 0.000 0.184 0.008
#> GSM648637 4 0.4946 0.745 0.004 0.088 0.124 0.784
#> GSM648638 4 0.4946 0.745 0.004 0.088 0.124 0.784
#> GSM648641 3 0.4603 0.638 0.160 0.032 0.796 0.012
#> GSM648672 2 0.4222 0.262 0.000 0.728 0.000 0.272
#> GSM648674 4 0.4794 0.758 0.016 0.100 0.076 0.808
#> GSM648703 2 0.3024 0.442 0.000 0.852 0.000 0.148
#> GSM648631 3 0.0000 0.860 0.000 0.000 1.000 0.000
#> GSM648669 4 0.4343 0.758 0.004 0.264 0.000 0.732
#> GSM648671 4 0.4343 0.758 0.004 0.264 0.000 0.732
#> GSM648678 2 0.3024 0.431 0.000 0.852 0.000 0.148
#> GSM648679 4 0.4368 0.767 0.004 0.244 0.004 0.748
#> GSM648681 1 0.7005 0.316 0.572 0.172 0.000 0.256
#> GSM648686 3 0.0188 0.859 0.000 0.000 0.996 0.004
#> GSM648689 3 0.4038 0.683 0.136 0.032 0.828 0.004
#> GSM648690 3 0.0188 0.859 0.000 0.000 0.996 0.004
#> GSM648691 3 0.0000 0.860 0.000 0.000 1.000 0.000
#> GSM648693 3 0.0000 0.860 0.000 0.000 1.000 0.000
#> GSM648700 2 0.3024 0.442 0.000 0.852 0.000 0.148
#> GSM648630 3 0.0000 0.860 0.000 0.000 1.000 0.000
#> GSM648632 3 0.0000 0.860 0.000 0.000 1.000 0.000
#> GSM648639 3 0.4382 0.614 0.000 0.000 0.704 0.296
#> GSM648640 3 0.4382 0.614 0.000 0.000 0.704 0.296
#> GSM648668 2 0.4222 0.262 0.000 0.728 0.000 0.272
#> GSM648676 2 0.3024 0.442 0.000 0.852 0.000 0.148
#> GSM648692 3 0.0000 0.860 0.000 0.000 1.000 0.000
#> GSM648694 3 0.0000 0.860 0.000 0.000 1.000 0.000
#> GSM648699 2 0.3024 0.442 0.000 0.852 0.000 0.148
#> GSM648701 2 0.3024 0.442 0.000 0.852 0.000 0.148
#> GSM648673 4 0.4343 0.758 0.004 0.264 0.000 0.732
#> GSM648677 2 0.3610 0.378 0.000 0.800 0.000 0.200
#> GSM648687 3 0.3597 0.685 0.148 0.000 0.836 0.016
#> GSM648688 3 0.3597 0.685 0.148 0.000 0.836 0.016
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM648605 2 0.1851 0.710 0.088 0.912 0.000 0.000 0.000
#> GSM648618 1 0.5680 0.261 0.508 0.012 0.000 0.052 0.428
#> GSM648620 2 0.1851 0.710 0.088 0.912 0.000 0.000 0.000
#> GSM648646 2 0.2674 0.652 0.012 0.868 0.000 0.120 0.000
#> GSM648649 1 0.2153 0.847 0.916 0.044 0.000 0.040 0.000
#> GSM648675 1 0.5769 0.258 0.504 0.016 0.000 0.052 0.428
#> GSM648682 2 0.1478 0.705 0.064 0.936 0.000 0.000 0.000
#> GSM648698 2 0.1851 0.710 0.088 0.912 0.000 0.000 0.000
#> GSM648708 2 0.1851 0.710 0.088 0.912 0.000 0.000 0.000
#> GSM648628 1 0.4780 0.710 0.744 0.008 0.176 0.068 0.004
#> GSM648595 1 0.1753 0.853 0.936 0.032 0.000 0.032 0.000
#> GSM648635 1 0.1915 0.849 0.928 0.032 0.000 0.040 0.000
#> GSM648645 1 0.1701 0.854 0.944 0.016 0.000 0.028 0.012
#> GSM648647 2 0.2020 0.704 0.100 0.900 0.000 0.000 0.000
#> GSM648667 2 0.4374 0.486 0.272 0.700 0.000 0.028 0.000
#> GSM648695 2 0.2127 0.697 0.108 0.892 0.000 0.000 0.000
#> GSM648704 2 0.3586 0.575 0.000 0.736 0.000 0.264 0.000
#> GSM648706 2 0.3039 0.614 0.000 0.808 0.000 0.192 0.000
#> GSM648593 1 0.2438 0.838 0.900 0.060 0.000 0.040 0.000
#> GSM648594 1 0.2409 0.847 0.912 0.016 0.000 0.028 0.044
#> GSM648600 1 0.1753 0.850 0.936 0.032 0.000 0.032 0.000
#> GSM648621 1 0.1331 0.855 0.952 0.008 0.000 0.040 0.000
#> GSM648622 1 0.0404 0.857 0.988 0.000 0.000 0.012 0.000
#> GSM648623 1 0.0510 0.857 0.984 0.000 0.000 0.016 0.000
#> GSM648636 1 0.2228 0.844 0.912 0.048 0.000 0.040 0.000
#> GSM648655 1 0.2632 0.832 0.888 0.072 0.000 0.040 0.000
#> GSM648661 1 0.2464 0.847 0.908 0.048 0.012 0.032 0.000
#> GSM648664 1 0.1710 0.855 0.944 0.020 0.012 0.024 0.000
#> GSM648683 1 0.1948 0.857 0.932 0.024 0.008 0.036 0.000
#> GSM648685 1 0.1710 0.855 0.944 0.020 0.012 0.024 0.000
#> GSM648702 1 0.2228 0.844 0.912 0.048 0.000 0.040 0.000
#> GSM648597 1 0.2409 0.847 0.912 0.016 0.000 0.028 0.044
#> GSM648603 1 0.0609 0.857 0.980 0.000 0.000 0.020 0.000
#> GSM648606 1 0.7051 0.123 0.444 0.108 0.400 0.040 0.008
#> GSM648613 1 0.7051 0.123 0.444 0.108 0.400 0.040 0.008
#> GSM648619 1 0.3512 0.795 0.840 0.000 0.088 0.068 0.004
#> GSM648654 1 0.4547 0.640 0.712 0.252 0.012 0.024 0.000
#> GSM648663 1 0.5851 0.403 0.560 0.044 0.368 0.024 0.004
#> GSM648670 1 0.5084 0.260 0.520 0.016 0.000 0.012 0.452
#> GSM648707 5 0.2363 0.399 0.024 0.000 0.012 0.052 0.912
#> GSM648615 2 0.1792 0.710 0.084 0.916 0.000 0.000 0.000
#> GSM648643 2 0.1764 0.704 0.064 0.928 0.000 0.008 0.000
#> GSM648650 1 0.4193 0.692 0.748 0.212 0.000 0.040 0.000
#> GSM648656 2 0.1485 0.677 0.020 0.948 0.000 0.032 0.000
#> GSM648715 2 0.4374 0.486 0.272 0.700 0.000 0.028 0.000
#> GSM648598 1 0.0404 0.857 0.988 0.000 0.000 0.012 0.000
#> GSM648601 1 0.0609 0.858 0.980 0.000 0.000 0.020 0.000
#> GSM648602 1 0.1121 0.855 0.956 0.000 0.000 0.044 0.000
#> GSM648604 1 0.1393 0.854 0.956 0.008 0.012 0.024 0.000
#> GSM648614 1 0.7051 0.123 0.444 0.108 0.400 0.040 0.008
#> GSM648624 1 0.0404 0.857 0.988 0.000 0.000 0.012 0.000
#> GSM648625 1 0.2331 0.835 0.900 0.080 0.000 0.020 0.000
#> GSM648629 1 0.1393 0.854 0.956 0.008 0.012 0.024 0.000
#> GSM648634 1 0.1741 0.860 0.936 0.024 0.000 0.040 0.000
#> GSM648648 1 0.1915 0.849 0.928 0.032 0.000 0.040 0.000
#> GSM648651 1 0.0609 0.858 0.980 0.000 0.000 0.020 0.000
#> GSM648657 1 0.1682 0.855 0.944 0.012 0.000 0.032 0.012
#> GSM648660 1 0.0404 0.857 0.988 0.000 0.000 0.012 0.000
#> GSM648697 1 0.1741 0.858 0.936 0.024 0.000 0.040 0.000
#> GSM648710 1 0.1393 0.854 0.956 0.008 0.012 0.024 0.000
#> GSM648591 1 0.5738 0.218 0.496 0.008 0.004 0.052 0.440
#> GSM648592 1 0.2409 0.847 0.912 0.016 0.000 0.028 0.044
#> GSM648607 1 0.3317 0.802 0.852 0.000 0.088 0.056 0.004
#> GSM648611 1 0.4780 0.710 0.744 0.008 0.176 0.068 0.004
#> GSM648612 1 0.3574 0.793 0.836 0.000 0.088 0.072 0.004
#> GSM648616 5 0.0451 0.414 0.000 0.000 0.008 0.004 0.988
#> GSM648617 1 0.1818 0.853 0.932 0.024 0.000 0.044 0.000
#> GSM648626 1 0.0703 0.856 0.976 0.000 0.000 0.024 0.000
#> GSM648711 1 0.3317 0.802 0.852 0.000 0.088 0.056 0.004
#> GSM648712 1 0.3574 0.793 0.836 0.000 0.088 0.072 0.004
#> GSM648713 1 0.3449 0.798 0.844 0.000 0.088 0.064 0.004
#> GSM648714 1 0.7051 0.123 0.444 0.108 0.400 0.040 0.008
#> GSM648716 1 0.3512 0.795 0.840 0.000 0.088 0.068 0.004
#> GSM648717 1 0.6369 0.259 0.504 0.048 0.400 0.040 0.008
#> GSM648590 1 0.2632 0.833 0.888 0.072 0.000 0.040 0.000
#> GSM648596 2 0.4734 0.465 0.288 0.676 0.000 0.028 0.008
#> GSM648642 2 0.1851 0.710 0.088 0.912 0.000 0.000 0.000
#> GSM648696 1 0.1836 0.851 0.932 0.036 0.000 0.032 0.000
#> GSM648705 1 0.1915 0.849 0.928 0.032 0.000 0.040 0.000
#> GSM648718 2 0.1792 0.710 0.084 0.916 0.000 0.000 0.000
#> GSM648599 1 0.1331 0.856 0.952 0.008 0.000 0.040 0.000
#> GSM648608 1 0.1893 0.856 0.936 0.024 0.012 0.028 0.000
#> GSM648609 1 0.1393 0.854 0.956 0.008 0.012 0.024 0.000
#> GSM648610 1 0.1331 0.856 0.952 0.008 0.000 0.040 0.000
#> GSM648633 1 0.1106 0.857 0.964 0.012 0.000 0.024 0.000
#> GSM648644 2 0.3586 0.575 0.000 0.736 0.000 0.264 0.000
#> GSM648652 1 0.1915 0.849 0.928 0.032 0.000 0.040 0.000
#> GSM648653 1 0.1121 0.853 0.956 0.000 0.000 0.044 0.000
#> GSM648658 1 0.2370 0.840 0.904 0.056 0.000 0.040 0.000
#> GSM648659 1 0.5095 0.321 0.560 0.400 0.000 0.040 0.000
#> GSM648662 1 0.2536 0.846 0.904 0.052 0.012 0.032 0.000
#> GSM648665 1 0.2536 0.846 0.904 0.052 0.012 0.032 0.000
#> GSM648666 1 0.1568 0.858 0.944 0.020 0.000 0.036 0.000
#> GSM648680 1 0.1915 0.849 0.928 0.032 0.000 0.040 0.000
#> GSM648684 1 0.1948 0.857 0.932 0.024 0.008 0.036 0.000
#> GSM648709 2 0.2179 0.694 0.112 0.888 0.000 0.000 0.000
#> GSM648719 1 0.0404 0.857 0.988 0.000 0.000 0.012 0.000
#> GSM648627 1 0.4505 0.715 0.752 0.000 0.176 0.068 0.004
#> GSM648637 5 0.4515 0.295 0.000 0.056 0.028 0.136 0.780
#> GSM648638 5 0.4515 0.295 0.000 0.056 0.028 0.136 0.780
#> GSM648641 3 0.5129 0.682 0.104 0.056 0.772 0.040 0.028
#> GSM648672 2 0.4824 0.282 0.000 0.512 0.000 0.468 0.020
#> GSM648674 5 0.4010 0.196 0.000 0.056 0.000 0.160 0.784
#> GSM648703 2 0.4074 0.501 0.000 0.636 0.000 0.364 0.000
#> GSM648631 3 0.0000 0.896 0.000 0.000 1.000 0.000 0.000
#> GSM648669 4 0.4982 1.000 0.000 0.032 0.000 0.556 0.412
#> GSM648671 4 0.4982 1.000 0.000 0.032 0.000 0.556 0.412
#> GSM648678 2 0.3999 0.503 0.000 0.656 0.000 0.344 0.000
#> GSM648679 5 0.5165 -0.812 0.000 0.040 0.000 0.448 0.512
#> GSM648681 1 0.7349 0.019 0.392 0.316 0.000 0.028 0.264
#> GSM648686 3 0.0290 0.893 0.000 0.000 0.992 0.000 0.008
#> GSM648689 3 0.4310 0.731 0.084 0.056 0.816 0.036 0.008
#> GSM648690 3 0.0290 0.893 0.000 0.000 0.992 0.000 0.008
#> GSM648691 3 0.0000 0.896 0.000 0.000 1.000 0.000 0.000
#> GSM648693 3 0.0000 0.896 0.000 0.000 1.000 0.000 0.000
#> GSM648700 2 0.4074 0.501 0.000 0.636 0.000 0.364 0.000
#> GSM648630 3 0.0000 0.896 0.000 0.000 1.000 0.000 0.000
#> GSM648632 3 0.0000 0.896 0.000 0.000 1.000 0.000 0.000
#> GSM648639 5 0.6491 0.333 0.000 0.000 0.228 0.284 0.488
#> GSM648640 5 0.6491 0.333 0.000 0.000 0.228 0.284 0.488
#> GSM648668 2 0.4824 0.282 0.000 0.512 0.000 0.468 0.020
#> GSM648676 2 0.4074 0.501 0.000 0.636 0.000 0.364 0.000
#> GSM648692 3 0.0000 0.896 0.000 0.000 1.000 0.000 0.000
#> GSM648694 3 0.0000 0.896 0.000 0.000 1.000 0.000 0.000
#> GSM648699 2 0.4074 0.501 0.000 0.636 0.000 0.364 0.000
#> GSM648701 2 0.4074 0.501 0.000 0.636 0.000 0.364 0.000
#> GSM648673 4 0.4982 1.000 0.000 0.032 0.000 0.556 0.412
#> GSM648677 2 0.4182 0.436 0.000 0.600 0.000 0.400 0.000
#> GSM648687 3 0.4272 0.682 0.124 0.000 0.796 0.060 0.020
#> GSM648688 3 0.4272 0.682 0.124 0.000 0.796 0.060 0.020
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM648605 2 0.0632 0.6793 0.024 0.976 0.000 0.000 0.000 0.000
#> GSM648618 1 0.6264 0.1469 0.444 0.016 0.000 0.012 0.388 0.140
#> GSM648620 2 0.0632 0.6793 0.024 0.976 0.000 0.000 0.000 0.000
#> GSM648646 2 0.3308 0.5805 0.000 0.828 0.000 0.096 0.004 0.072
#> GSM648649 1 0.2513 0.8133 0.888 0.060 0.000 0.008 0.000 0.044
#> GSM648675 1 0.6335 0.1427 0.440 0.020 0.000 0.012 0.388 0.140
#> GSM648682 2 0.0976 0.6741 0.016 0.968 0.000 0.008 0.000 0.008
#> GSM648698 2 0.0632 0.6793 0.024 0.976 0.000 0.000 0.000 0.000
#> GSM648708 2 0.0632 0.6793 0.024 0.976 0.000 0.000 0.000 0.000
#> GSM648628 1 0.5308 0.6143 0.620 0.008 0.112 0.000 0.004 0.256
#> GSM648595 1 0.2457 0.8104 0.880 0.036 0.000 0.000 0.000 0.084
#> GSM648635 1 0.2259 0.8159 0.904 0.044 0.000 0.008 0.000 0.044
#> GSM648645 1 0.1922 0.8224 0.924 0.024 0.000 0.000 0.012 0.040
#> GSM648647 2 0.0865 0.6752 0.036 0.964 0.000 0.000 0.000 0.000
#> GSM648667 2 0.3529 0.4638 0.208 0.764 0.000 0.000 0.000 0.028
#> GSM648695 2 0.1007 0.6692 0.044 0.956 0.000 0.000 0.000 0.000
#> GSM648704 2 0.4818 0.4293 0.000 0.672 0.000 0.212 0.004 0.112
#> GSM648706 2 0.4256 0.5146 0.000 0.744 0.000 0.140 0.004 0.112
#> GSM648593 1 0.2966 0.8021 0.864 0.072 0.000 0.020 0.000 0.044
#> GSM648594 1 0.2688 0.8148 0.884 0.024 0.000 0.000 0.044 0.048
#> GSM648600 1 0.2003 0.8185 0.912 0.044 0.000 0.000 0.000 0.044
#> GSM648621 1 0.2212 0.8089 0.880 0.008 0.000 0.000 0.000 0.112
#> GSM648622 1 0.0458 0.8277 0.984 0.000 0.000 0.000 0.000 0.016
#> GSM648623 1 0.0632 0.8282 0.976 0.000 0.000 0.000 0.000 0.024
#> GSM648636 1 0.2583 0.8126 0.888 0.052 0.000 0.016 0.000 0.044
#> GSM648655 1 0.3178 0.7942 0.848 0.088 0.000 0.020 0.000 0.044
#> GSM648661 1 0.2733 0.8148 0.864 0.056 0.000 0.000 0.000 0.080
#> GSM648664 1 0.2221 0.8235 0.896 0.032 0.000 0.000 0.000 0.072
#> GSM648683 1 0.2263 0.8277 0.900 0.036 0.000 0.004 0.000 0.060
#> GSM648685 1 0.2221 0.8235 0.896 0.032 0.000 0.000 0.000 0.072
#> GSM648702 1 0.2583 0.8126 0.888 0.052 0.000 0.016 0.000 0.044
#> GSM648597 1 0.2519 0.8152 0.892 0.016 0.000 0.000 0.044 0.048
#> GSM648603 1 0.0777 0.8276 0.972 0.000 0.000 0.000 0.004 0.024
#> GSM648606 1 0.7261 0.0448 0.368 0.068 0.284 0.008 0.000 0.272
#> GSM648613 1 0.7261 0.0448 0.368 0.068 0.284 0.008 0.000 0.272
#> GSM648619 1 0.3823 0.7389 0.760 0.000 0.044 0.000 0.004 0.192
#> GSM648654 1 0.4720 0.5514 0.624 0.304 0.000 0.000 0.000 0.072
#> GSM648663 1 0.6632 0.3542 0.496 0.044 0.268 0.008 0.000 0.184
#> GSM648670 1 0.5509 0.1432 0.464 0.016 0.000 0.004 0.448 0.068
#> GSM648707 5 0.2697 0.5784 0.012 0.000 0.004 0.020 0.876 0.088
#> GSM648615 2 0.0777 0.6794 0.024 0.972 0.000 0.000 0.000 0.004
#> GSM648643 2 0.1350 0.6705 0.020 0.952 0.000 0.020 0.000 0.008
#> GSM648650 1 0.4283 0.6427 0.704 0.244 0.000 0.008 0.000 0.044
#> GSM648656 2 0.1780 0.6331 0.000 0.924 0.000 0.048 0.000 0.028
#> GSM648715 2 0.3529 0.4638 0.208 0.764 0.000 0.000 0.000 0.028
#> GSM648598 1 0.0458 0.8277 0.984 0.000 0.000 0.000 0.000 0.016
#> GSM648601 1 0.1010 0.8282 0.960 0.004 0.000 0.000 0.000 0.036
#> GSM648602 1 0.1644 0.8230 0.920 0.004 0.000 0.000 0.000 0.076
#> GSM648604 1 0.1895 0.8228 0.912 0.016 0.000 0.000 0.000 0.072
#> GSM648614 1 0.7291 0.0462 0.368 0.072 0.284 0.008 0.000 0.268
#> GSM648624 1 0.0458 0.8277 0.984 0.000 0.000 0.000 0.000 0.016
#> GSM648625 1 0.2361 0.8122 0.884 0.088 0.000 0.000 0.000 0.028
#> GSM648629 1 0.1895 0.8228 0.912 0.016 0.000 0.000 0.000 0.072
#> GSM648634 1 0.2331 0.8316 0.888 0.032 0.000 0.000 0.000 0.080
#> GSM648648 1 0.2259 0.8159 0.904 0.044 0.000 0.008 0.000 0.044
#> GSM648651 1 0.1010 0.8282 0.960 0.004 0.000 0.000 0.000 0.036
#> GSM648657 1 0.1820 0.8237 0.928 0.016 0.000 0.000 0.012 0.044
#> GSM648660 1 0.0458 0.8277 0.984 0.000 0.000 0.000 0.000 0.016
#> GSM648697 1 0.2251 0.8282 0.904 0.036 0.000 0.008 0.000 0.052
#> GSM648710 1 0.1895 0.8228 0.912 0.016 0.000 0.000 0.000 0.072
#> GSM648591 1 0.6206 0.1030 0.440 0.008 0.000 0.020 0.400 0.132
#> GSM648592 1 0.2688 0.8148 0.884 0.024 0.000 0.000 0.044 0.048
#> GSM648607 1 0.3628 0.7537 0.784 0.000 0.044 0.000 0.004 0.168
#> GSM648611 1 0.5308 0.6143 0.620 0.008 0.112 0.000 0.004 0.256
#> GSM648612 1 0.3853 0.7365 0.756 0.000 0.044 0.000 0.004 0.196
#> GSM648616 5 0.0291 0.6058 0.000 0.000 0.004 0.004 0.992 0.000
#> GSM648617 1 0.1970 0.8220 0.912 0.028 0.000 0.000 0.000 0.060
#> GSM648626 1 0.0858 0.8279 0.968 0.000 0.000 0.000 0.004 0.028
#> GSM648711 1 0.3628 0.7537 0.784 0.000 0.044 0.000 0.004 0.168
#> GSM648712 1 0.3883 0.7344 0.752 0.000 0.044 0.000 0.004 0.200
#> GSM648713 1 0.3728 0.7465 0.772 0.000 0.044 0.000 0.004 0.180
#> GSM648714 1 0.7261 0.0448 0.368 0.068 0.284 0.008 0.000 0.272
#> GSM648716 1 0.3823 0.7389 0.760 0.000 0.044 0.000 0.004 0.192
#> GSM648717 1 0.6650 0.1735 0.420 0.020 0.284 0.008 0.000 0.268
#> GSM648590 1 0.3127 0.7974 0.852 0.084 0.000 0.020 0.000 0.044
#> GSM648596 2 0.3888 0.4427 0.224 0.740 0.000 0.000 0.008 0.028
#> GSM648642 2 0.0632 0.6793 0.024 0.976 0.000 0.000 0.000 0.000
#> GSM648696 1 0.2070 0.8186 0.908 0.048 0.000 0.000 0.000 0.044
#> GSM648705 1 0.2259 0.8159 0.904 0.044 0.000 0.008 0.000 0.044
#> GSM648718 2 0.0777 0.6794 0.024 0.972 0.000 0.000 0.000 0.004
#> GSM648599 1 0.1895 0.8264 0.912 0.016 0.000 0.000 0.000 0.072
#> GSM648608 1 0.2237 0.8261 0.896 0.036 0.000 0.000 0.000 0.068
#> GSM648609 1 0.1895 0.8228 0.912 0.016 0.000 0.000 0.000 0.072
#> GSM648610 1 0.1895 0.8264 0.912 0.016 0.000 0.000 0.000 0.072
#> GSM648633 1 0.1151 0.8276 0.956 0.012 0.000 0.000 0.000 0.032
#> GSM648644 2 0.4818 0.4293 0.000 0.672 0.000 0.212 0.004 0.112
#> GSM648652 1 0.2259 0.8159 0.904 0.044 0.000 0.008 0.000 0.044
#> GSM648653 1 0.1700 0.8215 0.916 0.004 0.000 0.000 0.000 0.080
#> GSM648658 1 0.2852 0.8063 0.872 0.064 0.000 0.020 0.000 0.044
#> GSM648659 1 0.5163 0.1767 0.492 0.444 0.000 0.020 0.000 0.044
#> GSM648662 1 0.2852 0.8118 0.856 0.064 0.000 0.000 0.000 0.080
#> GSM648665 1 0.2852 0.8118 0.856 0.064 0.000 0.000 0.000 0.080
#> GSM648666 1 0.2046 0.8279 0.916 0.032 0.000 0.008 0.000 0.044
#> GSM648680 1 0.2259 0.8159 0.904 0.044 0.000 0.008 0.000 0.044
#> GSM648684 1 0.2263 0.8277 0.900 0.036 0.000 0.004 0.000 0.060
#> GSM648709 2 0.1075 0.6667 0.048 0.952 0.000 0.000 0.000 0.000
#> GSM648719 1 0.0458 0.8277 0.984 0.000 0.000 0.000 0.000 0.016
#> GSM648627 1 0.4818 0.6626 0.672 0.000 0.112 0.000 0.004 0.212
#> GSM648637 5 0.4191 0.5763 0.000 0.024 0.020 0.152 0.776 0.028
#> GSM648638 5 0.4191 0.5763 0.000 0.024 0.020 0.152 0.776 0.028
#> GSM648641 3 0.5134 0.5522 0.020 0.016 0.580 0.008 0.012 0.364
#> GSM648672 4 0.4303 0.0529 0.000 0.460 0.000 0.524 0.012 0.004
#> GSM648674 5 0.3648 0.5360 0.000 0.024 0.000 0.188 0.776 0.012
#> GSM648703 2 0.4894 0.2131 0.000 0.556 0.000 0.376 0.000 0.068
#> GSM648631 3 0.0000 0.8617 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648669 4 0.3934 0.3150 0.000 0.000 0.000 0.676 0.304 0.020
#> GSM648671 4 0.3934 0.3150 0.000 0.000 0.000 0.676 0.304 0.020
#> GSM648678 2 0.5230 0.2830 0.000 0.592 0.000 0.292 0.004 0.112
#> GSM648679 5 0.4184 -0.1185 0.000 0.000 0.000 0.484 0.504 0.012
#> GSM648681 2 0.6929 -0.0264 0.316 0.376 0.000 0.004 0.260 0.044
#> GSM648686 3 0.1610 0.8324 0.000 0.000 0.916 0.000 0.000 0.084
#> GSM648689 3 0.4642 0.5965 0.016 0.016 0.624 0.008 0.000 0.336
#> GSM648690 3 0.1610 0.8324 0.000 0.000 0.916 0.000 0.000 0.084
#> GSM648691 3 0.0000 0.8617 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648693 3 0.0000 0.8617 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648700 2 0.4894 0.2131 0.000 0.556 0.000 0.376 0.000 0.068
#> GSM648630 3 0.0000 0.8617 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648632 3 0.0000 0.8617 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648639 5 0.6467 0.4476 0.000 0.000 0.040 0.256 0.488 0.216
#> GSM648640 5 0.6467 0.4476 0.000 0.000 0.040 0.256 0.488 0.216
#> GSM648668 4 0.4303 0.0529 0.000 0.460 0.000 0.524 0.012 0.004
#> GSM648676 2 0.4894 0.2131 0.000 0.556 0.000 0.376 0.000 0.068
#> GSM648692 3 0.0000 0.8617 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648694 3 0.0000 0.8617 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648699 2 0.4894 0.2131 0.000 0.556 0.000 0.376 0.000 0.068
#> GSM648701 2 0.4894 0.2131 0.000 0.556 0.000 0.376 0.000 0.068
#> GSM648673 4 0.3934 0.3150 0.000 0.000 0.000 0.676 0.304 0.020
#> GSM648677 2 0.4845 0.1006 0.000 0.540 0.000 0.400 0.000 0.060
#> GSM648687 3 0.4605 0.6093 0.096 0.000 0.736 0.012 0.008 0.148
#> GSM648688 3 0.4605 0.6093 0.096 0.000 0.736 0.012 0.008 0.148
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)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
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) development.stage(p) other(p) k
#> SD:hclust 121 1.66e-03 0.000155 2.89e-09 2
#> SD:hclust 104 1.93e-17 0.259845 2.13e-18 3
#> SD:hclust 105 1.37e-20 0.002788 7.97e-28 4
#> SD:hclust 104 1.10e-16 0.025770 7.66e-21 5
#> SD:hclust 98 3.23e-19 0.000678 4.96e-26 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 51941 rows and 130 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 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 0.479 0.730 0.849 0.4474 0.544 0.544
#> 3 3 0.730 0.849 0.915 0.3382 0.749 0.576
#> 4 4 0.572 0.660 0.791 0.1489 0.862 0.680
#> 5 5 0.641 0.669 0.802 0.0891 0.862 0.612
#> 6 6 0.628 0.614 0.757 0.0565 0.957 0.831
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
#> GSM648605 2 0.9983 -0.0511 0.476 0.524
#> GSM648618 1 0.0000 0.8525 1.000 0.000
#> GSM648620 1 0.8327 0.6839 0.736 0.264
#> GSM648646 2 0.4298 0.7589 0.088 0.912
#> GSM648649 1 0.7883 0.7135 0.764 0.236
#> GSM648675 1 0.9866 0.3524 0.568 0.432
#> GSM648682 2 0.6438 0.6914 0.164 0.836
#> GSM648698 2 0.9522 0.3150 0.372 0.628
#> GSM648708 1 0.8443 0.6741 0.728 0.272
#> GSM648628 1 0.4298 0.7771 0.912 0.088
#> GSM648595 1 0.8267 0.6884 0.740 0.260
#> GSM648635 1 0.7815 0.7169 0.768 0.232
#> GSM648645 1 0.1184 0.8514 0.984 0.016
#> GSM648647 1 0.8555 0.6640 0.720 0.280
#> GSM648667 1 0.8327 0.6839 0.736 0.264
#> GSM648695 1 0.8443 0.6741 0.728 0.272
#> GSM648704 2 0.4298 0.7589 0.088 0.912
#> GSM648706 2 0.4298 0.7589 0.088 0.912
#> GSM648593 1 0.7883 0.7135 0.764 0.236
#> GSM648594 1 0.7815 0.7169 0.768 0.232
#> GSM648600 1 0.1184 0.8514 0.984 0.016
#> GSM648621 1 0.0000 0.8525 1.000 0.000
#> GSM648622 1 0.0000 0.8525 1.000 0.000
#> GSM648623 1 0.0376 0.8505 0.996 0.004
#> GSM648636 1 0.7883 0.7135 0.764 0.236
#> GSM648655 1 0.7883 0.7135 0.764 0.236
#> GSM648661 1 0.0376 0.8505 0.996 0.004
#> GSM648664 1 0.0000 0.8525 1.000 0.000
#> GSM648683 1 0.0000 0.8525 1.000 0.000
#> GSM648685 1 0.0000 0.8525 1.000 0.000
#> GSM648702 1 0.7883 0.7135 0.764 0.236
#> GSM648597 1 0.1184 0.8514 0.984 0.016
#> GSM648603 1 0.0000 0.8525 1.000 0.000
#> GSM648606 1 0.2948 0.8153 0.948 0.052
#> GSM648613 1 0.4431 0.7744 0.908 0.092
#> GSM648619 1 0.2948 0.8153 0.948 0.052
#> GSM648654 1 0.1843 0.8344 0.972 0.028
#> GSM648663 1 0.2948 0.8153 0.948 0.052
#> GSM648670 2 0.9922 0.0633 0.448 0.552
#> GSM648707 2 0.8555 0.6756 0.280 0.720
#> GSM648615 2 0.9970 -0.0179 0.468 0.532
#> GSM648643 2 0.9491 0.3258 0.368 0.632
#> GSM648650 1 0.8327 0.6839 0.736 0.264
#> GSM648656 2 0.4298 0.7589 0.088 0.912
#> GSM648715 1 0.8555 0.6640 0.720 0.280
#> GSM648598 1 0.1184 0.8514 0.984 0.016
#> GSM648601 1 0.1184 0.8514 0.984 0.016
#> GSM648602 1 0.0000 0.8525 1.000 0.000
#> GSM648604 1 0.0000 0.8525 1.000 0.000
#> GSM648614 1 0.0000 0.8525 1.000 0.000
#> GSM648624 1 0.0000 0.8525 1.000 0.000
#> GSM648625 1 0.1184 0.8514 0.984 0.016
#> GSM648629 1 0.0000 0.8525 1.000 0.000
#> GSM648634 1 0.1184 0.8514 0.984 0.016
#> GSM648648 1 0.7883 0.7135 0.764 0.236
#> GSM648651 1 0.0000 0.8525 1.000 0.000
#> GSM648657 1 0.1184 0.8514 0.984 0.016
#> GSM648660 1 0.1184 0.8514 0.984 0.016
#> GSM648697 1 0.1184 0.8514 0.984 0.016
#> GSM648710 1 0.0000 0.8525 1.000 0.000
#> GSM648591 1 0.0376 0.8505 0.996 0.004
#> GSM648592 1 0.1633 0.8483 0.976 0.024
#> GSM648607 1 0.2236 0.8283 0.964 0.036
#> GSM648611 1 0.4298 0.7771 0.912 0.088
#> GSM648612 1 0.2948 0.8153 0.948 0.052
#> GSM648616 2 0.8499 0.6788 0.276 0.724
#> GSM648617 1 0.1184 0.8514 0.984 0.016
#> GSM648626 1 0.0376 0.8505 0.996 0.004
#> GSM648711 1 0.2423 0.8252 0.960 0.040
#> GSM648712 1 0.2948 0.8153 0.948 0.052
#> GSM648713 1 0.2948 0.8153 0.948 0.052
#> GSM648714 1 0.4298 0.7908 0.912 0.088
#> GSM648716 1 0.2948 0.8153 0.948 0.052
#> GSM648717 1 0.4298 0.7771 0.912 0.088
#> GSM648590 1 0.8555 0.6640 0.720 0.280
#> GSM648596 1 0.9661 0.4586 0.608 0.392
#> GSM648642 1 0.8555 0.6640 0.720 0.280
#> GSM648696 1 0.7883 0.7135 0.764 0.236
#> GSM648705 1 0.8267 0.6884 0.740 0.260
#> GSM648718 2 0.9998 -0.1159 0.492 0.508
#> GSM648599 1 0.0000 0.8525 1.000 0.000
#> GSM648608 1 0.0000 0.8525 1.000 0.000
#> GSM648609 1 0.0000 0.8525 1.000 0.000
#> GSM648610 1 0.0000 0.8525 1.000 0.000
#> GSM648633 1 0.1184 0.8514 0.984 0.016
#> GSM648644 2 0.4298 0.7589 0.088 0.912
#> GSM648652 1 0.7815 0.7169 0.768 0.232
#> GSM648653 1 0.0000 0.8525 1.000 0.000
#> GSM648658 1 0.7745 0.7200 0.772 0.228
#> GSM648659 1 0.8555 0.6640 0.720 0.280
#> GSM648662 1 0.0000 0.8525 1.000 0.000
#> GSM648665 1 0.0000 0.8525 1.000 0.000
#> GSM648666 1 0.0000 0.8525 1.000 0.000
#> GSM648680 1 0.7745 0.7200 0.772 0.228
#> GSM648684 1 0.0000 0.8525 1.000 0.000
#> GSM648709 1 0.8499 0.6694 0.724 0.276
#> GSM648719 1 0.1184 0.8514 0.984 0.016
#> GSM648627 1 0.2948 0.8153 0.948 0.052
#> GSM648637 2 0.4298 0.7589 0.088 0.912
#> GSM648638 2 0.0000 0.7366 0.000 1.000
#> GSM648641 2 0.8555 0.6756 0.280 0.720
#> GSM648672 2 0.4298 0.7589 0.088 0.912
#> GSM648674 2 0.4298 0.7589 0.088 0.912
#> GSM648703 2 0.4298 0.7589 0.088 0.912
#> GSM648631 2 0.8555 0.6756 0.280 0.720
#> GSM648669 2 0.2778 0.7524 0.048 0.952
#> GSM648671 2 0.2778 0.7524 0.048 0.952
#> GSM648678 2 0.4298 0.7589 0.088 0.912
#> GSM648679 2 0.2778 0.7524 0.048 0.952
#> GSM648681 1 0.9944 0.2785 0.544 0.456
#> GSM648686 2 0.8081 0.6885 0.248 0.752
#> GSM648689 2 0.8499 0.6788 0.276 0.724
#> GSM648690 2 0.8267 0.6845 0.260 0.740
#> GSM648691 2 0.8499 0.6788 0.276 0.724
#> GSM648693 2 0.8555 0.6756 0.280 0.720
#> GSM648700 2 0.4298 0.7589 0.088 0.912
#> GSM648630 2 0.8499 0.6788 0.276 0.724
#> GSM648632 2 0.8555 0.6756 0.280 0.720
#> GSM648639 2 0.7883 0.6915 0.236 0.764
#> GSM648640 2 0.8499 0.6788 0.276 0.724
#> GSM648668 2 0.4298 0.7589 0.088 0.912
#> GSM648676 2 0.4298 0.7589 0.088 0.912
#> GSM648692 2 0.8499 0.6788 0.276 0.724
#> GSM648694 2 0.8499 0.6788 0.276 0.724
#> GSM648699 2 0.4298 0.7589 0.088 0.912
#> GSM648701 2 0.4298 0.7589 0.088 0.912
#> GSM648673 2 0.2778 0.7524 0.048 0.952
#> GSM648677 2 0.4298 0.7589 0.088 0.912
#> GSM648687 2 0.8499 0.6788 0.276 0.724
#> GSM648688 2 0.8555 0.6756 0.280 0.720
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM648605 2 0.3112 0.789 0.096 0.900 0.004
#> GSM648618 1 0.1315 0.949 0.972 0.008 0.020
#> GSM648620 1 0.5678 0.551 0.684 0.316 0.000
#> GSM648646 2 0.0592 0.809 0.000 0.988 0.012
#> GSM648649 1 0.0424 0.957 0.992 0.008 0.000
#> GSM648675 2 0.5201 0.683 0.236 0.760 0.004
#> GSM648682 2 0.0829 0.809 0.004 0.984 0.012
#> GSM648698 2 0.2496 0.802 0.068 0.928 0.004
#> GSM648708 1 0.5733 0.533 0.676 0.324 0.000
#> GSM648628 3 0.6180 0.554 0.332 0.008 0.660
#> GSM648595 1 0.3038 0.872 0.896 0.104 0.000
#> GSM648635 1 0.0424 0.957 0.992 0.008 0.000
#> GSM648645 1 0.0000 0.959 1.000 0.000 0.000
#> GSM648647 2 0.4235 0.724 0.176 0.824 0.000
#> GSM648667 1 0.5363 0.629 0.724 0.276 0.000
#> GSM648695 2 0.6286 0.150 0.464 0.536 0.000
#> GSM648704 2 0.0892 0.809 0.000 0.980 0.020
#> GSM648706 2 0.0892 0.809 0.000 0.980 0.020
#> GSM648593 1 0.0424 0.957 0.992 0.008 0.000
#> GSM648594 1 0.0424 0.957 0.992 0.008 0.000
#> GSM648600 1 0.1015 0.954 0.980 0.008 0.012
#> GSM648621 1 0.1315 0.949 0.972 0.008 0.020
#> GSM648622 1 0.0000 0.959 1.000 0.000 0.000
#> GSM648623 1 0.0892 0.951 0.980 0.000 0.020
#> GSM648636 1 0.0747 0.956 0.984 0.016 0.000
#> GSM648655 1 0.0747 0.956 0.984 0.016 0.000
#> GSM648661 1 0.0000 0.959 1.000 0.000 0.000
#> GSM648664 1 0.0000 0.959 1.000 0.000 0.000
#> GSM648683 1 0.0424 0.958 0.992 0.008 0.000
#> GSM648685 1 0.0000 0.959 1.000 0.000 0.000
#> GSM648702 1 0.0747 0.956 0.984 0.016 0.000
#> GSM648597 1 0.0592 0.956 0.988 0.000 0.012
#> GSM648603 1 0.0592 0.956 0.988 0.000 0.012
#> GSM648606 3 0.7595 0.652 0.176 0.136 0.688
#> GSM648613 3 0.7510 0.654 0.184 0.124 0.692
#> GSM648619 1 0.2537 0.903 0.920 0.000 0.080
#> GSM648654 1 0.4045 0.850 0.872 0.104 0.024
#> GSM648663 3 0.8132 0.554 0.284 0.104 0.612
#> GSM648670 2 0.5574 0.717 0.184 0.784 0.032
#> GSM648707 3 0.0983 0.846 0.016 0.004 0.980
#> GSM648615 2 0.3030 0.792 0.092 0.904 0.004
#> GSM648643 2 0.2261 0.802 0.068 0.932 0.000
#> GSM648650 1 0.5016 0.694 0.760 0.240 0.000
#> GSM648656 2 0.0592 0.809 0.000 0.988 0.012
#> GSM648715 2 0.4399 0.710 0.188 0.812 0.000
#> GSM648598 1 0.0000 0.959 1.000 0.000 0.000
#> GSM648601 1 0.0000 0.959 1.000 0.000 0.000
#> GSM648602 1 0.0424 0.958 0.992 0.008 0.000
#> GSM648604 1 0.0000 0.959 1.000 0.000 0.000
#> GSM648614 1 0.4748 0.813 0.832 0.144 0.024
#> GSM648624 1 0.0000 0.959 1.000 0.000 0.000
#> GSM648625 1 0.0000 0.959 1.000 0.000 0.000
#> GSM648629 1 0.0000 0.959 1.000 0.000 0.000
#> GSM648634 1 0.0424 0.958 0.992 0.008 0.000
#> GSM648648 1 0.0424 0.957 0.992 0.008 0.000
#> GSM648651 1 0.0000 0.959 1.000 0.000 0.000
#> GSM648657 1 0.0000 0.959 1.000 0.000 0.000
#> GSM648660 1 0.0000 0.959 1.000 0.000 0.000
#> GSM648697 1 0.0424 0.958 0.992 0.008 0.000
#> GSM648710 1 0.0000 0.959 1.000 0.000 0.000
#> GSM648591 1 0.1315 0.949 0.972 0.008 0.020
#> GSM648592 1 0.0592 0.956 0.988 0.000 0.012
#> GSM648607 1 0.0592 0.956 0.988 0.000 0.012
#> GSM648611 3 0.5988 0.607 0.304 0.008 0.688
#> GSM648612 1 0.2625 0.899 0.916 0.000 0.084
#> GSM648616 3 0.1529 0.847 0.000 0.040 0.960
#> GSM648617 1 0.0892 0.951 0.980 0.000 0.020
#> GSM648626 1 0.0592 0.956 0.988 0.000 0.012
#> GSM648711 1 0.0592 0.956 0.988 0.000 0.012
#> GSM648712 1 0.2955 0.900 0.912 0.008 0.080
#> GSM648713 1 0.1163 0.947 0.972 0.000 0.028
#> GSM648714 3 0.7759 0.638 0.180 0.144 0.676
#> GSM648716 1 0.2537 0.903 0.920 0.000 0.080
#> GSM648717 3 0.5465 0.633 0.288 0.000 0.712
#> GSM648590 2 0.6140 0.429 0.404 0.596 0.000
#> GSM648596 2 0.2878 0.791 0.096 0.904 0.000
#> GSM648642 2 0.4235 0.724 0.176 0.824 0.000
#> GSM648696 1 0.0747 0.956 0.984 0.016 0.000
#> GSM648705 1 0.0424 0.957 0.992 0.008 0.000
#> GSM648718 2 0.2878 0.791 0.096 0.904 0.000
#> GSM648599 1 0.1015 0.954 0.980 0.008 0.012
#> GSM648608 1 0.0424 0.958 0.992 0.008 0.000
#> GSM648609 1 0.0000 0.959 1.000 0.000 0.000
#> GSM648610 1 0.1015 0.954 0.980 0.008 0.012
#> GSM648633 1 0.0000 0.959 1.000 0.000 0.000
#> GSM648644 2 0.0892 0.809 0.000 0.980 0.020
#> GSM648652 1 0.0424 0.957 0.992 0.008 0.000
#> GSM648653 1 0.0424 0.958 0.992 0.008 0.000
#> GSM648658 1 0.0747 0.956 0.984 0.016 0.000
#> GSM648659 2 0.4002 0.734 0.160 0.840 0.000
#> GSM648662 1 0.0000 0.959 1.000 0.000 0.000
#> GSM648665 1 0.3038 0.866 0.896 0.104 0.000
#> GSM648666 1 0.0424 0.958 0.992 0.008 0.000
#> GSM648680 1 0.0237 0.958 0.996 0.004 0.000
#> GSM648684 1 0.0424 0.958 0.992 0.008 0.000
#> GSM648709 2 0.5835 0.501 0.340 0.660 0.000
#> GSM648719 1 0.0000 0.959 1.000 0.000 0.000
#> GSM648627 1 0.2955 0.900 0.912 0.008 0.080
#> GSM648637 2 0.3941 0.799 0.000 0.844 0.156
#> GSM648638 2 0.4504 0.773 0.000 0.804 0.196
#> GSM648641 3 0.0424 0.855 0.000 0.008 0.992
#> GSM648672 2 0.3941 0.799 0.000 0.844 0.156
#> GSM648674 2 0.3941 0.799 0.000 0.844 0.156
#> GSM648703 2 0.3752 0.802 0.000 0.856 0.144
#> GSM648631 3 0.0424 0.855 0.000 0.008 0.992
#> GSM648669 2 0.4062 0.794 0.000 0.836 0.164
#> GSM648671 2 0.4062 0.794 0.000 0.836 0.164
#> GSM648678 2 0.3340 0.807 0.000 0.880 0.120
#> GSM648679 2 0.4002 0.797 0.000 0.840 0.160
#> GSM648681 2 0.2356 0.801 0.072 0.928 0.000
#> GSM648686 3 0.1031 0.858 0.000 0.024 0.976
#> GSM648689 3 0.1031 0.858 0.000 0.024 0.976
#> GSM648690 3 0.1031 0.858 0.000 0.024 0.976
#> GSM648691 3 0.1031 0.858 0.000 0.024 0.976
#> GSM648693 3 0.0747 0.858 0.000 0.016 0.984
#> GSM648700 2 0.3551 0.806 0.000 0.868 0.132
#> GSM648630 3 0.1031 0.858 0.000 0.024 0.976
#> GSM648632 3 0.0747 0.858 0.000 0.016 0.984
#> GSM648639 3 0.1031 0.855 0.000 0.024 0.976
#> GSM648640 3 0.0892 0.857 0.000 0.020 0.980
#> GSM648668 2 0.3941 0.799 0.000 0.844 0.156
#> GSM648676 2 0.3619 0.805 0.000 0.864 0.136
#> GSM648692 3 0.1031 0.858 0.000 0.024 0.976
#> GSM648694 3 0.1031 0.858 0.000 0.024 0.976
#> GSM648699 2 0.3752 0.802 0.000 0.856 0.144
#> GSM648701 2 0.3752 0.802 0.000 0.856 0.144
#> GSM648673 2 0.4002 0.797 0.000 0.840 0.160
#> GSM648677 2 0.3941 0.799 0.000 0.844 0.156
#> GSM648687 3 0.1163 0.857 0.000 0.028 0.972
#> GSM648688 3 0.1031 0.858 0.000 0.024 0.976
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM648605 2 0.5464 0.564772 0.072 0.716 0.000 0.212
#> GSM648618 1 0.5180 0.709850 0.740 0.196 0.064 0.000
#> GSM648620 2 0.4059 0.583100 0.200 0.788 0.000 0.012
#> GSM648646 2 0.4888 0.344171 0.000 0.588 0.000 0.412
#> GSM648649 1 0.3610 0.735716 0.800 0.200 0.000 0.000
#> GSM648675 2 0.8905 0.220343 0.240 0.428 0.064 0.268
#> GSM648682 2 0.4888 0.344171 0.000 0.588 0.000 0.412
#> GSM648698 2 0.4855 0.527292 0.020 0.712 0.000 0.268
#> GSM648708 2 0.4175 0.584307 0.200 0.784 0.000 0.016
#> GSM648628 1 0.7648 0.014069 0.400 0.208 0.392 0.000
#> GSM648595 1 0.4692 0.723870 0.756 0.212 0.032 0.000
#> GSM648635 1 0.3791 0.735161 0.796 0.200 0.004 0.000
#> GSM648645 1 0.1940 0.805936 0.924 0.076 0.000 0.000
#> GSM648647 2 0.4791 0.603819 0.136 0.784 0.000 0.080
#> GSM648667 2 0.4477 0.485105 0.312 0.688 0.000 0.000
#> GSM648695 2 0.4466 0.593898 0.180 0.784 0.000 0.036
#> GSM648704 2 0.4888 0.344171 0.000 0.588 0.000 0.412
#> GSM648706 2 0.4888 0.344171 0.000 0.588 0.000 0.412
#> GSM648593 1 0.3751 0.734162 0.800 0.196 0.000 0.004
#> GSM648594 1 0.3610 0.735716 0.800 0.200 0.000 0.000
#> GSM648600 1 0.4244 0.794358 0.804 0.160 0.036 0.000
#> GSM648621 1 0.4663 0.743814 0.788 0.148 0.064 0.000
#> GSM648622 1 0.0000 0.810002 1.000 0.000 0.000 0.000
#> GSM648623 1 0.4163 0.726064 0.792 0.188 0.020 0.000
#> GSM648636 1 0.4798 0.725550 0.760 0.204 0.032 0.004
#> GSM648655 1 0.4798 0.725550 0.760 0.204 0.032 0.004
#> GSM648661 1 0.1004 0.807501 0.972 0.024 0.004 0.000
#> GSM648664 1 0.0592 0.807618 0.984 0.016 0.000 0.000
#> GSM648683 1 0.1833 0.805158 0.944 0.024 0.032 0.000
#> GSM648685 1 0.2530 0.801799 0.896 0.100 0.004 0.000
#> GSM648702 1 0.4617 0.728257 0.764 0.204 0.032 0.000
#> GSM648597 1 0.5498 0.735868 0.704 0.252 0.020 0.024
#> GSM648603 1 0.4163 0.726064 0.792 0.188 0.020 0.000
#> GSM648606 2 0.7426 -0.141308 0.172 0.452 0.376 0.000
#> GSM648613 2 0.7472 -0.171138 0.176 0.428 0.396 0.000
#> GSM648619 1 0.4669 0.708286 0.764 0.200 0.036 0.000
#> GSM648654 1 0.4908 0.550585 0.692 0.292 0.016 0.000
#> GSM648663 2 0.7733 -0.002280 0.356 0.412 0.232 0.000
#> GSM648670 4 0.5652 0.567685 0.096 0.068 0.064 0.772
#> GSM648707 3 0.9486 0.352293 0.172 0.192 0.420 0.216
#> GSM648615 2 0.4635 0.523297 0.012 0.720 0.000 0.268
#> GSM648643 2 0.5306 0.445383 0.020 0.632 0.000 0.348
#> GSM648650 2 0.4382 0.496478 0.296 0.704 0.000 0.000
#> GSM648656 2 0.4888 0.344171 0.000 0.588 0.000 0.412
#> GSM648715 2 0.4735 0.604598 0.148 0.784 0.000 0.068
#> GSM648598 1 0.1867 0.805699 0.928 0.072 0.000 0.000
#> GSM648601 1 0.1867 0.805699 0.928 0.072 0.000 0.000
#> GSM648602 1 0.2565 0.808282 0.912 0.056 0.032 0.000
#> GSM648604 1 0.1022 0.805809 0.968 0.032 0.000 0.000
#> GSM648614 2 0.4699 0.300791 0.320 0.676 0.004 0.000
#> GSM648624 1 0.0000 0.810002 1.000 0.000 0.000 0.000
#> GSM648625 1 0.2921 0.783590 0.860 0.140 0.000 0.000
#> GSM648629 1 0.1022 0.805809 0.968 0.032 0.000 0.000
#> GSM648634 1 0.3013 0.803153 0.888 0.080 0.032 0.000
#> GSM648648 1 0.3751 0.735772 0.800 0.196 0.004 0.000
#> GSM648651 1 0.1389 0.809753 0.952 0.048 0.000 0.000
#> GSM648657 1 0.2081 0.808128 0.916 0.084 0.000 0.000
#> GSM648660 1 0.1940 0.805936 0.924 0.076 0.000 0.000
#> GSM648697 1 0.2714 0.794935 0.884 0.112 0.004 0.000
#> GSM648710 1 0.1022 0.805809 0.968 0.032 0.000 0.000
#> GSM648591 1 0.5944 0.692787 0.716 0.196 0.064 0.024
#> GSM648592 1 0.5193 0.724102 0.656 0.324 0.020 0.000
#> GSM648607 1 0.4284 0.719087 0.780 0.200 0.020 0.000
#> GSM648611 3 0.7641 0.000972 0.376 0.208 0.416 0.000
#> GSM648612 1 0.4957 0.697961 0.748 0.204 0.048 0.000
#> GSM648616 4 0.7704 -0.283166 0.004 0.188 0.388 0.420
#> GSM648617 1 0.4675 0.744826 0.736 0.244 0.020 0.000
#> GSM648626 1 0.4163 0.726064 0.792 0.188 0.020 0.000
#> GSM648711 1 0.3893 0.729756 0.796 0.196 0.008 0.000
#> GSM648712 1 0.5530 0.688183 0.712 0.212 0.076 0.000
#> GSM648713 1 0.4387 0.716520 0.776 0.200 0.024 0.000
#> GSM648714 2 0.4507 0.420993 0.168 0.788 0.044 0.000
#> GSM648716 1 0.4839 0.702184 0.756 0.200 0.044 0.000
#> GSM648717 3 0.7526 0.244138 0.332 0.200 0.468 0.000
#> GSM648590 1 0.6790 0.106601 0.476 0.456 0.032 0.036
#> GSM648596 2 0.4606 0.527030 0.012 0.724 0.000 0.264
#> GSM648642 2 0.4735 0.604598 0.148 0.784 0.000 0.068
#> GSM648696 1 0.5169 0.649877 0.696 0.272 0.032 0.000
#> GSM648705 1 0.3791 0.735161 0.796 0.200 0.004 0.000
#> GSM648718 2 0.4898 0.534197 0.024 0.716 0.000 0.260
#> GSM648599 1 0.4100 0.758037 0.816 0.148 0.036 0.000
#> GSM648608 1 0.2224 0.803006 0.928 0.040 0.032 0.000
#> GSM648609 1 0.0592 0.807618 0.984 0.016 0.000 0.000
#> GSM648610 1 0.2224 0.803006 0.928 0.040 0.032 0.000
#> GSM648633 1 0.1940 0.805936 0.924 0.076 0.000 0.000
#> GSM648644 2 0.4916 0.318420 0.000 0.576 0.000 0.424
#> GSM648652 1 0.3751 0.735772 0.800 0.196 0.004 0.000
#> GSM648653 1 0.2943 0.804270 0.892 0.076 0.032 0.000
#> GSM648658 1 0.4640 0.739707 0.776 0.188 0.032 0.004
#> GSM648659 2 0.4706 0.602492 0.140 0.788 0.000 0.072
#> GSM648662 1 0.1474 0.801807 0.948 0.052 0.000 0.000
#> GSM648665 1 0.4331 0.479815 0.712 0.288 0.000 0.000
#> GSM648666 1 0.2125 0.807166 0.920 0.076 0.004 0.000
#> GSM648680 1 0.3583 0.747998 0.816 0.180 0.004 0.000
#> GSM648684 1 0.1833 0.805158 0.944 0.024 0.032 0.000
#> GSM648709 2 0.4711 0.603949 0.152 0.784 0.000 0.064
#> GSM648719 1 0.1867 0.805699 0.928 0.072 0.000 0.000
#> GSM648627 1 0.5558 0.689097 0.712 0.208 0.080 0.000
#> GSM648637 4 0.0895 0.837900 0.000 0.020 0.004 0.976
#> GSM648638 4 0.1297 0.835463 0.000 0.020 0.016 0.964
#> GSM648641 3 0.2844 0.807595 0.000 0.048 0.900 0.052
#> GSM648672 4 0.1109 0.838041 0.000 0.028 0.004 0.968
#> GSM648674 4 0.0524 0.834757 0.000 0.008 0.004 0.988
#> GSM648703 4 0.3123 0.785844 0.000 0.156 0.000 0.844
#> GSM648631 3 0.1474 0.837189 0.000 0.000 0.948 0.052
#> GSM648669 4 0.0657 0.831455 0.000 0.004 0.012 0.984
#> GSM648671 4 0.0657 0.831455 0.000 0.004 0.012 0.984
#> GSM648678 4 0.3172 0.783389 0.000 0.160 0.000 0.840
#> GSM648679 4 0.0524 0.834757 0.000 0.008 0.004 0.988
#> GSM648681 2 0.5436 0.459482 0.024 0.620 0.000 0.356
#> GSM648686 3 0.1716 0.842814 0.000 0.000 0.936 0.064
#> GSM648689 3 0.1716 0.842814 0.000 0.000 0.936 0.064
#> GSM648690 3 0.1716 0.842814 0.000 0.000 0.936 0.064
#> GSM648691 3 0.1716 0.842814 0.000 0.000 0.936 0.064
#> GSM648693 3 0.1474 0.837189 0.000 0.000 0.948 0.052
#> GSM648700 4 0.3123 0.785844 0.000 0.156 0.000 0.844
#> GSM648630 3 0.1716 0.842814 0.000 0.000 0.936 0.064
#> GSM648632 3 0.1716 0.842814 0.000 0.000 0.936 0.064
#> GSM648639 3 0.5920 0.516930 0.000 0.052 0.612 0.336
#> GSM648640 3 0.1902 0.841407 0.000 0.004 0.932 0.064
#> GSM648668 4 0.1109 0.838041 0.000 0.028 0.004 0.968
#> GSM648676 4 0.3123 0.785844 0.000 0.156 0.000 0.844
#> GSM648692 3 0.1716 0.842814 0.000 0.000 0.936 0.064
#> GSM648694 3 0.1716 0.842814 0.000 0.000 0.936 0.064
#> GSM648699 4 0.3123 0.785844 0.000 0.156 0.000 0.844
#> GSM648701 4 0.3123 0.785844 0.000 0.156 0.000 0.844
#> GSM648673 4 0.0376 0.835518 0.000 0.004 0.004 0.992
#> GSM648677 4 0.3024 0.791958 0.000 0.148 0.000 0.852
#> GSM648687 3 0.2704 0.797628 0.000 0.000 0.876 0.124
#> GSM648688 3 0.1716 0.842814 0.000 0.000 0.936 0.064
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM648605 2 0.1173 0.83555 0.012 0.964 0.000 0.020 0.004
#> GSM648618 5 0.5066 0.57597 0.384 0.004 0.004 0.024 0.584
#> GSM648620 2 0.1628 0.82444 0.056 0.936 0.000 0.000 0.008
#> GSM648646 2 0.2209 0.81496 0.000 0.912 0.000 0.056 0.032
#> GSM648649 1 0.2060 0.75643 0.924 0.052 0.000 0.008 0.016
#> GSM648675 5 0.8332 0.13948 0.264 0.264 0.000 0.136 0.336
#> GSM648682 2 0.2209 0.81400 0.000 0.912 0.000 0.056 0.032
#> GSM648698 2 0.1211 0.83229 0.000 0.960 0.000 0.024 0.016
#> GSM648708 2 0.1788 0.82223 0.056 0.932 0.000 0.004 0.008
#> GSM648628 5 0.5127 0.68047 0.156 0.000 0.104 0.016 0.724
#> GSM648595 1 0.3646 0.71991 0.836 0.052 0.000 0.012 0.100
#> GSM648635 1 0.1830 0.75670 0.932 0.052 0.000 0.004 0.012
#> GSM648645 1 0.0727 0.76286 0.980 0.004 0.000 0.004 0.012
#> GSM648647 2 0.1408 0.82980 0.044 0.948 0.000 0.000 0.008
#> GSM648667 1 0.4702 0.02114 0.512 0.476 0.000 0.004 0.008
#> GSM648695 2 0.1628 0.82444 0.056 0.936 0.000 0.000 0.008
#> GSM648704 2 0.2209 0.81496 0.000 0.912 0.000 0.056 0.032
#> GSM648706 2 0.2209 0.81496 0.000 0.912 0.000 0.056 0.032
#> GSM648593 1 0.2214 0.74766 0.916 0.052 0.000 0.004 0.028
#> GSM648594 1 0.1913 0.75776 0.932 0.044 0.000 0.008 0.016
#> GSM648600 1 0.4061 0.49490 0.740 0.004 0.000 0.016 0.240
#> GSM648621 1 0.4288 0.30995 0.664 0.000 0.000 0.012 0.324
#> GSM648622 1 0.1124 0.75064 0.960 0.000 0.000 0.004 0.036
#> GSM648623 5 0.4560 0.53176 0.484 0.000 0.000 0.008 0.508
#> GSM648636 1 0.3372 0.72587 0.852 0.052 0.000 0.008 0.088
#> GSM648655 1 0.3372 0.72587 0.852 0.052 0.000 0.008 0.088
#> GSM648661 1 0.3242 0.58890 0.784 0.000 0.000 0.000 0.216
#> GSM648664 1 0.3003 0.62574 0.812 0.000 0.000 0.000 0.188
#> GSM648683 1 0.3662 0.60511 0.744 0.000 0.000 0.004 0.252
#> GSM648685 1 0.2513 0.70376 0.876 0.008 0.000 0.000 0.116
#> GSM648702 1 0.3009 0.73953 0.876 0.052 0.000 0.008 0.064
#> GSM648597 1 0.5591 -0.45772 0.496 0.004 0.000 0.060 0.440
#> GSM648603 5 0.4727 0.57279 0.452 0.000 0.000 0.016 0.532
#> GSM648606 5 0.5816 0.53904 0.036 0.148 0.136 0.000 0.680
#> GSM648613 5 0.5934 0.52908 0.028 0.136 0.148 0.008 0.680
#> GSM648619 5 0.4166 0.67222 0.348 0.000 0.004 0.000 0.648
#> GSM648654 5 0.6680 0.39167 0.352 0.204 0.004 0.000 0.440
#> GSM648663 5 0.5994 0.65059 0.136 0.132 0.056 0.000 0.676
#> GSM648670 4 0.5877 0.43289 0.060 0.028 0.004 0.620 0.288
#> GSM648707 5 0.6623 0.24409 0.076 0.004 0.052 0.320 0.548
#> GSM648615 2 0.1485 0.82887 0.000 0.948 0.000 0.032 0.020
#> GSM648643 2 0.1626 0.82463 0.000 0.940 0.000 0.044 0.016
#> GSM648650 2 0.4870 0.14076 0.448 0.532 0.000 0.004 0.016
#> GSM648656 2 0.2291 0.81317 0.000 0.908 0.000 0.056 0.036
#> GSM648715 2 0.1557 0.82660 0.052 0.940 0.000 0.000 0.008
#> GSM648598 1 0.0162 0.76361 0.996 0.000 0.000 0.000 0.004
#> GSM648601 1 0.0324 0.76391 0.992 0.004 0.000 0.000 0.004
#> GSM648602 1 0.1894 0.74768 0.920 0.000 0.000 0.008 0.072
#> GSM648604 1 0.3274 0.58647 0.780 0.000 0.000 0.000 0.220
#> GSM648614 2 0.5651 0.06447 0.056 0.492 0.008 0.000 0.444
#> GSM648624 1 0.1043 0.74953 0.960 0.000 0.000 0.000 0.040
#> GSM648625 1 0.1492 0.75860 0.948 0.040 0.004 0.000 0.008
#> GSM648629 1 0.3242 0.59251 0.784 0.000 0.000 0.000 0.216
#> GSM648634 1 0.1731 0.75132 0.932 0.004 0.000 0.004 0.060
#> GSM648648 1 0.1591 0.75869 0.940 0.052 0.000 0.004 0.004
#> GSM648651 1 0.0771 0.75813 0.976 0.000 0.000 0.004 0.020
#> GSM648657 1 0.0727 0.76286 0.980 0.004 0.000 0.004 0.012
#> GSM648660 1 0.0486 0.76343 0.988 0.004 0.000 0.004 0.004
#> GSM648697 1 0.1281 0.76620 0.956 0.032 0.000 0.000 0.012
#> GSM648710 1 0.3274 0.58647 0.780 0.000 0.000 0.000 0.220
#> GSM648591 5 0.5613 0.58028 0.348 0.004 0.004 0.064 0.580
#> GSM648592 5 0.5709 0.49158 0.468 0.036 0.000 0.024 0.472
#> GSM648607 5 0.4030 0.66887 0.352 0.000 0.000 0.000 0.648
#> GSM648611 5 0.4905 0.65964 0.116 0.000 0.152 0.004 0.728
#> GSM648612 5 0.4299 0.69083 0.316 0.000 0.008 0.004 0.672
#> GSM648616 4 0.5803 0.38374 0.012 0.012 0.052 0.596 0.328
#> GSM648617 1 0.4700 -0.47937 0.516 0.000 0.004 0.008 0.472
#> GSM648626 5 0.4727 0.57279 0.452 0.000 0.000 0.016 0.532
#> GSM648711 5 0.4161 0.61865 0.392 0.000 0.000 0.000 0.608
#> GSM648712 5 0.3989 0.68777 0.260 0.000 0.008 0.004 0.728
#> GSM648713 5 0.4030 0.66887 0.352 0.000 0.000 0.000 0.648
#> GSM648714 2 0.5189 0.32590 0.028 0.584 0.012 0.000 0.376
#> GSM648716 5 0.4084 0.68503 0.328 0.000 0.004 0.000 0.668
#> GSM648717 5 0.5699 0.62980 0.128 0.020 0.180 0.000 0.672
#> GSM648590 1 0.5822 0.44418 0.628 0.232 0.000 0.008 0.132
#> GSM648596 2 0.1356 0.83197 0.000 0.956 0.004 0.028 0.012
#> GSM648642 2 0.1357 0.82913 0.048 0.948 0.000 0.000 0.004
#> GSM648696 1 0.3919 0.69571 0.816 0.100 0.000 0.008 0.076
#> GSM648705 1 0.1830 0.75670 0.932 0.052 0.000 0.004 0.012
#> GSM648718 2 0.1095 0.83437 0.008 0.968 0.000 0.012 0.012
#> GSM648599 1 0.4249 0.38359 0.688 0.000 0.000 0.016 0.296
#> GSM648608 1 0.3814 0.56904 0.720 0.000 0.000 0.004 0.276
#> GSM648609 1 0.3074 0.61933 0.804 0.000 0.000 0.000 0.196
#> GSM648610 1 0.3906 0.54625 0.704 0.000 0.000 0.004 0.292
#> GSM648633 1 0.0486 0.76343 0.988 0.004 0.000 0.004 0.004
#> GSM648644 2 0.2359 0.81001 0.000 0.904 0.000 0.060 0.036
#> GSM648652 1 0.1591 0.75869 0.940 0.052 0.000 0.004 0.004
#> GSM648653 1 0.1571 0.75133 0.936 0.000 0.000 0.004 0.060
#> GSM648658 1 0.3229 0.73051 0.860 0.044 0.000 0.008 0.088
#> GSM648659 2 0.2949 0.79908 0.052 0.876 0.000 0.004 0.068
#> GSM648662 1 0.5070 -0.00767 0.568 0.024 0.008 0.000 0.400
#> GSM648665 1 0.6298 0.28139 0.572 0.212 0.008 0.000 0.208
#> GSM648666 1 0.0290 0.76593 0.992 0.000 0.000 0.000 0.008
#> GSM648680 1 0.1443 0.76145 0.948 0.044 0.000 0.004 0.004
#> GSM648684 1 0.3430 0.64722 0.776 0.000 0.000 0.004 0.220
#> GSM648709 2 0.1670 0.82723 0.052 0.936 0.000 0.000 0.012
#> GSM648719 1 0.0486 0.76343 0.988 0.004 0.000 0.004 0.004
#> GSM648627 5 0.3989 0.68527 0.260 0.000 0.008 0.004 0.728
#> GSM648637 4 0.2900 0.77736 0.000 0.064 0.012 0.884 0.040
#> GSM648638 4 0.2766 0.77552 0.000 0.056 0.012 0.892 0.040
#> GSM648641 3 0.2806 0.79773 0.000 0.000 0.844 0.004 0.152
#> GSM648672 4 0.2507 0.78281 0.000 0.072 0.016 0.900 0.012
#> GSM648674 4 0.2515 0.76770 0.000 0.044 0.008 0.904 0.044
#> GSM648703 4 0.6020 0.70249 0.000 0.204 0.016 0.628 0.152
#> GSM648631 3 0.0290 0.97369 0.000 0.000 0.992 0.008 0.000
#> GSM648669 4 0.1891 0.77556 0.000 0.032 0.016 0.936 0.016
#> GSM648671 4 0.1891 0.77556 0.000 0.032 0.016 0.936 0.016
#> GSM648678 4 0.5945 0.65479 0.000 0.256 0.012 0.612 0.120
#> GSM648679 4 0.2546 0.76522 0.000 0.036 0.012 0.904 0.048
#> GSM648681 2 0.5477 0.47531 0.036 0.652 0.000 0.272 0.040
#> GSM648686 3 0.0693 0.97290 0.000 0.000 0.980 0.012 0.008
#> GSM648689 3 0.0451 0.96909 0.000 0.000 0.988 0.004 0.008
#> GSM648690 3 0.0693 0.97290 0.000 0.000 0.980 0.012 0.008
#> GSM648691 3 0.0404 0.97441 0.000 0.000 0.988 0.012 0.000
#> GSM648693 3 0.0290 0.97369 0.000 0.000 0.992 0.008 0.000
#> GSM648700 4 0.5981 0.69555 0.000 0.212 0.012 0.624 0.152
#> GSM648630 3 0.0404 0.97441 0.000 0.000 0.988 0.012 0.000
#> GSM648632 3 0.0290 0.97369 0.000 0.000 0.992 0.008 0.000
#> GSM648639 4 0.6434 0.15210 0.000 0.012 0.344 0.508 0.136
#> GSM648640 3 0.1579 0.94668 0.000 0.000 0.944 0.032 0.024
#> GSM648668 4 0.2507 0.78281 0.000 0.072 0.016 0.900 0.012
#> GSM648676 4 0.5992 0.70396 0.000 0.200 0.016 0.632 0.152
#> GSM648692 3 0.0404 0.97441 0.000 0.000 0.988 0.012 0.000
#> GSM648694 3 0.0404 0.97441 0.000 0.000 0.988 0.012 0.000
#> GSM648699 4 0.5992 0.70396 0.000 0.200 0.016 0.632 0.152
#> GSM648701 4 0.5992 0.70396 0.000 0.200 0.016 0.632 0.152
#> GSM648673 4 0.1989 0.77596 0.000 0.032 0.016 0.932 0.020
#> GSM648677 4 0.6020 0.70768 0.000 0.204 0.016 0.628 0.152
#> GSM648687 3 0.1557 0.93667 0.000 0.000 0.940 0.052 0.008
#> GSM648688 3 0.0566 0.97337 0.000 0.000 0.984 0.012 0.004
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM648605 2 0.2571 0.7962 0.004 0.892 0.000 0.024 0.020 0.060
#> GSM648618 5 0.6002 0.2190 0.224 0.000 0.000 0.012 0.516 0.248
#> GSM648620 2 0.1679 0.7914 0.016 0.936 0.000 0.000 0.036 0.012
#> GSM648646 2 0.3910 0.7555 0.000 0.784 0.000 0.072 0.012 0.132
#> GSM648649 1 0.4157 0.6598 0.772 0.124 0.000 0.000 0.020 0.084
#> GSM648675 6 0.8055 0.2765 0.256 0.208 0.000 0.040 0.136 0.360
#> GSM648682 2 0.3438 0.7686 0.000 0.816 0.000 0.068 0.004 0.112
#> GSM648698 2 0.2437 0.7949 0.000 0.888 0.000 0.036 0.004 0.072
#> GSM648708 2 0.1503 0.7930 0.016 0.944 0.000 0.000 0.032 0.008
#> GSM648628 5 0.4410 0.5309 0.072 0.000 0.020 0.008 0.760 0.140
#> GSM648595 1 0.5428 0.5734 0.648 0.116 0.000 0.000 0.036 0.200
#> GSM648635 1 0.3221 0.6941 0.828 0.096 0.000 0.000 0.000 0.076
#> GSM648645 1 0.2699 0.7190 0.880 0.020 0.000 0.000 0.032 0.068
#> GSM648647 2 0.1245 0.7956 0.016 0.952 0.000 0.000 0.032 0.000
#> GSM648667 2 0.5569 0.0572 0.400 0.504 0.000 0.000 0.032 0.064
#> GSM648695 2 0.1503 0.7930 0.016 0.944 0.000 0.000 0.032 0.008
#> GSM648704 2 0.4078 0.7487 0.000 0.772 0.000 0.072 0.016 0.140
#> GSM648706 2 0.4000 0.7539 0.000 0.780 0.000 0.072 0.016 0.132
#> GSM648593 1 0.2997 0.7019 0.844 0.096 0.000 0.000 0.000 0.060
#> GSM648594 1 0.3970 0.6744 0.800 0.080 0.000 0.000 0.040 0.080
#> GSM648600 1 0.4892 0.6030 0.696 0.016 0.000 0.000 0.144 0.144
#> GSM648621 1 0.4915 0.5187 0.652 0.000 0.000 0.000 0.208 0.140
#> GSM648622 1 0.2420 0.7193 0.884 0.000 0.000 0.000 0.076 0.040
#> GSM648623 5 0.4703 0.4057 0.408 0.000 0.000 0.000 0.544 0.048
#> GSM648636 1 0.4426 0.6607 0.748 0.100 0.000 0.000 0.020 0.132
#> GSM648655 1 0.4464 0.6569 0.744 0.100 0.000 0.000 0.020 0.136
#> GSM648661 1 0.4051 0.5759 0.728 0.004 0.000 0.000 0.224 0.044
#> GSM648664 1 0.3819 0.5994 0.756 0.004 0.000 0.000 0.200 0.040
#> GSM648683 1 0.4908 0.5806 0.660 0.004 0.000 0.000 0.220 0.116
#> GSM648685 1 0.3207 0.7030 0.844 0.016 0.000 0.000 0.092 0.048
#> GSM648702 1 0.4380 0.6645 0.752 0.096 0.000 0.000 0.020 0.132
#> GSM648597 1 0.6329 -0.2568 0.412 0.000 0.000 0.016 0.348 0.224
#> GSM648603 5 0.5100 0.4640 0.284 0.000 0.000 0.000 0.600 0.116
#> GSM648606 5 0.4127 0.4947 0.004 0.084 0.020 0.016 0.804 0.072
#> GSM648613 5 0.4243 0.4858 0.004 0.064 0.024 0.016 0.796 0.096
#> GSM648619 5 0.2772 0.6186 0.180 0.000 0.004 0.000 0.816 0.000
#> GSM648654 5 0.6474 0.3866 0.196 0.204 0.004 0.000 0.540 0.056
#> GSM648663 5 0.4073 0.5145 0.016 0.088 0.016 0.012 0.812 0.056
#> GSM648670 6 0.7237 0.3126 0.084 0.020 0.000 0.368 0.140 0.388
#> GSM648707 6 0.7071 0.2239 0.020 0.000 0.040 0.204 0.368 0.368
#> GSM648615 2 0.2627 0.7962 0.000 0.884 0.000 0.036 0.016 0.064
#> GSM648643 2 0.2744 0.7853 0.000 0.864 0.000 0.064 0.000 0.072
#> GSM648650 2 0.5562 0.1347 0.348 0.548 0.000 0.000 0.032 0.072
#> GSM648656 2 0.3949 0.7522 0.000 0.780 0.000 0.072 0.012 0.136
#> GSM648715 2 0.1503 0.7930 0.016 0.944 0.000 0.000 0.032 0.008
#> GSM648598 1 0.0508 0.7363 0.984 0.000 0.000 0.000 0.012 0.004
#> GSM648601 1 0.1826 0.7276 0.924 0.004 0.000 0.000 0.052 0.020
#> GSM648602 1 0.2542 0.7164 0.876 0.000 0.000 0.000 0.044 0.080
#> GSM648604 1 0.3987 0.5767 0.732 0.004 0.000 0.000 0.224 0.040
#> GSM648614 5 0.4923 0.3577 0.012 0.260 0.000 0.012 0.664 0.052
#> GSM648624 1 0.2579 0.7059 0.872 0.000 0.000 0.000 0.088 0.040
#> GSM648625 1 0.4941 0.6106 0.728 0.092 0.000 0.008 0.132 0.040
#> GSM648629 1 0.3987 0.5771 0.732 0.004 0.000 0.000 0.224 0.040
#> GSM648634 1 0.2904 0.7154 0.852 0.008 0.000 0.000 0.028 0.112
#> GSM648648 1 0.2537 0.7100 0.872 0.096 0.000 0.000 0.000 0.032
#> GSM648651 1 0.2201 0.7232 0.896 0.000 0.000 0.000 0.076 0.028
#> GSM648657 1 0.3177 0.7087 0.852 0.024 0.000 0.000 0.052 0.072
#> GSM648660 1 0.1713 0.7304 0.928 0.000 0.000 0.000 0.028 0.044
#> GSM648697 1 0.2361 0.7347 0.896 0.032 0.000 0.000 0.008 0.064
#> GSM648710 1 0.3987 0.5767 0.732 0.004 0.000 0.000 0.224 0.040
#> GSM648591 5 0.6031 0.1473 0.188 0.000 0.000 0.016 0.512 0.284
#> GSM648592 5 0.6435 0.1828 0.276 0.040 0.000 0.004 0.508 0.172
#> GSM648607 5 0.3619 0.5863 0.232 0.000 0.000 0.000 0.744 0.024
#> GSM648611 5 0.4169 0.5507 0.068 0.000 0.024 0.004 0.780 0.124
#> GSM648612 5 0.2714 0.6179 0.136 0.000 0.004 0.000 0.848 0.012
#> GSM648616 4 0.7159 -0.1380 0.016 0.000 0.060 0.416 0.196 0.312
#> GSM648617 5 0.4666 0.3431 0.388 0.000 0.000 0.000 0.564 0.048
#> GSM648626 5 0.5066 0.4690 0.276 0.000 0.000 0.000 0.608 0.116
#> GSM648711 5 0.4392 0.4884 0.332 0.000 0.000 0.000 0.628 0.040
#> GSM648712 5 0.3510 0.5860 0.088 0.000 0.004 0.004 0.820 0.084
#> GSM648713 5 0.2738 0.6193 0.176 0.000 0.000 0.000 0.820 0.004
#> GSM648714 5 0.5156 0.2199 0.004 0.356 0.000 0.012 0.572 0.056
#> GSM648716 5 0.2913 0.6181 0.180 0.000 0.004 0.000 0.812 0.004
#> GSM648717 5 0.3454 0.5625 0.048 0.016 0.024 0.008 0.856 0.048
#> GSM648590 1 0.5887 0.4279 0.572 0.212 0.000 0.000 0.024 0.192
#> GSM648596 2 0.3754 0.7838 0.004 0.824 0.000 0.052 0.064 0.056
#> GSM648642 2 0.1059 0.7968 0.016 0.964 0.000 0.000 0.016 0.004
#> GSM648696 1 0.5153 0.5989 0.676 0.148 0.000 0.000 0.024 0.152
#> GSM648705 1 0.3481 0.6732 0.804 0.124 0.000 0.000 0.000 0.072
#> GSM648718 2 0.0665 0.7990 0.008 0.980 0.000 0.004 0.000 0.008
#> GSM648599 1 0.4624 0.5760 0.688 0.000 0.000 0.000 0.192 0.120
#> GSM648608 1 0.5001 0.5611 0.644 0.004 0.000 0.000 0.236 0.116
#> GSM648609 1 0.3960 0.5813 0.736 0.004 0.000 0.000 0.220 0.040
#> GSM648610 1 0.5061 0.5548 0.636 0.004 0.000 0.000 0.240 0.120
#> GSM648633 1 0.2594 0.7263 0.888 0.020 0.000 0.000 0.036 0.056
#> GSM648644 2 0.4078 0.7487 0.000 0.772 0.000 0.072 0.016 0.140
#> GSM648652 1 0.2997 0.7007 0.844 0.096 0.000 0.000 0.000 0.060
#> GSM648653 1 0.2436 0.7186 0.880 0.000 0.000 0.000 0.032 0.088
#> GSM648658 1 0.4030 0.6835 0.780 0.068 0.000 0.000 0.020 0.132
#> GSM648659 2 0.2815 0.7456 0.012 0.864 0.000 0.000 0.028 0.096
#> GSM648662 5 0.5811 0.4365 0.268 0.052 0.000 0.008 0.600 0.072
#> GSM648665 1 0.6976 0.1752 0.464 0.184 0.000 0.008 0.272 0.072
#> GSM648666 1 0.1700 0.7326 0.928 0.000 0.000 0.000 0.024 0.048
#> GSM648680 1 0.1970 0.7276 0.912 0.060 0.000 0.000 0.000 0.028
#> GSM648684 1 0.4722 0.6178 0.688 0.004 0.000 0.000 0.192 0.116
#> GSM648709 2 0.1577 0.7935 0.016 0.940 0.000 0.000 0.036 0.008
#> GSM648719 1 0.1257 0.7318 0.952 0.000 0.000 0.000 0.028 0.020
#> GSM648627 5 0.3754 0.5768 0.096 0.000 0.004 0.004 0.800 0.096
#> GSM648637 4 0.4297 0.5775 0.000 0.016 0.020 0.732 0.016 0.216
#> GSM648638 4 0.4875 0.5411 0.000 0.016 0.036 0.688 0.024 0.236
#> GSM648641 3 0.4704 0.4534 0.000 0.004 0.632 0.000 0.304 0.060
#> GSM648672 4 0.2196 0.6659 0.000 0.016 0.020 0.908 0.000 0.056
#> GSM648674 4 0.3859 0.5483 0.000 0.000 0.016 0.756 0.024 0.204
#> GSM648703 4 0.5139 0.6369 0.000 0.084 0.020 0.640 0.000 0.256
#> GSM648631 3 0.0291 0.9448 0.000 0.000 0.992 0.000 0.004 0.004
#> GSM648669 4 0.1974 0.6433 0.000 0.000 0.020 0.920 0.012 0.048
#> GSM648671 4 0.1974 0.6433 0.000 0.000 0.020 0.920 0.012 0.048
#> GSM648678 4 0.6069 0.5275 0.000 0.176 0.016 0.548 0.008 0.252
#> GSM648679 4 0.3593 0.5701 0.000 0.000 0.020 0.784 0.016 0.180
#> GSM648681 2 0.7035 0.2544 0.076 0.536 0.000 0.188 0.036 0.164
#> GSM648686 3 0.0603 0.9400 0.000 0.004 0.980 0.000 0.000 0.016
#> GSM648689 3 0.1003 0.9312 0.000 0.004 0.964 0.000 0.004 0.028
#> GSM648690 3 0.0603 0.9400 0.000 0.004 0.980 0.000 0.000 0.016
#> GSM648691 3 0.0000 0.9452 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648693 3 0.0000 0.9452 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648700 4 0.5425 0.6026 0.000 0.112 0.016 0.600 0.000 0.272
#> GSM648630 3 0.0000 0.9452 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648632 3 0.0291 0.9448 0.000 0.000 0.992 0.000 0.004 0.004
#> GSM648639 4 0.7368 -0.0681 0.000 0.000 0.212 0.364 0.128 0.296
#> GSM648640 3 0.2748 0.8181 0.000 0.000 0.848 0.000 0.024 0.128
#> GSM648668 4 0.2196 0.6659 0.000 0.016 0.020 0.908 0.000 0.056
#> GSM648676 4 0.5133 0.6339 0.000 0.080 0.020 0.636 0.000 0.264
#> GSM648692 3 0.0000 0.9452 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648694 3 0.0291 0.9448 0.000 0.000 0.992 0.000 0.004 0.004
#> GSM648699 4 0.5139 0.6369 0.000 0.084 0.020 0.640 0.000 0.256
#> GSM648701 4 0.5139 0.6369 0.000 0.084 0.020 0.640 0.000 0.256
#> GSM648673 4 0.1908 0.6441 0.000 0.000 0.020 0.924 0.012 0.044
#> GSM648677 4 0.5125 0.6346 0.000 0.076 0.020 0.632 0.000 0.272
#> GSM648687 3 0.1251 0.9234 0.000 0.000 0.956 0.024 0.008 0.012
#> GSM648688 3 0.0405 0.9438 0.000 0.000 0.988 0.000 0.004 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)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
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) development.stage(p) other(p) k
#> SD:kmeans 121 6.34e-20 0.0570 1.56e-19 2
#> SD:kmeans 128 1.29e-11 0.0207 3.03e-17 3
#> SD:kmeans 107 4.67e-22 0.0164 2.32e-27 4
#> SD:kmeans 110 7.28e-23 0.0305 1.50e-47 5
#> SD:kmeans 104 1.38e-21 0.0120 3.86e-48 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 51941 rows and 130 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 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 0.521 0.791 0.892 0.4964 0.504 0.504
#> 3 3 0.705 0.826 0.907 0.3124 0.720 0.502
#> 4 4 0.571 0.625 0.690 0.1310 0.871 0.662
#> 5 5 0.674 0.537 0.757 0.0764 0.858 0.554
#> 6 6 0.753 0.728 0.819 0.0471 0.903 0.579
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
#> GSM648605 2 0.0000 0.844 0.000 1.000
#> GSM648618 1 0.6712 0.665 0.824 0.176
#> GSM648620 1 0.9933 0.391 0.548 0.452
#> GSM648646 2 0.0000 0.844 0.000 1.000
#> GSM648649 1 0.8608 0.685 0.716 0.284
#> GSM648675 2 0.0672 0.839 0.008 0.992
#> GSM648682 2 0.0000 0.844 0.000 1.000
#> GSM648698 2 0.0000 0.844 0.000 1.000
#> GSM648708 1 0.9522 0.560 0.628 0.372
#> GSM648628 1 0.0000 0.876 1.000 0.000
#> GSM648595 1 0.8608 0.685 0.716 0.284
#> GSM648635 1 0.8608 0.685 0.716 0.284
#> GSM648645 1 0.0000 0.876 1.000 0.000
#> GSM648647 2 0.6343 0.691 0.160 0.840
#> GSM648667 1 0.8608 0.685 0.716 0.284
#> GSM648695 1 0.9710 0.508 0.600 0.400
#> GSM648704 2 0.0000 0.844 0.000 1.000
#> GSM648706 2 0.0000 0.844 0.000 1.000
#> GSM648593 1 0.8608 0.685 0.716 0.284
#> GSM648594 1 0.8608 0.685 0.716 0.284
#> GSM648600 1 0.0000 0.876 1.000 0.000
#> GSM648621 1 0.0000 0.876 1.000 0.000
#> GSM648622 1 0.0000 0.876 1.000 0.000
#> GSM648623 1 0.0000 0.876 1.000 0.000
#> GSM648636 1 0.8608 0.685 0.716 0.284
#> GSM648655 1 0.8608 0.685 0.716 0.284
#> GSM648661 1 0.0000 0.876 1.000 0.000
#> GSM648664 1 0.0000 0.876 1.000 0.000
#> GSM648683 1 0.0000 0.876 1.000 0.000
#> GSM648685 1 0.0000 0.876 1.000 0.000
#> GSM648702 1 0.8608 0.685 0.716 0.284
#> GSM648597 1 0.0000 0.876 1.000 0.000
#> GSM648603 1 0.0000 0.876 1.000 0.000
#> GSM648606 2 0.8608 0.720 0.284 0.716
#> GSM648613 2 0.8608 0.720 0.284 0.716
#> GSM648619 1 0.0000 0.876 1.000 0.000
#> GSM648654 1 0.0000 0.876 1.000 0.000
#> GSM648663 1 0.6712 0.664 0.824 0.176
#> GSM648670 2 0.0000 0.844 0.000 1.000
#> GSM648707 2 0.8608 0.720 0.284 0.716
#> GSM648615 2 0.0000 0.844 0.000 1.000
#> GSM648643 2 0.0000 0.844 0.000 1.000
#> GSM648650 1 0.8608 0.685 0.716 0.284
#> GSM648656 2 0.0000 0.844 0.000 1.000
#> GSM648715 2 0.8555 0.469 0.280 0.720
#> GSM648598 1 0.0000 0.876 1.000 0.000
#> GSM648601 1 0.0000 0.876 1.000 0.000
#> GSM648602 1 0.0000 0.876 1.000 0.000
#> GSM648604 1 0.0000 0.876 1.000 0.000
#> GSM648614 2 0.9881 0.480 0.436 0.564
#> GSM648624 1 0.0000 0.876 1.000 0.000
#> GSM648625 1 0.7219 0.749 0.800 0.200
#> GSM648629 1 0.0000 0.876 1.000 0.000
#> GSM648634 1 0.0000 0.876 1.000 0.000
#> GSM648648 1 0.8608 0.685 0.716 0.284
#> GSM648651 1 0.0000 0.876 1.000 0.000
#> GSM648657 1 0.0000 0.876 1.000 0.000
#> GSM648660 1 0.0000 0.876 1.000 0.000
#> GSM648697 1 0.0000 0.876 1.000 0.000
#> GSM648710 1 0.0000 0.876 1.000 0.000
#> GSM648591 1 0.0000 0.876 1.000 0.000
#> GSM648592 1 0.8608 0.685 0.716 0.284
#> GSM648607 1 0.0000 0.876 1.000 0.000
#> GSM648611 1 0.0376 0.873 0.996 0.004
#> GSM648612 1 0.0000 0.876 1.000 0.000
#> GSM648616 2 0.8608 0.720 0.284 0.716
#> GSM648617 1 0.0000 0.876 1.000 0.000
#> GSM648626 1 0.0000 0.876 1.000 0.000
#> GSM648711 1 0.0000 0.876 1.000 0.000
#> GSM648712 1 0.0000 0.876 1.000 0.000
#> GSM648713 1 0.0000 0.876 1.000 0.000
#> GSM648714 2 0.8608 0.720 0.284 0.716
#> GSM648716 1 0.0000 0.876 1.000 0.000
#> GSM648717 1 0.3114 0.826 0.944 0.056
#> GSM648590 1 0.9732 0.500 0.596 0.404
#> GSM648596 2 0.0376 0.842 0.004 0.996
#> GSM648642 2 0.6531 0.680 0.168 0.832
#> GSM648696 1 0.8608 0.685 0.716 0.284
#> GSM648705 1 0.8608 0.685 0.716 0.284
#> GSM648718 2 0.0000 0.844 0.000 1.000
#> GSM648599 1 0.0000 0.876 1.000 0.000
#> GSM648608 1 0.0000 0.876 1.000 0.000
#> GSM648609 1 0.0000 0.876 1.000 0.000
#> GSM648610 1 0.0000 0.876 1.000 0.000
#> GSM648633 1 0.0000 0.876 1.000 0.000
#> GSM648644 2 0.0000 0.844 0.000 1.000
#> GSM648652 1 0.8608 0.685 0.716 0.284
#> GSM648653 1 0.0000 0.876 1.000 0.000
#> GSM648658 1 0.8608 0.685 0.716 0.284
#> GSM648659 2 0.1633 0.829 0.024 0.976
#> GSM648662 1 0.0000 0.876 1.000 0.000
#> GSM648665 1 0.0000 0.876 1.000 0.000
#> GSM648666 1 0.0000 0.876 1.000 0.000
#> GSM648680 1 0.8608 0.685 0.716 0.284
#> GSM648684 1 0.0000 0.876 1.000 0.000
#> GSM648709 2 0.5946 0.712 0.144 0.856
#> GSM648719 1 0.0000 0.876 1.000 0.000
#> GSM648627 1 0.0000 0.876 1.000 0.000
#> GSM648637 2 0.0000 0.844 0.000 1.000
#> GSM648638 2 0.0000 0.844 0.000 1.000
#> GSM648641 2 0.8608 0.720 0.284 0.716
#> GSM648672 2 0.0000 0.844 0.000 1.000
#> GSM648674 2 0.0000 0.844 0.000 1.000
#> GSM648703 2 0.0000 0.844 0.000 1.000
#> GSM648631 2 0.9522 0.593 0.372 0.628
#> GSM648669 2 0.0000 0.844 0.000 1.000
#> GSM648671 2 0.0000 0.844 0.000 1.000
#> GSM648678 2 0.0000 0.844 0.000 1.000
#> GSM648679 2 0.0000 0.844 0.000 1.000
#> GSM648681 2 0.0000 0.844 0.000 1.000
#> GSM648686 2 0.8608 0.720 0.284 0.716
#> GSM648689 2 0.8608 0.720 0.284 0.716
#> GSM648690 2 0.8608 0.720 0.284 0.716
#> GSM648691 2 0.8608 0.720 0.284 0.716
#> GSM648693 2 0.8608 0.720 0.284 0.716
#> GSM648700 2 0.0000 0.844 0.000 1.000
#> GSM648630 2 0.8608 0.720 0.284 0.716
#> GSM648632 2 0.8713 0.711 0.292 0.708
#> GSM648639 2 0.8267 0.733 0.260 0.740
#> GSM648640 2 0.8608 0.720 0.284 0.716
#> GSM648668 2 0.0000 0.844 0.000 1.000
#> GSM648676 2 0.0000 0.844 0.000 1.000
#> GSM648692 2 0.8608 0.720 0.284 0.716
#> GSM648694 2 0.8608 0.720 0.284 0.716
#> GSM648699 2 0.0000 0.844 0.000 1.000
#> GSM648701 2 0.0000 0.844 0.000 1.000
#> GSM648673 2 0.0000 0.844 0.000 1.000
#> GSM648677 2 0.0000 0.844 0.000 1.000
#> GSM648687 2 0.8608 0.720 0.284 0.716
#> GSM648688 2 0.8608 0.720 0.284 0.716
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM648605 2 0.0000 0.846 0.000 1.000 0.000
#> GSM648618 3 0.2959 0.786 0.100 0.000 0.900
#> GSM648620 2 0.0237 0.844 0.004 0.996 0.000
#> GSM648646 2 0.0000 0.846 0.000 1.000 0.000
#> GSM648649 1 0.0424 0.955 0.992 0.008 0.000
#> GSM648675 2 0.5406 0.805 0.012 0.764 0.224
#> GSM648682 2 0.0892 0.844 0.000 0.980 0.020
#> GSM648698 2 0.0000 0.846 0.000 1.000 0.000
#> GSM648708 2 0.0237 0.844 0.004 0.996 0.000
#> GSM648628 3 0.4974 0.710 0.236 0.000 0.764
#> GSM648595 1 0.5882 0.439 0.652 0.348 0.000
#> GSM648635 1 0.0000 0.962 1.000 0.000 0.000
#> GSM648645 1 0.0000 0.962 1.000 0.000 0.000
#> GSM648647 2 0.0000 0.846 0.000 1.000 0.000
#> GSM648667 2 0.5968 0.329 0.364 0.636 0.000
#> GSM648695 2 0.0237 0.844 0.004 0.996 0.000
#> GSM648704 2 0.0000 0.846 0.000 1.000 0.000
#> GSM648706 2 0.0000 0.846 0.000 1.000 0.000
#> GSM648593 1 0.0000 0.962 1.000 0.000 0.000
#> GSM648594 1 0.0000 0.962 1.000 0.000 0.000
#> GSM648600 1 0.0000 0.962 1.000 0.000 0.000
#> GSM648621 1 0.0000 0.962 1.000 0.000 0.000
#> GSM648622 1 0.0000 0.962 1.000 0.000 0.000
#> GSM648623 1 0.0000 0.962 1.000 0.000 0.000
#> GSM648636 1 0.0000 0.962 1.000 0.000 0.000
#> GSM648655 1 0.0000 0.962 1.000 0.000 0.000
#> GSM648661 1 0.0000 0.962 1.000 0.000 0.000
#> GSM648664 1 0.0000 0.962 1.000 0.000 0.000
#> GSM648683 1 0.0000 0.962 1.000 0.000 0.000
#> GSM648685 1 0.0000 0.962 1.000 0.000 0.000
#> GSM648702 1 0.0000 0.962 1.000 0.000 0.000
#> GSM648597 1 0.0000 0.962 1.000 0.000 0.000
#> GSM648603 1 0.1163 0.935 0.972 0.000 0.028
#> GSM648606 3 0.4974 0.691 0.000 0.236 0.764
#> GSM648613 3 0.4974 0.691 0.000 0.236 0.764
#> GSM648619 3 0.6126 0.485 0.400 0.000 0.600
#> GSM648654 3 0.8590 0.615 0.164 0.236 0.600
#> GSM648663 3 0.4974 0.691 0.000 0.236 0.764
#> GSM648670 2 0.4974 0.808 0.000 0.764 0.236
#> GSM648707 3 0.0000 0.806 0.000 0.000 1.000
#> GSM648615 2 0.0000 0.846 0.000 1.000 0.000
#> GSM648643 2 0.0000 0.846 0.000 1.000 0.000
#> GSM648650 2 0.5968 0.329 0.364 0.636 0.000
#> GSM648656 2 0.0000 0.846 0.000 1.000 0.000
#> GSM648715 2 0.0000 0.846 0.000 1.000 0.000
#> GSM648598 1 0.0000 0.962 1.000 0.000 0.000
#> GSM648601 1 0.0000 0.962 1.000 0.000 0.000
#> GSM648602 1 0.0000 0.962 1.000 0.000 0.000
#> GSM648604 1 0.0000 0.962 1.000 0.000 0.000
#> GSM648614 3 0.4974 0.691 0.000 0.236 0.764
#> GSM648624 1 0.0000 0.962 1.000 0.000 0.000
#> GSM648625 1 0.0424 0.955 0.992 0.008 0.000
#> GSM648629 1 0.0000 0.962 1.000 0.000 0.000
#> GSM648634 1 0.0000 0.962 1.000 0.000 0.000
#> GSM648648 1 0.0000 0.962 1.000 0.000 0.000
#> GSM648651 1 0.0000 0.962 1.000 0.000 0.000
#> GSM648657 1 0.0000 0.962 1.000 0.000 0.000
#> GSM648660 1 0.0000 0.962 1.000 0.000 0.000
#> GSM648697 1 0.0000 0.962 1.000 0.000 0.000
#> GSM648710 1 0.0000 0.962 1.000 0.000 0.000
#> GSM648591 3 0.6062 0.514 0.384 0.000 0.616
#> GSM648592 1 0.6299 0.169 0.524 0.476 0.000
#> GSM648607 1 0.3551 0.809 0.868 0.000 0.132
#> GSM648611 3 0.4346 0.754 0.184 0.000 0.816
#> GSM648612 3 0.5138 0.694 0.252 0.000 0.748
#> GSM648616 3 0.0000 0.806 0.000 0.000 1.000
#> GSM648617 1 0.0000 0.962 1.000 0.000 0.000
#> GSM648626 1 0.3412 0.820 0.876 0.000 0.124
#> GSM648711 1 0.0000 0.962 1.000 0.000 0.000
#> GSM648712 3 0.6126 0.485 0.400 0.000 0.600
#> GSM648713 3 0.6126 0.485 0.400 0.000 0.600
#> GSM648714 3 0.4974 0.691 0.000 0.236 0.764
#> GSM648716 3 0.6126 0.485 0.400 0.000 0.600
#> GSM648717 3 0.6148 0.750 0.148 0.076 0.776
#> GSM648590 2 0.5016 0.641 0.240 0.760 0.000
#> GSM648596 2 0.0000 0.846 0.000 1.000 0.000
#> GSM648642 2 0.0000 0.846 0.000 1.000 0.000
#> GSM648696 1 0.4555 0.722 0.800 0.200 0.000
#> GSM648705 1 0.0424 0.955 0.992 0.008 0.000
#> GSM648718 2 0.0000 0.846 0.000 1.000 0.000
#> GSM648599 1 0.0000 0.962 1.000 0.000 0.000
#> GSM648608 1 0.0000 0.962 1.000 0.000 0.000
#> GSM648609 1 0.0000 0.962 1.000 0.000 0.000
#> GSM648610 1 0.0000 0.962 1.000 0.000 0.000
#> GSM648633 1 0.0000 0.962 1.000 0.000 0.000
#> GSM648644 2 0.0000 0.846 0.000 1.000 0.000
#> GSM648652 1 0.0000 0.962 1.000 0.000 0.000
#> GSM648653 1 0.0000 0.962 1.000 0.000 0.000
#> GSM648658 1 0.0000 0.962 1.000 0.000 0.000
#> GSM648659 2 0.0000 0.846 0.000 1.000 0.000
#> GSM648662 1 0.4206 0.836 0.872 0.088 0.040
#> GSM648665 1 0.4974 0.669 0.764 0.236 0.000
#> GSM648666 1 0.0000 0.962 1.000 0.000 0.000
#> GSM648680 1 0.0000 0.962 1.000 0.000 0.000
#> GSM648684 1 0.0000 0.962 1.000 0.000 0.000
#> GSM648709 2 0.0000 0.846 0.000 1.000 0.000
#> GSM648719 1 0.0000 0.962 1.000 0.000 0.000
#> GSM648627 3 0.6126 0.485 0.400 0.000 0.600
#> GSM648637 2 0.4974 0.808 0.000 0.764 0.236
#> GSM648638 2 0.6126 0.607 0.000 0.600 0.400
#> GSM648641 3 0.0000 0.806 0.000 0.000 1.000
#> GSM648672 2 0.4974 0.808 0.000 0.764 0.236
#> GSM648674 2 0.4974 0.808 0.000 0.764 0.236
#> GSM648703 2 0.4974 0.808 0.000 0.764 0.236
#> GSM648631 3 0.0000 0.806 0.000 0.000 1.000
#> GSM648669 2 0.4974 0.808 0.000 0.764 0.236
#> GSM648671 2 0.4974 0.808 0.000 0.764 0.236
#> GSM648678 2 0.4974 0.808 0.000 0.764 0.236
#> GSM648679 2 0.4974 0.808 0.000 0.764 0.236
#> GSM648681 2 0.0000 0.846 0.000 1.000 0.000
#> GSM648686 3 0.0000 0.806 0.000 0.000 1.000
#> GSM648689 3 0.0000 0.806 0.000 0.000 1.000
#> GSM648690 3 0.0000 0.806 0.000 0.000 1.000
#> GSM648691 3 0.0000 0.806 0.000 0.000 1.000
#> GSM648693 3 0.0000 0.806 0.000 0.000 1.000
#> GSM648700 2 0.4974 0.808 0.000 0.764 0.236
#> GSM648630 3 0.0000 0.806 0.000 0.000 1.000
#> GSM648632 3 0.0000 0.806 0.000 0.000 1.000
#> GSM648639 3 0.0000 0.806 0.000 0.000 1.000
#> GSM648640 3 0.0000 0.806 0.000 0.000 1.000
#> GSM648668 2 0.4974 0.808 0.000 0.764 0.236
#> GSM648676 2 0.4974 0.808 0.000 0.764 0.236
#> GSM648692 3 0.0000 0.806 0.000 0.000 1.000
#> GSM648694 3 0.0000 0.806 0.000 0.000 1.000
#> GSM648699 2 0.4974 0.808 0.000 0.764 0.236
#> GSM648701 2 0.4974 0.808 0.000 0.764 0.236
#> GSM648673 2 0.4974 0.808 0.000 0.764 0.236
#> GSM648677 2 0.4974 0.808 0.000 0.764 0.236
#> GSM648687 3 0.0000 0.806 0.000 0.000 1.000
#> GSM648688 3 0.0000 0.806 0.000 0.000 1.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM648605 2 0.2868 0.61204 0.000 0.864 0.000 0.136
#> GSM648618 3 0.4304 0.71185 0.000 0.000 0.716 0.284
#> GSM648620 2 0.0000 0.64094 0.000 1.000 0.000 0.000
#> GSM648646 2 0.3400 0.58099 0.000 0.820 0.000 0.180
#> GSM648649 1 0.3791 0.64367 0.796 0.200 0.000 0.004
#> GSM648675 4 0.6182 0.53987 0.000 0.308 0.076 0.616
#> GSM648682 2 0.4382 0.38861 0.000 0.704 0.000 0.296
#> GSM648698 2 0.2868 0.61204 0.000 0.864 0.000 0.136
#> GSM648708 2 0.0000 0.64094 0.000 1.000 0.000 0.000
#> GSM648628 3 0.4304 0.71028 0.000 0.000 0.716 0.284
#> GSM648595 1 0.6848 0.51033 0.592 0.160 0.000 0.248
#> GSM648635 1 0.3208 0.68191 0.848 0.148 0.000 0.004
#> GSM648645 1 0.0469 0.74473 0.988 0.000 0.000 0.012
#> GSM648647 2 0.0000 0.64094 0.000 1.000 0.000 0.000
#> GSM648667 2 0.4522 0.34569 0.320 0.680 0.000 0.000
#> GSM648695 2 0.0000 0.64094 0.000 1.000 0.000 0.000
#> GSM648704 2 0.3569 0.56300 0.000 0.804 0.000 0.196
#> GSM648706 2 0.3074 0.60283 0.000 0.848 0.000 0.152
#> GSM648593 1 0.3208 0.68191 0.848 0.148 0.000 0.004
#> GSM648594 1 0.3862 0.67596 0.824 0.152 0.000 0.024
#> GSM648600 1 0.3649 0.71307 0.796 0.000 0.000 0.204
#> GSM648621 1 0.4697 0.71572 0.644 0.000 0.000 0.356
#> GSM648622 1 0.4008 0.73558 0.756 0.000 0.000 0.244
#> GSM648623 1 0.4843 0.67254 0.604 0.000 0.000 0.396
#> GSM648636 1 0.5619 0.63971 0.724 0.152 0.000 0.124
#> GSM648655 1 0.5619 0.63971 0.724 0.152 0.000 0.124
#> GSM648661 1 0.4193 0.72991 0.732 0.000 0.000 0.268
#> GSM648664 1 0.4193 0.72991 0.732 0.000 0.000 0.268
#> GSM648683 1 0.4817 0.70664 0.612 0.000 0.000 0.388
#> GSM648685 1 0.5471 0.72992 0.684 0.048 0.000 0.268
#> GSM648702 1 0.5619 0.63971 0.724 0.152 0.000 0.124
#> GSM648597 1 0.3311 0.70365 0.828 0.000 0.000 0.172
#> GSM648603 1 0.4990 0.68437 0.640 0.000 0.008 0.352
#> GSM648606 3 0.5568 0.68556 0.000 0.152 0.728 0.120
#> GSM648613 3 0.5483 0.70065 0.000 0.128 0.736 0.136
#> GSM648619 1 0.7446 0.45513 0.432 0.000 0.172 0.396
#> GSM648654 2 0.8231 -0.00694 0.012 0.392 0.312 0.284
#> GSM648663 3 0.5568 0.68556 0.000 0.152 0.728 0.120
#> GSM648670 4 0.6597 0.58766 0.000 0.304 0.108 0.588
#> GSM648707 3 0.3123 0.78983 0.000 0.000 0.844 0.156
#> GSM648615 2 0.3123 0.60039 0.000 0.844 0.000 0.156
#> GSM648643 2 0.3486 0.57262 0.000 0.812 0.000 0.188
#> GSM648650 2 0.4406 0.37656 0.300 0.700 0.000 0.000
#> GSM648656 2 0.4250 0.43308 0.000 0.724 0.000 0.276
#> GSM648715 2 0.0000 0.64094 0.000 1.000 0.000 0.000
#> GSM648598 1 0.0188 0.74391 0.996 0.000 0.000 0.004
#> GSM648601 1 0.0469 0.74473 0.988 0.000 0.000 0.012
#> GSM648602 1 0.3726 0.73712 0.788 0.000 0.000 0.212
#> GSM648604 1 0.4164 0.72947 0.736 0.000 0.000 0.264
#> GSM648614 2 0.7175 0.08486 0.000 0.496 0.360 0.144
#> GSM648624 1 0.4103 0.73366 0.744 0.000 0.000 0.256
#> GSM648625 1 0.3450 0.68129 0.836 0.156 0.000 0.008
#> GSM648629 1 0.4164 0.72947 0.736 0.000 0.000 0.264
#> GSM648634 1 0.2704 0.72156 0.876 0.000 0.000 0.124
#> GSM648648 1 0.3257 0.67923 0.844 0.152 0.000 0.004
#> GSM648651 1 0.3123 0.75246 0.844 0.000 0.000 0.156
#> GSM648657 1 0.1022 0.74513 0.968 0.000 0.000 0.032
#> GSM648660 1 0.0000 0.74388 1.000 0.000 0.000 0.000
#> GSM648697 1 0.4633 0.75057 0.780 0.048 0.000 0.172
#> GSM648710 1 0.4164 0.72947 0.736 0.000 0.000 0.264
#> GSM648591 3 0.7904 -0.03085 0.324 0.000 0.368 0.308
#> GSM648592 1 0.7358 0.11526 0.448 0.392 0.000 0.160
#> GSM648607 1 0.5865 0.63239 0.552 0.000 0.036 0.412
#> GSM648611 3 0.4134 0.72237 0.000 0.000 0.740 0.260
#> GSM648612 1 0.7808 0.32394 0.400 0.000 0.256 0.344
#> GSM648616 3 0.3074 0.70622 0.000 0.000 0.848 0.152
#> GSM648617 1 0.3024 0.71434 0.852 0.000 0.000 0.148
#> GSM648626 1 0.5110 0.68151 0.636 0.000 0.012 0.352
#> GSM648711 1 0.5060 0.65934 0.584 0.000 0.004 0.412
#> GSM648712 4 0.7330 -0.49111 0.312 0.000 0.180 0.508
#> GSM648713 1 0.7342 0.47058 0.432 0.000 0.156 0.412
#> GSM648714 2 0.5859 -0.09392 0.000 0.496 0.472 0.032
#> GSM648716 1 0.7446 0.45513 0.432 0.000 0.172 0.396
#> GSM648717 3 0.5432 0.69374 0.124 0.000 0.740 0.136
#> GSM648590 2 0.7012 -0.05048 0.124 0.504 0.000 0.372
#> GSM648596 2 0.3074 0.60283 0.000 0.848 0.000 0.152
#> GSM648642 2 0.0000 0.64094 0.000 1.000 0.000 0.000
#> GSM648696 1 0.6571 0.51461 0.612 0.264 0.000 0.124
#> GSM648705 1 0.3610 0.64543 0.800 0.200 0.000 0.000
#> GSM648718 2 0.3400 0.58164 0.000 0.820 0.000 0.180
#> GSM648599 1 0.4843 0.70564 0.604 0.000 0.000 0.396
#> GSM648608 1 0.4817 0.70664 0.612 0.000 0.000 0.388
#> GSM648609 1 0.4164 0.72947 0.736 0.000 0.000 0.264
#> GSM648610 1 0.4817 0.70664 0.612 0.000 0.000 0.388
#> GSM648633 1 0.0000 0.74388 1.000 0.000 0.000 0.000
#> GSM648644 2 0.4277 0.42496 0.000 0.720 0.000 0.280
#> GSM648652 1 0.3074 0.67949 0.848 0.152 0.000 0.000
#> GSM648653 1 0.3486 0.73507 0.812 0.000 0.000 0.188
#> GSM648658 1 0.5574 0.64277 0.728 0.148 0.000 0.124
#> GSM648659 2 0.0000 0.64094 0.000 1.000 0.000 0.000
#> GSM648662 1 0.7216 0.64229 0.564 0.080 0.032 0.324
#> GSM648665 2 0.7798 -0.20600 0.320 0.416 0.000 0.264
#> GSM648666 1 0.4164 0.74528 0.736 0.000 0.000 0.264
#> GSM648680 1 0.3208 0.68191 0.848 0.148 0.000 0.004
#> GSM648684 1 0.4817 0.70664 0.612 0.000 0.000 0.388
#> GSM648709 2 0.0000 0.64094 0.000 1.000 0.000 0.000
#> GSM648719 1 0.0000 0.74388 1.000 0.000 0.000 0.000
#> GSM648627 4 0.7359 -0.48254 0.304 0.000 0.188 0.508
#> GSM648637 4 0.7649 0.77032 0.000 0.312 0.232 0.456
#> GSM648638 4 0.7537 0.63373 0.000 0.196 0.348 0.456
#> GSM648641 3 0.0000 0.84598 0.000 0.000 1.000 0.000
#> GSM648672 4 0.7640 0.77009 0.000 0.316 0.228 0.456
#> GSM648674 4 0.7626 0.76840 0.000 0.304 0.232 0.464
#> GSM648703 4 0.7640 0.77009 0.000 0.316 0.228 0.456
#> GSM648631 3 0.0000 0.84598 0.000 0.000 1.000 0.000
#> GSM648669 4 0.7660 0.74966 0.000 0.276 0.260 0.464
#> GSM648671 4 0.7660 0.74966 0.000 0.276 0.260 0.464
#> GSM648678 4 0.7617 0.74639 0.000 0.332 0.216 0.452
#> GSM648679 4 0.7634 0.76714 0.000 0.300 0.236 0.464
#> GSM648681 2 0.4977 -0.13625 0.000 0.540 0.000 0.460
#> GSM648686 3 0.0000 0.84598 0.000 0.000 1.000 0.000
#> GSM648689 3 0.0000 0.84598 0.000 0.000 1.000 0.000
#> GSM648690 3 0.0000 0.84598 0.000 0.000 1.000 0.000
#> GSM648691 3 0.0000 0.84598 0.000 0.000 1.000 0.000
#> GSM648693 3 0.0000 0.84598 0.000 0.000 1.000 0.000
#> GSM648700 4 0.7640 0.77009 0.000 0.316 0.228 0.456
#> GSM648630 3 0.0000 0.84598 0.000 0.000 1.000 0.000
#> GSM648632 3 0.0000 0.84598 0.000 0.000 1.000 0.000
#> GSM648639 3 0.1716 0.78982 0.000 0.000 0.936 0.064
#> GSM648640 3 0.0000 0.84598 0.000 0.000 1.000 0.000
#> GSM648668 4 0.7649 0.77032 0.000 0.312 0.232 0.456
#> GSM648676 4 0.7640 0.77009 0.000 0.316 0.228 0.456
#> GSM648692 3 0.0000 0.84598 0.000 0.000 1.000 0.000
#> GSM648694 3 0.0000 0.84598 0.000 0.000 1.000 0.000
#> GSM648699 4 0.7640 0.77009 0.000 0.316 0.228 0.456
#> GSM648701 4 0.7640 0.77009 0.000 0.316 0.228 0.456
#> GSM648673 4 0.7651 0.75968 0.000 0.288 0.248 0.464
#> GSM648677 4 0.7640 0.77009 0.000 0.316 0.228 0.456
#> GSM648687 3 0.2408 0.73908 0.000 0.000 0.896 0.104
#> GSM648688 3 0.0000 0.84598 0.000 0.000 1.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM648605 2 0.1270 0.8711 0.000 0.948 0.000 0.052 0.000
#> GSM648618 5 0.5418 -0.0774 0.000 0.000 0.364 0.068 0.568
#> GSM648620 2 0.0510 0.8727 0.000 0.984 0.000 0.016 0.000
#> GSM648646 2 0.1544 0.8672 0.000 0.932 0.000 0.068 0.000
#> GSM648649 1 0.5856 0.3785 0.504 0.100 0.000 0.000 0.396
#> GSM648675 4 0.3566 0.7523 0.000 0.024 0.004 0.812 0.160
#> GSM648682 2 0.3837 0.5734 0.000 0.692 0.000 0.308 0.000
#> GSM648698 2 0.1270 0.8711 0.000 0.948 0.000 0.052 0.000
#> GSM648708 2 0.0510 0.8727 0.000 0.984 0.000 0.016 0.000
#> GSM648628 5 0.4249 -0.1813 0.000 0.000 0.432 0.000 0.568
#> GSM648595 5 0.7448 -0.1420 0.276 0.056 0.004 0.180 0.484
#> GSM648635 1 0.5069 0.4697 0.620 0.052 0.000 0.000 0.328
#> GSM648645 5 0.4718 -0.2868 0.444 0.016 0.000 0.000 0.540
#> GSM648647 2 0.0510 0.8727 0.000 0.984 0.000 0.016 0.000
#> GSM648667 2 0.3454 0.6921 0.028 0.816 0.000 0.000 0.156
#> GSM648695 2 0.0510 0.8727 0.000 0.984 0.000 0.016 0.000
#> GSM648704 2 0.2074 0.8461 0.000 0.896 0.000 0.104 0.000
#> GSM648706 2 0.1410 0.8697 0.000 0.940 0.000 0.060 0.000
#> GSM648593 1 0.5069 0.4697 0.620 0.052 0.000 0.000 0.328
#> GSM648594 5 0.6261 -0.3032 0.452 0.036 0.012 0.036 0.464
#> GSM648600 5 0.2393 0.2750 0.080 0.016 0.004 0.000 0.900
#> GSM648621 5 0.3300 0.3119 0.204 0.000 0.004 0.000 0.792
#> GSM648622 1 0.2690 0.4855 0.844 0.000 0.000 0.000 0.156
#> GSM648623 5 0.4808 0.3217 0.400 0.000 0.024 0.000 0.576
#> GSM648636 5 0.5405 -0.3251 0.460 0.056 0.000 0.000 0.484
#> GSM648655 5 0.5405 -0.3251 0.460 0.056 0.000 0.000 0.484
#> GSM648661 1 0.0000 0.5196 1.000 0.000 0.000 0.000 0.000
#> GSM648664 1 0.0000 0.5196 1.000 0.000 0.000 0.000 0.000
#> GSM648683 1 0.2732 0.4163 0.840 0.000 0.000 0.000 0.160
#> GSM648685 1 0.0290 0.5220 0.992 0.000 0.000 0.000 0.008
#> GSM648702 5 0.5350 -0.3258 0.460 0.052 0.000 0.000 0.488
#> GSM648597 5 0.4716 0.3400 0.148 0.000 0.020 0.072 0.760
#> GSM648603 5 0.5412 0.3500 0.340 0.000 0.024 0.032 0.604
#> GSM648606 3 0.3956 0.7653 0.004 0.080 0.808 0.000 0.108
#> GSM648613 3 0.3667 0.7576 0.000 0.048 0.812 0.000 0.140
#> GSM648619 5 0.5803 0.3304 0.420 0.000 0.092 0.000 0.488
#> GSM648654 2 0.7204 0.1534 0.380 0.388 0.204 0.000 0.028
#> GSM648663 3 0.4658 0.7360 0.036 0.076 0.780 0.000 0.108
#> GSM648670 4 0.2890 0.7477 0.000 0.000 0.004 0.836 0.160
#> GSM648707 3 0.6477 0.2749 0.000 0.000 0.424 0.184 0.392
#> GSM648615 2 0.1410 0.8698 0.000 0.940 0.000 0.060 0.000
#> GSM648643 2 0.1851 0.8570 0.000 0.912 0.000 0.088 0.000
#> GSM648650 2 0.2773 0.7109 0.000 0.836 0.000 0.000 0.164
#> GSM648656 2 0.2929 0.7724 0.000 0.820 0.000 0.180 0.000
#> GSM648715 2 0.0510 0.8727 0.000 0.984 0.000 0.016 0.000
#> GSM648598 1 0.4497 0.4758 0.632 0.016 0.000 0.000 0.352
#> GSM648601 1 0.4936 0.4155 0.560 0.016 0.008 0.000 0.416
#> GSM648602 1 0.4658 0.3860 0.576 0.016 0.000 0.000 0.408
#> GSM648604 1 0.0794 0.5067 0.972 0.000 0.000 0.000 0.028
#> GSM648614 2 0.5165 0.5967 0.064 0.684 0.240 0.000 0.012
#> GSM648624 1 0.1732 0.5204 0.920 0.000 0.000 0.000 0.080
#> GSM648625 1 0.6509 0.3667 0.464 0.072 0.044 0.000 0.420
#> GSM648629 1 0.0794 0.5067 0.972 0.000 0.000 0.000 0.028
#> GSM648634 5 0.4744 -0.3448 0.476 0.016 0.000 0.000 0.508
#> GSM648648 1 0.5069 0.4697 0.620 0.052 0.000 0.000 0.328
#> GSM648651 1 0.3885 0.4845 0.724 0.000 0.008 0.000 0.268
#> GSM648657 5 0.4579 0.0247 0.308 0.016 0.008 0.000 0.668
#> GSM648660 1 0.4674 0.4437 0.568 0.016 0.000 0.000 0.416
#> GSM648697 1 0.3596 0.5337 0.784 0.016 0.000 0.000 0.200
#> GSM648710 1 0.0510 0.5128 0.984 0.000 0.000 0.000 0.016
#> GSM648591 5 0.4684 0.3272 0.008 0.000 0.176 0.072 0.744
#> GSM648592 5 0.5279 0.3580 0.112 0.016 0.044 0.072 0.756
#> GSM648607 1 0.5049 -0.3230 0.488 0.000 0.032 0.000 0.480
#> GSM648611 3 0.3838 0.6654 0.004 0.000 0.716 0.000 0.280
#> GSM648612 5 0.6094 0.3407 0.384 0.000 0.128 0.000 0.488
#> GSM648616 4 0.6315 -0.1734 0.000 0.000 0.396 0.448 0.156
#> GSM648617 5 0.3437 0.3276 0.120 0.000 0.048 0.000 0.832
#> GSM648626 5 0.5729 0.3575 0.320 0.000 0.024 0.056 0.600
#> GSM648711 1 0.4821 -0.3009 0.516 0.000 0.020 0.000 0.464
#> GSM648712 5 0.5487 0.3515 0.280 0.000 0.100 0.000 0.620
#> GSM648713 5 0.5504 0.3053 0.448 0.000 0.064 0.000 0.488
#> GSM648714 2 0.4016 0.6085 0.000 0.716 0.272 0.000 0.012
#> GSM648716 5 0.5803 0.3304 0.420 0.000 0.092 0.000 0.488
#> GSM648717 3 0.1831 0.8277 0.004 0.000 0.920 0.000 0.076
#> GSM648590 4 0.8117 0.1981 0.220 0.136 0.000 0.416 0.228
#> GSM648596 2 0.2104 0.8655 0.000 0.916 0.024 0.060 0.000
#> GSM648642 2 0.0510 0.8727 0.000 0.984 0.000 0.016 0.000
#> GSM648696 5 0.6908 -0.1555 0.316 0.288 0.004 0.000 0.392
#> GSM648705 1 0.5878 0.4147 0.556 0.120 0.000 0.000 0.324
#> GSM648718 2 0.1792 0.8593 0.000 0.916 0.000 0.084 0.000
#> GSM648599 5 0.3662 0.3278 0.252 0.000 0.004 0.000 0.744
#> GSM648608 1 0.2813 0.4123 0.832 0.000 0.000 0.000 0.168
#> GSM648609 1 0.0162 0.5185 0.996 0.000 0.000 0.000 0.004
#> GSM648610 1 0.3048 0.4039 0.820 0.000 0.004 0.000 0.176
#> GSM648633 1 0.4760 0.4412 0.564 0.020 0.000 0.000 0.416
#> GSM648644 2 0.3336 0.7113 0.000 0.772 0.000 0.228 0.000
#> GSM648652 1 0.5069 0.4697 0.620 0.052 0.000 0.000 0.328
#> GSM648653 1 0.4682 0.3766 0.564 0.016 0.000 0.000 0.420
#> GSM648658 5 0.5237 -0.3322 0.468 0.044 0.000 0.000 0.488
#> GSM648659 2 0.0510 0.8727 0.000 0.984 0.000 0.016 0.000
#> GSM648662 1 0.4689 0.3014 0.784 0.080 0.052 0.000 0.084
#> GSM648665 1 0.4608 0.1461 0.640 0.336 0.024 0.000 0.000
#> GSM648666 1 0.3109 0.5318 0.800 0.000 0.000 0.000 0.200
#> GSM648680 1 0.4874 0.4751 0.632 0.040 0.000 0.000 0.328
#> GSM648684 1 0.2732 0.4163 0.840 0.000 0.000 0.000 0.160
#> GSM648709 2 0.0510 0.8727 0.000 0.984 0.000 0.016 0.000
#> GSM648719 1 0.4674 0.4437 0.568 0.016 0.000 0.000 0.416
#> GSM648627 5 0.5296 0.3471 0.280 0.000 0.084 0.000 0.636
#> GSM648637 4 0.1197 0.8811 0.000 0.048 0.000 0.952 0.000
#> GSM648638 4 0.1357 0.8807 0.000 0.048 0.004 0.948 0.000
#> GSM648641 3 0.1270 0.9013 0.000 0.000 0.948 0.052 0.000
#> GSM648672 4 0.1544 0.8820 0.000 0.068 0.000 0.932 0.000
#> GSM648674 4 0.0000 0.8675 0.000 0.000 0.000 1.000 0.000
#> GSM648703 4 0.1544 0.8820 0.000 0.068 0.000 0.932 0.000
#> GSM648631 3 0.1270 0.9013 0.000 0.000 0.948 0.052 0.000
#> GSM648669 4 0.0162 0.8651 0.000 0.000 0.004 0.996 0.000
#> GSM648671 4 0.0162 0.8651 0.000 0.000 0.004 0.996 0.000
#> GSM648678 4 0.1608 0.8789 0.000 0.072 0.000 0.928 0.000
#> GSM648679 4 0.0000 0.8675 0.000 0.000 0.000 1.000 0.000
#> GSM648681 4 0.2732 0.7297 0.000 0.160 0.000 0.840 0.000
#> GSM648686 3 0.1270 0.9013 0.000 0.000 0.948 0.052 0.000
#> GSM648689 3 0.1270 0.9013 0.000 0.000 0.948 0.052 0.000
#> GSM648690 3 0.1270 0.9013 0.000 0.000 0.948 0.052 0.000
#> GSM648691 3 0.1270 0.9013 0.000 0.000 0.948 0.052 0.000
#> GSM648693 3 0.1270 0.9013 0.000 0.000 0.948 0.052 0.000
#> GSM648700 4 0.1544 0.8820 0.000 0.068 0.000 0.932 0.000
#> GSM648630 3 0.1270 0.9013 0.000 0.000 0.948 0.052 0.000
#> GSM648632 3 0.1270 0.9013 0.000 0.000 0.948 0.052 0.000
#> GSM648639 3 0.3366 0.7495 0.000 0.000 0.768 0.232 0.000
#> GSM648640 3 0.1270 0.9013 0.000 0.000 0.948 0.052 0.000
#> GSM648668 4 0.1544 0.8820 0.000 0.068 0.000 0.932 0.000
#> GSM648676 4 0.1544 0.8820 0.000 0.068 0.000 0.932 0.000
#> GSM648692 3 0.1270 0.9013 0.000 0.000 0.948 0.052 0.000
#> GSM648694 3 0.1270 0.9013 0.000 0.000 0.948 0.052 0.000
#> GSM648699 4 0.1544 0.8820 0.000 0.068 0.000 0.932 0.000
#> GSM648701 4 0.1544 0.8820 0.000 0.068 0.000 0.932 0.000
#> GSM648673 4 0.0000 0.8675 0.000 0.000 0.000 1.000 0.000
#> GSM648677 4 0.1544 0.8820 0.000 0.068 0.000 0.932 0.000
#> GSM648687 3 0.3039 0.7832 0.000 0.000 0.808 0.192 0.000
#> GSM648688 3 0.1270 0.9013 0.000 0.000 0.948 0.052 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM648605 2 0.0146 0.8917 0.004 0.996 0.000 0.000 0.000 0.000
#> GSM648618 5 0.4352 0.7408 0.016 0.000 0.080 0.008 0.764 0.132
#> GSM648620 2 0.0508 0.8913 0.012 0.984 0.000 0.000 0.000 0.004
#> GSM648646 2 0.0547 0.8884 0.000 0.980 0.000 0.020 0.000 0.000
#> GSM648649 6 0.3628 0.7287 0.168 0.004 0.000 0.000 0.044 0.784
#> GSM648675 4 0.3523 0.7783 0.016 0.008 0.000 0.812 0.020 0.144
#> GSM648682 2 0.2178 0.8029 0.000 0.868 0.000 0.132 0.000 0.000
#> GSM648698 2 0.0000 0.8914 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM648708 2 0.0603 0.8906 0.016 0.980 0.000 0.000 0.000 0.004
#> GSM648628 5 0.5085 0.6921 0.028 0.000 0.152 0.000 0.688 0.132
#> GSM648595 6 0.1448 0.6850 0.024 0.000 0.000 0.016 0.012 0.948
#> GSM648635 6 0.2597 0.7290 0.176 0.000 0.000 0.000 0.000 0.824
#> GSM648645 6 0.4860 0.6653 0.160 0.000 0.000 0.000 0.176 0.664
#> GSM648647 2 0.0508 0.8913 0.012 0.984 0.000 0.000 0.000 0.004
#> GSM648667 2 0.5022 0.0808 0.060 0.496 0.000 0.000 0.004 0.440
#> GSM648695 2 0.0653 0.8908 0.012 0.980 0.000 0.000 0.004 0.004
#> GSM648704 2 0.1007 0.8780 0.000 0.956 0.000 0.044 0.000 0.000
#> GSM648706 2 0.0146 0.8915 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM648593 6 0.3121 0.7224 0.192 0.000 0.000 0.004 0.008 0.796
#> GSM648594 6 0.5125 0.6261 0.132 0.004 0.000 0.000 0.232 0.632
#> GSM648600 6 0.4230 0.1536 0.024 0.000 0.000 0.000 0.364 0.612
#> GSM648621 5 0.4868 0.5742 0.076 0.000 0.000 0.000 0.592 0.332
#> GSM648622 1 0.4487 0.5343 0.668 0.000 0.000 0.000 0.068 0.264
#> GSM648623 5 0.2709 0.7771 0.132 0.000 0.000 0.000 0.848 0.020
#> GSM648636 6 0.1644 0.6802 0.052 0.000 0.000 0.004 0.012 0.932
#> GSM648655 6 0.1707 0.6825 0.056 0.000 0.000 0.004 0.012 0.928
#> GSM648661 1 0.2595 0.7463 0.836 0.000 0.000 0.000 0.004 0.160
#> GSM648664 1 0.2558 0.7457 0.840 0.000 0.000 0.000 0.004 0.156
#> GSM648683 1 0.3758 0.6643 0.668 0.000 0.000 0.000 0.008 0.324
#> GSM648685 1 0.2558 0.7457 0.840 0.000 0.000 0.000 0.004 0.156
#> GSM648702 6 0.1082 0.6889 0.040 0.000 0.000 0.000 0.004 0.956
#> GSM648597 5 0.3721 0.7545 0.108 0.000 0.000 0.020 0.808 0.064
#> GSM648603 5 0.2491 0.7831 0.112 0.000 0.000 0.000 0.868 0.020
#> GSM648606 3 0.5950 0.5411 0.124 0.044 0.572 0.000 0.260 0.000
#> GSM648613 3 0.5924 0.4863 0.124 0.032 0.544 0.000 0.300 0.000
#> GSM648619 5 0.3543 0.7637 0.200 0.000 0.032 0.000 0.768 0.000
#> GSM648654 1 0.3766 0.4894 0.736 0.232 0.000 0.000 0.032 0.000
#> GSM648663 3 0.6062 0.5277 0.140 0.044 0.560 0.000 0.256 0.000
#> GSM648670 4 0.3192 0.7858 0.016 0.000 0.000 0.828 0.020 0.136
#> GSM648707 5 0.4340 0.6572 0.012 0.000 0.168 0.080 0.740 0.000
#> GSM648615 2 0.0260 0.8912 0.000 0.992 0.000 0.008 0.000 0.000
#> GSM648643 2 0.0547 0.8883 0.000 0.980 0.000 0.020 0.000 0.000
#> GSM648650 2 0.4318 0.1715 0.020 0.532 0.000 0.000 0.000 0.448
#> GSM648656 2 0.1007 0.8773 0.000 0.956 0.000 0.044 0.000 0.000
#> GSM648715 2 0.0508 0.8913 0.012 0.984 0.000 0.000 0.000 0.004
#> GSM648598 6 0.3964 0.6884 0.232 0.000 0.000 0.000 0.044 0.724
#> GSM648601 6 0.4795 0.6617 0.176 0.000 0.000 0.000 0.152 0.672
#> GSM648602 6 0.3512 0.5011 0.196 0.000 0.000 0.000 0.032 0.772
#> GSM648604 1 0.2520 0.7468 0.844 0.000 0.000 0.000 0.004 0.152
#> GSM648614 2 0.6335 0.5061 0.152 0.584 0.132 0.000 0.132 0.000
#> GSM648624 1 0.3202 0.7240 0.800 0.000 0.000 0.000 0.024 0.176
#> GSM648625 6 0.5661 0.5572 0.196 0.012 0.008 0.000 0.172 0.612
#> GSM648629 1 0.2558 0.7449 0.840 0.000 0.000 0.000 0.004 0.156
#> GSM648634 6 0.1462 0.6835 0.056 0.000 0.000 0.000 0.008 0.936
#> GSM648648 6 0.2762 0.7227 0.196 0.000 0.000 0.000 0.000 0.804
#> GSM648651 1 0.5300 0.1027 0.496 0.000 0.000 0.000 0.104 0.400
#> GSM648657 6 0.4823 0.6386 0.124 0.000 0.000 0.000 0.216 0.660
#> GSM648660 6 0.3971 0.7149 0.184 0.000 0.000 0.000 0.068 0.748
#> GSM648697 1 0.3805 0.5349 0.664 0.000 0.000 0.004 0.004 0.328
#> GSM648710 1 0.2520 0.7468 0.844 0.000 0.000 0.000 0.004 0.152
#> GSM648591 5 0.3348 0.7596 0.016 0.000 0.000 0.020 0.812 0.152
#> GSM648592 5 0.2216 0.7775 0.024 0.000 0.000 0.016 0.908 0.052
#> GSM648607 5 0.3807 0.5535 0.368 0.000 0.000 0.000 0.628 0.004
#> GSM648611 3 0.5502 0.5153 0.028 0.000 0.632 0.000 0.204 0.136
#> GSM648612 5 0.2474 0.7759 0.080 0.000 0.040 0.000 0.880 0.000
#> GSM648616 4 0.5521 0.0520 0.000 0.000 0.132 0.468 0.400 0.000
#> GSM648617 5 0.2723 0.7543 0.016 0.000 0.004 0.000 0.852 0.128
#> GSM648626 5 0.2445 0.7832 0.108 0.000 0.000 0.000 0.872 0.020
#> GSM648711 1 0.4184 -0.2667 0.504 0.000 0.000 0.000 0.484 0.012
#> GSM648712 5 0.4129 0.7796 0.088 0.000 0.032 0.000 0.784 0.096
#> GSM648713 5 0.2768 0.7564 0.156 0.000 0.012 0.000 0.832 0.000
#> GSM648714 2 0.6304 0.5014 0.132 0.588 0.148 0.000 0.132 0.000
#> GSM648716 5 0.3572 0.7600 0.204 0.000 0.032 0.000 0.764 0.000
#> GSM648717 3 0.4734 0.6483 0.120 0.000 0.672 0.000 0.208 0.000
#> GSM648590 6 0.5829 0.2529 0.036 0.072 0.000 0.308 0.012 0.572
#> GSM648596 2 0.2996 0.8187 0.044 0.864 0.008 0.008 0.076 0.000
#> GSM648642 2 0.0508 0.8913 0.012 0.984 0.000 0.000 0.000 0.004
#> GSM648696 6 0.2932 0.5901 0.024 0.132 0.000 0.000 0.004 0.840
#> GSM648705 6 0.3053 0.7308 0.172 0.012 0.000 0.000 0.004 0.812
#> GSM648718 2 0.0603 0.8890 0.000 0.980 0.000 0.016 0.000 0.004
#> GSM648599 5 0.4436 0.6323 0.048 0.000 0.000 0.000 0.640 0.312
#> GSM648608 1 0.3619 0.6702 0.680 0.000 0.000 0.000 0.004 0.316
#> GSM648609 1 0.2520 0.7468 0.844 0.000 0.000 0.000 0.004 0.152
#> GSM648610 1 0.3707 0.6570 0.680 0.000 0.000 0.000 0.008 0.312
#> GSM648633 6 0.3960 0.7162 0.176 0.000 0.000 0.000 0.072 0.752
#> GSM648644 2 0.1765 0.8429 0.000 0.904 0.000 0.096 0.000 0.000
#> GSM648652 6 0.2703 0.7302 0.172 0.000 0.000 0.000 0.004 0.824
#> GSM648653 6 0.2915 0.5246 0.184 0.000 0.000 0.000 0.008 0.808
#> GSM648658 6 0.1707 0.6825 0.056 0.000 0.000 0.004 0.012 0.928
#> GSM648659 2 0.1140 0.8888 0.012 0.964 0.000 0.008 0.008 0.008
#> GSM648662 1 0.3147 0.5272 0.816 0.016 0.008 0.000 0.160 0.000
#> GSM648665 1 0.3950 0.5418 0.792 0.116 0.008 0.000 0.076 0.008
#> GSM648666 1 0.4052 0.5554 0.628 0.000 0.000 0.000 0.016 0.356
#> GSM648680 6 0.2730 0.7236 0.192 0.000 0.000 0.000 0.000 0.808
#> GSM648684 1 0.3774 0.6611 0.664 0.000 0.000 0.000 0.008 0.328
#> GSM648709 2 0.0748 0.8900 0.016 0.976 0.000 0.000 0.004 0.004
#> GSM648719 6 0.4024 0.7126 0.184 0.000 0.000 0.000 0.072 0.744
#> GSM648627 5 0.5013 0.7409 0.140 0.000 0.032 0.000 0.700 0.128
#> GSM648637 4 0.0363 0.9347 0.000 0.012 0.000 0.988 0.000 0.000
#> GSM648638 4 0.0622 0.9333 0.000 0.012 0.008 0.980 0.000 0.000
#> GSM648641 3 0.0551 0.8807 0.004 0.000 0.984 0.008 0.004 0.000
#> GSM648672 4 0.0458 0.9347 0.000 0.016 0.000 0.984 0.000 0.000
#> GSM648674 4 0.0260 0.9313 0.000 0.000 0.000 0.992 0.008 0.000
#> GSM648703 4 0.0622 0.9347 0.000 0.012 0.000 0.980 0.008 0.000
#> GSM648631 3 0.0260 0.8845 0.000 0.000 0.992 0.008 0.000 0.000
#> GSM648669 4 0.0260 0.9313 0.000 0.000 0.000 0.992 0.008 0.000
#> GSM648671 4 0.0260 0.9313 0.000 0.000 0.000 0.992 0.008 0.000
#> GSM648678 4 0.0937 0.9230 0.000 0.040 0.000 0.960 0.000 0.000
#> GSM648679 4 0.0260 0.9313 0.000 0.000 0.000 0.992 0.008 0.000
#> GSM648681 4 0.2313 0.8481 0.004 0.100 0.000 0.884 0.012 0.000
#> GSM648686 3 0.0260 0.8845 0.000 0.000 0.992 0.008 0.000 0.000
#> GSM648689 3 0.0260 0.8845 0.000 0.000 0.992 0.008 0.000 0.000
#> GSM648690 3 0.0260 0.8845 0.000 0.000 0.992 0.008 0.000 0.000
#> GSM648691 3 0.0260 0.8845 0.000 0.000 0.992 0.008 0.000 0.000
#> GSM648693 3 0.0260 0.8845 0.000 0.000 0.992 0.008 0.000 0.000
#> GSM648700 4 0.0622 0.9347 0.000 0.012 0.000 0.980 0.008 0.000
#> GSM648630 3 0.0260 0.8845 0.000 0.000 0.992 0.008 0.000 0.000
#> GSM648632 3 0.0260 0.8845 0.000 0.000 0.992 0.008 0.000 0.000
#> GSM648639 3 0.3013 0.7840 0.000 0.000 0.844 0.088 0.068 0.000
#> GSM648640 3 0.0260 0.8845 0.000 0.000 0.992 0.008 0.000 0.000
#> GSM648668 4 0.0458 0.9347 0.000 0.016 0.000 0.984 0.000 0.000
#> GSM648676 4 0.0622 0.9347 0.000 0.012 0.000 0.980 0.008 0.000
#> GSM648692 3 0.0260 0.8845 0.000 0.000 0.992 0.008 0.000 0.000
#> GSM648694 3 0.0260 0.8845 0.000 0.000 0.992 0.008 0.000 0.000
#> GSM648699 4 0.0622 0.9347 0.000 0.012 0.000 0.980 0.008 0.000
#> GSM648701 4 0.0622 0.9347 0.000 0.012 0.000 0.980 0.008 0.000
#> GSM648673 4 0.0260 0.9313 0.000 0.000 0.000 0.992 0.008 0.000
#> GSM648677 4 0.0717 0.9342 0.000 0.016 0.000 0.976 0.008 0.000
#> GSM648687 3 0.1444 0.8400 0.000 0.000 0.928 0.072 0.000 0.000
#> GSM648688 3 0.0260 0.8845 0.000 0.000 0.992 0.008 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) development.stage(p) other(p) k
#> SD:skmeans 127 3.41e-13 0.06533 2.99e-15 2
#> SD:skmeans 121 5.26e-08 0.00101 1.66e-20 3
#> SD:skmeans 111 5.32e-16 0.01194 1.90e-27 4
#> SD:skmeans 73 6.25e-11 0.18269 5.50e-20 5
#> SD:skmeans 121 4.54e-19 0.04426 4.89e-38 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 51941 rows and 130 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'pam' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.378 0.587 0.841 0.4322 0.577 0.577
#> 3 3 0.499 0.798 0.863 0.2829 0.797 0.676
#> 4 4 0.591 0.570 0.785 0.2448 0.812 0.609
#> 5 5 0.598 0.565 0.764 0.1219 0.827 0.503
#> 6 6 0.618 0.521 0.733 0.0462 0.883 0.550
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
#> GSM648605 2 0.8499 0.59792 0.276 0.724
#> GSM648618 1 0.1843 0.77829 0.972 0.028
#> GSM648620 1 0.9988 -0.07538 0.520 0.480
#> GSM648646 2 0.6801 0.66720 0.180 0.820
#> GSM648649 1 0.9209 0.41036 0.664 0.336
#> GSM648675 1 0.9922 0.05493 0.552 0.448
#> GSM648682 2 0.5629 0.69173 0.132 0.868
#> GSM648698 2 0.8081 0.61462 0.248 0.752
#> GSM648708 1 0.9323 0.38214 0.652 0.348
#> GSM648628 1 0.0000 0.78857 1.000 0.000
#> GSM648595 1 0.9209 0.41036 0.664 0.336
#> GSM648635 1 0.9209 0.41036 0.664 0.336
#> GSM648645 1 0.1843 0.77829 0.972 0.028
#> GSM648647 2 0.8081 0.61462 0.248 0.752
#> GSM648667 1 0.9209 0.41036 0.664 0.336
#> GSM648695 1 0.9522 0.31620 0.628 0.372
#> GSM648704 2 0.0000 0.72523 0.000 1.000
#> GSM648706 2 0.0000 0.72523 0.000 1.000
#> GSM648593 1 0.9209 0.41036 0.664 0.336
#> GSM648594 1 0.9129 0.42196 0.672 0.328
#> GSM648600 1 0.2236 0.77322 0.964 0.036
#> GSM648621 1 0.0672 0.78668 0.992 0.008
#> GSM648622 1 0.0000 0.78857 1.000 0.000
#> GSM648623 1 0.0000 0.78857 1.000 0.000
#> GSM648636 1 0.1414 0.78217 0.980 0.020
#> GSM648655 1 0.9000 0.43639 0.684 0.316
#> GSM648661 1 0.0000 0.78857 1.000 0.000
#> GSM648664 1 0.0000 0.78857 1.000 0.000
#> GSM648683 1 0.0000 0.78857 1.000 0.000
#> GSM648685 1 0.0000 0.78857 1.000 0.000
#> GSM648702 1 0.4431 0.73026 0.908 0.092
#> GSM648597 1 0.6048 0.67596 0.852 0.148
#> GSM648603 1 0.1843 0.77829 0.972 0.028
#> GSM648606 1 0.0376 0.78771 0.996 0.004
#> GSM648613 1 0.0000 0.78857 1.000 0.000
#> GSM648619 1 0.0000 0.78857 1.000 0.000
#> GSM648654 1 0.0000 0.78857 1.000 0.000
#> GSM648663 1 0.0000 0.78857 1.000 0.000
#> GSM648670 2 0.9993 0.14960 0.484 0.516
#> GSM648707 1 0.6623 0.63839 0.828 0.172
#> GSM648615 2 0.9970 0.21676 0.468 0.532
#> GSM648643 2 0.7056 0.65927 0.192 0.808
#> GSM648650 1 0.9209 0.41036 0.664 0.336
#> GSM648656 2 0.1414 0.72343 0.020 0.980
#> GSM648715 2 0.9998 0.14840 0.492 0.508
#> GSM648598 1 0.0000 0.78857 1.000 0.000
#> GSM648601 1 0.1843 0.77829 0.972 0.028
#> GSM648602 1 0.0000 0.78857 1.000 0.000
#> GSM648604 1 0.0000 0.78857 1.000 0.000
#> GSM648614 1 0.0672 0.78668 0.992 0.008
#> GSM648624 1 0.0000 0.78857 1.000 0.000
#> GSM648625 1 0.9209 0.41036 0.664 0.336
#> GSM648629 1 0.0000 0.78857 1.000 0.000
#> GSM648634 1 0.1414 0.78217 0.980 0.020
#> GSM648648 1 0.9129 0.42196 0.672 0.328
#> GSM648651 1 0.0000 0.78857 1.000 0.000
#> GSM648657 1 0.9209 0.41036 0.664 0.336
#> GSM648660 1 0.3733 0.74954 0.928 0.072
#> GSM648697 1 0.0000 0.78857 1.000 0.000
#> GSM648710 1 0.0000 0.78857 1.000 0.000
#> GSM648591 1 0.1633 0.78107 0.976 0.024
#> GSM648592 1 0.9209 0.41036 0.664 0.336
#> GSM648607 1 0.0000 0.78857 1.000 0.000
#> GSM648611 1 0.0000 0.78857 1.000 0.000
#> GSM648612 1 0.0000 0.78857 1.000 0.000
#> GSM648616 2 0.9909 0.18232 0.444 0.556
#> GSM648617 1 0.9209 0.41036 0.664 0.336
#> GSM648626 1 0.1843 0.77829 0.972 0.028
#> GSM648711 1 0.0000 0.78857 1.000 0.000
#> GSM648712 1 0.0000 0.78857 1.000 0.000
#> GSM648713 1 0.0000 0.78857 1.000 0.000
#> GSM648714 2 0.8499 0.59792 0.276 0.724
#> GSM648716 1 0.0000 0.78857 1.000 0.000
#> GSM648717 1 0.0000 0.78857 1.000 0.000
#> GSM648590 1 0.9580 0.29270 0.620 0.380
#> GSM648596 2 0.9922 0.26523 0.448 0.552
#> GSM648642 2 0.8081 0.61462 0.248 0.752
#> GSM648696 1 0.9209 0.41036 0.664 0.336
#> GSM648705 1 0.9209 0.41036 0.664 0.336
#> GSM648718 2 0.9998 0.14840 0.492 0.508
#> GSM648599 1 0.0672 0.78668 0.992 0.008
#> GSM648608 1 0.0000 0.78857 1.000 0.000
#> GSM648609 1 0.0000 0.78857 1.000 0.000
#> GSM648610 1 0.0000 0.78857 1.000 0.000
#> GSM648633 1 0.9209 0.41036 0.664 0.336
#> GSM648644 2 0.0000 0.72523 0.000 1.000
#> GSM648652 1 0.9209 0.41036 0.664 0.336
#> GSM648653 1 0.0000 0.78857 1.000 0.000
#> GSM648658 1 0.0672 0.78668 0.992 0.008
#> GSM648659 1 0.9993 -0.09138 0.516 0.484
#> GSM648662 1 0.0000 0.78857 1.000 0.000
#> GSM648665 1 0.0000 0.78857 1.000 0.000
#> GSM648666 1 0.0000 0.78857 1.000 0.000
#> GSM648680 1 0.5519 0.69884 0.872 0.128
#> GSM648684 1 0.0000 0.78857 1.000 0.000
#> GSM648709 2 0.9998 0.14840 0.492 0.508
#> GSM648719 1 0.1843 0.77829 0.972 0.028
#> GSM648627 1 0.0000 0.78857 1.000 0.000
#> GSM648637 2 0.0000 0.72523 0.000 1.000
#> GSM648638 2 0.0000 0.72523 0.000 1.000
#> GSM648641 1 0.7453 0.54986 0.788 0.212
#> GSM648672 2 0.0000 0.72523 0.000 1.000
#> GSM648674 2 0.8327 0.55991 0.264 0.736
#> GSM648703 2 0.0000 0.72523 0.000 1.000
#> GSM648631 1 0.1414 0.77445 0.980 0.020
#> GSM648669 2 0.1843 0.72069 0.028 0.972
#> GSM648671 2 0.0000 0.72523 0.000 1.000
#> GSM648678 2 0.0000 0.72523 0.000 1.000
#> GSM648679 2 0.0000 0.72523 0.000 1.000
#> GSM648681 2 0.9993 0.17187 0.484 0.516
#> GSM648686 2 0.9993 0.04433 0.484 0.516
#> GSM648689 1 0.9866 0.11905 0.568 0.432
#> GSM648690 2 0.9954 0.10178 0.460 0.540
#> GSM648691 1 0.9922 0.08662 0.552 0.448
#> GSM648693 1 0.8608 0.43022 0.716 0.284
#> GSM648700 2 0.9909 0.15778 0.444 0.556
#> GSM648630 1 0.9996 -0.00632 0.512 0.488
#> GSM648632 1 0.2043 0.76543 0.968 0.032
#> GSM648639 2 0.0938 0.71896 0.012 0.988
#> GSM648640 1 0.9996 -0.00632 0.512 0.488
#> GSM648668 2 0.7376 0.62495 0.208 0.792
#> GSM648676 2 0.8327 0.55991 0.264 0.736
#> GSM648692 1 0.9996 -0.00632 0.512 0.488
#> GSM648694 1 0.9944 0.06874 0.544 0.456
#> GSM648699 2 0.0000 0.72523 0.000 1.000
#> GSM648701 2 0.0000 0.72523 0.000 1.000
#> GSM648673 2 0.0000 0.72523 0.000 1.000
#> GSM648677 2 0.0000 0.72523 0.000 1.000
#> GSM648687 1 0.7299 0.55945 0.796 0.204
#> GSM648688 1 0.6973 0.58308 0.812 0.188
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM648605 2 0.5519 0.750 0.120 0.812 0.068
#> GSM648618 1 0.2711 0.850 0.912 0.000 0.088
#> GSM648620 1 0.7764 0.390 0.604 0.328 0.068
#> GSM648646 2 0.3888 0.796 0.064 0.888 0.048
#> GSM648649 1 0.0000 0.853 1.000 0.000 0.000
#> GSM648675 1 0.0000 0.853 1.000 0.000 0.000
#> GSM648682 2 0.3340 0.786 0.120 0.880 0.000
#> GSM648698 2 0.5519 0.750 0.120 0.812 0.068
#> GSM648708 1 0.4569 0.821 0.860 0.072 0.068
#> GSM648628 1 0.5621 0.677 0.692 0.000 0.308
#> GSM648595 1 0.0000 0.853 1.000 0.000 0.000
#> GSM648635 1 0.0000 0.853 1.000 0.000 0.000
#> GSM648645 1 0.2261 0.853 0.932 0.000 0.068
#> GSM648647 2 0.5787 0.732 0.136 0.796 0.068
#> GSM648667 1 0.4087 0.834 0.880 0.052 0.068
#> GSM648695 1 0.4288 0.830 0.872 0.060 0.068
#> GSM648704 2 0.0000 0.827 0.000 1.000 0.000
#> GSM648706 2 0.0424 0.826 0.000 0.992 0.008
#> GSM648593 1 0.2261 0.855 0.932 0.000 0.068
#> GSM648594 1 0.2796 0.856 0.908 0.000 0.092
#> GSM648600 1 0.0000 0.853 1.000 0.000 0.000
#> GSM648621 1 0.0237 0.853 0.996 0.000 0.004
#> GSM648622 1 0.5098 0.810 0.752 0.000 0.248
#> GSM648623 1 0.5098 0.810 0.752 0.000 0.248
#> GSM648636 1 0.0000 0.853 1.000 0.000 0.000
#> GSM648655 1 0.0000 0.853 1.000 0.000 0.000
#> GSM648661 1 0.5138 0.808 0.748 0.000 0.252
#> GSM648664 1 0.5138 0.808 0.748 0.000 0.252
#> GSM648683 1 0.0237 0.853 0.996 0.000 0.004
#> GSM648685 1 0.2448 0.856 0.924 0.000 0.076
#> GSM648702 1 0.0237 0.853 0.996 0.000 0.004
#> GSM648597 1 0.3340 0.842 0.880 0.000 0.120
#> GSM648603 1 0.4452 0.816 0.808 0.000 0.192
#> GSM648606 1 0.5977 0.800 0.728 0.020 0.252
#> GSM648613 1 0.5948 0.689 0.640 0.000 0.360
#> GSM648619 1 0.5138 0.808 0.748 0.000 0.252
#> GSM648654 1 0.6950 0.777 0.692 0.056 0.252
#> GSM648663 1 0.5138 0.808 0.748 0.000 0.252
#> GSM648670 1 0.0000 0.853 1.000 0.000 0.000
#> GSM648707 1 0.6982 0.740 0.708 0.072 0.220
#> GSM648615 2 0.5538 0.748 0.132 0.808 0.060
#> GSM648643 2 0.4569 0.781 0.072 0.860 0.068
#> GSM648650 1 0.1860 0.832 0.948 0.052 0.000
#> GSM648656 2 0.0747 0.825 0.000 0.984 0.016
#> GSM648715 2 0.6981 0.586 0.228 0.704 0.068
#> GSM648598 1 0.2261 0.855 0.932 0.000 0.068
#> GSM648601 1 0.2261 0.855 0.932 0.000 0.068
#> GSM648602 1 0.0237 0.853 0.996 0.000 0.004
#> GSM648604 1 0.5138 0.808 0.748 0.000 0.252
#> GSM648614 1 0.6820 0.782 0.700 0.052 0.248
#> GSM648624 1 0.5058 0.813 0.756 0.000 0.244
#> GSM648625 1 0.3141 0.850 0.912 0.020 0.068
#> GSM648629 1 0.5138 0.808 0.748 0.000 0.252
#> GSM648634 1 0.0237 0.853 0.996 0.000 0.004
#> GSM648648 1 0.2261 0.855 0.932 0.000 0.068
#> GSM648651 1 0.4235 0.838 0.824 0.000 0.176
#> GSM648657 1 0.0000 0.853 1.000 0.000 0.000
#> GSM648660 1 0.2261 0.855 0.932 0.000 0.068
#> GSM648697 1 0.1289 0.857 0.968 0.000 0.032
#> GSM648710 1 0.5138 0.808 0.748 0.000 0.252
#> GSM648591 1 0.4291 0.815 0.820 0.000 0.180
#> GSM648592 1 0.3499 0.848 0.900 0.028 0.072
#> GSM648607 1 0.5138 0.808 0.748 0.000 0.252
#> GSM648611 1 0.5948 0.578 0.640 0.000 0.360
#> GSM648612 1 0.5138 0.808 0.748 0.000 0.252
#> GSM648616 1 0.8129 0.634 0.632 0.124 0.244
#> GSM648617 1 0.1860 0.857 0.948 0.000 0.052
#> GSM648626 1 0.5098 0.810 0.752 0.000 0.248
#> GSM648711 1 0.5138 0.808 0.748 0.000 0.252
#> GSM648712 1 0.4346 0.814 0.816 0.000 0.184
#> GSM648713 1 0.5138 0.808 0.748 0.000 0.252
#> GSM648714 2 0.7164 0.636 0.140 0.720 0.140
#> GSM648716 1 0.5138 0.808 0.748 0.000 0.252
#> GSM648717 3 0.5882 0.179 0.348 0.000 0.652
#> GSM648590 1 0.0000 0.853 1.000 0.000 0.000
#> GSM648596 2 0.5722 0.739 0.132 0.800 0.068
#> GSM648642 2 0.5588 0.746 0.124 0.808 0.068
#> GSM648696 1 0.0000 0.853 1.000 0.000 0.000
#> GSM648705 1 0.0000 0.853 1.000 0.000 0.000
#> GSM648718 2 0.5656 0.742 0.128 0.804 0.068
#> GSM648599 1 0.0000 0.853 1.000 0.000 0.000
#> GSM648608 1 0.3038 0.852 0.896 0.000 0.104
#> GSM648609 1 0.5138 0.808 0.748 0.000 0.252
#> GSM648610 1 0.0237 0.853 0.996 0.000 0.004
#> GSM648633 1 0.2261 0.855 0.932 0.000 0.068
#> GSM648644 2 0.0000 0.827 0.000 1.000 0.000
#> GSM648652 1 0.0237 0.854 0.996 0.000 0.004
#> GSM648653 1 0.0237 0.853 0.996 0.000 0.004
#> GSM648658 1 0.0000 0.853 1.000 0.000 0.000
#> GSM648659 1 0.1964 0.829 0.944 0.056 0.000
#> GSM648662 1 0.5763 0.809 0.740 0.016 0.244
#> GSM648665 1 0.6857 0.780 0.696 0.052 0.252
#> GSM648666 1 0.0237 0.853 0.996 0.000 0.004
#> GSM648680 1 0.2261 0.855 0.932 0.000 0.068
#> GSM648684 1 0.0237 0.853 0.996 0.000 0.004
#> GSM648709 1 0.7248 0.671 0.676 0.256 0.068
#> GSM648719 1 0.2261 0.855 0.932 0.000 0.068
#> GSM648627 1 0.4346 0.814 0.816 0.000 0.184
#> GSM648637 2 0.1964 0.828 0.000 0.944 0.056
#> GSM648638 2 0.1964 0.828 0.000 0.944 0.056
#> GSM648641 3 0.2165 0.865 0.000 0.064 0.936
#> GSM648672 2 0.1860 0.830 0.000 0.948 0.052
#> GSM648674 2 0.2096 0.830 0.004 0.944 0.052
#> GSM648703 2 0.2096 0.830 0.004 0.944 0.052
#> GSM648631 3 0.1860 0.818 0.052 0.000 0.948
#> GSM648669 2 0.3267 0.780 0.000 0.884 0.116
#> GSM648671 2 0.1964 0.828 0.000 0.944 0.056
#> GSM648678 2 0.1643 0.831 0.000 0.956 0.044
#> GSM648679 2 0.1860 0.830 0.000 0.948 0.052
#> GSM648681 1 0.7567 0.405 0.576 0.376 0.048
#> GSM648686 3 0.2261 0.864 0.000 0.068 0.932
#> GSM648689 3 0.1860 0.815 0.000 0.052 0.948
#> GSM648690 3 0.2261 0.864 0.000 0.068 0.932
#> GSM648691 3 0.2261 0.865 0.000 0.068 0.932
#> GSM648693 3 0.1643 0.825 0.044 0.000 0.956
#> GSM648700 1 0.3989 0.732 0.864 0.124 0.012
#> GSM648630 3 0.2261 0.864 0.000 0.068 0.932
#> GSM648632 3 0.1860 0.818 0.052 0.000 0.948
#> GSM648639 3 0.5905 0.388 0.000 0.352 0.648
#> GSM648640 3 0.2537 0.858 0.000 0.080 0.920
#> GSM648668 2 0.2096 0.830 0.004 0.944 0.052
#> GSM648676 2 0.7274 0.416 0.304 0.644 0.052
#> GSM648692 3 0.2261 0.864 0.000 0.068 0.932
#> GSM648694 3 0.1860 0.865 0.000 0.052 0.948
#> GSM648699 2 0.1860 0.830 0.000 0.948 0.052
#> GSM648701 2 0.1860 0.830 0.000 0.948 0.052
#> GSM648673 2 0.1964 0.828 0.000 0.944 0.056
#> GSM648677 2 0.1860 0.830 0.000 0.948 0.052
#> GSM648687 3 0.5576 0.777 0.104 0.084 0.812
#> GSM648688 3 0.0000 0.845 0.000 0.000 1.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM648605 2 0.4730 0.7710 0.000 0.636 0.000 0.364
#> GSM648618 1 0.3873 0.4050 0.772 0.000 0.000 0.228
#> GSM648620 4 0.7810 -0.2801 0.364 0.252 0.000 0.384
#> GSM648646 2 0.4643 0.7786 0.000 0.656 0.000 0.344
#> GSM648649 1 0.0188 0.6400 0.996 0.000 0.000 0.004
#> GSM648675 1 0.0000 0.6394 1.000 0.000 0.000 0.000
#> GSM648682 2 0.5549 0.7770 0.048 0.672 0.000 0.280
#> GSM648698 2 0.4730 0.7710 0.000 0.636 0.000 0.364
#> GSM648708 1 0.5050 0.3171 0.588 0.004 0.000 0.408
#> GSM648628 4 0.4830 0.6044 0.392 0.000 0.000 0.608
#> GSM648595 1 0.0188 0.6385 0.996 0.000 0.000 0.004
#> GSM648635 1 0.0336 0.6402 0.992 0.000 0.000 0.008
#> GSM648645 1 0.4164 0.3773 0.736 0.000 0.000 0.264
#> GSM648647 2 0.5040 0.7668 0.008 0.628 0.000 0.364
#> GSM648667 1 0.4697 0.3508 0.644 0.000 0.000 0.356
#> GSM648695 1 0.5217 0.3320 0.608 0.012 0.000 0.380
#> GSM648704 2 0.4500 0.7841 0.000 0.684 0.000 0.316
#> GSM648706 2 0.4936 0.7804 0.000 0.672 0.012 0.316
#> GSM648593 1 0.1557 0.6346 0.944 0.000 0.000 0.056
#> GSM648594 1 0.2408 0.6176 0.896 0.000 0.000 0.104
#> GSM648600 1 0.2814 0.5322 0.868 0.000 0.000 0.132
#> GSM648621 1 0.4697 0.0899 0.644 0.000 0.000 0.356
#> GSM648622 1 0.4477 0.3343 0.688 0.000 0.000 0.312
#> GSM648623 1 0.4564 0.2991 0.672 0.000 0.000 0.328
#> GSM648636 1 0.3942 0.3618 0.764 0.000 0.000 0.236
#> GSM648655 1 0.0000 0.6394 1.000 0.000 0.000 0.000
#> GSM648661 4 0.4500 0.6955 0.316 0.000 0.000 0.684
#> GSM648664 4 0.4500 0.6955 0.316 0.000 0.000 0.684
#> GSM648683 1 0.4713 0.0863 0.640 0.000 0.000 0.360
#> GSM648685 1 0.4981 -0.1311 0.536 0.000 0.000 0.464
#> GSM648702 1 0.4679 0.1002 0.648 0.000 0.000 0.352
#> GSM648597 1 0.1557 0.6202 0.944 0.000 0.000 0.056
#> GSM648603 1 0.4304 0.3449 0.716 0.000 0.000 0.284
#> GSM648606 4 0.3907 0.6072 0.232 0.000 0.000 0.768
#> GSM648613 4 0.4624 0.6670 0.340 0.000 0.000 0.660
#> GSM648619 4 0.4522 0.6929 0.320 0.000 0.000 0.680
#> GSM648654 4 0.0592 0.4038 0.016 0.000 0.000 0.984
#> GSM648663 4 0.4776 0.5986 0.376 0.000 0.000 0.624
#> GSM648670 1 0.0336 0.6374 0.992 0.008 0.000 0.000
#> GSM648707 1 0.5644 0.3771 0.708 0.068 0.004 0.220
#> GSM648615 2 0.6373 0.7010 0.116 0.636 0.000 0.248
#> GSM648643 2 0.4730 0.7710 0.000 0.636 0.000 0.364
#> GSM648650 1 0.2868 0.5531 0.864 0.000 0.000 0.136
#> GSM648656 2 0.4454 0.7865 0.000 0.692 0.000 0.308
#> GSM648715 2 0.5159 0.7645 0.012 0.624 0.000 0.364
#> GSM648598 1 0.2408 0.6176 0.896 0.000 0.000 0.104
#> GSM648601 1 0.2281 0.6212 0.904 0.000 0.000 0.096
#> GSM648602 1 0.4697 0.0899 0.644 0.000 0.000 0.356
#> GSM648604 4 0.4500 0.6955 0.316 0.000 0.000 0.684
#> GSM648614 4 0.4761 -0.0965 0.372 0.000 0.000 0.628
#> GSM648624 4 0.4776 0.5931 0.376 0.000 0.000 0.624
#> GSM648625 1 0.3172 0.5898 0.840 0.000 0.000 0.160
#> GSM648629 4 0.4500 0.6955 0.316 0.000 0.000 0.684
#> GSM648634 1 0.4697 0.0899 0.644 0.000 0.000 0.356
#> GSM648648 1 0.2081 0.6265 0.916 0.000 0.000 0.084
#> GSM648651 1 0.2408 0.6174 0.896 0.000 0.000 0.104
#> GSM648657 1 0.0000 0.6394 1.000 0.000 0.000 0.000
#> GSM648660 1 0.2408 0.6176 0.896 0.000 0.000 0.104
#> GSM648697 1 0.4817 0.0400 0.612 0.000 0.000 0.388
#> GSM648710 4 0.4500 0.6955 0.316 0.000 0.000 0.684
#> GSM648591 1 0.3801 0.4153 0.780 0.000 0.000 0.220
#> GSM648592 1 0.4382 0.3719 0.704 0.000 0.000 0.296
#> GSM648607 4 0.4522 0.6929 0.320 0.000 0.000 0.680
#> GSM648611 4 0.4855 0.5925 0.400 0.000 0.000 0.600
#> GSM648612 4 0.4543 0.6888 0.324 0.000 0.000 0.676
#> GSM648616 1 0.7533 0.2638 0.580 0.176 0.024 0.220
#> GSM648617 1 0.1211 0.6384 0.960 0.000 0.000 0.040
#> GSM648626 1 0.4543 0.3023 0.676 0.000 0.000 0.324
#> GSM648711 4 0.4500 0.6955 0.316 0.000 0.000 0.684
#> GSM648712 4 0.4907 0.5566 0.420 0.000 0.000 0.580
#> GSM648713 4 0.4500 0.6955 0.316 0.000 0.000 0.684
#> GSM648714 4 0.5611 -0.4996 0.024 0.412 0.000 0.564
#> GSM648716 4 0.4522 0.6929 0.320 0.000 0.000 0.680
#> GSM648717 4 0.5038 0.6835 0.296 0.000 0.020 0.684
#> GSM648590 1 0.0000 0.6394 1.000 0.000 0.000 0.000
#> GSM648596 2 0.5878 0.7461 0.056 0.632 0.000 0.312
#> GSM648642 2 0.4730 0.7710 0.000 0.636 0.000 0.364
#> GSM648696 1 0.0000 0.6394 1.000 0.000 0.000 0.000
#> GSM648705 1 0.1022 0.6393 0.968 0.000 0.000 0.032
#> GSM648718 2 0.4905 0.7696 0.004 0.632 0.000 0.364
#> GSM648599 1 0.0000 0.6394 1.000 0.000 0.000 0.000
#> GSM648608 1 0.4916 -0.0802 0.576 0.000 0.000 0.424
#> GSM648609 4 0.4500 0.6955 0.316 0.000 0.000 0.684
#> GSM648610 1 0.4697 0.0899 0.644 0.000 0.000 0.356
#> GSM648633 1 0.1557 0.6346 0.944 0.000 0.000 0.056
#> GSM648644 2 0.3528 0.8012 0.000 0.808 0.000 0.192
#> GSM648652 1 0.0469 0.6408 0.988 0.000 0.000 0.012
#> GSM648653 1 0.4697 0.0899 0.644 0.000 0.000 0.356
#> GSM648658 1 0.0000 0.6394 1.000 0.000 0.000 0.000
#> GSM648659 1 0.4477 0.3576 0.688 0.000 0.000 0.312
#> GSM648662 4 0.4356 0.6805 0.292 0.000 0.000 0.708
#> GSM648665 4 0.0707 0.4062 0.020 0.000 0.000 0.980
#> GSM648666 1 0.4776 0.0511 0.624 0.000 0.000 0.376
#> GSM648680 1 0.2216 0.6236 0.908 0.000 0.000 0.092
#> GSM648684 1 0.4697 0.0899 0.644 0.000 0.000 0.356
#> GSM648709 4 0.7877 -0.3333 0.356 0.280 0.000 0.364
#> GSM648719 1 0.2469 0.6149 0.892 0.000 0.000 0.108
#> GSM648627 4 0.4776 0.6242 0.376 0.000 0.000 0.624
#> GSM648637 2 0.0188 0.8031 0.000 0.996 0.004 0.000
#> GSM648638 2 0.0336 0.8004 0.000 0.992 0.008 0.000
#> GSM648641 3 0.3486 0.7522 0.000 0.000 0.812 0.188
#> GSM648672 2 0.0188 0.8031 0.000 0.996 0.004 0.000
#> GSM648674 2 0.0188 0.8031 0.000 0.996 0.004 0.000
#> GSM648703 2 0.0188 0.8031 0.000 0.996 0.004 0.000
#> GSM648631 3 0.0188 0.9356 0.000 0.000 0.996 0.004
#> GSM648669 2 0.0817 0.7884 0.000 0.976 0.024 0.000
#> GSM648671 2 0.0188 0.8031 0.000 0.996 0.004 0.000
#> GSM648678 2 0.0000 0.8031 0.000 1.000 0.000 0.000
#> GSM648679 2 0.0188 0.8031 0.000 0.996 0.004 0.000
#> GSM648681 1 0.7896 -0.2563 0.368 0.296 0.000 0.336
#> GSM648686 3 0.0000 0.9366 0.000 0.000 1.000 0.000
#> GSM648689 3 0.0188 0.9354 0.000 0.004 0.996 0.000
#> GSM648690 3 0.0000 0.9366 0.000 0.000 1.000 0.000
#> GSM648691 3 0.0188 0.9354 0.000 0.004 0.996 0.000
#> GSM648693 3 0.0188 0.9349 0.004 0.000 0.996 0.000
#> GSM648700 1 0.4522 0.3593 0.680 0.320 0.000 0.000
#> GSM648630 3 0.0000 0.9366 0.000 0.000 1.000 0.000
#> GSM648632 3 0.0188 0.9356 0.000 0.000 0.996 0.004
#> GSM648639 3 0.1940 0.8861 0.000 0.076 0.924 0.000
#> GSM648640 3 0.1716 0.8958 0.000 0.064 0.936 0.000
#> GSM648668 2 0.0188 0.8031 0.000 0.996 0.004 0.000
#> GSM648676 2 0.3208 0.6618 0.148 0.848 0.004 0.000
#> GSM648692 3 0.0000 0.9366 0.000 0.000 1.000 0.000
#> GSM648694 3 0.0000 0.9366 0.000 0.000 1.000 0.000
#> GSM648699 2 0.0188 0.8031 0.000 0.996 0.004 0.000
#> GSM648701 2 0.0188 0.8031 0.000 0.996 0.004 0.000
#> GSM648673 2 0.0188 0.8031 0.000 0.996 0.004 0.000
#> GSM648677 2 0.0188 0.8031 0.000 0.996 0.004 0.000
#> GSM648687 3 0.8085 0.3288 0.156 0.040 0.512 0.292
#> GSM648688 3 0.0188 0.9356 0.000 0.000 0.996 0.004
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM648605 2 0.1608 0.758768 0.000 0.928 0.000 0.072 0.000
#> GSM648618 1 0.3109 0.481132 0.800 0.000 0.000 0.000 0.200
#> GSM648620 2 0.2690 0.698624 0.156 0.844 0.000 0.000 0.000
#> GSM648646 2 0.1608 0.758768 0.000 0.928 0.000 0.072 0.000
#> GSM648649 1 0.4479 0.579024 0.744 0.072 0.000 0.000 0.184
#> GSM648675 1 0.0290 0.599566 0.992 0.000 0.000 0.000 0.008
#> GSM648682 2 0.3741 0.531618 0.004 0.732 0.000 0.264 0.000
#> GSM648698 2 0.1608 0.758768 0.000 0.928 0.000 0.072 0.000
#> GSM648708 2 0.2929 0.685972 0.180 0.820 0.000 0.000 0.000
#> GSM648628 5 0.5334 0.482485 0.244 0.000 0.000 0.104 0.652
#> GSM648595 1 0.3921 0.492873 0.800 0.072 0.000 0.000 0.128
#> GSM648635 1 0.3875 0.594306 0.804 0.072 0.000 0.000 0.124
#> GSM648645 1 0.4256 0.280786 0.564 0.000 0.000 0.000 0.436
#> GSM648647 2 0.0000 0.762865 0.000 1.000 0.000 0.000 0.000
#> GSM648667 2 0.4026 0.618280 0.244 0.736 0.000 0.000 0.020
#> GSM648695 2 0.3821 0.655556 0.216 0.764 0.000 0.000 0.020
#> GSM648704 2 0.1608 0.758768 0.000 0.928 0.000 0.072 0.000
#> GSM648706 2 0.1608 0.758768 0.000 0.928 0.000 0.072 0.000
#> GSM648593 1 0.3353 0.583486 0.796 0.008 0.000 0.000 0.196
#> GSM648594 1 0.3752 0.512500 0.708 0.000 0.000 0.000 0.292
#> GSM648600 1 0.3661 0.311508 0.724 0.000 0.000 0.000 0.276
#> GSM648621 1 0.4249 0.009127 0.568 0.000 0.000 0.000 0.432
#> GSM648622 5 0.4268 -0.114731 0.444 0.000 0.000 0.000 0.556
#> GSM648623 5 0.5929 -0.086480 0.432 0.000 0.000 0.104 0.464
#> GSM648636 1 0.4060 0.148861 0.640 0.000 0.000 0.000 0.360
#> GSM648655 1 0.0609 0.607485 0.980 0.000 0.000 0.000 0.020
#> GSM648661 5 0.0000 0.650589 0.000 0.000 0.000 0.000 1.000
#> GSM648664 5 0.0000 0.650589 0.000 0.000 0.000 0.000 1.000
#> GSM648683 5 0.4307 0.105603 0.496 0.000 0.000 0.000 0.504
#> GSM648685 5 0.3109 0.452200 0.200 0.000 0.000 0.000 0.800
#> GSM648702 1 0.4825 0.045620 0.568 0.024 0.000 0.000 0.408
#> GSM648597 1 0.1608 0.603657 0.928 0.000 0.000 0.000 0.072
#> GSM648603 5 0.5933 -0.104114 0.444 0.000 0.000 0.104 0.452
#> GSM648606 5 0.5572 0.554322 0.104 0.072 0.000 0.104 0.720
#> GSM648613 5 0.4648 0.497909 0.156 0.000 0.000 0.104 0.740
#> GSM648619 5 0.3164 0.613611 0.044 0.000 0.000 0.104 0.852
#> GSM648654 5 0.0703 0.644027 0.000 0.024 0.000 0.000 0.976
#> GSM648663 5 0.5030 0.423721 0.200 0.000 0.000 0.104 0.696
#> GSM648670 1 0.2054 0.586033 0.916 0.072 0.000 0.008 0.004
#> GSM648707 1 0.5848 0.316467 0.608 0.000 0.000 0.192 0.200
#> GSM648615 2 0.3640 0.721478 0.084 0.836 0.000 0.072 0.008
#> GSM648643 2 0.1608 0.758768 0.000 0.928 0.000 0.072 0.000
#> GSM648650 1 0.4740 0.000546 0.516 0.468 0.000 0.000 0.016
#> GSM648656 2 0.3534 0.535542 0.000 0.744 0.000 0.256 0.000
#> GSM648715 2 0.0000 0.762865 0.000 1.000 0.000 0.000 0.000
#> GSM648598 1 0.3949 0.478066 0.668 0.000 0.000 0.000 0.332
#> GSM648601 1 0.2690 0.593001 0.844 0.000 0.000 0.000 0.156
#> GSM648602 1 0.4249 0.009127 0.568 0.000 0.000 0.000 0.432
#> GSM648604 5 0.0000 0.650589 0.000 0.000 0.000 0.000 1.000
#> GSM648614 2 0.6445 0.345648 0.216 0.496 0.000 0.000 0.288
#> GSM648624 5 0.1341 0.625403 0.056 0.000 0.000 0.000 0.944
#> GSM648625 1 0.4201 0.380263 0.592 0.000 0.000 0.000 0.408
#> GSM648629 5 0.0162 0.649536 0.004 0.000 0.000 0.000 0.996
#> GSM648634 1 0.4249 0.009127 0.568 0.000 0.000 0.000 0.432
#> GSM648648 1 0.4060 0.447605 0.640 0.000 0.000 0.000 0.360
#> GSM648651 1 0.3561 0.509205 0.740 0.000 0.000 0.000 0.260
#> GSM648657 1 0.0609 0.607485 0.980 0.000 0.000 0.000 0.020
#> GSM648660 1 0.3684 0.520797 0.720 0.000 0.000 0.000 0.280
#> GSM648697 5 0.4287 0.172683 0.460 0.000 0.000 0.000 0.540
#> GSM648710 5 0.0000 0.650589 0.000 0.000 0.000 0.000 1.000
#> GSM648591 1 0.3109 0.492329 0.800 0.000 0.000 0.000 0.200
#> GSM648592 1 0.5352 0.241125 0.536 0.000 0.000 0.056 0.408
#> GSM648607 5 0.2280 0.561101 0.120 0.000 0.000 0.000 0.880
#> GSM648611 5 0.5405 0.468842 0.256 0.000 0.000 0.104 0.640
#> GSM648612 5 0.4569 0.510650 0.148 0.000 0.000 0.104 0.748
#> GSM648616 1 0.6806 0.196897 0.436 0.000 0.016 0.380 0.168
#> GSM648617 1 0.3770 0.572094 0.832 0.040 0.000 0.104 0.024
#> GSM648626 5 0.5929 -0.086480 0.432 0.000 0.000 0.104 0.464
#> GSM648711 5 0.0000 0.650589 0.000 0.000 0.000 0.000 1.000
#> GSM648712 5 0.5512 0.444163 0.276 0.000 0.000 0.104 0.620
#> GSM648713 5 0.3569 0.597026 0.068 0.000 0.000 0.104 0.828
#> GSM648714 2 0.6817 0.524417 0.060 0.584 0.000 0.172 0.184
#> GSM648716 5 0.2074 0.629232 0.000 0.000 0.000 0.104 0.896
#> GSM648717 5 0.2074 0.629232 0.000 0.000 0.000 0.104 0.896
#> GSM648590 1 0.1571 0.592210 0.936 0.060 0.000 0.000 0.004
#> GSM648596 2 0.2300 0.739686 0.072 0.904 0.000 0.000 0.024
#> GSM648642 2 0.0000 0.762865 0.000 1.000 0.000 0.000 0.000
#> GSM648696 1 0.1608 0.589416 0.928 0.072 0.000 0.000 0.000
#> GSM648705 1 0.4877 0.549236 0.692 0.072 0.000 0.000 0.236
#> GSM648718 2 0.1608 0.758768 0.000 0.928 0.000 0.072 0.000
#> GSM648599 1 0.0000 0.601299 1.000 0.000 0.000 0.000 0.000
#> GSM648608 5 0.4074 0.324925 0.364 0.000 0.000 0.000 0.636
#> GSM648609 5 0.0000 0.650589 0.000 0.000 0.000 0.000 1.000
#> GSM648610 1 0.4249 0.009127 0.568 0.000 0.000 0.000 0.432
#> GSM648633 1 0.3074 0.582350 0.804 0.000 0.000 0.000 0.196
#> GSM648644 2 0.4307 -0.217094 0.000 0.504 0.000 0.496 0.000
#> GSM648652 1 0.3246 0.588041 0.808 0.008 0.000 0.000 0.184
#> GSM648653 1 0.4249 0.009127 0.568 0.000 0.000 0.000 0.432
#> GSM648658 1 0.0609 0.607485 0.980 0.000 0.000 0.000 0.020
#> GSM648659 2 0.3305 0.661077 0.224 0.776 0.000 0.000 0.000
#> GSM648662 5 0.0162 0.649536 0.004 0.000 0.000 0.000 0.996
#> GSM648665 5 0.4060 0.264029 0.000 0.360 0.000 0.000 0.640
#> GSM648666 5 0.4227 0.233510 0.420 0.000 0.000 0.000 0.580
#> GSM648680 1 0.3561 0.538221 0.740 0.000 0.000 0.000 0.260
#> GSM648684 5 0.4291 0.166848 0.464 0.000 0.000 0.000 0.536
#> GSM648709 2 0.0703 0.762517 0.024 0.976 0.000 0.000 0.000
#> GSM648719 1 0.4126 0.419002 0.620 0.000 0.000 0.000 0.380
#> GSM648627 5 0.5032 0.512242 0.220 0.000 0.000 0.092 0.688
#> GSM648637 4 0.2929 0.898527 0.000 0.180 0.000 0.820 0.000
#> GSM648638 4 0.2929 0.898527 0.000 0.180 0.000 0.820 0.000
#> GSM648641 3 0.4528 0.683861 0.000 0.000 0.752 0.104 0.144
#> GSM648672 4 0.2929 0.898527 0.000 0.180 0.000 0.820 0.000
#> GSM648674 4 0.2929 0.898527 0.000 0.180 0.000 0.820 0.000
#> GSM648703 4 0.2074 0.888388 0.000 0.104 0.000 0.896 0.000
#> GSM648631 3 0.0000 0.919799 0.000 0.000 1.000 0.000 0.000
#> GSM648669 4 0.2813 0.851627 0.000 0.168 0.000 0.832 0.000
#> GSM648671 4 0.2074 0.888388 0.000 0.104 0.000 0.896 0.000
#> GSM648678 4 0.3003 0.892450 0.000 0.188 0.000 0.812 0.000
#> GSM648679 4 0.2929 0.898527 0.000 0.180 0.000 0.820 0.000
#> GSM648681 2 0.3246 0.660202 0.184 0.808 0.000 0.000 0.008
#> GSM648686 3 0.0000 0.919799 0.000 0.000 1.000 0.000 0.000
#> GSM648689 3 0.0000 0.919799 0.000 0.000 1.000 0.000 0.000
#> GSM648690 3 0.0000 0.919799 0.000 0.000 1.000 0.000 0.000
#> GSM648691 3 0.0000 0.919799 0.000 0.000 1.000 0.000 0.000
#> GSM648693 3 0.0000 0.919799 0.000 0.000 1.000 0.000 0.000
#> GSM648700 4 0.3932 0.488586 0.328 0.000 0.000 0.672 0.000
#> GSM648630 3 0.0000 0.919799 0.000 0.000 1.000 0.000 0.000
#> GSM648632 3 0.0000 0.919799 0.000 0.000 1.000 0.000 0.000
#> GSM648639 3 0.3480 0.723616 0.000 0.000 0.752 0.248 0.000
#> GSM648640 3 0.1671 0.861755 0.000 0.000 0.924 0.076 0.000
#> GSM648668 4 0.3480 0.856877 0.000 0.248 0.000 0.752 0.000
#> GSM648676 4 0.3366 0.813025 0.000 0.232 0.000 0.768 0.000
#> GSM648692 3 0.0000 0.919799 0.000 0.000 1.000 0.000 0.000
#> GSM648694 3 0.0000 0.919799 0.000 0.000 1.000 0.000 0.000
#> GSM648699 4 0.2732 0.870651 0.000 0.160 0.000 0.840 0.000
#> GSM648701 4 0.2732 0.870651 0.000 0.160 0.000 0.840 0.000
#> GSM648673 4 0.2074 0.888388 0.000 0.104 0.000 0.896 0.000
#> GSM648677 4 0.2929 0.898527 0.000 0.180 0.000 0.820 0.000
#> GSM648687 3 0.6712 0.256071 0.108 0.000 0.508 0.040 0.344
#> GSM648688 3 0.0000 0.919799 0.000 0.000 1.000 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM648605 2 0.3930 0.6687 0.000 0.764 0.000 0.092 0.144 0.000
#> GSM648618 6 0.3859 0.5642 0.024 0.056 0.000 0.000 0.124 0.796
#> GSM648620 2 0.2135 0.6823 0.000 0.872 0.000 0.000 0.000 0.128
#> GSM648646 2 0.4728 0.6248 0.000 0.652 0.000 0.092 0.256 0.000
#> GSM648649 6 0.6214 0.4522 0.092 0.232 0.000 0.000 0.104 0.572
#> GSM648675 6 0.2632 0.5552 0.004 0.164 0.000 0.000 0.000 0.832
#> GSM648682 2 0.5513 0.4959 0.000 0.596 0.000 0.188 0.208 0.008
#> GSM648698 2 0.3930 0.6687 0.000 0.764 0.000 0.092 0.144 0.000
#> GSM648708 2 0.2135 0.6823 0.000 0.872 0.000 0.000 0.000 0.128
#> GSM648628 5 0.5291 0.4114 0.328 0.000 0.000 0.000 0.552 0.120
#> GSM648595 6 0.4787 0.4736 0.108 0.236 0.000 0.000 0.000 0.656
#> GSM648635 6 0.4173 0.5223 0.060 0.228 0.000 0.000 0.000 0.712
#> GSM648645 6 0.4125 0.4656 0.128 0.000 0.000 0.000 0.124 0.748
#> GSM648647 2 0.0000 0.7262 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM648667 2 0.2950 0.6611 0.024 0.828 0.000 0.000 0.000 0.148
#> GSM648695 2 0.2748 0.6736 0.024 0.848 0.000 0.000 0.000 0.128
#> GSM648704 2 0.4728 0.6248 0.000 0.652 0.000 0.092 0.256 0.000
#> GSM648706 2 0.4728 0.6248 0.000 0.652 0.000 0.092 0.256 0.000
#> GSM648593 6 0.3511 0.5028 0.216 0.024 0.000 0.000 0.000 0.760
#> GSM648594 6 0.4125 0.4656 0.128 0.000 0.000 0.000 0.124 0.748
#> GSM648600 6 0.3288 0.4505 0.276 0.000 0.000 0.000 0.000 0.724
#> GSM648621 6 0.3547 0.3885 0.332 0.000 0.000 0.000 0.000 0.668
#> GSM648622 1 0.3804 0.2463 0.576 0.000 0.000 0.000 0.000 0.424
#> GSM648623 6 0.6007 -0.2799 0.252 0.000 0.000 0.000 0.324 0.424
#> GSM648636 6 0.3601 0.4111 0.312 0.004 0.000 0.000 0.000 0.684
#> GSM648655 6 0.0458 0.5877 0.016 0.000 0.000 0.000 0.000 0.984
#> GSM648661 1 0.0000 0.6008 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM648664 1 0.0000 0.6008 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM648683 1 0.3866 -0.0584 0.516 0.000 0.000 0.000 0.000 0.484
#> GSM648685 1 0.0291 0.5998 0.992 0.004 0.000 0.000 0.000 0.004
#> GSM648702 6 0.5122 0.3486 0.320 0.104 0.000 0.000 0.000 0.576
#> GSM648597 6 0.2234 0.5495 0.004 0.000 0.000 0.000 0.124 0.872
#> GSM648603 5 0.5507 0.2705 0.128 0.000 0.000 0.000 0.448 0.424
#> GSM648606 1 0.5773 -0.3322 0.460 0.060 0.000 0.000 0.432 0.048
#> GSM648613 5 0.5440 0.5366 0.296 0.000 0.000 0.000 0.552 0.152
#> GSM648619 5 0.4654 0.4631 0.412 0.000 0.000 0.000 0.544 0.044
#> GSM648654 1 0.1663 0.5571 0.912 0.088 0.000 0.000 0.000 0.000
#> GSM648663 5 0.5848 0.4178 0.380 0.000 0.000 0.000 0.428 0.192
#> GSM648670 6 0.3163 0.5237 0.004 0.232 0.000 0.000 0.000 0.764
#> GSM648707 5 0.3955 0.4255 0.004 0.000 0.000 0.008 0.648 0.340
#> GSM648615 2 0.4879 0.6362 0.000 0.712 0.000 0.092 0.036 0.160
#> GSM648643 2 0.3172 0.6733 0.000 0.816 0.000 0.148 0.036 0.000
#> GSM648650 2 0.3717 0.2906 0.000 0.616 0.000 0.000 0.000 0.384
#> GSM648656 2 0.5802 0.3930 0.000 0.500 0.000 0.244 0.256 0.000
#> GSM648715 2 0.0000 0.7262 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM648598 1 0.3868 0.0811 0.504 0.000 0.000 0.000 0.000 0.496
#> GSM648601 6 0.3426 0.5144 0.068 0.000 0.000 0.000 0.124 0.808
#> GSM648602 6 0.3547 0.3885 0.332 0.000 0.000 0.000 0.000 0.668
#> GSM648604 1 0.0000 0.6008 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM648614 1 0.6123 0.2130 0.464 0.280 0.000 0.000 0.008 0.248
#> GSM648624 1 0.0260 0.5983 0.992 0.000 0.000 0.000 0.000 0.008
#> GSM648625 1 0.3804 0.2463 0.576 0.000 0.000 0.000 0.000 0.424
#> GSM648629 1 0.0146 0.5997 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM648634 6 0.3547 0.3885 0.332 0.000 0.000 0.000 0.000 0.668
#> GSM648648 1 0.4491 0.2602 0.576 0.036 0.000 0.000 0.000 0.388
#> GSM648651 1 0.3867 0.1889 0.512 0.000 0.000 0.000 0.000 0.488
#> GSM648657 6 0.2234 0.5495 0.004 0.000 0.000 0.000 0.124 0.872
#> GSM648660 6 0.3175 0.4548 0.256 0.000 0.000 0.000 0.000 0.744
#> GSM648697 1 0.2838 0.4853 0.808 0.004 0.000 0.000 0.000 0.188
#> GSM648710 1 0.0000 0.6008 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM648591 6 0.1219 0.5805 0.004 0.000 0.000 0.000 0.048 0.948
#> GSM648592 6 0.5468 -0.1616 0.128 0.000 0.000 0.000 0.380 0.492
#> GSM648607 1 0.4045 0.4045 0.756 0.000 0.000 0.000 0.124 0.120
#> GSM648611 5 0.6018 0.2907 0.332 0.000 0.000 0.000 0.416 0.252
#> GSM648612 5 0.5425 0.5338 0.300 0.000 0.000 0.000 0.552 0.148
#> GSM648616 5 0.4085 0.4513 0.000 0.000 0.000 0.052 0.716 0.232
#> GSM648617 5 0.4697 0.3453 0.028 0.008 0.000 0.000 0.500 0.464
#> GSM648626 5 0.5456 0.3618 0.128 0.000 0.000 0.000 0.500 0.372
#> GSM648711 1 0.0000 0.6008 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM648712 5 0.5919 0.3284 0.320 0.000 0.000 0.000 0.452 0.228
#> GSM648713 5 0.4903 0.4921 0.380 0.000 0.000 0.000 0.552 0.068
#> GSM648714 5 0.3956 -0.0424 0.024 0.292 0.000 0.000 0.684 0.000
#> GSM648716 5 0.3838 0.4122 0.448 0.000 0.000 0.000 0.552 0.000
#> GSM648717 1 0.3810 -0.2806 0.572 0.000 0.000 0.000 0.428 0.000
#> GSM648590 6 0.1958 0.5769 0.004 0.100 0.000 0.000 0.000 0.896
#> GSM648596 2 0.2822 0.7076 0.000 0.852 0.000 0.000 0.108 0.040
#> GSM648642 2 0.0000 0.7262 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM648696 6 0.2912 0.5334 0.000 0.216 0.000 0.000 0.000 0.784
#> GSM648705 6 0.6824 0.3536 0.128 0.340 0.000 0.000 0.100 0.432
#> GSM648718 2 0.2250 0.7072 0.000 0.888 0.000 0.092 0.020 0.000
#> GSM648599 6 0.0000 0.5866 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM648608 1 0.2562 0.4957 0.828 0.000 0.000 0.000 0.000 0.172
#> GSM648609 1 0.0000 0.6008 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM648610 6 0.3804 0.2406 0.424 0.000 0.000 0.000 0.000 0.576
#> GSM648633 6 0.2793 0.5108 0.200 0.000 0.000 0.000 0.000 0.800
#> GSM648644 2 0.6093 0.0792 0.000 0.380 0.000 0.336 0.284 0.000
#> GSM648652 6 0.2218 0.5703 0.104 0.012 0.000 0.000 0.000 0.884
#> GSM648653 6 0.3547 0.3885 0.332 0.000 0.000 0.000 0.000 0.668
#> GSM648658 6 0.0291 0.5877 0.004 0.004 0.000 0.000 0.000 0.992
#> GSM648659 2 0.2378 0.6683 0.000 0.848 0.000 0.000 0.000 0.152
#> GSM648662 1 0.0146 0.5997 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM648665 1 0.2631 0.4790 0.820 0.180 0.000 0.000 0.000 0.000
#> GSM648666 1 0.2854 0.4670 0.792 0.000 0.000 0.000 0.000 0.208
#> GSM648680 6 0.3360 0.4475 0.264 0.004 0.000 0.000 0.000 0.732
#> GSM648684 1 0.3531 0.3326 0.672 0.000 0.000 0.000 0.000 0.328
#> GSM648709 2 0.0146 0.7265 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM648719 1 0.3851 0.1735 0.540 0.000 0.000 0.000 0.000 0.460
#> GSM648627 6 0.6049 -0.1079 0.356 0.000 0.000 0.000 0.256 0.388
#> GSM648637 4 0.4884 0.7611 0.000 0.128 0.000 0.652 0.220 0.000
#> GSM648638 4 0.5081 0.7359 0.000 0.128 0.000 0.616 0.256 0.000
#> GSM648641 3 0.3810 0.2380 0.000 0.000 0.572 0.000 0.428 0.000
#> GSM648672 4 0.4125 0.7832 0.000 0.128 0.000 0.748 0.124 0.000
#> GSM648674 4 0.4884 0.7611 0.000 0.128 0.000 0.652 0.220 0.000
#> GSM648703 4 0.0000 0.8054 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM648631 3 0.0000 0.9489 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648669 4 0.3586 0.7707 0.000 0.080 0.000 0.796 0.124 0.000
#> GSM648671 4 0.1663 0.8094 0.000 0.000 0.000 0.912 0.088 0.000
#> GSM648678 4 0.4977 0.6011 0.000 0.128 0.000 0.636 0.236 0.000
#> GSM648679 4 0.4884 0.7611 0.000 0.128 0.000 0.652 0.220 0.000
#> GSM648681 2 0.2219 0.6815 0.000 0.864 0.000 0.000 0.000 0.136
#> GSM648686 3 0.0000 0.9489 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648689 3 0.0000 0.9489 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648690 3 0.0000 0.9489 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648691 3 0.0000 0.9489 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648693 3 0.0000 0.9489 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648700 4 0.2520 0.6814 0.000 0.004 0.000 0.844 0.000 0.152
#> GSM648630 3 0.0000 0.9489 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648632 3 0.0000 0.9489 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648639 5 0.4606 -0.0252 0.000 0.000 0.344 0.052 0.604 0.000
#> GSM648640 3 0.2282 0.8476 0.000 0.000 0.888 0.024 0.088 0.000
#> GSM648668 4 0.4641 0.7365 0.000 0.240 0.000 0.668 0.092 0.000
#> GSM648676 4 0.1957 0.7340 0.000 0.112 0.000 0.888 0.000 0.000
#> GSM648692 3 0.0000 0.9489 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648694 3 0.0000 0.9489 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648699 4 0.0146 0.8048 0.000 0.004 0.000 0.996 0.000 0.000
#> GSM648701 4 0.0146 0.8048 0.000 0.004 0.000 0.996 0.000 0.000
#> GSM648673 4 0.0000 0.8054 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM648677 4 0.3426 0.7882 0.000 0.124 0.000 0.808 0.068 0.000
#> GSM648687 1 0.4762 -0.0721 0.488 0.000 0.472 0.032 0.008 0.000
#> GSM648688 3 0.0000 0.9489 0.000 0.000 1.000 0.000 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) development.stage(p) other(p) k
#> SD:pam 89 1.74e-06 0.00628 8.03e-12 2
#> SD:pam 125 1.45e-17 0.04099 3.43e-20 3
#> SD:pam 94 1.04e-12 0.06078 4.85e-20 4
#> SD:pam 90 1.08e-17 0.08397 3.95e-24 5
#> SD:pam 76 3.96e-14 0.31412 2.13e-20 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 51941 rows and 130 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'mclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.472 0.825 0.874 0.4898 0.500 0.500
#> 3 3 0.398 0.183 0.618 0.2797 0.713 0.511
#> 4 4 0.591 0.562 0.759 0.1123 0.737 0.485
#> 5 5 0.604 0.639 0.791 0.0583 0.788 0.480
#> 6 6 0.622 0.579 0.681 0.0557 0.950 0.804
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
#> GSM648605 2 0.7376 0.8097 0.208 0.792
#> GSM648618 2 0.8813 0.3403 0.300 0.700
#> GSM648620 2 0.0000 0.8747 0.000 1.000
#> GSM648646 2 0.0000 0.8747 0.000 1.000
#> GSM648649 1 0.7376 0.8876 0.792 0.208
#> GSM648675 2 0.0000 0.8747 0.000 1.000
#> GSM648682 2 0.0000 0.8747 0.000 1.000
#> GSM648698 2 0.0000 0.8747 0.000 1.000
#> GSM648708 2 0.0000 0.8747 0.000 1.000
#> GSM648628 2 0.9087 0.7101 0.324 0.676
#> GSM648595 2 0.9552 0.0663 0.376 0.624
#> GSM648635 1 0.7376 0.8876 0.792 0.208
#> GSM648645 1 0.7376 0.8876 0.792 0.208
#> GSM648647 2 0.0000 0.8747 0.000 1.000
#> GSM648667 1 0.9686 0.6434 0.604 0.396
#> GSM648695 2 0.0000 0.8747 0.000 1.000
#> GSM648704 2 0.0000 0.8747 0.000 1.000
#> GSM648706 2 0.7376 0.8097 0.208 0.792
#> GSM648593 1 0.7376 0.8876 0.792 0.208
#> GSM648594 1 0.7453 0.8853 0.788 0.212
#> GSM648600 1 0.7376 0.8876 0.792 0.208
#> GSM648621 1 0.7602 0.8813 0.780 0.220
#> GSM648622 1 0.7376 0.8876 0.792 0.208
#> GSM648623 1 0.7602 0.8813 0.780 0.220
#> GSM648636 1 0.7376 0.8876 0.792 0.208
#> GSM648655 1 0.7376 0.8876 0.792 0.208
#> GSM648661 1 0.0672 0.7983 0.992 0.008
#> GSM648664 1 0.0672 0.7983 0.992 0.008
#> GSM648683 1 0.0672 0.7983 0.992 0.008
#> GSM648685 1 0.0672 0.7983 0.992 0.008
#> GSM648702 1 0.7376 0.8876 0.792 0.208
#> GSM648597 1 0.9922 0.5363 0.552 0.448
#> GSM648603 1 0.7528 0.8836 0.784 0.216
#> GSM648606 2 0.7376 0.8097 0.208 0.792
#> GSM648613 2 0.7376 0.8097 0.208 0.792
#> GSM648619 1 0.0938 0.7969 0.988 0.012
#> GSM648654 2 0.7745 0.7990 0.228 0.772
#> GSM648663 2 0.7376 0.8097 0.208 0.792
#> GSM648670 2 0.0000 0.8747 0.000 1.000
#> GSM648707 2 0.0000 0.8747 0.000 1.000
#> GSM648615 2 0.0000 0.8747 0.000 1.000
#> GSM648643 2 0.0000 0.8747 0.000 1.000
#> GSM648650 1 0.9732 0.6237 0.596 0.404
#> GSM648656 2 0.0000 0.8747 0.000 1.000
#> GSM648715 2 0.0000 0.8747 0.000 1.000
#> GSM648598 1 0.7376 0.8876 0.792 0.208
#> GSM648601 1 0.7376 0.8876 0.792 0.208
#> GSM648602 1 0.7376 0.8876 0.792 0.208
#> GSM648604 1 0.0672 0.7983 0.992 0.008
#> GSM648614 2 0.7376 0.8097 0.208 0.792
#> GSM648624 1 0.7376 0.8876 0.792 0.208
#> GSM648625 1 0.7376 0.8876 0.792 0.208
#> GSM648629 1 0.0672 0.7983 0.992 0.008
#> GSM648634 1 0.7376 0.8876 0.792 0.208
#> GSM648648 1 0.7376 0.8876 0.792 0.208
#> GSM648651 1 0.7376 0.8876 0.792 0.208
#> GSM648657 1 0.7376 0.8876 0.792 0.208
#> GSM648660 1 0.7376 0.8876 0.792 0.208
#> GSM648697 1 0.7376 0.8876 0.792 0.208
#> GSM648710 1 0.0672 0.7983 0.992 0.008
#> GSM648591 2 0.9460 0.1088 0.364 0.636
#> GSM648592 1 0.9970 0.4869 0.532 0.468
#> GSM648607 1 0.0672 0.7983 0.992 0.008
#> GSM648611 2 0.7528 0.8056 0.216 0.784
#> GSM648612 1 0.3879 0.7445 0.924 0.076
#> GSM648616 2 0.0000 0.8747 0.000 1.000
#> GSM648617 1 0.7674 0.8778 0.776 0.224
#> GSM648626 1 0.7602 0.8813 0.780 0.220
#> GSM648711 1 0.0672 0.7983 0.992 0.008
#> GSM648712 1 0.0938 0.7969 0.988 0.012
#> GSM648713 1 0.0938 0.7969 0.988 0.012
#> GSM648714 2 0.7376 0.8097 0.208 0.792
#> GSM648716 1 0.0938 0.7969 0.988 0.012
#> GSM648717 2 0.7376 0.8097 0.208 0.792
#> GSM648590 2 0.4815 0.7599 0.104 0.896
#> GSM648596 2 0.0000 0.8747 0.000 1.000
#> GSM648642 2 0.0000 0.8747 0.000 1.000
#> GSM648696 1 0.7453 0.8851 0.788 0.212
#> GSM648705 1 0.7376 0.8876 0.792 0.208
#> GSM648718 2 0.0000 0.8747 0.000 1.000
#> GSM648599 1 0.7376 0.8876 0.792 0.208
#> GSM648608 1 0.0672 0.7983 0.992 0.008
#> GSM648609 1 0.0672 0.7983 0.992 0.008
#> GSM648610 1 0.0672 0.7983 0.992 0.008
#> GSM648633 1 0.7376 0.8876 0.792 0.208
#> GSM648644 2 0.0000 0.8747 0.000 1.000
#> GSM648652 1 0.7376 0.8876 0.792 0.208
#> GSM648653 1 0.7376 0.8876 0.792 0.208
#> GSM648658 1 0.7376 0.8876 0.792 0.208
#> GSM648659 2 0.0000 0.8747 0.000 1.000
#> GSM648662 2 0.8909 0.7296 0.308 0.692
#> GSM648665 2 0.8081 0.7856 0.248 0.752
#> GSM648666 1 0.7376 0.8876 0.792 0.208
#> GSM648680 1 0.7376 0.8876 0.792 0.208
#> GSM648684 1 0.0672 0.7983 0.992 0.008
#> GSM648709 2 0.0000 0.8747 0.000 1.000
#> GSM648719 1 0.7376 0.8876 0.792 0.208
#> GSM648627 1 0.0938 0.7969 0.988 0.012
#> GSM648637 2 0.0000 0.8747 0.000 1.000
#> GSM648638 2 0.0000 0.8747 0.000 1.000
#> GSM648641 2 0.7376 0.8097 0.208 0.792
#> GSM648672 2 0.0000 0.8747 0.000 1.000
#> GSM648674 2 0.0000 0.8747 0.000 1.000
#> GSM648703 2 0.0000 0.8747 0.000 1.000
#> GSM648631 2 0.7376 0.8097 0.208 0.792
#> GSM648669 2 0.0000 0.8747 0.000 1.000
#> GSM648671 2 0.0000 0.8747 0.000 1.000
#> GSM648678 2 0.0000 0.8747 0.000 1.000
#> GSM648679 2 0.0000 0.8747 0.000 1.000
#> GSM648681 2 0.0000 0.8747 0.000 1.000
#> GSM648686 2 0.7376 0.8097 0.208 0.792
#> GSM648689 2 0.7376 0.8097 0.208 0.792
#> GSM648690 2 0.7376 0.8097 0.208 0.792
#> GSM648691 2 0.7376 0.8097 0.208 0.792
#> GSM648693 2 0.7376 0.8097 0.208 0.792
#> GSM648700 2 0.0000 0.8747 0.000 1.000
#> GSM648630 2 0.7376 0.8097 0.208 0.792
#> GSM648632 2 0.7376 0.8097 0.208 0.792
#> GSM648639 2 0.0000 0.8747 0.000 1.000
#> GSM648640 2 0.7376 0.8097 0.208 0.792
#> GSM648668 2 0.0000 0.8747 0.000 1.000
#> GSM648676 2 0.0000 0.8747 0.000 1.000
#> GSM648692 2 0.7376 0.8097 0.208 0.792
#> GSM648694 2 0.7376 0.8097 0.208 0.792
#> GSM648699 2 0.0000 0.8747 0.000 1.000
#> GSM648701 2 0.0000 0.8747 0.000 1.000
#> GSM648673 2 0.0000 0.8747 0.000 1.000
#> GSM648677 2 0.0000 0.8747 0.000 1.000
#> GSM648687 2 0.1843 0.8671 0.028 0.972
#> GSM648688 2 0.7376 0.8097 0.208 0.792
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM648605 2 0.3116 0.5238 0.108 0.892 0.000
#> GSM648618 2 0.8222 0.2209 0.100 0.592 0.308
#> GSM648620 2 0.6416 0.1938 0.008 0.616 0.376
#> GSM648646 2 0.5905 0.6047 0.000 0.648 0.352
#> GSM648649 3 0.6252 0.5897 0.444 0.000 0.556
#> GSM648675 2 0.6180 0.4068 0.000 0.584 0.416
#> GSM648682 2 0.4062 0.5793 0.000 0.836 0.164
#> GSM648698 2 0.3686 0.4959 0.000 0.860 0.140
#> GSM648708 2 0.6416 0.1938 0.008 0.616 0.376
#> GSM648628 1 0.7353 -0.0895 0.568 0.396 0.036
#> GSM648595 3 0.9712 0.3998 0.232 0.332 0.436
#> GSM648635 3 0.6252 0.5897 0.444 0.000 0.556
#> GSM648645 1 0.6154 -0.3104 0.592 0.000 0.408
#> GSM648647 2 0.6026 0.2085 0.000 0.624 0.376
#> GSM648667 3 0.9579 0.4626 0.352 0.204 0.444
#> GSM648695 2 0.6416 0.1938 0.008 0.616 0.376
#> GSM648704 2 0.5785 0.6091 0.000 0.668 0.332
#> GSM648706 2 0.5650 0.6089 0.000 0.688 0.312
#> GSM648593 3 0.6252 0.5897 0.444 0.000 0.556
#> GSM648594 1 0.8938 -0.4817 0.444 0.124 0.432
#> GSM648600 1 0.6154 -0.3104 0.592 0.000 0.408
#> GSM648621 1 0.6154 -0.3104 0.592 0.000 0.408
#> GSM648622 1 0.6154 -0.3104 0.592 0.000 0.408
#> GSM648623 1 0.6154 -0.3104 0.592 0.000 0.408
#> GSM648636 3 0.6252 0.5897 0.444 0.000 0.556
#> GSM648655 3 0.6252 0.5897 0.444 0.000 0.556
#> GSM648661 1 0.0000 0.3273 1.000 0.000 0.000
#> GSM648664 1 0.0892 0.3154 0.980 0.000 0.020
#> GSM648683 1 0.0747 0.3183 0.984 0.000 0.016
#> GSM648685 1 0.1411 0.3013 0.964 0.000 0.036
#> GSM648702 3 0.6252 0.5897 0.444 0.000 0.556
#> GSM648597 1 0.9825 -0.4009 0.424 0.268 0.308
#> GSM648603 1 0.6154 -0.3104 0.592 0.000 0.408
#> GSM648606 2 0.7203 0.2638 0.416 0.556 0.028
#> GSM648613 2 0.7411 0.2648 0.416 0.548 0.036
#> GSM648619 1 0.0000 0.3273 1.000 0.000 0.000
#> GSM648654 2 0.7240 0.2504 0.432 0.540 0.028
#> GSM648663 2 0.7203 0.2638 0.416 0.556 0.028
#> GSM648670 2 0.6008 0.5053 0.000 0.628 0.372
#> GSM648707 2 0.5706 0.6092 0.000 0.680 0.320
#> GSM648615 2 0.4452 0.4511 0.000 0.808 0.192
#> GSM648643 2 0.5678 0.4934 0.000 0.684 0.316
#> GSM648650 3 0.8933 0.4827 0.276 0.168 0.556
#> GSM648656 2 0.5785 0.6091 0.000 0.668 0.332
#> GSM648715 2 0.6026 0.2085 0.000 0.624 0.376
#> GSM648598 1 0.6154 -0.3104 0.592 0.000 0.408
#> GSM648601 1 0.6154 -0.3104 0.592 0.000 0.408
#> GSM648602 1 0.6154 -0.3104 0.592 0.000 0.408
#> GSM648604 1 0.0424 0.3233 0.992 0.000 0.008
#> GSM648614 2 0.7203 0.2638 0.416 0.556 0.028
#> GSM648624 1 0.6154 -0.3104 0.592 0.000 0.408
#> GSM648625 1 0.7841 -0.4163 0.536 0.056 0.408
#> GSM648629 1 0.0237 0.3255 0.996 0.000 0.004
#> GSM648634 1 0.6154 -0.3104 0.592 0.000 0.408
#> GSM648648 3 0.6252 0.5897 0.444 0.000 0.556
#> GSM648651 1 0.6154 -0.3104 0.592 0.000 0.408
#> GSM648657 1 0.6154 -0.3104 0.592 0.000 0.408
#> GSM648660 1 0.6154 -0.3104 0.592 0.000 0.408
#> GSM648697 1 0.6154 -0.3104 0.592 0.000 0.408
#> GSM648710 1 0.0000 0.3273 1.000 0.000 0.000
#> GSM648591 2 0.8889 0.0707 0.164 0.560 0.276
#> GSM648592 3 0.9885 0.3807 0.260 0.368 0.372
#> GSM648607 1 0.0000 0.3273 1.000 0.000 0.000
#> GSM648611 1 0.7487 -0.1176 0.552 0.408 0.040
#> GSM648612 1 0.3539 0.2795 0.888 0.100 0.012
#> GSM648616 2 0.5733 0.6082 0.000 0.676 0.324
#> GSM648617 1 0.9334 -0.4676 0.428 0.164 0.408
#> GSM648626 1 0.6154 -0.3104 0.592 0.000 0.408
#> GSM648711 1 0.0000 0.3273 1.000 0.000 0.000
#> GSM648712 1 0.0000 0.3273 1.000 0.000 0.000
#> GSM648713 1 0.0592 0.3227 0.988 0.012 0.000
#> GSM648714 2 0.7203 0.2638 0.416 0.556 0.028
#> GSM648716 1 0.0000 0.3273 1.000 0.000 0.000
#> GSM648717 2 0.7203 0.2638 0.416 0.556 0.028
#> GSM648590 3 0.9402 0.2801 0.172 0.408 0.420
#> GSM648596 2 0.5397 0.3593 0.000 0.720 0.280
#> GSM648642 2 0.6026 0.2085 0.000 0.624 0.376
#> GSM648696 1 0.8938 -0.4817 0.444 0.124 0.432
#> GSM648705 3 0.6252 0.5897 0.444 0.000 0.556
#> GSM648718 2 0.5650 0.3631 0.000 0.688 0.312
#> GSM648599 1 0.6154 -0.3104 0.592 0.000 0.408
#> GSM648608 1 0.0000 0.3273 1.000 0.000 0.000
#> GSM648609 1 0.0000 0.3273 1.000 0.000 0.000
#> GSM648610 1 0.1289 0.3053 0.968 0.000 0.032
#> GSM648633 1 0.6154 -0.3104 0.592 0.000 0.408
#> GSM648644 2 0.5785 0.6091 0.000 0.668 0.332
#> GSM648652 3 0.6252 0.5897 0.444 0.000 0.556
#> GSM648653 1 0.6154 -0.3104 0.592 0.000 0.408
#> GSM648658 3 0.6252 0.5897 0.444 0.000 0.556
#> GSM648659 2 0.6026 0.2085 0.000 0.624 0.376
#> GSM648662 2 0.7283 0.2102 0.460 0.512 0.028
#> GSM648665 2 0.7240 0.2504 0.432 0.540 0.028
#> GSM648666 1 0.6154 -0.3104 0.592 0.000 0.408
#> GSM648680 3 0.6260 0.5807 0.448 0.000 0.552
#> GSM648684 1 0.1163 0.3091 0.972 0.000 0.028
#> GSM648709 2 0.6416 0.1938 0.008 0.616 0.376
#> GSM648719 1 0.6154 -0.3104 0.592 0.000 0.408
#> GSM648627 1 0.0000 0.3273 1.000 0.000 0.000
#> GSM648637 2 0.6204 0.5853 0.000 0.576 0.424
#> GSM648638 2 0.5706 0.6082 0.000 0.680 0.320
#> GSM648641 1 0.9806 -0.2455 0.408 0.244 0.348
#> GSM648672 2 0.6215 0.5844 0.000 0.572 0.428
#> GSM648674 2 0.6168 0.5859 0.000 0.588 0.412
#> GSM648703 2 0.6252 0.5844 0.000 0.556 0.444
#> GSM648631 1 0.9806 -0.2455 0.408 0.244 0.348
#> GSM648669 2 0.6154 0.5858 0.000 0.592 0.408
#> GSM648671 2 0.6154 0.5858 0.000 0.592 0.408
#> GSM648678 2 0.5785 0.6091 0.000 0.668 0.332
#> GSM648679 2 0.6154 0.5858 0.000 0.592 0.408
#> GSM648681 2 0.5785 0.4849 0.000 0.668 0.332
#> GSM648686 1 0.9946 -0.3003 0.368 0.284 0.348
#> GSM648689 1 0.9806 -0.2455 0.408 0.244 0.348
#> GSM648690 1 0.9806 -0.2455 0.408 0.244 0.348
#> GSM648691 1 0.9806 -0.2455 0.408 0.244 0.348
#> GSM648693 1 0.9806 -0.2455 0.408 0.244 0.348
#> GSM648700 3 0.6291 -0.5348 0.000 0.468 0.532
#> GSM648630 1 0.9806 -0.2455 0.408 0.244 0.348
#> GSM648632 1 0.9806 -0.2455 0.408 0.244 0.348
#> GSM648639 2 0.5706 0.6082 0.000 0.680 0.320
#> GSM648640 1 0.9806 -0.2455 0.408 0.244 0.348
#> GSM648668 2 0.6305 0.5726 0.000 0.516 0.484
#> GSM648676 3 0.6302 -0.5433 0.000 0.480 0.520
#> GSM648692 1 0.9806 -0.2455 0.408 0.244 0.348
#> GSM648694 1 0.9806 -0.2455 0.408 0.244 0.348
#> GSM648699 2 0.6252 0.5844 0.000 0.556 0.444
#> GSM648701 2 0.6280 0.5812 0.000 0.540 0.460
#> GSM648673 2 0.6154 0.5858 0.000 0.592 0.408
#> GSM648677 2 0.6260 0.5839 0.000 0.552 0.448
#> GSM648687 2 0.5733 0.6082 0.000 0.676 0.324
#> GSM648688 1 0.9806 -0.2455 0.408 0.244 0.348
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM648605 2 0.5666 -0.0578 0.000 0.616 0.348 0.036
#> GSM648618 1 0.7393 0.2907 0.612 0.228 0.116 0.044
#> GSM648620 2 0.2830 0.5938 0.040 0.900 0.000 0.060
#> GSM648646 2 0.1389 0.6640 0.000 0.952 0.048 0.000
#> GSM648649 1 0.2984 0.6968 0.888 0.028 0.000 0.084
#> GSM648675 2 0.7238 0.6026 0.112 0.624 0.040 0.224
#> GSM648682 2 0.0921 0.6569 0.000 0.972 0.028 0.000
#> GSM648698 2 0.0376 0.6471 0.000 0.992 0.004 0.004
#> GSM648708 2 0.2739 0.5980 0.036 0.904 0.000 0.060
#> GSM648628 1 0.8447 -0.1003 0.400 0.032 0.208 0.360
#> GSM648595 1 0.5010 0.5867 0.772 0.120 0.000 0.108
#> GSM648635 1 0.2530 0.7028 0.896 0.004 0.000 0.100
#> GSM648645 1 0.0707 0.7267 0.980 0.000 0.000 0.020
#> GSM648647 2 0.2483 0.6075 0.032 0.916 0.000 0.052
#> GSM648667 1 0.5941 0.3571 0.652 0.276 0.000 0.072
#> GSM648695 2 0.2739 0.5980 0.036 0.904 0.000 0.060
#> GSM648704 2 0.1557 0.6653 0.000 0.944 0.056 0.000
#> GSM648706 2 0.3583 0.5598 0.000 0.816 0.180 0.004
#> GSM648593 1 0.2542 0.7028 0.904 0.012 0.000 0.084
#> GSM648594 1 0.3239 0.6879 0.880 0.068 0.000 0.052
#> GSM648600 1 0.0469 0.7277 0.988 0.000 0.000 0.012
#> GSM648621 1 0.1576 0.7211 0.948 0.004 0.000 0.048
#> GSM648622 1 0.0921 0.7264 0.972 0.000 0.000 0.028
#> GSM648623 1 0.1576 0.7211 0.948 0.004 0.000 0.048
#> GSM648636 1 0.2984 0.6960 0.888 0.028 0.000 0.084
#> GSM648655 1 0.2882 0.6974 0.892 0.024 0.000 0.084
#> GSM648661 1 0.6932 0.2797 0.532 0.004 0.104 0.360
#> GSM648664 1 0.6228 0.3521 0.572 0.000 0.064 0.364
#> GSM648683 1 0.6228 0.3521 0.572 0.000 0.064 0.364
#> GSM648685 1 0.6163 0.3582 0.576 0.000 0.060 0.364
#> GSM648702 1 0.2412 0.7046 0.908 0.008 0.000 0.084
#> GSM648597 1 0.4185 0.6360 0.832 0.120 0.012 0.036
#> GSM648603 1 0.1398 0.7227 0.956 0.004 0.000 0.040
#> GSM648606 3 0.6217 0.3919 0.012 0.340 0.604 0.044
#> GSM648613 3 0.6004 0.4107 0.008 0.336 0.616 0.040
#> GSM648619 1 0.6921 0.2846 0.536 0.004 0.104 0.356
#> GSM648654 3 0.9232 -0.5920 0.076 0.320 0.336 0.268
#> GSM648663 3 0.7377 0.2463 0.068 0.332 0.552 0.048
#> GSM648670 2 0.6864 0.6546 0.024 0.584 0.068 0.324
#> GSM648707 2 0.8046 0.5590 0.016 0.456 0.208 0.320
#> GSM648615 2 0.1182 0.6475 0.000 0.968 0.016 0.016
#> GSM648643 2 0.0188 0.6485 0.000 0.996 0.004 0.000
#> GSM648650 1 0.5875 0.4492 0.692 0.204 0.000 0.104
#> GSM648656 2 0.1557 0.6653 0.000 0.944 0.056 0.000
#> GSM648715 2 0.2565 0.6045 0.032 0.912 0.000 0.056
#> GSM648598 1 0.0592 0.7269 0.984 0.000 0.000 0.016
#> GSM648601 1 0.0000 0.7269 1.000 0.000 0.000 0.000
#> GSM648602 1 0.1022 0.7262 0.968 0.000 0.000 0.032
#> GSM648604 1 0.6574 0.3099 0.548 0.000 0.088 0.364
#> GSM648614 3 0.7344 0.2483 0.064 0.340 0.548 0.048
#> GSM648624 1 0.0817 0.7261 0.976 0.000 0.000 0.024
#> GSM648625 1 0.2101 0.7042 0.928 0.060 0.000 0.012
#> GSM648629 1 0.6562 0.3141 0.552 0.000 0.088 0.360
#> GSM648634 1 0.0592 0.7269 0.984 0.000 0.000 0.016
#> GSM648648 1 0.2412 0.7046 0.908 0.008 0.000 0.084
#> GSM648651 1 0.1022 0.7262 0.968 0.000 0.000 0.032
#> GSM648657 1 0.0707 0.7267 0.980 0.000 0.000 0.020
#> GSM648660 1 0.0707 0.7267 0.980 0.000 0.000 0.020
#> GSM648697 1 0.0707 0.7267 0.980 0.000 0.000 0.020
#> GSM648710 1 0.6574 0.3099 0.548 0.000 0.088 0.364
#> GSM648591 1 0.6491 0.4573 0.700 0.164 0.096 0.040
#> GSM648592 1 0.4001 0.6490 0.844 0.112 0.016 0.028
#> GSM648607 1 0.6614 0.3078 0.548 0.000 0.092 0.360
#> GSM648611 3 0.8125 -0.2391 0.312 0.080 0.516 0.092
#> GSM648612 1 0.7398 0.1790 0.496 0.008 0.136 0.360
#> GSM648616 2 0.7739 0.5591 0.004 0.456 0.208 0.332
#> GSM648617 1 0.2861 0.7071 0.908 0.032 0.012 0.048
#> GSM648626 1 0.1489 0.7219 0.952 0.004 0.000 0.044
#> GSM648711 1 0.6773 0.3070 0.548 0.004 0.092 0.356
#> GSM648712 1 0.6932 0.2795 0.532 0.004 0.104 0.360
#> GSM648713 1 0.6932 0.2795 0.532 0.004 0.104 0.360
#> GSM648714 3 0.6140 0.3982 0.012 0.340 0.608 0.040
#> GSM648716 1 0.6932 0.2795 0.532 0.004 0.104 0.360
#> GSM648717 3 0.6120 0.4103 0.008 0.328 0.616 0.048
#> GSM648590 2 0.6327 -0.0173 0.444 0.496 0.000 0.060
#> GSM648596 2 0.0000 0.6471 0.000 1.000 0.000 0.000
#> GSM648642 2 0.2565 0.6045 0.032 0.912 0.000 0.056
#> GSM648696 1 0.3157 0.6465 0.852 0.144 0.000 0.004
#> GSM648705 1 0.2984 0.6968 0.888 0.028 0.000 0.084
#> GSM648718 2 0.1296 0.6395 0.028 0.964 0.004 0.004
#> GSM648599 1 0.1022 0.7262 0.968 0.000 0.000 0.032
#> GSM648608 1 0.6574 0.3099 0.548 0.000 0.088 0.364
#> GSM648609 1 0.6574 0.3099 0.548 0.000 0.088 0.364
#> GSM648610 1 0.6308 0.3615 0.580 0.004 0.060 0.356
#> GSM648633 1 0.0592 0.7269 0.984 0.000 0.000 0.016
#> GSM648644 2 0.1557 0.6653 0.000 0.944 0.056 0.000
#> GSM648652 1 0.2466 0.7040 0.900 0.004 0.000 0.096
#> GSM648653 1 0.0921 0.7267 0.972 0.000 0.000 0.028
#> GSM648658 1 0.2266 0.7060 0.912 0.004 0.000 0.084
#> GSM648659 2 0.2036 0.6213 0.032 0.936 0.000 0.032
#> GSM648662 4 0.9376 0.8589 0.152 0.312 0.148 0.388
#> GSM648665 4 0.9227 0.8461 0.100 0.340 0.188 0.372
#> GSM648666 1 0.0707 0.7263 0.980 0.000 0.000 0.020
#> GSM648680 1 0.2530 0.7028 0.896 0.004 0.000 0.100
#> GSM648684 1 0.6176 0.3575 0.572 0.000 0.060 0.368
#> GSM648709 2 0.2644 0.6011 0.032 0.908 0.000 0.060
#> GSM648719 1 0.0592 0.7269 0.984 0.000 0.000 0.016
#> GSM648627 1 0.6932 0.2795 0.532 0.004 0.104 0.360
#> GSM648637 2 0.6064 0.6290 0.000 0.512 0.044 0.444
#> GSM648638 2 0.7830 0.4976 0.000 0.404 0.272 0.324
#> GSM648641 3 0.0000 0.7229 0.000 0.000 1.000 0.000
#> GSM648672 2 0.5681 0.6503 0.000 0.568 0.028 0.404
#> GSM648674 2 0.6211 0.6197 0.000 0.488 0.052 0.460
#> GSM648703 2 0.5511 0.6634 0.000 0.620 0.028 0.352
#> GSM648631 3 0.0000 0.7229 0.000 0.000 1.000 0.000
#> GSM648669 2 0.6214 0.6148 0.000 0.476 0.052 0.472
#> GSM648671 2 0.6214 0.6148 0.000 0.476 0.052 0.472
#> GSM648678 2 0.1743 0.6663 0.000 0.940 0.056 0.004
#> GSM648679 2 0.6214 0.6148 0.000 0.476 0.052 0.472
#> GSM648681 2 0.2992 0.6732 0.008 0.892 0.016 0.084
#> GSM648686 3 0.1118 0.6935 0.000 0.036 0.964 0.000
#> GSM648689 3 0.4353 0.5461 0.000 0.232 0.756 0.012
#> GSM648690 3 0.0000 0.7229 0.000 0.000 1.000 0.000
#> GSM648691 3 0.0000 0.7229 0.000 0.000 1.000 0.000
#> GSM648693 3 0.0000 0.7229 0.000 0.000 1.000 0.000
#> GSM648700 2 0.5980 0.6740 0.008 0.644 0.048 0.300
#> GSM648630 3 0.0000 0.7229 0.000 0.000 1.000 0.000
#> GSM648632 3 0.0000 0.7229 0.000 0.000 1.000 0.000
#> GSM648639 2 0.7845 0.4713 0.000 0.404 0.304 0.292
#> GSM648640 3 0.0188 0.7200 0.000 0.000 0.996 0.004
#> GSM648668 2 0.5686 0.6583 0.000 0.592 0.032 0.376
#> GSM648676 2 0.5720 0.6742 0.000 0.652 0.052 0.296
#> GSM648692 3 0.0000 0.7229 0.000 0.000 1.000 0.000
#> GSM648694 3 0.0000 0.7229 0.000 0.000 1.000 0.000
#> GSM648699 2 0.5543 0.6621 0.000 0.612 0.028 0.360
#> GSM648701 2 0.5478 0.6650 0.000 0.628 0.028 0.344
#> GSM648673 2 0.6214 0.6148 0.000 0.476 0.052 0.472
#> GSM648677 2 0.5511 0.6634 0.000 0.620 0.028 0.352
#> GSM648687 2 0.7899 0.5463 0.008 0.448 0.216 0.328
#> GSM648688 3 0.0000 0.7229 0.000 0.000 1.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM648605 5 0.6778 -0.1846 0.000 0.316 0.216 0.008 0.460
#> GSM648618 1 0.5071 0.6665 0.724 0.048 0.000 0.192 0.036
#> GSM648620 2 0.6114 0.5187 0.152 0.536 0.000 0.000 0.312
#> GSM648646 2 0.1341 0.7537 0.000 0.944 0.000 0.056 0.000
#> GSM648649 1 0.0693 0.8313 0.980 0.008 0.000 0.000 0.012
#> GSM648675 4 0.6421 0.3760 0.244 0.160 0.000 0.576 0.020
#> GSM648682 2 0.2813 0.7416 0.000 0.876 0.000 0.084 0.040
#> GSM648698 2 0.2304 0.7614 0.000 0.908 0.000 0.048 0.044
#> GSM648708 2 0.4237 0.7072 0.152 0.772 0.000 0.000 0.076
#> GSM648628 5 0.4537 0.5752 0.396 0.012 0.000 0.000 0.592
#> GSM648595 1 0.5163 0.6405 0.712 0.136 0.000 0.144 0.008
#> GSM648635 1 0.0404 0.8316 0.988 0.000 0.000 0.000 0.012
#> GSM648645 1 0.0000 0.8330 1.000 0.000 0.000 0.000 0.000
#> GSM648647 2 0.5163 0.6634 0.152 0.692 0.000 0.000 0.156
#> GSM648667 1 0.4327 0.3752 0.632 0.360 0.000 0.000 0.008
#> GSM648695 2 0.4277 0.7045 0.156 0.768 0.000 0.000 0.076
#> GSM648704 2 0.1341 0.7537 0.000 0.944 0.000 0.056 0.000
#> GSM648706 2 0.2835 0.6795 0.000 0.868 0.112 0.016 0.004
#> GSM648593 1 0.0566 0.8310 0.984 0.004 0.000 0.000 0.012
#> GSM648594 1 0.3289 0.7358 0.816 0.008 0.000 0.172 0.004
#> GSM648600 1 0.1251 0.8231 0.956 0.000 0.000 0.008 0.036
#> GSM648621 1 0.3953 0.7227 0.784 0.000 0.000 0.168 0.048
#> GSM648622 1 0.1357 0.8167 0.948 0.000 0.000 0.004 0.048
#> GSM648623 1 0.3953 0.7227 0.784 0.000 0.000 0.168 0.048
#> GSM648636 1 0.1106 0.8233 0.964 0.024 0.000 0.000 0.012
#> GSM648655 1 0.0912 0.8275 0.972 0.016 0.000 0.000 0.012
#> GSM648661 5 0.4201 0.5824 0.408 0.000 0.000 0.000 0.592
#> GSM648664 5 0.4210 0.5777 0.412 0.000 0.000 0.000 0.588
#> GSM648683 5 0.4201 0.5824 0.408 0.000 0.000 0.000 0.592
#> GSM648685 1 0.4291 -0.3571 0.536 0.000 0.000 0.000 0.464
#> GSM648702 1 0.0566 0.8310 0.984 0.004 0.000 0.000 0.012
#> GSM648597 1 0.4690 0.6551 0.724 0.036 0.000 0.224 0.016
#> GSM648603 1 0.3953 0.7227 0.784 0.000 0.000 0.168 0.048
#> GSM648606 5 0.5156 -0.0798 0.000 0.060 0.320 0.000 0.620
#> GSM648613 5 0.5203 -0.1026 0.000 0.060 0.332 0.000 0.608
#> GSM648619 5 0.4201 0.5824 0.408 0.000 0.000 0.000 0.592
#> GSM648654 5 0.3823 0.2461 0.008 0.060 0.112 0.000 0.820
#> GSM648663 5 0.5156 -0.0798 0.000 0.060 0.320 0.000 0.620
#> GSM648670 4 0.2900 0.7092 0.020 0.092 0.000 0.876 0.012
#> GSM648707 4 0.2656 0.7122 0.000 0.064 0.028 0.896 0.012
#> GSM648615 2 0.3840 0.7042 0.000 0.808 0.000 0.116 0.076
#> GSM648643 2 0.2221 0.7609 0.000 0.912 0.000 0.052 0.036
#> GSM648650 1 0.3885 0.5721 0.724 0.268 0.000 0.000 0.008
#> GSM648656 2 0.1341 0.7537 0.000 0.944 0.000 0.056 0.000
#> GSM648715 2 0.4509 0.6979 0.152 0.752 0.000 0.000 0.096
#> GSM648598 1 0.0000 0.8330 1.000 0.000 0.000 0.000 0.000
#> GSM648601 1 0.0865 0.8275 0.972 0.000 0.000 0.004 0.024
#> GSM648602 1 0.1357 0.8167 0.948 0.000 0.000 0.004 0.048
#> GSM648604 5 0.4210 0.5777 0.412 0.000 0.000 0.000 0.588
#> GSM648614 5 0.5421 -0.0915 0.000 0.060 0.320 0.008 0.612
#> GSM648624 1 0.1357 0.8167 0.948 0.000 0.000 0.004 0.048
#> GSM648625 1 0.2928 0.7757 0.872 0.092 0.000 0.004 0.032
#> GSM648629 5 0.4210 0.5777 0.412 0.000 0.000 0.000 0.588
#> GSM648634 1 0.0162 0.8329 0.996 0.000 0.000 0.004 0.000
#> GSM648648 1 0.0566 0.8310 0.984 0.004 0.000 0.000 0.012
#> GSM648651 1 0.1357 0.8167 0.948 0.000 0.000 0.004 0.048
#> GSM648657 1 0.0000 0.8330 1.000 0.000 0.000 0.000 0.000
#> GSM648660 1 0.0000 0.8330 1.000 0.000 0.000 0.000 0.000
#> GSM648697 1 0.0324 0.8332 0.992 0.000 0.000 0.004 0.004
#> GSM648710 5 0.4201 0.5824 0.408 0.000 0.000 0.000 0.592
#> GSM648591 1 0.4910 0.6614 0.724 0.036 0.000 0.208 0.032
#> GSM648592 1 0.4530 0.6893 0.752 0.036 0.000 0.192 0.020
#> GSM648607 5 0.4201 0.5824 0.408 0.000 0.000 0.000 0.592
#> GSM648611 1 0.7391 -0.4346 0.384 0.044 0.192 0.000 0.380
#> GSM648612 5 0.4331 0.5811 0.400 0.004 0.000 0.000 0.596
#> GSM648616 4 0.2597 0.7127 0.000 0.060 0.040 0.896 0.004
#> GSM648617 1 0.4291 0.7047 0.760 0.004 0.000 0.188 0.048
#> GSM648626 1 0.4136 0.7049 0.764 0.000 0.000 0.188 0.048
#> GSM648711 5 0.4201 0.5824 0.408 0.000 0.000 0.000 0.592
#> GSM648712 5 0.4341 0.5819 0.404 0.004 0.000 0.000 0.592
#> GSM648713 5 0.4331 0.5811 0.400 0.004 0.000 0.000 0.596
#> GSM648714 5 0.5478 -0.0972 0.000 0.064 0.320 0.008 0.608
#> GSM648716 5 0.4201 0.5824 0.408 0.000 0.000 0.000 0.592
#> GSM648717 5 0.5113 -0.0856 0.000 0.056 0.324 0.000 0.620
#> GSM648590 1 0.5726 0.5280 0.640 0.240 0.000 0.108 0.012
#> GSM648596 2 0.2804 0.7551 0.004 0.884 0.000 0.068 0.044
#> GSM648642 2 0.4119 0.7084 0.152 0.780 0.000 0.000 0.068
#> GSM648696 1 0.2179 0.7633 0.888 0.112 0.000 0.000 0.000
#> GSM648705 1 0.1012 0.8251 0.968 0.020 0.000 0.000 0.012
#> GSM648718 2 0.3288 0.7640 0.028 0.868 0.000 0.060 0.044
#> GSM648599 1 0.2149 0.8093 0.916 0.000 0.000 0.036 0.048
#> GSM648608 5 0.4210 0.5777 0.412 0.000 0.000 0.000 0.588
#> GSM648609 5 0.4201 0.5824 0.408 0.000 0.000 0.000 0.592
#> GSM648610 5 0.4219 0.5698 0.416 0.000 0.000 0.000 0.584
#> GSM648633 1 0.0000 0.8330 1.000 0.000 0.000 0.000 0.000
#> GSM648644 2 0.1341 0.7537 0.000 0.944 0.000 0.056 0.000
#> GSM648652 1 0.0404 0.8316 0.988 0.000 0.000 0.000 0.012
#> GSM648653 1 0.1205 0.8210 0.956 0.000 0.000 0.004 0.040
#> GSM648658 1 0.0404 0.8316 0.988 0.000 0.000 0.000 0.012
#> GSM648659 2 0.3649 0.7099 0.152 0.808 0.000 0.000 0.040
#> GSM648662 5 0.4219 0.3509 0.056 0.060 0.068 0.000 0.816
#> GSM648665 5 0.3209 0.2914 0.008 0.060 0.068 0.000 0.864
#> GSM648666 1 0.0771 0.8286 0.976 0.000 0.000 0.004 0.020
#> GSM648680 1 0.0404 0.8316 0.988 0.000 0.000 0.000 0.012
#> GSM648684 5 0.4304 0.4277 0.484 0.000 0.000 0.000 0.516
#> GSM648709 2 0.5583 0.6282 0.152 0.640 0.000 0.000 0.208
#> GSM648719 1 0.0162 0.8329 0.996 0.000 0.000 0.004 0.000
#> GSM648627 5 0.4341 0.5819 0.404 0.004 0.000 0.000 0.592
#> GSM648637 4 0.1608 0.7213 0.000 0.072 0.000 0.928 0.000
#> GSM648638 4 0.6510 0.4585 0.000 0.256 0.256 0.488 0.000
#> GSM648641 3 0.0000 0.9800 0.000 0.000 1.000 0.000 0.000
#> GSM648672 4 0.2280 0.7077 0.000 0.120 0.000 0.880 0.000
#> GSM648674 4 0.0510 0.7196 0.000 0.016 0.000 0.984 0.000
#> GSM648703 4 0.4291 0.3587 0.000 0.464 0.000 0.536 0.000
#> GSM648631 3 0.0000 0.9800 0.000 0.000 1.000 0.000 0.000
#> GSM648669 4 0.0162 0.7172 0.000 0.004 0.000 0.996 0.000
#> GSM648671 4 0.0162 0.7172 0.000 0.004 0.000 0.996 0.000
#> GSM648678 2 0.1478 0.7487 0.000 0.936 0.000 0.064 0.000
#> GSM648679 4 0.0162 0.7172 0.000 0.004 0.000 0.996 0.000
#> GSM648681 2 0.5839 0.2079 0.072 0.576 0.000 0.336 0.016
#> GSM648686 3 0.0162 0.9750 0.000 0.004 0.996 0.000 0.000
#> GSM648689 3 0.4221 0.7466 0.000 0.044 0.764 0.004 0.188
#> GSM648690 3 0.0000 0.9800 0.000 0.000 1.000 0.000 0.000
#> GSM648691 3 0.0000 0.9800 0.000 0.000 1.000 0.000 0.000
#> GSM648693 3 0.0000 0.9800 0.000 0.000 1.000 0.000 0.000
#> GSM648700 4 0.5508 0.5310 0.096 0.264 0.000 0.636 0.004
#> GSM648630 3 0.0000 0.9800 0.000 0.000 1.000 0.000 0.000
#> GSM648632 3 0.0000 0.9800 0.000 0.000 1.000 0.000 0.000
#> GSM648639 4 0.5104 0.4738 0.000 0.068 0.284 0.648 0.000
#> GSM648640 3 0.0000 0.9800 0.000 0.000 1.000 0.000 0.000
#> GSM648668 4 0.3612 0.6335 0.000 0.268 0.000 0.732 0.000
#> GSM648676 4 0.4449 0.3269 0.000 0.484 0.000 0.512 0.004
#> GSM648692 3 0.0000 0.9800 0.000 0.000 1.000 0.000 0.000
#> GSM648694 3 0.0000 0.9800 0.000 0.000 1.000 0.000 0.000
#> GSM648699 4 0.4268 0.4010 0.000 0.444 0.000 0.556 0.000
#> GSM648701 4 0.4297 0.3398 0.000 0.472 0.000 0.528 0.000
#> GSM648673 4 0.0162 0.7172 0.000 0.004 0.000 0.996 0.000
#> GSM648677 4 0.4287 0.3641 0.000 0.460 0.000 0.540 0.000
#> GSM648687 4 0.6693 0.5911 0.036 0.104 0.076 0.664 0.120
#> GSM648688 3 0.0000 0.9800 0.000 0.000 1.000 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM648605 2 0.7613 0.3619 0.228 0.360 0.156 0.004 NA 0.000
#> GSM648618 6 0.7358 0.4415 0.164 0.248 0.000 0.024 NA 0.464
#> GSM648620 2 0.7668 0.4762 0.232 0.384 0.000 0.108 NA 0.020
#> GSM648646 2 0.2048 0.3544 0.000 0.880 0.000 0.120 NA 0.000
#> GSM648649 6 0.3996 0.6669 0.004 0.008 0.000 0.000 NA 0.636
#> GSM648675 2 0.7692 0.0473 0.012 0.392 0.000 0.184 NA 0.176
#> GSM648682 2 0.2476 0.3844 0.004 0.880 0.000 0.092 NA 0.000
#> GSM648698 2 0.4187 0.5355 0.076 0.756 0.000 0.012 NA 0.000
#> GSM648708 2 0.7007 0.5309 0.116 0.512 0.000 0.108 NA 0.020
#> GSM648628 1 0.4818 0.6694 0.636 0.000 0.000 0.004 NA 0.284
#> GSM648595 6 0.6377 0.3953 0.008 0.276 0.000 0.040 NA 0.528
#> GSM648635 6 0.3647 0.6647 0.000 0.000 0.000 0.000 NA 0.640
#> GSM648645 6 0.2743 0.7006 0.000 0.008 0.000 0.000 NA 0.828
#> GSM648647 2 0.7342 0.5153 0.164 0.464 0.000 0.108 NA 0.020
#> GSM648667 2 0.6178 0.0956 0.004 0.396 0.000 0.000 NA 0.264
#> GSM648695 2 0.7076 0.5353 0.100 0.524 0.000 0.108 NA 0.036
#> GSM648704 2 0.2178 0.3384 0.000 0.868 0.000 0.132 NA 0.000
#> GSM648706 2 0.4140 0.4412 0.000 0.784 0.036 0.076 NA 0.000
#> GSM648593 6 0.3717 0.6557 0.000 0.000 0.000 0.000 NA 0.616
#> GSM648594 6 0.5237 0.6406 0.004 0.140 0.000 0.000 NA 0.616
#> GSM648600 6 0.1701 0.6784 0.072 0.000 0.000 0.000 NA 0.920
#> GSM648621 6 0.2562 0.6182 0.172 0.000 0.000 0.000 NA 0.828
#> GSM648622 6 0.2092 0.6472 0.124 0.000 0.000 0.000 NA 0.876
#> GSM648623 6 0.2730 0.5932 0.192 0.000 0.000 0.000 NA 0.808
#> GSM648636 6 0.3706 0.6566 0.000 0.000 0.000 0.000 NA 0.620
#> GSM648655 6 0.3717 0.6551 0.000 0.000 0.000 0.000 NA 0.616
#> GSM648661 1 0.3508 0.6843 0.704 0.000 0.000 0.000 NA 0.292
#> GSM648664 1 0.3547 0.6838 0.696 0.000 0.000 0.000 NA 0.300
#> GSM648683 1 0.3547 0.6838 0.696 0.000 0.000 0.000 NA 0.300
#> GSM648685 1 0.3699 0.6515 0.660 0.000 0.000 0.000 NA 0.336
#> GSM648702 6 0.3695 0.6594 0.000 0.000 0.000 0.000 NA 0.624
#> GSM648597 6 0.6996 0.3957 0.080 0.312 0.000 0.084 NA 0.484
#> GSM648603 6 0.2597 0.6084 0.176 0.000 0.000 0.000 NA 0.824
#> GSM648606 1 0.6131 0.2563 0.632 0.040 0.176 0.040 NA 0.000
#> GSM648613 1 0.6742 -0.0279 0.508 0.040 0.300 0.040 NA 0.000
#> GSM648619 1 0.4291 0.6815 0.664 0.000 0.000 0.000 NA 0.292
#> GSM648654 1 0.4549 0.4114 0.784 0.040 0.036 0.040 NA 0.004
#> GSM648663 1 0.5947 0.2922 0.656 0.040 0.152 0.040 NA 0.000
#> GSM648670 4 0.5608 0.4494 0.008 0.348 0.000 0.520 NA 0.000
#> GSM648707 4 0.6368 0.3529 0.000 0.316 0.032 0.468 NA 0.000
#> GSM648615 2 0.2876 0.5208 0.016 0.844 0.000 0.008 NA 0.000
#> GSM648643 2 0.0858 0.4583 0.004 0.968 0.000 0.028 NA 0.000
#> GSM648650 6 0.6243 0.2035 0.004 0.320 0.000 0.000 NA 0.360
#> GSM648656 2 0.2260 0.3264 0.000 0.860 0.000 0.140 NA 0.000
#> GSM648715 2 0.7174 0.5241 0.140 0.492 0.000 0.108 NA 0.020
#> GSM648598 6 0.0146 0.6970 0.000 0.000 0.000 0.000 NA 0.996
#> GSM648601 6 0.1387 0.6875 0.068 0.000 0.000 0.000 NA 0.932
#> GSM648602 6 0.1714 0.6618 0.092 0.000 0.000 0.000 NA 0.908
#> GSM648604 1 0.3547 0.6838 0.696 0.000 0.000 0.000 NA 0.300
#> GSM648614 1 0.6036 0.2836 0.652 0.052 0.156 0.040 NA 0.000
#> GSM648624 6 0.2562 0.6122 0.172 0.000 0.000 0.000 NA 0.828
#> GSM648625 6 0.3477 0.6537 0.112 0.040 0.000 0.000 NA 0.824
#> GSM648629 1 0.3547 0.6838 0.696 0.000 0.000 0.000 NA 0.300
#> GSM648634 6 0.0000 0.6960 0.000 0.000 0.000 0.000 NA 1.000
#> GSM648648 6 0.3659 0.6635 0.000 0.000 0.000 0.000 NA 0.636
#> GSM648651 6 0.1714 0.6618 0.092 0.000 0.000 0.000 NA 0.908
#> GSM648657 6 0.3098 0.7002 0.000 0.024 0.000 0.000 NA 0.812
#> GSM648660 6 0.2454 0.7006 0.000 0.000 0.000 0.000 NA 0.840
#> GSM648697 6 0.0806 0.6984 0.020 0.000 0.000 0.000 NA 0.972
#> GSM648710 1 0.3547 0.6838 0.696 0.000 0.000 0.000 NA 0.300
#> GSM648591 6 0.7669 0.3219 0.096 0.296 0.000 0.076 NA 0.432
#> GSM648592 6 0.5455 0.5812 0.096 0.216 0.000 0.012 NA 0.652
#> GSM648607 1 0.3409 0.6841 0.700 0.000 0.000 0.000 NA 0.300
#> GSM648611 1 0.7247 0.5217 0.480 0.020 0.176 0.004 NA 0.244
#> GSM648612 1 0.4291 0.6815 0.664 0.000 0.000 0.000 NA 0.292
#> GSM648616 4 0.6232 0.4006 0.000 0.312 0.040 0.504 NA 0.000
#> GSM648617 6 0.4290 0.6006 0.180 0.076 0.000 0.000 NA 0.736
#> GSM648626 6 0.3104 0.6060 0.184 0.016 0.000 0.000 NA 0.800
#> GSM648711 1 0.4326 0.6796 0.656 0.000 0.000 0.000 NA 0.300
#> GSM648712 1 0.4291 0.6815 0.664 0.000 0.000 0.000 NA 0.292
#> GSM648713 1 0.4291 0.6815 0.664 0.000 0.000 0.000 NA 0.292
#> GSM648714 1 0.6828 0.1743 0.572 0.080 0.176 0.040 NA 0.000
#> GSM648716 1 0.4291 0.6815 0.664 0.000 0.000 0.000 NA 0.292
#> GSM648717 1 0.6265 0.2255 0.612 0.040 0.196 0.040 NA 0.000
#> GSM648590 6 0.6514 0.1988 0.008 0.316 0.000 0.020 NA 0.448
#> GSM648596 2 0.3090 0.5094 0.004 0.828 0.000 0.028 NA 0.000
#> GSM648642 2 0.6849 0.5339 0.104 0.536 0.000 0.108 NA 0.020
#> GSM648696 6 0.5109 0.6272 0.012 0.136 0.000 0.000 NA 0.660
#> GSM648705 6 0.3672 0.6621 0.000 0.000 0.000 0.000 NA 0.632
#> GSM648718 2 0.2631 0.5232 0.004 0.860 0.000 0.008 NA 0.004
#> GSM648599 6 0.2135 0.6447 0.128 0.000 0.000 0.000 NA 0.872
#> GSM648608 1 0.3547 0.6838 0.696 0.000 0.000 0.000 NA 0.300
#> GSM648609 1 0.3547 0.6838 0.696 0.000 0.000 0.000 NA 0.300
#> GSM648610 1 0.3482 0.6674 0.684 0.000 0.000 0.000 NA 0.316
#> GSM648633 6 0.2482 0.7017 0.004 0.000 0.000 0.000 NA 0.848
#> GSM648644 2 0.2219 0.3304 0.000 0.864 0.000 0.136 NA 0.000
#> GSM648652 6 0.3647 0.6647 0.000 0.000 0.000 0.000 NA 0.640
#> GSM648653 6 0.1714 0.6618 0.092 0.000 0.000 0.000 NA 0.908
#> GSM648658 6 0.3717 0.6549 0.000 0.000 0.000 0.000 NA 0.616
#> GSM648659 2 0.5310 0.5410 0.004 0.644 0.000 0.088 NA 0.024
#> GSM648662 1 0.4936 0.4247 0.764 0.040 0.036 0.040 NA 0.016
#> GSM648665 1 0.4723 0.4054 0.772 0.048 0.036 0.040 NA 0.004
#> GSM648666 6 0.1806 0.6742 0.088 0.000 0.000 0.000 NA 0.908
#> GSM648680 6 0.3659 0.6638 0.000 0.000 0.000 0.000 NA 0.636
#> GSM648684 1 0.3636 0.6687 0.676 0.000 0.000 0.000 NA 0.320
#> GSM648709 2 0.7506 0.5051 0.188 0.428 0.000 0.108 NA 0.020
#> GSM648719 6 0.0260 0.6951 0.008 0.000 0.000 0.000 NA 0.992
#> GSM648627 1 0.4291 0.6815 0.664 0.000 0.000 0.000 NA 0.292
#> GSM648637 4 0.3772 0.7384 0.000 0.296 0.008 0.692 NA 0.000
#> GSM648638 2 0.6587 -0.2533 0.000 0.452 0.136 0.344 NA 0.000
#> GSM648641 3 0.0260 0.9785 0.000 0.000 0.992 0.000 NA 0.000
#> GSM648672 4 0.3652 0.7322 0.000 0.324 0.004 0.672 NA 0.000
#> GSM648674 4 0.2562 0.7308 0.000 0.172 0.000 0.828 NA 0.000
#> GSM648703 4 0.3727 0.7088 0.000 0.388 0.000 0.612 NA 0.000
#> GSM648631 3 0.0000 0.9833 0.000 0.000 1.000 0.000 NA 0.000
#> GSM648669 4 0.2340 0.7256 0.000 0.148 0.000 0.852 NA 0.000
#> GSM648671 4 0.2340 0.7256 0.000 0.148 0.000 0.852 NA 0.000
#> GSM648678 2 0.3076 0.0779 0.000 0.760 0.000 0.240 NA 0.000
#> GSM648679 4 0.2340 0.7256 0.000 0.148 0.000 0.852 NA 0.000
#> GSM648681 2 0.5438 0.3370 0.004 0.672 0.000 0.160 NA 0.044
#> GSM648686 3 0.1458 0.9402 0.000 0.016 0.948 0.016 NA 0.000
#> GSM648689 3 0.2733 0.8565 0.080 0.056 0.864 0.000 NA 0.000
#> GSM648690 3 0.0000 0.9833 0.000 0.000 1.000 0.000 NA 0.000
#> GSM648691 3 0.0000 0.9833 0.000 0.000 1.000 0.000 NA 0.000
#> GSM648693 3 0.0000 0.9833 0.000 0.000 1.000 0.000 NA 0.000
#> GSM648700 2 0.6258 -0.3320 0.000 0.412 0.004 0.408 NA 0.020
#> GSM648630 3 0.0000 0.9833 0.000 0.000 1.000 0.000 NA 0.000
#> GSM648632 3 0.0146 0.9803 0.004 0.000 0.996 0.000 NA 0.000
#> GSM648639 4 0.6827 0.2581 0.000 0.312 0.220 0.412 NA 0.000
#> GSM648640 3 0.0000 0.9833 0.000 0.000 1.000 0.000 NA 0.000
#> GSM648668 4 0.3684 0.7186 0.000 0.372 0.000 0.628 NA 0.000
#> GSM648676 4 0.4591 0.6688 0.000 0.408 0.000 0.552 NA 0.000
#> GSM648692 3 0.0000 0.9833 0.000 0.000 1.000 0.000 NA 0.000
#> GSM648694 3 0.0000 0.9833 0.000 0.000 1.000 0.000 NA 0.000
#> GSM648699 4 0.3727 0.7088 0.000 0.388 0.000 0.612 NA 0.000
#> GSM648701 4 0.3765 0.6934 0.000 0.404 0.000 0.596 NA 0.000
#> GSM648673 4 0.2340 0.7256 0.000 0.148 0.000 0.852 NA 0.000
#> GSM648677 4 0.3706 0.7141 0.000 0.380 0.000 0.620 NA 0.000
#> GSM648687 2 0.8601 -0.1480 0.112 0.372 0.048 0.264 NA 0.044
#> GSM648688 3 0.0000 0.9833 0.000 0.000 1.000 0.000 NA 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) development.stage(p) other(p) k
#> SD:mclust 126 5.12e-09 0.0103 2.99e-13 2
#> SD:mclust 37 2.00e-03 0.2270 3.72e-05 3
#> SD:mclust 94 1.30e-10 0.0301 8.18e-13 4
#> SD:mclust 107 3.06e-18 0.1884 3.37e-25 5
#> SD:mclust 95 1.14e-19 0.1297 1.34e-26 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 51941 rows and 130 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 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 0.387 0.525 0.821 0.4671 0.565 0.565
#> 3 3 0.903 0.918 0.965 0.3144 0.654 0.468
#> 4 4 0.810 0.812 0.914 0.1129 0.881 0.709
#> 5 5 0.864 0.859 0.937 0.0741 0.889 0.676
#> 6 6 0.676 0.646 0.817 0.0713 0.901 0.656
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
#> GSM648605 2 0.9954 0.21665 0.460 0.540
#> GSM648618 2 0.9866 0.03516 0.432 0.568
#> GSM648620 2 0.0000 0.73697 0.000 1.000
#> GSM648646 2 0.9954 0.21665 0.460 0.540
#> GSM648649 2 0.0000 0.73697 0.000 1.000
#> GSM648675 2 0.0000 0.73697 0.000 1.000
#> GSM648682 2 0.2043 0.71746 0.032 0.968
#> GSM648698 2 0.9635 0.31634 0.388 0.612
#> GSM648708 2 0.0000 0.73697 0.000 1.000
#> GSM648628 1 0.9954 0.27372 0.540 0.460
#> GSM648595 2 0.0000 0.73697 0.000 1.000
#> GSM648635 2 0.0000 0.73697 0.000 1.000
#> GSM648645 2 0.0000 0.73697 0.000 1.000
#> GSM648647 2 0.0000 0.73697 0.000 1.000
#> GSM648667 2 0.0000 0.73697 0.000 1.000
#> GSM648695 2 0.0000 0.73697 0.000 1.000
#> GSM648704 2 0.9954 0.21665 0.460 0.540
#> GSM648706 2 0.9954 0.21665 0.460 0.540
#> GSM648593 2 0.0000 0.73697 0.000 1.000
#> GSM648594 2 0.0000 0.73697 0.000 1.000
#> GSM648600 2 0.0000 0.73697 0.000 1.000
#> GSM648621 2 0.9427 0.25261 0.360 0.640
#> GSM648622 2 0.7453 0.53954 0.212 0.788
#> GSM648623 1 0.9970 0.25525 0.532 0.468
#> GSM648636 2 0.0000 0.73697 0.000 1.000
#> GSM648655 2 0.0000 0.73697 0.000 1.000
#> GSM648661 2 0.9754 0.11663 0.408 0.592
#> GSM648664 2 0.8443 0.44151 0.272 0.728
#> GSM648683 2 0.7528 0.53395 0.216 0.784
#> GSM648685 2 0.5842 0.62446 0.140 0.860
#> GSM648702 2 0.0000 0.73697 0.000 1.000
#> GSM648597 2 0.2603 0.70979 0.044 0.956
#> GSM648603 2 0.9286 0.29138 0.344 0.656
#> GSM648606 1 0.0000 0.71172 1.000 0.000
#> GSM648613 1 0.0000 0.71172 1.000 0.000
#> GSM648619 1 0.9954 0.27372 0.540 0.460
#> GSM648654 1 0.9996 0.20306 0.512 0.488
#> GSM648663 1 0.0376 0.70995 0.996 0.004
#> GSM648670 2 0.0000 0.73697 0.000 1.000
#> GSM648707 1 0.8555 0.48943 0.720 0.280
#> GSM648615 2 0.9754 0.28902 0.408 0.592
#> GSM648643 2 0.0376 0.73510 0.004 0.996
#> GSM648650 2 0.0000 0.73697 0.000 1.000
#> GSM648656 2 0.9954 0.21665 0.460 0.540
#> GSM648715 2 0.0000 0.73697 0.000 1.000
#> GSM648598 2 0.0000 0.73697 0.000 1.000
#> GSM648601 2 0.0000 0.73697 0.000 1.000
#> GSM648602 2 0.6148 0.61176 0.152 0.848
#> GSM648604 2 0.9608 0.18939 0.384 0.616
#> GSM648614 1 0.8608 0.37841 0.716 0.284
#> GSM648624 2 0.7602 0.52808 0.220 0.780
#> GSM648625 2 0.0000 0.73697 0.000 1.000
#> GSM648629 2 0.9608 0.18939 0.384 0.616
#> GSM648634 2 0.0000 0.73697 0.000 1.000
#> GSM648648 2 0.0000 0.73697 0.000 1.000
#> GSM648651 2 0.7056 0.56528 0.192 0.808
#> GSM648657 2 0.0000 0.73697 0.000 1.000
#> GSM648660 2 0.0000 0.73697 0.000 1.000
#> GSM648697 2 0.0000 0.73697 0.000 1.000
#> GSM648710 2 0.9686 0.15423 0.396 0.604
#> GSM648591 1 0.9963 0.26485 0.536 0.464
#> GSM648592 2 0.0000 0.73697 0.000 1.000
#> GSM648607 1 0.9996 0.20306 0.512 0.488
#> GSM648611 1 0.9522 0.38417 0.628 0.372
#> GSM648612 1 0.9954 0.27372 0.540 0.460
#> GSM648616 1 0.0000 0.71172 1.000 0.000
#> GSM648617 2 0.0672 0.73273 0.008 0.992
#> GSM648626 2 0.9977 -0.10711 0.472 0.528
#> GSM648711 1 0.9954 0.27372 0.540 0.460
#> GSM648712 1 0.9954 0.27372 0.540 0.460
#> GSM648713 1 0.9954 0.27372 0.540 0.460
#> GSM648714 1 0.0000 0.71172 1.000 0.000
#> GSM648716 1 0.9954 0.27372 0.540 0.460
#> GSM648717 1 0.0672 0.70795 0.992 0.008
#> GSM648590 2 0.0000 0.73697 0.000 1.000
#> GSM648596 2 0.9393 0.35878 0.356 0.644
#> GSM648642 2 0.0000 0.73697 0.000 1.000
#> GSM648696 2 0.0000 0.73697 0.000 1.000
#> GSM648705 2 0.0000 0.73697 0.000 1.000
#> GSM648718 2 0.0000 0.73697 0.000 1.000
#> GSM648599 2 0.7528 0.53405 0.216 0.784
#> GSM648608 2 0.9661 0.16620 0.392 0.608
#> GSM648609 2 0.8327 0.45601 0.264 0.736
#> GSM648610 2 0.9552 0.21119 0.376 0.624
#> GSM648633 2 0.0000 0.73697 0.000 1.000
#> GSM648644 2 0.9954 0.21665 0.460 0.540
#> GSM648652 2 0.0000 0.73697 0.000 1.000
#> GSM648653 2 0.2423 0.71252 0.040 0.960
#> GSM648658 2 0.0000 0.73697 0.000 1.000
#> GSM648659 2 0.1184 0.72810 0.016 0.984
#> GSM648662 2 0.9323 0.28198 0.348 0.652
#> GSM648665 2 0.7376 0.54482 0.208 0.792
#> GSM648666 2 0.3114 0.70067 0.056 0.944
#> GSM648680 2 0.0000 0.73697 0.000 1.000
#> GSM648684 2 0.7453 0.53954 0.212 0.788
#> GSM648709 2 0.4815 0.65772 0.104 0.896
#> GSM648719 2 0.0000 0.73697 0.000 1.000
#> GSM648627 1 0.9954 0.27372 0.540 0.460
#> GSM648637 2 0.9954 0.21665 0.460 0.540
#> GSM648638 1 0.0000 0.71172 1.000 0.000
#> GSM648641 1 0.0000 0.71172 1.000 0.000
#> GSM648672 2 0.9954 0.21665 0.460 0.540
#> GSM648674 2 0.9954 0.21665 0.460 0.540
#> GSM648703 2 0.9954 0.21665 0.460 0.540
#> GSM648631 1 0.0000 0.71172 1.000 0.000
#> GSM648669 1 0.7376 0.47328 0.792 0.208
#> GSM648671 1 0.7745 0.44383 0.772 0.228
#> GSM648678 2 0.9954 0.21665 0.460 0.540
#> GSM648679 1 0.9881 0.01309 0.564 0.436
#> GSM648681 2 0.9795 0.27791 0.416 0.584
#> GSM648686 1 0.0000 0.71172 1.000 0.000
#> GSM648689 1 0.0000 0.71172 1.000 0.000
#> GSM648690 1 0.0000 0.71172 1.000 0.000
#> GSM648691 1 0.0000 0.71172 1.000 0.000
#> GSM648693 1 0.0000 0.71172 1.000 0.000
#> GSM648700 2 0.0376 0.73509 0.004 0.996
#> GSM648630 1 0.0000 0.71172 1.000 0.000
#> GSM648632 1 0.0000 0.71172 1.000 0.000
#> GSM648639 1 0.0000 0.71172 1.000 0.000
#> GSM648640 1 0.0000 0.71172 1.000 0.000
#> GSM648668 2 0.9954 0.21665 0.460 0.540
#> GSM648676 2 0.0376 0.73509 0.004 0.996
#> GSM648692 1 0.0000 0.71172 1.000 0.000
#> GSM648694 1 0.0000 0.71172 1.000 0.000
#> GSM648699 2 0.9954 0.21665 0.460 0.540
#> GSM648701 2 0.9954 0.21665 0.460 0.540
#> GSM648673 1 0.9896 0.00216 0.560 0.440
#> GSM648677 2 0.9954 0.21665 0.460 0.540
#> GSM648687 1 0.0000 0.71172 1.000 0.000
#> GSM648688 1 0.0000 0.71172 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM648605 2 0.0237 0.943 0.000 0.996 0.004
#> GSM648618 1 0.0000 0.966 1.000 0.000 0.000
#> GSM648620 1 0.5216 0.639 0.740 0.260 0.000
#> GSM648646 2 0.0237 0.943 0.000 0.996 0.004
#> GSM648649 1 0.0000 0.966 1.000 0.000 0.000
#> GSM648675 1 0.2066 0.917 0.940 0.060 0.000
#> GSM648682 2 0.2625 0.880 0.084 0.916 0.000
#> GSM648698 2 0.0237 0.943 0.000 0.996 0.004
#> GSM648708 1 0.0237 0.964 0.996 0.004 0.000
#> GSM648628 3 0.0892 0.948 0.020 0.000 0.980
#> GSM648595 1 0.0424 0.961 0.992 0.008 0.000
#> GSM648635 1 0.0000 0.966 1.000 0.000 0.000
#> GSM648645 1 0.0000 0.966 1.000 0.000 0.000
#> GSM648647 2 0.0592 0.939 0.012 0.988 0.000
#> GSM648667 1 0.0592 0.958 0.988 0.012 0.000
#> GSM648695 1 0.3816 0.815 0.852 0.148 0.000
#> GSM648704 2 0.0237 0.943 0.000 0.996 0.004
#> GSM648706 2 0.0237 0.943 0.000 0.996 0.004
#> GSM648593 1 0.0000 0.966 1.000 0.000 0.000
#> GSM648594 1 0.0000 0.966 1.000 0.000 0.000
#> GSM648600 1 0.0000 0.966 1.000 0.000 0.000
#> GSM648621 1 0.0000 0.966 1.000 0.000 0.000
#> GSM648622 1 0.0000 0.966 1.000 0.000 0.000
#> GSM648623 1 0.0000 0.966 1.000 0.000 0.000
#> GSM648636 1 0.0000 0.966 1.000 0.000 0.000
#> GSM648655 1 0.0000 0.966 1.000 0.000 0.000
#> GSM648661 1 0.0000 0.966 1.000 0.000 0.000
#> GSM648664 1 0.0000 0.966 1.000 0.000 0.000
#> GSM648683 1 0.0000 0.966 1.000 0.000 0.000
#> GSM648685 1 0.0000 0.966 1.000 0.000 0.000
#> GSM648702 1 0.0000 0.966 1.000 0.000 0.000
#> GSM648597 1 0.0000 0.966 1.000 0.000 0.000
#> GSM648603 1 0.0000 0.966 1.000 0.000 0.000
#> GSM648606 3 0.0000 0.966 0.000 0.000 1.000
#> GSM648613 3 0.0000 0.966 0.000 0.000 1.000
#> GSM648619 1 0.1411 0.937 0.964 0.000 0.036
#> GSM648654 1 0.6274 0.181 0.544 0.000 0.456
#> GSM648663 3 0.1163 0.938 0.028 0.000 0.972
#> GSM648670 2 0.7236 0.346 0.392 0.576 0.032
#> GSM648707 3 0.0237 0.963 0.004 0.000 0.996
#> GSM648615 2 0.0237 0.943 0.000 0.996 0.004
#> GSM648643 2 0.3192 0.851 0.112 0.888 0.000
#> GSM648650 1 0.1411 0.938 0.964 0.036 0.000
#> GSM648656 2 0.0237 0.943 0.000 0.996 0.004
#> GSM648715 2 0.2066 0.903 0.060 0.940 0.000
#> GSM648598 1 0.0000 0.966 1.000 0.000 0.000
#> GSM648601 1 0.0000 0.966 1.000 0.000 0.000
#> GSM648602 1 0.0000 0.966 1.000 0.000 0.000
#> GSM648604 1 0.0000 0.966 1.000 0.000 0.000
#> GSM648614 1 0.3587 0.871 0.892 0.088 0.020
#> GSM648624 1 0.0000 0.966 1.000 0.000 0.000
#> GSM648625 1 0.0000 0.966 1.000 0.000 0.000
#> GSM648629 1 0.0000 0.966 1.000 0.000 0.000
#> GSM648634 1 0.0000 0.966 1.000 0.000 0.000
#> GSM648648 1 0.0000 0.966 1.000 0.000 0.000
#> GSM648651 1 0.0000 0.966 1.000 0.000 0.000
#> GSM648657 1 0.0000 0.966 1.000 0.000 0.000
#> GSM648660 1 0.0000 0.966 1.000 0.000 0.000
#> GSM648697 1 0.0000 0.966 1.000 0.000 0.000
#> GSM648710 1 0.0000 0.966 1.000 0.000 0.000
#> GSM648591 1 0.3879 0.816 0.848 0.000 0.152
#> GSM648592 1 0.0237 0.964 0.996 0.004 0.000
#> GSM648607 1 0.0000 0.966 1.000 0.000 0.000
#> GSM648611 3 0.0237 0.963 0.004 0.000 0.996
#> GSM648612 3 0.0592 0.957 0.012 0.000 0.988
#> GSM648616 3 0.0237 0.963 0.004 0.000 0.996
#> GSM648617 1 0.0000 0.966 1.000 0.000 0.000
#> GSM648626 1 0.0000 0.966 1.000 0.000 0.000
#> GSM648711 1 0.0000 0.966 1.000 0.000 0.000
#> GSM648712 1 0.5988 0.437 0.632 0.000 0.368
#> GSM648713 1 0.4178 0.789 0.828 0.000 0.172
#> GSM648714 3 0.4974 0.692 0.000 0.236 0.764
#> GSM648716 1 0.6079 0.383 0.612 0.000 0.388
#> GSM648717 3 0.0000 0.966 0.000 0.000 1.000
#> GSM648590 1 0.0237 0.964 0.996 0.004 0.000
#> GSM648596 2 0.0829 0.939 0.012 0.984 0.004
#> GSM648642 2 0.1753 0.913 0.048 0.952 0.000
#> GSM648696 1 0.0000 0.966 1.000 0.000 0.000
#> GSM648705 1 0.0000 0.966 1.000 0.000 0.000
#> GSM648718 2 0.4399 0.758 0.188 0.812 0.000
#> GSM648599 1 0.0000 0.966 1.000 0.000 0.000
#> GSM648608 1 0.0000 0.966 1.000 0.000 0.000
#> GSM648609 1 0.0000 0.966 1.000 0.000 0.000
#> GSM648610 1 0.0000 0.966 1.000 0.000 0.000
#> GSM648633 1 0.0000 0.966 1.000 0.000 0.000
#> GSM648644 2 0.0237 0.943 0.000 0.996 0.004
#> GSM648652 1 0.0000 0.966 1.000 0.000 0.000
#> GSM648653 1 0.0000 0.966 1.000 0.000 0.000
#> GSM648658 1 0.0000 0.966 1.000 0.000 0.000
#> GSM648659 2 0.0000 0.943 0.000 1.000 0.000
#> GSM648662 1 0.0000 0.966 1.000 0.000 0.000
#> GSM648665 1 0.0237 0.963 0.996 0.000 0.004
#> GSM648666 1 0.0000 0.966 1.000 0.000 0.000
#> GSM648680 1 0.0000 0.966 1.000 0.000 0.000
#> GSM648684 1 0.0000 0.966 1.000 0.000 0.000
#> GSM648709 2 0.0475 0.942 0.004 0.992 0.004
#> GSM648719 1 0.0000 0.966 1.000 0.000 0.000
#> GSM648627 3 0.6180 0.246 0.416 0.000 0.584
#> GSM648637 2 0.4452 0.769 0.000 0.808 0.192
#> GSM648638 3 0.0000 0.966 0.000 0.000 1.000
#> GSM648641 3 0.0000 0.966 0.000 0.000 1.000
#> GSM648672 2 0.0000 0.943 0.000 1.000 0.000
#> GSM648674 2 0.0424 0.941 0.000 0.992 0.008
#> GSM648703 2 0.0000 0.943 0.000 1.000 0.000
#> GSM648631 3 0.0000 0.966 0.000 0.000 1.000
#> GSM648669 2 0.4452 0.766 0.000 0.808 0.192
#> GSM648671 2 0.1031 0.932 0.000 0.976 0.024
#> GSM648678 2 0.0237 0.943 0.000 0.996 0.004
#> GSM648679 2 0.0592 0.939 0.000 0.988 0.012
#> GSM648681 2 0.0000 0.943 0.000 1.000 0.000
#> GSM648686 3 0.0000 0.966 0.000 0.000 1.000
#> GSM648689 3 0.0000 0.966 0.000 0.000 1.000
#> GSM648690 3 0.0000 0.966 0.000 0.000 1.000
#> GSM648691 3 0.0000 0.966 0.000 0.000 1.000
#> GSM648693 3 0.0000 0.966 0.000 0.000 1.000
#> GSM648700 2 0.3879 0.802 0.152 0.848 0.000
#> GSM648630 3 0.0000 0.966 0.000 0.000 1.000
#> GSM648632 3 0.0000 0.966 0.000 0.000 1.000
#> GSM648639 3 0.0000 0.966 0.000 0.000 1.000
#> GSM648640 3 0.0000 0.966 0.000 0.000 1.000
#> GSM648668 2 0.0237 0.942 0.000 0.996 0.004
#> GSM648676 2 0.0592 0.939 0.012 0.988 0.000
#> GSM648692 3 0.0000 0.966 0.000 0.000 1.000
#> GSM648694 3 0.0000 0.966 0.000 0.000 1.000
#> GSM648699 2 0.0000 0.943 0.000 1.000 0.000
#> GSM648701 2 0.0000 0.943 0.000 1.000 0.000
#> GSM648673 2 0.0237 0.942 0.000 0.996 0.004
#> GSM648677 2 0.0000 0.943 0.000 1.000 0.000
#> GSM648687 3 0.0000 0.966 0.000 0.000 1.000
#> GSM648688 3 0.0000 0.966 0.000 0.000 1.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM648605 2 0.0336 0.811 0.000 0.992 0.008 0.000
#> GSM648618 1 0.1792 0.907 0.932 0.000 0.000 0.068
#> GSM648620 2 0.4008 0.595 0.244 0.756 0.000 0.000
#> GSM648646 2 0.0000 0.814 0.000 1.000 0.000 0.000
#> GSM648649 1 0.0336 0.945 0.992 0.000 0.000 0.008
#> GSM648675 4 0.4095 0.650 0.192 0.016 0.000 0.792
#> GSM648682 2 0.1211 0.802 0.040 0.960 0.000 0.000
#> GSM648698 2 0.0000 0.814 0.000 1.000 0.000 0.000
#> GSM648708 1 0.5000 -0.123 0.504 0.496 0.000 0.000
#> GSM648628 3 0.1109 0.868 0.028 0.000 0.968 0.004
#> GSM648595 4 0.4103 0.578 0.256 0.000 0.000 0.744
#> GSM648635 1 0.0188 0.946 0.996 0.000 0.000 0.004
#> GSM648645 1 0.0336 0.945 0.992 0.000 0.000 0.008
#> GSM648647 2 0.0000 0.814 0.000 1.000 0.000 0.000
#> GSM648667 1 0.2216 0.870 0.908 0.092 0.000 0.000
#> GSM648695 2 0.4985 0.182 0.468 0.532 0.000 0.000
#> GSM648704 2 0.0000 0.814 0.000 1.000 0.000 0.000
#> GSM648706 2 0.0000 0.814 0.000 1.000 0.000 0.000
#> GSM648593 1 0.0000 0.946 1.000 0.000 0.000 0.000
#> GSM648594 1 0.3266 0.812 0.832 0.000 0.000 0.168
#> GSM648600 1 0.0336 0.945 0.992 0.000 0.000 0.008
#> GSM648621 1 0.0336 0.945 0.992 0.000 0.000 0.008
#> GSM648622 1 0.0188 0.946 0.996 0.000 0.000 0.004
#> GSM648623 1 0.3400 0.801 0.820 0.000 0.000 0.180
#> GSM648636 1 0.0000 0.946 1.000 0.000 0.000 0.000
#> GSM648655 1 0.0000 0.946 1.000 0.000 0.000 0.000
#> GSM648661 1 0.1022 0.925 0.968 0.000 0.032 0.000
#> GSM648664 1 0.0000 0.946 1.000 0.000 0.000 0.000
#> GSM648683 1 0.0000 0.946 1.000 0.000 0.000 0.000
#> GSM648685 1 0.0000 0.946 1.000 0.000 0.000 0.000
#> GSM648702 1 0.0000 0.946 1.000 0.000 0.000 0.000
#> GSM648597 1 0.4989 0.250 0.528 0.000 0.000 0.472
#> GSM648603 1 0.2704 0.858 0.876 0.000 0.000 0.124
#> GSM648606 3 0.0188 0.889 0.000 0.004 0.996 0.000
#> GSM648613 3 0.0000 0.892 0.000 0.000 1.000 0.000
#> GSM648619 1 0.1305 0.921 0.960 0.000 0.036 0.004
#> GSM648654 1 0.4916 0.274 0.576 0.000 0.424 0.000
#> GSM648663 3 0.0524 0.885 0.008 0.004 0.988 0.000
#> GSM648670 4 0.0000 0.771 0.000 0.000 0.000 1.000
#> GSM648707 3 0.4522 0.588 0.000 0.000 0.680 0.320
#> GSM648615 2 0.0188 0.813 0.000 0.996 0.000 0.004
#> GSM648643 2 0.2921 0.712 0.140 0.860 0.000 0.000
#> GSM648650 1 0.1557 0.905 0.944 0.056 0.000 0.000
#> GSM648656 2 0.0000 0.814 0.000 1.000 0.000 0.000
#> GSM648715 2 0.1389 0.798 0.048 0.952 0.000 0.000
#> GSM648598 1 0.0188 0.946 0.996 0.000 0.000 0.004
#> GSM648601 1 0.0188 0.946 0.996 0.000 0.000 0.004
#> GSM648602 1 0.0188 0.946 0.996 0.000 0.000 0.004
#> GSM648604 1 0.0000 0.946 1.000 0.000 0.000 0.000
#> GSM648614 2 0.5950 0.574 0.156 0.696 0.148 0.000
#> GSM648624 1 0.0188 0.946 0.996 0.000 0.000 0.004
#> GSM648625 1 0.0188 0.946 0.996 0.000 0.000 0.004
#> GSM648629 1 0.0000 0.946 1.000 0.000 0.000 0.000
#> GSM648634 1 0.0188 0.946 0.996 0.000 0.000 0.004
#> GSM648648 1 0.0000 0.946 1.000 0.000 0.000 0.000
#> GSM648651 1 0.0188 0.946 0.996 0.000 0.000 0.004
#> GSM648657 1 0.1792 0.906 0.932 0.000 0.000 0.068
#> GSM648660 1 0.0336 0.945 0.992 0.000 0.000 0.008
#> GSM648697 1 0.0000 0.946 1.000 0.000 0.000 0.000
#> GSM648710 1 0.0000 0.946 1.000 0.000 0.000 0.000
#> GSM648591 4 0.4919 0.539 0.200 0.000 0.048 0.752
#> GSM648592 1 0.4382 0.641 0.704 0.000 0.000 0.296
#> GSM648607 1 0.0188 0.946 0.996 0.000 0.000 0.004
#> GSM648611 3 0.0000 0.892 0.000 0.000 1.000 0.000
#> GSM648612 3 0.1610 0.867 0.016 0.000 0.952 0.032
#> GSM648616 3 0.4564 0.575 0.000 0.000 0.672 0.328
#> GSM648617 1 0.1474 0.918 0.948 0.000 0.000 0.052
#> GSM648626 1 0.3942 0.731 0.764 0.000 0.000 0.236
#> GSM648711 1 0.0188 0.946 0.996 0.000 0.000 0.004
#> GSM648712 3 0.5816 0.362 0.392 0.000 0.572 0.036
#> GSM648713 1 0.3402 0.789 0.832 0.000 0.164 0.004
#> GSM648714 2 0.4406 0.494 0.000 0.700 0.300 0.000
#> GSM648716 3 0.4955 0.198 0.444 0.000 0.556 0.000
#> GSM648717 3 0.0000 0.892 0.000 0.000 1.000 0.000
#> GSM648590 1 0.0524 0.941 0.988 0.008 0.000 0.004
#> GSM648596 2 0.2334 0.764 0.088 0.908 0.000 0.004
#> GSM648642 2 0.0707 0.812 0.020 0.980 0.000 0.000
#> GSM648696 1 0.0188 0.946 0.996 0.000 0.000 0.004
#> GSM648705 1 0.0000 0.946 1.000 0.000 0.000 0.000
#> GSM648718 2 0.5807 0.441 0.312 0.636 0.000 0.052
#> GSM648599 1 0.0336 0.945 0.992 0.000 0.000 0.008
#> GSM648608 1 0.0000 0.946 1.000 0.000 0.000 0.000
#> GSM648609 1 0.0000 0.946 1.000 0.000 0.000 0.000
#> GSM648610 1 0.0000 0.946 1.000 0.000 0.000 0.000
#> GSM648633 1 0.0336 0.945 0.992 0.000 0.000 0.008
#> GSM648644 2 0.0000 0.814 0.000 1.000 0.000 0.000
#> GSM648652 1 0.0000 0.946 1.000 0.000 0.000 0.000
#> GSM648653 1 0.0000 0.946 1.000 0.000 0.000 0.000
#> GSM648658 1 0.0000 0.946 1.000 0.000 0.000 0.000
#> GSM648659 2 0.6709 0.380 0.212 0.616 0.000 0.172
#> GSM648662 1 0.0000 0.946 1.000 0.000 0.000 0.000
#> GSM648665 1 0.0000 0.946 1.000 0.000 0.000 0.000
#> GSM648666 1 0.0000 0.946 1.000 0.000 0.000 0.000
#> GSM648680 1 0.0000 0.946 1.000 0.000 0.000 0.000
#> GSM648684 1 0.0000 0.946 1.000 0.000 0.000 0.000
#> GSM648709 2 0.0336 0.814 0.008 0.992 0.000 0.000
#> GSM648719 1 0.0188 0.946 0.996 0.000 0.000 0.004
#> GSM648627 3 0.4679 0.435 0.352 0.000 0.648 0.000
#> GSM648637 4 0.2224 0.776 0.000 0.040 0.032 0.928
#> GSM648638 3 0.2011 0.843 0.000 0.000 0.920 0.080
#> GSM648641 3 0.0000 0.892 0.000 0.000 1.000 0.000
#> GSM648672 4 0.4331 0.724 0.000 0.288 0.000 0.712
#> GSM648674 4 0.0707 0.781 0.000 0.020 0.000 0.980
#> GSM648703 4 0.4431 0.711 0.000 0.304 0.000 0.696
#> GSM648631 3 0.0000 0.892 0.000 0.000 1.000 0.000
#> GSM648669 4 0.0779 0.780 0.000 0.016 0.004 0.980
#> GSM648671 4 0.0592 0.779 0.000 0.016 0.000 0.984
#> GSM648678 2 0.0188 0.811 0.000 0.996 0.000 0.004
#> GSM648679 4 0.0336 0.775 0.000 0.008 0.000 0.992
#> GSM648681 4 0.2973 0.786 0.000 0.144 0.000 0.856
#> GSM648686 3 0.0707 0.879 0.000 0.000 0.980 0.020
#> GSM648689 3 0.0000 0.892 0.000 0.000 1.000 0.000
#> GSM648690 3 0.0000 0.892 0.000 0.000 1.000 0.000
#> GSM648691 3 0.0000 0.892 0.000 0.000 1.000 0.000
#> GSM648693 3 0.0000 0.892 0.000 0.000 1.000 0.000
#> GSM648700 4 0.4539 0.732 0.008 0.272 0.000 0.720
#> GSM648630 3 0.0000 0.892 0.000 0.000 1.000 0.000
#> GSM648632 3 0.0000 0.892 0.000 0.000 1.000 0.000
#> GSM648639 3 0.4277 0.639 0.000 0.000 0.720 0.280
#> GSM648640 3 0.0000 0.892 0.000 0.000 1.000 0.000
#> GSM648668 4 0.3219 0.780 0.000 0.164 0.000 0.836
#> GSM648676 4 0.4431 0.711 0.000 0.304 0.000 0.696
#> GSM648692 3 0.0000 0.892 0.000 0.000 1.000 0.000
#> GSM648694 3 0.0000 0.892 0.000 0.000 1.000 0.000
#> GSM648699 4 0.4431 0.711 0.000 0.304 0.000 0.696
#> GSM648701 4 0.4431 0.711 0.000 0.304 0.000 0.696
#> GSM648673 4 0.2216 0.789 0.000 0.092 0.000 0.908
#> GSM648677 4 0.4431 0.711 0.000 0.304 0.000 0.696
#> GSM648687 3 0.0000 0.892 0.000 0.000 1.000 0.000
#> GSM648688 3 0.0000 0.892 0.000 0.000 1.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM648605 2 0.0000 0.8911 0.000 1.000 0.000 0.000 0.000
#> GSM648618 1 0.0404 0.9489 0.988 0.000 0.000 0.000 0.012
#> GSM648620 2 0.2966 0.7305 0.184 0.816 0.000 0.000 0.000
#> GSM648646 2 0.0000 0.8911 0.000 1.000 0.000 0.000 0.000
#> GSM648649 1 0.0404 0.9489 0.988 0.000 0.000 0.000 0.012
#> GSM648675 4 0.0566 0.9472 0.012 0.000 0.000 0.984 0.004
#> GSM648682 2 0.2127 0.8211 0.108 0.892 0.000 0.000 0.000
#> GSM648698 2 0.0000 0.8911 0.000 1.000 0.000 0.000 0.000
#> GSM648708 1 0.3612 0.6205 0.732 0.268 0.000 0.000 0.000
#> GSM648628 3 0.1668 0.8634 0.028 0.000 0.940 0.000 0.032
#> GSM648595 4 0.1041 0.9276 0.032 0.000 0.000 0.964 0.004
#> GSM648635 1 0.0000 0.9531 1.000 0.000 0.000 0.000 0.000
#> GSM648645 1 0.0162 0.9519 0.996 0.000 0.000 0.000 0.004
#> GSM648647 2 0.0000 0.8911 0.000 1.000 0.000 0.000 0.000
#> GSM648667 1 0.1908 0.8793 0.908 0.092 0.000 0.000 0.000
#> GSM648695 2 0.4287 0.1707 0.460 0.540 0.000 0.000 0.000
#> GSM648704 2 0.0000 0.8911 0.000 1.000 0.000 0.000 0.000
#> GSM648706 2 0.0000 0.8911 0.000 1.000 0.000 0.000 0.000
#> GSM648593 1 0.0000 0.9531 1.000 0.000 0.000 0.000 0.000
#> GSM648594 1 0.1893 0.9065 0.928 0.000 0.000 0.048 0.024
#> GSM648600 1 0.0880 0.9368 0.968 0.000 0.000 0.000 0.032
#> GSM648621 1 0.1981 0.9078 0.924 0.000 0.000 0.028 0.048
#> GSM648622 1 0.0000 0.9531 1.000 0.000 0.000 0.000 0.000
#> GSM648623 5 0.1544 0.8301 0.068 0.000 0.000 0.000 0.932
#> GSM648636 1 0.0000 0.9531 1.000 0.000 0.000 0.000 0.000
#> GSM648655 1 0.0794 0.9352 0.972 0.000 0.000 0.028 0.000
#> GSM648661 1 0.2732 0.7965 0.840 0.000 0.160 0.000 0.000
#> GSM648664 1 0.0000 0.9531 1.000 0.000 0.000 0.000 0.000
#> GSM648683 1 0.0000 0.9531 1.000 0.000 0.000 0.000 0.000
#> GSM648685 1 0.0000 0.9531 1.000 0.000 0.000 0.000 0.000
#> GSM648702 1 0.0162 0.9514 0.996 0.000 0.000 0.004 0.000
#> GSM648597 5 0.0693 0.8357 0.008 0.000 0.000 0.012 0.980
#> GSM648603 5 0.3395 0.6573 0.236 0.000 0.000 0.000 0.764
#> GSM648606 3 0.5373 0.5783 0.000 0.236 0.652 0.000 0.112
#> GSM648613 5 0.3621 0.6894 0.000 0.020 0.192 0.000 0.788
#> GSM648619 1 0.4109 0.5673 0.700 0.000 0.012 0.000 0.288
#> GSM648654 3 0.4219 0.2566 0.416 0.000 0.584 0.000 0.000
#> GSM648663 3 0.5233 0.6373 0.004 0.164 0.696 0.000 0.136
#> GSM648670 4 0.1043 0.9444 0.000 0.000 0.000 0.960 0.040
#> GSM648707 5 0.0000 0.8371 0.000 0.000 0.000 0.000 1.000
#> GSM648615 2 0.0000 0.8911 0.000 1.000 0.000 0.000 0.000
#> GSM648643 2 0.2813 0.7535 0.168 0.832 0.000 0.000 0.000
#> GSM648650 1 0.1043 0.9278 0.960 0.040 0.000 0.000 0.000
#> GSM648656 2 0.0000 0.8911 0.000 1.000 0.000 0.000 0.000
#> GSM648715 2 0.2020 0.8286 0.100 0.900 0.000 0.000 0.000
#> GSM648598 1 0.0000 0.9531 1.000 0.000 0.000 0.000 0.000
#> GSM648601 1 0.0000 0.9531 1.000 0.000 0.000 0.000 0.000
#> GSM648602 1 0.0000 0.9531 1.000 0.000 0.000 0.000 0.000
#> GSM648604 1 0.0000 0.9531 1.000 0.000 0.000 0.000 0.000
#> GSM648614 2 0.1195 0.8744 0.028 0.960 0.012 0.000 0.000
#> GSM648624 1 0.0000 0.9531 1.000 0.000 0.000 0.000 0.000
#> GSM648625 1 0.0510 0.9472 0.984 0.000 0.000 0.000 0.016
#> GSM648629 1 0.0000 0.9531 1.000 0.000 0.000 0.000 0.000
#> GSM648634 1 0.0000 0.9531 1.000 0.000 0.000 0.000 0.000
#> GSM648648 1 0.0000 0.9531 1.000 0.000 0.000 0.000 0.000
#> GSM648651 1 0.0162 0.9516 0.996 0.000 0.000 0.000 0.004
#> GSM648657 1 0.1043 0.9323 0.960 0.000 0.000 0.000 0.040
#> GSM648660 1 0.0404 0.9489 0.988 0.000 0.000 0.000 0.012
#> GSM648697 1 0.0000 0.9531 1.000 0.000 0.000 0.000 0.000
#> GSM648710 1 0.0000 0.9531 1.000 0.000 0.000 0.000 0.000
#> GSM648591 5 0.0609 0.8350 0.000 0.000 0.000 0.020 0.980
#> GSM648592 5 0.0703 0.8427 0.024 0.000 0.000 0.000 0.976
#> GSM648607 1 0.0404 0.9489 0.988 0.000 0.000 0.000 0.012
#> GSM648611 3 0.0162 0.8942 0.000 0.000 0.996 0.000 0.004
#> GSM648612 5 0.1792 0.8087 0.000 0.000 0.084 0.000 0.916
#> GSM648616 5 0.0000 0.8371 0.000 0.000 0.000 0.000 1.000
#> GSM648617 5 0.1478 0.8322 0.064 0.000 0.000 0.000 0.936
#> GSM648626 5 0.2732 0.7428 0.160 0.000 0.000 0.000 0.840
#> GSM648711 1 0.0000 0.9531 1.000 0.000 0.000 0.000 0.000
#> GSM648712 5 0.2592 0.8207 0.052 0.000 0.056 0.000 0.892
#> GSM648713 5 0.4306 0.5374 0.328 0.000 0.012 0.000 0.660
#> GSM648714 2 0.0404 0.8829 0.000 0.988 0.012 0.000 0.000
#> GSM648716 1 0.4547 0.6650 0.736 0.000 0.192 0.000 0.072
#> GSM648717 3 0.1197 0.8710 0.000 0.000 0.952 0.000 0.048
#> GSM648590 1 0.1410 0.9124 0.940 0.000 0.000 0.060 0.000
#> GSM648596 2 0.3010 0.7583 0.000 0.824 0.000 0.004 0.172
#> GSM648642 2 0.2605 0.7796 0.148 0.852 0.000 0.000 0.000
#> GSM648696 1 0.0324 0.9509 0.992 0.004 0.000 0.000 0.004
#> GSM648705 1 0.0000 0.9531 1.000 0.000 0.000 0.000 0.000
#> GSM648718 1 0.6289 0.2768 0.536 0.236 0.000 0.228 0.000
#> GSM648599 1 0.1270 0.9214 0.948 0.000 0.000 0.000 0.052
#> GSM648608 1 0.0000 0.9531 1.000 0.000 0.000 0.000 0.000
#> GSM648609 1 0.0000 0.9531 1.000 0.000 0.000 0.000 0.000
#> GSM648610 1 0.0000 0.9531 1.000 0.000 0.000 0.000 0.000
#> GSM648633 1 0.0510 0.9472 0.984 0.000 0.000 0.000 0.016
#> GSM648644 2 0.0000 0.8911 0.000 1.000 0.000 0.000 0.000
#> GSM648652 1 0.0000 0.9531 1.000 0.000 0.000 0.000 0.000
#> GSM648653 1 0.0000 0.9531 1.000 0.000 0.000 0.000 0.000
#> GSM648658 1 0.0000 0.9531 1.000 0.000 0.000 0.000 0.000
#> GSM648659 1 0.4444 0.4233 0.624 0.012 0.000 0.364 0.000
#> GSM648662 1 0.0324 0.9502 0.992 0.000 0.004 0.000 0.004
#> GSM648665 1 0.0000 0.9531 1.000 0.000 0.000 0.000 0.000
#> GSM648666 1 0.0000 0.9531 1.000 0.000 0.000 0.000 0.000
#> GSM648680 1 0.0000 0.9531 1.000 0.000 0.000 0.000 0.000
#> GSM648684 1 0.0000 0.9531 1.000 0.000 0.000 0.000 0.000
#> GSM648709 2 0.0000 0.8911 0.000 1.000 0.000 0.000 0.000
#> GSM648719 1 0.0404 0.9489 0.988 0.000 0.000 0.000 0.012
#> GSM648627 3 0.3395 0.5742 0.236 0.000 0.764 0.000 0.000
#> GSM648637 5 0.4249 0.0427 0.000 0.000 0.000 0.432 0.568
#> GSM648638 5 0.1168 0.8327 0.000 0.008 0.032 0.000 0.960
#> GSM648641 3 0.0794 0.8833 0.000 0.000 0.972 0.000 0.028
#> GSM648672 4 0.2230 0.9307 0.000 0.044 0.000 0.912 0.044
#> GSM648674 4 0.3074 0.8191 0.000 0.000 0.000 0.804 0.196
#> GSM648703 4 0.0000 0.9519 0.000 0.000 0.000 1.000 0.000
#> GSM648631 3 0.0000 0.8950 0.000 0.000 1.000 0.000 0.000
#> GSM648669 4 0.1121 0.9467 0.000 0.000 0.000 0.956 0.044
#> GSM648671 4 0.1043 0.9479 0.000 0.000 0.000 0.960 0.040
#> GSM648678 2 0.0000 0.8911 0.000 1.000 0.000 0.000 0.000
#> GSM648679 4 0.2690 0.8675 0.000 0.000 0.000 0.844 0.156
#> GSM648681 4 0.2719 0.9082 0.000 0.068 0.000 0.884 0.048
#> GSM648686 3 0.0404 0.8902 0.000 0.000 0.988 0.012 0.000
#> GSM648689 3 0.0000 0.8950 0.000 0.000 1.000 0.000 0.000
#> GSM648690 3 0.0000 0.8950 0.000 0.000 1.000 0.000 0.000
#> GSM648691 3 0.0000 0.8950 0.000 0.000 1.000 0.000 0.000
#> GSM648693 3 0.0000 0.8950 0.000 0.000 1.000 0.000 0.000
#> GSM648700 4 0.0162 0.9517 0.000 0.000 0.004 0.996 0.000
#> GSM648630 3 0.0162 0.8942 0.000 0.000 0.996 0.000 0.004
#> GSM648632 3 0.0000 0.8950 0.000 0.000 1.000 0.000 0.000
#> GSM648639 5 0.0609 0.8350 0.000 0.000 0.020 0.000 0.980
#> GSM648640 3 0.3177 0.7010 0.000 0.000 0.792 0.000 0.208
#> GSM648668 4 0.1764 0.9381 0.000 0.008 0.000 0.928 0.064
#> GSM648676 4 0.0000 0.9519 0.000 0.000 0.000 1.000 0.000
#> GSM648692 3 0.0162 0.8942 0.000 0.000 0.996 0.000 0.004
#> GSM648694 3 0.0000 0.8950 0.000 0.000 1.000 0.000 0.000
#> GSM648699 4 0.0162 0.9517 0.000 0.000 0.004 0.996 0.000
#> GSM648701 4 0.0162 0.9517 0.000 0.000 0.004 0.996 0.000
#> GSM648673 4 0.0404 0.9527 0.000 0.000 0.000 0.988 0.012
#> GSM648677 4 0.0566 0.9508 0.000 0.012 0.000 0.984 0.004
#> GSM648687 3 0.0510 0.8872 0.000 0.000 0.984 0.016 0.000
#> GSM648688 3 0.0162 0.8936 0.000 0.000 0.996 0.004 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM648605 2 0.1124 0.82643 0.008 0.956 0.000 0.000 0.000 0.036
#> GSM648618 6 0.5597 0.15171 0.380 0.000 0.000 0.016 0.096 0.508
#> GSM648620 2 0.4474 0.63616 0.188 0.704 0.000 0.000 0.000 0.108
#> GSM648646 2 0.0000 0.82951 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM648649 1 0.2655 0.81637 0.848 0.000 0.000 0.008 0.004 0.140
#> GSM648675 6 0.2176 0.36039 0.024 0.000 0.000 0.080 0.000 0.896
#> GSM648682 2 0.3394 0.73987 0.052 0.804 0.000 0.000 0.000 0.144
#> GSM648698 2 0.1686 0.81496 0.012 0.924 0.000 0.000 0.000 0.064
#> GSM648708 1 0.3985 0.73777 0.760 0.100 0.000 0.000 0.000 0.140
#> GSM648628 6 0.5879 0.11304 0.004 0.000 0.228 0.000 0.260 0.508
#> GSM648595 6 0.4907 0.23593 0.100 0.000 0.000 0.248 0.004 0.648
#> GSM648635 1 0.2219 0.82293 0.864 0.000 0.000 0.000 0.000 0.136
#> GSM648645 1 0.2260 0.82060 0.860 0.000 0.000 0.000 0.000 0.140
#> GSM648647 2 0.0790 0.82678 0.032 0.968 0.000 0.000 0.000 0.000
#> GSM648667 1 0.2066 0.83966 0.904 0.072 0.000 0.000 0.000 0.024
#> GSM648695 2 0.3869 -0.01829 0.500 0.500 0.000 0.000 0.000 0.000
#> GSM648704 2 0.0000 0.82951 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM648706 2 0.0000 0.82951 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM648593 1 0.1858 0.83731 0.904 0.000 0.000 0.000 0.004 0.092
#> GSM648594 1 0.4702 0.70933 0.736 0.000 0.000 0.140 0.048 0.076
#> GSM648600 1 0.5393 0.36332 0.576 0.000 0.000 0.000 0.256 0.168
#> GSM648621 6 0.5835 0.09792 0.232 0.000 0.000 0.000 0.280 0.488
#> GSM648622 1 0.0520 0.85990 0.984 0.000 0.000 0.000 0.008 0.008
#> GSM648623 5 0.3679 0.53184 0.260 0.000 0.000 0.012 0.724 0.004
#> GSM648636 6 0.3854 -0.12451 0.464 0.000 0.000 0.000 0.000 0.536
#> GSM648655 6 0.4130 0.36161 0.300 0.000 0.000 0.024 0.004 0.672
#> GSM648661 1 0.3125 0.76730 0.828 0.000 0.136 0.000 0.004 0.032
#> GSM648664 1 0.0790 0.86014 0.968 0.000 0.000 0.000 0.000 0.032
#> GSM648683 1 0.1908 0.83245 0.900 0.000 0.000 0.000 0.004 0.096
#> GSM648685 1 0.0713 0.86131 0.972 0.000 0.000 0.000 0.000 0.028
#> GSM648702 1 0.1926 0.85411 0.912 0.000 0.000 0.020 0.000 0.068
#> GSM648597 5 0.2941 0.50908 0.000 0.000 0.000 0.220 0.780 0.000
#> GSM648603 5 0.4331 0.52795 0.192 0.000 0.000 0.008 0.728 0.072
#> GSM648606 6 0.7509 -0.07040 0.016 0.116 0.164 0.000 0.324 0.380
#> GSM648613 5 0.3207 0.65739 0.000 0.004 0.124 0.000 0.828 0.044
#> GSM648619 5 0.5621 0.33096 0.380 0.000 0.052 0.000 0.520 0.048
#> GSM648654 1 0.5698 0.17886 0.452 0.000 0.424 0.012 0.000 0.112
#> GSM648663 5 0.7481 0.36712 0.064 0.276 0.140 0.000 0.456 0.064
#> GSM648670 6 0.4066 0.20436 0.000 0.000 0.000 0.272 0.036 0.692
#> GSM648707 5 0.0937 0.68714 0.000 0.000 0.000 0.040 0.960 0.000
#> GSM648615 2 0.1471 0.81718 0.004 0.932 0.000 0.000 0.000 0.064
#> GSM648643 2 0.3202 0.70178 0.176 0.800 0.000 0.000 0.000 0.024
#> GSM648650 1 0.3236 0.79736 0.820 0.036 0.000 0.004 0.000 0.140
#> GSM648656 2 0.0000 0.82951 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM648715 2 0.3198 0.59437 0.260 0.740 0.000 0.000 0.000 0.000
#> GSM648598 1 0.0935 0.85568 0.964 0.000 0.000 0.000 0.004 0.032
#> GSM648601 1 0.1531 0.85367 0.928 0.000 0.000 0.000 0.004 0.068
#> GSM648602 1 0.1958 0.84624 0.896 0.000 0.000 0.000 0.004 0.100
#> GSM648604 1 0.1204 0.85479 0.944 0.000 0.000 0.000 0.000 0.056
#> GSM648614 2 0.3527 0.73508 0.040 0.840 0.072 0.000 0.008 0.040
#> GSM648624 1 0.0777 0.85718 0.972 0.000 0.000 0.000 0.004 0.024
#> GSM648625 1 0.1720 0.84527 0.928 0.000 0.000 0.000 0.032 0.040
#> GSM648629 1 0.1765 0.84072 0.904 0.000 0.000 0.000 0.000 0.096
#> GSM648634 1 0.1610 0.85593 0.916 0.000 0.000 0.000 0.000 0.084
#> GSM648648 1 0.1327 0.85297 0.936 0.000 0.000 0.000 0.000 0.064
#> GSM648651 1 0.1434 0.84906 0.940 0.000 0.000 0.000 0.012 0.048
#> GSM648657 1 0.3329 0.75904 0.796 0.000 0.000 0.012 0.180 0.012
#> GSM648660 1 0.0717 0.86090 0.976 0.000 0.000 0.000 0.008 0.016
#> GSM648697 1 0.1007 0.85811 0.956 0.000 0.000 0.000 0.000 0.044
#> GSM648710 1 0.0713 0.85969 0.972 0.000 0.000 0.000 0.000 0.028
#> GSM648591 5 0.3879 0.42091 0.000 0.000 0.000 0.020 0.688 0.292
#> GSM648592 5 0.0937 0.68743 0.000 0.000 0.000 0.040 0.960 0.000
#> GSM648607 1 0.3505 0.80707 0.812 0.000 0.008 0.000 0.056 0.124
#> GSM648611 6 0.5223 -0.00148 0.000 0.000 0.436 0.000 0.092 0.472
#> GSM648612 5 0.3406 0.68013 0.036 0.000 0.068 0.000 0.840 0.056
#> GSM648616 5 0.1007 0.68621 0.000 0.000 0.000 0.044 0.956 0.000
#> GSM648617 5 0.3032 0.67952 0.104 0.000 0.000 0.000 0.840 0.056
#> GSM648626 5 0.1633 0.69594 0.044 0.000 0.000 0.024 0.932 0.000
#> GSM648711 1 0.1245 0.85401 0.952 0.000 0.000 0.000 0.016 0.032
#> GSM648712 5 0.3841 0.67333 0.052 0.000 0.064 0.000 0.812 0.072
#> GSM648713 5 0.4332 0.64550 0.144 0.000 0.060 0.000 0.760 0.036
#> GSM648714 2 0.1434 0.79895 0.000 0.940 0.048 0.000 0.012 0.000
#> GSM648716 5 0.6285 0.46822 0.176 0.000 0.216 0.000 0.552 0.056
#> GSM648717 3 0.4998 0.20384 0.008 0.000 0.552 0.000 0.384 0.056
#> GSM648590 6 0.4303 0.35027 0.132 0.004 0.000 0.124 0.000 0.740
#> GSM648596 2 0.2595 0.72956 0.004 0.836 0.000 0.000 0.160 0.000
#> GSM648642 2 0.4890 0.58183 0.204 0.656 0.000 0.000 0.000 0.140
#> GSM648696 1 0.3014 0.79650 0.804 0.012 0.000 0.000 0.000 0.184
#> GSM648705 1 0.2260 0.82060 0.860 0.000 0.000 0.000 0.000 0.140
#> GSM648718 1 0.6005 0.41388 0.560 0.256 0.000 0.036 0.000 0.148
#> GSM648599 5 0.5973 0.18513 0.360 0.000 0.000 0.000 0.412 0.228
#> GSM648608 1 0.1588 0.84425 0.924 0.000 0.000 0.000 0.004 0.072
#> GSM648609 1 0.0935 0.85494 0.964 0.000 0.000 0.000 0.004 0.032
#> GSM648610 1 0.4116 0.25374 0.572 0.000 0.000 0.000 0.012 0.416
#> GSM648633 1 0.1003 0.85771 0.964 0.000 0.000 0.000 0.016 0.020
#> GSM648644 2 0.0000 0.82951 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM648652 1 0.2135 0.82711 0.872 0.000 0.000 0.000 0.000 0.128
#> GSM648653 1 0.1152 0.85967 0.952 0.000 0.000 0.000 0.004 0.044
#> GSM648658 1 0.4015 0.36365 0.616 0.000 0.000 0.012 0.000 0.372
#> GSM648659 6 0.3617 0.32770 0.044 0.012 0.000 0.144 0.000 0.800
#> GSM648662 1 0.2872 0.81407 0.868 0.004 0.016 0.000 0.024 0.088
#> GSM648665 1 0.1226 0.85206 0.952 0.000 0.004 0.000 0.004 0.040
#> GSM648666 1 0.1501 0.86069 0.924 0.000 0.000 0.000 0.000 0.076
#> GSM648680 1 0.1910 0.83751 0.892 0.000 0.000 0.000 0.000 0.108
#> GSM648684 1 0.2442 0.79256 0.852 0.000 0.000 0.000 0.004 0.144
#> GSM648709 2 0.0000 0.82951 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM648719 1 0.0622 0.86107 0.980 0.000 0.000 0.000 0.008 0.012
#> GSM648627 6 0.5682 0.01557 0.048 0.000 0.448 0.000 0.052 0.452
#> GSM648637 4 0.4358 0.44925 0.000 0.016 0.000 0.624 0.348 0.012
#> GSM648638 5 0.2613 0.68057 0.000 0.032 0.068 0.016 0.884 0.000
#> GSM648641 3 0.4099 0.34109 0.000 0.000 0.612 0.000 0.372 0.016
#> GSM648672 4 0.2982 0.74387 0.000 0.060 0.000 0.860 0.012 0.068
#> GSM648674 4 0.4062 0.71053 0.000 0.000 0.000 0.744 0.080 0.176
#> GSM648703 6 0.3938 0.05324 0.000 0.016 0.000 0.324 0.000 0.660
#> GSM648631 3 0.0000 0.87984 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648669 4 0.0909 0.74195 0.000 0.000 0.020 0.968 0.012 0.000
#> GSM648671 4 0.0820 0.74314 0.000 0.000 0.016 0.972 0.012 0.000
#> GSM648678 2 0.0146 0.82802 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM648679 4 0.2572 0.70494 0.000 0.000 0.000 0.852 0.136 0.012
#> GSM648681 4 0.2776 0.74478 0.004 0.040 0.000 0.884 0.044 0.028
#> GSM648686 3 0.1267 0.85422 0.000 0.000 0.940 0.060 0.000 0.000
#> GSM648689 3 0.0363 0.87332 0.000 0.000 0.988 0.000 0.012 0.000
#> GSM648690 3 0.0000 0.87984 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648691 3 0.1501 0.84594 0.000 0.000 0.924 0.076 0.000 0.000
#> GSM648693 3 0.0000 0.87984 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648700 6 0.3198 0.20518 0.000 0.000 0.000 0.260 0.000 0.740
#> GSM648630 3 0.0000 0.87984 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648632 3 0.0458 0.87567 0.000 0.000 0.984 0.016 0.000 0.000
#> GSM648639 5 0.1411 0.67791 0.000 0.000 0.004 0.060 0.936 0.000
#> GSM648640 3 0.2697 0.71723 0.000 0.000 0.812 0.000 0.188 0.000
#> GSM648668 4 0.3018 0.74632 0.000 0.024 0.000 0.848 0.016 0.112
#> GSM648676 4 0.3854 0.36132 0.000 0.000 0.000 0.536 0.000 0.464
#> GSM648692 3 0.0146 0.87804 0.000 0.000 0.996 0.000 0.004 0.000
#> GSM648694 3 0.0000 0.87984 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648699 6 0.3868 -0.35307 0.000 0.000 0.000 0.496 0.000 0.504
#> GSM648701 4 0.3986 0.35077 0.000 0.004 0.000 0.532 0.000 0.464
#> GSM648673 4 0.0993 0.74168 0.000 0.000 0.024 0.964 0.000 0.012
#> GSM648677 4 0.3984 0.55332 0.000 0.016 0.000 0.648 0.000 0.336
#> GSM648687 3 0.1814 0.82642 0.000 0.000 0.900 0.100 0.000 0.000
#> GSM648688 3 0.1556 0.84285 0.000 0.000 0.920 0.080 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) development.stage(p) other(p) k
#> SD:NMF 79 3.48e-10 0.2797 1.42e-12 2
#> SD:NMF 125 1.86e-11 0.1322 1.14e-17 3
#> SD:NMF 120 5.28e-15 0.0475 4.25e-24 4
#> SD:NMF 125 2.14e-15 0.0759 7.19e-30 5
#> SD:NMF 98 2.48e-17 0.0753 1.70e-30 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 51941 rows and 130 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 0.409 0.660 0.826 0.3345 0.706 0.706
#> 3 3 0.480 0.739 0.838 0.4332 0.695 0.584
#> 4 4 0.599 0.804 0.900 0.2271 0.938 0.866
#> 5 5 0.653 0.792 0.891 0.0421 0.979 0.948
#> 6 6 0.691 0.780 0.883 0.0331 0.997 0.992
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
#> GSM648605 2 0.8661 0.59941 0.288 0.712
#> GSM648618 2 0.7602 0.61881 0.220 0.780
#> GSM648620 2 0.8661 0.59941 0.288 0.712
#> GSM648646 2 0.9491 0.49993 0.368 0.632
#> GSM648649 2 0.0672 0.78911 0.008 0.992
#> GSM648675 2 0.7602 0.61881 0.220 0.780
#> GSM648682 2 0.7815 0.64627 0.232 0.768
#> GSM648698 2 0.8661 0.59941 0.288 0.712
#> GSM648708 2 0.8661 0.59941 0.288 0.712
#> GSM648628 2 0.2948 0.74801 0.052 0.948
#> GSM648595 2 0.4022 0.75525 0.080 0.920
#> GSM648635 2 0.0376 0.78963 0.004 0.996
#> GSM648645 2 0.0000 0.78964 0.000 1.000
#> GSM648647 2 0.8661 0.59941 0.288 0.712
#> GSM648667 2 0.8661 0.59941 0.288 0.712
#> GSM648695 2 0.8661 0.59941 0.288 0.712
#> GSM648704 2 0.9491 0.49993 0.368 0.632
#> GSM648706 2 0.8661 0.59941 0.288 0.712
#> GSM648593 2 0.1184 0.78718 0.016 0.984
#> GSM648594 2 0.7674 0.57604 0.224 0.776
#> GSM648600 2 0.0000 0.78964 0.000 1.000
#> GSM648621 2 0.0672 0.78913 0.008 0.992
#> GSM648622 2 0.0000 0.78964 0.000 1.000
#> GSM648623 2 0.1633 0.77438 0.024 0.976
#> GSM648636 2 0.0000 0.78964 0.000 1.000
#> GSM648655 2 0.1184 0.78718 0.016 0.984
#> GSM648661 2 0.0000 0.78964 0.000 1.000
#> GSM648664 2 0.0000 0.78964 0.000 1.000
#> GSM648683 2 0.0000 0.78964 0.000 1.000
#> GSM648685 2 0.0000 0.78964 0.000 1.000
#> GSM648702 2 0.0000 0.78964 0.000 1.000
#> GSM648597 2 0.7674 0.57604 0.224 0.776
#> GSM648603 2 0.3274 0.73510 0.060 0.940
#> GSM648606 2 0.2948 0.75197 0.052 0.948
#> GSM648613 2 0.2948 0.75197 0.052 0.948
#> GSM648619 2 0.2423 0.76014 0.040 0.960
#> GSM648654 2 0.1414 0.78570 0.020 0.980
#> GSM648663 2 0.2423 0.76378 0.040 0.960
#> GSM648670 2 0.8713 0.48386 0.292 0.708
#> GSM648707 2 0.9996 -0.34119 0.488 0.512
#> GSM648615 2 0.8661 0.59941 0.288 0.712
#> GSM648643 2 0.9358 0.52149 0.352 0.648
#> GSM648650 2 0.4939 0.73243 0.108 0.892
#> GSM648656 2 0.9358 0.52149 0.352 0.648
#> GSM648715 2 0.8661 0.59941 0.288 0.712
#> GSM648598 2 0.0000 0.78964 0.000 1.000
#> GSM648601 2 0.0000 0.78964 0.000 1.000
#> GSM648602 2 0.0000 0.78964 0.000 1.000
#> GSM648604 2 0.0000 0.78964 0.000 1.000
#> GSM648614 2 0.1843 0.77592 0.028 0.972
#> GSM648624 2 0.0000 0.78964 0.000 1.000
#> GSM648625 2 0.0376 0.78989 0.004 0.996
#> GSM648629 2 0.0000 0.78964 0.000 1.000
#> GSM648634 2 0.0000 0.78964 0.000 1.000
#> GSM648648 2 0.0376 0.78963 0.004 0.996
#> GSM648651 2 0.0000 0.78964 0.000 1.000
#> GSM648657 2 0.0000 0.78964 0.000 1.000
#> GSM648660 2 0.0000 0.78964 0.000 1.000
#> GSM648697 2 0.0000 0.78964 0.000 1.000
#> GSM648710 2 0.0000 0.78964 0.000 1.000
#> GSM648591 2 0.9522 0.16185 0.372 0.628
#> GSM648592 2 0.7602 0.55189 0.220 0.780
#> GSM648607 2 0.2423 0.76014 0.040 0.960
#> GSM648611 2 0.3274 0.73855 0.060 0.940
#> GSM648612 2 0.2423 0.76014 0.040 0.960
#> GSM648616 1 0.9866 0.37211 0.568 0.432
#> GSM648617 2 0.0000 0.78964 0.000 1.000
#> GSM648626 2 0.3274 0.73510 0.060 0.940
#> GSM648711 2 0.2423 0.76014 0.040 0.960
#> GSM648712 2 0.2423 0.76014 0.040 0.960
#> GSM648713 2 0.2423 0.76014 0.040 0.960
#> GSM648714 2 0.1843 0.77592 0.028 0.972
#> GSM648716 2 0.2423 0.76014 0.040 0.960
#> GSM648717 2 0.3733 0.72173 0.072 0.928
#> GSM648590 2 0.3879 0.75728 0.076 0.924
#> GSM648596 2 0.8661 0.59941 0.288 0.712
#> GSM648642 2 0.8661 0.59941 0.288 0.712
#> GSM648696 2 0.0376 0.78987 0.004 0.996
#> GSM648705 2 0.0376 0.78963 0.004 0.996
#> GSM648718 2 0.8555 0.60632 0.280 0.720
#> GSM648599 2 0.0000 0.78964 0.000 1.000
#> GSM648608 2 0.0000 0.78964 0.000 1.000
#> GSM648609 2 0.0000 0.78964 0.000 1.000
#> GSM648610 2 0.0000 0.78964 0.000 1.000
#> GSM648633 2 0.0000 0.78964 0.000 1.000
#> GSM648644 2 0.9491 0.49993 0.368 0.632
#> GSM648652 2 0.0376 0.78963 0.004 0.996
#> GSM648653 2 0.0000 0.78964 0.000 1.000
#> GSM648658 2 0.1184 0.78718 0.016 0.984
#> GSM648659 2 0.7376 0.66216 0.208 0.792
#> GSM648662 2 0.0376 0.78917 0.004 0.996
#> GSM648665 2 0.0376 0.78917 0.004 0.996
#> GSM648666 2 0.0000 0.78964 0.000 1.000
#> GSM648680 2 0.0376 0.78963 0.004 0.996
#> GSM648684 2 0.0000 0.78964 0.000 1.000
#> GSM648709 2 0.8661 0.59941 0.288 0.712
#> GSM648719 2 0.0000 0.78964 0.000 1.000
#> GSM648627 2 0.2948 0.74801 0.052 0.948
#> GSM648637 1 0.9998 0.00361 0.508 0.492
#> GSM648638 1 0.9998 0.00361 0.508 0.492
#> GSM648641 1 0.9977 0.65217 0.528 0.472
#> GSM648672 2 0.9732 0.44275 0.404 0.596
#> GSM648674 1 0.9998 -0.00945 0.508 0.492
#> GSM648703 2 0.9833 0.40529 0.424 0.576
#> GSM648631 1 0.9608 0.75156 0.616 0.384
#> GSM648669 1 0.3431 0.53116 0.936 0.064
#> GSM648671 1 0.3431 0.53116 0.936 0.064
#> GSM648678 2 0.9608 0.47613 0.384 0.616
#> GSM648679 1 0.9129 0.40523 0.672 0.328
#> GSM648681 2 0.9909 0.18369 0.444 0.556
#> GSM648686 1 0.9608 0.75156 0.616 0.384
#> GSM648689 1 0.9896 0.69693 0.560 0.440
#> GSM648690 1 0.9608 0.75156 0.616 0.384
#> GSM648691 1 0.9608 0.75156 0.616 0.384
#> GSM648693 1 0.9608 0.75156 0.616 0.384
#> GSM648700 2 0.9833 0.40529 0.424 0.576
#> GSM648630 1 0.9608 0.75156 0.616 0.384
#> GSM648632 1 0.9608 0.75156 0.616 0.384
#> GSM648639 1 0.9833 0.72098 0.576 0.424
#> GSM648640 1 0.9833 0.72098 0.576 0.424
#> GSM648668 2 0.9732 0.44275 0.404 0.596
#> GSM648676 2 0.9833 0.40529 0.424 0.576
#> GSM648692 1 0.9608 0.75156 0.616 0.384
#> GSM648694 1 0.9608 0.75156 0.616 0.384
#> GSM648699 2 0.9833 0.40529 0.424 0.576
#> GSM648701 2 0.9833 0.40529 0.424 0.576
#> GSM648673 1 0.3431 0.53116 0.936 0.064
#> GSM648677 2 0.9686 0.45614 0.396 0.604
#> GSM648687 1 0.9635 0.74461 0.612 0.388
#> GSM648688 1 0.9635 0.74461 0.612 0.388
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM648605 2 0.6267 0.713 0.452 0.548 0.000
#> GSM648618 1 0.6302 0.547 0.744 0.208 0.048
#> GSM648620 2 0.6267 0.713 0.452 0.548 0.000
#> GSM648646 2 0.5968 0.755 0.364 0.636 0.000
#> GSM648649 1 0.0747 0.876 0.984 0.016 0.000
#> GSM648675 1 0.6302 0.547 0.744 0.208 0.048
#> GSM648682 1 0.6260 -0.481 0.552 0.448 0.000
#> GSM648698 2 0.6267 0.713 0.452 0.548 0.000
#> GSM648708 2 0.6267 0.713 0.452 0.548 0.000
#> GSM648628 1 0.2280 0.848 0.940 0.008 0.052
#> GSM648595 1 0.3038 0.771 0.896 0.104 0.000
#> GSM648635 1 0.0237 0.883 0.996 0.004 0.000
#> GSM648645 1 0.0000 0.884 1.000 0.000 0.000
#> GSM648647 2 0.6260 0.717 0.448 0.552 0.000
#> GSM648667 2 0.6260 0.717 0.448 0.552 0.000
#> GSM648695 2 0.6267 0.713 0.452 0.548 0.000
#> GSM648704 2 0.5968 0.755 0.364 0.636 0.000
#> GSM648706 2 0.6267 0.713 0.452 0.548 0.000
#> GSM648593 1 0.1031 0.869 0.976 0.024 0.000
#> GSM648594 1 0.6124 0.517 0.744 0.220 0.036
#> GSM648600 1 0.0000 0.884 1.000 0.000 0.000
#> GSM648621 1 0.0592 0.882 0.988 0.012 0.000
#> GSM648622 1 0.0237 0.884 0.996 0.004 0.000
#> GSM648623 1 0.1482 0.871 0.968 0.012 0.020
#> GSM648636 1 0.0000 0.884 1.000 0.000 0.000
#> GSM648655 1 0.1031 0.869 0.976 0.024 0.000
#> GSM648661 1 0.0592 0.878 0.988 0.012 0.000
#> GSM648664 1 0.0000 0.884 1.000 0.000 0.000
#> GSM648683 1 0.0000 0.884 1.000 0.000 0.000
#> GSM648685 1 0.0000 0.884 1.000 0.000 0.000
#> GSM648702 1 0.0000 0.884 1.000 0.000 0.000
#> GSM648597 1 0.6124 0.517 0.744 0.220 0.036
#> GSM648603 1 0.3120 0.820 0.908 0.012 0.080
#> GSM648606 1 0.2599 0.848 0.932 0.016 0.052
#> GSM648613 1 0.2599 0.848 0.932 0.016 0.052
#> GSM648619 1 0.1950 0.858 0.952 0.008 0.040
#> GSM648654 1 0.1643 0.849 0.956 0.044 0.000
#> GSM648663 1 0.2681 0.852 0.932 0.028 0.040
#> GSM648670 1 0.7250 0.319 0.656 0.288 0.056
#> GSM648707 1 0.9887 -0.158 0.408 0.304 0.288
#> GSM648615 2 0.6260 0.717 0.448 0.552 0.000
#> GSM648643 2 0.6045 0.751 0.380 0.620 0.000
#> GSM648650 1 0.4178 0.605 0.828 0.172 0.000
#> GSM648656 2 0.6045 0.751 0.380 0.620 0.000
#> GSM648715 2 0.6260 0.717 0.448 0.552 0.000
#> GSM648598 1 0.0237 0.884 0.996 0.004 0.000
#> GSM648601 1 0.0237 0.884 0.996 0.004 0.000
#> GSM648602 1 0.0000 0.884 1.000 0.000 0.000
#> GSM648604 1 0.0000 0.884 1.000 0.000 0.000
#> GSM648614 1 0.3550 0.790 0.896 0.080 0.024
#> GSM648624 1 0.0237 0.884 0.996 0.004 0.000
#> GSM648625 1 0.0592 0.882 0.988 0.012 0.000
#> GSM648629 1 0.0000 0.884 1.000 0.000 0.000
#> GSM648634 1 0.0000 0.884 1.000 0.000 0.000
#> GSM648648 1 0.0237 0.883 0.996 0.004 0.000
#> GSM648651 1 0.0237 0.884 0.996 0.004 0.000
#> GSM648657 1 0.0237 0.884 0.996 0.004 0.000
#> GSM648660 1 0.0237 0.884 0.996 0.004 0.000
#> GSM648697 1 0.0000 0.884 1.000 0.000 0.000
#> GSM648710 1 0.0000 0.884 1.000 0.000 0.000
#> GSM648591 1 0.8958 0.107 0.552 0.280 0.168
#> GSM648592 1 0.6625 0.537 0.744 0.176 0.080
#> GSM648607 1 0.1950 0.858 0.952 0.008 0.040
#> GSM648611 1 0.2486 0.843 0.932 0.008 0.060
#> GSM648612 1 0.1950 0.858 0.952 0.008 0.040
#> GSM648616 2 0.9863 0.161 0.340 0.400 0.260
#> GSM648617 1 0.0237 0.884 0.996 0.004 0.000
#> GSM648626 1 0.3120 0.820 0.908 0.012 0.080
#> GSM648711 1 0.1950 0.858 0.952 0.008 0.040
#> GSM648712 1 0.1950 0.858 0.952 0.008 0.040
#> GSM648713 1 0.1950 0.858 0.952 0.008 0.040
#> GSM648714 1 0.3550 0.790 0.896 0.080 0.024
#> GSM648716 1 0.1950 0.858 0.952 0.008 0.040
#> GSM648717 1 0.3129 0.815 0.904 0.008 0.088
#> GSM648590 1 0.3267 0.737 0.884 0.116 0.000
#> GSM648596 2 0.6260 0.717 0.448 0.552 0.000
#> GSM648642 2 0.6267 0.713 0.452 0.548 0.000
#> GSM648696 1 0.0592 0.879 0.988 0.012 0.000
#> GSM648705 1 0.0237 0.883 0.996 0.004 0.000
#> GSM648718 2 0.6274 0.705 0.456 0.544 0.000
#> GSM648599 1 0.0000 0.884 1.000 0.000 0.000
#> GSM648608 1 0.0000 0.884 1.000 0.000 0.000
#> GSM648609 1 0.0000 0.884 1.000 0.000 0.000
#> GSM648610 1 0.0000 0.884 1.000 0.000 0.000
#> GSM648633 1 0.0237 0.884 0.996 0.004 0.000
#> GSM648644 2 0.5968 0.755 0.364 0.636 0.000
#> GSM648652 1 0.0237 0.883 0.996 0.004 0.000
#> GSM648653 1 0.0000 0.884 1.000 0.000 0.000
#> GSM648658 1 0.1031 0.869 0.976 0.024 0.000
#> GSM648659 1 0.6111 -0.300 0.604 0.396 0.000
#> GSM648662 1 0.1031 0.869 0.976 0.024 0.000
#> GSM648665 1 0.1031 0.869 0.976 0.024 0.000
#> GSM648666 1 0.0000 0.884 1.000 0.000 0.000
#> GSM648680 1 0.0237 0.883 0.996 0.004 0.000
#> GSM648684 1 0.0000 0.884 1.000 0.000 0.000
#> GSM648709 2 0.6267 0.713 0.452 0.548 0.000
#> GSM648719 1 0.0237 0.884 0.996 0.004 0.000
#> GSM648627 1 0.2280 0.848 0.940 0.008 0.052
#> GSM648637 2 0.8257 0.428 0.372 0.544 0.084
#> GSM648638 2 0.8257 0.428 0.372 0.544 0.084
#> GSM648641 3 0.4605 0.710 0.204 0.000 0.796
#> GSM648672 2 0.6008 0.749 0.332 0.664 0.004
#> GSM648674 2 0.8131 0.426 0.376 0.548 0.076
#> GSM648703 2 0.5815 0.737 0.304 0.692 0.004
#> GSM648631 3 0.0592 0.951 0.012 0.000 0.988
#> GSM648669 2 0.6090 -0.121 0.020 0.716 0.264
#> GSM648671 2 0.6090 -0.121 0.020 0.716 0.264
#> GSM648678 2 0.5882 0.754 0.348 0.652 0.000
#> GSM648679 2 0.7741 0.299 0.236 0.660 0.104
#> GSM648681 1 0.8069 -0.308 0.476 0.460 0.064
#> GSM648686 3 0.0592 0.951 0.012 0.000 0.988
#> GSM648689 3 0.3192 0.855 0.112 0.000 0.888
#> GSM648690 3 0.0592 0.951 0.012 0.000 0.988
#> GSM648691 3 0.0592 0.951 0.012 0.000 0.988
#> GSM648693 3 0.0592 0.951 0.012 0.000 0.988
#> GSM648700 2 0.5815 0.737 0.304 0.692 0.004
#> GSM648630 3 0.0592 0.951 0.012 0.000 0.988
#> GSM648632 3 0.0592 0.951 0.012 0.000 0.988
#> GSM648639 3 0.3875 0.897 0.068 0.044 0.888
#> GSM648640 3 0.3875 0.897 0.068 0.044 0.888
#> GSM648668 2 0.6008 0.749 0.332 0.664 0.004
#> GSM648676 2 0.5815 0.737 0.304 0.692 0.004
#> GSM648692 3 0.0592 0.951 0.012 0.000 0.988
#> GSM648694 3 0.0592 0.951 0.012 0.000 0.988
#> GSM648699 2 0.5815 0.737 0.304 0.692 0.004
#> GSM648701 2 0.5815 0.737 0.304 0.692 0.004
#> GSM648673 2 0.6090 -0.121 0.020 0.716 0.264
#> GSM648677 2 0.5835 0.751 0.340 0.660 0.000
#> GSM648687 3 0.2318 0.935 0.028 0.028 0.944
#> GSM648688 3 0.2318 0.935 0.028 0.028 0.944
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM648605 2 0.2888 0.8619 0.124 0.872 0.000 0.004
#> GSM648618 1 0.5172 0.5815 0.736 0.036 0.008 0.220
#> GSM648620 2 0.2888 0.8619 0.124 0.872 0.000 0.004
#> GSM648646 2 0.0817 0.8328 0.024 0.976 0.000 0.000
#> GSM648649 1 0.1389 0.8823 0.952 0.048 0.000 0.000
#> GSM648675 1 0.5172 0.5815 0.736 0.036 0.008 0.220
#> GSM648682 2 0.4088 0.6738 0.232 0.764 0.000 0.004
#> GSM648698 2 0.2888 0.8619 0.124 0.872 0.000 0.004
#> GSM648708 2 0.2888 0.8619 0.124 0.872 0.000 0.004
#> GSM648628 1 0.2099 0.8805 0.936 0.008 0.044 0.012
#> GSM648595 1 0.3009 0.8235 0.892 0.056 0.000 0.052
#> GSM648635 1 0.0336 0.9093 0.992 0.008 0.000 0.000
#> GSM648645 1 0.0188 0.9101 0.996 0.004 0.000 0.000
#> GSM648647 2 0.2704 0.8622 0.124 0.876 0.000 0.000
#> GSM648667 2 0.2704 0.8622 0.124 0.876 0.000 0.000
#> GSM648695 2 0.2888 0.8619 0.124 0.872 0.000 0.004
#> GSM648704 2 0.0817 0.8328 0.024 0.976 0.000 0.000
#> GSM648706 2 0.2888 0.8619 0.124 0.872 0.000 0.004
#> GSM648593 1 0.0921 0.8995 0.972 0.028 0.000 0.000
#> GSM648594 1 0.5432 0.5219 0.716 0.068 0.000 0.216
#> GSM648600 1 0.0188 0.9101 0.996 0.004 0.000 0.000
#> GSM648621 1 0.0592 0.9045 0.984 0.000 0.000 0.016
#> GSM648622 1 0.0000 0.9095 1.000 0.000 0.000 0.000
#> GSM648623 1 0.1262 0.8972 0.968 0.008 0.008 0.016
#> GSM648636 1 0.0188 0.9101 0.996 0.004 0.000 0.000
#> GSM648655 1 0.0921 0.8995 0.972 0.028 0.000 0.000
#> GSM648661 1 0.0779 0.9040 0.980 0.016 0.000 0.004
#> GSM648664 1 0.0188 0.9101 0.996 0.004 0.000 0.000
#> GSM648683 1 0.0188 0.9101 0.996 0.004 0.000 0.000
#> GSM648685 1 0.0188 0.9101 0.996 0.004 0.000 0.000
#> GSM648702 1 0.0188 0.9101 0.996 0.004 0.000 0.000
#> GSM648597 1 0.5432 0.5219 0.716 0.068 0.000 0.216
#> GSM648603 1 0.2707 0.8512 0.908 0.008 0.068 0.016
#> GSM648606 1 0.2352 0.8775 0.928 0.016 0.044 0.012
#> GSM648613 1 0.2352 0.8775 0.928 0.016 0.044 0.012
#> GSM648619 1 0.1690 0.8894 0.952 0.008 0.032 0.008
#> GSM648654 1 0.1576 0.8802 0.948 0.048 0.000 0.004
#> GSM648663 1 0.2400 0.8797 0.928 0.028 0.032 0.012
#> GSM648670 1 0.6028 0.3342 0.644 0.076 0.000 0.280
#> GSM648707 4 0.7912 0.4530 0.392 0.008 0.204 0.396
#> GSM648615 2 0.2704 0.8622 0.124 0.876 0.000 0.000
#> GSM648643 2 0.1474 0.8479 0.052 0.948 0.000 0.000
#> GSM648650 1 0.3837 0.6137 0.776 0.224 0.000 0.000
#> GSM648656 2 0.1474 0.8479 0.052 0.948 0.000 0.000
#> GSM648715 2 0.2704 0.8622 0.124 0.876 0.000 0.000
#> GSM648598 1 0.0000 0.9095 1.000 0.000 0.000 0.000
#> GSM648601 1 0.0000 0.9095 1.000 0.000 0.000 0.000
#> GSM648602 1 0.0188 0.9101 0.996 0.004 0.000 0.000
#> GSM648604 1 0.0188 0.9101 0.996 0.004 0.000 0.000
#> GSM648614 1 0.4890 0.5567 0.736 0.236 0.024 0.004
#> GSM648624 1 0.0000 0.9095 1.000 0.000 0.000 0.000
#> GSM648625 1 0.0336 0.9090 0.992 0.008 0.000 0.000
#> GSM648629 1 0.0188 0.9101 0.996 0.004 0.000 0.000
#> GSM648634 1 0.0188 0.9101 0.996 0.004 0.000 0.000
#> GSM648648 1 0.0336 0.9093 0.992 0.008 0.000 0.000
#> GSM648651 1 0.0000 0.9095 1.000 0.000 0.000 0.000
#> GSM648657 1 0.0000 0.9095 1.000 0.000 0.000 0.000
#> GSM648660 1 0.0000 0.9095 1.000 0.000 0.000 0.000
#> GSM648697 1 0.0188 0.9101 0.996 0.004 0.000 0.000
#> GSM648710 1 0.0188 0.9101 0.996 0.004 0.000 0.000
#> GSM648591 1 0.6927 -0.0813 0.536 0.008 0.092 0.364
#> GSM648592 1 0.6161 0.5353 0.716 0.044 0.060 0.180
#> GSM648607 1 0.1690 0.8894 0.952 0.008 0.032 0.008
#> GSM648611 1 0.2271 0.8753 0.928 0.008 0.052 0.012
#> GSM648612 1 0.1690 0.8894 0.952 0.008 0.032 0.008
#> GSM648616 4 0.7909 0.5410 0.312 0.020 0.176 0.492
#> GSM648617 1 0.0000 0.9095 1.000 0.000 0.000 0.000
#> GSM648626 1 0.2707 0.8512 0.908 0.008 0.068 0.016
#> GSM648711 1 0.1690 0.8894 0.952 0.008 0.032 0.008
#> GSM648712 1 0.1690 0.8894 0.952 0.008 0.032 0.008
#> GSM648713 1 0.1690 0.8894 0.952 0.008 0.032 0.008
#> GSM648714 1 0.4890 0.5567 0.736 0.236 0.024 0.004
#> GSM648716 1 0.1690 0.8894 0.952 0.008 0.032 0.008
#> GSM648717 1 0.2803 0.8476 0.900 0.008 0.080 0.012
#> GSM648590 1 0.2973 0.7615 0.856 0.144 0.000 0.000
#> GSM648596 2 0.2704 0.8622 0.124 0.876 0.000 0.000
#> GSM648642 2 0.2888 0.8619 0.124 0.872 0.000 0.004
#> GSM648696 1 0.1302 0.8857 0.956 0.044 0.000 0.000
#> GSM648705 1 0.0336 0.9093 0.992 0.008 0.000 0.000
#> GSM648718 2 0.2814 0.8533 0.132 0.868 0.000 0.000
#> GSM648599 1 0.0188 0.9101 0.996 0.004 0.000 0.000
#> GSM648608 1 0.0188 0.9101 0.996 0.004 0.000 0.000
#> GSM648609 1 0.0188 0.9101 0.996 0.004 0.000 0.000
#> GSM648610 1 0.0188 0.9101 0.996 0.004 0.000 0.000
#> GSM648633 1 0.0000 0.9095 1.000 0.000 0.000 0.000
#> GSM648644 2 0.0817 0.8328 0.024 0.976 0.000 0.000
#> GSM648652 1 0.0336 0.9093 0.992 0.008 0.000 0.000
#> GSM648653 1 0.0188 0.9101 0.996 0.004 0.000 0.000
#> GSM648658 1 0.0921 0.8995 0.972 0.028 0.000 0.000
#> GSM648659 2 0.4697 0.4022 0.356 0.644 0.000 0.000
#> GSM648662 1 0.1305 0.8907 0.960 0.036 0.000 0.004
#> GSM648665 1 0.1305 0.8907 0.960 0.036 0.000 0.004
#> GSM648666 1 0.0188 0.9101 0.996 0.004 0.000 0.000
#> GSM648680 1 0.0336 0.9093 0.992 0.008 0.000 0.000
#> GSM648684 1 0.0188 0.9101 0.996 0.004 0.000 0.000
#> GSM648709 2 0.2888 0.8619 0.124 0.872 0.000 0.004
#> GSM648719 1 0.0000 0.9095 1.000 0.000 0.000 0.000
#> GSM648627 1 0.1968 0.8831 0.940 0.008 0.044 0.008
#> GSM648637 4 0.7804 0.6339 0.272 0.232 0.008 0.488
#> GSM648638 4 0.7804 0.6339 0.272 0.232 0.008 0.488
#> GSM648641 3 0.3751 0.5809 0.196 0.000 0.800 0.004
#> GSM648672 2 0.3271 0.7497 0.012 0.856 0.000 0.132
#> GSM648674 4 0.7547 0.6284 0.276 0.236 0.000 0.488
#> GSM648703 2 0.3280 0.7684 0.016 0.860 0.000 0.124
#> GSM648631 3 0.0000 0.9158 0.000 0.000 1.000 0.000
#> GSM648669 4 0.1767 0.4279 0.000 0.044 0.012 0.944
#> GSM648671 4 0.1767 0.4279 0.000 0.044 0.012 0.944
#> GSM648678 2 0.0657 0.8214 0.012 0.984 0.000 0.004
#> GSM648679 4 0.6386 0.6434 0.212 0.140 0.000 0.648
#> GSM648681 1 0.7249 -0.4195 0.444 0.144 0.000 0.412
#> GSM648686 3 0.0000 0.9158 0.000 0.000 1.000 0.000
#> GSM648689 3 0.2408 0.7839 0.104 0.000 0.896 0.000
#> GSM648690 3 0.0000 0.9158 0.000 0.000 1.000 0.000
#> GSM648691 3 0.0000 0.9158 0.000 0.000 1.000 0.000
#> GSM648693 3 0.0000 0.9158 0.000 0.000 1.000 0.000
#> GSM648700 2 0.3280 0.7684 0.016 0.860 0.000 0.124
#> GSM648630 3 0.0000 0.9158 0.000 0.000 1.000 0.000
#> GSM648632 3 0.0000 0.9158 0.000 0.000 1.000 0.000
#> GSM648639 3 0.3697 0.8178 0.048 0.000 0.852 0.100
#> GSM648640 3 0.3697 0.8178 0.048 0.000 0.852 0.100
#> GSM648668 2 0.3271 0.7497 0.012 0.856 0.000 0.132
#> GSM648676 2 0.3280 0.7684 0.016 0.860 0.000 0.124
#> GSM648692 3 0.0000 0.9158 0.000 0.000 1.000 0.000
#> GSM648694 3 0.0000 0.9158 0.000 0.000 1.000 0.000
#> GSM648699 2 0.3280 0.7684 0.016 0.860 0.000 0.124
#> GSM648701 2 0.3280 0.7684 0.016 0.860 0.000 0.124
#> GSM648673 4 0.1767 0.4279 0.000 0.044 0.012 0.944
#> GSM648677 2 0.1975 0.8090 0.016 0.936 0.000 0.048
#> GSM648687 3 0.3123 0.8359 0.000 0.000 0.844 0.156
#> GSM648688 3 0.3123 0.8359 0.000 0.000 0.844 0.156
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM648605 2 0.2488 0.840 0.124 0.872 0.000 0.000 0.004
#> GSM648618 1 0.4789 0.355 0.644 0.028 0.000 0.004 0.324
#> GSM648620 2 0.2488 0.840 0.124 0.872 0.000 0.000 0.004
#> GSM648646 2 0.0740 0.805 0.008 0.980 0.000 0.004 0.008
#> GSM648649 1 0.1121 0.888 0.956 0.044 0.000 0.000 0.000
#> GSM648675 1 0.4789 0.355 0.644 0.028 0.000 0.004 0.324
#> GSM648682 2 0.3521 0.648 0.232 0.764 0.000 0.000 0.004
#> GSM648698 2 0.2488 0.840 0.124 0.872 0.000 0.000 0.004
#> GSM648708 2 0.2488 0.840 0.124 0.872 0.000 0.000 0.004
#> GSM648628 1 0.2075 0.882 0.924 0.000 0.032 0.004 0.040
#> GSM648595 1 0.3191 0.791 0.860 0.052 0.000 0.004 0.084
#> GSM648635 1 0.0162 0.915 0.996 0.004 0.000 0.000 0.000
#> GSM648645 1 0.0000 0.916 1.000 0.000 0.000 0.000 0.000
#> GSM648647 2 0.2329 0.840 0.124 0.876 0.000 0.000 0.000
#> GSM648667 2 0.2329 0.840 0.124 0.876 0.000 0.000 0.000
#> GSM648695 2 0.2488 0.840 0.124 0.872 0.000 0.000 0.004
#> GSM648704 2 0.0740 0.805 0.008 0.980 0.000 0.004 0.008
#> GSM648706 2 0.2488 0.840 0.124 0.872 0.000 0.000 0.004
#> GSM648593 1 0.0703 0.905 0.976 0.024 0.000 0.000 0.000
#> GSM648594 1 0.5411 0.269 0.632 0.068 0.000 0.008 0.292
#> GSM648600 1 0.0000 0.916 1.000 0.000 0.000 0.000 0.000
#> GSM648621 1 0.0703 0.909 0.976 0.000 0.000 0.000 0.024
#> GSM648622 1 0.0162 0.916 0.996 0.000 0.000 0.000 0.004
#> GSM648623 1 0.1124 0.901 0.960 0.000 0.000 0.004 0.036
#> GSM648636 1 0.0000 0.916 1.000 0.000 0.000 0.000 0.000
#> GSM648655 1 0.0703 0.905 0.976 0.024 0.000 0.000 0.000
#> GSM648661 1 0.0566 0.910 0.984 0.012 0.000 0.000 0.004
#> GSM648664 1 0.0000 0.916 1.000 0.000 0.000 0.000 0.000
#> GSM648683 1 0.0000 0.916 1.000 0.000 0.000 0.000 0.000
#> GSM648685 1 0.0000 0.916 1.000 0.000 0.000 0.000 0.000
#> GSM648702 1 0.0000 0.916 1.000 0.000 0.000 0.000 0.000
#> GSM648597 1 0.5411 0.269 0.632 0.068 0.000 0.008 0.292
#> GSM648603 1 0.2124 0.858 0.900 0.000 0.000 0.004 0.096
#> GSM648606 1 0.2282 0.882 0.920 0.008 0.032 0.004 0.036
#> GSM648613 1 0.2282 0.882 0.920 0.008 0.032 0.004 0.036
#> GSM648619 1 0.1653 0.893 0.944 0.000 0.024 0.004 0.028
#> GSM648654 1 0.1282 0.887 0.952 0.044 0.000 0.000 0.004
#> GSM648663 1 0.2341 0.884 0.920 0.020 0.024 0.004 0.032
#> GSM648670 1 0.5941 -0.067 0.540 0.072 0.000 0.016 0.372
#> GSM648707 5 0.4065 0.520 0.224 0.000 0.016 0.008 0.752
#> GSM648615 2 0.2329 0.840 0.124 0.876 0.000 0.000 0.000
#> GSM648643 2 0.1412 0.821 0.036 0.952 0.000 0.004 0.008
#> GSM648650 1 0.3274 0.625 0.780 0.220 0.000 0.000 0.000
#> GSM648656 2 0.1124 0.821 0.036 0.960 0.000 0.004 0.000
#> GSM648715 2 0.2329 0.840 0.124 0.876 0.000 0.000 0.000
#> GSM648598 1 0.0162 0.916 0.996 0.000 0.000 0.000 0.004
#> GSM648601 1 0.0162 0.916 0.996 0.000 0.000 0.000 0.004
#> GSM648602 1 0.0000 0.916 1.000 0.000 0.000 0.000 0.000
#> GSM648604 1 0.0000 0.916 1.000 0.000 0.000 0.000 0.000
#> GSM648614 1 0.4212 0.564 0.736 0.236 0.024 0.000 0.004
#> GSM648624 1 0.0162 0.916 0.996 0.000 0.000 0.000 0.004
#> GSM648625 1 0.0451 0.915 0.988 0.008 0.000 0.000 0.004
#> GSM648629 1 0.0000 0.916 1.000 0.000 0.000 0.000 0.000
#> GSM648634 1 0.0000 0.916 1.000 0.000 0.000 0.000 0.000
#> GSM648648 1 0.0162 0.915 0.996 0.004 0.000 0.000 0.000
#> GSM648651 1 0.0162 0.916 0.996 0.000 0.000 0.000 0.004
#> GSM648657 1 0.0162 0.916 0.996 0.000 0.000 0.000 0.004
#> GSM648660 1 0.0162 0.916 0.996 0.000 0.000 0.000 0.004
#> GSM648697 1 0.0000 0.916 1.000 0.000 0.000 0.000 0.000
#> GSM648710 1 0.0000 0.916 1.000 0.000 0.000 0.000 0.000
#> GSM648591 5 0.4810 0.440 0.400 0.000 0.012 0.008 0.580
#> GSM648592 1 0.5089 0.302 0.636 0.048 0.000 0.004 0.312
#> GSM648607 1 0.1653 0.893 0.944 0.000 0.024 0.004 0.028
#> GSM648611 1 0.2234 0.877 0.916 0.000 0.036 0.004 0.044
#> GSM648612 1 0.1653 0.893 0.944 0.000 0.024 0.004 0.028
#> GSM648616 5 0.3471 0.492 0.124 0.020 0.004 0.012 0.840
#> GSM648617 1 0.0162 0.916 0.996 0.000 0.000 0.000 0.004
#> GSM648626 1 0.2124 0.858 0.900 0.000 0.000 0.004 0.096
#> GSM648711 1 0.1653 0.893 0.944 0.000 0.024 0.004 0.028
#> GSM648712 1 0.1739 0.892 0.940 0.000 0.024 0.004 0.032
#> GSM648713 1 0.1653 0.893 0.944 0.000 0.024 0.004 0.028
#> GSM648714 1 0.4212 0.564 0.736 0.236 0.024 0.000 0.004
#> GSM648716 1 0.1653 0.893 0.944 0.000 0.024 0.004 0.028
#> GSM648717 1 0.2756 0.855 0.892 0.000 0.036 0.012 0.060
#> GSM648590 1 0.2516 0.766 0.860 0.140 0.000 0.000 0.000
#> GSM648596 2 0.2329 0.840 0.124 0.876 0.000 0.000 0.000
#> GSM648642 2 0.2488 0.840 0.124 0.872 0.000 0.000 0.004
#> GSM648696 1 0.1043 0.891 0.960 0.040 0.000 0.000 0.000
#> GSM648705 1 0.0162 0.915 0.996 0.004 0.000 0.000 0.000
#> GSM648718 2 0.2424 0.831 0.132 0.868 0.000 0.000 0.000
#> GSM648599 1 0.0000 0.916 1.000 0.000 0.000 0.000 0.000
#> GSM648608 1 0.0000 0.916 1.000 0.000 0.000 0.000 0.000
#> GSM648609 1 0.0000 0.916 1.000 0.000 0.000 0.000 0.000
#> GSM648610 1 0.0000 0.916 1.000 0.000 0.000 0.000 0.000
#> GSM648633 1 0.0162 0.916 0.996 0.000 0.000 0.000 0.004
#> GSM648644 2 0.0740 0.805 0.008 0.980 0.000 0.004 0.008
#> GSM648652 1 0.0162 0.915 0.996 0.004 0.000 0.000 0.000
#> GSM648653 1 0.0000 0.916 1.000 0.000 0.000 0.000 0.000
#> GSM648658 1 0.0703 0.905 0.976 0.024 0.000 0.000 0.000
#> GSM648659 2 0.4045 0.379 0.356 0.644 0.000 0.000 0.000
#> GSM648662 1 0.1041 0.897 0.964 0.032 0.000 0.000 0.004
#> GSM648665 1 0.1041 0.897 0.964 0.032 0.000 0.000 0.004
#> GSM648666 1 0.0000 0.916 1.000 0.000 0.000 0.000 0.000
#> GSM648680 1 0.0162 0.915 0.996 0.004 0.000 0.000 0.000
#> GSM648684 1 0.0000 0.916 1.000 0.000 0.000 0.000 0.000
#> GSM648709 2 0.2488 0.840 0.124 0.872 0.000 0.000 0.004
#> GSM648719 1 0.0162 0.916 0.996 0.000 0.000 0.000 0.004
#> GSM648627 1 0.1996 0.885 0.928 0.000 0.032 0.004 0.036
#> GSM648637 5 0.5815 0.601 0.112 0.232 0.000 0.016 0.640
#> GSM648638 5 0.5815 0.601 0.112 0.232 0.000 0.016 0.640
#> GSM648641 3 0.4187 0.517 0.196 0.000 0.764 0.008 0.032
#> GSM648672 2 0.3359 0.712 0.000 0.844 0.000 0.072 0.084
#> GSM648674 5 0.5883 0.603 0.116 0.236 0.000 0.016 0.632
#> GSM648703 2 0.3304 0.723 0.004 0.840 0.000 0.128 0.028
#> GSM648631 3 0.0000 0.834 0.000 0.000 1.000 0.000 0.000
#> GSM648669 4 0.2439 1.000 0.000 0.004 0.000 0.876 0.120
#> GSM648671 4 0.2439 1.000 0.000 0.004 0.000 0.876 0.120
#> GSM648678 2 0.0912 0.793 0.000 0.972 0.000 0.012 0.016
#> GSM648679 5 0.6745 0.278 0.056 0.124 0.000 0.248 0.572
#> GSM648681 5 0.6410 0.565 0.288 0.144 0.000 0.016 0.552
#> GSM648686 3 0.0000 0.834 0.000 0.000 1.000 0.000 0.000
#> GSM648689 3 0.2074 0.692 0.104 0.000 0.896 0.000 0.000
#> GSM648690 3 0.0000 0.834 0.000 0.000 1.000 0.000 0.000
#> GSM648691 3 0.0000 0.834 0.000 0.000 1.000 0.000 0.000
#> GSM648693 3 0.0000 0.834 0.000 0.000 1.000 0.000 0.000
#> GSM648700 2 0.3304 0.723 0.004 0.840 0.000 0.128 0.028
#> GSM648630 3 0.0000 0.834 0.000 0.000 1.000 0.000 0.000
#> GSM648632 3 0.0000 0.834 0.000 0.000 1.000 0.000 0.000
#> GSM648639 3 0.6032 0.370 0.000 0.000 0.460 0.116 0.424
#> GSM648640 3 0.6032 0.370 0.000 0.000 0.460 0.116 0.424
#> GSM648668 2 0.3359 0.712 0.000 0.844 0.000 0.072 0.084
#> GSM648676 2 0.3304 0.723 0.004 0.840 0.000 0.128 0.028
#> GSM648692 3 0.0000 0.834 0.000 0.000 1.000 0.000 0.000
#> GSM648694 3 0.0000 0.834 0.000 0.000 1.000 0.000 0.000
#> GSM648699 2 0.3304 0.723 0.004 0.840 0.000 0.128 0.028
#> GSM648701 2 0.3304 0.723 0.004 0.840 0.000 0.128 0.028
#> GSM648673 4 0.2439 1.000 0.000 0.004 0.000 0.876 0.120
#> GSM648677 2 0.2103 0.775 0.004 0.920 0.000 0.056 0.020
#> GSM648687 3 0.4933 0.630 0.000 0.000 0.692 0.228 0.080
#> GSM648688 3 0.4933 0.630 0.000 0.000 0.692 0.228 0.080
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM648605 2 0.2053 0.794 0.108 0.888 0.000 0.000 0.000 0.004
#> GSM648618 1 0.4861 0.333 0.604 0.024 0.000 0.000 0.032 0.340
#> GSM648620 2 0.2053 0.794 0.108 0.888 0.000 0.000 0.000 0.004
#> GSM648646 2 0.1801 0.736 0.004 0.924 0.000 0.000 0.016 0.056
#> GSM648649 1 0.1152 0.893 0.952 0.044 0.000 0.000 0.000 0.004
#> GSM648675 1 0.4861 0.333 0.604 0.024 0.000 0.000 0.032 0.340
#> GSM648682 2 0.3052 0.637 0.216 0.780 0.000 0.000 0.000 0.004
#> GSM648698 2 0.2053 0.794 0.108 0.888 0.000 0.000 0.000 0.004
#> GSM648708 2 0.2053 0.794 0.108 0.888 0.000 0.000 0.000 0.004
#> GSM648628 1 0.2302 0.870 0.900 0.000 0.008 0.000 0.060 0.032
#> GSM648595 1 0.3130 0.770 0.824 0.028 0.000 0.000 0.004 0.144
#> GSM648635 1 0.0291 0.915 0.992 0.004 0.000 0.000 0.000 0.004
#> GSM648645 1 0.0146 0.914 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM648647 2 0.1910 0.794 0.108 0.892 0.000 0.000 0.000 0.000
#> GSM648667 2 0.1910 0.794 0.108 0.892 0.000 0.000 0.000 0.000
#> GSM648695 2 0.2053 0.794 0.108 0.888 0.000 0.000 0.000 0.004
#> GSM648704 2 0.1801 0.736 0.004 0.924 0.000 0.000 0.016 0.056
#> GSM648706 2 0.2053 0.794 0.108 0.888 0.000 0.000 0.000 0.004
#> GSM648593 1 0.0858 0.906 0.968 0.028 0.000 0.000 0.000 0.004
#> GSM648594 1 0.3852 0.304 0.612 0.000 0.000 0.004 0.000 0.384
#> GSM648600 1 0.0146 0.914 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM648621 1 0.1285 0.894 0.944 0.000 0.000 0.000 0.004 0.052
#> GSM648622 1 0.0146 0.914 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM648623 1 0.1124 0.901 0.956 0.000 0.000 0.000 0.036 0.008
#> GSM648636 1 0.0291 0.915 0.992 0.004 0.000 0.000 0.000 0.004
#> GSM648655 1 0.0858 0.906 0.968 0.028 0.000 0.000 0.000 0.004
#> GSM648661 1 0.0692 0.909 0.976 0.020 0.000 0.000 0.000 0.004
#> GSM648664 1 0.0260 0.914 0.992 0.008 0.000 0.000 0.000 0.000
#> GSM648683 1 0.0291 0.915 0.992 0.004 0.000 0.000 0.000 0.004
#> GSM648685 1 0.0260 0.914 0.992 0.008 0.000 0.000 0.000 0.000
#> GSM648702 1 0.0291 0.915 0.992 0.004 0.000 0.000 0.000 0.004
#> GSM648597 1 0.3852 0.304 0.612 0.000 0.000 0.004 0.000 0.384
#> GSM648603 1 0.2221 0.863 0.896 0.000 0.000 0.000 0.072 0.032
#> GSM648606 1 0.1841 0.887 0.920 0.008 0.000 0.000 0.064 0.008
#> GSM648613 1 0.1841 0.887 0.920 0.008 0.000 0.000 0.064 0.008
#> GSM648619 1 0.1349 0.894 0.940 0.000 0.000 0.000 0.056 0.004
#> GSM648654 1 0.1285 0.888 0.944 0.052 0.000 0.000 0.000 0.004
#> GSM648663 1 0.1938 0.888 0.920 0.020 0.000 0.000 0.052 0.008
#> GSM648670 1 0.5200 -0.055 0.504 0.036 0.000 0.008 0.016 0.436
#> GSM648707 6 0.4792 0.452 0.148 0.000 0.000 0.000 0.180 0.672
#> GSM648615 2 0.1910 0.794 0.108 0.892 0.000 0.000 0.000 0.000
#> GSM648643 2 0.2271 0.761 0.032 0.908 0.000 0.000 0.024 0.036
#> GSM648650 1 0.3081 0.662 0.776 0.220 0.000 0.000 0.000 0.004
#> GSM648656 2 0.1777 0.764 0.032 0.932 0.000 0.000 0.012 0.024
#> GSM648715 2 0.1910 0.794 0.108 0.892 0.000 0.000 0.000 0.000
#> GSM648598 1 0.0146 0.914 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM648601 1 0.0291 0.914 0.992 0.000 0.000 0.000 0.004 0.004
#> GSM648602 1 0.0291 0.915 0.992 0.004 0.000 0.000 0.000 0.004
#> GSM648604 1 0.0260 0.914 0.992 0.008 0.000 0.000 0.000 0.000
#> GSM648614 1 0.3858 0.590 0.724 0.248 0.000 0.000 0.024 0.004
#> GSM648624 1 0.0146 0.914 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM648625 1 0.0405 0.915 0.988 0.008 0.000 0.000 0.004 0.000
#> GSM648629 1 0.0260 0.914 0.992 0.008 0.000 0.000 0.000 0.000
#> GSM648634 1 0.0291 0.915 0.992 0.004 0.000 0.000 0.000 0.004
#> GSM648648 1 0.0291 0.915 0.992 0.004 0.000 0.000 0.000 0.004
#> GSM648651 1 0.0291 0.914 0.992 0.000 0.000 0.000 0.004 0.004
#> GSM648657 1 0.0291 0.914 0.992 0.000 0.000 0.000 0.004 0.004
#> GSM648660 1 0.0146 0.914 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM648697 1 0.0291 0.915 0.992 0.004 0.000 0.000 0.000 0.004
#> GSM648710 1 0.0260 0.914 0.992 0.008 0.000 0.000 0.000 0.000
#> GSM648591 6 0.5070 0.372 0.328 0.000 0.000 0.000 0.096 0.576
#> GSM648592 1 0.4433 0.334 0.616 0.000 0.000 0.000 0.040 0.344
#> GSM648607 1 0.1349 0.894 0.940 0.000 0.000 0.000 0.056 0.004
#> GSM648611 1 0.2420 0.865 0.892 0.000 0.008 0.000 0.068 0.032
#> GSM648612 1 0.1349 0.894 0.940 0.000 0.000 0.000 0.056 0.004
#> GSM648616 6 0.3229 0.465 0.048 0.000 0.000 0.004 0.120 0.828
#> GSM648617 1 0.0146 0.914 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM648626 1 0.2221 0.863 0.896 0.000 0.000 0.000 0.072 0.032
#> GSM648711 1 0.1349 0.894 0.940 0.000 0.000 0.000 0.056 0.004
#> GSM648712 1 0.1563 0.891 0.932 0.000 0.000 0.000 0.056 0.012
#> GSM648713 1 0.1349 0.894 0.940 0.000 0.000 0.000 0.056 0.004
#> GSM648714 1 0.3858 0.590 0.724 0.248 0.000 0.000 0.024 0.004
#> GSM648716 1 0.1349 0.894 0.940 0.000 0.000 0.000 0.056 0.004
#> GSM648717 1 0.2006 0.864 0.892 0.000 0.000 0.000 0.104 0.004
#> GSM648590 1 0.2442 0.782 0.852 0.144 0.000 0.000 0.000 0.004
#> GSM648596 2 0.1910 0.794 0.108 0.892 0.000 0.000 0.000 0.000
#> GSM648642 2 0.2053 0.794 0.108 0.888 0.000 0.000 0.000 0.004
#> GSM648696 1 0.1082 0.896 0.956 0.040 0.000 0.000 0.000 0.004
#> GSM648705 1 0.0291 0.915 0.992 0.004 0.000 0.000 0.000 0.004
#> GSM648718 2 0.2003 0.787 0.116 0.884 0.000 0.000 0.000 0.000
#> GSM648599 1 0.0146 0.914 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM648608 1 0.0291 0.915 0.992 0.004 0.000 0.000 0.000 0.004
#> GSM648609 1 0.0260 0.914 0.992 0.008 0.000 0.000 0.000 0.000
#> GSM648610 1 0.0291 0.915 0.992 0.004 0.000 0.000 0.000 0.004
#> GSM648633 1 0.0146 0.914 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM648644 2 0.1801 0.736 0.004 0.924 0.000 0.000 0.016 0.056
#> GSM648652 1 0.0291 0.915 0.992 0.004 0.000 0.000 0.000 0.004
#> GSM648653 1 0.0291 0.915 0.992 0.004 0.000 0.000 0.000 0.004
#> GSM648658 1 0.0858 0.906 0.968 0.028 0.000 0.000 0.000 0.004
#> GSM648659 2 0.3850 0.392 0.340 0.652 0.000 0.000 0.004 0.004
#> GSM648662 1 0.1082 0.897 0.956 0.040 0.000 0.000 0.000 0.004
#> GSM648665 1 0.1082 0.897 0.956 0.040 0.000 0.000 0.000 0.004
#> GSM648666 1 0.0291 0.915 0.992 0.004 0.000 0.000 0.000 0.004
#> GSM648680 1 0.0291 0.915 0.992 0.004 0.000 0.000 0.000 0.004
#> GSM648684 1 0.0291 0.915 0.992 0.004 0.000 0.000 0.000 0.004
#> GSM648709 2 0.2053 0.794 0.108 0.888 0.000 0.000 0.000 0.004
#> GSM648719 1 0.0146 0.914 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM648627 1 0.1976 0.882 0.916 0.000 0.008 0.000 0.060 0.016
#> GSM648637 6 0.3218 0.606 0.044 0.112 0.000 0.004 0.004 0.836
#> GSM648638 6 0.3218 0.606 0.044 0.112 0.000 0.004 0.004 0.836
#> GSM648641 3 0.4215 0.394 0.196 0.000 0.724 0.000 0.080 0.000
#> GSM648672 2 0.4951 0.594 0.000 0.712 0.000 0.112 0.040 0.136
#> GSM648674 6 0.3121 0.604 0.044 0.116 0.000 0.004 0.000 0.836
#> GSM648703 2 0.6274 0.489 0.004 0.596 0.000 0.092 0.172 0.136
#> GSM648631 3 0.0000 0.856 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648669 4 0.0632 1.000 0.000 0.000 0.000 0.976 0.000 0.024
#> GSM648671 4 0.0632 1.000 0.000 0.000 0.000 0.976 0.000 0.024
#> GSM648678 2 0.2821 0.705 0.000 0.860 0.000 0.004 0.040 0.096
#> GSM648679 6 0.4624 0.421 0.024 0.044 0.000 0.244 0.000 0.688
#> GSM648681 6 0.4361 0.512 0.236 0.060 0.000 0.004 0.000 0.700
#> GSM648686 3 0.0146 0.854 0.000 0.000 0.996 0.000 0.004 0.000
#> GSM648689 3 0.2558 0.642 0.104 0.000 0.868 0.000 0.028 0.000
#> GSM648690 3 0.0146 0.854 0.000 0.000 0.996 0.000 0.004 0.000
#> GSM648691 3 0.0000 0.856 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648693 3 0.0000 0.856 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648700 2 0.6301 0.484 0.004 0.592 0.000 0.092 0.176 0.136
#> GSM648630 3 0.0000 0.856 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648632 3 0.0000 0.856 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648639 5 0.4030 1.000 0.000 0.000 0.104 0.000 0.756 0.140
#> GSM648640 5 0.4030 1.000 0.000 0.000 0.104 0.000 0.756 0.140
#> GSM648668 2 0.4951 0.594 0.000 0.712 0.000 0.112 0.040 0.136
#> GSM648676 2 0.6301 0.484 0.004 0.592 0.000 0.092 0.176 0.136
#> GSM648692 3 0.0000 0.856 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648694 3 0.0000 0.856 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648699 2 0.6301 0.484 0.004 0.592 0.000 0.092 0.176 0.136
#> GSM648701 2 0.6301 0.484 0.004 0.592 0.000 0.092 0.176 0.136
#> GSM648673 4 0.0632 1.000 0.000 0.000 0.000 0.976 0.000 0.024
#> GSM648677 2 0.4742 0.634 0.004 0.744 0.000 0.044 0.096 0.112
#> GSM648687 3 0.5136 0.452 0.000 0.000 0.640 0.160 0.196 0.004
#> GSM648688 3 0.5136 0.452 0.000 0.000 0.640 0.160 0.196 0.004
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) development.stage(p) other(p) k
#> CV:hclust 109 5.94e-24 0.612640 1.80e-23 2
#> CV:hclust 116 7.81e-18 0.136327 3.96e-23 3
#> CV:hclust 122 9.49e-18 0.013655 7.46e-23 4
#> CV:hclust 118 1.58e-16 0.003419 2.26e-21 5
#> CV:hclust 111 4.77e-18 0.000109 1.58e-22 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 51941 rows and 130 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#> Subgroups are detected by 'kmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.297 0.583 0.786 0.4332 0.565 0.565
#> 3 3 0.634 0.777 0.898 0.3877 0.598 0.412
#> 4 4 0.623 0.731 0.829 0.1340 0.853 0.674
#> 5 5 0.658 0.730 0.833 0.0943 0.856 0.612
#> 6 6 0.698 0.650 0.808 0.0545 0.942 0.777
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
#> GSM648605 2 0.0938 0.7582 0.012 0.988
#> GSM648618 2 0.9909 0.1365 0.444 0.556
#> GSM648620 2 0.0376 0.7595 0.004 0.996
#> GSM648646 2 0.0938 0.7582 0.012 0.988
#> GSM648649 2 0.2043 0.7621 0.032 0.968
#> GSM648675 2 0.2236 0.7622 0.036 0.964
#> GSM648682 2 0.0376 0.7592 0.004 0.996
#> GSM648698 2 0.0938 0.7582 0.012 0.988
#> GSM648708 2 0.0000 0.7597 0.000 1.000
#> GSM648628 1 0.7745 0.6718 0.772 0.228
#> GSM648595 2 0.2043 0.7621 0.032 0.968
#> GSM648635 2 0.2043 0.7621 0.032 0.968
#> GSM648645 2 0.6148 0.6960 0.152 0.848
#> GSM648647 2 0.0938 0.7582 0.012 0.988
#> GSM648667 2 0.0000 0.7597 0.000 1.000
#> GSM648695 2 0.0000 0.7597 0.000 1.000
#> GSM648704 2 0.2423 0.7444 0.040 0.960
#> GSM648706 2 0.2423 0.7444 0.040 0.960
#> GSM648593 2 0.2043 0.7621 0.032 0.968
#> GSM648594 2 0.2043 0.7621 0.032 0.968
#> GSM648600 2 0.6247 0.6925 0.156 0.844
#> GSM648621 2 0.9970 0.0479 0.468 0.532
#> GSM648622 2 0.9881 0.1635 0.436 0.564
#> GSM648623 1 0.8909 0.6206 0.692 0.308
#> GSM648636 2 0.2043 0.7621 0.032 0.968
#> GSM648655 2 0.1414 0.7624 0.020 0.980
#> GSM648661 1 0.9491 0.5062 0.632 0.368
#> GSM648664 2 0.9970 0.0473 0.468 0.532
#> GSM648683 2 0.9881 0.1635 0.436 0.564
#> GSM648685 2 0.9754 0.2402 0.408 0.592
#> GSM648702 2 0.2043 0.7621 0.032 0.968
#> GSM648597 2 0.6247 0.6925 0.156 0.844
#> GSM648603 2 0.9970 0.0479 0.468 0.532
#> GSM648606 1 0.8955 0.6312 0.688 0.312
#> GSM648613 1 0.8861 0.6386 0.696 0.304
#> GSM648619 1 0.8763 0.6359 0.704 0.296
#> GSM648654 1 0.9427 0.5422 0.640 0.360
#> GSM648663 1 0.9087 0.6169 0.676 0.324
#> GSM648670 2 0.2236 0.7622 0.036 0.964
#> GSM648707 1 0.2778 0.7165 0.952 0.048
#> GSM648615 2 0.0938 0.7582 0.012 0.988
#> GSM648643 2 0.0938 0.7582 0.012 0.988
#> GSM648650 2 0.0000 0.7597 0.000 1.000
#> GSM648656 2 0.2423 0.7444 0.040 0.960
#> GSM648715 2 0.0938 0.7582 0.012 0.988
#> GSM648598 2 0.6247 0.6925 0.156 0.844
#> GSM648601 2 0.6148 0.6960 0.152 0.848
#> GSM648602 2 0.9881 0.1635 0.436 0.564
#> GSM648604 2 0.9970 0.0479 0.468 0.532
#> GSM648614 2 0.9732 0.2011 0.404 0.596
#> GSM648624 2 0.9881 0.1635 0.436 0.564
#> GSM648625 2 0.4298 0.7335 0.088 0.912
#> GSM648629 1 0.9998 0.1002 0.508 0.492
#> GSM648634 2 0.6048 0.6995 0.148 0.852
#> GSM648648 2 0.2043 0.7621 0.032 0.968
#> GSM648651 2 0.9881 0.1635 0.436 0.564
#> GSM648657 2 0.4939 0.7276 0.108 0.892
#> GSM648660 2 0.6148 0.6960 0.152 0.848
#> GSM648697 2 0.6247 0.6925 0.156 0.844
#> GSM648710 1 0.9732 0.4151 0.596 0.404
#> GSM648591 1 0.8713 0.6394 0.708 0.292
#> GSM648592 2 0.1843 0.7619 0.028 0.972
#> GSM648607 1 0.9661 0.4477 0.608 0.392
#> GSM648611 1 0.2236 0.7176 0.964 0.036
#> GSM648612 1 0.8763 0.6359 0.704 0.296
#> GSM648616 1 0.2423 0.7164 0.960 0.040
#> GSM648617 2 0.6247 0.6925 0.156 0.844
#> GSM648626 1 0.9896 0.3021 0.560 0.440
#> GSM648711 1 0.8909 0.6206 0.692 0.308
#> GSM648712 1 0.8763 0.6359 0.704 0.296
#> GSM648713 1 0.8909 0.6206 0.692 0.308
#> GSM648714 2 0.9866 0.1080 0.432 0.568
#> GSM648716 1 0.8661 0.6425 0.712 0.288
#> GSM648717 1 0.8144 0.6616 0.748 0.252
#> GSM648590 2 0.2043 0.7622 0.032 0.968
#> GSM648596 2 0.0938 0.7582 0.012 0.988
#> GSM648642 2 0.0672 0.7589 0.008 0.992
#> GSM648696 2 0.1414 0.7624 0.020 0.980
#> GSM648705 2 0.2043 0.7621 0.032 0.968
#> GSM648718 2 0.0672 0.7589 0.008 0.992
#> GSM648599 2 0.9881 0.1635 0.436 0.564
#> GSM648608 2 0.9977 0.0305 0.472 0.528
#> GSM648609 2 0.9963 0.0635 0.464 0.536
#> GSM648610 2 0.9970 0.0479 0.468 0.532
#> GSM648633 2 0.4939 0.7276 0.108 0.892
#> GSM648644 2 0.2423 0.7444 0.040 0.960
#> GSM648652 2 0.2043 0.7621 0.032 0.968
#> GSM648653 2 0.6247 0.6925 0.156 0.844
#> GSM648658 2 0.2043 0.7621 0.032 0.968
#> GSM648659 2 0.0938 0.7582 0.012 0.988
#> GSM648662 2 0.9922 0.0609 0.448 0.552
#> GSM648665 2 0.9732 0.2011 0.404 0.596
#> GSM648666 2 0.9732 0.2505 0.404 0.596
#> GSM648680 2 0.2043 0.7621 0.032 0.968
#> GSM648684 2 0.9881 0.1635 0.436 0.564
#> GSM648709 2 0.0672 0.7591 0.008 0.992
#> GSM648719 2 0.6148 0.6960 0.152 0.848
#> GSM648627 1 0.8763 0.6359 0.704 0.296
#> GSM648637 2 0.6887 0.6369 0.184 0.816
#> GSM648638 2 0.9209 0.4225 0.336 0.664
#> GSM648641 1 0.0376 0.7191 0.996 0.004
#> GSM648672 2 0.6438 0.6328 0.164 0.836
#> GSM648674 2 0.6887 0.6369 0.184 0.816
#> GSM648703 2 0.6623 0.6350 0.172 0.828
#> GSM648631 1 0.0376 0.7191 0.996 0.004
#> GSM648669 1 0.9909 0.0626 0.556 0.444
#> GSM648671 1 0.9909 0.0626 0.556 0.444
#> GSM648678 2 0.6438 0.6328 0.164 0.836
#> GSM648679 2 0.9129 0.4189 0.328 0.672
#> GSM648681 2 0.0938 0.7582 0.012 0.988
#> GSM648686 1 0.0672 0.7126 0.992 0.008
#> GSM648689 1 0.2043 0.6975 0.968 0.032
#> GSM648690 1 0.0000 0.7180 1.000 0.000
#> GSM648691 1 0.0000 0.7180 1.000 0.000
#> GSM648693 1 0.0376 0.7191 0.996 0.004
#> GSM648700 2 0.6973 0.6371 0.188 0.812
#> GSM648630 1 0.0000 0.7180 1.000 0.000
#> GSM648632 1 0.0376 0.7191 0.996 0.004
#> GSM648639 1 0.0000 0.7180 1.000 0.000
#> GSM648640 1 0.0000 0.7180 1.000 0.000
#> GSM648668 2 0.6801 0.6366 0.180 0.820
#> GSM648676 2 0.6801 0.6366 0.180 0.820
#> GSM648692 1 0.0000 0.7180 1.000 0.000
#> GSM648694 1 0.0376 0.7191 0.996 0.004
#> GSM648699 2 0.6623 0.6350 0.172 0.828
#> GSM648701 2 0.6623 0.6350 0.172 0.828
#> GSM648673 1 0.9909 0.0626 0.556 0.444
#> GSM648677 2 0.6531 0.6395 0.168 0.832
#> GSM648687 1 0.0000 0.7180 1.000 0.000
#> GSM648688 1 0.0376 0.7191 0.996 0.004
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM648605 2 0.1989 0.848 0.048 0.948 0.004
#> GSM648618 1 0.0237 0.894 0.996 0.000 0.004
#> GSM648620 2 0.6033 0.546 0.336 0.660 0.004
#> GSM648646 2 0.1647 0.852 0.036 0.960 0.004
#> GSM648649 1 0.3816 0.769 0.852 0.148 0.000
#> GSM648675 2 0.6111 0.415 0.396 0.604 0.000
#> GSM648682 2 0.1643 0.851 0.044 0.956 0.000
#> GSM648698 2 0.1647 0.852 0.036 0.960 0.004
#> GSM648708 2 0.5835 0.541 0.340 0.660 0.000
#> GSM648628 1 0.6299 0.126 0.524 0.000 0.476
#> GSM648595 1 0.5882 0.373 0.652 0.348 0.000
#> GSM648635 1 0.1031 0.884 0.976 0.024 0.000
#> GSM648645 1 0.0237 0.894 0.996 0.000 0.004
#> GSM648647 2 0.1647 0.852 0.036 0.960 0.004
#> GSM648667 2 0.5835 0.541 0.340 0.660 0.000
#> GSM648695 2 0.5810 0.549 0.336 0.664 0.000
#> GSM648704 2 0.1129 0.845 0.020 0.976 0.004
#> GSM648706 2 0.1647 0.852 0.036 0.960 0.004
#> GSM648593 1 0.3941 0.758 0.844 0.156 0.000
#> GSM648594 1 0.0237 0.894 0.996 0.000 0.004
#> GSM648600 1 0.0237 0.894 0.996 0.004 0.000
#> GSM648621 1 0.0000 0.895 1.000 0.000 0.000
#> GSM648622 1 0.0000 0.895 1.000 0.000 0.000
#> GSM648623 1 0.0237 0.894 0.996 0.000 0.004
#> GSM648636 1 0.4062 0.748 0.836 0.164 0.000
#> GSM648655 1 0.4062 0.748 0.836 0.164 0.000
#> GSM648661 1 0.0000 0.895 1.000 0.000 0.000
#> GSM648664 1 0.0000 0.895 1.000 0.000 0.000
#> GSM648683 1 0.0000 0.895 1.000 0.000 0.000
#> GSM648685 1 0.0000 0.895 1.000 0.000 0.000
#> GSM648702 1 0.3482 0.791 0.872 0.128 0.000
#> GSM648597 1 0.0237 0.894 0.996 0.000 0.004
#> GSM648603 1 0.0237 0.894 0.996 0.000 0.004
#> GSM648606 1 0.8941 0.350 0.544 0.156 0.300
#> GSM648613 1 0.9017 0.270 0.516 0.148 0.336
#> GSM648619 1 0.4504 0.719 0.804 0.000 0.196
#> GSM648654 1 0.6410 0.716 0.764 0.092 0.144
#> GSM648663 1 0.8607 0.458 0.592 0.152 0.256
#> GSM648670 2 0.6169 0.486 0.360 0.636 0.004
#> GSM648707 3 0.6215 0.172 0.428 0.000 0.572
#> GSM648615 2 0.1647 0.852 0.036 0.960 0.004
#> GSM648643 2 0.1529 0.852 0.040 0.960 0.000
#> GSM648650 2 0.5835 0.541 0.340 0.660 0.000
#> GSM648656 2 0.1399 0.849 0.028 0.968 0.004
#> GSM648715 2 0.1647 0.852 0.036 0.960 0.004
#> GSM648598 1 0.0237 0.894 0.996 0.004 0.000
#> GSM648601 1 0.0237 0.894 0.996 0.004 0.000
#> GSM648602 1 0.0000 0.895 1.000 0.000 0.000
#> GSM648604 1 0.0000 0.895 1.000 0.000 0.000
#> GSM648614 1 0.5754 0.530 0.700 0.296 0.004
#> GSM648624 1 0.0000 0.895 1.000 0.000 0.000
#> GSM648625 1 0.2356 0.852 0.928 0.072 0.000
#> GSM648629 1 0.0000 0.895 1.000 0.000 0.000
#> GSM648634 1 0.0237 0.894 0.996 0.004 0.000
#> GSM648648 1 0.1163 0.881 0.972 0.028 0.000
#> GSM648651 1 0.0000 0.895 1.000 0.000 0.000
#> GSM648657 1 0.0237 0.894 0.996 0.000 0.004
#> GSM648660 1 0.0237 0.894 0.996 0.000 0.004
#> GSM648697 1 0.0237 0.894 0.996 0.004 0.000
#> GSM648710 1 0.0000 0.895 1.000 0.000 0.000
#> GSM648591 1 0.0237 0.894 0.996 0.000 0.004
#> GSM648592 1 0.1525 0.878 0.964 0.032 0.004
#> GSM648607 1 0.0000 0.895 1.000 0.000 0.000
#> GSM648611 3 0.3879 0.735 0.152 0.000 0.848
#> GSM648612 1 0.5178 0.636 0.744 0.000 0.256
#> GSM648616 3 0.6973 0.208 0.416 0.020 0.564
#> GSM648617 1 0.0237 0.894 0.996 0.004 0.000
#> GSM648626 1 0.0237 0.894 0.996 0.000 0.004
#> GSM648711 1 0.1411 0.873 0.964 0.000 0.036
#> GSM648712 1 0.5178 0.636 0.744 0.000 0.256
#> GSM648713 1 0.0000 0.895 1.000 0.000 0.000
#> GSM648714 1 0.5929 0.479 0.676 0.320 0.004
#> GSM648716 1 0.5178 0.636 0.744 0.000 0.256
#> GSM648717 1 0.7353 0.202 0.532 0.032 0.436
#> GSM648590 2 0.5254 0.668 0.264 0.736 0.000
#> GSM648596 2 0.1647 0.852 0.036 0.960 0.004
#> GSM648642 2 0.1860 0.847 0.052 0.948 0.000
#> GSM648696 1 0.3941 0.759 0.844 0.156 0.000
#> GSM648705 1 0.4121 0.742 0.832 0.168 0.000
#> GSM648718 2 0.1529 0.852 0.040 0.960 0.000
#> GSM648599 1 0.0000 0.895 1.000 0.000 0.000
#> GSM648608 1 0.0000 0.895 1.000 0.000 0.000
#> GSM648609 1 0.0000 0.895 1.000 0.000 0.000
#> GSM648610 1 0.0000 0.895 1.000 0.000 0.000
#> GSM648633 1 0.0237 0.894 0.996 0.004 0.000
#> GSM648644 2 0.0829 0.840 0.012 0.984 0.004
#> GSM648652 1 0.1031 0.884 0.976 0.024 0.000
#> GSM648653 1 0.0237 0.894 0.996 0.004 0.000
#> GSM648658 1 0.0237 0.894 0.996 0.004 0.000
#> GSM648659 2 0.1525 0.851 0.032 0.964 0.004
#> GSM648662 1 0.4047 0.773 0.848 0.148 0.004
#> GSM648665 1 0.4172 0.764 0.840 0.156 0.004
#> GSM648666 1 0.0000 0.895 1.000 0.000 0.000
#> GSM648680 1 0.0237 0.894 0.996 0.004 0.000
#> GSM648684 1 0.0000 0.895 1.000 0.000 0.000
#> GSM648709 2 0.5785 0.608 0.300 0.696 0.004
#> GSM648719 1 0.0237 0.894 0.996 0.004 0.000
#> GSM648627 1 0.5178 0.636 0.744 0.000 0.256
#> GSM648637 2 0.3028 0.824 0.032 0.920 0.048
#> GSM648638 2 0.3237 0.824 0.032 0.912 0.056
#> GSM648641 3 0.0237 0.871 0.004 0.000 0.996
#> GSM648672 2 0.1643 0.819 0.000 0.956 0.044
#> GSM648674 2 0.3028 0.824 0.032 0.920 0.048
#> GSM648703 2 0.2918 0.825 0.032 0.924 0.044
#> GSM648631 3 0.0237 0.871 0.004 0.000 0.996
#> GSM648669 3 0.6045 0.409 0.000 0.380 0.620
#> GSM648671 3 0.6045 0.409 0.000 0.380 0.620
#> GSM648678 2 0.0000 0.831 0.000 1.000 0.000
#> GSM648679 2 0.3028 0.824 0.032 0.920 0.048
#> GSM648681 2 0.1765 0.852 0.040 0.956 0.004
#> GSM648686 3 0.0237 0.871 0.004 0.000 0.996
#> GSM648689 3 0.0237 0.871 0.004 0.000 0.996
#> GSM648690 3 0.0237 0.871 0.004 0.000 0.996
#> GSM648691 3 0.0237 0.871 0.004 0.000 0.996
#> GSM648693 3 0.0237 0.871 0.004 0.000 0.996
#> GSM648700 2 0.3481 0.814 0.052 0.904 0.044
#> GSM648630 3 0.0237 0.871 0.004 0.000 0.996
#> GSM648632 3 0.0237 0.871 0.004 0.000 0.996
#> GSM648639 3 0.0000 0.868 0.000 0.000 1.000
#> GSM648640 3 0.0237 0.871 0.004 0.000 0.996
#> GSM648668 2 0.2918 0.825 0.032 0.924 0.044
#> GSM648676 2 0.2918 0.825 0.032 0.924 0.044
#> GSM648692 3 0.0237 0.871 0.004 0.000 0.996
#> GSM648694 3 0.0237 0.871 0.004 0.000 0.996
#> GSM648699 2 0.2918 0.825 0.032 0.924 0.044
#> GSM648701 2 0.2793 0.826 0.028 0.928 0.044
#> GSM648673 3 0.6126 0.362 0.000 0.400 0.600
#> GSM648677 2 0.2918 0.825 0.032 0.924 0.044
#> GSM648687 3 0.0475 0.868 0.004 0.004 0.992
#> GSM648688 3 0.0237 0.871 0.004 0.000 0.996
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM648605 2 0.4423 0.8114 0.036 0.788 0.000 0.176
#> GSM648618 1 0.3142 0.7905 0.860 0.132 0.000 0.008
#> GSM648620 2 0.4638 0.7993 0.060 0.788 0.000 0.152
#> GSM648646 2 0.3907 0.7883 0.000 0.768 0.000 0.232
#> GSM648649 1 0.3668 0.7138 0.808 0.188 0.000 0.004
#> GSM648675 1 0.7196 0.3673 0.552 0.236 0.000 0.212
#> GSM648682 2 0.4011 0.8079 0.008 0.784 0.000 0.208
#> GSM648698 2 0.4011 0.8079 0.008 0.784 0.000 0.208
#> GSM648708 2 0.4638 0.7993 0.060 0.788 0.000 0.152
#> GSM648628 1 0.8158 0.3915 0.504 0.208 0.256 0.032
#> GSM648595 1 0.3893 0.7047 0.796 0.196 0.000 0.008
#> GSM648635 1 0.1978 0.8075 0.928 0.068 0.000 0.004
#> GSM648645 1 0.0817 0.8281 0.976 0.024 0.000 0.000
#> GSM648647 2 0.4423 0.8114 0.036 0.788 0.000 0.176
#> GSM648667 2 0.4711 0.6980 0.152 0.784 0.000 0.064
#> GSM648695 2 0.4638 0.7993 0.060 0.788 0.000 0.152
#> GSM648704 2 0.3907 0.7883 0.000 0.768 0.000 0.232
#> GSM648706 2 0.3907 0.7883 0.000 0.768 0.000 0.232
#> GSM648593 1 0.3257 0.7476 0.844 0.152 0.000 0.004
#> GSM648594 1 0.2521 0.8136 0.912 0.024 0.000 0.064
#> GSM648600 1 0.0895 0.8275 0.976 0.020 0.000 0.004
#> GSM648621 1 0.1305 0.8269 0.960 0.036 0.000 0.004
#> GSM648622 1 0.0000 0.8290 1.000 0.000 0.000 0.000
#> GSM648623 1 0.3708 0.7756 0.832 0.148 0.000 0.020
#> GSM648636 1 0.3498 0.7393 0.832 0.160 0.000 0.008
#> GSM648655 1 0.3450 0.7429 0.836 0.156 0.000 0.008
#> GSM648661 1 0.1792 0.8217 0.932 0.068 0.000 0.000
#> GSM648664 1 0.1792 0.8217 0.932 0.068 0.000 0.000
#> GSM648683 1 0.1978 0.8216 0.928 0.068 0.000 0.004
#> GSM648685 1 0.1792 0.8257 0.932 0.068 0.000 0.000
#> GSM648702 1 0.3401 0.7465 0.840 0.152 0.000 0.008
#> GSM648597 1 0.5092 0.7403 0.764 0.140 0.000 0.096
#> GSM648603 1 0.3597 0.7778 0.836 0.148 0.000 0.016
#> GSM648606 2 0.7791 0.0258 0.240 0.544 0.192 0.024
#> GSM648613 2 0.8468 -0.1995 0.336 0.400 0.236 0.028
#> GSM648619 1 0.6714 0.6483 0.660 0.208 0.108 0.024
#> GSM648654 1 0.7016 0.4716 0.516 0.380 0.096 0.008
#> GSM648663 1 0.7673 0.3776 0.456 0.404 0.116 0.024
#> GSM648670 4 0.5970 0.3194 0.348 0.052 0.000 0.600
#> GSM648707 1 0.8779 0.3654 0.508 0.144 0.224 0.124
#> GSM648615 2 0.3972 0.8083 0.008 0.788 0.000 0.204
#> GSM648643 2 0.3907 0.7883 0.000 0.768 0.000 0.232
#> GSM648650 2 0.4669 0.6780 0.168 0.780 0.000 0.052
#> GSM648656 2 0.3907 0.7883 0.000 0.768 0.000 0.232
#> GSM648715 2 0.4423 0.8114 0.036 0.788 0.000 0.176
#> GSM648598 1 0.0707 0.8275 0.980 0.020 0.000 0.000
#> GSM648601 1 0.0707 0.8275 0.980 0.020 0.000 0.000
#> GSM648602 1 0.0376 0.8292 0.992 0.004 0.000 0.004
#> GSM648604 1 0.1792 0.8217 0.932 0.068 0.000 0.000
#> GSM648614 2 0.4158 0.5357 0.224 0.768 0.000 0.008
#> GSM648624 1 0.0000 0.8290 1.000 0.000 0.000 0.000
#> GSM648625 1 0.4072 0.6304 0.748 0.252 0.000 0.000
#> GSM648629 1 0.1792 0.8217 0.932 0.068 0.000 0.000
#> GSM648634 1 0.0895 0.8275 0.976 0.020 0.000 0.004
#> GSM648648 1 0.2831 0.7736 0.876 0.120 0.000 0.004
#> GSM648651 1 0.0000 0.8290 1.000 0.000 0.000 0.000
#> GSM648657 1 0.0817 0.8281 0.976 0.024 0.000 0.000
#> GSM648660 1 0.0707 0.8275 0.980 0.020 0.000 0.000
#> GSM648697 1 0.1042 0.8269 0.972 0.020 0.000 0.008
#> GSM648710 1 0.1792 0.8217 0.932 0.068 0.000 0.000
#> GSM648591 1 0.5369 0.7275 0.744 0.144 0.000 0.112
#> GSM648592 1 0.4610 0.7460 0.744 0.236 0.000 0.020
#> GSM648607 1 0.4098 0.7585 0.784 0.204 0.000 0.012
#> GSM648611 3 0.5612 0.6904 0.032 0.208 0.728 0.032
#> GSM648612 1 0.6858 0.6391 0.652 0.208 0.112 0.028
#> GSM648616 1 0.9354 0.0695 0.364 0.144 0.148 0.344
#> GSM648617 1 0.2222 0.8259 0.924 0.060 0.000 0.016
#> GSM648626 1 0.3757 0.7734 0.828 0.152 0.000 0.020
#> GSM648711 1 0.4279 0.7558 0.780 0.204 0.004 0.012
#> GSM648712 1 0.6947 0.6383 0.648 0.208 0.112 0.032
#> GSM648713 1 0.4542 0.7479 0.768 0.208 0.004 0.020
#> GSM648714 2 0.2401 0.5249 0.092 0.904 0.000 0.004
#> GSM648716 1 0.6765 0.6434 0.656 0.208 0.112 0.024
#> GSM648717 3 0.8289 0.1931 0.312 0.208 0.452 0.028
#> GSM648590 1 0.6949 0.2221 0.528 0.348 0.000 0.124
#> GSM648596 2 0.3764 0.8010 0.000 0.784 0.000 0.216
#> GSM648642 2 0.4423 0.8114 0.036 0.788 0.000 0.176
#> GSM648696 1 0.4632 0.5498 0.688 0.308 0.000 0.004
#> GSM648705 1 0.3710 0.7096 0.804 0.192 0.000 0.004
#> GSM648718 2 0.4011 0.8079 0.008 0.784 0.000 0.208
#> GSM648599 1 0.0188 0.8292 0.996 0.000 0.000 0.004
#> GSM648608 1 0.1978 0.8216 0.928 0.068 0.000 0.004
#> GSM648609 1 0.1792 0.8217 0.932 0.068 0.000 0.000
#> GSM648610 1 0.1978 0.8216 0.928 0.068 0.000 0.004
#> GSM648633 1 0.0707 0.8275 0.980 0.020 0.000 0.000
#> GSM648644 2 0.3907 0.7883 0.000 0.768 0.000 0.232
#> GSM648652 1 0.2773 0.7764 0.880 0.116 0.000 0.004
#> GSM648653 1 0.0895 0.8275 0.976 0.020 0.000 0.004
#> GSM648658 1 0.1042 0.8269 0.972 0.020 0.000 0.008
#> GSM648659 2 0.4011 0.8085 0.008 0.784 0.000 0.208
#> GSM648662 1 0.4567 0.6309 0.716 0.276 0.000 0.008
#> GSM648665 1 0.4941 0.2615 0.564 0.436 0.000 0.000
#> GSM648666 1 0.0336 0.8289 0.992 0.008 0.000 0.000
#> GSM648680 1 0.0895 0.8269 0.976 0.020 0.000 0.004
#> GSM648684 1 0.1902 0.8227 0.932 0.064 0.000 0.004
#> GSM648709 2 0.4609 0.8016 0.056 0.788 0.000 0.156
#> GSM648719 1 0.0707 0.8275 0.980 0.020 0.000 0.000
#> GSM648627 1 0.6858 0.6427 0.652 0.208 0.112 0.028
#> GSM648637 4 0.2973 0.7848 0.000 0.144 0.000 0.856
#> GSM648638 4 0.3249 0.7834 0.000 0.140 0.008 0.852
#> GSM648641 3 0.3913 0.7737 0.000 0.148 0.824 0.028
#> GSM648672 4 0.2408 0.8204 0.000 0.104 0.000 0.896
#> GSM648674 4 0.0921 0.8127 0.000 0.028 0.000 0.972
#> GSM648703 4 0.2654 0.8186 0.004 0.108 0.000 0.888
#> GSM648631 3 0.0000 0.8975 0.000 0.000 1.000 0.000
#> GSM648669 4 0.3486 0.6421 0.000 0.000 0.188 0.812
#> GSM648671 4 0.3486 0.6421 0.000 0.000 0.188 0.812
#> GSM648678 4 0.3444 0.7427 0.000 0.184 0.000 0.816
#> GSM648679 4 0.0707 0.8089 0.000 0.020 0.000 0.980
#> GSM648681 4 0.5596 0.3300 0.036 0.332 0.000 0.632
#> GSM648686 3 0.0000 0.8975 0.000 0.000 1.000 0.000
#> GSM648689 3 0.0000 0.8975 0.000 0.000 1.000 0.000
#> GSM648690 3 0.0000 0.8975 0.000 0.000 1.000 0.000
#> GSM648691 3 0.0000 0.8975 0.000 0.000 1.000 0.000
#> GSM648693 3 0.0000 0.8975 0.000 0.000 1.000 0.000
#> GSM648700 4 0.1767 0.8192 0.012 0.044 0.000 0.944
#> GSM648630 3 0.0000 0.8975 0.000 0.000 1.000 0.000
#> GSM648632 3 0.0000 0.8975 0.000 0.000 1.000 0.000
#> GSM648639 3 0.6492 0.6106 0.000 0.144 0.636 0.220
#> GSM648640 3 0.1629 0.8706 0.000 0.024 0.952 0.024
#> GSM648668 4 0.2888 0.8102 0.004 0.124 0.000 0.872
#> GSM648676 4 0.2593 0.8206 0.004 0.104 0.000 0.892
#> GSM648692 3 0.0000 0.8975 0.000 0.000 1.000 0.000
#> GSM648694 3 0.0000 0.8975 0.000 0.000 1.000 0.000
#> GSM648699 4 0.1576 0.8192 0.004 0.048 0.000 0.948
#> GSM648701 4 0.2593 0.8206 0.004 0.104 0.000 0.892
#> GSM648673 4 0.3356 0.6592 0.000 0.000 0.176 0.824
#> GSM648677 4 0.2944 0.8067 0.004 0.128 0.000 0.868
#> GSM648687 3 0.1118 0.8703 0.000 0.000 0.964 0.036
#> GSM648688 3 0.0000 0.8975 0.000 0.000 1.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM648605 2 0.0162 0.9447 0.004 0.996 0.000 0.000 0.000
#> GSM648618 1 0.4565 -0.0574 0.580 0.000 0.000 0.012 0.408
#> GSM648620 2 0.0609 0.9358 0.020 0.980 0.000 0.000 0.000
#> GSM648646 2 0.0451 0.9410 0.000 0.988 0.000 0.004 0.008
#> GSM648649 1 0.1731 0.7831 0.932 0.060 0.000 0.004 0.004
#> GSM648675 1 0.6253 0.4799 0.656 0.088 0.000 0.164 0.092
#> GSM648682 2 0.0000 0.9447 0.000 1.000 0.000 0.000 0.000
#> GSM648698 2 0.0000 0.9447 0.000 1.000 0.000 0.000 0.000
#> GSM648708 2 0.0510 0.9395 0.016 0.984 0.000 0.000 0.000
#> GSM648628 5 0.5072 0.7109 0.188 0.000 0.116 0.000 0.696
#> GSM648595 1 0.2674 0.7700 0.896 0.060 0.000 0.012 0.032
#> GSM648635 1 0.0880 0.7980 0.968 0.032 0.000 0.000 0.000
#> GSM648645 1 0.0613 0.8008 0.984 0.004 0.000 0.008 0.004
#> GSM648647 2 0.0162 0.9447 0.004 0.996 0.000 0.000 0.000
#> GSM648667 2 0.1965 0.8442 0.096 0.904 0.000 0.000 0.000
#> GSM648695 2 0.0510 0.9395 0.016 0.984 0.000 0.000 0.000
#> GSM648704 2 0.0579 0.9385 0.000 0.984 0.000 0.008 0.008
#> GSM648706 2 0.0162 0.9431 0.000 0.996 0.000 0.004 0.000
#> GSM648593 1 0.1444 0.7907 0.948 0.040 0.000 0.012 0.000
#> GSM648594 1 0.4412 0.5777 0.756 0.012 0.000 0.040 0.192
#> GSM648600 1 0.1243 0.8006 0.960 0.004 0.000 0.008 0.028
#> GSM648621 1 0.1597 0.7961 0.940 0.000 0.000 0.012 0.048
#> GSM648622 1 0.0880 0.7970 0.968 0.000 0.000 0.032 0.000
#> GSM648623 5 0.4897 0.5062 0.460 0.000 0.000 0.024 0.516
#> GSM648636 1 0.2591 0.7766 0.904 0.044 0.000 0.020 0.032
#> GSM648655 1 0.2515 0.7796 0.908 0.040 0.000 0.020 0.032
#> GSM648661 1 0.3909 0.5869 0.760 0.000 0.000 0.024 0.216
#> GSM648664 1 0.3779 0.6111 0.776 0.000 0.000 0.024 0.200
#> GSM648683 1 0.3687 0.6735 0.792 0.000 0.000 0.028 0.180
#> GSM648685 1 0.2362 0.7513 0.900 0.000 0.000 0.024 0.076
#> GSM648702 1 0.2313 0.7842 0.916 0.040 0.000 0.012 0.032
#> GSM648597 5 0.5735 0.4735 0.376 0.000 0.000 0.092 0.532
#> GSM648603 5 0.4517 0.5541 0.436 0.000 0.000 0.008 0.556
#> GSM648606 5 0.6729 0.5850 0.116 0.212 0.076 0.000 0.596
#> GSM648613 5 0.5692 0.6261 0.088 0.096 0.104 0.000 0.712
#> GSM648619 5 0.4967 0.7104 0.280 0.000 0.060 0.000 0.660
#> GSM648654 5 0.7726 0.6153 0.204 0.196 0.060 0.024 0.516
#> GSM648663 5 0.6811 0.6338 0.164 0.192 0.060 0.000 0.584
#> GSM648670 1 0.7322 -0.1643 0.368 0.028 0.000 0.364 0.240
#> GSM648707 5 0.4677 0.4936 0.132 0.000 0.008 0.104 0.756
#> GSM648615 2 0.0000 0.9447 0.000 1.000 0.000 0.000 0.000
#> GSM648643 2 0.0579 0.9385 0.000 0.984 0.000 0.008 0.008
#> GSM648650 2 0.2773 0.7476 0.164 0.836 0.000 0.000 0.000
#> GSM648656 2 0.0579 0.9385 0.000 0.984 0.000 0.008 0.008
#> GSM648715 2 0.0290 0.9439 0.008 0.992 0.000 0.000 0.000
#> GSM648598 1 0.0000 0.8017 1.000 0.000 0.000 0.000 0.000
#> GSM648601 1 0.0162 0.8013 0.996 0.000 0.000 0.004 0.000
#> GSM648602 1 0.1082 0.8007 0.964 0.000 0.000 0.008 0.028
#> GSM648604 1 0.3779 0.6111 0.776 0.000 0.000 0.024 0.200
#> GSM648614 2 0.4552 0.6714 0.040 0.760 0.000 0.024 0.176
#> GSM648624 1 0.0955 0.7965 0.968 0.000 0.000 0.028 0.004
#> GSM648625 1 0.3511 0.6182 0.800 0.184 0.000 0.004 0.012
#> GSM648629 1 0.3909 0.5869 0.760 0.000 0.000 0.024 0.216
#> GSM648634 1 0.1116 0.8005 0.964 0.004 0.000 0.004 0.028
#> GSM648648 1 0.1043 0.7947 0.960 0.040 0.000 0.000 0.000
#> GSM648651 1 0.0703 0.7990 0.976 0.000 0.000 0.024 0.000
#> GSM648657 1 0.0740 0.8000 0.980 0.004 0.000 0.008 0.008
#> GSM648660 1 0.0290 0.8006 0.992 0.000 0.000 0.008 0.000
#> GSM648697 1 0.0992 0.8019 0.968 0.000 0.000 0.024 0.008
#> GSM648710 1 0.3909 0.5869 0.760 0.000 0.000 0.024 0.216
#> GSM648591 5 0.4944 0.6107 0.208 0.000 0.000 0.092 0.700
#> GSM648592 5 0.5894 0.4177 0.432 0.068 0.000 0.012 0.488
#> GSM648607 5 0.4757 0.5920 0.380 0.000 0.000 0.024 0.596
#> GSM648611 5 0.4147 0.3769 0.008 0.000 0.316 0.000 0.676
#> GSM648612 5 0.4946 0.7121 0.276 0.000 0.060 0.000 0.664
#> GSM648616 5 0.4863 0.4404 0.116 0.000 0.008 0.136 0.740
#> GSM648617 1 0.3675 0.4962 0.772 0.004 0.000 0.008 0.216
#> GSM648626 5 0.4489 0.5748 0.420 0.000 0.000 0.008 0.572
#> GSM648711 5 0.4895 0.5982 0.376 0.000 0.004 0.024 0.596
#> GSM648712 5 0.4806 0.7100 0.252 0.000 0.060 0.000 0.688
#> GSM648713 5 0.4270 0.6616 0.336 0.000 0.004 0.004 0.656
#> GSM648714 2 0.3282 0.7338 0.008 0.804 0.000 0.000 0.188
#> GSM648716 5 0.4967 0.7104 0.280 0.000 0.060 0.000 0.660
#> GSM648717 5 0.5500 0.6457 0.144 0.008 0.172 0.000 0.676
#> GSM648590 1 0.5145 0.4806 0.664 0.280 0.000 0.024 0.032
#> GSM648596 2 0.0290 0.9431 0.000 0.992 0.000 0.000 0.008
#> GSM648642 2 0.0290 0.9439 0.008 0.992 0.000 0.000 0.000
#> GSM648696 1 0.3916 0.6387 0.780 0.188 0.000 0.004 0.028
#> GSM648705 1 0.1478 0.7814 0.936 0.064 0.000 0.000 0.000
#> GSM648718 2 0.0290 0.9431 0.000 0.992 0.000 0.000 0.008
#> GSM648599 1 0.1195 0.8003 0.960 0.000 0.000 0.012 0.028
#> GSM648608 1 0.4083 0.6078 0.744 0.000 0.000 0.028 0.228
#> GSM648609 1 0.3745 0.6174 0.780 0.000 0.000 0.024 0.196
#> GSM648610 1 0.4024 0.6208 0.752 0.000 0.000 0.028 0.220
#> GSM648633 1 0.0324 0.8013 0.992 0.004 0.000 0.004 0.000
#> GSM648644 2 0.0579 0.9385 0.000 0.984 0.000 0.008 0.008
#> GSM648652 1 0.1043 0.7947 0.960 0.040 0.000 0.000 0.000
#> GSM648653 1 0.0955 0.8005 0.968 0.000 0.000 0.004 0.028
#> GSM648658 1 0.1701 0.7976 0.944 0.012 0.000 0.016 0.028
#> GSM648659 2 0.0932 0.9324 0.004 0.972 0.000 0.020 0.004
#> GSM648662 1 0.6984 -0.0210 0.480 0.200 0.000 0.024 0.296
#> GSM648665 1 0.7165 -0.0676 0.412 0.348 0.000 0.024 0.216
#> GSM648666 1 0.0898 0.8015 0.972 0.000 0.000 0.020 0.008
#> GSM648680 1 0.0404 0.8019 0.988 0.012 0.000 0.000 0.000
#> GSM648684 1 0.3002 0.7401 0.856 0.000 0.000 0.028 0.116
#> GSM648709 2 0.0404 0.9421 0.012 0.988 0.000 0.000 0.000
#> GSM648719 1 0.0162 0.8013 0.996 0.000 0.000 0.004 0.000
#> GSM648627 5 0.4806 0.7100 0.252 0.000 0.060 0.000 0.688
#> GSM648637 4 0.5900 0.7543 0.000 0.212 0.000 0.600 0.188
#> GSM648638 4 0.6519 0.6288 0.000 0.204 0.000 0.456 0.340
#> GSM648641 3 0.4341 0.2946 0.000 0.000 0.592 0.004 0.404
#> GSM648672 4 0.3975 0.8315 0.000 0.144 0.000 0.792 0.064
#> GSM648674 4 0.4204 0.7801 0.000 0.048 0.000 0.756 0.196
#> GSM648703 4 0.2583 0.8268 0.000 0.132 0.000 0.864 0.004
#> GSM648631 3 0.0000 0.9505 0.000 0.000 1.000 0.000 0.000
#> GSM648669 4 0.4046 0.7613 0.000 0.008 0.032 0.780 0.180
#> GSM648671 4 0.4046 0.7613 0.000 0.008 0.032 0.780 0.180
#> GSM648678 4 0.3756 0.7535 0.000 0.248 0.000 0.744 0.008
#> GSM648679 4 0.4644 0.7491 0.000 0.040 0.000 0.680 0.280
#> GSM648681 4 0.7762 0.3175 0.068 0.352 0.000 0.364 0.216
#> GSM648686 3 0.0162 0.9492 0.000 0.000 0.996 0.004 0.000
#> GSM648689 3 0.0162 0.9492 0.000 0.000 0.996 0.004 0.000
#> GSM648690 3 0.0162 0.9492 0.000 0.000 0.996 0.004 0.000
#> GSM648691 3 0.0000 0.9505 0.000 0.000 1.000 0.000 0.000
#> GSM648693 3 0.0000 0.9505 0.000 0.000 1.000 0.000 0.000
#> GSM648700 4 0.2170 0.8241 0.004 0.088 0.000 0.904 0.004
#> GSM648630 3 0.0000 0.9505 0.000 0.000 1.000 0.000 0.000
#> GSM648632 3 0.0000 0.9505 0.000 0.000 1.000 0.000 0.000
#> GSM648639 5 0.5233 0.2106 0.000 0.000 0.192 0.128 0.680
#> GSM648640 3 0.1908 0.8729 0.000 0.000 0.908 0.000 0.092
#> GSM648668 4 0.4215 0.8248 0.000 0.168 0.000 0.768 0.064
#> GSM648676 4 0.2424 0.8270 0.000 0.132 0.000 0.868 0.000
#> GSM648692 3 0.0000 0.9505 0.000 0.000 1.000 0.000 0.000
#> GSM648694 3 0.0000 0.9505 0.000 0.000 1.000 0.000 0.000
#> GSM648699 4 0.1851 0.8244 0.000 0.088 0.000 0.912 0.000
#> GSM648701 4 0.2424 0.8270 0.000 0.132 0.000 0.868 0.000
#> GSM648673 4 0.3934 0.7638 0.000 0.008 0.032 0.792 0.168
#> GSM648677 4 0.3053 0.8186 0.000 0.164 0.000 0.828 0.008
#> GSM648687 3 0.1251 0.9134 0.000 0.000 0.956 0.008 0.036
#> GSM648688 3 0.0000 0.9505 0.000 0.000 1.000 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM648605 2 0.0436 0.9324 0.004 0.988 0.000 0.004 0.004 0.000
#> GSM648618 1 0.5410 0.3418 0.576 0.000 0.000 0.000 0.248 0.176
#> GSM648620 2 0.0551 0.9300 0.008 0.984 0.000 0.004 0.004 0.000
#> GSM648646 2 0.0622 0.9291 0.000 0.980 0.000 0.000 0.008 0.012
#> GSM648649 1 0.0717 0.7819 0.976 0.016 0.000 0.000 0.000 0.008
#> GSM648675 1 0.4836 0.6111 0.736 0.024 0.000 0.056 0.028 0.156
#> GSM648682 2 0.0146 0.9329 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM648698 2 0.0146 0.9331 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM648708 2 0.0291 0.9335 0.004 0.992 0.000 0.000 0.004 0.000
#> GSM648628 5 0.4600 0.6511 0.056 0.000 0.024 0.000 0.708 0.212
#> GSM648595 1 0.2314 0.7690 0.908 0.012 0.000 0.008 0.024 0.048
#> GSM648635 1 0.0405 0.7844 0.988 0.008 0.000 0.000 0.000 0.004
#> GSM648645 1 0.0692 0.7825 0.976 0.000 0.000 0.000 0.004 0.020
#> GSM648647 2 0.0291 0.9335 0.004 0.992 0.000 0.000 0.004 0.000
#> GSM648667 2 0.1349 0.8824 0.056 0.940 0.000 0.000 0.004 0.000
#> GSM648695 2 0.0291 0.9335 0.004 0.992 0.000 0.000 0.004 0.000
#> GSM648704 2 0.0717 0.9272 0.000 0.976 0.000 0.000 0.008 0.016
#> GSM648706 2 0.0405 0.9311 0.000 0.988 0.000 0.000 0.008 0.004
#> GSM648593 1 0.0405 0.7845 0.988 0.008 0.000 0.004 0.000 0.000
#> GSM648594 1 0.4460 0.3358 0.644 0.000 0.000 0.000 0.052 0.304
#> GSM648600 1 0.1700 0.7759 0.928 0.000 0.000 0.000 0.024 0.048
#> GSM648621 1 0.2852 0.7583 0.856 0.000 0.000 0.000 0.064 0.080
#> GSM648622 1 0.2755 0.7198 0.844 0.000 0.000 0.012 0.140 0.004
#> GSM648623 5 0.5424 0.4543 0.288 0.000 0.000 0.008 0.580 0.124
#> GSM648636 1 0.2034 0.7729 0.920 0.008 0.000 0.004 0.024 0.044
#> GSM648655 1 0.2146 0.7714 0.916 0.008 0.000 0.008 0.024 0.044
#> GSM648661 5 0.4262 -0.0719 0.476 0.000 0.000 0.016 0.508 0.000
#> GSM648664 1 0.4192 0.2993 0.572 0.000 0.000 0.016 0.412 0.000
#> GSM648683 1 0.5017 0.3955 0.552 0.000 0.000 0.016 0.388 0.044
#> GSM648685 1 0.3534 0.6038 0.740 0.000 0.000 0.016 0.244 0.000
#> GSM648702 1 0.2034 0.7729 0.920 0.008 0.000 0.004 0.024 0.044
#> GSM648597 6 0.5919 0.2726 0.364 0.000 0.000 0.000 0.212 0.424
#> GSM648603 5 0.5805 0.4079 0.276 0.000 0.000 0.000 0.496 0.228
#> GSM648606 5 0.5317 0.5709 0.012 0.104 0.024 0.008 0.700 0.152
#> GSM648613 5 0.4744 0.5917 0.012 0.044 0.024 0.008 0.736 0.176
#> GSM648619 5 0.4543 0.6813 0.108 0.000 0.016 0.000 0.732 0.144
#> GSM648654 5 0.4106 0.5754 0.060 0.116 0.016 0.016 0.792 0.000
#> GSM648663 5 0.5391 0.5927 0.028 0.096 0.016 0.008 0.700 0.152
#> GSM648670 6 0.5821 0.2793 0.372 0.004 0.000 0.068 0.040 0.516
#> GSM648707 6 0.4023 0.4065 0.036 0.000 0.000 0.004 0.240 0.720
#> GSM648615 2 0.0551 0.9332 0.004 0.984 0.000 0.004 0.000 0.008
#> GSM648643 2 0.0508 0.9292 0.000 0.984 0.000 0.000 0.004 0.012
#> GSM648650 2 0.3380 0.5711 0.244 0.748 0.000 0.000 0.004 0.004
#> GSM648656 2 0.0603 0.9280 0.000 0.980 0.000 0.000 0.004 0.016
#> GSM648715 2 0.0405 0.9333 0.004 0.988 0.000 0.000 0.008 0.000
#> GSM648598 1 0.0405 0.7852 0.988 0.000 0.000 0.000 0.008 0.004
#> GSM648601 1 0.0520 0.7850 0.984 0.000 0.000 0.000 0.008 0.008
#> GSM648602 1 0.2001 0.7781 0.912 0.000 0.000 0.000 0.040 0.048
#> GSM648604 1 0.4192 0.2993 0.572 0.000 0.000 0.016 0.412 0.000
#> GSM648614 2 0.4274 0.4670 0.008 0.640 0.000 0.012 0.336 0.004
#> GSM648624 1 0.2886 0.7140 0.836 0.000 0.000 0.016 0.144 0.004
#> GSM648625 1 0.3465 0.6784 0.828 0.084 0.000 0.004 0.076 0.008
#> GSM648629 1 0.4258 0.1368 0.516 0.000 0.000 0.016 0.468 0.000
#> GSM648634 1 0.1633 0.7765 0.932 0.000 0.000 0.000 0.024 0.044
#> GSM648648 1 0.0363 0.7844 0.988 0.012 0.000 0.000 0.000 0.000
#> GSM648651 1 0.2213 0.7489 0.888 0.000 0.000 0.008 0.100 0.004
#> GSM648657 1 0.0858 0.7803 0.968 0.000 0.000 0.000 0.004 0.028
#> GSM648660 1 0.0508 0.7842 0.984 0.000 0.000 0.000 0.004 0.012
#> GSM648697 1 0.2939 0.7393 0.848 0.000 0.000 0.016 0.120 0.016
#> GSM648710 1 0.4264 0.0807 0.500 0.000 0.000 0.016 0.484 0.000
#> GSM648591 6 0.5183 0.2944 0.140 0.000 0.000 0.000 0.256 0.604
#> GSM648592 1 0.6764 -0.3217 0.400 0.032 0.000 0.004 0.268 0.296
#> GSM648607 5 0.3247 0.6358 0.156 0.000 0.000 0.000 0.808 0.036
#> GSM648611 5 0.4574 0.6012 0.008 0.000 0.092 0.000 0.708 0.192
#> GSM648612 5 0.4723 0.6726 0.096 0.000 0.016 0.004 0.720 0.164
#> GSM648616 6 0.3460 0.4742 0.036 0.000 0.000 0.004 0.164 0.796
#> GSM648617 1 0.3698 0.6117 0.788 0.004 0.000 0.004 0.160 0.044
#> GSM648626 5 0.5901 0.3572 0.272 0.000 0.000 0.000 0.472 0.256
#> GSM648711 5 0.3631 0.6262 0.160 0.000 0.000 0.012 0.792 0.036
#> GSM648712 5 0.4349 0.6664 0.064 0.000 0.016 0.000 0.736 0.184
#> GSM648713 5 0.4204 0.6774 0.132 0.000 0.000 0.000 0.740 0.128
#> GSM648714 2 0.4549 0.5089 0.008 0.656 0.000 0.008 0.300 0.028
#> GSM648716 5 0.4455 0.6809 0.100 0.000 0.016 0.000 0.740 0.144
#> GSM648717 5 0.4415 0.6295 0.040 0.000 0.040 0.008 0.760 0.152
#> GSM648590 1 0.4028 0.6741 0.792 0.128 0.000 0.008 0.024 0.048
#> GSM648596 2 0.0767 0.9310 0.004 0.976 0.000 0.000 0.008 0.012
#> GSM648642 2 0.0291 0.9335 0.004 0.992 0.000 0.000 0.004 0.000
#> GSM648696 1 0.2880 0.7460 0.872 0.056 0.000 0.000 0.024 0.048
#> GSM648705 1 0.0692 0.7818 0.976 0.020 0.000 0.000 0.000 0.004
#> GSM648718 2 0.0405 0.9331 0.004 0.988 0.000 0.000 0.000 0.008
#> GSM648599 1 0.1995 0.7779 0.912 0.000 0.000 0.000 0.036 0.052
#> GSM648608 1 0.5084 0.2909 0.504 0.000 0.000 0.016 0.436 0.044
#> GSM648609 1 0.4205 0.2796 0.564 0.000 0.000 0.016 0.420 0.000
#> GSM648610 1 0.5052 0.3510 0.532 0.000 0.000 0.016 0.408 0.044
#> GSM648633 1 0.0260 0.7851 0.992 0.000 0.000 0.000 0.000 0.008
#> GSM648644 2 0.0717 0.9272 0.000 0.976 0.000 0.000 0.008 0.016
#> GSM648652 1 0.0508 0.7835 0.984 0.012 0.000 0.000 0.000 0.004
#> GSM648653 1 0.1789 0.7778 0.924 0.000 0.000 0.000 0.032 0.044
#> GSM648658 1 0.1852 0.7759 0.928 0.004 0.000 0.004 0.024 0.040
#> GSM648659 2 0.1067 0.9230 0.004 0.964 0.000 0.024 0.004 0.004
#> GSM648662 5 0.5221 0.4689 0.200 0.116 0.000 0.024 0.660 0.000
#> GSM648665 5 0.6129 0.2870 0.224 0.260 0.000 0.016 0.500 0.000
#> GSM648666 1 0.2806 0.7238 0.844 0.000 0.000 0.016 0.136 0.004
#> GSM648680 1 0.0260 0.7851 0.992 0.008 0.000 0.000 0.000 0.000
#> GSM648684 1 0.4656 0.5827 0.660 0.000 0.000 0.016 0.280 0.044
#> GSM648709 2 0.0436 0.9324 0.004 0.988 0.000 0.004 0.004 0.000
#> GSM648719 1 0.0405 0.7846 0.988 0.000 0.000 0.000 0.004 0.008
#> GSM648627 5 0.4349 0.6692 0.064 0.000 0.016 0.000 0.736 0.184
#> GSM648637 6 0.5981 -0.3017 0.000 0.156 0.000 0.396 0.012 0.436
#> GSM648638 6 0.6109 0.1591 0.000 0.140 0.000 0.204 0.068 0.588
#> GSM648641 3 0.6141 0.0314 0.000 0.000 0.432 0.012 0.364 0.192
#> GSM648672 4 0.4556 0.6981 0.000 0.120 0.000 0.732 0.016 0.132
#> GSM648674 4 0.3997 0.2562 0.000 0.004 0.000 0.508 0.000 0.488
#> GSM648703 4 0.0937 0.7497 0.000 0.040 0.000 0.960 0.000 0.000
#> GSM648631 3 0.0146 0.9154 0.000 0.000 0.996 0.000 0.000 0.004
#> GSM648669 4 0.4743 0.5654 0.000 0.000 0.004 0.584 0.048 0.364
#> GSM648671 4 0.4743 0.5654 0.000 0.000 0.004 0.584 0.048 0.364
#> GSM648678 4 0.4228 0.4869 0.000 0.316 0.000 0.656 0.008 0.020
#> GSM648679 6 0.4343 -0.2762 0.000 0.004 0.000 0.384 0.020 0.592
#> GSM648681 6 0.6999 0.2716 0.192 0.272 0.000 0.080 0.004 0.452
#> GSM648686 3 0.0717 0.9097 0.000 0.000 0.976 0.008 0.000 0.016
#> GSM648689 3 0.0717 0.9097 0.000 0.000 0.976 0.008 0.000 0.016
#> GSM648690 3 0.0717 0.9097 0.000 0.000 0.976 0.008 0.000 0.016
#> GSM648691 3 0.0000 0.9158 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648693 3 0.0000 0.9158 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648700 4 0.1003 0.7437 0.000 0.020 0.000 0.964 0.000 0.016
#> GSM648630 3 0.0000 0.9158 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648632 3 0.0146 0.9154 0.000 0.000 0.996 0.000 0.000 0.004
#> GSM648639 6 0.3595 0.4501 0.000 0.000 0.056 0.004 0.144 0.796
#> GSM648640 3 0.4332 0.6000 0.000 0.000 0.688 0.008 0.040 0.264
#> GSM648668 4 0.4747 0.6835 0.000 0.140 0.000 0.712 0.016 0.132
#> GSM648676 4 0.0937 0.7497 0.000 0.040 0.000 0.960 0.000 0.000
#> GSM648692 3 0.0000 0.9158 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648694 3 0.0146 0.9154 0.000 0.000 0.996 0.000 0.000 0.004
#> GSM648699 4 0.1003 0.7437 0.000 0.020 0.000 0.964 0.000 0.016
#> GSM648701 4 0.0937 0.7497 0.000 0.040 0.000 0.960 0.000 0.000
#> GSM648673 4 0.4697 0.5782 0.000 0.000 0.004 0.600 0.048 0.348
#> GSM648677 4 0.2678 0.7112 0.000 0.116 0.000 0.860 0.004 0.020
#> GSM648687 3 0.1716 0.8727 0.000 0.000 0.932 0.004 0.028 0.036
#> GSM648688 3 0.0405 0.9127 0.000 0.000 0.988 0.004 0.000 0.008
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
get_signatures(res, k = 5, scale_rows = FALSE)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
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) development.stage(p) other(p) k
#> CV:kmeans 100 1.14e-02 0.04972 2.51e-10 2
#> CV:kmeans 116 9.45e-15 0.00629 1.05e-21 3
#> CV:kmeans 117 3.71e-24 0.05082 1.36e-30 4
#> CV:kmeans 115 6.24e-24 0.02024 7.94e-50 5
#> CV:kmeans 99 1.61e-20 0.22161 1.80e-40 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 51941 rows and 130 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 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 0.511 0.852 0.903 0.5023 0.498 0.498
#> 3 3 0.674 0.799 0.901 0.3231 0.696 0.463
#> 4 4 0.840 0.799 0.909 0.1126 0.866 0.631
#> 5 5 0.759 0.595 0.798 0.0758 0.874 0.569
#> 6 6 0.833 0.765 0.875 0.0472 0.914 0.615
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
#> GSM648605 2 0.0000 0.878 0.000 1.000
#> GSM648618 1 0.0376 0.891 0.996 0.004
#> GSM648620 2 0.0000 0.878 0.000 1.000
#> GSM648646 2 0.0000 0.878 0.000 1.000
#> GSM648649 2 0.7139 0.850 0.196 0.804
#> GSM648675 2 0.7139 0.850 0.196 0.804
#> GSM648682 2 0.0938 0.878 0.012 0.988
#> GSM648698 2 0.0000 0.878 0.000 1.000
#> GSM648708 2 0.1184 0.878 0.016 0.984
#> GSM648628 1 0.0000 0.890 1.000 0.000
#> GSM648595 2 0.7139 0.850 0.196 0.804
#> GSM648635 2 0.7139 0.850 0.196 0.804
#> GSM648645 2 0.7528 0.838 0.216 0.784
#> GSM648647 2 0.0000 0.878 0.000 1.000
#> GSM648667 2 0.6887 0.854 0.184 0.816
#> GSM648695 2 0.0376 0.879 0.004 0.996
#> GSM648704 2 0.0000 0.878 0.000 1.000
#> GSM648706 2 0.0000 0.878 0.000 1.000
#> GSM648593 2 0.7139 0.850 0.196 0.804
#> GSM648594 2 0.7139 0.850 0.196 0.804
#> GSM648600 2 0.9686 0.589 0.396 0.604
#> GSM648621 1 0.0000 0.890 1.000 0.000
#> GSM648622 1 0.0376 0.891 0.996 0.004
#> GSM648623 1 0.0000 0.890 1.000 0.000
#> GSM648636 2 0.7139 0.850 0.196 0.804
#> GSM648655 2 0.7139 0.850 0.196 0.804
#> GSM648661 1 0.0376 0.891 0.996 0.004
#> GSM648664 1 0.0376 0.891 0.996 0.004
#> GSM648683 1 0.0376 0.891 0.996 0.004
#> GSM648685 1 0.0376 0.891 0.996 0.004
#> GSM648702 2 0.7139 0.850 0.196 0.804
#> GSM648597 1 0.9983 -0.265 0.524 0.476
#> GSM648603 1 0.0376 0.891 0.996 0.004
#> GSM648606 1 0.7139 0.838 0.804 0.196
#> GSM648613 1 0.7139 0.838 0.804 0.196
#> GSM648619 1 0.0376 0.891 0.996 0.004
#> GSM648654 1 0.7219 0.838 0.800 0.200
#> GSM648663 1 0.7139 0.838 0.804 0.196
#> GSM648670 2 0.7219 0.850 0.200 0.800
#> GSM648707 1 0.0000 0.890 1.000 0.000
#> GSM648615 2 0.0000 0.878 0.000 1.000
#> GSM648643 2 0.0000 0.878 0.000 1.000
#> GSM648650 2 0.6712 0.855 0.176 0.824
#> GSM648656 2 0.0000 0.878 0.000 1.000
#> GSM648715 2 0.0000 0.878 0.000 1.000
#> GSM648598 2 0.8016 0.813 0.244 0.756
#> GSM648601 2 0.8327 0.793 0.264 0.736
#> GSM648602 1 0.0376 0.891 0.996 0.004
#> GSM648604 1 0.0376 0.891 0.996 0.004
#> GSM648614 1 0.7528 0.825 0.784 0.216
#> GSM648624 1 0.0376 0.891 0.996 0.004
#> GSM648625 2 0.7139 0.850 0.196 0.804
#> GSM648629 1 0.0376 0.891 0.996 0.004
#> GSM648634 2 0.7602 0.834 0.220 0.780
#> GSM648648 2 0.7139 0.850 0.196 0.804
#> GSM648651 1 0.0376 0.891 0.996 0.004
#> GSM648657 2 0.7376 0.843 0.208 0.792
#> GSM648660 2 0.7528 0.838 0.216 0.784
#> GSM648697 2 0.8763 0.755 0.296 0.704
#> GSM648710 1 0.0376 0.891 0.996 0.004
#> GSM648591 1 0.0000 0.890 1.000 0.000
#> GSM648592 2 0.7139 0.850 0.196 0.804
#> GSM648607 1 0.0376 0.891 0.996 0.004
#> GSM648611 1 0.1184 0.888 0.984 0.016
#> GSM648612 1 0.0000 0.890 1.000 0.000
#> GSM648616 1 0.1843 0.885 0.972 0.028
#> GSM648617 2 0.9710 0.581 0.400 0.600
#> GSM648626 1 0.0376 0.891 0.996 0.004
#> GSM648711 1 0.0376 0.891 0.996 0.004
#> GSM648712 1 0.0000 0.890 1.000 0.000
#> GSM648713 1 0.0376 0.891 0.996 0.004
#> GSM648714 1 0.7528 0.825 0.784 0.216
#> GSM648716 1 0.0000 0.890 1.000 0.000
#> GSM648717 1 0.7139 0.838 0.804 0.196
#> GSM648590 2 0.7139 0.850 0.196 0.804
#> GSM648596 2 0.0000 0.878 0.000 1.000
#> GSM648642 2 0.0000 0.878 0.000 1.000
#> GSM648696 2 0.7139 0.850 0.196 0.804
#> GSM648705 2 0.7139 0.850 0.196 0.804
#> GSM648718 2 0.0000 0.878 0.000 1.000
#> GSM648599 1 0.0376 0.891 0.996 0.004
#> GSM648608 1 0.0376 0.891 0.996 0.004
#> GSM648609 1 0.0376 0.891 0.996 0.004
#> GSM648610 1 0.0376 0.891 0.996 0.004
#> GSM648633 2 0.7219 0.848 0.200 0.800
#> GSM648644 2 0.0000 0.878 0.000 1.000
#> GSM648652 2 0.7139 0.850 0.196 0.804
#> GSM648653 1 0.0376 0.891 0.996 0.004
#> GSM648658 2 0.7139 0.850 0.196 0.804
#> GSM648659 2 0.0000 0.878 0.000 1.000
#> GSM648662 1 0.7219 0.838 0.800 0.200
#> GSM648665 1 0.7219 0.838 0.800 0.200
#> GSM648666 1 0.0376 0.891 0.996 0.004
#> GSM648680 2 0.7139 0.850 0.196 0.804
#> GSM648684 1 0.0376 0.891 0.996 0.004
#> GSM648709 2 0.0000 0.878 0.000 1.000
#> GSM648719 2 0.7528 0.838 0.216 0.784
#> GSM648627 1 0.0000 0.890 1.000 0.000
#> GSM648637 2 0.0376 0.877 0.004 0.996
#> GSM648638 2 0.4161 0.811 0.084 0.916
#> GSM648641 1 0.7139 0.838 0.804 0.196
#> GSM648672 2 0.0376 0.877 0.004 0.996
#> GSM648674 2 0.0376 0.877 0.004 0.996
#> GSM648703 2 0.0376 0.877 0.004 0.996
#> GSM648631 1 0.7139 0.838 0.804 0.196
#> GSM648669 2 0.0376 0.877 0.004 0.996
#> GSM648671 2 0.0376 0.877 0.004 0.996
#> GSM648678 2 0.0376 0.877 0.004 0.996
#> GSM648679 2 0.0376 0.877 0.004 0.996
#> GSM648681 2 0.0000 0.878 0.000 1.000
#> GSM648686 1 0.7139 0.838 0.804 0.196
#> GSM648689 1 0.7139 0.838 0.804 0.196
#> GSM648690 1 0.7139 0.838 0.804 0.196
#> GSM648691 1 0.7139 0.838 0.804 0.196
#> GSM648693 1 0.7139 0.838 0.804 0.196
#> GSM648700 2 0.1184 0.878 0.016 0.984
#> GSM648630 1 0.7139 0.838 0.804 0.196
#> GSM648632 1 0.7139 0.838 0.804 0.196
#> GSM648639 1 0.7139 0.838 0.804 0.196
#> GSM648640 1 0.7139 0.838 0.804 0.196
#> GSM648668 2 0.0376 0.877 0.004 0.996
#> GSM648676 2 0.0376 0.877 0.004 0.996
#> GSM648692 1 0.7139 0.838 0.804 0.196
#> GSM648694 1 0.7139 0.838 0.804 0.196
#> GSM648699 2 0.0376 0.877 0.004 0.996
#> GSM648701 2 0.0376 0.877 0.004 0.996
#> GSM648673 2 0.0376 0.877 0.004 0.996
#> GSM648677 2 0.0376 0.877 0.004 0.996
#> GSM648687 1 0.7139 0.838 0.804 0.196
#> GSM648688 1 0.7139 0.838 0.804 0.196
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM648605 2 0.0000 0.89156 0.000 1.000 0.000
#> GSM648618 3 0.6252 0.33318 0.444 0.000 0.556
#> GSM648620 2 0.0000 0.89156 0.000 1.000 0.000
#> GSM648646 2 0.0000 0.89156 0.000 1.000 0.000
#> GSM648649 1 0.5016 0.67213 0.760 0.240 0.000
#> GSM648675 2 0.4605 0.71390 0.204 0.796 0.000
#> GSM648682 2 0.0000 0.89156 0.000 1.000 0.000
#> GSM648698 2 0.0000 0.89156 0.000 1.000 0.000
#> GSM648708 2 0.0000 0.89156 0.000 1.000 0.000
#> GSM648628 3 0.4121 0.76308 0.168 0.000 0.832
#> GSM648595 1 0.6309 -0.00082 0.504 0.496 0.000
#> GSM648635 1 0.0000 0.92656 1.000 0.000 0.000
#> GSM648645 1 0.0000 0.92656 1.000 0.000 0.000
#> GSM648647 2 0.0000 0.89156 0.000 1.000 0.000
#> GSM648667 2 0.0000 0.89156 0.000 1.000 0.000
#> GSM648695 2 0.0000 0.89156 0.000 1.000 0.000
#> GSM648704 2 0.0000 0.89156 0.000 1.000 0.000
#> GSM648706 2 0.0000 0.89156 0.000 1.000 0.000
#> GSM648593 1 0.0000 0.92656 1.000 0.000 0.000
#> GSM648594 1 0.2796 0.84287 0.908 0.092 0.000
#> GSM648600 1 0.0000 0.92656 1.000 0.000 0.000
#> GSM648621 1 0.0000 0.92656 1.000 0.000 0.000
#> GSM648622 1 0.0000 0.92656 1.000 0.000 0.000
#> GSM648623 1 0.6225 0.05313 0.568 0.000 0.432
#> GSM648636 1 0.0237 0.92336 0.996 0.004 0.000
#> GSM648655 1 0.1163 0.90321 0.972 0.028 0.000
#> GSM648661 3 0.6126 0.45546 0.400 0.000 0.600
#> GSM648664 1 0.0000 0.92656 1.000 0.000 0.000
#> GSM648683 1 0.0000 0.92656 1.000 0.000 0.000
#> GSM648685 1 0.0000 0.92656 1.000 0.000 0.000
#> GSM648702 1 0.0000 0.92656 1.000 0.000 0.000
#> GSM648597 1 0.3816 0.75988 0.852 0.000 0.148
#> GSM648603 1 0.6309 -0.20056 0.500 0.000 0.500
#> GSM648606 3 0.4555 0.72967 0.000 0.200 0.800
#> GSM648613 3 0.4555 0.72967 0.000 0.200 0.800
#> GSM648619 3 0.4555 0.73928 0.200 0.000 0.800
#> GSM648654 3 0.4555 0.72967 0.000 0.200 0.800
#> GSM648663 3 0.4555 0.72967 0.000 0.200 0.800
#> GSM648670 2 0.5020 0.82430 0.012 0.796 0.192
#> GSM648707 3 0.0000 0.82213 0.000 0.000 1.000
#> GSM648615 2 0.0000 0.89156 0.000 1.000 0.000
#> GSM648643 2 0.0000 0.89156 0.000 1.000 0.000
#> GSM648650 2 0.0000 0.89156 0.000 1.000 0.000
#> GSM648656 2 0.0000 0.89156 0.000 1.000 0.000
#> GSM648715 2 0.0000 0.89156 0.000 1.000 0.000
#> GSM648598 1 0.0000 0.92656 1.000 0.000 0.000
#> GSM648601 1 0.0000 0.92656 1.000 0.000 0.000
#> GSM648602 1 0.0000 0.92656 1.000 0.000 0.000
#> GSM648604 1 0.0000 0.92656 1.000 0.000 0.000
#> GSM648614 3 0.7395 0.47134 0.040 0.380 0.580
#> GSM648624 1 0.0000 0.92656 1.000 0.000 0.000
#> GSM648625 1 0.4555 0.70211 0.800 0.200 0.000
#> GSM648629 1 0.0000 0.92656 1.000 0.000 0.000
#> GSM648634 1 0.0000 0.92656 1.000 0.000 0.000
#> GSM648648 1 0.0000 0.92656 1.000 0.000 0.000
#> GSM648651 1 0.0000 0.92656 1.000 0.000 0.000
#> GSM648657 1 0.0000 0.92656 1.000 0.000 0.000
#> GSM648660 1 0.0000 0.92656 1.000 0.000 0.000
#> GSM648697 1 0.0000 0.92656 1.000 0.000 0.000
#> GSM648710 1 0.0000 0.92656 1.000 0.000 0.000
#> GSM648591 3 0.0592 0.82086 0.012 0.000 0.988
#> GSM648592 2 0.1031 0.87474 0.024 0.976 0.000
#> GSM648607 3 0.6225 0.36786 0.432 0.000 0.568
#> GSM648611 3 0.0000 0.82213 0.000 0.000 1.000
#> GSM648612 3 0.4555 0.73928 0.200 0.000 0.800
#> GSM648616 3 0.0000 0.82213 0.000 0.000 1.000
#> GSM648617 1 0.0000 0.92656 1.000 0.000 0.000
#> GSM648626 3 0.6309 0.17597 0.496 0.000 0.504
#> GSM648711 3 0.6215 0.37749 0.428 0.000 0.572
#> GSM648712 3 0.4555 0.73928 0.200 0.000 0.800
#> GSM648713 3 0.4842 0.71627 0.224 0.000 0.776
#> GSM648714 3 0.6008 0.51562 0.000 0.372 0.628
#> GSM648716 3 0.4555 0.73928 0.200 0.000 0.800
#> GSM648717 3 0.3686 0.76640 0.000 0.140 0.860
#> GSM648590 2 0.4605 0.71390 0.204 0.796 0.000
#> GSM648596 2 0.0000 0.89156 0.000 1.000 0.000
#> GSM648642 2 0.0000 0.89156 0.000 1.000 0.000
#> GSM648696 1 0.4796 0.70489 0.780 0.220 0.000
#> GSM648705 1 0.4555 0.72244 0.800 0.200 0.000
#> GSM648718 2 0.0000 0.89156 0.000 1.000 0.000
#> GSM648599 1 0.0000 0.92656 1.000 0.000 0.000
#> GSM648608 1 0.0000 0.92656 1.000 0.000 0.000
#> GSM648609 1 0.0000 0.92656 1.000 0.000 0.000
#> GSM648610 1 0.0000 0.92656 1.000 0.000 0.000
#> GSM648633 1 0.0000 0.92656 1.000 0.000 0.000
#> GSM648644 2 0.0000 0.89156 0.000 1.000 0.000
#> GSM648652 1 0.0000 0.92656 1.000 0.000 0.000
#> GSM648653 1 0.0000 0.92656 1.000 0.000 0.000
#> GSM648658 1 0.0000 0.92656 1.000 0.000 0.000
#> GSM648659 2 0.0000 0.89156 0.000 1.000 0.000
#> GSM648662 3 0.9425 0.42083 0.312 0.200 0.488
#> GSM648665 1 0.6767 0.60749 0.720 0.216 0.064
#> GSM648666 1 0.0000 0.92656 1.000 0.000 0.000
#> GSM648680 1 0.0000 0.92656 1.000 0.000 0.000
#> GSM648684 1 0.0000 0.92656 1.000 0.000 0.000
#> GSM648709 2 0.0000 0.89156 0.000 1.000 0.000
#> GSM648719 1 0.0000 0.92656 1.000 0.000 0.000
#> GSM648627 3 0.4504 0.74263 0.196 0.000 0.804
#> GSM648637 2 0.4555 0.82654 0.000 0.800 0.200
#> GSM648638 2 0.6215 0.51225 0.000 0.572 0.428
#> GSM648641 3 0.0000 0.82213 0.000 0.000 1.000
#> GSM648672 2 0.4555 0.82654 0.000 0.800 0.200
#> GSM648674 2 0.4555 0.82654 0.000 0.800 0.200
#> GSM648703 2 0.4555 0.82654 0.000 0.800 0.200
#> GSM648631 3 0.0000 0.82213 0.000 0.000 1.000
#> GSM648669 2 0.5988 0.62119 0.000 0.632 0.368
#> GSM648671 2 0.5988 0.62119 0.000 0.632 0.368
#> GSM648678 2 0.0000 0.89156 0.000 1.000 0.000
#> GSM648679 2 0.4555 0.82654 0.000 0.800 0.200
#> GSM648681 2 0.0000 0.89156 0.000 1.000 0.000
#> GSM648686 3 0.0000 0.82213 0.000 0.000 1.000
#> GSM648689 3 0.0000 0.82213 0.000 0.000 1.000
#> GSM648690 3 0.0000 0.82213 0.000 0.000 1.000
#> GSM648691 3 0.0000 0.82213 0.000 0.000 1.000
#> GSM648693 3 0.0000 0.82213 0.000 0.000 1.000
#> GSM648700 2 0.4555 0.82654 0.000 0.800 0.200
#> GSM648630 3 0.0000 0.82213 0.000 0.000 1.000
#> GSM648632 3 0.0000 0.82213 0.000 0.000 1.000
#> GSM648639 3 0.0000 0.82213 0.000 0.000 1.000
#> GSM648640 3 0.0000 0.82213 0.000 0.000 1.000
#> GSM648668 2 0.4555 0.82654 0.000 0.800 0.200
#> GSM648676 2 0.4555 0.82654 0.000 0.800 0.200
#> GSM648692 3 0.0000 0.82213 0.000 0.000 1.000
#> GSM648694 3 0.0000 0.82213 0.000 0.000 1.000
#> GSM648699 2 0.4555 0.82654 0.000 0.800 0.200
#> GSM648701 2 0.4555 0.82654 0.000 0.800 0.200
#> GSM648673 2 0.4702 0.81698 0.000 0.788 0.212
#> GSM648677 2 0.4555 0.82654 0.000 0.800 0.200
#> GSM648687 3 0.0000 0.82213 0.000 0.000 1.000
#> GSM648688 3 0.0000 0.82213 0.000 0.000 1.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM648605 2 0.0000 0.932104 0.000 1.000 0.000 0.000
#> GSM648618 3 0.5971 0.192502 0.428 0.000 0.532 0.040
#> GSM648620 2 0.0000 0.932104 0.000 1.000 0.000 0.000
#> GSM648646 2 0.0000 0.932104 0.000 1.000 0.000 0.000
#> GSM648649 1 0.4998 0.063096 0.512 0.488 0.000 0.000
#> GSM648675 4 0.1545 0.905451 0.008 0.040 0.000 0.952
#> GSM648682 2 0.0000 0.932104 0.000 1.000 0.000 0.000
#> GSM648698 2 0.0000 0.932104 0.000 1.000 0.000 0.000
#> GSM648708 2 0.0000 0.932104 0.000 1.000 0.000 0.000
#> GSM648628 3 0.0707 0.875783 0.000 0.000 0.980 0.020
#> GSM648595 4 0.3286 0.849612 0.044 0.080 0.000 0.876
#> GSM648635 1 0.0000 0.895328 1.000 0.000 0.000 0.000
#> GSM648645 1 0.0000 0.895328 1.000 0.000 0.000 0.000
#> GSM648647 2 0.0000 0.932104 0.000 1.000 0.000 0.000
#> GSM648667 2 0.0000 0.932104 0.000 1.000 0.000 0.000
#> GSM648695 2 0.0000 0.932104 0.000 1.000 0.000 0.000
#> GSM648704 2 0.0000 0.932104 0.000 1.000 0.000 0.000
#> GSM648706 2 0.0000 0.932104 0.000 1.000 0.000 0.000
#> GSM648593 1 0.0000 0.895328 1.000 0.000 0.000 0.000
#> GSM648594 4 0.4543 0.524322 0.324 0.000 0.000 0.676
#> GSM648600 1 0.0707 0.892610 0.980 0.000 0.000 0.020
#> GSM648621 1 0.0707 0.892610 0.980 0.000 0.000 0.020
#> GSM648622 1 0.0000 0.895328 1.000 0.000 0.000 0.000
#> GSM648623 1 0.5203 0.197638 0.576 0.000 0.416 0.008
#> GSM648636 1 0.0707 0.892610 0.980 0.000 0.000 0.020
#> GSM648655 1 0.0895 0.891443 0.976 0.004 0.000 0.020
#> GSM648661 1 0.5858 -0.000402 0.500 0.000 0.468 0.032
#> GSM648664 1 0.1209 0.883679 0.964 0.000 0.004 0.032
#> GSM648683 1 0.1661 0.881410 0.944 0.000 0.004 0.052
#> GSM648685 1 0.0921 0.887040 0.972 0.000 0.000 0.028
#> GSM648702 1 0.0707 0.892610 0.980 0.000 0.000 0.020
#> GSM648597 4 0.4655 0.545204 0.312 0.000 0.004 0.684
#> GSM648603 1 0.5472 0.100321 0.544 0.000 0.440 0.016
#> GSM648606 3 0.2722 0.844246 0.000 0.064 0.904 0.032
#> GSM648613 3 0.1629 0.869317 0.000 0.024 0.952 0.024
#> GSM648619 3 0.4332 0.735916 0.176 0.000 0.792 0.032
#> GSM648654 3 0.4579 0.714299 0.000 0.200 0.768 0.032
#> GSM648663 3 0.3342 0.817349 0.000 0.100 0.868 0.032
#> GSM648670 4 0.1356 0.905366 0.008 0.032 0.000 0.960
#> GSM648707 3 0.2408 0.816390 0.000 0.000 0.896 0.104
#> GSM648615 2 0.0000 0.932104 0.000 1.000 0.000 0.000
#> GSM648643 2 0.0000 0.932104 0.000 1.000 0.000 0.000
#> GSM648650 2 0.0000 0.932104 0.000 1.000 0.000 0.000
#> GSM648656 2 0.0000 0.932104 0.000 1.000 0.000 0.000
#> GSM648715 2 0.0000 0.932104 0.000 1.000 0.000 0.000
#> GSM648598 1 0.0000 0.895328 1.000 0.000 0.000 0.000
#> GSM648601 1 0.0000 0.895328 1.000 0.000 0.000 0.000
#> GSM648602 1 0.0707 0.892610 0.980 0.000 0.000 0.020
#> GSM648604 1 0.1209 0.883679 0.964 0.000 0.004 0.032
#> GSM648614 2 0.1488 0.897163 0.000 0.956 0.012 0.032
#> GSM648624 1 0.0000 0.895328 1.000 0.000 0.000 0.000
#> GSM648625 2 0.3764 0.700768 0.216 0.784 0.000 0.000
#> GSM648629 1 0.1209 0.883679 0.964 0.000 0.004 0.032
#> GSM648634 1 0.0707 0.892610 0.980 0.000 0.000 0.020
#> GSM648648 1 0.0000 0.895328 1.000 0.000 0.000 0.000
#> GSM648651 1 0.0000 0.895328 1.000 0.000 0.000 0.000
#> GSM648657 1 0.0000 0.895328 1.000 0.000 0.000 0.000
#> GSM648660 1 0.0000 0.895328 1.000 0.000 0.000 0.000
#> GSM648697 1 0.0188 0.895126 0.996 0.000 0.000 0.004
#> GSM648710 1 0.1209 0.883679 0.964 0.000 0.004 0.032
#> GSM648591 3 0.4776 0.466644 0.000 0.000 0.624 0.376
#> GSM648592 2 0.2924 0.860823 0.036 0.900 0.004 0.060
#> GSM648607 3 0.5821 0.211415 0.432 0.000 0.536 0.032
#> GSM648611 3 0.0921 0.875239 0.000 0.000 0.972 0.028
#> GSM648612 3 0.1724 0.868082 0.020 0.000 0.948 0.032
#> GSM648616 3 0.4713 0.429029 0.000 0.000 0.640 0.360
#> GSM648617 1 0.3591 0.733894 0.824 0.168 0.008 0.000
#> GSM648626 1 0.5581 0.063282 0.532 0.000 0.448 0.020
#> GSM648711 3 0.5792 0.258664 0.416 0.000 0.552 0.032
#> GSM648712 3 0.1474 0.869734 0.000 0.000 0.948 0.052
#> GSM648713 3 0.3279 0.816832 0.096 0.000 0.872 0.032
#> GSM648714 2 0.1488 0.897163 0.000 0.956 0.012 0.032
#> GSM648716 3 0.1724 0.868082 0.020 0.000 0.948 0.032
#> GSM648717 3 0.1209 0.873075 0.000 0.004 0.964 0.032
#> GSM648590 2 0.6621 0.088207 0.084 0.508 0.000 0.408
#> GSM648596 2 0.0000 0.932104 0.000 1.000 0.000 0.000
#> GSM648642 2 0.0000 0.932104 0.000 1.000 0.000 0.000
#> GSM648696 2 0.4204 0.695848 0.192 0.788 0.000 0.020
#> GSM648705 1 0.4996 0.075893 0.516 0.484 0.000 0.000
#> GSM648718 2 0.0000 0.932104 0.000 1.000 0.000 0.000
#> GSM648599 1 0.0707 0.892610 0.980 0.000 0.000 0.020
#> GSM648608 1 0.1661 0.881410 0.944 0.000 0.004 0.052
#> GSM648609 1 0.1209 0.883679 0.964 0.000 0.004 0.032
#> GSM648610 1 0.1661 0.881410 0.944 0.000 0.004 0.052
#> GSM648633 1 0.0000 0.895328 1.000 0.000 0.000 0.000
#> GSM648644 2 0.0000 0.932104 0.000 1.000 0.000 0.000
#> GSM648652 1 0.0000 0.895328 1.000 0.000 0.000 0.000
#> GSM648653 1 0.0707 0.892610 0.980 0.000 0.000 0.020
#> GSM648658 1 0.0707 0.892610 0.980 0.000 0.000 0.020
#> GSM648659 2 0.0469 0.922619 0.000 0.988 0.000 0.012
#> GSM648662 2 0.7717 0.085719 0.412 0.452 0.104 0.032
#> GSM648665 1 0.5861 -0.037927 0.488 0.480 0.000 0.032
#> GSM648666 1 0.0592 0.893498 0.984 0.000 0.000 0.016
#> GSM648680 1 0.0000 0.895328 1.000 0.000 0.000 0.000
#> GSM648684 1 0.1389 0.884678 0.952 0.000 0.000 0.048
#> GSM648709 2 0.0000 0.932104 0.000 1.000 0.000 0.000
#> GSM648719 1 0.0000 0.895328 1.000 0.000 0.000 0.000
#> GSM648627 3 0.1474 0.869734 0.000 0.000 0.948 0.052
#> GSM648637 4 0.2174 0.914064 0.000 0.052 0.020 0.928
#> GSM648638 4 0.3037 0.886067 0.000 0.036 0.076 0.888
#> GSM648641 3 0.0336 0.880151 0.000 0.000 0.992 0.008
#> GSM648672 4 0.2174 0.914064 0.000 0.052 0.020 0.928
#> GSM648674 4 0.1820 0.913569 0.000 0.036 0.020 0.944
#> GSM648703 4 0.2174 0.914064 0.000 0.052 0.020 0.928
#> GSM648631 3 0.0336 0.880151 0.000 0.000 0.992 0.008
#> GSM648669 4 0.1474 0.895894 0.000 0.000 0.052 0.948
#> GSM648671 4 0.1474 0.895894 0.000 0.000 0.052 0.948
#> GSM648678 4 0.4877 0.372625 0.000 0.408 0.000 0.592
#> GSM648679 4 0.1724 0.908514 0.000 0.020 0.032 0.948
#> GSM648681 4 0.2814 0.843312 0.000 0.132 0.000 0.868
#> GSM648686 3 0.0336 0.880151 0.000 0.000 0.992 0.008
#> GSM648689 3 0.0336 0.880151 0.000 0.000 0.992 0.008
#> GSM648690 3 0.0336 0.880151 0.000 0.000 0.992 0.008
#> GSM648691 3 0.0336 0.880151 0.000 0.000 0.992 0.008
#> GSM648693 3 0.0336 0.880151 0.000 0.000 0.992 0.008
#> GSM648700 4 0.1913 0.914492 0.000 0.040 0.020 0.940
#> GSM648630 3 0.0336 0.880151 0.000 0.000 0.992 0.008
#> GSM648632 3 0.0336 0.880151 0.000 0.000 0.992 0.008
#> GSM648639 3 0.2814 0.788394 0.000 0.000 0.868 0.132
#> GSM648640 3 0.0336 0.880151 0.000 0.000 0.992 0.008
#> GSM648668 4 0.2256 0.912382 0.000 0.056 0.020 0.924
#> GSM648676 4 0.2089 0.914780 0.000 0.048 0.020 0.932
#> GSM648692 3 0.0336 0.880151 0.000 0.000 0.992 0.008
#> GSM648694 3 0.0336 0.880151 0.000 0.000 0.992 0.008
#> GSM648699 4 0.1913 0.914492 0.000 0.040 0.020 0.940
#> GSM648701 4 0.2089 0.914780 0.000 0.048 0.020 0.932
#> GSM648673 4 0.1474 0.895894 0.000 0.000 0.052 0.948
#> GSM648677 4 0.2335 0.910183 0.000 0.060 0.020 0.920
#> GSM648687 3 0.1637 0.855964 0.000 0.000 0.940 0.060
#> GSM648688 3 0.0336 0.880151 0.000 0.000 0.992 0.008
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM648605 2 0.0000 0.9267 0.000 1.000 0.000 0.000 0.000
#> GSM648618 5 0.5431 -0.1678 0.016 0.000 0.304 0.052 0.628
#> GSM648620 2 0.0000 0.9267 0.000 1.000 0.000 0.000 0.000
#> GSM648646 2 0.0000 0.9267 0.000 1.000 0.000 0.000 0.000
#> GSM648649 5 0.5715 0.4181 0.388 0.088 0.000 0.000 0.524
#> GSM648675 4 0.3169 0.8363 0.004 0.016 0.000 0.840 0.140
#> GSM648682 2 0.0000 0.9267 0.000 1.000 0.000 0.000 0.000
#> GSM648698 2 0.0000 0.9267 0.000 1.000 0.000 0.000 0.000
#> GSM648708 2 0.0000 0.9267 0.000 1.000 0.000 0.000 0.000
#> GSM648628 3 0.3816 0.6960 0.000 0.000 0.696 0.000 0.304
#> GSM648595 5 0.4182 0.1902 0.000 0.000 0.000 0.400 0.600
#> GSM648635 5 0.4305 0.4022 0.488 0.000 0.000 0.000 0.512
#> GSM648645 5 0.4242 0.4303 0.428 0.000 0.000 0.000 0.572
#> GSM648647 2 0.0000 0.9267 0.000 1.000 0.000 0.000 0.000
#> GSM648667 2 0.0963 0.8969 0.000 0.964 0.000 0.000 0.036
#> GSM648695 2 0.0000 0.9267 0.000 1.000 0.000 0.000 0.000
#> GSM648704 2 0.0000 0.9267 0.000 1.000 0.000 0.000 0.000
#> GSM648706 2 0.0000 0.9267 0.000 1.000 0.000 0.000 0.000
#> GSM648593 1 0.4306 -0.4190 0.508 0.000 0.000 0.000 0.492
#> GSM648594 5 0.4503 0.3726 0.120 0.000 0.000 0.124 0.756
#> GSM648600 5 0.4088 0.4415 0.368 0.000 0.000 0.000 0.632
#> GSM648621 5 0.1792 0.3945 0.084 0.000 0.000 0.000 0.916
#> GSM648622 1 0.3177 0.2164 0.792 0.000 0.000 0.000 0.208
#> GSM648623 5 0.4640 0.0168 0.400 0.000 0.000 0.016 0.584
#> GSM648636 5 0.4161 0.4299 0.392 0.000 0.000 0.000 0.608
#> GSM648655 5 0.4161 0.4299 0.392 0.000 0.000 0.000 0.608
#> GSM648661 1 0.3550 0.3935 0.760 0.000 0.236 0.000 0.004
#> GSM648664 1 0.0000 0.5257 1.000 0.000 0.000 0.000 0.000
#> GSM648683 1 0.2280 0.4943 0.880 0.000 0.000 0.000 0.120
#> GSM648685 1 0.0000 0.5257 1.000 0.000 0.000 0.000 0.000
#> GSM648702 5 0.4161 0.4299 0.392 0.000 0.000 0.000 0.608
#> GSM648597 5 0.4111 0.3760 0.120 0.000 0.000 0.092 0.788
#> GSM648603 5 0.4124 0.3492 0.180 0.000 0.008 0.036 0.776
#> GSM648606 3 0.1168 0.8505 0.000 0.032 0.960 0.000 0.008
#> GSM648613 3 0.1195 0.8582 0.000 0.012 0.960 0.000 0.028
#> GSM648619 3 0.6503 0.4299 0.204 0.000 0.464 0.000 0.332
#> GSM648654 1 0.6444 0.1269 0.484 0.200 0.316 0.000 0.000
#> GSM648663 3 0.2199 0.8245 0.016 0.060 0.916 0.000 0.008
#> GSM648670 4 0.2280 0.8530 0.000 0.000 0.000 0.880 0.120
#> GSM648707 3 0.5360 0.5810 0.000 0.000 0.556 0.060 0.384
#> GSM648615 2 0.0000 0.9267 0.000 1.000 0.000 0.000 0.000
#> GSM648643 2 0.0000 0.9267 0.000 1.000 0.000 0.000 0.000
#> GSM648650 2 0.1341 0.8771 0.000 0.944 0.000 0.000 0.056
#> GSM648656 2 0.0000 0.9267 0.000 1.000 0.000 0.000 0.000
#> GSM648715 2 0.0000 0.9267 0.000 1.000 0.000 0.000 0.000
#> GSM648598 1 0.4306 -0.4190 0.508 0.000 0.000 0.000 0.492
#> GSM648601 5 0.4306 0.3927 0.492 0.000 0.000 0.000 0.508
#> GSM648602 5 0.4171 0.4262 0.396 0.000 0.000 0.000 0.604
#> GSM648604 1 0.0000 0.5257 1.000 0.000 0.000 0.000 0.000
#> GSM648614 2 0.0771 0.9088 0.020 0.976 0.000 0.000 0.004
#> GSM648624 1 0.0510 0.5147 0.984 0.000 0.000 0.000 0.016
#> GSM648625 2 0.5359 0.1220 0.056 0.532 0.000 0.000 0.412
#> GSM648629 1 0.0000 0.5257 1.000 0.000 0.000 0.000 0.000
#> GSM648634 5 0.4161 0.4299 0.392 0.000 0.000 0.000 0.608
#> GSM648648 1 0.4306 -0.4190 0.508 0.000 0.000 0.000 0.492
#> GSM648651 1 0.4287 -0.2573 0.540 0.000 0.000 0.000 0.460
#> GSM648657 5 0.2516 0.4155 0.140 0.000 0.000 0.000 0.860
#> GSM648660 5 0.4304 0.4063 0.484 0.000 0.000 0.000 0.516
#> GSM648697 1 0.2966 0.4284 0.816 0.000 0.000 0.000 0.184
#> GSM648710 1 0.0000 0.5257 1.000 0.000 0.000 0.000 0.000
#> GSM648591 5 0.5538 -0.4582 0.000 0.000 0.428 0.068 0.504
#> GSM648592 5 0.4866 0.3348 0.072 0.112 0.000 0.048 0.768
#> GSM648607 1 0.5037 0.2592 0.616 0.000 0.048 0.000 0.336
#> GSM648611 3 0.0000 0.8677 0.000 0.000 1.000 0.000 0.000
#> GSM648612 3 0.5145 0.6364 0.056 0.000 0.612 0.000 0.332
#> GSM648616 3 0.6356 0.4859 0.000 0.000 0.452 0.164 0.384
#> GSM648617 5 0.2377 0.4058 0.128 0.000 0.000 0.000 0.872
#> GSM648626 5 0.4500 0.3386 0.180 0.000 0.020 0.040 0.760
#> GSM648711 1 0.5037 0.2592 0.616 0.000 0.048 0.000 0.336
#> GSM648712 3 0.5284 0.5977 0.056 0.000 0.568 0.000 0.376
#> GSM648713 1 0.5203 0.2549 0.608 0.000 0.060 0.000 0.332
#> GSM648714 2 0.0566 0.9148 0.000 0.984 0.012 0.000 0.004
#> GSM648716 3 0.5145 0.6364 0.056 0.000 0.612 0.000 0.332
#> GSM648717 3 0.0290 0.8658 0.000 0.000 0.992 0.000 0.008
#> GSM648590 2 0.8116 0.0193 0.216 0.412 0.000 0.132 0.240
#> GSM648596 2 0.0000 0.9267 0.000 1.000 0.000 0.000 0.000
#> GSM648642 2 0.0000 0.9267 0.000 1.000 0.000 0.000 0.000
#> GSM648696 2 0.6483 0.0790 0.216 0.484 0.000 0.000 0.300
#> GSM648705 5 0.5867 0.4040 0.404 0.100 0.000 0.000 0.496
#> GSM648718 2 0.0000 0.9267 0.000 1.000 0.000 0.000 0.000
#> GSM648599 5 0.3983 0.4465 0.340 0.000 0.000 0.000 0.660
#> GSM648608 1 0.2280 0.4943 0.880 0.000 0.000 0.000 0.120
#> GSM648609 1 0.0000 0.5257 1.000 0.000 0.000 0.000 0.000
#> GSM648610 1 0.2280 0.4943 0.880 0.000 0.000 0.000 0.120
#> GSM648633 5 0.4304 0.4063 0.484 0.000 0.000 0.000 0.516
#> GSM648644 2 0.0000 0.9267 0.000 1.000 0.000 0.000 0.000
#> GSM648652 1 0.4307 -0.4232 0.504 0.000 0.000 0.000 0.496
#> GSM648653 5 0.4161 0.4299 0.392 0.000 0.000 0.000 0.608
#> GSM648658 5 0.4161 0.4299 0.392 0.000 0.000 0.000 0.608
#> GSM648659 2 0.0000 0.9267 0.000 1.000 0.000 0.000 0.000
#> GSM648662 1 0.5167 0.1168 0.564 0.396 0.036 0.000 0.004
#> GSM648665 1 0.4219 0.1114 0.584 0.416 0.000 0.000 0.000
#> GSM648666 1 0.2966 0.4423 0.816 0.000 0.000 0.000 0.184
#> GSM648680 1 0.4306 -0.4190 0.508 0.000 0.000 0.000 0.492
#> GSM648684 1 0.2280 0.4943 0.880 0.000 0.000 0.000 0.120
#> GSM648709 2 0.0000 0.9267 0.000 1.000 0.000 0.000 0.000
#> GSM648719 5 0.4304 0.4063 0.484 0.000 0.000 0.000 0.516
#> GSM648627 3 0.4290 0.6873 0.016 0.000 0.680 0.000 0.304
#> GSM648637 4 0.1270 0.9434 0.000 0.052 0.000 0.948 0.000
#> GSM648638 4 0.1628 0.9396 0.000 0.056 0.008 0.936 0.000
#> GSM648641 3 0.0000 0.8677 0.000 0.000 1.000 0.000 0.000
#> GSM648672 4 0.1270 0.9434 0.000 0.052 0.000 0.948 0.000
#> GSM648674 4 0.0000 0.9363 0.000 0.000 0.000 1.000 0.000
#> GSM648703 4 0.1043 0.9464 0.000 0.040 0.000 0.960 0.000
#> GSM648631 3 0.0000 0.8677 0.000 0.000 1.000 0.000 0.000
#> GSM648669 4 0.0000 0.9363 0.000 0.000 0.000 1.000 0.000
#> GSM648671 4 0.0000 0.9363 0.000 0.000 0.000 1.000 0.000
#> GSM648678 4 0.3336 0.7547 0.000 0.228 0.000 0.772 0.000
#> GSM648679 4 0.0000 0.9363 0.000 0.000 0.000 1.000 0.000
#> GSM648681 4 0.2280 0.8615 0.000 0.120 0.000 0.880 0.000
#> GSM648686 3 0.0000 0.8677 0.000 0.000 1.000 0.000 0.000
#> GSM648689 3 0.0000 0.8677 0.000 0.000 1.000 0.000 0.000
#> GSM648690 3 0.0000 0.8677 0.000 0.000 1.000 0.000 0.000
#> GSM648691 3 0.0000 0.8677 0.000 0.000 1.000 0.000 0.000
#> GSM648693 3 0.0000 0.8677 0.000 0.000 1.000 0.000 0.000
#> GSM648700 4 0.0794 0.9455 0.000 0.028 0.000 0.972 0.000
#> GSM648630 3 0.0000 0.8677 0.000 0.000 1.000 0.000 0.000
#> GSM648632 3 0.0000 0.8677 0.000 0.000 1.000 0.000 0.000
#> GSM648639 3 0.2378 0.8259 0.000 0.000 0.904 0.048 0.048
#> GSM648640 3 0.0000 0.8677 0.000 0.000 1.000 0.000 0.000
#> GSM648668 4 0.1270 0.9434 0.000 0.052 0.000 0.948 0.000
#> GSM648676 4 0.1043 0.9464 0.000 0.040 0.000 0.960 0.000
#> GSM648692 3 0.0000 0.8677 0.000 0.000 1.000 0.000 0.000
#> GSM648694 3 0.0000 0.8677 0.000 0.000 1.000 0.000 0.000
#> GSM648699 4 0.0794 0.9455 0.000 0.028 0.000 0.972 0.000
#> GSM648701 4 0.1043 0.9464 0.000 0.040 0.000 0.960 0.000
#> GSM648673 4 0.0000 0.9363 0.000 0.000 0.000 1.000 0.000
#> GSM648677 4 0.1341 0.9411 0.000 0.056 0.000 0.944 0.000
#> GSM648687 3 0.1908 0.8077 0.000 0.000 0.908 0.092 0.000
#> GSM648688 3 0.0000 0.8677 0.000 0.000 1.000 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM648605 2 0.0000 0.9662 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM648618 5 0.3010 0.7054 0.000 0.000 0.020 0.004 0.828 0.148
#> GSM648620 2 0.0000 0.9662 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM648646 2 0.0000 0.9662 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM648649 6 0.4176 0.8080 0.200 0.004 0.000 0.000 0.064 0.732
#> GSM648675 4 0.3351 0.6481 0.000 0.000 0.000 0.712 0.000 0.288
#> GSM648682 2 0.0000 0.9662 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM648698 2 0.0000 0.9662 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM648708 2 0.0000 0.9662 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM648628 3 0.4466 -0.0832 0.004 0.000 0.500 0.000 0.476 0.020
#> GSM648595 6 0.0858 0.7806 0.000 0.000 0.000 0.028 0.004 0.968
#> GSM648635 6 0.3738 0.8106 0.208 0.000 0.000 0.000 0.040 0.752
#> GSM648645 6 0.4518 0.7851 0.200 0.000 0.000 0.000 0.104 0.696
#> GSM648647 2 0.0000 0.9662 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM648667 2 0.0865 0.9357 0.000 0.964 0.000 0.000 0.000 0.036
#> GSM648695 2 0.0000 0.9662 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM648704 2 0.0000 0.9662 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM648706 2 0.0000 0.9662 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM648593 6 0.4187 0.8054 0.208 0.000 0.000 0.020 0.036 0.736
#> GSM648594 5 0.5626 0.2735 0.052 0.000 0.000 0.072 0.596 0.280
#> GSM648600 6 0.0520 0.7881 0.008 0.000 0.000 0.000 0.008 0.984
#> GSM648621 5 0.4246 0.3499 0.016 0.000 0.000 0.000 0.532 0.452
#> GSM648622 1 0.3381 0.6073 0.800 0.000 0.000 0.000 0.044 0.156
#> GSM648623 5 0.1471 0.7456 0.064 0.000 0.000 0.000 0.932 0.004
#> GSM648636 6 0.0547 0.7855 0.000 0.000 0.000 0.020 0.000 0.980
#> GSM648655 6 0.0547 0.7855 0.000 0.000 0.000 0.020 0.000 0.980
#> GSM648661 1 0.1232 0.7277 0.956 0.000 0.024 0.000 0.016 0.004
#> GSM648664 1 0.0547 0.7439 0.980 0.000 0.000 0.000 0.000 0.020
#> GSM648683 1 0.3198 0.6626 0.740 0.000 0.000 0.000 0.000 0.260
#> GSM648685 1 0.0891 0.7424 0.968 0.000 0.000 0.000 0.008 0.024
#> GSM648702 6 0.0146 0.7891 0.000 0.000 0.000 0.004 0.000 0.996
#> GSM648597 5 0.0964 0.7493 0.016 0.000 0.000 0.012 0.968 0.004
#> GSM648603 5 0.1297 0.7498 0.040 0.000 0.000 0.000 0.948 0.012
#> GSM648606 3 0.2811 0.8085 0.020 0.032 0.872 0.000 0.076 0.000
#> GSM648613 3 0.4245 0.5373 0.020 0.016 0.684 0.000 0.280 0.000
#> GSM648619 5 0.4789 0.5614 0.092 0.000 0.268 0.000 0.640 0.000
#> GSM648654 1 0.5111 0.5491 0.672 0.184 0.124 0.000 0.020 0.000
#> GSM648663 3 0.3259 0.7841 0.024 0.044 0.844 0.000 0.088 0.000
#> GSM648670 4 0.3539 0.7287 0.000 0.000 0.000 0.756 0.024 0.220
#> GSM648707 5 0.1866 0.7426 0.000 0.000 0.084 0.008 0.908 0.000
#> GSM648615 2 0.0000 0.9662 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM648643 2 0.0000 0.9662 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM648650 2 0.1610 0.8832 0.000 0.916 0.000 0.000 0.000 0.084
#> GSM648656 2 0.0000 0.9662 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM648715 2 0.0000 0.9662 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM648598 6 0.3892 0.8075 0.212 0.000 0.000 0.000 0.048 0.740
#> GSM648601 6 0.4037 0.8091 0.200 0.000 0.000 0.000 0.064 0.736
#> GSM648602 6 0.1245 0.7758 0.032 0.000 0.000 0.000 0.016 0.952
#> GSM648604 1 0.0547 0.7439 0.980 0.000 0.000 0.000 0.000 0.020
#> GSM648614 2 0.1088 0.9371 0.016 0.960 0.000 0.000 0.024 0.000
#> GSM648624 1 0.1398 0.7332 0.940 0.000 0.000 0.000 0.008 0.052
#> GSM648625 2 0.5343 0.0169 0.028 0.492 0.000 0.000 0.048 0.432
#> GSM648629 1 0.0363 0.7421 0.988 0.000 0.000 0.000 0.000 0.012
#> GSM648634 6 0.0405 0.7880 0.008 0.000 0.000 0.000 0.004 0.988
#> GSM648648 6 0.3671 0.8102 0.208 0.000 0.000 0.000 0.036 0.756
#> GSM648651 1 0.5492 0.2048 0.536 0.000 0.000 0.000 0.152 0.312
#> GSM648657 6 0.4487 0.6616 0.068 0.000 0.000 0.000 0.264 0.668
#> GSM648660 6 0.4251 0.7994 0.208 0.000 0.000 0.000 0.076 0.716
#> GSM648697 1 0.3565 0.4197 0.692 0.000 0.000 0.000 0.004 0.304
#> GSM648710 1 0.0363 0.7421 0.988 0.000 0.000 0.000 0.000 0.012
#> GSM648591 5 0.2652 0.7279 0.000 0.000 0.020 0.008 0.868 0.104
#> GSM648592 5 0.0692 0.7494 0.000 0.020 0.000 0.004 0.976 0.000
#> GSM648607 1 0.3866 -0.0486 0.516 0.000 0.000 0.000 0.484 0.000
#> GSM648611 3 0.0363 0.8820 0.000 0.000 0.988 0.000 0.012 0.000
#> GSM648612 5 0.4291 0.5620 0.052 0.000 0.268 0.000 0.680 0.000
#> GSM648616 5 0.2070 0.7460 0.000 0.000 0.044 0.048 0.908 0.000
#> GSM648617 5 0.3686 0.6086 0.032 0.000 0.000 0.000 0.748 0.220
#> GSM648626 5 0.1333 0.7496 0.048 0.000 0.000 0.000 0.944 0.008
#> GSM648711 1 0.3869 -0.1155 0.500 0.000 0.000 0.000 0.500 0.000
#> GSM648712 5 0.4934 0.5712 0.048 0.000 0.264 0.000 0.656 0.032
#> GSM648713 5 0.3727 0.3173 0.388 0.000 0.000 0.000 0.612 0.000
#> GSM648714 2 0.1168 0.9341 0.016 0.956 0.000 0.000 0.028 0.000
#> GSM648716 5 0.4621 0.5106 0.064 0.000 0.304 0.000 0.632 0.000
#> GSM648717 3 0.1644 0.8515 0.028 0.000 0.932 0.000 0.040 0.000
#> GSM648590 6 0.2605 0.6914 0.000 0.108 0.000 0.028 0.000 0.864
#> GSM648596 2 0.0000 0.9662 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM648642 2 0.0000 0.9662 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM648696 6 0.2003 0.7019 0.000 0.116 0.000 0.000 0.000 0.884
#> GSM648705 6 0.3956 0.8105 0.204 0.008 0.000 0.000 0.040 0.748
#> GSM648718 2 0.0000 0.9662 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM648599 6 0.2389 0.6895 0.008 0.000 0.000 0.000 0.128 0.864
#> GSM648608 1 0.3126 0.6660 0.752 0.000 0.000 0.000 0.000 0.248
#> GSM648609 1 0.0547 0.7439 0.980 0.000 0.000 0.000 0.000 0.020
#> GSM648610 1 0.3244 0.6573 0.732 0.000 0.000 0.000 0.000 0.268
#> GSM648633 6 0.3983 0.8077 0.208 0.000 0.000 0.000 0.056 0.736
#> GSM648644 2 0.0000 0.9662 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM648652 6 0.3709 0.8112 0.204 0.000 0.000 0.000 0.040 0.756
#> GSM648653 6 0.0692 0.7840 0.020 0.000 0.000 0.000 0.004 0.976
#> GSM648658 6 0.0909 0.7909 0.012 0.000 0.000 0.020 0.000 0.968
#> GSM648659 2 0.0547 0.9516 0.000 0.980 0.000 0.020 0.000 0.000
#> GSM648662 1 0.3614 0.5959 0.752 0.220 0.000 0.000 0.028 0.000
#> GSM648665 1 0.3617 0.5823 0.736 0.244 0.000 0.000 0.020 0.000
#> GSM648666 1 0.3789 0.6484 0.716 0.000 0.000 0.000 0.024 0.260
#> GSM648680 6 0.3671 0.8102 0.208 0.000 0.000 0.000 0.036 0.756
#> GSM648684 1 0.3198 0.6626 0.740 0.000 0.000 0.000 0.000 0.260
#> GSM648709 2 0.0000 0.9662 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM648719 6 0.4200 0.8015 0.208 0.000 0.000 0.000 0.072 0.720
#> GSM648627 3 0.5063 -0.0654 0.032 0.000 0.496 0.000 0.448 0.024
#> GSM648637 4 0.0717 0.9425 0.000 0.016 0.000 0.976 0.008 0.000
#> GSM648638 4 0.0984 0.9397 0.000 0.012 0.012 0.968 0.008 0.000
#> GSM648641 3 0.0000 0.8870 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648672 4 0.0717 0.9425 0.000 0.016 0.000 0.976 0.008 0.000
#> GSM648674 4 0.0547 0.9422 0.000 0.000 0.000 0.980 0.020 0.000
#> GSM648703 4 0.0146 0.9431 0.000 0.004 0.000 0.996 0.000 0.000
#> GSM648631 3 0.0000 0.8870 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648669 4 0.0547 0.9422 0.000 0.000 0.000 0.980 0.020 0.000
#> GSM648671 4 0.0547 0.9422 0.000 0.000 0.000 0.980 0.020 0.000
#> GSM648678 4 0.1957 0.8606 0.000 0.112 0.000 0.888 0.000 0.000
#> GSM648679 4 0.0547 0.9422 0.000 0.000 0.000 0.980 0.020 0.000
#> GSM648681 4 0.2350 0.8610 0.000 0.100 0.000 0.880 0.020 0.000
#> GSM648686 3 0.0000 0.8870 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648689 3 0.0000 0.8870 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648690 3 0.0000 0.8870 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648691 3 0.0000 0.8870 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648693 3 0.0000 0.8870 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648700 4 0.0000 0.9428 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM648630 3 0.0000 0.8870 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648632 3 0.0000 0.8870 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648639 3 0.3714 0.4490 0.000 0.000 0.656 0.004 0.340 0.000
#> GSM648640 3 0.0000 0.8870 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648668 4 0.0717 0.9425 0.000 0.016 0.000 0.976 0.008 0.000
#> GSM648676 4 0.0146 0.9431 0.000 0.004 0.000 0.996 0.000 0.000
#> GSM648692 3 0.0000 0.8870 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648694 3 0.0000 0.8870 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648699 4 0.0000 0.9428 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM648701 4 0.0146 0.9431 0.000 0.004 0.000 0.996 0.000 0.000
#> GSM648673 4 0.0547 0.9422 0.000 0.000 0.000 0.980 0.020 0.000
#> GSM648677 4 0.0260 0.9419 0.000 0.008 0.000 0.992 0.000 0.000
#> GSM648687 3 0.1075 0.8507 0.000 0.000 0.952 0.048 0.000 0.000
#> GSM648688 3 0.0000 0.8870 0.000 0.000 1.000 0.000 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) development.stage(p) other(p) k
#> CV:skmeans 129 1.00e+00 0.06968 3.89e-08 2
#> CV:skmeans 120 1.60e-06 0.00154 2.48e-25 3
#> CV:skmeans 115 1.02e-12 0.00788 1.88e-31 4
#> CV:skmeans 76 2.96e-09 0.25166 2.39e-21 5
#> CV:skmeans 119 5.06e-18 0.00854 2.26e-36 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 51941 rows and 130 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 0.588 0.897 0.939 0.2757 0.771 0.771
#> 3 3 0.527 0.820 0.902 0.9965 0.685 0.592
#> 4 4 0.578 0.700 0.833 0.3139 0.737 0.470
#> 5 5 0.618 0.680 0.829 0.0569 0.898 0.662
#> 6 6 0.626 0.654 0.805 0.0464 0.836 0.434
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
#> GSM648605 2 0.0000 0.926 0.000 1.000
#> GSM648618 2 0.6801 0.846 0.180 0.820
#> GSM648620 2 0.0000 0.926 0.000 1.000
#> GSM648646 2 0.0000 0.926 0.000 1.000
#> GSM648649 2 0.0000 0.926 0.000 1.000
#> GSM648675 2 0.0000 0.926 0.000 1.000
#> GSM648682 2 0.0000 0.926 0.000 1.000
#> GSM648698 2 0.0000 0.926 0.000 1.000
#> GSM648708 2 0.0000 0.926 0.000 1.000
#> GSM648628 2 0.9815 0.423 0.420 0.580
#> GSM648595 2 0.0000 0.926 0.000 1.000
#> GSM648635 2 0.0000 0.926 0.000 1.000
#> GSM648645 2 0.6801 0.846 0.180 0.820
#> GSM648647 2 0.0000 0.926 0.000 1.000
#> GSM648667 2 0.0000 0.926 0.000 1.000
#> GSM648695 2 0.0000 0.926 0.000 1.000
#> GSM648704 2 0.0000 0.926 0.000 1.000
#> GSM648706 2 0.0000 0.926 0.000 1.000
#> GSM648593 2 0.0000 0.926 0.000 1.000
#> GSM648594 2 0.6623 0.849 0.172 0.828
#> GSM648600 2 0.0000 0.926 0.000 1.000
#> GSM648621 2 0.1633 0.919 0.024 0.976
#> GSM648622 2 0.6801 0.846 0.180 0.820
#> GSM648623 2 0.6801 0.846 0.180 0.820
#> GSM648636 2 0.0000 0.926 0.000 1.000
#> GSM648655 2 0.0000 0.926 0.000 1.000
#> GSM648661 2 0.6801 0.846 0.180 0.820
#> GSM648664 2 0.6801 0.846 0.180 0.820
#> GSM648683 2 0.0672 0.925 0.008 0.992
#> GSM648685 2 0.6801 0.846 0.180 0.820
#> GSM648702 2 0.0000 0.926 0.000 1.000
#> GSM648597 2 0.6801 0.846 0.180 0.820
#> GSM648603 2 0.6801 0.846 0.180 0.820
#> GSM648606 2 0.6801 0.846 0.180 0.820
#> GSM648613 2 0.6801 0.846 0.180 0.820
#> GSM648619 2 0.6801 0.846 0.180 0.820
#> GSM648654 2 0.6801 0.846 0.180 0.820
#> GSM648663 2 0.6801 0.846 0.180 0.820
#> GSM648670 2 0.0000 0.926 0.000 1.000
#> GSM648707 2 0.6801 0.846 0.180 0.820
#> GSM648615 2 0.0000 0.926 0.000 1.000
#> GSM648643 2 0.0000 0.926 0.000 1.000
#> GSM648650 2 0.0000 0.926 0.000 1.000
#> GSM648656 2 0.0000 0.926 0.000 1.000
#> GSM648715 2 0.0000 0.926 0.000 1.000
#> GSM648598 2 0.0376 0.926 0.004 0.996
#> GSM648601 2 0.0376 0.926 0.004 0.996
#> GSM648602 2 0.0672 0.925 0.008 0.992
#> GSM648604 2 0.6801 0.846 0.180 0.820
#> GSM648614 2 0.0376 0.926 0.004 0.996
#> GSM648624 2 0.6801 0.846 0.180 0.820
#> GSM648625 2 0.0000 0.926 0.000 1.000
#> GSM648629 2 0.6801 0.846 0.180 0.820
#> GSM648634 2 0.0000 0.926 0.000 1.000
#> GSM648648 2 0.0000 0.926 0.000 1.000
#> GSM648651 2 0.6801 0.846 0.180 0.820
#> GSM648657 2 0.0000 0.926 0.000 1.000
#> GSM648660 2 0.0376 0.926 0.004 0.996
#> GSM648697 2 0.0672 0.925 0.008 0.992
#> GSM648710 2 0.6801 0.846 0.180 0.820
#> GSM648591 2 0.6801 0.846 0.180 0.820
#> GSM648592 2 0.0000 0.926 0.000 1.000
#> GSM648607 2 0.6801 0.846 0.180 0.820
#> GSM648611 1 0.3879 0.894 0.924 0.076
#> GSM648612 2 0.6801 0.846 0.180 0.820
#> GSM648616 2 0.6801 0.846 0.180 0.820
#> GSM648617 2 0.0000 0.926 0.000 1.000
#> GSM648626 2 0.6801 0.846 0.180 0.820
#> GSM648711 2 0.6801 0.846 0.180 0.820
#> GSM648712 2 0.6801 0.846 0.180 0.820
#> GSM648713 2 0.6801 0.846 0.180 0.820
#> GSM648714 2 0.0000 0.926 0.000 1.000
#> GSM648716 2 0.6801 0.846 0.180 0.820
#> GSM648717 2 0.7883 0.784 0.236 0.764
#> GSM648590 2 0.0000 0.926 0.000 1.000
#> GSM648596 2 0.0000 0.926 0.000 1.000
#> GSM648642 2 0.0000 0.926 0.000 1.000
#> GSM648696 2 0.0000 0.926 0.000 1.000
#> GSM648705 2 0.0000 0.926 0.000 1.000
#> GSM648718 2 0.0000 0.926 0.000 1.000
#> GSM648599 2 0.0672 0.925 0.008 0.992
#> GSM648608 2 0.6801 0.846 0.180 0.820
#> GSM648609 2 0.6801 0.846 0.180 0.820
#> GSM648610 2 0.0672 0.925 0.008 0.992
#> GSM648633 2 0.0000 0.926 0.000 1.000
#> GSM648644 2 0.0000 0.926 0.000 1.000
#> GSM648652 2 0.0000 0.926 0.000 1.000
#> GSM648653 2 0.0672 0.925 0.008 0.992
#> GSM648658 2 0.0000 0.926 0.000 1.000
#> GSM648659 2 0.0000 0.926 0.000 1.000
#> GSM648662 2 0.0672 0.925 0.008 0.992
#> GSM648665 2 0.6623 0.850 0.172 0.828
#> GSM648666 2 0.6801 0.846 0.180 0.820
#> GSM648680 2 0.0376 0.926 0.004 0.996
#> GSM648684 2 0.0672 0.925 0.008 0.992
#> GSM648709 2 0.0000 0.926 0.000 1.000
#> GSM648719 2 0.0376 0.926 0.004 0.996
#> GSM648627 2 0.6801 0.846 0.180 0.820
#> GSM648637 2 0.0000 0.926 0.000 1.000
#> GSM648638 2 0.0000 0.926 0.000 1.000
#> GSM648641 1 0.0000 0.963 1.000 0.000
#> GSM648672 2 0.0000 0.926 0.000 1.000
#> GSM648674 2 0.0000 0.926 0.000 1.000
#> GSM648703 2 0.0000 0.926 0.000 1.000
#> GSM648631 1 0.0000 0.963 1.000 0.000
#> GSM648669 1 0.3114 0.924 0.944 0.056
#> GSM648671 1 0.9460 0.324 0.636 0.364
#> GSM648678 2 0.0000 0.926 0.000 1.000
#> GSM648679 2 0.0000 0.926 0.000 1.000
#> GSM648681 2 0.0000 0.926 0.000 1.000
#> GSM648686 1 0.0000 0.963 1.000 0.000
#> GSM648689 1 0.0000 0.963 1.000 0.000
#> GSM648690 1 0.0000 0.963 1.000 0.000
#> GSM648691 1 0.0000 0.963 1.000 0.000
#> GSM648693 1 0.0000 0.963 1.000 0.000
#> GSM648700 2 0.0000 0.926 0.000 1.000
#> GSM648630 1 0.0000 0.963 1.000 0.000
#> GSM648632 1 0.0000 0.963 1.000 0.000
#> GSM648639 2 0.6247 0.828 0.156 0.844
#> GSM648640 1 0.0000 0.963 1.000 0.000
#> GSM648668 2 0.0000 0.926 0.000 1.000
#> GSM648676 2 0.0000 0.926 0.000 1.000
#> GSM648692 1 0.0000 0.963 1.000 0.000
#> GSM648694 1 0.0000 0.963 1.000 0.000
#> GSM648699 2 0.0000 0.926 0.000 1.000
#> GSM648701 2 0.0000 0.926 0.000 1.000
#> GSM648673 2 0.6531 0.851 0.168 0.832
#> GSM648677 2 0.0000 0.926 0.000 1.000
#> GSM648687 1 0.1843 0.943 0.972 0.028
#> GSM648688 1 0.0000 0.963 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM648605 2 0.0000 0.812 0.000 1.000 0.000
#> GSM648618 1 0.2878 0.878 0.904 0.000 0.096
#> GSM648620 2 0.2959 0.775 0.100 0.900 0.000
#> GSM648646 2 0.0000 0.812 0.000 1.000 0.000
#> GSM648649 1 0.1163 0.886 0.972 0.028 0.000
#> GSM648675 1 0.1163 0.886 0.972 0.028 0.000
#> GSM648682 2 0.5098 0.665 0.248 0.752 0.000
#> GSM648698 2 0.0000 0.812 0.000 1.000 0.000
#> GSM648708 2 0.3116 0.773 0.108 0.892 0.000
#> GSM648628 1 0.6095 0.494 0.608 0.000 0.392
#> GSM648595 1 0.3686 0.775 0.860 0.140 0.000
#> GSM648635 1 0.1163 0.886 0.972 0.028 0.000
#> GSM648645 1 0.0000 0.891 1.000 0.000 0.000
#> GSM648647 2 0.0000 0.812 0.000 1.000 0.000
#> GSM648667 2 0.4796 0.695 0.220 0.780 0.000
#> GSM648695 2 0.4235 0.736 0.176 0.824 0.000
#> GSM648704 2 0.0000 0.812 0.000 1.000 0.000
#> GSM648706 2 0.0000 0.812 0.000 1.000 0.000
#> GSM648593 1 0.1289 0.884 0.968 0.032 0.000
#> GSM648594 1 0.4483 0.870 0.848 0.024 0.128
#> GSM648600 1 0.1163 0.886 0.972 0.028 0.000
#> GSM648621 1 0.0237 0.891 0.996 0.004 0.000
#> GSM648622 1 0.3752 0.862 0.856 0.000 0.144
#> GSM648623 1 0.3752 0.862 0.856 0.000 0.144
#> GSM648636 1 0.1163 0.886 0.972 0.028 0.000
#> GSM648655 1 0.1289 0.886 0.968 0.032 0.000
#> GSM648661 1 0.3752 0.862 0.856 0.000 0.144
#> GSM648664 1 0.3752 0.862 0.856 0.000 0.144
#> GSM648683 1 0.0000 0.891 1.000 0.000 0.000
#> GSM648685 1 0.0000 0.891 1.000 0.000 0.000
#> GSM648702 1 0.1163 0.886 0.972 0.028 0.000
#> GSM648597 1 0.3752 0.862 0.856 0.000 0.144
#> GSM648603 1 0.2625 0.882 0.916 0.000 0.084
#> GSM648606 1 0.7988 0.633 0.656 0.200 0.144
#> GSM648613 1 0.4164 0.859 0.848 0.008 0.144
#> GSM648619 1 0.3752 0.862 0.856 0.000 0.144
#> GSM648654 1 0.8030 0.626 0.652 0.204 0.144
#> GSM648663 1 0.4164 0.859 0.848 0.008 0.144
#> GSM648670 1 0.1163 0.886 0.972 0.028 0.000
#> GSM648707 1 0.3752 0.862 0.856 0.000 0.144
#> GSM648615 1 0.5835 0.552 0.660 0.340 0.000
#> GSM648643 2 0.0000 0.812 0.000 1.000 0.000
#> GSM648650 2 0.6026 0.541 0.376 0.624 0.000
#> GSM648656 2 0.0000 0.812 0.000 1.000 0.000
#> GSM648715 2 0.0000 0.812 0.000 1.000 0.000
#> GSM648598 1 0.0000 0.891 1.000 0.000 0.000
#> GSM648601 1 0.0237 0.891 0.996 0.004 0.000
#> GSM648602 1 0.0237 0.891 0.996 0.004 0.000
#> GSM648604 1 0.3752 0.862 0.856 0.000 0.144
#> GSM648614 2 0.5016 0.681 0.240 0.760 0.000
#> GSM648624 1 0.0000 0.891 1.000 0.000 0.000
#> GSM648625 1 0.2625 0.862 0.916 0.084 0.000
#> GSM648629 1 0.3752 0.862 0.856 0.000 0.144
#> GSM648634 1 0.1031 0.887 0.976 0.024 0.000
#> GSM648648 1 0.1163 0.886 0.972 0.028 0.000
#> GSM648651 1 0.3752 0.862 0.856 0.000 0.144
#> GSM648657 1 0.1163 0.886 0.972 0.028 0.000
#> GSM648660 1 0.0000 0.891 1.000 0.000 0.000
#> GSM648697 1 0.0000 0.891 1.000 0.000 0.000
#> GSM648710 1 0.3752 0.862 0.856 0.000 0.144
#> GSM648591 1 0.3752 0.862 0.856 0.000 0.144
#> GSM648592 1 0.1163 0.886 0.972 0.028 0.000
#> GSM648607 1 0.3752 0.862 0.856 0.000 0.144
#> GSM648611 3 0.3116 0.826 0.108 0.000 0.892
#> GSM648612 1 0.3752 0.862 0.856 0.000 0.144
#> GSM648616 1 0.3851 0.866 0.860 0.004 0.136
#> GSM648617 1 0.1163 0.886 0.972 0.028 0.000
#> GSM648626 1 0.3752 0.862 0.856 0.000 0.144
#> GSM648711 1 0.3752 0.862 0.856 0.000 0.144
#> GSM648712 1 0.3752 0.862 0.856 0.000 0.144
#> GSM648713 1 0.3752 0.862 0.856 0.000 0.144
#> GSM648714 2 0.0000 0.812 0.000 1.000 0.000
#> GSM648716 1 0.3752 0.862 0.856 0.000 0.144
#> GSM648717 1 0.4605 0.813 0.796 0.000 0.204
#> GSM648590 1 0.1289 0.884 0.968 0.032 0.000
#> GSM648596 2 0.0000 0.812 0.000 1.000 0.000
#> GSM648642 2 0.0000 0.812 0.000 1.000 0.000
#> GSM648696 1 0.1163 0.886 0.972 0.028 0.000
#> GSM648705 1 0.1163 0.886 0.972 0.028 0.000
#> GSM648718 2 0.4504 0.708 0.196 0.804 0.000
#> GSM648599 1 0.0237 0.891 0.996 0.004 0.000
#> GSM648608 1 0.0000 0.891 1.000 0.000 0.000
#> GSM648609 1 0.3752 0.862 0.856 0.000 0.144
#> GSM648610 1 0.0000 0.891 1.000 0.000 0.000
#> GSM648633 1 0.1163 0.886 0.972 0.028 0.000
#> GSM648644 2 0.0000 0.812 0.000 1.000 0.000
#> GSM648652 1 0.1163 0.886 0.972 0.028 0.000
#> GSM648653 1 0.0237 0.891 0.996 0.004 0.000
#> GSM648658 1 0.1031 0.887 0.976 0.024 0.000
#> GSM648659 2 0.3816 0.755 0.148 0.852 0.000
#> GSM648662 1 0.1289 0.880 0.968 0.032 0.000
#> GSM648665 2 0.8604 0.345 0.348 0.540 0.112
#> GSM648666 1 0.0000 0.891 1.000 0.000 0.000
#> GSM648680 1 0.0237 0.891 0.996 0.004 0.000
#> GSM648684 1 0.0000 0.891 1.000 0.000 0.000
#> GSM648709 2 0.3116 0.770 0.108 0.892 0.000
#> GSM648719 1 0.0000 0.891 1.000 0.000 0.000
#> GSM648627 1 0.3752 0.862 0.856 0.000 0.144
#> GSM648637 2 0.6286 0.184 0.464 0.536 0.000
#> GSM648638 1 0.5291 0.653 0.732 0.268 0.000
#> GSM648641 3 0.0000 0.943 0.000 0.000 1.000
#> GSM648672 2 0.0000 0.812 0.000 1.000 0.000
#> GSM648674 1 0.5138 0.685 0.748 0.252 0.000
#> GSM648703 2 0.4555 0.704 0.200 0.800 0.000
#> GSM648631 3 0.0000 0.943 0.000 0.000 1.000
#> GSM648669 3 0.3484 0.870 0.048 0.048 0.904
#> GSM648671 3 0.8900 0.273 0.356 0.132 0.512
#> GSM648678 2 0.0000 0.812 0.000 1.000 0.000
#> GSM648679 1 0.5178 0.676 0.744 0.256 0.000
#> GSM648681 1 0.4702 0.771 0.788 0.212 0.000
#> GSM648686 3 0.0000 0.943 0.000 0.000 1.000
#> GSM648689 3 0.0000 0.943 0.000 0.000 1.000
#> GSM648690 3 0.0000 0.943 0.000 0.000 1.000
#> GSM648691 3 0.0000 0.943 0.000 0.000 1.000
#> GSM648693 3 0.0000 0.943 0.000 0.000 1.000
#> GSM648700 1 0.1163 0.886 0.972 0.028 0.000
#> GSM648630 3 0.0000 0.943 0.000 0.000 1.000
#> GSM648632 3 0.0000 0.943 0.000 0.000 1.000
#> GSM648639 1 0.5588 0.741 0.720 0.004 0.276
#> GSM648640 3 0.0000 0.943 0.000 0.000 1.000
#> GSM648668 2 0.5178 0.645 0.256 0.744 0.000
#> GSM648676 2 0.6274 0.224 0.456 0.544 0.000
#> GSM648692 3 0.0000 0.943 0.000 0.000 1.000
#> GSM648694 3 0.0000 0.943 0.000 0.000 1.000
#> GSM648699 2 0.3551 0.755 0.132 0.868 0.000
#> GSM648701 2 0.0000 0.812 0.000 1.000 0.000
#> GSM648673 1 0.6662 0.673 0.704 0.252 0.044
#> GSM648677 2 0.4504 0.707 0.196 0.804 0.000
#> GSM648687 3 0.2066 0.891 0.060 0.000 0.940
#> GSM648688 3 0.0000 0.943 0.000 0.000 1.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM648605 2 0.0000 0.8466 0.000 1.000 0.000 0.000
#> GSM648618 4 0.2281 0.7015 0.096 0.000 0.000 0.904
#> GSM648620 2 0.3266 0.7611 0.000 0.832 0.000 0.168
#> GSM648646 2 0.0000 0.8466 0.000 1.000 0.000 0.000
#> GSM648649 4 0.3311 0.7538 0.172 0.000 0.000 0.828
#> GSM648675 4 0.0000 0.7141 0.000 0.000 0.000 1.000
#> GSM648682 2 0.4134 0.6612 0.000 0.740 0.000 0.260
#> GSM648698 2 0.0000 0.8466 0.000 1.000 0.000 0.000
#> GSM648708 2 0.3356 0.7556 0.000 0.824 0.000 0.176
#> GSM648628 1 0.3569 0.7199 0.804 0.000 0.000 0.196
#> GSM648595 4 0.0000 0.7141 0.000 0.000 0.000 1.000
#> GSM648635 4 0.0000 0.7141 0.000 0.000 0.000 1.000
#> GSM648645 4 0.4661 0.6189 0.348 0.000 0.000 0.652
#> GSM648647 2 0.0000 0.8466 0.000 1.000 0.000 0.000
#> GSM648667 2 0.3873 0.7044 0.000 0.772 0.000 0.228
#> GSM648695 2 0.3688 0.7274 0.000 0.792 0.000 0.208
#> GSM648704 2 0.0000 0.8466 0.000 1.000 0.000 0.000
#> GSM648706 2 0.0000 0.8466 0.000 1.000 0.000 0.000
#> GSM648593 4 0.1792 0.7479 0.068 0.000 0.000 0.932
#> GSM648594 4 0.3610 0.7444 0.200 0.000 0.000 0.800
#> GSM648600 4 0.4804 -0.1913 0.384 0.000 0.000 0.616
#> GSM648621 1 0.4933 0.5944 0.568 0.000 0.000 0.432
#> GSM648622 1 0.2973 0.6787 0.856 0.000 0.000 0.144
#> GSM648623 4 0.4933 0.5228 0.432 0.000 0.000 0.568
#> GSM648636 1 0.4989 0.5343 0.528 0.000 0.000 0.472
#> GSM648655 4 0.6148 -0.2320 0.048 0.468 0.000 0.484
#> GSM648661 1 0.0000 0.7614 1.000 0.000 0.000 0.000
#> GSM648664 1 0.0000 0.7614 1.000 0.000 0.000 0.000
#> GSM648683 1 0.4933 0.5944 0.568 0.000 0.000 0.432
#> GSM648685 1 0.2408 0.7314 0.896 0.000 0.000 0.104
#> GSM648702 1 0.4967 0.5665 0.548 0.000 0.000 0.452
#> GSM648597 4 0.2011 0.7516 0.080 0.000 0.000 0.920
#> GSM648603 4 0.4933 0.5228 0.432 0.000 0.000 0.568
#> GSM648606 1 0.3610 0.6539 0.800 0.200 0.000 0.000
#> GSM648613 1 0.0000 0.7614 1.000 0.000 0.000 0.000
#> GSM648619 1 0.0000 0.7614 1.000 0.000 0.000 0.000
#> GSM648654 1 0.1637 0.7413 0.940 0.060 0.000 0.000
#> GSM648663 1 0.0707 0.7492 0.980 0.000 0.000 0.020
#> GSM648670 4 0.0000 0.7141 0.000 0.000 0.000 1.000
#> GSM648707 4 0.4477 0.6688 0.312 0.000 0.000 0.688
#> GSM648615 2 0.1389 0.8322 0.048 0.952 0.000 0.000
#> GSM648643 2 0.0000 0.8466 0.000 1.000 0.000 0.000
#> GSM648650 2 0.4833 0.6806 0.032 0.740 0.000 0.228
#> GSM648656 2 0.0000 0.8466 0.000 1.000 0.000 0.000
#> GSM648715 2 0.0000 0.8466 0.000 1.000 0.000 0.000
#> GSM648598 4 0.3837 0.7324 0.224 0.000 0.000 0.776
#> GSM648601 4 0.3610 0.7444 0.200 0.000 0.000 0.800
#> GSM648602 1 0.4933 0.5944 0.568 0.000 0.000 0.432
#> GSM648604 1 0.0000 0.7614 1.000 0.000 0.000 0.000
#> GSM648614 2 0.4820 0.7098 0.168 0.772 0.000 0.060
#> GSM648624 1 0.1867 0.7528 0.928 0.000 0.000 0.072
#> GSM648625 2 0.7363 0.3123 0.200 0.516 0.000 0.284
#> GSM648629 1 0.0000 0.7614 1.000 0.000 0.000 0.000
#> GSM648634 1 0.4933 0.5944 0.568 0.000 0.000 0.432
#> GSM648648 4 0.3610 0.7444 0.200 0.000 0.000 0.800
#> GSM648651 4 0.3528 0.7504 0.192 0.000 0.000 0.808
#> GSM648657 4 0.1637 0.7447 0.060 0.000 0.000 0.940
#> GSM648660 4 0.3610 0.7444 0.200 0.000 0.000 0.800
#> GSM648697 1 0.4933 0.5944 0.568 0.000 0.000 0.432
#> GSM648710 1 0.0000 0.7614 1.000 0.000 0.000 0.000
#> GSM648591 4 0.3024 0.7291 0.148 0.000 0.000 0.852
#> GSM648592 4 0.4008 0.7245 0.244 0.000 0.000 0.756
#> GSM648607 1 0.0336 0.7572 0.992 0.000 0.000 0.008
#> GSM648611 1 0.5839 0.6783 0.696 0.000 0.104 0.200
#> GSM648612 1 0.0336 0.7572 0.992 0.000 0.000 0.008
#> GSM648616 4 0.3528 0.7502 0.192 0.000 0.000 0.808
#> GSM648617 4 0.1637 0.7447 0.060 0.000 0.000 0.940
#> GSM648626 4 0.4933 0.5228 0.432 0.000 0.000 0.568
#> GSM648711 1 0.0000 0.7614 1.000 0.000 0.000 0.000
#> GSM648712 1 0.3610 0.7181 0.800 0.000 0.000 0.200
#> GSM648713 1 0.0000 0.7614 1.000 0.000 0.000 0.000
#> GSM648714 2 0.2011 0.8067 0.080 0.920 0.000 0.000
#> GSM648716 1 0.0000 0.7614 1.000 0.000 0.000 0.000
#> GSM648717 1 0.0000 0.7614 1.000 0.000 0.000 0.000
#> GSM648590 4 0.4353 0.4431 0.012 0.232 0.000 0.756
#> GSM648596 2 0.0000 0.8466 0.000 1.000 0.000 0.000
#> GSM648642 2 0.0000 0.8466 0.000 1.000 0.000 0.000
#> GSM648696 4 0.7429 0.0092 0.192 0.316 0.000 0.492
#> GSM648705 4 0.3569 0.7463 0.196 0.000 0.000 0.804
#> GSM648718 2 0.1824 0.8255 0.060 0.936 0.000 0.004
#> GSM648599 1 0.4999 0.5004 0.508 0.000 0.000 0.492
#> GSM648608 1 0.4277 0.6981 0.720 0.000 0.000 0.280
#> GSM648609 1 0.0000 0.7614 1.000 0.000 0.000 0.000
#> GSM648610 1 0.4933 0.5944 0.568 0.000 0.000 0.432
#> GSM648633 4 0.2814 0.7576 0.132 0.000 0.000 0.868
#> GSM648644 2 0.0000 0.8466 0.000 1.000 0.000 0.000
#> GSM648652 4 0.2345 0.7554 0.100 0.000 0.000 0.900
#> GSM648653 1 0.4933 0.5944 0.568 0.000 0.000 0.432
#> GSM648658 4 0.0921 0.7306 0.028 0.000 0.000 0.972
#> GSM648659 2 0.3569 0.7426 0.000 0.804 0.000 0.196
#> GSM648662 1 0.0779 0.7580 0.980 0.016 0.000 0.004
#> GSM648665 1 0.4991 0.3154 0.608 0.388 0.000 0.004
#> GSM648666 1 0.4817 0.6315 0.612 0.000 0.000 0.388
#> GSM648680 4 0.3311 0.7537 0.172 0.000 0.000 0.828
#> GSM648684 1 0.4933 0.5944 0.568 0.000 0.000 0.432
#> GSM648709 2 0.1389 0.8358 0.000 0.952 0.000 0.048
#> GSM648719 4 0.3649 0.7427 0.204 0.000 0.000 0.796
#> GSM648627 1 0.3610 0.7181 0.800 0.000 0.000 0.200
#> GSM648637 4 0.5343 0.5912 0.052 0.240 0.000 0.708
#> GSM648638 4 0.6374 0.6171 0.128 0.228 0.000 0.644
#> GSM648641 3 0.2281 0.8567 0.096 0.000 0.904 0.000
#> GSM648672 2 0.1211 0.8369 0.000 0.960 0.000 0.040
#> GSM648674 4 0.5327 0.6179 0.060 0.220 0.000 0.720
#> GSM648703 2 0.3610 0.7297 0.000 0.800 0.000 0.200
#> GSM648631 3 0.0000 0.9504 0.000 0.000 1.000 0.000
#> GSM648669 4 0.6198 0.2886 0.020 0.024 0.396 0.560
#> GSM648671 4 0.8584 0.4701 0.168 0.080 0.244 0.508
#> GSM648678 2 0.0000 0.8466 0.000 1.000 0.000 0.000
#> GSM648679 4 0.5123 0.5972 0.044 0.232 0.000 0.724
#> GSM648681 2 0.5148 0.6308 0.056 0.736 0.000 0.208
#> GSM648686 3 0.0000 0.9504 0.000 0.000 1.000 0.000
#> GSM648689 3 0.0000 0.9504 0.000 0.000 1.000 0.000
#> GSM648690 3 0.0000 0.9504 0.000 0.000 1.000 0.000
#> GSM648691 3 0.0000 0.9504 0.000 0.000 1.000 0.000
#> GSM648693 3 0.0000 0.9504 0.000 0.000 1.000 0.000
#> GSM648700 4 0.0000 0.7141 0.000 0.000 0.000 1.000
#> GSM648630 3 0.0000 0.9504 0.000 0.000 1.000 0.000
#> GSM648632 3 0.0000 0.9504 0.000 0.000 1.000 0.000
#> GSM648639 3 0.8835 0.0667 0.240 0.060 0.436 0.264
#> GSM648640 3 0.0000 0.9504 0.000 0.000 1.000 0.000
#> GSM648668 2 0.5913 0.3970 0.048 0.600 0.000 0.352
#> GSM648676 2 0.5767 0.5753 0.060 0.660 0.000 0.280
#> GSM648692 3 0.0000 0.9504 0.000 0.000 1.000 0.000
#> GSM648694 3 0.0000 0.9504 0.000 0.000 1.000 0.000
#> GSM648699 2 0.3610 0.7297 0.000 0.800 0.000 0.200
#> GSM648701 2 0.0000 0.8466 0.000 1.000 0.000 0.000
#> GSM648673 4 0.5938 0.5346 0.016 0.236 0.056 0.692
#> GSM648677 2 0.4916 0.6971 0.056 0.760 0.000 0.184
#> GSM648687 1 0.4304 0.5498 0.716 0.000 0.284 0.000
#> GSM648688 3 0.0000 0.9504 0.000 0.000 1.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM648605 2 0.0000 0.8021 0.000 1.000 0.000 0.000 0.000
#> GSM648618 5 0.2929 0.6710 0.152 0.000 0.000 0.008 0.840
#> GSM648620 2 0.2732 0.7102 0.000 0.840 0.000 0.000 0.160
#> GSM648646 2 0.0000 0.8021 0.000 1.000 0.000 0.000 0.000
#> GSM648649 5 0.2970 0.7686 0.168 0.004 0.000 0.000 0.828
#> GSM648675 5 0.0162 0.7474 0.000 0.000 0.000 0.004 0.996
#> GSM648682 2 0.3816 0.4174 0.000 0.696 0.000 0.000 0.304
#> GSM648698 2 0.0000 0.8021 0.000 1.000 0.000 0.000 0.000
#> GSM648708 2 0.2773 0.7079 0.000 0.836 0.000 0.000 0.164
#> GSM648628 1 0.3745 0.6951 0.780 0.000 0.000 0.024 0.196
#> GSM648595 5 0.1544 0.7153 0.000 0.068 0.000 0.000 0.932
#> GSM648635 5 0.0162 0.7475 0.000 0.004 0.000 0.000 0.996
#> GSM648645 5 0.4015 0.6259 0.348 0.000 0.000 0.000 0.652
#> GSM648647 2 0.0000 0.8021 0.000 1.000 0.000 0.000 0.000
#> GSM648667 2 0.3143 0.6715 0.000 0.796 0.000 0.000 0.204
#> GSM648695 2 0.3003 0.6888 0.000 0.812 0.000 0.000 0.188
#> GSM648704 2 0.0000 0.8021 0.000 1.000 0.000 0.000 0.000
#> GSM648706 2 0.0000 0.8021 0.000 1.000 0.000 0.000 0.000
#> GSM648593 5 0.0771 0.7583 0.020 0.004 0.000 0.000 0.976
#> GSM648594 5 0.3779 0.7523 0.200 0.000 0.000 0.024 0.776
#> GSM648600 5 0.3074 0.4928 0.196 0.000 0.000 0.000 0.804
#> GSM648621 1 0.4283 0.5133 0.544 0.000 0.000 0.000 0.456
#> GSM648622 5 0.4192 0.5578 0.404 0.000 0.000 0.000 0.596
#> GSM648623 5 0.5065 0.4942 0.420 0.000 0.000 0.036 0.544
#> GSM648636 5 0.4201 -0.2011 0.408 0.000 0.000 0.000 0.592
#> GSM648655 5 0.1597 0.7436 0.012 0.048 0.000 0.000 0.940
#> GSM648661 1 0.0000 0.7448 1.000 0.000 0.000 0.000 0.000
#> GSM648664 1 0.0000 0.7448 1.000 0.000 0.000 0.000 0.000
#> GSM648683 1 0.4283 0.5133 0.544 0.000 0.000 0.000 0.456
#> GSM648685 1 0.2074 0.6957 0.896 0.000 0.000 0.000 0.104
#> GSM648702 1 0.4446 0.4703 0.520 0.004 0.000 0.000 0.476
#> GSM648597 5 0.1661 0.7667 0.036 0.000 0.000 0.024 0.940
#> GSM648603 5 0.4867 0.4828 0.432 0.000 0.000 0.024 0.544
#> GSM648606 1 0.3779 0.6146 0.776 0.200 0.000 0.024 0.000
#> GSM648613 1 0.1211 0.7342 0.960 0.000 0.000 0.024 0.016
#> GSM648619 1 0.0703 0.7417 0.976 0.000 0.000 0.024 0.000
#> GSM648654 1 0.0404 0.7436 0.988 0.012 0.000 0.000 0.000
#> GSM648663 1 0.4696 -0.0303 0.616 0.000 0.000 0.024 0.360
#> GSM648670 5 0.0162 0.7475 0.000 0.004 0.000 0.000 0.996
#> GSM648707 5 0.5002 0.6338 0.312 0.000 0.000 0.052 0.636
#> GSM648615 2 0.4641 0.0430 0.012 0.532 0.000 0.000 0.456
#> GSM648643 2 0.0000 0.8021 0.000 1.000 0.000 0.000 0.000
#> GSM648650 2 0.4397 0.2750 0.004 0.564 0.000 0.000 0.432
#> GSM648656 2 0.0000 0.8021 0.000 1.000 0.000 0.000 0.000
#> GSM648715 2 0.0000 0.8021 0.000 1.000 0.000 0.000 0.000
#> GSM648598 5 0.3143 0.7568 0.204 0.000 0.000 0.000 0.796
#> GSM648601 5 0.3109 0.7577 0.200 0.000 0.000 0.000 0.800
#> GSM648602 1 0.4283 0.5133 0.544 0.000 0.000 0.000 0.456
#> GSM648604 1 0.0000 0.7448 1.000 0.000 0.000 0.000 0.000
#> GSM648614 2 0.4101 0.6269 0.184 0.768 0.000 0.000 0.048
#> GSM648624 1 0.1478 0.7253 0.936 0.000 0.000 0.000 0.064
#> GSM648625 5 0.5396 0.6761 0.220 0.124 0.000 0.000 0.656
#> GSM648629 1 0.0000 0.7448 1.000 0.000 0.000 0.000 0.000
#> GSM648634 1 0.4283 0.5133 0.544 0.000 0.000 0.000 0.456
#> GSM648648 5 0.3305 0.7482 0.224 0.000 0.000 0.000 0.776
#> GSM648651 5 0.3177 0.7586 0.208 0.000 0.000 0.000 0.792
#> GSM648657 5 0.0693 0.7561 0.012 0.000 0.000 0.008 0.980
#> GSM648660 5 0.3109 0.7577 0.200 0.000 0.000 0.000 0.800
#> GSM648697 1 0.4278 0.5169 0.548 0.000 0.000 0.000 0.452
#> GSM648710 1 0.0000 0.7448 1.000 0.000 0.000 0.000 0.000
#> GSM648591 5 0.3085 0.7125 0.116 0.000 0.000 0.032 0.852
#> GSM648592 5 0.4167 0.7198 0.252 0.000 0.000 0.024 0.724
#> GSM648607 1 0.0609 0.7351 0.980 0.000 0.000 0.000 0.020
#> GSM648611 1 0.5029 0.6754 0.720 0.000 0.056 0.024 0.200
#> GSM648612 1 0.2236 0.6863 0.908 0.000 0.000 0.024 0.068
#> GSM648616 5 0.5272 0.4913 0.072 0.000 0.000 0.308 0.620
#> GSM648617 5 0.1012 0.7571 0.012 0.000 0.000 0.020 0.968
#> GSM648626 5 0.5236 0.5043 0.408 0.000 0.000 0.048 0.544
#> GSM648711 1 0.0000 0.7448 1.000 0.000 0.000 0.000 0.000
#> GSM648712 1 0.3779 0.6932 0.776 0.000 0.000 0.024 0.200
#> GSM648713 1 0.0703 0.7417 0.976 0.000 0.000 0.024 0.000
#> GSM648714 2 0.2351 0.7282 0.088 0.896 0.000 0.016 0.000
#> GSM648716 1 0.0703 0.7417 0.976 0.000 0.000 0.024 0.000
#> GSM648717 1 0.0703 0.7417 0.976 0.000 0.000 0.024 0.000
#> GSM648590 5 0.0963 0.7388 0.000 0.036 0.000 0.000 0.964
#> GSM648596 2 0.0000 0.8021 0.000 1.000 0.000 0.000 0.000
#> GSM648642 2 0.0000 0.8021 0.000 1.000 0.000 0.000 0.000
#> GSM648696 5 0.1251 0.7355 0.008 0.036 0.000 0.000 0.956
#> GSM648705 5 0.3231 0.7599 0.196 0.004 0.000 0.000 0.800
#> GSM648718 2 0.1597 0.7691 0.012 0.940 0.000 0.000 0.048
#> GSM648599 5 0.1043 0.7288 0.040 0.000 0.000 0.000 0.960
#> GSM648608 1 0.3752 0.6562 0.708 0.000 0.000 0.000 0.292
#> GSM648609 1 0.0000 0.7448 1.000 0.000 0.000 0.000 0.000
#> GSM648610 1 0.4283 0.5133 0.544 0.000 0.000 0.000 0.456
#> GSM648633 5 0.2127 0.7736 0.108 0.000 0.000 0.000 0.892
#> GSM648644 2 0.0000 0.8021 0.000 1.000 0.000 0.000 0.000
#> GSM648652 5 0.1638 0.7705 0.064 0.004 0.000 0.000 0.932
#> GSM648653 1 0.4283 0.5133 0.544 0.000 0.000 0.000 0.456
#> GSM648658 5 0.0290 0.7522 0.008 0.000 0.000 0.000 0.992
#> GSM648659 2 0.3209 0.6928 0.000 0.812 0.000 0.008 0.180
#> GSM648662 1 0.0671 0.7391 0.980 0.004 0.000 0.000 0.016
#> GSM648665 1 0.4182 0.2535 0.600 0.400 0.000 0.000 0.000
#> GSM648666 1 0.4161 0.5748 0.608 0.000 0.000 0.000 0.392
#> GSM648680 5 0.2773 0.7688 0.164 0.000 0.000 0.000 0.836
#> GSM648684 1 0.4249 0.5329 0.568 0.000 0.000 0.000 0.432
#> GSM648709 2 0.1121 0.7852 0.000 0.956 0.000 0.000 0.044
#> GSM648719 5 0.3143 0.7564 0.204 0.000 0.000 0.000 0.796
#> GSM648627 1 0.3779 0.6932 0.776 0.000 0.000 0.024 0.200
#> GSM648637 4 0.5466 0.7135 0.000 0.244 0.000 0.640 0.116
#> GSM648638 4 0.5500 0.7101 0.000 0.212 0.000 0.648 0.140
#> GSM648641 3 0.2813 0.7950 0.108 0.000 0.868 0.024 0.000
#> GSM648672 4 0.4030 0.6457 0.000 0.352 0.000 0.648 0.000
#> GSM648674 4 0.5210 0.7304 0.000 0.200 0.000 0.680 0.120
#> GSM648703 4 0.4803 0.6601 0.000 0.096 0.000 0.720 0.184
#> GSM648631 3 0.0000 0.9409 0.000 0.000 1.000 0.000 0.000
#> GSM648669 4 0.2654 0.6928 0.000 0.000 0.084 0.884 0.032
#> GSM648671 4 0.1668 0.7165 0.000 0.000 0.028 0.940 0.032
#> GSM648678 2 0.4060 0.0882 0.000 0.640 0.000 0.360 0.000
#> GSM648679 4 0.4676 0.7391 0.000 0.208 0.000 0.720 0.072
#> GSM648681 5 0.5225 0.1882 0.012 0.432 0.000 0.024 0.532
#> GSM648686 3 0.0000 0.9409 0.000 0.000 1.000 0.000 0.000
#> GSM648689 3 0.0000 0.9409 0.000 0.000 1.000 0.000 0.000
#> GSM648690 3 0.0000 0.9409 0.000 0.000 1.000 0.000 0.000
#> GSM648691 3 0.0000 0.9409 0.000 0.000 1.000 0.000 0.000
#> GSM648693 3 0.0000 0.9409 0.000 0.000 1.000 0.000 0.000
#> GSM648700 4 0.4074 0.4787 0.000 0.000 0.000 0.636 0.364
#> GSM648630 3 0.0000 0.9409 0.000 0.000 1.000 0.000 0.000
#> GSM648632 3 0.0000 0.9409 0.000 0.000 1.000 0.000 0.000
#> GSM648639 3 0.7887 0.2125 0.116 0.036 0.472 0.308 0.068
#> GSM648640 3 0.1671 0.8827 0.000 0.000 0.924 0.076 0.000
#> GSM648668 4 0.4225 0.6307 0.000 0.364 0.000 0.632 0.004
#> GSM648676 4 0.2074 0.7492 0.000 0.104 0.000 0.896 0.000
#> GSM648692 3 0.0000 0.9409 0.000 0.000 1.000 0.000 0.000
#> GSM648694 3 0.0000 0.9409 0.000 0.000 1.000 0.000 0.000
#> GSM648699 4 0.3641 0.7025 0.000 0.060 0.000 0.820 0.120
#> GSM648701 4 0.2471 0.7401 0.000 0.136 0.000 0.864 0.000
#> GSM648673 4 0.0798 0.7231 0.000 0.016 0.008 0.976 0.000
#> GSM648677 4 0.3999 0.6544 0.000 0.344 0.000 0.656 0.000
#> GSM648687 1 0.3835 0.5651 0.732 0.000 0.260 0.008 0.000
#> GSM648688 3 0.0000 0.9409 0.000 0.000 1.000 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM648605 2 0.0000 0.7862 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM648618 6 0.1391 0.7591 0.040 0.000 0.000 0.000 0.016 0.944
#> GSM648620 2 0.2793 0.6600 0.000 0.800 0.000 0.000 0.000 0.200
#> GSM648646 2 0.0000 0.7862 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM648649 6 0.3293 0.7201 0.140 0.000 0.000 0.000 0.048 0.812
#> GSM648675 6 0.0000 0.7687 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM648682 6 0.3756 0.2722 0.000 0.400 0.000 0.000 0.000 0.600
#> GSM648698 2 0.0000 0.7862 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM648708 2 0.2823 0.6564 0.000 0.796 0.000 0.000 0.000 0.204
#> GSM648628 5 0.5348 0.5966 0.216 0.000 0.000 0.000 0.592 0.192
#> GSM648595 6 0.0146 0.7681 0.000 0.004 0.000 0.000 0.000 0.996
#> GSM648635 6 0.0000 0.7687 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM648645 6 0.3752 0.6922 0.164 0.000 0.000 0.000 0.064 0.772
#> GSM648647 2 0.0000 0.7862 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM648667 2 0.2823 0.6564 0.000 0.796 0.000 0.000 0.000 0.204
#> GSM648695 2 0.2823 0.6564 0.000 0.796 0.000 0.000 0.000 0.204
#> GSM648704 2 0.0000 0.7862 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM648706 2 0.0000 0.7862 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM648593 6 0.1863 0.7640 0.036 0.000 0.000 0.000 0.044 0.920
#> GSM648594 6 0.3752 0.6922 0.164 0.000 0.000 0.000 0.064 0.772
#> GSM648600 6 0.2454 0.6907 0.160 0.000 0.000 0.000 0.000 0.840
#> GSM648621 6 0.2823 0.6519 0.204 0.000 0.000 0.000 0.000 0.796
#> GSM648622 1 0.3773 0.5263 0.752 0.000 0.000 0.000 0.044 0.204
#> GSM648623 5 0.4996 0.5890 0.156 0.000 0.000 0.000 0.644 0.200
#> GSM648636 6 0.2762 0.6596 0.196 0.000 0.000 0.000 0.000 0.804
#> GSM648655 6 0.1693 0.7671 0.020 0.012 0.000 0.000 0.032 0.936
#> GSM648661 1 0.0000 0.7523 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM648664 1 0.0000 0.7523 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM648683 6 0.3266 0.5686 0.272 0.000 0.000 0.000 0.000 0.728
#> GSM648685 1 0.0146 0.7511 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM648702 6 0.2823 0.6519 0.204 0.000 0.000 0.000 0.000 0.796
#> GSM648597 6 0.2384 0.7593 0.048 0.000 0.000 0.000 0.064 0.888
#> GSM648603 5 0.5352 0.5981 0.204 0.000 0.000 0.000 0.592 0.204
#> GSM648606 5 0.5186 0.5857 0.216 0.168 0.000 0.000 0.616 0.000
#> GSM648613 5 0.3887 0.6576 0.360 0.000 0.000 0.000 0.632 0.008
#> GSM648619 5 0.3684 0.6508 0.372 0.000 0.000 0.000 0.628 0.000
#> GSM648654 1 0.0713 0.7348 0.972 0.028 0.000 0.000 0.000 0.000
#> GSM648663 5 0.5319 0.6193 0.220 0.000 0.000 0.000 0.596 0.184
#> GSM648670 6 0.0000 0.7687 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM648707 5 0.4680 0.6131 0.120 0.000 0.000 0.000 0.680 0.200
#> GSM648615 2 0.4303 0.5211 0.024 0.732 0.000 0.000 0.040 0.204
#> GSM648643 2 0.0000 0.7862 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM648650 2 0.4464 0.4609 0.008 0.624 0.000 0.000 0.028 0.340
#> GSM648656 2 0.0000 0.7862 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM648715 2 0.0000 0.7862 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM648598 6 0.4552 0.4019 0.364 0.000 0.000 0.000 0.044 0.592
#> GSM648601 6 0.3672 0.6936 0.168 0.000 0.000 0.000 0.056 0.776
#> GSM648602 6 0.2823 0.6519 0.204 0.000 0.000 0.000 0.000 0.796
#> GSM648604 1 0.0000 0.7523 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM648614 1 0.3828 0.1895 0.560 0.440 0.000 0.000 0.000 0.000
#> GSM648624 1 0.0146 0.7511 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM648625 1 0.5449 0.3882 0.604 0.064 0.000 0.000 0.044 0.288
#> GSM648629 1 0.0000 0.7523 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM648634 6 0.2823 0.6519 0.204 0.000 0.000 0.000 0.000 0.796
#> GSM648648 1 0.4433 0.3296 0.616 0.000 0.000 0.000 0.040 0.344
#> GSM648651 1 0.3938 0.5251 0.728 0.000 0.000 0.000 0.044 0.228
#> GSM648657 6 0.2030 0.7621 0.028 0.000 0.000 0.000 0.064 0.908
#> GSM648660 6 0.3487 0.6990 0.168 0.000 0.000 0.000 0.044 0.788
#> GSM648697 6 0.3390 0.5318 0.296 0.000 0.000 0.000 0.000 0.704
#> GSM648710 1 0.0000 0.7523 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM648591 6 0.3279 0.6719 0.028 0.000 0.000 0.000 0.176 0.796
#> GSM648592 6 0.5108 0.5006 0.164 0.000 0.000 0.000 0.208 0.628
#> GSM648607 1 0.0622 0.7435 0.980 0.000 0.000 0.000 0.008 0.012
#> GSM648611 5 0.5487 0.6016 0.208 0.000 0.008 0.000 0.600 0.184
#> GSM648612 5 0.4548 0.6677 0.312 0.000 0.000 0.000 0.632 0.056
#> GSM648616 5 0.2706 0.5385 0.000 0.000 0.000 0.024 0.852 0.124
#> GSM648617 6 0.3854 -0.0174 0.000 0.000 0.000 0.000 0.464 0.536
#> GSM648626 5 0.5253 0.6119 0.200 0.000 0.000 0.000 0.608 0.192
#> GSM648711 1 0.0713 0.7278 0.972 0.000 0.000 0.000 0.028 0.000
#> GSM648712 5 0.5113 0.6188 0.204 0.000 0.000 0.000 0.628 0.168
#> GSM648713 5 0.3706 0.6446 0.380 0.000 0.000 0.000 0.620 0.000
#> GSM648714 2 0.3371 0.4912 0.000 0.708 0.000 0.000 0.292 0.000
#> GSM648716 5 0.3672 0.6527 0.368 0.000 0.000 0.000 0.632 0.000
#> GSM648717 5 0.3727 0.6372 0.388 0.000 0.000 0.000 0.612 0.000
#> GSM648590 6 0.0000 0.7687 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM648596 2 0.0000 0.7862 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM648642 2 0.0000 0.7862 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM648696 6 0.0000 0.7687 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM648705 6 0.3248 0.7090 0.164 0.000 0.000 0.000 0.032 0.804
#> GSM648718 2 0.2421 0.7217 0.028 0.900 0.000 0.000 0.040 0.032
#> GSM648599 6 0.0000 0.7687 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM648608 1 0.2793 0.5774 0.800 0.000 0.000 0.000 0.000 0.200
#> GSM648609 1 0.0000 0.7523 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM648610 6 0.2823 0.6519 0.204 0.000 0.000 0.000 0.000 0.796
#> GSM648633 6 0.2795 0.7454 0.100 0.000 0.000 0.000 0.044 0.856
#> GSM648644 2 0.0000 0.7862 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM648652 6 0.2433 0.7568 0.072 0.000 0.000 0.000 0.044 0.884
#> GSM648653 6 0.2823 0.6519 0.204 0.000 0.000 0.000 0.000 0.796
#> GSM648658 6 0.0909 0.7690 0.012 0.000 0.000 0.000 0.020 0.968
#> GSM648659 2 0.3073 0.6520 0.000 0.788 0.000 0.008 0.000 0.204
#> GSM648662 1 0.0291 0.7514 0.992 0.004 0.000 0.000 0.000 0.004
#> GSM648665 1 0.3659 0.3718 0.636 0.364 0.000 0.000 0.000 0.000
#> GSM648666 1 0.3076 0.5464 0.760 0.000 0.000 0.000 0.000 0.240
#> GSM648680 6 0.3229 0.7226 0.140 0.000 0.000 0.000 0.044 0.816
#> GSM648684 1 0.3756 0.2885 0.600 0.000 0.000 0.000 0.000 0.400
#> GSM648709 2 0.0000 0.7862 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM648719 6 0.4453 0.4700 0.332 0.000 0.000 0.000 0.044 0.624
#> GSM648627 5 0.5509 0.5690 0.220 0.000 0.000 0.000 0.564 0.216
#> GSM648637 4 0.7465 0.5264 0.000 0.204 0.000 0.392 0.200 0.204
#> GSM648638 5 0.5684 -0.2419 0.000 0.164 0.000 0.276 0.552 0.008
#> GSM648641 5 0.3727 0.3127 0.000 0.000 0.388 0.000 0.612 0.000
#> GSM648672 4 0.5837 0.5142 0.000 0.340 0.000 0.460 0.200 0.000
#> GSM648674 4 0.7066 0.5958 0.000 0.204 0.000 0.464 0.208 0.124
#> GSM648703 4 0.2706 0.6986 0.000 0.124 0.000 0.852 0.000 0.024
#> GSM648631 3 0.0000 0.9828 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648669 4 0.4594 0.6196 0.000 0.000 0.092 0.676 0.232 0.000
#> GSM648671 4 0.3151 0.6752 0.000 0.000 0.000 0.748 0.252 0.000
#> GSM648678 2 0.3695 0.1179 0.000 0.624 0.000 0.376 0.000 0.000
#> GSM648679 4 0.6052 0.6142 0.000 0.204 0.000 0.464 0.324 0.008
#> GSM648681 2 0.4303 0.5212 0.024 0.732 0.000 0.000 0.040 0.204
#> GSM648686 3 0.0000 0.9828 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648689 3 0.0000 0.9828 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648690 3 0.0000 0.9828 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648691 3 0.0000 0.9828 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648693 3 0.0000 0.9828 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648700 4 0.2491 0.6377 0.000 0.000 0.000 0.836 0.000 0.164
#> GSM648630 3 0.0000 0.9828 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648632 3 0.0000 0.9828 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648639 5 0.3334 0.4893 0.004 0.000 0.120 0.024 0.832 0.020
#> GSM648640 3 0.2743 0.7918 0.000 0.000 0.828 0.008 0.164 0.000
#> GSM648668 2 0.5887 -0.4491 0.000 0.408 0.000 0.392 0.200 0.000
#> GSM648676 4 0.2219 0.6965 0.000 0.136 0.000 0.864 0.000 0.000
#> GSM648692 3 0.0000 0.9828 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648694 3 0.0000 0.9828 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648699 4 0.0363 0.6920 0.000 0.000 0.000 0.988 0.000 0.012
#> GSM648701 4 0.2092 0.7032 0.000 0.124 0.000 0.876 0.000 0.000
#> GSM648673 4 0.2092 0.6620 0.000 0.000 0.000 0.876 0.124 0.000
#> GSM648677 4 0.5837 0.5142 0.000 0.340 0.000 0.460 0.200 0.000
#> GSM648687 1 0.3456 0.6051 0.788 0.000 0.172 0.000 0.040 0.000
#> GSM648688 3 0.0000 0.9828 0.000 0.000 1.000 0.000 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) development.stage(p) other(p) k
#> CV:pam 128 3.39e-11 0.5647 1.70e-10 2
#> CV:pam 125 2.06e-12 0.0708 2.22e-17 3
#> CV:pam 120 1.78e-11 0.2607 4.32e-18 4
#> CV:pam 115 8.31e-23 0.1543 6.90e-28 5
#> CV:pam 114 9.55e-21 0.1128 5.82e-37 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 51941 rows and 130 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 0.573 0.884 0.928 0.4894 0.497 0.497
#> 3 3 0.454 0.489 0.755 0.2538 0.814 0.653
#> 4 4 0.481 0.443 0.703 0.1230 0.749 0.483
#> 5 5 0.647 0.715 0.799 0.1119 0.767 0.409
#> 6 6 0.677 0.676 0.771 0.0423 0.958 0.825
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
#> GSM648605 2 0.4815 0.8910 0.104 0.896
#> GSM648618 1 0.0672 0.9482 0.992 0.008
#> GSM648620 2 0.6048 0.8925 0.148 0.852
#> GSM648646 2 0.5946 0.8946 0.144 0.856
#> GSM648649 1 0.0000 0.9532 1.000 0.000
#> GSM648675 2 0.9393 0.6031 0.356 0.644
#> GSM648682 2 0.5946 0.8946 0.144 0.856
#> GSM648698 2 0.5946 0.8946 0.144 0.856
#> GSM648708 2 0.6343 0.8841 0.160 0.840
#> GSM648628 2 0.9815 0.3510 0.420 0.580
#> GSM648595 1 0.3274 0.9013 0.940 0.060
#> GSM648635 1 0.0000 0.9532 1.000 0.000
#> GSM648645 1 0.0000 0.9532 1.000 0.000
#> GSM648647 2 0.5946 0.8946 0.144 0.856
#> GSM648667 2 0.9933 0.3825 0.452 0.548
#> GSM648695 2 0.6048 0.8925 0.148 0.852
#> GSM648704 2 0.5946 0.8946 0.144 0.856
#> GSM648706 2 0.4815 0.8910 0.104 0.896
#> GSM648593 1 0.0000 0.9532 1.000 0.000
#> GSM648594 1 0.8016 0.6203 0.756 0.244
#> GSM648600 1 0.0000 0.9532 1.000 0.000
#> GSM648621 1 0.0000 0.9532 1.000 0.000
#> GSM648622 1 0.0000 0.9532 1.000 0.000
#> GSM648623 1 0.0000 0.9532 1.000 0.000
#> GSM648636 1 0.0000 0.9532 1.000 0.000
#> GSM648655 1 0.0000 0.9532 1.000 0.000
#> GSM648661 1 0.2423 0.9395 0.960 0.040
#> GSM648664 1 0.2423 0.9395 0.960 0.040
#> GSM648683 1 0.2423 0.9395 0.960 0.040
#> GSM648685 1 0.2423 0.9395 0.960 0.040
#> GSM648702 1 0.0000 0.9532 1.000 0.000
#> GSM648597 1 0.7815 0.6438 0.768 0.232
#> GSM648603 1 0.0000 0.9532 1.000 0.000
#> GSM648606 2 0.4815 0.8910 0.104 0.896
#> GSM648613 2 0.4815 0.8910 0.104 0.896
#> GSM648619 1 0.2423 0.9395 0.960 0.040
#> GSM648654 2 0.9209 0.5742 0.336 0.664
#> GSM648663 2 0.5294 0.8820 0.120 0.880
#> GSM648670 2 0.6438 0.8779 0.164 0.836
#> GSM648707 2 0.6148 0.8682 0.152 0.848
#> GSM648615 2 0.5946 0.8946 0.144 0.856
#> GSM648643 2 0.5946 0.8946 0.144 0.856
#> GSM648650 1 0.7815 0.6383 0.768 0.232
#> GSM648656 2 0.5946 0.8946 0.144 0.856
#> GSM648715 2 0.5946 0.8946 0.144 0.856
#> GSM648598 1 0.0000 0.9532 1.000 0.000
#> GSM648601 1 0.0000 0.9532 1.000 0.000
#> GSM648602 1 0.0000 0.9532 1.000 0.000
#> GSM648604 1 0.2423 0.9395 0.960 0.040
#> GSM648614 2 0.4815 0.8910 0.104 0.896
#> GSM648624 1 0.0000 0.9532 1.000 0.000
#> GSM648625 1 0.3879 0.8832 0.924 0.076
#> GSM648629 1 0.2423 0.9395 0.960 0.040
#> GSM648634 1 0.0000 0.9532 1.000 0.000
#> GSM648648 1 0.0000 0.9532 1.000 0.000
#> GSM648651 1 0.0000 0.9532 1.000 0.000
#> GSM648657 1 0.0000 0.9532 1.000 0.000
#> GSM648660 1 0.0000 0.9532 1.000 0.000
#> GSM648697 1 0.0000 0.9532 1.000 0.000
#> GSM648710 1 0.2423 0.9395 0.960 0.040
#> GSM648591 1 0.8713 0.5153 0.708 0.292
#> GSM648592 1 0.3114 0.9071 0.944 0.056
#> GSM648607 1 0.2423 0.9395 0.960 0.040
#> GSM648611 2 0.1414 0.8942 0.020 0.980
#> GSM648612 1 0.2423 0.9395 0.960 0.040
#> GSM648616 2 0.2423 0.9038 0.040 0.960
#> GSM648617 1 0.0000 0.9532 1.000 0.000
#> GSM648626 1 0.0000 0.9532 1.000 0.000
#> GSM648711 1 0.2423 0.9395 0.960 0.040
#> GSM648712 1 0.2423 0.9395 0.960 0.040
#> GSM648713 1 0.2423 0.9395 0.960 0.040
#> GSM648714 2 0.4815 0.8910 0.104 0.896
#> GSM648716 1 0.2423 0.9395 0.960 0.040
#> GSM648717 2 0.4815 0.8910 0.104 0.896
#> GSM648590 1 0.9850 0.0781 0.572 0.428
#> GSM648596 2 0.5946 0.8946 0.144 0.856
#> GSM648642 2 0.5946 0.8946 0.144 0.856
#> GSM648696 1 0.0000 0.9532 1.000 0.000
#> GSM648705 1 0.0000 0.9532 1.000 0.000
#> GSM648718 2 0.5946 0.8946 0.144 0.856
#> GSM648599 1 0.0000 0.9532 1.000 0.000
#> GSM648608 1 0.2423 0.9395 0.960 0.040
#> GSM648609 1 0.2423 0.9395 0.960 0.040
#> GSM648610 1 0.2423 0.9395 0.960 0.040
#> GSM648633 1 0.0000 0.9532 1.000 0.000
#> GSM648644 2 0.5946 0.8946 0.144 0.856
#> GSM648652 1 0.0000 0.9532 1.000 0.000
#> GSM648653 1 0.0000 0.9532 1.000 0.000
#> GSM648658 1 0.0000 0.9532 1.000 0.000
#> GSM648659 2 0.5946 0.8946 0.144 0.856
#> GSM648662 2 0.9323 0.5494 0.348 0.652
#> GSM648665 2 0.9286 0.5583 0.344 0.656
#> GSM648666 1 0.0000 0.9532 1.000 0.000
#> GSM648680 1 0.0000 0.9532 1.000 0.000
#> GSM648684 1 0.2423 0.9395 0.960 0.040
#> GSM648709 2 0.6048 0.8925 0.148 0.852
#> GSM648719 1 0.0000 0.9532 1.000 0.000
#> GSM648627 1 0.2423 0.9395 0.960 0.040
#> GSM648637 2 0.2423 0.9038 0.040 0.960
#> GSM648638 2 0.2423 0.9038 0.040 0.960
#> GSM648641 2 0.0000 0.8945 0.000 1.000
#> GSM648672 2 0.2423 0.9038 0.040 0.960
#> GSM648674 2 0.2423 0.9038 0.040 0.960
#> GSM648703 2 0.2423 0.9038 0.040 0.960
#> GSM648631 2 0.0000 0.8945 0.000 1.000
#> GSM648669 2 0.2423 0.9038 0.040 0.960
#> GSM648671 2 0.2423 0.9038 0.040 0.960
#> GSM648678 2 0.2423 0.9038 0.040 0.960
#> GSM648679 2 0.2423 0.9038 0.040 0.960
#> GSM648681 2 0.5946 0.8946 0.144 0.856
#> GSM648686 2 0.0000 0.8945 0.000 1.000
#> GSM648689 2 0.0000 0.8945 0.000 1.000
#> GSM648690 2 0.0000 0.8945 0.000 1.000
#> GSM648691 2 0.0000 0.8945 0.000 1.000
#> GSM648693 2 0.0000 0.8945 0.000 1.000
#> GSM648700 2 0.2423 0.9038 0.040 0.960
#> GSM648630 2 0.0000 0.8945 0.000 1.000
#> GSM648632 2 0.0000 0.8945 0.000 1.000
#> GSM648639 2 0.2423 0.9038 0.040 0.960
#> GSM648640 2 0.0000 0.8945 0.000 1.000
#> GSM648668 2 0.2423 0.9038 0.040 0.960
#> GSM648676 2 0.2423 0.9038 0.040 0.960
#> GSM648692 2 0.0000 0.8945 0.000 1.000
#> GSM648694 2 0.0000 0.8945 0.000 1.000
#> GSM648699 2 0.2423 0.9038 0.040 0.960
#> GSM648701 2 0.2423 0.9038 0.040 0.960
#> GSM648673 2 0.2423 0.9038 0.040 0.960
#> GSM648677 2 0.2423 0.9038 0.040 0.960
#> GSM648687 2 0.2423 0.9038 0.040 0.960
#> GSM648688 2 0.0000 0.8945 0.000 1.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM648605 2 0.6669 0.0936 0.468 0.524 0.008
#> GSM648618 1 0.1525 0.7873 0.964 0.032 0.004
#> GSM648620 1 0.6309 -0.0845 0.504 0.496 0.000
#> GSM648646 2 0.5325 0.4578 0.248 0.748 0.004
#> GSM648649 1 0.0424 0.8021 0.992 0.008 0.000
#> GSM648675 1 0.8137 0.1962 0.592 0.316 0.092
#> GSM648682 2 0.5325 0.4578 0.248 0.748 0.004
#> GSM648698 2 0.5982 0.3735 0.328 0.668 0.004
#> GSM648708 1 0.6309 -0.0845 0.504 0.496 0.000
#> GSM648628 3 0.6252 0.4009 0.268 0.024 0.708
#> GSM648595 1 0.6019 0.4062 0.700 0.288 0.012
#> GSM648635 1 0.0424 0.8021 0.992 0.008 0.000
#> GSM648645 1 0.0000 0.8035 1.000 0.000 0.000
#> GSM648647 2 0.5178 0.4494 0.256 0.744 0.000
#> GSM648667 2 0.6308 0.0847 0.492 0.508 0.000
#> GSM648695 1 0.6309 -0.0845 0.504 0.496 0.000
#> GSM648704 2 0.5502 0.4585 0.248 0.744 0.008
#> GSM648706 2 0.6659 0.1161 0.460 0.532 0.008
#> GSM648593 1 0.0424 0.8021 0.992 0.008 0.000
#> GSM648594 1 0.5845 0.3927 0.688 0.308 0.004
#> GSM648600 1 0.0424 0.8021 0.992 0.008 0.000
#> GSM648621 1 0.0592 0.7999 0.988 0.012 0.000
#> GSM648622 1 0.0000 0.8035 1.000 0.000 0.000
#> GSM648623 1 0.1529 0.7865 0.960 0.040 0.000
#> GSM648636 1 0.0424 0.8021 0.992 0.008 0.000
#> GSM648655 1 0.0424 0.8021 0.992 0.008 0.000
#> GSM648661 1 0.5363 0.6432 0.724 0.000 0.276
#> GSM648664 1 0.4974 0.6746 0.764 0.000 0.236
#> GSM648683 1 0.4796 0.6865 0.780 0.000 0.220
#> GSM648685 1 0.2537 0.7728 0.920 0.000 0.080
#> GSM648702 1 0.0424 0.8021 0.992 0.008 0.000
#> GSM648597 1 0.9573 -0.1321 0.460 0.328 0.212
#> GSM648603 1 0.0237 0.8025 0.996 0.004 0.000
#> GSM648606 2 0.9560 0.2280 0.256 0.484 0.260
#> GSM648613 2 0.9560 0.2280 0.256 0.484 0.260
#> GSM648619 1 0.5363 0.6432 0.724 0.000 0.276
#> GSM648654 2 0.9559 0.2171 0.264 0.484 0.252
#> GSM648663 2 0.9560 0.2280 0.256 0.484 0.260
#> GSM648670 2 0.7489 -0.1844 0.036 0.496 0.468
#> GSM648707 3 0.5553 0.6471 0.004 0.272 0.724
#> GSM648615 2 0.6468 0.1873 0.444 0.552 0.004
#> GSM648643 2 0.5325 0.4578 0.248 0.748 0.004
#> GSM648650 1 0.5291 0.4680 0.732 0.268 0.000
#> GSM648656 2 0.5502 0.4585 0.248 0.744 0.008
#> GSM648715 2 0.5178 0.4494 0.256 0.744 0.000
#> GSM648598 1 0.0000 0.8035 1.000 0.000 0.000
#> GSM648601 1 0.0000 0.8035 1.000 0.000 0.000
#> GSM648602 1 0.0000 0.8035 1.000 0.000 0.000
#> GSM648604 1 0.5058 0.6682 0.756 0.000 0.244
#> GSM648614 2 0.9528 0.2110 0.288 0.484 0.228
#> GSM648624 1 0.0000 0.8035 1.000 0.000 0.000
#> GSM648625 1 0.4887 0.5419 0.772 0.228 0.000
#> GSM648629 1 0.5058 0.6682 0.756 0.000 0.244
#> GSM648634 1 0.0237 0.8031 0.996 0.004 0.000
#> GSM648648 1 0.0424 0.8021 0.992 0.008 0.000
#> GSM648651 1 0.0000 0.8035 1.000 0.000 0.000
#> GSM648657 1 0.0237 0.8031 0.996 0.004 0.000
#> GSM648660 1 0.0000 0.8035 1.000 0.000 0.000
#> GSM648697 1 0.0000 0.8035 1.000 0.000 0.000
#> GSM648710 1 0.5327 0.6465 0.728 0.000 0.272
#> GSM648591 3 0.6443 0.6211 0.040 0.240 0.720
#> GSM648592 1 0.1411 0.7861 0.964 0.036 0.000
#> GSM648607 1 0.5363 0.6432 0.724 0.000 0.276
#> GSM648611 3 0.6066 0.4026 0.248 0.024 0.728
#> GSM648612 1 0.5363 0.6432 0.724 0.000 0.276
#> GSM648616 3 0.5553 0.6471 0.004 0.272 0.724
#> GSM648617 1 0.0592 0.8009 0.988 0.012 0.000
#> GSM648626 1 0.1163 0.7911 0.972 0.028 0.000
#> GSM648711 1 0.5363 0.6432 0.724 0.000 0.276
#> GSM648712 1 0.5363 0.6432 0.724 0.000 0.276
#> GSM648713 1 0.5363 0.6432 0.724 0.000 0.276
#> GSM648714 2 0.9528 0.2110 0.288 0.484 0.228
#> GSM648716 1 0.5363 0.6432 0.724 0.000 0.276
#> GSM648717 2 0.9560 0.2280 0.256 0.484 0.260
#> GSM648590 1 0.5465 0.4254 0.712 0.288 0.000
#> GSM648596 2 0.5098 0.4553 0.248 0.752 0.000
#> GSM648642 2 0.6309 0.0690 0.496 0.504 0.000
#> GSM648696 1 0.1163 0.7909 0.972 0.028 0.000
#> GSM648705 1 0.0424 0.8021 0.992 0.008 0.000
#> GSM648718 2 0.5325 0.4578 0.248 0.748 0.004
#> GSM648599 1 0.0000 0.8035 1.000 0.000 0.000
#> GSM648608 1 0.5098 0.6654 0.752 0.000 0.248
#> GSM648609 1 0.5058 0.6682 0.756 0.000 0.244
#> GSM648610 1 0.3752 0.7379 0.856 0.000 0.144
#> GSM648633 1 0.0424 0.8021 0.992 0.008 0.000
#> GSM648644 2 0.5938 0.4591 0.248 0.732 0.020
#> GSM648652 1 0.0424 0.8021 0.992 0.008 0.000
#> GSM648653 1 0.0000 0.8035 1.000 0.000 0.000
#> GSM648658 1 0.0237 0.8031 0.996 0.004 0.000
#> GSM648659 2 0.5098 0.4553 0.248 0.752 0.000
#> GSM648662 2 0.9528 0.2110 0.288 0.484 0.228
#> GSM648665 2 0.9509 0.1990 0.296 0.484 0.220
#> GSM648666 1 0.0000 0.8035 1.000 0.000 0.000
#> GSM648680 1 0.0237 0.8031 0.996 0.004 0.000
#> GSM648684 1 0.3619 0.7428 0.864 0.000 0.136
#> GSM648709 1 0.6309 -0.0845 0.504 0.496 0.000
#> GSM648719 1 0.0000 0.8035 1.000 0.000 0.000
#> GSM648627 1 0.5363 0.6432 0.724 0.000 0.276
#> GSM648637 2 0.7489 -0.1844 0.036 0.496 0.468
#> GSM648638 3 0.5588 0.6459 0.004 0.276 0.720
#> GSM648641 3 0.1031 0.8338 0.000 0.024 0.976
#> GSM648672 2 0.7392 -0.1894 0.032 0.500 0.468
#> GSM648674 2 0.7489 -0.1844 0.036 0.496 0.468
#> GSM648703 2 0.7489 -0.1844 0.036 0.496 0.468
#> GSM648631 3 0.1031 0.8338 0.000 0.024 0.976
#> GSM648669 2 0.6299 -0.2516 0.000 0.524 0.476
#> GSM648671 2 0.6299 -0.2516 0.000 0.524 0.476
#> GSM648678 2 0.7489 -0.1844 0.036 0.496 0.468
#> GSM648679 2 0.6291 -0.2365 0.000 0.532 0.468
#> GSM648681 2 0.6962 -0.0298 0.036 0.648 0.316
#> GSM648686 3 0.1031 0.8338 0.000 0.024 0.976
#> GSM648689 3 0.1031 0.8338 0.000 0.024 0.976
#> GSM648690 3 0.1031 0.8338 0.000 0.024 0.976
#> GSM648691 3 0.1031 0.8338 0.000 0.024 0.976
#> GSM648693 3 0.1031 0.8338 0.000 0.024 0.976
#> GSM648700 2 0.7489 -0.1844 0.036 0.496 0.468
#> GSM648630 3 0.1031 0.8338 0.000 0.024 0.976
#> GSM648632 3 0.1031 0.8338 0.000 0.024 0.976
#> GSM648639 3 0.5327 0.6514 0.000 0.272 0.728
#> GSM648640 3 0.1031 0.8338 0.000 0.024 0.976
#> GSM648668 2 0.7489 -0.1844 0.036 0.496 0.468
#> GSM648676 2 0.7489 -0.1844 0.036 0.496 0.468
#> GSM648692 3 0.1031 0.8338 0.000 0.024 0.976
#> GSM648694 3 0.1031 0.8338 0.000 0.024 0.976
#> GSM648699 2 0.6291 -0.2365 0.000 0.532 0.468
#> GSM648701 2 0.7392 -0.1894 0.032 0.500 0.468
#> GSM648673 2 0.6291 -0.2365 0.000 0.532 0.468
#> GSM648677 2 0.7489 -0.1844 0.036 0.496 0.468
#> GSM648687 3 0.5363 0.6476 0.000 0.276 0.724
#> GSM648688 3 0.1031 0.8338 0.000 0.024 0.976
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM648605 2 0.6757 0.1556 0.360 0.556 0.012 0.072
#> GSM648618 1 0.7713 -0.0320 0.488 0.068 0.060 0.384
#> GSM648620 2 0.5417 0.1686 0.412 0.572 0.000 0.016
#> GSM648646 2 0.1109 0.5077 0.028 0.968 0.004 0.000
#> GSM648649 1 0.2319 0.6582 0.924 0.040 0.000 0.036
#> GSM648675 2 0.8064 0.1285 0.284 0.492 0.024 0.200
#> GSM648682 2 0.3024 0.4802 0.148 0.852 0.000 0.000
#> GSM648698 2 0.5093 0.2981 0.348 0.640 0.000 0.012
#> GSM648708 2 0.5256 0.2215 0.392 0.596 0.000 0.012
#> GSM648628 4 0.7889 0.1612 0.352 0.016 0.172 0.460
#> GSM648595 2 0.8153 0.1002 0.340 0.448 0.024 0.188
#> GSM648635 1 0.0524 0.6892 0.988 0.004 0.000 0.008
#> GSM648645 1 0.3528 0.5487 0.808 0.000 0.000 0.192
#> GSM648647 2 0.5038 0.3247 0.336 0.652 0.000 0.012
#> GSM648667 1 0.5792 0.1432 0.552 0.416 0.000 0.032
#> GSM648695 2 0.5231 0.2414 0.384 0.604 0.000 0.012
#> GSM648704 2 0.1109 0.5077 0.028 0.968 0.004 0.000
#> GSM648706 2 0.5464 0.2985 0.344 0.632 0.004 0.020
#> GSM648593 1 0.1174 0.6855 0.968 0.012 0.000 0.020
#> GSM648594 2 0.8590 -0.0727 0.340 0.352 0.028 0.280
#> GSM648600 1 0.0000 0.6890 1.000 0.000 0.000 0.000
#> GSM648621 1 0.6968 0.0883 0.540 0.048 0.036 0.376
#> GSM648622 1 0.0592 0.6869 0.984 0.000 0.000 0.016
#> GSM648623 1 0.7370 0.0477 0.524 0.052 0.056 0.368
#> GSM648636 1 0.2739 0.6655 0.904 0.060 0.000 0.036
#> GSM648655 1 0.3081 0.6566 0.888 0.064 0.000 0.048
#> GSM648661 1 0.6823 0.4011 0.576 0.016 0.076 0.332
#> GSM648664 1 0.5648 0.5324 0.684 0.000 0.064 0.252
#> GSM648683 1 0.4758 0.6135 0.780 0.000 0.064 0.156
#> GSM648685 1 0.3840 0.6500 0.844 0.000 0.052 0.104
#> GSM648702 1 0.1388 0.6814 0.960 0.012 0.000 0.028
#> GSM648597 4 0.7629 0.2913 0.300 0.076 0.064 0.560
#> GSM648603 1 0.4868 0.4897 0.748 0.040 0.000 0.212
#> GSM648606 1 0.8713 0.4097 0.496 0.256 0.096 0.152
#> GSM648613 1 0.8713 0.4120 0.496 0.256 0.096 0.152
#> GSM648619 4 0.7051 -0.0206 0.428 0.016 0.076 0.480
#> GSM648654 1 0.9305 0.2635 0.364 0.256 0.088 0.292
#> GSM648663 1 0.8747 0.4090 0.492 0.256 0.096 0.156
#> GSM648670 2 0.7928 0.2364 0.104 0.488 0.048 0.360
#> GSM648707 4 0.7835 -0.1071 0.096 0.056 0.316 0.532
#> GSM648615 2 0.4837 0.3049 0.348 0.648 0.000 0.004
#> GSM648643 2 0.1109 0.5077 0.028 0.968 0.004 0.000
#> GSM648650 1 0.5894 0.1594 0.568 0.392 0.000 0.040
#> GSM648656 2 0.0967 0.5045 0.016 0.976 0.004 0.004
#> GSM648715 2 0.4353 0.4503 0.232 0.756 0.000 0.012
#> GSM648598 1 0.0000 0.6890 1.000 0.000 0.000 0.000
#> GSM648601 1 0.0336 0.6895 0.992 0.000 0.000 0.008
#> GSM648602 1 0.0817 0.6848 0.976 0.000 0.000 0.024
#> GSM648604 1 0.6277 0.3594 0.572 0.000 0.068 0.360
#> GSM648614 1 0.8496 0.4005 0.508 0.264 0.076 0.152
#> GSM648624 1 0.1022 0.6877 0.968 0.000 0.000 0.032
#> GSM648625 1 0.4630 0.5131 0.732 0.252 0.000 0.016
#> GSM648629 1 0.6058 0.4523 0.624 0.000 0.068 0.308
#> GSM648634 1 0.0000 0.6890 1.000 0.000 0.000 0.000
#> GSM648648 1 0.0779 0.6871 0.980 0.004 0.000 0.016
#> GSM648651 1 0.3486 0.5639 0.812 0.000 0.000 0.188
#> GSM648657 1 0.3852 0.5319 0.800 0.008 0.000 0.192
#> GSM648660 1 0.0817 0.6855 0.976 0.000 0.000 0.024
#> GSM648697 1 0.1637 0.6688 0.940 0.000 0.000 0.060
#> GSM648710 1 0.6367 0.4465 0.616 0.008 0.068 0.308
#> GSM648591 4 0.6839 0.2043 0.124 0.060 0.128 0.688
#> GSM648592 1 0.4069 0.6337 0.840 0.116 0.020 0.024
#> GSM648607 1 0.6460 0.4136 0.596 0.008 0.068 0.328
#> GSM648611 4 0.7356 0.3092 0.200 0.016 0.196 0.588
#> GSM648612 4 0.7029 0.0277 0.408 0.016 0.076 0.500
#> GSM648616 3 0.7947 0.2497 0.088 0.056 0.428 0.428
#> GSM648617 1 0.3000 0.6622 0.900 0.052 0.008 0.040
#> GSM648626 1 0.6966 0.1203 0.560 0.052 0.036 0.352
#> GSM648711 1 0.6952 0.3248 0.552 0.016 0.080 0.352
#> GSM648712 4 0.7029 0.0277 0.408 0.016 0.076 0.500
#> GSM648713 1 0.6853 0.3698 0.568 0.016 0.076 0.340
#> GSM648714 1 0.8496 0.4089 0.508 0.264 0.076 0.152
#> GSM648716 4 0.7029 0.0277 0.408 0.016 0.076 0.500
#> GSM648717 1 0.8741 0.4117 0.496 0.252 0.100 0.152
#> GSM648590 2 0.7179 0.2068 0.276 0.544 0.000 0.180
#> GSM648596 2 0.1118 0.5068 0.036 0.964 0.000 0.000
#> GSM648642 2 0.5143 0.2855 0.360 0.628 0.000 0.012
#> GSM648696 1 0.2662 0.6658 0.900 0.084 0.000 0.016
#> GSM648705 1 0.2300 0.6636 0.924 0.028 0.000 0.048
#> GSM648718 2 0.3128 0.4971 0.032 0.888 0.004 0.076
#> GSM648599 1 0.0469 0.6878 0.988 0.000 0.000 0.012
#> GSM648608 1 0.6234 0.3828 0.584 0.000 0.068 0.348
#> GSM648609 1 0.6367 0.4465 0.616 0.008 0.068 0.308
#> GSM648610 1 0.4663 0.6144 0.788 0.000 0.064 0.148
#> GSM648633 1 0.0188 0.6889 0.996 0.000 0.000 0.004
#> GSM648644 2 0.1004 0.5070 0.024 0.972 0.004 0.000
#> GSM648652 1 0.0657 0.6879 0.984 0.004 0.000 0.012
#> GSM648653 1 0.0336 0.6881 0.992 0.000 0.000 0.008
#> GSM648658 1 0.0336 0.6890 0.992 0.000 0.000 0.008
#> GSM648659 2 0.1837 0.5056 0.028 0.944 0.000 0.028
#> GSM648662 1 0.8488 0.4271 0.520 0.248 0.084 0.148
#> GSM648665 1 0.8434 0.4298 0.524 0.248 0.080 0.148
#> GSM648666 1 0.3448 0.5787 0.828 0.004 0.000 0.168
#> GSM648680 1 0.0657 0.6888 0.984 0.012 0.000 0.004
#> GSM648684 1 0.4227 0.6341 0.820 0.000 0.060 0.120
#> GSM648709 2 0.5204 0.2532 0.376 0.612 0.000 0.012
#> GSM648719 1 0.0000 0.6890 1.000 0.000 0.000 0.000
#> GSM648627 4 0.7016 0.0433 0.400 0.016 0.076 0.508
#> GSM648637 2 0.6360 0.2916 0.000 0.516 0.064 0.420
#> GSM648638 3 0.6756 0.5666 0.012 0.108 0.624 0.256
#> GSM648641 3 0.1211 0.8659 0.000 0.000 0.960 0.040
#> GSM648672 2 0.6376 0.2865 0.000 0.504 0.064 0.432
#> GSM648674 2 0.6484 0.2873 0.004 0.504 0.060 0.432
#> GSM648703 2 0.6319 0.2881 0.000 0.504 0.060 0.436
#> GSM648631 3 0.0336 0.8870 0.000 0.000 0.992 0.008
#> GSM648669 4 0.7265 -0.2246 0.004 0.400 0.128 0.468
#> GSM648671 4 0.7265 -0.2246 0.004 0.400 0.128 0.468
#> GSM648678 2 0.4925 0.4053 0.004 0.752 0.036 0.208
#> GSM648679 4 0.7009 -0.2785 0.004 0.440 0.100 0.456
#> GSM648681 2 0.6434 0.3812 0.056 0.652 0.028 0.264
#> GSM648686 3 0.0921 0.8703 0.000 0.000 0.972 0.028
#> GSM648689 3 0.1211 0.8659 0.000 0.000 0.960 0.040
#> GSM648690 3 0.0000 0.8883 0.000 0.000 1.000 0.000
#> GSM648691 3 0.0000 0.8883 0.000 0.000 1.000 0.000
#> GSM648693 3 0.0336 0.8870 0.000 0.000 0.992 0.008
#> GSM648700 2 0.7422 0.2672 0.044 0.492 0.064 0.400
#> GSM648630 3 0.0000 0.8883 0.000 0.000 1.000 0.000
#> GSM648632 3 0.0336 0.8870 0.000 0.000 0.992 0.008
#> GSM648639 3 0.4181 0.7549 0.000 0.052 0.820 0.128
#> GSM648640 3 0.0000 0.8883 0.000 0.000 1.000 0.000
#> GSM648668 2 0.6319 0.2881 0.000 0.504 0.060 0.436
#> GSM648676 2 0.6488 0.2866 0.004 0.500 0.060 0.436
#> GSM648692 3 0.0000 0.8883 0.000 0.000 1.000 0.000
#> GSM648694 3 0.0000 0.8883 0.000 0.000 1.000 0.000
#> GSM648699 2 0.6449 0.2598 0.000 0.480 0.068 0.452
#> GSM648701 2 0.6319 0.2881 0.000 0.504 0.060 0.436
#> GSM648673 4 0.7265 -0.2246 0.004 0.400 0.128 0.468
#> GSM648677 2 0.6055 0.3000 0.000 0.520 0.044 0.436
#> GSM648687 3 0.6844 0.4532 0.028 0.048 0.532 0.392
#> GSM648688 3 0.0336 0.8870 0.000 0.000 0.992 0.008
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM648605 2 0.3355 0.8409 0.036 0.832 0.000 0.000 0.132
#> GSM648618 1 0.5956 0.6110 0.644 0.020 0.000 0.148 0.188
#> GSM648620 2 0.2230 0.9041 0.044 0.912 0.000 0.000 0.044
#> GSM648646 2 0.0000 0.9063 0.000 1.000 0.000 0.000 0.000
#> GSM648649 1 0.1792 0.7663 0.916 0.084 0.000 0.000 0.000
#> GSM648675 1 0.7393 0.2428 0.468 0.092 0.000 0.324 0.116
#> GSM648682 2 0.2270 0.8360 0.020 0.904 0.000 0.000 0.076
#> GSM648698 2 0.0794 0.9016 0.000 0.972 0.000 0.000 0.028
#> GSM648708 2 0.2153 0.9056 0.044 0.916 0.000 0.000 0.040
#> GSM648628 5 0.3248 0.7770 0.088 0.052 0.004 0.000 0.856
#> GSM648595 1 0.6847 0.5821 0.596 0.096 0.000 0.188 0.120
#> GSM648635 1 0.0703 0.7962 0.976 0.024 0.000 0.000 0.000
#> GSM648645 1 0.0000 0.7985 1.000 0.000 0.000 0.000 0.000
#> GSM648647 2 0.1626 0.9112 0.044 0.940 0.000 0.000 0.016
#> GSM648667 2 0.2964 0.8199 0.120 0.856 0.000 0.000 0.024
#> GSM648695 2 0.1818 0.9106 0.044 0.932 0.000 0.000 0.024
#> GSM648704 2 0.0000 0.9063 0.000 1.000 0.000 0.000 0.000
#> GSM648706 2 0.1197 0.8931 0.000 0.952 0.000 0.000 0.048
#> GSM648593 1 0.1043 0.7923 0.960 0.040 0.000 0.000 0.000
#> GSM648594 1 0.6180 0.5491 0.612 0.028 0.000 0.244 0.116
#> GSM648600 1 0.0955 0.7955 0.968 0.028 0.000 0.000 0.004
#> GSM648621 1 0.5942 0.6071 0.644 0.020 0.000 0.140 0.196
#> GSM648622 1 0.1121 0.7868 0.956 0.000 0.000 0.000 0.044
#> GSM648623 1 0.6045 0.5946 0.632 0.020 0.000 0.148 0.200
#> GSM648636 1 0.2074 0.7558 0.896 0.104 0.000 0.000 0.000
#> GSM648655 1 0.3527 0.7547 0.828 0.116 0.000 0.000 0.056
#> GSM648661 5 0.2448 0.7822 0.088 0.020 0.000 0.000 0.892
#> GSM648664 5 0.3210 0.7504 0.212 0.000 0.000 0.000 0.788
#> GSM648683 5 0.3452 0.7096 0.244 0.000 0.000 0.000 0.756
#> GSM648685 1 0.4549 -0.0234 0.528 0.008 0.000 0.000 0.464
#> GSM648702 1 0.1544 0.7766 0.932 0.068 0.000 0.000 0.000
#> GSM648597 1 0.6241 0.4811 0.576 0.020 0.000 0.288 0.116
#> GSM648603 1 0.5286 0.6408 0.696 0.016 0.000 0.084 0.204
#> GSM648606 5 0.3333 0.6425 0.000 0.208 0.004 0.000 0.788
#> GSM648613 5 0.3333 0.6425 0.000 0.208 0.004 0.000 0.788
#> GSM648619 5 0.3586 0.7707 0.188 0.020 0.000 0.000 0.792
#> GSM648654 5 0.3929 0.6628 0.028 0.208 0.000 0.000 0.764
#> GSM648663 5 0.3333 0.6425 0.000 0.208 0.004 0.000 0.788
#> GSM648670 4 0.5841 0.1053 0.428 0.064 0.000 0.496 0.012
#> GSM648707 4 0.7116 0.0927 0.392 0.000 0.160 0.412 0.036
#> GSM648615 2 0.0579 0.9079 0.008 0.984 0.000 0.000 0.008
#> GSM648643 2 0.0000 0.9063 0.000 1.000 0.000 0.000 0.000
#> GSM648650 2 0.4883 0.4806 0.300 0.652 0.000 0.000 0.048
#> GSM648656 2 0.0000 0.9063 0.000 1.000 0.000 0.000 0.000
#> GSM648715 2 0.1121 0.9099 0.044 0.956 0.000 0.000 0.000
#> GSM648598 1 0.0000 0.7985 1.000 0.000 0.000 0.000 0.000
#> GSM648601 1 0.2915 0.7650 0.860 0.024 0.000 0.000 0.116
#> GSM648602 1 0.0609 0.7959 0.980 0.000 0.000 0.000 0.020
#> GSM648604 5 0.3143 0.7580 0.204 0.000 0.000 0.000 0.796
#> GSM648614 5 0.3989 0.5676 0.008 0.260 0.004 0.000 0.728
#> GSM648624 1 0.2516 0.7416 0.860 0.000 0.000 0.000 0.140
#> GSM648625 1 0.6081 0.1767 0.476 0.400 0.000 0.000 0.124
#> GSM648629 5 0.3109 0.7598 0.200 0.000 0.000 0.000 0.800
#> GSM648634 1 0.0794 0.7962 0.972 0.028 0.000 0.000 0.000
#> GSM648648 1 0.1410 0.7814 0.940 0.060 0.000 0.000 0.000
#> GSM648651 1 0.2516 0.7443 0.860 0.000 0.000 0.000 0.140
#> GSM648657 1 0.0000 0.7985 1.000 0.000 0.000 0.000 0.000
#> GSM648660 1 0.0000 0.7985 1.000 0.000 0.000 0.000 0.000
#> GSM648697 1 0.0000 0.7985 1.000 0.000 0.000 0.000 0.000
#> GSM648710 5 0.2852 0.7710 0.172 0.000 0.000 0.000 0.828
#> GSM648591 1 0.6936 0.1012 0.480 0.020 0.084 0.384 0.032
#> GSM648592 1 0.6406 0.6665 0.648 0.104 0.000 0.104 0.144
#> GSM648607 5 0.3039 0.7647 0.192 0.000 0.000 0.000 0.808
#> GSM648611 5 0.5828 0.6691 0.076 0.052 0.028 0.120 0.724
#> GSM648612 5 0.2616 0.7832 0.100 0.020 0.000 0.000 0.880
#> GSM648616 4 0.7278 0.0975 0.388 0.004 0.164 0.408 0.036
#> GSM648617 1 0.5843 0.6900 0.688 0.056 0.000 0.104 0.152
#> GSM648626 1 0.6045 0.5946 0.632 0.020 0.000 0.148 0.200
#> GSM648711 5 0.3690 0.7626 0.200 0.020 0.000 0.000 0.780
#> GSM648712 5 0.3586 0.7705 0.188 0.020 0.000 0.000 0.792
#> GSM648713 5 0.3586 0.7700 0.188 0.020 0.000 0.000 0.792
#> GSM648714 5 0.3790 0.5521 0.000 0.272 0.004 0.000 0.724
#> GSM648716 5 0.2722 0.7832 0.108 0.020 0.000 0.000 0.872
#> GSM648717 5 0.3621 0.6477 0.000 0.192 0.020 0.000 0.788
#> GSM648590 1 0.7850 0.2626 0.392 0.348 0.000 0.144 0.116
#> GSM648596 2 0.1121 0.9099 0.044 0.956 0.000 0.000 0.000
#> GSM648642 2 0.1818 0.9101 0.044 0.932 0.000 0.000 0.024
#> GSM648696 1 0.6095 0.1987 0.460 0.416 0.000 0.000 0.124
#> GSM648705 1 0.1792 0.7663 0.916 0.084 0.000 0.000 0.000
#> GSM648718 2 0.1569 0.8775 0.008 0.944 0.000 0.004 0.044
#> GSM648599 1 0.0703 0.7945 0.976 0.000 0.000 0.000 0.024
#> GSM648608 5 0.3143 0.7580 0.204 0.000 0.000 0.000 0.796
#> GSM648609 5 0.3003 0.7667 0.188 0.000 0.000 0.000 0.812
#> GSM648610 5 0.4060 0.5003 0.360 0.000 0.000 0.000 0.640
#> GSM648633 1 0.0794 0.7962 0.972 0.028 0.000 0.000 0.000
#> GSM648644 2 0.0000 0.9063 0.000 1.000 0.000 0.000 0.000
#> GSM648652 1 0.0880 0.7951 0.968 0.032 0.000 0.000 0.000
#> GSM648653 1 0.0162 0.7983 0.996 0.000 0.000 0.000 0.004
#> GSM648658 1 0.0000 0.7985 1.000 0.000 0.000 0.000 0.000
#> GSM648659 2 0.1121 0.9099 0.044 0.956 0.000 0.000 0.000
#> GSM648662 5 0.3305 0.6382 0.000 0.224 0.000 0.000 0.776
#> GSM648665 5 0.3970 0.6382 0.024 0.224 0.000 0.000 0.752
#> GSM648666 1 0.2074 0.7637 0.896 0.000 0.000 0.000 0.104
#> GSM648680 1 0.0290 0.7990 0.992 0.008 0.000 0.000 0.000
#> GSM648684 5 0.4297 0.2520 0.472 0.000 0.000 0.000 0.528
#> GSM648709 2 0.2153 0.9056 0.044 0.916 0.000 0.000 0.040
#> GSM648719 1 0.0000 0.7985 1.000 0.000 0.000 0.000 0.000
#> GSM648627 5 0.2616 0.7830 0.100 0.020 0.000 0.000 0.880
#> GSM648637 4 0.3403 0.7280 0.000 0.160 0.012 0.820 0.008
#> GSM648638 3 0.7131 0.2286 0.008 0.140 0.492 0.324 0.036
#> GSM648641 3 0.2648 0.7973 0.000 0.000 0.848 0.000 0.152
#> GSM648672 4 0.1851 0.7718 0.000 0.088 0.000 0.912 0.000
#> GSM648674 4 0.1197 0.7808 0.000 0.048 0.000 0.952 0.000
#> GSM648703 4 0.1270 0.7806 0.000 0.052 0.000 0.948 0.000
#> GSM648631 3 0.0000 0.9158 0.000 0.000 1.000 0.000 0.000
#> GSM648669 4 0.0000 0.7796 0.000 0.000 0.000 1.000 0.000
#> GSM648671 4 0.0000 0.7796 0.000 0.000 0.000 1.000 0.000
#> GSM648678 2 0.3636 0.5372 0.000 0.728 0.000 0.272 0.000
#> GSM648679 4 0.0000 0.7796 0.000 0.000 0.000 1.000 0.000
#> GSM648681 4 0.6612 0.5405 0.156 0.208 0.000 0.592 0.044
#> GSM648686 3 0.0000 0.9158 0.000 0.000 1.000 0.000 0.000
#> GSM648689 3 0.3003 0.7566 0.000 0.000 0.812 0.000 0.188
#> GSM648690 3 0.0000 0.9158 0.000 0.000 1.000 0.000 0.000
#> GSM648691 3 0.0000 0.9158 0.000 0.000 1.000 0.000 0.000
#> GSM648693 3 0.0000 0.9158 0.000 0.000 1.000 0.000 0.000
#> GSM648700 4 0.3317 0.7297 0.116 0.044 0.000 0.840 0.000
#> GSM648630 3 0.0000 0.9158 0.000 0.000 1.000 0.000 0.000
#> GSM648632 3 0.0404 0.9075 0.000 0.000 0.988 0.000 0.012
#> GSM648639 3 0.3480 0.6428 0.000 0.000 0.752 0.248 0.000
#> GSM648640 3 0.0000 0.9158 0.000 0.000 1.000 0.000 0.000
#> GSM648668 4 0.2179 0.7624 0.000 0.112 0.000 0.888 0.000
#> GSM648676 4 0.1981 0.7800 0.028 0.048 0.000 0.924 0.000
#> GSM648692 3 0.0000 0.9158 0.000 0.000 1.000 0.000 0.000
#> GSM648694 3 0.0000 0.9158 0.000 0.000 1.000 0.000 0.000
#> GSM648699 4 0.0000 0.7796 0.000 0.000 0.000 1.000 0.000
#> GSM648701 4 0.1197 0.7808 0.000 0.048 0.000 0.952 0.000
#> GSM648673 4 0.0000 0.7796 0.000 0.000 0.000 1.000 0.000
#> GSM648677 4 0.2732 0.7313 0.000 0.160 0.000 0.840 0.000
#> GSM648687 4 0.4774 0.0621 0.020 0.000 0.424 0.556 0.000
#> GSM648688 3 0.0000 0.9158 0.000 0.000 1.000 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM648605 2 0.6157 0.4061 0.236 0.488 0.000 0.004 NA 0.008
#> GSM648618 6 0.5822 0.4538 0.248 0.000 0.000 0.068 NA 0.600
#> GSM648620 2 0.3264 0.8017 0.136 0.820 0.000 0.000 NA 0.040
#> GSM648646 2 0.0146 0.8417 0.000 0.996 0.000 0.004 NA 0.000
#> GSM648649 6 0.4503 0.6979 0.000 0.080 0.000 0.000 NA 0.680
#> GSM648675 4 0.6965 0.4809 0.004 0.152 0.000 0.492 NA 0.236
#> GSM648682 2 0.0862 0.8386 0.000 0.972 0.000 0.004 NA 0.016
#> GSM648698 2 0.1700 0.8294 0.080 0.916 0.000 0.004 NA 0.000
#> GSM648708 2 0.2928 0.8263 0.084 0.856 0.000 0.000 NA 0.056
#> GSM648628 1 0.3751 0.6740 0.792 0.000 0.000 0.004 NA 0.096
#> GSM648595 6 0.7053 0.4471 0.004 0.228 0.000 0.092 NA 0.464
#> GSM648635 6 0.3109 0.7363 0.000 0.004 0.000 0.000 NA 0.772
#> GSM648645 6 0.1644 0.7620 0.000 0.000 0.000 0.004 NA 0.920
#> GSM648647 2 0.3044 0.8304 0.052 0.864 0.000 0.000 NA 0.048
#> GSM648667 2 0.4663 0.4763 0.000 0.660 0.000 0.000 NA 0.252
#> GSM648695 2 0.2696 0.8317 0.076 0.872 0.000 0.000 NA 0.048
#> GSM648704 2 0.0405 0.8415 0.000 0.988 0.000 0.004 NA 0.000
#> GSM648706 2 0.4411 0.7188 0.120 0.728 0.000 0.004 NA 0.000
#> GSM648593 6 0.3215 0.7310 0.000 0.004 0.000 0.000 NA 0.756
#> GSM648594 4 0.6412 0.2033 0.008 0.044 0.000 0.444 NA 0.392
#> GSM648600 6 0.0146 0.7615 0.000 0.004 0.000 0.000 NA 0.996
#> GSM648621 6 0.5826 0.4908 0.220 0.000 0.000 0.052 NA 0.608
#> GSM648622 6 0.1908 0.7103 0.096 0.000 0.000 0.000 NA 0.900
#> GSM648623 6 0.5986 0.3470 0.308 0.000 0.000 0.052 NA 0.544
#> GSM648636 6 0.4686 0.6859 0.000 0.092 0.000 0.000 NA 0.660
#> GSM648655 6 0.5187 0.6333 0.000 0.136 0.000 0.000 NA 0.600
#> GSM648661 1 0.2743 0.6938 0.828 0.000 0.000 0.000 NA 0.164
#> GSM648664 1 0.3888 0.5699 0.672 0.000 0.000 0.000 NA 0.312
#> GSM648683 1 0.3912 0.5253 0.648 0.000 0.000 0.000 NA 0.340
#> GSM648685 6 0.4037 0.1727 0.380 0.000 0.000 0.000 NA 0.608
#> GSM648702 6 0.4832 0.6773 0.000 0.108 0.000 0.000 NA 0.648
#> GSM648597 4 0.6280 0.4968 0.024 0.016 0.000 0.540 NA 0.272
#> GSM648603 6 0.4012 0.5494 0.232 0.000 0.000 0.008 NA 0.728
#> GSM648606 1 0.4435 0.5021 0.604 0.028 0.000 0.004 NA 0.000
#> GSM648613 1 0.4423 0.5051 0.608 0.028 0.000 0.004 NA 0.000
#> GSM648619 1 0.2454 0.6914 0.840 0.000 0.000 0.000 NA 0.160
#> GSM648654 1 0.4270 0.5301 0.652 0.028 0.000 0.004 NA 0.000
#> GSM648663 1 0.4423 0.5051 0.608 0.028 0.000 0.004 NA 0.000
#> GSM648670 4 0.5823 0.6560 0.004 0.024 0.000 0.588 NA 0.140
#> GSM648707 4 0.6009 0.6246 0.052 0.000 0.060 0.536 NA 0.012
#> GSM648615 2 0.1938 0.8385 0.036 0.920 0.000 0.004 NA 0.000
#> GSM648643 2 0.0146 0.8417 0.000 0.996 0.000 0.004 NA 0.000
#> GSM648650 2 0.5515 0.0202 0.000 0.492 0.000 0.000 NA 0.372
#> GSM648656 2 0.0603 0.8375 0.000 0.980 0.000 0.016 NA 0.000
#> GSM648715 2 0.2287 0.8283 0.012 0.904 0.000 0.000 NA 0.048
#> GSM648598 6 0.0405 0.7587 0.008 0.000 0.000 0.000 NA 0.988
#> GSM648601 6 0.0146 0.7600 0.004 0.000 0.000 0.000 NA 0.996
#> GSM648602 6 0.1700 0.7217 0.080 0.000 0.000 0.000 NA 0.916
#> GSM648604 1 0.3717 0.6202 0.708 0.000 0.000 0.000 NA 0.276
#> GSM648614 1 0.6169 0.2010 0.432 0.188 0.000 0.004 NA 0.008
#> GSM648624 6 0.1957 0.7016 0.112 0.000 0.000 0.000 NA 0.888
#> GSM648625 6 0.5456 0.4473 0.152 0.244 0.000 0.000 NA 0.596
#> GSM648629 1 0.3652 0.6308 0.720 0.000 0.000 0.000 NA 0.264
#> GSM648634 6 0.0146 0.7615 0.000 0.004 0.000 0.000 NA 0.996
#> GSM648648 6 0.3770 0.7216 0.000 0.028 0.000 0.000 NA 0.728
#> GSM648651 6 0.2100 0.7004 0.112 0.000 0.000 0.000 NA 0.884
#> GSM648657 6 0.1753 0.7614 0.000 0.000 0.000 0.004 NA 0.912
#> GSM648660 6 0.1588 0.7623 0.000 0.000 0.000 0.004 NA 0.924
#> GSM648697 6 0.0000 0.7605 0.000 0.000 0.000 0.000 NA 1.000
#> GSM648710 1 0.3050 0.6569 0.764 0.000 0.000 0.000 NA 0.236
#> GSM648591 4 0.6113 0.6422 0.056 0.000 0.020 0.548 NA 0.056
#> GSM648592 6 0.6388 0.6040 0.060 0.196 0.000 0.052 NA 0.612
#> GSM648607 1 0.3240 0.6513 0.752 0.000 0.000 0.000 NA 0.244
#> GSM648611 1 0.4798 0.6057 0.712 0.000 0.060 0.004 NA 0.032
#> GSM648612 1 0.2362 0.6934 0.860 0.000 0.000 0.000 NA 0.136
#> GSM648616 4 0.5870 0.6241 0.044 0.000 0.064 0.540 NA 0.008
#> GSM648617 6 0.4580 0.6968 0.064 0.072 0.000 0.056 NA 0.780
#> GSM648626 6 0.5638 0.4282 0.272 0.000 0.000 0.052 NA 0.600
#> GSM648711 1 0.2838 0.6830 0.808 0.000 0.000 0.000 NA 0.188
#> GSM648712 1 0.2482 0.6924 0.848 0.000 0.000 0.000 NA 0.148
#> GSM648713 1 0.2631 0.6871 0.820 0.000 0.000 0.000 NA 0.180
#> GSM648714 1 0.5968 0.1884 0.432 0.192 0.000 0.004 NA 0.000
#> GSM648716 1 0.2431 0.6929 0.860 0.000 0.000 0.000 NA 0.132
#> GSM648717 1 0.4423 0.5051 0.608 0.028 0.000 0.004 NA 0.000
#> GSM648590 6 0.6388 0.3964 0.004 0.336 0.000 0.044 NA 0.484
#> GSM648596 2 0.0508 0.8425 0.000 0.984 0.000 0.000 NA 0.012
#> GSM648642 2 0.2176 0.8337 0.080 0.896 0.000 0.000 NA 0.024
#> GSM648696 6 0.3965 0.4281 0.000 0.388 0.000 0.000 NA 0.604
#> GSM648705 6 0.4750 0.6827 0.000 0.100 0.000 0.000 NA 0.656
#> GSM648718 2 0.0436 0.8419 0.000 0.988 0.000 0.004 NA 0.004
#> GSM648599 6 0.1958 0.7083 0.100 0.000 0.000 0.000 NA 0.896
#> GSM648608 1 0.3695 0.6237 0.712 0.000 0.000 0.000 NA 0.272
#> GSM648609 1 0.3483 0.6473 0.748 0.000 0.000 0.000 NA 0.236
#> GSM648610 1 0.4150 0.3947 0.592 0.000 0.000 0.000 NA 0.392
#> GSM648633 6 0.1644 0.7626 0.000 0.004 0.000 0.000 NA 0.920
#> GSM648644 2 0.0405 0.8415 0.000 0.988 0.000 0.004 NA 0.000
#> GSM648652 6 0.3349 0.7283 0.000 0.008 0.000 0.000 NA 0.748
#> GSM648653 6 0.0000 0.7605 0.000 0.000 0.000 0.000 NA 1.000
#> GSM648658 6 0.3149 0.7484 0.000 0.044 0.000 0.000 NA 0.824
#> GSM648659 2 0.0508 0.8424 0.000 0.984 0.000 0.000 NA 0.012
#> GSM648662 1 0.4595 0.5126 0.608 0.028 0.000 0.000 NA 0.012
#> GSM648665 1 0.4709 0.5312 0.632 0.028 0.000 0.000 NA 0.024
#> GSM648666 6 0.1245 0.7482 0.032 0.000 0.000 0.000 NA 0.952
#> GSM648680 6 0.2146 0.7584 0.000 0.004 0.000 0.000 NA 0.880
#> GSM648684 1 0.4129 0.3295 0.564 0.000 0.000 0.000 NA 0.424
#> GSM648709 2 0.2926 0.8103 0.124 0.844 0.000 0.000 NA 0.028
#> GSM648719 6 0.0000 0.7605 0.000 0.000 0.000 0.000 NA 1.000
#> GSM648627 1 0.2362 0.6934 0.860 0.000 0.000 0.000 NA 0.136
#> GSM648637 4 0.4053 0.7002 0.000 0.140 0.020 0.776 NA 0.000
#> GSM648638 2 0.7819 0.0851 0.016 0.368 0.252 0.172 NA 0.000
#> GSM648641 3 0.2173 0.9035 0.064 0.000 0.904 0.004 NA 0.000
#> GSM648672 4 0.0777 0.7788 0.000 0.024 0.000 0.972 NA 0.000
#> GSM648674 4 0.0260 0.7793 0.000 0.008 0.000 0.992 NA 0.000
#> GSM648703 4 0.0458 0.7791 0.000 0.016 0.000 0.984 NA 0.000
#> GSM648631 3 0.0000 0.9771 0.000 0.000 1.000 0.000 NA 0.000
#> GSM648669 4 0.0937 0.7774 0.000 0.000 0.000 0.960 NA 0.000
#> GSM648671 4 0.0937 0.7774 0.000 0.000 0.000 0.960 NA 0.000
#> GSM648678 4 0.4157 0.3058 0.000 0.444 0.000 0.544 NA 0.000
#> GSM648679 4 0.0363 0.7797 0.000 0.000 0.000 0.988 NA 0.000
#> GSM648681 4 0.5306 0.6446 0.000 0.196 0.000 0.668 NA 0.084
#> GSM648686 3 0.1141 0.9469 0.000 0.000 0.948 0.000 NA 0.000
#> GSM648689 3 0.1701 0.9117 0.072 0.000 0.920 0.000 NA 0.000
#> GSM648690 3 0.0000 0.9771 0.000 0.000 1.000 0.000 NA 0.000
#> GSM648691 3 0.0000 0.9771 0.000 0.000 1.000 0.000 NA 0.000
#> GSM648693 3 0.0000 0.9771 0.000 0.000 1.000 0.000 NA 0.000
#> GSM648700 4 0.0551 0.7805 0.000 0.008 0.000 0.984 NA 0.004
#> GSM648630 3 0.0000 0.9771 0.000 0.000 1.000 0.000 NA 0.000
#> GSM648632 3 0.0291 0.9739 0.004 0.000 0.992 0.000 NA 0.000
#> GSM648639 4 0.5611 0.3546 0.000 0.000 0.364 0.484 NA 0.000
#> GSM648640 3 0.0000 0.9771 0.000 0.000 1.000 0.000 NA 0.000
#> GSM648668 4 0.1610 0.7511 0.000 0.084 0.000 0.916 NA 0.000
#> GSM648676 4 0.0405 0.7800 0.000 0.008 0.000 0.988 NA 0.004
#> GSM648692 3 0.0000 0.9771 0.000 0.000 1.000 0.000 NA 0.000
#> GSM648694 3 0.0000 0.9771 0.000 0.000 1.000 0.000 NA 0.000
#> GSM648699 4 0.0458 0.7796 0.000 0.000 0.000 0.984 NA 0.000
#> GSM648701 4 0.0363 0.7795 0.000 0.012 0.000 0.988 NA 0.000
#> GSM648673 4 0.0937 0.7774 0.000 0.000 0.000 0.960 NA 0.000
#> GSM648677 4 0.2219 0.7057 0.000 0.136 0.000 0.864 NA 0.000
#> GSM648687 4 0.6119 0.5234 0.020 0.000 0.216 0.516 NA 0.000
#> GSM648688 3 0.0790 0.9597 0.000 0.000 0.968 0.000 NA 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) development.stage(p) other(p) k
#> CV:mclust 127 1.18e-09 0.0261 4.35e-13 2
#> CV:mclust 75 1.18e-13 0.3531 1.88e-13 3
#> CV:mclust 59 1.54e-13 0.3197 5.75e-14 4
#> CV:mclust 116 4.41e-23 0.1580 1.08e-35 5
#> CV:mclust 108 5.71e-19 0.2329 3.44e-32 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 51941 rows and 130 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 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 0.703 0.885 0.947 0.4390 0.571 0.571
#> 3 3 0.846 0.893 0.955 0.4432 0.693 0.508
#> 4 4 0.859 0.869 0.932 0.1180 0.874 0.682
#> 5 5 0.798 0.841 0.913 0.0794 0.908 0.703
#> 6 6 0.740 0.630 0.806 0.0465 0.927 0.719
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
#> GSM648605 2 0.000 0.946 0.000 1.000
#> GSM648618 2 0.671 0.765 0.176 0.824
#> GSM648620 2 0.000 0.946 0.000 1.000
#> GSM648646 2 0.000 0.946 0.000 1.000
#> GSM648649 2 0.000 0.946 0.000 1.000
#> GSM648675 2 0.000 0.946 0.000 1.000
#> GSM648682 2 0.000 0.946 0.000 1.000
#> GSM648698 2 0.000 0.946 0.000 1.000
#> GSM648708 2 0.000 0.946 0.000 1.000
#> GSM648628 1 0.000 0.928 1.000 0.000
#> GSM648595 2 0.000 0.946 0.000 1.000
#> GSM648635 2 0.000 0.946 0.000 1.000
#> GSM648645 2 0.000 0.946 0.000 1.000
#> GSM648647 2 0.000 0.946 0.000 1.000
#> GSM648667 2 0.000 0.946 0.000 1.000
#> GSM648695 2 0.000 0.946 0.000 1.000
#> GSM648704 2 0.000 0.946 0.000 1.000
#> GSM648706 2 0.000 0.946 0.000 1.000
#> GSM648593 2 0.000 0.946 0.000 1.000
#> GSM648594 2 0.000 0.946 0.000 1.000
#> GSM648600 2 0.000 0.946 0.000 1.000
#> GSM648621 2 0.552 0.832 0.128 0.872
#> GSM648622 2 0.000 0.946 0.000 1.000
#> GSM648623 1 0.634 0.821 0.840 0.160
#> GSM648636 2 0.000 0.946 0.000 1.000
#> GSM648655 2 0.000 0.946 0.000 1.000
#> GSM648661 1 0.781 0.740 0.768 0.232
#> GSM648664 2 0.706 0.742 0.192 0.808
#> GSM648683 2 0.000 0.946 0.000 1.000
#> GSM648685 2 0.000 0.946 0.000 1.000
#> GSM648702 2 0.000 0.946 0.000 1.000
#> GSM648597 2 0.000 0.946 0.000 1.000
#> GSM648603 2 0.000 0.946 0.000 1.000
#> GSM648606 1 0.456 0.876 0.904 0.096
#> GSM648613 1 0.662 0.809 0.828 0.172
#> GSM648619 1 0.722 0.778 0.800 0.200
#> GSM648654 1 0.949 0.492 0.632 0.368
#> GSM648663 1 0.738 0.770 0.792 0.208
#> GSM648670 2 0.625 0.807 0.156 0.844
#> GSM648707 1 0.000 0.928 1.000 0.000
#> GSM648615 2 0.000 0.946 0.000 1.000
#> GSM648643 2 0.000 0.946 0.000 1.000
#> GSM648650 2 0.000 0.946 0.000 1.000
#> GSM648656 2 0.000 0.946 0.000 1.000
#> GSM648715 2 0.000 0.946 0.000 1.000
#> GSM648598 2 0.000 0.946 0.000 1.000
#> GSM648601 2 0.000 0.946 0.000 1.000
#> GSM648602 2 0.000 0.946 0.000 1.000
#> GSM648604 2 0.242 0.913 0.040 0.960
#> GSM648614 2 0.000 0.946 0.000 1.000
#> GSM648624 2 0.000 0.946 0.000 1.000
#> GSM648625 2 0.000 0.946 0.000 1.000
#> GSM648629 2 0.745 0.711 0.212 0.788
#> GSM648634 2 0.000 0.946 0.000 1.000
#> GSM648648 2 0.000 0.946 0.000 1.000
#> GSM648651 2 0.000 0.946 0.000 1.000
#> GSM648657 2 0.000 0.946 0.000 1.000
#> GSM648660 2 0.000 0.946 0.000 1.000
#> GSM648697 2 0.000 0.946 0.000 1.000
#> GSM648710 2 0.958 0.343 0.380 0.620
#> GSM648591 1 0.000 0.928 1.000 0.000
#> GSM648592 2 0.000 0.946 0.000 1.000
#> GSM648607 2 0.981 0.215 0.420 0.580
#> GSM648611 1 0.000 0.928 1.000 0.000
#> GSM648612 1 0.000 0.928 1.000 0.000
#> GSM648616 1 0.000 0.928 1.000 0.000
#> GSM648617 2 0.000 0.946 0.000 1.000
#> GSM648626 2 0.949 0.376 0.368 0.632
#> GSM648711 1 0.738 0.770 0.792 0.208
#> GSM648712 1 0.456 0.876 0.904 0.096
#> GSM648713 1 0.925 0.555 0.660 0.340
#> GSM648714 2 0.000 0.946 0.000 1.000
#> GSM648716 1 0.311 0.901 0.944 0.056
#> GSM648717 1 0.000 0.928 1.000 0.000
#> GSM648590 2 0.000 0.946 0.000 1.000
#> GSM648596 2 0.000 0.946 0.000 1.000
#> GSM648642 2 0.000 0.946 0.000 1.000
#> GSM648696 2 0.000 0.946 0.000 1.000
#> GSM648705 2 0.000 0.946 0.000 1.000
#> GSM648718 2 0.000 0.946 0.000 1.000
#> GSM648599 2 0.000 0.946 0.000 1.000
#> GSM648608 2 0.680 0.759 0.180 0.820
#> GSM648609 2 0.000 0.946 0.000 1.000
#> GSM648610 2 0.000 0.946 0.000 1.000
#> GSM648633 2 0.000 0.946 0.000 1.000
#> GSM648644 2 0.000 0.946 0.000 1.000
#> GSM648652 2 0.000 0.946 0.000 1.000
#> GSM648653 2 0.000 0.946 0.000 1.000
#> GSM648658 2 0.000 0.946 0.000 1.000
#> GSM648659 2 0.000 0.946 0.000 1.000
#> GSM648662 2 0.000 0.946 0.000 1.000
#> GSM648665 2 0.000 0.946 0.000 1.000
#> GSM648666 2 0.000 0.946 0.000 1.000
#> GSM648680 2 0.000 0.946 0.000 1.000
#> GSM648684 2 0.000 0.946 0.000 1.000
#> GSM648709 2 0.000 0.946 0.000 1.000
#> GSM648719 2 0.000 0.946 0.000 1.000
#> GSM648627 1 0.373 0.892 0.928 0.072
#> GSM648637 2 0.788 0.709 0.236 0.764
#> GSM648638 1 0.000 0.928 1.000 0.000
#> GSM648641 1 0.000 0.928 1.000 0.000
#> GSM648672 2 0.730 0.752 0.204 0.796
#> GSM648674 2 0.730 0.752 0.204 0.796
#> GSM648703 2 0.722 0.757 0.200 0.800
#> GSM648631 1 0.000 0.928 1.000 0.000
#> GSM648669 1 0.000 0.928 1.000 0.000
#> GSM648671 1 0.000 0.928 1.000 0.000
#> GSM648678 2 0.722 0.757 0.200 0.800
#> GSM648679 1 0.402 0.871 0.920 0.080
#> GSM648681 2 0.000 0.946 0.000 1.000
#> GSM648686 1 0.000 0.928 1.000 0.000
#> GSM648689 1 0.000 0.928 1.000 0.000
#> GSM648690 1 0.000 0.928 1.000 0.000
#> GSM648691 1 0.000 0.928 1.000 0.000
#> GSM648693 1 0.000 0.928 1.000 0.000
#> GSM648700 2 0.730 0.752 0.204 0.796
#> GSM648630 1 0.000 0.928 1.000 0.000
#> GSM648632 1 0.000 0.928 1.000 0.000
#> GSM648639 1 0.000 0.928 1.000 0.000
#> GSM648640 1 0.000 0.928 1.000 0.000
#> GSM648668 2 0.722 0.757 0.200 0.800
#> GSM648676 2 0.722 0.757 0.200 0.800
#> GSM648692 1 0.000 0.928 1.000 0.000
#> GSM648694 1 0.000 0.928 1.000 0.000
#> GSM648699 1 0.839 0.617 0.732 0.268
#> GSM648701 2 0.753 0.737 0.216 0.784
#> GSM648673 1 0.000 0.928 1.000 0.000
#> GSM648677 2 0.722 0.757 0.200 0.800
#> GSM648687 1 0.000 0.928 1.000 0.000
#> GSM648688 1 0.000 0.928 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM648605 2 0.0747 0.92726 0.016 0.984 0.000
#> GSM648618 1 0.0000 0.94790 1.000 0.000 0.000
#> GSM648620 1 0.6225 0.26642 0.568 0.432 0.000
#> GSM648646 2 0.0000 0.93812 0.000 1.000 0.000
#> GSM648649 1 0.0000 0.94790 1.000 0.000 0.000
#> GSM648675 1 0.6291 0.13209 0.532 0.468 0.000
#> GSM648682 2 0.4121 0.76953 0.168 0.832 0.000
#> GSM648698 2 0.0000 0.93812 0.000 1.000 0.000
#> GSM648708 1 0.5291 0.65164 0.732 0.268 0.000
#> GSM648628 3 0.0000 0.96252 0.000 0.000 1.000
#> GSM648595 1 0.5650 0.55495 0.688 0.312 0.000
#> GSM648635 1 0.0000 0.94790 1.000 0.000 0.000
#> GSM648645 1 0.0000 0.94790 1.000 0.000 0.000
#> GSM648647 2 0.0000 0.93812 0.000 1.000 0.000
#> GSM648667 1 0.4062 0.79747 0.836 0.164 0.000
#> GSM648695 2 0.4452 0.73785 0.192 0.808 0.000
#> GSM648704 2 0.0000 0.93812 0.000 1.000 0.000
#> GSM648706 2 0.0000 0.93812 0.000 1.000 0.000
#> GSM648593 1 0.0000 0.94790 1.000 0.000 0.000
#> GSM648594 1 0.2796 0.87139 0.908 0.092 0.000
#> GSM648600 1 0.0000 0.94790 1.000 0.000 0.000
#> GSM648621 1 0.0000 0.94790 1.000 0.000 0.000
#> GSM648622 1 0.0000 0.94790 1.000 0.000 0.000
#> GSM648623 1 0.0000 0.94790 1.000 0.000 0.000
#> GSM648636 1 0.0000 0.94790 1.000 0.000 0.000
#> GSM648655 1 0.0000 0.94790 1.000 0.000 0.000
#> GSM648661 1 0.3816 0.80934 0.852 0.000 0.148
#> GSM648664 1 0.0000 0.94790 1.000 0.000 0.000
#> GSM648683 1 0.0000 0.94790 1.000 0.000 0.000
#> GSM648685 1 0.0000 0.94790 1.000 0.000 0.000
#> GSM648702 1 0.0000 0.94790 1.000 0.000 0.000
#> GSM648597 1 0.0000 0.94790 1.000 0.000 0.000
#> GSM648603 1 0.0000 0.94790 1.000 0.000 0.000
#> GSM648606 3 0.2796 0.88116 0.000 0.092 0.908
#> GSM648613 3 0.0000 0.96252 0.000 0.000 1.000
#> GSM648619 1 0.2796 0.86994 0.908 0.000 0.092
#> GSM648654 3 0.6026 0.39309 0.376 0.000 0.624
#> GSM648663 3 0.3989 0.83920 0.012 0.124 0.864
#> GSM648670 2 0.5254 0.62685 0.264 0.736 0.000
#> GSM648707 3 0.0000 0.96252 0.000 0.000 1.000
#> GSM648615 2 0.0000 0.93812 0.000 1.000 0.000
#> GSM648643 2 0.0000 0.93812 0.000 1.000 0.000
#> GSM648650 1 0.4121 0.79238 0.832 0.168 0.000
#> GSM648656 2 0.0000 0.93812 0.000 1.000 0.000
#> GSM648715 2 0.0000 0.93812 0.000 1.000 0.000
#> GSM648598 1 0.0000 0.94790 1.000 0.000 0.000
#> GSM648601 1 0.0000 0.94790 1.000 0.000 0.000
#> GSM648602 1 0.0000 0.94790 1.000 0.000 0.000
#> GSM648604 1 0.0000 0.94790 1.000 0.000 0.000
#> GSM648614 1 0.5760 0.51607 0.672 0.328 0.000
#> GSM648624 1 0.0000 0.94790 1.000 0.000 0.000
#> GSM648625 1 0.0000 0.94790 1.000 0.000 0.000
#> GSM648629 1 0.0000 0.94790 1.000 0.000 0.000
#> GSM648634 1 0.0000 0.94790 1.000 0.000 0.000
#> GSM648648 1 0.0000 0.94790 1.000 0.000 0.000
#> GSM648651 1 0.0000 0.94790 1.000 0.000 0.000
#> GSM648657 1 0.0000 0.94790 1.000 0.000 0.000
#> GSM648660 1 0.0000 0.94790 1.000 0.000 0.000
#> GSM648697 1 0.0000 0.94790 1.000 0.000 0.000
#> GSM648710 1 0.0000 0.94790 1.000 0.000 0.000
#> GSM648591 3 0.2066 0.90855 0.060 0.000 0.940
#> GSM648592 1 0.0000 0.94790 1.000 0.000 0.000
#> GSM648607 1 0.0000 0.94790 1.000 0.000 0.000
#> GSM648611 3 0.0000 0.96252 0.000 0.000 1.000
#> GSM648612 3 0.3038 0.86052 0.104 0.000 0.896
#> GSM648616 3 0.0000 0.96252 0.000 0.000 1.000
#> GSM648617 1 0.0000 0.94790 1.000 0.000 0.000
#> GSM648626 1 0.0000 0.94790 1.000 0.000 0.000
#> GSM648711 1 0.0424 0.94216 0.992 0.000 0.008
#> GSM648712 1 0.3816 0.80881 0.852 0.000 0.148
#> GSM648713 1 0.0000 0.94790 1.000 0.000 0.000
#> GSM648714 2 0.7395 0.00542 0.476 0.492 0.032
#> GSM648716 3 0.3941 0.80069 0.156 0.000 0.844
#> GSM648717 3 0.0000 0.96252 0.000 0.000 1.000
#> GSM648590 1 0.5591 0.56981 0.696 0.304 0.000
#> GSM648596 2 0.0000 0.93812 0.000 1.000 0.000
#> GSM648642 2 0.0592 0.93026 0.012 0.988 0.000
#> GSM648696 1 0.0000 0.94790 1.000 0.000 0.000
#> GSM648705 1 0.0000 0.94790 1.000 0.000 0.000
#> GSM648718 2 0.0000 0.93812 0.000 1.000 0.000
#> GSM648599 1 0.0000 0.94790 1.000 0.000 0.000
#> GSM648608 1 0.0000 0.94790 1.000 0.000 0.000
#> GSM648609 1 0.0000 0.94790 1.000 0.000 0.000
#> GSM648610 1 0.0000 0.94790 1.000 0.000 0.000
#> GSM648633 1 0.0000 0.94790 1.000 0.000 0.000
#> GSM648644 2 0.0000 0.93812 0.000 1.000 0.000
#> GSM648652 1 0.0000 0.94790 1.000 0.000 0.000
#> GSM648653 1 0.0000 0.94790 1.000 0.000 0.000
#> GSM648658 1 0.0000 0.94790 1.000 0.000 0.000
#> GSM648659 2 0.0000 0.93812 0.000 1.000 0.000
#> GSM648662 1 0.1031 0.92992 0.976 0.024 0.000
#> GSM648665 1 0.4452 0.75067 0.808 0.192 0.000
#> GSM648666 1 0.0000 0.94790 1.000 0.000 0.000
#> GSM648680 1 0.0000 0.94790 1.000 0.000 0.000
#> GSM648684 1 0.0000 0.94790 1.000 0.000 0.000
#> GSM648709 2 0.1031 0.92051 0.024 0.976 0.000
#> GSM648719 1 0.0000 0.94790 1.000 0.000 0.000
#> GSM648627 3 0.0892 0.94673 0.020 0.000 0.980
#> GSM648637 2 0.1964 0.89811 0.000 0.944 0.056
#> GSM648638 3 0.0000 0.96252 0.000 0.000 1.000
#> GSM648641 3 0.0000 0.96252 0.000 0.000 1.000
#> GSM648672 2 0.0000 0.93812 0.000 1.000 0.000
#> GSM648674 2 0.0237 0.93584 0.000 0.996 0.004
#> GSM648703 2 0.0000 0.93812 0.000 1.000 0.000
#> GSM648631 3 0.0000 0.96252 0.000 0.000 1.000
#> GSM648669 3 0.0000 0.96252 0.000 0.000 1.000
#> GSM648671 3 0.0000 0.96252 0.000 0.000 1.000
#> GSM648678 2 0.0000 0.93812 0.000 1.000 0.000
#> GSM648679 2 0.3686 0.81356 0.000 0.860 0.140
#> GSM648681 2 0.0000 0.93812 0.000 1.000 0.000
#> GSM648686 3 0.0000 0.96252 0.000 0.000 1.000
#> GSM648689 3 0.0000 0.96252 0.000 0.000 1.000
#> GSM648690 3 0.0000 0.96252 0.000 0.000 1.000
#> GSM648691 3 0.0000 0.96252 0.000 0.000 1.000
#> GSM648693 3 0.0000 0.96252 0.000 0.000 1.000
#> GSM648700 2 0.1860 0.90148 0.000 0.948 0.052
#> GSM648630 3 0.0000 0.96252 0.000 0.000 1.000
#> GSM648632 3 0.0000 0.96252 0.000 0.000 1.000
#> GSM648639 3 0.0000 0.96252 0.000 0.000 1.000
#> GSM648640 3 0.0000 0.96252 0.000 0.000 1.000
#> GSM648668 2 0.0000 0.93812 0.000 1.000 0.000
#> GSM648676 2 0.0000 0.93812 0.000 1.000 0.000
#> GSM648692 3 0.0000 0.96252 0.000 0.000 1.000
#> GSM648694 3 0.0000 0.96252 0.000 0.000 1.000
#> GSM648699 2 0.0000 0.93812 0.000 1.000 0.000
#> GSM648701 2 0.0000 0.93812 0.000 1.000 0.000
#> GSM648673 2 0.5678 0.54633 0.000 0.684 0.316
#> GSM648677 2 0.0000 0.93812 0.000 1.000 0.000
#> GSM648687 3 0.0000 0.96252 0.000 0.000 1.000
#> GSM648688 3 0.0000 0.96252 0.000 0.000 1.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM648605 2 0.0524 0.9415 0.008 0.988 0.004 0.000
#> GSM648618 1 0.0469 0.9377 0.988 0.000 0.000 0.012
#> GSM648620 2 0.0817 0.9309 0.024 0.976 0.000 0.000
#> GSM648646 2 0.0188 0.9447 0.000 0.996 0.000 0.004
#> GSM648649 1 0.0469 0.9377 0.988 0.000 0.000 0.012
#> GSM648675 4 0.4477 0.7943 0.108 0.084 0.000 0.808
#> GSM648682 2 0.3390 0.7689 0.132 0.852 0.000 0.016
#> GSM648698 2 0.0000 0.9443 0.000 1.000 0.000 0.000
#> GSM648708 2 0.1902 0.8917 0.064 0.932 0.000 0.004
#> GSM648628 3 0.1211 0.9239 0.000 0.000 0.960 0.040
#> GSM648595 4 0.3144 0.8403 0.072 0.044 0.000 0.884
#> GSM648635 1 0.0000 0.9439 1.000 0.000 0.000 0.000
#> GSM648645 1 0.0188 0.9419 0.996 0.000 0.000 0.004
#> GSM648647 2 0.0188 0.9447 0.000 0.996 0.000 0.004
#> GSM648667 1 0.5126 0.2178 0.552 0.444 0.000 0.004
#> GSM648695 2 0.0779 0.9393 0.016 0.980 0.000 0.004
#> GSM648704 2 0.0188 0.9447 0.000 0.996 0.000 0.004
#> GSM648706 2 0.0000 0.9443 0.000 1.000 0.000 0.000
#> GSM648593 1 0.0000 0.9439 1.000 0.000 0.000 0.000
#> GSM648594 4 0.4792 0.5355 0.312 0.008 0.000 0.680
#> GSM648600 1 0.0000 0.9439 1.000 0.000 0.000 0.000
#> GSM648621 1 0.0707 0.9334 0.980 0.000 0.000 0.020
#> GSM648622 1 0.0000 0.9439 1.000 0.000 0.000 0.000
#> GSM648623 1 0.2546 0.8747 0.900 0.000 0.008 0.092
#> GSM648636 1 0.0000 0.9439 1.000 0.000 0.000 0.000
#> GSM648655 1 0.0817 0.9279 0.976 0.024 0.000 0.000
#> GSM648661 1 0.4585 0.5123 0.668 0.000 0.332 0.000
#> GSM648664 1 0.0000 0.9439 1.000 0.000 0.000 0.000
#> GSM648683 1 0.0000 0.9439 1.000 0.000 0.000 0.000
#> GSM648685 1 0.0000 0.9439 1.000 0.000 0.000 0.000
#> GSM648702 1 0.0000 0.9439 1.000 0.000 0.000 0.000
#> GSM648597 4 0.5000 -0.0652 0.500 0.000 0.000 0.500
#> GSM648603 1 0.1716 0.9031 0.936 0.000 0.000 0.064
#> GSM648606 3 0.0921 0.9226 0.000 0.028 0.972 0.000
#> GSM648613 3 0.1706 0.9212 0.000 0.016 0.948 0.036
#> GSM648619 1 0.5284 0.3835 0.616 0.000 0.368 0.016
#> GSM648654 3 0.4485 0.6813 0.200 0.028 0.772 0.000
#> GSM648663 3 0.3688 0.7220 0.000 0.208 0.792 0.000
#> GSM648670 4 0.1211 0.8563 0.000 0.040 0.000 0.960
#> GSM648707 3 0.2921 0.8708 0.000 0.000 0.860 0.140
#> GSM648615 2 0.0336 0.9441 0.000 0.992 0.000 0.008
#> GSM648643 2 0.0707 0.9366 0.000 0.980 0.000 0.020
#> GSM648650 1 0.2773 0.8424 0.880 0.116 0.000 0.004
#> GSM648656 2 0.0592 0.9390 0.000 0.984 0.000 0.016
#> GSM648715 2 0.0188 0.9447 0.000 0.996 0.000 0.004
#> GSM648598 1 0.0000 0.9439 1.000 0.000 0.000 0.000
#> GSM648601 1 0.0000 0.9439 1.000 0.000 0.000 0.000
#> GSM648602 1 0.0000 0.9439 1.000 0.000 0.000 0.000
#> GSM648604 1 0.0000 0.9439 1.000 0.000 0.000 0.000
#> GSM648614 2 0.3873 0.8075 0.096 0.844 0.060 0.000
#> GSM648624 1 0.0000 0.9439 1.000 0.000 0.000 0.000
#> GSM648625 1 0.0000 0.9439 1.000 0.000 0.000 0.000
#> GSM648629 1 0.0000 0.9439 1.000 0.000 0.000 0.000
#> GSM648634 1 0.0000 0.9439 1.000 0.000 0.000 0.000
#> GSM648648 1 0.0000 0.9439 1.000 0.000 0.000 0.000
#> GSM648651 1 0.0000 0.9439 1.000 0.000 0.000 0.000
#> GSM648657 1 0.1302 0.9164 0.956 0.000 0.000 0.044
#> GSM648660 1 0.0000 0.9439 1.000 0.000 0.000 0.000
#> GSM648697 1 0.0000 0.9439 1.000 0.000 0.000 0.000
#> GSM648710 1 0.0000 0.9439 1.000 0.000 0.000 0.000
#> GSM648591 4 0.2926 0.7880 0.056 0.000 0.048 0.896
#> GSM648592 1 0.3463 0.8444 0.864 0.040 0.000 0.096
#> GSM648607 1 0.0000 0.9439 1.000 0.000 0.000 0.000
#> GSM648611 3 0.0000 0.9304 0.000 0.000 1.000 0.000
#> GSM648612 3 0.2174 0.9094 0.020 0.000 0.928 0.052
#> GSM648616 3 0.2647 0.8793 0.000 0.000 0.880 0.120
#> GSM648617 1 0.1398 0.9187 0.956 0.000 0.004 0.040
#> GSM648626 1 0.2466 0.8743 0.900 0.000 0.004 0.096
#> GSM648711 1 0.0000 0.9439 1.000 0.000 0.000 0.000
#> GSM648712 3 0.6016 0.2473 0.412 0.000 0.544 0.044
#> GSM648713 1 0.1637 0.8986 0.940 0.000 0.060 0.000
#> GSM648714 2 0.3556 0.8347 0.036 0.864 0.096 0.004
#> GSM648716 3 0.1398 0.9108 0.040 0.000 0.956 0.004
#> GSM648717 3 0.0469 0.9283 0.000 0.012 0.988 0.000
#> GSM648590 1 0.6394 0.4359 0.636 0.120 0.000 0.244
#> GSM648596 2 0.0707 0.9366 0.000 0.980 0.000 0.020
#> GSM648642 2 0.0336 0.9427 0.008 0.992 0.000 0.000
#> GSM648696 1 0.0000 0.9439 1.000 0.000 0.000 0.000
#> GSM648705 1 0.0000 0.9439 1.000 0.000 0.000 0.000
#> GSM648718 4 0.3528 0.8184 0.000 0.192 0.000 0.808
#> GSM648599 1 0.0000 0.9439 1.000 0.000 0.000 0.000
#> GSM648608 1 0.0000 0.9439 1.000 0.000 0.000 0.000
#> GSM648609 1 0.0000 0.9439 1.000 0.000 0.000 0.000
#> GSM648610 1 0.0000 0.9439 1.000 0.000 0.000 0.000
#> GSM648633 1 0.0000 0.9439 1.000 0.000 0.000 0.000
#> GSM648644 2 0.0188 0.9447 0.000 0.996 0.000 0.004
#> GSM648652 1 0.0000 0.9439 1.000 0.000 0.000 0.000
#> GSM648653 1 0.0000 0.9439 1.000 0.000 0.000 0.000
#> GSM648658 1 0.0000 0.9439 1.000 0.000 0.000 0.000
#> GSM648659 4 0.3444 0.8263 0.000 0.184 0.000 0.816
#> GSM648662 1 0.5472 0.5396 0.676 0.280 0.044 0.000
#> GSM648665 1 0.4936 0.3882 0.624 0.372 0.004 0.000
#> GSM648666 1 0.0000 0.9439 1.000 0.000 0.000 0.000
#> GSM648680 1 0.0000 0.9439 1.000 0.000 0.000 0.000
#> GSM648684 1 0.0000 0.9439 1.000 0.000 0.000 0.000
#> GSM648709 2 0.0188 0.9442 0.004 0.996 0.000 0.000
#> GSM648719 1 0.0000 0.9439 1.000 0.000 0.000 0.000
#> GSM648627 3 0.0469 0.9290 0.012 0.000 0.988 0.000
#> GSM648637 4 0.2408 0.8680 0.000 0.104 0.000 0.896
#> GSM648638 3 0.1824 0.9140 0.000 0.004 0.936 0.060
#> GSM648641 3 0.0000 0.9304 0.000 0.000 1.000 0.000
#> GSM648672 4 0.2814 0.8627 0.000 0.132 0.000 0.868
#> GSM648674 4 0.2081 0.8695 0.000 0.084 0.000 0.916
#> GSM648703 4 0.3024 0.8539 0.000 0.148 0.000 0.852
#> GSM648631 3 0.0921 0.9265 0.000 0.000 0.972 0.028
#> GSM648669 4 0.1557 0.8355 0.000 0.000 0.056 0.944
#> GSM648671 4 0.1302 0.8383 0.000 0.000 0.044 0.956
#> GSM648678 2 0.3801 0.6636 0.000 0.780 0.000 0.220
#> GSM648679 4 0.0817 0.8547 0.000 0.024 0.000 0.976
#> GSM648681 4 0.2281 0.8715 0.000 0.096 0.000 0.904
#> GSM648686 3 0.1474 0.9164 0.000 0.000 0.948 0.052
#> GSM648689 3 0.0000 0.9304 0.000 0.000 1.000 0.000
#> GSM648690 3 0.0707 0.9283 0.000 0.000 0.980 0.020
#> GSM648691 3 0.1302 0.9202 0.000 0.000 0.956 0.044
#> GSM648693 3 0.0469 0.9301 0.000 0.000 0.988 0.012
#> GSM648700 4 0.2281 0.8682 0.000 0.096 0.000 0.904
#> GSM648630 3 0.0188 0.9305 0.000 0.000 0.996 0.004
#> GSM648632 3 0.0188 0.9305 0.000 0.000 0.996 0.004
#> GSM648639 3 0.2345 0.8919 0.000 0.000 0.900 0.100
#> GSM648640 3 0.0592 0.9295 0.000 0.000 0.984 0.016
#> GSM648668 4 0.2760 0.8642 0.000 0.128 0.000 0.872
#> GSM648676 4 0.2345 0.8681 0.000 0.100 0.000 0.900
#> GSM648692 3 0.0000 0.9304 0.000 0.000 1.000 0.000
#> GSM648694 3 0.0000 0.9304 0.000 0.000 1.000 0.000
#> GSM648699 4 0.2401 0.8677 0.000 0.092 0.004 0.904
#> GSM648701 4 0.2345 0.8681 0.000 0.100 0.000 0.900
#> GSM648673 4 0.1929 0.8593 0.000 0.036 0.024 0.940
#> GSM648677 4 0.3123 0.8486 0.000 0.156 0.000 0.844
#> GSM648687 3 0.1637 0.9117 0.000 0.000 0.940 0.060
#> GSM648688 3 0.1389 0.9184 0.000 0.000 0.952 0.048
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM648605 2 0.0000 0.9071 0.000 1.000 0.000 0.000 0.000
#> GSM648618 1 0.0703 0.9315 0.976 0.000 0.000 0.000 0.024
#> GSM648620 2 0.0000 0.9071 0.000 1.000 0.000 0.000 0.000
#> GSM648646 2 0.0000 0.9071 0.000 1.000 0.000 0.000 0.000
#> GSM648649 1 0.2068 0.8660 0.904 0.000 0.000 0.004 0.092
#> GSM648675 4 0.4062 0.8237 0.040 0.000 0.000 0.764 0.196
#> GSM648682 2 0.2179 0.7804 0.112 0.888 0.000 0.000 0.000
#> GSM648698 2 0.0000 0.9071 0.000 1.000 0.000 0.000 0.000
#> GSM648708 2 0.3366 0.6036 0.232 0.768 0.000 0.000 0.000
#> GSM648628 3 0.2280 0.8444 0.000 0.000 0.880 0.000 0.120
#> GSM648595 4 0.3242 0.8544 0.012 0.000 0.000 0.816 0.172
#> GSM648635 1 0.0162 0.9378 0.996 0.000 0.000 0.000 0.004
#> GSM648645 1 0.0290 0.9366 0.992 0.000 0.000 0.000 0.008
#> GSM648647 2 0.0000 0.9071 0.000 1.000 0.000 0.000 0.000
#> GSM648667 1 0.4227 0.3092 0.580 0.420 0.000 0.000 0.000
#> GSM648695 2 0.0000 0.9071 0.000 1.000 0.000 0.000 0.000
#> GSM648704 2 0.0000 0.9071 0.000 1.000 0.000 0.000 0.000
#> GSM648706 2 0.0000 0.9071 0.000 1.000 0.000 0.000 0.000
#> GSM648593 1 0.0000 0.9386 1.000 0.000 0.000 0.000 0.000
#> GSM648594 4 0.5180 0.6138 0.196 0.000 0.000 0.684 0.120
#> GSM648600 1 0.0703 0.9288 0.976 0.000 0.000 0.000 0.024
#> GSM648621 1 0.3707 0.6707 0.716 0.000 0.000 0.000 0.284
#> GSM648622 1 0.0162 0.9378 0.996 0.000 0.000 0.000 0.004
#> GSM648623 5 0.2424 0.8238 0.132 0.000 0.000 0.000 0.868
#> GSM648636 1 0.1043 0.9175 0.960 0.000 0.000 0.000 0.040
#> GSM648655 1 0.2074 0.8649 0.896 0.000 0.000 0.000 0.104
#> GSM648661 3 0.3707 0.5285 0.284 0.000 0.716 0.000 0.000
#> GSM648664 1 0.0000 0.9386 1.000 0.000 0.000 0.000 0.000
#> GSM648683 1 0.0794 0.9244 0.972 0.000 0.000 0.000 0.028
#> GSM648685 1 0.0000 0.9386 1.000 0.000 0.000 0.000 0.000
#> GSM648702 1 0.1671 0.8887 0.924 0.000 0.000 0.000 0.076
#> GSM648597 5 0.3758 0.7878 0.096 0.000 0.000 0.088 0.816
#> GSM648603 5 0.2929 0.7834 0.180 0.000 0.000 0.000 0.820
#> GSM648606 2 0.6377 0.0823 0.000 0.452 0.380 0.000 0.168
#> GSM648613 5 0.5155 0.6814 0.000 0.140 0.168 0.000 0.692
#> GSM648619 5 0.4901 0.7581 0.168 0.000 0.116 0.000 0.716
#> GSM648654 3 0.3305 0.6116 0.224 0.000 0.776 0.000 0.000
#> GSM648663 2 0.6697 -0.1663 0.000 0.384 0.240 0.000 0.376
#> GSM648670 4 0.3143 0.8418 0.000 0.000 0.000 0.796 0.204
#> GSM648707 5 0.2230 0.8049 0.000 0.000 0.116 0.000 0.884
#> GSM648615 2 0.0000 0.9071 0.000 1.000 0.000 0.000 0.000
#> GSM648643 2 0.0162 0.9047 0.000 0.996 0.000 0.004 0.000
#> GSM648650 1 0.3684 0.6275 0.720 0.280 0.000 0.000 0.000
#> GSM648656 2 0.0000 0.9071 0.000 1.000 0.000 0.000 0.000
#> GSM648715 2 0.0000 0.9071 0.000 1.000 0.000 0.000 0.000
#> GSM648598 1 0.0000 0.9386 1.000 0.000 0.000 0.000 0.000
#> GSM648601 1 0.0162 0.9378 0.996 0.000 0.000 0.000 0.004
#> GSM648602 1 0.0162 0.9378 0.996 0.000 0.000 0.000 0.004
#> GSM648604 1 0.0000 0.9386 1.000 0.000 0.000 0.000 0.000
#> GSM648614 2 0.1851 0.8306 0.000 0.912 0.088 0.000 0.000
#> GSM648624 1 0.0000 0.9386 1.000 0.000 0.000 0.000 0.000
#> GSM648625 1 0.1430 0.9023 0.944 0.052 0.000 0.000 0.004
#> GSM648629 1 0.0000 0.9386 1.000 0.000 0.000 0.000 0.000
#> GSM648634 1 0.0162 0.9378 0.996 0.000 0.000 0.000 0.004
#> GSM648648 1 0.0000 0.9386 1.000 0.000 0.000 0.000 0.000
#> GSM648651 1 0.0000 0.9386 1.000 0.000 0.000 0.000 0.000
#> GSM648657 1 0.2377 0.8329 0.872 0.000 0.000 0.000 0.128
#> GSM648660 1 0.0290 0.9366 0.992 0.000 0.000 0.000 0.008
#> GSM648697 1 0.0000 0.9386 1.000 0.000 0.000 0.000 0.000
#> GSM648710 1 0.0000 0.9386 1.000 0.000 0.000 0.000 0.000
#> GSM648591 5 0.1671 0.7381 0.000 0.000 0.000 0.076 0.924
#> GSM648592 5 0.2574 0.8290 0.112 0.000 0.000 0.012 0.876
#> GSM648607 1 0.0324 0.9367 0.992 0.000 0.004 0.000 0.004
#> GSM648611 3 0.2127 0.8457 0.000 0.000 0.892 0.000 0.108
#> GSM648612 5 0.3177 0.7539 0.000 0.000 0.208 0.000 0.792
#> GSM648616 5 0.2411 0.8061 0.000 0.000 0.108 0.008 0.884
#> GSM648617 5 0.2424 0.8238 0.132 0.000 0.000 0.000 0.868
#> GSM648626 5 0.2280 0.8288 0.120 0.000 0.000 0.000 0.880
#> GSM648711 1 0.0000 0.9386 1.000 0.000 0.000 0.000 0.000
#> GSM648712 5 0.3562 0.7681 0.016 0.000 0.196 0.000 0.788
#> GSM648713 5 0.5037 0.7077 0.228 0.000 0.088 0.000 0.684
#> GSM648714 2 0.1851 0.8306 0.000 0.912 0.088 0.000 0.000
#> GSM648716 3 0.2351 0.8417 0.016 0.000 0.896 0.000 0.088
#> GSM648717 3 0.4047 0.4680 0.000 0.004 0.676 0.000 0.320
#> GSM648590 1 0.5684 0.6519 0.700 0.048 0.000 0.144 0.108
#> GSM648596 2 0.0162 0.9047 0.000 0.996 0.000 0.004 0.000
#> GSM648642 2 0.0000 0.9071 0.000 1.000 0.000 0.000 0.000
#> GSM648696 1 0.0290 0.9361 0.992 0.008 0.000 0.000 0.000
#> GSM648705 1 0.0000 0.9386 1.000 0.000 0.000 0.000 0.000
#> GSM648718 4 0.2864 0.8544 0.024 0.112 0.000 0.864 0.000
#> GSM648599 1 0.0880 0.9235 0.968 0.000 0.000 0.000 0.032
#> GSM648608 1 0.0000 0.9386 1.000 0.000 0.000 0.000 0.000
#> GSM648609 1 0.0000 0.9386 1.000 0.000 0.000 0.000 0.000
#> GSM648610 1 0.2074 0.8644 0.896 0.000 0.000 0.000 0.104
#> GSM648633 1 0.0290 0.9366 0.992 0.000 0.000 0.000 0.008
#> GSM648644 2 0.0000 0.9071 0.000 1.000 0.000 0.000 0.000
#> GSM648652 1 0.0000 0.9386 1.000 0.000 0.000 0.000 0.000
#> GSM648653 1 0.0000 0.9386 1.000 0.000 0.000 0.000 0.000
#> GSM648658 1 0.0162 0.9375 0.996 0.000 0.000 0.000 0.004
#> GSM648659 4 0.4473 0.8286 0.004 0.112 0.000 0.768 0.116
#> GSM648662 1 0.5325 0.5053 0.636 0.276 0.088 0.000 0.000
#> GSM648665 1 0.3274 0.7121 0.780 0.220 0.000 0.000 0.000
#> GSM648666 1 0.0000 0.9386 1.000 0.000 0.000 0.000 0.000
#> GSM648680 1 0.0000 0.9386 1.000 0.000 0.000 0.000 0.000
#> GSM648684 1 0.1341 0.9046 0.944 0.000 0.000 0.000 0.056
#> GSM648709 2 0.0000 0.9071 0.000 1.000 0.000 0.000 0.000
#> GSM648719 1 0.0162 0.9378 0.996 0.000 0.000 0.000 0.004
#> GSM648627 3 0.1444 0.8812 0.012 0.000 0.948 0.000 0.040
#> GSM648637 4 0.3487 0.7849 0.000 0.008 0.000 0.780 0.212
#> GSM648638 5 0.3300 0.7558 0.000 0.000 0.204 0.004 0.792
#> GSM648641 3 0.1121 0.8763 0.000 0.000 0.956 0.000 0.044
#> GSM648672 4 0.1851 0.8770 0.000 0.088 0.000 0.912 0.000
#> GSM648674 4 0.1965 0.8796 0.000 0.000 0.000 0.904 0.096
#> GSM648703 4 0.1282 0.8938 0.000 0.004 0.000 0.952 0.044
#> GSM648631 3 0.0000 0.8952 0.000 0.000 1.000 0.000 0.000
#> GSM648669 4 0.0963 0.8866 0.000 0.000 0.036 0.964 0.000
#> GSM648671 4 0.0880 0.8885 0.000 0.000 0.032 0.968 0.000
#> GSM648678 2 0.2127 0.8134 0.000 0.892 0.000 0.108 0.000
#> GSM648679 4 0.1965 0.8796 0.000 0.000 0.000 0.904 0.096
#> GSM648681 4 0.1952 0.8833 0.000 0.004 0.000 0.912 0.084
#> GSM648686 3 0.2136 0.8385 0.000 0.000 0.904 0.088 0.008
#> GSM648689 3 0.0000 0.8952 0.000 0.000 1.000 0.000 0.000
#> GSM648690 3 0.0000 0.8952 0.000 0.000 1.000 0.000 0.000
#> GSM648691 3 0.0000 0.8952 0.000 0.000 1.000 0.000 0.000
#> GSM648693 3 0.0000 0.8952 0.000 0.000 1.000 0.000 0.000
#> GSM648700 4 0.1082 0.8934 0.000 0.000 0.008 0.964 0.028
#> GSM648630 3 0.0000 0.8952 0.000 0.000 1.000 0.000 0.000
#> GSM648632 3 0.0000 0.8952 0.000 0.000 1.000 0.000 0.000
#> GSM648639 5 0.2329 0.8015 0.000 0.000 0.124 0.000 0.876
#> GSM648640 3 0.2561 0.7955 0.000 0.000 0.856 0.000 0.144
#> GSM648668 4 0.2110 0.8842 0.000 0.072 0.000 0.912 0.016
#> GSM648676 4 0.0671 0.8933 0.000 0.000 0.004 0.980 0.016
#> GSM648692 3 0.0162 0.8939 0.000 0.000 0.996 0.000 0.004
#> GSM648694 3 0.0000 0.8952 0.000 0.000 1.000 0.000 0.000
#> GSM648699 4 0.1211 0.8893 0.000 0.000 0.024 0.960 0.016
#> GSM648701 4 0.0912 0.8923 0.000 0.000 0.012 0.972 0.016
#> GSM648673 4 0.0609 0.8919 0.000 0.000 0.020 0.980 0.000
#> GSM648677 4 0.2011 0.8770 0.000 0.088 0.000 0.908 0.004
#> GSM648687 3 0.1851 0.8437 0.000 0.000 0.912 0.088 0.000
#> GSM648688 3 0.1732 0.8501 0.000 0.000 0.920 0.080 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM648605 2 0.0146 0.92917 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM648618 1 0.5583 0.10423 0.456 0.000 0.000 0.008 0.108 0.428
#> GSM648620 2 0.0547 0.92238 0.000 0.980 0.000 0.000 0.000 0.020
#> GSM648646 2 0.0000 0.93019 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM648649 1 0.4809 0.53629 0.628 0.000 0.000 0.308 0.012 0.052
#> GSM648675 6 0.1196 0.48351 0.000 0.000 0.000 0.040 0.008 0.952
#> GSM648682 2 0.3276 0.70942 0.132 0.816 0.000 0.000 0.000 0.052
#> GSM648698 2 0.0000 0.93019 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM648708 1 0.4584 0.31327 0.556 0.404 0.000 0.000 0.000 0.040
#> GSM648628 5 0.5108 0.08086 0.000 0.000 0.080 0.000 0.484 0.436
#> GSM648595 6 0.4115 0.07386 0.012 0.000 0.000 0.360 0.004 0.624
#> GSM648635 1 0.1398 0.85902 0.940 0.000 0.000 0.008 0.000 0.052
#> GSM648645 1 0.1219 0.86066 0.948 0.000 0.000 0.004 0.000 0.048
#> GSM648647 2 0.0000 0.93019 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM648667 1 0.3820 0.69417 0.756 0.204 0.000 0.008 0.000 0.032
#> GSM648695 2 0.1411 0.86974 0.060 0.936 0.000 0.000 0.000 0.004
#> GSM648704 2 0.0000 0.93019 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM648706 2 0.0000 0.93019 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM648593 1 0.1863 0.82186 0.896 0.000 0.000 0.000 0.000 0.104
#> GSM648594 4 0.4135 0.17754 0.292 0.000 0.000 0.680 0.012 0.016
#> GSM648600 1 0.2812 0.82419 0.856 0.000 0.000 0.000 0.048 0.096
#> GSM648621 6 0.6106 -0.01120 0.156 0.000 0.000 0.020 0.368 0.456
#> GSM648622 1 0.0146 0.86580 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM648623 5 0.5454 0.44442 0.180 0.000 0.000 0.252 0.568 0.000
#> GSM648636 1 0.3774 0.46019 0.592 0.000 0.000 0.000 0.000 0.408
#> GSM648655 6 0.3163 0.36754 0.232 0.004 0.000 0.000 0.000 0.764
#> GSM648661 3 0.4057 0.23336 0.436 0.000 0.556 0.000 0.000 0.008
#> GSM648664 1 0.0603 0.86513 0.980 0.000 0.004 0.000 0.000 0.016
#> GSM648683 1 0.1757 0.84203 0.916 0.000 0.000 0.000 0.008 0.076
#> GSM648685 1 0.0547 0.86461 0.980 0.000 0.000 0.000 0.000 0.020
#> GSM648702 1 0.2219 0.82486 0.864 0.000 0.000 0.000 0.000 0.136
#> GSM648597 5 0.3823 0.43923 0.000 0.000 0.000 0.436 0.564 0.000
#> GSM648603 5 0.5008 0.52044 0.148 0.000 0.000 0.212 0.640 0.000
#> GSM648606 5 0.6259 0.18300 0.000 0.204 0.024 0.000 0.488 0.284
#> GSM648613 5 0.0806 0.62257 0.000 0.008 0.020 0.000 0.972 0.000
#> GSM648619 5 0.2402 0.56554 0.120 0.000 0.012 0.000 0.868 0.000
#> GSM648654 3 0.4100 0.31440 0.388 0.000 0.600 0.000 0.008 0.004
#> GSM648663 5 0.4691 0.42106 0.012 0.272 0.028 0.000 0.672 0.016
#> GSM648670 6 0.3470 0.38595 0.000 0.000 0.000 0.248 0.012 0.740
#> GSM648707 5 0.3175 0.60495 0.000 0.000 0.000 0.256 0.744 0.000
#> GSM648615 2 0.0146 0.92925 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM648643 2 0.0603 0.92066 0.000 0.980 0.000 0.004 0.000 0.016
#> GSM648650 1 0.4408 0.73022 0.764 0.120 0.000 0.064 0.000 0.052
#> GSM648656 2 0.0000 0.93019 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM648715 2 0.0000 0.93019 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM648598 1 0.0000 0.86567 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM648601 1 0.0937 0.86271 0.960 0.000 0.000 0.000 0.000 0.040
#> GSM648602 1 0.2053 0.83693 0.888 0.000 0.000 0.000 0.004 0.108
#> GSM648604 1 0.0547 0.86461 0.980 0.000 0.000 0.000 0.000 0.020
#> GSM648614 2 0.1577 0.89018 0.000 0.940 0.008 0.000 0.036 0.016
#> GSM648624 1 0.0458 0.86393 0.984 0.000 0.000 0.000 0.000 0.016
#> GSM648625 1 0.0508 0.86699 0.984 0.012 0.000 0.000 0.000 0.004
#> GSM648629 1 0.0363 0.86566 0.988 0.000 0.000 0.000 0.000 0.012
#> GSM648634 1 0.1753 0.85046 0.912 0.000 0.000 0.000 0.004 0.084
#> GSM648648 1 0.0790 0.86418 0.968 0.000 0.000 0.000 0.000 0.032
#> GSM648651 1 0.0363 0.86487 0.988 0.000 0.000 0.000 0.000 0.012
#> GSM648657 1 0.5264 0.39036 0.548 0.000 0.000 0.376 0.028 0.048
#> GSM648660 1 0.0603 0.86595 0.980 0.000 0.000 0.000 0.004 0.016
#> GSM648697 1 0.0458 0.86393 0.984 0.000 0.000 0.000 0.000 0.016
#> GSM648710 1 0.0547 0.86461 0.980 0.000 0.000 0.000 0.000 0.020
#> GSM648591 5 0.5807 0.33106 0.000 0.000 0.000 0.200 0.476 0.324
#> GSM648592 5 0.3807 0.52527 0.004 0.000 0.000 0.368 0.628 0.000
#> GSM648607 1 0.2262 0.82491 0.896 0.000 0.008 0.000 0.080 0.016
#> GSM648611 6 0.5450 -0.13820 0.000 0.000 0.120 0.000 0.428 0.452
#> GSM648612 5 0.0508 0.62305 0.000 0.000 0.012 0.000 0.984 0.004
#> GSM648616 5 0.3390 0.58474 0.000 0.000 0.000 0.296 0.704 0.000
#> GSM648617 5 0.3314 0.61039 0.012 0.000 0.000 0.224 0.764 0.000
#> GSM648626 5 0.3126 0.60744 0.000 0.000 0.000 0.248 0.752 0.000
#> GSM648711 1 0.1003 0.86112 0.964 0.000 0.000 0.000 0.016 0.020
#> GSM648712 5 0.0820 0.62101 0.000 0.000 0.012 0.000 0.972 0.016
#> GSM648713 5 0.2094 0.60676 0.068 0.000 0.016 0.000 0.908 0.008
#> GSM648714 2 0.0891 0.90943 0.000 0.968 0.008 0.000 0.024 0.000
#> GSM648716 5 0.4375 -0.00206 0.012 0.000 0.432 0.000 0.548 0.008
#> GSM648717 5 0.2473 0.56516 0.000 0.000 0.136 0.000 0.856 0.008
#> GSM648590 6 0.1829 0.48346 0.028 0.008 0.000 0.036 0.000 0.928
#> GSM648596 2 0.0405 0.92706 0.000 0.988 0.000 0.004 0.000 0.008
#> GSM648642 2 0.0547 0.92204 0.000 0.980 0.000 0.000 0.000 0.020
#> GSM648696 1 0.1728 0.85456 0.924 0.008 0.000 0.000 0.004 0.064
#> GSM648705 1 0.1429 0.85874 0.940 0.004 0.000 0.004 0.000 0.052
#> GSM648718 2 0.6936 -0.05969 0.196 0.392 0.000 0.340 0.000 0.072
#> GSM648599 1 0.5450 0.40716 0.560 0.000 0.000 0.000 0.164 0.276
#> GSM648608 1 0.0790 0.86074 0.968 0.000 0.000 0.000 0.000 0.032
#> GSM648609 1 0.0458 0.86393 0.984 0.000 0.000 0.000 0.000 0.016
#> GSM648610 1 0.4256 0.23247 0.520 0.000 0.000 0.000 0.016 0.464
#> GSM648633 1 0.1049 0.86441 0.960 0.000 0.000 0.000 0.008 0.032
#> GSM648644 2 0.0000 0.93019 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM648652 1 0.1141 0.85975 0.948 0.000 0.000 0.000 0.000 0.052
#> GSM648653 1 0.0937 0.86449 0.960 0.000 0.000 0.000 0.000 0.040
#> GSM648658 1 0.3390 0.59390 0.704 0.000 0.000 0.000 0.000 0.296
#> GSM648659 6 0.1866 0.45420 0.000 0.008 0.000 0.084 0.000 0.908
#> GSM648662 1 0.4761 0.62360 0.716 0.200 0.012 0.000 0.040 0.032
#> GSM648665 1 0.3312 0.70315 0.792 0.180 0.000 0.000 0.000 0.028
#> GSM648666 1 0.0458 0.86393 0.984 0.000 0.000 0.000 0.000 0.016
#> GSM648680 1 0.1007 0.86162 0.956 0.000 0.000 0.000 0.000 0.044
#> GSM648684 1 0.1610 0.84184 0.916 0.000 0.000 0.000 0.000 0.084
#> GSM648709 2 0.0146 0.92917 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM648719 1 0.0508 0.86618 0.984 0.000 0.000 0.000 0.004 0.012
#> GSM648627 5 0.6131 0.13019 0.004 0.000 0.276 0.000 0.432 0.288
#> GSM648637 4 0.1693 0.56217 0.000 0.004 0.000 0.932 0.020 0.044
#> GSM648638 5 0.1807 0.63166 0.000 0.000 0.020 0.060 0.920 0.000
#> GSM648641 5 0.3868 -0.15930 0.000 0.000 0.492 0.000 0.508 0.000
#> GSM648672 4 0.3168 0.59310 0.000 0.016 0.000 0.792 0.000 0.192
#> GSM648674 4 0.3448 0.55461 0.000 0.000 0.000 0.716 0.004 0.280
#> GSM648703 6 0.3999 -0.44187 0.000 0.004 0.000 0.496 0.000 0.500
#> GSM648631 3 0.1863 0.77991 0.000 0.000 0.896 0.000 0.104 0.000
#> GSM648669 4 0.3710 0.51348 0.000 0.000 0.292 0.696 0.000 0.012
#> GSM648671 4 0.3470 0.54532 0.000 0.000 0.248 0.740 0.000 0.012
#> GSM648678 2 0.3522 0.73102 0.000 0.800 0.000 0.128 0.000 0.072
#> GSM648679 4 0.0603 0.55446 0.000 0.000 0.000 0.980 0.004 0.016
#> GSM648681 4 0.1363 0.57374 0.004 0.004 0.012 0.952 0.000 0.028
#> GSM648686 3 0.0405 0.77372 0.000 0.000 0.988 0.008 0.000 0.004
#> GSM648689 3 0.2631 0.73752 0.000 0.000 0.820 0.000 0.180 0.000
#> GSM648690 3 0.0405 0.77617 0.000 0.000 0.988 0.008 0.004 0.000
#> GSM648691 3 0.0632 0.76825 0.000 0.000 0.976 0.024 0.000 0.000
#> GSM648693 3 0.2664 0.73357 0.000 0.000 0.816 0.000 0.184 0.000
#> GSM648700 6 0.3737 -0.18623 0.000 0.000 0.000 0.392 0.000 0.608
#> GSM648630 3 0.1610 0.78387 0.000 0.000 0.916 0.000 0.084 0.000
#> GSM648632 3 0.1531 0.78481 0.000 0.000 0.928 0.004 0.068 0.000
#> GSM648639 5 0.3244 0.60077 0.000 0.000 0.000 0.268 0.732 0.000
#> GSM648640 3 0.3998 0.13601 0.000 0.000 0.504 0.004 0.492 0.000
#> GSM648668 4 0.3245 0.58433 0.000 0.008 0.000 0.764 0.000 0.228
#> GSM648676 4 0.3867 0.36008 0.000 0.000 0.000 0.512 0.000 0.488
#> GSM648692 3 0.2178 0.76845 0.000 0.000 0.868 0.000 0.132 0.000
#> GSM648694 3 0.2491 0.74969 0.000 0.000 0.836 0.000 0.164 0.000
#> GSM648699 4 0.4962 0.40364 0.000 0.000 0.068 0.516 0.000 0.416
#> GSM648701 4 0.3867 0.36008 0.000 0.000 0.000 0.512 0.000 0.488
#> GSM648673 4 0.4239 0.54239 0.000 0.000 0.248 0.696 0.000 0.056
#> GSM648677 4 0.4083 0.38414 0.000 0.008 0.000 0.532 0.000 0.460
#> GSM648687 3 0.0777 0.76603 0.000 0.000 0.972 0.024 0.000 0.004
#> GSM648688 3 0.0547 0.77031 0.000 0.000 0.980 0.020 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)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
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) development.stage(p) other(p) k
#> CV:NMF 126 2.72e-06 0.06423 1.43e-13 2
#> CV:NMF 126 1.35e-10 0.05521 1.29e-18 3
#> CV:NMF 124 1.37e-12 0.09094 1.93e-24 4
#> CV:NMF 126 9.99e-14 0.02393 2.80e-31 5
#> CV:NMF 96 7.27e-17 0.00881 3.95e-32 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 51941 rows and 130 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'hclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.533 0.776 0.886 0.3889 0.706 0.706
#> 3 3 0.625 0.749 0.872 0.3830 0.766 0.673
#> 4 4 0.522 0.685 0.778 0.2249 0.855 0.710
#> 5 5 0.514 0.578 0.730 0.0912 0.943 0.845
#> 6 6 0.551 0.505 0.705 0.0404 0.937 0.806
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
#> GSM648605 1 0.9833 0.406 0.576 0.424
#> GSM648618 1 0.3584 0.839 0.932 0.068
#> GSM648620 1 0.9552 0.504 0.624 0.376
#> GSM648646 2 0.4690 0.875 0.100 0.900
#> GSM648649 1 0.3114 0.842 0.944 0.056
#> GSM648675 1 0.5842 0.797 0.860 0.140
#> GSM648682 2 0.5294 0.857 0.120 0.880
#> GSM648698 1 0.9833 0.406 0.576 0.424
#> GSM648708 1 0.9686 0.471 0.604 0.396
#> GSM648628 1 0.0376 0.857 0.996 0.004
#> GSM648595 1 0.3733 0.838 0.928 0.072
#> GSM648635 1 0.1843 0.851 0.972 0.028
#> GSM648645 1 0.0000 0.857 1.000 0.000
#> GSM648647 1 0.9635 0.486 0.612 0.388
#> GSM648667 1 0.8144 0.683 0.748 0.252
#> GSM648695 1 0.9129 0.580 0.672 0.328
#> GSM648704 2 0.0000 0.951 0.000 1.000
#> GSM648706 2 0.0938 0.953 0.012 0.988
#> GSM648593 1 0.4298 0.825 0.912 0.088
#> GSM648594 1 0.2603 0.850 0.956 0.044
#> GSM648600 1 0.1633 0.852 0.976 0.024
#> GSM648621 1 0.0000 0.857 1.000 0.000
#> GSM648622 1 0.0000 0.857 1.000 0.000
#> GSM648623 1 0.0000 0.857 1.000 0.000
#> GSM648636 1 0.2423 0.847 0.960 0.040
#> GSM648655 1 0.4298 0.825 0.912 0.088
#> GSM648661 1 0.0000 0.857 1.000 0.000
#> GSM648664 1 0.0000 0.857 1.000 0.000
#> GSM648683 1 0.0000 0.857 1.000 0.000
#> GSM648685 1 0.0000 0.857 1.000 0.000
#> GSM648702 1 0.2236 0.849 0.964 0.036
#> GSM648597 1 0.0938 0.856 0.988 0.012
#> GSM648603 1 0.0672 0.856 0.992 0.008
#> GSM648606 1 0.3584 0.836 0.932 0.068
#> GSM648613 1 0.3584 0.836 0.932 0.068
#> GSM648619 1 0.0000 0.857 1.000 0.000
#> GSM648654 1 0.1843 0.851 0.972 0.028
#> GSM648663 1 0.3584 0.836 0.932 0.068
#> GSM648670 1 0.9881 0.313 0.564 0.436
#> GSM648707 1 0.9635 0.487 0.612 0.388
#> GSM648615 1 0.9896 0.375 0.560 0.440
#> GSM648643 2 0.7602 0.685 0.220 0.780
#> GSM648650 1 0.4022 0.833 0.920 0.080
#> GSM648656 2 0.4690 0.875 0.100 0.900
#> GSM648715 1 0.8144 0.683 0.748 0.252
#> GSM648598 1 0.0000 0.857 1.000 0.000
#> GSM648601 1 0.0000 0.857 1.000 0.000
#> GSM648602 1 0.0000 0.857 1.000 0.000
#> GSM648604 1 0.0000 0.857 1.000 0.000
#> GSM648614 1 0.3584 0.836 0.932 0.068
#> GSM648624 1 0.0000 0.857 1.000 0.000
#> GSM648625 1 0.3114 0.840 0.944 0.056
#> GSM648629 1 0.0000 0.857 1.000 0.000
#> GSM648634 1 0.0000 0.857 1.000 0.000
#> GSM648648 1 0.1843 0.851 0.972 0.028
#> GSM648651 1 0.0000 0.857 1.000 0.000
#> GSM648657 1 0.0000 0.857 1.000 0.000
#> GSM648660 1 0.0000 0.857 1.000 0.000
#> GSM648697 1 0.0000 0.857 1.000 0.000
#> GSM648710 1 0.0000 0.857 1.000 0.000
#> GSM648591 1 0.0938 0.856 0.988 0.012
#> GSM648592 1 0.0938 0.856 0.988 0.012
#> GSM648607 1 0.0000 0.857 1.000 0.000
#> GSM648611 1 0.0000 0.857 1.000 0.000
#> GSM648612 1 0.0000 0.857 1.000 0.000
#> GSM648616 1 0.9661 0.479 0.608 0.392
#> GSM648617 1 0.2948 0.848 0.948 0.052
#> GSM648626 1 0.0672 0.856 0.992 0.008
#> GSM648711 1 0.0000 0.857 1.000 0.000
#> GSM648712 1 0.0000 0.857 1.000 0.000
#> GSM648713 1 0.0000 0.857 1.000 0.000
#> GSM648714 1 0.3584 0.836 0.932 0.068
#> GSM648716 1 0.0000 0.857 1.000 0.000
#> GSM648717 1 0.3274 0.840 0.940 0.060
#> GSM648590 1 0.7056 0.754 0.808 0.192
#> GSM648596 1 0.9044 0.610 0.680 0.320
#> GSM648642 1 0.9686 0.471 0.604 0.396
#> GSM648696 1 0.2236 0.851 0.964 0.036
#> GSM648705 1 0.2236 0.848 0.964 0.036
#> GSM648718 1 0.9896 0.375 0.560 0.440
#> GSM648599 1 0.0000 0.857 1.000 0.000
#> GSM648608 1 0.0000 0.857 1.000 0.000
#> GSM648609 1 0.0000 0.857 1.000 0.000
#> GSM648610 1 0.0000 0.857 1.000 0.000
#> GSM648633 1 0.0000 0.857 1.000 0.000
#> GSM648644 2 0.0000 0.951 0.000 1.000
#> GSM648652 1 0.1843 0.851 0.972 0.028
#> GSM648653 1 0.0000 0.857 1.000 0.000
#> GSM648658 1 0.4298 0.825 0.912 0.088
#> GSM648659 1 0.7602 0.733 0.780 0.220
#> GSM648662 1 0.0000 0.857 1.000 0.000
#> GSM648665 1 0.0000 0.857 1.000 0.000
#> GSM648666 1 0.0000 0.857 1.000 0.000
#> GSM648680 1 0.1843 0.851 0.972 0.028
#> GSM648684 1 0.0000 0.857 1.000 0.000
#> GSM648709 1 0.9170 0.575 0.668 0.332
#> GSM648719 1 0.0000 0.857 1.000 0.000
#> GSM648627 1 0.0000 0.857 1.000 0.000
#> GSM648637 2 0.1843 0.949 0.028 0.972
#> GSM648638 2 0.1843 0.949 0.028 0.972
#> GSM648641 1 0.7376 0.723 0.792 0.208
#> GSM648672 2 0.0000 0.951 0.000 1.000
#> GSM648674 2 0.2236 0.942 0.036 0.964
#> GSM648703 2 0.0672 0.954 0.008 0.992
#> GSM648631 1 0.9491 0.521 0.632 0.368
#> GSM648669 2 0.1414 0.951 0.020 0.980
#> GSM648671 2 0.1414 0.951 0.020 0.980
#> GSM648678 2 0.0000 0.951 0.000 1.000
#> GSM648679 2 0.2043 0.945 0.032 0.968
#> GSM648681 1 0.9881 0.353 0.564 0.436
#> GSM648686 1 0.9580 0.501 0.620 0.380
#> GSM648689 1 0.9427 0.534 0.640 0.360
#> GSM648690 1 0.9580 0.501 0.620 0.380
#> GSM648691 1 0.9580 0.501 0.620 0.380
#> GSM648693 1 0.9491 0.521 0.632 0.368
#> GSM648700 2 0.0672 0.954 0.008 0.992
#> GSM648630 1 0.9580 0.501 0.620 0.380
#> GSM648632 1 0.9491 0.521 0.632 0.368
#> GSM648639 1 0.9661 0.479 0.608 0.392
#> GSM648640 1 0.9661 0.479 0.608 0.392
#> GSM648668 2 0.3733 0.909 0.072 0.928
#> GSM648676 2 0.0672 0.954 0.008 0.992
#> GSM648692 1 0.9580 0.501 0.620 0.380
#> GSM648694 1 0.9491 0.521 0.632 0.368
#> GSM648699 2 0.0672 0.954 0.008 0.992
#> GSM648701 2 0.0672 0.954 0.008 0.992
#> GSM648673 2 0.1414 0.951 0.020 0.980
#> GSM648677 2 0.0000 0.951 0.000 1.000
#> GSM648687 1 0.9608 0.494 0.616 0.384
#> GSM648688 1 0.9608 0.494 0.616 0.384
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM648605 2 0.6955 -0.1525 0.488 0.496 0.016
#> GSM648618 1 0.3966 0.8410 0.876 0.100 0.024
#> GSM648620 1 0.6912 0.2831 0.540 0.444 0.016
#> GSM648646 2 0.2031 0.7371 0.016 0.952 0.032
#> GSM648649 1 0.2066 0.8648 0.940 0.060 0.000
#> GSM648675 1 0.4755 0.7688 0.808 0.184 0.008
#> GSM648682 2 0.3993 0.7323 0.052 0.884 0.064
#> GSM648698 2 0.6955 -0.1525 0.488 0.496 0.016
#> GSM648708 1 0.6944 0.2125 0.516 0.468 0.016
#> GSM648628 1 0.2918 0.8673 0.924 0.044 0.032
#> GSM648595 1 0.3682 0.8333 0.876 0.116 0.008
#> GSM648635 1 0.1289 0.8756 0.968 0.032 0.000
#> GSM648645 1 0.0475 0.8811 0.992 0.004 0.004
#> GSM648647 1 0.6799 0.2566 0.532 0.456 0.012
#> GSM648667 1 0.5397 0.6347 0.720 0.280 0.000
#> GSM648695 1 0.5988 0.4748 0.632 0.368 0.000
#> GSM648704 2 0.2959 0.7808 0.000 0.900 0.100
#> GSM648706 2 0.3539 0.7819 0.012 0.888 0.100
#> GSM648593 1 0.3116 0.8373 0.892 0.108 0.000
#> GSM648594 1 0.2261 0.8652 0.932 0.068 0.000
#> GSM648600 1 0.1585 0.8782 0.964 0.028 0.008
#> GSM648621 1 0.1905 0.8781 0.956 0.028 0.016
#> GSM648622 1 0.0424 0.8804 0.992 0.000 0.008
#> GSM648623 1 0.0424 0.8804 0.992 0.000 0.008
#> GSM648636 1 0.1753 0.8719 0.952 0.048 0.000
#> GSM648655 1 0.3116 0.8373 0.892 0.108 0.000
#> GSM648661 1 0.0237 0.8805 0.996 0.004 0.000
#> GSM648664 1 0.0424 0.8802 0.992 0.008 0.000
#> GSM648683 1 0.0424 0.8802 0.992 0.008 0.000
#> GSM648685 1 0.0424 0.8802 0.992 0.008 0.000
#> GSM648702 1 0.1643 0.8733 0.956 0.044 0.000
#> GSM648597 1 0.1919 0.8777 0.956 0.020 0.024
#> GSM648603 1 0.0892 0.8795 0.980 0.000 0.020
#> GSM648606 1 0.6209 0.4487 0.628 0.004 0.368
#> GSM648613 1 0.6209 0.4487 0.628 0.004 0.368
#> GSM648619 1 0.1289 0.8765 0.968 0.000 0.032
#> GSM648654 1 0.3445 0.8409 0.896 0.088 0.016
#> GSM648663 1 0.5815 0.5790 0.692 0.004 0.304
#> GSM648670 1 0.7395 0.1031 0.492 0.476 0.032
#> GSM648707 3 0.0237 0.8993 0.000 0.004 0.996
#> GSM648615 2 0.7069 -0.1037 0.472 0.508 0.020
#> GSM648643 2 0.5618 0.6203 0.156 0.796 0.048
#> GSM648650 1 0.4345 0.8101 0.848 0.136 0.016
#> GSM648656 2 0.2031 0.7371 0.016 0.952 0.032
#> GSM648715 1 0.5397 0.6347 0.720 0.280 0.000
#> GSM648598 1 0.0424 0.8804 0.992 0.000 0.008
#> GSM648601 1 0.0424 0.8804 0.992 0.000 0.008
#> GSM648602 1 0.0424 0.8804 0.992 0.000 0.008
#> GSM648604 1 0.0237 0.8805 0.996 0.000 0.004
#> GSM648614 1 0.5929 0.5459 0.676 0.004 0.320
#> GSM648624 1 0.0424 0.8804 0.992 0.000 0.008
#> GSM648625 1 0.2200 0.8675 0.940 0.056 0.004
#> GSM648629 1 0.0237 0.8805 0.996 0.000 0.004
#> GSM648634 1 0.1182 0.8808 0.976 0.012 0.012
#> GSM648648 1 0.1411 0.8746 0.964 0.036 0.000
#> GSM648651 1 0.0424 0.8804 0.992 0.000 0.008
#> GSM648657 1 0.0000 0.8803 1.000 0.000 0.000
#> GSM648660 1 0.0424 0.8804 0.992 0.000 0.008
#> GSM648697 1 0.0424 0.8802 0.992 0.008 0.000
#> GSM648710 1 0.0237 0.8805 0.996 0.000 0.004
#> GSM648591 1 0.2313 0.8742 0.944 0.032 0.024
#> GSM648592 1 0.1919 0.8781 0.956 0.024 0.020
#> GSM648607 1 0.0424 0.8804 0.992 0.000 0.008
#> GSM648611 1 0.2689 0.8703 0.932 0.032 0.036
#> GSM648612 1 0.1289 0.8765 0.968 0.000 0.032
#> GSM648616 3 0.0424 0.8974 0.000 0.008 0.992
#> GSM648617 1 0.2414 0.8708 0.940 0.020 0.040
#> GSM648626 1 0.0892 0.8795 0.980 0.000 0.020
#> GSM648711 1 0.0424 0.8804 0.992 0.000 0.008
#> GSM648712 1 0.1289 0.8765 0.968 0.000 0.032
#> GSM648713 1 0.1289 0.8765 0.968 0.000 0.032
#> GSM648714 1 0.6209 0.4487 0.628 0.004 0.368
#> GSM648716 1 0.1289 0.8765 0.968 0.000 0.032
#> GSM648717 1 0.5650 0.5660 0.688 0.000 0.312
#> GSM648590 1 0.5247 0.7156 0.768 0.224 0.008
#> GSM648596 1 0.6879 0.4876 0.616 0.360 0.024
#> GSM648642 1 0.6944 0.2125 0.516 0.468 0.016
#> GSM648696 1 0.2173 0.8721 0.944 0.048 0.008
#> GSM648705 1 0.1753 0.8709 0.952 0.048 0.000
#> GSM648718 2 0.7069 -0.1037 0.472 0.508 0.020
#> GSM648599 1 0.1337 0.8805 0.972 0.012 0.016
#> GSM648608 1 0.0237 0.8805 0.996 0.000 0.004
#> GSM648609 1 0.0237 0.8805 0.996 0.000 0.004
#> GSM648610 1 0.1015 0.8807 0.980 0.008 0.012
#> GSM648633 1 0.0237 0.8805 0.996 0.000 0.004
#> GSM648644 2 0.3038 0.7803 0.000 0.896 0.104
#> GSM648652 1 0.1289 0.8756 0.968 0.032 0.000
#> GSM648653 1 0.0424 0.8804 0.992 0.000 0.008
#> GSM648658 1 0.3116 0.8373 0.892 0.108 0.000
#> GSM648659 1 0.5363 0.6648 0.724 0.276 0.000
#> GSM648662 1 0.2301 0.8621 0.936 0.004 0.060
#> GSM648665 1 0.0237 0.8805 0.996 0.004 0.000
#> GSM648666 1 0.0424 0.8804 0.992 0.000 0.008
#> GSM648680 1 0.1411 0.8746 0.964 0.036 0.000
#> GSM648684 1 0.0424 0.8802 0.992 0.008 0.000
#> GSM648709 1 0.6062 0.4490 0.616 0.384 0.000
#> GSM648719 1 0.0424 0.8804 0.992 0.000 0.008
#> GSM648627 1 0.1289 0.8767 0.968 0.000 0.032
#> GSM648637 2 0.5138 0.6940 0.000 0.748 0.252
#> GSM648638 2 0.5138 0.6940 0.000 0.748 0.252
#> GSM648641 3 0.6513 -0.0459 0.476 0.004 0.520
#> GSM648672 2 0.3482 0.7726 0.000 0.872 0.128
#> GSM648674 2 0.5216 0.6896 0.000 0.740 0.260
#> GSM648703 2 0.2945 0.7824 0.004 0.908 0.088
#> GSM648631 3 0.0747 0.9043 0.016 0.000 0.984
#> GSM648669 2 0.4121 0.7531 0.000 0.832 0.168
#> GSM648671 2 0.4121 0.7531 0.000 0.832 0.168
#> GSM648678 2 0.2959 0.7804 0.000 0.900 0.100
#> GSM648679 2 0.4178 0.7495 0.000 0.828 0.172
#> GSM648681 1 0.6955 0.1235 0.496 0.488 0.016
#> GSM648686 3 0.0983 0.9041 0.004 0.016 0.980
#> GSM648689 3 0.1031 0.8967 0.024 0.000 0.976
#> GSM648690 3 0.0983 0.9041 0.004 0.016 0.980
#> GSM648691 3 0.0983 0.9041 0.004 0.016 0.980
#> GSM648693 3 0.0747 0.9043 0.016 0.000 0.984
#> GSM648700 2 0.2945 0.7824 0.004 0.908 0.088
#> GSM648630 3 0.0983 0.9041 0.004 0.016 0.980
#> GSM648632 3 0.0747 0.9043 0.016 0.000 0.984
#> GSM648639 3 0.0424 0.8974 0.000 0.008 0.992
#> GSM648640 3 0.0424 0.8974 0.000 0.008 0.992
#> GSM648668 2 0.5744 0.7351 0.072 0.800 0.128
#> GSM648676 2 0.2945 0.7824 0.004 0.908 0.088
#> GSM648692 3 0.0983 0.9041 0.004 0.016 0.980
#> GSM648694 3 0.0747 0.9043 0.016 0.000 0.984
#> GSM648699 2 0.2945 0.7824 0.004 0.908 0.088
#> GSM648701 2 0.2945 0.7824 0.004 0.908 0.088
#> GSM648673 2 0.4121 0.7531 0.000 0.832 0.168
#> GSM648677 2 0.3038 0.7805 0.000 0.896 0.104
#> GSM648687 3 0.5094 0.7345 0.136 0.040 0.824
#> GSM648688 3 0.5094 0.7345 0.136 0.040 0.824
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM648605 2 0.3858 0.6469 0.056 0.844 0.000 0.100
#> GSM648618 1 0.5476 0.3746 0.584 0.396 0.020 0.000
#> GSM648620 2 0.4022 0.7054 0.096 0.836 0.000 0.068
#> GSM648646 4 0.4594 0.7701 0.000 0.280 0.008 0.712
#> GSM648649 1 0.4661 0.4875 0.652 0.348 0.000 0.000
#> GSM648675 2 0.5605 0.2757 0.408 0.572 0.008 0.012
#> GSM648682 4 0.4579 0.7620 0.004 0.272 0.004 0.720
#> GSM648698 2 0.3858 0.6469 0.056 0.844 0.000 0.100
#> GSM648708 2 0.3471 0.6792 0.060 0.868 0.000 0.072
#> GSM648628 1 0.3598 0.7143 0.848 0.124 0.028 0.000
#> GSM648595 2 0.5155 0.0792 0.468 0.528 0.004 0.000
#> GSM648635 1 0.4477 0.5536 0.688 0.312 0.000 0.000
#> GSM648645 1 0.2011 0.7780 0.920 0.080 0.000 0.000
#> GSM648647 2 0.3828 0.6984 0.084 0.848 0.000 0.068
#> GSM648667 2 0.5359 0.6265 0.288 0.676 0.000 0.036
#> GSM648695 2 0.4959 0.7203 0.196 0.752 0.000 0.052
#> GSM648704 4 0.3052 0.8248 0.000 0.136 0.004 0.860
#> GSM648706 4 0.3355 0.8221 0.000 0.160 0.004 0.836
#> GSM648593 1 0.4877 0.3572 0.592 0.408 0.000 0.000
#> GSM648594 1 0.4483 0.6247 0.712 0.284 0.000 0.004
#> GSM648600 1 0.4837 0.4676 0.648 0.348 0.004 0.000
#> GSM648621 1 0.2796 0.7455 0.892 0.092 0.016 0.000
#> GSM648622 1 0.1302 0.7808 0.956 0.044 0.000 0.000
#> GSM648623 1 0.0895 0.7742 0.976 0.020 0.004 0.000
#> GSM648636 1 0.4250 0.6111 0.724 0.276 0.000 0.000
#> GSM648655 1 0.4898 0.3478 0.584 0.416 0.000 0.000
#> GSM648661 1 0.1902 0.7769 0.932 0.064 0.004 0.000
#> GSM648664 1 0.2704 0.7541 0.876 0.124 0.000 0.000
#> GSM648683 1 0.2704 0.7541 0.876 0.124 0.000 0.000
#> GSM648685 1 0.2704 0.7541 0.876 0.124 0.000 0.000
#> GSM648702 1 0.4222 0.6154 0.728 0.272 0.000 0.000
#> GSM648597 1 0.3881 0.7273 0.812 0.172 0.016 0.000
#> GSM648603 1 0.3047 0.7615 0.872 0.116 0.012 0.000
#> GSM648606 1 0.6439 0.3639 0.576 0.084 0.340 0.000
#> GSM648613 1 0.6439 0.3639 0.576 0.084 0.340 0.000
#> GSM648619 1 0.2124 0.7609 0.932 0.040 0.028 0.000
#> GSM648654 1 0.3831 0.6857 0.792 0.204 0.004 0.000
#> GSM648663 1 0.6238 0.4528 0.632 0.092 0.276 0.000
#> GSM648670 2 0.7933 0.3642 0.244 0.404 0.004 0.348
#> GSM648707 3 0.2411 0.8764 0.000 0.040 0.920 0.040
#> GSM648615 2 0.4220 0.6334 0.056 0.828 0.004 0.112
#> GSM648643 4 0.5971 0.5203 0.032 0.420 0.004 0.544
#> GSM648650 1 0.4994 0.1485 0.520 0.480 0.000 0.000
#> GSM648656 4 0.4594 0.7701 0.000 0.280 0.008 0.712
#> GSM648715 2 0.5359 0.6265 0.288 0.676 0.000 0.036
#> GSM648598 1 0.1302 0.7808 0.956 0.044 0.000 0.000
#> GSM648601 1 0.0921 0.7826 0.972 0.028 0.000 0.000
#> GSM648602 1 0.0895 0.7738 0.976 0.020 0.004 0.000
#> GSM648604 1 0.1489 0.7804 0.952 0.044 0.004 0.000
#> GSM648614 1 0.6329 0.4344 0.616 0.092 0.292 0.000
#> GSM648624 1 0.1302 0.7808 0.956 0.044 0.000 0.000
#> GSM648625 1 0.4699 0.5243 0.676 0.320 0.004 0.000
#> GSM648629 1 0.1489 0.7804 0.952 0.044 0.004 0.000
#> GSM648634 1 0.1722 0.7730 0.944 0.048 0.008 0.000
#> GSM648648 1 0.4477 0.5538 0.688 0.312 0.000 0.000
#> GSM648651 1 0.0921 0.7826 0.972 0.028 0.000 0.000
#> GSM648657 1 0.2868 0.7503 0.864 0.136 0.000 0.000
#> GSM648660 1 0.1302 0.7808 0.956 0.044 0.000 0.000
#> GSM648697 1 0.2704 0.7541 0.876 0.124 0.000 0.000
#> GSM648710 1 0.1489 0.7804 0.952 0.044 0.004 0.000
#> GSM648591 1 0.4035 0.7205 0.804 0.176 0.020 0.000
#> GSM648592 1 0.3852 0.7230 0.808 0.180 0.012 0.000
#> GSM648607 1 0.1209 0.7709 0.964 0.032 0.004 0.000
#> GSM648611 1 0.3435 0.7269 0.864 0.100 0.036 0.000
#> GSM648612 1 0.2399 0.7561 0.920 0.048 0.032 0.000
#> GSM648616 3 0.2500 0.8743 0.000 0.044 0.916 0.040
#> GSM648617 1 0.5435 0.3129 0.564 0.420 0.016 0.000
#> GSM648626 1 0.3047 0.7615 0.872 0.116 0.012 0.000
#> GSM648711 1 0.0895 0.7728 0.976 0.020 0.004 0.000
#> GSM648712 1 0.2399 0.7561 0.920 0.048 0.032 0.000
#> GSM648713 1 0.2124 0.7609 0.932 0.040 0.028 0.000
#> GSM648714 1 0.6439 0.3639 0.576 0.084 0.340 0.000
#> GSM648716 1 0.2124 0.7609 0.932 0.040 0.028 0.000
#> GSM648717 1 0.6172 0.4568 0.632 0.084 0.284 0.000
#> GSM648590 2 0.5660 0.3775 0.400 0.576 0.004 0.020
#> GSM648596 2 0.5567 0.7063 0.164 0.740 0.008 0.088
#> GSM648642 2 0.3471 0.6792 0.060 0.868 0.000 0.072
#> GSM648696 1 0.5016 0.3470 0.600 0.396 0.004 0.000
#> GSM648705 1 0.4585 0.5191 0.668 0.332 0.000 0.000
#> GSM648718 2 0.4220 0.6334 0.056 0.828 0.004 0.112
#> GSM648599 1 0.2101 0.7647 0.928 0.060 0.012 0.000
#> GSM648608 1 0.1489 0.7804 0.952 0.044 0.004 0.000
#> GSM648609 1 0.1489 0.7804 0.952 0.044 0.004 0.000
#> GSM648610 1 0.2101 0.7630 0.928 0.060 0.012 0.000
#> GSM648633 1 0.2589 0.7630 0.884 0.116 0.000 0.000
#> GSM648644 4 0.2999 0.8249 0.000 0.132 0.004 0.864
#> GSM648652 1 0.4477 0.5536 0.688 0.312 0.000 0.000
#> GSM648653 1 0.0895 0.7738 0.976 0.020 0.004 0.000
#> GSM648658 1 0.4877 0.3572 0.592 0.408 0.000 0.000
#> GSM648659 2 0.5816 0.3761 0.392 0.572 0.000 0.036
#> GSM648662 1 0.3088 0.7639 0.888 0.052 0.060 0.000
#> GSM648665 1 0.1902 0.7769 0.932 0.064 0.004 0.000
#> GSM648666 1 0.0921 0.7819 0.972 0.028 0.000 0.000
#> GSM648680 1 0.4477 0.5538 0.688 0.312 0.000 0.000
#> GSM648684 1 0.2704 0.7541 0.876 0.124 0.000 0.000
#> GSM648709 2 0.4669 0.7244 0.168 0.780 0.000 0.052
#> GSM648719 1 0.1302 0.7808 0.956 0.044 0.000 0.000
#> GSM648627 1 0.2032 0.7626 0.936 0.036 0.028 0.000
#> GSM648637 4 0.4617 0.6958 0.000 0.032 0.204 0.764
#> GSM648638 4 0.4617 0.6958 0.000 0.032 0.204 0.764
#> GSM648641 3 0.6542 0.0720 0.428 0.076 0.496 0.000
#> GSM648672 4 0.3697 0.8231 0.000 0.100 0.048 0.852
#> GSM648674 4 0.4098 0.6860 0.000 0.012 0.204 0.784
#> GSM648703 4 0.4406 0.7536 0.000 0.300 0.000 0.700
#> GSM648631 3 0.0336 0.9008 0.008 0.000 0.992 0.000
#> GSM648669 4 0.2256 0.7948 0.000 0.020 0.056 0.924
#> GSM648671 4 0.2256 0.7948 0.000 0.020 0.056 0.924
#> GSM648678 4 0.2469 0.8243 0.000 0.108 0.000 0.892
#> GSM648679 4 0.2222 0.7857 0.000 0.016 0.060 0.924
#> GSM648681 2 0.6351 0.6460 0.160 0.680 0.008 0.152
#> GSM648686 3 0.0779 0.8983 0.000 0.004 0.980 0.016
#> GSM648689 3 0.0592 0.8957 0.016 0.000 0.984 0.000
#> GSM648690 3 0.0779 0.8983 0.000 0.004 0.980 0.016
#> GSM648691 3 0.0779 0.8983 0.000 0.004 0.980 0.016
#> GSM648693 3 0.0336 0.9008 0.008 0.000 0.992 0.000
#> GSM648700 4 0.4406 0.7536 0.000 0.300 0.000 0.700
#> GSM648630 3 0.0779 0.8983 0.000 0.004 0.980 0.016
#> GSM648632 3 0.0336 0.9008 0.008 0.000 0.992 0.000
#> GSM648639 3 0.2500 0.8743 0.000 0.044 0.916 0.040
#> GSM648640 3 0.2500 0.8743 0.000 0.044 0.916 0.040
#> GSM648668 4 0.5325 0.8007 0.012 0.196 0.048 0.744
#> GSM648676 4 0.4406 0.7536 0.000 0.300 0.000 0.700
#> GSM648692 3 0.0779 0.8983 0.000 0.004 0.980 0.016
#> GSM648694 3 0.0336 0.9008 0.008 0.000 0.992 0.000
#> GSM648699 4 0.4406 0.7536 0.000 0.300 0.000 0.700
#> GSM648701 4 0.4406 0.7536 0.000 0.300 0.000 0.700
#> GSM648673 4 0.2256 0.7948 0.000 0.020 0.056 0.924
#> GSM648677 4 0.2773 0.8264 0.000 0.116 0.004 0.880
#> GSM648687 3 0.4786 0.7530 0.128 0.012 0.800 0.060
#> GSM648688 3 0.4786 0.7530 0.128 0.012 0.800 0.060
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM648605 2 0.1485 0.6614 0.020 0.948 0.000 0.032 0.000
#> GSM648618 1 0.6908 0.1156 0.392 0.316 0.004 0.000 0.288
#> GSM648620 2 0.2017 0.7030 0.080 0.912 0.000 0.008 0.000
#> GSM648646 4 0.4236 0.6535 0.000 0.328 0.004 0.664 0.004
#> GSM648649 1 0.4602 0.4362 0.656 0.316 0.000 0.000 0.028
#> GSM648675 2 0.6672 0.3680 0.216 0.496 0.000 0.008 0.280
#> GSM648682 4 0.4857 0.6437 0.000 0.324 0.000 0.636 0.040
#> GSM648698 2 0.1485 0.6614 0.020 0.948 0.000 0.032 0.000
#> GSM648708 2 0.1331 0.6849 0.040 0.952 0.000 0.008 0.000
#> GSM648628 1 0.5322 0.1032 0.580 0.036 0.012 0.000 0.372
#> GSM648595 2 0.6662 0.2281 0.280 0.444 0.000 0.000 0.276
#> GSM648635 1 0.4420 0.4922 0.692 0.280 0.000 0.000 0.028
#> GSM648645 1 0.1282 0.6734 0.952 0.044 0.000 0.000 0.004
#> GSM648647 2 0.1704 0.6996 0.068 0.928 0.000 0.004 0.000
#> GSM648667 2 0.4452 0.5991 0.272 0.696 0.000 0.000 0.032
#> GSM648695 2 0.3196 0.6908 0.192 0.804 0.000 0.000 0.004
#> GSM648704 4 0.3123 0.7284 0.000 0.184 0.004 0.812 0.000
#> GSM648706 4 0.3333 0.7217 0.000 0.208 0.004 0.788 0.000
#> GSM648593 1 0.5329 0.3257 0.596 0.336 0.000 0.000 0.068
#> GSM648594 1 0.4649 0.5677 0.720 0.212 0.000 0.000 0.068
#> GSM648600 1 0.6306 0.2994 0.500 0.328 0.000 0.000 0.172
#> GSM648621 1 0.4538 0.2577 0.636 0.012 0.004 0.000 0.348
#> GSM648622 1 0.0579 0.6701 0.984 0.008 0.000 0.000 0.008
#> GSM648623 1 0.1671 0.6413 0.924 0.000 0.000 0.000 0.076
#> GSM648636 1 0.4333 0.5672 0.740 0.212 0.000 0.000 0.048
#> GSM648655 1 0.5357 0.3112 0.588 0.344 0.000 0.000 0.068
#> GSM648661 1 0.1485 0.6679 0.948 0.032 0.000 0.000 0.020
#> GSM648664 1 0.2011 0.6620 0.908 0.088 0.000 0.000 0.004
#> GSM648683 1 0.2011 0.6620 0.908 0.088 0.000 0.000 0.004
#> GSM648685 1 0.2011 0.6620 0.908 0.088 0.000 0.000 0.004
#> GSM648702 1 0.4302 0.5711 0.744 0.208 0.000 0.000 0.048
#> GSM648597 1 0.5408 0.5127 0.668 0.116 0.004 0.000 0.212
#> GSM648603 1 0.4973 0.5547 0.712 0.092 0.004 0.000 0.192
#> GSM648606 5 0.6729 0.7749 0.304 0.004 0.236 0.000 0.456
#> GSM648613 5 0.6729 0.7749 0.304 0.004 0.236 0.000 0.456
#> GSM648619 1 0.4220 0.3571 0.688 0.004 0.008 0.000 0.300
#> GSM648654 1 0.3476 0.5856 0.804 0.176 0.000 0.000 0.020
#> GSM648663 5 0.6642 0.6864 0.372 0.008 0.172 0.000 0.448
#> GSM648670 5 0.7868 -0.2703 0.068 0.320 0.000 0.264 0.348
#> GSM648707 3 0.4118 0.7915 0.000 0.008 0.772 0.032 0.188
#> GSM648615 2 0.2120 0.6486 0.020 0.924 0.004 0.048 0.004
#> GSM648643 2 0.4559 -0.4013 0.008 0.512 0.000 0.480 0.000
#> GSM648650 1 0.4894 0.1249 0.520 0.456 0.000 0.000 0.024
#> GSM648656 4 0.4236 0.6535 0.000 0.328 0.004 0.664 0.004
#> GSM648715 2 0.4452 0.5991 0.272 0.696 0.000 0.000 0.032
#> GSM648598 1 0.0579 0.6701 0.984 0.008 0.000 0.000 0.008
#> GSM648601 1 0.1300 0.6644 0.956 0.016 0.000 0.000 0.028
#> GSM648602 1 0.2890 0.5716 0.836 0.004 0.000 0.000 0.160
#> GSM648604 1 0.1012 0.6664 0.968 0.012 0.000 0.000 0.020
#> GSM648614 5 0.6699 0.7387 0.336 0.008 0.192 0.000 0.464
#> GSM648624 1 0.0579 0.6701 0.984 0.008 0.000 0.000 0.008
#> GSM648625 1 0.4988 0.5164 0.656 0.284 0.000 0.000 0.060
#> GSM648629 1 0.1012 0.6664 0.968 0.012 0.000 0.000 0.020
#> GSM648634 1 0.3596 0.5343 0.784 0.016 0.000 0.000 0.200
#> GSM648648 1 0.4420 0.4928 0.692 0.280 0.000 0.000 0.028
#> GSM648651 1 0.1300 0.6644 0.956 0.016 0.000 0.000 0.028
#> GSM648657 1 0.2470 0.6621 0.884 0.104 0.000 0.000 0.012
#> GSM648660 1 0.0579 0.6701 0.984 0.008 0.000 0.000 0.008
#> GSM648697 1 0.2011 0.6620 0.908 0.088 0.000 0.000 0.004
#> GSM648710 1 0.1012 0.6664 0.968 0.012 0.000 0.000 0.020
#> GSM648591 1 0.5772 0.3581 0.592 0.104 0.004 0.000 0.300
#> GSM648592 1 0.5476 0.5101 0.664 0.128 0.004 0.000 0.204
#> GSM648607 1 0.3143 0.5338 0.796 0.000 0.000 0.000 0.204
#> GSM648611 1 0.4965 0.1080 0.588 0.016 0.012 0.000 0.384
#> GSM648612 1 0.4434 0.2400 0.640 0.004 0.008 0.000 0.348
#> GSM648616 3 0.4153 0.7903 0.000 0.008 0.768 0.032 0.192
#> GSM648617 1 0.6792 0.0193 0.372 0.340 0.000 0.000 0.288
#> GSM648626 1 0.4973 0.5547 0.712 0.092 0.004 0.000 0.192
#> GSM648711 1 0.2813 0.5663 0.832 0.000 0.000 0.000 0.168
#> GSM648712 1 0.4434 0.2400 0.640 0.004 0.008 0.000 0.348
#> GSM648713 1 0.4111 0.3993 0.708 0.004 0.008 0.000 0.280
#> GSM648714 5 0.6729 0.7749 0.304 0.004 0.236 0.000 0.456
#> GSM648716 1 0.4353 0.2920 0.660 0.004 0.008 0.000 0.328
#> GSM648717 5 0.6662 0.7211 0.340 0.008 0.184 0.000 0.468
#> GSM648590 2 0.6460 0.4400 0.284 0.548 0.000 0.016 0.152
#> GSM648596 2 0.5748 0.6573 0.092 0.704 0.004 0.052 0.148
#> GSM648642 2 0.1331 0.6849 0.040 0.952 0.000 0.008 0.000
#> GSM648696 1 0.6282 0.1811 0.476 0.368 0.000 0.000 0.156
#> GSM648705 1 0.4445 0.4714 0.676 0.300 0.000 0.000 0.024
#> GSM648718 2 0.2120 0.6486 0.020 0.924 0.004 0.048 0.004
#> GSM648599 1 0.4142 0.4543 0.728 0.016 0.004 0.000 0.252
#> GSM648608 1 0.1012 0.6664 0.968 0.012 0.000 0.000 0.020
#> GSM648609 1 0.1012 0.6664 0.968 0.012 0.000 0.000 0.020
#> GSM648610 1 0.4015 0.4382 0.724 0.008 0.004 0.000 0.264
#> GSM648633 1 0.2889 0.6637 0.872 0.084 0.000 0.000 0.044
#> GSM648644 4 0.3086 0.7291 0.000 0.180 0.004 0.816 0.000
#> GSM648652 1 0.4420 0.4922 0.692 0.280 0.000 0.000 0.028
#> GSM648653 1 0.2890 0.5716 0.836 0.004 0.000 0.000 0.160
#> GSM648658 1 0.5329 0.3257 0.596 0.336 0.000 0.000 0.068
#> GSM648659 2 0.6142 0.3370 0.380 0.512 0.000 0.012 0.096
#> GSM648662 1 0.4368 0.5917 0.796 0.032 0.056 0.000 0.116
#> GSM648665 1 0.1485 0.6679 0.948 0.032 0.000 0.000 0.020
#> GSM648666 1 0.1211 0.6657 0.960 0.016 0.000 0.000 0.024
#> GSM648680 1 0.4420 0.4928 0.692 0.280 0.000 0.000 0.028
#> GSM648684 1 0.2011 0.6620 0.908 0.088 0.000 0.000 0.004
#> GSM648709 2 0.3123 0.7047 0.160 0.828 0.000 0.000 0.012
#> GSM648719 1 0.0579 0.6701 0.984 0.008 0.000 0.000 0.008
#> GSM648627 1 0.4194 0.3935 0.708 0.004 0.012 0.000 0.276
#> GSM648637 4 0.5596 0.5882 0.000 0.016 0.160 0.680 0.144
#> GSM648638 4 0.5596 0.5882 0.000 0.016 0.160 0.680 0.144
#> GSM648641 5 0.6437 0.4483 0.156 0.004 0.376 0.000 0.464
#> GSM648672 4 0.3688 0.7363 0.000 0.124 0.028 0.828 0.020
#> GSM648674 4 0.5494 0.5518 0.000 0.004 0.172 0.668 0.156
#> GSM648703 4 0.6562 0.5600 0.000 0.264 0.004 0.504 0.228
#> GSM648631 3 0.0404 0.8953 0.000 0.000 0.988 0.000 0.012
#> GSM648669 4 0.3563 0.6794 0.000 0.008 0.028 0.824 0.140
#> GSM648671 4 0.3563 0.6794 0.000 0.008 0.028 0.824 0.140
#> GSM648678 4 0.2719 0.7350 0.000 0.144 0.004 0.852 0.000
#> GSM648679 4 0.3730 0.6705 0.000 0.004 0.036 0.808 0.152
#> GSM648681 2 0.6349 0.6036 0.120 0.660 0.004 0.076 0.140
#> GSM648686 3 0.0798 0.8941 0.000 0.000 0.976 0.016 0.008
#> GSM648689 3 0.0794 0.8902 0.000 0.000 0.972 0.000 0.028
#> GSM648690 3 0.0798 0.8941 0.000 0.000 0.976 0.016 0.008
#> GSM648691 3 0.0510 0.8947 0.000 0.000 0.984 0.016 0.000
#> GSM648693 3 0.0404 0.8953 0.000 0.000 0.988 0.000 0.012
#> GSM648700 4 0.6562 0.5600 0.000 0.264 0.004 0.504 0.228
#> GSM648630 3 0.0510 0.8947 0.000 0.000 0.984 0.016 0.000
#> GSM648632 3 0.0404 0.8953 0.000 0.000 0.988 0.000 0.012
#> GSM648639 3 0.4153 0.7903 0.000 0.008 0.768 0.032 0.192
#> GSM648640 3 0.4153 0.7903 0.000 0.008 0.768 0.032 0.192
#> GSM648668 4 0.4944 0.7084 0.000 0.204 0.028 0.724 0.044
#> GSM648676 4 0.6562 0.5600 0.000 0.264 0.004 0.504 0.228
#> GSM648692 3 0.0510 0.8947 0.000 0.000 0.984 0.016 0.000
#> GSM648694 3 0.0404 0.8953 0.000 0.000 0.988 0.000 0.012
#> GSM648699 4 0.6562 0.5600 0.000 0.264 0.004 0.504 0.228
#> GSM648701 4 0.6562 0.5600 0.000 0.264 0.004 0.504 0.228
#> GSM648673 4 0.3563 0.6794 0.000 0.008 0.028 0.824 0.140
#> GSM648677 4 0.3239 0.7358 0.000 0.156 0.004 0.828 0.012
#> GSM648687 3 0.3937 0.6872 0.132 0.000 0.804 0.060 0.004
#> GSM648688 3 0.3937 0.6872 0.132 0.000 0.804 0.060 0.004
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM648605 2 0.1124 0.6195 0.008 0.956 0.000 0.036 0.000 0.000
#> GSM648618 5 0.6861 -0.0331 0.324 0.296 0.000 0.000 0.336 0.044
#> GSM648620 2 0.1524 0.6545 0.060 0.932 0.000 0.008 0.000 0.000
#> GSM648646 4 0.4042 0.5299 0.000 0.316 0.004 0.664 0.000 0.016
#> GSM648649 1 0.4569 0.4409 0.636 0.320 0.000 0.000 0.028 0.016
#> GSM648675 2 0.6692 0.3785 0.160 0.480 0.000 0.008 0.300 0.052
#> GSM648682 4 0.4508 0.5318 0.000 0.316 0.000 0.632 0.000 0.052
#> GSM648698 2 0.1124 0.6195 0.008 0.956 0.000 0.036 0.000 0.000
#> GSM648708 2 0.0806 0.6367 0.020 0.972 0.000 0.008 0.000 0.000
#> GSM648628 5 0.4632 0.3636 0.436 0.016 0.000 0.000 0.532 0.016
#> GSM648595 2 0.6841 0.2624 0.232 0.432 0.000 0.000 0.276 0.060
#> GSM648635 1 0.4383 0.5005 0.680 0.276 0.000 0.000 0.024 0.020
#> GSM648645 1 0.1461 0.6399 0.940 0.044 0.000 0.000 0.016 0.000
#> GSM648647 2 0.1285 0.6520 0.052 0.944 0.000 0.004 0.000 0.000
#> GSM648667 2 0.4113 0.5718 0.244 0.712 0.000 0.000 0.040 0.004
#> GSM648695 2 0.2738 0.6490 0.176 0.820 0.000 0.000 0.004 0.000
#> GSM648704 4 0.2879 0.5895 0.000 0.176 0.004 0.816 0.000 0.004
#> GSM648706 4 0.3074 0.5799 0.000 0.200 0.004 0.792 0.000 0.004
#> GSM648593 1 0.5367 0.3577 0.584 0.320 0.000 0.000 0.028 0.068
#> GSM648594 1 0.4683 0.5295 0.704 0.204 0.000 0.000 0.072 0.020
#> GSM648600 1 0.6193 0.1982 0.456 0.332 0.000 0.000 0.196 0.016
#> GSM648621 1 0.4185 -0.2997 0.496 0.000 0.000 0.000 0.492 0.012
#> GSM648622 1 0.0806 0.6320 0.972 0.008 0.000 0.000 0.020 0.000
#> GSM648623 1 0.1765 0.5870 0.904 0.000 0.000 0.000 0.096 0.000
#> GSM648636 1 0.4179 0.5754 0.736 0.204 0.000 0.000 0.012 0.048
#> GSM648655 1 0.5393 0.3436 0.576 0.328 0.000 0.000 0.028 0.068
#> GSM648661 1 0.1565 0.6288 0.940 0.028 0.000 0.000 0.028 0.004
#> GSM648664 1 0.2009 0.6291 0.904 0.084 0.000 0.000 0.008 0.004
#> GSM648683 1 0.2009 0.6291 0.904 0.084 0.000 0.000 0.008 0.004
#> GSM648685 1 0.2009 0.6291 0.904 0.084 0.000 0.000 0.008 0.004
#> GSM648702 1 0.4117 0.5771 0.740 0.204 0.000 0.000 0.012 0.044
#> GSM648597 1 0.5598 0.2979 0.576 0.104 0.000 0.000 0.296 0.024
#> GSM648603 1 0.5327 0.3453 0.616 0.096 0.000 0.000 0.268 0.020
#> GSM648606 5 0.4200 0.6795 0.164 0.000 0.088 0.000 0.744 0.004
#> GSM648613 5 0.4200 0.6795 0.164 0.000 0.088 0.000 0.744 0.004
#> GSM648619 1 0.3847 -0.1785 0.544 0.000 0.000 0.000 0.456 0.000
#> GSM648654 1 0.3385 0.5317 0.796 0.172 0.000 0.000 0.028 0.004
#> GSM648663 5 0.4393 0.6631 0.228 0.004 0.056 0.000 0.708 0.004
#> GSM648670 2 0.8081 0.0911 0.028 0.300 0.000 0.252 0.268 0.152
#> GSM648707 3 0.6903 0.4390 0.000 0.016 0.392 0.028 0.336 0.228
#> GSM648615 2 0.1901 0.6065 0.008 0.924 0.004 0.052 0.000 0.012
#> GSM648643 2 0.4127 -0.3771 0.004 0.508 0.000 0.484 0.000 0.004
#> GSM648650 1 0.4797 0.1327 0.500 0.460 0.000 0.000 0.024 0.016
#> GSM648656 4 0.4042 0.5299 0.000 0.316 0.004 0.664 0.000 0.016
#> GSM648715 2 0.4113 0.5718 0.244 0.712 0.000 0.000 0.040 0.004
#> GSM648598 1 0.0806 0.6320 0.972 0.008 0.000 0.000 0.020 0.000
#> GSM648601 1 0.1461 0.6268 0.940 0.016 0.000 0.000 0.044 0.000
#> GSM648602 1 0.3330 0.3510 0.716 0.000 0.000 0.000 0.284 0.000
#> GSM648604 1 0.0972 0.6256 0.964 0.008 0.000 0.000 0.028 0.000
#> GSM648614 5 0.4226 0.6796 0.188 0.004 0.064 0.000 0.740 0.004
#> GSM648624 1 0.0806 0.6320 0.972 0.008 0.000 0.000 0.020 0.000
#> GSM648625 1 0.4814 0.4593 0.616 0.304 0.000 0.000 0.080 0.000
#> GSM648629 1 0.0972 0.6256 0.964 0.008 0.000 0.000 0.028 0.000
#> GSM648634 1 0.3805 0.2631 0.664 0.004 0.000 0.000 0.328 0.004
#> GSM648648 1 0.4383 0.5018 0.680 0.276 0.000 0.000 0.024 0.020
#> GSM648651 1 0.1461 0.6268 0.940 0.016 0.000 0.000 0.044 0.000
#> GSM648657 1 0.2405 0.6337 0.880 0.100 0.000 0.000 0.016 0.004
#> GSM648660 1 0.0806 0.6320 0.972 0.008 0.000 0.000 0.020 0.000
#> GSM648697 1 0.2009 0.6291 0.904 0.084 0.000 0.000 0.008 0.004
#> GSM648710 1 0.0972 0.6256 0.964 0.008 0.000 0.000 0.028 0.000
#> GSM648591 1 0.5858 0.0536 0.500 0.096 0.000 0.000 0.372 0.032
#> GSM648592 1 0.5732 0.3027 0.572 0.116 0.000 0.000 0.284 0.028
#> GSM648607 1 0.3531 0.2625 0.672 0.000 0.000 0.000 0.328 0.000
#> GSM648611 5 0.4157 0.3725 0.444 0.000 0.000 0.000 0.544 0.012
#> GSM648612 5 0.3868 0.2726 0.496 0.000 0.000 0.000 0.504 0.000
#> GSM648616 3 0.6903 0.4396 0.000 0.016 0.392 0.028 0.336 0.228
#> GSM648617 2 0.6481 -0.0468 0.328 0.352 0.000 0.000 0.304 0.016
#> GSM648626 1 0.5327 0.3453 0.616 0.096 0.000 0.000 0.268 0.020
#> GSM648711 1 0.3330 0.3494 0.716 0.000 0.000 0.000 0.284 0.000
#> GSM648712 5 0.3868 0.2726 0.496 0.000 0.000 0.000 0.504 0.000
#> GSM648713 1 0.3823 -0.0979 0.564 0.000 0.000 0.000 0.436 0.000
#> GSM648714 5 0.4200 0.6795 0.164 0.000 0.088 0.000 0.744 0.004
#> GSM648716 1 0.3866 -0.2776 0.516 0.000 0.000 0.000 0.484 0.000
#> GSM648717 5 0.4256 0.6745 0.192 0.004 0.064 0.000 0.736 0.004
#> GSM648590 2 0.6549 0.4506 0.248 0.536 0.000 0.016 0.156 0.044
#> GSM648596 2 0.5716 0.6070 0.060 0.692 0.004 0.052 0.144 0.048
#> GSM648642 2 0.0806 0.6367 0.020 0.972 0.000 0.008 0.000 0.000
#> GSM648696 1 0.6199 0.1070 0.436 0.372 0.000 0.000 0.172 0.020
#> GSM648705 1 0.4433 0.4747 0.656 0.304 0.000 0.000 0.024 0.016
#> GSM648718 2 0.1901 0.6065 0.008 0.924 0.004 0.052 0.000 0.012
#> GSM648599 1 0.4006 0.0647 0.600 0.004 0.000 0.000 0.392 0.004
#> GSM648608 1 0.0972 0.6256 0.964 0.008 0.000 0.000 0.028 0.000
#> GSM648609 1 0.0972 0.6256 0.964 0.008 0.000 0.000 0.028 0.000
#> GSM648610 1 0.4002 0.0110 0.588 0.000 0.000 0.000 0.404 0.008
#> GSM648633 1 0.2876 0.6215 0.860 0.080 0.000 0.000 0.056 0.004
#> GSM648644 4 0.2845 0.5890 0.000 0.172 0.004 0.820 0.000 0.004
#> GSM648652 1 0.4383 0.5005 0.680 0.276 0.000 0.000 0.024 0.020
#> GSM648653 1 0.3330 0.3510 0.716 0.000 0.000 0.000 0.284 0.000
#> GSM648658 1 0.5367 0.3577 0.584 0.320 0.000 0.000 0.028 0.068
#> GSM648659 2 0.6300 0.2606 0.364 0.476 0.000 0.012 0.028 0.120
#> GSM648662 1 0.4963 0.4233 0.672 0.032 0.048 0.000 0.244 0.004
#> GSM648665 1 0.1565 0.6288 0.940 0.028 0.000 0.000 0.028 0.004
#> GSM648666 1 0.1536 0.6309 0.940 0.016 0.000 0.000 0.040 0.004
#> GSM648680 1 0.4383 0.5018 0.680 0.276 0.000 0.000 0.024 0.020
#> GSM648684 1 0.2009 0.6291 0.904 0.084 0.000 0.000 0.008 0.004
#> GSM648709 2 0.2704 0.6593 0.140 0.844 0.000 0.000 0.016 0.000
#> GSM648719 1 0.0806 0.6320 0.972 0.008 0.000 0.000 0.020 0.000
#> GSM648627 1 0.3823 -0.1108 0.564 0.000 0.000 0.000 0.436 0.000
#> GSM648637 4 0.5387 0.5567 0.000 0.016 0.076 0.656 0.024 0.228
#> GSM648638 4 0.5387 0.5567 0.000 0.016 0.076 0.656 0.024 0.228
#> GSM648641 5 0.4256 0.3518 0.032 0.000 0.196 0.000 0.740 0.032
#> GSM648672 4 0.3150 0.6227 0.000 0.120 0.000 0.828 0.000 0.052
#> GSM648674 4 0.5129 0.5209 0.000 0.000 0.080 0.624 0.016 0.280
#> GSM648703 6 0.4964 1.0000 0.000 0.072 0.000 0.388 0.000 0.540
#> GSM648631 3 0.0363 0.8021 0.000 0.000 0.988 0.000 0.012 0.000
#> GSM648669 4 0.2969 0.5930 0.000 0.000 0.000 0.776 0.000 0.224
#> GSM648671 4 0.2941 0.5946 0.000 0.000 0.000 0.780 0.000 0.220
#> GSM648678 4 0.2362 0.5963 0.000 0.136 0.004 0.860 0.000 0.000
#> GSM648679 4 0.3050 0.5922 0.000 0.000 0.000 0.764 0.000 0.236
#> GSM648681 2 0.6417 0.5534 0.096 0.644 0.004 0.056 0.084 0.116
#> GSM648686 3 0.1262 0.7945 0.000 0.000 0.956 0.016 0.020 0.008
#> GSM648689 3 0.1196 0.7919 0.000 0.000 0.952 0.000 0.040 0.008
#> GSM648690 3 0.1262 0.7945 0.000 0.000 0.956 0.016 0.020 0.008
#> GSM648691 3 0.0458 0.7998 0.000 0.000 0.984 0.016 0.000 0.000
#> GSM648693 3 0.0363 0.8021 0.000 0.000 0.988 0.000 0.012 0.000
#> GSM648700 6 0.4964 1.0000 0.000 0.072 0.000 0.388 0.000 0.540
#> GSM648630 3 0.0458 0.7998 0.000 0.000 0.984 0.016 0.000 0.000
#> GSM648632 3 0.0363 0.8021 0.000 0.000 0.988 0.000 0.012 0.000
#> GSM648639 3 0.6903 0.4396 0.000 0.016 0.392 0.028 0.336 0.228
#> GSM648640 3 0.6903 0.4396 0.000 0.016 0.392 0.028 0.336 0.228
#> GSM648668 4 0.4680 0.5531 0.000 0.200 0.000 0.700 0.012 0.088
#> GSM648676 6 0.4964 1.0000 0.000 0.072 0.000 0.388 0.000 0.540
#> GSM648692 3 0.0458 0.7998 0.000 0.000 0.984 0.016 0.000 0.000
#> GSM648694 3 0.0363 0.8021 0.000 0.000 0.988 0.000 0.012 0.000
#> GSM648699 6 0.4964 1.0000 0.000 0.072 0.000 0.388 0.000 0.540
#> GSM648701 6 0.4964 1.0000 0.000 0.072 0.000 0.388 0.000 0.540
#> GSM648673 4 0.2941 0.5946 0.000 0.000 0.000 0.780 0.000 0.220
#> GSM648677 4 0.3332 0.5586 0.000 0.144 0.000 0.808 0.000 0.048
#> GSM648687 3 0.3871 0.6623 0.128 0.000 0.800 0.032 0.004 0.036
#> GSM648688 3 0.3871 0.6623 0.128 0.000 0.800 0.032 0.004 0.036
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
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) development.stage(p) other(p) k
#> MAD:hclust 115 2.78e-08 0.01265 5.48e-09 2
#> MAD:hclust 113 6.82e-18 0.00946 4.92e-17 3
#> MAD:hclust 109 1.66e-15 0.00333 2.11e-24 4
#> MAD:hclust 97 3.10e-13 0.00817 5.49e-26 5
#> MAD:hclust 86 1.44e-12 0.02909 1.46e-28 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 51941 rows and 130 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 0.635 0.812 0.920 0.4682 0.527 0.527
#> 3 3 0.620 0.785 0.859 0.3242 0.792 0.629
#> 4 4 0.590 0.627 0.766 0.1512 0.808 0.554
#> 5 5 0.672 0.767 0.826 0.0816 0.816 0.458
#> 6 6 0.701 0.678 0.804 0.0475 0.995 0.979
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
#> GSM648605 2 0.6712 0.717 0.176 0.824
#> GSM648618 1 0.0000 0.931 1.000 0.000
#> GSM648620 1 0.8661 0.585 0.712 0.288
#> GSM648646 2 0.0000 0.862 0.000 1.000
#> GSM648649 1 0.6343 0.766 0.840 0.160
#> GSM648675 2 0.0672 0.858 0.008 0.992
#> GSM648682 2 0.0000 0.862 0.000 1.000
#> GSM648698 2 0.0000 0.862 0.000 1.000
#> GSM648708 1 0.8661 0.585 0.712 0.288
#> GSM648628 1 0.0000 0.931 1.000 0.000
#> GSM648595 1 0.8661 0.585 0.712 0.288
#> GSM648635 1 0.0000 0.931 1.000 0.000
#> GSM648645 1 0.0000 0.931 1.000 0.000
#> GSM648647 2 0.9933 0.128 0.452 0.548
#> GSM648667 1 0.8661 0.585 0.712 0.288
#> GSM648695 1 0.9710 0.351 0.600 0.400
#> GSM648704 2 0.0000 0.862 0.000 1.000
#> GSM648706 2 0.0000 0.862 0.000 1.000
#> GSM648593 1 0.0000 0.931 1.000 0.000
#> GSM648594 1 0.0000 0.931 1.000 0.000
#> GSM648600 1 0.0000 0.931 1.000 0.000
#> GSM648621 1 0.0000 0.931 1.000 0.000
#> GSM648622 1 0.0000 0.931 1.000 0.000
#> GSM648623 1 0.0000 0.931 1.000 0.000
#> GSM648636 1 0.0000 0.931 1.000 0.000
#> GSM648655 1 0.0000 0.931 1.000 0.000
#> GSM648661 1 0.0000 0.931 1.000 0.000
#> GSM648664 1 0.0000 0.931 1.000 0.000
#> GSM648683 1 0.0000 0.931 1.000 0.000
#> GSM648685 1 0.0000 0.931 1.000 0.000
#> GSM648702 1 0.0000 0.931 1.000 0.000
#> GSM648597 1 0.0000 0.931 1.000 0.000
#> GSM648603 1 0.0000 0.931 1.000 0.000
#> GSM648606 1 0.0000 0.931 1.000 0.000
#> GSM648613 1 0.0000 0.931 1.000 0.000
#> GSM648619 1 0.0000 0.931 1.000 0.000
#> GSM648654 1 0.0000 0.931 1.000 0.000
#> GSM648663 1 0.0000 0.931 1.000 0.000
#> GSM648670 2 0.0000 0.862 0.000 1.000
#> GSM648707 2 0.9732 0.453 0.404 0.596
#> GSM648615 2 0.0000 0.862 0.000 1.000
#> GSM648643 2 0.0000 0.862 0.000 1.000
#> GSM648650 1 0.8661 0.585 0.712 0.288
#> GSM648656 2 0.0000 0.862 0.000 1.000
#> GSM648715 1 0.9732 0.341 0.596 0.404
#> GSM648598 1 0.0000 0.931 1.000 0.000
#> GSM648601 1 0.0000 0.931 1.000 0.000
#> GSM648602 1 0.0000 0.931 1.000 0.000
#> GSM648604 1 0.0000 0.931 1.000 0.000
#> GSM648614 1 0.0000 0.931 1.000 0.000
#> GSM648624 1 0.0000 0.931 1.000 0.000
#> GSM648625 1 0.0000 0.931 1.000 0.000
#> GSM648629 1 0.0000 0.931 1.000 0.000
#> GSM648634 1 0.0000 0.931 1.000 0.000
#> GSM648648 1 0.0000 0.931 1.000 0.000
#> GSM648651 1 0.0000 0.931 1.000 0.000
#> GSM648657 1 0.0000 0.931 1.000 0.000
#> GSM648660 1 0.0000 0.931 1.000 0.000
#> GSM648697 1 0.0000 0.931 1.000 0.000
#> GSM648710 1 0.0000 0.931 1.000 0.000
#> GSM648591 1 0.0000 0.931 1.000 0.000
#> GSM648592 1 0.0000 0.931 1.000 0.000
#> GSM648607 1 0.0000 0.931 1.000 0.000
#> GSM648611 1 0.0000 0.931 1.000 0.000
#> GSM648612 1 0.0000 0.931 1.000 0.000
#> GSM648616 2 0.0000 0.862 0.000 1.000
#> GSM648617 1 0.0000 0.931 1.000 0.000
#> GSM648626 1 0.0000 0.931 1.000 0.000
#> GSM648711 1 0.0000 0.931 1.000 0.000
#> GSM648712 1 0.0000 0.931 1.000 0.000
#> GSM648713 1 0.0000 0.931 1.000 0.000
#> GSM648714 2 0.9993 0.142 0.484 0.516
#> GSM648716 1 0.0000 0.931 1.000 0.000
#> GSM648717 1 0.0000 0.931 1.000 0.000
#> GSM648590 1 0.9775 0.319 0.588 0.412
#> GSM648596 2 0.7219 0.687 0.200 0.800
#> GSM648642 2 0.9933 0.128 0.452 0.548
#> GSM648696 1 0.7376 0.705 0.792 0.208
#> GSM648705 1 0.6343 0.766 0.840 0.160
#> GSM648718 2 0.1414 0.851 0.020 0.980
#> GSM648599 1 0.0000 0.931 1.000 0.000
#> GSM648608 1 0.0000 0.931 1.000 0.000
#> GSM648609 1 0.0000 0.931 1.000 0.000
#> GSM648610 1 0.0000 0.931 1.000 0.000
#> GSM648633 1 0.0000 0.931 1.000 0.000
#> GSM648644 2 0.0000 0.862 0.000 1.000
#> GSM648652 1 0.0000 0.931 1.000 0.000
#> GSM648653 1 0.0000 0.931 1.000 0.000
#> GSM648658 1 0.0000 0.931 1.000 0.000
#> GSM648659 1 0.9732 0.341 0.596 0.404
#> GSM648662 1 0.0000 0.931 1.000 0.000
#> GSM648665 1 0.0000 0.931 1.000 0.000
#> GSM648666 1 0.0000 0.931 1.000 0.000
#> GSM648680 1 0.0000 0.931 1.000 0.000
#> GSM648684 1 0.0000 0.931 1.000 0.000
#> GSM648709 1 0.9732 0.341 0.596 0.404
#> GSM648719 1 0.0000 0.931 1.000 0.000
#> GSM648627 1 0.0000 0.931 1.000 0.000
#> GSM648637 2 0.0000 0.862 0.000 1.000
#> GSM648638 2 0.0000 0.862 0.000 1.000
#> GSM648641 2 0.9732 0.453 0.404 0.596
#> GSM648672 2 0.0000 0.862 0.000 1.000
#> GSM648674 2 0.0000 0.862 0.000 1.000
#> GSM648703 2 0.0000 0.862 0.000 1.000
#> GSM648631 1 0.8386 0.535 0.732 0.268
#> GSM648669 2 0.0000 0.862 0.000 1.000
#> GSM648671 2 0.0000 0.862 0.000 1.000
#> GSM648678 2 0.0000 0.862 0.000 1.000
#> GSM648679 2 0.0000 0.862 0.000 1.000
#> GSM648681 2 0.4161 0.808 0.084 0.916
#> GSM648686 2 0.6343 0.767 0.160 0.840
#> GSM648689 2 0.9129 0.585 0.328 0.672
#> GSM648690 2 0.6343 0.767 0.160 0.840
#> GSM648691 2 0.9087 0.591 0.324 0.676
#> GSM648693 2 0.9732 0.453 0.404 0.596
#> GSM648700 2 0.0000 0.862 0.000 1.000
#> GSM648630 2 0.8909 0.612 0.308 0.692
#> GSM648632 1 0.8386 0.535 0.732 0.268
#> GSM648639 2 0.0000 0.862 0.000 1.000
#> GSM648640 2 0.6438 0.764 0.164 0.836
#> GSM648668 2 0.0000 0.862 0.000 1.000
#> GSM648676 2 0.0000 0.862 0.000 1.000
#> GSM648692 2 0.7219 0.732 0.200 0.800
#> GSM648694 2 0.9087 0.591 0.324 0.676
#> GSM648699 2 0.0000 0.862 0.000 1.000
#> GSM648701 2 0.0000 0.862 0.000 1.000
#> GSM648673 2 0.0000 0.862 0.000 1.000
#> GSM648677 2 0.0000 0.862 0.000 1.000
#> GSM648687 2 0.9661 0.477 0.392 0.608
#> GSM648688 2 0.9795 0.427 0.416 0.584
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM648605 2 0.3765 0.732 0.084 0.888 0.028
#> GSM648618 1 0.5731 0.793 0.804 0.088 0.108
#> GSM648620 1 0.6295 0.317 0.528 0.472 0.000
#> GSM648646 2 0.3879 0.805 0.000 0.848 0.152
#> GSM648649 1 0.4062 0.815 0.836 0.164 0.000
#> GSM648675 2 0.1860 0.746 0.000 0.948 0.052
#> GSM648682 2 0.3941 0.805 0.000 0.844 0.156
#> GSM648698 2 0.0829 0.770 0.004 0.984 0.012
#> GSM648708 1 0.6299 0.306 0.524 0.476 0.000
#> GSM648628 3 0.5560 0.641 0.300 0.000 0.700
#> GSM648595 1 0.6079 0.466 0.612 0.388 0.000
#> GSM648635 1 0.3879 0.824 0.848 0.152 0.000
#> GSM648645 1 0.0747 0.884 0.984 0.016 0.000
#> GSM648647 2 0.3619 0.671 0.136 0.864 0.000
#> GSM648667 1 0.6192 0.425 0.580 0.420 0.000
#> GSM648695 2 0.5882 0.301 0.348 0.652 0.000
#> GSM648704 2 0.4842 0.805 0.000 0.776 0.224
#> GSM648706 2 0.4750 0.807 0.000 0.784 0.216
#> GSM648593 1 0.3879 0.824 0.848 0.152 0.000
#> GSM648594 1 0.3879 0.824 0.848 0.152 0.000
#> GSM648600 1 0.2261 0.860 0.932 0.000 0.068
#> GSM648621 1 0.2261 0.860 0.932 0.000 0.068
#> GSM648622 1 0.0000 0.886 1.000 0.000 0.000
#> GSM648623 1 0.2261 0.860 0.932 0.000 0.068
#> GSM648636 1 0.3941 0.821 0.844 0.156 0.000
#> GSM648655 1 0.3879 0.824 0.848 0.152 0.000
#> GSM648661 1 0.1163 0.882 0.972 0.028 0.000
#> GSM648664 1 0.1163 0.882 0.972 0.028 0.000
#> GSM648683 1 0.1163 0.882 0.972 0.028 0.000
#> GSM648685 1 0.3192 0.846 0.888 0.112 0.000
#> GSM648702 1 0.3879 0.824 0.848 0.152 0.000
#> GSM648597 1 0.2261 0.860 0.932 0.000 0.068
#> GSM648603 1 0.2261 0.860 0.932 0.000 0.068
#> GSM648606 3 0.6437 0.710 0.220 0.048 0.732
#> GSM648613 3 0.6437 0.710 0.220 0.048 0.732
#> GSM648619 1 0.2261 0.860 0.932 0.000 0.068
#> GSM648654 1 0.2356 0.868 0.928 0.072 0.000
#> GSM648663 3 0.7306 0.526 0.340 0.044 0.616
#> GSM648670 2 0.4974 0.791 0.000 0.764 0.236
#> GSM648707 3 0.0592 0.805 0.012 0.000 0.988
#> GSM648615 2 0.4235 0.794 0.000 0.824 0.176
#> GSM648643 2 0.0829 0.770 0.004 0.984 0.012
#> GSM648650 1 0.6215 0.434 0.572 0.428 0.000
#> GSM648656 2 0.4605 0.809 0.000 0.796 0.204
#> GSM648715 2 0.5178 0.529 0.256 0.744 0.000
#> GSM648598 1 0.0237 0.886 0.996 0.004 0.000
#> GSM648601 1 0.0000 0.886 1.000 0.000 0.000
#> GSM648602 1 0.0000 0.886 1.000 0.000 0.000
#> GSM648604 1 0.0237 0.886 0.996 0.004 0.000
#> GSM648614 1 0.6798 0.571 0.696 0.048 0.256
#> GSM648624 1 0.0237 0.886 0.996 0.004 0.000
#> GSM648625 1 0.0000 0.886 1.000 0.000 0.000
#> GSM648629 1 0.0237 0.886 0.996 0.004 0.000
#> GSM648634 1 0.0237 0.886 0.996 0.004 0.000
#> GSM648648 1 0.3879 0.824 0.848 0.152 0.000
#> GSM648651 1 0.0000 0.886 1.000 0.000 0.000
#> GSM648657 1 0.0000 0.886 1.000 0.000 0.000
#> GSM648660 1 0.0237 0.886 0.996 0.004 0.000
#> GSM648697 1 0.3340 0.842 0.880 0.120 0.000
#> GSM648710 1 0.0237 0.886 0.996 0.004 0.000
#> GSM648591 1 0.2356 0.857 0.928 0.000 0.072
#> GSM648592 1 0.4087 0.836 0.880 0.052 0.068
#> GSM648607 1 0.2261 0.860 0.932 0.000 0.068
#> GSM648611 3 0.5560 0.641 0.300 0.000 0.700
#> GSM648612 1 0.4931 0.669 0.768 0.000 0.232
#> GSM648616 3 0.2356 0.791 0.000 0.072 0.928
#> GSM648617 1 0.2261 0.860 0.932 0.000 0.068
#> GSM648626 1 0.2261 0.860 0.932 0.000 0.068
#> GSM648711 1 0.1031 0.879 0.976 0.000 0.024
#> GSM648712 1 0.2261 0.860 0.932 0.000 0.068
#> GSM648713 1 0.2261 0.860 0.932 0.000 0.068
#> GSM648714 3 0.6138 0.729 0.172 0.060 0.768
#> GSM648716 1 0.2261 0.860 0.932 0.000 0.068
#> GSM648717 3 0.5327 0.681 0.272 0.000 0.728
#> GSM648590 2 0.4346 0.620 0.184 0.816 0.000
#> GSM648596 2 0.3805 0.755 0.024 0.884 0.092
#> GSM648642 2 0.3192 0.692 0.112 0.888 0.000
#> GSM648696 1 0.5948 0.548 0.640 0.360 0.000
#> GSM648705 1 0.4235 0.806 0.824 0.176 0.000
#> GSM648718 2 0.1643 0.744 0.044 0.956 0.000
#> GSM648599 1 0.2261 0.860 0.932 0.000 0.068
#> GSM648608 1 0.0237 0.886 0.996 0.004 0.000
#> GSM648609 1 0.0237 0.886 0.996 0.004 0.000
#> GSM648610 1 0.0592 0.883 0.988 0.000 0.012
#> GSM648633 1 0.0000 0.886 1.000 0.000 0.000
#> GSM648644 2 0.4887 0.804 0.000 0.772 0.228
#> GSM648652 1 0.3879 0.824 0.848 0.152 0.000
#> GSM648653 1 0.0237 0.886 0.996 0.004 0.000
#> GSM648658 1 0.3879 0.824 0.848 0.152 0.000
#> GSM648659 2 0.4682 0.609 0.192 0.804 0.004
#> GSM648662 1 0.0000 0.886 1.000 0.000 0.000
#> GSM648665 1 0.1163 0.882 0.972 0.028 0.000
#> GSM648666 1 0.1163 0.882 0.972 0.028 0.000
#> GSM648680 1 0.3879 0.824 0.848 0.152 0.000
#> GSM648684 1 0.0237 0.886 0.996 0.004 0.000
#> GSM648709 2 0.5216 0.523 0.260 0.740 0.000
#> GSM648719 1 0.0000 0.886 1.000 0.000 0.000
#> GSM648627 1 0.2261 0.860 0.932 0.000 0.068
#> GSM648637 2 0.5016 0.800 0.000 0.760 0.240
#> GSM648638 2 0.5098 0.794 0.000 0.752 0.248
#> GSM648641 3 0.2356 0.803 0.072 0.000 0.928
#> GSM648672 2 0.5016 0.800 0.000 0.760 0.240
#> GSM648674 2 0.5016 0.800 0.000 0.760 0.240
#> GSM648703 2 0.4796 0.806 0.000 0.780 0.220
#> GSM648631 3 0.3412 0.783 0.124 0.000 0.876
#> GSM648669 2 0.5058 0.798 0.000 0.756 0.244
#> GSM648671 2 0.5058 0.798 0.000 0.756 0.244
#> GSM648678 2 0.4887 0.804 0.000 0.772 0.228
#> GSM648679 2 0.5016 0.800 0.000 0.760 0.240
#> GSM648681 2 0.1643 0.744 0.044 0.956 0.000
#> GSM648686 3 0.2356 0.788 0.000 0.072 0.928
#> GSM648689 3 0.3406 0.808 0.028 0.068 0.904
#> GSM648690 3 0.2356 0.788 0.000 0.072 0.928
#> GSM648691 3 0.2845 0.802 0.012 0.068 0.920
#> GSM648693 3 0.3896 0.813 0.060 0.052 0.888
#> GSM648700 2 0.2878 0.793 0.000 0.904 0.096
#> GSM648630 3 0.2845 0.802 0.012 0.068 0.920
#> GSM648632 3 0.5000 0.796 0.124 0.044 0.832
#> GSM648639 3 0.2356 0.791 0.000 0.072 0.928
#> GSM648640 3 0.2496 0.795 0.004 0.068 0.928
#> GSM648668 2 0.5016 0.800 0.000 0.760 0.240
#> GSM648676 2 0.2878 0.793 0.000 0.904 0.096
#> GSM648692 3 0.2496 0.795 0.004 0.068 0.928
#> GSM648694 3 0.2845 0.802 0.012 0.068 0.920
#> GSM648699 2 0.4605 0.809 0.000 0.796 0.204
#> GSM648701 2 0.4605 0.809 0.000 0.796 0.204
#> GSM648673 2 0.5058 0.798 0.000 0.756 0.244
#> GSM648677 2 0.4931 0.803 0.000 0.768 0.232
#> GSM648687 3 0.3406 0.808 0.028 0.068 0.904
#> GSM648688 3 0.4658 0.811 0.076 0.068 0.856
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM648605 4 0.5277 -0.3319 0.000 0.460 0.008 0.532
#> GSM648618 1 0.4469 0.5782 0.808 0.000 0.112 0.080
#> GSM648620 4 0.1584 0.6585 0.012 0.036 0.000 0.952
#> GSM648646 2 0.3810 0.8129 0.000 0.804 0.008 0.188
#> GSM648649 4 0.3528 0.5944 0.192 0.000 0.000 0.808
#> GSM648675 4 0.5351 0.1435 0.008 0.280 0.024 0.688
#> GSM648682 2 0.3545 0.8303 0.000 0.828 0.008 0.164
#> GSM648698 2 0.5288 0.4450 0.000 0.520 0.008 0.472
#> GSM648708 4 0.1610 0.6610 0.016 0.032 0.000 0.952
#> GSM648628 1 0.4972 -0.1449 0.544 0.000 0.456 0.000
#> GSM648595 4 0.3932 0.6628 0.128 0.032 0.004 0.836
#> GSM648635 4 0.4679 0.4066 0.352 0.000 0.000 0.648
#> GSM648645 1 0.4277 0.6591 0.720 0.000 0.000 0.280
#> GSM648647 4 0.2408 0.5964 0.000 0.104 0.000 0.896
#> GSM648667 4 0.3523 0.6705 0.112 0.032 0.000 0.856
#> GSM648695 4 0.2271 0.6288 0.008 0.076 0.000 0.916
#> GSM648704 2 0.2412 0.8616 0.000 0.908 0.008 0.084
#> GSM648706 2 0.2412 0.8609 0.000 0.908 0.008 0.084
#> GSM648593 4 0.4605 0.4356 0.336 0.000 0.000 0.664
#> GSM648594 4 0.4697 0.3996 0.356 0.000 0.000 0.644
#> GSM648600 1 0.3198 0.6362 0.880 0.000 0.080 0.040
#> GSM648621 1 0.2197 0.6502 0.916 0.000 0.080 0.004
#> GSM648622 1 0.4008 0.6787 0.756 0.000 0.000 0.244
#> GSM648623 1 0.2011 0.6503 0.920 0.000 0.080 0.000
#> GSM648636 4 0.4585 0.4428 0.332 0.000 0.000 0.668
#> GSM648655 4 0.4679 0.4083 0.352 0.000 0.000 0.648
#> GSM648661 1 0.4331 0.6451 0.712 0.000 0.000 0.288
#> GSM648664 1 0.4382 0.6341 0.704 0.000 0.000 0.296
#> GSM648683 1 0.4331 0.6451 0.712 0.000 0.000 0.288
#> GSM648685 1 0.4972 0.2489 0.544 0.000 0.000 0.456
#> GSM648702 4 0.4624 0.4309 0.340 0.000 0.000 0.660
#> GSM648597 1 0.3263 0.6331 0.876 0.012 0.100 0.012
#> GSM648603 1 0.2266 0.6488 0.912 0.000 0.084 0.004
#> GSM648606 3 0.5560 0.5710 0.344 0.004 0.628 0.024
#> GSM648613 3 0.5543 0.5772 0.340 0.004 0.632 0.024
#> GSM648619 1 0.2149 0.6475 0.912 0.000 0.088 0.000
#> GSM648654 1 0.4564 0.6095 0.672 0.000 0.000 0.328
#> GSM648663 1 0.5691 -0.2284 0.508 0.000 0.468 0.024
#> GSM648670 2 0.6374 0.6736 0.040 0.704 0.080 0.176
#> GSM648707 3 0.5783 0.6739 0.220 0.088 0.692 0.000
#> GSM648615 2 0.5919 0.5740 0.008 0.584 0.028 0.380
#> GSM648643 2 0.5038 0.6678 0.000 0.652 0.012 0.336
#> GSM648650 4 0.2224 0.6721 0.040 0.032 0.000 0.928
#> GSM648656 2 0.3032 0.8506 0.000 0.868 0.008 0.124
#> GSM648715 4 0.2081 0.6199 0.000 0.084 0.000 0.916
#> GSM648598 1 0.4222 0.6650 0.728 0.000 0.000 0.272
#> GSM648601 1 0.4193 0.6681 0.732 0.000 0.000 0.268
#> GSM648602 1 0.4134 0.6708 0.740 0.000 0.000 0.260
#> GSM648604 1 0.4008 0.6787 0.756 0.000 0.000 0.244
#> GSM648614 1 0.4951 0.4579 0.744 0.000 0.212 0.044
#> GSM648624 1 0.4008 0.6787 0.756 0.000 0.000 0.244
#> GSM648625 1 0.4643 0.5717 0.656 0.000 0.000 0.344
#> GSM648629 1 0.4008 0.6787 0.756 0.000 0.000 0.244
#> GSM648634 1 0.4277 0.6590 0.720 0.000 0.000 0.280
#> GSM648648 4 0.4730 0.3806 0.364 0.000 0.000 0.636
#> GSM648651 1 0.4134 0.6708 0.740 0.000 0.000 0.260
#> GSM648657 1 0.4164 0.6584 0.736 0.000 0.000 0.264
#> GSM648660 1 0.4193 0.6681 0.732 0.000 0.000 0.268
#> GSM648697 1 0.4967 0.2608 0.548 0.000 0.000 0.452
#> GSM648710 1 0.4008 0.6787 0.756 0.000 0.000 0.244
#> GSM648591 1 0.3052 0.6326 0.880 0.012 0.104 0.004
#> GSM648592 1 0.6599 0.3394 0.640 0.012 0.100 0.248
#> GSM648607 1 0.0921 0.6617 0.972 0.000 0.028 0.000
#> GSM648611 1 0.4977 -0.1570 0.540 0.000 0.460 0.000
#> GSM648612 1 0.2589 0.6316 0.884 0.000 0.116 0.000
#> GSM648616 3 0.5142 0.7698 0.064 0.192 0.744 0.000
#> GSM648617 1 0.3858 0.6128 0.844 0.000 0.100 0.056
#> GSM648626 1 0.2266 0.6488 0.912 0.000 0.084 0.004
#> GSM648711 1 0.1211 0.6697 0.960 0.000 0.000 0.040
#> GSM648712 1 0.2216 0.6457 0.908 0.000 0.092 0.000
#> GSM648713 1 0.2149 0.6475 0.912 0.000 0.088 0.000
#> GSM648714 3 0.5660 0.5808 0.336 0.008 0.632 0.024
#> GSM648716 1 0.2149 0.6475 0.912 0.000 0.088 0.000
#> GSM648717 3 0.4761 0.5380 0.372 0.000 0.628 0.000
#> GSM648590 4 0.2469 0.5908 0.000 0.108 0.000 0.892
#> GSM648596 2 0.7793 0.5269 0.076 0.516 0.064 0.344
#> GSM648642 4 0.2408 0.5964 0.000 0.104 0.000 0.896
#> GSM648696 4 0.3694 0.6655 0.124 0.032 0.000 0.844
#> GSM648705 4 0.3400 0.6041 0.180 0.000 0.000 0.820
#> GSM648718 4 0.4792 0.0938 0.000 0.312 0.008 0.680
#> GSM648599 1 0.1824 0.6558 0.936 0.000 0.060 0.004
#> GSM648608 1 0.4008 0.6787 0.756 0.000 0.000 0.244
#> GSM648609 1 0.4193 0.6654 0.732 0.000 0.000 0.268
#> GSM648610 1 0.3024 0.6792 0.852 0.000 0.000 0.148
#> GSM648633 1 0.4406 0.6364 0.700 0.000 0.000 0.300
#> GSM648644 2 0.2542 0.8614 0.000 0.904 0.012 0.084
#> GSM648652 4 0.4730 0.3806 0.364 0.000 0.000 0.636
#> GSM648653 1 0.4164 0.6683 0.736 0.000 0.000 0.264
#> GSM648658 4 0.4804 0.3215 0.384 0.000 0.000 0.616
#> GSM648659 4 0.1557 0.6383 0.000 0.056 0.000 0.944
#> GSM648662 1 0.3873 0.6811 0.772 0.000 0.000 0.228
#> GSM648665 1 0.4406 0.6293 0.700 0.000 0.000 0.300
#> GSM648666 1 0.4356 0.6398 0.708 0.000 0.000 0.292
#> GSM648680 4 0.4790 0.3343 0.380 0.000 0.000 0.620
#> GSM648684 1 0.4250 0.6580 0.724 0.000 0.000 0.276
#> GSM648709 4 0.2412 0.6221 0.008 0.084 0.000 0.908
#> GSM648719 1 0.4193 0.6681 0.732 0.000 0.000 0.268
#> GSM648627 1 0.2149 0.6475 0.912 0.000 0.088 0.000
#> GSM648637 2 0.0779 0.8423 0.000 0.980 0.016 0.004
#> GSM648638 2 0.0779 0.8423 0.000 0.980 0.016 0.004
#> GSM648641 3 0.2011 0.7814 0.080 0.000 0.920 0.000
#> GSM648672 2 0.1182 0.8470 0.000 0.968 0.016 0.016
#> GSM648674 2 0.0779 0.8423 0.000 0.980 0.016 0.004
#> GSM648703 2 0.2611 0.8588 0.000 0.896 0.008 0.096
#> GSM648631 3 0.0469 0.8053 0.012 0.000 0.988 0.000
#> GSM648669 2 0.0779 0.8432 0.000 0.980 0.016 0.004
#> GSM648671 2 0.0779 0.8432 0.000 0.980 0.016 0.004
#> GSM648678 2 0.2662 0.8617 0.000 0.900 0.016 0.084
#> GSM648679 2 0.0779 0.8423 0.000 0.980 0.016 0.004
#> GSM648681 4 0.4456 0.2128 0.000 0.280 0.004 0.716
#> GSM648686 3 0.2530 0.8302 0.000 0.112 0.888 0.000
#> GSM648689 3 0.2466 0.8344 0.004 0.096 0.900 0.000
#> GSM648690 3 0.2530 0.8302 0.000 0.112 0.888 0.000
#> GSM648691 3 0.2469 0.8322 0.000 0.108 0.892 0.000
#> GSM648693 3 0.1743 0.8298 0.004 0.056 0.940 0.000
#> GSM648700 2 0.4955 0.5855 0.000 0.648 0.008 0.344
#> GSM648630 3 0.2469 0.8322 0.000 0.108 0.892 0.000
#> GSM648632 3 0.1576 0.8279 0.004 0.048 0.948 0.000
#> GSM648639 3 0.4399 0.7660 0.020 0.212 0.768 0.000
#> GSM648640 3 0.2647 0.8301 0.000 0.120 0.880 0.000
#> GSM648668 2 0.1182 0.8470 0.000 0.968 0.016 0.016
#> GSM648676 2 0.3933 0.7936 0.000 0.792 0.008 0.200
#> GSM648692 3 0.2469 0.8322 0.000 0.108 0.892 0.000
#> GSM648694 3 0.2408 0.8337 0.000 0.104 0.896 0.000
#> GSM648699 2 0.2737 0.8581 0.000 0.888 0.008 0.104
#> GSM648701 2 0.2737 0.8581 0.000 0.888 0.008 0.104
#> GSM648673 2 0.0779 0.8432 0.000 0.980 0.016 0.004
#> GSM648677 2 0.2593 0.8615 0.000 0.904 0.016 0.080
#> GSM648687 3 0.2593 0.8332 0.004 0.104 0.892 0.000
#> GSM648688 3 0.2530 0.8342 0.004 0.100 0.896 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM648605 2 0.3966 0.661 0.000 0.784 0.004 0.176 0.036
#> GSM648618 5 0.5086 0.811 0.200 0.096 0.004 0.000 0.700
#> GSM648620 2 0.2214 0.810 0.052 0.916 0.000 0.004 0.028
#> GSM648646 4 0.3846 0.739 0.000 0.200 0.004 0.776 0.020
#> GSM648649 2 0.4625 0.622 0.244 0.712 0.008 0.000 0.036
#> GSM648675 2 0.2914 0.756 0.000 0.872 0.000 0.052 0.076
#> GSM648682 4 0.4089 0.732 0.000 0.204 0.008 0.764 0.024
#> GSM648698 2 0.4085 0.625 0.000 0.760 0.004 0.208 0.028
#> GSM648708 2 0.1697 0.811 0.060 0.932 0.000 0.008 0.000
#> GSM648628 5 0.4886 0.779 0.124 0.012 0.120 0.000 0.744
#> GSM648595 2 0.5079 0.702 0.180 0.732 0.024 0.004 0.060
#> GSM648635 1 0.5170 0.588 0.648 0.296 0.012 0.000 0.044
#> GSM648645 1 0.1538 0.846 0.948 0.008 0.008 0.000 0.036
#> GSM648647 2 0.1934 0.811 0.040 0.932 0.000 0.020 0.008
#> GSM648667 2 0.4244 0.701 0.204 0.760 0.008 0.004 0.024
#> GSM648695 2 0.1956 0.813 0.052 0.928 0.000 0.012 0.008
#> GSM648704 4 0.2921 0.840 0.000 0.068 0.028 0.884 0.020
#> GSM648706 4 0.2984 0.839 0.000 0.072 0.028 0.880 0.020
#> GSM648593 1 0.5195 0.572 0.644 0.296 0.008 0.000 0.052
#> GSM648594 1 0.5228 0.611 0.660 0.276 0.016 0.000 0.048
#> GSM648600 5 0.4603 0.825 0.248 0.028 0.012 0.000 0.712
#> GSM648621 5 0.4402 0.816 0.292 0.012 0.008 0.000 0.688
#> GSM648622 1 0.0609 0.850 0.980 0.000 0.000 0.000 0.020
#> GSM648623 5 0.4415 0.613 0.444 0.000 0.004 0.000 0.552
#> GSM648636 1 0.5436 0.566 0.636 0.292 0.016 0.000 0.056
#> GSM648655 1 0.5321 0.576 0.644 0.288 0.012 0.000 0.056
#> GSM648661 1 0.0898 0.854 0.972 0.008 0.000 0.000 0.020
#> GSM648664 1 0.1117 0.854 0.964 0.016 0.000 0.000 0.020
#> GSM648683 1 0.1299 0.854 0.960 0.012 0.008 0.000 0.020
#> GSM648685 1 0.3031 0.800 0.856 0.120 0.004 0.000 0.020
#> GSM648702 1 0.5225 0.593 0.652 0.288 0.016 0.000 0.044
#> GSM648597 5 0.4379 0.827 0.220 0.032 0.008 0.000 0.740
#> GSM648603 5 0.4146 0.831 0.268 0.012 0.004 0.000 0.716
#> GSM648606 5 0.4601 0.631 0.032 0.012 0.236 0.000 0.720
#> GSM648613 5 0.4628 0.628 0.032 0.012 0.240 0.000 0.716
#> GSM648619 5 0.3684 0.831 0.280 0.000 0.000 0.000 0.720
#> GSM648654 1 0.2864 0.805 0.864 0.112 0.000 0.000 0.024
#> GSM648663 5 0.4951 0.750 0.104 0.012 0.148 0.000 0.736
#> GSM648670 4 0.6891 0.222 0.000 0.308 0.012 0.456 0.224
#> GSM648707 5 0.5014 0.528 0.000 0.040 0.212 0.032 0.716
#> GSM648615 2 0.5179 0.413 0.000 0.640 0.000 0.288 0.072
#> GSM648643 2 0.5044 0.334 0.000 0.608 0.004 0.352 0.036
#> GSM648650 2 0.2866 0.796 0.080 0.884 0.008 0.004 0.024
#> GSM648656 4 0.3031 0.812 0.000 0.120 0.004 0.856 0.020
#> GSM648715 2 0.1701 0.813 0.048 0.936 0.000 0.016 0.000
#> GSM648598 1 0.0404 0.854 0.988 0.000 0.000 0.000 0.012
#> GSM648601 1 0.0955 0.851 0.968 0.000 0.004 0.000 0.028
#> GSM648602 1 0.1082 0.852 0.964 0.000 0.008 0.000 0.028
#> GSM648604 1 0.0703 0.850 0.976 0.000 0.000 0.000 0.024
#> GSM648614 5 0.5177 0.802 0.160 0.040 0.068 0.000 0.732
#> GSM648624 1 0.0609 0.850 0.980 0.000 0.000 0.000 0.020
#> GSM648625 1 0.4218 0.720 0.760 0.196 0.004 0.000 0.040
#> GSM648629 1 0.0703 0.850 0.976 0.000 0.000 0.000 0.024
#> GSM648634 1 0.1764 0.846 0.940 0.012 0.012 0.000 0.036
#> GSM648648 1 0.4741 0.664 0.708 0.240 0.008 0.000 0.044
#> GSM648651 1 0.0703 0.849 0.976 0.000 0.000 0.000 0.024
#> GSM648657 1 0.2824 0.807 0.880 0.024 0.008 0.000 0.088
#> GSM648660 1 0.0955 0.851 0.968 0.000 0.004 0.000 0.028
#> GSM648697 1 0.3031 0.797 0.856 0.120 0.004 0.000 0.020
#> GSM648710 1 0.0703 0.850 0.976 0.000 0.000 0.000 0.024
#> GSM648591 5 0.4116 0.834 0.212 0.028 0.004 0.000 0.756
#> GSM648592 5 0.4811 0.747 0.108 0.140 0.008 0.000 0.744
#> GSM648607 5 0.4262 0.620 0.440 0.000 0.000 0.000 0.560
#> GSM648611 5 0.5035 0.766 0.124 0.008 0.144 0.000 0.724
#> GSM648612 5 0.3480 0.838 0.248 0.000 0.000 0.000 0.752
#> GSM648616 3 0.6983 0.467 0.000 0.036 0.500 0.164 0.300
#> GSM648617 5 0.4203 0.821 0.188 0.052 0.000 0.000 0.760
#> GSM648626 5 0.4121 0.833 0.264 0.012 0.004 0.000 0.720
#> GSM648711 1 0.1965 0.775 0.904 0.000 0.000 0.000 0.096
#> GSM648712 5 0.3508 0.838 0.252 0.000 0.000 0.000 0.748
#> GSM648713 5 0.3752 0.824 0.292 0.000 0.000 0.000 0.708
#> GSM648714 5 0.5023 0.646 0.032 0.044 0.204 0.000 0.720
#> GSM648716 5 0.3752 0.824 0.292 0.000 0.000 0.000 0.708
#> GSM648717 5 0.4552 0.637 0.040 0.004 0.240 0.000 0.716
#> GSM648590 2 0.2539 0.806 0.028 0.912 0.008 0.016 0.036
#> GSM648596 2 0.6647 0.302 0.004 0.520 0.008 0.284 0.184
#> GSM648642 2 0.1934 0.811 0.040 0.932 0.000 0.020 0.008
#> GSM648696 2 0.4469 0.699 0.196 0.756 0.016 0.004 0.028
#> GSM648705 2 0.4522 0.632 0.240 0.720 0.008 0.000 0.032
#> GSM648718 2 0.2900 0.737 0.000 0.864 0.000 0.108 0.028
#> GSM648599 5 0.4464 0.817 0.284 0.012 0.012 0.000 0.692
#> GSM648608 1 0.0865 0.850 0.972 0.000 0.004 0.000 0.024
#> GSM648609 1 0.0771 0.853 0.976 0.004 0.000 0.000 0.020
#> GSM648610 1 0.1857 0.812 0.928 0.004 0.008 0.000 0.060
#> GSM648633 1 0.1934 0.841 0.932 0.020 0.008 0.000 0.040
#> GSM648644 4 0.2921 0.840 0.000 0.068 0.028 0.884 0.020
#> GSM648652 1 0.4874 0.657 0.700 0.244 0.012 0.000 0.044
#> GSM648653 1 0.0771 0.853 0.976 0.000 0.004 0.000 0.020
#> GSM648658 1 0.4552 0.714 0.752 0.184 0.012 0.000 0.052
#> GSM648659 2 0.3733 0.785 0.056 0.844 0.004 0.020 0.076
#> GSM648662 1 0.1608 0.812 0.928 0.000 0.000 0.000 0.072
#> GSM648665 1 0.1216 0.854 0.960 0.020 0.000 0.000 0.020
#> GSM648666 1 0.0912 0.856 0.972 0.012 0.000 0.000 0.016
#> GSM648680 1 0.4235 0.731 0.772 0.176 0.008 0.000 0.044
#> GSM648684 1 0.1059 0.852 0.968 0.004 0.008 0.000 0.020
#> GSM648709 2 0.1921 0.812 0.044 0.932 0.000 0.012 0.012
#> GSM648719 1 0.1041 0.850 0.964 0.000 0.004 0.000 0.032
#> GSM648627 5 0.3752 0.824 0.292 0.000 0.000 0.000 0.708
#> GSM648637 4 0.2966 0.835 0.000 0.020 0.040 0.884 0.056
#> GSM648638 4 0.2966 0.835 0.000 0.020 0.040 0.884 0.056
#> GSM648641 3 0.4347 0.410 0.000 0.004 0.636 0.004 0.356
#> GSM648672 4 0.2305 0.844 0.000 0.012 0.044 0.916 0.028
#> GSM648674 4 0.3297 0.828 0.000 0.032 0.040 0.868 0.060
#> GSM648703 4 0.3720 0.811 0.000 0.048 0.020 0.836 0.096
#> GSM648631 3 0.1830 0.852 0.000 0.008 0.924 0.000 0.068
#> GSM648669 4 0.2514 0.837 0.000 0.000 0.044 0.896 0.060
#> GSM648671 4 0.2514 0.837 0.000 0.000 0.044 0.896 0.060
#> GSM648678 4 0.2312 0.843 0.000 0.060 0.016 0.912 0.012
#> GSM648679 4 0.3100 0.832 0.000 0.020 0.040 0.876 0.064
#> GSM648681 2 0.1901 0.789 0.000 0.928 0.004 0.056 0.012
#> GSM648686 3 0.0963 0.904 0.000 0.000 0.964 0.036 0.000
#> GSM648689 3 0.1153 0.898 0.000 0.004 0.964 0.024 0.008
#> GSM648690 3 0.0963 0.904 0.000 0.000 0.964 0.036 0.000
#> GSM648691 3 0.0963 0.904 0.000 0.000 0.964 0.036 0.000
#> GSM648693 3 0.1179 0.895 0.000 0.004 0.964 0.016 0.016
#> GSM648700 4 0.6425 0.545 0.008 0.240 0.024 0.604 0.124
#> GSM648630 3 0.0963 0.904 0.000 0.000 0.964 0.036 0.000
#> GSM648632 3 0.1168 0.884 0.000 0.008 0.960 0.000 0.032
#> GSM648639 3 0.5819 0.560 0.000 0.020 0.632 0.256 0.092
#> GSM648640 3 0.1956 0.889 0.000 0.008 0.928 0.052 0.012
#> GSM648668 4 0.2228 0.844 0.000 0.012 0.040 0.920 0.028
#> GSM648676 4 0.5589 0.676 0.000 0.172 0.024 0.688 0.116
#> GSM648692 3 0.0963 0.904 0.000 0.000 0.964 0.036 0.000
#> GSM648694 3 0.0963 0.904 0.000 0.000 0.964 0.036 0.000
#> GSM648699 4 0.4602 0.771 0.000 0.100 0.020 0.776 0.104
#> GSM648701 4 0.4602 0.771 0.000 0.100 0.020 0.776 0.104
#> GSM648673 4 0.2514 0.837 0.000 0.000 0.044 0.896 0.060
#> GSM648677 4 0.2053 0.841 0.000 0.040 0.016 0.928 0.016
#> GSM648687 3 0.1124 0.904 0.000 0.004 0.960 0.036 0.000
#> GSM648688 3 0.1124 0.904 0.000 0.004 0.960 0.036 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM648605 2 0.3975 0.5891 0.000 0.744 0.000 0.204 0.004 0.048
#> GSM648618 5 0.5290 0.6964 0.060 0.064 0.016 0.000 0.704 0.156
#> GSM648620 2 0.1116 0.7656 0.004 0.960 0.000 0.000 0.008 0.028
#> GSM648646 4 0.3344 0.5351 0.000 0.152 0.000 0.804 0.000 0.044
#> GSM648649 2 0.5277 0.5845 0.148 0.680 0.000 0.000 0.044 0.128
#> GSM648675 2 0.4139 0.6889 0.000 0.760 0.008 0.036 0.016 0.180
#> GSM648682 4 0.3417 0.5248 0.000 0.160 0.000 0.796 0.000 0.044
#> GSM648698 2 0.3885 0.5750 0.000 0.736 0.000 0.220 0.000 0.044
#> GSM648708 2 0.0767 0.7685 0.008 0.976 0.000 0.004 0.000 0.012
#> GSM648628 5 0.3286 0.7772 0.044 0.000 0.068 0.000 0.848 0.040
#> GSM648595 2 0.5537 0.6030 0.096 0.668 0.004 0.000 0.064 0.168
#> GSM648635 1 0.6010 0.5121 0.544 0.280 0.000 0.000 0.032 0.144
#> GSM648645 1 0.4007 0.7766 0.776 0.016 0.000 0.000 0.064 0.144
#> GSM648647 2 0.1036 0.7636 0.004 0.964 0.000 0.024 0.000 0.008
#> GSM648667 2 0.4352 0.6531 0.124 0.752 0.000 0.000 0.016 0.108
#> GSM648695 2 0.0862 0.7682 0.004 0.972 0.000 0.008 0.000 0.016
#> GSM648704 4 0.1864 0.6474 0.000 0.040 0.004 0.924 0.000 0.032
#> GSM648706 4 0.2433 0.6199 0.000 0.072 0.000 0.884 0.000 0.044
#> GSM648593 1 0.6147 0.4932 0.520 0.256 0.004 0.000 0.016 0.204
#> GSM648594 1 0.6522 0.4909 0.492 0.252 0.000 0.000 0.048 0.208
#> GSM648600 5 0.3777 0.7682 0.072 0.036 0.004 0.000 0.820 0.068
#> GSM648621 5 0.3575 0.7728 0.140 0.004 0.004 0.000 0.804 0.048
#> GSM648622 1 0.1074 0.8197 0.960 0.000 0.000 0.000 0.028 0.012
#> GSM648623 5 0.4493 0.5229 0.364 0.000 0.000 0.000 0.596 0.040
#> GSM648636 1 0.5840 0.5585 0.576 0.236 0.004 0.000 0.016 0.168
#> GSM648655 1 0.5920 0.5464 0.564 0.236 0.004 0.000 0.016 0.180
#> GSM648661 1 0.1078 0.8235 0.964 0.008 0.000 0.000 0.016 0.012
#> GSM648664 1 0.0984 0.8240 0.968 0.008 0.000 0.000 0.012 0.012
#> GSM648683 1 0.1129 0.8239 0.964 0.008 0.004 0.000 0.012 0.012
#> GSM648685 1 0.1856 0.8075 0.920 0.048 0.000 0.000 0.000 0.032
#> GSM648702 1 0.5898 0.5667 0.580 0.252 0.004 0.000 0.028 0.136
#> GSM648597 5 0.4267 0.6741 0.048 0.016 0.000 0.000 0.732 0.204
#> GSM648603 5 0.3002 0.7884 0.100 0.004 0.000 0.000 0.848 0.048
#> GSM648606 5 0.4102 0.6844 0.004 0.004 0.128 0.000 0.768 0.096
#> GSM648613 5 0.4095 0.6808 0.004 0.004 0.132 0.000 0.768 0.092
#> GSM648619 5 0.2178 0.7898 0.132 0.000 0.000 0.000 0.868 0.000
#> GSM648654 1 0.2926 0.7580 0.844 0.124 0.000 0.000 0.028 0.004
#> GSM648663 5 0.4081 0.7385 0.036 0.004 0.096 0.000 0.796 0.068
#> GSM648670 6 0.7183 0.3266 0.000 0.128 0.012 0.332 0.108 0.420
#> GSM648707 5 0.5521 0.2151 0.000 0.004 0.080 0.016 0.544 0.356
#> GSM648615 2 0.5404 0.4285 0.000 0.624 0.000 0.244 0.024 0.108
#> GSM648643 2 0.4563 0.3842 0.000 0.604 0.000 0.348 0.000 0.048
#> GSM648650 2 0.3103 0.7206 0.036 0.848 0.000 0.000 0.016 0.100
#> GSM648656 4 0.2542 0.6138 0.000 0.080 0.000 0.876 0.000 0.044
#> GSM648715 2 0.0862 0.7684 0.008 0.972 0.000 0.004 0.000 0.016
#> GSM648598 1 0.1151 0.8268 0.956 0.000 0.000 0.000 0.012 0.032
#> GSM648601 1 0.2801 0.8095 0.860 0.000 0.000 0.000 0.072 0.068
#> GSM648602 1 0.1693 0.8251 0.932 0.000 0.004 0.000 0.044 0.020
#> GSM648604 1 0.0632 0.8212 0.976 0.000 0.000 0.000 0.024 0.000
#> GSM648614 5 0.4216 0.7392 0.040 0.008 0.088 0.000 0.792 0.072
#> GSM648624 1 0.0891 0.8207 0.968 0.000 0.000 0.000 0.024 0.008
#> GSM648625 1 0.6365 0.5661 0.564 0.208 0.000 0.000 0.092 0.136
#> GSM648629 1 0.0632 0.8212 0.976 0.000 0.000 0.000 0.024 0.000
#> GSM648634 1 0.3547 0.7961 0.828 0.024 0.004 0.000 0.044 0.100
#> GSM648648 1 0.5467 0.6344 0.636 0.204 0.000 0.000 0.028 0.132
#> GSM648651 1 0.1480 0.8177 0.940 0.000 0.000 0.000 0.040 0.020
#> GSM648657 1 0.5734 0.6581 0.628 0.048 0.000 0.000 0.156 0.168
#> GSM648660 1 0.2822 0.8110 0.864 0.004 0.000 0.000 0.056 0.076
#> GSM648697 1 0.2134 0.8065 0.904 0.044 0.000 0.000 0.000 0.052
#> GSM648710 1 0.0777 0.8209 0.972 0.000 0.000 0.000 0.024 0.004
#> GSM648591 5 0.4071 0.7063 0.048 0.008 0.004 0.000 0.756 0.184
#> GSM648592 5 0.4138 0.6552 0.020 0.044 0.000 0.000 0.752 0.184
#> GSM648607 5 0.3528 0.6344 0.296 0.000 0.000 0.000 0.700 0.004
#> GSM648611 5 0.3491 0.7620 0.040 0.000 0.100 0.000 0.828 0.032
#> GSM648612 5 0.2006 0.7936 0.104 0.000 0.000 0.000 0.892 0.004
#> GSM648616 6 0.7498 0.4431 0.000 0.004 0.244 0.188 0.164 0.400
#> GSM648617 5 0.2906 0.7754 0.044 0.032 0.000 0.000 0.872 0.052
#> GSM648626 5 0.3186 0.7865 0.100 0.004 0.000 0.000 0.836 0.060
#> GSM648711 1 0.2446 0.7538 0.864 0.000 0.000 0.000 0.124 0.012
#> GSM648712 5 0.2053 0.7937 0.108 0.000 0.000 0.000 0.888 0.004
#> GSM648713 5 0.2668 0.7722 0.168 0.000 0.000 0.000 0.828 0.004
#> GSM648714 5 0.4357 0.6769 0.004 0.016 0.124 0.000 0.760 0.096
#> GSM648716 5 0.2527 0.7736 0.168 0.000 0.000 0.000 0.832 0.000
#> GSM648717 5 0.3840 0.7069 0.012 0.000 0.136 0.000 0.788 0.064
#> GSM648590 2 0.2468 0.7527 0.000 0.884 0.008 0.012 0.004 0.092
#> GSM648596 2 0.6286 0.3681 0.000 0.560 0.000 0.240 0.088 0.112
#> GSM648642 2 0.1036 0.7636 0.004 0.964 0.000 0.024 0.000 0.008
#> GSM648696 2 0.4855 0.6345 0.132 0.720 0.004 0.000 0.024 0.120
#> GSM648705 2 0.4419 0.6585 0.100 0.756 0.000 0.000 0.028 0.116
#> GSM648718 2 0.2728 0.7021 0.000 0.860 0.000 0.100 0.000 0.040
#> GSM648599 5 0.3536 0.7787 0.124 0.004 0.004 0.000 0.812 0.056
#> GSM648608 1 0.0922 0.8206 0.968 0.000 0.004 0.000 0.024 0.004
#> GSM648609 1 0.0622 0.8230 0.980 0.000 0.000 0.000 0.012 0.008
#> GSM648610 1 0.1555 0.8027 0.932 0.000 0.004 0.000 0.060 0.004
#> GSM648633 1 0.4613 0.7569 0.740 0.040 0.000 0.000 0.076 0.144
#> GSM648644 4 0.1716 0.6500 0.000 0.036 0.004 0.932 0.000 0.028
#> GSM648652 1 0.5832 0.5731 0.580 0.240 0.000 0.000 0.028 0.152
#> GSM648653 1 0.0862 0.8276 0.972 0.000 0.004 0.000 0.008 0.016
#> GSM648658 1 0.4937 0.7079 0.696 0.104 0.004 0.000 0.016 0.180
#> GSM648659 2 0.3404 0.6948 0.012 0.792 0.004 0.008 0.000 0.184
#> GSM648662 1 0.3314 0.6224 0.764 0.000 0.000 0.000 0.224 0.012
#> GSM648665 1 0.1173 0.8240 0.960 0.008 0.000 0.000 0.016 0.016
#> GSM648666 1 0.1049 0.8256 0.960 0.008 0.000 0.000 0.000 0.032
#> GSM648680 1 0.4303 0.7512 0.764 0.080 0.000 0.000 0.028 0.128
#> GSM648684 1 0.0870 0.8233 0.972 0.000 0.004 0.000 0.012 0.012
#> GSM648709 2 0.0665 0.7654 0.000 0.980 0.000 0.008 0.004 0.008
#> GSM648719 1 0.2685 0.8103 0.868 0.000 0.000 0.000 0.072 0.060
#> GSM648627 5 0.2841 0.7766 0.164 0.000 0.000 0.000 0.824 0.012
#> GSM648637 4 0.3484 0.5474 0.000 0.000 0.016 0.784 0.012 0.188
#> GSM648638 4 0.3892 0.5104 0.000 0.000 0.020 0.752 0.020 0.208
#> GSM648641 3 0.4962 0.0392 0.000 0.000 0.516 0.000 0.416 0.068
#> GSM648672 4 0.2264 0.6410 0.000 0.000 0.012 0.888 0.004 0.096
#> GSM648674 4 0.4586 0.4235 0.000 0.004 0.016 0.680 0.036 0.264
#> GSM648703 4 0.4586 0.5448 0.000 0.040 0.004 0.688 0.016 0.252
#> GSM648631 3 0.0820 0.8290 0.000 0.000 0.972 0.000 0.016 0.012
#> GSM648669 4 0.3893 0.5682 0.000 0.000 0.016 0.744 0.020 0.220
#> GSM648671 4 0.3893 0.5682 0.000 0.000 0.016 0.744 0.020 0.220
#> GSM648678 4 0.1313 0.6564 0.000 0.016 0.004 0.952 0.000 0.028
#> GSM648679 4 0.4221 0.4936 0.000 0.000 0.016 0.716 0.032 0.236
#> GSM648681 2 0.2878 0.7290 0.000 0.860 0.000 0.016 0.024 0.100
#> GSM648686 3 0.1010 0.8579 0.000 0.000 0.960 0.036 0.000 0.004
#> GSM648689 3 0.0767 0.8474 0.000 0.000 0.976 0.012 0.008 0.004
#> GSM648690 3 0.1010 0.8579 0.000 0.000 0.960 0.036 0.000 0.004
#> GSM648691 3 0.0865 0.8584 0.000 0.000 0.964 0.036 0.000 0.000
#> GSM648693 3 0.0260 0.8481 0.000 0.000 0.992 0.008 0.000 0.000
#> GSM648700 4 0.6384 0.3055 0.008 0.148 0.008 0.484 0.016 0.336
#> GSM648630 3 0.0865 0.8584 0.000 0.000 0.964 0.036 0.000 0.000
#> GSM648632 3 0.0665 0.8453 0.000 0.000 0.980 0.008 0.004 0.008
#> GSM648639 3 0.7155 -0.5128 0.000 0.000 0.352 0.236 0.084 0.328
#> GSM648640 3 0.3757 0.6754 0.000 0.000 0.784 0.036 0.016 0.164
#> GSM648668 4 0.2405 0.6402 0.000 0.000 0.016 0.880 0.004 0.100
#> GSM648676 4 0.5688 0.4095 0.000 0.096 0.008 0.560 0.016 0.320
#> GSM648692 3 0.0865 0.8584 0.000 0.000 0.964 0.036 0.000 0.000
#> GSM648694 3 0.0865 0.8584 0.000 0.000 0.964 0.036 0.000 0.000
#> GSM648699 4 0.4945 0.5083 0.000 0.052 0.004 0.640 0.016 0.288
#> GSM648701 4 0.4871 0.5183 0.000 0.052 0.004 0.656 0.016 0.272
#> GSM648673 4 0.3893 0.5682 0.000 0.000 0.016 0.744 0.020 0.220
#> GSM648677 4 0.2417 0.6548 0.000 0.008 0.004 0.888 0.012 0.088
#> GSM648687 3 0.1296 0.8559 0.000 0.000 0.952 0.032 0.004 0.012
#> GSM648688 3 0.1296 0.8559 0.000 0.000 0.952 0.032 0.004 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)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
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) development.stage(p) other(p) k
#> MAD:kmeans 117 1.04e-12 0.038842 2.23e-15 2
#> MAD:kmeans 124 7.50e-11 0.005313 9.50e-19 3
#> MAD:kmeans 108 2.59e-12 0.012207 1.63e-22 4
#> MAD:kmeans 124 8.25e-20 0.000705 1.28e-44 5
#> MAD:kmeans 116 4.39e-18 0.000986 1.03e-40 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 51941 rows and 130 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.639 0.825 0.929 0.4938 0.502 0.502
#> 3 3 0.657 0.769 0.889 0.3199 0.717 0.494
#> 4 4 0.673 0.738 0.822 0.1310 0.802 0.498
#> 5 5 0.847 0.808 0.905 0.0712 0.822 0.453
#> 6 6 0.755 0.730 0.821 0.0450 0.930 0.692
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 2
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM648605 2 0.000 0.8880 0.000 1.000
#> GSM648618 1 0.973 0.2263 0.596 0.404
#> GSM648620 2 0.971 0.3485 0.400 0.600
#> GSM648646 2 0.000 0.8880 0.000 1.000
#> GSM648649 1 0.722 0.6984 0.800 0.200
#> GSM648675 2 0.000 0.8880 0.000 1.000
#> GSM648682 2 0.000 0.8880 0.000 1.000
#> GSM648698 2 0.000 0.8880 0.000 1.000
#> GSM648708 2 0.999 0.0992 0.480 0.520
#> GSM648628 1 0.000 0.9400 1.000 0.000
#> GSM648595 1 0.971 0.2999 0.600 0.400
#> GSM648635 1 0.000 0.9400 1.000 0.000
#> GSM648645 1 0.000 0.9400 1.000 0.000
#> GSM648647 2 0.722 0.7207 0.200 0.800
#> GSM648667 1 0.971 0.2999 0.600 0.400
#> GSM648695 2 0.971 0.3485 0.400 0.600
#> GSM648704 2 0.000 0.8880 0.000 1.000
#> GSM648706 2 0.000 0.8880 0.000 1.000
#> GSM648593 1 0.000 0.9400 1.000 0.000
#> GSM648594 1 0.000 0.9400 1.000 0.000
#> GSM648600 1 0.000 0.9400 1.000 0.000
#> GSM648621 1 0.000 0.9400 1.000 0.000
#> GSM648622 1 0.000 0.9400 1.000 0.000
#> GSM648623 1 0.000 0.9400 1.000 0.000
#> GSM648636 1 0.000 0.9400 1.000 0.000
#> GSM648655 1 0.000 0.9400 1.000 0.000
#> GSM648661 1 0.000 0.9400 1.000 0.000
#> GSM648664 1 0.000 0.9400 1.000 0.000
#> GSM648683 1 0.000 0.9400 1.000 0.000
#> GSM648685 1 0.000 0.9400 1.000 0.000
#> GSM648702 1 0.000 0.9400 1.000 0.000
#> GSM648597 1 0.000 0.9400 1.000 0.000
#> GSM648603 1 0.000 0.9400 1.000 0.000
#> GSM648606 2 0.961 0.4299 0.384 0.616
#> GSM648613 2 0.760 0.7148 0.220 0.780
#> GSM648619 1 0.000 0.9400 1.000 0.000
#> GSM648654 1 0.000 0.9400 1.000 0.000
#> GSM648663 1 0.000 0.9400 1.000 0.000
#> GSM648670 2 0.000 0.8880 0.000 1.000
#> GSM648707 2 0.722 0.7383 0.200 0.800
#> GSM648615 2 0.000 0.8880 0.000 1.000
#> GSM648643 2 0.000 0.8880 0.000 1.000
#> GSM648650 1 0.971 0.2999 0.600 0.400
#> GSM648656 2 0.000 0.8880 0.000 1.000
#> GSM648715 2 0.971 0.3485 0.400 0.600
#> GSM648598 1 0.000 0.9400 1.000 0.000
#> GSM648601 1 0.000 0.9400 1.000 0.000
#> GSM648602 1 0.000 0.9400 1.000 0.000
#> GSM648604 1 0.000 0.9400 1.000 0.000
#> GSM648614 1 0.000 0.9400 1.000 0.000
#> GSM648624 1 0.000 0.9400 1.000 0.000
#> GSM648625 1 0.000 0.9400 1.000 0.000
#> GSM648629 1 0.000 0.9400 1.000 0.000
#> GSM648634 1 0.000 0.9400 1.000 0.000
#> GSM648648 1 0.000 0.9400 1.000 0.000
#> GSM648651 1 0.000 0.9400 1.000 0.000
#> GSM648657 1 0.000 0.9400 1.000 0.000
#> GSM648660 1 0.000 0.9400 1.000 0.000
#> GSM648697 1 0.000 0.9400 1.000 0.000
#> GSM648710 1 0.000 0.9400 1.000 0.000
#> GSM648591 1 0.000 0.9400 1.000 0.000
#> GSM648592 1 0.963 0.3312 0.612 0.388
#> GSM648607 1 0.000 0.9400 1.000 0.000
#> GSM648611 1 0.000 0.9400 1.000 0.000
#> GSM648612 1 0.000 0.9400 1.000 0.000
#> GSM648616 2 0.000 0.8880 0.000 1.000
#> GSM648617 1 0.000 0.9400 1.000 0.000
#> GSM648626 1 0.000 0.9400 1.000 0.000
#> GSM648711 1 0.000 0.9400 1.000 0.000
#> GSM648712 1 0.000 0.9400 1.000 0.000
#> GSM648713 1 0.000 0.9400 1.000 0.000
#> GSM648714 2 0.000 0.8880 0.000 1.000
#> GSM648716 1 0.000 0.9400 1.000 0.000
#> GSM648717 1 0.000 0.9400 1.000 0.000
#> GSM648590 2 0.722 0.7207 0.200 0.800
#> GSM648596 2 0.000 0.8880 0.000 1.000
#> GSM648642 2 0.706 0.7301 0.192 0.808
#> GSM648696 1 0.971 0.2999 0.600 0.400
#> GSM648705 1 0.722 0.6984 0.800 0.200
#> GSM648718 2 0.000 0.8880 0.000 1.000
#> GSM648599 1 0.000 0.9400 1.000 0.000
#> GSM648608 1 0.000 0.9400 1.000 0.000
#> GSM648609 1 0.000 0.9400 1.000 0.000
#> GSM648610 1 0.000 0.9400 1.000 0.000
#> GSM648633 1 0.000 0.9400 1.000 0.000
#> GSM648644 2 0.000 0.8880 0.000 1.000
#> GSM648652 1 0.000 0.9400 1.000 0.000
#> GSM648653 1 0.000 0.9400 1.000 0.000
#> GSM648658 1 0.000 0.9400 1.000 0.000
#> GSM648659 2 0.871 0.5810 0.292 0.708
#> GSM648662 1 0.000 0.9400 1.000 0.000
#> GSM648665 1 0.000 0.9400 1.000 0.000
#> GSM648666 1 0.000 0.9400 1.000 0.000
#> GSM648680 1 0.000 0.9400 1.000 0.000
#> GSM648684 1 0.000 0.9400 1.000 0.000
#> GSM648709 2 0.722 0.7207 0.200 0.800
#> GSM648719 1 0.000 0.9400 1.000 0.000
#> GSM648627 1 0.000 0.9400 1.000 0.000
#> GSM648637 2 0.000 0.8880 0.000 1.000
#> GSM648638 2 0.000 0.8880 0.000 1.000
#> GSM648641 2 0.760 0.7148 0.220 0.780
#> GSM648672 2 0.000 0.8880 0.000 1.000
#> GSM648674 2 0.000 0.8880 0.000 1.000
#> GSM648703 2 0.000 0.8880 0.000 1.000
#> GSM648631 1 0.971 0.2382 0.600 0.400
#> GSM648669 2 0.000 0.8880 0.000 1.000
#> GSM648671 2 0.000 0.8880 0.000 1.000
#> GSM648678 2 0.000 0.8880 0.000 1.000
#> GSM648679 2 0.000 0.8880 0.000 1.000
#> GSM648681 2 0.000 0.8880 0.000 1.000
#> GSM648686 2 0.000 0.8880 0.000 1.000
#> GSM648689 2 0.722 0.7383 0.200 0.800
#> GSM648690 2 0.000 0.8880 0.000 1.000
#> GSM648691 2 0.722 0.7383 0.200 0.800
#> GSM648693 2 0.971 0.3901 0.400 0.600
#> GSM648700 2 0.000 0.8880 0.000 1.000
#> GSM648630 2 0.722 0.7383 0.200 0.800
#> GSM648632 1 0.980 0.1886 0.584 0.416
#> GSM648639 2 0.000 0.8880 0.000 1.000
#> GSM648640 2 0.000 0.8880 0.000 1.000
#> GSM648668 2 0.000 0.8880 0.000 1.000
#> GSM648676 2 0.000 0.8880 0.000 1.000
#> GSM648692 2 0.000 0.8880 0.000 1.000
#> GSM648694 2 0.722 0.7383 0.200 0.800
#> GSM648699 2 0.000 0.8880 0.000 1.000
#> GSM648701 2 0.000 0.8880 0.000 1.000
#> GSM648673 2 0.000 0.8880 0.000 1.000
#> GSM648677 2 0.000 0.8880 0.000 1.000
#> GSM648687 2 0.730 0.7339 0.204 0.796
#> GSM648688 2 0.971 0.3901 0.400 0.600
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM648605 2 0.0237 0.8850 0.004 0.996 0.000
#> GSM648618 3 0.4047 0.7403 0.004 0.148 0.848
#> GSM648620 2 0.5061 0.7596 0.208 0.784 0.008
#> GSM648646 2 0.0000 0.8862 0.000 1.000 0.000
#> GSM648649 1 0.4963 0.6880 0.792 0.200 0.008
#> GSM648675 2 0.0000 0.8862 0.000 1.000 0.000
#> GSM648682 2 0.0000 0.8862 0.000 1.000 0.000
#> GSM648698 2 0.0237 0.8850 0.004 0.996 0.000
#> GSM648708 2 0.5061 0.7596 0.208 0.784 0.008
#> GSM648628 3 0.0424 0.7354 0.008 0.000 0.992
#> GSM648595 2 0.6632 0.4693 0.392 0.596 0.012
#> GSM648635 1 0.0424 0.9277 0.992 0.000 0.008
#> GSM648645 1 0.0237 0.9313 0.996 0.000 0.004
#> GSM648647 2 0.5061 0.7596 0.208 0.784 0.008
#> GSM648667 2 0.6498 0.4716 0.396 0.596 0.008
#> GSM648695 2 0.5061 0.7596 0.208 0.784 0.008
#> GSM648704 2 0.0000 0.8862 0.000 1.000 0.000
#> GSM648706 2 0.0000 0.8862 0.000 1.000 0.000
#> GSM648593 1 0.0424 0.9277 0.992 0.000 0.008
#> GSM648594 1 0.0424 0.9277 0.992 0.000 0.008
#> GSM648600 1 0.4654 0.7509 0.792 0.000 0.208
#> GSM648621 1 0.4654 0.7509 0.792 0.000 0.208
#> GSM648622 1 0.0237 0.9313 0.996 0.000 0.004
#> GSM648623 1 0.4654 0.7509 0.792 0.000 0.208
#> GSM648636 1 0.0424 0.9277 0.992 0.000 0.008
#> GSM648655 1 0.0424 0.9277 0.992 0.000 0.008
#> GSM648661 1 0.0000 0.9305 1.000 0.000 0.000
#> GSM648664 1 0.0000 0.9305 1.000 0.000 0.000
#> GSM648683 1 0.0000 0.9305 1.000 0.000 0.000
#> GSM648685 1 0.0424 0.9277 0.992 0.000 0.008
#> GSM648702 1 0.0424 0.9277 0.992 0.000 0.008
#> GSM648597 1 0.4654 0.7509 0.792 0.000 0.208
#> GSM648603 1 0.6280 0.1959 0.540 0.000 0.460
#> GSM648606 3 0.0424 0.7354 0.008 0.000 0.992
#> GSM648613 3 0.0424 0.7354 0.008 0.000 0.992
#> GSM648619 3 0.6308 -0.0644 0.492 0.000 0.508
#> GSM648654 1 0.0747 0.9217 0.984 0.000 0.016
#> GSM648663 3 0.0424 0.7354 0.008 0.000 0.992
#> GSM648670 2 0.0000 0.8862 0.000 1.000 0.000
#> GSM648707 3 0.4555 0.7373 0.000 0.200 0.800
#> GSM648615 2 0.0000 0.8862 0.000 1.000 0.000
#> GSM648643 2 0.0237 0.8850 0.004 0.996 0.000
#> GSM648650 2 0.6498 0.4716 0.396 0.596 0.008
#> GSM648656 2 0.0000 0.8862 0.000 1.000 0.000
#> GSM648715 2 0.5061 0.7596 0.208 0.784 0.008
#> GSM648598 1 0.0237 0.9313 0.996 0.000 0.004
#> GSM648601 1 0.0237 0.9313 0.996 0.000 0.004
#> GSM648602 1 0.0237 0.9313 0.996 0.000 0.004
#> GSM648604 1 0.0237 0.9313 0.996 0.000 0.004
#> GSM648614 3 0.0424 0.7354 0.008 0.000 0.992
#> GSM648624 1 0.0237 0.9313 0.996 0.000 0.004
#> GSM648625 1 0.0592 0.9272 0.988 0.000 0.012
#> GSM648629 1 0.0237 0.9313 0.996 0.000 0.004
#> GSM648634 1 0.0237 0.9313 0.996 0.000 0.004
#> GSM648648 1 0.0424 0.9277 0.992 0.000 0.008
#> GSM648651 1 0.0237 0.9313 0.996 0.000 0.004
#> GSM648657 1 0.2356 0.8833 0.928 0.000 0.072
#> GSM648660 1 0.0237 0.9313 0.996 0.000 0.004
#> GSM648697 1 0.0424 0.9277 0.992 0.000 0.008
#> GSM648710 1 0.0237 0.9313 0.996 0.000 0.004
#> GSM648591 3 0.3192 0.6701 0.112 0.000 0.888
#> GSM648592 3 0.9098 0.0226 0.404 0.140 0.456
#> GSM648607 1 0.4654 0.7509 0.792 0.000 0.208
#> GSM648611 3 0.0424 0.7354 0.008 0.000 0.992
#> GSM648612 3 0.0592 0.7343 0.012 0.000 0.988
#> GSM648616 3 0.4750 0.7323 0.000 0.216 0.784
#> GSM648617 3 0.6308 -0.0644 0.492 0.000 0.508
#> GSM648626 3 0.6308 -0.0644 0.492 0.000 0.508
#> GSM648711 1 0.4654 0.7509 0.792 0.000 0.208
#> GSM648712 3 0.6308 -0.0644 0.492 0.000 0.508
#> GSM648713 3 0.6308 -0.0644 0.492 0.000 0.508
#> GSM648714 3 0.0592 0.7353 0.000 0.012 0.988
#> GSM648716 3 0.6308 -0.0644 0.492 0.000 0.508
#> GSM648717 3 0.0424 0.7354 0.008 0.000 0.992
#> GSM648590 2 0.5012 0.7625 0.204 0.788 0.008
#> GSM648596 2 0.2878 0.8136 0.000 0.904 0.096
#> GSM648642 2 0.5012 0.7625 0.204 0.788 0.008
#> GSM648696 2 0.6577 0.4119 0.420 0.572 0.008
#> GSM648705 1 0.3965 0.7824 0.860 0.132 0.008
#> GSM648718 2 0.0661 0.8813 0.004 0.988 0.008
#> GSM648599 1 0.4654 0.7509 0.792 0.000 0.208
#> GSM648608 1 0.0237 0.9313 0.996 0.000 0.004
#> GSM648609 1 0.0237 0.9313 0.996 0.000 0.004
#> GSM648610 1 0.3941 0.8073 0.844 0.000 0.156
#> GSM648633 1 0.0237 0.9313 0.996 0.000 0.004
#> GSM648644 2 0.0000 0.8862 0.000 1.000 0.000
#> GSM648652 1 0.0424 0.9277 0.992 0.000 0.008
#> GSM648653 1 0.0237 0.9313 0.996 0.000 0.004
#> GSM648658 1 0.0424 0.9277 0.992 0.000 0.008
#> GSM648659 2 0.5061 0.7596 0.208 0.784 0.008
#> GSM648662 1 0.3879 0.8117 0.848 0.000 0.152
#> GSM648665 1 0.0000 0.9305 1.000 0.000 0.000
#> GSM648666 1 0.0000 0.9305 1.000 0.000 0.000
#> GSM648680 1 0.0424 0.9277 0.992 0.000 0.008
#> GSM648684 1 0.0237 0.9313 0.996 0.000 0.004
#> GSM648709 2 0.4963 0.7650 0.200 0.792 0.008
#> GSM648719 1 0.0237 0.9313 0.996 0.000 0.004
#> GSM648627 3 0.6308 -0.0644 0.492 0.000 0.508
#> GSM648637 2 0.0000 0.8862 0.000 1.000 0.000
#> GSM648638 2 0.0000 0.8862 0.000 1.000 0.000
#> GSM648641 3 0.4605 0.7365 0.000 0.204 0.796
#> GSM648672 2 0.0000 0.8862 0.000 1.000 0.000
#> GSM648674 2 0.0000 0.8862 0.000 1.000 0.000
#> GSM648703 2 0.0000 0.8862 0.000 1.000 0.000
#> GSM648631 3 0.4733 0.7380 0.004 0.196 0.800
#> GSM648669 2 0.0000 0.8862 0.000 1.000 0.000
#> GSM648671 2 0.0000 0.8862 0.000 1.000 0.000
#> GSM648678 2 0.0000 0.8862 0.000 1.000 0.000
#> GSM648679 2 0.0000 0.8862 0.000 1.000 0.000
#> GSM648681 2 0.0237 0.8849 0.000 0.996 0.004
#> GSM648686 3 0.4750 0.7323 0.000 0.216 0.784
#> GSM648689 3 0.4750 0.7323 0.000 0.216 0.784
#> GSM648690 3 0.4750 0.7323 0.000 0.216 0.784
#> GSM648691 3 0.4750 0.7323 0.000 0.216 0.784
#> GSM648693 3 0.4654 0.7353 0.000 0.208 0.792
#> GSM648700 2 0.0000 0.8862 0.000 1.000 0.000
#> GSM648630 3 0.4750 0.7323 0.000 0.216 0.784
#> GSM648632 3 0.4834 0.7367 0.004 0.204 0.792
#> GSM648639 3 0.4750 0.7323 0.000 0.216 0.784
#> GSM648640 3 0.4750 0.7323 0.000 0.216 0.784
#> GSM648668 2 0.0000 0.8862 0.000 1.000 0.000
#> GSM648676 2 0.0000 0.8862 0.000 1.000 0.000
#> GSM648692 3 0.4750 0.7323 0.000 0.216 0.784
#> GSM648694 3 0.4750 0.7323 0.000 0.216 0.784
#> GSM648699 2 0.0000 0.8862 0.000 1.000 0.000
#> GSM648701 2 0.0000 0.8862 0.000 1.000 0.000
#> GSM648673 2 0.0000 0.8862 0.000 1.000 0.000
#> GSM648677 2 0.0000 0.8862 0.000 1.000 0.000
#> GSM648687 3 0.4750 0.7323 0.000 0.216 0.784
#> GSM648688 3 0.4750 0.7323 0.000 0.216 0.784
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM648605 2 0.0000 0.7690 0.000 1.000 0.000 0.000
#> GSM648618 3 0.4155 0.7790 0.240 0.004 0.756 0.000
#> GSM648620 4 0.4817 0.5651 0.000 0.388 0.000 0.612
#> GSM648646 2 0.2469 0.8602 0.000 0.892 0.108 0.000
#> GSM648649 4 0.2216 0.7204 0.000 0.092 0.000 0.908
#> GSM648675 2 0.3528 0.9062 0.000 0.808 0.192 0.000
#> GSM648682 2 0.3528 0.9062 0.000 0.808 0.192 0.000
#> GSM648698 2 0.0000 0.7690 0.000 1.000 0.000 0.000
#> GSM648708 4 0.4817 0.5651 0.000 0.388 0.000 0.612
#> GSM648628 3 0.4761 0.6929 0.372 0.000 0.628 0.000
#> GSM648595 4 0.3074 0.7159 0.000 0.152 0.000 0.848
#> GSM648635 4 0.0000 0.7075 0.000 0.000 0.000 1.000
#> GSM648645 1 0.4866 0.7347 0.596 0.000 0.000 0.404
#> GSM648647 4 0.4996 0.4168 0.000 0.484 0.000 0.516
#> GSM648667 4 0.4103 0.6670 0.000 0.256 0.000 0.744
#> GSM648695 4 0.4989 0.4432 0.000 0.472 0.000 0.528
#> GSM648704 2 0.3569 0.9068 0.000 0.804 0.196 0.000
#> GSM648706 2 0.3569 0.9068 0.000 0.804 0.196 0.000
#> GSM648593 4 0.0000 0.7075 0.000 0.000 0.000 1.000
#> GSM648594 4 0.0000 0.7075 0.000 0.000 0.000 1.000
#> GSM648600 1 0.2469 0.6145 0.892 0.000 0.000 0.108
#> GSM648621 1 0.0921 0.6960 0.972 0.000 0.000 0.028
#> GSM648622 1 0.4761 0.7586 0.628 0.000 0.000 0.372
#> GSM648623 1 0.0000 0.6872 1.000 0.000 0.000 0.000
#> GSM648636 4 0.0000 0.7075 0.000 0.000 0.000 1.000
#> GSM648655 4 0.0000 0.7075 0.000 0.000 0.000 1.000
#> GSM648661 1 0.4776 0.7569 0.624 0.000 0.000 0.376
#> GSM648664 1 0.4776 0.7569 0.624 0.000 0.000 0.376
#> GSM648683 1 0.4776 0.7569 0.624 0.000 0.000 0.376
#> GSM648685 4 0.3486 0.4299 0.188 0.000 0.000 0.812
#> GSM648702 4 0.0000 0.7075 0.000 0.000 0.000 1.000
#> GSM648597 1 0.1022 0.6731 0.968 0.000 0.000 0.032
#> GSM648603 1 0.0000 0.6872 1.000 0.000 0.000 0.000
#> GSM648606 3 0.4304 0.7642 0.284 0.000 0.716 0.000
#> GSM648613 3 0.4304 0.7642 0.284 0.000 0.716 0.000
#> GSM648619 1 0.0000 0.6872 1.000 0.000 0.000 0.000
#> GSM648654 1 0.6617 0.6919 0.628 0.124 0.004 0.244
#> GSM648663 3 0.4304 0.7642 0.284 0.000 0.716 0.000
#> GSM648670 2 0.3610 0.9056 0.000 0.800 0.200 0.000
#> GSM648707 3 0.2281 0.8224 0.096 0.000 0.904 0.000
#> GSM648615 2 0.0469 0.7811 0.000 0.988 0.012 0.000
#> GSM648643 2 0.0000 0.7690 0.000 1.000 0.000 0.000
#> GSM648650 4 0.4661 0.6010 0.000 0.348 0.000 0.652
#> GSM648656 2 0.3356 0.9000 0.000 0.824 0.176 0.000
#> GSM648715 4 0.4866 0.5401 0.000 0.404 0.000 0.596
#> GSM648598 1 0.4776 0.7569 0.624 0.000 0.000 0.376
#> GSM648601 1 0.4761 0.7586 0.628 0.000 0.000 0.372
#> GSM648602 1 0.4761 0.7586 0.628 0.000 0.000 0.372
#> GSM648604 1 0.4761 0.7586 0.628 0.000 0.000 0.372
#> GSM648614 3 0.4304 0.7642 0.284 0.000 0.716 0.000
#> GSM648624 1 0.4761 0.7586 0.628 0.000 0.000 0.372
#> GSM648625 4 0.4955 -0.5278 0.444 0.000 0.000 0.556
#> GSM648629 1 0.4761 0.7586 0.628 0.000 0.000 0.372
#> GSM648634 1 0.4996 0.6392 0.516 0.000 0.000 0.484
#> GSM648648 4 0.0000 0.7075 0.000 0.000 0.000 1.000
#> GSM648651 1 0.4761 0.7586 0.628 0.000 0.000 0.372
#> GSM648657 1 0.4830 0.6666 0.608 0.000 0.000 0.392
#> GSM648660 1 0.4866 0.7347 0.596 0.000 0.000 0.404
#> GSM648697 4 0.3486 0.4299 0.188 0.000 0.000 0.812
#> GSM648710 1 0.4761 0.7586 0.628 0.000 0.000 0.372
#> GSM648591 1 0.0336 0.6824 0.992 0.000 0.008 0.000
#> GSM648592 1 0.3356 0.5281 0.824 0.000 0.000 0.176
#> GSM648607 1 0.0000 0.6872 1.000 0.000 0.000 0.000
#> GSM648611 3 0.4331 0.7616 0.288 0.000 0.712 0.000
#> GSM648612 1 0.0336 0.6824 0.992 0.000 0.008 0.000
#> GSM648616 3 0.0188 0.8686 0.004 0.000 0.996 0.000
#> GSM648617 1 0.2469 0.6145 0.892 0.000 0.000 0.108
#> GSM648626 1 0.0000 0.6872 1.000 0.000 0.000 0.000
#> GSM648711 1 0.3764 0.7404 0.784 0.000 0.000 0.216
#> GSM648712 1 0.0000 0.6872 1.000 0.000 0.000 0.000
#> GSM648713 1 0.0000 0.6872 1.000 0.000 0.000 0.000
#> GSM648714 3 0.4304 0.7642 0.284 0.000 0.716 0.000
#> GSM648716 1 0.0000 0.6872 1.000 0.000 0.000 0.000
#> GSM648717 3 0.4304 0.7642 0.284 0.000 0.716 0.000
#> GSM648590 2 0.4697 -0.0139 0.000 0.644 0.000 0.356
#> GSM648596 2 0.3320 0.8195 0.068 0.876 0.056 0.000
#> GSM648642 4 0.4996 0.4168 0.000 0.484 0.000 0.516
#> GSM648696 4 0.3074 0.7159 0.000 0.152 0.000 0.848
#> GSM648705 4 0.2530 0.7115 0.000 0.112 0.000 0.888
#> GSM648718 2 0.0000 0.7690 0.000 1.000 0.000 0.000
#> GSM648599 1 0.0000 0.6872 1.000 0.000 0.000 0.000
#> GSM648608 1 0.4761 0.7586 0.628 0.000 0.000 0.372
#> GSM648609 1 0.4776 0.7569 0.624 0.000 0.000 0.376
#> GSM648610 1 0.4477 0.7568 0.688 0.000 0.000 0.312
#> GSM648633 1 0.4994 0.6431 0.520 0.000 0.000 0.480
#> GSM648644 2 0.3569 0.9068 0.000 0.804 0.196 0.000
#> GSM648652 4 0.0000 0.7075 0.000 0.000 0.000 1.000
#> GSM648653 1 0.4776 0.7569 0.624 0.000 0.000 0.376
#> GSM648658 4 0.0707 0.6864 0.020 0.000 0.000 0.980
#> GSM648659 4 0.4955 0.4851 0.000 0.444 0.000 0.556
#> GSM648662 1 0.4250 0.7520 0.724 0.000 0.000 0.276
#> GSM648665 1 0.4776 0.7569 0.624 0.000 0.000 0.376
#> GSM648666 1 0.4776 0.7569 0.624 0.000 0.000 0.376
#> GSM648680 4 0.0000 0.7075 0.000 0.000 0.000 1.000
#> GSM648684 1 0.4776 0.7569 0.624 0.000 0.000 0.376
#> GSM648709 2 0.4304 0.2402 0.000 0.716 0.000 0.284
#> GSM648719 1 0.4761 0.7586 0.628 0.000 0.000 0.372
#> GSM648627 1 0.0000 0.6872 1.000 0.000 0.000 0.000
#> GSM648637 2 0.3688 0.9009 0.000 0.792 0.208 0.000
#> GSM648638 2 0.3942 0.8759 0.000 0.764 0.236 0.000
#> GSM648641 3 0.0921 0.8616 0.028 0.000 0.972 0.000
#> GSM648672 2 0.3610 0.9056 0.000 0.800 0.200 0.000
#> GSM648674 2 0.3610 0.9056 0.000 0.800 0.200 0.000
#> GSM648703 2 0.3569 0.9068 0.000 0.804 0.196 0.000
#> GSM648631 3 0.0000 0.8695 0.000 0.000 1.000 0.000
#> GSM648669 2 0.4134 0.8500 0.000 0.740 0.260 0.000
#> GSM648671 2 0.4134 0.8500 0.000 0.740 0.260 0.000
#> GSM648678 2 0.3569 0.9068 0.000 0.804 0.196 0.000
#> GSM648679 2 0.3688 0.9009 0.000 0.792 0.208 0.000
#> GSM648681 2 0.2011 0.8402 0.000 0.920 0.080 0.000
#> GSM648686 3 0.0000 0.8695 0.000 0.000 1.000 0.000
#> GSM648689 3 0.0000 0.8695 0.000 0.000 1.000 0.000
#> GSM648690 3 0.0000 0.8695 0.000 0.000 1.000 0.000
#> GSM648691 3 0.0000 0.8695 0.000 0.000 1.000 0.000
#> GSM648693 3 0.0000 0.8695 0.000 0.000 1.000 0.000
#> GSM648700 2 0.3751 0.9054 0.000 0.800 0.196 0.004
#> GSM648630 3 0.0000 0.8695 0.000 0.000 1.000 0.000
#> GSM648632 3 0.0000 0.8695 0.000 0.000 1.000 0.000
#> GSM648639 3 0.0000 0.8695 0.000 0.000 1.000 0.000
#> GSM648640 3 0.0000 0.8695 0.000 0.000 1.000 0.000
#> GSM648668 2 0.3610 0.9056 0.000 0.800 0.200 0.000
#> GSM648676 2 0.3569 0.9068 0.000 0.804 0.196 0.000
#> GSM648692 3 0.0000 0.8695 0.000 0.000 1.000 0.000
#> GSM648694 3 0.0000 0.8695 0.000 0.000 1.000 0.000
#> GSM648699 2 0.3569 0.9068 0.000 0.804 0.196 0.000
#> GSM648701 2 0.3569 0.9068 0.000 0.804 0.196 0.000
#> GSM648673 2 0.3688 0.9009 0.000 0.792 0.208 0.000
#> GSM648677 2 0.3569 0.9068 0.000 0.804 0.196 0.000
#> GSM648687 3 0.0000 0.8695 0.000 0.000 1.000 0.000
#> GSM648688 3 0.0000 0.8695 0.000 0.000 1.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM648605 2 0.4060 0.456 0.000 0.640 0.000 0.360 0.000
#> GSM648618 3 0.4262 0.202 0.000 0.000 0.560 0.000 0.440
#> GSM648620 2 0.0000 0.830 0.000 1.000 0.000 0.000 0.000
#> GSM648646 4 0.0404 0.976 0.000 0.012 0.000 0.988 0.000
#> GSM648649 2 0.3196 0.653 0.192 0.804 0.000 0.000 0.004
#> GSM648675 4 0.0671 0.968 0.000 0.004 0.016 0.980 0.000
#> GSM648682 4 0.0290 0.978 0.000 0.008 0.000 0.992 0.000
#> GSM648698 2 0.4060 0.456 0.000 0.640 0.000 0.360 0.000
#> GSM648708 2 0.0000 0.830 0.000 1.000 0.000 0.000 0.000
#> GSM648628 5 0.1043 0.832 0.000 0.000 0.040 0.000 0.960
#> GSM648595 2 0.3612 0.667 0.184 0.796 0.016 0.000 0.004
#> GSM648635 1 0.4299 0.498 0.608 0.388 0.000 0.000 0.004
#> GSM648645 1 0.1942 0.848 0.920 0.012 0.000 0.000 0.068
#> GSM648647 2 0.0880 0.831 0.000 0.968 0.000 0.032 0.000
#> GSM648667 2 0.1205 0.815 0.040 0.956 0.000 0.000 0.004
#> GSM648695 2 0.0880 0.831 0.000 0.968 0.000 0.032 0.000
#> GSM648704 4 0.0290 0.978 0.000 0.008 0.000 0.992 0.000
#> GSM648706 4 0.0290 0.978 0.000 0.008 0.000 0.992 0.000
#> GSM648593 1 0.4182 0.560 0.644 0.352 0.000 0.000 0.004
#> GSM648594 1 0.4268 0.566 0.648 0.344 0.000 0.000 0.008
#> GSM648600 5 0.0968 0.837 0.004 0.012 0.012 0.000 0.972
#> GSM648621 5 0.2189 0.783 0.084 0.000 0.012 0.000 0.904
#> GSM648622 1 0.0290 0.876 0.992 0.000 0.000 0.000 0.008
#> GSM648623 5 0.2561 0.729 0.144 0.000 0.000 0.000 0.856
#> GSM648636 1 0.4066 0.595 0.672 0.324 0.000 0.000 0.004
#> GSM648655 1 0.3990 0.616 0.688 0.308 0.000 0.000 0.004
#> GSM648661 1 0.0000 0.876 1.000 0.000 0.000 0.000 0.000
#> GSM648664 1 0.0290 0.875 0.992 0.008 0.000 0.000 0.000
#> GSM648683 1 0.0162 0.876 0.996 0.004 0.000 0.000 0.000
#> GSM648685 1 0.0290 0.875 0.992 0.008 0.000 0.000 0.000
#> GSM648702 1 0.4196 0.554 0.640 0.356 0.000 0.000 0.004
#> GSM648597 5 0.0451 0.840 0.000 0.000 0.008 0.004 0.988
#> GSM648603 5 0.0162 0.842 0.004 0.000 0.000 0.000 0.996
#> GSM648606 5 0.4210 0.454 0.000 0.000 0.412 0.000 0.588
#> GSM648613 5 0.4192 0.468 0.000 0.000 0.404 0.000 0.596
#> GSM648619 5 0.0162 0.842 0.004 0.000 0.000 0.000 0.996
#> GSM648654 1 0.4140 0.664 0.764 0.200 0.028 0.000 0.008
#> GSM648663 5 0.4192 0.469 0.000 0.000 0.404 0.000 0.596
#> GSM648670 4 0.0609 0.968 0.000 0.000 0.020 0.980 0.000
#> GSM648707 3 0.4817 0.313 0.000 0.000 0.572 0.024 0.404
#> GSM648615 4 0.1043 0.948 0.000 0.040 0.000 0.960 0.000
#> GSM648643 4 0.3774 0.542 0.000 0.296 0.000 0.704 0.000
#> GSM648650 2 0.0162 0.829 0.000 0.996 0.000 0.000 0.004
#> GSM648656 4 0.0290 0.978 0.000 0.008 0.000 0.992 0.000
#> GSM648715 2 0.0000 0.830 0.000 1.000 0.000 0.000 0.000
#> GSM648598 1 0.0000 0.876 1.000 0.000 0.000 0.000 0.000
#> GSM648601 1 0.0404 0.876 0.988 0.000 0.000 0.000 0.012
#> GSM648602 1 0.0290 0.876 0.992 0.000 0.000 0.000 0.008
#> GSM648604 1 0.0290 0.876 0.992 0.000 0.000 0.000 0.008
#> GSM648614 5 0.4341 0.465 0.000 0.004 0.404 0.000 0.592
#> GSM648624 1 0.0290 0.876 0.992 0.000 0.000 0.000 0.008
#> GSM648625 1 0.4546 0.587 0.668 0.304 0.000 0.000 0.028
#> GSM648629 1 0.0290 0.876 0.992 0.000 0.000 0.000 0.008
#> GSM648634 1 0.1082 0.869 0.964 0.028 0.000 0.000 0.008
#> GSM648648 1 0.4101 0.585 0.664 0.332 0.000 0.000 0.004
#> GSM648651 1 0.0290 0.876 0.992 0.000 0.000 0.000 0.008
#> GSM648657 1 0.4707 0.414 0.588 0.020 0.000 0.000 0.392
#> GSM648660 1 0.0609 0.875 0.980 0.000 0.000 0.000 0.020
#> GSM648697 1 0.0290 0.875 0.992 0.008 0.000 0.000 0.000
#> GSM648710 1 0.0290 0.876 0.992 0.000 0.000 0.000 0.008
#> GSM648591 5 0.0609 0.838 0.000 0.000 0.020 0.000 0.980
#> GSM648592 5 0.0451 0.839 0.000 0.008 0.000 0.004 0.988
#> GSM648607 5 0.1671 0.805 0.076 0.000 0.000 0.000 0.924
#> GSM648611 5 0.3913 0.594 0.000 0.000 0.324 0.000 0.676
#> GSM648612 5 0.0162 0.842 0.004 0.000 0.000 0.000 0.996
#> GSM648616 3 0.3051 0.820 0.000 0.000 0.852 0.120 0.028
#> GSM648617 5 0.0671 0.837 0.000 0.016 0.004 0.000 0.980
#> GSM648626 5 0.0162 0.842 0.004 0.000 0.000 0.000 0.996
#> GSM648711 1 0.3508 0.640 0.748 0.000 0.000 0.000 0.252
#> GSM648712 5 0.0162 0.842 0.004 0.000 0.000 0.000 0.996
#> GSM648713 5 0.0404 0.841 0.012 0.000 0.000 0.000 0.988
#> GSM648714 5 0.4350 0.457 0.000 0.004 0.408 0.000 0.588
#> GSM648716 5 0.0290 0.842 0.008 0.000 0.000 0.000 0.992
#> GSM648717 5 0.4210 0.454 0.000 0.000 0.412 0.000 0.588
#> GSM648590 2 0.4734 0.498 0.008 0.632 0.016 0.344 0.000
#> GSM648596 4 0.1372 0.950 0.000 0.016 0.004 0.956 0.024
#> GSM648642 2 0.0880 0.831 0.000 0.968 0.000 0.032 0.000
#> GSM648696 2 0.3355 0.671 0.184 0.804 0.012 0.000 0.000
#> GSM648705 2 0.0865 0.823 0.024 0.972 0.000 0.000 0.004
#> GSM648718 2 0.4101 0.431 0.000 0.628 0.000 0.372 0.000
#> GSM648599 5 0.1012 0.835 0.020 0.000 0.012 0.000 0.968
#> GSM648608 1 0.0290 0.876 0.992 0.000 0.000 0.000 0.008
#> GSM648609 1 0.0000 0.876 1.000 0.000 0.000 0.000 0.000
#> GSM648610 1 0.1012 0.866 0.968 0.000 0.012 0.000 0.020
#> GSM648633 1 0.1907 0.856 0.928 0.044 0.000 0.000 0.028
#> GSM648644 4 0.0290 0.978 0.000 0.008 0.000 0.992 0.000
#> GSM648652 1 0.4196 0.554 0.640 0.356 0.000 0.000 0.004
#> GSM648653 1 0.0162 0.876 0.996 0.000 0.000 0.000 0.004
#> GSM648658 1 0.0865 0.870 0.972 0.024 0.000 0.000 0.004
#> GSM648659 2 0.0798 0.832 0.008 0.976 0.000 0.016 0.000
#> GSM648662 1 0.2389 0.791 0.880 0.000 0.004 0.000 0.116
#> GSM648665 1 0.0290 0.875 0.992 0.008 0.000 0.000 0.000
#> GSM648666 1 0.0290 0.875 0.992 0.008 0.000 0.000 0.000
#> GSM648680 1 0.0955 0.868 0.968 0.028 0.000 0.000 0.004
#> GSM648684 1 0.0000 0.876 1.000 0.000 0.000 0.000 0.000
#> GSM648709 2 0.1478 0.816 0.000 0.936 0.000 0.064 0.000
#> GSM648719 1 0.0510 0.875 0.984 0.000 0.000 0.000 0.016
#> GSM648627 5 0.0912 0.839 0.016 0.000 0.012 0.000 0.972
#> GSM648637 4 0.0162 0.977 0.000 0.000 0.004 0.996 0.000
#> GSM648638 4 0.0162 0.977 0.000 0.000 0.004 0.996 0.000
#> GSM648641 3 0.1914 0.860 0.000 0.000 0.924 0.016 0.060
#> GSM648672 4 0.0162 0.977 0.000 0.000 0.004 0.996 0.000
#> GSM648674 4 0.0162 0.977 0.000 0.000 0.004 0.996 0.000
#> GSM648703 4 0.0162 0.979 0.000 0.004 0.000 0.996 0.000
#> GSM648631 3 0.0609 0.915 0.000 0.000 0.980 0.020 0.000
#> GSM648669 4 0.0162 0.977 0.000 0.000 0.004 0.996 0.000
#> GSM648671 4 0.0162 0.977 0.000 0.000 0.004 0.996 0.000
#> GSM648678 4 0.0162 0.979 0.000 0.004 0.000 0.996 0.000
#> GSM648679 4 0.0162 0.977 0.000 0.000 0.004 0.996 0.000
#> GSM648681 4 0.0566 0.969 0.000 0.012 0.004 0.984 0.000
#> GSM648686 3 0.0963 0.909 0.000 0.000 0.964 0.036 0.000
#> GSM648689 3 0.0609 0.915 0.000 0.000 0.980 0.020 0.000
#> GSM648690 3 0.0963 0.909 0.000 0.000 0.964 0.036 0.000
#> GSM648691 3 0.0609 0.915 0.000 0.000 0.980 0.020 0.000
#> GSM648693 3 0.0609 0.915 0.000 0.000 0.980 0.020 0.000
#> GSM648700 4 0.0162 0.979 0.000 0.004 0.000 0.996 0.000
#> GSM648630 3 0.0609 0.915 0.000 0.000 0.980 0.020 0.000
#> GSM648632 3 0.0609 0.915 0.000 0.000 0.980 0.020 0.000
#> GSM648639 3 0.2280 0.834 0.000 0.000 0.880 0.120 0.000
#> GSM648640 3 0.0609 0.915 0.000 0.000 0.980 0.020 0.000
#> GSM648668 4 0.0324 0.978 0.000 0.004 0.004 0.992 0.000
#> GSM648676 4 0.0162 0.979 0.000 0.004 0.000 0.996 0.000
#> GSM648692 3 0.0609 0.915 0.000 0.000 0.980 0.020 0.000
#> GSM648694 3 0.0609 0.915 0.000 0.000 0.980 0.020 0.000
#> GSM648699 4 0.0162 0.979 0.000 0.004 0.000 0.996 0.000
#> GSM648701 4 0.0162 0.979 0.000 0.004 0.000 0.996 0.000
#> GSM648673 4 0.0162 0.977 0.000 0.000 0.004 0.996 0.000
#> GSM648677 4 0.0162 0.979 0.000 0.004 0.000 0.996 0.000
#> GSM648687 3 0.0880 0.911 0.000 0.000 0.968 0.032 0.000
#> GSM648688 3 0.0703 0.914 0.000 0.000 0.976 0.024 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM648605 2 0.1918 0.7892 0.000 0.904 0.000 0.088 0.000 0.008
#> GSM648618 5 0.4649 0.1120 0.000 0.000 0.468 0.000 0.492 0.040
#> GSM648620 2 0.1049 0.8060 0.000 0.960 0.000 0.008 0.000 0.032
#> GSM648646 4 0.2814 0.8445 0.000 0.172 0.000 0.820 0.000 0.008
#> GSM648649 6 0.5307 0.5370 0.128 0.276 0.000 0.000 0.004 0.592
#> GSM648675 4 0.4460 0.7918 0.000 0.108 0.008 0.740 0.004 0.140
#> GSM648682 4 0.2631 0.8597 0.000 0.152 0.000 0.840 0.000 0.008
#> GSM648698 2 0.1918 0.7892 0.000 0.904 0.000 0.088 0.000 0.008
#> GSM648708 2 0.0790 0.8039 0.000 0.968 0.000 0.000 0.000 0.032
#> GSM648628 5 0.1268 0.7619 0.004 0.000 0.008 0.000 0.952 0.036
#> GSM648595 6 0.5084 0.5250 0.096 0.244 0.004 0.000 0.008 0.648
#> GSM648635 6 0.4793 0.7484 0.288 0.084 0.000 0.000 0.000 0.628
#> GSM648645 6 0.4493 0.6460 0.344 0.000 0.000 0.000 0.044 0.612
#> GSM648647 2 0.0717 0.8129 0.000 0.976 0.000 0.016 0.000 0.008
#> GSM648667 2 0.4144 0.3643 0.020 0.620 0.000 0.000 0.000 0.360
#> GSM648695 2 0.0820 0.8123 0.000 0.972 0.000 0.016 0.000 0.012
#> GSM648704 4 0.2595 0.8558 0.000 0.160 0.000 0.836 0.000 0.004
#> GSM648706 4 0.2848 0.8416 0.000 0.176 0.000 0.816 0.000 0.008
#> GSM648593 6 0.4428 0.7264 0.268 0.052 0.000 0.000 0.004 0.676
#> GSM648594 6 0.4657 0.7340 0.224 0.056 0.004 0.000 0.016 0.700
#> GSM648600 5 0.2679 0.7525 0.040 0.000 0.000 0.000 0.864 0.096
#> GSM648621 5 0.3770 0.6839 0.148 0.000 0.000 0.000 0.776 0.076
#> GSM648622 1 0.1398 0.8121 0.940 0.000 0.000 0.000 0.008 0.052
#> GSM648623 5 0.4823 0.3913 0.348 0.000 0.000 0.000 0.584 0.068
#> GSM648636 6 0.4956 0.6691 0.332 0.072 0.000 0.000 0.004 0.592
#> GSM648655 6 0.4782 0.6250 0.380 0.048 0.000 0.000 0.004 0.568
#> GSM648661 1 0.0937 0.8334 0.960 0.000 0.000 0.000 0.000 0.040
#> GSM648664 1 0.0937 0.8334 0.960 0.000 0.000 0.000 0.000 0.040
#> GSM648683 1 0.1204 0.8298 0.944 0.000 0.000 0.000 0.000 0.056
#> GSM648685 1 0.1075 0.8298 0.952 0.000 0.000 0.000 0.000 0.048
#> GSM648702 6 0.4634 0.7481 0.284 0.072 0.000 0.000 0.000 0.644
#> GSM648597 5 0.3862 0.7015 0.016 0.000 0.008 0.036 0.792 0.148
#> GSM648603 5 0.2058 0.7621 0.036 0.000 0.000 0.000 0.908 0.056
#> GSM648606 5 0.5351 0.4829 0.000 0.000 0.288 0.000 0.568 0.144
#> GSM648613 5 0.5287 0.5031 0.000 0.000 0.272 0.000 0.584 0.144
#> GSM648619 5 0.1225 0.7683 0.036 0.000 0.000 0.000 0.952 0.012
#> GSM648654 1 0.3250 0.6193 0.788 0.196 0.012 0.000 0.000 0.004
#> GSM648663 5 0.5351 0.4829 0.000 0.000 0.288 0.000 0.568 0.144
#> GSM648670 4 0.1196 0.8771 0.000 0.000 0.008 0.952 0.000 0.040
#> GSM648707 5 0.5598 0.0909 0.000 0.000 0.432 0.064 0.472 0.032
#> GSM648615 4 0.3454 0.7709 0.000 0.208 0.000 0.768 0.000 0.024
#> GSM648643 2 0.3936 0.4649 0.000 0.688 0.000 0.288 0.000 0.024
#> GSM648650 2 0.3578 0.4378 0.000 0.660 0.000 0.000 0.000 0.340
#> GSM648656 4 0.2706 0.8538 0.000 0.160 0.000 0.832 0.000 0.008
#> GSM648715 2 0.0858 0.8065 0.000 0.968 0.000 0.004 0.000 0.028
#> GSM648598 1 0.2631 0.7153 0.820 0.000 0.000 0.000 0.000 0.180
#> GSM648601 1 0.3136 0.6845 0.796 0.000 0.000 0.000 0.016 0.188
#> GSM648602 1 0.1663 0.8040 0.912 0.000 0.000 0.000 0.000 0.088
#> GSM648604 1 0.0000 0.8383 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM648614 5 0.5758 0.4688 0.000 0.016 0.288 0.000 0.552 0.144
#> GSM648624 1 0.0260 0.8372 0.992 0.000 0.000 0.000 0.000 0.008
#> GSM648625 1 0.5724 -0.1774 0.484 0.084 0.000 0.000 0.028 0.404
#> GSM648629 1 0.0000 0.8383 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM648634 6 0.3847 0.5824 0.456 0.000 0.000 0.000 0.000 0.544
#> GSM648648 6 0.4795 0.7374 0.324 0.072 0.000 0.000 0.000 0.604
#> GSM648651 1 0.1913 0.8009 0.908 0.000 0.000 0.000 0.012 0.080
#> GSM648657 6 0.5193 0.5605 0.164 0.004 0.000 0.000 0.200 0.632
#> GSM648660 1 0.3584 0.4073 0.688 0.000 0.000 0.000 0.004 0.308
#> GSM648697 1 0.1501 0.8174 0.924 0.000 0.000 0.000 0.000 0.076
#> GSM648710 1 0.0000 0.8383 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM648591 5 0.2291 0.7622 0.008 0.000 0.008 0.016 0.904 0.064
#> GSM648592 5 0.2752 0.7547 0.000 0.000 0.004 0.036 0.864 0.096
#> GSM648607 5 0.3999 0.5890 0.272 0.000 0.000 0.000 0.696 0.032
#> GSM648611 5 0.4550 0.5899 0.000 0.000 0.240 0.000 0.676 0.084
#> GSM648612 5 0.0405 0.7629 0.004 0.000 0.000 0.000 0.988 0.008
#> GSM648616 3 0.5214 0.5370 0.000 0.000 0.588 0.332 0.048 0.032
#> GSM648617 5 0.1531 0.7582 0.000 0.000 0.004 0.000 0.928 0.068
#> GSM648626 5 0.2058 0.7621 0.036 0.000 0.000 0.000 0.908 0.056
#> GSM648711 1 0.3254 0.6601 0.816 0.000 0.000 0.000 0.136 0.048
#> GSM648712 5 0.0520 0.7637 0.008 0.000 0.000 0.000 0.984 0.008
#> GSM648713 5 0.1616 0.7698 0.048 0.000 0.000 0.000 0.932 0.020
#> GSM648714 5 0.5772 0.4622 0.000 0.016 0.292 0.000 0.548 0.144
#> GSM648716 5 0.1500 0.7691 0.052 0.000 0.000 0.000 0.936 0.012
#> GSM648717 5 0.5408 0.4582 0.000 0.000 0.304 0.000 0.552 0.144
#> GSM648590 2 0.6023 0.5439 0.032 0.596 0.004 0.156 0.004 0.208
#> GSM648596 4 0.3950 0.8190 0.000 0.140 0.008 0.792 0.024 0.036
#> GSM648642 2 0.0717 0.8129 0.000 0.976 0.000 0.016 0.000 0.008
#> GSM648696 2 0.5244 0.2204 0.112 0.552 0.000 0.000 0.000 0.336
#> GSM648705 6 0.4152 0.1171 0.012 0.440 0.000 0.000 0.000 0.548
#> GSM648718 2 0.2020 0.7842 0.000 0.896 0.000 0.096 0.000 0.008
#> GSM648599 5 0.2937 0.7462 0.056 0.000 0.000 0.000 0.848 0.096
#> GSM648608 1 0.0363 0.8356 0.988 0.000 0.000 0.000 0.000 0.012
#> GSM648609 1 0.0713 0.8373 0.972 0.000 0.000 0.000 0.000 0.028
#> GSM648610 1 0.1723 0.7943 0.928 0.000 0.000 0.000 0.036 0.036
#> GSM648633 6 0.4486 0.6401 0.364 0.020 0.000 0.000 0.012 0.604
#> GSM648644 4 0.2558 0.8585 0.000 0.156 0.000 0.840 0.000 0.004
#> GSM648652 6 0.4704 0.7472 0.300 0.072 0.000 0.000 0.000 0.628
#> GSM648653 1 0.1714 0.8025 0.908 0.000 0.000 0.000 0.000 0.092
#> GSM648658 6 0.4332 0.5629 0.416 0.016 0.000 0.000 0.004 0.564
#> GSM648659 2 0.2558 0.7375 0.000 0.840 0.000 0.000 0.004 0.156
#> GSM648662 1 0.4141 0.5925 0.756 0.000 0.004 0.000 0.128 0.112
#> GSM648665 1 0.1007 0.8341 0.956 0.000 0.000 0.000 0.000 0.044
#> GSM648666 1 0.1327 0.8288 0.936 0.000 0.000 0.000 0.000 0.064
#> GSM648680 6 0.4010 0.6512 0.408 0.008 0.000 0.000 0.000 0.584
#> GSM648684 1 0.1204 0.8298 0.944 0.000 0.000 0.000 0.000 0.056
#> GSM648709 2 0.1196 0.8094 0.000 0.952 0.000 0.040 0.000 0.008
#> GSM648719 1 0.3582 0.5608 0.732 0.000 0.000 0.000 0.016 0.252
#> GSM648627 5 0.1970 0.7678 0.060 0.000 0.000 0.000 0.912 0.028
#> GSM648637 4 0.0146 0.8895 0.000 0.000 0.000 0.996 0.000 0.004
#> GSM648638 4 0.0146 0.8895 0.000 0.000 0.000 0.996 0.000 0.004
#> GSM648641 3 0.3190 0.7309 0.000 0.000 0.820 0.000 0.136 0.044
#> GSM648672 4 0.0713 0.8942 0.000 0.028 0.000 0.972 0.000 0.000
#> GSM648674 4 0.0146 0.8895 0.000 0.000 0.000 0.996 0.000 0.004
#> GSM648703 4 0.2474 0.8811 0.000 0.080 0.000 0.884 0.004 0.032
#> GSM648631 3 0.0405 0.9259 0.000 0.000 0.988 0.008 0.004 0.000
#> GSM648669 4 0.0000 0.8902 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM648671 4 0.0000 0.8902 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM648678 4 0.1471 0.8912 0.000 0.064 0.000 0.932 0.000 0.004
#> GSM648679 4 0.0146 0.8895 0.000 0.000 0.000 0.996 0.000 0.004
#> GSM648681 4 0.0665 0.8876 0.000 0.004 0.008 0.980 0.000 0.008
#> GSM648686 3 0.0363 0.9294 0.000 0.000 0.988 0.012 0.000 0.000
#> GSM648689 3 0.0508 0.9274 0.000 0.000 0.984 0.012 0.000 0.004
#> GSM648690 3 0.0363 0.9294 0.000 0.000 0.988 0.012 0.000 0.000
#> GSM648691 3 0.0363 0.9294 0.000 0.000 0.988 0.012 0.000 0.000
#> GSM648693 3 0.0363 0.9294 0.000 0.000 0.988 0.012 0.000 0.000
#> GSM648700 4 0.3885 0.8167 0.000 0.100 0.000 0.780 0.004 0.116
#> GSM648630 3 0.0363 0.9294 0.000 0.000 0.988 0.012 0.000 0.000
#> GSM648632 3 0.0363 0.9294 0.000 0.000 0.988 0.012 0.000 0.000
#> GSM648639 3 0.4198 0.6024 0.000 0.000 0.656 0.316 0.004 0.024
#> GSM648640 3 0.1088 0.9134 0.000 0.000 0.960 0.016 0.000 0.024
#> GSM648668 4 0.0713 0.8942 0.000 0.028 0.000 0.972 0.000 0.000
#> GSM648676 4 0.3843 0.8216 0.000 0.104 0.000 0.784 0.004 0.108
#> GSM648692 3 0.0363 0.9294 0.000 0.000 0.988 0.012 0.000 0.000
#> GSM648694 3 0.0363 0.9294 0.000 0.000 0.988 0.012 0.000 0.000
#> GSM648699 4 0.3658 0.8345 0.000 0.104 0.000 0.800 0.004 0.092
#> GSM648701 4 0.3658 0.8345 0.000 0.104 0.000 0.800 0.004 0.092
#> GSM648673 4 0.0000 0.8902 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM648677 4 0.1471 0.8912 0.000 0.064 0.000 0.932 0.000 0.004
#> GSM648687 3 0.0458 0.9270 0.000 0.000 0.984 0.016 0.000 0.000
#> GSM648688 3 0.0363 0.9294 0.000 0.000 0.988 0.012 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) development.stage(p) other(p) k
#> MAD:skmeans 115 4.21e-11 0.01602 3.61e-16 2
#> MAD:skmeans 117 7.71e-08 0.00157 3.99e-21 3
#> MAD:skmeans 121 1.36e-10 0.00278 8.27e-19 4
#> MAD:skmeans 116 4.91e-16 0.01526 6.22e-38 5
#> MAD:skmeans 115 1.37e-15 0.00645 1.26e-34 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 51941 rows and 130 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'pam' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.802 0.918 0.959 0.4438 0.554 0.554
#> 3 3 0.517 0.716 0.816 0.3448 0.805 0.669
#> 4 4 0.657 0.814 0.883 0.2146 0.795 0.542
#> 5 5 0.630 0.578 0.789 0.0803 0.807 0.420
#> 6 6 0.787 0.728 0.866 0.0576 0.892 0.554
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
#> GSM648605 2 0.5737 0.848 0.136 0.864
#> GSM648618 1 0.0000 0.969 1.000 0.000
#> GSM648620 2 0.9710 0.426 0.400 0.600
#> GSM648646 2 0.0000 0.927 0.000 1.000
#> GSM648649 1 0.2948 0.928 0.948 0.052
#> GSM648675 1 0.5178 0.873 0.884 0.116
#> GSM648682 2 0.0000 0.927 0.000 1.000
#> GSM648698 2 0.5294 0.861 0.120 0.880
#> GSM648708 1 0.3274 0.922 0.940 0.060
#> GSM648628 1 0.0000 0.969 1.000 0.000
#> GSM648595 1 0.0376 0.967 0.996 0.004
#> GSM648635 1 0.0000 0.969 1.000 0.000
#> GSM648645 1 0.0000 0.969 1.000 0.000
#> GSM648647 2 0.7602 0.758 0.220 0.780
#> GSM648667 1 0.3114 0.925 0.944 0.056
#> GSM648695 1 0.4022 0.904 0.920 0.080
#> GSM648704 2 0.0000 0.927 0.000 1.000
#> GSM648706 2 0.0000 0.927 0.000 1.000
#> GSM648593 1 0.0000 0.969 1.000 0.000
#> GSM648594 1 0.0000 0.969 1.000 0.000
#> GSM648600 1 0.0000 0.969 1.000 0.000
#> GSM648621 1 0.0000 0.969 1.000 0.000
#> GSM648622 1 0.0000 0.969 1.000 0.000
#> GSM648623 1 0.0000 0.969 1.000 0.000
#> GSM648636 1 0.0000 0.969 1.000 0.000
#> GSM648655 1 0.0000 0.969 1.000 0.000
#> GSM648661 1 0.0000 0.969 1.000 0.000
#> GSM648664 1 0.0000 0.969 1.000 0.000
#> GSM648683 1 0.0000 0.969 1.000 0.000
#> GSM648685 1 0.0000 0.969 1.000 0.000
#> GSM648702 1 0.0000 0.969 1.000 0.000
#> GSM648597 1 0.0000 0.969 1.000 0.000
#> GSM648603 1 0.0000 0.969 1.000 0.000
#> GSM648606 1 0.0000 0.969 1.000 0.000
#> GSM648613 1 0.0000 0.969 1.000 0.000
#> GSM648619 1 0.0000 0.969 1.000 0.000
#> GSM648654 1 0.0000 0.969 1.000 0.000
#> GSM648663 1 0.0000 0.969 1.000 0.000
#> GSM648670 1 0.7602 0.746 0.780 0.220
#> GSM648707 1 0.6247 0.810 0.844 0.156
#> GSM648615 2 0.2948 0.907 0.052 0.948
#> GSM648643 2 0.0000 0.927 0.000 1.000
#> GSM648650 1 0.3114 0.925 0.944 0.056
#> GSM648656 2 0.0000 0.927 0.000 1.000
#> GSM648715 2 0.9393 0.531 0.356 0.644
#> GSM648598 1 0.0000 0.969 1.000 0.000
#> GSM648601 1 0.0000 0.969 1.000 0.000
#> GSM648602 1 0.0000 0.969 1.000 0.000
#> GSM648604 1 0.0000 0.969 1.000 0.000
#> GSM648614 1 0.3879 0.901 0.924 0.076
#> GSM648624 1 0.0000 0.969 1.000 0.000
#> GSM648625 1 0.0000 0.969 1.000 0.000
#> GSM648629 1 0.0000 0.969 1.000 0.000
#> GSM648634 1 0.0000 0.969 1.000 0.000
#> GSM648648 1 0.0000 0.969 1.000 0.000
#> GSM648651 1 0.0000 0.969 1.000 0.000
#> GSM648657 1 0.0000 0.969 1.000 0.000
#> GSM648660 1 0.0000 0.969 1.000 0.000
#> GSM648697 1 0.0000 0.969 1.000 0.000
#> GSM648710 1 0.0000 0.969 1.000 0.000
#> GSM648591 1 0.0000 0.969 1.000 0.000
#> GSM648592 1 0.3114 0.925 0.944 0.056
#> GSM648607 1 0.0000 0.969 1.000 0.000
#> GSM648611 1 0.0000 0.969 1.000 0.000
#> GSM648612 1 0.0000 0.969 1.000 0.000
#> GSM648616 2 0.3274 0.901 0.060 0.940
#> GSM648617 1 0.0000 0.969 1.000 0.000
#> GSM648626 1 0.0000 0.969 1.000 0.000
#> GSM648711 1 0.0000 0.969 1.000 0.000
#> GSM648712 1 0.0000 0.969 1.000 0.000
#> GSM648713 1 0.0000 0.969 1.000 0.000
#> GSM648714 2 0.7056 0.793 0.192 0.808
#> GSM648716 1 0.0000 0.969 1.000 0.000
#> GSM648717 1 0.0000 0.969 1.000 0.000
#> GSM648590 1 0.3431 0.918 0.936 0.064
#> GSM648596 2 0.7056 0.794 0.192 0.808
#> GSM648642 2 0.6623 0.815 0.172 0.828
#> GSM648696 1 0.0000 0.969 1.000 0.000
#> GSM648705 1 0.0000 0.969 1.000 0.000
#> GSM648718 2 0.6343 0.827 0.160 0.840
#> GSM648599 1 0.0000 0.969 1.000 0.000
#> GSM648608 1 0.0000 0.969 1.000 0.000
#> GSM648609 1 0.0000 0.969 1.000 0.000
#> GSM648610 1 0.0000 0.969 1.000 0.000
#> GSM648633 1 0.0000 0.969 1.000 0.000
#> GSM648644 2 0.0000 0.927 0.000 1.000
#> GSM648652 1 0.0000 0.969 1.000 0.000
#> GSM648653 1 0.0000 0.969 1.000 0.000
#> GSM648658 1 0.0000 0.969 1.000 0.000
#> GSM648659 1 0.0938 0.961 0.988 0.012
#> GSM648662 1 0.0000 0.969 1.000 0.000
#> GSM648665 1 0.0000 0.969 1.000 0.000
#> GSM648666 1 0.0000 0.969 1.000 0.000
#> GSM648680 1 0.0000 0.969 1.000 0.000
#> GSM648684 1 0.0000 0.969 1.000 0.000
#> GSM648709 2 0.9170 0.580 0.332 0.668
#> GSM648719 1 0.0000 0.969 1.000 0.000
#> GSM648627 1 0.0000 0.969 1.000 0.000
#> GSM648637 2 0.0000 0.927 0.000 1.000
#> GSM648638 2 0.0000 0.927 0.000 1.000
#> GSM648641 1 0.8813 0.582 0.700 0.300
#> GSM648672 2 0.0000 0.927 0.000 1.000
#> GSM648674 2 0.0000 0.927 0.000 1.000
#> GSM648703 2 0.0000 0.927 0.000 1.000
#> GSM648631 1 0.2043 0.945 0.968 0.032
#> GSM648669 2 0.0000 0.927 0.000 1.000
#> GSM648671 2 0.0000 0.927 0.000 1.000
#> GSM648678 2 0.0000 0.927 0.000 1.000
#> GSM648679 2 0.0000 0.927 0.000 1.000
#> GSM648681 1 0.5946 0.831 0.856 0.144
#> GSM648686 2 0.3431 0.901 0.064 0.936
#> GSM648689 2 0.3431 0.901 0.064 0.936
#> GSM648690 2 0.3114 0.905 0.056 0.944
#> GSM648691 2 0.5178 0.863 0.116 0.884
#> GSM648693 1 0.7815 0.705 0.768 0.232
#> GSM648700 1 0.7602 0.723 0.780 0.220
#> GSM648630 2 0.3431 0.901 0.064 0.936
#> GSM648632 1 0.3114 0.924 0.944 0.056
#> GSM648639 2 0.0000 0.927 0.000 1.000
#> GSM648640 2 0.0672 0.925 0.008 0.992
#> GSM648668 2 0.0000 0.927 0.000 1.000
#> GSM648676 2 0.0000 0.927 0.000 1.000
#> GSM648692 2 0.3114 0.905 0.056 0.944
#> GSM648694 2 0.4298 0.886 0.088 0.912
#> GSM648699 2 0.0000 0.927 0.000 1.000
#> GSM648701 2 0.0000 0.927 0.000 1.000
#> GSM648673 2 0.0000 0.927 0.000 1.000
#> GSM648677 2 0.0000 0.927 0.000 1.000
#> GSM648687 1 0.7602 0.723 0.780 0.220
#> GSM648688 1 0.7453 0.735 0.788 0.212
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM648605 2 0.3886 0.7962 0.096 0.880 0.024
#> GSM648618 1 0.6267 0.4880 0.548 0.000 0.452
#> GSM648620 1 0.4015 0.7484 0.876 0.028 0.096
#> GSM648646 2 0.0747 0.8708 0.000 0.984 0.016
#> GSM648649 1 0.3116 0.7579 0.892 0.000 0.108
#> GSM648675 1 0.5166 0.7121 0.828 0.056 0.116
#> GSM648682 2 0.0424 0.8739 0.000 0.992 0.008
#> GSM648698 2 0.3722 0.8022 0.088 0.888 0.024
#> GSM648708 1 0.1453 0.7791 0.968 0.008 0.024
#> GSM648628 3 0.6225 -0.1160 0.432 0.000 0.568
#> GSM648595 1 0.0747 0.7867 0.984 0.000 0.016
#> GSM648635 1 0.0237 0.7879 0.996 0.000 0.004
#> GSM648645 1 0.5650 0.6983 0.688 0.000 0.312
#> GSM648647 2 0.5559 0.6785 0.192 0.780 0.028
#> GSM648667 1 0.0592 0.7850 0.988 0.000 0.012
#> GSM648695 1 0.4277 0.7070 0.852 0.132 0.016
#> GSM648704 2 0.0237 0.8764 0.000 0.996 0.004
#> GSM648706 2 0.0424 0.8739 0.000 0.992 0.008
#> GSM648593 1 0.0424 0.7880 0.992 0.000 0.008
#> GSM648594 1 0.3038 0.7667 0.896 0.000 0.104
#> GSM648600 1 0.2878 0.7621 0.904 0.000 0.096
#> GSM648621 1 0.0592 0.7875 0.988 0.000 0.012
#> GSM648622 1 0.5431 0.7156 0.716 0.000 0.284
#> GSM648623 1 0.5465 0.7151 0.712 0.000 0.288
#> GSM648636 1 0.0000 0.7877 1.000 0.000 0.000
#> GSM648655 1 0.1964 0.7826 0.944 0.000 0.056
#> GSM648661 1 0.5431 0.7156 0.716 0.000 0.284
#> GSM648664 1 0.5431 0.7156 0.716 0.000 0.284
#> GSM648683 1 0.0000 0.7877 1.000 0.000 0.000
#> GSM648685 1 0.1964 0.7826 0.944 0.000 0.056
#> GSM648702 1 0.0000 0.7877 1.000 0.000 0.000
#> GSM648597 1 0.5733 0.6921 0.676 0.000 0.324
#> GSM648603 1 0.5733 0.6921 0.676 0.000 0.324
#> GSM648606 3 0.5968 -0.0044 0.364 0.000 0.636
#> GSM648613 3 0.4931 0.5065 0.232 0.000 0.768
#> GSM648619 1 0.5733 0.6921 0.676 0.000 0.324
#> GSM648654 1 0.5497 0.7141 0.708 0.000 0.292
#> GSM648663 1 0.6079 0.6756 0.612 0.000 0.388
#> GSM648670 1 0.7013 0.0409 0.548 0.432 0.020
#> GSM648707 3 0.4902 0.7483 0.064 0.092 0.844
#> GSM648615 2 0.3375 0.8090 0.008 0.892 0.100
#> GSM648643 2 0.1878 0.8564 0.004 0.952 0.044
#> GSM648650 1 0.0592 0.7850 0.988 0.000 0.012
#> GSM648656 2 0.0424 0.8739 0.000 0.992 0.008
#> GSM648715 2 0.7274 0.1677 0.452 0.520 0.028
#> GSM648598 1 0.2448 0.7844 0.924 0.000 0.076
#> GSM648601 1 0.2165 0.7821 0.936 0.000 0.064
#> GSM648602 1 0.0237 0.7879 0.996 0.000 0.004
#> GSM648604 1 0.5431 0.7156 0.716 0.000 0.284
#> GSM648614 1 0.5529 0.7136 0.704 0.000 0.296
#> GSM648624 1 0.5431 0.7156 0.716 0.000 0.284
#> GSM648625 1 0.2261 0.7829 0.932 0.000 0.068
#> GSM648629 1 0.5431 0.7156 0.716 0.000 0.284
#> GSM648634 1 0.0592 0.7875 0.988 0.000 0.012
#> GSM648648 1 0.1964 0.7826 0.944 0.000 0.056
#> GSM648651 1 0.5397 0.7179 0.720 0.000 0.280
#> GSM648657 1 0.2878 0.7621 0.904 0.000 0.096
#> GSM648660 1 0.1964 0.7826 0.944 0.000 0.056
#> GSM648697 1 0.1964 0.7826 0.944 0.000 0.056
#> GSM648710 1 0.5431 0.7156 0.716 0.000 0.284
#> GSM648591 1 0.5733 0.6921 0.676 0.000 0.324
#> GSM648592 1 0.5810 0.6874 0.664 0.000 0.336
#> GSM648607 1 0.5988 0.6829 0.632 0.000 0.368
#> GSM648611 3 0.6286 -0.1208 0.464 0.000 0.536
#> GSM648612 1 0.5835 0.6898 0.660 0.000 0.340
#> GSM648616 3 0.8322 0.4451 0.120 0.276 0.604
#> GSM648617 1 0.3038 0.7594 0.896 0.000 0.104
#> GSM648626 1 0.5733 0.6921 0.676 0.000 0.324
#> GSM648711 1 0.5431 0.7156 0.716 0.000 0.284
#> GSM648712 1 0.5733 0.6921 0.676 0.000 0.324
#> GSM648713 1 0.5988 0.6829 0.632 0.000 0.368
#> GSM648714 2 0.5915 0.7112 0.080 0.792 0.128
#> GSM648716 1 0.6026 0.6782 0.624 0.000 0.376
#> GSM648717 3 0.3482 0.6554 0.128 0.000 0.872
#> GSM648590 1 0.3045 0.7701 0.916 0.020 0.064
#> GSM648596 2 0.5657 0.7247 0.088 0.808 0.104
#> GSM648642 2 0.5977 0.6281 0.252 0.728 0.020
#> GSM648696 1 0.3116 0.7579 0.892 0.000 0.108
#> GSM648705 1 0.1643 0.7829 0.956 0.000 0.044
#> GSM648718 2 0.4483 0.7690 0.128 0.848 0.024
#> GSM648599 1 0.2878 0.7621 0.904 0.000 0.096
#> GSM648608 1 0.5363 0.7202 0.724 0.000 0.276
#> GSM648609 1 0.5431 0.7156 0.716 0.000 0.284
#> GSM648610 1 0.0592 0.7875 0.988 0.000 0.012
#> GSM648633 1 0.0000 0.7877 1.000 0.000 0.000
#> GSM648644 2 0.0237 0.8764 0.000 0.996 0.004
#> GSM648652 1 0.0000 0.7877 1.000 0.000 0.000
#> GSM648653 1 0.0000 0.7877 1.000 0.000 0.000
#> GSM648658 1 0.1860 0.7835 0.948 0.000 0.052
#> GSM648659 1 0.1129 0.7804 0.976 0.004 0.020
#> GSM648662 1 0.5431 0.7156 0.716 0.000 0.284
#> GSM648665 1 0.5431 0.7156 0.716 0.000 0.284
#> GSM648666 1 0.3482 0.7783 0.872 0.000 0.128
#> GSM648680 1 0.1964 0.7826 0.944 0.000 0.056
#> GSM648684 1 0.1860 0.7835 0.948 0.000 0.052
#> GSM648709 1 0.8573 0.3557 0.524 0.372 0.104
#> GSM648719 1 0.5431 0.7156 0.716 0.000 0.284
#> GSM648627 1 0.5733 0.6921 0.676 0.000 0.324
#> GSM648637 2 0.0424 0.8751 0.000 0.992 0.008
#> GSM648638 2 0.1289 0.8620 0.000 0.968 0.032
#> GSM648641 3 0.5036 0.7546 0.048 0.120 0.832
#> GSM648672 2 0.0237 0.8764 0.000 0.996 0.004
#> GSM648674 2 0.0237 0.8764 0.000 0.996 0.004
#> GSM648703 2 0.0237 0.8764 0.000 0.996 0.004
#> GSM648631 3 0.3340 0.7158 0.120 0.000 0.880
#> GSM648669 2 0.1031 0.8669 0.000 0.976 0.024
#> GSM648671 2 0.0424 0.8751 0.000 0.992 0.008
#> GSM648678 2 0.0237 0.8764 0.000 0.996 0.004
#> GSM648679 2 0.0237 0.8764 0.000 0.996 0.004
#> GSM648681 2 0.6825 -0.0447 0.492 0.496 0.012
#> GSM648686 3 0.4504 0.7346 0.000 0.196 0.804
#> GSM648689 3 0.3941 0.7508 0.000 0.156 0.844
#> GSM648690 3 0.4399 0.7412 0.000 0.188 0.812
#> GSM648691 3 0.4121 0.7530 0.000 0.168 0.832
#> GSM648693 3 0.3500 0.7573 0.004 0.116 0.880
#> GSM648700 1 0.5331 0.5911 0.792 0.184 0.024
#> GSM648630 3 0.4291 0.7480 0.000 0.180 0.820
#> GSM648632 3 0.3267 0.7180 0.116 0.000 0.884
#> GSM648639 2 0.5948 0.3762 0.000 0.640 0.360
#> GSM648640 3 0.4346 0.7452 0.000 0.184 0.816
#> GSM648668 2 0.1031 0.8669 0.000 0.976 0.024
#> GSM648676 2 0.0237 0.8764 0.000 0.996 0.004
#> GSM648692 3 0.4346 0.7452 0.000 0.184 0.816
#> GSM648694 3 0.4121 0.7537 0.000 0.168 0.832
#> GSM648699 2 0.0237 0.8764 0.000 0.996 0.004
#> GSM648701 2 0.0237 0.8764 0.000 0.996 0.004
#> GSM648673 2 0.0592 0.8733 0.000 0.988 0.012
#> GSM648677 2 0.0237 0.8764 0.000 0.996 0.004
#> GSM648687 3 0.7898 0.5359 0.232 0.116 0.652
#> GSM648688 3 0.3500 0.7573 0.004 0.116 0.880
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM648605 2 0.4364 0.7779 0.056 0.808 0.000 0.136
#> GSM648618 1 0.3266 0.7898 0.832 0.000 0.000 0.168
#> GSM648620 1 0.1661 0.8409 0.944 0.004 0.000 0.052
#> GSM648646 2 0.1302 0.8993 0.044 0.956 0.000 0.000
#> GSM648649 1 0.1557 0.8492 0.944 0.000 0.000 0.056
#> GSM648675 1 0.0376 0.8321 0.992 0.004 0.000 0.004
#> GSM648682 2 0.1302 0.8993 0.044 0.956 0.000 0.000
#> GSM648698 2 0.1635 0.8969 0.044 0.948 0.000 0.008
#> GSM648708 1 0.3052 0.8194 0.860 0.004 0.000 0.136
#> GSM648628 1 0.3810 0.7885 0.804 0.000 0.008 0.188
#> GSM648595 1 0.3024 0.8491 0.852 0.000 0.000 0.148
#> GSM648635 1 0.3311 0.8400 0.828 0.000 0.000 0.172
#> GSM648645 1 0.3764 0.7944 0.784 0.000 0.000 0.216
#> GSM648647 2 0.5321 0.6793 0.056 0.716 0.000 0.228
#> GSM648667 1 0.3356 0.8374 0.824 0.000 0.000 0.176
#> GSM648695 1 0.4956 0.8078 0.756 0.056 0.000 0.188
#> GSM648704 2 0.0188 0.9109 0.000 0.996 0.004 0.000
#> GSM648706 2 0.1302 0.8993 0.044 0.956 0.000 0.000
#> GSM648593 1 0.3975 0.7832 0.760 0.000 0.000 0.240
#> GSM648594 1 0.3074 0.8380 0.848 0.000 0.000 0.152
#> GSM648600 1 0.1557 0.8497 0.944 0.000 0.000 0.056
#> GSM648621 1 0.3123 0.8510 0.844 0.000 0.000 0.156
#> GSM648622 4 0.0188 0.8802 0.004 0.000 0.000 0.996
#> GSM648623 4 0.0336 0.8783 0.008 0.000 0.000 0.992
#> GSM648636 1 0.3400 0.8365 0.820 0.000 0.000 0.180
#> GSM648655 4 0.2868 0.8383 0.136 0.000 0.000 0.864
#> GSM648661 4 0.1792 0.8741 0.068 0.000 0.000 0.932
#> GSM648664 4 0.1867 0.8730 0.072 0.000 0.000 0.928
#> GSM648683 1 0.3975 0.7849 0.760 0.000 0.000 0.240
#> GSM648685 4 0.2973 0.8322 0.144 0.000 0.000 0.856
#> GSM648702 1 0.3400 0.8365 0.820 0.000 0.000 0.180
#> GSM648597 1 0.3486 0.7915 0.812 0.000 0.000 0.188
#> GSM648603 1 0.3486 0.7915 0.812 0.000 0.000 0.188
#> GSM648606 4 0.7519 -0.0955 0.184 0.000 0.392 0.424
#> GSM648613 3 0.6936 0.4978 0.224 0.000 0.588 0.188
#> GSM648619 1 0.3801 0.7715 0.780 0.000 0.000 0.220
#> GSM648654 4 0.1118 0.8645 0.036 0.000 0.000 0.964
#> GSM648663 4 0.2814 0.7642 0.132 0.000 0.000 0.868
#> GSM648670 1 0.1994 0.8278 0.936 0.052 0.004 0.008
#> GSM648707 3 0.5669 0.7433 0.104 0.048 0.768 0.080
#> GSM648615 2 0.3105 0.8342 0.140 0.856 0.000 0.004
#> GSM648643 2 0.1389 0.8982 0.048 0.952 0.000 0.000
#> GSM648650 1 0.3356 0.8374 0.824 0.000 0.000 0.176
#> GSM648656 2 0.1302 0.8993 0.044 0.956 0.000 0.000
#> GSM648715 2 0.7702 0.2455 0.288 0.452 0.000 0.260
#> GSM648598 4 0.2704 0.8476 0.124 0.000 0.000 0.876
#> GSM648601 1 0.3356 0.8463 0.824 0.000 0.000 0.176
#> GSM648602 1 0.3311 0.8461 0.828 0.000 0.000 0.172
#> GSM648604 4 0.0188 0.8802 0.004 0.000 0.000 0.996
#> GSM648614 4 0.0188 0.8802 0.004 0.000 0.000 0.996
#> GSM648624 4 0.0188 0.8802 0.004 0.000 0.000 0.996
#> GSM648625 4 0.1940 0.8694 0.076 0.000 0.000 0.924
#> GSM648629 4 0.0188 0.8802 0.004 0.000 0.000 0.996
#> GSM648634 1 0.3123 0.8510 0.844 0.000 0.000 0.156
#> GSM648648 4 0.3024 0.8303 0.148 0.000 0.000 0.852
#> GSM648651 4 0.1867 0.8742 0.072 0.000 0.000 0.928
#> GSM648657 1 0.1557 0.8492 0.944 0.000 0.000 0.056
#> GSM648660 4 0.4193 0.6547 0.268 0.000 0.000 0.732
#> GSM648697 4 0.2973 0.8322 0.144 0.000 0.000 0.856
#> GSM648710 4 0.0000 0.8802 0.000 0.000 0.000 1.000
#> GSM648591 1 0.3528 0.7902 0.808 0.000 0.000 0.192
#> GSM648592 1 0.3486 0.7915 0.812 0.000 0.000 0.188
#> GSM648607 4 0.2408 0.7924 0.104 0.000 0.000 0.896
#> GSM648611 3 0.7684 0.0651 0.216 0.000 0.392 0.392
#> GSM648612 1 0.4500 0.6478 0.684 0.000 0.000 0.316
#> GSM648616 3 0.1389 0.8599 0.000 0.048 0.952 0.000
#> GSM648617 1 0.1474 0.8492 0.948 0.000 0.000 0.052
#> GSM648626 1 0.3486 0.7915 0.812 0.000 0.000 0.188
#> GSM648711 4 0.0188 0.8802 0.004 0.000 0.000 0.996
#> GSM648712 1 0.3486 0.7922 0.812 0.000 0.000 0.188
#> GSM648713 4 0.2408 0.7924 0.104 0.000 0.000 0.896
#> GSM648714 2 0.5582 0.6727 0.136 0.728 0.000 0.136
#> GSM648716 4 0.3024 0.7552 0.148 0.000 0.000 0.852
#> GSM648717 3 0.6445 0.4613 0.096 0.000 0.600 0.304
#> GSM648590 1 0.2011 0.8567 0.920 0.000 0.000 0.080
#> GSM648596 2 0.4205 0.7757 0.124 0.820 0.000 0.056
#> GSM648642 1 0.5116 0.7390 0.764 0.108 0.000 0.128
#> GSM648696 1 0.1474 0.8492 0.948 0.000 0.000 0.052
#> GSM648705 1 0.3074 0.8505 0.848 0.000 0.000 0.152
#> GSM648718 2 0.1890 0.8921 0.056 0.936 0.000 0.008
#> GSM648599 1 0.1557 0.8497 0.944 0.000 0.000 0.056
#> GSM648608 4 0.1940 0.8733 0.076 0.000 0.000 0.924
#> GSM648609 4 0.0336 0.8791 0.008 0.000 0.000 0.992
#> GSM648610 1 0.3123 0.8510 0.844 0.000 0.000 0.156
#> GSM648633 1 0.3444 0.8408 0.816 0.000 0.000 0.184
#> GSM648644 2 0.0188 0.9109 0.000 0.996 0.004 0.000
#> GSM648652 1 0.3356 0.8374 0.824 0.000 0.000 0.176
#> GSM648653 1 0.3444 0.8398 0.816 0.000 0.000 0.184
#> GSM648658 4 0.3311 0.8075 0.172 0.000 0.000 0.828
#> GSM648659 1 0.2944 0.8229 0.868 0.004 0.000 0.128
#> GSM648662 4 0.0188 0.8802 0.004 0.000 0.000 0.996
#> GSM648665 4 0.0336 0.8791 0.008 0.000 0.000 0.992
#> GSM648666 4 0.2921 0.8358 0.140 0.000 0.000 0.860
#> GSM648680 4 0.2973 0.8327 0.144 0.000 0.000 0.856
#> GSM648684 4 0.3024 0.8303 0.148 0.000 0.000 0.852
#> GSM648709 2 0.6733 0.4153 0.112 0.564 0.000 0.324
#> GSM648719 4 0.0469 0.8820 0.012 0.000 0.000 0.988
#> GSM648627 1 0.4103 0.7347 0.744 0.000 0.000 0.256
#> GSM648637 2 0.0188 0.9109 0.000 0.996 0.004 0.000
#> GSM648638 2 0.1118 0.8939 0.000 0.964 0.036 0.000
#> GSM648641 3 0.0000 0.8854 0.000 0.000 1.000 0.000
#> GSM648672 2 0.0188 0.9109 0.000 0.996 0.004 0.000
#> GSM648674 2 0.0188 0.9109 0.000 0.996 0.004 0.000
#> GSM648703 2 0.0188 0.9109 0.000 0.996 0.004 0.000
#> GSM648631 3 0.0188 0.8832 0.000 0.000 0.996 0.004
#> GSM648669 2 0.0188 0.9109 0.000 0.996 0.004 0.000
#> GSM648671 2 0.0188 0.9109 0.000 0.996 0.004 0.000
#> GSM648678 2 0.0188 0.9109 0.000 0.996 0.004 0.000
#> GSM648679 2 0.0188 0.9109 0.000 0.996 0.004 0.000
#> GSM648681 2 0.2924 0.8317 0.016 0.884 0.000 0.100
#> GSM648686 3 0.0188 0.8837 0.000 0.004 0.996 0.000
#> GSM648689 3 0.0000 0.8854 0.000 0.000 1.000 0.000
#> GSM648690 3 0.0000 0.8854 0.000 0.000 1.000 0.000
#> GSM648691 3 0.0000 0.8854 0.000 0.000 1.000 0.000
#> GSM648693 3 0.0000 0.8854 0.000 0.000 1.000 0.000
#> GSM648700 1 0.4229 0.7691 0.824 0.124 0.048 0.004
#> GSM648630 3 0.0000 0.8854 0.000 0.000 1.000 0.000
#> GSM648632 3 0.0188 0.8832 0.000 0.000 0.996 0.004
#> GSM648639 3 0.4925 0.1791 0.000 0.428 0.572 0.000
#> GSM648640 3 0.1302 0.8624 0.000 0.044 0.956 0.000
#> GSM648668 2 0.0188 0.9109 0.000 0.996 0.004 0.000
#> GSM648676 2 0.0188 0.9109 0.000 0.996 0.004 0.000
#> GSM648692 3 0.0000 0.8854 0.000 0.000 1.000 0.000
#> GSM648694 3 0.0000 0.8854 0.000 0.000 1.000 0.000
#> GSM648699 2 0.0188 0.9109 0.000 0.996 0.004 0.000
#> GSM648701 2 0.0188 0.9109 0.000 0.996 0.004 0.000
#> GSM648673 2 0.0188 0.9109 0.000 0.996 0.004 0.000
#> GSM648677 2 0.0188 0.9109 0.000 0.996 0.004 0.000
#> GSM648687 3 0.0000 0.8854 0.000 0.000 1.000 0.000
#> GSM648688 3 0.0000 0.8854 0.000 0.000 1.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM648605 2 0.4307 -0.47069 0.000 0.504 0.000 0.496 0.000
#> GSM648618 5 0.3876 0.44038 0.000 0.316 0.000 0.000 0.684
#> GSM648620 2 0.0703 0.51208 0.000 0.976 0.000 0.000 0.024
#> GSM648646 4 0.3684 0.71115 0.000 0.280 0.000 0.720 0.000
#> GSM648649 2 0.5447 0.30824 0.072 0.572 0.000 0.000 0.356
#> GSM648675 2 0.5562 0.03428 0.072 0.520 0.000 0.000 0.408
#> GSM648682 4 0.3508 0.72996 0.000 0.252 0.000 0.748 0.000
#> GSM648698 4 0.4273 0.52982 0.000 0.448 0.000 0.552 0.000
#> GSM648708 2 0.0000 0.52298 0.000 1.000 0.000 0.000 0.000
#> GSM648628 5 0.1410 0.65782 0.060 0.000 0.000 0.000 0.940
#> GSM648595 2 0.6023 0.45713 0.248 0.576 0.000 0.000 0.176
#> GSM648635 2 0.6072 0.46420 0.252 0.568 0.000 0.000 0.180
#> GSM648645 5 0.4756 0.41790 0.044 0.288 0.000 0.000 0.668
#> GSM648647 2 0.3730 0.04533 0.000 0.712 0.000 0.288 0.000
#> GSM648667 2 0.4466 0.53845 0.176 0.748 0.000 0.000 0.076
#> GSM648695 2 0.4875 0.53895 0.136 0.760 0.000 0.040 0.064
#> GSM648704 4 0.0000 0.85631 0.000 0.000 0.000 1.000 0.000
#> GSM648706 4 0.3508 0.72996 0.000 0.252 0.000 0.748 0.000
#> GSM648593 2 0.6163 0.40900 0.300 0.536 0.000 0.000 0.164
#> GSM648594 5 0.5785 0.09056 0.092 0.404 0.000 0.000 0.504
#> GSM648600 5 0.5655 0.33789 0.112 0.288 0.000 0.000 0.600
#> GSM648621 1 0.6496 0.08159 0.488 0.280 0.000 0.000 0.232
#> GSM648622 1 0.2377 0.74216 0.872 0.000 0.000 0.000 0.128
#> GSM648623 1 0.4235 0.40290 0.576 0.000 0.000 0.000 0.424
#> GSM648636 1 0.5673 0.26746 0.596 0.292 0.000 0.000 0.112
#> GSM648655 1 0.2104 0.73826 0.916 0.024 0.000 0.000 0.060
#> GSM648661 1 0.0510 0.76218 0.984 0.000 0.000 0.000 0.016
#> GSM648664 1 0.0510 0.76218 0.984 0.000 0.000 0.000 0.016
#> GSM648683 1 0.5284 0.38506 0.660 0.236 0.000 0.000 0.104
#> GSM648685 1 0.0162 0.75761 0.996 0.004 0.000 0.000 0.000
#> GSM648702 2 0.5971 0.27616 0.396 0.492 0.000 0.000 0.112
#> GSM648597 5 0.3730 0.44929 0.000 0.288 0.000 0.000 0.712
#> GSM648603 5 0.0000 0.66777 0.000 0.000 0.000 0.000 1.000
#> GSM648606 5 0.3493 0.61690 0.060 0.000 0.108 0.000 0.832
#> GSM648613 5 0.2179 0.63137 0.000 0.000 0.112 0.000 0.888
#> GSM648619 5 0.0162 0.66836 0.004 0.000 0.000 0.000 0.996
#> GSM648654 1 0.1768 0.75695 0.924 0.004 0.000 0.000 0.072
#> GSM648663 5 0.2891 0.59313 0.176 0.000 0.000 0.000 0.824
#> GSM648670 2 0.7172 0.39256 0.072 0.544 0.000 0.192 0.192
#> GSM648707 5 0.5150 0.46396 0.000 0.000 0.136 0.172 0.692
#> GSM648615 4 0.4201 0.64821 0.000 0.328 0.000 0.664 0.008
#> GSM648643 4 0.4262 0.54190 0.000 0.440 0.000 0.560 0.000
#> GSM648650 2 0.4486 0.53866 0.172 0.748 0.000 0.000 0.080
#> GSM648656 4 0.3508 0.72996 0.000 0.252 0.000 0.748 0.000
#> GSM648715 2 0.5532 0.17354 0.104 0.616 0.000 0.280 0.000
#> GSM648598 1 0.1478 0.74933 0.936 0.000 0.000 0.000 0.064
#> GSM648601 1 0.6790 0.00424 0.384 0.300 0.000 0.000 0.316
#> GSM648602 1 0.5923 0.24603 0.576 0.280 0.000 0.000 0.144
#> GSM648604 1 0.1608 0.75721 0.928 0.000 0.000 0.000 0.072
#> GSM648614 1 0.4227 0.41132 0.580 0.000 0.000 0.000 0.420
#> GSM648624 1 0.1544 0.75925 0.932 0.000 0.000 0.000 0.068
#> GSM648625 1 0.5006 0.55358 0.704 0.180 0.000 0.000 0.116
#> GSM648629 1 0.1608 0.75721 0.928 0.000 0.000 0.000 0.072
#> GSM648634 1 0.6117 0.17341 0.540 0.304 0.000 0.000 0.156
#> GSM648648 1 0.0865 0.75228 0.972 0.024 0.000 0.000 0.004
#> GSM648651 1 0.1671 0.75208 0.924 0.000 0.000 0.000 0.076
#> GSM648657 5 0.5272 0.31101 0.072 0.308 0.000 0.000 0.620
#> GSM648660 1 0.4016 0.63934 0.796 0.112 0.000 0.000 0.092
#> GSM648697 1 0.0162 0.75761 0.996 0.004 0.000 0.000 0.000
#> GSM648710 1 0.1544 0.75925 0.932 0.000 0.000 0.000 0.068
#> GSM648591 5 0.3508 0.49438 0.000 0.252 0.000 0.000 0.748
#> GSM648592 5 0.0290 0.66667 0.000 0.008 0.000 0.000 0.992
#> GSM648607 5 0.4287 0.11740 0.460 0.000 0.000 0.000 0.540
#> GSM648611 1 0.6085 0.17990 0.472 0.000 0.124 0.000 0.404
#> GSM648612 5 0.0703 0.66748 0.024 0.000 0.000 0.000 0.976
#> GSM648616 5 0.6601 0.02476 0.000 0.000 0.292 0.248 0.460
#> GSM648617 5 0.4444 0.43426 0.072 0.180 0.000 0.000 0.748
#> GSM648626 5 0.0000 0.66777 0.000 0.000 0.000 0.000 1.000
#> GSM648711 1 0.2230 0.74750 0.884 0.000 0.000 0.000 0.116
#> GSM648712 5 0.1341 0.65962 0.056 0.000 0.000 0.000 0.944
#> GSM648713 5 0.2891 0.59234 0.176 0.000 0.000 0.000 0.824
#> GSM648714 5 0.3267 0.62004 0.016 0.044 0.000 0.076 0.864
#> GSM648716 5 0.2813 0.61980 0.168 0.000 0.000 0.000 0.832
#> GSM648717 5 0.6112 0.17902 0.140 0.000 0.344 0.000 0.516
#> GSM648590 2 0.5496 0.46562 0.152 0.652 0.000 0.000 0.196
#> GSM648596 4 0.6333 0.30489 0.008 0.236 0.000 0.564 0.192
#> GSM648642 2 0.1544 0.49179 0.000 0.932 0.000 0.068 0.000
#> GSM648696 2 0.5498 0.30640 0.076 0.568 0.000 0.000 0.356
#> GSM648705 2 0.5759 0.48263 0.180 0.620 0.000 0.000 0.200
#> GSM648718 2 0.3752 0.03487 0.000 0.708 0.000 0.292 0.000
#> GSM648599 5 0.5139 0.35858 0.072 0.280 0.000 0.000 0.648
#> GSM648608 1 0.0510 0.76218 0.984 0.000 0.000 0.000 0.016
#> GSM648609 1 0.1544 0.75925 0.932 0.000 0.000 0.000 0.068
#> GSM648610 1 0.6022 0.22731 0.564 0.280 0.000 0.000 0.156
#> GSM648633 2 0.6415 0.17843 0.400 0.428 0.000 0.000 0.172
#> GSM648644 4 0.0000 0.85631 0.000 0.000 0.000 1.000 0.000
#> GSM648652 2 0.6317 0.34138 0.332 0.496 0.000 0.000 0.172
#> GSM648653 1 0.5637 0.28208 0.604 0.284 0.000 0.000 0.112
#> GSM648658 1 0.2171 0.73718 0.912 0.024 0.000 0.000 0.064
#> GSM648659 2 0.0898 0.53081 0.020 0.972 0.000 0.000 0.008
#> GSM648662 1 0.1671 0.75739 0.924 0.000 0.000 0.000 0.076
#> GSM648665 1 0.1608 0.75721 0.928 0.000 0.000 0.000 0.072
#> GSM648666 1 0.0000 0.75788 1.000 0.000 0.000 0.000 0.000
#> GSM648680 1 0.2104 0.73826 0.916 0.024 0.000 0.000 0.060
#> GSM648684 1 0.0000 0.75788 1.000 0.000 0.000 0.000 0.000
#> GSM648709 2 0.5083 0.13405 0.000 0.652 0.000 0.280 0.068
#> GSM648719 1 0.2329 0.74467 0.876 0.000 0.000 0.000 0.124
#> GSM648627 5 0.5779 0.45517 0.172 0.212 0.000 0.000 0.616
#> GSM648637 4 0.0000 0.85631 0.000 0.000 0.000 1.000 0.000
#> GSM648638 4 0.0000 0.85631 0.000 0.000 0.000 1.000 0.000
#> GSM648641 3 0.3730 0.57967 0.000 0.000 0.712 0.000 0.288
#> GSM648672 4 0.0000 0.85631 0.000 0.000 0.000 1.000 0.000
#> GSM648674 4 0.0000 0.85631 0.000 0.000 0.000 1.000 0.000
#> GSM648703 4 0.0000 0.85631 0.000 0.000 0.000 1.000 0.000
#> GSM648631 3 0.0000 0.92456 0.000 0.000 1.000 0.000 0.000
#> GSM648669 4 0.0000 0.85631 0.000 0.000 0.000 1.000 0.000
#> GSM648671 4 0.0000 0.85631 0.000 0.000 0.000 1.000 0.000
#> GSM648678 4 0.0000 0.85631 0.000 0.000 0.000 1.000 0.000
#> GSM648679 4 0.0000 0.85631 0.000 0.000 0.000 1.000 0.000
#> GSM648681 2 0.4907 0.13188 0.000 0.664 0.000 0.280 0.056
#> GSM648686 3 0.0000 0.92456 0.000 0.000 1.000 0.000 0.000
#> GSM648689 3 0.0000 0.92456 0.000 0.000 1.000 0.000 0.000
#> GSM648690 3 0.0000 0.92456 0.000 0.000 1.000 0.000 0.000
#> GSM648691 3 0.0000 0.92456 0.000 0.000 1.000 0.000 0.000
#> GSM648693 3 0.0000 0.92456 0.000 0.000 1.000 0.000 0.000
#> GSM648700 2 0.6042 0.45277 0.040 0.632 0.004 0.256 0.068
#> GSM648630 3 0.0000 0.92456 0.000 0.000 1.000 0.000 0.000
#> GSM648632 3 0.0000 0.92456 0.000 0.000 1.000 0.000 0.000
#> GSM648639 3 0.4451 0.52681 0.000 0.000 0.644 0.340 0.016
#> GSM648640 3 0.2732 0.79161 0.000 0.000 0.840 0.160 0.000
#> GSM648668 4 0.0290 0.85185 0.000 0.008 0.000 0.992 0.000
#> GSM648676 4 0.3336 0.69590 0.000 0.228 0.000 0.772 0.000
#> GSM648692 3 0.0000 0.92456 0.000 0.000 1.000 0.000 0.000
#> GSM648694 3 0.0000 0.92456 0.000 0.000 1.000 0.000 0.000
#> GSM648699 4 0.2561 0.78498 0.000 0.144 0.000 0.856 0.000
#> GSM648701 4 0.2127 0.80342 0.000 0.108 0.000 0.892 0.000
#> GSM648673 4 0.0000 0.85631 0.000 0.000 0.000 1.000 0.000
#> GSM648677 4 0.0000 0.85631 0.000 0.000 0.000 1.000 0.000
#> GSM648687 3 0.2605 0.77913 0.148 0.000 0.852 0.000 0.000
#> GSM648688 3 0.0000 0.92456 0.000 0.000 1.000 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM648605 2 0.0713 0.7772 0.000 0.972 0.000 0.028 0.000 0.000
#> GSM648618 5 0.4076 0.2729 0.000 0.012 0.000 0.000 0.592 0.396
#> GSM648620 2 0.1141 0.8145 0.000 0.948 0.000 0.000 0.000 0.052
#> GSM648646 4 0.3765 0.5222 0.000 0.404 0.000 0.596 0.000 0.000
#> GSM648649 6 0.0692 0.8272 0.000 0.020 0.000 0.000 0.004 0.976
#> GSM648675 6 0.2680 0.8021 0.000 0.108 0.000 0.000 0.032 0.860
#> GSM648682 4 0.3756 0.5282 0.000 0.400 0.000 0.600 0.000 0.000
#> GSM648698 2 0.0713 0.7772 0.000 0.972 0.000 0.028 0.000 0.000
#> GSM648708 2 0.1141 0.8145 0.000 0.948 0.000 0.000 0.000 0.052
#> GSM648628 5 0.0632 0.7638 0.024 0.000 0.000 0.000 0.976 0.000
#> GSM648595 6 0.1262 0.8301 0.016 0.020 0.000 0.000 0.008 0.956
#> GSM648635 6 0.0547 0.8267 0.000 0.020 0.000 0.000 0.000 0.980
#> GSM648645 5 0.3789 0.2666 0.000 0.000 0.000 0.000 0.584 0.416
#> GSM648647 2 0.1219 0.8139 0.004 0.948 0.000 0.000 0.000 0.048
#> GSM648667 2 0.4835 0.5626 0.072 0.592 0.000 0.000 0.000 0.336
#> GSM648695 2 0.4671 0.6901 0.160 0.688 0.000 0.000 0.000 0.152
#> GSM648704 4 0.1075 0.8227 0.000 0.048 0.000 0.952 0.000 0.000
#> GSM648706 4 0.3756 0.5282 0.000 0.400 0.000 0.600 0.000 0.000
#> GSM648593 6 0.3668 0.6486 0.228 0.028 0.000 0.000 0.000 0.744
#> GSM648594 6 0.3868 -0.1505 0.000 0.000 0.000 0.000 0.496 0.504
#> GSM648600 6 0.2445 0.7944 0.020 0.000 0.000 0.000 0.108 0.872
#> GSM648621 6 0.2491 0.8195 0.112 0.000 0.000 0.000 0.020 0.868
#> GSM648622 1 0.1082 0.9055 0.956 0.000 0.000 0.000 0.004 0.040
#> GSM648623 1 0.4584 0.2738 0.556 0.000 0.000 0.000 0.404 0.040
#> GSM648636 6 0.2053 0.8261 0.108 0.004 0.000 0.000 0.000 0.888
#> GSM648655 1 0.2001 0.8762 0.900 0.004 0.000 0.000 0.004 0.092
#> GSM648661 1 0.0000 0.9164 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM648664 1 0.0146 0.9163 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM648683 6 0.2912 0.7496 0.216 0.000 0.000 0.000 0.000 0.784
#> GSM648685 1 0.0260 0.9157 0.992 0.000 0.000 0.000 0.000 0.008
#> GSM648702 6 0.1480 0.8344 0.040 0.020 0.000 0.000 0.000 0.940
#> GSM648597 5 0.3782 0.2702 0.000 0.000 0.000 0.000 0.588 0.412
#> GSM648603 5 0.0937 0.7642 0.000 0.000 0.000 0.000 0.960 0.040
#> GSM648606 5 0.0260 0.7630 0.008 0.000 0.000 0.000 0.992 0.000
#> GSM648613 5 0.0146 0.7634 0.000 0.000 0.000 0.000 0.996 0.004
#> GSM648619 5 0.0547 0.7656 0.000 0.000 0.000 0.000 0.980 0.020
#> GSM648654 1 0.0632 0.9062 0.976 0.000 0.000 0.000 0.024 0.000
#> GSM648663 5 0.1806 0.7251 0.088 0.000 0.000 0.000 0.908 0.004
#> GSM648670 6 0.2920 0.7784 0.000 0.020 0.000 0.128 0.008 0.844
#> GSM648707 5 0.2346 0.6908 0.000 0.000 0.008 0.124 0.868 0.000
#> GSM648615 4 0.4310 0.4767 0.000 0.404 0.000 0.576 0.004 0.016
#> GSM648643 2 0.0713 0.7772 0.000 0.972 0.000 0.028 0.000 0.000
#> GSM648650 2 0.3782 0.4841 0.000 0.588 0.000 0.000 0.000 0.412
#> GSM648656 4 0.3756 0.5282 0.000 0.400 0.000 0.600 0.000 0.000
#> GSM648715 2 0.4040 0.7330 0.132 0.756 0.000 0.000 0.000 0.112
#> GSM648598 1 0.1082 0.9055 0.956 0.000 0.000 0.000 0.004 0.040
#> GSM648601 6 0.4014 0.6791 0.096 0.000 0.000 0.000 0.148 0.756
#> GSM648602 6 0.2278 0.8143 0.128 0.000 0.000 0.000 0.004 0.868
#> GSM648604 1 0.0000 0.9164 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM648614 1 0.3955 0.2591 0.560 0.000 0.000 0.000 0.436 0.004
#> GSM648624 1 0.0000 0.9164 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM648625 1 0.1753 0.8847 0.912 0.000 0.000 0.000 0.004 0.084
#> GSM648629 1 0.0000 0.9164 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM648634 6 0.1970 0.8299 0.092 0.000 0.000 0.000 0.008 0.900
#> GSM648648 1 0.1327 0.8867 0.936 0.000 0.000 0.000 0.000 0.064
#> GSM648651 1 0.1152 0.9047 0.952 0.000 0.000 0.000 0.004 0.044
#> GSM648657 6 0.2996 0.6271 0.000 0.000 0.000 0.000 0.228 0.772
#> GSM648660 1 0.2994 0.7207 0.788 0.000 0.000 0.000 0.004 0.208
#> GSM648697 1 0.0260 0.9157 0.992 0.000 0.000 0.000 0.000 0.008
#> GSM648710 1 0.0000 0.9164 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM648591 5 0.3706 0.3374 0.000 0.000 0.000 0.000 0.620 0.380
#> GSM648592 5 0.1007 0.7635 0.000 0.000 0.000 0.000 0.956 0.044
#> GSM648607 5 0.4254 0.2550 0.404 0.000 0.000 0.000 0.576 0.020
#> GSM648611 5 0.6381 0.1199 0.256 0.000 0.016 0.000 0.416 0.312
#> GSM648612 5 0.0458 0.7656 0.000 0.000 0.000 0.000 0.984 0.016
#> GSM648616 5 0.5160 0.3228 0.000 0.000 0.104 0.332 0.564 0.000
#> GSM648617 5 0.3843 0.1605 0.000 0.000 0.000 0.000 0.548 0.452
#> GSM648626 5 0.0713 0.7661 0.000 0.000 0.000 0.000 0.972 0.028
#> GSM648711 1 0.1074 0.9053 0.960 0.000 0.000 0.000 0.028 0.012
#> GSM648712 5 0.1088 0.7619 0.024 0.000 0.000 0.000 0.960 0.016
#> GSM648713 5 0.1003 0.7655 0.020 0.000 0.000 0.000 0.964 0.016
#> GSM648714 5 0.0777 0.7578 0.000 0.024 0.000 0.000 0.972 0.004
#> GSM648716 5 0.0777 0.7653 0.024 0.000 0.000 0.000 0.972 0.004
#> GSM648717 5 0.5100 0.3286 0.116 0.000 0.284 0.000 0.600 0.000
#> GSM648590 6 0.2022 0.8182 0.024 0.052 0.000 0.000 0.008 0.916
#> GSM648596 4 0.4840 0.6025 0.000 0.036 0.000 0.700 0.200 0.064
#> GSM648642 2 0.1141 0.8145 0.000 0.948 0.000 0.000 0.000 0.052
#> GSM648696 6 0.0951 0.8294 0.004 0.020 0.000 0.000 0.008 0.968
#> GSM648705 6 0.0547 0.8267 0.000 0.020 0.000 0.000 0.000 0.980
#> GSM648718 2 0.1141 0.8145 0.000 0.948 0.000 0.000 0.000 0.052
#> GSM648599 6 0.1957 0.7900 0.000 0.000 0.000 0.000 0.112 0.888
#> GSM648608 1 0.0000 0.9164 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM648609 1 0.0000 0.9164 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM648610 6 0.2553 0.8087 0.144 0.000 0.000 0.000 0.008 0.848
#> GSM648633 6 0.1082 0.8325 0.040 0.000 0.000 0.000 0.004 0.956
#> GSM648644 4 0.1007 0.8237 0.000 0.044 0.000 0.956 0.000 0.000
#> GSM648652 6 0.0458 0.8325 0.016 0.000 0.000 0.000 0.000 0.984
#> GSM648653 6 0.2003 0.8227 0.116 0.000 0.000 0.000 0.000 0.884
#> GSM648658 1 0.2902 0.7910 0.800 0.004 0.000 0.000 0.000 0.196
#> GSM648659 2 0.2912 0.7507 0.000 0.784 0.000 0.000 0.000 0.216
#> GSM648662 1 0.0146 0.9165 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM648665 1 0.0000 0.9164 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM648666 1 0.0146 0.9165 0.996 0.000 0.000 0.000 0.000 0.004
#> GSM648680 1 0.1863 0.8722 0.896 0.000 0.000 0.000 0.000 0.104
#> GSM648684 1 0.2491 0.7513 0.836 0.000 0.000 0.000 0.000 0.164
#> GSM648709 2 0.1745 0.8098 0.000 0.920 0.000 0.000 0.012 0.068
#> GSM648719 1 0.1196 0.9043 0.952 0.000 0.000 0.000 0.008 0.040
#> GSM648627 6 0.4273 0.7280 0.148 0.000 0.000 0.000 0.120 0.732
#> GSM648637 4 0.0000 0.8333 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM648638 4 0.0146 0.8330 0.000 0.004 0.000 0.996 0.000 0.000
#> GSM648641 3 0.3727 0.3387 0.000 0.000 0.612 0.000 0.388 0.000
#> GSM648672 4 0.0000 0.8333 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM648674 4 0.0000 0.8333 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM648703 4 0.0777 0.8290 0.000 0.024 0.000 0.972 0.004 0.000
#> GSM648631 3 0.0000 0.9108 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648669 4 0.0000 0.8333 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM648671 4 0.0000 0.8333 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM648678 4 0.1007 0.8237 0.000 0.044 0.000 0.956 0.000 0.000
#> GSM648679 4 0.0000 0.8333 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM648681 2 0.2234 0.7983 0.000 0.872 0.000 0.000 0.004 0.124
#> GSM648686 3 0.0000 0.9108 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648689 3 0.0000 0.9108 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648690 3 0.0000 0.9108 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648691 3 0.0000 0.9108 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648693 3 0.0000 0.9108 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648700 6 0.6003 0.1222 0.000 0.252 0.000 0.268 0.004 0.476
#> GSM648630 3 0.0000 0.9108 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648632 3 0.0000 0.9108 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648639 3 0.3819 0.4888 0.000 0.000 0.652 0.340 0.008 0.000
#> GSM648640 3 0.2300 0.7839 0.000 0.000 0.856 0.144 0.000 0.000
#> GSM648668 4 0.0363 0.8262 0.000 0.012 0.000 0.988 0.000 0.000
#> GSM648676 2 0.4015 0.3699 0.000 0.596 0.000 0.396 0.004 0.004
#> GSM648692 3 0.0000 0.9108 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648694 3 0.0000 0.9108 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648699 2 0.3742 0.4117 0.000 0.648 0.000 0.348 0.004 0.000
#> GSM648701 4 0.3997 -0.0669 0.000 0.488 0.000 0.508 0.004 0.000
#> GSM648673 4 0.0146 0.8318 0.000 0.004 0.000 0.996 0.000 0.000
#> GSM648677 4 0.0000 0.8333 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM648687 3 0.2854 0.6977 0.208 0.000 0.792 0.000 0.000 0.000
#> GSM648688 3 0.0000 0.9108 0.000 0.000 1.000 0.000 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) development.stage(p) other(p) k
#> MAD:pam 129 1.26e-08 0.08227 1.18e-11 2
#> MAD:pam 120 4.28e-14 0.01801 1.66e-16 3
#> MAD:pam 123 7.26e-15 0.03136 3.28e-19 4
#> MAD:pam 84 3.69e-12 0.02793 6.25e-31 5
#> MAD:pam 110 7.05e-16 0.00284 1.28e-35 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 51941 rows and 130 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'mclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.806 0.881 0.930 0.4942 0.497 0.497
#> 3 3 0.637 0.785 0.889 0.2641 0.814 0.644
#> 4 4 0.681 0.816 0.884 0.1358 0.710 0.382
#> 5 5 0.756 0.867 0.915 0.0587 0.904 0.694
#> 6 6 0.710 0.572 0.785 0.0635 0.956 0.827
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
#> GSM648605 2 0.0938 0.891 0.012 0.988
#> GSM648618 2 0.8861 0.700 0.304 0.696
#> GSM648620 2 0.9686 0.443 0.396 0.604
#> GSM648646 2 0.0672 0.891 0.008 0.992
#> GSM648649 1 0.3431 0.946 0.936 0.064
#> GSM648675 2 0.0938 0.891 0.012 0.988
#> GSM648682 2 0.0672 0.891 0.008 0.992
#> GSM648698 2 0.0938 0.891 0.012 0.988
#> GSM648708 2 0.9815 0.380 0.420 0.580
#> GSM648628 2 0.8861 0.700 0.304 0.696
#> GSM648595 1 0.3584 0.944 0.932 0.068
#> GSM648635 1 0.3431 0.946 0.936 0.064
#> GSM648645 1 0.2778 0.953 0.952 0.048
#> GSM648647 2 0.8016 0.714 0.244 0.756
#> GSM648667 1 0.3431 0.946 0.936 0.064
#> GSM648695 2 0.9710 0.433 0.400 0.600
#> GSM648704 2 0.0672 0.891 0.008 0.992
#> GSM648706 2 0.0672 0.891 0.008 0.992
#> GSM648593 1 0.3431 0.946 0.936 0.064
#> GSM648594 1 0.3584 0.944 0.932 0.068
#> GSM648600 1 0.2948 0.952 0.948 0.052
#> GSM648621 1 0.0000 0.964 1.000 0.000
#> GSM648622 1 0.0000 0.964 1.000 0.000
#> GSM648623 1 0.0000 0.964 1.000 0.000
#> GSM648636 1 0.3431 0.946 0.936 0.064
#> GSM648655 1 0.3431 0.946 0.936 0.064
#> GSM648661 1 0.0000 0.964 1.000 0.000
#> GSM648664 1 0.0000 0.964 1.000 0.000
#> GSM648683 1 0.0000 0.964 1.000 0.000
#> GSM648685 1 0.0000 0.964 1.000 0.000
#> GSM648702 1 0.3431 0.946 0.936 0.064
#> GSM648597 1 0.2778 0.954 0.952 0.048
#> GSM648603 1 0.0000 0.964 1.000 0.000
#> GSM648606 2 0.8443 0.738 0.272 0.728
#> GSM648613 2 0.7815 0.777 0.232 0.768
#> GSM648619 1 0.0000 0.964 1.000 0.000
#> GSM648654 2 0.9944 0.412 0.456 0.544
#> GSM648663 2 0.8861 0.700 0.304 0.696
#> GSM648670 2 0.0938 0.891 0.012 0.988
#> GSM648707 2 0.3733 0.878 0.072 0.928
#> GSM648615 2 0.0938 0.891 0.012 0.988
#> GSM648643 2 0.0938 0.891 0.012 0.988
#> GSM648650 1 0.3431 0.946 0.936 0.064
#> GSM648656 2 0.0672 0.891 0.008 0.992
#> GSM648715 2 0.9686 0.443 0.396 0.604
#> GSM648598 1 0.0376 0.964 0.996 0.004
#> GSM648601 1 0.0000 0.964 1.000 0.000
#> GSM648602 1 0.0000 0.964 1.000 0.000
#> GSM648604 1 0.0000 0.964 1.000 0.000
#> GSM648614 2 0.8861 0.700 0.304 0.696
#> GSM648624 1 0.0000 0.964 1.000 0.000
#> GSM648625 1 0.3431 0.946 0.936 0.064
#> GSM648629 1 0.0000 0.964 1.000 0.000
#> GSM648634 1 0.2423 0.956 0.960 0.040
#> GSM648648 1 0.3431 0.946 0.936 0.064
#> GSM648651 1 0.0000 0.964 1.000 0.000
#> GSM648657 1 0.3431 0.946 0.936 0.064
#> GSM648660 1 0.2043 0.958 0.968 0.032
#> GSM648697 1 0.0000 0.964 1.000 0.000
#> GSM648710 1 0.0000 0.964 1.000 0.000
#> GSM648591 1 0.7376 0.679 0.792 0.208
#> GSM648592 1 0.6438 0.835 0.836 0.164
#> GSM648607 1 0.0000 0.964 1.000 0.000
#> GSM648611 2 0.8861 0.700 0.304 0.696
#> GSM648612 1 0.0672 0.960 0.992 0.008
#> GSM648616 2 0.3431 0.881 0.064 0.936
#> GSM648617 1 0.3114 0.951 0.944 0.056
#> GSM648626 1 0.0000 0.964 1.000 0.000
#> GSM648711 1 0.0000 0.964 1.000 0.000
#> GSM648712 1 0.0000 0.964 1.000 0.000
#> GSM648713 1 0.0000 0.964 1.000 0.000
#> GSM648714 2 0.3733 0.881 0.072 0.928
#> GSM648716 1 0.0000 0.964 1.000 0.000
#> GSM648717 2 0.8661 0.720 0.288 0.712
#> GSM648590 2 0.9358 0.540 0.352 0.648
#> GSM648596 2 0.0938 0.891 0.012 0.988
#> GSM648642 2 0.7674 0.736 0.224 0.776
#> GSM648696 1 0.3584 0.944 0.932 0.068
#> GSM648705 1 0.3431 0.946 0.936 0.064
#> GSM648718 2 0.0938 0.891 0.012 0.988
#> GSM648599 1 0.0000 0.964 1.000 0.000
#> GSM648608 1 0.0000 0.964 1.000 0.000
#> GSM648609 1 0.0000 0.964 1.000 0.000
#> GSM648610 1 0.0000 0.964 1.000 0.000
#> GSM648633 1 0.3431 0.946 0.936 0.064
#> GSM648644 2 0.0672 0.891 0.008 0.992
#> GSM648652 1 0.3431 0.946 0.936 0.064
#> GSM648653 1 0.0000 0.964 1.000 0.000
#> GSM648658 1 0.3431 0.946 0.936 0.064
#> GSM648659 2 0.9286 0.556 0.344 0.656
#> GSM648662 1 0.0000 0.964 1.000 0.000
#> GSM648665 1 0.0000 0.964 1.000 0.000
#> GSM648666 1 0.0000 0.964 1.000 0.000
#> GSM648680 1 0.3431 0.946 0.936 0.064
#> GSM648684 1 0.0000 0.964 1.000 0.000
#> GSM648709 2 0.7950 0.718 0.240 0.760
#> GSM648719 1 0.0000 0.964 1.000 0.000
#> GSM648627 1 0.0000 0.964 1.000 0.000
#> GSM648637 2 0.0672 0.891 0.008 0.992
#> GSM648638 2 0.0672 0.891 0.008 0.992
#> GSM648641 2 0.3733 0.878 0.072 0.928
#> GSM648672 2 0.0672 0.891 0.008 0.992
#> GSM648674 2 0.0672 0.891 0.008 0.992
#> GSM648703 2 0.0672 0.891 0.008 0.992
#> GSM648631 2 0.3431 0.877 0.064 0.936
#> GSM648669 2 0.0672 0.891 0.008 0.992
#> GSM648671 2 0.0672 0.891 0.008 0.992
#> GSM648678 2 0.0672 0.891 0.008 0.992
#> GSM648679 2 0.0672 0.891 0.008 0.992
#> GSM648681 2 0.1184 0.890 0.016 0.984
#> GSM648686 2 0.3431 0.877 0.064 0.936
#> GSM648689 2 0.3431 0.877 0.064 0.936
#> GSM648690 2 0.3431 0.877 0.064 0.936
#> GSM648691 2 0.3431 0.877 0.064 0.936
#> GSM648693 2 0.3431 0.877 0.064 0.936
#> GSM648700 2 0.0672 0.891 0.008 0.992
#> GSM648630 2 0.3431 0.877 0.064 0.936
#> GSM648632 2 0.3431 0.877 0.064 0.936
#> GSM648639 2 0.3431 0.881 0.064 0.936
#> GSM648640 2 0.3733 0.878 0.072 0.928
#> GSM648668 2 0.0672 0.891 0.008 0.992
#> GSM648676 2 0.0672 0.891 0.008 0.992
#> GSM648692 2 0.3431 0.877 0.064 0.936
#> GSM648694 2 0.3431 0.877 0.064 0.936
#> GSM648699 2 0.0672 0.891 0.008 0.992
#> GSM648701 2 0.0672 0.891 0.008 0.992
#> GSM648673 2 0.0672 0.891 0.008 0.992
#> GSM648677 2 0.0672 0.891 0.008 0.992
#> GSM648687 2 0.3584 0.878 0.068 0.932
#> GSM648688 2 0.3431 0.877 0.064 0.936
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM648605 2 0.4475 0.77211 0.064 0.864 0.072
#> GSM648618 3 0.8949 0.51333 0.320 0.148 0.532
#> GSM648620 2 0.6075 0.61058 0.316 0.676 0.008
#> GSM648646 2 0.1860 0.84866 0.000 0.948 0.052
#> GSM648649 1 0.6079 0.28207 0.612 0.388 0.000
#> GSM648675 2 0.1289 0.83384 0.032 0.968 0.000
#> GSM648682 2 0.2261 0.84981 0.000 0.932 0.068
#> GSM648698 2 0.0829 0.84117 0.004 0.984 0.012
#> GSM648708 2 0.5363 0.66546 0.276 0.724 0.000
#> GSM648628 3 0.8830 0.22970 0.416 0.116 0.468
#> GSM648595 2 0.6082 0.60236 0.296 0.692 0.012
#> GSM648635 1 0.2711 0.84832 0.912 0.088 0.000
#> GSM648645 1 0.0000 0.91058 1.000 0.000 0.000
#> GSM648647 2 0.3412 0.80264 0.124 0.876 0.000
#> GSM648667 2 0.6168 0.40551 0.412 0.588 0.000
#> GSM648695 2 0.5216 0.68420 0.260 0.740 0.000
#> GSM648704 2 0.2537 0.84935 0.000 0.920 0.080
#> GSM648706 2 0.2711 0.84634 0.000 0.912 0.088
#> GSM648593 1 0.5016 0.64442 0.760 0.240 0.000
#> GSM648594 1 0.3752 0.78972 0.856 0.144 0.000
#> GSM648600 1 0.1289 0.90112 0.968 0.000 0.032
#> GSM648621 1 0.1289 0.90112 0.968 0.000 0.032
#> GSM648622 1 0.0237 0.91035 0.996 0.000 0.004
#> GSM648623 1 0.1289 0.90112 0.968 0.000 0.032
#> GSM648636 1 0.2165 0.86995 0.936 0.064 0.000
#> GSM648655 1 0.2066 0.87312 0.940 0.060 0.000
#> GSM648661 1 0.0000 0.91058 1.000 0.000 0.000
#> GSM648664 1 0.0000 0.91058 1.000 0.000 0.000
#> GSM648683 1 0.0000 0.91058 1.000 0.000 0.000
#> GSM648685 1 0.0000 0.91058 1.000 0.000 0.000
#> GSM648702 1 0.3116 0.82873 0.892 0.108 0.000
#> GSM648597 1 0.7419 0.55664 0.680 0.088 0.232
#> GSM648603 1 0.1289 0.90112 0.968 0.000 0.032
#> GSM648606 3 0.6325 0.75528 0.112 0.116 0.772
#> GSM648613 3 0.5695 0.77821 0.076 0.120 0.804
#> GSM648619 1 0.1289 0.90112 0.968 0.000 0.032
#> GSM648654 1 0.6527 0.41844 0.660 0.020 0.320
#> GSM648663 3 0.8468 0.50771 0.308 0.116 0.576
#> GSM648670 2 0.1711 0.83241 0.032 0.960 0.008
#> GSM648707 3 0.4931 0.80125 0.032 0.140 0.828
#> GSM648615 2 0.1482 0.83498 0.020 0.968 0.012
#> GSM648643 2 0.0848 0.84029 0.008 0.984 0.008
#> GSM648650 2 0.6008 0.50040 0.372 0.628 0.000
#> GSM648656 2 0.2537 0.84935 0.000 0.920 0.080
#> GSM648715 2 0.4605 0.74271 0.204 0.796 0.000
#> GSM648598 1 0.0000 0.91058 1.000 0.000 0.000
#> GSM648601 1 0.0000 0.91058 1.000 0.000 0.000
#> GSM648602 1 0.0237 0.91035 0.996 0.000 0.004
#> GSM648604 1 0.0000 0.91058 1.000 0.000 0.000
#> GSM648614 3 0.8513 0.49206 0.316 0.116 0.568
#> GSM648624 1 0.0237 0.91035 0.996 0.000 0.004
#> GSM648625 1 0.0237 0.91035 0.996 0.000 0.004
#> GSM648629 1 0.0000 0.91058 1.000 0.000 0.000
#> GSM648634 1 0.0000 0.91058 1.000 0.000 0.000
#> GSM648648 1 0.2356 0.86292 0.928 0.072 0.000
#> GSM648651 1 0.0237 0.91035 0.996 0.000 0.004
#> GSM648657 1 0.0892 0.90588 0.980 0.000 0.020
#> GSM648660 1 0.0000 0.91058 1.000 0.000 0.000
#> GSM648697 1 0.0000 0.91058 1.000 0.000 0.000
#> GSM648710 1 0.0000 0.91058 1.000 0.000 0.000
#> GSM648591 1 0.8550 0.00364 0.492 0.096 0.412
#> GSM648592 1 0.8765 0.09679 0.504 0.116 0.380
#> GSM648607 1 0.1289 0.90112 0.968 0.000 0.032
#> GSM648611 3 0.8765 0.33882 0.380 0.116 0.504
#> GSM648612 1 0.4745 0.80447 0.852 0.080 0.068
#> GSM648616 3 0.3690 0.81048 0.016 0.100 0.884
#> GSM648617 1 0.8202 0.19378 0.544 0.080 0.376
#> GSM648626 1 0.1643 0.89614 0.956 0.000 0.044
#> GSM648711 1 0.1289 0.90112 0.968 0.000 0.032
#> GSM648712 1 0.3134 0.86318 0.916 0.052 0.032
#> GSM648713 1 0.2689 0.87727 0.932 0.036 0.032
#> GSM648714 3 0.5998 0.77524 0.084 0.128 0.788
#> GSM648716 1 0.2176 0.88967 0.948 0.020 0.032
#> GSM648717 3 0.6462 0.75073 0.120 0.116 0.764
#> GSM648590 2 0.3686 0.77205 0.140 0.860 0.000
#> GSM648596 2 0.1620 0.83387 0.024 0.964 0.012
#> GSM648642 2 0.2066 0.82255 0.060 0.940 0.000
#> GSM648696 2 0.6309 0.16195 0.496 0.504 0.000
#> GSM648705 1 0.6302 -0.07031 0.520 0.480 0.000
#> GSM648718 2 0.0592 0.83757 0.012 0.988 0.000
#> GSM648599 1 0.1289 0.90112 0.968 0.000 0.032
#> GSM648608 1 0.0000 0.91058 1.000 0.000 0.000
#> GSM648609 1 0.0000 0.91058 1.000 0.000 0.000
#> GSM648610 1 0.0592 0.90849 0.988 0.000 0.012
#> GSM648633 1 0.0424 0.90957 0.992 0.000 0.008
#> GSM648644 2 0.2537 0.84935 0.000 0.920 0.080
#> GSM648652 1 0.1643 0.88551 0.956 0.044 0.000
#> GSM648653 1 0.0000 0.91058 1.000 0.000 0.000
#> GSM648658 1 0.0000 0.91058 1.000 0.000 0.000
#> GSM648659 2 0.4235 0.73354 0.176 0.824 0.000
#> GSM648662 1 0.0424 0.90957 0.992 0.000 0.008
#> GSM648665 1 0.0000 0.91058 1.000 0.000 0.000
#> GSM648666 1 0.0000 0.91058 1.000 0.000 0.000
#> GSM648680 1 0.1289 0.89202 0.968 0.032 0.000
#> GSM648684 1 0.0000 0.91058 1.000 0.000 0.000
#> GSM648709 2 0.4346 0.72647 0.184 0.816 0.000
#> GSM648719 1 0.0237 0.91035 0.996 0.000 0.004
#> GSM648627 1 0.1711 0.89718 0.960 0.008 0.032
#> GSM648637 2 0.2796 0.84501 0.000 0.908 0.092
#> GSM648638 2 0.5560 0.58315 0.000 0.700 0.300
#> GSM648641 3 0.3272 0.82445 0.016 0.080 0.904
#> GSM648672 2 0.2537 0.84935 0.000 0.920 0.080
#> GSM648674 2 0.2711 0.84684 0.000 0.912 0.088
#> GSM648703 2 0.2537 0.84935 0.000 0.920 0.080
#> GSM648631 3 0.1289 0.83779 0.000 0.032 0.968
#> GSM648669 2 0.3116 0.83329 0.000 0.892 0.108
#> GSM648671 2 0.2537 0.84935 0.000 0.920 0.080
#> GSM648678 2 0.2537 0.84935 0.000 0.920 0.080
#> GSM648679 2 0.2711 0.84684 0.000 0.912 0.088
#> GSM648681 2 0.1163 0.83511 0.028 0.972 0.000
#> GSM648686 3 0.1411 0.83687 0.000 0.036 0.964
#> GSM648689 3 0.1289 0.83779 0.000 0.032 0.968
#> GSM648690 3 0.1411 0.83687 0.000 0.036 0.964
#> GSM648691 3 0.1289 0.83779 0.000 0.032 0.968
#> GSM648693 3 0.1289 0.83779 0.000 0.032 0.968
#> GSM648700 2 0.0747 0.84272 0.000 0.984 0.016
#> GSM648630 3 0.1289 0.83779 0.000 0.032 0.968
#> GSM648632 3 0.1289 0.83779 0.000 0.032 0.968
#> GSM648639 3 0.3193 0.81094 0.004 0.100 0.896
#> GSM648640 3 0.2590 0.82446 0.004 0.072 0.924
#> GSM648668 2 0.2625 0.84793 0.000 0.916 0.084
#> GSM648676 2 0.2537 0.84935 0.000 0.920 0.080
#> GSM648692 3 0.1289 0.83779 0.000 0.032 0.968
#> GSM648694 3 0.1289 0.83779 0.000 0.032 0.968
#> GSM648699 2 0.2537 0.84935 0.000 0.920 0.080
#> GSM648701 2 0.2537 0.84935 0.000 0.920 0.080
#> GSM648673 2 0.2537 0.84935 0.000 0.920 0.080
#> GSM648677 2 0.2537 0.84935 0.000 0.920 0.080
#> GSM648687 3 0.1711 0.83678 0.008 0.032 0.960
#> GSM648688 3 0.1289 0.83779 0.000 0.032 0.968
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM648605 2 0.3176 0.832 0.000 0.880 0.084 0.036
#> GSM648618 3 0.0712 0.814 0.004 0.008 0.984 0.004
#> GSM648620 1 0.2921 0.816 0.860 0.140 0.000 0.000
#> GSM648646 2 0.0000 0.934 0.000 1.000 0.000 0.000
#> GSM648649 1 0.2469 0.831 0.892 0.108 0.000 0.000
#> GSM648675 1 0.3972 0.762 0.788 0.204 0.008 0.000
#> GSM648682 2 0.0000 0.934 0.000 1.000 0.000 0.000
#> GSM648698 2 0.0376 0.930 0.000 0.992 0.004 0.004
#> GSM648708 1 0.3266 0.799 0.832 0.168 0.000 0.000
#> GSM648628 3 0.0376 0.814 0.004 0.000 0.992 0.004
#> GSM648595 1 0.3266 0.799 0.832 0.168 0.000 0.000
#> GSM648635 1 0.0188 0.831 0.996 0.004 0.000 0.000
#> GSM648645 1 0.0188 0.829 0.996 0.000 0.000 0.004
#> GSM648647 1 0.5060 0.409 0.584 0.412 0.004 0.000
#> GSM648667 1 0.2589 0.828 0.884 0.116 0.000 0.000
#> GSM648695 1 0.3266 0.799 0.832 0.168 0.000 0.000
#> GSM648704 2 0.0000 0.934 0.000 1.000 0.000 0.000
#> GSM648706 2 0.0592 0.922 0.000 0.984 0.000 0.016
#> GSM648593 1 0.2704 0.826 0.876 0.124 0.000 0.000
#> GSM648594 1 0.2408 0.832 0.896 0.104 0.000 0.000
#> GSM648600 1 0.1792 0.817 0.932 0.000 0.068 0.000
#> GSM648621 3 0.3219 0.881 0.164 0.000 0.836 0.000
#> GSM648622 3 0.3908 0.887 0.212 0.000 0.784 0.004
#> GSM648623 3 0.2760 0.879 0.128 0.000 0.872 0.000
#> GSM648636 1 0.0000 0.830 1.000 0.000 0.000 0.000
#> GSM648655 1 0.0000 0.830 1.000 0.000 0.000 0.000
#> GSM648661 3 0.3870 0.889 0.208 0.000 0.788 0.004
#> GSM648664 3 0.3870 0.889 0.208 0.000 0.788 0.004
#> GSM648683 3 0.3870 0.889 0.208 0.000 0.788 0.004
#> GSM648685 3 0.3870 0.889 0.208 0.000 0.788 0.004
#> GSM648702 1 0.0817 0.834 0.976 0.024 0.000 0.000
#> GSM648597 1 0.3831 0.744 0.792 0.004 0.204 0.000
#> GSM648603 1 0.4193 0.531 0.732 0.000 0.268 0.000
#> GSM648606 3 0.0188 0.811 0.000 0.000 0.996 0.004
#> GSM648613 3 0.0188 0.811 0.000 0.000 0.996 0.004
#> GSM648619 3 0.2814 0.880 0.132 0.000 0.868 0.000
#> GSM648654 3 0.3791 0.888 0.200 0.000 0.796 0.004
#> GSM648663 3 0.0376 0.814 0.004 0.000 0.992 0.004
#> GSM648670 1 0.4544 0.779 0.788 0.164 0.048 0.000
#> GSM648707 3 0.2718 0.765 0.012 0.056 0.912 0.020
#> GSM648615 1 0.5148 0.554 0.640 0.348 0.008 0.004
#> GSM648643 2 0.2010 0.871 0.060 0.932 0.004 0.004
#> GSM648650 1 0.2921 0.817 0.860 0.140 0.000 0.000
#> GSM648656 2 0.0000 0.934 0.000 1.000 0.000 0.000
#> GSM648715 1 0.3266 0.799 0.832 0.168 0.000 0.000
#> GSM648598 1 0.0376 0.827 0.992 0.000 0.004 0.004
#> GSM648601 1 0.0895 0.817 0.976 0.000 0.020 0.004
#> GSM648602 1 0.5137 -0.251 0.544 0.000 0.452 0.004
#> GSM648604 3 0.3870 0.889 0.208 0.000 0.788 0.004
#> GSM648614 3 0.0524 0.817 0.008 0.000 0.988 0.004
#> GSM648624 3 0.3870 0.889 0.208 0.000 0.788 0.004
#> GSM648625 1 0.0524 0.825 0.988 0.000 0.008 0.004
#> GSM648629 3 0.3870 0.889 0.208 0.000 0.788 0.004
#> GSM648634 1 0.0376 0.827 0.992 0.000 0.004 0.004
#> GSM648648 1 0.0000 0.830 1.000 0.000 0.000 0.000
#> GSM648651 3 0.4456 0.826 0.280 0.000 0.716 0.004
#> GSM648657 1 0.0469 0.829 0.988 0.000 0.012 0.000
#> GSM648660 1 0.0188 0.829 0.996 0.000 0.000 0.004
#> GSM648697 3 0.4188 0.863 0.244 0.000 0.752 0.004
#> GSM648710 3 0.3870 0.889 0.208 0.000 0.788 0.004
#> GSM648591 3 0.3569 0.643 0.196 0.000 0.804 0.000
#> GSM648592 1 0.4019 0.749 0.792 0.012 0.196 0.000
#> GSM648607 3 0.3688 0.889 0.208 0.000 0.792 0.000
#> GSM648611 3 0.0376 0.814 0.004 0.000 0.992 0.004
#> GSM648612 3 0.2647 0.877 0.120 0.000 0.880 0.000
#> GSM648616 3 0.4988 0.455 0.000 0.288 0.692 0.020
#> GSM648617 1 0.3688 0.745 0.792 0.000 0.208 0.000
#> GSM648626 3 0.4193 0.719 0.268 0.000 0.732 0.000
#> GSM648711 3 0.3870 0.889 0.208 0.000 0.788 0.004
#> GSM648712 3 0.2704 0.878 0.124 0.000 0.876 0.000
#> GSM648713 3 0.2647 0.877 0.120 0.000 0.880 0.000
#> GSM648714 3 0.0188 0.811 0.000 0.000 0.996 0.004
#> GSM648716 3 0.2760 0.879 0.128 0.000 0.872 0.000
#> GSM648717 3 0.0376 0.814 0.004 0.000 0.992 0.004
#> GSM648590 1 0.3626 0.783 0.812 0.184 0.004 0.000
#> GSM648596 1 0.4578 0.780 0.788 0.160 0.052 0.000
#> GSM648642 2 0.4687 0.508 0.288 0.704 0.004 0.004
#> GSM648696 1 0.2281 0.833 0.904 0.096 0.000 0.000
#> GSM648705 1 0.2704 0.826 0.876 0.124 0.000 0.000
#> GSM648718 2 0.4832 0.447 0.312 0.680 0.004 0.004
#> GSM648599 1 0.4730 0.192 0.636 0.000 0.364 0.000
#> GSM648608 3 0.3870 0.889 0.208 0.000 0.788 0.004
#> GSM648609 3 0.3870 0.889 0.208 0.000 0.788 0.004
#> GSM648610 3 0.3870 0.889 0.208 0.000 0.788 0.004
#> GSM648633 1 0.0336 0.828 0.992 0.000 0.008 0.000
#> GSM648644 2 0.0000 0.934 0.000 1.000 0.000 0.000
#> GSM648652 1 0.0000 0.830 1.000 0.000 0.000 0.000
#> GSM648653 1 0.5151 -0.290 0.532 0.000 0.464 0.004
#> GSM648658 1 0.0000 0.830 1.000 0.000 0.000 0.000
#> GSM648659 1 0.4283 0.708 0.740 0.256 0.000 0.004
#> GSM648662 3 0.3870 0.889 0.208 0.000 0.788 0.004
#> GSM648665 3 0.3870 0.889 0.208 0.000 0.788 0.004
#> GSM648666 3 0.4018 0.879 0.224 0.000 0.772 0.004
#> GSM648680 1 0.0000 0.830 1.000 0.000 0.000 0.000
#> GSM648684 3 0.3870 0.889 0.208 0.000 0.788 0.004
#> GSM648709 1 0.3852 0.774 0.800 0.192 0.008 0.000
#> GSM648719 1 0.0376 0.827 0.992 0.000 0.004 0.004
#> GSM648627 3 0.2921 0.882 0.140 0.000 0.860 0.000
#> GSM648637 2 0.0188 0.933 0.000 0.996 0.004 0.000
#> GSM648638 2 0.2124 0.873 0.000 0.932 0.040 0.028
#> GSM648641 3 0.4511 0.463 0.000 0.008 0.724 0.268
#> GSM648672 2 0.0188 0.933 0.000 0.996 0.004 0.000
#> GSM648674 2 0.0188 0.933 0.000 0.996 0.004 0.000
#> GSM648703 2 0.0000 0.934 0.000 1.000 0.000 0.000
#> GSM648631 4 0.0336 0.973 0.000 0.008 0.000 0.992
#> GSM648669 2 0.0524 0.929 0.000 0.988 0.004 0.008
#> GSM648671 2 0.0524 0.929 0.000 0.988 0.004 0.008
#> GSM648678 2 0.0000 0.934 0.000 1.000 0.000 0.000
#> GSM648679 2 0.0188 0.933 0.000 0.996 0.004 0.000
#> GSM648681 1 0.4012 0.762 0.788 0.204 0.004 0.004
#> GSM648686 4 0.0336 0.973 0.000 0.008 0.000 0.992
#> GSM648689 4 0.0336 0.973 0.000 0.008 0.000 0.992
#> GSM648690 4 0.0336 0.973 0.000 0.008 0.000 0.992
#> GSM648691 4 0.0336 0.973 0.000 0.008 0.000 0.992
#> GSM648693 4 0.0336 0.973 0.000 0.008 0.000 0.992
#> GSM648700 2 0.0188 0.933 0.000 0.996 0.000 0.004
#> GSM648630 4 0.0336 0.973 0.000 0.008 0.000 0.992
#> GSM648632 4 0.0336 0.973 0.000 0.008 0.000 0.992
#> GSM648639 2 0.6650 0.222 0.000 0.484 0.432 0.084
#> GSM648640 4 0.3808 0.832 0.000 0.012 0.176 0.812
#> GSM648668 2 0.0188 0.933 0.000 0.996 0.004 0.000
#> GSM648676 2 0.0188 0.933 0.000 0.996 0.000 0.004
#> GSM648692 4 0.0336 0.973 0.000 0.008 0.000 0.992
#> GSM648694 4 0.0336 0.973 0.000 0.008 0.000 0.992
#> GSM648699 2 0.0188 0.933 0.000 0.996 0.000 0.004
#> GSM648701 2 0.0188 0.933 0.000 0.996 0.000 0.004
#> GSM648673 2 0.0376 0.931 0.000 0.992 0.004 0.004
#> GSM648677 2 0.0000 0.934 0.000 1.000 0.000 0.000
#> GSM648687 4 0.3088 0.839 0.000 0.008 0.128 0.864
#> GSM648688 4 0.0336 0.973 0.000 0.008 0.000 0.992
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM648605 4 0.2699 0.909 0.008 0.100 0.012 0.880 0.000
#> GSM648618 1 0.2646 0.812 0.868 0.000 0.004 0.004 0.124
#> GSM648620 2 0.1168 0.847 0.008 0.960 0.000 0.032 0.000
#> GSM648646 4 0.1341 0.948 0.000 0.056 0.000 0.944 0.000
#> GSM648649 2 0.2127 0.881 0.108 0.892 0.000 0.000 0.000
#> GSM648675 2 0.1043 0.838 0.000 0.960 0.000 0.040 0.000
#> GSM648682 4 0.1043 0.958 0.000 0.040 0.000 0.960 0.000
#> GSM648698 4 0.1965 0.918 0.000 0.096 0.000 0.904 0.000
#> GSM648708 2 0.1041 0.845 0.004 0.964 0.000 0.032 0.000
#> GSM648628 5 0.3086 0.731 0.180 0.000 0.004 0.000 0.816
#> GSM648595 2 0.3114 0.871 0.076 0.872 0.000 0.016 0.036
#> GSM648635 2 0.2127 0.881 0.108 0.892 0.000 0.000 0.000
#> GSM648645 2 0.2732 0.860 0.160 0.840 0.000 0.000 0.000
#> GSM648647 2 0.1124 0.843 0.004 0.960 0.000 0.036 0.000
#> GSM648667 2 0.2416 0.882 0.100 0.888 0.000 0.012 0.000
#> GSM648695 2 0.1168 0.847 0.008 0.960 0.000 0.032 0.000
#> GSM648704 4 0.0880 0.960 0.000 0.032 0.000 0.968 0.000
#> GSM648706 4 0.0880 0.960 0.000 0.032 0.000 0.968 0.000
#> GSM648593 2 0.2130 0.879 0.080 0.908 0.000 0.012 0.000
#> GSM648594 2 0.2669 0.881 0.104 0.876 0.000 0.020 0.000
#> GSM648600 2 0.3741 0.775 0.264 0.732 0.000 0.000 0.004
#> GSM648621 1 0.0404 0.933 0.988 0.000 0.000 0.000 0.012
#> GSM648622 1 0.0703 0.934 0.976 0.024 0.000 0.000 0.000
#> GSM648623 1 0.0880 0.925 0.968 0.000 0.000 0.000 0.032
#> GSM648636 2 0.2127 0.881 0.108 0.892 0.000 0.000 0.000
#> GSM648655 2 0.2230 0.880 0.116 0.884 0.000 0.000 0.000
#> GSM648661 1 0.0404 0.941 0.988 0.012 0.000 0.000 0.000
#> GSM648664 1 0.0404 0.941 0.988 0.012 0.000 0.000 0.000
#> GSM648683 1 0.0404 0.941 0.988 0.012 0.000 0.000 0.000
#> GSM648685 1 0.0404 0.941 0.988 0.012 0.000 0.000 0.000
#> GSM648702 2 0.2127 0.881 0.108 0.892 0.000 0.000 0.000
#> GSM648597 2 0.3579 0.851 0.100 0.828 0.000 0.000 0.072
#> GSM648603 1 0.1764 0.888 0.928 0.064 0.000 0.000 0.008
#> GSM648606 5 0.1502 0.833 0.056 0.000 0.004 0.000 0.940
#> GSM648613 5 0.0771 0.827 0.020 0.000 0.004 0.000 0.976
#> GSM648619 1 0.0609 0.930 0.980 0.000 0.000 0.000 0.020
#> GSM648654 1 0.1670 0.901 0.936 0.012 0.000 0.000 0.052
#> GSM648663 5 0.4440 0.117 0.468 0.000 0.004 0.000 0.528
#> GSM648670 2 0.2450 0.823 0.000 0.900 0.000 0.048 0.052
#> GSM648707 5 0.0510 0.813 0.000 0.016 0.000 0.000 0.984
#> GSM648615 2 0.5112 -0.015 0.000 0.496 0.000 0.468 0.036
#> GSM648643 4 0.2605 0.855 0.000 0.148 0.000 0.852 0.000
#> GSM648650 2 0.2293 0.881 0.084 0.900 0.000 0.016 0.000
#> GSM648656 4 0.0880 0.960 0.000 0.032 0.000 0.968 0.000
#> GSM648715 2 0.1041 0.845 0.004 0.964 0.000 0.032 0.000
#> GSM648598 2 0.3143 0.830 0.204 0.796 0.000 0.000 0.000
#> GSM648601 2 0.3932 0.674 0.328 0.672 0.000 0.000 0.000
#> GSM648602 1 0.2605 0.788 0.852 0.148 0.000 0.000 0.000
#> GSM648604 1 0.0290 0.940 0.992 0.008 0.000 0.000 0.000
#> GSM648614 1 0.3300 0.716 0.792 0.000 0.004 0.000 0.204
#> GSM648624 1 0.0404 0.939 0.988 0.012 0.000 0.000 0.000
#> GSM648625 2 0.3561 0.780 0.260 0.740 0.000 0.000 0.000
#> GSM648629 1 0.0404 0.941 0.988 0.012 0.000 0.000 0.000
#> GSM648634 2 0.3336 0.808 0.228 0.772 0.000 0.000 0.000
#> GSM648648 2 0.2179 0.880 0.112 0.888 0.000 0.000 0.000
#> GSM648651 1 0.0703 0.934 0.976 0.024 0.000 0.000 0.000
#> GSM648657 2 0.2773 0.861 0.164 0.836 0.000 0.000 0.000
#> GSM648660 2 0.2813 0.856 0.168 0.832 0.000 0.000 0.000
#> GSM648697 1 0.1043 0.922 0.960 0.040 0.000 0.000 0.000
#> GSM648710 1 0.0404 0.941 0.988 0.012 0.000 0.000 0.000
#> GSM648591 1 0.2597 0.865 0.884 0.024 0.000 0.000 0.092
#> GSM648592 2 0.3535 0.850 0.080 0.832 0.000 0.000 0.088
#> GSM648607 1 0.0162 0.939 0.996 0.004 0.000 0.000 0.000
#> GSM648611 5 0.2068 0.816 0.092 0.000 0.004 0.000 0.904
#> GSM648612 1 0.2329 0.849 0.876 0.000 0.000 0.000 0.124
#> GSM648616 5 0.1756 0.796 0.000 0.016 0.008 0.036 0.940
#> GSM648617 2 0.4670 0.770 0.200 0.724 0.000 0.000 0.076
#> GSM648626 1 0.0880 0.928 0.968 0.000 0.000 0.000 0.032
#> GSM648711 1 0.0290 0.940 0.992 0.008 0.000 0.000 0.000
#> GSM648712 1 0.2230 0.857 0.884 0.000 0.000 0.000 0.116
#> GSM648713 1 0.2230 0.857 0.884 0.000 0.000 0.000 0.116
#> GSM648714 5 0.1124 0.833 0.036 0.000 0.004 0.000 0.960
#> GSM648716 1 0.2230 0.857 0.884 0.000 0.000 0.000 0.116
#> GSM648717 5 0.1768 0.828 0.072 0.000 0.004 0.000 0.924
#> GSM648590 2 0.1331 0.843 0.008 0.952 0.000 0.040 0.000
#> GSM648596 2 0.2536 0.823 0.004 0.900 0.000 0.044 0.052
#> GSM648642 2 0.3612 0.548 0.000 0.732 0.000 0.268 0.000
#> GSM648696 2 0.2179 0.881 0.112 0.888 0.000 0.000 0.000
#> GSM648705 2 0.2077 0.880 0.084 0.908 0.000 0.008 0.000
#> GSM648718 2 0.2516 0.748 0.000 0.860 0.000 0.140 0.000
#> GSM648599 1 0.2020 0.851 0.900 0.100 0.000 0.000 0.000
#> GSM648608 1 0.0404 0.941 0.988 0.012 0.000 0.000 0.000
#> GSM648609 1 0.0404 0.941 0.988 0.012 0.000 0.000 0.000
#> GSM648610 1 0.0162 0.939 0.996 0.004 0.000 0.000 0.000
#> GSM648633 2 0.3242 0.824 0.216 0.784 0.000 0.000 0.000
#> GSM648644 4 0.0880 0.960 0.000 0.032 0.000 0.968 0.000
#> GSM648652 2 0.2179 0.880 0.112 0.888 0.000 0.000 0.000
#> GSM648653 1 0.2471 0.810 0.864 0.136 0.000 0.000 0.000
#> GSM648658 2 0.2329 0.877 0.124 0.876 0.000 0.000 0.000
#> GSM648659 2 0.1205 0.841 0.004 0.956 0.000 0.040 0.000
#> GSM648662 1 0.0162 0.939 0.996 0.004 0.000 0.000 0.000
#> GSM648665 1 0.0404 0.941 0.988 0.012 0.000 0.000 0.000
#> GSM648666 1 0.0404 0.941 0.988 0.012 0.000 0.000 0.000
#> GSM648680 2 0.2230 0.879 0.116 0.884 0.000 0.000 0.000
#> GSM648684 1 0.0404 0.941 0.988 0.012 0.000 0.000 0.000
#> GSM648709 2 0.1364 0.845 0.012 0.952 0.000 0.036 0.000
#> GSM648719 2 0.3336 0.810 0.228 0.772 0.000 0.000 0.000
#> GSM648627 1 0.0510 0.931 0.984 0.000 0.000 0.000 0.016
#> GSM648637 4 0.0798 0.938 0.000 0.016 0.000 0.976 0.008
#> GSM648638 4 0.1018 0.935 0.000 0.016 0.000 0.968 0.016
#> GSM648641 5 0.2612 0.725 0.000 0.008 0.124 0.000 0.868
#> GSM648672 4 0.0290 0.947 0.000 0.008 0.000 0.992 0.000
#> GSM648674 4 0.0798 0.938 0.000 0.016 0.000 0.976 0.008
#> GSM648703 4 0.0880 0.960 0.000 0.032 0.000 0.968 0.000
#> GSM648631 3 0.0000 0.957 0.000 0.000 1.000 0.000 0.000
#> GSM648669 4 0.0000 0.950 0.000 0.000 0.000 1.000 0.000
#> GSM648671 4 0.0510 0.957 0.000 0.016 0.000 0.984 0.000
#> GSM648678 4 0.0880 0.960 0.000 0.032 0.000 0.968 0.000
#> GSM648679 4 0.0798 0.938 0.000 0.016 0.000 0.976 0.008
#> GSM648681 2 0.1121 0.837 0.000 0.956 0.000 0.044 0.000
#> GSM648686 3 0.0000 0.957 0.000 0.000 1.000 0.000 0.000
#> GSM648689 3 0.0000 0.957 0.000 0.000 1.000 0.000 0.000
#> GSM648690 3 0.0000 0.957 0.000 0.000 1.000 0.000 0.000
#> GSM648691 3 0.0000 0.957 0.000 0.000 1.000 0.000 0.000
#> GSM648693 3 0.0000 0.957 0.000 0.000 1.000 0.000 0.000
#> GSM648700 4 0.2280 0.906 0.000 0.120 0.000 0.880 0.000
#> GSM648630 3 0.0000 0.957 0.000 0.000 1.000 0.000 0.000
#> GSM648632 3 0.0000 0.957 0.000 0.000 1.000 0.000 0.000
#> GSM648639 5 0.4394 0.672 0.000 0.016 0.112 0.084 0.788
#> GSM648640 3 0.2995 0.847 0.000 0.008 0.872 0.032 0.088
#> GSM648668 4 0.1830 0.921 0.000 0.068 0.000 0.924 0.008
#> GSM648676 4 0.2230 0.908 0.000 0.116 0.000 0.884 0.000
#> GSM648692 3 0.0000 0.957 0.000 0.000 1.000 0.000 0.000
#> GSM648694 3 0.0000 0.957 0.000 0.000 1.000 0.000 0.000
#> GSM648699 4 0.0880 0.960 0.000 0.032 0.000 0.968 0.000
#> GSM648701 4 0.0880 0.960 0.000 0.032 0.000 0.968 0.000
#> GSM648673 4 0.0609 0.958 0.000 0.020 0.000 0.980 0.000
#> GSM648677 4 0.0703 0.959 0.000 0.024 0.000 0.976 0.000
#> GSM648687 3 0.4074 0.390 0.000 0.000 0.636 0.000 0.364
#> GSM648688 3 0.0000 0.957 0.000 0.000 1.000 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM648605 4 0.4267 0.63937 0.044 0.040 0.000 0.760 0.000 0.156
#> GSM648618 5 0.3733 0.47069 0.288 0.008 0.000 0.004 0.700 0.000
#> GSM648620 6 0.4467 0.20503 0.048 0.320 0.000 0.000 0.000 0.632
#> GSM648646 4 0.0508 0.88645 0.000 0.012 0.000 0.984 0.000 0.004
#> GSM648649 6 0.1075 0.58234 0.048 0.000 0.000 0.000 0.000 0.952
#> GSM648675 6 0.4032 -0.07089 0.000 0.420 0.000 0.008 0.000 0.572
#> GSM648682 4 0.1257 0.86489 0.000 0.020 0.000 0.952 0.000 0.028
#> GSM648698 4 0.3578 0.64470 0.000 0.052 0.000 0.784 0.000 0.164
#> GSM648708 6 0.4491 0.07146 0.036 0.388 0.000 0.000 0.000 0.576
#> GSM648628 5 0.0865 0.76915 0.036 0.000 0.000 0.000 0.964 0.000
#> GSM648595 6 0.1285 0.58342 0.052 0.004 0.000 0.000 0.000 0.944
#> GSM648635 6 0.1814 0.59647 0.100 0.000 0.000 0.000 0.000 0.900
#> GSM648645 6 0.4215 0.55346 0.276 0.012 0.000 0.000 0.024 0.688
#> GSM648647 6 0.4101 -0.04726 0.000 0.408 0.000 0.012 0.000 0.580
#> GSM648667 6 0.1075 0.58234 0.048 0.000 0.000 0.000 0.000 0.952
#> GSM648695 6 0.4530 0.13930 0.044 0.356 0.000 0.000 0.000 0.600
#> GSM648704 4 0.0000 0.89323 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM648706 4 0.0146 0.89135 0.000 0.004 0.000 0.996 0.000 0.000
#> GSM648593 6 0.1075 0.58234 0.048 0.000 0.000 0.000 0.000 0.952
#> GSM648594 6 0.1285 0.58397 0.052 0.004 0.000 0.000 0.000 0.944
#> GSM648600 6 0.5517 0.38931 0.128 0.012 0.000 0.000 0.280 0.580
#> GSM648621 1 0.3198 0.61231 0.740 0.000 0.000 0.000 0.260 0.000
#> GSM648622 1 0.0146 0.79342 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM648623 1 0.3371 0.57679 0.708 0.000 0.000 0.000 0.292 0.000
#> GSM648636 6 0.2118 0.59716 0.104 0.008 0.000 0.000 0.000 0.888
#> GSM648655 6 0.2146 0.59748 0.116 0.004 0.000 0.000 0.000 0.880
#> GSM648661 1 0.0146 0.79492 0.996 0.004 0.000 0.000 0.000 0.000
#> GSM648664 1 0.0146 0.79492 0.996 0.004 0.000 0.000 0.000 0.000
#> GSM648683 1 0.0146 0.79492 0.996 0.004 0.000 0.000 0.000 0.000
#> GSM648685 1 0.0260 0.79345 0.992 0.008 0.000 0.000 0.000 0.000
#> GSM648702 6 0.1958 0.59656 0.100 0.004 0.000 0.000 0.000 0.896
#> GSM648597 6 0.3855 0.45676 0.004 0.016 0.000 0.000 0.276 0.704
#> GSM648603 6 0.6387 -0.16624 0.344 0.012 0.000 0.000 0.280 0.364
#> GSM648606 5 0.0632 0.76890 0.024 0.000 0.000 0.000 0.976 0.000
#> GSM648613 5 0.2778 0.68842 0.008 0.168 0.000 0.000 0.824 0.000
#> GSM648619 1 0.3563 0.52365 0.664 0.000 0.000 0.000 0.336 0.000
#> GSM648654 1 0.2933 0.59862 0.796 0.004 0.000 0.000 0.200 0.000
#> GSM648663 5 0.1204 0.75951 0.056 0.000 0.000 0.000 0.944 0.000
#> GSM648670 2 0.5700 0.27433 0.000 0.460 0.000 0.140 0.004 0.396
#> GSM648707 5 0.3989 0.40551 0.000 0.468 0.000 0.004 0.528 0.000
#> GSM648615 2 0.6037 0.45222 0.000 0.420 0.000 0.276 0.000 0.304
#> GSM648643 2 0.5867 0.31207 0.000 0.420 0.000 0.384 0.000 0.196
#> GSM648650 6 0.1075 0.58234 0.048 0.000 0.000 0.000 0.000 0.952
#> GSM648656 4 0.0363 0.88844 0.000 0.012 0.000 0.988 0.000 0.000
#> GSM648715 6 0.4066 -0.01807 0.000 0.392 0.000 0.012 0.000 0.596
#> GSM648598 6 0.4485 0.51111 0.340 0.012 0.000 0.000 0.024 0.624
#> GSM648601 6 0.4717 0.26511 0.460 0.012 0.000 0.000 0.024 0.504
#> GSM648602 1 0.4253 0.32866 0.664 0.008 0.000 0.000 0.024 0.304
#> GSM648604 1 0.0000 0.79495 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM648614 5 0.3266 0.48292 0.272 0.000 0.000 0.000 0.728 0.000
#> GSM648624 1 0.0000 0.79495 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM648625 6 0.4365 0.53672 0.292 0.012 0.000 0.000 0.028 0.668
#> GSM648629 1 0.0000 0.79495 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM648634 6 0.4601 0.44802 0.380 0.012 0.000 0.000 0.024 0.584
#> GSM648648 6 0.2838 0.58816 0.188 0.004 0.000 0.000 0.000 0.808
#> GSM648651 1 0.1757 0.73031 0.916 0.000 0.000 0.000 0.008 0.076
#> GSM648657 6 0.4801 0.52879 0.096 0.016 0.000 0.000 0.192 0.696
#> GSM648660 6 0.4255 0.54969 0.284 0.012 0.000 0.000 0.024 0.680
#> GSM648697 1 0.0260 0.79345 0.992 0.008 0.000 0.000 0.000 0.000
#> GSM648710 1 0.0000 0.79495 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM648591 1 0.4288 0.55563 0.660 0.020 0.000 0.000 0.308 0.012
#> GSM648592 6 0.3799 0.45488 0.000 0.020 0.000 0.000 0.276 0.704
#> GSM648607 1 0.2300 0.71211 0.856 0.000 0.000 0.000 0.144 0.000
#> GSM648611 5 0.0790 0.76972 0.032 0.000 0.000 0.000 0.968 0.000
#> GSM648612 1 0.3854 0.28795 0.536 0.000 0.000 0.000 0.464 0.000
#> GSM648616 2 0.4158 -0.45035 0.000 0.572 0.004 0.008 0.416 0.000
#> GSM648617 6 0.5485 0.37681 0.108 0.016 0.000 0.000 0.296 0.580
#> GSM648626 1 0.4274 0.57802 0.676 0.012 0.000 0.000 0.288 0.024
#> GSM648711 1 0.0713 0.78090 0.972 0.000 0.000 0.000 0.028 0.000
#> GSM648712 1 0.3782 0.40042 0.588 0.000 0.000 0.000 0.412 0.000
#> GSM648713 1 0.3789 0.39375 0.584 0.000 0.000 0.000 0.416 0.000
#> GSM648714 5 0.2558 0.69809 0.004 0.156 0.000 0.000 0.840 0.000
#> GSM648716 1 0.3765 0.41458 0.596 0.000 0.000 0.000 0.404 0.000
#> GSM648717 5 0.0632 0.76890 0.024 0.000 0.000 0.000 0.976 0.000
#> GSM648590 6 0.3774 -0.03492 0.000 0.408 0.000 0.000 0.000 0.592
#> GSM648596 2 0.5633 0.23824 0.000 0.448 0.000 0.128 0.004 0.420
#> GSM648642 6 0.4410 -0.10259 0.000 0.412 0.000 0.028 0.000 0.560
#> GSM648696 6 0.1285 0.58356 0.052 0.004 0.000 0.000 0.000 0.944
#> GSM648705 6 0.1141 0.58416 0.052 0.000 0.000 0.000 0.000 0.948
#> GSM648718 6 0.4423 -0.11272 0.000 0.420 0.000 0.028 0.000 0.552
#> GSM648599 1 0.6371 0.21196 0.388 0.012 0.000 0.000 0.280 0.320
#> GSM648608 1 0.0000 0.79495 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM648609 1 0.0146 0.79492 0.996 0.004 0.000 0.000 0.000 0.000
#> GSM648610 1 0.0000 0.79495 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM648633 6 0.4840 0.52017 0.316 0.012 0.000 0.000 0.052 0.620
#> GSM648644 4 0.0000 0.89323 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM648652 6 0.2703 0.59151 0.172 0.004 0.000 0.000 0.000 0.824
#> GSM648653 1 0.4096 0.34207 0.672 0.008 0.000 0.000 0.016 0.304
#> GSM648658 6 0.3383 0.56302 0.268 0.004 0.000 0.000 0.000 0.728
#> GSM648659 6 0.4109 -0.05459 0.000 0.412 0.000 0.012 0.000 0.576
#> GSM648662 1 0.0000 0.79495 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM648665 1 0.0146 0.79492 0.996 0.004 0.000 0.000 0.000 0.000
#> GSM648666 1 0.0291 0.79388 0.992 0.004 0.000 0.000 0.004 0.000
#> GSM648680 6 0.3421 0.56613 0.256 0.008 0.000 0.000 0.000 0.736
#> GSM648684 1 0.0146 0.79492 0.996 0.004 0.000 0.000 0.000 0.000
#> GSM648709 6 0.4261 0.00387 0.020 0.408 0.000 0.000 0.000 0.572
#> GSM648719 6 0.4581 0.46159 0.372 0.012 0.000 0.000 0.024 0.592
#> GSM648627 1 0.3266 0.59745 0.728 0.000 0.000 0.000 0.272 0.000
#> GSM648637 4 0.0713 0.87997 0.000 0.028 0.000 0.972 0.000 0.000
#> GSM648638 4 0.3515 0.56022 0.000 0.324 0.000 0.676 0.000 0.000
#> GSM648641 5 0.5762 0.38330 0.000 0.260 0.204 0.004 0.532 0.000
#> GSM648672 4 0.0000 0.89323 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM648674 4 0.1141 0.86635 0.000 0.052 0.000 0.948 0.000 0.000
#> GSM648703 4 0.0000 0.89323 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM648631 3 0.0000 0.94547 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648669 4 0.0000 0.89323 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM648671 4 0.0000 0.89323 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM648678 4 0.0000 0.89323 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM648679 4 0.0547 0.88366 0.000 0.020 0.000 0.980 0.000 0.000
#> GSM648681 6 0.3930 -0.06259 0.000 0.420 0.000 0.004 0.000 0.576
#> GSM648686 3 0.0260 0.93997 0.000 0.000 0.992 0.008 0.000 0.000
#> GSM648689 3 0.0000 0.94547 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648690 3 0.0146 0.94302 0.000 0.000 0.996 0.004 0.000 0.000
#> GSM648691 3 0.0000 0.94547 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648693 3 0.0000 0.94547 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648700 2 0.6034 0.36924 0.000 0.412 0.000 0.328 0.000 0.260
#> GSM648630 3 0.0000 0.94547 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648632 3 0.0000 0.94547 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648639 2 0.5183 -0.39625 0.000 0.568 0.020 0.056 0.356 0.000
#> GSM648640 3 0.3265 0.71623 0.000 0.248 0.748 0.004 0.000 0.000
#> GSM648668 4 0.3804 0.14386 0.000 0.424 0.000 0.576 0.000 0.000
#> GSM648676 4 0.5834 -0.18493 0.000 0.304 0.000 0.480 0.000 0.216
#> GSM648692 3 0.0146 0.94302 0.000 0.000 0.996 0.004 0.000 0.000
#> GSM648694 3 0.0000 0.94547 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648699 4 0.0000 0.89323 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM648701 4 0.0000 0.89323 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM648673 4 0.0000 0.89323 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM648677 4 0.0000 0.89323 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM648687 3 0.3940 0.42225 0.000 0.012 0.640 0.000 0.348 0.000
#> GSM648688 3 0.0000 0.94547 0.000 0.000 1.000 0.000 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) development.stage(p) other(p) k
#> MAD:mclust 125 1.49e-10 0.005323 3.46e-14 2
#> MAD:mclust 119 1.26e-09 0.000849 5.58e-21 3
#> MAD:mclust 122 5.31e-18 0.024983 7.80e-22 4
#> MAD:mclust 127 7.18e-17 0.006301 5.49e-24 5
#> MAD:mclust 89 3.31e-13 0.002433 6.29e-22 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 51941 rows and 130 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 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 0.516 0.881 0.893 0.4847 0.508 0.508
#> 3 3 0.841 0.871 0.947 0.2662 0.611 0.396
#> 4 4 0.589 0.482 0.703 0.1355 0.809 0.554
#> 5 5 0.627 0.630 0.806 0.0608 0.798 0.445
#> 6 6 0.622 0.646 0.798 0.0633 0.904 0.668
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
#> GSM648605 2 0.5946 0.913 0.144 0.856
#> GSM648618 1 0.3114 0.908 0.944 0.056
#> GSM648620 2 0.5946 0.913 0.144 0.856
#> GSM648646 2 0.0000 0.878 0.000 1.000
#> GSM648649 2 0.5946 0.913 0.144 0.856
#> GSM648675 2 0.5946 0.913 0.144 0.856
#> GSM648682 2 0.5946 0.913 0.144 0.856
#> GSM648698 2 0.5946 0.913 0.144 0.856
#> GSM648708 2 0.5946 0.913 0.144 0.856
#> GSM648628 1 0.0000 0.906 1.000 0.000
#> GSM648595 2 0.5946 0.913 0.144 0.856
#> GSM648635 2 0.6247 0.904 0.156 0.844
#> GSM648645 1 0.4022 0.895 0.920 0.080
#> GSM648647 2 0.5946 0.913 0.144 0.856
#> GSM648667 2 0.5946 0.913 0.144 0.856
#> GSM648695 2 0.5946 0.913 0.144 0.856
#> GSM648704 2 0.0672 0.875 0.008 0.992
#> GSM648706 2 0.0000 0.878 0.000 1.000
#> GSM648593 2 0.5946 0.913 0.144 0.856
#> GSM648594 2 0.5946 0.913 0.144 0.856
#> GSM648600 1 0.3114 0.908 0.944 0.056
#> GSM648621 1 0.1414 0.909 0.980 0.020
#> GSM648622 1 0.3114 0.908 0.944 0.056
#> GSM648623 1 0.0376 0.907 0.996 0.004
#> GSM648636 2 0.5946 0.913 0.144 0.856
#> GSM648655 2 0.5946 0.913 0.144 0.856
#> GSM648661 1 0.3114 0.908 0.944 0.056
#> GSM648664 1 0.3733 0.901 0.928 0.072
#> GSM648683 1 0.3733 0.901 0.928 0.072
#> GSM648685 1 0.6343 0.808 0.840 0.160
#> GSM648702 2 0.5946 0.913 0.144 0.856
#> GSM648597 1 0.2236 0.910 0.964 0.036
#> GSM648603 1 0.2948 0.909 0.948 0.052
#> GSM648606 1 0.5946 0.841 0.856 0.144
#> GSM648613 1 0.5946 0.841 0.856 0.144
#> GSM648619 1 0.0672 0.907 0.992 0.008
#> GSM648654 1 0.3114 0.908 0.944 0.056
#> GSM648663 1 0.4939 0.864 0.892 0.108
#> GSM648670 2 0.3114 0.843 0.056 0.944
#> GSM648707 1 0.5737 0.846 0.864 0.136
#> GSM648615 2 0.0672 0.875 0.008 0.992
#> GSM648643 2 0.5946 0.913 0.144 0.856
#> GSM648650 2 0.5946 0.913 0.144 0.856
#> GSM648656 2 0.0376 0.880 0.004 0.996
#> GSM648715 2 0.5946 0.913 0.144 0.856
#> GSM648598 1 0.4022 0.895 0.920 0.080
#> GSM648601 1 0.3431 0.905 0.936 0.064
#> GSM648602 1 0.3114 0.908 0.944 0.056
#> GSM648604 1 0.3114 0.908 0.944 0.056
#> GSM648614 1 0.2603 0.895 0.956 0.044
#> GSM648624 1 0.3114 0.908 0.944 0.056
#> GSM648625 1 0.6148 0.821 0.848 0.152
#> GSM648629 1 0.3114 0.908 0.944 0.056
#> GSM648634 1 0.4022 0.895 0.920 0.080
#> GSM648648 2 0.5946 0.913 0.144 0.856
#> GSM648651 1 0.3114 0.908 0.944 0.056
#> GSM648657 1 0.3733 0.901 0.928 0.072
#> GSM648660 1 0.3733 0.901 0.928 0.072
#> GSM648697 1 0.6712 0.786 0.824 0.176
#> GSM648710 1 0.3114 0.908 0.944 0.056
#> GSM648591 1 0.1184 0.903 0.984 0.016
#> GSM648592 1 0.7376 0.716 0.792 0.208
#> GSM648607 1 0.2778 0.909 0.952 0.048
#> GSM648611 1 0.1184 0.903 0.984 0.016
#> GSM648612 1 0.0938 0.903 0.988 0.012
#> GSM648616 1 0.5946 0.841 0.856 0.144
#> GSM648617 1 0.0000 0.906 1.000 0.000
#> GSM648626 1 0.1184 0.908 0.984 0.016
#> GSM648711 1 0.2043 0.910 0.968 0.032
#> GSM648712 1 0.0000 0.906 1.000 0.000
#> GSM648713 1 0.0000 0.906 1.000 0.000
#> GSM648714 1 0.5946 0.841 0.856 0.144
#> GSM648716 1 0.0376 0.907 0.996 0.004
#> GSM648717 1 0.4161 0.877 0.916 0.084
#> GSM648590 2 0.5946 0.913 0.144 0.856
#> GSM648596 2 0.2236 0.859 0.036 0.964
#> GSM648642 2 0.5946 0.913 0.144 0.856
#> GSM648696 2 0.6247 0.904 0.156 0.844
#> GSM648705 2 0.5946 0.913 0.144 0.856
#> GSM648718 2 0.5946 0.913 0.144 0.856
#> GSM648599 1 0.2948 0.909 0.948 0.052
#> GSM648608 1 0.3114 0.908 0.944 0.056
#> GSM648609 1 0.3114 0.908 0.944 0.056
#> GSM648610 1 0.2948 0.909 0.948 0.052
#> GSM648633 1 0.3431 0.905 0.936 0.064
#> GSM648644 2 0.0672 0.875 0.008 0.992
#> GSM648652 2 0.6148 0.907 0.152 0.848
#> GSM648653 1 0.3431 0.905 0.936 0.064
#> GSM648658 2 0.9608 0.528 0.384 0.616
#> GSM648659 2 0.5946 0.913 0.144 0.856
#> GSM648662 1 0.2778 0.909 0.952 0.048
#> GSM648665 1 0.3733 0.901 0.928 0.072
#> GSM648666 1 0.3879 0.898 0.924 0.076
#> GSM648680 2 0.7674 0.828 0.224 0.776
#> GSM648684 1 0.3114 0.908 0.944 0.056
#> GSM648709 2 0.5294 0.909 0.120 0.880
#> GSM648719 1 0.3431 0.905 0.936 0.064
#> GSM648627 1 0.0672 0.907 0.992 0.008
#> GSM648637 2 0.3274 0.841 0.060 0.940
#> GSM648638 1 0.9732 0.459 0.596 0.404
#> GSM648641 1 0.5946 0.841 0.856 0.144
#> GSM648672 2 0.1843 0.863 0.028 0.972
#> GSM648674 2 0.3114 0.843 0.056 0.944
#> GSM648703 2 0.0938 0.883 0.012 0.988
#> GSM648631 1 0.4562 0.870 0.904 0.096
#> GSM648669 2 0.3274 0.841 0.060 0.940
#> GSM648671 2 0.2778 0.850 0.048 0.952
#> GSM648678 2 0.0672 0.875 0.008 0.992
#> GSM648679 2 0.3114 0.843 0.056 0.944
#> GSM648681 2 0.0938 0.883 0.012 0.988
#> GSM648686 1 0.5946 0.841 0.856 0.144
#> GSM648689 1 0.5946 0.841 0.856 0.144
#> GSM648690 1 0.5946 0.841 0.856 0.144
#> GSM648691 1 0.5946 0.841 0.856 0.144
#> GSM648693 1 0.5946 0.841 0.856 0.144
#> GSM648700 2 0.5946 0.913 0.144 0.856
#> GSM648630 1 0.5946 0.841 0.856 0.144
#> GSM648632 1 0.2778 0.892 0.952 0.048
#> GSM648639 1 0.5946 0.841 0.856 0.144
#> GSM648640 1 0.5946 0.841 0.856 0.144
#> GSM648668 2 0.2948 0.847 0.052 0.948
#> GSM648676 2 0.5946 0.913 0.144 0.856
#> GSM648692 1 0.5946 0.841 0.856 0.144
#> GSM648694 1 0.5946 0.841 0.856 0.144
#> GSM648699 2 0.1414 0.886 0.020 0.980
#> GSM648701 2 0.2236 0.890 0.036 0.964
#> GSM648673 2 0.0672 0.875 0.008 0.992
#> GSM648677 2 0.0672 0.882 0.008 0.992
#> GSM648687 1 0.4161 0.877 0.916 0.084
#> GSM648688 1 0.4431 0.873 0.908 0.092
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM648605 2 0.6267 0.2246 0.452 0.548 0.000
#> GSM648618 1 0.1163 0.9188 0.972 0.000 0.028
#> GSM648620 1 0.3941 0.7829 0.844 0.156 0.000
#> GSM648646 2 0.0000 0.9020 0.000 1.000 0.000
#> GSM648649 1 0.0000 0.9360 1.000 0.000 0.000
#> GSM648675 1 0.0592 0.9289 0.988 0.012 0.000
#> GSM648682 2 0.4002 0.7850 0.160 0.840 0.000
#> GSM648698 2 0.1289 0.8837 0.032 0.968 0.000
#> GSM648708 1 0.0000 0.9360 1.000 0.000 0.000
#> GSM648628 3 0.0000 0.9744 0.000 0.000 1.000
#> GSM648595 1 0.3038 0.8452 0.896 0.104 0.000
#> GSM648635 1 0.0000 0.9360 1.000 0.000 0.000
#> GSM648645 1 0.0000 0.9360 1.000 0.000 0.000
#> GSM648647 2 0.5431 0.6282 0.284 0.716 0.000
#> GSM648667 1 0.2448 0.8747 0.924 0.076 0.000
#> GSM648695 1 0.1163 0.9175 0.972 0.028 0.000
#> GSM648704 2 0.0000 0.9020 0.000 1.000 0.000
#> GSM648706 2 0.0000 0.9020 0.000 1.000 0.000
#> GSM648593 1 0.0000 0.9360 1.000 0.000 0.000
#> GSM648594 1 0.0000 0.9360 1.000 0.000 0.000
#> GSM648600 1 0.0000 0.9360 1.000 0.000 0.000
#> GSM648621 1 0.1529 0.9080 0.960 0.000 0.040
#> GSM648622 1 0.0000 0.9360 1.000 0.000 0.000
#> GSM648623 1 0.5968 0.4569 0.636 0.000 0.364
#> GSM648636 1 0.0000 0.9360 1.000 0.000 0.000
#> GSM648655 1 0.0000 0.9360 1.000 0.000 0.000
#> GSM648661 1 0.0000 0.9360 1.000 0.000 0.000
#> GSM648664 1 0.0000 0.9360 1.000 0.000 0.000
#> GSM648683 1 0.0000 0.9360 1.000 0.000 0.000
#> GSM648685 1 0.0000 0.9360 1.000 0.000 0.000
#> GSM648702 1 0.0000 0.9360 1.000 0.000 0.000
#> GSM648597 1 0.1964 0.8975 0.944 0.000 0.056
#> GSM648603 1 0.2448 0.8793 0.924 0.000 0.076
#> GSM648606 3 0.0000 0.9744 0.000 0.000 1.000
#> GSM648613 3 0.0000 0.9744 0.000 0.000 1.000
#> GSM648619 1 0.5968 0.4617 0.636 0.000 0.364
#> GSM648654 1 0.1529 0.9102 0.960 0.000 0.040
#> GSM648663 3 0.0000 0.9744 0.000 0.000 1.000
#> GSM648670 2 0.4504 0.7205 0.000 0.804 0.196
#> GSM648707 3 0.0000 0.9744 0.000 0.000 1.000
#> GSM648615 2 0.0000 0.9020 0.000 1.000 0.000
#> GSM648643 2 0.4555 0.7499 0.200 0.800 0.000
#> GSM648650 1 0.4002 0.7777 0.840 0.160 0.000
#> GSM648656 2 0.0000 0.9020 0.000 1.000 0.000
#> GSM648715 1 0.6244 0.1454 0.560 0.440 0.000
#> GSM648598 1 0.0000 0.9360 1.000 0.000 0.000
#> GSM648601 1 0.0000 0.9360 1.000 0.000 0.000
#> GSM648602 1 0.0000 0.9360 1.000 0.000 0.000
#> GSM648604 1 0.0000 0.9360 1.000 0.000 0.000
#> GSM648614 1 0.5810 0.5116 0.664 0.000 0.336
#> GSM648624 1 0.0000 0.9360 1.000 0.000 0.000
#> GSM648625 1 0.0000 0.9360 1.000 0.000 0.000
#> GSM648629 1 0.0000 0.9360 1.000 0.000 0.000
#> GSM648634 1 0.0000 0.9360 1.000 0.000 0.000
#> GSM648648 1 0.0000 0.9360 1.000 0.000 0.000
#> GSM648651 1 0.0000 0.9360 1.000 0.000 0.000
#> GSM648657 1 0.0000 0.9360 1.000 0.000 0.000
#> GSM648660 1 0.0000 0.9360 1.000 0.000 0.000
#> GSM648697 1 0.0000 0.9360 1.000 0.000 0.000
#> GSM648710 1 0.0000 0.9360 1.000 0.000 0.000
#> GSM648591 3 0.0000 0.9744 0.000 0.000 1.000
#> GSM648592 3 0.6046 0.7318 0.080 0.136 0.784
#> GSM648607 1 0.0237 0.9337 0.996 0.000 0.004
#> GSM648611 3 0.0000 0.9744 0.000 0.000 1.000
#> GSM648612 3 0.0000 0.9744 0.000 0.000 1.000
#> GSM648616 3 0.0000 0.9744 0.000 0.000 1.000
#> GSM648617 1 0.5058 0.6719 0.756 0.000 0.244
#> GSM648626 1 0.6192 0.3245 0.580 0.000 0.420
#> GSM648711 1 0.0000 0.9360 1.000 0.000 0.000
#> GSM648712 3 0.0424 0.9658 0.008 0.000 0.992
#> GSM648713 3 0.0000 0.9744 0.000 0.000 1.000
#> GSM648714 3 0.0000 0.9744 0.000 0.000 1.000
#> GSM648716 3 0.5254 0.6125 0.264 0.000 0.736
#> GSM648717 3 0.0000 0.9744 0.000 0.000 1.000
#> GSM648590 1 0.0592 0.9288 0.988 0.012 0.000
#> GSM648596 2 0.0237 0.9000 0.000 0.996 0.004
#> GSM648642 1 0.1529 0.9086 0.960 0.040 0.000
#> GSM648696 1 0.0000 0.9360 1.000 0.000 0.000
#> GSM648705 1 0.0000 0.9360 1.000 0.000 0.000
#> GSM648718 2 0.4796 0.7273 0.220 0.780 0.000
#> GSM648599 1 0.0000 0.9360 1.000 0.000 0.000
#> GSM648608 1 0.0000 0.9360 1.000 0.000 0.000
#> GSM648609 1 0.0000 0.9360 1.000 0.000 0.000
#> GSM648610 1 0.0000 0.9360 1.000 0.000 0.000
#> GSM648633 1 0.0000 0.9360 1.000 0.000 0.000
#> GSM648644 2 0.0000 0.9020 0.000 1.000 0.000
#> GSM648652 1 0.0000 0.9360 1.000 0.000 0.000
#> GSM648653 1 0.0000 0.9360 1.000 0.000 0.000
#> GSM648658 1 0.0000 0.9360 1.000 0.000 0.000
#> GSM648659 1 0.1289 0.9162 0.968 0.032 0.000
#> GSM648662 1 0.0237 0.9337 0.996 0.000 0.004
#> GSM648665 1 0.0000 0.9360 1.000 0.000 0.000
#> GSM648666 1 0.0000 0.9360 1.000 0.000 0.000
#> GSM648680 1 0.0000 0.9360 1.000 0.000 0.000
#> GSM648684 1 0.0000 0.9360 1.000 0.000 0.000
#> GSM648709 1 0.6204 0.2096 0.576 0.424 0.000
#> GSM648719 1 0.0000 0.9360 1.000 0.000 0.000
#> GSM648627 1 0.6274 0.2105 0.544 0.000 0.456
#> GSM648637 2 0.3752 0.7819 0.000 0.856 0.144
#> GSM648638 3 0.0000 0.9744 0.000 0.000 1.000
#> GSM648641 3 0.0000 0.9744 0.000 0.000 1.000
#> GSM648672 2 0.0000 0.9020 0.000 1.000 0.000
#> GSM648674 2 0.0000 0.9020 0.000 1.000 0.000
#> GSM648703 2 0.0000 0.9020 0.000 1.000 0.000
#> GSM648631 3 0.0000 0.9744 0.000 0.000 1.000
#> GSM648669 2 0.0892 0.8891 0.000 0.980 0.020
#> GSM648671 2 0.0000 0.9020 0.000 1.000 0.000
#> GSM648678 2 0.0000 0.9020 0.000 1.000 0.000
#> GSM648679 2 0.0000 0.9020 0.000 1.000 0.000
#> GSM648681 2 0.0000 0.9020 0.000 1.000 0.000
#> GSM648686 3 0.4121 0.7946 0.000 0.168 0.832
#> GSM648689 3 0.0237 0.9715 0.000 0.004 0.996
#> GSM648690 3 0.0237 0.9715 0.000 0.004 0.996
#> GSM648691 3 0.0237 0.9715 0.000 0.004 0.996
#> GSM648693 3 0.0000 0.9744 0.000 0.000 1.000
#> GSM648700 1 0.1860 0.8985 0.948 0.052 0.000
#> GSM648630 3 0.0000 0.9744 0.000 0.000 1.000
#> GSM648632 3 0.0000 0.9744 0.000 0.000 1.000
#> GSM648639 3 0.0000 0.9744 0.000 0.000 1.000
#> GSM648640 3 0.0000 0.9744 0.000 0.000 1.000
#> GSM648668 2 0.0892 0.8908 0.000 0.980 0.020
#> GSM648676 2 0.6309 0.0617 0.496 0.504 0.000
#> GSM648692 3 0.0000 0.9744 0.000 0.000 1.000
#> GSM648694 3 0.0000 0.9744 0.000 0.000 1.000
#> GSM648699 2 0.0000 0.9020 0.000 1.000 0.000
#> GSM648701 2 0.0000 0.9020 0.000 1.000 0.000
#> GSM648673 2 0.0000 0.9020 0.000 1.000 0.000
#> GSM648677 2 0.0000 0.9020 0.000 1.000 0.000
#> GSM648687 3 0.0000 0.9744 0.000 0.000 1.000
#> GSM648688 3 0.0000 0.9744 0.000 0.000 1.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM648605 2 0.7738 0.39128 0.072 0.604 0.120 0.204
#> GSM648618 1 0.5517 0.55150 0.568 0.000 0.020 0.412
#> GSM648620 1 0.7733 0.13425 0.440 0.256 0.000 0.304
#> GSM648646 2 0.0000 0.74634 0.000 1.000 0.000 0.000
#> GSM648649 1 0.4817 0.57284 0.612 0.000 0.000 0.388
#> GSM648675 4 0.5277 -0.26276 0.460 0.008 0.000 0.532
#> GSM648682 2 0.3505 0.70344 0.088 0.864 0.000 0.048
#> GSM648698 2 0.2060 0.73568 0.016 0.932 0.000 0.052
#> GSM648708 1 0.5508 0.56078 0.572 0.020 0.000 0.408
#> GSM648628 3 0.4477 0.68379 0.312 0.000 0.688 0.000
#> GSM648595 4 0.6332 0.00776 0.404 0.064 0.000 0.532
#> GSM648635 1 0.4877 0.58429 0.592 0.000 0.000 0.408
#> GSM648645 1 0.4866 0.58421 0.596 0.000 0.000 0.404
#> GSM648647 2 0.5719 0.51027 0.132 0.716 0.000 0.152
#> GSM648667 1 0.6136 0.49678 0.584 0.060 0.000 0.356
#> GSM648695 1 0.6903 0.38635 0.508 0.112 0.000 0.380
#> GSM648704 2 0.0000 0.74634 0.000 1.000 0.000 0.000
#> GSM648706 2 0.0000 0.74634 0.000 1.000 0.000 0.000
#> GSM648593 1 0.4877 0.58429 0.592 0.000 0.000 0.408
#> GSM648594 1 0.4877 0.58429 0.592 0.000 0.000 0.408
#> GSM648600 1 0.2281 0.25892 0.904 0.000 0.000 0.096
#> GSM648621 1 0.5185 0.22783 0.748 0.000 0.076 0.176
#> GSM648622 1 0.4877 0.58429 0.592 0.000 0.000 0.408
#> GSM648623 1 0.4454 -0.33200 0.692 0.000 0.308 0.000
#> GSM648636 4 0.4998 -0.27313 0.488 0.000 0.000 0.512
#> GSM648655 4 0.4277 0.45764 0.280 0.000 0.000 0.720
#> GSM648661 4 0.5772 0.44559 0.260 0.000 0.068 0.672
#> GSM648664 4 0.4356 0.44651 0.292 0.000 0.000 0.708
#> GSM648683 4 0.4955 -0.05015 0.444 0.000 0.000 0.556
#> GSM648685 4 0.4356 0.44701 0.292 0.000 0.000 0.708
#> GSM648702 1 0.4925 0.53946 0.572 0.000 0.000 0.428
#> GSM648597 1 0.3474 0.10478 0.868 0.000 0.064 0.068
#> GSM648603 1 0.1488 0.18119 0.956 0.000 0.032 0.012
#> GSM648606 3 0.0336 0.74357 0.008 0.000 0.992 0.000
#> GSM648613 3 0.4643 0.67343 0.344 0.000 0.656 0.000
#> GSM648619 1 0.3521 0.16936 0.864 0.000 0.084 0.052
#> GSM648654 3 0.7877 -0.17012 0.312 0.000 0.388 0.300
#> GSM648663 3 0.0188 0.74290 0.004 0.000 0.996 0.000
#> GSM648670 1 0.8297 -0.49984 0.452 0.264 0.024 0.260
#> GSM648707 3 0.4877 0.64358 0.408 0.000 0.592 0.000
#> GSM648615 2 0.3024 0.68978 0.148 0.852 0.000 0.000
#> GSM648643 2 0.4462 0.64856 0.132 0.804 0.000 0.064
#> GSM648650 1 0.6309 0.45512 0.588 0.076 0.000 0.336
#> GSM648656 2 0.0000 0.74634 0.000 1.000 0.000 0.000
#> GSM648715 2 0.7416 -0.00722 0.244 0.516 0.000 0.240
#> GSM648598 1 0.4877 0.58429 0.592 0.000 0.000 0.408
#> GSM648601 1 0.4877 0.58429 0.592 0.000 0.000 0.408
#> GSM648602 1 0.4877 0.58429 0.592 0.000 0.000 0.408
#> GSM648604 1 0.4877 0.58429 0.592 0.000 0.000 0.408
#> GSM648614 3 0.8004 0.20234 0.316 0.164 0.492 0.028
#> GSM648624 1 0.4877 0.58429 0.592 0.000 0.000 0.408
#> GSM648625 1 0.4843 0.57961 0.604 0.000 0.000 0.396
#> GSM648629 1 0.4888 0.57734 0.588 0.000 0.000 0.412
#> GSM648634 1 0.4866 0.58421 0.596 0.000 0.000 0.404
#> GSM648648 1 0.4955 0.49388 0.556 0.000 0.000 0.444
#> GSM648651 1 0.4877 0.58429 0.592 0.000 0.000 0.408
#> GSM648657 1 0.3219 0.30782 0.836 0.000 0.000 0.164
#> GSM648660 1 0.4866 0.58421 0.596 0.000 0.000 0.404
#> GSM648697 4 0.4356 0.44712 0.292 0.000 0.000 0.708
#> GSM648710 4 0.4933 0.01545 0.432 0.000 0.000 0.568
#> GSM648591 3 0.4888 0.64226 0.412 0.000 0.588 0.000
#> GSM648592 1 0.3751 -0.03993 0.800 0.004 0.196 0.000
#> GSM648607 1 0.4877 0.58429 0.592 0.000 0.000 0.408
#> GSM648611 3 0.0376 0.74281 0.004 0.000 0.992 0.004
#> GSM648612 3 0.4907 0.63829 0.420 0.000 0.580 0.000
#> GSM648616 3 0.4877 0.64358 0.408 0.000 0.592 0.000
#> GSM648617 1 0.3123 0.09071 0.844 0.000 0.156 0.000
#> GSM648626 1 0.2888 0.12254 0.872 0.000 0.124 0.004
#> GSM648711 1 0.4888 0.57734 0.588 0.000 0.000 0.412
#> GSM648712 3 0.4998 0.58960 0.488 0.000 0.512 0.000
#> GSM648713 3 0.3810 0.71979 0.188 0.000 0.804 0.008
#> GSM648714 3 0.7102 0.58794 0.288 0.164 0.548 0.000
#> GSM648716 3 0.6980 0.49105 0.400 0.000 0.484 0.116
#> GSM648717 3 0.0188 0.74290 0.004 0.000 0.996 0.000
#> GSM648590 1 0.5183 0.56813 0.584 0.008 0.000 0.408
#> GSM648596 2 0.4006 0.72993 0.084 0.848 0.008 0.060
#> GSM648642 2 0.7458 -0.25672 0.176 0.444 0.000 0.380
#> GSM648696 1 0.4843 0.57961 0.604 0.000 0.000 0.396
#> GSM648705 1 0.4877 0.58429 0.592 0.000 0.000 0.408
#> GSM648718 2 0.5159 0.60142 0.156 0.756 0.000 0.088
#> GSM648599 1 0.2216 0.25609 0.908 0.000 0.000 0.092
#> GSM648608 1 0.4992 0.37748 0.524 0.000 0.000 0.476
#> GSM648609 4 0.4790 0.23182 0.380 0.000 0.000 0.620
#> GSM648610 1 0.4877 0.58429 0.592 0.000 0.000 0.408
#> GSM648633 1 0.4761 0.55412 0.628 0.000 0.000 0.372
#> GSM648644 2 0.0000 0.74634 0.000 1.000 0.000 0.000
#> GSM648652 1 0.4877 0.58429 0.592 0.000 0.000 0.408
#> GSM648653 1 0.4877 0.58429 0.592 0.000 0.000 0.408
#> GSM648658 4 0.4277 0.45836 0.280 0.000 0.000 0.720
#> GSM648659 4 0.4035 0.44874 0.176 0.020 0.000 0.804
#> GSM648662 1 0.6646 0.35263 0.584 0.000 0.112 0.304
#> GSM648665 4 0.6921 0.38535 0.260 0.000 0.160 0.580
#> GSM648666 4 0.4164 0.46384 0.264 0.000 0.000 0.736
#> GSM648680 1 0.4877 0.58429 0.592 0.000 0.000 0.408
#> GSM648684 4 0.4981 -0.15821 0.464 0.000 0.000 0.536
#> GSM648709 2 0.6637 0.29461 0.240 0.616 0.000 0.144
#> GSM648719 1 0.4866 0.58421 0.596 0.000 0.000 0.404
#> GSM648627 3 0.6805 0.19534 0.260 0.000 0.592 0.148
#> GSM648637 2 0.7955 0.63226 0.140 0.580 0.068 0.212
#> GSM648638 3 0.5172 0.64275 0.404 0.008 0.588 0.000
#> GSM648641 3 0.1389 0.74271 0.048 0.000 0.952 0.000
#> GSM648672 2 0.4040 0.74923 0.000 0.752 0.000 0.248
#> GSM648674 2 0.4220 0.74924 0.004 0.748 0.000 0.248
#> GSM648703 2 0.4193 0.74643 0.000 0.732 0.000 0.268
#> GSM648631 3 0.0707 0.73957 0.000 0.000 0.980 0.020
#> GSM648669 2 0.5810 0.71305 0.000 0.672 0.072 0.256
#> GSM648671 2 0.5508 0.72555 0.000 0.692 0.056 0.252
#> GSM648678 2 0.0000 0.74634 0.000 1.000 0.000 0.000
#> GSM648679 2 0.4485 0.74750 0.012 0.740 0.000 0.248
#> GSM648681 2 0.4103 0.74845 0.000 0.744 0.000 0.256
#> GSM648686 3 0.6340 0.51429 0.000 0.096 0.620 0.284
#> GSM648689 3 0.3105 0.69832 0.000 0.012 0.868 0.120
#> GSM648690 3 0.2256 0.71896 0.000 0.056 0.924 0.020
#> GSM648691 3 0.2256 0.71914 0.000 0.056 0.924 0.020
#> GSM648693 3 0.0592 0.74019 0.000 0.000 0.984 0.016
#> GSM648700 4 0.1489 0.33843 0.000 0.044 0.004 0.952
#> GSM648630 3 0.0524 0.74096 0.000 0.004 0.988 0.008
#> GSM648632 3 0.3024 0.68839 0.000 0.000 0.852 0.148
#> GSM648639 3 0.4855 0.64779 0.400 0.000 0.600 0.000
#> GSM648640 3 0.1940 0.73999 0.076 0.000 0.924 0.000
#> GSM648668 2 0.4946 0.74442 0.020 0.720 0.004 0.256
#> GSM648676 4 0.3764 -0.15301 0.000 0.216 0.000 0.784
#> GSM648692 3 0.0376 0.74146 0.000 0.004 0.992 0.004
#> GSM648694 3 0.0336 0.74153 0.000 0.000 0.992 0.008
#> GSM648699 4 0.5050 -0.51823 0.000 0.408 0.004 0.588
#> GSM648701 2 0.4955 0.63427 0.000 0.556 0.000 0.444
#> GSM648673 2 0.4134 0.74777 0.000 0.740 0.000 0.260
#> GSM648677 2 0.4134 0.74777 0.000 0.740 0.000 0.260
#> GSM648687 3 0.4866 0.47803 0.000 0.000 0.596 0.404
#> GSM648688 3 0.4624 0.54360 0.000 0.000 0.660 0.340
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM648605 2 0.2011 0.7083 0.088 0.908 0.004 0.000 0.000
#> GSM648618 1 0.2670 0.8429 0.904 0.004 0.024 0.044 0.024
#> GSM648620 2 0.4278 0.3222 0.452 0.548 0.000 0.000 0.000
#> GSM648646 2 0.0510 0.6947 0.000 0.984 0.000 0.016 0.000
#> GSM648649 1 0.0162 0.8784 0.996 0.004 0.000 0.000 0.000
#> GSM648675 4 0.4286 0.3882 0.340 0.004 0.004 0.652 0.000
#> GSM648682 2 0.2969 0.6990 0.128 0.852 0.000 0.020 0.000
#> GSM648698 2 0.1121 0.7088 0.044 0.956 0.000 0.000 0.000
#> GSM648708 1 0.2516 0.7700 0.860 0.140 0.000 0.000 0.000
#> GSM648628 5 0.3635 0.3919 0.000 0.004 0.248 0.000 0.748
#> GSM648595 4 0.3080 0.6820 0.124 0.000 0.004 0.852 0.020
#> GSM648635 1 0.0162 0.8784 0.996 0.004 0.000 0.000 0.000
#> GSM648645 1 0.1153 0.8722 0.964 0.004 0.000 0.024 0.008
#> GSM648647 2 0.3689 0.6387 0.256 0.740 0.000 0.004 0.000
#> GSM648667 1 0.1043 0.8625 0.960 0.040 0.000 0.000 0.000
#> GSM648695 1 0.3480 0.5970 0.752 0.248 0.000 0.000 0.000
#> GSM648704 2 0.0404 0.6953 0.000 0.988 0.000 0.012 0.000
#> GSM648706 2 0.0162 0.6950 0.000 0.996 0.000 0.004 0.000
#> GSM648593 1 0.1410 0.8630 0.940 0.000 0.000 0.060 0.000
#> GSM648594 1 0.3521 0.7070 0.764 0.000 0.000 0.232 0.004
#> GSM648600 1 0.2773 0.7597 0.836 0.000 0.000 0.000 0.164
#> GSM648621 1 0.4979 0.6068 0.708 0.000 0.036 0.028 0.228
#> GSM648622 1 0.0162 0.8790 0.996 0.000 0.000 0.004 0.000
#> GSM648623 5 0.2871 0.6141 0.088 0.000 0.004 0.032 0.876
#> GSM648636 1 0.1915 0.8578 0.928 0.000 0.040 0.032 0.000
#> GSM648655 1 0.5700 0.5374 0.628 0.000 0.176 0.196 0.000
#> GSM648661 3 0.3728 0.3719 0.244 0.000 0.748 0.008 0.000
#> GSM648664 1 0.3003 0.7398 0.812 0.000 0.188 0.000 0.000
#> GSM648683 1 0.1041 0.8725 0.964 0.000 0.032 0.004 0.000
#> GSM648685 1 0.3561 0.6505 0.740 0.000 0.260 0.000 0.000
#> GSM648702 1 0.1549 0.8669 0.944 0.000 0.016 0.040 0.000
#> GSM648597 5 0.5577 0.4161 0.256 0.000 0.000 0.120 0.624
#> GSM648603 1 0.4307 -0.0261 0.500 0.000 0.000 0.000 0.500
#> GSM648606 5 0.4759 0.0371 0.000 0.016 0.388 0.004 0.592
#> GSM648613 5 0.1571 0.6014 0.000 0.004 0.060 0.000 0.936
#> GSM648619 1 0.3969 0.5195 0.692 0.000 0.000 0.004 0.304
#> GSM648654 3 0.5485 0.4243 0.256 0.012 0.652 0.000 0.080
#> GSM648663 5 0.5086 -0.0435 0.000 0.040 0.396 0.000 0.564
#> GSM648670 4 0.3607 0.5959 0.000 0.004 0.000 0.752 0.244
#> GSM648707 5 0.1908 0.6156 0.000 0.000 0.000 0.092 0.908
#> GSM648615 2 0.0579 0.6995 0.008 0.984 0.000 0.008 0.000
#> GSM648643 2 0.3944 0.6619 0.200 0.768 0.000 0.032 0.000
#> GSM648650 1 0.0703 0.8722 0.976 0.024 0.000 0.000 0.000
#> GSM648656 2 0.0609 0.6938 0.000 0.980 0.000 0.020 0.000
#> GSM648715 1 0.4306 -0.1757 0.508 0.492 0.000 0.000 0.000
#> GSM648598 1 0.0290 0.8793 0.992 0.000 0.000 0.008 0.000
#> GSM648601 1 0.0000 0.8786 1.000 0.000 0.000 0.000 0.000
#> GSM648602 1 0.0000 0.8786 1.000 0.000 0.000 0.000 0.000
#> GSM648604 1 0.0000 0.8786 1.000 0.000 0.000 0.000 0.000
#> GSM648614 2 0.7682 0.3212 0.296 0.468 0.128 0.004 0.104
#> GSM648624 1 0.0324 0.8792 0.992 0.000 0.004 0.004 0.000
#> GSM648625 1 0.0162 0.8788 0.996 0.000 0.000 0.004 0.000
#> GSM648629 1 0.0162 0.8787 0.996 0.000 0.004 0.000 0.000
#> GSM648634 1 0.0162 0.8784 0.996 0.004 0.000 0.000 0.000
#> GSM648648 1 0.0510 0.8771 0.984 0.000 0.016 0.000 0.000
#> GSM648651 1 0.1461 0.8684 0.952 0.000 0.004 0.028 0.016
#> GSM648657 1 0.3086 0.7382 0.816 0.000 0.000 0.004 0.180
#> GSM648660 1 0.0000 0.8786 1.000 0.000 0.000 0.000 0.000
#> GSM648697 1 0.4921 0.4898 0.620 0.000 0.340 0.040 0.000
#> GSM648710 1 0.1043 0.8707 0.960 0.000 0.040 0.000 0.000
#> GSM648591 5 0.2416 0.6118 0.012 0.000 0.000 0.100 0.888
#> GSM648592 5 0.5304 0.4224 0.292 0.000 0.000 0.080 0.628
#> GSM648607 1 0.0324 0.8791 0.992 0.004 0.000 0.000 0.004
#> GSM648611 3 0.4489 0.4210 0.000 0.000 0.572 0.008 0.420
#> GSM648612 5 0.1267 0.6258 0.012 0.000 0.024 0.004 0.960
#> GSM648616 5 0.1851 0.6171 0.000 0.000 0.000 0.088 0.912
#> GSM648617 5 0.4288 0.3474 0.384 0.000 0.000 0.004 0.612
#> GSM648626 5 0.4963 0.3964 0.352 0.000 0.000 0.040 0.608
#> GSM648711 1 0.0671 0.8765 0.980 0.000 0.000 0.004 0.016
#> GSM648712 5 0.1547 0.6323 0.032 0.000 0.016 0.004 0.948
#> GSM648713 5 0.3861 0.5184 0.068 0.000 0.128 0.000 0.804
#> GSM648714 2 0.5431 0.1489 0.000 0.516 0.060 0.000 0.424
#> GSM648716 5 0.5178 0.3505 0.304 0.000 0.056 0.004 0.636
#> GSM648717 5 0.4333 0.1598 0.000 0.004 0.352 0.004 0.640
#> GSM648590 1 0.1205 0.8681 0.956 0.004 0.000 0.040 0.000
#> GSM648596 2 0.6022 0.3535 0.004 0.596 0.000 0.232 0.168
#> GSM648642 2 0.4138 0.4867 0.384 0.616 0.000 0.000 0.000
#> GSM648696 1 0.0290 0.8780 0.992 0.008 0.000 0.000 0.000
#> GSM648705 1 0.0162 0.8784 0.996 0.004 0.000 0.000 0.000
#> GSM648718 2 0.5672 0.4654 0.368 0.544 0.000 0.088 0.000
#> GSM648599 1 0.2970 0.7577 0.828 0.004 0.000 0.000 0.168
#> GSM648608 1 0.0451 0.8784 0.988 0.000 0.008 0.004 0.000
#> GSM648609 1 0.0880 0.8729 0.968 0.000 0.032 0.000 0.000
#> GSM648610 1 0.0162 0.8788 0.996 0.000 0.000 0.004 0.000
#> GSM648633 1 0.0290 0.8784 0.992 0.000 0.000 0.000 0.008
#> GSM648644 2 0.0609 0.6938 0.000 0.980 0.000 0.020 0.000
#> GSM648652 1 0.0000 0.8786 1.000 0.000 0.000 0.000 0.000
#> GSM648653 1 0.0000 0.8786 1.000 0.000 0.000 0.000 0.000
#> GSM648658 1 0.5692 0.5455 0.628 0.000 0.204 0.168 0.000
#> GSM648659 4 0.7841 0.1859 0.276 0.064 0.312 0.348 0.000
#> GSM648662 1 0.2024 0.8438 0.920 0.004 0.068 0.004 0.004
#> GSM648665 3 0.4101 0.2032 0.372 0.000 0.628 0.000 0.000
#> GSM648666 1 0.5221 0.3533 0.552 0.000 0.400 0.048 0.000
#> GSM648680 1 0.0566 0.8787 0.984 0.000 0.004 0.012 0.000
#> GSM648684 1 0.1444 0.8673 0.948 0.000 0.040 0.012 0.000
#> GSM648709 2 0.3461 0.6622 0.224 0.772 0.000 0.004 0.000
#> GSM648719 1 0.0000 0.8786 1.000 0.000 0.000 0.000 0.000
#> GSM648627 3 0.6533 0.1890 0.400 0.000 0.428 0.004 0.168
#> GSM648637 4 0.6277 0.2293 0.000 0.152 0.000 0.464 0.384
#> GSM648638 5 0.0693 0.6280 0.000 0.008 0.000 0.012 0.980
#> GSM648641 5 0.4114 0.1011 0.000 0.000 0.376 0.000 0.624
#> GSM648672 4 0.4047 0.5328 0.000 0.320 0.000 0.676 0.004
#> GSM648674 4 0.3323 0.7130 0.000 0.056 0.000 0.844 0.100
#> GSM648703 4 0.2519 0.7090 0.000 0.100 0.016 0.884 0.000
#> GSM648631 3 0.3366 0.6160 0.000 0.000 0.768 0.000 0.232
#> GSM648669 4 0.1770 0.7395 0.000 0.048 0.008 0.936 0.008
#> GSM648671 4 0.1798 0.7376 0.000 0.064 0.004 0.928 0.004
#> GSM648678 2 0.1197 0.6777 0.000 0.952 0.000 0.048 0.000
#> GSM648679 4 0.4599 0.6615 0.000 0.100 0.000 0.744 0.156
#> GSM648681 4 0.1357 0.7389 0.000 0.048 0.000 0.948 0.004
#> GSM648686 3 0.2812 0.6252 0.000 0.024 0.876 0.004 0.096
#> GSM648689 3 0.2951 0.6302 0.000 0.028 0.860 0.000 0.112
#> GSM648690 3 0.4865 0.5900 0.000 0.064 0.684 0.000 0.252
#> GSM648691 3 0.4413 0.6090 0.000 0.044 0.724 0.000 0.232
#> GSM648693 3 0.4074 0.5170 0.000 0.000 0.636 0.000 0.364
#> GSM648700 4 0.4101 0.5716 0.004 0.000 0.332 0.664 0.000
#> GSM648630 3 0.4551 0.5065 0.000 0.016 0.616 0.000 0.368
#> GSM648632 3 0.2424 0.6309 0.000 0.000 0.868 0.000 0.132
#> GSM648639 5 0.0703 0.6294 0.000 0.000 0.000 0.024 0.976
#> GSM648640 5 0.2516 0.5398 0.000 0.000 0.140 0.000 0.860
#> GSM648668 4 0.2921 0.7103 0.000 0.124 0.000 0.856 0.020
#> GSM648676 4 0.4017 0.6507 0.004 0.012 0.248 0.736 0.000
#> GSM648692 3 0.4736 0.4484 0.000 0.020 0.576 0.000 0.404
#> GSM648694 3 0.4101 0.5072 0.000 0.000 0.628 0.000 0.372
#> GSM648699 4 0.4161 0.5089 0.000 0.000 0.392 0.608 0.000
#> GSM648701 4 0.3845 0.6754 0.000 0.024 0.208 0.768 0.000
#> GSM648673 4 0.1205 0.7392 0.000 0.040 0.004 0.956 0.000
#> GSM648677 4 0.2763 0.6938 0.000 0.148 0.004 0.848 0.000
#> GSM648687 3 0.1270 0.5403 0.000 0.000 0.948 0.052 0.000
#> GSM648688 3 0.1357 0.6015 0.000 0.000 0.948 0.004 0.048
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM648605 2 0.3317 0.72059 0.168 0.804 0.016 0.000 0.000 0.012
#> GSM648618 1 0.6312 0.20925 0.496 0.012 0.372 0.072 0.012 0.036
#> GSM648620 1 0.3969 0.41888 0.652 0.332 0.000 0.000 0.000 0.016
#> GSM648646 2 0.1267 0.73735 0.000 0.940 0.000 0.060 0.000 0.000
#> GSM648649 1 0.0820 0.79760 0.972 0.012 0.000 0.000 0.000 0.016
#> GSM648675 6 0.6026 0.29792 0.256 0.000 0.000 0.180 0.024 0.540
#> GSM648682 2 0.4618 0.57399 0.312 0.640 0.000 0.028 0.000 0.020
#> GSM648698 2 0.2704 0.73914 0.140 0.844 0.000 0.000 0.000 0.016
#> GSM648708 1 0.1951 0.77406 0.908 0.076 0.000 0.000 0.000 0.016
#> GSM648628 5 0.4996 0.51705 0.004 0.004 0.260 0.028 0.664 0.040
#> GSM648595 4 0.5930 0.38516 0.056 0.000 0.000 0.564 0.092 0.288
#> GSM648635 1 0.0820 0.79760 0.972 0.012 0.000 0.000 0.000 0.016
#> GSM648645 1 0.1275 0.79832 0.956 0.012 0.000 0.016 0.000 0.016
#> GSM648647 2 0.3636 0.56437 0.320 0.676 0.000 0.004 0.000 0.000
#> GSM648667 1 0.1080 0.80289 0.960 0.032 0.000 0.004 0.000 0.004
#> GSM648695 1 0.2848 0.70987 0.816 0.176 0.000 0.000 0.000 0.008
#> GSM648704 2 0.1075 0.73776 0.000 0.952 0.000 0.048 0.000 0.000
#> GSM648706 2 0.0717 0.73348 0.000 0.976 0.008 0.016 0.000 0.000
#> GSM648593 6 0.3896 0.52600 0.204 0.000 0.000 0.000 0.052 0.744
#> GSM648594 1 0.5035 0.60303 0.668 0.004 0.000 0.148 0.004 0.176
#> GSM648600 1 0.4263 0.43303 0.600 0.000 0.000 0.000 0.376 0.024
#> GSM648621 5 0.5051 0.58320 0.112 0.000 0.000 0.020 0.676 0.192
#> GSM648622 1 0.2282 0.78923 0.908 0.008 0.004 0.008 0.012 0.060
#> GSM648623 5 0.4075 0.69150 0.100 0.000 0.000 0.080 0.788 0.032
#> GSM648636 1 0.4181 0.13856 0.512 0.012 0.000 0.000 0.000 0.476
#> GSM648655 6 0.1572 0.66583 0.036 0.000 0.000 0.000 0.028 0.936
#> GSM648661 3 0.6246 0.22282 0.184 0.012 0.552 0.024 0.000 0.228
#> GSM648664 1 0.2510 0.77561 0.884 0.008 0.088 0.004 0.000 0.016
#> GSM648683 1 0.3048 0.74566 0.824 0.004 0.000 0.000 0.020 0.152
#> GSM648685 1 0.2469 0.79126 0.896 0.008 0.048 0.004 0.000 0.044
#> GSM648702 1 0.1471 0.79842 0.932 0.000 0.000 0.004 0.000 0.064
#> GSM648597 5 0.4231 0.45546 0.012 0.000 0.008 0.364 0.616 0.000
#> GSM648603 5 0.4576 0.38011 0.368 0.000 0.000 0.036 0.592 0.004
#> GSM648606 5 0.4137 0.67720 0.000 0.036 0.144 0.008 0.780 0.032
#> GSM648613 5 0.2264 0.72117 0.000 0.004 0.096 0.012 0.888 0.000
#> GSM648619 5 0.3309 0.64107 0.192 0.000 0.004 0.000 0.788 0.016
#> GSM648654 3 0.4119 0.42934 0.280 0.004 0.692 0.016 0.000 0.008
#> GSM648663 5 0.3917 0.66758 0.004 0.056 0.164 0.000 0.772 0.004
#> GSM648670 4 0.3736 0.70650 0.000 0.000 0.000 0.776 0.068 0.156
#> GSM648707 5 0.3617 0.62845 0.000 0.000 0.020 0.244 0.736 0.000
#> GSM648615 2 0.3100 0.74781 0.108 0.848 0.000 0.028 0.004 0.012
#> GSM648643 2 0.3839 0.68652 0.212 0.748 0.000 0.036 0.000 0.004
#> GSM648650 1 0.2001 0.78821 0.920 0.044 0.000 0.020 0.000 0.016
#> GSM648656 2 0.1444 0.73297 0.000 0.928 0.000 0.072 0.000 0.000
#> GSM648715 1 0.4700 0.00863 0.488 0.476 0.000 0.008 0.000 0.028
#> GSM648598 1 0.2806 0.75543 0.844 0.000 0.000 0.004 0.016 0.136
#> GSM648601 1 0.1572 0.79667 0.936 0.000 0.000 0.000 0.028 0.036
#> GSM648602 1 0.1829 0.79197 0.920 0.000 0.000 0.000 0.056 0.024
#> GSM648604 1 0.0520 0.79974 0.984 0.008 0.000 0.000 0.000 0.008
#> GSM648614 2 0.6422 0.06504 0.052 0.468 0.076 0.000 0.384 0.020
#> GSM648624 1 0.1196 0.80018 0.952 0.008 0.000 0.000 0.000 0.040
#> GSM648625 1 0.4953 0.65107 0.704 0.012 0.004 0.004 0.160 0.116
#> GSM648629 1 0.0767 0.79916 0.976 0.008 0.004 0.000 0.000 0.012
#> GSM648634 1 0.1269 0.79895 0.956 0.012 0.000 0.000 0.012 0.020
#> GSM648648 1 0.0810 0.80281 0.976 0.004 0.008 0.004 0.000 0.008
#> GSM648651 1 0.5685 0.43492 0.568 0.012 0.004 0.008 0.092 0.316
#> GSM648657 1 0.4790 0.57912 0.656 0.000 0.000 0.056 0.272 0.016
#> GSM648660 1 0.0837 0.80080 0.972 0.000 0.000 0.004 0.004 0.020
#> GSM648697 1 0.4577 0.39073 0.572 0.004 0.024 0.004 0.000 0.396
#> GSM648710 1 0.1155 0.80310 0.956 0.004 0.036 0.000 0.000 0.004
#> GSM648591 5 0.3254 0.69393 0.000 0.000 0.008 0.172 0.804 0.016
#> GSM648592 5 0.4364 0.59733 0.052 0.000 0.004 0.256 0.688 0.000
#> GSM648607 1 0.2959 0.77350 0.864 0.012 0.020 0.000 0.092 0.012
#> GSM648611 5 0.5504 0.35216 0.000 0.000 0.328 0.024 0.564 0.084
#> GSM648612 5 0.0508 0.73824 0.000 0.000 0.004 0.000 0.984 0.012
#> GSM648616 5 0.3245 0.64417 0.000 0.000 0.008 0.228 0.764 0.000
#> GSM648617 5 0.3419 0.64599 0.172 0.000 0.000 0.008 0.796 0.024
#> GSM648626 5 0.4494 0.63748 0.136 0.000 0.004 0.140 0.720 0.000
#> GSM648711 1 0.4518 0.62505 0.688 0.000 0.000 0.004 0.236 0.072
#> GSM648712 5 0.0922 0.73854 0.004 0.000 0.004 0.000 0.968 0.024
#> GSM648713 5 0.2570 0.72902 0.024 0.000 0.076 0.000 0.884 0.016
#> GSM648714 5 0.4889 0.44119 0.000 0.312 0.084 0.000 0.604 0.000
#> GSM648716 5 0.2164 0.73596 0.044 0.000 0.028 0.000 0.912 0.016
#> GSM648717 5 0.3291 0.70811 0.016 0.012 0.120 0.000 0.836 0.016
#> GSM648590 1 0.4994 0.43617 0.624 0.020 0.000 0.044 0.004 0.308
#> GSM648596 2 0.4950 0.51534 0.000 0.652 0.000 0.184 0.164 0.000
#> GSM648642 1 0.4076 0.32721 0.620 0.364 0.000 0.000 0.000 0.016
#> GSM648696 1 0.1448 0.79717 0.948 0.016 0.000 0.000 0.012 0.024
#> GSM648705 1 0.0964 0.79759 0.968 0.012 0.004 0.000 0.000 0.016
#> GSM648718 1 0.3543 0.61597 0.756 0.224 0.000 0.004 0.000 0.016
#> GSM648599 1 0.4204 0.56983 0.676 0.004 0.000 0.012 0.296 0.012
#> GSM648608 1 0.1003 0.80314 0.964 0.000 0.004 0.000 0.004 0.028
#> GSM648609 1 0.2257 0.78506 0.900 0.008 0.012 0.004 0.000 0.076
#> GSM648610 1 0.3161 0.75101 0.828 0.008 0.000 0.000 0.136 0.028
#> GSM648633 1 0.2588 0.76067 0.860 0.000 0.000 0.004 0.124 0.012
#> GSM648644 2 0.1267 0.73735 0.000 0.940 0.000 0.060 0.000 0.000
#> GSM648652 1 0.0508 0.80122 0.984 0.000 0.000 0.004 0.000 0.012
#> GSM648653 1 0.0622 0.80113 0.980 0.000 0.000 0.000 0.012 0.008
#> GSM648658 6 0.2056 0.65044 0.080 0.000 0.004 0.000 0.012 0.904
#> GSM648659 6 0.1622 0.67757 0.016 0.016 0.000 0.028 0.000 0.940
#> GSM648662 1 0.7014 0.43377 0.548 0.048 0.056 0.004 0.136 0.208
#> GSM648665 6 0.6677 0.06757 0.348 0.012 0.276 0.012 0.000 0.352
#> GSM648666 1 0.5347 0.15578 0.484 0.012 0.036 0.012 0.004 0.452
#> GSM648680 1 0.0551 0.79990 0.984 0.004 0.004 0.000 0.000 0.008
#> GSM648684 1 0.4337 0.61730 0.700 0.004 0.000 0.004 0.044 0.248
#> GSM648709 2 0.2704 0.72852 0.140 0.844 0.000 0.016 0.000 0.000
#> GSM648719 1 0.1268 0.80239 0.952 0.004 0.000 0.000 0.036 0.008
#> GSM648627 5 0.6648 0.01318 0.148 0.000 0.384 0.008 0.416 0.044
#> GSM648637 4 0.4719 0.60120 0.000 0.100 0.000 0.680 0.216 0.004
#> GSM648638 5 0.2638 0.73041 0.000 0.032 0.036 0.044 0.888 0.000
#> GSM648641 5 0.3046 0.66903 0.000 0.000 0.188 0.000 0.800 0.012
#> GSM648672 4 0.3175 0.75220 0.000 0.164 0.000 0.808 0.000 0.028
#> GSM648674 4 0.3344 0.78437 0.000 0.020 0.000 0.828 0.032 0.120
#> GSM648703 6 0.4299 0.36831 0.000 0.040 0.000 0.308 0.000 0.652
#> GSM648631 3 0.2543 0.82640 0.000 0.008 0.868 0.004 0.116 0.004
#> GSM648669 4 0.3214 0.77903 0.000 0.024 0.084 0.852 0.004 0.036
#> GSM648671 4 0.2867 0.79435 0.000 0.024 0.064 0.872 0.000 0.040
#> GSM648678 2 0.1663 0.72414 0.000 0.912 0.000 0.088 0.000 0.000
#> GSM648679 4 0.1594 0.80108 0.000 0.052 0.000 0.932 0.016 0.000
#> GSM648681 4 0.2129 0.81161 0.000 0.056 0.000 0.904 0.000 0.040
#> GSM648686 3 0.2202 0.81924 0.000 0.016 0.916 0.016 0.040 0.012
#> GSM648689 3 0.3590 0.80809 0.000 0.064 0.812 0.000 0.112 0.012
#> GSM648690 3 0.2622 0.82607 0.000 0.024 0.868 0.000 0.104 0.004
#> GSM648691 3 0.1434 0.80821 0.000 0.008 0.948 0.020 0.024 0.000
#> GSM648693 3 0.2340 0.80650 0.000 0.000 0.852 0.000 0.148 0.000
#> GSM648700 6 0.1531 0.67213 0.000 0.000 0.000 0.068 0.004 0.928
#> GSM648630 3 0.2278 0.81912 0.000 0.004 0.868 0.000 0.128 0.000
#> GSM648632 3 0.1985 0.82766 0.000 0.008 0.916 0.004 0.064 0.008
#> GSM648639 5 0.3156 0.68169 0.000 0.000 0.020 0.180 0.800 0.000
#> GSM648640 5 0.3514 0.63122 0.000 0.000 0.228 0.020 0.752 0.000
#> GSM648668 4 0.3123 0.79171 0.000 0.088 0.000 0.836 0.000 0.076
#> GSM648676 6 0.1411 0.67304 0.000 0.000 0.004 0.060 0.000 0.936
#> GSM648692 3 0.3012 0.75197 0.000 0.008 0.796 0.000 0.196 0.000
#> GSM648694 3 0.2300 0.81122 0.000 0.000 0.856 0.000 0.144 0.000
#> GSM648699 6 0.2553 0.62641 0.000 0.000 0.008 0.144 0.000 0.848
#> GSM648701 6 0.2488 0.64052 0.000 0.008 0.004 0.124 0.000 0.864
#> GSM648673 4 0.3263 0.78593 0.000 0.028 0.024 0.836 0.000 0.112
#> GSM648677 6 0.4823 0.12243 0.000 0.060 0.000 0.388 0.000 0.552
#> GSM648687 3 0.3142 0.70624 0.004 0.012 0.852 0.044 0.000 0.088
#> GSM648688 3 0.2294 0.76353 0.004 0.012 0.912 0.032 0.004 0.036
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) development.stage(p) other(p) k
#> MAD:NMF 129 3.41e-01 0.000926 8.61e-11 2
#> MAD:NMF 122 1.69e-09 0.019900 5.07e-19 3
#> MAD:NMF 83 6.36e-06 0.032218 7.97e-15 4
#> MAD:NMF 101 6.56e-16 0.090942 3.61e-24 5
#> MAD:NMF 106 6.33e-14 0.032664 9.08e-27 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 51941 rows and 130 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 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.996 0.997 0.4345 0.565 0.565
#> 3 3 0.819 0.920 0.945 0.5062 0.764 0.582
#> 4 4 0.782 0.844 0.888 0.0927 0.940 0.819
#> 5 5 0.764 0.826 0.839 0.0591 0.953 0.831
#> 6 6 0.785 0.804 0.888 0.0435 0.973 0.883
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
#> GSM648605 2 0.0376 0.997 0.004 0.996
#> GSM648618 2 0.0672 0.994 0.008 0.992
#> GSM648620 2 0.0000 0.998 0.000 1.000
#> GSM648646 2 0.0000 0.998 0.000 1.000
#> GSM648649 2 0.0000 0.998 0.000 1.000
#> GSM648675 2 0.0376 0.997 0.004 0.996
#> GSM648682 2 0.0000 0.998 0.000 1.000
#> GSM648698 2 0.0000 0.998 0.000 1.000
#> GSM648708 2 0.0000 0.998 0.000 1.000
#> GSM648628 1 0.0000 0.994 1.000 0.000
#> GSM648595 2 0.0000 0.998 0.000 1.000
#> GSM648635 2 0.0000 0.998 0.000 1.000
#> GSM648645 2 0.0376 0.997 0.004 0.996
#> GSM648647 2 0.0000 0.998 0.000 1.000
#> GSM648667 2 0.0000 0.998 0.000 1.000
#> GSM648695 2 0.0000 0.998 0.000 1.000
#> GSM648704 2 0.0000 0.998 0.000 1.000
#> GSM648706 2 0.0376 0.997 0.004 0.996
#> GSM648593 2 0.0000 0.998 0.000 1.000
#> GSM648594 2 0.0376 0.997 0.004 0.996
#> GSM648600 2 0.0000 0.998 0.000 1.000
#> GSM648621 2 0.0000 0.998 0.000 1.000
#> GSM648622 2 0.0672 0.994 0.008 0.992
#> GSM648623 2 0.0672 0.994 0.008 0.992
#> GSM648636 2 0.0000 0.998 0.000 1.000
#> GSM648655 2 0.0000 0.998 0.000 1.000
#> GSM648661 1 0.0000 0.994 1.000 0.000
#> GSM648664 1 0.0000 0.994 1.000 0.000
#> GSM648683 1 0.1414 0.985 0.980 0.020
#> GSM648685 1 0.1414 0.985 0.980 0.020
#> GSM648702 2 0.0000 0.998 0.000 1.000
#> GSM648597 2 0.0376 0.997 0.004 0.996
#> GSM648603 2 0.0000 0.998 0.000 1.000
#> GSM648606 1 0.1414 0.985 0.980 0.020
#> GSM648613 1 0.1414 0.985 0.980 0.020
#> GSM648619 1 0.0000 0.994 1.000 0.000
#> GSM648654 1 0.0000 0.994 1.000 0.000
#> GSM648663 1 0.0000 0.994 1.000 0.000
#> GSM648670 2 0.0000 0.998 0.000 1.000
#> GSM648707 2 0.0672 0.994 0.008 0.992
#> GSM648615 2 0.0000 0.998 0.000 1.000
#> GSM648643 2 0.0000 0.998 0.000 1.000
#> GSM648650 2 0.0000 0.998 0.000 1.000
#> GSM648656 2 0.0000 0.998 0.000 1.000
#> GSM648715 2 0.0000 0.998 0.000 1.000
#> GSM648598 2 0.0000 0.998 0.000 1.000
#> GSM648601 2 0.0000 0.998 0.000 1.000
#> GSM648602 2 0.0000 0.998 0.000 1.000
#> GSM648604 1 0.1184 0.987 0.984 0.016
#> GSM648614 2 0.0376 0.997 0.004 0.996
#> GSM648624 2 0.0672 0.994 0.008 0.992
#> GSM648625 2 0.0000 0.998 0.000 1.000
#> GSM648629 1 0.0000 0.994 1.000 0.000
#> GSM648634 2 0.0000 0.998 0.000 1.000
#> GSM648648 2 0.0376 0.997 0.004 0.996
#> GSM648651 2 0.0672 0.994 0.008 0.992
#> GSM648657 2 0.0376 0.997 0.004 0.996
#> GSM648660 2 0.0376 0.997 0.004 0.996
#> GSM648697 2 0.0672 0.994 0.008 0.992
#> GSM648710 1 0.0000 0.994 1.000 0.000
#> GSM648591 2 0.0672 0.994 0.008 0.992
#> GSM648592 2 0.0000 0.998 0.000 1.000
#> GSM648607 1 0.0000 0.994 1.000 0.000
#> GSM648611 1 0.0000 0.994 1.000 0.000
#> GSM648612 1 0.0000 0.994 1.000 0.000
#> GSM648616 2 0.0000 0.998 0.000 1.000
#> GSM648617 2 0.0000 0.998 0.000 1.000
#> GSM648626 2 0.0000 0.998 0.000 1.000
#> GSM648711 1 0.0000 0.994 1.000 0.000
#> GSM648712 1 0.0000 0.994 1.000 0.000
#> GSM648713 1 0.0000 0.994 1.000 0.000
#> GSM648714 2 0.0376 0.997 0.004 0.996
#> GSM648716 1 0.0000 0.994 1.000 0.000
#> GSM648717 1 0.1414 0.985 0.980 0.020
#> GSM648590 2 0.0000 0.998 0.000 1.000
#> GSM648596 2 0.0000 0.998 0.000 1.000
#> GSM648642 2 0.0376 0.997 0.004 0.996
#> GSM648696 2 0.0000 0.998 0.000 1.000
#> GSM648705 2 0.0000 0.998 0.000 1.000
#> GSM648718 2 0.0000 0.998 0.000 1.000
#> GSM648599 2 0.0000 0.998 0.000 1.000
#> GSM648608 1 0.1184 0.987 0.984 0.016
#> GSM648609 1 0.1184 0.987 0.984 0.016
#> GSM648610 1 0.1843 0.977 0.972 0.028
#> GSM648633 2 0.0000 0.998 0.000 1.000
#> GSM648644 2 0.0000 0.998 0.000 1.000
#> GSM648652 2 0.0000 0.998 0.000 1.000
#> GSM648653 2 0.0000 0.998 0.000 1.000
#> GSM648658 2 0.0376 0.997 0.004 0.996
#> GSM648659 2 0.0000 0.998 0.000 1.000
#> GSM648662 1 0.1414 0.985 0.980 0.020
#> GSM648665 1 0.1414 0.985 0.980 0.020
#> GSM648666 2 0.0672 0.994 0.008 0.992
#> GSM648680 2 0.0000 0.998 0.000 1.000
#> GSM648684 1 0.1414 0.985 0.980 0.020
#> GSM648709 2 0.0000 0.998 0.000 1.000
#> GSM648719 2 0.0376 0.997 0.004 0.996
#> GSM648627 1 0.0000 0.994 1.000 0.000
#> GSM648637 2 0.0000 0.998 0.000 1.000
#> GSM648638 2 0.0376 0.997 0.004 0.996
#> GSM648641 1 0.0000 0.994 1.000 0.000
#> GSM648672 2 0.0000 0.998 0.000 1.000
#> GSM648674 2 0.0000 0.998 0.000 1.000
#> GSM648703 2 0.0000 0.998 0.000 1.000
#> GSM648631 1 0.0000 0.994 1.000 0.000
#> GSM648669 2 0.0376 0.997 0.004 0.996
#> GSM648671 2 0.0376 0.997 0.004 0.996
#> GSM648678 2 0.0000 0.998 0.000 1.000
#> GSM648679 2 0.0000 0.998 0.000 1.000
#> GSM648681 2 0.0000 0.998 0.000 1.000
#> GSM648686 1 0.0000 0.994 1.000 0.000
#> GSM648689 1 0.0000 0.994 1.000 0.000
#> GSM648690 1 0.0000 0.994 1.000 0.000
#> GSM648691 1 0.0000 0.994 1.000 0.000
#> GSM648693 1 0.0000 0.994 1.000 0.000
#> GSM648700 2 0.0376 0.997 0.004 0.996
#> GSM648630 1 0.0000 0.994 1.000 0.000
#> GSM648632 1 0.0000 0.994 1.000 0.000
#> GSM648639 2 0.0000 0.998 0.000 1.000
#> GSM648640 1 0.0000 0.994 1.000 0.000
#> GSM648668 2 0.0000 0.998 0.000 1.000
#> GSM648676 2 0.0000 0.998 0.000 1.000
#> GSM648692 1 0.0000 0.994 1.000 0.000
#> GSM648694 1 0.0000 0.994 1.000 0.000
#> GSM648699 2 0.0376 0.997 0.004 0.996
#> GSM648701 2 0.0000 0.998 0.000 1.000
#> GSM648673 2 0.0376 0.997 0.004 0.996
#> GSM648677 2 0.0000 0.998 0.000 1.000
#> GSM648687 2 0.0672 0.994 0.008 0.992
#> GSM648688 1 0.0000 0.994 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM648605 1 0.0237 0.942 0.996 0.004 0.000
#> GSM648618 1 0.0000 0.940 1.000 0.000 0.000
#> GSM648620 2 0.3340 0.923 0.120 0.880 0.000
#> GSM648646 2 0.0000 0.885 0.000 1.000 0.000
#> GSM648649 2 0.3116 0.926 0.108 0.892 0.000
#> GSM648675 1 0.1529 0.939 0.960 0.040 0.000
#> GSM648682 2 0.3267 0.924 0.116 0.884 0.000
#> GSM648698 2 0.3267 0.924 0.116 0.884 0.000
#> GSM648708 2 0.3340 0.923 0.120 0.880 0.000
#> GSM648628 3 0.0000 0.992 0.000 0.000 1.000
#> GSM648595 2 0.2878 0.927 0.096 0.904 0.000
#> GSM648635 2 0.4235 0.882 0.176 0.824 0.000
#> GSM648645 1 0.1411 0.941 0.964 0.036 0.000
#> GSM648647 2 0.2878 0.927 0.096 0.904 0.000
#> GSM648667 2 0.2537 0.921 0.080 0.920 0.000
#> GSM648695 2 0.3192 0.925 0.112 0.888 0.000
#> GSM648704 2 0.0000 0.885 0.000 1.000 0.000
#> GSM648706 1 0.0237 0.942 0.996 0.004 0.000
#> GSM648593 2 0.4235 0.882 0.176 0.824 0.000
#> GSM648594 1 0.1411 0.941 0.964 0.036 0.000
#> GSM648600 1 0.5835 0.448 0.660 0.340 0.000
#> GSM648621 1 0.1529 0.940 0.960 0.040 0.000
#> GSM648622 1 0.0000 0.940 1.000 0.000 0.000
#> GSM648623 1 0.0000 0.940 1.000 0.000 0.000
#> GSM648636 2 0.4178 0.887 0.172 0.828 0.000
#> GSM648655 2 0.4178 0.887 0.172 0.828 0.000
#> GSM648661 3 0.0000 0.992 0.000 0.000 1.000
#> GSM648664 3 0.0237 0.991 0.004 0.000 0.996
#> GSM648683 3 0.1163 0.981 0.028 0.000 0.972
#> GSM648685 3 0.1163 0.981 0.028 0.000 0.972
#> GSM648702 2 0.4235 0.882 0.176 0.824 0.000
#> GSM648597 1 0.1411 0.941 0.964 0.036 0.000
#> GSM648603 1 0.1529 0.940 0.960 0.040 0.000
#> GSM648606 3 0.1163 0.981 0.028 0.000 0.972
#> GSM648613 3 0.1163 0.981 0.028 0.000 0.972
#> GSM648619 3 0.0237 0.991 0.004 0.000 0.996
#> GSM648654 3 0.0000 0.992 0.000 0.000 1.000
#> GSM648663 3 0.0237 0.991 0.004 0.000 0.996
#> GSM648670 2 0.3267 0.924 0.116 0.884 0.000
#> GSM648707 1 0.0000 0.940 1.000 0.000 0.000
#> GSM648615 2 0.3267 0.924 0.116 0.884 0.000
#> GSM648643 2 0.0000 0.885 0.000 1.000 0.000
#> GSM648650 2 0.3116 0.926 0.108 0.892 0.000
#> GSM648656 2 0.0000 0.885 0.000 1.000 0.000
#> GSM648715 2 0.2878 0.927 0.096 0.904 0.000
#> GSM648598 1 0.1529 0.940 0.960 0.040 0.000
#> GSM648601 1 0.1529 0.940 0.960 0.040 0.000
#> GSM648602 1 0.1529 0.940 0.960 0.040 0.000
#> GSM648604 3 0.1031 0.983 0.024 0.000 0.976
#> GSM648614 1 0.0237 0.942 0.996 0.004 0.000
#> GSM648624 1 0.0000 0.940 1.000 0.000 0.000
#> GSM648625 2 0.3267 0.924 0.116 0.884 0.000
#> GSM648629 3 0.0237 0.991 0.004 0.000 0.996
#> GSM648634 1 0.1529 0.940 0.960 0.040 0.000
#> GSM648648 1 0.1289 0.942 0.968 0.032 0.000
#> GSM648651 1 0.0000 0.940 1.000 0.000 0.000
#> GSM648657 1 0.1289 0.942 0.968 0.032 0.000
#> GSM648660 1 0.1411 0.941 0.964 0.036 0.000
#> GSM648697 1 0.0000 0.940 1.000 0.000 0.000
#> GSM648710 3 0.0237 0.991 0.004 0.000 0.996
#> GSM648591 1 0.0000 0.940 1.000 0.000 0.000
#> GSM648592 2 0.5327 0.748 0.272 0.728 0.000
#> GSM648607 3 0.0237 0.991 0.004 0.000 0.996
#> GSM648611 3 0.0000 0.992 0.000 0.000 1.000
#> GSM648612 3 0.0237 0.991 0.004 0.000 0.996
#> GSM648616 1 0.1529 0.940 0.960 0.040 0.000
#> GSM648617 1 0.5560 0.548 0.700 0.300 0.000
#> GSM648626 1 0.1529 0.940 0.960 0.040 0.000
#> GSM648711 3 0.0000 0.992 0.000 0.000 1.000
#> GSM648712 3 0.0237 0.991 0.004 0.000 0.996
#> GSM648713 3 0.0237 0.991 0.004 0.000 0.996
#> GSM648714 1 0.0237 0.942 0.996 0.004 0.000
#> GSM648716 3 0.0000 0.992 0.000 0.000 1.000
#> GSM648717 3 0.1163 0.981 0.028 0.000 0.972
#> GSM648590 2 0.2878 0.927 0.096 0.904 0.000
#> GSM648596 2 0.0000 0.885 0.000 1.000 0.000
#> GSM648642 1 0.0237 0.942 0.996 0.004 0.000
#> GSM648696 2 0.6307 0.166 0.488 0.512 0.000
#> GSM648705 2 0.3116 0.926 0.108 0.892 0.000
#> GSM648718 2 0.2878 0.927 0.096 0.904 0.000
#> GSM648599 1 0.1529 0.940 0.960 0.040 0.000
#> GSM648608 3 0.1031 0.983 0.024 0.000 0.976
#> GSM648609 3 0.1031 0.983 0.024 0.000 0.976
#> GSM648610 3 0.1411 0.974 0.036 0.000 0.964
#> GSM648633 2 0.4235 0.882 0.176 0.824 0.000
#> GSM648644 2 0.0000 0.885 0.000 1.000 0.000
#> GSM648652 2 0.4605 0.850 0.204 0.796 0.000
#> GSM648653 1 0.1529 0.940 0.960 0.040 0.000
#> GSM648658 1 0.1289 0.942 0.968 0.032 0.000
#> GSM648659 2 0.4002 0.896 0.160 0.840 0.000
#> GSM648662 3 0.1163 0.981 0.028 0.000 0.972
#> GSM648665 3 0.1163 0.981 0.028 0.000 0.972
#> GSM648666 1 0.0000 0.940 1.000 0.000 0.000
#> GSM648680 1 0.6235 0.108 0.564 0.436 0.000
#> GSM648684 3 0.1163 0.981 0.028 0.000 0.972
#> GSM648709 1 0.5591 0.539 0.696 0.304 0.000
#> GSM648719 1 0.1289 0.942 0.968 0.032 0.000
#> GSM648627 3 0.0000 0.992 0.000 0.000 1.000
#> GSM648637 2 0.0000 0.885 0.000 1.000 0.000
#> GSM648638 1 0.0237 0.942 0.996 0.004 0.000
#> GSM648641 3 0.0000 0.992 0.000 0.000 1.000
#> GSM648672 2 0.0000 0.885 0.000 1.000 0.000
#> GSM648674 2 0.2878 0.927 0.096 0.904 0.000
#> GSM648703 2 0.0000 0.885 0.000 1.000 0.000
#> GSM648631 3 0.0000 0.992 0.000 0.000 1.000
#> GSM648669 1 0.0237 0.942 0.996 0.004 0.000
#> GSM648671 1 0.0237 0.942 0.996 0.004 0.000
#> GSM648678 2 0.0000 0.885 0.000 1.000 0.000
#> GSM648679 2 0.2878 0.927 0.096 0.904 0.000
#> GSM648681 2 0.3551 0.915 0.132 0.868 0.000
#> GSM648686 3 0.0000 0.992 0.000 0.000 1.000
#> GSM648689 3 0.0000 0.992 0.000 0.000 1.000
#> GSM648690 3 0.0000 0.992 0.000 0.000 1.000
#> GSM648691 3 0.0000 0.992 0.000 0.000 1.000
#> GSM648693 3 0.0000 0.992 0.000 0.000 1.000
#> GSM648700 1 0.0424 0.943 0.992 0.008 0.000
#> GSM648630 3 0.0000 0.992 0.000 0.000 1.000
#> GSM648632 3 0.0000 0.992 0.000 0.000 1.000
#> GSM648639 1 0.3752 0.823 0.856 0.144 0.000
#> GSM648640 3 0.0000 0.992 0.000 0.000 1.000
#> GSM648668 2 0.0000 0.885 0.000 1.000 0.000
#> GSM648676 2 0.2878 0.927 0.096 0.904 0.000
#> GSM648692 3 0.0000 0.992 0.000 0.000 1.000
#> GSM648694 3 0.0000 0.992 0.000 0.000 1.000
#> GSM648699 1 0.0237 0.942 0.996 0.004 0.000
#> GSM648701 2 0.2959 0.926 0.100 0.900 0.000
#> GSM648673 1 0.0237 0.942 0.996 0.004 0.000
#> GSM648677 2 0.0000 0.885 0.000 1.000 0.000
#> GSM648687 1 0.0000 0.940 1.000 0.000 0.000
#> GSM648688 3 0.0000 0.992 0.000 0.000 1.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM648605 1 0.0817 0.8945 0.976 0.000 0.000 0.024
#> GSM648618 1 0.0336 0.9008 0.992 0.000 0.000 0.008
#> GSM648620 2 0.0336 0.9066 0.008 0.992 0.000 0.000
#> GSM648646 2 0.3311 0.8441 0.000 0.828 0.000 0.172
#> GSM648649 2 0.0524 0.9081 0.004 0.988 0.000 0.008
#> GSM648675 1 0.1867 0.9036 0.928 0.072 0.000 0.000
#> GSM648682 2 0.0188 0.9070 0.004 0.996 0.000 0.000
#> GSM648698 2 0.0188 0.9070 0.004 0.996 0.000 0.000
#> GSM648708 2 0.0336 0.9066 0.008 0.992 0.000 0.000
#> GSM648628 3 0.4072 0.6731 0.000 0.000 0.748 0.252
#> GSM648595 2 0.0895 0.9080 0.004 0.976 0.000 0.020
#> GSM648635 2 0.1716 0.8779 0.064 0.936 0.000 0.000
#> GSM648645 1 0.1867 0.9034 0.928 0.072 0.000 0.000
#> GSM648647 2 0.0895 0.9080 0.004 0.976 0.000 0.020
#> GSM648667 2 0.1824 0.8972 0.004 0.936 0.000 0.060
#> GSM648695 2 0.0376 0.9078 0.004 0.992 0.000 0.004
#> GSM648704 2 0.3311 0.8441 0.000 0.828 0.000 0.172
#> GSM648706 1 0.0188 0.9022 0.996 0.000 0.000 0.004
#> GSM648593 2 0.1716 0.8779 0.064 0.936 0.000 0.000
#> GSM648594 1 0.1867 0.9034 0.928 0.072 0.000 0.000
#> GSM648600 1 0.4967 0.3363 0.548 0.452 0.000 0.000
#> GSM648621 1 0.3074 0.8703 0.848 0.152 0.000 0.000
#> GSM648622 1 0.0336 0.9008 0.992 0.000 0.000 0.008
#> GSM648623 1 0.0336 0.9008 0.992 0.000 0.000 0.008
#> GSM648636 2 0.1637 0.8827 0.060 0.940 0.000 0.000
#> GSM648655 2 0.1637 0.8827 0.060 0.940 0.000 0.000
#> GSM648661 3 0.4522 0.5117 0.000 0.000 0.680 0.320
#> GSM648664 4 0.4222 0.8740 0.000 0.000 0.272 0.728
#> GSM648683 4 0.3494 0.9179 0.004 0.000 0.172 0.824
#> GSM648685 4 0.3494 0.9179 0.004 0.000 0.172 0.824
#> GSM648702 2 0.1716 0.8779 0.064 0.936 0.000 0.000
#> GSM648597 1 0.1867 0.9034 0.928 0.072 0.000 0.000
#> GSM648603 1 0.3074 0.8703 0.848 0.152 0.000 0.000
#> GSM648606 4 0.3494 0.9179 0.004 0.000 0.172 0.824
#> GSM648613 4 0.3494 0.9179 0.004 0.000 0.172 0.824
#> GSM648619 4 0.4250 0.8715 0.000 0.000 0.276 0.724
#> GSM648654 3 0.4193 0.6411 0.000 0.000 0.732 0.268
#> GSM648663 4 0.3610 0.9132 0.000 0.000 0.200 0.800
#> GSM648670 2 0.0188 0.9070 0.004 0.996 0.000 0.000
#> GSM648707 1 0.0336 0.9008 0.992 0.000 0.000 0.008
#> GSM648615 2 0.0188 0.9070 0.004 0.996 0.000 0.000
#> GSM648643 2 0.3311 0.8441 0.000 0.828 0.000 0.172
#> GSM648650 2 0.0524 0.9081 0.004 0.988 0.000 0.008
#> GSM648656 2 0.3311 0.8441 0.000 0.828 0.000 0.172
#> GSM648715 2 0.0895 0.9080 0.004 0.976 0.000 0.020
#> GSM648598 1 0.3074 0.8703 0.848 0.152 0.000 0.000
#> GSM648601 1 0.3074 0.8703 0.848 0.152 0.000 0.000
#> GSM648602 1 0.3074 0.8703 0.848 0.152 0.000 0.000
#> GSM648604 4 0.3583 0.9189 0.004 0.000 0.180 0.816
#> GSM648614 1 0.0817 0.8945 0.976 0.000 0.000 0.024
#> GSM648624 1 0.0336 0.9008 0.992 0.000 0.000 0.008
#> GSM648625 2 0.0188 0.9070 0.004 0.996 0.000 0.000
#> GSM648629 4 0.4356 0.8518 0.000 0.000 0.292 0.708
#> GSM648634 1 0.3074 0.8703 0.848 0.152 0.000 0.000
#> GSM648648 1 0.1792 0.9041 0.932 0.068 0.000 0.000
#> GSM648651 1 0.0336 0.9008 0.992 0.000 0.000 0.008
#> GSM648657 1 0.1792 0.9041 0.932 0.068 0.000 0.000
#> GSM648660 1 0.1867 0.9034 0.928 0.072 0.000 0.000
#> GSM648697 1 0.0336 0.9008 0.992 0.000 0.000 0.008
#> GSM648710 4 0.4406 0.8401 0.000 0.000 0.300 0.700
#> GSM648591 1 0.0188 0.9016 0.996 0.000 0.000 0.004
#> GSM648592 2 0.3172 0.7665 0.160 0.840 0.000 0.000
#> GSM648607 4 0.4250 0.8715 0.000 0.000 0.276 0.724
#> GSM648611 3 0.3975 0.6872 0.000 0.000 0.760 0.240
#> GSM648612 4 0.3873 0.9021 0.000 0.000 0.228 0.772
#> GSM648616 1 0.3074 0.8703 0.848 0.152 0.000 0.000
#> GSM648617 1 0.4888 0.4483 0.588 0.412 0.000 0.000
#> GSM648626 1 0.3074 0.8703 0.848 0.152 0.000 0.000
#> GSM648711 3 0.4072 0.6731 0.000 0.000 0.748 0.252
#> GSM648712 4 0.4250 0.8715 0.000 0.000 0.276 0.724
#> GSM648713 4 0.4250 0.8715 0.000 0.000 0.276 0.724
#> GSM648714 1 0.0817 0.8945 0.976 0.000 0.000 0.024
#> GSM648716 3 0.4072 0.6731 0.000 0.000 0.748 0.252
#> GSM648717 4 0.3494 0.9179 0.004 0.000 0.172 0.824
#> GSM648590 2 0.0895 0.9080 0.004 0.976 0.000 0.020
#> GSM648596 2 0.3311 0.8441 0.000 0.828 0.000 0.172
#> GSM648642 1 0.0188 0.9022 0.996 0.000 0.000 0.004
#> GSM648696 2 0.4776 0.2700 0.376 0.624 0.000 0.000
#> GSM648705 2 0.0524 0.9081 0.004 0.988 0.000 0.008
#> GSM648718 2 0.0895 0.9080 0.004 0.976 0.000 0.020
#> GSM648599 1 0.3074 0.8703 0.848 0.152 0.000 0.000
#> GSM648608 4 0.3583 0.9189 0.004 0.000 0.180 0.816
#> GSM648609 4 0.3583 0.9189 0.004 0.000 0.180 0.816
#> GSM648610 4 0.3591 0.9110 0.008 0.000 0.168 0.824
#> GSM648633 2 0.1716 0.8779 0.064 0.936 0.000 0.000
#> GSM648644 2 0.3311 0.8441 0.000 0.828 0.000 0.172
#> GSM648652 2 0.2216 0.8531 0.092 0.908 0.000 0.000
#> GSM648653 1 0.3074 0.8703 0.848 0.152 0.000 0.000
#> GSM648658 1 0.1792 0.9041 0.932 0.068 0.000 0.000
#> GSM648659 2 0.1389 0.8883 0.048 0.952 0.000 0.000
#> GSM648662 4 0.3494 0.9179 0.004 0.000 0.172 0.824
#> GSM648665 4 0.3494 0.9179 0.004 0.000 0.172 0.824
#> GSM648666 1 0.0336 0.9008 0.992 0.000 0.000 0.008
#> GSM648680 2 0.4977 -0.0138 0.460 0.540 0.000 0.000
#> GSM648684 4 0.3494 0.9179 0.004 0.000 0.172 0.824
#> GSM648709 1 0.4898 0.4383 0.584 0.416 0.000 0.000
#> GSM648719 1 0.1792 0.9041 0.932 0.068 0.000 0.000
#> GSM648627 3 0.4072 0.6731 0.000 0.000 0.748 0.252
#> GSM648637 2 0.3311 0.8441 0.000 0.828 0.000 0.172
#> GSM648638 1 0.0188 0.9022 0.996 0.000 0.000 0.004
#> GSM648641 4 0.4967 0.4626 0.000 0.000 0.452 0.548
#> GSM648672 2 0.3311 0.8441 0.000 0.828 0.000 0.172
#> GSM648674 2 0.1004 0.9075 0.004 0.972 0.000 0.024
#> GSM648703 2 0.3311 0.8441 0.000 0.828 0.000 0.172
#> GSM648631 3 0.0000 0.8369 0.000 0.000 1.000 0.000
#> GSM648669 1 0.0188 0.9040 0.996 0.004 0.000 0.000
#> GSM648671 1 0.0188 0.9040 0.996 0.004 0.000 0.000
#> GSM648678 2 0.3311 0.8441 0.000 0.828 0.000 0.172
#> GSM648679 2 0.1004 0.9075 0.004 0.972 0.000 0.024
#> GSM648681 2 0.0707 0.9016 0.020 0.980 0.000 0.000
#> GSM648686 3 0.0000 0.8369 0.000 0.000 1.000 0.000
#> GSM648689 3 0.0469 0.8330 0.000 0.000 0.988 0.012
#> GSM648690 3 0.0000 0.8369 0.000 0.000 1.000 0.000
#> GSM648691 3 0.0000 0.8369 0.000 0.000 1.000 0.000
#> GSM648693 3 0.0000 0.8369 0.000 0.000 1.000 0.000
#> GSM648700 1 0.0336 0.9045 0.992 0.008 0.000 0.000
#> GSM648630 3 0.0000 0.8369 0.000 0.000 1.000 0.000
#> GSM648632 3 0.0000 0.8369 0.000 0.000 1.000 0.000
#> GSM648639 1 0.4103 0.7501 0.744 0.256 0.000 0.000
#> GSM648640 3 0.3400 0.7407 0.000 0.000 0.820 0.180
#> GSM648668 2 0.3311 0.8441 0.000 0.828 0.000 0.172
#> GSM648676 2 0.0895 0.9080 0.004 0.976 0.000 0.020
#> GSM648692 3 0.0000 0.8369 0.000 0.000 1.000 0.000
#> GSM648694 3 0.0000 0.8369 0.000 0.000 1.000 0.000
#> GSM648699 1 0.0188 0.9040 0.996 0.004 0.000 0.000
#> GSM648701 2 0.0779 0.9080 0.004 0.980 0.000 0.016
#> GSM648673 1 0.0188 0.9040 0.996 0.004 0.000 0.000
#> GSM648677 2 0.3311 0.8441 0.000 0.828 0.000 0.172
#> GSM648687 1 0.0336 0.9008 0.992 0.000 0.000 0.008
#> GSM648688 3 0.0000 0.8369 0.000 0.000 1.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM648605 5 0.4421 0.688 0.024 0.000 0.004 0.268 0.704
#> GSM648618 5 0.0833 0.852 0.004 0.000 0.004 0.016 0.976
#> GSM648620 2 0.0162 0.894 0.000 0.996 0.000 0.000 0.004
#> GSM648646 4 0.3876 0.975 0.000 0.316 0.000 0.684 0.000
#> GSM648649 2 0.0290 0.893 0.000 0.992 0.000 0.008 0.000
#> GSM648675 5 0.1831 0.861 0.000 0.076 0.000 0.004 0.920
#> GSM648682 2 0.0000 0.894 0.000 1.000 0.000 0.000 0.000
#> GSM648698 2 0.0000 0.894 0.000 1.000 0.000 0.000 0.000
#> GSM648708 2 0.0162 0.894 0.000 0.996 0.000 0.000 0.004
#> GSM648628 3 0.3932 0.640 0.328 0.000 0.672 0.000 0.000
#> GSM648595 2 0.0703 0.884 0.000 0.976 0.000 0.024 0.000
#> GSM648635 2 0.1410 0.863 0.000 0.940 0.000 0.000 0.060
#> GSM648645 5 0.1671 0.860 0.000 0.076 0.000 0.000 0.924
#> GSM648647 2 0.0703 0.884 0.000 0.976 0.000 0.024 0.000
#> GSM648667 2 0.3039 0.591 0.000 0.808 0.000 0.192 0.000
#> GSM648695 2 0.0510 0.891 0.000 0.984 0.000 0.016 0.000
#> GSM648704 4 0.3876 0.975 0.000 0.316 0.000 0.684 0.000
#> GSM648706 5 0.3766 0.708 0.000 0.000 0.004 0.268 0.728
#> GSM648593 2 0.1410 0.863 0.000 0.940 0.000 0.000 0.060
#> GSM648594 5 0.1671 0.860 0.000 0.076 0.000 0.000 0.924
#> GSM648600 5 0.4968 0.277 0.000 0.456 0.000 0.028 0.516
#> GSM648621 5 0.3454 0.824 0.000 0.156 0.000 0.028 0.816
#> GSM648622 5 0.0833 0.852 0.004 0.000 0.004 0.016 0.976
#> GSM648623 5 0.0833 0.852 0.004 0.000 0.004 0.016 0.976
#> GSM648636 2 0.1341 0.868 0.000 0.944 0.000 0.000 0.056
#> GSM648655 2 0.1341 0.868 0.000 0.944 0.000 0.000 0.056
#> GSM648661 3 0.4210 0.461 0.412 0.000 0.588 0.000 0.000
#> GSM648664 1 0.2179 0.882 0.888 0.000 0.112 0.000 0.000
#> GSM648683 1 0.0000 0.924 1.000 0.000 0.000 0.000 0.000
#> GSM648685 1 0.0000 0.924 1.000 0.000 0.000 0.000 0.000
#> GSM648702 2 0.1410 0.863 0.000 0.940 0.000 0.000 0.060
#> GSM648597 5 0.1671 0.860 0.000 0.076 0.000 0.000 0.924
#> GSM648603 5 0.3454 0.824 0.000 0.156 0.000 0.028 0.816
#> GSM648606 1 0.0000 0.924 1.000 0.000 0.000 0.000 0.000
#> GSM648613 1 0.0000 0.924 1.000 0.000 0.000 0.000 0.000
#> GSM648619 1 0.2179 0.884 0.888 0.000 0.112 0.000 0.000
#> GSM648654 3 0.3999 0.612 0.344 0.000 0.656 0.000 0.000
#> GSM648663 1 0.0880 0.921 0.968 0.000 0.032 0.000 0.000
#> GSM648670 2 0.0000 0.894 0.000 1.000 0.000 0.000 0.000
#> GSM648707 5 0.0833 0.852 0.004 0.000 0.004 0.016 0.976
#> GSM648615 2 0.0000 0.894 0.000 1.000 0.000 0.000 0.000
#> GSM648643 4 0.3876 0.975 0.000 0.316 0.000 0.684 0.000
#> GSM648650 2 0.0290 0.893 0.000 0.992 0.000 0.008 0.000
#> GSM648656 4 0.3876 0.975 0.000 0.316 0.000 0.684 0.000
#> GSM648715 2 0.0703 0.884 0.000 0.976 0.000 0.024 0.000
#> GSM648598 5 0.3454 0.824 0.000 0.156 0.000 0.028 0.816
#> GSM648601 5 0.3454 0.824 0.000 0.156 0.000 0.028 0.816
#> GSM648602 5 0.3454 0.824 0.000 0.156 0.000 0.028 0.816
#> GSM648604 1 0.0404 0.925 0.988 0.000 0.012 0.000 0.000
#> GSM648614 5 0.4421 0.688 0.024 0.000 0.004 0.268 0.704
#> GSM648624 5 0.0833 0.852 0.004 0.000 0.004 0.016 0.976
#> GSM648625 2 0.0000 0.894 0.000 1.000 0.000 0.000 0.000
#> GSM648629 1 0.2424 0.865 0.868 0.000 0.132 0.000 0.000
#> GSM648634 5 0.3454 0.824 0.000 0.156 0.000 0.028 0.816
#> GSM648648 5 0.1608 0.861 0.000 0.072 0.000 0.000 0.928
#> GSM648651 5 0.0833 0.852 0.004 0.000 0.004 0.016 0.976
#> GSM648657 5 0.1608 0.861 0.000 0.072 0.000 0.000 0.928
#> GSM648660 5 0.1671 0.860 0.000 0.076 0.000 0.000 0.924
#> GSM648697 5 0.0833 0.852 0.004 0.000 0.004 0.016 0.976
#> GSM648710 1 0.2516 0.855 0.860 0.000 0.140 0.000 0.000
#> GSM648591 5 0.0671 0.853 0.004 0.000 0.000 0.016 0.980
#> GSM648592 2 0.2690 0.725 0.000 0.844 0.000 0.000 0.156
#> GSM648607 1 0.2179 0.884 0.888 0.000 0.112 0.000 0.000
#> GSM648611 3 0.3876 0.652 0.316 0.000 0.684 0.000 0.000
#> GSM648612 1 0.1410 0.911 0.940 0.000 0.060 0.000 0.000
#> GSM648616 5 0.3454 0.824 0.000 0.156 0.000 0.028 0.816
#> GSM648617 5 0.4917 0.395 0.000 0.416 0.000 0.028 0.556
#> GSM648626 5 0.3454 0.824 0.000 0.156 0.000 0.028 0.816
#> GSM648711 3 0.3932 0.640 0.328 0.000 0.672 0.000 0.000
#> GSM648712 1 0.2179 0.884 0.888 0.000 0.112 0.000 0.000
#> GSM648713 1 0.2179 0.884 0.888 0.000 0.112 0.000 0.000
#> GSM648714 5 0.4421 0.688 0.024 0.000 0.004 0.268 0.704
#> GSM648716 3 0.3932 0.640 0.328 0.000 0.672 0.000 0.000
#> GSM648717 1 0.0000 0.924 1.000 0.000 0.000 0.000 0.000
#> GSM648590 2 0.0703 0.884 0.000 0.976 0.000 0.024 0.000
#> GSM648596 4 0.3876 0.975 0.000 0.316 0.000 0.684 0.000
#> GSM648642 5 0.3766 0.708 0.000 0.000 0.004 0.268 0.728
#> GSM648696 2 0.4718 0.322 0.000 0.628 0.000 0.028 0.344
#> GSM648705 2 0.0290 0.893 0.000 0.992 0.000 0.008 0.000
#> GSM648718 2 0.0703 0.884 0.000 0.976 0.000 0.024 0.000
#> GSM648599 5 0.3454 0.824 0.000 0.156 0.000 0.028 0.816
#> GSM648608 1 0.0404 0.925 0.988 0.000 0.012 0.000 0.000
#> GSM648609 1 0.0404 0.925 0.988 0.000 0.012 0.000 0.000
#> GSM648610 1 0.0324 0.918 0.992 0.000 0.004 0.000 0.004
#> GSM648633 2 0.1410 0.863 0.000 0.940 0.000 0.000 0.060
#> GSM648644 4 0.3876 0.975 0.000 0.316 0.000 0.684 0.000
#> GSM648652 2 0.1851 0.830 0.000 0.912 0.000 0.000 0.088
#> GSM648653 5 0.3454 0.824 0.000 0.156 0.000 0.028 0.816
#> GSM648658 5 0.1608 0.861 0.000 0.072 0.000 0.000 0.928
#> GSM648659 2 0.1121 0.875 0.000 0.956 0.000 0.000 0.044
#> GSM648662 1 0.0000 0.924 1.000 0.000 0.000 0.000 0.000
#> GSM648665 1 0.0000 0.924 1.000 0.000 0.000 0.000 0.000
#> GSM648666 5 0.0833 0.852 0.004 0.000 0.004 0.016 0.976
#> GSM648680 2 0.4283 0.028 0.000 0.544 0.000 0.000 0.456
#> GSM648684 1 0.0000 0.924 1.000 0.000 0.000 0.000 0.000
#> GSM648709 5 0.4924 0.385 0.000 0.420 0.000 0.028 0.552
#> GSM648719 5 0.1608 0.861 0.000 0.072 0.000 0.000 0.928
#> GSM648627 3 0.3932 0.640 0.328 0.000 0.672 0.000 0.000
#> GSM648637 4 0.3707 0.975 0.000 0.284 0.000 0.716 0.000
#> GSM648638 5 0.3766 0.708 0.000 0.000 0.004 0.268 0.728
#> GSM648641 1 0.3932 0.475 0.672 0.000 0.328 0.000 0.000
#> GSM648672 4 0.3707 0.975 0.000 0.284 0.000 0.716 0.000
#> GSM648674 2 0.1197 0.864 0.000 0.952 0.000 0.048 0.000
#> GSM648703 4 0.3730 0.976 0.000 0.288 0.000 0.712 0.000
#> GSM648631 3 0.0162 0.829 0.004 0.000 0.996 0.000 0.000
#> GSM648669 5 0.0798 0.856 0.000 0.008 0.000 0.016 0.976
#> GSM648671 5 0.0798 0.856 0.000 0.008 0.000 0.016 0.976
#> GSM648678 4 0.3707 0.975 0.000 0.284 0.000 0.716 0.000
#> GSM648679 2 0.1043 0.871 0.000 0.960 0.000 0.040 0.000
#> GSM648681 2 0.0510 0.889 0.000 0.984 0.000 0.000 0.016
#> GSM648686 3 0.0162 0.829 0.004 0.000 0.996 0.000 0.000
#> GSM648689 3 0.0510 0.826 0.016 0.000 0.984 0.000 0.000
#> GSM648690 3 0.0162 0.829 0.004 0.000 0.996 0.000 0.000
#> GSM648691 3 0.0162 0.829 0.004 0.000 0.996 0.000 0.000
#> GSM648693 3 0.0162 0.829 0.004 0.000 0.996 0.000 0.000
#> GSM648700 5 0.0912 0.856 0.000 0.012 0.000 0.016 0.972
#> GSM648630 3 0.0162 0.829 0.004 0.000 0.996 0.000 0.000
#> GSM648632 3 0.0162 0.829 0.004 0.000 0.996 0.000 0.000
#> GSM648639 5 0.4301 0.705 0.000 0.260 0.000 0.028 0.712
#> GSM648640 3 0.3109 0.742 0.200 0.000 0.800 0.000 0.000
#> GSM648668 4 0.3707 0.975 0.000 0.284 0.000 0.716 0.000
#> GSM648676 2 0.0703 0.884 0.000 0.976 0.000 0.024 0.000
#> GSM648692 3 0.0162 0.829 0.004 0.000 0.996 0.000 0.000
#> GSM648694 3 0.0162 0.829 0.004 0.000 0.996 0.000 0.000
#> GSM648699 5 0.0798 0.856 0.000 0.008 0.000 0.016 0.976
#> GSM648701 2 0.0510 0.888 0.000 0.984 0.000 0.016 0.000
#> GSM648673 5 0.0798 0.856 0.000 0.008 0.000 0.016 0.976
#> GSM648677 4 0.3707 0.975 0.000 0.284 0.000 0.716 0.000
#> GSM648687 5 0.0833 0.852 0.004 0.000 0.004 0.016 0.976
#> GSM648688 3 0.0162 0.829 0.004 0.000 0.996 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM648605 5 0.3454 0.9844 0.024 0.000 0.000 0.000 0.768 0.208
#> GSM648618 6 0.2823 0.6104 0.000 0.000 0.000 0.000 0.204 0.796
#> GSM648620 2 0.0363 0.9256 0.000 0.988 0.000 0.000 0.000 0.012
#> GSM648646 4 0.0865 0.9553 0.000 0.036 0.000 0.964 0.000 0.000
#> GSM648649 2 0.0000 0.9247 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM648675 6 0.1584 0.7586 0.000 0.064 0.000 0.000 0.008 0.928
#> GSM648682 2 0.0260 0.9256 0.000 0.992 0.000 0.000 0.000 0.008
#> GSM648698 2 0.0260 0.9256 0.000 0.992 0.000 0.000 0.000 0.008
#> GSM648708 2 0.0363 0.9256 0.000 0.988 0.000 0.000 0.000 0.012
#> GSM648628 3 0.3515 0.6420 0.324 0.000 0.676 0.000 0.000 0.000
#> GSM648595 2 0.0458 0.9202 0.000 0.984 0.000 0.016 0.000 0.000
#> GSM648635 2 0.1387 0.9019 0.000 0.932 0.000 0.000 0.000 0.068
#> GSM648645 6 0.1204 0.7602 0.000 0.056 0.000 0.000 0.000 0.944
#> GSM648647 2 0.0458 0.9202 0.000 0.984 0.000 0.016 0.000 0.000
#> GSM648667 2 0.2793 0.7219 0.000 0.800 0.000 0.200 0.000 0.000
#> GSM648695 2 0.0717 0.9241 0.000 0.976 0.000 0.016 0.000 0.008
#> GSM648704 4 0.0865 0.9553 0.000 0.036 0.000 0.964 0.000 0.000
#> GSM648706 5 0.2912 0.9843 0.000 0.000 0.000 0.000 0.784 0.216
#> GSM648593 2 0.1387 0.9019 0.000 0.932 0.000 0.000 0.000 0.068
#> GSM648594 6 0.1204 0.7602 0.000 0.056 0.000 0.000 0.000 0.944
#> GSM648600 6 0.4739 0.2359 0.000 0.436 0.000 0.000 0.048 0.516
#> GSM648621 6 0.3254 0.7168 0.000 0.136 0.000 0.000 0.048 0.816
#> GSM648622 6 0.2823 0.6104 0.000 0.000 0.000 0.000 0.204 0.796
#> GSM648623 6 0.2793 0.6147 0.000 0.000 0.000 0.000 0.200 0.800
#> GSM648636 2 0.1387 0.9031 0.000 0.932 0.000 0.000 0.000 0.068
#> GSM648655 2 0.1387 0.9031 0.000 0.932 0.000 0.000 0.000 0.068
#> GSM648661 3 0.3774 0.4652 0.408 0.000 0.592 0.000 0.000 0.000
#> GSM648664 1 0.1957 0.8830 0.888 0.000 0.112 0.000 0.000 0.000
#> GSM648683 1 0.0000 0.9243 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM648685 1 0.0000 0.9243 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM648702 2 0.1387 0.9019 0.000 0.932 0.000 0.000 0.000 0.068
#> GSM648597 6 0.1204 0.7602 0.000 0.056 0.000 0.000 0.000 0.944
#> GSM648603 6 0.3316 0.7166 0.000 0.136 0.000 0.000 0.052 0.812
#> GSM648606 1 0.0000 0.9243 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM648613 1 0.0000 0.9243 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM648619 1 0.1957 0.8852 0.888 0.000 0.112 0.000 0.000 0.000
#> GSM648654 3 0.3578 0.6143 0.340 0.000 0.660 0.000 0.000 0.000
#> GSM648663 1 0.0790 0.9211 0.968 0.000 0.032 0.000 0.000 0.000
#> GSM648670 2 0.0260 0.9256 0.000 0.992 0.000 0.000 0.000 0.008
#> GSM648707 6 0.2823 0.6104 0.000 0.000 0.000 0.000 0.204 0.796
#> GSM648615 2 0.0260 0.9256 0.000 0.992 0.000 0.000 0.000 0.008
#> GSM648643 4 0.0865 0.9553 0.000 0.036 0.000 0.964 0.000 0.000
#> GSM648650 2 0.0000 0.9247 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM648656 4 0.0865 0.9553 0.000 0.036 0.000 0.964 0.000 0.000
#> GSM648715 2 0.0458 0.9202 0.000 0.984 0.000 0.016 0.000 0.000
#> GSM648598 6 0.3254 0.7168 0.000 0.136 0.000 0.000 0.048 0.816
#> GSM648601 6 0.3316 0.7166 0.000 0.136 0.000 0.000 0.052 0.812
#> GSM648602 6 0.3316 0.7166 0.000 0.136 0.000 0.000 0.052 0.812
#> GSM648604 1 0.0363 0.9256 0.988 0.000 0.012 0.000 0.000 0.000
#> GSM648614 5 0.3454 0.9844 0.024 0.000 0.000 0.000 0.768 0.208
#> GSM648624 6 0.2823 0.6104 0.000 0.000 0.000 0.000 0.204 0.796
#> GSM648625 2 0.0260 0.9256 0.000 0.992 0.000 0.000 0.000 0.008
#> GSM648629 1 0.2178 0.8655 0.868 0.000 0.132 0.000 0.000 0.000
#> GSM648634 6 0.3254 0.7168 0.000 0.136 0.000 0.000 0.048 0.816
#> GSM648648 6 0.1349 0.7603 0.000 0.056 0.000 0.000 0.004 0.940
#> GSM648651 6 0.2823 0.6104 0.000 0.000 0.000 0.000 0.204 0.796
#> GSM648657 6 0.1349 0.7603 0.000 0.056 0.000 0.000 0.004 0.940
#> GSM648660 6 0.1204 0.7602 0.000 0.056 0.000 0.000 0.000 0.944
#> GSM648697 6 0.2823 0.6104 0.000 0.000 0.000 0.000 0.204 0.796
#> GSM648710 1 0.2260 0.8560 0.860 0.000 0.140 0.000 0.000 0.000
#> GSM648591 6 0.1501 0.7122 0.000 0.000 0.000 0.000 0.076 0.924
#> GSM648592 2 0.2527 0.7933 0.000 0.832 0.000 0.000 0.000 0.168
#> GSM648607 1 0.1957 0.8852 0.888 0.000 0.112 0.000 0.000 0.000
#> GSM648611 3 0.3464 0.6545 0.312 0.000 0.688 0.000 0.000 0.000
#> GSM648612 1 0.1267 0.9120 0.940 0.000 0.060 0.000 0.000 0.000
#> GSM648616 6 0.3254 0.7168 0.000 0.136 0.000 0.000 0.048 0.816
#> GSM648617 6 0.4682 0.3551 0.000 0.396 0.000 0.000 0.048 0.556
#> GSM648626 6 0.3316 0.7166 0.000 0.136 0.000 0.000 0.052 0.812
#> GSM648711 3 0.3515 0.6420 0.324 0.000 0.676 0.000 0.000 0.000
#> GSM648712 1 0.1957 0.8852 0.888 0.000 0.112 0.000 0.000 0.000
#> GSM648713 1 0.1957 0.8852 0.888 0.000 0.112 0.000 0.000 0.000
#> GSM648714 5 0.3454 0.9844 0.024 0.000 0.000 0.000 0.768 0.208
#> GSM648716 3 0.3515 0.6420 0.324 0.000 0.676 0.000 0.000 0.000
#> GSM648717 1 0.0000 0.9243 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM648590 2 0.0458 0.9202 0.000 0.984 0.000 0.016 0.000 0.000
#> GSM648596 4 0.0865 0.9553 0.000 0.036 0.000 0.964 0.000 0.000
#> GSM648642 5 0.2912 0.9843 0.000 0.000 0.000 0.000 0.784 0.216
#> GSM648696 2 0.4524 0.3326 0.000 0.616 0.000 0.000 0.048 0.336
#> GSM648705 2 0.0000 0.9247 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM648718 2 0.0458 0.9202 0.000 0.984 0.000 0.016 0.000 0.000
#> GSM648599 6 0.3316 0.7166 0.000 0.136 0.000 0.000 0.052 0.812
#> GSM648608 1 0.0363 0.9256 0.988 0.000 0.012 0.000 0.000 0.000
#> GSM648609 1 0.0363 0.9256 0.988 0.000 0.012 0.000 0.000 0.000
#> GSM648610 1 0.0260 0.9214 0.992 0.000 0.000 0.000 0.008 0.000
#> GSM648633 2 0.1387 0.9019 0.000 0.932 0.000 0.000 0.000 0.068
#> GSM648644 4 0.0865 0.9553 0.000 0.036 0.000 0.964 0.000 0.000
#> GSM648652 2 0.1765 0.8788 0.000 0.904 0.000 0.000 0.000 0.096
#> GSM648653 6 0.3254 0.7168 0.000 0.136 0.000 0.000 0.048 0.816
#> GSM648658 6 0.1349 0.7603 0.000 0.056 0.000 0.000 0.004 0.940
#> GSM648659 2 0.1204 0.9096 0.000 0.944 0.000 0.000 0.000 0.056
#> GSM648662 1 0.0000 0.9243 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM648665 1 0.0000 0.9243 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM648666 6 0.2823 0.6104 0.000 0.000 0.000 0.000 0.204 0.796
#> GSM648680 2 0.3857 0.0481 0.000 0.532 0.000 0.000 0.000 0.468
#> GSM648684 1 0.0000 0.9243 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM648709 6 0.4690 0.3446 0.000 0.400 0.000 0.000 0.048 0.552
#> GSM648719 6 0.1349 0.7603 0.000 0.056 0.000 0.000 0.004 0.940
#> GSM648627 3 0.3515 0.6420 0.324 0.000 0.676 0.000 0.000 0.000
#> GSM648637 4 0.2527 0.8579 0.000 0.000 0.000 0.832 0.168 0.000
#> GSM648638 5 0.2912 0.9843 0.000 0.000 0.000 0.000 0.784 0.216
#> GSM648641 1 0.3531 0.4788 0.672 0.000 0.328 0.000 0.000 0.000
#> GSM648672 4 0.0260 0.9449 0.000 0.000 0.000 0.992 0.008 0.000
#> GSM648674 2 0.0937 0.9085 0.000 0.960 0.000 0.040 0.000 0.000
#> GSM648703 4 0.2897 0.8653 0.000 0.088 0.000 0.852 0.060 0.000
#> GSM648631 3 0.0000 0.8152 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648669 6 0.1757 0.7198 0.000 0.008 0.000 0.000 0.076 0.916
#> GSM648671 6 0.1757 0.7198 0.000 0.008 0.000 0.000 0.076 0.916
#> GSM648678 4 0.0260 0.9449 0.000 0.000 0.000 0.992 0.008 0.000
#> GSM648679 2 0.0790 0.9129 0.000 0.968 0.000 0.032 0.000 0.000
#> GSM648681 2 0.0632 0.9218 0.000 0.976 0.000 0.000 0.000 0.024
#> GSM648686 3 0.0000 0.8152 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648689 3 0.0363 0.8133 0.012 0.000 0.988 0.000 0.000 0.000
#> GSM648690 3 0.0000 0.8152 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648691 3 0.0000 0.8152 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648693 3 0.0000 0.8152 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648700 6 0.1701 0.7213 0.000 0.008 0.000 0.000 0.072 0.920
#> GSM648630 3 0.0000 0.8152 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648632 3 0.0000 0.8152 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648639 6 0.4075 0.5817 0.000 0.240 0.000 0.000 0.048 0.712
#> GSM648640 3 0.2762 0.7430 0.196 0.000 0.804 0.000 0.000 0.000
#> GSM648668 4 0.0260 0.9449 0.000 0.000 0.000 0.992 0.008 0.000
#> GSM648676 2 0.0458 0.9202 0.000 0.984 0.000 0.016 0.000 0.000
#> GSM648692 3 0.0000 0.8152 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648694 3 0.0000 0.8152 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648699 6 0.1757 0.7198 0.000 0.008 0.000 0.000 0.076 0.916
#> GSM648701 2 0.0363 0.9218 0.000 0.988 0.000 0.012 0.000 0.000
#> GSM648673 6 0.1757 0.7198 0.000 0.008 0.000 0.000 0.076 0.916
#> GSM648677 4 0.0547 0.9422 0.000 0.000 0.000 0.980 0.020 0.000
#> GSM648687 6 0.2823 0.6104 0.000 0.000 0.000 0.000 0.204 0.796
#> GSM648688 3 0.0000 0.8152 0.000 0.000 1.000 0.000 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) development.stage(p) other(p) k
#> ATC:hclust 130 2.91e-01 0.306 2.96e-05 2
#> ATC:hclust 127 3.84e-01 0.163 1.43e-07 3
#> ATC:hclust 124 5.33e-05 0.176 8.86e-11 4
#> ATC:hclust 123 6.29e-06 0.236 5.12e-11 5
#> ATC:hclust 123 1.85e-05 0.223 5.80e-11 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["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 51941 rows and 130 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'kmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.994 0.998 0.4396 0.559 0.559
#> 3 3 1.000 0.969 0.989 0.5260 0.759 0.574
#> 4 4 0.711 0.631 0.743 0.0922 0.959 0.880
#> 5 5 0.673 0.564 0.697 0.0621 0.828 0.498
#> 6 6 0.700 0.628 0.746 0.0470 0.912 0.613
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 3
#> attr(,"optional")
#> [1] 2
There is also optional best \(k\) = 2 that is worth to check.
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM648605 2 0.0000 1.000 0.000 1.000
#> GSM648618 2 0.0000 1.000 0.000 1.000
#> GSM648620 2 0.0000 1.000 0.000 1.000
#> GSM648646 2 0.0000 1.000 0.000 1.000
#> GSM648649 2 0.0000 1.000 0.000 1.000
#> GSM648675 2 0.0000 1.000 0.000 1.000
#> GSM648682 2 0.0000 1.000 0.000 1.000
#> GSM648698 2 0.0000 1.000 0.000 1.000
#> GSM648708 2 0.0000 1.000 0.000 1.000
#> GSM648628 1 0.0000 0.992 1.000 0.000
#> GSM648595 2 0.0000 1.000 0.000 1.000
#> GSM648635 2 0.0000 1.000 0.000 1.000
#> GSM648645 2 0.0000 1.000 0.000 1.000
#> GSM648647 2 0.0000 1.000 0.000 1.000
#> GSM648667 2 0.0000 1.000 0.000 1.000
#> GSM648695 2 0.0000 1.000 0.000 1.000
#> GSM648704 2 0.0000 1.000 0.000 1.000
#> GSM648706 2 0.0000 1.000 0.000 1.000
#> GSM648593 2 0.0000 1.000 0.000 1.000
#> GSM648594 2 0.0000 1.000 0.000 1.000
#> GSM648600 2 0.0000 1.000 0.000 1.000
#> GSM648621 2 0.0000 1.000 0.000 1.000
#> GSM648622 2 0.0000 1.000 0.000 1.000
#> GSM648623 2 0.0000 1.000 0.000 1.000
#> GSM648636 2 0.0000 1.000 0.000 1.000
#> GSM648655 2 0.0000 1.000 0.000 1.000
#> GSM648661 1 0.0000 0.992 1.000 0.000
#> GSM648664 1 0.0000 0.992 1.000 0.000
#> GSM648683 1 0.0000 0.992 1.000 0.000
#> GSM648685 1 0.0000 0.992 1.000 0.000
#> GSM648702 2 0.0000 1.000 0.000 1.000
#> GSM648597 2 0.0000 1.000 0.000 1.000
#> GSM648603 2 0.0000 1.000 0.000 1.000
#> GSM648606 1 0.0000 0.992 1.000 0.000
#> GSM648613 1 0.0000 0.992 1.000 0.000
#> GSM648619 1 0.0000 0.992 1.000 0.000
#> GSM648654 1 0.0000 0.992 1.000 0.000
#> GSM648663 1 0.0000 0.992 1.000 0.000
#> GSM648670 2 0.0000 1.000 0.000 1.000
#> GSM648707 2 0.0000 1.000 0.000 1.000
#> GSM648615 2 0.0000 1.000 0.000 1.000
#> GSM648643 2 0.0000 1.000 0.000 1.000
#> GSM648650 2 0.0000 1.000 0.000 1.000
#> GSM648656 2 0.0000 1.000 0.000 1.000
#> GSM648715 2 0.0000 1.000 0.000 1.000
#> GSM648598 2 0.0000 1.000 0.000 1.000
#> GSM648601 2 0.0000 1.000 0.000 1.000
#> GSM648602 2 0.0000 1.000 0.000 1.000
#> GSM648604 1 0.0000 0.992 1.000 0.000
#> GSM648614 1 0.8909 0.555 0.692 0.308
#> GSM648624 2 0.0000 1.000 0.000 1.000
#> GSM648625 2 0.0000 1.000 0.000 1.000
#> GSM648629 1 0.0000 0.992 1.000 0.000
#> GSM648634 2 0.0000 1.000 0.000 1.000
#> GSM648648 2 0.0000 1.000 0.000 1.000
#> GSM648651 2 0.0000 1.000 0.000 1.000
#> GSM648657 2 0.0000 1.000 0.000 1.000
#> GSM648660 2 0.0000 1.000 0.000 1.000
#> GSM648697 2 0.0000 1.000 0.000 1.000
#> GSM648710 1 0.0000 0.992 1.000 0.000
#> GSM648591 2 0.0000 1.000 0.000 1.000
#> GSM648592 2 0.0000 1.000 0.000 1.000
#> GSM648607 1 0.0000 0.992 1.000 0.000
#> GSM648611 1 0.0000 0.992 1.000 0.000
#> GSM648612 1 0.0000 0.992 1.000 0.000
#> GSM648616 2 0.0000 1.000 0.000 1.000
#> GSM648617 2 0.0000 1.000 0.000 1.000
#> GSM648626 2 0.0000 1.000 0.000 1.000
#> GSM648711 1 0.0000 0.992 1.000 0.000
#> GSM648712 1 0.0000 0.992 1.000 0.000
#> GSM648713 1 0.0000 0.992 1.000 0.000
#> GSM648714 2 0.0376 0.996 0.004 0.996
#> GSM648716 1 0.0000 0.992 1.000 0.000
#> GSM648717 1 0.0000 0.992 1.000 0.000
#> GSM648590 2 0.0000 1.000 0.000 1.000
#> GSM648596 2 0.0000 1.000 0.000 1.000
#> GSM648642 2 0.0000 1.000 0.000 1.000
#> GSM648696 2 0.0000 1.000 0.000 1.000
#> GSM648705 2 0.0000 1.000 0.000 1.000
#> GSM648718 2 0.0000 1.000 0.000 1.000
#> GSM648599 2 0.0000 1.000 0.000 1.000
#> GSM648608 1 0.0000 0.992 1.000 0.000
#> GSM648609 1 0.0000 0.992 1.000 0.000
#> GSM648610 1 0.0000 0.992 1.000 0.000
#> GSM648633 2 0.0000 1.000 0.000 1.000
#> GSM648644 2 0.0000 1.000 0.000 1.000
#> GSM648652 2 0.0000 1.000 0.000 1.000
#> GSM648653 2 0.0000 1.000 0.000 1.000
#> GSM648658 2 0.0000 1.000 0.000 1.000
#> GSM648659 2 0.0000 1.000 0.000 1.000
#> GSM648662 1 0.0000 0.992 1.000 0.000
#> GSM648665 1 0.0000 0.992 1.000 0.000
#> GSM648666 2 0.0000 1.000 0.000 1.000
#> GSM648680 2 0.0000 1.000 0.000 1.000
#> GSM648684 1 0.0000 0.992 1.000 0.000
#> GSM648709 2 0.0000 1.000 0.000 1.000
#> GSM648719 2 0.0000 1.000 0.000 1.000
#> GSM648627 1 0.0000 0.992 1.000 0.000
#> GSM648637 2 0.0000 1.000 0.000 1.000
#> GSM648638 2 0.0000 1.000 0.000 1.000
#> GSM648641 1 0.0000 0.992 1.000 0.000
#> GSM648672 2 0.0000 1.000 0.000 1.000
#> GSM648674 2 0.0000 1.000 0.000 1.000
#> GSM648703 2 0.0000 1.000 0.000 1.000
#> GSM648631 1 0.0000 0.992 1.000 0.000
#> GSM648669 2 0.0000 1.000 0.000 1.000
#> GSM648671 2 0.0000 1.000 0.000 1.000
#> GSM648678 2 0.0000 1.000 0.000 1.000
#> GSM648679 2 0.0000 1.000 0.000 1.000
#> GSM648681 2 0.0000 1.000 0.000 1.000
#> GSM648686 1 0.0000 0.992 1.000 0.000
#> GSM648689 1 0.0000 0.992 1.000 0.000
#> GSM648690 1 0.0000 0.992 1.000 0.000
#> GSM648691 1 0.0000 0.992 1.000 0.000
#> GSM648693 1 0.0000 0.992 1.000 0.000
#> GSM648700 2 0.0000 1.000 0.000 1.000
#> GSM648630 1 0.0000 0.992 1.000 0.000
#> GSM648632 1 0.0000 0.992 1.000 0.000
#> GSM648639 2 0.0000 1.000 0.000 1.000
#> GSM648640 1 0.0000 0.992 1.000 0.000
#> GSM648668 2 0.0000 1.000 0.000 1.000
#> GSM648676 2 0.0000 1.000 0.000 1.000
#> GSM648692 1 0.0000 0.992 1.000 0.000
#> GSM648694 1 0.0000 0.992 1.000 0.000
#> GSM648699 2 0.0000 1.000 0.000 1.000
#> GSM648701 2 0.0000 1.000 0.000 1.000
#> GSM648673 2 0.0000 1.000 0.000 1.000
#> GSM648677 2 0.0000 1.000 0.000 1.000
#> GSM648687 2 0.0000 1.000 0.000 1.000
#> GSM648688 1 0.0000 0.992 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM648605 1 0.0000 0.9797 1.000 0.000 0.000
#> GSM648618 1 0.0000 0.9797 1.000 0.000 0.000
#> GSM648620 2 0.0000 0.9965 0.000 1.000 0.000
#> GSM648646 2 0.0000 0.9965 0.000 1.000 0.000
#> GSM648649 2 0.0000 0.9965 0.000 1.000 0.000
#> GSM648675 1 0.0000 0.9797 1.000 0.000 0.000
#> GSM648682 2 0.0000 0.9965 0.000 1.000 0.000
#> GSM648698 2 0.0000 0.9965 0.000 1.000 0.000
#> GSM648708 2 0.0000 0.9965 0.000 1.000 0.000
#> GSM648628 3 0.0000 0.9889 0.000 0.000 1.000
#> GSM648595 2 0.0000 0.9965 0.000 1.000 0.000
#> GSM648635 2 0.0237 0.9928 0.004 0.996 0.000
#> GSM648645 1 0.0000 0.9797 1.000 0.000 0.000
#> GSM648647 2 0.0000 0.9965 0.000 1.000 0.000
#> GSM648667 2 0.0000 0.9965 0.000 1.000 0.000
#> GSM648695 2 0.0000 0.9965 0.000 1.000 0.000
#> GSM648704 2 0.0000 0.9965 0.000 1.000 0.000
#> GSM648706 1 0.6299 0.0977 0.524 0.476 0.000
#> GSM648593 2 0.0000 0.9965 0.000 1.000 0.000
#> GSM648594 1 0.0000 0.9797 1.000 0.000 0.000
#> GSM648600 1 0.0000 0.9797 1.000 0.000 0.000
#> GSM648621 1 0.0000 0.9797 1.000 0.000 0.000
#> GSM648622 1 0.0000 0.9797 1.000 0.000 0.000
#> GSM648623 1 0.0000 0.9797 1.000 0.000 0.000
#> GSM648636 2 0.2261 0.9269 0.068 0.932 0.000
#> GSM648655 2 0.2261 0.9269 0.068 0.932 0.000
#> GSM648661 3 0.0000 0.9889 0.000 0.000 1.000
#> GSM648664 3 0.0000 0.9889 0.000 0.000 1.000
#> GSM648683 3 0.0000 0.9889 0.000 0.000 1.000
#> GSM648685 3 0.0000 0.9889 0.000 0.000 1.000
#> GSM648702 2 0.0000 0.9965 0.000 1.000 0.000
#> GSM648597 1 0.0000 0.9797 1.000 0.000 0.000
#> GSM648603 1 0.0000 0.9797 1.000 0.000 0.000
#> GSM648606 3 0.0000 0.9889 0.000 0.000 1.000
#> GSM648613 3 0.0000 0.9889 0.000 0.000 1.000
#> GSM648619 3 0.0000 0.9889 0.000 0.000 1.000
#> GSM648654 3 0.0000 0.9889 0.000 0.000 1.000
#> GSM648663 3 0.0000 0.9889 0.000 0.000 1.000
#> GSM648670 2 0.0000 0.9965 0.000 1.000 0.000
#> GSM648707 1 0.0000 0.9797 1.000 0.000 0.000
#> GSM648615 2 0.0000 0.9965 0.000 1.000 0.000
#> GSM648643 2 0.0000 0.9965 0.000 1.000 0.000
#> GSM648650 2 0.0000 0.9965 0.000 1.000 0.000
#> GSM648656 2 0.0000 0.9965 0.000 1.000 0.000
#> GSM648715 2 0.0000 0.9965 0.000 1.000 0.000
#> GSM648598 1 0.0000 0.9797 1.000 0.000 0.000
#> GSM648601 1 0.0000 0.9797 1.000 0.000 0.000
#> GSM648602 1 0.0000 0.9797 1.000 0.000 0.000
#> GSM648604 3 0.0000 0.9889 0.000 0.000 1.000
#> GSM648614 1 0.0000 0.9797 1.000 0.000 0.000
#> GSM648624 1 0.0000 0.9797 1.000 0.000 0.000
#> GSM648625 2 0.0000 0.9965 0.000 1.000 0.000
#> GSM648629 3 0.0000 0.9889 0.000 0.000 1.000
#> GSM648634 1 0.0000 0.9797 1.000 0.000 0.000
#> GSM648648 1 0.0000 0.9797 1.000 0.000 0.000
#> GSM648651 1 0.0000 0.9797 1.000 0.000 0.000
#> GSM648657 1 0.0000 0.9797 1.000 0.000 0.000
#> GSM648660 1 0.0000 0.9797 1.000 0.000 0.000
#> GSM648697 1 0.0000 0.9797 1.000 0.000 0.000
#> GSM648710 3 0.0000 0.9889 0.000 0.000 1.000
#> GSM648591 1 0.0000 0.9797 1.000 0.000 0.000
#> GSM648592 1 0.0000 0.9797 1.000 0.000 0.000
#> GSM648607 3 0.0000 0.9889 0.000 0.000 1.000
#> GSM648611 3 0.0000 0.9889 0.000 0.000 1.000
#> GSM648612 3 0.0000 0.9889 0.000 0.000 1.000
#> GSM648616 1 0.0000 0.9797 1.000 0.000 0.000
#> GSM648617 1 0.0000 0.9797 1.000 0.000 0.000
#> GSM648626 1 0.0000 0.9797 1.000 0.000 0.000
#> GSM648711 3 0.0000 0.9889 0.000 0.000 1.000
#> GSM648712 3 0.0000 0.9889 0.000 0.000 1.000
#> GSM648713 3 0.0000 0.9889 0.000 0.000 1.000
#> GSM648714 1 0.0000 0.9797 1.000 0.000 0.000
#> GSM648716 3 0.0000 0.9889 0.000 0.000 1.000
#> GSM648717 3 0.0000 0.9889 0.000 0.000 1.000
#> GSM648590 2 0.0000 0.9965 0.000 1.000 0.000
#> GSM648596 2 0.0000 0.9965 0.000 1.000 0.000
#> GSM648642 1 0.0000 0.9797 1.000 0.000 0.000
#> GSM648696 2 0.0000 0.9965 0.000 1.000 0.000
#> GSM648705 2 0.0000 0.9965 0.000 1.000 0.000
#> GSM648718 2 0.0000 0.9965 0.000 1.000 0.000
#> GSM648599 1 0.0000 0.9797 1.000 0.000 0.000
#> GSM648608 3 0.0000 0.9889 0.000 0.000 1.000
#> GSM648609 3 0.0000 0.9889 0.000 0.000 1.000
#> GSM648610 3 0.6235 0.2331 0.436 0.000 0.564
#> GSM648633 2 0.0000 0.9965 0.000 1.000 0.000
#> GSM648644 2 0.0000 0.9965 0.000 1.000 0.000
#> GSM648652 1 0.0000 0.9797 1.000 0.000 0.000
#> GSM648653 1 0.0000 0.9797 1.000 0.000 0.000
#> GSM648658 1 0.0000 0.9797 1.000 0.000 0.000
#> GSM648659 2 0.0000 0.9965 0.000 1.000 0.000
#> GSM648662 3 0.0000 0.9889 0.000 0.000 1.000
#> GSM648665 3 0.0000 0.9889 0.000 0.000 1.000
#> GSM648666 1 0.0000 0.9797 1.000 0.000 0.000
#> GSM648680 1 0.0000 0.9797 1.000 0.000 0.000
#> GSM648684 3 0.0000 0.9889 0.000 0.000 1.000
#> GSM648709 1 0.6215 0.2534 0.572 0.428 0.000
#> GSM648719 1 0.0000 0.9797 1.000 0.000 0.000
#> GSM648627 3 0.0000 0.9889 0.000 0.000 1.000
#> GSM648637 2 0.0000 0.9965 0.000 1.000 0.000
#> GSM648638 1 0.0000 0.9797 1.000 0.000 0.000
#> GSM648641 3 0.0000 0.9889 0.000 0.000 1.000
#> GSM648672 2 0.0000 0.9965 0.000 1.000 0.000
#> GSM648674 2 0.0000 0.9965 0.000 1.000 0.000
#> GSM648703 2 0.0000 0.9965 0.000 1.000 0.000
#> GSM648631 3 0.0000 0.9889 0.000 0.000 1.000
#> GSM648669 1 0.0000 0.9797 1.000 0.000 0.000
#> GSM648671 1 0.0000 0.9797 1.000 0.000 0.000
#> GSM648678 2 0.0000 0.9965 0.000 1.000 0.000
#> GSM648679 2 0.0000 0.9965 0.000 1.000 0.000
#> GSM648681 2 0.0000 0.9965 0.000 1.000 0.000
#> GSM648686 3 0.0000 0.9889 0.000 0.000 1.000
#> GSM648689 3 0.0000 0.9889 0.000 0.000 1.000
#> GSM648690 3 0.0000 0.9889 0.000 0.000 1.000
#> GSM648691 3 0.0000 0.9889 0.000 0.000 1.000
#> GSM648693 3 0.0000 0.9889 0.000 0.000 1.000
#> GSM648700 1 0.0000 0.9797 1.000 0.000 0.000
#> GSM648630 3 0.0000 0.9889 0.000 0.000 1.000
#> GSM648632 3 0.0000 0.9889 0.000 0.000 1.000
#> GSM648639 1 0.0000 0.9797 1.000 0.000 0.000
#> GSM648640 3 0.0000 0.9889 0.000 0.000 1.000
#> GSM648668 2 0.0000 0.9965 0.000 1.000 0.000
#> GSM648676 2 0.0000 0.9965 0.000 1.000 0.000
#> GSM648692 3 0.0000 0.9889 0.000 0.000 1.000
#> GSM648694 3 0.0000 0.9889 0.000 0.000 1.000
#> GSM648699 1 0.0000 0.9797 1.000 0.000 0.000
#> GSM648701 2 0.0000 0.9965 0.000 1.000 0.000
#> GSM648673 1 0.0000 0.9797 1.000 0.000 0.000
#> GSM648677 2 0.0000 0.9965 0.000 1.000 0.000
#> GSM648687 1 0.0000 0.9797 1.000 0.000 0.000
#> GSM648688 3 0.0000 0.9889 0.000 0.000 1.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM648605 4 0.5000 -0.4139 0.496 0.000 0.000 0.504
#> GSM648618 1 0.4907 0.4539 0.580 0.000 0.000 0.420
#> GSM648620 2 0.4250 0.7605 0.276 0.724 0.000 0.000
#> GSM648646 2 0.2530 0.7641 0.000 0.888 0.000 0.112
#> GSM648649 2 0.4040 0.7744 0.248 0.752 0.000 0.000
#> GSM648675 1 0.1474 0.5755 0.948 0.000 0.000 0.052
#> GSM648682 2 0.3610 0.7887 0.200 0.800 0.000 0.000
#> GSM648698 2 0.3726 0.7867 0.212 0.788 0.000 0.000
#> GSM648708 2 0.4948 0.5935 0.440 0.560 0.000 0.000
#> GSM648628 3 0.0000 0.7582 0.000 0.000 1.000 0.000
#> GSM648595 2 0.0188 0.7895 0.000 0.996 0.000 0.004
#> GSM648635 2 0.4998 0.5004 0.488 0.512 0.000 0.000
#> GSM648645 1 0.4164 0.6047 0.736 0.000 0.000 0.264
#> GSM648647 2 0.0000 0.7901 0.000 1.000 0.000 0.000
#> GSM648667 2 0.2216 0.7702 0.000 0.908 0.000 0.092
#> GSM648695 2 0.1389 0.7924 0.048 0.952 0.000 0.000
#> GSM648704 2 0.2530 0.7641 0.000 0.888 0.000 0.112
#> GSM648706 1 0.5306 -0.0129 0.632 0.348 0.000 0.020
#> GSM648593 2 0.4948 0.5935 0.440 0.560 0.000 0.000
#> GSM648594 1 0.3610 0.6189 0.800 0.000 0.000 0.200
#> GSM648600 1 0.2530 0.4784 0.888 0.112 0.000 0.000
#> GSM648621 1 0.4454 0.5818 0.692 0.000 0.000 0.308
#> GSM648622 1 0.4907 0.4539 0.580 0.000 0.000 0.420
#> GSM648623 1 0.4907 0.4539 0.580 0.000 0.000 0.420
#> GSM648636 1 0.4776 -0.1431 0.624 0.376 0.000 0.000
#> GSM648655 1 0.4790 -0.1570 0.620 0.380 0.000 0.000
#> GSM648661 3 0.0000 0.7582 0.000 0.000 1.000 0.000
#> GSM648664 3 0.4961 0.6665 0.000 0.000 0.552 0.448
#> GSM648683 3 0.5000 0.6273 0.000 0.000 0.504 0.496
#> GSM648685 3 0.5000 0.6273 0.000 0.000 0.504 0.496
#> GSM648702 2 0.4941 0.5999 0.436 0.564 0.000 0.000
#> GSM648597 1 0.4008 0.6130 0.756 0.000 0.000 0.244
#> GSM648603 1 0.4830 0.4952 0.608 0.000 0.000 0.392
#> GSM648606 3 0.5000 0.6273 0.000 0.000 0.504 0.496
#> GSM648613 3 0.4985 0.6552 0.000 0.000 0.532 0.468
#> GSM648619 3 0.4382 0.7278 0.000 0.000 0.704 0.296
#> GSM648654 3 0.0000 0.7582 0.000 0.000 1.000 0.000
#> GSM648663 3 0.4985 0.6552 0.000 0.000 0.532 0.468
#> GSM648670 2 0.3942 0.7791 0.236 0.764 0.000 0.000
#> GSM648707 1 0.4907 0.4539 0.580 0.000 0.000 0.420
#> GSM648615 2 0.3688 0.7877 0.208 0.792 0.000 0.000
#> GSM648643 2 0.2530 0.7641 0.000 0.888 0.000 0.112
#> GSM648650 2 0.3649 0.7883 0.204 0.796 0.000 0.000
#> GSM648656 2 0.2530 0.7641 0.000 0.888 0.000 0.112
#> GSM648715 2 0.0000 0.7901 0.000 1.000 0.000 0.000
#> GSM648598 1 0.3837 0.6179 0.776 0.000 0.000 0.224
#> GSM648601 1 0.4477 0.5791 0.688 0.000 0.000 0.312
#> GSM648602 1 0.4697 0.5384 0.644 0.000 0.000 0.356
#> GSM648604 3 0.4989 0.6516 0.000 0.000 0.528 0.472
#> GSM648614 4 0.2530 0.6921 0.112 0.000 0.000 0.888
#> GSM648624 1 0.4907 0.4539 0.580 0.000 0.000 0.420
#> GSM648625 2 0.4103 0.7711 0.256 0.744 0.000 0.000
#> GSM648629 3 0.4382 0.7278 0.000 0.000 0.704 0.296
#> GSM648634 1 0.0336 0.5479 0.992 0.008 0.000 0.000
#> GSM648648 1 0.0524 0.5544 0.988 0.004 0.000 0.008
#> GSM648651 1 0.4907 0.4539 0.580 0.000 0.000 0.420
#> GSM648657 1 0.1792 0.5814 0.932 0.000 0.000 0.068
#> GSM648660 1 0.3610 0.6189 0.800 0.000 0.000 0.200
#> GSM648697 1 0.4907 0.4539 0.580 0.000 0.000 0.420
#> GSM648710 3 0.3837 0.7383 0.000 0.000 0.776 0.224
#> GSM648591 1 0.4907 0.4539 0.580 0.000 0.000 0.420
#> GSM648592 1 0.2530 0.4784 0.888 0.112 0.000 0.000
#> GSM648607 3 0.4543 0.7203 0.000 0.000 0.676 0.324
#> GSM648611 3 0.0000 0.7582 0.000 0.000 1.000 0.000
#> GSM648612 3 0.4985 0.6552 0.000 0.000 0.532 0.468
#> GSM648616 1 0.4406 0.5867 0.700 0.000 0.000 0.300
#> GSM648617 1 0.0921 0.5359 0.972 0.028 0.000 0.000
#> GSM648626 1 0.4605 0.5591 0.664 0.000 0.000 0.336
#> GSM648711 3 0.0000 0.7582 0.000 0.000 1.000 0.000
#> GSM648712 3 0.4564 0.7189 0.000 0.000 0.672 0.328
#> GSM648713 3 0.4543 0.7203 0.000 0.000 0.676 0.324
#> GSM648714 4 0.2530 0.6921 0.112 0.000 0.000 0.888
#> GSM648716 3 0.0000 0.7582 0.000 0.000 1.000 0.000
#> GSM648717 3 0.4985 0.6552 0.000 0.000 0.532 0.468
#> GSM648590 2 0.0000 0.7901 0.000 1.000 0.000 0.000
#> GSM648596 2 0.2530 0.7641 0.000 0.888 0.000 0.112
#> GSM648642 1 0.3463 0.5043 0.864 0.096 0.000 0.040
#> GSM648696 2 0.4103 0.7711 0.256 0.744 0.000 0.000
#> GSM648705 2 0.4454 0.7379 0.308 0.692 0.000 0.000
#> GSM648718 2 0.3726 0.7867 0.212 0.788 0.000 0.000
#> GSM648599 1 0.4624 0.5553 0.660 0.000 0.000 0.340
#> GSM648608 3 0.4985 0.6552 0.000 0.000 0.532 0.468
#> GSM648609 3 0.4985 0.6552 0.000 0.000 0.532 0.468
#> GSM648610 4 0.3308 0.5660 0.036 0.000 0.092 0.872
#> GSM648633 2 0.4916 0.6179 0.424 0.576 0.000 0.000
#> GSM648644 2 0.2530 0.7641 0.000 0.888 0.000 0.112
#> GSM648652 1 0.2530 0.4784 0.888 0.112 0.000 0.000
#> GSM648653 1 0.4477 0.5791 0.688 0.000 0.000 0.312
#> GSM648658 1 0.3942 0.6156 0.764 0.000 0.000 0.236
#> GSM648659 2 0.4907 0.6235 0.420 0.580 0.000 0.000
#> GSM648662 3 0.5000 0.6273 0.000 0.000 0.504 0.496
#> GSM648665 3 0.5000 0.6273 0.000 0.000 0.504 0.496
#> GSM648666 1 0.4907 0.4539 0.580 0.000 0.000 0.420
#> GSM648680 1 0.2408 0.4844 0.896 0.104 0.000 0.000
#> GSM648684 3 0.5000 0.6273 0.000 0.000 0.504 0.496
#> GSM648709 1 0.3873 0.3458 0.772 0.228 0.000 0.000
#> GSM648719 1 0.3726 0.6187 0.788 0.000 0.000 0.212
#> GSM648627 3 0.0000 0.7582 0.000 0.000 1.000 0.000
#> GSM648637 2 0.2530 0.7641 0.000 0.888 0.000 0.112
#> GSM648638 1 0.4996 0.2190 0.516 0.000 0.000 0.484
#> GSM648641 3 0.4382 0.7278 0.000 0.000 0.704 0.296
#> GSM648672 2 0.2530 0.7641 0.000 0.888 0.000 0.112
#> GSM648674 2 0.2081 0.7724 0.000 0.916 0.000 0.084
#> GSM648703 2 0.2530 0.7641 0.000 0.888 0.000 0.112
#> GSM648631 3 0.0000 0.7582 0.000 0.000 1.000 0.000
#> GSM648669 1 0.3907 0.6168 0.768 0.000 0.000 0.232
#> GSM648671 1 0.3975 0.6144 0.760 0.000 0.000 0.240
#> GSM648678 2 0.2530 0.7641 0.000 0.888 0.000 0.112
#> GSM648679 2 0.0188 0.7895 0.000 0.996 0.000 0.004
#> GSM648681 2 0.4790 0.6703 0.380 0.620 0.000 0.000
#> GSM648686 3 0.0000 0.7582 0.000 0.000 1.000 0.000
#> GSM648689 3 0.0000 0.7582 0.000 0.000 1.000 0.000
#> GSM648690 3 0.0000 0.7582 0.000 0.000 1.000 0.000
#> GSM648691 3 0.0000 0.7582 0.000 0.000 1.000 0.000
#> GSM648693 3 0.0000 0.7582 0.000 0.000 1.000 0.000
#> GSM648700 1 0.2300 0.5622 0.924 0.028 0.000 0.048
#> GSM648630 3 0.0000 0.7582 0.000 0.000 1.000 0.000
#> GSM648632 3 0.0000 0.7582 0.000 0.000 1.000 0.000
#> GSM648639 1 0.2530 0.4784 0.888 0.112 0.000 0.000
#> GSM648640 3 0.0000 0.7582 0.000 0.000 1.000 0.000
#> GSM648668 2 0.2530 0.7641 0.000 0.888 0.000 0.112
#> GSM648676 2 0.4277 0.7584 0.280 0.720 0.000 0.000
#> GSM648692 3 0.0000 0.7582 0.000 0.000 1.000 0.000
#> GSM648694 3 0.0000 0.7582 0.000 0.000 1.000 0.000
#> GSM648699 1 0.3486 0.6175 0.812 0.000 0.000 0.188
#> GSM648701 2 0.4304 0.7557 0.284 0.716 0.000 0.000
#> GSM648673 1 0.3400 0.6160 0.820 0.000 0.000 0.180
#> GSM648677 2 0.2530 0.7641 0.000 0.888 0.000 0.112
#> GSM648687 1 0.4907 0.4539 0.580 0.000 0.000 0.420
#> GSM648688 3 0.0000 0.7582 0.000 0.000 1.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM648605 4 0.5990 0.32265 0.116 0.000 0.000 0.500 0.384
#> GSM648618 4 0.2561 0.69260 0.000 0.000 0.000 0.856 0.144
#> GSM648620 1 0.6231 0.15238 0.508 0.380 0.000 0.016 0.096
#> GSM648646 2 0.0000 0.77745 0.000 1.000 0.000 0.000 0.000
#> GSM648649 1 0.5810 -0.00989 0.480 0.428 0.000 0.000 0.092
#> GSM648675 4 0.5048 0.49087 0.380 0.000 0.000 0.580 0.040
#> GSM648682 2 0.5844 0.17062 0.420 0.484 0.000 0.000 0.096
#> GSM648698 2 0.5847 0.15743 0.424 0.480 0.000 0.000 0.096
#> GSM648708 1 0.5724 0.51367 0.668 0.208 0.000 0.028 0.096
#> GSM648628 3 0.0880 0.75874 0.032 0.000 0.968 0.000 0.000
#> GSM648595 2 0.3236 0.73376 0.152 0.828 0.000 0.000 0.020
#> GSM648635 1 0.3847 0.57603 0.784 0.180 0.000 0.036 0.000
#> GSM648645 4 0.4224 0.64700 0.216 0.000 0.000 0.744 0.040
#> GSM648647 2 0.3236 0.73376 0.152 0.828 0.000 0.000 0.020
#> GSM648667 2 0.2390 0.76039 0.084 0.896 0.000 0.000 0.020
#> GSM648695 2 0.5741 0.32380 0.360 0.544 0.000 0.000 0.096
#> GSM648704 2 0.0000 0.77745 0.000 1.000 0.000 0.000 0.000
#> GSM648706 1 0.6898 0.53080 0.572 0.096 0.000 0.236 0.096
#> GSM648593 1 0.3455 0.55649 0.784 0.208 0.000 0.008 0.000
#> GSM648594 4 0.4935 0.55183 0.344 0.000 0.000 0.616 0.040
#> GSM648600 1 0.5534 0.47990 0.604 0.000 0.000 0.300 0.096
#> GSM648621 4 0.1270 0.71436 0.052 0.000 0.000 0.948 0.000
#> GSM648622 4 0.2516 0.69453 0.000 0.000 0.000 0.860 0.140
#> GSM648623 4 0.2516 0.69453 0.000 0.000 0.000 0.860 0.140
#> GSM648636 1 0.4791 0.58551 0.736 0.100 0.000 0.160 0.004
#> GSM648655 1 0.4779 0.58163 0.736 0.096 0.000 0.164 0.004
#> GSM648661 3 0.0404 0.74293 0.000 0.000 0.988 0.000 0.012
#> GSM648664 5 0.4294 0.56604 0.000 0.000 0.468 0.000 0.532
#> GSM648683 5 0.3752 0.78159 0.000 0.000 0.292 0.000 0.708
#> GSM648685 5 0.3752 0.78159 0.000 0.000 0.292 0.000 0.708
#> GSM648702 1 0.3455 0.55649 0.784 0.208 0.000 0.008 0.000
#> GSM648597 4 0.4503 0.62541 0.256 0.000 0.000 0.704 0.040
#> GSM648603 4 0.1704 0.71008 0.004 0.000 0.000 0.928 0.068
#> GSM648606 5 0.3752 0.78159 0.000 0.000 0.292 0.000 0.708
#> GSM648613 5 0.4030 0.76257 0.000 0.000 0.352 0.000 0.648
#> GSM648619 3 0.4015 0.13348 0.000 0.000 0.652 0.000 0.348
#> GSM648654 3 0.0000 0.75059 0.000 0.000 1.000 0.000 0.000
#> GSM648663 5 0.4045 0.75991 0.000 0.000 0.356 0.000 0.644
#> GSM648670 1 0.5814 -0.04232 0.472 0.436 0.000 0.000 0.092
#> GSM648707 4 0.2561 0.69260 0.000 0.000 0.000 0.856 0.144
#> GSM648615 2 0.5847 0.15743 0.424 0.480 0.000 0.000 0.096
#> GSM648643 2 0.0000 0.77745 0.000 1.000 0.000 0.000 0.000
#> GSM648650 2 0.5435 0.22319 0.428 0.512 0.000 0.000 0.060
#> GSM648656 2 0.0000 0.77745 0.000 1.000 0.000 0.000 0.000
#> GSM648715 2 0.3236 0.73376 0.152 0.828 0.000 0.000 0.020
#> GSM648598 4 0.4644 0.60074 0.280 0.000 0.000 0.680 0.040
#> GSM648601 4 0.0963 0.71650 0.036 0.000 0.000 0.964 0.000
#> GSM648602 4 0.0451 0.71568 0.004 0.000 0.000 0.988 0.008
#> GSM648604 5 0.4030 0.76330 0.000 0.000 0.352 0.000 0.648
#> GSM648614 5 0.4901 0.35266 0.060 0.000 0.000 0.268 0.672
#> GSM648624 4 0.2516 0.69453 0.000 0.000 0.000 0.860 0.140
#> GSM648625 1 0.6251 0.11232 0.496 0.392 0.000 0.016 0.096
#> GSM648629 3 0.4015 0.13348 0.000 0.000 0.652 0.000 0.348
#> GSM648634 1 0.5125 0.16348 0.544 0.000 0.000 0.416 0.040
#> GSM648648 1 0.5168 -0.16583 0.508 0.000 0.000 0.452 0.040
#> GSM648651 4 0.2561 0.69260 0.000 0.000 0.000 0.856 0.144
#> GSM648657 4 0.5048 0.49087 0.380 0.000 0.000 0.580 0.040
#> GSM648660 4 0.4921 0.55427 0.340 0.000 0.000 0.620 0.040
#> GSM648697 4 0.2516 0.69453 0.000 0.000 0.000 0.860 0.140
#> GSM648710 3 0.3752 0.29241 0.000 0.000 0.708 0.000 0.292
#> GSM648591 4 0.2719 0.70668 0.004 0.000 0.000 0.852 0.144
#> GSM648592 1 0.3906 0.39786 0.704 0.000 0.000 0.292 0.004
#> GSM648607 3 0.4045 0.10282 0.000 0.000 0.644 0.000 0.356
#> GSM648611 3 0.2230 0.76209 0.116 0.000 0.884 0.000 0.000
#> GSM648612 5 0.4150 0.71941 0.000 0.000 0.388 0.000 0.612
#> GSM648616 4 0.1341 0.71349 0.056 0.000 0.000 0.944 0.000
#> GSM648617 1 0.5401 0.22756 0.536 0.000 0.000 0.404 0.060
#> GSM648626 4 0.1082 0.71739 0.028 0.000 0.000 0.964 0.008
#> GSM648711 3 0.0000 0.75059 0.000 0.000 1.000 0.000 0.000
#> GSM648712 3 0.4045 0.10282 0.000 0.000 0.644 0.000 0.356
#> GSM648713 3 0.4045 0.10282 0.000 0.000 0.644 0.000 0.356
#> GSM648714 5 0.5172 0.23950 0.060 0.000 0.000 0.324 0.616
#> GSM648716 3 0.0000 0.75059 0.000 0.000 1.000 0.000 0.000
#> GSM648717 5 0.3837 0.77597 0.000 0.000 0.308 0.000 0.692
#> GSM648590 2 0.3476 0.71223 0.176 0.804 0.000 0.000 0.020
#> GSM648596 2 0.0000 0.77745 0.000 1.000 0.000 0.000 0.000
#> GSM648642 1 0.5659 0.23965 0.580 0.016 0.000 0.348 0.056
#> GSM648696 1 0.6245 0.12675 0.500 0.388 0.000 0.016 0.096
#> GSM648705 1 0.5051 0.44652 0.664 0.264 0.000 0.000 0.072
#> GSM648718 2 0.5447 0.18633 0.440 0.500 0.000 0.000 0.060
#> GSM648599 4 0.1082 0.71739 0.028 0.000 0.000 0.964 0.008
#> GSM648608 5 0.4114 0.73665 0.000 0.000 0.376 0.000 0.624
#> GSM648609 5 0.4045 0.75990 0.000 0.000 0.356 0.000 0.644
#> GSM648610 5 0.3039 0.51241 0.000 0.000 0.012 0.152 0.836
#> GSM648633 1 0.4643 0.53848 0.732 0.208 0.000 0.008 0.052
#> GSM648644 2 0.0000 0.77745 0.000 1.000 0.000 0.000 0.000
#> GSM648652 1 0.4874 0.27884 0.632 0.000 0.000 0.328 0.040
#> GSM648653 4 0.1522 0.71477 0.044 0.000 0.000 0.944 0.012
#> GSM648658 4 0.4728 0.60195 0.296 0.000 0.000 0.664 0.040
#> GSM648659 1 0.4205 0.54769 0.756 0.208 0.000 0.008 0.028
#> GSM648662 5 0.3752 0.78159 0.000 0.000 0.292 0.000 0.708
#> GSM648665 5 0.3774 0.78154 0.000 0.000 0.296 0.000 0.704
#> GSM648666 4 0.2561 0.69260 0.000 0.000 0.000 0.856 0.144
#> GSM648680 1 0.4990 0.18487 0.600 0.000 0.000 0.360 0.040
#> GSM648684 5 0.3752 0.78159 0.000 0.000 0.292 0.000 0.708
#> GSM648709 1 0.6006 0.54073 0.624 0.028 0.000 0.252 0.096
#> GSM648719 4 0.4905 0.56207 0.336 0.000 0.000 0.624 0.040
#> GSM648627 3 0.0000 0.75059 0.000 0.000 1.000 0.000 0.000
#> GSM648637 2 0.0510 0.77390 0.000 0.984 0.000 0.000 0.016
#> GSM648638 4 0.6142 0.29708 0.132 0.000 0.000 0.472 0.396
#> GSM648641 3 0.4166 0.12743 0.004 0.000 0.648 0.000 0.348
#> GSM648672 2 0.0290 0.77672 0.000 0.992 0.000 0.000 0.008
#> GSM648674 2 0.2761 0.75412 0.104 0.872 0.000 0.000 0.024
#> GSM648703 2 0.0510 0.77512 0.000 0.984 0.000 0.000 0.016
#> GSM648631 3 0.2648 0.75758 0.152 0.000 0.848 0.000 0.000
#> GSM648669 4 0.4786 0.58861 0.308 0.000 0.000 0.652 0.040
#> GSM648671 4 0.4708 0.60284 0.292 0.000 0.000 0.668 0.040
#> GSM648678 2 0.0162 0.77691 0.000 0.996 0.000 0.000 0.004
#> GSM648679 2 0.3236 0.73376 0.152 0.828 0.000 0.000 0.020
#> GSM648681 1 0.4466 0.51860 0.728 0.232 0.000 0.008 0.032
#> GSM648686 3 0.2280 0.76177 0.120 0.000 0.880 0.000 0.000
#> GSM648689 3 0.1043 0.75903 0.040 0.000 0.960 0.000 0.000
#> GSM648690 3 0.2690 0.75558 0.156 0.000 0.844 0.000 0.000
#> GSM648691 3 0.2648 0.75758 0.152 0.000 0.848 0.000 0.000
#> GSM648693 3 0.2648 0.75758 0.152 0.000 0.848 0.000 0.000
#> GSM648700 4 0.5077 0.45937 0.392 0.000 0.000 0.568 0.040
#> GSM648630 3 0.2648 0.75758 0.152 0.000 0.848 0.000 0.000
#> GSM648632 3 0.2648 0.75758 0.152 0.000 0.848 0.000 0.000
#> GSM648639 1 0.5534 0.47990 0.604 0.000 0.000 0.300 0.096
#> GSM648640 3 0.0609 0.75656 0.020 0.000 0.980 0.000 0.000
#> GSM648668 2 0.0290 0.77672 0.000 0.992 0.000 0.000 0.008
#> GSM648676 1 0.5423 0.16270 0.548 0.388 0.000 0.000 0.064
#> GSM648692 3 0.2648 0.75758 0.152 0.000 0.848 0.000 0.000
#> GSM648694 3 0.2648 0.75758 0.152 0.000 0.848 0.000 0.000
#> GSM648699 4 0.4977 0.52967 0.356 0.000 0.000 0.604 0.040
#> GSM648701 1 0.5405 0.18767 0.556 0.380 0.000 0.000 0.064
#> GSM648673 4 0.4977 0.52967 0.356 0.000 0.000 0.604 0.040
#> GSM648677 2 0.0404 0.77536 0.000 0.988 0.000 0.000 0.012
#> GSM648687 4 0.2561 0.69260 0.000 0.000 0.000 0.856 0.144
#> GSM648688 3 0.2648 0.75758 0.152 0.000 0.848 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM648605 1 0.6752 0.2955 0.492 0.180 0.000 0.000 0.244 0.084
#> GSM648618 1 0.0000 0.7801 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM648620 2 0.4011 0.6767 0.000 0.780 0.000 0.144 0.048 0.028
#> GSM648646 4 0.0000 0.8439 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM648649 2 0.3897 0.6460 0.000 0.760 0.000 0.192 0.012 0.036
#> GSM648675 6 0.3980 0.7802 0.216 0.052 0.000 0.000 0.000 0.732
#> GSM648682 2 0.4121 0.6068 0.000 0.720 0.000 0.220 0.060 0.000
#> GSM648698 2 0.4039 0.6151 0.000 0.732 0.000 0.208 0.060 0.000
#> GSM648708 2 0.4038 0.6923 0.000 0.784 0.000 0.036 0.048 0.132
#> GSM648628 3 0.0291 0.7215 0.000 0.004 0.992 0.000 0.000 0.004
#> GSM648595 4 0.4308 0.6189 0.000 0.280 0.000 0.680 0.012 0.028
#> GSM648635 2 0.4531 0.6048 0.004 0.608 0.000 0.036 0.000 0.352
#> GSM648645 6 0.4093 0.3697 0.440 0.004 0.000 0.000 0.004 0.552
#> GSM648647 4 0.4288 0.6257 0.000 0.276 0.000 0.684 0.012 0.028
#> GSM648667 4 0.3219 0.7345 0.000 0.192 0.000 0.792 0.012 0.004
#> GSM648695 2 0.4535 0.4929 0.000 0.644 0.000 0.296 0.060 0.000
#> GSM648704 4 0.0000 0.8439 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM648706 2 0.5387 0.3956 0.020 0.624 0.000 0.024 0.048 0.284
#> GSM648593 2 0.4470 0.6063 0.000 0.604 0.000 0.040 0.000 0.356
#> GSM648594 6 0.3745 0.7783 0.240 0.028 0.000 0.000 0.000 0.732
#> GSM648600 2 0.4944 0.5405 0.036 0.680 0.000 0.000 0.060 0.224
#> GSM648621 1 0.4058 0.5583 0.708 0.016 0.000 0.000 0.016 0.260
#> GSM648622 1 0.0000 0.7801 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM648623 1 0.0000 0.7801 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM648636 2 0.4775 0.4457 0.016 0.528 0.000 0.024 0.000 0.432
#> GSM648655 2 0.4779 0.4364 0.016 0.524 0.000 0.024 0.000 0.436
#> GSM648661 3 0.0458 0.7100 0.000 0.000 0.984 0.000 0.016 0.000
#> GSM648664 5 0.3795 0.6237 0.000 0.000 0.364 0.000 0.632 0.004
#> GSM648683 5 0.2527 0.8310 0.000 0.000 0.168 0.000 0.832 0.000
#> GSM648685 5 0.2527 0.8310 0.000 0.000 0.168 0.000 0.832 0.000
#> GSM648702 2 0.4470 0.6063 0.000 0.604 0.000 0.040 0.000 0.356
#> GSM648597 6 0.4131 0.5405 0.384 0.000 0.000 0.000 0.016 0.600
#> GSM648603 1 0.2095 0.7491 0.904 0.004 0.000 0.000 0.016 0.076
#> GSM648606 5 0.2527 0.8310 0.000 0.000 0.168 0.000 0.832 0.000
#> GSM648613 5 0.2912 0.8177 0.000 0.000 0.216 0.000 0.784 0.000
#> GSM648619 3 0.3828 -0.0645 0.000 0.000 0.560 0.000 0.440 0.000
#> GSM648654 3 0.0000 0.7194 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648663 5 0.3192 0.8170 0.000 0.004 0.216 0.000 0.776 0.004
#> GSM648670 2 0.3917 0.6336 0.000 0.752 0.000 0.204 0.012 0.032
#> GSM648707 1 0.0000 0.7801 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM648615 2 0.4095 0.6100 0.000 0.724 0.000 0.216 0.060 0.000
#> GSM648643 4 0.0291 0.8428 0.000 0.000 0.000 0.992 0.004 0.004
#> GSM648650 2 0.4499 0.4708 0.000 0.636 0.000 0.324 0.012 0.028
#> GSM648656 4 0.0000 0.8439 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM648715 4 0.4215 0.6304 0.000 0.276 0.000 0.688 0.012 0.024
#> GSM648598 6 0.4509 0.6437 0.344 0.020 0.000 0.000 0.016 0.620
#> GSM648601 1 0.3780 0.5941 0.732 0.008 0.000 0.000 0.016 0.244
#> GSM648602 1 0.3354 0.6709 0.792 0.008 0.000 0.000 0.016 0.184
#> GSM648604 5 0.2941 0.8157 0.000 0.000 0.220 0.000 0.780 0.000
#> GSM648614 5 0.5941 0.2727 0.344 0.068 0.000 0.000 0.524 0.064
#> GSM648624 1 0.0000 0.7801 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM648625 2 0.3828 0.6658 0.000 0.780 0.000 0.160 0.048 0.012
#> GSM648629 3 0.3828 -0.0645 0.000 0.000 0.560 0.000 0.440 0.000
#> GSM648634 6 0.5951 0.3317 0.104 0.388 0.000 0.000 0.032 0.476
#> GSM648648 6 0.4209 0.7507 0.160 0.104 0.000 0.000 0.000 0.736
#> GSM648651 1 0.0000 0.7801 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM648657 6 0.3952 0.7795 0.212 0.052 0.000 0.000 0.000 0.736
#> GSM648660 6 0.4175 0.7754 0.240 0.028 0.000 0.000 0.016 0.716
#> GSM648697 1 0.0000 0.7801 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM648710 3 0.3756 0.0550 0.000 0.000 0.600 0.000 0.400 0.000
#> GSM648591 1 0.2982 0.6563 0.820 0.004 0.000 0.000 0.012 0.164
#> GSM648592 6 0.4847 -0.1001 0.040 0.424 0.000 0.000 0.008 0.528
#> GSM648607 3 0.3847 -0.1208 0.000 0.000 0.544 0.000 0.456 0.000
#> GSM648611 3 0.2384 0.7262 0.000 0.048 0.888 0.000 0.000 0.064
#> GSM648612 5 0.3428 0.7194 0.000 0.000 0.304 0.000 0.696 0.000
#> GSM648616 1 0.4035 0.5656 0.712 0.016 0.000 0.000 0.016 0.256
#> GSM648617 2 0.5840 -0.2202 0.076 0.444 0.000 0.000 0.040 0.440
#> GSM648626 1 0.3480 0.6535 0.776 0.008 0.000 0.000 0.016 0.200
#> GSM648711 3 0.0000 0.7194 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648712 3 0.3847 -0.1208 0.000 0.000 0.544 0.000 0.456 0.000
#> GSM648713 3 0.3847 -0.1208 0.000 0.000 0.544 0.000 0.456 0.000
#> GSM648714 5 0.6098 0.1632 0.384 0.076 0.000 0.000 0.476 0.064
#> GSM648716 3 0.0000 0.7194 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648717 5 0.2527 0.8310 0.000 0.000 0.168 0.000 0.832 0.000
#> GSM648590 4 0.4556 0.4842 0.000 0.340 0.000 0.620 0.012 0.028
#> GSM648596 4 0.0000 0.8439 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM648642 6 0.5235 0.3761 0.064 0.368 0.000 0.000 0.016 0.552
#> GSM648696 2 0.3918 0.6667 0.000 0.776 0.000 0.160 0.048 0.016
#> GSM648705 2 0.4646 0.7057 0.000 0.692 0.000 0.072 0.012 0.224
#> GSM648718 2 0.4484 0.4793 0.000 0.640 0.000 0.320 0.012 0.028
#> GSM648599 1 0.3480 0.6535 0.776 0.008 0.000 0.000 0.016 0.200
#> GSM648608 5 0.3309 0.7525 0.000 0.000 0.280 0.000 0.720 0.000
#> GSM648609 5 0.2941 0.8157 0.000 0.000 0.220 0.000 0.780 0.000
#> GSM648610 5 0.2706 0.6720 0.160 0.000 0.008 0.000 0.832 0.000
#> GSM648633 2 0.4229 0.6668 0.000 0.668 0.000 0.040 0.000 0.292
#> GSM648644 4 0.0000 0.8439 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM648652 6 0.3766 0.5511 0.040 0.212 0.000 0.000 0.000 0.748
#> GSM648653 1 0.3961 0.5326 0.700 0.008 0.000 0.000 0.016 0.276
#> GSM648658 6 0.3288 0.7479 0.276 0.000 0.000 0.000 0.000 0.724
#> GSM648659 2 0.4299 0.6549 0.000 0.652 0.000 0.040 0.000 0.308
#> GSM648662 5 0.2668 0.8300 0.000 0.000 0.168 0.000 0.828 0.004
#> GSM648665 5 0.2668 0.8300 0.000 0.000 0.168 0.000 0.828 0.004
#> GSM648666 1 0.0000 0.7801 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM648680 6 0.3923 0.6008 0.060 0.192 0.000 0.000 0.000 0.748
#> GSM648684 5 0.2527 0.8310 0.000 0.000 0.168 0.000 0.832 0.000
#> GSM648709 2 0.4796 0.6161 0.024 0.720 0.000 0.012 0.060 0.184
#> GSM648719 6 0.3695 0.7768 0.244 0.024 0.000 0.000 0.000 0.732
#> GSM648627 3 0.0000 0.7194 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648637 4 0.1401 0.8326 0.000 0.004 0.000 0.948 0.028 0.020
#> GSM648638 1 0.6879 0.2635 0.464 0.196 0.000 0.000 0.256 0.084
#> GSM648641 3 0.4189 -0.0653 0.000 0.008 0.552 0.000 0.436 0.004
#> GSM648672 4 0.1265 0.8364 0.000 0.000 0.000 0.948 0.044 0.008
#> GSM648674 4 0.4017 0.7108 0.000 0.204 0.000 0.748 0.020 0.028
#> GSM648703 4 0.1382 0.8335 0.000 0.008 0.000 0.948 0.036 0.008
#> GSM648631 3 0.3563 0.7167 0.000 0.072 0.796 0.000 0.000 0.132
#> GSM648669 6 0.3788 0.7413 0.280 0.004 0.000 0.000 0.012 0.704
#> GSM648671 6 0.3788 0.7413 0.280 0.004 0.000 0.000 0.012 0.704
#> GSM648678 4 0.1124 0.8371 0.000 0.000 0.000 0.956 0.036 0.008
#> GSM648679 4 0.4308 0.6189 0.000 0.280 0.000 0.680 0.012 0.028
#> GSM648681 2 0.4685 0.6689 0.000 0.648 0.000 0.048 0.012 0.292
#> GSM648686 3 0.2685 0.7252 0.000 0.060 0.868 0.000 0.000 0.072
#> GSM648689 3 0.0914 0.7233 0.000 0.016 0.968 0.000 0.000 0.016
#> GSM648690 3 0.3586 0.7163 0.000 0.080 0.796 0.000 0.000 0.124
#> GSM648691 3 0.3563 0.7167 0.000 0.072 0.796 0.000 0.000 0.132
#> GSM648693 3 0.3563 0.7167 0.000 0.072 0.796 0.000 0.000 0.132
#> GSM648700 6 0.4255 0.7734 0.196 0.064 0.000 0.000 0.008 0.732
#> GSM648630 3 0.3563 0.7167 0.000 0.072 0.796 0.000 0.000 0.132
#> GSM648632 3 0.3563 0.7167 0.000 0.072 0.796 0.000 0.000 0.132
#> GSM648639 2 0.4841 0.5632 0.036 0.696 0.000 0.000 0.060 0.208
#> GSM648640 3 0.0291 0.7210 0.000 0.004 0.992 0.000 0.000 0.004
#> GSM648668 4 0.1265 0.8364 0.000 0.000 0.000 0.948 0.044 0.008
#> GSM648676 2 0.5204 0.6858 0.000 0.660 0.000 0.128 0.020 0.192
#> GSM648692 3 0.3563 0.7167 0.000 0.072 0.796 0.000 0.000 0.132
#> GSM648694 3 0.3563 0.7167 0.000 0.072 0.796 0.000 0.000 0.132
#> GSM648699 6 0.4182 0.7756 0.244 0.032 0.000 0.000 0.012 0.712
#> GSM648701 2 0.5248 0.6864 0.000 0.652 0.000 0.124 0.020 0.204
#> GSM648673 6 0.4182 0.7756 0.244 0.032 0.000 0.000 0.012 0.712
#> GSM648677 4 0.1391 0.8341 0.000 0.000 0.000 0.944 0.040 0.016
#> GSM648687 1 0.0146 0.7793 0.996 0.000 0.000 0.000 0.004 0.000
#> GSM648688 3 0.3563 0.7167 0.000 0.072 0.796 0.000 0.000 0.132
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) development.stage(p) other(p) k
#> ATC:kmeans 130 0.346667 0.2532 3.72e-05 2
#> ATC:kmeans 127 0.291116 0.0961 2.51e-09 3
#> ATC:kmeans 108 0.501680 0.1044 4.87e-08 4
#> ATC:kmeans 93 0.000127 0.0531 8.65e-10 5
#> ATC:kmeans 107 0.000122 0.2790 8.77e-10 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 51941 rows and 130 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 6.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.959 0.984 0.4828 0.516 0.516
#> 3 3 0.979 0.953 0.980 0.2631 0.851 0.717
#> 4 4 0.977 0.944 0.949 0.1452 0.898 0.742
#> 5 5 0.933 0.833 0.915 0.0391 0.972 0.908
#> 6 6 0.925 0.878 0.946 0.0339 0.939 0.793
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 6
#> attr(,"optional")
#> [1] 2 3 4 5
There is also optional best \(k\) = 2 3 4 5 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
#> GSM648605 1 0.0000 0.97602 1.000 0.000
#> GSM648618 1 0.0000 0.97602 1.000 0.000
#> GSM648620 2 0.0000 0.98876 0.000 1.000
#> GSM648646 2 0.0000 0.98876 0.000 1.000
#> GSM648649 2 0.0000 0.98876 0.000 1.000
#> GSM648675 2 0.0000 0.98876 0.000 1.000
#> GSM648682 2 0.0000 0.98876 0.000 1.000
#> GSM648698 2 0.0000 0.98876 0.000 1.000
#> GSM648708 2 0.0000 0.98876 0.000 1.000
#> GSM648628 1 0.0000 0.97602 1.000 0.000
#> GSM648595 2 0.0000 0.98876 0.000 1.000
#> GSM648635 2 0.0000 0.98876 0.000 1.000
#> GSM648645 2 0.0000 0.98876 0.000 1.000
#> GSM648647 2 0.0000 0.98876 0.000 1.000
#> GSM648667 2 0.0000 0.98876 0.000 1.000
#> GSM648695 2 0.0000 0.98876 0.000 1.000
#> GSM648704 2 0.0000 0.98876 0.000 1.000
#> GSM648706 2 0.0000 0.98876 0.000 1.000
#> GSM648593 2 0.0000 0.98876 0.000 1.000
#> GSM648594 2 0.0000 0.98876 0.000 1.000
#> GSM648600 2 0.0000 0.98876 0.000 1.000
#> GSM648621 2 0.0000 0.98876 0.000 1.000
#> GSM648622 2 0.8267 0.64262 0.260 0.740
#> GSM648623 2 0.9170 0.49648 0.332 0.668
#> GSM648636 2 0.0000 0.98876 0.000 1.000
#> GSM648655 2 0.0000 0.98876 0.000 1.000
#> GSM648661 1 0.0000 0.97602 1.000 0.000
#> GSM648664 1 0.0000 0.97602 1.000 0.000
#> GSM648683 1 0.0000 0.97602 1.000 0.000
#> GSM648685 1 0.0000 0.97602 1.000 0.000
#> GSM648702 2 0.0000 0.98876 0.000 1.000
#> GSM648597 2 0.0000 0.98876 0.000 1.000
#> GSM648603 2 0.0000 0.98876 0.000 1.000
#> GSM648606 1 0.0000 0.97602 1.000 0.000
#> GSM648613 1 0.0000 0.97602 1.000 0.000
#> GSM648619 1 0.0000 0.97602 1.000 0.000
#> GSM648654 1 0.0000 0.97602 1.000 0.000
#> GSM648663 1 0.0000 0.97602 1.000 0.000
#> GSM648670 2 0.0000 0.98876 0.000 1.000
#> GSM648707 1 0.0000 0.97602 1.000 0.000
#> GSM648615 2 0.0000 0.98876 0.000 1.000
#> GSM648643 2 0.0000 0.98876 0.000 1.000
#> GSM648650 2 0.0000 0.98876 0.000 1.000
#> GSM648656 2 0.0000 0.98876 0.000 1.000
#> GSM648715 2 0.0000 0.98876 0.000 1.000
#> GSM648598 2 0.0000 0.98876 0.000 1.000
#> GSM648601 2 0.0000 0.98876 0.000 1.000
#> GSM648602 2 0.0000 0.98876 0.000 1.000
#> GSM648604 1 0.0000 0.97602 1.000 0.000
#> GSM648614 1 0.0000 0.97602 1.000 0.000
#> GSM648624 1 1.0000 0.00774 0.504 0.496
#> GSM648625 2 0.0000 0.98876 0.000 1.000
#> GSM648629 1 0.0000 0.97602 1.000 0.000
#> GSM648634 2 0.0000 0.98876 0.000 1.000
#> GSM648648 2 0.0000 0.98876 0.000 1.000
#> GSM648651 1 0.0000 0.97602 1.000 0.000
#> GSM648657 2 0.0000 0.98876 0.000 1.000
#> GSM648660 2 0.0000 0.98876 0.000 1.000
#> GSM648697 2 0.7950 0.67784 0.240 0.760
#> GSM648710 1 0.0000 0.97602 1.000 0.000
#> GSM648591 1 0.9754 0.30477 0.592 0.408
#> GSM648592 2 0.0000 0.98876 0.000 1.000
#> GSM648607 1 0.0000 0.97602 1.000 0.000
#> GSM648611 1 0.0000 0.97602 1.000 0.000
#> GSM648612 1 0.0000 0.97602 1.000 0.000
#> GSM648616 2 0.0000 0.98876 0.000 1.000
#> GSM648617 2 0.0000 0.98876 0.000 1.000
#> GSM648626 2 0.0000 0.98876 0.000 1.000
#> GSM648711 1 0.0000 0.97602 1.000 0.000
#> GSM648712 1 0.0000 0.97602 1.000 0.000
#> GSM648713 1 0.0000 0.97602 1.000 0.000
#> GSM648714 1 0.0000 0.97602 1.000 0.000
#> GSM648716 1 0.0000 0.97602 1.000 0.000
#> GSM648717 1 0.0000 0.97602 1.000 0.000
#> GSM648590 2 0.0000 0.98876 0.000 1.000
#> GSM648596 2 0.0000 0.98876 0.000 1.000
#> GSM648642 2 0.0000 0.98876 0.000 1.000
#> GSM648696 2 0.0000 0.98876 0.000 1.000
#> GSM648705 2 0.0000 0.98876 0.000 1.000
#> GSM648718 2 0.0000 0.98876 0.000 1.000
#> GSM648599 2 0.0000 0.98876 0.000 1.000
#> GSM648608 1 0.0000 0.97602 1.000 0.000
#> GSM648609 1 0.0000 0.97602 1.000 0.000
#> GSM648610 1 0.0000 0.97602 1.000 0.000
#> GSM648633 2 0.0000 0.98876 0.000 1.000
#> GSM648644 2 0.0000 0.98876 0.000 1.000
#> GSM648652 2 0.0000 0.98876 0.000 1.000
#> GSM648653 2 0.0000 0.98876 0.000 1.000
#> GSM648658 2 0.0000 0.98876 0.000 1.000
#> GSM648659 2 0.0000 0.98876 0.000 1.000
#> GSM648662 1 0.0000 0.97602 1.000 0.000
#> GSM648665 1 0.0000 0.97602 1.000 0.000
#> GSM648666 1 0.0000 0.97602 1.000 0.000
#> GSM648680 2 0.0000 0.98876 0.000 1.000
#> GSM648684 1 0.0000 0.97602 1.000 0.000
#> GSM648709 2 0.0000 0.98876 0.000 1.000
#> GSM648719 2 0.0000 0.98876 0.000 1.000
#> GSM648627 1 0.0000 0.97602 1.000 0.000
#> GSM648637 2 0.0000 0.98876 0.000 1.000
#> GSM648638 1 0.8555 0.60677 0.720 0.280
#> GSM648641 1 0.0000 0.97602 1.000 0.000
#> GSM648672 2 0.0000 0.98876 0.000 1.000
#> GSM648674 2 0.0000 0.98876 0.000 1.000
#> GSM648703 2 0.0000 0.98876 0.000 1.000
#> GSM648631 1 0.0000 0.97602 1.000 0.000
#> GSM648669 2 0.0000 0.98876 0.000 1.000
#> GSM648671 2 0.0000 0.98876 0.000 1.000
#> GSM648678 2 0.0000 0.98876 0.000 1.000
#> GSM648679 2 0.0000 0.98876 0.000 1.000
#> GSM648681 2 0.0000 0.98876 0.000 1.000
#> GSM648686 1 0.0000 0.97602 1.000 0.000
#> GSM648689 1 0.0000 0.97602 1.000 0.000
#> GSM648690 1 0.0000 0.97602 1.000 0.000
#> GSM648691 1 0.0000 0.97602 1.000 0.000
#> GSM648693 1 0.0000 0.97602 1.000 0.000
#> GSM648700 2 0.0000 0.98876 0.000 1.000
#> GSM648630 1 0.0000 0.97602 1.000 0.000
#> GSM648632 1 0.0000 0.97602 1.000 0.000
#> GSM648639 2 0.0000 0.98876 0.000 1.000
#> GSM648640 1 0.0000 0.97602 1.000 0.000
#> GSM648668 2 0.0000 0.98876 0.000 1.000
#> GSM648676 2 0.0000 0.98876 0.000 1.000
#> GSM648692 1 0.0000 0.97602 1.000 0.000
#> GSM648694 1 0.0000 0.97602 1.000 0.000
#> GSM648699 2 0.0000 0.98876 0.000 1.000
#> GSM648701 2 0.0000 0.98876 0.000 1.000
#> GSM648673 2 0.0000 0.98876 0.000 1.000
#> GSM648677 2 0.0000 0.98876 0.000 1.000
#> GSM648687 1 0.0376 0.97234 0.996 0.004
#> GSM648688 1 0.0000 0.97602 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM648605 3 0.0000 0.9941 0.000 0.000 1.0
#> GSM648618 1 0.0000 0.9277 1.000 0.000 0.0
#> GSM648620 2 0.0000 0.9804 0.000 1.000 0.0
#> GSM648646 2 0.0000 0.9804 0.000 1.000 0.0
#> GSM648649 2 0.0000 0.9804 0.000 1.000 0.0
#> GSM648675 2 0.0000 0.9804 0.000 1.000 0.0
#> GSM648682 2 0.0000 0.9804 0.000 1.000 0.0
#> GSM648698 2 0.0000 0.9804 0.000 1.000 0.0
#> GSM648708 2 0.0000 0.9804 0.000 1.000 0.0
#> GSM648628 3 0.0000 0.9941 0.000 0.000 1.0
#> GSM648595 2 0.0000 0.9804 0.000 1.000 0.0
#> GSM648635 2 0.0000 0.9804 0.000 1.000 0.0
#> GSM648645 1 0.4702 0.7785 0.788 0.212 0.0
#> GSM648647 2 0.0000 0.9804 0.000 1.000 0.0
#> GSM648667 2 0.0000 0.9804 0.000 1.000 0.0
#> GSM648695 2 0.0000 0.9804 0.000 1.000 0.0
#> GSM648704 2 0.0000 0.9804 0.000 1.000 0.0
#> GSM648706 2 0.0000 0.9804 0.000 1.000 0.0
#> GSM648593 2 0.0000 0.9804 0.000 1.000 0.0
#> GSM648594 2 0.0424 0.9727 0.008 0.992 0.0
#> GSM648600 2 0.0000 0.9804 0.000 1.000 0.0
#> GSM648621 2 0.6267 0.0822 0.452 0.548 0.0
#> GSM648622 1 0.0000 0.9277 1.000 0.000 0.0
#> GSM648623 1 0.0000 0.9277 1.000 0.000 0.0
#> GSM648636 2 0.0000 0.9804 0.000 1.000 0.0
#> GSM648655 2 0.0000 0.9804 0.000 1.000 0.0
#> GSM648661 3 0.0000 0.9941 0.000 0.000 1.0
#> GSM648664 3 0.0000 0.9941 0.000 0.000 1.0
#> GSM648683 3 0.0000 0.9941 0.000 0.000 1.0
#> GSM648685 3 0.0000 0.9941 0.000 0.000 1.0
#> GSM648702 2 0.0000 0.9804 0.000 1.000 0.0
#> GSM648597 1 0.4887 0.7588 0.772 0.228 0.0
#> GSM648603 1 0.0000 0.9277 1.000 0.000 0.0
#> GSM648606 3 0.0000 0.9941 0.000 0.000 1.0
#> GSM648613 3 0.0000 0.9941 0.000 0.000 1.0
#> GSM648619 3 0.0000 0.9941 0.000 0.000 1.0
#> GSM648654 3 0.0000 0.9941 0.000 0.000 1.0
#> GSM648663 3 0.0000 0.9941 0.000 0.000 1.0
#> GSM648670 2 0.0000 0.9804 0.000 1.000 0.0
#> GSM648707 1 0.0000 0.9277 1.000 0.000 0.0
#> GSM648615 2 0.0000 0.9804 0.000 1.000 0.0
#> GSM648643 2 0.0000 0.9804 0.000 1.000 0.0
#> GSM648650 2 0.0000 0.9804 0.000 1.000 0.0
#> GSM648656 2 0.0000 0.9804 0.000 1.000 0.0
#> GSM648715 2 0.0000 0.9804 0.000 1.000 0.0
#> GSM648598 2 0.6079 0.3014 0.388 0.612 0.0
#> GSM648601 1 0.1031 0.9213 0.976 0.024 0.0
#> GSM648602 1 0.0000 0.9277 1.000 0.000 0.0
#> GSM648604 3 0.0000 0.9941 0.000 0.000 1.0
#> GSM648614 3 0.0000 0.9941 0.000 0.000 1.0
#> GSM648624 1 0.0000 0.9277 1.000 0.000 0.0
#> GSM648625 2 0.0000 0.9804 0.000 1.000 0.0
#> GSM648629 3 0.0000 0.9941 0.000 0.000 1.0
#> GSM648634 2 0.0000 0.9804 0.000 1.000 0.0
#> GSM648648 2 0.0000 0.9804 0.000 1.000 0.0
#> GSM648651 1 0.0000 0.9277 1.000 0.000 0.0
#> GSM648657 2 0.0000 0.9804 0.000 1.000 0.0
#> GSM648660 2 0.2356 0.9026 0.072 0.928 0.0
#> GSM648697 1 0.0000 0.9277 1.000 0.000 0.0
#> GSM648710 3 0.0000 0.9941 0.000 0.000 1.0
#> GSM648591 1 0.0000 0.9277 1.000 0.000 0.0
#> GSM648592 2 0.0000 0.9804 0.000 1.000 0.0
#> GSM648607 3 0.0000 0.9941 0.000 0.000 1.0
#> GSM648611 3 0.0000 0.9941 0.000 0.000 1.0
#> GSM648612 3 0.0000 0.9941 0.000 0.000 1.0
#> GSM648616 1 0.4504 0.7954 0.804 0.196 0.0
#> GSM648617 2 0.0000 0.9804 0.000 1.000 0.0
#> GSM648626 1 0.1031 0.9213 0.976 0.024 0.0
#> GSM648711 3 0.0000 0.9941 0.000 0.000 1.0
#> GSM648712 3 0.0000 0.9941 0.000 0.000 1.0
#> GSM648713 3 0.0000 0.9941 0.000 0.000 1.0
#> GSM648714 3 0.0000 0.9941 0.000 0.000 1.0
#> GSM648716 3 0.0000 0.9941 0.000 0.000 1.0
#> GSM648717 3 0.0000 0.9941 0.000 0.000 1.0
#> GSM648590 2 0.0000 0.9804 0.000 1.000 0.0
#> GSM648596 2 0.0000 0.9804 0.000 1.000 0.0
#> GSM648642 2 0.0000 0.9804 0.000 1.000 0.0
#> GSM648696 2 0.0000 0.9804 0.000 1.000 0.0
#> GSM648705 2 0.0000 0.9804 0.000 1.000 0.0
#> GSM648718 2 0.0000 0.9804 0.000 1.000 0.0
#> GSM648599 1 0.1031 0.9213 0.976 0.024 0.0
#> GSM648608 3 0.0000 0.9941 0.000 0.000 1.0
#> GSM648609 3 0.0000 0.9941 0.000 0.000 1.0
#> GSM648610 3 0.0000 0.9941 0.000 0.000 1.0
#> GSM648633 2 0.0000 0.9804 0.000 1.000 0.0
#> GSM648644 2 0.0000 0.9804 0.000 1.000 0.0
#> GSM648652 2 0.0000 0.9804 0.000 1.000 0.0
#> GSM648653 1 0.4887 0.7588 0.772 0.228 0.0
#> GSM648658 1 0.5178 0.7152 0.744 0.256 0.0
#> GSM648659 2 0.0000 0.9804 0.000 1.000 0.0
#> GSM648662 3 0.0000 0.9941 0.000 0.000 1.0
#> GSM648665 3 0.0000 0.9941 0.000 0.000 1.0
#> GSM648666 1 0.0000 0.9277 1.000 0.000 0.0
#> GSM648680 2 0.0000 0.9804 0.000 1.000 0.0
#> GSM648684 3 0.0000 0.9941 0.000 0.000 1.0
#> GSM648709 2 0.0000 0.9804 0.000 1.000 0.0
#> GSM648719 2 0.0000 0.9804 0.000 1.000 0.0
#> GSM648627 3 0.0000 0.9941 0.000 0.000 1.0
#> GSM648637 2 0.0000 0.9804 0.000 1.000 0.0
#> GSM648638 3 0.4555 0.7034 0.000 0.200 0.8
#> GSM648641 3 0.0000 0.9941 0.000 0.000 1.0
#> GSM648672 2 0.0000 0.9804 0.000 1.000 0.0
#> GSM648674 2 0.0000 0.9804 0.000 1.000 0.0
#> GSM648703 2 0.0000 0.9804 0.000 1.000 0.0
#> GSM648631 3 0.0000 0.9941 0.000 0.000 1.0
#> GSM648669 2 0.0237 0.9766 0.004 0.996 0.0
#> GSM648671 2 0.4842 0.6820 0.224 0.776 0.0
#> GSM648678 2 0.0000 0.9804 0.000 1.000 0.0
#> GSM648679 2 0.0000 0.9804 0.000 1.000 0.0
#> GSM648681 2 0.0000 0.9804 0.000 1.000 0.0
#> GSM648686 3 0.0000 0.9941 0.000 0.000 1.0
#> GSM648689 3 0.0000 0.9941 0.000 0.000 1.0
#> GSM648690 3 0.0000 0.9941 0.000 0.000 1.0
#> GSM648691 3 0.0000 0.9941 0.000 0.000 1.0
#> GSM648693 3 0.0000 0.9941 0.000 0.000 1.0
#> GSM648700 2 0.0000 0.9804 0.000 1.000 0.0
#> GSM648630 3 0.0000 0.9941 0.000 0.000 1.0
#> GSM648632 3 0.0000 0.9941 0.000 0.000 1.0
#> GSM648639 2 0.0000 0.9804 0.000 1.000 0.0
#> GSM648640 3 0.0000 0.9941 0.000 0.000 1.0
#> GSM648668 2 0.0000 0.9804 0.000 1.000 0.0
#> GSM648676 2 0.0000 0.9804 0.000 1.000 0.0
#> GSM648692 3 0.0000 0.9941 0.000 0.000 1.0
#> GSM648694 3 0.0000 0.9941 0.000 0.000 1.0
#> GSM648699 2 0.0000 0.9804 0.000 1.000 0.0
#> GSM648701 2 0.0000 0.9804 0.000 1.000 0.0
#> GSM648673 2 0.0000 0.9804 0.000 1.000 0.0
#> GSM648677 2 0.0000 0.9804 0.000 1.000 0.0
#> GSM648687 1 0.0000 0.9277 1.000 0.000 0.0
#> GSM648688 3 0.0000 0.9941 0.000 0.000 1.0
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM648605 3 0.2060 0.914 0.000 0.052 0.932 0.016
#> GSM648618 1 0.0000 0.948 1.000 0.000 0.000 0.000
#> GSM648620 2 0.0000 0.972 0.000 1.000 0.000 0.000
#> GSM648646 2 0.0000 0.972 0.000 1.000 0.000 0.000
#> GSM648649 2 0.0000 0.972 0.000 1.000 0.000 0.000
#> GSM648675 4 0.1118 0.942 0.000 0.036 0.000 0.964
#> GSM648682 2 0.0000 0.972 0.000 1.000 0.000 0.000
#> GSM648698 2 0.0000 0.972 0.000 1.000 0.000 0.000
#> GSM648708 2 0.0000 0.972 0.000 1.000 0.000 0.000
#> GSM648628 3 0.0000 0.987 0.000 0.000 1.000 0.000
#> GSM648595 2 0.0000 0.972 0.000 1.000 0.000 0.000
#> GSM648635 2 0.1637 0.936 0.000 0.940 0.000 0.060
#> GSM648645 4 0.0592 0.919 0.016 0.000 0.000 0.984
#> GSM648647 2 0.0000 0.972 0.000 1.000 0.000 0.000
#> GSM648667 2 0.0000 0.972 0.000 1.000 0.000 0.000
#> GSM648695 2 0.0000 0.972 0.000 1.000 0.000 0.000
#> GSM648704 2 0.0000 0.972 0.000 1.000 0.000 0.000
#> GSM648706 2 0.0592 0.962 0.000 0.984 0.000 0.016
#> GSM648593 2 0.1940 0.923 0.000 0.924 0.000 0.076
#> GSM648594 4 0.0921 0.939 0.000 0.028 0.000 0.972
#> GSM648600 2 0.0469 0.965 0.000 0.988 0.000 0.012
#> GSM648621 2 0.6514 0.114 0.408 0.516 0.000 0.076
#> GSM648622 1 0.0000 0.948 1.000 0.000 0.000 0.000
#> GSM648623 1 0.0000 0.948 1.000 0.000 0.000 0.000
#> GSM648636 2 0.1716 0.933 0.000 0.936 0.000 0.064
#> GSM648655 2 0.2011 0.919 0.000 0.920 0.000 0.080
#> GSM648661 3 0.0000 0.987 0.000 0.000 1.000 0.000
#> GSM648664 3 0.0000 0.987 0.000 0.000 1.000 0.000
#> GSM648683 3 0.0000 0.987 0.000 0.000 1.000 0.000
#> GSM648685 3 0.0000 0.987 0.000 0.000 1.000 0.000
#> GSM648702 2 0.1792 0.929 0.000 0.932 0.000 0.068
#> GSM648597 4 0.0592 0.919 0.016 0.000 0.000 0.984
#> GSM648603 1 0.2408 0.918 0.896 0.000 0.000 0.104
#> GSM648606 3 0.0000 0.987 0.000 0.000 1.000 0.000
#> GSM648613 3 0.0000 0.987 0.000 0.000 1.000 0.000
#> GSM648619 3 0.0000 0.987 0.000 0.000 1.000 0.000
#> GSM648654 3 0.0000 0.987 0.000 0.000 1.000 0.000
#> GSM648663 3 0.0000 0.987 0.000 0.000 1.000 0.000
#> GSM648670 2 0.0000 0.972 0.000 1.000 0.000 0.000
#> GSM648707 1 0.0000 0.948 1.000 0.000 0.000 0.000
#> GSM648615 2 0.0000 0.972 0.000 1.000 0.000 0.000
#> GSM648643 2 0.0000 0.972 0.000 1.000 0.000 0.000
#> GSM648650 2 0.0000 0.972 0.000 1.000 0.000 0.000
#> GSM648656 2 0.0000 0.972 0.000 1.000 0.000 0.000
#> GSM648715 2 0.0000 0.972 0.000 1.000 0.000 0.000
#> GSM648598 4 0.0657 0.930 0.004 0.012 0.000 0.984
#> GSM648601 1 0.2704 0.905 0.876 0.000 0.000 0.124
#> GSM648602 1 0.2469 0.916 0.892 0.000 0.000 0.108
#> GSM648604 3 0.0000 0.987 0.000 0.000 1.000 0.000
#> GSM648614 3 0.0000 0.987 0.000 0.000 1.000 0.000
#> GSM648624 1 0.0000 0.948 1.000 0.000 0.000 0.000
#> GSM648625 2 0.0000 0.972 0.000 1.000 0.000 0.000
#> GSM648629 3 0.0000 0.987 0.000 0.000 1.000 0.000
#> GSM648634 2 0.2149 0.912 0.000 0.912 0.000 0.088
#> GSM648648 4 0.1302 0.937 0.000 0.044 0.000 0.956
#> GSM648651 1 0.0000 0.948 1.000 0.000 0.000 0.000
#> GSM648657 4 0.1118 0.942 0.000 0.036 0.000 0.964
#> GSM648660 4 0.0592 0.933 0.000 0.016 0.000 0.984
#> GSM648697 1 0.0000 0.948 1.000 0.000 0.000 0.000
#> GSM648710 3 0.0000 0.987 0.000 0.000 1.000 0.000
#> GSM648591 4 0.2469 0.847 0.108 0.000 0.000 0.892
#> GSM648592 2 0.2281 0.904 0.000 0.904 0.000 0.096
#> GSM648607 3 0.0000 0.987 0.000 0.000 1.000 0.000
#> GSM648611 3 0.0000 0.987 0.000 0.000 1.000 0.000
#> GSM648612 3 0.0000 0.987 0.000 0.000 1.000 0.000
#> GSM648616 1 0.3863 0.863 0.828 0.028 0.000 0.144
#> GSM648617 2 0.0817 0.958 0.000 0.976 0.000 0.024
#> GSM648626 1 0.2589 0.911 0.884 0.000 0.000 0.116
#> GSM648711 3 0.0000 0.987 0.000 0.000 1.000 0.000
#> GSM648712 3 0.0000 0.987 0.000 0.000 1.000 0.000
#> GSM648713 3 0.0000 0.987 0.000 0.000 1.000 0.000
#> GSM648714 3 0.0592 0.973 0.000 0.000 0.984 0.016
#> GSM648716 3 0.0000 0.987 0.000 0.000 1.000 0.000
#> GSM648717 3 0.0000 0.987 0.000 0.000 1.000 0.000
#> GSM648590 2 0.0000 0.972 0.000 1.000 0.000 0.000
#> GSM648596 2 0.0000 0.972 0.000 1.000 0.000 0.000
#> GSM648642 2 0.0592 0.962 0.000 0.984 0.000 0.016
#> GSM648696 2 0.0000 0.972 0.000 1.000 0.000 0.000
#> GSM648705 2 0.0000 0.972 0.000 1.000 0.000 0.000
#> GSM648718 2 0.0000 0.972 0.000 1.000 0.000 0.000
#> GSM648599 1 0.2647 0.908 0.880 0.000 0.000 0.120
#> GSM648608 3 0.0000 0.987 0.000 0.000 1.000 0.000
#> GSM648609 3 0.0000 0.987 0.000 0.000 1.000 0.000
#> GSM648610 3 0.0000 0.987 0.000 0.000 1.000 0.000
#> GSM648633 2 0.0000 0.972 0.000 1.000 0.000 0.000
#> GSM648644 2 0.0000 0.972 0.000 1.000 0.000 0.000
#> GSM648652 4 0.2973 0.800 0.000 0.144 0.000 0.856
#> GSM648653 4 0.4817 0.265 0.388 0.000 0.000 0.612
#> GSM648658 4 0.0657 0.924 0.012 0.004 0.000 0.984
#> GSM648659 2 0.1716 0.933 0.000 0.936 0.000 0.064
#> GSM648662 3 0.0000 0.987 0.000 0.000 1.000 0.000
#> GSM648665 3 0.0000 0.987 0.000 0.000 1.000 0.000
#> GSM648666 1 0.0000 0.948 1.000 0.000 0.000 0.000
#> GSM648680 4 0.1389 0.933 0.000 0.048 0.000 0.952
#> GSM648684 3 0.0000 0.987 0.000 0.000 1.000 0.000
#> GSM648709 2 0.0817 0.958 0.000 0.976 0.000 0.024
#> GSM648719 4 0.1118 0.942 0.000 0.036 0.000 0.964
#> GSM648627 3 0.0000 0.987 0.000 0.000 1.000 0.000
#> GSM648637 2 0.0000 0.972 0.000 1.000 0.000 0.000
#> GSM648638 3 0.5364 0.341 0.000 0.392 0.592 0.016
#> GSM648641 3 0.0000 0.987 0.000 0.000 1.000 0.000
#> GSM648672 2 0.0000 0.972 0.000 1.000 0.000 0.000
#> GSM648674 2 0.0000 0.972 0.000 1.000 0.000 0.000
#> GSM648703 2 0.0000 0.972 0.000 1.000 0.000 0.000
#> GSM648631 3 0.0000 0.987 0.000 0.000 1.000 0.000
#> GSM648669 4 0.1302 0.937 0.000 0.044 0.000 0.956
#> GSM648671 4 0.1118 0.942 0.000 0.036 0.000 0.964
#> GSM648678 2 0.0000 0.972 0.000 1.000 0.000 0.000
#> GSM648679 2 0.0000 0.972 0.000 1.000 0.000 0.000
#> GSM648681 2 0.1716 0.933 0.000 0.936 0.000 0.064
#> GSM648686 3 0.0000 0.987 0.000 0.000 1.000 0.000
#> GSM648689 3 0.0000 0.987 0.000 0.000 1.000 0.000
#> GSM648690 3 0.0000 0.987 0.000 0.000 1.000 0.000
#> GSM648691 3 0.0000 0.987 0.000 0.000 1.000 0.000
#> GSM648693 3 0.0000 0.987 0.000 0.000 1.000 0.000
#> GSM648700 4 0.1118 0.942 0.000 0.036 0.000 0.964
#> GSM648630 3 0.0000 0.987 0.000 0.000 1.000 0.000
#> GSM648632 3 0.0000 0.987 0.000 0.000 1.000 0.000
#> GSM648639 2 0.0469 0.965 0.000 0.988 0.000 0.012
#> GSM648640 3 0.0000 0.987 0.000 0.000 1.000 0.000
#> GSM648668 2 0.0000 0.972 0.000 1.000 0.000 0.000
#> GSM648676 2 0.1637 0.936 0.000 0.940 0.000 0.060
#> GSM648692 3 0.0000 0.987 0.000 0.000 1.000 0.000
#> GSM648694 3 0.0000 0.987 0.000 0.000 1.000 0.000
#> GSM648699 4 0.1118 0.942 0.000 0.036 0.000 0.964
#> GSM648701 2 0.1474 0.941 0.000 0.948 0.000 0.052
#> GSM648673 4 0.1302 0.937 0.000 0.044 0.000 0.956
#> GSM648677 2 0.0000 0.972 0.000 1.000 0.000 0.000
#> GSM648687 1 0.0000 0.948 1.000 0.000 0.000 0.000
#> GSM648688 3 0.0000 0.987 0.000 0.000 1.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM648605 4 0.4909 0.281 0.008 0.012 0.472 0.508 0.000
#> GSM648618 5 0.4305 0.745 0.000 0.000 0.000 0.488 0.512
#> GSM648620 2 0.0000 0.929 0.000 1.000 0.000 0.000 0.000
#> GSM648646 2 0.0000 0.929 0.000 1.000 0.000 0.000 0.000
#> GSM648649 2 0.0000 0.929 0.000 1.000 0.000 0.000 0.000
#> GSM648675 1 0.0451 0.871 0.988 0.008 0.000 0.000 0.004
#> GSM648682 2 0.0000 0.929 0.000 1.000 0.000 0.000 0.000
#> GSM648698 2 0.0000 0.929 0.000 1.000 0.000 0.000 0.000
#> GSM648708 2 0.0000 0.929 0.000 1.000 0.000 0.000 0.000
#> GSM648628 3 0.0000 0.977 0.000 0.000 1.000 0.000 0.000
#> GSM648595 2 0.0000 0.929 0.000 1.000 0.000 0.000 0.000
#> GSM648635 2 0.0963 0.906 0.036 0.964 0.000 0.000 0.000
#> GSM648645 1 0.0404 0.867 0.988 0.000 0.000 0.000 0.012
#> GSM648647 2 0.0000 0.929 0.000 1.000 0.000 0.000 0.000
#> GSM648667 2 0.0000 0.929 0.000 1.000 0.000 0.000 0.000
#> GSM648695 2 0.0000 0.929 0.000 1.000 0.000 0.000 0.000
#> GSM648704 2 0.0000 0.929 0.000 1.000 0.000 0.000 0.000
#> GSM648706 4 0.4560 -0.163 0.008 0.484 0.000 0.508 0.000
#> GSM648593 2 0.1121 0.899 0.044 0.956 0.000 0.000 0.000
#> GSM648594 1 0.0404 0.867 0.988 0.000 0.000 0.000 0.012
#> GSM648600 2 0.3966 0.503 0.000 0.664 0.000 0.000 0.336
#> GSM648621 5 0.2732 0.507 0.000 0.160 0.000 0.000 0.840
#> GSM648622 5 0.4305 0.745 0.000 0.000 0.000 0.488 0.512
#> GSM648623 5 0.4305 0.745 0.000 0.000 0.000 0.488 0.512
#> GSM648636 2 0.0963 0.906 0.036 0.964 0.000 0.000 0.000
#> GSM648655 2 0.1121 0.899 0.044 0.956 0.000 0.000 0.000
#> GSM648661 3 0.0000 0.977 0.000 0.000 1.000 0.000 0.000
#> GSM648664 3 0.0000 0.977 0.000 0.000 1.000 0.000 0.000
#> GSM648683 3 0.0000 0.977 0.000 0.000 1.000 0.000 0.000
#> GSM648685 3 0.0000 0.977 0.000 0.000 1.000 0.000 0.000
#> GSM648702 2 0.1043 0.903 0.040 0.960 0.000 0.000 0.000
#> GSM648597 1 0.3452 0.682 0.756 0.000 0.000 0.000 0.244
#> GSM648603 5 0.0000 0.693 0.000 0.000 0.000 0.000 1.000
#> GSM648606 3 0.0000 0.977 0.000 0.000 1.000 0.000 0.000
#> GSM648613 3 0.0000 0.977 0.000 0.000 1.000 0.000 0.000
#> GSM648619 3 0.0000 0.977 0.000 0.000 1.000 0.000 0.000
#> GSM648654 3 0.0000 0.977 0.000 0.000 1.000 0.000 0.000
#> GSM648663 3 0.0000 0.977 0.000 0.000 1.000 0.000 0.000
#> GSM648670 2 0.0000 0.929 0.000 1.000 0.000 0.000 0.000
#> GSM648707 5 0.4305 0.745 0.000 0.000 0.000 0.488 0.512
#> GSM648615 2 0.0000 0.929 0.000 1.000 0.000 0.000 0.000
#> GSM648643 2 0.0000 0.929 0.000 1.000 0.000 0.000 0.000
#> GSM648650 2 0.0000 0.929 0.000 1.000 0.000 0.000 0.000
#> GSM648656 2 0.0000 0.929 0.000 1.000 0.000 0.000 0.000
#> GSM648715 2 0.0000 0.929 0.000 1.000 0.000 0.000 0.000
#> GSM648598 1 0.4305 0.298 0.512 0.000 0.000 0.000 0.488
#> GSM648601 5 0.0000 0.693 0.000 0.000 0.000 0.000 1.000
#> GSM648602 5 0.0000 0.693 0.000 0.000 0.000 0.000 1.000
#> GSM648604 3 0.0000 0.977 0.000 0.000 1.000 0.000 0.000
#> GSM648614 3 0.3424 0.523 0.000 0.000 0.760 0.240 0.000
#> GSM648624 5 0.4305 0.745 0.000 0.000 0.000 0.488 0.512
#> GSM648625 2 0.0000 0.929 0.000 1.000 0.000 0.000 0.000
#> GSM648629 3 0.0000 0.977 0.000 0.000 1.000 0.000 0.000
#> GSM648634 2 0.4450 0.236 0.004 0.508 0.000 0.000 0.488
#> GSM648648 1 0.0404 0.869 0.988 0.012 0.000 0.000 0.000
#> GSM648651 5 0.4305 0.745 0.000 0.000 0.000 0.488 0.512
#> GSM648657 1 0.0451 0.871 0.988 0.008 0.000 0.000 0.004
#> GSM648660 1 0.3452 0.682 0.756 0.000 0.000 0.000 0.244
#> GSM648697 5 0.4305 0.745 0.000 0.000 0.000 0.488 0.512
#> GSM648710 3 0.0000 0.977 0.000 0.000 1.000 0.000 0.000
#> GSM648591 1 0.1281 0.840 0.956 0.000 0.000 0.032 0.012
#> GSM648592 2 0.1124 0.904 0.036 0.960 0.000 0.000 0.004
#> GSM648607 3 0.0000 0.977 0.000 0.000 1.000 0.000 0.000
#> GSM648611 3 0.0000 0.977 0.000 0.000 1.000 0.000 0.000
#> GSM648612 3 0.0000 0.977 0.000 0.000 1.000 0.000 0.000
#> GSM648616 5 0.0290 0.687 0.000 0.008 0.000 0.000 0.992
#> GSM648617 2 0.4304 0.251 0.000 0.516 0.000 0.000 0.484
#> GSM648626 5 0.0000 0.693 0.000 0.000 0.000 0.000 1.000
#> GSM648711 3 0.0000 0.977 0.000 0.000 1.000 0.000 0.000
#> GSM648712 3 0.0000 0.977 0.000 0.000 1.000 0.000 0.000
#> GSM648713 3 0.0000 0.977 0.000 0.000 1.000 0.000 0.000
#> GSM648714 3 0.4278 -0.298 0.000 0.000 0.548 0.452 0.000
#> GSM648716 3 0.0000 0.977 0.000 0.000 1.000 0.000 0.000
#> GSM648717 3 0.0000 0.977 0.000 0.000 1.000 0.000 0.000
#> GSM648590 2 0.0000 0.929 0.000 1.000 0.000 0.000 0.000
#> GSM648596 2 0.0000 0.929 0.000 1.000 0.000 0.000 0.000
#> GSM648642 2 0.3700 0.587 0.008 0.752 0.000 0.240 0.000
#> GSM648696 2 0.0000 0.929 0.000 1.000 0.000 0.000 0.000
#> GSM648705 2 0.0000 0.929 0.000 1.000 0.000 0.000 0.000
#> GSM648718 2 0.0000 0.929 0.000 1.000 0.000 0.000 0.000
#> GSM648599 5 0.0000 0.693 0.000 0.000 0.000 0.000 1.000
#> GSM648608 3 0.0000 0.977 0.000 0.000 1.000 0.000 0.000
#> GSM648609 3 0.0000 0.977 0.000 0.000 1.000 0.000 0.000
#> GSM648610 3 0.0000 0.977 0.000 0.000 1.000 0.000 0.000
#> GSM648633 2 0.0000 0.929 0.000 1.000 0.000 0.000 0.000
#> GSM648644 2 0.0000 0.929 0.000 1.000 0.000 0.000 0.000
#> GSM648652 1 0.4300 0.100 0.524 0.476 0.000 0.000 0.000
#> GSM648653 5 0.2852 0.489 0.172 0.000 0.000 0.000 0.828
#> GSM648658 1 0.0404 0.867 0.988 0.000 0.000 0.000 0.012
#> GSM648659 2 0.0963 0.906 0.036 0.964 0.000 0.000 0.000
#> GSM648662 3 0.0000 0.977 0.000 0.000 1.000 0.000 0.000
#> GSM648665 3 0.0000 0.977 0.000 0.000 1.000 0.000 0.000
#> GSM648666 5 0.4305 0.745 0.000 0.000 0.000 0.488 0.512
#> GSM648680 1 0.3274 0.616 0.780 0.220 0.000 0.000 0.000
#> GSM648684 3 0.0000 0.977 0.000 0.000 1.000 0.000 0.000
#> GSM648709 2 0.4305 0.242 0.000 0.512 0.000 0.000 0.488
#> GSM648719 1 0.0290 0.871 0.992 0.008 0.000 0.000 0.000
#> GSM648627 3 0.0000 0.977 0.000 0.000 1.000 0.000 0.000
#> GSM648637 2 0.0000 0.929 0.000 1.000 0.000 0.000 0.000
#> GSM648638 4 0.5687 0.401 0.008 0.060 0.424 0.508 0.000
#> GSM648641 3 0.0000 0.977 0.000 0.000 1.000 0.000 0.000
#> GSM648672 2 0.0000 0.929 0.000 1.000 0.000 0.000 0.000
#> GSM648674 2 0.0000 0.929 0.000 1.000 0.000 0.000 0.000
#> GSM648703 2 0.0000 0.929 0.000 1.000 0.000 0.000 0.000
#> GSM648631 3 0.0000 0.977 0.000 0.000 1.000 0.000 0.000
#> GSM648669 1 0.0451 0.870 0.988 0.008 0.000 0.004 0.000
#> GSM648671 1 0.0451 0.870 0.988 0.008 0.000 0.004 0.000
#> GSM648678 2 0.0000 0.929 0.000 1.000 0.000 0.000 0.000
#> GSM648679 2 0.0000 0.929 0.000 1.000 0.000 0.000 0.000
#> GSM648681 2 0.0963 0.906 0.036 0.964 0.000 0.000 0.000
#> GSM648686 3 0.0000 0.977 0.000 0.000 1.000 0.000 0.000
#> GSM648689 3 0.0000 0.977 0.000 0.000 1.000 0.000 0.000
#> GSM648690 3 0.0000 0.977 0.000 0.000 1.000 0.000 0.000
#> GSM648691 3 0.0000 0.977 0.000 0.000 1.000 0.000 0.000
#> GSM648693 3 0.0000 0.977 0.000 0.000 1.000 0.000 0.000
#> GSM648700 1 0.0290 0.871 0.992 0.008 0.000 0.000 0.000
#> GSM648630 3 0.0000 0.977 0.000 0.000 1.000 0.000 0.000
#> GSM648632 3 0.0000 0.977 0.000 0.000 1.000 0.000 0.000
#> GSM648639 2 0.3999 0.490 0.000 0.656 0.000 0.000 0.344
#> GSM648640 3 0.0000 0.977 0.000 0.000 1.000 0.000 0.000
#> GSM648668 2 0.0000 0.929 0.000 1.000 0.000 0.000 0.000
#> GSM648676 2 0.0703 0.914 0.024 0.976 0.000 0.000 0.000
#> GSM648692 3 0.0000 0.977 0.000 0.000 1.000 0.000 0.000
#> GSM648694 3 0.0000 0.977 0.000 0.000 1.000 0.000 0.000
#> GSM648699 1 0.0451 0.870 0.988 0.008 0.000 0.004 0.000
#> GSM648701 2 0.0609 0.917 0.020 0.980 0.000 0.000 0.000
#> GSM648673 1 0.0451 0.870 0.988 0.008 0.000 0.004 0.000
#> GSM648677 2 0.0000 0.929 0.000 1.000 0.000 0.000 0.000
#> GSM648687 5 0.4305 0.745 0.000 0.000 0.000 0.488 0.512
#> GSM648688 3 0.0000 0.977 0.000 0.000 1.000 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM648605 4 0.1410 0.5685 0.000 0.008 0.044 0.944 0.004 0.000
#> GSM648618 1 0.0000 1.0000 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM648620 2 0.1059 0.9336 0.000 0.964 0.000 0.016 0.016 0.004
#> GSM648646 2 0.0000 0.9502 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM648649 2 0.0000 0.9502 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM648675 6 0.1340 0.8314 0.000 0.004 0.000 0.008 0.040 0.948
#> GSM648682 2 0.1059 0.9336 0.000 0.964 0.000 0.016 0.016 0.004
#> GSM648698 2 0.0964 0.9359 0.000 0.968 0.000 0.016 0.012 0.004
#> GSM648708 2 0.1059 0.9336 0.000 0.964 0.000 0.016 0.016 0.004
#> GSM648628 3 0.0000 0.9923 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648595 2 0.0000 0.9502 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM648635 2 0.2728 0.8482 0.000 0.864 0.000 0.004 0.032 0.100
#> GSM648645 6 0.1152 0.8318 0.000 0.000 0.000 0.004 0.044 0.952
#> GSM648647 2 0.0000 0.9502 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM648667 2 0.0000 0.9502 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM648695 2 0.0964 0.9359 0.000 0.968 0.000 0.016 0.012 0.004
#> GSM648704 2 0.0000 0.9502 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM648706 4 0.1700 0.5566 0.000 0.080 0.000 0.916 0.004 0.000
#> GSM648593 2 0.3114 0.8112 0.000 0.832 0.000 0.004 0.036 0.128
#> GSM648594 6 0.1226 0.8324 0.000 0.004 0.000 0.004 0.040 0.952
#> GSM648600 5 0.4344 0.2821 0.000 0.424 0.000 0.016 0.556 0.004
#> GSM648621 5 0.1340 0.8227 0.040 0.008 0.000 0.004 0.948 0.000
#> GSM648622 1 0.0000 1.0000 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM648623 1 0.0000 1.0000 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM648636 2 0.2101 0.8766 0.000 0.892 0.000 0.004 0.004 0.100
#> GSM648655 2 0.2445 0.8530 0.000 0.868 0.000 0.004 0.008 0.120
#> GSM648661 3 0.0000 0.9923 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648664 3 0.0000 0.9923 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648683 3 0.0000 0.9923 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648685 3 0.0000 0.9923 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648702 2 0.2261 0.8690 0.000 0.884 0.000 0.004 0.008 0.104
#> GSM648597 6 0.4093 0.2342 0.000 0.000 0.000 0.008 0.476 0.516
#> GSM648603 5 0.1663 0.8107 0.088 0.000 0.000 0.000 0.912 0.000
#> GSM648606 3 0.0000 0.9923 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648613 3 0.0000 0.9923 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648619 3 0.0000 0.9923 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648654 3 0.0000 0.9923 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648663 3 0.0000 0.9923 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648670 2 0.0000 0.9502 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM648707 1 0.0000 1.0000 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM648615 2 0.1059 0.9336 0.000 0.964 0.000 0.016 0.016 0.004
#> GSM648643 2 0.0000 0.9502 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM648650 2 0.0000 0.9502 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM648656 2 0.0000 0.9502 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM648715 2 0.0000 0.9502 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM648598 5 0.1049 0.8005 0.000 0.000 0.000 0.008 0.960 0.032
#> GSM648601 5 0.1141 0.8235 0.052 0.000 0.000 0.000 0.948 0.000
#> GSM648602 5 0.1501 0.8173 0.076 0.000 0.000 0.000 0.924 0.000
#> GSM648604 3 0.0000 0.9923 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648614 3 0.3351 0.5542 0.000 0.000 0.712 0.288 0.000 0.000
#> GSM648624 1 0.0000 1.0000 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM648625 2 0.1059 0.9336 0.000 0.964 0.000 0.016 0.016 0.004
#> GSM648629 3 0.0000 0.9923 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648634 5 0.0993 0.8049 0.000 0.000 0.000 0.012 0.964 0.024
#> GSM648648 6 0.1226 0.8325 0.000 0.004 0.000 0.004 0.040 0.952
#> GSM648651 1 0.0000 1.0000 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM648657 6 0.1226 0.8325 0.000 0.004 0.000 0.004 0.040 0.952
#> GSM648660 6 0.4095 0.2218 0.000 0.000 0.000 0.008 0.480 0.512
#> GSM648697 1 0.0000 1.0000 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM648710 3 0.0000 0.9923 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648591 6 0.3041 0.7324 0.088 0.000 0.000 0.036 0.020 0.856
#> GSM648592 2 0.3165 0.8164 0.000 0.836 0.000 0.008 0.040 0.116
#> GSM648607 3 0.0000 0.9923 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648611 3 0.0000 0.9923 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648612 3 0.0000 0.9923 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648616 5 0.1141 0.8235 0.052 0.000 0.000 0.000 0.948 0.000
#> GSM648617 5 0.2886 0.7074 0.000 0.144 0.000 0.016 0.836 0.004
#> GSM648626 5 0.1444 0.8193 0.072 0.000 0.000 0.000 0.928 0.000
#> GSM648711 3 0.0000 0.9923 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648712 3 0.0000 0.9923 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648713 3 0.0000 0.9923 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648714 4 0.3717 0.2940 0.000 0.000 0.384 0.616 0.000 0.000
#> GSM648716 3 0.0000 0.9923 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648717 3 0.0000 0.9923 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648590 2 0.0000 0.9502 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM648596 2 0.0000 0.9502 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM648642 4 0.3864 0.0699 0.000 0.480 0.000 0.520 0.000 0.000
#> GSM648696 2 0.1059 0.9336 0.000 0.964 0.000 0.016 0.016 0.004
#> GSM648705 2 0.0146 0.9490 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM648718 2 0.0000 0.9502 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM648599 5 0.1444 0.8193 0.072 0.000 0.000 0.000 0.928 0.000
#> GSM648608 3 0.0000 0.9923 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648609 3 0.0000 0.9923 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648610 3 0.0000 0.9923 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648633 2 0.0146 0.9490 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM648644 2 0.0000 0.9502 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM648652 2 0.4347 0.5410 0.000 0.668 0.000 0.004 0.040 0.288
#> GSM648653 5 0.0922 0.8053 0.004 0.000 0.000 0.004 0.968 0.024
#> GSM648658 6 0.1152 0.8318 0.000 0.000 0.000 0.004 0.044 0.952
#> GSM648659 2 0.1949 0.8865 0.000 0.904 0.000 0.004 0.004 0.088
#> GSM648662 3 0.0000 0.9923 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648665 3 0.0000 0.9923 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648666 1 0.0000 1.0000 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM648680 6 0.4863 0.1013 0.000 0.440 0.000 0.008 0.040 0.512
#> GSM648684 3 0.0000 0.9923 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648709 5 0.3622 0.5922 0.000 0.236 0.000 0.016 0.744 0.004
#> GSM648719 6 0.1226 0.8325 0.000 0.004 0.000 0.004 0.040 0.952
#> GSM648627 3 0.0000 0.9923 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648637 2 0.0964 0.9359 0.000 0.968 0.000 0.016 0.012 0.004
#> GSM648638 4 0.1485 0.5765 0.000 0.024 0.028 0.944 0.004 0.000
#> GSM648641 3 0.0000 0.9923 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648672 2 0.0000 0.9502 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM648674 2 0.0146 0.9490 0.000 0.996 0.000 0.004 0.000 0.000
#> GSM648703 2 0.0000 0.9502 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM648631 3 0.0000 0.9923 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648669 6 0.1552 0.8107 0.000 0.004 0.000 0.036 0.020 0.940
#> GSM648671 6 0.1552 0.8084 0.004 0.000 0.000 0.036 0.020 0.940
#> GSM648678 2 0.0000 0.9502 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM648679 2 0.0000 0.9502 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM648681 2 0.2213 0.8727 0.000 0.888 0.000 0.004 0.008 0.100
#> GSM648686 3 0.0000 0.9923 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648689 3 0.0000 0.9923 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648690 3 0.0000 0.9923 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648691 3 0.0000 0.9923 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648693 3 0.0000 0.9923 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648700 6 0.0508 0.8240 0.000 0.004 0.000 0.012 0.000 0.984
#> GSM648630 3 0.0000 0.9923 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648632 3 0.0000 0.9923 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648639 5 0.3855 0.5289 0.000 0.276 0.000 0.016 0.704 0.004
#> GSM648640 3 0.0000 0.9923 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648668 2 0.0000 0.9502 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM648676 2 0.1674 0.9027 0.000 0.924 0.000 0.004 0.004 0.068
#> GSM648692 3 0.0000 0.9923 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648694 3 0.0000 0.9923 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648699 6 0.1552 0.8107 0.000 0.004 0.000 0.036 0.020 0.940
#> GSM648701 2 0.0935 0.9315 0.000 0.964 0.000 0.004 0.000 0.032
#> GSM648673 6 0.1552 0.8107 0.000 0.004 0.000 0.036 0.020 0.940
#> GSM648677 2 0.0000 0.9502 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM648687 1 0.0000 1.0000 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM648688 3 0.0000 0.9923 0.000 0.000 1.000 0.000 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) development.stage(p) other(p) k
#> ATC:skmeans 127 0.4263 0.7514 4.09e-05 2
#> ATC:skmeans 128 0.0708 0.5676 5.22e-07 3
#> ATC:skmeans 127 0.3472 0.5657 4.42e-07 4
#> ATC:skmeans 119 0.2500 0.3469 7.31e-08 5
#> ATC:skmeans 124 0.4141 0.0934 4.48e-07 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["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 51941 rows and 130 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'pam' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 6.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.969 0.988 0.4531 0.549 0.549
#> 3 3 0.787 0.933 0.949 0.3999 0.790 0.626
#> 4 4 0.782 0.784 0.837 0.1035 0.940 0.838
#> 5 5 0.946 0.918 0.965 0.1054 0.834 0.534
#> 6 6 0.957 0.927 0.960 0.0306 0.951 0.794
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 6
#> attr(,"optional")
#> [1] 2 5
There is also optional best \(k\) = 2 5 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
#> GSM648605 2 0.000 0.988 0.000 1.000
#> GSM648618 1 0.980 0.271 0.584 0.416
#> GSM648620 2 0.000 0.988 0.000 1.000
#> GSM648646 2 0.000 0.988 0.000 1.000
#> GSM648649 2 0.000 0.988 0.000 1.000
#> GSM648675 2 0.000 0.988 0.000 1.000
#> GSM648682 2 0.000 0.988 0.000 1.000
#> GSM648698 2 0.000 0.988 0.000 1.000
#> GSM648708 2 0.000 0.988 0.000 1.000
#> GSM648628 1 0.000 0.985 1.000 0.000
#> GSM648595 2 0.000 0.988 0.000 1.000
#> GSM648635 2 0.000 0.988 0.000 1.000
#> GSM648645 2 0.000 0.988 0.000 1.000
#> GSM648647 2 0.000 0.988 0.000 1.000
#> GSM648667 2 0.000 0.988 0.000 1.000
#> GSM648695 2 0.000 0.988 0.000 1.000
#> GSM648704 2 0.000 0.988 0.000 1.000
#> GSM648706 2 0.000 0.988 0.000 1.000
#> GSM648593 2 0.000 0.988 0.000 1.000
#> GSM648594 2 0.000 0.988 0.000 1.000
#> GSM648600 2 0.000 0.988 0.000 1.000
#> GSM648621 2 0.000 0.988 0.000 1.000
#> GSM648622 2 0.000 0.988 0.000 1.000
#> GSM648623 2 0.000 0.988 0.000 1.000
#> GSM648636 2 0.000 0.988 0.000 1.000
#> GSM648655 2 0.000 0.988 0.000 1.000
#> GSM648661 1 0.000 0.985 1.000 0.000
#> GSM648664 1 0.000 0.985 1.000 0.000
#> GSM648683 1 0.000 0.985 1.000 0.000
#> GSM648685 1 0.000 0.985 1.000 0.000
#> GSM648702 2 0.000 0.988 0.000 1.000
#> GSM648597 2 0.000 0.988 0.000 1.000
#> GSM648603 2 0.000 0.988 0.000 1.000
#> GSM648606 1 0.000 0.985 1.000 0.000
#> GSM648613 1 0.000 0.985 1.000 0.000
#> GSM648619 1 0.000 0.985 1.000 0.000
#> GSM648654 1 0.000 0.985 1.000 0.000
#> GSM648663 1 0.000 0.985 1.000 0.000
#> GSM648670 2 0.000 0.988 0.000 1.000
#> GSM648707 2 0.994 0.160 0.456 0.544
#> GSM648615 2 0.000 0.988 0.000 1.000
#> GSM648643 2 0.000 0.988 0.000 1.000
#> GSM648650 2 0.000 0.988 0.000 1.000
#> GSM648656 2 0.000 0.988 0.000 1.000
#> GSM648715 2 0.000 0.988 0.000 1.000
#> GSM648598 2 0.000 0.988 0.000 1.000
#> GSM648601 2 0.000 0.988 0.000 1.000
#> GSM648602 2 0.000 0.988 0.000 1.000
#> GSM648604 1 0.000 0.985 1.000 0.000
#> GSM648614 1 0.000 0.985 1.000 0.000
#> GSM648624 2 0.000 0.988 0.000 1.000
#> GSM648625 2 0.000 0.988 0.000 1.000
#> GSM648629 1 0.000 0.985 1.000 0.000
#> GSM648634 2 0.000 0.988 0.000 1.000
#> GSM648648 2 0.000 0.988 0.000 1.000
#> GSM648651 2 0.671 0.784 0.176 0.824
#> GSM648657 2 0.000 0.988 0.000 1.000
#> GSM648660 2 0.000 0.988 0.000 1.000
#> GSM648697 2 0.000 0.988 0.000 1.000
#> GSM648710 1 0.000 0.985 1.000 0.000
#> GSM648591 2 0.000 0.988 0.000 1.000
#> GSM648592 2 0.000 0.988 0.000 1.000
#> GSM648607 1 0.000 0.985 1.000 0.000
#> GSM648611 1 0.000 0.985 1.000 0.000
#> GSM648612 1 0.000 0.985 1.000 0.000
#> GSM648616 2 0.000 0.988 0.000 1.000
#> GSM648617 2 0.000 0.988 0.000 1.000
#> GSM648626 2 0.000 0.988 0.000 1.000
#> GSM648711 1 0.000 0.985 1.000 0.000
#> GSM648712 1 0.000 0.985 1.000 0.000
#> GSM648713 1 0.000 0.985 1.000 0.000
#> GSM648714 1 0.760 0.711 0.780 0.220
#> GSM648716 1 0.000 0.985 1.000 0.000
#> GSM648717 1 0.000 0.985 1.000 0.000
#> GSM648590 2 0.000 0.988 0.000 1.000
#> GSM648596 2 0.000 0.988 0.000 1.000
#> GSM648642 2 0.000 0.988 0.000 1.000
#> GSM648696 2 0.000 0.988 0.000 1.000
#> GSM648705 2 0.000 0.988 0.000 1.000
#> GSM648718 2 0.000 0.988 0.000 1.000
#> GSM648599 2 0.000 0.988 0.000 1.000
#> GSM648608 1 0.000 0.985 1.000 0.000
#> GSM648609 1 0.000 0.985 1.000 0.000
#> GSM648610 1 0.000 0.985 1.000 0.000
#> GSM648633 2 0.000 0.988 0.000 1.000
#> GSM648644 2 0.000 0.988 0.000 1.000
#> GSM648652 2 0.000 0.988 0.000 1.000
#> GSM648653 2 0.000 0.988 0.000 1.000
#> GSM648658 2 0.000 0.988 0.000 1.000
#> GSM648659 2 0.000 0.988 0.000 1.000
#> GSM648662 1 0.000 0.985 1.000 0.000
#> GSM648665 1 0.000 0.985 1.000 0.000
#> GSM648666 2 0.644 0.801 0.164 0.836
#> GSM648680 2 0.000 0.988 0.000 1.000
#> GSM648684 1 0.000 0.985 1.000 0.000
#> GSM648709 2 0.000 0.988 0.000 1.000
#> GSM648719 2 0.000 0.988 0.000 1.000
#> GSM648627 1 0.000 0.985 1.000 0.000
#> GSM648637 2 0.000 0.988 0.000 1.000
#> GSM648638 2 0.000 0.988 0.000 1.000
#> GSM648641 1 0.000 0.985 1.000 0.000
#> GSM648672 2 0.000 0.988 0.000 1.000
#> GSM648674 2 0.000 0.988 0.000 1.000
#> GSM648703 2 0.000 0.988 0.000 1.000
#> GSM648631 1 0.000 0.985 1.000 0.000
#> GSM648669 2 0.000 0.988 0.000 1.000
#> GSM648671 2 0.000 0.988 0.000 1.000
#> GSM648678 2 0.000 0.988 0.000 1.000
#> GSM648679 2 0.000 0.988 0.000 1.000
#> GSM648681 2 0.000 0.988 0.000 1.000
#> GSM648686 1 0.000 0.985 1.000 0.000
#> GSM648689 1 0.000 0.985 1.000 0.000
#> GSM648690 1 0.000 0.985 1.000 0.000
#> GSM648691 1 0.000 0.985 1.000 0.000
#> GSM648693 1 0.000 0.985 1.000 0.000
#> GSM648700 2 0.000 0.988 0.000 1.000
#> GSM648630 1 0.000 0.985 1.000 0.000
#> GSM648632 1 0.000 0.985 1.000 0.000
#> GSM648639 2 0.000 0.988 0.000 1.000
#> GSM648640 1 0.000 0.985 1.000 0.000
#> GSM648668 2 0.000 0.988 0.000 1.000
#> GSM648676 2 0.000 0.988 0.000 1.000
#> GSM648692 1 0.000 0.985 1.000 0.000
#> GSM648694 1 0.000 0.985 1.000 0.000
#> GSM648699 2 0.000 0.988 0.000 1.000
#> GSM648701 2 0.000 0.988 0.000 1.000
#> GSM648673 2 0.000 0.988 0.000 1.000
#> GSM648677 2 0.000 0.988 0.000 1.000
#> GSM648687 2 0.644 0.801 0.164 0.836
#> GSM648688 1 0.000 0.985 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM648605 1 0.0000 0.891 1.000 0.000 0.000
#> GSM648618 1 0.0000 0.891 1.000 0.000 0.000
#> GSM648620 1 0.4002 0.901 0.840 0.160 0.000
#> GSM648646 2 0.0000 0.977 0.000 1.000 0.000
#> GSM648649 1 0.4796 0.843 0.780 0.220 0.000
#> GSM648675 1 0.3816 0.908 0.852 0.148 0.000
#> GSM648682 2 0.0000 0.977 0.000 1.000 0.000
#> GSM648698 2 0.0000 0.977 0.000 1.000 0.000
#> GSM648708 1 0.3879 0.905 0.848 0.152 0.000
#> GSM648628 3 0.0000 0.995 0.000 0.000 1.000
#> GSM648595 2 0.0000 0.977 0.000 1.000 0.000
#> GSM648635 1 0.3879 0.905 0.848 0.152 0.000
#> GSM648645 1 0.0000 0.891 1.000 0.000 0.000
#> GSM648647 2 0.0000 0.977 0.000 1.000 0.000
#> GSM648667 2 0.0000 0.977 0.000 1.000 0.000
#> GSM648695 2 0.0000 0.977 0.000 1.000 0.000
#> GSM648704 2 0.0000 0.977 0.000 1.000 0.000
#> GSM648706 1 0.5905 0.636 0.648 0.352 0.000
#> GSM648593 1 0.3879 0.905 0.848 0.152 0.000
#> GSM648594 1 0.3752 0.908 0.856 0.144 0.000
#> GSM648600 1 0.3816 0.908 0.852 0.148 0.000
#> GSM648621 1 0.0000 0.891 1.000 0.000 0.000
#> GSM648622 1 0.0000 0.891 1.000 0.000 0.000
#> GSM648623 1 0.0000 0.891 1.000 0.000 0.000
#> GSM648636 1 0.3816 0.908 0.852 0.148 0.000
#> GSM648655 1 0.3816 0.908 0.852 0.148 0.000
#> GSM648661 3 0.0000 0.995 0.000 0.000 1.000
#> GSM648664 3 0.0000 0.995 0.000 0.000 1.000
#> GSM648683 3 0.0000 0.995 0.000 0.000 1.000
#> GSM648685 3 0.0000 0.995 0.000 0.000 1.000
#> GSM648702 1 0.3941 0.903 0.844 0.156 0.000
#> GSM648597 1 0.0000 0.891 1.000 0.000 0.000
#> GSM648603 1 0.0000 0.891 1.000 0.000 0.000
#> GSM648606 3 0.0000 0.995 0.000 0.000 1.000
#> GSM648613 3 0.0000 0.995 0.000 0.000 1.000
#> GSM648619 3 0.0000 0.995 0.000 0.000 1.000
#> GSM648654 3 0.0000 0.995 0.000 0.000 1.000
#> GSM648663 3 0.0000 0.995 0.000 0.000 1.000
#> GSM648670 2 0.4702 0.679 0.212 0.788 0.000
#> GSM648707 1 0.0000 0.891 1.000 0.000 0.000
#> GSM648615 1 0.5733 0.691 0.676 0.324 0.000
#> GSM648643 2 0.0000 0.977 0.000 1.000 0.000
#> GSM648650 2 0.0000 0.977 0.000 1.000 0.000
#> GSM648656 2 0.0000 0.977 0.000 1.000 0.000
#> GSM648715 2 0.0000 0.977 0.000 1.000 0.000
#> GSM648598 1 0.0000 0.891 1.000 0.000 0.000
#> GSM648601 1 0.0000 0.891 1.000 0.000 0.000
#> GSM648602 1 0.0000 0.891 1.000 0.000 0.000
#> GSM648604 3 0.0000 0.995 0.000 0.000 1.000
#> GSM648614 3 0.4702 0.754 0.212 0.000 0.788
#> GSM648624 1 0.0000 0.891 1.000 0.000 0.000
#> GSM648625 1 0.4062 0.898 0.836 0.164 0.000
#> GSM648629 3 0.0000 0.995 0.000 0.000 1.000
#> GSM648634 1 0.3816 0.908 0.852 0.148 0.000
#> GSM648648 1 0.3816 0.908 0.852 0.148 0.000
#> GSM648651 1 0.0000 0.891 1.000 0.000 0.000
#> GSM648657 1 0.3816 0.908 0.852 0.148 0.000
#> GSM648660 1 0.3752 0.908 0.856 0.144 0.000
#> GSM648697 1 0.0000 0.891 1.000 0.000 0.000
#> GSM648710 3 0.0000 0.995 0.000 0.000 1.000
#> GSM648591 1 0.0000 0.891 1.000 0.000 0.000
#> GSM648592 1 0.3816 0.908 0.852 0.148 0.000
#> GSM648607 3 0.0000 0.995 0.000 0.000 1.000
#> GSM648611 3 0.0000 0.995 0.000 0.000 1.000
#> GSM648612 3 0.0000 0.995 0.000 0.000 1.000
#> GSM648616 1 0.0000 0.891 1.000 0.000 0.000
#> GSM648617 1 0.3816 0.908 0.852 0.148 0.000
#> GSM648626 1 0.0000 0.891 1.000 0.000 0.000
#> GSM648711 3 0.0000 0.995 0.000 0.000 1.000
#> GSM648712 3 0.0000 0.995 0.000 0.000 1.000
#> GSM648713 3 0.0000 0.995 0.000 0.000 1.000
#> GSM648714 1 0.0000 0.891 1.000 0.000 0.000
#> GSM648716 3 0.0000 0.995 0.000 0.000 1.000
#> GSM648717 3 0.0000 0.995 0.000 0.000 1.000
#> GSM648590 2 0.0000 0.977 0.000 1.000 0.000
#> GSM648596 2 0.0000 0.977 0.000 1.000 0.000
#> GSM648642 1 0.3816 0.908 0.852 0.148 0.000
#> GSM648696 1 0.4002 0.901 0.840 0.160 0.000
#> GSM648705 1 0.4235 0.888 0.824 0.176 0.000
#> GSM648718 2 0.0000 0.977 0.000 1.000 0.000
#> GSM648599 1 0.0000 0.891 1.000 0.000 0.000
#> GSM648608 3 0.0000 0.995 0.000 0.000 1.000
#> GSM648609 3 0.0000 0.995 0.000 0.000 1.000
#> GSM648610 3 0.0237 0.991 0.004 0.000 0.996
#> GSM648633 1 0.3941 0.903 0.844 0.156 0.000
#> GSM648644 2 0.0000 0.977 0.000 1.000 0.000
#> GSM648652 1 0.3816 0.908 0.852 0.148 0.000
#> GSM648653 1 0.0000 0.891 1.000 0.000 0.000
#> GSM648658 1 0.0000 0.891 1.000 0.000 0.000
#> GSM648659 1 0.4002 0.901 0.840 0.160 0.000
#> GSM648662 3 0.0000 0.995 0.000 0.000 1.000
#> GSM648665 3 0.0000 0.995 0.000 0.000 1.000
#> GSM648666 1 0.0000 0.891 1.000 0.000 0.000
#> GSM648680 1 0.3816 0.908 0.852 0.148 0.000
#> GSM648684 3 0.0000 0.995 0.000 0.000 1.000
#> GSM648709 1 0.3816 0.908 0.852 0.148 0.000
#> GSM648719 1 0.3192 0.905 0.888 0.112 0.000
#> GSM648627 3 0.0000 0.995 0.000 0.000 1.000
#> GSM648637 2 0.0000 0.977 0.000 1.000 0.000
#> GSM648638 1 0.0000 0.891 1.000 0.000 0.000
#> GSM648641 3 0.0000 0.995 0.000 0.000 1.000
#> GSM648672 2 0.0000 0.977 0.000 1.000 0.000
#> GSM648674 2 0.0000 0.977 0.000 1.000 0.000
#> GSM648703 2 0.0000 0.977 0.000 1.000 0.000
#> GSM648631 3 0.0000 0.995 0.000 0.000 1.000
#> GSM648669 1 0.3816 0.908 0.852 0.148 0.000
#> GSM648671 1 0.0000 0.891 1.000 0.000 0.000
#> GSM648678 2 0.0000 0.977 0.000 1.000 0.000
#> GSM648679 2 0.0000 0.977 0.000 1.000 0.000
#> GSM648681 1 0.4002 0.901 0.840 0.160 0.000
#> GSM648686 3 0.0000 0.995 0.000 0.000 1.000
#> GSM648689 3 0.0000 0.995 0.000 0.000 1.000
#> GSM648690 3 0.0000 0.995 0.000 0.000 1.000
#> GSM648691 3 0.0000 0.995 0.000 0.000 1.000
#> GSM648693 3 0.0000 0.995 0.000 0.000 1.000
#> GSM648700 1 0.3816 0.908 0.852 0.148 0.000
#> GSM648630 3 0.0000 0.995 0.000 0.000 1.000
#> GSM648632 3 0.0000 0.995 0.000 0.000 1.000
#> GSM648639 1 0.3816 0.908 0.852 0.148 0.000
#> GSM648640 3 0.0000 0.995 0.000 0.000 1.000
#> GSM648668 2 0.0000 0.977 0.000 1.000 0.000
#> GSM648676 2 0.5327 0.550 0.272 0.728 0.000
#> GSM648692 3 0.0000 0.995 0.000 0.000 1.000
#> GSM648694 3 0.0000 0.995 0.000 0.000 1.000
#> GSM648699 1 0.3816 0.908 0.852 0.148 0.000
#> GSM648701 1 0.5431 0.757 0.716 0.284 0.000
#> GSM648673 1 0.3816 0.908 0.852 0.148 0.000
#> GSM648677 2 0.0000 0.977 0.000 1.000 0.000
#> GSM648687 1 0.0000 0.891 1.000 0.000 0.000
#> GSM648688 3 0.0000 0.995 0.000 0.000 1.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM648605 1 0.0000 0.7166 1.000 0.000 0.000 0.000
#> GSM648618 1 0.3444 0.4874 0.816 0.000 0.000 0.184
#> GSM648620 1 0.5150 0.7865 0.596 0.008 0.396 0.000
#> GSM648646 2 0.0000 0.8238 0.000 1.000 0.000 0.000
#> GSM648649 1 0.5150 0.7865 0.596 0.008 0.396 0.000
#> GSM648675 1 0.4843 0.7908 0.604 0.000 0.396 0.000
#> GSM648682 2 0.0000 0.8238 0.000 1.000 0.000 0.000
#> GSM648698 2 0.4843 0.6143 0.000 0.604 0.396 0.000
#> GSM648708 1 0.5016 0.7888 0.600 0.004 0.396 0.000
#> GSM648628 3 0.4843 0.8732 0.000 0.000 0.604 0.396
#> GSM648595 2 0.4356 0.6936 0.000 0.708 0.292 0.000
#> GSM648635 1 0.5016 0.7888 0.600 0.004 0.396 0.000
#> GSM648645 1 0.0000 0.7166 1.000 0.000 0.000 0.000
#> GSM648647 2 0.4624 0.6616 0.000 0.660 0.340 0.000
#> GSM648667 2 0.0000 0.8238 0.000 1.000 0.000 0.000
#> GSM648695 2 0.0000 0.8238 0.000 1.000 0.000 0.000
#> GSM648704 2 0.0000 0.8238 0.000 1.000 0.000 0.000
#> GSM648706 1 0.7609 0.5036 0.404 0.200 0.396 0.000
#> GSM648593 1 0.5016 0.7888 0.600 0.004 0.396 0.000
#> GSM648594 1 0.4804 0.7898 0.616 0.000 0.384 0.000
#> GSM648600 1 0.4843 0.7908 0.604 0.000 0.396 0.000
#> GSM648621 1 0.0000 0.7166 1.000 0.000 0.000 0.000
#> GSM648622 1 0.0000 0.7166 1.000 0.000 0.000 0.000
#> GSM648623 1 0.0000 0.7166 1.000 0.000 0.000 0.000
#> GSM648636 1 0.4843 0.7908 0.604 0.000 0.396 0.000
#> GSM648655 1 0.4843 0.7908 0.604 0.000 0.396 0.000
#> GSM648661 4 0.0000 0.9399 0.000 0.000 0.000 1.000
#> GSM648664 4 0.0000 0.9399 0.000 0.000 0.000 1.000
#> GSM648683 4 0.0000 0.9399 0.000 0.000 0.000 1.000
#> GSM648685 4 0.0000 0.9399 0.000 0.000 0.000 1.000
#> GSM648702 1 0.5150 0.7865 0.596 0.008 0.396 0.000
#> GSM648597 1 0.0000 0.7166 1.000 0.000 0.000 0.000
#> GSM648603 1 0.0000 0.7166 1.000 0.000 0.000 0.000
#> GSM648606 4 0.0000 0.9399 0.000 0.000 0.000 1.000
#> GSM648613 4 0.0000 0.9399 0.000 0.000 0.000 1.000
#> GSM648619 4 0.0000 0.9399 0.000 0.000 0.000 1.000
#> GSM648654 4 0.0921 0.9053 0.000 0.000 0.028 0.972
#> GSM648663 4 0.0000 0.9399 0.000 0.000 0.000 1.000
#> GSM648670 2 0.7648 0.0834 0.208 0.396 0.396 0.000
#> GSM648707 1 0.0000 0.7166 1.000 0.000 0.000 0.000
#> GSM648615 1 0.5571 0.7718 0.580 0.024 0.396 0.000
#> GSM648643 2 0.0000 0.8238 0.000 1.000 0.000 0.000
#> GSM648650 2 0.4843 0.6143 0.000 0.604 0.396 0.000
#> GSM648656 2 0.0000 0.8238 0.000 1.000 0.000 0.000
#> GSM648715 2 0.4250 0.7022 0.000 0.724 0.276 0.000
#> GSM648598 1 0.0000 0.7166 1.000 0.000 0.000 0.000
#> GSM648601 1 0.0000 0.7166 1.000 0.000 0.000 0.000
#> GSM648602 1 0.0000 0.7166 1.000 0.000 0.000 0.000
#> GSM648604 4 0.0000 0.9399 0.000 0.000 0.000 1.000
#> GSM648614 4 0.4843 0.3149 0.396 0.000 0.000 0.604
#> GSM648624 1 0.0000 0.7166 1.000 0.000 0.000 0.000
#> GSM648625 1 0.5150 0.7865 0.596 0.008 0.396 0.000
#> GSM648629 4 0.0000 0.9399 0.000 0.000 0.000 1.000
#> GSM648634 1 0.4843 0.7908 0.604 0.000 0.396 0.000
#> GSM648648 1 0.4843 0.7908 0.604 0.000 0.396 0.000
#> GSM648651 1 0.0000 0.7166 1.000 0.000 0.000 0.000
#> GSM648657 1 0.4843 0.7908 0.604 0.000 0.396 0.000
#> GSM648660 1 0.4830 0.7907 0.608 0.000 0.392 0.000
#> GSM648697 1 0.0000 0.7166 1.000 0.000 0.000 0.000
#> GSM648710 4 0.0000 0.9399 0.000 0.000 0.000 1.000
#> GSM648591 1 0.0000 0.7166 1.000 0.000 0.000 0.000
#> GSM648592 1 0.4843 0.7908 0.604 0.000 0.396 0.000
#> GSM648607 4 0.0000 0.9399 0.000 0.000 0.000 1.000
#> GSM648611 3 0.4843 0.8732 0.000 0.000 0.604 0.396
#> GSM648612 4 0.0000 0.9399 0.000 0.000 0.000 1.000
#> GSM648616 1 0.0000 0.7166 1.000 0.000 0.000 0.000
#> GSM648617 1 0.4843 0.7908 0.604 0.000 0.396 0.000
#> GSM648626 1 0.0000 0.7166 1.000 0.000 0.000 0.000
#> GSM648711 4 0.1022 0.8997 0.000 0.000 0.032 0.968
#> GSM648712 4 0.0000 0.9399 0.000 0.000 0.000 1.000
#> GSM648713 4 0.0000 0.9399 0.000 0.000 0.000 1.000
#> GSM648714 4 0.4855 0.3099 0.400 0.000 0.000 0.600
#> GSM648716 4 0.1118 0.8937 0.000 0.000 0.036 0.964
#> GSM648717 4 0.0000 0.9399 0.000 0.000 0.000 1.000
#> GSM648590 2 0.4790 0.6323 0.000 0.620 0.380 0.000
#> GSM648596 2 0.0000 0.8238 0.000 1.000 0.000 0.000
#> GSM648642 1 0.4843 0.7908 0.604 0.000 0.396 0.000
#> GSM648696 1 0.5150 0.7865 0.596 0.008 0.396 0.000
#> GSM648705 1 0.5150 0.7865 0.596 0.008 0.396 0.000
#> GSM648718 2 0.4843 0.6143 0.000 0.604 0.396 0.000
#> GSM648599 1 0.0000 0.7166 1.000 0.000 0.000 0.000
#> GSM648608 4 0.0000 0.9399 0.000 0.000 0.000 1.000
#> GSM648609 4 0.0000 0.9399 0.000 0.000 0.000 1.000
#> GSM648610 4 0.0188 0.9343 0.004 0.000 0.000 0.996
#> GSM648633 1 0.5150 0.7865 0.596 0.008 0.396 0.000
#> GSM648644 2 0.0000 0.8238 0.000 1.000 0.000 0.000
#> GSM648652 1 0.4843 0.7908 0.604 0.000 0.396 0.000
#> GSM648653 1 0.0000 0.7166 1.000 0.000 0.000 0.000
#> GSM648658 1 0.0000 0.7166 1.000 0.000 0.000 0.000
#> GSM648659 1 0.5150 0.7865 0.596 0.008 0.396 0.000
#> GSM648662 4 0.0000 0.9399 0.000 0.000 0.000 1.000
#> GSM648665 4 0.0000 0.9399 0.000 0.000 0.000 1.000
#> GSM648666 1 0.0000 0.7166 1.000 0.000 0.000 0.000
#> GSM648680 1 0.4843 0.7908 0.604 0.000 0.396 0.000
#> GSM648684 4 0.0000 0.9399 0.000 0.000 0.000 1.000
#> GSM648709 1 0.4843 0.7908 0.604 0.000 0.396 0.000
#> GSM648719 1 0.4277 0.7689 0.720 0.000 0.280 0.000
#> GSM648627 4 0.0469 0.9261 0.000 0.000 0.012 0.988
#> GSM648637 2 0.0000 0.8238 0.000 1.000 0.000 0.000
#> GSM648638 1 0.0188 0.7172 0.996 0.000 0.004 0.000
#> GSM648641 4 0.0000 0.9399 0.000 0.000 0.000 1.000
#> GSM648672 2 0.0000 0.8238 0.000 1.000 0.000 0.000
#> GSM648674 2 0.0000 0.8238 0.000 1.000 0.000 0.000
#> GSM648703 2 0.0000 0.8238 0.000 1.000 0.000 0.000
#> GSM648631 3 0.4843 0.8732 0.000 0.000 0.604 0.396
#> GSM648669 1 0.4843 0.7908 0.604 0.000 0.396 0.000
#> GSM648671 1 0.0000 0.7166 1.000 0.000 0.000 0.000
#> GSM648678 2 0.0000 0.8238 0.000 1.000 0.000 0.000
#> GSM648679 2 0.4134 0.7107 0.000 0.740 0.260 0.000
#> GSM648681 1 0.5150 0.7865 0.596 0.008 0.396 0.000
#> GSM648686 3 0.4843 0.8732 0.000 0.000 0.604 0.396
#> GSM648689 3 0.4855 0.8667 0.000 0.000 0.600 0.400
#> GSM648690 3 0.4843 0.8732 0.000 0.000 0.604 0.396
#> GSM648691 3 0.4843 0.8732 0.000 0.000 0.604 0.396
#> GSM648693 3 0.4843 0.8732 0.000 0.000 0.604 0.396
#> GSM648700 1 0.4843 0.7908 0.604 0.000 0.396 0.000
#> GSM648630 3 0.4843 0.8732 0.000 0.000 0.604 0.396
#> GSM648632 3 0.4843 0.8732 0.000 0.000 0.604 0.396
#> GSM648639 1 0.4843 0.7908 0.604 0.000 0.396 0.000
#> GSM648640 4 0.0000 0.9399 0.000 0.000 0.000 1.000
#> GSM648668 2 0.0000 0.8238 0.000 1.000 0.000 0.000
#> GSM648676 3 0.7835 -0.4401 0.268 0.336 0.396 0.000
#> GSM648692 3 0.4843 0.8732 0.000 0.000 0.604 0.396
#> GSM648694 3 0.4843 0.8732 0.000 0.000 0.604 0.396
#> GSM648699 1 0.4843 0.7908 0.604 0.000 0.396 0.000
#> GSM648701 1 0.5376 0.7797 0.588 0.016 0.396 0.000
#> GSM648673 1 0.4843 0.7908 0.604 0.000 0.396 0.000
#> GSM648677 2 0.0000 0.8238 0.000 1.000 0.000 0.000
#> GSM648687 1 0.0000 0.7166 1.000 0.000 0.000 0.000
#> GSM648688 3 0.4843 0.8732 0.000 0.000 0.604 0.396
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM648605 5 0.1410 0.884 0.000 0.000 0.000 0.060 0.940
#> GSM648618 5 0.0000 0.954 0.000 0.000 0.000 0.000 1.000
#> GSM648620 4 0.0000 0.986 0.000 0.000 0.000 1.000 0.000
#> GSM648646 2 0.0000 0.947 0.000 1.000 0.000 0.000 0.000
#> GSM648649 4 0.0000 0.986 0.000 0.000 0.000 1.000 0.000
#> GSM648675 4 0.0000 0.986 0.000 0.000 0.000 1.000 0.000
#> GSM648682 2 0.0000 0.947 0.000 1.000 0.000 0.000 0.000
#> GSM648698 4 0.0000 0.986 0.000 0.000 0.000 1.000 0.000
#> GSM648708 4 0.0000 0.986 0.000 0.000 0.000 1.000 0.000
#> GSM648628 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000
#> GSM648595 4 0.2074 0.882 0.000 0.104 0.000 0.896 0.000
#> GSM648635 4 0.0000 0.986 0.000 0.000 0.000 1.000 0.000
#> GSM648645 5 0.0000 0.954 0.000 0.000 0.000 0.000 1.000
#> GSM648647 2 0.4101 0.437 0.000 0.628 0.000 0.372 0.000
#> GSM648667 2 0.0000 0.947 0.000 1.000 0.000 0.000 0.000
#> GSM648695 2 0.0000 0.947 0.000 1.000 0.000 0.000 0.000
#> GSM648704 2 0.0000 0.947 0.000 1.000 0.000 0.000 0.000
#> GSM648706 4 0.0000 0.986 0.000 0.000 0.000 1.000 0.000
#> GSM648593 4 0.0000 0.986 0.000 0.000 0.000 1.000 0.000
#> GSM648594 4 0.0404 0.975 0.000 0.000 0.000 0.988 0.012
#> GSM648600 4 0.0000 0.986 0.000 0.000 0.000 1.000 0.000
#> GSM648621 5 0.0000 0.954 0.000 0.000 0.000 0.000 1.000
#> GSM648622 5 0.0000 0.954 0.000 0.000 0.000 0.000 1.000
#> GSM648623 5 0.0000 0.954 0.000 0.000 0.000 0.000 1.000
#> GSM648636 4 0.0000 0.986 0.000 0.000 0.000 1.000 0.000
#> GSM648655 4 0.0000 0.986 0.000 0.000 0.000 1.000 0.000
#> GSM648661 1 0.3336 0.731 0.772 0.000 0.228 0.000 0.000
#> GSM648664 1 0.0000 0.912 1.000 0.000 0.000 0.000 0.000
#> GSM648683 1 0.0000 0.912 1.000 0.000 0.000 0.000 0.000
#> GSM648685 1 0.0000 0.912 1.000 0.000 0.000 0.000 0.000
#> GSM648702 4 0.0000 0.986 0.000 0.000 0.000 1.000 0.000
#> GSM648597 5 0.0000 0.954 0.000 0.000 0.000 0.000 1.000
#> GSM648603 5 0.0000 0.954 0.000 0.000 0.000 0.000 1.000
#> GSM648606 1 0.0000 0.912 1.000 0.000 0.000 0.000 0.000
#> GSM648613 1 0.0000 0.912 1.000 0.000 0.000 0.000 0.000
#> GSM648619 1 0.0000 0.912 1.000 0.000 0.000 0.000 0.000
#> GSM648654 1 0.4201 0.449 0.592 0.000 0.408 0.000 0.000
#> GSM648663 1 0.0000 0.912 1.000 0.000 0.000 0.000 0.000
#> GSM648670 4 0.0000 0.986 0.000 0.000 0.000 1.000 0.000
#> GSM648707 5 0.0000 0.954 0.000 0.000 0.000 0.000 1.000
#> GSM648615 4 0.0000 0.986 0.000 0.000 0.000 1.000 0.000
#> GSM648643 2 0.0000 0.947 0.000 1.000 0.000 0.000 0.000
#> GSM648650 4 0.0000 0.986 0.000 0.000 0.000 1.000 0.000
#> GSM648656 2 0.0000 0.947 0.000 1.000 0.000 0.000 0.000
#> GSM648715 2 0.3707 0.599 0.000 0.716 0.000 0.284 0.000
#> GSM648598 5 0.0000 0.954 0.000 0.000 0.000 0.000 1.000
#> GSM648601 5 0.0000 0.954 0.000 0.000 0.000 0.000 1.000
#> GSM648602 5 0.0000 0.954 0.000 0.000 0.000 0.000 1.000
#> GSM648604 1 0.0000 0.912 1.000 0.000 0.000 0.000 0.000
#> GSM648614 1 0.0000 0.912 1.000 0.000 0.000 0.000 0.000
#> GSM648624 5 0.0000 0.954 0.000 0.000 0.000 0.000 1.000
#> GSM648625 4 0.0000 0.986 0.000 0.000 0.000 1.000 0.000
#> GSM648629 1 0.0000 0.912 1.000 0.000 0.000 0.000 0.000
#> GSM648634 4 0.0000 0.986 0.000 0.000 0.000 1.000 0.000
#> GSM648648 4 0.0000 0.986 0.000 0.000 0.000 1.000 0.000
#> GSM648651 5 0.0000 0.954 0.000 0.000 0.000 0.000 1.000
#> GSM648657 4 0.0000 0.986 0.000 0.000 0.000 1.000 0.000
#> GSM648660 4 0.0162 0.983 0.000 0.000 0.000 0.996 0.004
#> GSM648697 5 0.0000 0.954 0.000 0.000 0.000 0.000 1.000
#> GSM648710 1 0.3074 0.763 0.804 0.000 0.196 0.000 0.000
#> GSM648591 5 0.0000 0.954 0.000 0.000 0.000 0.000 1.000
#> GSM648592 4 0.0000 0.986 0.000 0.000 0.000 1.000 0.000
#> GSM648607 1 0.0000 0.912 1.000 0.000 0.000 0.000 0.000
#> GSM648611 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000
#> GSM648612 1 0.0000 0.912 1.000 0.000 0.000 0.000 0.000
#> GSM648616 5 0.0000 0.954 0.000 0.000 0.000 0.000 1.000
#> GSM648617 4 0.0000 0.986 0.000 0.000 0.000 1.000 0.000
#> GSM648626 5 0.0000 0.954 0.000 0.000 0.000 0.000 1.000
#> GSM648711 1 0.4201 0.449 0.592 0.000 0.408 0.000 0.000
#> GSM648712 1 0.0000 0.912 1.000 0.000 0.000 0.000 0.000
#> GSM648713 1 0.0000 0.912 1.000 0.000 0.000 0.000 0.000
#> GSM648714 5 0.4294 0.110 0.468 0.000 0.000 0.000 0.532
#> GSM648716 1 0.4201 0.449 0.592 0.000 0.408 0.000 0.000
#> GSM648717 1 0.0000 0.912 1.000 0.000 0.000 0.000 0.000
#> GSM648590 4 0.2891 0.778 0.000 0.176 0.000 0.824 0.000
#> GSM648596 2 0.0000 0.947 0.000 1.000 0.000 0.000 0.000
#> GSM648642 4 0.0000 0.986 0.000 0.000 0.000 1.000 0.000
#> GSM648696 4 0.0000 0.986 0.000 0.000 0.000 1.000 0.000
#> GSM648705 4 0.0000 0.986 0.000 0.000 0.000 1.000 0.000
#> GSM648718 4 0.0000 0.986 0.000 0.000 0.000 1.000 0.000
#> GSM648599 5 0.0000 0.954 0.000 0.000 0.000 0.000 1.000
#> GSM648608 1 0.0000 0.912 1.000 0.000 0.000 0.000 0.000
#> GSM648609 1 0.0000 0.912 1.000 0.000 0.000 0.000 0.000
#> GSM648610 1 0.0000 0.912 1.000 0.000 0.000 0.000 0.000
#> GSM648633 4 0.0000 0.986 0.000 0.000 0.000 1.000 0.000
#> GSM648644 2 0.0000 0.947 0.000 1.000 0.000 0.000 0.000
#> GSM648652 4 0.0000 0.986 0.000 0.000 0.000 1.000 0.000
#> GSM648653 5 0.0000 0.954 0.000 0.000 0.000 0.000 1.000
#> GSM648658 5 0.0000 0.954 0.000 0.000 0.000 0.000 1.000
#> GSM648659 4 0.0000 0.986 0.000 0.000 0.000 1.000 0.000
#> GSM648662 1 0.0000 0.912 1.000 0.000 0.000 0.000 0.000
#> GSM648665 1 0.0000 0.912 1.000 0.000 0.000 0.000 0.000
#> GSM648666 5 0.0000 0.954 0.000 0.000 0.000 0.000 1.000
#> GSM648680 4 0.0000 0.986 0.000 0.000 0.000 1.000 0.000
#> GSM648684 1 0.0000 0.912 1.000 0.000 0.000 0.000 0.000
#> GSM648709 4 0.0000 0.986 0.000 0.000 0.000 1.000 0.000
#> GSM648719 4 0.2230 0.860 0.000 0.000 0.000 0.884 0.116
#> GSM648627 1 0.4182 0.466 0.600 0.000 0.400 0.000 0.000
#> GSM648637 2 0.0000 0.947 0.000 1.000 0.000 0.000 0.000
#> GSM648638 5 0.4182 0.328 0.000 0.000 0.000 0.400 0.600
#> GSM648641 1 0.1544 0.870 0.932 0.000 0.068 0.000 0.000
#> GSM648672 2 0.0000 0.947 0.000 1.000 0.000 0.000 0.000
#> GSM648674 2 0.0000 0.947 0.000 1.000 0.000 0.000 0.000
#> GSM648703 2 0.0000 0.947 0.000 1.000 0.000 0.000 0.000
#> GSM648631 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000
#> GSM648669 4 0.0000 0.986 0.000 0.000 0.000 1.000 0.000
#> GSM648671 5 0.0000 0.954 0.000 0.000 0.000 0.000 1.000
#> GSM648678 2 0.0000 0.947 0.000 1.000 0.000 0.000 0.000
#> GSM648679 4 0.2516 0.837 0.000 0.140 0.000 0.860 0.000
#> GSM648681 4 0.0000 0.986 0.000 0.000 0.000 1.000 0.000
#> GSM648686 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000
#> GSM648689 3 0.0162 0.995 0.004 0.000 0.996 0.000 0.000
#> GSM648690 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000
#> GSM648691 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000
#> GSM648693 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000
#> GSM648700 4 0.0000 0.986 0.000 0.000 0.000 1.000 0.000
#> GSM648630 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000
#> GSM648632 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000
#> GSM648639 4 0.0000 0.986 0.000 0.000 0.000 1.000 0.000
#> GSM648640 1 0.3366 0.726 0.768 0.000 0.232 0.000 0.000
#> GSM648668 2 0.0000 0.947 0.000 1.000 0.000 0.000 0.000
#> GSM648676 4 0.0000 0.986 0.000 0.000 0.000 1.000 0.000
#> GSM648692 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000
#> GSM648694 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000
#> GSM648699 4 0.0000 0.986 0.000 0.000 0.000 1.000 0.000
#> GSM648701 4 0.0000 0.986 0.000 0.000 0.000 1.000 0.000
#> GSM648673 4 0.0000 0.986 0.000 0.000 0.000 1.000 0.000
#> GSM648677 2 0.0000 0.947 0.000 1.000 0.000 0.000 0.000
#> GSM648687 5 0.0000 0.954 0.000 0.000 0.000 0.000 1.000
#> GSM648688 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM648605 1 0.4985 0.5296 0.628 0.000 0.012 0.288 0.072 0.000
#> GSM648618 4 0.0000 1.0000 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM648620 6 0.0000 0.9824 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM648646 2 0.0000 0.9425 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM648649 6 0.0000 0.9824 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM648675 6 0.0000 0.9824 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM648682 2 0.0000 0.9425 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM648698 6 0.0000 0.9824 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM648708 6 0.0000 0.9824 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM648628 5 0.2300 0.8670 0.000 0.000 0.144 0.000 0.856 0.000
#> GSM648595 6 0.1863 0.8848 0.000 0.104 0.000 0.000 0.000 0.896
#> GSM648635 6 0.0000 0.9824 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM648645 4 0.0000 1.0000 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM648647 2 0.3684 0.4375 0.000 0.628 0.000 0.000 0.000 0.372
#> GSM648667 2 0.0000 0.9425 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM648695 2 0.0000 0.9425 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM648704 2 0.0000 0.9425 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM648706 6 0.1802 0.9149 0.000 0.000 0.012 0.000 0.072 0.916
#> GSM648593 6 0.0000 0.9824 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM648594 6 0.0363 0.9724 0.000 0.000 0.000 0.012 0.000 0.988
#> GSM648600 6 0.0000 0.9824 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM648621 4 0.0000 1.0000 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM648622 4 0.0000 1.0000 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM648623 4 0.0000 1.0000 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM648636 6 0.0000 0.9824 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM648655 6 0.0000 0.9824 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM648661 5 0.1531 0.9198 0.004 0.000 0.068 0.000 0.928 0.000
#> GSM648664 1 0.2003 0.8379 0.884 0.000 0.000 0.000 0.116 0.000
#> GSM648683 1 0.0000 0.8803 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM648685 1 0.0000 0.8803 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM648702 6 0.0000 0.9824 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM648597 4 0.0000 1.0000 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM648603 4 0.0000 1.0000 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM648606 1 0.0000 0.8803 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM648613 1 0.1663 0.8600 0.912 0.000 0.000 0.000 0.088 0.000
#> GSM648619 5 0.1610 0.8927 0.084 0.000 0.000 0.000 0.916 0.000
#> GSM648654 5 0.1444 0.9192 0.000 0.000 0.072 0.000 0.928 0.000
#> GSM648663 1 0.1610 0.8620 0.916 0.000 0.000 0.000 0.084 0.000
#> GSM648670 6 0.0000 0.9824 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM648707 4 0.0000 1.0000 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM648615 6 0.0000 0.9824 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM648643 2 0.0000 0.9425 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM648650 6 0.0000 0.9824 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM648656 2 0.0000 0.9425 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM648715 2 0.3330 0.5623 0.000 0.716 0.000 0.000 0.000 0.284
#> GSM648598 4 0.0000 1.0000 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM648601 4 0.0000 1.0000 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM648602 4 0.0000 1.0000 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM648604 1 0.1610 0.8620 0.916 0.000 0.000 0.000 0.084 0.000
#> GSM648614 1 0.1802 0.8338 0.916 0.000 0.012 0.000 0.072 0.000
#> GSM648624 4 0.0000 1.0000 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM648625 6 0.0000 0.9824 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM648629 5 0.1610 0.8927 0.084 0.000 0.000 0.000 0.916 0.000
#> GSM648634 6 0.0000 0.9824 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM648648 6 0.0000 0.9824 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM648651 4 0.0000 1.0000 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM648657 6 0.0000 0.9824 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM648660 6 0.0146 0.9791 0.000 0.000 0.000 0.004 0.000 0.996
#> GSM648697 4 0.0000 1.0000 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM648710 5 0.1625 0.9190 0.012 0.000 0.060 0.000 0.928 0.000
#> GSM648591 4 0.0000 1.0000 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM648592 6 0.0000 0.9824 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM648607 5 0.2219 0.8510 0.136 0.000 0.000 0.000 0.864 0.000
#> GSM648611 5 0.2562 0.8391 0.000 0.000 0.172 0.000 0.828 0.000
#> GSM648612 1 0.3864 0.0275 0.520 0.000 0.000 0.000 0.480 0.000
#> GSM648616 4 0.0000 1.0000 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM648617 6 0.0000 0.9824 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM648626 4 0.0000 1.0000 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM648711 5 0.1444 0.9192 0.000 0.000 0.072 0.000 0.928 0.000
#> GSM648712 5 0.1610 0.8927 0.084 0.000 0.000 0.000 0.916 0.000
#> GSM648713 5 0.2219 0.8510 0.136 0.000 0.000 0.000 0.864 0.000
#> GSM648714 1 0.1802 0.8338 0.916 0.000 0.012 0.000 0.072 0.000
#> GSM648716 5 0.1444 0.9192 0.000 0.000 0.072 0.000 0.928 0.000
#> GSM648717 1 0.0000 0.8803 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM648590 6 0.2597 0.7810 0.000 0.176 0.000 0.000 0.000 0.824
#> GSM648596 2 0.0000 0.9425 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM648642 6 0.1802 0.9149 0.000 0.000 0.012 0.000 0.072 0.916
#> GSM648696 6 0.0000 0.9824 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM648705 6 0.0000 0.9824 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM648718 6 0.0000 0.9824 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM648599 4 0.0000 1.0000 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM648608 1 0.1663 0.8600 0.912 0.000 0.000 0.000 0.088 0.000
#> GSM648609 1 0.1663 0.8600 0.912 0.000 0.000 0.000 0.088 0.000
#> GSM648610 1 0.0000 0.8803 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM648633 6 0.0000 0.9824 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM648644 2 0.0000 0.9425 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM648652 6 0.0000 0.9824 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM648653 4 0.0000 1.0000 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM648658 4 0.0000 1.0000 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM648659 6 0.0000 0.9824 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM648662 1 0.0000 0.8803 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM648665 1 0.0000 0.8803 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM648666 4 0.0000 1.0000 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM648680 6 0.0000 0.9824 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM648684 1 0.0000 0.8803 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM648709 6 0.0000 0.9824 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM648719 6 0.2003 0.8591 0.000 0.000 0.000 0.116 0.000 0.884
#> GSM648627 5 0.1444 0.9192 0.000 0.000 0.072 0.000 0.928 0.000
#> GSM648637 2 0.0000 0.9425 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM648638 1 0.4949 0.5443 0.636 0.000 0.012 0.280 0.072 0.000
#> GSM648641 5 0.1531 0.9008 0.068 0.000 0.004 0.000 0.928 0.000
#> GSM648672 2 0.0000 0.9425 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM648674 2 0.0000 0.9425 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM648703 2 0.0000 0.9425 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM648631 3 0.0363 1.0000 0.000 0.000 0.988 0.000 0.012 0.000
#> GSM648669 6 0.0000 0.9824 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM648671 4 0.0000 1.0000 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM648678 2 0.0000 0.9425 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM648679 6 0.2260 0.8402 0.000 0.140 0.000 0.000 0.000 0.860
#> GSM648681 6 0.0000 0.9824 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM648686 5 0.3288 0.7070 0.000 0.000 0.276 0.000 0.724 0.000
#> GSM648689 5 0.2003 0.8918 0.000 0.000 0.116 0.000 0.884 0.000
#> GSM648690 3 0.0363 1.0000 0.000 0.000 0.988 0.000 0.012 0.000
#> GSM648691 3 0.0363 1.0000 0.000 0.000 0.988 0.000 0.012 0.000
#> GSM648693 3 0.0363 1.0000 0.000 0.000 0.988 0.000 0.012 0.000
#> GSM648700 6 0.0000 0.9824 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM648630 3 0.0363 1.0000 0.000 0.000 0.988 0.000 0.012 0.000
#> GSM648632 3 0.0363 1.0000 0.000 0.000 0.988 0.000 0.012 0.000
#> GSM648639 6 0.0000 0.9824 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM648640 5 0.1531 0.9198 0.004 0.000 0.068 0.000 0.928 0.000
#> GSM648668 2 0.0000 0.9425 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM648676 6 0.0000 0.9824 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM648692 3 0.0363 1.0000 0.000 0.000 0.988 0.000 0.012 0.000
#> GSM648694 3 0.0363 1.0000 0.000 0.000 0.988 0.000 0.012 0.000
#> GSM648699 6 0.0000 0.9824 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM648701 6 0.0000 0.9824 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM648673 6 0.0000 0.9824 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM648677 2 0.0000 0.9425 0.000 1.000 0.000 0.000 0.000 0.000
#> GSM648687 4 0.0000 1.0000 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM648688 3 0.0363 1.0000 0.000 0.000 0.988 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)
#> Error in mat[ceiling(1:nr/h_ratio), ceiling(1:nc/w_ratio), drop = FALSE]: subscript out of bounds
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) development.stage(p) other(p) k
#> ATC:pam 128 4.49e-01 0.231 8.41e-06 2
#> ATC:pam 130 9.46e-02 0.200 8.30e-09 3
#> ATC:pam 125 3.75e-07 0.221 1.46e-14 4
#> ATC:pam 123 5.31e-07 0.317 1.49e-10 5
#> ATC:pam 128 6.67e-07 0.236 2.25e-12 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 51941 rows and 130 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'mclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 1.000 1.000 0.4658 0.535 0.535
#> 3 3 1.000 0.991 0.982 0.4067 0.805 0.636
#> 4 4 0.893 0.860 0.940 0.0964 0.953 0.861
#> 5 5 0.790 0.704 0.830 0.0813 0.887 0.628
#> 6 6 0.771 0.682 0.813 0.0285 0.931 0.714
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 3
#> attr(,"optional")
#> [1] 2
There is also optional best \(k\) = 2 that is worth to check.
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM648605 1 0 1 1 0
#> GSM648618 2 0 1 0 1
#> GSM648620 2 0 1 0 1
#> GSM648646 2 0 1 0 1
#> GSM648649 2 0 1 0 1
#> GSM648675 2 0 1 0 1
#> GSM648682 2 0 1 0 1
#> GSM648698 2 0 1 0 1
#> GSM648708 2 0 1 0 1
#> GSM648628 1 0 1 1 0
#> GSM648595 2 0 1 0 1
#> GSM648635 2 0 1 0 1
#> GSM648645 2 0 1 0 1
#> GSM648647 2 0 1 0 1
#> GSM648667 2 0 1 0 1
#> GSM648695 2 0 1 0 1
#> GSM648704 2 0 1 0 1
#> GSM648706 1 0 1 1 0
#> GSM648593 2 0 1 0 1
#> GSM648594 2 0 1 0 1
#> GSM648600 2 0 1 0 1
#> GSM648621 2 0 1 0 1
#> GSM648622 2 0 1 0 1
#> GSM648623 2 0 1 0 1
#> GSM648636 2 0 1 0 1
#> GSM648655 2 0 1 0 1
#> GSM648661 1 0 1 1 0
#> GSM648664 1 0 1 1 0
#> GSM648683 1 0 1 1 0
#> GSM648685 1 0 1 1 0
#> GSM648702 2 0 1 0 1
#> GSM648597 2 0 1 0 1
#> GSM648603 2 0 1 0 1
#> GSM648606 1 0 1 1 0
#> GSM648613 1 0 1 1 0
#> GSM648619 1 0 1 1 0
#> GSM648654 1 0 1 1 0
#> GSM648663 1 0 1 1 0
#> GSM648670 2 0 1 0 1
#> GSM648707 2 0 1 0 1
#> GSM648615 2 0 1 0 1
#> GSM648643 2 0 1 0 1
#> GSM648650 2 0 1 0 1
#> GSM648656 2 0 1 0 1
#> GSM648715 2 0 1 0 1
#> GSM648598 2 0 1 0 1
#> GSM648601 2 0 1 0 1
#> GSM648602 2 0 1 0 1
#> GSM648604 1 0 1 1 0
#> GSM648614 1 0 1 1 0
#> GSM648624 2 0 1 0 1
#> GSM648625 2 0 1 0 1
#> GSM648629 1 0 1 1 0
#> GSM648634 2 0 1 0 1
#> GSM648648 2 0 1 0 1
#> GSM648651 2 0 1 0 1
#> GSM648657 2 0 1 0 1
#> GSM648660 2 0 1 0 1
#> GSM648697 2 0 1 0 1
#> GSM648710 1 0 1 1 0
#> GSM648591 2 0 1 0 1
#> GSM648592 2 0 1 0 1
#> GSM648607 1 0 1 1 0
#> GSM648611 1 0 1 1 0
#> GSM648612 1 0 1 1 0
#> GSM648616 2 0 1 0 1
#> GSM648617 2 0 1 0 1
#> GSM648626 2 0 1 0 1
#> GSM648711 1 0 1 1 0
#> GSM648712 1 0 1 1 0
#> GSM648713 1 0 1 1 0
#> GSM648714 1 0 1 1 0
#> GSM648716 1 0 1 1 0
#> GSM648717 1 0 1 1 0
#> GSM648590 2 0 1 0 1
#> GSM648596 2 0 1 0 1
#> GSM648642 1 0 1 1 0
#> GSM648696 2 0 1 0 1
#> GSM648705 2 0 1 0 1
#> GSM648718 2 0 1 0 1
#> GSM648599 2 0 1 0 1
#> GSM648608 1 0 1 1 0
#> GSM648609 1 0 1 1 0
#> GSM648610 1 0 1 1 0
#> GSM648633 2 0 1 0 1
#> GSM648644 2 0 1 0 1
#> GSM648652 2 0 1 0 1
#> GSM648653 2 0 1 0 1
#> GSM648658 2 0 1 0 1
#> GSM648659 2 0 1 0 1
#> GSM648662 1 0 1 1 0
#> GSM648665 1 0 1 1 0
#> GSM648666 2 0 1 0 1
#> GSM648680 2 0 1 0 1
#> GSM648684 1 0 1 1 0
#> GSM648709 2 0 1 0 1
#> GSM648719 2 0 1 0 1
#> GSM648627 1 0 1 1 0
#> GSM648637 2 0 1 0 1
#> GSM648638 1 0 1 1 0
#> GSM648641 1 0 1 1 0
#> GSM648672 2 0 1 0 1
#> GSM648674 2 0 1 0 1
#> GSM648703 2 0 1 0 1
#> GSM648631 1 0 1 1 0
#> GSM648669 2 0 1 0 1
#> GSM648671 2 0 1 0 1
#> GSM648678 2 0 1 0 1
#> GSM648679 2 0 1 0 1
#> GSM648681 2 0 1 0 1
#> GSM648686 1 0 1 1 0
#> GSM648689 1 0 1 1 0
#> GSM648690 1 0 1 1 0
#> GSM648691 1 0 1 1 0
#> GSM648693 1 0 1 1 0
#> GSM648700 2 0 1 0 1
#> GSM648630 1 0 1 1 0
#> GSM648632 1 0 1 1 0
#> GSM648639 2 0 1 0 1
#> GSM648640 1 0 1 1 0
#> GSM648668 2 0 1 0 1
#> GSM648676 2 0 1 0 1
#> GSM648692 1 0 1 1 0
#> GSM648694 1 0 1 1 0
#> GSM648699 2 0 1 0 1
#> GSM648701 2 0 1 0 1
#> GSM648673 2 0 1 0 1
#> GSM648677 2 0 1 0 1
#> GSM648687 2 0 1 0 1
#> GSM648688 1 0 1 1 0
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM648605 3 0.1529 0.981 0.040 0.000 0.960
#> GSM648618 1 0.1643 0.998 0.956 0.044 0.000
#> GSM648620 1 0.1529 0.997 0.960 0.040 0.000
#> GSM648646 2 0.0237 0.994 0.004 0.996 0.000
#> GSM648649 2 0.0000 0.995 0.000 1.000 0.000
#> GSM648675 2 0.0000 0.995 0.000 1.000 0.000
#> GSM648682 1 0.1529 0.997 0.960 0.040 0.000
#> GSM648698 1 0.1643 0.995 0.956 0.044 0.000
#> GSM648708 1 0.1529 0.997 0.960 0.040 0.000
#> GSM648628 3 0.0000 0.989 0.000 0.000 1.000
#> GSM648595 2 0.0237 0.994 0.004 0.996 0.000
#> GSM648635 2 0.0000 0.995 0.000 1.000 0.000
#> GSM648645 2 0.0000 0.995 0.000 1.000 0.000
#> GSM648647 2 0.0237 0.994 0.004 0.996 0.000
#> GSM648667 2 0.0237 0.994 0.004 0.996 0.000
#> GSM648695 1 0.1529 0.997 0.960 0.040 0.000
#> GSM648704 2 0.0237 0.994 0.004 0.996 0.000
#> GSM648706 3 0.1529 0.981 0.040 0.000 0.960
#> GSM648593 2 0.0000 0.995 0.000 1.000 0.000
#> GSM648594 2 0.0000 0.995 0.000 1.000 0.000
#> GSM648600 1 0.1529 0.997 0.960 0.040 0.000
#> GSM648621 1 0.1643 0.998 0.956 0.044 0.000
#> GSM648622 1 0.1643 0.998 0.956 0.044 0.000
#> GSM648623 1 0.1643 0.998 0.956 0.044 0.000
#> GSM648636 2 0.0000 0.995 0.000 1.000 0.000
#> GSM648655 2 0.0000 0.995 0.000 1.000 0.000
#> GSM648661 3 0.1411 0.982 0.036 0.000 0.964
#> GSM648664 3 0.1529 0.981 0.040 0.000 0.960
#> GSM648683 3 0.0000 0.989 0.000 0.000 1.000
#> GSM648685 3 0.0000 0.989 0.000 0.000 1.000
#> GSM648702 2 0.0000 0.995 0.000 1.000 0.000
#> GSM648597 2 0.3192 0.869 0.112 0.888 0.000
#> GSM648603 1 0.1643 0.998 0.956 0.044 0.000
#> GSM648606 3 0.0237 0.988 0.004 0.000 0.996
#> GSM648613 3 0.1529 0.981 0.040 0.000 0.960
#> GSM648619 3 0.0000 0.989 0.000 0.000 1.000
#> GSM648654 3 0.0892 0.986 0.020 0.000 0.980
#> GSM648663 3 0.1411 0.982 0.036 0.000 0.964
#> GSM648670 2 0.0237 0.994 0.004 0.996 0.000
#> GSM648707 1 0.1643 0.998 0.956 0.044 0.000
#> GSM648615 1 0.1529 0.997 0.960 0.040 0.000
#> GSM648643 2 0.0237 0.994 0.004 0.996 0.000
#> GSM648650 2 0.0237 0.994 0.004 0.996 0.000
#> GSM648656 2 0.0237 0.994 0.004 0.996 0.000
#> GSM648715 2 0.0237 0.994 0.004 0.996 0.000
#> GSM648598 1 0.1643 0.998 0.956 0.044 0.000
#> GSM648601 1 0.1643 0.998 0.956 0.044 0.000
#> GSM648602 1 0.1643 0.998 0.956 0.044 0.000
#> GSM648604 3 0.0000 0.989 0.000 0.000 1.000
#> GSM648614 3 0.1529 0.981 0.040 0.000 0.960
#> GSM648624 1 0.1643 0.998 0.956 0.044 0.000
#> GSM648625 1 0.1529 0.997 0.960 0.040 0.000
#> GSM648629 3 0.0000 0.989 0.000 0.000 1.000
#> GSM648634 1 0.1643 0.998 0.956 0.044 0.000
#> GSM648648 2 0.0000 0.995 0.000 1.000 0.000
#> GSM648651 1 0.1643 0.998 0.956 0.044 0.000
#> GSM648657 2 0.0000 0.995 0.000 1.000 0.000
#> GSM648660 2 0.1860 0.942 0.052 0.948 0.000
#> GSM648697 1 0.1643 0.998 0.956 0.044 0.000
#> GSM648710 3 0.0000 0.989 0.000 0.000 1.000
#> GSM648591 2 0.0000 0.995 0.000 1.000 0.000
#> GSM648592 2 0.0237 0.992 0.004 0.996 0.000
#> GSM648607 3 0.0000 0.989 0.000 0.000 1.000
#> GSM648611 3 0.0000 0.989 0.000 0.000 1.000
#> GSM648612 3 0.0000 0.989 0.000 0.000 1.000
#> GSM648616 1 0.1643 0.998 0.956 0.044 0.000
#> GSM648617 1 0.1529 0.997 0.960 0.040 0.000
#> GSM648626 1 0.1643 0.998 0.956 0.044 0.000
#> GSM648711 3 0.0000 0.989 0.000 0.000 1.000
#> GSM648712 3 0.0000 0.989 0.000 0.000 1.000
#> GSM648713 3 0.0000 0.989 0.000 0.000 1.000
#> GSM648714 3 0.1529 0.981 0.040 0.000 0.960
#> GSM648716 3 0.0000 0.989 0.000 0.000 1.000
#> GSM648717 3 0.0892 0.986 0.020 0.000 0.980
#> GSM648590 2 0.0237 0.994 0.004 0.996 0.000
#> GSM648596 2 0.0237 0.994 0.004 0.996 0.000
#> GSM648642 3 0.1529 0.981 0.040 0.000 0.960
#> GSM648696 1 0.1529 0.997 0.960 0.040 0.000
#> GSM648705 2 0.0237 0.994 0.004 0.996 0.000
#> GSM648718 2 0.0237 0.994 0.004 0.996 0.000
#> GSM648599 1 0.1643 0.998 0.956 0.044 0.000
#> GSM648608 3 0.0000 0.989 0.000 0.000 1.000
#> GSM648609 3 0.0000 0.989 0.000 0.000 1.000
#> GSM648610 3 0.0000 0.989 0.000 0.000 1.000
#> GSM648633 2 0.0237 0.994 0.004 0.996 0.000
#> GSM648644 2 0.0237 0.994 0.004 0.996 0.000
#> GSM648652 2 0.0000 0.995 0.000 1.000 0.000
#> GSM648653 1 0.1643 0.998 0.956 0.044 0.000
#> GSM648658 2 0.0000 0.995 0.000 1.000 0.000
#> GSM648659 2 0.0000 0.995 0.000 1.000 0.000
#> GSM648662 3 0.1411 0.982 0.036 0.000 0.964
#> GSM648665 3 0.1411 0.982 0.036 0.000 0.964
#> GSM648666 1 0.1643 0.998 0.956 0.044 0.000
#> GSM648680 2 0.0000 0.995 0.000 1.000 0.000
#> GSM648684 3 0.0000 0.989 0.000 0.000 1.000
#> GSM648709 1 0.1529 0.997 0.960 0.040 0.000
#> GSM648719 2 0.0000 0.995 0.000 1.000 0.000
#> GSM648627 3 0.0000 0.989 0.000 0.000 1.000
#> GSM648637 1 0.1643 0.995 0.956 0.044 0.000
#> GSM648638 3 0.1529 0.981 0.040 0.000 0.960
#> GSM648641 3 0.1411 0.982 0.036 0.000 0.964
#> GSM648672 2 0.0237 0.994 0.004 0.996 0.000
#> GSM648674 2 0.0000 0.995 0.000 1.000 0.000
#> GSM648703 2 0.0237 0.994 0.004 0.996 0.000
#> GSM648631 3 0.0000 0.989 0.000 0.000 1.000
#> GSM648669 2 0.0000 0.995 0.000 1.000 0.000
#> GSM648671 2 0.0000 0.995 0.000 1.000 0.000
#> GSM648678 2 0.0237 0.994 0.004 0.996 0.000
#> GSM648679 2 0.0237 0.994 0.004 0.996 0.000
#> GSM648681 2 0.0000 0.995 0.000 1.000 0.000
#> GSM648686 3 0.1411 0.982 0.036 0.000 0.964
#> GSM648689 3 0.1411 0.982 0.036 0.000 0.964
#> GSM648690 3 0.1411 0.982 0.036 0.000 0.964
#> GSM648691 3 0.0000 0.989 0.000 0.000 1.000
#> GSM648693 3 0.0000 0.989 0.000 0.000 1.000
#> GSM648700 2 0.0000 0.995 0.000 1.000 0.000
#> GSM648630 3 0.0000 0.989 0.000 0.000 1.000
#> GSM648632 3 0.0000 0.989 0.000 0.000 1.000
#> GSM648639 1 0.1529 0.997 0.960 0.040 0.000
#> GSM648640 3 0.1529 0.981 0.040 0.000 0.960
#> GSM648668 2 0.0237 0.994 0.004 0.996 0.000
#> GSM648676 2 0.0000 0.995 0.000 1.000 0.000
#> GSM648692 3 0.0237 0.988 0.004 0.000 0.996
#> GSM648694 3 0.0237 0.988 0.004 0.000 0.996
#> GSM648699 2 0.0000 0.995 0.000 1.000 0.000
#> GSM648701 2 0.0000 0.995 0.000 1.000 0.000
#> GSM648673 2 0.0000 0.995 0.000 1.000 0.000
#> GSM648677 2 0.0237 0.994 0.004 0.996 0.000
#> GSM648687 1 0.1643 0.998 0.956 0.044 0.000
#> GSM648688 3 0.0000 0.989 0.000 0.000 1.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM648605 4 0.1474 0.8890 0.000 0.000 0.052 0.948
#> GSM648618 1 0.0000 0.9830 1.000 0.000 0.000 0.000
#> GSM648620 1 0.0188 0.9824 0.996 0.000 0.000 0.004
#> GSM648646 2 0.3311 0.8546 0.000 0.828 0.000 0.172
#> GSM648649 2 0.0000 0.9701 0.000 1.000 0.000 0.000
#> GSM648675 2 0.0000 0.9701 0.000 1.000 0.000 0.000
#> GSM648682 1 0.0188 0.9824 0.996 0.000 0.000 0.004
#> GSM648698 1 0.0188 0.9824 0.996 0.000 0.000 0.004
#> GSM648708 1 0.0188 0.9824 0.996 0.000 0.000 0.004
#> GSM648628 3 0.0000 0.8456 0.000 0.000 1.000 0.000
#> GSM648595 2 0.0592 0.9661 0.000 0.984 0.000 0.016
#> GSM648635 2 0.0000 0.9701 0.000 1.000 0.000 0.000
#> GSM648645 2 0.0000 0.9701 0.000 1.000 0.000 0.000
#> GSM648647 2 0.1389 0.9552 0.000 0.952 0.000 0.048
#> GSM648667 2 0.1389 0.9552 0.000 0.952 0.000 0.048
#> GSM648695 1 0.0336 0.9793 0.992 0.000 0.000 0.008
#> GSM648704 2 0.3311 0.8546 0.000 0.828 0.000 0.172
#> GSM648706 4 0.1474 0.8890 0.000 0.000 0.052 0.948
#> GSM648593 2 0.0000 0.9701 0.000 1.000 0.000 0.000
#> GSM648594 2 0.0000 0.9701 0.000 1.000 0.000 0.000
#> GSM648600 1 0.0188 0.9824 0.996 0.000 0.000 0.004
#> GSM648621 1 0.0188 0.9824 0.996 0.000 0.000 0.004
#> GSM648622 1 0.0000 0.9830 1.000 0.000 0.000 0.000
#> GSM648623 1 0.0000 0.9830 1.000 0.000 0.000 0.000
#> GSM648636 2 0.0000 0.9701 0.000 1.000 0.000 0.000
#> GSM648655 2 0.0000 0.9701 0.000 1.000 0.000 0.000
#> GSM648661 3 0.4193 0.5477 0.000 0.000 0.732 0.268
#> GSM648664 3 0.4977 0.1025 0.000 0.000 0.540 0.460
#> GSM648683 3 0.0000 0.8456 0.000 0.000 1.000 0.000
#> GSM648685 3 0.0000 0.8456 0.000 0.000 1.000 0.000
#> GSM648702 2 0.0000 0.9701 0.000 1.000 0.000 0.000
#> GSM648597 2 0.2589 0.8559 0.116 0.884 0.000 0.000
#> GSM648603 1 0.0000 0.9830 1.000 0.000 0.000 0.000
#> GSM648606 4 0.2973 0.8296 0.000 0.000 0.144 0.856
#> GSM648613 4 0.3610 0.7713 0.000 0.000 0.200 0.800
#> GSM648619 3 0.0000 0.8456 0.000 0.000 1.000 0.000
#> GSM648654 3 0.1474 0.8088 0.000 0.000 0.948 0.052
#> GSM648663 3 0.4977 0.1025 0.000 0.000 0.540 0.460
#> GSM648670 2 0.0188 0.9693 0.000 0.996 0.000 0.004
#> GSM648707 1 0.0000 0.9830 1.000 0.000 0.000 0.000
#> GSM648615 1 0.0188 0.9824 0.996 0.000 0.000 0.004
#> GSM648643 2 0.1389 0.9552 0.000 0.952 0.000 0.048
#> GSM648650 2 0.0188 0.9693 0.000 0.996 0.000 0.004
#> GSM648656 2 0.3311 0.8546 0.000 0.828 0.000 0.172
#> GSM648715 2 0.1389 0.9552 0.000 0.952 0.000 0.048
#> GSM648598 1 0.0000 0.9830 1.000 0.000 0.000 0.000
#> GSM648601 1 0.0000 0.9830 1.000 0.000 0.000 0.000
#> GSM648602 1 0.0000 0.9830 1.000 0.000 0.000 0.000
#> GSM648604 3 0.0000 0.8456 0.000 0.000 1.000 0.000
#> GSM648614 4 0.1474 0.8890 0.000 0.000 0.052 0.948
#> GSM648624 1 0.0000 0.9830 1.000 0.000 0.000 0.000
#> GSM648625 1 0.0188 0.9824 0.996 0.000 0.000 0.004
#> GSM648629 3 0.0188 0.8434 0.000 0.000 0.996 0.004
#> GSM648634 1 0.0000 0.9830 1.000 0.000 0.000 0.000
#> GSM648648 2 0.0000 0.9701 0.000 1.000 0.000 0.000
#> GSM648651 1 0.0000 0.9830 1.000 0.000 0.000 0.000
#> GSM648657 2 0.0000 0.9701 0.000 1.000 0.000 0.000
#> GSM648660 2 0.1940 0.9034 0.076 0.924 0.000 0.000
#> GSM648697 1 0.0000 0.9830 1.000 0.000 0.000 0.000
#> GSM648710 3 0.0000 0.8456 0.000 0.000 1.000 0.000
#> GSM648591 2 0.0000 0.9701 0.000 1.000 0.000 0.000
#> GSM648592 2 0.0817 0.9541 0.024 0.976 0.000 0.000
#> GSM648607 3 0.0000 0.8456 0.000 0.000 1.000 0.000
#> GSM648611 3 0.0000 0.8456 0.000 0.000 1.000 0.000
#> GSM648612 3 0.0000 0.8456 0.000 0.000 1.000 0.000
#> GSM648616 1 0.0000 0.9830 1.000 0.000 0.000 0.000
#> GSM648617 1 0.0188 0.9824 0.996 0.000 0.000 0.004
#> GSM648626 1 0.0000 0.9830 1.000 0.000 0.000 0.000
#> GSM648711 3 0.0000 0.8456 0.000 0.000 1.000 0.000
#> GSM648712 3 0.0000 0.8456 0.000 0.000 1.000 0.000
#> GSM648713 3 0.0000 0.8456 0.000 0.000 1.000 0.000
#> GSM648714 4 0.1474 0.8890 0.000 0.000 0.052 0.948
#> GSM648716 3 0.0000 0.8456 0.000 0.000 1.000 0.000
#> GSM648717 4 0.1474 0.8890 0.000 0.000 0.052 0.948
#> GSM648590 2 0.1118 0.9598 0.000 0.964 0.000 0.036
#> GSM648596 2 0.3172 0.8664 0.000 0.840 0.000 0.160
#> GSM648642 4 0.1474 0.8890 0.000 0.000 0.052 0.948
#> GSM648696 1 0.0188 0.9824 0.996 0.000 0.000 0.004
#> GSM648705 2 0.0000 0.9701 0.000 1.000 0.000 0.000
#> GSM648718 2 0.1118 0.9598 0.000 0.964 0.000 0.036
#> GSM648599 1 0.0000 0.9830 1.000 0.000 0.000 0.000
#> GSM648608 3 0.0000 0.8456 0.000 0.000 1.000 0.000
#> GSM648609 3 0.0000 0.8456 0.000 0.000 1.000 0.000
#> GSM648610 3 0.0000 0.8456 0.000 0.000 1.000 0.000
#> GSM648633 2 0.0000 0.9701 0.000 1.000 0.000 0.000
#> GSM648644 2 0.3311 0.8546 0.000 0.828 0.000 0.172
#> GSM648652 2 0.0000 0.9701 0.000 1.000 0.000 0.000
#> GSM648653 1 0.0188 0.9824 0.996 0.000 0.000 0.004
#> GSM648658 2 0.0000 0.9701 0.000 1.000 0.000 0.000
#> GSM648659 2 0.0000 0.9701 0.000 1.000 0.000 0.000
#> GSM648662 4 0.4992 0.0512 0.000 0.000 0.476 0.524
#> GSM648665 3 0.4977 0.1025 0.000 0.000 0.540 0.460
#> GSM648666 1 0.0000 0.9830 1.000 0.000 0.000 0.000
#> GSM648680 2 0.0000 0.9701 0.000 1.000 0.000 0.000
#> GSM648684 3 0.0000 0.8456 0.000 0.000 1.000 0.000
#> GSM648709 1 0.0000 0.9830 1.000 0.000 0.000 0.000
#> GSM648719 2 0.0000 0.9701 0.000 1.000 0.000 0.000
#> GSM648627 3 0.0336 0.8410 0.000 0.000 0.992 0.008
#> GSM648637 1 0.4985 0.1311 0.532 0.000 0.000 0.468
#> GSM648638 4 0.1474 0.8890 0.000 0.000 0.052 0.948
#> GSM648641 3 0.4977 0.1025 0.000 0.000 0.540 0.460
#> GSM648672 2 0.1389 0.9552 0.000 0.952 0.000 0.048
#> GSM648674 2 0.0000 0.9701 0.000 1.000 0.000 0.000
#> GSM648703 2 0.1389 0.9552 0.000 0.952 0.000 0.048
#> GSM648631 3 0.0000 0.8456 0.000 0.000 1.000 0.000
#> GSM648669 2 0.0000 0.9701 0.000 1.000 0.000 0.000
#> GSM648671 2 0.0000 0.9701 0.000 1.000 0.000 0.000
#> GSM648678 2 0.1389 0.9552 0.000 0.952 0.000 0.048
#> GSM648679 2 0.1211 0.9583 0.000 0.960 0.000 0.040
#> GSM648681 2 0.0000 0.9701 0.000 1.000 0.000 0.000
#> GSM648686 3 0.4977 0.1025 0.000 0.000 0.540 0.460
#> GSM648689 3 0.4977 0.1025 0.000 0.000 0.540 0.460
#> GSM648690 3 0.4977 0.1025 0.000 0.000 0.540 0.460
#> GSM648691 3 0.0000 0.8456 0.000 0.000 1.000 0.000
#> GSM648693 3 0.0000 0.8456 0.000 0.000 1.000 0.000
#> GSM648700 2 0.0000 0.9701 0.000 1.000 0.000 0.000
#> GSM648630 3 0.0707 0.8327 0.000 0.000 0.980 0.020
#> GSM648632 3 0.0000 0.8456 0.000 0.000 1.000 0.000
#> GSM648639 1 0.0188 0.9824 0.996 0.000 0.000 0.004
#> GSM648640 4 0.3610 0.7713 0.000 0.000 0.200 0.800
#> GSM648668 2 0.1389 0.9552 0.000 0.952 0.000 0.048
#> GSM648676 2 0.0000 0.9701 0.000 1.000 0.000 0.000
#> GSM648692 3 0.4500 0.4391 0.000 0.000 0.684 0.316
#> GSM648694 3 0.4679 0.3701 0.000 0.000 0.648 0.352
#> GSM648699 2 0.0000 0.9701 0.000 1.000 0.000 0.000
#> GSM648701 2 0.0000 0.9701 0.000 1.000 0.000 0.000
#> GSM648673 2 0.0000 0.9701 0.000 1.000 0.000 0.000
#> GSM648677 2 0.1389 0.9552 0.000 0.952 0.000 0.048
#> GSM648687 1 0.0000 0.9830 1.000 0.000 0.000 0.000
#> GSM648688 3 0.0000 0.8456 0.000 0.000 1.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM648605 3 0.2648 0.73018 0.152 0.000 0.848 0.000 0.000
#> GSM648618 5 0.0000 0.97496 0.000 0.000 0.000 0.000 1.000
#> GSM648620 5 0.1364 0.96096 0.000 0.012 0.036 0.000 0.952
#> GSM648646 2 0.0510 0.66638 0.000 0.984 0.000 0.016 0.000
#> GSM648649 4 0.3561 0.54863 0.000 0.260 0.000 0.740 0.000
#> GSM648675 4 0.4249 0.37189 0.000 0.432 0.000 0.568 0.000
#> GSM648682 5 0.1469 0.95906 0.000 0.016 0.036 0.000 0.948
#> GSM648698 5 0.1469 0.95906 0.000 0.016 0.036 0.000 0.948
#> GSM648708 5 0.1251 0.96274 0.000 0.008 0.036 0.000 0.956
#> GSM648628 1 0.0510 0.90857 0.984 0.000 0.016 0.000 0.000
#> GSM648595 2 0.4101 0.43239 0.000 0.628 0.000 0.372 0.000
#> GSM648635 4 0.4256 0.36319 0.000 0.436 0.000 0.564 0.000
#> GSM648645 4 0.4242 0.37733 0.000 0.428 0.000 0.572 0.000
#> GSM648647 2 0.2732 0.72258 0.000 0.840 0.000 0.160 0.000
#> GSM648667 2 0.3730 0.61260 0.000 0.712 0.000 0.288 0.000
#> GSM648695 5 0.1469 0.95906 0.000 0.016 0.036 0.000 0.948
#> GSM648704 2 0.0510 0.66638 0.000 0.984 0.000 0.016 0.000
#> GSM648706 3 0.3141 0.72725 0.152 0.016 0.832 0.000 0.000
#> GSM648593 4 0.0290 0.59984 0.000 0.008 0.000 0.992 0.000
#> GSM648594 4 0.4249 0.37189 0.000 0.432 0.000 0.568 0.000
#> GSM648600 5 0.0880 0.96708 0.000 0.000 0.032 0.000 0.968
#> GSM648621 5 0.0000 0.97496 0.000 0.000 0.000 0.000 1.000
#> GSM648622 5 0.0000 0.97496 0.000 0.000 0.000 0.000 1.000
#> GSM648623 5 0.0000 0.97496 0.000 0.000 0.000 0.000 1.000
#> GSM648636 4 0.3661 0.54057 0.000 0.276 0.000 0.724 0.000
#> GSM648655 4 0.1043 0.60326 0.000 0.040 0.000 0.960 0.000
#> GSM648661 1 0.4060 0.00289 0.640 0.000 0.360 0.000 0.000
#> GSM648664 3 0.4305 0.49592 0.488 0.000 0.512 0.000 0.000
#> GSM648683 1 0.0000 0.91427 1.000 0.000 0.000 0.000 0.000
#> GSM648685 1 0.0000 0.91427 1.000 0.000 0.000 0.000 0.000
#> GSM648702 4 0.3876 0.50721 0.000 0.316 0.000 0.684 0.000
#> GSM648597 2 0.6465 -0.01306 0.000 0.440 0.000 0.376 0.184
#> GSM648603 5 0.0000 0.97496 0.000 0.000 0.000 0.000 1.000
#> GSM648606 3 0.2648 0.73018 0.152 0.000 0.848 0.000 0.000
#> GSM648613 3 0.2773 0.72860 0.164 0.000 0.836 0.000 0.000
#> GSM648619 1 0.0162 0.91200 0.996 0.000 0.004 0.000 0.000
#> GSM648654 1 0.2891 0.65435 0.824 0.000 0.176 0.000 0.000
#> GSM648663 3 0.4305 0.49592 0.488 0.000 0.512 0.000 0.000
#> GSM648670 4 0.4538 0.31639 0.000 0.452 0.000 0.540 0.008
#> GSM648707 5 0.0000 0.97496 0.000 0.000 0.000 0.000 1.000
#> GSM648615 5 0.1124 0.96435 0.000 0.004 0.036 0.000 0.960
#> GSM648643 2 0.3305 0.66713 0.000 0.776 0.000 0.224 0.000
#> GSM648650 4 0.4283 0.31563 0.000 0.456 0.000 0.544 0.000
#> GSM648656 2 0.0510 0.66638 0.000 0.984 0.000 0.016 0.000
#> GSM648715 2 0.2605 0.72128 0.000 0.852 0.000 0.148 0.000
#> GSM648598 5 0.0000 0.97496 0.000 0.000 0.000 0.000 1.000
#> GSM648601 5 0.0000 0.97496 0.000 0.000 0.000 0.000 1.000
#> GSM648602 5 0.0000 0.97496 0.000 0.000 0.000 0.000 1.000
#> GSM648604 1 0.0000 0.91427 1.000 0.000 0.000 0.000 0.000
#> GSM648614 3 0.2648 0.73018 0.152 0.000 0.848 0.000 0.000
#> GSM648624 5 0.0000 0.97496 0.000 0.000 0.000 0.000 1.000
#> GSM648625 5 0.1364 0.96096 0.000 0.012 0.036 0.000 0.952
#> GSM648629 1 0.0162 0.91200 0.996 0.000 0.004 0.000 0.000
#> GSM648634 5 0.0000 0.97496 0.000 0.000 0.000 0.000 1.000
#> GSM648648 4 0.0000 0.59634 0.000 0.000 0.000 1.000 0.000
#> GSM648651 5 0.0000 0.97496 0.000 0.000 0.000 0.000 1.000
#> GSM648657 4 0.3336 0.56211 0.000 0.228 0.000 0.772 0.000
#> GSM648660 4 0.6102 0.07896 0.000 0.436 0.000 0.440 0.124
#> GSM648697 5 0.0000 0.97496 0.000 0.000 0.000 0.000 1.000
#> GSM648710 1 0.0162 0.91269 0.996 0.000 0.004 0.000 0.000
#> GSM648591 4 0.0000 0.59634 0.000 0.000 0.000 1.000 0.000
#> GSM648592 4 0.5019 0.30677 0.000 0.436 0.000 0.532 0.032
#> GSM648607 1 0.0000 0.91427 1.000 0.000 0.000 0.000 0.000
#> GSM648611 1 0.2329 0.83131 0.876 0.000 0.124 0.000 0.000
#> GSM648612 1 0.0162 0.91269 0.996 0.000 0.004 0.000 0.000
#> GSM648616 5 0.0000 0.97496 0.000 0.000 0.000 0.000 1.000
#> GSM648617 5 0.0880 0.96708 0.000 0.000 0.032 0.000 0.968
#> GSM648626 5 0.0000 0.97496 0.000 0.000 0.000 0.000 1.000
#> GSM648711 1 0.0000 0.91427 1.000 0.000 0.000 0.000 0.000
#> GSM648712 1 0.0000 0.91427 1.000 0.000 0.000 0.000 0.000
#> GSM648713 1 0.0162 0.91269 0.996 0.000 0.004 0.000 0.000
#> GSM648714 3 0.2648 0.73018 0.152 0.000 0.848 0.000 0.000
#> GSM648716 1 0.0000 0.91427 1.000 0.000 0.000 0.000 0.000
#> GSM648717 3 0.2648 0.73018 0.152 0.000 0.848 0.000 0.000
#> GSM648590 2 0.3949 0.54596 0.000 0.668 0.000 0.332 0.000
#> GSM648596 2 0.0703 0.67071 0.000 0.976 0.000 0.024 0.000
#> GSM648642 3 0.4540 0.52978 0.020 0.340 0.640 0.000 0.000
#> GSM648696 5 0.1124 0.96435 0.000 0.004 0.036 0.000 0.960
#> GSM648705 4 0.4249 0.37109 0.000 0.432 0.000 0.568 0.000
#> GSM648718 2 0.3983 0.52771 0.000 0.660 0.000 0.340 0.000
#> GSM648599 5 0.0000 0.97496 0.000 0.000 0.000 0.000 1.000
#> GSM648608 1 0.0000 0.91427 1.000 0.000 0.000 0.000 0.000
#> GSM648609 1 0.0000 0.91427 1.000 0.000 0.000 0.000 0.000
#> GSM648610 1 0.0000 0.91427 1.000 0.000 0.000 0.000 0.000
#> GSM648633 4 0.4283 0.31611 0.000 0.456 0.000 0.544 0.000
#> GSM648644 2 0.0510 0.66638 0.000 0.984 0.000 0.016 0.000
#> GSM648652 4 0.3661 0.53955 0.000 0.276 0.000 0.724 0.000
#> GSM648653 5 0.0000 0.97496 0.000 0.000 0.000 0.000 1.000
#> GSM648658 4 0.4256 0.36319 0.000 0.436 0.000 0.564 0.000
#> GSM648659 4 0.4283 0.31588 0.000 0.456 0.000 0.544 0.000
#> GSM648662 3 0.4182 0.60870 0.400 0.000 0.600 0.000 0.000
#> GSM648665 3 0.4305 0.49592 0.488 0.000 0.512 0.000 0.000
#> GSM648666 5 0.0000 0.97496 0.000 0.000 0.000 0.000 1.000
#> GSM648680 4 0.4235 0.38535 0.000 0.424 0.000 0.576 0.000
#> GSM648684 1 0.0000 0.91427 1.000 0.000 0.000 0.000 0.000
#> GSM648709 5 0.0290 0.97327 0.000 0.000 0.008 0.000 0.992
#> GSM648719 4 0.0404 0.60089 0.000 0.012 0.000 0.988 0.000
#> GSM648627 1 0.0510 0.90857 0.984 0.000 0.016 0.000 0.000
#> GSM648637 5 0.4610 0.40466 0.000 0.016 0.388 0.000 0.596
#> GSM648638 3 0.2648 0.73018 0.152 0.000 0.848 0.000 0.000
#> GSM648641 3 0.4101 0.52802 0.372 0.000 0.628 0.000 0.000
#> GSM648672 2 0.2690 0.72294 0.000 0.844 0.000 0.156 0.000
#> GSM648674 4 0.0000 0.59634 0.000 0.000 0.000 1.000 0.000
#> GSM648703 2 0.3999 0.52306 0.000 0.656 0.000 0.344 0.000
#> GSM648631 1 0.2280 0.83211 0.880 0.000 0.120 0.000 0.000
#> GSM648669 4 0.0609 0.59195 0.000 0.020 0.000 0.980 0.000
#> GSM648671 4 0.0880 0.58477 0.000 0.032 0.000 0.968 0.000
#> GSM648678 2 0.2690 0.72294 0.000 0.844 0.000 0.156 0.000
#> GSM648679 2 0.4015 0.50592 0.000 0.652 0.000 0.348 0.000
#> GSM648681 4 0.0290 0.59956 0.000 0.008 0.000 0.992 0.000
#> GSM648686 3 0.4101 0.52802 0.372 0.000 0.628 0.000 0.000
#> GSM648689 3 0.4101 0.52802 0.372 0.000 0.628 0.000 0.000
#> GSM648690 3 0.4101 0.52802 0.372 0.000 0.628 0.000 0.000
#> GSM648691 1 0.2280 0.83211 0.880 0.000 0.120 0.000 0.000
#> GSM648693 1 0.2280 0.83211 0.880 0.000 0.120 0.000 0.000
#> GSM648700 4 0.3039 0.51756 0.000 0.192 0.000 0.808 0.000
#> GSM648630 1 0.3003 0.76621 0.812 0.000 0.188 0.000 0.000
#> GSM648632 1 0.2280 0.83211 0.880 0.000 0.120 0.000 0.000
#> GSM648639 5 0.0880 0.96708 0.000 0.000 0.032 0.000 0.968
#> GSM648640 3 0.1197 0.67769 0.048 0.000 0.952 0.000 0.000
#> GSM648668 2 0.2813 0.71951 0.000 0.832 0.000 0.168 0.000
#> GSM648676 4 0.0000 0.59634 0.000 0.000 0.000 1.000 0.000
#> GSM648692 3 0.4278 0.11149 0.452 0.000 0.548 0.000 0.000
#> GSM648694 3 0.4126 0.29631 0.380 0.000 0.620 0.000 0.000
#> GSM648699 4 0.0609 0.59182 0.000 0.020 0.000 0.980 0.000
#> GSM648701 4 0.3109 0.53306 0.000 0.200 0.000 0.800 0.000
#> GSM648673 4 0.1671 0.54817 0.000 0.076 0.000 0.924 0.000
#> GSM648677 2 0.3424 0.65360 0.000 0.760 0.000 0.240 0.000
#> GSM648687 5 0.0000 0.97496 0.000 0.000 0.000 0.000 1.000
#> GSM648688 1 0.2329 0.83131 0.876 0.000 0.124 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM648605 5 0.1327 0.6734 0.000 0.000 0.064 0.000 0.936 0.000
#> GSM648618 1 0.0260 0.9308 0.992 0.000 0.000 0.008 0.000 0.000
#> GSM648620 1 0.3225 0.8852 0.828 0.080 0.000 0.092 0.000 0.000
#> GSM648646 2 0.2454 0.8253 0.000 0.840 0.000 0.000 0.000 0.160
#> GSM648649 6 0.4785 0.4786 0.000 0.120 0.000 0.216 0.000 0.664
#> GSM648675 6 0.0000 0.6298 0.000 0.000 0.000 0.000 0.000 1.000
#> GSM648682 1 0.3366 0.8831 0.824 0.080 0.000 0.092 0.004 0.000
#> GSM648698 1 0.3277 0.8831 0.824 0.084 0.000 0.092 0.000 0.000
#> GSM648708 1 0.3225 0.8852 0.828 0.080 0.000 0.092 0.000 0.000
#> GSM648628 3 0.1245 0.8814 0.000 0.016 0.952 0.032 0.000 0.000
#> GSM648595 6 0.2491 0.5909 0.000 0.164 0.000 0.000 0.000 0.836
#> GSM648635 6 0.2389 0.6200 0.000 0.128 0.000 0.008 0.000 0.864
#> GSM648645 6 0.0937 0.6050 0.000 0.000 0.000 0.040 0.000 0.960
#> GSM648647 6 0.3765 0.1975 0.000 0.404 0.000 0.000 0.000 0.596
#> GSM648667 2 0.3833 0.3584 0.000 0.556 0.000 0.000 0.000 0.444
#> GSM648695 1 0.3225 0.8852 0.828 0.080 0.000 0.092 0.000 0.000
#> GSM648704 2 0.2454 0.8253 0.000 0.840 0.000 0.000 0.000 0.160
#> GSM648706 5 0.2571 0.6595 0.000 0.060 0.064 0.000 0.876 0.000
#> GSM648593 6 0.3584 0.2241 0.000 0.004 0.000 0.308 0.000 0.688
#> GSM648594 6 0.0820 0.6328 0.000 0.016 0.000 0.012 0.000 0.972
#> GSM648600 1 0.2912 0.8946 0.852 0.076 0.000 0.072 0.000 0.000
#> GSM648621 1 0.0405 0.9324 0.988 0.008 0.000 0.004 0.000 0.000
#> GSM648622 1 0.0146 0.9322 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM648623 1 0.0146 0.9322 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM648636 6 0.2531 0.5482 0.000 0.012 0.000 0.132 0.000 0.856
#> GSM648655 6 0.3565 0.2363 0.000 0.004 0.000 0.304 0.000 0.692
#> GSM648661 3 0.6454 -0.2232 0.000 0.068 0.496 0.128 0.308 0.000
#> GSM648664 5 0.6869 0.4936 0.000 0.076 0.368 0.168 0.388 0.000
#> GSM648683 3 0.0000 0.8967 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648685 3 0.0000 0.8967 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648702 6 0.3453 0.5456 0.000 0.044 0.000 0.164 0.000 0.792
#> GSM648597 6 0.4216 0.5064 0.084 0.148 0.000 0.012 0.000 0.756
#> GSM648603 1 0.0146 0.9322 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM648606 5 0.1610 0.6771 0.000 0.000 0.084 0.000 0.916 0.000
#> GSM648613 5 0.2378 0.6756 0.000 0.000 0.152 0.000 0.848 0.000
#> GSM648619 3 0.0000 0.8967 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648654 3 0.4775 0.5711 0.000 0.052 0.732 0.080 0.136 0.000
#> GSM648663 5 0.6521 0.5456 0.000 0.060 0.360 0.136 0.444 0.000
#> GSM648670 6 0.4187 0.5972 0.012 0.156 0.000 0.076 0.000 0.756
#> GSM648707 1 0.0146 0.9322 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM648615 1 0.3123 0.8886 0.836 0.076 0.000 0.088 0.000 0.000
#> GSM648643 2 0.3838 0.4399 0.000 0.552 0.000 0.000 0.000 0.448
#> GSM648650 6 0.2613 0.6138 0.000 0.140 0.000 0.012 0.000 0.848
#> GSM648656 2 0.2595 0.8243 0.000 0.836 0.000 0.004 0.000 0.160
#> GSM648715 6 0.3804 0.1298 0.000 0.424 0.000 0.000 0.000 0.576
#> GSM648598 1 0.0405 0.9327 0.988 0.008 0.000 0.004 0.000 0.000
#> GSM648601 1 0.0000 0.9327 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM648602 1 0.0000 0.9327 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM648604 3 0.0000 0.8967 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648614 5 0.1327 0.6734 0.000 0.000 0.064 0.000 0.936 0.000
#> GSM648624 1 0.0146 0.9322 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM648625 1 0.3225 0.8852 0.828 0.080 0.000 0.092 0.000 0.000
#> GSM648629 3 0.1245 0.8814 0.000 0.016 0.952 0.032 0.000 0.000
#> GSM648634 1 0.0458 0.9318 0.984 0.016 0.000 0.000 0.000 0.000
#> GSM648648 6 0.3584 0.2207 0.000 0.004 0.000 0.308 0.000 0.688
#> GSM648651 1 0.0146 0.9322 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM648657 6 0.3508 0.2661 0.000 0.004 0.000 0.292 0.000 0.704
#> GSM648660 6 0.3521 0.5728 0.036 0.148 0.000 0.012 0.000 0.804
#> GSM648697 1 0.0000 0.9327 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM648710 3 0.0000 0.8967 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648591 4 0.3360 0.9066 0.000 0.004 0.000 0.732 0.000 0.264
#> GSM648592 6 0.2482 0.6058 0.000 0.148 0.000 0.004 0.000 0.848
#> GSM648607 3 0.0000 0.8967 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648611 3 0.2724 0.8465 0.000 0.016 0.876 0.032 0.076 0.000
#> GSM648612 3 0.0146 0.8950 0.000 0.000 0.996 0.000 0.004 0.000
#> GSM648616 1 0.0603 0.9313 0.980 0.016 0.000 0.004 0.000 0.000
#> GSM648617 1 0.2190 0.9099 0.900 0.060 0.000 0.040 0.000 0.000
#> GSM648626 1 0.0000 0.9327 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM648711 3 0.0000 0.8967 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648712 3 0.0000 0.8967 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648713 3 0.0000 0.8967 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648714 5 0.1327 0.6734 0.000 0.000 0.064 0.000 0.936 0.000
#> GSM648716 3 0.0000 0.8967 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648717 5 0.1663 0.6778 0.000 0.000 0.088 0.000 0.912 0.000
#> GSM648590 6 0.3672 0.2882 0.000 0.368 0.000 0.000 0.000 0.632
#> GSM648596 2 0.2454 0.8253 0.000 0.840 0.000 0.000 0.000 0.160
#> GSM648642 5 0.5160 0.4560 0.000 0.396 0.060 0.012 0.532 0.000
#> GSM648696 1 0.3225 0.8852 0.828 0.080 0.000 0.092 0.000 0.000
#> GSM648705 6 0.2362 0.6133 0.000 0.136 0.000 0.004 0.000 0.860
#> GSM648718 6 0.3695 0.2755 0.000 0.376 0.000 0.000 0.000 0.624
#> GSM648599 1 0.0000 0.9327 1.000 0.000 0.000 0.000 0.000 0.000
#> GSM648608 3 0.0000 0.8967 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648609 3 0.0000 0.8967 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648610 3 0.0000 0.8967 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648633 6 0.2520 0.6037 0.000 0.152 0.000 0.004 0.000 0.844
#> GSM648644 2 0.2595 0.8243 0.000 0.836 0.000 0.004 0.000 0.160
#> GSM648652 6 0.2402 0.5151 0.000 0.004 0.000 0.140 0.000 0.856
#> GSM648653 1 0.0291 0.9329 0.992 0.004 0.000 0.004 0.000 0.000
#> GSM648658 6 0.1124 0.6422 0.000 0.036 0.000 0.008 0.000 0.956
#> GSM648659 6 0.1500 0.6433 0.000 0.052 0.000 0.012 0.000 0.936
#> GSM648662 5 0.6446 0.5699 0.000 0.056 0.340 0.136 0.468 0.000
#> GSM648665 5 0.6850 0.4981 0.000 0.076 0.368 0.164 0.392 0.000
#> GSM648666 1 0.0146 0.9322 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM648680 6 0.1341 0.6285 0.000 0.024 0.000 0.028 0.000 0.948
#> GSM648684 3 0.0000 0.8967 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648709 1 0.0458 0.9318 0.984 0.016 0.000 0.000 0.000 0.000
#> GSM648719 6 0.3619 0.1996 0.000 0.004 0.000 0.316 0.000 0.680
#> GSM648627 3 0.1245 0.8814 0.000 0.016 0.952 0.032 0.000 0.000
#> GSM648637 1 0.6084 0.4191 0.528 0.060 0.000 0.092 0.320 0.000
#> GSM648638 5 0.1327 0.6734 0.000 0.000 0.064 0.000 0.936 0.000
#> GSM648641 5 0.6442 0.5511 0.000 0.060 0.316 0.136 0.488 0.000
#> GSM648672 6 0.3765 0.1975 0.000 0.404 0.000 0.000 0.000 0.596
#> GSM648674 6 0.3619 0.2120 0.000 0.004 0.000 0.316 0.000 0.680
#> GSM648703 6 0.3499 0.3610 0.000 0.320 0.000 0.000 0.000 0.680
#> GSM648631 3 0.2724 0.8465 0.000 0.016 0.876 0.032 0.076 0.000
#> GSM648669 4 0.4270 0.9049 0.000 0.052 0.000 0.684 0.000 0.264
#> GSM648671 4 0.3564 0.9116 0.000 0.012 0.000 0.724 0.000 0.264
#> GSM648678 6 0.3765 0.1975 0.000 0.404 0.000 0.000 0.000 0.596
#> GSM648679 6 0.3563 0.3553 0.000 0.336 0.000 0.000 0.000 0.664
#> GSM648681 6 0.3468 0.2674 0.000 0.004 0.000 0.284 0.000 0.712
#> GSM648686 5 0.6411 0.5511 0.000 0.060 0.304 0.136 0.500 0.000
#> GSM648689 5 0.6411 0.5511 0.000 0.060 0.304 0.136 0.500 0.000
#> GSM648690 5 0.6411 0.5511 0.000 0.060 0.304 0.136 0.500 0.000
#> GSM648691 3 0.2724 0.8465 0.000 0.016 0.876 0.032 0.076 0.000
#> GSM648693 3 0.2724 0.8465 0.000 0.016 0.876 0.032 0.076 0.000
#> GSM648700 4 0.4578 0.7721 0.000 0.056 0.000 0.624 0.000 0.320
#> GSM648630 3 0.3424 0.7917 0.000 0.016 0.816 0.032 0.136 0.000
#> GSM648632 3 0.2724 0.8465 0.000 0.016 0.876 0.032 0.076 0.000
#> GSM648639 1 0.3123 0.8886 0.836 0.076 0.000 0.088 0.000 0.000
#> GSM648640 5 0.1663 0.6530 0.000 0.000 0.088 0.000 0.912 0.000
#> GSM648668 6 0.3765 0.1975 0.000 0.404 0.000 0.000 0.000 0.596
#> GSM648676 6 0.3802 0.2099 0.000 0.012 0.000 0.312 0.000 0.676
#> GSM648692 3 0.4927 0.0852 0.000 0.016 0.484 0.032 0.468 0.000
#> GSM648694 5 0.4832 -0.0114 0.000 0.012 0.440 0.032 0.516 0.000
#> GSM648699 4 0.3490 0.9092 0.000 0.008 0.000 0.724 0.000 0.268
#> GSM648701 6 0.2706 0.5457 0.000 0.036 0.000 0.104 0.000 0.860
#> GSM648673 4 0.4814 0.8693 0.000 0.100 0.000 0.644 0.000 0.256
#> GSM648677 2 0.3647 0.6424 0.000 0.640 0.000 0.000 0.000 0.360
#> GSM648687 1 0.0146 0.9322 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM648688 3 0.2724 0.8465 0.000 0.016 0.876 0.032 0.076 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) development.stage(p) other(p) k
#> ATC:mclust 130 0.413232 0.669 5.46e-04 2
#> ATC:mclust 130 0.070115 0.479 4.39e-04 3
#> ATC:mclust 119 0.104139 0.589 4.51e-04 4
#> ATC:mclust 108 0.012479 0.747 7.32e-07 5
#> ATC:mclust 103 0.000155 0.366 1.23e-05 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["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 51941 rows and 130 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 0.965 0.986 0.4127 0.590 0.590
#> 3 3 0.776 0.860 0.930 0.5842 0.723 0.537
#> 4 4 0.697 0.742 0.861 0.1008 0.883 0.678
#> 5 5 0.622 0.616 0.794 0.0661 0.842 0.528
#> 6 6 0.713 0.629 0.811 0.0336 0.875 0.573
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
#> GSM648605 2 0.0000 0.988 0.000 1.000
#> GSM648618 2 0.0000 0.988 0.000 1.000
#> GSM648620 2 0.0000 0.988 0.000 1.000
#> GSM648646 2 0.0000 0.988 0.000 1.000
#> GSM648649 2 0.0000 0.988 0.000 1.000
#> GSM648675 2 0.0000 0.988 0.000 1.000
#> GSM648682 2 0.0000 0.988 0.000 1.000
#> GSM648698 2 0.0000 0.988 0.000 1.000
#> GSM648708 2 0.0000 0.988 0.000 1.000
#> GSM648628 1 0.0000 0.980 1.000 0.000
#> GSM648595 2 0.0000 0.988 0.000 1.000
#> GSM648635 2 0.0000 0.988 0.000 1.000
#> GSM648645 2 0.0000 0.988 0.000 1.000
#> GSM648647 2 0.0000 0.988 0.000 1.000
#> GSM648667 2 0.0000 0.988 0.000 1.000
#> GSM648695 2 0.0000 0.988 0.000 1.000
#> GSM648704 2 0.0000 0.988 0.000 1.000
#> GSM648706 2 0.0000 0.988 0.000 1.000
#> GSM648593 2 0.0000 0.988 0.000 1.000
#> GSM648594 2 0.0000 0.988 0.000 1.000
#> GSM648600 2 0.0000 0.988 0.000 1.000
#> GSM648621 2 0.0000 0.988 0.000 1.000
#> GSM648622 2 0.0000 0.988 0.000 1.000
#> GSM648623 2 0.0000 0.988 0.000 1.000
#> GSM648636 2 0.0000 0.988 0.000 1.000
#> GSM648655 2 0.0000 0.988 0.000 1.000
#> GSM648661 1 0.0000 0.980 1.000 0.000
#> GSM648664 1 0.0000 0.980 1.000 0.000
#> GSM648683 1 0.8955 0.554 0.688 0.312
#> GSM648685 2 0.9881 0.206 0.436 0.564
#> GSM648702 2 0.0000 0.988 0.000 1.000
#> GSM648597 2 0.0000 0.988 0.000 1.000
#> GSM648603 2 0.0000 0.988 0.000 1.000
#> GSM648606 1 0.6438 0.803 0.836 0.164
#> GSM648613 1 0.0000 0.980 1.000 0.000
#> GSM648619 1 0.0000 0.980 1.000 0.000
#> GSM648654 1 0.0000 0.980 1.000 0.000
#> GSM648663 1 0.0000 0.980 1.000 0.000
#> GSM648670 2 0.0000 0.988 0.000 1.000
#> GSM648707 2 0.0000 0.988 0.000 1.000
#> GSM648615 2 0.0000 0.988 0.000 1.000
#> GSM648643 2 0.0000 0.988 0.000 1.000
#> GSM648650 2 0.0000 0.988 0.000 1.000
#> GSM648656 2 0.0000 0.988 0.000 1.000
#> GSM648715 2 0.0000 0.988 0.000 1.000
#> GSM648598 2 0.0000 0.988 0.000 1.000
#> GSM648601 2 0.0000 0.988 0.000 1.000
#> GSM648602 2 0.0000 0.988 0.000 1.000
#> GSM648604 1 0.0376 0.976 0.996 0.004
#> GSM648614 2 0.0000 0.988 0.000 1.000
#> GSM648624 2 0.0000 0.988 0.000 1.000
#> GSM648625 2 0.0000 0.988 0.000 1.000
#> GSM648629 1 0.0000 0.980 1.000 0.000
#> GSM648634 2 0.0000 0.988 0.000 1.000
#> GSM648648 2 0.0000 0.988 0.000 1.000
#> GSM648651 2 0.0000 0.988 0.000 1.000
#> GSM648657 2 0.0000 0.988 0.000 1.000
#> GSM648660 2 0.0000 0.988 0.000 1.000
#> GSM648697 2 0.0000 0.988 0.000 1.000
#> GSM648710 1 0.0000 0.980 1.000 0.000
#> GSM648591 2 0.0000 0.988 0.000 1.000
#> GSM648592 2 0.0000 0.988 0.000 1.000
#> GSM648607 1 0.0000 0.980 1.000 0.000
#> GSM648611 1 0.0000 0.980 1.000 0.000
#> GSM648612 1 0.0000 0.980 1.000 0.000
#> GSM648616 2 0.0000 0.988 0.000 1.000
#> GSM648617 2 0.0000 0.988 0.000 1.000
#> GSM648626 2 0.0000 0.988 0.000 1.000
#> GSM648711 1 0.0000 0.980 1.000 0.000
#> GSM648712 1 0.0000 0.980 1.000 0.000
#> GSM648713 1 0.0000 0.980 1.000 0.000
#> GSM648714 2 0.0000 0.988 0.000 1.000
#> GSM648716 1 0.0000 0.980 1.000 0.000
#> GSM648717 1 0.0000 0.980 1.000 0.000
#> GSM648590 2 0.0000 0.988 0.000 1.000
#> GSM648596 2 0.0000 0.988 0.000 1.000
#> GSM648642 2 0.0000 0.988 0.000 1.000
#> GSM648696 2 0.0000 0.988 0.000 1.000
#> GSM648705 2 0.0000 0.988 0.000 1.000
#> GSM648718 2 0.0000 0.988 0.000 1.000
#> GSM648599 2 0.0000 0.988 0.000 1.000
#> GSM648608 1 0.0000 0.980 1.000 0.000
#> GSM648609 1 0.0000 0.980 1.000 0.000
#> GSM648610 2 0.0000 0.988 0.000 1.000
#> GSM648633 2 0.0000 0.988 0.000 1.000
#> GSM648644 2 0.0000 0.988 0.000 1.000
#> GSM648652 2 0.0000 0.988 0.000 1.000
#> GSM648653 2 0.0000 0.988 0.000 1.000
#> GSM648658 2 0.0000 0.988 0.000 1.000
#> GSM648659 2 0.0000 0.988 0.000 1.000
#> GSM648662 2 0.6343 0.799 0.160 0.840
#> GSM648665 1 0.7745 0.709 0.772 0.228
#> GSM648666 2 0.0000 0.988 0.000 1.000
#> GSM648680 2 0.0000 0.988 0.000 1.000
#> GSM648684 2 0.9933 0.152 0.452 0.548
#> GSM648709 2 0.0000 0.988 0.000 1.000
#> GSM648719 2 0.0000 0.988 0.000 1.000
#> GSM648627 1 0.0000 0.980 1.000 0.000
#> GSM648637 2 0.0000 0.988 0.000 1.000
#> GSM648638 2 0.0000 0.988 0.000 1.000
#> GSM648641 1 0.0000 0.980 1.000 0.000
#> GSM648672 2 0.0000 0.988 0.000 1.000
#> GSM648674 2 0.0000 0.988 0.000 1.000
#> GSM648703 2 0.0000 0.988 0.000 1.000
#> GSM648631 1 0.0000 0.980 1.000 0.000
#> GSM648669 2 0.0000 0.988 0.000 1.000
#> GSM648671 2 0.0000 0.988 0.000 1.000
#> GSM648678 2 0.0000 0.988 0.000 1.000
#> GSM648679 2 0.0000 0.988 0.000 1.000
#> GSM648681 2 0.0000 0.988 0.000 1.000
#> GSM648686 1 0.0000 0.980 1.000 0.000
#> GSM648689 1 0.0000 0.980 1.000 0.000
#> GSM648690 1 0.0000 0.980 1.000 0.000
#> GSM648691 1 0.0000 0.980 1.000 0.000
#> GSM648693 1 0.0000 0.980 1.000 0.000
#> GSM648700 2 0.0000 0.988 0.000 1.000
#> GSM648630 1 0.0000 0.980 1.000 0.000
#> GSM648632 1 0.0000 0.980 1.000 0.000
#> GSM648639 2 0.0000 0.988 0.000 1.000
#> GSM648640 1 0.0000 0.980 1.000 0.000
#> GSM648668 2 0.0000 0.988 0.000 1.000
#> GSM648676 2 0.0000 0.988 0.000 1.000
#> GSM648692 1 0.0000 0.980 1.000 0.000
#> GSM648694 1 0.0000 0.980 1.000 0.000
#> GSM648699 2 0.0000 0.988 0.000 1.000
#> GSM648701 2 0.0000 0.988 0.000 1.000
#> GSM648673 2 0.0000 0.988 0.000 1.000
#> GSM648677 2 0.0000 0.988 0.000 1.000
#> GSM648687 2 0.0000 0.988 0.000 1.000
#> GSM648688 1 0.0000 0.980 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM648605 2 0.0000 0.844 0.000 1.000 0.000
#> GSM648618 1 0.0000 0.949 1.000 0.000 0.000
#> GSM648620 2 0.4931 0.743 0.232 0.768 0.000
#> GSM648646 2 0.0000 0.844 0.000 1.000 0.000
#> GSM648649 2 0.6140 0.491 0.404 0.596 0.000
#> GSM648675 1 0.0000 0.949 1.000 0.000 0.000
#> GSM648682 2 0.4346 0.780 0.184 0.816 0.000
#> GSM648698 2 0.3340 0.817 0.120 0.880 0.000
#> GSM648708 2 0.5810 0.620 0.336 0.664 0.000
#> GSM648628 3 0.0000 0.969 0.000 0.000 1.000
#> GSM648595 2 0.3267 0.819 0.116 0.884 0.000
#> GSM648635 1 0.4842 0.683 0.776 0.224 0.000
#> GSM648645 1 0.0000 0.949 1.000 0.000 0.000
#> GSM648647 2 0.0000 0.844 0.000 1.000 0.000
#> GSM648667 2 0.1411 0.844 0.036 0.964 0.000
#> GSM648695 2 0.1643 0.844 0.044 0.956 0.000
#> GSM648704 2 0.0000 0.844 0.000 1.000 0.000
#> GSM648706 2 0.0000 0.844 0.000 1.000 0.000
#> GSM648593 1 0.4121 0.779 0.832 0.168 0.000
#> GSM648594 1 0.0000 0.949 1.000 0.000 0.000
#> GSM648600 1 0.4504 0.733 0.804 0.196 0.000
#> GSM648621 1 0.0000 0.949 1.000 0.000 0.000
#> GSM648622 1 0.0000 0.949 1.000 0.000 0.000
#> GSM648623 1 0.0000 0.949 1.000 0.000 0.000
#> GSM648636 1 0.4291 0.760 0.820 0.180 0.000
#> GSM648655 1 0.3192 0.850 0.888 0.112 0.000
#> GSM648661 3 0.0000 0.969 0.000 0.000 1.000
#> GSM648664 3 0.0000 0.969 0.000 0.000 1.000
#> GSM648683 3 0.2711 0.879 0.088 0.000 0.912
#> GSM648685 3 0.5905 0.438 0.352 0.000 0.648
#> GSM648702 2 0.6280 0.348 0.460 0.540 0.000
#> GSM648597 1 0.0000 0.949 1.000 0.000 0.000
#> GSM648603 1 0.0000 0.949 1.000 0.000 0.000
#> GSM648606 3 0.5098 0.686 0.000 0.248 0.752
#> GSM648613 3 0.0747 0.956 0.000 0.016 0.984
#> GSM648619 3 0.0000 0.969 0.000 0.000 1.000
#> GSM648654 3 0.0000 0.969 0.000 0.000 1.000
#> GSM648663 3 0.2356 0.912 0.000 0.072 0.928
#> GSM648670 2 0.5327 0.705 0.272 0.728 0.000
#> GSM648707 1 0.0000 0.949 1.000 0.000 0.000
#> GSM648615 2 0.5178 0.721 0.256 0.744 0.000
#> GSM648643 2 0.0237 0.844 0.004 0.996 0.000
#> GSM648650 2 0.4654 0.763 0.208 0.792 0.000
#> GSM648656 2 0.0000 0.844 0.000 1.000 0.000
#> GSM648715 2 0.0592 0.845 0.012 0.988 0.000
#> GSM648598 1 0.0000 0.949 1.000 0.000 0.000
#> GSM648601 1 0.0000 0.949 1.000 0.000 0.000
#> GSM648602 1 0.0000 0.949 1.000 0.000 0.000
#> GSM648604 3 0.0000 0.969 0.000 0.000 1.000
#> GSM648614 2 0.0000 0.844 0.000 1.000 0.000
#> GSM648624 1 0.0000 0.949 1.000 0.000 0.000
#> GSM648625 2 0.5431 0.691 0.284 0.716 0.000
#> GSM648629 3 0.0000 0.969 0.000 0.000 1.000
#> GSM648634 1 0.0237 0.947 0.996 0.004 0.000
#> GSM648648 1 0.0747 0.939 0.984 0.016 0.000
#> GSM648651 1 0.0000 0.949 1.000 0.000 0.000
#> GSM648657 1 0.0237 0.947 0.996 0.004 0.000
#> GSM648660 1 0.0000 0.949 1.000 0.000 0.000
#> GSM648697 1 0.0000 0.949 1.000 0.000 0.000
#> GSM648710 3 0.0000 0.969 0.000 0.000 1.000
#> GSM648591 1 0.0000 0.949 1.000 0.000 0.000
#> GSM648592 1 0.2165 0.901 0.936 0.064 0.000
#> GSM648607 3 0.0000 0.969 0.000 0.000 1.000
#> GSM648611 3 0.0000 0.969 0.000 0.000 1.000
#> GSM648612 3 0.0000 0.969 0.000 0.000 1.000
#> GSM648616 1 0.0000 0.949 1.000 0.000 0.000
#> GSM648617 1 0.1860 0.912 0.948 0.052 0.000
#> GSM648626 1 0.0000 0.949 1.000 0.000 0.000
#> GSM648711 3 0.0000 0.969 0.000 0.000 1.000
#> GSM648712 3 0.0000 0.969 0.000 0.000 1.000
#> GSM648713 3 0.0000 0.969 0.000 0.000 1.000
#> GSM648714 2 0.0000 0.844 0.000 1.000 0.000
#> GSM648716 3 0.0000 0.969 0.000 0.000 1.000
#> GSM648717 3 0.0000 0.969 0.000 0.000 1.000
#> GSM648590 2 0.2261 0.838 0.068 0.932 0.000
#> GSM648596 2 0.0237 0.844 0.004 0.996 0.000
#> GSM648642 2 0.0000 0.844 0.000 1.000 0.000
#> GSM648696 2 0.5968 0.572 0.364 0.636 0.000
#> GSM648705 2 0.5529 0.677 0.296 0.704 0.000
#> GSM648718 2 0.2711 0.831 0.088 0.912 0.000
#> GSM648599 1 0.0000 0.949 1.000 0.000 0.000
#> GSM648608 3 0.0000 0.969 0.000 0.000 1.000
#> GSM648609 3 0.0000 0.969 0.000 0.000 1.000
#> GSM648610 2 0.7600 0.561 0.344 0.600 0.056
#> GSM648633 2 0.6252 0.393 0.444 0.556 0.000
#> GSM648644 2 0.0000 0.844 0.000 1.000 0.000
#> GSM648652 1 0.1643 0.918 0.956 0.044 0.000
#> GSM648653 1 0.0000 0.949 1.000 0.000 0.000
#> GSM648658 1 0.0000 0.949 1.000 0.000 0.000
#> GSM648659 2 0.6307 0.259 0.488 0.512 0.000
#> GSM648662 2 0.0592 0.837 0.000 0.988 0.012
#> GSM648665 3 0.3619 0.846 0.000 0.136 0.864
#> GSM648666 1 0.0000 0.949 1.000 0.000 0.000
#> GSM648680 1 0.0237 0.947 0.996 0.004 0.000
#> GSM648684 3 0.4808 0.747 0.188 0.008 0.804
#> GSM648709 1 0.5178 0.615 0.744 0.256 0.000
#> GSM648719 1 0.0000 0.949 1.000 0.000 0.000
#> GSM648627 3 0.0000 0.969 0.000 0.000 1.000
#> GSM648637 2 0.0000 0.844 0.000 1.000 0.000
#> GSM648638 2 0.0000 0.844 0.000 1.000 0.000
#> GSM648641 3 0.0000 0.969 0.000 0.000 1.000
#> GSM648672 2 0.0000 0.844 0.000 1.000 0.000
#> GSM648674 2 0.5650 0.656 0.312 0.688 0.000
#> GSM648703 2 0.1643 0.843 0.044 0.956 0.000
#> GSM648631 3 0.0000 0.969 0.000 0.000 1.000
#> GSM648669 1 0.0000 0.949 1.000 0.000 0.000
#> GSM648671 1 0.0000 0.949 1.000 0.000 0.000
#> GSM648678 2 0.0000 0.844 0.000 1.000 0.000
#> GSM648679 2 0.1964 0.841 0.056 0.944 0.000
#> GSM648681 1 0.5706 0.448 0.680 0.320 0.000
#> GSM648686 3 0.0000 0.969 0.000 0.000 1.000
#> GSM648689 3 0.0000 0.969 0.000 0.000 1.000
#> GSM648690 3 0.0000 0.969 0.000 0.000 1.000
#> GSM648691 3 0.0000 0.969 0.000 0.000 1.000
#> GSM648693 3 0.0000 0.969 0.000 0.000 1.000
#> GSM648700 1 0.0000 0.949 1.000 0.000 0.000
#> GSM648630 3 0.0000 0.969 0.000 0.000 1.000
#> GSM648632 3 0.0000 0.969 0.000 0.000 1.000
#> GSM648639 1 0.2356 0.894 0.928 0.072 0.000
#> GSM648640 3 0.0000 0.969 0.000 0.000 1.000
#> GSM648668 2 0.0747 0.845 0.016 0.984 0.000
#> GSM648676 1 0.4346 0.752 0.816 0.184 0.000
#> GSM648692 3 0.0000 0.969 0.000 0.000 1.000
#> GSM648694 3 0.0000 0.969 0.000 0.000 1.000
#> GSM648699 1 0.0000 0.949 1.000 0.000 0.000
#> GSM648701 2 0.6291 0.322 0.468 0.532 0.000
#> GSM648673 1 0.0237 0.947 0.996 0.004 0.000
#> GSM648677 2 0.1163 0.845 0.028 0.972 0.000
#> GSM648687 1 0.0000 0.949 1.000 0.000 0.000
#> GSM648688 3 0.0000 0.969 0.000 0.000 1.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM648605 2 0.1557 0.699 0.000 0.944 0.000 0.056
#> GSM648618 1 0.0707 0.853 0.980 0.020 0.000 0.000
#> GSM648620 2 0.1978 0.713 0.068 0.928 0.000 0.004
#> GSM648646 2 0.4356 0.454 0.000 0.708 0.000 0.292
#> GSM648649 2 0.5411 0.442 0.312 0.656 0.000 0.032
#> GSM648675 1 0.2011 0.857 0.920 0.000 0.000 0.080
#> GSM648682 2 0.1389 0.715 0.048 0.952 0.000 0.000
#> GSM648698 2 0.1520 0.718 0.024 0.956 0.000 0.020
#> GSM648708 2 0.3142 0.682 0.132 0.860 0.000 0.008
#> GSM648628 3 0.0000 0.959 0.000 0.000 1.000 0.000
#> GSM648595 4 0.6071 0.181 0.044 0.452 0.000 0.504
#> GSM648635 1 0.2329 0.859 0.916 0.012 0.000 0.072
#> GSM648645 1 0.1940 0.857 0.924 0.000 0.000 0.076
#> GSM648647 4 0.2976 0.646 0.008 0.120 0.000 0.872
#> GSM648667 2 0.4382 0.469 0.000 0.704 0.000 0.296
#> GSM648695 2 0.1284 0.717 0.024 0.964 0.000 0.012
#> GSM648704 2 0.3873 0.560 0.000 0.772 0.000 0.228
#> GSM648706 2 0.3074 0.634 0.000 0.848 0.000 0.152
#> GSM648593 1 0.2654 0.853 0.888 0.004 0.000 0.108
#> GSM648594 1 0.2149 0.855 0.912 0.000 0.000 0.088
#> GSM648600 2 0.4356 0.508 0.292 0.708 0.000 0.000
#> GSM648621 1 0.4382 0.551 0.704 0.296 0.000 0.000
#> GSM648622 1 0.0817 0.852 0.976 0.024 0.000 0.000
#> GSM648623 1 0.1118 0.848 0.964 0.036 0.000 0.000
#> GSM648636 1 0.2868 0.840 0.864 0.000 0.000 0.136
#> GSM648655 1 0.2814 0.842 0.868 0.000 0.000 0.132
#> GSM648661 3 0.0000 0.959 0.000 0.000 1.000 0.000
#> GSM648664 3 0.0188 0.957 0.000 0.000 0.996 0.004
#> GSM648683 3 0.2670 0.875 0.040 0.052 0.908 0.000
#> GSM648685 3 0.6167 0.547 0.124 0.208 0.668 0.000
#> GSM648702 1 0.5365 0.681 0.692 0.044 0.000 0.264
#> GSM648597 1 0.0707 0.858 0.980 0.000 0.000 0.020
#> GSM648603 1 0.1637 0.839 0.940 0.060 0.000 0.000
#> GSM648606 2 0.2589 0.611 0.000 0.884 0.116 0.000
#> GSM648613 3 0.2011 0.889 0.000 0.080 0.920 0.000
#> GSM648619 3 0.0000 0.959 0.000 0.000 1.000 0.000
#> GSM648654 3 0.0000 0.959 0.000 0.000 1.000 0.000
#> GSM648663 3 0.2530 0.869 0.000 0.000 0.888 0.112
#> GSM648670 2 0.4701 0.626 0.164 0.780 0.000 0.056
#> GSM648707 1 0.1022 0.850 0.968 0.032 0.000 0.000
#> GSM648615 2 0.1661 0.716 0.052 0.944 0.000 0.004
#> GSM648643 2 0.3873 0.571 0.000 0.772 0.000 0.228
#> GSM648650 2 0.7128 0.165 0.152 0.528 0.000 0.320
#> GSM648656 2 0.3610 0.592 0.000 0.800 0.000 0.200
#> GSM648715 4 0.4122 0.643 0.004 0.236 0.000 0.760
#> GSM648598 1 0.1940 0.829 0.924 0.076 0.000 0.000
#> GSM648601 1 0.2530 0.804 0.888 0.112 0.000 0.000
#> GSM648602 1 0.2469 0.807 0.892 0.108 0.000 0.000
#> GSM648604 3 0.0000 0.959 0.000 0.000 1.000 0.000
#> GSM648614 2 0.2149 0.684 0.000 0.912 0.000 0.088
#> GSM648624 1 0.0817 0.852 0.976 0.024 0.000 0.000
#> GSM648625 2 0.2149 0.707 0.088 0.912 0.000 0.000
#> GSM648629 3 0.0000 0.959 0.000 0.000 1.000 0.000
#> GSM648634 1 0.4164 0.613 0.736 0.264 0.000 0.000
#> GSM648648 1 0.3400 0.818 0.820 0.000 0.000 0.180
#> GSM648651 1 0.0336 0.855 0.992 0.008 0.000 0.000
#> GSM648657 1 0.2760 0.842 0.872 0.000 0.000 0.128
#> GSM648660 1 0.1004 0.859 0.972 0.004 0.000 0.024
#> GSM648697 1 0.0592 0.854 0.984 0.016 0.000 0.000
#> GSM648710 3 0.0000 0.959 0.000 0.000 1.000 0.000
#> GSM648591 1 0.3311 0.822 0.828 0.000 0.000 0.172
#> GSM648592 1 0.2611 0.826 0.896 0.096 0.000 0.008
#> GSM648607 3 0.0000 0.959 0.000 0.000 1.000 0.000
#> GSM648611 3 0.0000 0.959 0.000 0.000 1.000 0.000
#> GSM648612 3 0.0000 0.959 0.000 0.000 1.000 0.000
#> GSM648616 1 0.3074 0.767 0.848 0.152 0.000 0.000
#> GSM648617 2 0.4941 0.284 0.436 0.564 0.000 0.000
#> GSM648626 1 0.3266 0.749 0.832 0.168 0.000 0.000
#> GSM648711 3 0.0000 0.959 0.000 0.000 1.000 0.000
#> GSM648712 3 0.0000 0.959 0.000 0.000 1.000 0.000
#> GSM648713 3 0.0000 0.959 0.000 0.000 1.000 0.000
#> GSM648714 2 0.1474 0.699 0.000 0.948 0.000 0.052
#> GSM648716 3 0.0000 0.959 0.000 0.000 1.000 0.000
#> GSM648717 3 0.4933 0.284 0.000 0.432 0.568 0.000
#> GSM648590 4 0.3881 0.664 0.016 0.172 0.000 0.812
#> GSM648596 2 0.4431 0.437 0.000 0.696 0.000 0.304
#> GSM648642 4 0.4661 0.516 0.000 0.348 0.000 0.652
#> GSM648696 2 0.2216 0.704 0.092 0.908 0.000 0.000
#> GSM648705 4 0.7773 0.295 0.284 0.284 0.000 0.432
#> GSM648718 4 0.4761 0.655 0.044 0.192 0.000 0.764
#> GSM648599 1 0.2530 0.804 0.888 0.112 0.000 0.000
#> GSM648608 3 0.0000 0.959 0.000 0.000 1.000 0.000
#> GSM648609 3 0.0000 0.959 0.000 0.000 1.000 0.000
#> GSM648610 2 0.2469 0.696 0.108 0.892 0.000 0.000
#> GSM648633 1 0.5862 0.655 0.704 0.148 0.000 0.148
#> GSM648644 2 0.3907 0.553 0.000 0.768 0.000 0.232
#> GSM648652 1 0.2530 0.849 0.888 0.000 0.000 0.112
#> GSM648653 1 0.0921 0.852 0.972 0.028 0.000 0.000
#> GSM648658 1 0.1867 0.857 0.928 0.000 0.000 0.072
#> GSM648659 1 0.5407 0.344 0.504 0.012 0.000 0.484
#> GSM648662 4 0.6949 0.129 0.000 0.112 0.408 0.480
#> GSM648665 3 0.2345 0.876 0.000 0.000 0.900 0.100
#> GSM648666 1 0.0336 0.855 0.992 0.008 0.000 0.000
#> GSM648680 1 0.2081 0.856 0.916 0.000 0.000 0.084
#> GSM648684 3 0.4776 0.717 0.060 0.164 0.776 0.000
#> GSM648709 2 0.4164 0.546 0.264 0.736 0.000 0.000
#> GSM648719 1 0.2647 0.845 0.880 0.000 0.000 0.120
#> GSM648627 3 0.0000 0.959 0.000 0.000 1.000 0.000
#> GSM648637 2 0.0779 0.715 0.016 0.980 0.000 0.004
#> GSM648638 2 0.1867 0.690 0.000 0.928 0.000 0.072
#> GSM648641 3 0.0188 0.957 0.000 0.000 0.996 0.004
#> GSM648672 4 0.3356 0.662 0.000 0.176 0.000 0.824
#> GSM648674 2 0.6881 0.359 0.236 0.592 0.000 0.172
#> GSM648703 4 0.5163 0.204 0.004 0.480 0.000 0.516
#> GSM648631 3 0.0000 0.959 0.000 0.000 1.000 0.000
#> GSM648669 1 0.4193 0.747 0.732 0.000 0.000 0.268
#> GSM648671 1 0.3610 0.808 0.800 0.000 0.000 0.200
#> GSM648678 4 0.4564 0.547 0.000 0.328 0.000 0.672
#> GSM648679 4 0.3497 0.643 0.024 0.124 0.000 0.852
#> GSM648681 1 0.3306 0.835 0.840 0.004 0.000 0.156
#> GSM648686 3 0.0000 0.959 0.000 0.000 1.000 0.000
#> GSM648689 3 0.0188 0.957 0.000 0.000 0.996 0.004
#> GSM648690 3 0.0000 0.959 0.000 0.000 1.000 0.000
#> GSM648691 3 0.0000 0.959 0.000 0.000 1.000 0.000
#> GSM648693 3 0.0000 0.959 0.000 0.000 1.000 0.000
#> GSM648700 1 0.3400 0.819 0.820 0.000 0.000 0.180
#> GSM648630 3 0.0000 0.959 0.000 0.000 1.000 0.000
#> GSM648632 3 0.0000 0.959 0.000 0.000 1.000 0.000
#> GSM648639 2 0.3873 0.591 0.228 0.772 0.000 0.000
#> GSM648640 3 0.0000 0.959 0.000 0.000 1.000 0.000
#> GSM648668 4 0.4088 0.646 0.004 0.232 0.000 0.764
#> GSM648676 1 0.3172 0.830 0.840 0.000 0.000 0.160
#> GSM648692 3 0.0000 0.959 0.000 0.000 1.000 0.000
#> GSM648694 3 0.0000 0.959 0.000 0.000 1.000 0.000
#> GSM648699 1 0.3942 0.780 0.764 0.000 0.000 0.236
#> GSM648701 4 0.5165 -0.358 0.484 0.004 0.000 0.512
#> GSM648673 1 0.4916 0.518 0.576 0.000 0.000 0.424
#> GSM648677 2 0.2469 0.684 0.000 0.892 0.000 0.108
#> GSM648687 1 0.1867 0.831 0.928 0.072 0.000 0.000
#> GSM648688 3 0.0000 0.959 0.000 0.000 1.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM648605 2 0.0510 0.5852 0.000 0.984 0.000 0.000 0.016
#> GSM648618 1 0.1628 0.7883 0.936 0.000 0.000 0.056 0.008
#> GSM648620 2 0.4033 0.5356 0.212 0.760 0.000 0.024 0.004
#> GSM648646 2 0.5292 0.3875 0.004 0.580 0.000 0.368 0.048
#> GSM648649 4 0.6384 0.1474 0.152 0.360 0.000 0.484 0.004
#> GSM648675 4 0.4734 0.4739 0.372 0.000 0.000 0.604 0.024
#> GSM648682 2 0.2517 0.5874 0.104 0.884 0.000 0.008 0.004
#> GSM648698 2 0.3774 0.5692 0.160 0.804 0.000 0.028 0.008
#> GSM648708 2 0.4860 0.4410 0.292 0.664 0.000 0.040 0.004
#> GSM648628 3 0.0000 0.8703 0.000 0.000 1.000 0.000 0.000
#> GSM648595 4 0.5681 0.5434 0.072 0.196 0.000 0.684 0.048
#> GSM648635 4 0.4773 0.6107 0.312 0.008 0.000 0.656 0.024
#> GSM648645 1 0.5104 0.4575 0.648 0.000 0.000 0.284 0.068
#> GSM648647 4 0.3736 0.5927 0.000 0.052 0.000 0.808 0.140
#> GSM648667 2 0.5616 0.2241 0.020 0.508 0.000 0.436 0.036
#> GSM648695 2 0.1041 0.5977 0.032 0.964 0.000 0.004 0.000
#> GSM648704 2 0.5106 0.3802 0.004 0.588 0.000 0.372 0.036
#> GSM648706 2 0.2423 0.5518 0.000 0.896 0.000 0.024 0.080
#> GSM648593 4 0.4233 0.7143 0.208 0.000 0.000 0.748 0.044
#> GSM648594 4 0.5176 0.1960 0.468 0.000 0.000 0.492 0.040
#> GSM648600 1 0.5106 0.3020 0.564 0.400 0.000 0.032 0.004
#> GSM648621 1 0.3934 0.7109 0.796 0.160 0.000 0.036 0.008
#> GSM648622 1 0.3106 0.7313 0.844 0.000 0.000 0.024 0.132
#> GSM648623 1 0.1549 0.7931 0.944 0.000 0.000 0.040 0.016
#> GSM648636 4 0.3196 0.7414 0.192 0.000 0.000 0.804 0.004
#> GSM648655 4 0.3266 0.7364 0.200 0.000 0.000 0.796 0.004
#> GSM648661 3 0.2852 0.7314 0.000 0.000 0.828 0.000 0.172
#> GSM648664 3 0.3969 0.5316 0.000 0.000 0.692 0.004 0.304
#> GSM648683 3 0.6903 0.3762 0.120 0.096 0.588 0.000 0.196
#> GSM648685 3 0.8526 0.0620 0.136 0.164 0.464 0.040 0.196
#> GSM648702 4 0.3257 0.7410 0.112 0.012 0.000 0.852 0.024
#> GSM648597 1 0.3736 0.6855 0.808 0.000 0.000 0.140 0.052
#> GSM648603 1 0.2387 0.7911 0.908 0.004 0.000 0.040 0.048
#> GSM648606 2 0.3400 0.4685 0.004 0.840 0.116 0.000 0.040
#> GSM648613 3 0.2408 0.7932 0.000 0.092 0.892 0.000 0.016
#> GSM648619 3 0.0290 0.8685 0.000 0.000 0.992 0.000 0.008
#> GSM648654 3 0.0609 0.8640 0.000 0.000 0.980 0.000 0.020
#> GSM648663 5 0.4451 0.5684 0.000 0.000 0.248 0.040 0.712
#> GSM648670 2 0.6065 0.4120 0.132 0.560 0.000 0.304 0.004
#> GSM648707 1 0.1579 0.7896 0.944 0.000 0.000 0.032 0.024
#> GSM648615 2 0.2462 0.5886 0.112 0.880 0.000 0.008 0.000
#> GSM648643 2 0.5225 0.2579 0.020 0.540 0.000 0.424 0.016
#> GSM648650 4 0.5324 0.5298 0.076 0.224 0.000 0.684 0.016
#> GSM648656 2 0.4752 0.4890 0.004 0.684 0.000 0.272 0.040
#> GSM648715 4 0.4509 0.5946 0.012 0.108 0.000 0.776 0.104
#> GSM648598 1 0.3355 0.7635 0.856 0.012 0.000 0.048 0.084
#> GSM648601 1 0.2768 0.7911 0.896 0.040 0.000 0.040 0.024
#> GSM648602 1 0.2420 0.7930 0.912 0.036 0.000 0.036 0.016
#> GSM648604 3 0.3880 0.7076 0.044 0.004 0.800 0.000 0.152
#> GSM648614 2 0.2813 0.4836 0.000 0.832 0.000 0.000 0.168
#> GSM648624 1 0.2900 0.7482 0.864 0.000 0.000 0.028 0.108
#> GSM648625 2 0.4007 0.5331 0.220 0.756 0.000 0.020 0.004
#> GSM648629 3 0.0000 0.8703 0.000 0.000 1.000 0.000 0.000
#> GSM648634 1 0.3669 0.7454 0.828 0.116 0.000 0.008 0.048
#> GSM648648 4 0.3795 0.7080 0.192 0.000 0.000 0.780 0.028
#> GSM648651 1 0.1648 0.7917 0.940 0.000 0.000 0.040 0.020
#> GSM648657 4 0.5238 0.1672 0.472 0.000 0.000 0.484 0.044
#> GSM648660 1 0.4155 0.6819 0.780 0.000 0.000 0.144 0.076
#> GSM648697 1 0.2278 0.7713 0.908 0.000 0.000 0.032 0.060
#> GSM648710 3 0.0703 0.8651 0.000 0.000 0.976 0.000 0.024
#> GSM648591 4 0.5084 0.4841 0.332 0.000 0.000 0.616 0.052
#> GSM648592 1 0.4901 0.5820 0.700 0.040 0.000 0.244 0.016
#> GSM648607 3 0.0880 0.8621 0.000 0.000 0.968 0.000 0.032
#> GSM648611 3 0.0162 0.8697 0.000 0.000 0.996 0.000 0.004
#> GSM648612 3 0.0880 0.8634 0.000 0.000 0.968 0.000 0.032
#> GSM648616 1 0.2853 0.7837 0.884 0.068 0.000 0.040 0.008
#> GSM648617 1 0.4625 0.6042 0.712 0.244 0.000 0.036 0.008
#> GSM648626 1 0.2938 0.7796 0.876 0.084 0.000 0.032 0.008
#> GSM648711 3 0.0290 0.8694 0.000 0.000 0.992 0.000 0.008
#> GSM648712 3 0.1484 0.8488 0.008 0.000 0.944 0.000 0.048
#> GSM648713 3 0.0609 0.8663 0.000 0.000 0.980 0.000 0.020
#> GSM648714 2 0.1121 0.5711 0.000 0.956 0.000 0.000 0.044
#> GSM648716 3 0.0000 0.8703 0.000 0.000 1.000 0.000 0.000
#> GSM648717 2 0.5157 -0.1115 0.000 0.520 0.440 0.000 0.040
#> GSM648590 4 0.3902 0.6416 0.016 0.068 0.000 0.824 0.092
#> GSM648596 2 0.5289 0.2573 0.004 0.528 0.000 0.428 0.040
#> GSM648642 5 0.6705 0.0697 0.000 0.292 0.000 0.280 0.428
#> GSM648696 2 0.4142 0.5053 0.252 0.728 0.000 0.016 0.004
#> GSM648705 4 0.4320 0.6930 0.088 0.076 0.000 0.804 0.032
#> GSM648718 4 0.3605 0.6688 0.024 0.056 0.000 0.848 0.072
#> GSM648599 1 0.3241 0.7810 0.872 0.040 0.000 0.036 0.052
#> GSM648608 3 0.2813 0.7856 0.024 0.000 0.868 0.000 0.108
#> GSM648609 3 0.1809 0.8385 0.012 0.000 0.928 0.000 0.060
#> GSM648610 2 0.5885 0.3108 0.152 0.632 0.004 0.004 0.208
#> GSM648633 4 0.4483 0.7117 0.156 0.064 0.000 0.768 0.012
#> GSM648644 2 0.5217 0.3916 0.004 0.588 0.000 0.364 0.044
#> GSM648652 4 0.4221 0.6815 0.236 0.000 0.000 0.732 0.032
#> GSM648653 1 0.3197 0.7266 0.836 0.000 0.000 0.024 0.140
#> GSM648658 1 0.4955 0.5143 0.680 0.000 0.000 0.248 0.072
#> GSM648659 4 0.2482 0.7295 0.084 0.000 0.000 0.892 0.024
#> GSM648662 5 0.4040 0.5685 0.004 0.076 0.028 0.064 0.828
#> GSM648665 5 0.5123 0.4658 0.024 0.000 0.276 0.032 0.668
#> GSM648666 1 0.1597 0.7906 0.940 0.000 0.000 0.048 0.012
#> GSM648680 1 0.5202 0.2711 0.596 0.000 0.000 0.348 0.056
#> GSM648684 3 0.7132 0.2991 0.060 0.180 0.556 0.004 0.200
#> GSM648709 1 0.5218 0.2246 0.536 0.424 0.000 0.036 0.004
#> GSM648719 4 0.5694 0.0715 0.460 0.000 0.000 0.460 0.080
#> GSM648627 3 0.0162 0.8694 0.000 0.000 0.996 0.000 0.004
#> GSM648637 2 0.0771 0.5945 0.020 0.976 0.000 0.004 0.000
#> GSM648638 2 0.1197 0.5704 0.000 0.952 0.000 0.000 0.048
#> GSM648641 3 0.4307 -0.0700 0.000 0.000 0.500 0.000 0.500
#> GSM648672 4 0.4185 0.6080 0.008 0.084 0.000 0.796 0.112
#> GSM648674 4 0.5240 0.6667 0.136 0.152 0.000 0.704 0.008
#> GSM648703 4 0.5237 0.4868 0.032 0.244 0.000 0.684 0.040
#> GSM648631 3 0.0000 0.8703 0.000 0.000 1.000 0.000 0.000
#> GSM648669 4 0.3055 0.7357 0.144 0.000 0.000 0.840 0.016
#> GSM648671 4 0.4360 0.7093 0.184 0.000 0.000 0.752 0.064
#> GSM648678 4 0.5020 0.5057 0.004 0.180 0.000 0.712 0.104
#> GSM648679 4 0.3426 0.6739 0.032 0.040 0.000 0.860 0.068
#> GSM648681 4 0.3583 0.7436 0.168 0.016 0.000 0.808 0.008
#> GSM648686 3 0.2605 0.7568 0.000 0.000 0.852 0.000 0.148
#> GSM648689 3 0.4045 0.4010 0.000 0.000 0.644 0.000 0.356
#> GSM648690 3 0.1851 0.8162 0.000 0.000 0.912 0.000 0.088
#> GSM648691 3 0.0000 0.8703 0.000 0.000 1.000 0.000 0.000
#> GSM648693 3 0.0000 0.8703 0.000 0.000 1.000 0.000 0.000
#> GSM648700 4 0.3970 0.7174 0.156 0.000 0.000 0.788 0.056
#> GSM648630 3 0.0000 0.8703 0.000 0.000 1.000 0.000 0.000
#> GSM648632 3 0.0000 0.8703 0.000 0.000 1.000 0.000 0.000
#> GSM648639 1 0.5384 0.2405 0.536 0.416 0.000 0.040 0.008
#> GSM648640 3 0.0703 0.8660 0.000 0.000 0.976 0.000 0.024
#> GSM648668 4 0.4350 0.6194 0.016 0.104 0.000 0.792 0.088
#> GSM648676 4 0.3768 0.7436 0.156 0.020 0.000 0.808 0.016
#> GSM648692 3 0.0000 0.8703 0.000 0.000 1.000 0.000 0.000
#> GSM648694 3 0.0000 0.8703 0.000 0.000 1.000 0.000 0.000
#> GSM648699 4 0.4036 0.7188 0.144 0.000 0.000 0.788 0.068
#> GSM648701 4 0.2871 0.7312 0.088 0.004 0.000 0.876 0.032
#> GSM648673 4 0.3477 0.7174 0.112 0.000 0.000 0.832 0.056
#> GSM648677 2 0.4846 0.3437 0.020 0.588 0.000 0.388 0.004
#> GSM648687 1 0.1913 0.7921 0.932 0.016 0.000 0.044 0.008
#> GSM648688 3 0.0000 0.8703 0.000 0.000 1.000 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM648605 2 0.1629 0.6884 0.004 0.940 0.000 0.004 0.028 0.024
#> GSM648618 1 0.1152 0.7397 0.952 0.000 0.000 0.004 0.000 0.044
#> GSM648620 1 0.4062 0.1701 0.552 0.440 0.000 0.000 0.000 0.008
#> GSM648646 4 0.4118 0.4149 0.000 0.396 0.000 0.592 0.004 0.008
#> GSM648649 4 0.4056 0.7249 0.056 0.144 0.000 0.776 0.000 0.024
#> GSM648675 4 0.5065 0.5330 0.192 0.000 0.000 0.636 0.000 0.172
#> GSM648682 2 0.4138 0.5373 0.276 0.692 0.000 0.020 0.000 0.012
#> GSM648698 2 0.5093 0.3399 0.372 0.560 0.000 0.052 0.000 0.016
#> GSM648708 1 0.4061 0.4367 0.664 0.316 0.000 0.012 0.000 0.008
#> GSM648628 3 0.0000 0.8789 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648595 4 0.1728 0.7979 0.004 0.064 0.000 0.924 0.000 0.008
#> GSM648635 4 0.3585 0.7358 0.048 0.004 0.000 0.792 0.000 0.156
#> GSM648645 6 0.5944 0.2657 0.304 0.000 0.000 0.244 0.000 0.452
#> GSM648647 4 0.0976 0.8051 0.000 0.016 0.000 0.968 0.008 0.008
#> GSM648667 4 0.3767 0.6112 0.004 0.276 0.000 0.708 0.000 0.012
#> GSM648695 2 0.2402 0.6929 0.084 0.888 0.000 0.020 0.000 0.008
#> GSM648704 4 0.4039 0.3556 0.000 0.424 0.000 0.568 0.000 0.008
#> GSM648706 2 0.2510 0.6680 0.000 0.892 0.000 0.024 0.060 0.024
#> GSM648593 4 0.3088 0.7406 0.020 0.000 0.000 0.808 0.000 0.172
#> GSM648594 4 0.5714 0.2502 0.184 0.000 0.000 0.496 0.000 0.320
#> GSM648600 1 0.2945 0.6822 0.824 0.156 0.000 0.000 0.000 0.020
#> GSM648621 1 0.1802 0.7325 0.916 0.072 0.000 0.000 0.000 0.012
#> GSM648622 1 0.3578 0.5135 0.660 0.000 0.000 0.000 0.000 0.340
#> GSM648623 1 0.1958 0.7179 0.896 0.000 0.000 0.004 0.000 0.100
#> GSM648636 4 0.1225 0.8011 0.012 0.000 0.000 0.952 0.000 0.036
#> GSM648655 4 0.1572 0.7989 0.028 0.000 0.000 0.936 0.000 0.036
#> GSM648661 3 0.3620 0.3965 0.000 0.000 0.648 0.000 0.352 0.000
#> GSM648664 5 0.4482 0.3230 0.000 0.000 0.384 0.000 0.580 0.036
#> GSM648683 6 0.5645 0.3513 0.020 0.104 0.160 0.000 0.044 0.672
#> GSM648685 6 0.2802 0.4130 0.016 0.100 0.004 0.004 0.008 0.868
#> GSM648702 4 0.1010 0.8041 0.004 0.000 0.000 0.960 0.000 0.036
#> GSM648597 6 0.4684 0.1863 0.372 0.000 0.000 0.052 0.000 0.576
#> GSM648603 1 0.2377 0.7238 0.868 0.000 0.000 0.004 0.004 0.124
#> GSM648606 2 0.3996 0.5323 0.000 0.776 0.112 0.000 0.008 0.104
#> GSM648613 3 0.2566 0.7921 0.000 0.112 0.868 0.000 0.008 0.012
#> GSM648619 3 0.0363 0.8753 0.000 0.000 0.988 0.000 0.012 0.000
#> GSM648654 3 0.0865 0.8621 0.000 0.000 0.964 0.000 0.036 0.000
#> GSM648663 5 0.0790 0.6508 0.000 0.000 0.032 0.000 0.968 0.000
#> GSM648670 4 0.6045 0.2767 0.164 0.288 0.000 0.524 0.000 0.024
#> GSM648707 1 0.1958 0.7158 0.896 0.000 0.000 0.004 0.000 0.100
#> GSM648615 2 0.4190 0.4893 0.304 0.668 0.000 0.012 0.000 0.016
#> GSM648643 4 0.3713 0.6108 0.000 0.284 0.000 0.704 0.004 0.008
#> GSM648650 4 0.1584 0.8012 0.008 0.064 0.000 0.928 0.000 0.000
#> GSM648656 2 0.4158 0.0439 0.004 0.572 0.000 0.416 0.000 0.008
#> GSM648715 4 0.1590 0.8009 0.000 0.048 0.000 0.936 0.008 0.008
#> GSM648598 1 0.3937 0.3373 0.572 0.004 0.000 0.000 0.000 0.424
#> GSM648601 1 0.0520 0.7474 0.984 0.008 0.000 0.000 0.000 0.008
#> GSM648602 1 0.1080 0.7472 0.960 0.004 0.000 0.004 0.000 0.032
#> GSM648604 6 0.4863 -0.0759 0.000 0.040 0.440 0.000 0.008 0.512
#> GSM648614 2 0.3986 0.3651 0.000 0.664 0.000 0.000 0.316 0.020
#> GSM648624 1 0.3309 0.5748 0.720 0.000 0.000 0.000 0.000 0.280
#> GSM648625 1 0.4212 0.1966 0.560 0.424 0.000 0.000 0.000 0.016
#> GSM648629 3 0.0405 0.8779 0.000 0.000 0.988 0.000 0.004 0.008
#> GSM648634 1 0.3319 0.7037 0.800 0.036 0.000 0.000 0.000 0.164
#> GSM648648 4 0.3285 0.7425 0.064 0.000 0.000 0.820 0.000 0.116
#> GSM648651 1 0.2070 0.7147 0.892 0.000 0.000 0.008 0.000 0.100
#> GSM648657 4 0.4503 0.6165 0.108 0.000 0.000 0.700 0.000 0.192
#> GSM648660 6 0.3715 0.4496 0.188 0.000 0.000 0.048 0.000 0.764
#> GSM648697 1 0.2823 0.6327 0.796 0.000 0.000 0.000 0.000 0.204
#> GSM648710 3 0.2069 0.8449 0.000 0.020 0.908 0.000 0.004 0.068
#> GSM648591 4 0.4575 0.6133 0.124 0.000 0.000 0.696 0.000 0.180
#> GSM648592 1 0.6417 -0.1801 0.340 0.012 0.000 0.336 0.000 0.312
#> GSM648607 3 0.2060 0.8371 0.000 0.016 0.900 0.000 0.000 0.084
#> GSM648611 3 0.0000 0.8789 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648612 3 0.2206 0.8392 0.000 0.024 0.904 0.000 0.008 0.064
#> GSM648616 1 0.0458 0.7460 0.984 0.016 0.000 0.000 0.000 0.000
#> GSM648617 1 0.2218 0.7155 0.884 0.104 0.000 0.000 0.000 0.012
#> GSM648626 1 0.1624 0.7489 0.936 0.020 0.000 0.004 0.000 0.040
#> GSM648711 3 0.0858 0.8715 0.000 0.000 0.968 0.000 0.004 0.028
#> GSM648712 3 0.3361 0.7373 0.000 0.020 0.788 0.000 0.004 0.188
#> GSM648713 3 0.2034 0.8447 0.000 0.024 0.912 0.000 0.004 0.060
#> GSM648714 2 0.2138 0.6733 0.000 0.908 0.000 0.004 0.036 0.052
#> GSM648716 3 0.0146 0.8785 0.000 0.000 0.996 0.000 0.000 0.004
#> GSM648717 3 0.4841 0.2592 0.000 0.412 0.536 0.000 0.004 0.048
#> GSM648590 4 0.0520 0.8061 0.000 0.008 0.000 0.984 0.008 0.000
#> GSM648596 4 0.3690 0.5777 0.000 0.308 0.000 0.684 0.000 0.008
#> GSM648642 5 0.5839 0.0326 0.000 0.236 0.000 0.276 0.488 0.000
#> GSM648696 1 0.4116 0.2326 0.572 0.416 0.000 0.000 0.000 0.012
#> GSM648705 4 0.1167 0.8086 0.008 0.020 0.000 0.960 0.000 0.012
#> GSM648718 4 0.1261 0.8052 0.004 0.028 0.000 0.956 0.004 0.008
#> GSM648599 1 0.1411 0.7459 0.936 0.004 0.000 0.000 0.000 0.060
#> GSM648608 3 0.4605 0.4220 0.000 0.032 0.596 0.000 0.008 0.364
#> GSM648609 3 0.4291 0.6403 0.000 0.040 0.708 0.000 0.012 0.240
#> GSM648610 6 0.6000 0.2405 0.044 0.272 0.000 0.000 0.124 0.560
#> GSM648633 4 0.1059 0.8084 0.016 0.016 0.000 0.964 0.000 0.004
#> GSM648644 4 0.4072 0.2943 0.000 0.448 0.000 0.544 0.000 0.008
#> GSM648652 4 0.2726 0.7655 0.032 0.000 0.000 0.856 0.000 0.112
#> GSM648653 1 0.3823 0.3400 0.564 0.000 0.000 0.000 0.000 0.436
#> GSM648658 6 0.5818 0.3310 0.296 0.000 0.000 0.160 0.012 0.532
#> GSM648659 4 0.0547 0.8047 0.000 0.000 0.000 0.980 0.000 0.020
#> GSM648662 5 0.1572 0.6204 0.000 0.036 0.000 0.000 0.936 0.028
#> GSM648665 5 0.3441 0.6359 0.004 0.012 0.060 0.000 0.832 0.092
#> GSM648666 1 0.1387 0.7348 0.932 0.000 0.000 0.000 0.000 0.068
#> GSM648680 4 0.5475 0.1426 0.124 0.000 0.000 0.460 0.000 0.416
#> GSM648684 6 0.6588 0.2354 0.004 0.132 0.196 0.000 0.112 0.556
#> GSM648709 1 0.2631 0.6898 0.840 0.152 0.000 0.000 0.000 0.008
#> GSM648719 4 0.5253 0.4101 0.128 0.000 0.000 0.576 0.000 0.296
#> GSM648627 3 0.0146 0.8785 0.000 0.000 0.996 0.000 0.000 0.004
#> GSM648637 2 0.2036 0.6963 0.064 0.912 0.000 0.016 0.000 0.008
#> GSM648638 2 0.2214 0.6576 0.000 0.888 0.000 0.000 0.016 0.096
#> GSM648641 5 0.2378 0.6685 0.000 0.000 0.152 0.000 0.848 0.000
#> GSM648672 4 0.1230 0.8042 0.000 0.028 0.000 0.956 0.008 0.008
#> GSM648674 4 0.1382 0.8082 0.008 0.036 0.000 0.948 0.000 0.008
#> GSM648703 4 0.2062 0.7891 0.004 0.088 0.000 0.900 0.000 0.008
#> GSM648631 3 0.0000 0.8789 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648669 4 0.2070 0.7922 0.048 0.000 0.000 0.908 0.000 0.044
#> GSM648671 4 0.3513 0.7357 0.084 0.000 0.000 0.812 0.004 0.100
#> GSM648678 4 0.2056 0.7878 0.000 0.080 0.000 0.904 0.004 0.012
#> GSM648679 4 0.0405 0.8061 0.000 0.008 0.000 0.988 0.000 0.004
#> GSM648681 4 0.0891 0.8047 0.008 0.000 0.000 0.968 0.000 0.024
#> GSM648686 3 0.3695 0.3366 0.000 0.000 0.624 0.000 0.376 0.000
#> GSM648689 5 0.2996 0.6218 0.000 0.000 0.228 0.000 0.772 0.000
#> GSM648690 3 0.2762 0.6946 0.000 0.000 0.804 0.000 0.196 0.000
#> GSM648691 3 0.0000 0.8789 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648693 3 0.0000 0.8789 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648700 4 0.3703 0.7199 0.072 0.000 0.000 0.792 0.004 0.132
#> GSM648630 3 0.0000 0.8789 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648632 3 0.0000 0.8789 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648639 1 0.2572 0.6982 0.852 0.136 0.000 0.000 0.000 0.012
#> GSM648640 3 0.0622 0.8746 0.000 0.012 0.980 0.000 0.000 0.008
#> GSM648668 4 0.1606 0.7992 0.000 0.056 0.000 0.932 0.004 0.008
#> GSM648676 4 0.0993 0.8042 0.012 0.000 0.000 0.964 0.000 0.024
#> GSM648692 3 0.0000 0.8789 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648694 3 0.0000 0.8789 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM648699 4 0.3995 0.7369 0.080 0.000 0.000 0.800 0.048 0.072
#> GSM648701 4 0.1074 0.8055 0.012 0.000 0.000 0.960 0.000 0.028
#> GSM648673 4 0.2036 0.7943 0.028 0.000 0.000 0.916 0.008 0.048
#> GSM648677 4 0.3833 0.5266 0.000 0.344 0.000 0.648 0.000 0.008
#> GSM648687 1 0.0653 0.7446 0.980 0.004 0.000 0.004 0.000 0.012
#> GSM648688 3 0.0000 0.8789 0.000 0.000 1.000 0.000 0.000 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n disease.state(p) development.stage(p) other(p) k
#> ATC:NMF 128 0.1434 0.15426 7.09e-06 2
#> ATC:NMF 123 0.1974 0.01069 9.97e-12 3
#> ATC:NMF 116 0.1035 0.15361 9.33e-08 4
#> ATC:NMF 97 0.0089 0.00163 3.21e-08 5
#> ATC:NMF 97 0.1403 0.02148 1.45e-06 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
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