Date: 2019-12-25 21:41:03 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 140 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 140
The density distribution for each sample is visualized as in one column in the following heatmap. The clustering is based on the distance which is the Kolmogorov-Smirnov statistic between two distributions.
library(ComplexHeatmap)
densityHeatmap(mat, top_annotation = HeatmapAnnotation(df = get_anno(res_list),
col = get_anno_col(res_list)), ylab = "value", cluster_columns = TRUE, show_column_names = FALSE,
mc.cores = 4)
Folowing table shows the best k (number of partitions) for each combination
of top-value methods and partition methods. Clicking on the method name in
the table goes to the section for a single combination of methods.
The cola vignette explains the definition of the metrics used for determining the best number of partitions.
suggest_best_k(res_list)
| The best k | 1-PAC | Mean silhouette | Concordance | Optional k | ||
|---|---|---|---|---|---|---|
| SD:skmeans | 2 | 1.000 | 0.976 | 0.991 | ** | |
| CV:hclust | 2 | 1.000 | 0.990 | 0.994 | ** | |
| CV:skmeans | 2 | 1.000 | 0.989 | 0.995 | ** | |
| MAD:skmeans | 2 | 1.000 | 0.962 | 0.986 | ** | |
| MAD:mclust | 3 | 1.000 | 0.987 | 0.993 | ** | |
| ATC:kmeans | 2 | 1.000 | 0.974 | 0.990 | ** | |
| ATC:skmeans | 2 | 1.000 | 0.979 | 0.993 | ** | |
| ATC:pam | 2 | 1.000 | 0.985 | 0.994 | ** | |
| ATC:mclust | 2 | 1.000 | 0.999 | 0.999 | ** | |
| ATC:NMF | 2 | 1.000 | 0.971 | 0.988 | ** | |
| SD:NMF | 3 | 0.999 | 0.936 | 0.976 | ** | |
| CV:NMF | 3 | 0.999 | 0.941 | 0.979 | ** | |
| MAD:pam | 6 | 0.993 | 0.960 | 0.982 | ** | 5 |
| CV:pam | 6 | 0.980 | 0.950 | 0.978 | ** | 5 |
| SD:pam | 6 | 0.978 | 0.947 | 0.977 | ** | 5 |
| ATC:hclust | 2 | 0.956 | 0.954 | 0.979 | ** | |
| MAD:hclust | 2 | 0.927 | 0.922 | 0.968 | * | |
| SD:mclust | 3 | 0.856 | 0.953 | 0.965 | ||
| MAD:NMF | 2 | 0.855 | 0.931 | 0.971 | ||
| SD:hclust | 4 | 0.618 | 0.878 | 0.915 | ||
| CV:mclust | 3 | 0.598 | 0.871 | 0.919 | ||
| MAD:kmeans | 2 | 0.483 | 0.895 | 0.902 | ||
| SD:kmeans | 5 | 0.476 | 0.666 | 0.693 | ||
| CV:kmeans | 5 | 0.468 | 0.659 | 0.652 |
**: 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 0.873 0.926 0.970 0.408 0.595 0.595
#> CV:NMF 2 0.845 0.926 0.963 0.392 0.589 0.589
#> MAD:NMF 2 0.855 0.931 0.971 0.478 0.526 0.526
#> ATC:NMF 2 1.000 0.971 0.988 0.490 0.509 0.509
#> SD:skmeans 2 1.000 0.976 0.991 0.501 0.500 0.500
#> CV:skmeans 2 1.000 0.989 0.995 0.500 0.500 0.500
#> MAD:skmeans 2 1.000 0.962 0.986 0.501 0.499 0.499
#> ATC:skmeans 2 1.000 0.979 0.993 0.497 0.503 0.503
#> SD:mclust 2 0.456 0.873 0.899 0.315 0.678 0.678
#> CV:mclust 2 0.456 0.711 0.841 0.295 0.819 0.819
#> MAD:mclust 2 0.307 0.694 0.801 0.378 0.602 0.602
#> ATC:mclust 2 1.000 0.999 0.999 0.183 0.819 0.819
#> SD:kmeans 2 0.323 0.802 0.846 0.398 0.514 0.514
#> CV:kmeans 2 0.223 0.757 0.827 0.399 0.514 0.514
#> MAD:kmeans 2 0.483 0.895 0.902 0.449 0.501 0.501
#> ATC:kmeans 2 1.000 0.974 0.990 0.483 0.520 0.520
#> SD:pam 2 0.661 0.929 0.959 0.230 0.819 0.819
#> CV:pam 2 0.663 0.883 0.900 0.319 0.571 0.571
#> MAD:pam 2 0.797 0.940 0.971 0.453 0.556 0.556
#> ATC:pam 2 1.000 0.985 0.994 0.497 0.503 0.503
#> SD:hclust 2 0.468 0.897 0.875 0.247 0.819 0.819
#> CV:hclust 2 1.000 0.990 0.994 0.190 0.819 0.819
#> MAD:hclust 2 0.927 0.922 0.968 0.500 0.500 0.500
#> ATC:hclust 2 0.956 0.954 0.979 0.474 0.534 0.534
get_stats(res_list, k = 3)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 3 0.999 0.936 0.976 0.396 0.721 0.573
#> CV:NMF 3 0.999 0.941 0.979 0.450 0.719 0.568
#> MAD:NMF 3 0.651 0.740 0.878 0.312 0.759 0.575
#> ATC:NMF 3 0.789 0.863 0.921 0.309 0.805 0.630
#> SD:skmeans 3 0.759 0.887 0.915 0.313 0.807 0.627
#> CV:skmeans 3 0.643 0.858 0.899 0.312 0.825 0.657
#> MAD:skmeans 3 0.860 0.905 0.937 0.298 0.819 0.648
#> ATC:skmeans 3 0.725 0.813 0.864 0.266 0.861 0.726
#> SD:mclust 3 0.856 0.953 0.965 0.538 0.617 0.521
#> CV:mclust 3 0.598 0.871 0.919 0.738 0.718 0.656
#> MAD:mclust 3 1.000 0.987 0.993 0.220 0.572 0.456
#> ATC:mclust 3 0.481 0.820 0.893 2.123 0.592 0.502
#> SD:kmeans 3 0.396 0.625 0.731 0.432 0.958 0.921
#> CV:kmeans 3 0.345 0.364 0.633 0.445 0.740 0.575
#> MAD:kmeans 3 0.444 0.499 0.673 0.360 0.884 0.774
#> ATC:kmeans 3 0.602 0.683 0.833 0.332 0.751 0.547
#> SD:pam 3 0.822 0.934 0.945 1.302 0.642 0.563
#> CV:pam 3 0.594 0.846 0.864 0.638 0.777 0.643
#> MAD:pam 3 0.638 0.854 0.890 0.264 0.803 0.673
#> ATC:pam 3 0.816 0.877 0.949 0.235 0.799 0.630
#> SD:hclust 3 0.551 0.891 0.914 1.167 0.597 0.508
#> CV:hclust 3 0.504 0.713 0.860 1.879 0.609 0.523
#> MAD:hclust 3 0.812 0.818 0.893 0.186 0.896 0.793
#> ATC:hclust 3 0.731 0.910 0.906 0.386 0.797 0.620
get_stats(res_list, k = 4)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 4 0.731 0.794 0.911 0.2515 0.784 0.532
#> CV:NMF 4 0.736 0.780 0.901 0.2523 0.805 0.567
#> MAD:NMF 4 0.736 0.811 0.900 0.1466 0.733 0.413
#> ATC:NMF 4 0.791 0.820 0.905 0.1091 0.873 0.673
#> SD:skmeans 4 0.706 0.769 0.853 0.1260 0.863 0.625
#> CV:skmeans 4 0.702 0.721 0.825 0.1241 0.877 0.664
#> MAD:skmeans 4 0.680 0.816 0.851 0.1341 0.885 0.679
#> ATC:skmeans 4 0.651 0.702 0.814 0.1129 0.923 0.794
#> SD:mclust 4 0.694 0.926 0.918 0.2599 0.879 0.775
#> CV:mclust 4 0.584 0.528 0.722 0.2634 0.828 0.680
#> MAD:mclust 4 0.772 0.893 0.913 0.2832 0.878 0.775
#> ATC:mclust 4 0.810 0.874 0.907 0.2027 0.705 0.406
#> SD:kmeans 4 0.383 0.643 0.685 0.1684 0.750 0.518
#> CV:kmeans 4 0.449 0.631 0.682 0.1664 0.711 0.441
#> MAD:kmeans 4 0.460 0.633 0.698 0.1329 0.763 0.495
#> ATC:kmeans 4 0.592 0.708 0.747 0.1160 0.827 0.547
#> SD:pam 4 0.839 0.885 0.945 0.3127 0.823 0.621
#> CV:pam 4 0.829 0.804 0.925 0.3222 0.706 0.452
#> MAD:pam 4 0.848 0.889 0.951 0.2307 0.803 0.580
#> ATC:pam 4 0.844 0.920 0.951 0.0837 0.947 0.865
#> SD:hclust 4 0.618 0.878 0.915 0.1852 0.940 0.855
#> CV:hclust 4 0.471 0.556 0.727 0.1964 0.759 0.520
#> MAD:hclust 4 0.637 0.804 0.817 0.1072 0.980 0.949
#> ATC:hclust 4 0.688 0.771 0.849 0.1060 0.938 0.811
get_stats(res_list, k = 5)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 5 0.728 0.698 0.848 0.0839 0.800 0.427
#> CV:NMF 5 0.770 0.842 0.904 0.0777 0.817 0.475
#> MAD:NMF 5 0.705 0.660 0.778 0.0831 0.845 0.528
#> ATC:NMF 5 0.766 0.812 0.881 0.0865 0.849 0.550
#> SD:skmeans 5 0.756 0.587 0.692 0.0620 0.830 0.483
#> CV:skmeans 5 0.784 0.679 0.763 0.0659 0.853 0.512
#> MAD:skmeans 5 0.758 0.696 0.777 0.0640 0.937 0.761
#> ATC:skmeans 5 0.786 0.750 0.847 0.0700 0.943 0.818
#> SD:mclust 5 0.798 0.842 0.914 0.2243 0.819 0.564
#> CV:mclust 5 0.740 0.823 0.871 0.1588 0.763 0.434
#> MAD:mclust 5 0.703 0.678 0.818 0.2349 0.900 0.764
#> ATC:mclust 5 0.709 0.871 0.919 -0.0589 0.710 0.383
#> SD:kmeans 5 0.476 0.666 0.693 0.1081 0.873 0.600
#> CV:kmeans 5 0.468 0.659 0.652 0.0975 0.934 0.777
#> MAD:kmeans 5 0.494 0.472 0.622 0.0881 0.977 0.922
#> ATC:kmeans 5 0.674 0.756 0.808 0.0754 0.927 0.732
#> SD:pam 5 0.947 0.921 0.967 0.0825 0.946 0.821
#> CV:pam 5 0.923 0.936 0.974 0.0948 0.882 0.659
#> MAD:pam 5 0.948 0.911 0.966 0.0788 0.926 0.755
#> ATC:pam 5 0.753 0.800 0.833 0.1418 0.851 0.585
#> SD:hclust 5 0.742 0.858 0.863 0.1158 0.960 0.887
#> CV:hclust 5 0.711 0.784 0.839 0.1656 0.758 0.389
#> MAD:hclust 5 0.757 0.698 0.820 0.0901 0.987 0.965
#> ATC:hclust 5 0.731 0.722 0.829 0.0540 0.919 0.718
get_stats(res_list, k = 6)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> SD:NMF 6 0.854 0.803 0.890 0.0573 0.879 0.543
#> CV:NMF 6 0.885 0.856 0.908 0.0631 0.928 0.711
#> MAD:NMF 6 0.841 0.756 0.882 0.0572 0.894 0.581
#> ATC:NMF 6 0.802 0.815 0.887 0.0556 0.907 0.630
#> SD:skmeans 6 0.841 0.776 0.809 0.0441 0.894 0.587
#> CV:skmeans 6 0.817 0.733 0.744 0.0404 0.851 0.441
#> MAD:skmeans 6 0.858 0.739 0.767 0.0449 0.892 0.558
#> ATC:skmeans 6 0.775 0.815 0.864 0.0506 0.938 0.775
#> SD:mclust 6 0.799 0.819 0.862 0.0730 0.901 0.620
#> CV:mclust 6 0.841 0.828 0.873 0.0778 0.908 0.634
#> MAD:mclust 6 0.819 0.614 0.852 0.0584 0.907 0.717
#> ATC:mclust 6 0.854 0.897 0.937 0.2138 0.829 0.535
#> SD:kmeans 6 0.612 0.744 0.695 0.0561 0.960 0.828
#> CV:kmeans 6 0.610 0.727 0.700 0.0620 0.951 0.796
#> MAD:kmeans 6 0.641 0.675 0.646 0.0483 0.897 0.636
#> ATC:kmeans 6 0.768 0.710 0.792 0.0509 0.981 0.914
#> SD:pam 6 0.978 0.947 0.977 0.0750 0.938 0.760
#> CV:pam 6 0.980 0.950 0.978 0.0731 0.941 0.763
#> MAD:pam 6 0.993 0.960 0.982 0.0655 0.921 0.690
#> ATC:pam 6 0.855 0.897 0.946 0.0706 0.840 0.441
#> SD:hclust 6 0.865 0.904 0.932 0.1366 0.879 0.616
#> CV:hclust 6 0.811 0.866 0.896 0.0437 0.980 0.906
#> MAD:hclust 6 0.897 0.865 0.900 0.1102 0.835 0.551
#> ATC:hclust 6 0.841 0.881 0.911 0.0455 0.965 0.846
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 temperature(p) time(p) specimen(p) k
#> SD:NMF 134 0.977 0.991 9.54e-21 2
#> CV:NMF 136 0.985 0.992 4.02e-21 2
#> MAD:NMF 137 0.987 0.974 4.32e-19 2
#> ATC:NMF 139 0.477 0.611 3.42e-14 2
#> SD:skmeans 138 0.869 0.998 1.28e-22 2
#> CV:skmeans 140 0.897 0.998 7.21e-23 2
#> MAD:skmeans 136 0.883 0.984 4.71e-22 2
#> ATC:skmeans 138 0.582 0.620 1.05e-13 2
#> SD:mclust 140 1.000 1.000 1.03e-25 2
#> CV:mclust 126 1.000 1.000 1.91e-23 2
#> MAD:mclust 129 0.838 0.954 1.03e-21 2
#> ATC:mclust 140 1.000 1.000 1.03e-25 2
#> SD:kmeans 135 0.987 0.994 7.66e-23 2
#> CV:kmeans 131 0.992 0.987 1.87e-22 2
#> MAD:kmeans 138 0.982 0.990 7.63e-22 2
#> ATC:kmeans 137 0.690 0.804 1.23e-15 2
#> SD:pam 140 1.000 1.000 1.03e-25 2
#> CV:pam 139 0.911 0.981 6.98e-19 2
#> MAD:pam 139 0.817 0.997 1.14e-17 2
#> ATC:pam 138 0.393 0.666 1.78e-13 2
#> SD:hclust 140 1.000 1.000 1.03e-25 2
#> CV:hclust 140 1.000 1.000 1.03e-25 2
#> MAD:hclust 130 0.996 1.000 1.19e-23 2
#> ATC:hclust 137 0.785 0.947 8.61e-17 2
test_to_known_factors(res_list, k = 3)
#> n temperature(p) time(p) specimen(p) k
#> SD:NMF 135 0.989 1.000 8.02e-44 3
#> CV:NMF 135 0.989 1.000 8.02e-44 3
#> MAD:NMF 121 0.787 0.990 4.88e-34 3
#> ATC:NMF 132 0.850 0.948 1.00e-22 3
#> SD:skmeans 137 0.998 1.000 1.24e-44 3
#> CV:skmeans 139 0.997 1.000 7.22e-44 3
#> MAD:skmeans 136 1.000 1.000 1.74e-42 3
#> ATC:skmeans 136 0.907 0.960 2.99e-29 3
#> SD:mclust 140 1.000 1.000 6.13e-49 3
#> CV:mclust 140 1.000 1.000 6.13e-49 3
#> MAD:mclust 139 1.000 1.000 1.57e-48 3
#> ATC:mclust 133 0.961 0.997 1.99e-34 3
#> SD:kmeans 120 0.989 1.000 2.12e-21 3
#> CV:kmeans 55 0.999 1.000 6.87e-12 3
#> MAD:kmeans 72 0.981 0.997 3.93e-14 3
#> ATC:kmeans 115 0.922 0.944 1.18e-20 3
#> SD:pam 140 0.996 1.000 1.51e-43 3
#> CV:pam 139 0.991 1.000 7.70e-43 3
#> MAD:pam 137 0.983 1.000 1.97e-42 3
#> ATC:pam 128 0.582 0.970 1.27e-28 3
#> SD:hclust 140 1.000 1.000 6.13e-49 3
#> CV:hclust 126 1.000 1.000 2.01e-44 3
#> MAD:hclust 130 1.000 1.000 7.50e-45 3
#> ATC:hclust 139 0.923 0.985 8.08e-26 3
test_to_known_factors(res_list, k = 4)
#> n temperature(p) time(p) specimen(p) k
#> SD:NMF 127 1.000 1.000 6.42e-60 4
#> CV:NMF 123 1.000 1.000 6.04e-58 4
#> MAD:NMF 131 1.000 1.000 1.82e-55 4
#> ATC:NMF 125 0.959 0.996 4.50e-42 4
#> SD:skmeans 133 1.000 1.000 1.69e-64 4
#> CV:skmeans 123 0.999 1.000 8.47e-60 4
#> MAD:skmeans 137 1.000 1.000 3.18e-67 4
#> ATC:skmeans 123 0.990 1.000 1.20e-46 4
#> SD:mclust 140 1.000 1.000 4.16e-72 4
#> CV:mclust 64 0.999 1.000 7.35e-24 4
#> MAD:mclust 139 1.000 1.000 1.71e-71 4
#> ATC:mclust 138 0.994 1.000 1.49e-55 4
#> SD:kmeans 118 1.000 1.000 1.92e-60 4
#> CV:kmeans 104 1.000 1.000 1.14e-53 4
#> MAD:kmeans 105 1.000 1.000 3.14e-52 4
#> ATC:kmeans 129 0.968 0.882 6.42e-34 4
#> SD:pam 137 1.000 1.000 7.38e-65 4
#> CV:pam 119 1.000 1.000 1.26e-55 4
#> MAD:pam 136 1.000 1.000 3.30e-62 4
#> ATC:pam 139 0.947 0.999 3.30e-51 4
#> SD:hclust 140 1.000 1.000 4.16e-72 4
#> CV:hclust 84 1.000 1.000 7.20e-31 4
#> MAD:hclust 130 1.000 1.000 5.41e-66 4
#> ATC:hclust 134 0.989 1.000 3.20e-47 4
test_to_known_factors(res_list, k = 5)
#> n temperature(p) time(p) specimen(p) k
#> SD:NMF 107 1.000 1.000 1.48e-66 5
#> CV:NMF 132 1.000 1.000 4.42e-86 5
#> MAD:NMF 114 1.000 1.000 1.62e-70 5
#> ATC:NMF 130 0.991 0.994 3.23e-64 5
#> SD:skmeans 84 1.000 1.000 3.39e-59 5
#> CV:skmeans 132 1.000 1.000 9.80e-89 5
#> MAD:skmeans 118 1.000 1.000 5.17e-76 5
#> ATC:skmeans 129 0.973 1.000 6.92e-57 5
#> SD:mclust 135 1.000 1.000 3.55e-91 5
#> CV:mclust 126 1.000 1.000 7.26e-84 5
#> MAD:mclust 85 1.000 1.000 1.36e-57 5
#> ATC:mclust 137 0.982 1.000 9.07e-78 5
#> SD:kmeans 97 1.000 1.000 5.30e-65 5
#> CV:kmeans 108 1.000 1.000 5.93e-74 5
#> MAD:kmeans 77 0.969 0.994 5.82e-39 5
#> ATC:kmeans 130 0.993 0.975 9.10e-54 5
#> SD:pam 136 1.000 1.000 6.48e-85 5
#> CV:pam 138 1.000 1.000 5.56e-81 5
#> MAD:pam 134 1.000 1.000 1.83e-79 5
#> ATC:pam 136 0.961 0.999 1.09e-62 5
#> SD:hclust 140 1.000 1.000 2.99e-95 5
#> CV:hclust 140 1.000 1.000 2.99e-95 5
#> MAD:hclust 124 0.966 1.000 3.02e-82 5
#> ATC:hclust 109 0.968 0.999 8.76e-31 5
test_to_known_factors(res_list, k = 6)
#> n temperature(p) time(p) specimen(p) k
#> SD:NMF 124 1.000 1.000 1.71e-98 6
#> CV:NMF 130 1.000 1.000 1.38e-106 6
#> MAD:NMF 119 0.997 1.000 1.30e-84 6
#> ATC:NMF 131 0.996 1.000 3.22e-84 6
#> SD:skmeans 139 1.000 1.000 2.31e-117 6
#> CV:skmeans 100 1.000 1.000 1.23e-83 6
#> MAD:skmeans 132 1.000 1.000 3.04e-110 6
#> ATC:skmeans 136 1.000 0.999 1.73e-78 6
#> SD:mclust 125 1.000 1.000 4.24e-106 6
#> CV:mclust 126 1.000 1.000 4.05e-107 6
#> MAD:mclust 82 0.993 1.000 3.70e-55 6
#> ATC:mclust 137 0.980 1.000 1.32e-97 6
#> SD:kmeans 122 1.000 1.000 4.84e-103 6
#> CV:kmeans 119 1.000 1.000 5.46e-100 6
#> MAD:kmeans 104 1.000 1.000 1.06e-87 6
#> ATC:kmeans 130 0.994 0.958 2.00e-54 6
#> SD:pam 138 1.000 1.000 1.51e-107 6
#> CV:pam 138 1.000 1.000 4.39e-104 6
#> MAD:pam 139 1.000 1.000 3.89e-105 6
#> ATC:pam 139 0.969 1.000 3.15e-74 6
#> SD:hclust 140 1.000 1.000 2.21e-118 6
#> CV:hclust 140 1.000 1.000 2.21e-118 6
#> MAD:hclust 134 1.000 1.000 2.83e-112 6
#> ATC:hclust 139 0.999 1.000 9.49e-84 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 140 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 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 0.468 0.897 0.875 0.247 0.819 0.819
#> 3 3 0.551 0.891 0.914 1.167 0.597 0.508
#> 4 4 0.618 0.878 0.915 0.185 0.940 0.855
#> 5 5 0.742 0.858 0.863 0.116 0.960 0.887
#> 6 6 0.865 0.904 0.932 0.137 0.879 0.616
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
#> GSM1009062 1 0.518 0.893 0.884 0.116
#> GSM1009076 1 0.343 0.886 0.936 0.064
#> GSM1009090 1 0.518 0.893 0.884 0.116
#> GSM1009104 1 0.563 0.832 0.868 0.132
#> GSM1009118 1 0.343 0.886 0.936 0.064
#> GSM1009132 1 0.518 0.893 0.884 0.116
#> GSM1009146 1 0.506 0.894 0.888 0.112
#> GSM1009160 2 0.518 1.000 0.116 0.884
#> GSM1009174 1 0.343 0.886 0.936 0.064
#> GSM1009188 1 0.000 0.900 1.000 0.000
#> GSM1009063 1 0.518 0.893 0.884 0.116
#> GSM1009077 1 0.343 0.886 0.936 0.064
#> GSM1009091 1 0.518 0.893 0.884 0.116
#> GSM1009105 1 0.563 0.832 0.868 0.132
#> GSM1009119 1 0.141 0.897 0.980 0.020
#> GSM1009133 1 0.518 0.893 0.884 0.116
#> GSM1009147 1 0.506 0.894 0.888 0.112
#> GSM1009161 2 0.518 1.000 0.116 0.884
#> GSM1009175 1 0.343 0.886 0.936 0.064
#> GSM1009189 1 0.000 0.900 1.000 0.000
#> GSM1009064 1 0.518 0.893 0.884 0.116
#> GSM1009078 1 0.343 0.886 0.936 0.064
#> GSM1009092 1 0.518 0.893 0.884 0.116
#> GSM1009106 1 0.563 0.832 0.868 0.132
#> GSM1009120 1 0.141 0.897 0.980 0.020
#> GSM1009134 1 0.518 0.893 0.884 0.116
#> GSM1009148 1 0.506 0.894 0.888 0.112
#> GSM1009162 2 0.518 1.000 0.116 0.884
#> GSM1009176 1 0.343 0.886 0.936 0.064
#> GSM1009190 1 0.000 0.900 1.000 0.000
#> GSM1009065 1 0.518 0.893 0.884 0.116
#> GSM1009079 1 0.343 0.886 0.936 0.064
#> GSM1009093 1 0.518 0.893 0.884 0.116
#> GSM1009107 1 0.563 0.832 0.868 0.132
#> GSM1009121 1 0.343 0.886 0.936 0.064
#> GSM1009135 1 0.518 0.893 0.884 0.116
#> GSM1009149 1 0.518 0.893 0.884 0.116
#> GSM1009163 2 0.518 1.000 0.116 0.884
#> GSM1009177 1 0.343 0.886 0.936 0.064
#> GSM1009191 1 0.000 0.900 1.000 0.000
#> GSM1009066 1 0.518 0.893 0.884 0.116
#> GSM1009080 1 0.343 0.886 0.936 0.064
#> GSM1009094 1 0.518 0.893 0.884 0.116
#> GSM1009108 1 0.563 0.832 0.868 0.132
#> GSM1009122 1 0.343 0.886 0.936 0.064
#> GSM1009136 1 0.518 0.893 0.884 0.116
#> GSM1009150 1 0.518 0.893 0.884 0.116
#> GSM1009164 2 0.518 1.000 0.116 0.884
#> GSM1009178 1 0.343 0.886 0.936 0.064
#> GSM1009192 1 0.000 0.900 1.000 0.000
#> GSM1009067 1 0.518 0.893 0.884 0.116
#> GSM1009081 1 0.343 0.886 0.936 0.064
#> GSM1009095 1 0.518 0.893 0.884 0.116
#> GSM1009109 1 0.563 0.832 0.868 0.132
#> GSM1009123 1 0.141 0.897 0.980 0.020
#> GSM1009137 1 0.518 0.893 0.884 0.116
#> GSM1009151 1 0.506 0.894 0.888 0.112
#> GSM1009165 2 0.518 1.000 0.116 0.884
#> GSM1009179 1 0.343 0.886 0.936 0.064
#> GSM1009193 1 0.000 0.900 1.000 0.000
#> GSM1009068 1 0.518 0.893 0.884 0.116
#> GSM1009082 1 0.343 0.886 0.936 0.064
#> GSM1009096 1 0.518 0.893 0.884 0.116
#> GSM1009110 1 0.563 0.832 0.868 0.132
#> GSM1009124 1 0.141 0.897 0.980 0.020
#> GSM1009138 1 0.518 0.893 0.884 0.116
#> GSM1009152 1 0.506 0.894 0.888 0.112
#> GSM1009166 2 0.518 1.000 0.116 0.884
#> GSM1009180 1 0.343 0.886 0.936 0.064
#> GSM1009194 1 0.000 0.900 1.000 0.000
#> GSM1009069 1 0.518 0.893 0.884 0.116
#> GSM1009083 1 0.343 0.886 0.936 0.064
#> GSM1009097 1 0.518 0.893 0.884 0.116
#> GSM1009111 1 0.563 0.832 0.868 0.132
#> GSM1009125 1 0.343 0.886 0.936 0.064
#> GSM1009139 1 0.518 0.893 0.884 0.116
#> GSM1009153 1 0.506 0.894 0.888 0.112
#> GSM1009167 2 0.518 1.000 0.116 0.884
#> GSM1009181 1 0.343 0.886 0.936 0.064
#> GSM1009195 1 0.000 0.900 1.000 0.000
#> GSM1009070 1 0.518 0.893 0.884 0.116
#> GSM1009084 1 0.343 0.886 0.936 0.064
#> GSM1009098 1 0.518 0.893 0.884 0.116
#> GSM1009112 1 0.563 0.832 0.868 0.132
#> GSM1009126 1 0.141 0.897 0.980 0.020
#> GSM1009140 1 0.518 0.893 0.884 0.116
#> GSM1009154 1 0.506 0.894 0.888 0.112
#> GSM1009168 2 0.518 1.000 0.116 0.884
#> GSM1009182 1 0.343 0.886 0.936 0.064
#> GSM1009196 1 0.000 0.900 1.000 0.000
#> GSM1009071 1 0.518 0.893 0.884 0.116
#> GSM1009085 1 0.343 0.886 0.936 0.064
#> GSM1009099 1 0.518 0.893 0.884 0.116
#> GSM1009113 1 0.563 0.832 0.868 0.132
#> GSM1009127 1 0.141 0.897 0.980 0.020
#> GSM1009141 1 0.518 0.893 0.884 0.116
#> GSM1009155 1 0.506 0.894 0.888 0.112
#> GSM1009169 2 0.518 1.000 0.116 0.884
#> GSM1009183 1 0.343 0.886 0.936 0.064
#> GSM1009197 1 0.000 0.900 1.000 0.000
#> GSM1009072 1 0.518 0.893 0.884 0.116
#> GSM1009086 1 0.343 0.886 0.936 0.064
#> GSM1009100 1 0.518 0.893 0.884 0.116
#> GSM1009114 1 0.563 0.832 0.868 0.132
#> GSM1009128 1 0.343 0.886 0.936 0.064
#> GSM1009142 1 0.518 0.893 0.884 0.116
#> GSM1009156 1 0.506 0.894 0.888 0.112
#> GSM1009170 2 0.518 1.000 0.116 0.884
#> GSM1009184 1 0.343 0.886 0.936 0.064
#> GSM1009198 1 0.000 0.900 1.000 0.000
#> GSM1009073 1 0.518 0.893 0.884 0.116
#> GSM1009087 1 0.343 0.886 0.936 0.064
#> GSM1009101 1 0.518 0.893 0.884 0.116
#> GSM1009115 1 0.563 0.832 0.868 0.132
#> GSM1009129 1 0.343 0.886 0.936 0.064
#> GSM1009143 1 0.518 0.893 0.884 0.116
#> GSM1009157 1 0.506 0.894 0.888 0.112
#> GSM1009171 2 0.518 1.000 0.116 0.884
#> GSM1009185 1 0.343 0.886 0.936 0.064
#> GSM1009199 1 0.000 0.900 1.000 0.000
#> GSM1009074 1 0.518 0.893 0.884 0.116
#> GSM1009088 1 0.343 0.886 0.936 0.064
#> GSM1009102 1 0.518 0.893 0.884 0.116
#> GSM1009116 1 0.563 0.832 0.868 0.132
#> GSM1009130 1 0.343 0.886 0.936 0.064
#> GSM1009144 1 0.518 0.893 0.884 0.116
#> GSM1009158 1 0.518 0.893 0.884 0.116
#> GSM1009172 2 0.518 1.000 0.116 0.884
#> GSM1009186 1 0.343 0.886 0.936 0.064
#> GSM1009200 1 0.000 0.900 1.000 0.000
#> GSM1009075 1 0.518 0.893 0.884 0.116
#> GSM1009089 1 0.343 0.886 0.936 0.064
#> GSM1009103 1 0.518 0.893 0.884 0.116
#> GSM1009117 1 0.563 0.832 0.868 0.132
#> GSM1009131 1 0.343 0.886 0.936 0.064
#> GSM1009145 1 0.518 0.893 0.884 0.116
#> GSM1009159 1 0.518 0.893 0.884 0.116
#> GSM1009173 2 0.518 1.000 0.116 0.884
#> GSM1009187 1 0.343 0.886 0.936 0.064
#> GSM1009201 1 0.000 0.900 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1009062 1 0.0000 0.957 1.000 0.000 0.000
#> GSM1009076 2 0.4399 0.890 0.188 0.812 0.000
#> GSM1009090 1 0.0000 0.957 1.000 0.000 0.000
#> GSM1009104 2 0.0237 0.740 0.000 0.996 0.004
#> GSM1009118 2 0.4702 0.888 0.212 0.788 0.000
#> GSM1009132 1 0.0000 0.957 1.000 0.000 0.000
#> GSM1009146 1 0.0237 0.955 0.996 0.004 0.000
#> GSM1009160 3 0.0000 1.000 0.000 0.000 1.000
#> GSM1009174 2 0.4654 0.890 0.208 0.792 0.000
#> GSM1009188 1 0.4002 0.796 0.840 0.160 0.000
#> GSM1009063 1 0.0000 0.957 1.000 0.000 0.000
#> GSM1009077 2 0.4399 0.890 0.188 0.812 0.000
#> GSM1009091 1 0.0000 0.957 1.000 0.000 0.000
#> GSM1009105 2 0.0237 0.740 0.000 0.996 0.004
#> GSM1009119 2 0.6126 0.606 0.400 0.600 0.000
#> GSM1009133 1 0.0000 0.957 1.000 0.000 0.000
#> GSM1009147 1 0.0237 0.955 0.996 0.004 0.000
#> GSM1009161 3 0.0000 1.000 0.000 0.000 1.000
#> GSM1009175 2 0.4654 0.890 0.208 0.792 0.000
#> GSM1009189 1 0.4002 0.796 0.840 0.160 0.000
#> GSM1009064 1 0.0000 0.957 1.000 0.000 0.000
#> GSM1009078 2 0.4399 0.890 0.188 0.812 0.000
#> GSM1009092 1 0.0000 0.957 1.000 0.000 0.000
#> GSM1009106 2 0.0237 0.740 0.000 0.996 0.004
#> GSM1009120 2 0.6126 0.606 0.400 0.600 0.000
#> GSM1009134 1 0.0000 0.957 1.000 0.000 0.000
#> GSM1009148 1 0.0237 0.955 0.996 0.004 0.000
#> GSM1009162 3 0.0000 1.000 0.000 0.000 1.000
#> GSM1009176 2 0.4654 0.890 0.208 0.792 0.000
#> GSM1009190 1 0.4002 0.796 0.840 0.160 0.000
#> GSM1009065 1 0.0000 0.957 1.000 0.000 0.000
#> GSM1009079 2 0.4399 0.890 0.188 0.812 0.000
#> GSM1009093 1 0.0000 0.957 1.000 0.000 0.000
#> GSM1009107 2 0.0237 0.740 0.000 0.996 0.004
#> GSM1009121 2 0.4702 0.888 0.212 0.788 0.000
#> GSM1009135 1 0.0000 0.957 1.000 0.000 0.000
#> GSM1009149 1 0.0000 0.957 1.000 0.000 0.000
#> GSM1009163 3 0.0000 1.000 0.000 0.000 1.000
#> GSM1009177 2 0.4654 0.890 0.208 0.792 0.000
#> GSM1009191 1 0.4002 0.796 0.840 0.160 0.000
#> GSM1009066 1 0.0000 0.957 1.000 0.000 0.000
#> GSM1009080 2 0.4399 0.890 0.188 0.812 0.000
#> GSM1009094 1 0.0000 0.957 1.000 0.000 0.000
#> GSM1009108 2 0.0237 0.740 0.000 0.996 0.004
#> GSM1009122 2 0.4702 0.888 0.212 0.788 0.000
#> GSM1009136 1 0.0000 0.957 1.000 0.000 0.000
#> GSM1009150 1 0.0000 0.957 1.000 0.000 0.000
#> GSM1009164 3 0.0000 1.000 0.000 0.000 1.000
#> GSM1009178 2 0.4654 0.890 0.208 0.792 0.000
#> GSM1009192 1 0.4002 0.796 0.840 0.160 0.000
#> GSM1009067 1 0.0000 0.957 1.000 0.000 0.000
#> GSM1009081 2 0.4399 0.890 0.188 0.812 0.000
#> GSM1009095 1 0.0000 0.957 1.000 0.000 0.000
#> GSM1009109 2 0.0237 0.740 0.000 0.996 0.004
#> GSM1009123 2 0.6126 0.606 0.400 0.600 0.000
#> GSM1009137 1 0.0000 0.957 1.000 0.000 0.000
#> GSM1009151 1 0.0237 0.955 0.996 0.004 0.000
#> GSM1009165 3 0.0000 1.000 0.000 0.000 1.000
#> GSM1009179 2 0.4654 0.890 0.208 0.792 0.000
#> GSM1009193 1 0.4002 0.796 0.840 0.160 0.000
#> GSM1009068 1 0.0000 0.957 1.000 0.000 0.000
#> GSM1009082 2 0.4399 0.890 0.188 0.812 0.000
#> GSM1009096 1 0.0000 0.957 1.000 0.000 0.000
#> GSM1009110 2 0.0237 0.740 0.000 0.996 0.004
#> GSM1009124 2 0.6126 0.606 0.400 0.600 0.000
#> GSM1009138 1 0.0000 0.957 1.000 0.000 0.000
#> GSM1009152 1 0.0237 0.955 0.996 0.004 0.000
#> GSM1009166 3 0.0000 1.000 0.000 0.000 1.000
#> GSM1009180 2 0.4654 0.890 0.208 0.792 0.000
#> GSM1009194 1 0.4002 0.796 0.840 0.160 0.000
#> GSM1009069 1 0.0000 0.957 1.000 0.000 0.000
#> GSM1009083 2 0.4399 0.890 0.188 0.812 0.000
#> GSM1009097 1 0.0000 0.957 1.000 0.000 0.000
#> GSM1009111 2 0.0237 0.740 0.000 0.996 0.004
#> GSM1009125 2 0.4702 0.888 0.212 0.788 0.000
#> GSM1009139 1 0.0000 0.957 1.000 0.000 0.000
#> GSM1009153 1 0.0237 0.955 0.996 0.004 0.000
#> GSM1009167 3 0.0000 1.000 0.000 0.000 1.000
#> GSM1009181 2 0.4654 0.890 0.208 0.792 0.000
#> GSM1009195 1 0.4002 0.796 0.840 0.160 0.000
#> GSM1009070 1 0.0000 0.957 1.000 0.000 0.000
#> GSM1009084 2 0.4399 0.890 0.188 0.812 0.000
#> GSM1009098 1 0.0000 0.957 1.000 0.000 0.000
#> GSM1009112 2 0.0237 0.740 0.000 0.996 0.004
#> GSM1009126 2 0.6126 0.606 0.400 0.600 0.000
#> GSM1009140 1 0.0000 0.957 1.000 0.000 0.000
#> GSM1009154 1 0.0237 0.955 0.996 0.004 0.000
#> GSM1009168 3 0.0000 1.000 0.000 0.000 1.000
#> GSM1009182 2 0.4654 0.890 0.208 0.792 0.000
#> GSM1009196 1 0.4002 0.796 0.840 0.160 0.000
#> GSM1009071 1 0.0000 0.957 1.000 0.000 0.000
#> GSM1009085 2 0.4399 0.890 0.188 0.812 0.000
#> GSM1009099 1 0.0000 0.957 1.000 0.000 0.000
#> GSM1009113 2 0.0237 0.740 0.000 0.996 0.004
#> GSM1009127 2 0.6126 0.606 0.400 0.600 0.000
#> GSM1009141 1 0.0000 0.957 1.000 0.000 0.000
#> GSM1009155 1 0.0237 0.955 0.996 0.004 0.000
#> GSM1009169 3 0.0000 1.000 0.000 0.000 1.000
#> GSM1009183 2 0.4654 0.890 0.208 0.792 0.000
#> GSM1009197 1 0.4002 0.796 0.840 0.160 0.000
#> GSM1009072 1 0.0000 0.957 1.000 0.000 0.000
#> GSM1009086 2 0.4399 0.890 0.188 0.812 0.000
#> GSM1009100 1 0.0000 0.957 1.000 0.000 0.000
#> GSM1009114 2 0.0237 0.740 0.000 0.996 0.004
#> GSM1009128 2 0.4702 0.888 0.212 0.788 0.000
#> GSM1009142 1 0.0000 0.957 1.000 0.000 0.000
#> GSM1009156 1 0.0237 0.955 0.996 0.004 0.000
#> GSM1009170 3 0.0000 1.000 0.000 0.000 1.000
#> GSM1009184 2 0.4654 0.890 0.208 0.792 0.000
#> GSM1009198 1 0.4002 0.796 0.840 0.160 0.000
#> GSM1009073 1 0.0000 0.957 1.000 0.000 0.000
#> GSM1009087 2 0.4399 0.890 0.188 0.812 0.000
#> GSM1009101 1 0.0000 0.957 1.000 0.000 0.000
#> GSM1009115 2 0.0237 0.740 0.000 0.996 0.004
#> GSM1009129 2 0.4702 0.888 0.212 0.788 0.000
#> GSM1009143 1 0.0000 0.957 1.000 0.000 0.000
#> GSM1009157 1 0.0237 0.955 0.996 0.004 0.000
#> GSM1009171 3 0.0000 1.000 0.000 0.000 1.000
#> GSM1009185 2 0.4654 0.890 0.208 0.792 0.000
#> GSM1009199 1 0.4002 0.796 0.840 0.160 0.000
#> GSM1009074 1 0.0000 0.957 1.000 0.000 0.000
#> GSM1009088 2 0.4399 0.890 0.188 0.812 0.000
#> GSM1009102 1 0.0000 0.957 1.000 0.000 0.000
#> GSM1009116 2 0.0237 0.740 0.000 0.996 0.004
#> GSM1009130 2 0.4702 0.888 0.212 0.788 0.000
#> GSM1009144 1 0.0000 0.957 1.000 0.000 0.000
#> GSM1009158 1 0.0000 0.957 1.000 0.000 0.000
#> GSM1009172 3 0.0000 1.000 0.000 0.000 1.000
#> GSM1009186 2 0.4654 0.890 0.208 0.792 0.000
#> GSM1009200 1 0.4002 0.796 0.840 0.160 0.000
#> GSM1009075 1 0.0000 0.957 1.000 0.000 0.000
#> GSM1009089 2 0.4399 0.890 0.188 0.812 0.000
#> GSM1009103 1 0.0000 0.957 1.000 0.000 0.000
#> GSM1009117 2 0.0237 0.740 0.000 0.996 0.004
#> GSM1009131 2 0.4702 0.888 0.212 0.788 0.000
#> GSM1009145 1 0.0000 0.957 1.000 0.000 0.000
#> GSM1009159 1 0.0000 0.957 1.000 0.000 0.000
#> GSM1009173 3 0.0000 1.000 0.000 0.000 1.000
#> GSM1009187 2 0.4654 0.890 0.208 0.792 0.000
#> GSM1009201 1 0.4002 0.796 0.840 0.160 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1009062 1 0.3528 0.849 0.808 0.192 0 0.000
#> GSM1009076 2 0.0817 0.948 0.000 0.976 0 0.024
#> GSM1009090 1 0.0000 0.819 1.000 0.000 0 0.000
#> GSM1009104 4 0.0000 1.000 0.000 0.000 0 1.000
#> GSM1009118 2 0.0000 0.951 0.000 1.000 0 0.000
#> GSM1009132 1 0.0000 0.819 1.000 0.000 0 0.000
#> GSM1009146 1 0.3569 0.847 0.804 0.196 0 0.000
#> GSM1009160 3 0.0000 1.000 0.000 0.000 1 0.000
#> GSM1009174 2 0.0188 0.952 0.000 0.996 0 0.004
#> GSM1009188 1 0.4679 0.691 0.648 0.352 0 0.000
#> GSM1009063 1 0.3528 0.849 0.808 0.192 0 0.000
#> GSM1009077 2 0.0817 0.948 0.000 0.976 0 0.024
#> GSM1009091 1 0.0000 0.819 1.000 0.000 0 0.000
#> GSM1009105 4 0.0000 1.000 0.000 0.000 0 1.000
#> GSM1009119 2 0.3486 0.720 0.188 0.812 0 0.000
#> GSM1009133 1 0.0000 0.819 1.000 0.000 0 0.000
#> GSM1009147 1 0.3569 0.847 0.804 0.196 0 0.000
#> GSM1009161 3 0.0000 1.000 0.000 0.000 1 0.000
#> GSM1009175 2 0.0188 0.952 0.000 0.996 0 0.004
#> GSM1009189 1 0.4679 0.691 0.648 0.352 0 0.000
#> GSM1009064 1 0.3528 0.849 0.808 0.192 0 0.000
#> GSM1009078 2 0.0817 0.948 0.000 0.976 0 0.024
#> GSM1009092 1 0.0000 0.819 1.000 0.000 0 0.000
#> GSM1009106 4 0.0000 1.000 0.000 0.000 0 1.000
#> GSM1009120 2 0.3486 0.720 0.188 0.812 0 0.000
#> GSM1009134 1 0.0000 0.819 1.000 0.000 0 0.000
#> GSM1009148 1 0.3569 0.847 0.804 0.196 0 0.000
#> GSM1009162 3 0.0000 1.000 0.000 0.000 1 0.000
#> GSM1009176 2 0.0188 0.952 0.000 0.996 0 0.004
#> GSM1009190 1 0.4679 0.691 0.648 0.352 0 0.000
#> GSM1009065 1 0.3528 0.849 0.808 0.192 0 0.000
#> GSM1009079 2 0.0817 0.948 0.000 0.976 0 0.024
#> GSM1009093 1 0.0000 0.819 1.000 0.000 0 0.000
#> GSM1009107 4 0.0000 1.000 0.000 0.000 0 1.000
#> GSM1009121 2 0.0000 0.951 0.000 1.000 0 0.000
#> GSM1009135 1 0.0000 0.819 1.000 0.000 0 0.000
#> GSM1009149 1 0.3528 0.849 0.808 0.192 0 0.000
#> GSM1009163 3 0.0000 1.000 0.000 0.000 1 0.000
#> GSM1009177 2 0.0188 0.952 0.000 0.996 0 0.004
#> GSM1009191 1 0.4679 0.691 0.648 0.352 0 0.000
#> GSM1009066 1 0.3528 0.849 0.808 0.192 0 0.000
#> GSM1009080 2 0.0817 0.948 0.000 0.976 0 0.024
#> GSM1009094 1 0.0000 0.819 1.000 0.000 0 0.000
#> GSM1009108 4 0.0000 1.000 0.000 0.000 0 1.000
#> GSM1009122 2 0.0000 0.951 0.000 1.000 0 0.000
#> GSM1009136 1 0.0000 0.819 1.000 0.000 0 0.000
#> GSM1009150 1 0.3528 0.849 0.808 0.192 0 0.000
#> GSM1009164 3 0.0000 1.000 0.000 0.000 1 0.000
#> GSM1009178 2 0.0188 0.952 0.000 0.996 0 0.004
#> GSM1009192 1 0.4679 0.691 0.648 0.352 0 0.000
#> GSM1009067 1 0.3528 0.849 0.808 0.192 0 0.000
#> GSM1009081 2 0.0817 0.948 0.000 0.976 0 0.024
#> GSM1009095 1 0.0000 0.819 1.000 0.000 0 0.000
#> GSM1009109 4 0.0000 1.000 0.000 0.000 0 1.000
#> GSM1009123 2 0.3486 0.720 0.188 0.812 0 0.000
#> GSM1009137 1 0.0000 0.819 1.000 0.000 0 0.000
#> GSM1009151 1 0.3569 0.847 0.804 0.196 0 0.000
#> GSM1009165 3 0.0000 1.000 0.000 0.000 1 0.000
#> GSM1009179 2 0.0188 0.952 0.000 0.996 0 0.004
#> GSM1009193 1 0.4679 0.691 0.648 0.352 0 0.000
#> GSM1009068 1 0.3528 0.849 0.808 0.192 0 0.000
#> GSM1009082 2 0.0817 0.948 0.000 0.976 0 0.024
#> GSM1009096 1 0.0000 0.819 1.000 0.000 0 0.000
#> GSM1009110 4 0.0000 1.000 0.000 0.000 0 1.000
#> GSM1009124 2 0.3486 0.720 0.188 0.812 0 0.000
#> GSM1009138 1 0.0000 0.819 1.000 0.000 0 0.000
#> GSM1009152 1 0.3569 0.847 0.804 0.196 0 0.000
#> GSM1009166 3 0.0000 1.000 0.000 0.000 1 0.000
#> GSM1009180 2 0.0188 0.952 0.000 0.996 0 0.004
#> GSM1009194 1 0.4679 0.691 0.648 0.352 0 0.000
#> GSM1009069 1 0.3528 0.849 0.808 0.192 0 0.000
#> GSM1009083 2 0.0817 0.948 0.000 0.976 0 0.024
#> GSM1009097 1 0.0000 0.819 1.000 0.000 0 0.000
#> GSM1009111 4 0.0000 1.000 0.000 0.000 0 1.000
#> GSM1009125 2 0.0000 0.951 0.000 1.000 0 0.000
#> GSM1009139 1 0.0000 0.819 1.000 0.000 0 0.000
#> GSM1009153 1 0.3569 0.847 0.804 0.196 0 0.000
#> GSM1009167 3 0.0000 1.000 0.000 0.000 1 0.000
#> GSM1009181 2 0.0188 0.952 0.000 0.996 0 0.004
#> GSM1009195 1 0.4679 0.691 0.648 0.352 0 0.000
#> GSM1009070 1 0.3528 0.849 0.808 0.192 0 0.000
#> GSM1009084 2 0.0817 0.948 0.000 0.976 0 0.024
#> GSM1009098 1 0.0000 0.819 1.000 0.000 0 0.000
#> GSM1009112 4 0.0000 1.000 0.000 0.000 0 1.000
#> GSM1009126 2 0.3486 0.720 0.188 0.812 0 0.000
#> GSM1009140 1 0.0000 0.819 1.000 0.000 0 0.000
#> GSM1009154 1 0.3569 0.847 0.804 0.196 0 0.000
#> GSM1009168 3 0.0000 1.000 0.000 0.000 1 0.000
#> GSM1009182 2 0.0188 0.952 0.000 0.996 0 0.004
#> GSM1009196 1 0.4679 0.691 0.648 0.352 0 0.000
#> GSM1009071 1 0.3528 0.849 0.808 0.192 0 0.000
#> GSM1009085 2 0.0817 0.948 0.000 0.976 0 0.024
#> GSM1009099 1 0.0000 0.819 1.000 0.000 0 0.000
#> GSM1009113 4 0.0000 1.000 0.000 0.000 0 1.000
#> GSM1009127 2 0.3486 0.720 0.188 0.812 0 0.000
#> GSM1009141 1 0.0000 0.819 1.000 0.000 0 0.000
#> GSM1009155 1 0.3569 0.847 0.804 0.196 0 0.000
#> GSM1009169 3 0.0000 1.000 0.000 0.000 1 0.000
#> GSM1009183 2 0.0188 0.952 0.000 0.996 0 0.004
#> GSM1009197 1 0.4679 0.691 0.648 0.352 0 0.000
#> GSM1009072 1 0.3528 0.849 0.808 0.192 0 0.000
#> GSM1009086 2 0.0817 0.948 0.000 0.976 0 0.024
#> GSM1009100 1 0.0000 0.819 1.000 0.000 0 0.000
#> GSM1009114 4 0.0000 1.000 0.000 0.000 0 1.000
#> GSM1009128 2 0.0000 0.951 0.000 1.000 0 0.000
#> GSM1009142 1 0.0000 0.819 1.000 0.000 0 0.000
#> GSM1009156 1 0.3569 0.847 0.804 0.196 0 0.000
#> GSM1009170 3 0.0000 1.000 0.000 0.000 1 0.000
#> GSM1009184 2 0.0188 0.952 0.000 0.996 0 0.004
#> GSM1009198 1 0.4679 0.691 0.648 0.352 0 0.000
#> GSM1009073 1 0.3528 0.849 0.808 0.192 0 0.000
#> GSM1009087 2 0.0817 0.948 0.000 0.976 0 0.024
#> GSM1009101 1 0.0000 0.819 1.000 0.000 0 0.000
#> GSM1009115 4 0.0000 1.000 0.000 0.000 0 1.000
#> GSM1009129 2 0.0000 0.951 0.000 1.000 0 0.000
#> GSM1009143 1 0.0000 0.819 1.000 0.000 0 0.000
#> GSM1009157 1 0.3569 0.847 0.804 0.196 0 0.000
#> GSM1009171 3 0.0000 1.000 0.000 0.000 1 0.000
#> GSM1009185 2 0.0188 0.952 0.000 0.996 0 0.004
#> GSM1009199 1 0.4679 0.691 0.648 0.352 0 0.000
#> GSM1009074 1 0.3528 0.849 0.808 0.192 0 0.000
#> GSM1009088 2 0.0817 0.948 0.000 0.976 0 0.024
#> GSM1009102 1 0.0000 0.819 1.000 0.000 0 0.000
#> GSM1009116 4 0.0000 1.000 0.000 0.000 0 1.000
#> GSM1009130 2 0.0000 0.951 0.000 1.000 0 0.000
#> GSM1009144 1 0.0000 0.819 1.000 0.000 0 0.000
#> GSM1009158 1 0.3528 0.849 0.808 0.192 0 0.000
#> GSM1009172 3 0.0000 1.000 0.000 0.000 1 0.000
#> GSM1009186 2 0.0188 0.952 0.000 0.996 0 0.004
#> GSM1009200 1 0.4679 0.691 0.648 0.352 0 0.000
#> GSM1009075 1 0.3528 0.849 0.808 0.192 0 0.000
#> GSM1009089 2 0.0817 0.948 0.000 0.976 0 0.024
#> GSM1009103 1 0.0000 0.819 1.000 0.000 0 0.000
#> GSM1009117 4 0.0000 1.000 0.000 0.000 0 1.000
#> GSM1009131 2 0.0000 0.951 0.000 1.000 0 0.000
#> GSM1009145 1 0.0000 0.819 1.000 0.000 0 0.000
#> GSM1009159 1 0.3528 0.849 0.808 0.192 0 0.000
#> GSM1009173 3 0.0000 1.000 0.000 0.000 1 0.000
#> GSM1009187 2 0.0188 0.952 0.000 0.996 0 0.004
#> GSM1009201 1 0.4679 0.691 0.648 0.352 0 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM1009062 1 0.374 0.832 0.732 0.004 0 0.264 0.000
#> GSM1009076 4 0.333 1.000 0.000 0.208 0 0.788 0.004
#> GSM1009090 1 0.000 0.810 1.000 0.000 0 0.000 0.000
#> GSM1009104 5 0.000 1.000 0.000 0.000 0 0.000 1.000
#> GSM1009118 2 0.000 0.825 0.000 1.000 0 0.000 0.000
#> GSM1009132 1 0.000 0.810 1.000 0.000 0 0.000 0.000
#> GSM1009146 1 0.453 0.831 0.728 0.060 0 0.212 0.000
#> GSM1009160 3 0.000 1.000 0.000 0.000 1 0.000 0.000
#> GSM1009174 2 0.223 0.837 0.000 0.884 0 0.116 0.000
#> GSM1009188 1 0.609 0.712 0.572 0.216 0 0.212 0.000
#> GSM1009063 1 0.374 0.832 0.732 0.004 0 0.264 0.000
#> GSM1009077 4 0.333 1.000 0.000 0.208 0 0.788 0.004
#> GSM1009091 1 0.000 0.810 1.000 0.000 0 0.000 0.000
#> GSM1009105 5 0.000 1.000 0.000 0.000 0 0.000 1.000
#> GSM1009119 2 0.300 0.642 0.188 0.812 0 0.000 0.000
#> GSM1009133 1 0.000 0.810 1.000 0.000 0 0.000 0.000
#> GSM1009147 1 0.453 0.831 0.728 0.060 0 0.212 0.000
#> GSM1009161 3 0.000 1.000 0.000 0.000 1 0.000 0.000
#> GSM1009175 2 0.223 0.837 0.000 0.884 0 0.116 0.000
#> GSM1009189 1 0.609 0.712 0.572 0.216 0 0.212 0.000
#> GSM1009064 1 0.374 0.832 0.732 0.004 0 0.264 0.000
#> GSM1009078 4 0.333 1.000 0.000 0.208 0 0.788 0.004
#> GSM1009092 1 0.000 0.810 1.000 0.000 0 0.000 0.000
#> GSM1009106 5 0.000 1.000 0.000 0.000 0 0.000 1.000
#> GSM1009120 2 0.300 0.642 0.188 0.812 0 0.000 0.000
#> GSM1009134 1 0.000 0.810 1.000 0.000 0 0.000 0.000
#> GSM1009148 1 0.453 0.831 0.728 0.060 0 0.212 0.000
#> GSM1009162 3 0.000 1.000 0.000 0.000 1 0.000 0.000
#> GSM1009176 2 0.223 0.837 0.000 0.884 0 0.116 0.000
#> GSM1009190 1 0.609 0.712 0.572 0.216 0 0.212 0.000
#> GSM1009065 1 0.374 0.832 0.732 0.004 0 0.264 0.000
#> GSM1009079 4 0.333 1.000 0.000 0.208 0 0.788 0.004
#> GSM1009093 1 0.000 0.810 1.000 0.000 0 0.000 0.000
#> GSM1009107 5 0.000 1.000 0.000 0.000 0 0.000 1.000
#> GSM1009121 2 0.000 0.825 0.000 1.000 0 0.000 0.000
#> GSM1009135 1 0.000 0.810 1.000 0.000 0 0.000 0.000
#> GSM1009149 1 0.447 0.832 0.732 0.056 0 0.212 0.000
#> GSM1009163 3 0.000 1.000 0.000 0.000 1 0.000 0.000
#> GSM1009177 2 0.223 0.837 0.000 0.884 0 0.116 0.000
#> GSM1009191 1 0.609 0.712 0.572 0.216 0 0.212 0.000
#> GSM1009066 1 0.374 0.832 0.732 0.004 0 0.264 0.000
#> GSM1009080 4 0.333 1.000 0.000 0.208 0 0.788 0.004
#> GSM1009094 1 0.000 0.810 1.000 0.000 0 0.000 0.000
#> GSM1009108 5 0.000 1.000 0.000 0.000 0 0.000 1.000
#> GSM1009122 2 0.000 0.825 0.000 1.000 0 0.000 0.000
#> GSM1009136 1 0.000 0.810 1.000 0.000 0 0.000 0.000
#> GSM1009150 1 0.447 0.832 0.732 0.056 0 0.212 0.000
#> GSM1009164 3 0.000 1.000 0.000 0.000 1 0.000 0.000
#> GSM1009178 2 0.223 0.837 0.000 0.884 0 0.116 0.000
#> GSM1009192 1 0.609 0.712 0.572 0.216 0 0.212 0.000
#> GSM1009067 1 0.374 0.832 0.732 0.004 0 0.264 0.000
#> GSM1009081 4 0.333 1.000 0.000 0.208 0 0.788 0.004
#> GSM1009095 1 0.000 0.810 1.000 0.000 0 0.000 0.000
#> GSM1009109 5 0.000 1.000 0.000 0.000 0 0.000 1.000
#> GSM1009123 2 0.300 0.642 0.188 0.812 0 0.000 0.000
#> GSM1009137 1 0.000 0.810 1.000 0.000 0 0.000 0.000
#> GSM1009151 1 0.453 0.831 0.728 0.060 0 0.212 0.000
#> GSM1009165 3 0.000 1.000 0.000 0.000 1 0.000 0.000
#> GSM1009179 2 0.223 0.837 0.000 0.884 0 0.116 0.000
#> GSM1009193 1 0.609 0.712 0.572 0.216 0 0.212 0.000
#> GSM1009068 1 0.374 0.832 0.732 0.004 0 0.264 0.000
#> GSM1009082 4 0.333 1.000 0.000 0.208 0 0.788 0.004
#> GSM1009096 1 0.000 0.810 1.000 0.000 0 0.000 0.000
#> GSM1009110 5 0.000 1.000 0.000 0.000 0 0.000 1.000
#> GSM1009124 2 0.300 0.642 0.188 0.812 0 0.000 0.000
#> GSM1009138 1 0.000 0.810 1.000 0.000 0 0.000 0.000
#> GSM1009152 1 0.453 0.831 0.728 0.060 0 0.212 0.000
#> GSM1009166 3 0.000 1.000 0.000 0.000 1 0.000 0.000
#> GSM1009180 2 0.223 0.837 0.000 0.884 0 0.116 0.000
#> GSM1009194 1 0.609 0.712 0.572 0.216 0 0.212 0.000
#> GSM1009069 1 0.374 0.832 0.732 0.004 0 0.264 0.000
#> GSM1009083 4 0.333 1.000 0.000 0.208 0 0.788 0.004
#> GSM1009097 1 0.000 0.810 1.000 0.000 0 0.000 0.000
#> GSM1009111 5 0.000 1.000 0.000 0.000 0 0.000 1.000
#> GSM1009125 2 0.000 0.825 0.000 1.000 0 0.000 0.000
#> GSM1009139 1 0.000 0.810 1.000 0.000 0 0.000 0.000
#> GSM1009153 1 0.453 0.831 0.728 0.060 0 0.212 0.000
#> GSM1009167 3 0.000 1.000 0.000 0.000 1 0.000 0.000
#> GSM1009181 2 0.223 0.837 0.000 0.884 0 0.116 0.000
#> GSM1009195 1 0.609 0.712 0.572 0.216 0 0.212 0.000
#> GSM1009070 1 0.374 0.832 0.732 0.004 0 0.264 0.000
#> GSM1009084 4 0.333 1.000 0.000 0.208 0 0.788 0.004
#> GSM1009098 1 0.000 0.810 1.000 0.000 0 0.000 0.000
#> GSM1009112 5 0.000 1.000 0.000 0.000 0 0.000 1.000
#> GSM1009126 2 0.300 0.642 0.188 0.812 0 0.000 0.000
#> GSM1009140 1 0.000 0.810 1.000 0.000 0 0.000 0.000
#> GSM1009154 1 0.453 0.831 0.728 0.060 0 0.212 0.000
#> GSM1009168 3 0.000 1.000 0.000 0.000 1 0.000 0.000
#> GSM1009182 2 0.223 0.837 0.000 0.884 0 0.116 0.000
#> GSM1009196 1 0.609 0.712 0.572 0.216 0 0.212 0.000
#> GSM1009071 1 0.374 0.832 0.732 0.004 0 0.264 0.000
#> GSM1009085 4 0.333 1.000 0.000 0.208 0 0.788 0.004
#> GSM1009099 1 0.000 0.810 1.000 0.000 0 0.000 0.000
#> GSM1009113 5 0.000 1.000 0.000 0.000 0 0.000 1.000
#> GSM1009127 2 0.300 0.642 0.188 0.812 0 0.000 0.000
#> GSM1009141 1 0.000 0.810 1.000 0.000 0 0.000 0.000
#> GSM1009155 1 0.453 0.831 0.728 0.060 0 0.212 0.000
#> GSM1009169 3 0.000 1.000 0.000 0.000 1 0.000 0.000
#> GSM1009183 2 0.223 0.837 0.000 0.884 0 0.116 0.000
#> GSM1009197 1 0.609 0.712 0.572 0.216 0 0.212 0.000
#> GSM1009072 1 0.374 0.832 0.732 0.004 0 0.264 0.000
#> GSM1009086 4 0.333 1.000 0.000 0.208 0 0.788 0.004
#> GSM1009100 1 0.000 0.810 1.000 0.000 0 0.000 0.000
#> GSM1009114 5 0.000 1.000 0.000 0.000 0 0.000 1.000
#> GSM1009128 2 0.000 0.825 0.000 1.000 0 0.000 0.000
#> GSM1009142 1 0.000 0.810 1.000 0.000 0 0.000 0.000
#> GSM1009156 1 0.453 0.831 0.728 0.060 0 0.212 0.000
#> GSM1009170 3 0.000 1.000 0.000 0.000 1 0.000 0.000
#> GSM1009184 2 0.223 0.837 0.000 0.884 0 0.116 0.000
#> GSM1009198 1 0.609 0.712 0.572 0.216 0 0.212 0.000
#> GSM1009073 1 0.374 0.832 0.732 0.004 0 0.264 0.000
#> GSM1009087 4 0.333 1.000 0.000 0.208 0 0.788 0.004
#> GSM1009101 1 0.000 0.810 1.000 0.000 0 0.000 0.000
#> GSM1009115 5 0.000 1.000 0.000 0.000 0 0.000 1.000
#> GSM1009129 2 0.000 0.825 0.000 1.000 0 0.000 0.000
#> GSM1009143 1 0.000 0.810 1.000 0.000 0 0.000 0.000
#> GSM1009157 1 0.453 0.831 0.728 0.060 0 0.212 0.000
#> GSM1009171 3 0.000 1.000 0.000 0.000 1 0.000 0.000
#> GSM1009185 2 0.223 0.837 0.000 0.884 0 0.116 0.000
#> GSM1009199 1 0.609 0.712 0.572 0.216 0 0.212 0.000
#> GSM1009074 1 0.374 0.832 0.732 0.004 0 0.264 0.000
#> GSM1009088 4 0.333 1.000 0.000 0.208 0 0.788 0.004
#> GSM1009102 1 0.000 0.810 1.000 0.000 0 0.000 0.000
#> GSM1009116 5 0.000 1.000 0.000 0.000 0 0.000 1.000
#> GSM1009130 2 0.000 0.825 0.000 1.000 0 0.000 0.000
#> GSM1009144 1 0.000 0.810 1.000 0.000 0 0.000 0.000
#> GSM1009158 1 0.447 0.832 0.732 0.056 0 0.212 0.000
#> GSM1009172 3 0.000 1.000 0.000 0.000 1 0.000 0.000
#> GSM1009186 2 0.223 0.837 0.000 0.884 0 0.116 0.000
#> GSM1009200 1 0.609 0.712 0.572 0.216 0 0.212 0.000
#> GSM1009075 1 0.374 0.832 0.732 0.004 0 0.264 0.000
#> GSM1009089 4 0.333 1.000 0.000 0.208 0 0.788 0.004
#> GSM1009103 1 0.000 0.810 1.000 0.000 0 0.000 0.000
#> GSM1009117 5 0.000 1.000 0.000 0.000 0 0.000 1.000
#> GSM1009131 2 0.000 0.825 0.000 1.000 0 0.000 0.000
#> GSM1009145 1 0.000 0.810 1.000 0.000 0 0.000 0.000
#> GSM1009159 1 0.447 0.832 0.732 0.056 0 0.212 0.000
#> GSM1009173 3 0.000 1.000 0.000 0.000 1 0.000 0.000
#> GSM1009187 2 0.223 0.837 0.000 0.884 0 0.116 0.000
#> GSM1009201 1 0.609 0.712 0.572 0.216 0 0.212 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1009062 1 0.114 0.889 0.948 0.000 0 0.000 0 0.052
#> GSM1009076 6 0.000 1.000 0.000 0.000 0 0.000 0 1.000
#> GSM1009090 4 0.000 1.000 0.000 0.000 0 1.000 0 0.000
#> GSM1009104 5 0.000 1.000 0.000 0.000 0 0.000 1 0.000
#> GSM1009118 2 0.000 0.734 0.000 1.000 0 0.000 0 0.000
#> GSM1009132 4 0.000 1.000 0.000 0.000 0 1.000 0 0.000
#> GSM1009146 1 0.141 0.896 0.936 0.004 0 0.060 0 0.000
#> GSM1009160 3 0.000 1.000 0.000 0.000 1 0.000 0 0.000
#> GSM1009174 2 0.371 0.715 0.008 0.680 0 0.000 0 0.312
#> GSM1009188 1 0.245 0.844 0.840 0.160 0 0.000 0 0.000
#> GSM1009063 1 0.114 0.889 0.948 0.000 0 0.000 0 0.052
#> GSM1009077 6 0.000 1.000 0.000 0.000 0 0.000 0 1.000
#> GSM1009091 4 0.000 1.000 0.000 0.000 0 1.000 0 0.000
#> GSM1009105 5 0.000 1.000 0.000 0.000 0 0.000 1 0.000
#> GSM1009119 2 0.270 0.637 0.188 0.812 0 0.000 0 0.000
#> GSM1009133 4 0.000 1.000 0.000 0.000 0 1.000 0 0.000
#> GSM1009147 1 0.141 0.896 0.936 0.004 0 0.060 0 0.000
#> GSM1009161 3 0.000 1.000 0.000 0.000 1 0.000 0 0.000
#> GSM1009175 2 0.371 0.715 0.008 0.680 0 0.000 0 0.312
#> GSM1009189 1 0.245 0.844 0.840 0.160 0 0.000 0 0.000
#> GSM1009064 1 0.114 0.889 0.948 0.000 0 0.000 0 0.052
#> GSM1009078 6 0.000 1.000 0.000 0.000 0 0.000 0 1.000
#> GSM1009092 4 0.000 1.000 0.000 0.000 0 1.000 0 0.000
#> GSM1009106 5 0.000 1.000 0.000 0.000 0 0.000 1 0.000
#> GSM1009120 2 0.270 0.637 0.188 0.812 0 0.000 0 0.000
#> GSM1009134 4 0.000 1.000 0.000 0.000 0 1.000 0 0.000
#> GSM1009148 1 0.141 0.896 0.936 0.004 0 0.060 0 0.000
#> GSM1009162 3 0.000 1.000 0.000 0.000 1 0.000 0 0.000
#> GSM1009176 2 0.371 0.715 0.008 0.680 0 0.000 0 0.312
#> GSM1009190 1 0.245 0.844 0.840 0.160 0 0.000 0 0.000
#> GSM1009065 1 0.114 0.889 0.948 0.000 0 0.000 0 0.052
#> GSM1009079 6 0.000 1.000 0.000 0.000 0 0.000 0 1.000
#> GSM1009093 4 0.000 1.000 0.000 0.000 0 1.000 0 0.000
#> GSM1009107 5 0.000 1.000 0.000 0.000 0 0.000 1 0.000
#> GSM1009121 2 0.000 0.734 0.000 1.000 0 0.000 0 0.000
#> GSM1009135 4 0.000 1.000 0.000 0.000 0 1.000 0 0.000
#> GSM1009149 1 0.133 0.894 0.936 0.000 0 0.064 0 0.000
#> GSM1009163 3 0.000 1.000 0.000 0.000 1 0.000 0 0.000
#> GSM1009177 2 0.371 0.715 0.008 0.680 0 0.000 0 0.312
#> GSM1009191 1 0.245 0.844 0.840 0.160 0 0.000 0 0.000
#> GSM1009066 1 0.114 0.889 0.948 0.000 0 0.000 0 0.052
#> GSM1009080 6 0.000 1.000 0.000 0.000 0 0.000 0 1.000
#> GSM1009094 4 0.000 1.000 0.000 0.000 0 1.000 0 0.000
#> GSM1009108 5 0.000 1.000 0.000 0.000 0 0.000 1 0.000
#> GSM1009122 2 0.000 0.734 0.000 1.000 0 0.000 0 0.000
#> GSM1009136 4 0.000 1.000 0.000 0.000 0 1.000 0 0.000
#> GSM1009150 1 0.133 0.894 0.936 0.000 0 0.064 0 0.000
#> GSM1009164 3 0.000 1.000 0.000 0.000 1 0.000 0 0.000
#> GSM1009178 2 0.371 0.715 0.008 0.680 0 0.000 0 0.312
#> GSM1009192 1 0.245 0.844 0.840 0.160 0 0.000 0 0.000
#> GSM1009067 1 0.114 0.889 0.948 0.000 0 0.000 0 0.052
#> GSM1009081 6 0.000 1.000 0.000 0.000 0 0.000 0 1.000
#> GSM1009095 4 0.000 1.000 0.000 0.000 0 1.000 0 0.000
#> GSM1009109 5 0.000 1.000 0.000 0.000 0 0.000 1 0.000
#> GSM1009123 2 0.270 0.637 0.188 0.812 0 0.000 0 0.000
#> GSM1009137 4 0.000 1.000 0.000 0.000 0 1.000 0 0.000
#> GSM1009151 1 0.141 0.896 0.936 0.004 0 0.060 0 0.000
#> GSM1009165 3 0.000 1.000 0.000 0.000 1 0.000 0 0.000
#> GSM1009179 2 0.371 0.715 0.008 0.680 0 0.000 0 0.312
#> GSM1009193 1 0.245 0.844 0.840 0.160 0 0.000 0 0.000
#> GSM1009068 1 0.114 0.889 0.948 0.000 0 0.000 0 0.052
#> GSM1009082 6 0.000 1.000 0.000 0.000 0 0.000 0 1.000
#> GSM1009096 4 0.000 1.000 0.000 0.000 0 1.000 0 0.000
#> GSM1009110 5 0.000 1.000 0.000 0.000 0 0.000 1 0.000
#> GSM1009124 2 0.270 0.637 0.188 0.812 0 0.000 0 0.000
#> GSM1009138 4 0.000 1.000 0.000 0.000 0 1.000 0 0.000
#> GSM1009152 1 0.141 0.896 0.936 0.004 0 0.060 0 0.000
#> GSM1009166 3 0.000 1.000 0.000 0.000 1 0.000 0 0.000
#> GSM1009180 2 0.371 0.715 0.008 0.680 0 0.000 0 0.312
#> GSM1009194 1 0.245 0.844 0.840 0.160 0 0.000 0 0.000
#> GSM1009069 1 0.114 0.889 0.948 0.000 0 0.000 0 0.052
#> GSM1009083 6 0.000 1.000 0.000 0.000 0 0.000 0 1.000
#> GSM1009097 4 0.000 1.000 0.000 0.000 0 1.000 0 0.000
#> GSM1009111 5 0.000 1.000 0.000 0.000 0 0.000 1 0.000
#> GSM1009125 2 0.000 0.734 0.000 1.000 0 0.000 0 0.000
#> GSM1009139 4 0.000 1.000 0.000 0.000 0 1.000 0 0.000
#> GSM1009153 1 0.141 0.896 0.936 0.004 0 0.060 0 0.000
#> GSM1009167 3 0.000 1.000 0.000 0.000 1 0.000 0 0.000
#> GSM1009181 2 0.371 0.715 0.008 0.680 0 0.000 0 0.312
#> GSM1009195 1 0.245 0.844 0.840 0.160 0 0.000 0 0.000
#> GSM1009070 1 0.114 0.889 0.948 0.000 0 0.000 0 0.052
#> GSM1009084 6 0.000 1.000 0.000 0.000 0 0.000 0 1.000
#> GSM1009098 4 0.000 1.000 0.000 0.000 0 1.000 0 0.000
#> GSM1009112 5 0.000 1.000 0.000 0.000 0 0.000 1 0.000
#> GSM1009126 2 0.270 0.637 0.188 0.812 0 0.000 0 0.000
#> GSM1009140 4 0.000 1.000 0.000 0.000 0 1.000 0 0.000
#> GSM1009154 1 0.141 0.896 0.936 0.004 0 0.060 0 0.000
#> GSM1009168 3 0.000 1.000 0.000 0.000 1 0.000 0 0.000
#> GSM1009182 2 0.371 0.715 0.008 0.680 0 0.000 0 0.312
#> GSM1009196 1 0.245 0.844 0.840 0.160 0 0.000 0 0.000
#> GSM1009071 1 0.114 0.889 0.948 0.000 0 0.000 0 0.052
#> GSM1009085 6 0.000 1.000 0.000 0.000 0 0.000 0 1.000
#> GSM1009099 4 0.000 1.000 0.000 0.000 0 1.000 0 0.000
#> GSM1009113 5 0.000 1.000 0.000 0.000 0 0.000 1 0.000
#> GSM1009127 2 0.270 0.637 0.188 0.812 0 0.000 0 0.000
#> GSM1009141 4 0.000 1.000 0.000 0.000 0 1.000 0 0.000
#> GSM1009155 1 0.141 0.896 0.936 0.004 0 0.060 0 0.000
#> GSM1009169 3 0.000 1.000 0.000 0.000 1 0.000 0 0.000
#> GSM1009183 2 0.371 0.715 0.008 0.680 0 0.000 0 0.312
#> GSM1009197 1 0.245 0.844 0.840 0.160 0 0.000 0 0.000
#> GSM1009072 1 0.114 0.889 0.948 0.000 0 0.000 0 0.052
#> GSM1009086 6 0.000 1.000 0.000 0.000 0 0.000 0 1.000
#> GSM1009100 4 0.000 1.000 0.000 0.000 0 1.000 0 0.000
#> GSM1009114 5 0.000 1.000 0.000 0.000 0 0.000 1 0.000
#> GSM1009128 2 0.000 0.734 0.000 1.000 0 0.000 0 0.000
#> GSM1009142 4 0.000 1.000 0.000 0.000 0 1.000 0 0.000
#> GSM1009156 1 0.141 0.896 0.936 0.004 0 0.060 0 0.000
#> GSM1009170 3 0.000 1.000 0.000 0.000 1 0.000 0 0.000
#> GSM1009184 2 0.371 0.715 0.008 0.680 0 0.000 0 0.312
#> GSM1009198 1 0.245 0.844 0.840 0.160 0 0.000 0 0.000
#> GSM1009073 1 0.114 0.889 0.948 0.000 0 0.000 0 0.052
#> GSM1009087 6 0.000 1.000 0.000 0.000 0 0.000 0 1.000
#> GSM1009101 4 0.000 1.000 0.000 0.000 0 1.000 0 0.000
#> GSM1009115 5 0.000 1.000 0.000 0.000 0 0.000 1 0.000
#> GSM1009129 2 0.000 0.734 0.000 1.000 0 0.000 0 0.000
#> GSM1009143 4 0.000 1.000 0.000 0.000 0 1.000 0 0.000
#> GSM1009157 1 0.141 0.896 0.936 0.004 0 0.060 0 0.000
#> GSM1009171 3 0.000 1.000 0.000 0.000 1 0.000 0 0.000
#> GSM1009185 2 0.371 0.715 0.008 0.680 0 0.000 0 0.312
#> GSM1009199 1 0.245 0.844 0.840 0.160 0 0.000 0 0.000
#> GSM1009074 1 0.114 0.889 0.948 0.000 0 0.000 0 0.052
#> GSM1009088 6 0.000 1.000 0.000 0.000 0 0.000 0 1.000
#> GSM1009102 4 0.000 1.000 0.000 0.000 0 1.000 0 0.000
#> GSM1009116 5 0.000 1.000 0.000 0.000 0 0.000 1 0.000
#> GSM1009130 2 0.000 0.734 0.000 1.000 0 0.000 0 0.000
#> GSM1009144 4 0.000 1.000 0.000 0.000 0 1.000 0 0.000
#> GSM1009158 1 0.133 0.894 0.936 0.000 0 0.064 0 0.000
#> GSM1009172 3 0.000 1.000 0.000 0.000 1 0.000 0 0.000
#> GSM1009186 2 0.371 0.715 0.008 0.680 0 0.000 0 0.312
#> GSM1009200 1 0.245 0.844 0.840 0.160 0 0.000 0 0.000
#> GSM1009075 1 0.114 0.889 0.948 0.000 0 0.000 0 0.052
#> GSM1009089 6 0.000 1.000 0.000 0.000 0 0.000 0 1.000
#> GSM1009103 4 0.000 1.000 0.000 0.000 0 1.000 0 0.000
#> GSM1009117 5 0.000 1.000 0.000 0.000 0 0.000 1 0.000
#> GSM1009131 2 0.000 0.734 0.000 1.000 0 0.000 0 0.000
#> GSM1009145 4 0.000 1.000 0.000 0.000 0 1.000 0 0.000
#> GSM1009159 1 0.133 0.894 0.936 0.000 0 0.064 0 0.000
#> GSM1009173 3 0.000 1.000 0.000 0.000 1 0.000 0 0.000
#> GSM1009187 2 0.371 0.715 0.008 0.680 0 0.000 0 0.312
#> GSM1009201 1 0.245 0.844 0.840 0.160 0 0.000 0 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 temperature(p) time(p) specimen(p) k
#> SD:hclust 140 1 1 1.03e-25 2
#> SD:hclust 140 1 1 6.13e-49 3
#> SD:hclust 140 1 1 4.16e-72 4
#> SD:hclust 140 1 1 2.99e-95 5
#> SD:hclust 140 1 1 2.21e-118 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 140 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 5.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.323 0.802 0.846 0.3979 0.514 0.514
#> 3 3 0.396 0.625 0.731 0.4323 0.958 0.921
#> 4 4 0.383 0.643 0.685 0.1684 0.750 0.518
#> 5 5 0.476 0.666 0.693 0.1081 0.873 0.600
#> 6 6 0.612 0.744 0.695 0.0561 0.960 0.828
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 5
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM1009062 1 0.482 0.882 0.896 0.104
#> GSM1009076 2 0.861 0.795 0.284 0.716
#> GSM1009090 1 0.224 0.888 0.964 0.036
#> GSM1009104 2 0.745 0.810 0.212 0.788
#> GSM1009118 1 0.634 0.792 0.840 0.160
#> GSM1009132 1 0.224 0.888 0.964 0.036
#> GSM1009146 1 0.327 0.907 0.940 0.060
#> GSM1009160 2 0.443 0.724 0.092 0.908
#> GSM1009174 2 0.987 0.646 0.432 0.568
#> GSM1009188 1 0.343 0.907 0.936 0.064
#> GSM1009063 1 0.482 0.882 0.896 0.104
#> GSM1009077 2 0.861 0.795 0.284 0.716
#> GSM1009091 1 0.224 0.888 0.964 0.036
#> GSM1009105 2 0.745 0.810 0.212 0.788
#> GSM1009119 1 0.343 0.907 0.936 0.064
#> GSM1009133 1 0.224 0.888 0.964 0.036
#> GSM1009147 1 0.327 0.907 0.940 0.060
#> GSM1009161 2 0.443 0.724 0.092 0.908
#> GSM1009175 2 0.987 0.646 0.432 0.568
#> GSM1009189 1 0.343 0.907 0.936 0.064
#> GSM1009064 1 0.482 0.882 0.896 0.104
#> GSM1009078 2 0.921 0.753 0.336 0.664
#> GSM1009092 1 0.224 0.888 0.964 0.036
#> GSM1009106 2 0.745 0.810 0.212 0.788
#> GSM1009120 1 0.327 0.907 0.940 0.060
#> GSM1009134 1 0.224 0.888 0.964 0.036
#> GSM1009148 1 0.327 0.907 0.940 0.060
#> GSM1009162 2 0.443 0.724 0.092 0.908
#> GSM1009176 2 0.980 0.673 0.416 0.584
#> GSM1009190 1 0.343 0.907 0.936 0.064
#> GSM1009065 1 0.482 0.882 0.896 0.104
#> GSM1009079 2 0.861 0.795 0.284 0.716
#> GSM1009093 1 0.224 0.888 0.964 0.036
#> GSM1009107 2 0.745 0.810 0.212 0.788
#> GSM1009121 1 0.634 0.792 0.840 0.160
#> GSM1009135 1 0.224 0.888 0.964 0.036
#> GSM1009149 1 0.311 0.908 0.944 0.056
#> GSM1009163 2 0.443 0.724 0.092 0.908
#> GSM1009177 2 0.980 0.673 0.416 0.584
#> GSM1009191 1 0.343 0.907 0.936 0.064
#> GSM1009066 1 0.482 0.882 0.896 0.104
#> GSM1009080 2 0.861 0.795 0.284 0.716
#> GSM1009094 1 0.224 0.888 0.964 0.036
#> GSM1009108 2 0.745 0.810 0.212 0.788
#> GSM1009122 1 0.980 -0.142 0.584 0.416
#> GSM1009136 1 0.224 0.888 0.964 0.036
#> GSM1009150 1 0.311 0.908 0.944 0.056
#> GSM1009164 2 0.443 0.724 0.092 0.908
#> GSM1009178 2 0.991 0.620 0.444 0.556
#> GSM1009192 1 0.327 0.907 0.940 0.060
#> GSM1009067 1 0.482 0.882 0.896 0.104
#> GSM1009081 2 0.861 0.795 0.284 0.716
#> GSM1009095 1 0.163 0.888 0.976 0.024
#> GSM1009109 2 0.745 0.810 0.212 0.788
#> GSM1009123 1 0.327 0.908 0.940 0.060
#> GSM1009137 1 0.224 0.888 0.964 0.036
#> GSM1009151 1 0.327 0.907 0.940 0.060
#> GSM1009165 2 0.443 0.724 0.092 0.908
#> GSM1009179 2 0.991 0.620 0.444 0.556
#> GSM1009193 1 0.343 0.907 0.936 0.064
#> GSM1009068 1 0.482 0.882 0.896 0.104
#> GSM1009082 2 0.861 0.795 0.284 0.716
#> GSM1009096 1 0.224 0.888 0.964 0.036
#> GSM1009110 2 0.745 0.810 0.212 0.788
#> GSM1009124 1 0.388 0.902 0.924 0.076
#> GSM1009138 1 0.224 0.888 0.964 0.036
#> GSM1009152 1 0.327 0.907 0.940 0.060
#> GSM1009166 2 0.443 0.724 0.092 0.908
#> GSM1009180 2 0.991 0.620 0.444 0.556
#> GSM1009194 1 0.327 0.907 0.940 0.060
#> GSM1009069 1 0.482 0.882 0.896 0.104
#> GSM1009083 2 0.861 0.795 0.284 0.716
#> GSM1009097 1 0.224 0.888 0.964 0.036
#> GSM1009111 2 0.745 0.810 0.212 0.788
#> GSM1009125 2 0.999 0.524 0.480 0.520
#> GSM1009139 1 0.224 0.888 0.964 0.036
#> GSM1009153 1 0.327 0.907 0.940 0.060
#> GSM1009167 2 0.443 0.724 0.092 0.908
#> GSM1009181 2 0.980 0.673 0.416 0.584
#> GSM1009195 1 0.358 0.905 0.932 0.068
#> GSM1009070 1 0.482 0.882 0.896 0.104
#> GSM1009084 2 0.861 0.795 0.284 0.716
#> GSM1009098 1 0.224 0.888 0.964 0.036
#> GSM1009112 2 0.745 0.810 0.212 0.788
#> GSM1009126 1 0.388 0.902 0.924 0.076
#> GSM1009140 1 0.224 0.888 0.964 0.036
#> GSM1009154 1 0.327 0.907 0.940 0.060
#> GSM1009168 2 0.443 0.724 0.092 0.908
#> GSM1009182 2 0.987 0.646 0.432 0.568
#> GSM1009196 1 0.327 0.907 0.940 0.060
#> GSM1009071 1 0.482 0.882 0.896 0.104
#> GSM1009085 2 0.861 0.795 0.284 0.716
#> GSM1009099 1 0.224 0.888 0.964 0.036
#> GSM1009113 2 0.745 0.810 0.212 0.788
#> GSM1009127 1 0.327 0.907 0.940 0.060
#> GSM1009141 1 0.224 0.888 0.964 0.036
#> GSM1009155 1 0.327 0.907 0.940 0.060
#> GSM1009169 2 0.443 0.724 0.092 0.908
#> GSM1009183 2 0.980 0.673 0.416 0.584
#> GSM1009197 1 0.327 0.907 0.940 0.060
#> GSM1009072 1 0.482 0.882 0.896 0.104
#> GSM1009086 2 0.861 0.795 0.284 0.716
#> GSM1009100 1 0.224 0.888 0.964 0.036
#> GSM1009114 2 0.745 0.810 0.212 0.788
#> GSM1009128 1 0.891 0.402 0.692 0.308
#> GSM1009142 1 0.224 0.888 0.964 0.036
#> GSM1009156 1 0.327 0.907 0.940 0.060
#> GSM1009170 2 0.443 0.724 0.092 0.908
#> GSM1009184 2 0.987 0.646 0.432 0.568
#> GSM1009198 1 0.343 0.907 0.936 0.064
#> GSM1009073 1 0.482 0.882 0.896 0.104
#> GSM1009087 2 0.921 0.753 0.336 0.664
#> GSM1009101 1 0.224 0.888 0.964 0.036
#> GSM1009115 2 0.745 0.810 0.212 0.788
#> GSM1009129 2 0.985 0.651 0.428 0.572
#> GSM1009143 1 0.224 0.888 0.964 0.036
#> GSM1009157 1 0.327 0.907 0.940 0.060
#> GSM1009171 2 0.443 0.724 0.092 0.908
#> GSM1009185 2 0.992 0.611 0.448 0.552
#> GSM1009199 1 0.343 0.907 0.936 0.064
#> GSM1009074 1 0.482 0.882 0.896 0.104
#> GSM1009088 2 0.900 0.772 0.316 0.684
#> GSM1009102 1 0.224 0.888 0.964 0.036
#> GSM1009116 2 0.745 0.810 0.212 0.788
#> GSM1009130 2 0.891 0.783 0.308 0.692
#> GSM1009144 1 0.224 0.888 0.964 0.036
#> GSM1009158 1 0.327 0.907 0.940 0.060
#> GSM1009172 2 0.443 0.724 0.092 0.908
#> GSM1009186 2 0.987 0.646 0.432 0.568
#> GSM1009200 1 0.343 0.907 0.936 0.064
#> GSM1009075 1 0.482 0.882 0.896 0.104
#> GSM1009089 1 0.992 -0.195 0.552 0.448
#> GSM1009103 1 0.224 0.888 0.964 0.036
#> GSM1009117 2 0.745 0.810 0.212 0.788
#> GSM1009131 1 0.904 0.343 0.680 0.320
#> GSM1009145 1 0.224 0.888 0.964 0.036
#> GSM1009159 1 0.311 0.908 0.944 0.056
#> GSM1009173 2 0.443 0.724 0.092 0.908
#> GSM1009187 1 0.946 0.159 0.636 0.364
#> GSM1009201 1 0.343 0.907 0.936 0.064
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1009062 1 0.7147 0.6344 0.696 0.076 NA
#> GSM1009076 2 0.7297 0.6717 0.188 0.704 NA
#> GSM1009090 1 0.6148 0.6624 0.640 0.004 NA
#> GSM1009104 2 0.3091 0.6971 0.072 0.912 NA
#> GSM1009118 1 0.4790 0.6201 0.848 0.096 NA
#> GSM1009132 1 0.6373 0.6498 0.588 0.004 NA
#> GSM1009146 1 0.2229 0.7345 0.944 0.012 NA
#> GSM1009160 2 0.7640 0.5809 0.056 0.592 NA
#> GSM1009174 2 0.9082 0.4837 0.392 0.468 NA
#> GSM1009188 1 0.0424 0.7372 0.992 0.008 NA
#> GSM1009063 1 0.7147 0.6344 0.696 0.076 NA
#> GSM1009077 2 0.7297 0.6717 0.188 0.704 NA
#> GSM1009091 1 0.6148 0.6624 0.640 0.004 NA
#> GSM1009105 2 0.3091 0.6971 0.072 0.912 NA
#> GSM1009119 1 0.0237 0.7383 0.996 0.000 NA
#> GSM1009133 1 0.6373 0.6498 0.588 0.004 NA
#> GSM1009147 1 0.1999 0.7329 0.952 0.012 NA
#> GSM1009161 2 0.7640 0.5809 0.056 0.592 NA
#> GSM1009175 2 0.9082 0.4837 0.392 0.468 NA
#> GSM1009189 1 0.0424 0.7372 0.992 0.008 NA
#> GSM1009064 1 0.7147 0.6344 0.696 0.076 NA
#> GSM1009078 2 0.8201 0.6110 0.276 0.612 NA
#> GSM1009092 1 0.6148 0.6624 0.640 0.004 NA
#> GSM1009106 2 0.3091 0.6971 0.072 0.912 NA
#> GSM1009120 1 0.0424 0.7391 0.992 0.000 NA
#> GSM1009134 1 0.6373 0.6498 0.588 0.004 NA
#> GSM1009148 1 0.2229 0.7345 0.944 0.012 NA
#> GSM1009162 2 0.7620 0.5809 0.056 0.596 NA
#> GSM1009176 2 0.9082 0.4837 0.392 0.468 NA
#> GSM1009190 1 0.0424 0.7372 0.992 0.008 NA
#> GSM1009065 1 0.7147 0.6344 0.696 0.076 NA
#> GSM1009079 2 0.7297 0.6717 0.188 0.704 NA
#> GSM1009093 1 0.6148 0.6624 0.640 0.004 NA
#> GSM1009107 2 0.3091 0.6971 0.072 0.912 NA
#> GSM1009121 1 0.4859 0.6012 0.840 0.116 NA
#> GSM1009135 1 0.6373 0.6498 0.588 0.004 NA
#> GSM1009149 1 0.2229 0.7345 0.944 0.012 NA
#> GSM1009163 2 0.7640 0.5809 0.056 0.592 NA
#> GSM1009177 2 0.9082 0.4837 0.392 0.468 NA
#> GSM1009191 1 0.0424 0.7372 0.992 0.008 NA
#> GSM1009066 1 0.7147 0.6344 0.696 0.076 NA
#> GSM1009080 2 0.7297 0.6717 0.188 0.704 NA
#> GSM1009094 1 0.6148 0.6624 0.640 0.004 NA
#> GSM1009108 2 0.3091 0.6971 0.072 0.912 NA
#> GSM1009122 1 0.7394 0.1732 0.652 0.284 NA
#> GSM1009136 1 0.6359 0.6502 0.592 0.004 NA
#> GSM1009150 1 0.2339 0.7355 0.940 0.012 NA
#> GSM1009164 2 0.7640 0.5809 0.056 0.592 NA
#> GSM1009178 2 0.9093 0.4685 0.400 0.460 NA
#> GSM1009192 1 0.0424 0.7372 0.992 0.008 NA
#> GSM1009067 1 0.7147 0.6344 0.696 0.076 NA
#> GSM1009081 2 0.7297 0.6717 0.188 0.704 NA
#> GSM1009095 1 0.6148 0.6624 0.640 0.004 NA
#> GSM1009109 2 0.3091 0.6971 0.072 0.912 NA
#> GSM1009123 1 0.0592 0.7379 0.988 0.000 NA
#> GSM1009137 1 0.6373 0.6498 0.588 0.004 NA
#> GSM1009151 1 0.2229 0.7345 0.944 0.012 NA
#> GSM1009165 2 0.7620 0.5809 0.056 0.596 NA
#> GSM1009179 2 0.9093 0.4685 0.400 0.460 NA
#> GSM1009193 1 0.0424 0.7372 0.992 0.008 NA
#> GSM1009068 1 0.7147 0.6344 0.696 0.076 NA
#> GSM1009082 2 0.7297 0.6717 0.188 0.704 NA
#> GSM1009096 1 0.6148 0.6624 0.640 0.004 NA
#> GSM1009110 2 0.3091 0.6971 0.072 0.912 NA
#> GSM1009124 1 0.1765 0.7260 0.956 0.004 NA
#> GSM1009138 1 0.6373 0.6498 0.588 0.004 NA
#> GSM1009152 1 0.2229 0.7345 0.944 0.012 NA
#> GSM1009166 2 0.7620 0.5809 0.056 0.596 NA
#> GSM1009180 2 0.9102 0.4517 0.408 0.452 NA
#> GSM1009194 1 0.0661 0.7374 0.988 0.008 NA
#> GSM1009069 1 0.7106 0.6331 0.700 0.076 NA
#> GSM1009083 2 0.7458 0.6653 0.196 0.692 NA
#> GSM1009097 1 0.6148 0.6624 0.640 0.004 NA
#> GSM1009111 2 0.3091 0.6971 0.072 0.912 NA
#> GSM1009125 1 0.7867 -0.0479 0.584 0.348 NA
#> GSM1009139 1 0.6373 0.6498 0.588 0.004 NA
#> GSM1009153 1 0.2229 0.7345 0.944 0.012 NA
#> GSM1009167 2 0.7620 0.5809 0.056 0.596 NA
#> GSM1009181 2 0.9082 0.4837 0.392 0.468 NA
#> GSM1009195 1 0.1905 0.7221 0.956 0.016 NA
#> GSM1009070 1 0.7064 0.6402 0.704 0.076 NA
#> GSM1009084 2 0.7297 0.6717 0.188 0.704 NA
#> GSM1009098 1 0.6148 0.6624 0.640 0.004 NA
#> GSM1009112 2 0.3091 0.6971 0.072 0.912 NA
#> GSM1009126 1 0.1765 0.7260 0.956 0.004 NA
#> GSM1009140 1 0.6373 0.6498 0.588 0.004 NA
#> GSM1009154 1 0.2229 0.7345 0.944 0.012 NA
#> GSM1009168 2 0.7620 0.5809 0.056 0.596 NA
#> GSM1009182 2 0.9082 0.4837 0.392 0.468 NA
#> GSM1009196 1 0.0592 0.7367 0.988 0.012 NA
#> GSM1009071 1 0.7147 0.6344 0.696 0.076 NA
#> GSM1009085 2 0.7297 0.6717 0.188 0.704 NA
#> GSM1009099 1 0.6148 0.6624 0.640 0.004 NA
#> GSM1009113 2 0.3091 0.6971 0.072 0.912 NA
#> GSM1009127 1 0.0424 0.7384 0.992 0.000 NA
#> GSM1009141 1 0.6373 0.6498 0.588 0.004 NA
#> GSM1009155 1 0.2229 0.7345 0.944 0.012 NA
#> GSM1009169 2 0.7620 0.5809 0.056 0.596 NA
#> GSM1009183 2 0.9082 0.4837 0.392 0.468 NA
#> GSM1009197 1 0.0424 0.7372 0.992 0.008 NA
#> GSM1009072 1 0.7147 0.6344 0.696 0.076 NA
#> GSM1009086 2 0.7297 0.6717 0.188 0.704 NA
#> GSM1009100 1 0.6148 0.6624 0.640 0.004 NA
#> GSM1009114 2 0.3091 0.6971 0.072 0.912 NA
#> GSM1009128 1 0.5955 0.4922 0.772 0.180 NA
#> GSM1009142 1 0.6373 0.6498 0.588 0.004 NA
#> GSM1009156 1 0.2414 0.7266 0.940 0.020 NA
#> GSM1009170 2 0.7640 0.5809 0.056 0.592 NA
#> GSM1009184 2 0.9082 0.4837 0.392 0.468 NA
#> GSM1009198 1 0.0424 0.7372 0.992 0.008 NA
#> GSM1009073 1 0.7147 0.6344 0.696 0.076 NA
#> GSM1009087 2 0.8201 0.6110 0.276 0.612 NA
#> GSM1009101 1 0.6148 0.6624 0.640 0.004 NA
#> GSM1009115 2 0.3091 0.6971 0.072 0.912 NA
#> GSM1009129 1 0.8208 -0.3644 0.476 0.452 NA
#> GSM1009143 1 0.6373 0.6498 0.588 0.004 NA
#> GSM1009157 1 0.3272 0.7080 0.904 0.016 NA
#> GSM1009171 2 0.7640 0.5809 0.056 0.592 NA
#> GSM1009185 2 0.9112 0.4156 0.428 0.432 NA
#> GSM1009199 1 0.1585 0.7275 0.964 0.008 NA
#> GSM1009074 1 0.7147 0.6344 0.696 0.076 NA
#> GSM1009088 2 0.8201 0.6110 0.276 0.612 NA
#> GSM1009102 1 0.6169 0.6615 0.636 0.004 NA
#> GSM1009116 2 0.3091 0.6971 0.072 0.912 NA
#> GSM1009130 2 0.7564 0.6234 0.296 0.636 NA
#> GSM1009144 1 0.6373 0.6498 0.588 0.004 NA
#> GSM1009158 1 0.2229 0.7345 0.944 0.012 NA
#> GSM1009172 2 0.7640 0.5809 0.056 0.592 NA
#> GSM1009186 2 0.9082 0.4837 0.392 0.468 NA
#> GSM1009200 1 0.0424 0.7372 0.992 0.008 NA
#> GSM1009075 1 0.7147 0.6344 0.696 0.076 NA
#> GSM1009089 2 0.8743 0.4811 0.372 0.512 NA
#> GSM1009103 1 0.6169 0.6615 0.636 0.004 NA
#> GSM1009117 2 0.3091 0.6971 0.072 0.912 NA
#> GSM1009131 1 0.6481 0.3854 0.728 0.224 NA
#> GSM1009145 1 0.6359 0.6502 0.592 0.004 NA
#> GSM1009159 1 0.2229 0.7345 0.944 0.012 NA
#> GSM1009173 2 0.7620 0.5809 0.056 0.596 NA
#> GSM1009187 1 0.8991 -0.3186 0.476 0.392 NA
#> GSM1009201 1 0.0424 0.7372 0.992 0.008 NA
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1009062 1 0.711 0.4550 0.596 0.220 0.008 0.176
#> GSM1009076 2 0.548 0.5629 0.104 0.732 0.164 0.000
#> GSM1009090 4 0.676 0.8457 0.408 0.072 0.008 0.512
#> GSM1009104 3 0.752 0.5504 0.020 0.420 0.452 0.108
#> GSM1009118 1 0.679 0.4691 0.676 0.156 0.036 0.132
#> GSM1009132 4 0.453 0.8554 0.292 0.004 0.000 0.704
#> GSM1009146 1 0.212 0.6686 0.932 0.028 0.000 0.040
#> GSM1009160 3 0.215 0.6601 0.020 0.008 0.936 0.036
#> GSM1009174 2 0.900 0.6742 0.320 0.428 0.112 0.140
#> GSM1009188 1 0.263 0.6690 0.912 0.024 0.004 0.060
#> GSM1009063 1 0.711 0.4550 0.596 0.220 0.008 0.176
#> GSM1009077 2 0.548 0.5629 0.104 0.732 0.164 0.000
#> GSM1009091 4 0.676 0.8457 0.408 0.072 0.008 0.512
#> GSM1009105 3 0.752 0.5504 0.020 0.420 0.452 0.108
#> GSM1009119 1 0.334 0.6476 0.876 0.024 0.008 0.092
#> GSM1009133 4 0.453 0.8554 0.292 0.004 0.000 0.704
#> GSM1009147 1 0.139 0.6778 0.960 0.028 0.000 0.012
#> GSM1009161 3 0.225 0.6602 0.020 0.008 0.932 0.040
#> GSM1009175 2 0.900 0.6742 0.320 0.428 0.112 0.140
#> GSM1009189 1 0.263 0.6690 0.912 0.024 0.004 0.060
#> GSM1009064 1 0.711 0.4550 0.596 0.220 0.008 0.176
#> GSM1009078 2 0.545 0.6102 0.172 0.732 0.096 0.000
#> GSM1009092 4 0.676 0.8457 0.408 0.072 0.008 0.512
#> GSM1009106 3 0.752 0.5504 0.020 0.420 0.452 0.108
#> GSM1009120 1 0.317 0.6601 0.884 0.028 0.004 0.084
#> GSM1009134 4 0.453 0.8554 0.292 0.004 0.000 0.704
#> GSM1009148 1 0.212 0.6686 0.932 0.028 0.000 0.040
#> GSM1009162 3 0.151 0.6602 0.020 0.008 0.960 0.012
#> GSM1009176 2 0.900 0.6742 0.320 0.428 0.112 0.140
#> GSM1009190 1 0.263 0.6690 0.912 0.024 0.004 0.060
#> GSM1009065 1 0.711 0.4550 0.596 0.220 0.008 0.176
#> GSM1009079 2 0.548 0.5629 0.104 0.732 0.164 0.000
#> GSM1009093 4 0.676 0.8457 0.408 0.072 0.008 0.512
#> GSM1009107 3 0.752 0.5504 0.020 0.420 0.452 0.108
#> GSM1009121 1 0.670 0.4808 0.684 0.148 0.036 0.132
#> GSM1009135 4 0.453 0.8554 0.292 0.004 0.000 0.704
#> GSM1009149 1 0.212 0.6686 0.932 0.028 0.000 0.040
#> GSM1009163 3 0.219 0.6601 0.020 0.012 0.936 0.032
#> GSM1009177 2 0.900 0.6742 0.320 0.428 0.112 0.140
#> GSM1009191 1 0.263 0.6690 0.912 0.024 0.004 0.060
#> GSM1009066 1 0.711 0.4550 0.596 0.220 0.008 0.176
#> GSM1009080 2 0.548 0.5629 0.104 0.732 0.164 0.000
#> GSM1009094 4 0.676 0.8457 0.408 0.072 0.008 0.512
#> GSM1009108 3 0.752 0.5504 0.020 0.420 0.452 0.108
#> GSM1009122 1 0.768 0.2841 0.604 0.204 0.060 0.132
#> GSM1009136 4 0.458 0.8548 0.300 0.004 0.000 0.696
#> GSM1009150 1 0.212 0.6686 0.932 0.028 0.000 0.040
#> GSM1009164 3 0.219 0.6601 0.020 0.012 0.936 0.032
#> GSM1009178 2 0.900 0.6742 0.320 0.428 0.112 0.140
#> GSM1009192 1 0.263 0.6690 0.912 0.024 0.004 0.060
#> GSM1009067 1 0.711 0.4550 0.596 0.220 0.008 0.176
#> GSM1009081 2 0.548 0.5629 0.104 0.732 0.164 0.000
#> GSM1009095 4 0.676 0.8457 0.408 0.072 0.008 0.512
#> GSM1009109 3 0.752 0.5504 0.020 0.420 0.452 0.108
#> GSM1009123 1 0.341 0.6474 0.872 0.024 0.008 0.096
#> GSM1009137 4 0.453 0.8554 0.292 0.004 0.000 0.704
#> GSM1009151 1 0.212 0.6686 0.932 0.028 0.000 0.040
#> GSM1009165 3 0.136 0.6604 0.020 0.012 0.964 0.004
#> GSM1009179 2 0.900 0.6742 0.320 0.428 0.112 0.140
#> GSM1009193 1 0.263 0.6690 0.912 0.024 0.004 0.060
#> GSM1009068 1 0.711 0.4550 0.596 0.220 0.008 0.176
#> GSM1009082 2 0.548 0.5629 0.104 0.732 0.164 0.000
#> GSM1009096 4 0.676 0.8457 0.408 0.072 0.008 0.512
#> GSM1009110 3 0.752 0.5504 0.020 0.420 0.452 0.108
#> GSM1009124 1 0.501 0.5997 0.784 0.080 0.008 0.128
#> GSM1009138 4 0.453 0.8554 0.292 0.004 0.000 0.704
#> GSM1009152 1 0.212 0.6686 0.932 0.028 0.000 0.040
#> GSM1009166 3 0.151 0.6602 0.020 0.008 0.960 0.012
#> GSM1009180 2 0.900 0.6742 0.320 0.428 0.112 0.140
#> GSM1009194 1 0.263 0.6690 0.912 0.024 0.004 0.060
#> GSM1009069 1 0.711 0.4550 0.596 0.220 0.008 0.176
#> GSM1009083 2 0.551 0.5722 0.112 0.732 0.156 0.000
#> GSM1009097 4 0.676 0.8457 0.408 0.072 0.008 0.512
#> GSM1009111 3 0.752 0.5504 0.020 0.420 0.452 0.108
#> GSM1009125 1 0.791 0.2277 0.584 0.212 0.068 0.136
#> GSM1009139 4 0.453 0.8554 0.292 0.004 0.000 0.704
#> GSM1009153 1 0.212 0.6686 0.932 0.028 0.000 0.040
#> GSM1009167 3 0.138 0.6603 0.020 0.008 0.964 0.008
#> GSM1009181 2 0.900 0.6742 0.320 0.428 0.112 0.140
#> GSM1009195 1 0.327 0.6668 0.884 0.056 0.004 0.056
#> GSM1009070 1 0.705 0.4553 0.604 0.212 0.008 0.176
#> GSM1009084 2 0.548 0.5629 0.104 0.732 0.164 0.000
#> GSM1009098 4 0.676 0.8457 0.408 0.072 0.008 0.512
#> GSM1009112 3 0.752 0.5504 0.020 0.420 0.452 0.108
#> GSM1009126 1 0.501 0.5997 0.784 0.080 0.008 0.128
#> GSM1009140 4 0.453 0.8554 0.292 0.004 0.000 0.704
#> GSM1009154 1 0.212 0.6686 0.932 0.028 0.000 0.040
#> GSM1009168 3 0.138 0.6603 0.020 0.008 0.964 0.008
#> GSM1009182 2 0.900 0.6742 0.320 0.428 0.112 0.140
#> GSM1009196 1 0.263 0.6690 0.912 0.024 0.004 0.060
#> GSM1009071 1 0.711 0.4550 0.596 0.220 0.008 0.176
#> GSM1009085 2 0.548 0.5629 0.104 0.732 0.164 0.000
#> GSM1009099 4 0.676 0.8457 0.408 0.072 0.008 0.512
#> GSM1009113 3 0.752 0.5504 0.020 0.420 0.452 0.108
#> GSM1009127 1 0.351 0.6495 0.868 0.028 0.008 0.096
#> GSM1009141 4 0.453 0.8554 0.292 0.004 0.000 0.704
#> GSM1009155 1 0.194 0.6727 0.940 0.028 0.000 0.032
#> GSM1009169 3 0.138 0.6603 0.020 0.008 0.964 0.008
#> GSM1009183 2 0.900 0.6742 0.320 0.428 0.112 0.140
#> GSM1009197 1 0.263 0.6690 0.912 0.024 0.004 0.060
#> GSM1009072 1 0.711 0.4550 0.596 0.220 0.008 0.176
#> GSM1009086 2 0.548 0.5629 0.104 0.732 0.164 0.000
#> GSM1009100 4 0.676 0.8457 0.408 0.072 0.008 0.512
#> GSM1009114 3 0.752 0.5504 0.020 0.420 0.452 0.108
#> GSM1009128 1 0.688 0.4585 0.672 0.152 0.040 0.136
#> GSM1009142 4 0.453 0.8554 0.292 0.004 0.000 0.704
#> GSM1009156 1 0.202 0.6813 0.932 0.056 0.000 0.012
#> GSM1009170 3 0.219 0.6601 0.020 0.012 0.936 0.032
#> GSM1009184 2 0.900 0.6742 0.320 0.428 0.112 0.140
#> GSM1009198 1 0.263 0.6690 0.912 0.024 0.004 0.060
#> GSM1009073 1 0.711 0.4550 0.596 0.220 0.008 0.176
#> GSM1009087 2 0.545 0.6102 0.172 0.732 0.096 0.000
#> GSM1009101 4 0.676 0.8457 0.408 0.072 0.008 0.512
#> GSM1009115 3 0.752 0.5504 0.020 0.420 0.452 0.108
#> GSM1009129 1 0.824 0.0638 0.548 0.244 0.088 0.120
#> GSM1009143 4 0.453 0.8554 0.292 0.004 0.000 0.704
#> GSM1009157 1 0.256 0.6783 0.908 0.072 0.000 0.020
#> GSM1009171 3 0.199 0.6606 0.020 0.012 0.944 0.024
#> GSM1009185 2 0.890 0.6573 0.328 0.432 0.100 0.140
#> GSM1009199 1 0.302 0.6676 0.896 0.044 0.004 0.056
#> GSM1009074 1 0.711 0.4550 0.596 0.220 0.008 0.176
#> GSM1009088 2 0.545 0.6102 0.172 0.732 0.096 0.000
#> GSM1009102 4 0.676 0.8457 0.408 0.072 0.008 0.512
#> GSM1009116 3 0.752 0.5504 0.020 0.420 0.452 0.108
#> GSM1009130 2 0.840 0.4649 0.388 0.416 0.144 0.052
#> GSM1009144 4 0.453 0.8554 0.292 0.004 0.000 0.704
#> GSM1009158 1 0.212 0.6686 0.932 0.028 0.000 0.040
#> GSM1009172 3 0.215 0.6601 0.020 0.008 0.936 0.036
#> GSM1009186 2 0.900 0.6742 0.320 0.428 0.112 0.140
#> GSM1009200 1 0.263 0.6690 0.912 0.024 0.004 0.060
#> GSM1009075 1 0.711 0.4550 0.596 0.220 0.008 0.176
#> GSM1009089 2 0.529 0.6084 0.208 0.728 0.064 0.000
#> GSM1009103 4 0.676 0.8457 0.408 0.072 0.008 0.512
#> GSM1009117 3 0.752 0.5504 0.020 0.420 0.452 0.108
#> GSM1009131 1 0.687 0.4310 0.664 0.172 0.032 0.132
#> GSM1009145 4 0.458 0.8548 0.300 0.004 0.000 0.696
#> GSM1009159 1 0.202 0.6679 0.936 0.024 0.000 0.040
#> GSM1009173 3 0.136 0.6604 0.020 0.012 0.964 0.004
#> GSM1009187 2 0.857 0.6038 0.360 0.432 0.068 0.140
#> GSM1009201 1 0.263 0.6690 0.912 0.024 0.004 0.060
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM1009062 5 0.5546 9.96e-01 0.364 0.020 0.000 0.040 0.576
#> GSM1009076 2 0.2700 5.27e-01 0.068 0.896 0.012 0.004 0.020
#> GSM1009090 4 0.3395 8.19e-01 0.236 0.000 0.000 0.764 0.000
#> GSM1009104 2 0.6752 3.31e-01 0.016 0.620 0.208 0.084 0.072
#> GSM1009118 1 0.5853 5.73e-01 0.716 0.076 0.020 0.056 0.132
#> GSM1009132 4 0.7156 8.17e-01 0.168 0.016 0.096 0.600 0.120
#> GSM1009146 1 0.4556 4.94e-01 0.756 0.008 0.016 0.028 0.192
#> GSM1009160 3 0.3088 9.86e-01 0.004 0.164 0.828 0.000 0.004
#> GSM1009174 2 0.8988 4.34e-01 0.260 0.368 0.108 0.056 0.208
#> GSM1009188 1 0.0771 7.07e-01 0.976 0.004 0.000 0.020 0.000
#> GSM1009063 5 0.5546 9.96e-01 0.364 0.020 0.000 0.040 0.576
#> GSM1009077 2 0.2700 5.27e-01 0.068 0.896 0.012 0.004 0.020
#> GSM1009091 4 0.3395 8.19e-01 0.236 0.000 0.000 0.764 0.000
#> GSM1009105 2 0.6752 3.31e-01 0.016 0.620 0.208 0.084 0.072
#> GSM1009119 1 0.3304 6.70e-01 0.872 0.012 0.012 0.044 0.060
#> GSM1009133 4 0.7156 8.17e-01 0.168 0.016 0.096 0.600 0.120
#> GSM1009147 1 0.4241 5.27e-01 0.780 0.008 0.016 0.020 0.176
#> GSM1009161 3 0.3088 9.86e-01 0.004 0.164 0.828 0.000 0.004
#> GSM1009175 2 0.8988 4.34e-01 0.260 0.368 0.108 0.056 0.208
#> GSM1009189 1 0.0771 7.07e-01 0.976 0.004 0.000 0.020 0.000
#> GSM1009064 5 0.5759 9.95e-01 0.364 0.020 0.004 0.044 0.568
#> GSM1009078 2 0.4040 5.25e-01 0.152 0.800 0.012 0.004 0.032
#> GSM1009092 4 0.3395 8.19e-01 0.236 0.000 0.000 0.764 0.000
#> GSM1009106 2 0.6752 3.31e-01 0.016 0.620 0.208 0.084 0.072
#> GSM1009120 1 0.3161 6.74e-01 0.880 0.012 0.012 0.040 0.056
#> GSM1009134 4 0.7156 8.17e-01 0.168 0.016 0.096 0.600 0.120
#> GSM1009148 1 0.4556 4.94e-01 0.756 0.008 0.016 0.028 0.192
#> GSM1009162 3 0.3516 9.85e-01 0.004 0.164 0.812 0.000 0.020
#> GSM1009176 2 0.8988 4.34e-01 0.260 0.368 0.108 0.056 0.208
#> GSM1009190 1 0.0771 7.07e-01 0.976 0.004 0.000 0.020 0.000
#> GSM1009065 5 0.5759 9.95e-01 0.364 0.020 0.004 0.044 0.568
#> GSM1009079 2 0.2700 5.27e-01 0.068 0.896 0.012 0.004 0.020
#> GSM1009093 4 0.3395 8.19e-01 0.236 0.000 0.000 0.764 0.000
#> GSM1009107 2 0.6752 3.31e-01 0.016 0.620 0.208 0.084 0.072
#> GSM1009121 1 0.6136 5.61e-01 0.696 0.088 0.020 0.064 0.132
#> GSM1009135 4 0.7156 8.17e-01 0.168 0.016 0.096 0.600 0.120
#> GSM1009149 1 0.4556 4.94e-01 0.756 0.008 0.016 0.028 0.192
#> GSM1009163 3 0.3477 9.86e-01 0.004 0.164 0.816 0.004 0.012
#> GSM1009177 2 0.8988 4.34e-01 0.260 0.368 0.108 0.056 0.208
#> GSM1009191 1 0.0771 7.07e-01 0.976 0.004 0.000 0.020 0.000
#> GSM1009066 5 0.5759 9.95e-01 0.364 0.020 0.004 0.044 0.568
#> GSM1009080 2 0.2700 5.27e-01 0.068 0.896 0.012 0.004 0.020
#> GSM1009094 4 0.3395 8.19e-01 0.236 0.000 0.000 0.764 0.000
#> GSM1009108 2 0.6752 3.31e-01 0.016 0.620 0.208 0.084 0.072
#> GSM1009122 1 0.6823 4.90e-01 0.632 0.156 0.024 0.056 0.132
#> GSM1009136 4 0.7172 8.16e-01 0.176 0.016 0.092 0.596 0.120
#> GSM1009150 1 0.4556 4.94e-01 0.756 0.008 0.016 0.028 0.192
#> GSM1009164 3 0.3477 9.86e-01 0.004 0.164 0.816 0.004 0.012
#> GSM1009178 2 0.8988 4.34e-01 0.260 0.368 0.108 0.056 0.208
#> GSM1009192 1 0.0771 7.07e-01 0.976 0.004 0.000 0.020 0.000
#> GSM1009067 5 0.5546 9.96e-01 0.364 0.020 0.000 0.040 0.576
#> GSM1009081 2 0.2700 5.27e-01 0.068 0.896 0.012 0.004 0.020
#> GSM1009095 4 0.3395 8.19e-01 0.236 0.000 0.000 0.764 0.000
#> GSM1009109 2 0.6752 3.31e-01 0.016 0.620 0.208 0.084 0.072
#> GSM1009123 1 0.3748 6.59e-01 0.848 0.016 0.012 0.056 0.068
#> GSM1009137 4 0.7156 8.17e-01 0.168 0.016 0.096 0.600 0.120
#> GSM1009151 1 0.4556 4.94e-01 0.756 0.008 0.016 0.028 0.192
#> GSM1009165 3 0.3929 9.85e-01 0.004 0.164 0.796 0.004 0.032
#> GSM1009179 2 0.8988 4.34e-01 0.260 0.368 0.108 0.056 0.208
#> GSM1009193 1 0.0771 7.07e-01 0.976 0.004 0.000 0.020 0.000
#> GSM1009068 5 0.5546 9.96e-01 0.364 0.020 0.000 0.040 0.576
#> GSM1009082 2 0.2700 5.27e-01 0.068 0.896 0.012 0.004 0.020
#> GSM1009096 4 0.3395 8.19e-01 0.236 0.000 0.000 0.764 0.000
#> GSM1009110 2 0.6778 3.30e-01 0.016 0.620 0.204 0.080 0.080
#> GSM1009124 1 0.5242 6.08e-01 0.756 0.032 0.020 0.072 0.120
#> GSM1009138 4 0.7156 8.17e-01 0.168 0.016 0.096 0.600 0.120
#> GSM1009152 1 0.4556 4.94e-01 0.756 0.008 0.016 0.028 0.192
#> GSM1009166 3 0.3516 9.85e-01 0.004 0.164 0.812 0.000 0.020
#> GSM1009180 2 0.8988 4.34e-01 0.260 0.368 0.108 0.056 0.208
#> GSM1009194 1 0.0771 7.07e-01 0.976 0.004 0.000 0.020 0.000
#> GSM1009069 5 0.5759 9.95e-01 0.364 0.020 0.004 0.044 0.568
#> GSM1009083 2 0.2917 5.28e-01 0.076 0.884 0.012 0.004 0.024
#> GSM1009097 4 0.3395 8.19e-01 0.236 0.000 0.000 0.764 0.000
#> GSM1009111 2 0.6752 3.31e-01 0.016 0.620 0.208 0.084 0.072
#> GSM1009125 1 0.7077 4.71e-01 0.612 0.168 0.032 0.056 0.132
#> GSM1009139 4 0.7156 8.17e-01 0.168 0.016 0.096 0.600 0.120
#> GSM1009153 1 0.4556 4.94e-01 0.756 0.008 0.016 0.028 0.192
#> GSM1009167 3 0.4545 9.67e-01 0.004 0.168 0.768 0.020 0.040
#> GSM1009181 2 0.8988 4.34e-01 0.260 0.368 0.108 0.056 0.208
#> GSM1009195 1 0.0955 7.01e-01 0.968 0.028 0.000 0.004 0.000
#> GSM1009070 5 0.5546 9.96e-01 0.364 0.020 0.000 0.040 0.576
#> GSM1009084 2 0.2700 5.27e-01 0.068 0.896 0.012 0.004 0.020
#> GSM1009098 4 0.3395 8.19e-01 0.236 0.000 0.000 0.764 0.000
#> GSM1009112 2 0.6752 3.31e-01 0.016 0.620 0.208 0.084 0.072
#> GSM1009126 1 0.5242 6.08e-01 0.756 0.032 0.020 0.072 0.120
#> GSM1009140 4 0.7156 8.17e-01 0.168 0.016 0.096 0.600 0.120
#> GSM1009154 1 0.4556 4.94e-01 0.756 0.008 0.016 0.028 0.192
#> GSM1009168 3 0.3968 9.79e-01 0.004 0.168 0.792 0.004 0.032
#> GSM1009182 2 0.8988 4.34e-01 0.260 0.368 0.108 0.056 0.208
#> GSM1009196 1 0.0771 7.07e-01 0.976 0.004 0.000 0.020 0.000
#> GSM1009071 5 0.5759 9.95e-01 0.364 0.020 0.004 0.044 0.568
#> GSM1009085 2 0.2700 5.27e-01 0.068 0.896 0.012 0.004 0.020
#> GSM1009099 4 0.3395 8.19e-01 0.236 0.000 0.000 0.764 0.000
#> GSM1009113 2 0.6752 3.31e-01 0.016 0.620 0.208 0.084 0.072
#> GSM1009127 1 0.3680 6.61e-01 0.852 0.016 0.012 0.052 0.068
#> GSM1009141 4 0.7156 8.17e-01 0.168 0.016 0.096 0.600 0.120
#> GSM1009155 1 0.4556 4.94e-01 0.756 0.008 0.016 0.028 0.192
#> GSM1009169 3 0.3970 9.82e-01 0.004 0.164 0.796 0.008 0.028
#> GSM1009183 2 0.8988 4.34e-01 0.260 0.368 0.108 0.056 0.208
#> GSM1009197 1 0.0771 7.07e-01 0.976 0.004 0.000 0.020 0.000
#> GSM1009072 5 0.5546 9.96e-01 0.364 0.020 0.000 0.040 0.576
#> GSM1009086 2 0.2700 5.27e-01 0.068 0.896 0.012 0.004 0.020
#> GSM1009100 4 0.3395 8.19e-01 0.236 0.000 0.000 0.764 0.000
#> GSM1009114 2 0.6778 3.30e-01 0.016 0.620 0.204 0.080 0.080
#> GSM1009128 1 0.6092 5.68e-01 0.700 0.076 0.020 0.072 0.132
#> GSM1009142 4 0.7156 8.17e-01 0.168 0.016 0.096 0.600 0.120
#> GSM1009156 1 0.4391 5.55e-01 0.784 0.020 0.016 0.020 0.160
#> GSM1009170 3 0.3477 9.86e-01 0.004 0.164 0.816 0.004 0.012
#> GSM1009184 2 0.9003 4.34e-01 0.260 0.368 0.112 0.056 0.204
#> GSM1009198 1 0.0771 7.07e-01 0.976 0.004 0.000 0.020 0.000
#> GSM1009073 5 0.5759 9.95e-01 0.364 0.020 0.004 0.044 0.568
#> GSM1009087 2 0.4040 5.25e-01 0.152 0.800 0.012 0.004 0.032
#> GSM1009101 4 0.3395 8.19e-01 0.236 0.000 0.000 0.764 0.000
#> GSM1009115 2 0.6752 3.31e-01 0.016 0.620 0.208 0.084 0.072
#> GSM1009129 1 0.6983 4.54e-01 0.604 0.192 0.024 0.048 0.132
#> GSM1009143 4 0.7156 8.17e-01 0.168 0.016 0.096 0.600 0.120
#> GSM1009157 1 0.4391 5.48e-01 0.784 0.020 0.016 0.020 0.160
#> GSM1009171 3 0.3477 9.86e-01 0.004 0.164 0.816 0.004 0.012
#> GSM1009185 2 0.8965 4.28e-01 0.264 0.368 0.104 0.056 0.208
#> GSM1009199 1 0.1012 7.04e-01 0.968 0.020 0.000 0.012 0.000
#> GSM1009074 5 0.5546 9.96e-01 0.364 0.020 0.000 0.040 0.576
#> GSM1009088 2 0.4040 5.25e-01 0.152 0.800 0.012 0.004 0.032
#> GSM1009102 4 0.3395 8.19e-01 0.236 0.000 0.000 0.764 0.000
#> GSM1009116 2 0.6752 3.31e-01 0.016 0.620 0.208 0.084 0.072
#> GSM1009130 1 0.6817 -5.08e-05 0.452 0.412 0.012 0.024 0.100
#> GSM1009144 4 0.7156 8.17e-01 0.168 0.016 0.096 0.600 0.120
#> GSM1009158 1 0.4556 4.94e-01 0.756 0.008 0.016 0.028 0.192
#> GSM1009172 3 0.3088 9.86e-01 0.004 0.164 0.828 0.000 0.004
#> GSM1009186 2 0.9003 4.34e-01 0.260 0.368 0.112 0.056 0.204
#> GSM1009200 1 0.0771 7.07e-01 0.976 0.004 0.000 0.020 0.000
#> GSM1009075 5 0.5546 9.96e-01 0.364 0.020 0.000 0.040 0.576
#> GSM1009089 2 0.4203 5.22e-01 0.156 0.792 0.012 0.008 0.032
#> GSM1009103 4 0.3395 8.19e-01 0.236 0.000 0.000 0.764 0.000
#> GSM1009117 2 0.6778 3.30e-01 0.016 0.620 0.204 0.080 0.080
#> GSM1009131 1 0.6100 5.55e-01 0.696 0.100 0.020 0.052 0.132
#> GSM1009145 4 0.7172 8.16e-01 0.176 0.016 0.092 0.596 0.120
#> GSM1009159 1 0.4556 4.94e-01 0.756 0.008 0.016 0.028 0.192
#> GSM1009173 3 0.3848 9.86e-01 0.004 0.164 0.800 0.004 0.028
#> GSM1009187 2 0.8982 4.28e-01 0.264 0.368 0.108 0.056 0.204
#> GSM1009201 1 0.0771 7.07e-01 0.976 0.004 0.000 0.020 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1009062 6 0.436 0.986 0.244 0.008 0.000 0.040 0.004 0.704
#> GSM1009076 2 0.722 0.460 0.040 0.456 0.052 0.024 0.352 0.076
#> GSM1009090 4 0.686 0.810 0.136 0.032 0.004 0.584 0.140 0.104
#> GSM1009104 5 0.541 0.995 0.000 0.176 0.248 0.000 0.576 0.000
#> GSM1009118 1 0.562 0.592 0.700 0.148 0.012 0.048 0.028 0.064
#> GSM1009132 4 0.233 0.807 0.076 0.004 0.000 0.896 0.004 0.020
#> GSM1009146 1 0.578 0.552 0.692 0.044 0.012 0.048 0.056 0.148
#> GSM1009160 3 0.208 0.957 0.000 0.040 0.916 0.012 0.000 0.032
#> GSM1009174 2 0.359 0.607 0.164 0.796 0.024 0.004 0.000 0.012
#> GSM1009188 1 0.181 0.712 0.928 0.020 0.000 0.044 0.000 0.008
#> GSM1009063 6 0.436 0.986 0.244 0.008 0.000 0.040 0.004 0.704
#> GSM1009077 2 0.722 0.460 0.040 0.456 0.052 0.024 0.352 0.076
#> GSM1009091 4 0.686 0.810 0.136 0.032 0.004 0.584 0.140 0.104
#> GSM1009105 5 0.541 0.995 0.000 0.176 0.248 0.000 0.576 0.000
#> GSM1009119 1 0.376 0.663 0.840 0.024 0.012 0.036 0.024 0.064
#> GSM1009133 4 0.218 0.807 0.076 0.004 0.000 0.900 0.000 0.020
#> GSM1009147 1 0.572 0.555 0.696 0.044 0.012 0.044 0.056 0.148
#> GSM1009161 3 0.208 0.957 0.000 0.040 0.916 0.012 0.000 0.032
#> GSM1009175 2 0.359 0.607 0.164 0.796 0.024 0.004 0.000 0.012
#> GSM1009189 1 0.181 0.712 0.928 0.020 0.000 0.044 0.000 0.008
#> GSM1009064 6 0.539 0.972 0.244 0.012 0.008 0.052 0.028 0.656
#> GSM1009078 2 0.721 0.466 0.064 0.456 0.028 0.028 0.352 0.072
#> GSM1009092 4 0.686 0.810 0.136 0.032 0.004 0.584 0.140 0.104
#> GSM1009106 5 0.541 0.995 0.000 0.176 0.248 0.000 0.576 0.000
#> GSM1009120 1 0.362 0.667 0.848 0.024 0.012 0.028 0.024 0.064
#> GSM1009134 4 0.218 0.807 0.076 0.004 0.000 0.900 0.000 0.020
#> GSM1009148 1 0.578 0.552 0.692 0.044 0.012 0.048 0.056 0.148
#> GSM1009162 3 0.240 0.959 0.000 0.048 0.900 0.008 0.004 0.040
#> GSM1009176 2 0.359 0.607 0.164 0.796 0.024 0.004 0.000 0.012
#> GSM1009190 1 0.181 0.712 0.928 0.020 0.000 0.044 0.000 0.008
#> GSM1009065 6 0.539 0.972 0.244 0.012 0.008 0.052 0.028 0.656
#> GSM1009079 2 0.722 0.460 0.040 0.456 0.052 0.024 0.352 0.076
#> GSM1009093 4 0.686 0.810 0.136 0.032 0.004 0.584 0.140 0.104
#> GSM1009107 5 0.543 0.994 0.000 0.176 0.252 0.000 0.572 0.000
#> GSM1009121 1 0.579 0.587 0.692 0.144 0.012 0.048 0.040 0.064
#> GSM1009135 4 0.218 0.807 0.076 0.004 0.000 0.900 0.000 0.020
#> GSM1009149 1 0.578 0.552 0.692 0.044 0.012 0.048 0.056 0.148
#> GSM1009163 3 0.166 0.958 0.000 0.040 0.936 0.012 0.000 0.012
#> GSM1009177 2 0.359 0.607 0.164 0.796 0.024 0.004 0.000 0.012
#> GSM1009191 1 0.181 0.712 0.928 0.020 0.000 0.044 0.000 0.008
#> GSM1009066 6 0.509 0.979 0.244 0.008 0.008 0.048 0.020 0.672
#> GSM1009080 2 0.722 0.460 0.040 0.456 0.052 0.024 0.352 0.076
#> GSM1009094 4 0.686 0.810 0.136 0.032 0.004 0.584 0.140 0.104
#> GSM1009108 5 0.543 0.994 0.000 0.176 0.252 0.000 0.572 0.000
#> GSM1009122 1 0.639 0.493 0.612 0.220 0.012 0.048 0.044 0.064
#> GSM1009136 4 0.243 0.807 0.080 0.008 0.000 0.888 0.000 0.024
#> GSM1009150 1 0.578 0.552 0.692 0.044 0.012 0.048 0.056 0.148
#> GSM1009164 3 0.166 0.958 0.000 0.040 0.936 0.012 0.000 0.012
#> GSM1009178 2 0.362 0.605 0.168 0.792 0.024 0.004 0.000 0.012
#> GSM1009192 1 0.181 0.712 0.928 0.020 0.000 0.044 0.000 0.008
#> GSM1009067 6 0.436 0.986 0.244 0.008 0.000 0.040 0.004 0.704
#> GSM1009081 2 0.722 0.460 0.040 0.456 0.052 0.024 0.352 0.076
#> GSM1009095 4 0.688 0.810 0.136 0.036 0.004 0.584 0.140 0.100
#> GSM1009109 5 0.541 0.995 0.000 0.176 0.248 0.000 0.576 0.000
#> GSM1009123 1 0.396 0.658 0.828 0.024 0.012 0.048 0.024 0.064
#> GSM1009137 4 0.218 0.807 0.076 0.004 0.000 0.900 0.000 0.020
#> GSM1009151 1 0.578 0.552 0.692 0.044 0.012 0.048 0.056 0.148
#> GSM1009165 3 0.220 0.957 0.000 0.048 0.912 0.012 0.004 0.024
#> GSM1009179 2 0.362 0.605 0.168 0.792 0.024 0.004 0.000 0.012
#> GSM1009193 1 0.181 0.712 0.928 0.020 0.000 0.044 0.000 0.008
#> GSM1009068 6 0.436 0.986 0.244 0.008 0.000 0.040 0.004 0.704
#> GSM1009082 2 0.722 0.460 0.040 0.456 0.052 0.024 0.352 0.076
#> GSM1009096 4 0.686 0.810 0.136 0.032 0.004 0.584 0.140 0.104
#> GSM1009110 5 0.599 0.983 0.000 0.176 0.252 0.008 0.552 0.012
#> GSM1009124 1 0.524 0.615 0.736 0.116 0.012 0.048 0.024 0.064
#> GSM1009138 4 0.218 0.807 0.076 0.004 0.000 0.900 0.000 0.020
#> GSM1009152 1 0.578 0.552 0.692 0.044 0.012 0.048 0.056 0.148
#> GSM1009166 3 0.240 0.959 0.000 0.048 0.900 0.008 0.004 0.040
#> GSM1009180 2 0.362 0.605 0.168 0.792 0.024 0.004 0.000 0.012
#> GSM1009194 1 0.181 0.712 0.928 0.020 0.000 0.044 0.000 0.008
#> GSM1009069 6 0.539 0.972 0.244 0.012 0.008 0.052 0.028 0.656
#> GSM1009083 2 0.722 0.460 0.040 0.456 0.052 0.024 0.352 0.076
#> GSM1009097 4 0.686 0.810 0.136 0.032 0.004 0.584 0.140 0.104
#> GSM1009111 5 0.543 0.994 0.000 0.176 0.252 0.000 0.572 0.000
#> GSM1009125 1 0.650 0.478 0.604 0.224 0.016 0.048 0.044 0.064
#> GSM1009139 4 0.233 0.807 0.076 0.004 0.000 0.896 0.004 0.020
#> GSM1009153 1 0.578 0.552 0.692 0.044 0.012 0.048 0.056 0.148
#> GSM1009167 3 0.336 0.938 0.000 0.056 0.856 0.020 0.032 0.036
#> GSM1009181 2 0.359 0.607 0.164 0.796 0.024 0.004 0.000 0.012
#> GSM1009195 1 0.189 0.710 0.924 0.024 0.000 0.044 0.000 0.008
#> GSM1009070 6 0.436 0.986 0.244 0.008 0.000 0.040 0.004 0.704
#> GSM1009084 2 0.722 0.460 0.040 0.456 0.052 0.024 0.352 0.076
#> GSM1009098 4 0.685 0.810 0.136 0.032 0.004 0.584 0.144 0.100
#> GSM1009112 5 0.541 0.995 0.000 0.176 0.248 0.000 0.576 0.000
#> GSM1009126 1 0.524 0.615 0.736 0.116 0.012 0.048 0.024 0.064
#> GSM1009140 4 0.218 0.807 0.076 0.004 0.000 0.900 0.000 0.020
#> GSM1009154 1 0.578 0.552 0.692 0.044 0.012 0.048 0.056 0.148
#> GSM1009168 3 0.286 0.952 0.000 0.056 0.880 0.016 0.012 0.036
#> GSM1009182 2 0.359 0.607 0.164 0.796 0.024 0.004 0.000 0.012
#> GSM1009196 1 0.181 0.712 0.928 0.020 0.000 0.044 0.000 0.008
#> GSM1009071 6 0.509 0.979 0.244 0.008 0.008 0.048 0.020 0.672
#> GSM1009085 2 0.722 0.460 0.040 0.456 0.052 0.024 0.352 0.076
#> GSM1009099 4 0.685 0.810 0.136 0.032 0.004 0.584 0.144 0.100
#> GSM1009113 5 0.543 0.994 0.000 0.176 0.252 0.000 0.572 0.000
#> GSM1009127 1 0.396 0.658 0.828 0.024 0.012 0.048 0.024 0.064
#> GSM1009141 4 0.233 0.807 0.076 0.004 0.000 0.896 0.004 0.020
#> GSM1009155 1 0.578 0.552 0.692 0.044 0.012 0.048 0.056 0.148
#> GSM1009169 3 0.260 0.952 0.000 0.044 0.896 0.016 0.012 0.032
#> GSM1009183 2 0.359 0.607 0.164 0.796 0.024 0.004 0.000 0.012
#> GSM1009197 1 0.181 0.712 0.928 0.020 0.000 0.044 0.000 0.008
#> GSM1009072 6 0.436 0.986 0.244 0.008 0.000 0.040 0.004 0.704
#> GSM1009086 2 0.722 0.460 0.040 0.456 0.052 0.024 0.352 0.076
#> GSM1009100 4 0.685 0.810 0.136 0.032 0.004 0.584 0.144 0.100
#> GSM1009114 5 0.588 0.986 0.000 0.176 0.248 0.008 0.560 0.008
#> GSM1009128 1 0.575 0.590 0.696 0.140 0.012 0.048 0.040 0.064
#> GSM1009142 4 0.233 0.807 0.076 0.004 0.000 0.896 0.004 0.020
#> GSM1009156 1 0.561 0.568 0.708 0.044 0.012 0.044 0.056 0.136
#> GSM1009170 3 0.166 0.958 0.000 0.040 0.936 0.012 0.000 0.012
#> GSM1009184 2 0.359 0.607 0.164 0.796 0.024 0.004 0.000 0.012
#> GSM1009198 1 0.181 0.712 0.928 0.020 0.000 0.044 0.000 0.008
#> GSM1009073 6 0.470 0.985 0.244 0.012 0.004 0.040 0.008 0.692
#> GSM1009087 2 0.721 0.466 0.064 0.456 0.028 0.028 0.352 0.072
#> GSM1009101 4 0.685 0.810 0.136 0.032 0.004 0.584 0.144 0.100
#> GSM1009115 5 0.541 0.995 0.000 0.176 0.248 0.000 0.576 0.000
#> GSM1009129 1 0.667 0.456 0.592 0.224 0.016 0.048 0.056 0.064
#> GSM1009143 4 0.256 0.805 0.076 0.012 0.000 0.884 0.000 0.028
#> GSM1009157 1 0.561 0.568 0.708 0.044 0.012 0.044 0.056 0.136
#> GSM1009171 3 0.123 0.961 0.000 0.040 0.952 0.004 0.000 0.004
#> GSM1009185 2 0.362 0.605 0.168 0.792 0.024 0.004 0.000 0.012
#> GSM1009199 1 0.189 0.710 0.924 0.024 0.000 0.044 0.000 0.008
#> GSM1009074 6 0.445 0.986 0.244 0.012 0.000 0.040 0.004 0.700
#> GSM1009088 2 0.721 0.466 0.064 0.456 0.028 0.028 0.352 0.072
#> GSM1009102 4 0.688 0.810 0.136 0.036 0.004 0.584 0.140 0.100
#> GSM1009116 5 0.541 0.995 0.000 0.176 0.248 0.000 0.576 0.000
#> GSM1009130 1 0.729 0.254 0.524 0.220 0.024 0.024 0.144 0.064
#> GSM1009144 4 0.270 0.805 0.076 0.012 0.000 0.880 0.004 0.028
#> GSM1009158 1 0.578 0.552 0.692 0.044 0.012 0.048 0.056 0.148
#> GSM1009172 3 0.208 0.957 0.000 0.040 0.916 0.012 0.000 0.032
#> GSM1009186 2 0.359 0.607 0.164 0.796 0.024 0.004 0.000 0.012
#> GSM1009200 1 0.181 0.712 0.928 0.020 0.000 0.044 0.000 0.008
#> GSM1009075 6 0.445 0.986 0.244 0.012 0.000 0.040 0.004 0.700
#> GSM1009089 2 0.727 0.460 0.068 0.448 0.024 0.028 0.352 0.080
#> GSM1009103 4 0.688 0.810 0.136 0.036 0.004 0.584 0.140 0.100
#> GSM1009117 5 0.588 0.986 0.000 0.176 0.248 0.008 0.560 0.008
#> GSM1009131 1 0.579 0.586 0.692 0.144 0.012 0.048 0.040 0.064
#> GSM1009145 4 0.243 0.807 0.080 0.008 0.000 0.888 0.000 0.024
#> GSM1009159 1 0.578 0.552 0.692 0.044 0.012 0.048 0.056 0.148
#> GSM1009173 3 0.240 0.955 0.000 0.044 0.904 0.012 0.008 0.032
#> GSM1009187 2 0.358 0.602 0.172 0.792 0.020 0.004 0.000 0.012
#> GSM1009201 1 0.181 0.712 0.928 0.020 0.000 0.044 0.000 0.008
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n temperature(p) time(p) specimen(p) k
#> SD:kmeans 135 0.987 0.994 7.66e-23 2
#> SD:kmeans 120 0.989 1.000 2.12e-21 3
#> SD:kmeans 118 1.000 1.000 1.92e-60 4
#> SD:kmeans 97 1.000 1.000 5.30e-65 5
#> SD:kmeans 122 1.000 1.000 4.84e-103 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 140 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.976 0.991 0.5007 0.500 0.500
#> 3 3 0.759 0.887 0.915 0.3129 0.807 0.627
#> 4 4 0.706 0.769 0.853 0.1260 0.863 0.625
#> 5 5 0.756 0.587 0.692 0.0620 0.830 0.483
#> 6 6 0.841 0.776 0.809 0.0441 0.894 0.587
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 2
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM1009062 1 0.0000 0.989 1.000 0.000
#> GSM1009076 2 0.0000 0.992 0.000 1.000
#> GSM1009090 1 0.0000 0.989 1.000 0.000
#> GSM1009104 2 0.0000 0.992 0.000 1.000
#> GSM1009118 2 0.9944 0.141 0.456 0.544
#> GSM1009132 1 0.0000 0.989 1.000 0.000
#> GSM1009146 1 0.0000 0.989 1.000 0.000
#> GSM1009160 2 0.0000 0.992 0.000 1.000
#> GSM1009174 2 0.0000 0.992 0.000 1.000
#> GSM1009188 1 0.0000 0.989 1.000 0.000
#> GSM1009063 1 0.0000 0.989 1.000 0.000
#> GSM1009077 2 0.0000 0.992 0.000 1.000
#> GSM1009091 1 0.0000 0.989 1.000 0.000
#> GSM1009105 2 0.0000 0.992 0.000 1.000
#> GSM1009119 1 0.0000 0.989 1.000 0.000
#> GSM1009133 1 0.0000 0.989 1.000 0.000
#> GSM1009147 1 0.0000 0.989 1.000 0.000
#> GSM1009161 2 0.0000 0.992 0.000 1.000
#> GSM1009175 2 0.0000 0.992 0.000 1.000
#> GSM1009189 1 0.0000 0.989 1.000 0.000
#> GSM1009064 1 0.0000 0.989 1.000 0.000
#> GSM1009078 2 0.0000 0.992 0.000 1.000
#> GSM1009092 1 0.0000 0.989 1.000 0.000
#> GSM1009106 2 0.0000 0.992 0.000 1.000
#> GSM1009120 1 0.0000 0.989 1.000 0.000
#> GSM1009134 1 0.0000 0.989 1.000 0.000
#> GSM1009148 1 0.0000 0.989 1.000 0.000
#> GSM1009162 2 0.0000 0.992 0.000 1.000
#> GSM1009176 2 0.0000 0.992 0.000 1.000
#> GSM1009190 1 0.0000 0.989 1.000 0.000
#> GSM1009065 1 0.0000 0.989 1.000 0.000
#> GSM1009079 2 0.0000 0.992 0.000 1.000
#> GSM1009093 1 0.0000 0.989 1.000 0.000
#> GSM1009107 2 0.0000 0.992 0.000 1.000
#> GSM1009121 2 0.0672 0.985 0.008 0.992
#> GSM1009135 1 0.0000 0.989 1.000 0.000
#> GSM1009149 1 0.0000 0.989 1.000 0.000
#> GSM1009163 2 0.0000 0.992 0.000 1.000
#> GSM1009177 2 0.0000 0.992 0.000 1.000
#> GSM1009191 1 0.0000 0.989 1.000 0.000
#> GSM1009066 1 0.0000 0.989 1.000 0.000
#> GSM1009080 2 0.0000 0.992 0.000 1.000
#> GSM1009094 1 0.0000 0.989 1.000 0.000
#> GSM1009108 2 0.0000 0.992 0.000 1.000
#> GSM1009122 2 0.0000 0.992 0.000 1.000
#> GSM1009136 1 0.0000 0.989 1.000 0.000
#> GSM1009150 1 0.0000 0.989 1.000 0.000
#> GSM1009164 2 0.0000 0.992 0.000 1.000
#> GSM1009178 2 0.0000 0.992 0.000 1.000
#> GSM1009192 1 0.0000 0.989 1.000 0.000
#> GSM1009067 1 0.0000 0.989 1.000 0.000
#> GSM1009081 2 0.0000 0.992 0.000 1.000
#> GSM1009095 1 0.0000 0.989 1.000 0.000
#> GSM1009109 2 0.0000 0.992 0.000 1.000
#> GSM1009123 1 0.0000 0.989 1.000 0.000
#> GSM1009137 1 0.0000 0.989 1.000 0.000
#> GSM1009151 1 0.0000 0.989 1.000 0.000
#> GSM1009165 2 0.0000 0.992 0.000 1.000
#> GSM1009179 2 0.0000 0.992 0.000 1.000
#> GSM1009193 1 0.0000 0.989 1.000 0.000
#> GSM1009068 1 0.0000 0.989 1.000 0.000
#> GSM1009082 2 0.0000 0.992 0.000 1.000
#> GSM1009096 1 0.0000 0.989 1.000 0.000
#> GSM1009110 2 0.0000 0.992 0.000 1.000
#> GSM1009124 1 0.0000 0.989 1.000 0.000
#> GSM1009138 1 0.0000 0.989 1.000 0.000
#> GSM1009152 1 0.0000 0.989 1.000 0.000
#> GSM1009166 2 0.0000 0.992 0.000 1.000
#> GSM1009180 2 0.0000 0.992 0.000 1.000
#> GSM1009194 1 0.0000 0.989 1.000 0.000
#> GSM1009069 1 0.0000 0.989 1.000 0.000
#> GSM1009083 2 0.0000 0.992 0.000 1.000
#> GSM1009097 1 0.0000 0.989 1.000 0.000
#> GSM1009111 2 0.0000 0.992 0.000 1.000
#> GSM1009125 2 0.0000 0.992 0.000 1.000
#> GSM1009139 1 0.0000 0.989 1.000 0.000
#> GSM1009153 1 0.0000 0.989 1.000 0.000
#> GSM1009167 2 0.0000 0.992 0.000 1.000
#> GSM1009181 2 0.0000 0.992 0.000 1.000
#> GSM1009195 1 0.7139 0.754 0.804 0.196
#> GSM1009070 1 0.0000 0.989 1.000 0.000
#> GSM1009084 2 0.0000 0.992 0.000 1.000
#> GSM1009098 1 0.0000 0.989 1.000 0.000
#> GSM1009112 2 0.0000 0.992 0.000 1.000
#> GSM1009126 1 0.0000 0.989 1.000 0.000
#> GSM1009140 1 0.0000 0.989 1.000 0.000
#> GSM1009154 1 0.0000 0.989 1.000 0.000
#> GSM1009168 2 0.0000 0.992 0.000 1.000
#> GSM1009182 2 0.0000 0.992 0.000 1.000
#> GSM1009196 1 0.0000 0.989 1.000 0.000
#> GSM1009071 1 0.0000 0.989 1.000 0.000
#> GSM1009085 2 0.0000 0.992 0.000 1.000
#> GSM1009099 1 0.0000 0.989 1.000 0.000
#> GSM1009113 2 0.0000 0.992 0.000 1.000
#> GSM1009127 1 0.0000 0.989 1.000 0.000
#> GSM1009141 1 0.0000 0.989 1.000 0.000
#> GSM1009155 1 0.0000 0.989 1.000 0.000
#> GSM1009169 2 0.0000 0.992 0.000 1.000
#> GSM1009183 2 0.0000 0.992 0.000 1.000
#> GSM1009197 1 0.0000 0.989 1.000 0.000
#> GSM1009072 1 0.0000 0.989 1.000 0.000
#> GSM1009086 2 0.0000 0.992 0.000 1.000
#> GSM1009100 1 0.0000 0.989 1.000 0.000
#> GSM1009114 2 0.0000 0.992 0.000 1.000
#> GSM1009128 2 0.0000 0.992 0.000 1.000
#> GSM1009142 1 0.0000 0.989 1.000 0.000
#> GSM1009156 1 0.8267 0.650 0.740 0.260
#> GSM1009170 2 0.0000 0.992 0.000 1.000
#> GSM1009184 2 0.0000 0.992 0.000 1.000
#> GSM1009198 1 0.0000 0.989 1.000 0.000
#> GSM1009073 1 0.0000 0.989 1.000 0.000
#> GSM1009087 2 0.0000 0.992 0.000 1.000
#> GSM1009101 1 0.0000 0.989 1.000 0.000
#> GSM1009115 2 0.0000 0.992 0.000 1.000
#> GSM1009129 2 0.0000 0.992 0.000 1.000
#> GSM1009143 1 0.0000 0.989 1.000 0.000
#> GSM1009157 1 0.9552 0.402 0.624 0.376
#> GSM1009171 2 0.0000 0.992 0.000 1.000
#> GSM1009185 2 0.0000 0.992 0.000 1.000
#> GSM1009199 1 0.0000 0.989 1.000 0.000
#> GSM1009074 1 0.0000 0.989 1.000 0.000
#> GSM1009088 2 0.0000 0.992 0.000 1.000
#> GSM1009102 1 0.0000 0.989 1.000 0.000
#> GSM1009116 2 0.0000 0.992 0.000 1.000
#> GSM1009130 2 0.0000 0.992 0.000 1.000
#> GSM1009144 1 0.0000 0.989 1.000 0.000
#> GSM1009158 1 0.0000 0.989 1.000 0.000
#> GSM1009172 2 0.0000 0.992 0.000 1.000
#> GSM1009186 2 0.0000 0.992 0.000 1.000
#> GSM1009200 1 0.0000 0.989 1.000 0.000
#> GSM1009075 1 0.0000 0.989 1.000 0.000
#> GSM1009089 2 0.0000 0.992 0.000 1.000
#> GSM1009103 1 0.0000 0.989 1.000 0.000
#> GSM1009117 2 0.0000 0.992 0.000 1.000
#> GSM1009131 2 0.0000 0.992 0.000 1.000
#> GSM1009145 1 0.0000 0.989 1.000 0.000
#> GSM1009159 1 0.0000 0.989 1.000 0.000
#> GSM1009173 2 0.0000 0.992 0.000 1.000
#> GSM1009187 2 0.0000 0.992 0.000 1.000
#> GSM1009201 1 0.0000 0.989 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1009062 1 0.4235 0.874 0.824 0.000 0.176
#> GSM1009076 2 0.1964 0.932 0.056 0.944 0.000
#> GSM1009090 3 0.0892 0.923 0.020 0.000 0.980
#> GSM1009104 2 0.0000 0.937 0.000 1.000 0.000
#> GSM1009118 3 0.5931 0.782 0.124 0.084 0.792
#> GSM1009132 3 0.0592 0.922 0.012 0.000 0.988
#> GSM1009146 1 0.2165 0.913 0.936 0.000 0.064
#> GSM1009160 2 0.1015 0.933 0.008 0.980 0.012
#> GSM1009174 2 0.3983 0.896 0.144 0.852 0.004
#> GSM1009188 1 0.3482 0.899 0.872 0.000 0.128
#> GSM1009063 1 0.4235 0.874 0.824 0.000 0.176
#> GSM1009077 2 0.1964 0.932 0.056 0.944 0.000
#> GSM1009091 3 0.0892 0.923 0.020 0.000 0.980
#> GSM1009105 2 0.0000 0.937 0.000 1.000 0.000
#> GSM1009119 3 0.4842 0.737 0.224 0.000 0.776
#> GSM1009133 3 0.0592 0.922 0.012 0.000 0.988
#> GSM1009147 1 0.2165 0.913 0.936 0.000 0.064
#> GSM1009161 2 0.1015 0.933 0.008 0.980 0.012
#> GSM1009175 2 0.3983 0.896 0.144 0.852 0.004
#> GSM1009189 1 0.3482 0.899 0.872 0.000 0.128
#> GSM1009064 1 0.4235 0.874 0.824 0.000 0.176
#> GSM1009078 2 0.1964 0.932 0.056 0.944 0.000
#> GSM1009092 3 0.0892 0.923 0.020 0.000 0.980
#> GSM1009106 2 0.0000 0.937 0.000 1.000 0.000
#> GSM1009120 1 0.3879 0.883 0.848 0.000 0.152
#> GSM1009134 3 0.0592 0.922 0.012 0.000 0.988
#> GSM1009148 1 0.2165 0.913 0.936 0.000 0.064
#> GSM1009162 2 0.1015 0.933 0.008 0.980 0.012
#> GSM1009176 2 0.3983 0.896 0.144 0.852 0.004
#> GSM1009190 1 0.3482 0.899 0.872 0.000 0.128
#> GSM1009065 1 0.4235 0.874 0.824 0.000 0.176
#> GSM1009079 2 0.1964 0.932 0.056 0.944 0.000
#> GSM1009093 3 0.0892 0.923 0.020 0.000 0.980
#> GSM1009107 2 0.0000 0.937 0.000 1.000 0.000
#> GSM1009121 3 0.5815 0.785 0.096 0.104 0.800
#> GSM1009135 3 0.0592 0.922 0.012 0.000 0.988
#> GSM1009149 1 0.2165 0.913 0.936 0.000 0.064
#> GSM1009163 2 0.1015 0.933 0.008 0.980 0.012
#> GSM1009177 2 0.3983 0.896 0.144 0.852 0.004
#> GSM1009191 1 0.3482 0.899 0.872 0.000 0.128
#> GSM1009066 1 0.4235 0.874 0.824 0.000 0.176
#> GSM1009080 2 0.1964 0.932 0.056 0.944 0.000
#> GSM1009094 3 0.0892 0.923 0.020 0.000 0.980
#> GSM1009108 2 0.0000 0.937 0.000 1.000 0.000
#> GSM1009122 3 0.8033 0.239 0.064 0.424 0.512
#> GSM1009136 3 0.0592 0.922 0.012 0.000 0.988
#> GSM1009150 1 0.2165 0.913 0.936 0.000 0.064
#> GSM1009164 2 0.1015 0.933 0.008 0.980 0.012
#> GSM1009178 2 0.3983 0.896 0.144 0.852 0.004
#> GSM1009192 1 0.3482 0.899 0.872 0.000 0.128
#> GSM1009067 1 0.4235 0.874 0.824 0.000 0.176
#> GSM1009081 2 0.1964 0.932 0.056 0.944 0.000
#> GSM1009095 3 0.0892 0.923 0.020 0.000 0.980
#> GSM1009109 2 0.0000 0.937 0.000 1.000 0.000
#> GSM1009123 3 0.3752 0.826 0.144 0.000 0.856
#> GSM1009137 3 0.0592 0.922 0.012 0.000 0.988
#> GSM1009151 1 0.2165 0.913 0.936 0.000 0.064
#> GSM1009165 2 0.1015 0.933 0.008 0.980 0.012
#> GSM1009179 2 0.3983 0.896 0.144 0.852 0.004
#> GSM1009193 1 0.3482 0.899 0.872 0.000 0.128
#> GSM1009068 1 0.4235 0.874 0.824 0.000 0.176
#> GSM1009082 2 0.1964 0.932 0.056 0.944 0.000
#> GSM1009096 3 0.0892 0.923 0.020 0.000 0.980
#> GSM1009110 2 0.0000 0.937 0.000 1.000 0.000
#> GSM1009124 3 0.3965 0.824 0.132 0.008 0.860
#> GSM1009138 3 0.0592 0.922 0.012 0.000 0.988
#> GSM1009152 1 0.2165 0.913 0.936 0.000 0.064
#> GSM1009166 2 0.1015 0.933 0.008 0.980 0.012
#> GSM1009180 2 0.3983 0.896 0.144 0.852 0.004
#> GSM1009194 1 0.3482 0.899 0.872 0.000 0.128
#> GSM1009069 1 0.3851 0.850 0.860 0.004 0.136
#> GSM1009083 2 0.1964 0.932 0.056 0.944 0.000
#> GSM1009097 3 0.0892 0.923 0.020 0.000 0.980
#> GSM1009111 2 0.0000 0.937 0.000 1.000 0.000
#> GSM1009125 3 0.7956 0.241 0.060 0.424 0.516
#> GSM1009139 3 0.0592 0.922 0.012 0.000 0.988
#> GSM1009153 1 0.2165 0.913 0.936 0.000 0.064
#> GSM1009167 2 0.1015 0.933 0.008 0.980 0.012
#> GSM1009181 2 0.3983 0.896 0.144 0.852 0.004
#> GSM1009195 1 0.3116 0.893 0.892 0.000 0.108
#> GSM1009070 1 0.4235 0.874 0.824 0.000 0.176
#> GSM1009084 2 0.1964 0.932 0.056 0.944 0.000
#> GSM1009098 3 0.0892 0.923 0.020 0.000 0.980
#> GSM1009112 2 0.0000 0.937 0.000 1.000 0.000
#> GSM1009126 3 0.3965 0.824 0.132 0.008 0.860
#> GSM1009140 3 0.0592 0.922 0.012 0.000 0.988
#> GSM1009154 1 0.2165 0.913 0.936 0.000 0.064
#> GSM1009168 2 0.1015 0.933 0.008 0.980 0.012
#> GSM1009182 2 0.3983 0.896 0.144 0.852 0.004
#> GSM1009196 1 0.3482 0.899 0.872 0.000 0.128
#> GSM1009071 1 0.4235 0.874 0.824 0.000 0.176
#> GSM1009085 2 0.1964 0.932 0.056 0.944 0.000
#> GSM1009099 3 0.0892 0.923 0.020 0.000 0.980
#> GSM1009113 2 0.0000 0.937 0.000 1.000 0.000
#> GSM1009127 3 0.4842 0.737 0.224 0.000 0.776
#> GSM1009141 3 0.0592 0.922 0.012 0.000 0.988
#> GSM1009155 1 0.2165 0.913 0.936 0.000 0.064
#> GSM1009169 2 0.1015 0.933 0.008 0.980 0.012
#> GSM1009183 2 0.3983 0.896 0.144 0.852 0.004
#> GSM1009197 1 0.3482 0.899 0.872 0.000 0.128
#> GSM1009072 1 0.4235 0.874 0.824 0.000 0.176
#> GSM1009086 2 0.1964 0.932 0.056 0.944 0.000
#> GSM1009100 3 0.0892 0.923 0.020 0.000 0.980
#> GSM1009114 2 0.0000 0.937 0.000 1.000 0.000
#> GSM1009128 3 0.5815 0.786 0.096 0.104 0.800
#> GSM1009142 3 0.0592 0.922 0.012 0.000 0.988
#> GSM1009156 1 0.1031 0.883 0.976 0.000 0.024
#> GSM1009170 2 0.1015 0.933 0.008 0.980 0.012
#> GSM1009184 2 0.3983 0.896 0.144 0.852 0.004
#> GSM1009198 1 0.3482 0.899 0.872 0.000 0.128
#> GSM1009073 1 0.4235 0.874 0.824 0.000 0.176
#> GSM1009087 2 0.1964 0.932 0.056 0.944 0.000
#> GSM1009101 3 0.0892 0.923 0.020 0.000 0.980
#> GSM1009115 2 0.0000 0.937 0.000 1.000 0.000
#> GSM1009129 2 0.4469 0.840 0.060 0.864 0.076
#> GSM1009143 3 0.0592 0.922 0.012 0.000 0.988
#> GSM1009157 1 0.0592 0.873 0.988 0.000 0.012
#> GSM1009171 2 0.1015 0.933 0.008 0.980 0.012
#> GSM1009185 2 0.3983 0.896 0.144 0.852 0.004
#> GSM1009199 1 0.3192 0.895 0.888 0.000 0.112
#> GSM1009074 1 0.4235 0.874 0.824 0.000 0.176
#> GSM1009088 2 0.1964 0.932 0.056 0.944 0.000
#> GSM1009102 3 0.0892 0.923 0.020 0.000 0.980
#> GSM1009116 2 0.0000 0.937 0.000 1.000 0.000
#> GSM1009130 2 0.2280 0.907 0.052 0.940 0.008
#> GSM1009144 3 0.0592 0.922 0.012 0.000 0.988
#> GSM1009158 1 0.2165 0.913 0.936 0.000 0.064
#> GSM1009172 2 0.1015 0.933 0.008 0.980 0.012
#> GSM1009186 2 0.3983 0.896 0.144 0.852 0.004
#> GSM1009200 1 0.3482 0.899 0.872 0.000 0.128
#> GSM1009075 1 0.4235 0.874 0.824 0.000 0.176
#> GSM1009089 2 0.5138 0.740 0.252 0.748 0.000
#> GSM1009103 3 0.0892 0.923 0.020 0.000 0.980
#> GSM1009117 2 0.0000 0.937 0.000 1.000 0.000
#> GSM1009131 2 0.8521 -0.112 0.092 0.468 0.440
#> GSM1009145 3 0.0592 0.922 0.012 0.000 0.988
#> GSM1009159 1 0.2165 0.913 0.936 0.000 0.064
#> GSM1009173 2 0.1015 0.933 0.008 0.980 0.012
#> GSM1009187 2 0.3983 0.896 0.144 0.852 0.004
#> GSM1009201 1 0.3482 0.899 0.872 0.000 0.128
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1009062 1 0.4917 0.797 0.768 0.040 0.008 0.184
#> GSM1009076 2 0.4222 0.733 0.000 0.728 0.272 0.000
#> GSM1009090 4 0.0707 0.942 0.020 0.000 0.000 0.980
#> GSM1009104 3 0.4661 0.520 0.000 0.348 0.652 0.000
#> GSM1009118 3 0.8224 0.398 0.096 0.176 0.572 0.156
#> GSM1009132 4 0.0000 0.942 0.000 0.000 0.000 1.000
#> GSM1009146 1 0.1059 0.878 0.972 0.012 0.000 0.016
#> GSM1009160 3 0.1867 0.706 0.000 0.072 0.928 0.000
#> GSM1009174 2 0.1584 0.777 0.012 0.952 0.036 0.000
#> GSM1009188 1 0.3038 0.863 0.904 0.036 0.032 0.028
#> GSM1009063 1 0.4917 0.797 0.768 0.040 0.008 0.184
#> GSM1009077 2 0.4222 0.733 0.000 0.728 0.272 0.000
#> GSM1009091 4 0.0707 0.942 0.020 0.000 0.000 0.980
#> GSM1009105 3 0.4661 0.520 0.000 0.348 0.652 0.000
#> GSM1009119 1 0.5027 0.751 0.784 0.028 0.036 0.152
#> GSM1009133 4 0.0000 0.942 0.000 0.000 0.000 1.000
#> GSM1009147 1 0.1059 0.878 0.972 0.012 0.000 0.016
#> GSM1009161 3 0.1867 0.706 0.000 0.072 0.928 0.000
#> GSM1009175 2 0.1584 0.777 0.012 0.952 0.036 0.000
#> GSM1009189 1 0.3038 0.863 0.904 0.036 0.032 0.028
#> GSM1009064 1 0.4917 0.797 0.768 0.040 0.008 0.184
#> GSM1009078 2 0.4164 0.731 0.000 0.736 0.264 0.000
#> GSM1009092 4 0.0707 0.942 0.020 0.000 0.000 0.980
#> GSM1009106 3 0.4661 0.520 0.000 0.348 0.652 0.000
#> GSM1009120 1 0.2940 0.863 0.908 0.028 0.036 0.028
#> GSM1009134 4 0.0000 0.942 0.000 0.000 0.000 1.000
#> GSM1009148 1 0.1059 0.878 0.972 0.012 0.000 0.016
#> GSM1009162 3 0.1867 0.706 0.000 0.072 0.928 0.000
#> GSM1009176 2 0.1584 0.777 0.012 0.952 0.036 0.000
#> GSM1009190 1 0.3038 0.863 0.904 0.036 0.032 0.028
#> GSM1009065 1 0.4917 0.797 0.768 0.040 0.008 0.184
#> GSM1009079 2 0.4222 0.733 0.000 0.728 0.272 0.000
#> GSM1009093 4 0.0707 0.942 0.020 0.000 0.000 0.980
#> GSM1009107 3 0.4661 0.520 0.000 0.348 0.652 0.000
#> GSM1009121 3 0.7656 0.445 0.072 0.164 0.620 0.144
#> GSM1009135 4 0.0000 0.942 0.000 0.000 0.000 1.000
#> GSM1009149 1 0.1059 0.878 0.972 0.012 0.000 0.016
#> GSM1009163 3 0.1867 0.706 0.000 0.072 0.928 0.000
#> GSM1009177 2 0.1584 0.777 0.012 0.952 0.036 0.000
#> GSM1009191 1 0.3038 0.863 0.904 0.036 0.032 0.028
#> GSM1009066 1 0.4917 0.797 0.768 0.040 0.008 0.184
#> GSM1009080 2 0.4222 0.733 0.000 0.728 0.272 0.000
#> GSM1009094 4 0.0707 0.942 0.020 0.000 0.000 0.980
#> GSM1009108 3 0.4661 0.520 0.000 0.348 0.652 0.000
#> GSM1009122 3 0.6918 0.493 0.056 0.188 0.668 0.088
#> GSM1009136 4 0.0000 0.942 0.000 0.000 0.000 1.000
#> GSM1009150 1 0.1059 0.878 0.972 0.012 0.000 0.016
#> GSM1009164 3 0.1867 0.706 0.000 0.072 0.928 0.000
#> GSM1009178 2 0.1584 0.777 0.012 0.952 0.036 0.000
#> GSM1009192 1 0.3038 0.863 0.904 0.036 0.032 0.028
#> GSM1009067 1 0.4917 0.797 0.768 0.040 0.008 0.184
#> GSM1009081 2 0.4222 0.733 0.000 0.728 0.272 0.000
#> GSM1009095 4 0.0707 0.942 0.020 0.000 0.000 0.980
#> GSM1009109 3 0.4661 0.520 0.000 0.348 0.652 0.000
#> GSM1009123 4 0.7082 0.515 0.264 0.032 0.092 0.612
#> GSM1009137 4 0.0000 0.942 0.000 0.000 0.000 1.000
#> GSM1009151 1 0.1059 0.878 0.972 0.012 0.000 0.016
#> GSM1009165 3 0.1867 0.706 0.000 0.072 0.928 0.000
#> GSM1009179 2 0.1584 0.777 0.012 0.952 0.036 0.000
#> GSM1009193 1 0.3038 0.863 0.904 0.036 0.032 0.028
#> GSM1009068 1 0.4917 0.797 0.768 0.040 0.008 0.184
#> GSM1009082 2 0.4222 0.733 0.000 0.728 0.272 0.000
#> GSM1009096 4 0.0707 0.942 0.020 0.000 0.000 0.980
#> GSM1009110 3 0.4661 0.520 0.000 0.348 0.652 0.000
#> GSM1009124 4 0.9335 0.278 0.168 0.144 0.264 0.424
#> GSM1009138 4 0.0000 0.942 0.000 0.000 0.000 1.000
#> GSM1009152 1 0.1059 0.878 0.972 0.012 0.000 0.016
#> GSM1009166 3 0.1867 0.706 0.000 0.072 0.928 0.000
#> GSM1009180 2 0.1584 0.777 0.012 0.952 0.036 0.000
#> GSM1009194 1 0.3038 0.863 0.904 0.036 0.032 0.028
#> GSM1009069 1 0.4917 0.797 0.768 0.040 0.008 0.184
#> GSM1009083 2 0.4222 0.733 0.000 0.728 0.272 0.000
#> GSM1009097 4 0.0707 0.942 0.020 0.000 0.000 0.980
#> GSM1009111 3 0.4661 0.520 0.000 0.348 0.652 0.000
#> GSM1009125 3 0.6952 0.493 0.052 0.200 0.660 0.088
#> GSM1009139 4 0.0000 0.942 0.000 0.000 0.000 1.000
#> GSM1009153 1 0.1059 0.878 0.972 0.012 0.000 0.016
#> GSM1009167 3 0.1867 0.706 0.000 0.072 0.928 0.000
#> GSM1009181 2 0.1584 0.777 0.012 0.952 0.036 0.000
#> GSM1009195 1 0.3038 0.863 0.904 0.036 0.032 0.028
#> GSM1009070 1 0.4917 0.797 0.768 0.040 0.008 0.184
#> GSM1009084 2 0.4222 0.733 0.000 0.728 0.272 0.000
#> GSM1009098 4 0.0707 0.942 0.020 0.000 0.000 0.980
#> GSM1009112 3 0.4661 0.520 0.000 0.348 0.652 0.000
#> GSM1009126 4 0.9321 0.287 0.168 0.144 0.260 0.428
#> GSM1009140 4 0.0000 0.942 0.000 0.000 0.000 1.000
#> GSM1009154 1 0.1059 0.878 0.972 0.012 0.000 0.016
#> GSM1009168 3 0.1867 0.706 0.000 0.072 0.928 0.000
#> GSM1009182 2 0.1584 0.777 0.012 0.952 0.036 0.000
#> GSM1009196 1 0.3038 0.863 0.904 0.036 0.032 0.028
#> GSM1009071 1 0.4917 0.797 0.768 0.040 0.008 0.184
#> GSM1009085 2 0.4222 0.733 0.000 0.728 0.272 0.000
#> GSM1009099 4 0.0707 0.942 0.020 0.000 0.000 0.980
#> GSM1009113 3 0.4661 0.520 0.000 0.348 0.652 0.000
#> GSM1009127 1 0.5482 0.735 0.764 0.032 0.056 0.148
#> GSM1009141 4 0.0000 0.942 0.000 0.000 0.000 1.000
#> GSM1009155 1 0.1059 0.878 0.972 0.012 0.000 0.016
#> GSM1009169 3 0.1867 0.706 0.000 0.072 0.928 0.000
#> GSM1009183 2 0.1584 0.777 0.012 0.952 0.036 0.000
#> GSM1009197 1 0.3038 0.863 0.904 0.036 0.032 0.028
#> GSM1009072 1 0.4917 0.797 0.768 0.040 0.008 0.184
#> GSM1009086 2 0.4222 0.733 0.000 0.728 0.272 0.000
#> GSM1009100 4 0.0707 0.942 0.020 0.000 0.000 0.980
#> GSM1009114 3 0.4661 0.520 0.000 0.348 0.652 0.000
#> GSM1009128 3 0.7750 0.432 0.068 0.152 0.608 0.172
#> GSM1009142 4 0.0000 0.942 0.000 0.000 0.000 1.000
#> GSM1009156 1 0.1004 0.874 0.972 0.024 0.004 0.000
#> GSM1009170 3 0.1867 0.706 0.000 0.072 0.928 0.000
#> GSM1009184 2 0.1584 0.777 0.012 0.952 0.036 0.000
#> GSM1009198 1 0.3038 0.863 0.904 0.036 0.032 0.028
#> GSM1009073 1 0.4917 0.797 0.768 0.040 0.008 0.184
#> GSM1009087 2 0.4164 0.731 0.000 0.736 0.264 0.000
#> GSM1009101 4 0.0707 0.942 0.020 0.000 0.000 0.980
#> GSM1009115 3 0.4661 0.520 0.000 0.348 0.652 0.000
#> GSM1009129 3 0.5317 0.554 0.052 0.200 0.740 0.008
#> GSM1009143 4 0.0000 0.942 0.000 0.000 0.000 1.000
#> GSM1009157 1 0.1109 0.873 0.968 0.028 0.004 0.000
#> GSM1009171 3 0.1867 0.706 0.000 0.072 0.928 0.000
#> GSM1009185 2 0.1584 0.777 0.012 0.952 0.036 0.000
#> GSM1009199 1 0.3038 0.863 0.904 0.036 0.032 0.028
#> GSM1009074 1 0.4917 0.797 0.768 0.040 0.008 0.184
#> GSM1009088 2 0.4164 0.731 0.000 0.736 0.264 0.000
#> GSM1009102 4 0.0707 0.942 0.020 0.000 0.000 0.980
#> GSM1009116 3 0.4661 0.520 0.000 0.348 0.652 0.000
#> GSM1009130 3 0.3266 0.653 0.040 0.084 0.876 0.000
#> GSM1009144 4 0.0000 0.942 0.000 0.000 0.000 1.000
#> GSM1009158 1 0.1059 0.878 0.972 0.012 0.000 0.016
#> GSM1009172 3 0.1867 0.706 0.000 0.072 0.928 0.000
#> GSM1009186 2 0.1584 0.777 0.012 0.952 0.036 0.000
#> GSM1009200 1 0.3038 0.863 0.904 0.036 0.032 0.028
#> GSM1009075 1 0.4917 0.797 0.768 0.040 0.008 0.184
#> GSM1009089 2 0.4576 0.725 0.012 0.728 0.260 0.000
#> GSM1009103 4 0.0707 0.942 0.020 0.000 0.000 0.980
#> GSM1009117 3 0.4661 0.520 0.000 0.348 0.652 0.000
#> GSM1009131 3 0.5737 0.549 0.064 0.196 0.724 0.016
#> GSM1009145 4 0.0000 0.942 0.000 0.000 0.000 1.000
#> GSM1009159 1 0.1059 0.878 0.972 0.012 0.000 0.016
#> GSM1009173 3 0.1867 0.706 0.000 0.072 0.928 0.000
#> GSM1009187 2 0.1584 0.777 0.012 0.952 0.036 0.000
#> GSM1009201 1 0.3038 0.863 0.904 0.036 0.032 0.028
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM1009062 3 0.5309 0.999 0.336 0.004 0.604 0.056 0.000
#> GSM1009076 5 0.5942 0.198 0.004 0.292 0.124 0.000 0.580
#> GSM1009090 4 0.0510 0.983 0.016 0.000 0.000 0.984 0.000
#> GSM1009104 5 0.0162 0.556 0.000 0.004 0.000 0.000 0.996
#> GSM1009118 1 0.8950 0.268 0.424 0.220 0.148 0.072 0.136
#> GSM1009132 4 0.0609 0.983 0.000 0.000 0.020 0.980 0.000
#> GSM1009146 1 0.4313 -0.157 0.636 0.008 0.356 0.000 0.000
#> GSM1009160 5 0.6381 0.495 0.000 0.212 0.276 0.000 0.512
#> GSM1009174 2 0.3652 1.000 0.012 0.784 0.004 0.000 0.200
#> GSM1009188 1 0.0609 0.519 0.980 0.000 0.000 0.020 0.000
#> GSM1009063 3 0.5309 0.999 0.336 0.004 0.604 0.056 0.000
#> GSM1009077 5 0.5942 0.198 0.004 0.292 0.124 0.000 0.580
#> GSM1009091 4 0.0510 0.983 0.016 0.000 0.000 0.984 0.000
#> GSM1009105 5 0.0162 0.556 0.000 0.004 0.000 0.000 0.996
#> GSM1009119 1 0.4055 0.476 0.820 0.028 0.064 0.088 0.000
#> GSM1009133 4 0.0609 0.983 0.000 0.000 0.020 0.980 0.000
#> GSM1009147 1 0.4313 -0.157 0.636 0.008 0.356 0.000 0.000
#> GSM1009161 5 0.6381 0.495 0.000 0.212 0.276 0.000 0.512
#> GSM1009175 2 0.3652 1.000 0.012 0.784 0.004 0.000 0.200
#> GSM1009189 1 0.0609 0.519 0.980 0.000 0.000 0.020 0.000
#> GSM1009064 3 0.5309 0.999 0.336 0.004 0.604 0.056 0.000
#> GSM1009078 5 0.5942 0.198 0.004 0.292 0.124 0.000 0.580
#> GSM1009092 4 0.0510 0.983 0.016 0.000 0.000 0.984 0.000
#> GSM1009106 5 0.0162 0.556 0.000 0.004 0.000 0.000 0.996
#> GSM1009120 1 0.3036 0.496 0.880 0.028 0.064 0.028 0.000
#> GSM1009134 4 0.0609 0.983 0.000 0.000 0.020 0.980 0.000
#> GSM1009148 1 0.4313 -0.157 0.636 0.008 0.356 0.000 0.000
#> GSM1009162 5 0.6381 0.495 0.000 0.212 0.276 0.000 0.512
#> GSM1009176 2 0.3652 1.000 0.012 0.784 0.004 0.000 0.200
#> GSM1009190 1 0.0609 0.519 0.980 0.000 0.000 0.020 0.000
#> GSM1009065 3 0.5309 0.999 0.336 0.004 0.604 0.056 0.000
#> GSM1009079 5 0.5942 0.198 0.004 0.292 0.124 0.000 0.580
#> GSM1009093 4 0.0510 0.983 0.016 0.000 0.000 0.984 0.000
#> GSM1009107 5 0.0162 0.556 0.000 0.004 0.000 0.000 0.996
#> GSM1009121 1 0.9104 0.230 0.396 0.220 0.148 0.068 0.168
#> GSM1009135 4 0.0609 0.983 0.000 0.000 0.020 0.980 0.000
#> GSM1009149 1 0.4313 -0.157 0.636 0.008 0.356 0.000 0.000
#> GSM1009163 5 0.6381 0.495 0.000 0.212 0.276 0.000 0.512
#> GSM1009177 2 0.3652 1.000 0.012 0.784 0.004 0.000 0.200
#> GSM1009191 1 0.0609 0.519 0.980 0.000 0.000 0.020 0.000
#> GSM1009066 3 0.5309 0.999 0.336 0.004 0.604 0.056 0.000
#> GSM1009080 5 0.5942 0.198 0.004 0.292 0.124 0.000 0.580
#> GSM1009094 4 0.0510 0.983 0.016 0.000 0.000 0.984 0.000
#> GSM1009108 5 0.0162 0.556 0.000 0.004 0.000 0.000 0.996
#> GSM1009122 1 0.9088 0.203 0.384 0.220 0.152 0.056 0.188
#> GSM1009136 4 0.0609 0.983 0.000 0.000 0.020 0.980 0.000
#> GSM1009150 1 0.4313 -0.157 0.636 0.008 0.356 0.000 0.000
#> GSM1009164 5 0.6381 0.495 0.000 0.212 0.276 0.000 0.512
#> GSM1009178 2 0.3652 1.000 0.012 0.784 0.004 0.000 0.200
#> GSM1009192 1 0.0609 0.519 0.980 0.000 0.000 0.020 0.000
#> GSM1009067 3 0.5309 0.999 0.336 0.004 0.604 0.056 0.000
#> GSM1009081 5 0.5942 0.198 0.004 0.292 0.124 0.000 0.580
#> GSM1009095 4 0.0510 0.983 0.016 0.000 0.000 0.984 0.000
#> GSM1009109 5 0.0162 0.556 0.000 0.004 0.000 0.000 0.996
#> GSM1009123 1 0.5946 0.379 0.632 0.040 0.072 0.256 0.000
#> GSM1009137 4 0.0609 0.983 0.000 0.000 0.020 0.980 0.000
#> GSM1009151 1 0.4313 -0.157 0.636 0.008 0.356 0.000 0.000
#> GSM1009165 5 0.6381 0.495 0.000 0.212 0.276 0.000 0.512
#> GSM1009179 2 0.3652 1.000 0.012 0.784 0.004 0.000 0.200
#> GSM1009193 1 0.0609 0.519 0.980 0.000 0.000 0.020 0.000
#> GSM1009068 3 0.5309 0.999 0.336 0.004 0.604 0.056 0.000
#> GSM1009082 5 0.5942 0.198 0.004 0.292 0.124 0.000 0.580
#> GSM1009096 4 0.0510 0.983 0.016 0.000 0.000 0.984 0.000
#> GSM1009110 5 0.0162 0.556 0.000 0.004 0.000 0.000 0.996
#> GSM1009124 1 0.7894 0.370 0.532 0.192 0.148 0.096 0.032
#> GSM1009138 4 0.0609 0.983 0.000 0.000 0.020 0.980 0.000
#> GSM1009152 1 0.4313 -0.157 0.636 0.008 0.356 0.000 0.000
#> GSM1009166 5 0.6381 0.495 0.000 0.212 0.276 0.000 0.512
#> GSM1009180 2 0.3652 1.000 0.012 0.784 0.004 0.000 0.200
#> GSM1009194 1 0.0609 0.519 0.980 0.000 0.000 0.020 0.000
#> GSM1009069 3 0.5399 0.991 0.336 0.004 0.604 0.052 0.004
#> GSM1009083 5 0.5942 0.198 0.004 0.292 0.124 0.000 0.580
#> GSM1009097 4 0.0510 0.983 0.016 0.000 0.000 0.984 0.000
#> GSM1009111 5 0.0162 0.556 0.000 0.004 0.000 0.000 0.996
#> GSM1009125 1 0.9107 0.206 0.384 0.224 0.148 0.060 0.184
#> GSM1009139 4 0.0609 0.983 0.000 0.000 0.020 0.980 0.000
#> GSM1009153 1 0.4313 -0.157 0.636 0.008 0.356 0.000 0.000
#> GSM1009167 5 0.6381 0.495 0.000 0.212 0.276 0.000 0.512
#> GSM1009181 2 0.3652 1.000 0.012 0.784 0.004 0.000 0.200
#> GSM1009195 1 0.0671 0.517 0.980 0.000 0.004 0.016 0.000
#> GSM1009070 3 0.5309 0.999 0.336 0.004 0.604 0.056 0.000
#> GSM1009084 5 0.5942 0.198 0.004 0.292 0.124 0.000 0.580
#> GSM1009098 4 0.0510 0.983 0.016 0.000 0.000 0.984 0.000
#> GSM1009112 5 0.0162 0.556 0.000 0.004 0.000 0.000 0.996
#> GSM1009126 1 0.7894 0.370 0.532 0.192 0.148 0.096 0.032
#> GSM1009140 4 0.0609 0.983 0.000 0.000 0.020 0.980 0.000
#> GSM1009154 1 0.4313 -0.157 0.636 0.008 0.356 0.000 0.000
#> GSM1009168 5 0.6381 0.495 0.000 0.212 0.276 0.000 0.512
#> GSM1009182 2 0.3652 1.000 0.012 0.784 0.004 0.000 0.200
#> GSM1009196 1 0.0609 0.519 0.980 0.000 0.000 0.020 0.000
#> GSM1009071 3 0.5309 0.999 0.336 0.004 0.604 0.056 0.000
#> GSM1009085 5 0.5942 0.198 0.004 0.292 0.124 0.000 0.580
#> GSM1009099 4 0.0510 0.983 0.016 0.000 0.000 0.984 0.000
#> GSM1009113 5 0.0162 0.556 0.000 0.004 0.000 0.000 0.996
#> GSM1009127 1 0.4355 0.472 0.804 0.040 0.068 0.088 0.000
#> GSM1009141 4 0.0609 0.983 0.000 0.000 0.020 0.980 0.000
#> GSM1009155 1 0.4313 -0.157 0.636 0.008 0.356 0.000 0.000
#> GSM1009169 5 0.6381 0.495 0.000 0.212 0.276 0.000 0.512
#> GSM1009183 2 0.3652 1.000 0.012 0.784 0.004 0.000 0.200
#> GSM1009197 1 0.0609 0.519 0.980 0.000 0.000 0.020 0.000
#> GSM1009072 3 0.5309 0.999 0.336 0.004 0.604 0.056 0.000
#> GSM1009086 5 0.5942 0.198 0.004 0.292 0.124 0.000 0.580
#> GSM1009100 4 0.0510 0.983 0.016 0.000 0.000 0.984 0.000
#> GSM1009114 5 0.0162 0.556 0.000 0.004 0.000 0.000 0.996
#> GSM1009128 1 0.9176 0.231 0.392 0.224 0.148 0.080 0.156
#> GSM1009142 4 0.0609 0.983 0.000 0.000 0.020 0.980 0.000
#> GSM1009156 1 0.4313 -0.157 0.636 0.008 0.356 0.000 0.000
#> GSM1009170 5 0.6381 0.495 0.000 0.212 0.276 0.000 0.512
#> GSM1009184 2 0.3652 1.000 0.012 0.784 0.004 0.000 0.200
#> GSM1009198 1 0.0609 0.519 0.980 0.000 0.000 0.020 0.000
#> GSM1009073 3 0.5309 0.999 0.336 0.004 0.604 0.056 0.000
#> GSM1009087 5 0.5942 0.198 0.004 0.292 0.124 0.000 0.580
#> GSM1009101 4 0.0510 0.983 0.016 0.000 0.000 0.984 0.000
#> GSM1009115 5 0.0162 0.556 0.000 0.004 0.000 0.000 0.996
#> GSM1009129 1 0.8589 0.144 0.380 0.216 0.156 0.012 0.236
#> GSM1009143 4 0.0609 0.983 0.000 0.000 0.020 0.980 0.000
#> GSM1009157 1 0.4313 -0.157 0.636 0.008 0.356 0.000 0.000
#> GSM1009171 5 0.6381 0.495 0.000 0.212 0.276 0.000 0.512
#> GSM1009185 2 0.3652 1.000 0.012 0.784 0.004 0.000 0.200
#> GSM1009199 1 0.0609 0.519 0.980 0.000 0.000 0.020 0.000
#> GSM1009074 3 0.5309 0.999 0.336 0.004 0.604 0.056 0.000
#> GSM1009088 5 0.5942 0.198 0.004 0.292 0.124 0.000 0.580
#> GSM1009102 4 0.0510 0.983 0.016 0.000 0.000 0.984 0.000
#> GSM1009116 5 0.0162 0.556 0.000 0.004 0.000 0.000 0.996
#> GSM1009130 5 0.7848 0.158 0.296 0.100 0.180 0.000 0.424
#> GSM1009144 4 0.0609 0.983 0.000 0.000 0.020 0.980 0.000
#> GSM1009158 1 0.4313 -0.157 0.636 0.008 0.356 0.000 0.000
#> GSM1009172 5 0.6381 0.495 0.000 0.212 0.276 0.000 0.512
#> GSM1009186 2 0.3652 1.000 0.012 0.784 0.004 0.000 0.200
#> GSM1009200 1 0.0609 0.519 0.980 0.000 0.000 0.020 0.000
#> GSM1009075 3 0.5309 0.999 0.336 0.004 0.604 0.056 0.000
#> GSM1009089 5 0.6053 0.173 0.004 0.292 0.136 0.000 0.568
#> GSM1009103 4 0.0510 0.983 0.016 0.000 0.000 0.984 0.000
#> GSM1009117 5 0.0162 0.556 0.000 0.004 0.000 0.000 0.996
#> GSM1009131 1 0.8633 0.167 0.388 0.216 0.156 0.016 0.224
#> GSM1009145 4 0.0609 0.983 0.000 0.000 0.020 0.980 0.000
#> GSM1009159 1 0.4313 -0.157 0.636 0.008 0.356 0.000 0.000
#> GSM1009173 5 0.6381 0.495 0.000 0.212 0.276 0.000 0.512
#> GSM1009187 2 0.3652 1.000 0.012 0.784 0.004 0.000 0.200
#> GSM1009201 1 0.0609 0.519 0.980 0.000 0.000 0.020 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1009062 6 0.1257 0.708 0.000 0.000 0.000 0.020 0.028 0.952
#> GSM1009076 5 0.1180 0.641 0.004 0.004 0.008 0.000 0.960 0.024
#> GSM1009090 4 0.0146 0.982 0.004 0.000 0.000 0.996 0.000 0.000
#> GSM1009104 5 0.5721 0.604 0.028 0.100 0.324 0.000 0.548 0.000
#> GSM1009118 1 0.6662 0.562 0.532 0.272 0.132 0.008 0.020 0.036
#> GSM1009132 4 0.1086 0.982 0.012 0.012 0.000 0.964 0.000 0.012
#> GSM1009146 6 0.5052 0.632 0.392 0.052 0.012 0.000 0.000 0.544
#> GSM1009160 3 0.0790 1.000 0.000 0.000 0.968 0.000 0.032 0.000
#> GSM1009174 2 0.3198 1.000 0.000 0.740 0.000 0.000 0.260 0.000
#> GSM1009188 1 0.2520 0.644 0.872 0.012 0.000 0.008 0.000 0.108
#> GSM1009063 6 0.1257 0.708 0.000 0.000 0.000 0.020 0.028 0.952
#> GSM1009077 5 0.1180 0.641 0.004 0.004 0.008 0.000 0.960 0.024
#> GSM1009091 4 0.0146 0.982 0.004 0.000 0.000 0.996 0.000 0.000
#> GSM1009105 5 0.5721 0.604 0.028 0.100 0.324 0.000 0.548 0.000
#> GSM1009119 1 0.4517 0.656 0.744 0.164 0.020 0.008 0.000 0.064
#> GSM1009133 4 0.1086 0.982 0.012 0.012 0.000 0.964 0.000 0.012
#> GSM1009147 6 0.5052 0.632 0.392 0.052 0.012 0.000 0.000 0.544
#> GSM1009161 3 0.0790 1.000 0.000 0.000 0.968 0.000 0.032 0.000
#> GSM1009175 2 0.3198 1.000 0.000 0.740 0.000 0.000 0.260 0.000
#> GSM1009189 1 0.2520 0.644 0.872 0.012 0.000 0.008 0.000 0.108
#> GSM1009064 6 0.1257 0.708 0.000 0.000 0.000 0.020 0.028 0.952
#> GSM1009078 5 0.1180 0.641 0.004 0.004 0.008 0.000 0.960 0.024
#> GSM1009092 4 0.0146 0.982 0.004 0.000 0.000 0.996 0.000 0.000
#> GSM1009106 5 0.5721 0.604 0.028 0.100 0.324 0.000 0.548 0.000
#> GSM1009120 1 0.4555 0.655 0.744 0.156 0.020 0.008 0.000 0.072
#> GSM1009134 4 0.1086 0.982 0.012 0.012 0.000 0.964 0.000 0.012
#> GSM1009148 6 0.5052 0.632 0.392 0.052 0.012 0.000 0.000 0.544
#> GSM1009162 3 0.0790 1.000 0.000 0.000 0.968 0.000 0.032 0.000
#> GSM1009176 2 0.3198 1.000 0.000 0.740 0.000 0.000 0.260 0.000
#> GSM1009190 1 0.2520 0.644 0.872 0.012 0.000 0.008 0.000 0.108
#> GSM1009065 6 0.1257 0.708 0.000 0.000 0.000 0.020 0.028 0.952
#> GSM1009079 5 0.1180 0.641 0.004 0.004 0.008 0.000 0.960 0.024
#> GSM1009093 4 0.0146 0.982 0.004 0.000 0.000 0.996 0.000 0.000
#> GSM1009107 5 0.5721 0.604 0.028 0.100 0.324 0.000 0.548 0.000
#> GSM1009121 1 0.6808 0.547 0.516 0.276 0.140 0.008 0.024 0.036
#> GSM1009135 4 0.1086 0.982 0.012 0.012 0.000 0.964 0.000 0.012
#> GSM1009149 6 0.5052 0.632 0.392 0.052 0.012 0.000 0.000 0.544
#> GSM1009163 3 0.0790 1.000 0.000 0.000 0.968 0.000 0.032 0.000
#> GSM1009177 2 0.3198 1.000 0.000 0.740 0.000 0.000 0.260 0.000
#> GSM1009191 1 0.2520 0.644 0.872 0.012 0.000 0.008 0.000 0.108
#> GSM1009066 6 0.1257 0.708 0.000 0.000 0.000 0.020 0.028 0.952
#> GSM1009080 5 0.1180 0.641 0.004 0.004 0.008 0.000 0.960 0.024
#> GSM1009094 4 0.0146 0.982 0.004 0.000 0.000 0.996 0.000 0.000
#> GSM1009108 5 0.5721 0.604 0.028 0.100 0.324 0.000 0.548 0.000
#> GSM1009122 1 0.6847 0.539 0.504 0.288 0.140 0.008 0.024 0.036
#> GSM1009136 4 0.1086 0.982 0.012 0.012 0.000 0.964 0.000 0.012
#> GSM1009150 6 0.5052 0.632 0.392 0.052 0.012 0.000 0.000 0.544
#> GSM1009164 3 0.0790 1.000 0.000 0.000 0.968 0.000 0.032 0.000
#> GSM1009178 2 0.3198 1.000 0.000 0.740 0.000 0.000 0.260 0.000
#> GSM1009192 1 0.2520 0.644 0.872 0.012 0.000 0.008 0.000 0.108
#> GSM1009067 6 0.1257 0.708 0.000 0.000 0.000 0.020 0.028 0.952
#> GSM1009081 5 0.1180 0.641 0.004 0.004 0.008 0.000 0.960 0.024
#> GSM1009095 4 0.0146 0.982 0.004 0.000 0.000 0.996 0.000 0.000
#> GSM1009109 5 0.5721 0.604 0.028 0.100 0.324 0.000 0.548 0.000
#> GSM1009123 1 0.4767 0.652 0.736 0.168 0.024 0.036 0.000 0.036
#> GSM1009137 4 0.1086 0.982 0.012 0.012 0.000 0.964 0.000 0.012
#> GSM1009151 6 0.5052 0.632 0.392 0.052 0.012 0.000 0.000 0.544
#> GSM1009165 3 0.0790 1.000 0.000 0.000 0.968 0.000 0.032 0.000
#> GSM1009179 2 0.3198 1.000 0.000 0.740 0.000 0.000 0.260 0.000
#> GSM1009193 1 0.2520 0.644 0.872 0.012 0.000 0.008 0.000 0.108
#> GSM1009068 6 0.1257 0.708 0.000 0.000 0.000 0.020 0.028 0.952
#> GSM1009082 5 0.1180 0.641 0.004 0.004 0.008 0.000 0.960 0.024
#> GSM1009096 4 0.0146 0.982 0.004 0.000 0.000 0.996 0.000 0.000
#> GSM1009110 5 0.5721 0.604 0.028 0.100 0.324 0.000 0.548 0.000
#> GSM1009124 1 0.5640 0.636 0.652 0.212 0.084 0.012 0.004 0.036
#> GSM1009138 4 0.1086 0.982 0.012 0.012 0.000 0.964 0.000 0.012
#> GSM1009152 6 0.5052 0.632 0.392 0.052 0.012 0.000 0.000 0.544
#> GSM1009166 3 0.0790 1.000 0.000 0.000 0.968 0.000 0.032 0.000
#> GSM1009180 2 0.3198 1.000 0.000 0.740 0.000 0.000 0.260 0.000
#> GSM1009194 1 0.2520 0.644 0.872 0.012 0.000 0.008 0.000 0.108
#> GSM1009069 6 0.1196 0.700 0.000 0.000 0.000 0.008 0.040 0.952
#> GSM1009083 5 0.1180 0.641 0.004 0.004 0.008 0.000 0.960 0.024
#> GSM1009097 4 0.0146 0.982 0.004 0.000 0.000 0.996 0.000 0.000
#> GSM1009111 5 0.5721 0.604 0.028 0.100 0.324 0.000 0.548 0.000
#> GSM1009125 1 0.6876 0.534 0.500 0.288 0.144 0.008 0.024 0.036
#> GSM1009139 4 0.1086 0.982 0.012 0.012 0.000 0.964 0.000 0.012
#> GSM1009153 6 0.5052 0.632 0.392 0.052 0.012 0.000 0.000 0.544
#> GSM1009167 3 0.0790 1.000 0.000 0.000 0.968 0.000 0.032 0.000
#> GSM1009181 2 0.3198 1.000 0.000 0.740 0.000 0.000 0.260 0.000
#> GSM1009195 1 0.2551 0.640 0.872 0.012 0.000 0.004 0.004 0.108
#> GSM1009070 6 0.1257 0.708 0.000 0.000 0.000 0.020 0.028 0.952
#> GSM1009084 5 0.1180 0.641 0.004 0.004 0.008 0.000 0.960 0.024
#> GSM1009098 4 0.0146 0.982 0.004 0.000 0.000 0.996 0.000 0.000
#> GSM1009112 5 0.5721 0.604 0.028 0.100 0.324 0.000 0.548 0.000
#> GSM1009126 1 0.5640 0.636 0.652 0.212 0.084 0.012 0.004 0.036
#> GSM1009140 4 0.1086 0.982 0.012 0.012 0.000 0.964 0.000 0.012
#> GSM1009154 6 0.5052 0.632 0.392 0.052 0.012 0.000 0.000 0.544
#> GSM1009168 3 0.0790 1.000 0.000 0.000 0.968 0.000 0.032 0.000
#> GSM1009182 2 0.3198 1.000 0.000 0.740 0.000 0.000 0.260 0.000
#> GSM1009196 1 0.2520 0.644 0.872 0.012 0.000 0.008 0.000 0.108
#> GSM1009071 6 0.1257 0.708 0.000 0.000 0.000 0.020 0.028 0.952
#> GSM1009085 5 0.1180 0.641 0.004 0.004 0.008 0.000 0.960 0.024
#> GSM1009099 4 0.0146 0.982 0.004 0.000 0.000 0.996 0.000 0.000
#> GSM1009113 5 0.5721 0.604 0.028 0.100 0.324 0.000 0.548 0.000
#> GSM1009127 1 0.4646 0.655 0.736 0.168 0.020 0.012 0.000 0.064
#> GSM1009141 4 0.1086 0.982 0.012 0.012 0.000 0.964 0.000 0.012
#> GSM1009155 6 0.5052 0.632 0.392 0.052 0.012 0.000 0.000 0.544
#> GSM1009169 3 0.0790 1.000 0.000 0.000 0.968 0.000 0.032 0.000
#> GSM1009183 2 0.3198 1.000 0.000 0.740 0.000 0.000 0.260 0.000
#> GSM1009197 1 0.2520 0.644 0.872 0.012 0.000 0.008 0.000 0.108
#> GSM1009072 6 0.1257 0.708 0.000 0.000 0.000 0.020 0.028 0.952
#> GSM1009086 5 0.1180 0.641 0.004 0.004 0.008 0.000 0.960 0.024
#> GSM1009100 4 0.0146 0.982 0.004 0.000 0.000 0.996 0.000 0.000
#> GSM1009114 5 0.5721 0.604 0.028 0.100 0.324 0.000 0.548 0.000
#> GSM1009128 1 0.6808 0.547 0.516 0.276 0.140 0.008 0.024 0.036
#> GSM1009142 4 0.1086 0.982 0.012 0.012 0.000 0.964 0.000 0.012
#> GSM1009156 6 0.5052 0.632 0.392 0.052 0.012 0.000 0.000 0.544
#> GSM1009170 3 0.0790 1.000 0.000 0.000 0.968 0.000 0.032 0.000
#> GSM1009184 2 0.3198 1.000 0.000 0.740 0.000 0.000 0.260 0.000
#> GSM1009198 1 0.2520 0.644 0.872 0.012 0.000 0.008 0.000 0.108
#> GSM1009073 6 0.1257 0.708 0.000 0.000 0.000 0.020 0.028 0.952
#> GSM1009087 5 0.1180 0.641 0.004 0.004 0.008 0.000 0.960 0.024
#> GSM1009101 4 0.0146 0.982 0.004 0.000 0.000 0.996 0.000 0.000
#> GSM1009115 5 0.5721 0.604 0.028 0.100 0.324 0.000 0.548 0.000
#> GSM1009129 1 0.6925 0.532 0.500 0.284 0.140 0.004 0.036 0.036
#> GSM1009143 4 0.1086 0.982 0.012 0.012 0.000 0.964 0.000 0.012
#> GSM1009157 6 0.5052 0.632 0.392 0.052 0.012 0.000 0.000 0.544
#> GSM1009171 3 0.0790 1.000 0.000 0.000 0.968 0.000 0.032 0.000
#> GSM1009185 2 0.3198 1.000 0.000 0.740 0.000 0.000 0.260 0.000
#> GSM1009199 1 0.2520 0.644 0.872 0.012 0.000 0.008 0.000 0.108
#> GSM1009074 6 0.1257 0.708 0.000 0.000 0.000 0.020 0.028 0.952
#> GSM1009088 5 0.1180 0.641 0.004 0.004 0.008 0.000 0.960 0.024
#> GSM1009102 4 0.0146 0.982 0.004 0.000 0.000 0.996 0.000 0.000
#> GSM1009116 5 0.5721 0.604 0.028 0.100 0.324 0.000 0.548 0.000
#> GSM1009130 1 0.8055 0.289 0.360 0.212 0.148 0.000 0.244 0.036
#> GSM1009144 4 0.1086 0.982 0.012 0.012 0.000 0.964 0.000 0.012
#> GSM1009158 6 0.5052 0.632 0.392 0.052 0.012 0.000 0.000 0.544
#> GSM1009172 3 0.0790 1.000 0.000 0.000 0.968 0.000 0.032 0.000
#> GSM1009186 2 0.3198 1.000 0.000 0.740 0.000 0.000 0.260 0.000
#> GSM1009200 1 0.2520 0.644 0.872 0.012 0.000 0.008 0.000 0.108
#> GSM1009075 6 0.1257 0.708 0.000 0.000 0.000 0.020 0.028 0.952
#> GSM1009089 5 0.1180 0.641 0.004 0.004 0.008 0.000 0.960 0.024
#> GSM1009103 4 0.0146 0.982 0.004 0.000 0.000 0.996 0.000 0.000
#> GSM1009117 5 0.5721 0.604 0.028 0.100 0.324 0.000 0.548 0.000
#> GSM1009131 1 0.6746 0.549 0.524 0.268 0.140 0.004 0.028 0.036
#> GSM1009145 4 0.1086 0.982 0.012 0.012 0.000 0.964 0.000 0.012
#> GSM1009159 6 0.5052 0.632 0.392 0.052 0.012 0.000 0.000 0.544
#> GSM1009173 3 0.0790 1.000 0.000 0.000 0.968 0.000 0.032 0.000
#> GSM1009187 2 0.3198 1.000 0.000 0.740 0.000 0.000 0.260 0.000
#> GSM1009201 1 0.2520 0.644 0.872 0.012 0.000 0.008 0.000 0.108
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 temperature(p) time(p) specimen(p) k
#> SD:skmeans 138 0.869 0.998 1.28e-22 2
#> SD:skmeans 137 0.998 1.000 1.24e-44 3
#> SD:skmeans 133 1.000 1.000 1.69e-64 4
#> SD:skmeans 84 1.000 1.000 3.39e-59 5
#> SD:skmeans 139 1.000 1.000 2.31e-117 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 140 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'pam' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 6.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.661 0.929 0.959 0.2297 0.819 0.819
#> 3 3 0.822 0.934 0.945 1.3025 0.642 0.563
#> 4 4 0.839 0.885 0.945 0.3127 0.823 0.621
#> 5 5 0.947 0.921 0.967 0.0825 0.946 0.821
#> 6 6 0.978 0.947 0.977 0.0750 0.938 0.760
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] 5
There is also optional best \(k\) = 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
#> GSM1009062 1 0.0000 0.951 1.000 0.000
#> GSM1009076 1 0.6973 0.825 0.812 0.188
#> GSM1009090 1 0.0000 0.951 1.000 0.000
#> GSM1009104 1 0.6973 0.825 0.812 0.188
#> GSM1009118 1 0.0376 0.950 0.996 0.004
#> GSM1009132 1 0.0000 0.951 1.000 0.000
#> GSM1009146 1 0.0000 0.951 1.000 0.000
#> GSM1009160 2 0.0000 1.000 0.000 1.000
#> GSM1009174 1 0.0938 0.946 0.988 0.012
#> GSM1009188 1 0.0000 0.951 1.000 0.000
#> GSM1009063 1 0.0000 0.951 1.000 0.000
#> GSM1009077 1 0.6973 0.825 0.812 0.188
#> GSM1009091 1 0.0000 0.951 1.000 0.000
#> GSM1009105 1 0.6973 0.825 0.812 0.188
#> GSM1009119 1 0.0000 0.951 1.000 0.000
#> GSM1009133 1 0.0000 0.951 1.000 0.000
#> GSM1009147 1 0.0000 0.951 1.000 0.000
#> GSM1009161 2 0.0000 1.000 0.000 1.000
#> GSM1009175 1 0.0938 0.946 0.988 0.012
#> GSM1009189 1 0.0000 0.951 1.000 0.000
#> GSM1009064 1 0.0000 0.951 1.000 0.000
#> GSM1009078 1 0.0000 0.951 1.000 0.000
#> GSM1009092 1 0.0000 0.951 1.000 0.000
#> GSM1009106 1 0.6973 0.825 0.812 0.188
#> GSM1009120 1 0.0000 0.951 1.000 0.000
#> GSM1009134 1 0.0000 0.951 1.000 0.000
#> GSM1009148 1 0.0000 0.951 1.000 0.000
#> GSM1009162 2 0.0000 1.000 0.000 1.000
#> GSM1009176 1 0.6973 0.825 0.812 0.188
#> GSM1009190 1 0.0000 0.951 1.000 0.000
#> GSM1009065 1 0.0000 0.951 1.000 0.000
#> GSM1009079 1 0.6973 0.825 0.812 0.188
#> GSM1009093 1 0.0000 0.951 1.000 0.000
#> GSM1009107 1 0.6973 0.825 0.812 0.188
#> GSM1009121 1 0.0000 0.951 1.000 0.000
#> GSM1009135 1 0.0000 0.951 1.000 0.000
#> GSM1009149 1 0.0000 0.951 1.000 0.000
#> GSM1009163 2 0.0000 1.000 0.000 1.000
#> GSM1009177 1 0.5946 0.859 0.856 0.144
#> GSM1009191 1 0.0000 0.951 1.000 0.000
#> GSM1009066 1 0.0000 0.951 1.000 0.000
#> GSM1009080 1 0.6973 0.825 0.812 0.188
#> GSM1009094 1 0.0000 0.951 1.000 0.000
#> GSM1009108 1 0.6973 0.825 0.812 0.188
#> GSM1009122 1 0.0938 0.946 0.988 0.012
#> GSM1009136 1 0.0000 0.951 1.000 0.000
#> GSM1009150 1 0.0000 0.951 1.000 0.000
#> GSM1009164 2 0.0000 1.000 0.000 1.000
#> GSM1009178 1 0.0938 0.946 0.988 0.012
#> GSM1009192 1 0.0000 0.951 1.000 0.000
#> GSM1009067 1 0.0000 0.951 1.000 0.000
#> GSM1009081 1 0.6973 0.825 0.812 0.188
#> GSM1009095 1 0.0000 0.951 1.000 0.000
#> GSM1009109 1 0.6973 0.825 0.812 0.188
#> GSM1009123 1 0.0000 0.951 1.000 0.000
#> GSM1009137 1 0.0000 0.951 1.000 0.000
#> GSM1009151 1 0.0000 0.951 1.000 0.000
#> GSM1009165 2 0.0000 1.000 0.000 1.000
#> GSM1009179 1 0.0938 0.946 0.988 0.012
#> GSM1009193 1 0.0000 0.951 1.000 0.000
#> GSM1009068 1 0.0000 0.951 1.000 0.000
#> GSM1009082 1 0.6973 0.825 0.812 0.188
#> GSM1009096 1 0.0000 0.951 1.000 0.000
#> GSM1009110 1 0.6973 0.825 0.812 0.188
#> GSM1009124 1 0.0000 0.951 1.000 0.000
#> GSM1009138 1 0.0000 0.951 1.000 0.000
#> GSM1009152 1 0.0000 0.951 1.000 0.000
#> GSM1009166 2 0.0000 1.000 0.000 1.000
#> GSM1009180 1 0.0938 0.946 0.988 0.012
#> GSM1009194 1 0.0000 0.951 1.000 0.000
#> GSM1009069 1 0.0376 0.950 0.996 0.004
#> GSM1009083 1 0.6973 0.825 0.812 0.188
#> GSM1009097 1 0.0000 0.951 1.000 0.000
#> GSM1009111 1 0.6973 0.825 0.812 0.188
#> GSM1009125 1 0.6973 0.825 0.812 0.188
#> GSM1009139 1 0.0000 0.951 1.000 0.000
#> GSM1009153 1 0.0000 0.951 1.000 0.000
#> GSM1009167 2 0.0000 1.000 0.000 1.000
#> GSM1009181 1 0.6973 0.825 0.812 0.188
#> GSM1009195 1 0.0000 0.951 1.000 0.000
#> GSM1009070 1 0.0000 0.951 1.000 0.000
#> GSM1009084 1 0.6973 0.825 0.812 0.188
#> GSM1009098 1 0.0000 0.951 1.000 0.000
#> GSM1009112 1 0.6973 0.825 0.812 0.188
#> GSM1009126 1 0.0000 0.951 1.000 0.000
#> GSM1009140 1 0.0000 0.951 1.000 0.000
#> GSM1009154 1 0.0000 0.951 1.000 0.000
#> GSM1009168 2 0.0000 1.000 0.000 1.000
#> GSM1009182 1 0.0938 0.946 0.988 0.012
#> GSM1009196 1 0.0000 0.951 1.000 0.000
#> GSM1009071 1 0.0000 0.951 1.000 0.000
#> GSM1009085 1 0.6973 0.825 0.812 0.188
#> GSM1009099 1 0.0000 0.951 1.000 0.000
#> GSM1009113 1 0.6973 0.825 0.812 0.188
#> GSM1009127 1 0.0000 0.951 1.000 0.000
#> GSM1009141 1 0.0000 0.951 1.000 0.000
#> GSM1009155 1 0.0000 0.951 1.000 0.000
#> GSM1009169 2 0.0000 1.000 0.000 1.000
#> GSM1009183 1 0.0938 0.946 0.988 0.012
#> GSM1009197 1 0.0000 0.951 1.000 0.000
#> GSM1009072 1 0.0000 0.951 1.000 0.000
#> GSM1009086 1 0.6973 0.825 0.812 0.188
#> GSM1009100 1 0.0000 0.951 1.000 0.000
#> GSM1009114 1 0.6973 0.825 0.812 0.188
#> GSM1009128 1 0.0000 0.951 1.000 0.000
#> GSM1009142 1 0.0000 0.951 1.000 0.000
#> GSM1009156 1 0.0000 0.951 1.000 0.000
#> GSM1009170 2 0.0000 1.000 0.000 1.000
#> GSM1009184 1 0.0938 0.946 0.988 0.012
#> GSM1009198 1 0.0000 0.951 1.000 0.000
#> GSM1009073 1 0.0000 0.951 1.000 0.000
#> GSM1009087 1 0.0000 0.951 1.000 0.000
#> GSM1009101 1 0.0000 0.951 1.000 0.000
#> GSM1009115 1 0.6973 0.825 0.812 0.188
#> GSM1009129 1 0.6973 0.825 0.812 0.188
#> GSM1009143 1 0.0000 0.951 1.000 0.000
#> GSM1009157 1 0.0000 0.951 1.000 0.000
#> GSM1009171 2 0.0000 1.000 0.000 1.000
#> GSM1009185 1 0.0938 0.946 0.988 0.012
#> GSM1009199 1 0.0000 0.951 1.000 0.000
#> GSM1009074 1 0.0000 0.951 1.000 0.000
#> GSM1009088 1 0.0000 0.951 1.000 0.000
#> GSM1009102 1 0.0000 0.951 1.000 0.000
#> GSM1009116 1 0.6973 0.825 0.812 0.188
#> GSM1009130 1 0.6712 0.833 0.824 0.176
#> GSM1009144 1 0.0000 0.951 1.000 0.000
#> GSM1009158 1 0.0000 0.951 1.000 0.000
#> GSM1009172 2 0.0000 1.000 0.000 1.000
#> GSM1009186 1 0.0938 0.946 0.988 0.012
#> GSM1009200 1 0.0000 0.951 1.000 0.000
#> GSM1009075 1 0.0000 0.951 1.000 0.000
#> GSM1009089 1 0.0000 0.951 1.000 0.000
#> GSM1009103 1 0.0000 0.951 1.000 0.000
#> GSM1009117 1 0.6973 0.825 0.812 0.188
#> GSM1009131 1 0.0000 0.951 1.000 0.000
#> GSM1009145 1 0.0000 0.951 1.000 0.000
#> GSM1009159 1 0.0000 0.951 1.000 0.000
#> GSM1009173 2 0.0000 1.000 0.000 1.000
#> GSM1009187 1 0.0938 0.946 0.988 0.012
#> GSM1009201 1 0.0000 0.951 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1009062 1 0.2356 0.955 0.928 0.072 0
#> GSM1009076 2 0.0000 0.927 0.000 1.000 0
#> GSM1009090 1 0.0000 0.938 1.000 0.000 0
#> GSM1009104 2 0.0000 0.927 0.000 1.000 0
#> GSM1009118 1 0.2448 0.953 0.924 0.076 0
#> GSM1009132 1 0.0892 0.931 0.980 0.020 0
#> GSM1009146 1 0.2165 0.958 0.936 0.064 0
#> GSM1009160 3 0.0000 1.000 0.000 0.000 1
#> GSM1009174 2 0.2537 0.908 0.080 0.920 0
#> GSM1009188 1 0.2066 0.958 0.940 0.060 0
#> GSM1009063 1 0.2537 0.951 0.920 0.080 0
#> GSM1009077 2 0.0000 0.927 0.000 1.000 0
#> GSM1009091 1 0.0000 0.938 1.000 0.000 0
#> GSM1009105 2 0.0000 0.927 0.000 1.000 0
#> GSM1009119 1 0.2066 0.958 0.940 0.060 0
#> GSM1009133 1 0.0000 0.938 1.000 0.000 0
#> GSM1009147 1 0.2165 0.958 0.936 0.064 0
#> GSM1009161 3 0.0000 1.000 0.000 0.000 1
#> GSM1009175 2 0.2537 0.908 0.080 0.920 0
#> GSM1009189 1 0.2165 0.958 0.936 0.064 0
#> GSM1009064 1 0.3116 0.932 0.892 0.108 0
#> GSM1009078 1 0.3686 0.906 0.860 0.140 0
#> GSM1009092 1 0.0000 0.938 1.000 0.000 0
#> GSM1009106 2 0.0000 0.927 0.000 1.000 0
#> GSM1009120 1 0.2165 0.958 0.936 0.064 0
#> GSM1009134 1 0.0000 0.938 1.000 0.000 0
#> GSM1009148 1 0.2165 0.958 0.936 0.064 0
#> GSM1009162 3 0.0000 1.000 0.000 0.000 1
#> GSM1009176 2 0.2448 0.910 0.076 0.924 0
#> GSM1009190 1 0.2165 0.958 0.936 0.064 0
#> GSM1009065 1 0.3340 0.922 0.880 0.120 0
#> GSM1009079 2 0.0000 0.927 0.000 1.000 0
#> GSM1009093 1 0.0000 0.938 1.000 0.000 0
#> GSM1009107 2 0.0000 0.927 0.000 1.000 0
#> GSM1009121 1 0.2165 0.958 0.936 0.064 0
#> GSM1009135 1 0.0000 0.938 1.000 0.000 0
#> GSM1009149 1 0.2165 0.958 0.936 0.064 0
#> GSM1009163 3 0.0000 1.000 0.000 0.000 1
#> GSM1009177 2 0.2537 0.908 0.080 0.920 0
#> GSM1009191 1 0.2165 0.958 0.936 0.064 0
#> GSM1009066 1 0.2448 0.953 0.924 0.076 0
#> GSM1009080 2 0.0000 0.927 0.000 1.000 0
#> GSM1009094 1 0.0000 0.938 1.000 0.000 0
#> GSM1009108 2 0.0000 0.927 0.000 1.000 0
#> GSM1009122 2 0.5591 0.607 0.304 0.696 0
#> GSM1009136 1 0.0000 0.938 1.000 0.000 0
#> GSM1009150 1 0.2066 0.958 0.940 0.060 0
#> GSM1009164 3 0.0000 1.000 0.000 0.000 1
#> GSM1009178 2 0.3038 0.889 0.104 0.896 0
#> GSM1009192 1 0.2165 0.958 0.936 0.064 0
#> GSM1009067 1 0.2356 0.955 0.928 0.072 0
#> GSM1009081 2 0.0000 0.927 0.000 1.000 0
#> GSM1009095 1 0.0000 0.938 1.000 0.000 0
#> GSM1009109 2 0.0000 0.927 0.000 1.000 0
#> GSM1009123 1 0.2066 0.958 0.940 0.060 0
#> GSM1009137 1 0.0000 0.938 1.000 0.000 0
#> GSM1009151 1 0.2165 0.958 0.936 0.064 0
#> GSM1009165 3 0.0000 1.000 0.000 0.000 1
#> GSM1009179 2 0.3038 0.889 0.104 0.896 0
#> GSM1009193 1 0.2165 0.958 0.936 0.064 0
#> GSM1009068 1 0.2356 0.955 0.928 0.072 0
#> GSM1009082 2 0.1529 0.920 0.040 0.960 0
#> GSM1009096 1 0.0000 0.938 1.000 0.000 0
#> GSM1009110 2 0.0000 0.927 0.000 1.000 0
#> GSM1009124 1 0.2165 0.958 0.936 0.064 0
#> GSM1009138 1 0.0000 0.938 1.000 0.000 0
#> GSM1009152 1 0.2165 0.958 0.936 0.064 0
#> GSM1009166 3 0.0000 1.000 0.000 0.000 1
#> GSM1009180 2 0.3038 0.889 0.104 0.896 0
#> GSM1009194 1 0.2165 0.958 0.936 0.064 0
#> GSM1009069 1 0.4842 0.793 0.776 0.224 0
#> GSM1009083 2 0.1411 0.921 0.036 0.964 0
#> GSM1009097 1 0.0000 0.938 1.000 0.000 0
#> GSM1009111 2 0.0000 0.927 0.000 1.000 0
#> GSM1009125 2 0.3619 0.852 0.136 0.864 0
#> GSM1009139 1 0.0000 0.938 1.000 0.000 0
#> GSM1009153 1 0.2165 0.958 0.936 0.064 0
#> GSM1009167 3 0.0000 1.000 0.000 0.000 1
#> GSM1009181 2 0.2448 0.910 0.076 0.924 0
#> GSM1009195 1 0.2165 0.958 0.936 0.064 0
#> GSM1009070 1 0.2959 0.938 0.900 0.100 0
#> GSM1009084 2 0.0000 0.927 0.000 1.000 0
#> GSM1009098 1 0.0000 0.938 1.000 0.000 0
#> GSM1009112 2 0.0000 0.927 0.000 1.000 0
#> GSM1009126 1 0.2165 0.958 0.936 0.064 0
#> GSM1009140 1 0.0000 0.938 1.000 0.000 0
#> GSM1009154 1 0.2165 0.958 0.936 0.064 0
#> GSM1009168 3 0.0000 1.000 0.000 0.000 1
#> GSM1009182 2 0.3038 0.889 0.104 0.896 0
#> GSM1009196 1 0.2165 0.958 0.936 0.064 0
#> GSM1009071 1 0.3340 0.922 0.880 0.120 0
#> GSM1009085 2 0.0000 0.927 0.000 1.000 0
#> GSM1009099 1 0.0000 0.938 1.000 0.000 0
#> GSM1009113 2 0.0000 0.927 0.000 1.000 0
#> GSM1009127 1 0.2165 0.958 0.936 0.064 0
#> GSM1009141 1 0.0000 0.938 1.000 0.000 0
#> GSM1009155 1 0.2165 0.958 0.936 0.064 0
#> GSM1009169 3 0.0000 1.000 0.000 0.000 1
#> GSM1009183 2 0.2448 0.910 0.076 0.924 0
#> GSM1009197 1 0.2165 0.958 0.936 0.064 0
#> GSM1009072 1 0.2796 0.944 0.908 0.092 0
#> GSM1009086 2 0.0000 0.927 0.000 1.000 0
#> GSM1009100 1 0.0000 0.938 1.000 0.000 0
#> GSM1009114 2 0.0000 0.927 0.000 1.000 0
#> GSM1009128 1 0.5760 0.562 0.672 0.328 0
#> GSM1009142 1 0.0000 0.938 1.000 0.000 0
#> GSM1009156 1 0.2165 0.958 0.936 0.064 0
#> GSM1009170 3 0.0000 1.000 0.000 0.000 1
#> GSM1009184 2 0.2537 0.908 0.080 0.920 0
#> GSM1009198 1 0.2165 0.958 0.936 0.064 0
#> GSM1009073 1 0.3038 0.935 0.896 0.104 0
#> GSM1009087 1 0.3752 0.902 0.856 0.144 0
#> GSM1009101 1 0.0000 0.938 1.000 0.000 0
#> GSM1009115 2 0.0000 0.927 0.000 1.000 0
#> GSM1009129 2 0.5216 0.660 0.260 0.740 0
#> GSM1009143 1 0.0000 0.938 1.000 0.000 0
#> GSM1009157 1 0.2165 0.958 0.936 0.064 0
#> GSM1009171 3 0.0000 1.000 0.000 0.000 1
#> GSM1009185 2 0.2537 0.908 0.080 0.920 0
#> GSM1009199 1 0.2165 0.958 0.936 0.064 0
#> GSM1009074 1 0.2959 0.938 0.900 0.100 0
#> GSM1009088 1 0.4605 0.828 0.796 0.204 0
#> GSM1009102 1 0.0000 0.938 1.000 0.000 0
#> GSM1009116 2 0.0000 0.927 0.000 1.000 0
#> GSM1009130 1 0.3551 0.913 0.868 0.132 0
#> GSM1009144 1 0.0000 0.938 1.000 0.000 0
#> GSM1009158 1 0.2165 0.958 0.936 0.064 0
#> GSM1009172 3 0.0000 1.000 0.000 0.000 1
#> GSM1009186 2 0.2537 0.908 0.080 0.920 0
#> GSM1009200 1 0.2165 0.958 0.936 0.064 0
#> GSM1009075 1 0.2959 0.938 0.900 0.100 0
#> GSM1009089 1 0.3619 0.909 0.864 0.136 0
#> GSM1009103 1 0.0000 0.938 1.000 0.000 0
#> GSM1009117 2 0.0000 0.927 0.000 1.000 0
#> GSM1009131 1 0.2625 0.949 0.916 0.084 0
#> GSM1009145 1 0.0000 0.938 1.000 0.000 0
#> GSM1009159 1 0.2165 0.958 0.936 0.064 0
#> GSM1009173 3 0.0000 1.000 0.000 0.000 1
#> GSM1009187 2 0.3116 0.885 0.108 0.892 0
#> GSM1009201 1 0.2165 0.958 0.936 0.064 0
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1009062 1 0.0000 0.950 1.000 0.000 0 0.000
#> GSM1009076 2 0.0000 0.833 0.000 1.000 0 0.000
#> GSM1009090 4 0.0000 0.979 0.000 0.000 0 1.000
#> GSM1009104 2 0.0000 0.833 0.000 1.000 0 0.000
#> GSM1009118 1 0.2868 0.807 0.864 0.136 0 0.000
#> GSM1009132 4 0.0000 0.979 0.000 0.000 0 1.000
#> GSM1009146 1 0.0000 0.950 1.000 0.000 0 0.000
#> GSM1009160 3 0.0000 1.000 0.000 0.000 1 0.000
#> GSM1009174 2 0.4431 0.709 0.304 0.696 0 0.000
#> GSM1009188 1 0.0000 0.950 1.000 0.000 0 0.000
#> GSM1009063 1 0.0000 0.950 1.000 0.000 0 0.000
#> GSM1009077 2 0.0000 0.833 0.000 1.000 0 0.000
#> GSM1009091 4 0.0000 0.979 0.000 0.000 0 1.000
#> GSM1009105 2 0.0000 0.833 0.000 1.000 0 0.000
#> GSM1009119 1 0.0000 0.950 1.000 0.000 0 0.000
#> GSM1009133 4 0.0000 0.979 0.000 0.000 0 1.000
#> GSM1009147 1 0.0000 0.950 1.000 0.000 0 0.000
#> GSM1009161 3 0.0000 1.000 0.000 0.000 1 0.000
#> GSM1009175 2 0.4431 0.709 0.304 0.696 0 0.000
#> GSM1009189 1 0.0000 0.950 1.000 0.000 0 0.000
#> GSM1009064 1 0.0000 0.950 1.000 0.000 0 0.000
#> GSM1009078 1 0.2149 0.873 0.912 0.088 0 0.000
#> GSM1009092 4 0.0000 0.979 0.000 0.000 0 1.000
#> GSM1009106 2 0.0000 0.833 0.000 1.000 0 0.000
#> GSM1009120 1 0.0000 0.950 1.000 0.000 0 0.000
#> GSM1009134 4 0.0000 0.979 0.000 0.000 0 1.000
#> GSM1009148 1 0.0000 0.950 1.000 0.000 0 0.000
#> GSM1009162 3 0.0000 1.000 0.000 0.000 1 0.000
#> GSM1009176 2 0.4072 0.749 0.252 0.748 0 0.000
#> GSM1009190 1 0.0000 0.950 1.000 0.000 0 0.000
#> GSM1009065 1 0.0336 0.945 0.992 0.008 0 0.000
#> GSM1009079 2 0.0000 0.833 0.000 1.000 0 0.000
#> GSM1009093 4 0.0000 0.979 0.000 0.000 0 1.000
#> GSM1009107 2 0.0000 0.833 0.000 1.000 0 0.000
#> GSM1009121 1 0.5582 0.671 0.728 0.136 0 0.136
#> GSM1009135 4 0.0000 0.979 0.000 0.000 0 1.000
#> GSM1009149 1 0.0000 0.950 1.000 0.000 0 0.000
#> GSM1009163 3 0.0000 1.000 0.000 0.000 1 0.000
#> GSM1009177 2 0.4406 0.712 0.300 0.700 0 0.000
#> GSM1009191 1 0.0000 0.950 1.000 0.000 0 0.000
#> GSM1009066 1 0.0000 0.950 1.000 0.000 0 0.000
#> GSM1009080 2 0.0000 0.833 0.000 1.000 0 0.000
#> GSM1009094 4 0.0000 0.979 0.000 0.000 0 1.000
#> GSM1009108 2 0.0000 0.833 0.000 1.000 0 0.000
#> GSM1009122 1 0.4999 -0.226 0.508 0.492 0 0.000
#> GSM1009136 4 0.0000 0.979 0.000 0.000 0 1.000
#> GSM1009150 1 0.0000 0.950 1.000 0.000 0 0.000
#> GSM1009164 3 0.0000 1.000 0.000 0.000 1 0.000
#> GSM1009178 2 0.4454 0.704 0.308 0.692 0 0.000
#> GSM1009192 1 0.0000 0.950 1.000 0.000 0 0.000
#> GSM1009067 1 0.0000 0.950 1.000 0.000 0 0.000
#> GSM1009081 2 0.0000 0.833 0.000 1.000 0 0.000
#> GSM1009095 4 0.0000 0.979 0.000 0.000 0 1.000
#> GSM1009109 2 0.0000 0.833 0.000 1.000 0 0.000
#> GSM1009123 1 0.4008 0.678 0.756 0.000 0 0.244
#> GSM1009137 4 0.0000 0.979 0.000 0.000 0 1.000
#> GSM1009151 1 0.0000 0.950 1.000 0.000 0 0.000
#> GSM1009165 3 0.0000 1.000 0.000 0.000 1 0.000
#> GSM1009179 2 0.4454 0.704 0.308 0.692 0 0.000
#> GSM1009193 1 0.0000 0.950 1.000 0.000 0 0.000
#> GSM1009068 1 0.0000 0.950 1.000 0.000 0 0.000
#> GSM1009082 2 0.1389 0.823 0.048 0.952 0 0.000
#> GSM1009096 4 0.0000 0.979 0.000 0.000 0 1.000
#> GSM1009110 2 0.0000 0.833 0.000 1.000 0 0.000
#> GSM1009124 1 0.0000 0.950 1.000 0.000 0 0.000
#> GSM1009138 4 0.0000 0.979 0.000 0.000 0 1.000
#> GSM1009152 1 0.0000 0.950 1.000 0.000 0 0.000
#> GSM1009166 3 0.0000 1.000 0.000 0.000 1 0.000
#> GSM1009180 2 0.4454 0.704 0.308 0.692 0 0.000
#> GSM1009194 1 0.0000 0.950 1.000 0.000 0 0.000
#> GSM1009069 1 0.1867 0.887 0.928 0.072 0 0.000
#> GSM1009083 2 0.1716 0.818 0.064 0.936 0 0.000
#> GSM1009097 4 0.0000 0.979 0.000 0.000 0 1.000
#> GSM1009111 2 0.0000 0.833 0.000 1.000 0 0.000
#> GSM1009125 2 0.3959 0.784 0.092 0.840 0 0.068
#> GSM1009139 4 0.0000 0.979 0.000 0.000 0 1.000
#> GSM1009153 1 0.0000 0.950 1.000 0.000 0 0.000
#> GSM1009167 3 0.0000 1.000 0.000 0.000 1 0.000
#> GSM1009181 2 0.4356 0.719 0.292 0.708 0 0.000
#> GSM1009195 1 0.1389 0.911 0.952 0.048 0 0.000
#> GSM1009070 1 0.0000 0.950 1.000 0.000 0 0.000
#> GSM1009084 2 0.0000 0.833 0.000 1.000 0 0.000
#> GSM1009098 4 0.0000 0.979 0.000 0.000 0 1.000
#> GSM1009112 2 0.0000 0.833 0.000 1.000 0 0.000
#> GSM1009126 1 0.1211 0.919 0.960 0.040 0 0.000
#> GSM1009140 4 0.0000 0.979 0.000 0.000 0 1.000
#> GSM1009154 1 0.0000 0.950 1.000 0.000 0 0.000
#> GSM1009168 3 0.0000 1.000 0.000 0.000 1 0.000
#> GSM1009182 2 0.4454 0.704 0.308 0.692 0 0.000
#> GSM1009196 1 0.0000 0.950 1.000 0.000 0 0.000
#> GSM1009071 1 0.0000 0.950 1.000 0.000 0 0.000
#> GSM1009085 2 0.0000 0.833 0.000 1.000 0 0.000
#> GSM1009099 4 0.0000 0.979 0.000 0.000 0 1.000
#> GSM1009113 2 0.0000 0.833 0.000 1.000 0 0.000
#> GSM1009127 1 0.0000 0.950 1.000 0.000 0 0.000
#> GSM1009141 4 0.0000 0.979 0.000 0.000 0 1.000
#> GSM1009155 1 0.0000 0.950 1.000 0.000 0 0.000
#> GSM1009169 3 0.0000 1.000 0.000 0.000 1 0.000
#> GSM1009183 2 0.4356 0.719 0.292 0.708 0 0.000
#> GSM1009197 1 0.0000 0.950 1.000 0.000 0 0.000
#> GSM1009072 1 0.0000 0.950 1.000 0.000 0 0.000
#> GSM1009086 2 0.0000 0.833 0.000 1.000 0 0.000
#> GSM1009100 4 0.0000 0.979 0.000 0.000 0 1.000
#> GSM1009114 2 0.0000 0.833 0.000 1.000 0 0.000
#> GSM1009128 4 0.7070 0.266 0.348 0.136 0 0.516
#> GSM1009142 4 0.0000 0.979 0.000 0.000 0 1.000
#> GSM1009156 1 0.0000 0.950 1.000 0.000 0 0.000
#> GSM1009170 3 0.0000 1.000 0.000 0.000 1 0.000
#> GSM1009184 2 0.4431 0.709 0.304 0.696 0 0.000
#> GSM1009198 1 0.1022 0.926 0.968 0.000 0 0.032
#> GSM1009073 1 0.0000 0.950 1.000 0.000 0 0.000
#> GSM1009087 1 0.2281 0.866 0.904 0.096 0 0.000
#> GSM1009101 4 0.0000 0.979 0.000 0.000 0 1.000
#> GSM1009115 2 0.0000 0.833 0.000 1.000 0 0.000
#> GSM1009129 2 0.4585 0.568 0.332 0.668 0 0.000
#> GSM1009143 4 0.0000 0.979 0.000 0.000 0 1.000
#> GSM1009157 1 0.0000 0.950 1.000 0.000 0 0.000
#> GSM1009171 3 0.0000 1.000 0.000 0.000 1 0.000
#> GSM1009185 2 0.4431 0.709 0.304 0.696 0 0.000
#> GSM1009199 1 0.2530 0.840 0.888 0.112 0 0.000
#> GSM1009074 1 0.0000 0.950 1.000 0.000 0 0.000
#> GSM1009088 1 0.3569 0.741 0.804 0.196 0 0.000
#> GSM1009102 4 0.0000 0.979 0.000 0.000 0 1.000
#> GSM1009116 2 0.0000 0.833 0.000 1.000 0 0.000
#> GSM1009130 1 0.4746 0.435 0.632 0.368 0 0.000
#> GSM1009144 4 0.0000 0.979 0.000 0.000 0 1.000
#> GSM1009158 1 0.0000 0.950 1.000 0.000 0 0.000
#> GSM1009172 3 0.0000 1.000 0.000 0.000 1 0.000
#> GSM1009186 2 0.4431 0.709 0.304 0.696 0 0.000
#> GSM1009200 1 0.0000 0.950 1.000 0.000 0 0.000
#> GSM1009075 1 0.0000 0.950 1.000 0.000 0 0.000
#> GSM1009089 1 0.1792 0.896 0.932 0.068 0 0.000
#> GSM1009103 4 0.0000 0.979 0.000 0.000 0 1.000
#> GSM1009117 2 0.0000 0.833 0.000 1.000 0 0.000
#> GSM1009131 1 0.3311 0.776 0.828 0.172 0 0.000
#> GSM1009145 4 0.0000 0.979 0.000 0.000 0 1.000
#> GSM1009159 1 0.0000 0.950 1.000 0.000 0 0.000
#> GSM1009173 3 0.0000 1.000 0.000 0.000 1 0.000
#> GSM1009187 2 0.4454 0.704 0.308 0.692 0 0.000
#> GSM1009201 1 0.0000 0.950 1.000 0.000 0 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM1009062 1 0.0000 0.9343 1.000 0.000 0 0.000 0.000
#> GSM1009076 2 0.0510 0.9351 0.000 0.984 0 0.000 0.016
#> GSM1009090 4 0.0000 1.0000 0.000 0.000 0 1.000 0.000
#> GSM1009104 5 0.0000 1.0000 0.000 0.000 0 0.000 1.000
#> GSM1009118 1 0.3774 0.5827 0.704 0.296 0 0.000 0.000
#> GSM1009132 4 0.0000 1.0000 0.000 0.000 0 1.000 0.000
#> GSM1009146 1 0.0000 0.9343 1.000 0.000 0 0.000 0.000
#> GSM1009160 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000
#> GSM1009174 2 0.0000 0.9420 0.000 1.000 0 0.000 0.000
#> GSM1009188 1 0.0000 0.9343 1.000 0.000 0 0.000 0.000
#> GSM1009063 1 0.0000 0.9343 1.000 0.000 0 0.000 0.000
#> GSM1009077 2 0.0404 0.9375 0.000 0.988 0 0.000 0.012
#> GSM1009091 4 0.0000 1.0000 0.000 0.000 0 1.000 0.000
#> GSM1009105 5 0.0000 1.0000 0.000 0.000 0 0.000 1.000
#> GSM1009119 1 0.0000 0.9343 1.000 0.000 0 0.000 0.000
#> GSM1009133 4 0.0000 1.0000 0.000 0.000 0 1.000 0.000
#> GSM1009147 1 0.0000 0.9343 1.000 0.000 0 0.000 0.000
#> GSM1009161 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000
#> GSM1009175 2 0.0000 0.9420 0.000 1.000 0 0.000 0.000
#> GSM1009189 1 0.0000 0.9343 1.000 0.000 0 0.000 0.000
#> GSM1009064 1 0.0000 0.9343 1.000 0.000 0 0.000 0.000
#> GSM1009078 1 0.1410 0.8879 0.940 0.000 0 0.000 0.060
#> GSM1009092 4 0.0000 1.0000 0.000 0.000 0 1.000 0.000
#> GSM1009106 5 0.0000 1.0000 0.000 0.000 0 0.000 1.000
#> GSM1009120 1 0.0000 0.9343 1.000 0.000 0 0.000 0.000
#> GSM1009134 4 0.0000 1.0000 0.000 0.000 0 1.000 0.000
#> GSM1009148 1 0.0000 0.9343 1.000 0.000 0 0.000 0.000
#> GSM1009162 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000
#> GSM1009176 2 0.0000 0.9420 0.000 1.000 0 0.000 0.000
#> GSM1009190 1 0.0000 0.9343 1.000 0.000 0 0.000 0.000
#> GSM1009065 1 0.0000 0.9343 1.000 0.000 0 0.000 0.000
#> GSM1009079 2 0.0162 0.9409 0.000 0.996 0 0.000 0.004
#> GSM1009093 4 0.0000 1.0000 0.000 0.000 0 1.000 0.000
#> GSM1009107 5 0.0000 1.0000 0.000 0.000 0 0.000 1.000
#> GSM1009121 1 0.4086 0.6128 0.704 0.012 0 0.284 0.000
#> GSM1009135 4 0.0000 1.0000 0.000 0.000 0 1.000 0.000
#> GSM1009149 1 0.0000 0.9343 1.000 0.000 0 0.000 0.000
#> GSM1009163 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000
#> GSM1009177 2 0.0000 0.9420 0.000 1.000 0 0.000 0.000
#> GSM1009191 1 0.0000 0.9343 1.000 0.000 0 0.000 0.000
#> GSM1009066 1 0.0000 0.9343 1.000 0.000 0 0.000 0.000
#> GSM1009080 2 0.0162 0.9409 0.000 0.996 0 0.000 0.004
#> GSM1009094 4 0.0000 1.0000 0.000 0.000 0 1.000 0.000
#> GSM1009108 5 0.0000 1.0000 0.000 0.000 0 0.000 1.000
#> GSM1009122 2 0.3949 0.4919 0.332 0.668 0 0.000 0.000
#> GSM1009136 4 0.0000 1.0000 0.000 0.000 0 1.000 0.000
#> GSM1009150 1 0.0000 0.9343 1.000 0.000 0 0.000 0.000
#> GSM1009164 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000
#> GSM1009178 2 0.0000 0.9420 0.000 1.000 0 0.000 0.000
#> GSM1009192 1 0.0000 0.9343 1.000 0.000 0 0.000 0.000
#> GSM1009067 1 0.0000 0.9343 1.000 0.000 0 0.000 0.000
#> GSM1009081 2 0.0404 0.9375 0.000 0.988 0 0.000 0.012
#> GSM1009095 4 0.0000 1.0000 0.000 0.000 0 1.000 0.000
#> GSM1009109 5 0.0000 1.0000 0.000 0.000 0 0.000 1.000
#> GSM1009123 1 0.3612 0.6507 0.732 0.000 0 0.268 0.000
#> GSM1009137 4 0.0000 1.0000 0.000 0.000 0 1.000 0.000
#> GSM1009151 1 0.0000 0.9343 1.000 0.000 0 0.000 0.000
#> GSM1009165 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000
#> GSM1009179 2 0.0000 0.9420 0.000 1.000 0 0.000 0.000
#> GSM1009193 1 0.0000 0.9343 1.000 0.000 0 0.000 0.000
#> GSM1009068 1 0.0000 0.9343 1.000 0.000 0 0.000 0.000
#> GSM1009082 2 0.0404 0.9375 0.000 0.988 0 0.000 0.012
#> GSM1009096 4 0.0000 1.0000 0.000 0.000 0 1.000 0.000
#> GSM1009110 5 0.0000 1.0000 0.000 0.000 0 0.000 1.000
#> GSM1009124 1 0.0000 0.9343 1.000 0.000 0 0.000 0.000
#> GSM1009138 4 0.0000 1.0000 0.000 0.000 0 1.000 0.000
#> GSM1009152 1 0.0000 0.9343 1.000 0.000 0 0.000 0.000
#> GSM1009166 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000
#> GSM1009180 2 0.0000 0.9420 0.000 1.000 0 0.000 0.000
#> GSM1009194 1 0.0000 0.9343 1.000 0.000 0 0.000 0.000
#> GSM1009069 1 0.4150 0.3473 0.612 0.388 0 0.000 0.000
#> GSM1009083 2 0.0290 0.9394 0.000 0.992 0 0.000 0.008
#> GSM1009097 4 0.0000 1.0000 0.000 0.000 0 1.000 0.000
#> GSM1009111 5 0.0000 1.0000 0.000 0.000 0 0.000 1.000
#> GSM1009125 2 0.0000 0.9420 0.000 1.000 0 0.000 0.000
#> GSM1009139 4 0.0000 1.0000 0.000 0.000 0 1.000 0.000
#> GSM1009153 1 0.0000 0.9343 1.000 0.000 0 0.000 0.000
#> GSM1009167 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000
#> GSM1009181 2 0.0000 0.9420 0.000 1.000 0 0.000 0.000
#> GSM1009195 1 0.3074 0.7409 0.804 0.196 0 0.000 0.000
#> GSM1009070 1 0.0000 0.9343 1.000 0.000 0 0.000 0.000
#> GSM1009084 2 0.2732 0.8045 0.000 0.840 0 0.000 0.160
#> GSM1009098 4 0.0000 1.0000 0.000 0.000 0 1.000 0.000
#> GSM1009112 5 0.0000 1.0000 0.000 0.000 0 0.000 1.000
#> GSM1009126 1 0.0000 0.9343 1.000 0.000 0 0.000 0.000
#> GSM1009140 4 0.0000 1.0000 0.000 0.000 0 1.000 0.000
#> GSM1009154 1 0.0000 0.9343 1.000 0.000 0 0.000 0.000
#> GSM1009168 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000
#> GSM1009182 2 0.0000 0.9420 0.000 1.000 0 0.000 0.000
#> GSM1009196 1 0.0000 0.9343 1.000 0.000 0 0.000 0.000
#> GSM1009071 1 0.0000 0.9343 1.000 0.000 0 0.000 0.000
#> GSM1009085 2 0.3074 0.7620 0.000 0.804 0 0.000 0.196
#> GSM1009099 4 0.0000 1.0000 0.000 0.000 0 1.000 0.000
#> GSM1009113 5 0.0000 1.0000 0.000 0.000 0 0.000 1.000
#> GSM1009127 1 0.0000 0.9343 1.000 0.000 0 0.000 0.000
#> GSM1009141 4 0.0000 1.0000 0.000 0.000 0 1.000 0.000
#> GSM1009155 1 0.0000 0.9343 1.000 0.000 0 0.000 0.000
#> GSM1009169 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000
#> GSM1009183 2 0.0000 0.9420 0.000 1.000 0 0.000 0.000
#> GSM1009197 1 0.0000 0.9343 1.000 0.000 0 0.000 0.000
#> GSM1009072 1 0.0000 0.9343 1.000 0.000 0 0.000 0.000
#> GSM1009086 2 0.2605 0.8177 0.000 0.852 0 0.000 0.148
#> GSM1009100 4 0.0000 1.0000 0.000 0.000 0 1.000 0.000
#> GSM1009114 5 0.0000 1.0000 0.000 0.000 0 0.000 1.000
#> GSM1009128 1 0.4747 0.1036 0.500 0.016 0 0.484 0.000
#> GSM1009142 4 0.0000 1.0000 0.000 0.000 0 1.000 0.000
#> GSM1009156 1 0.0000 0.9343 1.000 0.000 0 0.000 0.000
#> GSM1009170 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000
#> GSM1009184 2 0.0000 0.9420 0.000 1.000 0 0.000 0.000
#> GSM1009198 1 0.0880 0.9104 0.968 0.000 0 0.032 0.000
#> GSM1009073 1 0.0000 0.9343 1.000 0.000 0 0.000 0.000
#> GSM1009087 1 0.2011 0.8595 0.908 0.004 0 0.000 0.088
#> GSM1009101 4 0.0000 1.0000 0.000 0.000 0 1.000 0.000
#> GSM1009115 5 0.0000 1.0000 0.000 0.000 0 0.000 1.000
#> GSM1009129 2 0.3932 0.5013 0.328 0.672 0 0.000 0.000
#> GSM1009143 4 0.0000 1.0000 0.000 0.000 0 1.000 0.000
#> GSM1009157 1 0.0000 0.9343 1.000 0.000 0 0.000 0.000
#> GSM1009171 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000
#> GSM1009185 2 0.0000 0.9420 0.000 1.000 0 0.000 0.000
#> GSM1009199 1 0.3586 0.6394 0.736 0.264 0 0.000 0.000
#> GSM1009074 1 0.0000 0.9343 1.000 0.000 0 0.000 0.000
#> GSM1009088 1 0.3764 0.7417 0.800 0.044 0 0.000 0.156
#> GSM1009102 4 0.0000 1.0000 0.000 0.000 0 1.000 0.000
#> GSM1009116 5 0.0000 1.0000 0.000 0.000 0 0.000 1.000
#> GSM1009130 1 0.4307 0.0981 0.504 0.000 0 0.000 0.496
#> GSM1009144 4 0.0000 1.0000 0.000 0.000 0 1.000 0.000
#> GSM1009158 1 0.0000 0.9343 1.000 0.000 0 0.000 0.000
#> GSM1009172 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000
#> GSM1009186 2 0.0000 0.9420 0.000 1.000 0 0.000 0.000
#> GSM1009200 1 0.0000 0.9343 1.000 0.000 0 0.000 0.000
#> GSM1009075 1 0.0000 0.9343 1.000 0.000 0 0.000 0.000
#> GSM1009089 1 0.1697 0.8824 0.932 0.008 0 0.000 0.060
#> GSM1009103 4 0.0000 1.0000 0.000 0.000 0 1.000 0.000
#> GSM1009117 5 0.0000 1.0000 0.000 0.000 0 0.000 1.000
#> GSM1009131 1 0.3684 0.6291 0.720 0.000 0 0.000 0.280
#> GSM1009145 4 0.0000 1.0000 0.000 0.000 0 1.000 0.000
#> GSM1009159 1 0.0000 0.9343 1.000 0.000 0 0.000 0.000
#> GSM1009173 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000
#> GSM1009187 2 0.0000 0.9420 0.000 1.000 0 0.000 0.000
#> GSM1009201 1 0.0000 0.9343 1.000 0.000 0 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1009062 6 0.0000 1.0000 0.000 0.000 0 0.000 0.000 1.000
#> GSM1009076 2 0.1007 0.9185 0.000 0.956 0 0.000 0.044 0.000
#> GSM1009090 4 0.0000 1.0000 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009104 5 0.0000 1.0000 0.000 0.000 0 0.000 1.000 0.000
#> GSM1009118 1 0.2562 0.7667 0.828 0.172 0 0.000 0.000 0.000
#> GSM1009132 4 0.0000 1.0000 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009146 1 0.0000 0.9547 1.000 0.000 0 0.000 0.000 0.000
#> GSM1009160 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009174 2 0.0000 0.9389 0.000 1.000 0 0.000 0.000 0.000
#> GSM1009188 1 0.0000 0.9547 1.000 0.000 0 0.000 0.000 0.000
#> GSM1009063 6 0.0000 1.0000 0.000 0.000 0 0.000 0.000 1.000
#> GSM1009077 2 0.1074 0.9235 0.000 0.960 0 0.000 0.012 0.028
#> GSM1009091 4 0.0000 1.0000 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009105 5 0.0000 1.0000 0.000 0.000 0 0.000 1.000 0.000
#> GSM1009119 1 0.0000 0.9547 1.000 0.000 0 0.000 0.000 0.000
#> GSM1009133 4 0.0000 1.0000 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009147 1 0.0000 0.9547 1.000 0.000 0 0.000 0.000 0.000
#> GSM1009161 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009175 2 0.0000 0.9389 0.000 1.000 0 0.000 0.000 0.000
#> GSM1009189 1 0.0000 0.9547 1.000 0.000 0 0.000 0.000 0.000
#> GSM1009064 6 0.0000 1.0000 0.000 0.000 0 0.000 0.000 1.000
#> GSM1009078 1 0.2658 0.8360 0.864 0.000 0 0.000 0.036 0.100
#> GSM1009092 4 0.0000 1.0000 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009106 5 0.0000 1.0000 0.000 0.000 0 0.000 1.000 0.000
#> GSM1009120 1 0.0000 0.9547 1.000 0.000 0 0.000 0.000 0.000
#> GSM1009134 4 0.0000 1.0000 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009148 1 0.0000 0.9547 1.000 0.000 0 0.000 0.000 0.000
#> GSM1009162 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009176 2 0.0000 0.9389 0.000 1.000 0 0.000 0.000 0.000
#> GSM1009190 1 0.0000 0.9547 1.000 0.000 0 0.000 0.000 0.000
#> GSM1009065 6 0.0000 1.0000 0.000 0.000 0 0.000 0.000 1.000
#> GSM1009079 2 0.0363 0.9351 0.000 0.988 0 0.000 0.012 0.000
#> GSM1009093 4 0.0000 1.0000 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009107 5 0.0000 1.0000 0.000 0.000 0 0.000 1.000 0.000
#> GSM1009121 1 0.0000 0.9547 1.000 0.000 0 0.000 0.000 0.000
#> GSM1009135 4 0.0000 1.0000 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009149 1 0.0000 0.9547 1.000 0.000 0 0.000 0.000 0.000
#> GSM1009163 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009177 2 0.0000 0.9389 0.000 1.000 0 0.000 0.000 0.000
#> GSM1009191 1 0.0000 0.9547 1.000 0.000 0 0.000 0.000 0.000
#> GSM1009066 6 0.0000 1.0000 0.000 0.000 0 0.000 0.000 1.000
#> GSM1009080 2 0.0260 0.9366 0.000 0.992 0 0.000 0.008 0.000
#> GSM1009094 4 0.0000 1.0000 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009108 5 0.0000 1.0000 0.000 0.000 0 0.000 1.000 0.000
#> GSM1009122 2 0.3547 0.5318 0.332 0.668 0 0.000 0.000 0.000
#> GSM1009136 4 0.0000 1.0000 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009150 1 0.0000 0.9547 1.000 0.000 0 0.000 0.000 0.000
#> GSM1009164 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009178 2 0.0000 0.9389 0.000 1.000 0 0.000 0.000 0.000
#> GSM1009192 1 0.0000 0.9547 1.000 0.000 0 0.000 0.000 0.000
#> GSM1009067 6 0.0000 1.0000 0.000 0.000 0 0.000 0.000 1.000
#> GSM1009081 2 0.0865 0.9233 0.000 0.964 0 0.000 0.036 0.000
#> GSM1009095 4 0.0000 1.0000 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009109 5 0.0000 1.0000 0.000 0.000 0 0.000 1.000 0.000
#> GSM1009123 1 0.0000 0.9547 1.000 0.000 0 0.000 0.000 0.000
#> GSM1009137 4 0.0000 1.0000 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009151 1 0.0000 0.9547 1.000 0.000 0 0.000 0.000 0.000
#> GSM1009165 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009179 2 0.0000 0.9389 0.000 1.000 0 0.000 0.000 0.000
#> GSM1009193 1 0.0000 0.9547 1.000 0.000 0 0.000 0.000 0.000
#> GSM1009068 6 0.0000 1.0000 0.000 0.000 0 0.000 0.000 1.000
#> GSM1009082 2 0.1007 0.9158 0.000 0.956 0 0.000 0.000 0.044
#> GSM1009096 4 0.0000 1.0000 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009110 5 0.0000 1.0000 0.000 0.000 0 0.000 1.000 0.000
#> GSM1009124 1 0.0000 0.9547 1.000 0.000 0 0.000 0.000 0.000
#> GSM1009138 4 0.0000 1.0000 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009152 1 0.0000 0.9547 1.000 0.000 0 0.000 0.000 0.000
#> GSM1009166 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009180 2 0.0000 0.9389 0.000 1.000 0 0.000 0.000 0.000
#> GSM1009194 1 0.0000 0.9547 1.000 0.000 0 0.000 0.000 0.000
#> GSM1009069 6 0.0000 1.0000 0.000 0.000 0 0.000 0.000 1.000
#> GSM1009083 2 0.1267 0.9038 0.000 0.940 0 0.000 0.000 0.060
#> GSM1009097 4 0.0000 1.0000 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009111 5 0.0000 1.0000 0.000 0.000 0 0.000 1.000 0.000
#> GSM1009125 2 0.0146 0.9370 0.004 0.996 0 0.000 0.000 0.000
#> GSM1009139 4 0.0000 1.0000 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009153 1 0.0000 0.9547 1.000 0.000 0 0.000 0.000 0.000
#> GSM1009167 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009181 2 0.0000 0.9389 0.000 1.000 0 0.000 0.000 0.000
#> GSM1009195 1 0.0000 0.9547 1.000 0.000 0 0.000 0.000 0.000
#> GSM1009070 6 0.0000 1.0000 0.000 0.000 0 0.000 0.000 1.000
#> GSM1009084 2 0.2706 0.8123 0.000 0.832 0 0.000 0.160 0.008
#> GSM1009098 4 0.0000 1.0000 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009112 5 0.0000 1.0000 0.000 0.000 0 0.000 1.000 0.000
#> GSM1009126 1 0.0000 0.9547 1.000 0.000 0 0.000 0.000 0.000
#> GSM1009140 4 0.0000 1.0000 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009154 1 0.0000 0.9547 1.000 0.000 0 0.000 0.000 0.000
#> GSM1009168 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009182 2 0.0000 0.9389 0.000 1.000 0 0.000 0.000 0.000
#> GSM1009196 1 0.0000 0.9547 1.000 0.000 0 0.000 0.000 0.000
#> GSM1009071 6 0.0000 1.0000 0.000 0.000 0 0.000 0.000 1.000
#> GSM1009085 2 0.2980 0.7749 0.000 0.800 0 0.000 0.192 0.008
#> GSM1009099 4 0.0000 1.0000 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009113 5 0.0000 1.0000 0.000 0.000 0 0.000 1.000 0.000
#> GSM1009127 1 0.0000 0.9547 1.000 0.000 0 0.000 0.000 0.000
#> GSM1009141 4 0.0000 1.0000 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009155 1 0.0000 0.9547 1.000 0.000 0 0.000 0.000 0.000
#> GSM1009169 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009183 2 0.0000 0.9389 0.000 1.000 0 0.000 0.000 0.000
#> GSM1009197 1 0.0000 0.9547 1.000 0.000 0 0.000 0.000 0.000
#> GSM1009072 6 0.0000 1.0000 0.000 0.000 0 0.000 0.000 1.000
#> GSM1009086 2 0.2378 0.8255 0.000 0.848 0 0.000 0.152 0.000
#> GSM1009100 4 0.0000 1.0000 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009114 5 0.0000 1.0000 0.000 0.000 0 0.000 1.000 0.000
#> GSM1009128 1 0.4534 0.0618 0.496 0.032 0 0.472 0.000 0.000
#> GSM1009142 4 0.0000 1.0000 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009156 1 0.0000 0.9547 1.000 0.000 0 0.000 0.000 0.000
#> GSM1009170 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009184 2 0.0000 0.9389 0.000 1.000 0 0.000 0.000 0.000
#> GSM1009198 1 0.0000 0.9547 1.000 0.000 0 0.000 0.000 0.000
#> GSM1009073 6 0.0000 1.0000 0.000 0.000 0 0.000 0.000 1.000
#> GSM1009087 1 0.2998 0.8275 0.852 0.004 0 0.000 0.068 0.076
#> GSM1009101 4 0.0000 1.0000 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009115 5 0.0000 1.0000 0.000 0.000 0 0.000 1.000 0.000
#> GSM1009129 2 0.3531 0.5403 0.328 0.672 0 0.000 0.000 0.000
#> GSM1009143 4 0.0000 1.0000 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009157 1 0.0000 0.9547 1.000 0.000 0 0.000 0.000 0.000
#> GSM1009171 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009185 2 0.0000 0.9389 0.000 1.000 0 0.000 0.000 0.000
#> GSM1009199 1 0.0000 0.9547 1.000 0.000 0 0.000 0.000 0.000
#> GSM1009074 6 0.0000 1.0000 0.000 0.000 0 0.000 0.000 1.000
#> GSM1009088 1 0.3977 0.7660 0.796 0.036 0 0.000 0.104 0.064
#> GSM1009102 4 0.0000 1.0000 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009116 5 0.0000 1.0000 0.000 0.000 0 0.000 1.000 0.000
#> GSM1009130 1 0.3864 0.0994 0.520 0.000 0 0.000 0.480 0.000
#> GSM1009144 4 0.0000 1.0000 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009158 1 0.0000 0.9547 1.000 0.000 0 0.000 0.000 0.000
#> GSM1009172 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009186 2 0.0000 0.9389 0.000 1.000 0 0.000 0.000 0.000
#> GSM1009200 1 0.0000 0.9547 1.000 0.000 0 0.000 0.000 0.000
#> GSM1009075 6 0.0000 1.0000 0.000 0.000 0 0.000 0.000 1.000
#> GSM1009089 1 0.2946 0.7667 0.812 0.000 0 0.000 0.012 0.176
#> GSM1009103 4 0.0000 1.0000 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009117 5 0.0000 1.0000 0.000 0.000 0 0.000 1.000 0.000
#> GSM1009131 1 0.0000 0.9547 1.000 0.000 0 0.000 0.000 0.000
#> GSM1009145 4 0.0000 1.0000 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009159 1 0.0000 0.9547 1.000 0.000 0 0.000 0.000 0.000
#> GSM1009173 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009187 2 0.0000 0.9389 0.000 1.000 0 0.000 0.000 0.000
#> GSM1009201 1 0.0000 0.9547 1.000 0.000 0 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 temperature(p) time(p) specimen(p) k
#> SD:pam 140 1.000 1 1.03e-25 2
#> SD:pam 140 0.996 1 1.51e-43 3
#> SD:pam 137 1.000 1 7.38e-65 4
#> SD:pam 136 1.000 1 6.48e-85 5
#> SD:pam 138 1.000 1 1.51e-107 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 140 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'SD' method.
#> Subgroups are detected by 'mclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 3.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.456 0.873 0.899 0.315 0.678 0.678
#> 3 3 0.856 0.953 0.965 0.538 0.617 0.521
#> 4 4 0.694 0.926 0.918 0.260 0.879 0.775
#> 5 5 0.798 0.842 0.914 0.224 0.819 0.564
#> 6 6 0.799 0.819 0.862 0.073 0.901 0.620
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
#> GSM1009062 1 0.4431 0.862 0.908 0.092
#> GSM1009076 1 0.0000 0.916 1.000 0.000
#> GSM1009090 1 0.6531 0.814 0.832 0.168
#> GSM1009104 2 0.9580 0.818 0.380 0.620
#> GSM1009118 1 0.0672 0.917 0.992 0.008
#> GSM1009132 1 0.6531 0.814 0.832 0.168
#> GSM1009146 1 0.2236 0.904 0.964 0.036
#> GSM1009160 2 0.5519 0.856 0.128 0.872
#> GSM1009174 1 0.0000 0.916 1.000 0.000
#> GSM1009188 1 0.0938 0.916 0.988 0.012
#> GSM1009063 1 0.4431 0.862 0.908 0.092
#> GSM1009077 1 0.0000 0.916 1.000 0.000
#> GSM1009091 1 0.6531 0.814 0.832 0.168
#> GSM1009105 2 0.9580 0.818 0.380 0.620
#> GSM1009119 1 0.0672 0.917 0.992 0.008
#> GSM1009133 1 0.6531 0.814 0.832 0.168
#> GSM1009147 1 0.1184 0.915 0.984 0.016
#> GSM1009161 2 0.5519 0.856 0.128 0.872
#> GSM1009175 1 0.0000 0.916 1.000 0.000
#> GSM1009189 1 0.0938 0.916 0.988 0.012
#> GSM1009064 1 0.4431 0.862 0.908 0.092
#> GSM1009078 1 0.0000 0.916 1.000 0.000
#> GSM1009092 1 0.6531 0.814 0.832 0.168
#> GSM1009106 2 0.9580 0.818 0.380 0.620
#> GSM1009120 1 0.0938 0.916 0.988 0.012
#> GSM1009134 1 0.6531 0.814 0.832 0.168
#> GSM1009148 1 0.3274 0.888 0.940 0.060
#> GSM1009162 2 0.5519 0.856 0.128 0.872
#> GSM1009176 1 0.0000 0.916 1.000 0.000
#> GSM1009190 1 0.0938 0.916 0.988 0.012
#> GSM1009065 1 0.4431 0.862 0.908 0.092
#> GSM1009079 1 0.0000 0.916 1.000 0.000
#> GSM1009093 1 0.6531 0.814 0.832 0.168
#> GSM1009107 2 0.9580 0.818 0.380 0.620
#> GSM1009121 1 0.0672 0.917 0.992 0.008
#> GSM1009135 1 0.6531 0.814 0.832 0.168
#> GSM1009149 1 0.0938 0.916 0.988 0.012
#> GSM1009163 2 0.5519 0.856 0.128 0.872
#> GSM1009177 1 0.0000 0.916 1.000 0.000
#> GSM1009191 1 0.0938 0.916 0.988 0.012
#> GSM1009066 1 0.4431 0.862 0.908 0.092
#> GSM1009080 1 0.0000 0.916 1.000 0.000
#> GSM1009094 1 0.6531 0.814 0.832 0.168
#> GSM1009108 2 0.9580 0.818 0.380 0.620
#> GSM1009122 1 0.0672 0.917 0.992 0.008
#> GSM1009136 1 0.6531 0.814 0.832 0.168
#> GSM1009150 1 0.0938 0.916 0.988 0.012
#> GSM1009164 2 0.5519 0.856 0.128 0.872
#> GSM1009178 1 0.0000 0.916 1.000 0.000
#> GSM1009192 1 0.0938 0.916 0.988 0.012
#> GSM1009067 1 0.4431 0.862 0.908 0.092
#> GSM1009081 1 0.0000 0.916 1.000 0.000
#> GSM1009095 1 0.6531 0.814 0.832 0.168
#> GSM1009109 2 0.9580 0.818 0.380 0.620
#> GSM1009123 1 0.0000 0.916 1.000 0.000
#> GSM1009137 1 0.6531 0.814 0.832 0.168
#> GSM1009151 1 0.4431 0.862 0.908 0.092
#> GSM1009165 2 0.5519 0.856 0.128 0.872
#> GSM1009179 1 0.0000 0.916 1.000 0.000
#> GSM1009193 1 0.0938 0.916 0.988 0.012
#> GSM1009068 1 0.4431 0.862 0.908 0.092
#> GSM1009082 1 0.0000 0.916 1.000 0.000
#> GSM1009096 1 0.6531 0.814 0.832 0.168
#> GSM1009110 2 0.9580 0.818 0.380 0.620
#> GSM1009124 1 0.0938 0.916 0.988 0.012
#> GSM1009138 1 0.6531 0.814 0.832 0.168
#> GSM1009152 1 0.3584 0.882 0.932 0.068
#> GSM1009166 2 0.5519 0.856 0.128 0.872
#> GSM1009180 1 0.0000 0.916 1.000 0.000
#> GSM1009194 1 0.0938 0.916 0.988 0.012
#> GSM1009069 1 0.3274 0.886 0.940 0.060
#> GSM1009083 1 0.0000 0.916 1.000 0.000
#> GSM1009097 1 0.6531 0.814 0.832 0.168
#> GSM1009111 2 0.9580 0.818 0.380 0.620
#> GSM1009125 1 0.0672 0.917 0.992 0.008
#> GSM1009139 1 0.6531 0.814 0.832 0.168
#> GSM1009153 1 0.4431 0.862 0.908 0.092
#> GSM1009167 2 0.5519 0.856 0.128 0.872
#> GSM1009181 1 0.0000 0.916 1.000 0.000
#> GSM1009195 1 0.0938 0.916 0.988 0.012
#> GSM1009070 1 0.4431 0.862 0.908 0.092
#> GSM1009084 1 0.0000 0.916 1.000 0.000
#> GSM1009098 1 0.6531 0.814 0.832 0.168
#> GSM1009112 2 0.9580 0.818 0.380 0.620
#> GSM1009126 1 0.0672 0.917 0.992 0.008
#> GSM1009140 1 0.6531 0.814 0.832 0.168
#> GSM1009154 1 0.1184 0.915 0.984 0.016
#> GSM1009168 2 0.5519 0.856 0.128 0.872
#> GSM1009182 1 0.0000 0.916 1.000 0.000
#> GSM1009196 1 0.0938 0.916 0.988 0.012
#> GSM1009071 1 0.4431 0.862 0.908 0.092
#> GSM1009085 1 0.0000 0.916 1.000 0.000
#> GSM1009099 1 0.6531 0.814 0.832 0.168
#> GSM1009113 2 0.9580 0.818 0.380 0.620
#> GSM1009127 1 0.0672 0.917 0.992 0.008
#> GSM1009141 1 0.6531 0.814 0.832 0.168
#> GSM1009155 1 0.4431 0.862 0.908 0.092
#> GSM1009169 2 0.5519 0.856 0.128 0.872
#> GSM1009183 1 0.0000 0.916 1.000 0.000
#> GSM1009197 1 0.0938 0.916 0.988 0.012
#> GSM1009072 1 0.4431 0.862 0.908 0.092
#> GSM1009086 1 0.0000 0.916 1.000 0.000
#> GSM1009100 1 0.6531 0.814 0.832 0.168
#> GSM1009114 2 0.9580 0.818 0.380 0.620
#> GSM1009128 1 0.0672 0.917 0.992 0.008
#> GSM1009142 1 0.6531 0.814 0.832 0.168
#> GSM1009156 1 0.1633 0.912 0.976 0.024
#> GSM1009170 2 0.5519 0.856 0.128 0.872
#> GSM1009184 1 0.0000 0.916 1.000 0.000
#> GSM1009198 1 0.0938 0.916 0.988 0.012
#> GSM1009073 1 0.4431 0.862 0.908 0.092
#> GSM1009087 1 0.0000 0.916 1.000 0.000
#> GSM1009101 1 0.6531 0.814 0.832 0.168
#> GSM1009115 2 0.9580 0.818 0.380 0.620
#> GSM1009129 1 0.0938 0.916 0.988 0.012
#> GSM1009143 1 0.6531 0.814 0.832 0.168
#> GSM1009157 1 0.1843 0.909 0.972 0.028
#> GSM1009171 2 0.5519 0.856 0.128 0.872
#> GSM1009185 1 0.0000 0.916 1.000 0.000
#> GSM1009199 1 0.0938 0.916 0.988 0.012
#> GSM1009074 1 0.4431 0.862 0.908 0.092
#> GSM1009088 1 0.0000 0.916 1.000 0.000
#> GSM1009102 1 0.6531 0.814 0.832 0.168
#> GSM1009116 2 0.9580 0.818 0.380 0.620
#> GSM1009130 1 0.0938 0.916 0.988 0.012
#> GSM1009144 1 0.6531 0.814 0.832 0.168
#> GSM1009158 1 0.3431 0.885 0.936 0.064
#> GSM1009172 2 0.5519 0.856 0.128 0.872
#> GSM1009186 1 0.0000 0.916 1.000 0.000
#> GSM1009200 1 0.0938 0.916 0.988 0.012
#> GSM1009075 1 0.4431 0.862 0.908 0.092
#> GSM1009089 1 0.0000 0.916 1.000 0.000
#> GSM1009103 1 0.6531 0.814 0.832 0.168
#> GSM1009117 2 0.9580 0.818 0.380 0.620
#> GSM1009131 1 0.0938 0.916 0.988 0.012
#> GSM1009145 1 0.6531 0.814 0.832 0.168
#> GSM1009159 1 0.0938 0.916 0.988 0.012
#> GSM1009173 2 0.5519 0.856 0.128 0.872
#> GSM1009187 1 0.0000 0.916 1.000 0.000
#> GSM1009201 1 0.0938 0.916 0.988 0.012
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1009062 2 0.0000 0.951 0.000 1.000 0.000
#> GSM1009076 2 0.0000 0.951 0.000 1.000 0.000
#> GSM1009090 1 0.0000 0.999 1.000 0.000 0.000
#> GSM1009104 2 0.3038 0.901 0.000 0.896 0.104
#> GSM1009118 2 0.2537 0.937 0.080 0.920 0.000
#> GSM1009132 1 0.0000 0.999 1.000 0.000 0.000
#> GSM1009146 2 0.2066 0.946 0.060 0.940 0.000
#> GSM1009160 3 0.0000 1.000 0.000 0.000 1.000
#> GSM1009174 2 0.0000 0.951 0.000 1.000 0.000
#> GSM1009188 2 0.2066 0.946 0.060 0.940 0.000
#> GSM1009063 2 0.0000 0.951 0.000 1.000 0.000
#> GSM1009077 2 0.0000 0.951 0.000 1.000 0.000
#> GSM1009091 1 0.0000 0.999 1.000 0.000 0.000
#> GSM1009105 2 0.3038 0.901 0.000 0.896 0.104
#> GSM1009119 2 0.4974 0.761 0.236 0.764 0.000
#> GSM1009133 1 0.0000 0.999 1.000 0.000 0.000
#> GSM1009147 2 0.2066 0.946 0.060 0.940 0.000
#> GSM1009161 3 0.0000 1.000 0.000 0.000 1.000
#> GSM1009175 2 0.0000 0.951 0.000 1.000 0.000
#> GSM1009189 2 0.2066 0.946 0.060 0.940 0.000
#> GSM1009064 2 0.0000 0.951 0.000 1.000 0.000
#> GSM1009078 2 0.0000 0.951 0.000 1.000 0.000
#> GSM1009092 1 0.0000 0.999 1.000 0.000 0.000
#> GSM1009106 2 0.3038 0.901 0.000 0.896 0.104
#> GSM1009120 2 0.3816 0.841 0.148 0.852 0.000
#> GSM1009134 1 0.0000 0.999 1.000 0.000 0.000
#> GSM1009148 2 0.2066 0.946 0.060 0.940 0.000
#> GSM1009162 3 0.0000 1.000 0.000 0.000 1.000
#> GSM1009176 2 0.0000 0.951 0.000 1.000 0.000
#> GSM1009190 2 0.2066 0.946 0.060 0.940 0.000
#> GSM1009065 2 0.0000 0.951 0.000 1.000 0.000
#> GSM1009079 2 0.0000 0.951 0.000 1.000 0.000
#> GSM1009093 1 0.0000 0.999 1.000 0.000 0.000
#> GSM1009107 2 0.3038 0.901 0.000 0.896 0.104
#> GSM1009121 2 0.2537 0.937 0.080 0.920 0.000
#> GSM1009135 1 0.0000 0.999 1.000 0.000 0.000
#> GSM1009149 2 0.2066 0.946 0.060 0.940 0.000
#> GSM1009163 3 0.0000 1.000 0.000 0.000 1.000
#> GSM1009177 2 0.0000 0.951 0.000 1.000 0.000
#> GSM1009191 2 0.2066 0.946 0.060 0.940 0.000
#> GSM1009066 2 0.0000 0.951 0.000 1.000 0.000
#> GSM1009080 2 0.0000 0.951 0.000 1.000 0.000
#> GSM1009094 1 0.0000 0.999 1.000 0.000 0.000
#> GSM1009108 2 0.3038 0.901 0.000 0.896 0.104
#> GSM1009122 2 0.2537 0.937 0.080 0.920 0.000
#> GSM1009136 1 0.0000 0.999 1.000 0.000 0.000
#> GSM1009150 2 0.1964 0.947 0.056 0.944 0.000
#> GSM1009164 3 0.0000 1.000 0.000 0.000 1.000
#> GSM1009178 2 0.0000 0.951 0.000 1.000 0.000
#> GSM1009192 2 0.2066 0.946 0.060 0.940 0.000
#> GSM1009067 2 0.0000 0.951 0.000 1.000 0.000
#> GSM1009081 2 0.0000 0.951 0.000 1.000 0.000
#> GSM1009095 1 0.0892 0.965 0.980 0.020 0.000
#> GSM1009109 2 0.3038 0.901 0.000 0.896 0.104
#> GSM1009123 2 0.5706 0.621 0.320 0.680 0.000
#> GSM1009137 1 0.0000 0.999 1.000 0.000 0.000
#> GSM1009151 2 0.1964 0.947 0.056 0.944 0.000
#> GSM1009165 3 0.0000 1.000 0.000 0.000 1.000
#> GSM1009179 2 0.0000 0.951 0.000 1.000 0.000
#> GSM1009193 2 0.2066 0.946 0.060 0.940 0.000
#> GSM1009068 2 0.0000 0.951 0.000 1.000 0.000
#> GSM1009082 2 0.0000 0.951 0.000 1.000 0.000
#> GSM1009096 1 0.0000 0.999 1.000 0.000 0.000
#> GSM1009110 2 0.3038 0.901 0.000 0.896 0.104
#> GSM1009124 2 0.2625 0.935 0.084 0.916 0.000
#> GSM1009138 1 0.0000 0.999 1.000 0.000 0.000
#> GSM1009152 2 0.1964 0.947 0.056 0.944 0.000
#> GSM1009166 3 0.0000 1.000 0.000 0.000 1.000
#> GSM1009180 2 0.0000 0.951 0.000 1.000 0.000
#> GSM1009194 2 0.2066 0.946 0.060 0.940 0.000
#> GSM1009069 2 0.0000 0.951 0.000 1.000 0.000
#> GSM1009083 2 0.0000 0.951 0.000 1.000 0.000
#> GSM1009097 1 0.0000 0.999 1.000 0.000 0.000
#> GSM1009111 2 0.3038 0.901 0.000 0.896 0.104
#> GSM1009125 2 0.2537 0.937 0.080 0.920 0.000
#> GSM1009139 1 0.0000 0.999 1.000 0.000 0.000
#> GSM1009153 2 0.2066 0.946 0.060 0.940 0.000
#> GSM1009167 3 0.0000 1.000 0.000 0.000 1.000
#> GSM1009181 2 0.0000 0.951 0.000 1.000 0.000
#> GSM1009195 2 0.2066 0.946 0.060 0.940 0.000
#> GSM1009070 2 0.0000 0.951 0.000 1.000 0.000
#> GSM1009084 2 0.0000 0.951 0.000 1.000 0.000
#> GSM1009098 1 0.0000 0.999 1.000 0.000 0.000
#> GSM1009112 2 0.3038 0.901 0.000 0.896 0.104
#> GSM1009126 2 0.2625 0.935 0.084 0.916 0.000
#> GSM1009140 1 0.0000 0.999 1.000 0.000 0.000
#> GSM1009154 2 0.2066 0.946 0.060 0.940 0.000
#> GSM1009168 3 0.0000 1.000 0.000 0.000 1.000
#> GSM1009182 2 0.0000 0.951 0.000 1.000 0.000
#> GSM1009196 2 0.2066 0.946 0.060 0.940 0.000
#> GSM1009071 2 0.0000 0.951 0.000 1.000 0.000
#> GSM1009085 2 0.0000 0.951 0.000 1.000 0.000
#> GSM1009099 1 0.0000 0.999 1.000 0.000 0.000
#> GSM1009113 2 0.3038 0.901 0.000 0.896 0.104
#> GSM1009127 2 0.4555 0.809 0.200 0.800 0.000
#> GSM1009141 1 0.0000 0.999 1.000 0.000 0.000
#> GSM1009155 2 0.2066 0.946 0.060 0.940 0.000
#> GSM1009169 3 0.0000 1.000 0.000 0.000 1.000
#> GSM1009183 2 0.0000 0.951 0.000 1.000 0.000
#> GSM1009197 2 0.2066 0.946 0.060 0.940 0.000
#> GSM1009072 2 0.0000 0.951 0.000 1.000 0.000
#> GSM1009086 2 0.0000 0.951 0.000 1.000 0.000
#> GSM1009100 1 0.0000 0.999 1.000 0.000 0.000
#> GSM1009114 2 0.3038 0.901 0.000 0.896 0.104
#> GSM1009128 2 0.2537 0.937 0.080 0.920 0.000
#> GSM1009142 1 0.0000 0.999 1.000 0.000 0.000
#> GSM1009156 2 0.2165 0.945 0.064 0.936 0.000
#> GSM1009170 3 0.0000 1.000 0.000 0.000 1.000
#> GSM1009184 2 0.0000 0.951 0.000 1.000 0.000
#> GSM1009198 2 0.2066 0.946 0.060 0.940 0.000
#> GSM1009073 2 0.0000 0.951 0.000 1.000 0.000
#> GSM1009087 2 0.0000 0.951 0.000 1.000 0.000
#> GSM1009101 1 0.0000 0.999 1.000 0.000 0.000
#> GSM1009115 2 0.3038 0.901 0.000 0.896 0.104
#> GSM1009129 2 0.2537 0.937 0.080 0.920 0.000
#> GSM1009143 1 0.0000 0.999 1.000 0.000 0.000
#> GSM1009157 2 0.2165 0.945 0.064 0.936 0.000
#> GSM1009171 3 0.0000 1.000 0.000 0.000 1.000
#> GSM1009185 2 0.0000 0.951 0.000 1.000 0.000
#> GSM1009199 2 0.2066 0.946 0.060 0.940 0.000
#> GSM1009074 2 0.0000 0.951 0.000 1.000 0.000
#> GSM1009088 2 0.0000 0.951 0.000 1.000 0.000
#> GSM1009102 1 0.0000 0.999 1.000 0.000 0.000
#> GSM1009116 2 0.3038 0.901 0.000 0.896 0.104
#> GSM1009130 2 0.2537 0.937 0.080 0.920 0.000
#> GSM1009144 1 0.0000 0.999 1.000 0.000 0.000
#> GSM1009158 2 0.2066 0.946 0.060 0.940 0.000
#> GSM1009172 3 0.0000 1.000 0.000 0.000 1.000
#> GSM1009186 2 0.0000 0.951 0.000 1.000 0.000
#> GSM1009200 2 0.2066 0.946 0.060 0.940 0.000
#> GSM1009075 2 0.0000 0.951 0.000 1.000 0.000
#> GSM1009089 2 0.0000 0.951 0.000 1.000 0.000
#> GSM1009103 1 0.0000 0.999 1.000 0.000 0.000
#> GSM1009117 2 0.3038 0.901 0.000 0.896 0.104
#> GSM1009131 2 0.2537 0.937 0.080 0.920 0.000
#> GSM1009145 1 0.0000 0.999 1.000 0.000 0.000
#> GSM1009159 2 0.2066 0.946 0.060 0.940 0.000
#> GSM1009173 3 0.0000 1.000 0.000 0.000 1.000
#> GSM1009187 2 0.0000 0.951 0.000 1.000 0.000
#> GSM1009201 2 0.2066 0.946 0.060 0.940 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1009062 1 0.3219 0.881 0.836 0.164 0 0.000
#> GSM1009076 1 0.3266 0.875 0.832 0.168 0 0.000
#> GSM1009090 4 0.0000 1.000 0.000 0.000 0 1.000
#> GSM1009104 2 0.1716 0.999 0.064 0.936 0 0.000
#> GSM1009118 1 0.1398 0.892 0.956 0.040 0 0.004
#> GSM1009132 4 0.0000 1.000 0.000 0.000 0 1.000
#> GSM1009146 1 0.1716 0.870 0.936 0.064 0 0.000
#> GSM1009160 3 0.0000 1.000 0.000 0.000 1 0.000
#> GSM1009174 1 0.3219 0.877 0.836 0.164 0 0.000
#> GSM1009188 1 0.0657 0.892 0.984 0.012 0 0.004
#> GSM1009063 1 0.3219 0.881 0.836 0.164 0 0.000
#> GSM1009077 1 0.3266 0.875 0.832 0.168 0 0.000
#> GSM1009091 4 0.0000 1.000 0.000 0.000 0 1.000
#> GSM1009105 2 0.1716 0.999 0.064 0.936 0 0.000
#> GSM1009119 1 0.2909 0.841 0.888 0.020 0 0.092
#> GSM1009133 4 0.0000 1.000 0.000 0.000 0 1.000
#> GSM1009147 1 0.1489 0.880 0.952 0.044 0 0.004
#> GSM1009161 3 0.0000 1.000 0.000 0.000 1 0.000
#> GSM1009175 1 0.3219 0.877 0.836 0.164 0 0.000
#> GSM1009189 1 0.0779 0.891 0.980 0.016 0 0.004
#> GSM1009064 1 0.3219 0.881 0.836 0.164 0 0.000
#> GSM1009078 1 0.3266 0.875 0.832 0.168 0 0.000
#> GSM1009092 4 0.0000 1.000 0.000 0.000 0 1.000
#> GSM1009106 2 0.1716 0.999 0.064 0.936 0 0.000
#> GSM1009120 1 0.2483 0.877 0.916 0.032 0 0.052
#> GSM1009134 4 0.0000 1.000 0.000 0.000 0 1.000
#> GSM1009148 1 0.1716 0.870 0.936 0.064 0 0.000
#> GSM1009162 3 0.0000 1.000 0.000 0.000 1 0.000
#> GSM1009176 1 0.3219 0.877 0.836 0.164 0 0.000
#> GSM1009190 1 0.0524 0.891 0.988 0.008 0 0.004
#> GSM1009065 1 0.3219 0.881 0.836 0.164 0 0.000
#> GSM1009079 1 0.3266 0.875 0.832 0.168 0 0.000
#> GSM1009093 4 0.0000 1.000 0.000 0.000 0 1.000
#> GSM1009107 2 0.1716 0.999 0.064 0.936 0 0.000
#> GSM1009121 1 0.1398 0.892 0.956 0.040 0 0.004
#> GSM1009135 4 0.0000 1.000 0.000 0.000 0 1.000
#> GSM1009149 1 0.1637 0.872 0.940 0.060 0 0.000
#> GSM1009163 3 0.0000 1.000 0.000 0.000 1 0.000
#> GSM1009177 1 0.3219 0.877 0.836 0.164 0 0.000
#> GSM1009191 1 0.0524 0.891 0.988 0.008 0 0.004
#> GSM1009066 1 0.3219 0.881 0.836 0.164 0 0.000
#> GSM1009080 1 0.3266 0.875 0.832 0.168 0 0.000
#> GSM1009094 4 0.0000 1.000 0.000 0.000 0 1.000
#> GSM1009108 2 0.1716 0.999 0.064 0.936 0 0.000
#> GSM1009122 1 0.1398 0.892 0.956 0.040 0 0.004
#> GSM1009136 4 0.0000 1.000 0.000 0.000 0 1.000
#> GSM1009150 1 0.1637 0.872 0.940 0.060 0 0.000
#> GSM1009164 3 0.0000 1.000 0.000 0.000 1 0.000
#> GSM1009178 1 0.3123 0.880 0.844 0.156 0 0.000
#> GSM1009192 1 0.0657 0.890 0.984 0.012 0 0.004
#> GSM1009067 1 0.3219 0.881 0.836 0.164 0 0.000
#> GSM1009081 1 0.3266 0.875 0.832 0.168 0 0.000
#> GSM1009095 4 0.0188 0.993 0.004 0.000 0 0.996
#> GSM1009109 2 0.1716 0.999 0.064 0.936 0 0.000
#> GSM1009123 1 0.5008 0.639 0.732 0.040 0 0.228
#> GSM1009137 4 0.0000 1.000 0.000 0.000 0 1.000
#> GSM1009151 1 0.1716 0.870 0.936 0.064 0 0.000
#> GSM1009165 3 0.0000 1.000 0.000 0.000 1 0.000
#> GSM1009179 1 0.3123 0.880 0.844 0.156 0 0.000
#> GSM1009193 1 0.0657 0.890 0.984 0.012 0 0.004
#> GSM1009068 1 0.3219 0.881 0.836 0.164 0 0.000
#> GSM1009082 1 0.3266 0.875 0.832 0.168 0 0.000
#> GSM1009096 4 0.0000 1.000 0.000 0.000 0 1.000
#> GSM1009110 2 0.1792 0.993 0.068 0.932 0 0.000
#> GSM1009124 1 0.1398 0.892 0.956 0.040 0 0.004
#> GSM1009138 4 0.0000 1.000 0.000 0.000 0 1.000
#> GSM1009152 1 0.1716 0.870 0.936 0.064 0 0.000
#> GSM1009166 3 0.0000 1.000 0.000 0.000 1 0.000
#> GSM1009180 1 0.3123 0.880 0.844 0.156 0 0.000
#> GSM1009194 1 0.0657 0.890 0.984 0.012 0 0.004
#> GSM1009069 1 0.3569 0.880 0.804 0.196 0 0.000
#> GSM1009083 1 0.3266 0.875 0.832 0.168 0 0.000
#> GSM1009097 4 0.0000 1.000 0.000 0.000 0 1.000
#> GSM1009111 2 0.1716 0.999 0.064 0.936 0 0.000
#> GSM1009125 1 0.1398 0.892 0.956 0.040 0 0.004
#> GSM1009139 4 0.0000 1.000 0.000 0.000 0 1.000
#> GSM1009153 1 0.1716 0.870 0.936 0.064 0 0.000
#> GSM1009167 3 0.0000 1.000 0.000 0.000 1 0.000
#> GSM1009181 1 0.3219 0.877 0.836 0.164 0 0.000
#> GSM1009195 1 0.0524 0.891 0.988 0.008 0 0.004
#> GSM1009070 1 0.3219 0.881 0.836 0.164 0 0.000
#> GSM1009084 1 0.3266 0.875 0.832 0.168 0 0.000
#> GSM1009098 4 0.0000 1.000 0.000 0.000 0 1.000
#> GSM1009112 2 0.1716 0.999 0.064 0.936 0 0.000
#> GSM1009126 1 0.1398 0.892 0.956 0.040 0 0.004
#> GSM1009140 4 0.0000 1.000 0.000 0.000 0 1.000
#> GSM1009154 1 0.1716 0.870 0.936 0.064 0 0.000
#> GSM1009168 3 0.0000 1.000 0.000 0.000 1 0.000
#> GSM1009182 1 0.3219 0.877 0.836 0.164 0 0.000
#> GSM1009196 1 0.1489 0.880 0.952 0.044 0 0.004
#> GSM1009071 1 0.3219 0.881 0.836 0.164 0 0.000
#> GSM1009085 1 0.3266 0.875 0.832 0.168 0 0.000
#> GSM1009099 4 0.0000 1.000 0.000 0.000 0 1.000
#> GSM1009113 2 0.1716 0.999 0.064 0.936 0 0.000
#> GSM1009127 1 0.2174 0.876 0.928 0.020 0 0.052
#> GSM1009141 4 0.0000 1.000 0.000 0.000 0 1.000
#> GSM1009155 1 0.1716 0.875 0.936 0.064 0 0.000
#> GSM1009169 3 0.0000 1.000 0.000 0.000 1 0.000
#> GSM1009183 1 0.3219 0.877 0.836 0.164 0 0.000
#> GSM1009197 1 0.0657 0.890 0.984 0.012 0 0.004
#> GSM1009072 1 0.3219 0.881 0.836 0.164 0 0.000
#> GSM1009086 1 0.3266 0.875 0.832 0.168 0 0.000
#> GSM1009100 4 0.0000 1.000 0.000 0.000 0 1.000
#> GSM1009114 2 0.1716 0.999 0.064 0.936 0 0.000
#> GSM1009128 1 0.1398 0.892 0.956 0.040 0 0.004
#> GSM1009142 4 0.0000 1.000 0.000 0.000 0 1.000
#> GSM1009156 1 0.1576 0.882 0.948 0.048 0 0.004
#> GSM1009170 3 0.0000 1.000 0.000 0.000 1 0.000
#> GSM1009184 1 0.3219 0.877 0.836 0.164 0 0.000
#> GSM1009198 1 0.0779 0.892 0.980 0.016 0 0.004
#> GSM1009073 1 0.3219 0.881 0.836 0.164 0 0.000
#> GSM1009087 1 0.3266 0.875 0.832 0.168 0 0.000
#> GSM1009101 4 0.0000 1.000 0.000 0.000 0 1.000
#> GSM1009115 2 0.1792 0.993 0.068 0.932 0 0.000
#> GSM1009129 1 0.1398 0.892 0.956 0.040 0 0.004
#> GSM1009143 4 0.0000 1.000 0.000 0.000 0 1.000
#> GSM1009157 1 0.1661 0.884 0.944 0.052 0 0.004
#> GSM1009171 3 0.0000 1.000 0.000 0.000 1 0.000
#> GSM1009185 1 0.3123 0.880 0.844 0.156 0 0.000
#> GSM1009199 1 0.0524 0.891 0.988 0.008 0 0.004
#> GSM1009074 1 0.3219 0.881 0.836 0.164 0 0.000
#> GSM1009088 1 0.3266 0.875 0.832 0.168 0 0.000
#> GSM1009102 4 0.0000 1.000 0.000 0.000 0 1.000
#> GSM1009116 2 0.1716 0.999 0.064 0.936 0 0.000
#> GSM1009130 1 0.1398 0.892 0.956 0.040 0 0.004
#> GSM1009144 4 0.0000 1.000 0.000 0.000 0 1.000
#> GSM1009158 1 0.1716 0.870 0.936 0.064 0 0.000
#> GSM1009172 3 0.0000 1.000 0.000 0.000 1 0.000
#> GSM1009186 1 0.3219 0.877 0.836 0.164 0 0.000
#> GSM1009200 1 0.0376 0.891 0.992 0.004 0 0.004
#> GSM1009075 1 0.3219 0.881 0.836 0.164 0 0.000
#> GSM1009089 1 0.3219 0.877 0.836 0.164 0 0.000
#> GSM1009103 4 0.0000 1.000 0.000 0.000 0 1.000
#> GSM1009117 2 0.1716 0.999 0.064 0.936 0 0.000
#> GSM1009131 1 0.1398 0.892 0.956 0.040 0 0.004
#> GSM1009145 4 0.0000 1.000 0.000 0.000 0 1.000
#> GSM1009159 1 0.1637 0.872 0.940 0.060 0 0.000
#> GSM1009173 3 0.0000 1.000 0.000 0.000 1 0.000
#> GSM1009187 1 0.3123 0.880 0.844 0.156 0 0.000
#> GSM1009201 1 0.0524 0.891 0.988 0.008 0 0.004
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM1009062 1 0.3318 0.6928 0.808 0.180 0 0.000 0.012
#> GSM1009076 2 0.0000 0.9109 0.000 1.000 0 0.000 0.000
#> GSM1009090 4 0.0000 0.9934 0.000 0.000 0 1.000 0.000
#> GSM1009104 5 0.0404 1.0000 0.000 0.012 0 0.000 0.988
#> GSM1009118 2 0.1121 0.8816 0.044 0.956 0 0.000 0.000
#> GSM1009132 4 0.0794 0.9621 0.000 0.028 0 0.972 0.000
#> GSM1009146 1 0.1121 0.7349 0.956 0.044 0 0.000 0.000
#> GSM1009160 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000
#> GSM1009174 2 0.0000 0.9109 0.000 1.000 0 0.000 0.000
#> GSM1009188 1 0.4045 0.6163 0.644 0.356 0 0.000 0.000
#> GSM1009063 1 0.3318 0.6928 0.808 0.180 0 0.000 0.012
#> GSM1009077 2 0.0000 0.9109 0.000 1.000 0 0.000 0.000
#> GSM1009091 4 0.0000 0.9934 0.000 0.000 0 1.000 0.000
#> GSM1009105 5 0.0404 1.0000 0.000 0.012 0 0.000 0.988
#> GSM1009119 2 0.5756 0.1750 0.312 0.576 0 0.112 0.000
#> GSM1009133 4 0.0000 0.9934 0.000 0.000 0 1.000 0.000
#> GSM1009147 1 0.3837 0.6545 0.692 0.308 0 0.000 0.000
#> GSM1009161 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000
#> GSM1009175 2 0.0000 0.9109 0.000 1.000 0 0.000 0.000
#> GSM1009189 1 0.4045 0.6163 0.644 0.356 0 0.000 0.000
#> GSM1009064 1 0.3318 0.6928 0.808 0.180 0 0.000 0.012
#> GSM1009078 2 0.0000 0.9109 0.000 1.000 0 0.000 0.000
#> GSM1009092 4 0.0000 0.9934 0.000 0.000 0 1.000 0.000
#> GSM1009106 5 0.0404 1.0000 0.000 0.012 0 0.000 0.988
#> GSM1009120 2 0.5862 0.0439 0.344 0.544 0 0.112 0.000
#> GSM1009134 4 0.0000 0.9934 0.000 0.000 0 1.000 0.000
#> GSM1009148 1 0.1121 0.7349 0.956 0.044 0 0.000 0.000
#> GSM1009162 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000
#> GSM1009176 2 0.0000 0.9109 0.000 1.000 0 0.000 0.000
#> GSM1009190 1 0.4045 0.6163 0.644 0.356 0 0.000 0.000
#> GSM1009065 1 0.3318 0.6928 0.808 0.180 0 0.000 0.012
#> GSM1009079 2 0.0000 0.9109 0.000 1.000 0 0.000 0.000
#> GSM1009093 4 0.0000 0.9934 0.000 0.000 0 1.000 0.000
#> GSM1009107 5 0.0404 1.0000 0.000 0.012 0 0.000 0.988
#> GSM1009121 2 0.1197 0.8785 0.048 0.952 0 0.000 0.000
#> GSM1009135 4 0.0000 0.9934 0.000 0.000 0 1.000 0.000
#> GSM1009149 1 0.1121 0.7349 0.956 0.044 0 0.000 0.000
#> GSM1009163 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000
#> GSM1009177 2 0.0000 0.9109 0.000 1.000 0 0.000 0.000
#> GSM1009191 1 0.4045 0.6163 0.644 0.356 0 0.000 0.000
#> GSM1009066 1 0.3318 0.6928 0.808 0.180 0 0.000 0.012
#> GSM1009080 2 0.0000 0.9109 0.000 1.000 0 0.000 0.000
#> GSM1009094 4 0.0000 0.9934 0.000 0.000 0 1.000 0.000
#> GSM1009108 5 0.0404 1.0000 0.000 0.012 0 0.000 0.988
#> GSM1009122 2 0.1043 0.8842 0.040 0.960 0 0.000 0.000
#> GSM1009136 4 0.0000 0.9934 0.000 0.000 0 1.000 0.000
#> GSM1009150 1 0.1121 0.7349 0.956 0.044 0 0.000 0.000
#> GSM1009164 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000
#> GSM1009178 2 0.0000 0.9109 0.000 1.000 0 0.000 0.000
#> GSM1009192 1 0.4045 0.6163 0.644 0.356 0 0.000 0.000
#> GSM1009067 1 0.3318 0.6928 0.808 0.180 0 0.000 0.012
#> GSM1009081 2 0.0000 0.9109 0.000 1.000 0 0.000 0.000
#> GSM1009095 4 0.1877 0.9021 0.012 0.064 0 0.924 0.000
#> GSM1009109 5 0.0404 1.0000 0.000 0.012 0 0.000 0.988
#> GSM1009123 2 0.3944 0.6921 0.052 0.788 0 0.160 0.000
#> GSM1009137 4 0.0000 0.9934 0.000 0.000 0 1.000 0.000
#> GSM1009151 1 0.1121 0.7349 0.956 0.044 0 0.000 0.000
#> GSM1009165 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000
#> GSM1009179 2 0.0000 0.9109 0.000 1.000 0 0.000 0.000
#> GSM1009193 1 0.4045 0.6163 0.644 0.356 0 0.000 0.000
#> GSM1009068 1 0.3318 0.6928 0.808 0.180 0 0.000 0.012
#> GSM1009082 2 0.0000 0.9109 0.000 1.000 0 0.000 0.000
#> GSM1009096 4 0.0000 0.9934 0.000 0.000 0 1.000 0.000
#> GSM1009110 5 0.0404 1.0000 0.000 0.012 0 0.000 0.988
#> GSM1009124 2 0.3561 0.4966 0.260 0.740 0 0.000 0.000
#> GSM1009138 4 0.0000 0.9934 0.000 0.000 0 1.000 0.000
#> GSM1009152 1 0.1121 0.7349 0.956 0.044 0 0.000 0.000
#> GSM1009166 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000
#> GSM1009180 2 0.0000 0.9109 0.000 1.000 0 0.000 0.000
#> GSM1009194 1 0.4045 0.6163 0.644 0.356 0 0.000 0.000
#> GSM1009069 2 0.4273 -0.1955 0.448 0.552 0 0.000 0.000
#> GSM1009083 2 0.0000 0.9109 0.000 1.000 0 0.000 0.000
#> GSM1009097 4 0.0000 0.9934 0.000 0.000 0 1.000 0.000
#> GSM1009111 5 0.0404 1.0000 0.000 0.012 0 0.000 0.988
#> GSM1009125 2 0.1197 0.8785 0.048 0.952 0 0.000 0.000
#> GSM1009139 4 0.0671 0.9757 0.004 0.016 0 0.980 0.000
#> GSM1009153 1 0.1121 0.7349 0.956 0.044 0 0.000 0.000
#> GSM1009167 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000
#> GSM1009181 2 0.0000 0.9109 0.000 1.000 0 0.000 0.000
#> GSM1009195 1 0.4045 0.6163 0.644 0.356 0 0.000 0.000
#> GSM1009070 1 0.3318 0.6928 0.808 0.180 0 0.000 0.012
#> GSM1009084 2 0.0000 0.9109 0.000 1.000 0 0.000 0.000
#> GSM1009098 4 0.0000 0.9934 0.000 0.000 0 1.000 0.000
#> GSM1009112 5 0.0404 1.0000 0.000 0.012 0 0.000 0.988
#> GSM1009126 2 0.1341 0.8706 0.056 0.944 0 0.000 0.000
#> GSM1009140 4 0.0000 0.9934 0.000 0.000 0 1.000 0.000
#> GSM1009154 1 0.1121 0.7349 0.956 0.044 0 0.000 0.000
#> GSM1009168 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000
#> GSM1009182 2 0.0000 0.9109 0.000 1.000 0 0.000 0.000
#> GSM1009196 1 0.3837 0.6545 0.692 0.308 0 0.000 0.000
#> GSM1009071 1 0.3318 0.6928 0.808 0.180 0 0.000 0.012
#> GSM1009085 2 0.0000 0.9109 0.000 1.000 0 0.000 0.000
#> GSM1009099 4 0.0000 0.9934 0.000 0.000 0 1.000 0.000
#> GSM1009113 5 0.0404 1.0000 0.000 0.012 0 0.000 0.988
#> GSM1009127 2 0.5813 0.1140 0.328 0.560 0 0.112 0.000
#> GSM1009141 4 0.0162 0.9901 0.004 0.000 0 0.996 0.000
#> GSM1009155 1 0.1121 0.7349 0.956 0.044 0 0.000 0.000
#> GSM1009169 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000
#> GSM1009183 2 0.0000 0.9109 0.000 1.000 0 0.000 0.000
#> GSM1009197 1 0.4030 0.6201 0.648 0.352 0 0.000 0.000
#> GSM1009072 1 0.3318 0.6928 0.808 0.180 0 0.000 0.012
#> GSM1009086 2 0.0000 0.9109 0.000 1.000 0 0.000 0.000
#> GSM1009100 4 0.0000 0.9934 0.000 0.000 0 1.000 0.000
#> GSM1009114 5 0.0404 1.0000 0.000 0.012 0 0.000 0.988
#> GSM1009128 2 0.1197 0.8785 0.048 0.952 0 0.000 0.000
#> GSM1009142 4 0.0000 0.9934 0.000 0.000 0 1.000 0.000
#> GSM1009156 1 0.3999 0.6315 0.656 0.344 0 0.000 0.000
#> GSM1009170 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000
#> GSM1009184 2 0.0000 0.9109 0.000 1.000 0 0.000 0.000
#> GSM1009198 1 0.4045 0.6163 0.644 0.356 0 0.000 0.000
#> GSM1009073 1 0.3318 0.6928 0.808 0.180 0 0.000 0.012
#> GSM1009087 2 0.0000 0.9109 0.000 1.000 0 0.000 0.000
#> GSM1009101 4 0.0000 0.9934 0.000 0.000 0 1.000 0.000
#> GSM1009115 5 0.0404 1.0000 0.000 0.012 0 0.000 0.988
#> GSM1009129 2 0.1121 0.8816 0.044 0.956 0 0.000 0.000
#> GSM1009143 4 0.0000 0.9934 0.000 0.000 0 1.000 0.000
#> GSM1009157 1 0.3913 0.6476 0.676 0.324 0 0.000 0.000
#> GSM1009171 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000
#> GSM1009185 2 0.0000 0.9109 0.000 1.000 0 0.000 0.000
#> GSM1009199 1 0.4045 0.6163 0.644 0.356 0 0.000 0.000
#> GSM1009074 1 0.3318 0.6928 0.808 0.180 0 0.000 0.012
#> GSM1009088 2 0.0000 0.9109 0.000 1.000 0 0.000 0.000
#> GSM1009102 4 0.0162 0.9901 0.004 0.000 0 0.996 0.000
#> GSM1009116 5 0.0404 1.0000 0.000 0.012 0 0.000 0.988
#> GSM1009130 2 0.3177 0.6196 0.208 0.792 0 0.000 0.000
#> GSM1009144 4 0.0290 0.9861 0.000 0.008 0 0.992 0.000
#> GSM1009158 1 0.1121 0.7349 0.956 0.044 0 0.000 0.000
#> GSM1009172 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000
#> GSM1009186 2 0.0000 0.9109 0.000 1.000 0 0.000 0.000
#> GSM1009200 1 0.4045 0.6163 0.644 0.356 0 0.000 0.000
#> GSM1009075 1 0.3318 0.6928 0.808 0.180 0 0.000 0.012
#> GSM1009089 2 0.0000 0.9109 0.000 1.000 0 0.000 0.000
#> GSM1009103 4 0.0000 0.9934 0.000 0.000 0 1.000 0.000
#> GSM1009117 5 0.0404 1.0000 0.000 0.012 0 0.000 0.988
#> GSM1009131 2 0.1478 0.8617 0.064 0.936 0 0.000 0.000
#> GSM1009145 4 0.0000 0.9934 0.000 0.000 0 1.000 0.000
#> GSM1009159 1 0.1121 0.7349 0.956 0.044 0 0.000 0.000
#> GSM1009173 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000
#> GSM1009187 2 0.0000 0.9109 0.000 1.000 0 0.000 0.000
#> GSM1009201 1 0.4045 0.6163 0.644 0.356 0 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1009062 6 0.3320 0.966 0.212 0.016 0 0.000 0 0.772
#> GSM1009076 2 0.2454 0.698 0.000 0.840 0 0.000 0 0.160
#> GSM1009090 4 0.0000 0.979 0.000 0.000 0 1.000 0 0.000
#> GSM1009104 5 0.0000 1.000 0.000 0.000 0 0.000 1 0.000
#> GSM1009118 2 0.6017 0.234 0.316 0.424 0 0.000 0 0.260
#> GSM1009132 4 0.0865 0.952 0.036 0.000 0 0.964 0 0.000
#> GSM1009146 1 0.1765 0.757 0.904 0.000 0 0.000 0 0.096
#> GSM1009160 3 0.0000 1.000 0.000 0.000 1 0.000 0 0.000
#> GSM1009174 2 0.2730 0.743 0.192 0.808 0 0.000 0 0.000
#> GSM1009188 1 0.0146 0.808 0.996 0.004 0 0.000 0 0.000
#> GSM1009063 6 0.3320 0.966 0.212 0.016 0 0.000 0 0.772
#> GSM1009077 2 0.2454 0.698 0.000 0.840 0 0.000 0 0.160
#> GSM1009091 4 0.0000 0.979 0.000 0.000 0 1.000 0 0.000
#> GSM1009105 5 0.0000 1.000 0.000 0.000 0 0.000 1 0.000
#> GSM1009119 1 0.5909 0.410 0.596 0.056 0 0.116 0 0.232
#> GSM1009133 4 0.0000 0.979 0.000 0.000 0 1.000 0 0.000
#> GSM1009147 1 0.0146 0.807 0.996 0.000 0 0.000 0 0.004
#> GSM1009161 3 0.0000 1.000 0.000 0.000 1 0.000 0 0.000
#> GSM1009175 2 0.2730 0.743 0.192 0.808 0 0.000 0 0.000
#> GSM1009189 1 0.0146 0.808 0.996 0.004 0 0.000 0 0.000
#> GSM1009064 6 0.3320 0.966 0.212 0.016 0 0.000 0 0.772
#> GSM1009078 2 0.2402 0.705 0.004 0.856 0 0.000 0 0.140
#> GSM1009092 4 0.0000 0.979 0.000 0.000 0 1.000 0 0.000
#> GSM1009106 5 0.0000 1.000 0.000 0.000 0 0.000 1 0.000
#> GSM1009120 1 0.5299 0.437 0.648 0.024 0 0.116 0 0.212
#> GSM1009134 4 0.0000 0.979 0.000 0.000 0 1.000 0 0.000
#> GSM1009148 1 0.1863 0.751 0.896 0.000 0 0.000 0 0.104
#> GSM1009162 3 0.0000 1.000 0.000 0.000 1 0.000 0 0.000
#> GSM1009176 2 0.2730 0.743 0.192 0.808 0 0.000 0 0.000
#> GSM1009190 1 0.0146 0.808 0.996 0.004 0 0.000 0 0.000
#> GSM1009065 6 0.3320 0.966 0.212 0.016 0 0.000 0 0.772
#> GSM1009079 2 0.3506 0.713 0.052 0.792 0 0.000 0 0.156
#> GSM1009093 4 0.0000 0.979 0.000 0.000 0 1.000 0 0.000
#> GSM1009107 5 0.0000 1.000 0.000 0.000 0 0.000 1 0.000
#> GSM1009121 1 0.5926 0.236 0.464 0.276 0 0.000 0 0.260
#> GSM1009135 4 0.0000 0.979 0.000 0.000 0 1.000 0 0.000
#> GSM1009149 1 0.1765 0.757 0.904 0.000 0 0.000 0 0.096
#> GSM1009163 3 0.0000 1.000 0.000 0.000 1 0.000 0 0.000
#> GSM1009177 2 0.2730 0.743 0.192 0.808 0 0.000 0 0.000
#> GSM1009191 1 0.0146 0.808 0.996 0.004 0 0.000 0 0.000
#> GSM1009066 6 0.3320 0.966 0.212 0.016 0 0.000 0 0.772
#> GSM1009080 2 0.2454 0.698 0.000 0.840 0 0.000 0 0.160
#> GSM1009094 4 0.0000 0.979 0.000 0.000 0 1.000 0 0.000
#> GSM1009108 5 0.0000 1.000 0.000 0.000 0 0.000 1 0.000
#> GSM1009122 2 0.5969 0.299 0.292 0.448 0 0.000 0 0.260
#> GSM1009136 4 0.0000 0.979 0.000 0.000 0 1.000 0 0.000
#> GSM1009150 1 0.1814 0.754 0.900 0.000 0 0.000 0 0.100
#> GSM1009164 3 0.0000 1.000 0.000 0.000 1 0.000 0 0.000
#> GSM1009178 2 0.2730 0.743 0.192 0.808 0 0.000 0 0.000
#> GSM1009192 1 0.0000 0.807 1.000 0.000 0 0.000 0 0.000
#> GSM1009067 6 0.3320 0.966 0.212 0.016 0 0.000 0 0.772
#> GSM1009081 2 0.2416 0.699 0.000 0.844 0 0.000 0 0.156
#> GSM1009095 4 0.2994 0.730 0.208 0.004 0 0.788 0 0.000
#> GSM1009109 5 0.0000 1.000 0.000 0.000 0 0.000 1 0.000
#> GSM1009123 1 0.6966 0.307 0.472 0.144 0 0.132 0 0.252
#> GSM1009137 4 0.0000 0.979 0.000 0.000 0 1.000 0 0.000
#> GSM1009151 1 0.1863 0.751 0.896 0.000 0 0.000 0 0.104
#> GSM1009165 3 0.0000 1.000 0.000 0.000 1 0.000 0 0.000
#> GSM1009179 2 0.2730 0.743 0.192 0.808 0 0.000 0 0.000
#> GSM1009193 1 0.0146 0.808 0.996 0.004 0 0.000 0 0.000
#> GSM1009068 6 0.3320 0.966 0.212 0.016 0 0.000 0 0.772
#> GSM1009082 2 0.2454 0.698 0.000 0.840 0 0.000 0 0.160
#> GSM1009096 4 0.0000 0.979 0.000 0.000 0 1.000 0 0.000
#> GSM1009110 5 0.0000 1.000 0.000 0.000 0 0.000 1 0.000
#> GSM1009124 1 0.5258 0.458 0.596 0.152 0 0.000 0 0.252
#> GSM1009138 4 0.0000 0.979 0.000 0.000 0 1.000 0 0.000
#> GSM1009152 1 0.1863 0.751 0.896 0.000 0 0.000 0 0.104
#> GSM1009166 3 0.0000 1.000 0.000 0.000 1 0.000 0 0.000
#> GSM1009180 2 0.2969 0.712 0.224 0.776 0 0.000 0 0.000
#> GSM1009194 1 0.0000 0.807 1.000 0.000 0 0.000 0 0.000
#> GSM1009069 6 0.5974 0.475 0.248 0.312 0 0.000 0 0.440
#> GSM1009083 2 0.2416 0.699 0.000 0.844 0 0.000 0 0.156
#> GSM1009097 4 0.0000 0.979 0.000 0.000 0 1.000 0 0.000
#> GSM1009111 5 0.0000 1.000 0.000 0.000 0 0.000 1 0.000
#> GSM1009125 1 0.5902 0.258 0.472 0.268 0 0.000 0 0.260
#> GSM1009139 4 0.1387 0.926 0.068 0.000 0 0.932 0 0.000
#> GSM1009153 1 0.1765 0.757 0.904 0.000 0 0.000 0 0.096
#> GSM1009167 3 0.0000 1.000 0.000 0.000 1 0.000 0 0.000
#> GSM1009181 2 0.2730 0.743 0.192 0.808 0 0.000 0 0.000
#> GSM1009195 1 0.0146 0.808 0.996 0.004 0 0.000 0 0.000
#> GSM1009070 6 0.3320 0.966 0.212 0.016 0 0.000 0 0.772
#> GSM1009084 2 0.2454 0.698 0.000 0.840 0 0.000 0 0.160
#> GSM1009098 4 0.0000 0.979 0.000 0.000 0 1.000 0 0.000
#> GSM1009112 5 0.0000 1.000 0.000 0.000 0 0.000 1 0.000
#> GSM1009126 1 0.5586 0.409 0.544 0.196 0 0.000 0 0.260
#> GSM1009140 4 0.0000 0.979 0.000 0.000 0 1.000 0 0.000
#> GSM1009154 1 0.1814 0.754 0.900 0.000 0 0.000 0 0.100
#> GSM1009168 3 0.0000 1.000 0.000 0.000 1 0.000 0 0.000
#> GSM1009182 2 0.2730 0.743 0.192 0.808 0 0.000 0 0.000
#> GSM1009196 1 0.0000 0.807 1.000 0.000 0 0.000 0 0.000
#> GSM1009071 6 0.3320 0.966 0.212 0.016 0 0.000 0 0.772
#> GSM1009085 2 0.2454 0.698 0.000 0.840 0 0.000 0 0.160
#> GSM1009099 4 0.0000 0.979 0.000 0.000 0 1.000 0 0.000
#> GSM1009113 5 0.0000 1.000 0.000 0.000 0 0.000 1 0.000
#> GSM1009127 1 0.5909 0.410 0.596 0.056 0 0.116 0 0.232
#> GSM1009141 4 0.1387 0.926 0.068 0.000 0 0.932 0 0.000
#> GSM1009155 1 0.1007 0.791 0.956 0.000 0 0.000 0 0.044
#> GSM1009169 3 0.0000 1.000 0.000 0.000 1 0.000 0 0.000
#> GSM1009183 2 0.2730 0.743 0.192 0.808 0 0.000 0 0.000
#> GSM1009197 1 0.0000 0.807 1.000 0.000 0 0.000 0 0.000
#> GSM1009072 6 0.3320 0.966 0.212 0.016 0 0.000 0 0.772
#> GSM1009086 2 0.2454 0.698 0.000 0.840 0 0.000 0 0.160
#> GSM1009100 4 0.0000 0.979 0.000 0.000 0 1.000 0 0.000
#> GSM1009114 5 0.0000 1.000 0.000 0.000 0 0.000 1 0.000
#> GSM1009128 1 0.5948 0.212 0.456 0.284 0 0.000 0 0.260
#> GSM1009142 4 0.0000 0.979 0.000 0.000 0 1.000 0 0.000
#> GSM1009156 1 0.0713 0.798 0.972 0.000 0 0.000 0 0.028
#> GSM1009170 3 0.0000 1.000 0.000 0.000 1 0.000 0 0.000
#> GSM1009184 2 0.2730 0.743 0.192 0.808 0 0.000 0 0.000
#> GSM1009198 1 0.0146 0.808 0.996 0.004 0 0.000 0 0.000
#> GSM1009073 6 0.3320 0.966 0.212 0.016 0 0.000 0 0.772
#> GSM1009087 2 0.2520 0.703 0.004 0.844 0 0.000 0 0.152
#> GSM1009101 4 0.0000 0.979 0.000 0.000 0 1.000 0 0.000
#> GSM1009115 5 0.0000 1.000 0.000 0.000 0 0.000 1 0.000
#> GSM1009129 2 0.5969 0.299 0.292 0.448 0 0.000 0 0.260
#> GSM1009143 4 0.0000 0.979 0.000 0.000 0 1.000 0 0.000
#> GSM1009157 1 0.0632 0.800 0.976 0.000 0 0.000 0 0.024
#> GSM1009171 3 0.0000 1.000 0.000 0.000 1 0.000 0 0.000
#> GSM1009185 2 0.2941 0.717 0.220 0.780 0 0.000 0 0.000
#> GSM1009199 1 0.0146 0.808 0.996 0.004 0 0.000 0 0.000
#> GSM1009074 6 0.3320 0.966 0.212 0.016 0 0.000 0 0.772
#> GSM1009088 2 0.2520 0.703 0.004 0.844 0 0.000 0 0.152
#> GSM1009102 4 0.0790 0.959 0.032 0.000 0 0.968 0 0.000
#> GSM1009116 5 0.0000 1.000 0.000 0.000 0 0.000 1 0.000
#> GSM1009130 2 0.5938 0.327 0.280 0.460 0 0.000 0 0.260
#> GSM1009144 4 0.0937 0.953 0.040 0.000 0 0.960 0 0.000
#> GSM1009158 1 0.1765 0.759 0.904 0.000 0 0.000 0 0.096
#> GSM1009172 3 0.0000 1.000 0.000 0.000 1 0.000 0 0.000
#> GSM1009186 2 0.2730 0.743 0.192 0.808 0 0.000 0 0.000
#> GSM1009200 1 0.0146 0.808 0.996 0.004 0 0.000 0 0.000
#> GSM1009075 6 0.3320 0.966 0.212 0.016 0 0.000 0 0.772
#> GSM1009089 2 0.3167 0.726 0.072 0.832 0 0.000 0 0.096
#> GSM1009103 4 0.0790 0.959 0.032 0.000 0 0.968 0 0.000
#> GSM1009117 5 0.0000 1.000 0.000 0.000 0 0.000 1 0.000
#> GSM1009131 2 0.6039 0.187 0.332 0.408 0 0.000 0 0.260
#> GSM1009145 4 0.0000 0.979 0.000 0.000 0 1.000 0 0.000
#> GSM1009159 1 0.1610 0.767 0.916 0.000 0 0.000 0 0.084
#> GSM1009173 3 0.0000 1.000 0.000 0.000 1 0.000 0 0.000
#> GSM1009187 2 0.2854 0.729 0.208 0.792 0 0.000 0 0.000
#> GSM1009201 1 0.0146 0.808 0.996 0.004 0 0.000 0 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 temperature(p) time(p) specimen(p) k
#> SD:mclust 140 1 1 1.03e-25 2
#> SD:mclust 140 1 1 6.13e-49 3
#> SD:mclust 140 1 1 4.16e-72 4
#> SD:mclust 135 1 1 3.55e-91 5
#> SD:mclust 125 1 1 4.24e-106 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 140 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 0.873 0.926 0.970 0.4076 0.595 0.595
#> 3 3 0.999 0.936 0.976 0.3960 0.721 0.573
#> 4 4 0.731 0.794 0.911 0.2515 0.784 0.532
#> 5 5 0.728 0.698 0.848 0.0839 0.800 0.427
#> 6 6 0.854 0.803 0.890 0.0573 0.879 0.543
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
#> GSM1009062 1 0.0000 0.9733 1.000 0.000
#> GSM1009076 2 0.3431 0.9155 0.064 0.936
#> GSM1009090 1 0.0000 0.9733 1.000 0.000
#> GSM1009104 2 0.0000 0.9528 0.000 1.000
#> GSM1009118 1 0.0000 0.9733 1.000 0.000
#> GSM1009132 1 0.0000 0.9733 1.000 0.000
#> GSM1009146 1 0.0000 0.9733 1.000 0.000
#> GSM1009160 2 0.0000 0.9528 0.000 1.000
#> GSM1009174 1 0.0672 0.9660 0.992 0.008
#> GSM1009188 1 0.0000 0.9733 1.000 0.000
#> GSM1009063 1 0.0000 0.9733 1.000 0.000
#> GSM1009077 2 0.6712 0.8071 0.176 0.824
#> GSM1009091 1 0.0000 0.9733 1.000 0.000
#> GSM1009105 2 0.0000 0.9528 0.000 1.000
#> GSM1009119 1 0.0000 0.9733 1.000 0.000
#> GSM1009133 1 0.0000 0.9733 1.000 0.000
#> GSM1009147 1 0.0000 0.9733 1.000 0.000
#> GSM1009161 2 0.0000 0.9528 0.000 1.000
#> GSM1009175 1 0.0000 0.9733 1.000 0.000
#> GSM1009189 1 0.0000 0.9733 1.000 0.000
#> GSM1009064 1 0.0000 0.9733 1.000 0.000
#> GSM1009078 1 0.0376 0.9698 0.996 0.004
#> GSM1009092 1 0.0000 0.9733 1.000 0.000
#> GSM1009106 2 0.0000 0.9528 0.000 1.000
#> GSM1009120 1 0.0000 0.9733 1.000 0.000
#> GSM1009134 1 0.0000 0.9733 1.000 0.000
#> GSM1009148 1 0.0000 0.9733 1.000 0.000
#> GSM1009162 2 0.0000 0.9528 0.000 1.000
#> GSM1009176 1 0.9963 0.0875 0.536 0.464
#> GSM1009190 1 0.0000 0.9733 1.000 0.000
#> GSM1009065 1 0.0000 0.9733 1.000 0.000
#> GSM1009079 2 0.5737 0.8544 0.136 0.864
#> GSM1009093 1 0.0000 0.9733 1.000 0.000
#> GSM1009107 2 0.0000 0.9528 0.000 1.000
#> GSM1009121 1 0.0000 0.9733 1.000 0.000
#> GSM1009135 1 0.0000 0.9733 1.000 0.000
#> GSM1009149 1 0.0000 0.9733 1.000 0.000
#> GSM1009163 2 0.0000 0.9528 0.000 1.000
#> GSM1009177 1 0.9815 0.2409 0.580 0.420
#> GSM1009191 1 0.0000 0.9733 1.000 0.000
#> GSM1009066 1 0.0000 0.9733 1.000 0.000
#> GSM1009080 2 0.4690 0.8890 0.100 0.900
#> GSM1009094 1 0.0000 0.9733 1.000 0.000
#> GSM1009108 2 0.0000 0.9528 0.000 1.000
#> GSM1009122 1 0.0376 0.9698 0.996 0.004
#> GSM1009136 1 0.0000 0.9733 1.000 0.000
#> GSM1009150 1 0.0000 0.9733 1.000 0.000
#> GSM1009164 2 0.0000 0.9528 0.000 1.000
#> GSM1009178 1 0.0000 0.9733 1.000 0.000
#> GSM1009192 1 0.0000 0.9733 1.000 0.000
#> GSM1009067 1 0.0000 0.9733 1.000 0.000
#> GSM1009081 2 0.6712 0.8071 0.176 0.824
#> GSM1009095 1 0.0000 0.9733 1.000 0.000
#> GSM1009109 2 0.0000 0.9528 0.000 1.000
#> GSM1009123 1 0.0000 0.9733 1.000 0.000
#> GSM1009137 1 0.0000 0.9733 1.000 0.000
#> GSM1009151 1 0.0000 0.9733 1.000 0.000
#> GSM1009165 2 0.0000 0.9528 0.000 1.000
#> GSM1009179 1 0.0000 0.9733 1.000 0.000
#> GSM1009193 1 0.0000 0.9733 1.000 0.000
#> GSM1009068 1 0.0000 0.9733 1.000 0.000
#> GSM1009082 2 0.8144 0.6919 0.252 0.748
#> GSM1009096 1 0.0000 0.9733 1.000 0.000
#> GSM1009110 2 0.0000 0.9528 0.000 1.000
#> GSM1009124 1 0.0000 0.9733 1.000 0.000
#> GSM1009138 1 0.0000 0.9733 1.000 0.000
#> GSM1009152 1 0.0000 0.9733 1.000 0.000
#> GSM1009166 2 0.0000 0.9528 0.000 1.000
#> GSM1009180 1 0.0000 0.9733 1.000 0.000
#> GSM1009194 1 0.0000 0.9733 1.000 0.000
#> GSM1009069 1 0.0000 0.9733 1.000 0.000
#> GSM1009083 1 0.9866 0.2024 0.568 0.432
#> GSM1009097 1 0.0000 0.9733 1.000 0.000
#> GSM1009111 2 0.0000 0.9528 0.000 1.000
#> GSM1009125 1 0.8081 0.6476 0.752 0.248
#> GSM1009139 1 0.0000 0.9733 1.000 0.000
#> GSM1009153 1 0.0000 0.9733 1.000 0.000
#> GSM1009167 2 0.0000 0.9528 0.000 1.000
#> GSM1009181 1 0.9815 0.2409 0.580 0.420
#> GSM1009195 1 0.0000 0.9733 1.000 0.000
#> GSM1009070 1 0.0000 0.9733 1.000 0.000
#> GSM1009084 2 0.4815 0.8856 0.104 0.896
#> GSM1009098 1 0.0000 0.9733 1.000 0.000
#> GSM1009112 2 0.0000 0.9528 0.000 1.000
#> GSM1009126 1 0.0000 0.9733 1.000 0.000
#> GSM1009140 1 0.0000 0.9733 1.000 0.000
#> GSM1009154 1 0.0000 0.9733 1.000 0.000
#> GSM1009168 2 0.0000 0.9528 0.000 1.000
#> GSM1009182 1 0.0000 0.9733 1.000 0.000
#> GSM1009196 1 0.0000 0.9733 1.000 0.000
#> GSM1009071 1 0.0000 0.9733 1.000 0.000
#> GSM1009085 2 0.4690 0.8890 0.100 0.900
#> GSM1009099 1 0.0000 0.9733 1.000 0.000
#> GSM1009113 2 0.0000 0.9528 0.000 1.000
#> GSM1009127 1 0.0000 0.9733 1.000 0.000
#> GSM1009141 1 0.0000 0.9733 1.000 0.000
#> GSM1009155 1 0.0000 0.9733 1.000 0.000
#> GSM1009169 2 0.0000 0.9528 0.000 1.000
#> GSM1009183 1 0.9087 0.4956 0.676 0.324
#> GSM1009197 1 0.0000 0.9733 1.000 0.000
#> GSM1009072 1 0.0000 0.9733 1.000 0.000
#> GSM1009086 2 0.2423 0.9308 0.040 0.960
#> GSM1009100 1 0.0000 0.9733 1.000 0.000
#> GSM1009114 2 0.0000 0.9528 0.000 1.000
#> GSM1009128 1 0.0000 0.9733 1.000 0.000
#> GSM1009142 1 0.0000 0.9733 1.000 0.000
#> GSM1009156 1 0.0000 0.9733 1.000 0.000
#> GSM1009170 2 0.0000 0.9528 0.000 1.000
#> GSM1009184 1 0.0000 0.9733 1.000 0.000
#> GSM1009198 1 0.0000 0.9733 1.000 0.000
#> GSM1009073 1 0.0000 0.9733 1.000 0.000
#> GSM1009087 1 0.0672 0.9660 0.992 0.008
#> GSM1009101 1 0.0000 0.9733 1.000 0.000
#> GSM1009115 2 0.0000 0.9528 0.000 1.000
#> GSM1009129 2 0.9866 0.2709 0.432 0.568
#> GSM1009143 1 0.0000 0.9733 1.000 0.000
#> GSM1009157 1 0.0000 0.9733 1.000 0.000
#> GSM1009171 2 0.0000 0.9528 0.000 1.000
#> GSM1009185 1 0.0000 0.9733 1.000 0.000
#> GSM1009199 1 0.0000 0.9733 1.000 0.000
#> GSM1009074 1 0.0000 0.9733 1.000 0.000
#> GSM1009088 1 0.5946 0.8123 0.856 0.144
#> GSM1009102 1 0.0000 0.9733 1.000 0.000
#> GSM1009116 2 0.0000 0.9528 0.000 1.000
#> GSM1009130 2 0.5629 0.8587 0.132 0.868
#> GSM1009144 1 0.0000 0.9733 1.000 0.000
#> GSM1009158 1 0.0000 0.9733 1.000 0.000
#> GSM1009172 2 0.0000 0.9528 0.000 1.000
#> GSM1009186 1 0.0000 0.9733 1.000 0.000
#> GSM1009200 1 0.0000 0.9733 1.000 0.000
#> GSM1009075 1 0.0000 0.9733 1.000 0.000
#> GSM1009089 1 0.0000 0.9733 1.000 0.000
#> GSM1009103 1 0.0000 0.9733 1.000 0.000
#> GSM1009117 2 0.0000 0.9528 0.000 1.000
#> GSM1009131 1 0.0000 0.9733 1.000 0.000
#> GSM1009145 1 0.0000 0.9733 1.000 0.000
#> GSM1009159 1 0.0000 0.9733 1.000 0.000
#> GSM1009173 2 0.0000 0.9528 0.000 1.000
#> GSM1009187 1 0.0000 0.9733 1.000 0.000
#> GSM1009201 1 0.0000 0.9733 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1009062 1 0.0424 0.967 0.992 0.008 0.000
#> GSM1009076 2 0.0000 0.961 0.000 1.000 0.000
#> GSM1009090 1 0.0000 0.972 1.000 0.000 0.000
#> GSM1009104 2 0.0237 0.960 0.000 0.996 0.004
#> GSM1009118 1 0.1289 0.945 0.968 0.032 0.000
#> GSM1009132 1 0.0000 0.972 1.000 0.000 0.000
#> GSM1009146 1 0.0000 0.972 1.000 0.000 0.000
#> GSM1009160 3 0.0000 1.000 0.000 0.000 1.000
#> GSM1009174 2 0.0000 0.961 0.000 1.000 0.000
#> GSM1009188 1 0.0000 0.972 1.000 0.000 0.000
#> GSM1009063 1 0.0892 0.958 0.980 0.020 0.000
#> GSM1009077 2 0.0000 0.961 0.000 1.000 0.000
#> GSM1009091 1 0.0000 0.972 1.000 0.000 0.000
#> GSM1009105 2 0.0237 0.960 0.000 0.996 0.004
#> GSM1009119 1 0.0000 0.972 1.000 0.000 0.000
#> GSM1009133 1 0.0000 0.972 1.000 0.000 0.000
#> GSM1009147 1 0.0000 0.972 1.000 0.000 0.000
#> GSM1009161 3 0.0000 1.000 0.000 0.000 1.000
#> GSM1009175 2 0.0000 0.961 0.000 1.000 0.000
#> GSM1009189 1 0.0000 0.972 1.000 0.000 0.000
#> GSM1009064 1 0.2448 0.901 0.924 0.076 0.000
#> GSM1009078 2 0.0000 0.961 0.000 1.000 0.000
#> GSM1009092 1 0.0000 0.972 1.000 0.000 0.000
#> GSM1009106 2 0.0237 0.960 0.000 0.996 0.004
#> GSM1009120 1 0.0000 0.972 1.000 0.000 0.000
#> GSM1009134 1 0.0000 0.972 1.000 0.000 0.000
#> GSM1009148 1 0.0237 0.970 0.996 0.004 0.000
#> GSM1009162 3 0.0000 1.000 0.000 0.000 1.000
#> GSM1009176 2 0.0000 0.961 0.000 1.000 0.000
#> GSM1009190 1 0.0000 0.972 1.000 0.000 0.000
#> GSM1009065 1 0.3941 0.802 0.844 0.156 0.000
#> GSM1009079 2 0.0000 0.961 0.000 1.000 0.000
#> GSM1009093 1 0.0000 0.972 1.000 0.000 0.000
#> GSM1009107 2 0.0237 0.960 0.000 0.996 0.004
#> GSM1009121 1 0.0000 0.972 1.000 0.000 0.000
#> GSM1009135 1 0.0000 0.972 1.000 0.000 0.000
#> GSM1009149 1 0.0000 0.972 1.000 0.000 0.000
#> GSM1009163 3 0.0000 1.000 0.000 0.000 1.000
#> GSM1009177 2 0.0000 0.961 0.000 1.000 0.000
#> GSM1009191 1 0.1529 0.939 0.960 0.040 0.000
#> GSM1009066 1 0.1289 0.947 0.968 0.032 0.000
#> GSM1009080 2 0.0000 0.961 0.000 1.000 0.000
#> GSM1009094 1 0.0000 0.972 1.000 0.000 0.000
#> GSM1009108 2 0.0237 0.960 0.000 0.996 0.004
#> GSM1009122 1 0.6252 0.208 0.556 0.444 0.000
#> GSM1009136 1 0.0000 0.972 1.000 0.000 0.000
#> GSM1009150 1 0.0000 0.972 1.000 0.000 0.000
#> GSM1009164 3 0.0000 1.000 0.000 0.000 1.000
#> GSM1009178 2 0.0237 0.958 0.004 0.996 0.000
#> GSM1009192 1 0.0000 0.972 1.000 0.000 0.000
#> GSM1009067 1 0.0424 0.967 0.992 0.008 0.000
#> GSM1009081 2 0.0000 0.961 0.000 1.000 0.000
#> GSM1009095 1 0.0000 0.972 1.000 0.000 0.000
#> GSM1009109 2 0.0237 0.960 0.000 0.996 0.004
#> GSM1009123 1 0.0000 0.972 1.000 0.000 0.000
#> GSM1009137 1 0.0000 0.972 1.000 0.000 0.000
#> GSM1009151 1 0.0237 0.970 0.996 0.004 0.000
#> GSM1009165 3 0.0000 1.000 0.000 0.000 1.000
#> GSM1009179 2 0.0237 0.958 0.004 0.996 0.000
#> GSM1009193 1 0.0000 0.972 1.000 0.000 0.000
#> GSM1009068 1 0.0237 0.970 0.996 0.004 0.000
#> GSM1009082 2 0.0000 0.961 0.000 1.000 0.000
#> GSM1009096 1 0.0000 0.972 1.000 0.000 0.000
#> GSM1009110 2 0.0237 0.960 0.000 0.996 0.004
#> GSM1009124 1 0.0000 0.972 1.000 0.000 0.000
#> GSM1009138 1 0.0000 0.972 1.000 0.000 0.000
#> GSM1009152 1 0.0000 0.972 1.000 0.000 0.000
#> GSM1009166 3 0.0000 1.000 0.000 0.000 1.000
#> GSM1009180 2 0.0237 0.958 0.004 0.996 0.000
#> GSM1009194 1 0.0000 0.972 1.000 0.000 0.000
#> GSM1009069 2 0.1753 0.906 0.048 0.952 0.000
#> GSM1009083 2 0.0000 0.961 0.000 1.000 0.000
#> GSM1009097 1 0.0000 0.972 1.000 0.000 0.000
#> GSM1009111 2 0.0237 0.960 0.000 0.996 0.004
#> GSM1009125 1 0.9532 0.152 0.488 0.244 0.268
#> GSM1009139 1 0.0000 0.972 1.000 0.000 0.000
#> GSM1009153 1 0.0237 0.970 0.996 0.004 0.000
#> GSM1009167 3 0.0000 1.000 0.000 0.000 1.000
#> GSM1009181 2 0.0000 0.961 0.000 1.000 0.000
#> GSM1009195 1 0.6168 0.306 0.588 0.412 0.000
#> GSM1009070 1 0.0424 0.967 0.992 0.008 0.000
#> GSM1009084 2 0.0000 0.961 0.000 1.000 0.000
#> GSM1009098 1 0.0000 0.972 1.000 0.000 0.000
#> GSM1009112 2 0.0237 0.960 0.000 0.996 0.004
#> GSM1009126 1 0.0000 0.972 1.000 0.000 0.000
#> GSM1009140 1 0.0000 0.972 1.000 0.000 0.000
#> GSM1009154 1 0.0000 0.972 1.000 0.000 0.000
#> GSM1009168 3 0.0000 1.000 0.000 0.000 1.000
#> GSM1009182 2 0.0000 0.961 0.000 1.000 0.000
#> GSM1009196 1 0.0000 0.972 1.000 0.000 0.000
#> GSM1009071 1 0.2356 0.906 0.928 0.072 0.000
#> GSM1009085 2 0.0000 0.961 0.000 1.000 0.000
#> GSM1009099 1 0.0000 0.972 1.000 0.000 0.000
#> GSM1009113 2 0.0237 0.960 0.000 0.996 0.004
#> GSM1009127 1 0.0000 0.972 1.000 0.000 0.000
#> GSM1009141 1 0.0000 0.972 1.000 0.000 0.000
#> GSM1009155 1 0.0424 0.967 0.992 0.008 0.000
#> GSM1009169 3 0.0000 1.000 0.000 0.000 1.000
#> GSM1009183 2 0.0000 0.961 0.000 1.000 0.000
#> GSM1009197 1 0.0000 0.972 1.000 0.000 0.000
#> GSM1009072 1 0.0592 0.964 0.988 0.012 0.000
#> GSM1009086 2 0.0000 0.961 0.000 1.000 0.000
#> GSM1009100 1 0.0000 0.972 1.000 0.000 0.000
#> GSM1009114 2 0.0747 0.950 0.000 0.984 0.016
#> GSM1009128 1 0.0747 0.960 0.984 0.000 0.016
#> GSM1009142 1 0.0000 0.972 1.000 0.000 0.000
#> GSM1009156 2 0.6308 0.024 0.492 0.508 0.000
#> GSM1009170 3 0.0000 1.000 0.000 0.000 1.000
#> GSM1009184 2 0.0000 0.961 0.000 1.000 0.000
#> GSM1009198 1 0.0000 0.972 1.000 0.000 0.000
#> GSM1009073 1 0.1031 0.954 0.976 0.024 0.000
#> GSM1009087 2 0.0000 0.961 0.000 1.000 0.000
#> GSM1009101 1 0.0000 0.972 1.000 0.000 0.000
#> GSM1009115 2 0.0592 0.954 0.000 0.988 0.012
#> GSM1009129 2 0.1129 0.941 0.020 0.976 0.004
#> GSM1009143 1 0.0000 0.972 1.000 0.000 0.000
#> GSM1009157 2 0.6260 0.165 0.448 0.552 0.000
#> GSM1009171 3 0.0000 1.000 0.000 0.000 1.000
#> GSM1009185 2 0.0237 0.958 0.004 0.996 0.000
#> GSM1009199 1 0.2165 0.914 0.936 0.064 0.000
#> GSM1009074 1 0.0592 0.964 0.988 0.012 0.000
#> GSM1009088 2 0.0000 0.961 0.000 1.000 0.000
#> GSM1009102 1 0.0000 0.972 1.000 0.000 0.000
#> GSM1009116 2 0.0237 0.960 0.000 0.996 0.004
#> GSM1009130 2 0.0237 0.960 0.000 0.996 0.004
#> GSM1009144 1 0.0000 0.972 1.000 0.000 0.000
#> GSM1009158 1 0.0000 0.972 1.000 0.000 0.000
#> GSM1009172 3 0.0000 1.000 0.000 0.000 1.000
#> GSM1009186 2 0.0000 0.961 0.000 1.000 0.000
#> GSM1009200 1 0.0000 0.972 1.000 0.000 0.000
#> GSM1009075 1 0.0424 0.967 0.992 0.008 0.000
#> GSM1009089 2 0.0237 0.958 0.004 0.996 0.000
#> GSM1009103 1 0.0000 0.972 1.000 0.000 0.000
#> GSM1009117 2 0.0237 0.960 0.000 0.996 0.004
#> GSM1009131 2 0.4235 0.721 0.176 0.824 0.000
#> GSM1009145 1 0.0000 0.972 1.000 0.000 0.000
#> GSM1009159 1 0.0000 0.972 1.000 0.000 0.000
#> GSM1009173 3 0.0000 1.000 0.000 0.000 1.000
#> GSM1009187 2 0.1163 0.932 0.028 0.972 0.000
#> GSM1009201 1 0.0000 0.972 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1009062 1 0.1118 0.8749 0.964 0.000 0.000 0.036
#> GSM1009076 2 0.0000 0.8688 0.000 1.000 0.000 0.000
#> GSM1009090 4 0.0000 0.8665 0.000 0.000 0.000 1.000
#> GSM1009104 2 0.0000 0.8688 0.000 1.000 0.000 0.000
#> GSM1009118 4 0.3881 0.7423 0.172 0.016 0.000 0.812
#> GSM1009132 4 0.0000 0.8665 0.000 0.000 0.000 1.000
#> GSM1009146 1 0.0000 0.8903 1.000 0.000 0.000 0.000
#> GSM1009160 3 0.0000 1.0000 0.000 0.000 1.000 0.000
#> GSM1009174 2 0.4382 0.6704 0.296 0.704 0.000 0.000
#> GSM1009188 4 0.4697 0.4780 0.356 0.000 0.000 0.644
#> GSM1009063 1 0.0336 0.8899 0.992 0.000 0.000 0.008
#> GSM1009077 2 0.0188 0.8684 0.004 0.996 0.000 0.000
#> GSM1009091 4 0.0000 0.8665 0.000 0.000 0.000 1.000
#> GSM1009105 2 0.0000 0.8688 0.000 1.000 0.000 0.000
#> GSM1009119 4 0.4406 0.5811 0.300 0.000 0.000 0.700
#> GSM1009133 4 0.0000 0.8665 0.000 0.000 0.000 1.000
#> GSM1009147 1 0.0000 0.8903 1.000 0.000 0.000 0.000
#> GSM1009161 3 0.0000 1.0000 0.000 0.000 1.000 0.000
#> GSM1009175 2 0.4103 0.7237 0.256 0.744 0.000 0.000
#> GSM1009189 4 0.4977 0.2081 0.460 0.000 0.000 0.540
#> GSM1009064 1 0.0469 0.8892 0.988 0.000 0.000 0.012
#> GSM1009078 1 0.4941 0.1892 0.564 0.436 0.000 0.000
#> GSM1009092 4 0.0188 0.8644 0.004 0.000 0.000 0.996
#> GSM1009106 2 0.0000 0.8688 0.000 1.000 0.000 0.000
#> GSM1009120 1 0.4843 0.2800 0.604 0.000 0.000 0.396
#> GSM1009134 4 0.0000 0.8665 0.000 0.000 0.000 1.000
#> GSM1009148 1 0.0000 0.8903 1.000 0.000 0.000 0.000
#> GSM1009162 3 0.0000 1.0000 0.000 0.000 1.000 0.000
#> GSM1009176 2 0.2814 0.8333 0.132 0.868 0.000 0.000
#> GSM1009190 1 0.4996 -0.0411 0.516 0.000 0.000 0.484
#> GSM1009065 1 0.0336 0.8899 0.992 0.000 0.000 0.008
#> GSM1009079 2 0.0000 0.8688 0.000 1.000 0.000 0.000
#> GSM1009093 4 0.0000 0.8665 0.000 0.000 0.000 1.000
#> GSM1009107 2 0.0000 0.8688 0.000 1.000 0.000 0.000
#> GSM1009121 4 0.2281 0.8129 0.096 0.000 0.000 0.904
#> GSM1009135 4 0.0000 0.8665 0.000 0.000 0.000 1.000
#> GSM1009149 1 0.2589 0.8078 0.884 0.000 0.000 0.116
#> GSM1009163 3 0.0000 1.0000 0.000 0.000 1.000 0.000
#> GSM1009177 2 0.2814 0.8335 0.132 0.868 0.000 0.000
#> GSM1009191 1 0.2216 0.8325 0.908 0.000 0.000 0.092
#> GSM1009066 1 0.0707 0.8856 0.980 0.000 0.000 0.020
#> GSM1009080 2 0.0000 0.8688 0.000 1.000 0.000 0.000
#> GSM1009094 4 0.0000 0.8665 0.000 0.000 0.000 1.000
#> GSM1009108 2 0.0000 0.8688 0.000 1.000 0.000 0.000
#> GSM1009122 4 0.7497 -0.0331 0.180 0.396 0.000 0.424
#> GSM1009136 4 0.0000 0.8665 0.000 0.000 0.000 1.000
#> GSM1009150 1 0.0707 0.8843 0.980 0.000 0.000 0.020
#> GSM1009164 3 0.0000 1.0000 0.000 0.000 1.000 0.000
#> GSM1009178 2 0.4543 0.6261 0.324 0.676 0.000 0.000
#> GSM1009192 1 0.2216 0.8321 0.908 0.000 0.000 0.092
#> GSM1009067 1 0.0592 0.8875 0.984 0.000 0.000 0.016
#> GSM1009081 2 0.0000 0.8688 0.000 1.000 0.000 0.000
#> GSM1009095 4 0.0000 0.8665 0.000 0.000 0.000 1.000
#> GSM1009109 2 0.0000 0.8688 0.000 1.000 0.000 0.000
#> GSM1009123 4 0.3123 0.7666 0.156 0.000 0.000 0.844
#> GSM1009137 4 0.0000 0.8665 0.000 0.000 0.000 1.000
#> GSM1009151 1 0.0000 0.8903 1.000 0.000 0.000 0.000
#> GSM1009165 3 0.0000 1.0000 0.000 0.000 1.000 0.000
#> GSM1009179 2 0.4564 0.6187 0.328 0.672 0.000 0.000
#> GSM1009193 4 0.4999 0.0935 0.492 0.000 0.000 0.508
#> GSM1009068 1 0.1302 0.8685 0.956 0.000 0.000 0.044
#> GSM1009082 2 0.1474 0.8574 0.052 0.948 0.000 0.000
#> GSM1009096 4 0.0000 0.8665 0.000 0.000 0.000 1.000
#> GSM1009110 2 0.0000 0.8688 0.000 1.000 0.000 0.000
#> GSM1009124 4 0.4008 0.6648 0.244 0.000 0.000 0.756
#> GSM1009138 4 0.0000 0.8665 0.000 0.000 0.000 1.000
#> GSM1009152 1 0.0000 0.8903 1.000 0.000 0.000 0.000
#> GSM1009166 3 0.0000 1.0000 0.000 0.000 1.000 0.000
#> GSM1009180 2 0.4304 0.6887 0.284 0.716 0.000 0.000
#> GSM1009194 1 0.0000 0.8903 1.000 0.000 0.000 0.000
#> GSM1009069 1 0.0927 0.8826 0.976 0.016 0.000 0.008
#> GSM1009083 2 0.2216 0.8333 0.092 0.908 0.000 0.000
#> GSM1009097 4 0.0000 0.8665 0.000 0.000 0.000 1.000
#> GSM1009111 2 0.0000 0.8688 0.000 1.000 0.000 0.000
#> GSM1009125 4 0.4540 0.5719 0.004 0.248 0.008 0.740
#> GSM1009139 4 0.0000 0.8665 0.000 0.000 0.000 1.000
#> GSM1009153 1 0.0000 0.8903 1.000 0.000 0.000 0.000
#> GSM1009167 3 0.0000 1.0000 0.000 0.000 1.000 0.000
#> GSM1009181 2 0.2530 0.8405 0.112 0.888 0.000 0.000
#> GSM1009195 1 0.0000 0.8903 1.000 0.000 0.000 0.000
#> GSM1009070 1 0.0469 0.8892 0.988 0.000 0.000 0.012
#> GSM1009084 2 0.0188 0.8684 0.004 0.996 0.000 0.000
#> GSM1009098 4 0.0000 0.8665 0.000 0.000 0.000 1.000
#> GSM1009112 2 0.0000 0.8688 0.000 1.000 0.000 0.000
#> GSM1009126 4 0.3528 0.7290 0.192 0.000 0.000 0.808
#> GSM1009140 4 0.0000 0.8665 0.000 0.000 0.000 1.000
#> GSM1009154 1 0.0000 0.8903 1.000 0.000 0.000 0.000
#> GSM1009168 3 0.0000 1.0000 0.000 0.000 1.000 0.000
#> GSM1009182 2 0.4277 0.6932 0.280 0.720 0.000 0.000
#> GSM1009196 1 0.0000 0.8903 1.000 0.000 0.000 0.000
#> GSM1009071 1 0.0469 0.8892 0.988 0.000 0.000 0.012
#> GSM1009085 2 0.0592 0.8649 0.016 0.984 0.000 0.000
#> GSM1009099 4 0.0188 0.8644 0.004 0.000 0.000 0.996
#> GSM1009113 2 0.0000 0.8688 0.000 1.000 0.000 0.000
#> GSM1009127 4 0.4605 0.5211 0.336 0.000 0.000 0.664
#> GSM1009141 4 0.0000 0.8665 0.000 0.000 0.000 1.000
#> GSM1009155 1 0.0000 0.8903 1.000 0.000 0.000 0.000
#> GSM1009169 3 0.0000 1.0000 0.000 0.000 1.000 0.000
#> GSM1009183 2 0.2760 0.8346 0.128 0.872 0.000 0.000
#> GSM1009197 1 0.4713 0.3816 0.640 0.000 0.000 0.360
#> GSM1009072 1 0.1302 0.8685 0.956 0.000 0.000 0.044
#> GSM1009086 2 0.0000 0.8688 0.000 1.000 0.000 0.000
#> GSM1009100 4 0.0000 0.8665 0.000 0.000 0.000 1.000
#> GSM1009114 2 0.0000 0.8688 0.000 1.000 0.000 0.000
#> GSM1009128 4 0.0921 0.8531 0.028 0.000 0.000 0.972
#> GSM1009142 4 0.0000 0.8665 0.000 0.000 0.000 1.000
#> GSM1009156 1 0.0000 0.8903 1.000 0.000 0.000 0.000
#> GSM1009170 3 0.0000 1.0000 0.000 0.000 1.000 0.000
#> GSM1009184 2 0.4713 0.5548 0.360 0.640 0.000 0.000
#> GSM1009198 4 0.4790 0.4256 0.380 0.000 0.000 0.620
#> GSM1009073 1 0.0469 0.8892 0.988 0.000 0.000 0.012
#> GSM1009087 1 0.4713 0.4041 0.640 0.360 0.000 0.000
#> GSM1009101 4 0.0000 0.8665 0.000 0.000 0.000 1.000
#> GSM1009115 2 0.0000 0.8688 0.000 1.000 0.000 0.000
#> GSM1009129 2 0.3400 0.7954 0.180 0.820 0.000 0.000
#> GSM1009143 4 0.0000 0.8665 0.000 0.000 0.000 1.000
#> GSM1009157 1 0.0000 0.8903 1.000 0.000 0.000 0.000
#> GSM1009171 3 0.0000 1.0000 0.000 0.000 1.000 0.000
#> GSM1009185 2 0.4134 0.7202 0.260 0.740 0.000 0.000
#> GSM1009199 1 0.1022 0.8785 0.968 0.000 0.000 0.032
#> GSM1009074 1 0.0336 0.8899 0.992 0.000 0.000 0.008
#> GSM1009088 1 0.4907 0.2610 0.580 0.420 0.000 0.000
#> GSM1009102 4 0.0000 0.8665 0.000 0.000 0.000 1.000
#> GSM1009116 2 0.0000 0.8688 0.000 1.000 0.000 0.000
#> GSM1009130 2 0.3311 0.8019 0.172 0.828 0.000 0.000
#> GSM1009144 4 0.0000 0.8665 0.000 0.000 0.000 1.000
#> GSM1009158 1 0.0000 0.8903 1.000 0.000 0.000 0.000
#> GSM1009172 3 0.0000 1.0000 0.000 0.000 1.000 0.000
#> GSM1009186 2 0.4697 0.5632 0.356 0.644 0.000 0.000
#> GSM1009200 4 0.4941 0.2808 0.436 0.000 0.000 0.564
#> GSM1009075 1 0.0707 0.8855 0.980 0.000 0.000 0.020
#> GSM1009089 1 0.0817 0.8794 0.976 0.024 0.000 0.000
#> GSM1009103 4 0.0000 0.8665 0.000 0.000 0.000 1.000
#> GSM1009117 2 0.0000 0.8688 0.000 1.000 0.000 0.000
#> GSM1009131 2 0.5494 0.7103 0.208 0.716 0.000 0.076
#> GSM1009145 4 0.0000 0.8665 0.000 0.000 0.000 1.000
#> GSM1009159 1 0.2704 0.7989 0.876 0.000 0.000 0.124
#> GSM1009173 3 0.0000 1.0000 0.000 0.000 1.000 0.000
#> GSM1009187 1 0.4948 0.0440 0.560 0.440 0.000 0.000
#> GSM1009201 1 0.4134 0.6007 0.740 0.000 0.000 0.260
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM1009062 1 0.0000 0.8210 1.000 0.000 0 0.000 0.000
#> GSM1009076 5 0.2378 0.8852 0.048 0.048 0 0.000 0.904
#> GSM1009090 4 0.0290 0.9204 0.000 0.008 0 0.992 0.000
#> GSM1009104 5 0.0162 0.9018 0.000 0.004 0 0.000 0.996
#> GSM1009118 2 0.6497 0.2414 0.040 0.460 0 0.424 0.076
#> GSM1009132 4 0.0290 0.9217 0.008 0.000 0 0.992 0.000
#> GSM1009146 2 0.4300 0.2758 0.476 0.524 0 0.000 0.000
#> GSM1009160 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000
#> GSM1009174 2 0.2286 0.5667 0.004 0.888 0 0.000 0.108
#> GSM1009188 2 0.5355 0.5429 0.084 0.624 0 0.292 0.000
#> GSM1009063 1 0.0000 0.8210 1.000 0.000 0 0.000 0.000
#> GSM1009077 5 0.2863 0.8753 0.060 0.064 0 0.000 0.876
#> GSM1009091 4 0.0510 0.9186 0.000 0.016 0 0.984 0.000
#> GSM1009105 5 0.0162 0.9018 0.000 0.004 0 0.000 0.996
#> GSM1009119 2 0.6154 0.4706 0.144 0.508 0 0.348 0.000
#> GSM1009133 4 0.0290 0.9217 0.008 0.000 0 0.992 0.000
#> GSM1009147 2 0.3521 0.5614 0.232 0.764 0 0.004 0.000
#> GSM1009161 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000
#> GSM1009175 2 0.1831 0.5895 0.004 0.920 0 0.000 0.076
#> GSM1009189 2 0.5472 0.5723 0.156 0.656 0 0.188 0.000
#> GSM1009064 1 0.0000 0.8210 1.000 0.000 0 0.000 0.000
#> GSM1009078 5 0.4130 0.6825 0.292 0.012 0 0.000 0.696
#> GSM1009092 4 0.0880 0.9082 0.000 0.032 0 0.968 0.000
#> GSM1009106 5 0.0162 0.9018 0.000 0.004 0 0.000 0.996
#> GSM1009120 2 0.5922 0.3885 0.388 0.504 0 0.108 0.000
#> GSM1009134 4 0.0290 0.9217 0.008 0.000 0 0.992 0.000
#> GSM1009148 2 0.4304 0.2594 0.484 0.516 0 0.000 0.000
#> GSM1009162 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000
#> GSM1009176 2 0.3550 0.4369 0.004 0.760 0 0.000 0.236
#> GSM1009190 2 0.4322 0.6086 0.088 0.768 0 0.144 0.000
#> GSM1009065 1 0.0000 0.8210 1.000 0.000 0 0.000 0.000
#> GSM1009079 5 0.3039 0.8130 0.012 0.152 0 0.000 0.836
#> GSM1009093 4 0.0794 0.9111 0.000 0.028 0 0.972 0.000
#> GSM1009107 5 0.0162 0.9018 0.000 0.004 0 0.000 0.996
#> GSM1009121 4 0.4262 0.0189 0.000 0.440 0 0.560 0.000
#> GSM1009135 4 0.0290 0.9217 0.008 0.000 0 0.992 0.000
#> GSM1009149 2 0.4740 0.2942 0.468 0.516 0 0.016 0.000
#> GSM1009163 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000
#> GSM1009177 2 0.3521 0.4433 0.004 0.764 0 0.000 0.232
#> GSM1009191 2 0.2006 0.6209 0.072 0.916 0 0.012 0.000
#> GSM1009066 1 0.0000 0.8210 1.000 0.000 0 0.000 0.000
#> GSM1009080 5 0.2964 0.8439 0.024 0.120 0 0.000 0.856
#> GSM1009094 4 0.0510 0.9186 0.000 0.016 0 0.984 0.000
#> GSM1009108 5 0.0162 0.9018 0.000 0.004 0 0.000 0.996
#> GSM1009122 2 0.6763 0.3593 0.016 0.504 0 0.284 0.196
#> GSM1009136 4 0.0290 0.9217 0.008 0.000 0 0.992 0.000
#> GSM1009150 2 0.4559 0.2722 0.480 0.512 0 0.008 0.000
#> GSM1009164 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000
#> GSM1009178 2 0.0566 0.6114 0.004 0.984 0 0.000 0.012
#> GSM1009192 2 0.4793 0.3488 0.436 0.544 0 0.020 0.000
#> GSM1009067 1 0.0000 0.8210 1.000 0.000 0 0.000 0.000
#> GSM1009081 5 0.1750 0.8916 0.028 0.036 0 0.000 0.936
#> GSM1009095 4 0.0162 0.9211 0.000 0.004 0 0.996 0.000
#> GSM1009109 5 0.0162 0.9018 0.000 0.004 0 0.000 0.996
#> GSM1009123 4 0.4907 -0.2139 0.024 0.484 0 0.492 0.000
#> GSM1009137 4 0.0290 0.9217 0.008 0.000 0 0.992 0.000
#> GSM1009151 1 0.4306 -0.2580 0.508 0.492 0 0.000 0.000
#> GSM1009165 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000
#> GSM1009179 2 0.0771 0.6096 0.004 0.976 0 0.000 0.020
#> GSM1009193 2 0.6279 0.4730 0.280 0.528 0 0.192 0.000
#> GSM1009068 1 0.0162 0.8177 0.996 0.000 0 0.004 0.000
#> GSM1009082 5 0.3064 0.8635 0.108 0.036 0 0.000 0.856
#> GSM1009096 4 0.0510 0.9186 0.000 0.016 0 0.984 0.000
#> GSM1009110 5 0.0162 0.9018 0.000 0.004 0 0.000 0.996
#> GSM1009124 2 0.5111 0.3795 0.040 0.552 0 0.408 0.000
#> GSM1009138 4 0.0290 0.9217 0.008 0.000 0 0.992 0.000
#> GSM1009152 1 0.4300 -0.2181 0.524 0.476 0 0.000 0.000
#> GSM1009166 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000
#> GSM1009180 2 0.0671 0.6115 0.004 0.980 0 0.000 0.016
#> GSM1009194 2 0.4397 0.3564 0.432 0.564 0 0.004 0.000
#> GSM1009069 1 0.0162 0.8174 0.996 0.004 0 0.000 0.000
#> GSM1009083 5 0.3513 0.8114 0.180 0.020 0 0.000 0.800
#> GSM1009097 4 0.0880 0.9082 0.000 0.032 0 0.968 0.000
#> GSM1009111 5 0.0162 0.9018 0.000 0.004 0 0.000 0.996
#> GSM1009125 4 0.5752 0.4760 0.000 0.208 0 0.620 0.172
#> GSM1009139 4 0.0290 0.9217 0.008 0.000 0 0.992 0.000
#> GSM1009153 1 0.4300 -0.2175 0.524 0.476 0 0.000 0.000
#> GSM1009167 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000
#> GSM1009181 2 0.3579 0.4296 0.004 0.756 0 0.000 0.240
#> GSM1009195 2 0.2338 0.6129 0.112 0.884 0 0.000 0.004
#> GSM1009070 1 0.0000 0.8210 1.000 0.000 0 0.000 0.000
#> GSM1009084 5 0.2046 0.8851 0.068 0.016 0 0.000 0.916
#> GSM1009098 4 0.0510 0.9186 0.000 0.016 0 0.984 0.000
#> GSM1009112 5 0.0162 0.9018 0.000 0.004 0 0.000 0.996
#> GSM1009126 2 0.4744 0.2248 0.016 0.508 0 0.476 0.000
#> GSM1009140 4 0.0290 0.9217 0.008 0.000 0 0.992 0.000
#> GSM1009154 2 0.4451 0.2444 0.492 0.504 0 0.004 0.000
#> GSM1009168 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000
#> GSM1009182 2 0.1638 0.5953 0.004 0.932 0 0.000 0.064
#> GSM1009196 2 0.4118 0.4774 0.336 0.660 0 0.004 0.000
#> GSM1009071 1 0.0000 0.8210 1.000 0.000 0 0.000 0.000
#> GSM1009085 5 0.2351 0.8770 0.088 0.016 0 0.000 0.896
#> GSM1009099 4 0.0880 0.9082 0.000 0.032 0 0.968 0.000
#> GSM1009113 5 0.0162 0.9018 0.000 0.004 0 0.000 0.996
#> GSM1009127 2 0.6372 0.4613 0.184 0.492 0 0.324 0.000
#> GSM1009141 4 0.0290 0.9217 0.008 0.000 0 0.992 0.000
#> GSM1009155 1 0.4262 -0.1296 0.560 0.440 0 0.000 0.000
#> GSM1009169 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000
#> GSM1009183 2 0.2970 0.5225 0.004 0.828 0 0.000 0.168
#> GSM1009197 2 0.5683 0.4944 0.304 0.588 0 0.108 0.000
#> GSM1009072 1 0.0162 0.8177 0.996 0.000 0 0.004 0.000
#> GSM1009086 5 0.1661 0.8935 0.036 0.024 0 0.000 0.940
#> GSM1009100 4 0.0510 0.9186 0.000 0.016 0 0.984 0.000
#> GSM1009114 5 0.0162 0.9018 0.000 0.004 0 0.000 0.996
#> GSM1009128 4 0.4015 0.3363 0.000 0.348 0 0.652 0.000
#> GSM1009142 4 0.0290 0.9217 0.008 0.000 0 0.992 0.000
#> GSM1009156 2 0.2074 0.6085 0.104 0.896 0 0.000 0.000
#> GSM1009170 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000
#> GSM1009184 2 0.1894 0.5889 0.008 0.920 0 0.000 0.072
#> GSM1009198 2 0.4878 0.5649 0.060 0.676 0 0.264 0.000
#> GSM1009073 1 0.0000 0.8210 1.000 0.000 0 0.000 0.000
#> GSM1009087 5 0.4090 0.7105 0.268 0.016 0 0.000 0.716
#> GSM1009101 4 0.0510 0.9186 0.000 0.016 0 0.984 0.000
#> GSM1009115 5 0.0162 0.9018 0.000 0.004 0 0.000 0.996
#> GSM1009129 5 0.4323 0.6228 0.028 0.240 0 0.004 0.728
#> GSM1009143 4 0.0290 0.9217 0.008 0.000 0 0.992 0.000
#> GSM1009157 2 0.4294 0.2820 0.468 0.532 0 0.000 0.000
#> GSM1009171 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000
#> GSM1009185 2 0.0566 0.6114 0.004 0.984 0 0.000 0.012
#> GSM1009199 2 0.1638 0.6199 0.064 0.932 0 0.004 0.000
#> GSM1009074 1 0.0000 0.8210 1.000 0.000 0 0.000 0.000
#> GSM1009088 5 0.4065 0.7151 0.264 0.016 0 0.000 0.720
#> GSM1009102 4 0.0162 0.9211 0.000 0.004 0 0.996 0.000
#> GSM1009116 5 0.0162 0.9018 0.000 0.004 0 0.000 0.996
#> GSM1009130 5 0.2694 0.8427 0.032 0.076 0 0.004 0.888
#> GSM1009144 4 0.0290 0.9217 0.008 0.000 0 0.992 0.000
#> GSM1009158 2 0.4305 0.2508 0.488 0.512 0 0.000 0.000
#> GSM1009172 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000
#> GSM1009186 2 0.2017 0.5841 0.008 0.912 0 0.000 0.080
#> GSM1009200 2 0.4763 0.5878 0.076 0.712 0 0.212 0.000
#> GSM1009075 1 0.0000 0.8210 1.000 0.000 0 0.000 0.000
#> GSM1009089 1 0.4514 0.5803 0.740 0.072 0 0.000 0.188
#> GSM1009103 4 0.0324 0.9215 0.004 0.004 0 0.992 0.000
#> GSM1009117 5 0.0162 0.9018 0.000 0.004 0 0.000 0.996
#> GSM1009131 5 0.5858 0.2529 0.060 0.340 0 0.024 0.576
#> GSM1009145 4 0.0290 0.9217 0.008 0.000 0 0.992 0.000
#> GSM1009159 2 0.4740 0.2925 0.468 0.516 0 0.016 0.000
#> GSM1009173 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000
#> GSM1009187 2 0.0290 0.6124 0.008 0.992 0 0.000 0.000
#> GSM1009201 2 0.5773 0.4369 0.356 0.544 0 0.100 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1009062 6 0.0363 0.9236 0.012 0.000 0 0.000 0.000 0.988
#> GSM1009076 5 0.5911 0.2774 0.048 0.388 0 0.000 0.488 0.076
#> GSM1009090 4 0.0964 0.9738 0.012 0.016 0 0.968 0.004 0.000
#> GSM1009104 5 0.0000 0.8246 0.000 0.000 0 0.000 1.000 0.000
#> GSM1009118 1 0.5409 0.4919 0.584 0.284 0 0.124 0.000 0.008
#> GSM1009132 4 0.0000 0.9764 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009146 1 0.1501 0.8430 0.924 0.000 0 0.000 0.000 0.076
#> GSM1009160 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009174 2 0.1556 0.8533 0.080 0.920 0 0.000 0.000 0.000
#> GSM1009188 1 0.1265 0.8451 0.948 0.008 0 0.044 0.000 0.000
#> GSM1009063 6 0.0363 0.9236 0.012 0.000 0 0.000 0.000 0.988
#> GSM1009077 2 0.6238 -0.1569 0.048 0.448 0 0.000 0.392 0.112
#> GSM1009091 4 0.1464 0.9641 0.036 0.016 0 0.944 0.004 0.000
#> GSM1009105 5 0.0000 0.8246 0.000 0.000 0 0.000 1.000 0.000
#> GSM1009119 1 0.1923 0.8346 0.916 0.016 0 0.064 0.000 0.004
#> GSM1009133 4 0.0000 0.9764 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009147 1 0.1461 0.8426 0.940 0.044 0 0.000 0.000 0.016
#> GSM1009161 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009175 2 0.1556 0.8533 0.080 0.920 0 0.000 0.000 0.000
#> GSM1009189 1 0.1245 0.8464 0.952 0.016 0 0.032 0.000 0.000
#> GSM1009064 6 0.0363 0.9236 0.012 0.000 0 0.000 0.000 0.988
#> GSM1009078 5 0.5959 0.3925 0.056 0.076 0 0.000 0.512 0.356
#> GSM1009092 4 0.2501 0.8905 0.108 0.016 0 0.872 0.004 0.000
#> GSM1009106 5 0.0000 0.8246 0.000 0.000 0 0.000 1.000 0.000
#> GSM1009120 1 0.1959 0.8484 0.924 0.020 0 0.032 0.000 0.024
#> GSM1009134 4 0.0000 0.9764 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009148 1 0.1910 0.8307 0.892 0.000 0 0.000 0.000 0.108
#> GSM1009162 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009176 2 0.1779 0.8449 0.064 0.920 0 0.000 0.016 0.000
#> GSM1009190 1 0.0870 0.8484 0.972 0.012 0 0.012 0.000 0.004
#> GSM1009065 6 0.0363 0.9236 0.012 0.000 0 0.000 0.000 0.988
#> GSM1009079 2 0.3863 0.5985 0.048 0.776 0 0.000 0.164 0.012
#> GSM1009093 4 0.1464 0.9642 0.036 0.016 0 0.944 0.004 0.000
#> GSM1009107 5 0.0000 0.8246 0.000 0.000 0 0.000 1.000 0.000
#> GSM1009121 1 0.3569 0.7553 0.792 0.036 0 0.164 0.000 0.008
#> GSM1009135 4 0.0000 0.9764 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009149 1 0.1141 0.8483 0.948 0.000 0 0.000 0.000 0.052
#> GSM1009163 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009177 2 0.1745 0.8480 0.068 0.920 0 0.000 0.012 0.000
#> GSM1009191 1 0.0692 0.8454 0.976 0.020 0 0.000 0.000 0.004
#> GSM1009066 6 0.0363 0.9236 0.012 0.000 0 0.000 0.000 0.988
#> GSM1009080 2 0.4323 0.5566 0.048 0.744 0 0.000 0.180 0.028
#> GSM1009094 4 0.1313 0.9687 0.028 0.016 0 0.952 0.004 0.000
#> GSM1009108 5 0.0146 0.8222 0.000 0.004 0 0.000 0.996 0.000
#> GSM1009122 1 0.4905 0.1278 0.492 0.460 0 0.036 0.000 0.012
#> GSM1009136 4 0.0000 0.9764 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009150 1 0.1387 0.8459 0.932 0.000 0 0.000 0.000 0.068
#> GSM1009164 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009178 2 0.1610 0.8517 0.084 0.916 0 0.000 0.000 0.000
#> GSM1009192 1 0.1296 0.8509 0.952 0.004 0 0.012 0.000 0.032
#> GSM1009067 6 0.0363 0.9236 0.012 0.000 0 0.000 0.000 0.988
#> GSM1009081 2 0.5289 -0.1557 0.048 0.472 0 0.000 0.456 0.024
#> GSM1009095 4 0.1059 0.9730 0.016 0.016 0 0.964 0.004 0.000
#> GSM1009109 5 0.0000 0.8246 0.000 0.000 0 0.000 1.000 0.000
#> GSM1009123 1 0.2313 0.8141 0.884 0.012 0 0.100 0.000 0.004
#> GSM1009137 4 0.0000 0.9764 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009151 1 0.2883 0.7566 0.788 0.000 0 0.000 0.000 0.212
#> GSM1009165 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009179 2 0.1556 0.8533 0.080 0.920 0 0.000 0.000 0.000
#> GSM1009193 1 0.1391 0.8485 0.944 0.000 0 0.040 0.000 0.016
#> GSM1009068 6 0.0363 0.9236 0.012 0.000 0 0.000 0.000 0.988
#> GSM1009082 5 0.6704 0.2699 0.048 0.348 0 0.000 0.404 0.200
#> GSM1009096 4 0.1390 0.9665 0.032 0.016 0 0.948 0.004 0.000
#> GSM1009110 5 0.0000 0.8246 0.000 0.000 0 0.000 1.000 0.000
#> GSM1009124 1 0.2822 0.8180 0.868 0.056 0 0.068 0.000 0.008
#> GSM1009138 4 0.0000 0.9764 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009152 1 0.3175 0.7129 0.744 0.000 0 0.000 0.000 0.256
#> GSM1009166 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009180 2 0.1610 0.8517 0.084 0.916 0 0.000 0.000 0.000
#> GSM1009194 1 0.3023 0.8046 0.828 0.032 0 0.000 0.000 0.140
#> GSM1009069 6 0.0363 0.9236 0.012 0.000 0 0.000 0.000 0.988
#> GSM1009083 6 0.6881 -0.2321 0.048 0.276 0 0.000 0.316 0.360
#> GSM1009097 4 0.1738 0.9517 0.052 0.016 0 0.928 0.004 0.000
#> GSM1009111 5 0.0000 0.8246 0.000 0.000 0 0.000 1.000 0.000
#> GSM1009125 2 0.6004 0.0453 0.340 0.416 0 0.244 0.000 0.000
#> GSM1009139 4 0.0000 0.9764 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009153 1 0.3330 0.6778 0.716 0.000 0 0.000 0.000 0.284
#> GSM1009167 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009181 2 0.1829 0.8378 0.056 0.920 0 0.000 0.024 0.000
#> GSM1009195 1 0.3053 0.7253 0.812 0.168 0 0.000 0.000 0.020
#> GSM1009070 6 0.0363 0.9236 0.012 0.000 0 0.000 0.000 0.988
#> GSM1009084 5 0.4788 0.6990 0.048 0.124 0 0.000 0.732 0.096
#> GSM1009098 4 0.1059 0.9730 0.016 0.016 0 0.964 0.004 0.000
#> GSM1009112 5 0.0000 0.8246 0.000 0.000 0 0.000 1.000 0.000
#> GSM1009126 1 0.4329 0.7102 0.728 0.088 0 0.180 0.000 0.004
#> GSM1009140 4 0.0000 0.9764 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009154 1 0.1714 0.8390 0.908 0.000 0 0.000 0.000 0.092
#> GSM1009168 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009182 2 0.1556 0.8533 0.080 0.920 0 0.000 0.000 0.000
#> GSM1009196 1 0.1265 0.8490 0.948 0.008 0 0.000 0.000 0.044
#> GSM1009071 6 0.0363 0.9236 0.012 0.000 0 0.000 0.000 0.988
#> GSM1009085 5 0.4920 0.6965 0.048 0.108 0 0.000 0.720 0.124
#> GSM1009099 4 0.1863 0.9442 0.060 0.016 0 0.920 0.004 0.000
#> GSM1009113 5 0.0000 0.8246 0.000 0.000 0 0.000 1.000 0.000
#> GSM1009127 1 0.2402 0.8254 0.896 0.032 0 0.060 0.000 0.012
#> GSM1009141 4 0.0000 0.9764 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009155 1 0.3774 0.4659 0.592 0.000 0 0.000 0.000 0.408
#> GSM1009169 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009183 2 0.1701 0.8502 0.072 0.920 0 0.000 0.008 0.000
#> GSM1009197 1 0.1434 0.8502 0.948 0.008 0 0.024 0.000 0.020
#> GSM1009072 6 0.0508 0.9190 0.012 0.000 0 0.004 0.000 0.984
#> GSM1009086 5 0.4852 0.5460 0.048 0.276 0 0.000 0.652 0.024
#> GSM1009100 4 0.1148 0.9720 0.020 0.016 0 0.960 0.004 0.000
#> GSM1009114 5 0.0000 0.8246 0.000 0.000 0 0.000 1.000 0.000
#> GSM1009128 1 0.2592 0.8010 0.864 0.016 0 0.116 0.000 0.004
#> GSM1009142 4 0.0000 0.9764 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009156 1 0.1563 0.8381 0.932 0.056 0 0.000 0.000 0.012
#> GSM1009170 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009184 2 0.1556 0.8533 0.080 0.920 0 0.000 0.000 0.000
#> GSM1009198 1 0.1367 0.8444 0.944 0.012 0 0.044 0.000 0.000
#> GSM1009073 6 0.0363 0.9236 0.012 0.000 0 0.000 0.000 0.988
#> GSM1009087 5 0.5887 0.4754 0.056 0.080 0 0.000 0.552 0.312
#> GSM1009101 4 0.1232 0.9706 0.024 0.016 0 0.956 0.004 0.000
#> GSM1009115 5 0.0000 0.8246 0.000 0.000 0 0.000 1.000 0.000
#> GSM1009129 1 0.6608 -0.1456 0.380 0.240 0 0.012 0.356 0.012
#> GSM1009143 4 0.0000 0.9764 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009157 1 0.3738 0.6738 0.704 0.016 0 0.000 0.000 0.280
#> GSM1009171 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009185 2 0.1610 0.8517 0.084 0.916 0 0.000 0.000 0.000
#> GSM1009199 1 0.2994 0.6928 0.788 0.208 0 0.000 0.000 0.004
#> GSM1009074 6 0.0363 0.9236 0.012 0.000 0 0.000 0.000 0.988
#> GSM1009088 5 0.5997 0.4294 0.056 0.084 0 0.000 0.524 0.336
#> GSM1009102 4 0.0964 0.9735 0.012 0.016 0 0.968 0.004 0.000
#> GSM1009116 5 0.0000 0.8246 0.000 0.000 0 0.000 1.000 0.000
#> GSM1009130 5 0.5212 0.4071 0.340 0.064 0 0.004 0.580 0.012
#> GSM1009144 4 0.0000 0.9764 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009158 1 0.1501 0.8437 0.924 0.000 0 0.000 0.000 0.076
#> GSM1009172 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009186 2 0.1556 0.8533 0.080 0.920 0 0.000 0.000 0.000
#> GSM1009200 1 0.1225 0.8471 0.952 0.012 0 0.036 0.000 0.000
#> GSM1009075 6 0.0363 0.9236 0.012 0.000 0 0.000 0.000 0.988
#> GSM1009089 6 0.6432 0.3420 0.228 0.072 0 0.000 0.160 0.540
#> GSM1009103 4 0.0964 0.9735 0.012 0.016 0 0.968 0.004 0.000
#> GSM1009117 5 0.0000 0.8246 0.000 0.000 0 0.000 1.000 0.000
#> GSM1009131 1 0.5235 0.4371 0.620 0.064 0 0.012 0.292 0.012
#> GSM1009145 4 0.0000 0.9764 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009159 1 0.1267 0.8478 0.940 0.000 0 0.000 0.000 0.060
#> GSM1009173 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009187 2 0.1610 0.8517 0.084 0.916 0 0.000 0.000 0.000
#> GSM1009201 1 0.0976 0.8491 0.968 0.008 0 0.016 0.000 0.008
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n temperature(p) time(p) specimen(p) k
#> SD:NMF 134 0.977 0.991 9.54e-21 2
#> SD:NMF 135 0.989 1.000 8.02e-44 3
#> SD:NMF 127 1.000 1.000 6.42e-60 4
#> SD:NMF 107 1.000 1.000 1.48e-66 5
#> SD:NMF 124 1.000 1.000 1.71e-98 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 140 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#> Subgroups are detected by 'hclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.990 0.994 0.1899 0.819 0.819
#> 3 3 0.504 0.713 0.860 1.8790 0.609 0.523
#> 4 4 0.471 0.556 0.727 0.1964 0.759 0.520
#> 5 5 0.711 0.784 0.839 0.1656 0.758 0.389
#> 6 6 0.811 0.866 0.896 0.0437 0.980 0.906
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
#> GSM1009062 1 0.000 0.994 1.000 0.000
#> GSM1009076 1 0.000 0.994 1.000 0.000
#> GSM1009090 1 0.000 0.994 1.000 0.000
#> GSM1009104 1 0.311 0.947 0.944 0.056
#> GSM1009118 1 0.000 0.994 1.000 0.000
#> GSM1009132 1 0.000 0.994 1.000 0.000
#> GSM1009146 1 0.000 0.994 1.000 0.000
#> GSM1009160 2 0.000 1.000 0.000 1.000
#> GSM1009174 1 0.000 0.994 1.000 0.000
#> GSM1009188 1 0.000 0.994 1.000 0.000
#> GSM1009063 1 0.000 0.994 1.000 0.000
#> GSM1009077 1 0.000 0.994 1.000 0.000
#> GSM1009091 1 0.000 0.994 1.000 0.000
#> GSM1009105 1 0.311 0.947 0.944 0.056
#> GSM1009119 1 0.000 0.994 1.000 0.000
#> GSM1009133 1 0.000 0.994 1.000 0.000
#> GSM1009147 1 0.000 0.994 1.000 0.000
#> GSM1009161 2 0.000 1.000 0.000 1.000
#> GSM1009175 1 0.000 0.994 1.000 0.000
#> GSM1009189 1 0.000 0.994 1.000 0.000
#> GSM1009064 1 0.000 0.994 1.000 0.000
#> GSM1009078 1 0.000 0.994 1.000 0.000
#> GSM1009092 1 0.000 0.994 1.000 0.000
#> GSM1009106 1 0.311 0.947 0.944 0.056
#> GSM1009120 1 0.000 0.994 1.000 0.000
#> GSM1009134 1 0.000 0.994 1.000 0.000
#> GSM1009148 1 0.000 0.994 1.000 0.000
#> GSM1009162 2 0.000 1.000 0.000 1.000
#> GSM1009176 1 0.000 0.994 1.000 0.000
#> GSM1009190 1 0.000 0.994 1.000 0.000
#> GSM1009065 1 0.000 0.994 1.000 0.000
#> GSM1009079 1 0.000 0.994 1.000 0.000
#> GSM1009093 1 0.000 0.994 1.000 0.000
#> GSM1009107 1 0.311 0.947 0.944 0.056
#> GSM1009121 1 0.000 0.994 1.000 0.000
#> GSM1009135 1 0.000 0.994 1.000 0.000
#> GSM1009149 1 0.000 0.994 1.000 0.000
#> GSM1009163 2 0.000 1.000 0.000 1.000
#> GSM1009177 1 0.000 0.994 1.000 0.000
#> GSM1009191 1 0.000 0.994 1.000 0.000
#> GSM1009066 1 0.000 0.994 1.000 0.000
#> GSM1009080 1 0.000 0.994 1.000 0.000
#> GSM1009094 1 0.000 0.994 1.000 0.000
#> GSM1009108 1 0.311 0.947 0.944 0.056
#> GSM1009122 1 0.000 0.994 1.000 0.000
#> GSM1009136 1 0.000 0.994 1.000 0.000
#> GSM1009150 1 0.000 0.994 1.000 0.000
#> GSM1009164 2 0.000 1.000 0.000 1.000
#> GSM1009178 1 0.000 0.994 1.000 0.000
#> GSM1009192 1 0.000 0.994 1.000 0.000
#> GSM1009067 1 0.000 0.994 1.000 0.000
#> GSM1009081 1 0.000 0.994 1.000 0.000
#> GSM1009095 1 0.000 0.994 1.000 0.000
#> GSM1009109 1 0.311 0.947 0.944 0.056
#> GSM1009123 1 0.000 0.994 1.000 0.000
#> GSM1009137 1 0.000 0.994 1.000 0.000
#> GSM1009151 1 0.000 0.994 1.000 0.000
#> GSM1009165 2 0.000 1.000 0.000 1.000
#> GSM1009179 1 0.000 0.994 1.000 0.000
#> GSM1009193 1 0.000 0.994 1.000 0.000
#> GSM1009068 1 0.000 0.994 1.000 0.000
#> GSM1009082 1 0.000 0.994 1.000 0.000
#> GSM1009096 1 0.000 0.994 1.000 0.000
#> GSM1009110 1 0.311 0.947 0.944 0.056
#> GSM1009124 1 0.000 0.994 1.000 0.000
#> GSM1009138 1 0.000 0.994 1.000 0.000
#> GSM1009152 1 0.000 0.994 1.000 0.000
#> GSM1009166 2 0.000 1.000 0.000 1.000
#> GSM1009180 1 0.000 0.994 1.000 0.000
#> GSM1009194 1 0.000 0.994 1.000 0.000
#> GSM1009069 1 0.000 0.994 1.000 0.000
#> GSM1009083 1 0.000 0.994 1.000 0.000
#> GSM1009097 1 0.000 0.994 1.000 0.000
#> GSM1009111 1 0.311 0.947 0.944 0.056
#> GSM1009125 1 0.000 0.994 1.000 0.000
#> GSM1009139 1 0.000 0.994 1.000 0.000
#> GSM1009153 1 0.000 0.994 1.000 0.000
#> GSM1009167 2 0.000 1.000 0.000 1.000
#> GSM1009181 1 0.000 0.994 1.000 0.000
#> GSM1009195 1 0.000 0.994 1.000 0.000
#> GSM1009070 1 0.000 0.994 1.000 0.000
#> GSM1009084 1 0.000 0.994 1.000 0.000
#> GSM1009098 1 0.000 0.994 1.000 0.000
#> GSM1009112 1 0.311 0.947 0.944 0.056
#> GSM1009126 1 0.000 0.994 1.000 0.000
#> GSM1009140 1 0.000 0.994 1.000 0.000
#> GSM1009154 1 0.000 0.994 1.000 0.000
#> GSM1009168 2 0.000 1.000 0.000 1.000
#> GSM1009182 1 0.000 0.994 1.000 0.000
#> GSM1009196 1 0.000 0.994 1.000 0.000
#> GSM1009071 1 0.000 0.994 1.000 0.000
#> GSM1009085 1 0.000 0.994 1.000 0.000
#> GSM1009099 1 0.000 0.994 1.000 0.000
#> GSM1009113 1 0.311 0.947 0.944 0.056
#> GSM1009127 1 0.000 0.994 1.000 0.000
#> GSM1009141 1 0.000 0.994 1.000 0.000
#> GSM1009155 1 0.000 0.994 1.000 0.000
#> GSM1009169 2 0.000 1.000 0.000 1.000
#> GSM1009183 1 0.000 0.994 1.000 0.000
#> GSM1009197 1 0.000 0.994 1.000 0.000
#> GSM1009072 1 0.000 0.994 1.000 0.000
#> GSM1009086 1 0.000 0.994 1.000 0.000
#> GSM1009100 1 0.000 0.994 1.000 0.000
#> GSM1009114 1 0.311 0.947 0.944 0.056
#> GSM1009128 1 0.000 0.994 1.000 0.000
#> GSM1009142 1 0.000 0.994 1.000 0.000
#> GSM1009156 1 0.000 0.994 1.000 0.000
#> GSM1009170 2 0.000 1.000 0.000 1.000
#> GSM1009184 1 0.000 0.994 1.000 0.000
#> GSM1009198 1 0.000 0.994 1.000 0.000
#> GSM1009073 1 0.000 0.994 1.000 0.000
#> GSM1009087 1 0.000 0.994 1.000 0.000
#> GSM1009101 1 0.000 0.994 1.000 0.000
#> GSM1009115 1 0.311 0.947 0.944 0.056
#> GSM1009129 1 0.000 0.994 1.000 0.000
#> GSM1009143 1 0.000 0.994 1.000 0.000
#> GSM1009157 1 0.000 0.994 1.000 0.000
#> GSM1009171 2 0.000 1.000 0.000 1.000
#> GSM1009185 1 0.000 0.994 1.000 0.000
#> GSM1009199 1 0.000 0.994 1.000 0.000
#> GSM1009074 1 0.000 0.994 1.000 0.000
#> GSM1009088 1 0.000 0.994 1.000 0.000
#> GSM1009102 1 0.000 0.994 1.000 0.000
#> GSM1009116 1 0.311 0.947 0.944 0.056
#> GSM1009130 1 0.000 0.994 1.000 0.000
#> GSM1009144 1 0.000 0.994 1.000 0.000
#> GSM1009158 1 0.000 0.994 1.000 0.000
#> GSM1009172 2 0.000 1.000 0.000 1.000
#> GSM1009186 1 0.000 0.994 1.000 0.000
#> GSM1009200 1 0.000 0.994 1.000 0.000
#> GSM1009075 1 0.000 0.994 1.000 0.000
#> GSM1009089 1 0.000 0.994 1.000 0.000
#> GSM1009103 1 0.000 0.994 1.000 0.000
#> GSM1009117 1 0.311 0.947 0.944 0.056
#> GSM1009131 1 0.000 0.994 1.000 0.000
#> GSM1009145 1 0.000 0.994 1.000 0.000
#> GSM1009159 1 0.000 0.994 1.000 0.000
#> GSM1009173 2 0.000 1.000 0.000 1.000
#> GSM1009187 1 0.000 0.994 1.000 0.000
#> GSM1009201 1 0.000 0.994 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1009062 1 0.6062 0.521 0.616 0.384 0.000
#> GSM1009076 2 0.1163 0.768 0.028 0.972 0.000
#> GSM1009090 1 0.0000 0.820 1.000 0.000 0.000
#> GSM1009104 2 0.1964 0.741 0.000 0.944 0.056
#> GSM1009118 2 0.6286 0.294 0.464 0.536 0.000
#> GSM1009132 1 0.0000 0.820 1.000 0.000 0.000
#> GSM1009146 1 0.0237 0.820 0.996 0.004 0.000
#> GSM1009160 3 0.0000 1.000 0.000 0.000 1.000
#> GSM1009174 2 0.5529 0.660 0.296 0.704 0.000
#> GSM1009188 1 0.4399 0.722 0.812 0.188 0.000
#> GSM1009063 1 0.6062 0.521 0.616 0.384 0.000
#> GSM1009077 2 0.1163 0.768 0.028 0.972 0.000
#> GSM1009091 1 0.0000 0.820 1.000 0.000 0.000
#> GSM1009105 2 0.1964 0.741 0.000 0.944 0.056
#> GSM1009119 1 0.6168 0.217 0.588 0.412 0.000
#> GSM1009133 1 0.0000 0.820 1.000 0.000 0.000
#> GSM1009147 1 0.0237 0.820 0.996 0.004 0.000
#> GSM1009161 3 0.0000 1.000 0.000 0.000 1.000
#> GSM1009175 2 0.5529 0.660 0.296 0.704 0.000
#> GSM1009189 1 0.4399 0.722 0.812 0.188 0.000
#> GSM1009064 1 0.6062 0.521 0.616 0.384 0.000
#> GSM1009078 2 0.1163 0.768 0.028 0.972 0.000
#> GSM1009092 1 0.0000 0.820 1.000 0.000 0.000
#> GSM1009106 2 0.1964 0.741 0.000 0.944 0.056
#> GSM1009120 1 0.6168 0.217 0.588 0.412 0.000
#> GSM1009134 1 0.0000 0.820 1.000 0.000 0.000
#> GSM1009148 1 0.0237 0.820 0.996 0.004 0.000
#> GSM1009162 3 0.0000 1.000 0.000 0.000 1.000
#> GSM1009176 2 0.5529 0.660 0.296 0.704 0.000
#> GSM1009190 1 0.4399 0.722 0.812 0.188 0.000
#> GSM1009065 1 0.6062 0.521 0.616 0.384 0.000
#> GSM1009079 2 0.1163 0.768 0.028 0.972 0.000
#> GSM1009093 1 0.0000 0.820 1.000 0.000 0.000
#> GSM1009107 2 0.1964 0.741 0.000 0.944 0.056
#> GSM1009121 2 0.6286 0.294 0.464 0.536 0.000
#> GSM1009135 1 0.0000 0.820 1.000 0.000 0.000
#> GSM1009149 1 0.0237 0.820 0.996 0.004 0.000
#> GSM1009163 3 0.0000 1.000 0.000 0.000 1.000
#> GSM1009177 2 0.5529 0.660 0.296 0.704 0.000
#> GSM1009191 1 0.4399 0.722 0.812 0.188 0.000
#> GSM1009066 1 0.6062 0.521 0.616 0.384 0.000
#> GSM1009080 2 0.1163 0.768 0.028 0.972 0.000
#> GSM1009094 1 0.0000 0.820 1.000 0.000 0.000
#> GSM1009108 2 0.1964 0.741 0.000 0.944 0.056
#> GSM1009122 2 0.6286 0.294 0.464 0.536 0.000
#> GSM1009136 1 0.0000 0.820 1.000 0.000 0.000
#> GSM1009150 1 0.0237 0.820 0.996 0.004 0.000
#> GSM1009164 3 0.0000 1.000 0.000 0.000 1.000
#> GSM1009178 2 0.5529 0.660 0.296 0.704 0.000
#> GSM1009192 1 0.4399 0.722 0.812 0.188 0.000
#> GSM1009067 1 0.6062 0.521 0.616 0.384 0.000
#> GSM1009081 2 0.1163 0.768 0.028 0.972 0.000
#> GSM1009095 1 0.0000 0.820 1.000 0.000 0.000
#> GSM1009109 2 0.1964 0.741 0.000 0.944 0.056
#> GSM1009123 1 0.6168 0.217 0.588 0.412 0.000
#> GSM1009137 1 0.0000 0.820 1.000 0.000 0.000
#> GSM1009151 1 0.0237 0.820 0.996 0.004 0.000
#> GSM1009165 3 0.0000 1.000 0.000 0.000 1.000
#> GSM1009179 2 0.5529 0.660 0.296 0.704 0.000
#> GSM1009193 1 0.4399 0.722 0.812 0.188 0.000
#> GSM1009068 1 0.6062 0.521 0.616 0.384 0.000
#> GSM1009082 2 0.1163 0.768 0.028 0.972 0.000
#> GSM1009096 1 0.0000 0.820 1.000 0.000 0.000
#> GSM1009110 2 0.1964 0.741 0.000 0.944 0.056
#> GSM1009124 1 0.6168 0.217 0.588 0.412 0.000
#> GSM1009138 1 0.0000 0.820 1.000 0.000 0.000
#> GSM1009152 1 0.0237 0.820 0.996 0.004 0.000
#> GSM1009166 3 0.0000 1.000 0.000 0.000 1.000
#> GSM1009180 2 0.5529 0.660 0.296 0.704 0.000
#> GSM1009194 1 0.4399 0.722 0.812 0.188 0.000
#> GSM1009069 1 0.6062 0.521 0.616 0.384 0.000
#> GSM1009083 2 0.1163 0.768 0.028 0.972 0.000
#> GSM1009097 1 0.0000 0.820 1.000 0.000 0.000
#> GSM1009111 2 0.1964 0.741 0.000 0.944 0.056
#> GSM1009125 2 0.6286 0.294 0.464 0.536 0.000
#> GSM1009139 1 0.0000 0.820 1.000 0.000 0.000
#> GSM1009153 1 0.0237 0.820 0.996 0.004 0.000
#> GSM1009167 3 0.0000 1.000 0.000 0.000 1.000
#> GSM1009181 2 0.5529 0.660 0.296 0.704 0.000
#> GSM1009195 1 0.4399 0.722 0.812 0.188 0.000
#> GSM1009070 1 0.6062 0.521 0.616 0.384 0.000
#> GSM1009084 2 0.1163 0.768 0.028 0.972 0.000
#> GSM1009098 1 0.0000 0.820 1.000 0.000 0.000
#> GSM1009112 2 0.1964 0.741 0.000 0.944 0.056
#> GSM1009126 1 0.6168 0.217 0.588 0.412 0.000
#> GSM1009140 1 0.0000 0.820 1.000 0.000 0.000
#> GSM1009154 1 0.0237 0.820 0.996 0.004 0.000
#> GSM1009168 3 0.0000 1.000 0.000 0.000 1.000
#> GSM1009182 2 0.5529 0.660 0.296 0.704 0.000
#> GSM1009196 1 0.4399 0.722 0.812 0.188 0.000
#> GSM1009071 1 0.6062 0.521 0.616 0.384 0.000
#> GSM1009085 2 0.1163 0.768 0.028 0.972 0.000
#> GSM1009099 1 0.0000 0.820 1.000 0.000 0.000
#> GSM1009113 2 0.1964 0.741 0.000 0.944 0.056
#> GSM1009127 1 0.6168 0.217 0.588 0.412 0.000
#> GSM1009141 1 0.0000 0.820 1.000 0.000 0.000
#> GSM1009155 1 0.0592 0.818 0.988 0.012 0.000
#> GSM1009169 3 0.0000 1.000 0.000 0.000 1.000
#> GSM1009183 2 0.5529 0.660 0.296 0.704 0.000
#> GSM1009197 1 0.4399 0.722 0.812 0.188 0.000
#> GSM1009072 1 0.6062 0.521 0.616 0.384 0.000
#> GSM1009086 2 0.1163 0.768 0.028 0.972 0.000
#> GSM1009100 1 0.0000 0.820 1.000 0.000 0.000
#> GSM1009114 2 0.1964 0.741 0.000 0.944 0.056
#> GSM1009128 2 0.6286 0.294 0.464 0.536 0.000
#> GSM1009142 1 0.0000 0.820 1.000 0.000 0.000
#> GSM1009156 1 0.0592 0.818 0.988 0.012 0.000
#> GSM1009170 3 0.0000 1.000 0.000 0.000 1.000
#> GSM1009184 2 0.5529 0.660 0.296 0.704 0.000
#> GSM1009198 1 0.4399 0.722 0.812 0.188 0.000
#> GSM1009073 1 0.6062 0.521 0.616 0.384 0.000
#> GSM1009087 2 0.1163 0.768 0.028 0.972 0.000
#> GSM1009101 1 0.0000 0.820 1.000 0.000 0.000
#> GSM1009115 2 0.1964 0.741 0.000 0.944 0.056
#> GSM1009129 2 0.6286 0.294 0.464 0.536 0.000
#> GSM1009143 1 0.0000 0.820 1.000 0.000 0.000
#> GSM1009157 1 0.0592 0.818 0.988 0.012 0.000
#> GSM1009171 3 0.0000 1.000 0.000 0.000 1.000
#> GSM1009185 2 0.5529 0.660 0.296 0.704 0.000
#> GSM1009199 1 0.4399 0.722 0.812 0.188 0.000
#> GSM1009074 1 0.6062 0.521 0.616 0.384 0.000
#> GSM1009088 2 0.1163 0.768 0.028 0.972 0.000
#> GSM1009102 1 0.0000 0.820 1.000 0.000 0.000
#> GSM1009116 2 0.1964 0.741 0.000 0.944 0.056
#> GSM1009130 2 0.6286 0.294 0.464 0.536 0.000
#> GSM1009144 1 0.0000 0.820 1.000 0.000 0.000
#> GSM1009158 1 0.0237 0.820 0.996 0.004 0.000
#> GSM1009172 3 0.0000 1.000 0.000 0.000 1.000
#> GSM1009186 2 0.5529 0.660 0.296 0.704 0.000
#> GSM1009200 1 0.4399 0.722 0.812 0.188 0.000
#> GSM1009075 1 0.6062 0.521 0.616 0.384 0.000
#> GSM1009089 2 0.1163 0.768 0.028 0.972 0.000
#> GSM1009103 1 0.0000 0.820 1.000 0.000 0.000
#> GSM1009117 2 0.1964 0.741 0.000 0.944 0.056
#> GSM1009131 2 0.6286 0.294 0.464 0.536 0.000
#> GSM1009145 1 0.0000 0.820 1.000 0.000 0.000
#> GSM1009159 1 0.0237 0.820 0.996 0.004 0.000
#> GSM1009173 3 0.0000 1.000 0.000 0.000 1.000
#> GSM1009187 2 0.5529 0.660 0.296 0.704 0.000
#> GSM1009201 1 0.4399 0.722 0.812 0.188 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1009062 1 0.485 -0.113 0.600 0.000 0 0.400
#> GSM1009076 1 0.498 0.011 0.540 0.460 0 0.000
#> GSM1009090 4 0.000 0.779 0.000 0.000 0 1.000
#> GSM1009104 2 0.000 1.000 0.000 1.000 0 0.000
#> GSM1009118 1 0.625 0.432 0.592 0.072 0 0.336
#> GSM1009132 4 0.000 0.779 0.000 0.000 0 1.000
#> GSM1009146 4 0.380 0.740 0.220 0.000 0 0.780
#> GSM1009160 3 0.000 1.000 0.000 0.000 1 0.000
#> GSM1009174 1 0.705 0.461 0.572 0.232 0 0.196
#> GSM1009188 4 0.503 0.553 0.400 0.004 0 0.596
#> GSM1009063 1 0.485 -0.113 0.600 0.000 0 0.400
#> GSM1009077 1 0.498 0.011 0.540 0.460 0 0.000
#> GSM1009091 4 0.000 0.779 0.000 0.000 0 1.000
#> GSM1009105 2 0.000 1.000 0.000 1.000 0 0.000
#> GSM1009119 1 0.584 0.251 0.524 0.032 0 0.444
#> GSM1009133 4 0.000 0.779 0.000 0.000 0 1.000
#> GSM1009147 4 0.380 0.740 0.220 0.000 0 0.780
#> GSM1009161 3 0.000 1.000 0.000 0.000 1 0.000
#> GSM1009175 1 0.705 0.461 0.572 0.232 0 0.196
#> GSM1009189 4 0.503 0.553 0.400 0.004 0 0.596
#> GSM1009064 1 0.485 -0.113 0.600 0.000 0 0.400
#> GSM1009078 1 0.498 0.011 0.540 0.460 0 0.000
#> GSM1009092 4 0.000 0.779 0.000 0.000 0 1.000
#> GSM1009106 2 0.000 1.000 0.000 1.000 0 0.000
#> GSM1009120 1 0.584 0.251 0.524 0.032 0 0.444
#> GSM1009134 4 0.000 0.779 0.000 0.000 0 1.000
#> GSM1009148 4 0.380 0.740 0.220 0.000 0 0.780
#> GSM1009162 3 0.000 1.000 0.000 0.000 1 0.000
#> GSM1009176 1 0.705 0.461 0.572 0.232 0 0.196
#> GSM1009190 4 0.503 0.553 0.400 0.004 0 0.596
#> GSM1009065 1 0.485 -0.113 0.600 0.000 0 0.400
#> GSM1009079 1 0.498 0.011 0.540 0.460 0 0.000
#> GSM1009093 4 0.000 0.779 0.000 0.000 0 1.000
#> GSM1009107 2 0.000 1.000 0.000 1.000 0 0.000
#> GSM1009121 1 0.625 0.432 0.592 0.072 0 0.336
#> GSM1009135 4 0.000 0.779 0.000 0.000 0 1.000
#> GSM1009149 4 0.380 0.740 0.220 0.000 0 0.780
#> GSM1009163 3 0.000 1.000 0.000 0.000 1 0.000
#> GSM1009177 1 0.705 0.461 0.572 0.232 0 0.196
#> GSM1009191 4 0.503 0.553 0.400 0.004 0 0.596
#> GSM1009066 1 0.485 -0.113 0.600 0.000 0 0.400
#> GSM1009080 1 0.498 0.011 0.540 0.460 0 0.000
#> GSM1009094 4 0.000 0.779 0.000 0.000 0 1.000
#> GSM1009108 2 0.000 1.000 0.000 1.000 0 0.000
#> GSM1009122 1 0.625 0.432 0.592 0.072 0 0.336
#> GSM1009136 4 0.000 0.779 0.000 0.000 0 1.000
#> GSM1009150 4 0.380 0.740 0.220 0.000 0 0.780
#> GSM1009164 3 0.000 1.000 0.000 0.000 1 0.000
#> GSM1009178 1 0.705 0.461 0.572 0.232 0 0.196
#> GSM1009192 4 0.503 0.553 0.400 0.004 0 0.596
#> GSM1009067 1 0.485 -0.113 0.600 0.000 0 0.400
#> GSM1009081 1 0.498 0.011 0.540 0.460 0 0.000
#> GSM1009095 4 0.000 0.779 0.000 0.000 0 1.000
#> GSM1009109 2 0.000 1.000 0.000 1.000 0 0.000
#> GSM1009123 1 0.584 0.251 0.524 0.032 0 0.444
#> GSM1009137 4 0.000 0.779 0.000 0.000 0 1.000
#> GSM1009151 4 0.380 0.740 0.220 0.000 0 0.780
#> GSM1009165 3 0.000 1.000 0.000 0.000 1 0.000
#> GSM1009179 1 0.705 0.461 0.572 0.232 0 0.196
#> GSM1009193 4 0.503 0.553 0.400 0.004 0 0.596
#> GSM1009068 1 0.485 -0.113 0.600 0.000 0 0.400
#> GSM1009082 1 0.498 0.011 0.540 0.460 0 0.000
#> GSM1009096 4 0.000 0.779 0.000 0.000 0 1.000
#> GSM1009110 2 0.000 1.000 0.000 1.000 0 0.000
#> GSM1009124 1 0.584 0.251 0.524 0.032 0 0.444
#> GSM1009138 4 0.000 0.779 0.000 0.000 0 1.000
#> GSM1009152 4 0.380 0.740 0.220 0.000 0 0.780
#> GSM1009166 3 0.000 1.000 0.000 0.000 1 0.000
#> GSM1009180 1 0.705 0.461 0.572 0.232 0 0.196
#> GSM1009194 4 0.503 0.553 0.400 0.004 0 0.596
#> GSM1009069 1 0.485 -0.113 0.600 0.000 0 0.400
#> GSM1009083 1 0.498 0.011 0.540 0.460 0 0.000
#> GSM1009097 4 0.000 0.779 0.000 0.000 0 1.000
#> GSM1009111 2 0.000 1.000 0.000 1.000 0 0.000
#> GSM1009125 1 0.625 0.432 0.592 0.072 0 0.336
#> GSM1009139 4 0.000 0.779 0.000 0.000 0 1.000
#> GSM1009153 4 0.380 0.740 0.220 0.000 0 0.780
#> GSM1009167 3 0.000 1.000 0.000 0.000 1 0.000
#> GSM1009181 1 0.705 0.461 0.572 0.232 0 0.196
#> GSM1009195 4 0.503 0.553 0.400 0.004 0 0.596
#> GSM1009070 1 0.485 -0.113 0.600 0.000 0 0.400
#> GSM1009084 1 0.498 0.011 0.540 0.460 0 0.000
#> GSM1009098 4 0.000 0.779 0.000 0.000 0 1.000
#> GSM1009112 2 0.000 1.000 0.000 1.000 0 0.000
#> GSM1009126 1 0.584 0.251 0.524 0.032 0 0.444
#> GSM1009140 4 0.000 0.779 0.000 0.000 0 1.000
#> GSM1009154 4 0.380 0.740 0.220 0.000 0 0.780
#> GSM1009168 3 0.000 1.000 0.000 0.000 1 0.000
#> GSM1009182 1 0.705 0.461 0.572 0.232 0 0.196
#> GSM1009196 4 0.503 0.553 0.400 0.004 0 0.596
#> GSM1009071 1 0.485 -0.113 0.600 0.000 0 0.400
#> GSM1009085 1 0.498 0.011 0.540 0.460 0 0.000
#> GSM1009099 4 0.000 0.779 0.000 0.000 0 1.000
#> GSM1009113 2 0.000 1.000 0.000 1.000 0 0.000
#> GSM1009127 1 0.584 0.251 0.524 0.032 0 0.444
#> GSM1009141 4 0.000 0.779 0.000 0.000 0 1.000
#> GSM1009155 4 0.387 0.736 0.228 0.000 0 0.772
#> GSM1009169 3 0.000 1.000 0.000 0.000 1 0.000
#> GSM1009183 1 0.705 0.461 0.572 0.232 0 0.196
#> GSM1009197 4 0.503 0.553 0.400 0.004 0 0.596
#> GSM1009072 1 0.485 -0.113 0.600 0.000 0 0.400
#> GSM1009086 1 0.498 0.011 0.540 0.460 0 0.000
#> GSM1009100 4 0.000 0.779 0.000 0.000 0 1.000
#> GSM1009114 2 0.000 1.000 0.000 1.000 0 0.000
#> GSM1009128 1 0.625 0.432 0.592 0.072 0 0.336
#> GSM1009142 4 0.000 0.779 0.000 0.000 0 1.000
#> GSM1009156 4 0.387 0.736 0.228 0.000 0 0.772
#> GSM1009170 3 0.000 1.000 0.000 0.000 1 0.000
#> GSM1009184 1 0.705 0.461 0.572 0.232 0 0.196
#> GSM1009198 4 0.503 0.553 0.400 0.004 0 0.596
#> GSM1009073 1 0.485 -0.113 0.600 0.000 0 0.400
#> GSM1009087 1 0.498 0.011 0.540 0.460 0 0.000
#> GSM1009101 4 0.000 0.779 0.000 0.000 0 1.000
#> GSM1009115 2 0.000 1.000 0.000 1.000 0 0.000
#> GSM1009129 1 0.625 0.432 0.592 0.072 0 0.336
#> GSM1009143 4 0.000 0.779 0.000 0.000 0 1.000
#> GSM1009157 4 0.387 0.736 0.228 0.000 0 0.772
#> GSM1009171 3 0.000 1.000 0.000 0.000 1 0.000
#> GSM1009185 1 0.705 0.461 0.572 0.232 0 0.196
#> GSM1009199 4 0.503 0.553 0.400 0.004 0 0.596
#> GSM1009074 1 0.485 -0.113 0.600 0.000 0 0.400
#> GSM1009088 1 0.498 0.011 0.540 0.460 0 0.000
#> GSM1009102 4 0.000 0.779 0.000 0.000 0 1.000
#> GSM1009116 2 0.000 1.000 0.000 1.000 0 0.000
#> GSM1009130 1 0.625 0.432 0.592 0.072 0 0.336
#> GSM1009144 4 0.000 0.779 0.000 0.000 0 1.000
#> GSM1009158 4 0.380 0.740 0.220 0.000 0 0.780
#> GSM1009172 3 0.000 1.000 0.000 0.000 1 0.000
#> GSM1009186 1 0.705 0.461 0.572 0.232 0 0.196
#> GSM1009200 4 0.503 0.553 0.400 0.004 0 0.596
#> GSM1009075 1 0.485 -0.113 0.600 0.000 0 0.400
#> GSM1009089 1 0.498 0.011 0.540 0.460 0 0.000
#> GSM1009103 4 0.000 0.779 0.000 0.000 0 1.000
#> GSM1009117 2 0.000 1.000 0.000 1.000 0 0.000
#> GSM1009131 1 0.625 0.432 0.592 0.072 0 0.336
#> GSM1009145 4 0.000 0.779 0.000 0.000 0 1.000
#> GSM1009159 4 0.380 0.740 0.220 0.000 0 0.780
#> GSM1009173 3 0.000 1.000 0.000 0.000 1 0.000
#> GSM1009187 1 0.705 0.461 0.572 0.232 0 0.196
#> GSM1009201 4 0.503 0.553 0.400 0.004 0 0.596
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM1009062 1 0.0865 0.644 0.972 0.024 0 0.004 0.000
#> GSM1009076 5 0.6372 0.638 0.376 0.168 0 0.000 0.456
#> GSM1009090 4 0.0000 1.000 0.000 0.000 0 1.000 0.000
#> GSM1009104 5 0.0000 0.677 0.000 0.000 0 0.000 1.000
#> GSM1009118 2 0.0703 0.779 0.024 0.976 0 0.000 0.000
#> GSM1009132 4 0.0000 1.000 0.000 0.000 0 1.000 0.000
#> GSM1009146 1 0.4350 0.639 0.588 0.004 0 0.408 0.000
#> GSM1009160 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> GSM1009174 2 0.2732 0.784 0.000 0.840 0 0.000 0.160
#> GSM1009188 1 0.4400 0.772 0.736 0.052 0 0.212 0.000
#> GSM1009063 1 0.0865 0.644 0.972 0.024 0 0.004 0.000
#> GSM1009077 5 0.6372 0.638 0.376 0.168 0 0.000 0.456
#> GSM1009091 4 0.0000 1.000 0.000 0.000 0 1.000 0.000
#> GSM1009105 5 0.0000 0.677 0.000 0.000 0 0.000 1.000
#> GSM1009119 2 0.5162 0.561 0.148 0.692 0 0.160 0.000
#> GSM1009133 4 0.0000 1.000 0.000 0.000 0 1.000 0.000
#> GSM1009147 1 0.4350 0.639 0.588 0.004 0 0.408 0.000
#> GSM1009161 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> GSM1009175 2 0.2732 0.784 0.000 0.840 0 0.000 0.160
#> GSM1009189 1 0.4400 0.772 0.736 0.052 0 0.212 0.000
#> GSM1009064 1 0.0865 0.644 0.972 0.024 0 0.004 0.000
#> GSM1009078 5 0.6372 0.638 0.376 0.168 0 0.000 0.456
#> GSM1009092 4 0.0000 1.000 0.000 0.000 0 1.000 0.000
#> GSM1009106 5 0.0000 0.677 0.000 0.000 0 0.000 1.000
#> GSM1009120 2 0.5162 0.561 0.148 0.692 0 0.160 0.000
#> GSM1009134 4 0.0000 1.000 0.000 0.000 0 1.000 0.000
#> GSM1009148 1 0.4350 0.639 0.588 0.004 0 0.408 0.000
#> GSM1009162 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> GSM1009176 2 0.2732 0.784 0.000 0.840 0 0.000 0.160
#> GSM1009190 1 0.4400 0.772 0.736 0.052 0 0.212 0.000
#> GSM1009065 1 0.0865 0.644 0.972 0.024 0 0.004 0.000
#> GSM1009079 5 0.6372 0.638 0.376 0.168 0 0.000 0.456
#> GSM1009093 4 0.0000 1.000 0.000 0.000 0 1.000 0.000
#> GSM1009107 5 0.0000 0.677 0.000 0.000 0 0.000 1.000
#> GSM1009121 2 0.0703 0.779 0.024 0.976 0 0.000 0.000
#> GSM1009135 4 0.0000 1.000 0.000 0.000 0 1.000 0.000
#> GSM1009149 1 0.4350 0.639 0.588 0.004 0 0.408 0.000
#> GSM1009163 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> GSM1009177 2 0.2732 0.784 0.000 0.840 0 0.000 0.160
#> GSM1009191 1 0.4400 0.772 0.736 0.052 0 0.212 0.000
#> GSM1009066 1 0.0865 0.644 0.972 0.024 0 0.004 0.000
#> GSM1009080 5 0.6372 0.638 0.376 0.168 0 0.000 0.456
#> GSM1009094 4 0.0000 1.000 0.000 0.000 0 1.000 0.000
#> GSM1009108 5 0.0000 0.677 0.000 0.000 0 0.000 1.000
#> GSM1009122 2 0.0703 0.779 0.024 0.976 0 0.000 0.000
#> GSM1009136 4 0.0000 1.000 0.000 0.000 0 1.000 0.000
#> GSM1009150 1 0.4350 0.639 0.588 0.004 0 0.408 0.000
#> GSM1009164 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> GSM1009178 2 0.2732 0.784 0.000 0.840 0 0.000 0.160
#> GSM1009192 1 0.4400 0.772 0.736 0.052 0 0.212 0.000
#> GSM1009067 1 0.0865 0.644 0.972 0.024 0 0.004 0.000
#> GSM1009081 5 0.6372 0.638 0.376 0.168 0 0.000 0.456
#> GSM1009095 4 0.0000 1.000 0.000 0.000 0 1.000 0.000
#> GSM1009109 5 0.0000 0.677 0.000 0.000 0 0.000 1.000
#> GSM1009123 2 0.5162 0.561 0.148 0.692 0 0.160 0.000
#> GSM1009137 4 0.0000 1.000 0.000 0.000 0 1.000 0.000
#> GSM1009151 1 0.4350 0.639 0.588 0.004 0 0.408 0.000
#> GSM1009165 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> GSM1009179 2 0.2732 0.784 0.000 0.840 0 0.000 0.160
#> GSM1009193 1 0.4400 0.772 0.736 0.052 0 0.212 0.000
#> GSM1009068 1 0.0865 0.644 0.972 0.024 0 0.004 0.000
#> GSM1009082 5 0.6372 0.638 0.376 0.168 0 0.000 0.456
#> GSM1009096 4 0.0000 1.000 0.000 0.000 0 1.000 0.000
#> GSM1009110 5 0.0000 0.677 0.000 0.000 0 0.000 1.000
#> GSM1009124 2 0.5162 0.561 0.148 0.692 0 0.160 0.000
#> GSM1009138 4 0.0000 1.000 0.000 0.000 0 1.000 0.000
#> GSM1009152 1 0.4350 0.639 0.588 0.004 0 0.408 0.000
#> GSM1009166 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> GSM1009180 2 0.2732 0.784 0.000 0.840 0 0.000 0.160
#> GSM1009194 1 0.4400 0.772 0.736 0.052 0 0.212 0.000
#> GSM1009069 1 0.0865 0.644 0.972 0.024 0 0.004 0.000
#> GSM1009083 5 0.6372 0.638 0.376 0.168 0 0.000 0.456
#> GSM1009097 4 0.0000 1.000 0.000 0.000 0 1.000 0.000
#> GSM1009111 5 0.0000 0.677 0.000 0.000 0 0.000 1.000
#> GSM1009125 2 0.0703 0.779 0.024 0.976 0 0.000 0.000
#> GSM1009139 4 0.0000 1.000 0.000 0.000 0 1.000 0.000
#> GSM1009153 1 0.4350 0.639 0.588 0.004 0 0.408 0.000
#> GSM1009167 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> GSM1009181 2 0.2732 0.784 0.000 0.840 0 0.000 0.160
#> GSM1009195 1 0.4400 0.772 0.736 0.052 0 0.212 0.000
#> GSM1009070 1 0.0865 0.644 0.972 0.024 0 0.004 0.000
#> GSM1009084 5 0.6372 0.638 0.376 0.168 0 0.000 0.456
#> GSM1009098 4 0.0000 1.000 0.000 0.000 0 1.000 0.000
#> GSM1009112 5 0.0000 0.677 0.000 0.000 0 0.000 1.000
#> GSM1009126 2 0.5162 0.561 0.148 0.692 0 0.160 0.000
#> GSM1009140 4 0.0000 1.000 0.000 0.000 0 1.000 0.000
#> GSM1009154 1 0.4350 0.639 0.588 0.004 0 0.408 0.000
#> GSM1009168 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> GSM1009182 2 0.2732 0.784 0.000 0.840 0 0.000 0.160
#> GSM1009196 1 0.4400 0.772 0.736 0.052 0 0.212 0.000
#> GSM1009071 1 0.0865 0.644 0.972 0.024 0 0.004 0.000
#> GSM1009085 5 0.6372 0.638 0.376 0.168 0 0.000 0.456
#> GSM1009099 4 0.0000 1.000 0.000 0.000 0 1.000 0.000
#> GSM1009113 5 0.0000 0.677 0.000 0.000 0 0.000 1.000
#> GSM1009127 2 0.5162 0.561 0.148 0.692 0 0.160 0.000
#> GSM1009141 4 0.0000 1.000 0.000 0.000 0 1.000 0.000
#> GSM1009155 1 0.4446 0.646 0.592 0.008 0 0.400 0.000
#> GSM1009169 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> GSM1009183 2 0.2732 0.784 0.000 0.840 0 0.000 0.160
#> GSM1009197 1 0.4400 0.772 0.736 0.052 0 0.212 0.000
#> GSM1009072 1 0.0865 0.644 0.972 0.024 0 0.004 0.000
#> GSM1009086 5 0.6372 0.638 0.376 0.168 0 0.000 0.456
#> GSM1009100 4 0.0000 1.000 0.000 0.000 0 1.000 0.000
#> GSM1009114 5 0.0000 0.677 0.000 0.000 0 0.000 1.000
#> GSM1009128 2 0.0703 0.779 0.024 0.976 0 0.000 0.000
#> GSM1009142 4 0.0000 1.000 0.000 0.000 0 1.000 0.000
#> GSM1009156 1 0.4446 0.646 0.592 0.008 0 0.400 0.000
#> GSM1009170 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> GSM1009184 2 0.2732 0.784 0.000 0.840 0 0.000 0.160
#> GSM1009198 1 0.4400 0.772 0.736 0.052 0 0.212 0.000
#> GSM1009073 1 0.0865 0.644 0.972 0.024 0 0.004 0.000
#> GSM1009087 5 0.6372 0.638 0.376 0.168 0 0.000 0.456
#> GSM1009101 4 0.0000 1.000 0.000 0.000 0 1.000 0.000
#> GSM1009115 5 0.0000 0.677 0.000 0.000 0 0.000 1.000
#> GSM1009129 2 0.0703 0.779 0.024 0.976 0 0.000 0.000
#> GSM1009143 4 0.0000 1.000 0.000 0.000 0 1.000 0.000
#> GSM1009157 1 0.4446 0.646 0.592 0.008 0 0.400 0.000
#> GSM1009171 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> GSM1009185 2 0.2732 0.784 0.000 0.840 0 0.000 0.160
#> GSM1009199 1 0.4400 0.772 0.736 0.052 0 0.212 0.000
#> GSM1009074 1 0.0865 0.644 0.972 0.024 0 0.004 0.000
#> GSM1009088 5 0.6372 0.638 0.376 0.168 0 0.000 0.456
#> GSM1009102 4 0.0000 1.000 0.000 0.000 0 1.000 0.000
#> GSM1009116 5 0.0000 0.677 0.000 0.000 0 0.000 1.000
#> GSM1009130 2 0.0703 0.779 0.024 0.976 0 0.000 0.000
#> GSM1009144 4 0.0000 1.000 0.000 0.000 0 1.000 0.000
#> GSM1009158 1 0.4350 0.639 0.588 0.004 0 0.408 0.000
#> GSM1009172 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> GSM1009186 2 0.2732 0.784 0.000 0.840 0 0.000 0.160
#> GSM1009200 1 0.4400 0.772 0.736 0.052 0 0.212 0.000
#> GSM1009075 1 0.0865 0.644 0.972 0.024 0 0.004 0.000
#> GSM1009089 5 0.6372 0.638 0.376 0.168 0 0.000 0.456
#> GSM1009103 4 0.0000 1.000 0.000 0.000 0 1.000 0.000
#> GSM1009117 5 0.0000 0.677 0.000 0.000 0 0.000 1.000
#> GSM1009131 2 0.0703 0.779 0.024 0.976 0 0.000 0.000
#> GSM1009145 4 0.0000 1.000 0.000 0.000 0 1.000 0.000
#> GSM1009159 1 0.4350 0.639 0.588 0.004 0 0.408 0.000
#> GSM1009173 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> GSM1009187 2 0.2732 0.784 0.000 0.840 0 0.000 0.160
#> GSM1009201 1 0.4400 0.772 0.736 0.052 0 0.212 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1009062 1 0.2277 0.690 0.892 0.000 0 0.000 0.032 0.076
#> GSM1009076 6 0.0000 1.000 0.000 0.000 0 0.000 0.000 1.000
#> GSM1009090 4 0.0000 1.000 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009104 5 0.0790 1.000 0.000 0.000 0 0.000 0.968 0.032
#> GSM1009118 2 0.0146 0.787 0.004 0.996 0 0.000 0.000 0.000
#> GSM1009132 4 0.0000 1.000 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009146 1 0.3769 0.686 0.640 0.004 0 0.356 0.000 0.000
#> GSM1009160 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009174 2 0.2762 0.795 0.000 0.804 0 0.000 0.000 0.196
#> GSM1009188 1 0.3506 0.793 0.792 0.052 0 0.156 0.000 0.000
#> GSM1009063 1 0.2277 0.690 0.892 0.000 0 0.000 0.032 0.076
#> GSM1009077 6 0.0000 1.000 0.000 0.000 0 0.000 0.000 1.000
#> GSM1009091 4 0.0000 1.000 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009105 5 0.0790 1.000 0.000 0.000 0 0.000 0.968 0.032
#> GSM1009119 2 0.4464 0.568 0.140 0.712 0 0.148 0.000 0.000
#> GSM1009133 4 0.0000 1.000 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009147 1 0.3769 0.686 0.640 0.004 0 0.356 0.000 0.000
#> GSM1009161 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009175 2 0.2762 0.795 0.000 0.804 0 0.000 0.000 0.196
#> GSM1009189 1 0.3506 0.793 0.792 0.052 0 0.156 0.000 0.000
#> GSM1009064 1 0.2277 0.690 0.892 0.000 0 0.000 0.032 0.076
#> GSM1009078 6 0.0000 1.000 0.000 0.000 0 0.000 0.000 1.000
#> GSM1009092 4 0.0000 1.000 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009106 5 0.0790 1.000 0.000 0.000 0 0.000 0.968 0.032
#> GSM1009120 2 0.4464 0.568 0.140 0.712 0 0.148 0.000 0.000
#> GSM1009134 4 0.0000 1.000 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009148 1 0.3769 0.686 0.640 0.004 0 0.356 0.000 0.000
#> GSM1009162 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009176 2 0.2762 0.795 0.000 0.804 0 0.000 0.000 0.196
#> GSM1009190 1 0.3506 0.793 0.792 0.052 0 0.156 0.000 0.000
#> GSM1009065 1 0.2277 0.690 0.892 0.000 0 0.000 0.032 0.076
#> GSM1009079 6 0.0000 1.000 0.000 0.000 0 0.000 0.000 1.000
#> GSM1009093 4 0.0000 1.000 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009107 5 0.0790 1.000 0.000 0.000 0 0.000 0.968 0.032
#> GSM1009121 2 0.0146 0.787 0.004 0.996 0 0.000 0.000 0.000
#> GSM1009135 4 0.0000 1.000 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009149 1 0.3769 0.686 0.640 0.004 0 0.356 0.000 0.000
#> GSM1009163 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009177 2 0.2762 0.795 0.000 0.804 0 0.000 0.000 0.196
#> GSM1009191 1 0.3506 0.793 0.792 0.052 0 0.156 0.000 0.000
#> GSM1009066 1 0.2277 0.690 0.892 0.000 0 0.000 0.032 0.076
#> GSM1009080 6 0.0000 1.000 0.000 0.000 0 0.000 0.000 1.000
#> GSM1009094 4 0.0000 1.000 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009108 5 0.0790 1.000 0.000 0.000 0 0.000 0.968 0.032
#> GSM1009122 2 0.0146 0.787 0.004 0.996 0 0.000 0.000 0.000
#> GSM1009136 4 0.0000 1.000 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009150 1 0.3769 0.686 0.640 0.004 0 0.356 0.000 0.000
#> GSM1009164 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009178 2 0.2762 0.795 0.000 0.804 0 0.000 0.000 0.196
#> GSM1009192 1 0.3506 0.793 0.792 0.052 0 0.156 0.000 0.000
#> GSM1009067 1 0.2277 0.690 0.892 0.000 0 0.000 0.032 0.076
#> GSM1009081 6 0.0000 1.000 0.000 0.000 0 0.000 0.000 1.000
#> GSM1009095 4 0.0000 1.000 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009109 5 0.0790 1.000 0.000 0.000 0 0.000 0.968 0.032
#> GSM1009123 2 0.4464 0.568 0.140 0.712 0 0.148 0.000 0.000
#> GSM1009137 4 0.0000 1.000 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009151 1 0.3769 0.686 0.640 0.004 0 0.356 0.000 0.000
#> GSM1009165 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009179 2 0.2762 0.795 0.000 0.804 0 0.000 0.000 0.196
#> GSM1009193 1 0.3506 0.793 0.792 0.052 0 0.156 0.000 0.000
#> GSM1009068 1 0.2277 0.690 0.892 0.000 0 0.000 0.032 0.076
#> GSM1009082 6 0.0000 1.000 0.000 0.000 0 0.000 0.000 1.000
#> GSM1009096 4 0.0000 1.000 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009110 5 0.0790 1.000 0.000 0.000 0 0.000 0.968 0.032
#> GSM1009124 2 0.4464 0.568 0.140 0.712 0 0.148 0.000 0.000
#> GSM1009138 4 0.0000 1.000 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009152 1 0.3769 0.686 0.640 0.004 0 0.356 0.000 0.000
#> GSM1009166 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009180 2 0.2762 0.795 0.000 0.804 0 0.000 0.000 0.196
#> GSM1009194 1 0.3506 0.793 0.792 0.052 0 0.156 0.000 0.000
#> GSM1009069 1 0.2277 0.690 0.892 0.000 0 0.000 0.032 0.076
#> GSM1009083 6 0.0000 1.000 0.000 0.000 0 0.000 0.000 1.000
#> GSM1009097 4 0.0000 1.000 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009111 5 0.0790 1.000 0.000 0.000 0 0.000 0.968 0.032
#> GSM1009125 2 0.0146 0.787 0.004 0.996 0 0.000 0.000 0.000
#> GSM1009139 4 0.0000 1.000 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009153 1 0.3769 0.686 0.640 0.004 0 0.356 0.000 0.000
#> GSM1009167 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009181 2 0.2762 0.795 0.000 0.804 0 0.000 0.000 0.196
#> GSM1009195 1 0.3506 0.793 0.792 0.052 0 0.156 0.000 0.000
#> GSM1009070 1 0.2277 0.690 0.892 0.000 0 0.000 0.032 0.076
#> GSM1009084 6 0.0000 1.000 0.000 0.000 0 0.000 0.000 1.000
#> GSM1009098 4 0.0000 1.000 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009112 5 0.0790 1.000 0.000 0.000 0 0.000 0.968 0.032
#> GSM1009126 2 0.4464 0.568 0.140 0.712 0 0.148 0.000 0.000
#> GSM1009140 4 0.0000 1.000 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009154 1 0.3769 0.686 0.640 0.004 0 0.356 0.000 0.000
#> GSM1009168 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009182 2 0.2762 0.795 0.000 0.804 0 0.000 0.000 0.196
#> GSM1009196 1 0.3506 0.793 0.792 0.052 0 0.156 0.000 0.000
#> GSM1009071 1 0.2277 0.690 0.892 0.000 0 0.000 0.032 0.076
#> GSM1009085 6 0.0000 1.000 0.000 0.000 0 0.000 0.000 1.000
#> GSM1009099 4 0.0000 1.000 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009113 5 0.0790 1.000 0.000 0.000 0 0.000 0.968 0.032
#> GSM1009127 2 0.4464 0.568 0.140 0.712 0 0.148 0.000 0.000
#> GSM1009141 4 0.0000 1.000 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009155 1 0.3847 0.692 0.644 0.008 0 0.348 0.000 0.000
#> GSM1009169 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009183 2 0.2762 0.795 0.000 0.804 0 0.000 0.000 0.196
#> GSM1009197 1 0.3506 0.793 0.792 0.052 0 0.156 0.000 0.000
#> GSM1009072 1 0.2277 0.690 0.892 0.000 0 0.000 0.032 0.076
#> GSM1009086 6 0.0000 1.000 0.000 0.000 0 0.000 0.000 1.000
#> GSM1009100 4 0.0000 1.000 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009114 5 0.0790 1.000 0.000 0.000 0 0.000 0.968 0.032
#> GSM1009128 2 0.0146 0.787 0.004 0.996 0 0.000 0.000 0.000
#> GSM1009142 4 0.0000 1.000 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009156 1 0.3847 0.692 0.644 0.008 0 0.348 0.000 0.000
#> GSM1009170 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009184 2 0.2762 0.795 0.000 0.804 0 0.000 0.000 0.196
#> GSM1009198 1 0.3506 0.793 0.792 0.052 0 0.156 0.000 0.000
#> GSM1009073 1 0.2277 0.690 0.892 0.000 0 0.000 0.032 0.076
#> GSM1009087 6 0.0000 1.000 0.000 0.000 0 0.000 0.000 1.000
#> GSM1009101 4 0.0000 1.000 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009115 5 0.0790 1.000 0.000 0.000 0 0.000 0.968 0.032
#> GSM1009129 2 0.0146 0.787 0.004 0.996 0 0.000 0.000 0.000
#> GSM1009143 4 0.0000 1.000 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009157 1 0.3847 0.692 0.644 0.008 0 0.348 0.000 0.000
#> GSM1009171 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009185 2 0.2762 0.795 0.000 0.804 0 0.000 0.000 0.196
#> GSM1009199 1 0.3506 0.793 0.792 0.052 0 0.156 0.000 0.000
#> GSM1009074 1 0.2277 0.690 0.892 0.000 0 0.000 0.032 0.076
#> GSM1009088 6 0.0000 1.000 0.000 0.000 0 0.000 0.000 1.000
#> GSM1009102 4 0.0000 1.000 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009116 5 0.0790 1.000 0.000 0.000 0 0.000 0.968 0.032
#> GSM1009130 2 0.0146 0.787 0.004 0.996 0 0.000 0.000 0.000
#> GSM1009144 4 0.0000 1.000 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009158 1 0.3769 0.686 0.640 0.004 0 0.356 0.000 0.000
#> GSM1009172 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009186 2 0.2762 0.795 0.000 0.804 0 0.000 0.000 0.196
#> GSM1009200 1 0.3506 0.793 0.792 0.052 0 0.156 0.000 0.000
#> GSM1009075 1 0.2277 0.690 0.892 0.000 0 0.000 0.032 0.076
#> GSM1009089 6 0.0000 1.000 0.000 0.000 0 0.000 0.000 1.000
#> GSM1009103 4 0.0000 1.000 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009117 5 0.0790 1.000 0.000 0.000 0 0.000 0.968 0.032
#> GSM1009131 2 0.0146 0.787 0.004 0.996 0 0.000 0.000 0.000
#> GSM1009145 4 0.0000 1.000 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009159 1 0.3769 0.686 0.640 0.004 0 0.356 0.000 0.000
#> GSM1009173 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009187 2 0.2762 0.795 0.000 0.804 0 0.000 0.000 0.196
#> GSM1009201 1 0.3506 0.793 0.792 0.052 0 0.156 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 temperature(p) time(p) specimen(p) k
#> CV:hclust 140 1 1 1.03e-25 2
#> CV:hclust 126 1 1 2.01e-44 3
#> CV:hclust 84 1 1 7.20e-31 4
#> CV:hclust 140 1 1 2.99e-95 5
#> CV:hclust 140 1 1 2.21e-118 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 140 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 5.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.223 0.757 0.827 0.3994 0.514 0.514
#> 3 3 0.345 0.364 0.633 0.4453 0.740 0.575
#> 4 4 0.449 0.631 0.682 0.1664 0.711 0.441
#> 5 5 0.468 0.659 0.652 0.0975 0.934 0.777
#> 6 6 0.610 0.727 0.700 0.0620 0.951 0.796
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 5
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM1009062 1 0.541 0.855 0.876 0.124
#> GSM1009076 2 0.855 0.778 0.280 0.720
#> GSM1009090 1 0.327 0.823 0.940 0.060
#> GSM1009104 2 0.753 0.789 0.216 0.784
#> GSM1009118 1 0.671 0.770 0.824 0.176
#> GSM1009132 1 0.343 0.822 0.936 0.064
#> GSM1009146 1 0.456 0.871 0.904 0.096
#> GSM1009160 2 0.373 0.687 0.072 0.928
#> GSM1009174 2 0.990 0.583 0.440 0.560
#> GSM1009188 1 0.443 0.873 0.908 0.092
#> GSM1009063 1 0.541 0.855 0.876 0.124
#> GSM1009077 2 0.855 0.778 0.280 0.720
#> GSM1009091 1 0.327 0.823 0.940 0.060
#> GSM1009105 2 0.753 0.789 0.216 0.784
#> GSM1009119 1 0.430 0.873 0.912 0.088
#> GSM1009133 1 0.343 0.822 0.936 0.064
#> GSM1009147 1 0.430 0.873 0.912 0.088
#> GSM1009161 2 0.373 0.687 0.072 0.928
#> GSM1009175 2 0.990 0.583 0.440 0.560
#> GSM1009189 1 0.443 0.873 0.908 0.092
#> GSM1009064 1 0.541 0.855 0.876 0.124
#> GSM1009078 2 0.904 0.746 0.320 0.680
#> GSM1009092 1 0.327 0.823 0.940 0.060
#> GSM1009106 2 0.753 0.789 0.216 0.784
#> GSM1009120 1 0.416 0.873 0.916 0.084
#> GSM1009134 1 0.343 0.822 0.936 0.064
#> GSM1009148 1 0.456 0.871 0.904 0.096
#> GSM1009162 2 0.373 0.687 0.072 0.928
#> GSM1009176 2 0.985 0.606 0.428 0.572
#> GSM1009190 1 0.443 0.873 0.908 0.092
#> GSM1009065 1 0.541 0.855 0.876 0.124
#> GSM1009079 2 0.855 0.778 0.280 0.720
#> GSM1009093 1 0.327 0.823 0.940 0.060
#> GSM1009107 2 0.753 0.789 0.216 0.784
#> GSM1009121 1 0.680 0.762 0.820 0.180
#> GSM1009135 1 0.343 0.822 0.936 0.064
#> GSM1009149 1 0.456 0.871 0.904 0.096
#> GSM1009163 2 0.373 0.687 0.072 0.928
#> GSM1009177 2 0.985 0.606 0.428 0.572
#> GSM1009191 1 0.443 0.873 0.908 0.092
#> GSM1009066 1 0.541 0.855 0.876 0.124
#> GSM1009080 2 0.855 0.778 0.280 0.720
#> GSM1009094 1 0.327 0.823 0.940 0.060
#> GSM1009108 2 0.753 0.789 0.216 0.784
#> GSM1009122 1 0.999 -0.416 0.516 0.484
#> GSM1009136 1 0.327 0.819 0.940 0.060
#> GSM1009150 1 0.456 0.871 0.904 0.096
#> GSM1009164 2 0.373 0.687 0.072 0.928
#> GSM1009178 2 0.998 0.487 0.476 0.524
#> GSM1009192 1 0.443 0.873 0.908 0.092
#> GSM1009067 1 0.541 0.855 0.876 0.124
#> GSM1009081 2 0.855 0.778 0.280 0.720
#> GSM1009095 1 0.327 0.823 0.940 0.060
#> GSM1009109 2 0.753 0.789 0.216 0.784
#> GSM1009123 1 0.443 0.871 0.908 0.092
#> GSM1009137 1 0.343 0.822 0.936 0.064
#> GSM1009151 1 0.456 0.871 0.904 0.096
#> GSM1009165 2 0.373 0.687 0.072 0.928
#> GSM1009179 2 0.998 0.487 0.476 0.524
#> GSM1009193 1 0.443 0.873 0.908 0.092
#> GSM1009068 1 0.541 0.855 0.876 0.124
#> GSM1009082 2 0.855 0.778 0.280 0.720
#> GSM1009096 1 0.327 0.823 0.940 0.060
#> GSM1009110 2 0.753 0.789 0.216 0.784
#> GSM1009124 1 0.456 0.870 0.904 0.096
#> GSM1009138 1 0.343 0.822 0.936 0.064
#> GSM1009152 1 0.456 0.871 0.904 0.096
#> GSM1009166 2 0.373 0.687 0.072 0.928
#> GSM1009180 2 0.998 0.487 0.476 0.524
#> GSM1009194 1 0.443 0.873 0.908 0.092
#> GSM1009069 1 0.541 0.855 0.876 0.124
#> GSM1009083 2 0.855 0.778 0.280 0.720
#> GSM1009097 1 0.327 0.823 0.940 0.060
#> GSM1009111 2 0.753 0.789 0.216 0.784
#> GSM1009125 2 0.999 0.488 0.484 0.516
#> GSM1009139 1 0.343 0.822 0.936 0.064
#> GSM1009153 1 0.456 0.871 0.904 0.096
#> GSM1009167 2 0.373 0.687 0.072 0.928
#> GSM1009181 2 0.985 0.606 0.428 0.572
#> GSM1009195 1 0.443 0.873 0.908 0.092
#> GSM1009070 1 0.541 0.855 0.876 0.124
#> GSM1009084 2 0.855 0.778 0.280 0.720
#> GSM1009098 1 0.327 0.823 0.940 0.060
#> GSM1009112 2 0.753 0.789 0.216 0.784
#> GSM1009126 1 0.456 0.870 0.904 0.096
#> GSM1009140 1 0.343 0.822 0.936 0.064
#> GSM1009154 1 0.456 0.871 0.904 0.096
#> GSM1009168 2 0.373 0.687 0.072 0.928
#> GSM1009182 2 0.990 0.583 0.440 0.560
#> GSM1009196 1 0.443 0.873 0.908 0.092
#> GSM1009071 1 0.541 0.855 0.876 0.124
#> GSM1009085 2 0.855 0.778 0.280 0.720
#> GSM1009099 1 0.327 0.823 0.940 0.060
#> GSM1009113 2 0.753 0.789 0.216 0.784
#> GSM1009127 1 0.430 0.873 0.912 0.088
#> GSM1009141 1 0.343 0.822 0.936 0.064
#> GSM1009155 1 0.456 0.871 0.904 0.096
#> GSM1009169 2 0.373 0.687 0.072 0.928
#> GSM1009183 2 0.988 0.591 0.436 0.564
#> GSM1009197 1 0.443 0.873 0.908 0.092
#> GSM1009072 1 0.541 0.855 0.876 0.124
#> GSM1009086 2 0.855 0.778 0.280 0.720
#> GSM1009100 1 0.327 0.823 0.940 0.060
#> GSM1009114 2 0.753 0.789 0.216 0.784
#> GSM1009128 1 0.738 0.711 0.792 0.208
#> GSM1009142 1 0.343 0.822 0.936 0.064
#> GSM1009156 1 0.469 0.870 0.900 0.100
#> GSM1009170 2 0.373 0.687 0.072 0.928
#> GSM1009184 2 0.990 0.583 0.440 0.560
#> GSM1009198 1 0.443 0.873 0.908 0.092
#> GSM1009073 1 0.541 0.855 0.876 0.124
#> GSM1009087 2 0.904 0.746 0.320 0.680
#> GSM1009101 1 0.327 0.823 0.940 0.060
#> GSM1009115 2 0.753 0.789 0.216 0.784
#> GSM1009129 2 0.985 0.620 0.428 0.572
#> GSM1009143 1 0.343 0.822 0.936 0.064
#> GSM1009157 1 0.469 0.870 0.900 0.100
#> GSM1009171 2 0.373 0.687 0.072 0.928
#> GSM1009185 1 0.991 -0.191 0.556 0.444
#> GSM1009199 1 0.443 0.873 0.908 0.092
#> GSM1009074 1 0.541 0.855 0.876 0.124
#> GSM1009088 2 0.891 0.757 0.308 0.692
#> GSM1009102 1 0.327 0.823 0.940 0.060
#> GSM1009116 2 0.753 0.789 0.216 0.784
#> GSM1009130 2 0.939 0.716 0.356 0.644
#> GSM1009144 1 0.343 0.822 0.936 0.064
#> GSM1009158 1 0.456 0.871 0.904 0.096
#> GSM1009172 2 0.373 0.687 0.072 0.928
#> GSM1009186 2 0.990 0.583 0.440 0.560
#> GSM1009200 1 0.443 0.873 0.908 0.092
#> GSM1009075 1 0.541 0.855 0.876 0.124
#> GSM1009089 2 1.000 0.392 0.492 0.508
#> GSM1009103 1 0.327 0.823 0.940 0.060
#> GSM1009117 2 0.753 0.789 0.216 0.784
#> GSM1009131 1 0.939 0.199 0.644 0.356
#> GSM1009145 1 0.343 0.822 0.936 0.064
#> GSM1009159 1 0.456 0.871 0.904 0.096
#> GSM1009173 2 0.373 0.687 0.072 0.928
#> GSM1009187 1 0.988 -0.152 0.564 0.436
#> GSM1009201 1 0.443 0.873 0.908 0.092
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1009062 1 0.627 0.3724 0.768 0.076 0.156
#> GSM1009076 2 0.725 0.6471 0.196 0.704 0.100
#> GSM1009090 1 0.682 -0.8503 0.512 0.012 0.476
#> GSM1009104 2 0.364 0.7387 0.084 0.892 0.024
#> GSM1009118 1 0.653 0.4121 0.760 0.124 0.116
#> GSM1009132 3 0.627 0.9973 0.456 0.000 0.544
#> GSM1009146 1 0.175 0.4754 0.960 0.012 0.028
#> GSM1009160 2 0.790 0.6447 0.084 0.616 0.300
#> GSM1009174 1 0.879 -0.1268 0.456 0.432 0.112
#> GSM1009188 1 0.195 0.4535 0.952 0.008 0.040
#> GSM1009063 1 0.627 0.3724 0.768 0.076 0.156
#> GSM1009077 2 0.725 0.6471 0.196 0.704 0.100
#> GSM1009091 1 0.682 -0.8503 0.512 0.012 0.476
#> GSM1009105 2 0.364 0.7387 0.084 0.892 0.024
#> GSM1009119 1 0.220 0.4399 0.940 0.004 0.056
#> GSM1009133 3 0.627 0.9973 0.456 0.000 0.544
#> GSM1009147 1 0.162 0.4773 0.964 0.012 0.024
#> GSM1009161 2 0.790 0.6447 0.084 0.616 0.300
#> GSM1009175 1 0.879 -0.1268 0.456 0.432 0.112
#> GSM1009189 1 0.195 0.4535 0.952 0.008 0.040
#> GSM1009064 1 0.627 0.3724 0.768 0.076 0.156
#> GSM1009078 2 0.819 0.5000 0.292 0.604 0.104
#> GSM1009092 1 0.682 -0.8503 0.512 0.012 0.476
#> GSM1009106 2 0.364 0.7387 0.084 0.892 0.024
#> GSM1009120 1 0.210 0.4451 0.944 0.004 0.052
#> GSM1009134 3 0.627 0.9973 0.456 0.000 0.544
#> GSM1009148 1 0.175 0.4754 0.960 0.012 0.028
#> GSM1009162 2 0.787 0.6446 0.084 0.620 0.296
#> GSM1009176 1 0.879 -0.1268 0.456 0.432 0.112
#> GSM1009190 1 0.195 0.4535 0.952 0.008 0.040
#> GSM1009065 1 0.627 0.3724 0.768 0.076 0.156
#> GSM1009079 2 0.725 0.6471 0.196 0.704 0.100
#> GSM1009093 1 0.682 -0.8503 0.512 0.012 0.476
#> GSM1009107 2 0.364 0.7387 0.084 0.892 0.024
#> GSM1009121 1 0.739 0.3855 0.696 0.196 0.108
#> GSM1009135 3 0.627 0.9973 0.456 0.000 0.544
#> GSM1009149 1 0.188 0.4736 0.956 0.012 0.032
#> GSM1009163 2 0.790 0.6447 0.084 0.616 0.300
#> GSM1009177 1 0.879 -0.1268 0.456 0.432 0.112
#> GSM1009191 1 0.195 0.4535 0.952 0.008 0.040
#> GSM1009066 1 0.627 0.3724 0.768 0.076 0.156
#> GSM1009080 2 0.725 0.6471 0.196 0.704 0.100
#> GSM1009094 1 0.682 -0.8503 0.512 0.012 0.476
#> GSM1009108 2 0.364 0.7387 0.084 0.892 0.024
#> GSM1009122 1 0.863 0.1611 0.544 0.340 0.116
#> GSM1009136 3 0.629 0.9840 0.464 0.000 0.536
#> GSM1009150 1 0.188 0.4736 0.956 0.012 0.032
#> GSM1009164 2 0.790 0.6447 0.084 0.616 0.300
#> GSM1009178 1 0.879 -0.1045 0.464 0.424 0.112
#> GSM1009192 1 0.195 0.4535 0.952 0.008 0.040
#> GSM1009067 1 0.627 0.3724 0.768 0.076 0.156
#> GSM1009081 2 0.725 0.6471 0.196 0.704 0.100
#> GSM1009095 1 0.682 -0.8503 0.512 0.012 0.476
#> GSM1009109 2 0.364 0.7387 0.084 0.892 0.024
#> GSM1009123 1 0.220 0.4399 0.940 0.004 0.056
#> GSM1009137 3 0.627 0.9973 0.456 0.000 0.544
#> GSM1009151 1 0.175 0.4754 0.960 0.012 0.028
#> GSM1009165 2 0.787 0.6446 0.084 0.620 0.296
#> GSM1009179 1 0.879 -0.1045 0.464 0.424 0.112
#> GSM1009193 1 0.195 0.4535 0.952 0.008 0.040
#> GSM1009068 1 0.627 0.3724 0.768 0.076 0.156
#> GSM1009082 2 0.725 0.6471 0.196 0.704 0.100
#> GSM1009096 1 0.682 -0.8503 0.512 0.012 0.476
#> GSM1009110 2 0.364 0.7387 0.084 0.892 0.024
#> GSM1009124 1 0.296 0.4386 0.912 0.008 0.080
#> GSM1009138 3 0.627 0.9973 0.456 0.000 0.544
#> GSM1009152 1 0.175 0.4754 0.960 0.012 0.028
#> GSM1009166 2 0.787 0.6446 0.084 0.620 0.296
#> GSM1009180 1 0.879 -0.1045 0.464 0.424 0.112
#> GSM1009194 1 0.206 0.4540 0.948 0.008 0.044
#> GSM1009069 1 0.644 0.3745 0.756 0.076 0.168
#> GSM1009083 2 0.734 0.6368 0.204 0.696 0.100
#> GSM1009097 1 0.682 -0.8503 0.512 0.012 0.476
#> GSM1009111 2 0.364 0.7387 0.084 0.892 0.024
#> GSM1009125 1 0.874 0.0779 0.512 0.372 0.116
#> GSM1009139 3 0.627 0.9973 0.456 0.000 0.544
#> GSM1009153 1 0.175 0.4754 0.960 0.012 0.028
#> GSM1009167 2 0.790 0.6446 0.084 0.616 0.300
#> GSM1009181 1 0.879 -0.1268 0.456 0.432 0.112
#> GSM1009195 1 0.249 0.4617 0.932 0.008 0.060
#> GSM1009070 1 0.621 0.3698 0.772 0.076 0.152
#> GSM1009084 2 0.725 0.6471 0.196 0.704 0.100
#> GSM1009098 1 0.682 -0.8503 0.512 0.012 0.476
#> GSM1009112 2 0.364 0.7387 0.084 0.892 0.024
#> GSM1009126 1 0.296 0.4386 0.912 0.008 0.080
#> GSM1009140 3 0.627 0.9973 0.456 0.000 0.544
#> GSM1009154 1 0.175 0.4754 0.960 0.012 0.028
#> GSM1009168 2 0.787 0.6446 0.084 0.620 0.296
#> GSM1009182 1 0.879 -0.1268 0.456 0.432 0.112
#> GSM1009196 1 0.195 0.4535 0.952 0.008 0.040
#> GSM1009071 1 0.627 0.3724 0.768 0.076 0.156
#> GSM1009085 2 0.725 0.6471 0.196 0.704 0.100
#> GSM1009099 1 0.682 -0.8503 0.512 0.012 0.476
#> GSM1009113 2 0.364 0.7387 0.084 0.892 0.024
#> GSM1009127 1 0.210 0.4451 0.944 0.004 0.052
#> GSM1009141 3 0.627 0.9973 0.456 0.000 0.544
#> GSM1009155 1 0.162 0.4773 0.964 0.012 0.024
#> GSM1009169 2 0.787 0.6446 0.084 0.620 0.296
#> GSM1009183 1 0.879 -0.1268 0.456 0.432 0.112
#> GSM1009197 1 0.195 0.4535 0.952 0.008 0.040
#> GSM1009072 1 0.627 0.3724 0.768 0.076 0.156
#> GSM1009086 2 0.725 0.6471 0.196 0.704 0.100
#> GSM1009100 1 0.682 -0.8503 0.512 0.012 0.476
#> GSM1009114 2 0.364 0.7387 0.084 0.892 0.024
#> GSM1009128 1 0.754 0.3746 0.680 0.216 0.104
#> GSM1009142 3 0.627 0.9973 0.456 0.000 0.544
#> GSM1009156 1 0.148 0.4789 0.968 0.012 0.020
#> GSM1009170 2 0.790 0.6447 0.084 0.616 0.300
#> GSM1009184 1 0.879 -0.1268 0.456 0.432 0.112
#> GSM1009198 1 0.195 0.4535 0.952 0.008 0.040
#> GSM1009073 1 0.627 0.3724 0.768 0.076 0.156
#> GSM1009087 2 0.819 0.5000 0.292 0.604 0.104
#> GSM1009101 1 0.682 -0.8503 0.512 0.012 0.476
#> GSM1009115 2 0.364 0.7387 0.084 0.892 0.024
#> GSM1009129 1 0.879 -0.1341 0.452 0.436 0.112
#> GSM1009143 3 0.627 0.9973 0.456 0.000 0.544
#> GSM1009157 1 0.241 0.4832 0.940 0.020 0.040
#> GSM1009171 2 0.790 0.6447 0.084 0.616 0.300
#> GSM1009185 1 0.878 -0.0951 0.468 0.420 0.112
#> GSM1009199 1 0.249 0.4617 0.932 0.008 0.060
#> GSM1009074 1 0.627 0.3724 0.768 0.076 0.156
#> GSM1009088 2 0.813 0.5127 0.284 0.612 0.104
#> GSM1009102 1 0.682 -0.8503 0.512 0.012 0.476
#> GSM1009116 2 0.364 0.7387 0.084 0.892 0.024
#> GSM1009130 2 0.833 0.4679 0.328 0.572 0.100
#> GSM1009144 3 0.627 0.9973 0.456 0.000 0.544
#> GSM1009158 1 0.175 0.4754 0.960 0.012 0.028
#> GSM1009172 2 0.790 0.6447 0.084 0.616 0.300
#> GSM1009186 1 0.879 -0.1268 0.456 0.432 0.112
#> GSM1009200 1 0.195 0.4535 0.952 0.008 0.040
#> GSM1009075 1 0.627 0.3724 0.768 0.076 0.156
#> GSM1009089 2 0.855 0.3587 0.364 0.532 0.104
#> GSM1009103 1 0.682 -0.8503 0.512 0.012 0.476
#> GSM1009117 2 0.364 0.7387 0.084 0.892 0.024
#> GSM1009131 1 0.811 0.2871 0.604 0.300 0.096
#> GSM1009145 3 0.629 0.9840 0.464 0.000 0.536
#> GSM1009159 1 0.188 0.4736 0.956 0.012 0.032
#> GSM1009173 2 0.787 0.6446 0.084 0.620 0.296
#> GSM1009187 1 0.878 -0.0851 0.472 0.416 0.112
#> GSM1009201 1 0.195 0.4535 0.952 0.008 0.040
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1009062 1 0.819 0.4543 0.556 0.088 0.120 0.236
#> GSM1009076 2 0.238 0.5966 0.072 0.916 0.004 0.008
#> GSM1009090 4 0.736 0.7952 0.384 0.012 0.116 0.488
#> GSM1009104 2 0.546 0.3978 0.008 0.744 0.172 0.076
#> GSM1009118 1 0.760 0.4454 0.592 0.232 0.044 0.132
#> GSM1009132 4 0.416 0.8044 0.240 0.004 0.000 0.756
#> GSM1009146 1 0.197 0.6689 0.944 0.012 0.028 0.016
#> GSM1009160 3 0.559 0.9883 0.020 0.296 0.668 0.016
#> GSM1009174 2 0.765 0.5446 0.312 0.548 0.088 0.052
#> GSM1009188 1 0.307 0.6438 0.888 0.024 0.004 0.084
#> GSM1009063 1 0.819 0.4543 0.556 0.088 0.120 0.236
#> GSM1009077 2 0.238 0.5966 0.072 0.916 0.004 0.008
#> GSM1009091 4 0.736 0.7952 0.384 0.012 0.116 0.488
#> GSM1009105 2 0.546 0.3978 0.008 0.744 0.172 0.076
#> GSM1009119 1 0.391 0.6165 0.848 0.020 0.020 0.112
#> GSM1009133 4 0.416 0.8044 0.240 0.004 0.000 0.756
#> GSM1009147 1 0.185 0.6687 0.948 0.012 0.028 0.012
#> GSM1009161 3 0.569 0.9882 0.020 0.296 0.664 0.020
#> GSM1009175 2 0.765 0.5446 0.312 0.548 0.088 0.052
#> GSM1009189 1 0.307 0.6438 0.888 0.024 0.004 0.084
#> GSM1009064 1 0.819 0.4543 0.556 0.088 0.120 0.236
#> GSM1009078 2 0.382 0.6093 0.160 0.824 0.008 0.008
#> GSM1009092 4 0.736 0.7952 0.384 0.012 0.116 0.488
#> GSM1009106 2 0.546 0.3978 0.008 0.744 0.172 0.076
#> GSM1009120 1 0.373 0.6293 0.860 0.020 0.020 0.100
#> GSM1009134 4 0.416 0.8044 0.240 0.004 0.000 0.756
#> GSM1009148 1 0.197 0.6689 0.944 0.012 0.028 0.016
#> GSM1009162 3 0.597 0.9869 0.020 0.296 0.652 0.032
#> GSM1009176 2 0.758 0.5628 0.296 0.564 0.088 0.052
#> GSM1009190 1 0.307 0.6438 0.888 0.024 0.004 0.084
#> GSM1009065 1 0.819 0.4543 0.556 0.088 0.120 0.236
#> GSM1009079 2 0.238 0.5966 0.072 0.916 0.004 0.008
#> GSM1009093 4 0.736 0.7952 0.384 0.012 0.116 0.488
#> GSM1009107 2 0.546 0.3978 0.008 0.744 0.172 0.076
#> GSM1009121 1 0.752 0.4349 0.604 0.220 0.044 0.132
#> GSM1009135 4 0.416 0.8044 0.240 0.004 0.000 0.756
#> GSM1009149 1 0.197 0.6689 0.944 0.012 0.028 0.016
#> GSM1009163 3 0.569 0.9880 0.020 0.296 0.664 0.020
#> GSM1009177 2 0.758 0.5628 0.296 0.564 0.088 0.052
#> GSM1009191 1 0.307 0.6438 0.888 0.024 0.004 0.084
#> GSM1009066 1 0.819 0.4543 0.556 0.088 0.120 0.236
#> GSM1009080 2 0.238 0.5966 0.072 0.916 0.004 0.008
#> GSM1009094 4 0.736 0.7952 0.384 0.012 0.116 0.488
#> GSM1009108 2 0.546 0.3978 0.008 0.744 0.172 0.076
#> GSM1009122 1 0.800 -0.0473 0.444 0.400 0.044 0.112
#> GSM1009136 4 0.466 0.8037 0.264 0.004 0.008 0.724
#> GSM1009150 1 0.197 0.6689 0.944 0.012 0.028 0.016
#> GSM1009164 3 0.569 0.9880 0.020 0.296 0.664 0.020
#> GSM1009178 2 0.765 0.5446 0.312 0.548 0.088 0.052
#> GSM1009192 1 0.307 0.6438 0.888 0.024 0.004 0.084
#> GSM1009067 1 0.819 0.4543 0.556 0.088 0.120 0.236
#> GSM1009081 2 0.238 0.5966 0.072 0.916 0.004 0.008
#> GSM1009095 4 0.732 0.7952 0.384 0.012 0.112 0.492
#> GSM1009109 2 0.546 0.3978 0.008 0.744 0.172 0.076
#> GSM1009123 1 0.418 0.6057 0.832 0.020 0.024 0.124
#> GSM1009137 4 0.416 0.8044 0.240 0.004 0.000 0.756
#> GSM1009151 1 0.197 0.6689 0.944 0.012 0.028 0.016
#> GSM1009165 3 0.597 0.9878 0.020 0.296 0.652 0.032
#> GSM1009179 2 0.765 0.5446 0.312 0.548 0.088 0.052
#> GSM1009193 1 0.307 0.6438 0.888 0.024 0.004 0.084
#> GSM1009068 1 0.819 0.4543 0.556 0.088 0.120 0.236
#> GSM1009082 2 0.238 0.5966 0.072 0.916 0.004 0.008
#> GSM1009096 4 0.736 0.7952 0.384 0.012 0.116 0.488
#> GSM1009110 2 0.546 0.3978 0.008 0.744 0.172 0.076
#> GSM1009124 1 0.531 0.5841 0.780 0.048 0.040 0.132
#> GSM1009138 4 0.416 0.8044 0.240 0.004 0.000 0.756
#> GSM1009152 1 0.197 0.6689 0.944 0.012 0.028 0.016
#> GSM1009166 3 0.597 0.9869 0.020 0.296 0.652 0.032
#> GSM1009180 2 0.765 0.5446 0.312 0.548 0.088 0.052
#> GSM1009194 1 0.313 0.6441 0.884 0.024 0.004 0.088
#> GSM1009069 1 0.834 0.4489 0.544 0.100 0.120 0.236
#> GSM1009083 2 0.253 0.5992 0.080 0.908 0.004 0.008
#> GSM1009097 4 0.736 0.7952 0.384 0.012 0.116 0.488
#> GSM1009111 2 0.546 0.3978 0.008 0.744 0.172 0.076
#> GSM1009125 1 0.803 -0.0851 0.436 0.408 0.048 0.108
#> GSM1009139 4 0.416 0.8044 0.240 0.004 0.000 0.756
#> GSM1009153 1 0.197 0.6689 0.944 0.012 0.028 0.016
#> GSM1009167 3 0.664 0.9700 0.020 0.296 0.616 0.068
#> GSM1009181 2 0.758 0.5628 0.296 0.564 0.088 0.052
#> GSM1009195 1 0.407 0.6469 0.840 0.064 0.004 0.092
#> GSM1009070 1 0.800 0.4573 0.572 0.076 0.120 0.232
#> GSM1009084 2 0.238 0.5966 0.072 0.916 0.004 0.008
#> GSM1009098 4 0.732 0.7952 0.384 0.012 0.112 0.492
#> GSM1009112 2 0.546 0.3978 0.008 0.744 0.172 0.076
#> GSM1009126 1 0.531 0.5841 0.780 0.048 0.040 0.132
#> GSM1009140 4 0.416 0.8044 0.240 0.004 0.000 0.756
#> GSM1009154 1 0.197 0.6689 0.944 0.012 0.028 0.016
#> GSM1009168 3 0.644 0.9769 0.020 0.296 0.628 0.056
#> GSM1009182 2 0.765 0.5446 0.312 0.548 0.088 0.052
#> GSM1009196 1 0.307 0.6438 0.888 0.024 0.004 0.084
#> GSM1009071 1 0.819 0.4543 0.556 0.088 0.120 0.236
#> GSM1009085 2 0.238 0.5966 0.072 0.916 0.004 0.008
#> GSM1009099 4 0.732 0.7952 0.384 0.012 0.112 0.492
#> GSM1009113 2 0.546 0.3978 0.008 0.744 0.172 0.076
#> GSM1009127 1 0.413 0.6099 0.836 0.020 0.024 0.120
#> GSM1009141 4 0.416 0.8044 0.240 0.004 0.000 0.756
#> GSM1009155 1 0.197 0.6689 0.944 0.012 0.028 0.016
#> GSM1009169 3 0.644 0.9769 0.020 0.296 0.628 0.056
#> GSM1009183 2 0.762 0.5547 0.304 0.556 0.088 0.052
#> GSM1009197 1 0.307 0.6438 0.888 0.024 0.004 0.084
#> GSM1009072 1 0.819 0.4543 0.556 0.088 0.120 0.236
#> GSM1009086 2 0.238 0.5966 0.072 0.916 0.004 0.008
#> GSM1009100 4 0.732 0.7952 0.384 0.012 0.112 0.492
#> GSM1009114 2 0.546 0.3978 0.008 0.744 0.172 0.076
#> GSM1009128 1 0.708 0.4598 0.656 0.164 0.044 0.136
#> GSM1009142 4 0.416 0.8044 0.240 0.004 0.000 0.756
#> GSM1009156 1 0.196 0.6693 0.944 0.020 0.028 0.008
#> GSM1009170 3 0.569 0.9880 0.020 0.296 0.664 0.020
#> GSM1009184 2 0.765 0.5446 0.312 0.548 0.088 0.052
#> GSM1009198 1 0.307 0.6438 0.888 0.024 0.004 0.084
#> GSM1009073 1 0.819 0.4543 0.556 0.088 0.120 0.236
#> GSM1009087 2 0.382 0.6093 0.160 0.824 0.008 0.008
#> GSM1009101 4 0.732 0.7952 0.384 0.012 0.112 0.492
#> GSM1009115 2 0.546 0.3978 0.008 0.744 0.172 0.076
#> GSM1009129 2 0.780 0.2248 0.392 0.472 0.048 0.088
#> GSM1009143 4 0.434 0.8039 0.240 0.004 0.004 0.752
#> GSM1009157 1 0.317 0.6512 0.888 0.076 0.028 0.008
#> GSM1009171 3 0.569 0.9880 0.020 0.296 0.664 0.020
#> GSM1009185 2 0.763 0.5298 0.324 0.540 0.088 0.048
#> GSM1009199 1 0.365 0.6456 0.860 0.040 0.004 0.096
#> GSM1009074 1 0.819 0.4543 0.556 0.088 0.120 0.236
#> GSM1009088 2 0.382 0.6093 0.160 0.824 0.008 0.008
#> GSM1009102 4 0.732 0.7952 0.384 0.012 0.112 0.492
#> GSM1009116 2 0.546 0.3978 0.008 0.744 0.172 0.076
#> GSM1009130 2 0.619 0.4886 0.260 0.668 0.032 0.040
#> GSM1009144 4 0.434 0.8039 0.240 0.004 0.004 0.752
#> GSM1009158 1 0.197 0.6689 0.944 0.012 0.028 0.016
#> GSM1009172 3 0.559 0.9883 0.020 0.296 0.668 0.016
#> GSM1009186 2 0.765 0.5446 0.312 0.548 0.088 0.052
#> GSM1009200 1 0.307 0.6438 0.888 0.024 0.004 0.084
#> GSM1009075 1 0.819 0.4543 0.556 0.088 0.120 0.236
#> GSM1009089 2 0.448 0.5842 0.224 0.760 0.008 0.008
#> GSM1009103 4 0.732 0.7952 0.384 0.012 0.112 0.492
#> GSM1009117 2 0.546 0.3978 0.008 0.744 0.172 0.076
#> GSM1009131 1 0.762 0.3007 0.560 0.292 0.044 0.104
#> GSM1009145 4 0.466 0.8037 0.264 0.004 0.008 0.724
#> GSM1009159 1 0.197 0.6689 0.944 0.012 0.028 0.016
#> GSM1009173 3 0.597 0.9878 0.020 0.296 0.652 0.032
#> GSM1009187 2 0.767 0.5384 0.316 0.544 0.088 0.052
#> GSM1009201 1 0.307 0.6438 0.888 0.024 0.004 0.084
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM1009062 5 0.693 0.998 0.380 0.056 0.000 0.100 0.464
#> GSM1009076 2 0.551 0.557 0.044 0.720 0.020 0.040 0.176
#> GSM1009090 4 0.353 0.744 0.256 0.000 0.000 0.744 0.000
#> GSM1009104 2 0.400 0.318 0.004 0.748 0.232 0.016 0.000
#> GSM1009118 1 0.702 0.520 0.636 0.092 0.044 0.080 0.148
#> GSM1009132 4 0.635 0.736 0.120 0.004 0.032 0.616 0.228
#> GSM1009146 1 0.400 0.572 0.820 0.012 0.012 0.036 0.120
#> GSM1009160 3 0.291 0.983 0.012 0.104 0.872 0.004 0.008
#> GSM1009174 2 0.871 0.452 0.252 0.356 0.096 0.032 0.264
#> GSM1009188 1 0.218 0.717 0.920 0.008 0.024 0.048 0.000
#> GSM1009063 5 0.693 0.998 0.380 0.056 0.000 0.100 0.464
#> GSM1009077 2 0.551 0.557 0.044 0.720 0.020 0.040 0.176
#> GSM1009091 4 0.353 0.744 0.256 0.000 0.000 0.744 0.000
#> GSM1009105 2 0.400 0.318 0.004 0.748 0.232 0.016 0.000
#> GSM1009119 1 0.375 0.687 0.852 0.008 0.040 0.052 0.048
#> GSM1009133 4 0.635 0.736 0.120 0.004 0.032 0.616 0.228
#> GSM1009147 1 0.392 0.577 0.824 0.012 0.012 0.032 0.120
#> GSM1009161 3 0.291 0.983 0.012 0.104 0.872 0.004 0.008
#> GSM1009175 2 0.871 0.452 0.252 0.356 0.096 0.032 0.264
#> GSM1009189 1 0.218 0.717 0.920 0.008 0.024 0.048 0.000
#> GSM1009064 5 0.707 0.996 0.380 0.056 0.004 0.100 0.460
#> GSM1009078 2 0.591 0.560 0.076 0.692 0.016 0.040 0.176
#> GSM1009092 4 0.353 0.744 0.256 0.000 0.000 0.744 0.000
#> GSM1009106 2 0.400 0.318 0.004 0.748 0.232 0.016 0.000
#> GSM1009120 1 0.360 0.688 0.860 0.008 0.036 0.052 0.044
#> GSM1009134 4 0.635 0.736 0.120 0.004 0.032 0.616 0.228
#> GSM1009148 1 0.400 0.572 0.820 0.012 0.012 0.036 0.120
#> GSM1009162 3 0.344 0.981 0.012 0.104 0.852 0.008 0.024
#> GSM1009176 2 0.870 0.456 0.248 0.360 0.096 0.032 0.264
#> GSM1009190 1 0.218 0.717 0.920 0.008 0.024 0.048 0.000
#> GSM1009065 5 0.707 0.996 0.380 0.056 0.004 0.100 0.460
#> GSM1009079 2 0.551 0.557 0.044 0.720 0.020 0.040 0.176
#> GSM1009093 4 0.353 0.744 0.256 0.000 0.000 0.744 0.000
#> GSM1009107 2 0.400 0.318 0.004 0.748 0.232 0.016 0.000
#> GSM1009121 1 0.698 0.523 0.640 0.092 0.044 0.080 0.144
#> GSM1009135 4 0.635 0.736 0.120 0.004 0.032 0.616 0.228
#> GSM1009149 1 0.400 0.572 0.820 0.012 0.012 0.036 0.120
#> GSM1009163 3 0.279 0.983 0.012 0.104 0.876 0.004 0.004
#> GSM1009177 2 0.870 0.456 0.248 0.360 0.096 0.032 0.264
#> GSM1009191 1 0.218 0.717 0.920 0.008 0.024 0.048 0.000
#> GSM1009066 5 0.693 0.998 0.380 0.056 0.000 0.100 0.464
#> GSM1009080 2 0.551 0.557 0.044 0.720 0.020 0.040 0.176
#> GSM1009094 4 0.353 0.744 0.256 0.000 0.000 0.744 0.000
#> GSM1009108 2 0.400 0.318 0.004 0.748 0.232 0.016 0.000
#> GSM1009122 1 0.811 0.336 0.516 0.188 0.052 0.076 0.168
#> GSM1009136 4 0.647 0.736 0.128 0.008 0.032 0.616 0.216
#> GSM1009150 1 0.400 0.572 0.820 0.012 0.012 0.036 0.120
#> GSM1009164 3 0.279 0.983 0.012 0.104 0.876 0.004 0.004
#> GSM1009178 2 0.871 0.452 0.252 0.356 0.096 0.032 0.264
#> GSM1009192 1 0.218 0.717 0.920 0.008 0.024 0.048 0.000
#> GSM1009067 5 0.693 0.998 0.380 0.056 0.000 0.100 0.464
#> GSM1009081 2 0.551 0.557 0.044 0.720 0.020 0.040 0.176
#> GSM1009095 4 0.369 0.744 0.256 0.004 0.000 0.740 0.000
#> GSM1009109 2 0.400 0.318 0.004 0.748 0.232 0.016 0.000
#> GSM1009123 1 0.408 0.676 0.832 0.008 0.040 0.068 0.052
#> GSM1009137 4 0.635 0.736 0.120 0.004 0.032 0.616 0.228
#> GSM1009151 1 0.400 0.572 0.820 0.012 0.012 0.036 0.120
#> GSM1009165 3 0.335 0.981 0.012 0.104 0.856 0.008 0.020
#> GSM1009179 2 0.871 0.452 0.252 0.356 0.096 0.032 0.264
#> GSM1009193 1 0.218 0.717 0.920 0.008 0.024 0.048 0.000
#> GSM1009068 5 0.693 0.998 0.380 0.056 0.000 0.100 0.464
#> GSM1009082 2 0.551 0.557 0.044 0.720 0.020 0.040 0.176
#> GSM1009096 4 0.353 0.744 0.256 0.000 0.000 0.744 0.000
#> GSM1009110 2 0.400 0.318 0.004 0.748 0.232 0.016 0.000
#> GSM1009124 1 0.531 0.624 0.752 0.016 0.040 0.076 0.116
#> GSM1009138 4 0.635 0.736 0.120 0.004 0.032 0.616 0.228
#> GSM1009152 1 0.400 0.572 0.820 0.012 0.012 0.036 0.120
#> GSM1009166 3 0.344 0.981 0.012 0.104 0.852 0.008 0.024
#> GSM1009180 2 0.871 0.452 0.252 0.356 0.096 0.032 0.264
#> GSM1009194 1 0.234 0.715 0.916 0.008 0.024 0.048 0.004
#> GSM1009069 5 0.707 0.996 0.380 0.056 0.004 0.100 0.460
#> GSM1009083 2 0.551 0.557 0.044 0.720 0.020 0.040 0.176
#> GSM1009097 4 0.353 0.744 0.256 0.000 0.000 0.744 0.000
#> GSM1009111 2 0.400 0.318 0.004 0.748 0.232 0.016 0.000
#> GSM1009125 1 0.811 0.336 0.516 0.188 0.052 0.076 0.168
#> GSM1009139 4 0.635 0.736 0.120 0.004 0.032 0.616 0.228
#> GSM1009153 1 0.400 0.572 0.820 0.012 0.012 0.036 0.120
#> GSM1009167 3 0.392 0.973 0.012 0.104 0.832 0.024 0.028
#> GSM1009181 2 0.870 0.456 0.248 0.360 0.096 0.032 0.264
#> GSM1009195 1 0.266 0.710 0.908 0.012 0.024 0.036 0.020
#> GSM1009070 5 0.694 0.990 0.384 0.056 0.000 0.100 0.460
#> GSM1009084 2 0.551 0.557 0.044 0.720 0.020 0.040 0.176
#> GSM1009098 4 0.353 0.744 0.256 0.000 0.000 0.744 0.000
#> GSM1009112 2 0.400 0.318 0.004 0.748 0.232 0.016 0.000
#> GSM1009126 1 0.536 0.621 0.748 0.016 0.040 0.076 0.120
#> GSM1009140 4 0.635 0.736 0.120 0.004 0.032 0.616 0.228
#> GSM1009154 1 0.400 0.572 0.820 0.012 0.012 0.036 0.120
#> GSM1009168 3 0.374 0.977 0.012 0.104 0.840 0.020 0.024
#> GSM1009182 2 0.871 0.452 0.252 0.356 0.096 0.032 0.264
#> GSM1009196 1 0.229 0.715 0.916 0.012 0.024 0.048 0.000
#> GSM1009071 5 0.693 0.998 0.380 0.056 0.000 0.100 0.464
#> GSM1009085 2 0.551 0.557 0.044 0.720 0.020 0.040 0.176
#> GSM1009099 4 0.353 0.744 0.256 0.000 0.000 0.744 0.000
#> GSM1009113 2 0.400 0.318 0.004 0.748 0.232 0.016 0.000
#> GSM1009127 1 0.415 0.675 0.828 0.008 0.040 0.068 0.056
#> GSM1009141 4 0.635 0.736 0.120 0.004 0.032 0.616 0.228
#> GSM1009155 1 0.400 0.572 0.820 0.012 0.012 0.036 0.120
#> GSM1009169 3 0.384 0.977 0.012 0.104 0.836 0.024 0.024
#> GSM1009183 2 0.870 0.456 0.248 0.360 0.096 0.032 0.264
#> GSM1009197 1 0.218 0.717 0.920 0.008 0.024 0.048 0.000
#> GSM1009072 5 0.693 0.998 0.380 0.056 0.000 0.100 0.464
#> GSM1009086 2 0.551 0.557 0.044 0.720 0.020 0.040 0.176
#> GSM1009100 4 0.353 0.744 0.256 0.000 0.000 0.744 0.000
#> GSM1009114 2 0.400 0.318 0.004 0.748 0.232 0.016 0.000
#> GSM1009128 1 0.680 0.545 0.656 0.076 0.044 0.088 0.136
#> GSM1009142 4 0.635 0.736 0.120 0.004 0.032 0.616 0.228
#> GSM1009156 1 0.378 0.585 0.832 0.012 0.012 0.028 0.116
#> GSM1009170 3 0.279 0.983 0.012 0.104 0.876 0.004 0.004
#> GSM1009184 2 0.871 0.452 0.252 0.356 0.096 0.032 0.264
#> GSM1009198 1 0.218 0.717 0.920 0.008 0.024 0.048 0.000
#> GSM1009073 5 0.693 0.998 0.380 0.056 0.000 0.100 0.464
#> GSM1009087 2 0.591 0.560 0.076 0.692 0.016 0.040 0.176
#> GSM1009101 4 0.353 0.744 0.256 0.000 0.000 0.744 0.000
#> GSM1009115 2 0.400 0.318 0.004 0.748 0.232 0.016 0.000
#> GSM1009129 1 0.827 0.260 0.488 0.216 0.052 0.076 0.168
#> GSM1009143 4 0.644 0.735 0.120 0.008 0.032 0.616 0.224
#> GSM1009157 1 0.398 0.572 0.820 0.016 0.012 0.028 0.124
#> GSM1009171 3 0.247 0.984 0.012 0.104 0.884 0.000 0.000
#> GSM1009185 2 0.871 0.452 0.252 0.356 0.096 0.032 0.264
#> GSM1009199 1 0.250 0.714 0.912 0.012 0.024 0.044 0.008
#> GSM1009074 5 0.693 0.998 0.380 0.056 0.000 0.100 0.464
#> GSM1009088 2 0.591 0.560 0.076 0.692 0.016 0.040 0.176
#> GSM1009102 4 0.369 0.744 0.256 0.004 0.000 0.740 0.000
#> GSM1009116 2 0.400 0.318 0.004 0.748 0.232 0.016 0.000
#> GSM1009130 2 0.830 0.222 0.332 0.400 0.052 0.052 0.164
#> GSM1009144 4 0.644 0.735 0.120 0.008 0.032 0.616 0.224
#> GSM1009158 1 0.400 0.572 0.820 0.012 0.012 0.036 0.120
#> GSM1009172 3 0.291 0.983 0.012 0.104 0.872 0.004 0.008
#> GSM1009186 2 0.871 0.452 0.252 0.356 0.096 0.032 0.264
#> GSM1009200 1 0.218 0.717 0.920 0.008 0.024 0.048 0.000
#> GSM1009075 5 0.693 0.998 0.380 0.056 0.000 0.100 0.464
#> GSM1009089 2 0.607 0.558 0.088 0.680 0.016 0.040 0.176
#> GSM1009103 4 0.369 0.744 0.256 0.004 0.000 0.740 0.000
#> GSM1009117 2 0.400 0.318 0.004 0.748 0.232 0.016 0.000
#> GSM1009131 1 0.698 0.510 0.636 0.104 0.040 0.072 0.148
#> GSM1009145 4 0.647 0.736 0.128 0.008 0.032 0.616 0.216
#> GSM1009159 1 0.395 0.576 0.824 0.012 0.012 0.036 0.116
#> GSM1009173 3 0.304 0.983 0.012 0.104 0.868 0.008 0.008
#> GSM1009187 2 0.868 0.445 0.256 0.356 0.092 0.032 0.264
#> GSM1009201 1 0.218 0.717 0.920 0.008 0.024 0.048 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1009062 6 0.576 0.9945 0.216 0.040 0.000 0.084 0.020 0.640
#> GSM1009076 5 0.707 0.4980 0.024 0.360 0.072 0.012 0.448 0.084
#> GSM1009090 4 0.730 0.7563 0.172 0.060 0.000 0.524 0.080 0.164
#> GSM1009104 5 0.324 0.5703 0.004 0.000 0.244 0.000 0.752 0.000
#> GSM1009118 1 0.672 0.4458 0.564 0.236 0.000 0.076 0.068 0.056
#> GSM1009132 4 0.155 0.7557 0.044 0.008 0.000 0.940 0.004 0.004
#> GSM1009146 1 0.512 0.5879 0.728 0.072 0.004 0.028 0.028 0.140
#> GSM1009160 3 0.175 0.9652 0.008 0.024 0.940 0.004 0.008 0.016
#> GSM1009174 2 0.577 0.9969 0.148 0.664 0.024 0.004 0.132 0.028
#> GSM1009188 1 0.207 0.7018 0.916 0.012 0.000 0.056 0.004 0.012
#> GSM1009063 6 0.576 0.9945 0.216 0.040 0.000 0.084 0.020 0.640
#> GSM1009077 5 0.707 0.4980 0.024 0.360 0.072 0.012 0.448 0.084
#> GSM1009091 4 0.730 0.7563 0.172 0.060 0.000 0.524 0.080 0.164
#> GSM1009105 5 0.324 0.5703 0.004 0.000 0.244 0.000 0.752 0.000
#> GSM1009119 1 0.469 0.6422 0.768 0.100 0.000 0.056 0.028 0.048
#> GSM1009133 4 0.155 0.7557 0.044 0.008 0.000 0.940 0.004 0.004
#> GSM1009147 1 0.512 0.5879 0.728 0.072 0.004 0.028 0.028 0.140
#> GSM1009161 3 0.175 0.9652 0.008 0.024 0.940 0.004 0.008 0.016
#> GSM1009175 2 0.577 0.9969 0.148 0.664 0.024 0.004 0.132 0.028
#> GSM1009189 1 0.207 0.7018 0.916 0.012 0.000 0.056 0.004 0.012
#> GSM1009064 6 0.601 0.9919 0.216 0.052 0.000 0.084 0.024 0.624
#> GSM1009078 5 0.707 0.4430 0.044 0.364 0.040 0.012 0.448 0.092
#> GSM1009092 4 0.730 0.7563 0.172 0.060 0.000 0.524 0.080 0.164
#> GSM1009106 5 0.338 0.5702 0.004 0.000 0.244 0.004 0.748 0.000
#> GSM1009120 1 0.463 0.6436 0.772 0.100 0.000 0.052 0.028 0.048
#> GSM1009134 4 0.155 0.7557 0.044 0.008 0.000 0.940 0.004 0.004
#> GSM1009148 1 0.512 0.5879 0.728 0.072 0.004 0.028 0.028 0.140
#> GSM1009162 3 0.112 0.9611 0.008 0.004 0.964 0.000 0.008 0.016
#> GSM1009176 2 0.577 0.9916 0.144 0.664 0.024 0.004 0.136 0.028
#> GSM1009190 1 0.207 0.7018 0.916 0.012 0.000 0.056 0.004 0.012
#> GSM1009065 6 0.601 0.9919 0.216 0.052 0.000 0.084 0.024 0.624
#> GSM1009079 5 0.707 0.4980 0.024 0.360 0.072 0.012 0.448 0.084
#> GSM1009093 4 0.730 0.7563 0.172 0.060 0.000 0.524 0.080 0.164
#> GSM1009107 5 0.324 0.5703 0.004 0.000 0.244 0.000 0.752 0.000
#> GSM1009121 1 0.671 0.4659 0.576 0.216 0.000 0.076 0.076 0.056
#> GSM1009135 4 0.155 0.7557 0.044 0.008 0.000 0.940 0.004 0.004
#> GSM1009149 1 0.512 0.5879 0.728 0.072 0.004 0.028 0.028 0.140
#> GSM1009163 3 0.213 0.9631 0.008 0.024 0.924 0.012 0.008 0.024
#> GSM1009177 2 0.577 0.9916 0.144 0.664 0.024 0.004 0.136 0.028
#> GSM1009191 1 0.207 0.7018 0.916 0.012 0.000 0.056 0.004 0.012
#> GSM1009066 6 0.589 0.9939 0.216 0.044 0.000 0.084 0.024 0.632
#> GSM1009080 5 0.707 0.4980 0.024 0.360 0.072 0.012 0.448 0.084
#> GSM1009094 4 0.730 0.7563 0.172 0.060 0.000 0.524 0.080 0.164
#> GSM1009108 5 0.338 0.5702 0.004 0.000 0.244 0.004 0.748 0.000
#> GSM1009122 1 0.734 0.3283 0.504 0.248 0.004 0.076 0.112 0.056
#> GSM1009136 4 0.187 0.7549 0.044 0.012 0.004 0.928 0.012 0.000
#> GSM1009150 1 0.512 0.5879 0.728 0.072 0.004 0.028 0.028 0.140
#> GSM1009164 3 0.213 0.9631 0.008 0.024 0.924 0.012 0.008 0.024
#> GSM1009178 2 0.577 0.9969 0.148 0.664 0.024 0.004 0.132 0.028
#> GSM1009192 1 0.207 0.7018 0.916 0.012 0.000 0.056 0.004 0.012
#> GSM1009067 6 0.576 0.9945 0.216 0.040 0.000 0.084 0.020 0.640
#> GSM1009081 5 0.707 0.4980 0.024 0.360 0.072 0.012 0.448 0.084
#> GSM1009095 4 0.750 0.7530 0.172 0.072 0.004 0.524 0.092 0.136
#> GSM1009109 5 0.338 0.5702 0.004 0.000 0.244 0.004 0.748 0.000
#> GSM1009123 1 0.500 0.6326 0.744 0.108 0.000 0.072 0.028 0.048
#> GSM1009137 4 0.155 0.7557 0.044 0.008 0.000 0.940 0.004 0.004
#> GSM1009151 1 0.512 0.5879 0.728 0.072 0.004 0.028 0.028 0.140
#> GSM1009165 3 0.155 0.9618 0.008 0.004 0.948 0.008 0.008 0.024
#> GSM1009179 2 0.577 0.9969 0.148 0.664 0.024 0.004 0.132 0.028
#> GSM1009193 1 0.196 0.7014 0.920 0.008 0.000 0.056 0.004 0.012
#> GSM1009068 6 0.576 0.9945 0.216 0.040 0.000 0.084 0.020 0.640
#> GSM1009082 5 0.707 0.4980 0.024 0.360 0.072 0.012 0.448 0.084
#> GSM1009096 4 0.730 0.7563 0.172 0.060 0.000 0.524 0.080 0.164
#> GSM1009110 5 0.406 0.5643 0.004 0.004 0.244 0.008 0.724 0.016
#> GSM1009124 1 0.554 0.5895 0.676 0.184 0.000 0.072 0.024 0.044
#> GSM1009138 4 0.155 0.7557 0.044 0.008 0.000 0.940 0.004 0.004
#> GSM1009152 1 0.512 0.5879 0.728 0.072 0.004 0.028 0.028 0.140
#> GSM1009166 3 0.112 0.9611 0.008 0.004 0.964 0.000 0.008 0.016
#> GSM1009180 2 0.577 0.9969 0.148 0.664 0.024 0.004 0.132 0.028
#> GSM1009194 1 0.207 0.7018 0.916 0.012 0.000 0.056 0.004 0.012
#> GSM1009069 6 0.601 0.9919 0.216 0.052 0.000 0.084 0.024 0.624
#> GSM1009083 5 0.707 0.4980 0.024 0.360 0.072 0.012 0.448 0.084
#> GSM1009097 4 0.730 0.7563 0.172 0.060 0.000 0.524 0.080 0.164
#> GSM1009111 5 0.324 0.5703 0.004 0.000 0.244 0.000 0.752 0.000
#> GSM1009125 1 0.761 0.3111 0.492 0.248 0.016 0.076 0.112 0.056
#> GSM1009139 4 0.155 0.7557 0.044 0.008 0.000 0.940 0.004 0.004
#> GSM1009153 1 0.512 0.5879 0.728 0.072 0.004 0.028 0.028 0.140
#> GSM1009167 3 0.235 0.9410 0.008 0.032 0.912 0.008 0.008 0.032
#> GSM1009181 2 0.577 0.9916 0.144 0.664 0.024 0.004 0.136 0.028
#> GSM1009195 1 0.216 0.7006 0.912 0.016 0.000 0.056 0.004 0.012
#> GSM1009070 6 0.572 0.9897 0.220 0.036 0.000 0.084 0.020 0.640
#> GSM1009084 5 0.707 0.4980 0.024 0.360 0.072 0.012 0.448 0.084
#> GSM1009098 4 0.733 0.7562 0.172 0.064 0.000 0.524 0.080 0.160
#> GSM1009112 5 0.324 0.5703 0.004 0.000 0.244 0.000 0.752 0.000
#> GSM1009126 1 0.554 0.5895 0.676 0.184 0.000 0.072 0.024 0.044
#> GSM1009140 4 0.155 0.7557 0.044 0.008 0.000 0.940 0.004 0.004
#> GSM1009154 1 0.512 0.5879 0.728 0.072 0.004 0.028 0.028 0.140
#> GSM1009168 3 0.199 0.9487 0.008 0.032 0.928 0.004 0.008 0.020
#> GSM1009182 2 0.577 0.9969 0.148 0.664 0.024 0.004 0.132 0.028
#> GSM1009196 1 0.207 0.7018 0.916 0.012 0.000 0.056 0.004 0.012
#> GSM1009071 6 0.595 0.9932 0.216 0.048 0.000 0.084 0.024 0.628
#> GSM1009085 5 0.707 0.4980 0.024 0.360 0.072 0.012 0.448 0.084
#> GSM1009099 4 0.733 0.7562 0.172 0.064 0.000 0.524 0.080 0.160
#> GSM1009113 5 0.338 0.5702 0.004 0.000 0.244 0.004 0.748 0.000
#> GSM1009127 1 0.504 0.6305 0.740 0.112 0.000 0.072 0.028 0.048
#> GSM1009141 4 0.155 0.7557 0.044 0.008 0.000 0.940 0.004 0.004
#> GSM1009155 1 0.512 0.5879 0.728 0.072 0.004 0.028 0.028 0.140
#> GSM1009169 3 0.208 0.9470 0.008 0.032 0.924 0.004 0.008 0.024
#> GSM1009183 2 0.577 0.9969 0.148 0.664 0.024 0.004 0.132 0.028
#> GSM1009197 1 0.207 0.7018 0.916 0.012 0.000 0.056 0.004 0.012
#> GSM1009072 6 0.576 0.9945 0.216 0.040 0.000 0.084 0.020 0.640
#> GSM1009086 5 0.707 0.4980 0.024 0.360 0.072 0.012 0.448 0.084
#> GSM1009100 4 0.733 0.7562 0.172 0.064 0.000 0.524 0.080 0.160
#> GSM1009114 5 0.377 0.5668 0.004 0.004 0.244 0.004 0.736 0.008
#> GSM1009128 1 0.636 0.5174 0.612 0.200 0.000 0.076 0.064 0.048
#> GSM1009142 4 0.155 0.7557 0.044 0.008 0.000 0.940 0.004 0.004
#> GSM1009156 1 0.512 0.5879 0.728 0.072 0.004 0.028 0.028 0.140
#> GSM1009170 3 0.213 0.9631 0.008 0.024 0.924 0.012 0.008 0.024
#> GSM1009184 2 0.584 0.9954 0.148 0.660 0.024 0.004 0.132 0.032
#> GSM1009198 1 0.207 0.7018 0.916 0.012 0.000 0.056 0.004 0.012
#> GSM1009073 6 0.595 0.9932 0.216 0.048 0.000 0.084 0.024 0.628
#> GSM1009087 5 0.707 0.4430 0.044 0.364 0.040 0.012 0.448 0.092
#> GSM1009101 4 0.733 0.7562 0.172 0.064 0.000 0.524 0.080 0.160
#> GSM1009115 5 0.324 0.5703 0.004 0.000 0.244 0.000 0.752 0.000
#> GSM1009129 1 0.769 0.2862 0.480 0.248 0.016 0.072 0.128 0.056
#> GSM1009143 4 0.211 0.7534 0.044 0.016 0.004 0.920 0.012 0.004
#> GSM1009157 1 0.518 0.5846 0.724 0.076 0.004 0.028 0.028 0.140
#> GSM1009171 3 0.187 0.9652 0.008 0.012 0.936 0.012 0.008 0.024
#> GSM1009185 2 0.577 0.9969 0.148 0.664 0.024 0.004 0.132 0.028
#> GSM1009199 1 0.216 0.7006 0.912 0.016 0.000 0.056 0.004 0.012
#> GSM1009074 6 0.576 0.9945 0.216 0.040 0.000 0.084 0.020 0.640
#> GSM1009088 5 0.707 0.4430 0.044 0.364 0.040 0.012 0.448 0.092
#> GSM1009102 4 0.750 0.7530 0.172 0.072 0.004 0.524 0.092 0.136
#> GSM1009116 5 0.338 0.5702 0.004 0.000 0.244 0.004 0.748 0.000
#> GSM1009130 1 0.824 0.0108 0.372 0.236 0.032 0.032 0.248 0.080
#> GSM1009144 4 0.211 0.7534 0.044 0.016 0.004 0.920 0.012 0.004
#> GSM1009158 1 0.512 0.5879 0.728 0.072 0.004 0.028 0.028 0.140
#> GSM1009172 3 0.175 0.9652 0.008 0.024 0.940 0.004 0.008 0.016
#> GSM1009186 2 0.584 0.9954 0.148 0.660 0.024 0.004 0.132 0.032
#> GSM1009200 1 0.207 0.7018 0.916 0.012 0.000 0.056 0.004 0.012
#> GSM1009075 6 0.582 0.9943 0.216 0.044 0.000 0.084 0.020 0.636
#> GSM1009089 5 0.704 0.4217 0.056 0.364 0.028 0.012 0.448 0.092
#> GSM1009103 4 0.750 0.7530 0.172 0.072 0.004 0.524 0.092 0.136
#> GSM1009117 5 0.377 0.5668 0.004 0.004 0.244 0.004 0.736 0.008
#> GSM1009131 1 0.671 0.4642 0.576 0.216 0.000 0.072 0.080 0.056
#> GSM1009145 4 0.187 0.7549 0.044 0.012 0.004 0.928 0.012 0.000
#> GSM1009159 1 0.512 0.5879 0.728 0.072 0.004 0.028 0.028 0.140
#> GSM1009173 3 0.104 0.9648 0.008 0.000 0.968 0.008 0.008 0.008
#> GSM1009187 2 0.584 0.9954 0.148 0.660 0.024 0.004 0.132 0.032
#> GSM1009201 1 0.207 0.7018 0.916 0.012 0.000 0.056 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)
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 temperature(p) time(p) specimen(p) k
#> CV:kmeans 131 0.992 0.987 1.87e-22 2
#> CV:kmeans 55 0.999 1.000 6.87e-12 3
#> CV:kmeans 104 1.000 1.000 1.14e-53 4
#> CV:kmeans 108 1.000 1.000 5.93e-74 5
#> CV:kmeans 119 1.000 1.000 5.46e-100 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 140 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'CV' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.989 0.995 0.5005 0.500 0.500
#> 3 3 0.643 0.858 0.899 0.3118 0.825 0.657
#> 4 4 0.702 0.721 0.825 0.1241 0.877 0.664
#> 5 5 0.784 0.679 0.763 0.0659 0.853 0.512
#> 6 6 0.817 0.733 0.744 0.0404 0.851 0.441
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
#> GSM1009062 1 0.0000 0.995 1.000 0.000
#> GSM1009076 2 0.0000 0.996 0.000 1.000
#> GSM1009090 1 0.0000 0.995 1.000 0.000
#> GSM1009104 2 0.0000 0.996 0.000 1.000
#> GSM1009118 2 0.8267 0.645 0.260 0.740
#> GSM1009132 1 0.0000 0.995 1.000 0.000
#> GSM1009146 1 0.0000 0.995 1.000 0.000
#> GSM1009160 2 0.0000 0.996 0.000 1.000
#> GSM1009174 2 0.0000 0.996 0.000 1.000
#> GSM1009188 1 0.0000 0.995 1.000 0.000
#> GSM1009063 1 0.0000 0.995 1.000 0.000
#> GSM1009077 2 0.0000 0.996 0.000 1.000
#> GSM1009091 1 0.0000 0.995 1.000 0.000
#> GSM1009105 2 0.0000 0.996 0.000 1.000
#> GSM1009119 1 0.0000 0.995 1.000 0.000
#> GSM1009133 1 0.0000 0.995 1.000 0.000
#> GSM1009147 1 0.0000 0.995 1.000 0.000
#> GSM1009161 2 0.0000 0.996 0.000 1.000
#> GSM1009175 2 0.0000 0.996 0.000 1.000
#> GSM1009189 1 0.0000 0.995 1.000 0.000
#> GSM1009064 1 0.0000 0.995 1.000 0.000
#> GSM1009078 2 0.0000 0.996 0.000 1.000
#> GSM1009092 1 0.0000 0.995 1.000 0.000
#> GSM1009106 2 0.0000 0.996 0.000 1.000
#> GSM1009120 1 0.0000 0.995 1.000 0.000
#> GSM1009134 1 0.0000 0.995 1.000 0.000
#> GSM1009148 1 0.0000 0.995 1.000 0.000
#> GSM1009162 2 0.0000 0.996 0.000 1.000
#> GSM1009176 2 0.0000 0.996 0.000 1.000
#> GSM1009190 1 0.0000 0.995 1.000 0.000
#> GSM1009065 1 0.0000 0.995 1.000 0.000
#> GSM1009079 2 0.0000 0.996 0.000 1.000
#> GSM1009093 1 0.0000 0.995 1.000 0.000
#> GSM1009107 2 0.0000 0.996 0.000 1.000
#> GSM1009121 2 0.0376 0.992 0.004 0.996
#> GSM1009135 1 0.0000 0.995 1.000 0.000
#> GSM1009149 1 0.0000 0.995 1.000 0.000
#> GSM1009163 2 0.0000 0.996 0.000 1.000
#> GSM1009177 2 0.0000 0.996 0.000 1.000
#> GSM1009191 1 0.0000 0.995 1.000 0.000
#> GSM1009066 1 0.0000 0.995 1.000 0.000
#> GSM1009080 2 0.0000 0.996 0.000 1.000
#> GSM1009094 1 0.0000 0.995 1.000 0.000
#> GSM1009108 2 0.0000 0.996 0.000 1.000
#> GSM1009122 2 0.0000 0.996 0.000 1.000
#> GSM1009136 1 0.0000 0.995 1.000 0.000
#> GSM1009150 1 0.0000 0.995 1.000 0.000
#> GSM1009164 2 0.0000 0.996 0.000 1.000
#> GSM1009178 2 0.0000 0.996 0.000 1.000
#> GSM1009192 1 0.0000 0.995 1.000 0.000
#> GSM1009067 1 0.0000 0.995 1.000 0.000
#> GSM1009081 2 0.0000 0.996 0.000 1.000
#> GSM1009095 1 0.0000 0.995 1.000 0.000
#> GSM1009109 2 0.0000 0.996 0.000 1.000
#> GSM1009123 1 0.0000 0.995 1.000 0.000
#> GSM1009137 1 0.0000 0.995 1.000 0.000
#> GSM1009151 1 0.0000 0.995 1.000 0.000
#> GSM1009165 2 0.0000 0.996 0.000 1.000
#> GSM1009179 2 0.0000 0.996 0.000 1.000
#> GSM1009193 1 0.0000 0.995 1.000 0.000
#> GSM1009068 1 0.0000 0.995 1.000 0.000
#> GSM1009082 2 0.0000 0.996 0.000 1.000
#> GSM1009096 1 0.0000 0.995 1.000 0.000
#> GSM1009110 2 0.0000 0.996 0.000 1.000
#> GSM1009124 1 0.0000 0.995 1.000 0.000
#> GSM1009138 1 0.0000 0.995 1.000 0.000
#> GSM1009152 1 0.0000 0.995 1.000 0.000
#> GSM1009166 2 0.0000 0.996 0.000 1.000
#> GSM1009180 2 0.0000 0.996 0.000 1.000
#> GSM1009194 1 0.0000 0.995 1.000 0.000
#> GSM1009069 1 0.0000 0.995 1.000 0.000
#> GSM1009083 2 0.0000 0.996 0.000 1.000
#> GSM1009097 1 0.0000 0.995 1.000 0.000
#> GSM1009111 2 0.0000 0.996 0.000 1.000
#> GSM1009125 2 0.0000 0.996 0.000 1.000
#> GSM1009139 1 0.0000 0.995 1.000 0.000
#> GSM1009153 1 0.0000 0.995 1.000 0.000
#> GSM1009167 2 0.0000 0.996 0.000 1.000
#> GSM1009181 2 0.0000 0.996 0.000 1.000
#> GSM1009195 1 0.6247 0.815 0.844 0.156
#> GSM1009070 1 0.0000 0.995 1.000 0.000
#> GSM1009084 2 0.0000 0.996 0.000 1.000
#> GSM1009098 1 0.0000 0.995 1.000 0.000
#> GSM1009112 2 0.0000 0.996 0.000 1.000
#> GSM1009126 1 0.0000 0.995 1.000 0.000
#> GSM1009140 1 0.0000 0.995 1.000 0.000
#> GSM1009154 1 0.0000 0.995 1.000 0.000
#> GSM1009168 2 0.0000 0.996 0.000 1.000
#> GSM1009182 2 0.0000 0.996 0.000 1.000
#> GSM1009196 1 0.0000 0.995 1.000 0.000
#> GSM1009071 1 0.0000 0.995 1.000 0.000
#> GSM1009085 2 0.0000 0.996 0.000 1.000
#> GSM1009099 1 0.0000 0.995 1.000 0.000
#> GSM1009113 2 0.0000 0.996 0.000 1.000
#> GSM1009127 1 0.0000 0.995 1.000 0.000
#> GSM1009141 1 0.0000 0.995 1.000 0.000
#> GSM1009155 1 0.0000 0.995 1.000 0.000
#> GSM1009169 2 0.0000 0.996 0.000 1.000
#> GSM1009183 2 0.0000 0.996 0.000 1.000
#> GSM1009197 1 0.0000 0.995 1.000 0.000
#> GSM1009072 1 0.0000 0.995 1.000 0.000
#> GSM1009086 2 0.0000 0.996 0.000 1.000
#> GSM1009100 1 0.0000 0.995 1.000 0.000
#> GSM1009114 2 0.0000 0.996 0.000 1.000
#> GSM1009128 2 0.0000 0.996 0.000 1.000
#> GSM1009142 1 0.0000 0.995 1.000 0.000
#> GSM1009156 1 0.1414 0.975 0.980 0.020
#> GSM1009170 2 0.0000 0.996 0.000 1.000
#> GSM1009184 2 0.0000 0.996 0.000 1.000
#> GSM1009198 1 0.0000 0.995 1.000 0.000
#> GSM1009073 1 0.0000 0.995 1.000 0.000
#> GSM1009087 2 0.0000 0.996 0.000 1.000
#> GSM1009101 1 0.0000 0.995 1.000 0.000
#> GSM1009115 2 0.0000 0.996 0.000 1.000
#> GSM1009129 2 0.0000 0.996 0.000 1.000
#> GSM1009143 1 0.0000 0.995 1.000 0.000
#> GSM1009157 1 0.7528 0.726 0.784 0.216
#> GSM1009171 2 0.0000 0.996 0.000 1.000
#> GSM1009185 2 0.0000 0.996 0.000 1.000
#> GSM1009199 1 0.0000 0.995 1.000 0.000
#> GSM1009074 1 0.0000 0.995 1.000 0.000
#> GSM1009088 2 0.0000 0.996 0.000 1.000
#> GSM1009102 1 0.0000 0.995 1.000 0.000
#> GSM1009116 2 0.0000 0.996 0.000 1.000
#> GSM1009130 2 0.0000 0.996 0.000 1.000
#> GSM1009144 1 0.0000 0.995 1.000 0.000
#> GSM1009158 1 0.0000 0.995 1.000 0.000
#> GSM1009172 2 0.0000 0.996 0.000 1.000
#> GSM1009186 2 0.0000 0.996 0.000 1.000
#> GSM1009200 1 0.0000 0.995 1.000 0.000
#> GSM1009075 1 0.0000 0.995 1.000 0.000
#> GSM1009089 2 0.0000 0.996 0.000 1.000
#> GSM1009103 1 0.0000 0.995 1.000 0.000
#> GSM1009117 2 0.0000 0.996 0.000 1.000
#> GSM1009131 2 0.0000 0.996 0.000 1.000
#> GSM1009145 1 0.0000 0.995 1.000 0.000
#> GSM1009159 1 0.0000 0.995 1.000 0.000
#> GSM1009173 2 0.0000 0.996 0.000 1.000
#> GSM1009187 2 0.0000 0.996 0.000 1.000
#> GSM1009201 1 0.0000 0.995 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1009062 1 0.5201 0.779 0.760 0.004 0.236
#> GSM1009076 2 0.1529 0.912 0.040 0.960 0.000
#> GSM1009090 3 0.1163 0.916 0.028 0.000 0.972
#> GSM1009104 2 0.0000 0.914 0.000 1.000 0.000
#> GSM1009118 3 0.7525 0.640 0.228 0.096 0.676
#> GSM1009132 3 0.0892 0.915 0.020 0.000 0.980
#> GSM1009146 1 0.1753 0.875 0.952 0.000 0.048
#> GSM1009160 2 0.2486 0.898 0.008 0.932 0.060
#> GSM1009174 2 0.4784 0.831 0.200 0.796 0.004
#> GSM1009188 1 0.3116 0.866 0.892 0.000 0.108
#> GSM1009063 1 0.5201 0.779 0.760 0.004 0.236
#> GSM1009077 2 0.1529 0.912 0.040 0.960 0.000
#> GSM1009091 3 0.1163 0.916 0.028 0.000 0.972
#> GSM1009105 2 0.0000 0.914 0.000 1.000 0.000
#> GSM1009119 3 0.6008 0.497 0.372 0.000 0.628
#> GSM1009133 3 0.0892 0.915 0.020 0.000 0.980
#> GSM1009147 1 0.1753 0.875 0.952 0.000 0.048
#> GSM1009161 2 0.2486 0.898 0.008 0.932 0.060
#> GSM1009175 2 0.4784 0.831 0.200 0.796 0.004
#> GSM1009189 1 0.3116 0.866 0.892 0.000 0.108
#> GSM1009064 1 0.5201 0.779 0.760 0.004 0.236
#> GSM1009078 2 0.1529 0.912 0.040 0.960 0.000
#> GSM1009092 3 0.1163 0.916 0.028 0.000 0.972
#> GSM1009106 2 0.0000 0.914 0.000 1.000 0.000
#> GSM1009120 1 0.3941 0.818 0.844 0.000 0.156
#> GSM1009134 3 0.0892 0.915 0.020 0.000 0.980
#> GSM1009148 1 0.1753 0.875 0.952 0.000 0.048
#> GSM1009162 2 0.2486 0.898 0.008 0.932 0.060
#> GSM1009176 2 0.4784 0.831 0.200 0.796 0.004
#> GSM1009190 1 0.3116 0.866 0.892 0.000 0.108
#> GSM1009065 1 0.5201 0.779 0.760 0.004 0.236
#> GSM1009079 2 0.1529 0.912 0.040 0.960 0.000
#> GSM1009093 3 0.1163 0.916 0.028 0.000 0.972
#> GSM1009107 2 0.0000 0.914 0.000 1.000 0.000
#> GSM1009121 3 0.7766 0.643 0.176 0.148 0.676
#> GSM1009135 3 0.0892 0.915 0.020 0.000 0.980
#> GSM1009149 1 0.1753 0.875 0.952 0.000 0.048
#> GSM1009163 2 0.2486 0.898 0.008 0.932 0.060
#> GSM1009177 2 0.4784 0.831 0.200 0.796 0.004
#> GSM1009191 1 0.3116 0.866 0.892 0.000 0.108
#> GSM1009066 1 0.5201 0.779 0.760 0.004 0.236
#> GSM1009080 2 0.1529 0.912 0.040 0.960 0.000
#> GSM1009094 3 0.1163 0.916 0.028 0.000 0.972
#> GSM1009108 2 0.0000 0.914 0.000 1.000 0.000
#> GSM1009122 2 0.6239 0.749 0.072 0.768 0.160
#> GSM1009136 3 0.0892 0.915 0.020 0.000 0.980
#> GSM1009150 1 0.1753 0.875 0.952 0.000 0.048
#> GSM1009164 2 0.2486 0.898 0.008 0.932 0.060
#> GSM1009178 2 0.4784 0.831 0.200 0.796 0.004
#> GSM1009192 1 0.3116 0.866 0.892 0.000 0.108
#> GSM1009067 1 0.5201 0.779 0.760 0.004 0.236
#> GSM1009081 2 0.1529 0.912 0.040 0.960 0.000
#> GSM1009095 3 0.1163 0.916 0.028 0.000 0.972
#> GSM1009109 2 0.0000 0.914 0.000 1.000 0.000
#> GSM1009123 3 0.5291 0.661 0.268 0.000 0.732
#> GSM1009137 3 0.0892 0.915 0.020 0.000 0.980
#> GSM1009151 1 0.1753 0.875 0.952 0.000 0.048
#> GSM1009165 2 0.2486 0.898 0.008 0.932 0.060
#> GSM1009179 2 0.4784 0.831 0.200 0.796 0.004
#> GSM1009193 1 0.3116 0.866 0.892 0.000 0.108
#> GSM1009068 1 0.5201 0.779 0.760 0.004 0.236
#> GSM1009082 2 0.1529 0.912 0.040 0.960 0.000
#> GSM1009096 3 0.1163 0.916 0.028 0.000 0.972
#> GSM1009110 2 0.0000 0.914 0.000 1.000 0.000
#> GSM1009124 3 0.5443 0.661 0.260 0.004 0.736
#> GSM1009138 3 0.0892 0.915 0.020 0.000 0.980
#> GSM1009152 1 0.1753 0.875 0.952 0.000 0.048
#> GSM1009166 2 0.2486 0.898 0.008 0.932 0.060
#> GSM1009180 2 0.4784 0.831 0.200 0.796 0.004
#> GSM1009194 1 0.3116 0.866 0.892 0.000 0.108
#> GSM1009069 1 0.5551 0.759 0.768 0.020 0.212
#> GSM1009083 2 0.1529 0.912 0.040 0.960 0.000
#> GSM1009097 3 0.1163 0.916 0.028 0.000 0.972
#> GSM1009111 2 0.0000 0.914 0.000 1.000 0.000
#> GSM1009125 2 0.6062 0.758 0.064 0.776 0.160
#> GSM1009139 3 0.0892 0.915 0.020 0.000 0.980
#> GSM1009153 1 0.1753 0.875 0.952 0.000 0.048
#> GSM1009167 2 0.2486 0.898 0.008 0.932 0.060
#> GSM1009181 2 0.4784 0.831 0.200 0.796 0.004
#> GSM1009195 1 0.2959 0.864 0.900 0.000 0.100
#> GSM1009070 1 0.5201 0.779 0.760 0.004 0.236
#> GSM1009084 2 0.1529 0.912 0.040 0.960 0.000
#> GSM1009098 3 0.1163 0.916 0.028 0.000 0.972
#> GSM1009112 2 0.0000 0.914 0.000 1.000 0.000
#> GSM1009126 3 0.5443 0.661 0.260 0.004 0.736
#> GSM1009140 3 0.0892 0.915 0.020 0.000 0.980
#> GSM1009154 1 0.1753 0.875 0.952 0.000 0.048
#> GSM1009168 2 0.2486 0.898 0.008 0.932 0.060
#> GSM1009182 2 0.4784 0.831 0.200 0.796 0.004
#> GSM1009196 1 0.3116 0.866 0.892 0.000 0.108
#> GSM1009071 1 0.5201 0.779 0.760 0.004 0.236
#> GSM1009085 2 0.1529 0.912 0.040 0.960 0.000
#> GSM1009099 3 0.1163 0.916 0.028 0.000 0.972
#> GSM1009113 2 0.0000 0.914 0.000 1.000 0.000
#> GSM1009127 3 0.5948 0.501 0.360 0.000 0.640
#> GSM1009141 3 0.0892 0.915 0.020 0.000 0.980
#> GSM1009155 1 0.1753 0.875 0.952 0.000 0.048
#> GSM1009169 2 0.2486 0.898 0.008 0.932 0.060
#> GSM1009183 2 0.4784 0.831 0.200 0.796 0.004
#> GSM1009197 1 0.3116 0.866 0.892 0.000 0.108
#> GSM1009072 1 0.5201 0.779 0.760 0.004 0.236
#> GSM1009086 2 0.1529 0.912 0.040 0.960 0.000
#> GSM1009100 3 0.1163 0.916 0.028 0.000 0.972
#> GSM1009114 2 0.0000 0.914 0.000 1.000 0.000
#> GSM1009128 3 0.7552 0.659 0.168 0.140 0.692
#> GSM1009142 3 0.0892 0.915 0.020 0.000 0.980
#> GSM1009156 1 0.1711 0.859 0.960 0.008 0.032
#> GSM1009170 2 0.2486 0.898 0.008 0.932 0.060
#> GSM1009184 2 0.4784 0.831 0.200 0.796 0.004
#> GSM1009198 1 0.3116 0.866 0.892 0.000 0.108
#> GSM1009073 1 0.5201 0.779 0.760 0.004 0.236
#> GSM1009087 2 0.1529 0.912 0.040 0.960 0.000
#> GSM1009101 3 0.1163 0.916 0.028 0.000 0.972
#> GSM1009115 2 0.0000 0.914 0.000 1.000 0.000
#> GSM1009129 2 0.4475 0.852 0.064 0.864 0.072
#> GSM1009143 3 0.0892 0.915 0.020 0.000 0.980
#> GSM1009157 1 0.1015 0.844 0.980 0.008 0.012
#> GSM1009171 2 0.2486 0.898 0.008 0.932 0.060
#> GSM1009185 2 0.4784 0.831 0.200 0.796 0.004
#> GSM1009199 1 0.3038 0.865 0.896 0.000 0.104
#> GSM1009074 1 0.5201 0.779 0.760 0.004 0.236
#> GSM1009088 2 0.1529 0.912 0.040 0.960 0.000
#> GSM1009102 3 0.1163 0.916 0.028 0.000 0.972
#> GSM1009116 2 0.0000 0.914 0.000 1.000 0.000
#> GSM1009130 2 0.3083 0.885 0.060 0.916 0.024
#> GSM1009144 3 0.0892 0.915 0.020 0.000 0.980
#> GSM1009158 1 0.1753 0.875 0.952 0.000 0.048
#> GSM1009172 2 0.2486 0.898 0.008 0.932 0.060
#> GSM1009186 2 0.4784 0.831 0.200 0.796 0.004
#> GSM1009200 1 0.3116 0.866 0.892 0.000 0.108
#> GSM1009075 1 0.5201 0.779 0.760 0.004 0.236
#> GSM1009089 2 0.4452 0.800 0.192 0.808 0.000
#> GSM1009103 3 0.1163 0.916 0.028 0.000 0.972
#> GSM1009117 2 0.0000 0.914 0.000 1.000 0.000
#> GSM1009131 2 0.7278 0.674 0.152 0.712 0.136
#> GSM1009145 3 0.0892 0.915 0.020 0.000 0.980
#> GSM1009159 1 0.1860 0.875 0.948 0.000 0.052
#> GSM1009173 2 0.2486 0.898 0.008 0.932 0.060
#> GSM1009187 2 0.4784 0.831 0.200 0.796 0.004
#> GSM1009201 1 0.3116 0.866 0.892 0.000 0.108
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1009062 1 0.5542 0.75107 0.732 0.044 0.020 0.204
#> GSM1009076 2 0.2408 0.66186 0.000 0.896 0.104 0.000
#> GSM1009090 4 0.1389 0.94944 0.048 0.000 0.000 0.952
#> GSM1009104 2 0.4925 0.34655 0.000 0.572 0.428 0.000
#> GSM1009118 3 0.6742 0.54148 0.100 0.076 0.700 0.124
#> GSM1009132 4 0.0000 0.94855 0.000 0.000 0.000 1.000
#> GSM1009146 1 0.0524 0.86313 0.988 0.004 0.000 0.008
#> GSM1009160 3 0.3402 0.74128 0.000 0.164 0.832 0.004
#> GSM1009174 2 0.4633 0.59248 0.048 0.780 0.172 0.000
#> GSM1009188 1 0.2860 0.85282 0.904 0.004 0.048 0.044
#> GSM1009063 1 0.5542 0.75107 0.732 0.044 0.020 0.204
#> GSM1009077 2 0.2408 0.66186 0.000 0.896 0.104 0.000
#> GSM1009091 4 0.1389 0.94944 0.048 0.000 0.000 0.952
#> GSM1009105 2 0.4925 0.34655 0.000 0.572 0.428 0.000
#> GSM1009119 1 0.4819 0.74398 0.784 0.004 0.060 0.152
#> GSM1009133 4 0.0000 0.94855 0.000 0.000 0.000 1.000
#> GSM1009147 1 0.0524 0.86313 0.988 0.004 0.000 0.008
#> GSM1009161 3 0.3402 0.74128 0.000 0.164 0.832 0.004
#> GSM1009175 2 0.4633 0.59248 0.048 0.780 0.172 0.000
#> GSM1009189 1 0.2860 0.85282 0.904 0.004 0.048 0.044
#> GSM1009064 1 0.5542 0.75107 0.732 0.044 0.020 0.204
#> GSM1009078 2 0.2408 0.66186 0.000 0.896 0.104 0.000
#> GSM1009092 4 0.1389 0.94944 0.048 0.000 0.000 0.952
#> GSM1009106 2 0.4925 0.34655 0.000 0.572 0.428 0.000
#> GSM1009120 1 0.2944 0.85069 0.900 0.004 0.052 0.044
#> GSM1009134 4 0.0000 0.94855 0.000 0.000 0.000 1.000
#> GSM1009148 1 0.0524 0.86313 0.988 0.004 0.000 0.008
#> GSM1009162 3 0.3402 0.74128 0.000 0.164 0.832 0.004
#> GSM1009176 2 0.4633 0.59248 0.048 0.780 0.172 0.000
#> GSM1009190 1 0.2860 0.85282 0.904 0.004 0.048 0.044
#> GSM1009065 1 0.5542 0.75107 0.732 0.044 0.020 0.204
#> GSM1009079 2 0.2408 0.66186 0.000 0.896 0.104 0.000
#> GSM1009093 4 0.1389 0.94944 0.048 0.000 0.000 0.952
#> GSM1009107 2 0.4925 0.34655 0.000 0.572 0.428 0.000
#> GSM1009121 3 0.6453 0.55272 0.064 0.084 0.716 0.136
#> GSM1009135 4 0.0000 0.94855 0.000 0.000 0.000 1.000
#> GSM1009149 1 0.0524 0.86313 0.988 0.004 0.000 0.008
#> GSM1009163 3 0.3402 0.74128 0.000 0.164 0.832 0.004
#> GSM1009177 2 0.4633 0.59248 0.048 0.780 0.172 0.000
#> GSM1009191 1 0.2860 0.85282 0.904 0.004 0.048 0.044
#> GSM1009066 1 0.5542 0.75107 0.732 0.044 0.020 0.204
#> GSM1009080 2 0.2408 0.66186 0.000 0.896 0.104 0.000
#> GSM1009094 4 0.1389 0.94944 0.048 0.000 0.000 0.952
#> GSM1009108 2 0.4925 0.34655 0.000 0.572 0.428 0.000
#> GSM1009122 3 0.5194 0.59913 0.056 0.112 0.792 0.040
#> GSM1009136 4 0.0000 0.94855 0.000 0.000 0.000 1.000
#> GSM1009150 1 0.0524 0.86313 0.988 0.004 0.000 0.008
#> GSM1009164 3 0.3402 0.74128 0.000 0.164 0.832 0.004
#> GSM1009178 2 0.4633 0.59248 0.048 0.780 0.172 0.000
#> GSM1009192 1 0.2860 0.85282 0.904 0.004 0.048 0.044
#> GSM1009067 1 0.5542 0.75107 0.732 0.044 0.020 0.204
#> GSM1009081 2 0.2408 0.66186 0.000 0.896 0.104 0.000
#> GSM1009095 4 0.1389 0.94944 0.048 0.000 0.000 0.952
#> GSM1009109 2 0.4925 0.34655 0.000 0.572 0.428 0.000
#> GSM1009123 4 0.7372 0.12402 0.400 0.004 0.140 0.456
#> GSM1009137 4 0.0000 0.94855 0.000 0.000 0.000 1.000
#> GSM1009151 1 0.0524 0.86313 0.988 0.004 0.000 0.008
#> GSM1009165 3 0.3402 0.74128 0.000 0.164 0.832 0.004
#> GSM1009179 2 0.4633 0.59248 0.048 0.780 0.172 0.000
#> GSM1009193 1 0.2860 0.85282 0.904 0.004 0.048 0.044
#> GSM1009068 1 0.5542 0.75107 0.732 0.044 0.020 0.204
#> GSM1009082 2 0.2408 0.66186 0.000 0.896 0.104 0.000
#> GSM1009096 4 0.1389 0.94944 0.048 0.000 0.000 0.952
#> GSM1009110 2 0.4925 0.34655 0.000 0.572 0.428 0.000
#> GSM1009124 3 0.8235 0.00949 0.240 0.016 0.404 0.340
#> GSM1009138 4 0.0000 0.94855 0.000 0.000 0.000 1.000
#> GSM1009152 1 0.0524 0.86313 0.988 0.004 0.000 0.008
#> GSM1009166 3 0.3402 0.74128 0.000 0.164 0.832 0.004
#> GSM1009180 2 0.4633 0.59248 0.048 0.780 0.172 0.000
#> GSM1009194 1 0.2860 0.85282 0.904 0.004 0.048 0.044
#> GSM1009069 1 0.5542 0.75107 0.732 0.044 0.020 0.204
#> GSM1009083 2 0.2408 0.66186 0.000 0.896 0.104 0.000
#> GSM1009097 4 0.1389 0.94944 0.048 0.000 0.000 0.952
#> GSM1009111 2 0.4925 0.34655 0.000 0.572 0.428 0.000
#> GSM1009125 3 0.5194 0.59913 0.056 0.112 0.792 0.040
#> GSM1009139 4 0.0000 0.94855 0.000 0.000 0.000 1.000
#> GSM1009153 1 0.0524 0.86313 0.988 0.004 0.000 0.008
#> GSM1009167 3 0.3402 0.74128 0.000 0.164 0.832 0.004
#> GSM1009181 2 0.4633 0.59248 0.048 0.780 0.172 0.000
#> GSM1009195 1 0.2860 0.85282 0.904 0.004 0.048 0.044
#> GSM1009070 1 0.5542 0.75107 0.732 0.044 0.020 0.204
#> GSM1009084 2 0.2408 0.66186 0.000 0.896 0.104 0.000
#> GSM1009098 4 0.1389 0.94944 0.048 0.000 0.000 0.952
#> GSM1009112 2 0.4925 0.34655 0.000 0.572 0.428 0.000
#> GSM1009126 3 0.8235 0.00949 0.240 0.016 0.404 0.340
#> GSM1009140 4 0.0000 0.94855 0.000 0.000 0.000 1.000
#> GSM1009154 1 0.0524 0.86313 0.988 0.004 0.000 0.008
#> GSM1009168 3 0.3402 0.74128 0.000 0.164 0.832 0.004
#> GSM1009182 2 0.4633 0.59248 0.048 0.780 0.172 0.000
#> GSM1009196 1 0.2860 0.85282 0.904 0.004 0.048 0.044
#> GSM1009071 1 0.5542 0.75107 0.732 0.044 0.020 0.204
#> GSM1009085 2 0.2408 0.66186 0.000 0.896 0.104 0.000
#> GSM1009099 4 0.1389 0.94944 0.048 0.000 0.000 0.952
#> GSM1009113 2 0.4925 0.34655 0.000 0.572 0.428 0.000
#> GSM1009127 1 0.5540 0.70314 0.740 0.004 0.108 0.148
#> GSM1009141 4 0.0000 0.94855 0.000 0.000 0.000 1.000
#> GSM1009155 1 0.0524 0.86313 0.988 0.004 0.000 0.008
#> GSM1009169 3 0.3402 0.74128 0.000 0.164 0.832 0.004
#> GSM1009183 2 0.4633 0.59248 0.048 0.780 0.172 0.000
#> GSM1009197 1 0.2860 0.85282 0.904 0.004 0.048 0.044
#> GSM1009072 1 0.5542 0.75107 0.732 0.044 0.020 0.204
#> GSM1009086 2 0.2408 0.66186 0.000 0.896 0.104 0.000
#> GSM1009100 4 0.1389 0.94944 0.048 0.000 0.000 0.952
#> GSM1009114 2 0.4925 0.34655 0.000 0.572 0.428 0.000
#> GSM1009128 3 0.6507 0.53283 0.076 0.052 0.700 0.172
#> GSM1009142 4 0.0000 0.94855 0.000 0.000 0.000 1.000
#> GSM1009156 1 0.0524 0.86149 0.988 0.008 0.000 0.004
#> GSM1009170 3 0.3402 0.74128 0.000 0.164 0.832 0.004
#> GSM1009184 2 0.4633 0.59248 0.048 0.780 0.172 0.000
#> GSM1009198 1 0.2860 0.85282 0.904 0.004 0.048 0.044
#> GSM1009073 1 0.5542 0.75107 0.732 0.044 0.020 0.204
#> GSM1009087 2 0.2408 0.66186 0.000 0.896 0.104 0.000
#> GSM1009101 4 0.1389 0.94944 0.048 0.000 0.000 0.952
#> GSM1009115 2 0.4925 0.34655 0.000 0.572 0.428 0.000
#> GSM1009129 3 0.4720 0.59799 0.056 0.120 0.808 0.016
#> GSM1009143 4 0.0000 0.94855 0.000 0.000 0.000 1.000
#> GSM1009157 1 0.0657 0.86017 0.984 0.012 0.000 0.004
#> GSM1009171 3 0.3402 0.74128 0.000 0.164 0.832 0.004
#> GSM1009185 2 0.4633 0.59248 0.048 0.780 0.172 0.000
#> GSM1009199 1 0.2860 0.85282 0.904 0.004 0.048 0.044
#> GSM1009074 1 0.5542 0.75107 0.732 0.044 0.020 0.204
#> GSM1009088 2 0.2408 0.66186 0.000 0.896 0.104 0.000
#> GSM1009102 4 0.1389 0.94944 0.048 0.000 0.000 0.952
#> GSM1009116 2 0.4925 0.34655 0.000 0.572 0.428 0.000
#> GSM1009130 3 0.5537 0.60198 0.056 0.256 0.688 0.000
#> GSM1009144 4 0.0000 0.94855 0.000 0.000 0.000 1.000
#> GSM1009158 1 0.0524 0.86313 0.988 0.004 0.000 0.008
#> GSM1009172 3 0.3402 0.74128 0.000 0.164 0.832 0.004
#> GSM1009186 2 0.4633 0.59248 0.048 0.780 0.172 0.000
#> GSM1009200 1 0.2860 0.85282 0.904 0.004 0.048 0.044
#> GSM1009075 1 0.5542 0.75107 0.732 0.044 0.020 0.204
#> GSM1009089 2 0.2843 0.65310 0.020 0.892 0.088 0.000
#> GSM1009103 4 0.1389 0.94944 0.048 0.000 0.000 0.952
#> GSM1009117 2 0.4925 0.34655 0.000 0.572 0.428 0.000
#> GSM1009131 3 0.4903 0.59364 0.060 0.128 0.796 0.016
#> GSM1009145 4 0.0000 0.94855 0.000 0.000 0.000 1.000
#> GSM1009159 1 0.0524 0.86313 0.988 0.004 0.000 0.008
#> GSM1009173 3 0.3402 0.74128 0.000 0.164 0.832 0.004
#> GSM1009187 2 0.4633 0.59248 0.048 0.780 0.172 0.000
#> GSM1009201 1 0.2860 0.85282 0.904 0.004 0.048 0.044
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM1009062 1 0.1701 0.7031 0.936 0.016 0.000 0.048 0.000
#> GSM1009076 2 0.4361 0.6721 0.060 0.792 0.124 0.000 0.024
#> GSM1009090 4 0.0324 0.9832 0.004 0.000 0.000 0.992 0.004
#> GSM1009104 3 0.5568 0.5028 0.020 0.472 0.476 0.000 0.032
#> GSM1009118 5 0.7053 0.3310 0.032 0.108 0.280 0.028 0.552
#> GSM1009132 4 0.0794 0.9833 0.028 0.000 0.000 0.972 0.000
#> GSM1009146 1 0.4251 0.6058 0.624 0.000 0.000 0.004 0.372
#> GSM1009160 3 0.0324 0.6690 0.000 0.000 0.992 0.004 0.004
#> GSM1009174 2 0.4371 0.7267 0.032 0.776 0.028 0.000 0.164
#> GSM1009188 5 0.4080 0.5684 0.252 0.000 0.000 0.020 0.728
#> GSM1009063 1 0.1701 0.7031 0.936 0.016 0.000 0.048 0.000
#> GSM1009077 2 0.4361 0.6721 0.060 0.792 0.124 0.000 0.024
#> GSM1009091 4 0.0324 0.9832 0.004 0.000 0.000 0.992 0.004
#> GSM1009105 3 0.5568 0.5028 0.020 0.472 0.476 0.000 0.032
#> GSM1009119 5 0.4354 0.5655 0.180 0.000 0.004 0.056 0.760
#> GSM1009133 4 0.0794 0.9833 0.028 0.000 0.000 0.972 0.000
#> GSM1009147 1 0.4251 0.6058 0.624 0.000 0.000 0.004 0.372
#> GSM1009161 3 0.0324 0.6690 0.000 0.000 0.992 0.004 0.004
#> GSM1009175 2 0.4371 0.7267 0.032 0.776 0.028 0.000 0.164
#> GSM1009189 5 0.4080 0.5684 0.252 0.000 0.000 0.020 0.728
#> GSM1009064 1 0.1701 0.7031 0.936 0.016 0.000 0.048 0.000
#> GSM1009078 2 0.4361 0.6721 0.060 0.792 0.124 0.000 0.024
#> GSM1009092 4 0.0324 0.9832 0.004 0.000 0.000 0.992 0.004
#> GSM1009106 3 0.5568 0.5028 0.020 0.472 0.476 0.000 0.032
#> GSM1009120 5 0.3944 0.5601 0.212 0.000 0.004 0.020 0.764
#> GSM1009134 4 0.0794 0.9833 0.028 0.000 0.000 0.972 0.000
#> GSM1009148 1 0.4251 0.6058 0.624 0.000 0.000 0.004 0.372
#> GSM1009162 3 0.0324 0.6690 0.000 0.000 0.992 0.004 0.004
#> GSM1009176 2 0.4371 0.7267 0.032 0.776 0.028 0.000 0.164
#> GSM1009190 5 0.4080 0.5684 0.252 0.000 0.000 0.020 0.728
#> GSM1009065 1 0.1701 0.7031 0.936 0.016 0.000 0.048 0.000
#> GSM1009079 2 0.4361 0.6721 0.060 0.792 0.124 0.000 0.024
#> GSM1009093 4 0.0324 0.9832 0.004 0.000 0.000 0.992 0.004
#> GSM1009107 3 0.5568 0.5028 0.020 0.472 0.476 0.000 0.032
#> GSM1009121 5 0.7037 0.3175 0.028 0.112 0.284 0.028 0.548
#> GSM1009135 4 0.0794 0.9833 0.028 0.000 0.000 0.972 0.000
#> GSM1009149 1 0.4251 0.6058 0.624 0.000 0.000 0.004 0.372
#> GSM1009163 3 0.0324 0.6690 0.000 0.000 0.992 0.004 0.004
#> GSM1009177 2 0.4371 0.7267 0.032 0.776 0.028 0.000 0.164
#> GSM1009191 5 0.4080 0.5684 0.252 0.000 0.000 0.020 0.728
#> GSM1009066 1 0.1701 0.7031 0.936 0.016 0.000 0.048 0.000
#> GSM1009080 2 0.4361 0.6721 0.060 0.792 0.124 0.000 0.024
#> GSM1009094 4 0.0324 0.9832 0.004 0.000 0.000 0.992 0.004
#> GSM1009108 3 0.5568 0.5028 0.020 0.472 0.476 0.000 0.032
#> GSM1009122 5 0.7033 0.2758 0.028 0.120 0.304 0.020 0.528
#> GSM1009136 4 0.0794 0.9833 0.028 0.000 0.000 0.972 0.000
#> GSM1009150 1 0.4251 0.6058 0.624 0.000 0.000 0.004 0.372
#> GSM1009164 3 0.0324 0.6690 0.000 0.000 0.992 0.004 0.004
#> GSM1009178 2 0.4371 0.7267 0.032 0.776 0.028 0.000 0.164
#> GSM1009192 5 0.4080 0.5684 0.252 0.000 0.000 0.020 0.728
#> GSM1009067 1 0.1701 0.7031 0.936 0.016 0.000 0.048 0.000
#> GSM1009081 2 0.4361 0.6721 0.060 0.792 0.124 0.000 0.024
#> GSM1009095 4 0.0324 0.9832 0.004 0.000 0.000 0.992 0.004
#> GSM1009109 3 0.5568 0.5028 0.020 0.472 0.476 0.000 0.032
#> GSM1009123 5 0.4460 0.5204 0.060 0.000 0.012 0.160 0.768
#> GSM1009137 4 0.0794 0.9833 0.028 0.000 0.000 0.972 0.000
#> GSM1009151 1 0.4251 0.6058 0.624 0.000 0.000 0.004 0.372
#> GSM1009165 3 0.0324 0.6690 0.000 0.000 0.992 0.004 0.004
#> GSM1009179 2 0.4371 0.7267 0.032 0.776 0.028 0.000 0.164
#> GSM1009193 5 0.4080 0.5684 0.252 0.000 0.000 0.020 0.728
#> GSM1009068 1 0.1701 0.7031 0.936 0.016 0.000 0.048 0.000
#> GSM1009082 2 0.4361 0.6721 0.060 0.792 0.124 0.000 0.024
#> GSM1009096 4 0.0324 0.9832 0.004 0.000 0.000 0.992 0.004
#> GSM1009110 3 0.5568 0.5028 0.020 0.472 0.476 0.000 0.032
#> GSM1009124 5 0.5219 0.5239 0.016 0.052 0.096 0.072 0.764
#> GSM1009138 4 0.0794 0.9833 0.028 0.000 0.000 0.972 0.000
#> GSM1009152 1 0.4251 0.6058 0.624 0.000 0.000 0.004 0.372
#> GSM1009166 3 0.0324 0.6690 0.000 0.000 0.992 0.004 0.004
#> GSM1009180 2 0.4371 0.7267 0.032 0.776 0.028 0.000 0.164
#> GSM1009194 5 0.4080 0.5684 0.252 0.000 0.000 0.020 0.728
#> GSM1009069 1 0.1701 0.7031 0.936 0.016 0.000 0.048 0.000
#> GSM1009083 2 0.4361 0.6721 0.060 0.792 0.124 0.000 0.024
#> GSM1009097 4 0.0324 0.9832 0.004 0.000 0.000 0.992 0.004
#> GSM1009111 3 0.5568 0.5028 0.020 0.472 0.476 0.000 0.032
#> GSM1009125 5 0.6972 0.2705 0.024 0.120 0.308 0.020 0.528
#> GSM1009139 4 0.0794 0.9833 0.028 0.000 0.000 0.972 0.000
#> GSM1009153 1 0.4251 0.6058 0.624 0.000 0.000 0.004 0.372
#> GSM1009167 3 0.0324 0.6690 0.000 0.000 0.992 0.004 0.004
#> GSM1009181 2 0.4371 0.7267 0.032 0.776 0.028 0.000 0.164
#> GSM1009195 5 0.4080 0.5684 0.252 0.000 0.000 0.020 0.728
#> GSM1009070 1 0.1701 0.7031 0.936 0.016 0.000 0.048 0.000
#> GSM1009084 2 0.4361 0.6721 0.060 0.792 0.124 0.000 0.024
#> GSM1009098 4 0.0324 0.9832 0.004 0.000 0.000 0.992 0.004
#> GSM1009112 3 0.5568 0.5028 0.020 0.472 0.476 0.000 0.032
#> GSM1009126 5 0.5219 0.5239 0.016 0.052 0.096 0.072 0.764
#> GSM1009140 4 0.0794 0.9833 0.028 0.000 0.000 0.972 0.000
#> GSM1009154 1 0.4251 0.6058 0.624 0.000 0.000 0.004 0.372
#> GSM1009168 3 0.0324 0.6690 0.000 0.000 0.992 0.004 0.004
#> GSM1009182 2 0.4371 0.7267 0.032 0.776 0.028 0.000 0.164
#> GSM1009196 5 0.4080 0.5684 0.252 0.000 0.000 0.020 0.728
#> GSM1009071 1 0.1701 0.7031 0.936 0.016 0.000 0.048 0.000
#> GSM1009085 2 0.4361 0.6721 0.060 0.792 0.124 0.000 0.024
#> GSM1009099 4 0.0324 0.9832 0.004 0.000 0.000 0.992 0.004
#> GSM1009113 3 0.5568 0.5028 0.020 0.472 0.476 0.000 0.032
#> GSM1009127 5 0.4177 0.5652 0.168 0.000 0.004 0.052 0.776
#> GSM1009141 4 0.0794 0.9833 0.028 0.000 0.000 0.972 0.000
#> GSM1009155 1 0.4251 0.6058 0.624 0.000 0.000 0.004 0.372
#> GSM1009169 3 0.0324 0.6690 0.000 0.000 0.992 0.004 0.004
#> GSM1009183 2 0.4371 0.7267 0.032 0.776 0.028 0.000 0.164
#> GSM1009197 5 0.4080 0.5684 0.252 0.000 0.000 0.020 0.728
#> GSM1009072 1 0.1701 0.7031 0.936 0.016 0.000 0.048 0.000
#> GSM1009086 2 0.4361 0.6721 0.060 0.792 0.124 0.000 0.024
#> GSM1009100 4 0.0324 0.9832 0.004 0.000 0.000 0.992 0.004
#> GSM1009114 3 0.5568 0.5028 0.020 0.472 0.476 0.000 0.032
#> GSM1009128 5 0.7053 0.3411 0.024 0.084 0.284 0.052 0.556
#> GSM1009142 4 0.0794 0.9833 0.028 0.000 0.000 0.972 0.000
#> GSM1009156 1 0.4251 0.6058 0.624 0.000 0.000 0.004 0.372
#> GSM1009170 3 0.0324 0.6690 0.000 0.000 0.992 0.004 0.004
#> GSM1009184 2 0.4371 0.7267 0.032 0.776 0.028 0.000 0.164
#> GSM1009198 5 0.4080 0.5684 0.252 0.000 0.000 0.020 0.728
#> GSM1009073 1 0.1701 0.7031 0.936 0.016 0.000 0.048 0.000
#> GSM1009087 2 0.4361 0.6721 0.060 0.792 0.124 0.000 0.024
#> GSM1009101 4 0.0324 0.9832 0.004 0.000 0.000 0.992 0.004
#> GSM1009115 3 0.5568 0.5028 0.020 0.472 0.476 0.000 0.032
#> GSM1009129 5 0.6901 0.2611 0.024 0.120 0.312 0.016 0.528
#> GSM1009143 4 0.0794 0.9833 0.028 0.000 0.000 0.972 0.000
#> GSM1009157 1 0.4251 0.6058 0.624 0.000 0.000 0.004 0.372
#> GSM1009171 3 0.0324 0.6690 0.000 0.000 0.992 0.004 0.004
#> GSM1009185 2 0.4371 0.7267 0.032 0.776 0.028 0.000 0.164
#> GSM1009199 5 0.4080 0.5684 0.252 0.000 0.000 0.020 0.728
#> GSM1009074 1 0.1701 0.7031 0.936 0.016 0.000 0.048 0.000
#> GSM1009088 2 0.4361 0.6721 0.060 0.792 0.124 0.000 0.024
#> GSM1009102 4 0.0324 0.9832 0.004 0.000 0.000 0.992 0.004
#> GSM1009116 3 0.5568 0.5028 0.020 0.472 0.476 0.000 0.032
#> GSM1009130 5 0.6796 -0.0413 0.024 0.140 0.408 0.000 0.428
#> GSM1009144 4 0.0794 0.9833 0.028 0.000 0.000 0.972 0.000
#> GSM1009158 1 0.4251 0.6058 0.624 0.000 0.000 0.004 0.372
#> GSM1009172 3 0.0324 0.6690 0.000 0.000 0.992 0.004 0.004
#> GSM1009186 2 0.4371 0.7267 0.032 0.776 0.028 0.000 0.164
#> GSM1009200 5 0.4080 0.5684 0.252 0.000 0.000 0.020 0.728
#> GSM1009075 1 0.1701 0.7031 0.936 0.016 0.000 0.048 0.000
#> GSM1009089 2 0.4433 0.6692 0.060 0.792 0.116 0.000 0.032
#> GSM1009103 4 0.0324 0.9832 0.004 0.000 0.000 0.992 0.004
#> GSM1009117 3 0.5568 0.5028 0.020 0.472 0.476 0.000 0.032
#> GSM1009131 5 0.6935 0.2796 0.028 0.120 0.300 0.016 0.536
#> GSM1009145 4 0.0794 0.9833 0.028 0.000 0.000 0.972 0.000
#> GSM1009159 1 0.4251 0.6058 0.624 0.000 0.000 0.004 0.372
#> GSM1009173 3 0.0324 0.6690 0.000 0.000 0.992 0.004 0.004
#> GSM1009187 2 0.4371 0.7267 0.032 0.776 0.028 0.000 0.164
#> GSM1009201 5 0.4080 0.5684 0.252 0.000 0.000 0.020 0.728
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1009062 6 0.2243 1.00000 0.112 0.004 0.000 0.004 0.000 0.880
#> GSM1009076 5 0.5598 0.44842 0.000 0.332 0.024 0.000 0.552 0.092
#> GSM1009090 4 0.1082 0.95918 0.000 0.000 0.000 0.956 0.040 0.004
#> GSM1009104 5 0.5734 0.53389 0.000 0.196 0.264 0.000 0.536 0.004
#> GSM1009118 5 0.8221 0.05428 0.284 0.092 0.116 0.016 0.404 0.088
#> GSM1009132 4 0.1152 0.95820 0.000 0.000 0.000 0.952 0.004 0.044
#> GSM1009146 1 0.4898 0.49466 0.612 0.016 0.000 0.000 0.048 0.324
#> GSM1009160 3 0.0458 1.00000 0.000 0.016 0.984 0.000 0.000 0.000
#> GSM1009174 2 0.0291 1.00000 0.004 0.992 0.000 0.000 0.000 0.004
#> GSM1009188 1 0.0291 0.69137 0.992 0.000 0.000 0.004 0.000 0.004
#> GSM1009063 6 0.2243 1.00000 0.112 0.004 0.000 0.004 0.000 0.880
#> GSM1009077 5 0.5598 0.44842 0.000 0.332 0.024 0.000 0.552 0.092
#> GSM1009091 4 0.1082 0.95918 0.000 0.000 0.000 0.956 0.040 0.004
#> GSM1009105 5 0.5734 0.53389 0.000 0.196 0.264 0.000 0.536 0.004
#> GSM1009119 1 0.5574 0.50068 0.644 0.000 0.016 0.028 0.224 0.088
#> GSM1009133 4 0.1152 0.95820 0.000 0.000 0.000 0.952 0.004 0.044
#> GSM1009147 1 0.4898 0.49466 0.612 0.016 0.000 0.000 0.048 0.324
#> GSM1009161 3 0.0458 1.00000 0.000 0.016 0.984 0.000 0.000 0.000
#> GSM1009175 2 0.0291 1.00000 0.004 0.992 0.000 0.000 0.000 0.004
#> GSM1009189 1 0.0291 0.69137 0.992 0.000 0.000 0.004 0.000 0.004
#> GSM1009064 6 0.2243 1.00000 0.112 0.004 0.000 0.004 0.000 0.880
#> GSM1009078 5 0.5598 0.44842 0.000 0.332 0.024 0.000 0.552 0.092
#> GSM1009092 4 0.1082 0.95918 0.000 0.000 0.000 0.956 0.040 0.004
#> GSM1009106 5 0.5734 0.53389 0.000 0.196 0.264 0.000 0.536 0.004
#> GSM1009120 1 0.5550 0.50345 0.648 0.000 0.016 0.028 0.220 0.088
#> GSM1009134 4 0.1152 0.95820 0.000 0.000 0.000 0.952 0.004 0.044
#> GSM1009148 1 0.4898 0.49466 0.612 0.016 0.000 0.000 0.048 0.324
#> GSM1009162 3 0.0458 1.00000 0.000 0.016 0.984 0.000 0.000 0.000
#> GSM1009176 2 0.0291 1.00000 0.004 0.992 0.000 0.000 0.000 0.004
#> GSM1009190 1 0.0291 0.69137 0.992 0.000 0.000 0.004 0.000 0.004
#> GSM1009065 6 0.2243 1.00000 0.112 0.004 0.000 0.004 0.000 0.880
#> GSM1009079 5 0.5598 0.44842 0.000 0.332 0.024 0.000 0.552 0.092
#> GSM1009093 4 0.1082 0.95918 0.000 0.000 0.000 0.956 0.040 0.004
#> GSM1009107 5 0.5734 0.53389 0.000 0.196 0.264 0.000 0.536 0.004
#> GSM1009121 5 0.8348 0.05354 0.280 0.092 0.116 0.024 0.400 0.088
#> GSM1009135 4 0.1152 0.95820 0.000 0.000 0.000 0.952 0.004 0.044
#> GSM1009149 1 0.4898 0.49466 0.612 0.016 0.000 0.000 0.048 0.324
#> GSM1009163 3 0.0458 1.00000 0.000 0.016 0.984 0.000 0.000 0.000
#> GSM1009177 2 0.0291 1.00000 0.004 0.992 0.000 0.000 0.000 0.004
#> GSM1009191 1 0.0291 0.69137 0.992 0.000 0.000 0.004 0.000 0.004
#> GSM1009066 6 0.2243 1.00000 0.112 0.004 0.000 0.004 0.000 0.880
#> GSM1009080 5 0.5598 0.44842 0.000 0.332 0.024 0.000 0.552 0.092
#> GSM1009094 4 0.1082 0.95918 0.000 0.000 0.000 0.956 0.040 0.004
#> GSM1009108 5 0.5734 0.53389 0.000 0.196 0.264 0.000 0.536 0.004
#> GSM1009122 5 0.8072 0.11555 0.264 0.092 0.124 0.008 0.424 0.088
#> GSM1009136 4 0.1082 0.95845 0.000 0.000 0.000 0.956 0.004 0.040
#> GSM1009150 1 0.4898 0.49466 0.612 0.016 0.000 0.000 0.048 0.324
#> GSM1009164 3 0.0458 1.00000 0.000 0.016 0.984 0.000 0.000 0.000
#> GSM1009178 2 0.0291 1.00000 0.004 0.992 0.000 0.000 0.000 0.004
#> GSM1009192 1 0.0291 0.69137 0.992 0.000 0.000 0.004 0.000 0.004
#> GSM1009067 6 0.2243 1.00000 0.112 0.004 0.000 0.004 0.000 0.880
#> GSM1009081 5 0.5598 0.44842 0.000 0.332 0.024 0.000 0.552 0.092
#> GSM1009095 4 0.1082 0.95918 0.000 0.000 0.000 0.956 0.040 0.004
#> GSM1009109 5 0.5734 0.53389 0.000 0.196 0.264 0.000 0.536 0.004
#> GSM1009123 1 0.5933 0.47934 0.620 0.000 0.020 0.044 0.228 0.088
#> GSM1009137 4 0.1152 0.95820 0.000 0.000 0.000 0.952 0.004 0.044
#> GSM1009151 1 0.4898 0.49466 0.612 0.016 0.000 0.000 0.048 0.324
#> GSM1009165 3 0.0458 1.00000 0.000 0.016 0.984 0.000 0.000 0.000
#> GSM1009179 2 0.0291 1.00000 0.004 0.992 0.000 0.000 0.000 0.004
#> GSM1009193 1 0.0291 0.69137 0.992 0.000 0.000 0.004 0.000 0.004
#> GSM1009068 6 0.2243 1.00000 0.112 0.004 0.000 0.004 0.000 0.880
#> GSM1009082 5 0.5598 0.44842 0.000 0.332 0.024 0.000 0.552 0.092
#> GSM1009096 4 0.1082 0.95918 0.000 0.000 0.000 0.956 0.040 0.004
#> GSM1009110 5 0.5734 0.53389 0.000 0.196 0.264 0.000 0.536 0.004
#> GSM1009124 1 0.7500 0.32999 0.508 0.072 0.044 0.032 0.256 0.088
#> GSM1009138 4 0.1152 0.95820 0.000 0.000 0.000 0.952 0.004 0.044
#> GSM1009152 1 0.4898 0.49466 0.612 0.016 0.000 0.000 0.048 0.324
#> GSM1009166 3 0.0458 1.00000 0.000 0.016 0.984 0.000 0.000 0.000
#> GSM1009180 2 0.0291 1.00000 0.004 0.992 0.000 0.000 0.000 0.004
#> GSM1009194 1 0.0291 0.69137 0.992 0.000 0.000 0.004 0.000 0.004
#> GSM1009069 6 0.2243 1.00000 0.112 0.004 0.000 0.004 0.000 0.880
#> GSM1009083 5 0.5598 0.44842 0.000 0.332 0.024 0.000 0.552 0.092
#> GSM1009097 4 0.1082 0.95918 0.000 0.000 0.000 0.956 0.040 0.004
#> GSM1009111 5 0.5734 0.53389 0.000 0.196 0.264 0.000 0.536 0.004
#> GSM1009125 5 0.8072 0.11555 0.264 0.092 0.124 0.008 0.424 0.088
#> GSM1009139 4 0.1152 0.95820 0.000 0.000 0.000 0.952 0.004 0.044
#> GSM1009153 1 0.4898 0.49466 0.612 0.016 0.000 0.000 0.048 0.324
#> GSM1009167 3 0.0458 1.00000 0.000 0.016 0.984 0.000 0.000 0.000
#> GSM1009181 2 0.0291 1.00000 0.004 0.992 0.000 0.000 0.000 0.004
#> GSM1009195 1 0.0291 0.69137 0.992 0.000 0.000 0.004 0.000 0.004
#> GSM1009070 6 0.2243 1.00000 0.112 0.004 0.000 0.004 0.000 0.880
#> GSM1009084 5 0.5598 0.44842 0.000 0.332 0.024 0.000 0.552 0.092
#> GSM1009098 4 0.1082 0.95918 0.000 0.000 0.000 0.956 0.040 0.004
#> GSM1009112 5 0.5734 0.53389 0.000 0.196 0.264 0.000 0.536 0.004
#> GSM1009126 1 0.7543 0.32423 0.504 0.076 0.044 0.032 0.256 0.088
#> GSM1009140 4 0.1152 0.95820 0.000 0.000 0.000 0.952 0.004 0.044
#> GSM1009154 1 0.4898 0.49466 0.612 0.016 0.000 0.000 0.048 0.324
#> GSM1009168 3 0.0458 1.00000 0.000 0.016 0.984 0.000 0.000 0.000
#> GSM1009182 2 0.0291 1.00000 0.004 0.992 0.000 0.000 0.000 0.004
#> GSM1009196 1 0.0291 0.69137 0.992 0.000 0.000 0.004 0.000 0.004
#> GSM1009071 6 0.2243 1.00000 0.112 0.004 0.000 0.004 0.000 0.880
#> GSM1009085 5 0.5598 0.44842 0.000 0.332 0.024 0.000 0.552 0.092
#> GSM1009099 4 0.1082 0.95918 0.000 0.000 0.000 0.956 0.040 0.004
#> GSM1009113 5 0.5734 0.53389 0.000 0.196 0.264 0.000 0.536 0.004
#> GSM1009127 1 0.5699 0.49155 0.632 0.000 0.020 0.028 0.232 0.088
#> GSM1009141 4 0.1152 0.95820 0.000 0.000 0.000 0.952 0.004 0.044
#> GSM1009155 1 0.4898 0.49466 0.612 0.016 0.000 0.000 0.048 0.324
#> GSM1009169 3 0.0458 1.00000 0.000 0.016 0.984 0.000 0.000 0.000
#> GSM1009183 2 0.0291 1.00000 0.004 0.992 0.000 0.000 0.000 0.004
#> GSM1009197 1 0.0291 0.69137 0.992 0.000 0.000 0.004 0.000 0.004
#> GSM1009072 6 0.2243 1.00000 0.112 0.004 0.000 0.004 0.000 0.880
#> GSM1009086 5 0.5598 0.44842 0.000 0.332 0.024 0.000 0.552 0.092
#> GSM1009100 4 0.1082 0.95918 0.000 0.000 0.000 0.956 0.040 0.004
#> GSM1009114 5 0.5734 0.53389 0.000 0.196 0.264 0.000 0.536 0.004
#> GSM1009128 5 0.8523 -0.00276 0.304 0.088 0.116 0.036 0.368 0.088
#> GSM1009142 4 0.1152 0.95820 0.000 0.000 0.000 0.952 0.004 0.044
#> GSM1009156 1 0.4898 0.49466 0.612 0.016 0.000 0.000 0.048 0.324
#> GSM1009170 3 0.0458 1.00000 0.000 0.016 0.984 0.000 0.000 0.000
#> GSM1009184 2 0.0291 1.00000 0.004 0.992 0.000 0.000 0.000 0.004
#> GSM1009198 1 0.0291 0.69137 0.992 0.000 0.000 0.004 0.000 0.004
#> GSM1009073 6 0.2243 1.00000 0.112 0.004 0.000 0.004 0.000 0.880
#> GSM1009087 5 0.5598 0.44842 0.000 0.332 0.024 0.000 0.552 0.092
#> GSM1009101 4 0.1082 0.95918 0.000 0.000 0.000 0.956 0.040 0.004
#> GSM1009115 5 0.5734 0.53389 0.000 0.196 0.264 0.000 0.536 0.004
#> GSM1009129 5 0.8026 0.12432 0.260 0.088 0.124 0.008 0.432 0.088
#> GSM1009143 4 0.1152 0.95820 0.000 0.000 0.000 0.952 0.004 0.044
#> GSM1009157 1 0.4898 0.49466 0.612 0.016 0.000 0.000 0.048 0.324
#> GSM1009171 3 0.0458 1.00000 0.000 0.016 0.984 0.000 0.000 0.000
#> GSM1009185 2 0.0291 1.00000 0.004 0.992 0.000 0.000 0.000 0.004
#> GSM1009199 1 0.0291 0.69137 0.992 0.000 0.000 0.004 0.000 0.004
#> GSM1009074 6 0.2243 1.00000 0.112 0.004 0.000 0.004 0.000 0.880
#> GSM1009088 5 0.5598 0.44842 0.000 0.332 0.024 0.000 0.552 0.092
#> GSM1009102 4 0.1082 0.95918 0.000 0.000 0.000 0.956 0.040 0.004
#> GSM1009116 5 0.5734 0.53389 0.000 0.196 0.264 0.000 0.536 0.004
#> GSM1009130 5 0.6460 0.24712 0.156 0.008 0.144 0.004 0.596 0.092
#> GSM1009144 4 0.1152 0.95820 0.000 0.000 0.000 0.952 0.004 0.044
#> GSM1009158 1 0.4898 0.49466 0.612 0.016 0.000 0.000 0.048 0.324
#> GSM1009172 3 0.0458 1.00000 0.000 0.016 0.984 0.000 0.000 0.000
#> GSM1009186 2 0.0291 1.00000 0.004 0.992 0.000 0.000 0.000 0.004
#> GSM1009200 1 0.0291 0.69137 0.992 0.000 0.000 0.004 0.000 0.004
#> GSM1009075 6 0.2243 1.00000 0.112 0.004 0.000 0.004 0.000 0.880
#> GSM1009089 5 0.5598 0.44842 0.000 0.332 0.024 0.000 0.552 0.092
#> GSM1009103 4 0.1082 0.95918 0.000 0.000 0.000 0.956 0.040 0.004
#> GSM1009117 5 0.5734 0.53389 0.000 0.196 0.264 0.000 0.536 0.004
#> GSM1009131 5 0.7938 0.09911 0.276 0.080 0.116 0.008 0.432 0.088
#> GSM1009145 4 0.1082 0.95845 0.000 0.000 0.000 0.956 0.004 0.040
#> GSM1009159 1 0.4898 0.49466 0.612 0.016 0.000 0.000 0.048 0.324
#> GSM1009173 3 0.0458 1.00000 0.000 0.016 0.984 0.000 0.000 0.000
#> GSM1009187 2 0.0291 1.00000 0.004 0.992 0.000 0.000 0.000 0.004
#> GSM1009201 1 0.0291 0.69137 0.992 0.000 0.000 0.004 0.000 0.004
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n temperature(p) time(p) specimen(p) k
#> CV:skmeans 140 0.897 0.998 7.21e-23 2
#> CV:skmeans 139 0.997 1.000 7.22e-44 3
#> CV:skmeans 123 0.999 1.000 8.47e-60 4
#> CV:skmeans 132 1.000 1.000 9.80e-89 5
#> CV:skmeans 100 1.000 1.000 1.23e-83 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 140 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 6.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.663 0.883 0.900 0.3187 0.571 0.571
#> 3 3 0.594 0.846 0.864 0.6376 0.777 0.643
#> 4 4 0.829 0.804 0.925 0.3222 0.706 0.452
#> 5 5 0.923 0.936 0.974 0.0948 0.882 0.659
#> 6 6 0.980 0.950 0.978 0.0731 0.941 0.763
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] 5
There is also optional best \(k\) = 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
#> GSM1009062 1 0.0000 0.9892 1.000 0.000
#> GSM1009076 2 0.9954 0.7068 0.460 0.540
#> GSM1009090 1 0.0000 0.9892 1.000 0.000
#> GSM1009104 2 0.9954 0.7068 0.460 0.540
#> GSM1009118 1 0.0000 0.9892 1.000 0.000
#> GSM1009132 1 0.0000 0.9892 1.000 0.000
#> GSM1009146 1 0.0000 0.9892 1.000 0.000
#> GSM1009160 2 0.0000 0.6370 0.000 1.000
#> GSM1009174 1 0.0376 0.9850 0.996 0.004
#> GSM1009188 1 0.0000 0.9892 1.000 0.000
#> GSM1009063 1 0.0000 0.9892 1.000 0.000
#> GSM1009077 2 0.9954 0.7068 0.460 0.540
#> GSM1009091 1 0.0000 0.9892 1.000 0.000
#> GSM1009105 2 0.9954 0.7068 0.460 0.540
#> GSM1009119 1 0.0000 0.9892 1.000 0.000
#> GSM1009133 1 0.0000 0.9892 1.000 0.000
#> GSM1009147 1 0.0000 0.9892 1.000 0.000
#> GSM1009161 2 0.0000 0.6370 0.000 1.000
#> GSM1009175 1 0.0376 0.9850 0.996 0.004
#> GSM1009189 1 0.0000 0.9892 1.000 0.000
#> GSM1009064 1 0.0000 0.9892 1.000 0.000
#> GSM1009078 1 0.0376 0.9847 0.996 0.004
#> GSM1009092 1 0.0000 0.9892 1.000 0.000
#> GSM1009106 2 0.9954 0.7068 0.460 0.540
#> GSM1009120 1 0.0000 0.9892 1.000 0.000
#> GSM1009134 1 0.0000 0.9892 1.000 0.000
#> GSM1009148 1 0.0000 0.9892 1.000 0.000
#> GSM1009162 2 0.0000 0.6370 0.000 1.000
#> GSM1009176 2 0.9970 0.6913 0.468 0.532
#> GSM1009190 1 0.0000 0.9892 1.000 0.000
#> GSM1009065 1 0.0000 0.9892 1.000 0.000
#> GSM1009079 2 0.9954 0.7068 0.460 0.540
#> GSM1009093 1 0.0000 0.9892 1.000 0.000
#> GSM1009107 2 0.9954 0.7068 0.460 0.540
#> GSM1009121 1 0.0000 0.9892 1.000 0.000
#> GSM1009135 1 0.0000 0.9892 1.000 0.000
#> GSM1009149 1 0.0000 0.9892 1.000 0.000
#> GSM1009163 2 0.0000 0.6370 0.000 1.000
#> GSM1009177 1 0.9358 -0.0814 0.648 0.352
#> GSM1009191 1 0.0000 0.9892 1.000 0.000
#> GSM1009066 1 0.0000 0.9892 1.000 0.000
#> GSM1009080 2 0.9954 0.7068 0.460 0.540
#> GSM1009094 1 0.0000 0.9892 1.000 0.000
#> GSM1009108 2 0.9954 0.7068 0.460 0.540
#> GSM1009122 1 0.0376 0.9850 0.996 0.004
#> GSM1009136 1 0.0000 0.9892 1.000 0.000
#> GSM1009150 1 0.0000 0.9892 1.000 0.000
#> GSM1009164 2 0.0000 0.6370 0.000 1.000
#> GSM1009178 1 0.0376 0.9850 0.996 0.004
#> GSM1009192 1 0.0000 0.9892 1.000 0.000
#> GSM1009067 1 0.0000 0.9892 1.000 0.000
#> GSM1009081 2 0.9954 0.7068 0.460 0.540
#> GSM1009095 1 0.0000 0.9892 1.000 0.000
#> GSM1009109 2 0.9954 0.7068 0.460 0.540
#> GSM1009123 1 0.0000 0.9892 1.000 0.000
#> GSM1009137 1 0.0000 0.9892 1.000 0.000
#> GSM1009151 1 0.0000 0.9892 1.000 0.000
#> GSM1009165 2 0.0000 0.6370 0.000 1.000
#> GSM1009179 1 0.0376 0.9850 0.996 0.004
#> GSM1009193 1 0.0000 0.9892 1.000 0.000
#> GSM1009068 1 0.0000 0.9892 1.000 0.000
#> GSM1009082 2 0.9954 0.7068 0.460 0.540
#> GSM1009096 1 0.0000 0.9892 1.000 0.000
#> GSM1009110 2 0.9954 0.7068 0.460 0.540
#> GSM1009124 1 0.0000 0.9892 1.000 0.000
#> GSM1009138 1 0.0000 0.9892 1.000 0.000
#> GSM1009152 1 0.0000 0.9892 1.000 0.000
#> GSM1009166 2 0.0000 0.6370 0.000 1.000
#> GSM1009180 1 0.0376 0.9850 0.996 0.004
#> GSM1009194 1 0.0000 0.9892 1.000 0.000
#> GSM1009069 1 0.1633 0.9561 0.976 0.024
#> GSM1009083 2 0.9954 0.7068 0.460 0.540
#> GSM1009097 1 0.0000 0.9892 1.000 0.000
#> GSM1009111 2 0.9954 0.7068 0.460 0.540
#> GSM1009125 2 0.9970 0.6913 0.468 0.532
#> GSM1009139 1 0.0000 0.9892 1.000 0.000
#> GSM1009153 1 0.0000 0.9892 1.000 0.000
#> GSM1009167 2 0.0000 0.6370 0.000 1.000
#> GSM1009181 2 0.9954 0.7068 0.460 0.540
#> GSM1009195 1 0.0000 0.9892 1.000 0.000
#> GSM1009070 1 0.0000 0.9892 1.000 0.000
#> GSM1009084 2 0.9954 0.7068 0.460 0.540
#> GSM1009098 1 0.0000 0.9892 1.000 0.000
#> GSM1009112 2 0.9954 0.7068 0.460 0.540
#> GSM1009126 1 0.0000 0.9892 1.000 0.000
#> GSM1009140 1 0.0000 0.9892 1.000 0.000
#> GSM1009154 1 0.0000 0.9892 1.000 0.000
#> GSM1009168 2 0.0000 0.6370 0.000 1.000
#> GSM1009182 1 0.0376 0.9850 0.996 0.004
#> GSM1009196 1 0.0000 0.9892 1.000 0.000
#> GSM1009071 1 0.0000 0.9892 1.000 0.000
#> GSM1009085 2 0.9954 0.7068 0.460 0.540
#> GSM1009099 1 0.0000 0.9892 1.000 0.000
#> GSM1009113 2 0.9954 0.7068 0.460 0.540
#> GSM1009127 1 0.0000 0.9892 1.000 0.000
#> GSM1009141 1 0.0000 0.9892 1.000 0.000
#> GSM1009155 1 0.0000 0.9892 1.000 0.000
#> GSM1009169 2 0.0000 0.6370 0.000 1.000
#> GSM1009183 1 0.6712 0.6429 0.824 0.176
#> GSM1009197 1 0.0000 0.9892 1.000 0.000
#> GSM1009072 1 0.0000 0.9892 1.000 0.000
#> GSM1009086 2 0.9954 0.7068 0.460 0.540
#> GSM1009100 1 0.0000 0.9892 1.000 0.000
#> GSM1009114 2 0.9954 0.7068 0.460 0.540
#> GSM1009128 1 0.0000 0.9892 1.000 0.000
#> GSM1009142 1 0.0000 0.9892 1.000 0.000
#> GSM1009156 1 0.0000 0.9892 1.000 0.000
#> GSM1009170 2 0.0000 0.6370 0.000 1.000
#> GSM1009184 1 0.0376 0.9850 0.996 0.004
#> GSM1009198 1 0.0000 0.9892 1.000 0.000
#> GSM1009073 1 0.0000 0.9892 1.000 0.000
#> GSM1009087 1 0.0376 0.9847 0.996 0.004
#> GSM1009101 1 0.0000 0.9892 1.000 0.000
#> GSM1009115 2 0.9954 0.7068 0.460 0.540
#> GSM1009129 2 0.9998 0.6368 0.492 0.508
#> GSM1009143 1 0.0000 0.9892 1.000 0.000
#> GSM1009157 1 0.0000 0.9892 1.000 0.000
#> GSM1009171 2 0.0000 0.6370 0.000 1.000
#> GSM1009185 1 0.0376 0.9850 0.996 0.004
#> GSM1009199 1 0.0000 0.9892 1.000 0.000
#> GSM1009074 1 0.0000 0.9892 1.000 0.000
#> GSM1009088 1 0.3584 0.8811 0.932 0.068
#> GSM1009102 1 0.0000 0.9892 1.000 0.000
#> GSM1009116 2 0.9954 0.7068 0.460 0.540
#> GSM1009130 2 0.9963 0.6999 0.464 0.536
#> GSM1009144 1 0.0000 0.9892 1.000 0.000
#> GSM1009158 1 0.0000 0.9892 1.000 0.000
#> GSM1009172 2 0.0000 0.6370 0.000 1.000
#> GSM1009186 1 0.0376 0.9850 0.996 0.004
#> GSM1009200 1 0.0000 0.9892 1.000 0.000
#> GSM1009075 1 0.0000 0.9892 1.000 0.000
#> GSM1009089 1 0.0376 0.9847 0.996 0.004
#> GSM1009103 1 0.0000 0.9892 1.000 0.000
#> GSM1009117 2 0.9954 0.7068 0.460 0.540
#> GSM1009131 1 0.0000 0.9892 1.000 0.000
#> GSM1009145 1 0.0000 0.9892 1.000 0.000
#> GSM1009159 1 0.0000 0.9892 1.000 0.000
#> GSM1009173 2 0.0000 0.6370 0.000 1.000
#> GSM1009187 1 0.0376 0.9850 0.996 0.004
#> GSM1009201 1 0.0000 0.9892 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1009062 1 0.5621 0.825 0.692 0.308 0
#> GSM1009076 2 0.0000 0.963 0.000 1.000 0
#> GSM1009090 1 0.0000 0.725 1.000 0.000 0
#> GSM1009104 2 0.0000 0.963 0.000 1.000 0
#> GSM1009118 1 0.5810 0.793 0.664 0.336 0
#> GSM1009132 1 0.0592 0.718 0.988 0.012 0
#> GSM1009146 1 0.5621 0.825 0.692 0.308 0
#> GSM1009160 3 0.0000 1.000 0.000 0.000 1
#> GSM1009174 2 0.1031 0.954 0.024 0.976 0
#> GSM1009188 1 0.5591 0.825 0.696 0.304 0
#> GSM1009063 1 0.5621 0.825 0.692 0.308 0
#> GSM1009077 2 0.0000 0.963 0.000 1.000 0
#> GSM1009091 1 0.0000 0.725 1.000 0.000 0
#> GSM1009105 2 0.0000 0.963 0.000 1.000 0
#> GSM1009119 1 0.5591 0.825 0.696 0.304 0
#> GSM1009133 1 0.0000 0.725 1.000 0.000 0
#> GSM1009147 1 0.5621 0.825 0.692 0.308 0
#> GSM1009161 3 0.0000 1.000 0.000 0.000 1
#> GSM1009175 2 0.1031 0.954 0.024 0.976 0
#> GSM1009189 1 0.5621 0.825 0.692 0.308 0
#> GSM1009064 1 0.5621 0.825 0.692 0.308 0
#> GSM1009078 1 0.6126 0.695 0.600 0.400 0
#> GSM1009092 1 0.0000 0.725 1.000 0.000 0
#> GSM1009106 2 0.0000 0.963 0.000 1.000 0
#> GSM1009120 1 0.5621 0.825 0.692 0.308 0
#> GSM1009134 1 0.0000 0.725 1.000 0.000 0
#> GSM1009148 1 0.5621 0.825 0.692 0.308 0
#> GSM1009162 3 0.0000 1.000 0.000 0.000 1
#> GSM1009176 2 0.0000 0.963 0.000 1.000 0
#> GSM1009190 1 0.5621 0.825 0.692 0.308 0
#> GSM1009065 1 0.5650 0.821 0.688 0.312 0
#> GSM1009079 2 0.0000 0.963 0.000 1.000 0
#> GSM1009093 1 0.0000 0.725 1.000 0.000 0
#> GSM1009107 2 0.0000 0.963 0.000 1.000 0
#> GSM1009121 1 0.5621 0.825 0.692 0.308 0
#> GSM1009135 1 0.0000 0.725 1.000 0.000 0
#> GSM1009149 1 0.5621 0.825 0.692 0.308 0
#> GSM1009163 3 0.0000 1.000 0.000 0.000 1
#> GSM1009177 2 0.1031 0.954 0.024 0.976 0
#> GSM1009191 1 0.5621 0.825 0.692 0.308 0
#> GSM1009066 1 0.5621 0.825 0.692 0.308 0
#> GSM1009080 2 0.0000 0.963 0.000 1.000 0
#> GSM1009094 1 0.0000 0.725 1.000 0.000 0
#> GSM1009108 2 0.0000 0.963 0.000 1.000 0
#> GSM1009122 2 0.4121 0.723 0.168 0.832 0
#> GSM1009136 1 0.0000 0.725 1.000 0.000 0
#> GSM1009150 1 0.5621 0.825 0.692 0.308 0
#> GSM1009164 3 0.0000 1.000 0.000 0.000 1
#> GSM1009178 2 0.1031 0.954 0.024 0.976 0
#> GSM1009192 1 0.5621 0.825 0.692 0.308 0
#> GSM1009067 1 0.5621 0.825 0.692 0.308 0
#> GSM1009081 2 0.0000 0.963 0.000 1.000 0
#> GSM1009095 1 0.0000 0.725 1.000 0.000 0
#> GSM1009109 2 0.0000 0.963 0.000 1.000 0
#> GSM1009123 1 0.4750 0.799 0.784 0.216 0
#> GSM1009137 1 0.0000 0.725 1.000 0.000 0
#> GSM1009151 1 0.5621 0.825 0.692 0.308 0
#> GSM1009165 3 0.0000 1.000 0.000 0.000 1
#> GSM1009179 2 0.1031 0.954 0.024 0.976 0
#> GSM1009193 1 0.5621 0.825 0.692 0.308 0
#> GSM1009068 1 0.5621 0.825 0.692 0.308 0
#> GSM1009082 2 0.0000 0.963 0.000 1.000 0
#> GSM1009096 1 0.0000 0.725 1.000 0.000 0
#> GSM1009110 2 0.0000 0.963 0.000 1.000 0
#> GSM1009124 1 0.5621 0.825 0.692 0.308 0
#> GSM1009138 1 0.0000 0.725 1.000 0.000 0
#> GSM1009152 1 0.5621 0.825 0.692 0.308 0
#> GSM1009166 3 0.0000 1.000 0.000 0.000 1
#> GSM1009180 2 0.1031 0.954 0.024 0.976 0
#> GSM1009194 1 0.5621 0.825 0.692 0.308 0
#> GSM1009069 2 0.1411 0.941 0.036 0.964 0
#> GSM1009083 2 0.0000 0.963 0.000 1.000 0
#> GSM1009097 1 0.0000 0.725 1.000 0.000 0
#> GSM1009111 2 0.0000 0.963 0.000 1.000 0
#> GSM1009125 2 0.1163 0.943 0.028 0.972 0
#> GSM1009139 1 0.0000 0.725 1.000 0.000 0
#> GSM1009153 1 0.5621 0.825 0.692 0.308 0
#> GSM1009167 3 0.0000 1.000 0.000 0.000 1
#> GSM1009181 2 0.0000 0.963 0.000 1.000 0
#> GSM1009195 1 0.5621 0.825 0.692 0.308 0
#> GSM1009070 1 0.5621 0.825 0.692 0.308 0
#> GSM1009084 2 0.0000 0.963 0.000 1.000 0
#> GSM1009098 1 0.0000 0.725 1.000 0.000 0
#> GSM1009112 2 0.0000 0.963 0.000 1.000 0
#> GSM1009126 1 0.5621 0.825 0.692 0.308 0
#> GSM1009140 1 0.0000 0.725 1.000 0.000 0
#> GSM1009154 1 0.5621 0.825 0.692 0.308 0
#> GSM1009168 3 0.0000 1.000 0.000 0.000 1
#> GSM1009182 2 0.1031 0.954 0.024 0.976 0
#> GSM1009196 1 0.5621 0.825 0.692 0.308 0
#> GSM1009071 1 0.5621 0.825 0.692 0.308 0
#> GSM1009085 2 0.0000 0.963 0.000 1.000 0
#> GSM1009099 1 0.0000 0.725 1.000 0.000 0
#> GSM1009113 2 0.0000 0.963 0.000 1.000 0
#> GSM1009127 1 0.5621 0.825 0.692 0.308 0
#> GSM1009141 1 0.0000 0.725 1.000 0.000 0
#> GSM1009155 1 0.5621 0.825 0.692 0.308 0
#> GSM1009169 3 0.0000 1.000 0.000 0.000 1
#> GSM1009183 2 0.1031 0.954 0.024 0.976 0
#> GSM1009197 1 0.5621 0.825 0.692 0.308 0
#> GSM1009072 1 0.5138 0.811 0.748 0.252 0
#> GSM1009086 2 0.0000 0.963 0.000 1.000 0
#> GSM1009100 1 0.0000 0.725 1.000 0.000 0
#> GSM1009114 2 0.0000 0.963 0.000 1.000 0
#> GSM1009128 1 0.6140 0.567 0.596 0.404 0
#> GSM1009142 1 0.0000 0.725 1.000 0.000 0
#> GSM1009156 1 0.5926 0.766 0.644 0.356 0
#> GSM1009170 3 0.0000 1.000 0.000 0.000 1
#> GSM1009184 2 0.1031 0.954 0.024 0.976 0
#> GSM1009198 1 0.5621 0.825 0.692 0.308 0
#> GSM1009073 1 0.5621 0.825 0.692 0.308 0
#> GSM1009087 1 0.6180 0.665 0.584 0.416 0
#> GSM1009101 1 0.0000 0.725 1.000 0.000 0
#> GSM1009115 2 0.0000 0.963 0.000 1.000 0
#> GSM1009129 2 0.3619 0.776 0.136 0.864 0
#> GSM1009143 1 0.0000 0.725 1.000 0.000 0
#> GSM1009157 1 0.5733 0.808 0.676 0.324 0
#> GSM1009171 3 0.0000 1.000 0.000 0.000 1
#> GSM1009185 2 0.1031 0.954 0.024 0.976 0
#> GSM1009199 1 0.5621 0.825 0.692 0.308 0
#> GSM1009074 1 0.5621 0.825 0.692 0.308 0
#> GSM1009088 2 0.6215 -0.241 0.428 0.572 0
#> GSM1009102 1 0.0000 0.725 1.000 0.000 0
#> GSM1009116 2 0.0000 0.963 0.000 1.000 0
#> GSM1009130 1 0.5785 0.802 0.668 0.332 0
#> GSM1009144 1 0.0000 0.725 1.000 0.000 0
#> GSM1009158 1 0.5621 0.825 0.692 0.308 0
#> GSM1009172 3 0.0000 1.000 0.000 0.000 1
#> GSM1009186 2 0.1031 0.954 0.024 0.976 0
#> GSM1009200 1 0.5621 0.825 0.692 0.308 0
#> GSM1009075 1 0.5621 0.825 0.692 0.308 0
#> GSM1009089 1 0.5988 0.749 0.632 0.368 0
#> GSM1009103 1 0.0000 0.725 1.000 0.000 0
#> GSM1009117 2 0.0000 0.963 0.000 1.000 0
#> GSM1009131 1 0.5621 0.825 0.692 0.308 0
#> GSM1009145 1 0.0000 0.725 1.000 0.000 0
#> GSM1009159 1 0.4750 0.799 0.784 0.216 0
#> GSM1009173 3 0.0000 1.000 0.000 0.000 1
#> GSM1009187 2 0.1031 0.954 0.024 0.976 0
#> GSM1009201 1 0.5621 0.825 0.692 0.308 0
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1009062 1 0.0000 0.8545 1.000 0.000 0 0.000
#> GSM1009076 2 0.0000 0.9135 0.000 1.000 0 0.000
#> GSM1009090 4 0.0000 0.9840 0.000 0.000 0 1.000
#> GSM1009104 2 0.0000 0.9135 0.000 1.000 0 0.000
#> GSM1009118 1 0.0000 0.8545 1.000 0.000 0 0.000
#> GSM1009132 4 0.0000 0.9840 0.000 0.000 0 1.000
#> GSM1009146 1 0.0000 0.8545 1.000 0.000 0 0.000
#> GSM1009160 3 0.0000 1.0000 0.000 0.000 1 0.000
#> GSM1009174 1 0.4992 0.2230 0.524 0.476 0 0.000
#> GSM1009188 1 0.0000 0.8545 1.000 0.000 0 0.000
#> GSM1009063 1 0.0000 0.8545 1.000 0.000 0 0.000
#> GSM1009077 2 0.0000 0.9135 0.000 1.000 0 0.000
#> GSM1009091 4 0.0000 0.9840 0.000 0.000 0 1.000
#> GSM1009105 2 0.0000 0.9135 0.000 1.000 0 0.000
#> GSM1009119 1 0.0000 0.8545 1.000 0.000 0 0.000
#> GSM1009133 4 0.0000 0.9840 0.000 0.000 0 1.000
#> GSM1009147 1 0.0000 0.8545 1.000 0.000 0 0.000
#> GSM1009161 3 0.0000 1.0000 0.000 0.000 1 0.000
#> GSM1009175 1 0.4992 0.2230 0.524 0.476 0 0.000
#> GSM1009189 1 0.0000 0.8545 1.000 0.000 0 0.000
#> GSM1009064 1 0.0000 0.8545 1.000 0.000 0 0.000
#> GSM1009078 1 0.3172 0.7359 0.840 0.160 0 0.000
#> GSM1009092 4 0.0000 0.9840 0.000 0.000 0 1.000
#> GSM1009106 2 0.0000 0.9135 0.000 1.000 0 0.000
#> GSM1009120 1 0.0000 0.8545 1.000 0.000 0 0.000
#> GSM1009134 4 0.0000 0.9840 0.000 0.000 0 1.000
#> GSM1009148 1 0.0000 0.8545 1.000 0.000 0 0.000
#> GSM1009162 3 0.0000 1.0000 0.000 0.000 1 0.000
#> GSM1009176 2 0.4999 -0.1632 0.492 0.508 0 0.000
#> GSM1009190 1 0.0000 0.8545 1.000 0.000 0 0.000
#> GSM1009065 1 0.0000 0.8545 1.000 0.000 0 0.000
#> GSM1009079 2 0.0000 0.9135 0.000 1.000 0 0.000
#> GSM1009093 4 0.0000 0.9840 0.000 0.000 0 1.000
#> GSM1009107 2 0.0000 0.9135 0.000 1.000 0 0.000
#> GSM1009121 1 0.4888 0.2068 0.588 0.000 0 0.412
#> GSM1009135 4 0.0000 0.9840 0.000 0.000 0 1.000
#> GSM1009149 1 0.0000 0.8545 1.000 0.000 0 0.000
#> GSM1009163 3 0.0000 1.0000 0.000 0.000 1 0.000
#> GSM1009177 1 0.4992 0.2230 0.524 0.476 0 0.000
#> GSM1009191 1 0.0000 0.8545 1.000 0.000 0 0.000
#> GSM1009066 1 0.0000 0.8545 1.000 0.000 0 0.000
#> GSM1009080 2 0.0000 0.9135 0.000 1.000 0 0.000
#> GSM1009094 4 0.0000 0.9840 0.000 0.000 0 1.000
#> GSM1009108 2 0.0000 0.9135 0.000 1.000 0 0.000
#> GSM1009122 1 0.4697 0.4598 0.644 0.356 0 0.000
#> GSM1009136 4 0.0000 0.9840 0.000 0.000 0 1.000
#> GSM1009150 1 0.0000 0.8545 1.000 0.000 0 0.000
#> GSM1009164 3 0.0000 1.0000 0.000 0.000 1 0.000
#> GSM1009178 1 0.4992 0.2230 0.524 0.476 0 0.000
#> GSM1009192 1 0.0000 0.8545 1.000 0.000 0 0.000
#> GSM1009067 1 0.0000 0.8545 1.000 0.000 0 0.000
#> GSM1009081 2 0.0000 0.9135 0.000 1.000 0 0.000
#> GSM1009095 4 0.0000 0.9840 0.000 0.000 0 1.000
#> GSM1009109 2 0.0000 0.9135 0.000 1.000 0 0.000
#> GSM1009123 1 0.4888 0.2045 0.588 0.000 0 0.412
#> GSM1009137 4 0.0000 0.9840 0.000 0.000 0 1.000
#> GSM1009151 1 0.0000 0.8545 1.000 0.000 0 0.000
#> GSM1009165 3 0.0000 1.0000 0.000 0.000 1 0.000
#> GSM1009179 1 0.4992 0.2230 0.524 0.476 0 0.000
#> GSM1009193 1 0.0000 0.8545 1.000 0.000 0 0.000
#> GSM1009068 1 0.0000 0.8545 1.000 0.000 0 0.000
#> GSM1009082 2 0.0188 0.9095 0.004 0.996 0 0.000
#> GSM1009096 4 0.0000 0.9840 0.000 0.000 0 1.000
#> GSM1009110 2 0.0000 0.9135 0.000 1.000 0 0.000
#> GSM1009124 1 0.0000 0.8545 1.000 0.000 0 0.000
#> GSM1009138 4 0.0000 0.9840 0.000 0.000 0 1.000
#> GSM1009152 1 0.0000 0.8545 1.000 0.000 0 0.000
#> GSM1009166 3 0.0000 1.0000 0.000 0.000 1 0.000
#> GSM1009180 1 0.4992 0.2230 0.524 0.476 0 0.000
#> GSM1009194 1 0.0000 0.8545 1.000 0.000 0 0.000
#> GSM1009069 1 0.4985 0.2413 0.532 0.468 0 0.000
#> GSM1009083 2 0.0000 0.9135 0.000 1.000 0 0.000
#> GSM1009097 4 0.0000 0.9840 0.000 0.000 0 1.000
#> GSM1009111 2 0.0000 0.9135 0.000 1.000 0 0.000
#> GSM1009125 2 0.0469 0.9015 0.012 0.988 0 0.000
#> GSM1009139 4 0.0000 0.9840 0.000 0.000 0 1.000
#> GSM1009153 1 0.0000 0.8545 1.000 0.000 0 0.000
#> GSM1009167 3 0.0000 1.0000 0.000 0.000 1 0.000
#> GSM1009181 2 0.4999 -0.1628 0.492 0.508 0 0.000
#> GSM1009195 1 0.0000 0.8545 1.000 0.000 0 0.000
#> GSM1009070 1 0.0000 0.8545 1.000 0.000 0 0.000
#> GSM1009084 2 0.0000 0.9135 0.000 1.000 0 0.000
#> GSM1009098 4 0.0000 0.9840 0.000 0.000 0 1.000
#> GSM1009112 2 0.0000 0.9135 0.000 1.000 0 0.000
#> GSM1009126 1 0.0000 0.8545 1.000 0.000 0 0.000
#> GSM1009140 4 0.0000 0.9840 0.000 0.000 0 1.000
#> GSM1009154 1 0.0000 0.8545 1.000 0.000 0 0.000
#> GSM1009168 3 0.0000 1.0000 0.000 0.000 1 0.000
#> GSM1009182 1 0.4992 0.2230 0.524 0.476 0 0.000
#> GSM1009196 1 0.0000 0.8545 1.000 0.000 0 0.000
#> GSM1009071 1 0.0000 0.8545 1.000 0.000 0 0.000
#> GSM1009085 2 0.0000 0.9135 0.000 1.000 0 0.000
#> GSM1009099 4 0.0000 0.9840 0.000 0.000 0 1.000
#> GSM1009113 2 0.0000 0.9135 0.000 1.000 0 0.000
#> GSM1009127 1 0.0000 0.8545 1.000 0.000 0 0.000
#> GSM1009141 4 0.0000 0.9840 0.000 0.000 0 1.000
#> GSM1009155 1 0.0000 0.8545 1.000 0.000 0 0.000
#> GSM1009169 3 0.0000 1.0000 0.000 0.000 1 0.000
#> GSM1009183 1 0.4994 0.2116 0.520 0.480 0 0.000
#> GSM1009197 1 0.0000 0.8545 1.000 0.000 0 0.000
#> GSM1009072 1 0.0000 0.8545 1.000 0.000 0 0.000
#> GSM1009086 2 0.0000 0.9135 0.000 1.000 0 0.000
#> GSM1009100 4 0.0000 0.9840 0.000 0.000 0 1.000
#> GSM1009114 2 0.0000 0.9135 0.000 1.000 0 0.000
#> GSM1009128 4 0.6742 0.4674 0.232 0.160 0 0.608
#> GSM1009142 4 0.0000 0.9840 0.000 0.000 0 1.000
#> GSM1009156 1 0.1474 0.8214 0.948 0.052 0 0.000
#> GSM1009170 3 0.0000 1.0000 0.000 0.000 1 0.000
#> GSM1009184 1 0.4992 0.2230 0.524 0.476 0 0.000
#> GSM1009198 1 0.3074 0.7137 0.848 0.000 0 0.152
#> GSM1009073 1 0.0000 0.8545 1.000 0.000 0 0.000
#> GSM1009087 1 0.3219 0.7316 0.836 0.164 0 0.000
#> GSM1009101 4 0.0000 0.9840 0.000 0.000 0 1.000
#> GSM1009115 2 0.0000 0.9135 0.000 1.000 0 0.000
#> GSM1009129 2 0.3486 0.6654 0.188 0.812 0 0.000
#> GSM1009143 4 0.0000 0.9840 0.000 0.000 0 1.000
#> GSM1009157 1 0.0592 0.8451 0.984 0.016 0 0.000
#> GSM1009171 3 0.0000 1.0000 0.000 0.000 1 0.000
#> GSM1009185 1 0.4992 0.2230 0.524 0.476 0 0.000
#> GSM1009199 1 0.0000 0.8545 1.000 0.000 0 0.000
#> GSM1009074 1 0.0000 0.8545 1.000 0.000 0 0.000
#> GSM1009088 1 0.4746 0.4544 0.632 0.368 0 0.000
#> GSM1009102 4 0.0000 0.9840 0.000 0.000 0 1.000
#> GSM1009116 2 0.0000 0.9135 0.000 1.000 0 0.000
#> GSM1009130 2 0.4994 0.0432 0.480 0.520 0 0.000
#> GSM1009144 4 0.0000 0.9840 0.000 0.000 0 1.000
#> GSM1009158 1 0.0000 0.8545 1.000 0.000 0 0.000
#> GSM1009172 3 0.0000 1.0000 0.000 0.000 1 0.000
#> GSM1009186 1 0.4992 0.2230 0.524 0.476 0 0.000
#> GSM1009200 1 0.0000 0.8545 1.000 0.000 0 0.000
#> GSM1009075 1 0.0000 0.8545 1.000 0.000 0 0.000
#> GSM1009089 1 0.1557 0.8201 0.944 0.056 0 0.000
#> GSM1009103 4 0.0000 0.9840 0.000 0.000 0 1.000
#> GSM1009117 2 0.0000 0.9135 0.000 1.000 0 0.000
#> GSM1009131 1 0.1557 0.8130 0.944 0.056 0 0.000
#> GSM1009145 4 0.0000 0.9840 0.000 0.000 0 1.000
#> GSM1009159 1 0.0000 0.8545 1.000 0.000 0 0.000
#> GSM1009173 3 0.0000 1.0000 0.000 0.000 1 0.000
#> GSM1009187 1 0.4992 0.2230 0.524 0.476 0 0.000
#> GSM1009201 1 0.0000 0.8545 1.000 0.000 0 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM1009062 1 0.0000 0.961 1.000 0.000 0 0.000 0.000
#> GSM1009076 2 0.0290 0.956 0.000 0.992 0 0.000 0.008
#> GSM1009090 4 0.0000 0.982 0.000 0.000 0 1.000 0.000
#> GSM1009104 5 0.0000 0.955 0.000 0.000 0 0.000 1.000
#> GSM1009118 1 0.3837 0.564 0.692 0.308 0 0.000 0.000
#> GSM1009132 4 0.0000 0.982 0.000 0.000 0 1.000 0.000
#> GSM1009146 1 0.0000 0.961 1.000 0.000 0 0.000 0.000
#> GSM1009160 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> GSM1009174 2 0.0000 0.959 0.000 1.000 0 0.000 0.000
#> GSM1009188 1 0.0000 0.961 1.000 0.000 0 0.000 0.000
#> GSM1009063 1 0.0000 0.961 1.000 0.000 0 0.000 0.000
#> GSM1009077 2 0.0290 0.956 0.000 0.992 0 0.000 0.008
#> GSM1009091 4 0.0000 0.982 0.000 0.000 0 1.000 0.000
#> GSM1009105 5 0.0000 0.955 0.000 0.000 0 0.000 1.000
#> GSM1009119 1 0.0000 0.961 1.000 0.000 0 0.000 0.000
#> GSM1009133 4 0.0000 0.982 0.000 0.000 0 1.000 0.000
#> GSM1009147 1 0.0000 0.961 1.000 0.000 0 0.000 0.000
#> GSM1009161 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> GSM1009175 2 0.0000 0.959 0.000 1.000 0 0.000 0.000
#> GSM1009189 1 0.0000 0.961 1.000 0.000 0 0.000 0.000
#> GSM1009064 1 0.0000 0.961 1.000 0.000 0 0.000 0.000
#> GSM1009078 1 0.2358 0.860 0.888 0.104 0 0.000 0.008
#> GSM1009092 4 0.0000 0.982 0.000 0.000 0 1.000 0.000
#> GSM1009106 5 0.0000 0.955 0.000 0.000 0 0.000 1.000
#> GSM1009120 1 0.0000 0.961 1.000 0.000 0 0.000 0.000
#> GSM1009134 4 0.0000 0.982 0.000 0.000 0 1.000 0.000
#> GSM1009148 1 0.0000 0.961 1.000 0.000 0 0.000 0.000
#> GSM1009162 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> GSM1009176 2 0.0000 0.959 0.000 1.000 0 0.000 0.000
#> GSM1009190 1 0.0000 0.961 1.000 0.000 0 0.000 0.000
#> GSM1009065 1 0.0000 0.961 1.000 0.000 0 0.000 0.000
#> GSM1009079 2 0.0000 0.959 0.000 1.000 0 0.000 0.000
#> GSM1009093 4 0.0000 0.982 0.000 0.000 0 1.000 0.000
#> GSM1009107 5 0.0000 0.955 0.000 0.000 0 0.000 1.000
#> GSM1009121 1 0.3115 0.830 0.852 0.036 0 0.112 0.000
#> GSM1009135 4 0.0000 0.982 0.000 0.000 0 1.000 0.000
#> GSM1009149 1 0.0000 0.961 1.000 0.000 0 0.000 0.000
#> GSM1009163 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> GSM1009177 2 0.0000 0.959 0.000 1.000 0 0.000 0.000
#> GSM1009191 1 0.0000 0.961 1.000 0.000 0 0.000 0.000
#> GSM1009066 1 0.0000 0.961 1.000 0.000 0 0.000 0.000
#> GSM1009080 2 0.0162 0.957 0.000 0.996 0 0.000 0.004
#> GSM1009094 4 0.0000 0.982 0.000 0.000 0 1.000 0.000
#> GSM1009108 5 0.0000 0.955 0.000 0.000 0 0.000 1.000
#> GSM1009122 2 0.3003 0.723 0.188 0.812 0 0.000 0.000
#> GSM1009136 4 0.0000 0.982 0.000 0.000 0 1.000 0.000
#> GSM1009150 1 0.0000 0.961 1.000 0.000 0 0.000 0.000
#> GSM1009164 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> GSM1009178 2 0.0000 0.959 0.000 1.000 0 0.000 0.000
#> GSM1009192 1 0.0000 0.961 1.000 0.000 0 0.000 0.000
#> GSM1009067 1 0.0000 0.961 1.000 0.000 0 0.000 0.000
#> GSM1009081 2 0.0290 0.956 0.000 0.992 0 0.000 0.008
#> GSM1009095 4 0.0000 0.982 0.000 0.000 0 1.000 0.000
#> GSM1009109 5 0.0000 0.955 0.000 0.000 0 0.000 1.000
#> GSM1009123 1 0.3109 0.750 0.800 0.000 0 0.200 0.000
#> GSM1009137 4 0.0000 0.982 0.000 0.000 0 1.000 0.000
#> GSM1009151 1 0.0000 0.961 1.000 0.000 0 0.000 0.000
#> GSM1009165 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> GSM1009179 2 0.0000 0.959 0.000 1.000 0 0.000 0.000
#> GSM1009193 1 0.0000 0.961 1.000 0.000 0 0.000 0.000
#> GSM1009068 1 0.0000 0.961 1.000 0.000 0 0.000 0.000
#> GSM1009082 2 0.0290 0.956 0.000 0.992 0 0.000 0.008
#> GSM1009096 4 0.0000 0.982 0.000 0.000 0 1.000 0.000
#> GSM1009110 5 0.0000 0.955 0.000 0.000 0 0.000 1.000
#> GSM1009124 1 0.0000 0.961 1.000 0.000 0 0.000 0.000
#> GSM1009138 4 0.0000 0.982 0.000 0.000 0 1.000 0.000
#> GSM1009152 1 0.0000 0.961 1.000 0.000 0 0.000 0.000
#> GSM1009166 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> GSM1009180 2 0.0000 0.959 0.000 1.000 0 0.000 0.000
#> GSM1009194 1 0.0000 0.961 1.000 0.000 0 0.000 0.000
#> GSM1009069 2 0.3398 0.677 0.216 0.780 0 0.000 0.004
#> GSM1009083 2 0.0290 0.956 0.000 0.992 0 0.000 0.008
#> GSM1009097 4 0.0000 0.982 0.000 0.000 0 1.000 0.000
#> GSM1009111 5 0.0000 0.955 0.000 0.000 0 0.000 1.000
#> GSM1009125 2 0.0290 0.952 0.008 0.992 0 0.000 0.000
#> GSM1009139 4 0.0000 0.982 0.000 0.000 0 1.000 0.000
#> GSM1009153 1 0.0000 0.961 1.000 0.000 0 0.000 0.000
#> GSM1009167 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> GSM1009181 2 0.0000 0.959 0.000 1.000 0 0.000 0.000
#> GSM1009195 1 0.1043 0.930 0.960 0.040 0 0.000 0.000
#> GSM1009070 1 0.0000 0.961 1.000 0.000 0 0.000 0.000
#> GSM1009084 2 0.2424 0.830 0.000 0.868 0 0.000 0.132
#> GSM1009098 4 0.0000 0.982 0.000 0.000 0 1.000 0.000
#> GSM1009112 5 0.0000 0.955 0.000 0.000 0 0.000 1.000
#> GSM1009126 1 0.0000 0.961 1.000 0.000 0 0.000 0.000
#> GSM1009140 4 0.0000 0.982 0.000 0.000 0 1.000 0.000
#> GSM1009154 1 0.0000 0.961 1.000 0.000 0 0.000 0.000
#> GSM1009168 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> GSM1009182 2 0.0000 0.959 0.000 1.000 0 0.000 0.000
#> GSM1009196 1 0.0000 0.961 1.000 0.000 0 0.000 0.000
#> GSM1009071 1 0.0000 0.961 1.000 0.000 0 0.000 0.000
#> GSM1009085 5 0.3857 0.534 0.000 0.312 0 0.000 0.688
#> GSM1009099 4 0.0000 0.982 0.000 0.000 0 1.000 0.000
#> GSM1009113 5 0.0000 0.955 0.000 0.000 0 0.000 1.000
#> GSM1009127 1 0.0000 0.961 1.000 0.000 0 0.000 0.000
#> GSM1009141 4 0.0000 0.982 0.000 0.000 0 1.000 0.000
#> GSM1009155 1 0.0000 0.961 1.000 0.000 0 0.000 0.000
#> GSM1009169 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> GSM1009183 2 0.0000 0.959 0.000 1.000 0 0.000 0.000
#> GSM1009197 1 0.0000 0.961 1.000 0.000 0 0.000 0.000
#> GSM1009072 1 0.0000 0.961 1.000 0.000 0 0.000 0.000
#> GSM1009086 2 0.1043 0.931 0.000 0.960 0 0.000 0.040
#> GSM1009100 4 0.0000 0.982 0.000 0.000 0 1.000 0.000
#> GSM1009114 5 0.0000 0.955 0.000 0.000 0 0.000 1.000
#> GSM1009128 4 0.4225 0.396 0.364 0.004 0 0.632 0.000
#> GSM1009142 4 0.0000 0.982 0.000 0.000 0 1.000 0.000
#> GSM1009156 1 0.0162 0.959 0.996 0.004 0 0.000 0.000
#> GSM1009170 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> GSM1009184 2 0.0000 0.959 0.000 1.000 0 0.000 0.000
#> GSM1009198 1 0.2074 0.866 0.896 0.000 0 0.104 0.000
#> GSM1009073 1 0.0000 0.961 1.000 0.000 0 0.000 0.000
#> GSM1009087 1 0.2574 0.847 0.876 0.112 0 0.000 0.012
#> GSM1009101 4 0.0000 0.982 0.000 0.000 0 1.000 0.000
#> GSM1009115 5 0.0000 0.955 0.000 0.000 0 0.000 1.000
#> GSM1009129 2 0.3003 0.723 0.188 0.812 0 0.000 0.000
#> GSM1009143 4 0.0000 0.982 0.000 0.000 0 1.000 0.000
#> GSM1009157 1 0.0404 0.953 0.988 0.012 0 0.000 0.000
#> GSM1009171 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> GSM1009185 2 0.0000 0.959 0.000 1.000 0 0.000 0.000
#> GSM1009199 1 0.2648 0.816 0.848 0.152 0 0.000 0.000
#> GSM1009074 1 0.0000 0.961 1.000 0.000 0 0.000 0.000
#> GSM1009088 1 0.6191 0.245 0.536 0.172 0 0.000 0.292
#> GSM1009102 4 0.0000 0.982 0.000 0.000 0 1.000 0.000
#> GSM1009116 5 0.0000 0.955 0.000 0.000 0 0.000 1.000
#> GSM1009130 5 0.3109 0.671 0.200 0.000 0 0.000 0.800
#> GSM1009144 4 0.0000 0.982 0.000 0.000 0 1.000 0.000
#> GSM1009158 1 0.0000 0.961 1.000 0.000 0 0.000 0.000
#> GSM1009172 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> GSM1009186 2 0.0000 0.959 0.000 1.000 0 0.000 0.000
#> GSM1009200 1 0.0000 0.961 1.000 0.000 0 0.000 0.000
#> GSM1009075 1 0.0000 0.961 1.000 0.000 0 0.000 0.000
#> GSM1009089 1 0.1282 0.923 0.952 0.044 0 0.000 0.004
#> GSM1009103 4 0.0000 0.982 0.000 0.000 0 1.000 0.000
#> GSM1009117 5 0.0000 0.955 0.000 0.000 0 0.000 1.000
#> GSM1009131 1 0.3177 0.741 0.792 0.000 0 0.000 0.208
#> GSM1009145 4 0.0000 0.982 0.000 0.000 0 1.000 0.000
#> GSM1009159 1 0.0000 0.961 1.000 0.000 0 0.000 0.000
#> GSM1009173 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> GSM1009187 2 0.0000 0.959 0.000 1.000 0 0.000 0.000
#> GSM1009201 1 0.0000 0.961 1.000 0.000 0 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1009062 6 0.0146 0.999 0.004 0.000 0 0.000 0.000 0.996
#> GSM1009076 2 0.1391 0.937 0.000 0.944 0 0.000 0.040 0.016
#> GSM1009090 4 0.0000 0.984 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009104 5 0.0000 0.958 0.000 0.000 0 0.000 1.000 0.000
#> GSM1009118 1 0.3288 0.601 0.724 0.276 0 0.000 0.000 0.000
#> GSM1009132 4 0.0000 0.984 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009146 1 0.0000 0.967 1.000 0.000 0 0.000 0.000 0.000
#> GSM1009160 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009174 2 0.0000 0.962 0.000 1.000 0 0.000 0.000 0.000
#> GSM1009188 1 0.0000 0.967 1.000 0.000 0 0.000 0.000 0.000
#> GSM1009063 6 0.0146 0.999 0.004 0.000 0 0.000 0.000 0.996
#> GSM1009077 2 0.1261 0.943 0.000 0.952 0 0.000 0.024 0.024
#> GSM1009091 4 0.0000 0.984 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009105 5 0.0000 0.958 0.000 0.000 0 0.000 1.000 0.000
#> GSM1009119 1 0.0000 0.967 1.000 0.000 0 0.000 0.000 0.000
#> GSM1009133 4 0.0000 0.984 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009147 1 0.0000 0.967 1.000 0.000 0 0.000 0.000 0.000
#> GSM1009161 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009175 2 0.0000 0.962 0.000 1.000 0 0.000 0.000 0.000
#> GSM1009189 1 0.0000 0.967 1.000 0.000 0 0.000 0.000 0.000
#> GSM1009064 6 0.0146 0.999 0.004 0.000 0 0.000 0.000 0.996
#> GSM1009078 1 0.3402 0.795 0.820 0.052 0 0.000 0.008 0.120
#> GSM1009092 4 0.0000 0.984 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009106 5 0.0000 0.958 0.000 0.000 0 0.000 1.000 0.000
#> GSM1009120 1 0.0000 0.967 1.000 0.000 0 0.000 0.000 0.000
#> GSM1009134 4 0.0000 0.984 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009148 1 0.0000 0.967 1.000 0.000 0 0.000 0.000 0.000
#> GSM1009162 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009176 2 0.0000 0.962 0.000 1.000 0 0.000 0.000 0.000
#> GSM1009190 1 0.0000 0.967 1.000 0.000 0 0.000 0.000 0.000
#> GSM1009065 6 0.0000 0.994 0.000 0.000 0 0.000 0.000 1.000
#> GSM1009079 2 0.0146 0.961 0.000 0.996 0 0.000 0.000 0.004
#> GSM1009093 4 0.0000 0.984 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009107 5 0.0000 0.958 0.000 0.000 0 0.000 1.000 0.000
#> GSM1009121 1 0.0146 0.964 0.996 0.004 0 0.000 0.000 0.000
#> GSM1009135 4 0.0000 0.984 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009149 1 0.0000 0.967 1.000 0.000 0 0.000 0.000 0.000
#> GSM1009163 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009177 2 0.0000 0.962 0.000 1.000 0 0.000 0.000 0.000
#> GSM1009191 1 0.0000 0.967 1.000 0.000 0 0.000 0.000 0.000
#> GSM1009066 6 0.0146 0.999 0.004 0.000 0 0.000 0.000 0.996
#> GSM1009080 2 0.0405 0.958 0.000 0.988 0 0.000 0.008 0.004
#> GSM1009094 4 0.0000 0.984 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009108 5 0.0000 0.958 0.000 0.000 0 0.000 1.000 0.000
#> GSM1009122 2 0.2697 0.749 0.188 0.812 0 0.000 0.000 0.000
#> GSM1009136 4 0.0000 0.984 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009150 1 0.0000 0.967 1.000 0.000 0 0.000 0.000 0.000
#> GSM1009164 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009178 2 0.0000 0.962 0.000 1.000 0 0.000 0.000 0.000
#> GSM1009192 1 0.0000 0.967 1.000 0.000 0 0.000 0.000 0.000
#> GSM1009067 6 0.0146 0.999 0.004 0.000 0 0.000 0.000 0.996
#> GSM1009081 2 0.0935 0.946 0.000 0.964 0 0.000 0.032 0.004
#> GSM1009095 4 0.0000 0.984 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009109 5 0.0000 0.958 0.000 0.000 0 0.000 1.000 0.000
#> GSM1009123 1 0.0000 0.967 1.000 0.000 0 0.000 0.000 0.000
#> GSM1009137 4 0.0000 0.984 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009151 1 0.0000 0.967 1.000 0.000 0 0.000 0.000 0.000
#> GSM1009165 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009179 2 0.0000 0.962 0.000 1.000 0 0.000 0.000 0.000
#> GSM1009193 1 0.0000 0.967 1.000 0.000 0 0.000 0.000 0.000
#> GSM1009068 6 0.0146 0.999 0.004 0.000 0 0.000 0.000 0.996
#> GSM1009082 2 0.1320 0.939 0.000 0.948 0 0.000 0.016 0.036
#> GSM1009096 4 0.0000 0.984 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009110 5 0.0000 0.958 0.000 0.000 0 0.000 1.000 0.000
#> GSM1009124 1 0.0000 0.967 1.000 0.000 0 0.000 0.000 0.000
#> GSM1009138 4 0.0000 0.984 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009152 1 0.0000 0.967 1.000 0.000 0 0.000 0.000 0.000
#> GSM1009166 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009180 2 0.0000 0.962 0.000 1.000 0 0.000 0.000 0.000
#> GSM1009194 1 0.0000 0.967 1.000 0.000 0 0.000 0.000 0.000
#> GSM1009069 6 0.0000 0.994 0.000 0.000 0 0.000 0.000 1.000
#> GSM1009083 2 0.1297 0.939 0.000 0.948 0 0.000 0.012 0.040
#> GSM1009097 4 0.0000 0.984 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009111 5 0.0000 0.958 0.000 0.000 0 0.000 1.000 0.000
#> GSM1009125 2 0.0260 0.957 0.008 0.992 0 0.000 0.000 0.000
#> GSM1009139 4 0.0000 0.984 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009153 1 0.0000 0.967 1.000 0.000 0 0.000 0.000 0.000
#> GSM1009167 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009181 2 0.0000 0.962 0.000 1.000 0 0.000 0.000 0.000
#> GSM1009195 1 0.0000 0.967 1.000 0.000 0 0.000 0.000 0.000
#> GSM1009070 6 0.0146 0.999 0.004 0.000 0 0.000 0.000 0.996
#> GSM1009084 2 0.2783 0.821 0.000 0.836 0 0.000 0.148 0.016
#> GSM1009098 4 0.0000 0.984 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009112 5 0.0000 0.958 0.000 0.000 0 0.000 1.000 0.000
#> GSM1009126 1 0.0000 0.967 1.000 0.000 0 0.000 0.000 0.000
#> GSM1009140 4 0.0000 0.984 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009154 1 0.0000 0.967 1.000 0.000 0 0.000 0.000 0.000
#> GSM1009168 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009182 2 0.0000 0.962 0.000 1.000 0 0.000 0.000 0.000
#> GSM1009196 1 0.0000 0.967 1.000 0.000 0 0.000 0.000 0.000
#> GSM1009071 6 0.0146 0.999 0.004 0.000 0 0.000 0.000 0.996
#> GSM1009085 5 0.3898 0.541 0.000 0.296 0 0.000 0.684 0.020
#> GSM1009099 4 0.0000 0.984 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009113 5 0.0000 0.958 0.000 0.000 0 0.000 1.000 0.000
#> GSM1009127 1 0.0000 0.967 1.000 0.000 0 0.000 0.000 0.000
#> GSM1009141 4 0.0000 0.984 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009155 1 0.0000 0.967 1.000 0.000 0 0.000 0.000 0.000
#> GSM1009169 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009183 2 0.0000 0.962 0.000 1.000 0 0.000 0.000 0.000
#> GSM1009197 1 0.0000 0.967 1.000 0.000 0 0.000 0.000 0.000
#> GSM1009072 6 0.0146 0.999 0.004 0.000 0 0.000 0.000 0.996
#> GSM1009086 2 0.1531 0.921 0.000 0.928 0 0.000 0.068 0.004
#> GSM1009100 4 0.0000 0.984 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009114 5 0.0000 0.958 0.000 0.000 0 0.000 1.000 0.000
#> GSM1009128 4 0.4167 0.446 0.344 0.024 0 0.632 0.000 0.000
#> GSM1009142 4 0.0000 0.984 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009156 1 0.0146 0.964 0.996 0.004 0 0.000 0.000 0.000
#> GSM1009170 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009184 2 0.0000 0.962 0.000 1.000 0 0.000 0.000 0.000
#> GSM1009198 1 0.0000 0.967 1.000 0.000 0 0.000 0.000 0.000
#> GSM1009073 6 0.0146 0.999 0.004 0.000 0 0.000 0.000 0.996
#> GSM1009087 1 0.3297 0.809 0.832 0.060 0 0.000 0.008 0.100
#> GSM1009101 4 0.0000 0.984 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009115 5 0.0000 0.958 0.000 0.000 0 0.000 1.000 0.000
#> GSM1009129 2 0.2805 0.753 0.184 0.812 0 0.000 0.000 0.004
#> GSM1009143 4 0.0000 0.984 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009157 1 0.0363 0.957 0.988 0.012 0 0.000 0.000 0.000
#> GSM1009171 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009185 2 0.0000 0.962 0.000 1.000 0 0.000 0.000 0.000
#> GSM1009199 1 0.0000 0.967 1.000 0.000 0 0.000 0.000 0.000
#> GSM1009074 6 0.0146 0.999 0.004 0.000 0 0.000 0.000 0.996
#> GSM1009088 1 0.6417 0.315 0.536 0.096 0 0.000 0.260 0.108
#> GSM1009102 4 0.0000 0.984 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009116 5 0.0000 0.958 0.000 0.000 0 0.000 1.000 0.000
#> GSM1009130 5 0.2902 0.705 0.196 0.000 0 0.000 0.800 0.004
#> GSM1009144 4 0.0000 0.984 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009158 1 0.0000 0.967 1.000 0.000 0 0.000 0.000 0.000
#> GSM1009172 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009186 2 0.0000 0.962 0.000 1.000 0 0.000 0.000 0.000
#> GSM1009200 1 0.0000 0.967 1.000 0.000 0 0.000 0.000 0.000
#> GSM1009075 6 0.0146 0.999 0.004 0.000 0 0.000 0.000 0.996
#> GSM1009089 1 0.2805 0.770 0.812 0.000 0 0.000 0.004 0.184
#> GSM1009103 4 0.0000 0.984 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009117 5 0.0000 0.958 0.000 0.000 0 0.000 1.000 0.000
#> GSM1009131 1 0.0000 0.967 1.000 0.000 0 0.000 0.000 0.000
#> GSM1009145 4 0.0000 0.984 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009159 1 0.0000 0.967 1.000 0.000 0 0.000 0.000 0.000
#> GSM1009173 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009187 2 0.0000 0.962 0.000 1.000 0 0.000 0.000 0.000
#> GSM1009201 1 0.0000 0.967 1.000 0.000 0 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 temperature(p) time(p) specimen(p) k
#> CV:pam 139 0.911 0.981 6.98e-19 2
#> CV:pam 139 0.991 1.000 7.70e-43 3
#> CV:pam 119 1.000 1.000 1.26e-55 4
#> CV:pam 138 1.000 1.000 5.56e-81 5
#> CV:pam 138 1.000 1.000 4.39e-104 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 140 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 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 0.456 0.711 0.841 0.2950 0.819 0.819
#> 3 3 0.598 0.871 0.919 0.7377 0.718 0.656
#> 4 4 0.584 0.528 0.722 0.2634 0.828 0.680
#> 5 5 0.740 0.823 0.871 0.1588 0.763 0.434
#> 6 6 0.841 0.828 0.873 0.0778 0.908 0.634
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
#> GSM1009062 1 0.3733 0.813 0.928 0.072
#> GSM1009076 1 0.0000 0.834 1.000 0.000
#> GSM1009090 1 0.7528 0.705 0.784 0.216
#> GSM1009104 1 0.9944 -0.267 0.544 0.456
#> GSM1009118 1 0.0376 0.835 0.996 0.004
#> GSM1009132 1 0.7528 0.705 0.784 0.216
#> GSM1009146 1 0.2948 0.825 0.948 0.052
#> GSM1009160 2 0.7528 1.000 0.216 0.784
#> GSM1009174 1 0.0000 0.834 1.000 0.000
#> GSM1009188 1 0.2423 0.829 0.960 0.040
#> GSM1009063 1 0.3733 0.813 0.928 0.072
#> GSM1009077 1 0.0000 0.834 1.000 0.000
#> GSM1009091 1 0.7528 0.705 0.784 0.216
#> GSM1009105 1 0.9944 -0.267 0.544 0.456
#> GSM1009119 1 0.0376 0.835 0.996 0.004
#> GSM1009133 1 0.7528 0.705 0.784 0.216
#> GSM1009147 1 0.3431 0.818 0.936 0.064
#> GSM1009161 2 0.7528 1.000 0.216 0.784
#> GSM1009175 1 0.0000 0.834 1.000 0.000
#> GSM1009189 1 0.2603 0.828 0.956 0.044
#> GSM1009064 1 0.3733 0.813 0.928 0.072
#> GSM1009078 1 0.0000 0.834 1.000 0.000
#> GSM1009092 1 0.7528 0.705 0.784 0.216
#> GSM1009106 1 0.9944 -0.267 0.544 0.456
#> GSM1009120 1 0.0376 0.835 0.996 0.004
#> GSM1009134 1 0.7528 0.705 0.784 0.216
#> GSM1009148 1 0.2948 0.825 0.948 0.052
#> GSM1009162 2 0.7528 1.000 0.216 0.784
#> GSM1009176 1 0.0000 0.834 1.000 0.000
#> GSM1009190 1 0.2603 0.828 0.956 0.044
#> GSM1009065 1 0.3733 0.813 0.928 0.072
#> GSM1009079 1 0.0000 0.834 1.000 0.000
#> GSM1009093 1 0.7528 0.705 0.784 0.216
#> GSM1009107 1 0.9944 -0.267 0.544 0.456
#> GSM1009121 1 0.0376 0.835 0.996 0.004
#> GSM1009135 1 0.7528 0.705 0.784 0.216
#> GSM1009149 1 0.2603 0.828 0.956 0.044
#> GSM1009163 2 0.7528 1.000 0.216 0.784
#> GSM1009177 1 0.0000 0.834 1.000 0.000
#> GSM1009191 1 0.2778 0.826 0.952 0.048
#> GSM1009066 1 0.3733 0.813 0.928 0.072
#> GSM1009080 1 0.0000 0.834 1.000 0.000
#> GSM1009094 1 0.7528 0.705 0.784 0.216
#> GSM1009108 1 0.9944 -0.267 0.544 0.456
#> GSM1009122 1 0.0376 0.835 0.996 0.004
#> GSM1009136 1 0.7528 0.705 0.784 0.216
#> GSM1009150 1 0.2603 0.828 0.956 0.044
#> GSM1009164 2 0.7528 1.000 0.216 0.784
#> GSM1009178 1 0.0000 0.834 1.000 0.000
#> GSM1009192 1 0.2603 0.828 0.956 0.044
#> GSM1009067 1 0.3733 0.813 0.928 0.072
#> GSM1009081 1 0.0000 0.834 1.000 0.000
#> GSM1009095 1 0.7528 0.705 0.784 0.216
#> GSM1009109 1 0.9944 -0.267 0.544 0.456
#> GSM1009123 1 0.0376 0.835 0.996 0.004
#> GSM1009137 1 0.7528 0.705 0.784 0.216
#> GSM1009151 1 0.2778 0.827 0.952 0.048
#> GSM1009165 2 0.7528 1.000 0.216 0.784
#> GSM1009179 1 0.0000 0.834 1.000 0.000
#> GSM1009193 1 0.2603 0.828 0.956 0.044
#> GSM1009068 1 0.3733 0.813 0.928 0.072
#> GSM1009082 1 0.0000 0.834 1.000 0.000
#> GSM1009096 1 0.7528 0.705 0.784 0.216
#> GSM1009110 1 0.9944 -0.267 0.544 0.456
#> GSM1009124 1 0.0376 0.835 0.996 0.004
#> GSM1009138 1 0.7528 0.705 0.784 0.216
#> GSM1009152 1 0.2603 0.828 0.956 0.044
#> GSM1009166 2 0.7528 1.000 0.216 0.784
#> GSM1009180 1 0.0000 0.834 1.000 0.000
#> GSM1009194 1 0.2603 0.828 0.956 0.044
#> GSM1009069 1 0.2236 0.830 0.964 0.036
#> GSM1009083 1 0.0000 0.834 1.000 0.000
#> GSM1009097 1 0.7528 0.705 0.784 0.216
#> GSM1009111 1 0.9944 -0.267 0.544 0.456
#> GSM1009125 1 0.0376 0.835 0.996 0.004
#> GSM1009139 1 0.7528 0.705 0.784 0.216
#> GSM1009153 1 0.2778 0.827 0.952 0.048
#> GSM1009167 2 0.7528 1.000 0.216 0.784
#> GSM1009181 1 0.0000 0.834 1.000 0.000
#> GSM1009195 1 0.3431 0.818 0.936 0.064
#> GSM1009070 1 0.3733 0.813 0.928 0.072
#> GSM1009084 1 0.0000 0.834 1.000 0.000
#> GSM1009098 1 0.7528 0.705 0.784 0.216
#> GSM1009112 1 0.9944 -0.267 0.544 0.456
#> GSM1009126 1 0.0376 0.835 0.996 0.004
#> GSM1009140 1 0.7528 0.705 0.784 0.216
#> GSM1009154 1 0.2948 0.825 0.948 0.052
#> GSM1009168 2 0.7528 1.000 0.216 0.784
#> GSM1009182 1 0.0000 0.834 1.000 0.000
#> GSM1009196 1 0.3274 0.820 0.940 0.060
#> GSM1009071 1 0.3733 0.813 0.928 0.072
#> GSM1009085 1 0.0000 0.834 1.000 0.000
#> GSM1009099 1 0.7528 0.705 0.784 0.216
#> GSM1009113 1 0.9944 -0.267 0.544 0.456
#> GSM1009127 1 0.0376 0.835 0.996 0.004
#> GSM1009141 1 0.7528 0.705 0.784 0.216
#> GSM1009155 1 0.3114 0.823 0.944 0.056
#> GSM1009169 2 0.7528 1.000 0.216 0.784
#> GSM1009183 1 0.0000 0.834 1.000 0.000
#> GSM1009197 1 0.2603 0.828 0.956 0.044
#> GSM1009072 1 0.3733 0.813 0.928 0.072
#> GSM1009086 1 0.0000 0.834 1.000 0.000
#> GSM1009100 1 0.7528 0.705 0.784 0.216
#> GSM1009114 1 0.9944 -0.267 0.544 0.456
#> GSM1009128 1 0.0376 0.835 0.996 0.004
#> GSM1009142 1 0.7528 0.705 0.784 0.216
#> GSM1009156 1 0.3431 0.818 0.936 0.064
#> GSM1009170 2 0.7528 1.000 0.216 0.784
#> GSM1009184 1 0.0000 0.834 1.000 0.000
#> GSM1009198 1 0.2603 0.828 0.956 0.044
#> GSM1009073 1 0.3733 0.813 0.928 0.072
#> GSM1009087 1 0.0000 0.834 1.000 0.000
#> GSM1009101 1 0.7528 0.705 0.784 0.216
#> GSM1009115 1 0.9944 -0.267 0.544 0.456
#> GSM1009129 1 0.0938 0.834 0.988 0.012
#> GSM1009143 1 0.7528 0.705 0.784 0.216
#> GSM1009157 1 0.3733 0.813 0.928 0.072
#> GSM1009171 2 0.7528 1.000 0.216 0.784
#> GSM1009185 1 0.0000 0.834 1.000 0.000
#> GSM1009199 1 0.2603 0.828 0.956 0.044
#> GSM1009074 1 0.3733 0.813 0.928 0.072
#> GSM1009088 1 0.0000 0.834 1.000 0.000
#> GSM1009102 1 0.7528 0.705 0.784 0.216
#> GSM1009116 1 0.9944 -0.267 0.544 0.456
#> GSM1009130 1 0.1414 0.833 0.980 0.020
#> GSM1009144 1 0.7528 0.705 0.784 0.216
#> GSM1009158 1 0.3114 0.823 0.944 0.056
#> GSM1009172 2 0.7528 1.000 0.216 0.784
#> GSM1009186 1 0.0000 0.834 1.000 0.000
#> GSM1009200 1 0.2603 0.828 0.956 0.044
#> GSM1009075 1 0.3733 0.813 0.928 0.072
#> GSM1009089 1 0.0000 0.834 1.000 0.000
#> GSM1009103 1 0.7528 0.705 0.784 0.216
#> GSM1009117 1 0.9944 -0.267 0.544 0.456
#> GSM1009131 1 0.0672 0.834 0.992 0.008
#> GSM1009145 1 0.7528 0.705 0.784 0.216
#> GSM1009159 1 0.2603 0.828 0.956 0.044
#> GSM1009173 2 0.7528 1.000 0.216 0.784
#> GSM1009187 1 0.0000 0.834 1.000 0.000
#> GSM1009201 1 0.2603 0.828 0.956 0.044
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1009062 2 0.0424 0.873 0.008 0.992 0.000
#> GSM1009076 2 0.0000 0.873 0.000 1.000 0.000
#> GSM1009090 1 0.0000 0.996 1.000 0.000 0.000
#> GSM1009104 2 0.3267 0.806 0.000 0.884 0.116
#> GSM1009118 2 0.2878 0.858 0.096 0.904 0.000
#> GSM1009132 1 0.0237 0.996 0.996 0.004 0.000
#> GSM1009146 2 0.5016 0.768 0.240 0.760 0.000
#> GSM1009160 3 0.0000 1.000 0.000 0.000 1.000
#> GSM1009174 2 0.0000 0.873 0.000 1.000 0.000
#> GSM1009188 2 0.5591 0.706 0.304 0.696 0.000
#> GSM1009063 2 0.0424 0.873 0.008 0.992 0.000
#> GSM1009077 2 0.0000 0.873 0.000 1.000 0.000
#> GSM1009091 1 0.0000 0.996 1.000 0.000 0.000
#> GSM1009105 2 0.3267 0.806 0.000 0.884 0.116
#> GSM1009119 2 0.5706 0.691 0.320 0.680 0.000
#> GSM1009133 1 0.0237 0.996 0.996 0.004 0.000
#> GSM1009147 2 0.5465 0.726 0.288 0.712 0.000
#> GSM1009161 3 0.0000 1.000 0.000 0.000 1.000
#> GSM1009175 2 0.0000 0.873 0.000 1.000 0.000
#> GSM1009189 2 0.5465 0.726 0.288 0.712 0.000
#> GSM1009064 2 0.0424 0.873 0.008 0.992 0.000
#> GSM1009078 2 0.0000 0.873 0.000 1.000 0.000
#> GSM1009092 1 0.0000 0.996 1.000 0.000 0.000
#> GSM1009106 2 0.3267 0.806 0.000 0.884 0.116
#> GSM1009120 2 0.2711 0.859 0.088 0.912 0.000
#> GSM1009134 1 0.0237 0.996 0.996 0.004 0.000
#> GSM1009148 2 0.3192 0.850 0.112 0.888 0.000
#> GSM1009162 3 0.0000 1.000 0.000 0.000 1.000
#> GSM1009176 2 0.0000 0.873 0.000 1.000 0.000
#> GSM1009190 2 0.5465 0.726 0.288 0.712 0.000
#> GSM1009065 2 0.0424 0.873 0.008 0.992 0.000
#> GSM1009079 2 0.0000 0.873 0.000 1.000 0.000
#> GSM1009093 1 0.0000 0.996 1.000 0.000 0.000
#> GSM1009107 2 0.3267 0.806 0.000 0.884 0.116
#> GSM1009121 2 0.2878 0.858 0.096 0.904 0.000
#> GSM1009135 1 0.0237 0.996 0.996 0.004 0.000
#> GSM1009149 2 0.4654 0.795 0.208 0.792 0.000
#> GSM1009163 3 0.0000 1.000 0.000 0.000 1.000
#> GSM1009177 2 0.0000 0.873 0.000 1.000 0.000
#> GSM1009191 2 0.5465 0.726 0.288 0.712 0.000
#> GSM1009066 2 0.0424 0.873 0.008 0.992 0.000
#> GSM1009080 2 0.0000 0.873 0.000 1.000 0.000
#> GSM1009094 1 0.0000 0.996 1.000 0.000 0.000
#> GSM1009108 2 0.3267 0.806 0.000 0.884 0.116
#> GSM1009122 2 0.3030 0.859 0.092 0.904 0.004
#> GSM1009136 1 0.0237 0.996 0.996 0.004 0.000
#> GSM1009150 2 0.3482 0.842 0.128 0.872 0.000
#> GSM1009164 3 0.0000 1.000 0.000 0.000 1.000
#> GSM1009178 2 0.0000 0.873 0.000 1.000 0.000
#> GSM1009192 2 0.5465 0.726 0.288 0.712 0.000
#> GSM1009067 2 0.0424 0.873 0.008 0.992 0.000
#> GSM1009081 2 0.0000 0.873 0.000 1.000 0.000
#> GSM1009095 1 0.0424 0.987 0.992 0.008 0.000
#> GSM1009109 2 0.3267 0.806 0.000 0.884 0.116
#> GSM1009123 2 0.6204 0.506 0.424 0.576 0.000
#> GSM1009137 1 0.0237 0.996 0.996 0.004 0.000
#> GSM1009151 2 0.2537 0.861 0.080 0.920 0.000
#> GSM1009165 3 0.0000 1.000 0.000 0.000 1.000
#> GSM1009179 2 0.0000 0.873 0.000 1.000 0.000
#> GSM1009193 2 0.5497 0.722 0.292 0.708 0.000
#> GSM1009068 2 0.0424 0.873 0.008 0.992 0.000
#> GSM1009082 2 0.0000 0.873 0.000 1.000 0.000
#> GSM1009096 1 0.0000 0.996 1.000 0.000 0.000
#> GSM1009110 2 0.3267 0.806 0.000 0.884 0.116
#> GSM1009124 2 0.5650 0.703 0.312 0.688 0.000
#> GSM1009138 1 0.0237 0.996 0.996 0.004 0.000
#> GSM1009152 2 0.3038 0.853 0.104 0.896 0.000
#> GSM1009166 3 0.0000 1.000 0.000 0.000 1.000
#> GSM1009180 2 0.0000 0.873 0.000 1.000 0.000
#> GSM1009194 2 0.5465 0.726 0.288 0.712 0.000
#> GSM1009069 2 0.0424 0.873 0.008 0.992 0.000
#> GSM1009083 2 0.0000 0.873 0.000 1.000 0.000
#> GSM1009097 1 0.0000 0.996 1.000 0.000 0.000
#> GSM1009111 2 0.3267 0.806 0.000 0.884 0.116
#> GSM1009125 2 0.2878 0.858 0.096 0.904 0.000
#> GSM1009139 1 0.0237 0.996 0.996 0.004 0.000
#> GSM1009153 2 0.4842 0.781 0.224 0.776 0.000
#> GSM1009167 3 0.0000 1.000 0.000 0.000 1.000
#> GSM1009181 2 0.0000 0.873 0.000 1.000 0.000
#> GSM1009195 2 0.5465 0.726 0.288 0.712 0.000
#> GSM1009070 2 0.0424 0.873 0.008 0.992 0.000
#> GSM1009084 2 0.0000 0.873 0.000 1.000 0.000
#> GSM1009098 1 0.0000 0.996 1.000 0.000 0.000
#> GSM1009112 2 0.3267 0.806 0.000 0.884 0.116
#> GSM1009126 2 0.5560 0.719 0.300 0.700 0.000
#> GSM1009140 1 0.0237 0.996 0.996 0.004 0.000
#> GSM1009154 2 0.4887 0.778 0.228 0.772 0.000
#> GSM1009168 3 0.0000 1.000 0.000 0.000 1.000
#> GSM1009182 2 0.0000 0.873 0.000 1.000 0.000
#> GSM1009196 2 0.5465 0.726 0.288 0.712 0.000
#> GSM1009071 2 0.0424 0.873 0.008 0.992 0.000
#> GSM1009085 2 0.0000 0.873 0.000 1.000 0.000
#> GSM1009099 1 0.0000 0.996 1.000 0.000 0.000
#> GSM1009113 2 0.3267 0.806 0.000 0.884 0.116
#> GSM1009127 2 0.3551 0.843 0.132 0.868 0.000
#> GSM1009141 1 0.0237 0.996 0.996 0.004 0.000
#> GSM1009155 2 0.5397 0.734 0.280 0.720 0.000
#> GSM1009169 3 0.0000 1.000 0.000 0.000 1.000
#> GSM1009183 2 0.0000 0.873 0.000 1.000 0.000
#> GSM1009197 2 0.5465 0.726 0.288 0.712 0.000
#> GSM1009072 2 0.0424 0.873 0.008 0.992 0.000
#> GSM1009086 2 0.0000 0.873 0.000 1.000 0.000
#> GSM1009100 1 0.0000 0.996 1.000 0.000 0.000
#> GSM1009114 2 0.3267 0.806 0.000 0.884 0.116
#> GSM1009128 2 0.4974 0.778 0.236 0.764 0.000
#> GSM1009142 1 0.0237 0.996 0.996 0.004 0.000
#> GSM1009156 2 0.5465 0.726 0.288 0.712 0.000
#> GSM1009170 3 0.0000 1.000 0.000 0.000 1.000
#> GSM1009184 2 0.0000 0.873 0.000 1.000 0.000
#> GSM1009198 2 0.5529 0.717 0.296 0.704 0.000
#> GSM1009073 2 0.0424 0.873 0.008 0.992 0.000
#> GSM1009087 2 0.0000 0.873 0.000 1.000 0.000
#> GSM1009101 1 0.0000 0.996 1.000 0.000 0.000
#> GSM1009115 2 0.3267 0.806 0.000 0.884 0.116
#> GSM1009129 2 0.3030 0.859 0.092 0.904 0.004
#> GSM1009143 1 0.0237 0.996 0.996 0.004 0.000
#> GSM1009157 2 0.5465 0.726 0.288 0.712 0.000
#> GSM1009171 3 0.0000 1.000 0.000 0.000 1.000
#> GSM1009185 2 0.0000 0.873 0.000 1.000 0.000
#> GSM1009199 2 0.5465 0.726 0.288 0.712 0.000
#> GSM1009074 2 0.0424 0.873 0.008 0.992 0.000
#> GSM1009088 2 0.0000 0.873 0.000 1.000 0.000
#> GSM1009102 1 0.0000 0.996 1.000 0.000 0.000
#> GSM1009116 2 0.3267 0.806 0.000 0.884 0.116
#> GSM1009130 2 0.3030 0.859 0.092 0.904 0.004
#> GSM1009144 1 0.0237 0.996 0.996 0.004 0.000
#> GSM1009158 2 0.5465 0.726 0.288 0.712 0.000
#> GSM1009172 3 0.0000 1.000 0.000 0.000 1.000
#> GSM1009186 2 0.0000 0.873 0.000 1.000 0.000
#> GSM1009200 2 0.5465 0.726 0.288 0.712 0.000
#> GSM1009075 2 0.0424 0.873 0.008 0.992 0.000
#> GSM1009089 2 0.0000 0.873 0.000 1.000 0.000
#> GSM1009103 1 0.0000 0.996 1.000 0.000 0.000
#> GSM1009117 2 0.3267 0.806 0.000 0.884 0.116
#> GSM1009131 2 0.3030 0.859 0.092 0.904 0.004
#> GSM1009145 1 0.0237 0.996 0.996 0.004 0.000
#> GSM1009159 2 0.5291 0.745 0.268 0.732 0.000
#> GSM1009173 3 0.0000 1.000 0.000 0.000 1.000
#> GSM1009187 2 0.0237 0.873 0.004 0.996 0.000
#> GSM1009201 2 0.5465 0.726 0.288 0.712 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1009062 1 0.4994 0.8418 0.520 0.480 0 0.000
#> GSM1009076 2 0.0000 0.4781 0.000 1.000 0 0.000
#> GSM1009090 4 0.0000 0.9945 0.000 0.000 0 1.000
#> GSM1009104 2 0.4830 0.3400 0.392 0.608 0 0.000
#> GSM1009118 2 0.4688 0.2891 0.128 0.792 0 0.080
#> GSM1009132 4 0.0336 0.9947 0.008 0.000 0 0.992
#> GSM1009146 1 0.6626 0.7479 0.528 0.384 0 0.088
#> GSM1009160 3 0.0000 1.0000 0.000 0.000 1 0.000
#> GSM1009174 2 0.0336 0.4758 0.008 0.992 0 0.000
#> GSM1009188 2 0.7567 -0.2864 0.276 0.484 0 0.240
#> GSM1009063 1 0.4994 0.8418 0.520 0.480 0 0.000
#> GSM1009077 2 0.0000 0.4781 0.000 1.000 0 0.000
#> GSM1009091 4 0.0000 0.9945 0.000 0.000 0 1.000
#> GSM1009105 2 0.4830 0.3400 0.392 0.608 0 0.000
#> GSM1009119 2 0.6374 0.0955 0.128 0.644 0 0.228
#> GSM1009133 4 0.0336 0.9947 0.008 0.000 0 0.992
#> GSM1009147 2 0.7599 -0.3963 0.344 0.448 0 0.208
#> GSM1009161 3 0.0000 1.0000 0.000 0.000 1 0.000
#> GSM1009175 2 0.0336 0.4758 0.008 0.992 0 0.000
#> GSM1009189 2 0.7613 -0.3922 0.340 0.448 0 0.212
#> GSM1009064 1 0.4994 0.8418 0.520 0.480 0 0.000
#> GSM1009078 2 0.0000 0.4781 0.000 1.000 0 0.000
#> GSM1009092 4 0.0000 0.9945 0.000 0.000 0 1.000
#> GSM1009106 2 0.4830 0.3400 0.392 0.608 0 0.000
#> GSM1009120 2 0.6119 0.1338 0.152 0.680 0 0.168
#> GSM1009134 4 0.0336 0.9947 0.008 0.000 0 0.992
#> GSM1009148 1 0.6376 0.7722 0.536 0.396 0 0.068
#> GSM1009162 3 0.0000 1.0000 0.000 0.000 1 0.000
#> GSM1009176 2 0.0336 0.4758 0.008 0.992 0 0.000
#> GSM1009190 2 0.7582 -0.3825 0.336 0.456 0 0.208
#> GSM1009065 1 0.4994 0.8418 0.520 0.480 0 0.000
#> GSM1009079 2 0.0000 0.4781 0.000 1.000 0 0.000
#> GSM1009093 4 0.0000 0.9945 0.000 0.000 0 1.000
#> GSM1009107 2 0.4830 0.3400 0.392 0.608 0 0.000
#> GSM1009121 2 0.5226 0.2504 0.128 0.756 0 0.116
#> GSM1009135 4 0.0336 0.9947 0.008 0.000 0 0.992
#> GSM1009149 1 0.6985 0.6909 0.480 0.404 0 0.116
#> GSM1009163 3 0.0000 1.0000 0.000 0.000 1 0.000
#> GSM1009177 2 0.0336 0.4758 0.008 0.992 0 0.000
#> GSM1009191 2 0.7591 -0.3897 0.340 0.452 0 0.208
#> GSM1009066 1 0.4992 0.8407 0.524 0.476 0 0.000
#> GSM1009080 2 0.0000 0.4781 0.000 1.000 0 0.000
#> GSM1009094 4 0.0000 0.9945 0.000 0.000 0 1.000
#> GSM1009108 2 0.4830 0.3400 0.392 0.608 0 0.000
#> GSM1009122 2 0.4621 0.2938 0.128 0.796 0 0.076
#> GSM1009136 4 0.0336 0.9947 0.008 0.000 0 0.992
#> GSM1009150 1 0.6426 0.7682 0.536 0.392 0 0.072
#> GSM1009164 3 0.0000 1.0000 0.000 0.000 1 0.000
#> GSM1009178 2 0.0469 0.4723 0.012 0.988 0 0.000
#> GSM1009192 2 0.7599 -0.3963 0.344 0.448 0 0.208
#> GSM1009067 1 0.4994 0.8418 0.520 0.480 0 0.000
#> GSM1009081 2 0.0000 0.4781 0.000 1.000 0 0.000
#> GSM1009095 4 0.0188 0.9916 0.000 0.004 0 0.996
#> GSM1009109 2 0.4830 0.3400 0.392 0.608 0 0.000
#> GSM1009123 2 0.7098 0.0107 0.128 0.472 0 0.400
#> GSM1009137 4 0.0336 0.9947 0.008 0.000 0 0.992
#> GSM1009151 1 0.6376 0.7722 0.536 0.396 0 0.068
#> GSM1009165 3 0.0000 1.0000 0.000 0.000 1 0.000
#> GSM1009179 2 0.0469 0.4723 0.012 0.988 0 0.000
#> GSM1009193 2 0.7669 -0.3792 0.328 0.444 0 0.228
#> GSM1009068 1 0.4994 0.8418 0.520 0.480 0 0.000
#> GSM1009082 2 0.0000 0.4781 0.000 1.000 0 0.000
#> GSM1009096 4 0.0000 0.9945 0.000 0.000 0 1.000
#> GSM1009110 2 0.4790 0.3455 0.380 0.620 0 0.000
#> GSM1009124 2 0.6248 0.1165 0.128 0.660 0 0.212
#> GSM1009138 4 0.0336 0.9947 0.008 0.000 0 0.992
#> GSM1009152 1 0.6376 0.7722 0.536 0.396 0 0.068
#> GSM1009166 3 0.0000 1.0000 0.000 0.000 1 0.000
#> GSM1009180 2 0.0336 0.4758 0.008 0.992 0 0.000
#> GSM1009194 2 0.7599 -0.3963 0.344 0.448 0 0.208
#> GSM1009069 2 0.2530 0.3611 0.100 0.896 0 0.004
#> GSM1009083 2 0.0000 0.4781 0.000 1.000 0 0.000
#> GSM1009097 4 0.0000 0.9945 0.000 0.000 0 1.000
#> GSM1009111 2 0.4830 0.3400 0.392 0.608 0 0.000
#> GSM1009125 2 0.4621 0.2938 0.128 0.796 0 0.076
#> GSM1009139 4 0.0336 0.9947 0.008 0.000 0 0.992
#> GSM1009153 1 0.6552 0.7136 0.484 0.440 0 0.076
#> GSM1009167 3 0.0000 1.0000 0.000 0.000 1 0.000
#> GSM1009181 2 0.0336 0.4758 0.008 0.992 0 0.000
#> GSM1009195 2 0.7464 -0.3063 0.296 0.496 0 0.208
#> GSM1009070 1 0.5165 0.8366 0.512 0.484 0 0.004
#> GSM1009084 2 0.0000 0.4781 0.000 1.000 0 0.000
#> GSM1009098 4 0.0000 0.9945 0.000 0.000 0 1.000
#> GSM1009112 2 0.4830 0.3400 0.392 0.608 0 0.000
#> GSM1009126 2 0.6181 0.1296 0.128 0.668 0 0.204
#> GSM1009140 4 0.0336 0.9947 0.008 0.000 0 0.992
#> GSM1009154 1 0.6474 0.7633 0.536 0.388 0 0.076
#> GSM1009168 3 0.0000 1.0000 0.000 0.000 1 0.000
#> GSM1009182 2 0.0336 0.4758 0.008 0.992 0 0.000
#> GSM1009196 2 0.7599 -0.3963 0.344 0.448 0 0.208
#> GSM1009071 1 0.4994 0.8418 0.520 0.480 0 0.000
#> GSM1009085 2 0.0000 0.4781 0.000 1.000 0 0.000
#> GSM1009099 4 0.0000 0.9945 0.000 0.000 0 1.000
#> GSM1009113 2 0.4830 0.3400 0.392 0.608 0 0.000
#> GSM1009127 2 0.5923 0.1702 0.128 0.696 0 0.176
#> GSM1009141 4 0.0336 0.9947 0.008 0.000 0 0.992
#> GSM1009155 2 0.7265 -0.2846 0.288 0.528 0 0.184
#> GSM1009169 3 0.0000 1.0000 0.000 0.000 1 0.000
#> GSM1009183 2 0.0336 0.4758 0.008 0.992 0 0.000
#> GSM1009197 2 0.7599 -0.3963 0.344 0.448 0 0.208
#> GSM1009072 1 0.4994 0.8418 0.520 0.480 0 0.000
#> GSM1009086 2 0.0000 0.4781 0.000 1.000 0 0.000
#> GSM1009100 4 0.0000 0.9945 0.000 0.000 0 1.000
#> GSM1009114 2 0.4830 0.3400 0.392 0.608 0 0.000
#> GSM1009128 2 0.5923 0.1779 0.128 0.696 0 0.176
#> GSM1009142 4 0.0336 0.9947 0.008 0.000 0 0.992
#> GSM1009156 2 0.7382 -0.2789 0.276 0.516 0 0.208
#> GSM1009170 3 0.0000 1.0000 0.000 0.000 1 0.000
#> GSM1009184 2 0.0336 0.4758 0.008 0.992 0 0.000
#> GSM1009198 2 0.7621 -0.3224 0.296 0.468 0 0.236
#> GSM1009073 1 0.4994 0.8418 0.520 0.480 0 0.000
#> GSM1009087 2 0.0000 0.4781 0.000 1.000 0 0.000
#> GSM1009101 4 0.0000 0.9945 0.000 0.000 0 1.000
#> GSM1009115 2 0.4830 0.3400 0.392 0.608 0 0.000
#> GSM1009129 2 0.4621 0.2938 0.128 0.796 0 0.076
#> GSM1009143 4 0.0336 0.9947 0.008 0.000 0 0.992
#> GSM1009157 2 0.7304 -0.2422 0.260 0.532 0 0.208
#> GSM1009171 3 0.0000 1.0000 0.000 0.000 1 0.000
#> GSM1009185 2 0.0469 0.4723 0.012 0.988 0 0.000
#> GSM1009199 2 0.7591 -0.3897 0.340 0.452 0 0.208
#> GSM1009074 1 0.4994 0.8418 0.520 0.480 0 0.000
#> GSM1009088 2 0.0000 0.4781 0.000 1.000 0 0.000
#> GSM1009102 4 0.0000 0.9945 0.000 0.000 0 1.000
#> GSM1009116 2 0.4830 0.3400 0.392 0.608 0 0.000
#> GSM1009130 2 0.4621 0.2938 0.128 0.796 0 0.076
#> GSM1009144 4 0.0336 0.9947 0.008 0.000 0 0.992
#> GSM1009158 1 0.7292 0.6272 0.460 0.388 0 0.152
#> GSM1009172 3 0.0000 1.0000 0.000 0.000 1 0.000
#> GSM1009186 2 0.0336 0.4758 0.008 0.992 0 0.000
#> GSM1009200 2 0.7564 -0.3685 0.328 0.464 0 0.208
#> GSM1009075 1 0.4994 0.8418 0.520 0.480 0 0.000
#> GSM1009089 2 0.0188 0.4753 0.004 0.996 0 0.000
#> GSM1009103 4 0.0000 0.9945 0.000 0.000 0 1.000
#> GSM1009117 2 0.4830 0.3400 0.392 0.608 0 0.000
#> GSM1009131 2 0.4621 0.2938 0.128 0.796 0 0.076
#> GSM1009145 4 0.0336 0.9947 0.008 0.000 0 0.992
#> GSM1009159 2 0.7454 -0.4668 0.376 0.448 0 0.176
#> GSM1009173 3 0.0000 1.0000 0.000 0.000 1 0.000
#> GSM1009187 2 0.0804 0.4680 0.012 0.980 0 0.008
#> GSM1009201 2 0.7573 -0.3757 0.332 0.460 0 0.208
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM1009062 1 0.3639 0.76232 0.812 0.044 0 0.000 0.144
#> GSM1009076 2 0.0162 0.83521 0.000 0.996 0 0.000 0.004
#> GSM1009090 4 0.1671 0.92101 0.076 0.000 0 0.924 0.000
#> GSM1009104 5 0.2561 0.99897 0.000 0.144 0 0.000 0.856
#> GSM1009118 2 0.4354 0.46811 0.368 0.624 0 0.008 0.000
#> GSM1009132 4 0.2424 0.93229 0.132 0.000 0 0.868 0.000
#> GSM1009146 1 0.0510 0.82898 0.984 0.000 0 0.016 0.000
#> GSM1009160 3 0.0000 1.00000 0.000 0.000 1 0.000 0.000
#> GSM1009174 2 0.0000 0.83517 0.000 1.000 0 0.000 0.000
#> GSM1009188 1 0.3085 0.80925 0.852 0.032 0 0.116 0.000
#> GSM1009063 1 0.3639 0.76232 0.812 0.044 0 0.000 0.144
#> GSM1009077 2 0.0162 0.83521 0.000 0.996 0 0.000 0.004
#> GSM1009091 4 0.0000 0.89045 0.000 0.000 0 1.000 0.000
#> GSM1009105 5 0.2561 0.99897 0.000 0.144 0 0.000 0.856
#> GSM1009119 1 0.6202 0.23179 0.496 0.356 0 0.148 0.000
#> GSM1009133 4 0.2424 0.93229 0.132 0.000 0 0.868 0.000
#> GSM1009147 1 0.2740 0.82306 0.876 0.028 0 0.096 0.000
#> GSM1009161 3 0.0000 1.00000 0.000 0.000 1 0.000 0.000
#> GSM1009175 2 0.0000 0.83517 0.000 1.000 0 0.000 0.000
#> GSM1009189 1 0.2740 0.82306 0.876 0.028 0 0.096 0.000
#> GSM1009064 1 0.3639 0.76232 0.812 0.044 0 0.000 0.144
#> GSM1009078 2 0.0162 0.83521 0.000 0.996 0 0.000 0.004
#> GSM1009092 4 0.0000 0.89045 0.000 0.000 0 1.000 0.000
#> GSM1009106 5 0.2561 0.99897 0.000 0.144 0 0.000 0.856
#> GSM1009120 1 0.5357 0.54102 0.640 0.264 0 0.096 0.000
#> GSM1009134 4 0.2424 0.93229 0.132 0.000 0 0.868 0.000
#> GSM1009148 1 0.0510 0.82898 0.984 0.000 0 0.016 0.000
#> GSM1009162 3 0.0000 1.00000 0.000 0.000 1 0.000 0.000
#> GSM1009176 2 0.0000 0.83517 0.000 1.000 0 0.000 0.000
#> GSM1009190 1 0.2824 0.82189 0.872 0.032 0 0.096 0.000
#> GSM1009065 1 0.3710 0.76032 0.808 0.048 0 0.000 0.144
#> GSM1009079 2 0.0162 0.83521 0.000 0.996 0 0.000 0.004
#> GSM1009093 4 0.0000 0.89045 0.000 0.000 0 1.000 0.000
#> GSM1009107 5 0.2561 0.99897 0.000 0.144 0 0.000 0.856
#> GSM1009121 2 0.4367 0.46111 0.372 0.620 0 0.008 0.000
#> GSM1009135 4 0.2424 0.93229 0.132 0.000 0 0.868 0.000
#> GSM1009149 1 0.0510 0.82898 0.984 0.000 0 0.016 0.000
#> GSM1009163 3 0.0000 1.00000 0.000 0.000 1 0.000 0.000
#> GSM1009177 2 0.0000 0.83517 0.000 1.000 0 0.000 0.000
#> GSM1009191 1 0.2740 0.82306 0.876 0.028 0 0.096 0.000
#> GSM1009066 1 0.3639 0.76232 0.812 0.044 0 0.000 0.144
#> GSM1009080 2 0.0162 0.83521 0.000 0.996 0 0.000 0.004
#> GSM1009094 4 0.0000 0.89045 0.000 0.000 0 1.000 0.000
#> GSM1009108 5 0.2561 0.99897 0.000 0.144 0 0.000 0.856
#> GSM1009122 2 0.4354 0.46811 0.368 0.624 0 0.008 0.000
#> GSM1009136 4 0.2488 0.93178 0.124 0.004 0 0.872 0.000
#> GSM1009150 1 0.0510 0.82898 0.984 0.000 0 0.016 0.000
#> GSM1009164 3 0.0000 1.00000 0.000 0.000 1 0.000 0.000
#> GSM1009178 2 0.0404 0.83137 0.012 0.988 0 0.000 0.000
#> GSM1009192 1 0.2740 0.82306 0.876 0.028 0 0.096 0.000
#> GSM1009067 1 0.3639 0.76232 0.812 0.044 0 0.000 0.144
#> GSM1009081 2 0.0162 0.83521 0.000 0.996 0 0.000 0.004
#> GSM1009095 4 0.2753 0.91629 0.136 0.008 0 0.856 0.000
#> GSM1009109 5 0.2561 0.99897 0.000 0.144 0 0.000 0.856
#> GSM1009123 1 0.6729 0.00606 0.376 0.372 0 0.252 0.000
#> GSM1009137 4 0.2424 0.93229 0.132 0.000 0 0.868 0.000
#> GSM1009151 1 0.0510 0.82898 0.984 0.000 0 0.016 0.000
#> GSM1009165 3 0.0000 1.00000 0.000 0.000 1 0.000 0.000
#> GSM1009179 2 0.0404 0.83137 0.012 0.988 0 0.000 0.000
#> GSM1009193 1 0.2848 0.81784 0.868 0.028 0 0.104 0.000
#> GSM1009068 1 0.3639 0.76232 0.812 0.044 0 0.000 0.144
#> GSM1009082 2 0.0162 0.83521 0.000 0.996 0 0.000 0.004
#> GSM1009096 4 0.0000 0.89045 0.000 0.000 0 1.000 0.000
#> GSM1009110 5 0.2690 0.98654 0.000 0.156 0 0.000 0.844
#> GSM1009124 2 0.5591 0.25486 0.396 0.528 0 0.076 0.000
#> GSM1009138 4 0.2424 0.93229 0.132 0.000 0 0.868 0.000
#> GSM1009152 1 0.0510 0.82898 0.984 0.000 0 0.016 0.000
#> GSM1009166 3 0.0000 1.00000 0.000 0.000 1 0.000 0.000
#> GSM1009180 2 0.0290 0.83367 0.008 0.992 0 0.000 0.000
#> GSM1009194 1 0.2740 0.82306 0.876 0.028 0 0.096 0.000
#> GSM1009069 1 0.5385 0.17949 0.512 0.432 0 0.000 0.056
#> GSM1009083 2 0.0162 0.83521 0.000 0.996 0 0.000 0.004
#> GSM1009097 4 0.0000 0.89045 0.000 0.000 0 1.000 0.000
#> GSM1009111 5 0.2561 0.99897 0.000 0.144 0 0.000 0.856
#> GSM1009125 2 0.4367 0.46111 0.372 0.620 0 0.008 0.000
#> GSM1009139 4 0.2471 0.92915 0.136 0.000 0 0.864 0.000
#> GSM1009153 1 0.0510 0.82898 0.984 0.000 0 0.016 0.000
#> GSM1009167 3 0.0000 1.00000 0.000 0.000 1 0.000 0.000
#> GSM1009181 2 0.0000 0.83517 0.000 1.000 0 0.000 0.000
#> GSM1009195 1 0.2740 0.82306 0.876 0.028 0 0.096 0.000
#> GSM1009070 1 0.4084 0.77247 0.800 0.044 0 0.016 0.140
#> GSM1009084 2 0.0162 0.83521 0.000 0.996 0 0.000 0.004
#> GSM1009098 4 0.0000 0.89045 0.000 0.000 0 1.000 0.000
#> GSM1009112 5 0.2561 0.99897 0.000 0.144 0 0.000 0.856
#> GSM1009126 2 0.5165 0.37540 0.376 0.576 0 0.048 0.000
#> GSM1009140 4 0.2424 0.93229 0.132 0.000 0 0.868 0.000
#> GSM1009154 1 0.0510 0.82898 0.984 0.000 0 0.016 0.000
#> GSM1009168 3 0.0000 1.00000 0.000 0.000 1 0.000 0.000
#> GSM1009182 2 0.0162 0.83484 0.004 0.996 0 0.000 0.000
#> GSM1009196 1 0.2740 0.82306 0.876 0.028 0 0.096 0.000
#> GSM1009071 1 0.3639 0.76232 0.812 0.044 0 0.000 0.144
#> GSM1009085 2 0.0162 0.83521 0.000 0.996 0 0.000 0.004
#> GSM1009099 4 0.0000 0.89045 0.000 0.000 0 1.000 0.000
#> GSM1009113 5 0.2561 0.99897 0.000 0.144 0 0.000 0.856
#> GSM1009127 1 0.5856 0.16362 0.504 0.396 0 0.100 0.000
#> GSM1009141 4 0.2561 0.92209 0.144 0.000 0 0.856 0.000
#> GSM1009155 1 0.1965 0.81917 0.904 0.000 0 0.096 0.000
#> GSM1009169 3 0.0000 1.00000 0.000 0.000 1 0.000 0.000
#> GSM1009183 2 0.0000 0.83517 0.000 1.000 0 0.000 0.000
#> GSM1009197 1 0.2740 0.82306 0.876 0.028 0 0.096 0.000
#> GSM1009072 1 0.3639 0.76232 0.812 0.044 0 0.000 0.144
#> GSM1009086 2 0.0162 0.83521 0.000 0.996 0 0.000 0.004
#> GSM1009100 4 0.0000 0.89045 0.000 0.000 0 1.000 0.000
#> GSM1009114 5 0.2561 0.99897 0.000 0.144 0 0.000 0.856
#> GSM1009128 2 0.5968 0.23288 0.372 0.512 0 0.116 0.000
#> GSM1009142 4 0.2424 0.93229 0.132 0.000 0 0.868 0.000
#> GSM1009156 1 0.3130 0.81978 0.856 0.048 0 0.096 0.000
#> GSM1009170 3 0.0000 1.00000 0.000 0.000 1 0.000 0.000
#> GSM1009184 2 0.0000 0.83517 0.000 1.000 0 0.000 0.000
#> GSM1009198 1 0.3182 0.80567 0.844 0.032 0 0.124 0.000
#> GSM1009073 1 0.3639 0.76232 0.812 0.044 0 0.000 0.144
#> GSM1009087 2 0.0162 0.83521 0.000 0.996 0 0.000 0.004
#> GSM1009101 4 0.0000 0.89045 0.000 0.000 0 1.000 0.000
#> GSM1009115 5 0.2561 0.99897 0.000 0.144 0 0.000 0.856
#> GSM1009129 2 0.4354 0.46811 0.368 0.624 0 0.008 0.000
#> GSM1009143 4 0.2424 0.93229 0.132 0.000 0 0.868 0.000
#> GSM1009157 1 0.2983 0.82178 0.864 0.040 0 0.096 0.000
#> GSM1009171 3 0.0000 1.00000 0.000 0.000 1 0.000 0.000
#> GSM1009185 2 0.0609 0.82706 0.020 0.980 0 0.000 0.000
#> GSM1009199 1 0.2740 0.82306 0.876 0.028 0 0.096 0.000
#> GSM1009074 1 0.3639 0.76232 0.812 0.044 0 0.000 0.144
#> GSM1009088 2 0.0162 0.83521 0.000 0.996 0 0.000 0.004
#> GSM1009102 4 0.2179 0.92909 0.112 0.000 0 0.888 0.000
#> GSM1009116 5 0.2561 0.99897 0.000 0.144 0 0.000 0.856
#> GSM1009130 2 0.4354 0.46811 0.368 0.624 0 0.008 0.000
#> GSM1009144 4 0.2424 0.93229 0.132 0.000 0 0.868 0.000
#> GSM1009158 1 0.1478 0.82688 0.936 0.000 0 0.064 0.000
#> GSM1009172 3 0.0000 1.00000 0.000 0.000 1 0.000 0.000
#> GSM1009186 2 0.0162 0.83484 0.004 0.996 0 0.000 0.000
#> GSM1009200 1 0.2740 0.82306 0.876 0.028 0 0.096 0.000
#> GSM1009075 1 0.3639 0.76232 0.812 0.044 0 0.000 0.144
#> GSM1009089 2 0.0162 0.83521 0.000 0.996 0 0.000 0.004
#> GSM1009103 4 0.2179 0.92909 0.112 0.000 0 0.888 0.000
#> GSM1009117 5 0.2561 0.99897 0.000 0.144 0 0.000 0.856
#> GSM1009131 2 0.4367 0.46111 0.372 0.620 0 0.008 0.000
#> GSM1009145 4 0.2377 0.93237 0.128 0.000 0 0.872 0.000
#> GSM1009159 1 0.1942 0.82879 0.920 0.012 0 0.068 0.000
#> GSM1009173 3 0.0000 1.00000 0.000 0.000 1 0.000 0.000
#> GSM1009187 2 0.1121 0.80862 0.044 0.956 0 0.000 0.000
#> GSM1009201 1 0.2740 0.82306 0.876 0.028 0 0.096 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1009062 6 0.3927 0.618 0.172 0.072 0 0.000 0.000 0.756
#> GSM1009076 2 0.1007 0.953 0.000 0.956 0 0.000 0.000 0.044
#> GSM1009090 4 0.0713 0.940 0.028 0.000 0 0.972 0.000 0.000
#> GSM1009104 5 0.0000 0.996 0.000 0.000 0 0.000 1.000 0.000
#> GSM1009118 6 0.6441 0.326 0.172 0.368 0 0.036 0.000 0.424
#> GSM1009132 4 0.1714 0.941 0.092 0.000 0 0.908 0.000 0.000
#> GSM1009146 1 0.1434 0.850 0.940 0.000 0 0.012 0.000 0.048
#> GSM1009160 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009174 2 0.0622 0.973 0.008 0.980 0 0.000 0.000 0.012
#> GSM1009188 1 0.2778 0.712 0.824 0.008 0 0.168 0.000 0.000
#> GSM1009063 6 0.3927 0.618 0.172 0.072 0 0.000 0.000 0.756
#> GSM1009077 2 0.1007 0.953 0.000 0.956 0 0.000 0.000 0.044
#> GSM1009091 4 0.0000 0.939 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009105 5 0.0000 0.996 0.000 0.000 0 0.000 1.000 0.000
#> GSM1009119 1 0.6785 -0.154 0.440 0.128 0 0.096 0.000 0.336
#> GSM1009133 4 0.1501 0.946 0.076 0.000 0 0.924 0.000 0.000
#> GSM1009147 1 0.0790 0.860 0.968 0.000 0 0.032 0.000 0.000
#> GSM1009161 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009175 2 0.0622 0.973 0.008 0.980 0 0.000 0.000 0.012
#> GSM1009189 1 0.1204 0.846 0.944 0.000 0 0.056 0.000 0.000
#> GSM1009064 6 0.3927 0.618 0.172 0.072 0 0.000 0.000 0.756
#> GSM1009078 2 0.0000 0.971 0.000 1.000 0 0.000 0.000 0.000
#> GSM1009092 4 0.0000 0.939 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009106 5 0.0000 0.996 0.000 0.000 0 0.000 1.000 0.000
#> GSM1009120 1 0.6681 -0.136 0.456 0.136 0 0.080 0.000 0.328
#> GSM1009134 4 0.1556 0.945 0.080 0.000 0 0.920 0.000 0.000
#> GSM1009148 1 0.1434 0.850 0.940 0.000 0 0.012 0.000 0.048
#> GSM1009162 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009176 2 0.0622 0.973 0.008 0.980 0 0.000 0.000 0.012
#> GSM1009190 1 0.1010 0.860 0.960 0.004 0 0.036 0.000 0.000
#> GSM1009065 6 0.3927 0.618 0.172 0.072 0 0.000 0.000 0.756
#> GSM1009079 2 0.0603 0.968 0.004 0.980 0 0.000 0.000 0.016
#> GSM1009093 4 0.0000 0.939 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009107 5 0.0000 0.996 0.000 0.000 0 0.000 1.000 0.000
#> GSM1009121 6 0.6548 0.311 0.332 0.204 0 0.036 0.000 0.428
#> GSM1009135 4 0.1501 0.946 0.076 0.000 0 0.924 0.000 0.000
#> GSM1009149 1 0.1297 0.854 0.948 0.000 0 0.012 0.000 0.040
#> GSM1009163 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009177 2 0.0622 0.973 0.008 0.980 0 0.000 0.000 0.012
#> GSM1009191 1 0.0363 0.865 0.988 0.000 0 0.012 0.000 0.000
#> GSM1009066 6 0.3907 0.614 0.176 0.068 0 0.000 0.000 0.756
#> GSM1009080 2 0.0937 0.955 0.000 0.960 0 0.000 0.000 0.040
#> GSM1009094 4 0.0000 0.939 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009108 5 0.0000 0.996 0.000 0.000 0 0.000 1.000 0.000
#> GSM1009122 6 0.6365 0.306 0.156 0.380 0 0.036 0.000 0.428
#> GSM1009136 4 0.1814 0.938 0.100 0.000 0 0.900 0.000 0.000
#> GSM1009150 1 0.1434 0.850 0.940 0.000 0 0.012 0.000 0.048
#> GSM1009164 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009178 2 0.0622 0.973 0.008 0.980 0 0.000 0.000 0.012
#> GSM1009192 1 0.0363 0.865 0.988 0.000 0 0.012 0.000 0.000
#> GSM1009067 6 0.3927 0.618 0.172 0.072 0 0.000 0.000 0.756
#> GSM1009081 2 0.0713 0.962 0.000 0.972 0 0.000 0.000 0.028
#> GSM1009095 4 0.2513 0.838 0.140 0.008 0 0.852 0.000 0.000
#> GSM1009109 5 0.0000 0.996 0.000 0.000 0 0.000 1.000 0.000
#> GSM1009123 6 0.6958 0.217 0.260 0.068 0 0.256 0.000 0.416
#> GSM1009137 4 0.1501 0.946 0.076 0.000 0 0.924 0.000 0.000
#> GSM1009151 1 0.1434 0.850 0.940 0.000 0 0.012 0.000 0.048
#> GSM1009165 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009179 2 0.0622 0.973 0.008 0.980 0 0.000 0.000 0.012
#> GSM1009193 1 0.1610 0.825 0.916 0.000 0 0.084 0.000 0.000
#> GSM1009068 6 0.3927 0.618 0.172 0.072 0 0.000 0.000 0.756
#> GSM1009082 2 0.1007 0.953 0.000 0.956 0 0.000 0.000 0.044
#> GSM1009096 4 0.0000 0.939 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009110 5 0.1082 0.948 0.004 0.040 0 0.000 0.956 0.000
#> GSM1009124 6 0.6472 0.219 0.384 0.156 0 0.044 0.000 0.416
#> GSM1009138 4 0.1501 0.946 0.076 0.000 0 0.924 0.000 0.000
#> GSM1009152 1 0.1434 0.850 0.940 0.000 0 0.012 0.000 0.048
#> GSM1009166 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009180 2 0.0806 0.967 0.008 0.972 0 0.000 0.000 0.020
#> GSM1009194 1 0.0363 0.865 0.988 0.000 0 0.012 0.000 0.000
#> GSM1009069 6 0.5138 0.536 0.168 0.208 0 0.000 0.000 0.624
#> GSM1009083 2 0.0632 0.963 0.000 0.976 0 0.000 0.000 0.024
#> GSM1009097 4 0.0000 0.939 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009111 5 0.0000 0.996 0.000 0.000 0 0.000 1.000 0.000
#> GSM1009125 6 0.6548 0.311 0.332 0.204 0 0.036 0.000 0.428
#> GSM1009139 4 0.1957 0.928 0.112 0.000 0 0.888 0.000 0.000
#> GSM1009153 1 0.1225 0.855 0.952 0.000 0 0.012 0.000 0.036
#> GSM1009167 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009181 2 0.0622 0.973 0.008 0.980 0 0.000 0.000 0.012
#> GSM1009195 1 0.0363 0.865 0.988 0.000 0 0.012 0.000 0.000
#> GSM1009070 6 0.4742 0.562 0.240 0.072 0 0.012 0.000 0.676
#> GSM1009084 2 0.1007 0.953 0.000 0.956 0 0.000 0.000 0.044
#> GSM1009098 4 0.0000 0.939 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009112 5 0.0000 0.996 0.000 0.000 0 0.000 1.000 0.000
#> GSM1009126 6 0.6586 0.282 0.348 0.184 0 0.044 0.000 0.424
#> GSM1009140 4 0.1501 0.946 0.076 0.000 0 0.924 0.000 0.000
#> GSM1009154 1 0.1297 0.854 0.948 0.000 0 0.012 0.000 0.040
#> GSM1009168 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009182 2 0.0622 0.973 0.008 0.980 0 0.000 0.000 0.012
#> GSM1009196 1 0.0363 0.865 0.988 0.000 0 0.012 0.000 0.000
#> GSM1009071 6 0.3927 0.618 0.172 0.072 0 0.000 0.000 0.756
#> GSM1009085 2 0.1007 0.953 0.000 0.956 0 0.000 0.000 0.044
#> GSM1009099 4 0.0000 0.939 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009113 5 0.0000 0.996 0.000 0.000 0 0.000 1.000 0.000
#> GSM1009127 1 0.6789 -0.174 0.436 0.152 0 0.080 0.000 0.332
#> GSM1009141 4 0.1910 0.929 0.108 0.000 0 0.892 0.000 0.000
#> GSM1009155 1 0.1088 0.859 0.960 0.000 0 0.016 0.000 0.024
#> GSM1009169 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009183 2 0.0622 0.973 0.008 0.980 0 0.000 0.000 0.012
#> GSM1009197 1 0.1204 0.845 0.944 0.000 0 0.056 0.000 0.000
#> GSM1009072 6 0.3927 0.618 0.172 0.072 0 0.000 0.000 0.756
#> GSM1009086 2 0.1007 0.953 0.000 0.956 0 0.000 0.000 0.044
#> GSM1009100 4 0.0000 0.939 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009114 5 0.0000 0.996 0.000 0.000 0 0.000 1.000 0.000
#> GSM1009128 6 0.6704 0.196 0.364 0.112 0 0.096 0.000 0.428
#> GSM1009142 4 0.1610 0.944 0.084 0.000 0 0.916 0.000 0.000
#> GSM1009156 1 0.1719 0.823 0.924 0.000 0 0.016 0.000 0.060
#> GSM1009170 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009184 2 0.0622 0.973 0.008 0.980 0 0.000 0.000 0.012
#> GSM1009198 1 0.2848 0.700 0.816 0.008 0 0.176 0.000 0.000
#> GSM1009073 6 0.3927 0.618 0.172 0.072 0 0.000 0.000 0.756
#> GSM1009087 2 0.0146 0.970 0.000 0.996 0 0.000 0.000 0.004
#> GSM1009101 4 0.0000 0.939 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009115 5 0.0000 0.996 0.000 0.000 0 0.000 1.000 0.000
#> GSM1009129 6 0.6367 0.298 0.156 0.384 0 0.036 0.000 0.424
#> GSM1009143 4 0.1501 0.946 0.076 0.000 0 0.924 0.000 0.000
#> GSM1009157 1 0.1594 0.832 0.932 0.000 0 0.016 0.000 0.052
#> GSM1009171 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009185 2 0.0622 0.973 0.008 0.980 0 0.000 0.000 0.012
#> GSM1009199 1 0.0363 0.865 0.988 0.000 0 0.012 0.000 0.000
#> GSM1009074 6 0.3927 0.618 0.172 0.072 0 0.000 0.000 0.756
#> GSM1009088 2 0.0146 0.970 0.000 0.996 0 0.000 0.000 0.004
#> GSM1009102 4 0.1462 0.931 0.056 0.008 0 0.936 0.000 0.000
#> GSM1009116 5 0.0000 0.996 0.000 0.000 0 0.000 1.000 0.000
#> GSM1009130 6 0.6303 0.274 0.144 0.396 0 0.036 0.000 0.424
#> GSM1009144 4 0.2003 0.928 0.116 0.000 0 0.884 0.000 0.000
#> GSM1009158 1 0.1908 0.847 0.916 0.000 0 0.056 0.000 0.028
#> GSM1009172 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009186 2 0.0622 0.973 0.008 0.980 0 0.000 0.000 0.012
#> GSM1009200 1 0.0713 0.863 0.972 0.000 0 0.028 0.000 0.000
#> GSM1009075 6 0.3927 0.618 0.172 0.072 0 0.000 0.000 0.756
#> GSM1009089 2 0.0291 0.972 0.004 0.992 0 0.000 0.000 0.004
#> GSM1009103 4 0.1462 0.931 0.056 0.008 0 0.936 0.000 0.000
#> GSM1009117 5 0.0000 0.996 0.000 0.000 0 0.000 1.000 0.000
#> GSM1009131 6 0.6454 0.337 0.176 0.360 0 0.036 0.000 0.428
#> GSM1009145 4 0.1814 0.938 0.100 0.000 0 0.900 0.000 0.000
#> GSM1009159 1 0.1462 0.853 0.936 0.000 0 0.056 0.000 0.008
#> GSM1009173 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009187 2 0.0717 0.970 0.008 0.976 0 0.000 0.000 0.016
#> GSM1009201 1 0.0363 0.865 0.988 0.000 0 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 temperature(p) time(p) specimen(p) k
#> CV:mclust 126 1.000 1 1.91e-23 2
#> CV:mclust 140 1.000 1 6.13e-49 3
#> CV:mclust 64 0.999 1 7.35e-24 4
#> CV:mclust 126 1.000 1 7.26e-84 5
#> CV:mclust 126 1.000 1 4.05e-107 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 140 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 0.845 0.926 0.963 0.3918 0.589 0.589
#> 3 3 0.999 0.941 0.979 0.4499 0.719 0.568
#> 4 4 0.736 0.780 0.901 0.2523 0.805 0.567
#> 5 5 0.770 0.842 0.904 0.0777 0.817 0.475
#> 6 6 0.885 0.856 0.908 0.0631 0.928 0.711
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
#> GSM1009062 1 0.0000 0.983 1.000 0.000
#> GSM1009076 2 0.7056 0.819 0.192 0.808
#> GSM1009090 1 0.0000 0.983 1.000 0.000
#> GSM1009104 2 0.3879 0.895 0.076 0.924
#> GSM1009118 1 0.0000 0.983 1.000 0.000
#> GSM1009132 1 0.0000 0.983 1.000 0.000
#> GSM1009146 1 0.0000 0.983 1.000 0.000
#> GSM1009160 2 0.0000 0.901 0.000 1.000
#> GSM1009174 1 0.0000 0.983 1.000 0.000
#> GSM1009188 1 0.0000 0.983 1.000 0.000
#> GSM1009063 1 0.0000 0.983 1.000 0.000
#> GSM1009077 2 0.8267 0.733 0.260 0.740
#> GSM1009091 1 0.0000 0.983 1.000 0.000
#> GSM1009105 2 0.2603 0.906 0.044 0.956
#> GSM1009119 1 0.0000 0.983 1.000 0.000
#> GSM1009133 1 0.0000 0.983 1.000 0.000
#> GSM1009147 1 0.0000 0.983 1.000 0.000
#> GSM1009161 2 0.0000 0.901 0.000 1.000
#> GSM1009175 1 0.0000 0.983 1.000 0.000
#> GSM1009189 1 0.0000 0.983 1.000 0.000
#> GSM1009064 1 0.0000 0.983 1.000 0.000
#> GSM1009078 1 0.0000 0.983 1.000 0.000
#> GSM1009092 1 0.0000 0.983 1.000 0.000
#> GSM1009106 2 0.3879 0.895 0.076 0.924
#> GSM1009120 1 0.0000 0.983 1.000 0.000
#> GSM1009134 1 0.0000 0.983 1.000 0.000
#> GSM1009148 1 0.0000 0.983 1.000 0.000
#> GSM1009162 2 0.0000 0.901 0.000 1.000
#> GSM1009176 1 0.9323 0.381 0.652 0.348
#> GSM1009190 1 0.0000 0.983 1.000 0.000
#> GSM1009065 1 0.0000 0.983 1.000 0.000
#> GSM1009079 2 0.7139 0.816 0.196 0.804
#> GSM1009093 1 0.0000 0.983 1.000 0.000
#> GSM1009107 2 0.2236 0.907 0.036 0.964
#> GSM1009121 1 0.0000 0.983 1.000 0.000
#> GSM1009135 1 0.0000 0.983 1.000 0.000
#> GSM1009149 1 0.0000 0.983 1.000 0.000
#> GSM1009163 2 0.0000 0.901 0.000 1.000
#> GSM1009177 1 0.8207 0.608 0.744 0.256
#> GSM1009191 1 0.0000 0.983 1.000 0.000
#> GSM1009066 1 0.0000 0.983 1.000 0.000
#> GSM1009080 2 0.6887 0.826 0.184 0.816
#> GSM1009094 1 0.0000 0.983 1.000 0.000
#> GSM1009108 2 0.2778 0.905 0.048 0.952
#> GSM1009122 1 0.0000 0.983 1.000 0.000
#> GSM1009136 1 0.0000 0.983 1.000 0.000
#> GSM1009150 1 0.0000 0.983 1.000 0.000
#> GSM1009164 2 0.0000 0.901 0.000 1.000
#> GSM1009178 1 0.0000 0.983 1.000 0.000
#> GSM1009192 1 0.0000 0.983 1.000 0.000
#> GSM1009067 1 0.0000 0.983 1.000 0.000
#> GSM1009081 2 0.7376 0.803 0.208 0.792
#> GSM1009095 1 0.0000 0.983 1.000 0.000
#> GSM1009109 2 0.2423 0.907 0.040 0.960
#> GSM1009123 1 0.0000 0.983 1.000 0.000
#> GSM1009137 1 0.0000 0.983 1.000 0.000
#> GSM1009151 1 0.0000 0.983 1.000 0.000
#> GSM1009165 2 0.0000 0.901 0.000 1.000
#> GSM1009179 1 0.0000 0.983 1.000 0.000
#> GSM1009193 1 0.0000 0.983 1.000 0.000
#> GSM1009068 1 0.0000 0.983 1.000 0.000
#> GSM1009082 2 0.9087 0.626 0.324 0.676
#> GSM1009096 1 0.0000 0.983 1.000 0.000
#> GSM1009110 2 0.1184 0.904 0.016 0.984
#> GSM1009124 1 0.0000 0.983 1.000 0.000
#> GSM1009138 1 0.0000 0.983 1.000 0.000
#> GSM1009152 1 0.0000 0.983 1.000 0.000
#> GSM1009166 2 0.0000 0.901 0.000 1.000
#> GSM1009180 1 0.0000 0.983 1.000 0.000
#> GSM1009194 1 0.0000 0.983 1.000 0.000
#> GSM1009069 1 0.0000 0.983 1.000 0.000
#> GSM1009083 2 1.0000 0.173 0.496 0.504
#> GSM1009097 1 0.0000 0.983 1.000 0.000
#> GSM1009111 2 0.2236 0.907 0.036 0.964
#> GSM1009125 1 0.6712 0.756 0.824 0.176
#> GSM1009139 1 0.0000 0.983 1.000 0.000
#> GSM1009153 1 0.0000 0.983 1.000 0.000
#> GSM1009167 2 0.0000 0.901 0.000 1.000
#> GSM1009181 1 0.9248 0.404 0.660 0.340
#> GSM1009195 1 0.0000 0.983 1.000 0.000
#> GSM1009070 1 0.0000 0.983 1.000 0.000
#> GSM1009084 2 0.7139 0.816 0.196 0.804
#> GSM1009098 1 0.0000 0.983 1.000 0.000
#> GSM1009112 2 0.2236 0.907 0.036 0.964
#> GSM1009126 1 0.0000 0.983 1.000 0.000
#> GSM1009140 1 0.0000 0.983 1.000 0.000
#> GSM1009154 1 0.0000 0.983 1.000 0.000
#> GSM1009168 2 0.0000 0.901 0.000 1.000
#> GSM1009182 1 0.0000 0.983 1.000 0.000
#> GSM1009196 1 0.0000 0.983 1.000 0.000
#> GSM1009071 1 0.0000 0.983 1.000 0.000
#> GSM1009085 2 0.7299 0.807 0.204 0.796
#> GSM1009099 1 0.0000 0.983 1.000 0.000
#> GSM1009113 2 0.2236 0.907 0.036 0.964
#> GSM1009127 1 0.0000 0.983 1.000 0.000
#> GSM1009141 1 0.0000 0.983 1.000 0.000
#> GSM1009155 1 0.0000 0.983 1.000 0.000
#> GSM1009169 2 0.0000 0.901 0.000 1.000
#> GSM1009183 1 0.6148 0.794 0.848 0.152
#> GSM1009197 1 0.0000 0.983 1.000 0.000
#> GSM1009072 1 0.0000 0.983 1.000 0.000
#> GSM1009086 2 0.6343 0.844 0.160 0.840
#> GSM1009100 1 0.0000 0.983 1.000 0.000
#> GSM1009114 2 0.4161 0.891 0.084 0.916
#> GSM1009128 1 0.0000 0.983 1.000 0.000
#> GSM1009142 1 0.0000 0.983 1.000 0.000
#> GSM1009156 1 0.0000 0.983 1.000 0.000
#> GSM1009170 2 0.0000 0.901 0.000 1.000
#> GSM1009184 1 0.0000 0.983 1.000 0.000
#> GSM1009198 1 0.0000 0.983 1.000 0.000
#> GSM1009073 1 0.0000 0.983 1.000 0.000
#> GSM1009087 1 0.0376 0.979 0.996 0.004
#> GSM1009101 1 0.0000 0.983 1.000 0.000
#> GSM1009115 2 0.2423 0.907 0.040 0.960
#> GSM1009129 2 0.9944 0.308 0.456 0.544
#> GSM1009143 1 0.0000 0.983 1.000 0.000
#> GSM1009157 1 0.0000 0.983 1.000 0.000
#> GSM1009171 2 0.0000 0.901 0.000 1.000
#> GSM1009185 1 0.0000 0.983 1.000 0.000
#> GSM1009199 1 0.0000 0.983 1.000 0.000
#> GSM1009074 1 0.0000 0.983 1.000 0.000
#> GSM1009088 1 0.7299 0.710 0.796 0.204
#> GSM1009102 1 0.0000 0.983 1.000 0.000
#> GSM1009116 2 0.2236 0.907 0.036 0.964
#> GSM1009130 2 0.7299 0.807 0.204 0.796
#> GSM1009144 1 0.0000 0.983 1.000 0.000
#> GSM1009158 1 0.0000 0.983 1.000 0.000
#> GSM1009172 2 0.0000 0.901 0.000 1.000
#> GSM1009186 1 0.0000 0.983 1.000 0.000
#> GSM1009200 1 0.0000 0.983 1.000 0.000
#> GSM1009075 1 0.0000 0.983 1.000 0.000
#> GSM1009089 1 0.0000 0.983 1.000 0.000
#> GSM1009103 1 0.0000 0.983 1.000 0.000
#> GSM1009117 2 0.3114 0.903 0.056 0.944
#> GSM1009131 1 0.0000 0.983 1.000 0.000
#> GSM1009145 1 0.0000 0.983 1.000 0.000
#> GSM1009159 1 0.0000 0.983 1.000 0.000
#> GSM1009173 2 0.0000 0.901 0.000 1.000
#> GSM1009187 1 0.0000 0.983 1.000 0.000
#> GSM1009201 1 0.0000 0.983 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1009062 1 0.0000 0.9800 1.000 0.000 0.000
#> GSM1009076 2 0.0000 0.9525 0.000 1.000 0.000
#> GSM1009090 1 0.0000 0.9800 1.000 0.000 0.000
#> GSM1009104 2 0.0000 0.9525 0.000 1.000 0.000
#> GSM1009118 1 0.1643 0.9346 0.956 0.044 0.000
#> GSM1009132 1 0.0000 0.9800 1.000 0.000 0.000
#> GSM1009146 1 0.0000 0.9800 1.000 0.000 0.000
#> GSM1009160 3 0.0000 1.0000 0.000 0.000 1.000
#> GSM1009174 2 0.0000 0.9525 0.000 1.000 0.000
#> GSM1009188 1 0.0000 0.9800 1.000 0.000 0.000
#> GSM1009063 1 0.0237 0.9763 0.996 0.004 0.000
#> GSM1009077 2 0.0000 0.9525 0.000 1.000 0.000
#> GSM1009091 1 0.0000 0.9800 1.000 0.000 0.000
#> GSM1009105 2 0.0000 0.9525 0.000 1.000 0.000
#> GSM1009119 1 0.0000 0.9800 1.000 0.000 0.000
#> GSM1009133 1 0.0000 0.9800 1.000 0.000 0.000
#> GSM1009147 1 0.0000 0.9800 1.000 0.000 0.000
#> GSM1009161 3 0.0000 1.0000 0.000 0.000 1.000
#> GSM1009175 2 0.0000 0.9525 0.000 1.000 0.000
#> GSM1009189 1 0.0000 0.9800 1.000 0.000 0.000
#> GSM1009064 1 0.2625 0.8887 0.916 0.084 0.000
#> GSM1009078 2 0.0000 0.9525 0.000 1.000 0.000
#> GSM1009092 1 0.0000 0.9800 1.000 0.000 0.000
#> GSM1009106 2 0.0000 0.9525 0.000 1.000 0.000
#> GSM1009120 1 0.0000 0.9800 1.000 0.000 0.000
#> GSM1009134 1 0.0000 0.9800 1.000 0.000 0.000
#> GSM1009148 1 0.0000 0.9800 1.000 0.000 0.000
#> GSM1009162 3 0.0000 1.0000 0.000 0.000 1.000
#> GSM1009176 2 0.0000 0.9525 0.000 1.000 0.000
#> GSM1009190 1 0.0000 0.9800 1.000 0.000 0.000
#> GSM1009065 1 0.5497 0.5778 0.708 0.292 0.000
#> GSM1009079 2 0.0000 0.9525 0.000 1.000 0.000
#> GSM1009093 1 0.0000 0.9800 1.000 0.000 0.000
#> GSM1009107 2 0.0000 0.9525 0.000 1.000 0.000
#> GSM1009121 1 0.0000 0.9800 1.000 0.000 0.000
#> GSM1009135 1 0.0000 0.9800 1.000 0.000 0.000
#> GSM1009149 1 0.0000 0.9800 1.000 0.000 0.000
#> GSM1009163 3 0.0000 1.0000 0.000 0.000 1.000
#> GSM1009177 2 0.0000 0.9525 0.000 1.000 0.000
#> GSM1009191 1 0.0000 0.9800 1.000 0.000 0.000
#> GSM1009066 1 0.0592 0.9686 0.988 0.012 0.000
#> GSM1009080 2 0.0000 0.9525 0.000 1.000 0.000
#> GSM1009094 1 0.0000 0.9800 1.000 0.000 0.000
#> GSM1009108 2 0.0000 0.9525 0.000 1.000 0.000
#> GSM1009122 2 0.6260 0.2005 0.448 0.552 0.000
#> GSM1009136 1 0.0000 0.9800 1.000 0.000 0.000
#> GSM1009150 1 0.0000 0.9800 1.000 0.000 0.000
#> GSM1009164 3 0.0000 1.0000 0.000 0.000 1.000
#> GSM1009178 2 0.0000 0.9525 0.000 1.000 0.000
#> GSM1009192 1 0.0000 0.9800 1.000 0.000 0.000
#> GSM1009067 1 0.0000 0.9800 1.000 0.000 0.000
#> GSM1009081 2 0.0000 0.9525 0.000 1.000 0.000
#> GSM1009095 1 0.0000 0.9800 1.000 0.000 0.000
#> GSM1009109 2 0.0000 0.9525 0.000 1.000 0.000
#> GSM1009123 1 0.0000 0.9800 1.000 0.000 0.000
#> GSM1009137 1 0.0000 0.9800 1.000 0.000 0.000
#> GSM1009151 1 0.0000 0.9800 1.000 0.000 0.000
#> GSM1009165 3 0.0000 1.0000 0.000 0.000 1.000
#> GSM1009179 2 0.0000 0.9525 0.000 1.000 0.000
#> GSM1009193 1 0.0000 0.9800 1.000 0.000 0.000
#> GSM1009068 1 0.0000 0.9800 1.000 0.000 0.000
#> GSM1009082 2 0.0000 0.9525 0.000 1.000 0.000
#> GSM1009096 1 0.0000 0.9800 1.000 0.000 0.000
#> GSM1009110 2 0.0000 0.9525 0.000 1.000 0.000
#> GSM1009124 1 0.0000 0.9800 1.000 0.000 0.000
#> GSM1009138 1 0.0000 0.9800 1.000 0.000 0.000
#> GSM1009152 1 0.0000 0.9800 1.000 0.000 0.000
#> GSM1009166 3 0.0000 1.0000 0.000 0.000 1.000
#> GSM1009180 2 0.0000 0.9525 0.000 1.000 0.000
#> GSM1009194 1 0.0000 0.9800 1.000 0.000 0.000
#> GSM1009069 2 0.2356 0.8675 0.072 0.928 0.000
#> GSM1009083 2 0.0000 0.9525 0.000 1.000 0.000
#> GSM1009097 1 0.0000 0.9800 1.000 0.000 0.000
#> GSM1009111 2 0.0000 0.9525 0.000 1.000 0.000
#> GSM1009125 2 0.8209 0.0772 0.456 0.472 0.072
#> GSM1009139 1 0.0000 0.9800 1.000 0.000 0.000
#> GSM1009153 1 0.0000 0.9800 1.000 0.000 0.000
#> GSM1009167 3 0.0000 1.0000 0.000 0.000 1.000
#> GSM1009181 2 0.0000 0.9525 0.000 1.000 0.000
#> GSM1009195 1 0.5988 0.4012 0.632 0.368 0.000
#> GSM1009070 1 0.0000 0.9800 1.000 0.000 0.000
#> GSM1009084 2 0.0000 0.9525 0.000 1.000 0.000
#> GSM1009098 1 0.0000 0.9800 1.000 0.000 0.000
#> GSM1009112 2 0.0000 0.9525 0.000 1.000 0.000
#> GSM1009126 1 0.0000 0.9800 1.000 0.000 0.000
#> GSM1009140 1 0.0000 0.9800 1.000 0.000 0.000
#> GSM1009154 1 0.0000 0.9800 1.000 0.000 0.000
#> GSM1009168 3 0.0000 1.0000 0.000 0.000 1.000
#> GSM1009182 2 0.0000 0.9525 0.000 1.000 0.000
#> GSM1009196 1 0.0000 0.9800 1.000 0.000 0.000
#> GSM1009071 1 0.2878 0.8742 0.904 0.096 0.000
#> GSM1009085 2 0.0000 0.9525 0.000 1.000 0.000
#> GSM1009099 1 0.0000 0.9800 1.000 0.000 0.000
#> GSM1009113 2 0.0000 0.9525 0.000 1.000 0.000
#> GSM1009127 1 0.0000 0.9800 1.000 0.000 0.000
#> GSM1009141 1 0.0000 0.9800 1.000 0.000 0.000
#> GSM1009155 1 0.0000 0.9800 1.000 0.000 0.000
#> GSM1009169 3 0.0000 1.0000 0.000 0.000 1.000
#> GSM1009183 2 0.0000 0.9525 0.000 1.000 0.000
#> GSM1009197 1 0.0000 0.9800 1.000 0.000 0.000
#> GSM1009072 1 0.0000 0.9800 1.000 0.000 0.000
#> GSM1009086 2 0.0000 0.9525 0.000 1.000 0.000
#> GSM1009100 1 0.0000 0.9800 1.000 0.000 0.000
#> GSM1009114 2 0.0000 0.9525 0.000 1.000 0.000
#> GSM1009128 1 0.0000 0.9800 1.000 0.000 0.000
#> GSM1009142 1 0.0000 0.9800 1.000 0.000 0.000
#> GSM1009156 1 0.6095 0.3326 0.608 0.392 0.000
#> GSM1009170 3 0.0000 1.0000 0.000 0.000 1.000
#> GSM1009184 2 0.0000 0.9525 0.000 1.000 0.000
#> GSM1009198 1 0.0000 0.9800 1.000 0.000 0.000
#> GSM1009073 1 0.0237 0.9763 0.996 0.004 0.000
#> GSM1009087 2 0.0000 0.9525 0.000 1.000 0.000
#> GSM1009101 1 0.0000 0.9800 1.000 0.000 0.000
#> GSM1009115 2 0.0000 0.9525 0.000 1.000 0.000
#> GSM1009129 2 0.2448 0.8623 0.076 0.924 0.000
#> GSM1009143 1 0.0000 0.9800 1.000 0.000 0.000
#> GSM1009157 2 0.5926 0.4497 0.356 0.644 0.000
#> GSM1009171 3 0.0000 1.0000 0.000 0.000 1.000
#> GSM1009185 2 0.0000 0.9525 0.000 1.000 0.000
#> GSM1009199 1 0.0424 0.9725 0.992 0.008 0.000
#> GSM1009074 1 0.0000 0.9800 1.000 0.000 0.000
#> GSM1009088 2 0.0000 0.9525 0.000 1.000 0.000
#> GSM1009102 1 0.0000 0.9800 1.000 0.000 0.000
#> GSM1009116 2 0.0000 0.9525 0.000 1.000 0.000
#> GSM1009130 2 0.0237 0.9482 0.004 0.996 0.000
#> GSM1009144 1 0.0000 0.9800 1.000 0.000 0.000
#> GSM1009158 1 0.0000 0.9800 1.000 0.000 0.000
#> GSM1009172 3 0.0000 1.0000 0.000 0.000 1.000
#> GSM1009186 2 0.0000 0.9525 0.000 1.000 0.000
#> GSM1009200 1 0.0000 0.9800 1.000 0.000 0.000
#> GSM1009075 1 0.0000 0.9800 1.000 0.000 0.000
#> GSM1009089 2 0.0000 0.9525 0.000 1.000 0.000
#> GSM1009103 1 0.0000 0.9800 1.000 0.000 0.000
#> GSM1009117 2 0.0000 0.9525 0.000 1.000 0.000
#> GSM1009131 2 0.4062 0.7403 0.164 0.836 0.000
#> GSM1009145 1 0.0000 0.9800 1.000 0.000 0.000
#> GSM1009159 1 0.0000 0.9800 1.000 0.000 0.000
#> GSM1009173 3 0.0000 1.0000 0.000 0.000 1.000
#> GSM1009187 2 0.0000 0.9525 0.000 1.000 0.000
#> GSM1009201 1 0.0000 0.9800 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1009062 1 0.1302 0.8467 0.956 0.000 0.000 0.044
#> GSM1009076 2 0.0000 0.8671 0.000 1.000 0.000 0.000
#> GSM1009090 4 0.0188 0.8819 0.004 0.000 0.000 0.996
#> GSM1009104 2 0.0000 0.8671 0.000 1.000 0.000 0.000
#> GSM1009118 4 0.4988 0.5498 0.288 0.020 0.000 0.692
#> GSM1009132 4 0.0000 0.8819 0.000 0.000 0.000 1.000
#> GSM1009146 1 0.0188 0.8513 0.996 0.000 0.000 0.004
#> GSM1009160 3 0.0000 1.0000 0.000 0.000 1.000 0.000
#> GSM1009174 2 0.4356 0.6509 0.292 0.708 0.000 0.000
#> GSM1009188 4 0.5000 -0.0750 0.500 0.000 0.000 0.500
#> GSM1009063 1 0.1211 0.8479 0.960 0.000 0.000 0.040
#> GSM1009077 2 0.0000 0.8671 0.000 1.000 0.000 0.000
#> GSM1009091 4 0.0469 0.8797 0.012 0.000 0.000 0.988
#> GSM1009105 2 0.0000 0.8671 0.000 1.000 0.000 0.000
#> GSM1009119 4 0.4916 0.2260 0.424 0.000 0.000 0.576
#> GSM1009133 4 0.0000 0.8819 0.000 0.000 0.000 1.000
#> GSM1009147 1 0.1389 0.8381 0.952 0.000 0.000 0.048
#> GSM1009161 3 0.0000 1.0000 0.000 0.000 1.000 0.000
#> GSM1009175 2 0.3649 0.7565 0.204 0.796 0.000 0.000
#> GSM1009189 1 0.4855 0.3750 0.600 0.000 0.000 0.400
#> GSM1009064 1 0.1302 0.8467 0.956 0.000 0.000 0.044
#> GSM1009078 2 0.3219 0.7899 0.164 0.836 0.000 0.000
#> GSM1009092 4 0.0469 0.8797 0.012 0.000 0.000 0.988
#> GSM1009106 2 0.0000 0.8671 0.000 1.000 0.000 0.000
#> GSM1009120 1 0.4843 0.3858 0.604 0.000 0.000 0.396
#> GSM1009134 4 0.0000 0.8819 0.000 0.000 0.000 1.000
#> GSM1009148 1 0.0000 0.8510 1.000 0.000 0.000 0.000
#> GSM1009162 3 0.0000 1.0000 0.000 0.000 1.000 0.000
#> GSM1009176 2 0.1118 0.8593 0.036 0.964 0.000 0.000
#> GSM1009190 1 0.4761 0.4431 0.628 0.000 0.000 0.372
#> GSM1009065 1 0.1302 0.8467 0.956 0.000 0.000 0.044
#> GSM1009079 2 0.0000 0.8671 0.000 1.000 0.000 0.000
#> GSM1009093 4 0.0469 0.8797 0.012 0.000 0.000 0.988
#> GSM1009107 2 0.0000 0.8671 0.000 1.000 0.000 0.000
#> GSM1009121 4 0.3942 0.6549 0.236 0.000 0.000 0.764
#> GSM1009135 4 0.0000 0.8819 0.000 0.000 0.000 1.000
#> GSM1009149 1 0.3764 0.7030 0.784 0.000 0.000 0.216
#> GSM1009163 3 0.0000 1.0000 0.000 0.000 1.000 0.000
#> GSM1009177 2 0.1118 0.8593 0.036 0.964 0.000 0.000
#> GSM1009191 1 0.3569 0.7257 0.804 0.000 0.000 0.196
#> GSM1009066 1 0.1302 0.8467 0.956 0.000 0.000 0.044
#> GSM1009080 2 0.0000 0.8671 0.000 1.000 0.000 0.000
#> GSM1009094 4 0.0336 0.8813 0.008 0.000 0.000 0.992
#> GSM1009108 2 0.0000 0.8671 0.000 1.000 0.000 0.000
#> GSM1009122 2 0.7773 0.2071 0.264 0.428 0.000 0.308
#> GSM1009136 4 0.0000 0.8819 0.000 0.000 0.000 1.000
#> GSM1009150 1 0.1940 0.8226 0.924 0.000 0.000 0.076
#> GSM1009164 3 0.0000 1.0000 0.000 0.000 1.000 0.000
#> GSM1009178 2 0.4804 0.4906 0.384 0.616 0.000 0.000
#> GSM1009192 1 0.3649 0.7167 0.796 0.000 0.000 0.204
#> GSM1009067 1 0.1211 0.8479 0.960 0.000 0.000 0.040
#> GSM1009081 2 0.0000 0.8671 0.000 1.000 0.000 0.000
#> GSM1009095 4 0.0000 0.8819 0.000 0.000 0.000 1.000
#> GSM1009109 2 0.0000 0.8671 0.000 1.000 0.000 0.000
#> GSM1009123 4 0.4331 0.5713 0.288 0.000 0.000 0.712
#> GSM1009137 4 0.0000 0.8819 0.000 0.000 0.000 1.000
#> GSM1009151 1 0.0000 0.8510 1.000 0.000 0.000 0.000
#> GSM1009165 3 0.0000 1.0000 0.000 0.000 1.000 0.000
#> GSM1009179 2 0.4804 0.4892 0.384 0.616 0.000 0.000
#> GSM1009193 1 0.4817 0.4065 0.612 0.000 0.000 0.388
#> GSM1009068 1 0.1716 0.8325 0.936 0.000 0.000 0.064
#> GSM1009082 2 0.0469 0.8651 0.012 0.988 0.000 0.000
#> GSM1009096 4 0.0336 0.8813 0.008 0.000 0.000 0.992
#> GSM1009110 2 0.0000 0.8671 0.000 1.000 0.000 0.000
#> GSM1009124 4 0.4605 0.4696 0.336 0.000 0.000 0.664
#> GSM1009138 4 0.0000 0.8819 0.000 0.000 0.000 1.000
#> GSM1009152 1 0.0000 0.8510 1.000 0.000 0.000 0.000
#> GSM1009166 3 0.0000 1.0000 0.000 0.000 1.000 0.000
#> GSM1009180 2 0.4431 0.6354 0.304 0.696 0.000 0.000
#> GSM1009194 1 0.0000 0.8510 1.000 0.000 0.000 0.000
#> GSM1009069 1 0.1706 0.8418 0.948 0.016 0.000 0.036
#> GSM1009083 2 0.0336 0.8656 0.008 0.992 0.000 0.000
#> GSM1009097 4 0.0469 0.8797 0.012 0.000 0.000 0.988
#> GSM1009111 2 0.0000 0.8671 0.000 1.000 0.000 0.000
#> GSM1009125 4 0.6890 0.3443 0.104 0.312 0.008 0.576
#> GSM1009139 4 0.0000 0.8819 0.000 0.000 0.000 1.000
#> GSM1009153 1 0.0000 0.8510 1.000 0.000 0.000 0.000
#> GSM1009167 3 0.0000 1.0000 0.000 0.000 1.000 0.000
#> GSM1009181 2 0.0817 0.8623 0.024 0.976 0.000 0.000
#> GSM1009195 1 0.0336 0.8492 0.992 0.008 0.000 0.000
#> GSM1009070 1 0.1211 0.8479 0.960 0.000 0.000 0.040
#> GSM1009084 2 0.0000 0.8671 0.000 1.000 0.000 0.000
#> GSM1009098 4 0.0336 0.8813 0.008 0.000 0.000 0.992
#> GSM1009112 2 0.0000 0.8671 0.000 1.000 0.000 0.000
#> GSM1009126 4 0.4304 0.5787 0.284 0.000 0.000 0.716
#> GSM1009140 4 0.0000 0.8819 0.000 0.000 0.000 1.000
#> GSM1009154 1 0.0000 0.8510 1.000 0.000 0.000 0.000
#> GSM1009168 3 0.0000 1.0000 0.000 0.000 1.000 0.000
#> GSM1009182 2 0.4193 0.6854 0.268 0.732 0.000 0.000
#> GSM1009196 1 0.0188 0.8513 0.996 0.000 0.000 0.004
#> GSM1009071 1 0.1302 0.8467 0.956 0.000 0.000 0.044
#> GSM1009085 2 0.0000 0.8671 0.000 1.000 0.000 0.000
#> GSM1009099 4 0.0469 0.8797 0.012 0.000 0.000 0.988
#> GSM1009113 2 0.0000 0.8671 0.000 1.000 0.000 0.000
#> GSM1009127 4 0.4972 0.1101 0.456 0.000 0.000 0.544
#> GSM1009141 4 0.0000 0.8819 0.000 0.000 0.000 1.000
#> GSM1009155 1 0.0000 0.8510 1.000 0.000 0.000 0.000
#> GSM1009169 3 0.0000 1.0000 0.000 0.000 1.000 0.000
#> GSM1009183 2 0.1118 0.8587 0.036 0.964 0.000 0.000
#> GSM1009197 1 0.4605 0.5166 0.664 0.000 0.000 0.336
#> GSM1009072 1 0.1867 0.8259 0.928 0.000 0.000 0.072
#> GSM1009086 2 0.0000 0.8671 0.000 1.000 0.000 0.000
#> GSM1009100 4 0.0336 0.8813 0.008 0.000 0.000 0.992
#> GSM1009114 2 0.0000 0.8671 0.000 1.000 0.000 0.000
#> GSM1009128 4 0.3266 0.7422 0.168 0.000 0.000 0.832
#> GSM1009142 4 0.0000 0.8819 0.000 0.000 0.000 1.000
#> GSM1009156 1 0.0592 0.8499 0.984 0.000 0.000 0.016
#> GSM1009170 3 0.0000 1.0000 0.000 0.000 1.000 0.000
#> GSM1009184 2 0.4843 0.4625 0.396 0.604 0.000 0.000
#> GSM1009198 1 0.5000 0.0279 0.500 0.000 0.000 0.500
#> GSM1009073 1 0.1302 0.8467 0.956 0.000 0.000 0.044
#> GSM1009087 2 0.2760 0.8125 0.128 0.872 0.000 0.000
#> GSM1009101 4 0.0336 0.8813 0.008 0.000 0.000 0.992
#> GSM1009115 2 0.0000 0.8671 0.000 1.000 0.000 0.000
#> GSM1009129 2 0.3610 0.7526 0.200 0.800 0.000 0.000
#> GSM1009143 4 0.0000 0.8819 0.000 0.000 0.000 1.000
#> GSM1009157 1 0.0000 0.8510 1.000 0.000 0.000 0.000
#> GSM1009171 3 0.0000 1.0000 0.000 0.000 1.000 0.000
#> GSM1009185 2 0.4072 0.7062 0.252 0.748 0.000 0.000
#> GSM1009199 1 0.0336 0.8511 0.992 0.000 0.000 0.008
#> GSM1009074 1 0.1211 0.8479 0.960 0.000 0.000 0.040
#> GSM1009088 2 0.1637 0.8472 0.060 0.940 0.000 0.000
#> GSM1009102 4 0.0188 0.8819 0.004 0.000 0.000 0.996
#> GSM1009116 2 0.0000 0.8671 0.000 1.000 0.000 0.000
#> GSM1009130 2 0.3610 0.7526 0.200 0.800 0.000 0.000
#> GSM1009144 4 0.0000 0.8819 0.000 0.000 0.000 1.000
#> GSM1009158 1 0.0469 0.8507 0.988 0.000 0.000 0.012
#> GSM1009172 3 0.0000 1.0000 0.000 0.000 1.000 0.000
#> GSM1009186 2 0.4804 0.4907 0.384 0.616 0.000 0.000
#> GSM1009200 1 0.4817 0.4065 0.612 0.000 0.000 0.388
#> GSM1009075 1 0.1302 0.8467 0.956 0.000 0.000 0.044
#> GSM1009089 1 0.4972 -0.0141 0.544 0.456 0.000 0.000
#> GSM1009103 4 0.0000 0.8819 0.000 0.000 0.000 1.000
#> GSM1009117 2 0.0000 0.8671 0.000 1.000 0.000 0.000
#> GSM1009131 2 0.5577 0.5381 0.328 0.636 0.000 0.036
#> GSM1009145 4 0.0000 0.8819 0.000 0.000 0.000 1.000
#> GSM1009159 1 0.3837 0.6930 0.776 0.000 0.000 0.224
#> GSM1009173 3 0.0000 1.0000 0.000 0.000 1.000 0.000
#> GSM1009187 1 0.4193 0.5281 0.732 0.268 0.000 0.000
#> GSM1009201 1 0.3528 0.7293 0.808 0.000 0.000 0.192
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM1009062 2 0.0510 1.000 0.016 0.984 0.000 0.000 0.000
#> GSM1009076 5 0.2439 0.826 0.004 0.120 0.000 0.000 0.876
#> GSM1009090 4 0.0290 0.967 0.008 0.000 0.000 0.992 0.000
#> GSM1009104 5 0.0000 0.859 0.000 0.000 0.000 0.000 1.000
#> GSM1009118 1 0.5114 0.651 0.660 0.008 0.000 0.280 0.052
#> GSM1009132 4 0.0000 0.968 0.000 0.000 0.000 1.000 0.000
#> GSM1009146 1 0.2732 0.810 0.840 0.160 0.000 0.000 0.000
#> GSM1009160 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000
#> GSM1009174 1 0.4249 0.486 0.688 0.016 0.000 0.000 0.296
#> GSM1009188 1 0.2669 0.830 0.876 0.020 0.000 0.104 0.000
#> GSM1009063 2 0.0510 1.000 0.016 0.984 0.000 0.000 0.000
#> GSM1009077 5 0.2488 0.824 0.004 0.124 0.000 0.000 0.872
#> GSM1009091 4 0.0404 0.964 0.012 0.000 0.000 0.988 0.000
#> GSM1009105 5 0.0000 0.859 0.000 0.000 0.000 0.000 1.000
#> GSM1009119 1 0.3412 0.805 0.820 0.028 0.000 0.152 0.000
#> GSM1009133 4 0.0000 0.968 0.000 0.000 0.000 1.000 0.000
#> GSM1009147 1 0.0771 0.830 0.976 0.020 0.000 0.004 0.000
#> GSM1009161 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000
#> GSM1009175 1 0.4114 0.539 0.712 0.016 0.000 0.000 0.272
#> GSM1009189 1 0.2208 0.837 0.908 0.020 0.000 0.072 0.000
#> GSM1009064 2 0.0510 1.000 0.016 0.984 0.000 0.000 0.000
#> GSM1009078 5 0.3123 0.779 0.004 0.184 0.000 0.000 0.812
#> GSM1009092 4 0.1197 0.928 0.048 0.000 0.000 0.952 0.000
#> GSM1009106 5 0.0000 0.859 0.000 0.000 0.000 0.000 1.000
#> GSM1009120 1 0.3648 0.823 0.824 0.084 0.000 0.092 0.000
#> GSM1009134 4 0.0000 0.968 0.000 0.000 0.000 1.000 0.000
#> GSM1009148 1 0.2732 0.809 0.840 0.160 0.000 0.000 0.000
#> GSM1009162 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000
#> GSM1009176 5 0.4588 0.433 0.380 0.016 0.000 0.000 0.604
#> GSM1009190 1 0.1943 0.837 0.924 0.020 0.000 0.056 0.000
#> GSM1009065 2 0.0510 1.000 0.016 0.984 0.000 0.000 0.000
#> GSM1009079 5 0.1124 0.845 0.036 0.004 0.000 0.000 0.960
#> GSM1009093 4 0.0290 0.967 0.008 0.000 0.000 0.992 0.000
#> GSM1009107 5 0.0000 0.859 0.000 0.000 0.000 0.000 1.000
#> GSM1009121 1 0.3491 0.747 0.768 0.000 0.000 0.228 0.004
#> GSM1009135 4 0.0000 0.968 0.000 0.000 0.000 1.000 0.000
#> GSM1009149 1 0.3112 0.831 0.856 0.100 0.000 0.044 0.000
#> GSM1009163 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000
#> GSM1009177 5 0.4599 0.423 0.384 0.016 0.000 0.000 0.600
#> GSM1009191 1 0.0898 0.831 0.972 0.020 0.000 0.008 0.000
#> GSM1009066 2 0.0510 1.000 0.016 0.984 0.000 0.000 0.000
#> GSM1009080 5 0.1740 0.850 0.012 0.056 0.000 0.000 0.932
#> GSM1009094 4 0.0290 0.967 0.008 0.000 0.000 0.992 0.000
#> GSM1009108 5 0.0000 0.859 0.000 0.000 0.000 0.000 1.000
#> GSM1009122 1 0.6132 0.219 0.508 0.000 0.000 0.140 0.352
#> GSM1009136 4 0.0000 0.968 0.000 0.000 0.000 1.000 0.000
#> GSM1009150 1 0.2813 0.806 0.832 0.168 0.000 0.000 0.000
#> GSM1009164 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000
#> GSM1009178 1 0.1386 0.810 0.952 0.016 0.000 0.000 0.032
#> GSM1009192 1 0.2793 0.835 0.876 0.088 0.000 0.036 0.000
#> GSM1009067 2 0.0510 1.000 0.016 0.984 0.000 0.000 0.000
#> GSM1009081 5 0.1638 0.847 0.004 0.064 0.000 0.000 0.932
#> GSM1009095 4 0.0162 0.968 0.004 0.000 0.000 0.996 0.000
#> GSM1009109 5 0.0000 0.859 0.000 0.000 0.000 0.000 1.000
#> GSM1009123 1 0.3282 0.783 0.804 0.008 0.000 0.188 0.000
#> GSM1009137 4 0.0000 0.968 0.000 0.000 0.000 1.000 0.000
#> GSM1009151 1 0.3143 0.783 0.796 0.204 0.000 0.000 0.000
#> GSM1009165 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000
#> GSM1009179 1 0.1981 0.794 0.920 0.016 0.000 0.000 0.064
#> GSM1009193 1 0.3130 0.830 0.856 0.048 0.000 0.096 0.000
#> GSM1009068 2 0.0510 1.000 0.016 0.984 0.000 0.000 0.000
#> GSM1009082 5 0.2605 0.810 0.000 0.148 0.000 0.000 0.852
#> GSM1009096 4 0.0290 0.967 0.008 0.000 0.000 0.992 0.000
#> GSM1009110 5 0.0000 0.859 0.000 0.000 0.000 0.000 1.000
#> GSM1009124 1 0.2471 0.815 0.864 0.000 0.000 0.136 0.000
#> GSM1009138 4 0.0000 0.968 0.000 0.000 0.000 1.000 0.000
#> GSM1009152 1 0.3274 0.772 0.780 0.220 0.000 0.000 0.000
#> GSM1009166 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000
#> GSM1009180 1 0.1300 0.812 0.956 0.016 0.000 0.000 0.028
#> GSM1009194 1 0.2732 0.811 0.840 0.160 0.000 0.000 0.000
#> GSM1009069 2 0.0510 1.000 0.016 0.984 0.000 0.000 0.000
#> GSM1009083 5 0.2852 0.792 0.000 0.172 0.000 0.000 0.828
#> GSM1009097 4 0.0880 0.946 0.032 0.000 0.000 0.968 0.000
#> GSM1009111 5 0.0000 0.859 0.000 0.000 0.000 0.000 1.000
#> GSM1009125 4 0.6728 -0.105 0.368 0.000 0.000 0.380 0.252
#> GSM1009139 4 0.0000 0.968 0.000 0.000 0.000 1.000 0.000
#> GSM1009153 1 0.3508 0.746 0.748 0.252 0.000 0.000 0.000
#> GSM1009167 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000
#> GSM1009181 5 0.4418 0.535 0.332 0.016 0.000 0.000 0.652
#> GSM1009195 1 0.1197 0.828 0.952 0.048 0.000 0.000 0.000
#> GSM1009070 2 0.0510 1.000 0.016 0.984 0.000 0.000 0.000
#> GSM1009084 5 0.2424 0.819 0.000 0.132 0.000 0.000 0.868
#> GSM1009098 4 0.0290 0.967 0.008 0.000 0.000 0.992 0.000
#> GSM1009112 5 0.0000 0.859 0.000 0.000 0.000 0.000 1.000
#> GSM1009126 1 0.3074 0.776 0.804 0.000 0.000 0.196 0.000
#> GSM1009140 4 0.0000 0.968 0.000 0.000 0.000 1.000 0.000
#> GSM1009154 1 0.2891 0.802 0.824 0.176 0.000 0.000 0.000
#> GSM1009168 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000
#> GSM1009182 1 0.3663 0.638 0.776 0.016 0.000 0.000 0.208
#> GSM1009196 1 0.1908 0.830 0.908 0.092 0.000 0.000 0.000
#> GSM1009071 2 0.0510 1.000 0.016 0.984 0.000 0.000 0.000
#> GSM1009085 5 0.2516 0.815 0.000 0.140 0.000 0.000 0.860
#> GSM1009099 4 0.0794 0.950 0.028 0.000 0.000 0.972 0.000
#> GSM1009113 5 0.0000 0.859 0.000 0.000 0.000 0.000 1.000
#> GSM1009127 1 0.3309 0.818 0.836 0.036 0.000 0.128 0.000
#> GSM1009141 4 0.0000 0.968 0.000 0.000 0.000 1.000 0.000
#> GSM1009155 1 0.4060 0.608 0.640 0.360 0.000 0.000 0.000
#> GSM1009169 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000
#> GSM1009183 5 0.4718 0.256 0.444 0.016 0.000 0.000 0.540
#> GSM1009197 1 0.2171 0.838 0.912 0.024 0.000 0.064 0.000
#> GSM1009072 2 0.0510 1.000 0.016 0.984 0.000 0.000 0.000
#> GSM1009086 5 0.1608 0.845 0.000 0.072 0.000 0.000 0.928
#> GSM1009100 4 0.0290 0.967 0.008 0.000 0.000 0.992 0.000
#> GSM1009114 5 0.0000 0.859 0.000 0.000 0.000 0.000 1.000
#> GSM1009128 1 0.3586 0.700 0.736 0.000 0.000 0.264 0.000
#> GSM1009142 4 0.0000 0.968 0.000 0.000 0.000 1.000 0.000
#> GSM1009156 1 0.0955 0.825 0.968 0.028 0.000 0.004 0.000
#> GSM1009170 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000
#> GSM1009184 1 0.3878 0.590 0.748 0.016 0.000 0.000 0.236
#> GSM1009198 1 0.2464 0.832 0.888 0.016 0.000 0.096 0.000
#> GSM1009073 2 0.0510 1.000 0.016 0.984 0.000 0.000 0.000
#> GSM1009087 5 0.2890 0.801 0.004 0.160 0.000 0.000 0.836
#> GSM1009101 4 0.0290 0.967 0.008 0.000 0.000 0.992 0.000
#> GSM1009115 5 0.0000 0.859 0.000 0.000 0.000 0.000 1.000
#> GSM1009129 5 0.4478 0.386 0.360 0.008 0.004 0.000 0.628
#> GSM1009143 4 0.0000 0.968 0.000 0.000 0.000 1.000 0.000
#> GSM1009157 1 0.3707 0.707 0.716 0.284 0.000 0.000 0.000
#> GSM1009171 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000
#> GSM1009185 1 0.1914 0.796 0.924 0.016 0.000 0.000 0.060
#> GSM1009199 1 0.0609 0.829 0.980 0.020 0.000 0.000 0.000
#> GSM1009074 2 0.0510 1.000 0.016 0.984 0.000 0.000 0.000
#> GSM1009088 5 0.2813 0.795 0.000 0.168 0.000 0.000 0.832
#> GSM1009102 4 0.0162 0.968 0.004 0.000 0.000 0.996 0.000
#> GSM1009116 5 0.0000 0.859 0.000 0.000 0.000 0.000 1.000
#> GSM1009130 5 0.3852 0.662 0.220 0.020 0.000 0.000 0.760
#> GSM1009144 4 0.0000 0.968 0.000 0.000 0.000 1.000 0.000
#> GSM1009158 1 0.2891 0.801 0.824 0.176 0.000 0.000 0.000
#> GSM1009172 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000
#> GSM1009186 1 0.4404 0.488 0.684 0.024 0.000 0.000 0.292
#> GSM1009200 1 0.2208 0.837 0.908 0.020 0.000 0.072 0.000
#> GSM1009075 2 0.0510 1.000 0.016 0.984 0.000 0.000 0.000
#> GSM1009089 5 0.5562 0.579 0.156 0.200 0.000 0.000 0.644
#> GSM1009103 4 0.0162 0.968 0.004 0.000 0.000 0.996 0.000
#> GSM1009117 5 0.0000 0.859 0.000 0.000 0.000 0.000 1.000
#> GSM1009131 1 0.4686 0.528 0.644 0.016 0.000 0.008 0.332
#> GSM1009145 4 0.0000 0.968 0.000 0.000 0.000 1.000 0.000
#> GSM1009159 1 0.3506 0.819 0.824 0.132 0.000 0.044 0.000
#> GSM1009173 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000
#> GSM1009187 1 0.0798 0.817 0.976 0.016 0.000 0.000 0.008
#> GSM1009201 1 0.3037 0.832 0.860 0.100 0.000 0.040 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1009062 6 0.0146 1.000 0.004 0.000 0 0.000 0.000 0.996
#> GSM1009076 5 0.6107 0.440 0.036 0.364 0 0.000 0.480 0.120
#> GSM1009090 4 0.0000 0.983 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009104 5 0.0363 0.795 0.000 0.012 0 0.000 0.988 0.000
#> GSM1009118 1 0.5838 0.347 0.552 0.280 0 0.152 0.012 0.004
#> GSM1009132 4 0.0891 0.982 0.000 0.024 0 0.968 0.000 0.008
#> GSM1009146 1 0.0935 0.902 0.964 0.004 0 0.000 0.000 0.032
#> GSM1009160 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009174 2 0.1983 0.857 0.072 0.908 0 0.000 0.020 0.000
#> GSM1009188 1 0.0935 0.906 0.964 0.004 0 0.032 0.000 0.000
#> GSM1009063 6 0.0146 1.000 0.004 0.000 0 0.000 0.000 0.996
#> GSM1009077 5 0.6232 0.325 0.036 0.412 0 0.000 0.420 0.132
#> GSM1009091 4 0.0146 0.983 0.000 0.004 0 0.996 0.000 0.000
#> GSM1009105 5 0.0363 0.795 0.000 0.012 0 0.000 0.988 0.000
#> GSM1009119 1 0.1464 0.901 0.944 0.016 0 0.036 0.000 0.004
#> GSM1009133 4 0.0777 0.983 0.000 0.024 0 0.972 0.000 0.004
#> GSM1009147 1 0.0713 0.900 0.972 0.028 0 0.000 0.000 0.000
#> GSM1009161 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009175 2 0.1983 0.857 0.072 0.908 0 0.000 0.020 0.000
#> GSM1009189 1 0.0935 0.906 0.964 0.004 0 0.032 0.000 0.000
#> GSM1009064 6 0.0146 1.000 0.004 0.000 0 0.000 0.000 0.996
#> GSM1009078 5 0.4842 0.731 0.040 0.092 0 0.000 0.720 0.148
#> GSM1009092 4 0.1082 0.946 0.040 0.004 0 0.956 0.000 0.000
#> GSM1009106 5 0.0363 0.795 0.000 0.012 0 0.000 0.988 0.000
#> GSM1009120 1 0.1534 0.900 0.944 0.016 0 0.032 0.004 0.004
#> GSM1009134 4 0.0891 0.982 0.000 0.024 0 0.968 0.000 0.008
#> GSM1009148 1 0.1049 0.902 0.960 0.008 0 0.000 0.000 0.032
#> GSM1009162 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009176 2 0.2030 0.828 0.028 0.908 0 0.000 0.064 0.000
#> GSM1009190 1 0.0777 0.907 0.972 0.004 0 0.024 0.000 0.000
#> GSM1009065 6 0.0146 1.000 0.004 0.000 0 0.000 0.000 0.996
#> GSM1009079 2 0.4487 0.440 0.036 0.688 0 0.000 0.256 0.020
#> GSM1009093 4 0.0146 0.983 0.000 0.004 0 0.996 0.000 0.000
#> GSM1009107 5 0.0363 0.795 0.000 0.012 0 0.000 0.988 0.000
#> GSM1009121 1 0.3807 0.769 0.784 0.044 0 0.160 0.008 0.004
#> GSM1009135 4 0.0777 0.983 0.000 0.024 0 0.972 0.000 0.004
#> GSM1009149 1 0.1167 0.907 0.960 0.008 0 0.012 0.000 0.020
#> GSM1009163 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009177 2 0.2058 0.835 0.036 0.908 0 0.000 0.056 0.000
#> GSM1009191 1 0.0405 0.905 0.988 0.004 0 0.008 0.000 0.000
#> GSM1009066 6 0.0146 1.000 0.004 0.000 0 0.000 0.000 0.996
#> GSM1009080 2 0.5029 0.381 0.036 0.660 0 0.000 0.248 0.056
#> GSM1009094 4 0.0146 0.983 0.000 0.004 0 0.996 0.000 0.000
#> GSM1009108 5 0.0363 0.795 0.000 0.012 0 0.000 0.988 0.000
#> GSM1009122 2 0.5473 0.148 0.416 0.500 0 0.060 0.020 0.004
#> GSM1009136 4 0.0777 0.983 0.000 0.024 0 0.972 0.000 0.004
#> GSM1009150 1 0.0935 0.904 0.964 0.000 0 0.004 0.000 0.032
#> GSM1009164 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009178 2 0.1866 0.855 0.084 0.908 0 0.000 0.008 0.000
#> GSM1009192 1 0.0777 0.907 0.972 0.000 0 0.024 0.000 0.004
#> GSM1009067 6 0.0146 1.000 0.004 0.000 0 0.000 0.000 0.996
#> GSM1009081 5 0.5565 0.316 0.036 0.432 0 0.000 0.476 0.056
#> GSM1009095 4 0.0146 0.983 0.000 0.004 0 0.996 0.000 0.000
#> GSM1009109 5 0.0363 0.795 0.000 0.012 0 0.000 0.988 0.000
#> GSM1009123 1 0.2267 0.879 0.904 0.020 0 0.064 0.008 0.004
#> GSM1009137 4 0.0777 0.983 0.000 0.024 0 0.972 0.000 0.004
#> GSM1009151 1 0.1444 0.886 0.928 0.000 0 0.000 0.000 0.072
#> GSM1009165 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009179 2 0.1714 0.851 0.092 0.908 0 0.000 0.000 0.000
#> GSM1009193 1 0.1003 0.907 0.964 0.004 0 0.028 0.000 0.004
#> GSM1009068 6 0.0146 1.000 0.004 0.000 0 0.000 0.000 0.996
#> GSM1009082 5 0.5979 0.591 0.036 0.264 0 0.000 0.560 0.140
#> GSM1009096 4 0.0146 0.983 0.000 0.004 0 0.996 0.000 0.000
#> GSM1009110 5 0.0363 0.795 0.000 0.012 0 0.000 0.988 0.000
#> GSM1009124 1 0.2050 0.887 0.920 0.036 0 0.032 0.008 0.004
#> GSM1009138 4 0.0891 0.982 0.000 0.024 0 0.968 0.000 0.008
#> GSM1009152 1 0.1814 0.869 0.900 0.000 0 0.000 0.000 0.100
#> GSM1009166 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009180 2 0.1714 0.851 0.092 0.908 0 0.000 0.000 0.000
#> GSM1009194 1 0.2056 0.880 0.904 0.004 0 0.012 0.000 0.080
#> GSM1009069 6 0.0146 1.000 0.004 0.000 0 0.000 0.000 0.996
#> GSM1009083 5 0.6314 0.568 0.036 0.228 0 0.000 0.516 0.220
#> GSM1009097 4 0.0405 0.978 0.008 0.004 0 0.988 0.000 0.000
#> GSM1009111 5 0.0363 0.795 0.000 0.012 0 0.000 0.988 0.000
#> GSM1009125 2 0.6191 0.271 0.312 0.464 0 0.212 0.008 0.004
#> GSM1009139 4 0.0891 0.982 0.000 0.024 0 0.968 0.000 0.008
#> GSM1009153 1 0.2340 0.831 0.852 0.000 0 0.000 0.000 0.148
#> GSM1009167 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009181 2 0.1983 0.819 0.020 0.908 0 0.000 0.072 0.000
#> GSM1009195 1 0.2445 0.800 0.868 0.120 0 0.000 0.004 0.008
#> GSM1009070 6 0.0146 1.000 0.004 0.000 0 0.000 0.000 0.996
#> GSM1009084 5 0.4812 0.731 0.036 0.116 0 0.000 0.724 0.124
#> GSM1009098 4 0.0146 0.983 0.000 0.004 0 0.996 0.000 0.000
#> GSM1009112 5 0.0363 0.795 0.000 0.012 0 0.000 0.988 0.000
#> GSM1009126 1 0.3516 0.803 0.812 0.040 0 0.136 0.008 0.004
#> GSM1009140 4 0.0777 0.983 0.000 0.024 0 0.972 0.000 0.004
#> GSM1009154 1 0.0937 0.901 0.960 0.000 0 0.000 0.000 0.040
#> GSM1009168 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009182 2 0.1951 0.857 0.076 0.908 0 0.000 0.016 0.000
#> GSM1009196 1 0.0820 0.908 0.972 0.000 0 0.016 0.000 0.012
#> GSM1009071 6 0.0146 1.000 0.004 0.000 0 0.000 0.000 0.996
#> GSM1009085 5 0.4589 0.739 0.036 0.092 0 0.000 0.744 0.128
#> GSM1009099 4 0.0146 0.983 0.000 0.004 0 0.996 0.000 0.000
#> GSM1009113 5 0.0363 0.795 0.000 0.012 0 0.000 0.988 0.000
#> GSM1009127 1 0.2121 0.886 0.916 0.040 0 0.032 0.008 0.004
#> GSM1009141 4 0.0891 0.982 0.000 0.024 0 0.968 0.000 0.008
#> GSM1009155 1 0.3592 0.565 0.656 0.000 0 0.000 0.000 0.344
#> GSM1009169 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009183 2 0.2030 0.828 0.028 0.908 0 0.000 0.064 0.000
#> GSM1009197 1 0.0858 0.907 0.968 0.000 0 0.028 0.000 0.004
#> GSM1009072 6 0.0146 1.000 0.004 0.000 0 0.000 0.000 0.996
#> GSM1009086 5 0.4887 0.697 0.036 0.192 0 0.000 0.700 0.072
#> GSM1009100 4 0.0146 0.983 0.000 0.004 0 0.996 0.000 0.000
#> GSM1009114 5 0.0363 0.795 0.000 0.012 0 0.000 0.988 0.000
#> GSM1009128 1 0.2384 0.878 0.900 0.032 0 0.056 0.008 0.004
#> GSM1009142 4 0.0891 0.982 0.000 0.024 0 0.968 0.000 0.008
#> GSM1009156 1 0.0865 0.897 0.964 0.036 0 0.000 0.000 0.000
#> GSM1009170 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009184 2 0.1866 0.855 0.084 0.908 0 0.000 0.008 0.000
#> GSM1009198 1 0.0935 0.906 0.964 0.004 0 0.032 0.000 0.000
#> GSM1009073 6 0.0146 1.000 0.004 0.000 0 0.000 0.000 0.996
#> GSM1009087 5 0.4842 0.731 0.040 0.092 0 0.000 0.720 0.148
#> GSM1009101 4 0.0146 0.983 0.000 0.004 0 0.996 0.000 0.000
#> GSM1009115 5 0.0363 0.795 0.000 0.012 0 0.000 0.988 0.000
#> GSM1009129 5 0.6291 0.187 0.344 0.256 0 0.004 0.392 0.004
#> GSM1009143 4 0.0891 0.982 0.000 0.024 0 0.968 0.000 0.008
#> GSM1009157 1 0.3217 0.747 0.768 0.008 0 0.000 0.000 0.224
#> GSM1009171 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009185 2 0.2039 0.856 0.076 0.904 0 0.000 0.020 0.000
#> GSM1009199 1 0.2527 0.749 0.832 0.168 0 0.000 0.000 0.000
#> GSM1009074 6 0.0146 1.000 0.004 0.000 0 0.000 0.000 0.996
#> GSM1009088 5 0.4740 0.733 0.036 0.092 0 0.000 0.728 0.144
#> GSM1009102 4 0.0146 0.983 0.000 0.004 0 0.996 0.000 0.000
#> GSM1009116 5 0.0363 0.795 0.000 0.012 0 0.000 0.988 0.000
#> GSM1009130 5 0.4582 0.617 0.224 0.072 0 0.004 0.696 0.004
#> GSM1009144 4 0.0891 0.982 0.000 0.024 0 0.968 0.000 0.008
#> GSM1009158 1 0.0935 0.903 0.964 0.004 0 0.000 0.000 0.032
#> GSM1009172 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009186 2 0.1983 0.857 0.072 0.908 0 0.000 0.020 0.000
#> GSM1009200 1 0.0858 0.907 0.968 0.004 0 0.028 0.000 0.000
#> GSM1009075 6 0.0146 1.000 0.004 0.000 0 0.000 0.000 0.996
#> GSM1009089 5 0.6311 0.613 0.192 0.084 0 0.000 0.572 0.152
#> GSM1009103 4 0.0146 0.983 0.000 0.004 0 0.996 0.000 0.000
#> GSM1009117 5 0.0363 0.795 0.000 0.012 0 0.000 0.988 0.000
#> GSM1009131 1 0.5196 0.387 0.596 0.068 0 0.012 0.320 0.004
#> GSM1009145 4 0.0777 0.983 0.000 0.024 0 0.972 0.000 0.004
#> GSM1009159 1 0.1148 0.908 0.960 0.004 0 0.020 0.000 0.016
#> GSM1009173 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009187 2 0.1863 0.841 0.104 0.896 0 0.000 0.000 0.000
#> GSM1009201 1 0.0508 0.907 0.984 0.000 0 0.012 0.000 0.004
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n temperature(p) time(p) specimen(p) k
#> CV:NMF 136 0.985 0.992 4.02e-21 2
#> CV:NMF 135 0.989 1.000 8.02e-44 3
#> CV:NMF 123 1.000 1.000 6.04e-58 4
#> CV:NMF 132 1.000 1.000 4.42e-86 5
#> CV:NMF 130 1.000 1.000 1.38e-106 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 140 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 0.927 0.922 0.968 0.4996 0.500 0.500
#> 3 3 0.812 0.818 0.893 0.1857 0.896 0.793
#> 4 4 0.637 0.804 0.817 0.1072 0.980 0.949
#> 5 5 0.757 0.698 0.820 0.0901 0.987 0.965
#> 6 6 0.897 0.865 0.900 0.1102 0.835 0.551
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
#> GSM1009062 1 0.0000 0.963 1.000 0.000
#> GSM1009076 2 0.0000 0.970 0.000 1.000
#> GSM1009090 1 0.0000 0.963 1.000 0.000
#> GSM1009104 2 0.0000 0.970 0.000 1.000
#> GSM1009118 2 0.0938 0.961 0.012 0.988
#> GSM1009132 1 0.0000 0.963 1.000 0.000
#> GSM1009146 1 0.1184 0.957 0.984 0.016
#> GSM1009160 2 0.0000 0.970 0.000 1.000
#> GSM1009174 2 0.0000 0.970 0.000 1.000
#> GSM1009188 1 0.0000 0.963 1.000 0.000
#> GSM1009063 1 0.0000 0.963 1.000 0.000
#> GSM1009077 2 0.0000 0.970 0.000 1.000
#> GSM1009091 1 0.0000 0.963 1.000 0.000
#> GSM1009105 2 0.0000 0.970 0.000 1.000
#> GSM1009119 1 0.9552 0.411 0.624 0.376
#> GSM1009133 1 0.0000 0.963 1.000 0.000
#> GSM1009147 1 0.1184 0.957 0.984 0.016
#> GSM1009161 2 0.0000 0.970 0.000 1.000
#> GSM1009175 2 0.0000 0.970 0.000 1.000
#> GSM1009189 1 0.1184 0.957 0.984 0.016
#> GSM1009064 1 0.0000 0.963 1.000 0.000
#> GSM1009078 2 0.9710 0.348 0.400 0.600
#> GSM1009092 1 0.0000 0.963 1.000 0.000
#> GSM1009106 2 0.0000 0.970 0.000 1.000
#> GSM1009120 1 0.9552 0.411 0.624 0.376
#> GSM1009134 1 0.0000 0.963 1.000 0.000
#> GSM1009148 1 0.1184 0.957 0.984 0.016
#> GSM1009162 2 0.0000 0.970 0.000 1.000
#> GSM1009176 2 0.0000 0.970 0.000 1.000
#> GSM1009190 1 0.1184 0.957 0.984 0.016
#> GSM1009065 1 0.0000 0.963 1.000 0.000
#> GSM1009079 2 0.0000 0.970 0.000 1.000
#> GSM1009093 1 0.0000 0.963 1.000 0.000
#> GSM1009107 2 0.0000 0.970 0.000 1.000
#> GSM1009121 2 0.0938 0.961 0.012 0.988
#> GSM1009135 1 0.0000 0.963 1.000 0.000
#> GSM1009149 1 0.0000 0.963 1.000 0.000
#> GSM1009163 2 0.0000 0.970 0.000 1.000
#> GSM1009177 2 0.0000 0.970 0.000 1.000
#> GSM1009191 1 0.1184 0.957 0.984 0.016
#> GSM1009066 1 0.0000 0.963 1.000 0.000
#> GSM1009080 2 0.0000 0.970 0.000 1.000
#> GSM1009094 1 0.0000 0.963 1.000 0.000
#> GSM1009108 2 0.0000 0.970 0.000 1.000
#> GSM1009122 2 0.0938 0.961 0.012 0.988
#> GSM1009136 1 0.0000 0.963 1.000 0.000
#> GSM1009150 1 0.0000 0.963 1.000 0.000
#> GSM1009164 2 0.0000 0.970 0.000 1.000
#> GSM1009178 2 0.0000 0.970 0.000 1.000
#> GSM1009192 1 0.0376 0.962 0.996 0.004
#> GSM1009067 1 0.0000 0.963 1.000 0.000
#> GSM1009081 2 0.0000 0.970 0.000 1.000
#> GSM1009095 1 0.0000 0.963 1.000 0.000
#> GSM1009109 2 0.0000 0.970 0.000 1.000
#> GSM1009123 1 0.9552 0.411 0.624 0.376
#> GSM1009137 1 0.0000 0.963 1.000 0.000
#> GSM1009151 1 0.1184 0.957 0.984 0.016
#> GSM1009165 2 0.0000 0.970 0.000 1.000
#> GSM1009179 2 0.0000 0.970 0.000 1.000
#> GSM1009193 1 0.0000 0.963 1.000 0.000
#> GSM1009068 1 0.0000 0.963 1.000 0.000
#> GSM1009082 2 0.0000 0.970 0.000 1.000
#> GSM1009096 1 0.0000 0.963 1.000 0.000
#> GSM1009110 2 0.0000 0.970 0.000 1.000
#> GSM1009124 1 0.9661 0.383 0.608 0.392
#> GSM1009138 1 0.0000 0.963 1.000 0.000
#> GSM1009152 1 0.1184 0.957 0.984 0.016
#> GSM1009166 2 0.0000 0.970 0.000 1.000
#> GSM1009180 2 0.0000 0.970 0.000 1.000
#> GSM1009194 1 0.1184 0.957 0.984 0.016
#> GSM1009069 1 0.0000 0.963 1.000 0.000
#> GSM1009083 2 0.0000 0.970 0.000 1.000
#> GSM1009097 1 0.0000 0.963 1.000 0.000
#> GSM1009111 2 0.0000 0.970 0.000 1.000
#> GSM1009125 2 0.0938 0.961 0.012 0.988
#> GSM1009139 1 0.0000 0.963 1.000 0.000
#> GSM1009153 1 0.1184 0.957 0.984 0.016
#> GSM1009167 2 0.0000 0.970 0.000 1.000
#> GSM1009181 2 0.0000 0.970 0.000 1.000
#> GSM1009195 1 0.1184 0.957 0.984 0.016
#> GSM1009070 1 0.0000 0.963 1.000 0.000
#> GSM1009084 2 0.0000 0.970 0.000 1.000
#> GSM1009098 1 0.0000 0.963 1.000 0.000
#> GSM1009112 2 0.0000 0.970 0.000 1.000
#> GSM1009126 1 0.9661 0.383 0.608 0.392
#> GSM1009140 1 0.0000 0.963 1.000 0.000
#> GSM1009154 1 0.1184 0.957 0.984 0.016
#> GSM1009168 2 0.0000 0.970 0.000 1.000
#> GSM1009182 2 0.0000 0.970 0.000 1.000
#> GSM1009196 1 0.1184 0.957 0.984 0.016
#> GSM1009071 1 0.0000 0.963 1.000 0.000
#> GSM1009085 2 0.0000 0.970 0.000 1.000
#> GSM1009099 1 0.0000 0.963 1.000 0.000
#> GSM1009113 2 0.0000 0.970 0.000 1.000
#> GSM1009127 1 0.9552 0.411 0.624 0.376
#> GSM1009141 1 0.0000 0.963 1.000 0.000
#> GSM1009155 1 0.1184 0.957 0.984 0.016
#> GSM1009169 2 0.0000 0.970 0.000 1.000
#> GSM1009183 2 0.0000 0.970 0.000 1.000
#> GSM1009197 1 0.1184 0.957 0.984 0.016
#> GSM1009072 1 0.0000 0.963 1.000 0.000
#> GSM1009086 2 0.0000 0.970 0.000 1.000
#> GSM1009100 1 0.0000 0.963 1.000 0.000
#> GSM1009114 2 0.0000 0.970 0.000 1.000
#> GSM1009128 2 0.5408 0.839 0.124 0.876
#> GSM1009142 1 0.0000 0.963 1.000 0.000
#> GSM1009156 1 0.1184 0.957 0.984 0.016
#> GSM1009170 2 0.0000 0.970 0.000 1.000
#> GSM1009184 2 0.0000 0.970 0.000 1.000
#> GSM1009198 1 0.0000 0.963 1.000 0.000
#> GSM1009073 1 0.0000 0.963 1.000 0.000
#> GSM1009087 2 0.9710 0.348 0.400 0.600
#> GSM1009101 1 0.0000 0.963 1.000 0.000
#> GSM1009115 2 0.0000 0.970 0.000 1.000
#> GSM1009129 2 0.0938 0.961 0.012 0.988
#> GSM1009143 1 0.0000 0.963 1.000 0.000
#> GSM1009157 1 0.1184 0.957 0.984 0.016
#> GSM1009171 2 0.0000 0.970 0.000 1.000
#> GSM1009185 2 0.0000 0.970 0.000 1.000
#> GSM1009199 1 0.1184 0.957 0.984 0.016
#> GSM1009074 1 0.0000 0.963 1.000 0.000
#> GSM1009088 2 0.9710 0.348 0.400 0.600
#> GSM1009102 1 0.0000 0.963 1.000 0.000
#> GSM1009116 2 0.0000 0.970 0.000 1.000
#> GSM1009130 2 0.0938 0.961 0.012 0.988
#> GSM1009144 1 0.0000 0.963 1.000 0.000
#> GSM1009158 1 0.0000 0.963 1.000 0.000
#> GSM1009172 2 0.0000 0.970 0.000 1.000
#> GSM1009186 2 0.0000 0.970 0.000 1.000
#> GSM1009200 1 0.1184 0.957 0.984 0.016
#> GSM1009075 1 0.0000 0.963 1.000 0.000
#> GSM1009089 2 0.9710 0.348 0.400 0.600
#> GSM1009103 1 0.0000 0.963 1.000 0.000
#> GSM1009117 2 0.0000 0.970 0.000 1.000
#> GSM1009131 2 0.0938 0.961 0.012 0.988
#> GSM1009145 1 0.0000 0.963 1.000 0.000
#> GSM1009159 1 0.0000 0.963 1.000 0.000
#> GSM1009173 2 0.0000 0.970 0.000 1.000
#> GSM1009187 2 0.0000 0.970 0.000 1.000
#> GSM1009201 1 0.1184 0.957 0.984 0.016
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1009062 1 0.0747 0.951 0.984 0.016 0.000
#> GSM1009076 2 0.1289 0.675 0.000 0.968 0.032
#> GSM1009090 1 0.0424 0.955 0.992 0.008 0.000
#> GSM1009104 3 0.5363 0.754 0.000 0.276 0.724
#> GSM1009118 2 0.5953 0.730 0.012 0.708 0.280
#> GSM1009132 1 0.0424 0.955 0.992 0.008 0.000
#> GSM1009146 1 0.1015 0.950 0.980 0.008 0.012
#> GSM1009160 3 0.0000 0.804 0.000 0.000 1.000
#> GSM1009174 2 0.5363 0.736 0.000 0.724 0.276
#> GSM1009188 1 0.0237 0.954 0.996 0.004 0.000
#> GSM1009063 1 0.0747 0.951 0.984 0.016 0.000
#> GSM1009077 2 0.1289 0.675 0.000 0.968 0.032
#> GSM1009091 1 0.0424 0.955 0.992 0.008 0.000
#> GSM1009105 3 0.5363 0.754 0.000 0.276 0.724
#> GSM1009119 1 0.6045 0.381 0.620 0.380 0.000
#> GSM1009133 1 0.0424 0.955 0.992 0.008 0.000
#> GSM1009147 1 0.1015 0.950 0.980 0.008 0.012
#> GSM1009161 3 0.0000 0.804 0.000 0.000 1.000
#> GSM1009175 2 0.5363 0.736 0.000 0.724 0.276
#> GSM1009189 1 0.1015 0.950 0.980 0.008 0.012
#> GSM1009064 1 0.0747 0.951 0.984 0.016 0.000
#> GSM1009078 2 0.6045 0.258 0.380 0.620 0.000
#> GSM1009092 1 0.0424 0.955 0.992 0.008 0.000
#> GSM1009106 3 0.5363 0.754 0.000 0.276 0.724
#> GSM1009120 1 0.6045 0.381 0.620 0.380 0.000
#> GSM1009134 1 0.0424 0.955 0.992 0.008 0.000
#> GSM1009148 1 0.1015 0.950 0.980 0.008 0.012
#> GSM1009162 3 0.0000 0.804 0.000 0.000 1.000
#> GSM1009176 2 0.5363 0.736 0.000 0.724 0.276
#> GSM1009190 1 0.1015 0.950 0.980 0.008 0.012
#> GSM1009065 1 0.0747 0.951 0.984 0.016 0.000
#> GSM1009079 2 0.1289 0.675 0.000 0.968 0.032
#> GSM1009093 1 0.0424 0.955 0.992 0.008 0.000
#> GSM1009107 3 0.5363 0.754 0.000 0.276 0.724
#> GSM1009121 2 0.5953 0.730 0.012 0.708 0.280
#> GSM1009135 1 0.0424 0.955 0.992 0.008 0.000
#> GSM1009149 1 0.0892 0.951 0.980 0.020 0.000
#> GSM1009163 3 0.0000 0.804 0.000 0.000 1.000
#> GSM1009177 2 0.5363 0.736 0.000 0.724 0.276
#> GSM1009191 1 0.1015 0.950 0.980 0.008 0.012
#> GSM1009066 1 0.0747 0.951 0.984 0.016 0.000
#> GSM1009080 2 0.1289 0.675 0.000 0.968 0.032
#> GSM1009094 1 0.0424 0.955 0.992 0.008 0.000
#> GSM1009108 3 0.5363 0.754 0.000 0.276 0.724
#> GSM1009122 2 0.5953 0.730 0.012 0.708 0.280
#> GSM1009136 1 0.0424 0.955 0.992 0.008 0.000
#> GSM1009150 1 0.0892 0.951 0.980 0.020 0.000
#> GSM1009164 3 0.0000 0.804 0.000 0.000 1.000
#> GSM1009178 2 0.5363 0.736 0.000 0.724 0.276
#> GSM1009192 1 0.0475 0.954 0.992 0.004 0.004
#> GSM1009067 1 0.0747 0.951 0.984 0.016 0.000
#> GSM1009081 2 0.1289 0.675 0.000 0.968 0.032
#> GSM1009095 1 0.0424 0.955 0.992 0.008 0.000
#> GSM1009109 3 0.5363 0.754 0.000 0.276 0.724
#> GSM1009123 1 0.6045 0.381 0.620 0.380 0.000
#> GSM1009137 1 0.0424 0.955 0.992 0.008 0.000
#> GSM1009151 1 0.1015 0.950 0.980 0.008 0.012
#> GSM1009165 3 0.0000 0.804 0.000 0.000 1.000
#> GSM1009179 2 0.5363 0.736 0.000 0.724 0.276
#> GSM1009193 1 0.0237 0.954 0.996 0.004 0.000
#> GSM1009068 1 0.0747 0.951 0.984 0.016 0.000
#> GSM1009082 2 0.1289 0.675 0.000 0.968 0.032
#> GSM1009096 1 0.0424 0.955 0.992 0.008 0.000
#> GSM1009110 3 0.5363 0.754 0.000 0.276 0.724
#> GSM1009124 1 0.6600 0.360 0.604 0.384 0.012
#> GSM1009138 1 0.0424 0.955 0.992 0.008 0.000
#> GSM1009152 1 0.1015 0.950 0.980 0.008 0.012
#> GSM1009166 3 0.0000 0.804 0.000 0.000 1.000
#> GSM1009180 2 0.5363 0.736 0.000 0.724 0.276
#> GSM1009194 1 0.1015 0.950 0.980 0.008 0.012
#> GSM1009069 1 0.0747 0.951 0.984 0.016 0.000
#> GSM1009083 2 0.1289 0.675 0.000 0.968 0.032
#> GSM1009097 1 0.0424 0.955 0.992 0.008 0.000
#> GSM1009111 3 0.5363 0.754 0.000 0.276 0.724
#> GSM1009125 2 0.5953 0.730 0.012 0.708 0.280
#> GSM1009139 1 0.0424 0.955 0.992 0.008 0.000
#> GSM1009153 1 0.1015 0.950 0.980 0.008 0.012
#> GSM1009167 3 0.0000 0.804 0.000 0.000 1.000
#> GSM1009181 2 0.5363 0.736 0.000 0.724 0.276
#> GSM1009195 1 0.1015 0.950 0.980 0.008 0.012
#> GSM1009070 1 0.0747 0.951 0.984 0.016 0.000
#> GSM1009084 2 0.1289 0.675 0.000 0.968 0.032
#> GSM1009098 1 0.0424 0.955 0.992 0.008 0.000
#> GSM1009112 3 0.5363 0.754 0.000 0.276 0.724
#> GSM1009126 1 0.6600 0.360 0.604 0.384 0.012
#> GSM1009140 1 0.0424 0.955 0.992 0.008 0.000
#> GSM1009154 1 0.1015 0.950 0.980 0.008 0.012
#> GSM1009168 3 0.0000 0.804 0.000 0.000 1.000
#> GSM1009182 2 0.5363 0.736 0.000 0.724 0.276
#> GSM1009196 1 0.1015 0.950 0.980 0.008 0.012
#> GSM1009071 1 0.0747 0.951 0.984 0.016 0.000
#> GSM1009085 2 0.1289 0.675 0.000 0.968 0.032
#> GSM1009099 1 0.0424 0.955 0.992 0.008 0.000
#> GSM1009113 3 0.5363 0.754 0.000 0.276 0.724
#> GSM1009127 1 0.6045 0.381 0.620 0.380 0.000
#> GSM1009141 1 0.0424 0.955 0.992 0.008 0.000
#> GSM1009155 1 0.1015 0.950 0.980 0.008 0.012
#> GSM1009169 3 0.0000 0.804 0.000 0.000 1.000
#> GSM1009183 2 0.5363 0.736 0.000 0.724 0.276
#> GSM1009197 1 0.1015 0.950 0.980 0.008 0.012
#> GSM1009072 1 0.0747 0.951 0.984 0.016 0.000
#> GSM1009086 2 0.1289 0.675 0.000 0.968 0.032
#> GSM1009100 1 0.0424 0.955 0.992 0.008 0.000
#> GSM1009114 3 0.5363 0.754 0.000 0.276 0.724
#> GSM1009128 2 0.8202 0.597 0.120 0.620 0.260
#> GSM1009142 1 0.0424 0.955 0.992 0.008 0.000
#> GSM1009156 1 0.1015 0.950 0.980 0.008 0.012
#> GSM1009170 3 0.0000 0.804 0.000 0.000 1.000
#> GSM1009184 2 0.5363 0.736 0.000 0.724 0.276
#> GSM1009198 1 0.0237 0.954 0.996 0.004 0.000
#> GSM1009073 1 0.0747 0.951 0.984 0.016 0.000
#> GSM1009087 2 0.6045 0.258 0.380 0.620 0.000
#> GSM1009101 1 0.0424 0.955 0.992 0.008 0.000
#> GSM1009115 3 0.5363 0.754 0.000 0.276 0.724
#> GSM1009129 2 0.5953 0.730 0.012 0.708 0.280
#> GSM1009143 1 0.0424 0.955 0.992 0.008 0.000
#> GSM1009157 1 0.1015 0.950 0.980 0.008 0.012
#> GSM1009171 3 0.0000 0.804 0.000 0.000 1.000
#> GSM1009185 2 0.5363 0.736 0.000 0.724 0.276
#> GSM1009199 1 0.1015 0.950 0.980 0.008 0.012
#> GSM1009074 1 0.0747 0.951 0.984 0.016 0.000
#> GSM1009088 2 0.6045 0.258 0.380 0.620 0.000
#> GSM1009102 1 0.0424 0.955 0.992 0.008 0.000
#> GSM1009116 3 0.5363 0.754 0.000 0.276 0.724
#> GSM1009130 2 0.5953 0.730 0.012 0.708 0.280
#> GSM1009144 1 0.0424 0.955 0.992 0.008 0.000
#> GSM1009158 1 0.0892 0.951 0.980 0.020 0.000
#> GSM1009172 3 0.0000 0.804 0.000 0.000 1.000
#> GSM1009186 2 0.5363 0.736 0.000 0.724 0.276
#> GSM1009200 1 0.1015 0.950 0.980 0.008 0.012
#> GSM1009075 1 0.0747 0.951 0.984 0.016 0.000
#> GSM1009089 2 0.6045 0.258 0.380 0.620 0.000
#> GSM1009103 1 0.0424 0.955 0.992 0.008 0.000
#> GSM1009117 3 0.5363 0.754 0.000 0.276 0.724
#> GSM1009131 2 0.5953 0.730 0.012 0.708 0.280
#> GSM1009145 1 0.0424 0.955 0.992 0.008 0.000
#> GSM1009159 1 0.0892 0.951 0.980 0.020 0.000
#> GSM1009173 3 0.0000 0.804 0.000 0.000 1.000
#> GSM1009187 2 0.5363 0.736 0.000 0.724 0.276
#> GSM1009201 1 0.1015 0.950 0.980 0.008 0.012
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1009062 1 0.205 0.862 0.928 0.064 0.000 0.008
#> GSM1009076 2 0.265 0.551 0.000 0.880 0.000 0.120
#> GSM1009090 1 0.292 0.863 0.860 0.000 0.000 0.140
#> GSM1009104 4 0.573 1.000 0.000 0.064 0.264 0.672
#> GSM1009118 2 0.739 0.708 0.000 0.484 0.176 0.340
#> GSM1009132 1 0.292 0.863 0.860 0.000 0.000 0.140
#> GSM1009146 1 0.164 0.876 0.948 0.044 0.000 0.008
#> GSM1009160 3 0.000 1.000 0.000 0.000 1.000 0.000
#> GSM1009174 2 0.732 0.712 0.000 0.500 0.172 0.328
#> GSM1009188 1 0.126 0.876 0.964 0.028 0.000 0.008
#> GSM1009063 1 0.205 0.862 0.928 0.064 0.000 0.008
#> GSM1009077 2 0.265 0.551 0.000 0.880 0.000 0.120
#> GSM1009091 1 0.292 0.863 0.860 0.000 0.000 0.140
#> GSM1009105 4 0.573 1.000 0.000 0.064 0.264 0.672
#> GSM1009119 1 0.558 0.345 0.576 0.400 0.000 0.024
#> GSM1009133 1 0.292 0.863 0.860 0.000 0.000 0.140
#> GSM1009147 1 0.164 0.876 0.948 0.044 0.000 0.008
#> GSM1009161 3 0.000 1.000 0.000 0.000 1.000 0.000
#> GSM1009175 2 0.732 0.712 0.000 0.500 0.172 0.328
#> GSM1009189 1 0.164 0.876 0.948 0.044 0.000 0.008
#> GSM1009064 1 0.205 0.862 0.928 0.064 0.000 0.008
#> GSM1009078 2 0.559 0.226 0.264 0.680 0.000 0.056
#> GSM1009092 1 0.292 0.863 0.860 0.000 0.000 0.140
#> GSM1009106 4 0.573 1.000 0.000 0.064 0.264 0.672
#> GSM1009120 1 0.558 0.345 0.576 0.400 0.000 0.024
#> GSM1009134 1 0.292 0.863 0.860 0.000 0.000 0.140
#> GSM1009148 1 0.164 0.876 0.948 0.044 0.000 0.008
#> GSM1009162 3 0.000 1.000 0.000 0.000 1.000 0.000
#> GSM1009176 2 0.732 0.712 0.000 0.500 0.172 0.328
#> GSM1009190 1 0.164 0.876 0.948 0.044 0.000 0.008
#> GSM1009065 1 0.205 0.862 0.928 0.064 0.000 0.008
#> GSM1009079 2 0.265 0.551 0.000 0.880 0.000 0.120
#> GSM1009093 1 0.292 0.863 0.860 0.000 0.000 0.140
#> GSM1009107 4 0.573 1.000 0.000 0.064 0.264 0.672
#> GSM1009121 2 0.739 0.708 0.000 0.484 0.176 0.340
#> GSM1009135 1 0.292 0.863 0.860 0.000 0.000 0.140
#> GSM1009149 1 0.349 0.841 0.864 0.092 0.000 0.044
#> GSM1009163 3 0.000 1.000 0.000 0.000 1.000 0.000
#> GSM1009177 2 0.732 0.712 0.000 0.500 0.172 0.328
#> GSM1009191 1 0.164 0.876 0.948 0.044 0.000 0.008
#> GSM1009066 1 0.205 0.862 0.928 0.064 0.000 0.008
#> GSM1009080 2 0.265 0.551 0.000 0.880 0.000 0.120
#> GSM1009094 1 0.292 0.863 0.860 0.000 0.000 0.140
#> GSM1009108 4 0.573 1.000 0.000 0.064 0.264 0.672
#> GSM1009122 2 0.739 0.708 0.000 0.484 0.176 0.340
#> GSM1009136 1 0.292 0.863 0.860 0.000 0.000 0.140
#> GSM1009150 1 0.349 0.841 0.864 0.092 0.000 0.044
#> GSM1009164 3 0.000 1.000 0.000 0.000 1.000 0.000
#> GSM1009178 2 0.732 0.712 0.000 0.500 0.172 0.328
#> GSM1009192 1 0.136 0.876 0.960 0.032 0.000 0.008
#> GSM1009067 1 0.205 0.862 0.928 0.064 0.000 0.008
#> GSM1009081 2 0.265 0.551 0.000 0.880 0.000 0.120
#> GSM1009095 1 0.292 0.863 0.860 0.000 0.000 0.140
#> GSM1009109 4 0.573 1.000 0.000 0.064 0.264 0.672
#> GSM1009123 1 0.558 0.345 0.576 0.400 0.000 0.024
#> GSM1009137 1 0.292 0.863 0.860 0.000 0.000 0.140
#> GSM1009151 1 0.164 0.876 0.948 0.044 0.000 0.008
#> GSM1009165 3 0.000 1.000 0.000 0.000 1.000 0.000
#> GSM1009179 2 0.732 0.712 0.000 0.500 0.172 0.328
#> GSM1009193 1 0.126 0.876 0.964 0.028 0.000 0.008
#> GSM1009068 1 0.205 0.862 0.928 0.064 0.000 0.008
#> GSM1009082 2 0.265 0.551 0.000 0.880 0.000 0.120
#> GSM1009096 1 0.292 0.863 0.860 0.000 0.000 0.140
#> GSM1009110 4 0.573 1.000 0.000 0.064 0.264 0.672
#> GSM1009124 1 0.562 0.322 0.560 0.416 0.000 0.024
#> GSM1009138 1 0.292 0.863 0.860 0.000 0.000 0.140
#> GSM1009152 1 0.164 0.876 0.948 0.044 0.000 0.008
#> GSM1009166 3 0.000 1.000 0.000 0.000 1.000 0.000
#> GSM1009180 2 0.732 0.712 0.000 0.500 0.172 0.328
#> GSM1009194 1 0.164 0.876 0.948 0.044 0.000 0.008
#> GSM1009069 1 0.205 0.862 0.928 0.064 0.000 0.008
#> GSM1009083 2 0.265 0.551 0.000 0.880 0.000 0.120
#> GSM1009097 1 0.292 0.863 0.860 0.000 0.000 0.140
#> GSM1009111 4 0.573 1.000 0.000 0.064 0.264 0.672
#> GSM1009125 2 0.739 0.708 0.000 0.484 0.176 0.340
#> GSM1009139 1 0.292 0.863 0.860 0.000 0.000 0.140
#> GSM1009153 1 0.164 0.876 0.948 0.044 0.000 0.008
#> GSM1009167 3 0.000 1.000 0.000 0.000 1.000 0.000
#> GSM1009181 2 0.732 0.712 0.000 0.500 0.172 0.328
#> GSM1009195 1 0.164 0.876 0.948 0.044 0.000 0.008
#> GSM1009070 1 0.205 0.862 0.928 0.064 0.000 0.008
#> GSM1009084 2 0.265 0.551 0.000 0.880 0.000 0.120
#> GSM1009098 1 0.292 0.863 0.860 0.000 0.000 0.140
#> GSM1009112 4 0.573 1.000 0.000 0.064 0.264 0.672
#> GSM1009126 1 0.562 0.322 0.560 0.416 0.000 0.024
#> GSM1009140 1 0.292 0.863 0.860 0.000 0.000 0.140
#> GSM1009154 1 0.164 0.876 0.948 0.044 0.000 0.008
#> GSM1009168 3 0.000 1.000 0.000 0.000 1.000 0.000
#> GSM1009182 2 0.732 0.712 0.000 0.500 0.172 0.328
#> GSM1009196 1 0.164 0.876 0.948 0.044 0.000 0.008
#> GSM1009071 1 0.205 0.862 0.928 0.064 0.000 0.008
#> GSM1009085 2 0.265 0.551 0.000 0.880 0.000 0.120
#> GSM1009099 1 0.292 0.863 0.860 0.000 0.000 0.140
#> GSM1009113 4 0.573 1.000 0.000 0.064 0.264 0.672
#> GSM1009127 1 0.558 0.345 0.576 0.400 0.000 0.024
#> GSM1009141 1 0.292 0.863 0.860 0.000 0.000 0.140
#> GSM1009155 1 0.164 0.876 0.948 0.044 0.000 0.008
#> GSM1009169 3 0.000 1.000 0.000 0.000 1.000 0.000
#> GSM1009183 2 0.732 0.712 0.000 0.500 0.172 0.328
#> GSM1009197 1 0.164 0.876 0.948 0.044 0.000 0.008
#> GSM1009072 1 0.205 0.862 0.928 0.064 0.000 0.008
#> GSM1009086 2 0.265 0.551 0.000 0.880 0.000 0.120
#> GSM1009100 1 0.292 0.863 0.860 0.000 0.000 0.140
#> GSM1009114 4 0.573 1.000 0.000 0.064 0.264 0.672
#> GSM1009128 2 0.865 0.599 0.076 0.492 0.176 0.256
#> GSM1009142 1 0.292 0.863 0.860 0.000 0.000 0.140
#> GSM1009156 1 0.164 0.876 0.948 0.044 0.000 0.008
#> GSM1009170 3 0.000 1.000 0.000 0.000 1.000 0.000
#> GSM1009184 2 0.732 0.712 0.000 0.500 0.172 0.328
#> GSM1009198 1 0.126 0.876 0.964 0.028 0.000 0.008
#> GSM1009073 1 0.205 0.862 0.928 0.064 0.000 0.008
#> GSM1009087 2 0.559 0.226 0.264 0.680 0.000 0.056
#> GSM1009101 1 0.292 0.863 0.860 0.000 0.000 0.140
#> GSM1009115 4 0.573 1.000 0.000 0.064 0.264 0.672
#> GSM1009129 2 0.739 0.708 0.000 0.484 0.176 0.340
#> GSM1009143 1 0.292 0.863 0.860 0.000 0.000 0.140
#> GSM1009157 1 0.164 0.876 0.948 0.044 0.000 0.008
#> GSM1009171 3 0.000 1.000 0.000 0.000 1.000 0.000
#> GSM1009185 2 0.732 0.712 0.000 0.500 0.172 0.328
#> GSM1009199 1 0.164 0.876 0.948 0.044 0.000 0.008
#> GSM1009074 1 0.205 0.862 0.928 0.064 0.000 0.008
#> GSM1009088 2 0.559 0.226 0.264 0.680 0.000 0.056
#> GSM1009102 1 0.292 0.863 0.860 0.000 0.000 0.140
#> GSM1009116 4 0.573 1.000 0.000 0.064 0.264 0.672
#> GSM1009130 2 0.739 0.708 0.000 0.484 0.176 0.340
#> GSM1009144 1 0.292 0.863 0.860 0.000 0.000 0.140
#> GSM1009158 1 0.349 0.841 0.864 0.092 0.000 0.044
#> GSM1009172 3 0.000 1.000 0.000 0.000 1.000 0.000
#> GSM1009186 2 0.732 0.712 0.000 0.500 0.172 0.328
#> GSM1009200 1 0.164 0.876 0.948 0.044 0.000 0.008
#> GSM1009075 1 0.205 0.862 0.928 0.064 0.000 0.008
#> GSM1009089 2 0.559 0.226 0.264 0.680 0.000 0.056
#> GSM1009103 1 0.292 0.863 0.860 0.000 0.000 0.140
#> GSM1009117 4 0.573 1.000 0.000 0.064 0.264 0.672
#> GSM1009131 2 0.739 0.708 0.000 0.484 0.176 0.340
#> GSM1009145 1 0.292 0.863 0.860 0.000 0.000 0.140
#> GSM1009159 1 0.349 0.841 0.864 0.092 0.000 0.044
#> GSM1009173 3 0.000 1.000 0.000 0.000 1.000 0.000
#> GSM1009187 2 0.732 0.712 0.000 0.500 0.172 0.328
#> GSM1009201 1 0.164 0.876 0.948 0.044 0.000 0.008
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM1009062 1 0.2179 0.623 0.888 0.000 0.000 0.112 0.000
#> GSM1009076 2 0.5221 0.384 0.000 0.552 0.000 0.400 0.048
#> GSM1009090 1 0.4297 0.599 0.528 0.000 0.000 0.472 0.000
#> GSM1009104 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> GSM1009118 2 0.0566 0.787 0.000 0.984 0.004 0.012 0.000
#> GSM1009132 1 0.4297 0.599 0.528 0.000 0.000 0.472 0.000
#> GSM1009146 1 0.0510 0.661 0.984 0.016 0.000 0.000 0.000
#> GSM1009160 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000
#> GSM1009174 2 0.0404 0.798 0.000 0.988 0.000 0.000 0.012
#> GSM1009188 1 0.0000 0.659 1.000 0.000 0.000 0.000 0.000
#> GSM1009063 1 0.2179 0.623 0.888 0.000 0.000 0.112 0.000
#> GSM1009077 2 0.5221 0.384 0.000 0.552 0.000 0.400 0.048
#> GSM1009091 1 0.4297 0.599 0.528 0.000 0.000 0.472 0.000
#> GSM1009105 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> GSM1009119 1 0.4482 0.165 0.612 0.376 0.000 0.012 0.000
#> GSM1009133 1 0.4297 0.599 0.528 0.000 0.000 0.472 0.000
#> GSM1009147 1 0.0510 0.661 0.984 0.016 0.000 0.000 0.000
#> GSM1009161 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000
#> GSM1009175 2 0.0404 0.798 0.000 0.988 0.000 0.000 0.012
#> GSM1009189 1 0.0510 0.661 0.984 0.016 0.000 0.000 0.000
#> GSM1009064 1 0.2179 0.623 0.888 0.000 0.000 0.112 0.000
#> GSM1009078 4 0.6799 1.000 0.284 0.176 0.000 0.516 0.024
#> GSM1009092 1 0.4297 0.599 0.528 0.000 0.000 0.472 0.000
#> GSM1009106 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> GSM1009120 1 0.4482 0.165 0.612 0.376 0.000 0.012 0.000
#> GSM1009134 1 0.4297 0.599 0.528 0.000 0.000 0.472 0.000
#> GSM1009148 1 0.0510 0.661 0.984 0.016 0.000 0.000 0.000
#> GSM1009162 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000
#> GSM1009176 2 0.0404 0.798 0.000 0.988 0.000 0.000 0.012
#> GSM1009190 1 0.0510 0.661 0.984 0.016 0.000 0.000 0.000
#> GSM1009065 1 0.2179 0.623 0.888 0.000 0.000 0.112 0.000
#> GSM1009079 2 0.5221 0.384 0.000 0.552 0.000 0.400 0.048
#> GSM1009093 1 0.4297 0.599 0.528 0.000 0.000 0.472 0.000
#> GSM1009107 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> GSM1009121 2 0.0566 0.787 0.000 0.984 0.004 0.012 0.000
#> GSM1009135 1 0.4297 0.599 0.528 0.000 0.000 0.472 0.000
#> GSM1009149 1 0.2230 0.562 0.884 0.000 0.000 0.116 0.000
#> GSM1009163 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000
#> GSM1009177 2 0.0404 0.798 0.000 0.988 0.000 0.000 0.012
#> GSM1009191 1 0.0510 0.661 0.984 0.016 0.000 0.000 0.000
#> GSM1009066 1 0.2179 0.623 0.888 0.000 0.000 0.112 0.000
#> GSM1009080 2 0.5221 0.384 0.000 0.552 0.000 0.400 0.048
#> GSM1009094 1 0.4297 0.599 0.528 0.000 0.000 0.472 0.000
#> GSM1009108 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> GSM1009122 2 0.0566 0.787 0.000 0.984 0.004 0.012 0.000
#> GSM1009136 1 0.4297 0.599 0.528 0.000 0.000 0.472 0.000
#> GSM1009150 1 0.2230 0.562 0.884 0.000 0.000 0.116 0.000
#> GSM1009164 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000
#> GSM1009178 2 0.0404 0.798 0.000 0.988 0.000 0.000 0.012
#> GSM1009192 1 0.0162 0.659 0.996 0.004 0.000 0.000 0.000
#> GSM1009067 1 0.2179 0.623 0.888 0.000 0.000 0.112 0.000
#> GSM1009081 2 0.5221 0.384 0.000 0.552 0.000 0.400 0.048
#> GSM1009095 1 0.4297 0.599 0.528 0.000 0.000 0.472 0.000
#> GSM1009109 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> GSM1009123 1 0.4482 0.165 0.612 0.376 0.000 0.012 0.000
#> GSM1009137 1 0.4297 0.599 0.528 0.000 0.000 0.472 0.000
#> GSM1009151 1 0.0510 0.661 0.984 0.016 0.000 0.000 0.000
#> GSM1009165 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000
#> GSM1009179 2 0.0404 0.798 0.000 0.988 0.000 0.000 0.012
#> GSM1009193 1 0.0000 0.659 1.000 0.000 0.000 0.000 0.000
#> GSM1009068 1 0.2179 0.623 0.888 0.000 0.000 0.112 0.000
#> GSM1009082 2 0.5221 0.384 0.000 0.552 0.000 0.400 0.048
#> GSM1009096 1 0.4297 0.599 0.528 0.000 0.000 0.472 0.000
#> GSM1009110 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> GSM1009124 1 0.4527 0.163 0.596 0.392 0.000 0.012 0.000
#> GSM1009138 1 0.4297 0.599 0.528 0.000 0.000 0.472 0.000
#> GSM1009152 1 0.0510 0.661 0.984 0.016 0.000 0.000 0.000
#> GSM1009166 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000
#> GSM1009180 2 0.0404 0.798 0.000 0.988 0.000 0.000 0.012
#> GSM1009194 1 0.0510 0.661 0.984 0.016 0.000 0.000 0.000
#> GSM1009069 1 0.2179 0.623 0.888 0.000 0.000 0.112 0.000
#> GSM1009083 2 0.5221 0.384 0.000 0.552 0.000 0.400 0.048
#> GSM1009097 1 0.4297 0.599 0.528 0.000 0.000 0.472 0.000
#> GSM1009111 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> GSM1009125 2 0.0566 0.787 0.000 0.984 0.004 0.012 0.000
#> GSM1009139 1 0.4297 0.599 0.528 0.000 0.000 0.472 0.000
#> GSM1009153 1 0.0510 0.661 0.984 0.016 0.000 0.000 0.000
#> GSM1009167 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000
#> GSM1009181 2 0.0404 0.798 0.000 0.988 0.000 0.000 0.012
#> GSM1009195 1 0.0510 0.661 0.984 0.016 0.000 0.000 0.000
#> GSM1009070 1 0.2179 0.623 0.888 0.000 0.000 0.112 0.000
#> GSM1009084 2 0.5221 0.384 0.000 0.552 0.000 0.400 0.048
#> GSM1009098 1 0.4297 0.599 0.528 0.000 0.000 0.472 0.000
#> GSM1009112 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> GSM1009126 1 0.4527 0.163 0.596 0.392 0.000 0.012 0.000
#> GSM1009140 1 0.4297 0.599 0.528 0.000 0.000 0.472 0.000
#> GSM1009154 1 0.0510 0.661 0.984 0.016 0.000 0.000 0.000
#> GSM1009168 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000
#> GSM1009182 2 0.0404 0.798 0.000 0.988 0.000 0.000 0.012
#> GSM1009196 1 0.0510 0.661 0.984 0.016 0.000 0.000 0.000
#> GSM1009071 1 0.2179 0.623 0.888 0.000 0.000 0.112 0.000
#> GSM1009085 2 0.5221 0.384 0.000 0.552 0.000 0.400 0.048
#> GSM1009099 1 0.4297 0.599 0.528 0.000 0.000 0.472 0.000
#> GSM1009113 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> GSM1009127 1 0.4482 0.165 0.612 0.376 0.000 0.012 0.000
#> GSM1009141 1 0.4297 0.599 0.528 0.000 0.000 0.472 0.000
#> GSM1009155 1 0.0510 0.661 0.984 0.016 0.000 0.000 0.000
#> GSM1009169 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000
#> GSM1009183 2 0.0404 0.798 0.000 0.988 0.000 0.000 0.012
#> GSM1009197 1 0.0510 0.661 0.984 0.016 0.000 0.000 0.000
#> GSM1009072 1 0.2179 0.623 0.888 0.000 0.000 0.112 0.000
#> GSM1009086 2 0.5221 0.384 0.000 0.552 0.000 0.400 0.048
#> GSM1009100 1 0.4297 0.599 0.528 0.000 0.000 0.472 0.000
#> GSM1009114 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> GSM1009128 2 0.2733 0.616 0.112 0.872 0.004 0.012 0.000
#> GSM1009142 1 0.4297 0.599 0.528 0.000 0.000 0.472 0.000
#> GSM1009156 1 0.0510 0.661 0.984 0.016 0.000 0.000 0.000
#> GSM1009170 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000
#> GSM1009184 2 0.0404 0.798 0.000 0.988 0.000 0.000 0.012
#> GSM1009198 1 0.0000 0.659 1.000 0.000 0.000 0.000 0.000
#> GSM1009073 1 0.2179 0.623 0.888 0.000 0.000 0.112 0.000
#> GSM1009087 4 0.6799 1.000 0.284 0.176 0.000 0.516 0.024
#> GSM1009101 1 0.4297 0.599 0.528 0.000 0.000 0.472 0.000
#> GSM1009115 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> GSM1009129 2 0.0566 0.787 0.000 0.984 0.004 0.012 0.000
#> GSM1009143 1 0.4297 0.599 0.528 0.000 0.000 0.472 0.000
#> GSM1009157 1 0.0510 0.661 0.984 0.016 0.000 0.000 0.000
#> GSM1009171 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000
#> GSM1009185 2 0.0404 0.798 0.000 0.988 0.000 0.000 0.012
#> GSM1009199 1 0.0510 0.661 0.984 0.016 0.000 0.000 0.000
#> GSM1009074 1 0.2179 0.623 0.888 0.000 0.000 0.112 0.000
#> GSM1009088 4 0.6799 1.000 0.284 0.176 0.000 0.516 0.024
#> GSM1009102 1 0.4297 0.599 0.528 0.000 0.000 0.472 0.000
#> GSM1009116 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> GSM1009130 2 0.0566 0.787 0.000 0.984 0.004 0.012 0.000
#> GSM1009144 1 0.4297 0.599 0.528 0.000 0.000 0.472 0.000
#> GSM1009158 1 0.2230 0.562 0.884 0.000 0.000 0.116 0.000
#> GSM1009172 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000
#> GSM1009186 2 0.0404 0.798 0.000 0.988 0.000 0.000 0.012
#> GSM1009200 1 0.0510 0.661 0.984 0.016 0.000 0.000 0.000
#> GSM1009075 1 0.2179 0.623 0.888 0.000 0.000 0.112 0.000
#> GSM1009089 4 0.6799 1.000 0.284 0.176 0.000 0.516 0.024
#> GSM1009103 1 0.4297 0.599 0.528 0.000 0.000 0.472 0.000
#> GSM1009117 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000
#> GSM1009131 2 0.0566 0.787 0.000 0.984 0.004 0.012 0.000
#> GSM1009145 1 0.4297 0.599 0.528 0.000 0.000 0.472 0.000
#> GSM1009159 1 0.2230 0.562 0.884 0.000 0.000 0.116 0.000
#> GSM1009173 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000
#> GSM1009187 2 0.0404 0.798 0.000 0.988 0.000 0.000 0.012
#> GSM1009201 1 0.0510 0.661 0.984 0.016 0.000 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1009062 1 0.0865 0.847 0.964 0.000 0.000 0.000 0.000 0.036
#> GSM1009076 6 0.4263 0.729 0.000 0.376 0.000 0.000 0.024 0.600
#> GSM1009090 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1009104 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1009118 2 0.0146 0.748 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM1009132 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1009146 1 0.2095 0.876 0.904 0.016 0.000 0.076 0.000 0.004
#> GSM1009160 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1009174 2 0.2697 0.836 0.000 0.812 0.000 0.000 0.000 0.188
#> GSM1009188 1 0.1951 0.873 0.908 0.000 0.000 0.076 0.000 0.016
#> GSM1009063 1 0.0865 0.847 0.964 0.000 0.000 0.000 0.000 0.036
#> GSM1009077 6 0.4263 0.729 0.000 0.376 0.000 0.000 0.024 0.600
#> GSM1009091 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1009105 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1009119 1 0.5410 0.473 0.520 0.388 0.000 0.076 0.000 0.016
#> GSM1009133 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1009147 1 0.2095 0.876 0.904 0.016 0.000 0.076 0.000 0.004
#> GSM1009161 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1009175 2 0.2697 0.836 0.000 0.812 0.000 0.000 0.000 0.188
#> GSM1009189 1 0.1951 0.877 0.908 0.016 0.000 0.076 0.000 0.000
#> GSM1009064 1 0.0865 0.847 0.964 0.000 0.000 0.000 0.000 0.036
#> GSM1009078 6 0.0865 0.555 0.036 0.000 0.000 0.000 0.000 0.964
#> GSM1009092 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1009106 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1009120 1 0.5410 0.473 0.520 0.388 0.000 0.076 0.000 0.016
#> GSM1009134 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1009148 1 0.2095 0.876 0.904 0.016 0.000 0.076 0.000 0.004
#> GSM1009162 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1009176 2 0.2697 0.836 0.000 0.812 0.000 0.000 0.000 0.188
#> GSM1009190 1 0.1951 0.877 0.908 0.016 0.000 0.076 0.000 0.000
#> GSM1009065 1 0.0865 0.847 0.964 0.000 0.000 0.000 0.000 0.036
#> GSM1009079 6 0.4263 0.729 0.000 0.376 0.000 0.000 0.024 0.600
#> GSM1009093 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1009107 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1009121 2 0.0146 0.748 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM1009135 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1009149 1 0.3659 0.611 0.636 0.000 0.000 0.000 0.000 0.364
#> GSM1009163 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1009177 2 0.2697 0.836 0.000 0.812 0.000 0.000 0.000 0.188
#> GSM1009191 1 0.1951 0.877 0.908 0.016 0.000 0.076 0.000 0.000
#> GSM1009066 1 0.0865 0.847 0.964 0.000 0.000 0.000 0.000 0.036
#> GSM1009080 6 0.4263 0.729 0.000 0.376 0.000 0.000 0.024 0.600
#> GSM1009094 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1009108 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1009122 2 0.0146 0.748 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM1009136 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1009150 1 0.3659 0.611 0.636 0.000 0.000 0.000 0.000 0.364
#> GSM1009164 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1009178 2 0.2697 0.836 0.000 0.812 0.000 0.000 0.000 0.188
#> GSM1009192 1 0.2002 0.874 0.908 0.004 0.000 0.076 0.000 0.012
#> GSM1009067 1 0.0865 0.847 0.964 0.000 0.000 0.000 0.000 0.036
#> GSM1009081 6 0.4263 0.729 0.000 0.376 0.000 0.000 0.024 0.600
#> GSM1009095 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1009109 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1009123 1 0.5410 0.473 0.520 0.388 0.000 0.076 0.000 0.016
#> GSM1009137 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1009151 1 0.2095 0.876 0.904 0.016 0.000 0.076 0.000 0.004
#> GSM1009165 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1009179 2 0.2697 0.836 0.000 0.812 0.000 0.000 0.000 0.188
#> GSM1009193 1 0.1951 0.873 0.908 0.000 0.000 0.076 0.000 0.016
#> GSM1009068 1 0.0865 0.847 0.964 0.000 0.000 0.000 0.000 0.036
#> GSM1009082 6 0.4263 0.729 0.000 0.376 0.000 0.000 0.024 0.600
#> GSM1009096 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1009110 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1009124 1 0.5034 0.471 0.520 0.404 0.000 0.076 0.000 0.000
#> GSM1009138 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1009152 1 0.2095 0.876 0.904 0.016 0.000 0.076 0.000 0.004
#> GSM1009166 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1009180 2 0.2697 0.836 0.000 0.812 0.000 0.000 0.000 0.188
#> GSM1009194 1 0.1951 0.877 0.908 0.016 0.000 0.076 0.000 0.000
#> GSM1009069 1 0.0865 0.847 0.964 0.000 0.000 0.000 0.000 0.036
#> GSM1009083 6 0.4263 0.729 0.000 0.376 0.000 0.000 0.024 0.600
#> GSM1009097 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1009111 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1009125 2 0.0146 0.748 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM1009139 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1009153 1 0.2095 0.876 0.904 0.016 0.000 0.076 0.000 0.004
#> GSM1009167 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1009181 2 0.2697 0.836 0.000 0.812 0.000 0.000 0.000 0.188
#> GSM1009195 1 0.1951 0.877 0.908 0.016 0.000 0.076 0.000 0.000
#> GSM1009070 1 0.0865 0.847 0.964 0.000 0.000 0.000 0.000 0.036
#> GSM1009084 6 0.4263 0.729 0.000 0.376 0.000 0.000 0.024 0.600
#> GSM1009098 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1009112 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1009126 1 0.5034 0.471 0.520 0.404 0.000 0.076 0.000 0.000
#> GSM1009140 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1009154 1 0.2095 0.876 0.904 0.016 0.000 0.076 0.000 0.004
#> GSM1009168 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1009182 2 0.2697 0.836 0.000 0.812 0.000 0.000 0.000 0.188
#> GSM1009196 1 0.1951 0.877 0.908 0.016 0.000 0.076 0.000 0.000
#> GSM1009071 1 0.0865 0.847 0.964 0.000 0.000 0.000 0.000 0.036
#> GSM1009085 6 0.4263 0.729 0.000 0.376 0.000 0.000 0.024 0.600
#> GSM1009099 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1009113 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1009127 1 0.5410 0.473 0.520 0.388 0.000 0.076 0.000 0.016
#> GSM1009141 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1009155 1 0.2095 0.876 0.904 0.016 0.000 0.076 0.000 0.004
#> GSM1009169 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1009183 2 0.2697 0.836 0.000 0.812 0.000 0.000 0.000 0.188
#> GSM1009197 1 0.1951 0.877 0.908 0.016 0.000 0.076 0.000 0.000
#> GSM1009072 1 0.0865 0.847 0.964 0.000 0.000 0.000 0.000 0.036
#> GSM1009086 6 0.4263 0.729 0.000 0.376 0.000 0.000 0.024 0.600
#> GSM1009100 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1009114 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1009128 2 0.2493 0.554 0.036 0.884 0.004 0.076 0.000 0.000
#> GSM1009142 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1009156 1 0.2095 0.876 0.904 0.016 0.000 0.076 0.000 0.004
#> GSM1009170 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1009184 2 0.2697 0.836 0.000 0.812 0.000 0.000 0.000 0.188
#> GSM1009198 1 0.1951 0.873 0.908 0.000 0.000 0.076 0.000 0.016
#> GSM1009073 1 0.0865 0.847 0.964 0.000 0.000 0.000 0.000 0.036
#> GSM1009087 6 0.0865 0.555 0.036 0.000 0.000 0.000 0.000 0.964
#> GSM1009101 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1009115 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1009129 2 0.0146 0.748 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM1009143 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1009157 1 0.2095 0.876 0.904 0.016 0.000 0.076 0.000 0.004
#> GSM1009171 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1009185 2 0.2697 0.836 0.000 0.812 0.000 0.000 0.000 0.188
#> GSM1009199 1 0.1951 0.877 0.908 0.016 0.000 0.076 0.000 0.000
#> GSM1009074 1 0.0865 0.847 0.964 0.000 0.000 0.000 0.000 0.036
#> GSM1009088 6 0.0865 0.555 0.036 0.000 0.000 0.000 0.000 0.964
#> GSM1009102 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1009116 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1009130 2 0.0146 0.748 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM1009144 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1009158 1 0.3659 0.611 0.636 0.000 0.000 0.000 0.000 0.364
#> GSM1009172 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1009186 2 0.2697 0.836 0.000 0.812 0.000 0.000 0.000 0.188
#> GSM1009200 1 0.1951 0.877 0.908 0.016 0.000 0.076 0.000 0.000
#> GSM1009075 1 0.0865 0.847 0.964 0.000 0.000 0.000 0.000 0.036
#> GSM1009089 6 0.0865 0.555 0.036 0.000 0.000 0.000 0.000 0.964
#> GSM1009103 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1009117 5 0.0000 1.000 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1009131 2 0.0146 0.748 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM1009145 4 0.0000 1.000 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1009159 1 0.3659 0.611 0.636 0.000 0.000 0.000 0.000 0.364
#> GSM1009173 3 0.0000 1.000 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1009187 2 0.2697 0.836 0.000 0.812 0.000 0.000 0.000 0.188
#> GSM1009201 1 0.1951 0.877 0.908 0.016 0.000 0.076 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 temperature(p) time(p) specimen(p) k
#> MAD:hclust 130 0.996 1 1.19e-23 2
#> MAD:hclust 130 1.000 1 7.50e-45 3
#> MAD:hclust 130 1.000 1 5.41e-66 4
#> MAD:hclust 124 0.966 1 3.02e-82 5
#> MAD:hclust 134 1.000 1 2.83e-112 6
If matrix rows can be associated to genes, consider to use functional_enrichment(res,
...) to perform function enrichment for the signature genes. See this vignette for more detailed explanations.
The object with results only for a single top-value method and a single partition method can be extracted as:
res = res_list["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 140 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 0.483 0.895 0.902 0.4493 0.501 0.501
#> 3 3 0.444 0.499 0.673 0.3604 0.884 0.774
#> 4 4 0.460 0.633 0.698 0.1329 0.763 0.495
#> 5 5 0.494 0.472 0.622 0.0881 0.977 0.922
#> 6 6 0.641 0.675 0.646 0.0483 0.897 0.636
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
#> GSM1009062 1 0.3733 0.938 0.928 0.072
#> GSM1009076 2 0.6623 0.888 0.172 0.828
#> GSM1009090 1 0.1414 0.949 0.980 0.020
#> GSM1009104 2 0.4431 0.885 0.092 0.908
#> GSM1009118 2 0.9686 0.588 0.396 0.604
#> GSM1009132 1 0.1414 0.949 0.980 0.020
#> GSM1009146 1 0.2236 0.955 0.964 0.036
#> GSM1009160 2 0.2948 0.853 0.052 0.948
#> GSM1009174 2 0.7376 0.880 0.208 0.792
#> GSM1009188 1 0.2603 0.955 0.956 0.044
#> GSM1009063 1 0.3733 0.938 0.928 0.072
#> GSM1009077 2 0.6623 0.888 0.172 0.828
#> GSM1009091 1 0.1414 0.949 0.980 0.020
#> GSM1009105 2 0.4431 0.885 0.092 0.908
#> GSM1009119 1 0.2423 0.955 0.960 0.040
#> GSM1009133 1 0.1414 0.949 0.980 0.020
#> GSM1009147 1 0.2236 0.955 0.964 0.036
#> GSM1009161 2 0.2948 0.853 0.052 0.948
#> GSM1009175 2 0.7376 0.880 0.208 0.792
#> GSM1009189 1 0.2603 0.955 0.956 0.044
#> GSM1009064 1 0.3733 0.938 0.928 0.072
#> GSM1009078 2 0.7950 0.831 0.240 0.760
#> GSM1009092 1 0.1414 0.949 0.980 0.020
#> GSM1009106 2 0.4431 0.885 0.092 0.908
#> GSM1009120 1 0.2423 0.955 0.960 0.040
#> GSM1009134 1 0.1414 0.949 0.980 0.020
#> GSM1009148 1 0.2236 0.955 0.964 0.036
#> GSM1009162 2 0.2948 0.853 0.052 0.948
#> GSM1009176 2 0.7376 0.880 0.208 0.792
#> GSM1009190 1 0.2603 0.955 0.956 0.044
#> GSM1009065 1 0.3733 0.938 0.928 0.072
#> GSM1009079 2 0.6623 0.888 0.172 0.828
#> GSM1009093 1 0.1414 0.949 0.980 0.020
#> GSM1009107 2 0.4431 0.885 0.092 0.908
#> GSM1009121 2 0.8861 0.765 0.304 0.696
#> GSM1009135 1 0.1414 0.949 0.980 0.020
#> GSM1009149 1 0.2236 0.955 0.964 0.036
#> GSM1009163 2 0.2948 0.853 0.052 0.948
#> GSM1009177 2 0.7376 0.880 0.208 0.792
#> GSM1009191 1 0.2603 0.955 0.956 0.044
#> GSM1009066 1 0.3733 0.938 0.928 0.072
#> GSM1009080 2 0.6623 0.888 0.172 0.828
#> GSM1009094 1 0.1414 0.949 0.980 0.020
#> GSM1009108 2 0.4431 0.885 0.092 0.908
#> GSM1009122 2 0.7528 0.869 0.216 0.784
#> GSM1009136 1 0.1414 0.949 0.980 0.020
#> GSM1009150 1 0.2236 0.955 0.964 0.036
#> GSM1009164 2 0.2948 0.853 0.052 0.948
#> GSM1009178 2 0.7376 0.880 0.208 0.792
#> GSM1009192 1 0.2603 0.955 0.956 0.044
#> GSM1009067 1 0.3733 0.938 0.928 0.072
#> GSM1009081 2 0.6623 0.888 0.172 0.828
#> GSM1009095 1 0.0938 0.950 0.988 0.012
#> GSM1009109 2 0.4431 0.885 0.092 0.908
#> GSM1009123 1 0.2778 0.953 0.952 0.048
#> GSM1009137 1 0.1414 0.949 0.980 0.020
#> GSM1009151 1 0.2236 0.955 0.964 0.036
#> GSM1009165 2 0.2948 0.853 0.052 0.948
#> GSM1009179 2 0.7376 0.880 0.208 0.792
#> GSM1009193 1 0.2603 0.955 0.956 0.044
#> GSM1009068 1 0.3733 0.938 0.928 0.072
#> GSM1009082 2 0.6623 0.888 0.172 0.828
#> GSM1009096 1 0.1414 0.949 0.980 0.020
#> GSM1009110 2 0.4431 0.885 0.092 0.908
#> GSM1009124 1 0.2948 0.952 0.948 0.052
#> GSM1009138 1 0.1414 0.949 0.980 0.020
#> GSM1009152 1 0.2236 0.955 0.964 0.036
#> GSM1009166 2 0.2948 0.853 0.052 0.948
#> GSM1009180 2 0.7376 0.880 0.208 0.792
#> GSM1009194 1 0.2603 0.955 0.956 0.044
#> GSM1009069 1 0.3733 0.938 0.928 0.072
#> GSM1009083 2 0.6623 0.888 0.172 0.828
#> GSM1009097 1 0.1414 0.949 0.980 0.020
#> GSM1009111 2 0.4431 0.885 0.092 0.908
#> GSM1009125 2 0.7528 0.869 0.216 0.784
#> GSM1009139 1 0.1414 0.949 0.980 0.020
#> GSM1009153 1 0.2236 0.955 0.964 0.036
#> GSM1009167 2 0.2948 0.853 0.052 0.948
#> GSM1009181 2 0.7376 0.880 0.208 0.792
#> GSM1009195 1 0.9661 0.178 0.608 0.392
#> GSM1009070 1 0.3733 0.938 0.928 0.072
#> GSM1009084 2 0.6623 0.888 0.172 0.828
#> GSM1009098 1 0.1414 0.949 0.980 0.020
#> GSM1009112 2 0.4431 0.885 0.092 0.908
#> GSM1009126 1 0.2948 0.952 0.948 0.052
#> GSM1009140 1 0.1414 0.949 0.980 0.020
#> GSM1009154 1 0.2236 0.955 0.964 0.036
#> GSM1009168 2 0.2948 0.853 0.052 0.948
#> GSM1009182 2 0.7376 0.880 0.208 0.792
#> GSM1009196 1 0.2603 0.955 0.956 0.044
#> GSM1009071 1 0.3733 0.938 0.928 0.072
#> GSM1009085 2 0.6623 0.888 0.172 0.828
#> GSM1009099 1 0.1414 0.949 0.980 0.020
#> GSM1009113 2 0.4431 0.885 0.092 0.908
#> GSM1009127 1 0.2423 0.955 0.960 0.040
#> GSM1009141 1 0.1414 0.949 0.980 0.020
#> GSM1009155 1 0.2236 0.955 0.964 0.036
#> GSM1009169 2 0.2948 0.853 0.052 0.948
#> GSM1009183 2 0.7376 0.880 0.208 0.792
#> GSM1009197 1 0.2603 0.955 0.956 0.044
#> GSM1009072 1 0.3733 0.938 0.928 0.072
#> GSM1009086 2 0.6623 0.888 0.172 0.828
#> GSM1009100 1 0.1414 0.949 0.980 0.020
#> GSM1009114 2 0.4431 0.885 0.092 0.908
#> GSM1009128 2 0.9909 0.473 0.444 0.556
#> GSM1009142 1 0.1414 0.949 0.980 0.020
#> GSM1009156 1 0.2236 0.955 0.964 0.036
#> GSM1009170 2 0.2948 0.853 0.052 0.948
#> GSM1009184 2 0.7376 0.880 0.208 0.792
#> GSM1009198 1 0.2603 0.955 0.956 0.044
#> GSM1009073 1 0.3733 0.938 0.928 0.072
#> GSM1009087 2 0.7950 0.831 0.240 0.760
#> GSM1009101 1 0.1414 0.949 0.980 0.020
#> GSM1009115 2 0.4431 0.885 0.092 0.908
#> GSM1009129 2 0.7056 0.882 0.192 0.808
#> GSM1009143 1 0.1414 0.949 0.980 0.020
#> GSM1009157 1 0.3879 0.922 0.924 0.076
#> GSM1009171 2 0.2948 0.853 0.052 0.948
#> GSM1009185 2 0.9427 0.672 0.360 0.640
#> GSM1009199 1 0.6887 0.768 0.816 0.184
#> GSM1009074 1 0.3733 0.938 0.928 0.072
#> GSM1009088 2 0.6623 0.888 0.172 0.828
#> GSM1009102 1 0.1414 0.949 0.980 0.020
#> GSM1009116 2 0.4431 0.885 0.092 0.908
#> GSM1009130 2 0.6712 0.887 0.176 0.824
#> GSM1009144 1 0.1414 0.949 0.980 0.020
#> GSM1009158 1 0.2236 0.955 0.964 0.036
#> GSM1009172 2 0.2948 0.853 0.052 0.948
#> GSM1009186 2 0.7376 0.880 0.208 0.792
#> GSM1009200 1 0.2603 0.955 0.956 0.044
#> GSM1009075 1 0.3733 0.938 0.928 0.072
#> GSM1009089 1 0.8713 0.575 0.708 0.292
#> GSM1009103 1 0.1414 0.949 0.980 0.020
#> GSM1009117 2 0.4431 0.885 0.092 0.908
#> GSM1009131 2 0.9661 0.605 0.392 0.608
#> GSM1009145 1 0.1414 0.949 0.980 0.020
#> GSM1009159 1 0.2236 0.955 0.964 0.036
#> GSM1009173 2 0.2948 0.853 0.052 0.948
#> GSM1009187 2 0.9815 0.538 0.420 0.580
#> GSM1009201 1 0.2603 0.955 0.956 0.044
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1009062 3 0.7067 0.88029 0.468 0.020 0.512
#> GSM1009076 2 0.5843 0.75188 0.016 0.732 0.252
#> GSM1009090 1 0.0237 0.44559 0.996 0.004 0.000
#> GSM1009104 2 0.1337 0.76243 0.012 0.972 0.016
#> GSM1009118 2 0.9759 0.37185 0.284 0.444 0.272
#> GSM1009132 1 0.3644 0.34036 0.872 0.004 0.124
#> GSM1009146 1 0.6282 0.01635 0.612 0.004 0.384
#> GSM1009160 2 0.5578 0.70143 0.012 0.748 0.240
#> GSM1009174 2 0.7600 0.71723 0.056 0.600 0.344
#> GSM1009188 1 0.5656 0.32684 0.712 0.004 0.284
#> GSM1009063 3 0.7067 0.88029 0.468 0.020 0.512
#> GSM1009077 2 0.5843 0.75188 0.016 0.732 0.252
#> GSM1009091 1 0.0237 0.44559 0.996 0.004 0.000
#> GSM1009105 2 0.1337 0.76243 0.012 0.972 0.016
#> GSM1009119 1 0.5465 0.32696 0.712 0.000 0.288
#> GSM1009133 1 0.3644 0.34036 0.872 0.004 0.124
#> GSM1009147 1 0.6314 0.02104 0.604 0.004 0.392
#> GSM1009161 2 0.5578 0.70143 0.012 0.748 0.240
#> GSM1009175 2 0.7685 0.71461 0.060 0.596 0.344
#> GSM1009189 1 0.5656 0.32684 0.712 0.004 0.284
#> GSM1009064 3 0.7067 0.88029 0.468 0.020 0.512
#> GSM1009078 2 0.8355 0.52339 0.084 0.508 0.408
#> GSM1009092 1 0.0237 0.44559 0.996 0.004 0.000
#> GSM1009106 2 0.1337 0.76243 0.012 0.972 0.016
#> GSM1009120 1 0.5465 0.32696 0.712 0.000 0.288
#> GSM1009134 1 0.3644 0.34036 0.872 0.004 0.124
#> GSM1009148 1 0.6282 0.01635 0.612 0.004 0.384
#> GSM1009162 2 0.5578 0.70143 0.012 0.748 0.240
#> GSM1009176 2 0.7537 0.72474 0.056 0.612 0.332
#> GSM1009190 1 0.5656 0.32684 0.712 0.004 0.284
#> GSM1009065 3 0.7067 0.88029 0.468 0.020 0.512
#> GSM1009079 2 0.5763 0.75391 0.016 0.740 0.244
#> GSM1009093 1 0.0237 0.44559 0.996 0.004 0.000
#> GSM1009107 2 0.1337 0.76243 0.012 0.972 0.016
#> GSM1009121 1 0.9843 -0.25293 0.380 0.372 0.248
#> GSM1009135 1 0.3644 0.34036 0.872 0.004 0.124
#> GSM1009149 1 0.6264 0.03020 0.616 0.004 0.380
#> GSM1009163 2 0.5578 0.70143 0.012 0.748 0.240
#> GSM1009177 2 0.7537 0.72474 0.056 0.612 0.332
#> GSM1009191 1 0.5722 0.31076 0.704 0.004 0.292
#> GSM1009066 3 0.7067 0.88029 0.468 0.020 0.512
#> GSM1009080 2 0.5803 0.75293 0.016 0.736 0.248
#> GSM1009094 1 0.0237 0.44559 0.996 0.004 0.000
#> GSM1009108 2 0.1337 0.76243 0.012 0.972 0.016
#> GSM1009122 2 0.8201 0.70739 0.112 0.612 0.276
#> GSM1009136 1 0.3272 0.35691 0.892 0.004 0.104
#> GSM1009150 1 0.6264 0.03020 0.616 0.004 0.380
#> GSM1009164 2 0.5578 0.70143 0.012 0.748 0.240
#> GSM1009178 2 0.7920 0.69508 0.068 0.572 0.360
#> GSM1009192 1 0.5656 0.32684 0.712 0.004 0.284
#> GSM1009067 3 0.7067 0.88029 0.468 0.020 0.512
#> GSM1009081 2 0.5843 0.75188 0.016 0.732 0.252
#> GSM1009095 1 0.0237 0.44559 0.996 0.004 0.000
#> GSM1009109 2 0.1337 0.76243 0.012 0.972 0.016
#> GSM1009123 1 0.5254 0.34239 0.736 0.000 0.264
#> GSM1009137 1 0.3644 0.34036 0.872 0.004 0.124
#> GSM1009151 1 0.6282 0.01635 0.612 0.004 0.384
#> GSM1009165 2 0.5578 0.70143 0.012 0.748 0.240
#> GSM1009179 2 0.7841 0.69851 0.064 0.576 0.360
#> GSM1009193 1 0.5656 0.32684 0.712 0.004 0.284
#> GSM1009068 3 0.7069 0.86512 0.472 0.020 0.508
#> GSM1009082 2 0.5843 0.75188 0.016 0.732 0.252
#> GSM1009096 1 0.0237 0.44559 0.996 0.004 0.000
#> GSM1009110 2 0.1337 0.76243 0.012 0.972 0.016
#> GSM1009124 1 0.5678 0.28730 0.684 0.000 0.316
#> GSM1009138 1 0.3644 0.34036 0.872 0.004 0.124
#> GSM1009152 1 0.6282 0.01635 0.612 0.004 0.384
#> GSM1009166 2 0.5578 0.70143 0.012 0.748 0.240
#> GSM1009180 2 0.7920 0.69508 0.068 0.572 0.360
#> GSM1009194 1 0.5785 0.29597 0.696 0.004 0.300
#> GSM1009069 3 0.7043 0.70319 0.400 0.024 0.576
#> GSM1009083 2 0.5843 0.75188 0.016 0.732 0.252
#> GSM1009097 1 0.0237 0.44559 0.996 0.004 0.000
#> GSM1009111 2 0.1337 0.76243 0.012 0.972 0.016
#> GSM1009125 2 0.7770 0.72994 0.088 0.640 0.272
#> GSM1009139 1 0.3644 0.34036 0.872 0.004 0.124
#> GSM1009153 1 0.6282 0.01635 0.612 0.004 0.384
#> GSM1009167 2 0.5578 0.70143 0.012 0.748 0.240
#> GSM1009181 2 0.7537 0.72474 0.056 0.612 0.332
#> GSM1009195 1 0.9129 -0.11192 0.480 0.148 0.372
#> GSM1009070 1 0.7075 -0.80106 0.492 0.020 0.488
#> GSM1009084 2 0.5843 0.75188 0.016 0.732 0.252
#> GSM1009098 1 0.0237 0.44559 0.996 0.004 0.000
#> GSM1009112 2 0.1337 0.76243 0.012 0.972 0.016
#> GSM1009126 1 0.5706 0.28689 0.680 0.000 0.320
#> GSM1009140 1 0.3644 0.34036 0.872 0.004 0.124
#> GSM1009154 1 0.6282 0.01635 0.612 0.004 0.384
#> GSM1009168 2 0.5578 0.70143 0.012 0.748 0.240
#> GSM1009182 2 0.7705 0.71191 0.060 0.592 0.348
#> GSM1009196 1 0.5722 0.31076 0.704 0.004 0.292
#> GSM1009071 3 0.7067 0.88029 0.468 0.020 0.512
#> GSM1009085 2 0.5843 0.75188 0.016 0.732 0.252
#> GSM1009099 1 0.0237 0.44559 0.996 0.004 0.000
#> GSM1009113 2 0.1337 0.76243 0.012 0.972 0.016
#> GSM1009127 1 0.5497 0.32678 0.708 0.000 0.292
#> GSM1009141 1 0.3644 0.34036 0.872 0.004 0.124
#> GSM1009155 1 0.6282 0.01635 0.612 0.004 0.384
#> GSM1009169 2 0.5578 0.70143 0.012 0.748 0.240
#> GSM1009183 2 0.7537 0.72474 0.056 0.612 0.332
#> GSM1009197 1 0.5656 0.32684 0.712 0.004 0.284
#> GSM1009072 3 0.7067 0.88029 0.468 0.020 0.512
#> GSM1009086 2 0.5843 0.75188 0.016 0.732 0.252
#> GSM1009100 1 0.0237 0.44559 0.996 0.004 0.000
#> GSM1009114 2 0.1337 0.76243 0.012 0.972 0.016
#> GSM1009128 1 0.9641 -0.00446 0.456 0.316 0.228
#> GSM1009142 1 0.3644 0.34036 0.872 0.004 0.124
#> GSM1009156 1 0.6661 -0.01174 0.588 0.012 0.400
#> GSM1009170 2 0.5578 0.70143 0.012 0.748 0.240
#> GSM1009184 2 0.7685 0.71461 0.060 0.596 0.344
#> GSM1009198 1 0.5656 0.32684 0.712 0.004 0.284
#> GSM1009073 3 0.7067 0.88029 0.468 0.020 0.512
#> GSM1009087 2 0.8355 0.52339 0.084 0.508 0.408
#> GSM1009101 1 0.0237 0.44559 0.996 0.004 0.000
#> GSM1009115 2 0.1337 0.76243 0.012 0.972 0.016
#> GSM1009129 2 0.6904 0.75037 0.048 0.684 0.268
#> GSM1009143 1 0.3644 0.34036 0.872 0.004 0.124
#> GSM1009157 1 0.7293 -0.21365 0.496 0.028 0.476
#> GSM1009171 2 0.5578 0.70143 0.012 0.748 0.240
#> GSM1009185 2 0.9217 0.52659 0.152 0.448 0.400
#> GSM1009199 1 0.7141 0.11222 0.600 0.032 0.368
#> GSM1009074 3 0.7067 0.88029 0.468 0.020 0.512
#> GSM1009088 2 0.8220 0.53691 0.076 0.516 0.408
#> GSM1009102 1 0.0237 0.44559 0.996 0.004 0.000
#> GSM1009116 2 0.1337 0.76243 0.012 0.972 0.016
#> GSM1009130 2 0.6201 0.76232 0.044 0.748 0.208
#> GSM1009144 1 0.3644 0.34036 0.872 0.004 0.124
#> GSM1009158 1 0.6264 0.03020 0.616 0.004 0.380
#> GSM1009172 2 0.5578 0.70143 0.012 0.748 0.240
#> GSM1009186 2 0.7685 0.71461 0.060 0.596 0.344
#> GSM1009200 1 0.5656 0.32684 0.712 0.004 0.284
#> GSM1009075 3 0.7067 0.88029 0.468 0.020 0.512
#> GSM1009089 3 0.9693 0.17064 0.292 0.252 0.456
#> GSM1009103 1 0.0237 0.44559 0.996 0.004 0.000
#> GSM1009117 2 0.1337 0.76243 0.012 0.972 0.016
#> GSM1009131 2 0.9907 0.18277 0.356 0.376 0.268
#> GSM1009145 1 0.3272 0.35691 0.892 0.004 0.104
#> GSM1009159 1 0.6247 0.04216 0.620 0.004 0.376
#> GSM1009173 2 0.5578 0.70143 0.012 0.748 0.240
#> GSM1009187 2 0.9221 0.52101 0.152 0.444 0.404
#> GSM1009201 1 0.5656 0.32684 0.712 0.004 0.284
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1009062 1 0.761 0.4880 0.612 0.068 0.112 0.208
#> GSM1009076 2 0.166 0.6014 0.052 0.944 0.000 0.004
#> GSM1009090 4 0.501 0.8171 0.320 0.004 0.008 0.668
#> GSM1009104 2 0.577 0.2865 0.004 0.676 0.264 0.056
#> GSM1009118 2 0.853 0.2407 0.348 0.452 0.080 0.120
#> GSM1009132 4 0.610 0.8185 0.212 0.000 0.116 0.672
#> GSM1009146 1 0.171 0.6789 0.952 0.004 0.020 0.024
#> GSM1009160 3 0.576 0.9877 0.020 0.328 0.636 0.016
#> GSM1009174 2 0.707 0.5948 0.156 0.668 0.112 0.064
#> GSM1009188 1 0.378 0.6483 0.856 0.016 0.024 0.104
#> GSM1009063 1 0.761 0.4880 0.612 0.068 0.112 0.208
#> GSM1009077 2 0.166 0.6014 0.052 0.944 0.000 0.004
#> GSM1009091 4 0.501 0.8171 0.320 0.004 0.008 0.668
#> GSM1009105 2 0.577 0.2865 0.004 0.676 0.264 0.056
#> GSM1009119 1 0.436 0.6395 0.828 0.016 0.044 0.112
#> GSM1009133 4 0.610 0.8185 0.212 0.000 0.116 0.672
#> GSM1009147 1 0.125 0.6806 0.968 0.004 0.016 0.012
#> GSM1009161 3 0.576 0.9877 0.020 0.328 0.636 0.016
#> GSM1009175 2 0.707 0.5948 0.156 0.668 0.112 0.064
#> GSM1009189 1 0.378 0.6483 0.856 0.016 0.024 0.104
#> GSM1009064 1 0.761 0.4880 0.612 0.068 0.112 0.208
#> GSM1009078 2 0.375 0.5684 0.196 0.800 0.000 0.004
#> GSM1009092 4 0.501 0.8171 0.320 0.004 0.008 0.668
#> GSM1009106 2 0.577 0.2865 0.004 0.676 0.264 0.056
#> GSM1009120 1 0.394 0.6521 0.848 0.016 0.028 0.108
#> GSM1009134 4 0.610 0.8185 0.212 0.000 0.116 0.672
#> GSM1009148 1 0.171 0.6789 0.952 0.004 0.020 0.024
#> GSM1009162 3 0.519 0.9883 0.020 0.324 0.656 0.000
#> GSM1009176 2 0.703 0.5955 0.152 0.672 0.112 0.064
#> GSM1009190 1 0.378 0.6483 0.856 0.016 0.024 0.104
#> GSM1009065 1 0.761 0.4880 0.612 0.068 0.112 0.208
#> GSM1009079 2 0.166 0.6014 0.052 0.944 0.000 0.004
#> GSM1009093 4 0.501 0.8171 0.320 0.004 0.008 0.668
#> GSM1009107 2 0.577 0.2865 0.004 0.676 0.264 0.056
#> GSM1009121 1 0.869 0.0739 0.444 0.336 0.080 0.140
#> GSM1009135 4 0.610 0.8185 0.212 0.000 0.116 0.672
#> GSM1009149 1 0.181 0.6760 0.948 0.004 0.020 0.028
#> GSM1009163 3 0.576 0.9877 0.020 0.328 0.636 0.016
#> GSM1009177 2 0.703 0.5955 0.152 0.672 0.112 0.064
#> GSM1009191 1 0.378 0.6483 0.856 0.016 0.024 0.104
#> GSM1009066 1 0.761 0.4880 0.612 0.068 0.112 0.208
#> GSM1009080 2 0.166 0.6014 0.052 0.944 0.000 0.004
#> GSM1009094 4 0.501 0.8171 0.320 0.004 0.008 0.668
#> GSM1009108 2 0.577 0.2865 0.004 0.676 0.264 0.056
#> GSM1009122 2 0.768 0.4922 0.220 0.604 0.096 0.080
#> GSM1009136 4 0.627 0.8194 0.240 0.000 0.112 0.648
#> GSM1009150 1 0.181 0.6760 0.948 0.004 0.020 0.028
#> GSM1009164 3 0.576 0.9877 0.020 0.328 0.636 0.016
#> GSM1009178 2 0.723 0.5878 0.172 0.652 0.112 0.064
#> GSM1009192 1 0.372 0.6493 0.860 0.016 0.024 0.100
#> GSM1009067 1 0.761 0.4880 0.612 0.068 0.112 0.208
#> GSM1009081 2 0.166 0.6014 0.052 0.944 0.000 0.004
#> GSM1009095 4 0.501 0.8171 0.320 0.004 0.008 0.668
#> GSM1009109 2 0.577 0.2865 0.004 0.676 0.264 0.056
#> GSM1009123 1 0.453 0.6338 0.820 0.020 0.044 0.116
#> GSM1009137 4 0.610 0.8185 0.212 0.000 0.116 0.672
#> GSM1009151 1 0.171 0.6789 0.952 0.004 0.020 0.024
#> GSM1009165 3 0.519 0.9883 0.020 0.324 0.656 0.000
#> GSM1009179 2 0.723 0.5878 0.172 0.652 0.112 0.064
#> GSM1009193 1 0.368 0.6492 0.860 0.016 0.020 0.104
#> GSM1009068 1 0.748 0.4862 0.620 0.060 0.112 0.208
#> GSM1009082 2 0.166 0.6014 0.052 0.944 0.000 0.004
#> GSM1009096 4 0.501 0.8171 0.320 0.004 0.008 0.668
#> GSM1009110 2 0.577 0.2865 0.004 0.676 0.264 0.056
#> GSM1009124 1 0.617 0.5645 0.732 0.068 0.060 0.140
#> GSM1009138 4 0.610 0.8185 0.212 0.000 0.116 0.672
#> GSM1009152 1 0.171 0.6789 0.952 0.004 0.020 0.024
#> GSM1009166 3 0.519 0.9883 0.020 0.324 0.656 0.000
#> GSM1009180 2 0.723 0.5878 0.172 0.652 0.112 0.064
#> GSM1009194 1 0.378 0.6483 0.856 0.016 0.024 0.104
#> GSM1009069 1 0.761 0.4939 0.616 0.072 0.112 0.200
#> GSM1009083 2 0.166 0.6014 0.052 0.944 0.000 0.004
#> GSM1009097 4 0.501 0.8171 0.320 0.004 0.008 0.668
#> GSM1009111 2 0.577 0.2865 0.004 0.676 0.264 0.056
#> GSM1009125 2 0.758 0.4993 0.200 0.620 0.100 0.080
#> GSM1009139 4 0.610 0.8185 0.212 0.000 0.116 0.672
#> GSM1009153 1 0.171 0.6789 0.952 0.004 0.020 0.024
#> GSM1009167 3 0.563 0.9837 0.020 0.324 0.644 0.012
#> GSM1009181 2 0.703 0.5955 0.152 0.672 0.112 0.064
#> GSM1009195 1 0.576 0.6094 0.748 0.132 0.024 0.096
#> GSM1009070 1 0.703 0.4867 0.644 0.036 0.112 0.208
#> GSM1009084 2 0.166 0.6014 0.052 0.944 0.000 0.004
#> GSM1009098 4 0.501 0.8171 0.320 0.004 0.008 0.668
#> GSM1009112 2 0.577 0.2865 0.004 0.676 0.264 0.056
#> GSM1009126 1 0.617 0.5645 0.732 0.068 0.060 0.140
#> GSM1009140 4 0.610 0.8185 0.212 0.000 0.116 0.672
#> GSM1009154 1 0.171 0.6789 0.952 0.004 0.020 0.024
#> GSM1009168 3 0.519 0.9883 0.020 0.324 0.656 0.000
#> GSM1009182 2 0.715 0.5917 0.164 0.660 0.112 0.064
#> GSM1009196 1 0.372 0.6493 0.860 0.016 0.024 0.100
#> GSM1009071 1 0.761 0.4880 0.612 0.068 0.112 0.208
#> GSM1009085 2 0.166 0.6014 0.052 0.944 0.000 0.004
#> GSM1009099 4 0.501 0.8171 0.320 0.004 0.008 0.668
#> GSM1009113 2 0.577 0.2865 0.004 0.676 0.264 0.056
#> GSM1009127 1 0.448 0.6366 0.824 0.020 0.044 0.112
#> GSM1009141 4 0.610 0.8185 0.212 0.000 0.116 0.672
#> GSM1009155 1 0.171 0.6789 0.952 0.004 0.020 0.024
#> GSM1009169 3 0.563 0.9837 0.020 0.324 0.644 0.012
#> GSM1009183 2 0.703 0.5955 0.152 0.672 0.112 0.064
#> GSM1009197 1 0.361 0.6502 0.864 0.016 0.020 0.100
#> GSM1009072 1 0.761 0.4880 0.612 0.068 0.112 0.208
#> GSM1009086 2 0.166 0.6014 0.052 0.944 0.000 0.004
#> GSM1009100 4 0.501 0.8171 0.320 0.004 0.008 0.668
#> GSM1009114 2 0.577 0.2865 0.004 0.676 0.264 0.056
#> GSM1009128 1 0.855 0.2282 0.492 0.284 0.076 0.148
#> GSM1009142 4 0.610 0.8185 0.212 0.000 0.116 0.672
#> GSM1009156 1 0.152 0.6813 0.960 0.016 0.016 0.008
#> GSM1009170 3 0.576 0.9877 0.020 0.328 0.636 0.016
#> GSM1009184 2 0.707 0.5948 0.156 0.668 0.112 0.064
#> GSM1009198 1 0.378 0.6483 0.856 0.016 0.024 0.104
#> GSM1009073 1 0.761 0.4880 0.612 0.068 0.112 0.208
#> GSM1009087 2 0.375 0.5684 0.196 0.800 0.000 0.004
#> GSM1009101 4 0.501 0.8171 0.320 0.004 0.008 0.668
#> GSM1009115 2 0.577 0.2865 0.004 0.676 0.264 0.056
#> GSM1009129 2 0.705 0.5319 0.192 0.656 0.100 0.052
#> GSM1009143 4 0.610 0.8185 0.212 0.000 0.116 0.672
#> GSM1009157 1 0.305 0.6599 0.892 0.080 0.016 0.012
#> GSM1009171 3 0.551 0.9886 0.020 0.324 0.648 0.008
#> GSM1009185 2 0.765 0.5438 0.228 0.600 0.108 0.064
#> GSM1009199 1 0.544 0.6268 0.772 0.096 0.024 0.108
#> GSM1009074 1 0.761 0.4880 0.612 0.068 0.112 0.208
#> GSM1009088 2 0.371 0.5703 0.192 0.804 0.000 0.004
#> GSM1009102 4 0.501 0.8171 0.320 0.004 0.008 0.668
#> GSM1009116 2 0.577 0.2865 0.004 0.676 0.264 0.056
#> GSM1009130 2 0.585 0.5421 0.160 0.736 0.080 0.024
#> GSM1009144 4 0.610 0.8185 0.212 0.000 0.116 0.672
#> GSM1009158 1 0.171 0.6789 0.952 0.004 0.020 0.024
#> GSM1009172 3 0.576 0.9877 0.020 0.328 0.636 0.016
#> GSM1009186 2 0.707 0.5948 0.156 0.668 0.112 0.064
#> GSM1009200 1 0.378 0.6483 0.856 0.016 0.024 0.104
#> GSM1009075 1 0.761 0.4880 0.612 0.068 0.112 0.208
#> GSM1009089 1 0.557 0.1065 0.516 0.468 0.004 0.012
#> GSM1009103 4 0.501 0.8171 0.320 0.004 0.008 0.668
#> GSM1009117 2 0.577 0.2865 0.004 0.676 0.264 0.056
#> GSM1009131 1 0.854 0.1685 0.476 0.312 0.076 0.136
#> GSM1009145 4 0.627 0.8194 0.240 0.000 0.112 0.648
#> GSM1009159 1 0.170 0.6768 0.952 0.004 0.016 0.028
#> GSM1009173 3 0.563 0.9837 0.020 0.324 0.644 0.012
#> GSM1009187 2 0.740 0.5713 0.196 0.632 0.108 0.064
#> GSM1009201 1 0.378 0.6483 0.856 0.016 0.024 0.104
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM1009062 1 0.728 0.4431 0.572 0.040 0.036 0.140 0.212
#> GSM1009076 2 0.582 0.2713 0.016 0.672 0.084 0.016 0.212
#> GSM1009090 4 0.251 0.7955 0.116 0.008 0.000 0.876 0.000
#> GSM1009104 2 0.679 -1.0000 0.000 0.360 0.280 0.000 0.360
#> GSM1009118 2 0.873 -0.0252 0.312 0.376 0.044 0.116 0.152
#> GSM1009132 4 0.462 0.7977 0.040 0.004 0.004 0.724 0.228
#> GSM1009146 1 0.287 0.6210 0.872 0.024 0.004 0.100 0.000
#> GSM1009160 3 0.208 0.9841 0.008 0.064 0.920 0.004 0.004
#> GSM1009174 2 0.367 0.4491 0.092 0.836 0.060 0.012 0.000
#> GSM1009188 1 0.641 0.5688 0.632 0.048 0.012 0.224 0.084
#> GSM1009063 1 0.728 0.4431 0.572 0.040 0.036 0.140 0.212
#> GSM1009077 2 0.582 0.2713 0.016 0.672 0.084 0.016 0.212
#> GSM1009091 4 0.251 0.7955 0.116 0.008 0.000 0.876 0.000
#> GSM1009105 2 0.679 -1.0000 0.000 0.360 0.280 0.000 0.360
#> GSM1009119 1 0.704 0.5290 0.596 0.056 0.020 0.200 0.128
#> GSM1009133 4 0.450 0.7978 0.040 0.004 0.000 0.724 0.232
#> GSM1009147 1 0.295 0.6214 0.868 0.028 0.004 0.100 0.000
#> GSM1009161 3 0.208 0.9841 0.008 0.064 0.920 0.004 0.004
#> GSM1009175 2 0.367 0.4491 0.092 0.836 0.060 0.012 0.000
#> GSM1009189 1 0.639 0.5705 0.636 0.048 0.012 0.220 0.084
#> GSM1009064 1 0.731 0.4430 0.568 0.040 0.036 0.140 0.216
#> GSM1009078 2 0.619 0.3186 0.076 0.656 0.040 0.016 0.212
#> GSM1009092 4 0.251 0.7955 0.116 0.008 0.000 0.876 0.000
#> GSM1009106 5 0.679 1.0000 0.000 0.360 0.280 0.000 0.360
#> GSM1009120 1 0.691 0.5414 0.612 0.056 0.020 0.192 0.120
#> GSM1009134 4 0.450 0.7978 0.040 0.004 0.000 0.724 0.232
#> GSM1009148 1 0.287 0.6210 0.872 0.024 0.004 0.100 0.000
#> GSM1009162 3 0.236 0.9847 0.008 0.064 0.908 0.000 0.020
#> GSM1009176 2 0.367 0.4491 0.092 0.836 0.060 0.012 0.000
#> GSM1009190 1 0.639 0.5705 0.636 0.048 0.012 0.220 0.084
#> GSM1009065 1 0.731 0.4430 0.568 0.040 0.036 0.140 0.216
#> GSM1009079 2 0.582 0.2713 0.016 0.672 0.084 0.016 0.212
#> GSM1009093 4 0.251 0.7955 0.116 0.008 0.000 0.876 0.000
#> GSM1009107 2 0.679 -0.9896 0.000 0.364 0.284 0.000 0.352
#> GSM1009121 1 0.893 0.2367 0.356 0.288 0.040 0.172 0.144
#> GSM1009135 4 0.450 0.7978 0.040 0.004 0.000 0.724 0.232
#> GSM1009149 1 0.293 0.6175 0.864 0.020 0.004 0.112 0.000
#> GSM1009163 3 0.208 0.9841 0.008 0.064 0.920 0.004 0.004
#> GSM1009177 2 0.367 0.4491 0.092 0.836 0.060 0.012 0.000
#> GSM1009191 1 0.642 0.5720 0.636 0.052 0.012 0.216 0.084
#> GSM1009066 1 0.731 0.4430 0.568 0.040 0.036 0.140 0.216
#> GSM1009080 2 0.582 0.2713 0.016 0.672 0.084 0.016 0.212
#> GSM1009094 4 0.251 0.7955 0.116 0.008 0.000 0.876 0.000
#> GSM1009108 2 0.679 -0.9896 0.000 0.364 0.284 0.000 0.352
#> GSM1009122 2 0.851 0.2703 0.196 0.480 0.072 0.084 0.168
#> GSM1009136 4 0.443 0.7989 0.052 0.000 0.000 0.732 0.216
#> GSM1009150 1 0.293 0.6175 0.864 0.020 0.004 0.112 0.000
#> GSM1009164 3 0.208 0.9841 0.008 0.064 0.920 0.004 0.004
#> GSM1009178 2 0.383 0.4485 0.104 0.824 0.060 0.012 0.000
#> GSM1009192 1 0.639 0.5705 0.636 0.048 0.012 0.220 0.084
#> GSM1009067 1 0.728 0.4431 0.572 0.040 0.036 0.140 0.212
#> GSM1009081 2 0.582 0.2713 0.016 0.672 0.084 0.016 0.212
#> GSM1009095 4 0.251 0.7955 0.116 0.008 0.000 0.876 0.000
#> GSM1009109 2 0.679 -1.0000 0.000 0.360 0.280 0.000 0.360
#> GSM1009123 1 0.712 0.5218 0.584 0.056 0.020 0.212 0.128
#> GSM1009137 4 0.450 0.7978 0.040 0.004 0.000 0.724 0.232
#> GSM1009151 1 0.287 0.6210 0.872 0.024 0.004 0.100 0.000
#> GSM1009165 3 0.246 0.9837 0.008 0.064 0.904 0.000 0.024
#> GSM1009179 2 0.383 0.4485 0.104 0.824 0.060 0.012 0.000
#> GSM1009193 1 0.641 0.5688 0.632 0.048 0.012 0.224 0.084
#> GSM1009068 1 0.728 0.4431 0.572 0.040 0.036 0.140 0.212
#> GSM1009082 2 0.582 0.2713 0.016 0.672 0.084 0.016 0.212
#> GSM1009096 4 0.251 0.7955 0.116 0.008 0.000 0.876 0.000
#> GSM1009110 2 0.693 -0.9866 0.000 0.364 0.284 0.004 0.348
#> GSM1009124 1 0.820 0.4309 0.480 0.176 0.020 0.192 0.132
#> GSM1009138 4 0.450 0.7978 0.040 0.004 0.000 0.724 0.232
#> GSM1009152 1 0.287 0.6210 0.872 0.024 0.004 0.100 0.000
#> GSM1009166 3 0.236 0.9847 0.008 0.064 0.908 0.000 0.020
#> GSM1009180 2 0.383 0.4485 0.104 0.824 0.060 0.012 0.000
#> GSM1009194 1 0.646 0.5744 0.636 0.056 0.012 0.212 0.084
#> GSM1009069 1 0.737 0.4416 0.564 0.044 0.036 0.140 0.216
#> GSM1009083 2 0.586 0.2759 0.020 0.672 0.080 0.016 0.212
#> GSM1009097 4 0.251 0.7955 0.116 0.008 0.000 0.876 0.000
#> GSM1009111 2 0.679 -0.9896 0.000 0.364 0.284 0.000 0.352
#> GSM1009125 2 0.851 0.2689 0.188 0.484 0.080 0.080 0.168
#> GSM1009139 4 0.462 0.7977 0.040 0.004 0.004 0.724 0.228
#> GSM1009153 1 0.287 0.6210 0.872 0.024 0.004 0.100 0.000
#> GSM1009167 3 0.246 0.9837 0.008 0.064 0.904 0.000 0.024
#> GSM1009181 2 0.367 0.4491 0.092 0.836 0.060 0.012 0.000
#> GSM1009195 1 0.716 0.5643 0.604 0.140 0.020 0.152 0.084
#> GSM1009070 1 0.721 0.4426 0.576 0.036 0.036 0.140 0.212
#> GSM1009084 2 0.582 0.2713 0.016 0.672 0.084 0.016 0.212
#> GSM1009098 4 0.251 0.7955 0.116 0.008 0.000 0.876 0.000
#> GSM1009112 2 0.679 -1.0000 0.000 0.360 0.280 0.000 0.360
#> GSM1009126 1 0.820 0.4309 0.480 0.176 0.020 0.192 0.132
#> GSM1009140 4 0.450 0.7978 0.040 0.004 0.000 0.724 0.232
#> GSM1009154 1 0.287 0.6210 0.872 0.024 0.004 0.100 0.000
#> GSM1009168 3 0.236 0.9847 0.008 0.064 0.908 0.000 0.020
#> GSM1009182 2 0.378 0.4491 0.100 0.828 0.060 0.012 0.000
#> GSM1009196 1 0.636 0.5722 0.640 0.048 0.012 0.216 0.084
#> GSM1009071 1 0.731 0.4430 0.568 0.040 0.036 0.140 0.216
#> GSM1009085 2 0.582 0.2713 0.016 0.672 0.084 0.016 0.212
#> GSM1009099 4 0.251 0.7955 0.116 0.008 0.000 0.876 0.000
#> GSM1009113 2 0.679 -0.9896 0.000 0.364 0.284 0.000 0.352
#> GSM1009127 1 0.712 0.5218 0.584 0.056 0.020 0.212 0.128
#> GSM1009141 4 0.462 0.7977 0.040 0.004 0.004 0.724 0.228
#> GSM1009155 1 0.287 0.6210 0.872 0.024 0.004 0.100 0.000
#> GSM1009169 3 0.246 0.9837 0.008 0.064 0.904 0.000 0.024
#> GSM1009183 2 0.367 0.4491 0.092 0.836 0.060 0.012 0.000
#> GSM1009197 1 0.641 0.5688 0.632 0.048 0.012 0.224 0.084
#> GSM1009072 1 0.728 0.4431 0.572 0.040 0.036 0.140 0.212
#> GSM1009086 2 0.582 0.2713 0.016 0.672 0.084 0.016 0.212
#> GSM1009100 4 0.251 0.7955 0.116 0.008 0.000 0.876 0.000
#> GSM1009114 2 0.679 -1.0000 0.000 0.360 0.280 0.000 0.360
#> GSM1009128 1 0.890 0.3296 0.388 0.236 0.040 0.192 0.144
#> GSM1009142 4 0.462 0.7977 0.040 0.004 0.004 0.724 0.228
#> GSM1009156 1 0.306 0.6223 0.864 0.036 0.004 0.096 0.000
#> GSM1009170 3 0.208 0.9841 0.008 0.064 0.920 0.004 0.004
#> GSM1009184 2 0.367 0.4491 0.092 0.836 0.060 0.012 0.000
#> GSM1009198 1 0.641 0.5688 0.632 0.048 0.012 0.224 0.084
#> GSM1009073 1 0.731 0.4430 0.568 0.040 0.036 0.140 0.216
#> GSM1009087 2 0.619 0.3186 0.076 0.656 0.040 0.016 0.212
#> GSM1009101 4 0.251 0.7955 0.116 0.008 0.000 0.876 0.000
#> GSM1009115 5 0.679 1.0000 0.000 0.360 0.280 0.000 0.360
#> GSM1009129 2 0.842 0.2662 0.184 0.492 0.080 0.072 0.172
#> GSM1009143 4 0.450 0.7978 0.040 0.004 0.000 0.724 0.232
#> GSM1009157 1 0.358 0.6133 0.840 0.084 0.008 0.068 0.000
#> GSM1009171 3 0.176 0.9848 0.008 0.064 0.928 0.000 0.000
#> GSM1009185 2 0.423 0.4361 0.132 0.792 0.064 0.012 0.000
#> GSM1009199 1 0.708 0.5665 0.604 0.120 0.016 0.176 0.084
#> GSM1009074 1 0.728 0.4431 0.572 0.040 0.036 0.140 0.212
#> GSM1009088 2 0.619 0.3186 0.076 0.656 0.040 0.016 0.212
#> GSM1009102 4 0.251 0.7955 0.116 0.008 0.000 0.876 0.000
#> GSM1009116 5 0.679 1.0000 0.000 0.360 0.280 0.000 0.360
#> GSM1009130 2 0.855 0.2176 0.172 0.440 0.108 0.040 0.240
#> GSM1009144 4 0.462 0.7977 0.040 0.004 0.004 0.724 0.228
#> GSM1009158 1 0.297 0.6195 0.864 0.024 0.004 0.108 0.000
#> GSM1009172 3 0.208 0.9841 0.008 0.064 0.920 0.004 0.004
#> GSM1009186 2 0.367 0.4491 0.092 0.836 0.060 0.012 0.000
#> GSM1009200 1 0.639 0.5705 0.636 0.048 0.012 0.220 0.084
#> GSM1009075 1 0.728 0.4431 0.572 0.040 0.036 0.140 0.212
#> GSM1009089 2 0.785 0.2464 0.312 0.448 0.020 0.060 0.160
#> GSM1009103 4 0.251 0.7955 0.116 0.008 0.000 0.876 0.000
#> GSM1009117 2 0.679 -0.9946 0.000 0.360 0.284 0.000 0.356
#> GSM1009131 1 0.890 0.2922 0.388 0.256 0.044 0.168 0.144
#> GSM1009145 4 0.443 0.7989 0.052 0.000 0.000 0.732 0.216
#> GSM1009159 1 0.293 0.6175 0.864 0.020 0.004 0.112 0.000
#> GSM1009173 3 0.246 0.9837 0.008 0.064 0.904 0.000 0.024
#> GSM1009187 2 0.414 0.4401 0.124 0.800 0.064 0.012 0.000
#> GSM1009201 1 0.639 0.5705 0.636 0.048 0.012 0.220 0.084
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1009062 6 0.5054 0.992 0.224 0.008 0.000 0.104 0.004 0.660
#> GSM1009076 2 0.7653 0.423 0.008 0.388 0.100 0.024 0.336 0.144
#> GSM1009090 4 0.7116 0.723 0.208 0.016 0.008 0.508 0.192 0.068
#> GSM1009104 5 0.5704 0.916 0.004 0.136 0.336 0.000 0.520 0.004
#> GSM1009118 1 0.7639 0.107 0.440 0.328 0.024 0.056 0.068 0.084
#> GSM1009132 4 0.2237 0.725 0.068 0.000 0.000 0.896 0.000 0.036
#> GSM1009146 1 0.6159 0.445 0.620 0.060 0.004 0.036 0.056 0.224
#> GSM1009160 3 0.1481 0.969 0.008 0.012 0.952 0.016 0.004 0.008
#> GSM1009174 2 0.3405 0.571 0.068 0.832 0.084 0.000 0.000 0.016
#> GSM1009188 1 0.2067 0.661 0.912 0.016 0.004 0.064 0.004 0.000
#> GSM1009063 6 0.5054 0.992 0.224 0.008 0.000 0.104 0.004 0.660
#> GSM1009077 2 0.7653 0.423 0.008 0.388 0.100 0.024 0.336 0.144
#> GSM1009091 4 0.7116 0.723 0.208 0.016 0.008 0.508 0.192 0.068
#> GSM1009105 5 0.5573 0.918 0.004 0.136 0.336 0.000 0.524 0.000
#> GSM1009119 1 0.4922 0.590 0.764 0.068 0.004 0.060 0.036 0.068
#> GSM1009133 4 0.2237 0.725 0.068 0.000 0.000 0.896 0.000 0.036
#> GSM1009147 1 0.6159 0.445 0.620 0.060 0.004 0.036 0.056 0.224
#> GSM1009161 3 0.1481 0.969 0.008 0.012 0.952 0.016 0.004 0.008
#> GSM1009175 2 0.3405 0.571 0.068 0.832 0.084 0.000 0.000 0.016
#> GSM1009189 1 0.2067 0.661 0.912 0.016 0.004 0.064 0.004 0.000
#> GSM1009064 6 0.5474 0.990 0.224 0.008 0.004 0.104 0.016 0.644
#> GSM1009078 2 0.7869 0.417 0.064 0.384 0.032 0.032 0.344 0.144
#> GSM1009092 4 0.7116 0.723 0.208 0.016 0.008 0.508 0.192 0.068
#> GSM1009106 5 0.5573 0.918 0.004 0.136 0.336 0.000 0.524 0.000
#> GSM1009120 1 0.4750 0.599 0.776 0.064 0.004 0.052 0.036 0.068
#> GSM1009134 4 0.2237 0.725 0.068 0.000 0.000 0.896 0.000 0.036
#> GSM1009148 1 0.6159 0.445 0.620 0.060 0.004 0.036 0.056 0.224
#> GSM1009162 3 0.1777 0.970 0.008 0.020 0.940 0.008 0.008 0.016
#> GSM1009176 2 0.3405 0.571 0.068 0.832 0.084 0.000 0.000 0.016
#> GSM1009190 1 0.2067 0.661 0.912 0.016 0.004 0.064 0.004 0.000
#> GSM1009065 6 0.5474 0.990 0.224 0.008 0.004 0.104 0.016 0.644
#> GSM1009079 2 0.7653 0.423 0.008 0.388 0.100 0.024 0.336 0.144
#> GSM1009093 4 0.7116 0.723 0.208 0.016 0.008 0.508 0.192 0.068
#> GSM1009107 5 0.5573 0.918 0.004 0.136 0.336 0.000 0.524 0.000
#> GSM1009121 1 0.7315 0.297 0.516 0.264 0.020 0.056 0.064 0.080
#> GSM1009135 4 0.2237 0.725 0.068 0.000 0.000 0.896 0.000 0.036
#> GSM1009149 1 0.6106 0.446 0.624 0.056 0.004 0.036 0.056 0.224
#> GSM1009163 3 0.1337 0.968 0.008 0.012 0.956 0.016 0.000 0.008
#> GSM1009177 2 0.3405 0.571 0.068 0.832 0.084 0.000 0.000 0.016
#> GSM1009191 1 0.2154 0.660 0.908 0.020 0.004 0.064 0.004 0.000
#> GSM1009066 6 0.5474 0.990 0.224 0.008 0.004 0.104 0.016 0.644
#> GSM1009080 2 0.7653 0.423 0.008 0.388 0.100 0.024 0.336 0.144
#> GSM1009094 4 0.7116 0.723 0.208 0.016 0.008 0.508 0.192 0.068
#> GSM1009108 5 0.5573 0.918 0.004 0.136 0.336 0.000 0.524 0.000
#> GSM1009122 2 0.8303 0.175 0.328 0.380 0.072 0.044 0.092 0.084
#> GSM1009136 4 0.2342 0.730 0.088 0.004 0.000 0.888 0.000 0.020
#> GSM1009150 1 0.6106 0.446 0.624 0.056 0.004 0.036 0.056 0.224
#> GSM1009164 3 0.1337 0.968 0.008 0.012 0.956 0.016 0.000 0.008
#> GSM1009178 2 0.3459 0.571 0.072 0.832 0.080 0.004 0.000 0.012
#> GSM1009192 1 0.2067 0.661 0.912 0.016 0.004 0.064 0.004 0.000
#> GSM1009067 6 0.4972 0.986 0.228 0.004 0.000 0.104 0.004 0.660
#> GSM1009081 2 0.7653 0.423 0.008 0.388 0.100 0.024 0.336 0.144
#> GSM1009095 4 0.7229 0.720 0.208 0.020 0.008 0.508 0.176 0.080
#> GSM1009109 5 0.5573 0.918 0.004 0.136 0.336 0.000 0.524 0.000
#> GSM1009123 1 0.4922 0.590 0.764 0.068 0.004 0.060 0.036 0.068
#> GSM1009137 4 0.2237 0.725 0.068 0.000 0.000 0.896 0.000 0.036
#> GSM1009151 1 0.6159 0.445 0.620 0.060 0.004 0.036 0.056 0.224
#> GSM1009165 3 0.1664 0.970 0.008 0.020 0.944 0.008 0.004 0.016
#> GSM1009179 2 0.3459 0.571 0.072 0.832 0.080 0.004 0.000 0.012
#> GSM1009193 1 0.2067 0.661 0.912 0.016 0.004 0.064 0.004 0.000
#> GSM1009068 6 0.5054 0.992 0.224 0.008 0.000 0.104 0.004 0.660
#> GSM1009082 2 0.7653 0.423 0.008 0.388 0.100 0.024 0.336 0.144
#> GSM1009096 4 0.7116 0.723 0.208 0.016 0.008 0.508 0.192 0.068
#> GSM1009110 5 0.5935 0.911 0.004 0.136 0.336 0.004 0.512 0.008
#> GSM1009124 1 0.6136 0.525 0.648 0.172 0.004 0.060 0.040 0.076
#> GSM1009138 4 0.2237 0.725 0.068 0.000 0.000 0.896 0.000 0.036
#> GSM1009152 1 0.6159 0.445 0.620 0.060 0.004 0.036 0.056 0.224
#> GSM1009166 3 0.1777 0.970 0.008 0.020 0.940 0.008 0.008 0.016
#> GSM1009180 2 0.3459 0.571 0.072 0.832 0.080 0.004 0.000 0.012
#> GSM1009194 1 0.2154 0.660 0.908 0.020 0.004 0.064 0.004 0.000
#> GSM1009069 6 0.5526 0.985 0.224 0.012 0.004 0.100 0.016 0.644
#> GSM1009083 2 0.7653 0.423 0.008 0.388 0.100 0.024 0.336 0.144
#> GSM1009097 4 0.7116 0.723 0.208 0.016 0.008 0.508 0.192 0.068
#> GSM1009111 5 0.5573 0.918 0.004 0.136 0.336 0.000 0.524 0.000
#> GSM1009125 2 0.8398 0.197 0.316 0.380 0.084 0.044 0.092 0.084
#> GSM1009139 4 0.2237 0.725 0.068 0.000 0.000 0.896 0.000 0.036
#> GSM1009153 1 0.6159 0.445 0.620 0.060 0.004 0.036 0.056 0.224
#> GSM1009167 3 0.2051 0.966 0.008 0.020 0.928 0.008 0.012 0.024
#> GSM1009181 2 0.3405 0.571 0.068 0.832 0.084 0.000 0.000 0.016
#> GSM1009195 1 0.3078 0.631 0.852 0.080 0.004 0.060 0.004 0.000
#> GSM1009070 6 0.5054 0.992 0.224 0.008 0.000 0.104 0.004 0.660
#> GSM1009084 2 0.7653 0.423 0.008 0.388 0.100 0.024 0.336 0.144
#> GSM1009098 4 0.7116 0.723 0.208 0.016 0.008 0.508 0.192 0.068
#> GSM1009112 5 0.5704 0.916 0.004 0.136 0.336 0.000 0.520 0.004
#> GSM1009126 1 0.6136 0.525 0.648 0.172 0.004 0.060 0.040 0.076
#> GSM1009140 4 0.2237 0.725 0.068 0.000 0.000 0.896 0.000 0.036
#> GSM1009154 1 0.6159 0.445 0.620 0.060 0.004 0.036 0.056 0.224
#> GSM1009168 3 0.2051 0.966 0.008 0.020 0.928 0.008 0.012 0.024
#> GSM1009182 2 0.3455 0.571 0.068 0.832 0.084 0.004 0.000 0.012
#> GSM1009196 1 0.2154 0.660 0.908 0.020 0.004 0.064 0.004 0.000
#> GSM1009071 6 0.5474 0.990 0.224 0.008 0.004 0.104 0.016 0.644
#> GSM1009085 2 0.7653 0.423 0.008 0.388 0.100 0.024 0.336 0.144
#> GSM1009099 4 0.7116 0.723 0.208 0.016 0.008 0.508 0.192 0.068
#> GSM1009113 5 0.5573 0.918 0.004 0.136 0.336 0.000 0.524 0.000
#> GSM1009127 1 0.4922 0.590 0.764 0.068 0.004 0.060 0.036 0.068
#> GSM1009141 4 0.2237 0.725 0.068 0.000 0.000 0.896 0.000 0.036
#> GSM1009155 1 0.6159 0.445 0.620 0.060 0.004 0.036 0.056 0.224
#> GSM1009169 3 0.1860 0.968 0.008 0.020 0.936 0.016 0.004 0.016
#> GSM1009183 2 0.3405 0.571 0.068 0.832 0.084 0.000 0.000 0.016
#> GSM1009197 1 0.2067 0.661 0.912 0.016 0.004 0.064 0.004 0.000
#> GSM1009072 6 0.5054 0.992 0.224 0.008 0.000 0.104 0.004 0.660
#> GSM1009086 2 0.7653 0.423 0.008 0.388 0.100 0.024 0.336 0.144
#> GSM1009100 4 0.7116 0.723 0.208 0.016 0.008 0.508 0.192 0.068
#> GSM1009114 5 0.5935 0.912 0.004 0.136 0.336 0.004 0.512 0.008
#> GSM1009128 1 0.7145 0.366 0.552 0.232 0.024 0.048 0.064 0.080
#> GSM1009142 4 0.2237 0.725 0.068 0.000 0.000 0.896 0.000 0.036
#> GSM1009156 1 0.6136 0.450 0.624 0.060 0.004 0.036 0.056 0.220
#> GSM1009170 3 0.1337 0.968 0.008 0.012 0.956 0.016 0.000 0.008
#> GSM1009184 2 0.3405 0.571 0.068 0.832 0.084 0.000 0.000 0.016
#> GSM1009198 1 0.2067 0.661 0.912 0.016 0.004 0.064 0.004 0.000
#> GSM1009073 6 0.5474 0.990 0.224 0.008 0.004 0.104 0.016 0.644
#> GSM1009087 2 0.7869 0.417 0.064 0.384 0.032 0.032 0.344 0.144
#> GSM1009101 4 0.7116 0.723 0.208 0.016 0.008 0.508 0.192 0.068
#> GSM1009115 5 0.5704 0.916 0.004 0.136 0.336 0.000 0.520 0.004
#> GSM1009129 2 0.8413 0.231 0.308 0.380 0.088 0.036 0.104 0.084
#> GSM1009143 4 0.2380 0.725 0.068 0.004 0.000 0.892 0.000 0.036
#> GSM1009157 1 0.6423 0.417 0.600 0.084 0.004 0.036 0.056 0.220
#> GSM1009171 3 0.0912 0.972 0.008 0.012 0.972 0.004 0.004 0.000
#> GSM1009185 2 0.3651 0.553 0.100 0.816 0.068 0.004 0.000 0.012
#> GSM1009199 1 0.2970 0.636 0.860 0.072 0.004 0.060 0.004 0.000
#> GSM1009074 6 0.5054 0.992 0.224 0.008 0.000 0.104 0.004 0.660
#> GSM1009088 2 0.7869 0.417 0.064 0.384 0.032 0.032 0.344 0.144
#> GSM1009102 4 0.7229 0.720 0.208 0.020 0.008 0.508 0.176 0.080
#> GSM1009116 5 0.5573 0.918 0.004 0.136 0.336 0.000 0.524 0.000
#> GSM1009130 2 0.8766 0.251 0.288 0.328 0.108 0.024 0.152 0.100
#> GSM1009144 4 0.2380 0.725 0.068 0.004 0.000 0.892 0.000 0.036
#> GSM1009158 1 0.6106 0.446 0.624 0.056 0.004 0.036 0.056 0.224
#> GSM1009172 3 0.1481 0.969 0.008 0.012 0.952 0.016 0.004 0.008
#> GSM1009186 2 0.3405 0.571 0.068 0.832 0.084 0.000 0.000 0.016
#> GSM1009200 1 0.2067 0.661 0.912 0.016 0.004 0.064 0.004 0.000
#> GSM1009075 6 0.5054 0.992 0.224 0.008 0.000 0.104 0.004 0.660
#> GSM1009089 5 0.8464 -0.351 0.168 0.272 0.020 0.032 0.328 0.180
#> GSM1009103 4 0.7229 0.720 0.208 0.020 0.008 0.508 0.176 0.080
#> GSM1009117 5 0.5935 0.912 0.004 0.136 0.336 0.004 0.512 0.008
#> GSM1009131 1 0.7173 0.366 0.552 0.228 0.024 0.052 0.060 0.084
#> GSM1009145 4 0.2342 0.730 0.088 0.004 0.000 0.888 0.000 0.020
#> GSM1009159 1 0.6106 0.446 0.624 0.056 0.004 0.036 0.056 0.224
#> GSM1009173 3 0.1715 0.970 0.008 0.020 0.940 0.016 0.000 0.016
#> GSM1009187 2 0.3495 0.566 0.080 0.832 0.068 0.004 0.000 0.016
#> GSM1009201 1 0.2067 0.661 0.912 0.016 0.004 0.064 0.004 0.000
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n temperature(p) time(p) specimen(p) k
#> MAD:kmeans 138 0.982 0.990 7.63e-22 2
#> MAD:kmeans 72 0.981 0.997 3.93e-14 3
#> MAD:kmeans 105 1.000 1.000 3.14e-52 4
#> MAD:kmeans 77 0.969 0.994 5.82e-39 5
#> MAD:kmeans 104 1.000 1.000 1.06e-87 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 140 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.962 0.986 0.5013 0.499 0.499
#> 3 3 0.860 0.905 0.937 0.2981 0.819 0.648
#> 4 4 0.680 0.816 0.851 0.1341 0.885 0.679
#> 5 5 0.758 0.696 0.777 0.0640 0.937 0.761
#> 6 6 0.858 0.739 0.767 0.0449 0.892 0.558
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
#> GSM1009062 1 0.0000 0.9871 1.000 0.000
#> GSM1009076 2 0.0000 0.9832 0.000 1.000
#> GSM1009090 1 0.0000 0.9871 1.000 0.000
#> GSM1009104 2 0.0000 0.9832 0.000 1.000
#> GSM1009118 2 0.7219 0.7430 0.200 0.800
#> GSM1009132 1 0.0000 0.9871 1.000 0.000
#> GSM1009146 1 0.0000 0.9871 1.000 0.000
#> GSM1009160 2 0.0000 0.9832 0.000 1.000
#> GSM1009174 2 0.0000 0.9832 0.000 1.000
#> GSM1009188 1 0.0000 0.9871 1.000 0.000
#> GSM1009063 1 0.0000 0.9871 1.000 0.000
#> GSM1009077 2 0.0000 0.9832 0.000 1.000
#> GSM1009091 1 0.0000 0.9871 1.000 0.000
#> GSM1009105 2 0.0000 0.9832 0.000 1.000
#> GSM1009119 1 0.0000 0.9871 1.000 0.000
#> GSM1009133 1 0.0000 0.9871 1.000 0.000
#> GSM1009147 1 0.0000 0.9871 1.000 0.000
#> GSM1009161 2 0.0000 0.9832 0.000 1.000
#> GSM1009175 2 0.0000 0.9832 0.000 1.000
#> GSM1009189 1 0.0000 0.9871 1.000 0.000
#> GSM1009064 1 0.0000 0.9871 1.000 0.000
#> GSM1009078 2 0.0000 0.9832 0.000 1.000
#> GSM1009092 1 0.0000 0.9871 1.000 0.000
#> GSM1009106 2 0.0000 0.9832 0.000 1.000
#> GSM1009120 1 0.0000 0.9871 1.000 0.000
#> GSM1009134 1 0.0000 0.9871 1.000 0.000
#> GSM1009148 1 0.0000 0.9871 1.000 0.000
#> GSM1009162 2 0.0000 0.9832 0.000 1.000
#> GSM1009176 2 0.0000 0.9832 0.000 1.000
#> GSM1009190 1 0.0000 0.9871 1.000 0.000
#> GSM1009065 1 0.0000 0.9871 1.000 0.000
#> GSM1009079 2 0.0000 0.9832 0.000 1.000
#> GSM1009093 1 0.0000 0.9871 1.000 0.000
#> GSM1009107 2 0.0000 0.9832 0.000 1.000
#> GSM1009121 2 0.0000 0.9832 0.000 1.000
#> GSM1009135 1 0.0000 0.9871 1.000 0.000
#> GSM1009149 1 0.0000 0.9871 1.000 0.000
#> GSM1009163 2 0.0000 0.9832 0.000 1.000
#> GSM1009177 2 0.0000 0.9832 0.000 1.000
#> GSM1009191 1 0.0000 0.9871 1.000 0.000
#> GSM1009066 1 0.0000 0.9871 1.000 0.000
#> GSM1009080 2 0.0000 0.9832 0.000 1.000
#> GSM1009094 1 0.0000 0.9871 1.000 0.000
#> GSM1009108 2 0.0000 0.9832 0.000 1.000
#> GSM1009122 2 0.0000 0.9832 0.000 1.000
#> GSM1009136 1 0.0000 0.9871 1.000 0.000
#> GSM1009150 1 0.0000 0.9871 1.000 0.000
#> GSM1009164 2 0.0000 0.9832 0.000 1.000
#> GSM1009178 2 0.0000 0.9832 0.000 1.000
#> GSM1009192 1 0.0000 0.9871 1.000 0.000
#> GSM1009067 1 0.0000 0.9871 1.000 0.000
#> GSM1009081 2 0.0000 0.9832 0.000 1.000
#> GSM1009095 1 0.0000 0.9871 1.000 0.000
#> GSM1009109 2 0.0000 0.9832 0.000 1.000
#> GSM1009123 1 0.0000 0.9871 1.000 0.000
#> GSM1009137 1 0.0000 0.9871 1.000 0.000
#> GSM1009151 1 0.0000 0.9871 1.000 0.000
#> GSM1009165 2 0.0000 0.9832 0.000 1.000
#> GSM1009179 2 0.0000 0.9832 0.000 1.000
#> GSM1009193 1 0.0000 0.9871 1.000 0.000
#> GSM1009068 1 0.0000 0.9871 1.000 0.000
#> GSM1009082 2 0.0000 0.9832 0.000 1.000
#> GSM1009096 1 0.0000 0.9871 1.000 0.000
#> GSM1009110 2 0.0000 0.9832 0.000 1.000
#> GSM1009124 1 0.0000 0.9871 1.000 0.000
#> GSM1009138 1 0.0000 0.9871 1.000 0.000
#> GSM1009152 1 0.0000 0.9871 1.000 0.000
#> GSM1009166 2 0.0000 0.9832 0.000 1.000
#> GSM1009180 2 0.0000 0.9832 0.000 1.000
#> GSM1009194 1 0.0000 0.9871 1.000 0.000
#> GSM1009069 1 0.0376 0.9833 0.996 0.004
#> GSM1009083 2 0.0000 0.9832 0.000 1.000
#> GSM1009097 1 0.0000 0.9871 1.000 0.000
#> GSM1009111 2 0.0000 0.9832 0.000 1.000
#> GSM1009125 2 0.0000 0.9832 0.000 1.000
#> GSM1009139 1 0.0000 0.9871 1.000 0.000
#> GSM1009153 1 0.0000 0.9871 1.000 0.000
#> GSM1009167 2 0.0000 0.9832 0.000 1.000
#> GSM1009181 2 0.0000 0.9832 0.000 1.000
#> GSM1009195 2 0.9954 0.1517 0.460 0.540
#> GSM1009070 1 0.0000 0.9871 1.000 0.000
#> GSM1009084 2 0.0000 0.9832 0.000 1.000
#> GSM1009098 1 0.0000 0.9871 1.000 0.000
#> GSM1009112 2 0.0000 0.9832 0.000 1.000
#> GSM1009126 1 0.0000 0.9871 1.000 0.000
#> GSM1009140 1 0.0000 0.9871 1.000 0.000
#> GSM1009154 1 0.0000 0.9871 1.000 0.000
#> GSM1009168 2 0.0000 0.9832 0.000 1.000
#> GSM1009182 2 0.0000 0.9832 0.000 1.000
#> GSM1009196 1 0.0000 0.9871 1.000 0.000
#> GSM1009071 1 0.0000 0.9871 1.000 0.000
#> GSM1009085 2 0.0000 0.9832 0.000 1.000
#> GSM1009099 1 0.0000 0.9871 1.000 0.000
#> GSM1009113 2 0.0000 0.9832 0.000 1.000
#> GSM1009127 1 0.0000 0.9871 1.000 0.000
#> GSM1009141 1 0.0000 0.9871 1.000 0.000
#> GSM1009155 1 0.0000 0.9871 1.000 0.000
#> GSM1009169 2 0.0000 0.9832 0.000 1.000
#> GSM1009183 2 0.0000 0.9832 0.000 1.000
#> GSM1009197 1 0.0000 0.9871 1.000 0.000
#> GSM1009072 1 0.0000 0.9871 1.000 0.000
#> GSM1009086 2 0.0000 0.9832 0.000 1.000
#> GSM1009100 1 0.0000 0.9871 1.000 0.000
#> GSM1009114 2 0.0000 0.9832 0.000 1.000
#> GSM1009128 2 0.0000 0.9832 0.000 1.000
#> GSM1009142 1 0.0000 0.9871 1.000 0.000
#> GSM1009156 1 0.4431 0.8888 0.908 0.092
#> GSM1009170 2 0.0000 0.9832 0.000 1.000
#> GSM1009184 2 0.0000 0.9832 0.000 1.000
#> GSM1009198 1 0.0000 0.9871 1.000 0.000
#> GSM1009073 1 0.0000 0.9871 1.000 0.000
#> GSM1009087 2 0.0000 0.9832 0.000 1.000
#> GSM1009101 1 0.0000 0.9871 1.000 0.000
#> GSM1009115 2 0.0000 0.9832 0.000 1.000
#> GSM1009129 2 0.0000 0.9832 0.000 1.000
#> GSM1009143 1 0.0000 0.9871 1.000 0.000
#> GSM1009157 2 0.9661 0.3552 0.392 0.608
#> GSM1009171 2 0.0000 0.9832 0.000 1.000
#> GSM1009185 2 0.0000 0.9832 0.000 1.000
#> GSM1009199 1 0.9393 0.4307 0.644 0.356
#> GSM1009074 1 0.0000 0.9871 1.000 0.000
#> GSM1009088 2 0.0000 0.9832 0.000 1.000
#> GSM1009102 1 0.0000 0.9871 1.000 0.000
#> GSM1009116 2 0.0000 0.9832 0.000 1.000
#> GSM1009130 2 0.0000 0.9832 0.000 1.000
#> GSM1009144 1 0.0000 0.9871 1.000 0.000
#> GSM1009158 1 0.0000 0.9871 1.000 0.000
#> GSM1009172 2 0.0000 0.9832 0.000 1.000
#> GSM1009186 2 0.0000 0.9832 0.000 1.000
#> GSM1009200 1 0.0000 0.9871 1.000 0.000
#> GSM1009075 1 0.0000 0.9871 1.000 0.000
#> GSM1009089 1 0.9977 0.0977 0.528 0.472
#> GSM1009103 1 0.0000 0.9871 1.000 0.000
#> GSM1009117 2 0.0000 0.9832 0.000 1.000
#> GSM1009131 2 0.0000 0.9832 0.000 1.000
#> GSM1009145 1 0.0000 0.9871 1.000 0.000
#> GSM1009159 1 0.0000 0.9871 1.000 0.000
#> GSM1009173 2 0.0000 0.9832 0.000 1.000
#> GSM1009187 2 0.0000 0.9832 0.000 1.000
#> GSM1009201 1 0.0000 0.9871 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1009062 1 0.2066 0.889 0.940 0.000 0.060
#> GSM1009076 2 0.1163 0.964 0.000 0.972 0.028
#> GSM1009090 3 0.1529 0.962 0.040 0.000 0.960
#> GSM1009104 2 0.0000 0.967 0.000 1.000 0.000
#> GSM1009118 2 0.7285 0.442 0.048 0.632 0.320
#> GSM1009132 3 0.1289 0.960 0.032 0.000 0.968
#> GSM1009146 1 0.0592 0.893 0.988 0.000 0.012
#> GSM1009160 2 0.0000 0.967 0.000 1.000 0.000
#> GSM1009174 2 0.1711 0.961 0.008 0.960 0.032
#> GSM1009188 1 0.3941 0.834 0.844 0.000 0.156
#> GSM1009063 1 0.2066 0.889 0.940 0.000 0.060
#> GSM1009077 2 0.1163 0.964 0.000 0.972 0.028
#> GSM1009091 3 0.1529 0.962 0.040 0.000 0.960
#> GSM1009105 2 0.0000 0.967 0.000 1.000 0.000
#> GSM1009119 1 0.6252 0.262 0.556 0.000 0.444
#> GSM1009133 3 0.1289 0.960 0.032 0.000 0.968
#> GSM1009147 1 0.0592 0.893 0.988 0.000 0.012
#> GSM1009161 2 0.0000 0.967 0.000 1.000 0.000
#> GSM1009175 2 0.1711 0.961 0.008 0.960 0.032
#> GSM1009189 1 0.3941 0.834 0.844 0.000 0.156
#> GSM1009064 1 0.2066 0.889 0.940 0.000 0.060
#> GSM1009078 2 0.5178 0.808 0.164 0.808 0.028
#> GSM1009092 3 0.1529 0.962 0.040 0.000 0.960
#> GSM1009106 2 0.0000 0.967 0.000 1.000 0.000
#> GSM1009120 1 0.3941 0.835 0.844 0.000 0.156
#> GSM1009134 3 0.1289 0.960 0.032 0.000 0.968
#> GSM1009148 1 0.0592 0.893 0.988 0.000 0.012
#> GSM1009162 2 0.0000 0.967 0.000 1.000 0.000
#> GSM1009176 2 0.1711 0.961 0.008 0.960 0.032
#> GSM1009190 1 0.3941 0.834 0.844 0.000 0.156
#> GSM1009065 1 0.2066 0.889 0.940 0.000 0.060
#> GSM1009079 2 0.1163 0.964 0.000 0.972 0.028
#> GSM1009093 3 0.1529 0.962 0.040 0.000 0.960
#> GSM1009107 2 0.0000 0.967 0.000 1.000 0.000
#> GSM1009121 3 0.7337 0.222 0.032 0.428 0.540
#> GSM1009135 3 0.1289 0.960 0.032 0.000 0.968
#> GSM1009149 1 0.0592 0.893 0.988 0.000 0.012
#> GSM1009163 2 0.0000 0.967 0.000 1.000 0.000
#> GSM1009177 2 0.1711 0.961 0.008 0.960 0.032
#> GSM1009191 1 0.3941 0.834 0.844 0.000 0.156
#> GSM1009066 1 0.2066 0.889 0.940 0.000 0.060
#> GSM1009080 2 0.1163 0.964 0.000 0.972 0.028
#> GSM1009094 3 0.1529 0.962 0.040 0.000 0.960
#> GSM1009108 2 0.0000 0.967 0.000 1.000 0.000
#> GSM1009122 2 0.2846 0.912 0.020 0.924 0.056
#> GSM1009136 3 0.1529 0.962 0.040 0.000 0.960
#> GSM1009150 1 0.0592 0.893 0.988 0.000 0.012
#> GSM1009164 2 0.0000 0.967 0.000 1.000 0.000
#> GSM1009178 2 0.1711 0.961 0.008 0.960 0.032
#> GSM1009192 1 0.3941 0.834 0.844 0.000 0.156
#> GSM1009067 1 0.2066 0.889 0.940 0.000 0.060
#> GSM1009081 2 0.1163 0.964 0.000 0.972 0.028
#> GSM1009095 3 0.1529 0.962 0.040 0.000 0.960
#> GSM1009109 2 0.0000 0.967 0.000 1.000 0.000
#> GSM1009123 3 0.3412 0.889 0.124 0.000 0.876
#> GSM1009137 3 0.1289 0.960 0.032 0.000 0.968
#> GSM1009151 1 0.0592 0.893 0.988 0.000 0.012
#> GSM1009165 2 0.0000 0.967 0.000 1.000 0.000
#> GSM1009179 2 0.1711 0.961 0.008 0.960 0.032
#> GSM1009193 1 0.3941 0.834 0.844 0.000 0.156
#> GSM1009068 1 0.2066 0.889 0.940 0.000 0.060
#> GSM1009082 2 0.1163 0.964 0.000 0.972 0.028
#> GSM1009096 3 0.1529 0.962 0.040 0.000 0.960
#> GSM1009110 2 0.0000 0.967 0.000 1.000 0.000
#> GSM1009124 3 0.3340 0.889 0.120 0.000 0.880
#> GSM1009138 3 0.1289 0.960 0.032 0.000 0.968
#> GSM1009152 1 0.0592 0.893 0.988 0.000 0.012
#> GSM1009166 2 0.0000 0.967 0.000 1.000 0.000
#> GSM1009180 2 0.1711 0.961 0.008 0.960 0.032
#> GSM1009194 1 0.3941 0.834 0.844 0.000 0.156
#> GSM1009069 1 0.2711 0.875 0.912 0.000 0.088
#> GSM1009083 2 0.1163 0.964 0.000 0.972 0.028
#> GSM1009097 3 0.1529 0.962 0.040 0.000 0.960
#> GSM1009111 2 0.0000 0.967 0.000 1.000 0.000
#> GSM1009125 2 0.2550 0.918 0.012 0.932 0.056
#> GSM1009139 3 0.1289 0.960 0.032 0.000 0.968
#> GSM1009153 1 0.0592 0.893 0.988 0.000 0.012
#> GSM1009167 2 0.0000 0.967 0.000 1.000 0.000
#> GSM1009181 2 0.1711 0.961 0.008 0.960 0.032
#> GSM1009195 1 0.5585 0.804 0.812 0.092 0.096
#> GSM1009070 1 0.2066 0.889 0.940 0.000 0.060
#> GSM1009084 2 0.1163 0.964 0.000 0.972 0.028
#> GSM1009098 3 0.1529 0.962 0.040 0.000 0.960
#> GSM1009112 2 0.0000 0.967 0.000 1.000 0.000
#> GSM1009126 3 0.3340 0.889 0.120 0.000 0.880
#> GSM1009140 3 0.1289 0.960 0.032 0.000 0.968
#> GSM1009154 1 0.0592 0.893 0.988 0.000 0.012
#> GSM1009168 2 0.0000 0.967 0.000 1.000 0.000
#> GSM1009182 2 0.1711 0.961 0.008 0.960 0.032
#> GSM1009196 1 0.3482 0.850 0.872 0.000 0.128
#> GSM1009071 1 0.2066 0.889 0.940 0.000 0.060
#> GSM1009085 2 0.1163 0.964 0.000 0.972 0.028
#> GSM1009099 3 0.1529 0.962 0.040 0.000 0.960
#> GSM1009113 2 0.0000 0.967 0.000 1.000 0.000
#> GSM1009127 1 0.6095 0.419 0.608 0.000 0.392
#> GSM1009141 3 0.1289 0.960 0.032 0.000 0.968
#> GSM1009155 1 0.0592 0.893 0.988 0.000 0.012
#> GSM1009169 2 0.0000 0.967 0.000 1.000 0.000
#> GSM1009183 2 0.1711 0.961 0.008 0.960 0.032
#> GSM1009197 1 0.3941 0.834 0.844 0.000 0.156
#> GSM1009072 1 0.2066 0.889 0.940 0.000 0.060
#> GSM1009086 2 0.1163 0.964 0.000 0.972 0.028
#> GSM1009100 3 0.1529 0.962 0.040 0.000 0.960
#> GSM1009114 2 0.0000 0.967 0.000 1.000 0.000
#> GSM1009128 3 0.5955 0.727 0.048 0.180 0.772
#> GSM1009142 3 0.1289 0.960 0.032 0.000 0.968
#> GSM1009156 1 0.1267 0.884 0.972 0.004 0.024
#> GSM1009170 2 0.0000 0.967 0.000 1.000 0.000
#> GSM1009184 2 0.1711 0.961 0.008 0.960 0.032
#> GSM1009198 1 0.3941 0.834 0.844 0.000 0.156
#> GSM1009073 1 0.2066 0.889 0.940 0.000 0.060
#> GSM1009087 2 0.5236 0.804 0.168 0.804 0.028
#> GSM1009101 3 0.1529 0.962 0.040 0.000 0.960
#> GSM1009115 2 0.0000 0.967 0.000 1.000 0.000
#> GSM1009129 2 0.0829 0.961 0.012 0.984 0.004
#> GSM1009143 3 0.1289 0.960 0.032 0.000 0.968
#> GSM1009157 1 0.1878 0.873 0.952 0.004 0.044
#> GSM1009171 2 0.0000 0.967 0.000 1.000 0.000
#> GSM1009185 2 0.2176 0.955 0.020 0.948 0.032
#> GSM1009199 1 0.5267 0.826 0.816 0.044 0.140
#> GSM1009074 1 0.2066 0.889 0.940 0.000 0.060
#> GSM1009088 2 0.5178 0.808 0.164 0.808 0.028
#> GSM1009102 3 0.1529 0.962 0.040 0.000 0.960
#> GSM1009116 2 0.0000 0.967 0.000 1.000 0.000
#> GSM1009130 2 0.0592 0.961 0.012 0.988 0.000
#> GSM1009144 3 0.1289 0.960 0.032 0.000 0.968
#> GSM1009158 1 0.0592 0.893 0.988 0.000 0.012
#> GSM1009172 2 0.0000 0.967 0.000 1.000 0.000
#> GSM1009186 2 0.1711 0.961 0.008 0.960 0.032
#> GSM1009200 1 0.3941 0.834 0.844 0.000 0.156
#> GSM1009075 1 0.2066 0.889 0.940 0.000 0.060
#> GSM1009089 1 0.6337 0.591 0.708 0.264 0.028
#> GSM1009103 3 0.1529 0.962 0.040 0.000 0.960
#> GSM1009117 2 0.0000 0.967 0.000 1.000 0.000
#> GSM1009131 2 0.5798 0.727 0.040 0.776 0.184
#> GSM1009145 3 0.1529 0.962 0.040 0.000 0.960
#> GSM1009159 1 0.0592 0.893 0.988 0.000 0.012
#> GSM1009173 2 0.0000 0.967 0.000 1.000 0.000
#> GSM1009187 2 0.1711 0.961 0.008 0.960 0.032
#> GSM1009201 1 0.3941 0.834 0.844 0.000 0.156
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1009062 1 0.4552 0.768 0.800 0.072 0.000 0.128
#> GSM1009076 2 0.3764 0.805 0.000 0.784 0.216 0.000
#> GSM1009090 4 0.1302 0.932 0.044 0.000 0.000 0.956
#> GSM1009104 3 0.3942 0.832 0.000 0.236 0.764 0.000
#> GSM1009118 3 0.3977 0.612 0.052 0.084 0.852 0.012
#> GSM1009132 4 0.0000 0.926 0.000 0.000 0.000 1.000
#> GSM1009146 1 0.0188 0.823 0.996 0.004 0.000 0.000
#> GSM1009160 3 0.3172 0.865 0.000 0.160 0.840 0.000
#> GSM1009174 2 0.1940 0.850 0.000 0.924 0.076 0.000
#> GSM1009188 1 0.5710 0.745 0.728 0.004 0.140 0.128
#> GSM1009063 1 0.4552 0.768 0.800 0.072 0.000 0.128
#> GSM1009077 2 0.3764 0.805 0.000 0.784 0.216 0.000
#> GSM1009091 4 0.1302 0.932 0.044 0.000 0.000 0.956
#> GSM1009105 3 0.3942 0.832 0.000 0.236 0.764 0.000
#> GSM1009119 1 0.5630 0.739 0.724 0.000 0.140 0.136
#> GSM1009133 4 0.0000 0.926 0.000 0.000 0.000 1.000
#> GSM1009147 1 0.0188 0.823 0.996 0.004 0.000 0.000
#> GSM1009161 3 0.3172 0.865 0.000 0.160 0.840 0.000
#> GSM1009175 2 0.1940 0.850 0.000 0.924 0.076 0.000
#> GSM1009189 1 0.5710 0.745 0.728 0.004 0.140 0.128
#> GSM1009064 1 0.4552 0.768 0.800 0.072 0.000 0.128
#> GSM1009078 2 0.5307 0.753 0.076 0.736 0.188 0.000
#> GSM1009092 4 0.1302 0.932 0.044 0.000 0.000 0.956
#> GSM1009106 3 0.3942 0.832 0.000 0.236 0.764 0.000
#> GSM1009120 1 0.5321 0.755 0.748 0.000 0.140 0.112
#> GSM1009134 4 0.0000 0.926 0.000 0.000 0.000 1.000
#> GSM1009148 1 0.0188 0.823 0.996 0.004 0.000 0.000
#> GSM1009162 3 0.3172 0.865 0.000 0.160 0.840 0.000
#> GSM1009176 2 0.1940 0.850 0.000 0.924 0.076 0.000
#> GSM1009190 1 0.5710 0.745 0.728 0.004 0.140 0.128
#> GSM1009065 1 0.4552 0.768 0.800 0.072 0.000 0.128
#> GSM1009079 2 0.3764 0.805 0.000 0.784 0.216 0.000
#> GSM1009093 4 0.1302 0.932 0.044 0.000 0.000 0.956
#> GSM1009107 3 0.3942 0.832 0.000 0.236 0.764 0.000
#> GSM1009121 3 0.3105 0.656 0.020 0.084 0.888 0.008
#> GSM1009135 4 0.0000 0.926 0.000 0.000 0.000 1.000
#> GSM1009149 1 0.0188 0.823 0.996 0.004 0.000 0.000
#> GSM1009163 3 0.3172 0.865 0.000 0.160 0.840 0.000
#> GSM1009177 2 0.1940 0.850 0.000 0.924 0.076 0.000
#> GSM1009191 1 0.5710 0.745 0.728 0.004 0.140 0.128
#> GSM1009066 1 0.4552 0.768 0.800 0.072 0.000 0.128
#> GSM1009080 2 0.3764 0.805 0.000 0.784 0.216 0.000
#> GSM1009094 4 0.1302 0.932 0.044 0.000 0.000 0.956
#> GSM1009108 3 0.3942 0.832 0.000 0.236 0.764 0.000
#> GSM1009122 3 0.3306 0.761 0.004 0.156 0.840 0.000
#> GSM1009136 4 0.0921 0.931 0.028 0.000 0.000 0.972
#> GSM1009150 1 0.0188 0.823 0.996 0.004 0.000 0.000
#> GSM1009164 3 0.3172 0.865 0.000 0.160 0.840 0.000
#> GSM1009178 2 0.1940 0.850 0.000 0.924 0.076 0.000
#> GSM1009192 1 0.5710 0.745 0.728 0.004 0.140 0.128
#> GSM1009067 1 0.4552 0.768 0.800 0.072 0.000 0.128
#> GSM1009081 2 0.3764 0.805 0.000 0.784 0.216 0.000
#> GSM1009095 4 0.1302 0.932 0.044 0.000 0.000 0.956
#> GSM1009109 3 0.3942 0.832 0.000 0.236 0.764 0.000
#> GSM1009123 4 0.6792 0.436 0.272 0.000 0.140 0.588
#> GSM1009137 4 0.0000 0.926 0.000 0.000 0.000 1.000
#> GSM1009151 1 0.0188 0.823 0.996 0.004 0.000 0.000
#> GSM1009165 3 0.3172 0.865 0.000 0.160 0.840 0.000
#> GSM1009179 2 0.1940 0.850 0.000 0.924 0.076 0.000
#> GSM1009193 1 0.5710 0.745 0.728 0.004 0.140 0.128
#> GSM1009068 1 0.4552 0.768 0.800 0.072 0.000 0.128
#> GSM1009082 2 0.3764 0.805 0.000 0.784 0.216 0.000
#> GSM1009096 4 0.1302 0.932 0.044 0.000 0.000 0.956
#> GSM1009110 3 0.3942 0.832 0.000 0.236 0.764 0.000
#> GSM1009124 4 0.8796 0.383 0.204 0.080 0.236 0.480
#> GSM1009138 4 0.0000 0.926 0.000 0.000 0.000 1.000
#> GSM1009152 1 0.0188 0.823 0.996 0.004 0.000 0.000
#> GSM1009166 3 0.3172 0.865 0.000 0.160 0.840 0.000
#> GSM1009180 2 0.1940 0.850 0.000 0.924 0.076 0.000
#> GSM1009194 1 0.5710 0.745 0.728 0.004 0.140 0.128
#> GSM1009069 1 0.4552 0.768 0.800 0.072 0.000 0.128
#> GSM1009083 2 0.3764 0.805 0.000 0.784 0.216 0.000
#> GSM1009097 4 0.1302 0.932 0.044 0.000 0.000 0.956
#> GSM1009111 3 0.3942 0.832 0.000 0.236 0.764 0.000
#> GSM1009125 3 0.3306 0.761 0.004 0.156 0.840 0.000
#> GSM1009139 4 0.0000 0.926 0.000 0.000 0.000 1.000
#> GSM1009153 1 0.0188 0.823 0.996 0.004 0.000 0.000
#> GSM1009167 3 0.3172 0.865 0.000 0.160 0.840 0.000
#> GSM1009181 2 0.1940 0.850 0.000 0.924 0.076 0.000
#> GSM1009195 1 0.6665 0.738 0.700 0.072 0.148 0.080
#> GSM1009070 1 0.4482 0.769 0.804 0.068 0.000 0.128
#> GSM1009084 2 0.3764 0.805 0.000 0.784 0.216 0.000
#> GSM1009098 4 0.1302 0.932 0.044 0.000 0.000 0.956
#> GSM1009112 3 0.3942 0.832 0.000 0.236 0.764 0.000
#> GSM1009126 4 0.8796 0.383 0.204 0.080 0.236 0.480
#> GSM1009140 4 0.0000 0.926 0.000 0.000 0.000 1.000
#> GSM1009154 1 0.0188 0.823 0.996 0.004 0.000 0.000
#> GSM1009168 3 0.3172 0.865 0.000 0.160 0.840 0.000
#> GSM1009182 2 0.1940 0.850 0.000 0.924 0.076 0.000
#> GSM1009196 1 0.5608 0.750 0.736 0.004 0.140 0.120
#> GSM1009071 1 0.4552 0.768 0.800 0.072 0.000 0.128
#> GSM1009085 2 0.3764 0.805 0.000 0.784 0.216 0.000
#> GSM1009099 4 0.1302 0.932 0.044 0.000 0.000 0.956
#> GSM1009113 3 0.3942 0.832 0.000 0.236 0.764 0.000
#> GSM1009127 1 0.5630 0.739 0.724 0.000 0.140 0.136
#> GSM1009141 4 0.0000 0.926 0.000 0.000 0.000 1.000
#> GSM1009155 1 0.0188 0.823 0.996 0.004 0.000 0.000
#> GSM1009169 3 0.3172 0.865 0.000 0.160 0.840 0.000
#> GSM1009183 2 0.1940 0.850 0.000 0.924 0.076 0.000
#> GSM1009197 1 0.5710 0.745 0.728 0.004 0.140 0.128
#> GSM1009072 1 0.4552 0.768 0.800 0.072 0.000 0.128
#> GSM1009086 2 0.3764 0.805 0.000 0.784 0.216 0.000
#> GSM1009100 4 0.1302 0.932 0.044 0.000 0.000 0.956
#> GSM1009114 3 0.3942 0.832 0.000 0.236 0.764 0.000
#> GSM1009128 3 0.4585 0.582 0.020 0.080 0.824 0.076
#> GSM1009142 4 0.0000 0.926 0.000 0.000 0.000 1.000
#> GSM1009156 1 0.0188 0.823 0.996 0.004 0.000 0.000
#> GSM1009170 3 0.3172 0.865 0.000 0.160 0.840 0.000
#> GSM1009184 2 0.1940 0.850 0.000 0.924 0.076 0.000
#> GSM1009198 1 0.5710 0.745 0.728 0.004 0.140 0.128
#> GSM1009073 1 0.4552 0.768 0.800 0.072 0.000 0.128
#> GSM1009087 2 0.5307 0.753 0.076 0.736 0.188 0.000
#> GSM1009101 4 0.1302 0.932 0.044 0.000 0.000 0.956
#> GSM1009115 3 0.3942 0.832 0.000 0.236 0.764 0.000
#> GSM1009129 3 0.3306 0.761 0.004 0.156 0.840 0.000
#> GSM1009143 4 0.0000 0.926 0.000 0.000 0.000 1.000
#> GSM1009157 1 0.1302 0.810 0.956 0.044 0.000 0.000
#> GSM1009171 3 0.3172 0.865 0.000 0.160 0.840 0.000
#> GSM1009185 2 0.2124 0.843 0.008 0.924 0.068 0.000
#> GSM1009199 1 0.6665 0.739 0.700 0.060 0.140 0.100
#> GSM1009074 1 0.4552 0.768 0.800 0.072 0.000 0.128
#> GSM1009088 2 0.5279 0.756 0.072 0.736 0.192 0.000
#> GSM1009102 4 0.1302 0.932 0.044 0.000 0.000 0.956
#> GSM1009116 3 0.3942 0.832 0.000 0.236 0.764 0.000
#> GSM1009130 3 0.2334 0.803 0.004 0.088 0.908 0.000
#> GSM1009144 4 0.0000 0.926 0.000 0.000 0.000 1.000
#> GSM1009158 1 0.0188 0.823 0.996 0.004 0.000 0.000
#> GSM1009172 3 0.3172 0.865 0.000 0.160 0.840 0.000
#> GSM1009186 2 0.1940 0.850 0.000 0.924 0.076 0.000
#> GSM1009200 1 0.5710 0.745 0.728 0.004 0.140 0.128
#> GSM1009075 1 0.4552 0.768 0.800 0.072 0.000 0.128
#> GSM1009089 2 0.6158 0.545 0.292 0.628 0.080 0.000
#> GSM1009103 4 0.1302 0.932 0.044 0.000 0.000 0.956
#> GSM1009117 3 0.3942 0.832 0.000 0.236 0.764 0.000
#> GSM1009131 3 0.2882 0.658 0.024 0.084 0.892 0.000
#> GSM1009145 4 0.0921 0.931 0.028 0.000 0.000 0.972
#> GSM1009159 1 0.0188 0.823 0.996 0.004 0.000 0.000
#> GSM1009173 3 0.3172 0.865 0.000 0.160 0.840 0.000
#> GSM1009187 2 0.1940 0.850 0.000 0.924 0.076 0.000
#> GSM1009201 1 0.5710 0.745 0.728 0.004 0.140 0.128
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM1009062 3 0.4550 0.999 0.276 0.000 0.688 0.036 0.000
#> GSM1009076 2 0.6593 0.572 0.028 0.476 0.108 0.000 0.388
#> GSM1009090 4 0.0290 0.987 0.008 0.000 0.000 0.992 0.000
#> GSM1009104 5 0.2280 0.641 0.000 0.120 0.000 0.000 0.880
#> GSM1009118 5 0.7673 0.402 0.332 0.080 0.148 0.004 0.436
#> GSM1009132 4 0.0771 0.984 0.000 0.004 0.020 0.976 0.000
#> GSM1009146 1 0.4633 0.230 0.612 0.008 0.372 0.008 0.000
#> GSM1009160 5 0.4193 0.704 0.000 0.040 0.212 0.000 0.748
#> GSM1009174 2 0.0771 0.692 0.004 0.976 0.000 0.000 0.020
#> GSM1009188 1 0.1041 0.664 0.964 0.004 0.000 0.032 0.000
#> GSM1009063 3 0.4550 0.999 0.276 0.000 0.688 0.036 0.000
#> GSM1009077 2 0.6593 0.572 0.028 0.476 0.108 0.000 0.388
#> GSM1009091 4 0.0290 0.987 0.008 0.000 0.000 0.992 0.000
#> GSM1009105 5 0.2280 0.641 0.000 0.120 0.000 0.000 0.880
#> GSM1009119 1 0.2878 0.617 0.880 0.004 0.048 0.068 0.000
#> GSM1009133 4 0.0771 0.984 0.000 0.004 0.020 0.976 0.000
#> GSM1009147 1 0.4633 0.230 0.612 0.008 0.372 0.008 0.000
#> GSM1009161 5 0.4193 0.704 0.000 0.040 0.212 0.000 0.748
#> GSM1009175 2 0.0771 0.692 0.004 0.976 0.000 0.000 0.020
#> GSM1009189 1 0.1041 0.664 0.964 0.004 0.000 0.032 0.000
#> GSM1009064 3 0.4550 0.999 0.276 0.000 0.688 0.036 0.000
#> GSM1009078 2 0.6789 0.579 0.028 0.476 0.136 0.000 0.360
#> GSM1009092 4 0.0290 0.987 0.008 0.000 0.000 0.992 0.000
#> GSM1009106 5 0.2280 0.641 0.000 0.120 0.000 0.000 0.880
#> GSM1009120 1 0.2679 0.627 0.892 0.004 0.056 0.048 0.000
#> GSM1009134 4 0.0771 0.984 0.000 0.004 0.020 0.976 0.000
#> GSM1009148 1 0.4633 0.230 0.612 0.008 0.372 0.008 0.000
#> GSM1009162 5 0.4193 0.704 0.000 0.040 0.212 0.000 0.748
#> GSM1009176 2 0.0771 0.692 0.004 0.976 0.000 0.000 0.020
#> GSM1009190 1 0.1041 0.664 0.964 0.004 0.000 0.032 0.000
#> GSM1009065 3 0.4550 0.999 0.276 0.000 0.688 0.036 0.000
#> GSM1009079 2 0.6593 0.572 0.028 0.476 0.108 0.000 0.388
#> GSM1009093 4 0.0290 0.987 0.008 0.000 0.000 0.992 0.000
#> GSM1009107 5 0.2280 0.641 0.000 0.120 0.000 0.000 0.880
#> GSM1009121 5 0.7826 0.419 0.308 0.080 0.148 0.012 0.452
#> GSM1009135 4 0.0771 0.984 0.000 0.004 0.020 0.976 0.000
#> GSM1009149 1 0.4633 0.230 0.612 0.008 0.372 0.008 0.000
#> GSM1009163 5 0.4193 0.704 0.000 0.040 0.212 0.000 0.748
#> GSM1009177 2 0.0771 0.692 0.004 0.976 0.000 0.000 0.020
#> GSM1009191 1 0.1041 0.664 0.964 0.004 0.000 0.032 0.000
#> GSM1009066 3 0.4550 0.999 0.276 0.000 0.688 0.036 0.000
#> GSM1009080 2 0.6593 0.572 0.028 0.476 0.108 0.000 0.388
#> GSM1009094 4 0.0290 0.987 0.008 0.000 0.000 0.992 0.000
#> GSM1009108 5 0.2280 0.641 0.000 0.120 0.000 0.000 0.880
#> GSM1009122 5 0.6818 0.563 0.156 0.080 0.148 0.004 0.612
#> GSM1009136 4 0.0324 0.985 0.000 0.004 0.004 0.992 0.000
#> GSM1009150 1 0.4633 0.230 0.612 0.008 0.372 0.008 0.000
#> GSM1009164 5 0.4193 0.704 0.000 0.040 0.212 0.000 0.748
#> GSM1009178 2 0.0771 0.692 0.004 0.976 0.000 0.000 0.020
#> GSM1009192 1 0.1041 0.664 0.964 0.004 0.000 0.032 0.000
#> GSM1009067 3 0.4550 0.999 0.276 0.000 0.688 0.036 0.000
#> GSM1009081 2 0.6593 0.572 0.028 0.476 0.108 0.000 0.388
#> GSM1009095 4 0.0290 0.987 0.008 0.000 0.000 0.992 0.000
#> GSM1009109 5 0.2280 0.641 0.000 0.120 0.000 0.000 0.880
#> GSM1009123 1 0.4858 0.421 0.688 0.004 0.052 0.256 0.000
#> GSM1009137 4 0.0771 0.984 0.000 0.004 0.020 0.976 0.000
#> GSM1009151 1 0.4633 0.230 0.612 0.008 0.372 0.008 0.000
#> GSM1009165 5 0.4193 0.704 0.000 0.040 0.212 0.000 0.748
#> GSM1009179 2 0.0771 0.692 0.004 0.976 0.000 0.000 0.020
#> GSM1009193 1 0.1041 0.664 0.964 0.004 0.000 0.032 0.000
#> GSM1009068 3 0.4550 0.999 0.276 0.000 0.688 0.036 0.000
#> GSM1009082 2 0.6593 0.572 0.028 0.476 0.108 0.000 0.388
#> GSM1009096 4 0.0290 0.987 0.008 0.000 0.000 0.992 0.000
#> GSM1009110 5 0.2280 0.641 0.000 0.120 0.000 0.000 0.880
#> GSM1009124 1 0.7075 0.400 0.632 0.060 0.132 0.124 0.052
#> GSM1009138 4 0.0771 0.984 0.000 0.004 0.020 0.976 0.000
#> GSM1009152 1 0.4633 0.230 0.612 0.008 0.372 0.008 0.000
#> GSM1009166 5 0.4193 0.704 0.000 0.040 0.212 0.000 0.748
#> GSM1009180 2 0.0771 0.692 0.004 0.976 0.000 0.000 0.020
#> GSM1009194 1 0.1041 0.664 0.964 0.004 0.000 0.032 0.000
#> GSM1009069 3 0.4668 0.981 0.276 0.000 0.688 0.028 0.008
#> GSM1009083 2 0.6593 0.572 0.028 0.476 0.108 0.000 0.388
#> GSM1009097 4 0.0290 0.987 0.008 0.000 0.000 0.992 0.000
#> GSM1009111 5 0.2280 0.641 0.000 0.120 0.000 0.000 0.880
#> GSM1009125 5 0.6818 0.563 0.156 0.080 0.148 0.004 0.612
#> GSM1009139 4 0.0771 0.984 0.000 0.004 0.020 0.976 0.000
#> GSM1009153 1 0.4633 0.230 0.612 0.008 0.372 0.008 0.000
#> GSM1009167 5 0.4193 0.704 0.000 0.040 0.212 0.000 0.748
#> GSM1009181 2 0.0771 0.692 0.004 0.976 0.000 0.000 0.020
#> GSM1009195 1 0.0798 0.655 0.976 0.008 0.000 0.016 0.000
#> GSM1009070 3 0.4550 0.999 0.276 0.000 0.688 0.036 0.000
#> GSM1009084 2 0.6593 0.572 0.028 0.476 0.108 0.000 0.388
#> GSM1009098 4 0.0290 0.987 0.008 0.000 0.000 0.992 0.000
#> GSM1009112 5 0.2280 0.641 0.000 0.120 0.000 0.000 0.880
#> GSM1009126 1 0.7075 0.400 0.632 0.060 0.132 0.124 0.052
#> GSM1009140 4 0.0771 0.984 0.000 0.004 0.020 0.976 0.000
#> GSM1009154 1 0.4633 0.230 0.612 0.008 0.372 0.008 0.000
#> GSM1009168 5 0.4193 0.704 0.000 0.040 0.212 0.000 0.748
#> GSM1009182 2 0.0771 0.692 0.004 0.976 0.000 0.000 0.020
#> GSM1009196 1 0.1026 0.660 0.968 0.004 0.004 0.024 0.000
#> GSM1009071 3 0.4550 0.999 0.276 0.000 0.688 0.036 0.000
#> GSM1009085 2 0.6593 0.572 0.028 0.476 0.108 0.000 0.388
#> GSM1009099 4 0.0290 0.987 0.008 0.000 0.000 0.992 0.000
#> GSM1009113 5 0.2280 0.641 0.000 0.120 0.000 0.000 0.880
#> GSM1009127 1 0.2954 0.617 0.876 0.004 0.056 0.064 0.000
#> GSM1009141 4 0.0771 0.984 0.000 0.004 0.020 0.976 0.000
#> GSM1009155 1 0.4633 0.230 0.612 0.008 0.372 0.008 0.000
#> GSM1009169 5 0.4193 0.704 0.000 0.040 0.212 0.000 0.748
#> GSM1009183 2 0.0771 0.692 0.004 0.976 0.000 0.000 0.020
#> GSM1009197 1 0.1041 0.664 0.964 0.004 0.000 0.032 0.000
#> GSM1009072 3 0.4550 0.999 0.276 0.000 0.688 0.036 0.000
#> GSM1009086 2 0.6593 0.572 0.028 0.476 0.108 0.000 0.388
#> GSM1009100 4 0.0290 0.987 0.008 0.000 0.000 0.992 0.000
#> GSM1009114 5 0.2280 0.641 0.000 0.120 0.000 0.000 0.880
#> GSM1009128 5 0.8690 0.380 0.312 0.076 0.148 0.072 0.392
#> GSM1009142 4 0.0771 0.984 0.000 0.004 0.020 0.976 0.000
#> GSM1009156 1 0.4530 0.229 0.612 0.008 0.376 0.004 0.000
#> GSM1009170 5 0.4193 0.704 0.000 0.040 0.212 0.000 0.748
#> GSM1009184 2 0.0771 0.692 0.004 0.976 0.000 0.000 0.020
#> GSM1009198 1 0.1041 0.664 0.964 0.004 0.000 0.032 0.000
#> GSM1009073 3 0.4550 0.999 0.276 0.000 0.688 0.036 0.000
#> GSM1009087 2 0.6789 0.579 0.028 0.476 0.136 0.000 0.360
#> GSM1009101 4 0.0290 0.987 0.008 0.000 0.000 0.992 0.000
#> GSM1009115 5 0.2280 0.641 0.000 0.120 0.000 0.000 0.880
#> GSM1009129 5 0.6783 0.566 0.152 0.080 0.148 0.004 0.616
#> GSM1009143 4 0.0771 0.984 0.000 0.004 0.020 0.976 0.000
#> GSM1009157 1 0.4482 0.224 0.612 0.012 0.376 0.000 0.000
#> GSM1009171 5 0.4193 0.704 0.000 0.040 0.212 0.000 0.748
#> GSM1009185 2 0.0771 0.692 0.004 0.976 0.000 0.000 0.020
#> GSM1009199 1 0.0992 0.660 0.968 0.008 0.000 0.024 0.000
#> GSM1009074 3 0.4550 0.999 0.276 0.000 0.688 0.036 0.000
#> GSM1009088 2 0.6789 0.579 0.028 0.476 0.136 0.000 0.360
#> GSM1009102 4 0.0290 0.987 0.008 0.000 0.000 0.992 0.000
#> GSM1009116 5 0.2280 0.641 0.000 0.120 0.000 0.000 0.880
#> GSM1009130 5 0.5579 0.606 0.132 0.024 0.152 0.000 0.692
#> GSM1009144 4 0.0771 0.984 0.000 0.004 0.020 0.976 0.000
#> GSM1009158 1 0.4633 0.230 0.612 0.008 0.372 0.008 0.000
#> GSM1009172 5 0.4193 0.704 0.000 0.040 0.212 0.000 0.748
#> GSM1009186 2 0.0771 0.692 0.004 0.976 0.000 0.000 0.020
#> GSM1009200 1 0.1041 0.664 0.964 0.004 0.000 0.032 0.000
#> GSM1009075 3 0.4550 0.999 0.276 0.000 0.688 0.036 0.000
#> GSM1009089 2 0.8169 0.484 0.076 0.412 0.280 0.016 0.216
#> GSM1009103 4 0.0290 0.987 0.008 0.000 0.000 0.992 0.000
#> GSM1009117 5 0.2280 0.641 0.000 0.120 0.000 0.000 0.880
#> GSM1009131 5 0.7826 0.419 0.308 0.080 0.148 0.012 0.452
#> GSM1009145 4 0.0324 0.985 0.000 0.004 0.004 0.992 0.000
#> GSM1009159 1 0.4633 0.230 0.612 0.008 0.372 0.008 0.000
#> GSM1009173 5 0.4193 0.704 0.000 0.040 0.212 0.000 0.748
#> GSM1009187 2 0.0771 0.692 0.004 0.976 0.000 0.000 0.020
#> GSM1009201 1 0.1041 0.664 0.964 0.004 0.000 0.032 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1009062 6 0.0520 0.7050 0.000 0.000 0.000 0.008 0.008 0.984
#> GSM1009076 5 0.5170 0.7379 0.000 0.276 0.056 0.000 0.632 0.036
#> GSM1009090 4 0.1225 0.9745 0.012 0.000 0.000 0.952 0.036 0.000
#> GSM1009104 3 0.5336 0.6277 0.016 0.060 0.544 0.000 0.376 0.004
#> GSM1009118 1 0.6789 0.1694 0.372 0.032 0.260 0.000 0.332 0.004
#> GSM1009132 4 0.0363 0.9739 0.000 0.000 0.000 0.988 0.000 0.012
#> GSM1009146 6 0.5103 0.6011 0.392 0.004 0.000 0.000 0.072 0.532
#> GSM1009160 3 0.0146 0.7032 0.000 0.004 0.996 0.000 0.000 0.000
#> GSM1009174 2 0.0146 1.0000 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM1009188 1 0.1267 0.7640 0.940 0.000 0.000 0.000 0.000 0.060
#> GSM1009063 6 0.0520 0.7050 0.000 0.000 0.000 0.008 0.008 0.984
#> GSM1009077 5 0.5170 0.7379 0.000 0.276 0.056 0.000 0.632 0.036
#> GSM1009091 4 0.1225 0.9745 0.012 0.000 0.000 0.952 0.036 0.000
#> GSM1009105 3 0.5336 0.6277 0.016 0.060 0.544 0.000 0.376 0.004
#> GSM1009119 1 0.3502 0.6980 0.784 0.004 0.000 0.004 0.188 0.020
#> GSM1009133 4 0.0363 0.9739 0.000 0.000 0.000 0.988 0.000 0.012
#> GSM1009147 6 0.5103 0.6011 0.392 0.004 0.000 0.000 0.072 0.532
#> GSM1009161 3 0.0146 0.7032 0.000 0.004 0.996 0.000 0.000 0.000
#> GSM1009175 2 0.0146 1.0000 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM1009189 1 0.1267 0.7640 0.940 0.000 0.000 0.000 0.000 0.060
#> GSM1009064 6 0.0520 0.7050 0.000 0.000 0.000 0.008 0.008 0.984
#> GSM1009078 5 0.4875 0.7190 0.000 0.276 0.036 0.000 0.652 0.036
#> GSM1009092 4 0.1225 0.9745 0.012 0.000 0.000 0.952 0.036 0.000
#> GSM1009106 3 0.5336 0.6277 0.016 0.060 0.544 0.000 0.376 0.004
#> GSM1009120 1 0.3517 0.6978 0.780 0.004 0.000 0.000 0.188 0.028
#> GSM1009134 4 0.0363 0.9739 0.000 0.000 0.000 0.988 0.000 0.012
#> GSM1009148 6 0.5103 0.6011 0.392 0.004 0.000 0.000 0.072 0.532
#> GSM1009162 3 0.0146 0.7032 0.000 0.004 0.996 0.000 0.000 0.000
#> GSM1009176 2 0.0146 1.0000 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM1009190 1 0.1267 0.7640 0.940 0.000 0.000 0.000 0.000 0.060
#> GSM1009065 6 0.0520 0.7050 0.000 0.000 0.000 0.008 0.008 0.984
#> GSM1009079 5 0.5170 0.7379 0.000 0.276 0.056 0.000 0.632 0.036
#> GSM1009093 4 0.1225 0.9745 0.012 0.000 0.000 0.952 0.036 0.000
#> GSM1009107 3 0.5336 0.6277 0.016 0.060 0.544 0.000 0.376 0.004
#> GSM1009121 1 0.6809 0.1766 0.372 0.032 0.280 0.000 0.312 0.004
#> GSM1009135 4 0.0363 0.9739 0.000 0.000 0.000 0.988 0.000 0.012
#> GSM1009149 6 0.5103 0.6011 0.392 0.004 0.000 0.000 0.072 0.532
#> GSM1009163 3 0.0146 0.7032 0.000 0.004 0.996 0.000 0.000 0.000
#> GSM1009177 2 0.0146 1.0000 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM1009191 1 0.1267 0.7640 0.940 0.000 0.000 0.000 0.000 0.060
#> GSM1009066 6 0.0520 0.7050 0.000 0.000 0.000 0.008 0.008 0.984
#> GSM1009080 5 0.5170 0.7379 0.000 0.276 0.056 0.000 0.632 0.036
#> GSM1009094 4 0.1225 0.9745 0.012 0.000 0.000 0.952 0.036 0.000
#> GSM1009108 3 0.5336 0.6277 0.016 0.060 0.544 0.000 0.376 0.004
#> GSM1009122 5 0.6704 -0.0716 0.224 0.032 0.344 0.000 0.396 0.004
#> GSM1009136 4 0.0260 0.9740 0.000 0.000 0.000 0.992 0.000 0.008
#> GSM1009150 6 0.5103 0.6011 0.392 0.004 0.000 0.000 0.072 0.532
#> GSM1009164 3 0.0146 0.7032 0.000 0.004 0.996 0.000 0.000 0.000
#> GSM1009178 2 0.0146 1.0000 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM1009192 1 0.1267 0.7640 0.940 0.000 0.000 0.000 0.000 0.060
#> GSM1009067 6 0.0520 0.7050 0.000 0.000 0.000 0.008 0.008 0.984
#> GSM1009081 5 0.5170 0.7379 0.000 0.276 0.056 0.000 0.632 0.036
#> GSM1009095 4 0.1225 0.9745 0.012 0.000 0.000 0.952 0.036 0.000
#> GSM1009109 3 0.5336 0.6277 0.016 0.060 0.544 0.000 0.376 0.004
#> GSM1009123 1 0.3527 0.6937 0.784 0.004 0.000 0.016 0.188 0.008
#> GSM1009137 4 0.0363 0.9739 0.000 0.000 0.000 0.988 0.000 0.012
#> GSM1009151 6 0.5103 0.6011 0.392 0.004 0.000 0.000 0.072 0.532
#> GSM1009165 3 0.0146 0.7032 0.000 0.004 0.996 0.000 0.000 0.000
#> GSM1009179 2 0.0146 1.0000 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM1009193 1 0.1267 0.7640 0.940 0.000 0.000 0.000 0.000 0.060
#> GSM1009068 6 0.0520 0.7050 0.000 0.000 0.000 0.008 0.008 0.984
#> GSM1009082 5 0.5170 0.7379 0.000 0.276 0.056 0.000 0.632 0.036
#> GSM1009096 4 0.1225 0.9745 0.012 0.000 0.000 0.952 0.036 0.000
#> GSM1009110 3 0.5336 0.6277 0.016 0.060 0.544 0.000 0.376 0.004
#> GSM1009124 1 0.4908 0.6469 0.704 0.032 0.052 0.004 0.204 0.004
#> GSM1009138 4 0.0363 0.9739 0.000 0.000 0.000 0.988 0.000 0.012
#> GSM1009152 6 0.5103 0.6011 0.392 0.004 0.000 0.000 0.072 0.532
#> GSM1009166 3 0.0146 0.7032 0.000 0.004 0.996 0.000 0.000 0.000
#> GSM1009180 2 0.0146 1.0000 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM1009194 1 0.1267 0.7640 0.940 0.000 0.000 0.000 0.000 0.060
#> GSM1009069 6 0.0458 0.7005 0.000 0.000 0.000 0.000 0.016 0.984
#> GSM1009083 5 0.5170 0.7379 0.000 0.276 0.056 0.000 0.632 0.036
#> GSM1009097 4 0.1225 0.9745 0.012 0.000 0.000 0.952 0.036 0.000
#> GSM1009111 3 0.5336 0.6277 0.016 0.060 0.544 0.000 0.376 0.004
#> GSM1009125 5 0.6661 -0.0927 0.208 0.032 0.360 0.000 0.396 0.004
#> GSM1009139 4 0.0363 0.9739 0.000 0.000 0.000 0.988 0.000 0.012
#> GSM1009153 6 0.5103 0.6011 0.392 0.004 0.000 0.000 0.072 0.532
#> GSM1009167 3 0.0146 0.7032 0.000 0.004 0.996 0.000 0.000 0.000
#> GSM1009181 2 0.0146 1.0000 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM1009195 1 0.1267 0.7640 0.940 0.000 0.000 0.000 0.000 0.060
#> GSM1009070 6 0.0520 0.7050 0.000 0.000 0.000 0.008 0.008 0.984
#> GSM1009084 5 0.5170 0.7379 0.000 0.276 0.056 0.000 0.632 0.036
#> GSM1009098 4 0.1225 0.9745 0.012 0.000 0.000 0.952 0.036 0.000
#> GSM1009112 3 0.5336 0.6277 0.016 0.060 0.544 0.000 0.376 0.004
#> GSM1009126 1 0.5197 0.6404 0.692 0.032 0.052 0.016 0.204 0.004
#> GSM1009140 4 0.0363 0.9739 0.000 0.000 0.000 0.988 0.000 0.012
#> GSM1009154 6 0.5103 0.6011 0.392 0.004 0.000 0.000 0.072 0.532
#> GSM1009168 3 0.0146 0.7032 0.000 0.004 0.996 0.000 0.000 0.000
#> GSM1009182 2 0.0146 1.0000 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM1009196 1 0.1267 0.7640 0.940 0.000 0.000 0.000 0.000 0.060
#> GSM1009071 6 0.0520 0.7050 0.000 0.000 0.000 0.008 0.008 0.984
#> GSM1009085 5 0.5170 0.7379 0.000 0.276 0.056 0.000 0.632 0.036
#> GSM1009099 4 0.1225 0.9745 0.012 0.000 0.000 0.952 0.036 0.000
#> GSM1009113 3 0.5336 0.6277 0.016 0.060 0.544 0.000 0.376 0.004
#> GSM1009127 1 0.3581 0.6987 0.780 0.004 0.000 0.004 0.188 0.024
#> GSM1009141 4 0.0363 0.9739 0.000 0.000 0.000 0.988 0.000 0.012
#> GSM1009155 6 0.5103 0.6011 0.392 0.004 0.000 0.000 0.072 0.532
#> GSM1009169 3 0.0146 0.7032 0.000 0.004 0.996 0.000 0.000 0.000
#> GSM1009183 2 0.0146 1.0000 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM1009197 1 0.1267 0.7640 0.940 0.000 0.000 0.000 0.000 0.060
#> GSM1009072 6 0.0520 0.7050 0.000 0.000 0.000 0.008 0.008 0.984
#> GSM1009086 5 0.5170 0.7379 0.000 0.276 0.056 0.000 0.632 0.036
#> GSM1009100 4 0.1225 0.9745 0.012 0.000 0.000 0.952 0.036 0.000
#> GSM1009114 3 0.5336 0.6277 0.016 0.060 0.544 0.000 0.376 0.004
#> GSM1009128 1 0.6977 0.2399 0.396 0.032 0.268 0.008 0.292 0.004
#> GSM1009142 4 0.0363 0.9739 0.000 0.000 0.000 0.988 0.000 0.012
#> GSM1009156 6 0.5103 0.6011 0.392 0.004 0.000 0.000 0.072 0.532
#> GSM1009170 3 0.0146 0.7032 0.000 0.004 0.996 0.000 0.000 0.000
#> GSM1009184 2 0.0146 1.0000 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM1009198 1 0.1267 0.7640 0.940 0.000 0.000 0.000 0.000 0.060
#> GSM1009073 6 0.0520 0.7050 0.000 0.000 0.000 0.008 0.008 0.984
#> GSM1009087 5 0.4875 0.7190 0.000 0.276 0.036 0.000 0.652 0.036
#> GSM1009101 4 0.1225 0.9745 0.012 0.000 0.000 0.952 0.036 0.000
#> GSM1009115 3 0.5336 0.6277 0.016 0.060 0.544 0.000 0.376 0.004
#> GSM1009129 5 0.6644 -0.0858 0.204 0.032 0.356 0.000 0.404 0.004
#> GSM1009143 4 0.0363 0.9739 0.000 0.000 0.000 0.988 0.000 0.012
#> GSM1009157 6 0.5103 0.6011 0.392 0.004 0.000 0.000 0.072 0.532
#> GSM1009171 3 0.0146 0.7032 0.000 0.004 0.996 0.000 0.000 0.000
#> GSM1009185 2 0.0146 1.0000 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM1009199 1 0.1267 0.7640 0.940 0.000 0.000 0.000 0.000 0.060
#> GSM1009074 6 0.0520 0.7050 0.000 0.000 0.000 0.008 0.008 0.984
#> GSM1009088 5 0.4875 0.7190 0.000 0.276 0.036 0.000 0.652 0.036
#> GSM1009102 4 0.1225 0.9745 0.012 0.000 0.000 0.952 0.036 0.000
#> GSM1009116 3 0.5336 0.6277 0.016 0.060 0.544 0.000 0.376 0.004
#> GSM1009130 5 0.5993 -0.0954 0.176 0.004 0.352 0.000 0.464 0.004
#> GSM1009144 4 0.0363 0.9739 0.000 0.000 0.000 0.988 0.000 0.012
#> GSM1009158 6 0.5103 0.6011 0.392 0.004 0.000 0.000 0.072 0.532
#> GSM1009172 3 0.0146 0.7032 0.000 0.004 0.996 0.000 0.000 0.000
#> GSM1009186 2 0.0146 1.0000 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM1009200 1 0.1267 0.7640 0.940 0.000 0.000 0.000 0.000 0.060
#> GSM1009075 6 0.0520 0.7050 0.000 0.000 0.000 0.008 0.008 0.984
#> GSM1009089 5 0.5625 0.5752 0.004 0.232 0.024 0.000 0.616 0.124
#> GSM1009103 4 0.1225 0.9745 0.012 0.000 0.000 0.952 0.036 0.000
#> GSM1009117 3 0.5336 0.6277 0.016 0.060 0.544 0.000 0.376 0.004
#> GSM1009131 1 0.6745 0.1943 0.372 0.028 0.268 0.000 0.328 0.004
#> GSM1009145 4 0.0260 0.9740 0.000 0.000 0.000 0.992 0.000 0.008
#> GSM1009159 6 0.5103 0.6011 0.392 0.004 0.000 0.000 0.072 0.532
#> GSM1009173 3 0.0146 0.7032 0.000 0.004 0.996 0.000 0.000 0.000
#> GSM1009187 2 0.0146 1.0000 0.000 0.996 0.004 0.000 0.000 0.000
#> GSM1009201 1 0.1267 0.7640 0.940 0.000 0.000 0.000 0.000 0.060
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 temperature(p) time(p) specimen(p) k
#> MAD:skmeans 136 0.883 0.984 4.71e-22 2
#> MAD:skmeans 136 1.000 1.000 1.74e-42 3
#> MAD:skmeans 137 1.000 1.000 3.18e-67 4
#> MAD:skmeans 118 1.000 1.000 5.17e-76 5
#> MAD:skmeans 132 1.000 1.000 3.04e-110 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 140 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'pam' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 6.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.797 0.940 0.971 0.4526 0.556 0.556
#> 3 3 0.638 0.854 0.890 0.2642 0.803 0.673
#> 4 4 0.848 0.889 0.951 0.2307 0.803 0.580
#> 5 5 0.948 0.911 0.966 0.0788 0.926 0.755
#> 6 6 0.993 0.960 0.982 0.0655 0.921 0.690
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] 5
There is also optional best \(k\) = 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
#> GSM1009062 1 0.0000 0.964 1.000 0.000
#> GSM1009076 2 0.0000 0.980 0.000 1.000
#> GSM1009090 1 0.0000 0.964 1.000 0.000
#> GSM1009104 2 0.0000 0.980 0.000 1.000
#> GSM1009118 1 0.2043 0.941 0.968 0.032
#> GSM1009132 1 0.2948 0.926 0.948 0.052
#> GSM1009146 1 0.0000 0.964 1.000 0.000
#> GSM1009160 2 0.0000 0.980 0.000 1.000
#> GSM1009174 1 0.9522 0.464 0.628 0.372
#> GSM1009188 1 0.0000 0.964 1.000 0.000
#> GSM1009063 1 0.0000 0.964 1.000 0.000
#> GSM1009077 2 0.0000 0.980 0.000 1.000
#> GSM1009091 1 0.0000 0.964 1.000 0.000
#> GSM1009105 2 0.0000 0.980 0.000 1.000
#> GSM1009119 1 0.0000 0.964 1.000 0.000
#> GSM1009133 1 0.0000 0.964 1.000 0.000
#> GSM1009147 1 0.0000 0.964 1.000 0.000
#> GSM1009161 2 0.0000 0.980 0.000 1.000
#> GSM1009175 1 0.7602 0.752 0.780 0.220
#> GSM1009189 1 0.0000 0.964 1.000 0.000
#> GSM1009064 1 0.0000 0.964 1.000 0.000
#> GSM1009078 1 0.5294 0.866 0.880 0.120
#> GSM1009092 1 0.0000 0.964 1.000 0.000
#> GSM1009106 2 0.0000 0.980 0.000 1.000
#> GSM1009120 1 0.0000 0.964 1.000 0.000
#> GSM1009134 1 0.0000 0.964 1.000 0.000
#> GSM1009148 1 0.0000 0.964 1.000 0.000
#> GSM1009162 2 0.0000 0.980 0.000 1.000
#> GSM1009176 2 0.0672 0.974 0.008 0.992
#> GSM1009190 1 0.0000 0.964 1.000 0.000
#> GSM1009065 1 0.0000 0.964 1.000 0.000
#> GSM1009079 2 0.0000 0.980 0.000 1.000
#> GSM1009093 1 0.0000 0.964 1.000 0.000
#> GSM1009107 2 0.0000 0.980 0.000 1.000
#> GSM1009121 1 0.1414 0.950 0.980 0.020
#> GSM1009135 1 0.0000 0.964 1.000 0.000
#> GSM1009149 1 0.0000 0.964 1.000 0.000
#> GSM1009163 2 0.0000 0.980 0.000 1.000
#> GSM1009177 2 0.1184 0.967 0.016 0.984
#> GSM1009191 1 0.0000 0.964 1.000 0.000
#> GSM1009066 1 0.0000 0.964 1.000 0.000
#> GSM1009080 2 0.0000 0.980 0.000 1.000
#> GSM1009094 1 0.0000 0.964 1.000 0.000
#> GSM1009108 2 0.0000 0.980 0.000 1.000
#> GSM1009122 2 0.8861 0.543 0.304 0.696
#> GSM1009136 1 0.0000 0.964 1.000 0.000
#> GSM1009150 1 0.0000 0.964 1.000 0.000
#> GSM1009164 2 0.0000 0.980 0.000 1.000
#> GSM1009178 1 0.7602 0.752 0.780 0.220
#> GSM1009192 1 0.0000 0.964 1.000 0.000
#> GSM1009067 1 0.0000 0.964 1.000 0.000
#> GSM1009081 2 0.0000 0.980 0.000 1.000
#> GSM1009095 1 0.0000 0.964 1.000 0.000
#> GSM1009109 2 0.0000 0.980 0.000 1.000
#> GSM1009123 1 0.0000 0.964 1.000 0.000
#> GSM1009137 1 0.0000 0.964 1.000 0.000
#> GSM1009151 1 0.0000 0.964 1.000 0.000
#> GSM1009165 2 0.0000 0.980 0.000 1.000
#> GSM1009179 1 0.7602 0.752 0.780 0.220
#> GSM1009193 1 0.0000 0.964 1.000 0.000
#> GSM1009068 1 0.0000 0.964 1.000 0.000
#> GSM1009082 2 0.0000 0.980 0.000 1.000
#> GSM1009096 1 0.0000 0.964 1.000 0.000
#> GSM1009110 2 0.0000 0.980 0.000 1.000
#> GSM1009124 1 0.0000 0.964 1.000 0.000
#> GSM1009138 1 0.0000 0.964 1.000 0.000
#> GSM1009152 1 0.0000 0.964 1.000 0.000
#> GSM1009166 2 0.0000 0.980 0.000 1.000
#> GSM1009180 1 0.7602 0.752 0.780 0.220
#> GSM1009194 1 0.0000 0.964 1.000 0.000
#> GSM1009069 1 0.6623 0.810 0.828 0.172
#> GSM1009083 2 0.0000 0.980 0.000 1.000
#> GSM1009097 1 0.0000 0.964 1.000 0.000
#> GSM1009111 2 0.0000 0.980 0.000 1.000
#> GSM1009125 2 0.4022 0.904 0.080 0.920
#> GSM1009139 1 0.0000 0.964 1.000 0.000
#> GSM1009153 1 0.0000 0.964 1.000 0.000
#> GSM1009167 2 0.0000 0.980 0.000 1.000
#> GSM1009181 2 0.0376 0.977 0.004 0.996
#> GSM1009195 1 0.0000 0.964 1.000 0.000
#> GSM1009070 1 0.0000 0.964 1.000 0.000
#> GSM1009084 2 0.0000 0.980 0.000 1.000
#> GSM1009098 1 0.0000 0.964 1.000 0.000
#> GSM1009112 2 0.0000 0.980 0.000 1.000
#> GSM1009126 1 0.0000 0.964 1.000 0.000
#> GSM1009140 1 0.0000 0.964 1.000 0.000
#> GSM1009154 1 0.0000 0.964 1.000 0.000
#> GSM1009168 2 0.0000 0.980 0.000 1.000
#> GSM1009182 1 0.7602 0.752 0.780 0.220
#> GSM1009196 1 0.0000 0.964 1.000 0.000
#> GSM1009071 1 0.0000 0.964 1.000 0.000
#> GSM1009085 2 0.0000 0.980 0.000 1.000
#> GSM1009099 1 0.0000 0.964 1.000 0.000
#> GSM1009113 2 0.0000 0.980 0.000 1.000
#> GSM1009127 1 0.0000 0.964 1.000 0.000
#> GSM1009141 1 0.0000 0.964 1.000 0.000
#> GSM1009155 1 0.0000 0.964 1.000 0.000
#> GSM1009169 2 0.5629 0.840 0.132 0.868
#> GSM1009183 2 0.0672 0.974 0.008 0.992
#> GSM1009197 1 0.0000 0.964 1.000 0.000
#> GSM1009072 1 0.0000 0.964 1.000 0.000
#> GSM1009086 2 0.0000 0.980 0.000 1.000
#> GSM1009100 1 0.0000 0.964 1.000 0.000
#> GSM1009114 2 0.0000 0.980 0.000 1.000
#> GSM1009128 1 0.2603 0.933 0.956 0.044
#> GSM1009142 1 0.0000 0.964 1.000 0.000
#> GSM1009156 1 0.0000 0.964 1.000 0.000
#> GSM1009170 2 0.0000 0.980 0.000 1.000
#> GSM1009184 1 0.7602 0.752 0.780 0.220
#> GSM1009198 1 0.0000 0.964 1.000 0.000
#> GSM1009073 1 0.0000 0.964 1.000 0.000
#> GSM1009087 1 0.5294 0.866 0.880 0.120
#> GSM1009101 1 0.0000 0.964 1.000 0.000
#> GSM1009115 2 0.0000 0.980 0.000 1.000
#> GSM1009129 2 0.4161 0.899 0.084 0.916
#> GSM1009143 1 0.0000 0.964 1.000 0.000
#> GSM1009157 1 0.0000 0.964 1.000 0.000
#> GSM1009171 2 0.0000 0.980 0.000 1.000
#> GSM1009185 1 0.7602 0.752 0.780 0.220
#> GSM1009199 1 0.0000 0.964 1.000 0.000
#> GSM1009074 1 0.0000 0.964 1.000 0.000
#> GSM1009088 1 0.6801 0.804 0.820 0.180
#> GSM1009102 1 0.0000 0.964 1.000 0.000
#> GSM1009116 2 0.0000 0.980 0.000 1.000
#> GSM1009130 2 0.7883 0.704 0.236 0.764
#> GSM1009144 1 0.0000 0.964 1.000 0.000
#> GSM1009158 1 0.0000 0.964 1.000 0.000
#> GSM1009172 2 0.0000 0.980 0.000 1.000
#> GSM1009186 1 0.7602 0.752 0.780 0.220
#> GSM1009200 1 0.0000 0.964 1.000 0.000
#> GSM1009075 1 0.0000 0.964 1.000 0.000
#> GSM1009089 1 0.4939 0.876 0.892 0.108
#> GSM1009103 1 0.0000 0.964 1.000 0.000
#> GSM1009117 2 0.0000 0.980 0.000 1.000
#> GSM1009131 1 0.0376 0.961 0.996 0.004
#> GSM1009145 1 0.0000 0.964 1.000 0.000
#> GSM1009159 1 0.0000 0.964 1.000 0.000
#> GSM1009173 2 0.0000 0.980 0.000 1.000
#> GSM1009187 1 0.7602 0.752 0.780 0.220
#> GSM1009201 1 0.0000 0.964 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1009062 1 0.0000 0.894 1.000 0.000 0.000
#> GSM1009076 2 0.0000 0.867 0.000 1.000 0.000
#> GSM1009090 1 0.5216 0.812 0.740 0.000 0.260
#> GSM1009104 2 0.0000 0.867 0.000 1.000 0.000
#> GSM1009118 1 0.6126 0.139 0.600 0.400 0.000
#> GSM1009132 2 0.9646 0.200 0.272 0.468 0.260
#> GSM1009146 1 0.0000 0.894 1.000 0.000 0.000
#> GSM1009160 3 0.5216 1.000 0.000 0.260 0.740
#> GSM1009174 2 0.4235 0.771 0.176 0.824 0.000
#> GSM1009188 1 0.0000 0.894 1.000 0.000 0.000
#> GSM1009063 1 0.0000 0.894 1.000 0.000 0.000
#> GSM1009077 2 0.0000 0.867 0.000 1.000 0.000
#> GSM1009091 1 0.5216 0.812 0.740 0.000 0.260
#> GSM1009105 2 0.0000 0.867 0.000 1.000 0.000
#> GSM1009119 1 0.0000 0.894 1.000 0.000 0.000
#> GSM1009133 1 0.5216 0.812 0.740 0.000 0.260
#> GSM1009147 1 0.0000 0.894 1.000 0.000 0.000
#> GSM1009161 3 0.5216 1.000 0.000 0.260 0.740
#> GSM1009175 2 0.4235 0.771 0.176 0.824 0.000
#> GSM1009189 1 0.0000 0.894 1.000 0.000 0.000
#> GSM1009064 1 0.0000 0.894 1.000 0.000 0.000
#> GSM1009078 1 0.3116 0.830 0.892 0.108 0.000
#> GSM1009092 1 0.5216 0.812 0.740 0.000 0.260
#> GSM1009106 2 0.0000 0.867 0.000 1.000 0.000
#> GSM1009120 1 0.0000 0.894 1.000 0.000 0.000
#> GSM1009134 1 0.5216 0.812 0.740 0.000 0.260
#> GSM1009148 1 0.0000 0.894 1.000 0.000 0.000
#> GSM1009162 3 0.5216 1.000 0.000 0.260 0.740
#> GSM1009176 2 0.1860 0.851 0.052 0.948 0.000
#> GSM1009190 1 0.0000 0.894 1.000 0.000 0.000
#> GSM1009065 1 0.0237 0.892 0.996 0.004 0.000
#> GSM1009079 2 0.0000 0.867 0.000 1.000 0.000
#> GSM1009093 1 0.5216 0.812 0.740 0.000 0.260
#> GSM1009107 2 0.0000 0.867 0.000 1.000 0.000
#> GSM1009121 1 0.5731 0.801 0.804 0.108 0.088
#> GSM1009135 1 0.5216 0.812 0.740 0.000 0.260
#> GSM1009149 1 0.0000 0.894 1.000 0.000 0.000
#> GSM1009163 3 0.5216 1.000 0.000 0.260 0.740
#> GSM1009177 2 0.2625 0.834 0.084 0.916 0.000
#> GSM1009191 1 0.0000 0.894 1.000 0.000 0.000
#> GSM1009066 1 0.0000 0.894 1.000 0.000 0.000
#> GSM1009080 2 0.0000 0.867 0.000 1.000 0.000
#> GSM1009094 1 0.5216 0.812 0.740 0.000 0.260
#> GSM1009108 2 0.0000 0.867 0.000 1.000 0.000
#> GSM1009122 2 0.3752 0.789 0.144 0.856 0.000
#> GSM1009136 1 0.5216 0.812 0.740 0.000 0.260
#> GSM1009150 1 0.0000 0.894 1.000 0.000 0.000
#> GSM1009164 3 0.5216 1.000 0.000 0.260 0.740
#> GSM1009178 2 0.4235 0.771 0.176 0.824 0.000
#> GSM1009192 1 0.0000 0.894 1.000 0.000 0.000
#> GSM1009067 1 0.0000 0.894 1.000 0.000 0.000
#> GSM1009081 2 0.0000 0.867 0.000 1.000 0.000
#> GSM1009095 1 0.5216 0.812 0.740 0.000 0.260
#> GSM1009109 2 0.0000 0.867 0.000 1.000 0.000
#> GSM1009123 1 0.2448 0.873 0.924 0.000 0.076
#> GSM1009137 1 0.5216 0.812 0.740 0.000 0.260
#> GSM1009151 1 0.0000 0.894 1.000 0.000 0.000
#> GSM1009165 3 0.5216 1.000 0.000 0.260 0.740
#> GSM1009179 2 0.4235 0.771 0.176 0.824 0.000
#> GSM1009193 1 0.0000 0.894 1.000 0.000 0.000
#> GSM1009068 1 0.0000 0.894 1.000 0.000 0.000
#> GSM1009082 2 0.0000 0.867 0.000 1.000 0.000
#> GSM1009096 1 0.5216 0.812 0.740 0.000 0.260
#> GSM1009110 2 0.0000 0.867 0.000 1.000 0.000
#> GSM1009124 1 0.0000 0.894 1.000 0.000 0.000
#> GSM1009138 1 0.5216 0.812 0.740 0.000 0.260
#> GSM1009152 1 0.0000 0.894 1.000 0.000 0.000
#> GSM1009166 3 0.5216 1.000 0.000 0.260 0.740
#> GSM1009180 2 0.4235 0.771 0.176 0.824 0.000
#> GSM1009194 1 0.0000 0.894 1.000 0.000 0.000
#> GSM1009069 2 0.5948 0.476 0.360 0.640 0.000
#> GSM1009083 2 0.0592 0.864 0.012 0.988 0.000
#> GSM1009097 1 0.5216 0.812 0.740 0.000 0.260
#> GSM1009111 2 0.0000 0.867 0.000 1.000 0.000
#> GSM1009125 2 0.2173 0.849 0.048 0.944 0.008
#> GSM1009139 1 0.5216 0.812 0.740 0.000 0.260
#> GSM1009153 1 0.0000 0.894 1.000 0.000 0.000
#> GSM1009167 3 0.5216 1.000 0.000 0.260 0.740
#> GSM1009181 2 0.1163 0.860 0.028 0.972 0.000
#> GSM1009195 1 0.0237 0.892 0.996 0.004 0.000
#> GSM1009070 1 0.0000 0.894 1.000 0.000 0.000
#> GSM1009084 2 0.0000 0.867 0.000 1.000 0.000
#> GSM1009098 1 0.5216 0.812 0.740 0.000 0.260
#> GSM1009112 2 0.0000 0.867 0.000 1.000 0.000
#> GSM1009126 1 0.0000 0.894 1.000 0.000 0.000
#> GSM1009140 1 0.5216 0.812 0.740 0.000 0.260
#> GSM1009154 1 0.0000 0.894 1.000 0.000 0.000
#> GSM1009168 3 0.5216 1.000 0.000 0.260 0.740
#> GSM1009182 2 0.4235 0.771 0.176 0.824 0.000
#> GSM1009196 1 0.0000 0.894 1.000 0.000 0.000
#> GSM1009071 1 0.0000 0.894 1.000 0.000 0.000
#> GSM1009085 2 0.0000 0.867 0.000 1.000 0.000
#> GSM1009099 1 0.5216 0.812 0.740 0.000 0.260
#> GSM1009113 2 0.0000 0.867 0.000 1.000 0.000
#> GSM1009127 1 0.0000 0.894 1.000 0.000 0.000
#> GSM1009141 1 0.5216 0.812 0.740 0.000 0.260
#> GSM1009155 1 0.0000 0.894 1.000 0.000 0.000
#> GSM1009169 3 0.5216 1.000 0.000 0.260 0.740
#> GSM1009183 2 0.2261 0.843 0.068 0.932 0.000
#> GSM1009197 1 0.0000 0.894 1.000 0.000 0.000
#> GSM1009072 1 0.0000 0.894 1.000 0.000 0.000
#> GSM1009086 2 0.0000 0.867 0.000 1.000 0.000
#> GSM1009100 1 0.5216 0.812 0.740 0.000 0.260
#> GSM1009114 2 0.0000 0.867 0.000 1.000 0.000
#> GSM1009128 1 0.5737 0.815 0.804 0.092 0.104
#> GSM1009142 1 0.5216 0.812 0.740 0.000 0.260
#> GSM1009156 1 0.0000 0.894 1.000 0.000 0.000
#> GSM1009170 3 0.5216 1.000 0.000 0.260 0.740
#> GSM1009184 2 0.4235 0.771 0.176 0.824 0.000
#> GSM1009198 1 0.0000 0.894 1.000 0.000 0.000
#> GSM1009073 1 0.0000 0.894 1.000 0.000 0.000
#> GSM1009087 1 0.3116 0.830 0.892 0.108 0.000
#> GSM1009101 1 0.5216 0.812 0.740 0.000 0.260
#> GSM1009115 2 0.0000 0.867 0.000 1.000 0.000
#> GSM1009129 2 0.2625 0.824 0.084 0.916 0.000
#> GSM1009143 1 0.5216 0.812 0.740 0.000 0.260
#> GSM1009157 1 0.0000 0.894 1.000 0.000 0.000
#> GSM1009171 3 0.5216 1.000 0.000 0.260 0.740
#> GSM1009185 2 0.4235 0.771 0.176 0.824 0.000
#> GSM1009199 1 0.0237 0.892 0.996 0.004 0.000
#> GSM1009074 1 0.0000 0.894 1.000 0.000 0.000
#> GSM1009088 1 0.4178 0.776 0.828 0.172 0.000
#> GSM1009102 1 0.5216 0.812 0.740 0.000 0.260
#> GSM1009116 2 0.0000 0.867 0.000 1.000 0.000
#> GSM1009130 1 0.4346 0.771 0.816 0.184 0.000
#> GSM1009144 1 0.5216 0.812 0.740 0.000 0.260
#> GSM1009158 1 0.0000 0.894 1.000 0.000 0.000
#> GSM1009172 3 0.5216 1.000 0.000 0.260 0.740
#> GSM1009186 2 0.4235 0.771 0.176 0.824 0.000
#> GSM1009200 1 0.0000 0.894 1.000 0.000 0.000
#> GSM1009075 1 0.0000 0.894 1.000 0.000 0.000
#> GSM1009089 1 0.2711 0.840 0.912 0.088 0.000
#> GSM1009103 1 0.5216 0.812 0.740 0.000 0.260
#> GSM1009117 2 0.0000 0.867 0.000 1.000 0.000
#> GSM1009131 1 0.1529 0.877 0.960 0.040 0.000
#> GSM1009145 1 0.5216 0.812 0.740 0.000 0.260
#> GSM1009159 1 0.0000 0.894 1.000 0.000 0.000
#> GSM1009173 3 0.5216 1.000 0.000 0.260 0.740
#> GSM1009187 2 0.4235 0.771 0.176 0.824 0.000
#> GSM1009201 1 0.0000 0.894 1.000 0.000 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1009062 1 0.0000 0.9624 1.000 0.000 0.000 0.000
#> GSM1009076 2 0.0000 0.8607 0.000 1.000 0.000 0.000
#> GSM1009090 4 0.0000 0.9688 0.000 0.000 0.000 1.000
#> GSM1009104 2 0.0000 0.8607 0.000 1.000 0.000 0.000
#> GSM1009118 1 0.4776 0.2671 0.624 0.376 0.000 0.000
#> GSM1009132 4 0.0000 0.9688 0.000 0.000 0.000 1.000
#> GSM1009146 1 0.0000 0.9624 1.000 0.000 0.000 0.000
#> GSM1009160 3 0.0000 1.0000 0.000 0.000 1.000 0.000
#> GSM1009174 2 0.4331 0.6841 0.288 0.712 0.000 0.000
#> GSM1009188 1 0.0000 0.9624 1.000 0.000 0.000 0.000
#> GSM1009063 1 0.0000 0.9624 1.000 0.000 0.000 0.000
#> GSM1009077 2 0.0000 0.8607 0.000 1.000 0.000 0.000
#> GSM1009091 4 0.0000 0.9688 0.000 0.000 0.000 1.000
#> GSM1009105 2 0.0000 0.8607 0.000 1.000 0.000 0.000
#> GSM1009119 1 0.0000 0.9624 1.000 0.000 0.000 0.000
#> GSM1009133 4 0.0000 0.9688 0.000 0.000 0.000 1.000
#> GSM1009147 1 0.0000 0.9624 1.000 0.000 0.000 0.000
#> GSM1009161 3 0.0000 1.0000 0.000 0.000 1.000 0.000
#> GSM1009175 2 0.4331 0.6841 0.288 0.712 0.000 0.000
#> GSM1009189 1 0.0000 0.9624 1.000 0.000 0.000 0.000
#> GSM1009064 1 0.0188 0.9594 0.996 0.004 0.000 0.000
#> GSM1009078 1 0.1211 0.9301 0.960 0.040 0.000 0.000
#> GSM1009092 4 0.0000 0.9688 0.000 0.000 0.000 1.000
#> GSM1009106 2 0.0000 0.8607 0.000 1.000 0.000 0.000
#> GSM1009120 1 0.0000 0.9624 1.000 0.000 0.000 0.000
#> GSM1009134 4 0.0000 0.9688 0.000 0.000 0.000 1.000
#> GSM1009148 1 0.0000 0.9624 1.000 0.000 0.000 0.000
#> GSM1009162 3 0.0000 1.0000 0.000 0.000 1.000 0.000
#> GSM1009176 2 0.1022 0.8495 0.032 0.968 0.000 0.000
#> GSM1009190 1 0.0000 0.9624 1.000 0.000 0.000 0.000
#> GSM1009065 1 0.0469 0.9532 0.988 0.012 0.000 0.000
#> GSM1009079 2 0.0000 0.8607 0.000 1.000 0.000 0.000
#> GSM1009093 4 0.0000 0.9688 0.000 0.000 0.000 1.000
#> GSM1009107 2 0.0000 0.8607 0.000 1.000 0.000 0.000
#> GSM1009121 2 0.7849 0.1827 0.352 0.380 0.000 0.268
#> GSM1009135 4 0.0000 0.9688 0.000 0.000 0.000 1.000
#> GSM1009149 1 0.0000 0.9624 1.000 0.000 0.000 0.000
#> GSM1009163 3 0.0000 1.0000 0.000 0.000 1.000 0.000
#> GSM1009177 2 0.1118 0.8479 0.036 0.964 0.000 0.000
#> GSM1009191 1 0.0000 0.9624 1.000 0.000 0.000 0.000
#> GSM1009066 1 0.0000 0.9624 1.000 0.000 0.000 0.000
#> GSM1009080 2 0.0000 0.8607 0.000 1.000 0.000 0.000
#> GSM1009094 4 0.0000 0.9688 0.000 0.000 0.000 1.000
#> GSM1009108 2 0.0000 0.8607 0.000 1.000 0.000 0.000
#> GSM1009122 2 0.3123 0.7667 0.156 0.844 0.000 0.000
#> GSM1009136 4 0.0000 0.9688 0.000 0.000 0.000 1.000
#> GSM1009150 1 0.0000 0.9624 1.000 0.000 0.000 0.000
#> GSM1009164 3 0.0000 1.0000 0.000 0.000 1.000 0.000
#> GSM1009178 2 0.4356 0.6796 0.292 0.708 0.000 0.000
#> GSM1009192 1 0.0000 0.9624 1.000 0.000 0.000 0.000
#> GSM1009067 1 0.0000 0.9624 1.000 0.000 0.000 0.000
#> GSM1009081 2 0.0000 0.8607 0.000 1.000 0.000 0.000
#> GSM1009095 4 0.0000 0.9688 0.000 0.000 0.000 1.000
#> GSM1009109 2 0.0000 0.8607 0.000 1.000 0.000 0.000
#> GSM1009123 4 0.4776 0.3908 0.376 0.000 0.000 0.624
#> GSM1009137 4 0.0000 0.9688 0.000 0.000 0.000 1.000
#> GSM1009151 1 0.0000 0.9624 1.000 0.000 0.000 0.000
#> GSM1009165 3 0.0000 1.0000 0.000 0.000 1.000 0.000
#> GSM1009179 2 0.4356 0.6796 0.292 0.708 0.000 0.000
#> GSM1009193 1 0.0000 0.9624 1.000 0.000 0.000 0.000
#> GSM1009068 1 0.0000 0.9624 1.000 0.000 0.000 0.000
#> GSM1009082 2 0.0000 0.8607 0.000 1.000 0.000 0.000
#> GSM1009096 4 0.0000 0.9688 0.000 0.000 0.000 1.000
#> GSM1009110 2 0.0000 0.8607 0.000 1.000 0.000 0.000
#> GSM1009124 1 0.0000 0.9624 1.000 0.000 0.000 0.000
#> GSM1009138 4 0.0000 0.9688 0.000 0.000 0.000 1.000
#> GSM1009152 1 0.0000 0.9624 1.000 0.000 0.000 0.000
#> GSM1009166 3 0.0000 1.0000 0.000 0.000 1.000 0.000
#> GSM1009180 2 0.4356 0.6796 0.292 0.708 0.000 0.000
#> GSM1009194 1 0.0000 0.9624 1.000 0.000 0.000 0.000
#> GSM1009069 1 0.1940 0.8878 0.924 0.076 0.000 0.000
#> GSM1009083 2 0.0188 0.8598 0.004 0.996 0.000 0.000
#> GSM1009097 4 0.0000 0.9688 0.000 0.000 0.000 1.000
#> GSM1009111 2 0.0000 0.8607 0.000 1.000 0.000 0.000
#> GSM1009125 2 0.0859 0.8545 0.008 0.980 0.004 0.008
#> GSM1009139 4 0.0000 0.9688 0.000 0.000 0.000 1.000
#> GSM1009153 1 0.0000 0.9624 1.000 0.000 0.000 0.000
#> GSM1009167 3 0.0000 1.0000 0.000 0.000 1.000 0.000
#> GSM1009181 2 0.0592 0.8560 0.016 0.984 0.000 0.000
#> GSM1009195 1 0.2589 0.8355 0.884 0.116 0.000 0.000
#> GSM1009070 1 0.0000 0.9624 1.000 0.000 0.000 0.000
#> GSM1009084 2 0.0000 0.8607 0.000 1.000 0.000 0.000
#> GSM1009098 4 0.0000 0.9688 0.000 0.000 0.000 1.000
#> GSM1009112 2 0.0000 0.8607 0.000 1.000 0.000 0.000
#> GSM1009126 1 0.0000 0.9624 1.000 0.000 0.000 0.000
#> GSM1009140 4 0.0000 0.9688 0.000 0.000 0.000 1.000
#> GSM1009154 1 0.0000 0.9624 1.000 0.000 0.000 0.000
#> GSM1009168 3 0.0000 1.0000 0.000 0.000 1.000 0.000
#> GSM1009182 2 0.4331 0.6841 0.288 0.712 0.000 0.000
#> GSM1009196 1 0.0000 0.9624 1.000 0.000 0.000 0.000
#> GSM1009071 1 0.0000 0.9624 1.000 0.000 0.000 0.000
#> GSM1009085 2 0.0000 0.8607 0.000 1.000 0.000 0.000
#> GSM1009099 4 0.0000 0.9688 0.000 0.000 0.000 1.000
#> GSM1009113 2 0.0000 0.8607 0.000 1.000 0.000 0.000
#> GSM1009127 1 0.0000 0.9624 1.000 0.000 0.000 0.000
#> GSM1009141 4 0.0000 0.9688 0.000 0.000 0.000 1.000
#> GSM1009155 1 0.0000 0.9624 1.000 0.000 0.000 0.000
#> GSM1009169 3 0.0000 1.0000 0.000 0.000 1.000 0.000
#> GSM1009183 2 0.3975 0.7275 0.240 0.760 0.000 0.000
#> GSM1009197 1 0.0000 0.9624 1.000 0.000 0.000 0.000
#> GSM1009072 1 0.0000 0.9624 1.000 0.000 0.000 0.000
#> GSM1009086 2 0.0000 0.8607 0.000 1.000 0.000 0.000
#> GSM1009100 4 0.0000 0.9688 0.000 0.000 0.000 1.000
#> GSM1009114 2 0.0000 0.8607 0.000 1.000 0.000 0.000
#> GSM1009128 4 0.6240 0.5572 0.200 0.136 0.000 0.664
#> GSM1009142 4 0.0000 0.9688 0.000 0.000 0.000 1.000
#> GSM1009156 1 0.0000 0.9624 1.000 0.000 0.000 0.000
#> GSM1009170 3 0.0000 1.0000 0.000 0.000 1.000 0.000
#> GSM1009184 2 0.4331 0.6841 0.288 0.712 0.000 0.000
#> GSM1009198 1 0.4981 0.0919 0.536 0.000 0.000 0.464
#> GSM1009073 1 0.0000 0.9624 1.000 0.000 0.000 0.000
#> GSM1009087 1 0.1211 0.9301 0.960 0.040 0.000 0.000
#> GSM1009101 4 0.0000 0.9688 0.000 0.000 0.000 1.000
#> GSM1009115 2 0.0000 0.8607 0.000 1.000 0.000 0.000
#> GSM1009129 2 0.2814 0.7711 0.132 0.868 0.000 0.000
#> GSM1009143 4 0.0000 0.9688 0.000 0.000 0.000 1.000
#> GSM1009157 1 0.0000 0.9624 1.000 0.000 0.000 0.000
#> GSM1009171 3 0.0000 1.0000 0.000 0.000 1.000 0.000
#> GSM1009185 2 0.4356 0.6796 0.292 0.708 0.000 0.000
#> GSM1009199 1 0.3569 0.7113 0.804 0.196 0.000 0.000
#> GSM1009074 1 0.0000 0.9624 1.000 0.000 0.000 0.000
#> GSM1009088 1 0.3356 0.7600 0.824 0.176 0.000 0.000
#> GSM1009102 4 0.0000 0.9688 0.000 0.000 0.000 1.000
#> GSM1009116 2 0.0000 0.8607 0.000 1.000 0.000 0.000
#> GSM1009130 2 0.4564 0.5141 0.328 0.672 0.000 0.000
#> GSM1009144 4 0.0000 0.9688 0.000 0.000 0.000 1.000
#> GSM1009158 1 0.0000 0.9624 1.000 0.000 0.000 0.000
#> GSM1009172 3 0.0000 1.0000 0.000 0.000 1.000 0.000
#> GSM1009186 2 0.4331 0.6841 0.288 0.712 0.000 0.000
#> GSM1009200 1 0.0000 0.9624 1.000 0.000 0.000 0.000
#> GSM1009075 1 0.0000 0.9624 1.000 0.000 0.000 0.000
#> GSM1009089 1 0.0592 0.9498 0.984 0.016 0.000 0.000
#> GSM1009103 4 0.0000 0.9688 0.000 0.000 0.000 1.000
#> GSM1009117 2 0.0000 0.8607 0.000 1.000 0.000 0.000
#> GSM1009131 1 0.3266 0.7647 0.832 0.168 0.000 0.000
#> GSM1009145 4 0.0000 0.9688 0.000 0.000 0.000 1.000
#> GSM1009159 1 0.0000 0.9624 1.000 0.000 0.000 0.000
#> GSM1009173 3 0.0000 1.0000 0.000 0.000 1.000 0.000
#> GSM1009187 2 0.4356 0.6796 0.292 0.708 0.000 0.000
#> GSM1009201 1 0.0000 0.9624 1.000 0.000 0.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM1009062 1 0.0000 0.9599 1.000 0.000 0 0.000 0.000
#> GSM1009076 2 0.0963 0.9166 0.000 0.964 0 0.000 0.036
#> GSM1009090 4 0.0000 0.9695 0.000 0.000 0 1.000 0.000
#> GSM1009104 5 0.0000 0.9435 0.000 0.000 0 0.000 1.000
#> GSM1009118 2 0.3895 0.5282 0.320 0.680 0 0.000 0.000
#> GSM1009132 4 0.0000 0.9695 0.000 0.000 0 1.000 0.000
#> GSM1009146 1 0.0000 0.9599 1.000 0.000 0 0.000 0.000
#> GSM1009160 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000
#> GSM1009174 2 0.0000 0.9277 0.000 1.000 0 0.000 0.000
#> GSM1009188 1 0.0000 0.9599 1.000 0.000 0 0.000 0.000
#> GSM1009063 1 0.0000 0.9599 1.000 0.000 0 0.000 0.000
#> GSM1009077 2 0.0963 0.9166 0.000 0.964 0 0.000 0.036
#> GSM1009091 4 0.0000 0.9695 0.000 0.000 0 1.000 0.000
#> GSM1009105 5 0.0000 0.9435 0.000 0.000 0 0.000 1.000
#> GSM1009119 1 0.0000 0.9599 1.000 0.000 0 0.000 0.000
#> GSM1009133 4 0.0000 0.9695 0.000 0.000 0 1.000 0.000
#> GSM1009147 1 0.0000 0.9599 1.000 0.000 0 0.000 0.000
#> GSM1009161 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000
#> GSM1009175 2 0.0000 0.9277 0.000 1.000 0 0.000 0.000
#> GSM1009189 1 0.0000 0.9599 1.000 0.000 0 0.000 0.000
#> GSM1009064 1 0.0000 0.9599 1.000 0.000 0 0.000 0.000
#> GSM1009078 1 0.0290 0.9528 0.992 0.000 0 0.000 0.008
#> GSM1009092 4 0.0000 0.9695 0.000 0.000 0 1.000 0.000
#> GSM1009106 5 0.0000 0.9435 0.000 0.000 0 0.000 1.000
#> GSM1009120 1 0.0000 0.9599 1.000 0.000 0 0.000 0.000
#> GSM1009134 4 0.0000 0.9695 0.000 0.000 0 1.000 0.000
#> GSM1009148 1 0.0000 0.9599 1.000 0.000 0 0.000 0.000
#> GSM1009162 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000
#> GSM1009176 2 0.0000 0.9277 0.000 1.000 0 0.000 0.000
#> GSM1009190 1 0.0000 0.9599 1.000 0.000 0 0.000 0.000
#> GSM1009065 1 0.0000 0.9599 1.000 0.000 0 0.000 0.000
#> GSM1009079 2 0.0290 0.9257 0.000 0.992 0 0.000 0.008
#> GSM1009093 4 0.0000 0.9695 0.000 0.000 0 1.000 0.000
#> GSM1009107 5 0.0000 0.9435 0.000 0.000 0 0.000 1.000
#> GSM1009121 1 0.8342 -0.2556 0.320 0.140 0 0.236 0.304
#> GSM1009135 4 0.0000 0.9695 0.000 0.000 0 1.000 0.000
#> GSM1009149 1 0.0000 0.9599 1.000 0.000 0 0.000 0.000
#> GSM1009163 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000
#> GSM1009177 2 0.0000 0.9277 0.000 1.000 0 0.000 0.000
#> GSM1009191 1 0.0000 0.9599 1.000 0.000 0 0.000 0.000
#> GSM1009066 1 0.0000 0.9599 1.000 0.000 0 0.000 0.000
#> GSM1009080 2 0.0963 0.9166 0.000 0.964 0 0.000 0.036
#> GSM1009094 4 0.0000 0.9695 0.000 0.000 0 1.000 0.000
#> GSM1009108 5 0.0000 0.9435 0.000 0.000 0 0.000 1.000
#> GSM1009122 2 0.2561 0.7770 0.144 0.856 0 0.000 0.000
#> GSM1009136 4 0.0000 0.9695 0.000 0.000 0 1.000 0.000
#> GSM1009150 1 0.0000 0.9599 1.000 0.000 0 0.000 0.000
#> GSM1009164 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000
#> GSM1009178 2 0.0000 0.9277 0.000 1.000 0 0.000 0.000
#> GSM1009192 1 0.0000 0.9599 1.000 0.000 0 0.000 0.000
#> GSM1009067 1 0.0000 0.9599 1.000 0.000 0 0.000 0.000
#> GSM1009081 2 0.0963 0.9166 0.000 0.964 0 0.000 0.036
#> GSM1009095 4 0.0000 0.9695 0.000 0.000 0 1.000 0.000
#> GSM1009109 5 0.0000 0.9435 0.000 0.000 0 0.000 1.000
#> GSM1009123 4 0.4015 0.4502 0.348 0.000 0 0.652 0.000
#> GSM1009137 4 0.0000 0.9695 0.000 0.000 0 1.000 0.000
#> GSM1009151 1 0.0000 0.9599 1.000 0.000 0 0.000 0.000
#> GSM1009165 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000
#> GSM1009179 2 0.0000 0.9277 0.000 1.000 0 0.000 0.000
#> GSM1009193 1 0.0000 0.9599 1.000 0.000 0 0.000 0.000
#> GSM1009068 1 0.0000 0.9599 1.000 0.000 0 0.000 0.000
#> GSM1009082 2 0.0963 0.9166 0.000 0.964 0 0.000 0.036
#> GSM1009096 4 0.0000 0.9695 0.000 0.000 0 1.000 0.000
#> GSM1009110 5 0.0000 0.9435 0.000 0.000 0 0.000 1.000
#> GSM1009124 1 0.0000 0.9599 1.000 0.000 0 0.000 0.000
#> GSM1009138 4 0.0000 0.9695 0.000 0.000 0 1.000 0.000
#> GSM1009152 1 0.0000 0.9599 1.000 0.000 0 0.000 0.000
#> GSM1009166 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000
#> GSM1009180 2 0.0000 0.9277 0.000 1.000 0 0.000 0.000
#> GSM1009194 1 0.0000 0.9599 1.000 0.000 0 0.000 0.000
#> GSM1009069 1 0.2127 0.8418 0.892 0.108 0 0.000 0.000
#> GSM1009083 2 0.0963 0.9166 0.000 0.964 0 0.000 0.036
#> GSM1009097 4 0.0000 0.9695 0.000 0.000 0 1.000 0.000
#> GSM1009111 5 0.0000 0.9435 0.000 0.000 0 0.000 1.000
#> GSM1009125 2 0.0000 0.9277 0.000 1.000 0 0.000 0.000
#> GSM1009139 4 0.0000 0.9695 0.000 0.000 0 1.000 0.000
#> GSM1009153 1 0.0000 0.9599 1.000 0.000 0 0.000 0.000
#> GSM1009167 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000
#> GSM1009181 2 0.0000 0.9277 0.000 1.000 0 0.000 0.000
#> GSM1009195 1 0.4045 0.3994 0.644 0.356 0 0.000 0.000
#> GSM1009070 1 0.0000 0.9599 1.000 0.000 0 0.000 0.000
#> GSM1009084 2 0.1270 0.9075 0.000 0.948 0 0.000 0.052
#> GSM1009098 4 0.0000 0.9695 0.000 0.000 0 1.000 0.000
#> GSM1009112 5 0.0000 0.9435 0.000 0.000 0 0.000 1.000
#> GSM1009126 1 0.0794 0.9348 0.972 0.028 0 0.000 0.000
#> GSM1009140 4 0.0000 0.9695 0.000 0.000 0 1.000 0.000
#> GSM1009154 1 0.0000 0.9599 1.000 0.000 0 0.000 0.000
#> GSM1009168 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000
#> GSM1009182 2 0.0000 0.9277 0.000 1.000 0 0.000 0.000
#> GSM1009196 1 0.0000 0.9599 1.000 0.000 0 0.000 0.000
#> GSM1009071 1 0.0000 0.9599 1.000 0.000 0 0.000 0.000
#> GSM1009085 2 0.3274 0.7311 0.000 0.780 0 0.000 0.220
#> GSM1009099 4 0.0000 0.9695 0.000 0.000 0 1.000 0.000
#> GSM1009113 5 0.0000 0.9435 0.000 0.000 0 0.000 1.000
#> GSM1009127 1 0.0000 0.9599 1.000 0.000 0 0.000 0.000
#> GSM1009141 4 0.0000 0.9695 0.000 0.000 0 1.000 0.000
#> GSM1009155 1 0.0000 0.9599 1.000 0.000 0 0.000 0.000
#> GSM1009169 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000
#> GSM1009183 2 0.0000 0.9277 0.000 1.000 0 0.000 0.000
#> GSM1009197 1 0.0000 0.9599 1.000 0.000 0 0.000 0.000
#> GSM1009072 1 0.0000 0.9599 1.000 0.000 0 0.000 0.000
#> GSM1009086 2 0.1478 0.8989 0.000 0.936 0 0.000 0.064
#> GSM1009100 4 0.0000 0.9695 0.000 0.000 0 1.000 0.000
#> GSM1009114 5 0.0000 0.9435 0.000 0.000 0 0.000 1.000
#> GSM1009128 4 0.4840 0.5298 0.268 0.000 0 0.676 0.056
#> GSM1009142 4 0.0000 0.9695 0.000 0.000 0 1.000 0.000
#> GSM1009156 1 0.0000 0.9599 1.000 0.000 0 0.000 0.000
#> GSM1009170 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000
#> GSM1009184 2 0.0000 0.9277 0.000 1.000 0 0.000 0.000
#> GSM1009198 1 0.4294 0.0993 0.532 0.000 0 0.468 0.000
#> GSM1009073 1 0.0000 0.9599 1.000 0.000 0 0.000 0.000
#> GSM1009087 1 0.0510 0.9455 0.984 0.000 0 0.000 0.016
#> GSM1009101 4 0.0000 0.9695 0.000 0.000 0 1.000 0.000
#> GSM1009115 5 0.0000 0.9435 0.000 0.000 0 0.000 1.000
#> GSM1009129 2 0.3573 0.7580 0.152 0.812 0 0.000 0.036
#> GSM1009143 4 0.0000 0.9695 0.000 0.000 0 1.000 0.000
#> GSM1009157 1 0.0000 0.9599 1.000 0.000 0 0.000 0.000
#> GSM1009171 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000
#> GSM1009185 2 0.0000 0.9277 0.000 1.000 0 0.000 0.000
#> GSM1009199 2 0.4262 0.2403 0.440 0.560 0 0.000 0.000
#> GSM1009074 1 0.0000 0.9599 1.000 0.000 0 0.000 0.000
#> GSM1009088 1 0.3495 0.7450 0.816 0.032 0 0.000 0.152
#> GSM1009102 4 0.0000 0.9695 0.000 0.000 0 1.000 0.000
#> GSM1009116 5 0.0000 0.9435 0.000 0.000 0 0.000 1.000
#> GSM1009130 5 0.3074 0.6861 0.196 0.000 0 0.000 0.804
#> GSM1009144 4 0.0000 0.9695 0.000 0.000 0 1.000 0.000
#> GSM1009158 1 0.0000 0.9599 1.000 0.000 0 0.000 0.000
#> GSM1009172 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000
#> GSM1009186 2 0.0000 0.9277 0.000 1.000 0 0.000 0.000
#> GSM1009200 1 0.0000 0.9599 1.000 0.000 0 0.000 0.000
#> GSM1009075 1 0.0000 0.9599 1.000 0.000 0 0.000 0.000
#> GSM1009089 1 0.0000 0.9599 1.000 0.000 0 0.000 0.000
#> GSM1009103 4 0.0000 0.9695 0.000 0.000 0 1.000 0.000
#> GSM1009117 5 0.0000 0.9435 0.000 0.000 0 0.000 1.000
#> GSM1009131 5 0.4045 0.4394 0.356 0.000 0 0.000 0.644
#> GSM1009145 4 0.0000 0.9695 0.000 0.000 0 1.000 0.000
#> GSM1009159 1 0.0000 0.9599 1.000 0.000 0 0.000 0.000
#> GSM1009173 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000
#> GSM1009187 2 0.0000 0.9277 0.000 1.000 0 0.000 0.000
#> GSM1009201 1 0.0000 0.9599 1.000 0.000 0 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1009062 6 0.0260 0.999 0.008 0.000 0 0.00 0.000 0.992
#> GSM1009076 2 0.1124 0.950 0.000 0.956 0 0.00 0.036 0.008
#> GSM1009090 4 0.0000 0.986 0.000 0.000 0 1.00 0.000 0.000
#> GSM1009104 5 0.0000 0.981 0.000 0.000 0 0.00 1.000 0.000
#> GSM1009118 1 0.3851 0.125 0.540 0.460 0 0.00 0.000 0.000
#> GSM1009132 4 0.0000 0.986 0.000 0.000 0 1.00 0.000 0.000
#> GSM1009146 1 0.0000 0.974 1.000 0.000 0 0.00 0.000 0.000
#> GSM1009160 3 0.0000 1.000 0.000 0.000 1 0.00 0.000 0.000
#> GSM1009174 2 0.0000 0.962 0.000 1.000 0 0.00 0.000 0.000
#> GSM1009188 1 0.0000 0.974 1.000 0.000 0 0.00 0.000 0.000
#> GSM1009063 6 0.0260 0.999 0.008 0.000 0 0.00 0.000 0.992
#> GSM1009077 2 0.1124 0.950 0.000 0.956 0 0.00 0.036 0.008
#> GSM1009091 4 0.0000 0.986 0.000 0.000 0 1.00 0.000 0.000
#> GSM1009105 5 0.0000 0.981 0.000 0.000 0 0.00 1.000 0.000
#> GSM1009119 1 0.0000 0.974 1.000 0.000 0 0.00 0.000 0.000
#> GSM1009133 4 0.0000 0.986 0.000 0.000 0 1.00 0.000 0.000
#> GSM1009147 1 0.0000 0.974 1.000 0.000 0 0.00 0.000 0.000
#> GSM1009161 3 0.0000 1.000 0.000 0.000 1 0.00 0.000 0.000
#> GSM1009175 2 0.0000 0.962 0.000 1.000 0 0.00 0.000 0.000
#> GSM1009189 1 0.0000 0.974 1.000 0.000 0 0.00 0.000 0.000
#> GSM1009064 6 0.0260 0.999 0.008 0.000 0 0.00 0.000 0.992
#> GSM1009078 1 0.1398 0.925 0.940 0.000 0 0.00 0.008 0.052
#> GSM1009092 4 0.0000 0.986 0.000 0.000 0 1.00 0.000 0.000
#> GSM1009106 5 0.0000 0.981 0.000 0.000 0 0.00 1.000 0.000
#> GSM1009120 1 0.0000 0.974 1.000 0.000 0 0.00 0.000 0.000
#> GSM1009134 4 0.0000 0.986 0.000 0.000 0 1.00 0.000 0.000
#> GSM1009148 1 0.0000 0.974 1.000 0.000 0 0.00 0.000 0.000
#> GSM1009162 3 0.0000 1.000 0.000 0.000 1 0.00 0.000 0.000
#> GSM1009176 2 0.0000 0.962 0.000 1.000 0 0.00 0.000 0.000
#> GSM1009190 1 0.0000 0.974 1.000 0.000 0 0.00 0.000 0.000
#> GSM1009065 6 0.0146 0.994 0.004 0.000 0 0.00 0.000 0.996
#> GSM1009079 2 0.0520 0.958 0.000 0.984 0 0.00 0.008 0.008
#> GSM1009093 4 0.0000 0.986 0.000 0.000 0 1.00 0.000 0.000
#> GSM1009107 5 0.0000 0.981 0.000 0.000 0 0.00 1.000 0.000
#> GSM1009121 1 0.0713 0.950 0.972 0.028 0 0.00 0.000 0.000
#> GSM1009135 4 0.0000 0.986 0.000 0.000 0 1.00 0.000 0.000
#> GSM1009149 1 0.0000 0.974 1.000 0.000 0 0.00 0.000 0.000
#> GSM1009163 3 0.0000 1.000 0.000 0.000 1 0.00 0.000 0.000
#> GSM1009177 2 0.0000 0.962 0.000 1.000 0 0.00 0.000 0.000
#> GSM1009191 1 0.0000 0.974 1.000 0.000 0 0.00 0.000 0.000
#> GSM1009066 6 0.0260 0.999 0.008 0.000 0 0.00 0.000 0.992
#> GSM1009080 2 0.1124 0.950 0.000 0.956 0 0.00 0.036 0.008
#> GSM1009094 4 0.0000 0.986 0.000 0.000 0 1.00 0.000 0.000
#> GSM1009108 5 0.0000 0.981 0.000 0.000 0 0.00 1.000 0.000
#> GSM1009122 2 0.2378 0.796 0.152 0.848 0 0.00 0.000 0.000
#> GSM1009136 4 0.0000 0.986 0.000 0.000 0 1.00 0.000 0.000
#> GSM1009150 1 0.0000 0.974 1.000 0.000 0 0.00 0.000 0.000
#> GSM1009164 3 0.0000 1.000 0.000 0.000 1 0.00 0.000 0.000
#> GSM1009178 2 0.0000 0.962 0.000 1.000 0 0.00 0.000 0.000
#> GSM1009192 1 0.0000 0.974 1.000 0.000 0 0.00 0.000 0.000
#> GSM1009067 6 0.0260 0.999 0.008 0.000 0 0.00 0.000 0.992
#> GSM1009081 2 0.1124 0.950 0.000 0.956 0 0.00 0.036 0.008
#> GSM1009095 4 0.0000 0.986 0.000 0.000 0 1.00 0.000 0.000
#> GSM1009109 5 0.0000 0.981 0.000 0.000 0 0.00 1.000 0.000
#> GSM1009123 1 0.0000 0.974 1.000 0.000 0 0.00 0.000 0.000
#> GSM1009137 4 0.0000 0.986 0.000 0.000 0 1.00 0.000 0.000
#> GSM1009151 1 0.0000 0.974 1.000 0.000 0 0.00 0.000 0.000
#> GSM1009165 3 0.0000 1.000 0.000 0.000 1 0.00 0.000 0.000
#> GSM1009179 2 0.0000 0.962 0.000 1.000 0 0.00 0.000 0.000
#> GSM1009193 1 0.0000 0.974 1.000 0.000 0 0.00 0.000 0.000
#> GSM1009068 6 0.0260 0.999 0.008 0.000 0 0.00 0.000 0.992
#> GSM1009082 2 0.1124 0.950 0.000 0.956 0 0.00 0.036 0.008
#> GSM1009096 4 0.0000 0.986 0.000 0.000 0 1.00 0.000 0.000
#> GSM1009110 5 0.0000 0.981 0.000 0.000 0 0.00 1.000 0.000
#> GSM1009124 1 0.0000 0.974 1.000 0.000 0 0.00 0.000 0.000
#> GSM1009138 4 0.0000 0.986 0.000 0.000 0 1.00 0.000 0.000
#> GSM1009152 1 0.0000 0.974 1.000 0.000 0 0.00 0.000 0.000
#> GSM1009166 3 0.0000 1.000 0.000 0.000 1 0.00 0.000 0.000
#> GSM1009180 2 0.0000 0.962 0.000 1.000 0 0.00 0.000 0.000
#> GSM1009194 1 0.0000 0.974 1.000 0.000 0 0.00 0.000 0.000
#> GSM1009069 6 0.0000 0.988 0.000 0.000 0 0.00 0.000 1.000
#> GSM1009083 2 0.1124 0.950 0.000 0.956 0 0.00 0.036 0.008
#> GSM1009097 4 0.0000 0.986 0.000 0.000 0 1.00 0.000 0.000
#> GSM1009111 5 0.0000 0.981 0.000 0.000 0 0.00 1.000 0.000
#> GSM1009125 2 0.0000 0.962 0.000 1.000 0 0.00 0.000 0.000
#> GSM1009139 4 0.0000 0.986 0.000 0.000 0 1.00 0.000 0.000
#> GSM1009153 1 0.0000 0.974 1.000 0.000 0 0.00 0.000 0.000
#> GSM1009167 3 0.0000 1.000 0.000 0.000 1 0.00 0.000 0.000
#> GSM1009181 2 0.0000 0.962 0.000 1.000 0 0.00 0.000 0.000
#> GSM1009195 1 0.0000 0.974 1.000 0.000 0 0.00 0.000 0.000
#> GSM1009070 6 0.0260 0.999 0.008 0.000 0 0.00 0.000 0.992
#> GSM1009084 2 0.1398 0.940 0.000 0.940 0 0.00 0.052 0.008
#> GSM1009098 4 0.0000 0.986 0.000 0.000 0 1.00 0.000 0.000
#> GSM1009112 5 0.0000 0.981 0.000 0.000 0 0.00 1.000 0.000
#> GSM1009126 1 0.0000 0.974 1.000 0.000 0 0.00 0.000 0.000
#> GSM1009140 4 0.0000 0.986 0.000 0.000 0 1.00 0.000 0.000
#> GSM1009154 1 0.0000 0.974 1.000 0.000 0 0.00 0.000 0.000
#> GSM1009168 3 0.0000 1.000 0.000 0.000 1 0.00 0.000 0.000
#> GSM1009182 2 0.0000 0.962 0.000 1.000 0 0.00 0.000 0.000
#> GSM1009196 1 0.0000 0.974 1.000 0.000 0 0.00 0.000 0.000
#> GSM1009071 6 0.0260 0.999 0.008 0.000 0 0.00 0.000 0.992
#> GSM1009085 2 0.3161 0.757 0.000 0.776 0 0.00 0.216 0.008
#> GSM1009099 4 0.0000 0.986 0.000 0.000 0 1.00 0.000 0.000
#> GSM1009113 5 0.0000 0.981 0.000 0.000 0 0.00 1.000 0.000
#> GSM1009127 1 0.0000 0.974 1.000 0.000 0 0.00 0.000 0.000
#> GSM1009141 4 0.0000 0.986 0.000 0.000 0 1.00 0.000 0.000
#> GSM1009155 1 0.0000 0.974 1.000 0.000 0 0.00 0.000 0.000
#> GSM1009169 3 0.0000 1.000 0.000 0.000 1 0.00 0.000 0.000
#> GSM1009183 2 0.0000 0.962 0.000 1.000 0 0.00 0.000 0.000
#> GSM1009197 1 0.0000 0.974 1.000 0.000 0 0.00 0.000 0.000
#> GSM1009072 6 0.0260 0.999 0.008 0.000 0 0.00 0.000 0.992
#> GSM1009086 2 0.1524 0.934 0.000 0.932 0 0.00 0.060 0.008
#> GSM1009100 4 0.0000 0.986 0.000 0.000 0 1.00 0.000 0.000
#> GSM1009114 5 0.0000 0.981 0.000 0.000 0 0.00 1.000 0.000
#> GSM1009128 4 0.4309 0.515 0.296 0.000 0 0.66 0.044 0.000
#> GSM1009142 4 0.0000 0.986 0.000 0.000 0 1.00 0.000 0.000
#> GSM1009156 1 0.0000 0.974 1.000 0.000 0 0.00 0.000 0.000
#> GSM1009170 3 0.0000 1.000 0.000 0.000 1 0.00 0.000 0.000
#> GSM1009184 2 0.0000 0.962 0.000 1.000 0 0.00 0.000 0.000
#> GSM1009198 1 0.0000 0.974 1.000 0.000 0 0.00 0.000 0.000
#> GSM1009073 6 0.0260 0.999 0.008 0.000 0 0.00 0.000 0.992
#> GSM1009087 1 0.1168 0.941 0.956 0.000 0 0.00 0.016 0.028
#> GSM1009101 4 0.0000 0.986 0.000 0.000 0 1.00 0.000 0.000
#> GSM1009115 5 0.0000 0.981 0.000 0.000 0 0.00 1.000 0.000
#> GSM1009129 2 0.3385 0.793 0.144 0.812 0 0.00 0.036 0.008
#> GSM1009143 4 0.0000 0.986 0.000 0.000 0 1.00 0.000 0.000
#> GSM1009157 1 0.0000 0.974 1.000 0.000 0 0.00 0.000 0.000
#> GSM1009171 3 0.0000 1.000 0.000 0.000 1 0.00 0.000 0.000
#> GSM1009185 2 0.0000 0.962 0.000 1.000 0 0.00 0.000 0.000
#> GSM1009199 1 0.0000 0.974 1.000 0.000 0 0.00 0.000 0.000
#> GSM1009074 6 0.0260 0.999 0.008 0.000 0 0.00 0.000 0.992
#> GSM1009088 1 0.3603 0.781 0.808 0.008 0 0.00 0.112 0.072
#> GSM1009102 4 0.0000 0.986 0.000 0.000 0 1.00 0.000 0.000
#> GSM1009116 5 0.0000 0.981 0.000 0.000 0 0.00 1.000 0.000
#> GSM1009130 5 0.2948 0.717 0.188 0.000 0 0.00 0.804 0.008
#> GSM1009144 4 0.0000 0.986 0.000 0.000 0 1.00 0.000 0.000
#> GSM1009158 1 0.0000 0.974 1.000 0.000 0 0.00 0.000 0.000
#> GSM1009172 3 0.0000 1.000 0.000 0.000 1 0.00 0.000 0.000
#> GSM1009186 2 0.0000 0.962 0.000 1.000 0 0.00 0.000 0.000
#> GSM1009200 1 0.0000 0.974 1.000 0.000 0 0.00 0.000 0.000
#> GSM1009075 6 0.0260 0.999 0.008 0.000 0 0.00 0.000 0.992
#> GSM1009089 1 0.2762 0.756 0.804 0.000 0 0.00 0.000 0.196
#> GSM1009103 4 0.0000 0.986 0.000 0.000 0 1.00 0.000 0.000
#> GSM1009117 5 0.0000 0.981 0.000 0.000 0 0.00 1.000 0.000
#> GSM1009131 1 0.0000 0.974 1.000 0.000 0 0.00 0.000 0.000
#> GSM1009145 4 0.0000 0.986 0.000 0.000 0 1.00 0.000 0.000
#> GSM1009159 1 0.0000 0.974 1.000 0.000 0 0.00 0.000 0.000
#> GSM1009173 3 0.0000 1.000 0.000 0.000 1 0.00 0.000 0.000
#> GSM1009187 2 0.0000 0.962 0.000 1.000 0 0.00 0.000 0.000
#> GSM1009201 1 0.0000 0.974 1.000 0.000 0 0.00 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 temperature(p) time(p) specimen(p) k
#> MAD:pam 139 0.817 0.997 1.14e-17 2
#> MAD:pam 137 0.983 1.000 1.97e-42 3
#> MAD:pam 136 1.000 1.000 3.30e-62 4
#> MAD:pam 134 1.000 1.000 1.83e-79 5
#> MAD:pam 139 1.000 1.000 3.89e-105 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 140 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 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 0.307 0.694 0.801 0.3785 0.602 0.602
#> 3 3 1.000 0.987 0.993 0.2201 0.572 0.456
#> 4 4 0.772 0.893 0.913 0.2832 0.878 0.775
#> 5 5 0.703 0.678 0.818 0.2349 0.900 0.764
#> 6 6 0.819 0.614 0.852 0.0584 0.907 0.717
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
#> GSM1009062 2 0.9775 0.9039 0.412 0.588
#> GSM1009076 2 0.9866 0.8872 0.432 0.568
#> GSM1009090 1 0.8763 0.6323 0.704 0.296
#> GSM1009104 1 0.0000 0.7671 1.000 0.000
#> GSM1009118 1 0.0000 0.7671 1.000 0.000
#> GSM1009132 1 0.8763 0.6323 0.704 0.296
#> GSM1009146 1 0.7674 0.3535 0.776 0.224
#> GSM1009160 2 0.8763 0.8651 0.296 0.704
#> GSM1009174 1 0.3114 0.7229 0.944 0.056
#> GSM1009188 1 0.2778 0.7343 0.952 0.048
#> GSM1009063 2 0.9775 0.9039 0.412 0.588
#> GSM1009077 2 0.9866 0.8872 0.432 0.568
#> GSM1009091 1 0.8763 0.6323 0.704 0.296
#> GSM1009105 1 0.0000 0.7671 1.000 0.000
#> GSM1009119 1 0.0000 0.7671 1.000 0.000
#> GSM1009133 1 0.8763 0.6323 0.704 0.296
#> GSM1009147 1 0.8144 0.2425 0.748 0.252
#> GSM1009161 2 0.8763 0.8651 0.296 0.704
#> GSM1009175 1 0.1184 0.7590 0.984 0.016
#> GSM1009189 1 0.0672 0.7635 0.992 0.008
#> GSM1009064 2 0.9775 0.9039 0.412 0.588
#> GSM1009078 1 0.9988 -0.6960 0.520 0.480
#> GSM1009092 1 0.8763 0.6323 0.704 0.296
#> GSM1009106 1 0.0000 0.7671 1.000 0.000
#> GSM1009120 1 0.0672 0.7645 0.992 0.008
#> GSM1009134 1 0.8763 0.6323 0.704 0.296
#> GSM1009148 1 0.7674 0.3513 0.776 0.224
#> GSM1009162 2 0.8763 0.8651 0.296 0.704
#> GSM1009176 1 0.2778 0.7315 0.952 0.048
#> GSM1009190 1 0.5737 0.6023 0.864 0.136
#> GSM1009065 2 0.9775 0.9039 0.412 0.588
#> GSM1009079 1 0.4815 0.6564 0.896 0.104
#> GSM1009093 1 0.8763 0.6323 0.704 0.296
#> GSM1009107 1 0.0000 0.7671 1.000 0.000
#> GSM1009121 1 0.0000 0.7671 1.000 0.000
#> GSM1009135 1 0.8763 0.6323 0.704 0.296
#> GSM1009149 1 0.2423 0.7399 0.960 0.040
#> GSM1009163 2 0.8763 0.8651 0.296 0.704
#> GSM1009177 1 0.2778 0.7315 0.952 0.048
#> GSM1009191 1 0.6531 0.5297 0.832 0.168
#> GSM1009066 2 0.9775 0.9039 0.412 0.588
#> GSM1009080 1 0.9933 -0.6233 0.548 0.452
#> GSM1009094 1 0.8763 0.6323 0.704 0.296
#> GSM1009108 1 0.0000 0.7671 1.000 0.000
#> GSM1009122 1 0.0000 0.7671 1.000 0.000
#> GSM1009136 1 0.8763 0.6323 0.704 0.296
#> GSM1009150 1 0.5059 0.6409 0.888 0.112
#> GSM1009164 2 0.8763 0.8651 0.296 0.704
#> GSM1009178 1 0.1184 0.7590 0.984 0.016
#> GSM1009192 1 0.0938 0.7621 0.988 0.012
#> GSM1009067 2 0.9775 0.9039 0.412 0.588
#> GSM1009081 1 0.8813 0.0118 0.700 0.300
#> GSM1009095 1 0.8499 0.6404 0.724 0.276
#> GSM1009109 1 0.0000 0.7671 1.000 0.000
#> GSM1009123 1 0.0000 0.7671 1.000 0.000
#> GSM1009137 1 0.8763 0.6323 0.704 0.296
#> GSM1009151 1 0.5178 0.6298 0.884 0.116
#> GSM1009165 2 0.8763 0.8651 0.296 0.704
#> GSM1009179 1 0.1414 0.7562 0.980 0.020
#> GSM1009193 1 0.0672 0.7635 0.992 0.008
#> GSM1009068 2 0.9775 0.9039 0.412 0.588
#> GSM1009082 2 0.9850 0.8908 0.428 0.572
#> GSM1009096 1 0.8763 0.6323 0.704 0.296
#> GSM1009110 1 0.0000 0.7671 1.000 0.000
#> GSM1009124 1 0.0000 0.7671 1.000 0.000
#> GSM1009138 1 0.8763 0.6323 0.704 0.296
#> GSM1009152 1 0.9358 -0.2550 0.648 0.352
#> GSM1009166 2 0.8763 0.8651 0.296 0.704
#> GSM1009180 1 0.0000 0.7671 1.000 0.000
#> GSM1009194 1 0.1843 0.7520 0.972 0.028
#> GSM1009069 2 0.9775 0.9039 0.412 0.588
#> GSM1009083 2 0.9850 0.8908 0.428 0.572
#> GSM1009097 1 0.8763 0.6323 0.704 0.296
#> GSM1009111 1 0.0000 0.7671 1.000 0.000
#> GSM1009125 1 0.0000 0.7671 1.000 0.000
#> GSM1009139 1 0.8763 0.6323 0.704 0.296
#> GSM1009153 1 0.5519 0.6066 0.872 0.128
#> GSM1009167 2 0.8763 0.8651 0.296 0.704
#> GSM1009181 1 0.0672 0.7638 0.992 0.008
#> GSM1009195 1 0.5059 0.6474 0.888 0.112
#> GSM1009070 2 0.9775 0.9039 0.412 0.588
#> GSM1009084 2 0.9866 0.8872 0.432 0.568
#> GSM1009098 1 0.8763 0.6323 0.704 0.296
#> GSM1009112 1 0.0000 0.7671 1.000 0.000
#> GSM1009126 1 0.0000 0.7671 1.000 0.000
#> GSM1009140 1 0.8763 0.6323 0.704 0.296
#> GSM1009154 1 0.8661 0.0764 0.712 0.288
#> GSM1009168 2 0.8763 0.8651 0.296 0.704
#> GSM1009182 1 0.0376 0.7656 0.996 0.004
#> GSM1009196 1 0.2603 0.7387 0.956 0.044
#> GSM1009071 2 0.9775 0.9039 0.412 0.588
#> GSM1009085 2 0.9866 0.8872 0.432 0.568
#> GSM1009099 1 0.8763 0.6323 0.704 0.296
#> GSM1009113 1 0.0000 0.7671 1.000 0.000
#> GSM1009127 1 0.0000 0.7671 1.000 0.000
#> GSM1009141 1 0.8763 0.6323 0.704 0.296
#> GSM1009155 1 0.2423 0.7395 0.960 0.040
#> GSM1009169 2 0.8763 0.8651 0.296 0.704
#> GSM1009183 1 0.0000 0.7671 1.000 0.000
#> GSM1009197 1 0.0938 0.7621 0.988 0.012
#> GSM1009072 2 0.9775 0.9039 0.412 0.588
#> GSM1009086 2 0.9881 0.8811 0.436 0.564
#> GSM1009100 1 0.8763 0.6323 0.704 0.296
#> GSM1009114 1 0.0000 0.7671 1.000 0.000
#> GSM1009128 1 0.0000 0.7671 1.000 0.000
#> GSM1009142 1 0.8763 0.6323 0.704 0.296
#> GSM1009156 2 0.9988 0.7822 0.480 0.520
#> GSM1009170 2 0.8763 0.8651 0.296 0.704
#> GSM1009184 1 0.4690 0.6628 0.900 0.100
#> GSM1009198 1 0.3584 0.7122 0.932 0.068
#> GSM1009073 2 0.9775 0.9039 0.412 0.588
#> GSM1009087 2 0.9850 0.8908 0.428 0.572
#> GSM1009101 1 0.8763 0.6323 0.704 0.296
#> GSM1009115 1 0.0000 0.7671 1.000 0.000
#> GSM1009129 1 0.0000 0.7671 1.000 0.000
#> GSM1009143 1 0.8763 0.6323 0.704 0.296
#> GSM1009157 1 0.7674 0.3600 0.776 0.224
#> GSM1009171 2 0.8763 0.8651 0.296 0.704
#> GSM1009185 1 0.0376 0.7656 0.996 0.004
#> GSM1009199 1 0.4562 0.6734 0.904 0.096
#> GSM1009074 2 0.9775 0.9039 0.412 0.588
#> GSM1009088 2 0.9881 0.8793 0.436 0.564
#> GSM1009102 1 0.8763 0.6323 0.704 0.296
#> GSM1009116 1 0.0000 0.7671 1.000 0.000
#> GSM1009130 1 0.0376 0.7654 0.996 0.004
#> GSM1009144 1 0.8763 0.6323 0.704 0.296
#> GSM1009158 1 0.8081 0.2636 0.752 0.248
#> GSM1009172 2 0.8763 0.8651 0.296 0.704
#> GSM1009186 1 0.3733 0.7033 0.928 0.072
#> GSM1009200 1 0.3431 0.7168 0.936 0.064
#> GSM1009075 2 0.9775 0.9039 0.412 0.588
#> GSM1009089 1 0.9963 -0.6537 0.536 0.464
#> GSM1009103 1 0.8763 0.6323 0.704 0.296
#> GSM1009117 1 0.0000 0.7671 1.000 0.000
#> GSM1009131 1 0.0000 0.7671 1.000 0.000
#> GSM1009145 1 0.8763 0.6323 0.704 0.296
#> GSM1009159 1 0.0938 0.7616 0.988 0.012
#> GSM1009173 2 0.8763 0.8651 0.296 0.704
#> GSM1009187 1 0.0938 0.7614 0.988 0.012
#> GSM1009201 1 0.2948 0.7301 0.948 0.052
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1009062 2 0.0237 0.989 0.000 0.996 0.004
#> GSM1009076 2 0.0000 0.990 0.000 1.000 0.000
#> GSM1009090 1 0.0000 1.000 1.000 0.000 0.000
#> GSM1009104 2 0.0747 0.982 0.016 0.984 0.000
#> GSM1009118 2 0.0892 0.982 0.020 0.980 0.000
#> GSM1009132 1 0.0000 1.000 1.000 0.000 0.000
#> GSM1009146 2 0.0237 0.990 0.004 0.996 0.000
#> GSM1009160 3 0.0000 1.000 0.000 0.000 1.000
#> GSM1009174 2 0.0000 0.990 0.000 1.000 0.000
#> GSM1009188 2 0.0237 0.990 0.004 0.996 0.000
#> GSM1009063 2 0.0237 0.989 0.000 0.996 0.004
#> GSM1009077 2 0.0000 0.990 0.000 1.000 0.000
#> GSM1009091 1 0.0000 1.000 1.000 0.000 0.000
#> GSM1009105 2 0.0747 0.982 0.016 0.984 0.000
#> GSM1009119 2 0.0237 0.990 0.004 0.996 0.000
#> GSM1009133 1 0.0000 1.000 1.000 0.000 0.000
#> GSM1009147 2 0.0237 0.990 0.004 0.996 0.000
#> GSM1009161 3 0.0000 1.000 0.000 0.000 1.000
#> GSM1009175 2 0.0000 0.990 0.000 1.000 0.000
#> GSM1009189 2 0.0237 0.990 0.004 0.996 0.000
#> GSM1009064 2 0.0237 0.989 0.000 0.996 0.004
#> GSM1009078 2 0.0000 0.990 0.000 1.000 0.000
#> GSM1009092 1 0.0000 1.000 1.000 0.000 0.000
#> GSM1009106 2 0.0747 0.982 0.016 0.984 0.000
#> GSM1009120 2 0.0237 0.990 0.004 0.996 0.000
#> GSM1009134 1 0.0000 1.000 1.000 0.000 0.000
#> GSM1009148 2 0.0237 0.990 0.004 0.996 0.000
#> GSM1009162 3 0.0000 1.000 0.000 0.000 1.000
#> GSM1009176 2 0.0000 0.990 0.000 1.000 0.000
#> GSM1009190 2 0.0237 0.990 0.004 0.996 0.000
#> GSM1009065 2 0.0237 0.989 0.000 0.996 0.004
#> GSM1009079 2 0.0000 0.990 0.000 1.000 0.000
#> GSM1009093 1 0.0000 1.000 1.000 0.000 0.000
#> GSM1009107 2 0.0747 0.982 0.016 0.984 0.000
#> GSM1009121 2 0.0892 0.982 0.020 0.980 0.000
#> GSM1009135 1 0.0000 1.000 1.000 0.000 0.000
#> GSM1009149 2 0.0237 0.990 0.004 0.996 0.000
#> GSM1009163 3 0.0000 1.000 0.000 0.000 1.000
#> GSM1009177 2 0.0000 0.990 0.000 1.000 0.000
#> GSM1009191 2 0.0237 0.990 0.004 0.996 0.000
#> GSM1009066 2 0.0237 0.989 0.000 0.996 0.004
#> GSM1009080 2 0.0000 0.990 0.000 1.000 0.000
#> GSM1009094 1 0.0000 1.000 1.000 0.000 0.000
#> GSM1009108 2 0.0747 0.982 0.016 0.984 0.000
#> GSM1009122 2 0.0892 0.982 0.020 0.980 0.000
#> GSM1009136 1 0.0000 1.000 1.000 0.000 0.000
#> GSM1009150 2 0.0237 0.990 0.004 0.996 0.000
#> GSM1009164 3 0.0000 1.000 0.000 0.000 1.000
#> GSM1009178 2 0.0000 0.990 0.000 1.000 0.000
#> GSM1009192 2 0.0237 0.990 0.004 0.996 0.000
#> GSM1009067 2 0.0237 0.989 0.000 0.996 0.004
#> GSM1009081 2 0.0000 0.990 0.000 1.000 0.000
#> GSM1009095 2 0.6140 0.342 0.404 0.596 0.000
#> GSM1009109 2 0.0747 0.982 0.016 0.984 0.000
#> GSM1009123 2 0.0892 0.982 0.020 0.980 0.000
#> GSM1009137 1 0.0000 1.000 1.000 0.000 0.000
#> GSM1009151 2 0.0237 0.990 0.004 0.996 0.000
#> GSM1009165 3 0.0000 1.000 0.000 0.000 1.000
#> GSM1009179 2 0.0000 0.990 0.000 1.000 0.000
#> GSM1009193 2 0.0237 0.990 0.004 0.996 0.000
#> GSM1009068 2 0.0237 0.989 0.000 0.996 0.004
#> GSM1009082 2 0.0000 0.990 0.000 1.000 0.000
#> GSM1009096 1 0.0000 1.000 1.000 0.000 0.000
#> GSM1009110 2 0.0747 0.982 0.016 0.984 0.000
#> GSM1009124 2 0.0424 0.989 0.008 0.992 0.000
#> GSM1009138 1 0.0000 1.000 1.000 0.000 0.000
#> GSM1009152 2 0.0237 0.990 0.004 0.996 0.000
#> GSM1009166 3 0.0000 1.000 0.000 0.000 1.000
#> GSM1009180 2 0.0000 0.990 0.000 1.000 0.000
#> GSM1009194 2 0.0237 0.990 0.004 0.996 0.000
#> GSM1009069 2 0.0237 0.989 0.000 0.996 0.004
#> GSM1009083 2 0.0000 0.990 0.000 1.000 0.000
#> GSM1009097 1 0.0000 1.000 1.000 0.000 0.000
#> GSM1009111 2 0.0747 0.982 0.016 0.984 0.000
#> GSM1009125 2 0.0892 0.982 0.020 0.980 0.000
#> GSM1009139 1 0.0000 1.000 1.000 0.000 0.000
#> GSM1009153 2 0.0237 0.990 0.004 0.996 0.000
#> GSM1009167 3 0.0000 1.000 0.000 0.000 1.000
#> GSM1009181 2 0.0000 0.990 0.000 1.000 0.000
#> GSM1009195 2 0.0237 0.990 0.004 0.996 0.000
#> GSM1009070 2 0.0237 0.989 0.000 0.996 0.004
#> GSM1009084 2 0.0000 0.990 0.000 1.000 0.000
#> GSM1009098 1 0.0000 1.000 1.000 0.000 0.000
#> GSM1009112 2 0.0747 0.982 0.016 0.984 0.000
#> GSM1009126 2 0.0892 0.982 0.020 0.980 0.000
#> GSM1009140 1 0.0000 1.000 1.000 0.000 0.000
#> GSM1009154 2 0.0237 0.990 0.004 0.996 0.000
#> GSM1009168 3 0.0000 1.000 0.000 0.000 1.000
#> GSM1009182 2 0.0000 0.990 0.000 1.000 0.000
#> GSM1009196 2 0.0237 0.990 0.004 0.996 0.000
#> GSM1009071 2 0.0237 0.989 0.000 0.996 0.004
#> GSM1009085 2 0.0000 0.990 0.000 1.000 0.000
#> GSM1009099 1 0.0000 1.000 1.000 0.000 0.000
#> GSM1009113 2 0.0747 0.982 0.016 0.984 0.000
#> GSM1009127 2 0.0237 0.990 0.004 0.996 0.000
#> GSM1009141 1 0.0000 1.000 1.000 0.000 0.000
#> GSM1009155 2 0.0237 0.990 0.004 0.996 0.000
#> GSM1009169 3 0.0000 1.000 0.000 0.000 1.000
#> GSM1009183 2 0.0000 0.990 0.000 1.000 0.000
#> GSM1009197 2 0.0237 0.990 0.004 0.996 0.000
#> GSM1009072 2 0.0237 0.989 0.000 0.996 0.004
#> GSM1009086 2 0.0000 0.990 0.000 1.000 0.000
#> GSM1009100 1 0.0000 1.000 1.000 0.000 0.000
#> GSM1009114 2 0.0747 0.982 0.016 0.984 0.000
#> GSM1009128 2 0.0892 0.982 0.020 0.980 0.000
#> GSM1009142 1 0.0000 1.000 1.000 0.000 0.000
#> GSM1009156 2 0.0237 0.990 0.004 0.996 0.000
#> GSM1009170 3 0.0000 1.000 0.000 0.000 1.000
#> GSM1009184 2 0.0000 0.990 0.000 1.000 0.000
#> GSM1009198 2 0.0237 0.990 0.004 0.996 0.000
#> GSM1009073 2 0.0237 0.989 0.000 0.996 0.004
#> GSM1009087 2 0.0000 0.990 0.000 1.000 0.000
#> GSM1009101 1 0.0000 1.000 1.000 0.000 0.000
#> GSM1009115 2 0.0747 0.982 0.016 0.984 0.000
#> GSM1009129 2 0.0983 0.982 0.016 0.980 0.004
#> GSM1009143 1 0.0000 1.000 1.000 0.000 0.000
#> GSM1009157 2 0.0237 0.990 0.004 0.996 0.000
#> GSM1009171 3 0.0000 1.000 0.000 0.000 1.000
#> GSM1009185 2 0.0000 0.990 0.000 1.000 0.000
#> GSM1009199 2 0.0237 0.990 0.004 0.996 0.000
#> GSM1009074 2 0.0237 0.989 0.000 0.996 0.004
#> GSM1009088 2 0.0000 0.990 0.000 1.000 0.000
#> GSM1009102 1 0.0000 1.000 1.000 0.000 0.000
#> GSM1009116 2 0.0747 0.982 0.016 0.984 0.000
#> GSM1009130 2 0.0237 0.989 0.000 0.996 0.004
#> GSM1009144 1 0.0000 1.000 1.000 0.000 0.000
#> GSM1009158 2 0.0237 0.990 0.004 0.996 0.000
#> GSM1009172 3 0.0000 1.000 0.000 0.000 1.000
#> GSM1009186 2 0.0000 0.990 0.000 1.000 0.000
#> GSM1009200 2 0.0237 0.990 0.004 0.996 0.000
#> GSM1009075 2 0.0237 0.989 0.000 0.996 0.004
#> GSM1009089 2 0.0000 0.990 0.000 1.000 0.000
#> GSM1009103 1 0.0000 1.000 1.000 0.000 0.000
#> GSM1009117 2 0.0747 0.982 0.016 0.984 0.000
#> GSM1009131 2 0.0829 0.985 0.012 0.984 0.004
#> GSM1009145 1 0.0000 1.000 1.000 0.000 0.000
#> GSM1009159 2 0.0237 0.990 0.004 0.996 0.000
#> GSM1009173 3 0.0000 1.000 0.000 0.000 1.000
#> GSM1009187 2 0.0237 0.989 0.000 0.996 0.004
#> GSM1009201 2 0.0237 0.990 0.004 0.996 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1009062 1 0.4564 0.609 0.672 0.328 0 0.000
#> GSM1009076 1 0.1118 0.879 0.964 0.036 0 0.000
#> GSM1009090 4 0.0000 1.000 0.000 0.000 0 1.000
#> GSM1009104 2 0.4564 1.000 0.328 0.672 0 0.000
#> GSM1009118 1 0.0000 0.885 1.000 0.000 0 0.000
#> GSM1009132 4 0.0000 1.000 0.000 0.000 0 1.000
#> GSM1009146 1 0.1940 0.855 0.924 0.076 0 0.000
#> GSM1009160 3 0.0000 1.000 0.000 0.000 1 0.000
#> GSM1009174 1 0.0592 0.879 0.984 0.016 0 0.000
#> GSM1009188 1 0.0469 0.885 0.988 0.012 0 0.000
#> GSM1009063 1 0.4564 0.609 0.672 0.328 0 0.000
#> GSM1009077 1 0.1118 0.879 0.964 0.036 0 0.000
#> GSM1009091 4 0.0000 1.000 0.000 0.000 0 1.000
#> GSM1009105 2 0.4564 1.000 0.328 0.672 0 0.000
#> GSM1009119 1 0.1637 0.864 0.940 0.060 0 0.000
#> GSM1009133 4 0.0000 1.000 0.000 0.000 0 1.000
#> GSM1009147 1 0.0707 0.885 0.980 0.020 0 0.000
#> GSM1009161 3 0.0000 1.000 0.000 0.000 1 0.000
#> GSM1009175 1 0.0592 0.879 0.984 0.016 0 0.000
#> GSM1009189 1 0.0469 0.885 0.988 0.012 0 0.000
#> GSM1009064 1 0.4564 0.609 0.672 0.328 0 0.000
#> GSM1009078 1 0.0592 0.886 0.984 0.016 0 0.000
#> GSM1009092 4 0.0000 1.000 0.000 0.000 0 1.000
#> GSM1009106 2 0.4564 1.000 0.328 0.672 0 0.000
#> GSM1009120 1 0.2011 0.851 0.920 0.080 0 0.000
#> GSM1009134 4 0.0000 1.000 0.000 0.000 0 1.000
#> GSM1009148 1 0.1940 0.855 0.924 0.076 0 0.000
#> GSM1009162 3 0.0000 1.000 0.000 0.000 1 0.000
#> GSM1009176 1 0.0592 0.879 0.984 0.016 0 0.000
#> GSM1009190 1 0.1022 0.881 0.968 0.032 0 0.000
#> GSM1009065 1 0.4564 0.609 0.672 0.328 0 0.000
#> GSM1009079 1 0.0707 0.880 0.980 0.020 0 0.000
#> GSM1009093 4 0.0000 1.000 0.000 0.000 0 1.000
#> GSM1009107 2 0.4564 1.000 0.328 0.672 0 0.000
#> GSM1009121 1 0.0000 0.885 1.000 0.000 0 0.000
#> GSM1009135 4 0.0000 1.000 0.000 0.000 0 1.000
#> GSM1009149 1 0.0921 0.883 0.972 0.028 0 0.000
#> GSM1009163 3 0.0000 1.000 0.000 0.000 1 0.000
#> GSM1009177 1 0.0592 0.879 0.984 0.016 0 0.000
#> GSM1009191 1 0.0592 0.886 0.984 0.016 0 0.000
#> GSM1009066 1 0.4564 0.609 0.672 0.328 0 0.000
#> GSM1009080 1 0.1118 0.879 0.964 0.036 0 0.000
#> GSM1009094 4 0.0000 1.000 0.000 0.000 0 1.000
#> GSM1009108 2 0.4564 1.000 0.328 0.672 0 0.000
#> GSM1009122 1 0.1211 0.858 0.960 0.040 0 0.000
#> GSM1009136 4 0.0000 1.000 0.000 0.000 0 1.000
#> GSM1009150 1 0.1792 0.861 0.932 0.068 0 0.000
#> GSM1009164 3 0.0000 1.000 0.000 0.000 1 0.000
#> GSM1009178 1 0.0000 0.885 1.000 0.000 0 0.000
#> GSM1009192 1 0.0469 0.885 0.988 0.012 0 0.000
#> GSM1009067 1 0.4564 0.609 0.672 0.328 0 0.000
#> GSM1009081 1 0.1022 0.880 0.968 0.032 0 0.000
#> GSM1009095 1 0.6574 0.147 0.532 0.084 0 0.384
#> GSM1009109 2 0.4564 1.000 0.328 0.672 0 0.000
#> GSM1009123 1 0.2412 0.839 0.908 0.084 0 0.008
#> GSM1009137 4 0.0000 1.000 0.000 0.000 0 1.000
#> GSM1009151 1 0.1637 0.866 0.940 0.060 0 0.000
#> GSM1009165 3 0.0000 1.000 0.000 0.000 1 0.000
#> GSM1009179 1 0.0188 0.884 0.996 0.004 0 0.000
#> GSM1009193 1 0.0469 0.885 0.988 0.012 0 0.000
#> GSM1009068 1 0.4564 0.609 0.672 0.328 0 0.000
#> GSM1009082 1 0.1118 0.879 0.964 0.036 0 0.000
#> GSM1009096 4 0.0000 1.000 0.000 0.000 0 1.000
#> GSM1009110 2 0.4564 1.000 0.328 0.672 0 0.000
#> GSM1009124 1 0.0000 0.885 1.000 0.000 0 0.000
#> GSM1009138 4 0.0000 1.000 0.000 0.000 0 1.000
#> GSM1009152 1 0.1940 0.855 0.924 0.076 0 0.000
#> GSM1009166 3 0.0000 1.000 0.000 0.000 1 0.000
#> GSM1009180 1 0.0000 0.885 1.000 0.000 0 0.000
#> GSM1009194 1 0.0188 0.886 0.996 0.004 0 0.000
#> GSM1009069 1 0.4382 0.641 0.704 0.296 0 0.000
#> GSM1009083 1 0.1118 0.879 0.964 0.036 0 0.000
#> GSM1009097 4 0.0000 1.000 0.000 0.000 0 1.000
#> GSM1009111 2 0.4564 1.000 0.328 0.672 0 0.000
#> GSM1009125 1 0.2281 0.781 0.904 0.096 0 0.000
#> GSM1009139 4 0.0000 1.000 0.000 0.000 0 1.000
#> GSM1009153 1 0.1792 0.861 0.932 0.068 0 0.000
#> GSM1009167 3 0.0000 1.000 0.000 0.000 1 0.000
#> GSM1009181 1 0.0592 0.879 0.984 0.016 0 0.000
#> GSM1009195 1 0.0469 0.884 0.988 0.012 0 0.000
#> GSM1009070 1 0.4543 0.614 0.676 0.324 0 0.000
#> GSM1009084 1 0.1118 0.879 0.964 0.036 0 0.000
#> GSM1009098 4 0.0000 1.000 0.000 0.000 0 1.000
#> GSM1009112 2 0.4564 1.000 0.328 0.672 0 0.000
#> GSM1009126 1 0.0000 0.885 1.000 0.000 0 0.000
#> GSM1009140 4 0.0000 1.000 0.000 0.000 0 1.000
#> GSM1009154 1 0.1867 0.858 0.928 0.072 0 0.000
#> GSM1009168 3 0.0000 1.000 0.000 0.000 1 0.000
#> GSM1009182 1 0.0469 0.881 0.988 0.012 0 0.000
#> GSM1009196 1 0.0469 0.885 0.988 0.012 0 0.000
#> GSM1009071 1 0.4564 0.609 0.672 0.328 0 0.000
#> GSM1009085 1 0.1022 0.880 0.968 0.032 0 0.000
#> GSM1009099 4 0.0000 1.000 0.000 0.000 0 1.000
#> GSM1009113 2 0.4564 1.000 0.328 0.672 0 0.000
#> GSM1009127 1 0.2149 0.842 0.912 0.088 0 0.000
#> GSM1009141 4 0.0000 1.000 0.000 0.000 0 1.000
#> GSM1009155 1 0.0817 0.884 0.976 0.024 0 0.000
#> GSM1009169 3 0.0000 1.000 0.000 0.000 1 0.000
#> GSM1009183 1 0.0592 0.879 0.984 0.016 0 0.000
#> GSM1009197 1 0.0469 0.885 0.988 0.012 0 0.000
#> GSM1009072 1 0.4564 0.609 0.672 0.328 0 0.000
#> GSM1009086 1 0.1118 0.879 0.964 0.036 0 0.000
#> GSM1009100 4 0.0000 1.000 0.000 0.000 0 1.000
#> GSM1009114 2 0.4564 1.000 0.328 0.672 0 0.000
#> GSM1009128 1 0.0000 0.885 1.000 0.000 0 0.000
#> GSM1009142 4 0.0000 1.000 0.000 0.000 0 1.000
#> GSM1009156 1 0.1557 0.869 0.944 0.056 0 0.000
#> GSM1009170 3 0.0000 1.000 0.000 0.000 1 0.000
#> GSM1009184 1 0.0592 0.879 0.984 0.016 0 0.000
#> GSM1009198 1 0.0469 0.885 0.988 0.012 0 0.000
#> GSM1009073 1 0.4564 0.609 0.672 0.328 0 0.000
#> GSM1009087 1 0.0336 0.887 0.992 0.008 0 0.000
#> GSM1009101 4 0.0000 1.000 0.000 0.000 0 1.000
#> GSM1009115 2 0.4564 1.000 0.328 0.672 0 0.000
#> GSM1009129 1 0.1637 0.835 0.940 0.060 0 0.000
#> GSM1009143 4 0.0000 1.000 0.000 0.000 0 1.000
#> GSM1009157 1 0.1389 0.874 0.952 0.048 0 0.000
#> GSM1009171 3 0.0000 1.000 0.000 0.000 1 0.000
#> GSM1009185 1 0.0000 0.885 1.000 0.000 0 0.000
#> GSM1009199 1 0.0336 0.886 0.992 0.008 0 0.000
#> GSM1009074 1 0.4564 0.609 0.672 0.328 0 0.000
#> GSM1009088 1 0.0817 0.880 0.976 0.024 0 0.000
#> GSM1009102 4 0.0000 1.000 0.000 0.000 0 1.000
#> GSM1009116 2 0.4564 1.000 0.328 0.672 0 0.000
#> GSM1009130 1 0.0592 0.879 0.984 0.016 0 0.000
#> GSM1009144 4 0.0000 1.000 0.000 0.000 0 1.000
#> GSM1009158 1 0.1637 0.866 0.940 0.060 0 0.000
#> GSM1009172 3 0.0000 1.000 0.000 0.000 1 0.000
#> GSM1009186 1 0.0592 0.879 0.984 0.016 0 0.000
#> GSM1009200 1 0.0188 0.886 0.996 0.004 0 0.000
#> GSM1009075 1 0.4564 0.609 0.672 0.328 0 0.000
#> GSM1009089 1 0.0336 0.887 0.992 0.008 0 0.000
#> GSM1009103 4 0.0000 1.000 0.000 0.000 0 1.000
#> GSM1009117 2 0.4564 1.000 0.328 0.672 0 0.000
#> GSM1009131 1 0.0000 0.885 1.000 0.000 0 0.000
#> GSM1009145 4 0.0000 1.000 0.000 0.000 0 1.000
#> GSM1009159 1 0.0592 0.886 0.984 0.016 0 0.000
#> GSM1009173 3 0.0000 1.000 0.000 0.000 1 0.000
#> GSM1009187 1 0.0000 0.885 1.000 0.000 0 0.000
#> GSM1009201 1 0.0188 0.886 0.996 0.004 0 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM1009062 2 0.4273 1.000 0.448 0.552 0 0.000 0.000
#> GSM1009076 1 0.5052 0.143 0.552 0.412 0 0.000 0.036
#> GSM1009090 4 0.0000 0.989 0.000 0.000 0 1.000 0.000
#> GSM1009104 5 0.0000 1.000 0.000 0.000 0 0.000 1.000
#> GSM1009118 1 0.1205 0.469 0.956 0.004 0 0.000 0.040
#> GSM1009132 4 0.0000 0.989 0.000 0.000 0 1.000 0.000
#> GSM1009146 1 0.3999 0.436 0.656 0.344 0 0.000 0.000
#> GSM1009160 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> GSM1009174 1 0.4550 0.391 0.688 0.276 0 0.000 0.036
#> GSM1009188 1 0.3508 0.514 0.748 0.252 0 0.000 0.000
#> GSM1009063 2 0.4273 1.000 0.448 0.552 0 0.000 0.000
#> GSM1009077 1 0.5052 0.143 0.552 0.412 0 0.000 0.036
#> GSM1009091 4 0.0000 0.989 0.000 0.000 0 1.000 0.000
#> GSM1009105 5 0.0000 1.000 0.000 0.000 0 0.000 1.000
#> GSM1009119 1 0.0609 0.472 0.980 0.020 0 0.000 0.000
#> GSM1009133 4 0.0000 0.989 0.000 0.000 0 1.000 0.000
#> GSM1009147 1 0.3534 0.512 0.744 0.256 0 0.000 0.000
#> GSM1009161 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> GSM1009175 1 0.4269 0.432 0.732 0.232 0 0.000 0.036
#> GSM1009189 1 0.3508 0.514 0.748 0.252 0 0.000 0.000
#> GSM1009064 2 0.4273 1.000 0.448 0.552 0 0.000 0.000
#> GSM1009078 1 0.4849 0.196 0.608 0.360 0 0.000 0.032
#> GSM1009092 4 0.0000 0.989 0.000 0.000 0 1.000 0.000
#> GSM1009106 5 0.0000 1.000 0.000 0.000 0 0.000 1.000
#> GSM1009120 1 0.1478 0.482 0.936 0.064 0 0.000 0.000
#> GSM1009134 4 0.0000 0.989 0.000 0.000 0 1.000 0.000
#> GSM1009148 1 0.3999 0.436 0.656 0.344 0 0.000 0.000
#> GSM1009162 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> GSM1009176 1 0.4594 0.381 0.680 0.284 0 0.000 0.036
#> GSM1009190 1 0.3508 0.514 0.748 0.252 0 0.000 0.000
#> GSM1009065 2 0.4273 1.000 0.448 0.552 0 0.000 0.000
#> GSM1009079 1 0.4677 0.361 0.664 0.300 0 0.000 0.036
#> GSM1009093 4 0.0000 0.989 0.000 0.000 0 1.000 0.000
#> GSM1009107 5 0.0000 1.000 0.000 0.000 0 0.000 1.000
#> GSM1009121 1 0.2674 0.373 0.856 0.004 0 0.000 0.140
#> GSM1009135 4 0.0000 0.989 0.000 0.000 0 1.000 0.000
#> GSM1009149 1 0.3999 0.436 0.656 0.344 0 0.000 0.000
#> GSM1009163 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> GSM1009177 1 0.4572 0.386 0.684 0.280 0 0.000 0.036
#> GSM1009191 1 0.3561 0.515 0.740 0.260 0 0.000 0.000
#> GSM1009066 2 0.4273 1.000 0.448 0.552 0 0.000 0.000
#> GSM1009080 1 0.5010 0.191 0.572 0.392 0 0.000 0.036
#> GSM1009094 4 0.0000 0.989 0.000 0.000 0 1.000 0.000
#> GSM1009108 5 0.0000 1.000 0.000 0.000 0 0.000 1.000
#> GSM1009122 1 0.2488 0.396 0.872 0.004 0 0.000 0.124
#> GSM1009136 4 0.0000 0.989 0.000 0.000 0 1.000 0.000
#> GSM1009150 1 0.3999 0.436 0.656 0.344 0 0.000 0.000
#> GSM1009164 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> GSM1009178 1 0.3883 0.458 0.780 0.184 0 0.000 0.036
#> GSM1009192 1 0.3508 0.514 0.748 0.252 0 0.000 0.000
#> GSM1009067 2 0.4273 1.000 0.448 0.552 0 0.000 0.000
#> GSM1009081 1 0.4958 0.235 0.592 0.372 0 0.000 0.036
#> GSM1009095 4 0.3305 0.664 0.224 0.000 0 0.776 0.000
#> GSM1009109 5 0.0000 1.000 0.000 0.000 0 0.000 1.000
#> GSM1009123 1 0.0162 0.471 0.996 0.004 0 0.000 0.000
#> GSM1009137 4 0.0000 0.989 0.000 0.000 0 1.000 0.000
#> GSM1009151 1 0.3999 0.436 0.656 0.344 0 0.000 0.000
#> GSM1009165 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> GSM1009179 1 0.4021 0.452 0.764 0.200 0 0.000 0.036
#> GSM1009193 1 0.3508 0.514 0.748 0.252 0 0.000 0.000
#> GSM1009068 2 0.4273 1.000 0.448 0.552 0 0.000 0.000
#> GSM1009082 1 0.5052 0.143 0.552 0.412 0 0.000 0.036
#> GSM1009096 4 0.0000 0.989 0.000 0.000 0 1.000 0.000
#> GSM1009110 5 0.0000 1.000 0.000 0.000 0 0.000 1.000
#> GSM1009124 1 0.0955 0.473 0.968 0.004 0 0.000 0.028
#> GSM1009138 4 0.0000 0.989 0.000 0.000 0 1.000 0.000
#> GSM1009152 1 0.3999 0.436 0.656 0.344 0 0.000 0.000
#> GSM1009166 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> GSM1009180 1 0.3810 0.459 0.788 0.176 0 0.000 0.036
#> GSM1009194 1 0.3508 0.514 0.748 0.252 0 0.000 0.000
#> GSM1009069 1 0.4321 -0.265 0.600 0.396 0 0.000 0.004
#> GSM1009083 1 0.5044 0.147 0.556 0.408 0 0.000 0.036
#> GSM1009097 4 0.0000 0.989 0.000 0.000 0 1.000 0.000
#> GSM1009111 5 0.0000 1.000 0.000 0.000 0 0.000 1.000
#> GSM1009125 1 0.3838 0.101 0.716 0.004 0 0.000 0.280
#> GSM1009139 4 0.0000 0.989 0.000 0.000 0 1.000 0.000
#> GSM1009153 1 0.3999 0.436 0.656 0.344 0 0.000 0.000
#> GSM1009167 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> GSM1009181 1 0.4527 0.395 0.692 0.272 0 0.000 0.036
#> GSM1009195 1 0.4428 0.514 0.700 0.268 0 0.000 0.032
#> GSM1009070 1 0.4219 -0.650 0.584 0.416 0 0.000 0.000
#> GSM1009084 1 0.5052 0.143 0.552 0.412 0 0.000 0.036
#> GSM1009098 4 0.0000 0.989 0.000 0.000 0 1.000 0.000
#> GSM1009112 5 0.0000 1.000 0.000 0.000 0 0.000 1.000
#> GSM1009126 1 0.0324 0.473 0.992 0.004 0 0.000 0.004
#> GSM1009140 4 0.0000 0.989 0.000 0.000 0 1.000 0.000
#> GSM1009154 1 0.3999 0.436 0.656 0.344 0 0.000 0.000
#> GSM1009168 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> GSM1009182 1 0.4054 0.450 0.760 0.204 0 0.000 0.036
#> GSM1009196 1 0.3508 0.514 0.748 0.252 0 0.000 0.000
#> GSM1009071 2 0.4273 1.000 0.448 0.552 0 0.000 0.000
#> GSM1009085 1 0.4958 0.235 0.592 0.372 0 0.000 0.036
#> GSM1009099 4 0.0000 0.989 0.000 0.000 0 1.000 0.000
#> GSM1009113 5 0.0000 1.000 0.000 0.000 0 0.000 1.000
#> GSM1009127 1 0.0609 0.465 0.980 0.020 0 0.000 0.000
#> GSM1009141 4 0.0000 0.989 0.000 0.000 0 1.000 0.000
#> GSM1009155 1 0.3999 0.436 0.656 0.344 0 0.000 0.000
#> GSM1009169 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> GSM1009183 1 0.4297 0.429 0.728 0.236 0 0.000 0.036
#> GSM1009197 1 0.3508 0.514 0.748 0.252 0 0.000 0.000
#> GSM1009072 2 0.4273 1.000 0.448 0.552 0 0.000 0.000
#> GSM1009086 1 0.5052 0.143 0.552 0.412 0 0.000 0.036
#> GSM1009100 4 0.0000 0.989 0.000 0.000 0 1.000 0.000
#> GSM1009114 5 0.0000 1.000 0.000 0.000 0 0.000 1.000
#> GSM1009128 1 0.2674 0.373 0.856 0.004 0 0.000 0.140
#> GSM1009142 4 0.0000 0.989 0.000 0.000 0 1.000 0.000
#> GSM1009156 1 0.3508 0.514 0.748 0.252 0 0.000 0.000
#> GSM1009170 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> GSM1009184 1 0.4572 0.386 0.684 0.280 0 0.000 0.036
#> GSM1009198 1 0.3508 0.514 0.748 0.252 0 0.000 0.000
#> GSM1009073 2 0.4273 1.000 0.448 0.552 0 0.000 0.000
#> GSM1009087 1 0.4637 0.335 0.672 0.292 0 0.000 0.036
#> GSM1009101 4 0.0000 0.989 0.000 0.000 0 1.000 0.000
#> GSM1009115 5 0.0000 1.000 0.000 0.000 0 0.000 1.000
#> GSM1009129 1 0.3266 0.270 0.796 0.004 0 0.000 0.200
#> GSM1009143 4 0.0000 0.989 0.000 0.000 0 1.000 0.000
#> GSM1009157 1 0.3508 0.514 0.748 0.252 0 0.000 0.000
#> GSM1009171 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> GSM1009185 1 0.3209 0.466 0.812 0.180 0 0.000 0.008
#> GSM1009199 1 0.4380 0.516 0.708 0.260 0 0.000 0.032
#> GSM1009074 2 0.4273 1.000 0.448 0.552 0 0.000 0.000
#> GSM1009088 1 0.4734 0.300 0.652 0.312 0 0.000 0.036
#> GSM1009102 4 0.0000 0.989 0.000 0.000 0 1.000 0.000
#> GSM1009116 5 0.0000 1.000 0.000 0.000 0 0.000 1.000
#> GSM1009130 1 0.1836 0.477 0.932 0.032 0 0.000 0.036
#> GSM1009144 4 0.0000 0.989 0.000 0.000 0 1.000 0.000
#> GSM1009158 1 0.3999 0.436 0.656 0.344 0 0.000 0.000
#> GSM1009172 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> GSM1009186 1 0.4572 0.386 0.684 0.280 0 0.000 0.036
#> GSM1009200 1 0.3662 0.515 0.744 0.252 0 0.000 0.004
#> GSM1009075 2 0.4273 1.000 0.448 0.552 0 0.000 0.000
#> GSM1009089 1 0.3366 0.439 0.768 0.232 0 0.000 0.000
#> GSM1009103 4 0.0000 0.989 0.000 0.000 0 1.000 0.000
#> GSM1009117 5 0.0000 1.000 0.000 0.000 0 0.000 1.000
#> GSM1009131 1 0.1124 0.471 0.960 0.004 0 0.000 0.036
#> GSM1009145 4 0.0000 0.989 0.000 0.000 0 1.000 0.000
#> GSM1009159 1 0.3999 0.436 0.656 0.344 0 0.000 0.000
#> GSM1009173 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> GSM1009187 1 0.3280 0.466 0.812 0.176 0 0.000 0.012
#> GSM1009201 1 0.3508 0.514 0.748 0.252 0 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1009062 6 0.0000 0.9896 0.000 0.000 0 0.000 0.000 1.000
#> GSM1009076 2 0.3684 0.9044 0.372 0.628 0 0.000 0.000 0.000
#> GSM1009090 4 0.0000 0.9988 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009104 5 0.0000 0.9461 0.000 0.000 0 0.000 1.000 0.000
#> GSM1009118 1 0.3329 0.2125 0.756 0.236 0 0.000 0.004 0.004
#> GSM1009132 4 0.0000 0.9988 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009146 1 0.3819 0.3666 0.624 0.372 0 0.000 0.000 0.004
#> GSM1009160 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009174 2 0.3851 0.8410 0.460 0.540 0 0.000 0.000 0.000
#> GSM1009188 1 0.0146 0.4867 0.996 0.004 0 0.000 0.000 0.000
#> GSM1009063 6 0.0000 0.9896 0.000 0.000 0 0.000 0.000 1.000
#> GSM1009077 2 0.3684 0.9044 0.372 0.628 0 0.000 0.000 0.000
#> GSM1009091 4 0.0000 0.9988 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009105 5 0.0000 0.9461 0.000 0.000 0 0.000 1.000 0.000
#> GSM1009119 1 0.3023 0.2662 0.784 0.212 0 0.000 0.000 0.004
#> GSM1009133 4 0.0000 0.9988 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009147 1 0.0000 0.4872 1.000 0.000 0 0.000 0.000 0.000
#> GSM1009161 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009175 1 0.3854 -0.6013 0.536 0.464 0 0.000 0.000 0.000
#> GSM1009189 1 0.0000 0.4872 1.000 0.000 0 0.000 0.000 0.000
#> GSM1009064 6 0.0000 0.9896 0.000 0.000 0 0.000 0.000 1.000
#> GSM1009078 1 0.3823 -0.4975 0.564 0.436 0 0.000 0.000 0.000
#> GSM1009092 4 0.0000 0.9988 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009106 5 0.0000 0.9461 0.000 0.000 0 0.000 1.000 0.000
#> GSM1009120 1 0.2964 0.2786 0.792 0.204 0 0.000 0.000 0.004
#> GSM1009134 4 0.0000 0.9988 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009148 1 0.3819 0.3666 0.624 0.372 0 0.000 0.000 0.004
#> GSM1009162 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009176 2 0.3828 0.8918 0.440 0.560 0 0.000 0.000 0.000
#> GSM1009190 1 0.0146 0.4867 0.996 0.004 0 0.000 0.000 0.000
#> GSM1009065 6 0.0000 0.9896 0.000 0.000 0 0.000 0.000 1.000
#> GSM1009079 2 0.3828 0.8918 0.440 0.560 0 0.000 0.000 0.000
#> GSM1009093 4 0.0000 0.9988 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009107 5 0.0000 0.9461 0.000 0.000 0 0.000 1.000 0.000
#> GSM1009121 1 0.3437 0.2077 0.752 0.236 0 0.000 0.008 0.004
#> GSM1009135 4 0.0000 0.9988 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009149 1 0.3819 0.3666 0.624 0.372 0 0.000 0.000 0.004
#> GSM1009163 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009177 2 0.3828 0.8918 0.440 0.560 0 0.000 0.000 0.000
#> GSM1009191 1 0.0146 0.4867 0.996 0.004 0 0.000 0.000 0.000
#> GSM1009066 6 0.0000 0.9896 0.000 0.000 0 0.000 0.000 1.000
#> GSM1009080 2 0.3684 0.9044 0.372 0.628 0 0.000 0.000 0.000
#> GSM1009094 4 0.0000 0.9988 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009108 5 0.0000 0.9461 0.000 0.000 0 0.000 1.000 0.000
#> GSM1009122 1 0.3437 0.2077 0.752 0.236 0 0.000 0.008 0.004
#> GSM1009136 4 0.0146 0.9963 0.004 0.000 0 0.996 0.000 0.000
#> GSM1009150 1 0.3819 0.3666 0.624 0.372 0 0.000 0.000 0.004
#> GSM1009164 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009178 1 0.3823 -0.4975 0.564 0.436 0 0.000 0.000 0.000
#> GSM1009192 1 0.0000 0.4872 1.000 0.000 0 0.000 0.000 0.000
#> GSM1009067 6 0.0000 0.9896 0.000 0.000 0 0.000 0.000 1.000
#> GSM1009081 2 0.3789 0.9073 0.416 0.584 0 0.000 0.000 0.000
#> GSM1009095 4 0.0363 0.9872 0.012 0.000 0 0.988 0.000 0.000
#> GSM1009109 5 0.0000 0.9461 0.000 0.000 0 0.000 1.000 0.000
#> GSM1009123 1 0.3023 0.2662 0.784 0.212 0 0.000 0.000 0.004
#> GSM1009137 4 0.0000 0.9988 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009151 1 0.3819 0.3666 0.624 0.372 0 0.000 0.000 0.004
#> GSM1009165 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009179 1 0.3823 -0.4975 0.564 0.436 0 0.000 0.000 0.000
#> GSM1009193 1 0.0000 0.4872 1.000 0.000 0 0.000 0.000 0.000
#> GSM1009068 6 0.0000 0.9896 0.000 0.000 0 0.000 0.000 1.000
#> GSM1009082 2 0.3684 0.9044 0.372 0.628 0 0.000 0.000 0.000
#> GSM1009096 4 0.0000 0.9988 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009110 5 0.0000 0.9461 0.000 0.000 0 0.000 1.000 0.000
#> GSM1009124 1 0.3052 0.2628 0.780 0.216 0 0.000 0.000 0.004
#> GSM1009138 4 0.0000 0.9988 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009152 1 0.3819 0.3666 0.624 0.372 0 0.000 0.000 0.004
#> GSM1009166 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009180 1 0.3817 -0.4861 0.568 0.432 0 0.000 0.000 0.000
#> GSM1009194 1 0.0146 0.4867 0.996 0.004 0 0.000 0.000 0.000
#> GSM1009069 1 0.4675 -0.4693 0.560 0.392 0 0.000 0.000 0.048
#> GSM1009083 1 0.3833 -0.5279 0.556 0.444 0 0.000 0.000 0.000
#> GSM1009097 4 0.0000 0.9988 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009111 5 0.0000 0.9461 0.000 0.000 0 0.000 1.000 0.000
#> GSM1009125 5 0.6095 -0.1197 0.332 0.236 0 0.000 0.428 0.004
#> GSM1009139 4 0.0000 0.9988 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009153 1 0.3819 0.3666 0.624 0.372 0 0.000 0.000 0.004
#> GSM1009167 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009181 2 0.3833 0.8834 0.444 0.556 0 0.000 0.000 0.000
#> GSM1009195 1 0.0632 0.4754 0.976 0.024 0 0.000 0.000 0.000
#> GSM1009070 6 0.1644 0.8721 0.076 0.004 0 0.000 0.000 0.920
#> GSM1009084 2 0.3684 0.9044 0.372 0.628 0 0.000 0.000 0.000
#> GSM1009098 4 0.0000 0.9988 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009112 5 0.0000 0.9461 0.000 0.000 0 0.000 1.000 0.000
#> GSM1009126 1 0.3163 0.2650 0.780 0.212 0 0.000 0.004 0.004
#> GSM1009140 4 0.0000 0.9988 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009154 1 0.3819 0.3666 0.624 0.372 0 0.000 0.000 0.004
#> GSM1009168 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009182 1 0.3823 -0.4975 0.564 0.436 0 0.000 0.000 0.000
#> GSM1009196 1 0.0000 0.4872 1.000 0.000 0 0.000 0.000 0.000
#> GSM1009071 6 0.0000 0.9896 0.000 0.000 0 0.000 0.000 1.000
#> GSM1009085 2 0.3804 0.9045 0.424 0.576 0 0.000 0.000 0.000
#> GSM1009099 4 0.0000 0.9988 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009113 5 0.0000 0.9461 0.000 0.000 0 0.000 1.000 0.000
#> GSM1009127 1 0.3023 0.2662 0.784 0.212 0 0.000 0.000 0.004
#> GSM1009141 4 0.0000 0.9988 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009155 1 0.3819 0.3666 0.624 0.372 0 0.000 0.000 0.004
#> GSM1009169 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009183 1 0.3862 -0.6444 0.524 0.476 0 0.000 0.000 0.000
#> GSM1009197 1 0.0000 0.4872 1.000 0.000 0 0.000 0.000 0.000
#> GSM1009072 6 0.0000 0.9896 0.000 0.000 0 0.000 0.000 1.000
#> GSM1009086 2 0.3684 0.9044 0.372 0.628 0 0.000 0.000 0.000
#> GSM1009100 4 0.0000 0.9988 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009114 5 0.0000 0.9461 0.000 0.000 0 0.000 1.000 0.000
#> GSM1009128 1 0.3437 0.2077 0.752 0.236 0 0.000 0.008 0.004
#> GSM1009142 4 0.0000 0.9988 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009156 1 0.0000 0.4872 1.000 0.000 0 0.000 0.000 0.000
#> GSM1009170 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009184 1 0.3868 -0.7149 0.504 0.496 0 0.000 0.000 0.000
#> GSM1009198 1 0.0146 0.4867 0.996 0.004 0 0.000 0.000 0.000
#> GSM1009073 6 0.0000 0.9896 0.000 0.000 0 0.000 0.000 1.000
#> GSM1009087 1 0.3823 -0.4975 0.564 0.436 0 0.000 0.000 0.000
#> GSM1009101 4 0.0000 0.9988 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009115 5 0.0000 0.9461 0.000 0.000 0 0.000 1.000 0.000
#> GSM1009129 1 0.5401 -0.0846 0.596 0.236 0 0.000 0.164 0.004
#> GSM1009143 4 0.0146 0.9963 0.004 0.000 0 0.996 0.000 0.000
#> GSM1009157 1 0.0000 0.4872 1.000 0.000 0 0.000 0.000 0.000
#> GSM1009171 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009185 1 0.3817 -0.4861 0.568 0.432 0 0.000 0.000 0.000
#> GSM1009199 1 0.0713 0.4727 0.972 0.028 0 0.000 0.000 0.000
#> GSM1009074 6 0.0000 0.9896 0.000 0.000 0 0.000 0.000 1.000
#> GSM1009088 1 0.3823 -0.4975 0.564 0.436 0 0.000 0.000 0.000
#> GSM1009102 4 0.0146 0.9963 0.004 0.000 0 0.996 0.000 0.000
#> GSM1009116 5 0.0000 0.9461 0.000 0.000 0 0.000 1.000 0.000
#> GSM1009130 1 0.3240 0.1985 0.752 0.244 0 0.000 0.000 0.004
#> GSM1009144 4 0.0146 0.9963 0.004 0.000 0 0.996 0.000 0.000
#> GSM1009158 1 0.3819 0.3666 0.624 0.372 0 0.000 0.000 0.004
#> GSM1009172 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009186 1 0.3851 -0.5871 0.540 0.460 0 0.000 0.000 0.000
#> GSM1009200 1 0.0146 0.4867 0.996 0.004 0 0.000 0.000 0.000
#> GSM1009075 6 0.0000 0.9896 0.000 0.000 0 0.000 0.000 1.000
#> GSM1009089 1 0.3817 -0.4863 0.568 0.432 0 0.000 0.000 0.000
#> GSM1009103 4 0.0146 0.9963 0.004 0.000 0 0.996 0.000 0.000
#> GSM1009117 5 0.0000 0.9461 0.000 0.000 0 0.000 1.000 0.000
#> GSM1009131 1 0.3215 0.2094 0.756 0.240 0 0.000 0.000 0.004
#> GSM1009145 4 0.0000 0.9988 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009159 1 0.3819 0.3666 0.624 0.372 0 0.000 0.000 0.004
#> GSM1009173 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009187 1 0.3817 -0.4861 0.568 0.432 0 0.000 0.000 0.000
#> GSM1009201 1 0.0146 0.4867 0.996 0.004 0 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 temperature(p) time(p) specimen(p) k
#> MAD:mclust 129 0.838 0.954 1.03e-21 2
#> MAD:mclust 139 1.000 1.000 1.57e-48 3
#> MAD:mclust 139 1.000 1.000 1.71e-71 4
#> MAD:mclust 85 1.000 1.000 1.36e-57 5
#> MAD:mclust 82 0.993 1.000 3.70e-55 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 140 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'MAD' method.
#> Subgroups are detected by 'NMF' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 0.855 0.931 0.971 0.4779 0.526 0.526
#> 3 3 0.651 0.740 0.878 0.3119 0.759 0.575
#> 4 4 0.736 0.811 0.900 0.1466 0.733 0.413
#> 5 5 0.705 0.660 0.778 0.0831 0.845 0.528
#> 6 6 0.841 0.756 0.882 0.0572 0.894 0.581
suggest_best_k() suggests the best \(k\) based on these statistics. The rules are as follows:
suggest_best_k(res)
#> [1] 2
Following shows the table of the partitions (You need to click the show/hide
code output link to see it). The membership matrix (columns with name p*)
is inferred by
clue::cl_consensus()
function with the SE method. Basically the value in the membership matrix
represents the probability to belong to a certain group. The finall class
label for an item is determined with the group with highest probability it
belongs to.
In get_classes() function, the entropy is calculated from the membership
matrix and the silhouette score is calculated from the consensus matrix.
cbind(get_classes(res, k = 2), get_membership(res, k = 2))
#> class entropy silhouette p1 p2
#> GSM1009062 1 0.0000 0.966 1.000 0.000
#> GSM1009076 2 0.0000 0.974 0.000 1.000
#> GSM1009090 1 0.0000 0.966 1.000 0.000
#> GSM1009104 2 0.0000 0.974 0.000 1.000
#> GSM1009118 1 0.6148 0.814 0.848 0.152
#> GSM1009132 1 0.0000 0.966 1.000 0.000
#> GSM1009146 1 0.0000 0.966 1.000 0.000
#> GSM1009160 2 0.0000 0.974 0.000 1.000
#> GSM1009174 2 0.0000 0.974 0.000 1.000
#> GSM1009188 1 0.0000 0.966 1.000 0.000
#> GSM1009063 1 0.0000 0.966 1.000 0.000
#> GSM1009077 2 0.0000 0.974 0.000 1.000
#> GSM1009091 1 0.0000 0.966 1.000 0.000
#> GSM1009105 2 0.0000 0.974 0.000 1.000
#> GSM1009119 1 0.0000 0.966 1.000 0.000
#> GSM1009133 1 0.0000 0.966 1.000 0.000
#> GSM1009147 1 0.0000 0.966 1.000 0.000
#> GSM1009161 2 0.0000 0.974 0.000 1.000
#> GSM1009175 2 0.0672 0.967 0.008 0.992
#> GSM1009189 1 0.0000 0.966 1.000 0.000
#> GSM1009064 1 0.0000 0.966 1.000 0.000
#> GSM1009078 1 0.9427 0.463 0.640 0.360
#> GSM1009092 1 0.0000 0.966 1.000 0.000
#> GSM1009106 2 0.0000 0.974 0.000 1.000
#> GSM1009120 1 0.0000 0.966 1.000 0.000
#> GSM1009134 1 0.0000 0.966 1.000 0.000
#> GSM1009148 1 0.0000 0.966 1.000 0.000
#> GSM1009162 2 0.0000 0.974 0.000 1.000
#> GSM1009176 2 0.0000 0.974 0.000 1.000
#> GSM1009190 1 0.0000 0.966 1.000 0.000
#> GSM1009065 1 0.0000 0.966 1.000 0.000
#> GSM1009079 2 0.0000 0.974 0.000 1.000
#> GSM1009093 1 0.0000 0.966 1.000 0.000
#> GSM1009107 2 0.0000 0.974 0.000 1.000
#> GSM1009121 1 0.3879 0.901 0.924 0.076
#> GSM1009135 1 0.0000 0.966 1.000 0.000
#> GSM1009149 1 0.0000 0.966 1.000 0.000
#> GSM1009163 2 0.0000 0.974 0.000 1.000
#> GSM1009177 2 0.0000 0.974 0.000 1.000
#> GSM1009191 1 0.0000 0.966 1.000 0.000
#> GSM1009066 1 0.0000 0.966 1.000 0.000
#> GSM1009080 2 0.0000 0.974 0.000 1.000
#> GSM1009094 1 0.0000 0.966 1.000 0.000
#> GSM1009108 2 0.0000 0.974 0.000 1.000
#> GSM1009122 2 0.5178 0.861 0.116 0.884
#> GSM1009136 1 0.0000 0.966 1.000 0.000
#> GSM1009150 1 0.0000 0.966 1.000 0.000
#> GSM1009164 2 0.0000 0.974 0.000 1.000
#> GSM1009178 1 0.7056 0.769 0.808 0.192
#> GSM1009192 1 0.0000 0.966 1.000 0.000
#> GSM1009067 1 0.0000 0.966 1.000 0.000
#> GSM1009081 2 0.0000 0.974 0.000 1.000
#> GSM1009095 1 0.0000 0.966 1.000 0.000
#> GSM1009109 2 0.0000 0.974 0.000 1.000
#> GSM1009123 1 0.0000 0.966 1.000 0.000
#> GSM1009137 1 0.0000 0.966 1.000 0.000
#> GSM1009151 1 0.0000 0.966 1.000 0.000
#> GSM1009165 2 0.0000 0.974 0.000 1.000
#> GSM1009179 1 0.7528 0.736 0.784 0.216
#> GSM1009193 1 0.0000 0.966 1.000 0.000
#> GSM1009068 1 0.0000 0.966 1.000 0.000
#> GSM1009082 2 0.0000 0.974 0.000 1.000
#> GSM1009096 1 0.0000 0.966 1.000 0.000
#> GSM1009110 2 0.0000 0.974 0.000 1.000
#> GSM1009124 1 0.0000 0.966 1.000 0.000
#> GSM1009138 1 0.0000 0.966 1.000 0.000
#> GSM1009152 1 0.0000 0.966 1.000 0.000
#> GSM1009166 2 0.0000 0.974 0.000 1.000
#> GSM1009180 1 0.4815 0.871 0.896 0.104
#> GSM1009194 1 0.0000 0.966 1.000 0.000
#> GSM1009069 1 0.0000 0.966 1.000 0.000
#> GSM1009083 2 0.0000 0.974 0.000 1.000
#> GSM1009097 1 0.0000 0.966 1.000 0.000
#> GSM1009111 2 0.0000 0.974 0.000 1.000
#> GSM1009125 2 0.0000 0.974 0.000 1.000
#> GSM1009139 1 0.0000 0.966 1.000 0.000
#> GSM1009153 1 0.0000 0.966 1.000 0.000
#> GSM1009167 2 0.0000 0.974 0.000 1.000
#> GSM1009181 2 0.0000 0.974 0.000 1.000
#> GSM1009195 1 0.9044 0.533 0.680 0.320
#> GSM1009070 1 0.0000 0.966 1.000 0.000
#> GSM1009084 2 0.0000 0.974 0.000 1.000
#> GSM1009098 1 0.0000 0.966 1.000 0.000
#> GSM1009112 2 0.0000 0.974 0.000 1.000
#> GSM1009126 1 0.0000 0.966 1.000 0.000
#> GSM1009140 1 0.0000 0.966 1.000 0.000
#> GSM1009154 1 0.0000 0.966 1.000 0.000
#> GSM1009168 2 0.0000 0.974 0.000 1.000
#> GSM1009182 2 0.5059 0.864 0.112 0.888
#> GSM1009196 1 0.0000 0.966 1.000 0.000
#> GSM1009071 1 0.0000 0.966 1.000 0.000
#> GSM1009085 2 0.0000 0.974 0.000 1.000
#> GSM1009099 1 0.0000 0.966 1.000 0.000
#> GSM1009113 2 0.0000 0.974 0.000 1.000
#> GSM1009127 1 0.0000 0.966 1.000 0.000
#> GSM1009141 1 0.0000 0.966 1.000 0.000
#> GSM1009155 1 0.0000 0.966 1.000 0.000
#> GSM1009169 2 0.0000 0.974 0.000 1.000
#> GSM1009183 2 0.0000 0.974 0.000 1.000
#> GSM1009197 1 0.0000 0.966 1.000 0.000
#> GSM1009072 1 0.0000 0.966 1.000 0.000
#> GSM1009086 2 0.0000 0.974 0.000 1.000
#> GSM1009100 1 0.0000 0.966 1.000 0.000
#> GSM1009114 2 0.0000 0.974 0.000 1.000
#> GSM1009128 1 0.9170 0.524 0.668 0.332
#> GSM1009142 1 0.0000 0.966 1.000 0.000
#> GSM1009156 1 0.6973 0.774 0.812 0.188
#> GSM1009170 2 0.0000 0.974 0.000 1.000
#> GSM1009184 2 0.4690 0.878 0.100 0.900
#> GSM1009198 1 0.0000 0.966 1.000 0.000
#> GSM1009073 1 0.0000 0.966 1.000 0.000
#> GSM1009087 1 0.9833 0.292 0.576 0.424
#> GSM1009101 1 0.0000 0.966 1.000 0.000
#> GSM1009115 2 0.0000 0.974 0.000 1.000
#> GSM1009129 2 0.0000 0.974 0.000 1.000
#> GSM1009143 1 0.0000 0.966 1.000 0.000
#> GSM1009157 1 0.2948 0.923 0.948 0.052
#> GSM1009171 2 0.0000 0.974 0.000 1.000
#> GSM1009185 1 0.7528 0.734 0.784 0.216
#> GSM1009199 1 0.0000 0.966 1.000 0.000
#> GSM1009074 1 0.0000 0.966 1.000 0.000
#> GSM1009088 2 0.7299 0.736 0.204 0.796
#> GSM1009102 1 0.0000 0.966 1.000 0.000
#> GSM1009116 2 0.0000 0.974 0.000 1.000
#> GSM1009130 2 0.0000 0.974 0.000 1.000
#> GSM1009144 1 0.0000 0.966 1.000 0.000
#> GSM1009158 1 0.0000 0.966 1.000 0.000
#> GSM1009172 2 0.0000 0.974 0.000 1.000
#> GSM1009186 2 0.8499 0.613 0.276 0.724
#> GSM1009200 1 0.0000 0.966 1.000 0.000
#> GSM1009075 1 0.0000 0.966 1.000 0.000
#> GSM1009089 1 0.7056 0.769 0.808 0.192
#> GSM1009103 1 0.0000 0.966 1.000 0.000
#> GSM1009117 2 0.0000 0.974 0.000 1.000
#> GSM1009131 2 0.9954 0.106 0.460 0.540
#> GSM1009145 1 0.0000 0.966 1.000 0.000
#> GSM1009159 1 0.0000 0.966 1.000 0.000
#> GSM1009173 2 0.0000 0.974 0.000 1.000
#> GSM1009187 1 0.0000 0.966 1.000 0.000
#> GSM1009201 1 0.0000 0.966 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1009062 1 0.6045 0.4509 0.620 0.380 0.000
#> GSM1009076 2 0.1163 0.8074 0.000 0.972 0.028
#> GSM1009090 1 0.1289 0.8946 0.968 0.000 0.032
#> GSM1009104 3 0.5650 0.6643 0.000 0.312 0.688
#> GSM1009118 1 0.7683 0.4634 0.640 0.080 0.280
#> GSM1009132 1 0.1163 0.8967 0.972 0.000 0.028
#> GSM1009146 1 0.2356 0.8695 0.928 0.072 0.000
#> GSM1009160 3 0.0747 0.7731 0.000 0.016 0.984
#> GSM1009174 2 0.0747 0.8125 0.000 0.984 0.016
#> GSM1009188 1 0.0475 0.9010 0.992 0.004 0.004
#> GSM1009063 2 0.6307 -0.0696 0.488 0.512 0.000
#> GSM1009077 2 0.0747 0.8125 0.000 0.984 0.016
#> GSM1009091 1 0.1163 0.8967 0.972 0.000 0.028
#> GSM1009105 3 0.6140 0.5628 0.000 0.404 0.596
#> GSM1009119 1 0.0237 0.9005 0.996 0.004 0.000
#> GSM1009133 1 0.1163 0.8967 0.972 0.000 0.028
#> GSM1009147 1 0.1411 0.8901 0.964 0.036 0.000
#> GSM1009161 3 0.0424 0.7737 0.000 0.008 0.992
#> GSM1009175 2 0.1163 0.8078 0.000 0.972 0.028
#> GSM1009189 1 0.0424 0.8999 0.992 0.008 0.000
#> GSM1009064 2 0.5254 0.5949 0.264 0.736 0.000
#> GSM1009078 2 0.0592 0.8095 0.012 0.988 0.000
#> GSM1009092 1 0.1163 0.8967 0.972 0.000 0.028
#> GSM1009106 3 0.6302 0.4273 0.000 0.480 0.520
#> GSM1009120 1 0.0892 0.8969 0.980 0.020 0.000
#> GSM1009134 1 0.0892 0.8990 0.980 0.000 0.020
#> GSM1009148 1 0.3482 0.8254 0.872 0.128 0.000
#> GSM1009162 3 0.0000 0.7729 0.000 0.000 1.000
#> GSM1009176 2 0.1964 0.7845 0.000 0.944 0.056
#> GSM1009190 1 0.0475 0.9010 0.992 0.004 0.004
#> GSM1009065 2 0.4750 0.6436 0.216 0.784 0.000
#> GSM1009079 2 0.2711 0.7456 0.000 0.912 0.088
#> GSM1009093 1 0.1163 0.8967 0.972 0.000 0.028
#> GSM1009107 3 0.6192 0.5399 0.000 0.420 0.580
#> GSM1009121 1 0.5968 0.4357 0.636 0.000 0.364
#> GSM1009135 1 0.1163 0.8967 0.972 0.000 0.028
#> GSM1009149 1 0.0892 0.8969 0.980 0.020 0.000
#> GSM1009163 3 0.0000 0.7729 0.000 0.000 1.000
#> GSM1009177 2 0.1753 0.7921 0.000 0.952 0.048
#> GSM1009191 1 0.1643 0.8866 0.956 0.044 0.000
#> GSM1009066 2 0.5905 0.4030 0.352 0.648 0.000
#> GSM1009080 2 0.2066 0.7800 0.000 0.940 0.060
#> GSM1009094 1 0.1163 0.8967 0.972 0.000 0.028
#> GSM1009108 2 0.6140 -0.1175 0.000 0.596 0.404
#> GSM1009122 3 0.7960 0.5998 0.120 0.232 0.648
#> GSM1009136 1 0.0237 0.9008 0.996 0.000 0.004
#> GSM1009150 1 0.0892 0.8971 0.980 0.020 0.000
#> GSM1009164 3 0.0747 0.7731 0.000 0.016 0.984
#> GSM1009178 2 0.0424 0.8108 0.008 0.992 0.000
#> GSM1009192 1 0.0892 0.8969 0.980 0.020 0.000
#> GSM1009067 1 0.6026 0.4600 0.624 0.376 0.000
#> GSM1009081 2 0.1289 0.8048 0.000 0.968 0.032
#> GSM1009095 1 0.0237 0.9005 0.996 0.004 0.000
#> GSM1009109 2 0.6280 -0.2996 0.000 0.540 0.460
#> GSM1009123 1 0.0237 0.9005 0.996 0.004 0.000
#> GSM1009137 1 0.1031 0.8980 0.976 0.000 0.024
#> GSM1009151 1 0.4062 0.7902 0.836 0.164 0.000
#> GSM1009165 3 0.0000 0.7729 0.000 0.000 1.000
#> GSM1009179 2 0.0237 0.8117 0.004 0.996 0.000
#> GSM1009193 1 0.0237 0.9005 0.996 0.004 0.000
#> GSM1009068 1 0.5733 0.5641 0.676 0.324 0.000
#> GSM1009082 2 0.0747 0.8125 0.000 0.984 0.016
#> GSM1009096 1 0.1163 0.8967 0.972 0.000 0.028
#> GSM1009110 3 0.6274 0.4763 0.000 0.456 0.544
#> GSM1009124 1 0.0475 0.9010 0.992 0.004 0.004
#> GSM1009138 1 0.1031 0.8980 0.976 0.000 0.024
#> GSM1009152 1 0.3192 0.8391 0.888 0.112 0.000
#> GSM1009166 3 0.0237 0.7735 0.000 0.004 0.996
#> GSM1009180 2 0.1337 0.8115 0.012 0.972 0.016
#> GSM1009194 1 0.3816 0.8067 0.852 0.148 0.000
#> GSM1009069 2 0.2066 0.7836 0.060 0.940 0.000
#> GSM1009083 2 0.0424 0.8129 0.000 0.992 0.008
#> GSM1009097 1 0.1163 0.8967 0.972 0.000 0.028
#> GSM1009111 3 0.6308 0.3990 0.000 0.492 0.508
#> GSM1009125 3 0.1525 0.7720 0.004 0.032 0.964
#> GSM1009139 1 0.0747 0.8998 0.984 0.000 0.016
#> GSM1009153 1 0.4974 0.7038 0.764 0.236 0.000
#> GSM1009167 3 0.0000 0.7729 0.000 0.000 1.000
#> GSM1009181 2 0.2448 0.7614 0.000 0.924 0.076
#> GSM1009195 2 0.6897 0.5435 0.292 0.668 0.040
#> GSM1009070 1 0.5363 0.6454 0.724 0.276 0.000
#> GSM1009084 2 0.0747 0.8125 0.000 0.984 0.016
#> GSM1009098 1 0.1163 0.8967 0.972 0.000 0.028
#> GSM1009112 3 0.5835 0.6377 0.000 0.340 0.660
#> GSM1009126 1 0.0237 0.9010 0.996 0.000 0.004
#> GSM1009140 1 0.0892 0.8990 0.980 0.000 0.020
#> GSM1009154 1 0.2165 0.8745 0.936 0.064 0.000
#> GSM1009168 3 0.0237 0.7735 0.000 0.004 0.996
#> GSM1009182 2 0.1163 0.8078 0.000 0.972 0.028
#> GSM1009196 1 0.2796 0.8548 0.908 0.092 0.000
#> GSM1009071 2 0.5363 0.5718 0.276 0.724 0.000
#> GSM1009085 2 0.0747 0.8125 0.000 0.984 0.016
#> GSM1009099 1 0.1163 0.8967 0.972 0.000 0.028
#> GSM1009113 3 0.6291 0.4534 0.000 0.468 0.532
#> GSM1009127 1 0.0424 0.8999 0.992 0.008 0.000
#> GSM1009141 1 0.0747 0.8998 0.984 0.000 0.016
#> GSM1009155 1 0.6079 0.4336 0.612 0.388 0.000
#> GSM1009169 3 0.0000 0.7729 0.000 0.000 1.000
#> GSM1009183 2 0.2959 0.7295 0.000 0.900 0.100
#> GSM1009197 1 0.0424 0.8999 0.992 0.008 0.000
#> GSM1009072 1 0.6079 0.4325 0.612 0.388 0.000
#> GSM1009086 2 0.1163 0.8074 0.000 0.972 0.028
#> GSM1009100 1 0.1163 0.8967 0.972 0.000 0.028
#> GSM1009114 3 0.5553 0.6925 0.004 0.272 0.724
#> GSM1009128 3 0.5988 0.3132 0.368 0.000 0.632
#> GSM1009142 1 0.1031 0.8980 0.976 0.000 0.024
#> GSM1009156 1 0.6470 0.4549 0.632 0.356 0.012
#> GSM1009170 3 0.0424 0.7737 0.000 0.008 0.992
#> GSM1009184 2 0.0592 0.8128 0.000 0.988 0.012
#> GSM1009198 1 0.0829 0.9010 0.984 0.004 0.012
#> GSM1009073 2 0.6168 0.2234 0.412 0.588 0.000
#> GSM1009087 2 0.0747 0.8079 0.016 0.984 0.000
#> GSM1009101 1 0.1163 0.8967 0.972 0.000 0.028
#> GSM1009115 3 0.5363 0.6898 0.000 0.276 0.724
#> GSM1009129 3 0.4504 0.7246 0.000 0.196 0.804
#> GSM1009143 1 0.0747 0.8998 0.984 0.000 0.016
#> GSM1009157 2 0.4504 0.6629 0.196 0.804 0.000
#> GSM1009171 3 0.0000 0.7729 0.000 0.000 1.000
#> GSM1009185 2 0.3879 0.7064 0.152 0.848 0.000
#> GSM1009199 1 0.6986 0.6067 0.688 0.256 0.056
#> GSM1009074 1 0.6180 0.3602 0.584 0.416 0.000
#> GSM1009088 2 0.0237 0.8117 0.004 0.996 0.000
#> GSM1009102 1 0.0000 0.9007 1.000 0.000 0.000
#> GSM1009116 3 0.6215 0.5278 0.000 0.428 0.572
#> GSM1009130 3 0.5785 0.6245 0.000 0.332 0.668
#> GSM1009144 1 0.0237 0.9008 0.996 0.000 0.004
#> GSM1009158 1 0.1289 0.8920 0.968 0.032 0.000
#> GSM1009172 3 0.0000 0.7729 0.000 0.000 1.000
#> GSM1009186 2 0.0424 0.8132 0.000 0.992 0.008
#> GSM1009200 1 0.0237 0.9005 0.996 0.004 0.000
#> GSM1009075 1 0.6111 0.4125 0.604 0.396 0.000
#> GSM1009089 2 0.1529 0.7963 0.040 0.960 0.000
#> GSM1009103 1 0.0237 0.9008 0.996 0.000 0.004
#> GSM1009117 3 0.5621 0.6674 0.000 0.308 0.692
#> GSM1009131 3 0.6867 0.5202 0.288 0.040 0.672
#> GSM1009145 1 0.0592 0.9002 0.988 0.000 0.012
#> GSM1009159 1 0.0592 0.8992 0.988 0.012 0.000
#> GSM1009173 3 0.0000 0.7729 0.000 0.000 1.000
#> GSM1009187 2 0.3038 0.7520 0.104 0.896 0.000
#> GSM1009201 1 0.0747 0.8982 0.984 0.016 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1009062 1 0.1256 0.8927 0.964 0.008 0.000 0.028
#> GSM1009076 2 0.1389 0.8591 0.048 0.952 0.000 0.000
#> GSM1009090 4 0.0000 0.8867 0.000 0.000 0.000 1.000
#> GSM1009104 2 0.0817 0.8544 0.000 0.976 0.024 0.000
#> GSM1009118 4 0.5578 0.3315 0.024 0.348 0.004 0.624
#> GSM1009132 4 0.0188 0.8858 0.000 0.004 0.000 0.996
#> GSM1009146 1 0.0895 0.8887 0.976 0.000 0.004 0.020
#> GSM1009160 3 0.0707 0.9953 0.000 0.020 0.980 0.000
#> GSM1009174 2 0.4313 0.7317 0.260 0.736 0.000 0.004
#> GSM1009188 4 0.4542 0.6788 0.228 0.000 0.020 0.752
#> GSM1009063 1 0.1406 0.8923 0.960 0.016 0.000 0.024
#> GSM1009077 2 0.1716 0.8575 0.064 0.936 0.000 0.000
#> GSM1009091 4 0.0188 0.8865 0.004 0.000 0.000 0.996
#> GSM1009105 2 0.0817 0.8544 0.000 0.976 0.024 0.000
#> GSM1009119 4 0.2987 0.8229 0.104 0.000 0.016 0.880
#> GSM1009133 4 0.0188 0.8858 0.000 0.004 0.000 0.996
#> GSM1009147 1 0.1388 0.8803 0.960 0.000 0.012 0.028
#> GSM1009161 3 0.0707 0.9953 0.000 0.020 0.980 0.000
#> GSM1009175 2 0.4088 0.7617 0.232 0.764 0.000 0.004
#> GSM1009189 4 0.4980 0.5655 0.304 0.000 0.016 0.680
#> GSM1009064 1 0.1510 0.8881 0.956 0.028 0.000 0.016
#> GSM1009078 1 0.4898 0.2200 0.584 0.416 0.000 0.000
#> GSM1009092 4 0.0779 0.8818 0.016 0.000 0.004 0.980
#> GSM1009106 2 0.0817 0.8544 0.000 0.976 0.024 0.000
#> GSM1009120 4 0.5236 0.2594 0.432 0.000 0.008 0.560
#> GSM1009134 4 0.0188 0.8858 0.000 0.004 0.000 0.996
#> GSM1009148 1 0.0336 0.8910 0.992 0.000 0.000 0.008
#> GSM1009162 3 0.0592 0.9972 0.000 0.016 0.984 0.000
#> GSM1009176 2 0.3441 0.8274 0.152 0.840 0.004 0.004
#> GSM1009190 4 0.5615 0.4354 0.356 0.000 0.032 0.612
#> GSM1009065 1 0.1510 0.8881 0.956 0.028 0.000 0.016
#> GSM1009079 2 0.1174 0.8580 0.020 0.968 0.012 0.000
#> GSM1009093 4 0.0376 0.8859 0.004 0.000 0.004 0.992
#> GSM1009107 2 0.0817 0.8544 0.000 0.976 0.024 0.000
#> GSM1009121 4 0.1209 0.8681 0.000 0.004 0.032 0.964
#> GSM1009135 4 0.0188 0.8858 0.000 0.004 0.000 0.996
#> GSM1009149 1 0.4844 0.5456 0.688 0.000 0.012 0.300
#> GSM1009163 3 0.0592 0.9972 0.000 0.016 0.984 0.000
#> GSM1009177 2 0.3448 0.8170 0.168 0.828 0.000 0.004
#> GSM1009191 1 0.4098 0.7012 0.784 0.000 0.012 0.204
#> GSM1009066 1 0.1388 0.8923 0.960 0.012 0.000 0.028
#> GSM1009080 2 0.1209 0.8594 0.032 0.964 0.004 0.000
#> GSM1009094 4 0.0000 0.8867 0.000 0.000 0.000 1.000
#> GSM1009108 2 0.0336 0.8557 0.000 0.992 0.008 0.000
#> GSM1009122 2 0.5866 0.7135 0.020 0.736 0.100 0.144
#> GSM1009136 4 0.0000 0.8867 0.000 0.000 0.000 1.000
#> GSM1009150 1 0.2831 0.8174 0.876 0.000 0.004 0.120
#> GSM1009164 3 0.0707 0.9953 0.000 0.020 0.980 0.000
#> GSM1009178 2 0.4456 0.7032 0.280 0.716 0.000 0.004
#> GSM1009192 1 0.4844 0.5453 0.688 0.000 0.012 0.300
#> GSM1009067 1 0.1256 0.8927 0.964 0.008 0.000 0.028
#> GSM1009081 2 0.0817 0.8591 0.024 0.976 0.000 0.000
#> GSM1009095 4 0.0376 0.8862 0.004 0.000 0.004 0.992
#> GSM1009109 2 0.0592 0.8554 0.000 0.984 0.016 0.000
#> GSM1009123 4 0.1488 0.8724 0.032 0.000 0.012 0.956
#> GSM1009137 4 0.0188 0.8858 0.000 0.004 0.000 0.996
#> GSM1009151 1 0.0592 0.8918 0.984 0.000 0.000 0.016
#> GSM1009165 3 0.0592 0.9972 0.000 0.016 0.984 0.000
#> GSM1009179 2 0.4560 0.6802 0.296 0.700 0.000 0.004
#> GSM1009193 4 0.5331 0.4999 0.332 0.000 0.024 0.644
#> GSM1009068 1 0.1256 0.8927 0.964 0.008 0.000 0.028
#> GSM1009082 2 0.2345 0.8491 0.100 0.900 0.000 0.000
#> GSM1009096 4 0.0000 0.8867 0.000 0.000 0.000 1.000
#> GSM1009110 2 0.0817 0.8544 0.000 0.976 0.024 0.000
#> GSM1009124 4 0.1854 0.8641 0.048 0.000 0.012 0.940
#> GSM1009138 4 0.0188 0.8858 0.000 0.004 0.000 0.996
#> GSM1009152 1 0.0895 0.8920 0.976 0.004 0.000 0.020
#> GSM1009166 3 0.0592 0.9972 0.000 0.016 0.984 0.000
#> GSM1009180 2 0.4053 0.7642 0.228 0.768 0.000 0.004
#> GSM1009194 1 0.0524 0.8911 0.988 0.004 0.000 0.008
#> GSM1009069 1 0.1389 0.8778 0.952 0.048 0.000 0.000
#> GSM1009083 2 0.2647 0.8374 0.120 0.880 0.000 0.000
#> GSM1009097 4 0.0657 0.8831 0.012 0.000 0.004 0.984
#> GSM1009111 2 0.0817 0.8544 0.000 0.976 0.024 0.000
#> GSM1009125 2 0.6267 0.6427 0.008 0.684 0.124 0.184
#> GSM1009139 4 0.0188 0.8858 0.000 0.004 0.000 0.996
#> GSM1009153 1 0.0469 0.8916 0.988 0.000 0.000 0.012
#> GSM1009167 3 0.0469 0.9944 0.000 0.012 0.988 0.000
#> GSM1009181 2 0.2983 0.8465 0.108 0.880 0.008 0.004
#> GSM1009195 1 0.1151 0.8788 0.968 0.024 0.008 0.000
#> GSM1009070 1 0.1042 0.8925 0.972 0.008 0.000 0.020
#> GSM1009084 2 0.1557 0.8579 0.056 0.944 0.000 0.000
#> GSM1009098 4 0.0000 0.8867 0.000 0.000 0.000 1.000
#> GSM1009112 2 0.0817 0.8544 0.000 0.976 0.024 0.000
#> GSM1009126 4 0.0592 0.8839 0.016 0.000 0.000 0.984
#> GSM1009140 4 0.0000 0.8867 0.000 0.000 0.000 1.000
#> GSM1009154 1 0.1209 0.8838 0.964 0.000 0.004 0.032
#> GSM1009168 3 0.0592 0.9972 0.000 0.016 0.984 0.000
#> GSM1009182 2 0.4122 0.7572 0.236 0.760 0.000 0.004
#> GSM1009196 1 0.0895 0.8919 0.976 0.004 0.000 0.020
#> GSM1009071 1 0.1411 0.8913 0.960 0.020 0.000 0.020
#> GSM1009085 2 0.1792 0.8558 0.068 0.932 0.000 0.000
#> GSM1009099 4 0.0779 0.8818 0.016 0.000 0.004 0.980
#> GSM1009113 2 0.0817 0.8544 0.000 0.976 0.024 0.000
#> GSM1009127 4 0.3751 0.7366 0.196 0.000 0.004 0.800
#> GSM1009141 4 0.0188 0.8858 0.000 0.004 0.000 0.996
#> GSM1009155 1 0.0524 0.8912 0.988 0.004 0.000 0.008
#> GSM1009169 3 0.0336 0.9904 0.000 0.008 0.992 0.000
#> GSM1009183 2 0.3727 0.8188 0.164 0.824 0.008 0.004
#> GSM1009197 4 0.5606 0.0654 0.480 0.000 0.020 0.500
#> GSM1009072 1 0.1256 0.8927 0.964 0.008 0.000 0.028
#> GSM1009086 2 0.1118 0.8592 0.036 0.964 0.000 0.000
#> GSM1009100 4 0.0188 0.8865 0.004 0.000 0.000 0.996
#> GSM1009114 2 0.1004 0.8529 0.000 0.972 0.024 0.004
#> GSM1009128 4 0.2161 0.8570 0.016 0.004 0.048 0.932
#> GSM1009142 4 0.0188 0.8858 0.000 0.004 0.000 0.996
#> GSM1009156 1 0.4980 0.5837 0.680 0.000 0.304 0.016
#> GSM1009170 3 0.0707 0.9953 0.000 0.020 0.980 0.000
#> GSM1009184 2 0.5004 0.5029 0.392 0.604 0.000 0.004
#> GSM1009198 4 0.4706 0.6487 0.248 0.000 0.020 0.732
#> GSM1009073 1 0.1406 0.8923 0.960 0.016 0.000 0.024
#> GSM1009087 1 0.3528 0.7285 0.808 0.192 0.000 0.000
#> GSM1009101 4 0.0188 0.8865 0.004 0.000 0.000 0.996
#> GSM1009115 2 0.0817 0.8544 0.000 0.976 0.024 0.000
#> GSM1009129 2 0.3837 0.7207 0.000 0.776 0.224 0.000
#> GSM1009143 4 0.0000 0.8867 0.000 0.000 0.000 1.000
#> GSM1009157 1 0.0336 0.8870 0.992 0.008 0.000 0.000
#> GSM1009171 3 0.0592 0.9972 0.000 0.016 0.984 0.000
#> GSM1009185 2 0.6498 0.6123 0.272 0.624 0.004 0.100
#> GSM1009199 1 0.1617 0.8816 0.956 0.024 0.008 0.012
#> GSM1009074 1 0.1284 0.8929 0.964 0.012 0.000 0.024
#> GSM1009088 1 0.3907 0.6714 0.768 0.232 0.000 0.000
#> GSM1009102 4 0.0188 0.8866 0.000 0.000 0.004 0.996
#> GSM1009116 2 0.0817 0.8544 0.000 0.976 0.024 0.000
#> GSM1009130 2 0.3975 0.7016 0.000 0.760 0.240 0.000
#> GSM1009144 4 0.0000 0.8867 0.000 0.000 0.000 1.000
#> GSM1009158 1 0.2101 0.8636 0.928 0.000 0.012 0.060
#> GSM1009172 3 0.0592 0.9972 0.000 0.016 0.984 0.000
#> GSM1009186 2 0.5004 0.5035 0.392 0.604 0.000 0.004
#> GSM1009200 4 0.5173 0.5278 0.320 0.000 0.020 0.660
#> GSM1009075 1 0.1256 0.8927 0.964 0.008 0.000 0.028
#> GSM1009089 1 0.1118 0.8825 0.964 0.036 0.000 0.000
#> GSM1009103 4 0.0188 0.8866 0.000 0.000 0.004 0.996
#> GSM1009117 2 0.0817 0.8544 0.000 0.976 0.024 0.000
#> GSM1009131 4 0.7666 0.3295 0.012 0.272 0.192 0.524
#> GSM1009145 4 0.0000 0.8867 0.000 0.000 0.000 1.000
#> GSM1009159 1 0.5326 0.3441 0.604 0.000 0.016 0.380
#> GSM1009173 3 0.0469 0.9944 0.000 0.012 0.988 0.000
#> GSM1009187 1 0.3672 0.7283 0.824 0.164 0.000 0.012
#> GSM1009201 1 0.5203 0.2463 0.576 0.000 0.008 0.416
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM1009062 1 0.4297 0.6006 0.528 0.472 0.000 0.000 0.000
#> GSM1009076 5 0.2890 0.6506 0.004 0.160 0.000 0.000 0.836
#> GSM1009090 4 0.0404 0.8912 0.012 0.000 0.000 0.988 0.000
#> GSM1009104 5 0.0290 0.7712 0.000 0.000 0.000 0.008 0.992
#> GSM1009118 4 0.5016 0.6953 0.064 0.136 0.000 0.752 0.048
#> GSM1009132 4 0.0162 0.8892 0.000 0.000 0.000 0.996 0.004
#> GSM1009146 1 0.1082 0.5809 0.964 0.028 0.000 0.008 0.000
#> GSM1009160 3 0.0000 1.0000 0.000 0.000 1.000 0.000 0.000
#> GSM1009174 2 0.5425 0.6302 0.060 0.520 0.000 0.000 0.420
#> GSM1009188 1 0.5102 0.0837 0.580 0.044 0.000 0.376 0.000
#> GSM1009063 1 0.4297 0.6006 0.528 0.472 0.000 0.000 0.000
#> GSM1009077 5 0.3010 0.6337 0.004 0.172 0.000 0.000 0.824
#> GSM1009091 4 0.1341 0.8780 0.056 0.000 0.000 0.944 0.000
#> GSM1009105 5 0.0162 0.7728 0.000 0.000 0.000 0.004 0.996
#> GSM1009119 1 0.4288 0.1081 0.612 0.004 0.000 0.384 0.000
#> GSM1009133 4 0.0162 0.8892 0.000 0.000 0.000 0.996 0.004
#> GSM1009147 1 0.3366 0.4610 0.828 0.140 0.000 0.032 0.000
#> GSM1009161 3 0.0000 1.0000 0.000 0.000 1.000 0.000 0.000
#> GSM1009175 2 0.5820 0.6735 0.100 0.524 0.000 0.000 0.376
#> GSM1009189 1 0.4206 0.3417 0.696 0.016 0.000 0.288 0.000
#> GSM1009064 1 0.4297 0.6006 0.528 0.472 0.000 0.000 0.000
#> GSM1009078 5 0.5572 0.3420 0.124 0.248 0.000 0.000 0.628
#> GSM1009092 4 0.3452 0.7156 0.244 0.000 0.000 0.756 0.000
#> GSM1009106 5 0.0162 0.7728 0.000 0.000 0.000 0.004 0.996
#> GSM1009120 1 0.3663 0.4940 0.776 0.016 0.000 0.208 0.000
#> GSM1009134 4 0.0162 0.8892 0.000 0.000 0.000 0.996 0.004
#> GSM1009148 1 0.2848 0.6063 0.840 0.156 0.000 0.004 0.000
#> GSM1009162 3 0.0000 1.0000 0.000 0.000 1.000 0.000 0.000
#> GSM1009176 2 0.4894 0.5557 0.024 0.520 0.000 0.000 0.456
#> GSM1009190 1 0.5891 0.3204 0.624 0.108 0.016 0.252 0.000
#> GSM1009065 1 0.4297 0.6006 0.528 0.472 0.000 0.000 0.000
#> GSM1009079 5 0.3534 0.4646 0.000 0.256 0.000 0.000 0.744
#> GSM1009093 4 0.1965 0.8549 0.096 0.000 0.000 0.904 0.000
#> GSM1009107 5 0.0162 0.7728 0.000 0.000 0.000 0.004 0.996
#> GSM1009121 4 0.4246 0.7760 0.088 0.008 0.004 0.800 0.100
#> GSM1009135 4 0.0162 0.8892 0.000 0.000 0.000 0.996 0.004
#> GSM1009149 1 0.2124 0.5521 0.900 0.004 0.000 0.096 0.000
#> GSM1009163 3 0.0000 1.0000 0.000 0.000 1.000 0.000 0.000
#> GSM1009177 2 0.5032 0.5769 0.032 0.520 0.000 0.000 0.448
#> GSM1009191 1 0.5355 0.3376 0.688 0.184 0.008 0.120 0.000
#> GSM1009066 1 0.4297 0.6006 0.528 0.472 0.000 0.000 0.000
#> GSM1009080 5 0.3424 0.5031 0.000 0.240 0.000 0.000 0.760
#> GSM1009094 4 0.0963 0.8862 0.036 0.000 0.000 0.964 0.000
#> GSM1009108 5 0.0162 0.7728 0.000 0.000 0.000 0.004 0.996
#> GSM1009122 5 0.8243 -0.0992 0.012 0.244 0.092 0.244 0.408
#> GSM1009136 4 0.0404 0.8911 0.012 0.000 0.000 0.988 0.000
#> GSM1009150 1 0.1251 0.5731 0.956 0.008 0.000 0.036 0.000
#> GSM1009164 3 0.0000 1.0000 0.000 0.000 1.000 0.000 0.000
#> GSM1009178 2 0.6188 0.6739 0.160 0.524 0.000 0.000 0.316
#> GSM1009192 1 0.2193 0.5515 0.900 0.008 0.000 0.092 0.000
#> GSM1009067 1 0.4297 0.6006 0.528 0.472 0.000 0.000 0.000
#> GSM1009081 5 0.2536 0.6910 0.004 0.128 0.000 0.000 0.868
#> GSM1009095 4 0.0609 0.8903 0.020 0.000 0.000 0.980 0.000
#> GSM1009109 5 0.0162 0.7728 0.000 0.000 0.000 0.004 0.996
#> GSM1009123 4 0.4410 0.3860 0.440 0.004 0.000 0.556 0.000
#> GSM1009137 4 0.0162 0.8892 0.000 0.000 0.000 0.996 0.004
#> GSM1009151 1 0.3636 0.6158 0.728 0.272 0.000 0.000 0.000
#> GSM1009165 3 0.0000 1.0000 0.000 0.000 1.000 0.000 0.000
#> GSM1009179 2 0.6133 0.6776 0.148 0.524 0.000 0.000 0.328
#> GSM1009193 1 0.3491 0.4566 0.768 0.004 0.000 0.228 0.000
#> GSM1009068 1 0.4297 0.6006 0.528 0.472 0.000 0.000 0.000
#> GSM1009082 5 0.2886 0.6866 0.008 0.148 0.000 0.000 0.844
#> GSM1009096 4 0.1043 0.8849 0.040 0.000 0.000 0.960 0.000
#> GSM1009110 5 0.0162 0.7728 0.000 0.000 0.000 0.004 0.996
#> GSM1009124 4 0.4542 0.3442 0.456 0.008 0.000 0.536 0.000
#> GSM1009138 4 0.0162 0.8892 0.000 0.000 0.000 0.996 0.004
#> GSM1009152 1 0.3906 0.6155 0.704 0.292 0.000 0.004 0.000
#> GSM1009166 3 0.0000 1.0000 0.000 0.000 1.000 0.000 0.000
#> GSM1009180 2 0.6312 0.6525 0.200 0.524 0.000 0.000 0.276
#> GSM1009194 1 0.4173 0.6151 0.688 0.300 0.000 0.012 0.000
#> GSM1009069 1 0.4443 0.5975 0.524 0.472 0.000 0.000 0.004
#> GSM1009083 5 0.3039 0.6921 0.012 0.152 0.000 0.000 0.836
#> GSM1009097 4 0.3143 0.7597 0.204 0.000 0.000 0.796 0.000
#> GSM1009111 5 0.0324 0.7718 0.000 0.000 0.004 0.004 0.992
#> GSM1009125 4 0.7197 0.0325 0.012 0.196 0.020 0.488 0.284
#> GSM1009139 4 0.0162 0.8892 0.000 0.000 0.000 0.996 0.004
#> GSM1009153 1 0.3837 0.6135 0.692 0.308 0.000 0.000 0.000
#> GSM1009167 3 0.0000 1.0000 0.000 0.000 1.000 0.000 0.000
#> GSM1009181 2 0.4894 0.5557 0.024 0.520 0.000 0.000 0.456
#> GSM1009195 2 0.5681 -0.0700 0.360 0.572 0.024 0.000 0.044
#> GSM1009070 1 0.4297 0.6006 0.528 0.472 0.000 0.000 0.000
#> GSM1009084 5 0.2011 0.7270 0.004 0.088 0.000 0.000 0.908
#> GSM1009098 4 0.0609 0.8901 0.020 0.000 0.000 0.980 0.000
#> GSM1009112 5 0.0290 0.7712 0.000 0.000 0.000 0.008 0.992
#> GSM1009126 4 0.2389 0.8356 0.116 0.004 0.000 0.880 0.000
#> GSM1009140 4 0.0324 0.8901 0.004 0.000 0.000 0.992 0.004
#> GSM1009154 1 0.2124 0.6011 0.900 0.096 0.000 0.004 0.000
#> GSM1009168 3 0.0000 1.0000 0.000 0.000 1.000 0.000 0.000
#> GSM1009182 2 0.5913 0.6774 0.112 0.524 0.000 0.000 0.364
#> GSM1009196 1 0.3562 0.6088 0.788 0.196 0.000 0.016 0.000
#> GSM1009071 1 0.4297 0.6006 0.528 0.472 0.000 0.000 0.000
#> GSM1009085 5 0.1830 0.7424 0.008 0.068 0.000 0.000 0.924
#> GSM1009099 4 0.3210 0.7535 0.212 0.000 0.000 0.788 0.000
#> GSM1009113 5 0.0324 0.7718 0.000 0.000 0.004 0.004 0.992
#> GSM1009127 1 0.4380 0.3234 0.676 0.020 0.000 0.304 0.000
#> GSM1009141 4 0.0162 0.8892 0.000 0.000 0.000 0.996 0.004
#> GSM1009155 1 0.4101 0.6092 0.628 0.372 0.000 0.000 0.000
#> GSM1009169 3 0.0000 1.0000 0.000 0.000 1.000 0.000 0.000
#> GSM1009183 2 0.5216 0.6032 0.044 0.520 0.000 0.000 0.436
#> GSM1009197 1 0.3513 0.4966 0.800 0.020 0.000 0.180 0.000
#> GSM1009072 1 0.4297 0.6006 0.528 0.472 0.000 0.000 0.000
#> GSM1009086 5 0.2389 0.7035 0.004 0.116 0.000 0.000 0.880
#> GSM1009100 4 0.0963 0.8863 0.036 0.000 0.000 0.964 0.000
#> GSM1009114 5 0.0290 0.7712 0.000 0.000 0.000 0.008 0.992
#> GSM1009128 4 0.4714 0.5076 0.372 0.004 0.016 0.608 0.000
#> GSM1009142 4 0.0162 0.8892 0.000 0.000 0.000 0.996 0.004
#> GSM1009156 1 0.5386 0.2819 0.680 0.168 0.148 0.004 0.000
#> GSM1009170 3 0.0000 1.0000 0.000 0.000 1.000 0.000 0.000
#> GSM1009184 2 0.5639 0.6598 0.080 0.524 0.000 0.000 0.396
#> GSM1009198 1 0.5922 0.0692 0.532 0.116 0.000 0.352 0.000
#> GSM1009073 1 0.4297 0.6006 0.528 0.472 0.000 0.000 0.000
#> GSM1009087 5 0.6636 0.0834 0.232 0.336 0.000 0.000 0.432
#> GSM1009101 4 0.1197 0.8819 0.048 0.000 0.000 0.952 0.000
#> GSM1009115 5 0.0162 0.7728 0.000 0.000 0.000 0.004 0.996
#> GSM1009129 5 0.4204 0.5569 0.000 0.048 0.196 0.000 0.756
#> GSM1009143 4 0.0451 0.8907 0.008 0.000 0.000 0.988 0.004
#> GSM1009157 1 0.4537 0.5833 0.592 0.396 0.012 0.000 0.000
#> GSM1009171 3 0.0000 1.0000 0.000 0.000 1.000 0.000 0.000
#> GSM1009185 2 0.4961 0.3762 0.448 0.524 0.000 0.000 0.028
#> GSM1009199 2 0.5090 0.4345 0.264 0.668 0.000 0.004 0.064
#> GSM1009074 1 0.4297 0.6006 0.528 0.472 0.000 0.000 0.000
#> GSM1009088 5 0.6432 0.1819 0.204 0.304 0.000 0.000 0.492
#> GSM1009102 4 0.0510 0.8909 0.016 0.000 0.000 0.984 0.000
#> GSM1009116 5 0.0162 0.7728 0.000 0.000 0.000 0.004 0.996
#> GSM1009130 5 0.3461 0.5461 0.000 0.004 0.224 0.000 0.772
#> GSM1009144 4 0.0324 0.8901 0.004 0.000 0.000 0.992 0.004
#> GSM1009158 1 0.0798 0.5804 0.976 0.016 0.000 0.008 0.000
#> GSM1009172 3 0.0000 1.0000 0.000 0.000 1.000 0.000 0.000
#> GSM1009186 2 0.5556 0.6512 0.072 0.524 0.000 0.000 0.404
#> GSM1009200 1 0.5203 0.3214 0.648 0.080 0.000 0.272 0.000
#> GSM1009075 1 0.4297 0.6006 0.528 0.472 0.000 0.000 0.000
#> GSM1009089 1 0.4295 0.5863 0.740 0.216 0.000 0.000 0.044
#> GSM1009103 4 0.0404 0.8912 0.012 0.000 0.000 0.988 0.000
#> GSM1009117 5 0.0290 0.7712 0.000 0.000 0.000 0.008 0.992
#> GSM1009131 5 0.6565 0.3536 0.096 0.004 0.184 0.084 0.632
#> GSM1009145 4 0.0290 0.8908 0.008 0.000 0.000 0.992 0.000
#> GSM1009159 1 0.1908 0.5556 0.908 0.000 0.000 0.092 0.000
#> GSM1009173 3 0.0000 1.0000 0.000 0.000 1.000 0.000 0.000
#> GSM1009187 2 0.4627 0.3513 0.444 0.544 0.000 0.000 0.012
#> GSM1009201 1 0.2971 0.5189 0.836 0.008 0.000 0.156 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1009062 6 0.1082 0.8256 0.040 0.000 0.000 0.004 0.000 0.956
#> GSM1009076 2 0.5213 0.4039 0.024 0.564 0.000 0.000 0.360 0.052
#> GSM1009090 4 0.0547 0.8889 0.020 0.000 0.000 0.980 0.000 0.000
#> GSM1009104 5 0.0146 0.8412 0.000 0.004 0.000 0.000 0.996 0.000
#> GSM1009118 4 0.5113 0.4197 0.352 0.064 0.000 0.572 0.000 0.012
#> GSM1009132 4 0.0000 0.8876 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1009146 1 0.2214 0.8304 0.888 0.016 0.000 0.000 0.000 0.096
#> GSM1009160 3 0.0000 1.0000 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1009174 2 0.0405 0.8419 0.004 0.988 0.000 0.000 0.008 0.000
#> GSM1009188 1 0.0858 0.8836 0.968 0.004 0.000 0.028 0.000 0.000
#> GSM1009063 6 0.0937 0.8264 0.040 0.000 0.000 0.000 0.000 0.960
#> GSM1009077 2 0.5452 0.4111 0.024 0.560 0.000 0.000 0.340 0.076
#> GSM1009091 4 0.1219 0.8816 0.048 0.004 0.000 0.948 0.000 0.000
#> GSM1009105 5 0.0146 0.8412 0.000 0.004 0.000 0.000 0.996 0.000
#> GSM1009119 1 0.0937 0.8754 0.960 0.000 0.000 0.040 0.000 0.000
#> GSM1009133 4 0.0000 0.8876 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1009147 1 0.1498 0.8751 0.940 0.028 0.000 0.000 0.000 0.032
#> GSM1009161 3 0.0000 1.0000 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1009175 2 0.0508 0.8420 0.004 0.984 0.000 0.000 0.012 0.000
#> GSM1009189 1 0.0862 0.8870 0.972 0.008 0.000 0.016 0.000 0.004
#> GSM1009064 6 0.0790 0.8219 0.032 0.000 0.000 0.000 0.000 0.968
#> GSM1009078 5 0.4852 0.4385 0.024 0.024 0.000 0.000 0.564 0.388
#> GSM1009092 4 0.3575 0.6409 0.284 0.008 0.000 0.708 0.000 0.000
#> GSM1009106 5 0.0146 0.8412 0.000 0.004 0.000 0.000 0.996 0.000
#> GSM1009120 1 0.0725 0.8869 0.976 0.000 0.000 0.012 0.000 0.012
#> GSM1009134 4 0.0146 0.8870 0.000 0.000 0.000 0.996 0.000 0.004
#> GSM1009148 1 0.3403 0.6723 0.768 0.020 0.000 0.000 0.000 0.212
#> GSM1009162 3 0.0000 1.0000 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1009176 2 0.0547 0.8408 0.000 0.980 0.000 0.000 0.020 0.000
#> GSM1009190 1 0.0806 0.8841 0.972 0.008 0.000 0.020 0.000 0.000
#> GSM1009065 6 0.0790 0.8219 0.032 0.000 0.000 0.000 0.000 0.968
#> GSM1009079 2 0.4087 0.6944 0.024 0.764 0.000 0.000 0.168 0.044
#> GSM1009093 4 0.1753 0.8616 0.084 0.004 0.000 0.912 0.000 0.000
#> GSM1009107 5 0.0146 0.8412 0.000 0.004 0.000 0.000 0.996 0.000
#> GSM1009121 4 0.4662 0.3710 0.388 0.008 0.000 0.576 0.024 0.004
#> GSM1009135 4 0.0000 0.8876 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1009149 1 0.1297 0.8759 0.948 0.012 0.000 0.000 0.000 0.040
#> GSM1009163 3 0.0000 1.0000 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1009177 2 0.0632 0.8399 0.000 0.976 0.000 0.000 0.024 0.000
#> GSM1009191 1 0.0767 0.8844 0.976 0.012 0.000 0.008 0.000 0.004
#> GSM1009066 6 0.0937 0.8264 0.040 0.000 0.000 0.000 0.000 0.960
#> GSM1009080 2 0.4476 0.6392 0.024 0.712 0.000 0.000 0.220 0.044
#> GSM1009094 4 0.0713 0.8887 0.028 0.000 0.000 0.972 0.000 0.000
#> GSM1009108 5 0.0146 0.8412 0.000 0.004 0.000 0.000 0.996 0.000
#> GSM1009122 4 0.7580 0.1133 0.212 0.288 0.000 0.388 0.088 0.024
#> GSM1009136 4 0.0508 0.8896 0.012 0.000 0.000 0.984 0.000 0.004
#> GSM1009150 1 0.1434 0.8706 0.940 0.012 0.000 0.000 0.000 0.048
#> GSM1009164 3 0.0000 1.0000 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1009178 2 0.0603 0.8423 0.004 0.980 0.000 0.000 0.016 0.000
#> GSM1009192 1 0.0922 0.8845 0.968 0.004 0.000 0.004 0.000 0.024
#> GSM1009067 6 0.1007 0.8259 0.044 0.000 0.000 0.000 0.000 0.956
#> GSM1009081 2 0.5234 0.3175 0.024 0.532 0.000 0.000 0.396 0.048
#> GSM1009095 4 0.1075 0.8838 0.048 0.000 0.000 0.952 0.000 0.000
#> GSM1009109 5 0.0146 0.8412 0.000 0.004 0.000 0.000 0.996 0.000
#> GSM1009123 1 0.1327 0.8551 0.936 0.000 0.000 0.064 0.000 0.000
#> GSM1009137 4 0.0000 0.8876 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1009151 6 0.3915 0.3581 0.412 0.004 0.000 0.000 0.000 0.584
#> GSM1009165 3 0.0000 1.0000 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1009179 2 0.0508 0.8420 0.004 0.984 0.000 0.000 0.012 0.000
#> GSM1009193 1 0.0622 0.8867 0.980 0.000 0.000 0.012 0.000 0.008
#> GSM1009068 6 0.1152 0.8255 0.044 0.000 0.000 0.004 0.000 0.952
#> GSM1009082 2 0.6242 0.2680 0.024 0.476 0.000 0.000 0.316 0.184
#> GSM1009096 4 0.0790 0.8881 0.032 0.000 0.000 0.968 0.000 0.000
#> GSM1009110 5 0.0146 0.8412 0.000 0.004 0.000 0.000 0.996 0.000
#> GSM1009124 1 0.2883 0.8121 0.864 0.068 0.000 0.060 0.000 0.008
#> GSM1009138 4 0.0000 0.8876 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1009152 6 0.3852 0.4218 0.384 0.004 0.000 0.000 0.000 0.612
#> GSM1009166 3 0.0000 1.0000 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1009180 2 0.0622 0.8414 0.008 0.980 0.000 0.000 0.012 0.000
#> GSM1009194 6 0.4258 0.1793 0.468 0.016 0.000 0.000 0.000 0.516
#> GSM1009069 6 0.0713 0.8181 0.028 0.000 0.000 0.000 0.000 0.972
#> GSM1009083 5 0.6602 0.0510 0.024 0.320 0.000 0.000 0.356 0.300
#> GSM1009097 4 0.2980 0.7622 0.192 0.008 0.000 0.800 0.000 0.000
#> GSM1009111 5 0.0146 0.8412 0.000 0.004 0.000 0.000 0.996 0.000
#> GSM1009125 4 0.6957 0.2253 0.176 0.296 0.000 0.452 0.068 0.008
#> GSM1009139 4 0.0291 0.8886 0.004 0.000 0.000 0.992 0.000 0.004
#> GSM1009153 6 0.3699 0.5225 0.336 0.004 0.000 0.000 0.000 0.660
#> GSM1009167 3 0.0000 1.0000 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1009181 2 0.0632 0.8399 0.000 0.976 0.000 0.000 0.024 0.000
#> GSM1009195 1 0.5344 -0.0200 0.468 0.448 0.012 0.000 0.000 0.072
#> GSM1009070 6 0.1007 0.8259 0.044 0.000 0.000 0.000 0.000 0.956
#> GSM1009084 5 0.4187 0.6766 0.024 0.140 0.000 0.000 0.768 0.068
#> GSM1009098 4 0.0790 0.8880 0.032 0.000 0.000 0.968 0.000 0.000
#> GSM1009112 5 0.0146 0.8412 0.000 0.004 0.000 0.000 0.996 0.000
#> GSM1009126 4 0.4209 0.3981 0.384 0.020 0.000 0.596 0.000 0.000
#> GSM1009140 4 0.0146 0.8886 0.004 0.000 0.000 0.996 0.000 0.000
#> GSM1009154 1 0.3653 0.5091 0.692 0.008 0.000 0.000 0.000 0.300
#> GSM1009168 3 0.0000 1.0000 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1009182 2 0.0603 0.8423 0.004 0.980 0.000 0.000 0.016 0.000
#> GSM1009196 1 0.4062 0.0475 0.552 0.008 0.000 0.000 0.000 0.440
#> GSM1009071 6 0.0790 0.8219 0.032 0.000 0.000 0.000 0.000 0.968
#> GSM1009085 5 0.3732 0.7326 0.024 0.084 0.000 0.000 0.812 0.080
#> GSM1009099 4 0.2848 0.7821 0.176 0.008 0.000 0.816 0.000 0.000
#> GSM1009113 5 0.0146 0.8412 0.000 0.004 0.000 0.000 0.996 0.000
#> GSM1009127 1 0.1196 0.8748 0.952 0.000 0.000 0.040 0.000 0.008
#> GSM1009141 4 0.0146 0.8870 0.000 0.000 0.000 0.996 0.000 0.004
#> GSM1009155 6 0.2941 0.6820 0.220 0.000 0.000 0.000 0.000 0.780
#> GSM1009169 3 0.0000 1.0000 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1009183 2 0.0547 0.8412 0.000 0.980 0.000 0.000 0.020 0.000
#> GSM1009197 1 0.0748 0.8858 0.976 0.004 0.000 0.004 0.000 0.016
#> GSM1009072 6 0.1082 0.8256 0.040 0.000 0.000 0.004 0.000 0.956
#> GSM1009086 5 0.5287 0.0858 0.024 0.396 0.000 0.000 0.528 0.052
#> GSM1009100 4 0.0865 0.8874 0.036 0.000 0.000 0.964 0.000 0.000
#> GSM1009114 5 0.0291 0.8383 0.000 0.004 0.000 0.004 0.992 0.000
#> GSM1009128 1 0.2068 0.8351 0.904 0.008 0.000 0.080 0.008 0.000
#> GSM1009142 4 0.0000 0.8876 0.000 0.000 0.000 1.000 0.000 0.000
#> GSM1009156 1 0.2007 0.8646 0.920 0.032 0.012 0.000 0.000 0.036
#> GSM1009170 3 0.0000 1.0000 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1009184 2 0.0405 0.8419 0.004 0.988 0.000 0.000 0.008 0.000
#> GSM1009198 1 0.0891 0.8833 0.968 0.008 0.000 0.024 0.000 0.000
#> GSM1009073 6 0.0937 0.8264 0.040 0.000 0.000 0.000 0.000 0.960
#> GSM1009087 5 0.4983 0.3468 0.024 0.028 0.000 0.000 0.520 0.428
#> GSM1009101 4 0.0935 0.8868 0.032 0.004 0.000 0.964 0.000 0.000
#> GSM1009115 5 0.0146 0.8412 0.000 0.004 0.000 0.000 0.996 0.000
#> GSM1009129 5 0.5922 0.6052 0.164 0.112 0.024 0.016 0.660 0.024
#> GSM1009143 4 0.0291 0.8886 0.004 0.000 0.000 0.992 0.000 0.004
#> GSM1009157 6 0.3957 0.5935 0.280 0.020 0.004 0.000 0.000 0.696
#> GSM1009171 3 0.0000 1.0000 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1009185 2 0.0458 0.8324 0.016 0.984 0.000 0.000 0.000 0.000
#> GSM1009199 2 0.3955 0.1704 0.436 0.560 0.000 0.004 0.000 0.000
#> GSM1009074 6 0.0937 0.8264 0.040 0.000 0.000 0.000 0.000 0.960
#> GSM1009088 6 0.5070 -0.2971 0.024 0.032 0.000 0.000 0.472 0.472
#> GSM1009102 4 0.0865 0.8876 0.036 0.000 0.000 0.964 0.000 0.000
#> GSM1009116 5 0.0146 0.8412 0.000 0.004 0.000 0.000 0.996 0.000
#> GSM1009130 5 0.3916 0.7254 0.132 0.016 0.028 0.000 0.800 0.024
#> GSM1009144 4 0.0508 0.8896 0.012 0.000 0.000 0.984 0.000 0.004
#> GSM1009158 1 0.1967 0.8432 0.904 0.012 0.000 0.000 0.000 0.084
#> GSM1009172 3 0.0000 1.0000 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1009186 2 0.0405 0.8419 0.004 0.988 0.000 0.000 0.008 0.000
#> GSM1009200 1 0.0891 0.8837 0.968 0.008 0.000 0.024 0.000 0.000
#> GSM1009075 6 0.1082 0.8256 0.040 0.000 0.000 0.004 0.000 0.956
#> GSM1009089 6 0.5006 0.0802 0.460 0.028 0.000 0.000 0.024 0.488
#> GSM1009103 4 0.0458 0.8896 0.016 0.000 0.000 0.984 0.000 0.000
#> GSM1009117 5 0.0146 0.8412 0.000 0.004 0.000 0.000 0.996 0.000
#> GSM1009131 5 0.4616 0.5764 0.260 0.012 0.004 0.016 0.688 0.020
#> GSM1009145 4 0.0363 0.8899 0.012 0.000 0.000 0.988 0.000 0.000
#> GSM1009159 1 0.1297 0.8759 0.948 0.012 0.000 0.000 0.000 0.040
#> GSM1009173 3 0.0000 1.0000 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1009187 2 0.0363 0.8351 0.012 0.988 0.000 0.000 0.000 0.000
#> GSM1009201 1 0.1218 0.8835 0.956 0.004 0.000 0.012 0.000 0.028
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 temperature(p) time(p) specimen(p) k
#> MAD:NMF 137 0.987 0.974 4.32e-19 2
#> MAD:NMF 121 0.787 0.990 4.88e-34 3
#> MAD:NMF 131 1.000 1.000 1.82e-55 4
#> MAD:NMF 114 1.000 1.000 1.62e-70 5
#> MAD:NMF 119 0.997 1.000 1.30e-84 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 140 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 0.956 0.954 0.979 0.4742 0.534 0.534
#> 3 3 0.731 0.910 0.906 0.3858 0.797 0.620
#> 4 4 0.688 0.771 0.849 0.1060 0.938 0.811
#> 5 5 0.731 0.722 0.829 0.0540 0.919 0.718
#> 6 6 0.841 0.881 0.911 0.0455 0.965 0.846
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
#> GSM1009062 1 0.0000 0.969 1.000 0.000
#> GSM1009076 2 0.0000 0.994 0.000 1.000
#> GSM1009090 1 0.0000 0.969 1.000 0.000
#> GSM1009104 2 0.0000 0.994 0.000 1.000
#> GSM1009118 2 0.0000 0.994 0.000 1.000
#> GSM1009132 1 0.4939 0.875 0.892 0.108
#> GSM1009146 1 0.0000 0.969 1.000 0.000
#> GSM1009160 2 0.0000 0.994 0.000 1.000
#> GSM1009174 2 0.2778 0.950 0.048 0.952
#> GSM1009188 1 0.0000 0.969 1.000 0.000
#> GSM1009063 1 0.0000 0.969 1.000 0.000
#> GSM1009077 2 0.0000 0.994 0.000 1.000
#> GSM1009091 1 0.0000 0.969 1.000 0.000
#> GSM1009105 2 0.0000 0.994 0.000 1.000
#> GSM1009119 1 0.0000 0.969 1.000 0.000
#> GSM1009133 1 0.0000 0.969 1.000 0.000
#> GSM1009147 1 0.0000 0.969 1.000 0.000
#> GSM1009161 2 0.0000 0.994 0.000 1.000
#> GSM1009175 2 0.3431 0.933 0.064 0.936
#> GSM1009189 1 0.0672 0.964 0.992 0.008
#> GSM1009064 1 0.0000 0.969 1.000 0.000
#> GSM1009078 1 0.0000 0.969 1.000 0.000
#> GSM1009092 1 0.0000 0.969 1.000 0.000
#> GSM1009106 2 0.0000 0.994 0.000 1.000
#> GSM1009120 1 0.0000 0.969 1.000 0.000
#> GSM1009134 1 0.0000 0.969 1.000 0.000
#> GSM1009148 1 0.0000 0.969 1.000 0.000
#> GSM1009162 2 0.0000 0.994 0.000 1.000
#> GSM1009176 2 0.0000 0.994 0.000 1.000
#> GSM1009190 1 0.0672 0.964 0.992 0.008
#> GSM1009065 1 0.0000 0.969 1.000 0.000
#> GSM1009079 2 0.0000 0.994 0.000 1.000
#> GSM1009093 1 0.0000 0.969 1.000 0.000
#> GSM1009107 2 0.0000 0.994 0.000 1.000
#> GSM1009121 1 0.5946 0.839 0.856 0.144
#> GSM1009135 1 0.0000 0.969 1.000 0.000
#> GSM1009149 1 0.0000 0.969 1.000 0.000
#> GSM1009163 2 0.0000 0.994 0.000 1.000
#> GSM1009177 2 0.0000 0.994 0.000 1.000
#> GSM1009191 1 0.9087 0.560 0.676 0.324
#> GSM1009066 1 0.0000 0.969 1.000 0.000
#> GSM1009080 2 0.0000 0.994 0.000 1.000
#> GSM1009094 1 0.0000 0.969 1.000 0.000
#> GSM1009108 2 0.0000 0.994 0.000 1.000
#> GSM1009122 2 0.0000 0.994 0.000 1.000
#> GSM1009136 1 0.0000 0.969 1.000 0.000
#> GSM1009150 1 0.0000 0.969 1.000 0.000
#> GSM1009164 2 0.0000 0.994 0.000 1.000
#> GSM1009178 1 0.1633 0.952 0.976 0.024
#> GSM1009192 1 0.0672 0.964 0.992 0.008
#> GSM1009067 1 0.0000 0.969 1.000 0.000
#> GSM1009081 2 0.0000 0.994 0.000 1.000
#> GSM1009095 1 0.0000 0.969 1.000 0.000
#> GSM1009109 2 0.0000 0.994 0.000 1.000
#> GSM1009123 1 0.0000 0.969 1.000 0.000
#> GSM1009137 1 0.0000 0.969 1.000 0.000
#> GSM1009151 1 0.0000 0.969 1.000 0.000
#> GSM1009165 2 0.0000 0.994 0.000 1.000
#> GSM1009179 1 0.8861 0.597 0.696 0.304
#> GSM1009193 1 0.0000 0.969 1.000 0.000
#> GSM1009068 1 0.0000 0.969 1.000 0.000
#> GSM1009082 2 0.0000 0.994 0.000 1.000
#> GSM1009096 1 0.0000 0.969 1.000 0.000
#> GSM1009110 2 0.0000 0.994 0.000 1.000
#> GSM1009124 1 0.0938 0.962 0.988 0.012
#> GSM1009138 1 0.0000 0.969 1.000 0.000
#> GSM1009152 1 0.0000 0.969 1.000 0.000
#> GSM1009166 2 0.0000 0.994 0.000 1.000
#> GSM1009180 1 0.1633 0.952 0.976 0.024
#> GSM1009194 1 0.9393 0.493 0.644 0.356
#> GSM1009069 1 0.0000 0.969 1.000 0.000
#> GSM1009083 2 0.0000 0.994 0.000 1.000
#> GSM1009097 1 0.0000 0.969 1.000 0.000
#> GSM1009111 2 0.0000 0.994 0.000 1.000
#> GSM1009125 2 0.0000 0.994 0.000 1.000
#> GSM1009139 1 0.4939 0.875 0.892 0.108
#> GSM1009153 1 0.0000 0.969 1.000 0.000
#> GSM1009167 2 0.0000 0.994 0.000 1.000
#> GSM1009181 2 0.0000 0.994 0.000 1.000
#> GSM1009195 1 0.9393 0.493 0.644 0.356
#> GSM1009070 1 0.0000 0.969 1.000 0.000
#> GSM1009084 2 0.0000 0.994 0.000 1.000
#> GSM1009098 1 0.0000 0.969 1.000 0.000
#> GSM1009112 2 0.0000 0.994 0.000 1.000
#> GSM1009126 1 0.0938 0.962 0.988 0.012
#> GSM1009140 1 0.0000 0.969 1.000 0.000
#> GSM1009154 1 0.0000 0.969 1.000 0.000
#> GSM1009168 2 0.0000 0.994 0.000 1.000
#> GSM1009182 1 0.8861 0.597 0.696 0.304
#> GSM1009196 1 0.0000 0.969 1.000 0.000
#> GSM1009071 1 0.0000 0.969 1.000 0.000
#> GSM1009085 2 0.0000 0.994 0.000 1.000
#> GSM1009099 1 0.0000 0.969 1.000 0.000
#> GSM1009113 2 0.0000 0.994 0.000 1.000
#> GSM1009127 1 0.0000 0.969 1.000 0.000
#> GSM1009141 1 0.4939 0.875 0.892 0.108
#> GSM1009155 1 0.0000 0.969 1.000 0.000
#> GSM1009169 2 0.0000 0.994 0.000 1.000
#> GSM1009183 2 0.3431 0.933 0.064 0.936
#> GSM1009197 1 0.0000 0.969 1.000 0.000
#> GSM1009072 1 0.0000 0.969 1.000 0.000
#> GSM1009086 2 0.0000 0.994 0.000 1.000
#> GSM1009100 1 0.0000 0.969 1.000 0.000
#> GSM1009114 2 0.0000 0.994 0.000 1.000
#> GSM1009128 1 0.0938 0.962 0.988 0.012
#> GSM1009142 1 0.4161 0.899 0.916 0.084
#> GSM1009156 1 0.0000 0.969 1.000 0.000
#> GSM1009170 2 0.0000 0.994 0.000 1.000
#> GSM1009184 2 0.2948 0.946 0.052 0.948
#> GSM1009198 1 0.0000 0.969 1.000 0.000
#> GSM1009073 1 0.0000 0.969 1.000 0.000
#> GSM1009087 1 0.0000 0.969 1.000 0.000
#> GSM1009101 1 0.0000 0.969 1.000 0.000
#> GSM1009115 2 0.0000 0.994 0.000 1.000
#> GSM1009129 2 0.0000 0.994 0.000 1.000
#> GSM1009143 1 0.0000 0.969 1.000 0.000
#> GSM1009157 1 0.0000 0.969 1.000 0.000
#> GSM1009171 2 0.0000 0.994 0.000 1.000
#> GSM1009185 1 0.0000 0.969 1.000 0.000
#> GSM1009199 1 0.9393 0.493 0.644 0.356
#> GSM1009074 1 0.0000 0.969 1.000 0.000
#> GSM1009088 1 0.0000 0.969 1.000 0.000
#> GSM1009102 1 0.0000 0.969 1.000 0.000
#> GSM1009116 2 0.0000 0.994 0.000 1.000
#> GSM1009130 2 0.0000 0.994 0.000 1.000
#> GSM1009144 1 0.0000 0.969 1.000 0.000
#> GSM1009158 1 0.0000 0.969 1.000 0.000
#> GSM1009172 2 0.0000 0.994 0.000 1.000
#> GSM1009186 2 0.2948 0.946 0.052 0.948
#> GSM1009200 1 0.1414 0.956 0.980 0.020
#> GSM1009075 1 0.0000 0.969 1.000 0.000
#> GSM1009089 1 0.0000 0.969 1.000 0.000
#> GSM1009103 1 0.0000 0.969 1.000 0.000
#> GSM1009117 2 0.0000 0.994 0.000 1.000
#> GSM1009131 1 0.0938 0.962 0.988 0.012
#> GSM1009145 1 0.0000 0.969 1.000 0.000
#> GSM1009159 1 0.0000 0.969 1.000 0.000
#> GSM1009173 2 0.0000 0.994 0.000 1.000
#> GSM1009187 1 0.0000 0.969 1.000 0.000
#> GSM1009201 1 0.1414 0.956 0.980 0.020
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1009062 1 0.4654 0.866 0.792 0.000 0.208
#> GSM1009076 2 0.3267 0.935 0.116 0.884 0.000
#> GSM1009090 3 0.2066 0.955 0.060 0.000 0.940
#> GSM1009104 2 0.0000 0.950 0.000 1.000 0.000
#> GSM1009118 2 0.3267 0.935 0.116 0.884 0.000
#> GSM1009132 1 0.0829 0.844 0.984 0.004 0.012
#> GSM1009146 3 0.2066 0.955 0.060 0.000 0.940
#> GSM1009160 2 0.0000 0.950 0.000 1.000 0.000
#> GSM1009174 2 0.4062 0.900 0.164 0.836 0.000
#> GSM1009188 3 0.2066 0.955 0.060 0.000 0.940
#> GSM1009063 1 0.4654 0.866 0.792 0.000 0.208
#> GSM1009077 2 0.3267 0.935 0.116 0.884 0.000
#> GSM1009091 3 0.2066 0.955 0.060 0.000 0.940
#> GSM1009105 2 0.0000 0.950 0.000 1.000 0.000
#> GSM1009119 3 0.1860 0.957 0.052 0.000 0.948
#> GSM1009133 1 0.3752 0.899 0.856 0.000 0.144
#> GSM1009147 3 0.2066 0.955 0.060 0.000 0.940
#> GSM1009161 2 0.0000 0.950 0.000 1.000 0.000
#> GSM1009175 2 0.4291 0.886 0.180 0.820 0.000
#> GSM1009189 1 0.3192 0.907 0.888 0.000 0.112
#> GSM1009064 1 0.3267 0.906 0.884 0.000 0.116
#> GSM1009078 3 0.2711 0.933 0.088 0.000 0.912
#> GSM1009092 3 0.0000 0.950 0.000 0.000 1.000
#> GSM1009106 2 0.0000 0.950 0.000 1.000 0.000
#> GSM1009120 3 0.1860 0.957 0.052 0.000 0.948
#> GSM1009134 1 0.3752 0.899 0.856 0.000 0.144
#> GSM1009148 3 0.2066 0.955 0.060 0.000 0.940
#> GSM1009162 2 0.0000 0.950 0.000 1.000 0.000
#> GSM1009176 2 0.3267 0.935 0.116 0.884 0.000
#> GSM1009190 1 0.3192 0.907 0.888 0.000 0.112
#> GSM1009065 1 0.3267 0.906 0.884 0.000 0.116
#> GSM1009079 2 0.3267 0.935 0.116 0.884 0.000
#> GSM1009093 3 0.0000 0.950 0.000 0.000 1.000
#> GSM1009107 2 0.0000 0.950 0.000 1.000 0.000
#> GSM1009121 1 0.2116 0.826 0.948 0.040 0.012
#> GSM1009135 1 0.3752 0.899 0.856 0.000 0.144
#> GSM1009149 3 0.0000 0.950 0.000 0.000 1.000
#> GSM1009163 2 0.0000 0.950 0.000 1.000 0.000
#> GSM1009177 2 0.3267 0.935 0.116 0.884 0.000
#> GSM1009191 1 0.4654 0.617 0.792 0.208 0.000
#> GSM1009066 1 0.3267 0.906 0.884 0.000 0.116
#> GSM1009080 2 0.3267 0.935 0.116 0.884 0.000
#> GSM1009094 3 0.2066 0.955 0.060 0.000 0.940
#> GSM1009108 2 0.0000 0.950 0.000 1.000 0.000
#> GSM1009122 2 0.3267 0.935 0.116 0.884 0.000
#> GSM1009136 3 0.0000 0.950 0.000 0.000 1.000
#> GSM1009150 3 0.0000 0.950 0.000 0.000 1.000
#> GSM1009164 2 0.0000 0.950 0.000 1.000 0.000
#> GSM1009178 1 0.2796 0.901 0.908 0.000 0.092
#> GSM1009192 1 0.3192 0.907 0.888 0.000 0.112
#> GSM1009067 1 0.4654 0.866 0.792 0.000 0.208
#> GSM1009081 2 0.3267 0.935 0.116 0.884 0.000
#> GSM1009095 3 0.1289 0.955 0.032 0.000 0.968
#> GSM1009109 2 0.0000 0.950 0.000 1.000 0.000
#> GSM1009123 3 0.1860 0.957 0.052 0.000 0.948
#> GSM1009137 1 0.3752 0.899 0.856 0.000 0.144
#> GSM1009151 3 0.2066 0.955 0.060 0.000 0.940
#> GSM1009165 2 0.0000 0.950 0.000 1.000 0.000
#> GSM1009179 1 0.6012 0.640 0.748 0.220 0.032
#> GSM1009193 3 0.1860 0.957 0.052 0.000 0.948
#> GSM1009068 1 0.4654 0.866 0.792 0.000 0.208
#> GSM1009082 2 0.3267 0.935 0.116 0.884 0.000
#> GSM1009096 3 0.2066 0.955 0.060 0.000 0.940
#> GSM1009110 2 0.0000 0.950 0.000 1.000 0.000
#> GSM1009124 1 0.3038 0.906 0.896 0.000 0.104
#> GSM1009138 1 0.3752 0.899 0.856 0.000 0.144
#> GSM1009152 3 0.2066 0.955 0.060 0.000 0.940
#> GSM1009166 2 0.0000 0.950 0.000 1.000 0.000
#> GSM1009180 1 0.2796 0.901 0.908 0.000 0.092
#> GSM1009194 1 0.5016 0.560 0.760 0.240 0.000
#> GSM1009069 1 0.3267 0.906 0.884 0.000 0.116
#> GSM1009083 2 0.3267 0.935 0.116 0.884 0.000
#> GSM1009097 3 0.0000 0.950 0.000 0.000 1.000
#> GSM1009111 2 0.0000 0.950 0.000 1.000 0.000
#> GSM1009125 2 0.3267 0.935 0.116 0.884 0.000
#> GSM1009139 1 0.0829 0.844 0.984 0.004 0.012
#> GSM1009153 3 0.2066 0.955 0.060 0.000 0.940
#> GSM1009167 2 0.0000 0.950 0.000 1.000 0.000
#> GSM1009181 2 0.3267 0.935 0.116 0.884 0.000
#> GSM1009195 1 0.5016 0.560 0.760 0.240 0.000
#> GSM1009070 3 0.0000 0.950 0.000 0.000 1.000
#> GSM1009084 2 0.3267 0.935 0.116 0.884 0.000
#> GSM1009098 3 0.0000 0.950 0.000 0.000 1.000
#> GSM1009112 2 0.0000 0.950 0.000 1.000 0.000
#> GSM1009126 1 0.3038 0.906 0.896 0.000 0.104
#> GSM1009140 1 0.3752 0.899 0.856 0.000 0.144
#> GSM1009154 3 0.2066 0.955 0.060 0.000 0.940
#> GSM1009168 2 0.0000 0.950 0.000 1.000 0.000
#> GSM1009182 1 0.6012 0.640 0.748 0.220 0.032
#> GSM1009196 3 0.2356 0.948 0.072 0.000 0.928
#> GSM1009071 1 0.3267 0.906 0.884 0.000 0.116
#> GSM1009085 2 0.3267 0.935 0.116 0.884 0.000
#> GSM1009099 3 0.0000 0.950 0.000 0.000 1.000
#> GSM1009113 2 0.0000 0.950 0.000 1.000 0.000
#> GSM1009127 3 0.1860 0.957 0.052 0.000 0.948
#> GSM1009141 1 0.0829 0.844 0.984 0.004 0.012
#> GSM1009155 3 0.5926 0.447 0.356 0.000 0.644
#> GSM1009169 2 0.0000 0.950 0.000 1.000 0.000
#> GSM1009183 2 0.4291 0.886 0.180 0.820 0.000
#> GSM1009197 3 0.2165 0.953 0.064 0.000 0.936
#> GSM1009072 1 0.4654 0.866 0.792 0.000 0.208
#> GSM1009086 2 0.3267 0.935 0.116 0.884 0.000
#> GSM1009100 3 0.0000 0.950 0.000 0.000 1.000
#> GSM1009114 2 0.0237 0.949 0.004 0.996 0.000
#> GSM1009128 1 0.3038 0.906 0.896 0.000 0.104
#> GSM1009142 1 0.1647 0.862 0.960 0.004 0.036
#> GSM1009156 1 0.3267 0.906 0.884 0.000 0.116
#> GSM1009170 2 0.0000 0.950 0.000 1.000 0.000
#> GSM1009184 2 0.4121 0.897 0.168 0.832 0.000
#> GSM1009198 3 0.2066 0.955 0.060 0.000 0.940
#> GSM1009073 1 0.3482 0.905 0.872 0.000 0.128
#> GSM1009087 3 0.2711 0.933 0.088 0.000 0.912
#> GSM1009101 3 0.0000 0.950 0.000 0.000 1.000
#> GSM1009115 2 0.0000 0.950 0.000 1.000 0.000
#> GSM1009129 2 0.3267 0.935 0.116 0.884 0.000
#> GSM1009143 1 0.3752 0.899 0.856 0.000 0.144
#> GSM1009157 1 0.3267 0.906 0.884 0.000 0.116
#> GSM1009171 2 0.0000 0.950 0.000 1.000 0.000
#> GSM1009185 1 0.3686 0.900 0.860 0.000 0.140
#> GSM1009199 1 0.5016 0.560 0.760 0.240 0.000
#> GSM1009074 1 0.4654 0.866 0.792 0.000 0.208
#> GSM1009088 3 0.2711 0.933 0.088 0.000 0.912
#> GSM1009102 3 0.0000 0.950 0.000 0.000 1.000
#> GSM1009116 2 0.0000 0.950 0.000 1.000 0.000
#> GSM1009130 2 0.3267 0.935 0.116 0.884 0.000
#> GSM1009144 1 0.3752 0.899 0.856 0.000 0.144
#> GSM1009158 3 0.0000 0.950 0.000 0.000 1.000
#> GSM1009172 2 0.0000 0.950 0.000 1.000 0.000
#> GSM1009186 2 0.4121 0.897 0.168 0.832 0.000
#> GSM1009200 1 0.2878 0.903 0.904 0.000 0.096
#> GSM1009075 1 0.4654 0.866 0.792 0.000 0.208
#> GSM1009089 3 0.0000 0.950 0.000 0.000 1.000
#> GSM1009103 3 0.0000 0.950 0.000 0.000 1.000
#> GSM1009117 2 0.0237 0.949 0.004 0.996 0.000
#> GSM1009131 1 0.3038 0.906 0.896 0.000 0.104
#> GSM1009145 3 0.0000 0.950 0.000 0.000 1.000
#> GSM1009159 3 0.0000 0.950 0.000 0.000 1.000
#> GSM1009173 2 0.0000 0.950 0.000 1.000 0.000
#> GSM1009187 1 0.3686 0.900 0.860 0.000 0.140
#> GSM1009201 1 0.2878 0.903 0.904 0.000 0.096
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1009062 1 0.4731 0.8051 0.780 0.000 0.060 0.160
#> GSM1009076 3 0.3873 0.6774 0.000 0.228 0.772 0.000
#> GSM1009090 4 0.3123 0.9093 0.156 0.000 0.000 0.844
#> GSM1009104 2 0.0000 0.8015 0.000 1.000 0.000 0.000
#> GSM1009118 2 0.4998 0.0784 0.000 0.512 0.488 0.000
#> GSM1009132 1 0.2704 0.8374 0.876 0.000 0.124 0.000
#> GSM1009146 4 0.3219 0.9067 0.164 0.000 0.000 0.836
#> GSM1009160 3 0.4605 0.5902 0.000 0.336 0.664 0.000
#> GSM1009174 3 0.4540 0.6424 0.032 0.196 0.772 0.000
#> GSM1009188 4 0.3123 0.9090 0.156 0.000 0.000 0.844
#> GSM1009063 1 0.4731 0.8051 0.780 0.000 0.060 0.160
#> GSM1009077 3 0.3873 0.6774 0.000 0.228 0.772 0.000
#> GSM1009091 4 0.3123 0.9093 0.156 0.000 0.000 0.844
#> GSM1009105 2 0.0000 0.8015 0.000 1.000 0.000 0.000
#> GSM1009119 4 0.3024 0.9106 0.148 0.000 0.000 0.852
#> GSM1009133 1 0.0921 0.8857 0.972 0.000 0.000 0.028
#> GSM1009147 4 0.3219 0.9067 0.164 0.000 0.000 0.836
#> GSM1009161 3 0.4605 0.5902 0.000 0.336 0.664 0.000
#> GSM1009175 3 0.4842 0.6297 0.048 0.192 0.760 0.000
#> GSM1009189 1 0.0779 0.8892 0.980 0.000 0.016 0.004
#> GSM1009064 1 0.1637 0.8797 0.940 0.000 0.060 0.000
#> GSM1009078 4 0.3810 0.8804 0.188 0.000 0.008 0.804
#> GSM1009092 4 0.0000 0.8854 0.000 0.000 0.000 1.000
#> GSM1009106 2 0.0000 0.8015 0.000 1.000 0.000 0.000
#> GSM1009120 4 0.3024 0.9106 0.148 0.000 0.000 0.852
#> GSM1009134 1 0.0921 0.8857 0.972 0.000 0.000 0.028
#> GSM1009148 4 0.3219 0.9067 0.164 0.000 0.000 0.836
#> GSM1009162 3 0.4605 0.5902 0.000 0.336 0.664 0.000
#> GSM1009176 3 0.3837 0.6758 0.000 0.224 0.776 0.000
#> GSM1009190 1 0.0779 0.8892 0.980 0.000 0.016 0.004
#> GSM1009065 1 0.1637 0.8797 0.940 0.000 0.060 0.000
#> GSM1009079 3 0.3873 0.6774 0.000 0.228 0.772 0.000
#> GSM1009093 4 0.0000 0.8854 0.000 0.000 0.000 1.000
#> GSM1009107 2 0.0000 0.8015 0.000 1.000 0.000 0.000
#> GSM1009121 1 0.3552 0.8177 0.848 0.024 0.128 0.000
#> GSM1009135 1 0.0921 0.8857 0.972 0.000 0.000 0.028
#> GSM1009149 4 0.0000 0.8854 0.000 0.000 0.000 1.000
#> GSM1009163 3 0.4605 0.5902 0.000 0.336 0.664 0.000
#> GSM1009177 3 0.3837 0.6758 0.000 0.224 0.776 0.000
#> GSM1009191 1 0.6198 0.5952 0.660 0.116 0.224 0.000
#> GSM1009066 1 0.1637 0.8797 0.940 0.000 0.060 0.000
#> GSM1009080 3 0.3873 0.6774 0.000 0.228 0.772 0.000
#> GSM1009094 4 0.3123 0.9093 0.156 0.000 0.000 0.844
#> GSM1009108 2 0.0000 0.8015 0.000 1.000 0.000 0.000
#> GSM1009122 2 0.4998 0.0784 0.000 0.512 0.488 0.000
#> GSM1009136 4 0.0000 0.8854 0.000 0.000 0.000 1.000
#> GSM1009150 4 0.0000 0.8854 0.000 0.000 0.000 1.000
#> GSM1009164 3 0.4605 0.5902 0.000 0.336 0.664 0.000
#> GSM1009178 1 0.1211 0.8847 0.960 0.000 0.040 0.000
#> GSM1009192 1 0.0779 0.8892 0.980 0.000 0.016 0.004
#> GSM1009067 1 0.4731 0.8051 0.780 0.000 0.060 0.160
#> GSM1009081 3 0.3873 0.6774 0.000 0.228 0.772 0.000
#> GSM1009095 4 0.2281 0.9048 0.096 0.000 0.000 0.904
#> GSM1009109 2 0.0000 0.8015 0.000 1.000 0.000 0.000
#> GSM1009123 4 0.3024 0.9106 0.148 0.000 0.000 0.852
#> GSM1009137 1 0.0921 0.8857 0.972 0.000 0.000 0.028
#> GSM1009151 4 0.3219 0.9067 0.164 0.000 0.000 0.836
#> GSM1009165 3 0.4605 0.5902 0.000 0.336 0.664 0.000
#> GSM1009179 1 0.4891 0.6279 0.680 0.012 0.308 0.000
#> GSM1009193 4 0.3024 0.9106 0.148 0.000 0.000 0.852
#> GSM1009068 1 0.4731 0.8051 0.780 0.000 0.060 0.160
#> GSM1009082 3 0.3873 0.6774 0.000 0.228 0.772 0.000
#> GSM1009096 4 0.3123 0.9093 0.156 0.000 0.000 0.844
#> GSM1009110 2 0.0000 0.8015 0.000 1.000 0.000 0.000
#> GSM1009124 1 0.0707 0.8885 0.980 0.000 0.020 0.000
#> GSM1009138 1 0.0921 0.8857 0.972 0.000 0.000 0.028
#> GSM1009152 4 0.3219 0.9067 0.164 0.000 0.000 0.836
#> GSM1009166 3 0.4605 0.5902 0.000 0.336 0.664 0.000
#> GSM1009180 1 0.1211 0.8847 0.960 0.000 0.040 0.000
#> GSM1009194 1 0.6506 0.5439 0.628 0.132 0.240 0.000
#> GSM1009069 1 0.1637 0.8797 0.940 0.000 0.060 0.000
#> GSM1009083 3 0.3873 0.6774 0.000 0.228 0.772 0.000
#> GSM1009097 4 0.0000 0.8854 0.000 0.000 0.000 1.000
#> GSM1009111 2 0.0000 0.8015 0.000 1.000 0.000 0.000
#> GSM1009125 2 0.4998 0.0784 0.000 0.512 0.488 0.000
#> GSM1009139 1 0.2704 0.8374 0.876 0.000 0.124 0.000
#> GSM1009153 4 0.3219 0.9067 0.164 0.000 0.000 0.836
#> GSM1009167 3 0.4605 0.5902 0.000 0.336 0.664 0.000
#> GSM1009181 3 0.3837 0.6758 0.000 0.224 0.776 0.000
#> GSM1009195 1 0.6506 0.5439 0.628 0.132 0.240 0.000
#> GSM1009070 4 0.0000 0.8854 0.000 0.000 0.000 1.000
#> GSM1009084 3 0.3873 0.6774 0.000 0.228 0.772 0.000
#> GSM1009098 4 0.0000 0.8854 0.000 0.000 0.000 1.000
#> GSM1009112 2 0.0000 0.8015 0.000 1.000 0.000 0.000
#> GSM1009126 1 0.0707 0.8885 0.980 0.000 0.020 0.000
#> GSM1009140 1 0.0921 0.8857 0.972 0.000 0.000 0.028
#> GSM1009154 4 0.3219 0.9067 0.164 0.000 0.000 0.836
#> GSM1009168 3 0.4605 0.5902 0.000 0.336 0.664 0.000
#> GSM1009182 1 0.4891 0.6279 0.680 0.012 0.308 0.000
#> GSM1009196 4 0.3356 0.8993 0.176 0.000 0.000 0.824
#> GSM1009071 1 0.1637 0.8797 0.940 0.000 0.060 0.000
#> GSM1009085 3 0.3873 0.6774 0.000 0.228 0.772 0.000
#> GSM1009099 4 0.0000 0.8854 0.000 0.000 0.000 1.000
#> GSM1009113 2 0.0000 0.8015 0.000 1.000 0.000 0.000
#> GSM1009127 4 0.3024 0.9106 0.148 0.000 0.000 0.852
#> GSM1009141 1 0.2704 0.8374 0.876 0.000 0.124 0.000
#> GSM1009155 4 0.5281 0.3849 0.464 0.000 0.008 0.528
#> GSM1009169 3 0.4605 0.5902 0.000 0.336 0.664 0.000
#> GSM1009183 3 0.4842 0.6297 0.048 0.192 0.760 0.000
#> GSM1009197 4 0.3266 0.9042 0.168 0.000 0.000 0.832
#> GSM1009072 1 0.4731 0.8051 0.780 0.000 0.060 0.160
#> GSM1009086 3 0.3873 0.6774 0.000 0.228 0.772 0.000
#> GSM1009100 4 0.0000 0.8854 0.000 0.000 0.000 1.000
#> GSM1009114 2 0.0188 0.7984 0.000 0.996 0.004 0.000
#> GSM1009128 1 0.0707 0.8885 0.980 0.000 0.020 0.000
#> GSM1009142 1 0.2281 0.8553 0.904 0.000 0.096 0.000
#> GSM1009156 1 0.0336 0.8875 0.992 0.000 0.008 0.000
#> GSM1009170 3 0.4605 0.5902 0.000 0.336 0.664 0.000
#> GSM1009184 3 0.4590 0.6406 0.036 0.192 0.772 0.000
#> GSM1009198 4 0.3123 0.9090 0.156 0.000 0.000 0.844
#> GSM1009073 1 0.2101 0.8770 0.928 0.000 0.060 0.012
#> GSM1009087 4 0.3810 0.8804 0.188 0.000 0.008 0.804
#> GSM1009101 4 0.0000 0.8854 0.000 0.000 0.000 1.000
#> GSM1009115 2 0.0000 0.8015 0.000 1.000 0.000 0.000
#> GSM1009129 2 0.4998 0.0784 0.000 0.512 0.488 0.000
#> GSM1009143 1 0.0921 0.8857 0.972 0.000 0.000 0.028
#> GSM1009157 1 0.0336 0.8875 0.992 0.000 0.008 0.000
#> GSM1009171 3 0.4605 0.5902 0.000 0.336 0.664 0.000
#> GSM1009185 1 0.0817 0.8867 0.976 0.000 0.000 0.024
#> GSM1009199 1 0.6506 0.5439 0.628 0.132 0.240 0.000
#> GSM1009074 1 0.4731 0.8051 0.780 0.000 0.060 0.160
#> GSM1009088 4 0.3810 0.8804 0.188 0.000 0.008 0.804
#> GSM1009102 4 0.0000 0.8854 0.000 0.000 0.000 1.000
#> GSM1009116 2 0.0000 0.8015 0.000 1.000 0.000 0.000
#> GSM1009130 2 0.4998 0.0784 0.000 0.512 0.488 0.000
#> GSM1009144 1 0.0921 0.8857 0.972 0.000 0.000 0.028
#> GSM1009158 4 0.0000 0.8854 0.000 0.000 0.000 1.000
#> GSM1009172 3 0.4605 0.5902 0.000 0.336 0.664 0.000
#> GSM1009186 3 0.4590 0.6406 0.036 0.192 0.772 0.000
#> GSM1009200 1 0.1118 0.8866 0.964 0.000 0.036 0.000
#> GSM1009075 1 0.4731 0.8051 0.780 0.000 0.060 0.160
#> GSM1009089 4 0.0592 0.8894 0.016 0.000 0.000 0.984
#> GSM1009103 4 0.0000 0.8854 0.000 0.000 0.000 1.000
#> GSM1009117 2 0.0188 0.7984 0.000 0.996 0.004 0.000
#> GSM1009131 1 0.0707 0.8885 0.980 0.000 0.020 0.000
#> GSM1009145 4 0.0000 0.8854 0.000 0.000 0.000 1.000
#> GSM1009159 4 0.0000 0.8854 0.000 0.000 0.000 1.000
#> GSM1009173 3 0.4605 0.5902 0.000 0.336 0.664 0.000
#> GSM1009187 1 0.0817 0.8867 0.976 0.000 0.000 0.024
#> GSM1009201 1 0.1118 0.8866 0.964 0.000 0.036 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM1009062 3 0.6132 -0.164 0.388 0.000 0.480 0.132 0.000
#> GSM1009076 2 0.0703 0.871 0.000 0.976 0.000 0.000 0.024
#> GSM1009090 4 0.2629 0.903 0.136 0.000 0.004 0.860 0.000
#> GSM1009104 5 0.0000 0.999 0.000 0.000 0.000 0.000 1.000
#> GSM1009118 2 0.3837 0.616 0.000 0.692 0.000 0.000 0.308
#> GSM1009132 1 0.2329 0.744 0.876 0.124 0.000 0.000 0.000
#> GSM1009146 4 0.2719 0.900 0.144 0.000 0.004 0.852 0.000
#> GSM1009160 3 0.4653 0.408 0.000 0.472 0.516 0.000 0.012
#> GSM1009174 2 0.0880 0.840 0.032 0.968 0.000 0.000 0.000
#> GSM1009188 4 0.2629 0.902 0.136 0.000 0.004 0.860 0.000
#> GSM1009063 3 0.6132 -0.164 0.388 0.000 0.480 0.132 0.000
#> GSM1009077 2 0.0703 0.871 0.000 0.976 0.000 0.000 0.024
#> GSM1009091 4 0.2629 0.903 0.136 0.000 0.004 0.860 0.000
#> GSM1009105 5 0.0000 0.999 0.000 0.000 0.000 0.000 1.000
#> GSM1009119 4 0.2536 0.904 0.128 0.000 0.004 0.868 0.000
#> GSM1009133 1 0.1282 0.794 0.952 0.000 0.004 0.044 0.000
#> GSM1009147 4 0.2719 0.900 0.144 0.000 0.004 0.852 0.000
#> GSM1009161 3 0.4653 0.408 0.000 0.472 0.516 0.000 0.012
#> GSM1009175 2 0.1197 0.824 0.048 0.952 0.000 0.000 0.000
#> GSM1009189 1 0.0579 0.804 0.984 0.008 0.000 0.008 0.000
#> GSM1009064 1 0.4815 0.353 0.524 0.000 0.456 0.020 0.000
#> GSM1009078 4 0.3039 0.873 0.192 0.000 0.000 0.808 0.000
#> GSM1009092 4 0.0703 0.879 0.000 0.000 0.024 0.976 0.000
#> GSM1009106 5 0.0000 0.999 0.000 0.000 0.000 0.000 1.000
#> GSM1009120 4 0.2536 0.904 0.128 0.000 0.004 0.868 0.000
#> GSM1009134 1 0.1282 0.794 0.952 0.000 0.004 0.044 0.000
#> GSM1009148 4 0.2719 0.900 0.144 0.000 0.004 0.852 0.000
#> GSM1009162 3 0.4653 0.408 0.000 0.472 0.516 0.000 0.012
#> GSM1009176 2 0.0609 0.869 0.000 0.980 0.000 0.000 0.020
#> GSM1009190 1 0.0579 0.804 0.984 0.008 0.000 0.008 0.000
#> GSM1009065 1 0.4815 0.353 0.524 0.000 0.456 0.020 0.000
#> GSM1009079 2 0.0703 0.871 0.000 0.976 0.000 0.000 0.024
#> GSM1009093 4 0.0703 0.879 0.000 0.000 0.024 0.976 0.000
#> GSM1009107 5 0.0000 0.999 0.000 0.000 0.000 0.000 1.000
#> GSM1009121 1 0.2921 0.729 0.856 0.124 0.000 0.000 0.020
#> GSM1009135 1 0.1282 0.794 0.952 0.000 0.004 0.044 0.000
#> GSM1009149 4 0.0703 0.879 0.000 0.000 0.024 0.976 0.000
#> GSM1009163 3 0.4653 0.408 0.000 0.472 0.516 0.000 0.012
#> GSM1009177 2 0.0609 0.869 0.000 0.980 0.000 0.000 0.020
#> GSM1009191 1 0.3983 0.503 0.660 0.340 0.000 0.000 0.000
#> GSM1009066 1 0.4815 0.353 0.524 0.000 0.456 0.020 0.000
#> GSM1009080 2 0.0703 0.871 0.000 0.976 0.000 0.000 0.024
#> GSM1009094 4 0.2629 0.903 0.136 0.000 0.004 0.860 0.000
#> GSM1009108 5 0.0000 0.999 0.000 0.000 0.000 0.000 1.000
#> GSM1009122 2 0.3837 0.616 0.000 0.692 0.000 0.000 0.308
#> GSM1009136 4 0.0703 0.879 0.000 0.000 0.024 0.976 0.000
#> GSM1009150 4 0.0703 0.879 0.000 0.000 0.024 0.976 0.000
#> GSM1009164 3 0.4653 0.408 0.000 0.472 0.516 0.000 0.012
#> GSM1009178 1 0.1043 0.797 0.960 0.040 0.000 0.000 0.000
#> GSM1009192 1 0.0579 0.804 0.984 0.008 0.000 0.008 0.000
#> GSM1009067 3 0.6132 -0.164 0.388 0.000 0.480 0.132 0.000
#> GSM1009081 2 0.0703 0.871 0.000 0.976 0.000 0.000 0.024
#> GSM1009095 4 0.2293 0.898 0.084 0.000 0.016 0.900 0.000
#> GSM1009109 5 0.0000 0.999 0.000 0.000 0.000 0.000 1.000
#> GSM1009123 4 0.2536 0.904 0.128 0.000 0.004 0.868 0.000
#> GSM1009137 1 0.1282 0.794 0.952 0.000 0.004 0.044 0.000
#> GSM1009151 4 0.2719 0.900 0.144 0.000 0.004 0.852 0.000
#> GSM1009165 3 0.4653 0.408 0.000 0.472 0.516 0.000 0.012
#> GSM1009179 1 0.3895 0.543 0.680 0.320 0.000 0.000 0.000
#> GSM1009193 4 0.2536 0.904 0.128 0.000 0.004 0.868 0.000
#> GSM1009068 3 0.6132 -0.164 0.388 0.000 0.480 0.132 0.000
#> GSM1009082 2 0.0703 0.871 0.000 0.976 0.000 0.000 0.024
#> GSM1009096 4 0.2629 0.903 0.136 0.000 0.004 0.860 0.000
#> GSM1009110 5 0.0000 0.999 0.000 0.000 0.000 0.000 1.000
#> GSM1009124 1 0.0510 0.804 0.984 0.016 0.000 0.000 0.000
#> GSM1009138 1 0.1282 0.794 0.952 0.000 0.004 0.044 0.000
#> GSM1009152 4 0.2719 0.900 0.144 0.000 0.004 0.852 0.000
#> GSM1009166 3 0.4653 0.408 0.000 0.472 0.516 0.000 0.012
#> GSM1009180 1 0.1043 0.797 0.960 0.040 0.000 0.000 0.000
#> GSM1009194 1 0.4101 0.443 0.628 0.372 0.000 0.000 0.000
#> GSM1009069 1 0.4815 0.353 0.524 0.000 0.456 0.020 0.000
#> GSM1009083 2 0.0703 0.871 0.000 0.976 0.000 0.000 0.024
#> GSM1009097 4 0.0703 0.879 0.000 0.000 0.024 0.976 0.000
#> GSM1009111 5 0.0000 0.999 0.000 0.000 0.000 0.000 1.000
#> GSM1009125 2 0.3837 0.616 0.000 0.692 0.000 0.000 0.308
#> GSM1009139 1 0.2329 0.744 0.876 0.124 0.000 0.000 0.000
#> GSM1009153 4 0.2719 0.900 0.144 0.000 0.004 0.852 0.000
#> GSM1009167 3 0.4653 0.408 0.000 0.472 0.516 0.000 0.012
#> GSM1009181 2 0.0609 0.869 0.000 0.980 0.000 0.000 0.020
#> GSM1009195 1 0.4101 0.443 0.628 0.372 0.000 0.000 0.000
#> GSM1009070 4 0.0703 0.879 0.000 0.000 0.024 0.976 0.000
#> GSM1009084 2 0.0703 0.871 0.000 0.976 0.000 0.000 0.024
#> GSM1009098 4 0.0703 0.879 0.000 0.000 0.024 0.976 0.000
#> GSM1009112 5 0.0000 0.999 0.000 0.000 0.000 0.000 1.000
#> GSM1009126 1 0.0510 0.804 0.984 0.016 0.000 0.000 0.000
#> GSM1009140 1 0.1282 0.794 0.952 0.000 0.004 0.044 0.000
#> GSM1009154 4 0.2719 0.900 0.144 0.000 0.004 0.852 0.000
#> GSM1009168 3 0.4653 0.408 0.000 0.472 0.516 0.000 0.012
#> GSM1009182 1 0.3895 0.543 0.680 0.320 0.000 0.000 0.000
#> GSM1009196 4 0.2848 0.893 0.156 0.000 0.004 0.840 0.000
#> GSM1009071 1 0.4815 0.353 0.524 0.000 0.456 0.020 0.000
#> GSM1009085 2 0.0703 0.871 0.000 0.976 0.000 0.000 0.024
#> GSM1009099 4 0.0703 0.879 0.000 0.000 0.024 0.976 0.000
#> GSM1009113 5 0.0000 0.999 0.000 0.000 0.000 0.000 1.000
#> GSM1009127 4 0.2536 0.904 0.128 0.000 0.004 0.868 0.000
#> GSM1009141 1 0.2329 0.744 0.876 0.124 0.000 0.000 0.000
#> GSM1009155 4 0.4294 0.367 0.468 0.000 0.000 0.532 0.000
#> GSM1009169 3 0.4653 0.408 0.000 0.472 0.516 0.000 0.012
#> GSM1009183 2 0.1197 0.824 0.048 0.952 0.000 0.000 0.000
#> GSM1009197 4 0.2763 0.898 0.148 0.000 0.004 0.848 0.000
#> GSM1009072 3 0.6132 -0.164 0.388 0.000 0.480 0.132 0.000
#> GSM1009086 2 0.0703 0.871 0.000 0.976 0.000 0.000 0.024
#> GSM1009100 4 0.0703 0.879 0.000 0.000 0.024 0.976 0.000
#> GSM1009114 5 0.0162 0.995 0.000 0.004 0.000 0.000 0.996
#> GSM1009128 1 0.0510 0.804 0.984 0.016 0.000 0.000 0.000
#> GSM1009142 1 0.1908 0.765 0.908 0.092 0.000 0.000 0.000
#> GSM1009156 1 0.0162 0.802 0.996 0.000 0.000 0.004 0.000
#> GSM1009170 3 0.4653 0.408 0.000 0.472 0.516 0.000 0.012
#> GSM1009184 2 0.0963 0.837 0.036 0.964 0.000 0.000 0.000
#> GSM1009198 4 0.2629 0.902 0.136 0.000 0.004 0.860 0.000
#> GSM1009073 1 0.4900 0.341 0.512 0.000 0.464 0.024 0.000
#> GSM1009087 4 0.3039 0.873 0.192 0.000 0.000 0.808 0.000
#> GSM1009101 4 0.0703 0.879 0.000 0.000 0.024 0.976 0.000
#> GSM1009115 5 0.0000 0.999 0.000 0.000 0.000 0.000 1.000
#> GSM1009129 2 0.3837 0.616 0.000 0.692 0.000 0.000 0.308
#> GSM1009143 1 0.1282 0.794 0.952 0.000 0.004 0.044 0.000
#> GSM1009157 1 0.0162 0.802 0.996 0.000 0.000 0.004 0.000
#> GSM1009171 3 0.4653 0.408 0.000 0.472 0.516 0.000 0.012
#> GSM1009185 1 0.1205 0.795 0.956 0.000 0.004 0.040 0.000
#> GSM1009199 1 0.4101 0.443 0.628 0.372 0.000 0.000 0.000
#> GSM1009074 3 0.6132 -0.164 0.388 0.000 0.480 0.132 0.000
#> GSM1009088 4 0.3039 0.873 0.192 0.000 0.000 0.808 0.000
#> GSM1009102 4 0.0703 0.879 0.000 0.000 0.024 0.976 0.000
#> GSM1009116 5 0.0000 0.999 0.000 0.000 0.000 0.000 1.000
#> GSM1009130 2 0.3837 0.616 0.000 0.692 0.000 0.000 0.308
#> GSM1009144 1 0.1282 0.794 0.952 0.000 0.004 0.044 0.000
#> GSM1009158 4 0.0703 0.879 0.000 0.000 0.024 0.976 0.000
#> GSM1009172 3 0.4653 0.408 0.000 0.472 0.516 0.000 0.012
#> GSM1009186 2 0.0963 0.837 0.036 0.964 0.000 0.000 0.000
#> GSM1009200 1 0.0963 0.799 0.964 0.036 0.000 0.000 0.000
#> GSM1009075 3 0.6132 -0.164 0.388 0.000 0.480 0.132 0.000
#> GSM1009089 4 0.1211 0.883 0.016 0.000 0.024 0.960 0.000
#> GSM1009103 4 0.0703 0.879 0.000 0.000 0.024 0.976 0.000
#> GSM1009117 5 0.0162 0.995 0.000 0.004 0.000 0.000 0.996
#> GSM1009131 1 0.0510 0.804 0.984 0.016 0.000 0.000 0.000
#> GSM1009145 4 0.0703 0.879 0.000 0.000 0.024 0.976 0.000
#> GSM1009159 4 0.0703 0.879 0.000 0.000 0.024 0.976 0.000
#> GSM1009173 3 0.4653 0.408 0.000 0.472 0.516 0.000 0.012
#> GSM1009187 1 0.1205 0.795 0.956 0.000 0.004 0.040 0.000
#> GSM1009201 1 0.0963 0.799 0.964 0.036 0.000 0.000 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1009062 6 0.1765 0.912 0.096 0.000 0.000 0.000 0.000 0.904
#> GSM1009076 2 0.0146 0.913 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM1009090 1 0.2118 0.895 0.888 0.000 0.000 0.104 0.000 0.008
#> GSM1009104 5 0.0000 0.999 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1009118 2 0.3351 0.673 0.000 0.712 0.000 0.000 0.288 0.000
#> GSM1009132 4 0.2250 0.824 0.000 0.092 0.000 0.888 0.000 0.020
#> GSM1009146 1 0.2266 0.892 0.880 0.000 0.000 0.108 0.000 0.012
#> GSM1009160 3 0.0363 1.000 0.000 0.012 0.988 0.000 0.000 0.000
#> GSM1009174 2 0.1333 0.888 0.000 0.944 0.000 0.048 0.000 0.008
#> GSM1009188 1 0.2053 0.895 0.888 0.000 0.000 0.108 0.000 0.004
#> GSM1009063 6 0.1765 0.912 0.096 0.000 0.000 0.000 0.000 0.904
#> GSM1009077 2 0.0146 0.913 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM1009091 1 0.2118 0.895 0.888 0.000 0.000 0.104 0.000 0.008
#> GSM1009105 5 0.0000 0.999 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1009119 1 0.1958 0.896 0.896 0.000 0.000 0.100 0.000 0.004
#> GSM1009133 4 0.1838 0.862 0.068 0.000 0.000 0.916 0.000 0.016
#> GSM1009147 1 0.2266 0.892 0.880 0.000 0.000 0.108 0.000 0.012
#> GSM1009161 3 0.0363 1.000 0.000 0.012 0.988 0.000 0.000 0.000
#> GSM1009175 2 0.1584 0.877 0.000 0.928 0.000 0.064 0.000 0.008
#> GSM1009189 4 0.0972 0.876 0.028 0.000 0.000 0.964 0.000 0.008
#> GSM1009064 6 0.2007 0.894 0.036 0.000 0.012 0.032 0.000 0.920
#> GSM1009078 1 0.2706 0.865 0.832 0.000 0.000 0.160 0.000 0.008
#> GSM1009092 1 0.1267 0.866 0.940 0.000 0.000 0.000 0.000 0.060
#> GSM1009106 5 0.0000 0.999 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1009120 1 0.1958 0.896 0.896 0.000 0.000 0.100 0.000 0.004
#> GSM1009134 4 0.1838 0.862 0.068 0.000 0.000 0.916 0.000 0.016
#> GSM1009148 1 0.2266 0.892 0.880 0.000 0.000 0.108 0.000 0.012
#> GSM1009162 3 0.0363 1.000 0.000 0.012 0.988 0.000 0.000 0.000
#> GSM1009176 2 0.0146 0.911 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM1009190 4 0.0972 0.876 0.028 0.000 0.000 0.964 0.000 0.008
#> GSM1009065 6 0.2007 0.894 0.036 0.000 0.012 0.032 0.000 0.920
#> GSM1009079 2 0.0146 0.913 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM1009093 1 0.1267 0.866 0.940 0.000 0.000 0.000 0.000 0.060
#> GSM1009107 5 0.0000 0.999 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1009121 4 0.2219 0.810 0.000 0.136 0.000 0.864 0.000 0.000
#> GSM1009135 4 0.1838 0.862 0.068 0.000 0.000 0.916 0.000 0.016
#> GSM1009149 1 0.1267 0.866 0.940 0.000 0.000 0.000 0.000 0.060
#> GSM1009163 3 0.0363 1.000 0.000 0.012 0.988 0.000 0.000 0.000
#> GSM1009177 2 0.0146 0.911 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM1009191 4 0.3898 0.581 0.000 0.296 0.000 0.684 0.000 0.020
#> GSM1009066 6 0.2007 0.894 0.036 0.000 0.012 0.032 0.000 0.920
#> GSM1009080 2 0.0146 0.913 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM1009094 1 0.2118 0.895 0.888 0.000 0.000 0.104 0.000 0.008
#> GSM1009108 5 0.0000 0.999 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1009122 2 0.3351 0.673 0.000 0.712 0.000 0.000 0.288 0.000
#> GSM1009136 1 0.1267 0.866 0.940 0.000 0.000 0.000 0.000 0.060
#> GSM1009150 1 0.1267 0.866 0.940 0.000 0.000 0.000 0.000 0.060
#> GSM1009164 3 0.0363 1.000 0.000 0.012 0.988 0.000 0.000 0.000
#> GSM1009178 4 0.1262 0.871 0.008 0.020 0.000 0.956 0.000 0.016
#> GSM1009192 4 0.0972 0.876 0.028 0.000 0.000 0.964 0.000 0.008
#> GSM1009067 6 0.1765 0.912 0.096 0.000 0.000 0.000 0.000 0.904
#> GSM1009081 2 0.0146 0.913 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM1009095 1 0.2499 0.888 0.880 0.000 0.000 0.072 0.000 0.048
#> GSM1009109 5 0.0000 0.999 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1009123 1 0.1958 0.896 0.896 0.000 0.000 0.100 0.000 0.004
#> GSM1009137 4 0.1838 0.862 0.068 0.000 0.000 0.916 0.000 0.016
#> GSM1009151 1 0.2266 0.892 0.880 0.000 0.000 0.108 0.000 0.012
#> GSM1009165 3 0.0363 1.000 0.000 0.012 0.988 0.000 0.000 0.000
#> GSM1009179 4 0.3729 0.596 0.000 0.296 0.000 0.692 0.000 0.012
#> GSM1009193 1 0.1958 0.896 0.896 0.000 0.000 0.100 0.000 0.004
#> GSM1009068 6 0.1765 0.912 0.096 0.000 0.000 0.000 0.000 0.904
#> GSM1009082 2 0.0146 0.913 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM1009096 1 0.2118 0.895 0.888 0.000 0.000 0.104 0.000 0.008
#> GSM1009110 5 0.0000 0.999 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1009124 4 0.0458 0.876 0.016 0.000 0.000 0.984 0.000 0.000
#> GSM1009138 4 0.1838 0.862 0.068 0.000 0.000 0.916 0.000 0.016
#> GSM1009152 1 0.2266 0.892 0.880 0.000 0.000 0.108 0.000 0.012
#> GSM1009166 3 0.0363 1.000 0.000 0.012 0.988 0.000 0.000 0.000
#> GSM1009180 4 0.1262 0.871 0.008 0.020 0.000 0.956 0.000 0.016
#> GSM1009194 4 0.4034 0.524 0.000 0.328 0.000 0.652 0.000 0.020
#> GSM1009069 6 0.2007 0.894 0.036 0.000 0.012 0.032 0.000 0.920
#> GSM1009083 2 0.0146 0.913 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM1009097 1 0.1267 0.866 0.940 0.000 0.000 0.000 0.000 0.060
#> GSM1009111 5 0.0000 0.999 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1009125 2 0.3351 0.673 0.000 0.712 0.000 0.000 0.288 0.000
#> GSM1009139 4 0.2250 0.824 0.000 0.092 0.000 0.888 0.000 0.020
#> GSM1009153 1 0.2266 0.892 0.880 0.000 0.000 0.108 0.000 0.012
#> GSM1009167 3 0.0363 1.000 0.000 0.012 0.988 0.000 0.000 0.000
#> GSM1009181 2 0.0146 0.911 0.000 0.996 0.000 0.000 0.000 0.004
#> GSM1009195 4 0.4034 0.524 0.000 0.328 0.000 0.652 0.000 0.020
#> GSM1009070 1 0.1267 0.866 0.940 0.000 0.000 0.000 0.000 0.060
#> GSM1009084 2 0.0146 0.913 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM1009098 1 0.1267 0.866 0.940 0.000 0.000 0.000 0.000 0.060
#> GSM1009112 5 0.0000 0.999 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1009126 4 0.0458 0.876 0.016 0.000 0.000 0.984 0.000 0.000
#> GSM1009140 4 0.1838 0.862 0.068 0.000 0.000 0.916 0.000 0.016
#> GSM1009154 1 0.2266 0.892 0.880 0.000 0.000 0.108 0.000 0.012
#> GSM1009168 3 0.0363 1.000 0.000 0.012 0.988 0.000 0.000 0.000
#> GSM1009182 4 0.3729 0.596 0.000 0.296 0.000 0.692 0.000 0.012
#> GSM1009196 1 0.2450 0.885 0.868 0.000 0.000 0.116 0.000 0.016
#> GSM1009071 6 0.2007 0.894 0.036 0.000 0.012 0.032 0.000 0.920
#> GSM1009085 2 0.0146 0.913 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM1009099 1 0.1267 0.866 0.940 0.000 0.000 0.000 0.000 0.060
#> GSM1009113 5 0.0000 0.999 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1009127 1 0.1958 0.896 0.896 0.000 0.000 0.100 0.000 0.004
#> GSM1009141 4 0.2250 0.824 0.000 0.092 0.000 0.888 0.000 0.020
#> GSM1009155 1 0.4218 0.385 0.556 0.000 0.000 0.428 0.000 0.016
#> GSM1009169 3 0.0363 1.000 0.000 0.012 0.988 0.000 0.000 0.000
#> GSM1009183 2 0.1584 0.877 0.000 0.928 0.000 0.064 0.000 0.008
#> GSM1009197 1 0.2358 0.890 0.876 0.000 0.000 0.108 0.000 0.016
#> GSM1009072 6 0.1765 0.912 0.096 0.000 0.000 0.000 0.000 0.904
#> GSM1009086 2 0.0146 0.913 0.000 0.996 0.000 0.000 0.004 0.000
#> GSM1009100 1 0.1267 0.866 0.940 0.000 0.000 0.000 0.000 0.060
#> GSM1009114 5 0.0146 0.996 0.000 0.004 0.000 0.000 0.996 0.000
#> GSM1009128 4 0.0458 0.876 0.016 0.000 0.000 0.984 0.000 0.000
#> GSM1009142 4 0.2182 0.843 0.008 0.068 0.000 0.904 0.000 0.020
#> GSM1009156 4 0.1168 0.874 0.028 0.000 0.000 0.956 0.000 0.016
#> GSM1009170 3 0.0363 1.000 0.000 0.012 0.988 0.000 0.000 0.000
#> GSM1009184 2 0.1398 0.885 0.000 0.940 0.000 0.052 0.000 0.008
#> GSM1009198 1 0.2053 0.895 0.888 0.000 0.000 0.108 0.000 0.004
#> GSM1009073 6 0.1777 0.895 0.032 0.000 0.012 0.024 0.000 0.932
#> GSM1009087 1 0.2706 0.865 0.832 0.000 0.000 0.160 0.000 0.008
#> GSM1009101 1 0.1267 0.866 0.940 0.000 0.000 0.000 0.000 0.060
#> GSM1009115 5 0.0000 0.999 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1009129 2 0.3351 0.673 0.000 0.712 0.000 0.000 0.288 0.000
#> GSM1009143 4 0.1838 0.862 0.068 0.000 0.000 0.916 0.000 0.016
#> GSM1009157 4 0.1168 0.874 0.028 0.000 0.000 0.956 0.000 0.016
#> GSM1009171 3 0.0363 1.000 0.000 0.012 0.988 0.000 0.000 0.000
#> GSM1009185 4 0.1867 0.864 0.064 0.000 0.000 0.916 0.000 0.020
#> GSM1009199 4 0.4034 0.524 0.000 0.328 0.000 0.652 0.000 0.020
#> GSM1009074 6 0.1765 0.912 0.096 0.000 0.000 0.000 0.000 0.904
#> GSM1009088 1 0.2706 0.865 0.832 0.000 0.000 0.160 0.000 0.008
#> GSM1009102 1 0.1267 0.866 0.940 0.000 0.000 0.000 0.000 0.060
#> GSM1009116 5 0.0000 0.999 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1009130 2 0.3351 0.673 0.000 0.712 0.000 0.000 0.288 0.000
#> GSM1009144 4 0.1838 0.862 0.068 0.000 0.000 0.916 0.000 0.016
#> GSM1009158 1 0.1267 0.866 0.940 0.000 0.000 0.000 0.000 0.060
#> GSM1009172 3 0.0363 1.000 0.000 0.012 0.988 0.000 0.000 0.000
#> GSM1009186 2 0.1398 0.885 0.000 0.940 0.000 0.052 0.000 0.008
#> GSM1009200 4 0.0458 0.872 0.000 0.000 0.000 0.984 0.000 0.016
#> GSM1009075 6 0.1765 0.912 0.096 0.000 0.000 0.000 0.000 0.904
#> GSM1009089 1 0.1657 0.871 0.928 0.000 0.000 0.016 0.000 0.056
#> GSM1009103 1 0.1267 0.866 0.940 0.000 0.000 0.000 0.000 0.060
#> GSM1009117 5 0.0146 0.996 0.000 0.004 0.000 0.000 0.996 0.000
#> GSM1009131 4 0.0458 0.876 0.016 0.000 0.000 0.984 0.000 0.000
#> GSM1009145 1 0.1267 0.866 0.940 0.000 0.000 0.000 0.000 0.060
#> GSM1009159 1 0.1267 0.866 0.940 0.000 0.000 0.000 0.000 0.060
#> GSM1009173 3 0.0363 1.000 0.000 0.012 0.988 0.000 0.000 0.000
#> GSM1009187 4 0.1867 0.864 0.064 0.000 0.000 0.916 0.000 0.020
#> GSM1009201 4 0.0458 0.872 0.000 0.000 0.000 0.984 0.000 0.016
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n temperature(p) time(p) specimen(p) k
#> ATC:hclust 137 0.785 0.947 8.61e-17 2
#> ATC:hclust 139 0.923 0.985 8.08e-26 3
#> ATC:hclust 134 0.989 1.000 3.20e-47 4
#> ATC:hclust 109 0.968 0.999 8.76e-31 5
#> ATC:hclust 139 0.999 1.000 9.49e-84 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 140 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'kmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.974 0.990 0.4831 0.520 0.520
#> 3 3 0.602 0.683 0.833 0.3325 0.751 0.547
#> 4 4 0.592 0.708 0.747 0.1160 0.827 0.547
#> 5 5 0.674 0.756 0.808 0.0754 0.927 0.732
#> 6 6 0.768 0.710 0.792 0.0509 0.981 0.914
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
#> GSM1009062 1 0.000 0.986 1.000 0.000
#> GSM1009076 2 0.000 0.994 0.000 1.000
#> GSM1009090 1 0.000 0.986 1.000 0.000
#> GSM1009104 2 0.000 0.994 0.000 1.000
#> GSM1009118 2 0.000 0.994 0.000 1.000
#> GSM1009132 1 0.969 0.356 0.604 0.396
#> GSM1009146 1 0.000 0.986 1.000 0.000
#> GSM1009160 2 0.000 0.994 0.000 1.000
#> GSM1009174 2 0.000 0.994 0.000 1.000
#> GSM1009188 1 0.000 0.986 1.000 0.000
#> GSM1009063 1 0.000 0.986 1.000 0.000
#> GSM1009077 2 0.000 0.994 0.000 1.000
#> GSM1009091 1 0.000 0.986 1.000 0.000
#> GSM1009105 2 0.000 0.994 0.000 1.000
#> GSM1009119 1 0.000 0.986 1.000 0.000
#> GSM1009133 1 0.000 0.986 1.000 0.000
#> GSM1009147 1 0.000 0.986 1.000 0.000
#> GSM1009161 2 0.000 0.994 0.000 1.000
#> GSM1009175 2 0.000 0.994 0.000 1.000
#> GSM1009189 1 0.000 0.986 1.000 0.000
#> GSM1009064 1 0.000 0.986 1.000 0.000
#> GSM1009078 1 0.000 0.986 1.000 0.000
#> GSM1009092 1 0.000 0.986 1.000 0.000
#> GSM1009106 2 0.000 0.994 0.000 1.000
#> GSM1009120 1 0.000 0.986 1.000 0.000
#> GSM1009134 1 0.000 0.986 1.000 0.000
#> GSM1009148 1 0.000 0.986 1.000 0.000
#> GSM1009162 2 0.000 0.994 0.000 1.000
#> GSM1009176 2 0.000 0.994 0.000 1.000
#> GSM1009190 1 0.000 0.986 1.000 0.000
#> GSM1009065 1 0.000 0.986 1.000 0.000
#> GSM1009079 2 0.000 0.994 0.000 1.000
#> GSM1009093 1 0.000 0.986 1.000 0.000
#> GSM1009107 2 0.000 0.994 0.000 1.000
#> GSM1009121 2 0.000 0.994 0.000 1.000
#> GSM1009135 1 0.000 0.986 1.000 0.000
#> GSM1009149 1 0.000 0.986 1.000 0.000
#> GSM1009163 2 0.000 0.994 0.000 1.000
#> GSM1009177 2 0.000 0.994 0.000 1.000
#> GSM1009191 1 0.653 0.795 0.832 0.168
#> GSM1009066 1 0.000 0.986 1.000 0.000
#> GSM1009080 2 0.000 0.994 0.000 1.000
#> GSM1009094 1 0.000 0.986 1.000 0.000
#> GSM1009108 2 0.000 0.994 0.000 1.000
#> GSM1009122 2 0.000 0.994 0.000 1.000
#> GSM1009136 1 0.000 0.986 1.000 0.000
#> GSM1009150 1 0.000 0.986 1.000 0.000
#> GSM1009164 2 0.000 0.994 0.000 1.000
#> GSM1009178 1 0.000 0.986 1.000 0.000
#> GSM1009192 1 0.000 0.986 1.000 0.000
#> GSM1009067 1 0.000 0.986 1.000 0.000
#> GSM1009081 2 0.000 0.994 0.000 1.000
#> GSM1009095 1 0.000 0.986 1.000 0.000
#> GSM1009109 2 0.000 0.994 0.000 1.000
#> GSM1009123 1 0.000 0.986 1.000 0.000
#> GSM1009137 1 0.000 0.986 1.000 0.000
#> GSM1009151 1 0.000 0.986 1.000 0.000
#> GSM1009165 2 0.000 0.994 0.000 1.000
#> GSM1009179 1 0.000 0.986 1.000 0.000
#> GSM1009193 1 0.000 0.986 1.000 0.000
#> GSM1009068 1 0.000 0.986 1.000 0.000
#> GSM1009082 2 0.000 0.994 0.000 1.000
#> GSM1009096 1 0.000 0.986 1.000 0.000
#> GSM1009110 2 0.000 0.994 0.000 1.000
#> GSM1009124 1 0.000 0.986 1.000 0.000
#> GSM1009138 1 0.000 0.986 1.000 0.000
#> GSM1009152 1 0.000 0.986 1.000 0.000
#> GSM1009166 2 0.000 0.994 0.000 1.000
#> GSM1009180 1 0.000 0.986 1.000 0.000
#> GSM1009194 1 0.653 0.795 0.832 0.168
#> GSM1009069 1 0.000 0.986 1.000 0.000
#> GSM1009083 2 0.000 0.994 0.000 1.000
#> GSM1009097 1 0.000 0.986 1.000 0.000
#> GSM1009111 2 0.000 0.994 0.000 1.000
#> GSM1009125 2 0.000 0.994 0.000 1.000
#> GSM1009139 1 0.969 0.356 0.604 0.396
#> GSM1009153 1 0.000 0.986 1.000 0.000
#> GSM1009167 2 0.000 0.994 0.000 1.000
#> GSM1009181 2 0.000 0.994 0.000 1.000
#> GSM1009195 2 0.000 0.994 0.000 1.000
#> GSM1009070 1 0.000 0.986 1.000 0.000
#> GSM1009084 2 0.000 0.994 0.000 1.000
#> GSM1009098 1 0.000 0.986 1.000 0.000
#> GSM1009112 2 0.000 0.994 0.000 1.000
#> GSM1009126 1 0.000 0.986 1.000 0.000
#> GSM1009140 1 0.000 0.986 1.000 0.000
#> GSM1009154 1 0.000 0.986 1.000 0.000
#> GSM1009168 2 0.000 0.994 0.000 1.000
#> GSM1009182 2 0.921 0.480 0.336 0.664
#> GSM1009196 1 0.000 0.986 1.000 0.000
#> GSM1009071 1 0.000 0.986 1.000 0.000
#> GSM1009085 2 0.000 0.994 0.000 1.000
#> GSM1009099 1 0.000 0.986 1.000 0.000
#> GSM1009113 2 0.000 0.994 0.000 1.000
#> GSM1009127 1 0.000 0.986 1.000 0.000
#> GSM1009141 1 0.000 0.986 1.000 0.000
#> GSM1009155 1 0.000 0.986 1.000 0.000
#> GSM1009169 2 0.000 0.994 0.000 1.000
#> GSM1009183 2 0.000 0.994 0.000 1.000
#> GSM1009197 1 0.000 0.986 1.000 0.000
#> GSM1009072 1 0.000 0.986 1.000 0.000
#> GSM1009086 2 0.000 0.994 0.000 1.000
#> GSM1009100 1 0.000 0.986 1.000 0.000
#> GSM1009114 2 0.000 0.994 0.000 1.000
#> GSM1009128 1 0.000 0.986 1.000 0.000
#> GSM1009142 1 0.000 0.986 1.000 0.000
#> GSM1009156 1 0.000 0.986 1.000 0.000
#> GSM1009170 2 0.000 0.994 0.000 1.000
#> GSM1009184 2 0.000 0.994 0.000 1.000
#> GSM1009198 1 0.000 0.986 1.000 0.000
#> GSM1009073 1 0.000 0.986 1.000 0.000
#> GSM1009087 1 0.000 0.986 1.000 0.000
#> GSM1009101 1 0.000 0.986 1.000 0.000
#> GSM1009115 2 0.000 0.994 0.000 1.000
#> GSM1009129 2 0.000 0.994 0.000 1.000
#> GSM1009143 1 0.000 0.986 1.000 0.000
#> GSM1009157 1 0.000 0.986 1.000 0.000
#> GSM1009171 2 0.000 0.994 0.000 1.000
#> GSM1009185 1 0.000 0.986 1.000 0.000
#> GSM1009199 2 0.000 0.994 0.000 1.000
#> GSM1009074 1 0.000 0.986 1.000 0.000
#> GSM1009088 1 0.000 0.986 1.000 0.000
#> GSM1009102 1 0.000 0.986 1.000 0.000
#> GSM1009116 2 0.000 0.994 0.000 1.000
#> GSM1009130 2 0.000 0.994 0.000 1.000
#> GSM1009144 1 0.000 0.986 1.000 0.000
#> GSM1009158 1 0.000 0.986 1.000 0.000
#> GSM1009172 2 0.000 0.994 0.000 1.000
#> GSM1009186 2 0.000 0.994 0.000 1.000
#> GSM1009200 1 0.000 0.986 1.000 0.000
#> GSM1009075 1 0.000 0.986 1.000 0.000
#> GSM1009089 1 0.000 0.986 1.000 0.000
#> GSM1009103 1 0.000 0.986 1.000 0.000
#> GSM1009117 2 0.000 0.994 0.000 1.000
#> GSM1009131 1 0.000 0.986 1.000 0.000
#> GSM1009145 1 0.000 0.986 1.000 0.000
#> GSM1009159 1 0.000 0.986 1.000 0.000
#> GSM1009173 2 0.000 0.994 0.000 1.000
#> GSM1009187 1 0.000 0.986 1.000 0.000
#> GSM1009201 1 0.000 0.986 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1009062 1 0.1411 0.849 0.964 0.000 0.036
#> GSM1009076 2 0.6126 0.731 0.000 0.600 0.400
#> GSM1009090 3 0.6244 0.423 0.440 0.000 0.560
#> GSM1009104 2 0.3038 0.848 0.000 0.896 0.104
#> GSM1009118 2 0.6126 0.724 0.000 0.600 0.400
#> GSM1009132 3 0.1647 0.675 0.036 0.004 0.960
#> GSM1009146 1 0.0237 0.864 0.996 0.000 0.004
#> GSM1009160 2 0.0424 0.826 0.000 0.992 0.008
#> GSM1009174 3 0.3482 0.488 0.000 0.128 0.872
#> GSM1009188 1 0.6274 -0.138 0.544 0.000 0.456
#> GSM1009063 1 0.1411 0.849 0.964 0.000 0.036
#> GSM1009077 2 0.6140 0.728 0.000 0.596 0.404
#> GSM1009091 1 0.1031 0.867 0.976 0.000 0.024
#> GSM1009105 2 0.3038 0.848 0.000 0.896 0.104
#> GSM1009119 1 0.6309 -0.267 0.504 0.000 0.496
#> GSM1009133 3 0.6235 0.426 0.436 0.000 0.564
#> GSM1009147 1 0.4235 0.685 0.824 0.000 0.176
#> GSM1009161 2 0.0424 0.826 0.000 0.992 0.008
#> GSM1009175 3 0.2066 0.601 0.000 0.060 0.940
#> GSM1009189 3 0.6244 0.423 0.440 0.000 0.560
#> GSM1009064 3 0.3619 0.699 0.136 0.000 0.864
#> GSM1009078 3 0.6305 0.291 0.484 0.000 0.516
#> GSM1009092 1 0.1031 0.867 0.976 0.000 0.024
#> GSM1009106 2 0.3038 0.848 0.000 0.896 0.104
#> GSM1009120 1 0.1031 0.867 0.976 0.000 0.024
#> GSM1009134 3 0.6235 0.426 0.436 0.000 0.564
#> GSM1009148 1 0.0424 0.865 0.992 0.000 0.008
#> GSM1009162 2 0.0424 0.826 0.000 0.992 0.008
#> GSM1009176 2 0.6126 0.731 0.000 0.600 0.400
#> GSM1009190 3 0.6026 0.520 0.376 0.000 0.624
#> GSM1009065 3 0.6225 0.409 0.432 0.000 0.568
#> GSM1009079 2 0.6126 0.731 0.000 0.600 0.400
#> GSM1009093 1 0.1031 0.867 0.976 0.000 0.024
#> GSM1009107 2 0.3038 0.848 0.000 0.896 0.104
#> GSM1009121 3 0.1964 0.617 0.000 0.056 0.944
#> GSM1009135 3 0.6235 0.426 0.436 0.000 0.564
#> GSM1009149 1 0.0000 0.865 1.000 0.000 0.000
#> GSM1009163 2 0.0424 0.826 0.000 0.992 0.008
#> GSM1009177 2 0.6154 0.723 0.000 0.592 0.408
#> GSM1009191 3 0.1765 0.675 0.040 0.004 0.956
#> GSM1009066 3 0.6225 0.409 0.432 0.000 0.568
#> GSM1009080 2 0.5397 0.795 0.000 0.720 0.280
#> GSM1009094 1 0.1031 0.867 0.976 0.000 0.024
#> GSM1009108 2 0.3038 0.848 0.000 0.896 0.104
#> GSM1009122 2 0.6095 0.731 0.000 0.608 0.392
#> GSM1009136 1 0.0892 0.867 0.980 0.000 0.020
#> GSM1009150 1 0.0237 0.864 0.996 0.000 0.004
#> GSM1009164 2 0.0424 0.826 0.000 0.992 0.008
#> GSM1009178 3 0.3686 0.708 0.140 0.000 0.860
#> GSM1009192 1 0.6308 -0.254 0.508 0.000 0.492
#> GSM1009067 1 0.1411 0.849 0.964 0.000 0.036
#> GSM1009081 2 0.6126 0.731 0.000 0.600 0.400
#> GSM1009095 1 0.1031 0.867 0.976 0.000 0.024
#> GSM1009109 2 0.3038 0.848 0.000 0.896 0.104
#> GSM1009123 1 0.1031 0.867 0.976 0.000 0.024
#> GSM1009137 3 0.6235 0.426 0.436 0.000 0.564
#> GSM1009151 1 0.0592 0.863 0.988 0.000 0.012
#> GSM1009165 2 0.0424 0.826 0.000 0.992 0.008
#> GSM1009179 3 0.1529 0.678 0.040 0.000 0.960
#> GSM1009193 1 0.1031 0.867 0.976 0.000 0.024
#> GSM1009068 1 0.1411 0.849 0.964 0.000 0.036
#> GSM1009082 2 0.6126 0.731 0.000 0.600 0.400
#> GSM1009096 1 0.1031 0.867 0.976 0.000 0.024
#> GSM1009110 2 0.3038 0.848 0.000 0.896 0.104
#> GSM1009124 3 0.3752 0.708 0.144 0.000 0.856
#> GSM1009138 3 0.6235 0.426 0.436 0.000 0.564
#> GSM1009152 1 0.0592 0.863 0.988 0.000 0.012
#> GSM1009166 2 0.0424 0.826 0.000 0.992 0.008
#> GSM1009180 3 0.3752 0.708 0.144 0.000 0.856
#> GSM1009194 3 0.1647 0.675 0.036 0.004 0.960
#> GSM1009069 3 0.3619 0.699 0.136 0.000 0.864
#> GSM1009083 2 0.6140 0.728 0.000 0.596 0.404
#> GSM1009097 1 0.1031 0.867 0.976 0.000 0.024
#> GSM1009111 2 0.3038 0.848 0.000 0.896 0.104
#> GSM1009125 2 0.5016 0.809 0.000 0.760 0.240
#> GSM1009139 3 0.1647 0.675 0.036 0.004 0.960
#> GSM1009153 1 0.0592 0.863 0.988 0.000 0.012
#> GSM1009167 2 0.0424 0.826 0.000 0.992 0.008
#> GSM1009181 2 0.6154 0.723 0.000 0.592 0.408
#> GSM1009195 3 0.5291 0.110 0.000 0.268 0.732
#> GSM1009070 1 0.1163 0.850 0.972 0.000 0.028
#> GSM1009084 2 0.6126 0.731 0.000 0.600 0.400
#> GSM1009098 1 0.1031 0.867 0.976 0.000 0.024
#> GSM1009112 2 0.3038 0.848 0.000 0.896 0.104
#> GSM1009126 3 0.3752 0.708 0.144 0.000 0.856
#> GSM1009140 1 0.1163 0.866 0.972 0.000 0.028
#> GSM1009154 1 0.0424 0.865 0.992 0.000 0.008
#> GSM1009168 2 0.0424 0.826 0.000 0.992 0.008
#> GSM1009182 3 0.1585 0.641 0.008 0.028 0.964
#> GSM1009196 1 0.6305 -0.213 0.516 0.000 0.484
#> GSM1009071 1 0.3340 0.782 0.880 0.000 0.120
#> GSM1009085 2 0.6140 0.728 0.000 0.596 0.404
#> GSM1009099 1 0.1031 0.867 0.976 0.000 0.024
#> GSM1009113 2 0.3038 0.848 0.000 0.896 0.104
#> GSM1009127 1 0.1163 0.866 0.972 0.000 0.028
#> GSM1009141 3 0.3686 0.708 0.140 0.000 0.860
#> GSM1009155 1 0.6267 -0.153 0.548 0.000 0.452
#> GSM1009169 2 0.0424 0.826 0.000 0.992 0.008
#> GSM1009183 3 0.2165 0.595 0.000 0.064 0.936
#> GSM1009197 1 0.1031 0.867 0.976 0.000 0.024
#> GSM1009072 1 0.1411 0.849 0.964 0.000 0.036
#> GSM1009086 2 0.6126 0.731 0.000 0.600 0.400
#> GSM1009100 1 0.1031 0.867 0.976 0.000 0.024
#> GSM1009114 2 0.3038 0.848 0.000 0.896 0.104
#> GSM1009128 3 0.5678 0.587 0.316 0.000 0.684
#> GSM1009142 3 0.3686 0.708 0.140 0.000 0.860
#> GSM1009156 1 0.6305 -0.213 0.516 0.000 0.484
#> GSM1009170 2 0.0424 0.826 0.000 0.992 0.008
#> GSM1009184 3 0.2066 0.601 0.000 0.060 0.940
#> GSM1009198 1 0.6274 -0.138 0.544 0.000 0.456
#> GSM1009073 1 0.1411 0.849 0.964 0.000 0.036
#> GSM1009087 3 0.6302 0.304 0.480 0.000 0.520
#> GSM1009101 1 0.1031 0.867 0.976 0.000 0.024
#> GSM1009115 2 0.3038 0.848 0.000 0.896 0.104
#> GSM1009129 2 0.5016 0.809 0.000 0.760 0.240
#> GSM1009143 1 0.1964 0.846 0.944 0.000 0.056
#> GSM1009157 3 0.3619 0.708 0.136 0.000 0.864
#> GSM1009171 2 0.0424 0.826 0.000 0.992 0.008
#> GSM1009185 1 0.6305 -0.226 0.516 0.000 0.484
#> GSM1009199 3 0.5016 0.206 0.000 0.240 0.760
#> GSM1009074 1 0.1411 0.849 0.964 0.000 0.036
#> GSM1009088 3 0.6280 0.363 0.460 0.000 0.540
#> GSM1009102 1 0.0237 0.866 0.996 0.000 0.004
#> GSM1009116 2 0.3038 0.848 0.000 0.896 0.104
#> GSM1009130 2 0.6111 0.728 0.000 0.604 0.396
#> GSM1009144 3 0.6235 0.426 0.436 0.000 0.564
#> GSM1009158 1 0.0237 0.864 0.996 0.000 0.004
#> GSM1009172 2 0.0424 0.826 0.000 0.992 0.008
#> GSM1009186 3 0.2066 0.601 0.000 0.060 0.940
#> GSM1009200 3 0.1643 0.678 0.044 0.000 0.956
#> GSM1009075 1 0.1411 0.849 0.964 0.000 0.036
#> GSM1009089 1 0.0892 0.867 0.980 0.000 0.020
#> GSM1009103 1 0.0237 0.866 0.996 0.000 0.004
#> GSM1009117 2 0.3038 0.848 0.000 0.896 0.104
#> GSM1009131 3 0.4452 0.684 0.192 0.000 0.808
#> GSM1009145 1 0.0892 0.867 0.980 0.000 0.020
#> GSM1009159 1 0.0000 0.865 1.000 0.000 0.000
#> GSM1009173 2 0.0424 0.826 0.000 0.992 0.008
#> GSM1009187 3 0.6235 0.423 0.436 0.000 0.564
#> GSM1009201 3 0.5882 0.554 0.348 0.000 0.652
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1009062 4 0.6285 0.6755 0.168 0.000 0.168 0.664
#> GSM1009076 2 0.0469 0.7640 0.012 0.988 0.000 0.000
#> GSM1009090 1 0.4134 0.7258 0.740 0.000 0.000 0.260
#> GSM1009104 3 0.6788 0.7851 0.096 0.424 0.480 0.000
#> GSM1009118 2 0.0895 0.7630 0.020 0.976 0.004 0.000
#> GSM1009132 1 0.5141 0.5411 0.700 0.268 0.032 0.000
#> GSM1009146 4 0.0376 0.8628 0.004 0.000 0.004 0.992
#> GSM1009160 3 0.4356 0.8066 0.000 0.292 0.708 0.000
#> GSM1009174 2 0.4838 0.6057 0.252 0.724 0.024 0.000
#> GSM1009188 1 0.4697 0.6399 0.644 0.000 0.000 0.356
#> GSM1009063 4 0.6362 0.6679 0.168 0.000 0.176 0.656
#> GSM1009077 2 0.0469 0.7640 0.012 0.988 0.000 0.000
#> GSM1009091 4 0.0921 0.8660 0.028 0.000 0.000 0.972
#> GSM1009105 3 0.6788 0.7851 0.096 0.424 0.480 0.000
#> GSM1009119 1 0.4477 0.6885 0.688 0.000 0.000 0.312
#> GSM1009133 1 0.5156 0.7296 0.720 0.000 0.044 0.236
#> GSM1009147 1 0.5838 0.4369 0.524 0.000 0.032 0.444
#> GSM1009161 3 0.4356 0.8066 0.000 0.292 0.708 0.000
#> GSM1009175 2 0.5872 0.3185 0.384 0.576 0.040 0.000
#> GSM1009189 1 0.3942 0.7361 0.764 0.000 0.000 0.236
#> GSM1009064 1 0.6779 0.5318 0.652 0.108 0.216 0.024
#> GSM1009078 1 0.5836 0.6863 0.640 0.000 0.056 0.304
#> GSM1009092 4 0.0921 0.8660 0.028 0.000 0.000 0.972
#> GSM1009106 3 0.6788 0.7851 0.096 0.424 0.480 0.000
#> GSM1009120 4 0.0921 0.8660 0.028 0.000 0.000 0.972
#> GSM1009134 1 0.5156 0.7296 0.720 0.000 0.044 0.236
#> GSM1009148 4 0.3697 0.8039 0.048 0.000 0.100 0.852
#> GSM1009162 3 0.4356 0.8066 0.000 0.292 0.708 0.000
#> GSM1009176 2 0.0469 0.7640 0.012 0.988 0.000 0.000
#> GSM1009190 1 0.3837 0.7401 0.776 0.000 0.000 0.224
#> GSM1009065 1 0.6695 0.5353 0.616 0.000 0.220 0.164
#> GSM1009079 2 0.0469 0.7640 0.012 0.988 0.000 0.000
#> GSM1009093 4 0.0921 0.8660 0.028 0.000 0.000 0.972
#> GSM1009107 3 0.6788 0.7851 0.096 0.424 0.480 0.000
#> GSM1009121 2 0.5147 0.1932 0.460 0.536 0.004 0.000
#> GSM1009135 1 0.5156 0.7296 0.720 0.000 0.044 0.236
#> GSM1009149 4 0.0188 0.8635 0.000 0.000 0.004 0.996
#> GSM1009163 3 0.4356 0.8066 0.000 0.292 0.708 0.000
#> GSM1009177 2 0.1211 0.7569 0.040 0.960 0.000 0.000
#> GSM1009191 1 0.4222 0.5403 0.728 0.272 0.000 0.000
#> GSM1009066 1 0.6695 0.5353 0.616 0.000 0.220 0.164
#> GSM1009080 2 0.1004 0.7029 0.004 0.972 0.024 0.000
#> GSM1009094 4 0.0921 0.8660 0.028 0.000 0.000 0.972
#> GSM1009108 3 0.6788 0.7851 0.096 0.424 0.480 0.000
#> GSM1009122 2 0.0804 0.7604 0.012 0.980 0.008 0.000
#> GSM1009136 4 0.0707 0.8665 0.020 0.000 0.000 0.980
#> GSM1009150 4 0.0376 0.8628 0.004 0.000 0.004 0.992
#> GSM1009164 3 0.4356 0.8066 0.000 0.292 0.708 0.000
#> GSM1009178 1 0.5496 0.6112 0.724 0.220 0.040 0.016
#> GSM1009192 1 0.4454 0.6918 0.692 0.000 0.000 0.308
#> GSM1009067 4 0.6285 0.6755 0.168 0.000 0.168 0.664
#> GSM1009081 2 0.0469 0.7640 0.012 0.988 0.000 0.000
#> GSM1009095 4 0.0817 0.8661 0.024 0.000 0.000 0.976
#> GSM1009109 3 0.6788 0.7851 0.096 0.424 0.480 0.000
#> GSM1009123 4 0.0921 0.8660 0.028 0.000 0.000 0.972
#> GSM1009137 1 0.5156 0.7296 0.720 0.000 0.044 0.236
#> GSM1009151 4 0.3697 0.8039 0.048 0.000 0.100 0.852
#> GSM1009165 3 0.4356 0.8066 0.000 0.292 0.708 0.000
#> GSM1009179 1 0.5557 0.4730 0.652 0.308 0.040 0.000
#> GSM1009193 4 0.0921 0.8660 0.028 0.000 0.000 0.972
#> GSM1009068 4 0.6245 0.6777 0.164 0.000 0.168 0.668
#> GSM1009082 2 0.0469 0.7640 0.012 0.988 0.000 0.000
#> GSM1009096 4 0.0921 0.8660 0.028 0.000 0.000 0.972
#> GSM1009110 3 0.6788 0.7851 0.096 0.424 0.480 0.000
#> GSM1009124 1 0.4544 0.6137 0.760 0.220 0.004 0.016
#> GSM1009138 1 0.5156 0.7296 0.720 0.000 0.044 0.236
#> GSM1009152 4 0.3697 0.8039 0.048 0.000 0.100 0.852
#> GSM1009166 3 0.4356 0.8066 0.000 0.292 0.708 0.000
#> GSM1009180 1 0.5496 0.6112 0.724 0.220 0.040 0.016
#> GSM1009194 1 0.4222 0.5403 0.728 0.272 0.000 0.000
#> GSM1009069 1 0.6830 0.5279 0.648 0.112 0.216 0.024
#> GSM1009083 2 0.0469 0.7640 0.012 0.988 0.000 0.000
#> GSM1009097 4 0.0921 0.8660 0.028 0.000 0.000 0.972
#> GSM1009111 3 0.6788 0.7851 0.096 0.424 0.480 0.000
#> GSM1009125 2 0.2483 0.6139 0.052 0.916 0.032 0.000
#> GSM1009139 1 0.5312 0.5406 0.692 0.268 0.040 0.000
#> GSM1009153 4 0.3899 0.7959 0.052 0.000 0.108 0.840
#> GSM1009167 3 0.4356 0.8066 0.000 0.292 0.708 0.000
#> GSM1009181 2 0.1211 0.7569 0.040 0.960 0.000 0.000
#> GSM1009195 2 0.3764 0.6704 0.216 0.784 0.000 0.000
#> GSM1009070 4 0.2048 0.8311 0.064 0.000 0.008 0.928
#> GSM1009084 2 0.0469 0.7640 0.012 0.988 0.000 0.000
#> GSM1009098 4 0.0921 0.8660 0.028 0.000 0.000 0.972
#> GSM1009112 3 0.6788 0.7851 0.096 0.424 0.480 0.000
#> GSM1009126 1 0.4544 0.6137 0.760 0.220 0.004 0.016
#> GSM1009140 4 0.5546 0.4136 0.292 0.000 0.044 0.664
#> GSM1009154 4 0.3697 0.8039 0.048 0.000 0.100 0.852
#> GSM1009168 3 0.4356 0.8066 0.000 0.292 0.708 0.000
#> GSM1009182 1 0.6000 0.0682 0.508 0.452 0.040 0.000
#> GSM1009196 1 0.6033 0.6657 0.620 0.000 0.064 0.316
#> GSM1009071 1 0.7457 0.3077 0.504 0.000 0.220 0.276
#> GSM1009085 2 0.0469 0.7640 0.012 0.988 0.000 0.000
#> GSM1009099 4 0.0921 0.8660 0.028 0.000 0.000 0.972
#> GSM1009113 3 0.6788 0.7851 0.096 0.424 0.480 0.000
#> GSM1009127 4 0.1716 0.8462 0.064 0.000 0.000 0.936
#> GSM1009141 1 0.5245 0.6283 0.748 0.196 0.044 0.012
#> GSM1009155 1 0.6968 0.5849 0.552 0.000 0.140 0.308
#> GSM1009169 3 0.4356 0.8066 0.000 0.292 0.708 0.000
#> GSM1009183 2 0.5496 0.3802 0.372 0.604 0.024 0.000
#> GSM1009197 4 0.0921 0.8660 0.028 0.000 0.000 0.972
#> GSM1009072 4 0.6285 0.6755 0.168 0.000 0.168 0.664
#> GSM1009086 2 0.0469 0.7640 0.012 0.988 0.000 0.000
#> GSM1009100 4 0.0921 0.8660 0.028 0.000 0.000 0.972
#> GSM1009114 3 0.6788 0.7851 0.096 0.424 0.480 0.000
#> GSM1009128 1 0.4798 0.7420 0.760 0.032 0.004 0.204
#> GSM1009142 1 0.5245 0.6283 0.748 0.196 0.044 0.012
#> GSM1009156 1 0.5717 0.6698 0.632 0.000 0.044 0.324
#> GSM1009170 3 0.4356 0.8066 0.000 0.292 0.708 0.000
#> GSM1009184 2 0.5872 0.3185 0.384 0.576 0.040 0.000
#> GSM1009198 1 0.4713 0.6368 0.640 0.000 0.000 0.360
#> GSM1009073 4 0.6908 0.5926 0.188 0.000 0.220 0.592
#> GSM1009087 1 0.5836 0.6863 0.640 0.000 0.056 0.304
#> GSM1009101 4 0.0921 0.8660 0.028 0.000 0.000 0.972
#> GSM1009115 3 0.6788 0.7851 0.096 0.424 0.480 0.000
#> GSM1009129 2 0.2483 0.6139 0.052 0.916 0.032 0.000
#> GSM1009143 4 0.6082 -0.3038 0.476 0.000 0.044 0.480
#> GSM1009157 1 0.5461 0.6133 0.728 0.216 0.040 0.016
#> GSM1009171 3 0.4356 0.8066 0.000 0.292 0.708 0.000
#> GSM1009185 1 0.5677 0.6646 0.628 0.000 0.040 0.332
#> GSM1009199 2 0.3907 0.6608 0.232 0.768 0.000 0.000
#> GSM1009074 4 0.6285 0.6755 0.168 0.000 0.168 0.664
#> GSM1009088 1 0.5792 0.6935 0.648 0.000 0.056 0.296
#> GSM1009102 4 0.0336 0.8658 0.008 0.000 0.000 0.992
#> GSM1009116 3 0.6788 0.7851 0.096 0.424 0.480 0.000
#> GSM1009130 2 0.0804 0.7604 0.012 0.980 0.008 0.000
#> GSM1009144 1 0.5123 0.7312 0.724 0.000 0.044 0.232
#> GSM1009158 4 0.0376 0.8628 0.004 0.000 0.004 0.992
#> GSM1009172 3 0.4356 0.8066 0.000 0.292 0.708 0.000
#> GSM1009186 2 0.5872 0.3185 0.384 0.576 0.040 0.000
#> GSM1009200 1 0.3942 0.5936 0.764 0.236 0.000 0.000
#> GSM1009075 4 0.6285 0.6755 0.168 0.000 0.168 0.664
#> GSM1009089 4 0.1042 0.8654 0.020 0.000 0.008 0.972
#> GSM1009103 4 0.0336 0.8658 0.008 0.000 0.000 0.992
#> GSM1009117 3 0.6788 0.7851 0.096 0.424 0.480 0.000
#> GSM1009131 1 0.5223 0.6901 0.764 0.136 0.004 0.096
#> GSM1009145 4 0.0707 0.8665 0.020 0.000 0.000 0.980
#> GSM1009159 4 0.0188 0.8635 0.000 0.000 0.004 0.996
#> GSM1009173 3 0.4356 0.8066 0.000 0.292 0.708 0.000
#> GSM1009187 1 0.5444 0.7182 0.688 0.000 0.048 0.264
#> GSM1009201 1 0.3870 0.7437 0.788 0.004 0.000 0.208
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM1009062 3 0.4707 0.639 0.020 0.000 0.588 0.392 0.000
#> GSM1009076 2 0.1908 0.857 0.000 0.908 0.000 0.000 0.092
#> GSM1009090 1 0.2733 0.807 0.872 0.004 0.012 0.112 0.000
#> GSM1009104 5 0.6414 0.776 0.040 0.164 0.180 0.000 0.616
#> GSM1009118 2 0.2233 0.855 0.016 0.904 0.000 0.000 0.080
#> GSM1009132 1 0.4676 0.715 0.740 0.140 0.120 0.000 0.000
#> GSM1009146 4 0.1893 0.859 0.012 0.028 0.024 0.936 0.000
#> GSM1009160 5 0.1493 0.790 0.000 0.028 0.024 0.000 0.948
#> GSM1009174 2 0.3647 0.789 0.132 0.816 0.052 0.000 0.000
#> GSM1009188 1 0.3674 0.786 0.816 0.024 0.012 0.148 0.000
#> GSM1009063 3 0.4776 0.651 0.020 0.004 0.612 0.364 0.000
#> GSM1009077 2 0.1908 0.857 0.000 0.908 0.000 0.000 0.092
#> GSM1009091 4 0.1116 0.873 0.028 0.004 0.004 0.964 0.000
#> GSM1009105 5 0.6414 0.776 0.040 0.164 0.180 0.000 0.616
#> GSM1009119 1 0.3639 0.793 0.828 0.028 0.016 0.128 0.000
#> GSM1009133 1 0.4803 0.772 0.772 0.040 0.092 0.096 0.000
#> GSM1009147 1 0.5525 0.672 0.680 0.028 0.076 0.216 0.000
#> GSM1009161 5 0.1493 0.790 0.000 0.028 0.024 0.000 0.948
#> GSM1009175 2 0.4555 0.719 0.200 0.732 0.068 0.000 0.000
#> GSM1009189 1 0.2464 0.807 0.892 0.012 0.004 0.092 0.000
#> GSM1009064 3 0.4494 0.523 0.244 0.012 0.720 0.024 0.000
#> GSM1009078 1 0.5307 0.725 0.716 0.032 0.080 0.172 0.000
#> GSM1009092 4 0.0451 0.887 0.008 0.004 0.000 0.988 0.000
#> GSM1009106 5 0.6414 0.776 0.040 0.164 0.180 0.000 0.616
#> GSM1009120 4 0.1377 0.875 0.020 0.020 0.004 0.956 0.000
#> GSM1009134 1 0.4855 0.770 0.768 0.040 0.096 0.096 0.000
#> GSM1009148 4 0.4848 0.448 0.016 0.028 0.272 0.684 0.000
#> GSM1009162 5 0.0794 0.790 0.000 0.028 0.000 0.000 0.972
#> GSM1009176 2 0.2664 0.854 0.004 0.884 0.020 0.000 0.092
#> GSM1009190 1 0.2189 0.808 0.904 0.012 0.000 0.084 0.000
#> GSM1009065 3 0.4482 0.540 0.252 0.004 0.712 0.032 0.000
#> GSM1009079 2 0.1908 0.857 0.000 0.908 0.000 0.000 0.092
#> GSM1009093 4 0.0451 0.887 0.008 0.004 0.000 0.988 0.000
#> GSM1009107 5 0.6414 0.776 0.040 0.164 0.180 0.000 0.616
#> GSM1009121 2 0.5175 0.172 0.464 0.496 0.040 0.000 0.000
#> GSM1009135 1 0.4803 0.772 0.772 0.040 0.092 0.096 0.000
#> GSM1009149 4 0.0968 0.876 0.012 0.004 0.012 0.972 0.000
#> GSM1009163 5 0.1493 0.790 0.000 0.028 0.024 0.000 0.948
#> GSM1009177 2 0.3423 0.845 0.016 0.852 0.040 0.000 0.092
#> GSM1009191 1 0.3064 0.749 0.856 0.108 0.036 0.000 0.000
#> GSM1009066 3 0.4482 0.540 0.252 0.004 0.712 0.032 0.000
#> GSM1009080 2 0.2352 0.848 0.004 0.896 0.008 0.000 0.092
#> GSM1009094 4 0.1569 0.853 0.044 0.004 0.008 0.944 0.000
#> GSM1009108 5 0.6414 0.776 0.040 0.164 0.180 0.000 0.616
#> GSM1009122 2 0.1908 0.855 0.000 0.908 0.000 0.000 0.092
#> GSM1009136 4 0.0290 0.887 0.008 0.000 0.000 0.992 0.000
#> GSM1009150 4 0.0968 0.876 0.012 0.004 0.012 0.972 0.000
#> GSM1009164 5 0.1493 0.790 0.000 0.028 0.024 0.000 0.948
#> GSM1009178 1 0.4028 0.754 0.816 0.084 0.084 0.016 0.000
#> GSM1009192 1 0.3491 0.794 0.836 0.028 0.012 0.124 0.000
#> GSM1009067 3 0.4707 0.639 0.020 0.000 0.588 0.392 0.000
#> GSM1009081 2 0.1908 0.857 0.000 0.908 0.000 0.000 0.092
#> GSM1009095 4 0.0613 0.887 0.008 0.004 0.004 0.984 0.000
#> GSM1009109 5 0.6414 0.776 0.040 0.164 0.180 0.000 0.616
#> GSM1009123 4 0.1442 0.870 0.032 0.012 0.004 0.952 0.000
#> GSM1009137 1 0.4803 0.772 0.772 0.040 0.092 0.096 0.000
#> GSM1009151 4 0.4824 0.456 0.016 0.028 0.268 0.688 0.000
#> GSM1009165 5 0.0794 0.790 0.000 0.028 0.000 0.000 0.972
#> GSM1009179 1 0.4930 0.606 0.696 0.220 0.084 0.000 0.000
#> GSM1009193 4 0.1173 0.880 0.020 0.012 0.004 0.964 0.000
#> GSM1009068 3 0.4707 0.639 0.020 0.000 0.588 0.392 0.000
#> GSM1009082 2 0.1908 0.857 0.000 0.908 0.000 0.000 0.092
#> GSM1009096 4 0.0727 0.885 0.012 0.004 0.004 0.980 0.000
#> GSM1009110 5 0.6483 0.775 0.048 0.164 0.172 0.000 0.616
#> GSM1009124 1 0.2590 0.794 0.900 0.060 0.012 0.028 0.000
#> GSM1009138 1 0.4855 0.770 0.768 0.040 0.096 0.096 0.000
#> GSM1009152 4 0.4824 0.456 0.016 0.028 0.268 0.688 0.000
#> GSM1009166 5 0.0794 0.790 0.000 0.028 0.000 0.000 0.972
#> GSM1009180 1 0.3790 0.766 0.832 0.068 0.084 0.016 0.000
#> GSM1009194 1 0.3289 0.744 0.844 0.108 0.048 0.000 0.000
#> GSM1009069 3 0.4505 0.513 0.244 0.020 0.720 0.016 0.000
#> GSM1009083 2 0.1908 0.857 0.000 0.908 0.000 0.000 0.092
#> GSM1009097 4 0.0451 0.887 0.008 0.004 0.000 0.988 0.000
#> GSM1009111 5 0.6414 0.776 0.040 0.164 0.180 0.000 0.616
#> GSM1009125 2 0.3003 0.820 0.020 0.872 0.016 0.000 0.092
#> GSM1009139 1 0.4720 0.713 0.736 0.140 0.124 0.000 0.000
#> GSM1009153 4 0.5025 0.399 0.020 0.028 0.288 0.664 0.000
#> GSM1009167 5 0.1300 0.789 0.000 0.028 0.016 0.000 0.956
#> GSM1009181 2 0.3423 0.845 0.016 0.852 0.040 0.000 0.092
#> GSM1009195 2 0.3573 0.803 0.124 0.832 0.032 0.000 0.012
#> GSM1009070 4 0.1518 0.840 0.004 0.004 0.048 0.944 0.000
#> GSM1009084 2 0.1908 0.857 0.000 0.908 0.000 0.000 0.092
#> GSM1009098 4 0.0451 0.887 0.008 0.004 0.000 0.988 0.000
#> GSM1009112 5 0.6414 0.776 0.040 0.164 0.180 0.000 0.616
#> GSM1009126 1 0.2590 0.794 0.900 0.060 0.012 0.028 0.000
#> GSM1009140 1 0.6715 0.417 0.516 0.044 0.104 0.336 0.000
#> GSM1009154 4 0.4824 0.456 0.016 0.028 0.268 0.688 0.000
#> GSM1009168 5 0.1082 0.790 0.000 0.028 0.008 0.000 0.964
#> GSM1009182 2 0.5558 0.411 0.360 0.560 0.080 0.000 0.000
#> GSM1009196 1 0.5235 0.723 0.716 0.024 0.084 0.176 0.000
#> GSM1009071 3 0.5004 0.589 0.224 0.004 0.696 0.076 0.000
#> GSM1009085 2 0.1908 0.857 0.000 0.908 0.000 0.000 0.092
#> GSM1009099 4 0.0451 0.887 0.008 0.004 0.000 0.988 0.000
#> GSM1009113 5 0.6414 0.776 0.040 0.164 0.180 0.000 0.616
#> GSM1009127 4 0.4877 0.372 0.312 0.024 0.012 0.652 0.000
#> GSM1009141 1 0.4696 0.744 0.768 0.096 0.116 0.020 0.000
#> GSM1009155 1 0.6814 0.268 0.508 0.028 0.308 0.156 0.000
#> GSM1009169 5 0.1806 0.788 0.016 0.028 0.016 0.000 0.940
#> GSM1009183 2 0.4495 0.721 0.200 0.736 0.064 0.000 0.000
#> GSM1009197 4 0.1179 0.880 0.016 0.016 0.004 0.964 0.000
#> GSM1009072 3 0.4707 0.639 0.020 0.000 0.588 0.392 0.000
#> GSM1009086 2 0.1908 0.857 0.000 0.908 0.000 0.000 0.092
#> GSM1009100 4 0.0451 0.887 0.008 0.004 0.000 0.988 0.000
#> GSM1009114 5 0.6481 0.773 0.044 0.164 0.180 0.000 0.612
#> GSM1009128 1 0.2727 0.808 0.888 0.020 0.012 0.080 0.000
#> GSM1009142 1 0.4763 0.746 0.768 0.088 0.116 0.028 0.000
#> GSM1009156 1 0.5195 0.726 0.724 0.032 0.072 0.172 0.000
#> GSM1009170 5 0.1493 0.790 0.000 0.028 0.024 0.000 0.948
#> GSM1009184 2 0.4555 0.719 0.200 0.732 0.068 0.000 0.000
#> GSM1009198 1 0.3674 0.786 0.816 0.024 0.012 0.148 0.000
#> GSM1009073 3 0.4915 0.674 0.064 0.004 0.696 0.236 0.000
#> GSM1009087 1 0.5307 0.725 0.716 0.032 0.080 0.172 0.000
#> GSM1009101 4 0.0451 0.887 0.008 0.004 0.000 0.988 0.000
#> GSM1009115 5 0.6414 0.776 0.040 0.164 0.180 0.000 0.616
#> GSM1009129 2 0.3003 0.820 0.020 0.872 0.016 0.000 0.092
#> GSM1009143 1 0.6115 0.696 0.660 0.060 0.104 0.176 0.000
#> GSM1009157 1 0.4191 0.753 0.804 0.084 0.096 0.016 0.000
#> GSM1009171 5 0.0794 0.790 0.000 0.028 0.000 0.000 0.972
#> GSM1009185 1 0.4767 0.733 0.736 0.028 0.036 0.200 0.000
#> GSM1009199 2 0.3433 0.798 0.132 0.832 0.032 0.000 0.004
#> GSM1009074 3 0.4707 0.639 0.020 0.000 0.588 0.392 0.000
#> GSM1009088 1 0.5307 0.725 0.716 0.032 0.080 0.172 0.000
#> GSM1009102 4 0.0613 0.883 0.004 0.004 0.008 0.984 0.000
#> GSM1009116 5 0.6414 0.776 0.040 0.164 0.180 0.000 0.616
#> GSM1009130 2 0.1908 0.855 0.000 0.908 0.000 0.000 0.092
#> GSM1009144 1 0.4751 0.773 0.776 0.040 0.092 0.092 0.000
#> GSM1009158 4 0.0968 0.876 0.012 0.004 0.012 0.972 0.000
#> GSM1009172 5 0.1082 0.790 0.000 0.028 0.008 0.000 0.964
#> GSM1009186 2 0.4555 0.719 0.200 0.732 0.068 0.000 0.000
#> GSM1009200 1 0.2879 0.756 0.868 0.100 0.032 0.000 0.000
#> GSM1009075 3 0.4707 0.639 0.020 0.000 0.588 0.392 0.000
#> GSM1009089 4 0.1299 0.881 0.020 0.012 0.008 0.960 0.000
#> GSM1009103 4 0.0613 0.883 0.004 0.004 0.008 0.984 0.000
#> GSM1009117 5 0.6450 0.775 0.044 0.164 0.176 0.000 0.616
#> GSM1009131 1 0.2701 0.802 0.896 0.044 0.012 0.048 0.000
#> GSM1009145 4 0.0290 0.887 0.008 0.000 0.000 0.992 0.000
#> GSM1009159 4 0.0613 0.878 0.004 0.004 0.008 0.984 0.000
#> GSM1009173 5 0.1806 0.788 0.016 0.028 0.016 0.000 0.940
#> GSM1009187 1 0.4806 0.753 0.760 0.024 0.084 0.132 0.000
#> GSM1009201 1 0.1830 0.808 0.924 0.008 0.000 0.068 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1009062 6 0.3126 0.74343 0.000 0.000 NA 0.248 0.000 0.752
#> GSM1009076 2 0.0713 0.87211 0.000 0.972 NA 0.000 0.028 0.000
#> GSM1009090 1 0.3001 0.72695 0.868 0.004 NA 0.060 0.000 0.020
#> GSM1009104 5 0.5756 0.73328 0.000 0.160 NA 0.000 0.492 0.004
#> GSM1009118 2 0.1198 0.86451 0.004 0.960 NA 0.000 0.020 0.012
#> GSM1009132 1 0.5712 0.58428 0.552 0.020 NA 0.000 0.000 0.120
#> GSM1009146 4 0.3485 0.75015 0.028 0.000 NA 0.824 0.000 0.036
#> GSM1009160 5 0.0436 0.73701 0.004 0.004 NA 0.000 0.988 0.000
#> GSM1009174 2 0.4772 0.76687 0.044 0.708 NA 0.000 0.000 0.052
#> GSM1009188 1 0.2058 0.72430 0.916 0.004 NA 0.056 0.000 0.008
#> GSM1009063 6 0.3314 0.75247 0.000 0.000 NA 0.224 0.000 0.764
#> GSM1009077 2 0.0713 0.87211 0.000 0.972 NA 0.000 0.028 0.000
#> GSM1009091 4 0.1138 0.85063 0.024 0.004 NA 0.960 0.000 0.000
#> GSM1009105 5 0.5609 0.73599 0.000 0.156 NA 0.000 0.496 0.000
#> GSM1009119 1 0.2022 0.72440 0.916 0.000 NA 0.052 0.000 0.008
#> GSM1009133 1 0.5480 0.63455 0.636 0.000 NA 0.040 0.000 0.100
#> GSM1009147 1 0.5886 0.56308 0.632 0.000 NA 0.124 0.000 0.092
#> GSM1009161 5 0.0436 0.73701 0.004 0.004 NA 0.000 0.988 0.000
#> GSM1009175 2 0.5428 0.72674 0.076 0.656 NA 0.000 0.000 0.064
#> GSM1009189 1 0.1036 0.73064 0.964 0.004 NA 0.024 0.000 0.008
#> GSM1009064 6 0.2867 0.70615 0.076 0.016 NA 0.000 0.000 0.868
#> GSM1009078 1 0.5750 0.60453 0.656 0.008 NA 0.076 0.000 0.092
#> GSM1009092 4 0.0405 0.86275 0.000 0.004 NA 0.988 0.000 0.000
#> GSM1009106 5 0.5609 0.73599 0.000 0.156 NA 0.000 0.496 0.000
#> GSM1009120 4 0.1167 0.85869 0.012 0.000 NA 0.960 0.000 0.008
#> GSM1009134 1 0.5561 0.63154 0.628 0.000 NA 0.040 0.000 0.108
#> GSM1009148 4 0.5963 0.05630 0.032 0.000 NA 0.488 0.000 0.372
#> GSM1009162 5 0.1036 0.73695 0.000 0.004 NA 0.000 0.964 0.024
#> GSM1009176 2 0.3184 0.84459 0.000 0.836 NA 0.000 0.028 0.016
#> GSM1009190 1 0.1036 0.73116 0.964 0.004 NA 0.024 0.000 0.000
#> GSM1009065 6 0.3097 0.72290 0.088 0.004 NA 0.016 0.000 0.856
#> GSM1009079 2 0.0713 0.87211 0.000 0.972 NA 0.000 0.028 0.000
#> GSM1009093 4 0.0405 0.86275 0.000 0.004 NA 0.988 0.000 0.000
#> GSM1009107 5 0.5609 0.73599 0.000 0.156 NA 0.000 0.496 0.000
#> GSM1009121 1 0.5939 0.01955 0.484 0.388 NA 0.000 0.000 0.044
#> GSM1009135 1 0.5561 0.63154 0.628 0.000 NA 0.040 0.000 0.108
#> GSM1009149 4 0.1572 0.84830 0.000 0.000 NA 0.936 0.000 0.028
#> GSM1009163 5 0.0436 0.73701 0.004 0.004 NA 0.000 0.988 0.000
#> GSM1009177 2 0.3912 0.82753 0.008 0.784 NA 0.000 0.028 0.020
#> GSM1009191 1 0.3089 0.69322 0.856 0.024 NA 0.000 0.000 0.040
#> GSM1009066 6 0.3097 0.72290 0.088 0.004 NA 0.016 0.000 0.856
#> GSM1009080 2 0.0713 0.87211 0.000 0.972 NA 0.000 0.028 0.000
#> GSM1009094 4 0.2058 0.80908 0.056 0.004 NA 0.916 0.000 0.008
#> GSM1009108 5 0.5609 0.73599 0.000 0.156 NA 0.000 0.496 0.000
#> GSM1009122 2 0.1198 0.86451 0.004 0.960 NA 0.000 0.020 0.012
#> GSM1009136 4 0.0725 0.86155 0.000 0.000 NA 0.976 0.000 0.012
#> GSM1009150 4 0.1649 0.84621 0.000 0.000 NA 0.932 0.000 0.032
#> GSM1009164 5 0.0436 0.73701 0.004 0.004 NA 0.000 0.988 0.000
#> GSM1009178 1 0.5361 0.58219 0.608 0.032 NA 0.000 0.000 0.072
#> GSM1009192 1 0.1994 0.72477 0.920 0.004 NA 0.052 0.000 0.008
#> GSM1009067 6 0.3126 0.74343 0.000 0.000 NA 0.248 0.000 0.752
#> GSM1009081 2 0.0713 0.87211 0.000 0.972 NA 0.000 0.028 0.000
#> GSM1009095 4 0.0508 0.86260 0.000 0.004 NA 0.984 0.000 0.000
#> GSM1009109 5 0.5634 0.73333 0.000 0.160 NA 0.000 0.492 0.000
#> GSM1009123 4 0.1346 0.85282 0.024 0.000 NA 0.952 0.000 0.008
#> GSM1009137 1 0.5480 0.63455 0.636 0.000 NA 0.040 0.000 0.100
#> GSM1009151 4 0.5957 0.06776 0.032 0.000 NA 0.492 0.000 0.368
#> GSM1009165 5 0.1036 0.73695 0.000 0.004 NA 0.000 0.964 0.024
#> GSM1009179 1 0.6362 0.48934 0.524 0.120 NA 0.000 0.000 0.072
#> GSM1009193 4 0.1078 0.85965 0.012 0.000 NA 0.964 0.000 0.008
#> GSM1009068 6 0.3126 0.74343 0.000 0.000 NA 0.248 0.000 0.752
#> GSM1009082 2 0.0713 0.87211 0.000 0.972 NA 0.000 0.028 0.000
#> GSM1009096 4 0.1053 0.85355 0.020 0.004 NA 0.964 0.000 0.000
#> GSM1009110 5 0.6189 0.73375 0.012 0.156 NA 0.000 0.496 0.012
#> GSM1009124 1 0.1785 0.72706 0.936 0.012 NA 0.008 0.000 0.016
#> GSM1009138 1 0.5561 0.63154 0.628 0.000 NA 0.040 0.000 0.108
#> GSM1009152 4 0.5957 0.06776 0.032 0.000 NA 0.492 0.000 0.368
#> GSM1009166 5 0.1036 0.73695 0.000 0.004 NA 0.000 0.964 0.024
#> GSM1009180 1 0.5361 0.58219 0.608 0.032 NA 0.000 0.000 0.072
#> GSM1009194 1 0.3208 0.69057 0.848 0.024 NA 0.000 0.000 0.044
#> GSM1009069 6 0.3031 0.69476 0.072 0.020 NA 0.000 0.000 0.860
#> GSM1009083 2 0.0713 0.87211 0.000 0.972 NA 0.000 0.028 0.000
#> GSM1009097 4 0.0405 0.86275 0.000 0.004 NA 0.988 0.000 0.000
#> GSM1009111 5 0.5609 0.73599 0.000 0.156 NA 0.000 0.496 0.000
#> GSM1009125 2 0.1198 0.86451 0.004 0.960 NA 0.000 0.020 0.012
#> GSM1009139 1 0.5712 0.58428 0.552 0.020 NA 0.000 0.000 0.120
#> GSM1009153 4 0.5984 0.00780 0.032 0.000 NA 0.472 0.000 0.388
#> GSM1009167 5 0.1194 0.73653 0.000 0.004 NA 0.000 0.956 0.032
#> GSM1009181 2 0.3912 0.82753 0.008 0.784 NA 0.000 0.028 0.020
#> GSM1009195 2 0.3654 0.82720 0.056 0.820 NA 0.000 0.004 0.020
#> GSM1009070 4 0.1908 0.83570 0.000 0.000 NA 0.916 0.000 0.056
#> GSM1009084 2 0.0713 0.87211 0.000 0.972 NA 0.000 0.028 0.000
#> GSM1009098 4 0.0405 0.86275 0.000 0.004 NA 0.988 0.000 0.000
#> GSM1009112 5 0.5756 0.73328 0.000 0.160 NA 0.000 0.492 0.004
#> GSM1009126 1 0.1785 0.72706 0.936 0.012 NA 0.008 0.000 0.016
#> GSM1009140 1 0.6437 0.56676 0.552 0.000 NA 0.116 0.000 0.108
#> GSM1009154 4 0.5957 0.06776 0.032 0.000 NA 0.492 0.000 0.368
#> GSM1009168 5 0.0146 0.73715 0.000 0.004 NA 0.000 0.996 0.000
#> GSM1009182 2 0.6758 0.41414 0.240 0.468 NA 0.000 0.000 0.064
#> GSM1009196 1 0.5442 0.61622 0.680 0.000 NA 0.092 0.000 0.100
#> GSM1009071 6 0.3538 0.73268 0.092 0.004 NA 0.036 0.000 0.832
#> GSM1009085 2 0.0858 0.87203 0.000 0.968 NA 0.000 0.028 0.000
#> GSM1009099 4 0.0405 0.86275 0.000 0.004 NA 0.988 0.000 0.000
#> GSM1009113 5 0.5609 0.73599 0.000 0.156 NA 0.000 0.496 0.000
#> GSM1009127 1 0.5603 0.25927 0.504 0.000 NA 0.384 0.000 0.016
#> GSM1009141 1 0.5573 0.61182 0.596 0.020 NA 0.000 0.000 0.128
#> GSM1009155 6 0.6797 0.00787 0.380 0.000 NA 0.084 0.000 0.396
#> GSM1009169 5 0.1340 0.73647 0.000 0.004 NA 0.000 0.948 0.040
#> GSM1009183 2 0.5428 0.72674 0.076 0.656 NA 0.000 0.000 0.064
#> GSM1009197 4 0.0951 0.86058 0.004 0.000 NA 0.968 0.000 0.008
#> GSM1009072 6 0.3126 0.74343 0.000 0.000 NA 0.248 0.000 0.752
#> GSM1009086 2 0.0713 0.87211 0.000 0.972 NA 0.000 0.028 0.000
#> GSM1009100 4 0.0405 0.86275 0.000 0.004 NA 0.988 0.000 0.000
#> GSM1009114 5 0.6224 0.72884 0.012 0.160 NA 0.000 0.488 0.012
#> GSM1009128 1 0.1536 0.73109 0.944 0.000 NA 0.024 0.000 0.012
#> GSM1009142 1 0.5540 0.61747 0.608 0.016 NA 0.004 0.000 0.120
#> GSM1009156 1 0.5750 0.59948 0.660 0.008 NA 0.084 0.000 0.092
#> GSM1009170 5 0.0436 0.73701 0.004 0.004 NA 0.000 0.988 0.000
#> GSM1009184 2 0.5428 0.72674 0.076 0.656 NA 0.000 0.000 0.064
#> GSM1009198 1 0.2058 0.72430 0.916 0.004 NA 0.056 0.000 0.008
#> GSM1009073 6 0.3278 0.76668 0.020 0.004 NA 0.108 0.000 0.840
#> GSM1009087 1 0.5750 0.60453 0.656 0.008 NA 0.076 0.000 0.092
#> GSM1009101 4 0.0405 0.86275 0.000 0.004 NA 0.988 0.000 0.000
#> GSM1009115 5 0.5731 0.73581 0.000 0.156 NA 0.000 0.496 0.004
#> GSM1009129 2 0.1198 0.86451 0.004 0.960 NA 0.000 0.020 0.012
#> GSM1009143 1 0.5780 0.62540 0.608 0.000 NA 0.052 0.000 0.108
#> GSM1009157 1 0.5463 0.61351 0.640 0.032 NA 0.000 0.000 0.124
#> GSM1009171 5 0.1036 0.73695 0.000 0.004 NA 0.000 0.964 0.024
#> GSM1009185 1 0.5140 0.64103 0.704 0.008 NA 0.116 0.000 0.032
#> GSM1009199 2 0.3654 0.82720 0.056 0.820 NA 0.000 0.004 0.020
#> GSM1009074 6 0.3126 0.74343 0.000 0.000 NA 0.248 0.000 0.752
#> GSM1009088 1 0.5750 0.60453 0.656 0.008 NA 0.076 0.000 0.092
#> GSM1009102 4 0.0665 0.86181 0.000 0.004 NA 0.980 0.000 0.008
#> GSM1009116 5 0.5609 0.73599 0.000 0.156 NA 0.000 0.496 0.000
#> GSM1009130 2 0.1198 0.86451 0.004 0.960 NA 0.000 0.020 0.012
#> GSM1009144 1 0.5325 0.63563 0.644 0.000 NA 0.028 0.000 0.104
#> GSM1009158 4 0.1649 0.84621 0.000 0.000 NA 0.932 0.000 0.032
#> GSM1009172 5 0.0146 0.73715 0.000 0.004 NA 0.000 0.996 0.000
#> GSM1009186 2 0.5428 0.72674 0.076 0.656 NA 0.000 0.000 0.064
#> GSM1009200 1 0.2335 0.71406 0.904 0.024 NA 0.000 0.000 0.028
#> GSM1009075 6 0.3126 0.74343 0.000 0.000 NA 0.248 0.000 0.752
#> GSM1009089 4 0.1333 0.85161 0.000 0.000 NA 0.944 0.000 0.008
#> GSM1009103 4 0.0767 0.86168 0.000 0.004 NA 0.976 0.000 0.008
#> GSM1009117 5 0.6214 0.73135 0.012 0.160 NA 0.000 0.492 0.012
#> GSM1009131 1 0.1611 0.72934 0.944 0.008 NA 0.012 0.000 0.012
#> GSM1009145 4 0.0725 0.86155 0.000 0.000 NA 0.976 0.000 0.012
#> GSM1009159 4 0.1176 0.85540 0.000 0.000 NA 0.956 0.000 0.020
#> GSM1009173 5 0.1340 0.73647 0.000 0.004 NA 0.000 0.948 0.040
#> GSM1009187 1 0.5513 0.62501 0.680 0.008 NA 0.060 0.000 0.104
#> GSM1009201 1 0.1053 0.73119 0.964 0.004 NA 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)
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 temperature(p) time(p) specimen(p) k
#> ATC:kmeans 137 0.690 0.804 1.23e-15 2
#> ATC:kmeans 115 0.922 0.944 1.18e-20 3
#> ATC:kmeans 129 0.968 0.882 6.42e-34 4
#> ATC:kmeans 130 0.993 0.975 9.10e-54 5
#> ATC:kmeans 130 0.994 0.958 2.00e-54 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 140 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'skmeans' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.979 0.993 0.4974 0.503 0.503
#> 3 3 0.725 0.813 0.864 0.2656 0.861 0.726
#> 4 4 0.651 0.702 0.814 0.1129 0.923 0.794
#> 5 5 0.786 0.750 0.847 0.0700 0.943 0.818
#> 6 6 0.775 0.815 0.864 0.0506 0.938 0.775
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
#> GSM1009062 1 0.000 0.9935 1.000 0.000
#> GSM1009076 2 0.000 0.9919 0.000 1.000
#> GSM1009090 1 0.000 0.9935 1.000 0.000
#> GSM1009104 2 0.000 0.9919 0.000 1.000
#> GSM1009118 2 0.000 0.9919 0.000 1.000
#> GSM1009132 2 0.000 0.9919 0.000 1.000
#> GSM1009146 1 0.000 0.9935 1.000 0.000
#> GSM1009160 2 0.000 0.9919 0.000 1.000
#> GSM1009174 2 0.000 0.9919 0.000 1.000
#> GSM1009188 1 0.000 0.9935 1.000 0.000
#> GSM1009063 1 0.000 0.9935 1.000 0.000
#> GSM1009077 2 0.000 0.9919 0.000 1.000
#> GSM1009091 1 0.000 0.9935 1.000 0.000
#> GSM1009105 2 0.000 0.9919 0.000 1.000
#> GSM1009119 1 0.000 0.9935 1.000 0.000
#> GSM1009133 1 0.000 0.9935 1.000 0.000
#> GSM1009147 1 0.000 0.9935 1.000 0.000
#> GSM1009161 2 0.000 0.9919 0.000 1.000
#> GSM1009175 2 0.000 0.9919 0.000 1.000
#> GSM1009189 1 0.000 0.9935 1.000 0.000
#> GSM1009064 1 0.000 0.9935 1.000 0.000
#> GSM1009078 1 0.000 0.9935 1.000 0.000
#> GSM1009092 1 0.000 0.9935 1.000 0.000
#> GSM1009106 2 0.000 0.9919 0.000 1.000
#> GSM1009120 1 0.000 0.9935 1.000 0.000
#> GSM1009134 1 0.000 0.9935 1.000 0.000
#> GSM1009148 1 0.000 0.9935 1.000 0.000
#> GSM1009162 2 0.000 0.9919 0.000 1.000
#> GSM1009176 2 0.000 0.9919 0.000 1.000
#> GSM1009190 1 0.000 0.9935 1.000 0.000
#> GSM1009065 1 0.000 0.9935 1.000 0.000
#> GSM1009079 2 0.000 0.9919 0.000 1.000
#> GSM1009093 1 0.000 0.9935 1.000 0.000
#> GSM1009107 2 0.000 0.9919 0.000 1.000
#> GSM1009121 2 0.000 0.9919 0.000 1.000
#> GSM1009135 1 0.000 0.9935 1.000 0.000
#> GSM1009149 1 0.000 0.9935 1.000 0.000
#> GSM1009163 2 0.000 0.9919 0.000 1.000
#> GSM1009177 2 0.000 0.9919 0.000 1.000
#> GSM1009191 2 0.000 0.9919 0.000 1.000
#> GSM1009066 1 0.000 0.9935 1.000 0.000
#> GSM1009080 2 0.000 0.9919 0.000 1.000
#> GSM1009094 1 0.000 0.9935 1.000 0.000
#> GSM1009108 2 0.000 0.9919 0.000 1.000
#> GSM1009122 2 0.000 0.9919 0.000 1.000
#> GSM1009136 1 0.000 0.9935 1.000 0.000
#> GSM1009150 1 0.000 0.9935 1.000 0.000
#> GSM1009164 2 0.000 0.9919 0.000 1.000
#> GSM1009178 1 0.000 0.9935 1.000 0.000
#> GSM1009192 1 0.000 0.9935 1.000 0.000
#> GSM1009067 1 0.000 0.9935 1.000 0.000
#> GSM1009081 2 0.000 0.9919 0.000 1.000
#> GSM1009095 1 0.000 0.9935 1.000 0.000
#> GSM1009109 2 0.000 0.9919 0.000 1.000
#> GSM1009123 1 0.000 0.9935 1.000 0.000
#> GSM1009137 1 0.000 0.9935 1.000 0.000
#> GSM1009151 1 0.000 0.9935 1.000 0.000
#> GSM1009165 2 0.000 0.9919 0.000 1.000
#> GSM1009179 2 0.000 0.9919 0.000 1.000
#> GSM1009193 1 0.000 0.9935 1.000 0.000
#> GSM1009068 1 0.000 0.9935 1.000 0.000
#> GSM1009082 2 0.000 0.9919 0.000 1.000
#> GSM1009096 1 0.000 0.9935 1.000 0.000
#> GSM1009110 2 0.000 0.9919 0.000 1.000
#> GSM1009124 1 0.118 0.9775 0.984 0.016
#> GSM1009138 1 0.000 0.9935 1.000 0.000
#> GSM1009152 1 0.000 0.9935 1.000 0.000
#> GSM1009166 2 0.000 0.9919 0.000 1.000
#> GSM1009180 1 0.000 0.9935 1.000 0.000
#> GSM1009194 2 0.000 0.9919 0.000 1.000
#> GSM1009069 2 1.000 0.0347 0.488 0.512
#> GSM1009083 2 0.000 0.9919 0.000 1.000
#> GSM1009097 1 0.000 0.9935 1.000 0.000
#> GSM1009111 2 0.000 0.9919 0.000 1.000
#> GSM1009125 2 0.000 0.9919 0.000 1.000
#> GSM1009139 2 0.000 0.9919 0.000 1.000
#> GSM1009153 1 0.000 0.9935 1.000 0.000
#> GSM1009167 2 0.000 0.9919 0.000 1.000
#> GSM1009181 2 0.000 0.9919 0.000 1.000
#> GSM1009195 2 0.000 0.9919 0.000 1.000
#> GSM1009070 1 0.000 0.9935 1.000 0.000
#> GSM1009084 2 0.000 0.9919 0.000 1.000
#> GSM1009098 1 0.000 0.9935 1.000 0.000
#> GSM1009112 2 0.000 0.9919 0.000 1.000
#> GSM1009126 1 0.000 0.9935 1.000 0.000
#> GSM1009140 1 0.000 0.9935 1.000 0.000
#> GSM1009154 1 0.000 0.9935 1.000 0.000
#> GSM1009168 2 0.000 0.9919 0.000 1.000
#> GSM1009182 2 0.000 0.9919 0.000 1.000
#> GSM1009196 1 0.000 0.9935 1.000 0.000
#> GSM1009071 1 0.000 0.9935 1.000 0.000
#> GSM1009085 2 0.000 0.9919 0.000 1.000
#> GSM1009099 1 0.000 0.9935 1.000 0.000
#> GSM1009113 2 0.000 0.9919 0.000 1.000
#> GSM1009127 1 0.000 0.9935 1.000 0.000
#> GSM1009141 1 0.000 0.9935 1.000 0.000
#> GSM1009155 1 0.000 0.9935 1.000 0.000
#> GSM1009169 2 0.000 0.9919 0.000 1.000
#> GSM1009183 2 0.000 0.9919 0.000 1.000
#> GSM1009197 1 0.000 0.9935 1.000 0.000
#> GSM1009072 1 0.000 0.9935 1.000 0.000
#> GSM1009086 2 0.000 0.9919 0.000 1.000
#> GSM1009100 1 0.000 0.9935 1.000 0.000
#> GSM1009114 2 0.000 0.9919 0.000 1.000
#> GSM1009128 1 0.000 0.9935 1.000 0.000
#> GSM1009142 1 0.000 0.9935 1.000 0.000
#> GSM1009156 1 0.000 0.9935 1.000 0.000
#> GSM1009170 2 0.000 0.9919 0.000 1.000
#> GSM1009184 2 0.000 0.9919 0.000 1.000
#> GSM1009198 1 0.000 0.9935 1.000 0.000
#> GSM1009073 1 0.000 0.9935 1.000 0.000
#> GSM1009087 1 0.000 0.9935 1.000 0.000
#> GSM1009101 1 0.000 0.9935 1.000 0.000
#> GSM1009115 2 0.000 0.9919 0.000 1.000
#> GSM1009129 2 0.000 0.9919 0.000 1.000
#> GSM1009143 1 0.000 0.9935 1.000 0.000
#> GSM1009157 1 0.999 0.0621 0.520 0.480
#> GSM1009171 2 0.000 0.9919 0.000 1.000
#> GSM1009185 1 0.000 0.9935 1.000 0.000
#> GSM1009199 2 0.000 0.9919 0.000 1.000
#> GSM1009074 1 0.000 0.9935 1.000 0.000
#> GSM1009088 1 0.000 0.9935 1.000 0.000
#> GSM1009102 1 0.000 0.9935 1.000 0.000
#> GSM1009116 2 0.000 0.9919 0.000 1.000
#> GSM1009130 2 0.000 0.9919 0.000 1.000
#> GSM1009144 1 0.000 0.9935 1.000 0.000
#> GSM1009158 1 0.000 0.9935 1.000 0.000
#> GSM1009172 2 0.000 0.9919 0.000 1.000
#> GSM1009186 2 0.000 0.9919 0.000 1.000
#> GSM1009200 2 0.000 0.9919 0.000 1.000
#> GSM1009075 1 0.000 0.9935 1.000 0.000
#> GSM1009089 1 0.000 0.9935 1.000 0.000
#> GSM1009103 1 0.000 0.9935 1.000 0.000
#> GSM1009117 2 0.000 0.9919 0.000 1.000
#> GSM1009131 1 0.000 0.9935 1.000 0.000
#> GSM1009145 1 0.000 0.9935 1.000 0.000
#> GSM1009159 1 0.000 0.9935 1.000 0.000
#> GSM1009173 2 0.000 0.9919 0.000 1.000
#> GSM1009187 1 0.000 0.9935 1.000 0.000
#> GSM1009201 1 0.000 0.9935 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1009062 3 0.4750 0.760 0.216 0.000 0.784
#> GSM1009076 2 0.0237 0.958 0.000 0.996 0.004
#> GSM1009090 1 0.0000 0.805 1.000 0.000 0.000
#> GSM1009104 2 0.0000 0.959 0.000 1.000 0.000
#> GSM1009118 2 0.0000 0.959 0.000 1.000 0.000
#> GSM1009132 2 0.5560 0.621 0.000 0.700 0.300
#> GSM1009146 1 0.5254 0.676 0.736 0.000 0.264
#> GSM1009160 2 0.0000 0.959 0.000 1.000 0.000
#> GSM1009174 2 0.4235 0.849 0.000 0.824 0.176
#> GSM1009188 1 0.0747 0.794 0.984 0.000 0.016
#> GSM1009063 3 0.4750 0.760 0.216 0.000 0.784
#> GSM1009077 2 0.0237 0.958 0.000 0.996 0.004
#> GSM1009091 1 0.0000 0.805 1.000 0.000 0.000
#> GSM1009105 2 0.0000 0.959 0.000 1.000 0.000
#> GSM1009119 1 0.0237 0.803 0.996 0.000 0.004
#> GSM1009133 3 0.6280 0.656 0.460 0.000 0.540
#> GSM1009147 1 0.5254 0.676 0.736 0.000 0.264
#> GSM1009161 2 0.0000 0.959 0.000 1.000 0.000
#> GSM1009175 2 0.4235 0.849 0.000 0.824 0.176
#> GSM1009189 1 0.1031 0.787 0.976 0.000 0.024
#> GSM1009064 3 0.3412 0.700 0.124 0.000 0.876
#> GSM1009078 1 0.5254 0.676 0.736 0.000 0.264
#> GSM1009092 1 0.0000 0.805 1.000 0.000 0.000
#> GSM1009106 2 0.0000 0.959 0.000 1.000 0.000
#> GSM1009120 1 0.0000 0.805 1.000 0.000 0.000
#> GSM1009134 3 0.6267 0.664 0.452 0.000 0.548
#> GSM1009148 1 0.5291 0.672 0.732 0.000 0.268
#> GSM1009162 2 0.0000 0.959 0.000 1.000 0.000
#> GSM1009176 2 0.2711 0.910 0.000 0.912 0.088
#> GSM1009190 1 0.1031 0.787 0.976 0.000 0.024
#> GSM1009065 3 0.4750 0.760 0.216 0.000 0.784
#> GSM1009079 2 0.0237 0.958 0.000 0.996 0.004
#> GSM1009093 1 0.0000 0.805 1.000 0.000 0.000
#> GSM1009107 2 0.0000 0.959 0.000 1.000 0.000
#> GSM1009121 2 0.0000 0.959 0.000 1.000 0.000
#> GSM1009135 3 0.6267 0.664 0.452 0.000 0.548
#> GSM1009149 1 0.4974 0.694 0.764 0.000 0.236
#> GSM1009163 2 0.0000 0.959 0.000 1.000 0.000
#> GSM1009177 2 0.4178 0.852 0.000 0.828 0.172
#> GSM1009191 2 0.1163 0.945 0.000 0.972 0.028
#> GSM1009066 3 0.4750 0.760 0.216 0.000 0.784
#> GSM1009080 2 0.0237 0.958 0.000 0.996 0.004
#> GSM1009094 1 0.0000 0.805 1.000 0.000 0.000
#> GSM1009108 2 0.0000 0.959 0.000 1.000 0.000
#> GSM1009122 2 0.0000 0.959 0.000 1.000 0.000
#> GSM1009136 1 0.0000 0.805 1.000 0.000 0.000
#> GSM1009150 1 0.5254 0.676 0.736 0.000 0.264
#> GSM1009164 2 0.0000 0.959 0.000 1.000 0.000
#> GSM1009178 1 0.6252 0.432 0.556 0.000 0.444
#> GSM1009192 1 0.0237 0.803 0.996 0.000 0.004
#> GSM1009067 3 0.4750 0.760 0.216 0.000 0.784
#> GSM1009081 2 0.0237 0.958 0.000 0.996 0.004
#> GSM1009095 1 0.0000 0.805 1.000 0.000 0.000
#> GSM1009109 2 0.0000 0.959 0.000 1.000 0.000
#> GSM1009123 1 0.0000 0.805 1.000 0.000 0.000
#> GSM1009137 3 0.6280 0.656 0.460 0.000 0.540
#> GSM1009151 1 0.5291 0.672 0.732 0.000 0.268
#> GSM1009165 2 0.0000 0.959 0.000 1.000 0.000
#> GSM1009179 2 0.4235 0.849 0.000 0.824 0.176
#> GSM1009193 1 0.0000 0.805 1.000 0.000 0.000
#> GSM1009068 3 0.4750 0.760 0.216 0.000 0.784
#> GSM1009082 2 0.0237 0.958 0.000 0.996 0.004
#> GSM1009096 1 0.0000 0.805 1.000 0.000 0.000
#> GSM1009110 2 0.0000 0.959 0.000 1.000 0.000
#> GSM1009124 1 0.2261 0.744 0.932 0.000 0.068
#> GSM1009138 3 0.6267 0.664 0.452 0.000 0.548
#> GSM1009152 1 0.5291 0.672 0.732 0.000 0.268
#> GSM1009166 2 0.0000 0.959 0.000 1.000 0.000
#> GSM1009180 1 0.6244 0.438 0.560 0.000 0.440
#> GSM1009194 2 0.1643 0.938 0.000 0.956 0.044
#> GSM1009069 3 0.2165 0.653 0.064 0.000 0.936
#> GSM1009083 2 0.0237 0.958 0.000 0.996 0.004
#> GSM1009097 1 0.0000 0.805 1.000 0.000 0.000
#> GSM1009111 2 0.0000 0.959 0.000 1.000 0.000
#> GSM1009125 2 0.0000 0.959 0.000 1.000 0.000
#> GSM1009139 2 0.5948 0.496 0.000 0.640 0.360
#> GSM1009153 1 0.5431 0.650 0.716 0.000 0.284
#> GSM1009167 2 0.0000 0.959 0.000 1.000 0.000
#> GSM1009181 2 0.4178 0.852 0.000 0.828 0.172
#> GSM1009195 2 0.0237 0.958 0.000 0.996 0.004
#> GSM1009070 1 0.5254 0.676 0.736 0.000 0.264
#> GSM1009084 2 0.0237 0.958 0.000 0.996 0.004
#> GSM1009098 1 0.0000 0.805 1.000 0.000 0.000
#> GSM1009112 2 0.0000 0.959 0.000 1.000 0.000
#> GSM1009126 1 0.1753 0.763 0.952 0.000 0.048
#> GSM1009140 3 0.6299 0.651 0.476 0.000 0.524
#> GSM1009154 1 0.5291 0.672 0.732 0.000 0.268
#> GSM1009168 2 0.0000 0.959 0.000 1.000 0.000
#> GSM1009182 2 0.4235 0.849 0.000 0.824 0.176
#> GSM1009196 1 0.5327 0.667 0.728 0.000 0.272
#> GSM1009071 3 0.4750 0.760 0.216 0.000 0.784
#> GSM1009085 2 0.0237 0.958 0.000 0.996 0.004
#> GSM1009099 1 0.0000 0.805 1.000 0.000 0.000
#> GSM1009113 2 0.0000 0.959 0.000 1.000 0.000
#> GSM1009127 1 0.0000 0.805 1.000 0.000 0.000
#> GSM1009141 3 0.6252 0.669 0.444 0.000 0.556
#> GSM1009155 1 0.6126 0.399 0.600 0.000 0.400
#> GSM1009169 2 0.0000 0.959 0.000 1.000 0.000
#> GSM1009183 2 0.4235 0.849 0.000 0.824 0.176
#> GSM1009197 1 0.0000 0.805 1.000 0.000 0.000
#> GSM1009072 3 0.4750 0.760 0.216 0.000 0.784
#> GSM1009086 2 0.0237 0.958 0.000 0.996 0.004
#> GSM1009100 1 0.0000 0.805 1.000 0.000 0.000
#> GSM1009114 2 0.0000 0.959 0.000 1.000 0.000
#> GSM1009128 1 0.1289 0.780 0.968 0.000 0.032
#> GSM1009142 3 0.6267 0.664 0.452 0.000 0.548
#> GSM1009156 1 0.5254 0.676 0.736 0.000 0.264
#> GSM1009170 2 0.0000 0.959 0.000 1.000 0.000
#> GSM1009184 2 0.4235 0.849 0.000 0.824 0.176
#> GSM1009198 1 0.0747 0.794 0.984 0.000 0.016
#> GSM1009073 3 0.4750 0.760 0.216 0.000 0.784
#> GSM1009087 1 0.5254 0.676 0.736 0.000 0.264
#> GSM1009101 1 0.0000 0.805 1.000 0.000 0.000
#> GSM1009115 2 0.0000 0.959 0.000 1.000 0.000
#> GSM1009129 2 0.0000 0.959 0.000 1.000 0.000
#> GSM1009143 3 0.6299 0.651 0.476 0.000 0.524
#> GSM1009157 3 0.1643 0.635 0.044 0.000 0.956
#> GSM1009171 2 0.0000 0.959 0.000 1.000 0.000
#> GSM1009185 1 0.5058 0.689 0.756 0.000 0.244
#> GSM1009199 2 0.0237 0.958 0.000 0.996 0.004
#> GSM1009074 3 0.4750 0.760 0.216 0.000 0.784
#> GSM1009088 1 0.5291 0.672 0.732 0.000 0.268
#> GSM1009102 1 0.0000 0.805 1.000 0.000 0.000
#> GSM1009116 2 0.0000 0.959 0.000 1.000 0.000
#> GSM1009130 2 0.0000 0.959 0.000 1.000 0.000
#> GSM1009144 3 0.6274 0.661 0.456 0.000 0.544
#> GSM1009158 1 0.5254 0.676 0.736 0.000 0.264
#> GSM1009172 2 0.0000 0.959 0.000 1.000 0.000
#> GSM1009186 2 0.4235 0.849 0.000 0.824 0.176
#> GSM1009200 2 0.5524 0.739 0.164 0.796 0.040
#> GSM1009075 3 0.4750 0.760 0.216 0.000 0.784
#> GSM1009089 1 0.4974 0.694 0.764 0.000 0.236
#> GSM1009103 1 0.0000 0.805 1.000 0.000 0.000
#> GSM1009117 2 0.0000 0.959 0.000 1.000 0.000
#> GSM1009131 1 0.1289 0.780 0.968 0.000 0.032
#> GSM1009145 1 0.0000 0.805 1.000 0.000 0.000
#> GSM1009159 1 0.0000 0.805 1.000 0.000 0.000
#> GSM1009173 2 0.0000 0.959 0.000 1.000 0.000
#> GSM1009187 1 0.5327 0.667 0.728 0.000 0.272
#> GSM1009201 1 0.1529 0.769 0.960 0.000 0.040
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1009062 4 0.4072 0.5819 0.252 0.000 0.000 0.748
#> GSM1009076 2 0.3688 0.7375 0.000 0.792 0.208 0.000
#> GSM1009090 1 0.0000 0.7903 1.000 0.000 0.000 0.000
#> GSM1009104 2 0.0000 0.9028 0.000 1.000 0.000 0.000
#> GSM1009118 2 0.0000 0.9028 0.000 1.000 0.000 0.000
#> GSM1009132 3 0.7860 0.1897 0.000 0.276 0.384 0.340
#> GSM1009146 1 0.3873 0.6437 0.772 0.000 0.000 0.228
#> GSM1009160 2 0.0000 0.9028 0.000 1.000 0.000 0.000
#> GSM1009174 3 0.3219 0.8803 0.000 0.164 0.836 0.000
#> GSM1009188 1 0.3694 0.6657 0.844 0.000 0.032 0.124
#> GSM1009063 4 0.4040 0.5869 0.248 0.000 0.000 0.752
#> GSM1009077 2 0.3688 0.7375 0.000 0.792 0.208 0.000
#> GSM1009091 1 0.0000 0.7903 1.000 0.000 0.000 0.000
#> GSM1009105 2 0.0000 0.9028 0.000 1.000 0.000 0.000
#> GSM1009119 1 0.1151 0.7729 0.968 0.000 0.008 0.024
#> GSM1009133 4 0.7073 0.4504 0.364 0.000 0.132 0.504
#> GSM1009147 1 0.3873 0.6437 0.772 0.000 0.000 0.228
#> GSM1009161 2 0.0000 0.9028 0.000 1.000 0.000 0.000
#> GSM1009175 3 0.3219 0.8803 0.000 0.164 0.836 0.000
#> GSM1009189 1 0.4008 0.6376 0.820 0.000 0.032 0.148
#> GSM1009064 4 0.4786 0.5616 0.104 0.000 0.108 0.788
#> GSM1009078 1 0.3907 0.6396 0.768 0.000 0.000 0.232
#> GSM1009092 1 0.0000 0.7903 1.000 0.000 0.000 0.000
#> GSM1009106 2 0.0000 0.9028 0.000 1.000 0.000 0.000
#> GSM1009120 1 0.0000 0.7903 1.000 0.000 0.000 0.000
#> GSM1009134 4 0.6752 0.5377 0.280 0.000 0.132 0.588
#> GSM1009148 1 0.4605 0.5043 0.664 0.000 0.000 0.336
#> GSM1009162 2 0.0000 0.9028 0.000 1.000 0.000 0.000
#> GSM1009176 3 0.4543 0.6343 0.000 0.324 0.676 0.000
#> GSM1009190 1 0.4008 0.6376 0.820 0.000 0.032 0.148
#> GSM1009065 4 0.3764 0.6032 0.216 0.000 0.000 0.784
#> GSM1009079 2 0.3688 0.7375 0.000 0.792 0.208 0.000
#> GSM1009093 1 0.0000 0.7903 1.000 0.000 0.000 0.000
#> GSM1009107 2 0.0000 0.9028 0.000 1.000 0.000 0.000
#> GSM1009121 2 0.0000 0.9028 0.000 1.000 0.000 0.000
#> GSM1009135 4 0.6752 0.5377 0.280 0.000 0.132 0.588
#> GSM1009149 1 0.1118 0.7771 0.964 0.000 0.000 0.036
#> GSM1009163 2 0.0000 0.9028 0.000 1.000 0.000 0.000
#> GSM1009177 3 0.3219 0.8803 0.000 0.164 0.836 0.000
#> GSM1009191 2 0.6552 0.4788 0.000 0.628 0.228 0.144
#> GSM1009066 4 0.3764 0.6032 0.216 0.000 0.000 0.784
#> GSM1009080 2 0.3688 0.7375 0.000 0.792 0.208 0.000
#> GSM1009094 1 0.0000 0.7903 1.000 0.000 0.000 0.000
#> GSM1009108 2 0.0000 0.9028 0.000 1.000 0.000 0.000
#> GSM1009122 2 0.0000 0.9028 0.000 1.000 0.000 0.000
#> GSM1009136 1 0.0000 0.7903 1.000 0.000 0.000 0.000
#> GSM1009150 1 0.3873 0.6437 0.772 0.000 0.000 0.228
#> GSM1009164 2 0.0000 0.9028 0.000 1.000 0.000 0.000
#> GSM1009178 3 0.3931 0.6390 0.128 0.000 0.832 0.040
#> GSM1009192 1 0.2224 0.7446 0.928 0.000 0.032 0.040
#> GSM1009067 4 0.4040 0.5869 0.248 0.000 0.000 0.752
#> GSM1009081 2 0.3688 0.7375 0.000 0.792 0.208 0.000
#> GSM1009095 1 0.0000 0.7903 1.000 0.000 0.000 0.000
#> GSM1009109 2 0.0000 0.9028 0.000 1.000 0.000 0.000
#> GSM1009123 1 0.0000 0.7903 1.000 0.000 0.000 0.000
#> GSM1009137 4 0.7073 0.4504 0.364 0.000 0.132 0.504
#> GSM1009151 1 0.4605 0.5043 0.664 0.000 0.000 0.336
#> GSM1009165 2 0.0000 0.9028 0.000 1.000 0.000 0.000
#> GSM1009179 3 0.3219 0.8803 0.000 0.164 0.836 0.000
#> GSM1009193 1 0.0672 0.7824 0.984 0.000 0.008 0.008
#> GSM1009068 4 0.4072 0.5819 0.252 0.000 0.000 0.748
#> GSM1009082 2 0.3688 0.7375 0.000 0.792 0.208 0.000
#> GSM1009096 1 0.0000 0.7903 1.000 0.000 0.000 0.000
#> GSM1009110 2 0.0000 0.9028 0.000 1.000 0.000 0.000
#> GSM1009124 1 0.4707 0.5511 0.760 0.000 0.036 0.204
#> GSM1009138 4 0.6752 0.5377 0.280 0.000 0.132 0.588
#> GSM1009152 1 0.4605 0.5043 0.664 0.000 0.000 0.336
#> GSM1009166 2 0.0000 0.9028 0.000 1.000 0.000 0.000
#> GSM1009180 3 0.3400 0.6034 0.180 0.000 0.820 0.000
#> GSM1009194 2 0.6915 0.3100 0.000 0.564 0.296 0.140
#> GSM1009069 4 0.4589 0.4865 0.048 0.000 0.168 0.784
#> GSM1009083 2 0.3688 0.7375 0.000 0.792 0.208 0.000
#> GSM1009097 1 0.0000 0.7903 1.000 0.000 0.000 0.000
#> GSM1009111 2 0.0000 0.9028 0.000 1.000 0.000 0.000
#> GSM1009125 2 0.0000 0.9028 0.000 1.000 0.000 0.000
#> GSM1009139 4 0.7799 -0.1647 0.000 0.368 0.248 0.384
#> GSM1009153 1 0.4948 0.2739 0.560 0.000 0.000 0.440
#> GSM1009167 2 0.0000 0.9028 0.000 1.000 0.000 0.000
#> GSM1009181 3 0.3219 0.8803 0.000 0.164 0.836 0.000
#> GSM1009195 2 0.3649 0.7404 0.000 0.796 0.204 0.000
#> GSM1009070 1 0.4193 0.6003 0.732 0.000 0.000 0.268
#> GSM1009084 2 0.3688 0.7375 0.000 0.792 0.208 0.000
#> GSM1009098 1 0.0000 0.7903 1.000 0.000 0.000 0.000
#> GSM1009112 2 0.0000 0.9028 0.000 1.000 0.000 0.000
#> GSM1009126 1 0.6595 0.3386 0.628 0.000 0.160 0.212
#> GSM1009140 4 0.7135 0.4336 0.400 0.000 0.132 0.468
#> GSM1009154 1 0.4605 0.5043 0.664 0.000 0.000 0.336
#> GSM1009168 2 0.0000 0.9028 0.000 1.000 0.000 0.000
#> GSM1009182 3 0.3219 0.8803 0.000 0.164 0.836 0.000
#> GSM1009196 1 0.4454 0.5467 0.692 0.000 0.000 0.308
#> GSM1009071 4 0.3837 0.6004 0.224 0.000 0.000 0.776
#> GSM1009085 2 0.3688 0.7375 0.000 0.792 0.208 0.000
#> GSM1009099 1 0.0000 0.7903 1.000 0.000 0.000 0.000
#> GSM1009113 2 0.0000 0.9028 0.000 1.000 0.000 0.000
#> GSM1009127 1 0.0000 0.7903 1.000 0.000 0.000 0.000
#> GSM1009141 4 0.6661 0.5455 0.264 0.000 0.132 0.604
#> GSM1009155 1 0.4994 0.1532 0.520 0.000 0.000 0.480
#> GSM1009169 2 0.0000 0.9028 0.000 1.000 0.000 0.000
#> GSM1009183 3 0.3219 0.8803 0.000 0.164 0.836 0.000
#> GSM1009197 1 0.0000 0.7903 1.000 0.000 0.000 0.000
#> GSM1009072 4 0.4040 0.5869 0.248 0.000 0.000 0.752
#> GSM1009086 2 0.3688 0.7375 0.000 0.792 0.208 0.000
#> GSM1009100 1 0.0000 0.7903 1.000 0.000 0.000 0.000
#> GSM1009114 2 0.0000 0.9028 0.000 1.000 0.000 0.000
#> GSM1009128 1 0.3907 0.6479 0.828 0.000 0.032 0.140
#> GSM1009142 4 0.6730 0.5363 0.276 0.000 0.132 0.592
#> GSM1009156 1 0.3873 0.6437 0.772 0.000 0.000 0.228
#> GSM1009170 2 0.0000 0.9028 0.000 1.000 0.000 0.000
#> GSM1009184 3 0.3219 0.8803 0.000 0.164 0.836 0.000
#> GSM1009198 1 0.3694 0.6657 0.844 0.000 0.032 0.124
#> GSM1009073 4 0.3942 0.5945 0.236 0.000 0.000 0.764
#> GSM1009087 1 0.3907 0.6396 0.768 0.000 0.000 0.232
#> GSM1009101 1 0.0000 0.7903 1.000 0.000 0.000 0.000
#> GSM1009115 2 0.0000 0.9028 0.000 1.000 0.000 0.000
#> GSM1009129 2 0.0000 0.9028 0.000 1.000 0.000 0.000
#> GSM1009143 4 0.7135 0.4336 0.400 0.000 0.132 0.468
#> GSM1009157 4 0.5607 -0.0804 0.020 0.000 0.488 0.492
#> GSM1009171 2 0.0000 0.9028 0.000 1.000 0.000 0.000
#> GSM1009185 1 0.1118 0.7771 0.964 0.000 0.000 0.036
#> GSM1009199 2 0.3649 0.7404 0.000 0.796 0.204 0.000
#> GSM1009074 4 0.4040 0.5869 0.248 0.000 0.000 0.752
#> GSM1009088 1 0.4008 0.6273 0.756 0.000 0.000 0.244
#> GSM1009102 1 0.0000 0.7903 1.000 0.000 0.000 0.000
#> GSM1009116 2 0.0000 0.9028 0.000 1.000 0.000 0.000
#> GSM1009130 2 0.0000 0.9028 0.000 1.000 0.000 0.000
#> GSM1009144 4 0.7063 0.4558 0.360 0.000 0.132 0.508
#> GSM1009158 1 0.3873 0.6437 0.772 0.000 0.000 0.228
#> GSM1009172 2 0.0000 0.9028 0.000 1.000 0.000 0.000
#> GSM1009186 3 0.3219 0.8803 0.000 0.164 0.836 0.000
#> GSM1009200 2 0.7211 0.3218 0.012 0.596 0.184 0.208
#> GSM1009075 4 0.4040 0.5869 0.248 0.000 0.000 0.752
#> GSM1009089 1 0.1211 0.7752 0.960 0.000 0.000 0.040
#> GSM1009103 1 0.0000 0.7903 1.000 0.000 0.000 0.000
#> GSM1009117 2 0.0000 0.9028 0.000 1.000 0.000 0.000
#> GSM1009131 1 0.3907 0.6479 0.828 0.000 0.032 0.140
#> GSM1009145 1 0.0000 0.7903 1.000 0.000 0.000 0.000
#> GSM1009159 1 0.0000 0.7903 1.000 0.000 0.000 0.000
#> GSM1009173 2 0.0000 0.9028 0.000 1.000 0.000 0.000
#> GSM1009187 1 0.5883 0.3260 0.572 0.000 0.040 0.388
#> GSM1009201 1 0.6664 0.3217 0.620 0.000 0.164 0.216
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM1009062 3 0.5091 0.8737 0.236 0.000 0.676 0.088 0.000
#> GSM1009076 5 0.3996 0.7257 0.000 0.228 0.012 0.008 0.752
#> GSM1009090 1 0.0290 0.8024 0.992 0.000 0.000 0.008 0.000
#> GSM1009104 5 0.0000 0.8865 0.000 0.000 0.000 0.000 1.000
#> GSM1009118 5 0.0693 0.8826 0.000 0.000 0.012 0.008 0.980
#> GSM1009132 4 0.2992 0.7648 0.000 0.044 0.008 0.876 0.072
#> GSM1009146 1 0.1341 0.7726 0.944 0.000 0.056 0.000 0.000
#> GSM1009160 5 0.1503 0.8794 0.000 0.020 0.020 0.008 0.952
#> GSM1009174 2 0.1478 0.9465 0.000 0.936 0.000 0.000 0.064
#> GSM1009188 1 0.6096 0.5131 0.600 0.056 0.292 0.052 0.000
#> GSM1009063 3 0.5091 0.8737 0.236 0.000 0.676 0.088 0.000
#> GSM1009077 5 0.3996 0.7257 0.000 0.228 0.012 0.008 0.752
#> GSM1009091 1 0.0290 0.8024 0.992 0.000 0.000 0.008 0.000
#> GSM1009105 5 0.0000 0.8865 0.000 0.000 0.000 0.000 1.000
#> GSM1009119 1 0.4578 0.6525 0.748 0.028 0.196 0.028 0.000
#> GSM1009133 4 0.1732 0.8583 0.080 0.000 0.000 0.920 0.000
#> GSM1009147 1 0.1341 0.7726 0.944 0.000 0.056 0.000 0.000
#> GSM1009161 5 0.1503 0.8794 0.000 0.020 0.020 0.008 0.952
#> GSM1009175 2 0.1478 0.9465 0.000 0.936 0.000 0.000 0.064
#> GSM1009189 1 0.6428 0.4857 0.576 0.056 0.292 0.076 0.000
#> GSM1009064 3 0.5958 0.7174 0.072 0.080 0.676 0.172 0.000
#> GSM1009078 1 0.1341 0.7726 0.944 0.000 0.056 0.000 0.000
#> GSM1009092 1 0.0290 0.8024 0.992 0.000 0.000 0.008 0.000
#> GSM1009106 5 0.0000 0.8865 0.000 0.000 0.000 0.000 1.000
#> GSM1009120 1 0.2270 0.7619 0.904 0.000 0.076 0.020 0.000
#> GSM1009134 4 0.2036 0.8572 0.056 0.000 0.024 0.920 0.000
#> GSM1009148 1 0.4287 -0.2887 0.540 0.000 0.460 0.000 0.000
#> GSM1009162 5 0.1503 0.8794 0.000 0.020 0.020 0.008 0.952
#> GSM1009176 2 0.3461 0.7233 0.000 0.772 0.004 0.000 0.224
#> GSM1009190 1 0.6376 0.4913 0.580 0.056 0.292 0.072 0.000
#> GSM1009065 3 0.5309 0.8260 0.164 0.000 0.676 0.160 0.000
#> GSM1009079 5 0.3996 0.7257 0.000 0.228 0.012 0.008 0.752
#> GSM1009093 1 0.0290 0.8024 0.992 0.000 0.000 0.008 0.000
#> GSM1009107 5 0.0000 0.8865 0.000 0.000 0.000 0.000 1.000
#> GSM1009121 5 0.0693 0.8826 0.000 0.000 0.012 0.008 0.980
#> GSM1009135 4 0.2036 0.8572 0.056 0.000 0.024 0.920 0.000
#> GSM1009149 1 0.0510 0.7963 0.984 0.000 0.016 0.000 0.000
#> GSM1009163 5 0.1503 0.8794 0.000 0.020 0.020 0.008 0.952
#> GSM1009177 2 0.1478 0.9465 0.000 0.936 0.000 0.000 0.064
#> GSM1009191 5 0.7916 0.0347 0.000 0.216 0.304 0.088 0.392
#> GSM1009066 3 0.5307 0.8166 0.156 0.000 0.676 0.168 0.000
#> GSM1009080 5 0.3966 0.7298 0.000 0.224 0.012 0.008 0.756
#> GSM1009094 1 0.0290 0.8024 0.992 0.000 0.000 0.008 0.000
#> GSM1009108 5 0.0000 0.8865 0.000 0.000 0.000 0.000 1.000
#> GSM1009122 5 0.0693 0.8826 0.000 0.000 0.012 0.008 0.980
#> GSM1009136 1 0.0290 0.8024 0.992 0.000 0.000 0.008 0.000
#> GSM1009150 1 0.1270 0.7755 0.948 0.000 0.052 0.000 0.000
#> GSM1009164 5 0.1503 0.8794 0.000 0.020 0.020 0.008 0.952
#> GSM1009178 2 0.1831 0.8376 0.076 0.920 0.004 0.000 0.000
#> GSM1009192 1 0.5594 0.5782 0.656 0.044 0.256 0.044 0.000
#> GSM1009067 3 0.5091 0.8737 0.236 0.000 0.676 0.088 0.000
#> GSM1009081 5 0.3966 0.7298 0.000 0.224 0.012 0.008 0.756
#> GSM1009095 1 0.0290 0.8024 0.992 0.000 0.000 0.008 0.000
#> GSM1009109 5 0.0000 0.8865 0.000 0.000 0.000 0.000 1.000
#> GSM1009123 1 0.2362 0.7600 0.900 0.000 0.076 0.024 0.000
#> GSM1009137 4 0.1732 0.8583 0.080 0.000 0.000 0.920 0.000
#> GSM1009151 1 0.4287 -0.2887 0.540 0.000 0.460 0.000 0.000
#> GSM1009165 5 0.1503 0.8794 0.000 0.020 0.020 0.008 0.952
#> GSM1009179 2 0.1478 0.9465 0.000 0.936 0.000 0.000 0.064
#> GSM1009193 1 0.4121 0.6827 0.788 0.024 0.164 0.024 0.000
#> GSM1009068 3 0.5039 0.8675 0.244 0.000 0.676 0.080 0.000
#> GSM1009082 5 0.3996 0.7257 0.000 0.228 0.012 0.008 0.752
#> GSM1009096 1 0.0290 0.8024 0.992 0.000 0.000 0.008 0.000
#> GSM1009110 5 0.0000 0.8865 0.000 0.000 0.000 0.000 1.000
#> GSM1009124 1 0.6747 0.4427 0.548 0.056 0.292 0.104 0.000
#> GSM1009138 4 0.2036 0.8572 0.056 0.000 0.024 0.920 0.000
#> GSM1009152 1 0.4287 -0.2887 0.540 0.000 0.460 0.000 0.000
#> GSM1009166 5 0.1503 0.8794 0.000 0.020 0.020 0.008 0.952
#> GSM1009180 2 0.2074 0.8009 0.104 0.896 0.000 0.000 0.000
#> GSM1009194 5 0.8127 -0.1639 0.000 0.280 0.304 0.096 0.320
#> GSM1009069 3 0.5871 0.6805 0.048 0.108 0.680 0.164 0.000
#> GSM1009083 5 0.3996 0.7257 0.000 0.228 0.012 0.008 0.752
#> GSM1009097 1 0.0290 0.8024 0.992 0.000 0.000 0.008 0.000
#> GSM1009111 5 0.0000 0.8865 0.000 0.000 0.000 0.000 1.000
#> GSM1009125 5 0.0693 0.8826 0.000 0.000 0.012 0.008 0.980
#> GSM1009139 4 0.2577 0.7680 0.000 0.016 0.008 0.892 0.084
#> GSM1009153 3 0.4210 0.6269 0.412 0.000 0.588 0.000 0.000
#> GSM1009167 5 0.1503 0.8794 0.000 0.020 0.020 0.008 0.952
#> GSM1009181 2 0.1478 0.9465 0.000 0.936 0.000 0.000 0.064
#> GSM1009195 5 0.3883 0.7678 0.000 0.160 0.012 0.028 0.800
#> GSM1009070 1 0.3039 0.5866 0.808 0.000 0.192 0.000 0.000
#> GSM1009084 5 0.3996 0.7257 0.000 0.228 0.012 0.008 0.752
#> GSM1009098 1 0.0290 0.8024 0.992 0.000 0.000 0.008 0.000
#> GSM1009112 5 0.0000 0.8865 0.000 0.000 0.000 0.000 1.000
#> GSM1009126 4 0.6662 0.5490 0.096 0.056 0.292 0.556 0.000
#> GSM1009140 4 0.1792 0.8564 0.084 0.000 0.000 0.916 0.000
#> GSM1009154 1 0.4287 -0.2887 0.540 0.000 0.460 0.000 0.000
#> GSM1009168 5 0.1503 0.8794 0.000 0.020 0.020 0.008 0.952
#> GSM1009182 2 0.1478 0.9465 0.000 0.936 0.000 0.000 0.064
#> GSM1009196 1 0.3857 0.2969 0.688 0.000 0.312 0.000 0.000
#> GSM1009071 3 0.5283 0.8486 0.188 0.000 0.676 0.136 0.000
#> GSM1009085 5 0.3996 0.7257 0.000 0.228 0.012 0.008 0.752
#> GSM1009099 1 0.0290 0.8024 0.992 0.000 0.000 0.008 0.000
#> GSM1009113 5 0.0000 0.8865 0.000 0.000 0.000 0.000 1.000
#> GSM1009127 1 0.2362 0.7600 0.900 0.000 0.076 0.024 0.000
#> GSM1009141 4 0.1668 0.8421 0.028 0.000 0.032 0.940 0.000
#> GSM1009155 3 0.3966 0.7530 0.336 0.000 0.664 0.000 0.000
#> GSM1009169 5 0.1503 0.8794 0.000 0.020 0.020 0.008 0.952
#> GSM1009183 2 0.1478 0.9465 0.000 0.936 0.000 0.000 0.064
#> GSM1009197 1 0.0162 0.8018 0.996 0.000 0.000 0.004 0.000
#> GSM1009072 3 0.5091 0.8737 0.236 0.000 0.676 0.088 0.000
#> GSM1009086 5 0.3996 0.7257 0.000 0.228 0.012 0.008 0.752
#> GSM1009100 1 0.0290 0.8024 0.992 0.000 0.000 0.008 0.000
#> GSM1009114 5 0.0000 0.8865 0.000 0.000 0.000 0.000 1.000
#> GSM1009128 1 0.6096 0.5131 0.600 0.056 0.292 0.052 0.000
#> GSM1009142 4 0.1661 0.8498 0.036 0.000 0.024 0.940 0.000
#> GSM1009156 1 0.1341 0.7726 0.944 0.000 0.056 0.000 0.000
#> GSM1009170 5 0.1503 0.8794 0.000 0.020 0.020 0.008 0.952
#> GSM1009184 2 0.1478 0.9465 0.000 0.936 0.000 0.000 0.064
#> GSM1009198 1 0.6096 0.5131 0.600 0.056 0.292 0.052 0.000
#> GSM1009073 3 0.5240 0.8594 0.204 0.000 0.676 0.120 0.000
#> GSM1009087 1 0.1341 0.7726 0.944 0.000 0.056 0.000 0.000
#> GSM1009101 1 0.0290 0.8024 0.992 0.000 0.000 0.008 0.000
#> GSM1009115 5 0.0000 0.8865 0.000 0.000 0.000 0.000 1.000
#> GSM1009129 5 0.0693 0.8826 0.000 0.000 0.012 0.008 0.980
#> GSM1009143 4 0.1792 0.8564 0.084 0.000 0.000 0.916 0.000
#> GSM1009157 3 0.4588 0.4772 0.012 0.308 0.668 0.012 0.000
#> GSM1009171 5 0.1503 0.8794 0.000 0.020 0.020 0.008 0.952
#> GSM1009185 1 0.0510 0.7963 0.984 0.000 0.016 0.000 0.000
#> GSM1009199 5 0.3922 0.7643 0.000 0.164 0.012 0.028 0.796
#> GSM1009074 3 0.5091 0.8737 0.236 0.000 0.676 0.088 0.000
#> GSM1009088 1 0.1732 0.7508 0.920 0.000 0.080 0.000 0.000
#> GSM1009102 1 0.0290 0.8024 0.992 0.000 0.000 0.008 0.000
#> GSM1009116 5 0.0000 0.8865 0.000 0.000 0.000 0.000 1.000
#> GSM1009130 5 0.0693 0.8826 0.000 0.000 0.012 0.008 0.980
#> GSM1009144 4 0.1732 0.8583 0.080 0.000 0.000 0.920 0.000
#> GSM1009158 1 0.1270 0.7755 0.948 0.000 0.052 0.000 0.000
#> GSM1009172 5 0.1503 0.8794 0.000 0.020 0.020 0.008 0.952
#> GSM1009186 2 0.1478 0.9465 0.000 0.936 0.000 0.000 0.064
#> GSM1009200 4 0.6131 0.5620 0.000 0.056 0.300 0.592 0.052
#> GSM1009075 3 0.5091 0.8737 0.236 0.000 0.676 0.088 0.000
#> GSM1009089 1 0.0510 0.7963 0.984 0.000 0.016 0.000 0.000
#> GSM1009103 1 0.0290 0.8024 0.992 0.000 0.000 0.008 0.000
#> GSM1009117 5 0.0000 0.8865 0.000 0.000 0.000 0.000 1.000
#> GSM1009131 1 0.6096 0.5131 0.600 0.056 0.292 0.052 0.000
#> GSM1009145 1 0.0290 0.8024 0.992 0.000 0.000 0.008 0.000
#> GSM1009159 1 0.0000 0.8012 1.000 0.000 0.000 0.000 0.000
#> GSM1009173 5 0.1503 0.8794 0.000 0.020 0.020 0.008 0.952
#> GSM1009187 3 0.4767 0.5996 0.420 0.020 0.560 0.000 0.000
#> GSM1009201 4 0.6156 0.5815 0.056 0.056 0.292 0.596 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1009062 6 0.2346 0.942 0.124 0.000 0.000 0.008 0.000 0.868
#> GSM1009076 5 0.6420 0.648 0.000 0.196 0.116 0.040 0.600 0.048
#> GSM1009090 1 0.0622 0.887 0.980 0.000 0.012 0.008 0.000 0.000
#> GSM1009104 5 0.0000 0.810 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1009118 5 0.3296 0.773 0.000 0.000 0.084 0.036 0.844 0.036
#> GSM1009132 4 0.0922 0.921 0.000 0.004 0.004 0.968 0.024 0.000
#> GSM1009146 1 0.1176 0.880 0.956 0.000 0.024 0.000 0.000 0.020
#> GSM1009160 5 0.4442 0.763 0.000 0.012 0.128 0.020 0.764 0.076
#> GSM1009174 2 0.0260 0.966 0.000 0.992 0.000 0.000 0.008 0.000
#> GSM1009188 3 0.3314 0.812 0.256 0.000 0.740 0.004 0.000 0.000
#> GSM1009063 6 0.2346 0.942 0.124 0.000 0.000 0.008 0.000 0.868
#> GSM1009077 5 0.6420 0.648 0.000 0.196 0.116 0.040 0.600 0.048
#> GSM1009091 1 0.0622 0.887 0.980 0.000 0.012 0.008 0.000 0.000
#> GSM1009105 5 0.0000 0.810 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1009119 1 0.4076 0.236 0.636 0.000 0.348 0.004 0.000 0.012
#> GSM1009133 4 0.1418 0.977 0.024 0.000 0.000 0.944 0.000 0.032
#> GSM1009147 1 0.1176 0.880 0.956 0.000 0.024 0.000 0.000 0.020
#> GSM1009161 5 0.4442 0.763 0.000 0.012 0.128 0.020 0.764 0.076
#> GSM1009175 2 0.0260 0.966 0.000 0.992 0.000 0.000 0.008 0.000
#> GSM1009189 3 0.3314 0.812 0.256 0.000 0.740 0.004 0.000 0.000
#> GSM1009064 6 0.2972 0.871 0.032 0.052 0.000 0.048 0.000 0.868
#> GSM1009078 1 0.1257 0.879 0.952 0.000 0.028 0.000 0.000 0.020
#> GSM1009092 1 0.0622 0.887 0.980 0.000 0.012 0.008 0.000 0.000
#> GSM1009106 5 0.0000 0.810 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1009120 1 0.1757 0.831 0.916 0.000 0.076 0.000 0.000 0.008
#> GSM1009134 4 0.1391 0.977 0.016 0.000 0.000 0.944 0.000 0.040
#> GSM1009148 1 0.3619 0.658 0.744 0.000 0.024 0.000 0.000 0.232
#> GSM1009162 5 0.4442 0.763 0.000 0.012 0.128 0.020 0.764 0.076
#> GSM1009176 2 0.3445 0.743 0.000 0.816 0.016 0.012 0.144 0.012
#> GSM1009190 3 0.3314 0.812 0.256 0.000 0.740 0.004 0.000 0.000
#> GSM1009065 6 0.2647 0.927 0.088 0.000 0.000 0.044 0.000 0.868
#> GSM1009079 5 0.6420 0.648 0.000 0.196 0.116 0.040 0.600 0.048
#> GSM1009093 1 0.0622 0.887 0.980 0.000 0.012 0.008 0.000 0.000
#> GSM1009107 5 0.0000 0.810 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1009121 5 0.3411 0.771 0.000 0.000 0.088 0.032 0.836 0.044
#> GSM1009135 4 0.1391 0.977 0.016 0.000 0.000 0.944 0.000 0.040
#> GSM1009149 1 0.0806 0.884 0.972 0.000 0.020 0.000 0.000 0.008
#> GSM1009163 5 0.4442 0.763 0.000 0.012 0.128 0.020 0.764 0.076
#> GSM1009177 2 0.0405 0.964 0.000 0.988 0.000 0.004 0.008 0.000
#> GSM1009191 3 0.3214 0.594 0.000 0.016 0.812 0.004 0.164 0.004
#> GSM1009066 6 0.2660 0.924 0.084 0.000 0.000 0.048 0.000 0.868
#> GSM1009080 5 0.6420 0.648 0.000 0.196 0.116 0.040 0.600 0.048
#> GSM1009094 1 0.0622 0.887 0.980 0.000 0.012 0.008 0.000 0.000
#> GSM1009108 5 0.0000 0.810 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1009122 5 0.3296 0.773 0.000 0.000 0.084 0.036 0.844 0.036
#> GSM1009136 1 0.0622 0.886 0.980 0.000 0.012 0.008 0.000 0.000
#> GSM1009150 1 0.1088 0.881 0.960 0.000 0.024 0.000 0.000 0.016
#> GSM1009164 5 0.4442 0.763 0.000 0.012 0.128 0.020 0.764 0.076
#> GSM1009178 2 0.0935 0.926 0.032 0.964 0.004 0.000 0.000 0.000
#> GSM1009192 3 0.3742 0.684 0.348 0.000 0.648 0.004 0.000 0.000
#> GSM1009067 6 0.2346 0.942 0.124 0.000 0.000 0.008 0.000 0.868
#> GSM1009081 5 0.6420 0.648 0.000 0.196 0.116 0.040 0.600 0.048
#> GSM1009095 1 0.0622 0.887 0.980 0.000 0.012 0.008 0.000 0.000
#> GSM1009109 5 0.0000 0.810 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1009123 1 0.1901 0.828 0.912 0.000 0.076 0.004 0.000 0.008
#> GSM1009137 4 0.1418 0.977 0.024 0.000 0.000 0.944 0.000 0.032
#> GSM1009151 1 0.3645 0.652 0.740 0.000 0.024 0.000 0.000 0.236
#> GSM1009165 5 0.4442 0.763 0.000 0.012 0.128 0.020 0.764 0.076
#> GSM1009179 2 0.0260 0.966 0.000 0.992 0.000 0.000 0.008 0.000
#> GSM1009193 1 0.3668 0.333 0.668 0.000 0.328 0.004 0.000 0.000
#> GSM1009068 6 0.2431 0.933 0.132 0.000 0.000 0.008 0.000 0.860
#> GSM1009082 5 0.6420 0.648 0.000 0.196 0.116 0.040 0.600 0.048
#> GSM1009096 1 0.0622 0.887 0.980 0.000 0.012 0.008 0.000 0.000
#> GSM1009110 5 0.0000 0.810 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1009124 3 0.3780 0.806 0.244 0.000 0.732 0.008 0.000 0.016
#> GSM1009138 4 0.1391 0.977 0.016 0.000 0.000 0.944 0.000 0.040
#> GSM1009152 1 0.3645 0.652 0.740 0.000 0.024 0.000 0.000 0.236
#> GSM1009166 5 0.4442 0.763 0.000 0.012 0.128 0.020 0.764 0.076
#> GSM1009180 2 0.0935 0.926 0.032 0.964 0.004 0.000 0.000 0.000
#> GSM1009194 3 0.3214 0.594 0.000 0.016 0.812 0.004 0.164 0.004
#> GSM1009069 6 0.2972 0.871 0.032 0.052 0.000 0.048 0.000 0.868
#> GSM1009083 5 0.6420 0.648 0.000 0.196 0.116 0.040 0.600 0.048
#> GSM1009097 1 0.0622 0.887 0.980 0.000 0.012 0.008 0.000 0.000
#> GSM1009111 5 0.0000 0.810 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1009125 5 0.3191 0.776 0.000 0.000 0.076 0.036 0.852 0.036
#> GSM1009139 4 0.0922 0.921 0.000 0.004 0.004 0.968 0.024 0.000
#> GSM1009153 1 0.4237 0.274 0.584 0.000 0.020 0.000 0.000 0.396
#> GSM1009167 5 0.4442 0.763 0.000 0.012 0.128 0.020 0.764 0.076
#> GSM1009181 2 0.0405 0.964 0.000 0.988 0.000 0.004 0.008 0.000
#> GSM1009195 5 0.5364 0.704 0.000 0.112 0.092 0.036 0.716 0.044
#> GSM1009070 1 0.2445 0.820 0.872 0.000 0.020 0.000 0.000 0.108
#> GSM1009084 5 0.6420 0.648 0.000 0.196 0.116 0.040 0.600 0.048
#> GSM1009098 1 0.0622 0.887 0.980 0.000 0.012 0.008 0.000 0.000
#> GSM1009112 5 0.0000 0.810 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1009126 3 0.4252 0.613 0.040 0.000 0.724 0.220 0.000 0.016
#> GSM1009140 4 0.1418 0.977 0.024 0.000 0.000 0.944 0.000 0.032
#> GSM1009154 1 0.3645 0.652 0.740 0.000 0.024 0.000 0.000 0.236
#> GSM1009168 5 0.4442 0.763 0.000 0.012 0.128 0.020 0.764 0.076
#> GSM1009182 2 0.0260 0.966 0.000 0.992 0.000 0.000 0.008 0.000
#> GSM1009196 1 0.2667 0.800 0.852 0.000 0.020 0.000 0.000 0.128
#> GSM1009071 6 0.2609 0.933 0.096 0.000 0.000 0.036 0.000 0.868
#> GSM1009085 5 0.6420 0.648 0.000 0.196 0.116 0.040 0.600 0.048
#> GSM1009099 1 0.0622 0.887 0.980 0.000 0.012 0.008 0.000 0.000
#> GSM1009113 5 0.0000 0.810 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1009127 1 0.1901 0.828 0.912 0.000 0.076 0.004 0.000 0.008
#> GSM1009141 4 0.1605 0.973 0.016 0.000 0.004 0.936 0.000 0.044
#> GSM1009155 6 0.3315 0.833 0.200 0.000 0.020 0.000 0.000 0.780
#> GSM1009169 5 0.4442 0.763 0.000 0.012 0.128 0.020 0.764 0.076
#> GSM1009183 2 0.0260 0.966 0.000 0.992 0.000 0.000 0.008 0.000
#> GSM1009197 1 0.0458 0.884 0.984 0.000 0.016 0.000 0.000 0.000
#> GSM1009072 6 0.2346 0.942 0.124 0.000 0.000 0.008 0.000 0.868
#> GSM1009086 5 0.6420 0.648 0.000 0.196 0.116 0.040 0.600 0.048
#> GSM1009100 1 0.0622 0.887 0.980 0.000 0.012 0.008 0.000 0.000
#> GSM1009114 5 0.0000 0.810 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1009128 3 0.3833 0.796 0.272 0.000 0.708 0.004 0.000 0.016
#> GSM1009142 4 0.1536 0.975 0.016 0.000 0.004 0.940 0.000 0.040
#> GSM1009156 1 0.1176 0.880 0.956 0.000 0.024 0.000 0.000 0.020
#> GSM1009170 5 0.4442 0.763 0.000 0.012 0.128 0.020 0.764 0.076
#> GSM1009184 2 0.0260 0.966 0.000 0.992 0.000 0.000 0.008 0.000
#> GSM1009198 3 0.3314 0.812 0.256 0.000 0.740 0.004 0.000 0.000
#> GSM1009073 6 0.2558 0.937 0.104 0.000 0.000 0.028 0.000 0.868
#> GSM1009087 1 0.1421 0.875 0.944 0.000 0.028 0.000 0.000 0.028
#> GSM1009101 1 0.0622 0.887 0.980 0.000 0.012 0.008 0.000 0.000
#> GSM1009115 5 0.0000 0.810 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1009129 5 0.3191 0.776 0.000 0.000 0.076 0.036 0.852 0.036
#> GSM1009143 4 0.1418 0.977 0.024 0.000 0.000 0.944 0.000 0.032
#> GSM1009157 6 0.3428 0.787 0.024 0.152 0.016 0.000 0.000 0.808
#> GSM1009171 5 0.4442 0.763 0.000 0.012 0.128 0.020 0.764 0.076
#> GSM1009185 1 0.0806 0.884 0.972 0.000 0.020 0.000 0.000 0.008
#> GSM1009199 5 0.5484 0.694 0.000 0.124 0.092 0.036 0.704 0.044
#> GSM1009074 6 0.2346 0.942 0.124 0.000 0.000 0.008 0.000 0.868
#> GSM1009088 1 0.1498 0.873 0.940 0.000 0.028 0.000 0.000 0.032
#> GSM1009102 1 0.0622 0.887 0.980 0.000 0.012 0.008 0.000 0.000
#> GSM1009116 5 0.0000 0.810 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1009130 5 0.3191 0.776 0.000 0.000 0.076 0.036 0.852 0.036
#> GSM1009144 4 0.1418 0.977 0.024 0.000 0.000 0.944 0.000 0.032
#> GSM1009158 1 0.1088 0.881 0.960 0.000 0.024 0.000 0.000 0.016
#> GSM1009172 5 0.4442 0.763 0.000 0.012 0.128 0.020 0.764 0.076
#> GSM1009186 2 0.0260 0.966 0.000 0.992 0.000 0.000 0.008 0.000
#> GSM1009200 3 0.4056 0.590 0.000 0.000 0.748 0.184 0.064 0.004
#> GSM1009075 6 0.2346 0.942 0.124 0.000 0.000 0.008 0.000 0.868
#> GSM1009089 1 0.0993 0.883 0.964 0.000 0.024 0.000 0.000 0.012
#> GSM1009103 1 0.0622 0.887 0.980 0.000 0.012 0.008 0.000 0.000
#> GSM1009117 5 0.0000 0.810 0.000 0.000 0.000 0.000 1.000 0.000
#> GSM1009131 3 0.3833 0.796 0.272 0.000 0.708 0.004 0.000 0.016
#> GSM1009145 1 0.0622 0.886 0.980 0.000 0.012 0.008 0.000 0.000
#> GSM1009159 1 0.0291 0.886 0.992 0.000 0.004 0.000 0.000 0.004
#> GSM1009173 5 0.4442 0.763 0.000 0.012 0.128 0.020 0.764 0.076
#> GSM1009187 1 0.4660 0.469 0.644 0.028 0.024 0.000 0.000 0.304
#> GSM1009201 3 0.3583 0.544 0.008 0.000 0.728 0.260 0.000 0.004
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n temperature(p) time(p) specimen(p) k
#> ATC:skmeans 138 0.582 0.620 1.05e-13 2
#> ATC:skmeans 136 0.907 0.960 2.99e-29 3
#> ATC:skmeans 123 0.990 1.000 1.20e-46 4
#> ATC:skmeans 129 0.973 1.000 6.92e-57 5
#> ATC:skmeans 136 1.000 0.999 1.73e-78 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 140 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 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 0.985 0.994 0.4974 0.503 0.503
#> 3 3 0.816 0.877 0.949 0.2350 0.799 0.630
#> 4 4 0.844 0.920 0.951 0.0837 0.947 0.865
#> 5 5 0.753 0.800 0.833 0.1418 0.851 0.585
#> 6 6 0.855 0.897 0.946 0.0706 0.840 0.441
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
#> GSM1009062 1 0.0000 0.995 1.000 0.000
#> GSM1009076 2 0.0000 0.993 0.000 1.000
#> GSM1009090 1 0.0000 0.995 1.000 0.000
#> GSM1009104 2 0.0000 0.993 0.000 1.000
#> GSM1009118 2 0.0000 0.993 0.000 1.000
#> GSM1009132 2 0.0000 0.993 0.000 1.000
#> GSM1009146 1 0.0000 0.995 1.000 0.000
#> GSM1009160 2 0.0000 0.993 0.000 1.000
#> GSM1009174 2 0.0000 0.993 0.000 1.000
#> GSM1009188 1 0.0000 0.995 1.000 0.000
#> GSM1009063 1 0.0000 0.995 1.000 0.000
#> GSM1009077 2 0.0000 0.993 0.000 1.000
#> GSM1009091 1 0.0000 0.995 1.000 0.000
#> GSM1009105 2 0.0000 0.993 0.000 1.000
#> GSM1009119 1 0.0000 0.995 1.000 0.000
#> GSM1009133 1 0.0000 0.995 1.000 0.000
#> GSM1009147 1 0.0000 0.995 1.000 0.000
#> GSM1009161 2 0.0000 0.993 0.000 1.000
#> GSM1009175 2 0.0000 0.993 0.000 1.000
#> GSM1009189 1 0.0000 0.995 1.000 0.000
#> GSM1009064 1 0.0000 0.995 1.000 0.000
#> GSM1009078 1 0.0000 0.995 1.000 0.000
#> GSM1009092 1 0.0000 0.995 1.000 0.000
#> GSM1009106 2 0.0000 0.993 0.000 1.000
#> GSM1009120 1 0.0000 0.995 1.000 0.000
#> GSM1009134 1 0.0000 0.995 1.000 0.000
#> GSM1009148 1 0.0000 0.995 1.000 0.000
#> GSM1009162 2 0.0000 0.993 0.000 1.000
#> GSM1009176 2 0.0000 0.993 0.000 1.000
#> GSM1009190 1 0.0000 0.995 1.000 0.000
#> GSM1009065 1 0.0000 0.995 1.000 0.000
#> GSM1009079 2 0.0000 0.993 0.000 1.000
#> GSM1009093 1 0.0000 0.995 1.000 0.000
#> GSM1009107 2 0.0000 0.993 0.000 1.000
#> GSM1009121 2 0.0000 0.993 0.000 1.000
#> GSM1009135 1 0.0000 0.995 1.000 0.000
#> GSM1009149 1 0.0000 0.995 1.000 0.000
#> GSM1009163 2 0.0000 0.993 0.000 1.000
#> GSM1009177 2 0.0000 0.993 0.000 1.000
#> GSM1009191 2 0.0000 0.993 0.000 1.000
#> GSM1009066 1 0.0000 0.995 1.000 0.000
#> GSM1009080 2 0.0000 0.993 0.000 1.000
#> GSM1009094 1 0.0000 0.995 1.000 0.000
#> GSM1009108 2 0.0000 0.993 0.000 1.000
#> GSM1009122 2 0.0000 0.993 0.000 1.000
#> GSM1009136 1 0.0000 0.995 1.000 0.000
#> GSM1009150 1 0.0000 0.995 1.000 0.000
#> GSM1009164 2 0.0000 0.993 0.000 1.000
#> GSM1009178 1 0.0376 0.991 0.996 0.004
#> GSM1009192 1 0.0000 0.995 1.000 0.000
#> GSM1009067 1 0.0000 0.995 1.000 0.000
#> GSM1009081 2 0.0000 0.993 0.000 1.000
#> GSM1009095 1 0.0000 0.995 1.000 0.000
#> GSM1009109 2 0.0000 0.993 0.000 1.000
#> GSM1009123 1 0.0000 0.995 1.000 0.000
#> GSM1009137 1 0.0000 0.995 1.000 0.000
#> GSM1009151 1 0.0000 0.995 1.000 0.000
#> GSM1009165 2 0.0000 0.993 0.000 1.000
#> GSM1009179 2 0.0000 0.993 0.000 1.000
#> GSM1009193 1 0.0000 0.995 1.000 0.000
#> GSM1009068 1 0.0000 0.995 1.000 0.000
#> GSM1009082 2 0.0000 0.993 0.000 1.000
#> GSM1009096 1 0.0000 0.995 1.000 0.000
#> GSM1009110 2 0.0000 0.993 0.000 1.000
#> GSM1009124 2 0.9286 0.473 0.344 0.656
#> GSM1009138 1 0.0000 0.995 1.000 0.000
#> GSM1009152 1 0.0000 0.995 1.000 0.000
#> GSM1009166 2 0.0000 0.993 0.000 1.000
#> GSM1009180 1 0.0376 0.991 0.996 0.004
#> GSM1009194 2 0.0000 0.993 0.000 1.000
#> GSM1009069 2 0.4298 0.900 0.088 0.912
#> GSM1009083 2 0.0000 0.993 0.000 1.000
#> GSM1009097 1 0.0000 0.995 1.000 0.000
#> GSM1009111 2 0.0000 0.993 0.000 1.000
#> GSM1009125 2 0.0000 0.993 0.000 1.000
#> GSM1009139 2 0.0000 0.993 0.000 1.000
#> GSM1009153 1 0.0000 0.995 1.000 0.000
#> GSM1009167 2 0.0000 0.993 0.000 1.000
#> GSM1009181 2 0.0000 0.993 0.000 1.000
#> GSM1009195 2 0.0000 0.993 0.000 1.000
#> GSM1009070 1 0.0000 0.995 1.000 0.000
#> GSM1009084 2 0.0000 0.993 0.000 1.000
#> GSM1009098 1 0.0000 0.995 1.000 0.000
#> GSM1009112 2 0.0000 0.993 0.000 1.000
#> GSM1009126 1 0.1633 0.971 0.976 0.024
#> GSM1009140 1 0.0000 0.995 1.000 0.000
#> GSM1009154 1 0.0000 0.995 1.000 0.000
#> GSM1009168 2 0.0000 0.993 0.000 1.000
#> GSM1009182 2 0.0000 0.993 0.000 1.000
#> GSM1009196 1 0.0000 0.995 1.000 0.000
#> GSM1009071 1 0.0000 0.995 1.000 0.000
#> GSM1009085 2 0.0000 0.993 0.000 1.000
#> GSM1009099 1 0.0000 0.995 1.000 0.000
#> GSM1009113 2 0.0000 0.993 0.000 1.000
#> GSM1009127 1 0.0000 0.995 1.000 0.000
#> GSM1009141 1 0.0000 0.995 1.000 0.000
#> GSM1009155 1 0.0000 0.995 1.000 0.000
#> GSM1009169 2 0.0000 0.993 0.000 1.000
#> GSM1009183 2 0.0000 0.993 0.000 1.000
#> GSM1009197 1 0.0000 0.995 1.000 0.000
#> GSM1009072 1 0.0000 0.995 1.000 0.000
#> GSM1009086 2 0.0000 0.993 0.000 1.000
#> GSM1009100 1 0.0000 0.995 1.000 0.000
#> GSM1009114 2 0.0000 0.993 0.000 1.000
#> GSM1009128 1 0.0000 0.995 1.000 0.000
#> GSM1009142 1 0.0672 0.987 0.992 0.008
#> GSM1009156 1 0.0000 0.995 1.000 0.000
#> GSM1009170 2 0.0000 0.993 0.000 1.000
#> GSM1009184 2 0.0000 0.993 0.000 1.000
#> GSM1009198 1 0.0000 0.995 1.000 0.000
#> GSM1009073 1 0.0000 0.995 1.000 0.000
#> GSM1009087 1 0.0000 0.995 1.000 0.000
#> GSM1009101 1 0.0000 0.995 1.000 0.000
#> GSM1009115 2 0.0000 0.993 0.000 1.000
#> GSM1009129 2 0.0000 0.993 0.000 1.000
#> GSM1009143 1 0.0000 0.995 1.000 0.000
#> GSM1009157 1 0.0000 0.995 1.000 0.000
#> GSM1009171 2 0.0000 0.993 0.000 1.000
#> GSM1009185 1 0.0000 0.995 1.000 0.000
#> GSM1009199 2 0.0000 0.993 0.000 1.000
#> GSM1009074 1 0.0000 0.995 1.000 0.000
#> GSM1009088 1 0.0000 0.995 1.000 0.000
#> GSM1009102 1 0.0000 0.995 1.000 0.000
#> GSM1009116 2 0.0000 0.993 0.000 1.000
#> GSM1009130 2 0.0000 0.993 0.000 1.000
#> GSM1009144 1 0.0000 0.995 1.000 0.000
#> GSM1009158 1 0.0000 0.995 1.000 0.000
#> GSM1009172 2 0.0000 0.993 0.000 1.000
#> GSM1009186 2 0.0000 0.993 0.000 1.000
#> GSM1009200 1 0.9460 0.424 0.636 0.364
#> GSM1009075 1 0.0000 0.995 1.000 0.000
#> GSM1009089 1 0.0000 0.995 1.000 0.000
#> GSM1009103 1 0.0000 0.995 1.000 0.000
#> GSM1009117 2 0.0000 0.993 0.000 1.000
#> GSM1009131 1 0.0672 0.987 0.992 0.008
#> GSM1009145 1 0.0000 0.995 1.000 0.000
#> GSM1009159 1 0.0000 0.995 1.000 0.000
#> GSM1009173 2 0.0000 0.993 0.000 1.000
#> GSM1009187 1 0.0000 0.995 1.000 0.000
#> GSM1009201 1 0.0000 0.995 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1009062 1 0.0000 0.9925 1.000 0.000 0.000
#> GSM1009076 2 0.0237 0.8708 0.000 0.996 0.004
#> GSM1009090 1 0.0000 0.9925 1.000 0.000 0.000
#> GSM1009104 2 0.5760 0.4900 0.000 0.672 0.328
#> GSM1009118 2 0.0000 0.8729 0.000 1.000 0.000
#> GSM1009132 2 0.0000 0.8729 0.000 1.000 0.000
#> GSM1009146 1 0.0000 0.9925 1.000 0.000 0.000
#> GSM1009160 3 0.0000 0.8814 0.000 0.000 1.000
#> GSM1009174 2 0.0000 0.8729 0.000 1.000 0.000
#> GSM1009188 1 0.0000 0.9925 1.000 0.000 0.000
#> GSM1009063 1 0.0000 0.9925 1.000 0.000 0.000
#> GSM1009077 2 0.0000 0.8729 0.000 1.000 0.000
#> GSM1009091 1 0.0000 0.9925 1.000 0.000 0.000
#> GSM1009105 3 0.5529 0.5974 0.000 0.296 0.704
#> GSM1009119 1 0.0000 0.9925 1.000 0.000 0.000
#> GSM1009133 1 0.0000 0.9925 1.000 0.000 0.000
#> GSM1009147 1 0.0000 0.9925 1.000 0.000 0.000
#> GSM1009161 3 0.0000 0.8814 0.000 0.000 1.000
#> GSM1009175 2 0.0000 0.8729 0.000 1.000 0.000
#> GSM1009189 1 0.0237 0.9896 0.996 0.004 0.000
#> GSM1009064 2 0.6244 0.2379 0.440 0.560 0.000
#> GSM1009078 1 0.0000 0.9925 1.000 0.000 0.000
#> GSM1009092 1 0.0000 0.9925 1.000 0.000 0.000
#> GSM1009106 3 0.3686 0.8217 0.000 0.140 0.860
#> GSM1009120 1 0.0000 0.9925 1.000 0.000 0.000
#> GSM1009134 1 0.0000 0.9925 1.000 0.000 0.000
#> GSM1009148 1 0.0000 0.9925 1.000 0.000 0.000
#> GSM1009162 3 0.0000 0.8814 0.000 0.000 1.000
#> GSM1009176 2 0.0000 0.8729 0.000 1.000 0.000
#> GSM1009190 1 0.4931 0.6894 0.768 0.232 0.000
#> GSM1009065 1 0.0237 0.9896 0.996 0.004 0.000
#> GSM1009079 2 0.0000 0.8729 0.000 1.000 0.000
#> GSM1009093 1 0.0000 0.9925 1.000 0.000 0.000
#> GSM1009107 3 0.3686 0.8217 0.000 0.140 0.860
#> GSM1009121 2 0.0000 0.8729 0.000 1.000 0.000
#> GSM1009135 1 0.0000 0.9925 1.000 0.000 0.000
#> GSM1009149 1 0.0000 0.9925 1.000 0.000 0.000
#> GSM1009163 3 0.0000 0.8814 0.000 0.000 1.000
#> GSM1009177 2 0.0000 0.8729 0.000 1.000 0.000
#> GSM1009191 2 0.0000 0.8729 0.000 1.000 0.000
#> GSM1009066 1 0.0237 0.9896 0.996 0.004 0.000
#> GSM1009080 2 0.5760 0.4900 0.000 0.672 0.328
#> GSM1009094 1 0.0000 0.9925 1.000 0.000 0.000
#> GSM1009108 3 0.3619 0.8242 0.000 0.136 0.864
#> GSM1009122 2 0.0237 0.8708 0.000 0.996 0.004
#> GSM1009136 1 0.0000 0.9925 1.000 0.000 0.000
#> GSM1009150 1 0.0000 0.9925 1.000 0.000 0.000
#> GSM1009164 3 0.0000 0.8814 0.000 0.000 1.000
#> GSM1009178 2 0.1643 0.8414 0.044 0.956 0.000
#> GSM1009192 1 0.0000 0.9925 1.000 0.000 0.000
#> GSM1009067 1 0.0000 0.9925 1.000 0.000 0.000
#> GSM1009081 2 0.0237 0.8708 0.000 0.996 0.004
#> GSM1009095 1 0.0000 0.9925 1.000 0.000 0.000
#> GSM1009109 2 0.5760 0.4900 0.000 0.672 0.328
#> GSM1009123 1 0.0000 0.9925 1.000 0.000 0.000
#> GSM1009137 1 0.0237 0.9896 0.996 0.004 0.000
#> GSM1009151 1 0.0000 0.9925 1.000 0.000 0.000
#> GSM1009165 3 0.0000 0.8814 0.000 0.000 1.000
#> GSM1009179 2 0.0000 0.8729 0.000 1.000 0.000
#> GSM1009193 1 0.0000 0.9925 1.000 0.000 0.000
#> GSM1009068 1 0.0000 0.9925 1.000 0.000 0.000
#> GSM1009082 2 0.0237 0.8708 0.000 0.996 0.004
#> GSM1009096 1 0.0000 0.9925 1.000 0.000 0.000
#> GSM1009110 3 0.1643 0.8675 0.000 0.044 0.956
#> GSM1009124 2 0.2959 0.7909 0.100 0.900 0.000
#> GSM1009138 1 0.0000 0.9925 1.000 0.000 0.000
#> GSM1009152 1 0.0000 0.9925 1.000 0.000 0.000
#> GSM1009166 3 0.0000 0.8814 0.000 0.000 1.000
#> GSM1009180 2 0.1643 0.8414 0.044 0.956 0.000
#> GSM1009194 2 0.0000 0.8729 0.000 1.000 0.000
#> GSM1009069 2 0.0000 0.8729 0.000 1.000 0.000
#> GSM1009083 2 0.0000 0.8729 0.000 1.000 0.000
#> GSM1009097 1 0.0000 0.9925 1.000 0.000 0.000
#> GSM1009111 3 0.3686 0.8217 0.000 0.140 0.860
#> GSM1009125 2 0.5760 0.4900 0.000 0.672 0.328
#> GSM1009139 2 0.0000 0.8729 0.000 1.000 0.000
#> GSM1009153 1 0.0000 0.9925 1.000 0.000 0.000
#> GSM1009167 3 0.0000 0.8814 0.000 0.000 1.000
#> GSM1009181 2 0.0000 0.8729 0.000 1.000 0.000
#> GSM1009195 2 0.0000 0.8729 0.000 1.000 0.000
#> GSM1009070 1 0.0000 0.9925 1.000 0.000 0.000
#> GSM1009084 2 0.0000 0.8729 0.000 1.000 0.000
#> GSM1009098 1 0.0000 0.9925 1.000 0.000 0.000
#> GSM1009112 2 0.5760 0.4900 0.000 0.672 0.328
#> GSM1009126 2 0.3686 0.7485 0.140 0.860 0.000
#> GSM1009140 1 0.0000 0.9925 1.000 0.000 0.000
#> GSM1009154 1 0.0000 0.9925 1.000 0.000 0.000
#> GSM1009168 3 0.0000 0.8814 0.000 0.000 1.000
#> GSM1009182 2 0.0000 0.8729 0.000 1.000 0.000
#> GSM1009196 1 0.0000 0.9925 1.000 0.000 0.000
#> GSM1009071 1 0.0000 0.9925 1.000 0.000 0.000
#> GSM1009085 2 0.0000 0.8729 0.000 1.000 0.000
#> GSM1009099 1 0.0000 0.9925 1.000 0.000 0.000
#> GSM1009113 3 0.3686 0.8217 0.000 0.140 0.860
#> GSM1009127 1 0.0000 0.9925 1.000 0.000 0.000
#> GSM1009141 2 0.3686 0.7485 0.140 0.860 0.000
#> GSM1009155 1 0.0000 0.9925 1.000 0.000 0.000
#> GSM1009169 3 0.5760 0.4862 0.000 0.328 0.672
#> GSM1009183 2 0.0000 0.8729 0.000 1.000 0.000
#> GSM1009197 1 0.0000 0.9925 1.000 0.000 0.000
#> GSM1009072 1 0.0000 0.9925 1.000 0.000 0.000
#> GSM1009086 2 0.0237 0.8708 0.000 0.996 0.004
#> GSM1009100 1 0.0000 0.9925 1.000 0.000 0.000
#> GSM1009114 2 0.5760 0.4900 0.000 0.672 0.328
#> GSM1009128 2 0.4062 0.7171 0.164 0.836 0.000
#> GSM1009142 2 0.3686 0.7485 0.140 0.860 0.000
#> GSM1009156 1 0.0237 0.9896 0.996 0.004 0.000
#> GSM1009170 3 0.0000 0.8814 0.000 0.000 1.000
#> GSM1009184 2 0.0000 0.8729 0.000 1.000 0.000
#> GSM1009198 1 0.0000 0.9925 1.000 0.000 0.000
#> GSM1009073 1 0.0000 0.9925 1.000 0.000 0.000
#> GSM1009087 1 0.0237 0.9896 0.996 0.004 0.000
#> GSM1009101 1 0.0000 0.9925 1.000 0.000 0.000
#> GSM1009115 2 0.5785 0.4816 0.000 0.668 0.332
#> GSM1009129 2 0.5760 0.4900 0.000 0.672 0.328
#> GSM1009143 1 0.0000 0.9925 1.000 0.000 0.000
#> GSM1009157 2 0.3686 0.7485 0.140 0.860 0.000
#> GSM1009171 3 0.0000 0.8814 0.000 0.000 1.000
#> GSM1009185 1 0.0237 0.9896 0.996 0.004 0.000
#> GSM1009199 2 0.0000 0.8729 0.000 1.000 0.000
#> GSM1009074 1 0.0000 0.9925 1.000 0.000 0.000
#> GSM1009088 1 0.1643 0.9437 0.956 0.044 0.000
#> GSM1009102 1 0.0000 0.9925 1.000 0.000 0.000
#> GSM1009116 3 0.6305 0.0659 0.000 0.484 0.516
#> GSM1009130 2 0.0000 0.8729 0.000 1.000 0.000
#> GSM1009144 1 0.0237 0.9896 0.996 0.004 0.000
#> GSM1009158 1 0.0000 0.9925 1.000 0.000 0.000
#> GSM1009172 3 0.0000 0.8814 0.000 0.000 1.000
#> GSM1009186 2 0.0000 0.8729 0.000 1.000 0.000
#> GSM1009200 2 0.2448 0.8130 0.076 0.924 0.000
#> GSM1009075 1 0.0000 0.9925 1.000 0.000 0.000
#> GSM1009089 1 0.0000 0.9925 1.000 0.000 0.000
#> GSM1009103 1 0.0000 0.9925 1.000 0.000 0.000
#> GSM1009117 2 0.5760 0.4900 0.000 0.672 0.328
#> GSM1009131 2 0.3816 0.7386 0.148 0.852 0.000
#> GSM1009145 1 0.0000 0.9925 1.000 0.000 0.000
#> GSM1009159 1 0.0000 0.9925 1.000 0.000 0.000
#> GSM1009173 3 0.5706 0.5004 0.000 0.320 0.680
#> GSM1009187 1 0.0237 0.9896 0.996 0.004 0.000
#> GSM1009201 1 0.3551 0.8374 0.868 0.132 0.000
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1009062 1 0.0000 0.960 1.000 0.000 0.000 0.000
#> GSM1009076 2 0.3123 0.875 0.000 0.844 0.000 0.156
#> GSM1009090 1 0.2011 0.918 0.920 0.080 0.000 0.000
#> GSM1009104 4 0.0469 0.947 0.000 0.000 0.012 0.988
#> GSM1009118 2 0.3074 0.878 0.000 0.848 0.000 0.152
#> GSM1009132 2 0.0000 0.900 0.000 1.000 0.000 0.000
#> GSM1009146 1 0.0000 0.960 1.000 0.000 0.000 0.000
#> GSM1009160 3 0.0000 0.997 0.000 0.000 1.000 0.000
#> GSM1009174 2 0.0817 0.902 0.000 0.976 0.000 0.024
#> GSM1009188 1 0.0707 0.952 0.980 0.020 0.000 0.000
#> GSM1009063 1 0.0000 0.960 1.000 0.000 0.000 0.000
#> GSM1009077 2 0.3074 0.878 0.000 0.848 0.000 0.152
#> GSM1009091 1 0.0000 0.960 1.000 0.000 0.000 0.000
#> GSM1009105 4 0.0817 0.942 0.000 0.000 0.024 0.976
#> GSM1009119 1 0.3024 0.872 0.852 0.148 0.000 0.000
#> GSM1009133 1 0.3074 0.868 0.848 0.152 0.000 0.000
#> GSM1009147 1 0.1867 0.923 0.928 0.072 0.000 0.000
#> GSM1009161 3 0.0000 0.997 0.000 0.000 1.000 0.000
#> GSM1009175 2 0.0707 0.902 0.000 0.980 0.000 0.020
#> GSM1009189 1 0.3123 0.865 0.844 0.156 0.000 0.000
#> GSM1009064 2 0.4898 0.178 0.416 0.584 0.000 0.000
#> GSM1009078 1 0.3074 0.868 0.848 0.152 0.000 0.000
#> GSM1009092 1 0.0000 0.960 1.000 0.000 0.000 0.000
#> GSM1009106 4 0.3074 0.838 0.000 0.000 0.152 0.848
#> GSM1009120 1 0.0000 0.960 1.000 0.000 0.000 0.000
#> GSM1009134 1 0.0000 0.960 1.000 0.000 0.000 0.000
#> GSM1009148 1 0.0000 0.960 1.000 0.000 0.000 0.000
#> GSM1009162 3 0.0000 0.997 0.000 0.000 1.000 0.000
#> GSM1009176 2 0.3074 0.878 0.000 0.848 0.000 0.152
#> GSM1009190 1 0.4164 0.732 0.736 0.264 0.000 0.000
#> GSM1009065 1 0.1940 0.921 0.924 0.076 0.000 0.000
#> GSM1009079 2 0.3074 0.878 0.000 0.848 0.000 0.152
#> GSM1009093 1 0.0000 0.960 1.000 0.000 0.000 0.000
#> GSM1009107 4 0.2868 0.855 0.000 0.000 0.136 0.864
#> GSM1009121 2 0.0000 0.900 0.000 1.000 0.000 0.000
#> GSM1009135 1 0.2081 0.918 0.916 0.084 0.000 0.000
#> GSM1009149 1 0.0000 0.960 1.000 0.000 0.000 0.000
#> GSM1009163 3 0.0000 0.997 0.000 0.000 1.000 0.000
#> GSM1009177 2 0.3074 0.878 0.000 0.848 0.000 0.152
#> GSM1009191 2 0.0000 0.900 0.000 1.000 0.000 0.000
#> GSM1009066 1 0.1940 0.921 0.924 0.076 0.000 0.000
#> GSM1009080 2 0.4149 0.847 0.000 0.804 0.028 0.168
#> GSM1009094 1 0.0000 0.960 1.000 0.000 0.000 0.000
#> GSM1009108 4 0.3074 0.838 0.000 0.000 0.152 0.848
#> GSM1009122 2 0.3123 0.875 0.000 0.844 0.000 0.156
#> GSM1009136 1 0.0000 0.960 1.000 0.000 0.000 0.000
#> GSM1009150 1 0.0000 0.960 1.000 0.000 0.000 0.000
#> GSM1009164 3 0.0000 0.997 0.000 0.000 1.000 0.000
#> GSM1009178 2 0.0000 0.900 0.000 1.000 0.000 0.000
#> GSM1009192 1 0.0921 0.949 0.972 0.028 0.000 0.000
#> GSM1009067 1 0.0000 0.960 1.000 0.000 0.000 0.000
#> GSM1009081 2 0.3123 0.875 0.000 0.844 0.000 0.156
#> GSM1009095 1 0.0000 0.960 1.000 0.000 0.000 0.000
#> GSM1009109 4 0.0469 0.947 0.000 0.000 0.012 0.988
#> GSM1009123 1 0.0000 0.960 1.000 0.000 0.000 0.000
#> GSM1009137 1 0.3074 0.868 0.848 0.152 0.000 0.000
#> GSM1009151 1 0.0000 0.960 1.000 0.000 0.000 0.000
#> GSM1009165 3 0.0000 0.997 0.000 0.000 1.000 0.000
#> GSM1009179 2 0.0000 0.900 0.000 1.000 0.000 0.000
#> GSM1009193 1 0.0707 0.952 0.980 0.020 0.000 0.000
#> GSM1009068 1 0.0000 0.960 1.000 0.000 0.000 0.000
#> GSM1009082 2 0.3074 0.878 0.000 0.848 0.000 0.152
#> GSM1009096 1 0.0000 0.960 1.000 0.000 0.000 0.000
#> GSM1009110 4 0.3074 0.838 0.000 0.000 0.152 0.848
#> GSM1009124 2 0.0000 0.900 0.000 1.000 0.000 0.000
#> GSM1009138 1 0.0000 0.960 1.000 0.000 0.000 0.000
#> GSM1009152 1 0.0000 0.960 1.000 0.000 0.000 0.000
#> GSM1009166 3 0.0000 0.997 0.000 0.000 1.000 0.000
#> GSM1009180 2 0.0000 0.900 0.000 1.000 0.000 0.000
#> GSM1009194 2 0.0000 0.900 0.000 1.000 0.000 0.000
#> GSM1009069 2 0.0469 0.902 0.000 0.988 0.000 0.012
#> GSM1009083 2 0.3074 0.878 0.000 0.848 0.000 0.152
#> GSM1009097 1 0.0000 0.960 1.000 0.000 0.000 0.000
#> GSM1009111 4 0.0469 0.947 0.000 0.000 0.012 0.988
#> GSM1009125 4 0.0336 0.944 0.000 0.000 0.008 0.992
#> GSM1009139 2 0.0336 0.901 0.000 0.992 0.000 0.008
#> GSM1009153 1 0.0000 0.960 1.000 0.000 0.000 0.000
#> GSM1009167 3 0.0000 0.997 0.000 0.000 1.000 0.000
#> GSM1009181 2 0.3074 0.878 0.000 0.848 0.000 0.152
#> GSM1009195 2 0.3074 0.878 0.000 0.848 0.000 0.152
#> GSM1009070 1 0.0000 0.960 1.000 0.000 0.000 0.000
#> GSM1009084 2 0.3123 0.875 0.000 0.844 0.000 0.156
#> GSM1009098 1 0.0000 0.960 1.000 0.000 0.000 0.000
#> GSM1009112 4 0.0469 0.947 0.000 0.000 0.012 0.988
#> GSM1009126 2 0.0000 0.900 0.000 1.000 0.000 0.000
#> GSM1009140 1 0.0000 0.960 1.000 0.000 0.000 0.000
#> GSM1009154 1 0.0000 0.960 1.000 0.000 0.000 0.000
#> GSM1009168 3 0.0000 0.997 0.000 0.000 1.000 0.000
#> GSM1009182 2 0.0336 0.901 0.000 0.992 0.000 0.008
#> GSM1009196 1 0.0469 0.955 0.988 0.012 0.000 0.000
#> GSM1009071 1 0.0000 0.960 1.000 0.000 0.000 0.000
#> GSM1009085 2 0.3074 0.878 0.000 0.848 0.000 0.152
#> GSM1009099 1 0.0000 0.960 1.000 0.000 0.000 0.000
#> GSM1009113 4 0.1474 0.925 0.000 0.000 0.052 0.948
#> GSM1009127 1 0.0000 0.960 1.000 0.000 0.000 0.000
#> GSM1009141 2 0.0000 0.900 0.000 1.000 0.000 0.000
#> GSM1009155 1 0.1867 0.923 0.928 0.072 0.000 0.000
#> GSM1009169 3 0.0469 0.981 0.000 0.012 0.988 0.000
#> GSM1009183 2 0.0707 0.902 0.000 0.980 0.000 0.020
#> GSM1009197 1 0.0000 0.960 1.000 0.000 0.000 0.000
#> GSM1009072 1 0.0000 0.960 1.000 0.000 0.000 0.000
#> GSM1009086 2 0.3123 0.875 0.000 0.844 0.000 0.156
#> GSM1009100 1 0.0000 0.960 1.000 0.000 0.000 0.000
#> GSM1009114 4 0.0469 0.947 0.000 0.000 0.012 0.988
#> GSM1009128 2 0.1792 0.828 0.068 0.932 0.000 0.000
#> GSM1009142 2 0.0000 0.900 0.000 1.000 0.000 0.000
#> GSM1009156 1 0.1867 0.923 0.928 0.072 0.000 0.000
#> GSM1009170 3 0.0000 0.997 0.000 0.000 1.000 0.000
#> GSM1009184 2 0.0707 0.902 0.000 0.980 0.000 0.020
#> GSM1009198 1 0.1118 0.944 0.964 0.036 0.000 0.000
#> GSM1009073 1 0.0000 0.960 1.000 0.000 0.000 0.000
#> GSM1009087 1 0.3123 0.865 0.844 0.156 0.000 0.000
#> GSM1009101 1 0.0000 0.960 1.000 0.000 0.000 0.000
#> GSM1009115 4 0.0469 0.947 0.000 0.000 0.012 0.988
#> GSM1009129 4 0.0000 0.936 0.000 0.000 0.000 1.000
#> GSM1009143 1 0.0000 0.960 1.000 0.000 0.000 0.000
#> GSM1009157 2 0.0188 0.897 0.004 0.996 0.000 0.000
#> GSM1009171 3 0.0000 0.997 0.000 0.000 1.000 0.000
#> GSM1009185 1 0.2973 0.873 0.856 0.144 0.000 0.000
#> GSM1009199 2 0.3074 0.878 0.000 0.848 0.000 0.152
#> GSM1009074 1 0.0000 0.960 1.000 0.000 0.000 0.000
#> GSM1009088 1 0.3123 0.865 0.844 0.156 0.000 0.000
#> GSM1009102 1 0.0000 0.960 1.000 0.000 0.000 0.000
#> GSM1009116 4 0.0469 0.947 0.000 0.000 0.012 0.988
#> GSM1009130 2 0.3837 0.813 0.000 0.776 0.000 0.224
#> GSM1009144 1 0.3123 0.865 0.844 0.156 0.000 0.000
#> GSM1009158 1 0.0000 0.960 1.000 0.000 0.000 0.000
#> GSM1009172 3 0.0000 0.997 0.000 0.000 1.000 0.000
#> GSM1009186 2 0.0707 0.902 0.000 0.980 0.000 0.020
#> GSM1009200 2 0.0000 0.900 0.000 1.000 0.000 0.000
#> GSM1009075 1 0.0000 0.960 1.000 0.000 0.000 0.000
#> GSM1009089 1 0.0000 0.960 1.000 0.000 0.000 0.000
#> GSM1009103 1 0.0000 0.960 1.000 0.000 0.000 0.000
#> GSM1009117 4 0.0469 0.947 0.000 0.000 0.012 0.988
#> GSM1009131 2 0.0707 0.884 0.020 0.980 0.000 0.000
#> GSM1009145 1 0.0000 0.960 1.000 0.000 0.000 0.000
#> GSM1009159 1 0.0000 0.960 1.000 0.000 0.000 0.000
#> GSM1009173 3 0.0469 0.984 0.000 0.000 0.988 0.012
#> GSM1009187 1 0.2868 0.878 0.864 0.136 0.000 0.000
#> GSM1009201 1 0.3569 0.822 0.804 0.196 0.000 0.000
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM1009062 1 0.4150 0.782 0.612 0.000 0 0.388 0.000
#> GSM1009076 2 0.0000 0.779 0.000 1.000 0 0.000 0.000
#> GSM1009090 4 0.3816 0.546 0.304 0.000 0 0.696 0.000
#> GSM1009104 5 0.0000 0.956 0.000 0.000 0 0.000 1.000
#> GSM1009118 2 0.0000 0.779 0.000 1.000 0 0.000 0.000
#> GSM1009132 2 0.4114 0.801 0.376 0.624 0 0.000 0.000
#> GSM1009146 1 0.4150 0.782 0.612 0.000 0 0.388 0.000
#> GSM1009160 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> GSM1009174 2 0.3395 0.812 0.236 0.764 0 0.000 0.000
#> GSM1009188 4 0.2127 0.799 0.108 0.000 0 0.892 0.000
#> GSM1009063 1 0.4114 0.786 0.624 0.000 0 0.376 0.000
#> GSM1009077 2 0.0000 0.779 0.000 1.000 0 0.000 0.000
#> GSM1009091 4 0.0000 0.887 0.000 0.000 0 1.000 0.000
#> GSM1009105 5 0.0000 0.956 0.000 0.000 0 0.000 1.000
#> GSM1009119 1 0.3109 0.723 0.800 0.000 0 0.200 0.000
#> GSM1009133 4 0.3109 0.688 0.200 0.000 0 0.800 0.000
#> GSM1009147 1 0.3837 0.784 0.692 0.000 0 0.308 0.000
#> GSM1009161 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> GSM1009175 2 0.3684 0.813 0.280 0.720 0 0.000 0.000
#> GSM1009189 1 0.3003 0.724 0.812 0.000 0 0.188 0.000
#> GSM1009064 1 0.1965 0.637 0.904 0.000 0 0.096 0.000
#> GSM1009078 1 0.3003 0.724 0.812 0.000 0 0.188 0.000
#> GSM1009092 4 0.0000 0.887 0.000 0.000 0 1.000 0.000
#> GSM1009106 5 0.0000 0.956 0.000 0.000 0 0.000 1.000
#> GSM1009120 4 0.0000 0.887 0.000 0.000 0 1.000 0.000
#> GSM1009134 4 0.0404 0.879 0.012 0.000 0 0.988 0.000
#> GSM1009148 1 0.4114 0.786 0.624 0.000 0 0.376 0.000
#> GSM1009162 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> GSM1009176 2 0.0000 0.779 0.000 1.000 0 0.000 0.000
#> GSM1009190 1 0.0000 0.566 1.000 0.000 0 0.000 0.000
#> GSM1009065 1 0.3837 0.784 0.692 0.000 0 0.308 0.000
#> GSM1009079 2 0.0000 0.779 0.000 1.000 0 0.000 0.000
#> GSM1009093 4 0.0000 0.887 0.000 0.000 0 1.000 0.000
#> GSM1009107 5 0.0000 0.956 0.000 0.000 0 0.000 1.000
#> GSM1009121 2 0.4114 0.801 0.376 0.624 0 0.000 0.000
#> GSM1009135 4 0.2471 0.769 0.136 0.000 0 0.864 0.000
#> GSM1009149 1 0.4150 0.782 0.612 0.000 0 0.388 0.000
#> GSM1009163 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> GSM1009177 2 0.3003 0.807 0.188 0.812 0 0.000 0.000
#> GSM1009191 2 0.4150 0.796 0.388 0.612 0 0.000 0.000
#> GSM1009066 1 0.3837 0.784 0.692 0.000 0 0.308 0.000
#> GSM1009080 2 0.0000 0.779 0.000 1.000 0 0.000 0.000
#> GSM1009094 4 0.0162 0.885 0.004 0.000 0 0.996 0.000
#> GSM1009108 5 0.0000 0.956 0.000 0.000 0 0.000 1.000
#> GSM1009122 2 0.0000 0.779 0.000 1.000 0 0.000 0.000
#> GSM1009136 4 0.0000 0.887 0.000 0.000 0 1.000 0.000
#> GSM1009150 1 0.4150 0.782 0.612 0.000 0 0.388 0.000
#> GSM1009164 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> GSM1009178 2 0.4150 0.796 0.388 0.612 0 0.000 0.000
#> GSM1009192 1 0.3684 0.751 0.720 0.000 0 0.280 0.000
#> GSM1009067 1 0.4150 0.782 0.612 0.000 0 0.388 0.000
#> GSM1009081 2 0.0000 0.779 0.000 1.000 0 0.000 0.000
#> GSM1009095 4 0.0000 0.887 0.000 0.000 0 1.000 0.000
#> GSM1009109 5 0.0000 0.956 0.000 0.000 0 0.000 1.000
#> GSM1009123 4 0.0000 0.887 0.000 0.000 0 1.000 0.000
#> GSM1009137 4 0.3586 0.628 0.264 0.000 0 0.736 0.000
#> GSM1009151 1 0.4114 0.786 0.624 0.000 0 0.376 0.000
#> GSM1009165 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> GSM1009179 2 0.4126 0.799 0.380 0.620 0 0.000 0.000
#> GSM1009193 4 0.2074 0.803 0.104 0.000 0 0.896 0.000
#> GSM1009068 1 0.4150 0.782 0.612 0.000 0 0.388 0.000
#> GSM1009082 2 0.0000 0.779 0.000 1.000 0 0.000 0.000
#> GSM1009096 4 0.0000 0.887 0.000 0.000 0 1.000 0.000
#> GSM1009110 5 0.0000 0.956 0.000 0.000 0 0.000 1.000
#> GSM1009124 2 0.4150 0.796 0.388 0.612 0 0.000 0.000
#> GSM1009138 4 0.0404 0.879 0.012 0.000 0 0.988 0.000
#> GSM1009152 1 0.4150 0.782 0.612 0.000 0 0.388 0.000
#> GSM1009166 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> GSM1009180 2 0.4150 0.796 0.388 0.612 0 0.000 0.000
#> GSM1009194 2 0.4150 0.796 0.388 0.612 0 0.000 0.000
#> GSM1009069 1 0.4307 -0.594 0.500 0.500 0 0.000 0.000
#> GSM1009083 2 0.0000 0.779 0.000 1.000 0 0.000 0.000
#> GSM1009097 4 0.0000 0.887 0.000 0.000 0 1.000 0.000
#> GSM1009111 5 0.0000 0.956 0.000 0.000 0 0.000 1.000
#> GSM1009125 5 0.3561 0.712 0.000 0.260 0 0.000 0.740
#> GSM1009139 2 0.4114 0.801 0.376 0.624 0 0.000 0.000
#> GSM1009153 1 0.4114 0.786 0.624 0.000 0 0.376 0.000
#> GSM1009167 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> GSM1009181 2 0.3003 0.807 0.188 0.812 0 0.000 0.000
#> GSM1009195 2 0.0000 0.779 0.000 1.000 0 0.000 0.000
#> GSM1009070 4 0.4161 -0.271 0.392 0.000 0 0.608 0.000
#> GSM1009084 2 0.0000 0.779 0.000 1.000 0 0.000 0.000
#> GSM1009098 4 0.0000 0.887 0.000 0.000 0 1.000 0.000
#> GSM1009112 5 0.0000 0.956 0.000 0.000 0 0.000 1.000
#> GSM1009126 2 0.4161 0.793 0.392 0.608 0 0.000 0.000
#> GSM1009140 4 0.0000 0.887 0.000 0.000 0 1.000 0.000
#> GSM1009154 1 0.4150 0.782 0.612 0.000 0 0.388 0.000
#> GSM1009168 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> GSM1009182 2 0.4074 0.803 0.364 0.636 0 0.000 0.000
#> GSM1009196 1 0.3999 0.782 0.656 0.000 0 0.344 0.000
#> GSM1009071 1 0.4114 0.786 0.624 0.000 0 0.376 0.000
#> GSM1009085 2 0.0000 0.779 0.000 1.000 0 0.000 0.000
#> GSM1009099 4 0.0000 0.887 0.000 0.000 0 1.000 0.000
#> GSM1009113 5 0.0000 0.956 0.000 0.000 0 0.000 1.000
#> GSM1009127 1 0.4114 0.786 0.624 0.000 0 0.376 0.000
#> GSM1009141 2 0.4150 0.796 0.388 0.612 0 0.000 0.000
#> GSM1009155 1 0.3837 0.784 0.692 0.000 0 0.308 0.000
#> GSM1009169 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> GSM1009183 2 0.3684 0.813 0.280 0.720 0 0.000 0.000
#> GSM1009197 4 0.0404 0.879 0.012 0.000 0 0.988 0.000
#> GSM1009072 1 0.4150 0.782 0.612 0.000 0 0.388 0.000
#> GSM1009086 2 0.0000 0.779 0.000 1.000 0 0.000 0.000
#> GSM1009100 4 0.0000 0.887 0.000 0.000 0 1.000 0.000
#> GSM1009114 5 0.0000 0.956 0.000 0.000 0 0.000 1.000
#> GSM1009128 4 0.5922 0.204 0.388 0.108 0 0.504 0.000
#> GSM1009142 2 0.4150 0.796 0.388 0.612 0 0.000 0.000
#> GSM1009156 1 0.3837 0.784 0.692 0.000 0 0.308 0.000
#> GSM1009170 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> GSM1009184 2 0.3684 0.813 0.280 0.720 0 0.000 0.000
#> GSM1009198 4 0.2127 0.799 0.108 0.000 0 0.892 0.000
#> GSM1009073 1 0.4114 0.786 0.624 0.000 0 0.376 0.000
#> GSM1009087 1 0.3003 0.724 0.812 0.000 0 0.188 0.000
#> GSM1009101 4 0.0000 0.887 0.000 0.000 0 1.000 0.000
#> GSM1009115 5 0.0000 0.956 0.000 0.000 0 0.000 1.000
#> GSM1009129 5 0.4045 0.605 0.000 0.356 0 0.000 0.644
#> GSM1009143 4 0.3395 0.445 0.236 0.000 0 0.764 0.000
#> GSM1009157 1 0.0000 0.566 1.000 0.000 0 0.000 0.000
#> GSM1009171 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> GSM1009185 1 0.1121 0.607 0.956 0.000 0 0.044 0.000
#> GSM1009199 2 0.0000 0.779 0.000 1.000 0 0.000 0.000
#> GSM1009074 1 0.4150 0.782 0.612 0.000 0 0.388 0.000
#> GSM1009088 1 0.0510 0.590 0.984 0.000 0 0.016 0.000
#> GSM1009102 4 0.0000 0.887 0.000 0.000 0 1.000 0.000
#> GSM1009116 5 0.0000 0.956 0.000 0.000 0 0.000 1.000
#> GSM1009130 2 0.1544 0.716 0.000 0.932 0 0.000 0.068
#> GSM1009144 1 0.3003 0.724 0.812 0.000 0 0.188 0.000
#> GSM1009158 1 0.4150 0.782 0.612 0.000 0 0.388 0.000
#> GSM1009172 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> GSM1009186 2 0.3684 0.813 0.280 0.720 0 0.000 0.000
#> GSM1009200 2 0.4150 0.796 0.388 0.612 0 0.000 0.000
#> GSM1009075 1 0.4150 0.782 0.612 0.000 0 0.388 0.000
#> GSM1009089 4 0.0000 0.887 0.000 0.000 0 1.000 0.000
#> GSM1009103 4 0.0000 0.887 0.000 0.000 0 1.000 0.000
#> GSM1009117 5 0.0000 0.956 0.000 0.000 0 0.000 1.000
#> GSM1009131 2 0.4150 0.796 0.388 0.612 0 0.000 0.000
#> GSM1009145 4 0.0000 0.887 0.000 0.000 0 1.000 0.000
#> GSM1009159 4 0.0000 0.887 0.000 0.000 0 1.000 0.000
#> GSM1009173 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> GSM1009187 1 0.1965 0.637 0.904 0.000 0 0.096 0.000
#> GSM1009201 1 0.0898 0.548 0.972 0.008 0 0.020 0.000
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1009062 6 0.0000 0.9670 0.000 0.000 0 0.000 0.000 1.000
#> GSM1009076 2 0.0000 0.9674 0.000 1.000 0 0.000 0.000 0.000
#> GSM1009090 4 0.0000 0.9107 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009104 5 0.0000 1.0000 0.000 0.000 0 0.000 1.000 0.000
#> GSM1009118 2 0.0000 0.9674 0.000 1.000 0 0.000 0.000 0.000
#> GSM1009132 1 0.2454 0.8104 0.840 0.160 0 0.000 0.000 0.000
#> GSM1009146 4 0.2454 0.8410 0.160 0.000 0 0.840 0.000 0.000
#> GSM1009160 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009174 1 0.3446 0.6408 0.692 0.308 0 0.000 0.000 0.000
#> GSM1009188 4 0.3288 0.7341 0.276 0.000 0 0.724 0.000 0.000
#> GSM1009063 6 0.0000 0.9670 0.000 0.000 0 0.000 0.000 1.000
#> GSM1009077 2 0.0000 0.9674 0.000 1.000 0 0.000 0.000 0.000
#> GSM1009091 4 0.0000 0.9107 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009105 5 0.0000 1.0000 0.000 0.000 0 0.000 1.000 0.000
#> GSM1009119 4 0.3727 0.5458 0.388 0.000 0 0.612 0.000 0.000
#> GSM1009133 4 0.3647 0.6015 0.360 0.000 0 0.640 0.000 0.000
#> GSM1009147 1 0.0713 0.8664 0.972 0.000 0 0.028 0.000 0.000
#> GSM1009161 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009175 1 0.3288 0.6898 0.724 0.276 0 0.000 0.000 0.000
#> GSM1009189 1 0.0000 0.8807 1.000 0.000 0 0.000 0.000 0.000
#> GSM1009064 6 0.0000 0.9670 0.000 0.000 0 0.000 0.000 1.000
#> GSM1009078 1 0.0260 0.8756 0.992 0.000 0 0.008 0.000 0.000
#> GSM1009092 4 0.0000 0.9107 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009106 5 0.0000 1.0000 0.000 0.000 0 0.000 1.000 0.000
#> GSM1009120 4 0.2454 0.8410 0.160 0.000 0 0.840 0.000 0.000
#> GSM1009134 4 0.0547 0.9036 0.020 0.000 0 0.980 0.000 0.000
#> GSM1009148 6 0.2454 0.8067 0.160 0.000 0 0.000 0.000 0.840
#> GSM1009162 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009176 2 0.0146 0.9645 0.004 0.996 0 0.000 0.000 0.000
#> GSM1009190 1 0.0000 0.8807 1.000 0.000 0 0.000 0.000 0.000
#> GSM1009065 6 0.0000 0.9670 0.000 0.000 0 0.000 0.000 1.000
#> GSM1009079 2 0.0000 0.9674 0.000 1.000 0 0.000 0.000 0.000
#> GSM1009093 4 0.0000 0.9107 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009107 5 0.0000 1.0000 0.000 0.000 0 0.000 1.000 0.000
#> GSM1009121 1 0.3072 0.8131 0.840 0.076 0 0.000 0.084 0.000
#> GSM1009135 4 0.2793 0.7945 0.200 0.000 0 0.800 0.000 0.000
#> GSM1009149 4 0.0000 0.9107 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009163 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009177 2 0.2135 0.8229 0.128 0.872 0 0.000 0.000 0.000
#> GSM1009191 1 0.0000 0.8807 1.000 0.000 0 0.000 0.000 0.000
#> GSM1009066 6 0.0000 0.9670 0.000 0.000 0 0.000 0.000 1.000
#> GSM1009080 2 0.0000 0.9674 0.000 1.000 0 0.000 0.000 0.000
#> GSM1009094 4 0.0000 0.9107 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009108 5 0.0000 1.0000 0.000 0.000 0 0.000 1.000 0.000
#> GSM1009122 2 0.0000 0.9674 0.000 1.000 0 0.000 0.000 0.000
#> GSM1009136 4 0.0000 0.9107 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009150 4 0.0000 0.9107 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009164 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009178 1 0.2454 0.8104 0.840 0.160 0 0.000 0.000 0.000
#> GSM1009192 4 0.3351 0.7184 0.288 0.000 0 0.712 0.000 0.000
#> GSM1009067 6 0.0000 0.9670 0.000 0.000 0 0.000 0.000 1.000
#> GSM1009081 2 0.0000 0.9674 0.000 1.000 0 0.000 0.000 0.000
#> GSM1009095 4 0.0000 0.9107 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009109 5 0.0000 1.0000 0.000 0.000 0 0.000 1.000 0.000
#> GSM1009123 4 0.0000 0.9107 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009137 1 0.3823 -0.0437 0.564 0.000 0 0.436 0.000 0.000
#> GSM1009151 6 0.0000 0.9670 0.000 0.000 0 0.000 0.000 1.000
#> GSM1009165 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009179 1 0.2454 0.8104 0.840 0.160 0 0.000 0.000 0.000
#> GSM1009193 4 0.2454 0.8410 0.160 0.000 0 0.840 0.000 0.000
#> GSM1009068 6 0.0000 0.9670 0.000 0.000 0 0.000 0.000 1.000
#> GSM1009082 2 0.0000 0.9674 0.000 1.000 0 0.000 0.000 0.000
#> GSM1009096 4 0.0000 0.9107 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009110 5 0.0000 1.0000 0.000 0.000 0 0.000 1.000 0.000
#> GSM1009124 1 0.0000 0.8807 1.000 0.000 0 0.000 0.000 0.000
#> GSM1009138 4 0.0146 0.9094 0.004 0.000 0 0.996 0.000 0.000
#> GSM1009152 6 0.0000 0.9670 0.000 0.000 0 0.000 0.000 1.000
#> GSM1009166 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009180 1 0.2454 0.8104 0.840 0.160 0 0.000 0.000 0.000
#> GSM1009194 1 0.0000 0.8807 1.000 0.000 0 0.000 0.000 0.000
#> GSM1009069 6 0.0000 0.9670 0.000 0.000 0 0.000 0.000 1.000
#> GSM1009083 2 0.0000 0.9674 0.000 1.000 0 0.000 0.000 0.000
#> GSM1009097 4 0.0000 0.9107 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009111 5 0.0000 1.0000 0.000 0.000 0 0.000 1.000 0.000
#> GSM1009125 2 0.2941 0.7252 0.000 0.780 0 0.000 0.220 0.000
#> GSM1009139 1 0.2300 0.8213 0.856 0.144 0 0.000 0.000 0.000
#> GSM1009153 6 0.0000 0.9670 0.000 0.000 0 0.000 0.000 1.000
#> GSM1009167 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009181 2 0.0260 0.9612 0.008 0.992 0 0.000 0.000 0.000
#> GSM1009195 2 0.0000 0.9674 0.000 1.000 0 0.000 0.000 0.000
#> GSM1009070 6 0.0000 0.9670 0.000 0.000 0 0.000 0.000 1.000
#> GSM1009084 2 0.0000 0.9674 0.000 1.000 0 0.000 0.000 0.000
#> GSM1009098 4 0.0000 0.9107 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009112 5 0.0000 1.0000 0.000 0.000 0 0.000 1.000 0.000
#> GSM1009126 1 0.0000 0.8807 1.000 0.000 0 0.000 0.000 0.000
#> GSM1009140 4 0.0000 0.9107 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009154 6 0.3247 0.7826 0.156 0.000 0 0.036 0.000 0.808
#> GSM1009168 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009182 1 0.2664 0.7911 0.816 0.184 0 0.000 0.000 0.000
#> GSM1009196 4 0.3512 0.7337 0.272 0.000 0 0.720 0.000 0.008
#> GSM1009071 6 0.0000 0.9670 0.000 0.000 0 0.000 0.000 1.000
#> GSM1009085 2 0.0000 0.9674 0.000 1.000 0 0.000 0.000 0.000
#> GSM1009099 4 0.0000 0.9107 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009113 5 0.0000 1.0000 0.000 0.000 0 0.000 1.000 0.000
#> GSM1009127 4 0.2454 0.8410 0.160 0.000 0 0.840 0.000 0.000
#> GSM1009141 1 0.0000 0.8807 1.000 0.000 0 0.000 0.000 0.000
#> GSM1009155 6 0.2454 0.8067 0.160 0.000 0 0.000 0.000 0.840
#> GSM1009169 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009183 1 0.3288 0.6898 0.724 0.276 0 0.000 0.000 0.000
#> GSM1009197 4 0.2454 0.8410 0.160 0.000 0 0.840 0.000 0.000
#> GSM1009072 6 0.0000 0.9670 0.000 0.000 0 0.000 0.000 1.000
#> GSM1009086 2 0.0000 0.9674 0.000 1.000 0 0.000 0.000 0.000
#> GSM1009100 4 0.0000 0.9107 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009114 5 0.0000 1.0000 0.000 0.000 0 0.000 1.000 0.000
#> GSM1009128 1 0.0000 0.8807 1.000 0.000 0 0.000 0.000 0.000
#> GSM1009142 1 0.0000 0.8807 1.000 0.000 0 0.000 0.000 0.000
#> GSM1009156 1 0.0000 0.8807 1.000 0.000 0 0.000 0.000 0.000
#> GSM1009170 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009184 1 0.3288 0.6898 0.724 0.276 0 0.000 0.000 0.000
#> GSM1009198 4 0.3288 0.7341 0.276 0.000 0 0.724 0.000 0.000
#> GSM1009073 6 0.0000 0.9670 0.000 0.000 0 0.000 0.000 1.000
#> GSM1009087 1 0.0000 0.8807 1.000 0.000 0 0.000 0.000 0.000
#> GSM1009101 4 0.0000 0.9107 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009115 5 0.0000 1.0000 0.000 0.000 0 0.000 1.000 0.000
#> GSM1009129 2 0.1863 0.8747 0.000 0.896 0 0.000 0.104 0.000
#> GSM1009143 4 0.2454 0.8410 0.160 0.000 0 0.840 0.000 0.000
#> GSM1009157 1 0.1204 0.8639 0.944 0.056 0 0.000 0.000 0.000
#> GSM1009171 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009185 1 0.0000 0.8807 1.000 0.000 0 0.000 0.000 0.000
#> GSM1009199 2 0.0000 0.9674 0.000 1.000 0 0.000 0.000 0.000
#> GSM1009074 6 0.0000 0.9670 0.000 0.000 0 0.000 0.000 1.000
#> GSM1009088 1 0.0000 0.8807 1.000 0.000 0 0.000 0.000 0.000
#> GSM1009102 4 0.0000 0.9107 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009116 5 0.0000 1.0000 0.000 0.000 0 0.000 1.000 0.000
#> GSM1009130 2 0.1714 0.8779 0.000 0.908 0 0.000 0.092 0.000
#> GSM1009144 1 0.2378 0.7231 0.848 0.000 0 0.152 0.000 0.000
#> GSM1009158 4 0.0000 0.9107 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009172 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009186 1 0.3288 0.6898 0.724 0.276 0 0.000 0.000 0.000
#> GSM1009200 1 0.0000 0.8807 1.000 0.000 0 0.000 0.000 0.000
#> GSM1009075 6 0.0000 0.9670 0.000 0.000 0 0.000 0.000 1.000
#> GSM1009089 4 0.0000 0.9107 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009103 4 0.0000 0.9107 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009117 5 0.0000 1.0000 0.000 0.000 0 0.000 1.000 0.000
#> GSM1009131 1 0.0000 0.8807 1.000 0.000 0 0.000 0.000 0.000
#> GSM1009145 4 0.0000 0.9107 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009159 4 0.0000 0.9107 0.000 0.000 0 1.000 0.000 0.000
#> GSM1009173 3 0.0000 1.0000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009187 1 0.3536 0.8068 0.832 0.052 0 0.072 0.000 0.044
#> GSM1009201 1 0.0000 0.8807 1.000 0.000 0 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 temperature(p) time(p) specimen(p) k
#> ATC:pam 138 0.393 0.666 1.78e-13 2
#> ATC:pam 128 0.582 0.970 1.27e-28 3
#> ATC:pam 139 0.947 0.999 3.30e-51 4
#> ATC:pam 136 0.961 0.999 1.09e-62 5
#> ATC:pam 139 0.969 1.000 3.15e-74 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 140 columns.
#> Top rows (1000, 2000, 3000, 4000, 5000) are extracted by 'ATC' method.
#> Subgroups are detected by 'mclust' method.
#> Performed in total 1250 partitions by row resampling.
#> Best k for subgroups seems to be 2.
#>
#> Following methods can be applied to this 'ConsensusPartition' object:
#> [1] "cola_report" "collect_classes" "collect_plots"
#> [4] "collect_stats" "colnames" "compare_signatures"
#> [7] "consensus_heatmap" "dimension_reduction" "functional_enrichment"
#> [10] "get_anno_col" "get_anno" "get_classes"
#> [13] "get_consensus" "get_matrix" "get_membership"
#> [16] "get_param" "get_signatures" "get_stats"
#> [19] "is_best_k" "is_stable_k" "membership_heatmap"
#> [22] "ncol" "nrow" "plot_ecdf"
#> [25] "rownames" "select_partition_number" "show"
#> [28] "suggest_best_k" "test_to_known_factors"
collect_plots() function collects all the plots made from res for all k (number of partitions)
into one single page to provide an easy and fast comparison between different k.
collect_plots(res)
The plots are:
k and the heatmap of
predicted classes for each k.k.k.k.All the plots in panels can be made by individual functions and they are plotted later in this section.
select_partition_number() produces several plots showing different
statistics for choosing “optimized” k. There are following statistics:
k;k, the area increased is defined as \(A_k - A_{k-1}\).The detailed explanations of these statistics can be found in the cola vignette.
Generally speaking, lower PAC score, higher mean silhouette score or higher
concordance corresponds to better partition. Rand index and Jaccard index
measure how similar the current partition is compared to partition with k-1.
If they are too similar, we won't accept k is better than k-1.
select_partition_number(res)
The numeric values for all these statistics can be obtained by get_stats().
get_stats(res)
#> k 1-PAC mean_silhouette concordance area_increased Rand Jaccard
#> 2 2 1.000 0.999 0.999 0.1830 0.819 0.819
#> 3 3 0.481 0.820 0.893 2.1232 0.592 0.502
#> 4 4 0.810 0.874 0.907 0.2027 0.705 0.406
#> 5 5 0.709 0.871 0.919 -0.0589 0.710 0.383
#> 6 6 0.854 0.897 0.937 0.2138 0.829 0.535
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
#> GSM1009062 1 0.0000 0.999 1.000 0.000
#> GSM1009076 1 0.0000 0.999 1.000 0.000
#> GSM1009090 1 0.0000 0.999 1.000 0.000
#> GSM1009104 1 0.0672 0.993 0.992 0.008
#> GSM1009118 1 0.0000 0.999 1.000 0.000
#> GSM1009132 1 0.0000 0.999 1.000 0.000
#> GSM1009146 1 0.0000 0.999 1.000 0.000
#> GSM1009160 2 0.0000 1.000 0.000 1.000
#> GSM1009174 1 0.0000 0.999 1.000 0.000
#> GSM1009188 1 0.0000 0.999 1.000 0.000
#> GSM1009063 1 0.0000 0.999 1.000 0.000
#> GSM1009077 1 0.0000 0.999 1.000 0.000
#> GSM1009091 1 0.0000 0.999 1.000 0.000
#> GSM1009105 1 0.0672 0.993 0.992 0.008
#> GSM1009119 1 0.0000 0.999 1.000 0.000
#> GSM1009133 1 0.0000 0.999 1.000 0.000
#> GSM1009147 1 0.0000 0.999 1.000 0.000
#> GSM1009161 2 0.0000 1.000 0.000 1.000
#> GSM1009175 1 0.0000 0.999 1.000 0.000
#> GSM1009189 1 0.0000 0.999 1.000 0.000
#> GSM1009064 1 0.0000 0.999 1.000 0.000
#> GSM1009078 1 0.0000 0.999 1.000 0.000
#> GSM1009092 1 0.0000 0.999 1.000 0.000
#> GSM1009106 1 0.0672 0.993 0.992 0.008
#> GSM1009120 1 0.0000 0.999 1.000 0.000
#> GSM1009134 1 0.0000 0.999 1.000 0.000
#> GSM1009148 1 0.0000 0.999 1.000 0.000
#> GSM1009162 2 0.0000 1.000 0.000 1.000
#> GSM1009176 1 0.0000 0.999 1.000 0.000
#> GSM1009190 1 0.0000 0.999 1.000 0.000
#> GSM1009065 1 0.0000 0.999 1.000 0.000
#> GSM1009079 1 0.0000 0.999 1.000 0.000
#> GSM1009093 1 0.0000 0.999 1.000 0.000
#> GSM1009107 1 0.0672 0.993 0.992 0.008
#> GSM1009121 1 0.0000 0.999 1.000 0.000
#> GSM1009135 1 0.0000 0.999 1.000 0.000
#> GSM1009149 1 0.0000 0.999 1.000 0.000
#> GSM1009163 2 0.0000 1.000 0.000 1.000
#> GSM1009177 1 0.0000 0.999 1.000 0.000
#> GSM1009191 1 0.0000 0.999 1.000 0.000
#> GSM1009066 1 0.0000 0.999 1.000 0.000
#> GSM1009080 1 0.0000 0.999 1.000 0.000
#> GSM1009094 1 0.0000 0.999 1.000 0.000
#> GSM1009108 1 0.0672 0.993 0.992 0.008
#> GSM1009122 1 0.0000 0.999 1.000 0.000
#> GSM1009136 1 0.0000 0.999 1.000 0.000
#> GSM1009150 1 0.0000 0.999 1.000 0.000
#> GSM1009164 2 0.0000 1.000 0.000 1.000
#> GSM1009178 1 0.0000 0.999 1.000 0.000
#> GSM1009192 1 0.0000 0.999 1.000 0.000
#> GSM1009067 1 0.0000 0.999 1.000 0.000
#> GSM1009081 1 0.0000 0.999 1.000 0.000
#> GSM1009095 1 0.0000 0.999 1.000 0.000
#> GSM1009109 1 0.0000 0.999 1.000 0.000
#> GSM1009123 1 0.0000 0.999 1.000 0.000
#> GSM1009137 1 0.0000 0.999 1.000 0.000
#> GSM1009151 1 0.0000 0.999 1.000 0.000
#> GSM1009165 2 0.0000 1.000 0.000 1.000
#> GSM1009179 1 0.0000 0.999 1.000 0.000
#> GSM1009193 1 0.0000 0.999 1.000 0.000
#> GSM1009068 1 0.0000 0.999 1.000 0.000
#> GSM1009082 1 0.0000 0.999 1.000 0.000
#> GSM1009096 1 0.0000 0.999 1.000 0.000
#> GSM1009110 1 0.0672 0.993 0.992 0.008
#> GSM1009124 1 0.0000 0.999 1.000 0.000
#> GSM1009138 1 0.0000 0.999 1.000 0.000
#> GSM1009152 1 0.0000 0.999 1.000 0.000
#> GSM1009166 2 0.0000 1.000 0.000 1.000
#> GSM1009180 1 0.0000 0.999 1.000 0.000
#> GSM1009194 1 0.0000 0.999 1.000 0.000
#> GSM1009069 1 0.0000 0.999 1.000 0.000
#> GSM1009083 1 0.0000 0.999 1.000 0.000
#> GSM1009097 1 0.0000 0.999 1.000 0.000
#> GSM1009111 1 0.0672 0.993 0.992 0.008
#> GSM1009125 1 0.0000 0.999 1.000 0.000
#> GSM1009139 1 0.0000 0.999 1.000 0.000
#> GSM1009153 1 0.0000 0.999 1.000 0.000
#> GSM1009167 2 0.0000 1.000 0.000 1.000
#> GSM1009181 1 0.0000 0.999 1.000 0.000
#> GSM1009195 1 0.0000 0.999 1.000 0.000
#> GSM1009070 1 0.0000 0.999 1.000 0.000
#> GSM1009084 1 0.0000 0.999 1.000 0.000
#> GSM1009098 1 0.0000 0.999 1.000 0.000
#> GSM1009112 1 0.0672 0.993 0.992 0.008
#> GSM1009126 1 0.0000 0.999 1.000 0.000
#> GSM1009140 1 0.0000 0.999 1.000 0.000
#> GSM1009154 1 0.0000 0.999 1.000 0.000
#> GSM1009168 2 0.0000 1.000 0.000 1.000
#> GSM1009182 1 0.0000 0.999 1.000 0.000
#> GSM1009196 1 0.0000 0.999 1.000 0.000
#> GSM1009071 1 0.0000 0.999 1.000 0.000
#> GSM1009085 1 0.0000 0.999 1.000 0.000
#> GSM1009099 1 0.0000 0.999 1.000 0.000
#> GSM1009113 1 0.0672 0.993 0.992 0.008
#> GSM1009127 1 0.0000 0.999 1.000 0.000
#> GSM1009141 1 0.0000 0.999 1.000 0.000
#> GSM1009155 1 0.0000 0.999 1.000 0.000
#> GSM1009169 2 0.0000 1.000 0.000 1.000
#> GSM1009183 1 0.0000 0.999 1.000 0.000
#> GSM1009197 1 0.0000 0.999 1.000 0.000
#> GSM1009072 1 0.0000 0.999 1.000 0.000
#> GSM1009086 1 0.0000 0.999 1.000 0.000
#> GSM1009100 1 0.0000 0.999 1.000 0.000
#> GSM1009114 1 0.0000 0.999 1.000 0.000
#> GSM1009128 1 0.0000 0.999 1.000 0.000
#> GSM1009142 1 0.0000 0.999 1.000 0.000
#> GSM1009156 1 0.0000 0.999 1.000 0.000
#> GSM1009170 2 0.0000 1.000 0.000 1.000
#> GSM1009184 1 0.0000 0.999 1.000 0.000
#> GSM1009198 1 0.0000 0.999 1.000 0.000
#> GSM1009073 1 0.0000 0.999 1.000 0.000
#> GSM1009087 1 0.0000 0.999 1.000 0.000
#> GSM1009101 1 0.0000 0.999 1.000 0.000
#> GSM1009115 1 0.0672 0.993 0.992 0.008
#> GSM1009129 1 0.0000 0.999 1.000 0.000
#> GSM1009143 1 0.0000 0.999 1.000 0.000
#> GSM1009157 1 0.0000 0.999 1.000 0.000
#> GSM1009171 2 0.0000 1.000 0.000 1.000
#> GSM1009185 1 0.0000 0.999 1.000 0.000
#> GSM1009199 1 0.0000 0.999 1.000 0.000
#> GSM1009074 1 0.0000 0.999 1.000 0.000
#> GSM1009088 1 0.0000 0.999 1.000 0.000
#> GSM1009102 1 0.0000 0.999 1.000 0.000
#> GSM1009116 1 0.0672 0.993 0.992 0.008
#> GSM1009130 1 0.0000 0.999 1.000 0.000
#> GSM1009144 1 0.0000 0.999 1.000 0.000
#> GSM1009158 1 0.0000 0.999 1.000 0.000
#> GSM1009172 2 0.0000 1.000 0.000 1.000
#> GSM1009186 1 0.0000 0.999 1.000 0.000
#> GSM1009200 1 0.0000 0.999 1.000 0.000
#> GSM1009075 1 0.0000 0.999 1.000 0.000
#> GSM1009089 1 0.0000 0.999 1.000 0.000
#> GSM1009103 1 0.0000 0.999 1.000 0.000
#> GSM1009117 1 0.0672 0.993 0.992 0.008
#> GSM1009131 1 0.0000 0.999 1.000 0.000
#> GSM1009145 1 0.0000 0.999 1.000 0.000
#> GSM1009159 1 0.0000 0.999 1.000 0.000
#> GSM1009173 2 0.0000 1.000 0.000 1.000
#> GSM1009187 1 0.0000 0.999 1.000 0.000
#> GSM1009201 1 0.0000 0.999 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1009062 2 0.5465 0.6966 0.288 0.712 0
#> GSM1009076 2 0.3551 0.7965 0.132 0.868 0
#> GSM1009090 1 0.0000 0.8988 1.000 0.000 0
#> GSM1009104 2 0.0237 0.8090 0.004 0.996 0
#> GSM1009118 2 0.0592 0.8083 0.012 0.988 0
#> GSM1009132 2 0.5988 0.4854 0.368 0.632 0
#> GSM1009146 1 0.0000 0.8988 1.000 0.000 0
#> GSM1009160 3 0.0000 1.0000 0.000 0.000 1
#> GSM1009174 2 0.3551 0.7965 0.132 0.868 0
#> GSM1009188 1 0.2261 0.8941 0.932 0.068 0
#> GSM1009063 2 0.5465 0.6966 0.288 0.712 0
#> GSM1009077 2 0.3551 0.7965 0.132 0.868 0
#> GSM1009091 1 0.0000 0.8988 1.000 0.000 0
#> GSM1009105 2 0.0237 0.8090 0.004 0.996 0
#> GSM1009119 1 0.3340 0.8791 0.880 0.120 0
#> GSM1009133 1 0.3340 0.8791 0.880 0.120 0
#> GSM1009147 1 0.0000 0.8988 1.000 0.000 0
#> GSM1009161 3 0.0000 1.0000 0.000 0.000 1
#> GSM1009175 2 0.5591 0.7211 0.304 0.696 0
#> GSM1009189 1 0.3340 0.8791 0.880 0.120 0
#> GSM1009064 2 0.5465 0.6966 0.288 0.712 0
#> GSM1009078 1 0.0424 0.8985 0.992 0.008 0
#> GSM1009092 1 0.0000 0.8988 1.000 0.000 0
#> GSM1009106 2 0.0237 0.8090 0.004 0.996 0
#> GSM1009120 1 0.3340 0.8791 0.880 0.120 0
#> GSM1009134 1 0.3816 0.8571 0.852 0.148 0
#> GSM1009148 1 0.4121 0.8203 0.832 0.168 0
#> GSM1009162 3 0.0000 1.0000 0.000 0.000 1
#> GSM1009176 2 0.3551 0.7965 0.132 0.868 0
#> GSM1009190 1 0.2711 0.8935 0.912 0.088 0
#> GSM1009065 2 0.5465 0.6966 0.288 0.712 0
#> GSM1009079 2 0.3551 0.7965 0.132 0.868 0
#> GSM1009093 1 0.0000 0.8988 1.000 0.000 0
#> GSM1009107 2 0.0237 0.8090 0.004 0.996 0
#> GSM1009121 2 0.5016 0.7049 0.240 0.760 0
#> GSM1009135 1 0.3482 0.8736 0.872 0.128 0
#> GSM1009149 1 0.0237 0.8993 0.996 0.004 0
#> GSM1009163 3 0.0000 1.0000 0.000 0.000 1
#> GSM1009177 2 0.3551 0.7965 0.132 0.868 0
#> GSM1009191 2 0.5431 0.6729 0.284 0.716 0
#> GSM1009066 2 0.5465 0.6966 0.288 0.712 0
#> GSM1009080 2 0.3551 0.7965 0.132 0.868 0
#> GSM1009094 1 0.0000 0.8988 1.000 0.000 0
#> GSM1009108 2 0.0237 0.8090 0.004 0.996 0
#> GSM1009122 2 0.0237 0.8090 0.004 0.996 0
#> GSM1009136 1 0.3340 0.8791 0.880 0.120 0
#> GSM1009150 1 0.3412 0.8665 0.876 0.124 0
#> GSM1009164 3 0.0000 1.0000 0.000 0.000 1
#> GSM1009178 1 0.4654 0.6844 0.792 0.208 0
#> GSM1009192 1 0.2165 0.8972 0.936 0.064 0
#> GSM1009067 2 0.5465 0.6966 0.288 0.712 0
#> GSM1009081 2 0.3551 0.7965 0.132 0.868 0
#> GSM1009095 1 0.0000 0.8988 1.000 0.000 0
#> GSM1009109 2 0.0237 0.8090 0.004 0.996 0
#> GSM1009123 1 0.3340 0.8791 0.880 0.120 0
#> GSM1009137 1 0.3412 0.8765 0.876 0.124 0
#> GSM1009151 1 0.5835 0.4903 0.660 0.340 0
#> GSM1009165 3 0.0000 1.0000 0.000 0.000 1
#> GSM1009179 2 0.5733 0.7014 0.324 0.676 0
#> GSM1009193 1 0.1753 0.8948 0.952 0.048 0
#> GSM1009068 2 0.5465 0.6966 0.288 0.712 0
#> GSM1009082 2 0.3551 0.7965 0.132 0.868 0
#> GSM1009096 1 0.0000 0.8988 1.000 0.000 0
#> GSM1009110 2 0.0237 0.8090 0.004 0.996 0
#> GSM1009124 1 0.3879 0.8639 0.848 0.152 0
#> GSM1009138 1 0.3816 0.8571 0.852 0.148 0
#> GSM1009152 2 0.6095 0.4985 0.392 0.608 0
#> GSM1009166 3 0.0000 1.0000 0.000 0.000 1
#> GSM1009180 1 0.3116 0.8282 0.892 0.108 0
#> GSM1009194 2 0.5216 0.6821 0.260 0.740 0
#> GSM1009069 2 0.5016 0.7518 0.240 0.760 0
#> GSM1009083 2 0.2165 0.8121 0.064 0.936 0
#> GSM1009097 1 0.0000 0.8988 1.000 0.000 0
#> GSM1009111 2 0.0237 0.8090 0.004 0.996 0
#> GSM1009125 2 0.0237 0.8090 0.004 0.996 0
#> GSM1009139 1 0.3879 0.8548 0.848 0.152 0
#> GSM1009153 2 0.6286 0.3057 0.464 0.536 0
#> GSM1009167 3 0.0000 1.0000 0.000 0.000 1
#> GSM1009181 2 0.3551 0.7965 0.132 0.868 0
#> GSM1009195 2 0.1411 0.8109 0.036 0.964 0
#> GSM1009070 1 0.2356 0.8821 0.928 0.072 0
#> GSM1009084 2 0.2878 0.8076 0.096 0.904 0
#> GSM1009098 1 0.0000 0.8988 1.000 0.000 0
#> GSM1009112 2 0.0237 0.8090 0.004 0.996 0
#> GSM1009126 1 0.3340 0.8791 0.880 0.120 0
#> GSM1009140 1 0.3340 0.8791 0.880 0.120 0
#> GSM1009154 1 0.3192 0.8674 0.888 0.112 0
#> GSM1009168 3 0.0000 1.0000 0.000 0.000 1
#> GSM1009182 2 0.5733 0.7014 0.324 0.676 0
#> GSM1009196 1 0.0592 0.8958 0.988 0.012 0
#> GSM1009071 2 0.5465 0.6966 0.288 0.712 0
#> GSM1009085 2 0.3551 0.7965 0.132 0.868 0
#> GSM1009099 1 0.0000 0.8988 1.000 0.000 0
#> GSM1009113 2 0.0237 0.8090 0.004 0.996 0
#> GSM1009127 1 0.3340 0.8791 0.880 0.120 0
#> GSM1009141 1 0.4399 0.8240 0.812 0.188 0
#> GSM1009155 1 0.6111 0.0475 0.604 0.396 0
#> GSM1009169 3 0.0000 1.0000 0.000 0.000 1
#> GSM1009183 2 0.3752 0.7950 0.144 0.856 0
#> GSM1009197 1 0.0000 0.8988 1.000 0.000 0
#> GSM1009072 2 0.5465 0.6966 0.288 0.712 0
#> GSM1009086 2 0.3551 0.7965 0.132 0.868 0
#> GSM1009100 1 0.0000 0.8988 1.000 0.000 0
#> GSM1009114 2 0.0424 0.8097 0.008 0.992 0
#> GSM1009128 1 0.3340 0.8692 0.880 0.120 0
#> GSM1009142 1 0.3816 0.8571 0.852 0.148 0
#> GSM1009156 1 0.1289 0.8818 0.968 0.032 0
#> GSM1009170 3 0.0000 1.0000 0.000 0.000 1
#> GSM1009184 2 0.4291 0.7953 0.180 0.820 0
#> GSM1009198 1 0.1753 0.8948 0.952 0.048 0
#> GSM1009073 2 0.5465 0.6966 0.288 0.712 0
#> GSM1009087 1 0.1643 0.8804 0.956 0.044 0
#> GSM1009101 1 0.0000 0.8988 1.000 0.000 0
#> GSM1009115 2 0.0237 0.8090 0.004 0.996 0
#> GSM1009129 2 0.0237 0.8090 0.004 0.996 0
#> GSM1009143 1 0.3340 0.8791 0.880 0.120 0
#> GSM1009157 1 0.5678 0.4232 0.684 0.316 0
#> GSM1009171 3 0.0000 1.0000 0.000 0.000 1
#> GSM1009185 1 0.0000 0.8988 1.000 0.000 0
#> GSM1009199 2 0.0592 0.8099 0.012 0.988 0
#> GSM1009074 2 0.5465 0.6966 0.288 0.712 0
#> GSM1009088 1 0.2711 0.8446 0.912 0.088 0
#> GSM1009102 1 0.0000 0.8988 1.000 0.000 0
#> GSM1009116 2 0.0237 0.8090 0.004 0.996 0
#> GSM1009130 2 0.0424 0.8094 0.008 0.992 0
#> GSM1009144 1 0.3686 0.8642 0.860 0.140 0
#> GSM1009158 1 0.0000 0.8988 1.000 0.000 0
#> GSM1009172 3 0.0000 1.0000 0.000 0.000 1
#> GSM1009186 2 0.3816 0.7979 0.148 0.852 0
#> GSM1009200 2 0.6045 0.4725 0.380 0.620 0
#> GSM1009075 2 0.5465 0.6966 0.288 0.712 0
#> GSM1009089 1 0.0000 0.8988 1.000 0.000 0
#> GSM1009103 1 0.0000 0.8988 1.000 0.000 0
#> GSM1009117 2 0.0237 0.8090 0.004 0.996 0
#> GSM1009131 1 0.3267 0.8762 0.884 0.116 0
#> GSM1009145 1 0.1964 0.8947 0.944 0.056 0
#> GSM1009159 1 0.0000 0.8988 1.000 0.000 0
#> GSM1009173 3 0.0000 1.0000 0.000 0.000 1
#> GSM1009187 1 0.5058 0.5782 0.756 0.244 0
#> GSM1009201 1 0.3340 0.8791 0.880 0.120 0
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1009062 1 0.1576 0.917 0.948 0.048 0 0.004
#> GSM1009076 2 0.5152 0.727 0.316 0.664 0 0.020
#> GSM1009090 1 0.2048 0.938 0.928 0.008 0 0.064
#> GSM1009104 2 0.1557 0.692 0.000 0.944 0 0.056
#> GSM1009118 4 0.0937 0.950 0.012 0.012 0 0.976
#> GSM1009132 4 0.0469 0.971 0.012 0.000 0 0.988
#> GSM1009146 1 0.1807 0.945 0.940 0.008 0 0.052
#> GSM1009160 3 0.0000 1.000 0.000 0.000 1 0.000
#> GSM1009174 2 0.5250 0.725 0.316 0.660 0 0.024
#> GSM1009188 4 0.0817 0.965 0.024 0.000 0 0.976
#> GSM1009063 1 0.1389 0.915 0.952 0.048 0 0.000
#> GSM1009077 2 0.5250 0.725 0.316 0.660 0 0.024
#> GSM1009091 1 0.1722 0.946 0.944 0.008 0 0.048
#> GSM1009105 2 0.1557 0.692 0.000 0.944 0 0.056
#> GSM1009119 4 0.0336 0.971 0.008 0.000 0 0.992
#> GSM1009133 4 0.0336 0.971 0.008 0.000 0 0.992
#> GSM1009147 1 0.1389 0.948 0.952 0.000 0 0.048
#> GSM1009161 3 0.0000 1.000 0.000 0.000 1 0.000
#> GSM1009175 2 0.5403 0.684 0.348 0.628 0 0.024
#> GSM1009189 4 0.0592 0.969 0.016 0.000 0 0.984
#> GSM1009064 1 0.1389 0.915 0.952 0.048 0 0.000
#> GSM1009078 1 0.2101 0.943 0.928 0.012 0 0.060
#> GSM1009092 1 0.1890 0.941 0.936 0.008 0 0.056
#> GSM1009106 2 0.1557 0.692 0.000 0.944 0 0.056
#> GSM1009120 4 0.0336 0.971 0.008 0.000 0 0.992
#> GSM1009134 4 0.0336 0.971 0.008 0.000 0 0.992
#> GSM1009148 1 0.1854 0.947 0.940 0.012 0 0.048
#> GSM1009162 3 0.0000 1.000 0.000 0.000 1 0.000
#> GSM1009176 2 0.5152 0.727 0.316 0.664 0 0.020
#> GSM1009190 4 0.0921 0.962 0.028 0.000 0 0.972
#> GSM1009065 1 0.1389 0.915 0.952 0.048 0 0.000
#> GSM1009079 2 0.5349 0.708 0.336 0.640 0 0.024
#> GSM1009093 1 0.1722 0.946 0.944 0.008 0 0.048
#> GSM1009107 2 0.1557 0.692 0.000 0.944 0 0.056
#> GSM1009121 4 0.0469 0.971 0.012 0.000 0 0.988
#> GSM1009135 4 0.0336 0.971 0.008 0.000 0 0.992
#> GSM1009149 1 0.1807 0.945 0.940 0.008 0 0.052
#> GSM1009163 3 0.0000 1.000 0.000 0.000 1 0.000
#> GSM1009177 2 0.5250 0.725 0.316 0.660 0 0.024
#> GSM1009191 4 0.0592 0.969 0.016 0.000 0 0.984
#> GSM1009066 1 0.1389 0.915 0.952 0.048 0 0.000
#> GSM1009080 2 0.5291 0.716 0.324 0.652 0 0.024
#> GSM1009094 1 0.1722 0.946 0.944 0.008 0 0.048
#> GSM1009108 2 0.1557 0.692 0.000 0.944 0 0.056
#> GSM1009122 4 0.5105 0.473 0.028 0.276 0 0.696
#> GSM1009136 4 0.0336 0.971 0.008 0.000 0 0.992
#> GSM1009150 1 0.1807 0.945 0.940 0.008 0 0.052
#> GSM1009164 3 0.0000 1.000 0.000 0.000 1 0.000
#> GSM1009178 1 0.1854 0.945 0.940 0.012 0 0.048
#> GSM1009192 4 0.2704 0.848 0.124 0.000 0 0.876
#> GSM1009067 1 0.1389 0.915 0.952 0.048 0 0.000
#> GSM1009081 2 0.5152 0.727 0.316 0.664 0 0.020
#> GSM1009095 1 0.1389 0.948 0.952 0.000 0 0.048
#> GSM1009109 2 0.2345 0.687 0.000 0.900 0 0.100
#> GSM1009123 4 0.0336 0.971 0.008 0.000 0 0.992
#> GSM1009137 4 0.0336 0.971 0.008 0.000 0 0.992
#> GSM1009151 1 0.1388 0.942 0.960 0.012 0 0.028
#> GSM1009165 3 0.0000 1.000 0.000 0.000 1 0.000
#> GSM1009179 1 0.2494 0.911 0.916 0.048 0 0.036
#> GSM1009193 4 0.0921 0.962 0.028 0.000 0 0.972
#> GSM1009068 1 0.1576 0.917 0.948 0.048 0 0.004
#> GSM1009082 2 0.5152 0.727 0.316 0.664 0 0.020
#> GSM1009096 1 0.1722 0.946 0.944 0.008 0 0.048
#> GSM1009110 2 0.1557 0.692 0.000 0.944 0 0.056
#> GSM1009124 4 0.0469 0.971 0.012 0.000 0 0.988
#> GSM1009138 4 0.0336 0.971 0.008 0.000 0 0.992
#> GSM1009152 1 0.1584 0.945 0.952 0.012 0 0.036
#> GSM1009166 3 0.0000 1.000 0.000 0.000 1 0.000
#> GSM1009180 1 0.1970 0.943 0.932 0.008 0 0.060
#> GSM1009194 4 0.1297 0.956 0.016 0.020 0 0.964
#> GSM1009069 1 0.3801 0.639 0.780 0.220 0 0.000
#> GSM1009083 2 0.5250 0.725 0.316 0.660 0 0.024
#> GSM1009097 1 0.1722 0.946 0.944 0.008 0 0.048
#> GSM1009111 2 0.1557 0.692 0.000 0.944 0 0.056
#> GSM1009125 2 0.5231 0.580 0.028 0.676 0 0.296
#> GSM1009139 4 0.0336 0.971 0.008 0.000 0 0.992
#> GSM1009153 1 0.0657 0.932 0.984 0.012 0 0.004
#> GSM1009167 3 0.0000 1.000 0.000 0.000 1 0.000
#> GSM1009181 2 0.5250 0.725 0.316 0.660 0 0.024
#> GSM1009195 2 0.6098 0.572 0.068 0.616 0 0.316
#> GSM1009070 1 0.1389 0.948 0.952 0.000 0 0.048
#> GSM1009084 2 0.5623 0.730 0.292 0.660 0 0.048
#> GSM1009098 1 0.1722 0.946 0.944 0.008 0 0.048
#> GSM1009112 2 0.1557 0.692 0.000 0.944 0 0.056
#> GSM1009126 4 0.0336 0.971 0.008 0.000 0 0.992
#> GSM1009140 4 0.0336 0.971 0.008 0.000 0 0.992
#> GSM1009154 1 0.1854 0.947 0.940 0.012 0 0.048
#> GSM1009168 3 0.0000 1.000 0.000 0.000 1 0.000
#> GSM1009182 1 0.4399 0.599 0.768 0.212 0 0.020
#> GSM1009196 1 0.1059 0.938 0.972 0.012 0 0.016
#> GSM1009071 1 0.1389 0.915 0.952 0.048 0 0.000
#> GSM1009085 2 0.5250 0.725 0.316 0.660 0 0.024
#> GSM1009099 1 0.1722 0.946 0.944 0.008 0 0.048
#> GSM1009113 2 0.1557 0.692 0.000 0.944 0 0.056
#> GSM1009127 4 0.0336 0.971 0.008 0.000 0 0.992
#> GSM1009141 4 0.1118 0.943 0.036 0.000 0 0.964
#> GSM1009155 1 0.0657 0.932 0.984 0.012 0 0.004
#> GSM1009169 3 0.0000 1.000 0.000 0.000 1 0.000
#> GSM1009183 2 0.5311 0.711 0.328 0.648 0 0.024
#> GSM1009197 1 0.1722 0.946 0.944 0.008 0 0.048
#> GSM1009072 1 0.1389 0.915 0.952 0.048 0 0.000
#> GSM1009086 2 0.5152 0.727 0.316 0.664 0 0.020
#> GSM1009100 1 0.1722 0.946 0.944 0.008 0 0.048
#> GSM1009114 2 0.1557 0.692 0.000 0.944 0 0.056
#> GSM1009128 4 0.2345 0.868 0.100 0.000 0 0.900
#> GSM1009142 4 0.0336 0.971 0.008 0.000 0 0.992
#> GSM1009156 1 0.1389 0.948 0.952 0.000 0 0.048
#> GSM1009170 3 0.0000 1.000 0.000 0.000 1 0.000
#> GSM1009184 2 0.5233 0.707 0.332 0.648 0 0.020
#> GSM1009198 4 0.1302 0.946 0.044 0.000 0 0.956
#> GSM1009073 1 0.1389 0.915 0.952 0.048 0 0.000
#> GSM1009087 1 0.2101 0.943 0.928 0.012 0 0.060
#> GSM1009101 1 0.1722 0.946 0.944 0.008 0 0.048
#> GSM1009115 2 0.1557 0.692 0.000 0.944 0 0.056
#> GSM1009129 2 0.5300 0.576 0.028 0.664 0 0.308
#> GSM1009143 4 0.0336 0.971 0.008 0.000 0 0.992
#> GSM1009157 1 0.0657 0.932 0.984 0.012 0 0.004
#> GSM1009171 3 0.0000 1.000 0.000 0.000 1 0.000
#> GSM1009185 1 0.1389 0.948 0.952 0.000 0 0.048
#> GSM1009199 2 0.5764 0.310 0.028 0.520 0 0.452
#> GSM1009074 1 0.1389 0.915 0.952 0.048 0 0.000
#> GSM1009088 1 0.1059 0.938 0.972 0.012 0 0.016
#> GSM1009102 1 0.1576 0.947 0.948 0.004 0 0.048
#> GSM1009116 2 0.1557 0.692 0.000 0.944 0 0.056
#> GSM1009130 2 0.6162 0.595 0.076 0.620 0 0.304
#> GSM1009144 4 0.0336 0.971 0.008 0.000 0 0.992
#> GSM1009158 1 0.1389 0.948 0.952 0.000 0 0.048
#> GSM1009172 3 0.0000 1.000 0.000 0.000 1 0.000
#> GSM1009186 2 0.5193 0.717 0.324 0.656 0 0.020
#> GSM1009200 4 0.0469 0.971 0.012 0.000 0 0.988
#> GSM1009075 1 0.1389 0.915 0.952 0.048 0 0.000
#> GSM1009089 1 0.1389 0.948 0.952 0.000 0 0.048
#> GSM1009103 1 0.1722 0.946 0.944 0.008 0 0.048
#> GSM1009117 2 0.1557 0.692 0.000 0.944 0 0.056
#> GSM1009131 4 0.1211 0.949 0.040 0.000 0 0.960
#> GSM1009145 4 0.0817 0.965 0.024 0.000 0 0.976
#> GSM1009159 1 0.1807 0.945 0.940 0.008 0 0.052
#> GSM1009173 3 0.0000 1.000 0.000 0.000 1 0.000
#> GSM1009187 1 0.0657 0.932 0.984 0.012 0 0.004
#> GSM1009201 4 0.0469 0.971 0.012 0.000 0 0.988
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM1009062 4 0.0000 0.999 0.000 0.000 0 1.000 0.000
#> GSM1009076 2 0.0000 0.763 0.000 1.000 0 0.000 0.000
#> GSM1009090 1 0.3732 0.865 0.820 0.056 0 0.120 0.004
#> GSM1009104 5 0.0162 0.999 0.004 0.000 0 0.000 0.996
#> GSM1009118 1 0.1012 0.863 0.968 0.020 0 0.000 0.012
#> GSM1009132 1 0.0404 0.876 0.988 0.000 0 0.000 0.012
#> GSM1009146 1 0.3634 0.867 0.820 0.040 0 0.136 0.004
#> GSM1009160 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> GSM1009174 2 0.2424 0.754 0.132 0.868 0 0.000 0.000
#> GSM1009188 1 0.0404 0.876 0.988 0.000 0 0.000 0.012
#> GSM1009063 4 0.0000 0.999 0.000 0.000 0 1.000 0.000
#> GSM1009077 2 0.0000 0.763 0.000 1.000 0 0.000 0.000
#> GSM1009091 1 0.3732 0.865 0.820 0.056 0 0.120 0.004
#> GSM1009105 5 0.0162 0.999 0.004 0.000 0 0.000 0.996
#> GSM1009119 1 0.0404 0.876 0.988 0.000 0 0.000 0.012
#> GSM1009133 1 0.0404 0.876 0.988 0.000 0 0.000 0.012
#> GSM1009147 1 0.3641 0.864 0.820 0.060 0 0.120 0.000
#> GSM1009161 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> GSM1009175 2 0.2707 0.750 0.132 0.860 0 0.008 0.000
#> GSM1009189 1 0.0404 0.876 0.988 0.000 0 0.000 0.012
#> GSM1009064 4 0.0162 0.993 0.004 0.000 0 0.996 0.000
#> GSM1009078 1 0.3641 0.864 0.820 0.060 0 0.120 0.000
#> GSM1009092 1 0.3732 0.865 0.820 0.056 0 0.120 0.004
#> GSM1009106 5 0.0162 0.999 0.004 0.000 0 0.000 0.996
#> GSM1009120 1 0.0404 0.876 0.988 0.000 0 0.000 0.012
#> GSM1009134 1 0.0404 0.876 0.988 0.000 0 0.000 0.012
#> GSM1009148 1 0.3048 0.861 0.820 0.000 0 0.176 0.004
#> GSM1009162 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> GSM1009176 2 0.0162 0.763 0.004 0.996 0 0.000 0.000
#> GSM1009190 1 0.0404 0.876 0.988 0.000 0 0.000 0.012
#> GSM1009065 4 0.0000 0.999 0.000 0.000 0 1.000 0.000
#> GSM1009079 2 0.2329 0.758 0.124 0.876 0 0.000 0.000
#> GSM1009093 1 0.3732 0.865 0.820 0.056 0 0.120 0.004
#> GSM1009107 5 0.0162 0.999 0.004 0.000 0 0.000 0.996
#> GSM1009121 1 0.0404 0.876 0.988 0.000 0 0.000 0.012
#> GSM1009135 1 0.0404 0.876 0.988 0.000 0 0.000 0.012
#> GSM1009149 1 0.3634 0.866 0.820 0.040 0 0.136 0.004
#> GSM1009163 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> GSM1009177 2 0.0000 0.763 0.000 1.000 0 0.000 0.000
#> GSM1009191 1 0.0404 0.876 0.988 0.000 0 0.000 0.012
#> GSM1009066 4 0.0000 0.999 0.000 0.000 0 1.000 0.000
#> GSM1009080 2 0.2329 0.758 0.124 0.876 0 0.000 0.000
#> GSM1009094 1 0.3732 0.865 0.820 0.056 0 0.120 0.004
#> GSM1009108 5 0.0162 0.999 0.004 0.000 0 0.000 0.996
#> GSM1009122 1 0.2624 0.769 0.872 0.116 0 0.000 0.012
#> GSM1009136 1 0.0404 0.876 0.988 0.000 0 0.000 0.012
#> GSM1009150 1 0.3048 0.861 0.820 0.000 0 0.176 0.004
#> GSM1009164 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> GSM1009178 1 0.3682 0.861 0.820 0.072 0 0.108 0.000
#> GSM1009192 1 0.1087 0.877 0.968 0.016 0 0.008 0.008
#> GSM1009067 4 0.0000 0.999 0.000 0.000 0 1.000 0.000
#> GSM1009081 2 0.0000 0.763 0.000 1.000 0 0.000 0.000
#> GSM1009095 1 0.3132 0.862 0.820 0.008 0 0.172 0.000
#> GSM1009109 5 0.0510 0.982 0.016 0.000 0 0.000 0.984
#> GSM1009123 1 0.0404 0.876 0.988 0.000 0 0.000 0.012
#> GSM1009137 1 0.0404 0.876 0.988 0.000 0 0.000 0.012
#> GSM1009151 1 0.2929 0.860 0.820 0.000 0 0.180 0.000
#> GSM1009165 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> GSM1009179 2 0.5725 0.180 0.428 0.488 0 0.084 0.000
#> GSM1009193 1 0.0404 0.876 0.988 0.000 0 0.000 0.012
#> GSM1009068 4 0.0000 0.999 0.000 0.000 0 1.000 0.000
#> GSM1009082 2 0.0000 0.763 0.000 1.000 0 0.000 0.000
#> GSM1009096 1 0.3712 0.865 0.820 0.052 0 0.124 0.004
#> GSM1009110 5 0.0162 0.999 0.004 0.000 0 0.000 0.996
#> GSM1009124 1 0.0404 0.876 0.988 0.000 0 0.000 0.012
#> GSM1009138 1 0.0404 0.876 0.988 0.000 0 0.000 0.012
#> GSM1009152 1 0.2929 0.860 0.820 0.000 0 0.180 0.000
#> GSM1009166 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> GSM1009180 1 0.3682 0.861 0.820 0.072 0 0.108 0.000
#> GSM1009194 1 0.0404 0.876 0.988 0.000 0 0.000 0.012
#> GSM1009069 1 0.3109 0.851 0.800 0.000 0 0.200 0.000
#> GSM1009083 2 0.3752 0.582 0.292 0.708 0 0.000 0.000
#> GSM1009097 1 0.3732 0.865 0.820 0.056 0 0.120 0.004
#> GSM1009111 5 0.0162 0.999 0.004 0.000 0 0.000 0.996
#> GSM1009125 1 0.3586 0.710 0.792 0.020 0 0.000 0.188
#> GSM1009139 1 0.0404 0.876 0.988 0.000 0 0.000 0.012
#> GSM1009153 1 0.2929 0.860 0.820 0.000 0 0.180 0.000
#> GSM1009167 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> GSM1009181 2 0.0000 0.763 0.000 1.000 0 0.000 0.000
#> GSM1009195 1 0.3098 0.734 0.836 0.148 0 0.000 0.016
#> GSM1009070 1 0.3048 0.861 0.820 0.000 0 0.176 0.004
#> GSM1009084 2 0.1197 0.770 0.048 0.952 0 0.000 0.000
#> GSM1009098 1 0.3732 0.865 0.820 0.056 0 0.120 0.004
#> GSM1009112 5 0.0162 0.999 0.004 0.000 0 0.000 0.996
#> GSM1009126 1 0.0404 0.876 0.988 0.000 0 0.000 0.012
#> GSM1009140 1 0.0404 0.876 0.988 0.000 0 0.000 0.012
#> GSM1009154 1 0.2929 0.860 0.820 0.000 0 0.180 0.000
#> GSM1009168 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> GSM1009182 2 0.4113 0.689 0.140 0.784 0 0.076 0.000
#> GSM1009196 1 0.3203 0.863 0.820 0.012 0 0.168 0.000
#> GSM1009071 4 0.0000 0.999 0.000 0.000 0 1.000 0.000
#> GSM1009085 2 0.0000 0.763 0.000 1.000 0 0.000 0.000
#> GSM1009099 1 0.3712 0.865 0.820 0.052 0 0.124 0.004
#> GSM1009113 5 0.0162 0.999 0.004 0.000 0 0.000 0.996
#> GSM1009127 1 0.0404 0.876 0.988 0.000 0 0.000 0.012
#> GSM1009141 1 0.0404 0.876 0.988 0.000 0 0.000 0.012
#> GSM1009155 1 0.3132 0.862 0.820 0.008 0 0.172 0.000
#> GSM1009169 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> GSM1009183 2 0.1041 0.769 0.032 0.964 0 0.004 0.000
#> GSM1009197 1 0.3732 0.865 0.820 0.056 0 0.120 0.004
#> GSM1009072 4 0.0000 0.999 0.000 0.000 0 1.000 0.000
#> GSM1009086 2 0.0000 0.763 0.000 1.000 0 0.000 0.000
#> GSM1009100 1 0.3732 0.865 0.820 0.056 0 0.120 0.004
#> GSM1009114 5 0.0162 0.999 0.004 0.000 0 0.000 0.996
#> GSM1009128 1 0.0912 0.877 0.972 0.000 0 0.016 0.012
#> GSM1009142 1 0.0404 0.876 0.988 0.000 0 0.000 0.012
#> GSM1009156 1 0.3622 0.865 0.820 0.056 0 0.124 0.000
#> GSM1009170 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> GSM1009184 2 0.4376 0.668 0.144 0.764 0 0.092 0.000
#> GSM1009198 1 0.0693 0.877 0.980 0.000 0 0.008 0.012
#> GSM1009073 4 0.0000 0.999 0.000 0.000 0 1.000 0.000
#> GSM1009087 1 0.3657 0.863 0.820 0.064 0 0.116 0.000
#> GSM1009101 1 0.3712 0.865 0.820 0.052 0 0.124 0.004
#> GSM1009115 5 0.0162 0.999 0.004 0.000 0 0.000 0.996
#> GSM1009129 1 0.3526 0.753 0.832 0.072 0 0.000 0.096
#> GSM1009143 1 0.0404 0.876 0.988 0.000 0 0.000 0.012
#> GSM1009157 2 0.6124 0.170 0.412 0.460 0 0.128 0.000
#> GSM1009171 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> GSM1009185 1 0.3641 0.864 0.820 0.060 0 0.120 0.000
#> GSM1009199 1 0.2351 0.801 0.896 0.088 0 0.000 0.016
#> GSM1009074 4 0.0000 0.999 0.000 0.000 0 1.000 0.000
#> GSM1009088 1 0.3622 0.865 0.820 0.056 0 0.124 0.000
#> GSM1009102 1 0.3048 0.861 0.820 0.000 0 0.176 0.004
#> GSM1009116 5 0.0162 0.999 0.004 0.000 0 0.000 0.996
#> GSM1009130 1 0.2873 0.758 0.860 0.120 0 0.000 0.020
#> GSM1009144 1 0.0404 0.876 0.988 0.000 0 0.000 0.012
#> GSM1009158 1 0.3203 0.863 0.820 0.012 0 0.168 0.000
#> GSM1009172 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> GSM1009186 2 0.5815 0.374 0.356 0.540 0 0.104 0.000
#> GSM1009200 1 0.0404 0.876 0.988 0.000 0 0.000 0.012
#> GSM1009075 4 0.0000 0.999 0.000 0.000 0 1.000 0.000
#> GSM1009089 1 0.3521 0.866 0.820 0.040 0 0.140 0.000
#> GSM1009103 1 0.3048 0.861 0.820 0.000 0 0.176 0.004
#> GSM1009117 5 0.0162 0.999 0.004 0.000 0 0.000 0.996
#> GSM1009131 1 0.0404 0.876 0.988 0.000 0 0.000 0.012
#> GSM1009145 1 0.0404 0.876 0.988 0.000 0 0.000 0.012
#> GSM1009159 1 0.3663 0.866 0.820 0.044 0 0.132 0.004
#> GSM1009173 3 0.0000 1.000 0.000 0.000 1 0.000 0.000
#> GSM1009187 1 0.3203 0.863 0.820 0.012 0 0.168 0.000
#> GSM1009201 1 0.0404 0.876 0.988 0.000 0 0.000 0.012
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1009062 6 0.0260 0.962 0.000 0.000 0 0.008 0.000 0.992
#> GSM1009076 2 0.0000 0.808 0.000 1.000 0 0.000 0.000 0.000
#> GSM1009090 4 0.2454 0.819 0.160 0.000 0 0.840 0.000 0.000
#> GSM1009104 5 0.0000 0.993 0.000 0.000 0 0.000 1.000 0.000
#> GSM1009118 1 0.1895 0.928 0.912 0.016 0 0.072 0.000 0.000
#> GSM1009132 1 0.1501 0.931 0.924 0.000 0 0.076 0.000 0.000
#> GSM1009146 4 0.0937 0.925 0.040 0.000 0 0.960 0.000 0.000
#> GSM1009160 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009174 2 0.1542 0.815 0.004 0.936 0 0.052 0.008 0.000
#> GSM1009188 1 0.1556 0.930 0.920 0.000 0 0.080 0.000 0.000
#> GSM1009063 6 0.0000 0.967 0.000 0.000 0 0.000 0.000 1.000
#> GSM1009077 2 0.0000 0.808 0.000 1.000 0 0.000 0.000 0.000
#> GSM1009091 4 0.2378 0.820 0.152 0.000 0 0.848 0.000 0.000
#> GSM1009105 5 0.0000 0.993 0.000 0.000 0 0.000 1.000 0.000
#> GSM1009119 1 0.0000 0.918 1.000 0.000 0 0.000 0.000 0.000
#> GSM1009133 1 0.0000 0.918 1.000 0.000 0 0.000 0.000 0.000
#> GSM1009147 4 0.0692 0.924 0.020 0.004 0 0.976 0.000 0.000
#> GSM1009161 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009175 2 0.2980 0.765 0.008 0.800 0 0.192 0.000 0.000
#> GSM1009189 1 0.1501 0.931 0.924 0.000 0 0.076 0.000 0.000
#> GSM1009064 6 0.0146 0.963 0.000 0.000 0 0.004 0.000 0.996
#> GSM1009078 4 0.0692 0.925 0.020 0.004 0 0.976 0.000 0.000
#> GSM1009092 4 0.2562 0.803 0.172 0.000 0 0.828 0.000 0.000
#> GSM1009106 5 0.0000 0.993 0.000 0.000 0 0.000 1.000 0.000
#> GSM1009120 1 0.0146 0.920 0.996 0.000 0 0.004 0.000 0.000
#> GSM1009134 1 0.0000 0.918 1.000 0.000 0 0.000 0.000 0.000
#> GSM1009148 4 0.1010 0.923 0.036 0.000 0 0.960 0.000 0.004
#> GSM1009162 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009176 2 0.0405 0.812 0.008 0.988 0 0.004 0.000 0.000
#> GSM1009190 1 0.1501 0.931 0.924 0.000 0 0.076 0.000 0.000
#> GSM1009065 6 0.0000 0.967 0.000 0.000 0 0.000 0.000 1.000
#> GSM1009079 2 0.2933 0.761 0.004 0.796 0 0.200 0.000 0.000
#> GSM1009093 4 0.2048 0.857 0.120 0.000 0 0.880 0.000 0.000
#> GSM1009107 5 0.0000 0.993 0.000 0.000 0 0.000 1.000 0.000
#> GSM1009121 1 0.1501 0.931 0.924 0.000 0 0.076 0.000 0.000
#> GSM1009135 1 0.0000 0.918 1.000 0.000 0 0.000 0.000 0.000
#> GSM1009149 4 0.0865 0.924 0.036 0.000 0 0.964 0.000 0.000
#> GSM1009163 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009177 2 0.0291 0.812 0.004 0.992 0 0.004 0.000 0.000
#> GSM1009191 1 0.1501 0.931 0.924 0.000 0 0.076 0.000 0.000
#> GSM1009066 6 0.0000 0.967 0.000 0.000 0 0.000 0.000 1.000
#> GSM1009080 2 0.1644 0.816 0.004 0.920 0 0.076 0.000 0.000
#> GSM1009094 4 0.2048 0.857 0.120 0.000 0 0.880 0.000 0.000
#> GSM1009108 5 0.0146 0.990 0.000 0.004 0 0.000 0.996 0.000
#> GSM1009122 1 0.2978 0.886 0.856 0.084 0 0.052 0.008 0.000
#> GSM1009136 1 0.1075 0.929 0.952 0.000 0 0.048 0.000 0.000
#> GSM1009150 4 0.1010 0.923 0.036 0.000 0 0.960 0.000 0.004
#> GSM1009164 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009178 4 0.1829 0.889 0.024 0.056 0 0.920 0.000 0.000
#> GSM1009192 1 0.2793 0.808 0.800 0.000 0 0.200 0.000 0.000
#> GSM1009067 6 0.0000 0.967 0.000 0.000 0 0.000 0.000 1.000
#> GSM1009081 2 0.0000 0.808 0.000 1.000 0 0.000 0.000 0.000
#> GSM1009095 4 0.0790 0.925 0.032 0.000 0 0.968 0.000 0.000
#> GSM1009109 5 0.1434 0.925 0.048 0.012 0 0.000 0.940 0.000
#> GSM1009123 1 0.1141 0.929 0.948 0.000 0 0.052 0.000 0.000
#> GSM1009137 1 0.0000 0.918 1.000 0.000 0 0.000 0.000 0.000
#> GSM1009151 4 0.1010 0.923 0.036 0.000 0 0.960 0.000 0.004
#> GSM1009165 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009179 2 0.4157 0.403 0.012 0.544 0 0.444 0.000 0.000
#> GSM1009193 1 0.1556 0.930 0.920 0.000 0 0.080 0.000 0.000
#> GSM1009068 6 0.0260 0.962 0.000 0.000 0 0.008 0.000 0.992
#> GSM1009082 2 0.0000 0.808 0.000 1.000 0 0.000 0.000 0.000
#> GSM1009096 4 0.2092 0.853 0.124 0.000 0 0.876 0.000 0.000
#> GSM1009110 5 0.0363 0.983 0.000 0.012 0 0.000 0.988 0.000
#> GSM1009124 1 0.1501 0.931 0.924 0.000 0 0.076 0.000 0.000
#> GSM1009138 1 0.0000 0.918 1.000 0.000 0 0.000 0.000 0.000
#> GSM1009152 4 0.1010 0.923 0.036 0.000 0 0.960 0.000 0.004
#> GSM1009166 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009180 4 0.1649 0.903 0.032 0.036 0 0.932 0.000 0.000
#> GSM1009194 1 0.3522 0.849 0.800 0.128 0 0.072 0.000 0.000
#> GSM1009069 6 0.4231 0.579 0.028 0.012 0 0.224 0.008 0.728
#> GSM1009083 2 0.1841 0.814 0.008 0.920 0 0.064 0.008 0.000
#> GSM1009097 4 0.2048 0.857 0.120 0.000 0 0.880 0.000 0.000
#> GSM1009111 5 0.0000 0.993 0.000 0.000 0 0.000 1.000 0.000
#> GSM1009125 1 0.4494 0.765 0.732 0.036 0 0.048 0.184 0.000
#> GSM1009139 1 0.0713 0.927 0.972 0.000 0 0.028 0.000 0.000
#> GSM1009153 4 0.1124 0.922 0.036 0.000 0 0.956 0.000 0.008
#> GSM1009167 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009181 2 0.0260 0.812 0.000 0.992 0 0.008 0.000 0.000
#> GSM1009195 1 0.4643 0.606 0.640 0.304 0 0.048 0.008 0.000
#> GSM1009070 4 0.0790 0.924 0.032 0.000 0 0.968 0.000 0.000
#> GSM1009084 2 0.2078 0.794 0.044 0.912 0 0.040 0.004 0.000
#> GSM1009098 4 0.2048 0.857 0.120 0.000 0 0.880 0.000 0.000
#> GSM1009112 5 0.0000 0.993 0.000 0.000 0 0.000 1.000 0.000
#> GSM1009126 1 0.1444 0.931 0.928 0.000 0 0.072 0.000 0.000
#> GSM1009140 1 0.0000 0.918 1.000 0.000 0 0.000 0.000 0.000
#> GSM1009154 4 0.1010 0.923 0.036 0.000 0 0.960 0.000 0.004
#> GSM1009168 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009182 2 0.3690 0.640 0.008 0.684 0 0.308 0.000 0.000
#> GSM1009196 4 0.0692 0.925 0.020 0.004 0 0.976 0.000 0.000
#> GSM1009071 6 0.0000 0.967 0.000 0.000 0 0.000 0.000 1.000
#> GSM1009085 2 0.1957 0.799 0.000 0.888 0 0.112 0.000 0.000
#> GSM1009099 4 0.0260 0.920 0.008 0.000 0 0.992 0.000 0.000
#> GSM1009113 5 0.0000 0.993 0.000 0.000 0 0.000 1.000 0.000
#> GSM1009127 1 0.0000 0.918 1.000 0.000 0 0.000 0.000 0.000
#> GSM1009141 1 0.1141 0.929 0.948 0.000 0 0.052 0.000 0.000
#> GSM1009155 4 0.1080 0.924 0.032 0.004 0 0.960 0.000 0.004
#> GSM1009169 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009183 2 0.2513 0.790 0.008 0.852 0 0.140 0.000 0.000
#> GSM1009197 4 0.1610 0.890 0.084 0.000 0 0.916 0.000 0.000
#> GSM1009072 6 0.0000 0.967 0.000 0.000 0 0.000 0.000 1.000
#> GSM1009086 2 0.0000 0.808 0.000 1.000 0 0.000 0.000 0.000
#> GSM1009100 4 0.2048 0.857 0.120 0.000 0 0.880 0.000 0.000
#> GSM1009114 5 0.0000 0.993 0.000 0.000 0 0.000 1.000 0.000
#> GSM1009128 1 0.2542 0.913 0.876 0.000 0 0.080 0.044 0.000
#> GSM1009142 1 0.0146 0.920 0.996 0.000 0 0.004 0.000 0.000
#> GSM1009156 4 0.0603 0.923 0.016 0.004 0 0.980 0.000 0.000
#> GSM1009170 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009184 2 0.4076 0.391 0.008 0.540 0 0.452 0.000 0.000
#> GSM1009198 1 0.1556 0.930 0.920 0.000 0 0.080 0.000 0.000
#> GSM1009073 6 0.0000 0.967 0.000 0.000 0 0.000 0.000 1.000
#> GSM1009087 4 0.0806 0.925 0.020 0.008 0 0.972 0.000 0.000
#> GSM1009101 4 0.2048 0.857 0.120 0.000 0 0.880 0.000 0.000
#> GSM1009115 5 0.0000 0.993 0.000 0.000 0 0.000 1.000 0.000
#> GSM1009129 1 0.4467 0.805 0.756 0.064 0 0.048 0.132 0.000
#> GSM1009143 1 0.0000 0.918 1.000 0.000 0 0.000 0.000 0.000
#> GSM1009157 4 0.2896 0.755 0.016 0.160 0 0.824 0.000 0.000
#> GSM1009171 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009185 4 0.0603 0.923 0.016 0.004 0 0.980 0.000 0.000
#> GSM1009199 1 0.3895 0.805 0.768 0.172 0 0.052 0.008 0.000
#> GSM1009074 6 0.0000 0.967 0.000 0.000 0 0.000 0.000 1.000
#> GSM1009088 4 0.0806 0.925 0.020 0.008 0 0.972 0.000 0.000
#> GSM1009102 4 0.0713 0.925 0.028 0.000 0 0.972 0.000 0.000
#> GSM1009116 5 0.0000 0.993 0.000 0.000 0 0.000 1.000 0.000
#> GSM1009130 1 0.3681 0.824 0.796 0.144 0 0.048 0.012 0.000
#> GSM1009144 1 0.0000 0.918 1.000 0.000 0 0.000 0.000 0.000
#> GSM1009158 4 0.0777 0.925 0.024 0.004 0 0.972 0.000 0.000
#> GSM1009172 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009186 2 0.4389 0.393 0.012 0.536 0 0.444 0.008 0.000
#> GSM1009200 1 0.1501 0.931 0.924 0.000 0 0.076 0.000 0.000
#> GSM1009075 6 0.0000 0.967 0.000 0.000 0 0.000 0.000 1.000
#> GSM1009089 4 0.0291 0.920 0.004 0.004 0 0.992 0.000 0.000
#> GSM1009103 4 0.0713 0.925 0.028 0.000 0 0.972 0.000 0.000
#> GSM1009117 5 0.0000 0.993 0.000 0.000 0 0.000 1.000 0.000
#> GSM1009131 1 0.1501 0.931 0.924 0.000 0 0.076 0.000 0.000
#> GSM1009145 1 0.1387 0.927 0.932 0.000 0 0.068 0.000 0.000
#> GSM1009159 4 0.0790 0.924 0.032 0.000 0 0.968 0.000 0.000
#> GSM1009173 3 0.0000 1.000 0.000 0.000 1 0.000 0.000 0.000
#> GSM1009187 4 0.0748 0.925 0.016 0.004 0 0.976 0.000 0.004
#> GSM1009201 1 0.1501 0.931 0.924 0.000 0 0.076 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 temperature(p) time(p) specimen(p) k
#> ATC:mclust 140 1.000 1.000 1.03e-25 2
#> ATC:mclust 133 0.961 0.997 1.99e-34 3
#> ATC:mclust 138 0.994 1.000 1.49e-55 4
#> ATC:mclust 137 0.982 1.000 9.07e-78 5
#> ATC:mclust 137 0.980 1.000 1.32e-97 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 140 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 0.971 0.988 0.4896 0.509 0.509
#> 3 3 0.789 0.863 0.921 0.3092 0.805 0.630
#> 4 4 0.791 0.820 0.905 0.1091 0.873 0.673
#> 5 5 0.766 0.812 0.881 0.0865 0.849 0.550
#> 6 6 0.802 0.815 0.887 0.0556 0.907 0.630
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
#> GSM1009062 1 0.000 0.993 1.000 0.000
#> GSM1009076 2 0.000 0.979 0.000 1.000
#> GSM1009090 1 0.000 0.993 1.000 0.000
#> GSM1009104 2 0.000 0.979 0.000 1.000
#> GSM1009118 2 0.000 0.979 0.000 1.000
#> GSM1009132 2 0.821 0.667 0.256 0.744
#> GSM1009146 1 0.000 0.993 1.000 0.000
#> GSM1009160 2 0.000 0.979 0.000 1.000
#> GSM1009174 2 0.000 0.979 0.000 1.000
#> GSM1009188 1 0.000 0.993 1.000 0.000
#> GSM1009063 1 0.000 0.993 1.000 0.000
#> GSM1009077 2 0.000 0.979 0.000 1.000
#> GSM1009091 1 0.000 0.993 1.000 0.000
#> GSM1009105 2 0.000 0.979 0.000 1.000
#> GSM1009119 1 0.000 0.993 1.000 0.000
#> GSM1009133 1 0.000 0.993 1.000 0.000
#> GSM1009147 1 0.000 0.993 1.000 0.000
#> GSM1009161 2 0.000 0.979 0.000 1.000
#> GSM1009175 2 0.000 0.979 0.000 1.000
#> GSM1009189 1 0.000 0.993 1.000 0.000
#> GSM1009064 1 0.000 0.993 1.000 0.000
#> GSM1009078 1 0.000 0.993 1.000 0.000
#> GSM1009092 1 0.000 0.993 1.000 0.000
#> GSM1009106 2 0.000 0.979 0.000 1.000
#> GSM1009120 1 0.000 0.993 1.000 0.000
#> GSM1009134 1 0.000 0.993 1.000 0.000
#> GSM1009148 1 0.000 0.993 1.000 0.000
#> GSM1009162 2 0.000 0.979 0.000 1.000
#> GSM1009176 2 0.000 0.979 0.000 1.000
#> GSM1009190 1 0.000 0.993 1.000 0.000
#> GSM1009065 1 0.000 0.993 1.000 0.000
#> GSM1009079 2 0.000 0.979 0.000 1.000
#> GSM1009093 1 0.000 0.993 1.000 0.000
#> GSM1009107 2 0.000 0.979 0.000 1.000
#> GSM1009121 2 0.000 0.979 0.000 1.000
#> GSM1009135 1 0.000 0.993 1.000 0.000
#> GSM1009149 1 0.000 0.993 1.000 0.000
#> GSM1009163 2 0.000 0.979 0.000 1.000
#> GSM1009177 2 0.000 0.979 0.000 1.000
#> GSM1009191 2 0.886 0.579 0.304 0.696
#> GSM1009066 1 0.000 0.993 1.000 0.000
#> GSM1009080 2 0.000 0.979 0.000 1.000
#> GSM1009094 1 0.000 0.993 1.000 0.000
#> GSM1009108 2 0.000 0.979 0.000 1.000
#> GSM1009122 2 0.000 0.979 0.000 1.000
#> GSM1009136 1 0.000 0.993 1.000 0.000
#> GSM1009150 1 0.000 0.993 1.000 0.000
#> GSM1009164 2 0.000 0.979 0.000 1.000
#> GSM1009178 1 0.000 0.993 1.000 0.000
#> GSM1009192 1 0.000 0.993 1.000 0.000
#> GSM1009067 1 0.000 0.993 1.000 0.000
#> GSM1009081 2 0.000 0.979 0.000 1.000
#> GSM1009095 1 0.000 0.993 1.000 0.000
#> GSM1009109 2 0.000 0.979 0.000 1.000
#> GSM1009123 1 0.000 0.993 1.000 0.000
#> GSM1009137 1 0.000 0.993 1.000 0.000
#> GSM1009151 1 0.000 0.993 1.000 0.000
#> GSM1009165 2 0.000 0.979 0.000 1.000
#> GSM1009179 1 0.767 0.702 0.776 0.224
#> GSM1009193 1 0.000 0.993 1.000 0.000
#> GSM1009068 1 0.000 0.993 1.000 0.000
#> GSM1009082 2 0.000 0.979 0.000 1.000
#> GSM1009096 1 0.000 0.993 1.000 0.000
#> GSM1009110 2 0.000 0.979 0.000 1.000
#> GSM1009124 1 0.000 0.993 1.000 0.000
#> GSM1009138 1 0.000 0.993 1.000 0.000
#> GSM1009152 1 0.000 0.993 1.000 0.000
#> GSM1009166 2 0.000 0.979 0.000 1.000
#> GSM1009180 1 0.000 0.993 1.000 0.000
#> GSM1009194 2 0.932 0.485 0.348 0.652
#> GSM1009069 1 0.000 0.993 1.000 0.000
#> GSM1009083 2 0.000 0.979 0.000 1.000
#> GSM1009097 1 0.000 0.993 1.000 0.000
#> GSM1009111 2 0.000 0.979 0.000 1.000
#> GSM1009125 2 0.000 0.979 0.000 1.000
#> GSM1009139 2 0.781 0.706 0.232 0.768
#> GSM1009153 1 0.000 0.993 1.000 0.000
#> GSM1009167 2 0.000 0.979 0.000 1.000
#> GSM1009181 2 0.000 0.979 0.000 1.000
#> GSM1009195 2 0.000 0.979 0.000 1.000
#> GSM1009070 1 0.000 0.993 1.000 0.000
#> GSM1009084 2 0.000 0.979 0.000 1.000
#> GSM1009098 1 0.000 0.993 1.000 0.000
#> GSM1009112 2 0.000 0.979 0.000 1.000
#> GSM1009126 1 0.000 0.993 1.000 0.000
#> GSM1009140 1 0.000 0.993 1.000 0.000
#> GSM1009154 1 0.000 0.993 1.000 0.000
#> GSM1009168 2 0.000 0.979 0.000 1.000
#> GSM1009182 2 0.278 0.935 0.048 0.952
#> GSM1009196 1 0.000 0.993 1.000 0.000
#> GSM1009071 1 0.000 0.993 1.000 0.000
#> GSM1009085 2 0.000 0.979 0.000 1.000
#> GSM1009099 1 0.000 0.993 1.000 0.000
#> GSM1009113 2 0.000 0.979 0.000 1.000
#> GSM1009127 1 0.000 0.993 1.000 0.000
#> GSM1009141 1 0.000 0.993 1.000 0.000
#> GSM1009155 1 0.000 0.993 1.000 0.000
#> GSM1009169 2 0.000 0.979 0.000 1.000
#> GSM1009183 2 0.000 0.979 0.000 1.000
#> GSM1009197 1 0.000 0.993 1.000 0.000
#> GSM1009072 1 0.000 0.993 1.000 0.000
#> GSM1009086 2 0.000 0.979 0.000 1.000
#> GSM1009100 1 0.000 0.993 1.000 0.000
#> GSM1009114 2 0.000 0.979 0.000 1.000
#> GSM1009128 1 0.000 0.993 1.000 0.000
#> GSM1009142 1 0.000 0.993 1.000 0.000
#> GSM1009156 1 0.000 0.993 1.000 0.000
#> GSM1009170 2 0.000 0.979 0.000 1.000
#> GSM1009184 2 0.000 0.979 0.000 1.000
#> GSM1009198 1 0.000 0.993 1.000 0.000
#> GSM1009073 1 0.000 0.993 1.000 0.000
#> GSM1009087 1 0.000 0.993 1.000 0.000
#> GSM1009101 1 0.000 0.993 1.000 0.000
#> GSM1009115 2 0.000 0.979 0.000 1.000
#> GSM1009129 2 0.000 0.979 0.000 1.000
#> GSM1009143 1 0.000 0.993 1.000 0.000
#> GSM1009157 1 0.000 0.993 1.000 0.000
#> GSM1009171 2 0.000 0.979 0.000 1.000
#> GSM1009185 1 0.000 0.993 1.000 0.000
#> GSM1009199 2 0.000 0.979 0.000 1.000
#> GSM1009074 1 0.000 0.993 1.000 0.000
#> GSM1009088 1 0.000 0.993 1.000 0.000
#> GSM1009102 1 0.000 0.993 1.000 0.000
#> GSM1009116 2 0.000 0.979 0.000 1.000
#> GSM1009130 2 0.000 0.979 0.000 1.000
#> GSM1009144 1 0.000 0.993 1.000 0.000
#> GSM1009158 1 0.000 0.993 1.000 0.000
#> GSM1009172 2 0.000 0.979 0.000 1.000
#> GSM1009186 2 0.000 0.979 0.000 1.000
#> GSM1009200 1 0.871 0.575 0.708 0.292
#> GSM1009075 1 0.000 0.993 1.000 0.000
#> GSM1009089 1 0.000 0.993 1.000 0.000
#> GSM1009103 1 0.000 0.993 1.000 0.000
#> GSM1009117 2 0.000 0.979 0.000 1.000
#> GSM1009131 1 0.000 0.993 1.000 0.000
#> GSM1009145 1 0.000 0.993 1.000 0.000
#> GSM1009159 1 0.000 0.993 1.000 0.000
#> GSM1009173 2 0.000 0.979 0.000 1.000
#> GSM1009187 1 0.000 0.993 1.000 0.000
#> GSM1009201 1 0.000 0.993 1.000 0.000
cbind(get_classes(res, k = 3), get_membership(res, k = 3))
#> class entropy silhouette p1 p2 p3
#> GSM1009062 3 0.2959 0.848 0.100 0.000 0.900
#> GSM1009076 2 0.2537 0.896 0.000 0.920 0.080
#> GSM1009090 1 0.0000 0.966 1.000 0.000 0.000
#> GSM1009104 2 0.0000 0.921 0.000 1.000 0.000
#> GSM1009118 2 0.0747 0.919 0.000 0.984 0.016
#> GSM1009132 2 0.0424 0.921 0.000 0.992 0.008
#> GSM1009146 1 0.1529 0.953 0.960 0.000 0.040
#> GSM1009160 2 0.2261 0.913 0.000 0.932 0.068
#> GSM1009174 3 0.5905 0.328 0.000 0.352 0.648
#> GSM1009188 1 0.0000 0.966 1.000 0.000 0.000
#> GSM1009063 3 0.2537 0.853 0.080 0.000 0.920
#> GSM1009077 2 0.6235 0.382 0.000 0.564 0.436
#> GSM1009091 1 0.0000 0.966 1.000 0.000 0.000
#> GSM1009105 2 0.0000 0.921 0.000 1.000 0.000
#> GSM1009119 1 0.0000 0.966 1.000 0.000 0.000
#> GSM1009133 1 0.0424 0.961 0.992 0.008 0.000
#> GSM1009147 1 0.1163 0.960 0.972 0.000 0.028
#> GSM1009161 2 0.2261 0.913 0.000 0.932 0.068
#> GSM1009175 3 0.6309 -0.211 0.000 0.496 0.504
#> GSM1009189 1 0.0424 0.966 0.992 0.000 0.008
#> GSM1009064 3 0.2261 0.850 0.068 0.000 0.932
#> GSM1009078 1 0.1964 0.940 0.944 0.000 0.056
#> GSM1009092 1 0.0000 0.966 1.000 0.000 0.000
#> GSM1009106 2 0.0000 0.921 0.000 1.000 0.000
#> GSM1009120 1 0.0000 0.966 1.000 0.000 0.000
#> GSM1009134 1 0.1289 0.958 0.968 0.000 0.032
#> GSM1009148 1 0.6045 0.296 0.620 0.000 0.380
#> GSM1009162 2 0.2261 0.913 0.000 0.932 0.068
#> GSM1009176 2 0.2878 0.898 0.000 0.904 0.096
#> GSM1009190 1 0.0237 0.964 0.996 0.004 0.000
#> GSM1009065 3 0.2356 0.851 0.072 0.000 0.928
#> GSM1009079 2 0.2878 0.908 0.000 0.904 0.096
#> GSM1009093 1 0.0000 0.966 1.000 0.000 0.000
#> GSM1009107 2 0.0000 0.921 0.000 1.000 0.000
#> GSM1009121 2 0.0237 0.921 0.000 0.996 0.004
#> GSM1009135 1 0.1585 0.959 0.964 0.008 0.028
#> GSM1009149 1 0.0892 0.963 0.980 0.000 0.020
#> GSM1009163 2 0.2261 0.913 0.000 0.932 0.068
#> GSM1009177 2 0.4002 0.855 0.000 0.840 0.160
#> GSM1009191 2 0.0475 0.920 0.004 0.992 0.004
#> GSM1009066 3 0.2261 0.850 0.068 0.000 0.932
#> GSM1009080 2 0.2356 0.913 0.000 0.928 0.072
#> GSM1009094 1 0.0000 0.966 1.000 0.000 0.000
#> GSM1009108 2 0.0237 0.921 0.000 0.996 0.004
#> GSM1009122 2 0.1031 0.917 0.000 0.976 0.024
#> GSM1009136 1 0.0000 0.966 1.000 0.000 0.000
#> GSM1009150 1 0.1529 0.952 0.960 0.000 0.040
#> GSM1009164 2 0.2261 0.913 0.000 0.932 0.068
#> GSM1009178 3 0.8226 0.568 0.320 0.096 0.584
#> GSM1009192 1 0.1031 0.962 0.976 0.000 0.024
#> GSM1009067 3 0.2959 0.848 0.100 0.000 0.900
#> GSM1009081 2 0.2796 0.886 0.000 0.908 0.092
#> GSM1009095 1 0.1163 0.960 0.972 0.000 0.028
#> GSM1009109 2 0.0000 0.921 0.000 1.000 0.000
#> GSM1009123 1 0.0000 0.966 1.000 0.000 0.000
#> GSM1009137 1 0.0661 0.963 0.988 0.008 0.004
#> GSM1009151 3 0.6267 0.297 0.452 0.000 0.548
#> GSM1009165 2 0.2261 0.913 0.000 0.932 0.068
#> GSM1009179 3 0.6229 0.721 0.064 0.172 0.764
#> GSM1009193 1 0.0000 0.966 1.000 0.000 0.000
#> GSM1009068 3 0.4399 0.783 0.188 0.000 0.812
#> GSM1009082 2 0.4452 0.814 0.000 0.808 0.192
#> GSM1009096 1 0.0000 0.966 1.000 0.000 0.000
#> GSM1009110 2 0.0000 0.921 0.000 1.000 0.000
#> GSM1009124 1 0.1031 0.947 0.976 0.024 0.000
#> GSM1009138 1 0.1643 0.950 0.956 0.000 0.044
#> GSM1009152 3 0.6309 0.135 0.500 0.000 0.500
#> GSM1009166 2 0.2261 0.913 0.000 0.932 0.068
#> GSM1009180 1 0.2845 0.921 0.920 0.012 0.068
#> GSM1009194 2 0.6286 0.125 0.000 0.536 0.464
#> GSM1009069 3 0.1753 0.840 0.048 0.000 0.952
#> GSM1009083 3 0.4887 0.637 0.000 0.228 0.772
#> GSM1009097 1 0.0000 0.966 1.000 0.000 0.000
#> GSM1009111 2 0.0237 0.921 0.000 0.996 0.004
#> GSM1009125 2 0.0237 0.921 0.000 0.996 0.004
#> GSM1009139 2 0.5926 0.449 0.000 0.644 0.356
#> GSM1009153 3 0.4887 0.741 0.228 0.000 0.772
#> GSM1009167 2 0.2261 0.913 0.000 0.932 0.068
#> GSM1009181 2 0.5216 0.737 0.000 0.740 0.260
#> GSM1009195 2 0.1289 0.915 0.000 0.968 0.032
#> GSM1009070 1 0.2261 0.929 0.932 0.000 0.068
#> GSM1009084 2 0.3116 0.875 0.000 0.892 0.108
#> GSM1009098 1 0.0000 0.966 1.000 0.000 0.000
#> GSM1009112 2 0.0000 0.921 0.000 1.000 0.000
#> GSM1009126 1 0.2486 0.907 0.932 0.060 0.008
#> GSM1009140 1 0.0237 0.966 0.996 0.000 0.004
#> GSM1009154 1 0.3192 0.879 0.888 0.000 0.112
#> GSM1009168 2 0.2261 0.913 0.000 0.932 0.068
#> GSM1009182 3 0.4937 0.711 0.028 0.148 0.824
#> GSM1009196 1 0.3038 0.889 0.896 0.000 0.104
#> GSM1009071 3 0.2356 0.851 0.072 0.000 0.928
#> GSM1009085 2 0.3686 0.874 0.000 0.860 0.140
#> GSM1009099 1 0.0000 0.966 1.000 0.000 0.000
#> GSM1009113 2 0.0237 0.921 0.000 0.996 0.004
#> GSM1009127 1 0.0592 0.965 0.988 0.000 0.012
#> GSM1009141 3 0.2261 0.801 0.000 0.068 0.932
#> GSM1009155 3 0.3267 0.841 0.116 0.000 0.884
#> GSM1009169 2 0.3141 0.904 0.020 0.912 0.068
#> GSM1009183 2 0.5016 0.774 0.000 0.760 0.240
#> GSM1009197 1 0.0424 0.966 0.992 0.000 0.008
#> GSM1009072 3 0.3038 0.846 0.104 0.000 0.896
#> GSM1009086 2 0.4121 0.834 0.000 0.832 0.168
#> GSM1009100 1 0.0000 0.966 1.000 0.000 0.000
#> GSM1009114 2 0.0237 0.920 0.004 0.996 0.000
#> GSM1009128 1 0.0592 0.959 0.988 0.012 0.000
#> GSM1009142 1 0.5449 0.800 0.816 0.116 0.068
#> GSM1009156 1 0.0892 0.963 0.980 0.000 0.020
#> GSM1009170 2 0.2261 0.913 0.000 0.932 0.068
#> GSM1009184 3 0.1411 0.788 0.000 0.036 0.964
#> GSM1009198 1 0.0000 0.966 1.000 0.000 0.000
#> GSM1009073 3 0.2448 0.852 0.076 0.000 0.924
#> GSM1009087 1 0.3340 0.871 0.880 0.000 0.120
#> GSM1009101 1 0.0000 0.966 1.000 0.000 0.000
#> GSM1009115 2 0.0237 0.921 0.000 0.996 0.004
#> GSM1009129 2 0.0237 0.921 0.000 0.996 0.004
#> GSM1009143 1 0.0592 0.965 0.988 0.000 0.012
#> GSM1009157 3 0.2165 0.848 0.064 0.000 0.936
#> GSM1009171 2 0.2261 0.913 0.000 0.932 0.068
#> GSM1009185 1 0.0424 0.966 0.992 0.000 0.008
#> GSM1009199 2 0.1031 0.917 0.000 0.976 0.024
#> GSM1009074 3 0.2711 0.851 0.088 0.000 0.912
#> GSM1009088 3 0.5529 0.649 0.296 0.000 0.704
#> GSM1009102 1 0.0747 0.964 0.984 0.000 0.016
#> GSM1009116 2 0.0000 0.921 0.000 1.000 0.000
#> GSM1009130 2 0.0237 0.921 0.000 0.996 0.004
#> GSM1009144 1 0.1832 0.954 0.956 0.008 0.036
#> GSM1009158 1 0.1163 0.960 0.972 0.000 0.028
#> GSM1009172 2 0.2261 0.913 0.000 0.932 0.068
#> GSM1009186 3 0.1411 0.791 0.000 0.036 0.964
#> GSM1009200 2 0.4974 0.625 0.236 0.764 0.000
#> GSM1009075 3 0.3038 0.846 0.104 0.000 0.896
#> GSM1009089 1 0.0747 0.964 0.984 0.000 0.016
#> GSM1009103 1 0.0592 0.965 0.988 0.000 0.012
#> GSM1009117 2 0.0000 0.921 0.000 1.000 0.000
#> GSM1009131 1 0.0237 0.964 0.996 0.004 0.000
#> GSM1009145 1 0.0000 0.966 1.000 0.000 0.000
#> GSM1009159 1 0.0747 0.964 0.984 0.000 0.016
#> GSM1009173 2 0.2261 0.913 0.000 0.932 0.068
#> GSM1009187 3 0.3116 0.846 0.108 0.000 0.892
#> GSM1009201 1 0.1877 0.943 0.956 0.032 0.012
cbind(get_classes(res, k = 4), get_membership(res, k = 4))
#> class entropy silhouette p1 p2 p3 p4
#> GSM1009062 1 0.1302 0.811 0.956 0.000 0.000 0.044
#> GSM1009076 2 0.5464 0.668 0.064 0.708 0.228 0.000
#> GSM1009090 4 0.0188 0.924 0.004 0.000 0.000 0.996
#> GSM1009104 2 0.0707 0.891 0.000 0.980 0.020 0.000
#> GSM1009118 2 0.0469 0.884 0.000 0.988 0.012 0.000
#> GSM1009132 2 0.0779 0.890 0.000 0.980 0.016 0.004
#> GSM1009146 4 0.1474 0.911 0.052 0.000 0.000 0.948
#> GSM1009160 3 0.0921 1.000 0.000 0.028 0.972 0.000
#> GSM1009174 1 0.4100 0.742 0.816 0.148 0.036 0.000
#> GSM1009188 4 0.0188 0.923 0.004 0.000 0.000 0.996
#> GSM1009063 1 0.0188 0.824 0.996 0.000 0.000 0.004
#> GSM1009077 1 0.6310 0.371 0.576 0.352 0.072 0.000
#> GSM1009091 4 0.0188 0.923 0.004 0.000 0.000 0.996
#> GSM1009105 2 0.0817 0.891 0.000 0.976 0.024 0.000
#> GSM1009119 4 0.0336 0.924 0.008 0.000 0.000 0.992
#> GSM1009133 4 0.1488 0.909 0.012 0.032 0.000 0.956
#> GSM1009147 4 0.1118 0.918 0.036 0.000 0.000 0.964
#> GSM1009161 3 0.0921 1.000 0.000 0.028 0.972 0.000
#> GSM1009175 1 0.4834 0.731 0.784 0.096 0.120 0.000
#> GSM1009189 4 0.0188 0.924 0.004 0.000 0.000 0.996
#> GSM1009064 1 0.0188 0.824 0.996 0.000 0.000 0.004
#> GSM1009078 4 0.3127 0.882 0.060 0.016 0.028 0.896
#> GSM1009092 4 0.0188 0.923 0.004 0.000 0.000 0.996
#> GSM1009106 2 0.0817 0.891 0.000 0.976 0.024 0.000
#> GSM1009120 4 0.0188 0.924 0.004 0.000 0.000 0.996
#> GSM1009134 4 0.3015 0.880 0.092 0.024 0.000 0.884
#> GSM1009148 4 0.4454 0.595 0.308 0.000 0.000 0.692
#> GSM1009162 3 0.0921 1.000 0.000 0.028 0.972 0.000
#> GSM1009176 2 0.5380 0.708 0.120 0.744 0.136 0.000
#> GSM1009190 4 0.0188 0.923 0.004 0.000 0.000 0.996
#> GSM1009065 1 0.0188 0.824 0.996 0.000 0.000 0.004
#> GSM1009079 2 0.5838 0.294 0.032 0.524 0.444 0.000
#> GSM1009093 4 0.0000 0.924 0.000 0.000 0.000 1.000
#> GSM1009107 2 0.0817 0.891 0.000 0.976 0.024 0.000
#> GSM1009121 2 0.0524 0.888 0.000 0.988 0.004 0.008
#> GSM1009135 4 0.3547 0.861 0.064 0.072 0.000 0.864
#> GSM1009149 4 0.0817 0.922 0.024 0.000 0.000 0.976
#> GSM1009163 3 0.0921 1.000 0.000 0.028 0.972 0.000
#> GSM1009177 1 0.7561 0.125 0.424 0.384 0.192 0.000
#> GSM1009191 2 0.0779 0.885 0.000 0.980 0.004 0.016
#> GSM1009066 1 0.0188 0.824 0.996 0.000 0.000 0.004
#> GSM1009080 2 0.5112 0.357 0.004 0.560 0.436 0.000
#> GSM1009094 4 0.0000 0.924 0.000 0.000 0.000 1.000
#> GSM1009108 2 0.0817 0.891 0.000 0.976 0.024 0.000
#> GSM1009122 2 0.0469 0.886 0.000 0.988 0.012 0.000
#> GSM1009136 4 0.0000 0.924 0.000 0.000 0.000 1.000
#> GSM1009150 4 0.1557 0.909 0.056 0.000 0.000 0.944
#> GSM1009164 3 0.0921 1.000 0.000 0.028 0.972 0.000
#> GSM1009178 1 0.6480 0.334 0.568 0.024 0.036 0.372
#> GSM1009192 4 0.0921 0.921 0.028 0.000 0.000 0.972
#> GSM1009067 1 0.0188 0.824 0.996 0.000 0.000 0.004
#> GSM1009081 2 0.5849 0.668 0.132 0.704 0.164 0.000
#> GSM1009095 4 0.1389 0.913 0.048 0.000 0.000 0.952
#> GSM1009109 2 0.0592 0.891 0.000 0.984 0.016 0.000
#> GSM1009123 4 0.0188 0.923 0.004 0.000 0.000 0.996
#> GSM1009137 4 0.2706 0.871 0.020 0.080 0.000 0.900
#> GSM1009151 4 0.4948 0.289 0.440 0.000 0.000 0.560
#> GSM1009165 3 0.0921 1.000 0.000 0.028 0.972 0.000
#> GSM1009179 1 0.2830 0.801 0.900 0.040 0.060 0.000
#> GSM1009193 4 0.0000 0.924 0.000 0.000 0.000 1.000
#> GSM1009068 1 0.2973 0.732 0.856 0.000 0.000 0.144
#> GSM1009082 2 0.6468 0.338 0.348 0.568 0.084 0.000
#> GSM1009096 4 0.0188 0.923 0.004 0.000 0.000 0.996
#> GSM1009110 2 0.0921 0.887 0.000 0.972 0.028 0.000
#> GSM1009124 4 0.5039 0.322 0.004 0.404 0.000 0.592
#> GSM1009138 4 0.3080 0.877 0.096 0.024 0.000 0.880
#> GSM1009152 4 0.4776 0.468 0.376 0.000 0.000 0.624
#> GSM1009166 3 0.0921 1.000 0.000 0.028 0.972 0.000
#> GSM1009180 4 0.3600 0.867 0.068 0.028 0.028 0.876
#> GSM1009194 2 0.0817 0.885 0.024 0.976 0.000 0.000
#> GSM1009069 1 0.0188 0.824 0.996 0.000 0.000 0.004
#> GSM1009083 1 0.5728 0.379 0.600 0.364 0.036 0.000
#> GSM1009097 4 0.0188 0.923 0.004 0.000 0.000 0.996
#> GSM1009111 2 0.0817 0.891 0.000 0.976 0.024 0.000
#> GSM1009125 2 0.0469 0.889 0.000 0.988 0.012 0.000
#> GSM1009139 2 0.1211 0.875 0.040 0.960 0.000 0.000
#> GSM1009153 1 0.3801 0.648 0.780 0.000 0.000 0.220
#> GSM1009167 3 0.0921 1.000 0.000 0.028 0.972 0.000
#> GSM1009181 1 0.5875 0.635 0.692 0.204 0.104 0.000
#> GSM1009195 2 0.1510 0.883 0.028 0.956 0.016 0.000
#> GSM1009070 4 0.2921 0.850 0.140 0.000 0.000 0.860
#> GSM1009084 2 0.4832 0.707 0.176 0.768 0.056 0.000
#> GSM1009098 4 0.0000 0.924 0.000 0.000 0.000 1.000
#> GSM1009112 2 0.0817 0.891 0.000 0.976 0.024 0.000
#> GSM1009126 2 0.2466 0.807 0.004 0.900 0.000 0.096
#> GSM1009140 4 0.0779 0.924 0.016 0.004 0.000 0.980
#> GSM1009154 4 0.3123 0.832 0.156 0.000 0.000 0.844
#> GSM1009168 3 0.0921 1.000 0.000 0.028 0.972 0.000
#> GSM1009182 1 0.2660 0.804 0.908 0.036 0.056 0.000
#> GSM1009196 4 0.2469 0.874 0.108 0.000 0.000 0.892
#> GSM1009071 1 0.0188 0.824 0.996 0.000 0.000 0.004
#> GSM1009085 2 0.7155 0.346 0.292 0.540 0.168 0.000
#> GSM1009099 4 0.0000 0.924 0.000 0.000 0.000 1.000
#> GSM1009113 2 0.0817 0.891 0.000 0.976 0.024 0.000
#> GSM1009127 4 0.0817 0.922 0.024 0.000 0.000 0.976
#> GSM1009141 1 0.4843 0.328 0.604 0.396 0.000 0.000
#> GSM1009155 1 0.2216 0.780 0.908 0.000 0.000 0.092
#> GSM1009169 3 0.0921 1.000 0.000 0.028 0.972 0.000
#> GSM1009183 1 0.6115 0.646 0.680 0.148 0.172 0.000
#> GSM1009197 4 0.0336 0.924 0.008 0.000 0.000 0.992
#> GSM1009072 1 0.1557 0.805 0.944 0.000 0.000 0.056
#> GSM1009086 2 0.6764 0.465 0.260 0.596 0.144 0.000
#> GSM1009100 4 0.0000 0.924 0.000 0.000 0.000 1.000
#> GSM1009114 2 0.1004 0.890 0.004 0.972 0.024 0.000
#> GSM1009128 4 0.0188 0.923 0.004 0.000 0.000 0.996
#> GSM1009142 2 0.2075 0.862 0.044 0.936 0.004 0.016
#> GSM1009156 4 0.0707 0.923 0.020 0.000 0.000 0.980
#> GSM1009170 3 0.0921 1.000 0.000 0.028 0.972 0.000
#> GSM1009184 1 0.2131 0.811 0.932 0.036 0.032 0.000
#> GSM1009198 4 0.0188 0.923 0.004 0.000 0.000 0.996
#> GSM1009073 1 0.0188 0.824 0.996 0.000 0.000 0.004
#> GSM1009087 4 0.3279 0.876 0.068 0.016 0.028 0.888
#> GSM1009101 4 0.0188 0.923 0.004 0.000 0.000 0.996
#> GSM1009115 2 0.0817 0.891 0.000 0.976 0.024 0.000
#> GSM1009129 2 0.0469 0.891 0.000 0.988 0.012 0.000
#> GSM1009143 4 0.1109 0.921 0.028 0.004 0.000 0.968
#> GSM1009157 1 0.1229 0.821 0.968 0.008 0.020 0.004
#> GSM1009171 3 0.0921 1.000 0.000 0.028 0.972 0.000
#> GSM1009185 4 0.0804 0.924 0.012 0.000 0.008 0.980
#> GSM1009199 2 0.0336 0.888 0.000 0.992 0.008 0.000
#> GSM1009074 1 0.0188 0.824 0.996 0.000 0.000 0.004
#> GSM1009088 4 0.6369 0.115 0.444 0.020 0.028 0.508
#> GSM1009102 4 0.0707 0.923 0.020 0.000 0.000 0.980
#> GSM1009116 2 0.0817 0.891 0.000 0.976 0.024 0.000
#> GSM1009130 2 0.0592 0.891 0.000 0.984 0.016 0.000
#> GSM1009144 4 0.3471 0.866 0.060 0.072 0.000 0.868
#> GSM1009158 4 0.1302 0.915 0.044 0.000 0.000 0.956
#> GSM1009172 3 0.0921 1.000 0.000 0.028 0.972 0.000
#> GSM1009186 1 0.2032 0.812 0.936 0.036 0.028 0.000
#> GSM1009200 2 0.0804 0.889 0.000 0.980 0.012 0.008
#> GSM1009075 1 0.1716 0.800 0.936 0.000 0.000 0.064
#> GSM1009089 4 0.0592 0.924 0.016 0.000 0.000 0.984
#> GSM1009103 4 0.0592 0.924 0.016 0.000 0.000 0.984
#> GSM1009117 2 0.0921 0.889 0.000 0.972 0.028 0.000
#> GSM1009131 4 0.0188 0.923 0.004 0.000 0.000 0.996
#> GSM1009145 4 0.0000 0.924 0.000 0.000 0.000 1.000
#> GSM1009159 4 0.0592 0.924 0.016 0.000 0.000 0.984
#> GSM1009173 3 0.0921 1.000 0.000 0.028 0.972 0.000
#> GSM1009187 1 0.4482 0.735 0.808 0.016 0.028 0.148
#> GSM1009201 4 0.5040 0.454 0.008 0.364 0.000 0.628
cbind(get_classes(res, k = 5), get_membership(res, k = 5))
#> class entropy silhouette p1 p2 p3 p4 p5
#> GSM1009062 1 0.3037 0.830 0.860 0.100 0.000 0.040 0.000
#> GSM1009076 2 0.2707 0.839 0.008 0.860 0.000 0.000 0.132
#> GSM1009090 4 0.1768 0.895 0.072 0.000 0.000 0.924 0.004
#> GSM1009104 5 0.1205 0.848 0.000 0.040 0.004 0.000 0.956
#> GSM1009118 5 0.2690 0.816 0.000 0.156 0.000 0.000 0.844
#> GSM1009132 5 0.2763 0.708 0.148 0.000 0.000 0.004 0.848
#> GSM1009146 4 0.0609 0.917 0.020 0.000 0.000 0.980 0.000
#> GSM1009160 3 0.0290 0.997 0.000 0.008 0.992 0.000 0.000
#> GSM1009174 2 0.1502 0.861 0.004 0.940 0.000 0.000 0.056
#> GSM1009188 4 0.1106 0.917 0.024 0.000 0.000 0.964 0.012
#> GSM1009063 1 0.2536 0.828 0.868 0.128 0.000 0.004 0.000
#> GSM1009077 2 0.2248 0.863 0.012 0.900 0.000 0.000 0.088
#> GSM1009091 4 0.0510 0.914 0.016 0.000 0.000 0.984 0.000
#> GSM1009105 5 0.1502 0.853 0.000 0.056 0.004 0.000 0.940
#> GSM1009119 4 0.0162 0.917 0.004 0.000 0.000 0.996 0.000
#> GSM1009133 5 0.6586 0.119 0.300 0.004 0.000 0.208 0.488
#> GSM1009147 4 0.0865 0.908 0.024 0.004 0.000 0.972 0.000
#> GSM1009161 3 0.0290 0.997 0.000 0.008 0.992 0.000 0.000
#> GSM1009175 2 0.1408 0.857 0.008 0.948 0.000 0.000 0.044
#> GSM1009189 4 0.1830 0.907 0.040 0.000 0.000 0.932 0.028
#> GSM1009064 1 0.3074 0.777 0.804 0.196 0.000 0.000 0.000
#> GSM1009078 2 0.4781 0.249 0.020 0.552 0.000 0.428 0.000
#> GSM1009092 4 0.0000 0.916 0.000 0.000 0.000 1.000 0.000
#> GSM1009106 5 0.2124 0.849 0.000 0.096 0.004 0.000 0.900
#> GSM1009120 4 0.0404 0.917 0.012 0.000 0.000 0.988 0.000
#> GSM1009134 1 0.4303 0.682 0.772 0.004 0.004 0.048 0.172
#> GSM1009148 4 0.3639 0.795 0.044 0.144 0.000 0.812 0.000
#> GSM1009162 3 0.0162 0.997 0.000 0.004 0.996 0.000 0.000
#> GSM1009176 2 0.2488 0.845 0.004 0.872 0.000 0.000 0.124
#> GSM1009190 4 0.0955 0.914 0.004 0.000 0.000 0.968 0.028
#> GSM1009065 1 0.2648 0.815 0.848 0.152 0.000 0.000 0.000
#> GSM1009079 2 0.2570 0.853 0.004 0.880 0.008 0.000 0.108
#> GSM1009093 4 0.0404 0.918 0.012 0.000 0.000 0.988 0.000
#> GSM1009107 5 0.1704 0.853 0.000 0.068 0.004 0.000 0.928
#> GSM1009121 5 0.1608 0.854 0.000 0.072 0.000 0.000 0.928
#> GSM1009135 1 0.5411 0.299 0.552 0.004 0.000 0.052 0.392
#> GSM1009149 4 0.0794 0.916 0.028 0.000 0.000 0.972 0.000
#> GSM1009163 3 0.0290 0.997 0.000 0.008 0.992 0.000 0.000
#> GSM1009177 2 0.1851 0.862 0.000 0.912 0.000 0.000 0.088
#> GSM1009191 5 0.2516 0.830 0.000 0.140 0.000 0.000 0.860
#> GSM1009066 1 0.2516 0.821 0.860 0.140 0.000 0.000 0.000
#> GSM1009080 2 0.3694 0.783 0.000 0.796 0.032 0.000 0.172
#> GSM1009094 4 0.0510 0.914 0.016 0.000 0.000 0.984 0.000
#> GSM1009108 5 0.2513 0.841 0.000 0.116 0.008 0.000 0.876
#> GSM1009122 5 0.3109 0.768 0.000 0.200 0.000 0.000 0.800
#> GSM1009136 4 0.2329 0.867 0.124 0.000 0.000 0.876 0.000
#> GSM1009150 4 0.1270 0.910 0.052 0.000 0.000 0.948 0.000
#> GSM1009164 3 0.0290 0.997 0.000 0.008 0.992 0.000 0.000
#> GSM1009178 2 0.1331 0.813 0.008 0.952 0.000 0.040 0.000
#> GSM1009192 4 0.1544 0.904 0.068 0.000 0.000 0.932 0.000
#> GSM1009067 1 0.2864 0.832 0.864 0.112 0.000 0.024 0.000
#> GSM1009081 2 0.3805 0.766 0.016 0.784 0.008 0.000 0.192
#> GSM1009095 4 0.1270 0.913 0.052 0.000 0.000 0.948 0.000
#> GSM1009109 5 0.1282 0.850 0.000 0.044 0.004 0.000 0.952
#> GSM1009123 4 0.0510 0.918 0.016 0.000 0.000 0.984 0.000
#> GSM1009137 5 0.5699 0.300 0.308 0.000 0.000 0.108 0.584
#> GSM1009151 4 0.4901 0.666 0.104 0.184 0.000 0.712 0.000
#> GSM1009165 3 0.0162 0.997 0.000 0.004 0.996 0.000 0.000
#> GSM1009179 2 0.0727 0.832 0.012 0.980 0.000 0.004 0.004
#> GSM1009193 4 0.0880 0.916 0.032 0.000 0.000 0.968 0.000
#> GSM1009068 1 0.3201 0.826 0.852 0.096 0.000 0.052 0.000
#> GSM1009082 2 0.2824 0.850 0.020 0.864 0.000 0.000 0.116
#> GSM1009096 4 0.0404 0.914 0.012 0.000 0.000 0.988 0.000
#> GSM1009110 5 0.1357 0.852 0.000 0.048 0.004 0.000 0.948
#> GSM1009124 4 0.3891 0.777 0.020 0.076 0.000 0.828 0.076
#> GSM1009138 1 0.4044 0.696 0.792 0.004 0.004 0.040 0.160
#> GSM1009152 4 0.5264 0.592 0.196 0.128 0.000 0.676 0.000
#> GSM1009166 3 0.0162 0.997 0.000 0.004 0.996 0.000 0.000
#> GSM1009180 2 0.3293 0.800 0.012 0.852 0.000 0.108 0.028
#> GSM1009194 5 0.3106 0.777 0.132 0.024 0.000 0.000 0.844
#> GSM1009069 1 0.3305 0.746 0.776 0.224 0.000 0.000 0.000
#> GSM1009083 2 0.2813 0.855 0.024 0.868 0.000 0.000 0.108
#> GSM1009097 4 0.0000 0.916 0.000 0.000 0.000 1.000 0.000
#> GSM1009111 5 0.2439 0.840 0.000 0.120 0.004 0.000 0.876
#> GSM1009125 5 0.2806 0.820 0.000 0.152 0.004 0.000 0.844
#> GSM1009139 5 0.3109 0.657 0.200 0.000 0.000 0.000 0.800
#> GSM1009153 1 0.5700 0.672 0.628 0.196 0.000 0.176 0.000
#> GSM1009167 3 0.0162 0.997 0.000 0.004 0.996 0.000 0.000
#> GSM1009181 2 0.2017 0.863 0.008 0.912 0.000 0.000 0.080
#> GSM1009195 5 0.3304 0.823 0.052 0.092 0.004 0.000 0.852
#> GSM1009070 1 0.4339 0.479 0.652 0.012 0.000 0.336 0.000
#> GSM1009084 2 0.3011 0.831 0.016 0.844 0.000 0.000 0.140
#> GSM1009098 4 0.1341 0.912 0.056 0.000 0.000 0.944 0.000
#> GSM1009112 5 0.1502 0.853 0.000 0.056 0.004 0.000 0.940
#> GSM1009126 5 0.4352 0.603 0.000 0.036 0.000 0.244 0.720
#> GSM1009140 4 0.5799 0.396 0.324 0.000 0.000 0.564 0.112
#> GSM1009154 4 0.4972 0.437 0.336 0.044 0.000 0.620 0.000
#> GSM1009168 3 0.0290 0.997 0.000 0.008 0.992 0.000 0.000
#> GSM1009182 2 0.0880 0.813 0.032 0.968 0.000 0.000 0.000
#> GSM1009196 4 0.2450 0.875 0.028 0.076 0.000 0.896 0.000
#> GSM1009071 1 0.2280 0.828 0.880 0.120 0.000 0.000 0.000
#> GSM1009085 2 0.2193 0.861 0.008 0.900 0.000 0.000 0.092
#> GSM1009099 4 0.0404 0.914 0.012 0.000 0.000 0.988 0.000
#> GSM1009113 5 0.2389 0.842 0.000 0.116 0.004 0.000 0.880
#> GSM1009127 4 0.0794 0.916 0.028 0.000 0.000 0.972 0.000
#> GSM1009141 1 0.2771 0.729 0.860 0.000 0.000 0.012 0.128
#> GSM1009155 1 0.4325 0.730 0.724 0.240 0.000 0.036 0.000
#> GSM1009169 3 0.0162 0.997 0.000 0.004 0.996 0.000 0.000
#> GSM1009183 2 0.1638 0.863 0.000 0.932 0.004 0.000 0.064
#> GSM1009197 4 0.0703 0.917 0.024 0.000 0.000 0.976 0.000
#> GSM1009072 1 0.2972 0.826 0.872 0.084 0.004 0.040 0.000
#> GSM1009086 2 0.2777 0.848 0.016 0.864 0.000 0.000 0.120
#> GSM1009100 4 0.0162 0.916 0.004 0.000 0.000 0.996 0.000
#> GSM1009114 5 0.1282 0.850 0.000 0.044 0.004 0.000 0.952
#> GSM1009128 4 0.1173 0.902 0.020 0.012 0.000 0.964 0.004
#> GSM1009142 5 0.4213 0.467 0.308 0.000 0.000 0.012 0.680
#> GSM1009156 4 0.0703 0.910 0.024 0.000 0.000 0.976 0.000
#> GSM1009170 3 0.0290 0.997 0.000 0.008 0.992 0.000 0.000
#> GSM1009184 2 0.1121 0.807 0.044 0.956 0.000 0.000 0.000
#> GSM1009198 4 0.0566 0.917 0.004 0.000 0.000 0.984 0.012
#> GSM1009073 1 0.2280 0.828 0.880 0.120 0.000 0.000 0.000
#> GSM1009087 2 0.3878 0.634 0.016 0.748 0.000 0.236 0.000
#> GSM1009101 4 0.0000 0.916 0.000 0.000 0.000 1.000 0.000
#> GSM1009115 5 0.2068 0.850 0.000 0.092 0.004 0.000 0.904
#> GSM1009129 5 0.3081 0.814 0.000 0.156 0.012 0.000 0.832
#> GSM1009143 4 0.6084 0.253 0.360 0.000 0.000 0.508 0.132
#> GSM1009157 2 0.3280 0.655 0.184 0.808 0.004 0.004 0.000
#> GSM1009171 3 0.0162 0.997 0.000 0.004 0.996 0.000 0.000
#> GSM1009185 4 0.3550 0.722 0.020 0.184 0.000 0.796 0.000
#> GSM1009199 5 0.3790 0.623 0.004 0.272 0.000 0.000 0.724
#> GSM1009074 1 0.2813 0.832 0.868 0.108 0.000 0.024 0.000
#> GSM1009088 2 0.2727 0.736 0.016 0.868 0.000 0.116 0.000
#> GSM1009102 4 0.1341 0.910 0.056 0.000 0.000 0.944 0.000
#> GSM1009116 5 0.1831 0.853 0.000 0.076 0.004 0.000 0.920
#> GSM1009130 5 0.2719 0.826 0.000 0.144 0.004 0.000 0.852
#> GSM1009144 1 0.5492 0.275 0.536 0.000 0.000 0.068 0.396
#> GSM1009158 4 0.1043 0.914 0.040 0.000 0.000 0.960 0.000
#> GSM1009172 3 0.0290 0.997 0.000 0.008 0.992 0.000 0.000
#> GSM1009186 2 0.1792 0.778 0.084 0.916 0.000 0.000 0.000
#> GSM1009200 5 0.1671 0.768 0.076 0.000 0.000 0.000 0.924
#> GSM1009075 1 0.2813 0.827 0.876 0.084 0.000 0.040 0.000
#> GSM1009089 4 0.0609 0.912 0.020 0.000 0.000 0.980 0.000
#> GSM1009103 4 0.1544 0.904 0.068 0.000 0.000 0.932 0.000
#> GSM1009117 5 0.1205 0.849 0.000 0.040 0.004 0.000 0.956
#> GSM1009131 4 0.1725 0.884 0.000 0.020 0.000 0.936 0.044
#> GSM1009145 4 0.1671 0.902 0.076 0.000 0.000 0.924 0.000
#> GSM1009159 4 0.1121 0.913 0.044 0.000 0.000 0.956 0.000
#> GSM1009173 3 0.0162 0.997 0.000 0.004 0.996 0.000 0.000
#> GSM1009187 2 0.3267 0.707 0.044 0.844 0.000 0.112 0.000
#> GSM1009201 5 0.4119 0.593 0.212 0.000 0.000 0.036 0.752
cbind(get_classes(res, k = 6), get_membership(res, k = 6))
#> class entropy silhouette p1 p2 p3 p4 p5 p6
#> GSM1009062 6 0.0508 0.9136 0.012 0.000 0.000 0.004 0.000 0.984
#> GSM1009076 2 0.2999 0.8649 0.000 0.852 0.000 0.072 0.072 0.004
#> GSM1009090 4 0.4323 0.3947 0.312 0.032 0.000 0.652 0.004 0.000
#> GSM1009104 5 0.0713 0.8714 0.000 0.000 0.000 0.028 0.972 0.000
#> GSM1009118 5 0.4382 0.7269 0.000 0.124 0.000 0.156 0.720 0.000
#> GSM1009132 4 0.2488 0.8126 0.000 0.008 0.000 0.864 0.124 0.004
#> GSM1009146 1 0.0767 0.8694 0.976 0.004 0.000 0.012 0.000 0.008
#> GSM1009160 3 0.0458 0.9887 0.000 0.000 0.984 0.000 0.016 0.000
#> GSM1009174 2 0.0748 0.8823 0.000 0.976 0.000 0.004 0.016 0.004
#> GSM1009188 1 0.0653 0.8732 0.980 0.004 0.000 0.012 0.004 0.000
#> GSM1009063 6 0.0436 0.9130 0.004 0.004 0.000 0.004 0.000 0.988
#> GSM1009077 2 0.2853 0.8695 0.000 0.868 0.000 0.072 0.048 0.012
#> GSM1009091 1 0.2810 0.8305 0.832 0.008 0.000 0.156 0.000 0.004
#> GSM1009105 5 0.0291 0.8754 0.000 0.004 0.000 0.004 0.992 0.000
#> GSM1009119 1 0.0146 0.8724 0.996 0.000 0.000 0.004 0.000 0.000
#> GSM1009133 4 0.2624 0.8476 0.020 0.000 0.000 0.884 0.068 0.028
#> GSM1009147 1 0.0725 0.8712 0.976 0.012 0.000 0.012 0.000 0.000
#> GSM1009161 3 0.0363 0.9924 0.000 0.000 0.988 0.000 0.012 0.000
#> GSM1009175 2 0.0363 0.8822 0.000 0.988 0.000 0.000 0.012 0.000
#> GSM1009189 1 0.3232 0.8044 0.844 0.008 0.000 0.088 0.056 0.004
#> GSM1009064 6 0.0291 0.9117 0.000 0.004 0.000 0.004 0.000 0.992
#> GSM1009078 1 0.5206 0.6479 0.696 0.176 0.000 0.080 0.036 0.012
#> GSM1009092 1 0.2001 0.8596 0.900 0.004 0.000 0.092 0.000 0.004
#> GSM1009106 5 0.0405 0.8760 0.000 0.008 0.000 0.004 0.988 0.000
#> GSM1009120 1 0.0508 0.8691 0.984 0.004 0.000 0.012 0.000 0.000
#> GSM1009134 4 0.3181 0.8320 0.020 0.000 0.000 0.840 0.028 0.112
#> GSM1009148 1 0.4401 0.1015 0.512 0.024 0.000 0.000 0.000 0.464
#> GSM1009162 3 0.0146 0.9963 0.000 0.000 0.996 0.000 0.004 0.000
#> GSM1009176 2 0.0858 0.8831 0.000 0.968 0.000 0.004 0.028 0.000
#> GSM1009190 1 0.0820 0.8719 0.972 0.000 0.000 0.012 0.016 0.000
#> GSM1009065 6 0.0291 0.9117 0.000 0.004 0.000 0.004 0.000 0.992
#> GSM1009079 2 0.2692 0.8732 0.000 0.880 0.004 0.072 0.036 0.008
#> GSM1009093 1 0.2810 0.8305 0.832 0.008 0.000 0.156 0.000 0.004
#> GSM1009107 5 0.0405 0.8758 0.000 0.008 0.000 0.004 0.988 0.000
#> GSM1009121 5 0.2744 0.8052 0.000 0.016 0.000 0.144 0.840 0.000
#> GSM1009135 4 0.2775 0.8495 0.016 0.000 0.000 0.876 0.052 0.056
#> GSM1009149 1 0.0653 0.8690 0.980 0.004 0.000 0.012 0.000 0.004
#> GSM1009163 3 0.0146 0.9963 0.000 0.000 0.996 0.000 0.004 0.000
#> GSM1009177 2 0.0922 0.8831 0.000 0.968 0.004 0.004 0.024 0.000
#> GSM1009191 5 0.3676 0.8037 0.012 0.088 0.000 0.092 0.808 0.000
#> GSM1009066 6 0.0405 0.9116 0.000 0.004 0.000 0.008 0.000 0.988
#> GSM1009080 2 0.3469 0.8560 0.000 0.824 0.012 0.072 0.092 0.000
#> GSM1009094 1 0.3884 0.7077 0.708 0.012 0.000 0.272 0.004 0.004
#> GSM1009108 5 0.0405 0.8760 0.000 0.008 0.000 0.004 0.988 0.000
#> GSM1009122 5 0.4963 0.6079 0.000 0.240 0.000 0.124 0.636 0.000
#> GSM1009136 1 0.3615 0.6587 0.700 0.000 0.000 0.292 0.000 0.008
#> GSM1009150 1 0.2101 0.8332 0.892 0.004 0.000 0.004 0.000 0.100
#> GSM1009164 3 0.0363 0.9924 0.000 0.000 0.988 0.000 0.012 0.000
#> GSM1009178 2 0.0810 0.8777 0.008 0.976 0.000 0.004 0.008 0.004
#> GSM1009192 1 0.1572 0.8662 0.936 0.000 0.000 0.028 0.000 0.036
#> GSM1009067 6 0.0622 0.9138 0.012 0.000 0.000 0.008 0.000 0.980
#> GSM1009081 2 0.3754 0.8134 0.000 0.776 0.000 0.072 0.152 0.000
#> GSM1009095 1 0.3451 0.7886 0.776 0.004 0.000 0.204 0.004 0.012
#> GSM1009109 5 0.0790 0.8685 0.000 0.000 0.000 0.032 0.968 0.000
#> GSM1009123 1 0.0508 0.8733 0.984 0.004 0.000 0.012 0.000 0.000
#> GSM1009137 4 0.2647 0.8421 0.016 0.000 0.000 0.876 0.088 0.020
#> GSM1009151 6 0.3541 0.6403 0.260 0.012 0.000 0.000 0.000 0.728
#> GSM1009165 3 0.0146 0.9963 0.000 0.000 0.996 0.000 0.004 0.000
#> GSM1009179 2 0.0363 0.8822 0.000 0.988 0.000 0.000 0.012 0.000
#> GSM1009193 1 0.0291 0.8720 0.992 0.004 0.000 0.004 0.000 0.000
#> GSM1009068 6 0.0767 0.9130 0.012 0.004 0.000 0.008 0.000 0.976
#> GSM1009082 2 0.3473 0.8561 0.000 0.824 0.000 0.076 0.088 0.012
#> GSM1009096 1 0.2531 0.8444 0.860 0.008 0.000 0.128 0.000 0.004
#> GSM1009110 5 0.1556 0.8463 0.000 0.000 0.000 0.080 0.920 0.000
#> GSM1009124 1 0.3577 0.7698 0.816 0.088 0.000 0.012 0.084 0.000
#> GSM1009138 4 0.2961 0.8176 0.008 0.000 0.000 0.840 0.020 0.132
#> GSM1009152 6 0.3133 0.7125 0.212 0.008 0.000 0.000 0.000 0.780
#> GSM1009166 3 0.0146 0.9963 0.000 0.000 0.996 0.000 0.004 0.000
#> GSM1009180 2 0.1026 0.8766 0.008 0.968 0.000 0.008 0.012 0.004
#> GSM1009194 5 0.6132 0.5749 0.004 0.108 0.000 0.188 0.608 0.092
#> GSM1009069 6 0.0363 0.9061 0.000 0.012 0.000 0.000 0.000 0.988
#> GSM1009083 2 0.4000 0.8284 0.000 0.780 0.000 0.072 0.132 0.016
#> GSM1009097 1 0.1845 0.8646 0.916 0.008 0.000 0.072 0.000 0.004
#> GSM1009111 5 0.0363 0.8743 0.000 0.012 0.000 0.000 0.988 0.000
#> GSM1009125 5 0.2965 0.8301 0.000 0.080 0.000 0.072 0.848 0.000
#> GSM1009139 4 0.2389 0.8118 0.000 0.000 0.000 0.864 0.128 0.008
#> GSM1009153 6 0.1204 0.8886 0.056 0.000 0.000 0.000 0.000 0.944
#> GSM1009167 3 0.0146 0.9963 0.000 0.000 0.996 0.000 0.004 0.000
#> GSM1009181 2 0.0603 0.8831 0.000 0.980 0.000 0.004 0.016 0.000
#> GSM1009195 5 0.5502 0.4483 0.000 0.100 0.004 0.308 0.576 0.012
#> GSM1009070 6 0.1531 0.8783 0.068 0.004 0.000 0.000 0.000 0.928
#> GSM1009084 2 0.4166 0.7693 0.000 0.728 0.000 0.076 0.196 0.000
#> GSM1009098 1 0.3421 0.7393 0.736 0.008 0.000 0.256 0.000 0.000
#> GSM1009112 5 0.0291 0.8754 0.000 0.004 0.000 0.004 0.992 0.000
#> GSM1009126 5 0.6517 -0.0648 0.220 0.028 0.000 0.356 0.396 0.000
#> GSM1009140 4 0.2888 0.8031 0.092 0.000 0.000 0.852 0.000 0.056
#> GSM1009154 6 0.2738 0.7625 0.176 0.000 0.000 0.004 0.000 0.820
#> GSM1009168 3 0.0146 0.9963 0.000 0.000 0.996 0.000 0.004 0.000
#> GSM1009182 2 0.0260 0.8821 0.000 0.992 0.000 0.000 0.008 0.000
#> GSM1009196 1 0.3801 0.7843 0.800 0.136 0.000 0.036 0.004 0.024
#> GSM1009071 6 0.0405 0.9116 0.000 0.004 0.000 0.008 0.000 0.988
#> GSM1009085 2 0.3656 0.8437 0.000 0.804 0.000 0.076 0.112 0.008
#> GSM1009099 1 0.2261 0.8552 0.884 0.008 0.000 0.104 0.000 0.004
#> GSM1009113 5 0.0260 0.8749 0.000 0.008 0.000 0.000 0.992 0.000
#> GSM1009127 1 0.0653 0.8690 0.980 0.004 0.000 0.012 0.000 0.004
#> GSM1009141 4 0.2750 0.8167 0.000 0.000 0.000 0.844 0.020 0.136
#> GSM1009155 6 0.0935 0.9044 0.032 0.004 0.000 0.000 0.000 0.964
#> GSM1009169 3 0.0000 0.9938 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1009183 2 0.0748 0.8825 0.000 0.976 0.004 0.004 0.016 0.000
#> GSM1009197 1 0.0363 0.8729 0.988 0.000 0.000 0.012 0.000 0.000
#> GSM1009072 6 0.0806 0.9092 0.008 0.000 0.000 0.020 0.000 0.972
#> GSM1009086 2 0.3369 0.8588 0.000 0.832 0.000 0.072 0.084 0.012
#> GSM1009100 1 0.2062 0.8602 0.900 0.008 0.000 0.088 0.000 0.004
#> GSM1009114 5 0.0935 0.8714 0.000 0.004 0.000 0.032 0.964 0.000
#> GSM1009128 1 0.1067 0.8701 0.964 0.004 0.000 0.024 0.004 0.004
#> GSM1009142 4 0.2383 0.8372 0.000 0.000 0.000 0.880 0.096 0.024
#> GSM1009156 1 0.0363 0.8701 0.988 0.000 0.000 0.012 0.000 0.000
#> GSM1009170 3 0.0363 0.9924 0.000 0.000 0.988 0.000 0.012 0.000
#> GSM1009184 2 0.0458 0.8777 0.000 0.984 0.000 0.000 0.000 0.016
#> GSM1009198 1 0.0603 0.8730 0.980 0.004 0.000 0.016 0.000 0.000
#> GSM1009073 6 0.0405 0.9116 0.000 0.004 0.000 0.008 0.000 0.988
#> GSM1009087 1 0.6375 0.0281 0.456 0.400 0.000 0.080 0.044 0.020
#> GSM1009101 1 0.2308 0.8531 0.880 0.008 0.000 0.108 0.000 0.004
#> GSM1009115 5 0.0458 0.8730 0.000 0.016 0.000 0.000 0.984 0.000
#> GSM1009129 5 0.2452 0.8540 0.000 0.044 0.008 0.056 0.892 0.000
#> GSM1009143 4 0.3025 0.8182 0.080 0.000 0.000 0.856 0.012 0.052
#> GSM1009157 6 0.4488 0.4122 0.012 0.360 0.008 0.004 0.004 0.612
#> GSM1009171 3 0.0146 0.9963 0.000 0.000 0.996 0.000 0.004 0.000
#> GSM1009185 2 0.4199 0.0728 0.444 0.544 0.000 0.008 0.000 0.004
#> GSM1009199 2 0.5614 0.2776 0.000 0.544 0.000 0.160 0.292 0.004
#> GSM1009074 6 0.0622 0.9138 0.012 0.000 0.000 0.008 0.000 0.980
#> GSM1009088 2 0.5355 0.7415 0.120 0.716 0.000 0.080 0.052 0.032
#> GSM1009102 1 0.3010 0.8311 0.828 0.004 0.000 0.148 0.000 0.020
#> GSM1009116 5 0.0405 0.8756 0.000 0.008 0.000 0.004 0.988 0.000
#> GSM1009130 5 0.1562 0.8533 0.000 0.024 0.004 0.032 0.940 0.000
#> GSM1009144 4 0.2800 0.8500 0.020 0.000 0.000 0.876 0.052 0.052
#> GSM1009158 1 0.1218 0.8656 0.956 0.004 0.000 0.012 0.000 0.028
#> GSM1009172 3 0.0146 0.9963 0.000 0.000 0.996 0.000 0.004 0.000
#> GSM1009186 2 0.0692 0.8790 0.000 0.976 0.000 0.004 0.000 0.020
#> GSM1009200 4 0.4126 -0.0324 0.004 0.004 0.000 0.512 0.480 0.000
#> GSM1009075 6 0.0622 0.9138 0.012 0.000 0.000 0.008 0.000 0.980
#> GSM1009089 1 0.0862 0.8714 0.972 0.008 0.000 0.016 0.000 0.004
#> GSM1009103 1 0.3000 0.8292 0.824 0.004 0.000 0.156 0.000 0.016
#> GSM1009117 5 0.0777 0.8737 0.000 0.004 0.000 0.024 0.972 0.000
#> GSM1009131 1 0.3265 0.6591 0.748 0.004 0.000 0.000 0.248 0.000
#> GSM1009145 1 0.2946 0.7981 0.808 0.004 0.000 0.184 0.000 0.004
#> GSM1009159 1 0.0291 0.8716 0.992 0.004 0.000 0.000 0.000 0.004
#> GSM1009173 3 0.0000 0.9938 0.000 0.000 1.000 0.000 0.000 0.000
#> GSM1009187 2 0.1440 0.8605 0.012 0.948 0.000 0.004 0.004 0.032
#> GSM1009201 4 0.3539 0.7247 0.008 0.000 0.000 0.768 0.208 0.016
Heatmaps for the consensus matrix. It visualizes the probability of two samples to be in a same group.
consensus_heatmap(res, k = 2)
consensus_heatmap(res, k = 3)
consensus_heatmap(res, k = 4)
consensus_heatmap(res, k = 5)
consensus_heatmap(res, k = 6)
Heatmaps for the membership of samples in all partitions to see how consistent they are:
membership_heatmap(res, k = 2)
membership_heatmap(res, k = 3)
membership_heatmap(res, k = 4)
membership_heatmap(res, k = 5)
membership_heatmap(res, k = 6)
As soon as we have had the classes for columns, we can look for signatures which are significantly different between classes which can be candidate marks for certain classes. Following are the heatmaps for signatures.
Signature heatmaps where rows are scaled:
get_signatures(res, k = 2)
get_signatures(res, k = 3)
get_signatures(res, k = 4)
get_signatures(res, k = 5)
get_signatures(res, k = 6)
Signature heatmaps where rows are not scaled:
get_signatures(res, k = 2, scale_rows = FALSE)
get_signatures(res, k = 3, scale_rows = FALSE)
get_signatures(res, k = 4, scale_rows = FALSE)
get_signatures(res, k = 5, scale_rows = FALSE)
get_signatures(res, k = 6, scale_rows = FALSE)
Compare the overlap of signatures from different k:
compare_signatures(res)
get_signature() returns a data frame invisibly. TO get the list of signatures, the function
call should be assigned to a variable explicitly. In following code, if plot argument is set
to FALSE, no heatmap is plotted while only the differential analysis is performed.
# code only for demonstration
tb = get_signature(res, k = ..., plot = FALSE)
An example of the output of tb is:
#> which_row fdr mean_1 mean_2 scaled_mean_1 scaled_mean_2 km
#> 1 38 0.042760348 8.373488 9.131774 -0.5533452 0.5164555 1
#> 2 40 0.018707592 7.106213 8.469186 -0.6173731 0.5762149 1
#> 3 55 0.019134737 10.221463 11.207825 -0.6159697 0.5749050 1
#> 4 59 0.006059896 5.921854 7.869574 -0.6899429 0.6439467 1
#> 5 60 0.018055526 8.928898 10.211722 -0.6204761 0.5791110 1
#> 6 98 0.009384629 15.714769 14.887706 0.6635654 -0.6193277 2
...
The columns in tb are:
which_row: row indices corresponding to the input matrix.fdr: FDR for the differential test. mean_x: The mean value in group x.scaled_mean_x: The mean value in group x after rows are scaled.km: Row groups if k-means clustering is applied to rows.UMAP plot which shows how samples are separated.
dimension_reduction(res, k = 2, method = "UMAP")
dimension_reduction(res, k = 3, method = "UMAP")
dimension_reduction(res, k = 4, method = "UMAP")
dimension_reduction(res, k = 5, method = "UMAP")
dimension_reduction(res, k = 6, method = "UMAP")
Following heatmap shows how subgroups are split when increasing k:
collect_classes(res)
Test correlation between subgroups and known annotations. If the known annotation is numeric, one-way ANOVA test is applied, and if the known annotation is discrete, chi-squared contingency table test is applied.
test_to_known_factors(res)
#> n temperature(p) time(p) specimen(p) k
#> ATC:NMF 139 0.477 0.611 3.42e-14 2
#> ATC:NMF 132 0.850 0.948 1.00e-22 3
#> ATC:NMF 125 0.959 0.996 4.50e-42 4
#> ATC:NMF 130 0.991 0.994 3.23e-64 5
#> ATC:NMF 131 0.996 1.000 3.22e-84 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